{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3973","title":"ConnectionError  and SSLError","comments":"Hi ! You can download the `oscar.py` file from this repository at `\/datasets\/oscar\/oscar.py`.\r\n\r\nThen you can load the dataset by passing the local path to `oscar.py` to `load_dataset`:\r\n```python\r\nload_dataset(\"path\/to\/oscar.py\", \"unshuffled_deduplicated_it\")\r\n```","body":"code\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('oscar', 'unshuffled_deduplicated_it')\r\n```\r\nbug report\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionError                           Traceback (most recent call last)\r\n~\\AppData\\Local\\Temp\/ipykernel_29788\/2615425180.py in <module>\r\n----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)\r\n   1658 \r\n   1659     # Create a dataset builder\r\n-> 1660     builder_instance = load_dataset_builder(\r\n   1661         path=path,\r\n   1662         name=name,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)\r\n   1484         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1485         download_config.use_auth_token = use_auth_token\r\n-> 1486     dataset_module = dataset_module_factory(\r\n   1487         path,\r\n   1488         revision=revision,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n   1236                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n   1237                     ) from None\r\n-> 1238                 raise e1 from None\r\n   1239     else:\r\n   1240         raise FileNotFoundError(\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n   1173             if path.count(\"\/\") == 0:  # even though the dataset is on the Hub, we get it from GitHub for now\r\n   1174                 # TODO(QL): use a Hub dataset module factory instead of GitHub\r\n-> 1175                 return GithubDatasetModuleFactory(\r\n   1176                     path,\r\n   1177                     revision=revision,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in get_module(self)\r\n    531         revision = self.revision\r\n    532         try:\r\n--> 533             local_path = self.download_loading_script(revision)\r\n    534         except FileNotFoundError:\r\n    535             if revision is not None or os.getenv(\"HF_SCRIPTS_VERSION\", None) is not None:\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in download_loading_script(self, revision)\r\n    511         if download_config.download_desc is None:\r\n    512             download_config.download_desc = \"Downloading builder script\"\r\n--> 513         return cached_path(file_path, download_config=download_config)\r\n    514 \r\n    515     def download_dataset_infos_file(self, revision: Optional[str]) -> str:\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    232     if is_remote_url(url_or_filename):\r\n    233         # URL, so get it from the cache (downloading if necessary)\r\n--> 234         output_path = get_from_cache(\r\n    235             url_or_filename,\r\n    236             cache_dir=cache_dir,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\utils\\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)\r\n    580         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    581         if head_error is not None:\r\n--> 582             raise ConnectionError(f\"Couldn't reach {url} ({repr(head_error)})\")\r\n    583         elif response is not None:\r\n    584             raise ConnectionError(f\"Couldn't reach {url} (error {response.status_code})\")\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/2.0.0\/datasets\/oscar\/oscar.py (SSLError(MaxRetryError(\"HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/2.0.0\/datasets\/oscar\/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))\")))\r\n```\r\nIt may be caused by Caused by SSLError(in China?) because it works well on google colab.\r\nSo how can I download this dataset manually?\r\n","comment_length":32,"text":"ConnectionError  and SSLError \n code\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('oscar', 'unshuffled_deduplicated_it')\r\n```\r\nbug report\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionError                           Traceback (most recent call last)\r\n~\\AppData\\Local\\Temp\/ipykernel_29788\/2615425180.py in <module>\r\n----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)\r\n   1658 \r\n   1659     # Create a dataset builder\r\n-> 1660     builder_instance = load_dataset_builder(\r\n   1661         path=path,\r\n   1662         name=name,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)\r\n   1484         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1485         download_config.use_auth_token = use_auth_token\r\n-> 1486     dataset_module = dataset_module_factory(\r\n   1487         path,\r\n   1488         revision=revision,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n   1236                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n   1237                     ) from None\r\n-> 1238                 raise e1 from None\r\n   1239     else:\r\n   1240         raise FileNotFoundError(\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n   1173             if path.count(\"\/\") == 0:  # even though the dataset is on the Hub, we get it from GitHub for now\r\n   1174                 # TODO(QL): use a Hub dataset module factory instead of GitHub\r\n-> 1175                 return GithubDatasetModuleFactory(\r\n   1176                     path,\r\n   1177                     revision=revision,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in get_module(self)\r\n    531         revision = self.revision\r\n    532         try:\r\n--> 533             local_path = self.download_loading_script(revision)\r\n    534         except FileNotFoundError:\r\n    535             if revision is not None or os.getenv(\"HF_SCRIPTS_VERSION\", None) is not None:\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in download_loading_script(self, revision)\r\n    511         if download_config.download_desc is None:\r\n    512             download_config.download_desc = \"Downloading builder script\"\r\n--> 513         return cached_path(file_path, download_config=download_config)\r\n    514 \r\n    515     def download_dataset_infos_file(self, revision: Optional[str]) -> str:\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    232     if is_remote_url(url_or_filename):\r\n    233         # URL, so get it from the cache (downloading if necessary)\r\n--> 234         output_path = get_from_cache(\r\n    235             url_or_filename,\r\n    236             cache_dir=cache_dir,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\utils\\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)\r\n    580         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    581         if head_error is not None:\r\n--> 582             raise ConnectionError(f\"Couldn't reach {url} ({repr(head_error)})\")\r\n    583         elif response is not None:\r\n    584             raise ConnectionError(f\"Couldn't reach {url} (error {response.status_code})\")\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/2.0.0\/datasets\/oscar\/oscar.py (SSLError(MaxRetryError(\"HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/2.0.0\/datasets\/oscar\/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))\")))\r\n```\r\nIt may be caused by Caused by SSLError(in China?) because it works well on google colab.\r\nSo how can I download this dataset manually?\r\n \n Hi ! You can download the `oscar.py` file from this repository at `\/datasets\/oscar\/oscar.py`.\r\n\r\nThen you can load the dataset by passing the local path to `oscar.py` to `load_dataset`:\r\n```python\r\nload_dataset(\"path\/to\/oscar.py\", \"unshuffled_deduplicated_it\")\r\n```","embeddings":[-0.5248147249,0.0405230634,-0.1230327189,0.0725033507,0.3041602373,-0.0861735046,0.3794277012,0.3197903037,0.1657542586,0.1261250079,-0.0368023068,0.1544640511,0.0295699202,0.2415485531,-0.0804175362,0.0118007232,0.0215492398,0.2121128887,-0.2936364412,0.1434446275,-0.1580768973,0.0597132817,-0.1213339269,0.0628936142,0.0264170617,-0.0918866172,0.0635952353,0.09248963,-0.2721813619,-0.5847228765,0.3350659013,0.0544011779,0.1639633179,0.3337168694,-0.0001032296,0.1607512534,0.3830227554,0.0225937646,-0.4438838661,-0.0753620267,-0.2600414753,-0.1699307561,0.1725028008,-0.2509514093,-0.1271160692,-0.1427579224,0.0133180264,-0.1045694277,0.4266457558,0.5949547887,0.3087108433,0.2350473255,0.2211331576,-0.2260816693,-0.093868427,-0.0528600365,-0.0494220331,0.3206468523,-0.1541195363,-0.0526125655,-0.0211272147,0.1484285742,-0.0330743343,0.131769076,0.1853141338,-0.1362235844,0.2192585915,-0.2343800515,0.2789015472,0.1347588897,0.3769879937,-0.2704375386,-0.1939039081,-0.0663898513,0.0133975921,-0.1153049618,0.2672587931,-0.0227161665,-0.2561770678,0.1315240115,0.1152299568,-0.0420314372,-0.2920095026,0.1214196384,-0.2473967373,0.3689371347,-0.1031119749,0.1338785291,0.1406417042,-0.2849055231,0.3763561845,-0.265576601,-0.0630823895,-0.0249633025,-0.3652105033,-0.0172486659,0.0610821247,-0.1850283295,0.0179429464,0.2507582903,-0.1675679088,0.0015166609,0.2556134462,0.0941412598,0.1876465976,-0.0191309657,-0.0684208274,0.2050613612,0.2798160017,0.4794403911,-0.0046102297,-0.0193345509,0.0774071142,-0.3451925218,0.1494957656,0.2217476517,0.5676452518,-0.0497964099,-0.3642171621,0.0815469772,-0.0719113722,-0.0626080707,0.1049011424,0.5125083923,-0.1762953103,-0.0208250247,0.0871407911,0.0257266648,-0.1565681249,0.0333860219,-0.2656222582,0.0218052436,-0.0177687351,-0.0600029491,0.1233207211,-0.2167026997,0.0878132656,0.0247986671,0.0363383144,-0.0132873524,0.1787394583,-0.2246454358,-0.0337171294,0.3683737814,0.0790289044,0.1233741343,0.1865831465,-0.3182586432,0.0053789699,-0.0215358064,-0.1364102215,-0.368914634,-0.2977691889,0.3526279032,-0.099242948,-0.0255752541,0.1224341169,-0.1396495104,0.1118072271,-0.1843087524,-0.0066952514,-0.3930797875,-0.0140003776,-0.2388254553,0.4356188476,0.5765903592,-0.2544672191,-0.0194348674,-0.1086840257,-0.3297651112,0.1433623284,-0.0008665854,-0.1209413931,0.1959979683,-0.283337146,0.0140805123,0.4933345318,-0.2732447386,-0.474529624,0.2703975141,-0.0774072334,-0.0675550029,0.1488501728,0.0251265038,0.1717925221,0.1925431043,0.2184542567,0.0975835547,-0.132005766,0.0138857523,-0.110377647,-0.2811441422,0.086560525,0.2148653269,0.0964925736,0.0370329134,0.2573040128,-0.0018459745,0.2437432855,-0.0702360198,0.0848947614,0.1108255014,0.2402842641,0.2289813906,-0.0890921503,-0.0985051766,-0.3618351817,0.1667391062,0.0708520338,0.2703915834,-0.2953439355,-0.0068604504,-0.3324002028,0.0225030053,-0.3172858059,-0.2356597483,0.3202826977,0.3172685206,0.0873091444,0.1541065276,-0.1639429182,0.3611117601,-0.0898956805,0.1543644816,-0.3304589391,0.3856510222,-0.3553184867,-0.1974292696,0.1665094048,-0.0455648899,0.2814328969,-0.1167608425,-0.1832756102,0.2978258431,-0.1057495624,0.1631053835,-0.0631919801,-0.0845946819,0.0249384195,-0.2407684773,0.1089295,0.1619396359,0.2229104936,-0.0165450443,0.0845862702,0.3176093996,-0.0448932983,0.2343648672,0.1467713714,0.138876155,0.40602386,-0.0116489045,-0.05899553,0.022523215,0.0543649495,-0.1573626548,0.2438279837,-0.2047215849,-0.1547783166,0.1012208983,0.2893354297,-0.0325203091,0.1159038395,-0.0359678641,-0.1878464073,-0.0219852645,0.1158393696,0.3814059794,0.4593290389,0.0940860584,-0.1313472688,0.2244416177,0.0116805425,-0.0999060944,0.3175111711,-0.025184432,0.0247132182,0.2216822207,-0.0086906739,-0.0564420596,-0.3081172109,-0.4279347062,-0.2288101166,0.384462744,-0.2976517677,0.1923933178,-0.1080934033,-0.4097387195,-0.1233200654,0.1638267189,-0.1746722609,-0.2784341276,-0.204512164,0.2484969348,-0.0588205084,0.195192799,-0.3050100803,0.0053670285,0.203263998,-0.0682167709,-0.0573419482,-0.110627979,-0.0692984164,0.1607012749,0.161907196,0.089840278,0.4566483796,-0.1733338684,0.0086406097,-0.2272708863,-0.1802410185,0.020428136,0.0706614777,0.0958890319,0.4718353748,0.4023368657,0.1296416074,-0.4160859287,0.3316514194,-0.1155416444,-0.0953339264,0.0498097949,-0.1012485325,0.0574603081,-0.0175374411,-0.4779075384,-0.4305971265,-0.4179047346,0.0495835021,0.094447419,0.163816914,0.1572206467,0.0943631828,0.2408653647,-0.0294093676,0.0499746762,-0.1950811893,-0.2628124058,0.2003073692,-0.1548718512,-0.3877334595,0.1066555679,-0.0228675958,0.1981830001,-0.245931223,-0.5698668957,-0.3658428788,-0.1819875389,0.3429222405,-0.0642679855,-0.0980042294,0.289735198,0.1094275936,-0.1510211974,-0.0023643209,-0.3675800264,-0.0354960226,0.0333933271,0.0989801362,-0.0744050369,0.5484481454,-0.1492922306,0.4120691419,0.2601548433,-0.0247457903,0.2816315889,-0.1954274327,0.2761997283,-0.1008627191,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3969","title":"Cannot preview cnn_dailymail dataset","comments":"I guess the cache got corrupted due to a previous issue with Google Drive service.\r\n\r\nThe cache should be regenerated, e.g. by passing `download_mode=\"force_redownload\"`.\r\n\r\nCC: @severo ","body":"## Dataset viewer issue for '*cnn_dailymail*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/cnn_dailymail\r\n\r\n*short description of the issue*\r\n\r\nAm I the one who added this dataset ? Yes-No\r\n","comment_length":26,"text":"Cannot preview cnn_dailymail dataset \n ## Dataset viewer issue for '*cnn_dailymail*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/cnn_dailymail\r\n\r\n*short description of the issue*\r\n\r\nAm I the one who added this dataset ? Yes-No\r\n \n I guess the cache got corrupted due to a previous issue with Google Drive service.\r\n\r\nThe cache should be regenerated, e.g. by passing `download_mode=\"force_redownload\"`.\r\n\r\nCC: @severo ","embeddings":[-0.3001443148,0.1003711745,-0.0295108072,0.2527271807,-0.0365714543,0.4788869023,0.3518633544,0.1342158616,-0.1076103002,0.0816059932,0.0185798742,-0.0681077242,-0.1297189742,0.1330219954,-0.054589849,-0.0531774163,0.0362982601,0.0126727307,0.0645384118,0.1520994306,-0.328705281,0.1523635536,-0.1258555204,-0.1625272036,-0.1889630109,-0.2408405393,0.0509407073,-0.0764456764,-0.2410833836,-0.2481218129,0.0258545354,0.091694206,0.0761290938,0.286251992,-0.0001108677,0.1460110396,0.5770383477,0.0952286273,-0.4096316695,-0.1781215519,0.1109841019,-0.0640218556,0.065616928,-0.0470403694,-0.2066573799,-0.1666980237,0.0564076602,-0.2384856939,0.2563394308,0.2655278742,0.2397268862,0.2472810894,0.0292735044,-0.2963423133,-0.1173273325,-0.165860936,-0.2852419019,0.2716921568,-0.246544838,0.1303915083,0.1296731979,0.1965691596,0.0484308302,0.0150310528,-0.0918338671,0.1051229835,-0.1604434401,-0.3715656102,0.1897888482,-0.0067216209,0.5617846251,-0.0110142576,-0.2454853058,0.1766006202,0.2344596982,-0.1753761321,0.2967875898,0.1844427139,0.1404484808,0.2778817415,-0.40473634,-0.1989469975,-0.0632664263,0.31832847,-0.1603921652,0.0089877872,-0.1415831894,0.1223070323,-0.0212871321,-0.1049776301,0.5157182217,-0.1721924543,-0.0639269128,0.0498846173,-0.0117330188,-0.1290300488,0.0433034748,0.0984490141,-0.0437055975,0.116131939,-0.1133807674,-0.0721456781,-0.0635623336,0.0106569119,0.110333316,0.2751213312,0.0813373327,0.2554914057,0.3578922451,0.1652930081,0.3596162498,-0.0539102629,-0.1404289603,0.1071248204,-0.0932276174,-0.0819682181,0.4510961771,-0.0402333178,-0.241812259,0.1712124646,-0.2220128179,-0.0321245976,-0.0712794363,0.3751703799,-0.1875046045,-0.3492168486,-0.0499136858,0.1813981384,0.0645083115,-0.1590726376,-0.3080528378,-0.0439108647,-0.1557722688,0.252835542,0.3908677399,-0.2719049156,0.111952357,-0.0354322679,0.1177471876,-0.0882538632,-0.1107253432,0.0277161561,-0.2258161306,0.218535766,0.0410527289,0.3443533778,0.1177532896,0.1806509644,0.1035268009,0.2060120255,-0.1977183223,-0.4557098448,0.0034161727,0.1546727568,-0.4183003008,-0.1121838018,-0.0704928786,0.1604210734,-0.2096106857,-0.0834121779,-0.2386018932,0.2705118358,-0.0968362316,-0.1173521727,0.0767766237,0.3635947108,-0.3849908412,0.0475905649,-0.4673796594,-0.4734217227,0.1199678853,0.3018504381,-0.0998107865,-0.1450817138,-0.3228721619,0.0501847453,0.1767573506,-0.0054863323,-0.8023152947,-0.0368464924,-0.1421165913,-0.0080495309,-0.0756033212,0.1161130965,0.0739757046,-0.1442147344,-0.1413904727,0.4141310453,0.0430425145,-0.0266126748,-0.2092333287,-0.3023833036,0.2087436318,0.1786832511,0.2769963145,0.2069008648,0.1031349897,-0.2416488826,-0.0761631727,0.1954420805,0.014392525,0.1825558245,0.0907430351,-0.150275141,0.1358540356,0.1372164041,-0.567986846,0.0718719661,0.178058356,0.0281816069,-0.150418818,-0.129186064,-0.2626024783,0.070179455,-0.1377693564,-0.1789966226,0.2244574279,0.1023606732,-0.1249391586,0.2711905539,0.0309280604,0.0489558838,-0.0438947715,-0.1087173894,-0.117921114,0.3618579507,-0.0935049206,-0.0409866497,0.2035871446,-0.0774088576,0.2939368784,-0.083779335,-0.2664110065,0.1035315022,-0.1308322549,0.2918297648,0.2929287553,0.0693170428,0.0885813609,-0.3968087137,0.1386912763,0.2722697854,-0.1687529683,-0.0358427912,-0.2935991585,0.0438275002,0.1748094857,-0.1422575861,-0.1314187199,0.0886450112,0.1271843165,0.0016826026,0.167722702,-0.2345178574,0.1830431819,0.0120512024,0.5338984728,-0.1629336774,-0.221036911,0.1191416085,0.205720976,-0.2112344205,0.0331556574,0.1112066209,-0.4103272259,-0.2730825841,-0.2425906062,0.274220556,0.2535567582,0.0738537014,0.2500463128,0.2354700714,0.1809593439,-0.1370882243,0.110237062,0.071017459,0.0420132615,0.1230558008,0.2608272433,-0.0267975666,-0.4891771376,0.2632583976,0.4685604274,0.4192255139,-0.1656619757,-0.0913532972,-0.1788526177,-0.0869127214,0.2301268578,-0.2540560365,-0.1784055531,-0.3257436454,0.1554211229,0.2270703316,0.012674286,0.1348285228,0.1219554469,0.4795156419,0.3065283895,0.01757784,-0.2787468135,-0.0005129193,-0.0804369673,0.1368815303,0.360237509,-0.2678504586,0.2713237703,0.0690687671,0.0595741458,-0.7089738846,-0.2868512571,0.0434290394,0.0361276343,0.6266723871,-0.1880392432,0.3393319547,0.1618057489,0.1048678383,-0.0417827405,-0.2110104263,-0.2959507704,-0.0863000229,-0.1145061105,-0.0888173431,0.0027829404,-0.3537245691,-0.2696407735,-0.2092420161,0.1742672473,-0.1965801716,-0.0642950237,0.0015744645,-0.0416056588,-0.123150602,0.1943286657,-0.1102592796,-0.5379715562,-0.3815451264,0.4116377532,-0.5738167763,-0.5965697169,0.2564910054,0.1105611771,-0.1507775933,-0.1051835865,-0.543158412,-0.2485460937,-0.1045301035,-0.2073769122,0.0492331497,-0.5171288848,0.0807987154,-0.1011416167,-0.0628671944,-0.0649817288,-0.3293249309,-0.178177461,-0.129535988,0.0845281631,-0.1059697941,0.4432548285,-0.0378230922,0.5717325211,0.2785785496,0.0316255577,0.170555234,-0.1906147003,0.3731377721,-0.0227058399,-0.2890555263,-0.0133194253,0.1308900714,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3968","title":"Cannot preview 'indonesian-nlp\/eli5_id' dataset","comments":"Hi @cahya-wirawan, thanks for reporting.\r\n\r\nYour dataset is working OK in streaming mode:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n   ...: ds = load_dataset(\"indonesian-nlp\/eli5_id\", split=\"train\", streaming=True)\r\n   ...: item = next(iter(ds))\r\n   ...: item\r\nUsing custom data configuration indonesian-nlp--eli5_id-9fe728a7e760fb7b\r\n\r\nOut[1]: \r\n{'q_id': '1oy5tc',\r\n 'title': 'dalam sepak bola apa gunanya menyia-nyiakan dua permainan pertama dengan terburu-buru - di tengah - bukan permainan terburu-buru biasa saya mendapatkannya',\r\n 'selftext': '',\r\n 'document': '',\r\n 'subreddit': 'explainlikeimfive',\r\n 'answers': {'a_id': ['ccwtgnz', 'ccwtmho', 'ccwt946', 'ccwvj0u'],\r\n  'text': ['Jaga pertahanan tetap jujur, rasakan operan terburu-buru, buka permainan yang lewat. Pelanggaran yang terlalu satu dimensi akan gagal. Dan mereka yang bergegas ke tengah kadang-kadang dapat dibuka lebar-lebar untuk ukuran yard yang besar.',\r\n   'Jika Anda melempar bola sepanjang waktu, maka pertahanan akan beradaptasi untuk selalu menutupi umpan. Dengan melakukan permainan lari sederhana sesekali, Anda memaksa pertahanan untuk tetap dekat dan menjaga dari lari. Terkadang, pelanggaran dapat membuat pertahanan lengah dengan berpura-pura berlari dan membebaskan penerima mereka. Selain itu, Anda tidak perlu mendapatkan yard besar di setiap permainan. Terkadang, paling baik mendapatkan beberapa yard sekaligus. Selama Anda mendapatkan yang pertama, Anda dalam kondisi yang baik.',\r\n   'Dalam kebanyakan kasus, O-Line seharusnya membuat lubang untuk dilalui kembali. Jika Anda menjalankan terlalu banyak permainan ke luar \/ melempar, pertahanan akan mengejar. Juga, 2 permainan 5 yard memberi Anda satu set down baru.',\r\n   'Saya Anda tidak suka jenis drama itu, tonton CFL. Kami hanya mendapatkan 3 down sehingga Anda tidak bisa menyia-nyiakannya. Lebih banyak lagi yang lewat.'],\r\n  'score': [3, 2, 2, 2]},\r\n 'title_urls': {'url': []},\r\n 'selftext_urls': {'url': []},\r\n 'answers_urls': {'url': []}}\r\n```\r\nTherefore, it should be properly rendered in the previewer. Let me ping @severo to have a look at it.","body":"## Dataset viewer issue for '*indonesian-nlp\/eli5_id*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/indonesian-nlp\/eli5_id\r\n\r\nI can not see the dataset preview.\r\n```\r\nServer Error\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the dataset does not exist.\r\n```\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":271,"text":"Cannot preview 'indonesian-nlp\/eli5_id' dataset \n ## Dataset viewer issue for '*indonesian-nlp\/eli5_id*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/indonesian-nlp\/eli5_id\r\n\r\nI can not see the dataset preview.\r\n```\r\nServer Error\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the dataset does not exist.\r\n```\r\nAm I the one who added this dataset ? Yes\r\n \n Hi @cahya-wirawan, thanks for reporting.\r\n\r\nYour dataset is working OK in streaming mode:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n   ...: ds = load_dataset(\"indonesian-nlp\/eli5_id\", split=\"train\", streaming=True)\r\n   ...: item = next(iter(ds))\r\n   ...: item\r\nUsing custom data configuration indonesian-nlp--eli5_id-9fe728a7e760fb7b\r\n\r\nOut[1]: \r\n{'q_id': '1oy5tc',\r\n 'title': 'dalam sepak bola apa gunanya menyia-nyiakan dua permainan pertama dengan terburu-buru - di tengah - bukan permainan terburu-buru biasa saya mendapatkannya',\r\n 'selftext': '',\r\n 'document': '',\r\n 'subreddit': 'explainlikeimfive',\r\n 'answers': {'a_id': ['ccwtgnz', 'ccwtmho', 'ccwt946', 'ccwvj0u'],\r\n  'text': ['Jaga pertahanan tetap jujur, rasakan operan terburu-buru, buka permainan yang lewat. Pelanggaran yang terlalu satu dimensi akan gagal. Dan mereka yang bergegas ke tengah kadang-kadang dapat dibuka lebar-lebar untuk ukuran yard yang besar.',\r\n   'Jika Anda melempar bola sepanjang waktu, maka pertahanan akan beradaptasi untuk selalu menutupi umpan. Dengan melakukan permainan lari sederhana sesekali, Anda memaksa pertahanan untuk tetap dekat dan menjaga dari lari. Terkadang, pelanggaran dapat membuat pertahanan lengah dengan berpura-pura berlari dan membebaskan penerima mereka. Selain itu, Anda tidak perlu mendapatkan yard besar di setiap permainan. Terkadang, paling baik mendapatkan beberapa yard sekaligus. Selama Anda mendapatkan yang pertama, Anda dalam kondisi yang baik.',\r\n   'Dalam kebanyakan kasus, O-Line seharusnya membuat lubang untuk dilalui kembali. Jika Anda menjalankan terlalu banyak permainan ke luar \/ melempar, pertahanan akan mengejar. Juga, 2 permainan 5 yard memberi Anda satu set down baru.',\r\n   'Saya Anda tidak suka jenis drama itu, tonton CFL. Kami hanya mendapatkan 3 down sehingga Anda tidak bisa menyia-nyiakannya. Lebih banyak lagi yang lewat.'],\r\n  'score': [3, 2, 2, 2]},\r\n 'title_urls': {'url': []},\r\n 'selftext_urls': {'url': []},\r\n 'answers_urls': {'url': []}}\r\n```\r\nTherefore, it should be properly rendered in the previewer. Let me ping @severo to have a look at it.","embeddings":[-0.3884463906,-0.1418884844,-0.0735412017,0.1538683325,-0.0247687586,0.2311385423,0.0929410681,0.5587155223,0.0132091083,-0.0031359883,-0.2292771786,0.1413205415,-0.0089979507,0.0560641401,0.2455597967,-0.385282129,0.1090712696,0.2116556913,0.0661699772,0.2062216103,-0.2260432988,0.1690868884,-0.1779131442,0.0307424162,-0.1016645804,0.126808852,-0.2462918907,-0.1787736565,-0.2351306826,-0.4451823533,0.327098608,-0.0066394787,0.3465333879,0.3288263381,-0.0001176661,0.1015073955,0.4787644744,0.0469392464,-0.4361974895,-0.2867253423,-0.0695508793,-0.2629032135,0.1667048186,-0.3640157282,-0.3667900264,-0.1416674256,0.1614738852,-0.2330999523,0.44912377,0.2519759238,0.1802675724,0.1842023879,-0.0906575844,-0.1951971054,0.0046623503,0.0135075469,-0.340557456,-0.0311178211,0.0251057521,0.1483627409,0.0723093823,0.1425684243,0.0414824747,0.0002270873,-0.0983589888,-0.1774888933,-0.0742167607,-0.4272820652,0.2674390972,0.0889695287,0.829331398,-0.1354740709,-0.4231261909,0.0516598485,0.1803527772,-0.3652415574,0.0424412414,0.1807281226,0.0217582919,0.2970374525,-0.2148314714,-0.1728135645,-0.1344362646,0.352918148,0.0918898433,0.4469756186,-0.0729436353,0.1788550913,-0.1445162743,0.0110884197,0.3606284559,-0.2371483296,-0.0554233715,0.3523611426,-0.2338986397,-0.0025194369,-0.0487667806,0.0658835098,-0.0402618907,0.2348724902,0.0417962447,0.1268389076,0.2089369148,0.0888385028,-0.0419940986,0.2751559317,0.2118462622,0.1947982609,0.0795952007,0.1010092124,0.3155805171,-0.1507544965,-0.1895994395,0.0298122987,-0.0631499588,-0.1059169248,0.083505556,-0.0825968534,-0.2144390196,0.0875712782,-0.2015103996,-0.1049720645,-0.0790580288,0.2095071375,-0.2710109651,0.0359089598,-0.0197481364,0.3294924796,-0.1725652367,-0.3394614458,-0.204564169,-0.0959231481,-0.2350442708,0.107418634,0.2647384107,-0.056143038,0.354818821,-0.175687477,-0.0162342768,-0.0401391648,0.0868212953,-0.0773989707,-0.0552708022,0.2023692131,0.2660629749,0.24442783,0.2542658746,-0.1456144154,0.0261680279,0.2186499387,-0.0606235005,-0.339638859,-0.2691552043,0.129211843,-0.5351642966,0.0107726809,-0.4277229011,0.2442936897,0.0839643627,-0.2213212997,-0.0292767435,0.1889336556,-0.3994222879,-0.0839852542,0.1138235703,0.5005108118,-0.3949445188,-0.3168523014,-0.4791227579,-0.2337372899,0.2002006471,0.2949112952,-0.1533594131,0.0940814167,-0.4111356437,0.2337620705,0.6692294478,-0.1584241092,-0.8022477627,0.3397337794,-0.2535730898,0.1907269657,0.2465168983,0.187956214,0.2971201837,-0.2125584334,0.0285500977,0.1409574151,0.2370335311,-0.149567917,-0.2030353993,-0.1790860146,0.3119282424,0.1220059544,0.1393406242,0.0354034118,0.0201094951,-0.0297424663,0.3851157129,0.2866754532,0.1901659667,-0.0552024916,0.1308941841,0.1412426233,-0.0467300862,-0.1130552813,-0.1065964177,0.1706553549,0.0300967544,0.0040671732,-0.1335962862,-0.1195596159,-0.5475382209,0.1618610173,-0.4023771584,-0.2562200129,0.1501112431,0.1153146029,0.3233064115,0.2405787855,-0.0133459317,0.316526711,-0.1412055343,-0.1434650272,-0.3764167726,0.368545413,-0.0496472195,0.0896378756,0.2222929597,0.1586568505,0.369997561,0.0389926955,-0.1621093303,0.2774929404,-0.2628035843,0.1149619296,0.287896812,0.0580263175,0.1851752996,-0.6591469646,0.2241029739,0.0801993236,0.0848851204,-0.0888003483,-0.1891590059,0.1237821206,0.0863541439,0.0509520248,-0.1372753084,0.117165029,0.3348384798,0.0314216577,-0.1587203592,-0.158145383,0.2898854911,-0.2606801987,0.4740163982,-0.2403022796,-0.1924534887,0.1025101095,0.000977741,-0.090708755,0.0305853449,0.1434001178,-0.2073385566,-0.2956685722,0.0105985813,0.0880796835,0.4563913941,0.2363197207,0.2078634948,0.0893920735,0.0896414369,-0.1324800253,0.0991060734,0.0699365363,0.1454225332,0.1838043332,0.1430676877,-0.0732428506,-0.2717493474,0.0095777353,0.3296997547,0.2500695884,-0.0962308422,-0.0076894443,-0.2244861573,-0.5751373768,-0.0319591351,-0.2915560901,0.00723129,-0.4501574934,0.1041163132,0.0707953349,0.1665889025,0.3464949727,-0.4401084781,0.0872653574,0.1577487886,0.0515143424,-0.1567114443,0.0336695947,-0.0888903514,0.0692386702,0.2791858613,0.0393030979,0.4615880251,-0.2586062253,0.1123819724,-0.5453107357,-0.2779381275,0.0850230083,0.0678189695,0.4314616024,-0.1768881977,0.2624557018,0.1044780463,-0.1249225512,0.2560336292,-0.1020508334,-0.1849177182,0.1137405112,0.0292080306,-0.009925927,0.1951759905,-0.3396383524,-0.4454943836,-0.3778810203,0.0324128866,-0.1336253732,0.0466426052,0.0414326303,0.119172208,-0.0381572954,0.1233722642,-0.2101917565,-0.3526592255,-0.4695138037,0.3989985883,-0.4600352347,-0.439298749,0.1779684275,-0.0280604847,0.3165484369,-0.1425096542,-0.5763728023,0.1502581388,-0.0402829088,0.231717959,0.1383294016,-0.200196743,0.184401527,-0.186800316,-0.0026976971,-0.0738972947,-0.0629624128,-0.219480738,-0.3743027747,0.4920832217,-0.0524415001,0.5958861113,0.0800614506,0.7769434452,0.5118920207,0.1964694262,0.3109145463,-0.0558984168,0.1896193773,-0.1541992873,-0.3330961764,-0.030729454,-0.10418199,0.0561070479,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3968","title":"Cannot preview 'indonesian-nlp\/eli5_id' dataset","comments":"Thanks @albertvillanova for checking it. Btw, I have another dataset indonesian-nlp\/lfqa_id which has the same issue. However, this dataset is still private, is it the reason why the preview doesn't work?","body":"## Dataset viewer issue for '*indonesian-nlp\/eli5_id*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/indonesian-nlp\/eli5_id\r\n\r\nI can not see the dataset preview.\r\n```\r\nServer Error\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the dataset does not exist.\r\n```\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":31,"text":"Cannot preview 'indonesian-nlp\/eli5_id' dataset \n ## Dataset viewer issue for '*indonesian-nlp\/eli5_id*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/indonesian-nlp\/eli5_id\r\n\r\nI can not see the dataset preview.\r\n```\r\nServer Error\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the dataset does not exist.\r\n```\r\nAm I the one who added this dataset ? Yes\r\n \n Thanks @albertvillanova for checking it. Btw, I have another dataset indonesian-nlp\/lfqa_id which has the same issue. However, this dataset is still private, is it the reason why the preview doesn't work?","embeddings":[-0.335232228,-0.1239321679,-0.027136052,0.2779892683,-0.1499147117,0.2525175214,0.1689610779,0.4171839058,0.0262222327,0.1278894395,-0.209331423,0.0273422934,0.0728633255,-0.0633361638,0.2337566465,-0.2524942458,0.137003839,0.2076781541,0.041900117,0.1397501379,-0.3411981761,0.138701573,-0.0847396031,0.0956585556,-0.1752810329,0.1472758502,-0.2631250024,-0.162565738,-0.1750167161,-0.2988178432,0.3090737462,0.0094314171,0.3150721788,0.2825846076,-0.000120714,0.1024616584,0.5217805505,0.0814411044,-0.3983308971,-0.1704627275,-0.2927373946,-0.1974096596,0.1072620675,-0.2427866012,-0.3409104049,-0.0719875693,0.2031472921,-0.2895103991,0.3366419375,0.1013350785,0.1765974015,0.2822761536,0.0096484702,-0.418073386,0.0997695774,0.113939479,-0.3644113243,-0.0360721722,-0.0325783081,0.1766184419,0.1380995661,0.171203196,0.1845665872,-0.0496159792,-0.0524267964,-0.1137561798,-0.0809142143,-0.3823923171,0.3947344422,0.1050453484,0.9738980532,0.0537460819,-0.421857357,0.2185230106,0.165057376,-0.270213306,0.2160298377,0.077715151,0.2029744983,0.29381603,-0.284012109,-0.224087894,-0.0425119065,0.2671948373,0.048401244,0.3617527485,-0.1486109048,0.1761086583,-0.1185659394,-0.0315711126,0.2541719973,-0.2068041712,-0.1960059702,0.3550165296,-0.078970477,0.001830217,-0.0349565931,0.1232685447,0.0478699468,0.1794871092,-0.1306176633,0.1282430738,0.0576456115,0.1062515005,-0.0910091549,0.2693838477,0.2268246412,0.1627312303,0.2376191467,0.0461575687,0.3526900411,-0.1490909755,-0.2292901576,0.1273763776,-0.1262230873,-0.2348267138,0.1206289679,-0.1650118679,-0.2759542763,0.1054638028,-0.0866019577,-0.103412725,-0.1140597686,0.2603635788,-0.1627198309,-0.2183691412,0.0150112649,0.2727593482,-0.1524412632,-0.4064309895,-0.2044021785,-0.1434212923,-0.2629235089,0.2153827548,0.1966864169,0.0834170803,0.3332151473,-0.1632979512,0.0638503209,-0.0473395512,0.0570900366,0.0791114867,-0.2406433821,0.2372680157,0.1935512871,0.2929938436,0.1542376131,-0.1600528657,0.119665429,0.1881630272,-0.0789425373,-0.3005827367,-0.3701655865,0.0783779025,-0.5145670176,0.0544663668,-0.3898007572,0.1647766083,0.1224503815,-0.1828579307,0.0200936049,0.283948034,-0.3336379826,-0.0686913133,0.0581614561,0.5976068974,-0.4043647051,-0.2873674631,-0.5321161747,-0.3303726614,0.1480833292,0.3575612307,-0.167408362,-0.0633020923,-0.4827782214,0.1629850864,0.576540947,-0.0882451385,-0.7730315924,0.2138252407,-0.2996724248,0.0039665354,0.1571530849,0.1583943516,0.2135118246,-0.2919867337,-0.1313530058,-0.0370311774,0.2716798186,-0.2453710735,-0.1660526395,-0.2120825052,0.3313558996,0.1505402774,0.2093393207,0.0863539129,0.1533831507,-0.0644473732,0.1847374737,0.2670715451,0.2059558481,0.0518549345,0.1193992943,0.1918428242,-0.0156813301,-0.0267113894,-0.1420505345,0.1247741058,-0.1552666575,-0.0272433534,0.0034464574,-0.176190123,-0.4698728323,0.091563344,-0.3309877813,-0.2598211467,0.0590097457,0.2252207398,0.2295531631,0.2239294648,-0.0285141524,0.1453657448,-0.0788472965,-0.1175728962,-0.3168615401,0.1957642585,-0.038568601,0.1330084801,0.1202666312,0.1876130998,0.3888683915,-0.0624560863,-0.1487782151,0.2090479881,-0.3042759895,0.0499681532,0.4700348973,-0.002441087,0.1386856735,-0.6537967324,0.1642120183,-0.0789460838,0.0673483163,-0.0275915433,-0.194615379,0.0880391449,0.1664420515,-0.0703108311,-0.2632863522,0.150084585,0.3535473645,0.0582925528,-0.026063852,-0.2253334969,0.3618337214,-0.1910691559,0.4871994555,-0.2137669027,-0.253341347,0.1172369942,-0.0745110437,-0.1371079385,0.1531006992,0.1593272239,-0.0686936602,-0.1284013391,-0.0279647894,-0.0493271723,0.4077875018,0.1767100394,0.1648610532,-0.026566904,0.1069119275,-0.1371831596,0.0149037531,-0.0200791489,0.1377498209,-0.0249668956,0.1892005056,-0.0655183792,-0.2761457264,0.147875458,0.439067781,0.2086622715,-0.1248715594,0.0482875928,-0.1794449836,-0.5138228536,0.0877680406,-0.2043248862,-0.1417869925,-0.3560830653,0.1761588752,-0.0244356152,0.1562805772,0.301361829,-0.3635276556,0.3201647103,0.0134757999,0.2143827677,-0.229764387,0.1407666504,-0.0902791396,0.045907747,0.2752158344,-0.0245861281,0.3592140675,-0.2050981671,0.2490682006,-0.6150686145,-0.3445731401,0.0469790474,0.0729132742,0.4568885863,-0.137546882,0.2275836915,0.1145644039,-0.0123424986,0.1849180013,-0.0382079147,-0.2666500807,0.0514329597,-0.0535290763,-0.1655919999,0.0778845027,-0.1234854013,-0.3930378258,-0.2759178877,0.1000738814,-0.2051892579,-0.0067535318,-0.0844909996,-0.0700424388,-0.022473868,-0.0688115507,-0.2468435913,-0.4568462074,-0.5459211469,0.5053563118,-0.4959360063,-0.4490435421,0.2937420011,0.0267346688,0.3320818841,-0.2924139202,-0.5488402843,0.1215963885,-0.0819525719,0.2348625064,0.2194417268,-0.2633041143,0.3224680126,-0.2924363017,0.0128778787,-0.1233025119,-0.1479100138,-0.1501100957,-0.4924745858,0.6167436838,-0.1961087435,0.483294189,-0.0904608965,0.8399674296,0.423422873,0.2643404305,0.31518507,-0.0189050362,0.3202704191,-0.2186616361,-0.3000473678,-0.0788540095,-0.0497423522,0.067475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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3965","title":"TypeError: Couldn't cast array of type for JSONLines dataset","comments":"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype).","body":"## Describe the bug\r\nOne of the [course participants](https:\/\/discuss.huggingface.co\/t\/chapter-5-questions\/11744\/20?u=lewtun) is having trouble loading a JSONLines dataset that's composed of the GitHub issues from `spacy` (see stack trace below). \r\n\r\nThis reminds me a bit of #2799 where one can load the dataset in `pandas` but not in `datasets` and perhaps increasing the `block_size` is needed again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns 'https:\/\/huggingface.co\/datasets\/Evan\/spaCy-github-issues\/resolve\/main\/spacy-issues.jsonl'\r\ndata_files = hf_hub_url(repo_id=\"Evan\/spaCy-github-issues\", filename=\"spacy-issues.jsonl\", repo_type=\"dataset\")\r\n# throws TypeError: Couldn't cast array of type\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas - note this take a while as the file is >2GB\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to pandas.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 683, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1136, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 511, in write_table\r\n    pa_table = table_cast(pa_table, self._schema)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1121, in table_cast\r\n    return cast_table_to_features(table, Features.from_arrow_schema(schema))\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1102, in cast_table_to_features\r\n    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1102, in <listcomp>\r\n    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 944, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 918, in wrapper\r\n    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 918, in <listcomp>\r\n    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1086, in cast_array_to_feature\r\n    return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 944, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 920, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1019, in array_cast\r\n    raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{pa_type}\")\r\nTypeError: Couldn't cast array of type\r\nstruct<url: string, html_url: string, labels_url: string, id: int64, node_id: string, number: int64, title: string, description: string, creator: struct<login: string, id: int64, node_id: string, avatar_url: string, gravatar_id: string, url: string, html_url: string, followers_url: string, following_url: string, gists_url: string, starred_url: string, subscriptions_url: string, organizations_url: string, repos_url: string, events_url: string, received_events_url: string, type: string, site_admin: bool>, open_issues: int64, closed_issues: int64, state: string, created_at: timestamp[s], updated_at: timestamp[s], due_on: null, closed_at: timestamp[s]>\r\nto\r\nnull\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.1\r\n","comment_length":27,"text":"TypeError: Couldn't cast array of type for JSONLines dataset \n ## Describe the bug\r\nOne of the [course participants](https:\/\/discuss.huggingface.co\/t\/chapter-5-questions\/11744\/20?u=lewtun) is having trouble loading a JSONLines dataset that's composed of the GitHub issues from `spacy` (see stack trace below). \r\n\r\nThis reminds me a bit of #2799 where one can load the dataset in `pandas` but not in `datasets` and perhaps increasing the `block_size` is needed again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns 'https:\/\/huggingface.co\/datasets\/Evan\/spaCy-github-issues\/resolve\/main\/spacy-issues.jsonl'\r\ndata_files = hf_hub_url(repo_id=\"Evan\/spaCy-github-issues\", filename=\"spacy-issues.jsonl\", repo_type=\"dataset\")\r\n# throws TypeError: Couldn't cast array of type\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas - note this take a while as the file is >2GB\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to pandas.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 683, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1136, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 511, in write_table\r\n    pa_table = table_cast(pa_table, self._schema)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1121, in table_cast\r\n    return cast_table_to_features(table, Features.from_arrow_schema(schema))\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1102, in cast_table_to_features\r\n    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1102, in <listcomp>\r\n    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 944, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 918, in wrapper\r\n    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 918, in <listcomp>\r\n    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1086, in cast_array_to_feature\r\n    return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 944, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 920, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1019, in array_cast\r\n    raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{pa_type}\")\r\nTypeError: Couldn't cast array of type\r\nstruct<url: string, html_url: string, labels_url: string, id: int64, node_id: string, number: int64, title: string, description: string, creator: struct<login: string, id: int64, node_id: string, avatar_url: string, gravatar_id: string, url: string, html_url: string, followers_url: string, following_url: string, gists_url: string, starred_url: string, subscriptions_url: string, organizations_url: string, repos_url: string, events_url: string, received_events_url: string, type: string, site_admin: bool>, open_issues: int64, closed_issues: int64, state: string, created_at: timestamp[s], updated_at: timestamp[s], due_on: null, closed_at: timestamp[s]>\r\nto\r\nnull\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.1\r\n \n Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype).","embeddings":[0.0036059762,-0.0479997993,-0.0535864346,0.434281677,0.4592538476,0.1503392756,0.4873134792,0.3241958618,0.4770776629,-0.057500869,-0.061184179,0.1311786771,-0.1100012437,0.2816880941,-0.0008711191,-0.3941471875,-0.0146889417,-0.0445327871,0.1062430516,0.1749326438,-0.0933482721,0.1730911285,-0.1196654588,-0.1031807289,-0.1494211704,-0.085256055,0.1366049945,0.2853343785,-0.0558419898,-0.4664634764,0.5960640907,0.0274271145,0.2970753312,0.4774362147,-0.0001162325,0.2658969462,0.3623135686,0.0439485498,-0.2050976008,-0.3944782019,-0.2175051868,-0.2268907875,0.292797029,-0.2615489066,0.0292122662,0.0209290981,-0.2876492441,-0.280804038,0.631501019,0.3120798171,0.1566186994,0.5220170021,0.1360073388,-0.1679840684,-0.0224389546,0.2846999764,-0.2199214995,0.2597155571,-0.0267581884,0.3974964321,0.1206839457,-0.0854220763,-0.1513491422,-0.0770175233,0.4257461131,-0.1706186384,-0.0252256468,0.0552036688,-0.0201817993,0.2161801457,0.3892121911,-0.1799870282,-0.2810038626,-0.445832938,0.0304254517,0.2269043028,0.104608722,0.1048275903,-0.1074718237,0.1574066579,-0.3536053002,0.1600893289,-0.1817758977,0.0738727674,-0.0526402406,-0.3760051727,-0.1472248137,0.0713381842,0.0066733519,-0.1403694451,-0.1099321768,-0.0787994117,0.0000563304,0.2029625326,-0.0324040726,0.0440953523,0.2454863191,-0.4204905033,0.3482052684,0.0516690128,0.1613377184,0.1043186784,-0.2260720283,0.0543429218,0.5339517593,0.1073643565,-0.06462311,0.0745336041,0.0793801993,0.5198202729,-0.0296130534,-0.0402055867,-0.2337756604,-0.048777055,-0.1448587775,-0.112909779,0.4528776705,-0.3941109478,-0.3187656999,0.2627511919,-0.4356749356,-0.0382250696,0.1603553146,0.4267037809,-0.0415272787,0.4907935858,0.2890572548,0.3620336056,0.0117228385,0.1405922621,-0.1951085031,0.0639987215,-0.0529607162,-0.0929268524,0.1831150651,0.163158372,0.1911187619,-0.0758846626,-0.0173960738,-0.1847352982,-0.2162362784,0.081706427,-0.1178417355,0.2371089607,0.1023427844,0.1257822663,0.2100936919,-0.1883967966,-0.2573604286,-0.0501624979,-0.2659563422,-0.1070380211,-0.0492235683,0.0865136161,-0.2419669479,0.0422001407,-0.6630195379,-0.1053634062,0.0012046808,0.002678795,0.0476041362,-0.1705113649,0.2703705728,-0.2308913469,0.1184273511,0.3342460692,-0.1978952587,-0.1069839001,-0.1328890473,-0.0868328959,0.2841023207,0.3354702592,-0.3149739504,0.3115195036,-0.151019454,0.2302300185,0.1985109448,-0.1078666449,-0.316827327,0.3766020834,0.1159518808,0.4211955667,0.1875958592,-0.1109476015,0.2210971713,0.0574078672,0.0656334162,0.4336674511,0.0251350217,0.1517325491,-0.2819079459,-0.2880394757,0.0161611997,0.1849741042,0.1061124355,0.208267346,0.0661371425,0.4525848031,0.3530704081,-0.1095715538,-0.0313438997,0.2982585728,0.0087237153,0.1096552387,-0.0160198472,-0.2828069925,-0.3617673516,0.0373042151,0.0792266279,0.0482545346,-0.300780654,-0.2145833373,-0.4669224322,0.1430499554,-0.017001126,0.0824257433,0.080849275,-0.0262319613,0.0357583687,-0.0136144152,-0.2273827493,0.3854263127,-0.0392467529,0.0527988449,-0.3350836337,0.5943090916,0.0339025222,-0.1328176707,0.1310599893,0.2340805531,-0.0081726639,-0.1459142119,-0.2191980183,0.0569655821,0.0718324631,-0.1213673726,0.0456142388,0.2117891312,0.0988473222,-0.0642332807,-0.1028446779,0.1945102066,0.1439253986,0.0080830893,-0.0056003928,0.4861294031,-0.3273336887,0.412422508,0.0553850494,-0.0361753292,0.3704107404,0.0886540115,-0.1819515824,-0.1625491381,0.1414504051,0.1647379994,0.316264689,0.0625007451,-0.350074321,0.1661984771,0.4788588285,-0.1133032218,-0.0089869462,0.184374705,0.0940417945,0.0037376904,0.0713818297,0.054080721,0.2124791443,0.1124174744,-0.0267868415,0.0345435776,-0.1336924136,-0.1356707066,0.1111843213,0.212005645,0.1027932689,-0.1108708754,0.0721662417,0.0769232661,-0.3014950454,-0.1932430565,-0.2302246839,0.230689168,-0.5577501655,0.2543386221,-0.4477497041,-0.1062559038,-0.111947462,-0.1162648201,-0.1584255695,-0.369602412,-0.3457583785,0.1036254317,-0.2485160977,0.0584164634,-0.1589831114,0.3131408989,0.1099692211,-0.0202589501,-0.1929339319,0.0442026071,-0.0121294511,0.0135154622,0.4791285396,0.1433262378,0.2285078764,-0.1178401485,0.0981688723,-0.0482417457,-0.0546510071,-0.0053597251,0.0041049817,0.1908529103,0.3854108453,0.5826591849,-0.0693763196,-0.3032044172,0.2546191812,-0.125119701,-0.1720172316,0.1961242855,-0.043159347,-0.0881246179,-0.1345495731,-0.0127539076,-0.0794924721,-0.550662756,0.4509368539,-0.0443824418,0.0257021915,-0.1100879461,0.17030707,0.3367567956,0.0135358712,0.2070684284,-0.1416762024,-0.2518293262,0.0570570715,-0.0386795849,-0.3429827988,-0.1030455977,-0.0532400496,0.2181973606,-0.1152981967,-0.5638254881,0.0494283251,-0.1129122376,0.2226748765,0.0309357531,-0.1644446403,0.1530500054,-0.1596442759,0.0608240031,-0.1119984612,-0.0226334669,-0.0062943045,-0.1480904073,0.062577486,0.0717454553,0.6046342254,-0.0198420919,-0.0758411065,0.341966778,0.061121963,0.5457173586,0.0157763567,0.1968661398,-0.3442948759,-0.3486701548,-0.1388024241,-0.0335641913,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3960","title":"Load local dataset error","comments":"Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n```\r\n\r\n\r\nLet us know if that resolves the issue.","body":"When i used the datasets==1.11.0\uff0c it's all right. Util update the latest version, it get the error like this:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val']}\r\n>>> ds = load_dataset('nateraw\/image-folder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n[] https:\/\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\/ \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1671, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1521, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1031, in __init__\r\n    super().__init__(*args, **kwargs)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 255, in __init__\r\n    sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 584, in from_local_or_remote\r\n    if not isinstance(patterns_for_key, DataFilesList)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 546, in from_local_or_remote\r\n    data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 196, in resolve_patterns_locally_or_by_urls\r\n    for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 146, in _resolve_single_pattern_locally\r\n    raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '\/ssd\/datasets\/imagenet\/pytorch\/train' at \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\n```\r\nI need some help to solve the problem, thanks!","comment_length":40,"text":"Load local dataset error \n When i used the datasets==1.11.0\uff0c it's all right. Util update the latest version, it get the error like this:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val']}\r\n>>> ds = load_dataset('nateraw\/image-folder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n[] https:\/\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\/ \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1671, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1521, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1031, in __init__\r\n    super().__init__(*args, **kwargs)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 255, in __init__\r\n    sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 584, in from_local_or_remote\r\n    if not isinstance(patterns_for_key, DataFilesList)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 546, in from_local_or_remote\r\n    data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 196, in resolve_patterns_locally_or_by_urls\r\n    for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 146, in _resolve_single_pattern_locally\r\n    raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '\/ssd\/datasets\/imagenet\/pytorch\/train' at \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\n```\r\nI need some help to solve the problem, thanks! \n Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n```\r\n\r\n\r\nLet us know if that resolves the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3960","title":"Load local dataset error","comments":"> Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> >>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n> >>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n> ```\r\n> \r\n> Let us know if that resolves the issue.\r\n\r\nSorry, replied late.\r\nThanks a lot! It's worked for me. But it seems much slower than before, and now gets stuck.....\r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\nResolving data files: 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100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50001\/50001 [00:00<00:00, 89094.29it\/s]\r\nUsing custom data configuration default-baebca6347576b33\r\nDownloading and preparing dataset image_folder\/default to .\/image_folder\/default-baebca6347576b33\/0.0.0\/ee92df8e96c6907f3c851a987be3fd03d4b93b247e727b69a8e23ac94392a091...\r\nDownloading data files #0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 80073\/80073 [00:00<00:00, 82289.56obj\/s]\r\nDownloading data files #1: 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71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e                                 | 57032\/80073 [00:00<00:00, 79930.58obj\/s]\r\nDownloading data files #11:  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75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f                            | 60356\/80073 [00:00<00:00, 84833.35obj\/s]\r\nDownloading data files #13:  97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588    | 77368\/80073 [00:00<00:00, 84475.10obj\/s]\r\nDownloading data files #14:  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78%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b                         | 62724\/80072 [00:00<00:00, 78387.33obj\/s]\r\nDownloading data files #15:  99%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 78933\/80072 [00:01<00:00, 79353.63obj\/s]\r\n```","body":"When i used the datasets==1.11.0\uff0c it's all right. Util update the latest version, it get the error like this:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val']}\r\n>>> ds = load_dataset('nateraw\/image-folder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n[] https:\/\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\/ \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1671, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1521, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1031, in __init__\r\n    super().__init__(*args, **kwargs)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 255, in __init__\r\n    sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 584, in from_local_or_remote\r\n    if not isinstance(patterns_for_key, DataFilesList)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 546, in from_local_or_remote\r\n    data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 196, in resolve_patterns_locally_or_by_urls\r\n    for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 146, in _resolve_single_pattern_locally\r\n    raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '\/ssd\/datasets\/imagenet\/pytorch\/train' at \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\n```\r\nI need some help to solve the problem, thanks!","comment_length":378,"text":"Load local dataset error \n When i used the datasets==1.11.0\uff0c it's all right. Util update the latest version, it get the error like this:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val']}\r\n>>> ds = load_dataset('nateraw\/image-folder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n[] https:\/\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\/ \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1671, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1521, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1031, in __init__\r\n    super().__init__(*args, **kwargs)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 255, in __init__\r\n    sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 584, in from_local_or_remote\r\n    if not isinstance(patterns_for_key, DataFilesList)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 546, in from_local_or_remote\r\n    data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 196, in resolve_patterns_locally_or_by_urls\r\n    for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 146, in _resolve_single_pattern_locally\r\n    raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '\/ssd\/datasets\/imagenet\/pytorch\/train' at \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\n```\r\nI need some help to solve the problem, thanks! \n > Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> >>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n> >>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n> ```\r\n> \r\n> Let us know if that resolves the issue.\r\n\r\nSorry, replied late.\r\nThanks a lot! It's worked for me. But it seems much slower than before, and now gets stuck.....\r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\nResolving data files: 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100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50001\/50001 [00:00<00:00, 89094.29it\/s]\r\nUsing custom data configuration default-baebca6347576b33\r\nDownloading and preparing dataset image_folder\/default to .\/image_folder\/default-baebca6347576b33\/0.0.0\/ee92df8e96c6907f3c851a987be3fd03d4b93b247e727b69a8e23ac94392a091...\r\nDownloading data files #0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 80073\/80073 [00:00<00:00, 82289.56obj\/s]\r\nDownloading data files #1: 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75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b                             | 59995\/80073 [00:00<00:00, 84148.19obj\/s]\r\nDownloading data files #10:  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71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e                                 | 57032\/80073 [00:00<00:00, 79930.58obj\/s]\r\nDownloading data files #11:  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97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b   | 77803\/80073 [00:00<00:00, 85351.59obj\/s]\r\nDownloading data files #13:  75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f                            | 60356\/80073 [00:00<00:00, 84833.35obj\/s]\r\nDownloading data files #13:  97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588    | 77368\/80073 [00:00<00:00, 84475.10obj\/s]\r\nDownloading data files #14:  72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d                                | 57751\/80073 [00:00<00:00, 80727.33obj\/s]\r\nDownloading data files #14:  92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f        | 74022\/80073 [00:00<00:00, 78703.16obj\/s]\r\nDownloading data files #15:  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79353.63obj\/s]\r\n```","embeddings":[-0.3380225599,-0.114140451,-0.0211975258,0.2390871197,0.4253334999,-0.0225406867,0.1771003604,0.3576570451,0.0669671223,0.1895555854,-0.1314774305,0.1565939039,-0.0968019739,0.1201880723,-0.0979674831,-0.296218574,-0.0579133108,0.1971371919,-0.1178741753,-0.1511341929,-0.2569162548,0.1787683368,-0.1400129199,0.1847218424,-0.2956010401,0.0831508189,-0.1054894999,0.2142816186,-0.0965183303,-0.4482676089,0.4221608639,-0.0895105526,0.4329846501,0.4966243804,-0.0001023158,0.17225115,0.4993554652,-0.026760269,-0.1251298636,-0.4663240314,-0.1383082718,-0.1904052943,0.3075006902,-0.118566893,-0.2022633404,-0.4111450315,0.0497763902,-0.1912936866,0.4004446268,0.3993334472,0.3264052272,0.248587966,0.0299534686,-0.3245801926,-0.1635426283,0.1704921424,-0.1472791731,0.3505590856,0.256641537,-0.153898567,0.1403165758,0.3131349683,-0.055146616,0.1936384588,0.3182661235,-0.1083028242,-0.06152438,-0.1487922221,0.3644403219,0.0928643644,0.5512080193,-0.1445952952,-0.3069010079,0.0421254039,0.1144741029,-0.4257186055,0.2398095876,0.1540400982,-0.1403723359,0.2098692954,-0.2887387872,-0.0676197112,-0.2394699156,0.3054097295,-0.1165480837,0.1363257021,-0.1642613411,0.1148757786,0.2302117199,-0.0888859704,0.1679836065,0.1112330854,0.1810367256,0.2592576742,-0.3870074749,0.2529420853,-0.005841333,-0.074595429,-0.0328734703,-0.1497269273,-0.0505561009,-0.1193772107,-0.1094887704,0.2907733321,0.1241557002,0.2429410815,0.2186167091,0.3857802749,0.0354060158,0.0724390149,-0.1684983373,-0.0520526767,-0.3046111166,-0.3428444564,0.1724547297,0.0338618457,0.2183429152,-0.2477737665,-0.187506035,0.052968055,-0.0264608376,0.1310577989,0.0048450562,0.3974287212,-0.0720018819,0.1078411043,0.11357072,0.2105728835,-0.2100157589,0.0804998055,-0.3218249977,0.0064725401,-0.1483469158,-0.0503722616,0.1498716623,-0.3902206123,0.3461689949,-0.1646035314,-0.1027217954,0.0681311488,0.1059607565,-0.1619602293,-0.0051335753,0.5106235147,-0.1675314009,0.102222085,0.2465296388,0.0278735049,-0.0904969126,0.1305611879,-0.2611622214,-0.5131596923,-0.3502134681,0.2658132911,0.0356026106,-0.0744618401,-0.2554311156,0.0156026175,0.0914213285,0.2082774639,-0.2133510113,-0.2529437542,-0.3055506349,-0.0327625312,0.2802274525,0.3274256885,-0.3259852231,0.0016581784,-0.1004468948,-0.2188027948,0.0414027795,0.2085922211,-0.3016658127,0.2088793367,-0.104451701,0.1096088141,0.1563944966,-0.4672547579,-0.2618286908,0.2249240875,0.0080278758,-0.2027759999,-0.1378243417,-0.012152208,-0.058922112,0.1817512661,0.0981102139,0.3021401167,-0.0610940158,0.0481856167,-0.2115162909,-0.197669059,-0.1029630899,0.3457719386,0.0422711484,0.1390031278,0.2000285685,-0.2314841598,0.0671962127,-0.1334645003,-0.0854794532,0.2596311867,0.170796454,0.0681166425,0.0315433294,-0.046076566,-0.5464987159,0.2385165244,0.1520850807,0.2741982937,-0.2650343478,-0.0512846895,-0.4634175599,0.0776333138,-0.3745078444,-0.0832192674,0.1788612157,0.0678446814,0.066940628,0.1444493085,-0.19192186,0.2888279557,-0.3069467843,0.0401100479,-0.1483062506,0.185594067,-0.1667275578,-0.1352546215,0.1729490608,-0.103879638,0.0582589023,-0.2966474295,-0.2748553455,0.4825038314,0.0012197669,0.2237780094,0.002674374,0.05394933,0.1286351532,-0.2424883693,-0.0404466353,-0.0502867252,0.0433804132,0.0840272009,-0.0127479434,0.1394747198,0.0792034268,0.1023014262,0.0895428732,0.0503306985,0.3731085062,-0.0630995482,0.0525553897,-0.2071747482,0.1640108079,0.0410339087,0.2085024714,0.0219007451,-0.2507736087,-0.2435825318,0.2033375949,0.0275660157,-0.0100003937,0.2371495068,-0.0452149399,0.2026247531,0.2026037127,0.3036540747,0.3462533057,0.1128005683,-0.2387435287,0.2949957848,-0.1616530567,-0.0559896752,0.0445131473,-0.0829130337,0.3669479191,0.2743315697,0.0693969429,0.0740834028,-0.4737313986,-0.0784993023,-0.0236855894,0.4904199839,-0.1245588735,0.1172459498,-0.2654000521,-0.2774946094,-0.2419726402,-0.0375763476,-0.1377545148,-0.1387421489,-0.1552968025,0.066696398,-0.0139740342,0.2876707613,-0.2312740833,0.0697480962,0.1242867634,-0.2204566598,-0.1328823715,-0.2112715691,-0.3053153753,0.0875388831,0.3674649,0.0579072386,0.3674761951,-0.3410927951,0.0082324529,-0.0165083203,-0.3201085329,0.0844090581,-0.1093897298,0.1559102535,0.070500873,0.223752439,-0.1700755656,-0.2264109999,0.6276735067,-0.3136832416,-0.1328911334,0.0429497957,-0.0383523256,-0.2057213038,-0.1820735335,-0.5400707722,-0.3777373433,-0.2573294342,0.0813766196,0.2109511197,0.2434428781,0.4190809429,0.344676733,0.1439471543,-0.1057146415,-0.1207183972,-0.1763256937,-0.3949628174,0.1005408987,-0.2491053194,-0.4323080182,-0.0686041936,0.0622896366,0.5364884138,0.0186182894,-0.3495426774,-0.2764158249,-0.1668328047,0.1295659095,-0.121655412,0.0708092377,0.3206848502,-0.0917444155,-0.1171273738,-0.0888179541,-0.1010697708,0.1604620814,-0.0386743173,-0.0627672672,0.0926694497,0.3357470632,-0.0903460011,0.5322237015,-0.0121135712,-0.3363138735,0.4557913244,-0.1320487112,0.2421814948,-0.3542275727,-0.5656894445,0.07093001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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3956","title":"TypeError: __init__() missing 1 required positional argument: 'scheme'","comments":"Hi @amirj, thanks for reporting.\r\n\r\nAt first sight, your issue seems a version incompatibility between your Elasticsearch client and your Elasticsearch server.\r\n\r\nFeel free to have a look at Elasticsearch client docs: https:\/\/www.elastic.co\/guide\/en\/elasticsearch\/client\/python-api\/current\/overview.html#_compatibility\r\n> Language clients are forward compatible; meaning that clients support communicating with greater or equal minor versions of Elasticsearch. Elasticsearch language clients are only backwards compatible with default distributions and without guarantees made.","body":"## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n","comment_length":66,"text":"TypeError: __init__() missing 1 required positional argument: 'scheme' \n ## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n \n Hi @amirj, thanks for reporting.\r\n\r\nAt first sight, your issue seems a version incompatibility between your Elasticsearch client and your Elasticsearch server.\r\n\r\nFeel free to have a look at Elasticsearch client docs: https:\/\/www.elastic.co\/guide\/en\/elasticsearch\/client\/python-api\/current\/overview.html#_compatibility\r\n> Language clients are forward compatible; meaning that clients support communicating with greater or equal minor versions of Elasticsearch. Elasticsearch language clients are only backwards compatible with default distributions and without guarantees 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3956","title":"TypeError: __init__() missing 1 required positional argument: 'scheme'","comments":"@albertvillanova It doesn't seem a version incompatibility between the client and server, since the following code is working:\r\n\r\n```\r\nfrom elasticsearch import Elasticsearch\r\nes_client = Elasticsearch(\"http:\/\/localhost:9200\")\r\ndataset.add_elasticsearch_index(column=\"e1\", es_client=es_client, es_index_name=\"e1_index\")\r\n```","body":"## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n","comment_length":30,"text":"TypeError: __init__() missing 1 required positional argument: 'scheme' \n ## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n \n @albertvillanova It doesn't seem a version incompatibility between the client and server, since the following code is working:\r\n\r\n```\r\nfrom elasticsearch import Elasticsearch\r\nes_client = Elasticsearch(\"http:\/\/localhost:9200\")\r\ndataset.add_elasticsearch_index(column=\"e1\", es_client=es_client, es_index_name=\"e1_index\")\r\n```","embeddings":[-0.2016850263,-0.3632028699,-0.0670639575,-0.1154993474,-0.0156479795,0.2479981333,0.2341077775,0.2877420485,0.0638611168,0.0936895311,-0.0358448662,0.3917693198,-0.0215827525,-0.152334705,0.1281492412,-0.2309139818,0.121085614,0.087432608,0.1187869459,-0.0516799912,-0.2244480103,0.2019564062,-0.2399699688,-0.1863757074,0.0260687228,0.0416941904,-0.281126678,0.0370436236,-0.2842970788,-0.5741453171,0.5656750798,-0.3205862045,-0.0054031173,0.3283087611,-0.0001174942,0.2588958144,0.6181120276,-0.070870921,-0.1365975589,-0.3066091239,-0.2747555971,-0.1212207898,0.3674224615,-0.3745529652,-0.0447417274,0.0033120096,0.052850984,-0.3770344555,0.5422481894,0.474177748,0.1803839803,0.1715567857,0.4112608731,-0.5131973028,0.1866672337,0.0686088055,-0.1862741709,0.0758131891,-0.0755469725,0.3040466607,0.2885068357,0.1322864741,0.0819440186,-0.0990729779,0.0131786326,0.1307261139,0.1366858631,-0.1495376378,-0.2264224887,-0.194740817,0.2292087972,-0.1556185931,-0.4308762252,0.1329660714,0.1585823596,-0.2405288368,0.14714472,-0.0151985036,-0.0127603607,0.1177387908,0.3022183478,-0.1775995642,-0.3475022614,0.5092264414,0.0847174227,0.1819162667,-0.0808770731,0.0391932651,0.1065986156,-0.1730144322,0.0020133331,0.3552635908,0.0788009167,0.252709955,-0.2984156311,-0.0282034855,0.1122636944,-0.246192798,0.0884466097,-0.1922887713,-0.1135168672,-0.1603745669,0.5498575568,0.1669120193,-0.0451067202,0.1012818217,0.1200140268,0.1737442166,-0.049007941,-0.0631368831,0.2725943625,-0.0562142171,0.0272712186,-0.3945193589,0.1915056854,-0.0452345572,0.4555641115,-0.1275632977,-0.2430796176,-0.058577273,-0.31708166,-0.0847008899,0.0372951142,0.1921493709,0.232196629,-0.4963364899,0.4507412612,0.3782169223,-0.145913884,0.1989922971,-0.259350121,0.1355430633,-0.0163129698,-0.0615728125,0.1132288799,-0.58444947,0.4247122407,0.2325557917,-0.0374883786,0.0610244833,0.2157832682,0.0300101079,-0.2533140779,0.1263980567,-0.0347205289,-0.2597734928,0.1583043635,0.0415071212,-0.1471166164,-0.0897269845,-0.2763342261,-0.5810432434,-0.6209134459,0.1893595457,0.044520393,-0.0496433303,-0.1614013016,0.0976208001,0.2085873634,-0.0586843453,-0.1142825037,-0.1084868237,-0.1068545207,0.0529944897,0.2593818605,0.2752767503,-0.1447116137,-0.2105154395,-0.2179664671,0.3615582883,-0.1270294487,0.0429474637,-0.3336133957,0.330368042,-0.0799245983,0.0909015909,0.6981776357,-0.5474752784,-0.0536419302,-0.2091817707,-0.0626361072,-0.2886890769,0.0518553518,-0.0723175406,0.1295573115,0.2020718008,0.0836893022,0.0188737437,0.156858772,-0.1904112399,-0.301077038,-0.0207562335,-0.2133549154,0.2194907367,0.0688673705,0.0743892342,0.1130572036,-0.0949399248,-0.0289155561,0.0968277603,-0.0160929449,0.3530196846,0.447727114,0.0474989042,0.1451194733,-0.2445705682,-0.186145246,0.0887330323,0.1305076778,0.4066120088,-0.2754704654,-0.0054867994,-0.4557587206,-0.0125355404,-0.1999202669,-0.0404614061,0.047690399,-0.1804461032,0.0655059591,0.0815154389,-0.3663472235,0.0903836116,-0.2240103036,0.087416254,-0.4838053882,0.3780785501,-0.3399507403,-0.2848972082,-0.1005496234,-0.127281487,0.162595734,-0.2289761454,-0.252905786,0.3170164526,0.1585814059,-0.1993494779,-0.0094233891,-0.2196142226,0.0687942579,-0.3717128038,-0.1601184607,0.1146563739,0.0060175043,0.0075713098,-0.1409527957,0.0491281226,-0.0276780799,0.1236103997,-0.1865464598,0.0244979896,0.1674749106,0.0128466589,-0.2148431093,-0.1568399817,0.0762598366,0.0110410759,0.0965336114,-0.2560921013,-0.0806219056,-0.2556485534,0.1009649262,0.1908090562,0.0109869624,0.3315634727,0.0306276679,-0.0241756272,0.0200197026,-0.2649319768,0.5081581473,0.1863701046,-0.2549333572,0.1788088381,-0.1951348782,0.0434972234,0.3546721935,0.077606909,0.2782560587,-0.0410640687,0.2114385813,0.2027515471,-0.3051767051,-0.4304826856,-0.063165769,0.2208944857,-0.3955515921,0.1845768094,0.0268659834,0.0299323853,-0.0677968264,0.0289350152,0.140772447,-0.2932099402,-0.0042727776,0.2873460054,-0.0005063467,0.2911942005,-0.2152974308,0.1278512627,0.4924534261,-0.2807107568,-0.1180763692,0.0015201593,-0.1466072798,0.0087545374,0.0437357835,0.2254190445,0.2538788915,-0.2099373043,0.0073483945,-0.1291221082,-0.5952881575,0.075783588,-0.0007682078,0.4424199462,0.4109157324,0.1672627479,-0.5368112326,-0.416403681,0.3792355359,-0.047238417,-0.2670032084,0.0130983731,-0.2432999164,-0.1095611677,-0.0610098802,-0.6315124035,-0.1218555793,-0.4427924454,0.0928208604,0.0293398183,0.0403706655,0.4282228649,0.2001364976,0.1551534981,-0.0561065227,0.0692987591,-0.1925010979,-0.0702749565,0.3183072209,-0.365151912,-0.24668625,-0.1141716465,-0.1642150879,0.2686992586,0.260550648,-0.0881302282,-0.2447285503,-0.012627136,0.1801123023,-0.3956003189,0.1502328813,0.5313319564,0.0224416535,0.0291626509,-0.0647805333,-0.2079590112,0.4208350778,0.3675064445,0.1470497251,0.02519417,0.5940636396,-0.0903488398,0.6092325449,0.1436546445,-0.2190537751,0.4205082953,-0.2054324895,0.1942408234,-0.3542343676,-0.4843166769,-0.25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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3956","title":"TypeError: __init__() missing 1 required positional argument: 'scheme'","comments":"Hi @amirj, \r\n\r\nI really think it is a version incompatibility issue between your Elasticsearch client and server:\r\n- Your Elasticsearch server NodeConfig expects a positional argument named 'scheme'\r\n- Whereas your Elasticsearch client passes only keyword arguments: `NodeConfig(**options)`\r\n\r\nMoreover:\r\n- Looking at your stack trace, I deduce you are using Elasticsearch client **\"8\"** major version:\r\n  - the Elasticsearch file \"elasticsearch\/_sync\/client\/utils.py\" was created in version \"8.0.0a1\": https:\/\/github.com\/elastic\/elasticsearch-py\/commit\/21fa13b0f03b7b27ace9e19a1f763d40bd2e2ba4\r\n  - you can check your Elasticsearch client version by running this Python code:\r\n    ```python\r\n    import elasticsearch\r\n    print(elasticsearch.__version__)\r\n    ```\r\n\r\n- However, in the *Environment info*, you informed that the major version of your Eleasticsearch cluster server is **\"7\"** (\"7.10.2-SNAPSHOT\")\r\n\r\nCould you please align the Elasticsearch client\/server major versions (as pointed out in Elasticsearch docs) and check if the problem persists?","body":"## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n","comment_length":125,"text":"TypeError: __init__() missing 1 required positional argument: 'scheme' \n ## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n \n Hi @amirj, \r\n\r\nI really think it is a version incompatibility issue between your Elasticsearch client and server:\r\n- Your Elasticsearch server NodeConfig expects a positional argument named 'scheme'\r\n- Whereas your Elasticsearch client passes only keyword arguments: `NodeConfig(**options)`\r\n\r\nMoreover:\r\n- Looking at your stack trace, I deduce you are using Elasticsearch client **\"8\"** major version:\r\n  - the Elasticsearch file \"elasticsearch\/_sync\/client\/utils.py\" was created in version \"8.0.0a1\": https:\/\/github.com\/elastic\/elasticsearch-py\/commit\/21fa13b0f03b7b27ace9e19a1f763d40bd2e2ba4\r\n  - you can check your Elasticsearch client version by running this Python code:\r\n    ```python\r\n    import elasticsearch\r\n    print(elasticsearch.__version__)\r\n    ```\r\n\r\n- However, in the *Environment info*, you informed that the major version of your Eleasticsearch cluster server is **\"7\"** (\"7.10.2-SNAPSHOT\")\r\n\r\nCould you please align the Elasticsearch client\/server major versions (as pointed out in Elasticsearch docs) and check if the problem persists?","embeddings":[-0.2016850263,-0.3632028699,-0.0670639575,-0.1154993474,-0.0156479795,0.2479981333,0.2341077775,0.2877420485,0.0638611168,0.0936895311,-0.0358448662,0.3917693198,-0.0215827525,-0.152334705,0.1281492412,-0.2309139818,0.121085614,0.087432608,0.1187869459,-0.0516799912,-0.2244480103,0.2019564062,-0.2399699688,-0.1863757074,0.0260687228,0.0416941904,-0.281126678,0.0370436236,-0.2842970788,-0.5741453171,0.5656750798,-0.3205862045,-0.0054031173,0.3283087611,-0.0001174942,0.2588958144,0.6181120276,-0.070870921,-0.1365975589,-0.3066091239,-0.2747555971,-0.1212207898,0.3674224615,-0.3745529652,-0.0447417274,0.0033120096,0.052850984,-0.3770344555,0.5422481894,0.474177748,0.1803839803,0.1715567857,0.4112608731,-0.5131973028,0.1866672337,0.0686088055,-0.1862741709,0.0758131891,-0.0755469725,0.3040466607,0.2885068357,0.1322864741,0.0819440186,-0.0990729779,0.0131786326,0.1307261139,0.1366858631,-0.1495376378,-0.2264224887,-0.194740817,0.2292087972,-0.1556185931,-0.4308762252,0.1329660714,0.1585823596,-0.2405288368,0.14714472,-0.0151985036,-0.0127603607,0.1177387908,0.3022183478,-0.1775995642,-0.3475022614,0.5092264414,0.0847174227,0.1819162667,-0.0808770731,0.0391932651,0.1065986156,-0.1730144322,0.0020133331,0.3552635908,0.0788009167,0.252709955,-0.2984156311,-0.0282034855,0.1122636944,-0.246192798,0.0884466097,-0.1922887713,-0.1135168672,-0.1603745669,0.5498575568,0.1669120193,-0.0451067202,0.1012818217,0.1200140268,0.1737442166,-0.049007941,-0.0631368831,0.2725943625,-0.0562142171,0.0272712186,-0.3945193589,0.1915056854,-0.0452345572,0.4555641115,-0.1275632977,-0.2430796176,-0.058577273,-0.31708166,-0.0847008899,0.0372951142,0.1921493709,0.232196629,-0.4963364899,0.4507412612,0.3782169223,-0.145913884,0.1989922971,-0.259350121,0.1355430633,-0.0163129698,-0.0615728125,0.1132288799,-0.58444947,0.4247122407,0.2325557917,-0.0374883786,0.0610244833,0.2157832682,0.0300101079,-0.2533140779,0.1263980567,-0.0347205289,-0.2597734928,0.1583043635,0.0415071212,-0.1471166164,-0.0897269845,-0.2763342261,-0.5810432434,-0.6209134459,0.1893595457,0.044520393,-0.0496433303,-0.1614013016,0.0976208001,0.2085873634,-0.0586843453,-0.1142825037,-0.1084868237,-0.1068545207,0.0529944897,0.2593818605,0.2752767503,-0.1447116137,-0.2105154395,-0.2179664671,0.3615582883,-0.1270294487,0.0429474637,-0.3336133957,0.330368042,-0.0799245983,0.0909015909,0.6981776357,-0.5474752784,-0.0536419302,-0.2091817707,-0.0626361072,-0.2886890769,0.0518553518,-0.0723175406,0.1295573115,0.2020718008,0.0836893022,0.0188737437,0.156858772,-0.1904112399,-0.301077038,-0.0207562335,-0.2133549154,0.2194907367,0.0688673705,0.0743892342,0.1130572036,-0.0949399248,-0.0289155561,0.0968277603,-0.0160929449,0.3530196846,0.447727114,0.0474989042,0.1451194733,-0.2445705682,-0.186145246,0.0887330323,0.1305076778,0.4066120088,-0.2754704654,-0.0054867994,-0.4557587206,-0.0125355404,-0.1999202669,-0.0404614061,0.047690399,-0.1804461032,0.0655059591,0.0815154389,-0.3663472235,0.0903836116,-0.2240103036,0.087416254,-0.4838053882,0.3780785501,-0.3399507403,-0.2848972082,-0.1005496234,-0.127281487,0.162595734,-0.2289761454,-0.252905786,0.3170164526,0.1585814059,-0.1993494779,-0.0094233891,-0.2196142226,0.0687942579,-0.3717128038,-0.1601184607,0.1146563739,0.0060175043,0.0075713098,-0.1409527957,0.0491281226,-0.0276780799,0.1236103997,-0.1865464598,0.0244979896,0.1674749106,0.0128466589,-0.2148431093,-0.1568399817,0.0762598366,0.0110410759,0.0965336114,-0.2560921013,-0.0806219056,-0.2556485534,0.1009649262,0.1908090562,0.0109869624,0.3315634727,0.0306276679,-0.0241756272,0.0200197026,-0.2649319768,0.5081581473,0.1863701046,-0.2549333572,0.1788088381,-0.1951348782,0.0434972234,0.3546721935,0.077606909,0.2782560587,-0.0410640687,0.2114385813,0.2027515471,-0.3051767051,-0.4304826856,-0.063165769,0.2208944857,-0.3955515921,0.1845768094,0.0268659834,0.0299323853,-0.0677968264,0.0289350152,0.140772447,-0.2932099402,-0.0042727776,0.2873460054,-0.0005063467,0.2911942005,-0.2152974308,0.1278512627,0.4924534261,-0.2807107568,-0.1180763692,0.0015201593,-0.1466072798,0.0087545374,0.0437357835,0.2254190445,0.2538788915,-0.2099373043,0.0073483945,-0.1291221082,-0.5952881575,0.075783588,-0.0007682078,0.4424199462,0.4109157324,0.1672627479,-0.5368112326,-0.416403681,0.3792355359,-0.047238417,-0.2670032084,0.0130983731,-0.2432999164,-0.1095611677,-0.0610098802,-0.6315124035,-0.1218555793,-0.4427924454,0.0928208604,0.0293398183,0.0403706655,0.4282228649,0.2001364976,0.1551534981,-0.0561065227,0.0692987591,-0.1925010979,-0.0702749565,0.3183072209,-0.365151912,-0.24668625,-0.1141716465,-0.1642150879,0.2686992586,0.260550648,-0.0881302282,-0.2447285503,-0.012627136,0.1801123023,-0.3956003189,0.1502328813,0.5313319564,0.0224416535,0.0291626509,-0.0647805333,-0.2079590112,0.4208350778,0.3675064445,0.1470497251,0.02519417,0.5940636396,-0.0903488398,0.6092325449,0.1436546445,-0.2190537751,0.4205082953,-0.2054324895,0.1942408234,-0.3542343676,-0.4843166769,-0.2542829216,0.2180972546,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3954","title":"The dataset preview is not available for tdklab\/Hebrew_Squad_v1.1 dataset","comments":"Hi @MatanBenChorin, thanks for reporting.\r\n\r\nPlease, take into account that the preview may take some time until it properly renders (we are working to reduce this time).\r\n\r\nMaybe @severo can give more details on this.","body":"## Dataset viewer issue for 'tdklab\/Hebrew_Squad_v1.1'\r\n\r\n**Link:**  https:\/\/huggingface.co\/api\/datasets\/tdklab\/Hebrew_Squad_v1.1?full=true\r\n\r\nThe dataset preview is not available for this dataset.\r\n\r\nAm I the one who added this dataset ? Yes","comment_length":35,"text":"The dataset preview is not available for tdklab\/Hebrew_Squad_v1.1 dataset \n ## Dataset viewer issue for 'tdklab\/Hebrew_Squad_v1.1'\r\n\r\n**Link:**  https:\/\/huggingface.co\/api\/datasets\/tdklab\/Hebrew_Squad_v1.1?full=true\r\n\r\nThe dataset preview is not available for this dataset.\r\n\r\nAm I the one who added this dataset ? Yes \n Hi @MatanBenChorin, thanks for reporting.\r\n\r\nPlease, take into account that the preview may take some time until it properly renders (we are working to reduce this time).\r\n\r\nMaybe @severo can give more details on this.","embeddings":[-0.311417073,-0.4014883935,-0.0658439845,0.2669583857,0.0838584378,0.1116312519,0.282913208,0.2667755187,-0.0862871781,0.3120709956,-0.1596049517,-0.0146393748,0.0534692183,0.174065277,0.1613078713,-0.1715472639,0.0359722525,0.1212253198,-0.0750993937,-0.0402280726,-0.2085525393,0.2721411288,-0.1961265504,0.0811796337,-0.2160776258,-0.0268842969,-0.0728323907,-0.0551242158,-0.2894195914,-0.2867647409,0.2907398343,-0.0514758155,0.2851788998,0.139846921,-0.0001113655,0.0937947407,0.261030376,-0.0603734255,-0.2673738599,-0.1547252089,0.0836879238,-0.2515999377,0.0806364045,-0.0030499001,-0.4502523541,-0.2254164815,0.1948548704,-0.2031599879,0.3874706328,0.1095742285,0.2613073587,0.5152115226,0.0199661404,-0.3259842992,0.1277089566,0.1748391092,-0.4528559446,-0.0725076571,0.1961443126,0.0952185467,0.1359424442,0.3663333952,0.3392030299,0.0849600658,0.0812118202,0.2018366158,-0.0015254866,-0.4241366088,0.2338888794,0.0842058137,0.7123064399,-0.0491914712,-0.201416567,0.1472202986,0.2774580419,0.0545285642,0.1342963129,0.0669682771,-0.1154622734,0.1445250064,-0.2964590192,-0.1811362356,0.0075478372,0.2059744447,0.0780535862,-0.0244395789,-0.277379334,0.0922090709,-0.1193279848,0.1183669791,0.1304579377,-0.025730269,-0.1497566849,0.342898488,-0.2063551843,-0.1538840383,0.0097941905,0.0488988645,0.0551649593,0.1905864179,0.1900181174,0.0714691952,-0.1487370729,-0.0124958334,0.1132702529,0.1042587236,0.3729870617,0.2126194239,0.3135798275,0.2581315637,0.4647903144,-0.014693358,-0.0998043418,-0.0669217035,-0.2137878239,-0.1548792422,0.2970794439,-0.0986104831,-0.254658401,-0.0499985032,-0.0585315414,0.0071913265,-0.0907531828,0.4044861495,-0.1182073727,-0.4784182012,0.1883211732,0.1282232106,-0.1205221787,-0.4043055177,-0.3268052042,-0.03421022,0.0190725382,0.2060566992,0.1323945522,-0.2404393703,0.209087342,0.1146891788,0.2104792893,0.1642152071,-0.2199823111,0.1174659431,-0.0739343166,0.0999159664,0.1455454528,0.2429649383,0.0810941011,0.034124963,0.0242817551,0.260535121,-0.1091808677,-0.139604643,-0.3876098096,0.1073495597,-0.393030256,-0.0526122823,-0.4856953323,0.4448401332,-0.16121687,-0.1029474884,0.0858733729,0.1520816237,-0.3201351166,-0.0759900585,0.3708455563,0.52246207,-0.5629886985,-0.3431332707,0.0393035263,-0.3624466956,0.0630797148,0.3338085115,-0.1874682605,-0.1235264689,-0.3934738636,0.1184408888,0.2422900796,-0.402025938,-0.6250221729,0.132660985,-0.1800153702,0.0502875224,0.0214503445,0.0848658979,0.4433107078,0.0520148389,-0.3218519688,0.1607806683,0.1733347774,-0.1198523566,-0.2461257428,-0.1002230942,0.2543728054,0.1884293556,-0.0266235992,0.1197136939,0.0648285374,-0.0185182486,0.4133199751,0.2739727199,0.2073928267,0.1438471526,0.2722547352,0.0027252485,0.0078779021,-0.1696540862,-0.498452276,0.0623763204,0.0823321044,0.2828386128,-0.0431731939,-0.2127306014,-0.4552213252,0.025819188,-0.4027038217,-0.3439928293,0.1304973513,0.1178735867,0.0500583351,0.1583375484,0.0825508535,0.0865713432,-0.1460717469,0.0256743915,-0.162535727,0.3810801804,-0.027523784,0.0165362917,0.1925337762,0.0511125624,0.0506219231,-0.2914513946,-0.0400964208,0.2824089229,-0.1210748181,-0.0040253345,0.3039687276,-0.2232213318,0.248490572,-0.3490482271,0.1128165424,-0.081154421,-0.0760091692,0.0095010288,-0.0848271251,0.3645386994,0.2695686817,0.002818512,-0.2322318405,0.0730642602,0.2107012421,0.2549850047,-0.0426507555,-0.1491951197,0.0997676998,0.0987857282,0.3478735387,-0.2881268561,-0.1279271096,0.2332928777,0.3511849344,-0.2120281607,-0.0606025457,0.0380412117,-0.1264584213,-0.001607911,0.1337207705,-0.1474856436,0.1726687998,0.1064743474,0.0235909931,-0.0679185092,0.141197592,-0.0272674095,0.0557224676,-0.1213868484,0.0044170008,0.0701378733,0.2100199163,-0.1460223943,-0.6160922647,0.2807808518,0.1974073201,0.2429198325,-0.1518463641,-0.1309158653,-0.1582820266,-0.1487354636,0.0251721796,-0.2747641504,-0.3882994354,-0.1599146575,0.2342507839,-0.0912193581,0.3873554468,0.4110916555,0.2234779,0.3818232119,-0.0442448817,0.3038652241,-0.1901388317,0.1711225212,-0.1916797608,0.1673666686,0.1117504314,-0.1602656394,0.2302402109,-0.1125580072,0.0615534969,-0.470975101,-0.2829716206,0.1865496337,-0.1716091335,0.4791972935,0.0085646911,0.3409079015,-0.2217843533,0.0791374892,0.2285401672,-0.1378616095,-0.5370668769,-0.1701667458,-0.2172562927,-0.0694938973,-0.0172634032,-0.3079886436,-0.4341338277,-0.159680739,0.2873241901,0.0122511527,-0.1503362954,-0.254952997,0.0039484878,0.089370504,-0.0356414802,-0.1369201243,-0.427311331,-0.3670595586,0.3611653447,-0.4547727704,-0.5265399814,0.231230244,0.199977681,0.212443307,-0.3956668079,-0.6403865814,-0.215640828,-0.0392025784,0.1284010559,-0.0259771701,-0.1352202594,0.3031501472,-0.2349164188,-0.0528987348,-0.1281711012,-0.3473533392,-0.2101452798,-0.2186274976,0.4514725506,-0.2246676236,0.2805478871,0.0188514944,0.7145478725,0.4017541707,0.1220459417,0.1135827303,-0.3372505903,0.3376704454,-0.1450767815,-0.3311553597,0.0114813996,-0.1413028985,0.2859897017,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3952","title":"Checksum error for glue sst2, stsb, rte etc datasets","comments":"Hi, @ravindra-ut.\r\n\r\nI'm sorry but I can't reproduce your problem:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"glue\", \"sst2\")\r\nDownloading builder script: 28.8kB [00:00, 11.6MB\/s]                                                                                                                                                       \r\nDownloading metadata: 28.7kB [00:00, 12.9MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset glue\/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown size, total: 11.90 MiB) to ...\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7.44M\/7.44M [00:01<00:00, 5.82MB\/s]\r\nDataset glue downloaded and prepared to ...\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.  \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 895.96it\/s]\r\n\r\nIn [3]: ds\r\nOut[2]: \r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 67349\r\n    })\r\n    validation: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 872\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 1821\r\n    })\r\n})\r\n``` \r\n\r\nMoreover, I see in your traceback that your error was for an URL at https:\/\/firebasestorage.googleapis.com\r\nHowever, the URLs were updated on Sep 16, 2020 (`datasets` version 1.0.2) to https:\/\/dl.fbaipublicfiles.com: https:\/\/github.com\/huggingface\/datasets\/commit\/2f03041a21c03abaececb911760c3fe4f420c229\r\n\r\nCould you please try to update `datasets`\r\n```shell\r\npip install -U datasets\r\n```\r\nand then force redownload\r\n```python\r\nds = load_dataset(\"glue\", \"sst2\", download_mode=\"force_redownload\")\r\n```\r\nto update the cache?\r\n\r\nPlease, feel free to reopen this issue if the problem persists.","body":"## Describe the bug\r\nChecksum error for glue sst2, stsb, rte etc datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> nlp.load_dataset('glue', 'sst2')\r\nDownloading and preparing dataset glue\/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown sizetotal: 11.90 MiB) to \r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 73.0\/73.0 [00:00<00:00, 18.2kB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 521, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']\r\n```\r\n\r\n## Expected results\r\ndataset load should succeed without checksum error.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 521, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']\r\n```\r\n## Environment info\r\n- `datasets` version: '1.18.3'\r\n- Platform: Mac OS\r\n- Python version: Python 3.8.9\r\n- PyArrow version: '7.0.0'\r\n","comment_length":179,"text":"Checksum error for glue sst2, stsb, rte etc datasets \n ## Describe the bug\r\nChecksum error for glue sst2, stsb, rte etc datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> nlp.load_dataset('glue', 'sst2')\r\nDownloading and preparing dataset glue\/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown sizetotal: 11.90 MiB) to \r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 73.0\/73.0 [00:00<00:00, 18.2kB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 521, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']\r\n```\r\n\r\n## Expected results\r\ndataset load should succeed without checksum error.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 521, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']\r\n```\r\n## Environment info\r\n- `datasets` version: '1.18.3'\r\n- Platform: Mac OS\r\n- Python version: Python 3.8.9\r\n- PyArrow version: '7.0.0'\r\n \n Hi, @ravindra-ut.\r\n\r\nI'm sorry but I can't reproduce your problem:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"glue\", \"sst2\")\r\nDownloading builder script: 28.8kB [00:00, 11.6MB\/s]                                                                                                                                                       \r\nDownloading metadata: 28.7kB [00:00, 12.9MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset glue\/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown size, total: 11.90 MiB) to ...\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7.44M\/7.44M [00:01<00:00, 5.82MB\/s]\r\nDataset glue downloaded and prepared to ...\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.  \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 895.96it\/s]\r\n\r\nIn [3]: ds\r\nOut[2]: \r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 67349\r\n    })\r\n    validation: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 872\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 1821\r\n    })\r\n})\r\n``` \r\n\r\nMoreover, I see in your traceback that your error was for an URL at https:\/\/firebasestorage.googleapis.com\r\nHowever, the URLs were updated on Sep 16, 2020 (`datasets` version 1.0.2) to https:\/\/dl.fbaipublicfiles.com: https:\/\/github.com\/huggingface\/datasets\/commit\/2f03041a21c03abaececb911760c3fe4f420c229\r\n\r\nCould you please try to update `datasets`\r\n```shell\r\npip install -U datasets\r\n```\r\nand then force redownload\r\n```python\r\nds = load_dataset(\"glue\", \"sst2\", download_mode=\"force_redownload\")\r\n```\r\nto update the cache?\r\n\r\nPlease, feel free to reopen this issue if the problem persists.","embeddings":[-0.0671885833,0.1787162274,-0.0147208488,0.2622032464,0.2177488953,-0.1340350062,0.1646662652,0.3809952736,0.2130872011,-0.0059031658,-0.0861570239,0.0618881807,0.0476297326,0.214188233,0.0418719165,-0.0405456088,-0.0152425664,-0.052437339,-0.2798446715,0.1639460027,-0.1584367305,0.3463344872,-0.1569360942,-0.180052489,-0.1639233679,0.0982371047,0.0486511588,0.1676204354,-0.1455791444,-0.3067032397,0.0553164855,0.0415737703,0.0050722947,0.2880761921,-0.0001127216,0.0792040825,0.528539598,-0.1051177084,-0.2609041035,0.1533358246,-0.0594281852,-0.293854475,-0.0908590928,-0.2731427252,0.0386815742,0.2741303146,-0.0576671548,-0.1645520777,-0.0207887888,0.1585755348,0.2541366518,0.5122368336,0.2858022749,0.2648344636,0.4789220691,-0.4634759426,-0.0669462979,0.4398517609,0.1693459004,-0.1131617576,-0.0695996284,0.2199504524,-0.2075427473,0.0165758003,-0.0173020791,-0.0878705978,-0.0883627981,-0.1281586289,-0.0380858481,0.2778958678,0.184963882,-0.4158975482,-0.3692347705,-0.1844172627,-0.0087190233,0.2744037509,0.399384588,0.4050834179,-0.0587355942,-0.0569903404,-0.2348705828,0.2480697632,0.178845048,0.007956611,0.186606735,0.1626896411,0.0539450385,0.0292247627,0.1087220684,-0.1844329089,-0.2954387069,-0.0906714275,-0.2178560644,0.0153780691,-0.5871678591,-0.1529463083,0.0650218129,0.3687289655,0.127217114,0.3131500185,0.0500432216,0.0376670063,0.0620289445,0.0887447149,-0.0481866598,0.1607342809,0.0232840106,0.0993210673,0.3288429976,0.1968704462,-0.0617859177,0.0808813944,0.0559894666,-0.2374122143,0.080748409,0.1429014355,0.1524630636,-0.2870582044,-0.4671034217,0.2998577058,0.0216797721,0.0492876358,0.0984918624,0.196743235,-0.3022461534,0.005960532,0.1782279313,0.0296934955,-0.1722761244,-0.1047385931,-0.1682982147,0.1819144487,-0.1995644569,-0.0942768902,0.2441402674,-0.3175604939,0.3001098335,-0.0926740915,-0.0644674674,-0.0700125769,0.0059626983,-0.2478692234,-0.0336704105,0.2952056229,-0.0610093363,0.0594499409,0.0245097801,-0.1238793656,-0.0652926192,-0.0366829634,-0.2259282619,-0.1059480235,-0.4280591607,0.2123850733,-0.5210893154,0.0187124908,-0.0374256894,-0.3463670313,0.2859885991,-0.1537163258,0.1475514024,-0.2093326598,-0.3316103518,-0.3552064896,0.1609103978,0.2788506448,-0.1164488271,-0.0097814295,-0.0360126793,-0.3200135529,0.1861368716,0.2783738375,0.2153256536,0.2464402467,-0.3957540691,0.1882582158,0.2193171084,-0.2674520612,-0.5852255225,0.2623684108,0.0679152906,0.0631836355,0.0459108464,0.0869377181,0.0410211273,-0.2004756331,0.1557480395,0.3058222532,0.0642993599,0.3108571172,-0.5511221886,-0.2260424197,0.2275689244,0.1600197405,-0.1308973134,-0.1657432616,0.2566593587,0.1216816083,0.0984171182,0.128933832,0.0304951351,0.1782434583,0.2857558429,-0.2077768147,0.0670333654,-0.4600886405,-0.4718084037,0.27493155,-0.2038859874,0.2327622026,-0.1857413501,-0.0186843351,-0.3012114763,-0.1335271448,-0.0225120168,-0.0725781992,0.2280364633,0.2757361233,0.2199954689,0.1239229515,-0.0314606689,0.3188430369,-0.203610763,0.0596893318,-0.1580882221,0.5122644901,-0.062679477,-0.3088285625,0.1730995476,0.3362633288,0.0206828974,-0.0567381196,-0.2537503242,0.6166082621,0.051657252,0.0743113607,-0.0870448351,0.1346780807,0.0203005522,-0.2407770902,-0.0202232413,0.2986131907,0.1002102122,-0.0104841478,0.1652024835,0.2439779788,-0.2789328098,-0.0458980761,0.0711234361,0.013481521,0.1265432835,-0.3125060499,-0.1915133297,-0.2765546143,0.2906630635,0.1425997764,0.159190014,0.1545393765,0.0036098366,0.0135526676,0.5171052814,0.0430108383,0.1855411828,-0.0100924438,0.0480356477,-0.0127975084,0.0618535317,0.4849428833,0.4015761316,0.0660557002,0.1122709215,0.1525212526,-0.1818078011,-0.026630573,0.1443311572,-0.0386357531,-0.0555515997,0.4007842839,0.1236711368,0.1513870358,-0.2084614933,0.0684618354,0.0351409689,0.2023368776,-0.4411248863,-0.2767032683,-0.3105099797,-0.4415131807,-0.3549081981,0.1376703829,-0.3330470324,-0.3065845668,-0.0426150002,0.1029875502,-0.2490516752,0.0909171104,-0.4090116918,-0.0086511457,0.2510765493,-0.2255000174,0.2640661597,0.0976630375,-0.1357088685,0.0797717422,0.5637330413,0.3094772995,0.4695547521,-0.187748909,-0.0889442787,-0.3682776392,-0.2777637541,0.168939963,-0.1807103157,0.312771976,0.4396671653,0.0727538839,0.0740085915,-0.3028735518,0.2637736201,-0.1146069691,-0.3880486786,0.3122301102,-0.0413790047,-0.128575027,-0.2759366632,-0.0168489721,-0.0973513201,-0.4028921127,-0.0106558027,-0.1630793959,0.2472698241,0.0730063841,-0.1938678622,0.2199881822,-0.1373536736,0.343878597,-0.111646831,-0.4498354495,0.3288371265,0.0327135734,-0.4012394249,-0.0856254697,-0.0997942388,-0.0702849105,0.0490829945,-0.5313177705,-0.3595405221,-0.4158709049,0.0363902673,0.1391940862,0.0933345184,0.0729349181,-0.1139890105,-0.026902711,-0.322006911,-0.2428338975,0.1113379523,0.1950957179,0.3938701451,0.0438041277,0.2344286144,0.1719080806,0.0053074039,0.5169697404,-0.1140007153,0.4752208591,0.1185990199,0.3399907649,-0.0184927322,-0.2351238281,0.2634159327,-0.015512235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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3942","title":"reddit_tifu dataset: Checksums didn't match for dataset source files","comments":"Hi @XingxingZhang, \r\n\r\nWe have already fixed this. You should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nDuplicate of:\r\n- #3773","body":"## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n","comment_length":35,"text":"reddit_tifu dataset: Checksums didn't match for dataset source files \n ## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n \n Hi @XingxingZhang, \r\n\r\nWe have already fixed this. You should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nDuplicate of:\r\n- #3773","embeddings":[-0.3985462785,0.1268261671,-0.0900786668,0.1531641781,0.4119481146,-0.0722518563,0.1358965337,0.4609464705,-0.0700399131,-0.0869089812,-0.1713988632,0.4725554883,0.2960695922,0.2216591388,0.0006655382,0.148134321,0.113251023,0.1238125488,-0.2147902995,-0.1039822027,-0.051839903,0.0667048544,-0.02181888,-0.0644038394,-0.1209833845,0.2648584843,0.109420903,0.2050814331,-0.13820903,-0.1935888827,0.2310243845,0.0947568417,-0.1716674119,0.6253606677,-0.0001061642,0.1178650707,0.2978493869,-0.0738185272,-0.1075453758,-0.2903145254,-0.2285021096,-0.0266567487,-0.228690967,-0.0157380681,-0.0582639351,-0.0507716052,-0.1205493584,-0.1184360906,0.2054294199,0.3480323255,0.2619142234,0.3078448474,0.213805899,0.0326527618,0.3904974759,0.116725862,0.0546525158,0.3477866054,-0.0087808575,-0.1265859306,0.0680849478,0.0957550928,-0.1524364203,0.0942632109,-0.0499231033,-0.087837249,0.1393562704,-0.1794324666,-0.065368332,0.4148212373,0.3285357654,-0.1959819645,-0.3916213512,0.1505562812,0.0181435328,-0.1497636139,0.2844494283,0.2377644032,-0.1654654294,0.2523102462,-0.1200846806,0.1210448146,0.0244656336,0.1731975079,0.0333451554,-0.0579655431,0.0493322834,-0.0778755397,-0.1680637151,-0.12065579,0.1916184425,-0.011359266,-0.1268766224,-0.0633240789,-0.3275378346,0.0152147375,-0.0163262896,-0.0055162334,0.4443407059,0.169461444,0.1155322194,0.2310025096,-0.1730128974,0.2485596836,-0.029413566,0.061254289,-0.089982979,0.266808331,0.2934516966,0.0272206999,-0.1966457963,0.1666150391,-0.0261625107,-0.0619116575,0.3498398364,0.0617948771,0.2732435465,-0.3882815242,-0.3526941836,0.1378538758,0.1614838243,-0.2683421075,0.0340965278,0.321844399,-0.1196145564,0.2056097388,-0.2245728672,0.3239060938,-0.1389726996,-0.0496331155,-0.2525369227,-0.0312323906,-0.1228638664,-0.2130515575,0.1580700427,-0.3874782622,0.5200121999,0.1329913139,0.2709819973,-0.1481900513,0.0089211101,-0.0431148931,0.0243353993,0.3802427351,-0.2015050352,0.0493977889,0.255095154,0.0693126768,-0.0704425275,0.1141765937,-0.4392614663,-0.1480825096,-0.1808601171,0.3100427985,-0.2501482964,-0.181490764,-0.2639676929,-0.3084102869,0.2988123894,-0.1891636997,0.0385825448,-0.2646736801,-0.1646647602,-0.2092251629,0.2225359976,0.1276827902,-0.484706223,0.1347155124,-0.0505086593,-0.2308934629,0.2965950072,0.2197384536,-0.0891325995,0.0785434023,-0.2169452012,0.0927088112,0.3119708896,-0.5282865167,-0.6353443861,-0.2045747042,0.0392536968,0.199750632,0.136701405,0.0489972048,0.0670530498,-0.1192274019,0.2003705055,0.2554533482,0.1286742091,0.2997412086,-0.3779305518,-0.3374253511,-0.0244525149,0.1490076035,0.0705284998,0.0454091355,0.1995310187,-0.1780854017,0.2491868436,0.0048380303,-0.3049612045,0.0178339723,0.4241453111,0.1681174338,0.0189563315,-0.3989566565,-0.114322193,0.2084311992,-0.0606114641,0.1960173696,0.0218617171,-0.1804836392,-0.5836142302,-0.0075208996,-0.045009423,-0.0498922206,0.2237077057,0.3612641394,0.1590377092,0.0346530192,-0.2068693042,0.2542302012,-0.3873378038,0.0788437277,0.0390750505,0.5304793119,-0.2218928337,-0.1873175949,0.2554081976,-0.0189144593,0.0292931143,-0.1731526852,-0.1209300309,0.5735868216,0.2360803038,0.0379414819,0.0844285637,0.0684362948,0.1769237965,-0.3511034548,-0.2122675627,0.4654123783,-0.0064162025,0.0255096164,-0.0335024484,0.2390332818,-0.0899502337,-0.1070550606,-0.1001557931,0.0067662857,0.416274935,-0.03768792,0.0151178604,-0.3122467399,0.2221524417,0.0327106938,-0.1549657434,0.0455442555,-0.0278427862,0.0790932328,0.4024963677,-0.0163981523,0.084148109,0.0936824754,0.0919339508,-0.0063487026,-0.0205603987,0.3857573867,0.3906045258,0.2282775789,-0.0585578084,0.0642226711,-0.3530420661,-0.1700579077,0.086124748,0.1225321814,0.1640951037,0.2246408314,0.1111246496,0.1253666878,-0.3292346001,-0.0877719447,-0.0794124678,0.3189308345,-0.5176073313,-0.1456219107,-0.2637497485,-0.0641046539,0.0031651545,0.076881364,-0.1447908431,-0.2434421629,0.0333963335,0.122472629,-0.0742872953,0.2786252201,-0.1493641883,-0.0642516091,0.2559808493,-0.2241702974,0.0241990834,-0.1484817713,0.0180481039,0.1283254772,0.3840251267,0.176188305,0.3158918321,-0.4577540159,-0.071872972,-0.1982284635,-0.4003253281,0.0793983564,-0.2544686794,0.6606246233,-0.0155498683,0.3037824035,-0.0083889142,-0.1851842552,0.1895232797,-0.113524355,-0.3980981708,0.1655428112,0.0319788866,-0.1102654189,-0.0109364139,-0.1618316025,-0.0101669906,-0.3741977513,-0.1887770593,0.2284651101,0.1455118954,-0.022457717,0.3033699691,0.0544430278,0.007977928,0.2186071426,-0.2217547297,-0.6386207938,0.5002468228,-0.1377891302,-0.4190075994,-0.0380368829,0.1346495152,0.0600051992,0.2178351879,-0.4655655324,-0.4032658339,-0.1946605295,0.1383520067,0.1413580775,0.0781812593,0.0477098264,0.0300181285,-0.1848484427,-0.1977693886,0.0490745381,-0.0200139787,-0.2559481561,0.2273625284,-0.1955227405,0.2183904052,-0.1937243789,0.2075269818,0.3405131996,-0.0777283534,0.0695839524,-0.0228118263,0.4099595547,-0.1090231612,-0.2951969802,-0.0534316748,-0.0270303506,0.052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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3942","title":"reddit_tifu dataset: Checksums didn't match for dataset source files","comments":"thanks @albertvillanova . by upgrading to 1.18.4 and using `load_dataset(\"...\", download_mode=\"force_redownload\")` fixed \r\n the bug.\r\n\r\nusing the following as you suggested in another thread can also fixed the bug\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\n","body":"## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n","comment_length":33,"text":"reddit_tifu dataset: Checksums didn't match for dataset source files \n ## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n \n thanks @albertvillanova . by upgrading to 1.18.4 and using `load_dataset(\"...\", download_mode=\"force_redownload\")` fixed \r\n the bug.\r\n\r\nusing the following as you suggested in another thread can also fixed the bug\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\n","embeddings":[-0.4241079986,0.1216538996,-0.0851319879,0.1159736589,0.4104367197,-0.1015881076,0.161649555,0.4821439385,-0.0676114336,-0.0831582919,-0.1715805084,0.484701395,0.2961730957,0.260920167,-0.0052184002,0.1425730586,0.0813892782,0.0942176282,-0.1835354865,-0.1239801422,-0.0313977674,0.0742589831,-0.0655951723,-0.0387011953,-0.1272572875,0.2785607874,0.0962552354,0.1958373934,-0.1299274564,-0.2015249282,0.2113579065,0.1253212839,-0.2219691128,0.6362911463,-0.0001037592,0.1130173132,0.3000631034,-0.0719152987,-0.0833512545,-0.3188892603,-0.1930008084,-0.0129560223,-0.1795533895,-0.0037302237,-0.115698196,-0.0585101582,-0.0973990038,-0.0671985522,0.2122459114,0.3174713254,0.2753217816,0.3078344166,0.1872294694,0.0476778299,0.4358138144,0.1765980273,0.0376328528,0.3537876606,-0.0222580917,-0.120992817,0.0615175292,0.0806413889,-0.1599859893,0.1010159776,-0.0659198016,-0.0292650126,0.1396555305,-0.1895993799,-0.0617788211,0.4400642812,0.303653717,-0.2148500234,-0.3998056948,0.1274029315,0.014880253,-0.1722576767,0.2918680906,0.2763573527,-0.1804020256,0.2406665236,-0.1071696579,0.1765080243,0.0133623816,0.1725681871,0.0295837354,-0.0682585612,0.0082284734,-0.0568891317,-0.159471646,-0.1300682127,0.0715590641,-0.0321435444,-0.1241403893,-0.0463441722,-0.3199132085,0.0060781473,-0.0016880909,-0.0602719933,0.4826945662,0.1779183,0.1245702356,0.2734242678,-0.1492734849,0.2545270622,-0.0060308417,0.0516923703,-0.0779369324,0.2043065876,0.3689894676,-0.0066339909,-0.1574361473,0.1622216851,-0.039365001,-0.0339072868,0.348031342,0.0346651562,0.237737596,-0.3679087162,-0.3306000829,0.0805596262,0.1630642712,-0.2645067275,0.0781537592,0.3441263735,-0.0992540568,0.1183925271,-0.2678114772,0.3250126243,-0.1333276331,-0.0196578279,-0.257578373,-0.0487823263,-0.1573097259,-0.2214170545,0.1648215204,-0.3767096996,0.5168348551,0.0877229422,0.3125971556,-0.1008909643,-0.0099154394,-0.0704085454,0.068229571,0.4129202664,-0.1413104534,0.0304585006,0.2043717653,0.0644429624,-0.1120427921,0.0723024979,-0.3958519399,-0.1151546091,-0.1437643915,0.307682097,-0.278662473,-0.1811386943,-0.2636612356,-0.2843516171,0.2813166678,-0.1981930882,0.0266529322,-0.2471844554,-0.1655811071,-0.2202341408,0.2079783082,0.1195853278,-0.40086025,0.1340444684,0.0400185548,-0.2631473541,0.2815419137,0.2382414788,-0.0849270374,-0.0165697467,-0.184233591,0.166583702,0.2956678569,-0.5067376494,-0.5864641666,-0.1960976124,0.0866027996,0.1515453458,0.1235274747,0.0278241802,0.0812830105,-0.1529754698,0.1688821465,0.256315589,0.1470497847,0.3274729252,-0.3717185259,-0.3467594981,-0.0683312714,0.1492227316,0.0407869779,-0.027712062,0.1848412156,-0.2665777802,0.2314161062,-0.0082812663,-0.2588584721,-0.0013401586,0.4629286826,0.1236617565,0.0398438945,-0.3934967816,-0.1194251329,0.1840355992,-0.0369744785,0.2453461885,0.0259983186,-0.1655705571,-0.5125528574,0.0218798295,-0.0250609983,-0.043711707,0.2340406924,0.3688033223,0.1965874732,0.0488781929,-0.2205754966,0.2265203446,-0.3307691216,0.0873255655,0.1134692878,0.5346261263,-0.1987529993,-0.2265469134,0.2873022854,-0.0440327413,0.0147068985,-0.1954669505,-0.0823376924,0.5871126056,0.2365540564,0.0200580563,0.1091150939,0.1086096764,0.1948229074,-0.3378281593,-0.2032936811,0.4442740679,-0.0297802966,0.0060062567,-0.0732139796,0.2685295939,-0.0932854265,-0.0743135884,-0.0977487043,0.0180309862,0.3966173828,-0.0513017066,-0.0106148571,-0.2766389847,0.2215072811,0.0249375366,-0.164873004,0.0095847482,-0.0790589899,0.0905131921,0.4233700633,-0.0349309295,0.0830269083,0.1191070825,0.0372320674,-0.0334915034,-0.0523786098,0.3563751876,0.3615180552,0.2445400655,-0.0381733403,0.0521736108,-0.3511779308,-0.1828991324,0.0971592143,0.1120668054,0.1718709916,0.2004152983,0.1687261462,0.1054346859,-0.3583817184,-0.0585567504,-0.0790711269,0.2824410796,-0.4575935304,-0.0844145715,-0.232988745,-0.0539293401,0.0635260791,0.0643581301,-0.1851879507,-0.2677815557,0.0718844607,0.1106405929,-0.0896495506,0.2830824554,-0.1016329452,-0.0807155445,0.2854169607,-0.2564376891,-0.0086916862,-0.2279045582,-0.0060237879,0.1385422349,0.366851151,0.1844961047,0.3543100357,-0.4349477887,-0.0702895969,-0.1779164523,-0.4401725233,0.1225638092,-0.2930018902,0.6666305661,0.0256096404,0.2784463167,0.0197882429,-0.1328680217,0.2228195518,-0.2012175322,-0.3910889626,0.1868027151,0.0280624721,-0.1677422822,-0.0636750534,-0.106293641,-0.0331955887,-0.3610416949,-0.1088984236,0.2706436813,0.1203877032,-0.0087130861,0.312415719,0.0328514799,-0.0428843535,0.1993802041,-0.2519604266,-0.6342234612,0.5193831325,-0.1657375842,-0.4650086761,-0.0236073341,0.1615482569,0.0464696437,0.2168153375,-0.4444711208,-0.4417554736,-0.1710698307,0.1684521884,0.1846001297,0.065567933,0.0495572761,0.0156398918,-0.2220025212,-0.2117869258,0.0459698923,0.0188332051,-0.2363012582,0.190156132,-0.1714879125,0.1482187659,-0.1550745368,0.2010549754,0.2956931293,-0.1250995696,0.0375274941,-0.0760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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3942","title":"reddit_tifu dataset: Checksums didn't match for dataset source files","comments":"The latter solution (installing from GitHub) was proposed because the fix was not released yet. But last week we made the 1.18.4 patch release (with the fix), so no longer necessary to install from GitHub.\r\n\r\nYou can now install from PyPI, as usual:\r\n```shell\r\npip install -U datasets\r\n```\r\n","body":"## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n","comment_length":49,"text":"reddit_tifu dataset: Checksums didn't match for dataset source files \n ## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n \n The latter solution (installing from GitHub) was proposed because the fix was not released yet. But last week we made the 1.18.4 patch release (with the fix), so no longer necessary to install from GitHub.\r\n\r\nYou can now install from PyPI, as usual:\r\n```shell\r\npip install -U datasets\r\n```\r\n","embeddings":[-0.3972457945,0.1158501655,-0.0944005996,0.0743375272,0.3742969036,-0.1016561612,0.134125337,0.5053938031,-0.1505984366,-0.0394884273,-0.1530263275,0.4913249612,0.3067786694,0.2183292508,-0.0091935834,0.134451285,0.1148078814,0.1060608029,-0.1334550828,-0.0572964586,-0.0045508253,-0.0093496973,-0.0966604576,-0.0207301956,-0.0583633184,0.2276360095,0.0445015505,0.135986954,-0.2058945,-0.2403111756,0.2638667524,0.1483821422,-0.2442305535,0.5590673089,-0.00010491,0.1271552891,0.2830677629,-0.0792282224,-0.0613509305,-0.31683743,-0.2179539949,-0.0706221461,-0.1681921035,-0.0076873158,-0.0552161448,-0.10953857,-0.0527951717,0.0503651388,0.133496955,0.3430584371,0.2658721507,0.366440624,0.1584191918,0.0342426598,0.3947605491,0.1645510942,0.0348790586,0.3492319584,-0.062650986,-0.097888723,0.1093074605,-0.0030364573,-0.1994205415,0.0993498415,-0.1013718396,-0.0220341776,0.0463652685,-0.2138501406,-0.0981153026,0.4128343165,0.2350536585,-0.2419972271,-0.3816855252,0.1323263943,0.0004418562,-0.164333716,0.2986193299,0.2788708806,-0.2397130877,0.2224878669,-0.0347449891,0.199489817,-0.0440233648,0.177844137,0.0226801075,0.0216032229,0.0898499414,-0.0190123357,-0.0786939487,-0.1137216538,0.1954844594,0.0554828085,-0.067269139,-0.0375485905,-0.1989176422,0.0137191257,0.0118824085,-0.1319633126,0.4968379736,0.1359120458,0.0994018093,0.2437210083,-0.0761685371,0.3032625616,-0.0573111549,0.0137570798,-0.0470061824,0.2996734083,0.3338770866,0.0164612252,-0.0525902994,0.1817306578,-0.0304901004,-0.046069622,0.3226611316,0.0743711963,0.2300565243,-0.3679658175,-0.4308348,0.0870581567,0.1404775977,-0.2431651205,0.0388298854,0.3132538199,-0.0599289946,0.1402357221,-0.2776667476,0.3079220951,-0.1966086477,0.0513605997,-0.2339680493,0.0504702367,-0.1148832887,-0.1922035813,0.1268985868,-0.4038243592,0.4870954752,0.1144862324,0.316570282,-0.0361408964,-0.0162857454,-0.0618909188,0.0621015355,0.4958976507,-0.1525313705,0.0646699592,0.1975970864,0.1410731971,-0.1435844451,0.0806579888,-0.3774157763,-0.1210579127,-0.1983364671,0.3039913177,-0.3114700615,-0.1928068399,-0.1773220152,-0.3383498788,0.3129969537,-0.2297434807,0.0489845239,-0.2373637408,-0.0305993352,-0.2500886619,0.1779904217,0.1128632128,-0.5220831633,0.1453724951,0.0709230006,-0.2602553666,0.3192370534,0.2609838247,-0.0720183849,-0.0383889377,-0.1669498235,0.22831361,0.2290570885,-0.4691096246,-0.648625493,-0.2247489542,0.0988691077,0.0668754056,0.2018143982,0.0338719077,0.1469430476,-0.0930239484,0.178311795,0.1398080885,0.1675214767,0.2202978134,-0.396176368,-0.3666825593,0.0086012697,0.1462171376,0.09284731,-0.0688959658,0.1709890515,-0.2168817818,0.2603482902,-0.0071418011,-0.2701739073,-0.0111230584,0.5282152891,0.1565122753,0.0311639402,-0.4041968882,-0.0382097401,0.1638697237,0.0068689603,0.3009797335,-0.1075072587,-0.1852892637,-0.4991526902,0.0805613771,-0.0810400546,-0.0658172145,0.2241801471,0.3235909641,0.1962801516,0.03964708,-0.2381029129,0.2055830806,-0.2923541367,0.0459838361,0.1257136315,0.4918661118,-0.2841662169,-0.1985765547,0.2238285094,-0.0218977593,0.0338702835,-0.1906066388,-0.0358097591,0.5679984093,0.2216728628,-0.1000775173,0.0870040879,0.0638672262,0.2288838774,-0.4107523859,-0.138067767,0.4723104239,-0.058604259,0.0738959685,-0.0462402701,0.3331492543,-0.0237298496,-0.0715223253,-0.1091220975,0.0491441004,0.4476817846,-0.008242094,0.0055907951,-0.2900725007,0.2397475243,0.0696484298,-0.1521420926,0.0325714871,-0.0163992196,0.1328951567,0.4287265837,-0.0898688957,0.1032356545,0.0928947553,0.0883570164,-0.0231869128,0.0169912633,0.2769384086,0.2826452255,0.2031242996,-0.0718730241,0.0543081872,-0.4174272716,-0.2200956643,0.0408665724,0.1092732623,0.1560337991,0.1641885787,0.1498347819,0.1108972281,-0.4117979407,-0.0939693674,-0.0563563481,0.3467799127,-0.4742769897,-0.0733722895,-0.2659908533,-0.0773638114,0.1014615223,0.0134020951,-0.1797267199,-0.2796196938,0.1374929845,0.0663586631,-0.0962861925,0.2670513093,-0.1250310689,-0.1017081738,0.3037338257,-0.3043340147,-0.0180753563,-0.2500351667,0.009439487,0.1395905614,0.333696574,0.1813608408,0.3696864843,-0.4134679437,-0.1491151303,-0.1730808914,-0.4731228352,0.1068476066,-0.3054790199,0.5822451115,0.0626961142,0.29422912,0.045365002,-0.220229879,0.1857262254,-0.1878198534,-0.3979575038,0.2523879409,-0.0839364603,-0.1743398607,-0.1055322513,-0.1662874371,-0.0986048654,-0.301037401,-0.131566748,0.2938216329,0.1701860577,-0.009207068,0.3598101139,0.0320890173,-0.0901550353,0.1556848586,-0.1982466578,-0.546639502,0.4923823476,-0.1142811328,-0.4728689194,-0.0343177766,0.1789805889,-0.0162571222,0.2189088166,-0.4828189015,-0.4995726943,-0.1409182101,0.2771880627,0.1772972792,0.0657270402,0.0828181431,0.0465377122,-0.2038285881,-0.1848029345,0.0711306781,0.0187585689,-0.235979408,0.2017390877,-0.1863386333,0.2289091051,-0.081855543,0.2742735147,0.2685349882,-0.0731108114,0.0042001777,-0.0805646107,0.4213832319,-0.0970153064,-0.2580533922,0.0321565755,0.0074720518,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3941","title":"billsum dataset: Checksums didn't match for dataset source files:","comments":"Hi @XingxingZhang, thanks for reporting.\r\n\r\nThis was due to a change in Google Drive service:\r\n- #3786 \r\n\r\nWe have already fixed it:\r\n- #3787\r\n\r\nYou should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","body":"## Describe the bug\r\n\r\nWhen loading the `billsum` dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n```\r\n  File \"virtualenv_projects\/codex\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx']\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\nload_dataset('billsum')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n\r\n\r\n\r\n\r\n","comment_length":48,"text":"billsum dataset: Checksums didn't match for dataset source files: \n ## Describe the bug\r\n\r\nWhen loading the `billsum` dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n```\r\n  File \"virtualenv_projects\/codex\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx']\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\nload_dataset('billsum')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n\r\n\r\n\r\n\r\n \n Hi @XingxingZhang, thanks for reporting.\r\n\r\nThis was due to a change in Google Drive service:\r\n- #3786 \r\n\r\nWe have already fixed it:\r\n- #3787\r\n\r\nYou should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","embeddings":[-0.4439738691,0.3979528248,-0.1034802124,0.1886253506,0.2277504802,0.0679108351,0.2159260958,0.2985670269,0.1642130017,0.1222992986,0.0958597437,0.0360736698,0.1474190652,0.2202241868,-0.0004788893,0.1770035475,0.0542056262,-0.0288489033,-0.0223002993,-0.0352689512,-0.3512853086,0.2289222181,0.0162223671,-0.2879076898,0.0853461325,0.2226153463,0.1279373616,0.1907230318,-0.2450843304,-0.1924148053,0.1991567165,0.1335769892,-0.0073953597,0.4593857527,-0.0001127957,0.1864899993,0.4848975539,0.0287229922,-0.4654405117,-0.084063381,-0.2882366776,-0.206325084,-0.1947156936,-0.1314370781,0.1306875795,-0.0070895306,-0.1326556206,-0.1516025364,0.050739564,0.2921630442,0.2098049074,0.3013036549,0.1827804148,0.0766988173,0.5222919583,-0.1602871716,0.0872821808,0.5475760698,-0.0015224606,-0.0639345348,0.1201519817,0.1707801819,-0.2118850499,0.1150467247,0.1715529859,0.1248250529,-0.03692073,0.0423742458,0.0630678385,0.2080620378,0.3014431298,-0.1993893981,-0.3702297807,-0.1017953157,-0.0359057374,-0.2172555774,0.6341405511,0.2274866998,0.0735623166,0.0263714511,-0.5666072965,0.077351436,0.0355399325,0.1744483709,-0.1465765983,0.1413700879,0.0419780463,-0.0547221936,0.0333676301,-0.0838258043,0.1557453722,-0.254609108,-0.2240247875,-0.0415011831,-0.2506529391,-0.0584701858,0.0964481533,0.2282309532,0.3818089664,0.295438081,-0.0155849792,0.1652637273,-0.3365027905,0.185888797,0.0220103152,0.1599904746,-0.1169159189,0.151176393,0.4685677588,0.3264901638,0.2119035274,0.1329634488,0.0363318548,-0.1869616359,0.1759188771,0.1823223531,0.1848874241,-0.2980906665,-0.4048728645,0.1706672907,0.0492509156,-0.2403172255,0.1330958605,0.2846083641,-0.2474854589,-0.1176881865,0.0318560824,0.1040351614,-0.0670712218,0.3422398865,-0.1705227196,-0.0207442343,-0.0911357552,-0.0186963733,0.3730758131,-0.343816191,0.2726408243,0.0223871898,-0.0123666842,-0.0054004146,0.1697473377,-0.0315265581,-0.1972259134,0.3557344973,-0.0210166797,0.0881235152,0.1051382124,-0.0027710339,-0.1386390328,0.0815903544,-0.4535005987,-0.1019150391,-0.0580967329,0.3123090565,-0.2750694454,-0.1466859281,-0.2939595282,-0.4308347106,0.0257025156,-0.2684024274,-0.0789474547,-0.2064275444,-0.2950770855,-0.2398940772,0.2672673762,0.1245113462,-0.162611261,-0.0424724147,-0.2464709431,-0.2725116313,0.1295040548,0.2652706206,-0.0469176136,0.0531802662,-0.4091737866,-0.0250257235,-0.115003027,-0.3405149281,-0.6405295134,-0.0301493052,0.1324078143,-0.0595713928,0.0593252815,-0.1188477501,0.1125449762,0.0779316798,0.1620675772,0.186393559,0.1386142969,0.2338559926,-0.1820350289,-0.3929369748,0.0978237912,0.2020248324,0.0303073078,0.0208446737,0.3079658151,-0.3326949179,0.1134864166,-0.108789064,-0.1471491456,0.3346656263,0.5563585758,0.0699912384,0.1929026246,-0.042977348,-0.4167457521,0.3173339963,-0.2022904903,0.0091087278,-0.2194200307,-0.190119803,-0.2889499664,-0.1604917049,-0.0569011793,0.1140343025,0.1496287882,0.3731675744,0.0378662013,0.0841125175,0.1065787822,-0.075774312,-0.302161485,-0.0347997993,-0.1180203781,0.7207111716,-0.2065883726,-0.2001561821,0.3406206071,-0.0112095885,0.1953431666,-0.1817380339,-0.1705874205,0.4723944664,0.2709105611,0.2831824422,0.1877067834,0.561442256,0.2481345832,-0.2821190059,0.0549617968,0.4969735146,0.1127730384,0.0344053395,-0.4433575273,0.2786784172,-0.1827600598,-0.019771615,0.0459566191,0.1223966032,0.2940051258,-0.1368869841,0.1065027565,-0.3272482157,0.1007519364,0.4226847887,0.1944942623,-0.0070564565,0.1111246943,-0.0220980477,0.3812299073,-0.0466057956,0.09026476,0.0693548396,-0.0583516285,-0.0638910234,0.1664037704,0.4466325343,0.3047539294,0.1740949899,-0.0056575141,0.120159097,-0.121405907,-0.1634939909,0.1593515426,-0.0082265474,0.0087777972,0.3641691506,-0.0013633214,-0.0117337266,-0.4045173228,0.003959849,0.0633195117,0.2933954,-0.4859107733,-0.1301021278,-0.29378739,0.0642315075,-0.3407527804,0.0621950217,-0.2707392573,-0.4372276664,0.0047782878,0.4343088865,-0.2199890614,0.2267840207,0.0709044635,0.1152337044,0.1544796079,-0.4277650416,-0.105506137,-0.0595618561,0.0770658478,0.1029575467,0.5101454258,0.177814737,0.3525721729,-0.3003239036,-0.1154542565,-0.7003768086,-0.341155231,0.0723259225,-0.3449421227,0.4714392722,0.2795479596,0.0465645678,-0.0035760794,-0.1079631746,0.0734540373,-0.0575128794,-0.3673721552,0.1767672598,-0.1280110776,-0.1607517153,-0.0486229584,-0.2101787925,0.0654587969,-0.1770792753,-0.1525553316,0.2368773371,0.091890797,0.0638813525,0.1314509362,0.1555478275,0.207446754,0.1623739749,-0.2662161291,-0.2728484273,0.4294889867,-0.0594479032,-0.3758270741,0.0709474683,0.0414334871,-0.167716369,0.3445874751,-0.3872812092,-0.3581337631,-0.4112388194,0.1492066979,0.2963081598,0.1123657823,0.3224449158,0.1554400027,-0.12027511,-0.2137685716,-0.3296327293,-0.0757038966,-0.0299282111,0.2078787982,0.0278087854,0.2335329056,-0.3570574224,0.3795694411,0.4104430377,0.0316887163,0.1269768029,-0.1509151608,0.388938576,0.021634182,-0.072893247,0.0741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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"Thanks for reporting @qqaatw.\r\n\r\n@mishig25 @sgugger do you think this can be tweaked in the new doc framework?\r\n- From: https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/\r\n- To: https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":24,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n Thanks for reporting @qqaatw.\r\n\r\n@mishig25 @sgugger do you think this can be tweaked in the new doc framework?\r\n- From: https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/\r\n- To: https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/","embeddings":[-0.0187271331,-0.0063731926,0.0506370142,-0.0155851441,0.1034540683,0.0018574685,0.0943081155,0.4163026214,-0.3947233856,-0.0836842731,-0.0372577161,0.3179529607,-0.1664929092,0.2242850214,0.3080806434,-0.0525295697,0.048733592,0.2238046378,-0.2547568381,-0.0491920225,-0.2380856425,0.3419846892,-0.2553613782,0.1312544942,-0.335822165,0.2073529065,0.0516713262,0.273817867,-0.2153954953,-0.522408843,0.1666759998,0.1461387128,-0.0980079845,0.297453016,-0.000107693,0.0006841131,0.4079018533,0.1341495514,-0.448633045,-0.2668795586,-0.1302228719,-0.0143275484,-0.0388930067,0.0316008069,-0.0917570814,-0.3623400629,-0.0534400083,0.0620411895,0.1248966157,0.4276170433,0.267146647,0.2947669327,0.4018475115,0.0041196179,0.31690377,0.1281161457,-0.1532746255,0.2432874739,0.2216950655,-0.1291416883,0.0095920283,0.4768320024,-0.0680790469,-0.3794160485,0.4865565598,0.0933019966,-0.2182511091,-0.1806530952,0.1244002953,0.321747601,0.4607045949,-0.130715251,-0.4646189511,-0.0479646586,-0.1796613038,-0.0997242928,0.2587279379,0.1663856208,0.1724100262,0.373634845,-0.1632019281,-0.3525308371,-0.1811439544,0.1615774632,0.1899350882,0.0887923315,-0.2248000354,-0.1435865313,0.2506815791,-0.127245903,0.0898871943,0.0974664465,-0.3128813505,0.2352757305,0.3029549122,-0.0029521596,0.2264029831,0.3986922204,0.3898892999,0.0592280999,-0.2539223135,-0.1589945853,-0.2067804337,-0.0257673897,0.0218975078,0.0151034258,0.0302289594,0.2644266784,0.3953259289,0.4564833939,0.1249663979,-0.0697236285,0.0936110169,-0.2380833626,-0.351072669,-0.2569864392,0.2673898339,-0.2237783968,-0.2147223204,0.1401665509,-0.3454043567,0.1268763244,0.186518386,0.1045873091,-0.1278413832,0.3936744332,0.333807379,0.0770071745,-0.3253526092,-0.3007166982,-0.1939601898,0.1009992138,-0.2921341658,0.1147202328,0.1020905599,-0.2380368561,0.3687264323,0.2457109243,-0.0103546521,-0.3510748744,-0.1554171294,0.1139167771,0.0453462563,0.1353991181,-0.2145243138,0.0733347237,0.0992263407,-0.2943178713,0.0547437407,0.0642935559,-0.5767096877,-0.2669719756,-0.2404599339,0.2087740302,-0.0104366178,0.0827139467,0.0283074342,0.1178046614,-0.2028794736,-0.2205184996,-0.0355937853,0.0479941852,-0.2346374243,-0.0446825437,0.447042942,0.6542369127,0.0038461853,-0.2002677023,0.4292866588,0.2077867538,-0.2655270696,0.0403928272,-0.3147580922,0.077260986,-0.3521567583,-0.0752224922,0.0158991516,-0.4088335633,-0.1669686586,0.0403709039,-0.0717753321,0.1015604138,-0.0398607254,-0.2720073462,0.0598036498,-0.2486562133,0.1766275615,-0.1428523064,0.1704471111,0.254206419,-0.3809669316,-0.3537797034,-0.5163020492,-0.2521840036,0.0608098917,0.2332410216,0.084123522,0.2405653596,0.3700611591,-0.2897121906,0.0099810492,0.4187983871,0.2980928123,0.0733311698,0.1527809054,-0.1372025311,-0.4137676358,-0.0144111775,-0.0107094022,0.0819266066,-0.0175855514,-0.174079597,-0.2825234234,-0.0826976523,-0.0045411484,-0.5523074865,0.1927152276,-0.0065746419,0.0429887995,0.4545126855,-0.2057269663,0.1456053555,-0.1290303767,0.377671361,-0.091324091,0.5353217125,0.0404188931,-0.0633401126,0.0679041669,0.3209619224,0.179639563,0.0395846479,-0.0619940199,0.4921313822,0.0285408068,0.3934336305,0.2590904236,0.3861038685,0.3593552113,-0.5251443982,-0.1047118232,-0.0206501503,-0.0124324001,0.0503113531,-0.058868181,0.0821766034,-0.2177267671,0.1517742574,0.2938092947,0.2612450123,0.0162396431,-0.1683143526,-0.3037467897,-0.1937560737,0.079462707,0.0578259341,0.2957595587,0.0241561253,-0.2348698229,0.1241550371,0.2402562648,-0.01831544,-0.12097767,0.1407464892,-0.4795674086,-0.030586442,-0.060769774,0.2735624313,-0.0039077853,0.183672145,-0.0311644413,0.2403923422,-0.2020032108,-0.1329932362,0.3040081263,-0.0053365617,-0.05358788,0.1049759537,-0.0190359261,-0.0109797539,-0.4027560055,0.1428748369,-0.2491165847,0.0621053576,-0.6494679451,0.1374779493,-0.1472738683,0.0596090481,-0.4010846615,-0.1703273803,-0.2623657584,-0.3202089071,0.0230201837,0.4301061928,-0.0993966982,0.1361059994,-0.0358840078,0.2177260518,-0.0971841812,0.203785494,-0.0926349908,0.161842823,-0.1960624158,0.0989432335,0.1447135955,-0.0789839253,0.07299117,-0.4703900516,-0.1322035044,-0.3642755151,-0.5722596049,0.1921438575,0.0189025197,0.2950071096,0.2504951358,0.1046025231,0.0247422233,-0.2164057195,0.322450757,-0.1098167896,-0.0178805348,-0.1746653914,-0.1186217293,-0.0223934837,-0.1284303963,-0.3466691673,0.3226672411,-0.2008787394,0.1291579604,-0.073454015,-0.0414848663,0.3959862292,0.1823369861,0.1815573126,-0.0652907118,0.1858065873,0.0362912267,-0.368771255,0.2723689973,-0.4087771773,-0.3658388257,0.0640075877,0.1180800647,0.0521221161,-0.1150972247,-0.3908717632,-0.2551808655,-0.1362001896,-0.1333202869,0.1170573086,0.2899686694,0.1029919088,0.2729539275,-0.139807269,-0.1843677759,-0.1272682995,0.0618499704,-0.2787075043,0.2006921917,0.0618002303,0.0590312891,0.3185184002,0.6017562747,0.4573518038,0.1458194554,0.0502800383,-0.1485455036,0.5783555508,-0.183520793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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"@qqaatw thanks a lot for notifying about this issue!\r\n\r\nin comparison, transformers tags start with `v` like [this one](https:\/\/github.com\/huggingface\/transformers\/blob\/v4.17.0\/src\/transformers\/models\/bert\/configuration_bert.py#L54).\r\n\r\nTherefore, we have to do one of 2 options below:\r\n1. Make necessary changes on doc-builder side\r\nOR\r\n2. Make [datasets tags](https:\/\/github.com\/huggingface\/datasets\/tags) start with `v`, just like [transformers](https:\/\/github.com\/huggingface\/transformers\/tags) (so that tag naming can be consistent amongst hf repos)\r\n\r\nI'll let you decide @albertvillanova @lhoestq @sgugger ","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":64,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n @qqaatw thanks a lot for notifying about this issue!\r\n\r\nin comparison, transformers tags start with `v` like [this one](https:\/\/github.com\/huggingface\/transformers\/blob\/v4.17.0\/src\/transformers\/models\/bert\/configuration_bert.py#L54).\r\n\r\nTherefore, we have to do one of 2 options below:\r\n1. Make necessary changes on doc-builder side\r\nOR\r\n2. Make [datasets tags](https:\/\/github.com\/huggingface\/datasets\/tags) start with `v`, just like [transformers](https:\/\/github.com\/huggingface\/transformers\/tags) (so that tag naming can be consistent amongst hf repos)\r\n\r\nI'll let you decide @albertvillanova @lhoestq @sgugger 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"I think option 2 is the easiest and would provide harmony in the HF ecosystem but we can also add a doc config parameter to decide whether the default version has a v or not if `datasets` folks prefer their tags without a v :-)","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":45,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n I think option 2 is the easiest and would provide harmony in the HF ecosystem but we can also add a doc config parameter to decide whether the default version has a v or not if `datasets` folks prefer their tags without a v :-)","embeddings":[-0.0938099772,-0.0260814317,0.0604204051,-0.0434884131,0.1273478717,0.0210940521,0.0481096506,0.4152696729,-0.2235428691,-0.0596474335,-0.1113166511,0.2980236709,-0.0862364545,0.2317366898,0.2304339856,-0.0245840624,0.0244480819,0.2727003992,-0.2373473644,-0.1196464226,-0.2088505328,0.308724612,-0.1478628069,0.1997595131,-0.2349735051,0.1833885163,0.0328281261,0.2584998012,-0.161520645,-0.5289617777,0.1182913855,0.2092948109,0.0116788885,0.3349643946,-0.0001067179,0.0589274876,0.3170063794,0.1626368612,-0.4697597325,-0.2691056728,-0.0687209442,0.0060921861,-0.074226737,0.0837398618,-0.1293405145,-0.4473071396,-0.0449467897,0.0349623002,0.0986833721,0.2601931691,0.278313458,0.336794138,0.3182221949,-0.0664459765,0.4235322773,0.1754711419,-0.1066084802,0.2765163779,0.0671091005,-0.2146501392,-0.0356654935,0.5221790075,-0.0374240167,-0.2294415087,0.5925669074,0.0748155564,-0.2306540161,-0.1428269744,0.1135227829,0.3589001298,0.4462852776,-0.2040632218,-0.4455937147,-0.0123271002,-0.1961634606,-0.143974781,0.2808186114,0.1324786693,0.1828142107,0.3789462447,-0.026702337,-0.3251271248,-0.1416982412,0.1876940429,0.1829237789,0.0478197597,-0.1587758362,-0.1204466,0.219321698,-0.0920725986,0.0035388188,0.0445807874,-0.3210740387,0.1806866974,0.2929237187,0.0594237074,0.2188917696,0.2279134393,0.5009387732,-0.0982240513,-0.3371445537,-0.1453029215,-0.1811874509,0.0198490545,0.0221254621,-0.0441702381,0.0716973543,0.274343729,0.42422539,0.4228813946,0.0947222933,-0.0924699605,0.1771950722,-0.2170596272,-0.2569161654,-0.2759928107,0.20772098,-0.2495429665,-0.1821800917,0.1464055032,-0.3560432196,0.0826754421,0.0774414167,0.2412802726,-0.0906749889,0.3241419494,0.2872465253,0.073479481,-0.3544257879,-0.3064900339,-0.2401584238,0.0418641865,-0.2801040709,0.0955258906,0.0510749035,-0.3497385085,0.3087667227,0.2611150742,0.0672059432,-0.2071344256,-0.1649256796,0.0901015624,-0.0084886644,0.2701131105,-0.1356532723,0.1140801013,0.1679825038,-0.278372705,0.0206443798,0.0129680838,-0.5031883717,-0.1793837696,-0.2505913675,0.2505787611,0.037832275,0.0840973482,-0.0226692874,0.0678060278,-0.2331817448,-0.2102109641,-0.0148823624,0.0146440789,-0.1627744138,-0.1056721956,0.4456667304,0.611468792,-0.1278376281,-0.2162667662,0.4467181563,0.1495571733,-0.2754486799,0.0898848921,-0.2896924913,0.0201840959,-0.3396971822,-0.0371406376,0.0179261547,-0.3582273126,-0.2336691767,0.174030304,0.0296207182,0.0761552453,-0.0998117924,-0.2329990268,0.0555623397,-0.1966962367,0.1627610028,-0.1780527234,0.1555101573,0.1953388005,-0.4192001522,-0.3424870372,-0.4184395671,-0.1906795055,0.104263179,0.2388808727,0.1120271459,0.14968732,0.3964492977,-0.2935855389,0.001619012,0.4799332619,0.3468311429,0.2365270257,0.2016325146,-0.1711109579,-0.4050754309,-0.0027981214,-0.0923234671,0.1290829778,0.0436499715,-0.2718534768,-0.2950780988,-0.0758377239,0.0052120183,-0.4897664487,0.1882068664,0.0040580868,-0.0841534659,0.3724137545,-0.2133312672,0.2431949526,-0.0639813989,0.361189425,-0.0516379215,0.6402112246,-0.0565842949,-0.0539168939,0.0988237113,0.2673921585,0.2247012556,0.0055342405,-0.12586537,0.4742071927,-0.0502569415,0.3249878287,0.313418448,0.3151769042,0.3843261302,-0.5301796794,-0.0358466394,0.0382779129,-0.0142956451,0.0624511577,-0.042405922,0.0758528337,-0.1177836508,0.1185568422,0.2384513021,0.206801936,0.0174188912,-0.1224513352,-0.2819077969,-0.1586689353,0.1379288286,-0.0250326991,0.2805367112,0.0665382668,-0.2582451403,0.1283428371,0.4364575446,-0.0032925468,-0.1276251972,0.2597481906,-0.5415120721,0.0132140573,0.0497165322,0.1019376591,-0.0276826024,0.2440482974,-0.0807023421,0.2209555209,-0.3232031167,-0.0801903903,0.2584239542,-0.0073657893,0.0107590435,0.0914252102,0.0046745199,0.0482406095,-0.4385215342,0.0348989777,-0.3234518468,0.0315578952,-0.6536998749,0.1626318246,-0.2634989321,0.0121039078,-0.2666704655,-0.1537077725,-0.4200265706,-0.3341841102,0.061279688,0.3405522704,-0.1169091389,0.1726609617,-0.0413608439,0.1315213144,-0.1105312183,0.1321493685,-0.0888549015,0.1297267973,-0.2626478374,0.0790102854,0.0484266803,-0.0453161038,0.1310924441,-0.3703799546,-0.2198638618,-0.3529178202,-0.6234226227,0.2463837415,-0.0101766763,0.3135118484,0.3105318546,0.0921783,-0.0213553607,-0.1409447938,0.3786443174,-0.120101355,-0.0056829518,-0.1051732451,-0.1794451773,-0.061838299,-0.1545273811,-0.3004791439,0.276815027,-0.3093660772,0.0564941019,-0.1263272166,-0.0340287983,0.4534707069,0.1551070958,0.1416773498,-0.0414747149,0.1296435297,-0.0529725626,-0.2394369692,0.2339754999,-0.3955617249,-0.4028537869,0.1019027829,0.0771368295,0.0092872735,-0.0916307792,-0.4671472907,-0.2465723157,-0.1275961548,-0.0153947854,0.0862766951,0.3007089794,0.0404296033,0.235781759,-0.1616265327,-0.2027636617,-0.1491241306,0.093760252,-0.2770650685,0.1706627607,0.0628298447,0.1472318769,0.3029624522,0.6238446236,0.4332580864,0.1605636775,0.1675801426,-0.1096338704,0.5632911921,-0.198172763,-0.3579517305,0.1097210795,-0.0129497387,0.155657067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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"For me it is OK to conform to the rest of libraries and tag\/release with a preceding \"v\", rather than adding an extra argument to the doc builder just for `datasets`.\r\n\r\nLet me know if it is also OK for you @lhoestq. ","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":42,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n For me it is OK to conform to the rest of libraries and tag\/release with a preceding \"v\", rather than adding an extra argument to the doc builder just for `datasets`.\r\n\r\nLet me know if it is also OK for you @lhoestq. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"We could add a tag for each release without a 'v' but it could be confusing on github to see both tags `v2.0.0` and `2.0.0` IMO (not sure if many users check them though). Removing the tags without 'v' would break our versioning for github datasets: the library looks for dataset scripts at the URLs like `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/{revision}\/datasets\/{path}\/{name}` where `revision` is equal to `datasets.__version__` (which doesn't start with a 'v') for all released versions of `datasets`.\r\n\r\nI think we could just have a parameter for the documentation - and having different URLs schemes for the source links that the users don't even see (they simply click on a button) is probably fine","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":111,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n We could add a tag for each release without a 'v' but it could be confusing on github to see both tags `v2.0.0` and `2.0.0` IMO (not sure if many users check them though). Removing the tags without 'v' would break our versioning for github datasets: the library looks for dataset scripts at the URLs like `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/{revision}\/datasets\/{path}\/{name}` where `revision` is equal to `datasets.__version__` (which doesn't start with a 'v') for all released versions of `datasets`.\r\n\r\nI think we could just have a parameter for the documentation - and having different URLs schemes for the source links that the users don't even see (they simply click on a button) is probably fine","embeddings":[-0.054221414,0.0136878099,0.0050496417,-0.0114307534,0.1155416816,-0.0269931704,0.0938590616,0.4597058296,-0.3011182547,-0.0428777449,0.009345213,0.3782207966,-0.18527098,0.1949329972,0.1850505322,0.0363590643,-0.0211610738,0.2246724814,-0.1446908563,-0.0204798728,-0.1109918654,0.2455714941,-0.1002921462,0.1203873232,-0.1306868345,0.1420890391,0.0750569478,0.1642175615,-0.2008797228,-0.590864718,0.1809437871,0.1990986168,-0.0473841541,0.3812351227,-0.0001052928,0.1201697811,0.3078629076,0.0667871535,-0.5651860833,-0.3012249172,-0.0268747713,-0.112514779,0.0210579988,0.0310205538,-0.1715643257,-0.3184591532,-0.0153976781,-0.0091050705,0.2396462858,0.1511983871,0.2965371907,0.3428994715,0.2747197449,0.0389692634,0.424539417,0.1686266959,-0.1950582862,0.2710792124,0.2778761387,0.0112361731,0.0253735054,0.5503281355,-0.0539336726,-0.3083570302,0.4795606732,0.0461366884,-0.1021186486,-0.1716679335,0.0653163046,0.2752573192,0.3913716078,-0.3050712645,-0.5413457155,-0.1542346925,-0.1189399138,-0.1727122813,0.2240769863,0.1865039617,0.0220909473,0.3470360935,-0.2509979904,-0.3444195092,-0.2074721754,0.1031753272,0.3146031499,0.093835853,-0.202188313,-0.0418650135,0.1160954759,-0.1482353956,-0.1766381711,0.0847308263,-0.2427859902,0.2214248925,0.3431370556,-0.1051083431,0.2336750925,0.1466369331,0.537594676,0.1547502875,-0.2309925556,-0.1134039983,-0.2538582087,0.0126333823,0.1252824068,0.028732324,0.0497496687,0.302737236,0.4774960577,0.4509271979,0.1523098201,0.0032332574,0.1528264135,-0.2279386967,-0.2619708478,-0.1501891762,0.3943499625,-0.2247023433,-0.0735591501,0.0789219588,-0.3622938693,0.0866767019,0.0863626972,0.1619685143,-0.1515832841,0.3022723794,0.1939254999,0.0719939619,-0.2202887833,-0.2049559355,-0.2174598575,-0.0392497517,-0.2819617093,0.2269873023,0.0133958925,-0.3593467772,0.4161596,0.3444631994,-0.1101979092,-0.267772615,-0.1357262433,0.2479996383,0.0152178295,0.172095418,-0.1728611439,0.1544475704,0.0781944692,-0.4288077652,-0.1056361198,-0.0307912752,-0.4272589982,-0.2345656455,-0.2200474888,0.2096513212,-0.0790939704,0.1999441385,-0.0862919986,0.0730483234,-0.1882375628,-0.1438291818,0.0168609675,0.0818172768,-0.0446594507,-0.1591816097,0.4636858106,0.5729559064,0.1028031036,-0.2580921352,0.3721669316,0.145644784,-0.2716274858,0.0760374814,-0.2744162083,-0.0742287636,-0.2762053311,-0.2387370914,0.1267134547,-0.526727736,-0.168505609,0.2014665306,-0.0959832817,0.1630434096,-0.0242908895,-0.1367158443,0.1123338342,-0.3080492914,0.0888738111,-0.1551852077,0.0874286592,0.1594282538,-0.3915192485,-0.4566904902,-0.3765648603,-0.1531998664,-0.0531945787,0.2191495746,0.0897803754,0.1220666468,0.432831347,-0.299624294,0.0331355147,0.364916414,0.3787102997,0.1409219205,0.1150336191,-0.1485044658,-0.4050962627,0.0381333455,0.0813489929,0.1495905071,-0.0875000358,-0.3053787649,-0.2322246134,-0.1303720176,-0.0299280882,-0.5637584925,0.1992889792,0.0872493833,0.0565713644,0.3677258492,-0.216109708,0.0385982469,-0.0912366211,0.3434737325,-0.1919737607,0.48450616,-0.0167001095,0.0002172652,0.0913514942,0.3154968917,0.1559929848,-0.0745982751,-0.0344197154,0.4763198495,0.018971784,0.3283327818,0.3183338344,0.3700167537,0.3935618997,-0.4380132258,-0.056852553,0.1176652983,-0.082387656,0.1427531093,-0.073871389,0.1873866469,-0.1279742569,0.1506777406,0.227786243,0.2356332093,0.1827231348,-0.114689514,-0.4596430659,-0.2211456895,-0.0209889505,0.0680225939,0.3104145825,0.0393309034,-0.3383369446,0.1452131122,0.303358227,-0.0075369445,-0.0899386257,0.1617816389,-0.4471539259,0.0271517988,0.0191706829,0.3026750386,-0.1149839535,0.2538552284,-0.0608979501,0.2296644747,-0.2084737867,-0.1298716068,0.2393542528,0.0073408335,-0.0931047499,-0.0612492561,-0.0436510928,0.0710789487,-0.2412253469,0.0482486635,-0.3092395663,0.0353060216,-0.5924835801,0.1631026566,-0.2213118672,-0.0864244029,-0.3893520534,-0.2141112387,-0.3764783144,-0.140926823,0.065355286,0.296510905,-0.2333628237,0.2432199717,0.031894099,0.1190126017,-0.142689392,0.104295902,0.0345514603,0.0147474501,-0.157645762,0.1128982604,0.2009062022,-0.1181641743,0.1970475167,-0.4198921025,-0.2360130697,-0.4660208821,-0.6053329706,0.1902280003,-0.1535018682,0.3012955785,0.2898380458,0.1072126105,0.1266933084,-0.1856589764,0.302048862,-0.0724539161,0.0014364009,-0.2225417644,-0.0973568037,-0.1117891371,-0.2413486987,-0.2989282906,0.3168428838,-0.2898720205,0.2237966061,0.0320482999,0.0208561178,0.4790828526,0.1273510754,0.1041831896,0.0840647593,0.1887025088,-0.0077606901,-0.3454840779,0.1769047529,-0.403303057,-0.297393471,0.1109567285,0.1929228008,0.1309968978,0.0010931583,-0.5002476573,-0.182836026,-0.0578062981,-0.0478929542,0.222899884,0.2376237959,0.0752693415,0.3247595429,-0.135771811,-0.1508241594,-0.2767271996,0.0372971408,-0.2727071941,0.1897267103,0.0871313065,0.1349212676,0.2505810559,0.6639517546,0.5149468184,0.0622967817,0.0514857583,-0.1781602949,0.5719370246,-0.0726474896,-0.2306564301,0.1052415296,0.0177320205,0.0765632018,0.282995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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"This is done in #3943 to go along with [doc-builder#146](https:\/\/github.com\/huggingface\/doc-builder\/pull\/146).\r\n\r\nNote that this will only work for future versions, so once those two are merged, the actual v2.0.0 doc should be fixed. The easiest is to cherry-pick this commit on the v2.0.0 release branch (or on a new branch created from the 2.0.0 tag, with a name that triggers the doc building job, for instance v2.0.0-release)","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":66,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n This is done in #3943 to go along with [doc-builder#146](https:\/\/github.com\/huggingface\/doc-builder\/pull\/146).\r\n\r\nNote that this will only work for future versions, so once those two are merged, the actual v2.0.0 doc should be fixed. The easiest is to cherry-pick this commit on the v2.0.0 release branch (or on a new branch created from the 2.0.0 tag, with a name that triggers the doc building job, for instance v2.0.0-release)","embeddings":[-0.1071433127,-0.0372906737,0.0477761067,0.0057620401,0.1159641594,0.0030810323,0.125229001,0.4230194986,-0.3148477674,-0.0263506044,0.0007158918,0.3678081334,-0.0986706987,0.2560932338,0.210472092,-0.0194897894,-0.0013512948,0.2469568253,-0.2531590164,-0.0328656361,-0.2142949402,0.3160285354,-0.1937113255,0.1262152493,-0.3483327031,0.1634145379,0.0490950942,0.2235628515,-0.1971983165,-0.511272788,0.1294212788,0.1394853592,-0.13439174,0.3332173824,-0.0001022324,0.0552099645,0.3468748331,0.1372647136,-0.4601233602,-0.3032651842,0.0204030573,-0.0996507108,-0.0139368139,0.0026390029,-0.1639005393,-0.3710720837,0.0037496968,0.1029786244,0.1823265702,0.3079150319,0.3265562952,0.3916604221,0.3803835511,-0.0378035642,0.3094502687,0.11732319,-0.1556159258,0.2163527012,0.156303376,-0.1173389331,0.0127518391,0.4927563071,-0.0838645026,-0.3008239567,0.4644432962,0.0768599361,-0.2350217402,-0.1552544087,0.1582334042,0.273331821,0.3548756838,-0.2061711401,-0.4903328121,-0.1447439939,-0.1784856319,-0.0832839832,0.2891743779,0.1195666865,0.1420670152,0.3346913755,-0.1230040342,-0.2604273856,-0.1306667328,0.1025931388,0.1502468437,0.0501473062,-0.2547798753,-0.0900403559,0.2541618347,-0.1524027735,-0.0105203064,0.0536844097,-0.2337218523,0.1924676299,0.2942925096,0.0524608083,0.3222456574,0.2718269229,0.4008074701,0.0453940369,-0.226445511,-0.1080235094,-0.1441248953,0.0451004356,0.0487375185,0.0258510262,0.0195204541,0.2145218998,0.3920160532,0.439488709,0.0943796262,-0.1174977198,0.1307294369,-0.2378936857,-0.264936775,-0.247396037,0.3130307496,-0.2161940038,-0.1611794233,0.136413902,-0.2770585418,0.1908213049,0.0967978165,0.1543074846,-0.1117114127,0.3240162134,0.2667908072,0.1004949883,-0.31459409,-0.2243659347,-0.2666991949,0.0529441983,-0.2293559462,0.0853991881,0.1042206138,-0.2768050432,0.4117843211,0.2415241301,-0.0298953857,-0.3137990236,-0.1577792317,0.1638878286,-0.0176335592,0.2199803889,-0.1437368691,0.0919697955,0.1454605311,-0.2954696715,0.0302851945,0.0439090878,-0.4809795022,-0.2023004889,-0.1682680994,0.262914598,-0.0678285584,0.1145393252,-0.0105875488,0.0904821679,-0.1475963145,-0.207027927,-0.0477186851,0.0193930045,-0.147352159,-0.1184959784,0.5030521154,0.6115773916,-0.0449642166,-0.2201599926,0.4343182743,0.1521522999,-0.2516007423,0.0559660867,-0.2313720882,0.0011857474,-0.3202058077,-0.0150878271,-0.0472404584,-0.3331574202,-0.2149091214,0.0840318352,-0.0985764936,0.0950459242,-0.0305464566,-0.2089530528,0.0323258825,-0.1870996803,0.1124930605,-0.144200176,0.1425275654,0.2458458543,-0.3801752627,-0.3218501508,-0.4489344358,-0.1760313958,0.054382205,0.1899437904,0.0840316191,0.1246492863,0.2788216174,-0.260946542,0.0403302163,0.4124903977,0.2673463225,0.1624879539,0.1385267228,-0.1856293082,-0.4126905799,0.0219393186,-0.008496888,0.1084791645,-0.0857239142,-0.2280134559,-0.2862424254,-0.0653620437,-0.0148851154,-0.5016239285,0.2413314134,0.0393807553,0.0433535688,0.4598084092,-0.2105001062,0.1198688969,-0.1199142113,0.3309999704,-0.185864687,0.4658700824,-0.0102561563,-0.0916918665,0.1024843603,0.3219948411,0.158729136,0.0016074975,-0.0662004352,0.4905121922,-0.0442549922,0.3863055706,0.1834984422,0.2843047082,0.3086463213,-0.4547374547,-0.1141663641,0.0001430642,0.0350651219,0.0472924672,-0.0980220214,0.1702475101,-0.152406916,0.1341166943,0.2632459998,0.2405994684,0.0994580388,-0.1703149229,-0.3035512269,-0.1882561743,0.0578697287,0.0169623289,0.3196265399,0.0415263623,-0.2741637826,0.1383247375,0.3085360825,0.0166167822,-0.061990723,0.1238190681,-0.5089436173,0.0256401133,-0.0880164355,0.2399567366,-0.062647447,0.2277643234,-0.0153586278,0.2512654364,-0.1977048963,-0.1580945104,0.2575698793,0.0295447391,-0.0812300146,0.0868272856,-0.0244234316,-0.0407586358,-0.4203320444,0.1737301797,-0.2859551907,0.0819768757,-0.6088325977,0.0974345133,-0.1666130126,0.0196339488,-0.3458669782,-0.1531353593,-0.2905972302,-0.2852283716,0.0801589116,0.4142056406,-0.1749152988,0.1939690858,-0.0213949848,0.1683833152,-0.069265753,0.1146549284,-0.1127614528,0.0585967489,-0.1626858413,0.1369713396,0.1240982786,-0.0076016462,0.1384007782,-0.428360641,-0.1247859895,-0.3586524427,-0.5773410201,0.1289959401,-0.0664974004,0.2842065394,0.2798342407,0.1229058728,-0.0028931824,-0.2152701616,0.3932567835,-0.1551626772,-0.1043533608,-0.1494436413,-0.0961592495,-0.104682751,-0.2238079607,-0.338206321,0.3277105093,-0.2804459333,0.2350327671,-0.0195057914,-0.0394017734,0.3801999986,0.213974908,0.2209997177,-0.0814236775,0.2086212933,-0.0293289963,-0.3348321915,0.2482711524,-0.3973126411,-0.4279310107,0.0839229152,0.1231276691,0.054759521,-0.1407496333,-0.435520947,-0.2864557505,-0.1478250623,-0.0418114588,0.1820383817,0.2941394746,0.1477951258,0.2605451643,-0.2070947886,-0.2066696286,-0.1810037494,0.0862199292,-0.2901005149,0.1358250827,0.0799986124,0.132897675,0.2431131899,0.5736208558,0.4284619689,0.1309682578,0.0857001469,-0.1577617973,0.6485411525,-0.2301547825,-0.3381352127,0.1057180986,-0.0071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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3937","title":"Missing languages in lvwerra\/github-code dataset","comments":"That seems to be an oversight - I originally planned to include them in the dataset and for some reason they were in the list of languages but not in the query. Since there is an issue with the deduplication step I'll rerun the pipeline anyway and will double check the query.\r\n\r\nThanks for reporting this @Eytan-S!","body":"Hi,\r\n\r\nI'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!\r\nI've noticed that two languages are missing from the dataset: TypeScript and Scala. \r\nLooks like they're also omitted from the query you used to get the original code.\r\nAre there any plans to add them in the future?\r\n\r\nThanks!","comment_length":57,"text":"Missing languages in lvwerra\/github-code dataset \n Hi,\r\n\r\nI'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!\r\nI've noticed that two languages are missing from the dataset: TypeScript and Scala. \r\nLooks like they're also omitted from the query you used to get the original code.\r\nAre there any plans to add them in the future?\r\n\r\nThanks! \n That seems to be an oversight - I originally planned to include them in the dataset and for some reason they were in the list of languages but not in the query. Since there is an issue with the deduplication step I'll rerun the pipeline anyway and will double check the query.\r\n\r\nThanks for reporting this @Eytan-S!","embeddings":[0.0285871755,0.0836843252,-0.2411944419,0.1160970479,0.1939320266,0.0751118362,0.1134600341,0.3045021296,0.2181036174,-0.0445781015,0.0106748026,0.3104628026,-0.0661667287,0.1592541784,0.3111082315,-0.0231080912,0.1431610286,-0.082857497,-0.0986673161,-0.3723043203,0.1341448873,0.540225327,-0.0662390515,-0.2558536232,-0.0129761128,0.2341160774,-0.0335193649,0.1357935518,-0.1529674083,-0.2189214975,0.205270499,0.0797250941,0.1583462805,0.1356496811,-0.0001180935,-0.1313159317,0.1348675787,-0.2493512928,-0.0429882854,-0.1027954817,-0.2700395584,-0.2089757174,-0.0831201673,-0.0060102851,-0.0899348557,0.2168403268,0.0405476727,-0.9287718534,0.1554393768,0.4125950336,0.1402851641,-0.0482262708,0.0732947215,-0.0321506485,0.4961046875,0.2492422611,-0.0687647238,0.0762768164,0.8073577285,0.0435980372,0.0956179649,0.3410215974,0.1884566694,-0.4317694008,-0.2999332845,0.176067397,-0.0714580789,-0.4523974359,0.2324958891,0.220689252,0.489907831,-0.3061118722,-0.2455431372,-0.0579212494,0.0029785004,-0.1697540879,-0.0419923142,0.0415046886,0.1626274288,0.2922384739,-0.2264753729,-0.3839752972,-0.1396484673,0.0650667101,0.3619152009,0.177620247,0.1340553463,0.0172091685,-0.4431982636,0.2792977393,-0.1600619107,-0.190127492,0.0858417526,0.2896310985,-0.0576178841,-0.0959811732,0.338083148,-0.0180481113,0.0921969265,0.2171081007,-0.1146839112,0.0967346355,-0.1910435259,-0.0462737642,0.0141351791,0.164901346,0.1749161035,0.1733642668,0.2863581777,-0.2800834179,0.1415084749,0.0229826421,-0.3526426852,0.0414430276,-0.1964735836,0.3255061507,0.0776727051,-0.212597087,0.0086348411,0.2014260888,-0.2722543776,-0.4193371832,-0.1099285409,-0.0333082676,0.203388989,0.196843937,-0.1095567271,0.3547727168,-0.2986705005,-0.6461305618,-0.1396752,0.0649983287,-0.0952317193,-0.2280775607,0.1083459258,0.0493286513,0.2804523408,0.1642284095,0.1695809364,-0.0392054953,0.0367186926,-0.1218773723,-0.0848098621,0.0111296419,-0.0625397712,0.0549919084,0.3114725947,-0.7545150518,-0.1266690344,0.2793294191,0.0374707878,0.0303873885,-0.3034837246,0.1159187183,-0.189413026,-0.2170122266,-0.4007628262,0.4835268855,0.1327048838,0.0320464857,0.0811706111,-0.4108427763,0.4405936301,-0.1636562496,0.311288625,0.3433280587,-0.5936987996,-0.0742992535,-0.1995198131,-0.1825398058,0.4101724327,0.2085359544,-0.1639975458,0.4130247533,-0.2053508162,0.3845307827,0.6185679436,-0.2792361379,0.1131230146,0.1348580569,0.2056149691,0.0253865439,-0.1503004432,-0.0489830971,0.4721556008,-0.0425815359,-0.0612763986,0.0690193176,-0.2501829863,-0.278011024,-0.3073089421,-0.0232491959,0.4279970825,0.2604479194,0.4288309515,-0.0494788252,0.4264566004,-0.380641818,0.3495766819,-0.0971110314,0.1497046947,0.3568282425,0.3827942312,0.243526414,0.1485473961,-0.2218627334,-0.1117369086,0.1201246157,-0.0603378229,0.0743272081,0.1514178962,0.115287371,-0.0393782519,-0.0320705734,-0.1781095415,-0.1679106802,0.0785529688,0.0520369932,-0.2453866154,0.2314880937,-0.1384987533,-0.3390560448,0.0109714251,-0.0960007161,-0.4160979092,0.2616402805,-0.0883413851,-0.2472660691,0.0601825342,0.2442553788,-0.0026030703,-0.1305384934,-0.1159178019,-0.1717679948,0.468267411,-0.0463188589,0.8385347128,0.2590800822,0.1498354524,-0.3225875795,0.1770982444,0.1883222908,0.1248445585,0.0609680638,0.1995175779,0.2579246461,0.0794231892,-0.0838353857,0.0764779821,0.0746861249,0.5405638218,0.0535011515,-0.2232674211,-0.1248061135,0.4487493038,0.1348099262,0.1296194941,0.0085043246,-0.3771497905,0.3240275085,0.6538193226,-0.294018954,0.0539716631,-0.0464940071,-0.0106474487,-0.0842784494,0.0205616765,-0.0418116227,0.1210267022,0.220511049,0.0082281223,-0.3815179765,0.1313842982,-0.2564081252,0.0981414318,-0.0450078957,-0.0709216073,-0.0281966347,0.1349096149,-0.0259353705,-0.3911088407,-0.1439464688,0.0410104357,-0.1226796359,0.0842725113,0.1016535684,-0.4627666771,0.0928790495,-0.2698927224,0.107962884,-0.306260407,-0.2877420187,0.2937124074,-0.5787969828,-0.3027344644,0.2773122489,0.0761466175,-0.174793601,-0.4415943921,-0.0911059156,-0.102224879,-0.2954936028,-0.3739978969,0.0812617689,0.2521750927,-0.3914754689,0.3138927817,-0.2583538592,-0.0910449699,-0.2865504622,-0.6029033065,-0.0353766754,-0.3672944009,0.1301860511,-0.0210305844,-0.0376563109,0.1890175045,0.0696759,0.0834097788,0.1277468204,-0.1776200682,-0.0701806992,0.0626241267,-0.2352707386,-0.2267147899,-0.3963588476,-0.2663238645,0.0232639667,-0.0971490294,0.0184576083,-0.0747963712,-0.2524099946,-0.3073821664,0.0471215248,-0.2438136786,-0.0458291695,-0.1258288324,0.0781514123,0.1519199461,0.1529132873,-0.2765621543,0.0869561657,0.0236247759,0.1931100935,-0.1012751758,-0.4148791432,0.4246083796,-0.0227168631,0.419367075,-0.1215194464,-0.2013075352,0.4556480646,0.2991711497,0.0201995485,-0.2214746773,-0.0691010207,0.1342205852,0.1100416481,0.192746684,-0.3594231606,0.2671031058,-0.2063522786,0.5962945223,0.3068863153,0.3887251019,0.2321982831,0.208765015,0.206719026,-0.1601665169,0.1008153111,0.3125140965,0.0413771458,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3937","title":"Missing languages in lvwerra\/github-code dataset","comments":"Can confirm that the two languages are indeed missing from the dataset. Here are the file counts per language:\r\n```Python\r\n{'Assembly': 82847,\r\n 'Batchfile': 236755,\r\n 'C': 14127969,\r\n 'C#': 6793439,\r\n 'C++': 7368473,\r\n 'CMake': 175076,\r\n 'CSS': 1733625,\r\n 'Dockerfile': 331966,\r\n 'FORTRAN': 141963,\r\n 'GO': 2259363,\r\n 'Haskell': 340521,\r\n 'HTML': 11165464,\r\n 'Java': 19515696,\r\n 'JavaScript': 11829024,\r\n 'Julia': 58177,\r\n 'Lua': 576279,\r\n 'Makefile': 679338,\r\n 'Markdown': 8454049,\r\n 'PHP': 11181930,\r\n 'Perl': 497490,\r\n 'PowerShell': 136827,\r\n 'Python': 7203553,\r\n 'Ruby': 4479767,\r\n 'Rust': 321765,\r\n 'SQL': 655657,\r\n 'Scala': 0,\r\n 'Shell': 1382786,\r\n 'TypeScript': 0,\r\n 'TeX': 250764,\r\n 'Visual Basic': 155371}\r\n ```","body":"Hi,\r\n\r\nI'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!\r\nI've noticed that two languages are missing from the dataset: TypeScript and Scala. \r\nLooks like they're also omitted from the query you used to get the original code.\r\nAre there any plans to add them in the future?\r\n\r\nThanks!","comment_length":82,"text":"Missing languages in lvwerra\/github-code dataset \n Hi,\r\n\r\nI'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!\r\nI've noticed that two languages are missing from the dataset: TypeScript and Scala. \r\nLooks like they're also omitted from the query you used to get the original code.\r\nAre there any plans to add them in the future?\r\n\r\nThanks! \n Can confirm that the two languages are indeed missing from the dataset. Here are the file counts per language:\r\n```Python\r\n{'Assembly': 82847,\r\n 'Batchfile': 236755,\r\n 'C': 14127969,\r\n 'C#': 6793439,\r\n 'C++': 7368473,\r\n 'CMake': 175076,\r\n 'CSS': 1733625,\r\n 'Dockerfile': 331966,\r\n 'FORTRAN': 141963,\r\n 'GO': 2259363,\r\n 'Haskell': 340521,\r\n 'HTML': 11165464,\r\n 'Java': 19515696,\r\n 'JavaScript': 11829024,\r\n 'Julia': 58177,\r\n 'Lua': 576279,\r\n 'Makefile': 679338,\r\n 'Markdown': 8454049,\r\n 'PHP': 11181930,\r\n 'Perl': 497490,\r\n 'PowerShell': 136827,\r\n 'Python': 7203553,\r\n 'Ruby': 4479767,\r\n 'Rust': 321765,\r\n 'SQL': 655657,\r\n 'Scala': 0,\r\n 'Shell': 1382786,\r\n 'TypeScript': 0,\r\n 'TeX': 250764,\r\n 'Visual Basic': 155371}\r\n ```","embeddings":[-0.0256799217,0.1923888773,-0.272978425,0.0706949309,0.2020145953,0.0939294174,0.1608109772,0.4261847734,0.1612200886,0.0164100043,0.0299240425,0.1567168236,-0.1640650928,0.1871640235,0.35605371,0.0112819923,0.1720342785,-0.0363806635,-0.1442353129,-0.3623968661,0.0428690352,0.4893033504,-0.0448550358,-0.2011829019,-0.0201171059,0.2023858726,0.0501632281,0.1495665461,-0.1876768172,-0.3277837634,0.2157064378,0.0212305468,0.19919689,0.2118963301,-0.000114927,-0.0877813995,0.0863537267,-0.2238592505,-0.0232068244,-0.1382261515,-0.1704271883,-0.3886404634,-0.1896992177,-0.0729190111,-0.0765108317,0.2006596327,0.0185858458,-0.8684791327,0.167550534,0.396325618,0.165584445,-0.1049965173,0.0950197056,-0.0473390371,0.6005458832,0.2446626127,-0.1110946238,0.0045627132,0.6433558464,-0.0545374677,0.1017592773,0.3778309524,0.2307787538,-0.4238677621,-0.3097911775,0.2437201887,-0.1349988133,-0.4171084166,0.1595044434,0.209173128,0.5050122142,-0.3456083834,-0.3171154857,-0.1488676369,-0.0719931722,-0.1449185312,-0.013686575,0.2147448212,0.1787126064,0.3075534105,-0.2559271753,-0.3309674263,-0.1833507419,0.095074296,0.3122915924,0.2448309362,0.1176178008,0.0319051594,-0.3635489941,0.168726787,-0.2604123652,-0.2298236787,0.1810751408,0.3094374835,-0.0332331173,-0.1114740744,0.3894372582,-0.0867943019,0.1757959574,0.1250641197,-0.0898582414,0.0366789065,-0.2458774149,-0.0889191255,0.0052509736,0.1662537009,0.2426173091,0.1828615814,0.3056692481,-0.2173210979,0.1982505471,0.0411541015,-0.1946377903,-0.0528803729,-0.1924161911,0.2890437245,0.0939515233,-0.1790701449,0.0215352476,0.2261706293,-0.2377768159,-0.4298742115,-0.1804881841,-0.0255478267,0.0555436499,0.195237413,-0.1271903366,0.4023526907,-0.2323301435,-0.5169322491,-0.1151397899,0.0975297466,-0.1740338951,-0.2245048583,0.233991459,0.0474520177,0.279204756,0.1765708178,0.083045885,0.1021993086,0.0476592705,-0.1507477015,-0.0696634054,0.029823577,-0.0290440656,0.1349362284,0.2338401228,-0.6994519234,-0.1566347331,0.3318850696,-0.0260336325,0.052001033,-0.2703883648,0.1629122049,-0.3328405321,-0.243332237,-0.3135769665,0.3694373369,0.0746406838,-0.024840245,0.0366607979,-0.3730119765,0.3528101146,-0.1458717138,0.3113859892,0.2940367758,-0.5485295653,-0.099531889,-0.0809826255,-0.1537146866,0.3043380082,0.2470981926,-0.1908096522,0.4138155282,-0.1726740897,0.3982334733,0.6215548515,-0.2511278093,0.0729145184,0.0038430926,0.2812716663,0.0188065935,-0.1251240224,-0.0253281873,0.468603611,-0.0294928048,-0.1222667769,0.1291634142,-0.2078344077,-0.1983129382,-0.304921031,-0.0549535155,0.440033555,0.2910797,0.4913585782,-0.2154752016,0.4613809586,-0.3264811337,0.3861170411,-0.1471496373,0.1962706596,0.3553467095,0.3257569671,0.2856219709,0.0735528544,-0.2616325319,-0.2005247027,0.1921277046,0.0240815971,0.0889694542,0.0817538574,0.1580335349,-0.0704458207,-0.0647432506,-0.1479939222,-0.1840772331,0.0580396131,0.1112365499,-0.2758400738,0.2813232839,-0.1926245987,-0.3013668656,0.0202851389,-0.0587166548,-0.3397951722,0.2525525093,-0.1264894158,-0.2254974395,0.028132882,0.2714188099,-0.0345955081,-0.123894386,0.0004115612,-0.1363062263,0.4618397653,-0.1485310942,0.847786665,0.3481523097,0.214210853,-0.3438704908,0.2147151828,0.1516044289,0.143413052,0.0498107746,-0.022262983,0.3566561639,0.0604473613,-0.0389884673,0.0707647651,0.0205818675,0.4183607399,0.0849836245,-0.2500813603,-0.1012433469,0.4832189679,0.1584945768,0.1189161241,0.0062757167,-0.3107276857,0.1957835406,0.7553414702,-0.2609541118,0.0381716117,0.0694723129,-0.0108820433,-0.087542206,-0.0716680288,-0.0205269866,0.1116082594,0.2628958225,-0.0033216549,-0.3195087016,0.0447812937,-0.2486177534,0.0360364318,-0.0551719442,0.0059100217,0.0046011964,0.1698198617,0.0078158127,-0.3027003407,-0.1755026877,-0.0265127774,-0.1036216766,0.0604038872,0.0437187254,-0.4280203581,0.2008347511,-0.2700701356,0.011656899,-0.3343424797,-0.2698161006,0.2113119662,-0.6460710764,-0.3205455542,0.2955998778,-0.0201215707,-0.1181030944,-0.4078666866,-0.0454626381,-0.0128839621,-0.3261486888,-0.3053866625,0.1186985448,0.1231827214,-0.4179112911,0.252843231,-0.2978475988,-0.0519907437,-0.2662030756,-0.5618523359,-0.0176684037,-0.3140754104,0.1210411638,-0.0570675768,-0.0579199791,0.2267974615,-0.0060050283,0.1174776778,0.1087980941,-0.1501116604,-0.1005290747,0.0360424817,-0.223323524,-0.1995150745,-0.3810917139,-0.3201039135,0.0059551345,-0.0586553365,0.1406604499,-0.0274050552,-0.2067942917,-0.2288929969,0.0622484051,-0.3089595139,-0.0047116578,-0.0045248484,0.0339223631,0.1601254493,0.0732488334,-0.2625448108,0.1496671885,0.0491000749,0.0893125087,-0.0438601337,-0.3809745908,0.4687342644,-0.1299924105,0.4926301837,-0.1772530526,-0.0493053868,0.3978644609,0.3220377266,-0.0267269909,-0.2125959247,-0.1232167557,0.1198210195,0.1230963916,0.160566166,-0.3566540182,0.2434087098,-0.1420389712,0.6082155704,0.2452193946,0.377094835,0.2305626422,0.198453784,0.272120297,-0.1513032466,0.0578439198,0.3715667129,0.0746241435,-0.034449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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3929","title":"Load a local dataset twice","comments":"Hi @caush, thanks for reporting:\r\n\r\nIn order to load local CSV files, you can use our \"csv\" loading script: https:\/\/huggingface.co\/docs\/datasets\/loading#csv\r\n```python\r\ndataset = load_dataset(\"csv\", data_files=[\"data\/file1.csv\", \"data\/file2.csv\"])\r\n```\r\nOR:\r\n```python\r\ndataset = load_dataset(\"csv\", data_dir=\"data\")\r\n```\r\n\r\nAlternatively, you may also use:\r\n```python\r\ndataset = load_dataset(\"data\")","body":"## Describe the bug\r\nLoad a local \"dataset\" composed of two csv files twice.\r\n\r\n## Steps to reproduce the bug\r\nPut the two joined files in a repository named \"Data\".\r\nThen in python:\r\n\r\nimport datasets as ds\r\nds.load_dataset('Data', data_files = {'file1.csv', 'file2.csv'})\r\n\r\n## Expected results\r\nShould give something like (because files have only one data row):\r\nTitle, clicks\r\nTruc et astuce, 123\r\nMachin, 12\r\n\r\n## Actual results\r\nGives \r\nTitle, clicks\r\nTruc et astuce, 123\r\nMachin, 12\r\nTruc et astuce, 123\r\nMachin, 12\r\n\r\n## Environment info\r\n[file1.csv](https:\/\/github.com\/huggingface\/datasets\/files\/8256322\/file1.csv)\r\n[file2.csv](https:\/\/github.com\/huggingface\/datasets\/files\/8256323\/file2.csv)\r\n- `datasets` version: 2.0.0\r\n- Platform: Linux-5.4.0-65-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.12\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.1","comment_length":43,"text":"Load a local dataset twice \n ## Describe the bug\r\nLoad a local \"dataset\" composed of two csv files twice.\r\n\r\n## Steps to reproduce the bug\r\nPut the two joined files in a repository named \"Data\".\r\nThen in python:\r\n\r\nimport datasets as ds\r\nds.load_dataset('Data', data_files = {'file1.csv', 'file2.csv'})\r\n\r\n## Expected results\r\nShould give something like (because files have only one data row):\r\nTitle, clicks\r\nTruc et astuce, 123\r\nMachin, 12\r\n\r\n## Actual results\r\nGives \r\nTitle, clicks\r\nTruc et astuce, 123\r\nMachin, 12\r\nTruc et astuce, 123\r\nMachin, 12\r\n\r\n## Environment info\r\n[file1.csv](https:\/\/github.com\/huggingface\/datasets\/files\/8256322\/file1.csv)\r\n[file2.csv](https:\/\/github.com\/huggingface\/datasets\/files\/8256323\/file2.csv)\r\n- `datasets` version: 2.0.0\r\n- Platform: Linux-5.4.0-65-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.12\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.1 \n Hi @caush, thanks for reporting:\r\n\r\nIn order to load local CSV files, you can use our \"csv\" loading script: https:\/\/huggingface.co\/docs\/datasets\/loading#csv\r\n```python\r\ndataset = load_dataset(\"csv\", data_files=[\"data\/file1.csv\", \"data\/file2.csv\"])\r\n```\r\nOR:\r\n```python\r\ndataset = load_dataset(\"csv\", data_dir=\"data\")\r\n```\r\n\r\nAlternatively, you may also use:\r\n```python\r\ndataset = load_dataset(\"data\")","embeddings":[-0.0047103693,-0.2396460027,-0.0901846886,0.2367398888,0.1813366264,-0.0256923586,0.3377517462,0.3137400448,0.3259290755,0.4739157259,-0.135358721,0.0657345578,0.2889903486,0.0849127769,-0.1292003393,-0.0246550851,0.1108901054,0.3750528991,-0.4979867637,-0.1276559681,-0.0599836931,0.1558846086,-0.0555315018,0.0140052494,0.0218015965,0.1187570989,-0.1177623421,0.342132777,0.0803649873,-0.0798497126,0.2447361946,-0.0108566694,-0.2600645721,0.5470013618,-0.0000996255,0.0467818491,0.1137756929,0.0974404663,-0.0518645123,-0.2566027343,-0.0131813623,-0.2362201661,0.2924904525,-0.26614815,-0.0897424817,-0.1656882465,-0.1953888237,-0.1446096152,0.4296991229,-0.0061171683,0.3310914636,0.3042972386,-0.1212056279,-0.2316205949,0.2522759736,0.0968968272,-0.0116504859,0.4617191255,0.290522486,-0.2931227088,-0.0854262561,0.1257819682,-0.1210966557,0.1045168638,0.0094837174,0.3125209808,-0.0285934005,0.0582046211,0.1627658904,0.041473303,0.029043803,-0.1557943225,-0.055157423,-0.3824268878,-0.0111551033,-0.1577393562,0.0965088829,0.1607369334,-0.11406032,0.2203674763,0.0295508541,0.2224082798,0.0803386346,-0.1080937013,0.1144281924,-0.0805475265,-0.1729045361,0.0695256591,0.2517786622,-0.0901609585,-0.1792602986,-0.0389345698,-0.0455418713,0.1281986833,-0.3417593539,0.1355651617,0.345233947,0.0046561956,-0.0258540977,-0.0090740165,0.1109966636,-0.043571528,-0.0378703326,0.2711826265,0.2839873135,0.1514650136,-0.0505814217,-0.0359105878,0.3308827579,0.2138857991,-0.1920468062,-0.0142660644,0.1853339374,-0.396725893,-0.0480785109,-0.0322356261,0.3155704737,-0.144417569,-0.3964407146,0.3185766339,-0.0756179988,0.0642360821,-0.0776362866,0.3774129748,-0.2132586539,0.1070840061,0.0780620947,0.1251471937,-0.2004325241,-0.0869366899,-0.3048515618,0.0128177451,-0.1841704994,-0.0909783617,0.1953280866,-0.2779326737,0.0845676139,0.4328107536,0.1537929624,-0.2405355722,-0.066037178,-0.1104364619,-0.1011219323,0.2968145907,0.3218608201,0.2312332988,0.2191809863,-0.3751527667,0.0833429545,0.1481825113,-0.1747110933,-0.0479247235,-0.3080783486,0.3341524005,0.0400526635,0.2966656983,0.0062831207,0.0175572094,0.1565216035,-0.0611720234,0.076798521,0.1986530572,-0.2821182013,-0.1887473166,0.1655863822,0.3856534064,-0.2357445806,0.1085917428,0.2396053821,-0.1052378342,0.0915284678,0.3889173865,-0.272827208,0.0024754929,-0.3675377667,0.2460533977,0.0355188437,-0.3720150888,-0.3235742152,0.2362668365,-0.4288937151,0.1070050374,0.3266941905,-0.1531018466,-0.0544219054,-0.0642737448,0.2585344315,0.0789304152,0.0115897842,0.1504639834,-0.3756908476,-0.1237975284,0.0271944515,-0.1239465475,-0.2003208399,-0.0301454347,0.2965587974,-0.3555016816,0.4262883365,-0.2291593701,0.0737287849,0.1765636206,0.176980868,0.1662889421,0.2048566192,-0.1488470286,-0.4092324972,0.1284102947,-0.2241293043,-0.0464653857,-0.0598257519,-0.4873334169,-0.500721693,0.0193823799,-0.1815128773,-0.0848917365,0.2655279934,0.0896145254,-0.1299764514,0.0553810075,0.0232901908,0.0765483528,-0.1281058043,-0.0181133859,-0.2700134516,0.2697791755,-0.1623060405,-0.0178804658,-0.0501279384,0.1319673061,0.2050862014,-0.0291053504,0.0126267727,0.4310972095,-0.1858830005,0.5137690306,0.198597163,-0.2225104421,0.0997763127,0.0552196428,-0.1412791312,0.1407584846,0.280433476,-0.0787327066,-0.3607139885,0.4162096381,-0.1504447758,0.0369891897,0.1484325081,-0.1506400853,0.2041638792,-0.2732310295,0.0556389838,-0.3459355235,0.2634032369,0.2447438985,0.0985720828,0.2858408093,-0.6951723099,0.1613116264,0.225056842,-0.0620456412,0.0635553673,-0.0882728323,-0.1729615033,-0.0760081112,-0.122185871,0.2564500868,0.422717005,0.2277113646,0.1628244668,0.0713812634,-0.0438509844,-0.349973768,0.0266199708,-0.2437190115,-0.0794361383,0.5034421086,0.2667397559,-0.091555953,-0.455887109,0.0710245222,0.0228229258,0.1413589269,-0.3404091299,-0.098347649,-0.1996728778,-0.3038913906,-0.4504398108,-0.0599072613,0.1469738036,-0.1657044142,0.0457104705,0.4463579059,-0.3106680512,0.0832664818,0.0113632912,0.1508831084,0.0220251977,0.1434856951,0.061366383,0.0703353211,-0.2074864805,0.2287394553,0.1455427855,0.2019868046,0.2303654552,-0.0208170824,-0.0446992256,-0.1016834304,-0.2147222757,-0.0620657466,0.0438059457,0.2336656153,-0.0233602338,0.1378122121,-0.1526357532,-0.373069495,0.3301250637,0.0249604713,-0.2775484622,-0.1020512283,0.0398393348,-0.0137038529,-0.5017806888,-0.2886809409,-0.0118243834,-0.2671646774,0.3139075637,-0.0913456231,0.0927057639,0.0651019439,0.0210981611,0.1291800886,-0.0504977293,0.2391234189,-0.3536321819,-0.2593668997,0.1579990983,-0.3354128599,-0.4113503993,-0.2560961843,-0.0732127205,-0.0531082153,-0.3990424275,-0.2729157805,-0.3575667143,-0.284383893,0.1733621657,-0.0093268696,0.0898803324,0.1520979106,-0.0012281134,-0.3564863801,-0.2294594795,-0.0361367054,0.1663166285,0.0678069592,-0.3075997233,-0.0907313302,0.0960076451,0.0307710487,0.4480381012,0.2505360246,0.2274311185,0.2257124484,-0.1779827327,0.410353303,-0.0834574848,-0.5407409072,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3928","title":"Frugal score deprecations","comments":"Hi @Ierezell, thanks for reporting.\r\n\r\nI'm making a PR to suppress those logs from the terminal. ","body":"## Describe the bug\r\nThe frugal score returns a really verbose output with warnings that can be easily changed. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets.load import load_metric\r\n\r\nfrugal = load_metric(\"frugalscore\")\r\nfrugal.compute(predictions=[\"Do you like spinachis\"], references=[\"Do you like spinach\"])\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n```\r\n{'scores': [0.9946]}\r\n```\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nPyTorch: setting up devices\r\nThe default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 864.09ba\/s]\r\nUsing amp half precision backend\r\nThe following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1. If sentence2, sentence1 are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\r\n***** Running Prediction *****\r\n  Num examples = 1\r\n  Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 4644.85it\/s]\r\n {'scores': [0.9946]}\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n","comment_length":16,"text":"Frugal score deprecations \n ## Describe the bug\r\nThe frugal score returns a really verbose output with warnings that can be easily changed. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets.load import load_metric\r\n\r\nfrugal = load_metric(\"frugalscore\")\r\nfrugal.compute(predictions=[\"Do you like spinachis\"], references=[\"Do you like spinach\"])\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n```\r\n{'scores': [0.9946]}\r\n```\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nPyTorch: setting up devices\r\nThe default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 864.09ba\/s]\r\nUsing amp half precision backend\r\nThe following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1. If sentence2, sentence1 are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\r\n***** Running Prediction *****\r\n  Num examples = 1\r\n  Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 4644.85it\/s]\r\n {'scores': [0.9946]}\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n \n Hi @Ierezell, thanks for reporting.\r\n\r\nI'm making a PR to suppress those logs from the terminal. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3920","title":"'datasets.features' is not a package","comments":"Hi @Arij-Aladel,\r\n\r\nYou are using a very old version of our library `datasets`: 1.8.0\r\nCurrent version is 2.0.0 (and the previous one was 1.18.4)\r\n\r\nPlease, try to update `datasets` library and check if the problem persists:\r\n```shell\r\n\/env\/bin\/pip install -U datasets","body":"@albertvillanova \r\npython 3.9\r\nos: ubuntu 20.04\r\n\r\nIn conda environment\r\n\r\ntorch installed by\r\n\r\n\r\n```\/env\/bin\/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html```\r\ndatasets package is installed by \r\n```\r\n\/env\/bin\/pip  install datasets==1.8.0\r\n\r\n```\r\nDuring runing the code I have this error \r\n\r\n\r\n```\r\n[6]<stderr>:  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n[6]<stderr>:    return super().find_class(mod_name, name)\r\n[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n\r\n```\r\nprecisely this error appears when \r\n\r\ntorch.load('data_file.pt')\r\n```\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 607, in load\r\n    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 882, in _load\r\n    result = unpickler.load()\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n    return super().find_class(mod_name, name)\r\nModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n```\r\n\r\nWhy I am getting this error?\r\n","comment_length":41,"text":"'datasets.features' is not a package \n @albertvillanova \r\npython 3.9\r\nos: ubuntu 20.04\r\n\r\nIn conda environment\r\n\r\ntorch installed by\r\n\r\n\r\n```\/env\/bin\/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html```\r\ndatasets package is installed by \r\n```\r\n\/env\/bin\/pip  install datasets==1.8.0\r\n\r\n```\r\nDuring runing the code I have this error \r\n\r\n\r\n```\r\n[6]<stderr>:  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n[6]<stderr>:    return super().find_class(mod_name, name)\r\n[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n\r\n```\r\nprecisely this error appears when \r\n\r\ntorch.load('data_file.pt')\r\n```\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 607, in load\r\n    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 882, in _load\r\n    result = unpickler.load()\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n    return super().find_class(mod_name, name)\r\nModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n```\r\n\r\nWhy I am getting this error?\r\n \n Hi @Arij-Aladel,\r\n\r\nYou are using a very old version of our library `datasets`: 1.8.0\r\nCurrent version is 2.0.0 (and the previous one was 1.18.4)\r\n\r\nPlease, try to update `datasets` library and check if the problem persists:\r\n```shell\r\n\/env\/bin\/pip install -U datasets","embeddings":[-0.3741754293,-0.2259199321,-0.0957691446,0.3859058022,0.326772213,0.1350311935,0.2670206726,0.2414320111,-0.3361454606,-0.1415685117,0.2684737742,0.5509800911,-0.2597140074,-0.1125489175,-0.0667067692,-0.2506271899,0.1918473542,0.0886711031,-0.1753994673,0.0506082214,-0.1020834371,0.1772280037,-0.3086731732,0.1046833694,-0.4401827157,-0.1291004419,0.057662487,0.0011672718,-0.1308832616,-0.3993217945,0.1733085513,-0.1656587869,0.2008814514,0.5336257219,-0.0001102442,-0.102281861,0.3339243829,-0.235067144,-0.4051907361,-0.2696241438,0.2239958346,-0.344866246,0.1405482888,-0.320517689,-0.064861156,-0.1218972579,-0.1956815273,-0.3246592283,-0.0889194533,0.1999193728,0.2752568424,0.2673619986,0.341452539,-0.1077919304,0.1969591379,0.2647695243,-0.2433831096,0.087219663,0.2781101763,0.0932171717,0.185800463,0.3920754194,-0.4564202428,0.1995441765,0.5340098143,-0.1901662499,-0.4218783379,-0.3149923086,0.1190566719,0.1065387428,0.7960877419,-0.3633450866,-0.2871998549,-0.008440108,0.0209251028,-0.1926267892,0.177324757,0.2702406943,-0.1127576753,-0.065493755,-0.0351170972,-0.1235674918,0.1466792226,0.087388292,0.0234811753,0.2700716853,-0.2394395322,0.2198880762,-0.0778222308,-0.1512328684,0.2789128721,0.0992508009,0.2550493479,0.1950050294,-0.1274664551,0.0593905561,0.035875462,-0.5418815613,-0.0232456718,0.0867031664,-0.0389792211,-0.0078039696,0.0980109721,0.0533464327,-0.0426887944,0.2271229029,0.115377672,0.4202689528,0.0367598422,-0.1019454524,0.1479976177,-0.0050263316,-0.2203567326,-0.1163354963,-0.0239355918,0.2413001508,0.2839538753,-0.0653355047,-0.1946765035,0.0895602852,-0.1148720905,-0.1264280081,0.018678179,0.3936136663,0.0617713705,0.1892162114,0.2496029884,0.0898541361,-0.1363843083,0.1349422038,-0.1018899754,0.2601060569,-0.1946331859,-0.148253113,0.2276373804,-0.3055005968,0.3545936346,-0.2395033836,0.0195270181,0.0681515783,0.0189619567,-0.1080701649,0.0093236445,0.2394512445,-0.1829985231,0.2677110434,0.2797504067,0.6432611942,-0.3090865612,0.2902591228,-0.2692390382,-0.3517669737,-0.285200268,0.195899725,0.1038900316,0.0913799182,0.1703742445,-0.2150213867,0.2958919108,-0.3047992885,0.061851386,-0.1140145883,0.0703654066,-0.2477861792,0.090305917,0.2864120305,-0.4072782993,-0.2413600087,0.1788346022,-0.0406975783,0.0558572486,-0.3058131635,-0.2195417732,0.330498755,-0.2853322625,-0.1353888512,0.3486598134,-0.2800932229,-0.3074306846,0.0020025533,-0.081931673,-0.1549833566,0.0819935724,0.1981034428,0.0055795279,0.1080358997,0.1426399052,0.1517225951,0.3037721217,-0.199518621,-0.0355087332,-0.2358683646,0.3622281849,0.1398118585,0.3074507713,-0.0239192657,0.0582453012,0.0744278654,-0.0656486601,0.0496709719,-0.1429607719,0.3088869452,0.0822017714,0.3442079127,0.0646340102,-0.2338404804,-0.1789039522,0.1157387048,-0.0391768143,-0.0221634563,-0.4385868311,0.1039018184,0.0143684484,0.0288831703,-0.2115475386,-0.295609504,0.129686296,0.175942257,-0.1191499606,-0.1143187881,-0.3149427474,0.5230892897,0.1073751301,0.0615914278,-0.3357559443,0.2151219994,-0.3341527581,-0.3324249983,-0.0113322949,0.2416027784,0.452786237,-0.1976841092,-0.175453797,0.2943202853,-0.1112557799,-0.2471630722,-0.1432566792,-0.17965509,0.0906139314,-0.2848007977,0.2881484032,0.258849293,0.1336264759,0.0399493389,0.1715025604,0.1567478627,0.1792506725,0.042357605,0.1538583189,0.1563338786,0.1408084482,-0.013587215,0.0491760857,-0.0572556369,0.3017376661,0.0613723956,0.115772672,0.0887286812,-0.2141780853,-0.158018887,0.2411473542,-0.062527746,0.1126916632,0.0264636651,-0.3545201421,0.3017967641,0.306261301,-0.5455057025,0.4573797882,0.1624426395,-0.208591029,-0.0547160506,0.0878469646,0.1344629675,0.2282803357,0.1700317115,-0.070123069,0.2421507686,-0.1418666542,0.235649243,-0.0488916524,-0.277487427,-0.1123033985,0.0452267602,-0.3060614467,0.1804015487,-0.2832740843,0.0769763067,-0.3119251132,-0.3987560272,-0.1912996173,-0.0405015387,-0.1143326387,0.0967390463,0.2792671621,0.3615099192,-0.1512787491,-0.1177882105,0.3305978477,-0.5154699683,0.1445988715,0.078823179,-0.1144845411,0.0261661988,0.0128739541,0.0196784735,0.3810624778,-0.1107430831,0.1932116002,-0.3125234246,-0.1834489405,0.2537640035,-0.2077497989,-0.1284194142,0.2780032754,-0.0878008157,0.1190975681,-0.3930118978,0.2900563776,-0.1466547847,-0.1200963631,0.1572497785,0.0697195679,-0.2599533498,-0.0257712957,-0.7031169534,-0.4141612351,-0.4053668082,-0.1633957177,0.097940214,0.1934714764,0.1647307426,0.300988853,0.2713820934,0.2801977694,0.271680057,-0.0801217034,-0.1201182604,0.355294615,-0.1376410127,-0.281904459,0.0476395376,-0.0178221203,0.0919326022,0.3245996833,-0.3015490472,-0.0786707401,0.0980437323,0.2499044687,-0.3075302243,0.287167877,0.4976472259,0.2648240328,-0.0883166417,-0.1202771738,-0.1288150698,-0.2121380866,-0.2743762136,0.5441571474,0.0898794383,0.3591863811,-0.0874368697,0.7763411403,0.1134192124,-0.310706377,0.2981353402,0.0501732603,0.3941994011,-0.1232939661,-0.5466144085,0.1964439452,-0.1708875299,0.0900068507,-0.02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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3920","title":"'datasets.features' is not a package","comments":"The problem I can no I have build my project on this version and old version on transformers. I have preprocessed the data again to use it. Thank for your reply","body":"@albertvillanova \r\npython 3.9\r\nos: ubuntu 20.04\r\n\r\nIn conda environment\r\n\r\ntorch installed by\r\n\r\n\r\n```\/env\/bin\/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html```\r\ndatasets package is installed by \r\n```\r\n\/env\/bin\/pip  install datasets==1.8.0\r\n\r\n```\r\nDuring runing the code I have this error \r\n\r\n\r\n```\r\n[6]<stderr>:  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n[6]<stderr>:    return super().find_class(mod_name, name)\r\n[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n\r\n```\r\nprecisely this error appears when \r\n\r\ntorch.load('data_file.pt')\r\n```\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 607, in load\r\n    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 882, in _load\r\n    result = unpickler.load()\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n    return super().find_class(mod_name, name)\r\nModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n```\r\n\r\nWhy I am getting this error?\r\n","comment_length":31,"text":"'datasets.features' is not a package \n @albertvillanova \r\npython 3.9\r\nos: ubuntu 20.04\r\n\r\nIn conda environment\r\n\r\ntorch installed by\r\n\r\n\r\n```\/env\/bin\/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html```\r\ndatasets package is installed by \r\n```\r\n\/env\/bin\/pip  install datasets==1.8.0\r\n\r\n```\r\nDuring runing the code I have this error \r\n\r\n\r\n```\r\n[6]<stderr>:  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n[6]<stderr>:    return super().find_class(mod_name, name)\r\n[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n\r\n```\r\nprecisely this error appears when \r\n\r\ntorch.load('data_file.pt')\r\n```\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 607, in load\r\n    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 882, in _load\r\n    result = unpickler.load()\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n    return super().find_class(mod_name, name)\r\nModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n```\r\n\r\nWhy I am getting this error?\r\n \n The problem I can no I have build my project on this version and old version on transformers. I have preprocessed the data again to use it. Thank for your reply","embeddings":[-0.3741754293,-0.2259199321,-0.0957691446,0.3859058022,0.326772213,0.1350311935,0.2670206726,0.2414320111,-0.3361454606,-0.1415685117,0.2684737742,0.5509800911,-0.2597140074,-0.1125489175,-0.0667067692,-0.2506271899,0.1918473542,0.0886711031,-0.1753994673,0.0506082214,-0.1020834371,0.1772280037,-0.3086731732,0.1046833694,-0.4401827157,-0.1291004419,0.057662487,0.0011672718,-0.1308832616,-0.3993217945,0.1733085513,-0.1656587869,0.2008814514,0.5336257219,-0.0001102442,-0.102281861,0.3339243829,-0.235067144,-0.4051907361,-0.2696241438,0.2239958346,-0.344866246,0.1405482888,-0.320517689,-0.064861156,-0.1218972579,-0.1956815273,-0.3246592283,-0.0889194533,0.1999193728,0.2752568424,0.2673619986,0.341452539,-0.1077919304,0.1969591379,0.2647695243,-0.2433831096,0.087219663,0.2781101763,0.0932171717,0.185800463,0.3920754194,-0.4564202428,0.1995441765,0.5340098143,-0.1901662499,-0.4218783379,-0.3149923086,0.1190566719,0.1065387428,0.7960877419,-0.3633450866,-0.2871998549,-0.008440108,0.0209251028,-0.1926267892,0.177324757,0.2702406943,-0.1127576753,-0.065493755,-0.0351170972,-0.1235674918,0.1466792226,0.087388292,0.0234811753,0.2700716853,-0.2394395322,0.2198880762,-0.0778222308,-0.1512328684,0.2789128721,0.0992508009,0.2550493479,0.1950050294,-0.1274664551,0.0593905561,0.035875462,-0.5418815613,-0.0232456718,0.0867031664,-0.0389792211,-0.0078039696,0.0980109721,0.0533464327,-0.0426887944,0.2271229029,0.115377672,0.4202689528,0.0367598422,-0.1019454524,0.1479976177,-0.0050263316,-0.2203567326,-0.1163354963,-0.0239355918,0.2413001508,0.2839538753,-0.0653355047,-0.1946765035,0.0895602852,-0.1148720905,-0.1264280081,0.018678179,0.3936136663,0.0617713705,0.1892162114,0.2496029884,0.0898541361,-0.1363843083,0.1349422038,-0.1018899754,0.2601060569,-0.1946331859,-0.148253113,0.2276373804,-0.3055005968,0.3545936346,-0.2395033836,0.0195270181,0.0681515783,0.0189619567,-0.1080701649,0.0093236445,0.2394512445,-0.1829985231,0.2677110434,0.2797504067,0.6432611942,-0.3090865612,0.2902591228,-0.2692390382,-0.3517669737,-0.285200268,0.195899725,0.1038900316,0.0913799182,0.1703742445,-0.2150213867,0.2958919108,-0.3047992885,0.061851386,-0.1140145883,0.0703654066,-0.2477861792,0.090305917,0.2864120305,-0.4072782993,-0.2413600087,0.1788346022,-0.0406975783,0.0558572486,-0.3058131635,-0.2195417732,0.330498755,-0.2853322625,-0.1353888512,0.3486598134,-0.2800932229,-0.3074306846,0.0020025533,-0.081931673,-0.1549833566,0.0819935724,0.1981034428,0.0055795279,0.1080358997,0.1426399052,0.1517225951,0.3037721217,-0.199518621,-0.0355087332,-0.2358683646,0.3622281849,0.1398118585,0.3074507713,-0.0239192657,0.0582453012,0.0744278654,-0.0656486601,0.0496709719,-0.1429607719,0.3088869452,0.0822017714,0.3442079127,0.0646340102,-0.2338404804,-0.1789039522,0.1157387048,-0.0391768143,-0.0221634563,-0.4385868311,0.1039018184,0.0143684484,0.0288831703,-0.2115475386,-0.295609504,0.129686296,0.175942257,-0.1191499606,-0.1143187881,-0.3149427474,0.5230892897,0.1073751301,0.0615914278,-0.3357559443,0.2151219994,-0.3341527581,-0.3324249983,-0.0113322949,0.2416027784,0.452786237,-0.1976841092,-0.175453797,0.2943202853,-0.1112557799,-0.2471630722,-0.1432566792,-0.17965509,0.0906139314,-0.2848007977,0.2881484032,0.258849293,0.1336264759,0.0399493389,0.1715025604,0.1567478627,0.1792506725,0.042357605,0.1538583189,0.1563338786,0.1408084482,-0.013587215,0.0491760857,-0.0572556369,0.3017376661,0.0613723956,0.115772672,0.0887286812,-0.2141780853,-0.158018887,0.2411473542,-0.062527746,0.1126916632,0.0264636651,-0.3545201421,0.3017967641,0.306261301,-0.5455057025,0.4573797882,0.1624426395,-0.208591029,-0.0547160506,0.0878469646,0.1344629675,0.2282803357,0.1700317115,-0.070123069,0.2421507686,-0.1418666542,0.235649243,-0.0488916524,-0.277487427,-0.1123033985,0.0452267602,-0.3060614467,0.1804015487,-0.2832740843,0.0769763067,-0.3119251132,-0.3987560272,-0.1912996173,-0.0405015387,-0.1143326387,0.0967390463,0.2792671621,0.3615099192,-0.1512787491,-0.1177882105,0.3305978477,-0.5154699683,0.1445988715,0.078823179,-0.1144845411,0.0261661988,0.0128739541,0.0196784735,0.3810624778,-0.1107430831,0.1932116002,-0.3125234246,-0.1834489405,0.2537640035,-0.2077497989,-0.1284194142,0.2780032754,-0.0878008157,0.1190975681,-0.3930118978,0.2900563776,-0.1466547847,-0.1200963631,0.1572497785,0.0697195679,-0.2599533498,-0.0257712957,-0.7031169534,-0.4141612351,-0.4053668082,-0.1633957177,0.097940214,0.1934714764,0.1647307426,0.300988853,0.2713820934,0.2801977694,0.271680057,-0.0801217034,-0.1201182604,0.355294615,-0.1376410127,-0.281904459,0.0476395376,-0.0178221203,0.0919326022,0.3245996833,-0.3015490472,-0.0786707401,0.0980437323,0.2499044687,-0.3075302243,0.287167877,0.4976472259,0.2648240328,-0.0883166417,-0.1202771738,-0.1288150698,-0.2121380866,-0.2743762136,0.5441571474,0.0898794383,0.3591863811,-0.0874368697,0.7763411403,0.1134192124,-0.310706377,0.2981353402,0.0501732603,0.3941994011,-0.1232939661,-0.5466144085,0.1964439452,-0.1708875299,0.0900068507,-0.02504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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3919","title":"AttributeError: 'DatasetDict' object has no attribute 'features'","comments":"You are likely trying to get the `features` from a `DatasetDict`, a dictionary containing `Datasets`. You probably first want to index into a particular split from your `DatasetDict` i.e. `dataset['train'].features`. \r\n\r\nFor example \r\n\r\n```python \r\nds = load_dataset('mnist')\r\nds.features\r\n```\r\nReturns \r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError                            Traceback (most recent call last)\r\n\r\n[<ipython-input-39-791c1f9df6c2>](https:\/\/localhost:8080\/#) in <module>()\r\n----> 1 ds.features\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nIf we look at the dataset variable, we see it is a `DatasetDict`:\r\n\r\n```python \r\nprint(ds)\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['image', 'label'],\r\n        num_rows: 60000\r\n    })\r\n    test: Dataset({\r\n        features: ['image', 'label'],\r\n        num_rows: 10000\r\n    })\r\n})\r\n```\r\n\r\nWe can grab the features from a split by indexing into `train`:\r\n```python\r\nds['train'].features\r\n{'image': Image(decode=True, id=None),\r\n 'label': ClassLabel(num_classes=10, names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], id=None)}\r\n```\r\n\r\nHope that helps ","body":"## Describe the bug\r\nReceiving the error when trying to check for Dataset features\r\n\r\n## Steps to reproduce the bug\r\nfrom datasets import Dataset\r\ndataset = Dataset.from_pandas(df[['id', 'words', 'bboxes', 'ner_tags', 'image_path']])\r\n\r\ndataset.features\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nGetting the following errror\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n\r\n## Environment info\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 1.18.4\r\n- Platform: Linux-4.14.252-131.483.amzn1.x86_64-x86_64-with-glibc2.9\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n","comment_length":129,"text":"AttributeError: 'DatasetDict' object has no attribute 'features' \n ## Describe the bug\r\nReceiving the error when trying to check for Dataset features\r\n\r\n## Steps to reproduce the bug\r\nfrom datasets import Dataset\r\ndataset = Dataset.from_pandas(df[['id', 'words', 'bboxes', 'ner_tags', 'image_path']])\r\n\r\ndataset.features\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nGetting the following errror\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n\r\n## Environment info\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 1.18.4\r\n- Platform: Linux-4.14.252-131.483.amzn1.x86_64-x86_64-with-glibc2.9\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n \n You are likely trying to get the `features` from a `DatasetDict`, a dictionary containing `Datasets`. You probably first want to index into a particular split from your `DatasetDict` i.e. `dataset['train'].features`. \r\n\r\nFor example \r\n\r\n```python \r\nds = load_dataset('mnist')\r\nds.features\r\n```\r\nReturns \r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError                            Traceback (most recent call last)\r\n\r\n[<ipython-input-39-791c1f9df6c2>](https:\/\/localhost:8080\/#) in <module>()\r\n----> 1 ds.features\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nIf we look at the dataset variable, we see it is a `DatasetDict`:\r\n\r\n```python \r\nprint(ds)\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['image', 'label'],\r\n        num_rows: 60000\r\n    })\r\n    test: Dataset({\r\n        features: ['image', 'label'],\r\n        num_rows: 10000\r\n    })\r\n})\r\n```\r\n\r\nWe can grab the features from a split by indexing into `train`:\r\n```python\r\nds['train'].features\r\n{'image': Image(decode=True, id=None),\r\n 'label': ClassLabel(num_classes=10, names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], id=None)}\r\n```\r\n\r\nHope that helps 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3918","title":"datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files","comments":"Hi @willowdong! These issues were fixed on master. We will have a new release of `datasets` later today. In the meantime, you can avoid these issues by installing `datasets` from master as follows:\r\n```bash\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```","body":"## Describe the bug\r\nCan't load the dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('multi_news')\r\ndataset_2=load_dataset(\"reddit_tifu\", \"long\")\r\n\r\n\r\n## Actual results\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.4\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.0\r\n- PyArrow version: 6.0.1\r\n","comment_length":38,"text":"datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files \n ## Describe the bug\r\nCan't load the dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('multi_news')\r\ndataset_2=load_dataset(\"reddit_tifu\", \"long\")\r\n\r\n\r\n## Actual results\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.4\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.0\r\n- PyArrow version: 6.0.1\r\n \n Hi @willowdong! These issues were fixed on master. We will have a new release of `datasets` later today. In the meantime, you can avoid these issues by installing `datasets` from master as follows:\r\n```bash\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```","embeddings":[-0.3251985013,0.1044453755,-0.1208982319,0.194041878,0.2922667563,0.0498711243,0.121732451,0.3344368041,0.0943165347,0.1404136866,-0.1278533041,0.2029479444,0.0496360473,0.0912996754,-0.2518992126,0.2652892172,0.0160791725,-0.0456768498,-0.1795180738,-0.0238718539,-0.2421694994,0.2304715514,-0.1558197886,-0.1697168499,-0.1419748515,0.1738641113,0.0524106808,0.2872042656,-0.0756199062,-0.2301062196,0.1201064736,0.166470319,-0.0833464041,0.555454433,-0.000103766,0.1744946241,0.3435874879,-0.0208406001,-0.166346699,-0.3262065351,-0.4284004867,-0.3796931803,-0.1170199737,-0.2062368095,-0.01846857,0.078329578,-0.0458128415,-0.1120775267,0.214217782,0.4172297716,0.3292240202,0.4237022698,0.1001986712,-0.0719451308,0.3064060211,-0.1023229212,-0.031043686,0.1829224378,0.0206572451,-0.0657716691,-0.0972566977,0.3408417404,-0.1711777896,0.1619863659,-0.1531766057,-0.0198604539,0.0802557021,-0.1836100668,0.2516777813,0.3387814164,0.3033501208,-0.1283685118,-0.312238276,-0.0575103536,-0.1006444991,0.0861350447,0.3719235957,0.2933354676,-0.0934509262,0.1883303076,-0.4651943445,0.1557196975,0.0345456526,0.2467764169,0.093736589,0.0685843974,-0.0278183371,0.0301929433,0.0749739259,-0.1127654091,0.4349053204,-0.3108541071,0.057659395,-0.0511789247,-0.4282957315,-0.0063118339,0.1534122676,0.264636606,0.3667002916,0.1967823505,0.0601167046,0.1350749731,-0.320579797,0.2007720768,0.0059418157,-0.0350745507,-0.0690990537,0.1592850387,0.360861659,0.1941220909,-0.0950049907,0.1182544455,-0.0038035591,-0.2770170271,0.2882758677,0.0128536234,0.2886620462,-0.341440767,-0.3861628473,0.1806574762,0.0578779317,-0.2149212211,0.1329744756,0.4688464105,-0.279830426,0.1832853705,-0.1679286361,0.229931742,-0.1847196519,-0.0755023956,-0.3305081725,-0.0281458981,-0.1138716713,-0.0106404051,0.2079799324,-0.1856898963,0.337287128,0.0825712234,0.1155614182,-0.1586877853,0.1571108997,-0.1422626078,-0.0416096114,0.3967611194,0.1231288761,0.1533685774,0.2573885322,-0.0122001208,-0.0799766555,0.1779552102,-0.3896894455,-0.206582725,-0.0382684581,0.3383522928,-0.2754590809,-0.0832758546,-0.0892010033,-0.0960428342,0.2325134575,-0.1623981446,-0.0351919681,-0.1769384444,-0.1138744578,-0.2432812601,0.1807838529,0.2362620234,-0.0967481807,0.0320524983,0.0143092163,-0.1599157006,0.1947906315,0.2277045399,-0.1625549793,-0.0287955217,-0.1713344157,-0.017227523,0.2478479445,-0.2369933426,-0.5096578002,0.065347366,-0.0739465803,0.2944977283,0.1033963189,0.0532837845,-0.1902154982,-0.0483780913,0.1423814297,0.2900881469,0.1312149912,0.2479990721,-0.3040179014,-0.319929719,0.1130616218,0.1988230348,0.1305348426,-0.0116921719,0.2603779733,-0.2279060334,0.3221948147,-0.1326315105,-0.1002600193,0.2756309509,0.2547568977,0.0810410604,0.0635156184,-0.2972928286,-0.4121532142,0.2457030714,-0.0383021571,-0.016514685,-0.0944481045,-0.1573406011,-0.3147000074,-0.0892280489,-0.1588063538,-0.0179576762,0.2392259687,0.3178259134,0.2121145427,0.0424904563,-0.0707752705,0.2960853577,-0.2416674495,-0.0448920988,-0.2910603285,0.5378289223,-0.2003883868,-0.0581432283,0.2150563002,0.026643686,0.2615156174,-0.1005899906,-0.1682827175,0.5476431847,0.1651141346,0.1127801389,0.1127052829,0.2415401191,-0.0011959404,-0.4421369731,-0.0753177851,0.6118803024,0.0621359497,-0.0758353323,-0.3208577335,0.3685757816,-0.1385652125,0.1292603612,0.1297173351,0.0541939698,0.3685263395,-0.2475692779,-0.0145097272,-0.1644444764,0.2934492826,-0.1132425368,-0.0618822984,0.0249494277,-0.1543203741,-0.0227825399,0.4171395302,-0.0300076883,0.0559671149,0.1548723578,0.0113444841,-0.1598731428,-0.0979764238,0.4290879667,0.3949562013,0.2348757833,0.025972005,0.0385707058,-0.1639005542,-0.2216146141,0.1419147402,0.0329905748,0.1728031337,0.3657892644,0.2601340413,-0.0630248263,-0.3595382273,-0.1961605102,0.0646399856,0.2093335837,-0.437587738,-0.0943975747,-0.2645992041,-0.0252461899,-0.12750265,-0.0417168364,-0.188517496,-0.4284150898,0.008742135,0.2958369553,-0.0929614231,0.2532458603,-0.227134034,-0.0029734916,0.1672322154,-0.0196525604,0.111453414,-0.0922431797,0.0206035711,0.1587139219,0.377122432,0.0559357256,0.2599858344,-0.2830976546,-0.1039594561,-0.5253942609,-0.2716299891,0.0108476421,-0.1509878188,0.2281978428,0.1280445009,0.1844862103,-0.0059726047,-0.1572434604,0.3338563144,-0.2140404731,-0.2097415775,0.2373197675,0.0135373622,-0.0789201781,0.0980429426,-0.2553719878,-0.2656974792,-0.3681038916,-0.052588217,0.1545404941,0.1965331584,0.1734976023,0.2003933042,0.1242668927,0.1174331307,0.2431342155,-0.3164876699,-0.4715267718,0.5315617323,-0.2029938996,-0.4594942629,-0.1220105588,0.09769959,0.0731918067,0.1219043061,-0.5819919109,-0.3393842578,-0.343716085,0.0835421383,0.1165413335,0.102776818,0.1815557927,0.0183460489,-0.2554383278,-0.2616177201,-0.0955472961,-0.0749543905,-0.1663520783,0.2495718747,-0.1013023555,0.138745293,-0.0595767014,0.3682270944,0.391356647,0.0895824954,0.129749611,0.0178748779,0.4014896154,-0.1094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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3906","title":"NonMatchingChecksumError on Spider dataset ","comments":"Hi @kolk, thanks for reporting.\r\n\r\nIndeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change:\r\n- #3787 \r\n\r\nWe just made patch release last week: 1.18.4 https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.18.4\r\n\r\nPlease, feel free to update your local `datasets` version, so that you get the fix:\r\n```shell\r\npip install -U datasets\r\n```","body":"## Describe the bug\r\nFailure to generate dataset ```spider``` because of checksums error for dataset source files.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nspider = load_dataset(\"spider\")\r\n```\r\n\r\n\r\n## Expected results\r\nChecksums should match for files from url ['https:\/\/drive.google.com\/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']\r\n\r\n## Actual results\r\n```\r\n>>> load_dataset(\"spider\")\r\nload_dataset(\"spider\")\r\nDownloading and preparing dataset spider\/spider (download: 95.12 MiB, generated: 5.17 MiB, post-processed: Unknown size, total: 100.29 MiB) to \/home\/user\/.cache\/huggingface\/datasets\/spider\/spider\/1.0.0\/79778ebea87c59b19411f1eb3eda317e9dd5f7788a556d837ef25c3ae6e5e8b7...\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/IPython\/core\/interactiveshell.py\", line 3441, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n  File \"<ipython-input-5-d4cb54197348>\", line 1, in <module>\r\n    load_dataset(\"spider\")\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']\r\n```\r\n\r\n\r\n## Environment info\r\ndatasets version: 1.18.3\r\nPlatform: Ubuntu 20 LTS\r\nPython version: 3.8.10\r\nPyArrow version: 6.0.1 \r\n\r\n\r\n\r\n","comment_length":60,"text":"NonMatchingChecksumError on Spider dataset  \n ## Describe the bug\r\nFailure to generate dataset ```spider``` because of checksums error for dataset source files.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nspider = load_dataset(\"spider\")\r\n```\r\n\r\n\r\n## Expected results\r\nChecksums should match for files from url ['https:\/\/drive.google.com\/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']\r\n\r\n## Actual results\r\n```\r\n>>> load_dataset(\"spider\")\r\nload_dataset(\"spider\")\r\nDownloading and preparing dataset spider\/spider (download: 95.12 MiB, generated: 5.17 MiB, post-processed: Unknown size, total: 100.29 MiB) to \/home\/user\/.cache\/huggingface\/datasets\/spider\/spider\/1.0.0\/79778ebea87c59b19411f1eb3eda317e9dd5f7788a556d837ef25c3ae6e5e8b7...\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/IPython\/core\/interactiveshell.py\", line 3441, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n  File \"<ipython-input-5-d4cb54197348>\", line 1, in <module>\r\n    load_dataset(\"spider\")\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']\r\n```\r\n\r\n\r\n## Environment info\r\ndatasets version: 1.18.3\r\nPlatform: Ubuntu 20 LTS\r\nPython version: 3.8.10\r\nPyArrow version: 6.0.1 \r\n\r\n\r\n\r\n \n Hi @kolk, thanks for reporting.\r\n\r\nIndeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change:\r\n- #3787 \r\n\r\nWe just made patch release last week: 1.18.4 https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.18.4\r\n\r\nPlease, feel free to update your local `datasets` version, so that you get the fix:\r\n```shell\r\npip install -U datasets\r\n```","embeddings":[-0.1679656506,0.0254897103,-0.086360991,0.2954740524,0.3215499222,-0.1166108474,0.1364502758,0.4342499673,0.1861151159,0.2588540614,-0.2660089731,0.0177098159,-0.0366363712,0.1000536382,-0.1914369017,0.1708942652,-0.0017419427,0.0505830683,-0.4116186798,-0.1128830686,-0.2612490654,0.0663859695,0.007393091,-0.2859489322,0.131240353,0.1494043469,0.2227994651,0.1876972467,-0.1567740142,-0.2523317337,0.1642962694,0.0379325077,-0.0013270433,0.4155566096,-0.0001117713,0.1711553186,0.2956293821,-0.0187095813,-0.3775075972,-0.0154739358,-0.2517728508,-0.3479462266,-0.1685921103,-0.2898810506,-0.1090249121,0.139962554,0.047897011,-0.2327901572,0.0860856995,0.2851851583,0.2384159267,0.6125790477,0.213185519,0.0232694354,0.3841731846,0.0321945623,-0.1736340225,0.1957048923,0.2253403366,0.1989523172,-0.3688897789,0.1584017873,-0.0142050544,0.1721504331,0.1803523451,-0.0836847052,0.1250656843,-0.3748968542,0.1349820793,0.1953409165,0.3362746537,-0.1857327074,-0.3668503463,-0.0177901126,-0.2558866143,0.1457054764,0.2839543819,0.3837307096,-0.2814312279,-0.0129021462,-0.5050472021,0.3566196859,-0.1008981913,0.147763595,0.3273890018,0.2799341381,0.0756826997,0.0006087197,-0.1311927289,-0.2798725069,0.2466348857,-0.3080365658,-0.0511032827,0.0926309451,-0.4308572114,-0.1335953474,0.1100366935,0.3322862387,0.4337329566,0.6762366891,0.0887894034,0.077781342,-0.0821389407,-0.0311617032,0.0931549296,0.0517928898,-0.0663486272,0.1833837479,0.4966816604,0.4386210442,0.056514211,0.1035016626,0.1021684408,-0.2871927023,0.5880285501,0.1843371689,0.3593304753,-0.2924614251,-0.2455576807,0.2702584863,-0.1003122181,-0.0230431836,0.1609816104,0.1803759187,-0.3155960739,-0.0268610809,0.0883752629,0.1450075805,-0.1772433668,-0.0995495245,-0.2915545702,-0.0572170541,-0.149822101,-0.104149662,0.1891434938,-0.007183155,0.2658675313,0.1193881929,0.2343579233,-0.0817664415,0.1897697598,-0.0413553715,-0.1274435967,0.4126689732,0.063304767,-0.0233969837,0.185040921,0.0691346303,-0.0647882968,0.1271273196,0.0156463739,-0.2397377342,-0.0891631469,0.2631024718,-0.4621055126,0.0779600069,0.0981588736,-0.4419333637,0.2803555429,-0.0980943739,0.1347422153,-0.2901304364,-0.204136923,-0.287330091,0.2677721679,0.3509767652,0.3208661377,0.0927467495,0.066302903,-0.2560477257,0.2722521126,0.2439756393,-0.0363711789,0.0093617821,-0.2981927395,-0.1732172668,0.1313521266,-0.2259352058,-0.4142879844,-0.0106271952,-0.0069068829,0.4206263423,0.2157866806,0.1409053057,-0.0423574857,-0.0296328068,-0.0568707623,0.1014775336,-0.0508248247,0.2541747093,-0.2801446319,-0.167941153,0.0960812941,0.1012677476,0.0724208876,0.1218195856,0.2149815857,-0.2567163408,0.1568298787,-0.2339548916,0.0757908151,0.1255663633,0.4142953753,-0.0084637189,0.0831238553,-0.3101625144,-0.4560266435,0.4116443396,-0.2182246745,0.3407100439,-0.347061336,-0.1775736362,-0.3112812638,-0.0972140804,-0.1612679213,-0.1045205519,0.1421606392,0.223609373,0.1847404242,-0.1796045005,0.0860035792,0.1932879239,-0.3327668309,0.1498602182,-0.5810318589,0.4491330683,-0.1153194681,-0.1458493024,0.1303511709,0.1455845684,-0.0320716277,0.029165294,-0.1593019515,0.3726524115,0.1808288544,-0.0344035812,-0.0361247286,0.2395900339,-0.0390789956,-0.2366049439,0.1406241506,0.4901229143,0.0480915569,0.0115608759,-0.2281103581,0.302924633,-0.2631338239,0.084039934,0.0854123831,0.004577959,0.2411207259,-0.0643827617,-0.2080500871,-0.0729263499,0.1783101857,0.0101789767,0.2496869117,0.151643008,-0.2811576128,0.0313887745,0.1325372607,-0.0859318823,0.0630345643,0.1734752357,0.1342915446,-0.0889670104,0.1307595521,0.3538676798,0.2626610994,0.1438977122,0.1389660835,0.0689508095,-0.1038344204,-0.1060251147,0.0551614612,0.0143328784,-0.0144501105,0.4606788754,0.2667940855,-0.1306178868,-0.388602376,-0.1377413273,-0.1729715765,0.2812448442,-0.4171099663,-0.1218708828,-0.1354808807,-0.2149476856,-0.3599182069,0.1103953198,-0.2717369199,-0.2057739794,-0.1354443133,0.366210252,-0.2997896969,0.3312623799,-0.2938600779,-0.0639323741,0.1704417318,-0.1711730361,0.0319786705,0.1669487953,0.0391522013,0.0775909647,0.3167690933,0.1294442862,0.3987770379,-0.3342336714,0.0362009257,-0.5946778655,-0.2428197265,-0.1130498871,0.0139476471,0.1447071135,0.4299742877,0.1122180298,0.1420535445,-0.4234775007,0.40492028,-0.0649733096,-0.4167886674,0.2232038677,0.0242804252,-0.0845945701,0.0307878014,-0.1111186743,0.0374431051,-0.362168014,0.0427725129,0.1907773912,0.2233323306,0.1083918139,-0.007075632,-0.0144288614,0.2055198848,0.3901855052,-0.1245257705,-0.6085830927,0.4509720504,0.0148973865,-0.2213317752,-0.0627818927,-0.3001177609,0.0889879838,0.3230374753,-0.4250459373,-0.2102548927,-0.4416136742,0.4154333472,0.1584668458,0.1317279041,0.2263734043,0.1449032128,-0.1119664088,-0.1887500137,-0.2462440282,0.1474718899,-0.0600471534,0.3447145224,-0.1012158394,0.3811489046,-0.0756097361,0.4806392491,0.4218044579,0.1028437465,0.1447444856,0.0500761718,0.3370552957,-0.0827149898,-0.2198395431,0.0882623494,-0.075826346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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3904","title":"CONLL2003 Dataset not available","comments":"Thanks for reporting, @omarespejel.\r\n\r\nI'm sorry but I can't reproduce the issue: the loading of the dataset works perfecto for me and I can reach the data URL: https:\/\/data.deepai.org\/conll2003.zip\r\n\r\nMight it be due to a temporary problem in the data owner site (https:\/\/data.deepai.org\/) that is fixed now?\r\nCould you please try loading the dataset again and tell if the problem persists?","body":"## Describe the bug\r\n[CONLL2003](https:\/\/huggingface.co\/datasets\/conll2003) Dataset can no longer reach 'https:\/\/data.deepai.org\/conll2003.zip' \r\n![image](https:\/\/user-images.githubusercontent.com\/4755430\/158084483-ff83631c-5154-4823-892d-577bf1166db0.png)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndatasets = load_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nDownload the conll2003 dataset.\r\n\r\n## Actual results\r\nError: `ConnectionError: Couldn't reach https:\/\/data.deepai.org\/conll2003.zip (error 502)`\r\n","comment_length":61,"text":"CONLL2003 Dataset not available \n ## Describe the bug\r\n[CONLL2003](https:\/\/huggingface.co\/datasets\/conll2003) Dataset can no longer reach 'https:\/\/data.deepai.org\/conll2003.zip' \r\n![image](https:\/\/user-images.githubusercontent.com\/4755430\/158084483-ff83631c-5154-4823-892d-577bf1166db0.png)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndatasets = load_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nDownload the conll2003 dataset.\r\n\r\n## Actual results\r\nError: `ConnectionError: Couldn't reach https:\/\/data.deepai.org\/conll2003.zip (error 502)`\r\n \n Thanks for reporting, @omarespejel.\r\n\r\nI'm sorry but I can't reproduce the issue: the loading of the dataset works perfecto for me and I can reach the data URL: https:\/\/data.deepai.org\/conll2003.zip\r\n\r\nMight it be due to a temporary problem in the data owner site (https:\/\/data.deepai.org\/) that is fixed now?\r\nCould you please try loading the dataset again and tell if the problem persists?","embeddings":[-0.1911519617,0.0150686419,-0.0434647761,0.3382746875,0.1922972351,0.0039142501,0.2465220392,0.1154264212,-0.3758265078,0.1485977918,-0.0861061588,0.2452331036,0.4535978734,0.1852294654,-0.0381422117,-0.0447144099,0.0035966616,-0.012561569,-0.2133067399,0.060949713,-0.3082910776,0.1839654446,-0.324549377,-0.1091958806,-0.2825883031,0.1720990986,-0.0765397847,0.0668988675,-0.2217160612,-0.3594929874,0.6459475756,-0.1387488842,0.219104737,0.2534444034,-0.0001201601,0.1459243149,0.4636394978,-0.0202063359,-0.3350785673,-0.168982178,-0.5470793247,-0.3967566192,0.0409624055,-0.0647044778,-0.1748001575,0.2619145215,-0.1763678342,-0.302372992,0.2665678561,0.289865315,0.1705025733,0.4098833203,0.5369910598,-0.2640505135,0.2956158519,-0.4764294624,-0.1412767023,0.4444654882,0.1286043525,-0.1353069693,0.248010233,0.2859277427,0.1273665577,0.0120104263,-0.0046514124,0.0935135782,0.0199152231,-0.4266191721,0.0238515809,0.2839429975,0.6837103963,-0.0689505339,-0.5916917324,-0.1994310915,0.1127128005,-0.3133717775,0.3646003306,0.0124573307,-0.0910700485,0.2242885828,-0.0623500533,-0.1471731961,-0.1829299182,0.3587570488,-0.4926331639,0.4509404004,-0.1948852688,0.1381318271,-0.1549960375,-0.0961887166,0.4506497085,-0.0444488116,0.0477730781,0.1217945591,-0.4915871322,-0.0423258841,-0.1596913636,-0.0642005801,0.1879174262,0.1100337952,0.0488826297,-0.2731248438,-0.3385083973,0.0699989051,0.3205313981,-0.0032910826,-0.2013463974,0.3205688298,0.3586683869,0.2306410521,0.2811121643,-0.1395232826,-0.1319957227,-0.2044190764,-0.1598122716,0.1339294761,0.3498336077,-0.1771916449,-0.434140563,0.1273281425,-0.3241240978,-0.0766066611,-0.0758738369,0.2905044258,-0.2582842112,0.2126733959,0.1026142314,0.346885711,0.1174509227,-0.4192104638,-0.0438922048,-0.2295739204,-0.2155321687,-0.2688173652,0.0998635665,-0.2314803302,0.2051389813,-0.0196311269,-0.0973445699,-0.1232250854,-0.0330594741,-0.0409924127,-0.1482222229,0.1922344416,0.1004592851,0.0327275731,0.0971897915,0.0370222852,-0.0720090345,-0.0910898224,-0.3655590713,-0.135817498,-0.2648261786,0.0936969444,-0.0976727009,-0.1589882076,0.2533978522,-0.2123457938,0.020080803,-0.1046254113,-0.0279646888,-0.0463965423,-0.4827394485,-0.1759018749,0.3223654032,0.5480448008,-0.4749995172,-0.0362136252,-0.2915672064,-0.1059942245,0.0228661112,0.0332908779,-0.1720677316,0.0641270801,-0.4246785641,-0.2673769891,0.2539885044,-0.2158707976,-0.7834096551,0.3035688996,-0.2715330124,0.0399267301,-0.0451506861,-0.0306146648,0.0871112123,0.0591291673,0.3282382488,0.2745511532,0.1104793176,-0.1492652595,-0.1058249623,-0.2318632901,0.1638522595,0.0751705989,0.1366481036,0.2822562456,0.2140789032,-0.3216875494,0.3102396727,0.3172035217,0.1883398592,0.413141042,0.3903146982,0.1772895008,0.0249574743,0.0406509452,-0.3179346323,0.3623957634,-0.0240511373,0.026844671,-0.2266298532,0.1080952734,-0.4873018861,0.2320072651,-0.1956994832,0.0249787029,0.0417407304,0.1290781349,0.168920964,0.2547797859,-0.1289102584,0.4383784235,0.1867353469,0.1398229301,-0.4223473668,0.444260627,-0.0199257024,0.0001935926,0.1931785345,0.1566052139,0.2701599896,-0.0926455706,-0.1876108646,0.238639608,-0.0569416881,-0.0246095154,0.0934091732,0.1001346186,0.2624247372,-0.4059075117,0.1694059819,0.1568616331,0.0502152257,0.1710636467,0.1034444645,0.3641469479,0.2523148954,0.2222815752,-0.2074199617,0.3584577143,0.463054955,-0.2100827247,-0.0087658009,-0.1599047035,0.0558334403,0.1697687507,0.4409285486,-0.1146018058,-0.1469866931,0.0947866142,-0.0195964985,-0.1133601218,-0.0264153089,0.309071064,-0.1818431765,0.0792043433,0.0371022709,0.5061418414,0.3599759936,0.0418610983,0.0449689291,-0.0392539464,-0.0350655429,-0.1829446852,0.2657367587,0.0688483939,0.3184829354,0.2294777632,0.2030521333,0.1862416714,0.0453627594,-0.2390327603,0.042609144,0.0715352148,-0.2365992367,-0.0199941639,-0.2338448316,-0.4098167121,-0.0456623323,0.028428467,-0.3025399745,-0.2874476314,-0.2033434212,0.0506742857,0.0158919599,0.0153628075,-0.4120530188,0.1092397422,0.1572520882,0.1207511202,-0.0678470284,-0.0636951923,-0.0878445134,0.0082009286,0.0945231691,-0.5000411272,0.1805097312,-0.1945452988,0.007902178,-0.4518945813,-0.0643009916,0.3004820645,0.0559651852,0.3807739019,0.2168625742,0.2670297921,0.1977688968,0.1379681677,0.340552032,0.153391555,-0.1198728159,0.1192778051,0.0086197648,0.0763531774,0.2641281486,-0.0704363734,-0.3452371657,-0.2517300844,0.0568007417,-0.2325163186,0.0757075995,-0.0163783543,0.0946912095,-0.0341459773,-0.0891999155,0.0468294099,-0.2320617288,-0.5243464112,0.3111769557,-0.14749448,-0.3804163635,0.0246470757,-0.0843499154,0.2365965694,-0.0833291635,-0.5951570272,0.0443731472,-0.3468745351,0.4830993712,-0.1941372454,0.0033993672,0.1338635832,-0.1919364333,-0.0108312741,-0.1178724691,-0.3783442974,-0.2068364322,0.0041141603,0.5643001795,0.3332813978,0.3320019543,-0.026735656,0.2641961277,0.386924237,0.0395044461,0.5707175732,-0.0498799607,0.2450758219,-0.0015232161,-0.3920422792,-0.2506346405,-0.118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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3902","title":"Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils'","comments":"Update: `\"python3 -c \"from from datasets import Dataset, DatasetDict\"` works, but not if I import without the `python3 -c`","body":"## Describe the bug\r\nUnable to import datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset, DatasetDict\r\n```\r\n\r\n## Expected results\r\nThe import works without errors\r\n\r\n## Actual results\r\n```\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-37-c8cfcbe62127> in <module>\r\n     11 # from tqdm import tqdm\r\n     12 # import torch\r\n---> 13 from datasets import Dataset\r\n     14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling\r\n     15 # from sentence_transformers import SentenceTransformer\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     46 )\r\n     47 \r\n---> 48 import fsspec\r\n     49 import numpy as np\r\n     50 import pandas as pd\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/__init__.py in <module>\r\n     10 from . import _version, caching\r\n     11 from .callbacks import Callback\r\n---> 12 from .core import get_fs_token_paths, open, open_files, open_local\r\n     13 from .exceptions import FSTimeoutError\r\n     14 from .mapping import FSMap, get_mapper\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/core.py in <module>\r\n     16     caches,\r\n     17 )\r\n---> 18 from .compression import compr\r\n     19 from .registry import filesystem, get_filesystem_class\r\n     20 from .utils import (\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in <module>\r\n     68 \r\n     69 \r\n---> 70 register_compression(\"zip\", unzip, \"zip\")\r\n     71 register_compression(\"bz2\", BZ2File, \"bz2\")\r\n     72 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in register_compression(name, callback, extensions, force)\r\n     44 \r\n     45     for ext in extensions:\r\n---> 46         if ext in fsspec.utils.compressions and not force:\r\n     47             raise ValueError(\r\n     48                 \"Duplicate compression file extension: %s (%s)\" % (ext, name)\r\n\r\nAttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4\r\n- Platform: Jupyter notebook\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n","comment_length":19,"text":"Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils' \n ## Describe the bug\r\nUnable to import datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset, DatasetDict\r\n```\r\n\r\n## Expected results\r\nThe import works without errors\r\n\r\n## Actual results\r\n```\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-37-c8cfcbe62127> in <module>\r\n     11 # from tqdm import tqdm\r\n     12 # import torch\r\n---> 13 from datasets import Dataset\r\n     14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling\r\n     15 # from sentence_transformers import SentenceTransformer\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     46 )\r\n     47 \r\n---> 48 import fsspec\r\n     49 import numpy as np\r\n     50 import pandas as pd\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/__init__.py in <module>\r\n     10 from . import _version, caching\r\n     11 from .callbacks import Callback\r\n---> 12 from .core import get_fs_token_paths, open, open_files, open_local\r\n     13 from .exceptions import FSTimeoutError\r\n     14 from .mapping import FSMap, get_mapper\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/core.py in <module>\r\n     16     caches,\r\n     17 )\r\n---> 18 from .compression import compr\r\n     19 from .registry import filesystem, get_filesystem_class\r\n     20 from .utils import (\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in <module>\r\n     68 \r\n     69 \r\n---> 70 register_compression(\"zip\", unzip, \"zip\")\r\n     71 register_compression(\"bz2\", BZ2File, \"bz2\")\r\n     72 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in register_compression(name, callback, extensions, force)\r\n     44 \r\n     45     for ext in extensions:\r\n---> 46         if ext in fsspec.utils.compressions and not force:\r\n     47             raise ValueError(\r\n     48                 \"Duplicate compression file extension: %s (%s)\" % (ext, name)\r\n\r\nAttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4\r\n- Platform: Jupyter notebook\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n \n Update: `\"python3 -c \"from from datasets import Dataset, DatasetDict\"` works, but not if I import without the `python3 -c`","embeddings":[-0.3171516955,-0.0977633372,-0.060437385,0.250816226,0.5410323739,0.0515428744,0.2306825817,0.1784132868,0.1451839954,-0.0406591669,-0.0914043784,0.2070271969,-0.0849940628,0.0422019102,-0.0481128022,0.0800790861,0.0392681174,0.1573754847,-0.3625039756,-0.1490760148,-0.0667247623,0.1086276919,-0.2605908811,0.0229992531,-0.4667166173,-0.0420558229,0.0897894949,0.2693987489,-0.3021417558,-0.675156951,0.2488134503,-0.2023636401,0.1428641677,0.3091623187,-0.0001127279,0.0719646066,0.3168117702,-0.0403252691,-0.3669926524,0.0579778142,-0.1587973535,-0.2611021101,0.2014765292,-0.1858032495,0.0890876353,-0.2615580857,-0.0732190013,-0.5562984943,0.1979622245,0.6020060778,0.2539667785,0.2535479665,0.3767183721,-0.065631941,-0.0458863266,-0.0652860999,-0.0695680603,-0.0419695526,0.1539292783,-0.1513932049,0.1441338509,-0.0441076644,-0.0111272512,0.2427477539,0.1799446791,-0.1848336905,0.1125411466,-0.430542022,-0.0043802359,0.0089009423,0.4734683037,-0.2616320848,-0.4875103533,-0.0716140419,0.1318767816,-0.0342166126,0.077444233,0.0017766798,0.050103087,0.168234542,0.1930772066,-0.0962269083,-0.119060196,0.2657083273,-0.1976042986,0.0411211923,-0.2480921447,-0.0328236781,0.0241297632,-0.2687961459,0.1609052271,-0.1838238239,-0.0366592705,0.282142669,-0.223079294,0.0432266779,0.4131987393,-0.3409279883,-0.1540693045,0.3015023172,-0.2992433906,-0.006161103,-0.0515987016,0.2656780481,0.0537836663,0.0720264763,0.1819226891,0.4663425386,0.0702350736,0.0044090147,-0.1564638913,-0.0405409075,-0.1521216035,-0.2603019178,0.0962028652,0.1447868347,0.3706581593,-0.2103715986,-0.2649964988,0.1851914823,0.0192854386,-0.0430234559,-0.1079287753,0.2417486012,-0.0532334819,0.4904887378,0.0674702302,0.1611715108,-0.1824313402,-0.2234943956,-0.2142259777,0.1783576161,-0.1630671024,-0.2605764568,0.1102810279,-0.0998411328,0.1528036296,-0.0829320922,-0.0496405587,-0.2125434577,0.1854949147,-0.3258240819,0.1065762937,0.287884742,-0.031679906,0.3496369123,0.3111235201,-0.2670789063,-0.0803107694,-0.058453545,-0.3358172774,-0.2745758295,-0.2645739019,0.2715825737,-0.0893806666,-0.038603019,-0.2773093879,-0.1382635534,-0.0018734714,0.0657273307,-0.0957612842,-0.2059314698,0.0100183561,-0.1157741472,0.2959854007,0.4907724857,-0.2895367444,-0.1573784351,-0.1349968463,-0.1530815363,0.1031776145,0.2147765607,-0.1410847604,0.3906895816,-0.2649224102,0.094534725,0.4844045937,-0.3106129467,-0.3114543259,0.2260969579,0.2197265476,-0.0654390901,-0.0301990528,-0.227159813,0.1230243668,0.1487568021,0.2201774269,0.3329994678,0.0629456043,-0.1071567386,-0.1610435247,-0.2767029107,0.1084143147,0.1161678061,0.2522843778,0.0129441619,0.0879163742,-0.0649169013,0.0552031286,-0.053736221,-0.03042024,0.5308898091,0.2894537747,0.1546925306,-0.1256311983,-0.5731550455,-0.2998693883,0.3153097034,0.1423248351,-0.0670407712,-0.1783957034,0.1294166595,-0.2853480577,0.1269601136,-0.2079899758,-0.2001310587,0.1225437373,0.0760746673,-0.1545485109,0.101472877,-0.3925878108,0.0076310658,-0.0690085217,0.1811647564,-0.2581490278,0.3928671777,-0.303062439,-0.1876596361,0.0431398377,0.1343559176,0.1268025488,-0.1018529162,-0.2180567235,0.1174706519,0.3525497317,0.0994520634,-0.1108864024,0.1862249225,0.0937124044,-0.4421737194,-0.2014679909,0.4330585301,0.3560416996,-0.0344208218,-0.0025963043,0.0697676837,0.0524709076,0.159350425,0.1339353919,0.1726386249,0.2863733172,-0.0415172316,0.0041685109,-0.2934674323,0.3124761581,0.1163412184,0.2349733561,0.1087321341,-0.0224026889,-0.1354225427,0.1906784773,0.0591118932,0.0022931141,-0.0219512526,-0.3193933964,0.1661985815,0.2359825373,-0.0235642213,0.2860987782,0.1934305131,-0.1650980115,0.0464064926,0.0310792569,0.0036386051,0.2746046782,0.1606133878,-0.0208504796,0.2699983418,0.1434809268,0.2218672186,-0.1548241675,-0.3139861524,0.0116627924,0.3350784481,-0.6247816086,0.1250724345,-0.4319001138,-0.0962775871,-0.0814968646,-0.0048133433,0.1706407368,-0.2390512377,-0.3558322489,0.1845762581,-0.094914794,0.1931576878,-0.4526357055,-0.0501413718,0.0987260789,-0.1024632454,-0.0976242647,-0.0642575473,-0.0227902681,0.034170378,0.3636759222,0.1634949893,0.2452367991,0.03134121,0.0445542559,-0.1876611561,-0.2511563003,0.0158457235,0.2032201886,0.4261603057,0.1684318185,0.0959275439,0.2268284708,-0.1676134914,0.3005023897,0.0435412563,-0.1498238891,0.2119083256,-0.0704846606,-0.4356117845,-0.1026322171,-0.3086396456,-0.2543665171,-0.4043404758,-0.0180261526,0.2428501248,0.1552812755,0.2330589145,0.1010307074,0.1663682461,0.0441799425,0.1133504808,-0.0219150428,-0.1602955759,0.534722507,-0.1797955632,-0.4439277351,-0.0138777513,-0.2253858745,-0.0495800935,-0.004483196,-0.3404408693,-0.0417782217,-0.4150960445,0.436935991,-0.2352888584,0.200793162,0.2394403815,0.2682829797,-0.067236647,-0.1845626235,-0.0668199509,-0.2253563851,0.1414604932,-0.1335570216,-0.2373022884,0.2180665582,-0.0464896448,0.6331483126,0.3170133531,-0.0150260264,0.259219557,-0.1955890656,0.1963515133,-0.157507062,-0.6132749319,0.0977250859,-0.12901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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3902","title":"Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils'","comments":"Hi @arunasank, thanks for reporting.\r\n\r\nIt seems that this can be caused because you are using an old version of `fsspec`: the reason why it works if you run `python3` seems to be that `python3` runs in a Python virtual env (with an updated version of `fsspec`); whereas the error arises when you run the import from other Python virtual env (with an old version of `fsspec`).\r\n\r\nIn order to fix this, you should update `fsspec` from within the \"problematic\" Python virtual env:\r\n```\r\npip install -U \"fsspec[http]>=2021.05.0\"","body":"## Describe the bug\r\nUnable to import datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset, DatasetDict\r\n```\r\n\r\n## Expected results\r\nThe import works without errors\r\n\r\n## Actual results\r\n```\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-37-c8cfcbe62127> in <module>\r\n     11 # from tqdm import tqdm\r\n     12 # import torch\r\n---> 13 from datasets import Dataset\r\n     14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling\r\n     15 # from sentence_transformers import SentenceTransformer\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     46 )\r\n     47 \r\n---> 48 import fsspec\r\n     49 import numpy as np\r\n     50 import pandas as pd\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/__init__.py in <module>\r\n     10 from . import _version, caching\r\n     11 from .callbacks import Callback\r\n---> 12 from .core import get_fs_token_paths, open, open_files, open_local\r\n     13 from .exceptions import FSTimeoutError\r\n     14 from .mapping import FSMap, get_mapper\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/core.py in <module>\r\n     16     caches,\r\n     17 )\r\n---> 18 from .compression import compr\r\n     19 from .registry import filesystem, get_filesystem_class\r\n     20 from .utils import (\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in <module>\r\n     68 \r\n     69 \r\n---> 70 register_compression(\"zip\", unzip, \"zip\")\r\n     71 register_compression(\"bz2\", BZ2File, \"bz2\")\r\n     72 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in register_compression(name, callback, extensions, force)\r\n     44 \r\n     45     for ext in extensions:\r\n---> 46         if ext in fsspec.utils.compressions and not force:\r\n     47             raise ValueError(\r\n     48                 \"Duplicate compression file extension: %s (%s)\" % (ext, name)\r\n\r\nAttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4\r\n- Platform: Jupyter notebook\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n","comment_length":88,"text":"Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils' \n ## Describe the bug\r\nUnable to import datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset, DatasetDict\r\n```\r\n\r\n## Expected results\r\nThe import works without errors\r\n\r\n## Actual results\r\n```\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-37-c8cfcbe62127> in <module>\r\n     11 # from tqdm import tqdm\r\n     12 # import torch\r\n---> 13 from datasets import Dataset\r\n     14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling\r\n     15 # from sentence_transformers import SentenceTransformer\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     46 )\r\n     47 \r\n---> 48 import fsspec\r\n     49 import numpy as np\r\n     50 import pandas as pd\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/__init__.py in <module>\r\n     10 from . import _version, caching\r\n     11 from .callbacks import Callback\r\n---> 12 from .core import get_fs_token_paths, open, open_files, open_local\r\n     13 from .exceptions import FSTimeoutError\r\n     14 from .mapping import FSMap, get_mapper\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/core.py in <module>\r\n     16     caches,\r\n     17 )\r\n---> 18 from .compression import compr\r\n     19 from .registry import filesystem, get_filesystem_class\r\n     20 from .utils import (\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in <module>\r\n     68 \r\n     69 \r\n---> 70 register_compression(\"zip\", unzip, \"zip\")\r\n     71 register_compression(\"bz2\", BZ2File, \"bz2\")\r\n     72 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in register_compression(name, callback, extensions, force)\r\n     44 \r\n     45     for ext in extensions:\r\n---> 46         if ext in fsspec.utils.compressions and not force:\r\n     47             raise ValueError(\r\n     48                 \"Duplicate compression file extension: %s (%s)\" % (ext, name)\r\n\r\nAttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4\r\n- Platform: Jupyter notebook\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n \n Hi @arunasank, thanks for reporting.\r\n\r\nIt seems that this can be caused because you are using an old version of `fsspec`: the reason why it works if you run `python3` seems to be that `python3` runs in a Python virtual env (with an updated version of `fsspec`); whereas the error arises when you run the import from other Python virtual env (with an old version of `fsspec`).\r\n\r\nIn order to fix this, you should update `fsspec` from within the \"problematic\" Python virtual env:\r\n```\r\npip install -U \"fsspec[http]>=2021.05.0\"","embeddings":[-0.3171516955,-0.0977633372,-0.060437385,0.250816226,0.5410323739,0.0515428744,0.2306825817,0.1784132868,0.1451839954,-0.0406591669,-0.0914043784,0.2070271969,-0.0849940628,0.0422019102,-0.0481128022,0.0800790861,0.0392681174,0.1573754847,-0.3625039756,-0.1490760148,-0.0667247623,0.1086276919,-0.2605908811,0.0229992531,-0.4667166173,-0.0420558229,0.0897894949,0.2693987489,-0.3021417558,-0.675156951,0.2488134503,-0.2023636401,0.1428641677,0.3091623187,-0.0001127279,0.0719646066,0.3168117702,-0.0403252691,-0.3669926524,0.0579778142,-0.1587973535,-0.2611021101,0.2014765292,-0.1858032495,0.0890876353,-0.2615580857,-0.0732190013,-0.5562984943,0.1979622245,0.6020060778,0.2539667785,0.2535479665,0.3767183721,-0.065631941,-0.0458863266,-0.0652860999,-0.0695680603,-0.0419695526,0.1539292783,-0.1513932049,0.1441338509,-0.0441076644,-0.0111272512,0.2427477539,0.1799446791,-0.1848336905,0.1125411466,-0.430542022,-0.0043802359,0.0089009423,0.4734683037,-0.2616320848,-0.4875103533,-0.0716140419,0.1318767816,-0.0342166126,0.077444233,0.0017766798,0.050103087,0.168234542,0.1930772066,-0.0962269083,-0.119060196,0.2657083273,-0.1976042986,0.0411211923,-0.2480921447,-0.0328236781,0.0241297632,-0.2687961459,0.1609052271,-0.1838238239,-0.0366592705,0.282142669,-0.223079294,0.0432266779,0.4131987393,-0.3409279883,-0.1540693045,0.3015023172,-0.2992433906,-0.006161103,-0.0515987016,0.2656780481,0.0537836663,0.0720264763,0.1819226891,0.4663425386,0.0702350736,0.0044090147,-0.1564638913,-0.0405409075,-0.1521216035,-0.2603019178,0.0962028652,0.1447868347,0.3706581593,-0.2103715986,-0.2649964988,0.1851914823,0.0192854386,-0.0430234559,-0.1079287753,0.2417486012,-0.0532334819,0.4904887378,0.0674702302,0.1611715108,-0.1824313402,-0.2234943956,-0.2142259777,0.1783576161,-0.1630671024,-0.2605764568,0.1102810279,-0.0998411328,0.1528036296,-0.0829320922,-0.0496405587,-0.2125434577,0.1854949147,-0.3258240819,0.1065762937,0.287884742,-0.031679906,0.3496369123,0.3111235201,-0.2670789063,-0.0803107694,-0.058453545,-0.3358172774,-0.2745758295,-0.2645739019,0.2715825737,-0.0893806666,-0.038603019,-0.2773093879,-0.1382635534,-0.0018734714,0.0657273307,-0.0957612842,-0.2059314698,0.0100183561,-0.1157741472,0.2959854007,0.4907724857,-0.2895367444,-0.1573784351,-0.1349968463,-0.1530815363,0.1031776145,0.2147765607,-0.1410847604,0.3906895816,-0.2649224102,0.094534725,0.4844045937,-0.3106129467,-0.3114543259,0.2260969579,0.2197265476,-0.0654390901,-0.0301990528,-0.227159813,0.1230243668,0.1487568021,0.2201774269,0.3329994678,0.0629456043,-0.1071567386,-0.1610435247,-0.2767029107,0.1084143147,0.1161678061,0.2522843778,0.0129441619,0.0879163742,-0.0649169013,0.0552031286,-0.053736221,-0.03042024,0.5308898091,0.2894537747,0.1546925306,-0.1256311983,-0.5731550455,-0.2998693883,0.3153097034,0.1423248351,-0.0670407712,-0.1783957034,0.1294166595,-0.2853480577,0.1269601136,-0.2079899758,-0.2001310587,0.1225437373,0.0760746673,-0.1545485109,0.101472877,-0.3925878108,0.0076310658,-0.0690085217,0.1811647564,-0.2581490278,0.3928671777,-0.303062439,-0.1876596361,0.0431398377,0.1343559176,0.1268025488,-0.1018529162,-0.2180567235,0.1174706519,0.3525497317,0.0994520634,-0.1108864024,0.1862249225,0.0937124044,-0.4421737194,-0.2014679909,0.4330585301,0.3560416996,-0.0344208218,-0.0025963043,0.0697676837,0.0524709076,0.159350425,0.1339353919,0.1726386249,0.2863733172,-0.0415172316,0.0041685109,-0.2934674323,0.3124761581,0.1163412184,0.2349733561,0.1087321341,-0.0224026889,-0.1354225427,0.1906784773,0.0591118932,0.0022931141,-0.0219512526,-0.3193933964,0.1661985815,0.2359825373,-0.0235642213,0.2860987782,0.1934305131,-0.1650980115,0.0464064926,0.0310792569,0.0036386051,0.2746046782,0.1606133878,-0.0208504796,0.2699983418,0.1434809268,0.2218672186,-0.1548241675,-0.3139861524,0.0116627924,0.3350784481,-0.6247816086,0.1250724345,-0.4319001138,-0.0962775871,-0.0814968646,-0.0048133433,0.1706407368,-0.2390512377,-0.3558322489,0.1845762581,-0.094914794,0.1931576878,-0.4526357055,-0.0501413718,0.0987260789,-0.1024632454,-0.0976242647,-0.0642575473,-0.0227902681,0.034170378,0.3636759222,0.1634949893,0.2452367991,0.03134121,0.0445542559,-0.1876611561,-0.2511563003,0.0158457235,0.2032201886,0.4261603057,0.1684318185,0.0959275439,0.2268284708,-0.1676134914,0.3005023897,0.0435412563,-0.1498238891,0.2119083256,-0.0704846606,-0.4356117845,-0.1026322171,-0.3086396456,-0.2543665171,-0.4043404758,-0.0180261526,0.2428501248,0.1552812755,0.2330589145,0.1010307074,0.1663682461,0.0441799425,0.1133504808,-0.0219150428,-0.1602955759,0.534722507,-0.1797955632,-0.4439277351,-0.0138777513,-0.2253858745,-0.0495800935,-0.004483196,-0.3404408693,-0.0417782217,-0.4150960445,0.436935991,-0.2352888584,0.200793162,0.2394403815,0.2682829797,-0.067236647,-0.1845626235,-0.0668199509,-0.2253563851,0.1414604932,-0.1335570216,-0.2373022884,0.2180665582,-0.0464896448,0.6331483126,0.3170133531,-0.0150260264,0.259219557,-0.1955890656,0.1963515133,-0.157507062,-0.61327493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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3896","title":"Missing google file for `multi_news` dataset","comments":"`datasets` 1.18.4 fixes the issue when you load the dataset with `load_dataset`.\r\n\r\nWhen loading in streaming mode, the fix is indeed on https:\/\/github.com\/huggingface\/datasets\/pull\/3843 which will be merged soon :)","body":"## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n","comment_length":29,"text":"Missing google file for `multi_news` dataset \n ## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n \n `datasets` 1.18.4 fixes the issue when you load the dataset with `load_dataset`.\r\n\r\nWhen loading in streaming mode, the fix is indeed on https:\/\/github.com\/huggingface\/datasets\/pull\/3843 which will be merged soon :)","embeddings":[-0.2699741423,-0.0458398983,0.0497320034,0.1420214474,0.1276449263,0.2481628209,0.1934226304,0.2745967507,-0.0215559751,0.1579294354,0.0816370994,0.0792289525,-0.3635448813,0.3349537253,0.1228106022,-0.2137125283,0.1218471229,0.0056760493,0.1303979754,-0.0129607301,-0.2909452319,0.48308146,-0.1952868551,-0.3958175778,-0.496948868,0.0503465161,-0.1637780666,0.2913736701,-0.0822806433,-0.2260227054,-0.0992225558,0.0670764968,0.0633657798,0.5561423302,-0.0001087802,0.0966095775,0.1969502866,-0.016621517,-0.3042957485,-0.3295350671,-0.1191709414,-0.2145788819,0.084044531,0.0817018673,-0.2094341069,-0.1526982188,0.1739887893,-0.1258497387,0.1938049197,0.394449681,0.2137354016,0.1765259504,0.3621146679,-0.2409399301,0.1189704835,-0.1591909379,-0.0464449413,0.3128052652,-0.0731825233,0.1206492931,0.0118322605,0.6624206305,0.2969291508,-0.000695997,-0.1326517463,-0.0852346122,-0.2786018252,-0.2077054977,0.0824050009,0.3656952679,0.3241903484,0.0247237366,-0.2975257933,-0.1155707836,0.2260704637,0.0152590424,0.3099583685,0.2659318149,0.0102179609,0.2642276287,-0.2617853582,-0.4051489532,-0.0453688875,0.2854012251,-0.1519396752,0.4173752666,-0.0524607711,0.0434461497,0.1914957315,0.0440556481,-0.075037472,-0.0538571253,0.0096026203,-0.0354847834,-0.2180486023,0.0762828365,0.2372857779,-0.00741249,-0.0502682477,-0.0781644434,-0.2847441137,0.1482701302,-0.1795339882,0.1913747489,0.2886131406,-0.2734928131,-0.1140359789,0.2005846798,0.4426936209,0.3836375177,0.115444392,-0.0423417725,-0.0230799168,-0.185061723,-0.3568231165,-0.0876476988,0.3284963369,0.0430886596,-0.307120949,0.0704470798,-0.228169173,-0.0192242675,0.0640724301,0.2267341912,-0.0405922197,0.2543423176,0.0168917738,0.1298942417,-0.1590398997,-0.2027335912,-0.2411938906,0.0526433848,-0.0522894561,0.2572022974,0.252687186,-0.1416992098,0.2949465811,0.0210409872,0.1047536731,-0.1562040746,-0.0129451128,-0.0339324214,-0.262568295,0.4257769287,0.2072321326,0.1498874873,0.1915684342,-0.334259212,0.0919592604,0.0238727443,-0.2023177296,-0.5349404812,-0.3775516748,0.2433692962,-0.1930313706,0.0006407563,-0.0854162499,0.4926950932,-0.12587668,-0.200593859,-0.0741360411,0.1414724588,-0.3251979053,-0.036005877,0.2284482419,0.4770258963,-0.3275356889,-0.288528502,-0.1856211275,-0.1960795075,-0.0943301246,0.1733857989,-0.2498108149,0.087435469,-0.2971253693,-0.0471971892,0.2947886288,-0.0817531496,-0.2705627978,0.0774132162,-0.2476383746,0.1613091975,0.01842626,0.0087585505,-0.216787383,0.078478083,0.2718446851,0.1136029661,-0.0428930111,0.2028947026,-0.2328076214,-0.0976986587,-0.1189284697,0.2209886163,0.3705191016,-0.0847434849,0.305552274,-0.2494810969,0.2203667015,-0.0211676788,0.1663018465,0.450481385,0.4090561569,0.0482665561,0.0851075649,-0.0588616468,-0.6630219221,0.1824907511,0.0604587458,0.0548116565,-0.126583159,-0.2398706079,-0.2038579285,-0.2788566947,-0.3247821927,-0.2542050779,0.1308142096,0.0600290522,-0.2686506212,0.3854193091,-0.2169710845,0.1346040666,-0.0447409824,0.1121090055,-0.3367466033,0.2658316493,-0.249942109,0.0463997647,0.1564615965,0.0071089636,0.1120526195,-0.1856684536,-0.1147738546,0.5158991218,-0.0164389163,0.3071859777,0.407009989,0.0258425977,0.2801153362,-0.5864533186,0.0014757786,0.1969536096,-0.0329486392,-0.205010429,-0.5306293368,0.2777294815,0.0797254518,-0.0518693179,0.1031294242,0.3067663014,0.2608005702,-0.049142234,0.1582873166,-0.2414274365,0.1183762103,-0.0965613499,0.2608337998,-0.1759958267,-0.4331058562,0.1659525335,0.3494383097,0.0825655535,-0.0270134993,0.1943137944,-0.3193078339,-0.2418661565,0.0007051289,0.3982298672,0.3179396689,0.1797162592,0.1445576847,0.2854647934,0.2524555922,-0.3376318216,0.1855915338,-0.0335980132,0.3748424053,0.3180784881,0.07777749,0.0029972373,-0.4142679274,0.021108631,0.3318341076,0.1454630792,-0.3140525222,-0.0600810423,-0.2276221067,-0.2317850292,-0.2043213099,-0.0440888889,-0.4008327127,-0.3647614121,0.1441528052,0.2245456427,-0.1837060004,0.120805338,0.0933704525,0.1264437884,0.0041781315,0.126467526,-0.0736444592,-0.1099016219,-0.1484054476,0.1324982345,0.2351185679,-0.0280551221,0.1272785664,-0.2422489226,0.0189073365,-0.6473357081,-0.4950321913,0.1596491188,-0.1561375111,0.1320045739,0.1008690074,0.2379401177,-0.2871607542,-0.0246117581,0.2057286203,-0.1421523988,0.1015132293,0.0854821652,-0.0850513503,-0.0052082478,-0.0323273689,-0.4535266161,-0.2287196219,-0.3282014132,0.3167484105,-0.2446499765,0.0305211022,0.163333267,0.2659239471,0.1522564143,-0.0264348071,-0.1401323676,-0.2855236828,-0.2316874415,0.3149048686,-0.4753130972,-0.4785799384,0.2872186005,0.1915644109,-0.04286246,-0.0393915437,-0.5842483044,-0.1779858917,0.0073701399,-0.0607534647,0.0883076414,-0.0733661056,0.3424158394,-0.0894880444,-0.2021225542,-0.1193570644,-0.12852557,-0.1309719831,-0.1196522489,0.2961347103,-0.2446364015,0.2517604828,0.1309168786,0.8454495072,0.3763314188,0.1880951226,0.261441499,-0.076486133,0.5798196197,-0.0244065989,-0.1470611393,0.215679124,-0.0214543249,0.1336998194,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3896","title":"Missing google file for `multi_news` dataset","comments":"That is. The PR #3843 was just opened a bit later we had made our 1.18.4 patch release...\r\nOnce merged, that will fix this issue. ","body":"## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n","comment_length":25,"text":"Missing google file for `multi_news` dataset \n ## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n \n That is. The PR #3843 was just opened a bit later we had made our 1.18.4 patch release...\r\nOnce merged, that will fix this issue. ","embeddings":[-0.1872241795,-0.0048763808,0.0356897488,0.1828449368,0.0608461536,0.2791556716,0.2332007289,0.2045081854,-0.0366968475,0.1745186746,0.1645779461,0.0191534571,-0.3617120683,0.2578106821,0.207847625,-0.2329189479,0.1163955182,0.0118160276,0.2681148648,-0.0911784619,-0.3354702294,0.4920604229,-0.1394957751,-0.3474695683,-0.5592490435,0.0674447715,-0.2242628634,0.3208023012,-0.0488988273,-0.1438560635,-0.0915783942,0.0178269632,0.0637671649,0.5182000399,-0.0001100226,0.074409008,0.1599947661,-0.0728012174,-0.2432727069,-0.2922160327,-0.1795358956,-0.3841654658,-0.0315892212,0.063395001,-0.2299931049,-0.1248099506,0.2355206609,-0.038747061,0.0512416661,0.4330635071,0.2115032375,0.0479155108,0.4874853194,-0.2200936675,0.1155769229,-0.0778043941,-0.0510022566,0.2552134395,-0.0817326903,0.0770745724,0.1039974764,0.5462773442,0.304638803,-0.069516167,-0.1316421628,-0.0342438184,-0.1518393904,-0.2416981161,0.0863400176,0.3786845505,0.4296703637,0.1074757054,-0.2502039969,-0.0039475188,0.2750445604,0.0589222386,0.2631620169,0.369949609,-0.0290592313,0.2897690535,-0.1895841956,-0.4777516425,-0.0114069805,0.270226568,-0.1848279983,0.3734059632,0.0105295936,0.0573793575,0.0669681132,0.1050211936,-0.0994315892,-0.0402788147,-0.0326130688,0.0023558005,-0.1493077129,0.0730430335,0.2040159404,-0.0571354255,-0.0232475121,-0.0774524584,-0.3519572318,0.0225669071,-0.2316220999,0.210776329,0.3199984729,-0.2636631429,-0.1860022694,0.2886658907,0.4071512818,0.2384560555,0.1201669648,-0.0258683357,0.0091443025,-0.1525197774,-0.2817749083,0.041808553,0.2965108454,0.0076807057,-0.3023352325,0.0403850265,-0.2887432575,-0.060490571,-0.0300551429,0.1145807728,0.0022582794,0.2750249803,0.0942159444,0.0936123654,-0.1307660192,-0.2969667017,-0.22418046,0.1287459135,-0.0034952294,0.3261290789,0.2007310838,-0.0544315614,0.2158967108,-0.0277904626,0.0395586938,-0.2227405012,-0.0068461746,-0.013064188,-0.2587530315,0.4442702532,0.1846516877,0.1227163374,0.1658952683,-0.3463698626,0.0950521082,0.1327161938,-0.2355234921,-0.5962260962,-0.4397486746,0.2294386923,-0.1776950806,-0.0629325584,-0.0591413267,0.5101066232,-0.0654145926,-0.1904257387,-0.0086430609,0.1976844817,-0.3526614606,-0.060718786,0.1974289715,0.4896964729,-0.3454836011,-0.239469856,-0.3427885473,-0.2256973088,-0.0521435179,0.1951577067,-0.2483709157,0.2457916141,-0.3264862597,-0.1361158043,0.3167674243,-0.0719663352,-0.2423766106,-0.0625893921,-0.3118367791,0.0988035947,0.0554499999,0.0529271103,-0.1650902927,0.0587345511,0.2711957395,0.1168916374,-0.043060936,0.18805857,-0.2034879625,-0.0303610265,-0.0532486178,0.2304324508,0.4236678779,-0.070785284,0.3201605976,-0.3414313793,0.1870195866,-0.0118247699,0.1476411074,0.5195218921,0.4749770463,0.0739347264,0.1048127338,0.0054829069,-0.6059769988,0.1749014854,0.0899524763,0.0592468306,-0.0415676869,-0.1964131892,-0.2248484939,-0.3244538605,-0.3066115379,-0.2828349471,0.1422520131,0.0473343283,-0.3329624236,0.4180704653,-0.2121299207,0.1743275523,-0.0623030812,0.0349708423,-0.2749991417,0.2314402908,-0.2827828825,0.1294605136,0.1165911406,0.0248131957,0.1221664846,-0.149293676,-0.1036788672,0.4013264179,0.0372594967,0.1733629704,0.4822082818,-0.0054554814,0.2585506737,-0.7087469697,0.000439112,0.1740692854,-0.0236910954,-0.2458590418,-0.4859758317,0.2288686037,0.1894017756,-0.1759803593,-0.0261050593,0.3318282366,0.299470365,-0.1140444279,0.2522148788,-0.1744798422,0.0828067884,0.0041168341,0.2477096468,-0.1705700308,-0.3176648021,0.1946546733,0.416051507,-0.0094270343,0.0156620424,0.1871991903,-0.2291244417,-0.189079836,-0.0619778521,0.3554429412,0.3300711513,0.178344205,0.0890296698,0.232290417,0.2851096392,-0.2795419991,0.1586310714,0.0169706363,0.3599017859,0.2976170778,0.1143517196,0.0328885913,-0.3657296002,0.0612581894,0.403627038,0.218786329,-0.2972443998,-0.1190037131,-0.1616990417,-0.1548976451,-0.1404837966,0.0115751745,-0.3980472982,-0.3729676902,0.1576937288,0.0497477651,-0.0669739172,0.0677443445,0.1581225842,0.1308144331,-0.0415641703,0.1317182034,-0.0787145644,-0.0941478834,-0.1540471911,0.1254366785,0.2540756166,-0.0228356812,0.1249768808,-0.2394530028,0.068253018,-0.7043079138,-0.4395392239,0.092502594,-0.1899747998,0.0506546423,0.0765543133,0.2621290386,-0.2664461732,-0.0138535192,0.1472315043,-0.1100525931,-0.0209475141,0.0729540512,-0.1512774378,-0.0302422456,0.0135869337,-0.4395048022,-0.3328358829,-0.2127956301,0.3313548565,-0.1791113466,0.048629526,0.021221675,0.2010138184,0.1185043752,-0.0325611643,-0.188214004,-0.2785399556,-0.1579571962,0.2916122973,-0.4021892548,-0.398614198,0.3772142828,0.1471601427,-0.1747263223,-0.1544238776,-0.6030054688,-0.1053523868,0.0169254225,0.0033853636,0.0231465232,-0.0946335271,0.4681657851,-0.1171739846,-0.1710402817,-0.0588588193,-0.178869307,-0.1401715875,0.0170954093,0.4539295733,-0.275329411,0.2231184393,0.0799263865,0.9152948856,0.3518288434,0.1884813458,0.2041664273,-0.1072579995,0.5518421531,-0.0792363063,-0.1201916188,0.2751152217,-0.0374763682,0.1066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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3896","title":"Missing google file for `multi_news` dataset","comments":"OK. Should fix the viewer for 50 datasets\r\n\r\n<img width=\"148\" alt=\"Capture d\u2019e\u0301cran 2022-03-14 a\u0300 11 51 02\" src=\"https:\/\/user-images.githubusercontent.com\/1676121\/158157853-6c544a47-2d6d-4ac4-964a-6f10951ec36b.png\">\r\n","body":"## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n","comment_length":18,"text":"Missing google file for `multi_news` dataset \n ## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n \n OK. Should fix the viewer for 50 datasets\r\n\r\n<img width=\"148\" alt=\"Capture d\u2019e\u0301cran 2022-03-14 a\u0300 11 51 02\" src=\"https:\/\/user-images.githubusercontent.com\/1676121\/158157853-6c544a47-2d6d-4ac4-964a-6f10951ec36b.png\">\r\n","embeddings":[-0.1933975518,0.1477290988,0.0188400354,0.2966789603,0.0506261885,0.3109288216,0.2941398621,0.1884916127,-0.0731477886,0.1240582839,0.1497876495,-0.0864578187,-0.3482003212,0.2359734327,0.1940664202,-0.1597623229,0.0069946139,0.0499037988,0.1567207724,-0.0928815603,-0.3870481551,0.4132068753,-0.0313486122,-0.3563389182,-0.5102393031,-0.0329990387,-0.1349659562,0.3064566255,-0.1050367579,-0.1911119223,-0.1466589272,0.0352476165,0.1553139538,0.5570989251,-0.0001115682,0.1400071979,0.2059164047,-0.0818837285,-0.1803536415,-0.1162377968,-0.3221024275,-0.2358183712,-0.1057315394,-0.1040616632,-0.0874812081,-0.1110205799,0.1942804456,-0.1892816424,0.167338118,0.4233098328,0.2260954678,-0.0837061331,0.4143295884,-0.0836183503,0.1569638103,-0.0181507524,0.0868648589,0.2028197348,-0.2211886197,0.0388418473,0.0131307254,0.5771192312,0.1517453939,-0.069181107,-0.1997399479,-0.1929728091,-0.2381443381,-0.3023697734,0.1445631236,0.2396603376,0.5382723808,0.1348784268,-0.2633888125,0.0538243577,0.2205633521,0.0333982743,0.2443392873,0.4427041709,0.0467536002,0.1999877542,-0.3329994977,-0.378772527,-0.1341997832,0.2170260251,-0.3532187641,0.2478198856,-0.0400550775,0.1118904352,-0.0536293164,0.1300466657,-0.1430535913,-0.0610803962,-0.0797978565,-0.0073274267,-0.1237519607,-0.0030689167,0.1259953231,-0.0323797278,-0.0664397553,0.1231767088,-0.3546806574,-0.0045651281,-0.196668908,0.1291748285,0.2312476635,-0.264228642,-0.2307100743,0.2766657174,0.4023426175,0.1234374195,0.0920675695,-0.0263775215,-0.0049599917,-0.3208119273,-0.2527247071,-0.0089708548,0.3598236442,0.0441194102,-0.3617737889,0.1570187956,-0.3676458895,-0.1162530854,-0.0933945104,0.2400185168,-0.0025726298,0.1789370179,0.0582703166,0.0862540677,-0.100514859,-0.4063533247,-0.2330684215,0.259195447,0.0611420497,0.2163460553,0.1470379084,-0.0519426055,0.1378825158,-0.0710230395,0.0071859532,-0.2044921964,0.0477071889,-0.1214964166,-0.3182257712,0.3843327463,0.2175386399,0.1331340224,0.1397665292,-0.2936000228,0.1031841114,0.152900219,-0.3207333982,-0.3783464134,-0.4226319492,0.2303672582,-0.1800513715,-0.0467751026,-0.1888732016,0.3508194387,-0.1459731013,-0.1942494959,0.0030479727,0.2033005506,-0.2453912497,-0.1222520247,0.1510975212,0.6265177131,-0.4249155819,-0.1967709512,-0.3306944966,-0.1731228083,0.0129574956,0.1094381586,-0.1834160239,0.3391465545,-0.4214860499,-0.0499861762,0.4183912575,-0.1575907022,-0.3415843844,0.0134186707,-0.2451459318,0.1121573299,0.1690547466,0.106004864,-0.0735852197,-0.0287966765,0.084784776,0.0961102694,-0.0439546444,0.1152195558,-0.0948378667,-0.0212123226,-0.0877779424,0.3473652601,0.5229454637,-0.0642575026,0.451682359,-0.3917663693,0.1176191866,-0.0360614955,0.1515197307,0.5031406283,0.4215629399,0.0225729272,0.0581644401,-0.005333411,-0.6120933294,0.158170715,0.11792209,-0.0493541807,-0.187503159,-0.1399393827,-0.1834385991,-0.3869844973,-0.3619800806,-0.302595675,0.1464844793,0.0919080898,-0.3827188313,0.3406679332,-0.1360416561,0.3172214329,-0.0609804355,0.1744006127,-0.2450560778,0.2670666277,-0.2975046039,0.138832584,0.1471229643,-0.0174187515,0.1777623445,-0.1787783206,-0.089331314,0.3787342012,0.0940825865,0.2066822052,0.4688083827,-0.1009838283,0.2771246433,-0.7825940847,0.0343432203,0.1134519279,-0.014164336,-0.2199369818,-0.4153303206,0.1593326181,0.0949075073,-0.2169288397,-0.0367497876,0.2165375948,0.197375074,-0.1003537402,0.2343313396,-0.143856898,0.1092561707,0.1792021096,0.1956454664,-0.1516065598,-0.304712981,0.0346604176,0.3844015598,-0.0213315208,-0.0127327181,0.2074031979,-0.2463759184,0.0037434569,-0.0305374619,0.3902890086,0.351955086,0.1928465813,-0.0263642408,0.1137890145,0.3149711192,-0.2678753436,0.107470654,-0.0393528752,0.279212743,0.302575022,0.0775430351,0.1133673862,-0.3576546907,-0.0546948649,0.3388552368,0.1959892809,-0.3271508813,-0.037071418,-0.1829407662,-0.2859169245,-0.113967143,0.0535155572,-0.3040626943,-0.2806335092,0.0439461209,0.0334445052,-0.0097979642,0.0995761529,-0.009661451,0.1583256871,0.010761233,0.0862113237,-0.1488669217,-0.0266582966,-0.1367527097,0.1683765501,0.3545512557,-0.1417788118,0.2143007368,-0.2508836389,0.093230024,-0.7055172324,-0.3987385035,0.0841079503,-0.1574072242,0.1067790687,0.0167124961,0.2984663248,-0.2208357155,0.1146503165,0.0354875624,0.1096122488,-0.0795679092,0.027529683,-0.1064909846,0.0602412671,0.1396445781,-0.2922469676,-0.2657714188,-0.1923798919,0.2829860747,-0.0270374455,0.0903125554,0.0199677274,0.1817686558,0.0526220538,0.094734855,-0.1127860695,-0.2340850383,-0.1674276888,0.3372203708,-0.3695174754,-0.3251104057,0.4031918645,0.1352558285,-0.0931430385,-0.0218814872,-0.6589690447,-0.1537075639,-0.0204837844,-0.0274301078,-0.0118472595,-0.0351037271,0.3883838058,-0.0280543007,-0.0831823573,-0.0858014375,-0.1054191068,-0.225193873,-0.0430293232,0.3198215365,-0.222822547,0.3266832829,0.0098330257,0.8642650843,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3889","title":"Cannot load beans dataset (Couldn't reach the dataset)","comments":"Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)","body":"## Describe the bug\r\nThe beans dataset is unavailable to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('beans')\r\n```\r\n\r\n## Expected results\r\nThe dataset would be downloaded with no issue.\r\n\r\n## Actual results\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/ibeans\/train.zip (error 403)\r\n```\r\n\r\n[It looks like the billing of this project has been disabled because it is associated with a delinquent account.](https:\/\/storage.googleapis.com\/ibeans\/train.zip )\r\n\r\n## Environment info\r\nGoogle Colab\r\n","comment_length":23,"text":"Cannot load beans dataset (Couldn't reach the dataset) \n ## Describe the bug\r\nThe beans dataset is unavailable to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('beans')\r\n```\r\n\r\n## Expected results\r\nThe dataset would be downloaded with no issue.\r\n\r\n## Actual results\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/ibeans\/train.zip (error 403)\r\n```\r\n\r\n[It looks like the billing of this project has been disabled because it is associated with a delinquent account.](https:\/\/storage.googleapis.com\/ibeans\/train.zip )\r\n\r\n## Environment info\r\nGoogle Colab\r\n \n Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)","embeddings":[-0.4721034169,0.2937535346,-0.1632618159,0.4070334435,0.3040819168,0.0024879545,0.1432952583,0.0070785792,-0.0481522158,0.2847831845,-0.0638595298,0.3683096766,-0.0164078493,0.3610328436,-0.0237231459,0.0577074774,-0.1081279293,-0.1645550281,-0.5758637786,0.023881061,-0.2940049171,0.2738599777,-0.0233217739,-0.037474364,-0.2689076364,-0.0754930824,-0.0744953677,-0.0242402349,-0.4398058057,-0.2356318235,0.372954607,0.0063793166,0.140689075,0.5160427094,-0.0001106436,-0.0160402972,0.397513032,0.0894469693,-0.4802119434,-0.3550446332,-0.6385271549,-0.2208743542,0.1271834522,-0.0056030978,-0.3249291778,0.2984330952,0.1365662366,-0.1640415341,0.1632101983,0.3456363678,0.2839324176,-0.0932645053,0.0129738199,-0.2624518871,0.3314785957,-0.3533502519,-0.1359381974,0.4348132014,0.1738162339,-0.1343367547,0.2780838311,0.2461382449,0.1221311986,-0.1201090813,0.1030773297,-0.0395472199,-0.0103170257,-0.3759792745,0.2868287563,0.1133013815,1.080126524,-0.0898182243,-0.53858006,0.1489838362,0.1727022976,-0.1384823471,0.4577844441,0.1939825565,-0.0267166346,0.1473179907,-0.3441676497,-0.1730308533,-0.2207692713,0.2920139432,-0.1578496993,0.2300731987,-0.1040423959,0.0439440645,-0.0805236548,0.0574598908,0.4158937633,0.0981916487,0.0637681112,0.2207369506,-0.0257459469,0.1214711294,-0.009033802,0.2129358351,0.0016312037,0.4858339727,0.0665280148,-0.1074587852,0.0638756156,0.1107169688,0.4132048488,0.0794122964,0.0571545772,0.1571144015,0.3623424172,0.3630516827,-0.1413037926,-0.0843082219,-0.2615192831,-0.1784939766,0.1232367158,0.126542449,0.1174681783,-0.2192625403,-0.4947874546,0.1154305637,-0.2405761182,-0.0495441072,-0.0687739179,0.274515301,-0.3154754937,-0.2409535646,0.3581915498,0.1430877,0.0367650092,-0.1671223193,-0.2656650245,0.0400255434,-0.2496531606,0.102604121,0.2079625428,-0.3214920461,0.2372719347,-0.1805179864,-0.0788536817,-0.0867453739,0.1295170337,-0.1647742838,-0.3972166479,0.4750950336,0.2687705755,-0.0444381349,0.0200605448,0.0090056751,-0.0522282943,0.1811436564,-0.1661707461,-0.1524891108,0.1472980678,0.2620907426,-0.2511987388,-0.1091174856,-0.6378190517,0.2227010876,-0.0033721328,-0.0461200438,0.0216008369,-0.1372816712,-0.2391985506,-0.2655566633,0.1706481129,0.4565366805,-0.2245611101,-0.2256511599,-0.1552481651,-0.098114714,0.1407164037,0.1574579179,-0.2102051824,0.2902565002,-0.1073641554,0.046661254,0.2291445285,-0.0065964218,-0.7131419778,0.0040093851,-0.0644296929,0.0608281083,-0.1724887639,0.0499643944,-0.0024189418,0.1583202928,0.3343406022,0.5018733144,-0.1460793912,-0.1446336061,-0.1595896184,-0.3222993016,0.1664762199,0.1439738125,0.3315447867,0.141590476,0.2922763228,-0.2188187689,0.1172384247,0.2602131963,-0.1560252011,0.4014739394,0.2155708969,-0.0367309712,0.0442620143,-0.3375190198,-0.0303144045,0.3108627796,-0.1675970256,0.2984000146,-0.1555653214,-0.1185262054,-0.3878890872,0.0861207545,-0.0437087007,0.1628895998,0.1326960325,-0.1260160059,-0.0397692658,0.0969327465,-0.0658218637,0.4536839724,-0.0721083134,0.2146780491,-0.4441941381,0.5075232983,-0.0948171988,-0.2164162546,0.1601751149,-0.1268360764,0.2517758012,-0.1023758054,-0.2076101154,0.1164112687,0.082343854,0.1473176181,0.1911285371,0.2672754526,0.104579702,-0.3686168492,-0.0354872532,0.3077382147,0.1716676503,0.1133881062,0.1132223234,0.0175087601,-0.0653156564,-0.077568911,0.0607264526,0.0174866337,0.2353867441,-0.0136472881,-0.1798923463,0.0718199313,0.3981319666,-0.09911111,-0.229016602,-0.0178622622,-0.034087453,0.2495476007,0.2445355654,-0.0670033768,0.0538791046,0.0847787336,-0.0812266171,0.0165134426,0.1188027486,0.1258360595,0.2569776177,0.0632558763,0.0640148818,0.0012470603,0.1514450908,-0.1244456023,0.3070989549,0.0722188428,0.4070848227,0.0843797401,0.1470547616,0.1025902033,-0.0982528031,-0.1008221731,0.1055378765,0.2205802351,-0.1456204504,0.2648282647,-0.2310456336,-0.4061816037,0.0500538237,-0.2189503014,-0.1019000411,-0.0544711947,-0.0079943994,0.2230009437,0.0059520206,-0.0233863592,-0.0961436927,0.2160829604,0.1341499537,-0.2120065093,-0.1477357298,0.0866332948,-0.2356081009,0.1262641549,0.3402314782,-0.0133196106,0.376208216,-0.2595164776,-0.0783541575,-0.3196521401,-0.1683457643,0.0163611043,0.0965982676,0.5016034245,0.1734369993,0.2419324964,-0.1761866659,0.1051549762,0.2204367071,-0.1762995869,-0.1292262226,-0.1369809061,0.0521640554,-0.0436598808,0.0928468853,-0.5160238147,-0.4517393708,-0.1908639669,0.2059099376,0.1073965579,0.0475999452,-0.055874873,0.1187331527,0.0256141853,0.6462154388,-0.0350490659,-0.2899827957,-0.2966692448,0.2920725346,-0.0896096453,-0.3089390099,0.1453216821,0.0308607947,0.1981775463,0.139777258,-0.6269010305,0.0151537303,-0.1387936175,0.2227998823,-0.1545993537,-0.0364988707,0.1561367214,-0.0611620657,-0.0467450246,-0.0308073089,-0.3570403457,-0.2073605061,0.2021425962,0.4101080596,0.0098608946,0.3165043294,-0.1671999246,0.4252550602,-0.0655735284,0.1472411603,0.3459515274,0.0186855458,0.0972494707,-0.0367074274,-0.3450375497,0.0455223247,-0.186719074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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"Hi @INF800,\r\n\r\nPlease note that the `imagefolder` feature enhancement was just recently merged to our master branch (https:\/\/github.com\/huggingface\/datasets\/commit\/207be676bffe9d164740a41a883af6125edef135), but has not yet been released.\r\n\r\nWe are planning to make the 2.0 release of our library in the coming days and then that feature will be available by updating your `datasets` library from PyPI.\r\n\r\nIn the meantime, you can incorporate that feature if you install our library from our GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\n\r\nThen:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n        ds = load_dataset(\"imagefolder\", data_files=\"https:\/\/download.microsoft.com\/download\/3\/E\/1\/3E1C3F21-ECDB-4869-8368-6DEBA77B919F\/kagglecatsanddogs_3367a.zip\", split=\"train\")\r\nUsing custom data configuration default-7eb4e80d960deb18\r\nDownloading and preparing dataset image_folder\/default to ...\/.cache\/huggingface\/datasets\/image_folder\/default-7eb4e80d960deb18\/0.0.0\/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60...\r\nDownloading data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 690.19it\/s]\r\nExtracting data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 852.85it\/s]\r\nDataset image_folder downloaded and prepared to ...\/.cache\/huggingface\/datasets\/image_folder\/default-7eb4e80d960deb18\/0.0.0\/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60. Subsequent calls will reuse this data.\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDataset({\r\n    features: ['image', 'label'],\r\n    num_rows: 25000\r\n})\r\n```","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":140,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n Hi @INF800,\r\n\r\nPlease note that the `imagefolder` feature enhancement was just recently merged to our master branch (https:\/\/github.com\/huggingface\/datasets\/commit\/207be676bffe9d164740a41a883af6125edef135), but has not yet been released.\r\n\r\nWe are planning to make the 2.0 release of our library in the coming days and then that feature will be available by updating your `datasets` library from PyPI.\r\n\r\nIn the meantime, you can incorporate that feature if you install our library from our GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\n\r\nThen:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n        ds = load_dataset(\"imagefolder\", data_files=\"https:\/\/download.microsoft.com\/download\/3\/E\/1\/3E1C3F21-ECDB-4869-8368-6DEBA77B919F\/kagglecatsanddogs_3367a.zip\", split=\"train\")\r\nUsing custom data configuration default-7eb4e80d960deb18\r\nDownloading and preparing dataset image_folder\/default to ...\/.cache\/huggingface\/datasets\/image_folder\/default-7eb4e80d960deb18\/0.0.0\/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60...\r\nDownloading data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 690.19it\/s]\r\nExtracting data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 852.85it\/s]\r\nDataset image_folder downloaded and prepared to ...\/.cache\/huggingface\/datasets\/image_folder\/default-7eb4e80d960deb18\/0.0.0\/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60. Subsequent calls will reuse this data.\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDataset({\r\n    features: ['image', 'label'],\r\n    num_rows: 25000\r\n})\r\n```","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"Hey @albertvillanova. Does this load entire dataset in memory? Because I am facing huge trouble with loading very big datasets (OOM errors)","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":22,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n Hey @albertvillanova. Does this load entire dataset in memory? Because I am facing huge trouble with loading very big datasets (OOM errors)","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594,-0.0159561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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"Can you provide the error stack trace? The loader only stores the `data_files` dict, which can get big after globbing. Then, the OOM error would mean you don't have enough memory to keep all the paths to the image files. You can circumvent this by generating an archive and loading the dataset from there. Maybe we can optimize the globbing part in our data files resolution at some point, cc @lhoestq for visibility.","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":73,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n Can you provide the error stack trace? The loader only stores the `data_files` dict, which can get big after globbing. Then, the OOM error would mean you don't have enough memory to keep all the paths to the image files. You can circumvent this by generating an archive and loading the dataset from there. Maybe we can optimize the globbing part in our data files resolution at some point, cc @lhoestq for visibility.","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594,-0.015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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"Hey, memory error is resolved. It was fluke.\r\n\r\nBut there is another issue. Currently `load_dataset(\"imagefolder\", data_dir=\".\/path\/to\/train\",)` takes only `train` as arg to `split` parameter.\r\n\r\nI am creating vaildation dataset using\r\n\r\n```\r\nds_valid = datasets.DatasetDict(valid=load_dataset(\"imagefolder\", data_dir=\".\/path\/to\/valid\",)['train'])\r\n```","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":36,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n Hey, memory error is resolved. It was fluke.\r\n\r\nBut there is another issue. Currently `load_dataset(\"imagefolder\", data_dir=\".\/path\/to\/train\",)` takes only `train` as arg to `split` parameter.\r\n\r\nI am creating vaildation dataset using\r\n\r\n```\r\nds_valid = datasets.DatasetDict(valid=load_dataset(\"imagefolder\", data_dir=\".\/path\/to\/valid\",)['train'])\r\n```","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"`data_dir=\"path\/to\/folder\"` is a shorthand syntax fox `data_files={\"train\": \"path\/to\/folder\/**\"}`, so use `data_files` in that case instead:\r\n```python\r\nds = load_dataset(\"imagefolder\", data_files={\"train\": \"path\/to\/train\/**\", \"test\": \"path\/to\/test\/**\", \"valid\": \"path\/to\/valid\/**\"})\r\n```","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":26,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n `data_dir=\"path\/to\/folder\"` is a shorthand syntax fox `data_files={\"train\": \"path\/to\/folder\/**\"}`, so use `data_files` in that case instead:\r\n```python\r\nds = load_dataset(\"imagefolder\", data_files={\"train\": \"path\/to\/train\/**\", \"test\": \"path\/to\/test\/**\", \"valid\": \"path\/to\/valid\/**\"})\r\n```","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.173592686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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"And there was another issue. I loaded black and white images (jpeg file). Using load dataset. It reads it as PIL jpeg data format. But instead of converting it into 3 channel tensor, input to collator function is coming as a single channel tensor.","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":44,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n And there was another issue. I loaded black and white images (jpeg file). Using load dataset. It reads it as PIL jpeg data format. But instead of converting it into 3 channel tensor, input to collator function is coming as a single channel tensor.","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594,-0.0159561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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"We don't apply any additional preprocessing on top of `PIL.Image.open(image_file)`, so you need to do the conversion yourself:\r\n\r\n```python\r\ndef to_rgb(batch):\r\n    batch[\"image\"] = [img.convert(\"RGB\") for img in batch[\"image\"]]\r\n    return batch\r\n\r\nds_rgb = ds.map(to_rgb, batched=True)\r\n```\r\n\r\nPlease use our Forum for questions of this kind in the future.","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":47,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n We don't apply any additional preprocessing on top of `PIL.Image.open(image_file)`, so you need to do the conversion yourself:\r\n\r\n```python\r\ndef to_rgb(batch):\r\n    batch[\"image\"] = [img.convert(\"RGB\") for img in batch[\"image\"]]\r\n    return batch\r\n\r\nds_rgb = ds.map(to_rgb, batched=True)\r\n```\r\n\r\nPlease use our Forum for questions of this kind in the future.","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594,-0.0159561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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3973","title":"ConnectionError  and SSLError","comments":"Hi ! You can download the `oscar.py` file from this repository at `\/datasets\/oscar\/oscar.py`.\r\n\r\nThen you can load the dataset by passing the local path to `oscar.py` to `load_dataset`:\r\n```python\r\nload_dataset(\"path\/to\/oscar.py\", \"unshuffled_deduplicated_it\")\r\n```","body":"code\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('oscar', 'unshuffled_deduplicated_it')\r\n```\r\nbug report\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionError                           Traceback (most recent call last)\r\n~\\AppData\\Local\\Temp\/ipykernel_29788\/2615425180.py in <module>\r\n----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)\r\n   1658 \r\n   1659     # Create a dataset builder\r\n-> 1660     builder_instance = load_dataset_builder(\r\n   1661         path=path,\r\n   1662         name=name,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)\r\n   1484         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1485         download_config.use_auth_token = use_auth_token\r\n-> 1486     dataset_module = dataset_module_factory(\r\n   1487         path,\r\n   1488         revision=revision,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n   1236                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n   1237                     ) from None\r\n-> 1238                 raise e1 from None\r\n   1239     else:\r\n   1240         raise FileNotFoundError(\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n   1173             if path.count(\"\/\") == 0:  # even though the dataset is on the Hub, we get it from GitHub for now\r\n   1174                 # TODO(QL): use a Hub dataset module factory instead of GitHub\r\n-> 1175                 return GithubDatasetModuleFactory(\r\n   1176                     path,\r\n   1177                     revision=revision,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in get_module(self)\r\n    531         revision = self.revision\r\n    532         try:\r\n--> 533             local_path = self.download_loading_script(revision)\r\n    534         except FileNotFoundError:\r\n    535             if revision is not None or os.getenv(\"HF_SCRIPTS_VERSION\", None) is not None:\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in download_loading_script(self, revision)\r\n    511         if download_config.download_desc is None:\r\n    512             download_config.download_desc = \"Downloading builder script\"\r\n--> 513         return cached_path(file_path, download_config=download_config)\r\n    514 \r\n    515     def download_dataset_infos_file(self, revision: Optional[str]) -> str:\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    232     if is_remote_url(url_or_filename):\r\n    233         # URL, so get it from the cache (downloading if necessary)\r\n--> 234         output_path = get_from_cache(\r\n    235             url_or_filename,\r\n    236             cache_dir=cache_dir,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\utils\\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)\r\n    580         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    581         if head_error is not None:\r\n--> 582             raise ConnectionError(f\"Couldn't reach {url} ({repr(head_error)})\")\r\n    583         elif response is not None:\r\n    584             raise ConnectionError(f\"Couldn't reach {url} (error {response.status_code})\")\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/2.0.0\/datasets\/oscar\/oscar.py (SSLError(MaxRetryError(\"HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/2.0.0\/datasets\/oscar\/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))\")))\r\n```\r\nIt may be caused by Caused by SSLError(in China?) because it works well on google colab.\r\nSo how can I download this dataset manually?\r\n","comment_length":32,"text":"ConnectionError  and SSLError \n code\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('oscar', 'unshuffled_deduplicated_it')\r\n```\r\nbug report\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionError                           Traceback (most recent call last)\r\n~\\AppData\\Local\\Temp\/ipykernel_29788\/2615425180.py in <module>\r\n----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)\r\n   1658 \r\n   1659     # Create a dataset builder\r\n-> 1660     builder_instance = load_dataset_builder(\r\n   1661         path=path,\r\n   1662         name=name,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)\r\n   1484         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1485         download_config.use_auth_token = use_auth_token\r\n-> 1486     dataset_module = dataset_module_factory(\r\n   1487         path,\r\n   1488         revision=revision,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n   1236                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n   1237                     ) from None\r\n-> 1238                 raise e1 from None\r\n   1239     else:\r\n   1240         raise FileNotFoundError(\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)\r\n   1173             if path.count(\"\/\") == 0:  # even though the dataset is on the Hub, we get it from GitHub for now\r\n   1174                 # TODO(QL): use a Hub dataset module factory instead of GitHub\r\n-> 1175                 return GithubDatasetModuleFactory(\r\n   1176                     path,\r\n   1177                     revision=revision,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in get_module(self)\r\n    531         revision = self.revision\r\n    532         try:\r\n--> 533             local_path = self.download_loading_script(revision)\r\n    534         except FileNotFoundError:\r\n    535             if revision is not None or os.getenv(\"HF_SCRIPTS_VERSION\", None) is not None:\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\load.py in download_loading_script(self, revision)\r\n    511         if download_config.download_desc is None:\r\n    512             download_config.download_desc = \"Downloading builder script\"\r\n--> 513         return cached_path(file_path, download_config=download_config)\r\n    514 \r\n    515     def download_dataset_infos_file(self, revision: Optional[str]) -> str:\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    232     if is_remote_url(url_or_filename):\r\n    233         # URL, so get it from the cache (downloading if necessary)\r\n--> 234         output_path = get_from_cache(\r\n    235             url_or_filename,\r\n    236             cache_dir=cache_dir,\r\n\r\nD:\\DataScience\\PythonSet\\IDES\\anaconda\\lib\\site-packages\\datasets\\utils\\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)\r\n    580         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    581         if head_error is not None:\r\n--> 582             raise ConnectionError(f\"Couldn't reach {url} ({repr(head_error)})\")\r\n    583         elif response is not None:\r\n    584             raise ConnectionError(f\"Couldn't reach {url} (error {response.status_code})\")\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/2.0.0\/datasets\/oscar\/oscar.py (SSLError(MaxRetryError(\"HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/2.0.0\/datasets\/oscar\/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))\")))\r\n```\r\nIt may be caused by Caused by SSLError(in China?) because it works well on google colab.\r\nSo how can I download this dataset manually?\r\n \n Hi ! You can download the `oscar.py` file from this repository at `\/datasets\/oscar\/oscar.py`.\r\n\r\nThen you can load the dataset by passing the local path to `oscar.py` to `load_dataset`:\r\n```python\r\nload_dataset(\"path\/to\/oscar.py\", \"unshuffled_deduplicated_it\")\r\n```","embeddings":[-0.5248147249,0.0405230634,-0.1230327189,0.0725033507,0.3041602373,-0.0861735046,0.3794277012,0.3197903037,0.1657542586,0.1261250079,-0.0368023068,0.1544640511,0.0295699202,0.2415485531,-0.0804175362,0.0118007232,0.0215492398,0.2121128887,-0.2936364412,0.1434446275,-0.1580768973,0.0597132817,-0.1213339269,0.0628936142,0.0264170617,-0.0918866172,0.0635952353,0.09248963,-0.2721813619,-0.5847228765,0.3350659013,0.0544011779,0.1639633179,0.3337168694,-0.0001032296,0.1607512534,0.3830227554,0.0225937646,-0.4438838661,-0.0753620267,-0.2600414753,-0.1699307561,0.1725028008,-0.2509514093,-0.1271160692,-0.1427579224,0.0133180264,-0.1045694277,0.4266457558,0.5949547887,0.3087108433,0.2350473255,0.2211331576,-0.2260816693,-0.093868427,-0.0528600365,-0.0494220331,0.3206468523,-0.1541195363,-0.0526125655,-0.0211272147,0.1484285742,-0.0330743343,0.131769076,0.1853141338,-0.1362235844,0.2192585915,-0.2343800515,0.2789015472,0.1347588897,0.3769879937,-0.2704375386,-0.1939039081,-0.0663898513,0.0133975921,-0.1153049618,0.2672587931,-0.0227161665,-0.2561770678,0.1315240115,0.1152299568,-0.0420314372,-0.2920095026,0.1214196384,-0.2473967373,0.3689371347,-0.1031119749,0.1338785291,0.1406417042,-0.2849055231,0.3763561845,-0.265576601,-0.0630823895,-0.0249633025,-0.3652105033,-0.0172486659,0.0610821247,-0.1850283295,0.0179429464,0.2507582903,-0.1675679088,0.0015166609,0.2556134462,0.0941412598,0.1876465976,-0.0191309657,-0.0684208274,0.2050613612,0.2798160017,0.4794403911,-0.0046102297,-0.0193345509,0.0774071142,-0.3451925218,0.1494957656,0.2217476517,0.5676452518,-0.0497964099,-0.3642171621,0.0815469772,-0.0719113722,-0.0626080707,0.1049011424,0.5125083923,-0.1762953103,-0.0208250247,0.0871407911,0.0257266648,-0.1565681249,0.0333860219,-0.2656222582,0.0218052436,-0.0177687351,-0.0600029491,0.1233207211,-0.2167026997,0.0878132656,0.0247986671,0.0363383144,-0.0132873524,0.1787394583,-0.2246454358,-0.0337171294,0.3683737814,0.0790289044,0.1233741343,0.1865831465,-0.3182586432,0.0053789699,-0.0215358064,-0.1364102215,-0.368914634,-0.2977691889,0.3526279032,-0.099242948,-0.0255752541,0.1224341169,-0.1396495104,0.1118072271,-0.1843087524,-0.0066952514,-0.3930797875,-0.0140003776,-0.2388254553,0.4356188476,0.5765903592,-0.2544672191,-0.0194348674,-0.1086840257,-0.3297651112,0.1433623284,-0.0008665854,-0.1209413931,0.1959979683,-0.283337146,0.0140805123,0.4933345318,-0.2732447386,-0.474529624,0.2703975141,-0.0774072334,-0.0675550029,0.1488501728,0.0251265038,0.1717925221,0.1925431043,0.2184542567,0.0975835547,-0.132005766,0.0138857523,-0.110377647,-0.2811441422,0.086560525,0.2148653269,0.0964925736,0.0370329134,0.2573040128,-0.0018459745,0.2437432855,-0.0702360198,0.0848947614,0.1108255014,0.2402842641,0.2289813906,-0.0890921503,-0.0985051766,-0.3618351817,0.1667391062,0.0708520338,0.2703915834,-0.2953439355,-0.0068604504,-0.3324002028,0.0225030053,-0.3172858059,-0.2356597483,0.3202826977,0.3172685206,0.0873091444,0.1541065276,-0.1639429182,0.3611117601,-0.0898956805,0.1543644816,-0.3304589391,0.3856510222,-0.3553184867,-0.1974292696,0.1665094048,-0.0455648899,0.2814328969,-0.1167608425,-0.1832756102,0.2978258431,-0.1057495624,0.1631053835,-0.0631919801,-0.0845946819,0.0249384195,-0.2407684773,0.1089295,0.1619396359,0.2229104936,-0.0165450443,0.0845862702,0.3176093996,-0.0448932983,0.2343648672,0.1467713714,0.138876155,0.40602386,-0.0116489045,-0.05899553,0.022523215,0.0543649495,-0.1573626548,0.2438279837,-0.2047215849,-0.1547783166,0.1012208983,0.2893354297,-0.0325203091,0.1159038395,-0.0359678641,-0.1878464073,-0.0219852645,0.1158393696,0.3814059794,0.4593290389,0.0940860584,-0.1313472688,0.2244416177,0.0116805425,-0.0999060944,0.3175111711,-0.025184432,0.0247132182,0.2216822207,-0.0086906739,-0.0564420596,-0.3081172109,-0.4279347062,-0.2288101166,0.384462744,-0.2976517677,0.1923933178,-0.1080934033,-0.4097387195,-0.1233200654,0.1638267189,-0.1746722609,-0.2784341276,-0.204512164,0.2484969348,-0.0588205084,0.195192799,-0.3050100803,0.0053670285,0.203263998,-0.0682167709,-0.0573419482,-0.110627979,-0.0692984164,0.1607012749,0.161907196,0.089840278,0.4566483796,-0.1733338684,0.0086406097,-0.2272708863,-0.1802410185,0.020428136,0.0706614777,0.0958890319,0.4718353748,0.4023368657,0.1296416074,-0.4160859287,0.3316514194,-0.1155416444,-0.0953339264,0.0498097949,-0.1012485325,0.0574603081,-0.0175374411,-0.4779075384,-0.4305971265,-0.4179047346,0.0495835021,0.094447419,0.163816914,0.1572206467,0.0943631828,0.2408653647,-0.0294093676,0.0499746762,-0.1950811893,-0.2628124058,0.2003073692,-0.1548718512,-0.3877334595,0.1066555679,-0.0228675958,0.1981830001,-0.245931223,-0.5698668957,-0.3658428788,-0.1819875389,0.3429222405,-0.0642679855,-0.0980042294,0.289735198,0.1094275936,-0.1510211974,-0.0023643209,-0.3675800264,-0.0354960226,0.0333933271,0.0989801362,-0.0744050369,0.5484481454,-0.1492922306,0.4120691419,0.2601548433,-0.0247457903,0.2816315889,-0.1954274327,0.2761997283,-0.1008627191,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3969","title":"Cannot preview cnn_dailymail dataset","comments":"I guess the cache got corrupted due to a previous issue with Google Drive service.\r\n\r\nThe cache should be regenerated, e.g. by passing `download_mode=\"force_redownload\"`.\r\n\r\nCC: @severo ","body":"## Dataset viewer issue for '*cnn_dailymail*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/cnn_dailymail\r\n\r\n*short description of the issue*\r\n\r\nAm I the one who added this dataset ? Yes-No\r\n","comment_length":26,"text":"Cannot preview cnn_dailymail dataset \n ## Dataset viewer issue for '*cnn_dailymail*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/cnn_dailymail\r\n\r\n*short description of the issue*\r\n\r\nAm I the one who added this dataset ? Yes-No\r\n \n I guess the cache got corrupted due to a previous issue with Google Drive service.\r\n\r\nThe cache should be regenerated, e.g. by passing `download_mode=\"force_redownload\"`.\r\n\r\nCC: @severo ","embeddings":[-0.3001443148,0.1003711745,-0.0295108072,0.2527271807,-0.0365714543,0.4788869023,0.3518633544,0.1342158616,-0.1076103002,0.0816059932,0.0185798742,-0.0681077242,-0.1297189742,0.1330219954,-0.054589849,-0.0531774163,0.0362982601,0.0126727307,0.0645384118,0.1520994306,-0.328705281,0.1523635536,-0.1258555204,-0.1625272036,-0.1889630109,-0.2408405393,0.0509407073,-0.0764456764,-0.2410833836,-0.2481218129,0.0258545354,0.091694206,0.0761290938,0.286251992,-0.0001108677,0.1460110396,0.5770383477,0.0952286273,-0.4096316695,-0.1781215519,0.1109841019,-0.0640218556,0.065616928,-0.0470403694,-0.2066573799,-0.1666980237,0.0564076602,-0.2384856939,0.2563394308,0.2655278742,0.2397268862,0.2472810894,0.0292735044,-0.2963423133,-0.1173273325,-0.165860936,-0.2852419019,0.2716921568,-0.246544838,0.1303915083,0.1296731979,0.1965691596,0.0484308302,0.0150310528,-0.0918338671,0.1051229835,-0.1604434401,-0.3715656102,0.1897888482,-0.0067216209,0.5617846251,-0.0110142576,-0.2454853058,0.1766006202,0.2344596982,-0.1753761321,0.2967875898,0.1844427139,0.1404484808,0.2778817415,-0.40473634,-0.1989469975,-0.0632664263,0.31832847,-0.1603921652,0.0089877872,-0.1415831894,0.1223070323,-0.0212871321,-0.1049776301,0.5157182217,-0.1721924543,-0.0639269128,0.0498846173,-0.0117330188,-0.1290300488,0.0433034748,0.0984490141,-0.0437055975,0.116131939,-0.1133807674,-0.0721456781,-0.0635623336,0.0106569119,0.110333316,0.2751213312,0.0813373327,0.2554914057,0.3578922451,0.1652930081,0.3596162498,-0.0539102629,-0.1404289603,0.1071248204,-0.0932276174,-0.0819682181,0.4510961771,-0.0402333178,-0.241812259,0.1712124646,-0.2220128179,-0.0321245976,-0.0712794363,0.3751703799,-0.1875046045,-0.3492168486,-0.0499136858,0.1813981384,0.0645083115,-0.1590726376,-0.3080528378,-0.0439108647,-0.1557722688,0.252835542,0.3908677399,-0.2719049156,0.111952357,-0.0354322679,0.1177471876,-0.0882538632,-0.1107253432,0.0277161561,-0.2258161306,0.218535766,0.0410527289,0.3443533778,0.1177532896,0.1806509644,0.1035268009,0.2060120255,-0.1977183223,-0.4557098448,0.0034161727,0.1546727568,-0.4183003008,-0.1121838018,-0.0704928786,0.1604210734,-0.2096106857,-0.0834121779,-0.2386018932,0.2705118358,-0.0968362316,-0.1173521727,0.0767766237,0.3635947108,-0.3849908412,0.0475905649,-0.4673796594,-0.4734217227,0.1199678853,0.3018504381,-0.0998107865,-0.1450817138,-0.3228721619,0.0501847453,0.1767573506,-0.0054863323,-0.8023152947,-0.0368464924,-0.1421165913,-0.0080495309,-0.0756033212,0.1161130965,0.0739757046,-0.1442147344,-0.1413904727,0.4141310453,0.0430425145,-0.0266126748,-0.2092333287,-0.3023833036,0.2087436318,0.1786832511,0.2769963145,0.2069008648,0.1031349897,-0.2416488826,-0.0761631727,0.1954420805,0.014392525,0.1825558245,0.0907430351,-0.150275141,0.1358540356,0.1372164041,-0.567986846,0.0718719661,0.178058356,0.0281816069,-0.150418818,-0.129186064,-0.2626024783,0.070179455,-0.1377693564,-0.1789966226,0.2244574279,0.1023606732,-0.1249391586,0.2711905539,0.0309280604,0.0489558838,-0.0438947715,-0.1087173894,-0.117921114,0.3618579507,-0.0935049206,-0.0409866497,0.2035871446,-0.0774088576,0.2939368784,-0.083779335,-0.2664110065,0.1035315022,-0.1308322549,0.2918297648,0.2929287553,0.0693170428,0.0885813609,-0.3968087137,0.1386912763,0.2722697854,-0.1687529683,-0.0358427912,-0.2935991585,0.0438275002,0.1748094857,-0.1422575861,-0.1314187199,0.0886450112,0.1271843165,0.0016826026,0.167722702,-0.2345178574,0.1830431819,0.0120512024,0.5338984728,-0.1629336774,-0.221036911,0.1191416085,0.205720976,-0.2112344205,0.0331556574,0.1112066209,-0.4103272259,-0.2730825841,-0.2425906062,0.274220556,0.2535567582,0.0738537014,0.2500463128,0.2354700714,0.1809593439,-0.1370882243,0.110237062,0.071017459,0.0420132615,0.1230558008,0.2608272433,-0.0267975666,-0.4891771376,0.2632583976,0.4685604274,0.4192255139,-0.1656619757,-0.0913532972,-0.1788526177,-0.0869127214,0.2301268578,-0.2540560365,-0.1784055531,-0.3257436454,0.1554211229,0.2270703316,0.012674286,0.1348285228,0.1219554469,0.4795156419,0.3065283895,0.01757784,-0.2787468135,-0.0005129193,-0.0804369673,0.1368815303,0.360237509,-0.2678504586,0.2713237703,0.0690687671,0.0595741458,-0.7089738846,-0.2868512571,0.0434290394,0.0361276343,0.6266723871,-0.1880392432,0.3393319547,0.1618057489,0.1048678383,-0.0417827405,-0.2110104263,-0.2959507704,-0.0863000229,-0.1145061105,-0.0888173431,0.0027829404,-0.3537245691,-0.2696407735,-0.2092420161,0.1742672473,-0.1965801716,-0.0642950237,0.0015744645,-0.0416056588,-0.123150602,0.1943286657,-0.1102592796,-0.5379715562,-0.3815451264,0.4116377532,-0.5738167763,-0.5965697169,0.2564910054,0.1105611771,-0.1507775933,-0.1051835865,-0.543158412,-0.2485460937,-0.1045301035,-0.2073769122,0.0492331497,-0.5171288848,0.0807987154,-0.1011416167,-0.0628671944,-0.0649817288,-0.3293249309,-0.178177461,-0.129535988,0.0845281631,-0.1059697941,0.4432548285,-0.0378230922,0.5717325211,0.2785785496,0.0316255577,0.170555234,-0.1906147003,0.3731377721,-0.0227058399,-0.2890555263,-0.0133194253,0.1308900714,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3968","title":"Cannot preview 'indonesian-nlp\/eli5_id' dataset","comments":"Hi @cahya-wirawan, thanks for reporting.\r\n\r\nYour dataset is working OK in streaming mode:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n   ...: ds = load_dataset(\"indonesian-nlp\/eli5_id\", split=\"train\", streaming=True)\r\n   ...: item = next(iter(ds))\r\n   ...: item\r\nUsing custom data configuration indonesian-nlp--eli5_id-9fe728a7e760fb7b\r\n\r\nOut[1]: \r\n{'q_id': '1oy5tc',\r\n 'title': 'dalam sepak bola apa gunanya menyia-nyiakan dua permainan pertama dengan terburu-buru - di tengah - bukan permainan terburu-buru biasa saya mendapatkannya',\r\n 'selftext': '',\r\n 'document': '',\r\n 'subreddit': 'explainlikeimfive',\r\n 'answers': {'a_id': ['ccwtgnz', 'ccwtmho', 'ccwt946', 'ccwvj0u'],\r\n  'text': ['Jaga pertahanan tetap jujur, rasakan operan terburu-buru, buka permainan yang lewat. Pelanggaran yang terlalu satu dimensi akan gagal. Dan mereka yang bergegas ke tengah kadang-kadang dapat dibuka lebar-lebar untuk ukuran yard yang besar.',\r\n   'Jika Anda melempar bola sepanjang waktu, maka pertahanan akan beradaptasi untuk selalu menutupi umpan. Dengan melakukan permainan lari sederhana sesekali, Anda memaksa pertahanan untuk tetap dekat dan menjaga dari lari. Terkadang, pelanggaran dapat membuat pertahanan lengah dengan berpura-pura berlari dan membebaskan penerima mereka. Selain itu, Anda tidak perlu mendapatkan yard besar di setiap permainan. Terkadang, paling baik mendapatkan beberapa yard sekaligus. Selama Anda mendapatkan yang pertama, Anda dalam kondisi yang baik.',\r\n   'Dalam kebanyakan kasus, O-Line seharusnya membuat lubang untuk dilalui kembali. Jika Anda menjalankan terlalu banyak permainan ke luar \/ melempar, pertahanan akan mengejar. Juga, 2 permainan 5 yard memberi Anda satu set down baru.',\r\n   'Saya Anda tidak suka jenis drama itu, tonton CFL. Kami hanya mendapatkan 3 down sehingga Anda tidak bisa menyia-nyiakannya. Lebih banyak lagi yang lewat.'],\r\n  'score': [3, 2, 2, 2]},\r\n 'title_urls': {'url': []},\r\n 'selftext_urls': {'url': []},\r\n 'answers_urls': {'url': []}}\r\n```\r\nTherefore, it should be properly rendered in the previewer. Let me ping @severo to have a look at it.","body":"## Dataset viewer issue for '*indonesian-nlp\/eli5_id*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/indonesian-nlp\/eli5_id\r\n\r\nI can not see the dataset preview.\r\n```\r\nServer Error\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the dataset does not exist.\r\n```\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":271,"text":"Cannot preview 'indonesian-nlp\/eli5_id' dataset \n ## Dataset viewer issue for '*indonesian-nlp\/eli5_id*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/indonesian-nlp\/eli5_id\r\n\r\nI can not see the dataset preview.\r\n```\r\nServer Error\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the dataset does not exist.\r\n```\r\nAm I the one who added this dataset ? Yes\r\n \n Hi @cahya-wirawan, thanks for reporting.\r\n\r\nYour dataset is working OK in streaming mode:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n   ...: ds = load_dataset(\"indonesian-nlp\/eli5_id\", split=\"train\", streaming=True)\r\n   ...: item = next(iter(ds))\r\n   ...: item\r\nUsing custom data configuration indonesian-nlp--eli5_id-9fe728a7e760fb7b\r\n\r\nOut[1]: \r\n{'q_id': '1oy5tc',\r\n 'title': 'dalam sepak bola apa gunanya menyia-nyiakan dua permainan pertama dengan terburu-buru - di tengah - bukan permainan terburu-buru biasa saya mendapatkannya',\r\n 'selftext': '',\r\n 'document': '',\r\n 'subreddit': 'explainlikeimfive',\r\n 'answers': {'a_id': ['ccwtgnz', 'ccwtmho', 'ccwt946', 'ccwvj0u'],\r\n  'text': ['Jaga pertahanan tetap jujur, rasakan operan terburu-buru, buka permainan yang lewat. Pelanggaran yang terlalu satu dimensi akan gagal. Dan mereka yang bergegas ke tengah kadang-kadang dapat dibuka lebar-lebar untuk ukuran yard yang besar.',\r\n   'Jika Anda melempar bola sepanjang waktu, maka pertahanan akan beradaptasi untuk selalu menutupi umpan. Dengan melakukan permainan lari sederhana sesekali, Anda memaksa pertahanan untuk tetap dekat dan menjaga dari lari. Terkadang, pelanggaran dapat membuat pertahanan lengah dengan berpura-pura berlari dan membebaskan penerima mereka. Selain itu, Anda tidak perlu mendapatkan yard besar di setiap permainan. Terkadang, paling baik mendapatkan beberapa yard sekaligus. Selama Anda mendapatkan yang pertama, Anda dalam kondisi yang baik.',\r\n   'Dalam kebanyakan kasus, O-Line seharusnya membuat lubang untuk dilalui kembali. Jika Anda menjalankan terlalu banyak permainan ke luar \/ melempar, pertahanan akan mengejar. Juga, 2 permainan 5 yard memberi Anda satu set down baru.',\r\n   'Saya Anda tidak suka jenis drama itu, tonton CFL. Kami hanya mendapatkan 3 down sehingga Anda tidak bisa menyia-nyiakannya. Lebih banyak lagi yang lewat.'],\r\n  'score': [3, 2, 2, 2]},\r\n 'title_urls': {'url': []},\r\n 'selftext_urls': {'url': []},\r\n 'answers_urls': {'url': []}}\r\n```\r\nTherefore, it should be properly rendered in the previewer. Let me ping @severo to have a look at it.","embeddings":[-0.3884463906,-0.1418884844,-0.0735412017,0.1538683325,-0.0247687586,0.2311385423,0.0929410681,0.5587155223,0.0132091083,-0.0031359883,-0.2292771786,0.1413205415,-0.0089979507,0.0560641401,0.2455597967,-0.385282129,0.1090712696,0.2116556913,0.0661699772,0.2062216103,-0.2260432988,0.1690868884,-0.1779131442,0.0307424162,-0.1016645804,0.126808852,-0.2462918907,-0.1787736565,-0.2351306826,-0.4451823533,0.327098608,-0.0066394787,0.3465333879,0.3288263381,-0.0001176661,0.1015073955,0.4787644744,0.0469392464,-0.4361974895,-0.2867253423,-0.0695508793,-0.2629032135,0.1667048186,-0.3640157282,-0.3667900264,-0.1416674256,0.1614738852,-0.2330999523,0.44912377,0.2519759238,0.1802675724,0.1842023879,-0.0906575844,-0.1951971054,0.0046623503,0.0135075469,-0.340557456,-0.0311178211,0.0251057521,0.1483627409,0.0723093823,0.1425684243,0.0414824747,0.0002270873,-0.0983589888,-0.1774888933,-0.0742167607,-0.4272820652,0.2674390972,0.0889695287,0.829331398,-0.1354740709,-0.4231261909,0.0516598485,0.1803527772,-0.3652415574,0.0424412414,0.1807281226,0.0217582919,0.2970374525,-0.2148314714,-0.1728135645,-0.1344362646,0.352918148,0.0918898433,0.4469756186,-0.0729436353,0.1788550913,-0.1445162743,0.0110884197,0.3606284559,-0.2371483296,-0.0554233715,0.3523611426,-0.2338986397,-0.0025194369,-0.0487667806,0.0658835098,-0.0402618907,0.2348724902,0.0417962447,0.1268389076,0.2089369148,0.0888385028,-0.0419940986,0.2751559317,0.2118462622,0.1947982609,0.0795952007,0.1010092124,0.3155805171,-0.1507544965,-0.1895994395,0.0298122987,-0.0631499588,-0.1059169248,0.083505556,-0.0825968534,-0.2144390196,0.0875712782,-0.2015103996,-0.1049720645,-0.0790580288,0.2095071375,-0.2710109651,0.0359089598,-0.0197481364,0.3294924796,-0.1725652367,-0.3394614458,-0.204564169,-0.0959231481,-0.2350442708,0.107418634,0.2647384107,-0.056143038,0.354818821,-0.175687477,-0.0162342768,-0.0401391648,0.0868212953,-0.0773989707,-0.0552708022,0.2023692131,0.2660629749,0.24442783,0.2542658746,-0.1456144154,0.0261680279,0.2186499387,-0.0606235005,-0.339638859,-0.2691552043,0.129211843,-0.5351642966,0.0107726809,-0.4277229011,0.2442936897,0.0839643627,-0.2213212997,-0.0292767435,0.1889336556,-0.3994222879,-0.0839852542,0.1138235703,0.5005108118,-0.3949445188,-0.3168523014,-0.4791227579,-0.2337372899,0.2002006471,0.2949112952,-0.1533594131,0.0940814167,-0.4111356437,0.2337620705,0.6692294478,-0.1584241092,-0.8022477627,0.3397337794,-0.2535730898,0.1907269657,0.2465168983,0.187956214,0.2971201837,-0.2125584334,0.0285500977,0.1409574151,0.2370335311,-0.149567917,-0.2030353993,-0.1790860146,0.3119282424,0.1220059544,0.1393406242,0.0354034118,0.0201094951,-0.0297424663,0.3851157129,0.2866754532,0.1901659667,-0.0552024916,0.1308941841,0.1412426233,-0.0467300862,-0.1130552813,-0.1065964177,0.1706553549,0.0300967544,0.0040671732,-0.1335962862,-0.1195596159,-0.5475382209,0.1618610173,-0.4023771584,-0.2562200129,0.1501112431,0.1153146029,0.3233064115,0.2405787855,-0.0133459317,0.316526711,-0.1412055343,-0.1434650272,-0.3764167726,0.368545413,-0.0496472195,0.0896378756,0.2222929597,0.1586568505,0.369997561,0.0389926955,-0.1621093303,0.2774929404,-0.2628035843,0.1149619296,0.287896812,0.0580263175,0.1851752996,-0.6591469646,0.2241029739,0.0801993236,0.0848851204,-0.0888003483,-0.1891590059,0.1237821206,0.0863541439,0.0509520248,-0.1372753084,0.117165029,0.3348384798,0.0314216577,-0.1587203592,-0.158145383,0.2898854911,-0.2606801987,0.4740163982,-0.2403022796,-0.1924534887,0.1025101095,0.000977741,-0.090708755,0.0305853449,0.1434001178,-0.2073385566,-0.2956685722,0.0105985813,0.0880796835,0.4563913941,0.2363197207,0.2078634948,0.0893920735,0.0896414369,-0.1324800253,0.0991060734,0.0699365363,0.1454225332,0.1838043332,0.1430676877,-0.0732428506,-0.2717493474,0.0095777353,0.3296997547,0.2500695884,-0.0962308422,-0.0076894443,-0.2244861573,-0.5751373768,-0.0319591351,-0.2915560901,0.00723129,-0.4501574934,0.1041163132,0.0707953349,0.1665889025,0.3464949727,-0.4401084781,0.0872653574,0.1577487886,0.0515143424,-0.1567114443,0.0336695947,-0.0888903514,0.0692386702,0.2791858613,0.0393030979,0.4615880251,-0.2586062253,0.1123819724,-0.5453107357,-0.2779381275,0.0850230083,0.0678189695,0.4314616024,-0.1768881977,0.2624557018,0.1044780463,-0.1249225512,0.2560336292,-0.1020508334,-0.1849177182,0.1137405112,0.0292080306,-0.009925927,0.1951759905,-0.3396383524,-0.4454943836,-0.3778810203,0.0324128866,-0.1336253732,0.0466426052,0.0414326303,0.119172208,-0.0381572954,0.1233722642,-0.2101917565,-0.3526592255,-0.4695138037,0.3989985883,-0.4600352347,-0.439298749,0.1779684275,-0.0280604847,0.3165484369,-0.1425096542,-0.5763728023,0.1502581388,-0.0402829088,0.231717959,0.1383294016,-0.200196743,0.184401527,-0.186800316,-0.0026976971,-0.0738972947,-0.0629624128,-0.219480738,-0.3743027747,0.4920832217,-0.0524415001,0.5958861113,0.0800614506,0.7769434452,0.5118920207,0.1964694262,0.3109145463,-0.0558984168,0.1896193773,-0.1541992873,-0.3330961764,-0.030729454,-0.10418199,0.0561070479,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3968","title":"Cannot preview 'indonesian-nlp\/eli5_id' dataset","comments":"Thanks @albertvillanova for checking it. Btw, I have another dataset indonesian-nlp\/lfqa_id which has the same issue. However, this dataset is still private, is it the reason why the preview doesn't work?","body":"## Dataset viewer issue for '*indonesian-nlp\/eli5_id*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/indonesian-nlp\/eli5_id\r\n\r\nI can not see the dataset preview.\r\n```\r\nServer Error\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the dataset does not exist.\r\n```\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":31,"text":"Cannot preview 'indonesian-nlp\/eli5_id' dataset \n ## Dataset viewer issue for '*indonesian-nlp\/eli5_id*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/indonesian-nlp\/eli5_id\r\n\r\nI can not see the dataset preview.\r\n```\r\nServer Error\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the dataset does not exist.\r\n```\r\nAm I the one who added this dataset ? Yes\r\n \n Thanks @albertvillanova for checking it. Btw, I have another dataset indonesian-nlp\/lfqa_id which has the same issue. However, this dataset is still private, is it the reason why the preview doesn't work?","embeddings":[-0.335232228,-0.1239321679,-0.027136052,0.2779892683,-0.1499147117,0.2525175214,0.1689610779,0.4171839058,0.0262222327,0.1278894395,-0.209331423,0.0273422934,0.0728633255,-0.0633361638,0.2337566465,-0.2524942458,0.137003839,0.2076781541,0.041900117,0.1397501379,-0.3411981761,0.138701573,-0.0847396031,0.0956585556,-0.1752810329,0.1472758502,-0.2631250024,-0.162565738,-0.1750167161,-0.2988178432,0.3090737462,0.0094314171,0.3150721788,0.2825846076,-0.000120714,0.1024616584,0.5217805505,0.0814411044,-0.3983308971,-0.1704627275,-0.2927373946,-0.1974096596,0.1072620675,-0.2427866012,-0.3409104049,-0.0719875693,0.2031472921,-0.2895103991,0.3366419375,0.1013350785,0.1765974015,0.2822761536,0.0096484702,-0.418073386,0.0997695774,0.113939479,-0.3644113243,-0.0360721722,-0.0325783081,0.1766184419,0.1380995661,0.171203196,0.1845665872,-0.0496159792,-0.0524267964,-0.1137561798,-0.0809142143,-0.3823923171,0.3947344422,0.1050453484,0.9738980532,0.0537460819,-0.421857357,0.2185230106,0.165057376,-0.270213306,0.2160298377,0.077715151,0.2029744983,0.29381603,-0.284012109,-0.224087894,-0.0425119065,0.2671948373,0.048401244,0.3617527485,-0.1486109048,0.1761086583,-0.1185659394,-0.0315711126,0.2541719973,-0.2068041712,-0.1960059702,0.3550165296,-0.078970477,0.001830217,-0.0349565931,0.1232685447,0.0478699468,0.1794871092,-0.1306176633,0.1282430738,0.0576456115,0.1062515005,-0.0910091549,0.2693838477,0.2268246412,0.1627312303,0.2376191467,0.0461575687,0.3526900411,-0.1490909755,-0.2292901576,0.1273763776,-0.1262230873,-0.2348267138,0.1206289679,-0.1650118679,-0.2759542763,0.1054638028,-0.0866019577,-0.103412725,-0.1140597686,0.2603635788,-0.1627198309,-0.2183691412,0.0150112649,0.2727593482,-0.1524412632,-0.4064309895,-0.2044021785,-0.1434212923,-0.2629235089,0.2153827548,0.1966864169,0.0834170803,0.3332151473,-0.1632979512,0.0638503209,-0.0473395512,0.0570900366,0.0791114867,-0.2406433821,0.2372680157,0.1935512871,0.2929938436,0.1542376131,-0.1600528657,0.119665429,0.1881630272,-0.0789425373,-0.3005827367,-0.3701655865,0.0783779025,-0.5145670176,0.0544663668,-0.3898007572,0.1647766083,0.1224503815,-0.1828579307,0.0200936049,0.283948034,-0.3336379826,-0.0686913133,0.0581614561,0.5976068974,-0.4043647051,-0.2873674631,-0.5321161747,-0.3303726614,0.1480833292,0.3575612307,-0.167408362,-0.0633020923,-0.4827782214,0.1629850864,0.576540947,-0.0882451385,-0.7730315924,0.2138252407,-0.2996724248,0.0039665354,0.1571530849,0.1583943516,0.2135118246,-0.2919867337,-0.1313530058,-0.0370311774,0.2716798186,-0.2453710735,-0.1660526395,-0.2120825052,0.3313558996,0.1505402774,0.2093393207,0.0863539129,0.1533831507,-0.0644473732,0.1847374737,0.2670715451,0.2059558481,0.0518549345,0.1193992943,0.1918428242,-0.0156813301,-0.0267113894,-0.1420505345,0.1247741058,-0.1552666575,-0.0272433534,0.0034464574,-0.176190123,-0.4698728323,0.091563344,-0.3309877813,-0.2598211467,0.0590097457,0.2252207398,0.2295531631,0.2239294648,-0.0285141524,0.1453657448,-0.0788472965,-0.1175728962,-0.3168615401,0.1957642585,-0.038568601,0.1330084801,0.1202666312,0.1876130998,0.3888683915,-0.0624560863,-0.1487782151,0.2090479881,-0.3042759895,0.0499681532,0.4700348973,-0.002441087,0.1386856735,-0.6537967324,0.1642120183,-0.0789460838,0.0673483163,-0.0275915433,-0.194615379,0.0880391449,0.1664420515,-0.0703108311,-0.2632863522,0.150084585,0.3535473645,0.0582925528,-0.026063852,-0.2253334969,0.3618337214,-0.1910691559,0.4871994555,-0.2137669027,-0.253341347,0.1172369942,-0.0745110437,-0.1371079385,0.1531006992,0.1593272239,-0.0686936602,-0.1284013391,-0.0279647894,-0.0493271723,0.4077875018,0.1767100394,0.1648610532,-0.026566904,0.1069119275,-0.1371831596,0.0149037531,-0.0200791489,0.1377498209,-0.0249668956,0.1892005056,-0.0655183792,-0.2761457264,0.147875458,0.439067781,0.2086622715,-0.1248715594,0.0482875928,-0.1794449836,-0.5138228536,0.0877680406,-0.2043248862,-0.1417869925,-0.3560830653,0.1761588752,-0.0244356152,0.1562805772,0.301361829,-0.3635276556,0.3201647103,0.0134757999,0.2143827677,-0.229764387,0.1407666504,-0.0902791396,0.045907747,0.2752158344,-0.0245861281,0.3592140675,-0.2050981671,0.2490682006,-0.6150686145,-0.3445731401,0.0469790474,0.0729132742,0.4568885863,-0.137546882,0.2275836915,0.1145644039,-0.0123424986,0.1849180013,-0.0382079147,-0.2666500807,0.0514329597,-0.0535290763,-0.1655919999,0.0778845027,-0.1234854013,-0.3930378258,-0.2759178877,0.1000738814,-0.2051892579,-0.0067535318,-0.0844909996,-0.0700424388,-0.022473868,-0.0688115507,-0.2468435913,-0.4568462074,-0.5459211469,0.5053563118,-0.4959360063,-0.4490435421,0.2937420011,0.0267346688,0.3320818841,-0.2924139202,-0.5488402843,0.1215963885,-0.0819525719,0.2348625064,0.2194417268,-0.2633041143,0.3224680126,-0.2924363017,0.0128778787,-0.1233025119,-0.1479100138,-0.1501100957,-0.4924745858,0.6167436838,-0.1961087435,0.483294189,-0.0904608965,0.8399674296,0.423422873,0.2643404305,0.31518507,-0.0189050362,0.3202704191,-0.2186616361,-0.3000473678,-0.0788540095,-0.0497423522,0.067475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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3965","title":"TypeError: Couldn't cast array of type for JSONLines dataset","comments":"Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype).","body":"## Describe the bug\r\nOne of the [course participants](https:\/\/discuss.huggingface.co\/t\/chapter-5-questions\/11744\/20?u=lewtun) is having trouble loading a JSONLines dataset that's composed of the GitHub issues from `spacy` (see stack trace below). \r\n\r\nThis reminds me a bit of #2799 where one can load the dataset in `pandas` but not in `datasets` and perhaps increasing the `block_size` is needed again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns 'https:\/\/huggingface.co\/datasets\/Evan\/spaCy-github-issues\/resolve\/main\/spacy-issues.jsonl'\r\ndata_files = hf_hub_url(repo_id=\"Evan\/spaCy-github-issues\", filename=\"spacy-issues.jsonl\", repo_type=\"dataset\")\r\n# throws TypeError: Couldn't cast array of type\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas - note this take a while as the file is >2GB\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to pandas.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 683, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1136, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 511, in write_table\r\n    pa_table = table_cast(pa_table, self._schema)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1121, in table_cast\r\n    return cast_table_to_features(table, Features.from_arrow_schema(schema))\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1102, in cast_table_to_features\r\n    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1102, in <listcomp>\r\n    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 944, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 918, in wrapper\r\n    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 918, in <listcomp>\r\n    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1086, in cast_array_to_feature\r\n    return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 944, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 920, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1019, in array_cast\r\n    raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{pa_type}\")\r\nTypeError: Couldn't cast array of type\r\nstruct<url: string, html_url: string, labels_url: string, id: int64, node_id: string, number: int64, title: string, description: string, creator: struct<login: string, id: int64, node_id: string, avatar_url: string, gravatar_id: string, url: string, html_url: string, followers_url: string, following_url: string, gists_url: string, starred_url: string, subscriptions_url: string, organizations_url: string, repos_url: string, events_url: string, received_events_url: string, type: string, site_admin: bool>, open_issues: int64, closed_issues: int64, state: string, created_at: timestamp[s], updated_at: timestamp[s], due_on: null, closed_at: timestamp[s]>\r\nto\r\nnull\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.1\r\n","comment_length":27,"text":"TypeError: Couldn't cast array of type for JSONLines dataset \n ## Describe the bug\r\nOne of the [course participants](https:\/\/discuss.huggingface.co\/t\/chapter-5-questions\/11744\/20?u=lewtun) is having trouble loading a JSONLines dataset that's composed of the GitHub issues from `spacy` (see stack trace below). \r\n\r\nThis reminds me a bit of #2799 where one can load the dataset in `pandas` but not in `datasets` and perhaps increasing the `block_size` is needed again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns 'https:\/\/huggingface.co\/datasets\/Evan\/spaCy-github-issues\/resolve\/main\/spacy-issues.jsonl'\r\ndata_files = hf_hub_url(repo_id=\"Evan\/spaCy-github-issues\", filename=\"spacy-issues.jsonl\", repo_type=\"dataset\")\r\n# throws TypeError: Couldn't cast array of type\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas - note this take a while as the file is >2GB\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to pandas.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 683, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1136, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 511, in write_table\r\n    pa_table = table_cast(pa_table, self._schema)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1121, in table_cast\r\n    return cast_table_to_features(table, Features.from_arrow_schema(schema))\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1102, in cast_table_to_features\r\n    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1102, in <listcomp>\r\n    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 944, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 918, in wrapper\r\n    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 918, in <listcomp>\r\n    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1086, in cast_array_to_feature\r\n    return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 944, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 920, in wrapper\r\n    return func(array, *args, **kwargs)\r\n  File \"\/Users\/lewtun\/miniconda3\/envs\/hf\/lib\/python3.9\/site-packages\/datasets\/table.py\", line 1019, in array_cast\r\n    raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{pa_type}\")\r\nTypeError: Couldn't cast array of type\r\nstruct<url: string, html_url: string, labels_url: string, id: int64, node_id: string, number: int64, title: string, description: string, creator: struct<login: string, id: int64, node_id: string, avatar_url: string, gravatar_id: string, url: string, html_url: string, followers_url: string, following_url: string, gists_url: string, starred_url: string, subscriptions_url: string, organizations_url: string, repos_url: string, events_url: string, received_events_url: string, type: string, site_admin: bool>, open_issues: int64, closed_issues: int64, state: string, created_at: timestamp[s], updated_at: timestamp[s], due_on: null, closed_at: timestamp[s]>\r\nto\r\nnull\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.1\r\n \n Hi @lewtun, thanks for reporting.\r\n\r\nIt seems that our library fails at inferring the dtype of the columns:\r\n- `milestone`\r\n- `performed_via_github_app` \r\n\r\n(and assigns them `null` dtype).","embeddings":[0.0036059762,-0.0479997993,-0.0535864346,0.434281677,0.4592538476,0.1503392756,0.4873134792,0.3241958618,0.4770776629,-0.057500869,-0.061184179,0.1311786771,-0.1100012437,0.2816880941,-0.0008711191,-0.3941471875,-0.0146889417,-0.0445327871,0.1062430516,0.1749326438,-0.0933482721,0.1730911285,-0.1196654588,-0.1031807289,-0.1494211704,-0.085256055,0.1366049945,0.2853343785,-0.0558419898,-0.4664634764,0.5960640907,0.0274271145,0.2970753312,0.4774362147,-0.0001162325,0.2658969462,0.3623135686,0.0439485498,-0.2050976008,-0.3944782019,-0.2175051868,-0.2268907875,0.292797029,-0.2615489066,0.0292122662,0.0209290981,-0.2876492441,-0.280804038,0.631501019,0.3120798171,0.1566186994,0.5220170021,0.1360073388,-0.1679840684,-0.0224389546,0.2846999764,-0.2199214995,0.2597155571,-0.0267581884,0.3974964321,0.1206839457,-0.0854220763,-0.1513491422,-0.0770175233,0.4257461131,-0.1706186384,-0.0252256468,0.0552036688,-0.0201817993,0.2161801457,0.3892121911,-0.1799870282,-0.2810038626,-0.445832938,0.0304254517,0.2269043028,0.104608722,0.1048275903,-0.1074718237,0.1574066579,-0.3536053002,0.1600893289,-0.1817758977,0.0738727674,-0.0526402406,-0.3760051727,-0.1472248137,0.0713381842,0.0066733519,-0.1403694451,-0.1099321768,-0.0787994117,0.0000563304,0.2029625326,-0.0324040726,0.0440953523,0.2454863191,-0.4204905033,0.3482052684,0.0516690128,0.1613377184,0.1043186784,-0.2260720283,0.0543429218,0.5339517593,0.1073643565,-0.06462311,0.0745336041,0.0793801993,0.5198202729,-0.0296130534,-0.0402055867,-0.2337756604,-0.048777055,-0.1448587775,-0.112909779,0.4528776705,-0.3941109478,-0.3187656999,0.2627511919,-0.4356749356,-0.0382250696,0.1603553146,0.4267037809,-0.0415272787,0.4907935858,0.2890572548,0.3620336056,0.0117228385,0.1405922621,-0.1951085031,0.0639987215,-0.0529607162,-0.0929268524,0.1831150651,0.163158372,0.1911187619,-0.0758846626,-0.0173960738,-0.1847352982,-0.2162362784,0.081706427,-0.1178417355,0.2371089607,0.1023427844,0.1257822663,0.2100936919,-0.1883967966,-0.2573604286,-0.0501624979,-0.2659563422,-0.1070380211,-0.0492235683,0.0865136161,-0.2419669479,0.0422001407,-0.6630195379,-0.1053634062,0.0012046808,0.002678795,0.0476041362,-0.1705113649,0.2703705728,-0.2308913469,0.1184273511,0.3342460692,-0.1978952587,-0.1069839001,-0.1328890473,-0.0868328959,0.2841023207,0.3354702592,-0.3149739504,0.3115195036,-0.151019454,0.2302300185,0.1985109448,-0.1078666449,-0.316827327,0.3766020834,0.1159518808,0.4211955667,0.1875958592,-0.1109476015,0.2210971713,0.0574078672,0.0656334162,0.4336674511,0.0251350217,0.1517325491,-0.2819079459,-0.2880394757,0.0161611997,0.1849741042,0.1061124355,0.208267346,0.0661371425,0.4525848031,0.3530704081,-0.1095715538,-0.0313438997,0.2982585728,0.0087237153,0.1096552387,-0.0160198472,-0.2828069925,-0.3617673516,0.0373042151,0.0792266279,0.0482545346,-0.300780654,-0.2145833373,-0.4669224322,0.1430499554,-0.017001126,0.0824257433,0.080849275,-0.0262319613,0.0357583687,-0.0136144152,-0.2273827493,0.3854263127,-0.0392467529,0.0527988449,-0.3350836337,0.5943090916,0.0339025222,-0.1328176707,0.1310599893,0.2340805531,-0.0081726639,-0.1459142119,-0.2191980183,0.0569655821,0.0718324631,-0.1213673726,0.0456142388,0.2117891312,0.0988473222,-0.0642332807,-0.1028446779,0.1945102066,0.1439253986,0.0080830893,-0.0056003928,0.4861294031,-0.3273336887,0.412422508,0.0553850494,-0.0361753292,0.3704107404,0.0886540115,-0.1819515824,-0.1625491381,0.1414504051,0.1647379994,0.316264689,0.0625007451,-0.350074321,0.1661984771,0.4788588285,-0.1133032218,-0.0089869462,0.184374705,0.0940417945,0.0037376904,0.0713818297,0.054080721,0.2124791443,0.1124174744,-0.0267868415,0.0345435776,-0.1336924136,-0.1356707066,0.1111843213,0.212005645,0.1027932689,-0.1108708754,0.0721662417,0.0769232661,-0.3014950454,-0.1932430565,-0.2302246839,0.230689168,-0.5577501655,0.2543386221,-0.4477497041,-0.1062559038,-0.111947462,-0.1162648201,-0.1584255695,-0.369602412,-0.3457583785,0.1036254317,-0.2485160977,0.0584164634,-0.1589831114,0.3131408989,0.1099692211,-0.0202589501,-0.1929339319,0.0442026071,-0.0121294511,0.0135154622,0.4791285396,0.1433262378,0.2285078764,-0.1178401485,0.0981688723,-0.0482417457,-0.0546510071,-0.0053597251,0.0041049817,0.1908529103,0.3854108453,0.5826591849,-0.0693763196,-0.3032044172,0.2546191812,-0.125119701,-0.1720172316,0.1961242855,-0.043159347,-0.0881246179,-0.1345495731,-0.0127539076,-0.0794924721,-0.550662756,0.4509368539,-0.0443824418,0.0257021915,-0.1100879461,0.17030707,0.3367567956,0.0135358712,0.2070684284,-0.1416762024,-0.2518293262,0.0570570715,-0.0386795849,-0.3429827988,-0.1030455977,-0.0532400496,0.2181973606,-0.1152981967,-0.5638254881,0.0494283251,-0.1129122376,0.2226748765,0.0309357531,-0.1644446403,0.1530500054,-0.1596442759,0.0608240031,-0.1119984612,-0.0226334669,-0.0062943045,-0.1480904073,0.062577486,0.0717454553,0.6046342254,-0.0198420919,-0.0758411065,0.341966778,0.061121963,0.5457173586,0.0157763567,0.1968661398,-0.3442948759,-0.3486701548,-0.1388024241,-0.0335641913,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3960","title":"Load local dataset error","comments":"Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n```\r\n\r\n\r\nLet us know if that resolves the issue.","body":"When i used the datasets==1.11.0\uff0c it's all right. Util update the latest version, it get the error like this:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val']}\r\n>>> ds = load_dataset('nateraw\/image-folder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n[] https:\/\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\/ \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1671, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1521, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1031, in __init__\r\n    super().__init__(*args, **kwargs)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 255, in __init__\r\n    sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 584, in from_local_or_remote\r\n    if not isinstance(patterns_for_key, DataFilesList)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 546, in from_local_or_remote\r\n    data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 196, in resolve_patterns_locally_or_by_urls\r\n    for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 146, in _resolve_single_pattern_locally\r\n    raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '\/ssd\/datasets\/imagenet\/pytorch\/train' at \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\n```\r\nI need some help to solve the problem, thanks!","comment_length":40,"text":"Load local dataset error \n When i used the datasets==1.11.0\uff0c it's all right. Util update the latest version, it get the error like this:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val']}\r\n>>> ds = load_dataset('nateraw\/image-folder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n[] https:\/\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\/ \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1671, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1521, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1031, in __init__\r\n    super().__init__(*args, **kwargs)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 255, in __init__\r\n    sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 584, in from_local_or_remote\r\n    if not isinstance(patterns_for_key, DataFilesList)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 546, in from_local_or_remote\r\n    data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 196, in resolve_patterns_locally_or_by_urls\r\n    for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 146, in _resolve_single_pattern_locally\r\n    raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '\/ssd\/datasets\/imagenet\/pytorch\/train' at \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\n```\r\nI need some help to solve the problem, thanks! \n Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n```\r\n\r\n\r\nLet us know if that resolves the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3960","title":"Load local dataset error","comments":"> Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> >>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n> >>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n> ```\r\n> \r\n> Let us know if that resolves the issue.\r\n\r\nSorry, replied late.\r\nThanks a lot! It's worked for me. But it seems much slower than before, and now gets stuck.....\r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\nResolving data files: 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100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50001\/50001 [00:00<00:00, 89094.29it\/s]\r\nUsing custom data configuration default-baebca6347576b33\r\nDownloading and preparing dataset image_folder\/default to .\/image_folder\/default-baebca6347576b33\/0.0.0\/ee92df8e96c6907f3c851a987be3fd03d4b93b247e727b69a8e23ac94392a091...\r\nDownloading data files #0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 80073\/80073 [00:00<00:00, 82289.56obj\/s]\r\nDownloading data files #1: 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71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e                                 | 57032\/80073 [00:00<00:00, 79930.58obj\/s]\r\nDownloading data files #11:  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75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f                            | 60356\/80073 [00:00<00:00, 84833.35obj\/s]\r\nDownloading data files #13:  97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588    | 77368\/80073 [00:00<00:00, 84475.10obj\/s]\r\nDownloading data files #14:  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78%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b                         | 62724\/80072 [00:00<00:00, 78387.33obj\/s]\r\nDownloading data files #15:  99%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 78933\/80072 [00:01<00:00, 79353.63obj\/s]\r\n```","body":"When i used the datasets==1.11.0\uff0c it's all right. Util update the latest version, it get the error like this:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val']}\r\n>>> ds = load_dataset('nateraw\/image-folder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n[] https:\/\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\/ \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1671, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1521, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1031, in __init__\r\n    super().__init__(*args, **kwargs)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 255, in __init__\r\n    sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 584, in from_local_or_remote\r\n    if not isinstance(patterns_for_key, DataFilesList)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 546, in from_local_or_remote\r\n    data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 196, in resolve_patterns_locally_or_by_urls\r\n    for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 146, in _resolve_single_pattern_locally\r\n    raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '\/ssd\/datasets\/imagenet\/pytorch\/train' at \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\n```\r\nI need some help to solve the problem, thanks!","comment_length":378,"text":"Load local dataset error \n When i used the datasets==1.11.0\uff0c it's all right. Util update the latest version, it get the error like this:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val']}\r\n>>> ds = load_dataset('nateraw\/image-folder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n[] https:\/\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\/ \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1671, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1521, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1031, in __init__\r\n    super().__init__(*args, **kwargs)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 255, in __init__\r\n    sanitize_patterns(data_files), base_path=base_path, use_auth_token=use_auth_token\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 584, in from_local_or_remote\r\n    if not isinstance(patterns_for_key, DataFilesList)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 546, in from_local_or_remote\r\n    data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 196, in resolve_patterns_locally_or_by_urls\r\n    for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n  File \"\/gf3\/home\/txacs\/gv3\/anaconda3\/envs\/txacs\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 146, in _resolve_single_pattern_locally\r\n    raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '\/ssd\/datasets\/imagenet\/pytorch\/train' at \/dat\/txacs\/git\/txacs\/examples\/image-classification\/https:\/huggingface.co\/datasets\/nateraw\/image-folder\/resolve\/main\r\n```\r\nI need some help to solve the problem, thanks! \n > Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> >>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n> >>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\n> ```\r\n> \r\n> Let us know if that resolves the issue.\r\n\r\nSorry, replied late.\r\nThanks a lot! It's worked for me. But it seems much slower than before, and now gets stuck.....\r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['\/ssd\/datasets\/imagenet\/pytorch\/train\/**'], 'validation': ['\/ssd\/datasets\/imagenet\/pytorch\/val\/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='.\/', task='image-classification')\r\nResolving data files: 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100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50001\/50001 [00:00<00:00, 89094.29it\/s]\r\nUsing custom data configuration default-baebca6347576b33\r\nDownloading and preparing dataset image_folder\/default to .\/image_folder\/default-baebca6347576b33\/0.0.0\/ee92df8e96c6907f3c851a987be3fd03d4b93b247e727b69a8e23ac94392a091...\r\nDownloading data files #0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 80073\/80073 [00:00<00:00, 82289.56obj\/s]\r\nDownloading data files #1: 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75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b                             | 59995\/80073 [00:00<00:00, 84148.19obj\/s]\r\nDownloading data files #10:  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71%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e                                 | 57032\/80073 [00:00<00:00, 79930.58obj\/s]\r\nDownloading data files #11:  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97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b   | 77803\/80073 [00:00<00:00, 85351.59obj\/s]\r\nDownloading data files #13:  75%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f                            | 60356\/80073 [00:00<00:00, 84833.35obj\/s]\r\nDownloading data files #13:  97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588    | 77368\/80073 [00:00<00:00, 84475.10obj\/s]\r\nDownloading data files #14:  72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d                                | 57751\/80073 [00:00<00:00, 80727.33obj\/s]\r\nDownloading data files #14:  92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f        | 74022\/80073 [00:00<00:00, 78703.16obj\/s]\r\nDownloading data files #15:  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79353.63obj\/s]\r\n```","embeddings":[-0.3380225599,-0.114140451,-0.0211975258,0.2390871197,0.4253334999,-0.0225406867,0.1771003604,0.3576570451,0.0669671223,0.1895555854,-0.1314774305,0.1565939039,-0.0968019739,0.1201880723,-0.0979674831,-0.296218574,-0.0579133108,0.1971371919,-0.1178741753,-0.1511341929,-0.2569162548,0.1787683368,-0.1400129199,0.1847218424,-0.2956010401,0.0831508189,-0.1054894999,0.2142816186,-0.0965183303,-0.4482676089,0.4221608639,-0.0895105526,0.4329846501,0.4966243804,-0.0001023158,0.17225115,0.4993554652,-0.026760269,-0.1251298636,-0.4663240314,-0.1383082718,-0.1904052943,0.3075006902,-0.118566893,-0.2022633404,-0.4111450315,0.0497763902,-0.1912936866,0.4004446268,0.3993334472,0.3264052272,0.248587966,0.0299534686,-0.3245801926,-0.1635426283,0.1704921424,-0.1472791731,0.3505590856,0.256641537,-0.153898567,0.1403165758,0.3131349683,-0.055146616,0.1936384588,0.3182661235,-0.1083028242,-0.06152438,-0.1487922221,0.3644403219,0.0928643644,0.5512080193,-0.1445952952,-0.3069010079,0.0421254039,0.1144741029,-0.4257186055,0.2398095876,0.1540400982,-0.1403723359,0.2098692954,-0.2887387872,-0.0676197112,-0.2394699156,0.3054097295,-0.1165480837,0.1363257021,-0.1642613411,0.1148757786,0.2302117199,-0.0888859704,0.1679836065,0.1112330854,0.1810367256,0.2592576742,-0.3870074749,0.2529420853,-0.005841333,-0.074595429,-0.0328734703,-0.1497269273,-0.0505561009,-0.1193772107,-0.1094887704,0.2907733321,0.1241557002,0.2429410815,0.2186167091,0.3857802749,0.0354060158,0.0724390149,-0.1684983373,-0.0520526767,-0.3046111166,-0.3428444564,0.1724547297,0.0338618457,0.2183429152,-0.2477737665,-0.187506035,0.052968055,-0.0264608376,0.1310577989,0.0048450562,0.3974287212,-0.0720018819,0.1078411043,0.11357072,0.2105728835,-0.2100157589,0.0804998055,-0.3218249977,0.0064725401,-0.1483469158,-0.0503722616,0.1498716623,-0.3902206123,0.3461689949,-0.1646035314,-0.1027217954,0.0681311488,0.1059607565,-0.1619602293,-0.0051335753,0.5106235147,-0.1675314009,0.102222085,0.2465296388,0.0278735049,-0.0904969126,0.1305611879,-0.2611622214,-0.5131596923,-0.3502134681,0.2658132911,0.0356026106,-0.0744618401,-0.2554311156,0.0156026175,0.0914213285,0.2082774639,-0.2133510113,-0.2529437542,-0.3055506349,-0.0327625312,0.2802274525,0.3274256885,-0.3259852231,0.0016581784,-0.1004468948,-0.2188027948,0.0414027795,0.2085922211,-0.3016658127,0.2088793367,-0.104451701,0.1096088141,0.1563944966,-0.4672547579,-0.2618286908,0.2249240875,0.0080278758,-0.2027759999,-0.1378243417,-0.012152208,-0.058922112,0.1817512661,0.0981102139,0.3021401167,-0.0610940158,0.0481856167,-0.2115162909,-0.197669059,-0.1029630899,0.3457719386,0.0422711484,0.1390031278,0.2000285685,-0.2314841598,0.0671962127,-0.1334645003,-0.0854794532,0.2596311867,0.170796454,0.0681166425,0.0315433294,-0.046076566,-0.5464987159,0.2385165244,0.1520850807,0.2741982937,-0.2650343478,-0.0512846895,-0.4634175599,0.0776333138,-0.3745078444,-0.0832192674,0.1788612157,0.0678446814,0.066940628,0.1444493085,-0.19192186,0.2888279557,-0.3069467843,0.0401100479,-0.1483062506,0.185594067,-0.1667275578,-0.1352546215,0.1729490608,-0.103879638,0.0582589023,-0.2966474295,-0.2748553455,0.4825038314,0.0012197669,0.2237780094,0.002674374,0.05394933,0.1286351532,-0.2424883693,-0.0404466353,-0.0502867252,0.0433804132,0.0840272009,-0.0127479434,0.1394747198,0.0792034268,0.1023014262,0.0895428732,0.0503306985,0.3731085062,-0.0630995482,0.0525553897,-0.2071747482,0.1640108079,0.0410339087,0.2085024714,0.0219007451,-0.2507736087,-0.2435825318,0.2033375949,0.0275660157,-0.0100003937,0.2371495068,-0.0452149399,0.2026247531,0.2026037127,0.3036540747,0.3462533057,0.1128005683,-0.2387435287,0.2949957848,-0.1616530567,-0.0559896752,0.0445131473,-0.0829130337,0.3669479191,0.2743315697,0.0693969429,0.0740834028,-0.4737313986,-0.0784993023,-0.0236855894,0.4904199839,-0.1245588735,0.1172459498,-0.2654000521,-0.2774946094,-0.2419726402,-0.0375763476,-0.1377545148,-0.1387421489,-0.1552968025,0.066696398,-0.0139740342,0.2876707613,-0.2312740833,0.0697480962,0.1242867634,-0.2204566598,-0.1328823715,-0.2112715691,-0.3053153753,0.0875388831,0.3674649,0.0579072386,0.3674761951,-0.3410927951,0.0082324529,-0.0165083203,-0.3201085329,0.0844090581,-0.1093897298,0.1559102535,0.070500873,0.223752439,-0.1700755656,-0.2264109999,0.6276735067,-0.3136832416,-0.1328911334,0.0429497957,-0.0383523256,-0.2057213038,-0.1820735335,-0.5400707722,-0.3777373433,-0.2573294342,0.0813766196,0.2109511197,0.2434428781,0.4190809429,0.344676733,0.1439471543,-0.1057146415,-0.1207183972,-0.1763256937,-0.3949628174,0.1005408987,-0.2491053194,-0.4323080182,-0.0686041936,0.0622896366,0.5364884138,0.0186182894,-0.3495426774,-0.2764158249,-0.1668328047,0.1295659095,-0.121655412,0.0708092377,0.3206848502,-0.0917444155,-0.1171273738,-0.0888179541,-0.1010697708,0.1604620814,-0.0386743173,-0.0627672672,0.0926694497,0.3357470632,-0.0903460011,0.5322237015,-0.0121135712,-0.3363138735,0.4557913244,-0.1320487112,0.2421814948,-0.3542275727,-0.5656894445,0.07093001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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3956","title":"TypeError: __init__() missing 1 required positional argument: 'scheme'","comments":"Hi @amirj, thanks for reporting.\r\n\r\nAt first sight, your issue seems a version incompatibility between your Elasticsearch client and your Elasticsearch server.\r\n\r\nFeel free to have a look at Elasticsearch client docs: https:\/\/www.elastic.co\/guide\/en\/elasticsearch\/client\/python-api\/current\/overview.html#_compatibility\r\n> Language clients are forward compatible; meaning that clients support communicating with greater or equal minor versions of Elasticsearch. Elasticsearch language clients are only backwards compatible with default distributions and without guarantees made.","body":"## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n","comment_length":66,"text":"TypeError: __init__() missing 1 required positional argument: 'scheme' \n ## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n \n Hi @amirj, thanks for reporting.\r\n\r\nAt first sight, your issue seems a version incompatibility between your Elasticsearch client and your Elasticsearch server.\r\n\r\nFeel free to have a look at Elasticsearch client docs: https:\/\/www.elastic.co\/guide\/en\/elasticsearch\/client\/python-api\/current\/overview.html#_compatibility\r\n> Language clients are forward compatible; meaning that clients support communicating with greater or equal minor versions of Elasticsearch. Elasticsearch language clients are only backwards compatible with default distributions and without guarantees 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3956","title":"TypeError: __init__() missing 1 required positional argument: 'scheme'","comments":"@albertvillanova It doesn't seem a version incompatibility between the client and server, since the following code is working:\r\n\r\n```\r\nfrom elasticsearch import Elasticsearch\r\nes_client = Elasticsearch(\"http:\/\/localhost:9200\")\r\ndataset.add_elasticsearch_index(column=\"e1\", es_client=es_client, es_index_name=\"e1_index\")\r\n```","body":"## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n","comment_length":30,"text":"TypeError: __init__() missing 1 required positional argument: 'scheme' \n ## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n \n @albertvillanova It doesn't seem a version incompatibility between the client and server, since the following code is working:\r\n\r\n```\r\nfrom elasticsearch import Elasticsearch\r\nes_client = Elasticsearch(\"http:\/\/localhost:9200\")\r\ndataset.add_elasticsearch_index(column=\"e1\", es_client=es_client, es_index_name=\"e1_index\")\r\n```","embeddings":[-0.2016850263,-0.3632028699,-0.0670639575,-0.1154993474,-0.0156479795,0.2479981333,0.2341077775,0.2877420485,0.0638611168,0.0936895311,-0.0358448662,0.3917693198,-0.0215827525,-0.152334705,0.1281492412,-0.2309139818,0.121085614,0.087432608,0.1187869459,-0.0516799912,-0.2244480103,0.2019564062,-0.2399699688,-0.1863757074,0.0260687228,0.0416941904,-0.281126678,0.0370436236,-0.2842970788,-0.5741453171,0.5656750798,-0.3205862045,-0.0054031173,0.3283087611,-0.0001174942,0.2588958144,0.6181120276,-0.070870921,-0.1365975589,-0.3066091239,-0.2747555971,-0.1212207898,0.3674224615,-0.3745529652,-0.0447417274,0.0033120096,0.052850984,-0.3770344555,0.5422481894,0.474177748,0.1803839803,0.1715567857,0.4112608731,-0.5131973028,0.1866672337,0.0686088055,-0.1862741709,0.0758131891,-0.0755469725,0.3040466607,0.2885068357,0.1322864741,0.0819440186,-0.0990729779,0.0131786326,0.1307261139,0.1366858631,-0.1495376378,-0.2264224887,-0.194740817,0.2292087972,-0.1556185931,-0.4308762252,0.1329660714,0.1585823596,-0.2405288368,0.14714472,-0.0151985036,-0.0127603607,0.1177387908,0.3022183478,-0.1775995642,-0.3475022614,0.5092264414,0.0847174227,0.1819162667,-0.0808770731,0.0391932651,0.1065986156,-0.1730144322,0.0020133331,0.3552635908,0.0788009167,0.252709955,-0.2984156311,-0.0282034855,0.1122636944,-0.246192798,0.0884466097,-0.1922887713,-0.1135168672,-0.1603745669,0.5498575568,0.1669120193,-0.0451067202,0.1012818217,0.1200140268,0.1737442166,-0.049007941,-0.0631368831,0.2725943625,-0.0562142171,0.0272712186,-0.3945193589,0.1915056854,-0.0452345572,0.4555641115,-0.1275632977,-0.2430796176,-0.058577273,-0.31708166,-0.0847008899,0.0372951142,0.1921493709,0.232196629,-0.4963364899,0.4507412612,0.3782169223,-0.145913884,0.1989922971,-0.259350121,0.1355430633,-0.0163129698,-0.0615728125,0.1132288799,-0.58444947,0.4247122407,0.2325557917,-0.0374883786,0.0610244833,0.2157832682,0.0300101079,-0.2533140779,0.1263980567,-0.0347205289,-0.2597734928,0.1583043635,0.0415071212,-0.1471166164,-0.0897269845,-0.2763342261,-0.5810432434,-0.6209134459,0.1893595457,0.044520393,-0.0496433303,-0.1614013016,0.0976208001,0.2085873634,-0.0586843453,-0.1142825037,-0.1084868237,-0.1068545207,0.0529944897,0.2593818605,0.2752767503,-0.1447116137,-0.2105154395,-0.2179664671,0.3615582883,-0.1270294487,0.0429474637,-0.3336133957,0.330368042,-0.0799245983,0.0909015909,0.6981776357,-0.5474752784,-0.0536419302,-0.2091817707,-0.0626361072,-0.2886890769,0.0518553518,-0.0723175406,0.1295573115,0.2020718008,0.0836893022,0.0188737437,0.156858772,-0.1904112399,-0.301077038,-0.0207562335,-0.2133549154,0.2194907367,0.0688673705,0.0743892342,0.1130572036,-0.0949399248,-0.0289155561,0.0968277603,-0.0160929449,0.3530196846,0.447727114,0.0474989042,0.1451194733,-0.2445705682,-0.186145246,0.0887330323,0.1305076778,0.4066120088,-0.2754704654,-0.0054867994,-0.4557587206,-0.0125355404,-0.1999202669,-0.0404614061,0.047690399,-0.1804461032,0.0655059591,0.0815154389,-0.3663472235,0.0903836116,-0.2240103036,0.087416254,-0.4838053882,0.3780785501,-0.3399507403,-0.2848972082,-0.1005496234,-0.127281487,0.162595734,-0.2289761454,-0.252905786,0.3170164526,0.1585814059,-0.1993494779,-0.0094233891,-0.2196142226,0.0687942579,-0.3717128038,-0.1601184607,0.1146563739,0.0060175043,0.0075713098,-0.1409527957,0.0491281226,-0.0276780799,0.1236103997,-0.1865464598,0.0244979896,0.1674749106,0.0128466589,-0.2148431093,-0.1568399817,0.0762598366,0.0110410759,0.0965336114,-0.2560921013,-0.0806219056,-0.2556485534,0.1009649262,0.1908090562,0.0109869624,0.3315634727,0.0306276679,-0.0241756272,0.0200197026,-0.2649319768,0.5081581473,0.1863701046,-0.2549333572,0.1788088381,-0.1951348782,0.0434972234,0.3546721935,0.077606909,0.2782560587,-0.0410640687,0.2114385813,0.2027515471,-0.3051767051,-0.4304826856,-0.063165769,0.2208944857,-0.3955515921,0.1845768094,0.0268659834,0.0299323853,-0.0677968264,0.0289350152,0.140772447,-0.2932099402,-0.0042727776,0.2873460054,-0.0005063467,0.2911942005,-0.2152974308,0.1278512627,0.4924534261,-0.2807107568,-0.1180763692,0.0015201593,-0.1466072798,0.0087545374,0.0437357835,0.2254190445,0.2538788915,-0.2099373043,0.0073483945,-0.1291221082,-0.5952881575,0.075783588,-0.0007682078,0.4424199462,0.4109157324,0.1672627479,-0.5368112326,-0.416403681,0.3792355359,-0.047238417,-0.2670032084,0.0130983731,-0.2432999164,-0.1095611677,-0.0610098802,-0.6315124035,-0.1218555793,-0.4427924454,0.0928208604,0.0293398183,0.0403706655,0.4282228649,0.2001364976,0.1551534981,-0.0561065227,0.0692987591,-0.1925010979,-0.0702749565,0.3183072209,-0.365151912,-0.24668625,-0.1141716465,-0.1642150879,0.2686992586,0.260550648,-0.0881302282,-0.2447285503,-0.012627136,0.1801123023,-0.3956003189,0.1502328813,0.5313319564,0.0224416535,0.0291626509,-0.0647805333,-0.2079590112,0.4208350778,0.3675064445,0.1470497251,0.02519417,0.5940636396,-0.0903488398,0.6092325449,0.1436546445,-0.2190537751,0.4205082953,-0.2054324895,0.1942408234,-0.3542343676,-0.4843166769,-0.25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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3956","title":"TypeError: __init__() missing 1 required positional argument: 'scheme'","comments":"Hi @amirj, \r\n\r\nI really think it is a version incompatibility issue between your Elasticsearch client and server:\r\n- Your Elasticsearch server NodeConfig expects a positional argument named 'scheme'\r\n- Whereas your Elasticsearch client passes only keyword arguments: `NodeConfig(**options)`\r\n\r\nMoreover:\r\n- Looking at your stack trace, I deduce you are using Elasticsearch client **\"8\"** major version:\r\n  - the Elasticsearch file \"elasticsearch\/_sync\/client\/utils.py\" was created in version \"8.0.0a1\": https:\/\/github.com\/elastic\/elasticsearch-py\/commit\/21fa13b0f03b7b27ace9e19a1f763d40bd2e2ba4\r\n  - you can check your Elasticsearch client version by running this Python code:\r\n    ```python\r\n    import elasticsearch\r\n    print(elasticsearch.__version__)\r\n    ```\r\n\r\n- However, in the *Environment info*, you informed that the major version of your Eleasticsearch cluster server is **\"7\"** (\"7.10.2-SNAPSHOT\")\r\n\r\nCould you please align the Elasticsearch client\/server major versions (as pointed out in Elasticsearch docs) and check if the problem persists?","body":"## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n","comment_length":125,"text":"TypeError: __init__() missing 1 required positional argument: 'scheme' \n ## Describe the bug\r\nBased on [this tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch) the provided code should add Elasticsearch index but raised the following error, probably the new Elasticsearch version is not compatible though the tutorial doesn't provide any information about the supporting Elasticsearch version.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nsquad = load_dataset('squad', split='validation')\r\nsquad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n```\r\n\r\n## Expected results\r\n[Creating an elastic index based on the provided tutorial](https:\/\/huggingface.co\/docs\/datasets\/faiss_es#elasticsearch)\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-8fb51aa33961> in <module>\r\n      1 from datasets import load_dataset\r\n      2 squad = load_dataset('squad', split='validation')\r\n----> 3 squad.add_elasticsearch_index(\"context\", host=\"localhost\", port=\"9200\")\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n   3777         \"\"\"\r\n   3778         with self.formatted_as(type=None, columns=[column]):\r\n-> 3779             super().add_elasticsearch_index(\r\n   3780                 column=column,\r\n   3781                 index_name=index_name,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in add_elasticsearch_index(self, column, index_name, host, port, es_client, es_index_name, es_index_config)\r\n    587         \"\"\"\r\n    588         index_name = index_name if index_name is not None else column\r\n--> 589         es_index = ElasticSearchIndex(\r\n    590             host=host, port=port, es_client=es_client, es_index_name=es_index_name, es_index_config=es_index_config\r\n    591         )\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/search.py in __init__(self, host, port, es_client, es_index_name, es_index_config)\r\n    123         from elasticsearch import Elasticsearch  # noqa: F811\r\n    124 \r\n--> 125         self.es_client = es_client if es_client is not None else Elasticsearch([{\"host\": host, \"port\": str(port)}])\r\n    126         self.es_index_name = (\r\n    127             es_index_name\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/__init__.py in __init__(self, hosts, cloud_id, api_key, basic_auth, bearer_auth, opaque_id, headers, connections_per_node, http_compress, verify_certs, ca_certs, client_cert, client_key, ssl_assert_hostname, ssl_assert_fingerprint, ssl_version, ssl_context, ssl_show_warn, transport_class, request_timeout, node_class, node_pool_class, randomize_nodes_in_pool, node_selector_class, dead_node_backoff_factor, max_dead_node_backoff, serializer, serializers, default_mimetype, max_retries, retry_on_status, retry_on_timeout, sniff_on_start, sniff_before_requests, sniff_on_node_failure, sniff_timeout, min_delay_between_sniffing, sniffed_node_callback, meta_header, timeout, randomize_hosts, host_info_callback, sniffer_timeout, sniff_on_connection_fail, http_auth, maxsize, _transport)\r\n    310 \r\n    311         if _transport is None:\r\n--> 312             node_configs = client_node_configs(\r\n    313                 hosts,\r\n    314                 cloud_id=cloud_id,\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in client_node_configs(hosts, cloud_id, **kwargs)\r\n     99     else:\r\n    100         assert hosts is not None\r\n--> 101         node_configs = hosts_to_node_configs(hosts)\r\n    102 \r\n    103     # Remove all values which are 'DEFAULT' to avoid overwriting actual defaults.\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in hosts_to_node_configs(hosts)\r\n    142 \r\n    143         elif isinstance(host, Mapping):\r\n--> 144             node_configs.append(host_mapping_to_node_config(host))\r\n    145         else:\r\n    146             raise ValueError(\r\n\r\n~\/opt\/anaconda3\/lib\/python3.8\/site-packages\/elasticsearch\/_sync\/client\/utils.py in host_mapping_to_node_config(host)\r\n    209         options[\"path_prefix\"] = options.pop(\"url_prefix\")\r\n    210 \r\n--> 211     return NodeConfig(**options)  # type: ignore\r\n    212 \r\n    213 \r\n\r\nTypeError: __init__() missing 1 required positional argument: 'scheme'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 2.0.0\r\n- Platform: Mac\r\n- Python version: 3.8.0\r\n- PyArrow version: 7.0.0\r\n- ElaticSearch Info: \r\n{\r\n  \"name\" : \"byname\",\r\n  \"cluster_name\" : \"elasticsearch_brew\",\r\n  \"cluster_uuid\" : \"9xkjrltiQIG0J95ciWhqRA\",\r\n  \"version\" : {\r\n    \"number\" : \"7.10.2-SNAPSHOT\",\r\n    \"build_flavor\" : \"oss\",\r\n    \"build_type\" : \"tar\",\r\n    \"build_hash\" : \"unknown\",\r\n    \"build_date\" : \"2021-01-16T01:41:27.115673Z\",\r\n    \"build_snapshot\" : true,\r\n    \"lucene_version\" : \"8.7.0\",\r\n    \"minimum_wire_compatibility_version\" : \"6.8.0\",\r\n    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\r\n  },\r\n  \"tagline\" : \"You Know, for Search\"\r\n}\r\n \n Hi @amirj, \r\n\r\nI really think it is a version incompatibility issue between your Elasticsearch client and server:\r\n- Your Elasticsearch server NodeConfig expects a positional argument named 'scheme'\r\n- Whereas your Elasticsearch client passes only keyword arguments: `NodeConfig(**options)`\r\n\r\nMoreover:\r\n- Looking at your stack trace, I deduce you are using Elasticsearch client **\"8\"** major version:\r\n  - the Elasticsearch file \"elasticsearch\/_sync\/client\/utils.py\" was created in version \"8.0.0a1\": https:\/\/github.com\/elastic\/elasticsearch-py\/commit\/21fa13b0f03b7b27ace9e19a1f763d40bd2e2ba4\r\n  - you can check your Elasticsearch client version by running this Python code:\r\n    ```python\r\n    import elasticsearch\r\n    print(elasticsearch.__version__)\r\n    ```\r\n\r\n- However, in the *Environment info*, you informed that the major version of your Eleasticsearch cluster server is **\"7\"** (\"7.10.2-SNAPSHOT\")\r\n\r\nCould you please align the Elasticsearch client\/server major versions (as pointed out in Elasticsearch docs) and check if the problem persists?","embeddings":[-0.2016850263,-0.3632028699,-0.0670639575,-0.1154993474,-0.0156479795,0.2479981333,0.2341077775,0.2877420485,0.0638611168,0.0936895311,-0.0358448662,0.3917693198,-0.0215827525,-0.152334705,0.1281492412,-0.2309139818,0.121085614,0.087432608,0.1187869459,-0.0516799912,-0.2244480103,0.2019564062,-0.2399699688,-0.1863757074,0.0260687228,0.0416941904,-0.281126678,0.0370436236,-0.2842970788,-0.5741453171,0.5656750798,-0.3205862045,-0.0054031173,0.3283087611,-0.0001174942,0.2588958144,0.6181120276,-0.070870921,-0.1365975589,-0.3066091239,-0.2747555971,-0.1212207898,0.3674224615,-0.3745529652,-0.0447417274,0.0033120096,0.052850984,-0.3770344555,0.5422481894,0.474177748,0.1803839803,0.1715567857,0.4112608731,-0.5131973028,0.1866672337,0.0686088055,-0.1862741709,0.0758131891,-0.0755469725,0.3040466607,0.2885068357,0.1322864741,0.0819440186,-0.0990729779,0.0131786326,0.1307261139,0.1366858631,-0.1495376378,-0.2264224887,-0.194740817,0.2292087972,-0.1556185931,-0.4308762252,0.1329660714,0.1585823596,-0.2405288368,0.14714472,-0.0151985036,-0.0127603607,0.1177387908,0.3022183478,-0.1775995642,-0.3475022614,0.5092264414,0.0847174227,0.1819162667,-0.0808770731,0.0391932651,0.1065986156,-0.1730144322,0.0020133331,0.3552635908,0.0788009167,0.252709955,-0.2984156311,-0.0282034855,0.1122636944,-0.246192798,0.0884466097,-0.1922887713,-0.1135168672,-0.1603745669,0.5498575568,0.1669120193,-0.0451067202,0.1012818217,0.1200140268,0.1737442166,-0.049007941,-0.0631368831,0.2725943625,-0.0562142171,0.0272712186,-0.3945193589,0.1915056854,-0.0452345572,0.4555641115,-0.1275632977,-0.2430796176,-0.058577273,-0.31708166,-0.0847008899,0.0372951142,0.1921493709,0.232196629,-0.4963364899,0.4507412612,0.3782169223,-0.145913884,0.1989922971,-0.259350121,0.1355430633,-0.0163129698,-0.0615728125,0.1132288799,-0.58444947,0.4247122407,0.2325557917,-0.0374883786,0.0610244833,0.2157832682,0.0300101079,-0.2533140779,0.1263980567,-0.0347205289,-0.2597734928,0.1583043635,0.0415071212,-0.1471166164,-0.0897269845,-0.2763342261,-0.5810432434,-0.6209134459,0.1893595457,0.044520393,-0.0496433303,-0.1614013016,0.0976208001,0.2085873634,-0.0586843453,-0.1142825037,-0.1084868237,-0.1068545207,0.0529944897,0.2593818605,0.2752767503,-0.1447116137,-0.2105154395,-0.2179664671,0.3615582883,-0.1270294487,0.0429474637,-0.3336133957,0.330368042,-0.0799245983,0.0909015909,0.6981776357,-0.5474752784,-0.0536419302,-0.2091817707,-0.0626361072,-0.2886890769,0.0518553518,-0.0723175406,0.1295573115,0.2020718008,0.0836893022,0.0188737437,0.156858772,-0.1904112399,-0.301077038,-0.0207562335,-0.2133549154,0.2194907367,0.0688673705,0.0743892342,0.1130572036,-0.0949399248,-0.0289155561,0.0968277603,-0.0160929449,0.3530196846,0.447727114,0.0474989042,0.1451194733,-0.2445705682,-0.186145246,0.0887330323,0.1305076778,0.4066120088,-0.2754704654,-0.0054867994,-0.4557587206,-0.0125355404,-0.1999202669,-0.0404614061,0.047690399,-0.1804461032,0.0655059591,0.0815154389,-0.3663472235,0.0903836116,-0.2240103036,0.087416254,-0.4838053882,0.3780785501,-0.3399507403,-0.2848972082,-0.1005496234,-0.127281487,0.162595734,-0.2289761454,-0.252905786,0.3170164526,0.1585814059,-0.1993494779,-0.0094233891,-0.2196142226,0.0687942579,-0.3717128038,-0.1601184607,0.1146563739,0.0060175043,0.0075713098,-0.1409527957,0.0491281226,-0.0276780799,0.1236103997,-0.1865464598,0.0244979896,0.1674749106,0.0128466589,-0.2148431093,-0.1568399817,0.0762598366,0.0110410759,0.0965336114,-0.2560921013,-0.0806219056,-0.2556485534,0.1009649262,0.1908090562,0.0109869624,0.3315634727,0.0306276679,-0.0241756272,0.0200197026,-0.2649319768,0.5081581473,0.1863701046,-0.2549333572,0.1788088381,-0.1951348782,0.0434972234,0.3546721935,0.077606909,0.2782560587,-0.0410640687,0.2114385813,0.2027515471,-0.3051767051,-0.4304826856,-0.063165769,0.2208944857,-0.3955515921,0.1845768094,0.0268659834,0.0299323853,-0.0677968264,0.0289350152,0.140772447,-0.2932099402,-0.0042727776,0.2873460054,-0.0005063467,0.2911942005,-0.2152974308,0.1278512627,0.4924534261,-0.2807107568,-0.1180763692,0.0015201593,-0.1466072798,0.0087545374,0.0437357835,0.2254190445,0.2538788915,-0.2099373043,0.0073483945,-0.1291221082,-0.5952881575,0.075783588,-0.0007682078,0.4424199462,0.4109157324,0.1672627479,-0.5368112326,-0.416403681,0.3792355359,-0.047238417,-0.2670032084,0.0130983731,-0.2432999164,-0.1095611677,-0.0610098802,-0.6315124035,-0.1218555793,-0.4427924454,0.0928208604,0.0293398183,0.0403706655,0.4282228649,0.2001364976,0.1551534981,-0.0561065227,0.0692987591,-0.1925010979,-0.0702749565,0.3183072209,-0.365151912,-0.24668625,-0.1141716465,-0.1642150879,0.2686992586,0.260550648,-0.0881302282,-0.2447285503,-0.012627136,0.1801123023,-0.3956003189,0.1502328813,0.5313319564,0.0224416535,0.0291626509,-0.0647805333,-0.2079590112,0.4208350778,0.3675064445,0.1470497251,0.02519417,0.5940636396,-0.0903488398,0.6092325449,0.1436546445,-0.2190537751,0.4205082953,-0.2054324895,0.1942408234,-0.3542343676,-0.4843166769,-0.2542829216,0.2180972546,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3954","title":"The dataset preview is not available for tdklab\/Hebrew_Squad_v1.1 dataset","comments":"Hi @MatanBenChorin, thanks for reporting.\r\n\r\nPlease, take into account that the preview may take some time until it properly renders (we are working to reduce this time).\r\n\r\nMaybe @severo can give more details on this.","body":"## Dataset viewer issue for 'tdklab\/Hebrew_Squad_v1.1'\r\n\r\n**Link:**  https:\/\/huggingface.co\/api\/datasets\/tdklab\/Hebrew_Squad_v1.1?full=true\r\n\r\nThe dataset preview is not available for this dataset.\r\n\r\nAm I the one who added this dataset ? Yes","comment_length":35,"text":"The dataset preview is not available for tdklab\/Hebrew_Squad_v1.1 dataset \n ## Dataset viewer issue for 'tdklab\/Hebrew_Squad_v1.1'\r\n\r\n**Link:**  https:\/\/huggingface.co\/api\/datasets\/tdklab\/Hebrew_Squad_v1.1?full=true\r\n\r\nThe dataset preview is not available for this dataset.\r\n\r\nAm I the one who added this dataset ? Yes \n Hi @MatanBenChorin, thanks for reporting.\r\n\r\nPlease, take into account that the preview may take some time until it properly renders (we are working to reduce this time).\r\n\r\nMaybe @severo can give more details on this.","embeddings":[-0.311417073,-0.4014883935,-0.0658439845,0.2669583857,0.0838584378,0.1116312519,0.282913208,0.2667755187,-0.0862871781,0.3120709956,-0.1596049517,-0.0146393748,0.0534692183,0.174065277,0.1613078713,-0.1715472639,0.0359722525,0.1212253198,-0.0750993937,-0.0402280726,-0.2085525393,0.2721411288,-0.1961265504,0.0811796337,-0.2160776258,-0.0268842969,-0.0728323907,-0.0551242158,-0.2894195914,-0.2867647409,0.2907398343,-0.0514758155,0.2851788998,0.139846921,-0.0001113655,0.0937947407,0.261030376,-0.0603734255,-0.2673738599,-0.1547252089,0.0836879238,-0.2515999377,0.0806364045,-0.0030499001,-0.4502523541,-0.2254164815,0.1948548704,-0.2031599879,0.3874706328,0.1095742285,0.2613073587,0.5152115226,0.0199661404,-0.3259842992,0.1277089566,0.1748391092,-0.4528559446,-0.0725076571,0.1961443126,0.0952185467,0.1359424442,0.3663333952,0.3392030299,0.0849600658,0.0812118202,0.2018366158,-0.0015254866,-0.4241366088,0.2338888794,0.0842058137,0.7123064399,-0.0491914712,-0.201416567,0.1472202986,0.2774580419,0.0545285642,0.1342963129,0.0669682771,-0.1154622734,0.1445250064,-0.2964590192,-0.1811362356,0.0075478372,0.2059744447,0.0780535862,-0.0244395789,-0.277379334,0.0922090709,-0.1193279848,0.1183669791,0.1304579377,-0.025730269,-0.1497566849,0.342898488,-0.2063551843,-0.1538840383,0.0097941905,0.0488988645,0.0551649593,0.1905864179,0.1900181174,0.0714691952,-0.1487370729,-0.0124958334,0.1132702529,0.1042587236,0.3729870617,0.2126194239,0.3135798275,0.2581315637,0.4647903144,-0.014693358,-0.0998043418,-0.0669217035,-0.2137878239,-0.1548792422,0.2970794439,-0.0986104831,-0.254658401,-0.0499985032,-0.0585315414,0.0071913265,-0.0907531828,0.4044861495,-0.1182073727,-0.4784182012,0.1883211732,0.1282232106,-0.1205221787,-0.4043055177,-0.3268052042,-0.03421022,0.0190725382,0.2060566992,0.1323945522,-0.2404393703,0.209087342,0.1146891788,0.2104792893,0.1642152071,-0.2199823111,0.1174659431,-0.0739343166,0.0999159664,0.1455454528,0.2429649383,0.0810941011,0.034124963,0.0242817551,0.260535121,-0.1091808677,-0.139604643,-0.3876098096,0.1073495597,-0.393030256,-0.0526122823,-0.4856953323,0.4448401332,-0.16121687,-0.1029474884,0.0858733729,0.1520816237,-0.3201351166,-0.0759900585,0.3708455563,0.52246207,-0.5629886985,-0.3431332707,0.0393035263,-0.3624466956,0.0630797148,0.3338085115,-0.1874682605,-0.1235264689,-0.3934738636,0.1184408888,0.2422900796,-0.402025938,-0.6250221729,0.132660985,-0.1800153702,0.0502875224,0.0214503445,0.0848658979,0.4433107078,0.0520148389,-0.3218519688,0.1607806683,0.1733347774,-0.1198523566,-0.2461257428,-0.1002230942,0.2543728054,0.1884293556,-0.0266235992,0.1197136939,0.0648285374,-0.0185182486,0.4133199751,0.2739727199,0.2073928267,0.1438471526,0.2722547352,0.0027252485,0.0078779021,-0.1696540862,-0.498452276,0.0623763204,0.0823321044,0.2828386128,-0.0431731939,-0.2127306014,-0.4552213252,0.025819188,-0.4027038217,-0.3439928293,0.1304973513,0.1178735867,0.0500583351,0.1583375484,0.0825508535,0.0865713432,-0.1460717469,0.0256743915,-0.162535727,0.3810801804,-0.027523784,0.0165362917,0.1925337762,0.0511125624,0.0506219231,-0.2914513946,-0.0400964208,0.2824089229,-0.1210748181,-0.0040253345,0.3039687276,-0.2232213318,0.248490572,-0.3490482271,0.1128165424,-0.081154421,-0.0760091692,0.0095010288,-0.0848271251,0.3645386994,0.2695686817,0.002818512,-0.2322318405,0.0730642602,0.2107012421,0.2549850047,-0.0426507555,-0.1491951197,0.0997676998,0.0987857282,0.3478735387,-0.2881268561,-0.1279271096,0.2332928777,0.3511849344,-0.2120281607,-0.0606025457,0.0380412117,-0.1264584213,-0.001607911,0.1337207705,-0.1474856436,0.1726687998,0.1064743474,0.0235909931,-0.0679185092,0.141197592,-0.0272674095,0.0557224676,-0.1213868484,0.0044170008,0.0701378733,0.2100199163,-0.1460223943,-0.6160922647,0.2807808518,0.1974073201,0.2429198325,-0.1518463641,-0.1309158653,-0.1582820266,-0.1487354636,0.0251721796,-0.2747641504,-0.3882994354,-0.1599146575,0.2342507839,-0.0912193581,0.3873554468,0.4110916555,0.2234779,0.3818232119,-0.0442448817,0.3038652241,-0.1901388317,0.1711225212,-0.1916797608,0.1673666686,0.1117504314,-0.1602656394,0.2302402109,-0.1125580072,0.0615534969,-0.470975101,-0.2829716206,0.1865496337,-0.1716091335,0.4791972935,0.0085646911,0.3409079015,-0.2217843533,0.0791374892,0.2285401672,-0.1378616095,-0.5370668769,-0.1701667458,-0.2172562927,-0.0694938973,-0.0172634032,-0.3079886436,-0.4341338277,-0.159680739,0.2873241901,0.0122511527,-0.1503362954,-0.254952997,0.0039484878,0.089370504,-0.0356414802,-0.1369201243,-0.427311331,-0.3670595586,0.3611653447,-0.4547727704,-0.5265399814,0.231230244,0.199977681,0.212443307,-0.3956668079,-0.6403865814,-0.215640828,-0.0392025784,0.1284010559,-0.0259771701,-0.1352202594,0.3031501472,-0.2349164188,-0.0528987348,-0.1281711012,-0.3473533392,-0.2101452798,-0.2186274976,0.4514725506,-0.2246676236,0.2805478871,0.0188514944,0.7145478725,0.4017541707,0.1220459417,0.1135827303,-0.3372505903,0.3376704454,-0.1450767815,-0.3311553597,0.0114813996,-0.1413028985,0.2859897017,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3952","title":"Checksum error for glue sst2, stsb, rte etc datasets","comments":"Hi, @ravindra-ut.\r\n\r\nI'm sorry but I can't reproduce your problem:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"glue\", \"sst2\")\r\nDownloading builder script: 28.8kB [00:00, 11.6MB\/s]                                                                                                                                                       \r\nDownloading metadata: 28.7kB [00:00, 12.9MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset glue\/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown size, total: 11.90 MiB) to ...\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7.44M\/7.44M [00:01<00:00, 5.82MB\/s]\r\nDataset glue downloaded and prepared to ...\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.  \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 895.96it\/s]\r\n\r\nIn [3]: ds\r\nOut[2]: \r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 67349\r\n    })\r\n    validation: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 872\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 1821\r\n    })\r\n})\r\n``` \r\n\r\nMoreover, I see in your traceback that your error was for an URL at https:\/\/firebasestorage.googleapis.com\r\nHowever, the URLs were updated on Sep 16, 2020 (`datasets` version 1.0.2) to https:\/\/dl.fbaipublicfiles.com: https:\/\/github.com\/huggingface\/datasets\/commit\/2f03041a21c03abaececb911760c3fe4f420c229\r\n\r\nCould you please try to update `datasets`\r\n```shell\r\npip install -U datasets\r\n```\r\nand then force redownload\r\n```python\r\nds = load_dataset(\"glue\", \"sst2\", download_mode=\"force_redownload\")\r\n```\r\nto update the cache?\r\n\r\nPlease, feel free to reopen this issue if the problem persists.","body":"## Describe the bug\r\nChecksum error for glue sst2, stsb, rte etc datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> nlp.load_dataset('glue', 'sst2')\r\nDownloading and preparing dataset glue\/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown sizetotal: 11.90 MiB) to \r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 73.0\/73.0 [00:00<00:00, 18.2kB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 521, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']\r\n```\r\n\r\n## Expected results\r\ndataset load should succeed without checksum error.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 521, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']\r\n```\r\n## Environment info\r\n- `datasets` version: '1.18.3'\r\n- Platform: Mac OS\r\n- Python version: Python 3.8.9\r\n- PyArrow version: '7.0.0'\r\n","comment_length":179,"text":"Checksum error for glue sst2, stsb, rte etc datasets \n ## Describe the bug\r\nChecksum error for glue sst2, stsb, rte etc datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> nlp.load_dataset('glue', 'sst2')\r\nDownloading and preparing dataset glue\/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown sizetotal: 11.90 MiB) to \r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 73.0\/73.0 [00:00<00:00, 18.2kB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 521, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']\r\n```\r\n\r\n## Expected results\r\ndataset load should succeed without checksum error.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/builder.py\", line 521, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Library\/Python\/3.8\/lib\/python\/site-packages\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8']\r\n```\r\n## Environment info\r\n- `datasets` version: '1.18.3'\r\n- Platform: Mac OS\r\n- Python version: Python 3.8.9\r\n- PyArrow version: '7.0.0'\r\n \n Hi, @ravindra-ut.\r\n\r\nI'm sorry but I can't reproduce your problem:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"glue\", \"sst2\")\r\nDownloading builder script: 28.8kB [00:00, 11.6MB\/s]                                                                                                                                                       \r\nDownloading metadata: 28.7kB [00:00, 12.9MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset glue\/sst2 (download: 7.09 MiB, generated: 4.81 MiB, post-processed: Unknown size, total: 11.90 MiB) to ...\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7.44M\/7.44M [00:01<00:00, 5.82MB\/s]\r\nDataset glue downloaded and prepared to ...\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.  \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 895.96it\/s]\r\n\r\nIn [3]: ds\r\nOut[2]: \r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 67349\r\n    })\r\n    validation: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 872\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 1821\r\n    })\r\n})\r\n``` \r\n\r\nMoreover, I see in your traceback that your error was for an URL at https:\/\/firebasestorage.googleapis.com\r\nHowever, the URLs were updated on Sep 16, 2020 (`datasets` version 1.0.2) to https:\/\/dl.fbaipublicfiles.com: https:\/\/github.com\/huggingface\/datasets\/commit\/2f03041a21c03abaececb911760c3fe4f420c229\r\n\r\nCould you please try to update `datasets`\r\n```shell\r\npip install -U datasets\r\n```\r\nand then force redownload\r\n```python\r\nds = load_dataset(\"glue\", \"sst2\", download_mode=\"force_redownload\")\r\n```\r\nto update the cache?\r\n\r\nPlease, feel free to reopen this issue if the problem persists.","embeddings":[-0.0671885833,0.1787162274,-0.0147208488,0.2622032464,0.2177488953,-0.1340350062,0.1646662652,0.3809952736,0.2130872011,-0.0059031658,-0.0861570239,0.0618881807,0.0476297326,0.214188233,0.0418719165,-0.0405456088,-0.0152425664,-0.052437339,-0.2798446715,0.1639460027,-0.1584367305,0.3463344872,-0.1569360942,-0.180052489,-0.1639233679,0.0982371047,0.0486511588,0.1676204354,-0.1455791444,-0.3067032397,0.0553164855,0.0415737703,0.0050722947,0.2880761921,-0.0001127216,0.0792040825,0.528539598,-0.1051177084,-0.2609041035,0.1533358246,-0.0594281852,-0.293854475,-0.0908590928,-0.2731427252,0.0386815742,0.2741303146,-0.0576671548,-0.1645520777,-0.0207887888,0.1585755348,0.2541366518,0.5122368336,0.2858022749,0.2648344636,0.4789220691,-0.4634759426,-0.0669462979,0.4398517609,0.1693459004,-0.1131617576,-0.0695996284,0.2199504524,-0.2075427473,0.0165758003,-0.0173020791,-0.0878705978,-0.0883627981,-0.1281586289,-0.0380858481,0.2778958678,0.184963882,-0.4158975482,-0.3692347705,-0.1844172627,-0.0087190233,0.2744037509,0.399384588,0.4050834179,-0.0587355942,-0.0569903404,-0.2348705828,0.2480697632,0.178845048,0.007956611,0.186606735,0.1626896411,0.0539450385,0.0292247627,0.1087220684,-0.1844329089,-0.2954387069,-0.0906714275,-0.2178560644,0.0153780691,-0.5871678591,-0.1529463083,0.0650218129,0.3687289655,0.127217114,0.3131500185,0.0500432216,0.0376670063,0.0620289445,0.0887447149,-0.0481866598,0.1607342809,0.0232840106,0.0993210673,0.3288429976,0.1968704462,-0.0617859177,0.0808813944,0.0559894666,-0.2374122143,0.080748409,0.1429014355,0.1524630636,-0.2870582044,-0.4671034217,0.2998577058,0.0216797721,0.0492876358,0.0984918624,0.196743235,-0.3022461534,0.005960532,0.1782279313,0.0296934955,-0.1722761244,-0.1047385931,-0.1682982147,0.1819144487,-0.1995644569,-0.0942768902,0.2441402674,-0.3175604939,0.3001098335,-0.0926740915,-0.0644674674,-0.0700125769,0.0059626983,-0.2478692234,-0.0336704105,0.2952056229,-0.0610093363,0.0594499409,0.0245097801,-0.1238793656,-0.0652926192,-0.0366829634,-0.2259282619,-0.1059480235,-0.4280591607,0.2123850733,-0.5210893154,0.0187124908,-0.0374256894,-0.3463670313,0.2859885991,-0.1537163258,0.1475514024,-0.2093326598,-0.3316103518,-0.3552064896,0.1609103978,0.2788506448,-0.1164488271,-0.0097814295,-0.0360126793,-0.3200135529,0.1861368716,0.2783738375,0.2153256536,0.2464402467,-0.3957540691,0.1882582158,0.2193171084,-0.2674520612,-0.5852255225,0.2623684108,0.0679152906,0.0631836355,0.0459108464,0.0869377181,0.0410211273,-0.2004756331,0.1557480395,0.3058222532,0.0642993599,0.3108571172,-0.5511221886,-0.2260424197,0.2275689244,0.1600197405,-0.1308973134,-0.1657432616,0.2566593587,0.1216816083,0.0984171182,0.128933832,0.0304951351,0.1782434583,0.2857558429,-0.2077768147,0.0670333654,-0.4600886405,-0.4718084037,0.27493155,-0.2038859874,0.2327622026,-0.1857413501,-0.0186843351,-0.3012114763,-0.1335271448,-0.0225120168,-0.0725781992,0.2280364633,0.2757361233,0.2199954689,0.1239229515,-0.0314606689,0.3188430369,-0.203610763,0.0596893318,-0.1580882221,0.5122644901,-0.062679477,-0.3088285625,0.1730995476,0.3362633288,0.0206828974,-0.0567381196,-0.2537503242,0.6166082621,0.051657252,0.0743113607,-0.0870448351,0.1346780807,0.0203005522,-0.2407770902,-0.0202232413,0.2986131907,0.1002102122,-0.0104841478,0.1652024835,0.2439779788,-0.2789328098,-0.0458980761,0.0711234361,0.013481521,0.1265432835,-0.3125060499,-0.1915133297,-0.2765546143,0.2906630635,0.1425997764,0.159190014,0.1545393765,0.0036098366,0.0135526676,0.5171052814,0.0430108383,0.1855411828,-0.0100924438,0.0480356477,-0.0127975084,0.0618535317,0.4849428833,0.4015761316,0.0660557002,0.1122709215,0.1525212526,-0.1818078011,-0.026630573,0.1443311572,-0.0386357531,-0.0555515997,0.4007842839,0.1236711368,0.1513870358,-0.2084614933,0.0684618354,0.0351409689,0.2023368776,-0.4411248863,-0.2767032683,-0.3105099797,-0.4415131807,-0.3549081981,0.1376703829,-0.3330470324,-0.3065845668,-0.0426150002,0.1029875502,-0.2490516752,0.0909171104,-0.4090116918,-0.0086511457,0.2510765493,-0.2255000174,0.2640661597,0.0976630375,-0.1357088685,0.0797717422,0.5637330413,0.3094772995,0.4695547521,-0.187748909,-0.0889442787,-0.3682776392,-0.2777637541,0.168939963,-0.1807103157,0.312771976,0.4396671653,0.0727538839,0.0740085915,-0.3028735518,0.2637736201,-0.1146069691,-0.3880486786,0.3122301102,-0.0413790047,-0.128575027,-0.2759366632,-0.0168489721,-0.0973513201,-0.4028921127,-0.0106558027,-0.1630793959,0.2472698241,0.0730063841,-0.1938678622,0.2199881822,-0.1373536736,0.343878597,-0.111646831,-0.4498354495,0.3288371265,0.0327135734,-0.4012394249,-0.0856254697,-0.0997942388,-0.0702849105,0.0490829945,-0.5313177705,-0.3595405221,-0.4158709049,0.0363902673,0.1391940862,0.0933345184,0.0729349181,-0.1139890105,-0.026902711,-0.322006911,-0.2428338975,0.1113379523,0.1950957179,0.3938701451,0.0438041277,0.2344286144,0.1719080806,0.0053074039,0.5169697404,-0.1140007153,0.4752208591,0.1185990199,0.3399907649,-0.0184927322,-0.2351238281,0.2634159327,-0.015512235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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3942","title":"reddit_tifu dataset: Checksums didn't match for dataset source files","comments":"Hi @XingxingZhang, \r\n\r\nWe have already fixed this. You should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nDuplicate of:\r\n- #3773","body":"## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n","comment_length":35,"text":"reddit_tifu dataset: Checksums didn't match for dataset source files \n ## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n \n Hi @XingxingZhang, \r\n\r\nWe have already fixed this. You should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nDuplicate of:\r\n- #3773","embeddings":[-0.3985462785,0.1268261671,-0.0900786668,0.1531641781,0.4119481146,-0.0722518563,0.1358965337,0.4609464705,-0.0700399131,-0.0869089812,-0.1713988632,0.4725554883,0.2960695922,0.2216591388,0.0006655382,0.148134321,0.113251023,0.1238125488,-0.2147902995,-0.1039822027,-0.051839903,0.0667048544,-0.02181888,-0.0644038394,-0.1209833845,0.2648584843,0.109420903,0.2050814331,-0.13820903,-0.1935888827,0.2310243845,0.0947568417,-0.1716674119,0.6253606677,-0.0001061642,0.1178650707,0.2978493869,-0.0738185272,-0.1075453758,-0.2903145254,-0.2285021096,-0.0266567487,-0.228690967,-0.0157380681,-0.0582639351,-0.0507716052,-0.1205493584,-0.1184360906,0.2054294199,0.3480323255,0.2619142234,0.3078448474,0.213805899,0.0326527618,0.3904974759,0.116725862,0.0546525158,0.3477866054,-0.0087808575,-0.1265859306,0.0680849478,0.0957550928,-0.1524364203,0.0942632109,-0.0499231033,-0.087837249,0.1393562704,-0.1794324666,-0.065368332,0.4148212373,0.3285357654,-0.1959819645,-0.3916213512,0.1505562812,0.0181435328,-0.1497636139,0.2844494283,0.2377644032,-0.1654654294,0.2523102462,-0.1200846806,0.1210448146,0.0244656336,0.1731975079,0.0333451554,-0.0579655431,0.0493322834,-0.0778755397,-0.1680637151,-0.12065579,0.1916184425,-0.011359266,-0.1268766224,-0.0633240789,-0.3275378346,0.0152147375,-0.0163262896,-0.0055162334,0.4443407059,0.169461444,0.1155322194,0.2310025096,-0.1730128974,0.2485596836,-0.029413566,0.061254289,-0.089982979,0.266808331,0.2934516966,0.0272206999,-0.1966457963,0.1666150391,-0.0261625107,-0.0619116575,0.3498398364,0.0617948771,0.2732435465,-0.3882815242,-0.3526941836,0.1378538758,0.1614838243,-0.2683421075,0.0340965278,0.321844399,-0.1196145564,0.2056097388,-0.2245728672,0.3239060938,-0.1389726996,-0.0496331155,-0.2525369227,-0.0312323906,-0.1228638664,-0.2130515575,0.1580700427,-0.3874782622,0.5200121999,0.1329913139,0.2709819973,-0.1481900513,0.0089211101,-0.0431148931,0.0243353993,0.3802427351,-0.2015050352,0.0493977889,0.255095154,0.0693126768,-0.0704425275,0.1141765937,-0.4392614663,-0.1480825096,-0.1808601171,0.3100427985,-0.2501482964,-0.181490764,-0.2639676929,-0.3084102869,0.2988123894,-0.1891636997,0.0385825448,-0.2646736801,-0.1646647602,-0.2092251629,0.2225359976,0.1276827902,-0.484706223,0.1347155124,-0.0505086593,-0.2308934629,0.2965950072,0.2197384536,-0.0891325995,0.0785434023,-0.2169452012,0.0927088112,0.3119708896,-0.5282865167,-0.6353443861,-0.2045747042,0.0392536968,0.199750632,0.136701405,0.0489972048,0.0670530498,-0.1192274019,0.2003705055,0.2554533482,0.1286742091,0.2997412086,-0.3779305518,-0.3374253511,-0.0244525149,0.1490076035,0.0705284998,0.0454091355,0.1995310187,-0.1780854017,0.2491868436,0.0048380303,-0.3049612045,0.0178339723,0.4241453111,0.1681174338,0.0189563315,-0.3989566565,-0.114322193,0.2084311992,-0.0606114641,0.1960173696,0.0218617171,-0.1804836392,-0.5836142302,-0.0075208996,-0.045009423,-0.0498922206,0.2237077057,0.3612641394,0.1590377092,0.0346530192,-0.2068693042,0.2542302012,-0.3873378038,0.0788437277,0.0390750505,0.5304793119,-0.2218928337,-0.1873175949,0.2554081976,-0.0189144593,0.0292931143,-0.1731526852,-0.1209300309,0.5735868216,0.2360803038,0.0379414819,0.0844285637,0.0684362948,0.1769237965,-0.3511034548,-0.2122675627,0.4654123783,-0.0064162025,0.0255096164,-0.0335024484,0.2390332818,-0.0899502337,-0.1070550606,-0.1001557931,0.0067662857,0.416274935,-0.03768792,0.0151178604,-0.3122467399,0.2221524417,0.0327106938,-0.1549657434,0.0455442555,-0.0278427862,0.0790932328,0.4024963677,-0.0163981523,0.084148109,0.0936824754,0.0919339508,-0.0063487026,-0.0205603987,0.3857573867,0.3906045258,0.2282775789,-0.0585578084,0.0642226711,-0.3530420661,-0.1700579077,0.086124748,0.1225321814,0.1640951037,0.2246408314,0.1111246496,0.1253666878,-0.3292346001,-0.0877719447,-0.0794124678,0.3189308345,-0.5176073313,-0.1456219107,-0.2637497485,-0.0641046539,0.0031651545,0.076881364,-0.1447908431,-0.2434421629,0.0333963335,0.122472629,-0.0742872953,0.2786252201,-0.1493641883,-0.0642516091,0.2559808493,-0.2241702974,0.0241990834,-0.1484817713,0.0180481039,0.1283254772,0.3840251267,0.176188305,0.3158918321,-0.4577540159,-0.071872972,-0.1982284635,-0.4003253281,0.0793983564,-0.2544686794,0.6606246233,-0.0155498683,0.3037824035,-0.0083889142,-0.1851842552,0.1895232797,-0.113524355,-0.3980981708,0.1655428112,0.0319788866,-0.1102654189,-0.0109364139,-0.1618316025,-0.0101669906,-0.3741977513,-0.1887770593,0.2284651101,0.1455118954,-0.022457717,0.3033699691,0.0544430278,0.007977928,0.2186071426,-0.2217547297,-0.6386207938,0.5002468228,-0.1377891302,-0.4190075994,-0.0380368829,0.1346495152,0.0600051992,0.2178351879,-0.4655655324,-0.4032658339,-0.1946605295,0.1383520067,0.1413580775,0.0781812593,0.0477098264,0.0300181285,-0.1848484427,-0.1977693886,0.0490745381,-0.0200139787,-0.2559481561,0.2273625284,-0.1955227405,0.2183904052,-0.1937243789,0.2075269818,0.3405131996,-0.0777283534,0.0695839524,-0.0228118263,0.4099595547,-0.1090231612,-0.2951969802,-0.0534316748,-0.0270303506,0.052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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3942","title":"reddit_tifu dataset: Checksums didn't match for dataset source files","comments":"thanks @albertvillanova . by upgrading to 1.18.4 and using `load_dataset(\"...\", download_mode=\"force_redownload\")` fixed \r\n the bug.\r\n\r\nusing the following as you suggested in another thread can also fixed the bug\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\n","body":"## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n","comment_length":33,"text":"reddit_tifu dataset: Checksums didn't match for dataset source files \n ## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n \n thanks @albertvillanova . by upgrading to 1.18.4 and using `load_dataset(\"...\", download_mode=\"force_redownload\")` fixed \r\n the bug.\r\n\r\nusing the following as you suggested in another thread can also fixed the bug\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\n","embeddings":[-0.4241079986,0.1216538996,-0.0851319879,0.1159736589,0.4104367197,-0.1015881076,0.161649555,0.4821439385,-0.0676114336,-0.0831582919,-0.1715805084,0.484701395,0.2961730957,0.260920167,-0.0052184002,0.1425730586,0.0813892782,0.0942176282,-0.1835354865,-0.1239801422,-0.0313977674,0.0742589831,-0.0655951723,-0.0387011953,-0.1272572875,0.2785607874,0.0962552354,0.1958373934,-0.1299274564,-0.2015249282,0.2113579065,0.1253212839,-0.2219691128,0.6362911463,-0.0001037592,0.1130173132,0.3000631034,-0.0719152987,-0.0833512545,-0.3188892603,-0.1930008084,-0.0129560223,-0.1795533895,-0.0037302237,-0.115698196,-0.0585101582,-0.0973990038,-0.0671985522,0.2122459114,0.3174713254,0.2753217816,0.3078344166,0.1872294694,0.0476778299,0.4358138144,0.1765980273,0.0376328528,0.3537876606,-0.0222580917,-0.120992817,0.0615175292,0.0806413889,-0.1599859893,0.1010159776,-0.0659198016,-0.0292650126,0.1396555305,-0.1895993799,-0.0617788211,0.4400642812,0.303653717,-0.2148500234,-0.3998056948,0.1274029315,0.014880253,-0.1722576767,0.2918680906,0.2763573527,-0.1804020256,0.2406665236,-0.1071696579,0.1765080243,0.0133623816,0.1725681871,0.0295837354,-0.0682585612,0.0082284734,-0.0568891317,-0.159471646,-0.1300682127,0.0715590641,-0.0321435444,-0.1241403893,-0.0463441722,-0.3199132085,0.0060781473,-0.0016880909,-0.0602719933,0.4826945662,0.1779183,0.1245702356,0.2734242678,-0.1492734849,0.2545270622,-0.0060308417,0.0516923703,-0.0779369324,0.2043065876,0.3689894676,-0.0066339909,-0.1574361473,0.1622216851,-0.039365001,-0.0339072868,0.348031342,0.0346651562,0.237737596,-0.3679087162,-0.3306000829,0.0805596262,0.1630642712,-0.2645067275,0.0781537592,0.3441263735,-0.0992540568,0.1183925271,-0.2678114772,0.3250126243,-0.1333276331,-0.0196578279,-0.257578373,-0.0487823263,-0.1573097259,-0.2214170545,0.1648215204,-0.3767096996,0.5168348551,0.0877229422,0.3125971556,-0.1008909643,-0.0099154394,-0.0704085454,0.068229571,0.4129202664,-0.1413104534,0.0304585006,0.2043717653,0.0644429624,-0.1120427921,0.0723024979,-0.3958519399,-0.1151546091,-0.1437643915,0.307682097,-0.278662473,-0.1811386943,-0.2636612356,-0.2843516171,0.2813166678,-0.1981930882,0.0266529322,-0.2471844554,-0.1655811071,-0.2202341408,0.2079783082,0.1195853278,-0.40086025,0.1340444684,0.0400185548,-0.2631473541,0.2815419137,0.2382414788,-0.0849270374,-0.0165697467,-0.184233591,0.166583702,0.2956678569,-0.5067376494,-0.5864641666,-0.1960976124,0.0866027996,0.1515453458,0.1235274747,0.0278241802,0.0812830105,-0.1529754698,0.1688821465,0.256315589,0.1470497847,0.3274729252,-0.3717185259,-0.3467594981,-0.0683312714,0.1492227316,0.0407869779,-0.027712062,0.1848412156,-0.2665777802,0.2314161062,-0.0082812663,-0.2588584721,-0.0013401586,0.4629286826,0.1236617565,0.0398438945,-0.3934967816,-0.1194251329,0.1840355992,-0.0369744785,0.2453461885,0.0259983186,-0.1655705571,-0.5125528574,0.0218798295,-0.0250609983,-0.043711707,0.2340406924,0.3688033223,0.1965874732,0.0488781929,-0.2205754966,0.2265203446,-0.3307691216,0.0873255655,0.1134692878,0.5346261263,-0.1987529993,-0.2265469134,0.2873022854,-0.0440327413,0.0147068985,-0.1954669505,-0.0823376924,0.5871126056,0.2365540564,0.0200580563,0.1091150939,0.1086096764,0.1948229074,-0.3378281593,-0.2032936811,0.4442740679,-0.0297802966,0.0060062567,-0.0732139796,0.2685295939,-0.0932854265,-0.0743135884,-0.0977487043,0.0180309862,0.3966173828,-0.0513017066,-0.0106148571,-0.2766389847,0.2215072811,0.0249375366,-0.164873004,0.0095847482,-0.0790589899,0.0905131921,0.4233700633,-0.0349309295,0.0830269083,0.1191070825,0.0372320674,-0.0334915034,-0.0523786098,0.3563751876,0.3615180552,0.2445400655,-0.0381733403,0.0521736108,-0.3511779308,-0.1828991324,0.0971592143,0.1120668054,0.1718709916,0.2004152983,0.1687261462,0.1054346859,-0.3583817184,-0.0585567504,-0.0790711269,0.2824410796,-0.4575935304,-0.0844145715,-0.232988745,-0.0539293401,0.0635260791,0.0643581301,-0.1851879507,-0.2677815557,0.0718844607,0.1106405929,-0.0896495506,0.2830824554,-0.1016329452,-0.0807155445,0.2854169607,-0.2564376891,-0.0086916862,-0.2279045582,-0.0060237879,0.1385422349,0.366851151,0.1844961047,0.3543100357,-0.4349477887,-0.0702895969,-0.1779164523,-0.4401725233,0.1225638092,-0.2930018902,0.6666305661,0.0256096404,0.2784463167,0.0197882429,-0.1328680217,0.2228195518,-0.2012175322,-0.3910889626,0.1868027151,0.0280624721,-0.1677422822,-0.0636750534,-0.106293641,-0.0331955887,-0.3610416949,-0.1088984236,0.2706436813,0.1203877032,-0.0087130861,0.312415719,0.0328514799,-0.0428843535,0.1993802041,-0.2519604266,-0.6342234612,0.5193831325,-0.1657375842,-0.4650086761,-0.0236073341,0.1615482569,0.0464696437,0.2168153375,-0.4444711208,-0.4417554736,-0.1710698307,0.1684521884,0.1846001297,0.065567933,0.0495572761,0.0156398918,-0.2220025212,-0.2117869258,0.0459698923,0.0188332051,-0.2363012582,0.190156132,-0.1714879125,0.1482187659,-0.1550745368,0.2010549754,0.2956931293,-0.1250995696,0.0375274941,-0.0760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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3942","title":"reddit_tifu dataset: Checksums didn't match for dataset source files","comments":"The latter solution (installing from GitHub) was proposed because the fix was not released yet. But last week we made the 1.18.4 patch release (with the fix), so no longer necessary to install from GitHub.\r\n\r\nYou can now install from PyPI, as usual:\r\n```shell\r\npip install -U datasets\r\n```\r\n","body":"## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n","comment_length":49,"text":"reddit_tifu dataset: Checksums didn't match for dataset source files \n ## Describe the bug\r\nWhen loading the reddit_tifu dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\n# load_dataset('billsum')\r\n\r\nload_dataset('reddit_tifu', 'short')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n \n The latter solution (installing from GitHub) was proposed because the fix was not released yet. But last week we made the 1.18.4 patch release (with the fix), so no longer necessary to install from GitHub.\r\n\r\nYou can now install from PyPI, as usual:\r\n```shell\r\npip install -U datasets\r\n```\r\n","embeddings":[-0.3972457945,0.1158501655,-0.0944005996,0.0743375272,0.3742969036,-0.1016561612,0.134125337,0.5053938031,-0.1505984366,-0.0394884273,-0.1530263275,0.4913249612,0.3067786694,0.2183292508,-0.0091935834,0.134451285,0.1148078814,0.1060608029,-0.1334550828,-0.0572964586,-0.0045508253,-0.0093496973,-0.0966604576,-0.0207301956,-0.0583633184,0.2276360095,0.0445015505,0.135986954,-0.2058945,-0.2403111756,0.2638667524,0.1483821422,-0.2442305535,0.5590673089,-0.00010491,0.1271552891,0.2830677629,-0.0792282224,-0.0613509305,-0.31683743,-0.2179539949,-0.0706221461,-0.1681921035,-0.0076873158,-0.0552161448,-0.10953857,-0.0527951717,0.0503651388,0.133496955,0.3430584371,0.2658721507,0.366440624,0.1584191918,0.0342426598,0.3947605491,0.1645510942,0.0348790586,0.3492319584,-0.062650986,-0.097888723,0.1093074605,-0.0030364573,-0.1994205415,0.0993498415,-0.1013718396,-0.0220341776,0.0463652685,-0.2138501406,-0.0981153026,0.4128343165,0.2350536585,-0.2419972271,-0.3816855252,0.1323263943,0.0004418562,-0.164333716,0.2986193299,0.2788708806,-0.2397130877,0.2224878669,-0.0347449891,0.199489817,-0.0440233648,0.177844137,0.0226801075,0.0216032229,0.0898499414,-0.0190123357,-0.0786939487,-0.1137216538,0.1954844594,0.0554828085,-0.067269139,-0.0375485905,-0.1989176422,0.0137191257,0.0118824085,-0.1319633126,0.4968379736,0.1359120458,0.0994018093,0.2437210083,-0.0761685371,0.3032625616,-0.0573111549,0.0137570798,-0.0470061824,0.2996734083,0.3338770866,0.0164612252,-0.0525902994,0.1817306578,-0.0304901004,-0.046069622,0.3226611316,0.0743711963,0.2300565243,-0.3679658175,-0.4308348,0.0870581567,0.1404775977,-0.2431651205,0.0388298854,0.3132538199,-0.0599289946,0.1402357221,-0.2776667476,0.3079220951,-0.1966086477,0.0513605997,-0.2339680493,0.0504702367,-0.1148832887,-0.1922035813,0.1268985868,-0.4038243592,0.4870954752,0.1144862324,0.316570282,-0.0361408964,-0.0162857454,-0.0618909188,0.0621015355,0.4958976507,-0.1525313705,0.0646699592,0.1975970864,0.1410731971,-0.1435844451,0.0806579888,-0.3774157763,-0.1210579127,-0.1983364671,0.3039913177,-0.3114700615,-0.1928068399,-0.1773220152,-0.3383498788,0.3129969537,-0.2297434807,0.0489845239,-0.2373637408,-0.0305993352,-0.2500886619,0.1779904217,0.1128632128,-0.5220831633,0.1453724951,0.0709230006,-0.2602553666,0.3192370534,0.2609838247,-0.0720183849,-0.0383889377,-0.1669498235,0.22831361,0.2290570885,-0.4691096246,-0.648625493,-0.2247489542,0.0988691077,0.0668754056,0.2018143982,0.0338719077,0.1469430476,-0.0930239484,0.178311795,0.1398080885,0.1675214767,0.2202978134,-0.396176368,-0.3666825593,0.0086012697,0.1462171376,0.09284731,-0.0688959658,0.1709890515,-0.2168817818,0.2603482902,-0.0071418011,-0.2701739073,-0.0111230584,0.5282152891,0.1565122753,0.0311639402,-0.4041968882,-0.0382097401,0.1638697237,0.0068689603,0.3009797335,-0.1075072587,-0.1852892637,-0.4991526902,0.0805613771,-0.0810400546,-0.0658172145,0.2241801471,0.3235909641,0.1962801516,0.03964708,-0.2381029129,0.2055830806,-0.2923541367,0.0459838361,0.1257136315,0.4918661118,-0.2841662169,-0.1985765547,0.2238285094,-0.0218977593,0.0338702835,-0.1906066388,-0.0358097591,0.5679984093,0.2216728628,-0.1000775173,0.0870040879,0.0638672262,0.2288838774,-0.4107523859,-0.138067767,0.4723104239,-0.058604259,0.0738959685,-0.0462402701,0.3331492543,-0.0237298496,-0.0715223253,-0.1091220975,0.0491441004,0.4476817846,-0.008242094,0.0055907951,-0.2900725007,0.2397475243,0.0696484298,-0.1521420926,0.0325714871,-0.0163992196,0.1328951567,0.4287265837,-0.0898688957,0.1032356545,0.0928947553,0.0883570164,-0.0231869128,0.0169912633,0.2769384086,0.2826452255,0.2031242996,-0.0718730241,0.0543081872,-0.4174272716,-0.2200956643,0.0408665724,0.1092732623,0.1560337991,0.1641885787,0.1498347819,0.1108972281,-0.4117979407,-0.0939693674,-0.0563563481,0.3467799127,-0.4742769897,-0.0733722895,-0.2659908533,-0.0773638114,0.1014615223,0.0134020951,-0.1797267199,-0.2796196938,0.1374929845,0.0663586631,-0.0962861925,0.2670513093,-0.1250310689,-0.1017081738,0.3037338257,-0.3043340147,-0.0180753563,-0.2500351667,0.009439487,0.1395905614,0.333696574,0.1813608408,0.3696864843,-0.4134679437,-0.1491151303,-0.1730808914,-0.4731228352,0.1068476066,-0.3054790199,0.5822451115,0.0626961142,0.29422912,0.045365002,-0.220229879,0.1857262254,-0.1878198534,-0.3979575038,0.2523879409,-0.0839364603,-0.1743398607,-0.1055322513,-0.1662874371,-0.0986048654,-0.301037401,-0.131566748,0.2938216329,0.1701860577,-0.009207068,0.3598101139,0.0320890173,-0.0901550353,0.1556848586,-0.1982466578,-0.546639502,0.4923823476,-0.1142811328,-0.4728689194,-0.0343177766,0.1789805889,-0.0162571222,0.2189088166,-0.4828189015,-0.4995726943,-0.1409182101,0.2771880627,0.1772972792,0.0657270402,0.0828181431,0.0465377122,-0.2038285881,-0.1848029345,0.0711306781,0.0187585689,-0.235979408,0.2017390877,-0.1863386333,0.2289091051,-0.081855543,0.2742735147,0.2685349882,-0.0731108114,0.0042001777,-0.0805646107,0.4213832319,-0.0970153064,-0.2580533922,0.0321565755,0.0074720518,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3941","title":"billsum dataset: Checksums didn't match for dataset source files:","comments":"Hi @XingxingZhang, thanks for reporting.\r\n\r\nThis was due to a change in Google Drive service:\r\n- #3786 \r\n\r\nWe have already fixed it:\r\n- #3787\r\n\r\nYou should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","body":"## Describe the bug\r\n\r\nWhen loading the `billsum` dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n```\r\n  File \"virtualenv_projects\/codex\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx']\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\nload_dataset('billsum')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n\r\n\r\n\r\n\r\n","comment_length":48,"text":"billsum dataset: Checksums didn't match for dataset source files: \n ## Describe the bug\r\n\r\nWhen loading the `billsum` dataset, it throws the exception \"Checksums didn't match for dataset source files\"\r\n\r\n```\r\n  File \"virtualenv_projects\/codex\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx']\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nfrom datasets import load_dataset\r\n\r\nprint(datasets.__version__)\r\n\r\nload_dataset('billsum')\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n\r\n- Platform: mac os\r\n- Python version: Python 3.7.6\r\n- PyArrow version: 3.0.0\r\n\r\n\r\n\r\n\r\n \n Hi @XingxingZhang, thanks for reporting.\r\n\r\nThis was due to a change in Google Drive service:\r\n- #3786 \r\n\r\nWe have already fixed it:\r\n- #3787\r\n\r\nYou should update `datasets` version to at least 1.18.4:\r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then force the redownload:\r\n```python\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","embeddings":[-0.4439738691,0.3979528248,-0.1034802124,0.1886253506,0.2277504802,0.0679108351,0.2159260958,0.2985670269,0.1642130017,0.1222992986,0.0958597437,0.0360736698,0.1474190652,0.2202241868,-0.0004788893,0.1770035475,0.0542056262,-0.0288489033,-0.0223002993,-0.0352689512,-0.3512853086,0.2289222181,0.0162223671,-0.2879076898,0.0853461325,0.2226153463,0.1279373616,0.1907230318,-0.2450843304,-0.1924148053,0.1991567165,0.1335769892,-0.0073953597,0.4593857527,-0.0001127957,0.1864899993,0.4848975539,0.0287229922,-0.4654405117,-0.084063381,-0.2882366776,-0.206325084,-0.1947156936,-0.1314370781,0.1306875795,-0.0070895306,-0.1326556206,-0.1516025364,0.050739564,0.2921630442,0.2098049074,0.3013036549,0.1827804148,0.0766988173,0.5222919583,-0.1602871716,0.0872821808,0.5475760698,-0.0015224606,-0.0639345348,0.1201519817,0.1707801819,-0.2118850499,0.1150467247,0.1715529859,0.1248250529,-0.03692073,0.0423742458,0.0630678385,0.2080620378,0.3014431298,-0.1993893981,-0.3702297807,-0.1017953157,-0.0359057374,-0.2172555774,0.6341405511,0.2274866998,0.0735623166,0.0263714511,-0.5666072965,0.077351436,0.0355399325,0.1744483709,-0.1465765983,0.1413700879,0.0419780463,-0.0547221936,0.0333676301,-0.0838258043,0.1557453722,-0.254609108,-0.2240247875,-0.0415011831,-0.2506529391,-0.0584701858,0.0964481533,0.2282309532,0.3818089664,0.295438081,-0.0155849792,0.1652637273,-0.3365027905,0.185888797,0.0220103152,0.1599904746,-0.1169159189,0.151176393,0.4685677588,0.3264901638,0.2119035274,0.1329634488,0.0363318548,-0.1869616359,0.1759188771,0.1823223531,0.1848874241,-0.2980906665,-0.4048728645,0.1706672907,0.0492509156,-0.2403172255,0.1330958605,0.2846083641,-0.2474854589,-0.1176881865,0.0318560824,0.1040351614,-0.0670712218,0.3422398865,-0.1705227196,-0.0207442343,-0.0911357552,-0.0186963733,0.3730758131,-0.343816191,0.2726408243,0.0223871898,-0.0123666842,-0.0054004146,0.1697473377,-0.0315265581,-0.1972259134,0.3557344973,-0.0210166797,0.0881235152,0.1051382124,-0.0027710339,-0.1386390328,0.0815903544,-0.4535005987,-0.1019150391,-0.0580967329,0.3123090565,-0.2750694454,-0.1466859281,-0.2939595282,-0.4308347106,0.0257025156,-0.2684024274,-0.0789474547,-0.2064275444,-0.2950770855,-0.2398940772,0.2672673762,0.1245113462,-0.162611261,-0.0424724147,-0.2464709431,-0.2725116313,0.1295040548,0.2652706206,-0.0469176136,0.0531802662,-0.4091737866,-0.0250257235,-0.115003027,-0.3405149281,-0.6405295134,-0.0301493052,0.1324078143,-0.0595713928,0.0593252815,-0.1188477501,0.1125449762,0.0779316798,0.1620675772,0.186393559,0.1386142969,0.2338559926,-0.1820350289,-0.3929369748,0.0978237912,0.2020248324,0.0303073078,0.0208446737,0.3079658151,-0.3326949179,0.1134864166,-0.108789064,-0.1471491456,0.3346656263,0.5563585758,0.0699912384,0.1929026246,-0.042977348,-0.4167457521,0.3173339963,-0.2022904903,0.0091087278,-0.2194200307,-0.190119803,-0.2889499664,-0.1604917049,-0.0569011793,0.1140343025,0.1496287882,0.3731675744,0.0378662013,0.0841125175,0.1065787822,-0.075774312,-0.302161485,-0.0347997993,-0.1180203781,0.7207111716,-0.2065883726,-0.2001561821,0.3406206071,-0.0112095885,0.1953431666,-0.1817380339,-0.1705874205,0.4723944664,0.2709105611,0.2831824422,0.1877067834,0.561442256,0.2481345832,-0.2821190059,0.0549617968,0.4969735146,0.1127730384,0.0344053395,-0.4433575273,0.2786784172,-0.1827600598,-0.019771615,0.0459566191,0.1223966032,0.2940051258,-0.1368869841,0.1065027565,-0.3272482157,0.1007519364,0.4226847887,0.1944942623,-0.0070564565,0.1111246943,-0.0220980477,0.3812299073,-0.0466057956,0.09026476,0.0693548396,-0.0583516285,-0.0638910234,0.1664037704,0.4466325343,0.3047539294,0.1740949899,-0.0056575141,0.120159097,-0.121405907,-0.1634939909,0.1593515426,-0.0082265474,0.0087777972,0.3641691506,-0.0013633214,-0.0117337266,-0.4045173228,0.003959849,0.0633195117,0.2933954,-0.4859107733,-0.1301021278,-0.29378739,0.0642315075,-0.3407527804,0.0621950217,-0.2707392573,-0.4372276664,0.0047782878,0.4343088865,-0.2199890614,0.2267840207,0.0709044635,0.1152337044,0.1544796079,-0.4277650416,-0.105506137,-0.0595618561,0.0770658478,0.1029575467,0.5101454258,0.177814737,0.3525721729,-0.3003239036,-0.1154542565,-0.7003768086,-0.341155231,0.0723259225,-0.3449421227,0.4714392722,0.2795479596,0.0465645678,-0.0035760794,-0.1079631746,0.0734540373,-0.0575128794,-0.3673721552,0.1767672598,-0.1280110776,-0.1607517153,-0.0486229584,-0.2101787925,0.0654587969,-0.1770792753,-0.1525553316,0.2368773371,0.091890797,0.0638813525,0.1314509362,0.1555478275,0.207446754,0.1623739749,-0.2662161291,-0.2728484273,0.4294889867,-0.0594479032,-0.3758270741,0.0709474683,0.0414334871,-0.167716369,0.3445874751,-0.3872812092,-0.3581337631,-0.4112388194,0.1492066979,0.2963081598,0.1123657823,0.3224449158,0.1554400027,-0.12027511,-0.2137685716,-0.3296327293,-0.0757038966,-0.0299282111,0.2078787982,0.0278087854,0.2335329056,-0.3570574224,0.3795694411,0.4104430377,0.0316887163,0.1269768029,-0.1509151608,0.388938576,0.021634182,-0.072893247,0.0741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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"Thanks for reporting @qqaatw.\r\n\r\n@mishig25 @sgugger do you think this can be tweaked in the new doc framework?\r\n- From: https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/\r\n- To: https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":24,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n Thanks for reporting @qqaatw.\r\n\r\n@mishig25 @sgugger do you think this can be tweaked in the new doc framework?\r\n- From: https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/\r\n- To: https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/","embeddings":[-0.0187271331,-0.0063731926,0.0506370142,-0.0155851441,0.1034540683,0.0018574685,0.0943081155,0.4163026214,-0.3947233856,-0.0836842731,-0.0372577161,0.3179529607,-0.1664929092,0.2242850214,0.3080806434,-0.0525295697,0.048733592,0.2238046378,-0.2547568381,-0.0491920225,-0.2380856425,0.3419846892,-0.2553613782,0.1312544942,-0.335822165,0.2073529065,0.0516713262,0.273817867,-0.2153954953,-0.522408843,0.1666759998,0.1461387128,-0.0980079845,0.297453016,-0.000107693,0.0006841131,0.4079018533,0.1341495514,-0.448633045,-0.2668795586,-0.1302228719,-0.0143275484,-0.0388930067,0.0316008069,-0.0917570814,-0.3623400629,-0.0534400083,0.0620411895,0.1248966157,0.4276170433,0.267146647,0.2947669327,0.4018475115,0.0041196179,0.31690377,0.1281161457,-0.1532746255,0.2432874739,0.2216950655,-0.1291416883,0.0095920283,0.4768320024,-0.0680790469,-0.3794160485,0.4865565598,0.0933019966,-0.2182511091,-0.1806530952,0.1244002953,0.321747601,0.4607045949,-0.130715251,-0.4646189511,-0.0479646586,-0.1796613038,-0.0997242928,0.2587279379,0.1663856208,0.1724100262,0.373634845,-0.1632019281,-0.3525308371,-0.1811439544,0.1615774632,0.1899350882,0.0887923315,-0.2248000354,-0.1435865313,0.2506815791,-0.127245903,0.0898871943,0.0974664465,-0.3128813505,0.2352757305,0.3029549122,-0.0029521596,0.2264029831,0.3986922204,0.3898892999,0.0592280999,-0.2539223135,-0.1589945853,-0.2067804337,-0.0257673897,0.0218975078,0.0151034258,0.0302289594,0.2644266784,0.3953259289,0.4564833939,0.1249663979,-0.0697236285,0.0936110169,-0.2380833626,-0.351072669,-0.2569864392,0.2673898339,-0.2237783968,-0.2147223204,0.1401665509,-0.3454043567,0.1268763244,0.186518386,0.1045873091,-0.1278413832,0.3936744332,0.333807379,0.0770071745,-0.3253526092,-0.3007166982,-0.1939601898,0.1009992138,-0.2921341658,0.1147202328,0.1020905599,-0.2380368561,0.3687264323,0.2457109243,-0.0103546521,-0.3510748744,-0.1554171294,0.1139167771,0.0453462563,0.1353991181,-0.2145243138,0.0733347237,0.0992263407,-0.2943178713,0.0547437407,0.0642935559,-0.5767096877,-0.2669719756,-0.2404599339,0.2087740302,-0.0104366178,0.0827139467,0.0283074342,0.1178046614,-0.2028794736,-0.2205184996,-0.0355937853,0.0479941852,-0.2346374243,-0.0446825437,0.447042942,0.6542369127,0.0038461853,-0.2002677023,0.4292866588,0.2077867538,-0.2655270696,0.0403928272,-0.3147580922,0.077260986,-0.3521567583,-0.0752224922,0.0158991516,-0.4088335633,-0.1669686586,0.0403709039,-0.0717753321,0.1015604138,-0.0398607254,-0.2720073462,0.0598036498,-0.2486562133,0.1766275615,-0.1428523064,0.1704471111,0.254206419,-0.3809669316,-0.3537797034,-0.5163020492,-0.2521840036,0.0608098917,0.2332410216,0.084123522,0.2405653596,0.3700611591,-0.2897121906,0.0099810492,0.4187983871,0.2980928123,0.0733311698,0.1527809054,-0.1372025311,-0.4137676358,-0.0144111775,-0.0107094022,0.0819266066,-0.0175855514,-0.174079597,-0.2825234234,-0.0826976523,-0.0045411484,-0.5523074865,0.1927152276,-0.0065746419,0.0429887995,0.4545126855,-0.2057269663,0.1456053555,-0.1290303767,0.377671361,-0.091324091,0.5353217125,0.0404188931,-0.0633401126,0.0679041669,0.3209619224,0.179639563,0.0395846479,-0.0619940199,0.4921313822,0.0285408068,0.3934336305,0.2590904236,0.3861038685,0.3593552113,-0.5251443982,-0.1047118232,-0.0206501503,-0.0124324001,0.0503113531,-0.058868181,0.0821766034,-0.2177267671,0.1517742574,0.2938092947,0.2612450123,0.0162396431,-0.1683143526,-0.3037467897,-0.1937560737,0.079462707,0.0578259341,0.2957595587,0.0241561253,-0.2348698229,0.1241550371,0.2402562648,-0.01831544,-0.12097767,0.1407464892,-0.4795674086,-0.030586442,-0.060769774,0.2735624313,-0.0039077853,0.183672145,-0.0311644413,0.2403923422,-0.2020032108,-0.1329932362,0.3040081263,-0.0053365617,-0.05358788,0.1049759537,-0.0190359261,-0.0109797539,-0.4027560055,0.1428748369,-0.2491165847,0.0621053576,-0.6494679451,0.1374779493,-0.1472738683,0.0596090481,-0.4010846615,-0.1703273803,-0.2623657584,-0.3202089071,0.0230201837,0.4301061928,-0.0993966982,0.1361059994,-0.0358840078,0.2177260518,-0.0971841812,0.203785494,-0.0926349908,0.161842823,-0.1960624158,0.0989432335,0.1447135955,-0.0789839253,0.07299117,-0.4703900516,-0.1322035044,-0.3642755151,-0.5722596049,0.1921438575,0.0189025197,0.2950071096,0.2504951358,0.1046025231,0.0247422233,-0.2164057195,0.322450757,-0.1098167896,-0.0178805348,-0.1746653914,-0.1186217293,-0.0223934837,-0.1284303963,-0.3466691673,0.3226672411,-0.2008787394,0.1291579604,-0.073454015,-0.0414848663,0.3959862292,0.1823369861,0.1815573126,-0.0652907118,0.1858065873,0.0362912267,-0.368771255,0.2723689973,-0.4087771773,-0.3658388257,0.0640075877,0.1180800647,0.0521221161,-0.1150972247,-0.3908717632,-0.2551808655,-0.1362001896,-0.1333202869,0.1170573086,0.2899686694,0.1029919088,0.2729539275,-0.139807269,-0.1843677759,-0.1272682995,0.0618499704,-0.2787075043,0.2006921917,0.0618002303,0.0590312891,0.3185184002,0.6017562747,0.4573518038,0.1458194554,0.0502800383,-0.1485455036,0.5783555508,-0.183520793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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"@qqaatw thanks a lot for notifying about this issue!\r\n\r\nin comparison, transformers tags start with `v` like [this one](https:\/\/github.com\/huggingface\/transformers\/blob\/v4.17.0\/src\/transformers\/models\/bert\/configuration_bert.py#L54).\r\n\r\nTherefore, we have to do one of 2 options below:\r\n1. Make necessary changes on doc-builder side\r\nOR\r\n2. Make [datasets tags](https:\/\/github.com\/huggingface\/datasets\/tags) start with `v`, just like [transformers](https:\/\/github.com\/huggingface\/transformers\/tags) (so that tag naming can be consistent amongst hf repos)\r\n\r\nI'll let you decide @albertvillanova @lhoestq @sgugger ","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":64,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n @qqaatw thanks a lot for notifying about this issue!\r\n\r\nin comparison, transformers tags start with `v` like [this one](https:\/\/github.com\/huggingface\/transformers\/blob\/v4.17.0\/src\/transformers\/models\/bert\/configuration_bert.py#L54).\r\n\r\nTherefore, we have to do one of 2 options below:\r\n1. Make necessary changes on doc-builder side\r\nOR\r\n2. Make [datasets tags](https:\/\/github.com\/huggingface\/datasets\/tags) start with `v`, just like [transformers](https:\/\/github.com\/huggingface\/transformers\/tags) (so that tag naming can be consistent amongst hf repos)\r\n\r\nI'll let you decide @albertvillanova @lhoestq @sgugger 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"I think option 2 is the easiest and would provide harmony in the HF ecosystem but we can also add a doc config parameter to decide whether the default version has a v or not if `datasets` folks prefer their tags without a v :-)","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":45,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n I think option 2 is the easiest and would provide harmony in the HF ecosystem but we can also add a doc config parameter to decide whether the default version has a v or not if `datasets` folks prefer their tags without a v :-)","embeddings":[-0.0938099772,-0.0260814317,0.0604204051,-0.0434884131,0.1273478717,0.0210940521,0.0481096506,0.4152696729,-0.2235428691,-0.0596474335,-0.1113166511,0.2980236709,-0.0862364545,0.2317366898,0.2304339856,-0.0245840624,0.0244480819,0.2727003992,-0.2373473644,-0.1196464226,-0.2088505328,0.308724612,-0.1478628069,0.1997595131,-0.2349735051,0.1833885163,0.0328281261,0.2584998012,-0.161520645,-0.5289617777,0.1182913855,0.2092948109,0.0116788885,0.3349643946,-0.0001067179,0.0589274876,0.3170063794,0.1626368612,-0.4697597325,-0.2691056728,-0.0687209442,0.0060921861,-0.074226737,0.0837398618,-0.1293405145,-0.4473071396,-0.0449467897,0.0349623002,0.0986833721,0.2601931691,0.278313458,0.336794138,0.3182221949,-0.0664459765,0.4235322773,0.1754711419,-0.1066084802,0.2765163779,0.0671091005,-0.2146501392,-0.0356654935,0.5221790075,-0.0374240167,-0.2294415087,0.5925669074,0.0748155564,-0.2306540161,-0.1428269744,0.1135227829,0.3589001298,0.4462852776,-0.2040632218,-0.4455937147,-0.0123271002,-0.1961634606,-0.143974781,0.2808186114,0.1324786693,0.1828142107,0.3789462447,-0.026702337,-0.3251271248,-0.1416982412,0.1876940429,0.1829237789,0.0478197597,-0.1587758362,-0.1204466,0.219321698,-0.0920725986,0.0035388188,0.0445807874,-0.3210740387,0.1806866974,0.2929237187,0.0594237074,0.2188917696,0.2279134393,0.5009387732,-0.0982240513,-0.3371445537,-0.1453029215,-0.1811874509,0.0198490545,0.0221254621,-0.0441702381,0.0716973543,0.274343729,0.42422539,0.4228813946,0.0947222933,-0.0924699605,0.1771950722,-0.2170596272,-0.2569161654,-0.2759928107,0.20772098,-0.2495429665,-0.1821800917,0.1464055032,-0.3560432196,0.0826754421,0.0774414167,0.2412802726,-0.0906749889,0.3241419494,0.2872465253,0.073479481,-0.3544257879,-0.3064900339,-0.2401584238,0.0418641865,-0.2801040709,0.0955258906,0.0510749035,-0.3497385085,0.3087667227,0.2611150742,0.0672059432,-0.2071344256,-0.1649256796,0.0901015624,-0.0084886644,0.2701131105,-0.1356532723,0.1140801013,0.1679825038,-0.278372705,0.0206443798,0.0129680838,-0.5031883717,-0.1793837696,-0.2505913675,0.2505787611,0.037832275,0.0840973482,-0.0226692874,0.0678060278,-0.2331817448,-0.2102109641,-0.0148823624,0.0146440789,-0.1627744138,-0.1056721956,0.4456667304,0.611468792,-0.1278376281,-0.2162667662,0.4467181563,0.1495571733,-0.2754486799,0.0898848921,-0.2896924913,0.0201840959,-0.3396971822,-0.0371406376,0.0179261547,-0.3582273126,-0.2336691767,0.174030304,0.0296207182,0.0761552453,-0.0998117924,-0.2329990268,0.0555623397,-0.1966962367,0.1627610028,-0.1780527234,0.1555101573,0.1953388005,-0.4192001522,-0.3424870372,-0.4184395671,-0.1906795055,0.104263179,0.2388808727,0.1120271459,0.14968732,0.3964492977,-0.2935855389,0.001619012,0.4799332619,0.3468311429,0.2365270257,0.2016325146,-0.1711109579,-0.4050754309,-0.0027981214,-0.0923234671,0.1290829778,0.0436499715,-0.2718534768,-0.2950780988,-0.0758377239,0.0052120183,-0.4897664487,0.1882068664,0.0040580868,-0.0841534659,0.3724137545,-0.2133312672,0.2431949526,-0.0639813989,0.361189425,-0.0516379215,0.6402112246,-0.0565842949,-0.0539168939,0.0988237113,0.2673921585,0.2247012556,0.0055342405,-0.12586537,0.4742071927,-0.0502569415,0.3249878287,0.313418448,0.3151769042,0.3843261302,-0.5301796794,-0.0358466394,0.0382779129,-0.0142956451,0.0624511577,-0.042405922,0.0758528337,-0.1177836508,0.1185568422,0.2384513021,0.206801936,0.0174188912,-0.1224513352,-0.2819077969,-0.1586689353,0.1379288286,-0.0250326991,0.2805367112,0.0665382668,-0.2582451403,0.1283428371,0.4364575446,-0.0032925468,-0.1276251972,0.2597481906,-0.5415120721,0.0132140573,0.0497165322,0.1019376591,-0.0276826024,0.2440482974,-0.0807023421,0.2209555209,-0.3232031167,-0.0801903903,0.2584239542,-0.0073657893,0.0107590435,0.0914252102,0.0046745199,0.0482406095,-0.4385215342,0.0348989777,-0.3234518468,0.0315578952,-0.6536998749,0.1626318246,-0.2634989321,0.0121039078,-0.2666704655,-0.1537077725,-0.4200265706,-0.3341841102,0.061279688,0.3405522704,-0.1169091389,0.1726609617,-0.0413608439,0.1315213144,-0.1105312183,0.1321493685,-0.0888549015,0.1297267973,-0.2626478374,0.0790102854,0.0484266803,-0.0453161038,0.1310924441,-0.3703799546,-0.2198638618,-0.3529178202,-0.6234226227,0.2463837415,-0.0101766763,0.3135118484,0.3105318546,0.0921783,-0.0213553607,-0.1409447938,0.3786443174,-0.120101355,-0.0056829518,-0.1051732451,-0.1794451773,-0.061838299,-0.1545273811,-0.3004791439,0.276815027,-0.3093660772,0.0564941019,-0.1263272166,-0.0340287983,0.4534707069,0.1551070958,0.1416773498,-0.0414747149,0.1296435297,-0.0529725626,-0.2394369692,0.2339754999,-0.3955617249,-0.4028537869,0.1019027829,0.0771368295,0.0092872735,-0.0916307792,-0.4671472907,-0.2465723157,-0.1275961548,-0.0153947854,0.0862766951,0.3007089794,0.0404296033,0.235781759,-0.1616265327,-0.2027636617,-0.1491241306,0.093760252,-0.2770650685,0.1706627607,0.0628298447,0.1472318769,0.3029624522,0.6238446236,0.4332580864,0.1605636775,0.1675801426,-0.1096338704,0.5632911921,-0.198172763,-0.3579517305,0.1097210795,-0.0129497387,0.155657067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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"For me it is OK to conform to the rest of libraries and tag\/release with a preceding \"v\", rather than adding an extra argument to the doc builder just for `datasets`.\r\n\r\nLet me know if it is also OK for you @lhoestq. ","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":42,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n For me it is OK to conform to the rest of libraries and tag\/release with a preceding \"v\", rather than adding an extra argument to the doc builder just for `datasets`.\r\n\r\nLet me know if it is also OK for you @lhoestq. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"We could add a tag for each release without a 'v' but it could be confusing on github to see both tags `v2.0.0` and `2.0.0` IMO (not sure if many users check them though). Removing the tags without 'v' would break our versioning for github datasets: the library looks for dataset scripts at the URLs like `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/{revision}\/datasets\/{path}\/{name}` where `revision` is equal to `datasets.__version__` (which doesn't start with a 'v') for all released versions of `datasets`.\r\n\r\nI think we could just have a parameter for the documentation - and having different URLs schemes for the source links that the users don't even see (they simply click on a button) is probably fine","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":111,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n We could add a tag for each release without a 'v' but it could be confusing on github to see both tags `v2.0.0` and `2.0.0` IMO (not sure if many users check them though). Removing the tags without 'v' would break our versioning for github datasets: the library looks for dataset scripts at the URLs like `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/{revision}\/datasets\/{path}\/{name}` where `revision` is equal to `datasets.__version__` (which doesn't start with a 'v') for all released versions of `datasets`.\r\n\r\nI think we could just have a parameter for the documentation - and having different URLs schemes for the source links that the users don't even see (they simply click on a button) is probably fine","embeddings":[-0.054221414,0.0136878099,0.0050496417,-0.0114307534,0.1155416816,-0.0269931704,0.0938590616,0.4597058296,-0.3011182547,-0.0428777449,0.009345213,0.3782207966,-0.18527098,0.1949329972,0.1850505322,0.0363590643,-0.0211610738,0.2246724814,-0.1446908563,-0.0204798728,-0.1109918654,0.2455714941,-0.1002921462,0.1203873232,-0.1306868345,0.1420890391,0.0750569478,0.1642175615,-0.2008797228,-0.590864718,0.1809437871,0.1990986168,-0.0473841541,0.3812351227,-0.0001052928,0.1201697811,0.3078629076,0.0667871535,-0.5651860833,-0.3012249172,-0.0268747713,-0.112514779,0.0210579988,0.0310205538,-0.1715643257,-0.3184591532,-0.0153976781,-0.0091050705,0.2396462858,0.1511983871,0.2965371907,0.3428994715,0.2747197449,0.0389692634,0.424539417,0.1686266959,-0.1950582862,0.2710792124,0.2778761387,0.0112361731,0.0253735054,0.5503281355,-0.0539336726,-0.3083570302,0.4795606732,0.0461366884,-0.1021186486,-0.1716679335,0.0653163046,0.2752573192,0.3913716078,-0.3050712645,-0.5413457155,-0.1542346925,-0.1189399138,-0.1727122813,0.2240769863,0.1865039617,0.0220909473,0.3470360935,-0.2509979904,-0.3444195092,-0.2074721754,0.1031753272,0.3146031499,0.093835853,-0.202188313,-0.0418650135,0.1160954759,-0.1482353956,-0.1766381711,0.0847308263,-0.2427859902,0.2214248925,0.3431370556,-0.1051083431,0.2336750925,0.1466369331,0.537594676,0.1547502875,-0.2309925556,-0.1134039983,-0.2538582087,0.0126333823,0.1252824068,0.028732324,0.0497496687,0.302737236,0.4774960577,0.4509271979,0.1523098201,0.0032332574,0.1528264135,-0.2279386967,-0.2619708478,-0.1501891762,0.3943499625,-0.2247023433,-0.0735591501,0.0789219588,-0.3622938693,0.0866767019,0.0863626972,0.1619685143,-0.1515832841,0.3022723794,0.1939254999,0.0719939619,-0.2202887833,-0.2049559355,-0.2174598575,-0.0392497517,-0.2819617093,0.2269873023,0.0133958925,-0.3593467772,0.4161596,0.3444631994,-0.1101979092,-0.267772615,-0.1357262433,0.2479996383,0.0152178295,0.172095418,-0.1728611439,0.1544475704,0.0781944692,-0.4288077652,-0.1056361198,-0.0307912752,-0.4272589982,-0.2345656455,-0.2200474888,0.2096513212,-0.0790939704,0.1999441385,-0.0862919986,0.0730483234,-0.1882375628,-0.1438291818,0.0168609675,0.0818172768,-0.0446594507,-0.1591816097,0.4636858106,0.5729559064,0.1028031036,-0.2580921352,0.3721669316,0.145644784,-0.2716274858,0.0760374814,-0.2744162083,-0.0742287636,-0.2762053311,-0.2387370914,0.1267134547,-0.526727736,-0.168505609,0.2014665306,-0.0959832817,0.1630434096,-0.0242908895,-0.1367158443,0.1123338342,-0.3080492914,0.0888738111,-0.1551852077,0.0874286592,0.1594282538,-0.3915192485,-0.4566904902,-0.3765648603,-0.1531998664,-0.0531945787,0.2191495746,0.0897803754,0.1220666468,0.432831347,-0.299624294,0.0331355147,0.364916414,0.3787102997,0.1409219205,0.1150336191,-0.1485044658,-0.4050962627,0.0381333455,0.0813489929,0.1495905071,-0.0875000358,-0.3053787649,-0.2322246134,-0.1303720176,-0.0299280882,-0.5637584925,0.1992889792,0.0872493833,0.0565713644,0.3677258492,-0.216109708,0.0385982469,-0.0912366211,0.3434737325,-0.1919737607,0.48450616,-0.0167001095,0.0002172652,0.0913514942,0.3154968917,0.1559929848,-0.0745982751,-0.0344197154,0.4763198495,0.018971784,0.3283327818,0.3183338344,0.3700167537,0.3935618997,-0.4380132258,-0.056852553,0.1176652983,-0.082387656,0.1427531093,-0.073871389,0.1873866469,-0.1279742569,0.1506777406,0.227786243,0.2356332093,0.1827231348,-0.114689514,-0.4596430659,-0.2211456895,-0.0209889505,0.0680225939,0.3104145825,0.0393309034,-0.3383369446,0.1452131122,0.303358227,-0.0075369445,-0.0899386257,0.1617816389,-0.4471539259,0.0271517988,0.0191706829,0.3026750386,-0.1149839535,0.2538552284,-0.0608979501,0.2296644747,-0.2084737867,-0.1298716068,0.2393542528,0.0073408335,-0.0931047499,-0.0612492561,-0.0436510928,0.0710789487,-0.2412253469,0.0482486635,-0.3092395663,0.0353060216,-0.5924835801,0.1631026566,-0.2213118672,-0.0864244029,-0.3893520534,-0.2141112387,-0.3764783144,-0.140926823,0.065355286,0.296510905,-0.2333628237,0.2432199717,0.031894099,0.1190126017,-0.142689392,0.104295902,0.0345514603,0.0147474501,-0.157645762,0.1128982604,0.2009062022,-0.1181641743,0.1970475167,-0.4198921025,-0.2360130697,-0.4660208821,-0.6053329706,0.1902280003,-0.1535018682,0.3012955785,0.2898380458,0.1072126105,0.1266933084,-0.1856589764,0.302048862,-0.0724539161,0.0014364009,-0.2225417644,-0.0973568037,-0.1117891371,-0.2413486987,-0.2989282906,0.3168428838,-0.2898720205,0.2237966061,0.0320482999,0.0208561178,0.4790828526,0.1273510754,0.1041831896,0.0840647593,0.1887025088,-0.0077606901,-0.3454840779,0.1769047529,-0.403303057,-0.297393471,0.1109567285,0.1929228008,0.1309968978,0.0010931583,-0.5002476573,-0.182836026,-0.0578062981,-0.0478929542,0.222899884,0.2376237959,0.0752693415,0.3247595429,-0.135771811,-0.1508241594,-0.2767271996,0.0372971408,-0.2727071941,0.1897267103,0.0871313065,0.1349212676,0.2505810559,0.6639517546,0.5149468184,0.0622967817,0.0514857583,-0.1781602949,0.5719370246,-0.0726474896,-0.2306564301,0.1052415296,0.0177320205,0.0765632018,0.282995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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3939","title":"Source links broken","comments":"This is done in #3943 to go along with [doc-builder#146](https:\/\/github.com\/huggingface\/doc-builder\/pull\/146).\r\n\r\nNote that this will only work for future versions, so once those two are merged, the actual v2.0.0 doc should be fixed. The easiest is to cherry-pick this commit on the v2.0.0 release branch (or on a new branch created from the 2.0.0 tag, with a name that triggers the doc building job, for instance v2.0.0-release)","body":"## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":66,"text":"Source links broken \n ## Describe the bug\r\n\r\nThe source links of v2.0.0 docs are broken:\r\n\r\nFor exmaple, clicking the source button of this [class](https:\/\/huggingface.co\/docs\/datasets\/v2.0.0\/en\/package_reference\/main_classes#datasets.ClassLabel) will direct users to `https:\/\/github.com\/huggingface\/datasets\/blob\/v2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\nhere, the `v2.0.0` should be `2.0.0`.\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\n\r\nRedirecting to this link: `https:\/\/github.com\/huggingface\/datasets\/blob\/2.0.0\/src\/datasets\/features\/features.py#L747`\r\n\r\n## Actual results\r\nDescribed above.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n This is done in #3943 to go along with [doc-builder#146](https:\/\/github.com\/huggingface\/doc-builder\/pull\/146).\r\n\r\nNote that this will only work for future versions, so once those two are merged, the actual v2.0.0 doc should be fixed. The easiest is to cherry-pick this commit on the v2.0.0 release branch (or on a new branch created from the 2.0.0 tag, with a name that triggers the doc building job, for instance v2.0.0-release)","embeddings":[-0.1071433127,-0.0372906737,0.0477761067,0.0057620401,0.1159641594,0.0030810323,0.125229001,0.4230194986,-0.3148477674,-0.0263506044,0.0007158918,0.3678081334,-0.0986706987,0.2560932338,0.210472092,-0.0194897894,-0.0013512948,0.2469568253,-0.2531590164,-0.0328656361,-0.2142949402,0.3160285354,-0.1937113255,0.1262152493,-0.3483327031,0.1634145379,0.0490950942,0.2235628515,-0.1971983165,-0.511272788,0.1294212788,0.1394853592,-0.13439174,0.3332173824,-0.0001022324,0.0552099645,0.3468748331,0.1372647136,-0.4601233602,-0.3032651842,0.0204030573,-0.0996507108,-0.0139368139,0.0026390029,-0.1639005393,-0.3710720837,0.0037496968,0.1029786244,0.1823265702,0.3079150319,0.3265562952,0.3916604221,0.3803835511,-0.0378035642,0.3094502687,0.11732319,-0.1556159258,0.2163527012,0.156303376,-0.1173389331,0.0127518391,0.4927563071,-0.0838645026,-0.3008239567,0.4644432962,0.0768599361,-0.2350217402,-0.1552544087,0.1582334042,0.273331821,0.3548756838,-0.2061711401,-0.4903328121,-0.1447439939,-0.1784856319,-0.0832839832,0.2891743779,0.1195666865,0.1420670152,0.3346913755,-0.1230040342,-0.2604273856,-0.1306667328,0.1025931388,0.1502468437,0.0501473062,-0.2547798753,-0.0900403559,0.2541618347,-0.1524027735,-0.0105203064,0.0536844097,-0.2337218523,0.1924676299,0.2942925096,0.0524608083,0.3222456574,0.2718269229,0.4008074701,0.0453940369,-0.226445511,-0.1080235094,-0.1441248953,0.0451004356,0.0487375185,0.0258510262,0.0195204541,0.2145218998,0.3920160532,0.439488709,0.0943796262,-0.1174977198,0.1307294369,-0.2378936857,-0.264936775,-0.247396037,0.3130307496,-0.2161940038,-0.1611794233,0.136413902,-0.2770585418,0.1908213049,0.0967978165,0.1543074846,-0.1117114127,0.3240162134,0.2667908072,0.1004949883,-0.31459409,-0.2243659347,-0.2666991949,0.0529441983,-0.2293559462,0.0853991881,0.1042206138,-0.2768050432,0.4117843211,0.2415241301,-0.0298953857,-0.3137990236,-0.1577792317,0.1638878286,-0.0176335592,0.2199803889,-0.1437368691,0.0919697955,0.1454605311,-0.2954696715,0.0302851945,0.0439090878,-0.4809795022,-0.2023004889,-0.1682680994,0.262914598,-0.0678285584,0.1145393252,-0.0105875488,0.0904821679,-0.1475963145,-0.207027927,-0.0477186851,0.0193930045,-0.147352159,-0.1184959784,0.5030521154,0.6115773916,-0.0449642166,-0.2201599926,0.4343182743,0.1521522999,-0.2516007423,0.0559660867,-0.2313720882,0.0011857474,-0.3202058077,-0.0150878271,-0.0472404584,-0.3331574202,-0.2149091214,0.0840318352,-0.0985764936,0.0950459242,-0.0305464566,-0.2089530528,0.0323258825,-0.1870996803,0.1124930605,-0.144200176,0.1425275654,0.2458458543,-0.3801752627,-0.3218501508,-0.4489344358,-0.1760313958,0.054382205,0.1899437904,0.0840316191,0.1246492863,0.2788216174,-0.260946542,0.0403302163,0.4124903977,0.2673463225,0.1624879539,0.1385267228,-0.1856293082,-0.4126905799,0.0219393186,-0.008496888,0.1084791645,-0.0857239142,-0.2280134559,-0.2862424254,-0.0653620437,-0.0148851154,-0.5016239285,0.2413314134,0.0393807553,0.0433535688,0.4598084092,-0.2105001062,0.1198688969,-0.1199142113,0.3309999704,-0.185864687,0.4658700824,-0.0102561563,-0.0916918665,0.1024843603,0.3219948411,0.158729136,0.0016074975,-0.0662004352,0.4905121922,-0.0442549922,0.3863055706,0.1834984422,0.2843047082,0.3086463213,-0.4547374547,-0.1141663641,0.0001430642,0.0350651219,0.0472924672,-0.0980220214,0.1702475101,-0.152406916,0.1341166943,0.2632459998,0.2405994684,0.0994580388,-0.1703149229,-0.3035512269,-0.1882561743,0.0578697287,0.0169623289,0.3196265399,0.0415263623,-0.2741637826,0.1383247375,0.3085360825,0.0166167822,-0.061990723,0.1238190681,-0.5089436173,0.0256401133,-0.0880164355,0.2399567366,-0.062647447,0.2277643234,-0.0153586278,0.2512654364,-0.1977048963,-0.1580945104,0.2575698793,0.0295447391,-0.0812300146,0.0868272856,-0.0244234316,-0.0407586358,-0.4203320444,0.1737301797,-0.2859551907,0.0819768757,-0.6088325977,0.0974345133,-0.1666130126,0.0196339488,-0.3458669782,-0.1531353593,-0.2905972302,-0.2852283716,0.0801589116,0.4142056406,-0.1749152988,0.1939690858,-0.0213949848,0.1683833152,-0.069265753,0.1146549284,-0.1127614528,0.0585967489,-0.1626858413,0.1369713396,0.1240982786,-0.0076016462,0.1384007782,-0.428360641,-0.1247859895,-0.3586524427,-0.5773410201,0.1289959401,-0.0664974004,0.2842065394,0.2798342407,0.1229058728,-0.0028931824,-0.2152701616,0.3932567835,-0.1551626772,-0.1043533608,-0.1494436413,-0.0961592495,-0.104682751,-0.2238079607,-0.338206321,0.3277105093,-0.2804459333,0.2350327671,-0.0195057914,-0.0394017734,0.3801999986,0.213974908,0.2209997177,-0.0814236775,0.2086212933,-0.0293289963,-0.3348321915,0.2482711524,-0.3973126411,-0.4279310107,0.0839229152,0.1231276691,0.054759521,-0.1407496333,-0.435520947,-0.2864557505,-0.1478250623,-0.0418114588,0.1820383817,0.2941394746,0.1477951258,0.2605451643,-0.2070947886,-0.2066696286,-0.1810037494,0.0862199292,-0.2901005149,0.1358250827,0.0799986124,0.132897675,0.2431131899,0.5736208558,0.4284619689,0.1309682578,0.0857001469,-0.1577617973,0.6485411525,-0.2301547825,-0.3381352127,0.1057180986,-0.0071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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3937","title":"Missing languages in lvwerra\/github-code dataset","comments":"That seems to be an oversight - I originally planned to include them in the dataset and for some reason they were in the list of languages but not in the query. Since there is an issue with the deduplication step I'll rerun the pipeline anyway and will double check the query.\r\n\r\nThanks for reporting this @Eytan-S!","body":"Hi,\r\n\r\nI'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!\r\nI've noticed that two languages are missing from the dataset: TypeScript and Scala. \r\nLooks like they're also omitted from the query you used to get the original code.\r\nAre there any plans to add them in the future?\r\n\r\nThanks!","comment_length":57,"text":"Missing languages in lvwerra\/github-code dataset \n Hi,\r\n\r\nI'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!\r\nI've noticed that two languages are missing from the dataset: TypeScript and Scala. \r\nLooks like they're also omitted from the query you used to get the original code.\r\nAre there any plans to add them in the future?\r\n\r\nThanks! \n That seems to be an oversight - I originally planned to include them in the dataset and for some reason they were in the list of languages but not in the query. Since there is an issue with the deduplication step I'll rerun the pipeline anyway and will double check the query.\r\n\r\nThanks for reporting this @Eytan-S!","embeddings":[0.0285871755,0.0836843252,-0.2411944419,0.1160970479,0.1939320266,0.0751118362,0.1134600341,0.3045021296,0.2181036174,-0.0445781015,0.0106748026,0.3104628026,-0.0661667287,0.1592541784,0.3111082315,-0.0231080912,0.1431610286,-0.082857497,-0.0986673161,-0.3723043203,0.1341448873,0.540225327,-0.0662390515,-0.2558536232,-0.0129761128,0.2341160774,-0.0335193649,0.1357935518,-0.1529674083,-0.2189214975,0.205270499,0.0797250941,0.1583462805,0.1356496811,-0.0001180935,-0.1313159317,0.1348675787,-0.2493512928,-0.0429882854,-0.1027954817,-0.2700395584,-0.2089757174,-0.0831201673,-0.0060102851,-0.0899348557,0.2168403268,0.0405476727,-0.9287718534,0.1554393768,0.4125950336,0.1402851641,-0.0482262708,0.0732947215,-0.0321506485,0.4961046875,0.2492422611,-0.0687647238,0.0762768164,0.8073577285,0.0435980372,0.0956179649,0.3410215974,0.1884566694,-0.4317694008,-0.2999332845,0.176067397,-0.0714580789,-0.4523974359,0.2324958891,0.220689252,0.489907831,-0.3061118722,-0.2455431372,-0.0579212494,0.0029785004,-0.1697540879,-0.0419923142,0.0415046886,0.1626274288,0.2922384739,-0.2264753729,-0.3839752972,-0.1396484673,0.0650667101,0.3619152009,0.177620247,0.1340553463,0.0172091685,-0.4431982636,0.2792977393,-0.1600619107,-0.190127492,0.0858417526,0.2896310985,-0.0576178841,-0.0959811732,0.338083148,-0.0180481113,0.0921969265,0.2171081007,-0.1146839112,0.0967346355,-0.1910435259,-0.0462737642,0.0141351791,0.164901346,0.1749161035,0.1733642668,0.2863581777,-0.2800834179,0.1415084749,0.0229826421,-0.3526426852,0.0414430276,-0.1964735836,0.3255061507,0.0776727051,-0.212597087,0.0086348411,0.2014260888,-0.2722543776,-0.4193371832,-0.1099285409,-0.0333082676,0.203388989,0.196843937,-0.1095567271,0.3547727168,-0.2986705005,-0.6461305618,-0.1396752,0.0649983287,-0.0952317193,-0.2280775607,0.1083459258,0.0493286513,0.2804523408,0.1642284095,0.1695809364,-0.0392054953,0.0367186926,-0.1218773723,-0.0848098621,0.0111296419,-0.0625397712,0.0549919084,0.3114725947,-0.7545150518,-0.1266690344,0.2793294191,0.0374707878,0.0303873885,-0.3034837246,0.1159187183,-0.189413026,-0.2170122266,-0.4007628262,0.4835268855,0.1327048838,0.0320464857,0.0811706111,-0.4108427763,0.4405936301,-0.1636562496,0.311288625,0.3433280587,-0.5936987996,-0.0742992535,-0.1995198131,-0.1825398058,0.4101724327,0.2085359544,-0.1639975458,0.4130247533,-0.2053508162,0.3845307827,0.6185679436,-0.2792361379,0.1131230146,0.1348580569,0.2056149691,0.0253865439,-0.1503004432,-0.0489830971,0.4721556008,-0.0425815359,-0.0612763986,0.0690193176,-0.2501829863,-0.278011024,-0.3073089421,-0.0232491959,0.4279970825,0.2604479194,0.4288309515,-0.0494788252,0.4264566004,-0.380641818,0.3495766819,-0.0971110314,0.1497046947,0.3568282425,0.3827942312,0.243526414,0.1485473961,-0.2218627334,-0.1117369086,0.1201246157,-0.0603378229,0.0743272081,0.1514178962,0.115287371,-0.0393782519,-0.0320705734,-0.1781095415,-0.1679106802,0.0785529688,0.0520369932,-0.2453866154,0.2314880937,-0.1384987533,-0.3390560448,0.0109714251,-0.0960007161,-0.4160979092,0.2616402805,-0.0883413851,-0.2472660691,0.0601825342,0.2442553788,-0.0026030703,-0.1305384934,-0.1159178019,-0.1717679948,0.468267411,-0.0463188589,0.8385347128,0.2590800822,0.1498354524,-0.3225875795,0.1770982444,0.1883222908,0.1248445585,0.0609680638,0.1995175779,0.2579246461,0.0794231892,-0.0838353857,0.0764779821,0.0746861249,0.5405638218,0.0535011515,-0.2232674211,-0.1248061135,0.4487493038,0.1348099262,0.1296194941,0.0085043246,-0.3771497905,0.3240275085,0.6538193226,-0.294018954,0.0539716631,-0.0464940071,-0.0106474487,-0.0842784494,0.0205616765,-0.0418116227,0.1210267022,0.220511049,0.0082281223,-0.3815179765,0.1313842982,-0.2564081252,0.0981414318,-0.0450078957,-0.0709216073,-0.0281966347,0.1349096149,-0.0259353705,-0.3911088407,-0.1439464688,0.0410104357,-0.1226796359,0.0842725113,0.1016535684,-0.4627666771,0.0928790495,-0.2698927224,0.107962884,-0.306260407,-0.2877420187,0.2937124074,-0.5787969828,-0.3027344644,0.2773122489,0.0761466175,-0.174793601,-0.4415943921,-0.0911059156,-0.102224879,-0.2954936028,-0.3739978969,0.0812617689,0.2521750927,-0.3914754689,0.3138927817,-0.2583538592,-0.0910449699,-0.2865504622,-0.6029033065,-0.0353766754,-0.3672944009,0.1301860511,-0.0210305844,-0.0376563109,0.1890175045,0.0696759,0.0834097788,0.1277468204,-0.1776200682,-0.0701806992,0.0626241267,-0.2352707386,-0.2267147899,-0.3963588476,-0.2663238645,0.0232639667,-0.0971490294,0.0184576083,-0.0747963712,-0.2524099946,-0.3073821664,0.0471215248,-0.2438136786,-0.0458291695,-0.1258288324,0.0781514123,0.1519199461,0.1529132873,-0.2765621543,0.0869561657,0.0236247759,0.1931100935,-0.1012751758,-0.4148791432,0.4246083796,-0.0227168631,0.419367075,-0.1215194464,-0.2013075352,0.4556480646,0.2991711497,0.0201995485,-0.2214746773,-0.0691010207,0.1342205852,0.1100416481,0.192746684,-0.3594231606,0.2671031058,-0.2063522786,0.5962945223,0.3068863153,0.3887251019,0.2321982831,0.208765015,0.206719026,-0.1601665169,0.1008153111,0.3125140965,0.0413771458,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3937","title":"Missing languages in lvwerra\/github-code dataset","comments":"Can confirm that the two languages are indeed missing from the dataset. Here are the file counts per language:\r\n```Python\r\n{'Assembly': 82847,\r\n 'Batchfile': 236755,\r\n 'C': 14127969,\r\n 'C#': 6793439,\r\n 'C++': 7368473,\r\n 'CMake': 175076,\r\n 'CSS': 1733625,\r\n 'Dockerfile': 331966,\r\n 'FORTRAN': 141963,\r\n 'GO': 2259363,\r\n 'Haskell': 340521,\r\n 'HTML': 11165464,\r\n 'Java': 19515696,\r\n 'JavaScript': 11829024,\r\n 'Julia': 58177,\r\n 'Lua': 576279,\r\n 'Makefile': 679338,\r\n 'Markdown': 8454049,\r\n 'PHP': 11181930,\r\n 'Perl': 497490,\r\n 'PowerShell': 136827,\r\n 'Python': 7203553,\r\n 'Ruby': 4479767,\r\n 'Rust': 321765,\r\n 'SQL': 655657,\r\n 'Scala': 0,\r\n 'Shell': 1382786,\r\n 'TypeScript': 0,\r\n 'TeX': 250764,\r\n 'Visual Basic': 155371}\r\n ```","body":"Hi,\r\n\r\nI'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!\r\nI've noticed that two languages are missing from the dataset: TypeScript and Scala. \r\nLooks like they're also omitted from the query you used to get the original code.\r\nAre there any plans to add them in the future?\r\n\r\nThanks!","comment_length":82,"text":"Missing languages in lvwerra\/github-code dataset \n Hi,\r\n\r\nI'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!\r\nI've noticed that two languages are missing from the dataset: TypeScript and Scala. \r\nLooks like they're also omitted from the query you used to get the original code.\r\nAre there any plans to add them in the future?\r\n\r\nThanks! \n Can confirm that the two languages are indeed missing from the dataset. Here are the file counts per language:\r\n```Python\r\n{'Assembly': 82847,\r\n 'Batchfile': 236755,\r\n 'C': 14127969,\r\n 'C#': 6793439,\r\n 'C++': 7368473,\r\n 'CMake': 175076,\r\n 'CSS': 1733625,\r\n 'Dockerfile': 331966,\r\n 'FORTRAN': 141963,\r\n 'GO': 2259363,\r\n 'Haskell': 340521,\r\n 'HTML': 11165464,\r\n 'Java': 19515696,\r\n 'JavaScript': 11829024,\r\n 'Julia': 58177,\r\n 'Lua': 576279,\r\n 'Makefile': 679338,\r\n 'Markdown': 8454049,\r\n 'PHP': 11181930,\r\n 'Perl': 497490,\r\n 'PowerShell': 136827,\r\n 'Python': 7203553,\r\n 'Ruby': 4479767,\r\n 'Rust': 321765,\r\n 'SQL': 655657,\r\n 'Scala': 0,\r\n 'Shell': 1382786,\r\n 'TypeScript': 0,\r\n 'TeX': 250764,\r\n 'Visual Basic': 155371}\r\n ```","embeddings":[-0.0256799217,0.1923888773,-0.272978425,0.0706949309,0.2020145953,0.0939294174,0.1608109772,0.4261847734,0.1612200886,0.0164100043,0.0299240425,0.1567168236,-0.1640650928,0.1871640235,0.35605371,0.0112819923,0.1720342785,-0.0363806635,-0.1442353129,-0.3623968661,0.0428690352,0.4893033504,-0.0448550358,-0.2011829019,-0.0201171059,0.2023858726,0.0501632281,0.1495665461,-0.1876768172,-0.3277837634,0.2157064378,0.0212305468,0.19919689,0.2118963301,-0.000114927,-0.0877813995,0.0863537267,-0.2238592505,-0.0232068244,-0.1382261515,-0.1704271883,-0.3886404634,-0.1896992177,-0.0729190111,-0.0765108317,0.2006596327,0.0185858458,-0.8684791327,0.167550534,0.396325618,0.165584445,-0.1049965173,0.0950197056,-0.0473390371,0.6005458832,0.2446626127,-0.1110946238,0.0045627132,0.6433558464,-0.0545374677,0.1017592773,0.3778309524,0.2307787538,-0.4238677621,-0.3097911775,0.2437201887,-0.1349988133,-0.4171084166,0.1595044434,0.209173128,0.5050122142,-0.3456083834,-0.3171154857,-0.1488676369,-0.0719931722,-0.1449185312,-0.013686575,0.2147448212,0.1787126064,0.3075534105,-0.2559271753,-0.3309674263,-0.1833507419,0.095074296,0.3122915924,0.2448309362,0.1176178008,0.0319051594,-0.3635489941,0.168726787,-0.2604123652,-0.2298236787,0.1810751408,0.3094374835,-0.0332331173,-0.1114740744,0.3894372582,-0.0867943019,0.1757959574,0.1250641197,-0.0898582414,0.0366789065,-0.2458774149,-0.0889191255,0.0052509736,0.1662537009,0.2426173091,0.1828615814,0.3056692481,-0.2173210979,0.1982505471,0.0411541015,-0.1946377903,-0.0528803729,-0.1924161911,0.2890437245,0.0939515233,-0.1790701449,0.0215352476,0.2261706293,-0.2377768159,-0.4298742115,-0.1804881841,-0.0255478267,0.0555436499,0.195237413,-0.1271903366,0.4023526907,-0.2323301435,-0.5169322491,-0.1151397899,0.0975297466,-0.1740338951,-0.2245048583,0.233991459,0.0474520177,0.279204756,0.1765708178,0.083045885,0.1021993086,0.0476592705,-0.1507477015,-0.0696634054,0.029823577,-0.0290440656,0.1349362284,0.2338401228,-0.6994519234,-0.1566347331,0.3318850696,-0.0260336325,0.052001033,-0.2703883648,0.1629122049,-0.3328405321,-0.243332237,-0.3135769665,0.3694373369,0.0746406838,-0.024840245,0.0366607979,-0.3730119765,0.3528101146,-0.1458717138,0.3113859892,0.2940367758,-0.5485295653,-0.099531889,-0.0809826255,-0.1537146866,0.3043380082,0.2470981926,-0.1908096522,0.4138155282,-0.1726740897,0.3982334733,0.6215548515,-0.2511278093,0.0729145184,0.0038430926,0.2812716663,0.0188065935,-0.1251240224,-0.0253281873,0.468603611,-0.0294928048,-0.1222667769,0.1291634142,-0.2078344077,-0.1983129382,-0.304921031,-0.0549535155,0.440033555,0.2910797,0.4913585782,-0.2154752016,0.4613809586,-0.3264811337,0.3861170411,-0.1471496373,0.1962706596,0.3553467095,0.3257569671,0.2856219709,0.0735528544,-0.2616325319,-0.2005247027,0.1921277046,0.0240815971,0.0889694542,0.0817538574,0.1580335349,-0.0704458207,-0.0647432506,-0.1479939222,-0.1840772331,0.0580396131,0.1112365499,-0.2758400738,0.2813232839,-0.1926245987,-0.3013668656,0.0202851389,-0.0587166548,-0.3397951722,0.2525525093,-0.1264894158,-0.2254974395,0.028132882,0.2714188099,-0.0345955081,-0.123894386,0.0004115612,-0.1363062263,0.4618397653,-0.1485310942,0.847786665,0.3481523097,0.214210853,-0.3438704908,0.2147151828,0.1516044289,0.143413052,0.0498107746,-0.022262983,0.3566561639,0.0604473613,-0.0389884673,0.0707647651,0.0205818675,0.4183607399,0.0849836245,-0.2500813603,-0.1012433469,0.4832189679,0.1584945768,0.1189161241,0.0062757167,-0.3107276857,0.1957835406,0.7553414702,-0.2609541118,0.0381716117,0.0694723129,-0.0108820433,-0.087542206,-0.0716680288,-0.0205269866,0.1116082594,0.2628958225,-0.0033216549,-0.3195087016,0.0447812937,-0.2486177534,0.0360364318,-0.0551719442,0.0059100217,0.0046011964,0.1698198617,0.0078158127,-0.3027003407,-0.1755026877,-0.0265127774,-0.1036216766,0.0604038872,0.0437187254,-0.4280203581,0.2008347511,-0.2700701356,0.011656899,-0.3343424797,-0.2698161006,0.2113119662,-0.6460710764,-0.3205455542,0.2955998778,-0.0201215707,-0.1181030944,-0.4078666866,-0.0454626381,-0.0128839621,-0.3261486888,-0.3053866625,0.1186985448,0.1231827214,-0.4179112911,0.252843231,-0.2978475988,-0.0519907437,-0.2662030756,-0.5618523359,-0.0176684037,-0.3140754104,0.1210411638,-0.0570675768,-0.0579199791,0.2267974615,-0.0060050283,0.1174776778,0.1087980941,-0.1501116604,-0.1005290747,0.0360424817,-0.223323524,-0.1995150745,-0.3810917139,-0.3201039135,0.0059551345,-0.0586553365,0.1406604499,-0.0274050552,-0.2067942917,-0.2288929969,0.0622484051,-0.3089595139,-0.0047116578,-0.0045248484,0.0339223631,0.1601254493,0.0732488334,-0.2625448108,0.1496671885,0.0491000749,0.0893125087,-0.0438601337,-0.3809745908,0.4687342644,-0.1299924105,0.4926301837,-0.1772530526,-0.0493053868,0.3978644609,0.3220377266,-0.0267269909,-0.2125959247,-0.1232167557,0.1198210195,0.1230963916,0.160566166,-0.3566540182,0.2434087098,-0.1420389712,0.6082155704,0.2452193946,0.377094835,0.2305626422,0.198453784,0.272120297,-0.1513032466,0.0578439198,0.3715667129,0.0746241435,-0.034449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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3929","title":"Load a local dataset twice","comments":"Hi @caush, thanks for reporting:\r\n\r\nIn order to load local CSV files, you can use our \"csv\" loading script: https:\/\/huggingface.co\/docs\/datasets\/loading#csv\r\n```python\r\ndataset = load_dataset(\"csv\", data_files=[\"data\/file1.csv\", \"data\/file2.csv\"])\r\n```\r\nOR:\r\n```python\r\ndataset = load_dataset(\"csv\", data_dir=\"data\")\r\n```\r\n\r\nAlternatively, you may also use:\r\n```python\r\ndataset = load_dataset(\"data\")","body":"## Describe the bug\r\nLoad a local \"dataset\" composed of two csv files twice.\r\n\r\n## Steps to reproduce the bug\r\nPut the two joined files in a repository named \"Data\".\r\nThen in python:\r\n\r\nimport datasets as ds\r\nds.load_dataset('Data', data_files = {'file1.csv', 'file2.csv'})\r\n\r\n## Expected results\r\nShould give something like (because files have only one data row):\r\nTitle, clicks\r\nTruc et astuce, 123\r\nMachin, 12\r\n\r\n## Actual results\r\nGives \r\nTitle, clicks\r\nTruc et astuce, 123\r\nMachin, 12\r\nTruc et astuce, 123\r\nMachin, 12\r\n\r\n## Environment info\r\n[file1.csv](https:\/\/github.com\/huggingface\/datasets\/files\/8256322\/file1.csv)\r\n[file2.csv](https:\/\/github.com\/huggingface\/datasets\/files\/8256323\/file2.csv)\r\n- `datasets` version: 2.0.0\r\n- Platform: Linux-5.4.0-65-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.12\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.1","comment_length":43,"text":"Load a local dataset twice \n ## Describe the bug\r\nLoad a local \"dataset\" composed of two csv files twice.\r\n\r\n## Steps to reproduce the bug\r\nPut the two joined files in a repository named \"Data\".\r\nThen in python:\r\n\r\nimport datasets as ds\r\nds.load_dataset('Data', data_files = {'file1.csv', 'file2.csv'})\r\n\r\n## Expected results\r\nShould give something like (because files have only one data row):\r\nTitle, clicks\r\nTruc et astuce, 123\r\nMachin, 12\r\n\r\n## Actual results\r\nGives \r\nTitle, clicks\r\nTruc et astuce, 123\r\nMachin, 12\r\nTruc et astuce, 123\r\nMachin, 12\r\n\r\n## Environment info\r\n[file1.csv](https:\/\/github.com\/huggingface\/datasets\/files\/8256322\/file1.csv)\r\n[file2.csv](https:\/\/github.com\/huggingface\/datasets\/files\/8256323\/file2.csv)\r\n- `datasets` version: 2.0.0\r\n- Platform: Linux-5.4.0-65-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.12\r\n- PyArrow version: 7.0.0\r\n- Pandas version: 1.4.1 \n Hi @caush, thanks for reporting:\r\n\r\nIn order to load local CSV files, you can use our \"csv\" loading script: https:\/\/huggingface.co\/docs\/datasets\/loading#csv\r\n```python\r\ndataset = load_dataset(\"csv\", data_files=[\"data\/file1.csv\", \"data\/file2.csv\"])\r\n```\r\nOR:\r\n```python\r\ndataset = load_dataset(\"csv\", data_dir=\"data\")\r\n```\r\n\r\nAlternatively, you may also use:\r\n```python\r\ndataset = load_dataset(\"data\")","embeddings":[-0.0047103693,-0.2396460027,-0.0901846886,0.2367398888,0.1813366264,-0.0256923586,0.3377517462,0.3137400448,0.3259290755,0.4739157259,-0.135358721,0.0657345578,0.2889903486,0.0849127769,-0.1292003393,-0.0246550851,0.1108901054,0.3750528991,-0.4979867637,-0.1276559681,-0.0599836931,0.1558846086,-0.0555315018,0.0140052494,0.0218015965,0.1187570989,-0.1177623421,0.342132777,0.0803649873,-0.0798497126,0.2447361946,-0.0108566694,-0.2600645721,0.5470013618,-0.0000996255,0.0467818491,0.1137756929,0.0974404663,-0.0518645123,-0.2566027343,-0.0131813623,-0.2362201661,0.2924904525,-0.26614815,-0.0897424817,-0.1656882465,-0.1953888237,-0.1446096152,0.4296991229,-0.0061171683,0.3310914636,0.3042972386,-0.1212056279,-0.2316205949,0.2522759736,0.0968968272,-0.0116504859,0.4617191255,0.290522486,-0.2931227088,-0.0854262561,0.1257819682,-0.1210966557,0.1045168638,0.0094837174,0.3125209808,-0.0285934005,0.0582046211,0.1627658904,0.041473303,0.029043803,-0.1557943225,-0.055157423,-0.3824268878,-0.0111551033,-0.1577393562,0.0965088829,0.1607369334,-0.11406032,0.2203674763,0.0295508541,0.2224082798,0.0803386346,-0.1080937013,0.1144281924,-0.0805475265,-0.1729045361,0.0695256591,0.2517786622,-0.0901609585,-0.1792602986,-0.0389345698,-0.0455418713,0.1281986833,-0.3417593539,0.1355651617,0.345233947,0.0046561956,-0.0258540977,-0.0090740165,0.1109966636,-0.043571528,-0.0378703326,0.2711826265,0.2839873135,0.1514650136,-0.0505814217,-0.0359105878,0.3308827579,0.2138857991,-0.1920468062,-0.0142660644,0.1853339374,-0.396725893,-0.0480785109,-0.0322356261,0.3155704737,-0.144417569,-0.3964407146,0.3185766339,-0.0756179988,0.0642360821,-0.0776362866,0.3774129748,-0.2132586539,0.1070840061,0.0780620947,0.1251471937,-0.2004325241,-0.0869366899,-0.3048515618,0.0128177451,-0.1841704994,-0.0909783617,0.1953280866,-0.2779326737,0.0845676139,0.4328107536,0.1537929624,-0.2405355722,-0.066037178,-0.1104364619,-0.1011219323,0.2968145907,0.3218608201,0.2312332988,0.2191809863,-0.3751527667,0.0833429545,0.1481825113,-0.1747110933,-0.0479247235,-0.3080783486,0.3341524005,0.0400526635,0.2966656983,0.0062831207,0.0175572094,0.1565216035,-0.0611720234,0.076798521,0.1986530572,-0.2821182013,-0.1887473166,0.1655863822,0.3856534064,-0.2357445806,0.1085917428,0.2396053821,-0.1052378342,0.0915284678,0.3889173865,-0.272827208,0.0024754929,-0.3675377667,0.2460533977,0.0355188437,-0.3720150888,-0.3235742152,0.2362668365,-0.4288937151,0.1070050374,0.3266941905,-0.1531018466,-0.0544219054,-0.0642737448,0.2585344315,0.0789304152,0.0115897842,0.1504639834,-0.3756908476,-0.1237975284,0.0271944515,-0.1239465475,-0.2003208399,-0.0301454347,0.2965587974,-0.3555016816,0.4262883365,-0.2291593701,0.0737287849,0.1765636206,0.176980868,0.1662889421,0.2048566192,-0.1488470286,-0.4092324972,0.1284102947,-0.2241293043,-0.0464653857,-0.0598257519,-0.4873334169,-0.500721693,0.0193823799,-0.1815128773,-0.0848917365,0.2655279934,0.0896145254,-0.1299764514,0.0553810075,0.0232901908,0.0765483528,-0.1281058043,-0.0181133859,-0.2700134516,0.2697791755,-0.1623060405,-0.0178804658,-0.0501279384,0.1319673061,0.2050862014,-0.0291053504,0.0126267727,0.4310972095,-0.1858830005,0.5137690306,0.198597163,-0.2225104421,0.0997763127,0.0552196428,-0.1412791312,0.1407584846,0.280433476,-0.0787327066,-0.3607139885,0.4162096381,-0.1504447758,0.0369891897,0.1484325081,-0.1506400853,0.2041638792,-0.2732310295,0.0556389838,-0.3459355235,0.2634032369,0.2447438985,0.0985720828,0.2858408093,-0.6951723099,0.1613116264,0.225056842,-0.0620456412,0.0635553673,-0.0882728323,-0.1729615033,-0.0760081112,-0.122185871,0.2564500868,0.422717005,0.2277113646,0.1628244668,0.0713812634,-0.0438509844,-0.349973768,0.0266199708,-0.2437190115,-0.0794361383,0.5034421086,0.2667397559,-0.091555953,-0.455887109,0.0710245222,0.0228229258,0.1413589269,-0.3404091299,-0.098347649,-0.1996728778,-0.3038913906,-0.4504398108,-0.0599072613,0.1469738036,-0.1657044142,0.0457104705,0.4463579059,-0.3106680512,0.0832664818,0.0113632912,0.1508831084,0.0220251977,0.1434856951,0.061366383,0.0703353211,-0.2074864805,0.2287394553,0.1455427855,0.2019868046,0.2303654552,-0.0208170824,-0.0446992256,-0.1016834304,-0.2147222757,-0.0620657466,0.0438059457,0.2336656153,-0.0233602338,0.1378122121,-0.1526357532,-0.373069495,0.3301250637,0.0249604713,-0.2775484622,-0.1020512283,0.0398393348,-0.0137038529,-0.5017806888,-0.2886809409,-0.0118243834,-0.2671646774,0.3139075637,-0.0913456231,0.0927057639,0.0651019439,0.0210981611,0.1291800886,-0.0504977293,0.2391234189,-0.3536321819,-0.2593668997,0.1579990983,-0.3354128599,-0.4113503993,-0.2560961843,-0.0732127205,-0.0531082153,-0.3990424275,-0.2729157805,-0.3575667143,-0.284383893,0.1733621657,-0.0093268696,0.0898803324,0.1520979106,-0.0012281134,-0.3564863801,-0.2294594795,-0.0361367054,0.1663166285,0.0678069592,-0.3075997233,-0.0907313302,0.0960076451,0.0307710487,0.4480381012,0.2505360246,0.2274311185,0.2257124484,-0.1779827327,0.410353303,-0.0834574848,-0.5407409072,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3928","title":"Frugal score deprecations","comments":"Hi @Ierezell, thanks for reporting.\r\n\r\nI'm making a PR to suppress those logs from the terminal. ","body":"## Describe the bug\r\nThe frugal score returns a really verbose output with warnings that can be easily changed. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets.load import load_metric\r\n\r\nfrugal = load_metric(\"frugalscore\")\r\nfrugal.compute(predictions=[\"Do you like spinachis\"], references=[\"Do you like spinach\"])\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n```\r\n{'scores': [0.9946]}\r\n```\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nPyTorch: setting up devices\r\nThe default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 864.09ba\/s]\r\nUsing amp half precision backend\r\nThe following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1. If sentence2, sentence1 are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\r\n***** Running Prediction *****\r\n  Num examples = 1\r\n  Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 4644.85it\/s]\r\n {'scores': [0.9946]}\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n","comment_length":16,"text":"Frugal score deprecations \n ## Describe the bug\r\nThe frugal score returns a really verbose output with warnings that can be easily changed. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets.load import load_metric\r\n\r\nfrugal = load_metric(\"frugalscore\")\r\nfrugal.compute(predictions=[\"Do you like spinachis\"], references=[\"Do you like spinach\"])\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n```\r\n{'scores': [0.9946]}\r\n```\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nPyTorch: setting up devices\r\nThe default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 864.09ba\/s]\r\nUsing amp half precision backend\r\nThe following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, sentence1. If sentence2, sentence1 are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\r\n***** Running Prediction *****\r\n  Num examples = 1\r\n  Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 4644.85it\/s]\r\n {'scores': [0.9946]}\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n \n Hi @Ierezell, thanks for reporting.\r\n\r\nI'm making a PR to suppress those logs from the terminal. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3920","title":"'datasets.features' is not a package","comments":"Hi @Arij-Aladel,\r\n\r\nYou are using a very old version of our library `datasets`: 1.8.0\r\nCurrent version is 2.0.0 (and the previous one was 1.18.4)\r\n\r\nPlease, try to update `datasets` library and check if the problem persists:\r\n```shell\r\n\/env\/bin\/pip install -U datasets","body":"@albertvillanova \r\npython 3.9\r\nos: ubuntu 20.04\r\n\r\nIn conda environment\r\n\r\ntorch installed by\r\n\r\n\r\n```\/env\/bin\/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html```\r\ndatasets package is installed by \r\n```\r\n\/env\/bin\/pip  install datasets==1.8.0\r\n\r\n```\r\nDuring runing the code I have this error \r\n\r\n\r\n```\r\n[6]<stderr>:  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n[6]<stderr>:    return super().find_class(mod_name, name)\r\n[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n\r\n```\r\nprecisely this error appears when \r\n\r\ntorch.load('data_file.pt')\r\n```\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 607, in load\r\n    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 882, in _load\r\n    result = unpickler.load()\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n    return super().find_class(mod_name, name)\r\nModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n```\r\n\r\nWhy I am getting this error?\r\n","comment_length":41,"text":"'datasets.features' is not a package \n @albertvillanova \r\npython 3.9\r\nos: ubuntu 20.04\r\n\r\nIn conda environment\r\n\r\ntorch installed by\r\n\r\n\r\n```\/env\/bin\/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html```\r\ndatasets package is installed by \r\n```\r\n\/env\/bin\/pip  install datasets==1.8.0\r\n\r\n```\r\nDuring runing the code I have this error \r\n\r\n\r\n```\r\n[6]<stderr>:  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n[6]<stderr>:    return super().find_class(mod_name, name)\r\n[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n\r\n```\r\nprecisely this error appears when \r\n\r\ntorch.load('data_file.pt')\r\n```\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 607, in load\r\n    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 882, in _load\r\n    result = unpickler.load()\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n    return super().find_class(mod_name, name)\r\nModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n```\r\n\r\nWhy I am getting this error?\r\n \n Hi @Arij-Aladel,\r\n\r\nYou are using a very old version of our library `datasets`: 1.8.0\r\nCurrent version is 2.0.0 (and the previous one was 1.18.4)\r\n\r\nPlease, try to update `datasets` library and check if the problem persists:\r\n```shell\r\n\/env\/bin\/pip install -U datasets","embeddings":[-0.3741754293,-0.2259199321,-0.0957691446,0.3859058022,0.326772213,0.1350311935,0.2670206726,0.2414320111,-0.3361454606,-0.1415685117,0.2684737742,0.5509800911,-0.2597140074,-0.1125489175,-0.0667067692,-0.2506271899,0.1918473542,0.0886711031,-0.1753994673,0.0506082214,-0.1020834371,0.1772280037,-0.3086731732,0.1046833694,-0.4401827157,-0.1291004419,0.057662487,0.0011672718,-0.1308832616,-0.3993217945,0.1733085513,-0.1656587869,0.2008814514,0.5336257219,-0.0001102442,-0.102281861,0.3339243829,-0.235067144,-0.4051907361,-0.2696241438,0.2239958346,-0.344866246,0.1405482888,-0.320517689,-0.064861156,-0.1218972579,-0.1956815273,-0.3246592283,-0.0889194533,0.1999193728,0.2752568424,0.2673619986,0.341452539,-0.1077919304,0.1969591379,0.2647695243,-0.2433831096,0.087219663,0.2781101763,0.0932171717,0.185800463,0.3920754194,-0.4564202428,0.1995441765,0.5340098143,-0.1901662499,-0.4218783379,-0.3149923086,0.1190566719,0.1065387428,0.7960877419,-0.3633450866,-0.2871998549,-0.008440108,0.0209251028,-0.1926267892,0.177324757,0.2702406943,-0.1127576753,-0.065493755,-0.0351170972,-0.1235674918,0.1466792226,0.087388292,0.0234811753,0.2700716853,-0.2394395322,0.2198880762,-0.0778222308,-0.1512328684,0.2789128721,0.0992508009,0.2550493479,0.1950050294,-0.1274664551,0.0593905561,0.035875462,-0.5418815613,-0.0232456718,0.0867031664,-0.0389792211,-0.0078039696,0.0980109721,0.0533464327,-0.0426887944,0.2271229029,0.115377672,0.4202689528,0.0367598422,-0.1019454524,0.1479976177,-0.0050263316,-0.2203567326,-0.1163354963,-0.0239355918,0.2413001508,0.2839538753,-0.0653355047,-0.1946765035,0.0895602852,-0.1148720905,-0.1264280081,0.018678179,0.3936136663,0.0617713705,0.1892162114,0.2496029884,0.0898541361,-0.1363843083,0.1349422038,-0.1018899754,0.2601060569,-0.1946331859,-0.148253113,0.2276373804,-0.3055005968,0.3545936346,-0.2395033836,0.0195270181,0.0681515783,0.0189619567,-0.1080701649,0.0093236445,0.2394512445,-0.1829985231,0.2677110434,0.2797504067,0.6432611942,-0.3090865612,0.2902591228,-0.2692390382,-0.3517669737,-0.285200268,0.195899725,0.1038900316,0.0913799182,0.1703742445,-0.2150213867,0.2958919108,-0.3047992885,0.061851386,-0.1140145883,0.0703654066,-0.2477861792,0.090305917,0.2864120305,-0.4072782993,-0.2413600087,0.1788346022,-0.0406975783,0.0558572486,-0.3058131635,-0.2195417732,0.330498755,-0.2853322625,-0.1353888512,0.3486598134,-0.2800932229,-0.3074306846,0.0020025533,-0.081931673,-0.1549833566,0.0819935724,0.1981034428,0.0055795279,0.1080358997,0.1426399052,0.1517225951,0.3037721217,-0.199518621,-0.0355087332,-0.2358683646,0.3622281849,0.1398118585,0.3074507713,-0.0239192657,0.0582453012,0.0744278654,-0.0656486601,0.0496709719,-0.1429607719,0.3088869452,0.0822017714,0.3442079127,0.0646340102,-0.2338404804,-0.1789039522,0.1157387048,-0.0391768143,-0.0221634563,-0.4385868311,0.1039018184,0.0143684484,0.0288831703,-0.2115475386,-0.295609504,0.129686296,0.175942257,-0.1191499606,-0.1143187881,-0.3149427474,0.5230892897,0.1073751301,0.0615914278,-0.3357559443,0.2151219994,-0.3341527581,-0.3324249983,-0.0113322949,0.2416027784,0.452786237,-0.1976841092,-0.175453797,0.2943202853,-0.1112557799,-0.2471630722,-0.1432566792,-0.17965509,0.0906139314,-0.2848007977,0.2881484032,0.258849293,0.1336264759,0.0399493389,0.1715025604,0.1567478627,0.1792506725,0.042357605,0.1538583189,0.1563338786,0.1408084482,-0.013587215,0.0491760857,-0.0572556369,0.3017376661,0.0613723956,0.115772672,0.0887286812,-0.2141780853,-0.158018887,0.2411473542,-0.062527746,0.1126916632,0.0264636651,-0.3545201421,0.3017967641,0.306261301,-0.5455057025,0.4573797882,0.1624426395,-0.208591029,-0.0547160506,0.0878469646,0.1344629675,0.2282803357,0.1700317115,-0.070123069,0.2421507686,-0.1418666542,0.235649243,-0.0488916524,-0.277487427,-0.1123033985,0.0452267602,-0.3060614467,0.1804015487,-0.2832740843,0.0769763067,-0.3119251132,-0.3987560272,-0.1912996173,-0.0405015387,-0.1143326387,0.0967390463,0.2792671621,0.3615099192,-0.1512787491,-0.1177882105,0.3305978477,-0.5154699683,0.1445988715,0.078823179,-0.1144845411,0.0261661988,0.0128739541,0.0196784735,0.3810624778,-0.1107430831,0.1932116002,-0.3125234246,-0.1834489405,0.2537640035,-0.2077497989,-0.1284194142,0.2780032754,-0.0878008157,0.1190975681,-0.3930118978,0.2900563776,-0.1466547847,-0.1200963631,0.1572497785,0.0697195679,-0.2599533498,-0.0257712957,-0.7031169534,-0.4141612351,-0.4053668082,-0.1633957177,0.097940214,0.1934714764,0.1647307426,0.300988853,0.2713820934,0.2801977694,0.271680057,-0.0801217034,-0.1201182604,0.355294615,-0.1376410127,-0.281904459,0.0476395376,-0.0178221203,0.0919326022,0.3245996833,-0.3015490472,-0.0786707401,0.0980437323,0.2499044687,-0.3075302243,0.287167877,0.4976472259,0.2648240328,-0.0883166417,-0.1202771738,-0.1288150698,-0.2121380866,-0.2743762136,0.5441571474,0.0898794383,0.3591863811,-0.0874368697,0.7763411403,0.1134192124,-0.310706377,0.2981353402,0.0501732603,0.3941994011,-0.1232939661,-0.5466144085,0.1964439452,-0.1708875299,0.0900068507,-0.02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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3920","title":"'datasets.features' is not a package","comments":"The problem I can no I have build my project on this version and old version on transformers. I have preprocessed the data again to use it. Thank for your reply","body":"@albertvillanova \r\npython 3.9\r\nos: ubuntu 20.04\r\n\r\nIn conda environment\r\n\r\ntorch installed by\r\n\r\n\r\n```\/env\/bin\/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html```\r\ndatasets package is installed by \r\n```\r\n\/env\/bin\/pip  install datasets==1.8.0\r\n\r\n```\r\nDuring runing the code I have this error \r\n\r\n\r\n```\r\n[6]<stderr>:  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n[6]<stderr>:    return super().find_class(mod_name, name)\r\n[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n\r\n```\r\nprecisely this error appears when \r\n\r\ntorch.load('data_file.pt')\r\n```\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 607, in load\r\n    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 882, in _load\r\n    result = unpickler.load()\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n    return super().find_class(mod_name, name)\r\nModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n```\r\n\r\nWhy I am getting this error?\r\n","comment_length":31,"text":"'datasets.features' is not a package \n @albertvillanova \r\npython 3.9\r\nos: ubuntu 20.04\r\n\r\nIn conda environment\r\n\r\ntorch installed by\r\n\r\n\r\n```\/env\/bin\/pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https:\/\/download.pytorch.org\/whl\/torch_stable.html```\r\ndatasets package is installed by \r\n```\r\n\/env\/bin\/pip  install datasets==1.8.0\r\n\r\n```\r\nDuring runing the code I have this error \r\n\r\n\r\n```\r\n[6]<stderr>:  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n[6]<stderr>:    return super().find_class(mod_name, name)\r\n[6]<stderr>:ModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n\r\n```\r\nprecisely this error appears when \r\n\r\ntorch.load('data_file.pt')\r\n```\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 607, in load\r\n    return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 882, in _load\r\n    result = unpickler.load()\r\n  File \"\/home\/arij\/Memory-transformer-with-hierarchical-attention_MLM\/env\/lib\/python3.9\/site-packages\/torch\/serialization.py\", line 875, in find_class\r\n    return super().find_class(mod_name, name)\r\nModuleNotFoundError: No module named 'datasets.features.features'; 'datasets.features' is not a package\r\n```\r\n\r\nWhy I am getting this error?\r\n \n The problem I can no I have build my project on this version and old version on transformers. I have preprocessed the data again to use it. Thank for your reply","embeddings":[-0.3741754293,-0.2259199321,-0.0957691446,0.3859058022,0.326772213,0.1350311935,0.2670206726,0.2414320111,-0.3361454606,-0.1415685117,0.2684737742,0.5509800911,-0.2597140074,-0.1125489175,-0.0667067692,-0.2506271899,0.1918473542,0.0886711031,-0.1753994673,0.0506082214,-0.1020834371,0.1772280037,-0.3086731732,0.1046833694,-0.4401827157,-0.1291004419,0.057662487,0.0011672718,-0.1308832616,-0.3993217945,0.1733085513,-0.1656587869,0.2008814514,0.5336257219,-0.0001102442,-0.102281861,0.3339243829,-0.235067144,-0.4051907361,-0.2696241438,0.2239958346,-0.344866246,0.1405482888,-0.320517689,-0.064861156,-0.1218972579,-0.1956815273,-0.3246592283,-0.0889194533,0.1999193728,0.2752568424,0.2673619986,0.341452539,-0.1077919304,0.1969591379,0.2647695243,-0.2433831096,0.087219663,0.2781101763,0.0932171717,0.185800463,0.3920754194,-0.4564202428,0.1995441765,0.5340098143,-0.1901662499,-0.4218783379,-0.3149923086,0.1190566719,0.1065387428,0.7960877419,-0.3633450866,-0.2871998549,-0.008440108,0.0209251028,-0.1926267892,0.177324757,0.2702406943,-0.1127576753,-0.065493755,-0.0351170972,-0.1235674918,0.1466792226,0.087388292,0.0234811753,0.2700716853,-0.2394395322,0.2198880762,-0.0778222308,-0.1512328684,0.2789128721,0.0992508009,0.2550493479,0.1950050294,-0.1274664551,0.0593905561,0.035875462,-0.5418815613,-0.0232456718,0.0867031664,-0.0389792211,-0.0078039696,0.0980109721,0.0533464327,-0.0426887944,0.2271229029,0.115377672,0.4202689528,0.0367598422,-0.1019454524,0.1479976177,-0.0050263316,-0.2203567326,-0.1163354963,-0.0239355918,0.2413001508,0.2839538753,-0.0653355047,-0.1946765035,0.0895602852,-0.1148720905,-0.1264280081,0.018678179,0.3936136663,0.0617713705,0.1892162114,0.2496029884,0.0898541361,-0.1363843083,0.1349422038,-0.1018899754,0.2601060569,-0.1946331859,-0.148253113,0.2276373804,-0.3055005968,0.3545936346,-0.2395033836,0.0195270181,0.0681515783,0.0189619567,-0.1080701649,0.0093236445,0.2394512445,-0.1829985231,0.2677110434,0.2797504067,0.6432611942,-0.3090865612,0.2902591228,-0.2692390382,-0.3517669737,-0.285200268,0.195899725,0.1038900316,0.0913799182,0.1703742445,-0.2150213867,0.2958919108,-0.3047992885,0.061851386,-0.1140145883,0.0703654066,-0.2477861792,0.090305917,0.2864120305,-0.4072782993,-0.2413600087,0.1788346022,-0.0406975783,0.0558572486,-0.3058131635,-0.2195417732,0.330498755,-0.2853322625,-0.1353888512,0.3486598134,-0.2800932229,-0.3074306846,0.0020025533,-0.081931673,-0.1549833566,0.0819935724,0.1981034428,0.0055795279,0.1080358997,0.1426399052,0.1517225951,0.3037721217,-0.199518621,-0.0355087332,-0.2358683646,0.3622281849,0.1398118585,0.3074507713,-0.0239192657,0.0582453012,0.0744278654,-0.0656486601,0.0496709719,-0.1429607719,0.3088869452,0.0822017714,0.3442079127,0.0646340102,-0.2338404804,-0.1789039522,0.1157387048,-0.0391768143,-0.0221634563,-0.4385868311,0.1039018184,0.0143684484,0.0288831703,-0.2115475386,-0.295609504,0.129686296,0.175942257,-0.1191499606,-0.1143187881,-0.3149427474,0.5230892897,0.1073751301,0.0615914278,-0.3357559443,0.2151219994,-0.3341527581,-0.3324249983,-0.0113322949,0.2416027784,0.452786237,-0.1976841092,-0.175453797,0.2943202853,-0.1112557799,-0.2471630722,-0.1432566792,-0.17965509,0.0906139314,-0.2848007977,0.2881484032,0.258849293,0.1336264759,0.0399493389,0.1715025604,0.1567478627,0.1792506725,0.042357605,0.1538583189,0.1563338786,0.1408084482,-0.013587215,0.0491760857,-0.0572556369,0.3017376661,0.0613723956,0.115772672,0.0887286812,-0.2141780853,-0.158018887,0.2411473542,-0.062527746,0.1126916632,0.0264636651,-0.3545201421,0.3017967641,0.306261301,-0.5455057025,0.4573797882,0.1624426395,-0.208591029,-0.0547160506,0.0878469646,0.1344629675,0.2282803357,0.1700317115,-0.070123069,0.2421507686,-0.1418666542,0.235649243,-0.0488916524,-0.277487427,-0.1123033985,0.0452267602,-0.3060614467,0.1804015487,-0.2832740843,0.0769763067,-0.3119251132,-0.3987560272,-0.1912996173,-0.0405015387,-0.1143326387,0.0967390463,0.2792671621,0.3615099192,-0.1512787491,-0.1177882105,0.3305978477,-0.5154699683,0.1445988715,0.078823179,-0.1144845411,0.0261661988,0.0128739541,0.0196784735,0.3810624778,-0.1107430831,0.1932116002,-0.3125234246,-0.1834489405,0.2537640035,-0.2077497989,-0.1284194142,0.2780032754,-0.0878008157,0.1190975681,-0.3930118978,0.2900563776,-0.1466547847,-0.1200963631,0.1572497785,0.0697195679,-0.2599533498,-0.0257712957,-0.7031169534,-0.4141612351,-0.4053668082,-0.1633957177,0.097940214,0.1934714764,0.1647307426,0.300988853,0.2713820934,0.2801977694,0.271680057,-0.0801217034,-0.1201182604,0.355294615,-0.1376410127,-0.281904459,0.0476395376,-0.0178221203,0.0919326022,0.3245996833,-0.3015490472,-0.0786707401,0.0980437323,0.2499044687,-0.3075302243,0.287167877,0.4976472259,0.2648240328,-0.0883166417,-0.1202771738,-0.1288150698,-0.2121380866,-0.2743762136,0.5441571474,0.0898794383,0.3591863811,-0.0874368697,0.7763411403,0.1134192124,-0.310706377,0.2981353402,0.0501732603,0.3941994011,-0.1232939661,-0.5466144085,0.1964439452,-0.1708875299,0.0900068507,-0.02504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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3919","title":"AttributeError: 'DatasetDict' object has no attribute 'features'","comments":"You are likely trying to get the `features` from a `DatasetDict`, a dictionary containing `Datasets`. You probably first want to index into a particular split from your `DatasetDict` i.e. `dataset['train'].features`. \r\n\r\nFor example \r\n\r\n```python \r\nds = load_dataset('mnist')\r\nds.features\r\n```\r\nReturns \r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError                            Traceback (most recent call last)\r\n\r\n[<ipython-input-39-791c1f9df6c2>](https:\/\/localhost:8080\/#) in <module>()\r\n----> 1 ds.features\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nIf we look at the dataset variable, we see it is a `DatasetDict`:\r\n\r\n```python \r\nprint(ds)\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['image', 'label'],\r\n        num_rows: 60000\r\n    })\r\n    test: Dataset({\r\n        features: ['image', 'label'],\r\n        num_rows: 10000\r\n    })\r\n})\r\n```\r\n\r\nWe can grab the features from a split by indexing into `train`:\r\n```python\r\nds['train'].features\r\n{'image': Image(decode=True, id=None),\r\n 'label': ClassLabel(num_classes=10, names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], id=None)}\r\n```\r\n\r\nHope that helps ","body":"## Describe the bug\r\nReceiving the error when trying to check for Dataset features\r\n\r\n## Steps to reproduce the bug\r\nfrom datasets import Dataset\r\ndataset = Dataset.from_pandas(df[['id', 'words', 'bboxes', 'ner_tags', 'image_path']])\r\n\r\ndataset.features\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nGetting the following errror\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n\r\n## Environment info\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 1.18.4\r\n- Platform: Linux-4.14.252-131.483.amzn1.x86_64-x86_64-with-glibc2.9\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n","comment_length":129,"text":"AttributeError: 'DatasetDict' object has no attribute 'features' \n ## Describe the bug\r\nReceiving the error when trying to check for Dataset features\r\n\r\n## Steps to reproduce the bug\r\nfrom datasets import Dataset\r\ndataset = Dataset.from_pandas(df[['id', 'words', 'bboxes', 'ner_tags', 'image_path']])\r\n\r\ndataset.features\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nGetting the following errror\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n\r\n## Environment info\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 1.18.4\r\n- Platform: Linux-4.14.252-131.483.amzn1.x86_64-x86_64-with-glibc2.9\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n \n You are likely trying to get the `features` from a `DatasetDict`, a dictionary containing `Datasets`. You probably first want to index into a particular split from your `DatasetDict` i.e. `dataset['train'].features`. \r\n\r\nFor example \r\n\r\n```python \r\nds = load_dataset('mnist')\r\nds.features\r\n```\r\nReturns \r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError                            Traceback (most recent call last)\r\n\r\n[<ipython-input-39-791c1f9df6c2>](https:\/\/localhost:8080\/#) in <module>()\r\n----> 1 ds.features\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nIf we look at the dataset variable, we see it is a `DatasetDict`:\r\n\r\n```python \r\nprint(ds)\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['image', 'label'],\r\n        num_rows: 60000\r\n    })\r\n    test: Dataset({\r\n        features: ['image', 'label'],\r\n        num_rows: 10000\r\n    })\r\n})\r\n```\r\n\r\nWe can grab the features from a split by indexing into `train`:\r\n```python\r\nds['train'].features\r\n{'image': Image(decode=True, id=None),\r\n 'label': ClassLabel(num_classes=10, names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], id=None)}\r\n```\r\n\r\nHope that helps 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3918","title":"datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files","comments":"Hi @willowdong! These issues were fixed on master. We will have a new release of `datasets` later today. In the meantime, you can avoid these issues by installing `datasets` from master as follows:\r\n```bash\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```","body":"## Describe the bug\r\nCan't load the dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('multi_news')\r\ndataset_2=load_dataset(\"reddit_tifu\", \"long\")\r\n\r\n\r\n## Actual results\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.4\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.0\r\n- PyArrow version: 6.0.1\r\n","comment_length":38,"text":"datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files \n ## Describe the bug\r\nCan't load the dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('multi_news')\r\ndataset_2=load_dataset(\"reddit_tifu\", \"long\")\r\n\r\n\r\n## Actual results\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.4\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.0\r\n- PyArrow version: 6.0.1\r\n \n Hi @willowdong! These issues were fixed on master. We will have a new release of `datasets` later today. In the meantime, you can avoid these issues by installing `datasets` from master as follows:\r\n```bash\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```","embeddings":[-0.3251985013,0.1044453755,-0.1208982319,0.194041878,0.2922667563,0.0498711243,0.121732451,0.3344368041,0.0943165347,0.1404136866,-0.1278533041,0.2029479444,0.0496360473,0.0912996754,-0.2518992126,0.2652892172,0.0160791725,-0.0456768498,-0.1795180738,-0.0238718539,-0.2421694994,0.2304715514,-0.1558197886,-0.1697168499,-0.1419748515,0.1738641113,0.0524106808,0.2872042656,-0.0756199062,-0.2301062196,0.1201064736,0.166470319,-0.0833464041,0.555454433,-0.000103766,0.1744946241,0.3435874879,-0.0208406001,-0.166346699,-0.3262065351,-0.4284004867,-0.3796931803,-0.1170199737,-0.2062368095,-0.01846857,0.078329578,-0.0458128415,-0.1120775267,0.214217782,0.4172297716,0.3292240202,0.4237022698,0.1001986712,-0.0719451308,0.3064060211,-0.1023229212,-0.031043686,0.1829224378,0.0206572451,-0.0657716691,-0.0972566977,0.3408417404,-0.1711777896,0.1619863659,-0.1531766057,-0.0198604539,0.0802557021,-0.1836100668,0.2516777813,0.3387814164,0.3033501208,-0.1283685118,-0.312238276,-0.0575103536,-0.1006444991,0.0861350447,0.3719235957,0.2933354676,-0.0934509262,0.1883303076,-0.4651943445,0.1557196975,0.0345456526,0.2467764169,0.093736589,0.0685843974,-0.0278183371,0.0301929433,0.0749739259,-0.1127654091,0.4349053204,-0.3108541071,0.057659395,-0.0511789247,-0.4282957315,-0.0063118339,0.1534122676,0.264636606,0.3667002916,0.1967823505,0.0601167046,0.1350749731,-0.320579797,0.2007720768,0.0059418157,-0.0350745507,-0.0690990537,0.1592850387,0.360861659,0.1941220909,-0.0950049907,0.1182544455,-0.0038035591,-0.2770170271,0.2882758677,0.0128536234,0.2886620462,-0.341440767,-0.3861628473,0.1806574762,0.0578779317,-0.2149212211,0.1329744756,0.4688464105,-0.279830426,0.1832853705,-0.1679286361,0.229931742,-0.1847196519,-0.0755023956,-0.3305081725,-0.0281458981,-0.1138716713,-0.0106404051,0.2079799324,-0.1856898963,0.337287128,0.0825712234,0.1155614182,-0.1586877853,0.1571108997,-0.1422626078,-0.0416096114,0.3967611194,0.1231288761,0.1533685774,0.2573885322,-0.0122001208,-0.0799766555,0.1779552102,-0.3896894455,-0.206582725,-0.0382684581,0.3383522928,-0.2754590809,-0.0832758546,-0.0892010033,-0.0960428342,0.2325134575,-0.1623981446,-0.0351919681,-0.1769384444,-0.1138744578,-0.2432812601,0.1807838529,0.2362620234,-0.0967481807,0.0320524983,0.0143092163,-0.1599157006,0.1947906315,0.2277045399,-0.1625549793,-0.0287955217,-0.1713344157,-0.017227523,0.2478479445,-0.2369933426,-0.5096578002,0.065347366,-0.0739465803,0.2944977283,0.1033963189,0.0532837845,-0.1902154982,-0.0483780913,0.1423814297,0.2900881469,0.1312149912,0.2479990721,-0.3040179014,-0.319929719,0.1130616218,0.1988230348,0.1305348426,-0.0116921719,0.2603779733,-0.2279060334,0.3221948147,-0.1326315105,-0.1002600193,0.2756309509,0.2547568977,0.0810410604,0.0635156184,-0.2972928286,-0.4121532142,0.2457030714,-0.0383021571,-0.016514685,-0.0944481045,-0.1573406011,-0.3147000074,-0.0892280489,-0.1588063538,-0.0179576762,0.2392259687,0.3178259134,0.2121145427,0.0424904563,-0.0707752705,0.2960853577,-0.2416674495,-0.0448920988,-0.2910603285,0.5378289223,-0.2003883868,-0.0581432283,0.2150563002,0.026643686,0.2615156174,-0.1005899906,-0.1682827175,0.5476431847,0.1651141346,0.1127801389,0.1127052829,0.2415401191,-0.0011959404,-0.4421369731,-0.0753177851,0.6118803024,0.0621359497,-0.0758353323,-0.3208577335,0.3685757816,-0.1385652125,0.1292603612,0.1297173351,0.0541939698,0.3685263395,-0.2475692779,-0.0145097272,-0.1644444764,0.2934492826,-0.1132425368,-0.0618822984,0.0249494277,-0.1543203741,-0.0227825399,0.4171395302,-0.0300076883,0.0559671149,0.1548723578,0.0113444841,-0.1598731428,-0.0979764238,0.4290879667,0.3949562013,0.2348757833,0.025972005,0.0385707058,-0.1639005542,-0.2216146141,0.1419147402,0.0329905748,0.1728031337,0.3657892644,0.2601340413,-0.0630248263,-0.3595382273,-0.1961605102,0.0646399856,0.2093335837,-0.437587738,-0.0943975747,-0.2645992041,-0.0252461899,-0.12750265,-0.0417168364,-0.188517496,-0.4284150898,0.008742135,0.2958369553,-0.0929614231,0.2532458603,-0.227134034,-0.0029734916,0.1672322154,-0.0196525604,0.111453414,-0.0922431797,0.0206035711,0.1587139219,0.377122432,0.0559357256,0.2599858344,-0.2830976546,-0.1039594561,-0.5253942609,-0.2716299891,0.0108476421,-0.1509878188,0.2281978428,0.1280445009,0.1844862103,-0.0059726047,-0.1572434604,0.3338563144,-0.2140404731,-0.2097415775,0.2373197675,0.0135373622,-0.0789201781,0.0980429426,-0.2553719878,-0.2656974792,-0.3681038916,-0.052588217,0.1545404941,0.1965331584,0.1734976023,0.2003933042,0.1242668927,0.1174331307,0.2431342155,-0.3164876699,-0.4715267718,0.5315617323,-0.2029938996,-0.4594942629,-0.1220105588,0.09769959,0.0731918067,0.1219043061,-0.5819919109,-0.3393842578,-0.343716085,0.0835421383,0.1165413335,0.102776818,0.1815557927,0.0183460489,-0.2554383278,-0.2616177201,-0.0955472961,-0.0749543905,-0.1663520783,0.2495718747,-0.1013023555,0.138745293,-0.0595767014,0.3682270944,0.391356647,0.0895824954,0.129749611,0.0178748779,0.4014896154,-0.1094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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3906","title":"NonMatchingChecksumError on Spider dataset ","comments":"Hi @kolk, thanks for reporting.\r\n\r\nIndeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change:\r\n- #3787 \r\n\r\nWe just made patch release last week: 1.18.4 https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.18.4\r\n\r\nPlease, feel free to update your local `datasets` version, so that you get the fix:\r\n```shell\r\npip install -U datasets\r\n```","body":"## Describe the bug\r\nFailure to generate dataset ```spider``` because of checksums error for dataset source files.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nspider = load_dataset(\"spider\")\r\n```\r\n\r\n\r\n## Expected results\r\nChecksums should match for files from url ['https:\/\/drive.google.com\/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']\r\n\r\n## Actual results\r\n```\r\n>>> load_dataset(\"spider\")\r\nload_dataset(\"spider\")\r\nDownloading and preparing dataset spider\/spider (download: 95.12 MiB, generated: 5.17 MiB, post-processed: Unknown size, total: 100.29 MiB) to \/home\/user\/.cache\/huggingface\/datasets\/spider\/spider\/1.0.0\/79778ebea87c59b19411f1eb3eda317e9dd5f7788a556d837ef25c3ae6e5e8b7...\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/IPython\/core\/interactiveshell.py\", line 3441, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n  File \"<ipython-input-5-d4cb54197348>\", line 1, in <module>\r\n    load_dataset(\"spider\")\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']\r\n```\r\n\r\n\r\n## Environment info\r\ndatasets version: 1.18.3\r\nPlatform: Ubuntu 20 LTS\r\nPython version: 3.8.10\r\nPyArrow version: 6.0.1 \r\n\r\n\r\n\r\n","comment_length":60,"text":"NonMatchingChecksumError on Spider dataset  \n ## Describe the bug\r\nFailure to generate dataset ```spider``` because of checksums error for dataset source files.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nspider = load_dataset(\"spider\")\r\n```\r\n\r\n\r\n## Expected results\r\nChecksums should match for files from url ['https:\/\/drive.google.com\/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']\r\n\r\n## Actual results\r\n```\r\n>>> load_dataset(\"spider\")\r\nload_dataset(\"spider\")\r\nDownloading and preparing dataset spider\/spider (download: 95.12 MiB, generated: 5.17 MiB, post-processed: Unknown size, total: 100.29 MiB) to \/home\/user\/.cache\/huggingface\/datasets\/spider\/spider\/1.0.0\/79778ebea87c59b19411f1eb3eda317e9dd5f7788a556d837ef25c3ae6e5e8b7...\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/IPython\/core\/interactiveshell.py\", line 3441, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n  File \"<ipython-input-5-d4cb54197348>\", line 1, in <module>\r\n    load_dataset(\"spider\")\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/home\/user\/py3_env\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0']\r\n```\r\n\r\n\r\n## Environment info\r\ndatasets version: 1.18.3\r\nPlatform: Ubuntu 20 LTS\r\nPython version: 3.8.10\r\nPyArrow version: 6.0.1 \r\n\r\n\r\n\r\n \n Hi @kolk, thanks for reporting.\r\n\r\nIndeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change:\r\n- #3787 \r\n\r\nWe just made patch release last week: 1.18.4 https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.18.4\r\n\r\nPlease, feel free to update your local `datasets` version, so that you get the fix:\r\n```shell\r\npip install -U datasets\r\n```","embeddings":[-0.1679656506,0.0254897103,-0.086360991,0.2954740524,0.3215499222,-0.1166108474,0.1364502758,0.4342499673,0.1861151159,0.2588540614,-0.2660089731,0.0177098159,-0.0366363712,0.1000536382,-0.1914369017,0.1708942652,-0.0017419427,0.0505830683,-0.4116186798,-0.1128830686,-0.2612490654,0.0663859695,0.007393091,-0.2859489322,0.131240353,0.1494043469,0.2227994651,0.1876972467,-0.1567740142,-0.2523317337,0.1642962694,0.0379325077,-0.0013270433,0.4155566096,-0.0001117713,0.1711553186,0.2956293821,-0.0187095813,-0.3775075972,-0.0154739358,-0.2517728508,-0.3479462266,-0.1685921103,-0.2898810506,-0.1090249121,0.139962554,0.047897011,-0.2327901572,0.0860856995,0.2851851583,0.2384159267,0.6125790477,0.213185519,0.0232694354,0.3841731846,0.0321945623,-0.1736340225,0.1957048923,0.2253403366,0.1989523172,-0.3688897789,0.1584017873,-0.0142050544,0.1721504331,0.1803523451,-0.0836847052,0.1250656843,-0.3748968542,0.1349820793,0.1953409165,0.3362746537,-0.1857327074,-0.3668503463,-0.0177901126,-0.2558866143,0.1457054764,0.2839543819,0.3837307096,-0.2814312279,-0.0129021462,-0.5050472021,0.3566196859,-0.1008981913,0.147763595,0.3273890018,0.2799341381,0.0756826997,0.0006087197,-0.1311927289,-0.2798725069,0.2466348857,-0.3080365658,-0.0511032827,0.0926309451,-0.4308572114,-0.1335953474,0.1100366935,0.3322862387,0.4337329566,0.6762366891,0.0887894034,0.077781342,-0.0821389407,-0.0311617032,0.0931549296,0.0517928898,-0.0663486272,0.1833837479,0.4966816604,0.4386210442,0.056514211,0.1035016626,0.1021684408,-0.2871927023,0.5880285501,0.1843371689,0.3593304753,-0.2924614251,-0.2455576807,0.2702584863,-0.1003122181,-0.0230431836,0.1609816104,0.1803759187,-0.3155960739,-0.0268610809,0.0883752629,0.1450075805,-0.1772433668,-0.0995495245,-0.2915545702,-0.0572170541,-0.149822101,-0.104149662,0.1891434938,-0.007183155,0.2658675313,0.1193881929,0.2343579233,-0.0817664415,0.1897697598,-0.0413553715,-0.1274435967,0.4126689732,0.063304767,-0.0233969837,0.185040921,0.0691346303,-0.0647882968,0.1271273196,0.0156463739,-0.2397377342,-0.0891631469,0.2631024718,-0.4621055126,0.0779600069,0.0981588736,-0.4419333637,0.2803555429,-0.0980943739,0.1347422153,-0.2901304364,-0.204136923,-0.287330091,0.2677721679,0.3509767652,0.3208661377,0.0927467495,0.066302903,-0.2560477257,0.2722521126,0.2439756393,-0.0363711789,0.0093617821,-0.2981927395,-0.1732172668,0.1313521266,-0.2259352058,-0.4142879844,-0.0106271952,-0.0069068829,0.4206263423,0.2157866806,0.1409053057,-0.0423574857,-0.0296328068,-0.0568707623,0.1014775336,-0.0508248247,0.2541747093,-0.2801446319,-0.167941153,0.0960812941,0.1012677476,0.0724208876,0.1218195856,0.2149815857,-0.2567163408,0.1568298787,-0.2339548916,0.0757908151,0.1255663633,0.4142953753,-0.0084637189,0.0831238553,-0.3101625144,-0.4560266435,0.4116443396,-0.2182246745,0.3407100439,-0.347061336,-0.1775736362,-0.3112812638,-0.0972140804,-0.1612679213,-0.1045205519,0.1421606392,0.223609373,0.1847404242,-0.1796045005,0.0860035792,0.1932879239,-0.3327668309,0.1498602182,-0.5810318589,0.4491330683,-0.1153194681,-0.1458493024,0.1303511709,0.1455845684,-0.0320716277,0.029165294,-0.1593019515,0.3726524115,0.1808288544,-0.0344035812,-0.0361247286,0.2395900339,-0.0390789956,-0.2366049439,0.1406241506,0.4901229143,0.0480915569,0.0115608759,-0.2281103581,0.302924633,-0.2631338239,0.084039934,0.0854123831,0.004577959,0.2411207259,-0.0643827617,-0.2080500871,-0.0729263499,0.1783101857,0.0101789767,0.2496869117,0.151643008,-0.2811576128,0.0313887745,0.1325372607,-0.0859318823,0.0630345643,0.1734752357,0.1342915446,-0.0889670104,0.1307595521,0.3538676798,0.2626610994,0.1438977122,0.1389660835,0.0689508095,-0.1038344204,-0.1060251147,0.0551614612,0.0143328784,-0.0144501105,0.4606788754,0.2667940855,-0.1306178868,-0.388602376,-0.1377413273,-0.1729715765,0.2812448442,-0.4171099663,-0.1218708828,-0.1354808807,-0.2149476856,-0.3599182069,0.1103953198,-0.2717369199,-0.2057739794,-0.1354443133,0.366210252,-0.2997896969,0.3312623799,-0.2938600779,-0.0639323741,0.1704417318,-0.1711730361,0.0319786705,0.1669487953,0.0391522013,0.0775909647,0.3167690933,0.1294442862,0.3987770379,-0.3342336714,0.0362009257,-0.5946778655,-0.2428197265,-0.1130498871,0.0139476471,0.1447071135,0.4299742877,0.1122180298,0.1420535445,-0.4234775007,0.40492028,-0.0649733096,-0.4167886674,0.2232038677,0.0242804252,-0.0845945701,0.0307878014,-0.1111186743,0.0374431051,-0.362168014,0.0427725129,0.1907773912,0.2233323306,0.1083918139,-0.007075632,-0.0144288614,0.2055198848,0.3901855052,-0.1245257705,-0.6085830927,0.4509720504,0.0148973865,-0.2213317752,-0.0627818927,-0.3001177609,0.0889879838,0.3230374753,-0.4250459373,-0.2102548927,-0.4416136742,0.4154333472,0.1584668458,0.1317279041,0.2263734043,0.1449032128,-0.1119664088,-0.1887500137,-0.2462440282,0.1474718899,-0.0600471534,0.3447145224,-0.1012158394,0.3811489046,-0.0756097361,0.4806392491,0.4218044579,0.1028437465,0.1447444856,0.0500761718,0.3370552957,-0.0827149898,-0.2198395431,0.0882623494,-0.075826346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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3904","title":"CONLL2003 Dataset not available","comments":"Thanks for reporting, @omarespejel.\r\n\r\nI'm sorry but I can't reproduce the issue: the loading of the dataset works perfecto for me and I can reach the data URL: https:\/\/data.deepai.org\/conll2003.zip\r\n\r\nMight it be due to a temporary problem in the data owner site (https:\/\/data.deepai.org\/) that is fixed now?\r\nCould you please try loading the dataset again and tell if the problem persists?","body":"## Describe the bug\r\n[CONLL2003](https:\/\/huggingface.co\/datasets\/conll2003) Dataset can no longer reach 'https:\/\/data.deepai.org\/conll2003.zip' \r\n![image](https:\/\/user-images.githubusercontent.com\/4755430\/158084483-ff83631c-5154-4823-892d-577bf1166db0.png)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndatasets = load_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nDownload the conll2003 dataset.\r\n\r\n## Actual results\r\nError: `ConnectionError: Couldn't reach https:\/\/data.deepai.org\/conll2003.zip (error 502)`\r\n","comment_length":61,"text":"CONLL2003 Dataset not available \n ## Describe the bug\r\n[CONLL2003](https:\/\/huggingface.co\/datasets\/conll2003) Dataset can no longer reach 'https:\/\/data.deepai.org\/conll2003.zip' \r\n![image](https:\/\/user-images.githubusercontent.com\/4755430\/158084483-ff83631c-5154-4823-892d-577bf1166db0.png)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndatasets = load_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nDownload the conll2003 dataset.\r\n\r\n## Actual results\r\nError: `ConnectionError: Couldn't reach https:\/\/data.deepai.org\/conll2003.zip (error 502)`\r\n \n Thanks for reporting, @omarespejel.\r\n\r\nI'm sorry but I can't reproduce the issue: the loading of the dataset works perfecto for me and I can reach the data URL: https:\/\/data.deepai.org\/conll2003.zip\r\n\r\nMight it be due to a temporary problem in the data owner site (https:\/\/data.deepai.org\/) that is fixed now?\r\nCould you please try loading the dataset again and tell if the problem persists?","embeddings":[-0.1911519617,0.0150686419,-0.0434647761,0.3382746875,0.1922972351,0.0039142501,0.2465220392,0.1154264212,-0.3758265078,0.1485977918,-0.0861061588,0.2452331036,0.4535978734,0.1852294654,-0.0381422117,-0.0447144099,0.0035966616,-0.012561569,-0.2133067399,0.060949713,-0.3082910776,0.1839654446,-0.324549377,-0.1091958806,-0.2825883031,0.1720990986,-0.0765397847,0.0668988675,-0.2217160612,-0.3594929874,0.6459475756,-0.1387488842,0.219104737,0.2534444034,-0.0001201601,0.1459243149,0.4636394978,-0.0202063359,-0.3350785673,-0.168982178,-0.5470793247,-0.3967566192,0.0409624055,-0.0647044778,-0.1748001575,0.2619145215,-0.1763678342,-0.302372992,0.2665678561,0.289865315,0.1705025733,0.4098833203,0.5369910598,-0.2640505135,0.2956158519,-0.4764294624,-0.1412767023,0.4444654882,0.1286043525,-0.1353069693,0.248010233,0.2859277427,0.1273665577,0.0120104263,-0.0046514124,0.0935135782,0.0199152231,-0.4266191721,0.0238515809,0.2839429975,0.6837103963,-0.0689505339,-0.5916917324,-0.1994310915,0.1127128005,-0.3133717775,0.3646003306,0.0124573307,-0.0910700485,0.2242885828,-0.0623500533,-0.1471731961,-0.1829299182,0.3587570488,-0.4926331639,0.4509404004,-0.1948852688,0.1381318271,-0.1549960375,-0.0961887166,0.4506497085,-0.0444488116,0.0477730781,0.1217945591,-0.4915871322,-0.0423258841,-0.1596913636,-0.0642005801,0.1879174262,0.1100337952,0.0488826297,-0.2731248438,-0.3385083973,0.0699989051,0.3205313981,-0.0032910826,-0.2013463974,0.3205688298,0.3586683869,0.2306410521,0.2811121643,-0.1395232826,-0.1319957227,-0.2044190764,-0.1598122716,0.1339294761,0.3498336077,-0.1771916449,-0.434140563,0.1273281425,-0.3241240978,-0.0766066611,-0.0758738369,0.2905044258,-0.2582842112,0.2126733959,0.1026142314,0.346885711,0.1174509227,-0.4192104638,-0.0438922048,-0.2295739204,-0.2155321687,-0.2688173652,0.0998635665,-0.2314803302,0.2051389813,-0.0196311269,-0.0973445699,-0.1232250854,-0.0330594741,-0.0409924127,-0.1482222229,0.1922344416,0.1004592851,0.0327275731,0.0971897915,0.0370222852,-0.0720090345,-0.0910898224,-0.3655590713,-0.135817498,-0.2648261786,0.0936969444,-0.0976727009,-0.1589882076,0.2533978522,-0.2123457938,0.020080803,-0.1046254113,-0.0279646888,-0.0463965423,-0.4827394485,-0.1759018749,0.3223654032,0.5480448008,-0.4749995172,-0.0362136252,-0.2915672064,-0.1059942245,0.0228661112,0.0332908779,-0.1720677316,0.0641270801,-0.4246785641,-0.2673769891,0.2539885044,-0.2158707976,-0.7834096551,0.3035688996,-0.2715330124,0.0399267301,-0.0451506861,-0.0306146648,0.0871112123,0.0591291673,0.3282382488,0.2745511532,0.1104793176,-0.1492652595,-0.1058249623,-0.2318632901,0.1638522595,0.0751705989,0.1366481036,0.2822562456,0.2140789032,-0.3216875494,0.3102396727,0.3172035217,0.1883398592,0.413141042,0.3903146982,0.1772895008,0.0249574743,0.0406509452,-0.3179346323,0.3623957634,-0.0240511373,0.026844671,-0.2266298532,0.1080952734,-0.4873018861,0.2320072651,-0.1956994832,0.0249787029,0.0417407304,0.1290781349,0.168920964,0.2547797859,-0.1289102584,0.4383784235,0.1867353469,0.1398229301,-0.4223473668,0.444260627,-0.0199257024,0.0001935926,0.1931785345,0.1566052139,0.2701599896,-0.0926455706,-0.1876108646,0.238639608,-0.0569416881,-0.0246095154,0.0934091732,0.1001346186,0.2624247372,-0.4059075117,0.1694059819,0.1568616331,0.0502152257,0.1710636467,0.1034444645,0.3641469479,0.2523148954,0.2222815752,-0.2074199617,0.3584577143,0.463054955,-0.2100827247,-0.0087658009,-0.1599047035,0.0558334403,0.1697687507,0.4409285486,-0.1146018058,-0.1469866931,0.0947866142,-0.0195964985,-0.1133601218,-0.0264153089,0.309071064,-0.1818431765,0.0792043433,0.0371022709,0.5061418414,0.3599759936,0.0418610983,0.0449689291,-0.0392539464,-0.0350655429,-0.1829446852,0.2657367587,0.0688483939,0.3184829354,0.2294777632,0.2030521333,0.1862416714,0.0453627594,-0.2390327603,0.042609144,0.0715352148,-0.2365992367,-0.0199941639,-0.2338448316,-0.4098167121,-0.0456623323,0.028428467,-0.3025399745,-0.2874476314,-0.2033434212,0.0506742857,0.0158919599,0.0153628075,-0.4120530188,0.1092397422,0.1572520882,0.1207511202,-0.0678470284,-0.0636951923,-0.0878445134,0.0082009286,0.0945231691,-0.5000411272,0.1805097312,-0.1945452988,0.007902178,-0.4518945813,-0.0643009916,0.3004820645,0.0559651852,0.3807739019,0.2168625742,0.2670297921,0.1977688968,0.1379681677,0.340552032,0.153391555,-0.1198728159,0.1192778051,0.0086197648,0.0763531774,0.2641281486,-0.0704363734,-0.3452371657,-0.2517300844,0.0568007417,-0.2325163186,0.0757075995,-0.0163783543,0.0946912095,-0.0341459773,-0.0891999155,0.0468294099,-0.2320617288,-0.5243464112,0.3111769557,-0.14749448,-0.3804163635,0.0246470757,-0.0843499154,0.2365965694,-0.0833291635,-0.5951570272,0.0443731472,-0.3468745351,0.4830993712,-0.1941372454,0.0033993672,0.1338635832,-0.1919364333,-0.0108312741,-0.1178724691,-0.3783442974,-0.2068364322,0.0041141603,0.5643001795,0.3332813978,0.3320019543,-0.026735656,0.2641961277,0.386924237,0.0395044461,0.5707175732,-0.0498799607,0.2450758219,-0.0015232161,-0.3920422792,-0.2506346405,-0.118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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3902","title":"Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils'","comments":"Update: `\"python3 -c \"from from datasets import Dataset, DatasetDict\"` works, but not if I import without the `python3 -c`","body":"## Describe the bug\r\nUnable to import datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset, DatasetDict\r\n```\r\n\r\n## Expected results\r\nThe import works without errors\r\n\r\n## Actual results\r\n```\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-37-c8cfcbe62127> in <module>\r\n     11 # from tqdm import tqdm\r\n     12 # import torch\r\n---> 13 from datasets import Dataset\r\n     14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling\r\n     15 # from sentence_transformers import SentenceTransformer\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     46 )\r\n     47 \r\n---> 48 import fsspec\r\n     49 import numpy as np\r\n     50 import pandas as pd\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/__init__.py in <module>\r\n     10 from . import _version, caching\r\n     11 from .callbacks import Callback\r\n---> 12 from .core import get_fs_token_paths, open, open_files, open_local\r\n     13 from .exceptions import FSTimeoutError\r\n     14 from .mapping import FSMap, get_mapper\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/core.py in <module>\r\n     16     caches,\r\n     17 )\r\n---> 18 from .compression import compr\r\n     19 from .registry import filesystem, get_filesystem_class\r\n     20 from .utils import (\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in <module>\r\n     68 \r\n     69 \r\n---> 70 register_compression(\"zip\", unzip, \"zip\")\r\n     71 register_compression(\"bz2\", BZ2File, \"bz2\")\r\n     72 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in register_compression(name, callback, extensions, force)\r\n     44 \r\n     45     for ext in extensions:\r\n---> 46         if ext in fsspec.utils.compressions and not force:\r\n     47             raise ValueError(\r\n     48                 \"Duplicate compression file extension: %s (%s)\" % (ext, name)\r\n\r\nAttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4\r\n- Platform: Jupyter notebook\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n","comment_length":19,"text":"Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils' \n ## Describe the bug\r\nUnable to import datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset, DatasetDict\r\n```\r\n\r\n## Expected results\r\nThe import works without errors\r\n\r\n## Actual results\r\n```\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-37-c8cfcbe62127> in <module>\r\n     11 # from tqdm import tqdm\r\n     12 # import torch\r\n---> 13 from datasets import Dataset\r\n     14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling\r\n     15 # from sentence_transformers import SentenceTransformer\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     46 )\r\n     47 \r\n---> 48 import fsspec\r\n     49 import numpy as np\r\n     50 import pandas as pd\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/__init__.py in <module>\r\n     10 from . import _version, caching\r\n     11 from .callbacks import Callback\r\n---> 12 from .core import get_fs_token_paths, open, open_files, open_local\r\n     13 from .exceptions import FSTimeoutError\r\n     14 from .mapping import FSMap, get_mapper\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/core.py in <module>\r\n     16     caches,\r\n     17 )\r\n---> 18 from .compression import compr\r\n     19 from .registry import filesystem, get_filesystem_class\r\n     20 from .utils import (\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in <module>\r\n     68 \r\n     69 \r\n---> 70 register_compression(\"zip\", unzip, \"zip\")\r\n     71 register_compression(\"bz2\", BZ2File, \"bz2\")\r\n     72 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in register_compression(name, callback, extensions, force)\r\n     44 \r\n     45     for ext in extensions:\r\n---> 46         if ext in fsspec.utils.compressions and not force:\r\n     47             raise ValueError(\r\n     48                 \"Duplicate compression file extension: %s (%s)\" % (ext, name)\r\n\r\nAttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4\r\n- Platform: Jupyter notebook\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n \n Update: `\"python3 -c \"from from datasets import Dataset, DatasetDict\"` works, but not if I import without the `python3 -c`","embeddings":[-0.3171516955,-0.0977633372,-0.060437385,0.250816226,0.5410323739,0.0515428744,0.2306825817,0.1784132868,0.1451839954,-0.0406591669,-0.0914043784,0.2070271969,-0.0849940628,0.0422019102,-0.0481128022,0.0800790861,0.0392681174,0.1573754847,-0.3625039756,-0.1490760148,-0.0667247623,0.1086276919,-0.2605908811,0.0229992531,-0.4667166173,-0.0420558229,0.0897894949,0.2693987489,-0.3021417558,-0.675156951,0.2488134503,-0.2023636401,0.1428641677,0.3091623187,-0.0001127279,0.0719646066,0.3168117702,-0.0403252691,-0.3669926524,0.0579778142,-0.1587973535,-0.2611021101,0.2014765292,-0.1858032495,0.0890876353,-0.2615580857,-0.0732190013,-0.5562984943,0.1979622245,0.6020060778,0.2539667785,0.2535479665,0.3767183721,-0.065631941,-0.0458863266,-0.0652860999,-0.0695680603,-0.0419695526,0.1539292783,-0.1513932049,0.1441338509,-0.0441076644,-0.0111272512,0.2427477539,0.1799446791,-0.1848336905,0.1125411466,-0.430542022,-0.0043802359,0.0089009423,0.4734683037,-0.2616320848,-0.4875103533,-0.0716140419,0.1318767816,-0.0342166126,0.077444233,0.0017766798,0.050103087,0.168234542,0.1930772066,-0.0962269083,-0.119060196,0.2657083273,-0.1976042986,0.0411211923,-0.2480921447,-0.0328236781,0.0241297632,-0.2687961459,0.1609052271,-0.1838238239,-0.0366592705,0.282142669,-0.223079294,0.0432266779,0.4131987393,-0.3409279883,-0.1540693045,0.3015023172,-0.2992433906,-0.006161103,-0.0515987016,0.2656780481,0.0537836663,0.0720264763,0.1819226891,0.4663425386,0.0702350736,0.0044090147,-0.1564638913,-0.0405409075,-0.1521216035,-0.2603019178,0.0962028652,0.1447868347,0.3706581593,-0.2103715986,-0.2649964988,0.1851914823,0.0192854386,-0.0430234559,-0.1079287753,0.2417486012,-0.0532334819,0.4904887378,0.0674702302,0.1611715108,-0.1824313402,-0.2234943956,-0.2142259777,0.1783576161,-0.1630671024,-0.2605764568,0.1102810279,-0.0998411328,0.1528036296,-0.0829320922,-0.0496405587,-0.2125434577,0.1854949147,-0.3258240819,0.1065762937,0.287884742,-0.031679906,0.3496369123,0.3111235201,-0.2670789063,-0.0803107694,-0.058453545,-0.3358172774,-0.2745758295,-0.2645739019,0.2715825737,-0.0893806666,-0.038603019,-0.2773093879,-0.1382635534,-0.0018734714,0.0657273307,-0.0957612842,-0.2059314698,0.0100183561,-0.1157741472,0.2959854007,0.4907724857,-0.2895367444,-0.1573784351,-0.1349968463,-0.1530815363,0.1031776145,0.2147765607,-0.1410847604,0.3906895816,-0.2649224102,0.094534725,0.4844045937,-0.3106129467,-0.3114543259,0.2260969579,0.2197265476,-0.0654390901,-0.0301990528,-0.227159813,0.1230243668,0.1487568021,0.2201774269,0.3329994678,0.0629456043,-0.1071567386,-0.1610435247,-0.2767029107,0.1084143147,0.1161678061,0.2522843778,0.0129441619,0.0879163742,-0.0649169013,0.0552031286,-0.053736221,-0.03042024,0.5308898091,0.2894537747,0.1546925306,-0.1256311983,-0.5731550455,-0.2998693883,0.3153097034,0.1423248351,-0.0670407712,-0.1783957034,0.1294166595,-0.2853480577,0.1269601136,-0.2079899758,-0.2001310587,0.1225437373,0.0760746673,-0.1545485109,0.101472877,-0.3925878108,0.0076310658,-0.0690085217,0.1811647564,-0.2581490278,0.3928671777,-0.303062439,-0.1876596361,0.0431398377,0.1343559176,0.1268025488,-0.1018529162,-0.2180567235,0.1174706519,0.3525497317,0.0994520634,-0.1108864024,0.1862249225,0.0937124044,-0.4421737194,-0.2014679909,0.4330585301,0.3560416996,-0.0344208218,-0.0025963043,0.0697676837,0.0524709076,0.159350425,0.1339353919,0.1726386249,0.2863733172,-0.0415172316,0.0041685109,-0.2934674323,0.3124761581,0.1163412184,0.2349733561,0.1087321341,-0.0224026889,-0.1354225427,0.1906784773,0.0591118932,0.0022931141,-0.0219512526,-0.3193933964,0.1661985815,0.2359825373,-0.0235642213,0.2860987782,0.1934305131,-0.1650980115,0.0464064926,0.0310792569,0.0036386051,0.2746046782,0.1606133878,-0.0208504796,0.2699983418,0.1434809268,0.2218672186,-0.1548241675,-0.3139861524,0.0116627924,0.3350784481,-0.6247816086,0.1250724345,-0.4319001138,-0.0962775871,-0.0814968646,-0.0048133433,0.1706407368,-0.2390512377,-0.3558322489,0.1845762581,-0.094914794,0.1931576878,-0.4526357055,-0.0501413718,0.0987260789,-0.1024632454,-0.0976242647,-0.0642575473,-0.0227902681,0.034170378,0.3636759222,0.1634949893,0.2452367991,0.03134121,0.0445542559,-0.1876611561,-0.2511563003,0.0158457235,0.2032201886,0.4261603057,0.1684318185,0.0959275439,0.2268284708,-0.1676134914,0.3005023897,0.0435412563,-0.1498238891,0.2119083256,-0.0704846606,-0.4356117845,-0.1026322171,-0.3086396456,-0.2543665171,-0.4043404758,-0.0180261526,0.2428501248,0.1552812755,0.2330589145,0.1010307074,0.1663682461,0.0441799425,0.1133504808,-0.0219150428,-0.1602955759,0.534722507,-0.1797955632,-0.4439277351,-0.0138777513,-0.2253858745,-0.0495800935,-0.004483196,-0.3404408693,-0.0417782217,-0.4150960445,0.436935991,-0.2352888584,0.200793162,0.2394403815,0.2682829797,-0.067236647,-0.1845626235,-0.0668199509,-0.2253563851,0.1414604932,-0.1335570216,-0.2373022884,0.2180665582,-0.0464896448,0.6331483126,0.3170133531,-0.0150260264,0.259219557,-0.1955890656,0.1963515133,-0.157507062,-0.6132749319,0.0977250859,-0.12901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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3902","title":"Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils'","comments":"Hi @arunasank, thanks for reporting.\r\n\r\nIt seems that this can be caused because you are using an old version of `fsspec`: the reason why it works if you run `python3` seems to be that `python3` runs in a Python virtual env (with an updated version of `fsspec`); whereas the error arises when you run the import from other Python virtual env (with an old version of `fsspec`).\r\n\r\nIn order to fix this, you should update `fsspec` from within the \"problematic\" Python virtual env:\r\n```\r\npip install -U \"fsspec[http]>=2021.05.0\"","body":"## Describe the bug\r\nUnable to import datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset, DatasetDict\r\n```\r\n\r\n## Expected results\r\nThe import works without errors\r\n\r\n## Actual results\r\n```\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-37-c8cfcbe62127> in <module>\r\n     11 # from tqdm import tqdm\r\n     12 # import torch\r\n---> 13 from datasets import Dataset\r\n     14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling\r\n     15 # from sentence_transformers import SentenceTransformer\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     46 )\r\n     47 \r\n---> 48 import fsspec\r\n     49 import numpy as np\r\n     50 import pandas as pd\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/__init__.py in <module>\r\n     10 from . import _version, caching\r\n     11 from .callbacks import Callback\r\n---> 12 from .core import get_fs_token_paths, open, open_files, open_local\r\n     13 from .exceptions import FSTimeoutError\r\n     14 from .mapping import FSMap, get_mapper\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/core.py in <module>\r\n     16     caches,\r\n     17 )\r\n---> 18 from .compression import compr\r\n     19 from .registry import filesystem, get_filesystem_class\r\n     20 from .utils import (\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in <module>\r\n     68 \r\n     69 \r\n---> 70 register_compression(\"zip\", unzip, \"zip\")\r\n     71 register_compression(\"bz2\", BZ2File, \"bz2\")\r\n     72 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in register_compression(name, callback, extensions, force)\r\n     44 \r\n     45     for ext in extensions:\r\n---> 46         if ext in fsspec.utils.compressions and not force:\r\n     47             raise ValueError(\r\n     48                 \"Duplicate compression file extension: %s (%s)\" % (ext, name)\r\n\r\nAttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4\r\n- Platform: Jupyter notebook\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n","comment_length":88,"text":"Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils' \n ## Describe the bug\r\nUnable to import datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset, DatasetDict\r\n```\r\n\r\n## Expected results\r\nThe import works without errors\r\n\r\n## Actual results\r\n```\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-37-c8cfcbe62127> in <module>\r\n     11 # from tqdm import tqdm\r\n     12 # import torch\r\n---> 13 from datasets import Dataset\r\n     14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling\r\n     15 # from sentence_transformers import SentenceTransformer\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     46 )\r\n     47 \r\n---> 48 import fsspec\r\n     49 import numpy as np\r\n     50 import pandas as pd\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/__init__.py in <module>\r\n     10 from . import _version, caching\r\n     11 from .callbacks import Callback\r\n---> 12 from .core import get_fs_token_paths, open, open_files, open_local\r\n     13 from .exceptions import FSTimeoutError\r\n     14 from .mapping import FSMap, get_mapper\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/core.py in <module>\r\n     16     caches,\r\n     17 )\r\n---> 18 from .compression import compr\r\n     19 from .registry import filesystem, get_filesystem_class\r\n     20 from .utils import (\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in <module>\r\n     68 \r\n     69 \r\n---> 70 register_compression(\"zip\", unzip, \"zip\")\r\n     71 register_compression(\"bz2\", BZ2File, \"bz2\")\r\n     72 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/fsspec\/compression.py in register_compression(name, callback, extensions, force)\r\n     44 \r\n     45     for ext in extensions:\r\n---> 46         if ext in fsspec.utils.compressions and not force:\r\n     47             raise ValueError(\r\n     48                 \"Duplicate compression file extension: %s (%s)\" % (ext, name)\r\n\r\nAttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4\r\n- Platform: Jupyter notebook\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.0.0\r\n \n Hi @arunasank, thanks for reporting.\r\n\r\nIt seems that this can be caused because you are using an old version of `fsspec`: the reason why it works if you run `python3` seems to be that `python3` runs in a Python virtual env (with an updated version of `fsspec`); whereas the error arises when you run the import from other Python virtual env (with an old version of `fsspec`).\r\n\r\nIn order to fix this, you should update `fsspec` from within the \"problematic\" Python virtual env:\r\n```\r\npip install -U \"fsspec[http]>=2021.05.0\"","embeddings":[-0.3171516955,-0.0977633372,-0.060437385,0.250816226,0.5410323739,0.0515428744,0.2306825817,0.1784132868,0.1451839954,-0.0406591669,-0.0914043784,0.2070271969,-0.0849940628,0.0422019102,-0.0481128022,0.0800790861,0.0392681174,0.1573754847,-0.3625039756,-0.1490760148,-0.0667247623,0.1086276919,-0.2605908811,0.0229992531,-0.4667166173,-0.0420558229,0.0897894949,0.2693987489,-0.3021417558,-0.675156951,0.2488134503,-0.2023636401,0.1428641677,0.3091623187,-0.0001127279,0.0719646066,0.3168117702,-0.0403252691,-0.3669926524,0.0579778142,-0.1587973535,-0.2611021101,0.2014765292,-0.1858032495,0.0890876353,-0.2615580857,-0.0732190013,-0.5562984943,0.1979622245,0.6020060778,0.2539667785,0.2535479665,0.3767183721,-0.065631941,-0.0458863266,-0.0652860999,-0.0695680603,-0.0419695526,0.1539292783,-0.1513932049,0.1441338509,-0.0441076644,-0.0111272512,0.2427477539,0.1799446791,-0.1848336905,0.1125411466,-0.430542022,-0.0043802359,0.0089009423,0.4734683037,-0.2616320848,-0.4875103533,-0.0716140419,0.1318767816,-0.0342166126,0.077444233,0.0017766798,0.050103087,0.168234542,0.1930772066,-0.0962269083,-0.119060196,0.2657083273,-0.1976042986,0.0411211923,-0.2480921447,-0.0328236781,0.0241297632,-0.2687961459,0.1609052271,-0.1838238239,-0.0366592705,0.282142669,-0.223079294,0.0432266779,0.4131987393,-0.3409279883,-0.1540693045,0.3015023172,-0.2992433906,-0.006161103,-0.0515987016,0.2656780481,0.0537836663,0.0720264763,0.1819226891,0.4663425386,0.0702350736,0.0044090147,-0.1564638913,-0.0405409075,-0.1521216035,-0.2603019178,0.0962028652,0.1447868347,0.3706581593,-0.2103715986,-0.2649964988,0.1851914823,0.0192854386,-0.0430234559,-0.1079287753,0.2417486012,-0.0532334819,0.4904887378,0.0674702302,0.1611715108,-0.1824313402,-0.2234943956,-0.2142259777,0.1783576161,-0.1630671024,-0.2605764568,0.1102810279,-0.0998411328,0.1528036296,-0.0829320922,-0.0496405587,-0.2125434577,0.1854949147,-0.3258240819,0.1065762937,0.287884742,-0.031679906,0.3496369123,0.3111235201,-0.2670789063,-0.0803107694,-0.058453545,-0.3358172774,-0.2745758295,-0.2645739019,0.2715825737,-0.0893806666,-0.038603019,-0.2773093879,-0.1382635534,-0.0018734714,0.0657273307,-0.0957612842,-0.2059314698,0.0100183561,-0.1157741472,0.2959854007,0.4907724857,-0.2895367444,-0.1573784351,-0.1349968463,-0.1530815363,0.1031776145,0.2147765607,-0.1410847604,0.3906895816,-0.2649224102,0.094534725,0.4844045937,-0.3106129467,-0.3114543259,0.2260969579,0.2197265476,-0.0654390901,-0.0301990528,-0.227159813,0.1230243668,0.1487568021,0.2201774269,0.3329994678,0.0629456043,-0.1071567386,-0.1610435247,-0.2767029107,0.1084143147,0.1161678061,0.2522843778,0.0129441619,0.0879163742,-0.0649169013,0.0552031286,-0.053736221,-0.03042024,0.5308898091,0.2894537747,0.1546925306,-0.1256311983,-0.5731550455,-0.2998693883,0.3153097034,0.1423248351,-0.0670407712,-0.1783957034,0.1294166595,-0.2853480577,0.1269601136,-0.2079899758,-0.2001310587,0.1225437373,0.0760746673,-0.1545485109,0.101472877,-0.3925878108,0.0076310658,-0.0690085217,0.1811647564,-0.2581490278,0.3928671777,-0.303062439,-0.1876596361,0.0431398377,0.1343559176,0.1268025488,-0.1018529162,-0.2180567235,0.1174706519,0.3525497317,0.0994520634,-0.1108864024,0.1862249225,0.0937124044,-0.4421737194,-0.2014679909,0.4330585301,0.3560416996,-0.0344208218,-0.0025963043,0.0697676837,0.0524709076,0.159350425,0.1339353919,0.1726386249,0.2863733172,-0.0415172316,0.0041685109,-0.2934674323,0.3124761581,0.1163412184,0.2349733561,0.1087321341,-0.0224026889,-0.1354225427,0.1906784773,0.0591118932,0.0022931141,-0.0219512526,-0.3193933964,0.1661985815,0.2359825373,-0.0235642213,0.2860987782,0.1934305131,-0.1650980115,0.0464064926,0.0310792569,0.0036386051,0.2746046782,0.1606133878,-0.0208504796,0.2699983418,0.1434809268,0.2218672186,-0.1548241675,-0.3139861524,0.0116627924,0.3350784481,-0.6247816086,0.1250724345,-0.4319001138,-0.0962775871,-0.0814968646,-0.0048133433,0.1706407368,-0.2390512377,-0.3558322489,0.1845762581,-0.094914794,0.1931576878,-0.4526357055,-0.0501413718,0.0987260789,-0.1024632454,-0.0976242647,-0.0642575473,-0.0227902681,0.034170378,0.3636759222,0.1634949893,0.2452367991,0.03134121,0.0445542559,-0.1876611561,-0.2511563003,0.0158457235,0.2032201886,0.4261603057,0.1684318185,0.0959275439,0.2268284708,-0.1676134914,0.3005023897,0.0435412563,-0.1498238891,0.2119083256,-0.0704846606,-0.4356117845,-0.1026322171,-0.3086396456,-0.2543665171,-0.4043404758,-0.0180261526,0.2428501248,0.1552812755,0.2330589145,0.1010307074,0.1663682461,0.0441799425,0.1133504808,-0.0219150428,-0.1602955759,0.534722507,-0.1797955632,-0.4439277351,-0.0138777513,-0.2253858745,-0.0495800935,-0.004483196,-0.3404408693,-0.0417782217,-0.4150960445,0.436935991,-0.2352888584,0.200793162,0.2394403815,0.2682829797,-0.067236647,-0.1845626235,-0.0668199509,-0.2253563851,0.1414604932,-0.1335570216,-0.2373022884,0.2180665582,-0.0464896448,0.6331483126,0.3170133531,-0.0150260264,0.259219557,-0.1955890656,0.1963515133,-0.157507062,-0.61327493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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3896","title":"Missing google file for `multi_news` dataset","comments":"`datasets` 1.18.4 fixes the issue when you load the dataset with `load_dataset`.\r\n\r\nWhen loading in streaming mode, the fix is indeed on https:\/\/github.com\/huggingface\/datasets\/pull\/3843 which will be merged soon :)","body":"## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n","comment_length":29,"text":"Missing google file for `multi_news` dataset \n ## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n \n `datasets` 1.18.4 fixes the issue when you load the dataset with `load_dataset`.\r\n\r\nWhen loading in streaming mode, the fix is indeed on https:\/\/github.com\/huggingface\/datasets\/pull\/3843 which will be merged soon :)","embeddings":[-0.2699741423,-0.0458398983,0.0497320034,0.1420214474,0.1276449263,0.2481628209,0.1934226304,0.2745967507,-0.0215559751,0.1579294354,0.0816370994,0.0792289525,-0.3635448813,0.3349537253,0.1228106022,-0.2137125283,0.1218471229,0.0056760493,0.1303979754,-0.0129607301,-0.2909452319,0.48308146,-0.1952868551,-0.3958175778,-0.496948868,0.0503465161,-0.1637780666,0.2913736701,-0.0822806433,-0.2260227054,-0.0992225558,0.0670764968,0.0633657798,0.5561423302,-0.0001087802,0.0966095775,0.1969502866,-0.016621517,-0.3042957485,-0.3295350671,-0.1191709414,-0.2145788819,0.084044531,0.0817018673,-0.2094341069,-0.1526982188,0.1739887893,-0.1258497387,0.1938049197,0.394449681,0.2137354016,0.1765259504,0.3621146679,-0.2409399301,0.1189704835,-0.1591909379,-0.0464449413,0.3128052652,-0.0731825233,0.1206492931,0.0118322605,0.6624206305,0.2969291508,-0.000695997,-0.1326517463,-0.0852346122,-0.2786018252,-0.2077054977,0.0824050009,0.3656952679,0.3241903484,0.0247237366,-0.2975257933,-0.1155707836,0.2260704637,0.0152590424,0.3099583685,0.2659318149,0.0102179609,0.2642276287,-0.2617853582,-0.4051489532,-0.0453688875,0.2854012251,-0.1519396752,0.4173752666,-0.0524607711,0.0434461497,0.1914957315,0.0440556481,-0.075037472,-0.0538571253,0.0096026203,-0.0354847834,-0.2180486023,0.0762828365,0.2372857779,-0.00741249,-0.0502682477,-0.0781644434,-0.2847441137,0.1482701302,-0.1795339882,0.1913747489,0.2886131406,-0.2734928131,-0.1140359789,0.2005846798,0.4426936209,0.3836375177,0.115444392,-0.0423417725,-0.0230799168,-0.185061723,-0.3568231165,-0.0876476988,0.3284963369,0.0430886596,-0.307120949,0.0704470798,-0.228169173,-0.0192242675,0.0640724301,0.2267341912,-0.0405922197,0.2543423176,0.0168917738,0.1298942417,-0.1590398997,-0.2027335912,-0.2411938906,0.0526433848,-0.0522894561,0.2572022974,0.252687186,-0.1416992098,0.2949465811,0.0210409872,0.1047536731,-0.1562040746,-0.0129451128,-0.0339324214,-0.262568295,0.4257769287,0.2072321326,0.1498874873,0.1915684342,-0.334259212,0.0919592604,0.0238727443,-0.2023177296,-0.5349404812,-0.3775516748,0.2433692962,-0.1930313706,0.0006407563,-0.0854162499,0.4926950932,-0.12587668,-0.200593859,-0.0741360411,0.1414724588,-0.3251979053,-0.036005877,0.2284482419,0.4770258963,-0.3275356889,-0.288528502,-0.1856211275,-0.1960795075,-0.0943301246,0.1733857989,-0.2498108149,0.087435469,-0.2971253693,-0.0471971892,0.2947886288,-0.0817531496,-0.2705627978,0.0774132162,-0.2476383746,0.1613091975,0.01842626,0.0087585505,-0.216787383,0.078478083,0.2718446851,0.1136029661,-0.0428930111,0.2028947026,-0.2328076214,-0.0976986587,-0.1189284697,0.2209886163,0.3705191016,-0.0847434849,0.305552274,-0.2494810969,0.2203667015,-0.0211676788,0.1663018465,0.450481385,0.4090561569,0.0482665561,0.0851075649,-0.0588616468,-0.6630219221,0.1824907511,0.0604587458,0.0548116565,-0.126583159,-0.2398706079,-0.2038579285,-0.2788566947,-0.3247821927,-0.2542050779,0.1308142096,0.0600290522,-0.2686506212,0.3854193091,-0.2169710845,0.1346040666,-0.0447409824,0.1121090055,-0.3367466033,0.2658316493,-0.249942109,0.0463997647,0.1564615965,0.0071089636,0.1120526195,-0.1856684536,-0.1147738546,0.5158991218,-0.0164389163,0.3071859777,0.407009989,0.0258425977,0.2801153362,-0.5864533186,0.0014757786,0.1969536096,-0.0329486392,-0.205010429,-0.5306293368,0.2777294815,0.0797254518,-0.0518693179,0.1031294242,0.3067663014,0.2608005702,-0.049142234,0.1582873166,-0.2414274365,0.1183762103,-0.0965613499,0.2608337998,-0.1759958267,-0.4331058562,0.1659525335,0.3494383097,0.0825655535,-0.0270134993,0.1943137944,-0.3193078339,-0.2418661565,0.0007051289,0.3982298672,0.3179396689,0.1797162592,0.1445576847,0.2854647934,0.2524555922,-0.3376318216,0.1855915338,-0.0335980132,0.3748424053,0.3180784881,0.07777749,0.0029972373,-0.4142679274,0.021108631,0.3318341076,0.1454630792,-0.3140525222,-0.0600810423,-0.2276221067,-0.2317850292,-0.2043213099,-0.0440888889,-0.4008327127,-0.3647614121,0.1441528052,0.2245456427,-0.1837060004,0.120805338,0.0933704525,0.1264437884,0.0041781315,0.126467526,-0.0736444592,-0.1099016219,-0.1484054476,0.1324982345,0.2351185679,-0.0280551221,0.1272785664,-0.2422489226,0.0189073365,-0.6473357081,-0.4950321913,0.1596491188,-0.1561375111,0.1320045739,0.1008690074,0.2379401177,-0.2871607542,-0.0246117581,0.2057286203,-0.1421523988,0.1015132293,0.0854821652,-0.0850513503,-0.0052082478,-0.0323273689,-0.4535266161,-0.2287196219,-0.3282014132,0.3167484105,-0.2446499765,0.0305211022,0.163333267,0.2659239471,0.1522564143,-0.0264348071,-0.1401323676,-0.2855236828,-0.2316874415,0.3149048686,-0.4753130972,-0.4785799384,0.2872186005,0.1915644109,-0.04286246,-0.0393915437,-0.5842483044,-0.1779858917,0.0073701399,-0.0607534647,0.0883076414,-0.0733661056,0.3424158394,-0.0894880444,-0.2021225542,-0.1193570644,-0.12852557,-0.1309719831,-0.1196522489,0.2961347103,-0.2446364015,0.2517604828,0.1309168786,0.8454495072,0.3763314188,0.1880951226,0.261441499,-0.076486133,0.5798196197,-0.0244065989,-0.1470611393,0.215679124,-0.0214543249,0.1336998194,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3896","title":"Missing google file for `multi_news` dataset","comments":"That is. The PR #3843 was just opened a bit later we had made our 1.18.4 patch release...\r\nOnce merged, that will fix this issue. ","body":"## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n","comment_length":25,"text":"Missing google file for `multi_news` dataset \n ## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n \n That is. The PR #3843 was just opened a bit later we had made our 1.18.4 patch release...\r\nOnce merged, that will fix this issue. ","embeddings":[-0.1872241795,-0.0048763808,0.0356897488,0.1828449368,0.0608461536,0.2791556716,0.2332007289,0.2045081854,-0.0366968475,0.1745186746,0.1645779461,0.0191534571,-0.3617120683,0.2578106821,0.207847625,-0.2329189479,0.1163955182,0.0118160276,0.2681148648,-0.0911784619,-0.3354702294,0.4920604229,-0.1394957751,-0.3474695683,-0.5592490435,0.0674447715,-0.2242628634,0.3208023012,-0.0488988273,-0.1438560635,-0.0915783942,0.0178269632,0.0637671649,0.5182000399,-0.0001100226,0.074409008,0.1599947661,-0.0728012174,-0.2432727069,-0.2922160327,-0.1795358956,-0.3841654658,-0.0315892212,0.063395001,-0.2299931049,-0.1248099506,0.2355206609,-0.038747061,0.0512416661,0.4330635071,0.2115032375,0.0479155108,0.4874853194,-0.2200936675,0.1155769229,-0.0778043941,-0.0510022566,0.2552134395,-0.0817326903,0.0770745724,0.1039974764,0.5462773442,0.304638803,-0.069516167,-0.1316421628,-0.0342438184,-0.1518393904,-0.2416981161,0.0863400176,0.3786845505,0.4296703637,0.1074757054,-0.2502039969,-0.0039475188,0.2750445604,0.0589222386,0.2631620169,0.369949609,-0.0290592313,0.2897690535,-0.1895841956,-0.4777516425,-0.0114069805,0.270226568,-0.1848279983,0.3734059632,0.0105295936,0.0573793575,0.0669681132,0.1050211936,-0.0994315892,-0.0402788147,-0.0326130688,0.0023558005,-0.1493077129,0.0730430335,0.2040159404,-0.0571354255,-0.0232475121,-0.0774524584,-0.3519572318,0.0225669071,-0.2316220999,0.210776329,0.3199984729,-0.2636631429,-0.1860022694,0.2886658907,0.4071512818,0.2384560555,0.1201669648,-0.0258683357,0.0091443025,-0.1525197774,-0.2817749083,0.041808553,0.2965108454,0.0076807057,-0.3023352325,0.0403850265,-0.2887432575,-0.060490571,-0.0300551429,0.1145807728,0.0022582794,0.2750249803,0.0942159444,0.0936123654,-0.1307660192,-0.2969667017,-0.22418046,0.1287459135,-0.0034952294,0.3261290789,0.2007310838,-0.0544315614,0.2158967108,-0.0277904626,0.0395586938,-0.2227405012,-0.0068461746,-0.013064188,-0.2587530315,0.4442702532,0.1846516877,0.1227163374,0.1658952683,-0.3463698626,0.0950521082,0.1327161938,-0.2355234921,-0.5962260962,-0.4397486746,0.2294386923,-0.1776950806,-0.0629325584,-0.0591413267,0.5101066232,-0.0654145926,-0.1904257387,-0.0086430609,0.1976844817,-0.3526614606,-0.060718786,0.1974289715,0.4896964729,-0.3454836011,-0.239469856,-0.3427885473,-0.2256973088,-0.0521435179,0.1951577067,-0.2483709157,0.2457916141,-0.3264862597,-0.1361158043,0.3167674243,-0.0719663352,-0.2423766106,-0.0625893921,-0.3118367791,0.0988035947,0.0554499999,0.0529271103,-0.1650902927,0.0587345511,0.2711957395,0.1168916374,-0.043060936,0.18805857,-0.2034879625,-0.0303610265,-0.0532486178,0.2304324508,0.4236678779,-0.070785284,0.3201605976,-0.3414313793,0.1870195866,-0.0118247699,0.1476411074,0.5195218921,0.4749770463,0.0739347264,0.1048127338,0.0054829069,-0.6059769988,0.1749014854,0.0899524763,0.0592468306,-0.0415676869,-0.1964131892,-0.2248484939,-0.3244538605,-0.3066115379,-0.2828349471,0.1422520131,0.0473343283,-0.3329624236,0.4180704653,-0.2121299207,0.1743275523,-0.0623030812,0.0349708423,-0.2749991417,0.2314402908,-0.2827828825,0.1294605136,0.1165911406,0.0248131957,0.1221664846,-0.149293676,-0.1036788672,0.4013264179,0.0372594967,0.1733629704,0.4822082818,-0.0054554814,0.2585506737,-0.7087469697,0.000439112,0.1740692854,-0.0236910954,-0.2458590418,-0.4859758317,0.2288686037,0.1894017756,-0.1759803593,-0.0261050593,0.3318282366,0.299470365,-0.1140444279,0.2522148788,-0.1744798422,0.0828067884,0.0041168341,0.2477096468,-0.1705700308,-0.3176648021,0.1946546733,0.416051507,-0.0094270343,0.0156620424,0.1871991903,-0.2291244417,-0.189079836,-0.0619778521,0.3554429412,0.3300711513,0.178344205,0.0890296698,0.232290417,0.2851096392,-0.2795419991,0.1586310714,0.0169706363,0.3599017859,0.2976170778,0.1143517196,0.0328885913,-0.3657296002,0.0612581894,0.403627038,0.218786329,-0.2972443998,-0.1190037131,-0.1616990417,-0.1548976451,-0.1404837966,0.0115751745,-0.3980472982,-0.3729676902,0.1576937288,0.0497477651,-0.0669739172,0.0677443445,0.1581225842,0.1308144331,-0.0415641703,0.1317182034,-0.0787145644,-0.0941478834,-0.1540471911,0.1254366785,0.2540756166,-0.0228356812,0.1249768808,-0.2394530028,0.068253018,-0.7043079138,-0.4395392239,0.092502594,-0.1899747998,0.0506546423,0.0765543133,0.2621290386,-0.2664461732,-0.0138535192,0.1472315043,-0.1100525931,-0.0209475141,0.0729540512,-0.1512774378,-0.0302422456,0.0135869337,-0.4395048022,-0.3328358829,-0.2127956301,0.3313548565,-0.1791113466,0.048629526,0.021221675,0.2010138184,0.1185043752,-0.0325611643,-0.188214004,-0.2785399556,-0.1579571962,0.2916122973,-0.4021892548,-0.398614198,0.3772142828,0.1471601427,-0.1747263223,-0.1544238776,-0.6030054688,-0.1053523868,0.0169254225,0.0033853636,0.0231465232,-0.0946335271,0.4681657851,-0.1171739846,-0.1710402817,-0.0588588193,-0.178869307,-0.1401715875,0.0170954093,0.4539295733,-0.275329411,0.2231184393,0.0799263865,0.9152948856,0.3518288434,0.1884813458,0.2041664273,-0.1072579995,0.5518421531,-0.0792363063,-0.1201916188,0.2751152217,-0.0374763682,0.1066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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3896","title":"Missing google file for `multi_news` dataset","comments":"OK. Should fix the viewer for 50 datasets\r\n\r\n<img width=\"148\" alt=\"Capture d\u2019e\u0301cran 2022-03-14 a\u0300 11 51 02\" src=\"https:\/\/user-images.githubusercontent.com\/1676121\/158157853-6c544a47-2d6d-4ac4-964a-6f10951ec36b.png\">\r\n","body":"## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n","comment_length":18,"text":"Missing google file for `multi_news` dataset \n ## Dataset viewer issue for '*multi_news*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/multi_news\r\n\r\n```\r\nServer error\r\n\r\nStatus code:   400\r\nException:     FileNotFoundError\r\nMessage:       https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C\/multi-news-original\/train.src\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n \n OK. Should fix the viewer for 50 datasets\r\n\r\n<img width=\"148\" alt=\"Capture d\u2019e\u0301cran 2022-03-14 a\u0300 11 51 02\" src=\"https:\/\/user-images.githubusercontent.com\/1676121\/158157853-6c544a47-2d6d-4ac4-964a-6f10951ec36b.png\">\r\n","embeddings":[-0.1933975518,0.1477290988,0.0188400354,0.2966789603,0.0506261885,0.3109288216,0.2941398621,0.1884916127,-0.0731477886,0.1240582839,0.1497876495,-0.0864578187,-0.3482003212,0.2359734327,0.1940664202,-0.1597623229,0.0069946139,0.0499037988,0.1567207724,-0.0928815603,-0.3870481551,0.4132068753,-0.0313486122,-0.3563389182,-0.5102393031,-0.0329990387,-0.1349659562,0.3064566255,-0.1050367579,-0.1911119223,-0.1466589272,0.0352476165,0.1553139538,0.5570989251,-0.0001115682,0.1400071979,0.2059164047,-0.0818837285,-0.1803536415,-0.1162377968,-0.3221024275,-0.2358183712,-0.1057315394,-0.1040616632,-0.0874812081,-0.1110205799,0.1942804456,-0.1892816424,0.167338118,0.4233098328,0.2260954678,-0.0837061331,0.4143295884,-0.0836183503,0.1569638103,-0.0181507524,0.0868648589,0.2028197348,-0.2211886197,0.0388418473,0.0131307254,0.5771192312,0.1517453939,-0.069181107,-0.1997399479,-0.1929728091,-0.2381443381,-0.3023697734,0.1445631236,0.2396603376,0.5382723808,0.1348784268,-0.2633888125,0.0538243577,0.2205633521,0.0333982743,0.2443392873,0.4427041709,0.0467536002,0.1999877542,-0.3329994977,-0.378772527,-0.1341997832,0.2170260251,-0.3532187641,0.2478198856,-0.0400550775,0.1118904352,-0.0536293164,0.1300466657,-0.1430535913,-0.0610803962,-0.0797978565,-0.0073274267,-0.1237519607,-0.0030689167,0.1259953231,-0.0323797278,-0.0664397553,0.1231767088,-0.3546806574,-0.0045651281,-0.196668908,0.1291748285,0.2312476635,-0.264228642,-0.2307100743,0.2766657174,0.4023426175,0.1234374195,0.0920675695,-0.0263775215,-0.0049599917,-0.3208119273,-0.2527247071,-0.0089708548,0.3598236442,0.0441194102,-0.3617737889,0.1570187956,-0.3676458895,-0.1162530854,-0.0933945104,0.2400185168,-0.0025726298,0.1789370179,0.0582703166,0.0862540677,-0.100514859,-0.4063533247,-0.2330684215,0.259195447,0.0611420497,0.2163460553,0.1470379084,-0.0519426055,0.1378825158,-0.0710230395,0.0071859532,-0.2044921964,0.0477071889,-0.1214964166,-0.3182257712,0.3843327463,0.2175386399,0.1331340224,0.1397665292,-0.2936000228,0.1031841114,0.152900219,-0.3207333982,-0.3783464134,-0.4226319492,0.2303672582,-0.1800513715,-0.0467751026,-0.1888732016,0.3508194387,-0.1459731013,-0.1942494959,0.0030479727,0.2033005506,-0.2453912497,-0.1222520247,0.1510975212,0.6265177131,-0.4249155819,-0.1967709512,-0.3306944966,-0.1731228083,0.0129574956,0.1094381586,-0.1834160239,0.3391465545,-0.4214860499,-0.0499861762,0.4183912575,-0.1575907022,-0.3415843844,0.0134186707,-0.2451459318,0.1121573299,0.1690547466,0.106004864,-0.0735852197,-0.0287966765,0.084784776,0.0961102694,-0.0439546444,0.1152195558,-0.0948378667,-0.0212123226,-0.0877779424,0.3473652601,0.5229454637,-0.0642575026,0.451682359,-0.3917663693,0.1176191866,-0.0360614955,0.1515197307,0.5031406283,0.4215629399,0.0225729272,0.0581644401,-0.005333411,-0.6120933294,0.158170715,0.11792209,-0.0493541807,-0.187503159,-0.1399393827,-0.1834385991,-0.3869844973,-0.3619800806,-0.302595675,0.1464844793,0.0919080898,-0.3827188313,0.3406679332,-0.1360416561,0.3172214329,-0.0609804355,0.1744006127,-0.2450560778,0.2670666277,-0.2975046039,0.138832584,0.1471229643,-0.0174187515,0.1777623445,-0.1787783206,-0.089331314,0.3787342012,0.0940825865,0.2066822052,0.4688083827,-0.1009838283,0.2771246433,-0.7825940847,0.0343432203,0.1134519279,-0.014164336,-0.2199369818,-0.4153303206,0.1593326181,0.0949075073,-0.2169288397,-0.0367497876,0.2165375948,0.197375074,-0.1003537402,0.2343313396,-0.143856898,0.1092561707,0.1792021096,0.1956454664,-0.1516065598,-0.304712981,0.0346604176,0.3844015598,-0.0213315208,-0.0127327181,0.2074031979,-0.2463759184,0.0037434569,-0.0305374619,0.3902890086,0.351955086,0.1928465813,-0.0263642408,0.1137890145,0.3149711192,-0.2678753436,0.107470654,-0.0393528752,0.279212743,0.302575022,0.0775430351,0.1133673862,-0.3576546907,-0.0546948649,0.3388552368,0.1959892809,-0.3271508813,-0.037071418,-0.1829407662,-0.2859169245,-0.113967143,0.0535155572,-0.3040626943,-0.2806335092,0.0439461209,0.0334445052,-0.0097979642,0.0995761529,-0.009661451,0.1583256871,0.010761233,0.0862113237,-0.1488669217,-0.0266582966,-0.1367527097,0.1683765501,0.3545512557,-0.1417788118,0.2143007368,-0.2508836389,0.093230024,-0.7055172324,-0.3987385035,0.0841079503,-0.1574072242,0.1067790687,0.0167124961,0.2984663248,-0.2208357155,0.1146503165,0.0354875624,0.1096122488,-0.0795679092,0.027529683,-0.1064909846,0.0602412671,0.1396445781,-0.2922469676,-0.2657714188,-0.1923798919,0.2829860747,-0.0270374455,0.0903125554,0.0199677274,0.1817686558,0.0526220538,0.094734855,-0.1127860695,-0.2340850383,-0.1674276888,0.3372203708,-0.3695174754,-0.3251104057,0.4031918645,0.1352558285,-0.0931430385,-0.0218814872,-0.6589690447,-0.1537075639,-0.0204837844,-0.0274301078,-0.0118472595,-0.0351037271,0.3883838058,-0.0280543007,-0.0831823573,-0.0858014375,-0.1054191068,-0.225193873,-0.0430293232,0.3198215365,-0.222822547,0.3266832829,0.0098330257,0.8642650843,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3889","title":"Cannot load beans dataset (Couldn't reach the dataset)","comments":"Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)","body":"## Describe the bug\r\nThe beans dataset is unavailable to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('beans')\r\n```\r\n\r\n## Expected results\r\nThe dataset would be downloaded with no issue.\r\n\r\n## Actual results\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/ibeans\/train.zip (error 403)\r\n```\r\n\r\n[It looks like the billing of this project has been disabled because it is associated with a delinquent account.](https:\/\/storage.googleapis.com\/ibeans\/train.zip )\r\n\r\n## Environment info\r\nGoogle Colab\r\n","comment_length":23,"text":"Cannot load beans dataset (Couldn't reach the dataset) \n ## Describe the bug\r\nThe beans dataset is unavailable to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('beans')\r\n```\r\n\r\n## Expected results\r\nThe dataset would be downloaded with no issue.\r\n\r\n## Actual results\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/ibeans\/train.zip (error 403)\r\n```\r\n\r\n[It looks like the billing of this project has been disabled because it is associated with a delinquent account.](https:\/\/storage.googleapis.com\/ibeans\/train.zip )\r\n\r\n## Environment info\r\nGoogle Colab\r\n \n Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)","embeddings":[-0.4721034169,0.2937535346,-0.1632618159,0.4070334435,0.3040819168,0.0024879545,0.1432952583,0.0070785792,-0.0481522158,0.2847831845,-0.0638595298,0.3683096766,-0.0164078493,0.3610328436,-0.0237231459,0.0577074774,-0.1081279293,-0.1645550281,-0.5758637786,0.023881061,-0.2940049171,0.2738599777,-0.0233217739,-0.037474364,-0.2689076364,-0.0754930824,-0.0744953677,-0.0242402349,-0.4398058057,-0.2356318235,0.372954607,0.0063793166,0.140689075,0.5160427094,-0.0001106436,-0.0160402972,0.397513032,0.0894469693,-0.4802119434,-0.3550446332,-0.6385271549,-0.2208743542,0.1271834522,-0.0056030978,-0.3249291778,0.2984330952,0.1365662366,-0.1640415341,0.1632101983,0.3456363678,0.2839324176,-0.0932645053,0.0129738199,-0.2624518871,0.3314785957,-0.3533502519,-0.1359381974,0.4348132014,0.1738162339,-0.1343367547,0.2780838311,0.2461382449,0.1221311986,-0.1201090813,0.1030773297,-0.0395472199,-0.0103170257,-0.3759792745,0.2868287563,0.1133013815,1.080126524,-0.0898182243,-0.53858006,0.1489838362,0.1727022976,-0.1384823471,0.4577844441,0.1939825565,-0.0267166346,0.1473179907,-0.3441676497,-0.1730308533,-0.2207692713,0.2920139432,-0.1578496993,0.2300731987,-0.1040423959,0.0439440645,-0.0805236548,0.0574598908,0.4158937633,0.0981916487,0.0637681112,0.2207369506,-0.0257459469,0.1214711294,-0.009033802,0.2129358351,0.0016312037,0.4858339727,0.0665280148,-0.1074587852,0.0638756156,0.1107169688,0.4132048488,0.0794122964,0.0571545772,0.1571144015,0.3623424172,0.3630516827,-0.1413037926,-0.0843082219,-0.2615192831,-0.1784939766,0.1232367158,0.126542449,0.1174681783,-0.2192625403,-0.4947874546,0.1154305637,-0.2405761182,-0.0495441072,-0.0687739179,0.274515301,-0.3154754937,-0.2409535646,0.3581915498,0.1430877,0.0367650092,-0.1671223193,-0.2656650245,0.0400255434,-0.2496531606,0.102604121,0.2079625428,-0.3214920461,0.2372719347,-0.1805179864,-0.0788536817,-0.0867453739,0.1295170337,-0.1647742838,-0.3972166479,0.4750950336,0.2687705755,-0.0444381349,0.0200605448,0.0090056751,-0.0522282943,0.1811436564,-0.1661707461,-0.1524891108,0.1472980678,0.2620907426,-0.2511987388,-0.1091174856,-0.6378190517,0.2227010876,-0.0033721328,-0.0461200438,0.0216008369,-0.1372816712,-0.2391985506,-0.2655566633,0.1706481129,0.4565366805,-0.2245611101,-0.2256511599,-0.1552481651,-0.098114714,0.1407164037,0.1574579179,-0.2102051824,0.2902565002,-0.1073641554,0.046661254,0.2291445285,-0.0065964218,-0.7131419778,0.0040093851,-0.0644296929,0.0608281083,-0.1724887639,0.0499643944,-0.0024189418,0.1583202928,0.3343406022,0.5018733144,-0.1460793912,-0.1446336061,-0.1595896184,-0.3222993016,0.1664762199,0.1439738125,0.3315447867,0.141590476,0.2922763228,-0.2188187689,0.1172384247,0.2602131963,-0.1560252011,0.4014739394,0.2155708969,-0.0367309712,0.0442620143,-0.3375190198,-0.0303144045,0.3108627796,-0.1675970256,0.2984000146,-0.1555653214,-0.1185262054,-0.3878890872,0.0861207545,-0.0437087007,0.1628895998,0.1326960325,-0.1260160059,-0.0397692658,0.0969327465,-0.0658218637,0.4536839724,-0.0721083134,0.2146780491,-0.4441941381,0.5075232983,-0.0948171988,-0.2164162546,0.1601751149,-0.1268360764,0.2517758012,-0.1023758054,-0.2076101154,0.1164112687,0.082343854,0.1473176181,0.1911285371,0.2672754526,0.104579702,-0.3686168492,-0.0354872532,0.3077382147,0.1716676503,0.1133881062,0.1132223234,0.0175087601,-0.0653156564,-0.077568911,0.0607264526,0.0174866337,0.2353867441,-0.0136472881,-0.1798923463,0.0718199313,0.3981319666,-0.09911111,-0.229016602,-0.0178622622,-0.034087453,0.2495476007,0.2445355654,-0.0670033768,0.0538791046,0.0847787336,-0.0812266171,0.0165134426,0.1188027486,0.1258360595,0.2569776177,0.0632558763,0.0640148818,0.0012470603,0.1514450908,-0.1244456023,0.3070989549,0.0722188428,0.4070848227,0.0843797401,0.1470547616,0.1025902033,-0.0982528031,-0.1008221731,0.1055378765,0.2205802351,-0.1456204504,0.2648282647,-0.2310456336,-0.4061816037,0.0500538237,-0.2189503014,-0.1019000411,-0.0544711947,-0.0079943994,0.2230009437,0.0059520206,-0.0233863592,-0.0961436927,0.2160829604,0.1341499537,-0.2120065093,-0.1477357298,0.0866332948,-0.2356081009,0.1262641549,0.3402314782,-0.0133196106,0.376208216,-0.2595164776,-0.0783541575,-0.3196521401,-0.1683457643,0.0163611043,0.0965982676,0.5016034245,0.1734369993,0.2419324964,-0.1761866659,0.1051549762,0.2204367071,-0.1762995869,-0.1292262226,-0.1369809061,0.0521640554,-0.0436598808,0.0928468853,-0.5160238147,-0.4517393708,-0.1908639669,0.2059099376,0.1073965579,0.0475999452,-0.055874873,0.1187331527,0.0256141853,0.6462154388,-0.0350490659,-0.2899827957,-0.2966692448,0.2920725346,-0.0896096453,-0.3089390099,0.1453216821,0.0308607947,0.1981775463,0.139777258,-0.6269010305,0.0151537303,-0.1387936175,0.2227998823,-0.1545993537,-0.0364988707,0.1561367214,-0.0611620657,-0.0467450246,-0.0308073089,-0.3570403457,-0.2073605061,0.2021425962,0.4101080596,0.0098608946,0.3165043294,-0.1671999246,0.4252550602,-0.0655735284,0.1472411603,0.3459515274,0.0186855458,0.0972494707,-0.0367074274,-0.3450375497,0.0455223247,-0.186719074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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"Hi @INF800,\r\n\r\nPlease note that the `imagefolder` feature enhancement was just recently merged to our master branch (https:\/\/github.com\/huggingface\/datasets\/commit\/207be676bffe9d164740a41a883af6125edef135), but has not yet been released.\r\n\r\nWe are planning to make the 2.0 release of our library in the coming days and then that feature will be available by updating your `datasets` library from PyPI.\r\n\r\nIn the meantime, you can incorporate that feature if you install our library from our GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\n\r\nThen:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n        ds = load_dataset(\"imagefolder\", data_files=\"https:\/\/download.microsoft.com\/download\/3\/E\/1\/3E1C3F21-ECDB-4869-8368-6DEBA77B919F\/kagglecatsanddogs_3367a.zip\", split=\"train\")\r\nUsing custom data configuration default-7eb4e80d960deb18\r\nDownloading and preparing dataset image_folder\/default to ...\/.cache\/huggingface\/datasets\/image_folder\/default-7eb4e80d960deb18\/0.0.0\/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60...\r\nDownloading data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 690.19it\/s]\r\nExtracting data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 852.85it\/s]\r\nDataset image_folder downloaded and prepared to ...\/.cache\/huggingface\/datasets\/image_folder\/default-7eb4e80d960deb18\/0.0.0\/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60. Subsequent calls will reuse this data.\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDataset({\r\n    features: ['image', 'label'],\r\n    num_rows: 25000\r\n})\r\n```","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":140,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n Hi @INF800,\r\n\r\nPlease note that the `imagefolder` feature enhancement was just recently merged to our master branch (https:\/\/github.com\/huggingface\/datasets\/commit\/207be676bffe9d164740a41a883af6125edef135), but has not yet been released.\r\n\r\nWe are planning to make the 2.0 release of our library in the coming days and then that feature will be available by updating your `datasets` library from PyPI.\r\n\r\nIn the meantime, you can incorporate that feature if you install our library from our GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\n\r\nThen:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n        ds = load_dataset(\"imagefolder\", data_files=\"https:\/\/download.microsoft.com\/download\/3\/E\/1\/3E1C3F21-ECDB-4869-8368-6DEBA77B919F\/kagglecatsanddogs_3367a.zip\", split=\"train\")\r\nUsing custom data configuration default-7eb4e80d960deb18\r\nDownloading and preparing dataset image_folder\/default to ...\/.cache\/huggingface\/datasets\/image_folder\/default-7eb4e80d960deb18\/0.0.0\/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60...\r\nDownloading data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 690.19it\/s]\r\nExtracting data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 852.85it\/s]\r\nDataset image_folder downloaded and prepared to ...\/.cache\/huggingface\/datasets\/image_folder\/default-7eb4e80d960deb18\/0.0.0\/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60. Subsequent calls will reuse this data.\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDataset({\r\n    features: ['image', 'label'],\r\n    num_rows: 25000\r\n})\r\n```","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"Hey @albertvillanova. Does this load entire dataset in memory? Because I am facing huge trouble with loading very big datasets (OOM errors)","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":22,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n Hey @albertvillanova. Does this load entire dataset in memory? Because I am facing huge trouble with loading very big datasets (OOM errors)","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594,-0.0159561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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"Can you provide the error stack trace? The loader only stores the `data_files` dict, which can get big after globbing. Then, the OOM error would mean you don't have enough memory to keep all the paths to the image files. You can circumvent this by generating an archive and loading the dataset from there. Maybe we can optimize the globbing part in our data files resolution at some point, cc @lhoestq for visibility.","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":73,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n Can you provide the error stack trace? The loader only stores the `data_files` dict, which can get big after globbing. Then, the OOM error would mean you don't have enough memory to keep all the paths to the image files. You can circumvent this by generating an archive and loading the dataset from there. Maybe we can optimize the globbing part in our data files resolution at some point, cc @lhoestq for visibility.","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594,-0.015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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"Hey, memory error is resolved. It was fluke.\r\n\r\nBut there is another issue. Currently `load_dataset(\"imagefolder\", data_dir=\".\/path\/to\/train\",)` takes only `train` as arg to `split` parameter.\r\n\r\nI am creating vaildation dataset using\r\n\r\n```\r\nds_valid = datasets.DatasetDict(valid=load_dataset(\"imagefolder\", data_dir=\".\/path\/to\/valid\",)['train'])\r\n```","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":36,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n Hey, memory error is resolved. It was fluke.\r\n\r\nBut there is another issue. Currently `load_dataset(\"imagefolder\", data_dir=\".\/path\/to\/train\",)` takes only `train` as arg to `split` parameter.\r\n\r\nI am creating vaildation dataset using\r\n\r\n```\r\nds_valid = datasets.DatasetDict(valid=load_dataset(\"imagefolder\", data_dir=\".\/path\/to\/valid\",)['train'])\r\n```","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"`data_dir=\"path\/to\/folder\"` is a shorthand syntax fox `data_files={\"train\": \"path\/to\/folder\/**\"}`, so use `data_files` in that case instead:\r\n```python\r\nds = load_dataset(\"imagefolder\", data_files={\"train\": \"path\/to\/train\/**\", \"test\": \"path\/to\/test\/**\", \"valid\": \"path\/to\/valid\/**\"})\r\n```","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":26,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n `data_dir=\"path\/to\/folder\"` is a shorthand syntax fox `data_files={\"train\": \"path\/to\/folder\/**\"}`, so use `data_files` in that case instead:\r\n```python\r\nds = load_dataset(\"imagefolder\", data_files={\"train\": \"path\/to\/train\/**\", \"test\": \"path\/to\/test\/**\", \"valid\": \"path\/to\/valid\/**\"})\r\n```","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.173592686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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"And there was another issue. I loaded black and white images (jpeg file). Using load dataset. It reads it as PIL jpeg data format. But instead of converting it into 3 channel tensor, input to collator function is coming as a single channel tensor.","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":44,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n And there was another issue. I loaded black and white images (jpeg file). Using load dataset. It reads it as PIL jpeg data format. But instead of converting it into 3 channel tensor, input to collator function is coming as a single channel tensor.","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594,-0.0159561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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3881","title":"How to use Image folder","comments":"We don't apply any additional preprocessing on top of `PIL.Image.open(image_file)`, so you need to do the conversion yourself:\r\n\r\n```python\r\ndef to_rgb(batch):\r\n    batch[\"image\"] = [img.convert(\"RGB\") for img in batch[\"image\"]]\r\n    return batch\r\n\r\nds_rgb = ds.map(to_rgb, batched=True)\r\n```\r\n\r\nPlease use our Forum for questions of this kind in the future.","body":"Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n```","comment_length":47,"text":"How to use Image folder \n Ran this code\r\n```\r\n load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n```\r\n\r\n`https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py` missing\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/tmp\/ipykernel_33\/1648737256.py in <module>\r\n----> 1 load_dataset(\"imagefolder\", data_dir=\".\/my-dataset\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1684         revision=revision,\r\n   1685         use_auth_token=use_auth_token,\r\n-> 1686         **config_kwargs,\r\n   1687     )\r\n   1688 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1511         download_config.use_auth_token = use_auth_token\r\n   1512     dataset_module = dataset_module_factory(\r\n-> 1513         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1514     )\r\n   1515 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1200                         f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n   1201                         f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n-> 1202                     ) from None\r\n   1203                 raise e1 from None\r\n   1204     else:\r\n\r\nFileNotFoundError: Couldn't find a dataset script at \/kaggle\/working\/imagefolder\/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/imagefolder\/imagefolder.py\r\n``` \n We don't apply any additional preprocessing on top of `PIL.Image.open(image_file)`, so you need to do the conversion yourself:\r\n\r\n```python\r\ndef to_rgb(batch):\r\n    batch[\"image\"] = [img.convert(\"RGB\") for img in batch[\"image\"]]\r\n    return batch\r\n\r\nds_rgb = ds.map(to_rgb, batched=True)\r\n```\r\n\r\nPlease use our Forum for questions of this kind in the future.","embeddings":[-0.2858948708,0.0791946426,-0.1641437113,0.5192497969,0.3978692889,0.19424133,0.3166289926,0.1429089755,0.0455848053,0.1703680456,0.0783734024,-0.0138086444,-0.2019124031,0.0991082862,-0.027971765,-0.0610153899,-0.137442708,0.201635018,-0.1800703555,0.1053988934,-0.3243977129,0.2227627486,-0.199296385,0.0239701495,-0.3725244105,-0.1334149241,-0.2554858625,0.5537114143,-0.301984638,-0.2025518268,0.1425678879,-0.1484532803,0.2093805671,0.4515585899,-0.0001075645,0.2240584195,0.4101127684,-0.2311012149,-0.3290292025,-0.3769568503,-0.228266865,-0.0441721119,0.0047346358,-0.3819600344,-0.1225530952,-0.0782393292,0.2704272568,0.0254104696,0.3077074587,0.2765157223,0.2589990497,0.1214060858,0.1843478233,-0.0169015899,-0.1121836752,0.4930899143,0.0372766517,0.2362049818,0.0792014971,-0.0652794167,0.1625435948,0.2614591718,0.0358877704,-0.0124443481,0.2321919501,0.1105273589,-0.0068237293,-0.2155941725,0.1265500337,-0.1820921749,0.3849571049,-0.1616745293,-0.1817631423,-0.0181456879,-0.0395427793,-0.4064295888,0.1617155075,0.0836902708,-0.1187563688,0.0931640938,-0.1862467974,-0.2279615849,-0.3234365284,0.3092053831,0.0679564252,0.0877892375,-0.2053929716,0.0769239962,0.1150600836,0.0493061841,0.0970912576,-0.362165451,0.0442108065,0.1265060008,0.2593221068,0.146146819,-0.01951712,0.1517591774,0.0959282219,0.1028213501,-0.2983160019,-0.2555590868,-0.2526741028,0.0793466941,0.0006242411,0.2288815528,-0.0557621457,0.4583566487,0.2230264246,0.3975461423,-0.0246013198,-0.2443835586,-0.3011552393,-0.2005017102,0.0499580167,0.2011443973,0.3267679513,0.0799777582,-0.0135421371,-0.460842967,0.1270586848,-0.0686440393,0.2023273408,0.2879626453,0.227208361,-0.0218355898,0.1704128236,0.2375160605,0.1398355812,0.0984482616,-0.2554234862,0.2842488587,-0.2523291111,-0.005160897,-0.0439182371,-0.254650116,0.3056449294,-0.286896497,0.0526927561,0.0585399717,0.1553364843,-0.2169868648,-0.0509819761,0.2610358,-0.0916732103,-0.0187396556,0.2963454127,-0.0454201959,0.0666982532,0.0996685028,-0.448441565,-0.4122202992,-0.2043373734,0.2212548554,-0.0946172625,0.026381541,-0.5048220754,-0.2424771339,-0.0498434752,-0.2207406908,0.0285936091,0.0050321389,-0.2164946198,-0.1788631827,0.2506094277,0.5779446959,-0.1442596614,0.0715266243,-0.2689905763,-0.2302186936,0.1847218275,0.0917960629,-0.2062637061,0.4836918116,-0.6135778427,-0.0377430245,0.5469892621,-0.6308553219,-0.2642132044,-0.0194767378,-0.0160564333,-0.0283728242,0.1441946775,0.0491674915,0.0685672313,-0.078795217,-0.1048155352,0.2463382483,-0.0207467526,0.0393029712,0.0116023934,-0.2071622163,-0.1544005871,0.2210119069,0.0998416916,0.2198759615,0.2281200141,-0.0328827947,0.0621345602,-0.2988764942,0.1780728996,0.115188092,0.390630573,0.1229340509,-0.1748879999,-0.030899439,-0.3798305988,0.2845121026,0.1084419414,-0.0606032014,-0.4644959867,0.1427325159,-0.1616858542,-0.2617481947,-0.4119169116,-0.2486489266,0.1931683421,0.231127128,0.1035525799,0.0111389933,-0.1604461074,0.2848944366,-0.1841042042,0.1677675098,-0.1560896337,0.4557163119,-0.3871400952,-0.1786938459,0.1204003319,-0.0151078505,0.0775768906,-0.2544842064,-0.0591978543,0.2827380896,0.1352198422,0.1431296915,0.3067693114,-0.0705598444,0.1459953338,-0.3780553341,0.1258313656,-0.1305874139,0.3384917974,0.076620087,-0.0201673992,0.1809347719,-0.15699929,0.0884212777,0.0980383381,-0.1084254384,0.0385792553,-0.0328628272,0.1818051189,-0.1926600039,0.0655646846,0.2532230914,0.2033426315,-0.0350582376,0.011664737,-0.2331335992,0.3495358527,0.0617369376,0.1215711609,0.1810611337,-0.1412088424,0.1114946529,0.0055139344,0.1075901389,0.6850779653,0.2946434319,-0.2348813564,0.0732882768,0.2798925042,-0.0077906516,0.2949091792,0.0229829624,0.3190519512,0.1136220619,0.023056481,-0.0055124732,-0.2330718786,-0.5380800366,-0.1060534045,0.3599244654,-0.1671125144,0.0657726675,-0.2293881476,-0.4290291071,0.2275753021,0.1485639662,-0.0117521202,-0.13069731,-0.1375130564,0.042242907,0.1725978255,0.0048955311,-0.3100765646,0.0854400918,0.0750045627,-0.4491649866,-0.5388172269,-0.0349386185,-0.0489642918,0.1354590356,0.3568563163,-0.092357561,0.3888430297,-0.2904594243,0.1701116413,-0.51943928,-0.1685296595,0.0841429234,0.0003069938,0.1968117058,0.3385492265,0.3282364309,0.2073895484,-0.1462319642,0.4259983897,0.0774787068,-0.3267215788,-0.0126515543,-0.1966931075,-0.1362502873,-0.1642075628,-0.1284639388,-0.3573228717,-0.4335591495,-0.0684309155,0.2935099006,0.0824879259,0.4416903853,0.2030849606,0.2307364047,-0.1404051632,0.0298976302,-0.0397703312,-0.4825537205,0.2662041485,-0.1471428871,-0.2014241219,0.1132093593,-0.0971583724,0.2087316066,0.04969896,-0.3409630358,-0.6610171795,0.0980603173,0.2304313928,-0.0261130091,0.2105475515,0.2839563489,0.0462530553,-0.0669791549,-0.0725903139,-0.0761202201,-0.0332980677,-0.0082686013,0.2762325108,0.0866345093,0.4856369197,0.0010836117,0.7047639489,0.160201475,-0.0056630131,0.5541396737,-0.3467069864,0.5346217155,-0.3130922318,-0.2578807473,0.1735926867,0.3412323594,-0.0159561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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3872","title":"HTTP error 504 Server Error: Gateway Time-out","comments":"yes but is there any way you could try pushing with `git` command line directly instead of `push_to_hub`?","body":"I am trying to push a large dataset(450000+) records with the help of `push_to_hub()`\r\nWhile pushing, it gives some error like this.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"data_split_speech.py\", line 159, in <module>\r\n    data_new_2.push_to_hub(\"user-name\/dataset-name\",private=True)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/dataset_dict.py\", line 951, in push_to_hub\r\n    repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 3556, in _push_parquet_shards_to_hub\r\n    api.upload_file(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1017, in upload_file\r\n    raise err\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1008, in upload_file\r\n    r.raise_for_status()\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/requests\/models.py\", line 953, in raise_for_status\r\n    raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https:\/\/huggingface.co\/api\/datasets\/user-name\/dataset-name\/upload\/main\/data\/train2-00041-of-00064.parquet\r\n\r\n```\r\nCan anyone help me to resolve this issue.\r\n","comment_length":18,"text":"HTTP error 504 Server Error: Gateway Time-out \n I am trying to push a large dataset(450000+) records with the help of `push_to_hub()`\r\nWhile pushing, it gives some error like this.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"data_split_speech.py\", line 159, in <module>\r\n    data_new_2.push_to_hub(\"user-name\/dataset-name\",private=True)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/dataset_dict.py\", line 951, in push_to_hub\r\n    repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 3556, in _push_parquet_shards_to_hub\r\n    api.upload_file(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1017, in upload_file\r\n    raise err\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1008, in upload_file\r\n    r.raise_for_status()\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/requests\/models.py\", line 953, in raise_for_status\r\n    raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https:\/\/huggingface.co\/api\/datasets\/user-name\/dataset-name\/upload\/main\/data\/train2-00041-of-00064.parquet\r\n\r\n```\r\nCan anyone help me to resolve this issue.\r\n \n yes but is there any way you could try pushing with `git` command line directly instead of `push_to_hub`?","embeddings":[-0.4594688714,-0.3423293829,-0.0652930513,0.1371449828,0.1435393691,-0.0505814813,-0.0194971319,0.4111796916,-0.3377406597,-0.2412682474,0.0478665829,0.1072255373,0.1604776382,0.4615723491,0.0629626215,0.084594287,0.0672828034,-0.0497487932,-0.0619742833,0.0463443995,-0.1112953946,-0.097989805,0.1206012145,0.0700465143,-0.3712241352,-0.1621819139,0.117885761,0.3222818673,-0.1782839596,-0.2376813591,-0.0484963357,-0.0789998248,0.0556953251,0.5038568974,-0.000123036,0.0538012646,0.1469678134,-0.0013399177,-0.4750665426,-0.082999818,-0.4539307058,-0.3866649866,-0.070828259,-0.1189276204,0.0284198094,0.2812230289,-0.1359698027,0.1021412611,0.2842968702,0.4920177758,0.0959691703,-0.0434412919,0.4811983705,-0.0423652604,0.1924687922,-0.2092447579,-0.1680805087,0.3687007427,0.2202215642,0.2428538054,0.2456351072,-0.1315361857,0.0969926342,-0.0060395049,0.0635317564,-0.2721959352,0.2240208685,-0.2437978983,-0.0002724844,0.0724440366,0.1163826287,-0.212202996,-0.5422158837,-0.2486802489,-0.3864800334,-0.4973600507,0.1339166611,0.2904363573,-0.1851168573,0.1525425762,-0.1982710809,-0.4529161751,-0.0287060589,0.0408945456,0.0086747594,0.2050165534,0.0213705767,0.1482327729,0.0504419655,-0.1104723513,-0.118803665,0.0881051496,-0.1420343965,-0.2798287868,-0.5597026348,-0.2192979753,0.007290584,0.0760265067,0.2245848626,0.3849701881,-0.0560885221,0.0495186672,-0.0299313273,0.0557243265,0.4683922529,0.1254700422,-0.0146231456,0.0183218382,0.1798046082,0.2246981263,-0.0185641143,-0.0357363932,0.1515418142,-0.3674013317,0.0832112506,0.017220214,0.3115282655,-0.1838994175,-0.1052805409,-0.0698645636,0.0573119484,0.1503531933,0.0315767117,0.1721200198,-0.0175112691,0.2705176175,-0.2991518974,0.1185104549,-0.2656653523,-0.3054948747,-0.1560639441,0.2571674585,-0.0395300463,0.1732575148,0.0081984624,-0.4359897971,-0.1495836079,0.0356749892,0.1485536695,-0.1666675806,-0.091582939,-0.1941310167,-0.0917874798,0.2071214467,0.1778558642,0.2568475008,0.3273001909,-0.0330871232,-0.1217651367,-0.1881879568,-0.5727049112,-0.5500557423,0.1821069568,0.0593972579,0.0337621719,-0.0174244586,-0.826069653,-0.1005649641,0.0500261486,-0.0640473142,-0.0612553023,0.1435672492,0.0756101161,-0.0503599048,0.0638359115,0.2452341467,-0.2759172618,0.3127145171,0.2042500526,0.4145080447,0.1217302382,0.3683693409,-0.0384905413,0.1556811482,0.0053822109,-0.094386369,0.2585387826,-0.6565604806,-0.1934647858,0.135837391,-0.2571943402,-0.4090894461,-0.3261186182,-0.2345356345,0.4298559129,-0.0620924979,0.3848635256,0.1826003194,0.0465507433,0.2894309163,-0.320317179,-0.3113990128,-0.2534445226,0.1256612241,-0.0033071309,0.0263339132,0.2186239362,-0.08640185,-0.0574542955,0.1254656613,0.2504205704,0.2883915901,0.3329435885,-0.0393707491,0.0821881741,-0.0881392136,-0.0801143274,0.0744923055,0.0672905222,0.1455756426,-0.2961820364,0.2083791643,-0.3494784236,-0.0554159321,-0.0351561941,0.3873842359,-0.0087456172,-0.4085330665,0.261374861,0.1171610057,0.0819783956,0.3342568278,-0.2426544279,0.3391054869,-0.1309414357,0.3991455436,0.0545264073,-0.3109301627,0.1418356597,-0.0587573089,0.4184270203,-0.2182049304,-0.1748758256,0.1444712281,-0.0015289864,-0.0154877305,0.3226307631,0.2531341016,0.1836843193,0.0286287274,-0.1566276699,-0.1658302546,0.0391159169,0.0632997975,-0.0560715273,0.1272798777,0.0291248094,0.4284342229,0.1872791201,0.2653841078,0.2086433917,0.0619120561,0.3675251007,0.2661958337,0.0457269885,0.1447333395,0.063605316,-0.400850147,0.3576925993,0.1485633552,0.2496338636,0.1606439054,0.143526122,0.1142727658,0.0840797499,-0.1353592426,0.2016147375,0.0559896678,-0.1880717427,0.041147247,-0.101784952,-0.072929509,0.1412573904,-0.0097147916,0.1402814835,0.1210365444,0.1121575981,-0.1258271933,0.1406338215,-0.1666152626,-0.0512532592,-0.2159140855,0.3256112933,0.6453015208,-0.2706487775,-0.0661529899,-0.099436678,-0.0302894581,0.381377548,-0.1988293678,-0.4362564683,-0.4822511971,-0.0727711171,0.1216923445,-0.2395083904,0.1275833547,0.2182477564,0.0855302364,0.3413330019,-0.1394189596,0.1096699461,-0.1680203527,0.0577223338,-0.0128532238,0.2426443249,0.0127259102,0.1688940525,0.0366639867,-0.1379193664,-0.4979197681,0.1132404879,0.1936566681,-0.0989370048,0.1575779319,-0.2279056013,0.3994494677,-0.038652651,0.053909231,0.2807823122,-0.0570957996,0.022663122,-0.0019251971,-0.2050584704,0.0690857247,0.1663832515,0.0273342617,0.0185609479,-0.5421043634,0.3044731617,0.2487785816,0.227974847,-0.0608668625,0.0492309853,0.0659481734,0.2871103287,-0.1858739257,-0.0391795635,-0.4880356789,0.1621868312,-0.1053930372,-0.0331927314,0.0647962838,0.3226002753,0.1631536037,0.2033628523,-0.2839033008,-0.4911912978,-0.325258106,0.2968622446,-0.179274708,-0.1401677132,0.2536397576,-0.4748046398,-0.1070157215,0.2263578027,0.0760553926,0.2198491842,0.5247099996,-0.0732714757,0.0258314684,0.4122577906,0.0557195134,0.410305202,0.102832213,0.1694892496,0.5648216605,-0.072305046,0.2292646319,-0.1496031433,-0.0323726237,-0.1146644056,-0.0176307205,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3872","title":"HTTP error 504 Server Error: Gateway Time-out","comments":"Okay. I didnt saved the dataset to my local machine. So, I processed the dataset and pushed it directly to the hub. I think I should try saving those dataset to my local machine by `save_to_disk` and then push it with git command line","body":"I am trying to push a large dataset(450000+) records with the help of `push_to_hub()`\r\nWhile pushing, it gives some error like this.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"data_split_speech.py\", line 159, in <module>\r\n    data_new_2.push_to_hub(\"user-name\/dataset-name\",private=True)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/dataset_dict.py\", line 951, in push_to_hub\r\n    repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 3556, in _push_parquet_shards_to_hub\r\n    api.upload_file(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1017, in upload_file\r\n    raise err\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1008, in upload_file\r\n    r.raise_for_status()\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/requests\/models.py\", line 953, in raise_for_status\r\n    raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https:\/\/huggingface.co\/api\/datasets\/user-name\/dataset-name\/upload\/main\/data\/train2-00041-of-00064.parquet\r\n\r\n```\r\nCan anyone help me to resolve this issue.\r\n","comment_length":44,"text":"HTTP error 504 Server Error: Gateway Time-out \n I am trying to push a large dataset(450000+) records with the help of `push_to_hub()`\r\nWhile pushing, it gives some error like this.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"data_split_speech.py\", line 159, in <module>\r\n    data_new_2.push_to_hub(\"user-name\/dataset-name\",private=True)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/dataset_dict.py\", line 951, in push_to_hub\r\n    repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 3556, in _push_parquet_shards_to_hub\r\n    api.upload_file(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1017, in upload_file\r\n    raise err\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1008, in upload_file\r\n    r.raise_for_status()\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/requests\/models.py\", line 953, in raise_for_status\r\n    raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https:\/\/huggingface.co\/api\/datasets\/user-name\/dataset-name\/upload\/main\/data\/train2-00041-of-00064.parquet\r\n\r\n```\r\nCan anyone help me to resolve this issue.\r\n \n Okay. I didnt saved the dataset to my local machine. So, I processed the dataset and pushed it directly to the hub. I think I should try saving those dataset to my local machine by `save_to_disk` and then push it with git command 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3872","title":"HTTP error 504 Server Error: Gateway Time-out","comments":"`push_to_hub` is the preferred way of uploading a dataset to the Hub, which can then be reloaded with `load_dataset`. Feel free to try again and see if the server is working as expected now. Maybe we can add a retry mechanism in the meantime to workaround 504 errors.\r\n\r\nRegarding `save_to_disk`, this must only be used for local serialization (because it's uncompressed and compatible with memory-mapping). If you upload a dataset saved with `save_to_disk` to the Hub, then to reload it you will have to download\/clone the repository locally by yourself and use `load_from_disk`.","body":"I am trying to push a large dataset(450000+) records with the help of `push_to_hub()`\r\nWhile pushing, it gives some error like this.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"data_split_speech.py\", line 159, in <module>\r\n    data_new_2.push_to_hub(\"user-name\/dataset-name\",private=True)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/dataset_dict.py\", line 951, in push_to_hub\r\n    repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 3556, in _push_parquet_shards_to_hub\r\n    api.upload_file(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1017, in upload_file\r\n    raise err\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1008, in upload_file\r\n    r.raise_for_status()\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/requests\/models.py\", line 953, in raise_for_status\r\n    raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https:\/\/huggingface.co\/api\/datasets\/user-name\/dataset-name\/upload\/main\/data\/train2-00041-of-00064.parquet\r\n\r\n```\r\nCan anyone help me to resolve this issue.\r\n","comment_length":93,"text":"HTTP error 504 Server Error: Gateway Time-out \n I am trying to push a large dataset(450000+) records with the help of `push_to_hub()`\r\nWhile pushing, it gives some error like this.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"data_split_speech.py\", line 159, in <module>\r\n    data_new_2.push_to_hub(\"user-name\/dataset-name\",private=True)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/dataset_dict.py\", line 951, in push_to_hub\r\n    repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 3556, in _push_parquet_shards_to_hub\r\n    api.upload_file(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1017, in upload_file\r\n    raise err\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/huggingface_hub\/hf_api.py\", line 1008, in upload_file\r\n    r.raise_for_status()\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/requests\/models.py\", line 953, in raise_for_status\r\n    raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https:\/\/huggingface.co\/api\/datasets\/user-name\/dataset-name\/upload\/main\/data\/train2-00041-of-00064.parquet\r\n\r\n```\r\nCan anyone help me to resolve this issue.\r\n \n `push_to_hub` is the preferred way of uploading a dataset to the Hub, which can then be reloaded with `load_dataset`. Feel free to try again and see if the server is working as expected now. Maybe we can add a retry mechanism in the meantime to workaround 504 errors.\r\n\r\nRegarding `save_to_disk`, this must only be used for local serialization (because it's uncompressed and compatible with memory-mapping). If you upload a dataset saved with `save_to_disk` to the Hub, then to reload it you will have to download\/clone the repository locally by yourself and use `load_from_disk`.","embeddings":[-0.3772864044,-0.4499164522,-0.0528411977,0.1917433441,0.0723701864,-0.0209480301,-0.0438147113,0.3406640589,-0.312343508,-0.2927147448,0.0463634133,0.10223075,0.2157945186,0.4923057258,0.000318386,0.0172539242,0.0521851741,-0.0827598646,-0.0695908517,0.0098859379,-0.2126469314,-0.100777626,0.0573987737,0.1026083007,-0.504114151,-0.1804788262,0.0588860102,0.3174708188,-0.2073101103,-0.3262279332,-0.1001494899,-0.0635434315,0.1090135574,0.3926459849,-0.0001230279,-0.0216591638,0.233833164,0.0540989898,-0.4372362494,-0.0317660198,-0.5582006574,-0.3422931433,-0.0521945767,0.0205375087,0.1528576016,0.1326824129,-0.1972373426,0.0444327332,0.3663271666,0.4274432361,0.0835868418,-0.002781658,0.453722775,-0.0506938659,0.0479880646,-0.3276718855,-0.0777088031,0.36466223,0.2345074862,0.1147915721,0.1570212245,-0.1149135381,0.085269019,-0.0804212838,0.1531529874,-0.3478314579,0.1332237273,-0.3080156446,-0.0513095483,0.1136411428,0.2638614774,-0.1194803864,-0.4548139274,-0.2580797374,-0.3523033261,-0.3883285224,0.2454734296,0.2484698296,-0.0582684577,0.0855775923,-0.0945753753,-0.3569793403,-0.0088244267,0.0881061256,0.0153683936,0.1452086121,0.0376851298,0.2416586131,0.0324459858,-0.1029635221,-0.02027601,0.0545514636,-0.0840345994,-0.2581088543,-0.5741623044,-0.1249884591,0.0490086004,0.1909157038,0.1176391244,0.4242341816,0.0583348051,0.054947421,-0.0087028239,0.1903444827,0.5122082829,0.0397987626,0.0920365527,-0.0935678929,0.1221322045,0.3158600628,0.0388412252,-0.0440217294,0.1751118302,-0.3049294353,0.0441847667,-0.0188137889,0.3481652737,-0.1553938389,-0.1164544299,0.001817308,0.0745821595,0.1376432627,0.1933189332,0.1995637268,-0.1301386207,0.2826627493,-0.276420325,0.1464180648,-0.269144088,-0.3223247528,-0.2157492936,0.3450806737,-0.1025021672,0.186894834,0.0564509928,-0.4378084838,-0.2004394531,0.0410472117,0.1210928857,-0.2015158236,-0.0477600917,-0.2085365951,-0.1417894214,0.1327199936,0.1441736817,0.3217039406,0.3593088984,0.0129727423,-0.1133809015,-0.2661959231,-0.5282148123,-0.5475773215,0.17967619,0.0934400186,0.0455195941,-0.0095180934,-0.6094774604,-0.1043545753,0.0460516699,-0.1117109358,-0.0221506711,0.1141245589,0.0061957375,-0.0685900971,-0.0053344085,0.3328400552,-0.3689287901,0.334269017,0.2272829711,0.3300439715,0.0489416793,0.2875009477,-0.0070693628,0.0427448563,0.0023079563,0.0187265649,0.1254796386,-0.596765101,-0.1963545382,0.1106650606,-0.296675384,-0.4900279045,-0.3088032007,-0.1809915155,0.3949465454,-0.0236610919,0.44576478,0.1896989197,0.0662899911,0.1714083403,-0.3383087218,-0.1754685938,-0.2017689198,0.1565123945,-0.0494084023,-0.0386826769,0.1746988297,-0.0316733904,-0.0445857681,0.2224469483,0.2885765433,0.377268821,0.3414937258,0.0119064804,0.1740314811,-0.0245587248,-0.0152711114,0.0807048455,0.0001533835,0.0971040353,-0.263638556,0.1844610274,-0.458882302,0.0368184224,-0.1217057258,0.3685608506,0.0073169852,-0.4713347256,0.1921049654,0.1543774158,0.0744096711,0.4401382506,-0.3341596425,0.3136615455,-0.2858887911,0.4719209373,0.0496255904,-0.2516774535,0.1786368489,0.0064207292,0.4598091543,-0.1051868349,-0.1383992881,0.1695704311,0.0664445534,-0.0392947383,0.2962172925,0.2412147075,0.1962250322,-0.0406317078,-0.0043244497,-0.2264465988,0.0894439071,0.0042663259,-0.027693864,0.1333100498,-0.1059099957,0.4462148547,0.1466101557,0.3094755411,0.0979238749,0.1129171774,0.2934958041,0.2406595647,0.0499258675,0.0656433702,0.1304172128,-0.3172135353,0.4150717556,0.1408963352,0.105471462,0.0937837288,0.1073969901,0.0168834627,0.0300992802,-0.2142562121,0.2729049027,0.1389678866,-0.1399903297,0.0015077054,-0.0354145877,-0.0304887313,0.0872795507,0.0086763119,0.0706997737,0.0661747158,0.1159540564,-0.0652361214,0.0378656536,-0.1451430321,-0.2076569796,-0.2965587378,0.3471049368,0.5851219296,-0.2722334266,-0.0760577619,-0.0934426337,0.0025234008,0.3520659506,-0.1693079323,-0.498572737,-0.6248080134,-0.1138169467,0.2236975431,-0.1728803217,0.1125550047,0.1429991424,0.0158258155,0.3831649423,-0.1117196158,0.053079471,-0.1270461529,0.044587411,-0.0313243121,0.1610206217,0.103508234,0.1107820719,0.0527588725,-0.0737087876,-0.435056895,0.0797685534,0.1725901365,-0.1170037836,0.0852381811,-0.200963214,0.3914006948,-0.1197730675,0.0257479977,0.3599484563,0.0036057816,0.0367536135,0.0960720554,-0.1412959993,0.0780098215,0.3443878889,-0.0033613683,0.0245375969,-0.4813532531,0.3068944514,0.0519863851,0.0885201618,-0.0098563898,0.1157797202,0.1113718152,0.1694014072,-0.146959886,-0.1351215243,-0.4308348596,0.251093179,-0.0657343566,-0.0581844561,0.1055863798,0.2321406007,0.2221339345,0.1953172386,-0.2368520945,-0.6319936514,-0.3062055409,0.2223105133,-0.2175770849,-0.1988440752,0.314576,-0.4653941989,-0.0926438943,0.2337775528,0.022695668,0.1406391859,0.5354373455,0.0512211584,-0.0658343881,0.4104041159,0.0699982643,0.3227864802,0.1647175997,0.2501214147,0.6182129383,0.0641317517,0.2593074143,-0.0502898209,-0.0372433774,-0.0439008772,-0.0754936785,0.0896012932,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3869","title":"Making the Hub the place for datasets in Portuguese ","comments":"Hi @omarespejel! I think the philosophy for `datasets` issues is to create concrete issues with proposals to add a specific, individual dataset rather than umbrella issues for things such as datasets for a language, since we could end up with hundreds of issues (one per language). I see NILC - USP has many datasets, I would suggest to either create an issue for their datasets, or even better, we are trying to push to upload datasets as community datasets instead of adding them to the core library as guided in https:\/\/huggingface.co\/docs\/datasets\/share. That would have the additional benefit that the dataset would live under the NILC organization.\r\n\r\n@lhoestq correct me if I'm wrong please \ud83d\ude04  ","body":"Let's make Hugging Face Datasets the central hub for datasets in Portuguese :)\r\n\r\n**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the Portuguese speaking community.\r\n\r\nWhat are some datasets in Portuguese worth integrating into the Hugging Face hub?\r\n\r\nSpecial thanks to @augusnunes for his collaboration on identifying the first ones:\r\n\r\n-  [NILC - USP](http:\/\/www.nilc.icmc.usp.br\/nilc\/index.php\/tools-and-resources).\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\ncc @osanseviero\r\n\r\n\r\n","comment_length":114,"text":"Making the Hub the place for datasets in Portuguese  \n Let's make Hugging Face Datasets the central hub for datasets in Portuguese :)\r\n\r\n**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the Portuguese speaking community.\r\n\r\nWhat are some datasets in Portuguese worth integrating into the Hugging Face hub?\r\n\r\nSpecial thanks to @augusnunes for his collaboration on identifying the first ones:\r\n\r\n-  [NILC - USP](http:\/\/www.nilc.icmc.usp.br\/nilc\/index.php\/tools-and-resources).\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\ncc @osanseviero\r\n\r\n\r\n \n Hi @omarespejel! I think the philosophy for `datasets` issues is to create concrete issues with proposals to add a specific, individual dataset rather than umbrella issues for things such as datasets for a language, since we could end up with hundreds of issues (one per language). I see NILC - USP has many datasets, I would suggest to either create an issue for their datasets, or even better, we are trying to push to upload datasets as community datasets instead of adding them to the core library as guided in https:\/\/huggingface.co\/docs\/datasets\/share. That would have the additional benefit that the dataset would live under the NILC organization.\r\n\r\n@lhoestq correct me if I'm wrong please \ud83d\ude04  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3859","title":"Unable to dowload big_patent (FileNotFoundError)","comments":"Hi @slvcsl, thanks for reporting.\r\n\r\nYesterday we just made a patch release of our `datasets` library that fixes this issue: version 1.18.4.\r\nhttps:\/\/pypi.org\/project\/datasets\/#history\r\n\r\nPlease, feel free to update `datasets` library to the latest version: \r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then you should force redownload of the data file to update your local cache: \r\n```python\r\nds = load_dataset(\"big_patent\", \"g\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\n- Note that before the fix, you just downloaded and cached the Google Drive virus scan warning page, instead of the data file\r\n\r\nThis issue was already reported \r\n- #3784\r\n\r\nand its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe already fixed it. See:\r\n- #3787 \r\n","body":"## Describe the bug\r\nI am trying to download some splits of the big_patent dataset, using the following code:\r\n\r\n`ds = load_dataset(\"big_patent\", \"g\", split=\"validation\", download_mode=\"force_redownload\")\r\n`\r\nHowever, this leads to a FileNotFoundError.\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n[<ipython-input-3-8d8a745706a9>](https:\/\/localhost:8080\/#) in <module>()\r\n      1 from datasets import load_dataset\r\n----> 2 ds = load_dataset(\"big_patent\", \"g\", split=\"validation\", download_mode=\"force_redownload\")\r\n\r\n8 frames\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py](https:\/\/localhost:8080\/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1705         ignore_verifications=ignore_verifications,\r\n   1706         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1707         use_auth_token=use_auth_token,\r\n   1708     )\r\n   1709 \r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py](https:\/\/localhost:8080\/#) in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                     if not downloaded_from_gcs:\r\n    594                         self._download_and_prepare(\r\n--> 595                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    596                         )\r\n    597                     # Sync info\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py](https:\/\/localhost:8080\/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    659         split_dict = SplitDict(dataset_name=self.name)\r\n    660         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 661         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    662 \r\n    663         # Checksums verification\r\n\r\n[\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/big_patent\/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c\/big_patent.py](https:\/\/localhost:8080\/#) in _split_generators(self, dl_manager)\r\n    123         split_types = [\"train\", \"val\", \"test\"]\r\n    124         extract_paths = dl_manager.extract(\r\n--> 125             {k: os.path.join(dl_path, \"bigPatentData\", k + \".tar.gz\") for k in split_types}\r\n    126         )\r\n    127         extract_paths = {k: os.path.join(extract_paths[k], k) for k in split_types}\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py](https:\/\/localhost:8080\/#) in extract(self, path_or_paths, num_proc)\r\n    282         download_config.extract_compressed_file = True\r\n    283         extracted_paths = map_nested(\r\n--> 284             partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False\r\n    285         )\r\n    286         path_or_paths = NestedDataStructure(path_or_paths)\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/py_utils.py](https:\/\/localhost:8080\/#) in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm)\r\n    260         mapped = [\r\n    261             _single_map_nested((function, obj, types, None, True))\r\n--> 262             for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n    263         ]\r\n    264     else:\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/py_utils.py](https:\/\/localhost:8080\/#) in <listcomp>(.0)\r\n    260         mapped = [\r\n    261             _single_map_nested((function, obj, types, None, True))\r\n--> 262             for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n    263         ]\r\n    264     else:\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/py_utils.py](https:\/\/localhost:8080\/#) in _single_map_nested(args)\r\n    194     # Singleton first to spare some computation\r\n    195     if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 196         return function(data_struct)\r\n    197 \r\n    198     # Reduce logging to keep things readable in multiprocessing with tqdm\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py](https:\/\/localhost:8080\/#) in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    314     elif is_local_path(url_or_filename):\r\n    315         # File, but it doesn't exist.\r\n--> 316         raise FileNotFoundError(f\"Local file {url_or_filename} doesn't exist\")\r\n    317     else:\r\n    318         # Something unknown\r\n\r\nFileNotFoundError: Local file \/root\/.cache\/huggingface\/datasets\/downloads\/extracted\/ad068abb3e11f9f2f5440b62e37eb2b03ee515df9de1637c55cd1793b68668b2\/bigPatentData\/train.tar.gz doesn't exist\r\n\r\nI have tried this in a number of machines, including on Colab, so I think this is not environment dependent.\r\n\r\nHow do I load the bigPatent dataset?","comment_length":115,"text":"Unable to dowload big_patent (FileNotFoundError) \n ## Describe the bug\r\nI am trying to download some splits of the big_patent dataset, using the following code:\r\n\r\n`ds = load_dataset(\"big_patent\", \"g\", split=\"validation\", download_mode=\"force_redownload\")\r\n`\r\nHowever, this leads to a FileNotFoundError.\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n[<ipython-input-3-8d8a745706a9>](https:\/\/localhost:8080\/#) in <module>()\r\n      1 from datasets import load_dataset\r\n----> 2 ds = load_dataset(\"big_patent\", \"g\", split=\"validation\", download_mode=\"force_redownload\")\r\n\r\n8 frames\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py](https:\/\/localhost:8080\/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1705         ignore_verifications=ignore_verifications,\r\n   1706         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1707         use_auth_token=use_auth_token,\r\n   1708     )\r\n   1709 \r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py](https:\/\/localhost:8080\/#) in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                     if not downloaded_from_gcs:\r\n    594                         self._download_and_prepare(\r\n--> 595                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    596                         )\r\n    597                     # Sync info\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py](https:\/\/localhost:8080\/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    659         split_dict = SplitDict(dataset_name=self.name)\r\n    660         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 661         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    662 \r\n    663         # Checksums verification\r\n\r\n[\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/big_patent\/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c\/big_patent.py](https:\/\/localhost:8080\/#) in _split_generators(self, dl_manager)\r\n    123         split_types = [\"train\", \"val\", \"test\"]\r\n    124         extract_paths = dl_manager.extract(\r\n--> 125             {k: os.path.join(dl_path, \"bigPatentData\", k + \".tar.gz\") for k in split_types}\r\n    126         )\r\n    127         extract_paths = {k: os.path.join(extract_paths[k], k) for k in split_types}\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py](https:\/\/localhost:8080\/#) in extract(self, path_or_paths, num_proc)\r\n    282         download_config.extract_compressed_file = True\r\n    283         extracted_paths = map_nested(\r\n--> 284             partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False\r\n    285         )\r\n    286         path_or_paths = NestedDataStructure(path_or_paths)\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/py_utils.py](https:\/\/localhost:8080\/#) in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm)\r\n    260         mapped = [\r\n    261             _single_map_nested((function, obj, types, None, True))\r\n--> 262             for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n    263         ]\r\n    264     else:\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/py_utils.py](https:\/\/localhost:8080\/#) in <listcomp>(.0)\r\n    260         mapped = [\r\n    261             _single_map_nested((function, obj, types, None, True))\r\n--> 262             for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n    263         ]\r\n    264     else:\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/py_utils.py](https:\/\/localhost:8080\/#) in _single_map_nested(args)\r\n    194     # Singleton first to spare some computation\r\n    195     if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 196         return function(data_struct)\r\n    197 \r\n    198     # Reduce logging to keep things readable in multiprocessing with tqdm\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py](https:\/\/localhost:8080\/#) in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    314     elif is_local_path(url_or_filename):\r\n    315         # File, but it doesn't exist.\r\n--> 316         raise FileNotFoundError(f\"Local file {url_or_filename} doesn't exist\")\r\n    317     else:\r\n    318         # Something unknown\r\n\r\nFileNotFoundError: Local file \/root\/.cache\/huggingface\/datasets\/downloads\/extracted\/ad068abb3e11f9f2f5440b62e37eb2b03ee515df9de1637c55cd1793b68668b2\/bigPatentData\/train.tar.gz doesn't exist\r\n\r\nI have tried this in a number of machines, including on Colab, so I think this is not environment dependent.\r\n\r\nHow do I load the bigPatent dataset? \n Hi @slvcsl, thanks for reporting.\r\n\r\nYesterday we just made a patch release of our `datasets` library that fixes this issue: version 1.18.4.\r\nhttps:\/\/pypi.org\/project\/datasets\/#history\r\n\r\nPlease, feel free to update `datasets` library to the latest version: \r\n```shell\r\npip install -U datasets\r\n```\r\nAnd then you should force redownload of the data file to update your local cache: \r\n```python\r\nds = load_dataset(\"big_patent\", \"g\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\n- Note that before the fix, you just downloaded and cached the Google Drive virus scan warning page, instead of the data file\r\n\r\nThis issue was already reported \r\n- #3784\r\n\r\nand its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe already fixed it. See:\r\n- #3787 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3857","title":"Order of dataset changes due to glob.glob.","comments":"I agree using `glob.glob` alone is bad practice because it's not deterministic. Using `sorted` is a nice solution.\r\n\r\nNote that the `xglob` function you are referring to in the `streaming_download_manager.py` code just extends `glob.glob` for URLs - we don't change its behavior. That's why it has no `sorted()`","body":"## Describe the bug\r\n\r\nAfter discussion with @lhoestq, just want to mention here that `glob.glob(...)` should always be used in combination with `sorted(...)` to make sure the list of files returned by `glob.glob(...)` doesn't change depending on the OS system. \r\n\r\nThere are currently multiple datasets that use `glob.glob()` without making use of `sorted(...)` even the streaming download manager (if I'm not mistaken): \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c14bfeb4af89da14f870de5ddaa584b08aa08eeb\/src\/datasets\/utils\/streaming_download_manager.py#L483","comment_length":48,"text":"Order of dataset changes due to glob.glob. \n ## Describe the bug\r\n\r\nAfter discussion with @lhoestq, just want to mention here that `glob.glob(...)` should always be used in combination with `sorted(...)` to make sure the list of files returned by `glob.glob(...)` doesn't change depending on the OS system. \r\n\r\nThere are currently multiple datasets that use `glob.glob()` without making use of `sorted(...)` even the streaming download manager (if I'm not mistaken): \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c14bfeb4af89da14f870de5ddaa584b08aa08eeb\/src\/datasets\/utils\/streaming_download_manager.py#L483 \n I agree using `glob.glob` alone is bad practice because it's not deterministic. Using `sorted` is a nice solution.\r\n\r\nNote that the `xglob` function you are referring to in the `streaming_download_manager.py` code just extends `glob.glob` for URLs - we don't change its behavior. That's why it has no `sorted()`","embeddings":[-0.0024278788,-0.3010298014,-0.1189568862,0.1030942947,0.1896024942,-0.3035666943,0.2473374158,-0.0992213041,0.0932725295,0.1500161588,-0.2698244452,-0.0234249402,0.0356519297,0.0828587115,0.2031217217,0.0783789828,0.0705655292,0.1712586731,-0.299810499,-0.1218607649,-0.3105966747,0.1200750247,-0.2167549282,-0.1790893227,0.1936581731,0.0462515727,0.005676155,0.4292045534,-0.3670850098,-0.3727454543,-0.2406712919,0.3489483595,-0.1243047789,0.3379710019,-0.0001028676,-0.083656542,0.0129357716,0.0437236317,-0.2008921653,-0.1882362217,-0.2675737143,0.0309007969,-0.3639108837,0.0612706654,-0.0470190458,-0.2557655275,0.219542712,-0.1893503666,0.1566293985,0.267542243,0.278501451,0.2961478233,-0.0796055645,0.088737838,0.5229849815,0.0610817298,0.0155706238,0.2282912135,0.2922356725,0.299318254,-0.1464321017,0.3273721933,-0.118206434,-0.1036076173,0.3218753636,0.1478436738,-0.0689928085,-0.1347202212,-0.0509923361,0.1240315661,0.2547138333,-0.2356268764,-0.4690508544,-0.3165684342,0.1377796829,-0.3013277948,0.3187331557,0.2627427876,0.2551270723,0.0883730352,-0.0739430413,0.1611153185,0.0332602859,0.1405341327,-0.1492059827,0.3905848563,-0.1503960341,0.0256067831,0.1347731054,0.2214226872,0.1216893271,-0.2728053331,-0.2020950615,0.1126956791,-0.1821845174,0.0207657963,0.3582225442,0.4128097594,0.1720548868,0.2590597868,0.1746276021,0.0661374703,-0.17381455,0.0104107894,0.0517029352,0.4639857411,-0.0684362277,-0.1004230678,0.3889584541,0.1797581315,-0.0400288887,-0.0226429515,0.0884359851,-0.1022069603,-0.0471555032,0.1146285012,0.1994627714,0.0146946758,-0.303414464,-0.2451728582,0.0994824469,0.0477681719,0.1536649019,0.318390727,-0.2500185966,-0.0487566181,-0.0745517612,0.0934794992,0.0875718743,0.1235523075,-0.1519000083,-0.4035429657,-0.2463520169,0.1749241799,-0.222012803,-0.2110081315,0.1326159984,0.244417727,0.070275642,0.0103141349,0.1028052345,-0.125676915,0.3048266768,0.1124761179,0.1229911149,0.2755409181,-0.1385651529,0.1838986427,-0.2070230693,0.1691890508,-0.288123101,-0.4120264947,-0.0075717904,0.3168485761,-0.0576016791,0.0765498281,-0.0777204856,-0.0958045051,-0.0347949862,-0.3273604214,0.0196028575,0.0103108305,-0.004781242,-0.3278034329,-0.3227264583,0.0834190249,0.0223264452,-0.0751741454,-0.1281212717,0.0111903716,0.1748987734,0.0627151579,-0.1809512377,-0.1822589338,-0.2679776847,0.1382227689,0.12912637,-0.0907403678,-0.4851565957,0.1564984322,-0.4327246547,0.2601542175,0.1484549046,0.3623432219,0.2795422375,-0.3786308765,-0.0974713787,0.3717962801,0.2893102765,0.3647041619,-0.0786837265,-0.0308475848,-0.1979810297,-0.1440262794,-0.3000480831,0.1229887977,0.0626474321,-0.0461141579,0.4065995514,-0.1503685117,-0.2401465774,0.2809964716,0.3790040314,0.0950818881,0.1553979218,-0.0216881726,-0.2432284951,0.1683490574,-0.1660134345,-0.00799469,-0.1071996614,-0.4073869288,-0.0592596084,-0.1089068428,-0.1852240562,0.0675228909,0.2202925384,0.5063841343,0.0763627291,-0.0068671498,-0.0118607963,0.3149933517,-0.1914527267,-0.0448611565,-0.109645322,0.2080722898,-0.0834466144,0.0430358388,0.1640169322,-0.1757764518,0.0531349666,-0.0956596285,-0.0060093687,0.624889195,-0.0242801383,0.1941625327,0.0353236645,0.0052553229,0.1998539865,0.1118407026,0.0606410466,0.4743309319,-0.019369714,0.0421428159,0.0021496553,0.4418458641,0.2055894434,0.086261861,0.2136336863,-0.1761946976,-0.0095715476,0.1628030092,-0.1055845693,-0.1557449996,0.2065424323,0.2277876288,0.2814706862,0.3179132938,-0.4193255901,0.4482946694,0.8029441237,-0.1979241073,0.0793859065,0.0538402535,-0.1178063676,-0.1224348694,0.0804276466,0.2647716999,0.4423921406,0.3253397048,0.2410507053,-0.0520502962,0.04420707,-0.245423153,0.0843543932,-0.1077442318,-0.0363541096,0.2237228155,0.2957614958,-0.0280256215,-0.3833038807,-0.1459732503,0.0136879114,-0.1301027387,-0.1974382102,-0.0436572134,-0.1695073247,-0.4777666628,-0.2109214365,-0.3267460465,-0.4300376475,-0.218938604,0.1909625679,0.1763302386,-0.2032732368,0.1794314831,0.1448529065,0.0881595984,0.0879816785,-0.2213973999,-0.0608766973,0.2938155234,-0.1002674103,0.1316284239,0.0418625884,-0.1222731918,0.3301097751,-0.2791308463,-0.2254785597,-0.3218912482,-0.3011361063,-0.0547815487,-0.0526596196,-0.0332732908,0.0107229408,-0.0073156036,0.067955941,-0.0380981155,0.0550054349,-0.1560005844,-0.2122108191,0.0492785424,0.0231344327,-0.0033004801,-0.2606789172,-0.2389445305,-0.1619476229,-0.3508422077,0.0156305619,0.2190364599,0.3876532316,0.0268467907,-0.2463218719,-0.2387197316,-0.0084378095,-0.0622384585,-0.2017621398,-0.6392971873,0.3490802646,-0.2072499096,-0.2601878047,-0.3183811307,0.0293978788,-0.2741599381,0.3428639472,-0.2702059746,-0.3590189815,-0.1124824211,-0.0989151001,0.1192996204,0.0314329341,0.156312108,-0.1822867543,-0.1636268646,-0.2674310803,0.0840184391,0.0514327623,0.3413744569,0.3002271056,0.0775547847,-0.0430575646,0.1819957495,0.4467031658,-0.0830397531,0.0727400333,0.1969036758,-0.0603740029,0.4354903102,-0.2315902263,0.0404547341,0.0751066729,0.05072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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3855","title":"Bad error message when loading private dataset","comments":"We raise the error \u201c FileNotFoundError: can\u2019t find the dataset\u201d mainly to follow best practice in security (otherwise users could be able to guess what private repositories users\/orgs may have)\r\n\r\nWe can indeed reformulate this and add the \"If this is a private repository,...\" part !","body":"## Describe the bug\r\n\r\nA pretty common behavior of an interaction between the Hub and datasets is the following.\r\nAn organization adds a dataset in private mode and wants to load it afterward.\r\n\r\n\r\n```python\r\nfrom transformers import load_dataset\r\n\r\nds = load_dataset(\"NewT5\/dummy_data\", \"dummy\")\r\n```\r\n\r\nThis command then fails with:\r\n\r\n```bash\r\nFileNotFoundError: Couldn't find a dataset script at \/home\/patrick\/NewT5\/dummy_data\/dummy_data.py or any data file in the same directory. Couldn't find 'NewT5\/dummy_data' on the Hugging Face Hub either: FileNotFoundError: Dataset 'NewT5\/dummy_data' doesn't exist on the Hub\r\n```\r\n\r\n**even though** the user has access to the website `NewT5\/dummy_data` since she\/he is part of the org. \r\n\r\nWe need to improve the error message here similar to how @sgugger, @LysandreJik and @julien-c have done it for transformers IMO.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. execute the following code to see the different error messages between `transformes` and `datasets`.\r\n\r\n1. Transformers\r\n```python\r\nfrom transformers import BertModel\r\n\r\nBertModel.from_pretrained(\"NewT5\/dummy_model\")\r\n```\r\n\r\nThe error message is clearer here - it gives:\r\n\r\n```\r\nOSError: patrickvonplaten\/gpt2-xl is not a local folder and is not a valid model identifier listed on 'https:\/\/huggingface.co\/models'\r\nIf this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.\r\n```\r\n\r\nLet's maybe do the same for datasets? The PR was introduced to `transformers` here:\r\nhttps:\/\/github.com\/huggingface\/transformers\/pull\/15261\r\n\r\n## Expected results\r\n\r\nBetter error message\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4.dev0\r\n- Platform: Linux-5.15.15-76051515-generic-x86_64-with-glibc2.34\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n\r\n","comment_length":46,"text":"Bad error message when loading private dataset \n ## Describe the bug\r\n\r\nA pretty common behavior of an interaction between the Hub and datasets is the following.\r\nAn organization adds a dataset in private mode and wants to load it afterward.\r\n\r\n\r\n```python\r\nfrom transformers import load_dataset\r\n\r\nds = load_dataset(\"NewT5\/dummy_data\", \"dummy\")\r\n```\r\n\r\nThis command then fails with:\r\n\r\n```bash\r\nFileNotFoundError: Couldn't find a dataset script at \/home\/patrick\/NewT5\/dummy_data\/dummy_data.py or any data file in the same directory. Couldn't find 'NewT5\/dummy_data' on the Hugging Face Hub either: FileNotFoundError: Dataset 'NewT5\/dummy_data' doesn't exist on the Hub\r\n```\r\n\r\n**even though** the user has access to the website `NewT5\/dummy_data` since she\/he is part of the org. \r\n\r\nWe need to improve the error message here similar to how @sgugger, @LysandreJik and @julien-c have done it for transformers IMO.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. execute the following code to see the different error messages between `transformes` and `datasets`.\r\n\r\n1. Transformers\r\n```python\r\nfrom transformers import BertModel\r\n\r\nBertModel.from_pretrained(\"NewT5\/dummy_model\")\r\n```\r\n\r\nThe error message is clearer here - it gives:\r\n\r\n```\r\nOSError: patrickvonplaten\/gpt2-xl is not a local folder and is not a valid model identifier listed on 'https:\/\/huggingface.co\/models'\r\nIf this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.\r\n```\r\n\r\nLet's maybe do the same for datasets? The PR was introduced to `transformers` here:\r\nhttps:\/\/github.com\/huggingface\/transformers\/pull\/15261\r\n\r\n## Expected results\r\n\r\nBetter error message\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.4.dev0\r\n- Platform: Linux-5.15.15-76051515-generic-x86_64-with-glibc2.34\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n\r\n \n We raise the error \u201c FileNotFoundError: can\u2019t find the dataset\u201d mainly to follow best practice in security (otherwise users could be able to guess what private repositories users\/orgs may have)\r\n\r\nWe can indeed reformulate this and add the \"If this is a private repository,...\" part 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3854","title":"load only England English dataset from common voice english dataset","comments":"Hi @amanjaiswal777,\r\n\r\nFirst note that the dataset you are trying to load is deprecated: it was the Common Voice dataset release as of Dec 2020.\r\n\r\nCurrently, Common Voice dataset releases are directly hosted on the Hub, under the Mozilla Foundation organization: https:\/\/huggingface.co\/mozilla-foundation\r\n\r\nFor example, to get their latest Common Voice relase (8.0):\r\n- Go to the dataset page and request access permission (Mozilla Foundation requires this for people willing to use their datasets): https:\/\/huggingface.co\/datasets\/mozilla-foundation\/common_voice_8_0\r\n- Looking at the dataset card, you can check that data instances have, among other fields, the ones you are interested in: \"accent\", \"age\",... \r\n- Then you can load their \"en\" language dataset as usual, besides passing your authentication token (more info on auth token here: https:\/\/huggingface.co\/docs\/hub\/security)\r\n  ```python\r\n  from datasets import load_dataset\r\n  ds_en = load_dataset(\"mozilla-foundation\/common_voice_8_0\", \"en\", use_auth_token=True)\r\n  ```\r\n- Finally, you can filter only the data instances you are interested in (more info on `filter` here: https:\/\/huggingface.co\/docs\/datasets\/process#select-and-filter):\r\n  ```python\r\n  ds_england_en = ds_en.filter(lambda item: item[\"accent\"] == \"England English\")\r\n  ```\r\n\r\nFeel free to reopen this issue if you need further assistance.","body":"training_data = load_dataset(\"common_voice\", \"en\",split='train[:250]+validation[:250]')\r\ntesting_data = load_dataset(\"common_voice\", \"en\", split=\"test[:200]\")\r\n\r\nI'm trying to load only 8% of the English common voice data with accent == \"England English.\" Can somebody assist me with this?\r\n\r\n**Typical Voice Accent Proportions:**\r\n\r\n- 24% United States English \r\n- 8% England English \r\n- 5% India and South Asia (India, Pakistan, Sri Lanka) \r\n- 3% Australian English \r\n- 3% Canadian English \r\n- 2% Scottish English \r\n- 1% Irish English \r\n- 1% Southern African (South Africa, Zimbabwe, Namibia) \r\n- 1% New Zealand English\r\n\r\nCan we replicate this for Age as well?\r\n\r\n**Age proportions of the common voice:-**\r\n\r\n- 24% 19 - 29 \r\n- 14% 30 - 39 \r\n- 10% 40 - 49 \r\n- 6% < 19 \r\n- 4% 50 - 59 \r\n- 4% 60 - 69 \r\n- 1% 70 \u2013 79 ","comment_length":172,"text":"load only England English dataset from common voice english dataset \n training_data = load_dataset(\"common_voice\", \"en\",split='train[:250]+validation[:250]')\r\ntesting_data = load_dataset(\"common_voice\", \"en\", split=\"test[:200]\")\r\n\r\nI'm trying to load only 8% of the English common voice data with accent == \"England English.\" Can somebody assist me with this?\r\n\r\n**Typical Voice Accent Proportions:**\r\n\r\n- 24% United States English \r\n- 8% England English \r\n- 5% India and South Asia (India, Pakistan, Sri Lanka) \r\n- 3% Australian English \r\n- 3% Canadian English \r\n- 2% Scottish English \r\n- 1% Irish English \r\n- 1% Southern African (South Africa, Zimbabwe, Namibia) \r\n- 1% New Zealand English\r\n\r\nCan we replicate this for Age as well?\r\n\r\n**Age proportions of the common voice:-**\r\n\r\n- 24% 19 - 29 \r\n- 14% 30 - 39 \r\n- 10% 40 - 49 \r\n- 6% < 19 \r\n- 4% 50 - 59 \r\n- 4% 60 - 69 \r\n- 1% 70 \u2013 79  \n Hi @amanjaiswal777,\r\n\r\nFirst note that the dataset you are trying to load is deprecated: it was the Common Voice dataset release as of Dec 2020.\r\n\r\nCurrently, Common Voice dataset releases are directly hosted on the Hub, under the Mozilla Foundation organization: https:\/\/huggingface.co\/mozilla-foundation\r\n\r\nFor example, to get their latest Common Voice relase (8.0):\r\n- Go to the dataset page and request access permission (Mozilla Foundation requires this for people willing to use their datasets): https:\/\/huggingface.co\/datasets\/mozilla-foundation\/common_voice_8_0\r\n- Looking at the dataset card, you can check that data instances have, among other fields, the ones you are interested in: \"accent\", \"age\",... \r\n- Then you can load their \"en\" language dataset as usual, besides passing your authentication token (more info on auth token here: https:\/\/huggingface.co\/docs\/hub\/security)\r\n  ```python\r\n  from datasets import load_dataset\r\n  ds_en = load_dataset(\"mozilla-foundation\/common_voice_8_0\", \"en\", use_auth_token=True)\r\n  ```\r\n- Finally, you can filter only the data instances you are interested in (more info on `filter` here: https:\/\/huggingface.co\/docs\/datasets\/process#select-and-filter):\r\n  ```python\r\n  ds_england_en = ds_en.filter(lambda item: item[\"accent\"] == \"England English\")\r\n  ```\r\n\r\nFeel free to reopen this issue if you need further assistance.","embeddings":[-0.1626533121,0.006645842,-0.0680129007,-0.0724869892,0.1917734146,0.144611612,0.5200459361,0.2401535213,0.0146087687,0.08605811,-0.4945832491,-0.2602890134,0.2044374794,-0.1561595201,-0.2082675099,-0.0381299406,-0.1385681778,0.2314250022,0.3187884688,-0.3509130776,0.2503094375,-0.1115815043,-0.1239312142,0.1035627425,-0.3548627794,0.0593072735,0.1205281913,0.2372466922,-0.1382794082,0.0795694962,0.4342964292,0.0965993702,0.0610721409,0.1016036496,-0.0001166282,-0.112390615,0.1535633653,-0.1980030686,0.0334327295,-0.075373292,-0.1577675641,0.3177829087,-0.0563414246,-0.0603828728,-0.117915988,0.2991825044,-0.1959058046,-0.3585818112,0.6529741287,0.2741493285,0.1621575207,-0.0508313105,-0.4198023081,0.2071216702,0.0506918654,0.0018480628,0.1284767091,0.4298832715,0.0918025672,-0.0141987577,-0.0208566114,0.3105449975,-0.1517436355,-0.0386488102,-0.2900732458,0.0932241827,0.0189383402,-0.4673178196,0.2494369447,0.4235194623,0.3073986471,-0.0447184741,-0.2448137999,-0.1975494921,0.1060965061,-0.1420686692,0.021197442,0.1835011393,-0.1842531115,0.4176616371,-0.2250493765,-0.133966133,-0.0343175046,0.2777761519,-0.2427530587,0.3359062374,0.0418735668,-0.0250760168,0.2844216228,-0.2450345457,-0.1413979232,-0.0119018806,0.1014415845,0.5277354717,-0.2945689261,-0.1989199966,-0.3142452538,-0.1765630543,0.4934430122,-0.0614730231,0.0497606061,0.1731750965,-0.4262276292,0.0165638682,0.323664248,0.2762878537,0.3461343646,0.0343388133,0.0627195761,-0.0279852226,0.0469608419,0.1110305265,-0.3098829985,-0.2250702083,0.152113691,0.2015547603,0.0415371507,-0.5437754393,-0.5168312192,0.0325994454,0.0667411759,-0.1473622173,-0.0211261958,0.1896993518,0.18397066,0.7756183147,0.1477144212,0.4744293392,-0.4340798259,-0.4383442402,-0.0605360605,0.3679068685,-0.2703415751,-0.1553070843,0.3383284211,-0.4679102302,0.1132558435,0.3345911801,0.1416130066,-0.3451961279,-0.0416863114,-0.1509883553,-0.026467979,-0.0951770842,-0.0480397269,0.2167366594,0.2654091418,-0.1609548628,-0.2191589922,0.3500835299,-0.1333448887,-0.1169254184,-0.0519039258,0.187348336,-0.3149970472,-0.2320450395,-0.0798444152,0.4302381873,0.2331581265,-0.1281090081,0.1068847328,0.0623218119,0.0203429777,0.1786412895,0.3711982071,0.3264192045,-0.2245594263,-0.21575737,-0.3459850848,-0.1998174042,0.1475078613,0.3084806204,0.0394998901,-0.0654992536,-0.0214943569,0.1692523509,0.3668355644,-0.354665935,-0.3593708575,0.1119661331,-0.0073397448,-0.0199712645,-0.0594601966,-0.2629775405,0.1145817265,0.0816386342,0.2604739368,0.6405436397,0.1166642383,0.0464653559,0.0635703728,-0.2897794545,0.1354347914,0.2946453989,0.0333785601,0.0054166866,-0.0270138998,0.1330634654,0.3880604804,-0.1982971281,0.1911536008,-0.0703934059,0.0171639435,-0.2628725767,-0.2506577671,-0.2678254545,-0.1792818457,0.2207079679,0.1674523652,-0.0221699271,0.3807222545,0.0680767745,-0.3849970698,-0.2881516516,-0.2257810533,-0.0819552466,0.0204004571,-0.2865925729,0.0823283494,-0.2497664988,-0.2319968492,-0.0175440051,0.2953227758,-0.0202412549,-0.1007046774,0.2127567083,0.1210130602,-0.019425476,0.1057510898,0.320885092,0.182010591,0.2503682077,-0.1813220233,0.2155875713,0.0553352833,0.4699525833,-0.1964366436,-0.048060447,0.1691497564,-0.405813545,0.2731774151,-0.0944049805,0.0163115822,-0.02773582,0.0592834577,0.5545339584,0.3355201781,0.216299817,-0.1244542226,-0.127887547,-0.0384160057,-0.2165102512,-0.0246278755,-0.2768107355,0.290191561,0.1643131077,0.3405989408,-0.0332158059,-0.2013852298,-0.064400658,-0.0458472222,-0.2180229872,0.0917488337,0.1220283508,-0.2472975701,-0.0717211291,0.0344351381,-0.3670791686,0.3224883378,0.2227310687,0.2258178592,0.37736094,0.2488111556,0.0264352765,0.0326383896,-0.0946618766,0.0400480591,0.2485366464,0.0931701362,-0.0313228965,-0.5917181969,-0.2553531826,-0.1300874054,-0.1813628376,-0.4558275342,0.0074795098,-0.1831947714,0.4088342786,-0.0362979062,-0.3352953196,-0.2569250464,0.0566813722,0.0244082864,0.0162497219,-0.0187375098,0.0403886512,-0.4035811722,-0.1044735536,-0.1123176366,-0.2327421457,-0.2268542498,-0.0495365895,-0.1971724629,0.0756417438,-0.0328685008,0.2492743582,0.2373070866,-0.4021543562,-0.0943988264,0.2649933994,-0.1507182717,-0.0484308563,-0.0200698338,0.0618821532,-0.1169882566,0.2612067461,-0.140640229,-0.1266097575,0.0437246859,-0.2075289488,-0.1805600524,0.0290441513,-0.0726504847,0.3201825619,0.1418702304,-0.4972704351,-0.3525401652,-0.2220108956,-0.2046011686,-0.0786092132,0.1533882916,0.5186457038,-0.0775760114,-0.0707638115,0.0523242727,0.12112315,-0.5544793606,-0.0332393013,0.371290952,0.0187304653,-0.043872755,0.0232566241,-0.0721088573,-0.0657268614,-0.0684181377,-0.1123189107,0.1269305795,-0.2825592458,0.0221948158,-0.3234644234,0.3984655142,0.1402024031,-0.0734954551,-0.0533047244,-0.0397834107,-0.0898066536,0.0327421539,0.1973617226,-0.0578233935,-0.2501807809,0.2127292305,-0.0936787352,0.675146997,0.1347127855,0.1146573052,0.3498958647,-0.0549184941,0.1251315325,-0.1196512505,-0.146325767,0.053142257,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3851","title":"Load audio dataset error","comments":"Hi @lemoner20, thanks for reporting.\r\n\r\nI'm sorry but I cannot reproduce your problem:\r\n```python\r\nIn [1]: from datasets import load_dataset, load_metric, Audio\r\n   ...: raw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\n   ...: print(raw_datasets[0][\"audio\"])\r\nDownloading builder script: 30.2kB [00:00, 13.0MB\/s]                                                                                                                                                       \r\nDownloading metadata: 38.0kB [00:00, 16.6MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset superb\/ks (download: 1.45 GiB, generated: 9.64 MiB, post-processed: Unknown size, total: 1.46 GiB) to ...\/.cache\/huggingface\/datasets\/superb\/ks\/1.9.0\/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9...\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1.49G\/1.49G [00:37<00:00, 39.3MB\/s]\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 71.3M\/71.3M [00:01<00:00, 36.1MB\/s]\r\nDownloading data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:41<00:00, 20.67s\/it]\r\nExtracting data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:28<00:00, 14.24s\/it]\r\nDataset superb downloaded and prepared to ...\/.cache\/huggingface\/datasets\/superb\/ks\/1.9.0\/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9. Subsequent calls will reuse this data.\r\n{'path': '...\/.cache\/huggingface\/datasets\/downloads\/extracted\/8571921d3088b48f58f75b2e514815033e1ffbd06aa63fd4603691ac9f1c119f\/_background_noise_\/doing_the_dishes.wav', 'array': array([ 0.        ,  0.        ,  0.        , ..., -0.00592041,\r\n       -0.00405884, -0.00253296], dtype=float32), 'sampling_rate': 16000}\r\n``` \r\n\r\nWhich version of `datasets` are you using? Could you please fill in the environment info requested in the bug report template? You can run the command `datasets-cli env` and copy-and-paste its output below\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:","body":"## Load audio dataset error\r\n\r\nHi, when I load audio dataset following https:\/\/huggingface.co\/docs\/datasets\/audio_process and https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/superb,\r\n```\r\nfrom datasets import load_dataset, load_metric, Audio\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\nprint(raw_datasets[0][\"audio\"])\r\n```\r\nfollowing errors occur \r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-169-3f8253239fa0> in <module>\r\n----> 1 raw_datasets[0][\"audio\"]\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1924         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1925         return self._getitem(\r\n-> 1926             key,\r\n   1927         )\r\n   1928 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1909         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1910         formatted_output = format_table(\r\n-> 1911             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1912         )\r\n   1913         return formatted_output\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    530     python_formatter = PythonFormatter(features=None)\r\n    531     if format_columns is None:\r\n--> 532         return formatter(pa_table, query_type=query_type)\r\n    533     elif query_type == \"column\":\r\n    534         if key in format_columns:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    279     def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:\r\n    280         if query_type == \"row\":\r\n--> 281             return self.format_row(pa_table)\r\n    282         elif query_type == \"column\":\r\n    283             return self.format_column(pa_table)\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_row(self, pa_table)\r\n    310         row = self.python_arrow_extractor().extract_row(pa_table)\r\n    311         if self.decoded:\r\n--> 312             row = self.python_features_decoder.decode_row(row)\r\n    313         return row\r\n    314 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_row(self, row)\r\n    219 \r\n    220     def decode_row(self, row: dict) -> dict:\r\n--> 221         return self.features.decode_example(row) if self.features else row\r\n    222 \r\n    223     def decode_column(self, column: list, column_name: str) -> list:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_example(self, example)\r\n   1320             else value\r\n   1321             for column_name, (feature, value) in utils.zip_dict(\r\n-> 1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n   1324         }\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <dictcomp>(.0)\r\n   1319             if self._column_requires_decoding[column_name]\r\n   1320             else value\r\n-> 1321             for column_name, (feature, value) in utils.zip_dict(\r\n   1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_nested_example(schema, obj)\r\n   1053     # Object with special decoding:\r\n   1054     elif isinstance(schema, (Audio, Image)):\r\n-> 1055         return schema.decode_example(obj) if obj is not None else None\r\n   1056     return obj\r\n   1057 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n    100                 array, sampling_rate = self._decode_non_mp3_file_like(file)\r\n    101             else:\r\n--> 102                 array, sampling_rate = self._decode_non_mp3_path_like(path)\r\n    103         return {\"path\": path, \"array\": array, \"sampling_rate\": sampling_rate}\r\n    104 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in _decode_non_mp3_path_like(self, path)\r\n    143 \r\n    144         with xopen(path, \"rb\") as f:\r\n--> 145             array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)\r\n    146         return array, sampling_rate\r\n    147 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/librosa\/core\/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)\r\n    110 \r\n    111     y = []\r\n--> 112     with audioread.audio_open(os.path.realpath(path)) as input_file:\r\n    113         sr_native = input_file.samplerate\r\n    114         n_channels = input_file.channels\r\n\r\n\/usr\/lib\/python3.6\/posixpath.py in realpath(filename)\r\n    392     \"\"\"Return the canonical path of the specified filename, eliminating any\r\n    393 symbolic links encountered in the path.\"\"\"\r\n--> 394     filename = os.fspath(filename)\r\n    395     path, ok = _joinrealpath(filename[:0], filename, {})\r\n    396     return abspath(path)\r\n\r\nTypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader\r\n```\r\n\r\n## Expected results\r\n```\r\n>>> raw_datasets[0][\"audio\"]\r\n{'array': array([-0.0005188 , -0.00109863,  0.00030518, ...,  0.01730347,\r\n        0.01623535,  0.01724243]),\r\n'path': '\/root\/.cache\/huggingface\/datasets\/downloads\/extracted\/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7\/bed\/026290a7_nohash_0.wav',\r\n'sampling_rate': 16000}\r\n```","comment_length":178,"text":"Load audio dataset error \n ## Load audio dataset error\r\n\r\nHi, when I load audio dataset following https:\/\/huggingface.co\/docs\/datasets\/audio_process and https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/superb,\r\n```\r\nfrom datasets import load_dataset, load_metric, Audio\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\nprint(raw_datasets[0][\"audio\"])\r\n```\r\nfollowing errors occur \r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-169-3f8253239fa0> in <module>\r\n----> 1 raw_datasets[0][\"audio\"]\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1924         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1925         return self._getitem(\r\n-> 1926             key,\r\n   1927         )\r\n   1928 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1909         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1910         formatted_output = format_table(\r\n-> 1911             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1912         )\r\n   1913         return formatted_output\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    530     python_formatter = PythonFormatter(features=None)\r\n    531     if format_columns is None:\r\n--> 532         return formatter(pa_table, query_type=query_type)\r\n    533     elif query_type == \"column\":\r\n    534         if key in format_columns:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    279     def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:\r\n    280         if query_type == \"row\":\r\n--> 281             return self.format_row(pa_table)\r\n    282         elif query_type == \"column\":\r\n    283             return self.format_column(pa_table)\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_row(self, pa_table)\r\n    310         row = self.python_arrow_extractor().extract_row(pa_table)\r\n    311         if self.decoded:\r\n--> 312             row = self.python_features_decoder.decode_row(row)\r\n    313         return row\r\n    314 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_row(self, row)\r\n    219 \r\n    220     def decode_row(self, row: dict) -> dict:\r\n--> 221         return self.features.decode_example(row) if self.features else row\r\n    222 \r\n    223     def decode_column(self, column: list, column_name: str) -> list:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_example(self, example)\r\n   1320             else value\r\n   1321             for column_name, (feature, value) in utils.zip_dict(\r\n-> 1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n   1324         }\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <dictcomp>(.0)\r\n   1319             if self._column_requires_decoding[column_name]\r\n   1320             else value\r\n-> 1321             for column_name, (feature, value) in utils.zip_dict(\r\n   1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_nested_example(schema, obj)\r\n   1053     # Object with special decoding:\r\n   1054     elif isinstance(schema, (Audio, Image)):\r\n-> 1055         return schema.decode_example(obj) if obj is not None else None\r\n   1056     return obj\r\n   1057 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n    100                 array, sampling_rate = self._decode_non_mp3_file_like(file)\r\n    101             else:\r\n--> 102                 array, sampling_rate = self._decode_non_mp3_path_like(path)\r\n    103         return {\"path\": path, \"array\": array, \"sampling_rate\": sampling_rate}\r\n    104 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in _decode_non_mp3_path_like(self, path)\r\n    143 \r\n    144         with xopen(path, \"rb\") as f:\r\n--> 145             array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)\r\n    146         return array, sampling_rate\r\n    147 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/librosa\/core\/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)\r\n    110 \r\n    111     y = []\r\n--> 112     with audioread.audio_open(os.path.realpath(path)) as input_file:\r\n    113         sr_native = input_file.samplerate\r\n    114         n_channels = input_file.channels\r\n\r\n\/usr\/lib\/python3.6\/posixpath.py in realpath(filename)\r\n    392     \"\"\"Return the canonical path of the specified filename, eliminating any\r\n    393 symbolic links encountered in the path.\"\"\"\r\n--> 394     filename = os.fspath(filename)\r\n    395     path, ok = _joinrealpath(filename[:0], filename, {})\r\n    396     return abspath(path)\r\n\r\nTypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader\r\n```\r\n\r\n## Expected results\r\n```\r\n>>> raw_datasets[0][\"audio\"]\r\n{'array': array([-0.0005188 , -0.00109863,  0.00030518, ...,  0.01730347,\r\n        0.01623535,  0.01724243]),\r\n'path': '\/root\/.cache\/huggingface\/datasets\/downloads\/extracted\/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7\/bed\/026290a7_nohash_0.wav',\r\n'sampling_rate': 16000}\r\n``` \n Hi @lemoner20, thanks for reporting.\r\n\r\nI'm sorry but I cannot reproduce your problem:\r\n```python\r\nIn [1]: from datasets import load_dataset, load_metric, Audio\r\n   ...: raw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\n   ...: print(raw_datasets[0][\"audio\"])\r\nDownloading builder script: 30.2kB [00:00, 13.0MB\/s]                                                                                                                                                       \r\nDownloading metadata: 38.0kB [00:00, 16.6MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset superb\/ks (download: 1.45 GiB, generated: 9.64 MiB, post-processed: Unknown size, total: 1.46 GiB) to ...\/.cache\/huggingface\/datasets\/superb\/ks\/1.9.0\/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9...\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1.49G\/1.49G [00:37<00:00, 39.3MB\/s]\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 71.3M\/71.3M [00:01<00:00, 36.1MB\/s]\r\nDownloading data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:41<00:00, 20.67s\/it]\r\nExtracting data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:28<00:00, 14.24s\/it]\r\nDataset superb downloaded and prepared to ...\/.cache\/huggingface\/datasets\/superb\/ks\/1.9.0\/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9. Subsequent calls will reuse this data.\r\n{'path': '...\/.cache\/huggingface\/datasets\/downloads\/extracted\/8571921d3088b48f58f75b2e514815033e1ffbd06aa63fd4603691ac9f1c119f\/_background_noise_\/doing_the_dishes.wav', 'array': array([ 0.        ,  0.        ,  0.        , ..., -0.00592041,\r\n       -0.00405884, -0.00253296], dtype=float32), 'sampling_rate': 16000}\r\n``` \r\n\r\nWhich version of `datasets` are you using? Could you please fill in the environment info requested in the bug report template? You can run the command `datasets-cli env` and copy-and-paste its output below\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:","embeddings":[-0.376989007,-0.1154067442,-0.0308201592,0.3962246776,0.4579705,0.0699844882,0.2902420163,0.315089494,-0.0530283712,0.1748973876,-0.5356046557,0.317201376,-0.2974062562,0.0095626945,-0.0179306716,-0.4144312143,-0.0624868944,0.1607668251,-0.3674666882,0.0896921158,-0.3725435138,0.1743073761,-0.3261790574,0.2865290344,-0.2181515396,-0.10664922,0.1983113587,0.1052075475,-0.404435873,-0.5568415523,0.2095936835,-0.2148836255,0.2718895376,0.6587945819,-0.000109111,0.1062148288,0.3792859614,-0.0474429391,-0.4135835469,-0.2822248042,-0.4285669625,0.059622068,0.1489737183,-0.1274241656,-0.0034842102,-0.4431416392,-0.1901090294,-0.3268806934,0.4615101218,0.2684746981,0.2999238074,0.4350044727,0.1923298091,-0.2203475088,0.1194317862,0.1547943652,0.017395854,0.4202326536,0.0091918949,0.049166631,0.0069469907,0.2984599769,-0.173876524,0.1763353944,0.229850933,-0.1352755725,-0.1675219834,-0.1879023314,0.1613014638,0.1987244338,0.4726320505,-0.4815639853,-0.345949024,-0.0386826806,0.0690231472,-0.4702769816,0.0801246241,0.171560064,-0.0972005725,0.0952937007,-0.2051635236,0.0465240851,-0.0686939806,0.1682745963,0.2375473976,0.0064473674,-0.3387325704,0.0372266807,0.273814708,-0.2141977251,-0.1669427454,0.0483627655,-0.1655764282,0.3656981587,-0.4240424633,0.1116565242,0.0456984416,-0.1373231411,0.0146452794,-0.0403533019,0.0176255833,0.0415858664,0.0705679134,0.1646611989,0.2296693176,0.0986420959,0.1528075933,0.1120617241,0.1326157898,0.2336945534,0.092487976,-0.0318907984,-0.2244521081,-0.2344273031,0.0350971632,0.1130264923,0.456083864,-0.2372625768,-0.409147054,0.027019009,-0.0974412858,-0.0856006518,0.3154965639,0.2793029249,0.2499225587,0.10256318,0.2195274979,0.3914911151,-0.1359719932,0.0922218338,-0.134235695,0.083378993,-0.030907128,0.1335761994,0.2870156765,-0.2224531174,0.1550423354,0.2077090293,0.1261924654,-0.16103293,0.0189269856,-0.0493065715,-0.227455765,0.3239480853,-0.0960232988,0.1498952359,0.139388755,0.0410625748,-0.1044392884,0.0706788749,-0.1952906847,-0.302963227,-0.1049852967,0.2461148798,-0.0528141893,0.1340735853,-0.4590172768,-0.0732226595,0.1385058463,-0.3459163308,0.0648967326,-0.3205714822,-0.2848424911,0.0183808319,0.355086118,0.2735080421,-0.2584558725,-0.1789945662,-0.111605078,-0.2246555686,0.0354220718,0.3527233005,0.0290172044,0.2925767004,-0.149298802,-0.0259823166,0.297718823,-0.3080951869,-0.2015809566,-0.06839443,-0.0213344693,0.1206243411,-0.0819287077,-0.1163107902,0.1097762585,-0.04683052,0.3195449114,0.0579701215,0.0881781802,-0.0640451089,-0.0038452256,-0.1055636629,-0.0519391112,0.2132578343,-0.2208278775,0.0910755321,-0.0467555076,-0.0094854934,0.0476786196,-0.0992689729,-0.0255504418,0.1598064154,0.2196207047,0.0084250206,-0.0835880488,-0.4422390461,-0.2576403022,0.0173043888,-0.091310598,0.1824981868,-0.3626769483,-0.0666077808,-0.2478414625,0.137556091,-0.3986143172,0.058208283,0.1390697956,0.0923515409,-0.0479778275,0.0631794259,-0.2436275631,-0.0493089855,-0.0577989072,0.0540165901,-0.4655534029,0.0597283319,0.0191242341,-0.1688705683,0.2765914798,0.3562172651,0.2761615217,-0.0265946649,-0.2120907903,0.5099466443,0.1067440435,0.1462802887,-0.3429247439,0.0101277186,0.0833965987,-0.3226528764,-0.0090679778,-0.0623293482,0.293744266,-0.0997376814,-0.0672299415,0.4583495259,-0.1053345054,0.3200315535,0.004308546,-0.0589840673,0.1802778244,0.412814796,-0.0799909458,-0.1413908899,0.1623201221,-0.1702558994,0.1915431321,-0.0547325835,-0.3017842472,-0.3185960054,0.2729857862,-0.1987490207,0.0974563882,0.1345564872,-0.1964375377,0.0145389801,0.2190857232,-0.0600779429,0.3460257351,0.1377629042,-0.1827099323,0.0120346081,-0.0652056336,-0.0716543943,0.2884761095,0.133720845,0.0928261206,0.3119989038,0.1078322306,0.0081727179,-0.4893481731,-0.1266755462,-0.173090592,0.3339040279,-0.529899776,-0.1459741294,-0.3315993845,-0.2488905936,-0.1643964946,-0.3393754065,-0.0086977268,-0.1727548838,-0.0739424825,0.2299685031,-0.2515610456,0.3363698423,-0.0980426967,0.1093043759,0.1906432658,-0.0513187945,-0.1053448692,-0.0696240067,-0.0017023538,0.0683408827,0.4655221105,0.2131136656,0.299957931,-0.1394961476,0.0469153374,-0.1156080291,-0.0262309145,0.0053244438,-0.1554774344,0.222000584,0.0740114152,0.2549060285,-0.0642984584,-0.6536605358,0.4810533822,-0.0160904154,-0.1452650726,0.4130653143,-0.0233468972,0.0764258802,-0.1380885988,-0.3018046021,-0.1945600659,-0.5705913305,0.322735548,-0.125769943,0.2020571381,0.3859262168,-0.0013873472,0.3092149794,-0.015165925,0.3429301679,-0.2122425735,-0.1508434862,0.2838156223,-0.2811264694,-0.2468169034,-0.3224189878,0.05522383,0.2796715498,-0.0112670194,-0.3107510805,-0.1082311645,-0.0971762016,-0.1651502699,0.0173945874,0.0871351957,0.0340443514,0.2222821414,-0.0409988873,-0.071955733,-0.1744190603,-0.0211803447,-0.0760608315,-0.1083604917,0.0802405328,0.7200372815,-0.3476119339,0.5735509396,0.2487341911,0.0438383631,0.3965495527,-0.0171002746,0.25107494,-0.430244267,-0.4340074956,-0.0733803585,-0.118630439,0.0843298137,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3851","title":"Load audio dataset error","comments":"@albertvillanova Thanks for your reply. The environment info below\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-4.19.91-007.ali4000.alios7.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.12\r\n- PyArrow version: 6.0.1","body":"## Load audio dataset error\r\n\r\nHi, when I load audio dataset following https:\/\/huggingface.co\/docs\/datasets\/audio_process and https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/superb,\r\n```\r\nfrom datasets import load_dataset, load_metric, Audio\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\nprint(raw_datasets[0][\"audio\"])\r\n```\r\nfollowing errors occur \r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-169-3f8253239fa0> in <module>\r\n----> 1 raw_datasets[0][\"audio\"]\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1924         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1925         return self._getitem(\r\n-> 1926             key,\r\n   1927         )\r\n   1928 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1909         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1910         formatted_output = format_table(\r\n-> 1911             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1912         )\r\n   1913         return formatted_output\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    530     python_formatter = PythonFormatter(features=None)\r\n    531     if format_columns is None:\r\n--> 532         return formatter(pa_table, query_type=query_type)\r\n    533     elif query_type == \"column\":\r\n    534         if key in format_columns:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    279     def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:\r\n    280         if query_type == \"row\":\r\n--> 281             return self.format_row(pa_table)\r\n    282         elif query_type == \"column\":\r\n    283             return self.format_column(pa_table)\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_row(self, pa_table)\r\n    310         row = self.python_arrow_extractor().extract_row(pa_table)\r\n    311         if self.decoded:\r\n--> 312             row = self.python_features_decoder.decode_row(row)\r\n    313         return row\r\n    314 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_row(self, row)\r\n    219 \r\n    220     def decode_row(self, row: dict) -> dict:\r\n--> 221         return self.features.decode_example(row) if self.features else row\r\n    222 \r\n    223     def decode_column(self, column: list, column_name: str) -> list:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_example(self, example)\r\n   1320             else value\r\n   1321             for column_name, (feature, value) in utils.zip_dict(\r\n-> 1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n   1324         }\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <dictcomp>(.0)\r\n   1319             if self._column_requires_decoding[column_name]\r\n   1320             else value\r\n-> 1321             for column_name, (feature, value) in utils.zip_dict(\r\n   1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_nested_example(schema, obj)\r\n   1053     # Object with special decoding:\r\n   1054     elif isinstance(schema, (Audio, Image)):\r\n-> 1055         return schema.decode_example(obj) if obj is not None else None\r\n   1056     return obj\r\n   1057 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n    100                 array, sampling_rate = self._decode_non_mp3_file_like(file)\r\n    101             else:\r\n--> 102                 array, sampling_rate = self._decode_non_mp3_path_like(path)\r\n    103         return {\"path\": path, \"array\": array, \"sampling_rate\": sampling_rate}\r\n    104 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in _decode_non_mp3_path_like(self, path)\r\n    143 \r\n    144         with xopen(path, \"rb\") as f:\r\n--> 145             array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)\r\n    146         return array, sampling_rate\r\n    147 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/librosa\/core\/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)\r\n    110 \r\n    111     y = []\r\n--> 112     with audioread.audio_open(os.path.realpath(path)) as input_file:\r\n    113         sr_native = input_file.samplerate\r\n    114         n_channels = input_file.channels\r\n\r\n\/usr\/lib\/python3.6\/posixpath.py in realpath(filename)\r\n    392     \"\"\"Return the canonical path of the specified filename, eliminating any\r\n    393 symbolic links encountered in the path.\"\"\"\r\n--> 394     filename = os.fspath(filename)\r\n    395     path, ok = _joinrealpath(filename[:0], filename, {})\r\n    396     return abspath(path)\r\n\r\nTypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader\r\n```\r\n\r\n## Expected results\r\n```\r\n>>> raw_datasets[0][\"audio\"]\r\n{'array': array([-0.0005188 , -0.00109863,  0.00030518, ...,  0.01730347,\r\n        0.01623535,  0.01724243]),\r\n'path': '\/root\/.cache\/huggingface\/datasets\/downloads\/extracted\/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7\/bed\/026290a7_nohash_0.wav',\r\n'sampling_rate': 16000}\r\n```","comment_length":27,"text":"Load audio dataset error \n ## Load audio dataset error\r\n\r\nHi, when I load audio dataset following https:\/\/huggingface.co\/docs\/datasets\/audio_process and https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/superb,\r\n```\r\nfrom datasets import load_dataset, load_metric, Audio\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\nprint(raw_datasets[0][\"audio\"])\r\n```\r\nfollowing errors occur \r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-169-3f8253239fa0> in <module>\r\n----> 1 raw_datasets[0][\"audio\"]\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1924         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1925         return self._getitem(\r\n-> 1926             key,\r\n   1927         )\r\n   1928 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1909         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1910         formatted_output = format_table(\r\n-> 1911             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1912         )\r\n   1913         return formatted_output\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    530     python_formatter = PythonFormatter(features=None)\r\n    531     if format_columns is None:\r\n--> 532         return formatter(pa_table, query_type=query_type)\r\n    533     elif query_type == \"column\":\r\n    534         if key in format_columns:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    279     def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:\r\n    280         if query_type == \"row\":\r\n--> 281             return self.format_row(pa_table)\r\n    282         elif query_type == \"column\":\r\n    283             return self.format_column(pa_table)\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_row(self, pa_table)\r\n    310         row = self.python_arrow_extractor().extract_row(pa_table)\r\n    311         if self.decoded:\r\n--> 312             row = self.python_features_decoder.decode_row(row)\r\n    313         return row\r\n    314 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_row(self, row)\r\n    219 \r\n    220     def decode_row(self, row: dict) -> dict:\r\n--> 221         return self.features.decode_example(row) if self.features else row\r\n    222 \r\n    223     def decode_column(self, column: list, column_name: str) -> list:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_example(self, example)\r\n   1320             else value\r\n   1321             for column_name, (feature, value) in utils.zip_dict(\r\n-> 1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n   1324         }\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <dictcomp>(.0)\r\n   1319             if self._column_requires_decoding[column_name]\r\n   1320             else value\r\n-> 1321             for column_name, (feature, value) in utils.zip_dict(\r\n   1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_nested_example(schema, obj)\r\n   1053     # Object with special decoding:\r\n   1054     elif isinstance(schema, (Audio, Image)):\r\n-> 1055         return schema.decode_example(obj) if obj is not None else None\r\n   1056     return obj\r\n   1057 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n    100                 array, sampling_rate = self._decode_non_mp3_file_like(file)\r\n    101             else:\r\n--> 102                 array, sampling_rate = self._decode_non_mp3_path_like(path)\r\n    103         return {\"path\": path, \"array\": array, \"sampling_rate\": sampling_rate}\r\n    104 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in _decode_non_mp3_path_like(self, path)\r\n    143 \r\n    144         with xopen(path, \"rb\") as f:\r\n--> 145             array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)\r\n    146         return array, sampling_rate\r\n    147 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/librosa\/core\/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)\r\n    110 \r\n    111     y = []\r\n--> 112     with audioread.audio_open(os.path.realpath(path)) as input_file:\r\n    113         sr_native = input_file.samplerate\r\n    114         n_channels = input_file.channels\r\n\r\n\/usr\/lib\/python3.6\/posixpath.py in realpath(filename)\r\n    392     \"\"\"Return the canonical path of the specified filename, eliminating any\r\n    393 symbolic links encountered in the path.\"\"\"\r\n--> 394     filename = os.fspath(filename)\r\n    395     path, ok = _joinrealpath(filename[:0], filename, {})\r\n    396     return abspath(path)\r\n\r\nTypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader\r\n```\r\n\r\n## Expected results\r\n```\r\n>>> raw_datasets[0][\"audio\"]\r\n{'array': array([-0.0005188 , -0.00109863,  0.00030518, ...,  0.01730347,\r\n        0.01623535,  0.01724243]),\r\n'path': '\/root\/.cache\/huggingface\/datasets\/downloads\/extracted\/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7\/bed\/026290a7_nohash_0.wav',\r\n'sampling_rate': 16000}\r\n``` \n @albertvillanova Thanks for your reply. The environment info below\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-4.19.91-007.ali4000.alios7.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.12\r\n- PyArrow version: 6.0.1","embeddings":[-0.376989007,-0.1154067442,-0.0308201592,0.3962246776,0.4579705,0.0699844882,0.2902420163,0.315089494,-0.0530283712,0.1748973876,-0.5356046557,0.317201376,-0.2974062562,0.0095626945,-0.0179306716,-0.4144312143,-0.0624868944,0.1607668251,-0.3674666882,0.0896921158,-0.3725435138,0.1743073761,-0.3261790574,0.2865290344,-0.2181515396,-0.10664922,0.1983113587,0.1052075475,-0.404435873,-0.5568415523,0.2095936835,-0.2148836255,0.2718895376,0.6587945819,-0.000109111,0.1062148288,0.3792859614,-0.0474429391,-0.4135835469,-0.2822248042,-0.4285669625,0.059622068,0.1489737183,-0.1274241656,-0.0034842102,-0.4431416392,-0.1901090294,-0.3268806934,0.4615101218,0.2684746981,0.2999238074,0.4350044727,0.1923298091,-0.2203475088,0.1194317862,0.1547943652,0.017395854,0.4202326536,0.0091918949,0.049166631,0.0069469907,0.2984599769,-0.173876524,0.1763353944,0.229850933,-0.1352755725,-0.1675219834,-0.1879023314,0.1613014638,0.1987244338,0.4726320505,-0.4815639853,-0.345949024,-0.0386826806,0.0690231472,-0.4702769816,0.0801246241,0.171560064,-0.0972005725,0.0952937007,-0.2051635236,0.0465240851,-0.0686939806,0.1682745963,0.2375473976,0.0064473674,-0.3387325704,0.0372266807,0.273814708,-0.2141977251,-0.1669427454,0.0483627655,-0.1655764282,0.3656981587,-0.4240424633,0.1116565242,0.0456984416,-0.1373231411,0.0146452794,-0.0403533019,0.0176255833,0.0415858664,0.0705679134,0.1646611989,0.2296693176,0.0986420959,0.1528075933,0.1120617241,0.1326157898,0.2336945534,0.092487976,-0.0318907984,-0.2244521081,-0.2344273031,0.0350971632,0.1130264923,0.456083864,-0.2372625768,-0.409147054,0.027019009,-0.0974412858,-0.0856006518,0.3154965639,0.2793029249,0.2499225587,0.10256318,0.2195274979,0.3914911151,-0.1359719932,0.0922218338,-0.134235695,0.083378993,-0.030907128,0.1335761994,0.2870156765,-0.2224531174,0.1550423354,0.2077090293,0.1261924654,-0.16103293,0.0189269856,-0.0493065715,-0.227455765,0.3239480853,-0.0960232988,0.1498952359,0.139388755,0.0410625748,-0.1044392884,0.0706788749,-0.1952906847,-0.302963227,-0.1049852967,0.2461148798,-0.0528141893,0.1340735853,-0.4590172768,-0.0732226595,0.1385058463,-0.3459163308,0.0648967326,-0.3205714822,-0.2848424911,0.0183808319,0.355086118,0.2735080421,-0.2584558725,-0.1789945662,-0.111605078,-0.2246555686,0.0354220718,0.3527233005,0.0290172044,0.2925767004,-0.149298802,-0.0259823166,0.297718823,-0.3080951869,-0.2015809566,-0.06839443,-0.0213344693,0.1206243411,-0.0819287077,-0.1163107902,0.1097762585,-0.04683052,0.3195449114,0.0579701215,0.0881781802,-0.0640451089,-0.0038452256,-0.1055636629,-0.0519391112,0.2132578343,-0.2208278775,0.0910755321,-0.0467555076,-0.0094854934,0.0476786196,-0.0992689729,-0.0255504418,0.1598064154,0.2196207047,0.0084250206,-0.0835880488,-0.4422390461,-0.2576403022,0.0173043888,-0.091310598,0.1824981868,-0.3626769483,-0.0666077808,-0.2478414625,0.137556091,-0.3986143172,0.058208283,0.1390697956,0.0923515409,-0.0479778275,0.0631794259,-0.2436275631,-0.0493089855,-0.0577989072,0.0540165901,-0.4655534029,0.0597283319,0.0191242341,-0.1688705683,0.2765914798,0.3562172651,0.2761615217,-0.0265946649,-0.2120907903,0.5099466443,0.1067440435,0.1462802887,-0.3429247439,0.0101277186,0.0833965987,-0.3226528764,-0.0090679778,-0.0623293482,0.293744266,-0.0997376814,-0.0672299415,0.4583495259,-0.1053345054,0.3200315535,0.004308546,-0.0589840673,0.1802778244,0.412814796,-0.0799909458,-0.1413908899,0.1623201221,-0.1702558994,0.1915431321,-0.0547325835,-0.3017842472,-0.3185960054,0.2729857862,-0.1987490207,0.0974563882,0.1345564872,-0.1964375377,0.0145389801,0.2190857232,-0.0600779429,0.3460257351,0.1377629042,-0.1827099323,0.0120346081,-0.0652056336,-0.0716543943,0.2884761095,0.133720845,0.0928261206,0.3119989038,0.1078322306,0.0081727179,-0.4893481731,-0.1266755462,-0.173090592,0.3339040279,-0.529899776,-0.1459741294,-0.3315993845,-0.2488905936,-0.1643964946,-0.3393754065,-0.0086977268,-0.1727548838,-0.0739424825,0.2299685031,-0.2515610456,0.3363698423,-0.0980426967,0.1093043759,0.1906432658,-0.0513187945,-0.1053448692,-0.0696240067,-0.0017023538,0.0683408827,0.4655221105,0.2131136656,0.299957931,-0.1394961476,0.0469153374,-0.1156080291,-0.0262309145,0.0053244438,-0.1554774344,0.222000584,0.0740114152,0.2549060285,-0.0642984584,-0.6536605358,0.4810533822,-0.0160904154,-0.1452650726,0.4130653143,-0.0233468972,0.0764258802,-0.1380885988,-0.3018046021,-0.1945600659,-0.5705913305,0.322735548,-0.125769943,0.2020571381,0.3859262168,-0.0013873472,0.3092149794,-0.015165925,0.3429301679,-0.2122425735,-0.1508434862,0.2838156223,-0.2811264694,-0.2468169034,-0.3224189878,0.05522383,0.2796715498,-0.0112670194,-0.3107510805,-0.1082311645,-0.0971762016,-0.1651502699,0.0173945874,0.0871351957,0.0340443514,0.2222821414,-0.0409988873,-0.071955733,-0.1744190603,-0.0211803447,-0.0760608315,-0.1083604917,0.0802405328,0.7200372815,-0.3476119339,0.5735509396,0.2487341911,0.0438383631,0.3965495527,-0.0171002746,0.25107494,-0.430244267,-0.4340074956,-0.0733803585,-0.118630439,0.0843298137,0.0457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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3851","title":"Load audio dataset error","comments":"Thanks @lemoner20,\r\n\r\nI cannot reproduce your issue in datasets version 1.18.3 either.\r\n\r\nMaybe redownloading the data file may work if you had already cached this dataset previously. Could you please try passing \"force_redownload\"?\r\n```python\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\", download_mode=\"force_redownload\")","body":"## Load audio dataset error\r\n\r\nHi, when I load audio dataset following https:\/\/huggingface.co\/docs\/datasets\/audio_process and https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/superb,\r\n```\r\nfrom datasets import load_dataset, load_metric, Audio\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\nprint(raw_datasets[0][\"audio\"])\r\n```\r\nfollowing errors occur \r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-169-3f8253239fa0> in <module>\r\n----> 1 raw_datasets[0][\"audio\"]\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1924         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1925         return self._getitem(\r\n-> 1926             key,\r\n   1927         )\r\n   1928 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1909         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1910         formatted_output = format_table(\r\n-> 1911             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1912         )\r\n   1913         return formatted_output\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    530     python_formatter = PythonFormatter(features=None)\r\n    531     if format_columns is None:\r\n--> 532         return formatter(pa_table, query_type=query_type)\r\n    533     elif query_type == \"column\":\r\n    534         if key in format_columns:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    279     def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:\r\n    280         if query_type == \"row\":\r\n--> 281             return self.format_row(pa_table)\r\n    282         elif query_type == \"column\":\r\n    283             return self.format_column(pa_table)\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_row(self, pa_table)\r\n    310         row = self.python_arrow_extractor().extract_row(pa_table)\r\n    311         if self.decoded:\r\n--> 312             row = self.python_features_decoder.decode_row(row)\r\n    313         return row\r\n    314 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_row(self, row)\r\n    219 \r\n    220     def decode_row(self, row: dict) -> dict:\r\n--> 221         return self.features.decode_example(row) if self.features else row\r\n    222 \r\n    223     def decode_column(self, column: list, column_name: str) -> list:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_example(self, example)\r\n   1320             else value\r\n   1321             for column_name, (feature, value) in utils.zip_dict(\r\n-> 1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n   1324         }\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <dictcomp>(.0)\r\n   1319             if self._column_requires_decoding[column_name]\r\n   1320             else value\r\n-> 1321             for column_name, (feature, value) in utils.zip_dict(\r\n   1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_nested_example(schema, obj)\r\n   1053     # Object with special decoding:\r\n   1054     elif isinstance(schema, (Audio, Image)):\r\n-> 1055         return schema.decode_example(obj) if obj is not None else None\r\n   1056     return obj\r\n   1057 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n    100                 array, sampling_rate = self._decode_non_mp3_file_like(file)\r\n    101             else:\r\n--> 102                 array, sampling_rate = self._decode_non_mp3_path_like(path)\r\n    103         return {\"path\": path, \"array\": array, \"sampling_rate\": sampling_rate}\r\n    104 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in _decode_non_mp3_path_like(self, path)\r\n    143 \r\n    144         with xopen(path, \"rb\") as f:\r\n--> 145             array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)\r\n    146         return array, sampling_rate\r\n    147 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/librosa\/core\/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)\r\n    110 \r\n    111     y = []\r\n--> 112     with audioread.audio_open(os.path.realpath(path)) as input_file:\r\n    113         sr_native = input_file.samplerate\r\n    114         n_channels = input_file.channels\r\n\r\n\/usr\/lib\/python3.6\/posixpath.py in realpath(filename)\r\n    392     \"\"\"Return the canonical path of the specified filename, eliminating any\r\n    393 symbolic links encountered in the path.\"\"\"\r\n--> 394     filename = os.fspath(filename)\r\n    395     path, ok = _joinrealpath(filename[:0], filename, {})\r\n    396     return abspath(path)\r\n\r\nTypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader\r\n```\r\n\r\n## Expected results\r\n```\r\n>>> raw_datasets[0][\"audio\"]\r\n{'array': array([-0.0005188 , -0.00109863,  0.00030518, ...,  0.01730347,\r\n        0.01623535,  0.01724243]),\r\n'path': '\/root\/.cache\/huggingface\/datasets\/downloads\/extracted\/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7\/bed\/026290a7_nohash_0.wav',\r\n'sampling_rate': 16000}\r\n```","comment_length":40,"text":"Load audio dataset error \n ## Load audio dataset error\r\n\r\nHi, when I load audio dataset following https:\/\/huggingface.co\/docs\/datasets\/audio_process and https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/superb,\r\n```\r\nfrom datasets import load_dataset, load_metric, Audio\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\")\r\nprint(raw_datasets[0][\"audio\"])\r\n```\r\nfollowing errors occur \r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-169-3f8253239fa0> in <module>\r\n----> 1 raw_datasets[0][\"audio\"]\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1924         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1925         return self._getitem(\r\n-> 1926             key,\r\n   1927         )\r\n   1928 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1909         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1910         formatted_output = format_table(\r\n-> 1911             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1912         )\r\n   1913         return formatted_output\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    530     python_formatter = PythonFormatter(features=None)\r\n    531     if format_columns is None:\r\n--> 532         return formatter(pa_table, query_type=query_type)\r\n    533     elif query_type == \"column\":\r\n    534         if key in format_columns:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    279     def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:\r\n    280         if query_type == \"row\":\r\n--> 281             return self.format_row(pa_table)\r\n    282         elif query_type == \"column\":\r\n    283             return self.format_column(pa_table)\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_row(self, pa_table)\r\n    310         row = self.python_arrow_extractor().extract_row(pa_table)\r\n    311         if self.decoded:\r\n--> 312             row = self.python_features_decoder.decode_row(row)\r\n    313         return row\r\n    314 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_row(self, row)\r\n    219 \r\n    220     def decode_row(self, row: dict) -> dict:\r\n--> 221         return self.features.decode_example(row) if self.features else row\r\n    222 \r\n    223     def decode_column(self, column: list, column_name: str) -> list:\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_example(self, example)\r\n   1320             else value\r\n   1321             for column_name, (feature, value) in utils.zip_dict(\r\n-> 1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n   1324         }\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <dictcomp>(.0)\r\n   1319             if self._column_requires_decoding[column_name]\r\n   1320             else value\r\n-> 1321             for column_name, (feature, value) in utils.zip_dict(\r\n   1322                 {key: value for key, value in self.items() if key in example}, example\r\n   1323             )\r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_nested_example(schema, obj)\r\n   1053     # Object with special decoding:\r\n   1054     elif isinstance(schema, (Audio, Image)):\r\n-> 1055         return schema.decode_example(obj) if obj is not None else None\r\n   1056     return obj\r\n   1057 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n    100                 array, sampling_rate = self._decode_non_mp3_file_like(file)\r\n    101             else:\r\n--> 102                 array, sampling_rate = self._decode_non_mp3_path_like(path)\r\n    103         return {\"path\": path, \"array\": array, \"sampling_rate\": sampling_rate}\r\n    104 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in _decode_non_mp3_path_like(self, path)\r\n    143 \r\n    144         with xopen(path, \"rb\") as f:\r\n--> 145             array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)\r\n    146         return array, sampling_rate\r\n    147 \r\n\r\n\/usr\/lib\/python3.6\/site-packages\/librosa\/core\/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)\r\n    110 \r\n    111     y = []\r\n--> 112     with audioread.audio_open(os.path.realpath(path)) as input_file:\r\n    113         sr_native = input_file.samplerate\r\n    114         n_channels = input_file.channels\r\n\r\n\/usr\/lib\/python3.6\/posixpath.py in realpath(filename)\r\n    392     \"\"\"Return the canonical path of the specified filename, eliminating any\r\n    393 symbolic links encountered in the path.\"\"\"\r\n--> 394     filename = os.fspath(filename)\r\n    395     path, ok = _joinrealpath(filename[:0], filename, {})\r\n    396     return abspath(path)\r\n\r\nTypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader\r\n```\r\n\r\n## Expected results\r\n```\r\n>>> raw_datasets[0][\"audio\"]\r\n{'array': array([-0.0005188 , -0.00109863,  0.00030518, ...,  0.01730347,\r\n        0.01623535,  0.01724243]),\r\n'path': '\/root\/.cache\/huggingface\/datasets\/downloads\/extracted\/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7\/bed\/026290a7_nohash_0.wav',\r\n'sampling_rate': 16000}\r\n``` \n Thanks @lemoner20,\r\n\r\nI cannot reproduce your issue in datasets version 1.18.3 either.\r\n\r\nMaybe redownloading the data file may work if you had already cached this dataset previously. Could you please try passing \"force_redownload\"?\r\n```python\r\nraw_datasets = load_dataset(\"superb\", \"ks\", split=\"train\", download_mode=\"force_redownload\")","embeddings":[-0.376989007,-0.1154067442,-0.0308201592,0.3962246776,0.4579705,0.0699844882,0.2902420163,0.315089494,-0.0530283712,0.1748973876,-0.5356046557,0.317201376,-0.2974062562,0.0095626945,-0.0179306716,-0.4144312143,-0.0624868944,0.1607668251,-0.3674666882,0.0896921158,-0.3725435138,0.1743073761,-0.3261790574,0.2865290344,-0.2181515396,-0.10664922,0.1983113587,0.1052075475,-0.404435873,-0.5568415523,0.2095936835,-0.2148836255,0.2718895376,0.6587945819,-0.000109111,0.1062148288,0.3792859614,-0.0474429391,-0.4135835469,-0.2822248042,-0.4285669625,0.059622068,0.1489737183,-0.1274241656,-0.0034842102,-0.4431416392,-0.1901090294,-0.3268806934,0.4615101218,0.2684746981,0.2999238074,0.4350044727,0.1923298091,-0.2203475088,0.1194317862,0.1547943652,0.017395854,0.4202326536,0.0091918949,0.049166631,0.0069469907,0.2984599769,-0.173876524,0.1763353944,0.229850933,-0.1352755725,-0.1675219834,-0.1879023314,0.1613014638,0.1987244338,0.4726320505,-0.4815639853,-0.345949024,-0.0386826806,0.0690231472,-0.4702769816,0.0801246241,0.171560064,-0.0972005725,0.0952937007,-0.2051635236,0.0465240851,-0.0686939806,0.1682745963,0.2375473976,0.0064473674,-0.3387325704,0.0372266807,0.273814708,-0.2141977251,-0.1669427454,0.0483627655,-0.1655764282,0.3656981587,-0.4240424633,0.1116565242,0.0456984416,-0.1373231411,0.0146452794,-0.0403533019,0.0176255833,0.0415858664,0.0705679134,0.1646611989,0.2296693176,0.0986420959,0.1528075933,0.1120617241,0.1326157898,0.2336945534,0.092487976,-0.0318907984,-0.2244521081,-0.2344273031,0.0350971632,0.1130264923,0.456083864,-0.2372625768,-0.409147054,0.027019009,-0.0974412858,-0.0856006518,0.3154965639,0.2793029249,0.2499225587,0.10256318,0.2195274979,0.3914911151,-0.1359719932,0.0922218338,-0.134235695,0.083378993,-0.030907128,0.1335761994,0.2870156765,-0.2224531174,0.1550423354,0.2077090293,0.1261924654,-0.16103293,0.0189269856,-0.0493065715,-0.227455765,0.3239480853,-0.0960232988,0.1498952359,0.139388755,0.0410625748,-0.1044392884,0.0706788749,-0.1952906847,-0.302963227,-0.1049852967,0.2461148798,-0.0528141893,0.1340735853,-0.4590172768,-0.0732226595,0.1385058463,-0.3459163308,0.0648967326,-0.3205714822,-0.2848424911,0.0183808319,0.355086118,0.2735080421,-0.2584558725,-0.1789945662,-0.111605078,-0.2246555686,0.0354220718,0.3527233005,0.0290172044,0.2925767004,-0.149298802,-0.0259823166,0.297718823,-0.3080951869,-0.2015809566,-0.06839443,-0.0213344693,0.1206243411,-0.0819287077,-0.1163107902,0.1097762585,-0.04683052,0.3195449114,0.0579701215,0.0881781802,-0.0640451089,-0.0038452256,-0.1055636629,-0.0519391112,0.2132578343,-0.2208278775,0.0910755321,-0.0467555076,-0.0094854934,0.0476786196,-0.0992689729,-0.0255504418,0.1598064154,0.2196207047,0.0084250206,-0.0835880488,-0.4422390461,-0.2576403022,0.0173043888,-0.091310598,0.1824981868,-0.3626769483,-0.0666077808,-0.2478414625,0.137556091,-0.3986143172,0.058208283,0.1390697956,0.0923515409,-0.0479778275,0.0631794259,-0.2436275631,-0.0493089855,-0.0577989072,0.0540165901,-0.4655534029,0.0597283319,0.0191242341,-0.1688705683,0.2765914798,0.3562172651,0.2761615217,-0.0265946649,-0.2120907903,0.5099466443,0.1067440435,0.1462802887,-0.3429247439,0.0101277186,0.0833965987,-0.3226528764,-0.0090679778,-0.0623293482,0.293744266,-0.0997376814,-0.0672299415,0.4583495259,-0.1053345054,0.3200315535,0.004308546,-0.0589840673,0.1802778244,0.412814796,-0.0799909458,-0.1413908899,0.1623201221,-0.1702558994,0.1915431321,-0.0547325835,-0.3017842472,-0.3185960054,0.2729857862,-0.1987490207,0.0974563882,0.1345564872,-0.1964375377,0.0145389801,0.2190857232,-0.0600779429,0.3460257351,0.1377629042,-0.1827099323,0.0120346081,-0.0652056336,-0.0716543943,0.2884761095,0.133720845,0.0928261206,0.3119989038,0.1078322306,0.0081727179,-0.4893481731,-0.1266755462,-0.173090592,0.3339040279,-0.529899776,-0.1459741294,-0.3315993845,-0.2488905936,-0.1643964946,-0.3393754065,-0.0086977268,-0.1727548838,-0.0739424825,0.2299685031,-0.2515610456,0.3363698423,-0.0980426967,0.1093043759,0.1906432658,-0.0513187945,-0.1053448692,-0.0696240067,-0.0017023538,0.0683408827,0.4655221105,0.2131136656,0.299957931,-0.1394961476,0.0469153374,-0.1156080291,-0.0262309145,0.0053244438,-0.1554774344,0.222000584,0.0740114152,0.2549060285,-0.0642984584,-0.6536605358,0.4810533822,-0.0160904154,-0.1452650726,0.4130653143,-0.0233468972,0.0764258802,-0.1380885988,-0.3018046021,-0.1945600659,-0.5705913305,0.322735548,-0.125769943,0.2020571381,0.3859262168,-0.0013873472,0.3092149794,-0.015165925,0.3429301679,-0.2122425735,-0.1508434862,0.2838156223,-0.2811264694,-0.2468169034,-0.3224189878,0.05522383,0.2796715498,-0.0112670194,-0.3107510805,-0.1082311645,-0.0971762016,-0.1651502699,0.0173945874,0.0871351957,0.0340443514,0.2222821414,-0.0409988873,-0.071955733,-0.1744190603,-0.0211803447,-0.0760608315,-0.1083604917,0.0802405328,0.7200372815,-0.3476119339,0.5735509396,0.2487341911,0.0438383631,0.3965495527,-0.0171002746,0.25107494,-0.430244267,-0.4340074956,-0.0733803585,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3848","title":"NonMatchingChecksumError when checksum is None","comments":"Hi @jxmorris12, thanks for reporting.\r\n\r\nThe objective of `verify_checksums` is to check that both checksums are equal. Therefore if one is None and the other is non-None, they are not equal, and the function accordingly raises a NonMatchingChecksumError. That behavior is expected.\r\n\r\nThe question is: how did you generate the expected checksum? Normally, it should not be None. To properly generate it (it is contained in the `dataset_infos.json` file), you should have runned: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nOn the other hand, you should take into account that the generation of this file is NOT mandatory for personal\/community datasets (we only require it for \"canonical\" datasets, i.e., datasets added to our library GitHub repository: https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets). Therefore, other option would be just to delete the `dataset_infos.json` file. If that file is not present, the function `verify_checksums` is not executed.\r\n\r\nFinally, you can circumvent the `verify_checksums` function by passing `ignore_verifications=True` to `load_dataset`:\r\n```python\r\nload_dataset(..., ignore_verifications=True)\r\n``` ","body":"I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.","comment_length":157,"text":"NonMatchingChecksumError when checksum is None \n I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. \n Hi @jxmorris12, thanks for reporting.\r\n\r\nThe objective of `verify_checksums` is to check that both checksums are equal. Therefore if one is None and the other is non-None, they are not equal, and the function accordingly raises a NonMatchingChecksumError. That behavior is expected.\r\n\r\nThe question is: how did you generate the expected checksum? Normally, it should not be None. To properly generate it (it is contained in the `dataset_infos.json` file), you should have runned: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nOn the other hand, you should take into account that the generation of this file is NOT mandatory for personal\/community datasets (we only require it for \"canonical\" datasets, i.e., datasets added to our library GitHub repository: https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets). Therefore, other option would be just to delete the `dataset_infos.json` file. If that file is not present, the function `verify_checksums` is not executed.\r\n\r\nFinally, you can circumvent the `verify_checksums` function by passing `ignore_verifications=True` to `load_dataset`:\r\n```python\r\nload_dataset(..., ignore_verifications=True)\r\n``` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3848","title":"NonMatchingChecksumError when checksum is None","comments":"Thanks @albertvillanova!\r\n\r\nThat's fine. I did run that command when I was adding a new dataset. Maybe because the command crashed in the middle, the checksum wasn't stored properly. I don't know where the bug is happening. But either (i) `verify_checksums` should properly handle this edge case, where the passed checksum is None or (ii) the `datasets-cli test` shouldn't generate a corrupted dataset_infos.json file.\r\n\r\nJust a more high-level thing, I was trying to follow the instructions for adding a dataset in the CONTRIBUTING.md, so if running that command isn't even necessary, that should probably be mentioned in the document, right? But that's somewhat of a moot point, since something isn't working quite right internally if I was able to get into this corrupted state in the first place, just by following those instructions.","body":"I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.","comment_length":133,"text":"NonMatchingChecksumError when checksum is None \n I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. \n Thanks @albertvillanova!\r\n\r\nThat's fine. I did run that command when I was adding a new dataset. Maybe because the command crashed in the middle, the checksum wasn't stored properly. I don't know where the bug is happening. But either (i) `verify_checksums` should properly handle this edge case, where the passed checksum is None or (ii) the `datasets-cli test` shouldn't generate a corrupted dataset_infos.json file.\r\n\r\nJust a more high-level thing, I was trying to follow the instructions for adding a dataset in the CONTRIBUTING.md, so if running that command isn't even necessary, that should probably be mentioned in the document, right? But that's somewhat of a moot point, since something isn't working quite right internally if I was able to get into this corrupted state in the first place, just by following those instructions.","embeddings":[-0.2320744842,0.0051180013,-0.0630756393,0.0114171915,0.03937025,-0.0018277736,0.0637273043,0.213983655,0.2239055187,0.240778774,0.2089289129,-0.1715059429,-0.1793842763,-0.1686307043,-0.3194901645,0.5820991397,0.0091046058,0.1567642838,-0.0063254596,-0.1103414819,-0.259742856,0.0484449863,-0.0611694083,-0.5326397419,-0.1920528859,0.2473380268,-0.0098863598,0.1149757206,-0.1297463328,-0.3302526176,0.2777617574,0.1262668818,-0.2718398273,0.2832526863,-0.0001198954,0.2209150046,0.2281810045,-0.1096402854,-0.3899438381,-0.13719742,-0.6143530011,-0.4995467961,-0.3337577879,-0.1564416289,0.1629652679,0.205815345,-0.0214103926,-0.3387086689,-0.2293427885,0.119479008,0.1357061714,0.2888430059,-0.0406557508,-0.0627491474,0.2102416754,0.0540351644,-0.1934696585,-0.0633662641,0.2661625445,0.0005338599,-0.0912798494,0.1432641,-0.2583860457,0.1313908547,0.1104935184,-0.1003324166,0.2735524774,-0.0703482032,0.1362769306,0.4286722541,0.212308079,-0.0419761837,-0.4606053531,-0.2045884132,-0.0351583809,-0.2924728394,0.3948495686,0.0946060047,-0.1970760226,-0.1859196275,-0.5737665892,0.1739514321,-0.0133538246,0.2211709321,0.2296181321,0.2830680609,0.1889175922,-0.0384182185,0.1671478599,-0.2139754295,-0.0191501249,-0.3182002902,-0.0774727687,0.1109789908,-0.3909543157,-0.2279860377,0.0494965129,0.4995047152,0.4288211167,0.4684191346,0.1201374605,0.0688024834,-0.0366663709,0.1671061665,-0.0963462293,0.1400944144,0.1196580008,-0.0618377551,0.2501617968,0.1775500476,0.001888213,0.1504309326,0.2900310755,-0.3045158982,0.3938010931,0.2198646814,0.4815283716,-0.3492438495,-0.2934836447,0.3594183922,-0.0441873744,-0.1830992699,0.2943370342,0.200255394,-0.0999055207,0.1527583003,-0.2356730253,-0.0492413528,-0.2158452123,0.0130820684,-0.2533991039,-0.1017789319,-0.066580236,0.133494541,0.0993378386,0.0195720904,0.2893688679,0.0865936577,0.135745436,0.1964247972,0.2705962062,-0.2291410416,0.065513052,0.4084843099,-0.1550301313,0.0144534484,0.2092736661,-0.1412055343,-0.0850996748,0.2924254239,-0.1688417196,-0.2473763078,-0.0337585546,0.2199124545,-0.6749246716,-0.1524627954,0.2213704139,-0.2103562355,0.1287581623,-0.1970433891,-0.0122470865,-0.1919463724,-0.2976400852,-0.1756414175,-0.1062519476,0.4491339326,-0.2358754873,-0.1023653299,-0.1522547901,-0.1739755273,0.2358903289,0.1088120341,0.0516668037,-0.104314439,-0.3189382553,-0.139628157,0.0739300698,-0.2331832647,-0.5747233033,0.0857627988,-0.0583211146,0.4907502532,0.3521373868,0.3040938377,-0.2210258543,-0.2428819984,0.1296921074,-0.1301402003,0.0196330473,-0.0637225956,-0.2406348884,-0.2053636163,0.4354163408,-0.0076730475,0.06482438,-0.0114705488,0.0058360365,-0.2087116241,0.1196131408,-0.3051237166,-0.1553108096,0.1994118094,0.5661743879,0.0102962079,-0.0027463243,-0.0966397449,-0.4024398029,0.3852770925,-0.063539885,0.1093886793,0.0323587172,-0.2544021904,0.0724372789,-0.2285436094,-0.2381917238,0.173543483,0.1009774283,0.4710721374,0.1338863075,-0.2191171497,-0.0861188471,0.0738019794,-0.3849825859,0.0171771236,-0.2908610106,0.2074072957,0.0166566856,-0.0483402163,-0.1110702083,0.2855116725,0.167213738,0.1262550503,-0.0632704273,0.3517815471,0.3775028884,-0.1725124121,0.1655670255,0.4899955988,-0.0878477246,-0.1038005352,-0.1998687088,0.520735383,0.1144556701,0.0280811843,0.0062097921,0.4076439738,-0.0356270671,0.239261806,0.0936318338,-0.0190574937,0.2032479197,-0.3634719849,-0.1356293559,-0.2988702655,0.3530622423,0.1262719631,0.0400409959,0.1103011891,0.1196748689,-0.2106359005,0.0891733915,-0.0989120007,0.0566066876,-0.0305046663,0.271911025,-0.0270620584,-0.0882911086,0.6816493273,0.4652110338,0.2867638767,0.0402599424,0.0451144092,-0.1751432568,-0.102068834,-0.0405955277,0.0103077386,0.0922621414,0.6847397685,-0.0388810858,-0.2099470347,-0.3830485046,-0.3852018416,0.0503431074,0.3930249214,-0.3930547833,-0.1957711875,-0.3480162621,0.2055252641,-0.233498618,-0.1122873127,-0.3593842089,-0.4275899231,0.147363767,0.0306848977,-0.0991279557,0.1142359525,-0.5860802531,0.0029742885,-0.0607005544,-0.3298612237,-0.008157257,-0.0083160121,0.1915066987,0.0554616004,0.4833919406,-0.1673285067,0.3184070289,-0.1481002271,-0.107389465,-0.521630466,-0.3873057663,-0.1443323046,-0.2147364616,0.0527626239,0.3990139663,0.0594148003,0.0922621787,-0.2706478536,0.2820093632,0.0701401681,-0.365596503,0.1909026057,-0.149026081,-0.0551434495,0.0285817329,0.0042307517,0.1514241248,-0.0967821479,-0.1839743704,0.2876751721,0.2225278467,0.3205142319,0.0663739294,0.1733566374,0.0513740443,0.3008277118,-0.235499382,-0.5980967879,0.4360554516,0.1956647635,0.0150224138,0.1611694843,-0.1942097098,0.128142342,0.3130410314,-0.4280477762,-0.518540442,-0.2155809104,0.0225958638,0.1611423194,-0.0227837767,0.3931450844,0.1207485497,-0.2183138877,-0.2532044053,-0.2206777334,0.1966335177,-0.2012587488,0.5872361064,-0.1962158233,0.1474373043,0.0246154405,0.3689993918,0.3948135078,-0.0737774223,0.0684527308,0.0905947089,0.4791740477,0.1474192291,-0.1532063335,-0.1011933982,0.330464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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3848","title":"NonMatchingChecksumError when checksum is None","comments":"Hi @jxmorris12,\r\n\r\nDefinitely, your `dataset_infos.json` was corrupted (and wrongly contains expected None checksum). \r\n\r\nWhile we further investigate how this can happen and fix it, feel free to delete your `dataset_infos.json` file and recreate it with:\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nAlso note that `verify_checksum` is working as expected: if it receives a None and and a non-None checksums as input pair, it must raise an exception: they are not equal. That is not a bug.","body":"I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.","comment_length":77,"text":"NonMatchingChecksumError when checksum is None \n I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. \n Hi @jxmorris12,\r\n\r\nDefinitely, your `dataset_infos.json` was corrupted (and wrongly contains expected None checksum). \r\n\r\nWhile we further investigate how this can happen and fix it, feel free to delete your `dataset_infos.json` file and recreate it with:\r\n```shell\r\ndatasets-cli test <your-dataset-folder> --save_infos --all_configs\r\n```\r\n\r\nAlso note that `verify_checksum` is working as expected: if it receives a None and and a non-None checksums as input pair, it must raise an exception: they are not equal. That is not a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3848","title":"NonMatchingChecksumError when checksum is None","comments":"At a higher level, also note that we are preparing the release of `datasets` version 2.0, and some docs are being updated...\r\n\r\nIn order to add a dataset, I think the most updated instructions are in our official documentation pages: https:\/\/huggingface.co\/docs\/datasets\/share","body":"I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.","comment_length":41,"text":"NonMatchingChecksumError when checksum is None \n I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. \n At a higher level, also note that we are preparing the release of `datasets` version 2.0, and some docs are being updated...\r\n\r\nIn order to add a dataset, I think the most updated instructions are in our official documentation pages: https:\/\/huggingface.co\/docs\/datasets\/share","embeddings":[-0.2320744842,0.0051180013,-0.0630756393,0.0114171915,0.03937025,-0.0018277736,0.0637273043,0.213983655,0.2239055187,0.240778774,0.2089289129,-0.1715059429,-0.1793842763,-0.1686307043,-0.3194901645,0.5820991397,0.0091046058,0.1567642838,-0.0063254596,-0.1103414819,-0.259742856,0.0484449863,-0.0611694083,-0.5326397419,-0.1920528859,0.2473380268,-0.0098863598,0.1149757206,-0.1297463328,-0.3302526176,0.2777617574,0.1262668818,-0.2718398273,0.2832526863,-0.0001198954,0.2209150046,0.2281810045,-0.1096402854,-0.3899438381,-0.13719742,-0.6143530011,-0.4995467961,-0.3337577879,-0.1564416289,0.1629652679,0.205815345,-0.0214103926,-0.3387086689,-0.2293427885,0.119479008,0.1357061714,0.2888430059,-0.0406557508,-0.0627491474,0.2102416754,0.0540351644,-0.1934696585,-0.0633662641,0.2661625445,0.0005338599,-0.0912798494,0.1432641,-0.2583860457,0.1313908547,0.1104935184,-0.1003324166,0.2735524774,-0.0703482032,0.1362769306,0.4286722541,0.212308079,-0.0419761837,-0.4606053531,-0.2045884132,-0.0351583809,-0.2924728394,0.3948495686,0.0946060047,-0.1970760226,-0.1859196275,-0.5737665892,0.1739514321,-0.0133538246,0.2211709321,0.2296181321,0.2830680609,0.1889175922,-0.0384182185,0.1671478599,-0.2139754295,-0.0191501249,-0.3182002902,-0.0774727687,0.1109789908,-0.3909543157,-0.2279860377,0.0494965129,0.4995047152,0.4288211167,0.4684191346,0.1201374605,0.0688024834,-0.0366663709,0.1671061665,-0.0963462293,0.1400944144,0.1196580008,-0.0618377551,0.2501617968,0.1775500476,0.001888213,0.1504309326,0.2900310755,-0.3045158982,0.3938010931,0.2198646814,0.4815283716,-0.3492438495,-0.2934836447,0.3594183922,-0.0441873744,-0.1830992699,0.2943370342,0.200255394,-0.0999055207,0.1527583003,-0.2356730253,-0.0492413528,-0.2158452123,0.0130820684,-0.2533991039,-0.1017789319,-0.066580236,0.133494541,0.0993378386,0.0195720904,0.2893688679,0.0865936577,0.135745436,0.1964247972,0.2705962062,-0.2291410416,0.065513052,0.4084843099,-0.1550301313,0.0144534484,0.2092736661,-0.1412055343,-0.0850996748,0.2924254239,-0.1688417196,-0.2473763078,-0.0337585546,0.2199124545,-0.6749246716,-0.1524627954,0.2213704139,-0.2103562355,0.1287581623,-0.1970433891,-0.0122470865,-0.1919463724,-0.2976400852,-0.1756414175,-0.1062519476,0.4491339326,-0.2358754873,-0.1023653299,-0.1522547901,-0.1739755273,0.2358903289,0.1088120341,0.0516668037,-0.104314439,-0.3189382553,-0.139628157,0.0739300698,-0.2331832647,-0.5747233033,0.0857627988,-0.0583211146,0.4907502532,0.3521373868,0.3040938377,-0.2210258543,-0.2428819984,0.1296921074,-0.1301402003,0.0196330473,-0.0637225956,-0.2406348884,-0.2053636163,0.4354163408,-0.0076730475,0.06482438,-0.0114705488,0.0058360365,-0.2087116241,0.1196131408,-0.3051237166,-0.1553108096,0.1994118094,0.5661743879,0.0102962079,-0.0027463243,-0.0966397449,-0.4024398029,0.3852770925,-0.063539885,0.1093886793,0.0323587172,-0.2544021904,0.0724372789,-0.2285436094,-0.2381917238,0.173543483,0.1009774283,0.4710721374,0.1338863075,-0.2191171497,-0.0861188471,0.0738019794,-0.3849825859,0.0171771236,-0.2908610106,0.2074072957,0.0166566856,-0.0483402163,-0.1110702083,0.2855116725,0.167213738,0.1262550503,-0.0632704273,0.3517815471,0.3775028884,-0.1725124121,0.1655670255,0.4899955988,-0.0878477246,-0.1038005352,-0.1998687088,0.520735383,0.1144556701,0.0280811843,0.0062097921,0.4076439738,-0.0356270671,0.239261806,0.0936318338,-0.0190574937,0.2032479197,-0.3634719849,-0.1356293559,-0.2988702655,0.3530622423,0.1262719631,0.0400409959,0.1103011891,0.1196748689,-0.2106359005,0.0891733915,-0.0989120007,0.0566066876,-0.0305046663,0.271911025,-0.0270620584,-0.0882911086,0.6816493273,0.4652110338,0.2867638767,0.0402599424,0.0451144092,-0.1751432568,-0.102068834,-0.0405955277,0.0103077386,0.0922621414,0.6847397685,-0.0388810858,-0.2099470347,-0.3830485046,-0.3852018416,0.0503431074,0.3930249214,-0.3930547833,-0.1957711875,-0.3480162621,0.2055252641,-0.233498618,-0.1122873127,-0.3593842089,-0.4275899231,0.147363767,0.0306848977,-0.0991279557,0.1142359525,-0.5860802531,0.0029742885,-0.0607005544,-0.3298612237,-0.008157257,-0.0083160121,0.1915066987,0.0554616004,0.4833919406,-0.1673285067,0.3184070289,-0.1481002271,-0.107389465,-0.521630466,-0.3873057663,-0.1443323046,-0.2147364616,0.0527626239,0.3990139663,0.0594148003,0.0922621787,-0.2706478536,0.2820093632,0.0701401681,-0.365596503,0.1909026057,-0.149026081,-0.0551434495,0.0285817329,0.0042307517,0.1514241248,-0.0967821479,-0.1839743704,0.2876751721,0.2225278467,0.3205142319,0.0663739294,0.1733566374,0.0513740443,0.3008277118,-0.235499382,-0.5980967879,0.4360554516,0.1956647635,0.0150224138,0.1611694843,-0.1942097098,0.128142342,0.3130410314,-0.4280477762,-0.518540442,-0.2155809104,0.0225958638,0.1611423194,-0.0227837767,0.3931450844,0.1207485497,-0.2183138877,-0.2532044053,-0.2206777334,0.1966335177,-0.2012587488,0.5872361064,-0.1962158233,0.1474373043,0.0246154405,0.3689993918,0.3948135078,-0.0737774223,0.0684527308,0.0905947089,0.4791740477,0.1474192291,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3848","title":"NonMatchingChecksumError when checksum is None","comments":"Hi @jxmorris12, we have discovered the bug why `None` checksums wrongly appeared when generating the `dataset_infos.json` file:\r\n- #3892\r\n\r\nThe fix will be accessible once this PR merged. And we are planning to do our 2.0 release today.\r\n\r\nWe are also working on updating all our docs for our release today.","body":"I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.","comment_length":51,"text":"NonMatchingChecksumError when checksum is None \n I ran into the following error when adding a new dataset:\r\n\r\n```bash\r\nexpected_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': None, 'num_bytes': 40662}}\r\nrecorded_checksums = {'https:\/\/adversarialglue.github.io\/dataset\/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}}\r\nverification_name = 'dataset source files'\r\n\r\n    def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None):\r\n        if expected_checksums is None:\r\n            logger.info(\"Unable to verify checksums.\")\r\n            return\r\n        if len(set(expected_checksums) - set(recorded_checksums)) > 0:\r\n            raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\n        if len(set(recorded_checksums) - set(expected_checksums)) > 0:\r\n            raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))\r\n        bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n        for_verification_name = \" for \" + verification_name if verification_name is not None else \"\"\r\n        if len(bad_urls) > 0:\r\n            error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n>           raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nE           datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\nE           ['https:\/\/adversarialglue.github.io\/dataset\/dev.zip']\r\n\r\nsrc\/datasets\/utils\/info_utils.py:40: NonMatchingChecksumError\r\n```\r\n\r\n## Expected results\r\nThe dataset downloads correctly, and there is no error.\r\n\r\n## Actual results\r\nDatasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. \n Hi @jxmorris12, we have discovered the bug why `None` checksums wrongly appeared when generating the `dataset_infos.json` file:\r\n- #3892\r\n\r\nThe fix will be accessible once this PR merged. And we are planning to do our 2.0 release today.\r\n\r\nWe are also working on updating all our docs for our release today.","embeddings":[-0.2320744842,0.0051180013,-0.0630756393,0.0114171915,0.03937025,-0.0018277736,0.0637273043,0.213983655,0.2239055187,0.240778774,0.2089289129,-0.1715059429,-0.1793842763,-0.1686307043,-0.3194901645,0.5820991397,0.0091046058,0.1567642838,-0.0063254596,-0.1103414819,-0.259742856,0.0484449863,-0.0611694083,-0.5326397419,-0.1920528859,0.2473380268,-0.0098863598,0.1149757206,-0.1297463328,-0.3302526176,0.2777617574,0.1262668818,-0.2718398273,0.2832526863,-0.0001198954,0.2209150046,0.2281810045,-0.1096402854,-0.3899438381,-0.13719742,-0.6143530011,-0.4995467961,-0.3337577879,-0.1564416289,0.1629652679,0.205815345,-0.0214103926,-0.3387086689,-0.2293427885,0.119479008,0.1357061714,0.2888430059,-0.0406557508,-0.0627491474,0.2102416754,0.0540351644,-0.1934696585,-0.0633662641,0.2661625445,0.0005338599,-0.0912798494,0.1432641,-0.2583860457,0.1313908547,0.1104935184,-0.1003324166,0.2735524774,-0.0703482032,0.1362769306,0.4286722541,0.212308079,-0.0419761837,-0.4606053531,-0.2045884132,-0.0351583809,-0.2924728394,0.3948495686,0.0946060047,-0.1970760226,-0.1859196275,-0.5737665892,0.1739514321,-0.0133538246,0.2211709321,0.2296181321,0.2830680609,0.1889175922,-0.0384182185,0.1671478599,-0.2139754295,-0.0191501249,-0.3182002902,-0.0774727687,0.1109789908,-0.3909543157,-0.2279860377,0.0494965129,0.4995047152,0.4288211167,0.4684191346,0.1201374605,0.0688024834,-0.0366663709,0.1671061665,-0.0963462293,0.1400944144,0.1196580008,-0.0618377551,0.2501617968,0.1775500476,0.001888213,0.1504309326,0.2900310755,-0.3045158982,0.3938010931,0.2198646814,0.4815283716,-0.3492438495,-0.2934836447,0.3594183922,-0.0441873744,-0.1830992699,0.2943370342,0.200255394,-0.0999055207,0.1527583003,-0.2356730253,-0.0492413528,-0.2158452123,0.0130820684,-0.2533991039,-0.1017789319,-0.066580236,0.133494541,0.0993378386,0.0195720904,0.2893688679,0.0865936577,0.135745436,0.1964247972,0.2705962062,-0.2291410416,0.065513052,0.4084843099,-0.1550301313,0.0144534484,0.2092736661,-0.1412055343,-0.0850996748,0.2924254239,-0.1688417196,-0.2473763078,-0.0337585546,0.2199124545,-0.6749246716,-0.1524627954,0.2213704139,-0.2103562355,0.1287581623,-0.1970433891,-0.0122470865,-0.1919463724,-0.2976400852,-0.1756414175,-0.1062519476,0.4491339326,-0.2358754873,-0.1023653299,-0.1522547901,-0.1739755273,0.2358903289,0.1088120341,0.0516668037,-0.104314439,-0.3189382553,-0.139628157,0.0739300698,-0.2331832647,-0.5747233033,0.0857627988,-0.0583211146,0.4907502532,0.3521373868,0.3040938377,-0.2210258543,-0.2428819984,0.1296921074,-0.1301402003,0.0196330473,-0.0637225956,-0.2406348884,-0.2053636163,0.4354163408,-0.0076730475,0.06482438,-0.0114705488,0.0058360365,-0.2087116241,0.1196131408,-0.3051237166,-0.1553108096,0.1994118094,0.5661743879,0.0102962079,-0.0027463243,-0.0966397449,-0.4024398029,0.3852770925,-0.063539885,0.1093886793,0.0323587172,-0.2544021904,0.0724372789,-0.2285436094,-0.2381917238,0.173543483,0.1009774283,0.4710721374,0.1338863075,-0.2191171497,-0.0861188471,0.0738019794,-0.3849825859,0.0171771236,-0.2908610106,0.2074072957,0.0166566856,-0.0483402163,-0.1110702083,0.2855116725,0.167213738,0.1262550503,-0.0632704273,0.3517815471,0.3775028884,-0.1725124121,0.1655670255,0.4899955988,-0.0878477246,-0.1038005352,-0.1998687088,0.520735383,0.1144556701,0.0280811843,0.0062097921,0.4076439738,-0.0356270671,0.239261806,0.0936318338,-0.0190574937,0.2032479197,-0.3634719849,-0.1356293559,-0.2988702655,0.3530622423,0.1262719631,0.0400409959,0.1103011891,0.1196748689,-0.2106359005,0.0891733915,-0.0989120007,0.0566066876,-0.0305046663,0.271911025,-0.0270620584,-0.0882911086,0.6816493273,0.4652110338,0.2867638767,0.0402599424,0.0451144092,-0.1751432568,-0.102068834,-0.0405955277,0.0103077386,0.0922621414,0.6847397685,-0.0388810858,-0.2099470347,-0.3830485046,-0.3852018416,0.0503431074,0.3930249214,-0.3930547833,-0.1957711875,-0.3480162621,0.2055252641,-0.233498618,-0.1122873127,-0.3593842089,-0.4275899231,0.147363767,0.0306848977,-0.0991279557,0.1142359525,-0.5860802531,0.0029742885,-0.0607005544,-0.3298612237,-0.008157257,-0.0083160121,0.1915066987,0.0554616004,0.4833919406,-0.1673285067,0.3184070289,-0.1481002271,-0.107389465,-0.521630466,-0.3873057663,-0.1443323046,-0.2147364616,0.0527626239,0.3990139663,0.0594148003,0.0922621787,-0.2706478536,0.2820093632,0.0701401681,-0.365596503,0.1909026057,-0.149026081,-0.0551434495,0.0285817329,0.0042307517,0.1514241248,-0.0967821479,-0.1839743704,0.2876751721,0.2225278467,0.3205142319,0.0663739294,0.1733566374,0.0513740443,0.3008277118,-0.235499382,-0.5980967879,0.4360554516,0.1956647635,0.0150224138,0.1611694843,-0.1942097098,0.128142342,0.3130410314,-0.4280477762,-0.518540442,-0.2155809104,0.0225958638,0.1611423194,-0.0227837767,0.3931450844,0.1207485497,-0.2183138877,-0.2532044053,-0.2206777334,0.1966335177,-0.2012587488,0.5872361064,-0.1962158233,0.1474373043,0.0246154405,0.3689993918,0.3948135078,-0.0737774223,0.0684527308,0.0905947089,0.4791740477,0.1474192291,-0.1532063335,-0.1011933982,0.3304642737,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3847","title":"Datasets' cache not re-used","comments":"I think this is because the tokenizer is stateful and because the order in which the splits are processed is not deterministic. Because of that, the hash of the tokenizer may change for certain splits, which causes issues with caching.\r\n\r\nTo fix this we can try making the order of the splits deterministic for map.","body":"## Describe the bug\r\nFor most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory.\r\n\r\n## Steps to reproduce the bug\r\nHere is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example.\r\n```python\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\nraw_datasets = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\r\n# tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\r\ntokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")\r\ntext_column_name = \"text\"\r\ncolumn_names = raw_datasets[\"train\"].column_names\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = raw_datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n    desc=\"Running tokenizer on every text in dataset\",\r\n)\r\n```\r\n\r\n## Expected results\r\nNo tokenization would be required after the 1st run. Everything should be loaded from the cache.\r\n\r\n## Actual results\r\nTokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Ubuntu 18.04.6 LTS\r\n- Python version: 3.6.9\r\n- PyArrow version: 6.0.1\r\n","comment_length":55,"text":"Datasets' cache not re-used \n ## Describe the bug\r\nFor most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory.\r\n\r\n## Steps to reproduce the bug\r\nHere is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example.\r\n```python\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\nraw_datasets = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\r\n# tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\r\ntokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")\r\ntext_column_name = \"text\"\r\ncolumn_names = raw_datasets[\"train\"].column_names\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = raw_datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n    desc=\"Running tokenizer on every text in dataset\",\r\n)\r\n```\r\n\r\n## Expected results\r\nNo tokenization would be required after the 1st run. Everything should be loaded from the cache.\r\n\r\n## Actual results\r\nTokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Ubuntu 18.04.6 LTS\r\n- Python version: 3.6.9\r\n- PyArrow version: 6.0.1\r\n \n I think this is because the tokenizer is stateful and because the order in which the splits are processed is not deterministic. Because of that, the hash of the tokenizer may change for certain splits, which causes issues with caching.\r\n\r\nTo fix this we can try making the order of the splits deterministic for 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3847","title":"Datasets' cache not re-used","comments":"Actually this is not because of the order of the splits, but most likely because the tokenizer used to process the second split is in a state that has been modified by the first split.\r\n\r\nTherefore after reloading the first split from the cache, then the second split can't be reloaded since the tokenizer hasn't seen the first split (and therefore is considered a different tokenizer).\r\n\r\nThis is a bit trickier to fix, we can explore fixing this next week maybe","body":"## Describe the bug\r\nFor most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory.\r\n\r\n## Steps to reproduce the bug\r\nHere is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example.\r\n```python\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\nraw_datasets = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\r\n# tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\r\ntokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")\r\ntext_column_name = \"text\"\r\ncolumn_names = raw_datasets[\"train\"].column_names\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = raw_datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n    desc=\"Running tokenizer on every text in dataset\",\r\n)\r\n```\r\n\r\n## Expected results\r\nNo tokenization would be required after the 1st run. Everything should be loaded from the cache.\r\n\r\n## Actual results\r\nTokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Ubuntu 18.04.6 LTS\r\n- Python version: 3.6.9\r\n- PyArrow version: 6.0.1\r\n","comment_length":81,"text":"Datasets' cache not re-used \n ## Describe the bug\r\nFor most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory.\r\n\r\n## Steps to reproduce the bug\r\nHere is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example.\r\n```python\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\nraw_datasets = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\r\n# tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\r\ntokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")\r\ntext_column_name = \"text\"\r\ncolumn_names = raw_datasets[\"train\"].column_names\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = raw_datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n    desc=\"Running tokenizer on every text in dataset\",\r\n)\r\n```\r\n\r\n## Expected results\r\nNo tokenization would be required after the 1st run. Everything should be loaded from the cache.\r\n\r\n## Actual results\r\nTokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Ubuntu 18.04.6 LTS\r\n- Python version: 3.6.9\r\n- PyArrow version: 6.0.1\r\n \n Actually this is not because of the order of the splits, but most likely because the tokenizer used to process the second split is in a state that has been modified by the first split.\r\n\r\nTherefore after reloading the first split from the cache, then the second split can't be reloaded since the tokenizer hasn't seen the first split (and therefore is considered a different tokenizer).\r\n\r\nThis is a bit trickier to fix, we can explore fixing this next week maybe","embeddings":[-0.1986139715,0.2466835678,0.0669298396,0.1183695719,0.080037266,0.012468867,0.2719796002,0.2143709958,-0.1935909986,-0.1080534458,0.0886222199,0.4167427719,0.1809746325,-0.5377526283,-0.0903876945,-0.0427593328,0.1412885785,0.2917447388,0.2959861457,0.0640428513,0.1097168848,0.1906701326,-0.4057331979,0.070421122,-0.7545245886,0.0739881322,-0.1481440216,-0.1462657303,0.2047284693,-0.5908921361,0.1689056903,0.2595779002,0.2001173496,0.2852044106,-0.0001196654,-0.0867464244,0.1411748081,0.0526235253,-0.1707147658,0.1204955876,0.2111909688,-0.1910045892,-0.0383823887,-0.0498456657,-0.0368064232,-0.4331617355,-0.0786321387,-0.420848161,0.5780020356,-0.058157105,0.0969413668,0.2591757476,-0.1169558242,0.1271641552,0.1028003544,-0.2154768854,-0.1262718141,-0.4016621411,-0.0211598314,-0.2056728452,-0.2133861333,0.3187598586,-0.0963900387,0.1909554154,-0.0347307622,0.1991235167,-0.2444053888,-0.0330571979,0.205888167,0.0496276319,0.4405305684,-0.2741962969,-0.2419788539,-0.5149124861,-0.1330808997,-0.374530673,0.2267412245,-0.2042076439,0.073072277,0.2213133276,-0.1399079412,0.0891412348,0.0989517421,-0.1287356764,-0.0349249579,0.5847367048,0.0099366428,0.1250519753,-0.1420134455,-0.0792916566,0.3370870352,-0.2682816982,-0.2180351168,0.4121715426,-0.4101339579,0.0265909042,-0.0118189668,0.0121575398,-0.026250476,0.551140368,-0.0630173087,0.2254932672,0.1313028485,-0.0415939391,0.0266411696,0.4568917453,0.116132617,0.3488681316,0.1168064028,-0.2385695428,-0.3146237135,-0.2253877074,-0.1614961326,0.0269878991,0.5888153315,-0.0058410689,-0.3118107021,-0.0003038259,0.0084865,0.1413023174,-0.242304042,-0.0340621583,0.1819704771,0.1321225315,-0.070031032,0.3185286224,-0.3519311845,0.0457114913,-0.1780645549,-0.2316769958,-0.2925888002,-0.3107387722,-0.1810812056,0.4899430275,0.1706734002,0.1318376064,0.3035504222,0.1658536345,0.0606445037,-0.2821772695,0.3627636731,-0.1447577327,0.3338551223,0.1605362743,-0.1304642409,0.508590281,0.1579699069,0.0248627812,-0.1413265318,0.1234320551,-0.3401705921,-0.1505412459,0.3115956187,0.0881732553,-0.1358284205,-0.0182067119,-0.095885478,0.0745142102,0.7769221067,-0.0945151374,-0.0158475228,-0.1223431304,-0.1744953096,-0.092342779,0.2807446718,0.3410450518,0.0109003345,-0.1625839174,0.0517026521,0.2460907251,0.4139389992,0.3340788484,-0.2454839498,0.3515413702,-0.2834357917,0.6606817245,0.161466971,-0.1628277451,-0.5037252307,-0.1444267035,0.1136398166,0.2299574763,-0.0906316265,-0.2082571685,0.3524005115,-0.1944238096,-0.1165512726,0.3068514466,0.0670145601,0.0767698213,-0.3873151839,-0.0957920551,0.1263177842,-0.18333067,0.1591964662,-0.0987873077,-0.4068606198,-0.0252459794,0.0467833951,-0.0416569114,0.179851681,-0.0388617069,-0.0057351827,0.1389543265,0.2718569934,-0.087905243,-0.0031681033,0.2667992413,0.0274889302,-0.5433757901,-0.1068649217,-0.1762272865,-0.0915438235,-0.0271404162,-0.4331005514,-0.226607278,0.1392434388,0.3854080141,0.2420339286,0.058781635,-0.1209191084,0.2552437782,0.1672874391,0.1566624939,0.158709079,-0.0445005931,0.0627005249,-0.2388303429,-0.3361634016,0.0595778935,0.3235287368,-0.007461844,-0.1055858359,0.3222486377,0.2584587038,0.0905054808,-0.1185096577,-0.0426502936,0.0173140615,0.0908204615,-0.1965566278,-0.0049053752,-0.1352148503,-0.1392586678,0.1043124199,0.4631558657,0.153348729,0.2187498212,-0.2007429153,0.0072999969,0.0527473502,-0.0520291477,0.0671988577,-0.2550835609,-0.1028967425,-0.0584215596,0.3732785583,-0.0494304784,0.1537131816,0.4166162014,0.440703392,0.2286287844,-0.1198708713,-0.2013791353,-0.3755592406,-0.3949952126,0.050185781,0.0132806217,0.1498376876,0.0744452029,0.3507485688,-0.1952111423,-0.067659989,-0.1977310479,0.0408633053,0.1053514034,-0.0561087094,0.2733739316,0.1102679446,0.111708045,-0.1833277047,0.1229062751,0.2097740918,0.0982420817,-0.0068984604,0.222833544,-0.3755995929,0.3456408083,0.1019438356,-0.2880043089,0.1173820049,-0.2836714089,0.06360811,0.1541852653,0.0929389,0.422282815,-0.1402501464,0.3008969724,0.0507875606,0.0104565732,-0.3798061311,-0.1768654585,-0.3743945658,-0.0814044923,0.1589061022,-0.3389072716,0.0899123773,0.0020862136,0.0196651556,-0.2508191764,-0.2769839168,0.1574553251,0.120605506,0.0432880372,-0.2862045467,-0.2152857929,-0.1790947169,-0.1880661696,0.0562933832,-0.2978022397,-0.1772975177,0.0414557196,-0.0261546876,-0.0226895604,0.0218015779,-0.0258528311,0.0410149731,0.1434715986,-0.2004844695,-0.0810186788,-0.04349453,-0.1012005284,-0.3084027469,-0.1947136968,-0.464590013,-0.0675356612,-0.4340673983,-0.2563178837,0.4615660012,-0.2448830903,-0.117469579,-0.1471625268,-0.1874660105,0.3436487615,0.2376240194,-0.2583019733,0.1238426566,-0.2032196373,0.2383244634,-0.1623325348,-0.0352169275,0.4037556648,0.0084522516,0.0046835807,-0.1745022237,0.0674812943,0.1168954,-0.3396815062,0.0507073179,-0.2317256629,0.0983932763,0.2533621192,0.9920879602,0.3216715455,-0.0768560171,0.2035024315,0.0644132793,-0.0247605741,-0.287591815,-0.0832944512,-0.058900971,-0.1288049221,0.1359785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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3832","title":"Making Hugging Face the place to go for Graph NNs datasets","comments":"It will be indeed really great to add support to GNN datasets. Big :+1: for this initiative.","body":"Let's make Hugging Face Datasets the central hub for GNN datasets :)\r\n\r\n**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field.\r\n\r\nWhat are some datasets worth integrating into the Hugging Face hub?\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nSpecial thanks to @napoles-uach for his collaboration on identifying the first ones:\r\n\r\n- [ ] [SNAP-Stanford OGB Datasets](https:\/\/github.com\/snap-stanford\/ogb).\r\n- [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https:\/\/github.com\/snap-stanford\/pretrain-gnns).\r\n- [ ] [TUDatasets](https:\/\/chrsmrrs.github.io\/datasets\/) (A collection of benchmark datasets for graph classification and regression)\r\n\r\ncc @osanseviero \r\n","comment_length":17,"text":"Making Hugging Face the place to go for Graph NNs datasets \n Let's make Hugging Face Datasets the central hub for GNN datasets :)\r\n\r\n**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field.\r\n\r\nWhat are some datasets worth integrating into the Hugging Face hub?\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nSpecial thanks to @napoles-uach for his collaboration on identifying the first ones:\r\n\r\n- [ ] [SNAP-Stanford OGB Datasets](https:\/\/github.com\/snap-stanford\/ogb).\r\n- [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https:\/\/github.com\/snap-stanford\/pretrain-gnns).\r\n- [ ] [TUDatasets](https:\/\/chrsmrrs.github.io\/datasets\/) (A collection of benchmark datasets for graph classification and regression)\r\n\r\ncc @osanseviero \r\n \n It will be indeed really great to add support to GNN datasets. Big :+1: for this initiative.","embeddings":[-0.0234235283,-0.2333472222,-0.1094195396,-0.0523620062,-0.0944150165,-0.0435835384,-0.0281150118,0.1099213734,0.3266023993,0.1850887239,-0.1312942505,-0.084439829,-0.232024774,0.451104939,0.4168959856,-0.1545025706,0.3398022652,0.1104637906,0.2192725688,-0.0025326211,-0.2695412338,0.2994819283,-0.0809999481,-0.0671056136,-0.5245311856,0.0242109951,-0.1205038726,0.0994259492,-0.2501798868,-0.2618248761,0.0551650673,0.29652825,0.128969878,0.3990139961,-0.0001128217,-0.2206660956,0.1074072123,0.0552972704,-0.1619026661,0.1805570126,0.0956687555,-0.0669861734,-0.120657146,0.0956264958,-0.108474046,-0.0008087905,0.1654946953,-0.1503462046,0.017049944,0.0551261492,0.1392469853,0.2921247482,-0.0879428312,-0.2295959443,-0.2453842908,0.3886743486,-0.2929576337,0.203156665,0.3581440449,-0.0612001047,-0.164558053,0.2765050232,-0.0072288387,-0.4293164909,0.5624536276,0.136991933,-0.2511323392,-0.1757494956,-0.3110870719,0.2156855166,-0.1443588883,-0.039251551,-0.30567801,-0.471839726,-0.0524712764,-0.154211536,-0.2975980639,0.037596114,0.086866796,0.2878332734,-0.2444000691,-0.5392485857,-0.0592346527,0.2219287455,0.0701623037,0.1301753968,-0.0812686607,-0.0274148844,0.3999114335,0.0875995308,-0.2428833097,-0.0790505707,-0.0198866799,-0.1538605541,-0.3709764481,-0.2610402405,0.2003474236,0.6400768757,0.3712242842,0.5840640068,-0.1618957967,-0.0739836171,-0.2913262844,-0.09963765,-0.16319336,0.0098330174,-0.0532779582,0.0845638961,0.3618872464,-0.0739065856,0.1227043346,0.1353878379,-0.0595789216,-0.0763022304,-0.092030853,-0.4165196419,-0.073241502,-0.0100471359,0.1024673581,0.1092109755,0.4167848825,0.1870279759,0.1546116918,-0.0891544372,0.1864403635,0.3927560449,-0.1135211736,0.3840912282,0.0159060359,-0.3665124178,-0.2272325605,-0.0198425204,0.0629771426,0.5733380318,0.4738036394,-0.2146670967,0.3269357085,-0.1749045849,0.3883339763,0.2017089725,-0.179531917,0.2441057265,0.1639911979,0.0857873037,-0.2873264253,-0.1284167022,0.0778051913,0.0689965934,-0.2423447073,-0.4198279679,0.2821620107,-0.3629797101,-0.0811760128,0.0537455678,-0.1545815766,-0.0132204574,0.0054759807,0.1874282509,-0.2371592075,0.0826365277,0.3163538277,0.0419423617,-0.1424844563,-0.1246687397,0.4524648786,0.4214231074,0.2684029639,-0.2599221766,-0.079332307,0.1957058012,-0.3030687571,0.451100558,0.0435518697,-0.2493369579,-0.0818349048,0.1814568192,-0.1445280313,-0.4274068773,-0.2447238117,0.021074526,-0.4305689633,-0.0568214431,-0.0166086517,0.4939936399,-0.1594687402,0.0098485677,0.2929275036,0.5374083519,-0.2049236149,-0.2350042164,-0.1701624095,-0.1164645404,0.0045126956,0.5076349378,-0.0999404341,-0.0813326761,-0.2726167142,-0.4122262001,0.0261801872,-0.0606530681,0.0363545306,-0.1227231398,0.2241557389,-0.1409896314,-0.0221113376,0.030052539,-0.675883472,0.1726543605,0.036969278,0.1825154573,-0.2014204413,-0.2291575223,0.0920199752,-0.168723017,0.1585780084,-0.1452020556,0.0514304005,0.0329982638,0.3706387877,0.1985964477,-0.3138008416,0.4122317135,-0.3078444898,0.280166626,-0.2009654045,0.4001988173,-0.0070627374,-0.0146340169,0.0573706515,0.3544779122,-0.2154719681,-0.0368879139,0.2776973844,0.2596090734,-0.0890776962,0.3991345167,0.4834364355,0.5149629116,-0.0252329912,-0.4681952596,-0.0114720976,-0.1753763556,-0.2567226887,-0.1739605069,0.2097373754,0.4737678468,-0.1295677572,0.1249402314,-0.2376725972,0.2324970216,-0.4294071496,0.063312605,-0.3611107469,-0.0678467005,0.307412684,-0.1495144367,0.0022338759,0.153375864,-0.5056700706,0.1279099137,0.0646391958,-0.081730783,0.0431469567,-0.0208129957,-0.1544272453,-0.1516031325,0.1027506813,-0.1088036969,0.138062492,0.201677978,-0.084738791,0.1374889761,0.0455736853,-0.2825337648,0.0869328603,0.1590035409,0.1753515154,-0.0553475358,-0.1693840027,0.2838389874,-0.0986430272,-0.1005054265,-0.3518978357,-0.0992548689,-0.0335488878,0.010322853,0.264724046,-0.129560411,0.2143224776,-0.0523456298,-0.3891021013,-0.3053199053,-0.0037502258,-0.081274949,0.0966924578,0.020400051,-0.2523327172,1.0156260729,-0.3793948889,-0.2314542085,-0.5322870016,-0.3572649658,0.0551405549,0.0289307628,0.0217323694,0.0724615231,0.2583212256,-0.3679008484,0.5709317327,-0.2229535729,-0.4074282944,-0.0578043349,-0.1768966913,0.0055320663,0.1166601181,0.2535021007,-0.3335717916,0.039093446,-0.0907247514,-0.43514961,-0.0697351322,-0.1803048849,-0.1854144484,-0.2227858156,-0.1592585742,0.1067070365,-0.3368827403,-0.4096817374,0.4860121906,0.0082798731,-0.1271679103,0.4819794893,-0.308163017,0.0172966458,-0.4046180248,-0.0075008562,0.0755913854,-0.3832564652,0.4691114426,-0.3889317811,-0.118633315,-0.1864336133,-0.0995712578,0.1760892719,-0.0904717743,-0.496599555,-0.048432149,-0.2445757091,0.2675134242,0.255401969,-0.0953207463,-0.0358556174,-0.1258192956,0.0739168376,-0.0492745899,0.0186686348,0.0692384541,0.3589118719,-0.0883828476,0.1285729259,-0.3102230728,-0.0262042098,0.1105398238,0.2004532516,-0.1829913557,0.1018630415,0.0525923967,0.3965004385,-0.2478462011,-0.3260862231,0.1908594966,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3832","title":"Making Hugging Face the place to go for Graph NNs datasets","comments":"@napoles-uach identifies the [TUDatasets](https:\/\/chrsmrrs.github.io\/datasets\/) (A collection of benchmark datasets for graph classification and regression). \r\n\r\nAdded to the Tasks in the initial issue.","body":"Let's make Hugging Face Datasets the central hub for GNN datasets :)\r\n\r\n**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field.\r\n\r\nWhat are some datasets worth integrating into the Hugging Face hub?\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nSpecial thanks to @napoles-uach for his collaboration on identifying the first ones:\r\n\r\n- [ ] [SNAP-Stanford OGB Datasets](https:\/\/github.com\/snap-stanford\/ogb).\r\n- [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https:\/\/github.com\/snap-stanford\/pretrain-gnns).\r\n- [ ] [TUDatasets](https:\/\/chrsmrrs.github.io\/datasets\/) (A collection of benchmark datasets for graph classification and regression)\r\n\r\ncc @osanseviero \r\n","comment_length":22,"text":"Making Hugging Face the place to go for Graph NNs datasets \n Let's make Hugging Face Datasets the central hub for GNN datasets :)\r\n\r\n**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field.\r\n\r\nWhat are some datasets worth integrating into the Hugging Face hub?\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nSpecial thanks to @napoles-uach for his collaboration on identifying the first ones:\r\n\r\n- [ ] [SNAP-Stanford OGB Datasets](https:\/\/github.com\/snap-stanford\/ogb).\r\n- [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https:\/\/github.com\/snap-stanford\/pretrain-gnns).\r\n- [ ] [TUDatasets](https:\/\/chrsmrrs.github.io\/datasets\/) (A collection of benchmark datasets for graph classification and regression)\r\n\r\ncc @osanseviero \r\n \n @napoles-uach identifies the [TUDatasets](https:\/\/chrsmrrs.github.io\/datasets\/) (A collection of benchmark datasets for graph classification and regression). \r\n\r\nAdded to the Tasks in the initial issue.","embeddings":[-0.0233609602,-0.2450853139,-0.0979369953,-0.0057543805,-0.1070017219,-0.0364056453,-0.0293637775,0.1181741431,0.3275018036,0.1441018283,-0.1514698416,-0.1023743153,-0.2231214494,0.4196002483,0.4411780536,-0.1565523744,0.3331484497,0.0985796675,0.1610820889,-0.0428051949,-0.2738624513,0.3060706556,-0.0625943542,-0.1050333828,-0.5190338492,0.0456237681,-0.1384660602,0.1690508276,-0.2346580774,-0.2278612852,0.0522990189,0.3198702037,0.113336876,0.4515330493,-0.0001137931,-0.2179506868,0.0854682773,-0.0067805257,-0.198315233,0.196534127,0.0851241052,-0.0547658131,-0.1031997651,0.073767148,-0.0706423074,0.0080949673,0.17651245,-0.1631421,0.0584806651,0.0963978544,0.1242552623,0.3420622349,-0.1054976135,-0.2134554684,-0.2152913064,0.3499602675,-0.2723602951,0.2081030756,0.3574615419,-0.0453641899,-0.1874749213,0.2848186493,0.0022752613,-0.4445412457,0.5725594163,0.0943256393,-0.3197540343,-0.1687129587,-0.3043659031,0.2468399107,-0.1472280174,-0.0382358842,-0.2902912199,-0.5019684434,-0.0940566286,-0.1403455138,-0.2773576081,0.0202937685,0.0918725207,0.2887779474,-0.2512563169,-0.4706613123,-0.0564182401,0.2305878997,0.0445684157,0.0734838843,-0.072306253,-0.0390393324,0.378901422,0.0744159147,-0.2300553918,-0.1143108755,-0.0207077544,-0.1764051467,-0.4001747668,-0.26179263,0.2443186194,0.627116859,0.3334881961,0.6095017195,-0.1403456181,-0.0940977111,-0.2547819912,-0.1413640082,-0.1471498609,0.068113938,-0.0249355696,0.058340542,0.3155947924,-0.10759148,0.0951508433,0.1557854861,-0.0385557823,-0.0175573174,-0.0631007254,-0.4204910994,-0.0729584694,-0.0328545496,0.1098873317,0.1179100052,0.3972752094,0.1420386732,0.1769449264,-0.0746003315,0.1824149191,0.3754678071,-0.1290251762,0.372300446,-0.0091437632,-0.3626308143,-0.2317426205,0.0095557254,0.0982722044,0.5092727542,0.4926019907,-0.2240372747,0.3572075367,-0.1855000257,0.3667585254,0.2205149233,-0.146941334,0.2262598127,0.2278013974,0.1152421907,-0.2440516055,-0.1130532846,0.0744919106,0.0738721341,-0.2288151532,-0.4267303348,0.3132833838,-0.3727363944,-0.0093384273,0.0394669399,-0.1891999394,-0.0164181143,-0.0490655601,0.2004949301,-0.2101848274,0.1390502602,0.2855739295,0.0160090849,-0.069714047,-0.1360763907,0.4295977056,0.4751141667,0.2243054509,-0.2467336506,-0.0584567226,0.1948589683,-0.3182339966,0.4586291909,0.0432989933,-0.22370103,-0.0606564283,0.2129457444,-0.2075708359,-0.4393161833,-0.2643225491,0.0149455424,-0.3984659612,-0.0215950664,-0.0235941652,0.5437315106,-0.1660769731,-0.0162147414,0.3002969921,0.5359228849,-0.1805632263,-0.2231651694,-0.1595529914,-0.09705735,-0.0033764988,0.5075393319,-0.088957563,-0.1293688416,-0.327226162,-0.3996625841,-0.0040288419,-0.0502337068,-0.0017849706,-0.113921307,0.1833408922,-0.1577737629,-0.0013131325,0.0247624721,-0.6893976927,0.1813183874,0.0453994237,0.1824727803,-0.2089439183,-0.2216602713,0.0638182834,-0.1819539964,0.1680580229,-0.1352466345,0.0457422696,0.0436926782,0.387380898,0.1733853668,-0.324274689,0.4356336892,-0.3335012197,0.2972501814,-0.2227405459,0.401987195,0.0289467536,-0.0376200229,0.0973888412,0.3361044228,-0.2120353729,-0.0623086616,0.2646014392,0.2684752941,-0.068801485,0.386759609,0.5160742402,0.4843228757,-0.0246601887,-0.4256472886,-0.0321594067,-0.1668977588,-0.249656111,-0.1560572535,0.1963657141,0.482223928,-0.1367580891,0.1280923635,-0.1976244599,0.2223193496,-0.4207578301,0.0549089126,-0.3412322104,-0.0677173883,0.3625983894,-0.081845589,0.0277381055,0.2038395554,-0.4952783287,0.1713502109,0.0993867218,-0.149249211,0.0527756251,-0.0322260857,-0.1746987402,-0.1867622584,0.0965882614,-0.0862068757,0.1551426649,0.1880250126,-0.0737434477,0.09789408,0.0235999767,-0.3019615412,0.0656164959,0.1787194163,0.1272627711,-0.0837932676,-0.1827728599,0.2662578225,-0.1519951969,-0.0715187117,-0.3751090467,-0.0834874436,-0.0355341323,0.0202457998,0.2486352772,-0.112211667,0.24125126,-0.05371622,-0.4299564064,-0.3205106258,0.0027322071,-0.0881808698,0.0543246418,0.0093277432,-0.213837564,1.0346990824,-0.3860891163,-0.248020038,-0.5330561399,-0.393941164,0.0520405136,0.0223449003,0.0459770188,0.1065370142,0.2829063237,-0.4219708741,0.5744329095,-0.209837541,-0.4197376072,-0.0884745196,-0.2230726033,0.0659566596,0.0765048563,0.2899770141,-0.3516173363,0.0696178973,-0.1232649386,-0.3892052174,-0.1069501787,-0.1677799672,-0.1905222535,-0.2566027939,-0.1211204976,0.0570816249,-0.3225925863,-0.3757154346,0.4931261837,0.0110934135,-0.1195674837,0.4199029207,-0.3289011717,0.0388373807,-0.4195064604,-0.0141603928,0.0667815506,-0.3960446119,0.4426994026,-0.3666376472,-0.1355586052,-0.2370978892,-0.0956868082,0.1628172249,-0.071443297,-0.5246744752,-0.0688656271,-0.2792484164,0.2512847781,0.3150323629,-0.0994476974,0.0073297066,-0.1376239359,0.0754413083,-0.0691390336,0.0061830971,0.1133421659,0.3574836552,-0.1118012145,0.136077702,-0.2933054566,-0.0347734392,0.1813660115,0.1969319284,-0.2088232487,0.0446928926,0.0489498526,0.3792405427,-0.2641705573,-0.3291699886,0.1939218789,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3832","title":"Making Hugging Face the place to go for Graph NNs datasets","comments":"Great initiative! Let's keep this issue for these 3 datasets, but moving forward maybe let's create a new issue per dataset :rocket: great work @napoles-uach and @omarespejel!","body":"Let's make Hugging Face Datasets the central hub for GNN datasets :)\r\n\r\n**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field.\r\n\r\nWhat are some datasets worth integrating into the Hugging Face hub?\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nSpecial thanks to @napoles-uach for his collaboration on identifying the first ones:\r\n\r\n- [ ] [SNAP-Stanford OGB Datasets](https:\/\/github.com\/snap-stanford\/ogb).\r\n- [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https:\/\/github.com\/snap-stanford\/pretrain-gnns).\r\n- [ ] [TUDatasets](https:\/\/chrsmrrs.github.io\/datasets\/) (A collection of benchmark datasets for graph classification and regression)\r\n\r\ncc @osanseviero \r\n","comment_length":27,"text":"Making Hugging Face the place to go for Graph NNs datasets \n Let's make Hugging Face Datasets the central hub for GNN datasets :)\r\n\r\n**Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field.\r\n\r\nWhat are some datasets worth integrating into the Hugging Face hub?\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nSpecial thanks to @napoles-uach for his collaboration on identifying the first ones:\r\n\r\n- [ ] [SNAP-Stanford OGB Datasets](https:\/\/github.com\/snap-stanford\/ogb).\r\n- [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https:\/\/github.com\/snap-stanford\/pretrain-gnns).\r\n- [ ] [TUDatasets](https:\/\/chrsmrrs.github.io\/datasets\/) (A collection of benchmark datasets for graph classification and regression)\r\n\r\ncc @osanseviero \r\n \n Great initiative! Let's keep this issue for these 3 datasets, but moving forward maybe let's create a new issue per dataset :rocket: great work @napoles-uach and @omarespejel!","embeddings":[-0.0186398197,-0.2302568555,-0.0931066722,-0.0285473969,-0.122226432,-0.0637292936,0.0056518586,0.1148463637,0.3288172781,0.1683222353,-0.1298398823,-0.0805970952,-0.2303715348,0.4487099648,0.4454592168,-0.1293428093,0.3466874659,0.1192113534,0.2285957932,0.0034421233,-0.2893598974,0.2807302177,-0.0700318962,-0.0680466667,-0.5357472301,0.043321874,-0.1139806286,0.1229244247,-0.2512878478,-0.2653810084,0.0550239347,0.3174976707,0.1384718269,0.412987262,-0.0001124554,-0.2258913219,0.1241728663,0.0368224829,-0.1978865713,0.1738698184,0.0802010745,-0.0721594244,-0.1139427274,0.0908186063,-0.0948243067,0.0101961158,0.171884492,-0.1382856518,0.0330496803,0.0726307184,0.1397071481,0.3155460954,-0.1061960384,-0.2220097482,-0.2103468329,0.3641102314,-0.2708943486,0.1985775977,0.3390811086,-0.0717884377,-0.1666138321,0.2911741138,0.0018237671,-0.4482116401,0.5863304138,0.1152767688,-0.2714140713,-0.1581182182,-0.3135094941,0.2329480946,-0.1541870832,-0.0278255045,-0.2894389331,-0.5026134253,-0.0422282517,-0.1708832532,-0.2762459219,0.0263061859,0.0791872591,0.2687130272,-0.2497725189,-0.489913255,-0.0440931395,0.233781755,0.0506293885,0.1087306216,-0.1044622734,-0.028716417,0.3898029327,0.0723219216,-0.2257004082,-0.1083326563,-0.0096254079,-0.1697379351,-0.3979341686,-0.2541150451,0.2402843088,0.6615874171,0.3433295488,0.6073153019,-0.1653431207,-0.1073985025,-0.2254351079,-0.1189161837,-0.1313633472,0.03319785,-0.0255639646,0.0770246238,0.342394948,-0.0552436672,0.1093367338,0.1368563026,-0.0553173199,-0.0542908236,-0.0693296343,-0.4110111296,-0.0376611426,-0.0217593573,0.0938486606,0.1216982752,0.3986168504,0.1569367647,0.17420955,-0.0814301893,0.1973533332,0.3954193592,-0.0915808901,0.3732135296,0.0177149326,-0.3355209231,-0.2450400144,-0.0118585629,0.0768110454,0.5539177656,0.4719922245,-0.2066369802,0.3088727891,-0.1618185639,0.3798586428,0.1700962335,-0.1877235472,0.2427956015,0.1535172462,0.0895705,-0.2911784947,-0.1174892187,0.0891007856,0.050461024,-0.2515206635,-0.4046560526,0.2948519886,-0.3761080205,-0.0310837198,0.0646634996,-0.1627338678,-0.0091683995,-0.0206539594,0.2000062317,-0.2207282484,0.1168935597,0.3177727759,0.046401158,-0.0985935107,-0.1182750612,0.4515029788,0.466533035,0.2670098841,-0.2505664229,-0.0867218077,0.1905773431,-0.2997658849,0.4474501014,0.0162560437,-0.2244083583,-0.0868884251,0.212862432,-0.1735302955,-0.4372184277,-0.2461245507,0.0498727486,-0.4571654499,-0.0455301777,-0.0054080919,0.5096792579,-0.1646395624,-0.0219434202,0.3155058622,0.5374499559,-0.2076370716,-0.231816411,-0.1705037355,-0.1219889149,0.012245371,0.4985791445,-0.1094910502,-0.1055107787,-0.3241643906,-0.3981209695,0.0041222777,-0.0489005484,0.0134678939,-0.1045123264,0.214357391,-0.1489670128,-0.0385010056,0.0317337364,-0.6920562983,0.1713912338,0.0428792052,0.153984949,-0.196610868,-0.25061813,0.0588444807,-0.1815415919,0.1427150816,-0.1585981846,0.0567654371,0.0258782562,0.335873574,0.1762339771,-0.3432791531,0.4204112589,-0.328402251,0.2790558636,-0.2067564577,0.4110316634,-0.0135235758,-0.0285917241,0.043345809,0.3316638172,-0.201752618,-0.0250777956,0.2757624388,0.2574248016,-0.1065524369,0.3840558231,0.4867895544,0.4898571372,-0.0497057028,-0.4598687291,-0.0269942246,-0.1899919659,-0.2590203583,-0.1625639349,0.2172392011,0.4625552893,-0.1465929151,0.1452895552,-0.2177542597,0.2057461441,-0.4562290013,0.0164297484,-0.356697619,-0.0709606856,0.3321694434,-0.115744032,0.0286564846,0.2018540651,-0.4819090962,0.1383606642,0.1139452159,-0.1239217743,0.0382726789,-0.0081253862,-0.1458756626,-0.1637011915,0.0773818493,-0.0914034694,0.1387118697,0.1995055676,-0.0842095837,0.1148860306,0.0504638888,-0.3094615042,0.0928584412,0.1579541266,0.1607723236,-0.04723401,-0.1741517782,0.2815917134,-0.123162128,-0.0839244947,-0.3609775007,-0.1119123772,-0.0322391726,0.0121082962,0.2572003007,-0.1140261739,0.1871469766,-0.049780596,-0.4215137064,-0.3146457076,-0.0049814065,-0.0907122567,0.0791086927,0.0054480382,-0.2348264456,1.0043331385,-0.3869760633,-0.2163740098,-0.5498820543,-0.376711458,0.0545433611,0.0214037895,0.0311279725,0.1039146259,0.2846222818,-0.4173279107,0.5864564776,-0.2461652458,-0.4127749205,-0.0868065059,-0.185093984,0.0401351452,0.1091500074,0.2774034739,-0.3393736184,0.000042813,-0.0918159559,-0.3782729208,-0.0997165963,-0.1687418669,-0.1873462945,-0.2012339979,-0.131000638,0.0775087997,-0.3067908585,-0.4248397052,0.5007843375,-0.0477176979,-0.1170359477,0.4649668038,-0.3423024416,0.0398712941,-0.4230200052,-0.0311126355,0.0625880361,-0.3766846359,0.4299661517,-0.3609104753,-0.0862351358,-0.2224745303,-0.0831691027,0.1607104242,-0.0993160605,-0.5195980072,-0.0513488613,-0.2484766245,0.2611002922,0.2745940685,-0.0989924073,-0.0065973187,-0.1480500996,0.0550829135,-0.0594751686,0.0085992683,0.0814479962,0.3692734241,-0.0821862295,0.1484542191,-0.2546550035,-0.0252918433,0.1578257531,0.2039425224,-0.1858302504,0.0916253179,0.0718650073,0.4087697268,-0.2502826452,-0.3512640595,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3831","title":"when using to_tf_dataset with shuffle is true, not all completed batches are made","comments":"Hi @greenned, this is expected behaviour for `to_tf_dataset`. By default, we drop the smaller 'remainder' batch during training (i.e. when `shuffle=True`). If you really want to keep that batch, you can set `drop_remainder=False` when calling `to_tf_dataset()`.","body":"## Describe the bug\r\nwhen converting a dataset to tf_dataset by using to_tf_dataset with shuffle true, the remainder is not converted to one batch\r\n\r\n## Steps to reproduce the bug\r\nthis is the sample code below\r\nhttps:\/\/colab.research.google.com\/drive\/1_oRXWsR38ElO1EYF9ayFoCU7Ou1AAej4?usp=sharing\r\n\r\n\r\n## Expected results\r\nregardless of shuffle is true or not, 67 rows dataset should be 5 batches when batch size is 16.\r\n\r\n## Actual results\r\n4 batches\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n","comment_length":36,"text":"when using to_tf_dataset with shuffle is true, not all completed batches are made \n ## Describe the bug\r\nwhen converting a dataset to tf_dataset by using to_tf_dataset with shuffle true, the remainder is not converted to one batch\r\n\r\n## Steps to reproduce the bug\r\nthis is the sample code below\r\nhttps:\/\/colab.research.google.com\/drive\/1_oRXWsR38ElO1EYF9ayFoCU7Ou1AAej4?usp=sharing\r\n\r\n\r\n## Expected results\r\nregardless of shuffle is true or not, 67 rows dataset should be 5 batches when batch size is 16.\r\n\r\n## Actual results\r\n4 batches\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n \n Hi @greenned, this is expected behaviour for `to_tf_dataset`. By default, we drop the smaller 'remainder' batch during training (i.e. when `shuffle=True`). If you really want to keep that batch, you can set `drop_remainder=False` when calling `to_tf_dataset()`.","embeddings":[-0.2101609558,-0.1565532982,0.1008584052,0.109501034,0.3623253107,0.1720544696,0.0999243483,0.3753470778,-0.4610960186,0.4258148968,-0.1732450575,0.3522312343,0.0507793166,-0.0305817388,0.0872322097,0.1303315163,0.4016359746,-0.04188025,-0.3483785689,-0.2507770658,-0.2129975855,0.0240711756,-0.4630481899,-0.2681808174,-0.3795515299,0.0534661412,-0.0004540062,-0.1324293762,0.0985651314,0.1185760647,0.0071642743,0.0842508972,-0.048457969,0.6641512513,-0.0001149922,-0.0443853661,0.1291521341,-0.0472286046,-0.3840300739,0.0781278908,-0.2722941935,0.3542990685,-0.0494552888,0.0105825635,-0.002949252,-0.0420661718,0.0618720688,0.1616461873,0.1469322294,0.1686705351,0.1783306748,0.1592247933,0.0435215719,0.1571512818,0.5261585116,0.1009254456,-0.2237551361,0.1174941212,0.051142212,-0.0563640893,0.0690338612,0.2109211534,0.188196525,-0.0025160615,0.0795938075,0.04349491,0.0339075811,-0.4265289009,0.0041611562,0.2707496583,0.3294868171,-0.1710378528,-0.3079138398,-0.215056926,0.0574985854,-0.0192451589,-0.1576607078,0.0761367455,-0.2073668242,-0.1276507378,-0.4496008158,0.1997624338,-0.1855838299,-0.1281991303,-0.0981696174,0.4522594512,0.0467604101,0.062746875,0.1374316365,-0.1811993122,0.3976439238,-0.1726699024,0.004678478,0.1159965694,-0.5533165932,-0.2206910104,-0.0630645901,0.0250339769,0.3571752906,-0.0715067536,-0.1205955446,0.104949832,0.1056880802,-0.0098104831,0.2213849127,-0.0253059864,-0.3046863973,0.4243261814,0.057569392,-0.2200034261,-0.1627921015,0.1072137728,-0.0520222411,-0.0837988704,0.1242585257,-0.2192737162,-0.1689284593,-0.2298827618,-0.29360497,-0.1266325414,-0.0257421024,0.1067003906,-0.1465007067,0.1142191812,0.0585756935,0.0537425093,0.0016339772,0.0341750532,-0.4158667028,0.0767625943,-0.3324216008,-0.1855382621,-0.2726813853,-0.1292979121,0.0410892069,-0.1436268985,0.0376031362,0.2220435739,0.0978012085,0.1097993776,-0.0719805658,-0.3438981473,0.1481495202,0.2326765358,0.0983476192,0.1909242868,0.0847896934,0.1772270799,0.0122778853,0.4981276691,-0.2448166907,0.0318652876,0.3358406425,0.2177068144,-0.0413694084,-0.3718388379,-0.4808965623,0.3076965213,0.5394350886,0.015965974,-0.1312053949,-0.5934907198,-0.2205459774,-0.2656880617,0.118546851,0.2112939656,-0.272046566,0.1109274402,0.4417617321,-0.3732999265,0.5873150229,0.5287665725,0.1570784897,0.2953869998,-0.2372859865,-0.1077918857,0.1094994172,0.1917937994,-0.2076856196,-0.0346473865,-0.2131401747,0.105037652,-0.2020874619,-0.0828928873,0.0760055408,-0.2917896807,0.5760389566,0.3905271292,-0.405874908,0.3102740943,-0.4214007258,0.1288929582,0.2523376346,-0.0011611023,0.019637946,0.1392672956,-0.01399389,-0.1661165804,0.4232124686,0.0852231756,0.1128332391,-0.1058686823,0.1783780158,0.1461800039,0.0989807248,-0.0972493216,-0.1737001389,0.0351997502,0.1946460754,-0.0533729196,0.2260785997,-0.018429609,0.009624579,-0.1637152135,-0.0227603372,0.1599509716,0.0533388145,0.1665807962,0.1151717007,0.0766846016,-0.040101476,-0.2586226463,-0.2514570951,0.0490850657,-0.163468644,0.327504456,0.2180276662,-0.4094522893,0.0476958677,0.2396365404,0.2053247094,-0.2196432352,-0.1934143752,0.2814114988,0.0559857562,0.0149704423,-0.1482098997,-0.0664277375,0.0278343186,0.1572304219,-0.3681476712,-0.1498258859,0.0573852956,-0.1171422899,-0.0385737196,0.0636538342,-0.1382141709,0.2147266716,0.1854844391,-0.1266989559,0.0496385694,0.1972951442,0.0927772075,-0.0341079459,0.0300963148,-0.0616658777,-0.3536172807,0.0597371832,-0.3029875457,0.4018346965,0.7039234638,-0.1558255553,-0.1831423491,0.1673379093,-0.1333749145,-0.1988959759,-0.0278774258,0.1977152228,0.2501453459,0.3480067849,0.2588485181,-0.2263970822,0.0080189249,-0.2260936052,0.1142935753,0.0290048718,0.0295450743,0.3139496148,0.10345532,0.0543384291,-0.3334699869,-0.0876760259,0.1837738901,0.4879391789,-0.415607661,-0.3082158267,-0.3294088244,-0.0759217367,-0.2569134533,0.0410268791,0.2426648736,-0.3775737584,0.1372372359,0.4265151024,-0.2189571559,0.1763325036,0.0373731963,-0.0164315924,0.0395555794,0.0851954818,-0.2125041187,0.1054977998,-0.5505775213,0.1637596339,0.2325275093,-0.1417059451,0.2389282584,-0.3829708099,-0.2431970984,-0.1758555323,0.0496453084,0.131681025,-0.4701689184,0.0428504013,-0.1946887225,0.2371472418,-0.1721305102,-0.0847187415,0.1445052028,-0.3086988628,-0.207214877,0.3497442007,0.0035705359,-0.0838621557,-0.1532180607,-0.1952404231,0.0269905776,-0.1019688621,-0.2122154534,-0.099682577,0.113932848,0.456766963,0.0533058979,-0.3143669069,-0.3310249746,-0.3828105032,-0.1915974617,-0.4526929557,0.2719555795,-0.1121198609,-0.2837968469,-0.0241039321,0.1124822348,-0.2406271845,0.6917421222,-0.2306821793,0.3288361728,-0.2739790082,0.0598966591,-0.3209654093,0.1938394159,0.180528909,-0.1901185811,-0.0999694839,-0.1566762179,0.0409410037,0.0969257802,-0.0068074348,0.3754735291,-0.2390351295,0.176724866,0.1681838036,0.7660072446,0.1551320255,0.1033581793,-0.0546205081,-0.1835265756,0.1116340309,-0.2274734527,-0.0945961252,0.7235046625,0.19651681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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3830","title":"Got error when load cnn_dailymail dataset","comments":"Was able to reproduce the issue on Colab; full logs below. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError                        Traceback (most recent call last)\r\n[<ipython-input-2-39967739ba7f>](https:\/\/localhost:8080\/#) in <module>()\r\n      1 import datasets\r\n      2 \r\n----> 3 train_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n\r\n5 frames\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py](https:\/\/localhost:8080\/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1705         ignore_verifications=ignore_verifications,\r\n   1706         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1707         use_auth_token=use_auth_token,\r\n   1708     )\r\n   1709 \r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py](https:\/\/localhost:8080\/#) in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                     if not downloaded_from_gcs:\r\n    594                         self._download_and_prepare(\r\n--> 595                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    596                         )\r\n    597                     # Sync info\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py](https:\/\/localhost:8080\/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    659         split_dict = SplitDict(dataset_name=self.name)\r\n    660         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 661         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    662 \r\n    663         # Checksums verification\r\n\r\n[\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234\/cnn_dailymail.py](https:\/\/localhost:8080\/#) in _split_generators(self, dl_manager)\r\n    253     def _split_generators(self, dl_manager):\r\n    254         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 255         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    256         # Generate shared vocabulary\r\n    257 \r\n\r\n[\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234\/cnn_dailymail.py](https:\/\/localhost:8080\/#) in _subset_filenames(dl_paths, split)\r\n    154     else:\r\n    155         logger.fatal(\"Unsupported split: %s\", split)\r\n--> 156     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    157     dm = _find_files(dl_paths, \"dm\", urls)\r\n    158     return cnn + dm\r\n\r\n[\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234\/cnn_dailymail.py](https:\/\/localhost:8080\/#) in _find_files(dl_paths, publisher, url_dict)\r\n    133     else:\r\n    134         logger.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 135     files = sorted(os.listdir(top_dir))\r\n    136 \r\n    137     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```","body":"When using datasets.load_dataset method to load cnn_dailymail dataset,  got error as below:\r\n\r\n- windows os: FileNotFoundError: [WinError 3] \u7cfb\u7edf\u627e\u4e0d\u5230\u6307\u5b9a\u7684\u8def\u5f84\u3002: 'D:\\\\SourceCode\\\\DataScience\\\\HuggingFace\\\\Data\\\\downloads\\\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\\\cnn\\\\stories'\r\n- google colab: NotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\nThe code is to load dataset:\r\nwindows os:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", cache_dir=\"D:\\\\SourceCode\\\\DataScience\\\\HuggingFace\\\\Data\")\r\n\r\n```\r\ngoogle colab:\r\n```\r\nimport datasets\r\n\r\ntrain_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n```","comment_length":201,"text":"Got error when load cnn_dailymail dataset \n When using datasets.load_dataset method to load cnn_dailymail dataset,  got error as below:\r\n\r\n- windows os: FileNotFoundError: [WinError 3] \u7cfb\u7edf\u627e\u4e0d\u5230\u6307\u5b9a\u7684\u8def\u5f84\u3002: 'D:\\\\SourceCode\\\\DataScience\\\\HuggingFace\\\\Data\\\\downloads\\\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\\\cnn\\\\stories'\r\n- google colab: NotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\nThe code is to load dataset:\r\nwindows os:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", cache_dir=\"D:\\\\SourceCode\\\\DataScience\\\\HuggingFace\\\\Data\")\r\n\r\n```\r\ngoogle colab:\r\n```\r\nimport datasets\r\n\r\ntrain_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n``` \n Was able to reproduce the issue on Colab; full logs below. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError                        Traceback (most recent call last)\r\n[<ipython-input-2-39967739ba7f>](https:\/\/localhost:8080\/#) in <module>()\r\n      1 import datasets\r\n      2 \r\n----> 3 train_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n\r\n5 frames\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py](https:\/\/localhost:8080\/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1705         ignore_verifications=ignore_verifications,\r\n   1706         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1707         use_auth_token=use_auth_token,\r\n   1708     )\r\n   1709 \r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py](https:\/\/localhost:8080\/#) in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                     if not downloaded_from_gcs:\r\n    594                         self._download_and_prepare(\r\n--> 595                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    596                         )\r\n    597                     # Sync info\r\n\r\n[\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py](https:\/\/localhost:8080\/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    659         split_dict = SplitDict(dataset_name=self.name)\r\n    660         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 661         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    662 \r\n    663         # Checksums verification\r\n\r\n[\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234\/cnn_dailymail.py](https:\/\/localhost:8080\/#) in _split_generators(self, dl_manager)\r\n    253     def _split_generators(self, dl_manager):\r\n    254         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 255         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    256         # Generate shared vocabulary\r\n    257 \r\n\r\n[\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234\/cnn_dailymail.py](https:\/\/localhost:8080\/#) in _subset_filenames(dl_paths, split)\r\n    154     else:\r\n    155         logger.fatal(\"Unsupported split: %s\", split)\r\n--> 156     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    157     dm = _find_files(dl_paths, \"dm\", urls)\r\n    158     return cnn + dm\r\n\r\n[\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234\/cnn_dailymail.py](https:\/\/localhost:8080\/#) in _find_files(dl_paths, publisher, url_dict)\r\n    133     else:\r\n    134         logger.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 135     files = sorted(os.listdir(top_dir))\r\n    136 \r\n    137     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```","embeddings":[-0.3095068634,0.2098645717,0.0282028392,0.4526913762,0.2786128223,0.0300758779,0.6285235286,-0.0142823523,0.0125058144,0.2989324927,-0.3006661534,0.2344309986,-0.4058236182,0.135649994,-0.0893945023,0.0908746719,0.0534630083,0.162738055,-0.0562223271,0.0320773162,-0.2700074911,0.4004788101,-0.301060617,-0.0533442125,-0.6285268664,0.0138297388,-0.0953596979,0.4638507664,-0.1315477639,-0.0597177334,0.1083320975,-0.1623660177,0.3752065897,0.5299764872,-0.0001248304,0.2415371984,0.2318645865,-0.0633838102,-0.3524454534,-0.5094767809,-0.1255920082,-0.0482651033,0.1691559553,-0.1163774952,-0.1141695082,0.27736184,0.1407027394,0.1941359341,0.3674527705,0.5125661492,0.1052401438,0.1047104672,0.0365597084,0.0257800817,0.0601956695,0.1122562736,-0.1020159647,0.7109394073,-0.2355093807,0.0954458863,0.3148869872,0.2238556147,-0.1997052878,0.0569337606,0.0074757533,0.1964648515,-0.1544727087,-0.6135703921,0.2247476429,0.0434235521,0.5313556194,-0.1198203862,-0.2105577737,0.0643798187,0.1522720307,-0.3185528219,0.3862509131,0.3067632914,-0.145047918,0.0986967608,-0.195862934,-0.0804243833,-0.2782750428,0.4161159396,-0.1356514692,0.2995862663,-0.1916510463,0.0524695888,0.0913630277,-0.0304107573,0.3879628181,-0.090346463,0.0824392661,0.2340681553,-0.0359506868,0.0840247571,-0.2074853927,0.2534273267,-0.1279339939,0.1151793003,-0.1874554604,-0.1733553708,-0.0941245109,0.1420971006,-0.1086534411,0.129625082,-0.009419959,0.3359819949,0.3653140068,0.3486902118,-0.1976778209,-0.0737162977,-0.5522984266,-0.3752034009,0.0062364507,0.0427167714,0.6588177085,-0.2488256693,-0.4301328957,-0.1785049289,-0.577031374,0.1273433566,0.3073651791,0.6884992123,-0.090669468,-0.2485330999,0.0006124677,0.0779467747,-0.0009154449,0.1544563472,-0.1943496913,0.2483545095,-0.2779595256,0.241957128,0.3207553327,-0.3903261721,0.3200772107,0.0403801538,0.1246362999,-0.1580450684,-0.1141290292,-0.2772791982,-0.2260845304,0.4425128102,-0.0008917783,0.0171307139,0.1403041035,-0.1692491472,-0.1248302758,0.2865030766,-0.4726810455,-0.5606008768,-0.1279151142,0.0078634648,-0.2196883857,-0.1126105115,-0.172479406,-0.0071950713,0.0021513202,-0.1294339448,-0.0630452707,-0.009025963,-0.4937413037,-0.2018274665,0.310910821,0.5900986791,-0.561476171,-0.1409684569,-0.0356529057,-0.2114342898,0.2404618114,0.4673262835,-0.1799376309,0.1498856395,-0.2929301262,-0.1129491627,0.3088053763,-0.039694611,-0.5198440552,0.0493866019,0.0024257165,0.0991971791,0.0650312379,0.0823205858,-0.1055632606,-0.0592849813,0.2024238259,0.1964768767,0.0183852892,0.0749619976,-0.1724211723,-0.2078322917,0.0615353957,0.367541641,0.2675862908,0.1487717628,0.2101757079,-0.069795534,0.0327508673,-0.331834197,-0.1382556856,0.274363935,0.5242498517,0.1274564117,-0.0149251688,0.0737616941,-0.5138710737,0.1886540204,0.1215175539,0.3177192509,-0.2373701185,-0.0705838352,-0.123599574,-0.0034182521,-0.2920303345,-0.1643058211,-0.0287859682,0.0056134881,-0.0182575136,0.1839789748,-0.1737788171,0.5757718682,-0.1650727838,0.2158194631,-0.1507937908,0.4189091623,-0.1825194359,-0.1976023018,-0.1767901629,0.1962745637,0.1061785147,-0.2600624263,-0.1781481206,0.2192525715,0.0044811592,-0.0246918816,0.0200719312,-0.0260995906,0.1519243866,-0.0573946536,0.1103878841,0.2108823359,0.1341396719,0.0661738813,0.0295040458,0.3374632299,-0.0079795271,0.1355275512,-0.0155629255,-0.1064222753,-0.0251909383,-0.0373208486,0.0783377588,-0.0712004527,0.2821792066,-0.1807914674,0.4348880053,0.1113135815,-0.2562860847,-0.1900908053,0.0920441821,-0.1353327185,0.0936725363,0.1896373034,-0.3600356579,0.0499492809,-0.1369896084,0.1927137524,0.6719838381,0.0205483623,-0.053447444,0.2583379447,0.0439379811,-0.1488323212,0.2953349352,0.1547930837,0.3846693635,0.2565410733,-0.053167481,0.1039111391,-0.3355038762,-0.5095568299,0.1543495059,0.4287933409,-0.4389049411,0.0451397337,-0.0495077185,-0.4452630281,-0.0576046035,-0.3300298154,-0.1824723929,0.0079816282,-0.1696908772,0.3829991519,0.2046039551,0.0093017183,0.0044355583,0.0309412479,0.0462283082,-0.2844980359,-0.1010483429,0.0061764703,-0.0315188393,-0.0567340925,0.52067101,-0.1717217118,0.1327513456,-0.2523739934,-0.0040804869,-0.2433169186,-0.2447648793,0.0887608454,0.02960773,0.4573536515,-0.0251987092,0.0560984239,-0.1999283284,-0.2714459598,0.2580687702,0.0260042306,-0.1316272914,0.0171133745,-0.3006403148,-0.0000077797,-0.0525487512,-0.2995115519,-0.5992617011,-0.2427120656,-0.0157544818,-0.0655328333,0.1883067638,0.5018793344,-0.0082328329,0.1793191731,0.1717995107,0.0900170729,-0.0956965908,-0.2910320759,0.3816211224,-0.2746096849,-0.2307242155,0.2024276108,-0.0239230543,0.0335885249,-0.11995776,-0.4130155146,-0.4669873714,0.0137528284,-0.2432014495,-0.0563513078,-0.2628588378,0.1314911097,-0.0748455971,0.0652717352,0.1461220533,-0.2574116588,-0.0097228019,-0.1118962467,0.0733354092,-0.0635301247,0.5399028659,-0.1412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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3830","title":"Got error when load cnn_dailymail dataset","comments":"Hi @jon-tow, thanks for reporting. And hi @dynamicwebpaige, thanks for your investigation. \r\n\r\nThis issue was already reported \r\n- #3784\r\n\r\nand its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today (indeed, we were planning to do it last Friday).\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nCC: @lhoestq ","body":"When using datasets.load_dataset method to load cnn_dailymail dataset,  got error as below:\r\n\r\n- windows os: FileNotFoundError: [WinError 3] \u7cfb\u7edf\u627e\u4e0d\u5230\u6307\u5b9a\u7684\u8def\u5f84\u3002: 'D:\\\\SourceCode\\\\DataScience\\\\HuggingFace\\\\Data\\\\downloads\\\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\\\cnn\\\\stories'\r\n- google colab: NotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\nThe code is to load dataset:\r\nwindows os:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", cache_dir=\"D:\\\\SourceCode\\\\DataScience\\\\HuggingFace\\\\Data\")\r\n\r\n```\r\ngoogle colab:\r\n```\r\nimport datasets\r\n\r\ntrain_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n```","comment_length":129,"text":"Got error when load cnn_dailymail dataset \n When using datasets.load_dataset method to load cnn_dailymail dataset,  got error as below:\r\n\r\n- windows os: FileNotFoundError: [WinError 3] \u7cfb\u7edf\u627e\u4e0d\u5230\u6307\u5b9a\u7684\u8def\u5f84\u3002: 'D:\\\\SourceCode\\\\DataScience\\\\HuggingFace\\\\Data\\\\downloads\\\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\\\cnn\\\\stories'\r\n- google colab: NotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\nThe code is to load dataset:\r\nwindows os:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", cache_dir=\"D:\\\\SourceCode\\\\DataScience\\\\HuggingFace\\\\Data\")\r\n\r\n```\r\ngoogle colab:\r\n```\r\nimport datasets\r\n\r\ntrain_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n``` \n Hi @jon-tow, thanks for reporting. And hi @dynamicwebpaige, thanks for your investigation. \r\n\r\nThis issue was already reported \r\n- #3784\r\n\r\nand its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today (indeed, we were planning to do it last Friday).\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```\r\n\r\nCC: @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3829","title":"[\ud83d\udcc4 Docs] Create a `datasets` performance guide.","comments":"Hi ! Yes this is definitely something we'll explore, since optimizing processing pipelines can be challenging and because performance is key here: we want anyone to be able to play with large-scale datasets more easily.\r\n\r\nI think we'll start by documenting the performance of the dataset transforms we provide, and then we can have some tools to help debugging\/optimizing them","body":"## Brief Overview\r\nDownloading, saving, and preprocessing large datasets from the `datasets` library can often result in [performance bottlenecks](https:\/\/github.com\/huggingface\/datasets\/issues\/3735). These performance snags can be challenging to identify and to debug, especially for users who are less experienced with building deep learning experiments.\r\n\r\n## Feature Request\r\nCould we create a performance guide for using `datasets`, similar to:\r\n* [Better performance with the `tf.data` API](https:\/\/github.com\/huggingface\/datasets\/issues\/3735)\r\n* [Analyze `tf.data` performance with the TF Profiler](https:\/\/www.tensorflow.org\/guide\/data_performance_analysis)\r\n\r\nThis performance guide should detail practical options for improving performance with `datasets`, and enumerate any common best practices. It should also show how to use tools like the PyTorch Profiler or the TF Profiler to identify any performance bottlenecks (example below).\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/3712347\/156859152-a3cb9565-3ec6-4d39-8e77-56d0a75a4954.png)\r\n\r\n## Related Issues\r\n* [wiki_dpr pre-processing performance #1670](https:\/\/github.com\/huggingface\/datasets\/issues\/1670)\r\n* [Adjusting chunk size for streaming datasets #3499](https:\/\/github.com\/huggingface\/datasets\/issues\/3499)\r\n* [how large datasets are handled under the hood #1004](https:\/\/github.com\/huggingface\/datasets\/issues\/1004)\r\n* [using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? #1830](https:\/\/github.com\/huggingface\/datasets\/issues\/1830)\r\n* [Best way to batch a large dataset? #315](https:\/\/github.com\/huggingface\/datasets\/issues\/315)\r\n* [Saving processed dataset running infinitely #1911](https:\/\/github.com\/huggingface\/datasets\/issues\/1911)","comment_length":60,"text":"[\ud83d\udcc4 Docs] Create a `datasets` performance guide. \n ## Brief Overview\r\nDownloading, saving, and preprocessing large datasets from the `datasets` library can often result in [performance bottlenecks](https:\/\/github.com\/huggingface\/datasets\/issues\/3735). These performance snags can be challenging to identify and to debug, especially for users who are less experienced with building deep learning experiments.\r\n\r\n## Feature Request\r\nCould we create a performance guide for using `datasets`, similar to:\r\n* [Better performance with the `tf.data` API](https:\/\/github.com\/huggingface\/datasets\/issues\/3735)\r\n* [Analyze `tf.data` performance with the TF Profiler](https:\/\/www.tensorflow.org\/guide\/data_performance_analysis)\r\n\r\nThis performance guide should detail practical options for improving performance with `datasets`, and enumerate any common best practices. It should also show how to use tools like the PyTorch Profiler or the TF Profiler to identify any performance bottlenecks (example below).\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/3712347\/156859152-a3cb9565-3ec6-4d39-8e77-56d0a75a4954.png)\r\n\r\n## Related Issues\r\n* [wiki_dpr pre-processing performance #1670](https:\/\/github.com\/huggingface\/datasets\/issues\/1670)\r\n* [Adjusting chunk size for streaming datasets #3499](https:\/\/github.com\/huggingface\/datasets\/issues\/3499)\r\n* [how large datasets are handled under the hood #1004](https:\/\/github.com\/huggingface\/datasets\/issues\/1004)\r\n* [using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? #1830](https:\/\/github.com\/huggingface\/datasets\/issues\/1830)\r\n* [Best way to batch a large dataset? #315](https:\/\/github.com\/huggingface\/datasets\/issues\/315)\r\n* [Saving processed dataset running infinitely #1911](https:\/\/github.com\/huggingface\/datasets\/issues\/1911) \n Hi ! Yes this is definitely something we'll explore, since optimizing processing pipelines can be challenging and because performance is key here: we want anyone to be able to play with large-scale datasets more easily.\r\n\r\nI think we'll start by documenting the performance of the dataset transforms we provide, and then we can have some tools to help debugging\/optimizing them","embeddings":[-0.3264001608,-0.0672764257,-0.1491250545,0.0796749145,0.1978186816,0.1243428141,-0.0817879215,0.4515271187,-0.2909836173,0.0455368608,-0.0428124629,0.285545826,-0.328109175,0.4099059403,0.2774598598,-0.3230412602,-0.1052234247,0.0541577712,-0.0467096977,0.2728013992,0.2644215822,-0.0850740895,0.1813233942,0.0402290896,-0.2391879857,-0.1211326942,0.0761182085,-0.1559125334,-0.3433633745,-0.4508427382,0.1139681265,0.2355311364,0.1917216182,0.5568239689,-0.0001061001,0.0312976502,0.0062975795,0.0781592429,-0.0564728715,0.0241213255,-0.3465359211,-0.4599175155,0.1184705794,-0.2379987687,0.0715490058,-0.2084664851,-0.0563898161,-0.155621469,0.1767452955,0.4197971523,0.2017510384,0.5012520552,-0.0323937684,0.1390963346,-0.1044533551,0.2189443409,-0.5867086649,-0.0648491457,0.2524333596,0.0401321501,-0.1872048974,0.208473146,-0.0654991865,-0.0117916064,0.4738066792,-0.0849084035,-0.2327142656,-0.2614558935,-0.1477259398,0.5364258885,0.4791144729,-0.3459096253,-0.4647685289,-0.4730124772,-0.1995130479,-0.2476201355,-0.0866095498,-0.076305896,-0.0808994621,0.0736456513,-0.4500612319,-0.1940008104,-0.1187438294,-0.002709579,0.1636565328,-0.0273990668,-0.0348192565,-0.1289126873,0.2690831423,0.0132340426,-0.1243846789,-0.16106157,0.0885847062,-0.0345057398,-0.6336910129,-0.449490726,0.3769186139,-0.0405876078,0.4990196526,0.1062202826,0.2711893022,0.1955431998,-0.1856002212,0.0374262966,0.1465315372,0.1469934881,0.0162654519,-0.0837501884,0.2072175443,0.0128921568,0.0964670777,0.1816277504,0.2329196036,0.0560590327,-0.0620327666,-0.1192815602,-0.2027618885,-0.1952686906,-0.1679728925,-0.0070617585,0.0473815762,-0.3293756843,0.1967923045,0.2415254563,-0.080844909,-0.0381424055,-0.1095700711,-0.1094694287,-0.233158648,-0.1299682409,-0.1584755927,0.0585162714,-0.2665049732,0.2813127041,0.0878847316,-0.2203250825,0.1554387212,-0.1856840402,0.188073501,0.0675193369,0.3004237115,0.0011141385,-0.0532332063,0.1553984433,-0.1961368173,-0.0043530855,-0.0468521304,0.321988672,-0.3563618362,0.0999305025,-0.1478639245,-0.5457513332,0.0889049843,0.1789744049,-0.2666709721,-0.2046280354,-0.3942295909,0.7601329088,-0.1813787818,0.1888856143,0.0428415313,0.1493188441,-0.6066028476,-0.2440283597,0.2899289131,0.2103259414,-0.3450980484,-0.1088345423,-0.0102771157,-0.0206427239,-0.1500907987,0.4020774662,-0.2733123899,0.3129767179,-0.1208618954,0.2288607359,0.4433620274,-0.1086373925,0.0547268614,0.332076937,-0.1791610122,-0.0508073904,0.1727007926,0.1037734672,0.1674326509,-0.2862798274,0.0105300443,0.0672566742,-0.0796887651,0.000180873,-0.0740416497,-0.4640645981,-0.0060776961,0.0783305615,-0.0693699643,-0.10806375,-0.1384072155,-0.047011476,-0.0158425979,-0.1966395676,-0.1857056171,0.3850708008,0.1510401815,0.2176740617,0.0210486948,-0.1705007106,-0.3443332613,0.2474389523,0.3754791319,0.0141355423,0.2750578225,-0.2102042884,0.0766190588,-0.0887507051,0.0701818168,0.0209777262,0.0969838053,-0.3917255402,0.2942527533,0.0527806543,-0.2536871135,0.2526643872,-0.4835742116,0.0970924273,-0.2009820491,0.1117953286,0.1265898198,0.0206104722,0.1676586121,0.0972547308,-0.230777055,0.0939925388,-0.1601705104,0.1955580413,-0.0525113046,0.1757808179,0.0586967394,0.9100503325,0.2235710323,-0.0569514185,-0.0830823407,-0.0718042031,-0.15644674,-0.0429510623,-0.2149285227,0.3367483616,-0.0723712668,0.2215078771,0.1544151902,-0.197444126,-0.0061812242,0.0835220441,-0.0906367749,0.1149453744,0.4543447495,-0.0591668934,0.1857530177,0.0404203981,-0.3067844212,0.2421413213,0.3511918783,-0.1023898423,-0.0220310017,0.505648613,0.1731327176,-0.0750634521,0.0895357877,0.1644133031,0.3836788237,0.2676311731,0.0767658427,-0.1252445877,-0.0971909538,-0.0956021622,0.2376920283,0.2848193049,-0.1640800536,0.0028898902,-0.1599064618,0.0994388461,-0.3033535779,-0.4112361073,-0.1060041338,0.243767634,-0.3391506374,-0.2697493732,0.0259625148,-0.323650986,0.0707402751,0.0086298343,-0.056915056,-0.1723960191,0.0235008243,0.0125377653,0.0534062497,0.076799579,-0.2117454559,0.2614079416,-0.0640673265,-0.0514103249,-0.2297800183,-0.1596078277,-0.1065582857,0.1232894361,0.2051989138,0.0638158023,0.4398849905,0.0043704733,0.3025688827,-0.3117899001,-0.5977799296,0.0202933084,-0.2105545104,0.2067111582,0.0599435195,0.2758261263,-0.2165825218,-0.1545369476,0.0634708479,-0.1348529905,-0.0856625289,-0.1799231917,0.1030018106,-0.0418646075,0.0353492387,-0.3086977899,-0.0158612709,-0.4798069298,0.2777678967,-0.1838227212,0.1451109201,0.1535090655,0.1052519158,0.4430772662,-0.1762313396,0.2009651214,-0.0091128368,-0.338620156,0.188780725,-0.1472554058,-0.2319755405,-0.0633144155,0.1222564653,0.0408215597,0.0920946375,-0.5262892842,-0.1136243939,-0.490886867,0.1950223148,0.0036786983,0.1693670005,0.1843553334,-0.0214977078,-0.071279183,0.001419467,-0.3739381433,-0.1397010237,-0.1389691681,0.0188622158,0.0472338274,0.5734775066,0.3551222384,0.6103294492,0.2854109704,-0.1770605147,0.1909424365,0.0406640284,0.2482175082,0.0028378789,-0.2358932942,0.1446669847,-0.19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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3828","title":"The Pile's _FEATURE spec seems to be incorrect","comments":"Hi @dlwh, thanks for reporting.\r\n\r\nPlease note, that the source data files for \"all\" config are different from the other configurations.\r\n\r\nThe \"all\" config contains the official Pile data files, from https:\/\/mystic.the-eye.eu\/public\/AI\/pile\/\r\nAll data examples contain a \"meta\" dict with a single \"pile_set_name\" key:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n        ds = load_dataset(\"the_pile\", \"all\", split=\"train\", streaming=True)\r\n        item = next(iter(ds))\r\nDownloading builder script: 9.09kB [00:00, 4.42MB\/s]\r\n\r\nIn [3]: item[\"meta\"]\r\nOut[3]: {'pile_set_name': 'Pile-CC'}\r\n```\r\n\r\nOn the other hand, all the other subset configs data files come from the Pile preliminary components directory: https:\/\/mystic.the-eye.eu\/public\/AI\/pile_preliminary_components\/\r\nFor theses components, the \"meta\" field may have different keys depending on the subset: \"id\", \"language\", \"pmid\",... Because of that, if we had kept the `dict` data format for the \"meta\" field, we would have an error when trying to concatenate different subsets, whose \"meta\" keys are not identical. In order to avoid that, the \"meta\" field is cast to `str` in all these cases, so that there is no incompatibility in their \"meta\" data type when concatenating.\r\n\r\nYou can check, for example, that for \"pubmed_central\" the \"meta\" field is cast to `str`:\r\n```python\r\nIn [4]: from datasets import load_dataset\r\n        ds = load_dataset(\"the_pile\", \"pubmed_central\", split=\"train\", streaming=True)\r\n        item = next(iter(ds))\r\n\r\nIn [5]: item[\"meta\"]\r\nOut[5]: \"{'id': 'PMC6071596'}\"\r\n```\r\n\r\nFeel free to reopen this issue if you have further questions. ","body":"## Describe the bug\r\nIf you look at https:\/\/huggingface.co\/datasets\/the_pile\/blob\/main\/the_pile.py:\r\n\r\nFor \"all\"\r\n*  the pile_set_name is never set for data\r\n* there's actually an id field inside of \"meta\" \r\n\r\nFor subcorpora pubmed_central and hacker_news:\r\n* the meta is specified to be a string, but it's actually a dict with an id field inside.\r\n\r\n## Steps to reproduce the bug\r\n\r\n\r\n## Expected results\r\nFeature spec should match the data I'd think?\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":219,"text":"The Pile's _FEATURE spec seems to be incorrect \n ## Describe the bug\r\nIf you look at https:\/\/huggingface.co\/datasets\/the_pile\/blob\/main\/the_pile.py:\r\n\r\nFor \"all\"\r\n*  the pile_set_name is never set for data\r\n* there's actually an id field inside of \"meta\" \r\n\r\nFor subcorpora pubmed_central and hacker_news:\r\n* the meta is specified to be a string, but it's actually a dict with an id field inside.\r\n\r\n## Steps to reproduce the bug\r\n\r\n\r\n## Expected results\r\nFeature spec should match the data I'd think?\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n Hi @dlwh, thanks for reporting.\r\n\r\nPlease note, that the source data files for \"all\" config are different from the other configurations.\r\n\r\nThe \"all\" config contains the official Pile data files, from https:\/\/mystic.the-eye.eu\/public\/AI\/pile\/\r\nAll data examples contain a \"meta\" dict with a single \"pile_set_name\" key:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n        ds = load_dataset(\"the_pile\", \"all\", split=\"train\", streaming=True)\r\n        item = next(iter(ds))\r\nDownloading builder script: 9.09kB [00:00, 4.42MB\/s]\r\n\r\nIn [3]: item[\"meta\"]\r\nOut[3]: {'pile_set_name': 'Pile-CC'}\r\n```\r\n\r\nOn the other hand, all the other subset configs data files come from the Pile preliminary components directory: https:\/\/mystic.the-eye.eu\/public\/AI\/pile_preliminary_components\/\r\nFor theses components, the \"meta\" field may have different keys depending on the subset: \"id\", \"language\", \"pmid\",... Because of that, if we had kept the `dict` data format for the \"meta\" field, we would have an error when trying to concatenate different subsets, whose \"meta\" keys are not identical. In order to avoid that, the \"meta\" field is cast to `str` in all these cases, so that there is no incompatibility in their \"meta\" data type when concatenating.\r\n\r\nYou can check, for example, that for \"pubmed_central\" the \"meta\" field is cast to `str`:\r\n```python\r\nIn [4]: from datasets import load_dataset\r\n        ds = load_dataset(\"the_pile\", \"pubmed_central\", split=\"train\", streaming=True)\r\n        item = next(iter(ds))\r\n\r\nIn [5]: item[\"meta\"]\r\nOut[5]: \"{'id': 'PMC6071596'}\"\r\n```\r\n\r\nFeel free to reopen this issue if you have further questions. ","embeddings":[-0.0291285142,-0.0090105226,0.099736169,0.1450235993,0.325117439,-0.0145727322,0.5119672418,0.2684315145,-0.2205349505,0.1227263734,-0.1169433519,0.308239162,0.2474180609,0.3708835244,-0.074932009,0.138589114,0.216584444,-0.1402182579,0.1202759072,-0.1687899381,-0.0406840295,0.1051136106,-0.2738406658,-0.0485753678,-0.1679657549,0.2991250157,-0.2451973259,0.0311371237,-0.186047703,-0.3021289408,0.2103231102,0.0843930468,-0.1898959577,0.5995325446,-0.0001117417,0.0303297322,0.2264461219,-0.1954050064,-0.3208149374,-0.4287768602,-0.0424133688,-0.55054003,0.0012864689,-0.4069972634,0.0392622724,-0.0926215798,0.1107556894,-0.1663829237,-0.1735828221,0.216278106,0.1885695755,0.4782646596,0.0988763794,-0.0958582833,0.3971673846,0.0425354019,-0.5402694941,0.3837114871,0.0925672799,0.2168296427,0.0143314721,0.3321689665,0.0152877215,0.1324569136,0.220822379,0.3705256879,0.2862394452,-0.344337374,-0.150686726,0.4699770808,0.2301457226,-0.3511729836,-0.4661000669,-0.1572919637,-0.1161425784,-0.1404337585,0.1263165176,0.4039558768,-0.0097235208,0.0942723826,0.2184394449,0.3921533227,0.0956303626,0.2332691848,-0.0177282132,0.073370114,-0.1990641207,0.0699407384,-0.1886281371,-0.1055474058,-0.0844181925,0.0105937822,-0.0282079782,-0.0955801085,-0.4643936753,-0.1695881784,0.1245592088,0.000218182,0.2077024728,0.1627119929,0.2819620371,0.1857857406,0.1638554037,0.089447163,0.5152341127,0.0703875273,0.0403910689,-0.1580309272,0.3049031198,0.3515262604,0.027804276,-0.0296879355,0.1397052109,0.1050766557,0.0860834941,-0.2760535181,0.534230113,0.0454352424,-0.1121943817,0.0334010683,0.2784461379,-0.1266050041,-0.0984861702,0.3492751718,-0.2170657068,0.2983137071,-0.3323327303,0.3300401866,-0.0430384092,-0.1086184904,-0.3084416986,-0.0200205892,-0.2929323614,0.0555382557,0.2135956436,-0.3843566179,0.4038351178,0.0625975877,0.3123226762,0.0990020558,-0.1365767568,-0.2498092353,0.2847178578,0.163582474,0.063567698,0.0952843055,0.2144209892,0.2274407148,-0.1849108189,-0.0711426362,-0.2132178843,-0.1963565499,-0.3548826277,0.1677916795,-0.3532657325,0.0628075823,-0.2718473077,0.2239507437,0.1661539823,0.0975098535,0.0536107756,-0.1772306859,0.085431166,-0.3035125136,0.2661137283,0.2850615978,-0.1062532812,-0.1644148976,-0.2512855828,0.1637924612,0.189117521,0.070755817,0.0475107133,-0.0450149179,-0.1871285588,0.117684193,0.6021218896,-0.0311870836,-0.4138932526,0.0755238235,0.0501256064,-0.0500160418,0.1532352567,0.1134753525,0.001169201,-0.1751265377,-0.0638215691,0.1818766892,-0.0507822745,0.0276505183,-0.2598005831,-0.4174285233,-0.0578062832,0.016229393,-0.0506347828,0.088583678,0.1527716964,-0.2162072212,0.2929189801,0.2386127114,-0.0100935334,-0.2951330543,0.3379141986,0.0482273065,0.4168725014,-0.2778221667,-0.4659340084,0.29032287,-0.0871133581,0.3329507411,-0.1442107558,-0.2281321138,-0.3172802031,0.0180027541,-0.1170703322,-0.1281709373,0.1409193128,0.3256779015,-0.2292248607,-0.2725649178,-0.4675869048,-0.0796728954,-0.1614769995,0.1000157595,-0.4110552073,0.4026990533,-0.3840491176,-0.2857256234,-0.1490973681,0.0571565367,0.1006357521,-0.0479426645,0.0623502061,0.4887066185,-0.2160988599,-0.0291791297,0.1092039272,0.2147596627,0.1285681427,-0.2995304465,0.3153695166,0.0991046503,0.1577852517,-0.1199919209,-0.2572143674,0.1809552461,0.2282978296,0.2681288421,0.1786424667,-0.0016704958,0.2078131437,0.0055146939,-0.0954969749,-0.3759801686,-0.1951085627,0.0975383595,-0.239301905,0.0433827564,-0.1811479926,0.1885973364,0.6393433213,0.0694844723,0.2402867079,0.0406959876,-0.0371101163,-0.3657985926,-0.2272654474,-0.2080893219,0.3652798831,0.1584490389,0.0289772246,0.0032598895,0.1593939364,-0.1556950808,0.0938355774,0.2160117924,-0.2974951863,0.0824899897,0.3899601698,0.0721476153,-0.1692437232,-0.1407490224,0.2118919194,0.0093555422,-0.3722256422,-0.1019099355,-0.066400975,0.0301332176,-0.039465826,0.0289033763,-0.067406483,-0.3687912524,-0.2442298979,-0.0032643573,-0.1473221779,0.1637413353,-0.3513909876,0.0991055518,0.074735947,-0.0357750133,-0.2509861588,0.0143777756,-0.2329128683,0.0177971758,-0.1093472168,0.1887097955,0.0687681213,-0.211152792,-0.3601157367,-0.262821734,-0.449948132,0.3538540006,-0.2042381912,0.4392035007,0.2951973081,0.2371735722,0.0141070709,-0.2023884803,0.4782604873,-0.2401256859,-0.1555173248,0.1501457095,0.0889498517,-0.1404491961,-0.0871046633,-0.5808003545,-0.2035030127,-0.3904544711,0.1508892328,-0.0164652709,0.111192897,0.2342039943,0.0596039444,-0.0388655439,0.0556622073,0.0850800723,-0.2426033318,-0.3336433768,0.5162989497,-0.0555504225,-0.3999703825,-0.0267480593,-0.1135389283,-0.2588897347,-0.0092297038,-0.293051511,-0.1904014945,-0.3699251115,0.1749026775,0.310715735,0.2696798444,0.2099150568,-0.1605231613,-0.0834761262,-0.1340986341,-0.4975980818,0.2442676127,0.5948394537,0.1165995523,0.0531898513,0.1755328774,0.1662948877,0.5355654359,-0.0507582352,-0.0544781759,0.4430638552,-0.126119405,0.4311723411,-0.114050284,-0.3840386868,0.3135920465,-0.0002820509,0.0264414717,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3823","title":"500 internal server error when trying to open a dataset composed of Zarr stores","comments":"Hi @jacobbieker, thanks for reporting!\r\n\r\nI have transferred this issue to our Hub team and they are investigating it. I keep you informed. ","body":"## Describe the bug\r\nThe dataset [openclimatefix\/mrms](https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms) gives a 500 server error when trying to open it on the website, or through code.\r\n\r\nThe dataset doesn't have a loading script yet, and I did push two [xarray](https:\/\/docs.xarray.dev\/en\/stable\/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https:\/\/huggingface.co\/datasets\/openclimatefix\/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. \r\n\r\nIn general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? \r\n\r\nFor context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https:\/\/github.com\/openclimatefix\/metnet) [DGMR](https:\/\/github.com\/openclimatefix\/skillful_nowcasting), and [others](https:\/\/github.com\/openclimatefix\/graph_weather), which all have large, complex datasets.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"openclimatefix\/mrms\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be downloaded or open up\r\n\r\n## Actual results\r\nA 500 internal server error\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n","comment_length":23,"text":"500 internal server error when trying to open a dataset composed of Zarr stores \n ## Describe the bug\r\nThe dataset [openclimatefix\/mrms](https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms) gives a 500 server error when trying to open it on the website, or through code.\r\n\r\nThe dataset doesn't have a loading script yet, and I did push two [xarray](https:\/\/docs.xarray.dev\/en\/stable\/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https:\/\/huggingface.co\/datasets\/openclimatefix\/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. \r\n\r\nIn general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? \r\n\r\nFor context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https:\/\/github.com\/openclimatefix\/metnet) [DGMR](https:\/\/github.com\/openclimatefix\/skillful_nowcasting), and [others](https:\/\/github.com\/openclimatefix\/graph_weather), which all have large, complex datasets.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"openclimatefix\/mrms\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be downloaded or open up\r\n\r\n## Actual results\r\nA 500 internal server error\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n \n Hi @jacobbieker, thanks for reporting!\r\n\r\nI have transferred this issue to our Hub team and they are investigating it. I keep you informed. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3823","title":"500 internal server error when trying to open a dataset composed of Zarr stores","comments":"Hi @jacobbieker, we are investigating this issue on our side and we'll see if we can fix it, but please note that your repo is considered problematic for git. Here are the results of running https:\/\/github.com\/github\/git-sizer on it:\r\n\r\n```\r\nProcessing blobs: 147448                        \r\nProcessing trees: 27                        \r\nProcessing commits: 4                        \r\nMatching commits to trees: 4                        \r\nProcessing annotated tags: 0                        \r\nProcessing references: 3                        \r\n| Name                         | Value     | Level of concern               |\r\n| ---------------------------- | --------- | ------------------------------ |\r\n| Biggest objects              |           |                                |\r\n| * Trees                      |           |                                |\r\n|   * Maximum entries      [1] |   167 k   | !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! |\r\n|                              |           |                                |\r\n| Biggest checkouts            |           |                                |\r\n| * Number of files        [2] |   189 k   | ***                            |\r\n\r\n[1]  aa057d2667c34c70c6146efc631f5c9917ff326e (refs\/heads\/main:2016.zarr\/unknown)\r\n[2]  6897b7bf6440fdd16b2c39d08085a669e7eaa59d (refs\/heads\/main^{tree})\r\n```\r\n\r\nYou can check https:\/\/github.com\/github\/git-sizer for more information on how to avoid such pathological structures.","body":"## Describe the bug\r\nThe dataset [openclimatefix\/mrms](https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms) gives a 500 server error when trying to open it on the website, or through code.\r\n\r\nThe dataset doesn't have a loading script yet, and I did push two [xarray](https:\/\/docs.xarray.dev\/en\/stable\/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https:\/\/huggingface.co\/datasets\/openclimatefix\/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. \r\n\r\nIn general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? \r\n\r\nFor context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https:\/\/github.com\/openclimatefix\/metnet) [DGMR](https:\/\/github.com\/openclimatefix\/skillful_nowcasting), and [others](https:\/\/github.com\/openclimatefix\/graph_weather), which all have large, complex datasets.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"openclimatefix\/mrms\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be downloaded or open up\r\n\r\n## Actual results\r\nA 500 internal server error\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n","comment_length":142,"text":"500 internal server error when trying to open a dataset composed of Zarr stores \n ## Describe the bug\r\nThe dataset [openclimatefix\/mrms](https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms) gives a 500 server error when trying to open it on the website, or through code.\r\n\r\nThe dataset doesn't have a loading script yet, and I did push two [xarray](https:\/\/docs.xarray.dev\/en\/stable\/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https:\/\/huggingface.co\/datasets\/openclimatefix\/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. \r\n\r\nIn general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? \r\n\r\nFor context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https:\/\/github.com\/openclimatefix\/metnet) [DGMR](https:\/\/github.com\/openclimatefix\/skillful_nowcasting), and [others](https:\/\/github.com\/openclimatefix\/graph_weather), which all have large, complex datasets.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"openclimatefix\/mrms\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be downloaded or open up\r\n\r\n## Actual results\r\nA 500 internal server error\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n \n Hi @jacobbieker, we are investigating this issue on our side and we'll see if we can fix it, but please note that your repo is considered problematic for git. Here are the results of running https:\/\/github.com\/github\/git-sizer on it:\r\n\r\n```\r\nProcessing blobs: 147448                        \r\nProcessing trees: 27                        \r\nProcessing commits: 4                        \r\nMatching commits to trees: 4                        \r\nProcessing annotated tags: 0                        \r\nProcessing references: 3                        \r\n| Name                         | Value     | Level of concern               |\r\n| ---------------------------- | --------- | ------------------------------ |\r\n| Biggest objects              |           |                                |\r\n| * Trees                      |           |                                |\r\n|   * Maximum entries      [1] |   167 k   | !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! |\r\n|                              |           |                                |\r\n| Biggest checkouts            |           |                                |\r\n| * Number of files        [2] |   189 k   | ***                            |\r\n\r\n[1]  aa057d2667c34c70c6146efc631f5c9917ff326e (refs\/heads\/main:2016.zarr\/unknown)\r\n[2]  6897b7bf6440fdd16b2c39d08085a669e7eaa59d (refs\/heads\/main^{tree})\r\n```\r\n\r\nYou can check https:\/\/github.com\/github\/git-sizer for more information on how to avoid such pathological structures.","embeddings":[-0.3984357417,0.1099743173,0.0756589845,0.2678703666,0.3084730208,-0.0378658883,0.3065190017,0.1853011101,0.1879680902,0.1837240756,-0.3976975083,0.2528079152,0.0605284795,0.3931144476,-0.1333763003,0.1691716611,-0.0300630648,0.0935493708,-0.1518300921,0.0744264871,-0.2984042466,-0.0941732675,-0.0876861736,0.1554765552,-0.1840732396,-0.194451198,0.1473208219,0.4725471735,-0.303748548,-0.3931906521,-0.0050851502,-0.1785398126,0.5827432275,0.3816316426,-0.0001187077,0.2011728436,0.3711708188,-0.0413792208,-0.2092221826,-0.1555026174,-0.2414229959,-0.2809564769,-0.0461835191,0.1089150012,0.3819028139,-0.2584910095,0.018576812,-0.4012302458,0.3119853735,0.3165647089,0.0565305389,0.0047622197,-0.0007190772,-0.2311092764,0.3572831452,-0.0311906971,-0.1348472089,-0.0575641058,0.0255001672,0.1946513206,-0.2854172587,0.213011533,-0.2188452035,0.095358029,0.2666234374,-0.0507088229,-0.4268051684,-0.0319821835,0.1751129478,0.3958951831,0.946344018,-0.1077896953,-0.3681568205,-0.0370002314,-0.1416632384,-0.3509466052,0.2388872206,0.6036015749,0.1060877815,0.1252228171,-0.3666580319,0.0366460346,-0.2769367397,0.2287326306,-0.2805308104,0.4499380291,-0.0520050786,0.0792873427,0.3091824651,-0.1299463511,0.6826695204,-0.1095187142,-0.0622527748,-0.0981560126,-0.4054394066,0.2131051868,-0.050969243,-0.1125349104,0.4870853722,0.3105384409,-0.0315174237,0.1096204519,-0.1682975292,-0.0865038708,0.2324779183,0.3147866428,-0.3461244106,0.1032888964,0.1426674277,0.1056243777,-0.2557895184,-0.1429654062,-0.0894410834,-0.0408839136,-0.2812505066,-0.1094473153,0.3046711981,-0.3293418586,-0.0518089496,0.1893708706,-0.7700589299,-0.1698461771,-0.0743692294,0.3433123827,0.0922392011,0.2393713295,-0.1660357863,0.153554216,-0.0043023517,-0.243978709,0.0406889953,-0.1460739672,-0.2566801608,0.2699207962,-0.1131688505,-0.1292409599,-0.0489294119,0.0598608665,0.4044992328,-0.1443608701,0.4734460711,-0.054760728,0.1505469382,0.4587121606,0.2154912204,0.144797042,0.1125205606,0.1140173897,0.1160085872,0.0641443953,-0.5008354783,-0.6754108071,-0.2095429003,0.0050728223,-0.0345582217,-0.0692087784,0.2799401879,-0.3113851249,-0.1680425853,-0.4405532777,-0.130854696,0.0747524351,-0.1943155974,-0.0809453726,-0.046680972,0.4341385365,0.0101576028,0.4655015469,-0.3387539089,0.325824976,0.215999648,0.3854429424,-0.4366684556,0.1190592125,-0.2459863573,-0.1699960977,-0.0610178038,-0.3542780876,-0.4915455282,0.3505567014,0.0113619724,-0.1363778561,0.0153878611,-0.102577731,0.1342596114,0.112168625,0.2090385705,-0.0106125865,-0.1441091299,0.1603745222,-0.3339030445,-0.3214692771,-0.1609583795,0.2681575119,0.1380608678,0.0132281715,0.2260376215,-0.0840097368,0.1535701603,-0.1950404942,-0.308657825,0.1893524975,0.0060905223,-0.0449678265,0.0120658791,0.2088164538,-0.2857759893,0.0931548998,-0.449028492,0.2647958696,0.2011947781,0.1252955347,-0.3098519146,0.1922796816,-0.0508058108,-0.0774892718,-0.0087123308,0.2236857563,-0.2901361287,-0.1002730876,-0.0999596864,0.4725526273,0.2169274539,0.011342125,-0.2596185505,0.2975620627,-0.0867864564,0.1032017767,0.1341405064,-0.0667843297,0.1808625609,-0.0030179257,-0.4009098411,0.2010196298,0.1720314324,-0.0008749423,0.3186071813,0.3717554808,0.1363966018,-0.1478189528,0.1456035972,0.1837512851,0.0407226607,0.225951761,-0.1382213086,0.2717618644,-0.1824483871,0.1195554361,-0.2139184624,0.3813349307,0.1200454235,-0.0451892093,-0.1890976131,-0.1203495935,0.3008344471,0.1318986714,0.4435282052,0.1602237225,-0.2849342525,-0.1777033508,-0.0373542532,-0.0678613186,0.0508497804,0.0876593292,-0.2496498972,-0.0065746424,0.2016918361,0.474696368,0.3439954519,0.1430421323,-0.5446862578,0.1564785391,-0.2423101366,-0.0653164461,0.0419890508,0.4042559862,0.7995311022,0.3407514691,0.2375742793,0.1434384584,-0.1769735515,-0.5317544341,-0.0321369879,0.1390382349,-0.3055349588,0.1423567384,-0.1652137339,-0.0799652636,-0.0950530022,0.2459661663,-0.3469241261,-0.323918432,-0.2535281777,-0.0951101184,-0.3008131981,0.0498620309,-0.2183539718,0.1295088083,-0.1331196725,0.1629430354,0.0998251736,-0.259786278,-0.0271433853,-0.123823069,0.4707915485,-0.4197964072,0.0541695096,-0.0264255386,0.3818324506,-0.4889299273,-0.0351555571,0.338005662,0.2338466495,0.0094706398,0.0229982603,0.1243385673,-0.2394211888,0.1393587738,0.1937301606,0.0927357003,0.115707159,0.0888277516,-0.2749525905,0.0302496459,0.2273088843,-0.1381050497,-0.2600730062,-0.5794451237,-0.2574009299,-0.1094788611,0.0124932704,0.3392301202,0.0574957281,0.0196224134,-0.3005957603,-0.3893890083,-0.1530472934,-0.3725296557,0.3347474933,-0.2403354794,-0.3761213422,-0.0669837296,-0.0053695948,-0.1082744449,0.3252972662,-0.2586265206,0.1385419071,-0.1027742848,-0.3008290827,-0.3487482965,-0.2146426439,0.3086966276,-0.0066605881,0.0235156864,-0.142151773,-0.1536330879,-0.0326465182,0.1009541303,0.0798702762,-0.2318492532,0.1732764393,-0.0136065586,0.4899490774,0.0183367655,0.0410124734,0.353009969,-0.0754277483,0.2036314756,-0.0883295536,-0.0987284482,-0.08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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3823","title":"500 internal server error when trying to open a dataset composed of Zarr stores","comments":"Hi, thanks for getting back to me so quick! And yeah, I figured that was probably the problem. I was going to try to delete the repo, but couldn't through the website, so if that's the easiest way to solve it, I can regenerate the dataset in a different format with less tiny files, and you guys can delete the repo as it is. Zarr just saves everything as lots of small files to make chunks easy to load, which is why I was preferring that format, but maybne that just doesn't work well for HF datasets.","body":"## Describe the bug\r\nThe dataset [openclimatefix\/mrms](https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms) gives a 500 server error when trying to open it on the website, or through code.\r\n\r\nThe dataset doesn't have a loading script yet, and I did push two [xarray](https:\/\/docs.xarray.dev\/en\/stable\/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https:\/\/huggingface.co\/datasets\/openclimatefix\/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. \r\n\r\nIn general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? \r\n\r\nFor context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https:\/\/github.com\/openclimatefix\/metnet) [DGMR](https:\/\/github.com\/openclimatefix\/skillful_nowcasting), and [others](https:\/\/github.com\/openclimatefix\/graph_weather), which all have large, complex datasets.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"openclimatefix\/mrms\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be downloaded or open up\r\n\r\n## Actual results\r\nA 500 internal server error\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n","comment_length":97,"text":"500 internal server error when trying to open a dataset composed of Zarr stores \n ## Describe the bug\r\nThe dataset [openclimatefix\/mrms](https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms) gives a 500 server error when trying to open it on the website, or through code.\r\n\r\nThe dataset doesn't have a loading script yet, and I did push two [xarray](https:\/\/docs.xarray.dev\/en\/stable\/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https:\/\/huggingface.co\/datasets\/openclimatefix\/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. \r\n\r\nIn general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? \r\n\r\nFor context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https:\/\/github.com\/openclimatefix\/metnet) [DGMR](https:\/\/github.com\/openclimatefix\/skillful_nowcasting), and [others](https:\/\/github.com\/openclimatefix\/graph_weather), which all have large, complex datasets.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"openclimatefix\/mrms\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be downloaded or open up\r\n\r\n## Actual results\r\nA 500 internal server error\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n \n Hi, thanks for getting back to me so quick! And yeah, I figured that was probably the problem. I was going to try to delete the repo, but couldn't through the website, so if that's the easiest way to solve it, I can regenerate the dataset in a different format with less tiny files, and you guys can delete the repo as it is. Zarr just saves everything as lots of small files to make chunks easy to load, which is why I was preferring that format, but maybne that just doesn't work well for HF datasets.","embeddings":[-0.3984357417,0.1099743173,0.0756589845,0.2678703666,0.3084730208,-0.0378658883,0.3065190017,0.1853011101,0.1879680902,0.1837240756,-0.3976975083,0.2528079152,0.0605284795,0.3931144476,-0.1333763003,0.1691716611,-0.0300630648,0.0935493708,-0.1518300921,0.0744264871,-0.2984042466,-0.0941732675,-0.0876861736,0.1554765552,-0.1840732396,-0.194451198,0.1473208219,0.4725471735,-0.303748548,-0.3931906521,-0.0050851502,-0.1785398126,0.5827432275,0.3816316426,-0.0001187077,0.2011728436,0.3711708188,-0.0413792208,-0.2092221826,-0.1555026174,-0.2414229959,-0.2809564769,-0.0461835191,0.1089150012,0.3819028139,-0.2584910095,0.018576812,-0.4012302458,0.3119853735,0.3165647089,0.0565305389,0.0047622197,-0.0007190772,-0.2311092764,0.3572831452,-0.0311906971,-0.1348472089,-0.0575641058,0.0255001672,0.1946513206,-0.2854172587,0.213011533,-0.2188452035,0.095358029,0.2666234374,-0.0507088229,-0.4268051684,-0.0319821835,0.1751129478,0.3958951831,0.946344018,-0.1077896953,-0.3681568205,-0.0370002314,-0.1416632384,-0.3509466052,0.2388872206,0.6036015749,0.1060877815,0.1252228171,-0.3666580319,0.0366460346,-0.2769367397,0.2287326306,-0.2805308104,0.4499380291,-0.0520050786,0.0792873427,0.3091824651,-0.1299463511,0.6826695204,-0.1095187142,-0.0622527748,-0.0981560126,-0.4054394066,0.2131051868,-0.050969243,-0.1125349104,0.4870853722,0.3105384409,-0.0315174237,0.1096204519,-0.1682975292,-0.0865038708,0.2324779183,0.3147866428,-0.3461244106,0.1032888964,0.1426674277,0.1056243777,-0.2557895184,-0.1429654062,-0.0894410834,-0.0408839136,-0.2812505066,-0.1094473153,0.3046711981,-0.3293418586,-0.0518089496,0.1893708706,-0.7700589299,-0.1698461771,-0.0743692294,0.3433123827,0.0922392011,0.2393713295,-0.1660357863,0.153554216,-0.0043023517,-0.243978709,0.0406889953,-0.1460739672,-0.2566801608,0.2699207962,-0.1131688505,-0.1292409599,-0.0489294119,0.0598608665,0.4044992328,-0.1443608701,0.4734460711,-0.054760728,0.1505469382,0.4587121606,0.2154912204,0.144797042,0.1125205606,0.1140173897,0.1160085872,0.0641443953,-0.5008354783,-0.6754108071,-0.2095429003,0.0050728223,-0.0345582217,-0.0692087784,0.2799401879,-0.3113851249,-0.1680425853,-0.4405532777,-0.130854696,0.0747524351,-0.1943155974,-0.0809453726,-0.046680972,0.4341385365,0.0101576028,0.4655015469,-0.3387539089,0.325824976,0.215999648,0.3854429424,-0.4366684556,0.1190592125,-0.2459863573,-0.1699960977,-0.0610178038,-0.3542780876,-0.4915455282,0.3505567014,0.0113619724,-0.1363778561,0.0153878611,-0.102577731,0.1342596114,0.112168625,0.2090385705,-0.0106125865,-0.1441091299,0.1603745222,-0.3339030445,-0.3214692771,-0.1609583795,0.2681575119,0.1380608678,0.0132281715,0.2260376215,-0.0840097368,0.1535701603,-0.1950404942,-0.308657825,0.1893524975,0.0060905223,-0.0449678265,0.0120658791,0.2088164538,-0.2857759893,0.0931548998,-0.449028492,0.2647958696,0.2011947781,0.1252955347,-0.3098519146,0.1922796816,-0.0508058108,-0.0774892718,-0.0087123308,0.2236857563,-0.2901361287,-0.1002730876,-0.0999596864,0.4725526273,0.2169274539,0.011342125,-0.2596185505,0.2975620627,-0.0867864564,0.1032017767,0.1341405064,-0.0667843297,0.1808625609,-0.0030179257,-0.4009098411,0.2010196298,0.1720314324,-0.0008749423,0.3186071813,0.3717554808,0.1363966018,-0.1478189528,0.1456035972,0.1837512851,0.0407226607,0.225951761,-0.1382213086,0.2717618644,-0.1824483871,0.1195554361,-0.2139184624,0.3813349307,0.1200454235,-0.0451892093,-0.1890976131,-0.1203495935,0.3008344471,0.1318986714,0.4435282052,0.1602237225,-0.2849342525,-0.1777033508,-0.0373542532,-0.0678613186,0.0508497804,0.0876593292,-0.2496498972,-0.0065746424,0.2016918361,0.474696368,0.3439954519,0.1430421323,-0.5446862578,0.1564785391,-0.2423101366,-0.0653164461,0.0419890508,0.4042559862,0.7995311022,0.3407514691,0.2375742793,0.1434384584,-0.1769735515,-0.5317544341,-0.0321369879,0.1390382349,-0.3055349588,0.1423567384,-0.1652137339,-0.0799652636,-0.0950530022,0.2459661663,-0.3469241261,-0.323918432,-0.2535281777,-0.0951101184,-0.3008131981,0.0498620309,-0.2183539718,0.1295088083,-0.1331196725,0.1629430354,0.0998251736,-0.259786278,-0.0271433853,-0.123823069,0.4707915485,-0.4197964072,0.0541695096,-0.0264255386,0.3818324506,-0.4889299273,-0.0351555571,0.338005662,0.2338466495,0.0094706398,0.0229982603,0.1243385673,-0.2394211888,0.1393587738,0.1937301606,0.0927357003,0.115707159,0.0888277516,-0.2749525905,0.0302496459,0.2273088843,-0.1381050497,-0.2600730062,-0.5794451237,-0.2574009299,-0.1094788611,0.0124932704,0.3392301202,0.0574957281,0.0196224134,-0.3005957603,-0.3893890083,-0.1530472934,-0.3725296557,0.3347474933,-0.2403354794,-0.3761213422,-0.0669837296,-0.0053695948,-0.1082744449,0.3252972662,-0.2586265206,0.1385419071,-0.1027742848,-0.3008290827,-0.3487482965,-0.2146426439,0.3086966276,-0.0066605881,0.0235156864,-0.142151773,-0.1536330879,-0.0326465182,0.1009541303,0.0798702762,-0.2318492532,0.1732764393,-0.0136065586,0.4899490774,0.0183367655,0.0410124734,0.353009969,-0.0754277483,0.2036314756,-0.0883295536,-0.0987284482,-0.0810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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3823","title":"500 internal server error when trying to open a dataset composed of Zarr stores","comments":"Hi @jacobbieker,\r\n\r\nFor future use cases, our Hub team is still pondering whether to limit the maximum number of files per repo to avoid technical issues...\r\n\r\nOn the meantime, they have made a fix and your dataset is working: https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms","body":"## Describe the bug\r\nThe dataset [openclimatefix\/mrms](https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms) gives a 500 server error when trying to open it on the website, or through code.\r\n\r\nThe dataset doesn't have a loading script yet, and I did push two [xarray](https:\/\/docs.xarray.dev\/en\/stable\/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https:\/\/huggingface.co\/datasets\/openclimatefix\/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. \r\n\r\nIn general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? \r\n\r\nFor context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https:\/\/github.com\/openclimatefix\/metnet) [DGMR](https:\/\/github.com\/openclimatefix\/skillful_nowcasting), and [others](https:\/\/github.com\/openclimatefix\/graph_weather), which all have large, complex datasets.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"openclimatefix\/mrms\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be downloaded or open up\r\n\r\n## Actual results\r\nA 500 internal server error\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n","comment_length":40,"text":"500 internal server error when trying to open a dataset composed of Zarr stores \n ## Describe the bug\r\nThe dataset [openclimatefix\/mrms](https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms) gives a 500 server error when trying to open it on the website, or through code.\r\n\r\nThe dataset doesn't have a loading script yet, and I did push two [xarray](https:\/\/docs.xarray.dev\/en\/stable\/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https:\/\/huggingface.co\/datasets\/openclimatefix\/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. \r\n\r\nIn general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? \r\n\r\nFor context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https:\/\/github.com\/openclimatefix\/metnet) [DGMR](https:\/\/github.com\/openclimatefix\/skillful_nowcasting), and [others](https:\/\/github.com\/openclimatefix\/graph_weather), which all have large, complex datasets.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"openclimatefix\/mrms\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be downloaded or open up\r\n\r\n## Actual results\r\nA 500 internal server error\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n \n Hi @jacobbieker,\r\n\r\nFor future use cases, our Hub team is still pondering whether to limit the maximum number of files per repo to avoid technical issues...\r\n\r\nOn the meantime, they have made a fix and your dataset is working: https:\/\/huggingface.co\/datasets\/openclimatefix\/mrms","embeddings":[-0.3984357417,0.1099743173,0.0756589845,0.2678703666,0.3084730208,-0.0378658883,0.3065190017,0.1853011101,0.1879680902,0.1837240756,-0.3976975083,0.2528079152,0.0605284795,0.3931144476,-0.1333763003,0.1691716611,-0.0300630648,0.0935493708,-0.1518300921,0.0744264871,-0.2984042466,-0.0941732675,-0.0876861736,0.1554765552,-0.1840732396,-0.194451198,0.1473208219,0.4725471735,-0.303748548,-0.3931906521,-0.0050851502,-0.1785398126,0.5827432275,0.3816316426,-0.0001187077,0.2011728436,0.3711708188,-0.0413792208,-0.2092221826,-0.1555026174,-0.2414229959,-0.2809564769,-0.0461835191,0.1089150012,0.3819028139,-0.2584910095,0.018576812,-0.4012302458,0.3119853735,0.3165647089,0.0565305389,0.0047622197,-0.0007190772,-0.2311092764,0.3572831452,-0.0311906971,-0.1348472089,-0.0575641058,0.0255001672,0.1946513206,-0.2854172587,0.213011533,-0.2188452035,0.095358029,0.2666234374,-0.0507088229,-0.4268051684,-0.0319821835,0.1751129478,0.3958951831,0.946344018,-0.1077896953,-0.3681568205,-0.0370002314,-0.1416632384,-0.3509466052,0.2388872206,0.6036015749,0.1060877815,0.1252228171,-0.3666580319,0.0366460346,-0.2769367397,0.2287326306,-0.2805308104,0.4499380291,-0.0520050786,0.0792873427,0.3091824651,-0.1299463511,0.6826695204,-0.1095187142,-0.0622527748,-0.0981560126,-0.4054394066,0.2131051868,-0.050969243,-0.1125349104,0.4870853722,0.3105384409,-0.0315174237,0.1096204519,-0.1682975292,-0.0865038708,0.2324779183,0.3147866428,-0.3461244106,0.1032888964,0.1426674277,0.1056243777,-0.2557895184,-0.1429654062,-0.0894410834,-0.0408839136,-0.2812505066,-0.1094473153,0.3046711981,-0.3293418586,-0.0518089496,0.1893708706,-0.7700589299,-0.1698461771,-0.0743692294,0.3433123827,0.0922392011,0.2393713295,-0.1660357863,0.153554216,-0.0043023517,-0.243978709,0.0406889953,-0.1460739672,-0.2566801608,0.2699207962,-0.1131688505,-0.1292409599,-0.0489294119,0.0598608665,0.4044992328,-0.1443608701,0.4734460711,-0.054760728,0.1505469382,0.4587121606,0.2154912204,0.144797042,0.1125205606,0.1140173897,0.1160085872,0.0641443953,-0.5008354783,-0.6754108071,-0.2095429003,0.0050728223,-0.0345582217,-0.0692087784,0.2799401879,-0.3113851249,-0.1680425853,-0.4405532777,-0.130854696,0.0747524351,-0.1943155974,-0.0809453726,-0.046680972,0.4341385365,0.0101576028,0.4655015469,-0.3387539089,0.325824976,0.215999648,0.3854429424,-0.4366684556,0.1190592125,-0.2459863573,-0.1699960977,-0.0610178038,-0.3542780876,-0.4915455282,0.3505567014,0.0113619724,-0.1363778561,0.0153878611,-0.102577731,0.1342596114,0.112168625,0.2090385705,-0.0106125865,-0.1441091299,0.1603745222,-0.3339030445,-0.3214692771,-0.1609583795,0.2681575119,0.1380608678,0.0132281715,0.2260376215,-0.0840097368,0.1535701603,-0.1950404942,-0.308657825,0.1893524975,0.0060905223,-0.0449678265,0.0120658791,0.2088164538,-0.2857759893,0.0931548998,-0.449028492,0.2647958696,0.2011947781,0.1252955347,-0.3098519146,0.1922796816,-0.0508058108,-0.0774892718,-0.0087123308,0.2236857563,-0.2901361287,-0.1002730876,-0.0999596864,0.4725526273,0.2169274539,0.011342125,-0.2596185505,0.2975620627,-0.0867864564,0.1032017767,0.1341405064,-0.0667843297,0.1808625609,-0.0030179257,-0.4009098411,0.2010196298,0.1720314324,-0.0008749423,0.3186071813,0.3717554808,0.1363966018,-0.1478189528,0.1456035972,0.1837512851,0.0407226607,0.225951761,-0.1382213086,0.2717618644,-0.1824483871,0.1195554361,-0.2139184624,0.3813349307,0.1200454235,-0.0451892093,-0.1890976131,-0.1203495935,0.3008344471,0.1318986714,0.4435282052,0.1602237225,-0.2849342525,-0.1777033508,-0.0373542532,-0.0678613186,0.0508497804,0.0876593292,-0.2496498972,-0.0065746424,0.2016918361,0.474696368,0.3439954519,0.1430421323,-0.5446862578,0.1564785391,-0.2423101366,-0.0653164461,0.0419890508,0.4042559862,0.7995311022,0.3407514691,0.2375742793,0.1434384584,-0.1769735515,-0.5317544341,-0.0321369879,0.1390382349,-0.3055349588,0.1423567384,-0.1652137339,-0.0799652636,-0.0950530022,0.2459661663,-0.3469241261,-0.323918432,-0.2535281777,-0.0951101184,-0.3008131981,0.0498620309,-0.2183539718,0.1295088083,-0.1331196725,0.1629430354,0.0998251736,-0.259786278,-0.0271433853,-0.123823069,0.4707915485,-0.4197964072,0.0541695096,-0.0264255386,0.3818324506,-0.4889299273,-0.0351555571,0.338005662,0.2338466495,0.0094706398,0.0229982603,0.1243385673,-0.2394211888,0.1393587738,0.1937301606,0.0927357003,0.115707159,0.0888277516,-0.2749525905,0.0302496459,0.2273088843,-0.1381050497,-0.2600730062,-0.5794451237,-0.2574009299,-0.1094788611,0.0124932704,0.3392301202,0.0574957281,0.0196224134,-0.3005957603,-0.3893890083,-0.1530472934,-0.3725296557,0.3347474933,-0.2403354794,-0.3761213422,-0.0669837296,-0.0053695948,-0.1082744449,0.3252972662,-0.2586265206,0.1385419071,-0.1027742848,-0.3008290827,-0.3487482965,-0.2146426439,0.3086966276,-0.0066605881,0.0235156864,-0.142151773,-0.1536330879,-0.0326465182,0.1009541303,0.0798702762,-0.2318492532,0.1732764393,-0.0136065586,0.4899490774,0.0183367655,0.0410124734,0.353009969,-0.0754277483,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3822","title":"Add Biwi Kinect Head Pose Database","comments":"Official dataset location : https:\/\/icu.ee.ethz.ch\/research\/datsets.html\r\nIn the \"Biwi Kinect Head Pose Database\" section, I do not find any information regarding \"Downloading the dataset.\" . Do we mail the authors regarding this ?\r\n\r\nI found the dataset on Kaggle : [Link](https:\/\/www.kaggle.com\/kmader\/biwi-kinect-head-pose-database) , but since \ud83e\udd17 does not host any of the datasets, this would require the user to provide their Kaggle username and API key to download. \r\n\r\nAny inputs on how we could proceed ? Thank you.\r\n[ Need your inputs here, @lhoestq or @mariosasko ]","body":"## Adding a Dataset\r\n- **Name:** Biwi Kinect Head Pose Database\r\n- **Description:** Over 15K images of 20 people recorded with a Kinect while turning their heads around freely. For each frame, depth and rgb images are provided, together with ground in the form of the 3D location of the head and its rotation angles.\r\n- **Data:** [*link to the Github repository or current dataset location*](https:\/\/icu.ee.ethz.ch\/research\/datsets.html)\r\n- **Motivation:** Useful pose estimation dataset\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":85,"text":"Add Biwi Kinect Head Pose Database \n ## Adding a Dataset\r\n- **Name:** Biwi Kinect Head Pose Database\r\n- **Description:** Over 15K images of 20 people recorded with a Kinect while turning their heads around freely. For each frame, depth and rgb images are provided, together with ground in the form of the 3D location of the head and its rotation angles.\r\n- **Data:** [*link to the Github repository or current dataset location*](https:\/\/icu.ee.ethz.ch\/research\/datsets.html)\r\n- **Motivation:** Useful pose estimation dataset\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Official dataset location : https:\/\/icu.ee.ethz.ch\/research\/datsets.html\r\nIn the \"Biwi Kinect Head Pose Database\" section, I do not find any information regarding \"Downloading the dataset.\" . Do we mail the authors regarding this ?\r\n\r\nI found the dataset on Kaggle : [Link](https:\/\/www.kaggle.com\/kmader\/biwi-kinect-head-pose-database) , but since \ud83e\udd17 does not host any of the datasets, this would require the user to provide their Kaggle username and API key to download. \r\n\r\nAny inputs on how we could proceed ? Thank you.\r\n[ Need your inputs here, @lhoestq or @mariosasko ]","embeddings":[0.094134137,0.0514209233,-0.0333517864,0.2105915099,-0.3765191436,0.4054466486,0.1258432269,0.1778405011,0.2764805555,0.1559718549,-0.0106995367,-0.1799843162,0.1005426496,0.1961019635,0.1111485586,-0.0773012564,0.0561021939,-0.0838737562,0.2623635232,-0.210326001,-0.1619483978,0.0092647308,-0.0196097568,-0.2681522667,-0.2831790745,-0.4066944122,-0.2260457873,0.141568765,-0.3369924426,-0.263718158,0.443983525,0.1156710163,0.1898872852,0.0742283836,-0.0001192601,-0.1033370644,0.0527115129,-0.2356939167,-0.0110876029,0.0576065406,-0.575668931,-0.298925072,-0.2486609221,-0.3991960287,0.009730801,0.358046174,0.1494075209,-0.1827316284,0.0073733879,0.1614548117,0.1023986414,0.1191935241,0.1586507112,-0.297994107,0.2113945037,0.2969587743,-0.095624797,-0.0300701074,0.0477903597,0.2333857119,-0.0339677855,0.2189384103,0.0094929608,0.0492566004,0.1390036643,0.2067004293,0.1707311422,-0.5475084186,0.2872768939,0.093301028,0.6698589325,-0.0497611091,-0.2444587946,-0.0092349984,0.3526725173,0.0739816874,0.1538797021,0.5694825053,-0.3432711959,-0.2204057872,-0.3089030385,-0.4445103705,-0.2990525067,0.0922409967,-0.2237789482,0.4176446199,0.0990106612,-0.0752853528,-0.0728211403,0.0359473787,0.2629643679,-0.066000104,0.0584804714,0.1306064576,-0.0943219513,-0.026961619,0.0343292542,0.4317733347,0.3823195994,0.0465888456,0.1844284683,-0.1222571358,-0.1416309327,-0.0143680414,0.227415204,-0.2533210814,-0.0255414937,0.050907582,0.1600073725,0.3557755053,0.356431365,-0.1078537852,0.0042363703,0.0314131156,-0.3505025208,-0.1993352473,0.225106582,-0.160124138,-0.125595361,-0.105340302,-0.3025934994,0.0134798009,-0.072935462,0.2272932529,-0.2051982135,-0.1114992872,0.3086684644,0.0000371549,0.0147743812,0.1204609647,-0.2184101194,0.1873033941,0.0362239219,-0.0043383692,0.2221924663,-0.4249073267,0.2675152719,-0.1937243342,0.3881499171,0.0289629661,0.0126316519,0.206234768,0.250022471,0.3449640274,0.2377294451,0.085447751,-0.3491503298,-0.072381191,-0.2852598429,0.2127084285,-0.303506285,-0.0790792629,-0.3375252783,0.0435616821,-0.1865538061,-0.2252176851,0.0499529913,0.2323067039,-0.0675451532,-0.0884349793,0.2290563583,0.458598882,-0.3232206404,-0.3684560955,0.498970896,0.5692391992,-0.3336058557,-0.0739649907,-0.0151782315,0.2666883469,-0.1025395393,0.1621719003,-0.1619495153,0.3108871877,-0.2691166699,-0.1097044498,0.2958831191,-0.5843711495,-0.2913617194,-0.6096638441,-0.2002946436,-0.1860061735,0.0302005783,0.4294590056,0.0544611365,-0.2338531315,-0.3288489282,0.3962261677,-0.261200577,-0.048891183,0.1197784618,-0.3265258074,-0.1448609829,0.2697723508,0.0960686803,-0.2063264847,0.4805861115,0.4230885804,0.2369401306,-0.0414128266,0.0713610426,0.2087296695,-0.1076653004,-0.0816071704,-0.1300688535,0.1202685535,-0.4002217352,0.059050519,-0.0167299062,0.11451298,-0.302662313,-0.2708401978,-0.1511603296,0.1035117209,-0.2431451976,-0.0345088094,0.0656168982,0.0507444292,0.384244889,0.2510990202,-0.1198476329,0.1775939912,0.0061354917,-0.0580158308,-0.6463118196,0.3390845656,0.112093091,0.2071853429,-0.0424406677,0.301884234,0.0889112055,-0.0721217766,0.2147473246,0.2220048457,-0.0028942029,0.2063740194,0.3781712055,0.2083511651,0.3393723071,-0.3275669217,0.4322512746,-0.2370540202,0.0434241854,-0.1903114468,-0.3089826107,0.1673781127,-0.3071617186,-0.0096224928,-0.2100957632,0.1804734021,0.0208822303,0.1114385948,0.0605094396,-0.1304570287,-0.0773586705,0.1498487592,-0.037227843,-0.1536023319,-0.2907835543,-0.0094870795,0.4822549224,0.0490120985,0.2125630379,-0.1443144977,-0.1335878968,-0.0565045029,0.1677414477,0.084145017,0.0223952141,0.0121221431,0.2023932934,-0.1267769337,0.2541951537,-0.1143807098,0.0749027357,0.1586568058,-0.0223274399,-0.0721527934,0.058078371,-0.010735698,0.0067296964,0.0493991151,0.036541488,-0.1599122584,-0.0806244537,-0.3222396672,0.117851764,-0.1496367902,-0.1293908507,-0.2693133056,-0.2574304938,-0.1493404061,0.110975489,-0.0516190119,0.1877561957,0.05541775,-0.074429661,0.6403453946,-0.0959703177,-0.3285405338,-0.0282156356,-0.0771124884,0.0792464316,0.084590815,0.0377954394,0.0610029176,0.4288436174,-0.4109113514,0.563878715,-0.5343512297,-0.4746135771,-0.0950312614,0.139102757,-0.0571641847,0.2595438063,0.2581931055,-0.2353053093,-0.1266113371,-0.0746536329,0.2074791044,-0.1710094959,-0.0764355063,-0.1416880339,0.1064479798,0.1230902225,-0.0413631313,-0.4215524495,-0.1205441877,0.247908026,0.5945833921,0.0722369179,-0.3230954111,0.0641540587,0.1500743479,-0.1412392706,0.01079066,0.0966783315,-0.6578963995,0.4996113479,-0.2699284554,-0.3931700587,0.4361579418,0.0211407579,0.3017808497,-0.0139809735,-0.5926597714,-0.1209935844,-0.0033056529,0.781966269,0.1388585418,0.4464100003,0.4231777787,0.113144964,0.1028360873,-0.1593634188,-0.5519500375,0.0113379704,0.2923407853,0.363094151,-0.0941947624,-0.1171185747,0.0785181746,0.7384919524,0.3325369954,-0.2121267617,-0.0008039263,0.1544217467,0.2078674734,0.1882730722,-0.2735123932,0.1080589294,0.1936967522,-0.0679979995,0.095504716,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3820","title":"`pubmed_qa` checksum mismatch","comments":"Hi @jon-tow, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today.\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","body":"## Describe the bug\r\nLoading [`pubmed_qa`](https:\/\/huggingface.co\/datasets\/pubmed_qa) results in a mismatched checksum error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport datasets\r\ntry:\r\n    datasets.load_dataset(\"pubmed_qa\", \"pqa_labeled\")\r\nexcept Exception as e:\r\n    print(e)\r\n\r\ntry:\r\n    datasets.load_dataset(\"pubmed_qa\", \"pqa_unlabeled\")\r\nexcept Exception as e:\r\n    print(e)\r\n\r\ntry:\r\n    datasets.load_dataset(\"pubmed_qa\", \"pqa_artificial\")\r\nexcept Exception as e:\r\n    print(e)\r\n```\r\n\r\n## Expected results\r\nSuccessful download.\r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/usr\/local\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/usr\/local\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/usr\/local\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/usr\/local\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ', 'https:\/\/drive.google.com\/uc?export=download&id=15v1x6aQDlZymaHGP7cZJZZYFfeJt2NdS']\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: macOS\r\n- Python version: 3.8.1\r\n- PyArrow version: 3.0.0\r\n","comment_length":109,"text":"`pubmed_qa` checksum mismatch \n ## Describe the bug\r\nLoading [`pubmed_qa`](https:\/\/huggingface.co\/datasets\/pubmed_qa) results in a mismatched checksum error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport datasets\r\ntry:\r\n    datasets.load_dataset(\"pubmed_qa\", \"pqa_labeled\")\r\nexcept Exception as e:\r\n    print(e)\r\n\r\ntry:\r\n    datasets.load_dataset(\"pubmed_qa\", \"pqa_unlabeled\")\r\nexcept Exception as e:\r\n    print(e)\r\n\r\ntry:\r\n    datasets.load_dataset(\"pubmed_qa\", \"pqa_artificial\")\r\nexcept Exception as e:\r\n    print(e)\r\n```\r\n\r\n## Expected results\r\nSuccessful download.\r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/usr\/local\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/usr\/local\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/usr\/local\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/usr\/local\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ', 'https:\/\/drive.google.com\/uc?export=download&id=15v1x6aQDlZymaHGP7cZJZZYFfeJt2NdS']\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: macOS\r\n- Python version: 3.8.1\r\n- PyArrow version: 3.0.0\r\n \n Hi @jon-tow, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nWe are planning to make a patch release today.\r\n\r\nIn the meantime, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","embeddings":[-0.0026249711,0.1502340436,-0.0901137218,0.1827033609,0.3038474917,-0.0528592095,0.2118266821,0.3324444592,0.0343842395,0.1883316338,-0.0766895711,0.1153617278,0.3035595715,0.0669020414,0.0802898556,0.0465415269,0.0497063622,-0.0326127894,-0.0174246095,-0.0204585996,-0.2244304717,0.3450913727,-0.2007197738,-0.0885901004,0.4203203917,0.0858871043,0.2313582301,0.1695879549,-0.0631339177,-0.3160688281,0.122204192,0.0863687992,-0.261204958,0.5264265537,-0.0001103089,0.0344838016,0.3809787333,-0.0169944596,-0.2685242295,0.0333846509,-0.2106978446,-0.2016790509,-0.1557292938,-0.2083813399,0.0900617763,-0.1206649318,-0.0792786628,-0.1835966557,-0.1101812497,0.2240868807,0.2462407351,0.5687370896,0.127800405,-0.079131566,0.1922246665,-0.4594305158,0.1090765893,0.4872740209,0.4018702507,0.117817834,0.1158599257,0.2178651541,-0.1647207588,-0.0780973136,0.1283899248,0.0525799692,0.0142569318,-0.0145608447,0.1534482986,0.3935366273,0.1389754862,-0.3990601003,-0.3674913645,-0.1277901083,-0.0702857897,-0.0017431366,0.3739831746,0.0878920779,-0.1285222918,-0.0528368615,-0.1974687576,0.1455549747,0.1516219527,-0.021979237,-0.00037794,0.025903739,0.0400115773,0.0043400778,-0.0073263305,-0.0287414733,-0.0159016922,-0.1487453729,-0.0068127043,0.065295361,-0.3758668005,-0.0783467442,0.0088743009,0.1867604256,0.4948836267,0.4220801294,0.2280323505,0.1475583911,-0.0596161559,0.0582183488,0.0429624096,0.0027747781,-0.1335258335,0.2007655799,0.3923034668,0.551710248,0.0345010646,0.0858491138,0.1782536954,-0.1993532479,0.3639615476,0.1579547524,0.1357573718,-0.4726057947,-0.3926683664,0.2499887943,0.0634560958,-0.1459267437,-0.1217164323,0.301962316,-0.1898566931,-0.0783412531,0.0793357044,0.1711889803,-0.331210345,-0.0188760608,-0.2691606283,-0.1015061885,-0.0265912954,-0.0181247648,0.1537373215,-0.3991446197,0.3094807267,-0.1782109588,0.2519922256,-0.1236376092,0.0651648566,0.0155564398,-0.1878653318,0.381911397,-0.0464065112,0.0987263694,0.1738620996,-0.1328557432,-0.1422816366,0.0405218005,-0.1885634214,-0.0477264635,-0.1744932234,0.2967727482,-0.2863497734,-0.0524327457,-0.1831133664,-0.2661022544,0.2263147086,-0.3986724019,0.0449798517,-0.0893510133,-0.1263542473,-0.2974000871,0.4194730222,0.2189291716,0.0201689918,-0.1233552247,-0.0673747063,-0.1723155975,0.0426777303,0.215115875,-0.0483208485,0.0827600509,-0.2745404541,0.0193220116,0.1840738058,-0.5328791738,-0.7661895156,-0.132965222,0.0261426792,0.1692037731,0.0028227703,-0.0657259896,0.0554986075,-0.2078244686,0.156196326,0.2263736725,0.0469171181,0.2051308006,-0.4043137431,-0.3778974712,0.0578209758,0.1304064691,0.067955099,-0.0262514781,0.3796049356,-0.0651814863,0.3314708769,0.0231582224,-0.2612695396,0.1637845933,0.263399899,-0.085004814,0.1477188319,-0.33692348,-0.3175123334,0.4038661122,-0.3184611499,0.1423314959,-0.3370377421,-0.2060531527,-0.4266798496,-0.1250559241,-0.0500603728,-0.125036329,0.1738368124,0.2788876295,0.1509412229,0.3079708517,-0.0776695535,-0.0301154889,-0.2269475758,0.1371525675,-0.471403271,0.269125104,-0.1356675476,-0.0065417909,0.158838585,0.183266744,0.0389533862,0.0562338755,-0.1083953083,0.5022916198,-0.066712372,0.241855219,0.1468861997,0.1739688516,0.0576415733,-0.3471584916,-0.0905513242,0.3580245078,0.2346955538,0.0541057438,-0.1796023399,0.2575966716,-0.2281069458,0.0152469026,0.0334698372,-0.0984365866,0.2091045231,-0.0441809855,-0.1490540653,-0.2471700013,0.3133167028,0.3841618001,-0.119454436,-0.0515963994,0.0100295572,0.0925339237,0.480861932,-0.2687677443,-0.0293678883,-0.0976504833,-0.0070053637,0.075270623,-0.0591622442,0.3960860372,0.3434849381,0.1565676928,0.047935158,0.1654677242,-0.1657171547,-0.2134302706,0.013921272,0.0843564123,0.0144022722,0.3533497155,0.1549678892,0.0497136898,-0.1387683153,0.0805099905,0.0126546752,0.1844304204,-0.516888082,0.0289999265,-0.233452037,0.0703493878,-0.33595559,-0.0452868082,-0.2127526253,-0.3675152659,-0.057251066,0.2249247432,-0.0751382038,0.2436241508,-0.342550993,0.0516196787,-0.0065372833,-0.4962706864,0.2402106971,0.1743941903,-0.0533210672,0.111209251,0.3545040786,0.101308614,0.4781609774,-0.34720698,-0.1323990971,-0.3636715114,-0.4309234023,0.0484450758,-0.1348741353,0.529869318,0.2115858048,0.0810502619,0.0510997064,-0.1143639088,0.2148194462,-0.0267706551,-0.3574827611,0.2004785538,0.122952424,0.0308586285,-0.0013481928,-0.3538352549,0.0718201771,-0.2202579677,-0.333604157,-0.1184884831,0.1804867387,0.0028016979,-0.0813718587,0.1224871576,-0.0280112959,0.2799740136,-0.3093637824,-0.3594486415,0.3444041908,0.0307574645,-0.4610400796,-0.0786340237,0.0264562964,0.067783162,0.2043423951,-0.5702891946,-0.4192378819,-0.1666986197,0.069215931,0.2069515884,-0.0335253812,0.2707750797,-0.106651023,-0.101937294,-0.1528157741,-0.2389530987,0.0679609179,-0.2102670372,0.3185429573,-0.0928046405,0.181300804,-0.0767412707,0.5886957049,0.4875962436,0.0557856746,0.1298518479,0.0581743345,0.4549060762,-0.0365564153,-0.1437529474,-0.0306542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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3818","title":"Support for \"sources\" parameter in the add() and add_batch() methods in datasets.metric - SARI","comments":"Hi, thanks for reporting! We can add a `sources: datasets.Value(\"string\")` feature to the `Features` dict in the `SARI` script to fix this. Would you be interested in submitting a PR?","body":"**Is your feature request related to a problem? Please describe.**\r\nThe methods `add_batch` and `add` from the `Metric` [class](https:\/\/github.com\/huggingface\/datasets\/blob\/1675ad6a958435b675a849eafa8a7f10fe0f43bc\/src\/datasets\/metric.py) does not work with [SARI](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/sari\/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. \r\n\r\nFor example, when the `add_batch` method is used, then the `compute()` method fails:\r\n```\r\nmetric = load_metric(\"sari\")\r\nmetric.add_batch(\r\n    predictions=[\"About 95 you now get in .\"], \r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\nmetric.compute()\r\n> TypeError: _compute() missing 1 required positional argument: 'sources'\r\n```\r\n\r\nTherefore, the `compute() `method can only be used standalone:\r\n```\r\nmetric = load_metric(\"sari\")\r\nresult = metric.compute(\r\n    sources=[\"About 95 species are currently accepted .\"],\r\n    predictions=[\"About 95 you now get in .\"],\r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\n> {'sari': 26.953601953601954}\r\n```\r\n  \r\n**Describe the solution you'd like**\r\nSupport for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class.\r\n```\r\nadd_batch(*, sources=None, predictions=None, references=None, **kwargs)\r\nadd(*, sources=None, predictions=None, references=None, **kwargs)\r\ncompute()\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nI've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add) and [add_batch method](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add_batch). \r\n\r\n**Additional context**\r\nThese methods are used in the transformers [pytorch examples](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/summarization\/run_summarization_no_trainer.py).\r\n","comment_length":30,"text":"Support for \"sources\" parameter in the add() and add_batch() methods in datasets.metric - SARI \n **Is your feature request related to a problem? Please describe.**\r\nThe methods `add_batch` and `add` from the `Metric` [class](https:\/\/github.com\/huggingface\/datasets\/blob\/1675ad6a958435b675a849eafa8a7f10fe0f43bc\/src\/datasets\/metric.py) does not work with [SARI](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/sari\/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. \r\n\r\nFor example, when the `add_batch` method is used, then the `compute()` method fails:\r\n```\r\nmetric = load_metric(\"sari\")\r\nmetric.add_batch(\r\n    predictions=[\"About 95 you now get in .\"], \r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\nmetric.compute()\r\n> TypeError: _compute() missing 1 required positional argument: 'sources'\r\n```\r\n\r\nTherefore, the `compute() `method can only be used standalone:\r\n```\r\nmetric = load_metric(\"sari\")\r\nresult = metric.compute(\r\n    sources=[\"About 95 species are currently accepted .\"],\r\n    predictions=[\"About 95 you now get in .\"],\r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\n> {'sari': 26.953601953601954}\r\n```\r\n  \r\n**Describe the solution you'd like**\r\nSupport for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class.\r\n```\r\nadd_batch(*, sources=None, predictions=None, references=None, **kwargs)\r\nadd(*, sources=None, predictions=None, references=None, **kwargs)\r\ncompute()\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nI've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add) and [add_batch method](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add_batch). \r\n\r\n**Additional context**\r\nThese methods are used in the transformers [pytorch examples](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/summarization\/run_summarization_no_trainer.py).\r\n \n Hi, thanks for reporting! We can add a `sources: datasets.Value(\"string\")` feature to the `Features` dict in the `SARI` script to fix this. Would you be interested in submitting a PR?","embeddings":[-0.418857187,0.2724247873,-0.0092956405,-0.109503448,0.1228541508,-0.0708655789,0.2320676148,0.2151484042,-0.1837127805,0.2203429788,-0.2901641428,0.2855446935,0.1346973926,0.0324125364,0.0896025077,-0.3678045571,0.0503036194,0.1437146217,-0.0944620445,0.1511001587,-0.5576465726,0.0884080008,-0.1839939803,-0.2398210913,-0.1274917871,0.0271712467,-0.2369705588,-0.1809155643,-0.242304489,-0.5351161957,0.2128133774,0.184191525,0.030027695,0.328779608,-0.0001185521,0.1109369695,0.2762511969,-0.1328736842,-0.4383605421,-0.1820884794,-0.3840431273,-0.2386553288,-0.255456537,-0.2883082926,-0.066975221,0.1014839187,-0.2451184839,-0.0378114134,-0.1235950217,0.2556289136,0.1296489686,0.4364939034,0.1177339703,-0.3196769059,0.1126789674,0.1221246123,-0.2314319909,0.1621471196,0.2862774432,0.2523857653,0.1044625714,0.3575453758,-0.10116335,-0.1528925747,0.3361633718,0.1865538061,0.2177976221,-0.1124148741,0.0721455663,-0.0221289098,0.4007414281,-0.2246422172,-0.7027819753,-0.1128430516,0.0156833306,-0.4567686021,-0.1005972177,0.0173968282,-0.370431006,0.0589394197,-0.2042921931,-0.275709331,-0.4550824165,-0.0204551909,0.2664703429,0.480687052,-0.010899391,0.1277373433,0.2977893949,-0.1493104249,-0.2083183527,-0.2281209081,-0.0354343913,0.2015784234,-0.2583950758,-0.1311885566,0.0373128094,0.2627390027,0.3208867311,-0.1454826295,-0.1214293018,0.0887124017,0.1068656966,0.186365813,0.0315015242,0.1831670702,0.1211113781,0.128656134,0.1919880211,0.140594542,0.4344313741,-0.1230266392,0.3087745607,-0.587298274,0.1333309859,0.0711448193,0.1389213204,0.0042202417,-0.0917381495,-0.2361579686,0.197017327,-0.2742686868,0.3080248535,0.1325076669,0.080105193,0.2156480998,0.0563336834,0.3079833388,0.033877939,-0.2010417283,-0.1755243391,0.1349952966,-0.3487784564,0.3339532018,0.0842801556,-0.4368998706,0.2398524135,0.1866853982,0.3053173721,0.2414405793,-0.1046384722,-0.091588974,0.0677316487,0.2685288489,-0.1755870581,-0.1076954082,-0.0455673039,-0.0763387755,-0.1254886389,0.2220953405,-0.398018986,-0.2488846183,-0.0216519609,0.088848792,-0.2236714661,0.0860225931,-0.4042801559,0.4411891401,-0.053608343,-0.1159331277,-0.13124533,0.0245751608,-0.439569056,-0.2581740916,0.4780132174,0.3768750429,-0.270585537,-0.2682240605,0.0152603323,-0.2777920961,-0.000174235,-0.0487130992,-0.1349849999,0.226624608,-0.0900652409,0.0091337608,0.4235927165,-0.6044465303,-0.249181971,0.1749854088,0.2629358172,0.0213295035,0.2808436453,0.2441941351,0.3655673563,-0.1035799459,0.5832070112,-0.01007473,0.0577992648,-0.1951378435,-0.0983808413,-0.2154646814,-0.3887764812,0.1491802335,0.3146126568,0.2870127261,0.1172083169,-0.2743912041,0.1582759172,-0.2240352929,0.1490287185,0.2103224546,0.4284425676,-0.1786857396,0.0095951045,-0.4675133824,-0.1849878281,0.2353615165,-0.038783472,0.1429476589,-0.1661316305,-0.2337100059,-0.4295108914,0.2337548435,0.0730380267,0.1595888138,0.0083182538,0.0193767939,0.1887286901,-0.0650202259,-0.1504932046,-0.0511033162,-0.0218409449,0.1324141026,-0.0735514984,0.2290120572,0.0087169465,-0.2423764765,0.1229098886,0.2345895916,0.3444214463,-0.0058033983,0.0699786469,0.4360874593,-0.1422571689,0.2842975855,0.1106190607,0.4046298265,0.2912035286,0.1582604647,0.0404240526,-0.3556534946,0.1786972582,-0.0360753052,0.0573326461,0.3717138469,-0.0458056591,0.2234573662,0.0395647436,0.0530337095,0.3049707413,-0.0233941674,-0.3434014022,-0.3567788601,-0.1145392135,-0.3208931386,-0.0697284341,-0.0580247268,-0.165466845,-0.0241016131,0.1833823621,-0.0155967604,0.1204318106,0.0668271631,-0.0730015934,0.0985987484,-0.1357388794,0.0695260167,0.5584735274,0.1500831246,-0.0911995098,-0.0373592265,-0.0241184626,-0.2028726041,0.2230867296,0.2470092475,-0.2515798509,0.1927696168,-0.0019769694,0.0067640948,0.0543680489,0.1213758513,0.0380899869,0.0263195802,-0.3169482052,-0.0031067787,0.0437699929,0.1455572993,-0.2943174243,-0.4739550352,0.0568017513,-0.0962096304,0.0918493643,0.1123282015,0.0360778831,0.2308142483,-0.092454128,0.4944143295,-0.0593253002,-0.3651401997,-0.1701659262,-0.1873136014,0.1883337647,0.0158797111,-0.0531545356,0.0299402848,0.3106324077,-0.1446076483,0.2041735947,-0.4650370181,-0.4353862107,-0.0289641712,0.0004467398,0.750346899,0.2831505239,-0.0092198504,0.1365371644,-0.1327079237,0.2965182066,-0.0779575184,0.1647882015,-0.1711974889,-0.2469162345,-0.1490933448,-0.2791825533,-0.2390076518,-0.0450825542,-0.2869666517,-0.2676415741,-0.1203173995,-0.185465157,0.1274610162,0.4413746297,0.1953525841,0.3598144948,-0.0427214466,-0.0141195627,-0.307648927,0.6466607451,-0.3484894931,-0.2277261317,0.112713404,-0.22078982,0.315136224,0.4210939705,-0.1847179532,-0.6845133305,-0.1306389868,0.2173563838,0.0551277101,0.2106154114,0.0781694502,0.0405415371,-0.0389557481,-0.2910509109,-0.5418058634,0.1473374367,-0.2379997075,0.1455719173,0.2954826653,0.4121634066,-0.0041012759,1.1683142185,0.0577900372,-0.2287497222,0.1821614653,0.3092633188,0.438536346,0.3150938153,-0.3524233699,0.2687494159,0.1835827529,0.0522750616,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3818","title":"Support for \"sources\" parameter in the add() and add_batch() methods in datasets.metric - SARI","comments":"Hi Mario,\r\n\r\nThanks for your message. I did try to add `sources` into the `Features` dict using a script for the metric:\r\n```\r\n            features=datasets.Features(\r\n                {\r\n                    \"sources\": datasets.Value(\"string\", id=\"sequence\"),\r\n                    \"predictions\": datasets.Value(\"string\", id=\"sequence\"),\r\n                    \"references\": datasets.Sequence(datasets.Value(\"string\", id=\"sequence\"), id=\"references\"),\r\n                }\r\n            ),\r\n```\r\n\r\nBut that only avoids a failure in `encode_batch` in the `add_batch` method:\r\n```\r\n        batch = {\"predictions\": predictions, \"references\": references}\r\n        batch = self.info.features.encode_batch(batch)\r\n```\r\n\r\nThe real problem is that `add_batch()`, `add()` and `compute()` does not receive a `sources` param:\r\n```\r\ndef add_batch(self, *, predictions=None, references=None):\r\ndef add(self, *, prediction=None, reference=None):\r\ndef compute(self, *, predictions=None, references=None, **kwargs)\r\n```\r\n\r\nAnd then, it fails:\r\n`TypeError: add_batch() got an unexpected keyword argument sources`\r\n\r\nI need this for adding any metric based on SARI or alike, not only for sari.py :)\r\n\r\nLet me know if I understood correctly the proposed solution.\r\n","body":"**Is your feature request related to a problem? Please describe.**\r\nThe methods `add_batch` and `add` from the `Metric` [class](https:\/\/github.com\/huggingface\/datasets\/blob\/1675ad6a958435b675a849eafa8a7f10fe0f43bc\/src\/datasets\/metric.py) does not work with [SARI](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/sari\/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. \r\n\r\nFor example, when the `add_batch` method is used, then the `compute()` method fails:\r\n```\r\nmetric = load_metric(\"sari\")\r\nmetric.add_batch(\r\n    predictions=[\"About 95 you now get in .\"], \r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\nmetric.compute()\r\n> TypeError: _compute() missing 1 required positional argument: 'sources'\r\n```\r\n\r\nTherefore, the `compute() `method can only be used standalone:\r\n```\r\nmetric = load_metric(\"sari\")\r\nresult = metric.compute(\r\n    sources=[\"About 95 species are currently accepted .\"],\r\n    predictions=[\"About 95 you now get in .\"],\r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\n> {'sari': 26.953601953601954}\r\n```\r\n  \r\n**Describe the solution you'd like**\r\nSupport for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class.\r\n```\r\nadd_batch(*, sources=None, predictions=None, references=None, **kwargs)\r\nadd(*, sources=None, predictions=None, references=None, **kwargs)\r\ncompute()\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nI've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add) and [add_batch method](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add_batch). \r\n\r\n**Additional context**\r\nThese methods are used in the transformers [pytorch examples](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/summarization\/run_summarization_no_trainer.py).\r\n","comment_length":133,"text":"Support for \"sources\" parameter in the add() and add_batch() methods in datasets.metric - SARI \n **Is your feature request related to a problem? Please describe.**\r\nThe methods `add_batch` and `add` from the `Metric` [class](https:\/\/github.com\/huggingface\/datasets\/blob\/1675ad6a958435b675a849eafa8a7f10fe0f43bc\/src\/datasets\/metric.py) does not work with [SARI](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/sari\/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. \r\n\r\nFor example, when the `add_batch` method is used, then the `compute()` method fails:\r\n```\r\nmetric = load_metric(\"sari\")\r\nmetric.add_batch(\r\n    predictions=[\"About 95 you now get in .\"], \r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\nmetric.compute()\r\n> TypeError: _compute() missing 1 required positional argument: 'sources'\r\n```\r\n\r\nTherefore, the `compute() `method can only be used standalone:\r\n```\r\nmetric = load_metric(\"sari\")\r\nresult = metric.compute(\r\n    sources=[\"About 95 species are currently accepted .\"],\r\n    predictions=[\"About 95 you now get in .\"],\r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\n> {'sari': 26.953601953601954}\r\n```\r\n  \r\n**Describe the solution you'd like**\r\nSupport for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class.\r\n```\r\nadd_batch(*, sources=None, predictions=None, references=None, **kwargs)\r\nadd(*, sources=None, predictions=None, references=None, **kwargs)\r\ncompute()\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nI've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add) and [add_batch method](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add_batch). \r\n\r\n**Additional context**\r\nThese methods are used in the transformers [pytorch examples](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/summarization\/run_summarization_no_trainer.py).\r\n \n Hi Mario,\r\n\r\nThanks for your message. I did try to add `sources` into the `Features` dict using a script for the metric:\r\n```\r\n            features=datasets.Features(\r\n                {\r\n                    \"sources\": datasets.Value(\"string\", id=\"sequence\"),\r\n                    \"predictions\": datasets.Value(\"string\", id=\"sequence\"),\r\n                    \"references\": datasets.Sequence(datasets.Value(\"string\", id=\"sequence\"), id=\"references\"),\r\n                }\r\n            ),\r\n```\r\n\r\nBut that only avoids a failure in `encode_batch` in the `add_batch` method:\r\n```\r\n        batch = {\"predictions\": predictions, \"references\": references}\r\n        batch = self.info.features.encode_batch(batch)\r\n```\r\n\r\nThe real problem is that `add_batch()`, `add()` and `compute()` does not receive a `sources` param:\r\n```\r\ndef add_batch(self, *, predictions=None, references=None):\r\ndef add(self, *, prediction=None, reference=None):\r\ndef compute(self, *, predictions=None, references=None, **kwargs)\r\n```\r\n\r\nAnd then, it fails:\r\n`TypeError: add_batch() got an unexpected keyword argument sources`\r\n\r\nI need this for adding any metric based on SARI or alike, not only for sari.py :)\r\n\r\nLet me know if I understood correctly the proposed solution.\r\n","embeddings":[-0.418857187,0.2724247873,-0.0092956405,-0.109503448,0.1228541508,-0.0708655789,0.2320676148,0.2151484042,-0.1837127805,0.2203429788,-0.2901641428,0.2855446935,0.1346973926,0.0324125364,0.0896025077,-0.3678045571,0.0503036194,0.1437146217,-0.0944620445,0.1511001587,-0.5576465726,0.0884080008,-0.1839939803,-0.2398210913,-0.1274917871,0.0271712467,-0.2369705588,-0.1809155643,-0.242304489,-0.5351161957,0.2128133774,0.184191525,0.030027695,0.328779608,-0.0001185521,0.1109369695,0.2762511969,-0.1328736842,-0.4383605421,-0.1820884794,-0.3840431273,-0.2386553288,-0.255456537,-0.2883082926,-0.066975221,0.1014839187,-0.2451184839,-0.0378114134,-0.1235950217,0.2556289136,0.1296489686,0.4364939034,0.1177339703,-0.3196769059,0.1126789674,0.1221246123,-0.2314319909,0.1621471196,0.2862774432,0.2523857653,0.1044625714,0.3575453758,-0.10116335,-0.1528925747,0.3361633718,0.1865538061,0.2177976221,-0.1124148741,0.0721455663,-0.0221289098,0.4007414281,-0.2246422172,-0.7027819753,-0.1128430516,0.0156833306,-0.4567686021,-0.1005972177,0.0173968282,-0.370431006,0.0589394197,-0.2042921931,-0.275709331,-0.4550824165,-0.0204551909,0.2664703429,0.480687052,-0.010899391,0.1277373433,0.2977893949,-0.1493104249,-0.2083183527,-0.2281209081,-0.0354343913,0.2015784234,-0.2583950758,-0.1311885566,0.0373128094,0.2627390027,0.3208867311,-0.1454826295,-0.1214293018,0.0887124017,0.1068656966,0.186365813,0.0315015242,0.1831670702,0.1211113781,0.128656134,0.1919880211,0.140594542,0.4344313741,-0.1230266392,0.3087745607,-0.587298274,0.1333309859,0.0711448193,0.1389213204,0.0042202417,-0.0917381495,-0.2361579686,0.197017327,-0.2742686868,0.3080248535,0.1325076669,0.080105193,0.2156480998,0.0563336834,0.3079833388,0.033877939,-0.2010417283,-0.1755243391,0.1349952966,-0.3487784564,0.3339532018,0.0842801556,-0.4368998706,0.2398524135,0.1866853982,0.3053173721,0.2414405793,-0.1046384722,-0.091588974,0.0677316487,0.2685288489,-0.1755870581,-0.1076954082,-0.0455673039,-0.0763387755,-0.1254886389,0.2220953405,-0.398018986,-0.2488846183,-0.0216519609,0.088848792,-0.2236714661,0.0860225931,-0.4042801559,0.4411891401,-0.053608343,-0.1159331277,-0.13124533,0.0245751608,-0.439569056,-0.2581740916,0.4780132174,0.3768750429,-0.270585537,-0.2682240605,0.0152603323,-0.2777920961,-0.000174235,-0.0487130992,-0.1349849999,0.226624608,-0.0900652409,0.0091337608,0.4235927165,-0.6044465303,-0.249181971,0.1749854088,0.2629358172,0.0213295035,0.2808436453,0.2441941351,0.3655673563,-0.1035799459,0.5832070112,-0.01007473,0.0577992648,-0.1951378435,-0.0983808413,-0.2154646814,-0.3887764812,0.1491802335,0.3146126568,0.2870127261,0.1172083169,-0.2743912041,0.1582759172,-0.2240352929,0.1490287185,0.2103224546,0.4284425676,-0.1786857396,0.0095951045,-0.4675133824,-0.1849878281,0.2353615165,-0.038783472,0.1429476589,-0.1661316305,-0.2337100059,-0.4295108914,0.2337548435,0.0730380267,0.1595888138,0.0083182538,0.0193767939,0.1887286901,-0.0650202259,-0.1504932046,-0.0511033162,-0.0218409449,0.1324141026,-0.0735514984,0.2290120572,0.0087169465,-0.2423764765,0.1229098886,0.2345895916,0.3444214463,-0.0058033983,0.0699786469,0.4360874593,-0.1422571689,0.2842975855,0.1106190607,0.4046298265,0.2912035286,0.1582604647,0.0404240526,-0.3556534946,0.1786972582,-0.0360753052,0.0573326461,0.3717138469,-0.0458056591,0.2234573662,0.0395647436,0.0530337095,0.3049707413,-0.0233941674,-0.3434014022,-0.3567788601,-0.1145392135,-0.3208931386,-0.0697284341,-0.0580247268,-0.165466845,-0.0241016131,0.1833823621,-0.0155967604,0.1204318106,0.0668271631,-0.0730015934,0.0985987484,-0.1357388794,0.0695260167,0.5584735274,0.1500831246,-0.0911995098,-0.0373592265,-0.0241184626,-0.2028726041,0.2230867296,0.2470092475,-0.2515798509,0.1927696168,-0.0019769694,0.0067640948,0.0543680489,0.1213758513,0.0380899869,0.0263195802,-0.3169482052,-0.0031067787,0.0437699929,0.1455572993,-0.2943174243,-0.4739550352,0.0568017513,-0.0962096304,0.0918493643,0.1123282015,0.0360778831,0.2308142483,-0.092454128,0.4944143295,-0.0593253002,-0.3651401997,-0.1701659262,-0.1873136014,0.1883337647,0.0158797111,-0.0531545356,0.0299402848,0.3106324077,-0.1446076483,0.2041735947,-0.4650370181,-0.4353862107,-0.0289641712,0.0004467398,0.750346899,0.2831505239,-0.0092198504,0.1365371644,-0.1327079237,0.2965182066,-0.0779575184,0.1647882015,-0.1711974889,-0.2469162345,-0.1490933448,-0.2791825533,-0.2390076518,-0.0450825542,-0.2869666517,-0.2676415741,-0.1203173995,-0.185465157,0.1274610162,0.4413746297,0.1953525841,0.3598144948,-0.0427214466,-0.0141195627,-0.307648927,0.6466607451,-0.3484894931,-0.2277261317,0.112713404,-0.22078982,0.315136224,0.4210939705,-0.1847179532,-0.6845133305,-0.1306389868,0.2173563838,0.0551277101,0.2106154114,0.0781694502,0.0405415371,-0.0389557481,-0.2910509109,-0.5418058634,0.1473374367,-0.2379997075,0.1455719173,0.2954826653,0.4121634066,-0.0041012759,1.1683142185,0.0577900372,-0.2287497222,0.1821614653,0.3092633188,0.438536346,0.3150938153,-0.3524233699,0.2687494159,0.1835827529,0.05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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3818","title":"Support for \"sources\" parameter in the add() and add_batch() methods in datasets.metric - SARI","comments":"The `Metric` class has been modified recently to support this use-case, but the `add_batch` + `compute` pattern still doesn't work correctly. I'll open a PR.","body":"**Is your feature request related to a problem? Please describe.**\r\nThe methods `add_batch` and `add` from the `Metric` [class](https:\/\/github.com\/huggingface\/datasets\/blob\/1675ad6a958435b675a849eafa8a7f10fe0f43bc\/src\/datasets\/metric.py) does not work with [SARI](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/sari\/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. \r\n\r\nFor example, when the `add_batch` method is used, then the `compute()` method fails:\r\n```\r\nmetric = load_metric(\"sari\")\r\nmetric.add_batch(\r\n    predictions=[\"About 95 you now get in .\"], \r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\nmetric.compute()\r\n> TypeError: _compute() missing 1 required positional argument: 'sources'\r\n```\r\n\r\nTherefore, the `compute() `method can only be used standalone:\r\n```\r\nmetric = load_metric(\"sari\")\r\nresult = metric.compute(\r\n    sources=[\"About 95 species are currently accepted .\"],\r\n    predictions=[\"About 95 you now get in .\"],\r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\n> {'sari': 26.953601953601954}\r\n```\r\n  \r\n**Describe the solution you'd like**\r\nSupport for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class.\r\n```\r\nadd_batch(*, sources=None, predictions=None, references=None, **kwargs)\r\nadd(*, sources=None, predictions=None, references=None, **kwargs)\r\ncompute()\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nI've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add) and [add_batch method](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add_batch). \r\n\r\n**Additional context**\r\nThese methods are used in the transformers [pytorch examples](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/summarization\/run_summarization_no_trainer.py).\r\n","comment_length":25,"text":"Support for \"sources\" parameter in the add() and add_batch() methods in datasets.metric - SARI \n **Is your feature request related to a problem? Please describe.**\r\nThe methods `add_batch` and `add` from the `Metric` [class](https:\/\/github.com\/huggingface\/datasets\/blob\/1675ad6a958435b675a849eafa8a7f10fe0f43bc\/src\/datasets\/metric.py) does not work with [SARI](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/sari\/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. \r\n\r\nFor example, when the `add_batch` method is used, then the `compute()` method fails:\r\n```\r\nmetric = load_metric(\"sari\")\r\nmetric.add_batch(\r\n    predictions=[\"About 95 you now get in .\"], \r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\nmetric.compute()\r\n> TypeError: _compute() missing 1 required positional argument: 'sources'\r\n```\r\n\r\nTherefore, the `compute() `method can only be used standalone:\r\n```\r\nmetric = load_metric(\"sari\")\r\nresult = metric.compute(\r\n    sources=[\"About 95 species are currently accepted .\"],\r\n    predictions=[\"About 95 you now get in .\"],\r\n    references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]])\r\n> {'sari': 26.953601953601954}\r\n```\r\n  \r\n**Describe the solution you'd like**\r\nSupport for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class.\r\n```\r\nadd_batch(*, sources=None, predictions=None, references=None, **kwargs)\r\nadd(*, sources=None, predictions=None, references=None, **kwargs)\r\ncompute()\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nI've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add) and [add_batch method](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/metric.html#Metric.add_batch). \r\n\r\n**Additional context**\r\nThese methods are used in the transformers [pytorch examples](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/summarization\/run_summarization_no_trainer.py).\r\n \n The `Metric` class has been modified recently to support this use-case, but the `add_batch` + `compute` pattern still doesn't work correctly. I'll open a PR.","embeddings":[-0.418857187,0.2724247873,-0.0092956405,-0.109503448,0.1228541508,-0.0708655789,0.2320676148,0.2151484042,-0.1837127805,0.2203429788,-0.2901641428,0.2855446935,0.1346973926,0.0324125364,0.0896025077,-0.3678045571,0.0503036194,0.1437146217,-0.0944620445,0.1511001587,-0.5576465726,0.0884080008,-0.1839939803,-0.2398210913,-0.1274917871,0.0271712467,-0.2369705588,-0.1809155643,-0.242304489,-0.5351161957,0.2128133774,0.184191525,0.030027695,0.328779608,-0.0001185521,0.1109369695,0.2762511969,-0.1328736842,-0.4383605421,-0.1820884794,-0.3840431273,-0.2386553288,-0.255456537,-0.2883082926,-0.066975221,0.1014839187,-0.2451184839,-0.0378114134,-0.1235950217,0.2556289136,0.1296489686,0.4364939034,0.1177339703,-0.3196769059,0.1126789674,0.1221246123,-0.2314319909,0.1621471196,0.2862774432,0.2523857653,0.1044625714,0.3575453758,-0.10116335,-0.1528925747,0.3361633718,0.1865538061,0.2177976221,-0.1124148741,0.0721455663,-0.0221289098,0.4007414281,-0.2246422172,-0.7027819753,-0.1128430516,0.0156833306,-0.4567686021,-0.1005972177,0.0173968282,-0.370431006,0.0589394197,-0.2042921931,-0.275709331,-0.4550824165,-0.0204551909,0.2664703429,0.480687052,-0.010899391,0.1277373433,0.2977893949,-0.1493104249,-0.2083183527,-0.2281209081,-0.0354343913,0.2015784234,-0.2583950758,-0.1311885566,0.0373128094,0.2627390027,0.3208867311,-0.1454826295,-0.1214293018,0.0887124017,0.1068656966,0.186365813,0.0315015242,0.1831670702,0.1211113781,0.128656134,0.1919880211,0.140594542,0.4344313741,-0.1230266392,0.3087745607,-0.587298274,0.1333309859,0.0711448193,0.1389213204,0.0042202417,-0.0917381495,-0.2361579686,0.197017327,-0.2742686868,0.3080248535,0.1325076669,0.080105193,0.2156480998,0.0563336834,0.3079833388,0.033877939,-0.2010417283,-0.1755243391,0.1349952966,-0.3487784564,0.3339532018,0.0842801556,-0.4368998706,0.2398524135,0.1866853982,0.3053173721,0.2414405793,-0.1046384722,-0.091588974,0.0677316487,0.2685288489,-0.1755870581,-0.1076954082,-0.0455673039,-0.0763387755,-0.1254886389,0.2220953405,-0.398018986,-0.2488846183,-0.0216519609,0.088848792,-0.2236714661,0.0860225931,-0.4042801559,0.4411891401,-0.053608343,-0.1159331277,-0.13124533,0.0245751608,-0.439569056,-0.2581740916,0.4780132174,0.3768750429,-0.270585537,-0.2682240605,0.0152603323,-0.2777920961,-0.000174235,-0.0487130992,-0.1349849999,0.226624608,-0.0900652409,0.0091337608,0.4235927165,-0.6044465303,-0.249181971,0.1749854088,0.2629358172,0.0213295035,0.2808436453,0.2441941351,0.3655673563,-0.1035799459,0.5832070112,-0.01007473,0.0577992648,-0.1951378435,-0.0983808413,-0.2154646814,-0.3887764812,0.1491802335,0.3146126568,0.2870127261,0.1172083169,-0.2743912041,0.1582759172,-0.2240352929,0.1490287185,0.2103224546,0.4284425676,-0.1786857396,0.0095951045,-0.4675133824,-0.1849878281,0.2353615165,-0.038783472,0.1429476589,-0.1661316305,-0.2337100059,-0.4295108914,0.2337548435,0.0730380267,0.1595888138,0.0083182538,0.0193767939,0.1887286901,-0.0650202259,-0.1504932046,-0.0511033162,-0.0218409449,0.1324141026,-0.0735514984,0.2290120572,0.0087169465,-0.2423764765,0.1229098886,0.2345895916,0.3444214463,-0.0058033983,0.0699786469,0.4360874593,-0.1422571689,0.2842975855,0.1106190607,0.4046298265,0.2912035286,0.1582604647,0.0404240526,-0.3556534946,0.1786972582,-0.0360753052,0.0573326461,0.3717138469,-0.0458056591,0.2234573662,0.0395647436,0.0530337095,0.3049707413,-0.0233941674,-0.3434014022,-0.3567788601,-0.1145392135,-0.3208931386,-0.0697284341,-0.0580247268,-0.165466845,-0.0241016131,0.1833823621,-0.0155967604,0.1204318106,0.0668271631,-0.0730015934,0.0985987484,-0.1357388794,0.0695260167,0.5584735274,0.1500831246,-0.0911995098,-0.0373592265,-0.0241184626,-0.2028726041,0.2230867296,0.2470092475,-0.2515798509,0.1927696168,-0.0019769694,0.0067640948,0.0543680489,0.1213758513,0.0380899869,0.0263195802,-0.3169482052,-0.0031067787,0.0437699929,0.1455572993,-0.2943174243,-0.4739550352,0.0568017513,-0.0962096304,0.0918493643,0.1123282015,0.0360778831,0.2308142483,-0.092454128,0.4944143295,-0.0593253002,-0.3651401997,-0.1701659262,-0.1873136014,0.1883337647,0.0158797111,-0.0531545356,0.0299402848,0.3106324077,-0.1446076483,0.2041735947,-0.4650370181,-0.4353862107,-0.0289641712,0.0004467398,0.750346899,0.2831505239,-0.0092198504,0.1365371644,-0.1327079237,0.2965182066,-0.0779575184,0.1647882015,-0.1711974889,-0.2469162345,-0.1490933448,-0.2791825533,-0.2390076518,-0.0450825542,-0.2869666517,-0.2676415741,-0.1203173995,-0.185465157,0.1274610162,0.4413746297,0.1953525841,0.3598144948,-0.0427214466,-0.0141195627,-0.307648927,0.6466607451,-0.3484894931,-0.2277261317,0.112713404,-0.22078982,0.315136224,0.4210939705,-0.1847179532,-0.6845133305,-0.1306389868,0.2173563838,0.0551277101,0.2106154114,0.0781694502,0.0405415371,-0.0389557481,-0.2910509109,-0.5418058634,0.1473374367,-0.2379997075,0.1455719173,0.2954826653,0.4121634066,-0.0041012759,1.1683142185,0.0577900372,-0.2287497222,0.1821614653,0.3092633188,0.438536346,0.3150938153,-0.3524233699,0.2687494159,0.1835827529,0.0522750616,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3813","title":"Add MetaShift dataset","comments":"I would like to take this up and give it a shot. Any image specific - dataset guidelines to keep in mind ? Thank you.","body":"## Adding a Dataset\r\n- **Name:** MetaShift\r\n- **Description:**  collection of 12,868 sets of natural images across 410 classes-\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2202.06523v1\r\n- **Data:** https:\/\/github.com\/weixin-liang\/metashift\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":25,"text":"Add MetaShift dataset \n ## Adding a Dataset\r\n- **Name:** MetaShift\r\n- **Description:**  collection of 12,868 sets of natural images across 410 classes-\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2202.06523v1\r\n- **Data:** https:\/\/github.com\/weixin-liang\/metashift\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I would like to take this up and give it a shot. Any image specific - dataset guidelines to keep in mind ? Thank you.","embeddings":[-0.0806369334,-0.0981556475,-0.1193474755,0.0553123355,0.3078880012,-0.1292067468,0.0209145732,-0.0056073871,0.1324973553,-0.0477064215,0.0888742507,-0.033262074,-0.2426595688,0.2571824193,0.0837724209,-0.106322065,0.1582995653,-0.0881590694,-0.1022364348,-0.1867656261,-0.2449706197,-0.3292395771,0.2425886691,-0.2965027988,-0.3616740406,-0.1030900404,-0.2439588904,0.1631546021,-0.3346954584,-0.1375024766,-0.0018795427,0.2187566459,0.0880537406,0.5687717199,-0.0001072236,-0.0870248005,-0.0431333259,-0.2751207948,0.1904857159,-0.0804942846,-0.4750124514,-0.2762615085,-0.274995178,-0.2058663517,-0.0771298036,-0.1004363,0.0959042609,-0.1323484927,0.5059862137,0.270730257,0.2471280396,-0.3086114228,-0.1492785662,0.0247357674,0.2044592202,0.6788593531,-0.266726613,-0.211279124,0.2783688307,0.2591605783,0.018046461,0.2829157412,0.057910651,0.1840453893,0.6363854408,-0.1290773749,-0.2395906895,-0.3995333016,-0.1944017708,0.4912958443,0.3364048302,0.0559905171,-0.2499865144,-0.2782716155,-0.1760130078,0.0057297279,0.0431157611,0.4577920139,-0.0915554538,-0.1188665032,-0.3102502525,-0.128611207,-0.2617160976,0.116767846,0.0060852105,0.1250899434,-0.0367569961,0.0922939628,0.1488385499,-0.1808018088,-0.3503508568,-0.1428169608,-0.0255698524,0.0121923862,-0.0052332934,-0.116440922,0.0522360355,0.027493136,0.4315555692,-0.0517658107,-0.0143394154,-0.0293272771,-0.156759277,-0.0261766147,0.2056241035,-0.3562365174,-0.1891971231,-0.0611069873,0.0333326831,-0.0837024823,-0.0582029484,-0.0433064625,0.1015505195,-0.04478807,-0.2981718481,-0.365458101,0.2336863428,-0.2841867805,0.0996454507,-0.0581565611,0.3467344046,-0.2967643142,-0.0557835139,0.0594358332,0.339995116,-0.3247306645,-0.1192370728,0.3492453396,0.0014016681,-0.1533550471,-0.1125263274,0.1896640956,-0.2921923101,0.1068832055,0.1308776885,0.1000951305,0.0136616379,-0.0310706124,0.1026344076,0.1736071259,0.1944711506,-0.1298075318,0.5268257856,0.3511087298,-0.0813114122,-0.0806328356,-0.1648844332,0.30523476,-0.3161439002,0.2688781917,-0.076825738,-0.1164692938,-0.1298310161,0.1944045126,-0.3713923395,-0.1132453978,-0.2412797064,0.4663459659,-0.2068303823,0.1627429426,0.2716242671,0.1168861166,-0.283952415,-0.2506495416,-0.066366598,0.4393610358,-0.179412514,0.1206072271,-0.091252245,-0.0400545895,-0.1385915577,0.1362896115,-0.0775688365,0.1682338864,-0.0660634264,0.0870502368,0.1911992133,-0.0596693009,-0.0910243243,-0.0764427781,0.0007623163,-0.1495522708,0.1060772464,0.4495439529,0.1980768442,-0.366560787,-0.2991309166,0.34476915,-0.3825096488,0.1332362443,0.0128854671,-0.3783046901,-0.1573279053,0.193589896,-0.0133972093,-0.2210229784,0.1875384003,-0.0656864345,-0.2725299895,-0.334798336,0.0280284602,-0.0398728661,0.4994836152,0.0537786111,-0.1659391373,-0.0165581405,-0.3880640566,0.2039660811,0.4082158506,0.3886153102,-0.1101715043,-0.0821507424,0.2972352505,-0.2280924618,-0.2682796121,-0.073521018,0.1863560081,-0.0205724593,0.0863028169,-0.2469973713,-0.1352834255,-0.1253519058,-0.071989879,0.1984315813,0.0150129618,0.2216188312,0.0879129469,-0.0665144399,-0.0980481803,-0.0116570732,0.0965059623,-0.2961220443,-0.0308175255,0.1577616185,0.074945651,0.2068413943,0.2059378922,0.5141118169,0.3121541142,-0.4466294348,0.4281500578,-0.2516674995,-0.0421580821,-0.2014385313,-0.2720139921,0.0802598894,-0.273339659,0.1122061461,-0.1533187032,0.0226519294,0.1232229844,-0.0630741566,-0.0559188202,-0.163284719,0.1542351395,0.4310775399,-0.0820227191,0.048947785,-0.069018364,0.0351811685,0.1547570378,-0.0170402844,0.1516799927,0.2868845165,0.0593498945,0.1336822957,0.2283476144,0.1495655924,0.2751182616,0.3341164887,-0.1657230407,-0.0026583169,-0.0418216176,-0.1028193757,0.0143930055,0.3576337695,0.2339668125,-0.309107095,0.0760601386,0.1671631634,-0.4774659276,-0.1646919996,-0.1061156318,-0.0682458803,-0.0654435679,-0.0674615204,0.1684144288,-0.1984785199,-0.0375805721,-0.0642303303,-0.2903841436,-0.1140810475,0.0581376925,-0.2823921144,-0.2082513124,-0.0254747868,-0.1248731092,0.4227749705,-0.226755023,-0.2609773278,0.0212907363,-0.1984193772,-0.008394626,0.1850646734,0.4127252996,0.022687465,0.8276760578,-0.1950945258,0.2294580787,-0.2050996125,-0.6781932116,0.1127843335,-0.3867534399,0.1184290498,0.1493427902,0.4177560806,0.2646806538,0.1319990456,0.1613959074,-0.0676132292,-0.0069796881,-0.0183105767,-0.0761129409,-0.0493328087,-0.168832913,0.008013851,-0.0752814561,-0.1250628084,0.2304823101,0.3919714391,0.1168535054,0.1816571653,0.3658490479,-0.2039572448,-0.2600605488,0.2470443994,-0.1743068397,-0.4022272825,0.1596037447,-0.1208006814,-0.3903039098,0.3028558791,0.0191550553,-0.0608489215,0.2590591609,-0.4030801356,-0.271433413,-0.1416619718,0.1536109746,0.3207376599,0.1175633445,0.1091605276,0.0865797624,-0.1305451244,-0.2348518968,-0.1817722172,0.2536050975,0.1961795092,0.0717476755,0.1912804544,0.317602843,0.2021810412,0.4311878681,0.0350574479,-0.3972942233,0.4045544267,-0.095891133,0.2444385439,0.0072903777,-0.1448661834,0.6722423434,0.0678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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3813","title":"Add MetaShift dataset","comments":"I've started working on adding this dataset. I require some inputs on the following : \r\n\r\nRef for the initial draft [here](https:\/\/github.com\/dnaveenr\/datasets\/blob\/add_metashift_dataset\/datasets\/metashift\/metashift.py)\r\n1. The dataset does not have a typical - train\/test\/val split. What do we do for the _split_generators() function ? How do we go about this ?\r\n2. This dataset builds on the Visual Genome dataset, using a metadata file. The dataset is generated using generate_full_MetaShift.py script. By default, the authors choose to generate the dataset only for a SELECTED_CLASSES. The following script is used : \r\nCode : https:\/\/github.com\/Weixin-Liang\/MetaShift\/blob\/main\/dataset\/generate_full_MetaShift.py \r\nInfo : https:\/\/metashift.readthedocs.io\/en\/latest\/sub_pages\/download_MetaShift.html#generate-the-full-metashift-dataset\r\nCan I just copy over the required functions into the metashift.py to generate the dataset ?\r\n3. How do we complete the _generate_examples for this dataset ?\r\n\r\nThe user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here.\r\n\r\nInputs, suggestions would be helpful. Thank you.","body":"## Adding a Dataset\r\n- **Name:** MetaShift\r\n- **Description:**  collection of 12,868 sets of natural images across 410 classes-\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2202.06523v1\r\n- **Data:** https:\/\/github.com\/weixin-liang\/metashift\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":156,"text":"Add MetaShift dataset \n ## Adding a Dataset\r\n- **Name:** MetaShift\r\n- **Description:**  collection of 12,868 sets of natural images across 410 classes-\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2202.06523v1\r\n- **Data:** https:\/\/github.com\/weixin-liang\/metashift\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I've started working on adding this dataset. I require some inputs on the following : \r\n\r\nRef for the initial draft [here](https:\/\/github.com\/dnaveenr\/datasets\/blob\/add_metashift_dataset\/datasets\/metashift\/metashift.py)\r\n1. The dataset does not have a typical - train\/test\/val split. What do we do for the _split_generators() function ? How do we go about this ?\r\n2. This dataset builds on the Visual Genome dataset, using a metadata file. The dataset is generated using generate_full_MetaShift.py script. By default, the authors choose to generate the dataset only for a SELECTED_CLASSES. The following script is used : \r\nCode : https:\/\/github.com\/Weixin-Liang\/MetaShift\/blob\/main\/dataset\/generate_full_MetaShift.py \r\nInfo : https:\/\/metashift.readthedocs.io\/en\/latest\/sub_pages\/download_MetaShift.html#generate-the-full-metashift-dataset\r\nCan I just copy over the required functions into the metashift.py to generate the dataset ?\r\n3. How do we complete the _generate_examples for this dataset ?\r\n\r\nThe user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here.\r\n\r\nInputs, suggestions would be helpful. Thank 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3813","title":"Add MetaShift dataset","comments":"Hi ! Thanks for adding this dataset :) Let me answer your questions:\r\n\r\n1. in this case you can put everything in the \"train\" split\r\n2. Yes you can copy the script (provided you also include the MIT license of the code in the file header for example). Though we ideally try to not create new directories nor files when generating dataset, so if possible this script should be adapted to not create the file structure they mentioned, but instead yield the images one by one in `_generate_examples`. Let me know if you think this is feasible\r\n3. see point 2 haha\r\n\r\n> The user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here.\r\n\r\nYup ! We can also define a `selected_classes` parameter such that users can do\r\n```python\r\nload_dataset(\"metashift\", selected_classes=[\"cat\", \"dog\", ...])\r\n```","body":"## Adding a Dataset\r\n- **Name:** MetaShift\r\n- **Description:**  collection of 12,868 sets of natural images across 410 classes-\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2202.06523v1\r\n- **Data:** https:\/\/github.com\/weixin-liang\/metashift\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":152,"text":"Add MetaShift dataset \n ## Adding a Dataset\r\n- **Name:** MetaShift\r\n- **Description:**  collection of 12,868 sets of natural images across 410 classes-\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2202.06523v1\r\n- **Data:** https:\/\/github.com\/weixin-liang\/metashift\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hi ! Thanks for adding this dataset :) Let me answer your questions:\r\n\r\n1. in this case you can put everything in the \"train\" split\r\n2. Yes you can copy the script (provided you also include the MIT license of the code in the file header for example). Though we ideally try to not create new directories nor files when generating dataset, so if possible this script should be adapted to not create the file structure they mentioned, but instead yield the images one by one in `_generate_examples`. Let me know if you think this is feasible\r\n3. see point 2 haha\r\n\r\n> The user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here.\r\n\r\nYup ! We can also define a `selected_classes` parameter such that users can do\r\n```python\r\nload_dataset(\"metashift\", selected_classes=[\"cat\", \"dog\", ...])\r\n```","embeddings":[-0.1713748425,-0.0854927078,-0.1429996341,0.1220314503,0.2432815433,0.0219149925,0.505643189,0.2920891643,0.1803069711,0.1295088679,-0.1259088665,0.3187864721,-0.3814876974,0.3359365165,0.2225331068,-0.3426353037,-0.0453621782,-0.0169436354,-0.4357624948,-0.2787142694,-0.1351094544,-0.1734783351,0.1918793619,-0.1671461016,-0.1744369566,-0.1003690213,-0.278942287,0.2975198328,-0.2775051892,-0.2103960216,0.0295087527,0.1951223612,0.1212306544,0.4812795818,-0.000102626,-0.0359663218,-0.2641311884,-0.2260331064,0.0262498558,-0.2793749571,-0.1735372692,0.0321359411,-0.1853779852,-0.4760916531,-0.2596637607,0.0543114766,0.1492689699,-0.1845443994,0.6137756705,0.2783641517,0.2564154863,-0.2433260083,-0.255663991,-0.0286241584,0.2965321541,0.4603504837,-0.1727154404,0.0737155452,0.2140241563,0.1322762072,0.004336311,0.2569500804,0.1680525392,-0.2418022901,0.4714137018,0.1708839834,-0.0644063801,-0.6214824915,-0.1912195534,0.4629723132,0.17366606,-0.1054152176,-0.1764006019,-0.1933256835,-0.0077537047,-0.1363963932,-0.1740731746,0.5972149372,-0.2103251219,0.0638875589,-0.233710289,0.0149948336,-0.4762997627,0.0660966635,0.2260614336,0.0706620589,-0.0515924245,0.057423424,0.1356962174,0.0078311311,-0.0939606801,-0.1884717196,0.2775954306,0.2086522579,0.0953162089,-0.1730403751,-0.0506587401,-0.1825270951,0.3284851611,0.2355303317,0.088197507,0.0513958037,-0.0258587636,0.0604429692,0.2010171264,-0.2231749892,-0.186412707,0.0769947693,0.0346663073,0.1075370014,-0.1812854111,0.0072396426,-0.0626482293,-0.0196465794,-0.2486249954,0.0067529515,-0.02189661,0.1194235161,0.2223736346,0.0212793946,0.2213637978,-0.1038260311,-0.0980473086,0.4619571567,0.0358569622,-0.1679962128,0.1324815005,0.1421523541,-0.0541663803,-0.1343387961,-0.0755054057,0.269335717,-0.2499656975,0.1333349794,0.0659325048,0.0768385828,0.1974925995,-0.0878073573,0.3309428394,0.0629932359,0.2227968872,-0.1693210602,0.3840786815,0.1725316495,-0.0114068361,-0.1005855426,-0.0580081567,0.1741983294,-0.4390954971,0.1745888889,-0.0884143859,-0.1512622088,0.1041460931,0.2632454932,-0.052292157,-0.101066716,-0.1502703875,0.3087923825,-0.2092507184,0.258118391,0.1564034522,-0.1814828664,-0.165124774,-0.450486511,0.1124443561,0.5075385571,-0.0842101797,-0.1288881302,0.1051092818,-0.1167287976,-0.0043168496,-0.0736993998,-0.2457018793,0.3596146107,-0.1327020973,0.198087424,0.4703373015,-0.0575626753,-0.0710508898,-0.1511608809,0.0338464975,0.0658746064,0.2130269706,0.4191640913,0.3640280962,-0.2067690939,-0.1200643107,0.5442052484,-0.2732967436,0.0468799472,0.1305383742,-0.3632336557,-0.0433245376,0.3602813184,0.2186936438,-0.0541145355,0.1300497502,-0.0066275843,0.2039381415,-0.5015254617,0.0386704877,0.0448498838,0.3631575108,0.3604663312,-0.0297614243,-0.3296713829,-0.2181915939,0.1991685331,0.0754175782,0.2173470706,-0.0998863503,-0.3326768875,-0.0508977622,-0.1881195903,-0.3735206425,-0.0269674156,0.1971254498,-0.1125066951,0.0784415752,-0.2277383506,-0.2712686062,-0.1036008522,-0.3305698633,0.2257075012,-0.1282521188,0.2994076908,-0.2091212869,-0.0003203103,0.0119477883,0.0451567657,-0.1072061285,-0.3808164895,0.1719531268,0.3074436784,0.1491849124,-0.0086696474,0.0205512792,0.4042967558,0.1519865692,-0.4811360836,0.316934675,-0.0345087014,0.0450381748,0.0589548126,-0.2861289978,0.4348737895,-0.1321585029,0.0429566465,0.2507202625,0.0200449731,0.1081312075,-0.1908352375,-0.0535074323,-0.0968623832,-0.1193718538,0.2038119882,-0.1054713726,-0.0281321499,-0.2974167764,0.249284178,0.5039885044,0.0445759967,0.0181442685,0.0571760871,-0.0705772787,0.2856907845,-0.0116459653,0.1380454451,0.3094787598,0.3308018148,-0.0780396238,0.1010675877,0.0013264411,-0.0228293072,0.2193142623,0.1523889005,0.1907723248,-0.0635641292,-0.1739767343,0.1385087818,-0.4159290195,-0.3361175358,-0.1038813218,-0.1864596307,-0.0118742986,0.1094728187,0.1973594725,-0.146949321,-0.1646337211,0.0570019186,-0.077219598,0.1209650561,0.1977940798,-0.2975301147,-0.2712241709,0.0935020298,-0.155076772,0.4223380387,-0.0637417957,-0.133034274,0.1484808624,-0.0896102116,0.0195232444,0.2001469284,0.381111145,0.0093812635,0.698769927,-0.2212852389,-0.0354376994,-0.0886406228,-0.3641219735,0.1965439916,-0.2550824881,0.0815306753,0.2120423168,0.2983917892,0.2065593898,-0.1129943207,0.2723436356,-0.1536823958,-0.1097237244,-0.2439026684,0.2379827052,-0.0766616464,-0.3094433844,-0.5846554637,-0.3071856797,-0.2087841034,0.1934528053,0.6218825579,0.341974318,0.0532950535,0.399354279,-0.0739387646,-0.1481505632,0.4150904715,-0.1228411198,-0.347710669,0.1952228844,-0.3329266906,-0.4700809419,0.1345829219,-0.1847294718,-0.0553876087,0.0930156112,-0.3589762449,-0.3498623669,-0.1599855572,0.1779904068,-0.0883944184,0.3428149819,0.2566937506,0.0870727524,-0.155348435,-0.1967137456,0.0975374654,0.2550486028,-0.0637988225,0.0102863433,0.0288996454,0.2491102815,0.1602264196,0.6947693825,0.1918844134,-0.2577662766,0.1521679014,-0.1638443023,0.1408076882,-0.0512353778,-0.0637976229,0.374125331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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3813","title":"Add MetaShift dataset","comments":"Great. This is helpful. Thanks @lhoestq .\r\nRegarding Point 2, I'll try using yield instead of creating the directories and see if its feasible. selected_classes config sounds good.","body":"## Adding a Dataset\r\n- **Name:** MetaShift\r\n- **Description:**  collection of 12,868 sets of natural images across 410 classes-\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2202.06523v1\r\n- **Data:** https:\/\/github.com\/weixin-liang\/metashift\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":28,"text":"Add MetaShift dataset \n ## Adding a Dataset\r\n- **Name:** MetaShift\r\n- **Description:**  collection of 12,868 sets of natural images across 410 classes-\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2202.06523v1\r\n- **Data:** https:\/\/github.com\/weixin-liang\/metashift\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Great. This is helpful. Thanks @lhoestq .\r\nRegarding Point 2, I'll try using yield instead of creating the directories and see if its feasible. selected_classes config sounds good.","embeddings":[-0.3895171881,-0.0509431623,-0.1791784167,0.167194441,0.3236086965,-0.2388728708,0.246281296,0.2632823288,0.1178998798,0.3443956375,-0.1489604264,0.1883938909,-0.1833631694,0.2654517889,-0.0889897048,-0.170896098,0.0098174335,0.0219870433,-0.4625786543,-0.3382691443,-0.2971762419,-0.0264236983,0.1553830802,-0.277785778,-0.2578546405,0.1134651452,-0.177739948,0.320022881,-0.2286583036,-0.1323252469,0.0234170202,0.1727736443,0.0294273961,0.486774087,-0.0000973617,-0.0728516802,-0.1010104269,-0.2816464603,-0.0792611316,0.0239016116,-0.1853613108,-0.0264516994,-0.2507655621,-0.3703632653,-0.3569294512,0.064982906,0.0275258999,-0.1632046551,0.2688805163,0.0341937877,0.370120585,-0.2361430675,-0.1796728522,-0.1605500579,0.2042284012,0.6134074926,-0.3163789511,0.0039532869,0.2658745348,0.0525671095,-0.0340579562,0.4224968255,0.1473827958,-0.0329414196,0.4840038121,0.3195939064,-0.0030698241,-0.3683847487,-0.1413717866,0.138210088,0.1955311298,-0.012441799,-0.1594636142,-0.2242227644,-0.2817411125,-0.0663035512,0.110238947,0.4969938397,-0.0924629346,-0.0306903906,-0.2818903625,0.0445020124,-0.398039639,0.1095731258,0.3819840848,0.1387380213,-0.0635414273,-0.0686141253,0.2186118364,-0.0802064389,-0.3090288639,-0.2534581125,0.0862245783,0.0886058137,0.0791005269,-0.1761440784,0.2053524703,-0.041403953,0.3619939983,0.2026762664,-0.0681607723,0.1572469771,-0.020887237,0.0299311187,0.0183790736,-0.0034685575,0.0210332274,-0.0077195601,0.2574497461,0.3339688182,-0.1832004786,0.0611404553,0.167473346,-0.0677268282,-0.2930783033,-0.0372548997,-0.0814450458,-0.0802384913,0.2445183098,-0.0408575907,0.3105840981,-0.1755674332,-0.1019373164,0.4551906288,0.1626970917,-0.2059167773,0.1878748238,0.1487485766,-0.1627422273,-0.1761190891,-0.1838411391,0.0996400341,-0.3680684566,0.1892020851,0.0859310254,0.1859135181,0.1560721546,-0.1687855273,0.2741818726,0.1172120124,0.0070694652,-0.0109483032,0.2854577005,0.2076706141,-0.0818254724,-0.1442196369,0.0098516187,0.0835355744,-0.3049458265,0.0439243317,0.1504569054,-0.2718985081,0.0929071233,0.3082819283,0.2105264217,0.055008743,-0.1048372313,0.4233039021,-0.1487238705,0.2918606997,0.0914672613,0.1045936495,-0.0767086595,-0.2651067376,0.1456790715,0.546430409,-0.0344933718,-0.1847724468,0.1367258132,-0.1353978068,0.06199117,0.1142801568,-0.2255957127,0.0458992608,-0.2396719903,0.2657375932,0.2419467568,-0.1014278233,-0.1763137728,-0.1654966921,-0.0845165476,-0.2033248842,0.2644898295,0.3263711035,0.1442268938,-0.3142995536,-0.1236718372,0.4259985685,-0.3158285022,0.2198040485,-0.1339993477,-0.3348040283,-0.2860125303,0.2864803374,0.1712379307,-0.068422772,0.2690221965,-0.1139634475,0.1214432716,-0.5862209797,0.223813802,0.0973812491,0.4004169703,0.1138349399,-0.0757969394,-0.1372191906,-0.2899141908,0.1901641488,-0.1328291446,0.418366462,-0.0016060144,-0.2518885732,0.0940779075,-0.118757911,-0.1984105557,-0.0874865651,0.2967782021,0.0535657443,0.157241866,-0.2649131417,-0.0304405894,-0.3413158357,-0.2619585097,0.203200981,-0.0475011058,0.3176573515,-0.2450198382,-0.149438858,0.0399552807,-0.0973621681,-0.0049365438,-0.3266834617,0.0426662304,0.2407746166,-0.0444508679,0.0821467191,0.2627734542,0.5095670819,0.1272095889,-0.3854151666,0.2497275025,-0.0685307384,-0.1014344543,-0.000571525,-0.2917214632,0.3151196837,0.0013700685,0.1033903956,0.1957832128,-0.0733651072,0.18287386,-0.1554608494,0.1398690939,-0.1962130219,0.0307325982,0.2486646473,-0.2442180067,0.0985160321,-0.240111202,0.3804861903,0.2350235432,0.163030833,0.2268596888,0.3321473897,-0.0061088647,0.1834580302,0.0524327271,0.0845033377,0.3677015305,0.361326009,0.0345835499,-0.0593256988,-0.0538704693,-0.1421617717,0.3206610382,0.0245693307,0.1097796112,0.1540372968,-0.0031669394,-0.128677845,-0.5678732395,-0.1797193736,-0.2292711437,-0.1612043828,0.0926897377,0.07644324,0.2609697878,-0.155115813,0.1066301614,-0.1292443424,-0.0454933047,0.0618669949,0.3464685082,0.0185323488,-0.4552508295,0.1877953708,-0.046499908,0.4725929201,-0.0013100975,-0.0803340226,-0.0556896478,-0.0109870164,-0.0876669958,0.2106458396,0.2844857275,-0.1625899673,0.5464722514,-0.1218207702,0.1917263716,-0.3975758851,-0.3066880703,0.1365074068,-0.1862077117,0.1208979785,0.1627811193,0.1910033673,0.2794442773,-0.0860696658,0.513556838,-0.2169616818,-0.1126905978,-0.1972818226,0.1650841981,-0.0345436968,-0.3470689058,-0.4522973299,-0.1201257929,-0.4027176499,0.2353229374,0.4801425636,0.3007182479,0.1928516626,0.2721502781,-0.2154015899,-0.1439519376,0.3441705704,-0.1733070314,-0.2845410109,0.2100909501,-0.2663010657,-0.3854213953,0.0562639236,-0.2343224585,-0.1032488868,0.0305343159,-0.2833219767,-0.224044919,-0.1344722807,0.231527552,0.0754681304,0.2604391575,0.3394797742,0.1626918912,-0.3701765835,-0.16242145,0.0633871406,0.3434886336,-0.0051957159,0.1685742736,-0.1865460426,0.1025498658,-0.0169831906,0.5331882834,-0.0058510224,-0.1075249985,0.3847068846,0.0428630114,0.2067605406,-0.0129136201,-0.0409841314,0.3642288446,0.0409314372,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3809","title":"Checksums didn't match for datasets on Google Drive","comments":"Hi @muelletm, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nUntil our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","body":"## Describe the bug\r\n\r\nDatasets hosted on Google Drive do not seem to work right now.\r\nLoading them fails with a checksum error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfor dataset in [\"head_qa\", \"yelp_review_full\"]:\r\n  try:\r\n    load_dataset(dataset)\r\n  except Exception as exception:\r\n    print(\"Error\", dataset, exception)\r\n```\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1wOtHBmL8I65NmUYakzPV5zhVCtHhi7uQ#scrollTo=cDzdCLlk-Bo4).\r\n\r\n## Expected results\r\n\r\nThe datasets should be loaded.\r\n\r\n## Actual results\r\n\r\n```\r\nDownloading and preparing dataset head_qa\/es (download: 75.69 MiB, generated: 2.86 MiB, post-processed: Unknown size, total: 78.55 MiB) to \/root\/.cache\/huggingface\/datasets\/head_qa\/es\/1.1.0\/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9...\r\nError head_qa Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t']\r\nDownloading and preparing dataset yelp_review_full\/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to \/root\/.cache\/huggingface\/datasets\/yelp_review_full\/yelp_review_full\/1.0.0\/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43...\r\nError yelp_review_full Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n","comment_length":103,"text":"Checksums didn't match for datasets on Google Drive \n ## Describe the bug\r\n\r\nDatasets hosted on Google Drive do not seem to work right now.\r\nLoading them fails with a checksum error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfor dataset in [\"head_qa\", \"yelp_review_full\"]:\r\n  try:\r\n    load_dataset(dataset)\r\n  except Exception as exception:\r\n    print(\"Error\", dataset, exception)\r\n```\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1wOtHBmL8I65NmUYakzPV5zhVCtHhi7uQ#scrollTo=cDzdCLlk-Bo4).\r\n\r\n## Expected results\r\n\r\nThe datasets should be loaded.\r\n\r\n## Actual results\r\n\r\n```\r\nDownloading and preparing dataset head_qa\/es (download: 75.69 MiB, generated: 2.86 MiB, post-processed: Unknown size, total: 78.55 MiB) to \/root\/.cache\/huggingface\/datasets\/head_qa\/es\/1.1.0\/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9...\r\nError head_qa Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t']\r\nDownloading and preparing dataset yelp_review_full\/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to \/root\/.cache\/huggingface\/datasets\/yelp_review_full\/yelp_review_full\/1.0.0\/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43...\r\nError yelp_review_full Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n \n Hi @muelletm, thanks for reporting.\r\n\r\nThis issue was already reported and its root cause is a change in the Google Drive service. See:\r\n- #3786 \r\n\r\nWe have already fixed it. See:\r\n- #3787 \r\n\r\nUntil our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","embeddings":[-0.2004950494,0.1379579753,-0.0935081095,0.3851816654,0.1761962473,0.0669628531,0.2867942452,0.2709662318,0.3133201897,0.0352170989,-0.2274777144,-0.1330033094,0.0922783837,0.474976629,-0.0118395863,0.0952154696,0.2046583742,-0.207885325,-0.1187622175,-0.0767436624,-0.2440579385,0.2734455764,-0.1281362921,-0.2654185295,-0.0844998658,0.1922451109,0.1089082509,0.0303906668,-0.1364765167,-0.0768344104,0.3176573217,0.1080398113,-0.0438727662,0.6045651436,-0.0001152937,0.1691471338,0.5119219422,-0.1030113548,-0.2532036304,-0.1355905384,-0.2083259821,-0.2300936282,-0.1154296845,-0.0566904657,0.0270775519,-0.0767157823,-0.04180241,-0.2278389782,0.0343275145,0.165744558,0.2046627253,0.2291150689,0.1648141295,0.0777767673,0.3041205108,-0.023111606,-0.0295953155,0.5944210291,0.2160040289,-0.1312047392,0.1098718792,-0.0194702018,-0.1524477601,-0.1210944876,0.1067795977,-0.0484526493,-0.1900155246,-0.0585196391,0.194425717,0.3027798533,0.2271396369,-0.2340983301,-0.3918517828,-0.2272475213,-0.0718824118,-0.0303806532,0.4662078619,0.3633427918,0.0533336923,-0.0099207954,-0.6728940606,0.1784310192,0.1665133685,0.0341950208,-0.0722638816,0.2899852693,0.1073192805,-0.045937866,-0.0090741888,-0.0415419824,0.3304326236,-0.2977487147,-0.318459034,-0.0589790307,-0.4302060306,-0.0319447219,-0.0218981132,0.6578822732,0.379986614,0.4241061211,0.1344639361,0.14016819,-0.2275057435,0.1371592432,0.1466055512,0.1741494387,0.0507912412,0.15230003,0.45686391,0.463696897,0.0660020858,0.1024541259,0.2478535324,-0.2429818511,0.0757965073,0.1941560656,0.2457556874,-0.4055294096,-0.3348593712,0.2787911594,-0.2281613797,-0.2902029157,0.1522619724,0.3259157836,-0.1612098217,-0.1942272037,0.0551251061,0.2093652487,-0.0999331176,0.1333677322,-0.2791703939,0.0698437616,-0.0279424824,0.0597437285,0.3519744277,-0.5087801814,0.4866882861,-0.0784821212,0.2107606679,-0.2716502845,-0.1715469509,-0.1565554738,-0.12118873,0.2372383028,0.1546692848,-0.0397475511,0.0902509168,-0.1428681016,-0.0540083572,0.202021271,-0.3572333753,0.0197773445,-0.0073937993,0.1894222349,-0.5425913334,0.0429067202,-0.5800549388,-0.1823515445,0.1249126941,-0.1873633564,0.0450219773,-0.2815029025,-0.210622102,-0.1320472658,0.3063298166,0.3215180039,-0.189693898,0.0244252682,-0.0885229111,-0.1065398306,0.0131456302,0.3616098762,0.1162984446,0.1736361235,-0.4544726908,-0.0351739973,-0.1976670325,-0.3839850128,-0.5300045013,-0.2486233264,0.2050873041,0.1399909109,-0.1048648283,-0.2803535163,0.1224821433,-0.1864456981,0.040199697,0.3333052993,-0.000398833,0.2669268548,-0.1876935959,-0.2228947282,-0.1386245638,0.171887368,-0.0415618643,0.0259692241,0.3742716014,-0.3643811345,0.0710173324,-0.041315645,0.0141011011,0.3013989925,0.557610929,-0.107437335,0.1119196713,-0.061098814,-0.7031599879,0.4163667858,-0.1611442268,-0.0506533012,-0.23877123,0.0050090696,-0.4453287721,-0.1038249657,-0.1254402548,-0.0159322023,0.0952856913,0.1702819169,-0.0020620339,0.2192334086,0.0235986263,0.0235022828,-0.1645486653,-0.0052666753,-0.0646829531,0.6858771443,-0.1069711149,-0.2457262874,0.3047253489,0.0825412422,0.2195357233,-0.0828082934,-0.093741551,0.3717252314,-0.0015264198,0.4066149294,0.0590274334,0.2769157887,0.1922566444,-0.3241866529,0.0135881351,0.2911352217,0.2097778022,-0.2174917608,-0.3539370596,0.4361309111,-0.1927621365,0.1003941298,-0.1224751025,-0.0607693829,0.2534862757,-0.0373138078,-0.0905815512,-0.2046933174,0.2763153017,0.2808555961,0.3398906887,-0.0402721763,0.0056837862,-0.0251362864,0.4558697343,-0.1191228479,-0.0448765531,0.0108554875,0.0516641177,-0.1181828901,0.2301990539,0.3681416512,0.2318424433,0.0965977833,0.097878091,0.1922652423,-0.0474933572,-0.2538549602,0.0195681136,0.0056932927,0.0939343646,0.4377785921,0.1351940036,-0.1153586134,-0.2053841054,0.0138618322,-0.0341963656,0.1547343135,-0.5353252888,-0.0549461991,-0.1626689136,0.1231435314,-0.1970636845,0.0183842517,-0.337938875,-0.4040555358,-0.0577251464,0.3487561345,-0.1331400424,0.167565763,0.1188035235,0.2955754995,0.1296314746,-0.3248252869,-0.0774457604,-0.0039877514,-0.0432774313,0.0814709887,0.5646172762,0.0741605982,0.2197894156,-0.2283277214,-0.0163754094,-0.6965926886,-0.1423949599,0.1218652353,-0.277905196,0.6852679253,-0.046957463,0.1490630805,-0.2466881424,-0.1222143844,0.0865603536,0.1151097566,-0.4383168519,0.1449600011,0.0722432733,-0.0023824782,0.0772543699,0.088581644,0.1688695103,-0.1789821982,0.014176094,0.1095076427,0.1390978396,-0.1263242662,0.0315187685,0.0769812614,0.235553205,-0.0686870664,-0.2250458598,-0.5468575954,0.462374568,-0.0581725799,-0.3421655893,0.0213014167,0.1161955595,0.0273348168,0.1276450157,-0.5016132593,-0.3997257352,-0.2580124736,0.0086077526,0.3434936702,0.0918344781,0.1281291246,-0.0985984579,0.1352146119,-0.159944877,-0.3696451485,0.0605694018,0.0010758647,0.3646294773,-0.0184579063,0.2252977341,-0.2633546293,0.4675225914,0.4486221671,0.0984250605,0.3147240579,0.2940503061,0.3990002573,-0.2211803645,-0.16434216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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3808","title":"Pre-Processing Cache Fails when using a Factory pattern","comments":"Ok - this is still an issue but I believe the root cause is different than I originally thought. I'm now able to get caching to work consistently with the above example as long as I fix the python hash seed `export PYTHONHASHSEED=1234`","body":"## Describe the bug\r\nIf you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n    def preprocess_function_factory(augmentation=None):\r\n        def preprocess_function(examples):\r\n            # Tokenize the texts\r\n            if augmentation:\r\n                conversions1 = [\r\n                    augmentation(example)\r\n                    for example in examples[sentence1_key]\r\n                ]\r\n                if sentence2_key is None:\r\n                    args = (conversions1,)\r\n                else:\r\n                    conversions2 = [\r\n                        augmentation(example)\r\n                        for example in examples[sentence2_key]\r\n                    ]\r\n                    args = (conversions1, conversions2)\r\n            else:\r\n                args = (\r\n                    (examples[sentence1_key],)\r\n                    if sentence2_key is None\r\n                    else (examples[sentence1_key], examples[sentence2_key])\r\n                )\r\n            result = tokenizer(\r\n                *args, padding=padding, max_length=max_seq_length, truncation=True\r\n            )\r\n\r\n            # Map labels to IDs (not necessary for GLUE tasks)\r\n            if label_to_id is not None and \"label\" in examples:\r\n                result[\"label\"] = [\r\n                    (label_to_id[l] if l != -1 else -1) for l in examples[\"label\"]\r\n                ]\r\n            return result\r\n\r\n        return preprocess_function\r\n\r\ncapitalize = lambda x: x.capitalize()\r\npreprocess_function = preprocess_function_factory(augmentation=capitalize)\r\nprint(hash(preprocess_function)) # This will change on each run\r\nraw_datasets = raw_datasets.map(\r\n        preprocess_function,\r\n        batched=True,\r\n        load_from_cache_file=True,\r\n        desc=\"Running transformation and tokenizer on dataset\",\r\n)\r\n```\r\n\r\n## Expected results\r\nRunning the code twice will cause the cache to be re-used.\r\n\r\n## Actual results\r\nRunning the code twice causes the whole dataset to be re-processed\r\n","comment_length":43,"text":"Pre-Processing Cache Fails when using a Factory pattern \n ## Describe the bug\r\nIf you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n    def preprocess_function_factory(augmentation=None):\r\n        def preprocess_function(examples):\r\n            # Tokenize the texts\r\n            if augmentation:\r\n                conversions1 = [\r\n                    augmentation(example)\r\n                    for example in examples[sentence1_key]\r\n                ]\r\n                if sentence2_key is None:\r\n                    args = (conversions1,)\r\n                else:\r\n                    conversions2 = [\r\n                        augmentation(example)\r\n                        for example in examples[sentence2_key]\r\n                    ]\r\n                    args = (conversions1, conversions2)\r\n            else:\r\n                args = (\r\n                    (examples[sentence1_key],)\r\n                    if sentence2_key is None\r\n                    else (examples[sentence1_key], examples[sentence2_key])\r\n                )\r\n            result = tokenizer(\r\n                *args, padding=padding, max_length=max_seq_length, truncation=True\r\n            )\r\n\r\n            # Map labels to IDs (not necessary for GLUE tasks)\r\n            if label_to_id is not None and \"label\" in examples:\r\n                result[\"label\"] = [\r\n                    (label_to_id[l] if l != -1 else -1) for l in examples[\"label\"]\r\n                ]\r\n            return result\r\n\r\n        return preprocess_function\r\n\r\ncapitalize = lambda x: x.capitalize()\r\npreprocess_function = preprocess_function_factory(augmentation=capitalize)\r\nprint(hash(preprocess_function)) # This will change on each run\r\nraw_datasets = raw_datasets.map(\r\n        preprocess_function,\r\n        batched=True,\r\n        load_from_cache_file=True,\r\n        desc=\"Running transformation and tokenizer on dataset\",\r\n)\r\n```\r\n\r\n## Expected results\r\nRunning the code twice will cause the cache to be re-used.\r\n\r\n## Actual results\r\nRunning the code twice causes the whole dataset to be re-processed\r\n \n Ok - this is still an issue but I believe the root cause is different than I originally thought. I'm now able to get caching to work consistently with the above example as long as I fix the python hash seed `export 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3808","title":"Pre-Processing Cache Fails when using a Factory pattern","comments":"Hi! \r\n\r\nYes, our hasher should work with decorators. For instance, this dummy example:\r\n```python\r\ndef f(arg):\r\n    def f1(ex):\r\n        return {\"a\": ex[\"col1\"] + arg}\r\n    return f1\r\n```\r\ngives the same hash across different Python sessions (`datasets.fingerprint.Hasher.hash(f(\"string1\")` returns `\"408c9059f89dbd6c\"` on my machine).\r\n\r\nCould you please make the example self-contained? This way, we can reproduce the bug. Additionally, you can try to find the problematic object yourself by testing their hash with `datasets.fingerprint.Hasher.hash(obj)`\r\n\r\nThis could be related to https:\/\/github.com\/huggingface\/datasets\/issues\/3638.","body":"## Describe the bug\r\nIf you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n    def preprocess_function_factory(augmentation=None):\r\n        def preprocess_function(examples):\r\n            # Tokenize the texts\r\n            if augmentation:\r\n                conversions1 = [\r\n                    augmentation(example)\r\n                    for example in examples[sentence1_key]\r\n                ]\r\n                if sentence2_key is None:\r\n                    args = (conversions1,)\r\n                else:\r\n                    conversions2 = [\r\n                        augmentation(example)\r\n                        for example in examples[sentence2_key]\r\n                    ]\r\n                    args = (conversions1, conversions2)\r\n            else:\r\n                args = (\r\n                    (examples[sentence1_key],)\r\n                    if sentence2_key is None\r\n                    else (examples[sentence1_key], examples[sentence2_key])\r\n                )\r\n            result = tokenizer(\r\n                *args, padding=padding, max_length=max_seq_length, truncation=True\r\n            )\r\n\r\n            # Map labels to IDs (not necessary for GLUE tasks)\r\n            if label_to_id is not None and \"label\" in examples:\r\n                result[\"label\"] = [\r\n                    (label_to_id[l] if l != -1 else -1) for l in examples[\"label\"]\r\n                ]\r\n            return result\r\n\r\n        return preprocess_function\r\n\r\ncapitalize = lambda x: x.capitalize()\r\npreprocess_function = preprocess_function_factory(augmentation=capitalize)\r\nprint(hash(preprocess_function)) # This will change on each run\r\nraw_datasets = raw_datasets.map(\r\n        preprocess_function,\r\n        batched=True,\r\n        load_from_cache_file=True,\r\n        desc=\"Running transformation and tokenizer on dataset\",\r\n)\r\n```\r\n\r\n## Expected results\r\nRunning the code twice will cause the cache to be re-used.\r\n\r\n## Actual results\r\nRunning the code twice causes the whole dataset to be re-processed\r\n","comment_length":76,"text":"Pre-Processing Cache Fails when using a Factory pattern \n ## Describe the bug\r\nIf you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n    def preprocess_function_factory(augmentation=None):\r\n        def preprocess_function(examples):\r\n            # Tokenize the texts\r\n            if augmentation:\r\n                conversions1 = [\r\n                    augmentation(example)\r\n                    for example in examples[sentence1_key]\r\n                ]\r\n                if sentence2_key is None:\r\n                    args = (conversions1,)\r\n                else:\r\n                    conversions2 = [\r\n                        augmentation(example)\r\n                        for example in examples[sentence2_key]\r\n                    ]\r\n                    args = (conversions1, conversions2)\r\n            else:\r\n                args = (\r\n                    (examples[sentence1_key],)\r\n                    if sentence2_key is None\r\n                    else (examples[sentence1_key], examples[sentence2_key])\r\n                )\r\n            result = tokenizer(\r\n                *args, padding=padding, max_length=max_seq_length, truncation=True\r\n            )\r\n\r\n            # Map labels to IDs (not necessary for GLUE tasks)\r\n            if label_to_id is not None and \"label\" in examples:\r\n                result[\"label\"] = [\r\n                    (label_to_id[l] if l != -1 else -1) for l in examples[\"label\"]\r\n                ]\r\n            return result\r\n\r\n        return preprocess_function\r\n\r\ncapitalize = lambda x: x.capitalize()\r\npreprocess_function = preprocess_function_factory(augmentation=capitalize)\r\nprint(hash(preprocess_function)) # This will change on each run\r\nraw_datasets = raw_datasets.map(\r\n        preprocess_function,\r\n        batched=True,\r\n        load_from_cache_file=True,\r\n        desc=\"Running transformation and tokenizer on dataset\",\r\n)\r\n```\r\n\r\n## Expected results\r\nRunning the code twice will cause the cache to be re-used.\r\n\r\n## Actual results\r\nRunning the code twice causes the whole dataset to be re-processed\r\n \n Hi! \r\n\r\nYes, our hasher should work with decorators. For instance, this dummy example:\r\n```python\r\ndef f(arg):\r\n    def f1(ex):\r\n        return {\"a\": ex[\"col1\"] + arg}\r\n    return f1\r\n```\r\ngives the same hash across different Python sessions (`datasets.fingerprint.Hasher.hash(f(\"string1\")` returns `\"408c9059f89dbd6c\"` on my machine).\r\n\r\nCould you please make the example self-contained? This way, we can reproduce the bug. Additionally, you can try to find the problematic object yourself by testing their hash with `datasets.fingerprint.Hasher.hash(obj)`\r\n\r\nThis could be related to https:\/\/github.com\/huggingface\/datasets\/issues\/3638.","embeddings":[-0.0620611385,0.1236682087,-0.0902339444,0.0906540826,0.1542712599,-0.2390380651,0.3725136518,0.1926224232,-0.2423098385,0.1052957848,0.4187673926,0.1436961293,-0.2627626956,0.0627104938,-0.0321047381,0.2822054029,0.0156639498,0.1075156108,0.1408689916,0.1510554701,-0.0684495345,0.2015596926,0.0096532442,-0.1195453852,-0.5152248144,0.0751597658,-0.0482056513,0.0026572412,0.0664530993,-0.4019342959,0.1446282119,0.2565312982,-0.0128916549,0.3057130873,-0.0001054864,0.1628533304,0.0616887324,-0.0653815046,0.0152941952,0.1102629751,-0.0613919795,-0.0911327675,-0.2910897434,-0.1497635245,-0.1515439004,0.1063488871,-0.0768349767,0.0303307176,0.495439589,0.1672428846,0.226352036,-0.3021930754,-0.0579569079,0.1700790673,0.1229031235,-0.0066574872,-0.0178084429,0.2024968863,-0.0088677965,-0.100644134,-0.2753634155,0.4482295513,-0.1389077902,0.011037536,0.0025625082,0.1406016201,0.5876591802,-0.3276062906,0.2174799889,-0.0633458048,-0.0489889756,-0.3940421343,-0.2073916495,-0.1415274888,-0.2807801366,-0.6380515695,0.1990482211,-0.2292876989,0.1595846415,0.165512979,-0.0855662972,0.0525379963,0.2540952563,-0.1240687892,0.1573062092,0.0449583456,-0.1525186449,-0.0540120229,-0.0427495055,-0.0195492133,0.1485749036,-0.1385906786,-0.1261906624,0.1758725643,-0.1428729445,-0.1384238452,0.1148368716,0.0662834048,-0.2514893413,0.4752096832,-0.003888757,0.4604196548,0.1926216632,0.1259586066,-0.3425792754,0.4094690084,0.1100884154,0.0508370064,0.1101968512,-0.0030263052,-0.489580363,-0.0045208209,0.1759975702,-0.2412384748,0.5337760448,0.1409320086,-0.0929805115,0.0574243926,0.0273516849,0.2721283734,-0.3257071376,-0.0725228712,0.0561769865,0.3007163405,-0.0477878414,0.2964474261,0.0872787461,-0.0584968776,-0.0271752216,0.028148897,-0.2899160683,0.0254057273,-0.2662344277,0.3557652533,0.0624498948,0.0032460834,0.165595457,0.3438792229,0.2089624405,-0.2088247687,0.3667631149,-0.1252300739,0.241888836,0.0439813212,-0.1855017692,0.1805783063,0.1791797727,-0.1426582485,-0.0791163519,-0.0960297808,-0.2476249337,-0.3119928241,0.5255044103,0.2651924193,-0.2829459608,0.2601414025,0.1746836901,0.0032522939,0.614109993,0.0712556988,0.0233583692,-0.1973849684,-0.3598620892,-0.2974452972,0.0169788729,0.3339508474,-0.1431952715,-0.0853581056,0.0868127793,0.1032706201,0.2292219698,-0.0813163668,-0.0581668466,0.311614275,-0.2671949267,-0.1292686164,0.1042288244,-0.0283639468,-0.3186793327,0.2582259178,-0.2035990208,0.4261792004,0.121919468,-0.2674519122,-0.0920608416,-0.2959534526,0.0668024942,-0.0583799258,0.0240436755,-0.05184827,-0.4311166108,0.0940723345,0.0878733322,-0.0819050968,0.3880839646,0.1310264766,-0.4938380718,0.0326738395,-0.1932912916,-0.0768396333,0.2337358892,0.2223432809,-0.0394751988,0.3085139096,0.2856862545,-0.1450835019,0.215005219,0.371896863,-0.6784276962,0.0342337377,0.1212675273,-0.0517459139,0.0495641567,0.0456896275,-0.1298308671,-0.4243763983,0.2357674539,0.1588111371,0.1562712491,-0.2222517282,0.0366103649,0.6537880898,0.098853685,-0.0671386495,-0.0329095684,-0.1207574233,-0.1402735561,-0.3794024885,-0.3581218123,0.0821614861,0.0994128883,0.0159158129,-0.0206310004,0.2065404505,0.2557740211,-0.1629955173,-0.4880126715,0.3234144151,-0.1654229611,0.1393084675,-0.1825595796,0.1144193336,0.092304498,-0.2055995911,0.4152275622,0.3127865493,0.4920139611,0.0048488448,-0.2875194848,-0.1497833431,0.1791166812,-0.1749925613,-0.0633022413,-0.2371490896,0.0272278432,-0.3438032866,0.2941577137,0.1021298468,0.170202747,0.0991168618,0.1633831412,0.2768983841,0.1209581792,-0.0937047601,-0.2662439346,-0.2355816364,-0.2491426915,-0.1190615818,0.2770529389,0.08191742,-0.1813863516,0.2219793499,0.1601839066,-0.1306650639,0.047877498,-0.0490108468,0.1543429345,0.0672848895,0.0605300479,0.0448768698,-0.0029561894,-0.2299286872,0.2815257907,0.0445580408,-0.2940045893,-0.0055059087,-0.3183043897,0.1401886791,-0.1089272499,-0.0333260968,0.0795842186,-0.2421362251,0.0272102468,0.2224446535,-0.2469774783,0.0793835074,0.1280409992,0.1789474487,-0.0530031063,-0.1701988131,-0.2192520946,-0.1861510575,-0.4006759524,0.0312001035,0.1132157221,-0.2760976851,0.0940038413,0.0172625668,-0.0620532036,-0.0964810401,-0.3340960145,0.1603152156,0.0168768521,0.2541258335,0.1646113992,-0.2480516136,-0.1452465057,-0.1275636703,0.2842240036,0.0121314293,0.0082620513,0.1031867787,-0.069086045,-0.1105932221,-0.1872850507,0.1974332184,-0.0287268702,-0.0949207395,-0.3025699258,0.0238959119,-0.0273413751,-0.02688458,-0.1628063321,-0.115607664,-0.0303755403,0.0266759545,-0.3090045452,-0.5517365336,0.1979116648,-0.2744884789,0.0925374702,-0.1235140115,-0.3144634664,0.1956992,0.1724540144,-0.2606761754,-0.325397104,-0.0667716041,0.0666738674,0.168765381,-0.0827036873,0.3635723293,0.1551755965,-0.1329023093,-0.1831142455,-0.2263168842,0.196622014,-0.2118060142,0.1436378211,0.1152812988,-0.009881625,0.3127546012,0.8620081544,0.1895895004,-0.2615768313,0.2438985407,0.1799746752,0.237004444,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3807","title":"NonMatchingChecksumError in xcopa dataset","comments":"Hi @afcruzs-ms, thanks for opening this separate issue for your problem.\r\n\r\nThe root problem in the other issue (#3792) was a change in the service of Google Drive.\r\n\r\nBut in your case, the `xcopa` dataset is not hosted on Google Drive. Therefore, the root cause should be a different one.\r\n\r\nLet me look at it... ","body":"## Describe the bug\r\nLoading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"xcopa\", \"it\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be loaded correctly.\r\n\r\n## Actual results\r\nFails with:\r\n```python\r\nin verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/cambridgeltl\/xcopa\/archive\/master.zip']\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3, and 1.18.4.dev0\r\n- Platform:\r\n- Python version: 3.8\r\n- PyArrow version:\r\n\r\n","comment_length":55,"text":"NonMatchingChecksumError in xcopa dataset \n ## Describe the bug\r\nLoading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"xcopa\", \"it\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be loaded correctly.\r\n\r\n## Actual results\r\nFails with:\r\n```python\r\nin verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/cambridgeltl\/xcopa\/archive\/master.zip']\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3, and 1.18.4.dev0\r\n- Platform:\r\n- Python version: 3.8\r\n- PyArrow version:\r\n\r\n \n Hi @afcruzs-ms, thanks for opening this separate issue for your problem.\r\n\r\nThe root problem in the other issue (#3792) was a change in the service of Google Drive.\r\n\r\nBut in your case, the `xcopa` dataset is not hosted on Google Drive. Therefore, the root cause should be a different one.\r\n\r\nLet me look at it... 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3807","title":"NonMatchingChecksumError in xcopa dataset","comments":"@afcruzs-ms, I'm not able to reproduce the issue you reported:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n   ...: dataset = load_dataset(\"xcopa\", \"it\")\r\nDownloading builder script: 5.21kB [00:00, 2.75MB\/s]                                                                                                                                                       \r\nDownloading metadata: 28.6kB [00:00, 14.5MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset xcopa\/it (download: 627.09 KiB, generated: 76.43 KiB, post-processed: Unknown size, total: 703.52 KiB) to ...\/.cache\/huggingface\/datasets\/xcopa\/it\/1.0.0\/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6...\r\nDownloading data: 642kB [00:00, 5.42MB\/s]\r\nDataset xcopa downloaded and prepared to ...\/.cache\/huggingface\/datasets\/xcopa\/it\/1.0.0\/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6. Subsequent calls will reuse this data.  \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 733.27it\/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n    test: Dataset({\r\n        features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n        num_rows: 500\r\n    })\r\n    validation: Dataset({\r\n        features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n        num_rows: 100\r\n    })\r\n})\r\n```\r\n\r\nMaybe you have some issue with your cached data... Could you please try to force the redownload of the data?\r\n```python\r\ndataset = load_dataset(\"xcopa\", \"it\", download_mode=\"force_redownload\")\r\n```","body":"## Describe the bug\r\nLoading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"xcopa\", \"it\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be loaded correctly.\r\n\r\n## Actual results\r\nFails with:\r\n```python\r\nin verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/cambridgeltl\/xcopa\/archive\/master.zip']\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3, and 1.18.4.dev0\r\n- Platform:\r\n- Python version: 3.8\r\n- PyArrow version:\r\n\r\n","comment_length":134,"text":"NonMatchingChecksumError in xcopa dataset \n ## Describe the bug\r\nLoading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"xcopa\", \"it\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should be loaded correctly.\r\n\r\n## Actual results\r\nFails with:\r\n```python\r\nin verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/cambridgeltl\/xcopa\/archive\/master.zip']\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3, and 1.18.4.dev0\r\n- Platform:\r\n- Python version: 3.8\r\n- PyArrow version:\r\n\r\n \n @afcruzs-ms, I'm not able to reproduce the issue you reported:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n   ...: dataset = load_dataset(\"xcopa\", \"it\")\r\nDownloading builder script: 5.21kB [00:00, 2.75MB\/s]                                                                                                                                                       \r\nDownloading metadata: 28.6kB [00:00, 14.5MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset xcopa\/it (download: 627.09 KiB, generated: 76.43 KiB, post-processed: Unknown size, total: 703.52 KiB) to ...\/.cache\/huggingface\/datasets\/xcopa\/it\/1.0.0\/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6...\r\nDownloading data: 642kB [00:00, 5.42MB\/s]\r\nDataset xcopa downloaded and prepared to ...\/.cache\/huggingface\/datasets\/xcopa\/it\/1.0.0\/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6. Subsequent calls will reuse this data.  \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 733.27it\/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n    test: Dataset({\r\n        features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n        num_rows: 500\r\n    })\r\n    validation: Dataset({\r\n        features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n        num_rows: 100\r\n    })\r\n})\r\n```\r\n\r\nMaybe you have some issue with your cached data... Could you please try to force the redownload of the data?\r\n```python\r\ndataset = load_dataset(\"xcopa\", \"it\", download_mode=\"force_redownload\")\r\n```","embeddings":[-0.1630279273,0.0196219813,-0.0325447917,0.1558005661,0.0957747549,0.0057951692,-0.0534992442,0.3396128416,0.161243394,0.1424920261,0.0743284374,0.2626977861,0.2402615547,-0.0465792231,-0.2578477263,0.5077952147,0.018493114,0.0516379252,-0.0691113248,-0.0200470146,-0.338170886,0.1675314605,-0.106876567,-0.2577938139,-0.0699750334,0.3129392564,0.1021471024,0.0197592769,-0.0848325863,-0.4102775455,0.5081365108,0.1342600435,-0.1595923752,0.2846820951,-0.0001107674,0.1174912825,0.4348937273,0.0707352161,-0.1852411926,-0.1114563495,-0.6668503881,-0.4195703566,-0.1980729103,-0.1390823871,0.049085293,0.2461088896,-0.0244994853,-0.0086539686,0.0091818711,0.0390244946,0.219125554,0.4661894441,0.0923237726,-0.0671024472,0.2599259913,-0.132312566,-0.0411291793,0.1999804676,0.2612956762,0.0908373743,-0.1941878945,0.1393452138,-0.1561116576,-0.0067775617,-0.035189122,-0.0359178968,0.3015657961,-0.1108345017,0.1600984037,0.2649772465,0.1825530082,-0.3546313345,-0.2549881935,-0.1719811261,-0.2659864426,-0.3805515766,0.5082287192,0.1130217314,-0.1822007596,-0.0390745029,-0.3337227702,0.3940004408,0.1228013411,0.1146053597,0.1019062325,0.0496847667,0.060686212,-0.0057838573,-0.0462537594,-0.0275497288,0.4868713915,-0.2417344302,-0.0734856948,0.1453172565,-0.6300257444,-0.0187825579,-0.0471843146,0.433857739,0.5211066008,0.3476892412,0.1900653541,0.1839284003,-0.0309149884,0.2191929221,-0.0961961076,0.3911276758,-0.0302003585,0.2282932699,0.1320428699,0.2424547523,-0.1278648078,0.0215506069,0.1405365914,-0.2720236778,0.6138055325,0.2848964334,0.1505547911,-0.5430352092,-0.3608473539,0.273250252,0.0497499146,-0.1036083102,0.2006332874,0.291667372,-0.2110144198,0.3041430712,-0.0419300422,-0.054022871,-0.1170181781,-0.0257680472,-0.2434251904,-0.1080276966,-0.2720140517,0.1454720348,0.1451956034,-0.3726763725,0.300280869,0.0459558032,0.3244915605,-0.2155847847,0.2108271718,-0.0337594822,-0.0660990253,0.3783190548,0.0209685005,0.0132468268,0.1291052401,0.071054846,-0.1205466315,0.0651150495,-0.2788103223,-0.1115034223,0.1945991963,0.2823566496,-0.457955718,-0.0661634356,0.0016551346,-0.5158344507,0.3368554711,-0.2236979157,0.0099051315,-0.1620810032,-0.1049050912,-0.2464488,0.0138956681,0.0679682642,0.2439549863,-0.0104046259,0.0495399237,-0.0507252403,0.3463050425,-0.0009121206,0.0020238855,-0.0006273417,-0.300121665,0.0023423047,0.0401542112,-0.5451787114,-0.7369339466,0.0377642363,0.1057405993,0.4106001854,0.0402791612,0.0293904617,-0.1434262693,-0.2682661116,-0.1283813715,-0.0371711515,0.0196168553,0.1885872334,-0.3790684938,-0.251139015,0.160548389,0.0605142787,0.1122564599,0.0171211064,0.1704021394,-0.3401183188,0.1999276429,-0.0398573466,-0.1464656442,0.0066430108,0.3712928593,-0.099843204,0.116477333,-0.0991422907,-0.3164933622,0.4549769461,-0.2247021943,0.0204888061,0.1431206167,-0.1442416608,-0.2842843533,-0.1183460057,-0.2734475434,0.0490887277,0.1623864323,0.3422896266,0.3776508868,-0.1331894845,0.0296696685,0.3447775245,-0.3273167908,0.0948444232,-0.4133950472,0.1628824472,0.1240400299,-0.0393407829,0.0959394574,0.2538575828,0.2497411966,-0.0038448507,-0.1814047992,0.4333854616,0.236221686,0.1082585901,-0.1548808813,0.3445701599,-0.0450414978,-0.2898608148,-0.2100035399,0.5267946124,0.1590740234,-0.0288368445,-0.0378664285,0.4066285491,0.0937268808,0.1982611269,0.0780406073,-0.0627796724,0.2250871956,-0.3693935275,-0.3576959074,-0.0346528329,0.3355721533,-0.0124367252,0.1336476803,0.115209356,0.1392539144,-0.0599095188,0.2191553116,-0.1110502183,-0.0005875975,0.1385692805,0.15322496,0.0270947162,-0.0598817728,0.5775927305,0.3819383979,0.094723925,-0.1162263304,0.1021440253,-0.2558650672,0.0474639945,-0.0305191409,0.1065821797,0.0766427368,0.5775952339,0.1974823922,-0.1472819299,-0.2701243758,-0.1835308522,-0.1193983778,0.2002581656,-0.3634487689,-0.1279532313,-0.251643151,-0.2012146562,-0.2214488834,-0.0732321739,-0.0166904274,-0.4669903815,-0.081107296,0.1946985573,0.0422420651,0.271563679,-0.6162660718,-0.0091979783,-0.0808921531,-0.2440532446,0.023418352,0.1384817809,-0.0638386458,0.0574088618,0.3847287297,-0.0635788664,0.346532315,-0.4222986698,-0.0142082982,-0.3125957251,-0.3614790142,0.1421037316,-0.1112856343,-0.155439809,0.2120660394,-0.1255965531,0.0641670004,-0.2318175435,0.2537807524,-0.1121114939,-0.4089607,0.2661277056,-0.0828237683,-0.1111417636,0.0894846469,-0.1588228643,0.0174814425,-0.2103398889,-0.0565736406,0.1686772555,0.1754659861,0.1384278089,-0.0726519078,-0.0103398487,-0.1047431305,0.1048793867,-0.3309296966,-0.7593181729,0.3389309645,0.1170582101,-0.3031058609,0.0147879943,-0.1892269105,0.4114167392,0.1725504249,-0.3298171461,-0.1705539674,-0.3609612286,0.1544079483,0.2018371075,-0.0166147593,0.1844696403,0.044826109,-0.1298665255,-0.2199916095,-0.1843511462,0.0185372159,-0.0479692146,0.4424383342,-0.017908942,0.0763168633,-0.0220827479,0.2242678553,0.4968603551,0.0235717036,0.231322661,0.0661172643,0.2258277088,-0.0272848755,-0.13613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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3804","title":"Text builder with custom separator line boundaries","comments":"Hi ! Interresting :)\r\n\r\nCould you give more details on what kind of separators you would like to use instead ?","body":"**Is your feature request related to a problem? Please describe.**\r\nThe current [Text](https:\/\/github.com\/huggingface\/datasets\/blob\/207be676bffe9d164740a41a883af6125edef135\/src\/datasets\/packaged_modules\/text\/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. \r\n\r\n**Describe the solution you'd like**\r\n```python\r\n                if self.config.sample_by == \"line\":\r\n                    batch_idx = 0\r\n                    while True:\r\n                        batch = f.read(self.config.chunksize)\r\n                        if not batch:\r\n                            break\r\n                        batch += f.readline()  # finish current line\r\n                        if self.config.custom_newline is None: \r\n                            batch = batch.splitlines(keepends=self.config.keep_linebreaks)\r\n                        else:\r\n                            batch = batch.split(self.config.custom_newline)[:-1]\r\n                        pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema)\r\n                        # Uncomment for debugging (will print the Arrow table size and elements)\r\n                        # logger.warning(f\"pa_table: {pa_table} num rows: {pa_table.num_rows}\")\r\n                        # logger.warning('\\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))\r\n                        yield (file_idx, batch_idx), pa_table\r\n                        batch_idx += 1\r\n```\r\n\r\n**A clear and concise description of what you want to happen.**\r\nCreating the dataset rows with a subset of the `splitlines()` line boundaries.","comment_length":21,"text":"Text builder with custom separator line boundaries \n **Is your feature request related to a problem? Please describe.**\r\nThe current [Text](https:\/\/github.com\/huggingface\/datasets\/blob\/207be676bffe9d164740a41a883af6125edef135\/src\/datasets\/packaged_modules\/text\/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. \r\n\r\n**Describe the solution you'd like**\r\n```python\r\n                if self.config.sample_by == \"line\":\r\n                    batch_idx = 0\r\n                    while True:\r\n                        batch = f.read(self.config.chunksize)\r\n                        if not batch:\r\n                            break\r\n                        batch += f.readline()  # finish current line\r\n                        if self.config.custom_newline is None: \r\n                            batch = batch.splitlines(keepends=self.config.keep_linebreaks)\r\n                        else:\r\n                            batch = batch.split(self.config.custom_newline)[:-1]\r\n                        pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema)\r\n                        # Uncomment for debugging (will print the Arrow table size and elements)\r\n                        # logger.warning(f\"pa_table: {pa_table} num rows: {pa_table.num_rows}\")\r\n                        # logger.warning('\\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))\r\n                        yield (file_idx, batch_idx), pa_table\r\n                        batch_idx += 1\r\n```\r\n\r\n**A clear and concise description of what you want to happen.**\r\nCreating the dataset rows with a subset of the `splitlines()` line boundaries. \n Hi ! Interresting :)\r\n\r\nCould you give more details on what kind of separators you would like to use instead 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3804","title":"Text builder with custom separator line boundaries","comments":"Ok I see, maybe there can be a `sep` parameter to allow users to specify what line\/paragraph separator they'd like to use","body":"**Is your feature request related to a problem? Please describe.**\r\nThe current [Text](https:\/\/github.com\/huggingface\/datasets\/blob\/207be676bffe9d164740a41a883af6125edef135\/src\/datasets\/packaged_modules\/text\/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. \r\n\r\n**Describe the solution you'd like**\r\n```python\r\n                if self.config.sample_by == \"line\":\r\n                    batch_idx = 0\r\n                    while True:\r\n                        batch = f.read(self.config.chunksize)\r\n                        if not batch:\r\n                            break\r\n                        batch += f.readline()  # finish current line\r\n                        if self.config.custom_newline is None: \r\n                            batch = batch.splitlines(keepends=self.config.keep_linebreaks)\r\n                        else:\r\n                            batch = batch.split(self.config.custom_newline)[:-1]\r\n                        pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema)\r\n                        # Uncomment for debugging (will print the Arrow table size and elements)\r\n                        # logger.warning(f\"pa_table: {pa_table} num rows: {pa_table.num_rows}\")\r\n                        # logger.warning('\\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))\r\n                        yield (file_idx, batch_idx), pa_table\r\n                        batch_idx += 1\r\n```\r\n\r\n**A clear and concise description of what you want to happen.**\r\nCreating the dataset rows with a subset of the `splitlines()` line boundaries.","comment_length":22,"text":"Text builder with custom separator line boundaries \n **Is your feature request related to a problem? Please describe.**\r\nThe current [Text](https:\/\/github.com\/huggingface\/datasets\/blob\/207be676bffe9d164740a41a883af6125edef135\/src\/datasets\/packaged_modules\/text\/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. \r\n\r\n**Describe the solution you'd like**\r\n```python\r\n                if self.config.sample_by == \"line\":\r\n                    batch_idx = 0\r\n                    while True:\r\n                        batch = f.read(self.config.chunksize)\r\n                        if not batch:\r\n                            break\r\n                        batch += f.readline()  # finish current line\r\n                        if self.config.custom_newline is None: \r\n                            batch = batch.splitlines(keepends=self.config.keep_linebreaks)\r\n                        else:\r\n                            batch = batch.split(self.config.custom_newline)[:-1]\r\n                        pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema)\r\n                        # Uncomment for debugging (will print the Arrow table size and elements)\r\n                        # logger.warning(f\"pa_table: {pa_table} num rows: {pa_table.num_rows}\")\r\n                        # logger.warning('\\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))\r\n                        yield (file_idx, batch_idx), pa_table\r\n                        batch_idx += 1\r\n```\r\n\r\n**A clear and concise description of what you want to happen.**\r\nCreating the dataset rows with a subset of the `splitlines()` line boundaries. \n Ok I see, maybe there can be a `sep` parameter to allow users to specify what line\/paragraph separator they'd like to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3804","title":"Text builder with custom separator line boundaries","comments":"Thanks for requesting this enhancement. We have recently found a somehow related issue with another dataset:\r\n- #3704\r\n\r\nLet me make a PR proposal.","body":"**Is your feature request related to a problem? Please describe.**\r\nThe current [Text](https:\/\/github.com\/huggingface\/datasets\/blob\/207be676bffe9d164740a41a883af6125edef135\/src\/datasets\/packaged_modules\/text\/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. \r\n\r\n**Describe the solution you'd like**\r\n```python\r\n                if self.config.sample_by == \"line\":\r\n                    batch_idx = 0\r\n                    while True:\r\n                        batch = f.read(self.config.chunksize)\r\n                        if not batch:\r\n                            break\r\n                        batch += f.readline()  # finish current line\r\n                        if self.config.custom_newline is None: \r\n                            batch = batch.splitlines(keepends=self.config.keep_linebreaks)\r\n                        else:\r\n                            batch = batch.split(self.config.custom_newline)[:-1]\r\n                        pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema)\r\n                        # Uncomment for debugging (will print the Arrow table size and elements)\r\n                        # logger.warning(f\"pa_table: {pa_table} num rows: {pa_table.num_rows}\")\r\n                        # logger.warning('\\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))\r\n                        yield (file_idx, batch_idx), pa_table\r\n                        batch_idx += 1\r\n```\r\n\r\n**A clear and concise description of what you want to happen.**\r\nCreating the dataset rows with a subset of the `splitlines()` line boundaries.","comment_length":24,"text":"Text builder with custom separator line boundaries \n **Is your feature request related to a problem? Please describe.**\r\nThe current [Text](https:\/\/github.com\/huggingface\/datasets\/blob\/207be676bffe9d164740a41a883af6125edef135\/src\/datasets\/packaged_modules\/text\/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. \r\n\r\n**Describe the solution you'd like**\r\n```python\r\n                if self.config.sample_by == \"line\":\r\n                    batch_idx = 0\r\n                    while True:\r\n                        batch = f.read(self.config.chunksize)\r\n                        if not batch:\r\n                            break\r\n                        batch += f.readline()  # finish current line\r\n                        if self.config.custom_newline is None: \r\n                            batch = batch.splitlines(keepends=self.config.keep_linebreaks)\r\n                        else:\r\n                            batch = batch.split(self.config.custom_newline)[:-1]\r\n                        pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema)\r\n                        # Uncomment for debugging (will print the Arrow table size and elements)\r\n                        # logger.warning(f\"pa_table: {pa_table} num rows: {pa_table.num_rows}\")\r\n                        # logger.warning('\\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))\r\n                        yield (file_idx, batch_idx), pa_table\r\n                        batch_idx += 1\r\n```\r\n\r\n**A clear and concise description of what you want to happen.**\r\nCreating the dataset rows with a subset of the `splitlines()` line boundaries. \n Thanks for requesting this enhancement. We have recently found a somehow related issue with another dataset:\r\n- #3704\r\n\r\nLet me make a PR proposal.","embeddings":[-0.4503132999,0.0747966692,-0.1288340539,0.210461244,-0.1013786718,-0.1899402589,0.6217510104,0.223478362,0.0198683292,0.1670013815,0.065770641,0.143478334,-0.056949541,0.3055791557,0.002609235,-0.2434880137,-0.2827088833,0.2772157788,0.1772620827,0.2359316498,-0.2001570463,0.1065785959,-0.1743898094,-0.1252592951,-0.4215911627,-0.0156589914,-0.1075532213,-0.070725508,-0.0852708817,-0.256759733,0.1928037405,0.3010949194,-0.2299875468,0.430616945,-0.000096046,-0.1721263826,-0.2392147928,-0.0833008215,-0.2608181238,-0.2584207952,-0.1408493966,-0.4668595195,-0.0657953694,-0.1172913387,-0.2218179703,0.0509113893,-0.416074574,0.0028372505,0.2767480314,0.3362383842,0.3173391819,0.1585382819,-0.1395399272,-0.0423304029,0.4269616604,0.2250970006,-0.2683517933,-0.1177480444,0.0758822039,0.0283109713,-0.2942317724,0.3081378639,-0.1828732342,0.0808146894,0.1060440391,0.2269663513,-0.0707489476,-0.0007671482,0.1320342869,0.4570732117,0.2524113059,-0.2099049687,-0.1868516654,-0.5941711664,-0.0544026345,-0.3425969183,0.0383554511,0.2796129286,-0.2877872586,0.0532040708,-0.1744436771,-0.0608927682,-0.3819038868,-0.1624467969,-0.3151251972,0.0961749256,-0.2584426403,-0.1666646451,0.0774431154,0.0204651579,0.1514901072,-0.2565640807,0.0978810638,-0.0068050232,-0.0984373242,-0.0726691186,-0.1624204963,-0.2292208374,0.1241187751,0.1794795841,0.1801635027,0.0804708079,0.1959436387,-0.066999495,0.0582648031,0.0583137795,0.2712084353,0.1010802016,0.1700243652,0.3145928085,-0.0751638263,0.0013062708,-0.1068171859,-0.2777060866,0.1137463301,0.0123042287,0.1806453913,0.0163177513,-0.2668167353,0.1544482261,0.0951211676,-0.1067079529,0.001215937,0.0291098654,0.1699145734,-0.0984050035,-0.1137346998,-0.0848719105,-0.0873561651,-0.3259405494,-0.1712639183,-0.2313369066,-0.2053322643,0.1074969769,0.2783008814,-0.1235444769,-0.0523581468,0.2151487619,0.0323451944,-0.167509675,-0.1942752451,-0.2107167989,0.1764559895,0.0250365566,-0.0246795323,-0.1045438349,-0.0649843365,-0.0816358477,-0.0780768394,0.1871975213,0.0205197372,-0.1049850732,-0.2959202528,0.3364584446,-0.0194368884,0.0581445172,-0.0713791326,0.2770932317,0.4173926413,0.0800593719,0.2124153674,-0.0447965227,-0.331936866,-0.1318040788,0.1225213334,0.3104378879,-0.5823680758,-0.1604388058,-0.028523609,-0.0851991773,0.1969326735,0.2706176043,0.0590300635,0.2355438918,-0.0548669696,0.4100705087,0.2378142476,0.2568545341,-0.3561355174,0.3386347294,-0.002163151,0.1555816084,0.1728299856,-0.3054493368,0.6606842279,0.1149242073,0.2502601743,0.2612507045,0.1006112024,0.1078604534,-0.0808529034,-0.1648234576,-0.0192284565,0.1754954755,0.0389286578,-0.2970481217,-0.0394588932,-0.3648352623,0.2033339739,-0.1742991954,0.2555994093,0.0378709212,0.233272776,0.384727329,0.0157623366,-0.6501405835,-0.3218587041,0.0160228275,0.0509058125,0.1482609659,-0.1346403509,-0.2081367224,-0.1547171474,0.0150548052,-0.2668060958,-0.1678608805,0.2617924213,0.0704242587,0.0568113774,-0.2058555931,-0.2500184178,0.0871575326,-0.0944442898,0.2797544003,0.0738236159,0.2781914473,0.1051914245,-0.1759432852,-0.0275492053,0.6358810663,0.0061069042,0.0893457755,0.0251987129,0.2427850068,0.0425506011,-0.1191496477,-0.3129196465,-0.151259467,0.1671086699,0.0483945571,0.123519361,-0.100859724,-0.0999943614,0.0112664849,-0.2103914917,0.4641409218,-0.1178229451,0.3721243441,0.0056626787,-0.0773553401,0.0983833075,-0.3224308491,0.2388189435,-0.4719054103,0.2728684247,0.0464552119,0.3311283886,0.1743891388,-0.0801160559,0.3212556541,0.3898850381,-0.0095736589,0.1456764042,0.1718281955,-0.1322624832,0.0392074548,-0.1051184759,0.3313700855,0.0755306482,0.3149421215,0.0839982778,0.1368942261,-0.0685120597,-0.2613492608,0.33875072,0.0771786496,-0.2015601397,0.5223783851,0.2033178657,-0.0344460011,-0.3635410964,0.0059240814,-0.1225245073,-0.0140965711,-0.2295744419,-0.089075394,-0.2525794208,-0.2830494642,-0.3814736903,-0.2431195229,-0.1124623567,-0.0127149979,0.2188562006,-0.0475051962,-0.122694917,0.0922057107,0.2214662284,0.4175426066,-0.0993995443,-0.0161718093,0.0713793561,-0.2438623756,-0.0138112213,0.2630322576,0.0822929442,0.2670971155,0.3506157696,-0.0479389578,0.127702117,-0.1708741039,-0.1123291999,0.1972999126,-0.0963989198,0.0081846081,0.488471806,-0.1206296533,0.345754534,-0.2653593123,0.0419109613,-0.0667347908,-0.3116084635,-0.1618133336,-0.0521857403,-0.062033385,-0.2484727651,-0.2569599748,-0.121298708,-0.4216471314,0.4431913793,0.0485584512,0.2694498897,-0.1073792279,-0.0900108442,0.1225548759,-0.0568393692,-0.0344274528,-0.1762218326,-0.1278197765,0.104727149,-0.3778494895,-0.1156516001,0.0512747616,-0.0888803899,0.1113872454,0.0704650953,-0.2511370182,0.3246637881,-0.2252771556,0.4000568092,0.0825531334,0.1319589019,0.2089789957,0.1130009592,-0.1063686311,-0.1593797207,-0.0974484608,0.2232804447,-0.1906406879,-0.1244892478,-0.1843601614,0.4161757231,0.0551033206,0.3614323139,0.1238085032,0.0818406045,0.0759557933,-0.2364010513,0.1348820329,-0.189860329,-0.297033906,-0.0018105977,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"Same issue with `dataset = load_dataset(\"dbpedia_14\")`\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":16,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n Same issue with `dataset = load_dataset(\"dbpedia_14\")`\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']","embeddings":[-0.3649031818,0.2177053988,-0.1216041893,0.3296720386,0.1562201977,-0.0154086202,0.2091420889,0.3848864734,0.0918230563,0.039236147,-0.033991836,0.0775731355,0.0966648161,0.196764037,-0.0677060559,0.1878269017,0.2903296053,0.0128191179,-0.2121760845,-0.1110438704,-0.2556401789,0.0795855075,0.0540445559,-0.2795997262,-0.206144467,0.1816610545,0.0376119539,-0.2781576216,-0.21940054,-0.2239097208,0.4459527433,0.2312393337,0.0221346505,0.332960695,-0.0001146543,0.1809764951,0.2749058008,-0.0818748623,-0.1815721542,0.0576612279,-0.4351557791,-0.242260322,-0.1929508597,-0.1235463172,-0.127949819,0.0748817325,-0.0712577775,-0.1821267009,0.0285775643,0.2947609723,0.2457018048,0.2427361459,0.1084377095,-0.0278314073,0.1827470511,-0.0666249171,0.0569024757,0.0922835246,0.040926639,0.0948611945,-0.216037184,0.2534382343,-0.0407755375,-0.1019151136,0.1129590794,-0.329767853,-0.0108674765,-0.241048038,0.2879576385,0.4076489806,0.6579324007,0.0070104608,-0.3093769848,0.0387543589,-0.1418043226,0.0298879966,0.3959032297,0.1147580966,0.0535104647,0.0862997025,-0.3739296198,0.0844848976,0.0329431072,0.144281745,-0.1206048429,0.3449738622,0.0611071959,-0.0859795213,0.0656653643,-0.0771891475,0.5353932381,-0.2648794353,-0.3948878646,0.1479445994,-0.3148167133,-0.0398625322,-0.0611164719,0.5671243072,0.2706227899,0.1746229082,-0.1588137001,0.2994798422,-0.2750315368,0.0954769403,0.0121348314,0.0129723968,0.0373501889,0.3075268269,0.2601932585,0.1464447379,-0.0413884372,0.1546759009,0.1109937206,-0.3335037827,0.2497601062,-0.0206517298,-0.0673597082,-0.2686177194,-0.2288763076,0.3417773247,-0.0001855676,-0.3383468688,0.182690382,0.3188614547,-0.2053700686,0.2338353395,-0.2201315016,0.1664161235,-0.1131456718,-0.1544615626,-0.2869800031,0.0147218686,-0.0550081804,-0.1112675443,0.2444157153,-0.150823161,0.3748755455,-0.0179977734,0.0475723445,-0.2206418812,-0.0010539189,-0.1582076848,-0.0572836958,0.2545310259,-0.0531445406,0.1298472285,0.0727794468,-0.1604267359,0.0026081593,0.1967170537,-0.2892285585,-0.1030647457,-0.2647084296,0.2805671692,-0.4615334868,-0.1673068702,-0.263793081,-0.1185105741,0.2331908196,-0.3584415615,0.0120858746,-0.1307672411,-0.2124181688,-0.1538970768,0.1688150764,0.2531222105,-0.4453168511,0.08306811,-0.1450184435,-0.2161609679,0.1936572343,0.0866254941,-0.1379619688,0.303845495,-0.3273011446,-0.0220408048,0.3407531679,-0.4240174592,-0.622723341,-0.071707502,0.2478409261,0.2293115556,0.218871966,0.0245557111,0.1278188527,0.0889293477,-0.1366317272,0.000322144,0.2668471038,0.0986519456,-0.2074389607,-0.0505462587,0.2802022994,0.2321518213,0.1989942342,-0.1253507435,0.3417197466,-0.0179804973,0.3225956261,-0.2155803889,-0.0248203613,0.1675180197,0.349280566,-0.0511828326,0.0367113613,-0.1486386508,-0.2442848235,0.2688598335,-0.1106801704,0.0441485122,-0.0485202149,-0.0611571223,-0.2876093984,-0.2925641239,-0.5006023049,-0.0853026807,0.1696051061,0.3580898345,-0.0344224572,0.0460707285,0.0287741013,0.1205475405,-0.4129573405,-0.0433143862,-0.0909730271,0.3206452131,-0.1181434318,0.0078688646,0.1174002588,0.1525287032,0.0791538134,-0.0500100888,-0.198211059,0.3576850593,0.500259459,0.1929211617,0.1882385612,0.1937176436,0.0526381508,-0.4197148383,-0.0231960285,0.1176178381,0.0387948789,-0.2102441788,-0.197171405,0.3995685577,-0.0320381187,-0.083179161,-0.105879575,-0.0247243848,0.4654826522,0.0004974558,-0.0916764364,-0.2901568115,0.1740641743,0.1931730509,0.0682362244,0.2972057462,-0.0790610239,-0.1444645375,0.4361299574,-0.0851717591,-0.0075316075,0.0083236974,0.175778687,-0.0206256285,0.1431410909,0.4108365178,0.2403099835,0.0946326256,0.1471965164,0.0790064856,-0.1075948849,-0.1215286627,0.0327912308,-0.1278116405,0.2749180198,0.4040746093,-0.077067256,-0.1183871478,-0.5369077325,0.1161508709,0.0795434415,0.2669075727,-0.387856245,-0.2201352268,-0.2954816818,-0.1838358939,-0.3417542279,-0.0592276491,-0.3989603221,-0.408798188,-0.0720127374,0.0878604203,0.1517202705,0.285507232,-0.3101961911,-0.044495143,-0.114189595,-0.0902250335,0.0242443066,-0.0026127438,-0.100230664,0.1211646348,0.5357841849,0.1033028439,0.2642094195,-0.3780665398,0.1168261692,-0.3832770586,-0.3633136749,-0.0476146005,-0.1077932119,0.2839699388,0.0693664849,0.2792648077,-0.2618949711,-0.2803566754,0.0149791222,0.2981514931,-0.3457280099,0.1085592732,-0.1944727302,-0.1510723829,0.2430066615,-0.189678207,-0.1954576969,-0.197966665,-0.1396528184,0.2346077859,0.141179353,-0.1097821146,-0.0566728599,0.1576398164,0.0274013057,0.188432619,-0.3134538531,-0.5128033757,0.5660220981,-0.0861947015,-0.2589718699,0.1475934237,0.0026126783,0.395902276,-0.0370110944,-0.5061171055,-0.0590615347,-0.2451228946,0.2143708915,0.344083041,0.035833057,0.2615751624,0.1069056466,0.0754872859,-0.2287044674,-0.2131374627,0.009654711,-0.1612936556,0.5290948153,-0.2973439991,0.2049855292,-0.154060632,0.6229285598,0.4757351279,0.1157682762,0.314706862,0.1023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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"I think this is a side-effect of #3787. The checksums won't match because the URLs have changed. @rafikg @Y0mingZhang, while this is fixed, maybe you can load the datasets as such:\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", ignore_verifications=True)`\r\n`dataset = load_dataset(\"dbpedia_14\", ignore_verifications=True)`\r\n\r\nThis will, most probably, skip the verifications and integrity checks listed [here](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#integrity-verifications)","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":53,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n I think this is a side-effect of #3787. The checksums won't match because the URLs have changed. @rafikg @Y0mingZhang, while this is fixed, maybe you can load the datasets as such:\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", ignore_verifications=True)`\r\n`dataset = load_dataset(\"dbpedia_14\", ignore_verifications=True)`\r\n\r\nThis will, most probably, skip the verifications and integrity checks listed [here](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#integrity-verifications)","embeddings":[-0.2045925558,0.0499819182,-0.0409406908,0.3436925411,0.3134644628,-0.0442943946,0.131984368,0.3513386548,0.1909446269,0.1721865386,-0.0847846344,0.0363088101,0.2098383456,0.3477244973,0.0919781625,-0.1228857115,0.1445520073,-0.1180749387,-0.1569823772,-0.0561240353,-0.2454429269,0.2008165121,0.0101413596,-0.149865225,-0.161554724,0.149700284,-0.0422698781,0.0061745076,-0.2791846097,-0.3509845436,0.2837846875,0.25919649,0.0221888721,0.5167739391,-0.0001059076,0.1817240566,0.2788508534,-0.0544550605,-0.2185789794,0.0053355736,-0.1336797774,-0.2181914002,-0.0988242626,-0.1435140818,-0.1919460148,-0.0384768061,-0.0646072403,0.0666943938,0.2925494015,0.3108711243,0.2933385372,0.4503675997,0.1255127043,-0.0889574066,0.0439191759,-0.1734417081,-0.0084535666,0.2341273874,-0.1377679855,0.088231802,-0.1138206497,0.3634387553,-0.0031579263,-0.1495631337,0.0591812581,-0.1284624785,-0.0705365911,-0.2126499861,0.2750130892,0.3576304018,0.3475839496,0.0603179112,-0.3010663688,-0.115901053,-0.1458383203,-0.1816123426,0.4123067856,0.0660479516,0.0504796617,0.1404728442,-0.2799022794,0.0100352401,0.0810057521,0.0945101976,-0.1979288161,0.1856493801,-0.0088503435,-0.0758886635,0.2972292602,-0.0708639696,0.0819238648,-0.0453968197,-0.2888209522,0.0555689,-0.0609603375,-0.0368573219,0.2151448131,0.573831439,0.4007830322,0.1417341679,-0.1439328939,0.350808233,-0.311302036,0.0235247742,-0.0490990952,0.2295147479,-0.002649189,0.1744662672,0.4023959041,0.4902513921,-0.0269677322,0.0950661004,0.1599118412,-0.3208345473,-0.0654280782,0.0728862882,-0.0062204013,-0.2769302726,-0.1062520891,0.2125696242,0.0340953618,-0.1504904032,0.0487226397,0.4110956192,-0.1610012352,0.0875271633,-0.0169916842,0.2025012225,-0.2712935507,-0.1537721902,-0.2571966648,-0.0528937131,-0.1375508755,0.2296412289,0.2375564128,-0.1699535549,0.4323852062,0.0009103536,0.1297809035,-0.1600062996,-0.0894814953,-0.0671902746,0.0484970398,0.3409519196,0.0448330566,0.0326433256,0.1413496584,-0.3650875986,-0.0537731387,-0.0361864381,-0.2702430189,-0.238763541,-0.1221855208,0.2776730061,-0.3826920986,-0.1581919789,-0.3471926749,0.0815515295,0.0740454346,-0.2356493026,0.0212731808,-0.0552965328,-0.4379697442,-0.0690521002,0.297308147,0.4789752066,-0.1768089533,-0.1351740211,0.1087995917,-0.2522638142,-0.1322871447,0.2260910869,-0.1989189535,0.2180224508,-0.3373156786,0.0570313521,0.0185211487,-0.4569880366,-0.4088696837,-0.0932645425,0.0531147979,0.2925185561,0.0464234464,-0.2249037623,0.08042676,0.0488745421,-0.0867388397,0.1501296163,0.2505802512,0.1453009844,-0.4023270309,-0.1753377169,0.193177551,0.1485951394,0.1321051419,-0.0694797859,0.1069655269,-0.184757784,0.2373342961,-0.2791364789,-0.0247458797,0.2078899294,0.362611711,0.1107613891,0.0376187451,-0.0513957627,-0.3563480079,0.2752535939,-0.0532931685,0.4054986835,-0.1176222563,-0.2050201446,-0.2304691672,-0.197217539,-0.4626151025,-0.3365754783,0.2169029862,0.1790505946,0.0814639404,0.2475305945,0.0312343892,0.118984893,-0.3311958909,0.184115693,-0.2519909739,0.3930297792,-0.2363093495,0.1076006889,0.0512530729,0.1129641309,0.1015025154,-0.134288922,-0.0710751042,0.4831623733,0.261512816,0.1237619296,0.2876464128,0.3033978045,0.2425059974,-0.3200928271,-0.1311351955,0.0922806934,0.0067266799,0.04744564,-0.3048242331,0.596113205,-0.0612141564,0.0148725826,0.1740209609,-0.0298646241,0.5489341021,-0.0555429384,-0.2239997685,-0.3151458502,0.1672940403,0.0672291666,-0.116835773,0.1503597498,-0.2175281942,0.0019413875,0.4142994583,-0.1108246148,-0.0675584674,0.0943153501,0.1372823119,-0.0689139292,0.0870403051,0.3184420466,0.1427507401,0.1674799323,0.0931454897,0.2329990268,-0.1294826567,-0.1769054532,0.1441845298,-0.1496903002,0.1218137965,0.3058904111,-0.001751437,-0.0859408528,-0.6696029902,0.1511677206,0.0378383547,0.1872385293,-0.3923247457,-0.0262117665,-0.230526492,-0.1947762817,-0.3927036822,-0.075709492,-0.5519011021,-0.3827620745,0.090218693,0.1249615774,-0.1205915213,0.3284860253,-0.1108796522,0.1560670733,-0.0401973091,-0.0615164451,-0.0440316312,-0.1102376133,-0.1202027649,0.0723405033,0.5180629492,0.1345635504,0.3707781136,-0.4430663586,-0.0474795811,-0.5338808894,-0.5941340327,0.0855122954,-0.2172038555,0.3467795849,0.0718879998,0.3222483099,-0.1653076708,-0.2213834971,0.2801699936,-0.0119657004,-0.2537445724,0.0600436851,-0.0828090087,-0.0795691684,-0.0310913119,-0.287846297,-0.0753914714,-0.1612484604,0.1451287419,0.155924201,0.1318143457,0.2021944076,0.1026339754,0.1391443014,-0.1262352914,0.1383771151,-0.4692171514,-0.5948307514,0.3494369388,-0.3374530971,-0.429102093,0.1259705126,0.0387248434,0.3264408708,-0.105073221,-0.5396084785,-0.4145404994,-0.1647656262,0.3804258704,0.2890500724,0.1039528698,0.3047821522,0.0348582081,-0.106247291,-0.1404030919,-0.2749331892,0.0772826076,-0.162651211,0.3549124897,-0.3090600967,0.2292613834,-0.0377389975,0.6058033705,0.2294945419,0.1801404953,0.348363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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"Hi! Installing the `datasets` package from master (`pip install git+https:\/\/github.com\/huggingface\/datasets.git`) and then redownloading the datasets with `download_mode` set to `force_redownload` (e.g. `dataset = load_dataset(\"dbpedia_14\", download_mode=\"force_redownload\")`) should fix the issue.","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":29,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n Hi! Installing the `datasets` package from master (`pip install git+https:\/\/github.com\/huggingface\/datasets.git`) and then redownloading the datasets with `download_mode` set to `force_redownload` (e.g. `dataset = load_dataset(\"dbpedia_14\", download_mode=\"force_redownload\")`) should fix the issue.","embeddings":[-0.3843277097,0.1996201873,-0.114631176,0.3184938133,0.3418924212,-0.0549485609,0.2000973523,0.4718809128,0.2195709646,0.1365334541,-0.0418280326,0.1422181278,0.090307951,0.2628117502,-0.101556614,0.0394687802,0.1799624711,-0.0340432189,-0.2078187019,-0.076861307,-0.1915391833,0.1063364446,0.0344516374,-0.2217378467,-0.0929522961,0.0861785337,0.1301286966,-0.22223185,-0.2963681221,-0.2892341912,0.3145431876,0.1696397215,-0.0099380864,0.3620096147,-0.0001084645,0.1942800432,0.2652548254,-0.04762353,-0.14843826,0.0197356548,-0.2634498477,-0.276724726,-0.1532461196,-0.2055436373,-0.164462477,0.054340817,-0.066910401,-0.1006299406,0.1104783341,0.3617455363,0.2780793011,0.2687546313,0.0609445237,-0.0268979166,0.2514066696,-0.1411157101,-0.0722080395,0.102584295,0.0252454765,0.0471415035,-0.1745987535,0.3599443436,-0.0477110408,-0.1480884403,-0.0467779674,-0.2324600518,0.0243943613,-0.2691167295,0.3035478294,0.415912956,0.5679681301,-0.0935106054,-0.2494804561,-0.00435293,-0.0871255994,-0.0179787017,0.4270202518,0.1005474627,-0.0197124369,0.108871229,-0.1632165909,0.0853787884,0.0618807711,0.1550645381,-0.1376384199,0.4511947632,0.0409423858,-0.081325531,0.1466952562,-0.1026488245,0.3637425601,-0.0867576674,-0.3400505185,0.1782690436,-0.2492973655,-0.0034740705,0.1027373523,0.4441790283,0.2588038743,0.1230337843,-0.1921146065,0.3690150082,-0.2198885977,0.1032392457,0.0359564461,0.0342206284,0.0171559248,0.2700173557,0.3391151726,0.2140996009,-0.0472620055,0.0868132561,0.0238354728,-0.3039549589,0.1656917483,0.0427195542,-0.0595516637,-0.2528949976,-0.1355082542,0.2907100022,0.0695382357,-0.2092569321,0.158447355,0.3685164452,-0.2386262417,0.1860366464,-0.123336792,0.2094331831,-0.2423953712,-0.0541642159,-0.2264983952,-0.006935704,-0.133471787,-0.1894687563,0.2906647623,-0.1472396255,0.398457408,0.0116374455,0.1061040685,-0.1797090322,0.0394115411,-0.1450636983,-0.0774658844,0.3287844956,-0.0111268526,0.1284163296,0.147138536,-0.268966049,-0.049560044,0.1300310045,-0.2608909011,-0.0888917819,-0.2755616903,0.2991391718,-0.4207890928,-0.101716876,-0.2454640567,-0.0766066909,0.2398968786,-0.3894563913,0.040630769,-0.1682789326,-0.1784972548,-0.1528404951,0.2252547145,0.2241907418,-0.3964586258,-0.0315975323,-0.1463179886,-0.3034137785,0.190030694,0.1561888456,-0.1573348343,0.2516009212,-0.2913599014,0.0918926373,0.3398219347,-0.3996957839,-0.6684728861,-0.0990196243,0.1540710479,0.2284639925,0.1357443184,0.0018617646,0.1128628701,0.1486220956,-0.1454502493,0.0945101231,0.2838029861,0.1412256807,-0.3691073358,-0.0740118921,0.2144915909,0.2248090655,0.2869903743,-0.1816122234,0.3549657762,0.0223875754,0.2754331529,-0.2505272627,0.0659253448,0.1876997948,0.4286672473,0.0824068859,0.0638337582,-0.1808140427,-0.1734494418,0.2793299556,-0.182671383,0.1876136065,-0.2149441391,-0.1493430138,-0.2822322845,-0.2316586673,-0.4474293292,-0.123504214,0.2271803916,0.2625791132,-0.0763248354,0.2321339995,0.1237298697,0.0623152144,-0.3883274198,-0.0347068794,-0.0381856225,0.3368140459,-0.2364786714,-0.0294524003,0.0083059426,0.1850098968,-0.0096317753,-0.0642379522,-0.1920634806,0.3274856508,0.37438187,0.1920054555,0.1116970032,0.1408528388,0.0946310088,-0.3516256213,-0.0520003475,0.1021595001,0.0449458286,-0.1429371834,-0.3604608774,0.4549181759,-0.0137830414,-0.0226708725,-0.0332517438,0.0190723576,0.5954867601,-0.0272519849,-0.1094342619,-0.2923296094,0.182341665,0.1124070734,-0.0006337258,0.2079159617,-0.1104111448,-0.09569446,0.5075257421,-0.0016574045,0.1460801959,0.0597453043,0.1969854832,0.0265677217,0.150878042,0.3506385088,0.146579355,0.1382732987,0.1861891598,0.1440740824,-0.1056072637,-0.1167043,0.063647598,-0.1386869252,0.2452029288,0.4381197691,-0.0708657876,-0.1806443781,-0.5865635872,0.1104297489,0.062630482,0.3339016438,-0.274920553,-0.1332771927,-0.236347869,-0.1486181468,-0.2926533818,-0.0897348821,-0.4221866131,-0.3990253806,0.0064316848,0.0751575679,0.0113362903,0.3187352419,-0.2051644474,-0.0544299707,-0.0317235626,-0.1687525958,0.0801389143,-0.1552423835,-0.1114950627,0.1352194846,0.5495534539,0.1693426669,0.3197667599,-0.3259675801,0.0363656841,-0.3678114116,-0.523037374,0.0587065741,-0.1736362725,0.3409510255,0.1046387181,0.2746558785,-0.2745894194,-0.2693358362,0.1489979476,0.1421332657,-0.2392741591,0.183419764,-0.1712827832,-0.1189065203,0.0832990855,-0.3363425136,-0.2014178783,-0.2744336724,-0.0413240045,0.2167356014,0.1784880757,-0.0176531039,0.1119576767,0.1595247686,0.0263031833,0.1731659025,-0.3424067497,-0.493647635,0.5090579391,-0.1653393656,-0.3562130034,0.1753043532,0.0122891208,0.4438855648,0.0354995579,-0.5916407108,-0.1255012155,-0.1875085086,0.2895798087,0.288526088,0.0759868175,0.2897721231,0.1140789688,-0.0101583367,-0.2763186693,-0.0379831828,0.0185766481,-0.1822774559,0.4818850756,-0.3201198876,0.2641133666,-0.016828198,0.6856341362,0.3063667417,0.2493686527,0.2688688934,0.0281610209,0.4016679227,-0.2479357123,-0.2423769683,0.0629695803,-0.1685070097,-0.0092684487,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"Hi @rafikg and @Y0mingZhang, thanks for reporting.\r\n\r\nIndeed it seems that Google Drive changed their way to access their data files. We have recently handled that change:\r\n- #3787\r\n\r\nbut it will be accessible to users only in our next release of the `datasets` version.\r\n- Note that our latest release (version 1.18.3) was made before this fix: https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.18.3\r\n\r\nIn the meantime, as @mariosasko explained, you can incorporate this \"fix\" by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, you should force the redownload of the data (before the fix, you are just downloading\/caching the virus scan warning page, instead of the data file):\r\n```shell\r\ndata = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", download_mode=\"force_redownload\")","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":117,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n Hi @rafikg and @Y0mingZhang, thanks for reporting.\r\n\r\nIndeed it seems that Google Drive changed their way to access their data files. We have recently handled that change:\r\n- #3787\r\n\r\nbut it will be accessible to users only in our next release of the `datasets` version.\r\n- Note that our latest release (version 1.18.3) was made before this fix: https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.18.3\r\n\r\nIn the meantime, as @mariosasko explained, you can incorporate this \"fix\" by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, you should force the redownload of the data (before the fix, you are just downloading\/caching the virus scan warning page, instead of the data file):\r\n```shell\r\ndata = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", download_mode=\"force_redownload\")","embeddings":[-0.280697614,0.1842426509,-0.0431708023,0.2063536942,0.3406106532,0.1340316981,0.2423328459,0.301430732,0.2834566534,0.220218271,-0.0751205012,-0.1180784255,0.0470717065,0.4092030525,-0.1184176505,0.0272027571,0.1333746463,-0.1514434665,0.0380047373,0.0343718268,-0.3451867104,0.1651106924,-0.0846882537,-0.1403912008,-0.0485461205,0.0436250344,0.0023895935,-0.0327417739,-0.1069308594,-0.5085651278,0.2430501878,0.0947880596,0.138913244,0.4133019745,-0.0001084545,0.2546436489,0.4067674279,-0.0624668561,-0.1534775198,0.018075604,-0.0347886495,-0.0910984129,-0.1902339011,-0.2227662057,-0.11810866,-0.0754290372,0.0292310305,-0.1275217533,0.0166127179,0.4194630384,0.2399951369,0.2643740773,0.1732938737,0.0661038756,0.3712897003,-0.2725654244,-0.0888269022,0.3760095537,0.0611933619,0.2021663934,-0.1436437219,0.3161388338,-0.0341814756,-0.1472385526,0.0488390923,0.0372644588,-0.1075874195,-0.2263088077,0.3227770329,0.3171145916,0.3297155797,-0.0985495523,-0.2089858204,-0.1295852065,-0.0139484815,-0.1774871498,0.6740048528,0.0424484722,0.0781807378,0.1186480448,-0.3517786264,0.134769842,0.0834435895,0.018965343,-0.2183809429,0.2313467413,-0.0822959244,0.0327602997,0.208472386,-0.1423193067,0.1729555428,-0.0706114992,-0.297468096,0.1347073764,-0.1392706335,-0.0381055772,0.0945120528,0.3085157275,0.4922871292,0.0767579451,-0.3269588351,0.0419314988,-0.3647181988,-0.0257917549,0.1512061954,0.0453800894,-0.0666874498,0.1125382632,0.3515547216,0.4029657245,0.0724030435,0.0197230726,0.1376425326,-0.3669954538,0.3511634469,0.246380806,0.0305848494,-0.235047698,-0.1849406064,0.2504478097,0.0101536633,-0.2187473327,0.0613343939,0.3727704585,-0.0051797582,-0.1084930152,-0.0428546071,0.1350508332,-0.1006917283,-0.0495885499,-0.1773463935,0.0806766674,-0.2313710451,0.0178128425,0.2771867812,-0.3870124221,0.2303080559,-0.018027937,0.1170354411,-0.1404738575,-0.199759841,-0.1823632419,-0.0155405495,0.3104733825,0.1280609816,0.0866651982,0.153395474,-0.2373283505,-0.1051266268,0.1469208449,-0.3772841692,-0.0067259511,-0.0651958957,0.2036555707,-0.3899028897,-0.0674372092,-0.1547538489,-0.1299338937,0.0029346461,-0.3752027452,-0.1046101972,-0.2060654759,-0.405534029,-0.2473146766,0.2051641643,0.4027486145,-0.2309567034,0.1231143549,0.0566094927,-0.3296223283,-0.0897898972,0.1836728603,-0.0944328979,0.1881731302,-0.4774871171,0.1908774227,-0.1059469208,-0.2502759397,-0.5926556587,-0.1481301039,-0.0188872796,0.1667477936,-0.0521165766,-0.0712123141,0.023757128,0.0900956541,-0.078954801,0.1266408414,0.2598060966,0.2229453325,-0.2670016885,-0.3738026321,0.1871028394,0.1169929579,0.2048234791,0.0643000901,0.5371847153,-0.355163157,0.2681360245,-0.2922662497,0.0668217763,0.274322778,0.4069176018,0.1358005553,0.1353731155,0.0541509539,-0.5511983037,0.2991277575,-0.1138398498,0.0180061366,-0.3043980002,-0.1786101311,-0.3630911708,-0.3183590174,-0.4063474238,-0.1445735693,0.1915813684,0.2052730471,0.0947643965,0.3376879692,0.2154136598,-0.0525886454,-0.3357267678,-0.0082433149,-0.0837396607,0.5770990849,-0.282886833,-0.0813772231,0.1508817673,-0.004482626,0.1730348021,-0.0560906716,-0.1819313169,0.3738623559,0.2025898397,0.2947708964,0.1643804461,0.2622050345,0.1132966578,-0.3060264587,0.0486358255,0.1693634689,0.1462227851,-0.0118630212,-0.5752343535,0.418323338,-0.1716960818,0.0713652298,-0.0284683779,0.1201798245,0.4608344436,-0.1210544035,0.0017644766,-0.1878712475,-0.0006641138,0.3077031076,0.0985914022,-0.0435263552,-0.1037954241,-0.118484877,0.5224275589,0.0012731551,0.1268099993,0.14143987,0.0457557961,-0.0243496448,0.098507084,0.4228601456,0.1318577677,0.1070045009,0.1968848854,0.2121444941,-0.1387598366,-0.0896671414,0.0558624193,-0.1442407072,0.3520455658,0.5106645226,-0.0088530779,-0.0417730957,-0.6307204366,0.029203983,0.1313726753,0.3394635916,-0.4060431123,-0.0564499162,-0.0851310343,-0.0475081615,-0.1892308295,0.2275418192,-0.4634397626,-0.5150537491,-0.0582737699,0.1681042463,-0.2088488191,0.0605604053,-0.1874831468,0.0518670678,-0.0309048053,-0.2380191386,-0.0417054556,-0.1542359591,-0.1478381455,0.1115004793,0.5178375244,0.0236186031,0.3159516156,-0.2510509491,0.0593383126,-0.6393260956,-0.3777698874,0.1005240679,-0.2437069714,0.3291916847,0.0916448683,0.3190773427,-0.3589506745,-0.3353344202,0.1264745146,-0.1117309406,-0.3317470849,0.0628847256,-0.0461068042,-0.0360032357,0.0215901099,-0.3601584136,0.0591648109,-0.1522710174,-0.0953412876,0.0888911039,0.1657807082,0.0812890455,0.1759531647,0.2426643074,0.0841047838,0.0292827971,-0.4623934031,-0.4235394299,0.493886441,-0.1987027228,-0.4403117895,0.2561227977,-0.0506556816,0.0861608982,0.1215388998,-0.6318221688,-0.2949470282,-0.3726099133,0.2260109186,0.3153223097,0.0529728718,0.2384326756,0.0824150965,-0.0273637008,-0.0853573978,-0.2098316401,0.0102281366,-0.1507218331,0.2676159739,-0.0062824031,0.3313056529,0.0809748918,0.5084468722,0.2393155843,0.160880819,0.2514971793,-0.0708472803,0.5218967795,-0.2169974595,-0.1066775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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"@albertvillanova  by running:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\ndata = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", download_mode=\"force_redownload\", ignore_verifications=True)\r\n```\r\n\r\nI had a pickle error **UnpicklingError: invalid load key, '<'** in this part of code  both `locally and on google colab`:\r\n\r\n```\r\n\"\"\"Yields examples.\"\"\"\r\nwith open(filepath, \"rb\") as f:\r\n         data = pickle.load(f)\r\nfor id_, row in enumerate(data.items()):\r\n         yield id_, {\"url\": row[0], \"article\": self._process_article(row[1])}\r\n```\r\n","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":59,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n @albertvillanova  by running:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\ndata = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\", download_mode=\"force_redownload\", ignore_verifications=True)\r\n```\r\n\r\nI had a pickle error **UnpicklingError: invalid load key, '<'** in this part of code  both `locally and on google colab`:\r\n\r\n```\r\n\"\"\"Yields examples.\"\"\"\r\nwith open(filepath, \"rb\") as f:\r\n         data = pickle.load(f)\r\nfor id_, row in enumerate(data.items()):\r\n         yield id_, {\"url\": row[0], \"article\": self._process_article(row[1])}\r\n```\r\n","embeddings":[-0.2528679371,0.1900026947,-0.063642703,0.3140268326,0.0766307861,-0.0666102096,0.1628489643,0.2162665129,0.5342549086,0.1638111025,-0.0650472045,0.4586708844,-0.0562344566,0.4272354543,0.0337767303,0.088779673,0.1513222605,-0.0304993559,-0.2259760499,-0.1045603603,-0.2315224707,0.267427206,0.0334857553,-0.2563103139,-0.0576738268,-0.013430899,0.0152934352,0.0463458449,-0.2251960188,-0.3198206425,0.3860786557,0.21386832,-0.0832516849,0.4209759235,-0.0001190723,0.2136952281,0.3132317066,-0.1055534258,-0.4403299689,-0.1635105908,-0.2478757799,-0.3826873302,0.0440251194,-0.3404947221,-0.132297039,0.2774428427,-0.1330313832,-0.1618740708,0.1938681602,0.1893193871,0.177574411,0.3448016047,0.2914979756,0.1409096867,0.33001858,-0.1042358801,-0.0304452106,0.453050524,0.068127133,0.1778018028,-0.0668462291,0.2753111124,-0.0559270121,-0.1353753656,0.0447449759,-0.0413341634,-0.2361439019,-0.3221164346,0.1678913534,0.3900494576,0.5455671549,-0.2098401487,-0.4568276703,-0.1291813552,-0.1173151657,-0.0720624328,0.3100497127,0.2226850837,0.0060891663,0.137588039,-0.2757737935,-0.1037338674,0.1708634049,0.2109019011,0.1327082366,0.4865167737,0.0225964095,0.0699374676,0.135083124,-0.0442111678,0.1837435216,-0.1763031334,-0.2440820783,0.3063014448,-0.1139807776,-0.0929232687,0.0179314017,0.4175486863,0.2382950187,0.254982084,-0.1215653419,0.1396267265,-0.3940800726,0.1429608762,0.1983414888,0.0739567503,-0.12447799,0.3982487023,0.3807310462,0.1585371941,0.023955036,0.1444149911,0.0738767684,-0.2767380178,0.0143233687,0.0933526829,0.0566629246,-0.2888742685,-0.2668403089,0.1693912446,-0.0135262925,-0.3217413127,-0.0092743533,0.2382356077,-0.2409549206,0.0155597124,-0.1244163215,0.1569011956,-0.2585909963,-0.0233082678,-0.2268989384,0.2363652885,-0.0495735221,-0.0415499695,0.1220438108,-0.3013799787,0.4649293423,0.0178395212,0.0202266127,-0.1493333876,-0.1376052648,-0.1730495393,-0.2541310191,0.389659673,0.0117365178,-0.0446538068,0.3086327314,-0.4884026349,-0.1425709575,0.2305216193,-0.3667359948,-0.092772752,-0.2011180371,0.1401478499,-0.6305922866,-0.1462280601,-0.676422596,-0.1142531708,0.2172014862,-0.3185863793,0.0925927684,-0.1673716158,-0.1711813062,-0.2270736247,0.1974530816,0.3427009881,-0.3239112496,-0.1362417638,-0.1404350847,-0.0910111889,0.2624059021,0.1725545228,-0.1236182749,0.3524585068,-0.2878663242,0.0640756637,0.4019038975,-0.3472414613,-0.6560376883,-0.0823617652,0.095294632,0.3186149001,0.0613337569,0.0118045649,-0.0731987283,-0.0371899642,-0.0376703702,0.1327903271,0.2623271942,0.1589772701,-0.3625711501,-0.3252060711,0.1628906131,0.1653823853,0.2095088214,-0.1567005217,0.4243592918,-0.5061724782,0.3508394957,-0.2134560496,-0.0995201916,0.1817172915,0.5715590715,-0.0149304355,0.0156950001,-0.0553813539,-0.5021434426,0.3713897169,-0.2201079279,0.1815862358,-0.450658828,-0.0991212502,-0.3539779186,-0.1530148685,-0.4353387952,-0.0260328222,0.0306037217,0.231102407,0.1153796911,0.2346293181,0.0978080705,-0.0081236372,-0.2779420912,0.1182958633,-0.2313801199,0.3536604047,-0.1937054545,-0.1283809394,0.125419721,0.0892496407,0.0987062752,-0.2664654255,-0.1558886915,0.3263131082,0.4375668764,0.2257623225,0.2605549097,-0.014499547,0.1088796705,-0.4124882221,-0.0447093099,0.2842776179,0.2253895104,-0.1347850412,-0.3102371395,0.6110122204,-0.1842079312,-0.0670267642,-0.1094426066,-0.0608634949,0.2484120578,0.0308065377,-0.0784368366,-0.1149242073,0.1970295012,0.2878172398,0.1909892857,0.1616838425,-0.2053568363,0.0189958215,0.4041166902,-0.0457217805,0.156677261,0.2113799602,0.1638682485,-0.1065177843,0.1382417381,0.1597610265,0.2561878264,0.1205289811,-0.0183552615,0.0309296455,0.0087491963,-0.1049487963,0.1266597956,0.0003996137,0.0812383145,0.4319375455,-0.021975318,-0.0782154202,-0.4160600007,0.16817303,-0.0463723429,0.3713009357,-0.3852730989,0.0951862633,-0.1990466565,-0.0051379423,-0.1033417284,-0.1734513491,-0.2557071745,-0.4192435443,-0.1311055124,0.1337611079,-0.0148985097,0.2001955211,-0.2309560627,-0.0600641444,-0.0811314061,-0.2992195785,-0.0568285249,-0.3598300219,-0.1563221514,0.0772360563,0.6690936685,0.1273958534,0.3829645216,-0.2178860009,-0.0853122771,-0.3214855194,-0.3296702504,0.0051946728,-0.3213551939,0.6249520183,0.1242415234,0.2854340672,-0.4078520238,-0.2364944816,0.1086361259,0.1833010316,-0.3653050661,-0.0600073151,-0.0595201552,-0.1118507311,0.1301990449,-0.2831466794,-0.3330730796,-0.1814834923,0.1838848889,0.2822105885,0.2557506263,0.1599107534,0.0943194479,0.1940939128,0.3727245331,0.1214918792,-0.3112452626,-0.4904229939,0.4961253703,0.0050636111,-0.2876056731,0.0660957396,-0.170968771,0.0848919898,-0.0454867184,-0.5297892094,-0.0850809664,-0.2827373445,0.3480737209,0.2950773835,0.2237141877,0.2349916995,0.1556831896,0.0884662345,-0.0972446576,-0.1059788316,-0.0316697843,-0.0017052852,0.3416727185,0.0350564048,0.0747757629,-0.1968631744,1.0584331751,0.3284491301,0.1260665357,0.3611392677,0.0191697609,0.326151669,-0.2551502585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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"This issue impacts many more datasets than the ones mention in this thread.  Can we post # of downloads for each dataset by day (by successes and failures)?  If so, it should be obvious which ones are failing.","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":38,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n This issue impacts many more datasets than the ones mention in this thread.  Can we post # of downloads for each dataset by day (by successes and failures)?  If so, it should be obvious which ones are failing.","embeddings":[-0.3122649193,0.2210706621,-0.0906828716,0.2768564224,0.2292594761,-0.0886907578,0.2925547957,0.3545770347,0.1828719825,0.2068862021,0.1387794018,-0.0111588417,0.0016291483,0.1781063527,-0.0697038472,0.1052989811,0.3057501614,-0.0951659009,-0.1911695302,-0.0606807731,-0.3206844032,0.1676609963,0.0921663567,-0.294457078,-0.1731498986,0.1849992722,-0.0621065535,-0.2069591433,-0.2465740591,-0.4182297587,0.4067116976,0.2116328627,-0.0132813957,0.4017715454,-0.0001121963,0.1463092566,0.3609668016,-0.0118746283,-0.2743215263,0.0589583553,-0.3574695289,-0.4000295997,-0.2054273784,-0.1293691099,0.0501883328,0.1765696704,0.090959616,-0.0960203409,0.0456650332,0.3521710038,0.2322651595,0.3720600605,0.1206587777,-0.1288902611,0.3179793954,-0.0899647102,-0.0678585693,0.2471769303,0.1151352748,0.0584352911,-0.2333070785,0.2637361288,0.0712568462,-0.1435856074,0.1074528396,-0.1782395542,-0.0344056301,-0.3530973196,0.2641153038,0.4801808894,0.7626399994,0.0671481192,-0.3485156298,-0.0574800521,-0.1447699368,-0.0353270695,0.4738016725,0.1406771243,0.0308651049,0.0727357566,-0.2538784146,0.2222840935,-0.0102372319,0.22924079,-0.089599885,0.2683905661,0.025479259,0.0054686116,0.046784278,-0.0737857297,0.3437826335,-0.1402771622,-0.2881660163,0.0911717564,-0.2309808582,-0.0309533179,0.1132080778,0.3474107087,0.3019771576,0.1324877888,-0.0772627965,0.2700101733,-0.2145813107,0.142206043,0.1329952478,0.0609982535,0.0212313458,0.2251035869,0.389621377,0.2267389148,0.0899345204,0.1189350188,0.031090863,-0.3178121746,0.2106014341,-0.0249808524,-0.0529024117,-0.3350430131,-0.2397469729,0.3281226158,-0.0459095277,-0.3272869587,0.0117564183,0.2553394437,-0.2220915258,0.2956219912,-0.2900612354,0.2008787096,0.0116296727,-0.039369192,-0.2468170226,0.0392123908,-0.2411162555,-0.0887135342,0.2798561156,-0.0857619494,0.2759149075,-0.0155421495,0.055260241,-0.2210912555,-0.0446356013,-0.0334920622,-0.2132913619,0.3799152076,-0.0787840188,0.1663914174,0.0699701011,-0.1595645547,-0.0371518359,0.0764042735,-0.1545032412,-0.174736008,-0.1084295511,0.2518773079,-0.43210271,-0.2231098413,-0.1340698451,0.0163282193,0.117895551,-0.314943701,0.0129274931,-0.1260067523,-0.1943877041,-0.261854887,0.1870053858,0.4019260406,-0.3023399711,-0.0232705977,-0.1823227853,-0.2848272026,0.2355710417,0.16090253,-0.128933385,0.1814378351,-0.2432228327,-0.0052911295,0.2092022747,-0.290859282,-0.6658647656,0.0049505569,0.0380914323,0.1542330831,0.2416581362,0.0621549599,0.0861423761,-0.0564694367,-0.1621897072,0.0558740683,0.0746804699,0.0451989584,-0.327888608,-0.1714452952,0.2115719318,0.393810451,0.4312628806,-0.2194582969,0.4997037053,0.0074163573,0.1939439774,-0.2174015641,-0.0024494701,0.0845103487,0.397898972,-0.0799849033,0.0237133373,-0.0413204655,-0.3278672397,0.4429025352,-0.039146848,0.0405073762,-0.0520954654,-0.1266986281,-0.2712762952,-0.16231215,-0.3423414826,-0.0554054603,0.1371208131,0.3026048243,-0.0158608668,0.2044572681,0.027374221,0.1284896433,-0.5256736875,0.0420428105,0.0481819808,0.259793669,-0.1240633279,0.0058829305,0.1246684268,0.0992972255,0.0987911671,-0.0470510311,-0.1909763366,0.3082258701,0.2828398943,0.2788021564,0.1735418141,0.1547736079,0.1121040285,-0.3107188642,-0.0820143521,0.2652201056,-0.104679361,-0.2542715073,-0.3573019803,0.4060260057,-0.0496167876,-0.032520283,-0.0627221689,0.1177697182,0.3678382039,-0.0515308715,-0.1578397155,-0.2539518774,0.3428278863,0.0726509318,0.1073364094,0.1226062849,-0.2330145538,-0.0700386241,0.4974806905,-0.1455060691,-0.0020782237,0.1813168079,0.2159091234,-0.0173029955,0.1314570606,0.52135849,0.1450690329,0.0896787494,0.236161992,0.05181887,-0.1459528655,-0.1474684924,-0.0182254296,-0.0743849501,0.1252346933,0.4634186625,-0.0918282568,-0.0161519479,-0.6106398702,0.0051131202,0.0962445363,0.315115124,-0.2992806435,-0.2282571942,-0.08809378,-0.1382594705,-0.3154154718,-0.0629926473,-0.4115451574,-0.5413969755,-0.0040871087,0.036232166,0.0005866141,0.1515589654,-0.3392380178,0.0682108477,-0.1719213128,0.0220043063,0.0592254475,0.0131760174,-0.1600786,0.1361153275,0.6430556774,-0.0059279464,0.4341422915,-0.4331226647,0.0553327389,-0.4075479507,-0.4340152144,0.0458607711,-0.1136178523,0.1817967594,0.055339016,0.1882718205,-0.2814408839,-0.2978281081,0.0574826524,0.1158404127,-0.1857205331,0.2240148336,-0.1445132643,-0.1537796855,0.0844443664,-0.2872749269,-0.1813552678,-0.2275399119,0.0015405104,0.0912527889,0.2049351931,0.001929267,-0.029495243,0.2374272048,-0.074990198,0.1122090146,-0.2473246306,-0.5432507992,0.5415393114,-0.1286887228,-0.3952292204,0.2389933169,0.0579357296,0.1832264364,0.110275045,-0.6624621153,-0.0184654947,-0.2211354673,0.2416524291,0.256069839,0.0205152724,0.3066313565,0.0761023462,-0.0208981521,-0.2089378983,-0.0228604078,-0.0473887511,-0.1169830412,0.5548331738,-0.3558395505,0.282697618,0.0112472437,0.7115713954,0.3619564772,0.1010402367,0.2355141491,0.1196091473,0.4429763258,0.0093327807,-0.1098077521,0.2277192026,-0.3095940351,-0.1276212037,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"I can see this problem too in xcopa, unfortunately installing the latest master (1.18.4.dev0) doesn't work, @albertvillanova .\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"xcopa\", \"it\")\r\n```\r\n\r\nThrows\r\n\r\n```\r\nin verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/cambridgeltl\/xcopa\/archive\/master.zip']\r\n```","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":74,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n I can see this problem too in xcopa, unfortunately installing the latest master (1.18.4.dev0) doesn't work, @albertvillanova .\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"xcopa\", \"it\")\r\n```\r\n\r\nThrows\r\n\r\n```\r\nin verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/cambridgeltl\/xcopa\/archive\/master.zip']\r\n```","embeddings":[-0.3634566665,0.2217583358,-0.0390358083,0.1520664543,0.1960244179,-0.0046922453,-0.0165691655,0.480653733,0.0124928113,0.1067952663,0.0860227197,0.1600407064,0.2092341483,0.1283582598,-0.1657696813,0.3392212987,0.2206755131,0.0590418614,-0.2113899589,-0.0897830725,-0.3299863636,-0.009242163,0.0434562862,-0.3702940047,-0.2110364586,0.2245367169,0.0093346965,-0.3594246805,-0.3264991939,-0.4185554683,0.5466653109,0.2197135091,-0.0881340951,0.2510863245,-0.0001139345,0.2255785614,0.4916989207,-0.0334922001,-0.130114302,0.0573251024,-0.5727484226,-0.2505688965,-0.2062242925,-0.100093171,-0.0513585433,0.1637926251,-0.0833982602,-0.0672472864,-0.0723875538,0.2425497174,0.1950820386,0.3550715148,0.1313629597,-0.0388003923,0.2899055183,-0.0501925051,-0.0310711712,0.1560600698,0.1035280153,0.1247442514,-0.113500461,0.2756738961,-0.0733574033,-0.2580088377,-0.0850614086,-0.2370331138,0.1488453746,-0.183450222,0.2543841004,0.4078857601,0.5357618332,-0.1486240178,-0.3351545334,-0.0596357919,-0.1194317341,-0.1829402,0.4538167417,0.0964059383,-0.0209474657,0.0660037994,-0.1946440786,0.2606699169,0.0211788453,0.2225605696,-0.0251126774,0.3371692002,0.1283893883,-0.0029069753,0.1510206312,-0.0360058099,0.5746412873,-0.1349974722,-0.3604212105,0.1663499326,-0.3483098447,-0.0014671787,-0.0002135259,0.4169412553,0.4714282751,0.0882292762,-0.0827087238,0.2438014299,-0.1719440669,0.1578946114,-0.044778984,0.2244594395,-0.0235703643,0.3880789876,0.1165977344,0.188693285,-0.0718720481,0.1159352064,0.1248015389,-0.3089087307,0.2137677819,0.266844362,0.0246997178,-0.4375729561,-0.2234167904,0.3009352386,0.0163320005,-0.2949092686,0.2497538924,0.2732068598,-0.1022427902,0.5024850965,-0.2264558375,0.1637039781,-0.1313102096,-0.1101762727,-0.2292998135,-0.0158864483,-0.2489552647,-0.0269962586,0.2081590146,-0.328099072,0.37406075,-0.0025189514,0.2814953029,-0.2433121204,-0.0138610704,-0.1135364175,-0.1100086793,0.2595214546,-0.073294051,0.0175459366,0.0914088041,-0.0348763987,-0.0794271901,0.0835393146,-0.3359380364,-0.0633798912,0.0041351388,0.2597447634,-0.4431794882,-0.1055694148,-0.1516693085,-0.2823311687,0.2714230418,-0.1195514053,-0.0062138569,-0.1670936942,0.0071559884,-0.1993665695,0.0804664344,0.1566430479,-0.1580191553,0.0762370601,-0.0275206715,-0.1368149817,0.1813879758,-0.0126274377,-0.0944512039,0.3111427724,-0.1911142766,-0.0500597917,0.1041202173,-0.5962736011,-0.7689020038,-0.1865933985,0.3095909953,0.2092158198,0.1738779098,-0.0858601257,0.103318885,-0.1357975155,-0.3754762113,-0.0541892163,0.1690146029,0.0986005217,-0.2438964248,-0.1508104354,0.0396001786,0.2239178866,0.196277976,-0.2344999611,0.2876280844,-0.1017189249,0.245759204,-0.248596102,0.0269928779,0.0971400961,0.4311549962,-0.016864473,0.0510399491,0.0154453386,-0.0233340282,0.3862486482,-0.0511805899,0.1618931442,-0.0453089289,-0.1407322139,-0.279566586,-0.1633136421,-0.4493741989,-0.0144460015,0.1493973881,0.2919472158,0.0842829645,0.157036975,0.0710931197,0.1438407749,-0.5047531128,0.0067185271,-0.1339939535,0.1448830813,-0.0282200389,0.0071026571,0.1917278916,0.3070706427,0.1832426041,-0.0377740413,-0.2340926677,0.3226472437,0.3398549259,0.2355362475,-0.0184576809,0.2227361351,0.1116511896,-0.5854959488,-0.0470596217,0.2284664661,0.0506334268,-0.1292467266,-0.1104486659,0.4017414153,0.040965762,-0.0432968698,-0.0806946456,-0.0254894271,0.4137809575,-0.1971381754,-0.2287991345,-0.2732152343,0.2030034214,0.2050539255,0.0971652865,0.2035532445,0.2256783247,-0.0754480585,0.4232520163,-0.1062711328,0.0373562984,0.1138803288,0.2194246203,0.1228877306,-0.0364867114,0.5303395987,0.1804706901,0.1211035699,0.0328633972,0.1150273234,-0.3244568706,-0.0255212188,-0.00193592,-0.0347805321,0.2392189652,0.4284631312,-0.1507626176,-0.078721866,-0.4129519761,-0.0385007262,-0.0201823879,0.3083796203,-0.3606131375,-0.2553161383,-0.3567480147,-0.316880554,-0.1933459491,0.0418622382,-0.1216753125,-0.4632620513,-0.0543209612,-0.0195363928,-0.0061781672,0.3221709728,-0.5358046889,-0.0642517954,-0.1387615353,-0.1890026331,0.129436776,-0.102547735,-0.033603292,0.0914576277,0.5303916931,-0.0599392839,0.2663110197,-0.4404427707,0.1127863675,-0.2505290508,-0.4522188008,0.0925038233,-0.2644736469,-0.0153876878,0.0138087962,0.0914322361,-0.120202668,-0.3200675547,0.0816798806,0.1290531456,-0.3398220539,0.1617750973,-0.229786098,-0.2532001734,0.117239356,-0.2592996657,-0.0783351436,-0.1527514458,-0.19141379,0.2056282461,0.1268028021,-0.0792862922,0.0557546243,0.1161813587,-0.1673594266,-0.0227526166,-0.2798974514,-0.7397508621,0.5351415277,0.0111240223,-0.3585606217,0.1192596182,-0.1775107682,0.5888108015,0.0698417053,-0.4684118032,0.0231010243,-0.178436622,0.1969545037,0.4038404226,0.0500994809,0.2320293635,0.1058004722,0.0384809412,-0.2956575453,-0.0791116208,0.0240642279,-0.0895501301,0.5666341782,-0.2072359622,0.1848531365,-0.028026389,0.5037480593,0.4361937642,0.203081876,0.388571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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"Hi @rafikg, I think that is another different issue. Let me check it... \r\n\r\nI guess maybe you are using a different Python version that the one the dataset owner used to create the pickle file...","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":35,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n Hi @rafikg, I think that is another different issue. Let me check it... \r\n\r\nI guess maybe you are using a different Python version that the one the dataset owner used to create the pickle file...","embeddings":[-0.1837103516,0.1279492527,-0.0920476988,0.2596699595,0.2507976592,-0.0687122121,0.1381359696,0.4400163889,0.4555807412,0.1520559341,0.0166318957,0.4371010065,-0.0223844014,0.2981739342,-0.1252114326,0.0508492365,0.2061955929,0.0645046756,-0.3080223203,-0.1772566289,-0.1833800375,0.0459084399,0.0426425077,-0.25252074,-0.2048211992,0.019851502,0.0159437526,0.0702444836,-0.217963621,-0.3563278913,0.2247383893,0.080357179,0.0169105884,0.3639425933,-0.0001098583,0.1953138411,0.2266064435,-0.0610339791,-0.1925614625,0.0254562497,-0.2994517982,-0.3886967897,-0.1018152758,-0.2876324952,-0.1001746356,0.1234680489,-0.0243178885,-0.2047749162,0.2083332986,0.3270485699,0.2862396836,0.4228650928,0.1678298712,0.1190711781,0.2620047033,-0.1490420997,-0.0685954094,0.2557101548,-0.0603151992,0.0888667926,-0.2249458134,0.2761291564,0.0354711451,-0.1054695398,-0.0711274967,-0.1122571751,-0.1392607689,-0.2311639488,0.1794040799,0.4440361857,0.5254743099,-0.0637519732,-0.3858265579,-0.0568045117,-0.1382532716,-0.0540766492,0.3208704889,0.2015168369,0.0418239124,0.0703696162,0.0656211451,0.02752693,0.1179618314,0.1669707596,0.1519866288,0.4212054312,0.0426650271,-0.0297997165,0.1475411206,-0.1493612379,0.1190247759,-0.1159616411,-0.2355286181,0.2588365674,-0.1586143374,-0.0794006661,0.0832501277,0.2105809599,0.2272723913,0.1582143456,-0.2061163932,0.2631707191,-0.5139997005,0.0970155001,0.1250505149,-0.0995834321,-0.0054052868,0.4181596339,0.2440285087,0.1126215383,-0.2319514453,0.104294695,0.0145257609,-0.3301545978,0.1687527448,-0.037195567,-0.0530339293,-0.2394006699,-0.0520342924,0.2708117068,0.0187944919,-0.2278486192,0.0342284814,0.2991211712,-0.2435111254,0.1506122351,-0.1739813238,0.2190237343,-0.2628978193,-0.0296104383,-0.2792402804,0.1497242451,-0.1236694828,-0.1439272463,0.1465771198,-0.2184092104,0.4484280348,0.0282983165,0.0213186163,-0.0990248024,0.0034608142,-0.16309084,-0.1197389513,0.2568808496,-0.0386311337,0.0318731144,0.2937024236,-0.3306817412,-0.0754778087,0.121550478,-0.3162706196,-0.0432150289,-0.2383592576,0.2684853673,-0.3696346581,-0.1251151413,-0.1562822163,-0.2218886763,0.2508496344,-0.3410398066,0.0346543156,-0.2487425208,-0.1786746681,-0.2333725989,0.0210005958,0.2397781461,-0.4336731434,0.045538567,-0.0677610636,-0.1925331205,0.2785188258,0.1883743107,-0.0684211552,0.1143826917,-0.3408779204,0.3613523841,0.3370783925,-0.2554219067,-0.559656918,-0.0669448972,0.2126810402,0.1853820682,0.1174374819,0.0607459471,-0.133476302,0.0740352049,-0.0898513719,0.0596647412,0.3936367631,0.172651425,-0.3381372392,-0.0394435339,0.3509188592,0.1663109064,0.1363600492,-0.1765651852,0.2964217663,-0.3152950406,0.4309863746,-0.126952827,0.0001773164,0.1553294212,0.5322555304,0.0730095133,-0.0088884737,-0.1401404291,-0.0647079572,0.3137410581,-0.1866513491,0.1931518763,-0.1107610986,-0.0919199064,-0.2524580359,-0.142080158,-0.4565196335,-0.0252679605,0.1868506223,0.1929929256,0.0897424445,0.2391290665,0.0715041459,-0.0307856426,-0.5189889669,-0.0203907806,-0.1414521337,0.3249926865,-0.226724878,-0.1608237624,0.0063764015,0.1558340788,0.024845954,-0.0202005282,-0.2362953126,0.2243083417,0.641677618,0.0033626044,0.1569420546,0.0316944681,-0.1279967874,-0.2826164961,-0.0336736701,0.3249648511,0.1470149606,-0.0985000283,-0.276373148,0.5556148887,0.0352926739,-0.0694950745,-0.0094894934,0.0311426055,0.4056512415,0.0189015213,-0.1136312857,-0.2747990787,0.0642354041,0.0418616384,0.1189542934,0.1752710342,-0.2464984804,-0.0633244291,0.4687608182,-0.0300360899,0.2200410813,0.1378831118,0.2034180164,-0.0776547417,0.085702911,0.2691549659,0.2426616848,0.1494555324,0.1267057657,0.0099031543,-0.0842584744,-0.0640885234,0.0147191156,-0.1871045977,0.2280609906,0.4420960546,-0.0329043157,-0.1883387417,-0.4598093033,0.1113865003,-0.1529735178,0.2933872342,-0.2333507687,-0.0517874323,-0.331813395,-0.0445071869,-0.3081260622,-0.1152400598,-0.3320178092,-0.4242048264,0.0004073196,0.0024833053,-0.0777877122,0.2671741247,-0.4119128883,-0.1786642671,0.0536810979,-0.0781372264,0.0439313129,-0.2255787551,-0.1414557099,0.1240782142,0.5758264661,0.1559125036,0.3514201641,-0.1338611096,-0.1194980666,-0.3694281578,-0.2386049032,0.0068372204,-0.1855196357,0.3731229603,0.2028136104,0.3383087218,-0.2931666076,-0.1705391109,0.1574041396,0.0745806023,-0.3471678793,0.1264222562,-0.0103798639,-0.0779269636,0.1646076441,-0.3944224119,-0.259793371,-0.240537703,-0.0421295576,0.3098193407,0.2361100167,0.1788616478,0.1402592808,0.2043360621,0.1547923982,0.1862902939,-0.3219771981,-0.4618420303,0.5113166571,-0.1372172982,-0.4123413861,0.1191185638,-0.2202102989,0.2088123858,-0.1857352704,-0.4163479507,0.0272201858,-0.3612751067,0.3696192503,0.1945275515,0.2611677647,0.2279691249,0.1481056809,-0.0379107334,-0.2633203268,0.0542691126,0.0402426533,-0.0090992721,0.343387872,-0.1509934515,0.0712013468,-0.1638160348,0.7019244432,0.3543438613,0.0210630074,0.4377123415,-0.0859137028,0.2890711427,-0.2627668977,-0.2535893917,0.0583988987,-0.179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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"@kwchurch the datasets impacted for this specific issue are the ones which are hosted at Google Drive.","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":17,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n @kwchurch the datasets impacted for this specific issue are the ones which are hosted at Google Drive.","embeddings":[-0.2723341882,0.251368463,-0.0825370625,0.3092967272,0.1599653065,0.0824246258,0.3484656513,0.2746696472,0.1714854985,0.1092719585,0.0492886752,-0.0215493552,0.0095686279,0.1690100878,0.061832007,0.1273817271,0.3405564427,-0.0278941114,-0.0939085111,-0.1078557074,-0.3145876825,0.0975552797,0.1153275967,-0.3233899176,-0.2438773513,0.1783369631,-0.0035283035,-0.1137218028,-0.1283458173,-0.2508125007,0.4510060549,0.2048409432,-0.0686407089,0.2763004303,-0.0001145766,0.247797817,0.2989665568,-0.0793352053,-0.2047356665,0.0862704366,-0.3544012606,-0.2196479291,-0.265165031,-0.1107304916,-0.0322946124,0.1767450273,0.0624947511,-0.2090950161,-0.0070211985,0.3188165724,0.2185305506,0.2424392551,0.0754041523,0.0347164683,0.3117447793,-0.1165856719,0.0225455128,0.206290096,0.0305085015,0.0892168209,-0.2469039261,0.2390474826,-0.0375033021,-0.2095544189,0.1398573071,-0.197123006,-0.095473744,-0.3128155768,0.322643131,0.3903723657,0.6708311439,0.0972237587,-0.2421491593,0.0010652284,-0.1493716687,0.0949159563,0.5306344628,0.1641936898,0.0793966055,0.0428055935,-0.3659051061,0.023754064,0.0036729844,0.1766434014,-0.191241771,0.4262520969,0.0560175814,-0.018723039,-0.0107063521,-0.0029441637,0.3388336599,-0.2383149117,-0.4136508405,0.1551843286,-0.2419520915,-0.0156479236,0.0417980216,0.5411649346,0.2527109087,0.147537902,-0.1343055069,0.2664247453,-0.3098728955,0.0469848551,0.0392501168,0.0939101204,-0.0270286724,0.2620168924,0.3438663781,0.1067700312,0.0363251753,0.0938951597,0.0344168022,-0.3539592624,0.2297631055,0.0283338018,0.1067054942,-0.2699972093,-0.3008374572,0.3605436981,-0.1247452796,-0.3415850103,0.0710769743,0.2549729049,-0.1273253709,0.1563393325,-0.1915257275,0.2358928621,-0.10112831,-0.0106273517,-0.247839272,0.0527533032,-0.0950690806,-0.1324321926,0.2055669725,-0.1673240513,0.2335175574,0.0006798293,0.0993372872,-0.2083730549,-0.031718947,0.0061000511,-0.1578667909,0.2877129912,-0.0488172546,0.1319361329,0.1153222099,-0.2233643383,-0.0451095738,0.2538073361,-0.3779257238,-0.1248992756,-0.1644551605,0.2427510619,-0.5262765288,-0.1828803718,-0.1847947687,-0.1129160002,0.197696656,-0.3604329526,0.0837237611,-0.1343091279,-0.2451507598,-0.2365827709,0.0703604147,0.315767765,-0.4315350354,-0.0100125186,-0.1763361245,-0.2845640182,0.2102938443,0.0798701942,-0.0890826359,0.2305529416,-0.2634395957,0.0600470267,0.2795992494,-0.2271743417,-0.6469714046,-0.1539811492,0.0029058747,0.2863568366,0.2006396502,0.0970982388,0.0147224888,0.0208056942,-0.0249727257,0.0459176749,0.2296460718,0.0803336576,-0.1814255267,-0.1337170899,0.2289818227,0.3443884552,0.3398492038,-0.157630384,0.4693030119,-0.096197091,0.2432925701,-0.3280806541,0.0181432329,0.2173152268,0.4514325857,-0.0433380231,0.0666929707,-0.0817324743,-0.3092254698,0.3514639437,-0.0895543918,0.0807292387,-0.0832826868,-0.0657703355,-0.2799894214,-0.2635655105,-0.4767205715,-0.0782958791,0.1282375306,0.3685255945,-0.0606791563,0.1194753349,0.135912329,0.1021017209,-0.4171824157,-0.0279557183,-0.0955389068,0.3264546096,-0.0929247141,0.0225499365,0.0966106206,0.1502178907,0.074710004,-0.1519425064,-0.2158577144,0.3250206709,0.3738479316,0.364775002,0.132314235,0.2315996289,0.0471107066,-0.3811601698,0.133762151,0.2042320669,0.0145295719,-0.2559151053,-0.281360805,0.5126302838,-0.066272825,-0.0854257718,-0.1891553402,0.1124314964,0.3620568216,-0.0205364097,-0.0451588929,-0.2293643057,0.2820295393,0.2297661006,0.1275690943,0.2351493537,-0.198806867,-0.0717136338,0.477621913,-0.1367523372,-0.0243380815,0.1185947135,0.0629582554,0.017054325,0.0891247019,0.4566433728,0.235892579,0.0889461711,0.1303953379,0.12837632,-0.0516429543,-0.1467848569,0.0638571605,-0.0625068769,0.2599737942,0.440377295,0.0140715661,-0.0667078346,-0.6534082294,0.2014600337,0.0564733818,0.2341954857,-0.410315156,-0.044858899,-0.1236981452,-0.0358945243,-0.3355346322,-0.0356205143,-0.5250937939,-0.4948504865,0.0127809793,-0.0151128881,0.0592062958,0.1758829653,-0.2985755205,0.0677093863,-0.0812290087,-0.1393890381,0.0059019267,-0.1117298156,-0.1506037414,0.1320694387,0.6315668225,0.0748755187,0.3743740618,-0.2744452059,0.171920687,-0.4168557823,-0.4322859645,-0.0825858191,-0.2360452861,0.2696225643,0.0781117082,0.2739151418,-0.2623966336,-0.2785967886,0.0007493499,0.1780049056,-0.3387942612,0.0985371321,-0.1419025362,-0.1319001913,0.1496270895,-0.2089823484,-0.1171505302,-0.0974805802,0.0123608019,0.2124986649,0.1718710661,-0.0812839791,-0.0763619021,0.186664924,0.1223133504,0.0582357012,-0.3626247048,-0.5182885528,0.6088277102,-0.0402951315,-0.2906510532,0.2036103606,0.0250936113,0.2092010379,0.1131011397,-0.5993533134,-0.0930882916,-0.2408189923,0.2119827867,0.3013293445,0.0006270043,0.3328781128,0.0692608804,0.0650763363,-0.2517605424,-0.1411411613,-0.0585934371,-0.0899615213,0.4592781961,-0.3273323774,0.3235730529,-0.2007875443,0.7698401213,0.2856588364,0.2020162344,0.1788965315,0.083818756,0.4398254752,-0.1810165346,-0.1634222567,0.1356587112,-0.2291382849,-0.18569211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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"@afcruzs-ms I think your issue is a different one, because that dataset is not hosted at Google Drive. Would you mind open another issue for that other problem, please? Thanks! :)","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":31,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n @afcruzs-ms I think your issue is a different one, because that dataset is not hosted at Google Drive. Would you mind open another issue for that other problem, please? Thanks! :)","embeddings":[-0.3380447328,0.2343493253,-0.1028941348,0.248897776,0.2559024394,0.052229546,0.2988868356,0.3855466843,0.1882091612,0.1851579696,0.0704608411,-0.0634316877,0.0281984974,0.2961121202,-0.1326324344,0.0332360342,0.2472075224,-0.0536759123,-0.0801996514,-0.0937800705,-0.2452119589,0.1056140363,0.0454948768,-0.357878983,-0.1754431576,0.0557984933,0.0265297983,-0.1015067995,-0.1868115664,-0.1320729405,0.2757670581,0.1935142875,0.0177053418,0.3420615494,-0.0001092242,0.2025673687,0.3189062774,-0.039276503,-0.1414836943,0.0173738226,-0.2754639983,-0.1982258111,-0.3047448695,-0.1761559397,-0.1218796819,0.1576599032,-0.0245585833,-0.1557564735,0.0668033212,0.3204371631,0.2601001561,0.2424912304,-0.0063788877,0.0409337953,0.3197228611,-0.2059234232,-0.0037876903,0.2122244835,-0.0355359465,0.0667069852,-0.1772191375,0.3169079125,0.0364933684,-0.1566316038,-0.0380717963,-0.1515891552,-0.1121651232,-0.2184417099,0.3038474023,0.3827913105,0.5677098632,-0.0013477957,-0.2554209828,-0.0218641665,-0.1111452579,0.0108611928,0.5845531821,0.1662686616,0.0802182183,0.0621084385,-0.3437224329,0.0615605712,-0.0045966022,0.144080624,-0.1313807666,0.3763407767,0.0676313117,-0.0846863464,0.0635060817,-0.0921651274,0.267639935,-0.2179322392,-0.3177542388,0.1551634371,-0.2418033481,-0.0847037137,0.0666570216,0.5523623824,0.3174319863,0.2485667616,-0.087715961,0.2815699279,-0.3112304509,0.0842638761,0.0384590887,0.0540732741,0.0013163124,0.1828888506,0.3838077486,0.2167896926,-0.0529595129,0.0623809062,-0.0076656616,-0.3790988326,0.2950045466,0.1032406241,-0.0050801807,-0.2510635853,-0.1929875612,0.2914766371,-0.0182557739,-0.2955550551,0.1408369243,0.3574376106,-0.1777165681,0.0357861444,-0.1714901775,0.2136804461,-0.1469275951,0.0091007762,-0.2632764876,0.0831269324,-0.1656713486,-0.0656588599,0.2587563694,-0.1491791904,0.3266259432,-0.0343000703,0.0913025737,-0.0800335556,0.0065423027,-0.1011546254,-0.1937154382,0.2423563302,0.0273597222,0.1248834059,0.070965901,-0.2270925194,-0.0647624806,0.179940775,-0.3245857656,-0.0466017351,-0.1454952061,0.2965416908,-0.501467824,-0.1282917857,-0.1817837209,-0.1498234123,0.1220886707,-0.3781958818,0.0275919531,-0.1473522335,-0.2177443802,-0.2023107409,0.1200099513,0.185023129,-0.3365170658,0.0185868964,-0.0978593975,-0.3010311723,0.1582404822,0.1767712235,-0.0232075173,0.1248751655,-0.3458485603,0.239873752,0.226658985,-0.2644093633,-0.6744807959,-0.1033587903,0.0529531874,0.1326453388,0.1616999805,0.0370386802,0.0526808426,0.0841981322,-0.0524950549,0.1293567866,0.2292566895,0.1339053512,-0.2464106828,-0.0930181146,0.1905461252,0.3017821908,0.2347123027,-0.1220689863,0.5012937784,-0.1854778081,0.2362606376,-0.3278014362,0.0284674279,0.2979691625,0.5141527653,-0.0618611388,0.0391757824,-0.0910599828,-0.3323039412,0.3291970491,-0.1160505638,0.1131636798,-0.1256318986,-0.0741623938,-0.2423064411,-0.3082354963,-0.3783038557,0.0308119357,0.2030458301,0.3021928966,0.0019486191,0.1493043751,0.2016507387,-0.002147571,-0.3958661258,-0.0770234019,-0.0741963685,0.4360565841,-0.2015352696,-0.0990776047,0.1895360649,0.0805700645,0.0659323335,-0.0864226446,-0.2383992821,0.3621040583,0.3474031985,0.3443080783,0.1455613971,0.3726140261,0.091708459,-0.3004091382,0.0040680496,0.222158581,0.0175633319,-0.1593921036,-0.4458492994,0.5872706771,-0.0658743456,-0.0856307372,-0.0263806246,0.031644132,0.4443441331,-0.0717977881,-0.0807646737,-0.2925047278,0.208133325,0.2028685361,0.1410699338,0.150043875,-0.1758104116,-0.0862629041,0.4904077053,-0.0573464818,0.0717083365,0.0262976028,0.0738567486,-0.019248927,0.1963523179,0.4488587677,0.1767577529,0.1726195961,0.1972247213,0.1110131741,-0.0605816916,-0.1586007923,0.0087658064,-0.1965084523,0.3126210272,0.4605827034,0.0286643524,-0.1718840748,-0.55248487,0.1573194712,0.0522707701,0.2303788066,-0.3260909021,-0.1293133199,-0.182886675,-0.0011747121,-0.3017399907,0.0435382687,-0.4925371706,-0.3756002784,0.0499367863,0.0898952037,-0.0845504478,0.2213479131,-0.2084251493,0.005045393,0.0626790673,-0.2625906765,0.0597738959,-0.106989786,-0.0702153444,0.1544026881,0.5582308769,0.1363087445,0.3809381425,-0.2153822482,0.1323177814,-0.5136096478,-0.4490849078,0.0204251409,-0.2300570309,0.2673541307,0.0696013421,0.2216574997,-0.2743832767,-0.219632268,0.0923141614,0.087094754,-0.3543184698,0.1700355858,-0.0874857903,-0.0971321538,0.1093089059,-0.2499428391,-0.0489853099,-0.1354361326,-0.0572792739,0.266995132,0.1611167789,-0.063663505,0.1088097766,0.1369003356,0.1823074222,0.0659755543,-0.3855844438,-0.5822989345,0.6266951561,-0.1113214344,-0.4261171818,0.1962728202,-0.0671034977,0.2151007652,0.1369965822,-0.5321988463,-0.2014541477,-0.2606825829,0.2562443018,0.3483715355,0.0194807611,0.380553335,0.0557812825,-0.0154184615,-0.2793956399,-0.173429653,0.0915933326,-0.0232594721,0.4378794432,-0.3108063042,0.288120836,-0.1734597385,0.6061875224,0.184878096,0.1707120985,0.2367138416,-0.0025898898,0.3709447086,-0.1239984557,-0.0808344558,0.1754384786,-0.1410124898,-0.169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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"@albertvillanova just to let you know that I tried it locally and on colab and it is the same error","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":20,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n @albertvillanova just to let you know that I tried it locally and on colab and it is the same error","embeddings":[-0.3873744309,0.237312451,-0.0824100748,0.3719079792,0.2275024056,-0.0049790577,0.1810002625,0.3520690501,0.1753032357,0.1124962121,-0.0698556229,0.089103274,0.0238754991,0.2454140633,-0.0977154896,0.08050026,0.2709174752,0.0180108361,-0.2669126391,-0.038114205,-0.2875452936,0.1000635922,-0.0191114936,-0.285056144,-0.2242527008,0.1182512045,0.0407908186,-0.2390293777,-0.225737974,-0.1934922189,0.4914726317,0.1483865082,0.0737667456,0.3963165283,-0.0001144888,0.2044277638,0.2644136846,-0.0823888853,-0.153091535,-0.0043181106,-0.4366196096,-0.2904203534,-0.1879496276,-0.1636545211,-0.1195641756,0.2306779325,0.031902764,-0.1820704043,0.035710182,0.3506132364,0.2389425039,0.2096553743,0.1583422124,0.0147007378,0.2757311761,-0.087066941,0.0039271214,0.1537487209,0.0328370184,0.1501538008,-0.1278079003,0.2669779956,-0.0610046424,-0.1490532458,0.0947667807,-0.3035598695,-0.0367271379,-0.3440997005,0.3330574334,0.407084018,0.6930856705,-0.0349282548,-0.1812817752,0.1301519871,-0.0807467699,0.0314867347,0.4441695511,0.1426074356,-0.0203828812,0.0674295202,-0.2940243185,0.0506418943,-0.0415648445,0.1912656128,-0.0124070067,0.4687065184,0.0293728597,-0.0473023504,0.0440341458,-0.10663376,0.5398302674,-0.237836197,-0.3618006706,0.2516005039,-0.3341664672,-0.0062195966,-0.1466689259,0.5456804633,0.2707473338,0.0829086006,-0.1635315418,0.241856277,-0.2620737255,0.1147949547,-0.0159058943,0.0401257388,-0.0182713047,0.289616257,0.2473928779,0.1673131585,-0.0837496594,0.1173276082,-0.0154251475,-0.4389241636,0.1872455925,0.0529440939,-0.0747525841,-0.3144616187,-0.205425486,0.3248932064,-0.0657198876,-0.326200068,0.1804487109,0.3013953865,-0.1989020258,0.2046196312,-0.2174806446,0.1643425822,-0.0992891043,-0.0382628925,-0.2259388715,0.0966477394,-0.1288302392,-0.0826632529,0.2514098585,-0.1916666478,0.34080863,-0.057645496,0.1553331017,-0.1247642934,-0.0397526212,-0.1004401296,-0.1316548437,0.2832604647,-0.0129250493,0.1011033729,0.1221426427,-0.1848686785,-0.0257061161,0.2239614129,-0.3420382142,-0.1660688221,-0.2242328525,0.2477961779,-0.4321498275,-0.1552161425,-0.1788852215,-0.1366761178,0.2629215419,-0.3568663299,-0.0063428143,-0.1504597664,-0.2349771112,-0.1966187358,0.142405346,0.2320970744,-0.4391745329,0.03363543,-0.0732896328,-0.2484786361,0.2936116755,0.1112538949,-0.0975735113,0.1987709999,-0.2821417749,0.0312429499,0.2990908623,-0.2145706266,-0.6865375638,-0.0977107286,0.1778276563,0.1919944584,0.2213243544,0.1300691664,0.0369618647,0.0804641396,-0.117089428,-0.0139653319,0.2338560224,0.0996869057,-0.2158295363,-0.1193430498,0.3088148534,0.2696031332,0.2697467506,-0.1500303447,0.3792372048,-0.0486911759,0.2993228137,-0.313067168,-0.0086727999,0.1537794471,0.5309922695,-0.0587603115,0.042542655,-0.1099325716,-0.2123454362,0.3066335618,-0.0485866182,0.1309424937,-0.086683549,-0.0389373638,-0.2812834978,-0.2382824421,-0.4406086206,-0.0238168016,0.1255407631,0.2842606604,-0.0493335761,0.0638395101,0.0588163882,0.165235281,-0.4701365829,-0.0418399125,-0.0790293589,0.3589639664,-0.192935437,0.0297458339,0.0727653131,0.1384626478,0.034725666,-0.0994308889,-0.2661091387,0.2672634125,0.3873008192,0.1424125284,0.1312465072,0.1552255452,0.0994113758,-0.4390263855,-0.0338232107,0.135319382,0.0310638174,-0.1501327306,-0.1709096879,0.4189334214,-0.1106575057,-0.1052814946,-0.146572113,0.0208273847,0.4537898004,0.024179399,-0.054329019,-0.2178453058,0.2676340044,0.1426608115,-0.0064313631,0.2213168293,-0.0907421038,-0.1963499784,0.3994533122,-0.0053327139,0.0337553807,0.0288801733,0.128982842,0.0184783768,0.1656972319,0.3875589967,0.2157541066,0.0959018469,0.0755860582,0.1123893932,-0.1315509528,-0.099850595,0.0393902622,-0.1119802892,0.3893282115,0.4901269972,-0.1116554514,-0.0743087381,-0.5724046826,0.0944614038,0.0397410542,0.3518781364,-0.3241580427,-0.1709276289,-0.1657176018,-0.1974050999,-0.3489176035,-0.0736530349,-0.4027133286,-0.391633898,-0.0803682357,0.0265671443,0.1340557039,0.322730422,-0.2867651582,-0.0150850471,-0.0740040392,-0.1073404551,0.0995985568,-0.0938936472,-0.061401505,0.1278838813,0.6395922899,0.0901677161,0.2577966452,-0.3189165592,0.1206051111,-0.3440071344,-0.4535853267,0.0076004653,-0.1180509701,0.2545837164,0.0452968702,0.2363484949,-0.3233431578,-0.3459742069,0.0152931362,0.2156957239,-0.3002871871,0.1356076002,-0.2736188769,-0.1891297549,0.1971975416,-0.2516736388,-0.1967152357,-0.1636258066,-0.1499287188,0.2357472777,0.1922660619,-0.0657025129,0.0385861546,0.1781271398,0.0871257335,0.1671443284,-0.3002986908,-0.6020169854,0.6427317858,-0.0676674768,-0.3106471896,0.2161927074,-0.0425420031,0.3593716323,0.0508558862,-0.5779349208,-0.116436772,-0.1974352151,0.1735744029,0.3323958814,0.0768523514,0.2347913384,0.0364443064,0.092593573,-0.2479865402,-0.1317107677,-0.0184463318,-0.1263778657,0.5518276095,-0.2978221178,0.3197313547,-0.1686130017,0.6876214743,0.4003271163,0.1108196378,0.24985677,0.0610611103,0.3811839819,-0.187299937,-0.2483901531,0.1715606004,-0.2233639061,-0.092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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"There are many many datasets on HugggingFace that are receiving this checksum error.  Some of these datasets are very popular.  There must be a way to track these errors, or to do regression testing.  We don't want to catch each of these errors on each dataset, one at a time.","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":50,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n There are many many datasets on HugggingFace that are receiving this checksum error.  Some of these datasets are very popular.  There must be a way to track these errors, or to do regression testing.  We don't want to catch each of these errors on each dataset, one at a time.","embeddings":[-0.0298189297,-0.2039608061,0.021171147,0.4256592691,0.2904464602,0.0431600325,0.1427191198,0.1986544281,0.2408196628,0.2792514563,0.1181160808,-0.1039055586,-0.1298158169,0.2413559854,0.25999102,0.1816881895,0.3475731313,-0.0357043408,-0.1994272918,-0.0176085643,-0.0765747875,0.2514490485,0.1913542002,-0.0831412002,-0.2702049017,0.143540293,-0.037476413,-0.0458606519,-0.0961851552,-0.3156240284,0.4989827275,-0.058095675,-0.2143417448,0.4947701693,-0.0001180434,0.1260472536,0.202056855,-0.0915214047,-0.3524873853,0.1506556869,-0.1200501025,-0.2884407938,-0.2091126889,-0.1103005335,-0.0529426262,0.0407882929,0.078472957,-0.1119009405,0.1138559058,0.1694881767,0.1817380041,0.5020492077,0.2283353955,-0.0369313434,0.2585994303,0.0330209434,0.0187478792,0.3099552095,0.0157415792,0.1295992285,-0.2918395996,0.2564724088,-0.0213222858,-0.2818884254,0.1807877868,-0.2144158334,-0.0499077477,-0.2514431775,0.1440910846,0.5065052509,0.322216928,-0.0372436419,-0.3060801923,-0.2644797564,-0.0246055759,-0.033063069,0.4261021316,0.0601156093,0.0508360714,0.0170918424,-0.3233960569,0.023750402,0.0715180188,0.0460328981,0.109943442,0.0430752486,-0.0817602128,0.0820087418,0.2388519943,0.0794569328,-0.1852270067,-0.1573903859,-0.3557602465,0.1577069759,-0.4075605273,-0.0857751891,0.1556487978,0.6347828507,0.3955194056,0.2877072692,-0.1362099946,0.1935848743,-0.3457143903,0.1565325111,0.046228718,0.1461422145,-0.1365959942,0.0742464438,0.3876881003,0.1293323487,0.2060496956,0.1797098219,0.1156407967,-0.2909772694,0.2242577225,0.0114417048,0.0541279539,-0.4145081043,-0.2349790782,0.3199017346,-0.0710397884,-0.2167425305,0.3191268146,0.2758597136,-0.1634837538,0.0615207441,-0.2063799649,0.121839568,-0.1293509603,-0.0168274511,-0.2721393406,0.0369144753,-0.1433358043,0.0763732269,0.2201177329,-0.4969211221,0.2221266478,-0.0333806947,0.1537828743,-0.2805455029,-0.181665659,-0.0340405554,-0.1686597466,0.3325064182,-0.1957553029,0.095059067,0.2128852159,-0.0764771029,-0.1874417663,-0.1008344889,-0.2800804675,-0.4415451884,-0.1067212746,0.1462283134,-0.5534384847,-0.1503221691,-0.2834893167,-0.0062785838,0.0948242992,-0.1711052805,0.0709256157,0.00675719,-0.3037882745,-0.1643489897,0.2234363258,0.4157785177,-0.063897118,-0.197060734,0.1059156433,-0.3173187971,0.2351902127,0.2427196354,-0.0921293497,0.3261114061,-0.3456440568,-0.0237341039,0.1000944003,-0.2068561912,-0.4470391572,-0.2662116587,-0.0250874236,0.2893119752,0.2298903465,0.2039141506,-0.1643795818,-0.0036505186,-0.2043403387,-0.0546555929,0.149159357,0.09852615,-0.1257723272,-0.172331214,0.3890932202,0.3261504769,0.1021523997,-0.2429689765,0.1888466924,-0.0866186991,0.1704050004,-0.3913202584,-0.1349490881,0.147245422,0.4778808355,0.1857046336,0.1283027083,-0.1723976582,-0.2918384075,0.276045382,0.0223955363,0.1127857864,0.0589620247,-0.194996357,-0.26459077,-0.1874599606,-0.312007159,-0.0317353979,0.0349721871,0.3874686062,0.1872762442,0.1720817089,-0.1672074944,0.3712114096,-0.4883309901,0.1626034975,-0.4595715702,0.1617578268,-0.0656733885,-0.0087374421,0.1075223163,0.2758812606,0.0336646773,-0.1467995644,-0.0675571263,0.2927705348,0.2019956261,0.0657947958,0.1097145677,0.2085162848,0.0377951674,-0.304708302,-0.2492866963,0.1600956172,-0.0437267795,-0.1173709482,-0.050321918,0.2811786532,-0.1223789304,-0.0567919053,-0.1827742904,-0.0356062278,0.346724242,-0.0509491898,-0.239774406,-0.1529221237,0.4442636967,-0.1366994977,0.1288744211,0.1447470635,-0.3557025492,-0.3592733145,0.4614698291,-0.1522197872,0.0574386604,0.3024897575,0.1784007251,0.1565834284,0.0883850157,0.2087766528,0.1781833023,0.0877805576,-0.024772549,0.1294391453,-0.168941766,-0.0142710293,-0.0444664843,-0.0566512272,0.1929568052,0.3843280673,-0.1598247439,0.0096174041,-0.7090126276,0.0399220958,-0.2179626226,0.2752178609,-0.5447269678,-0.0173726305,-0.2277402133,-0.1776761413,-0.4768342376,-0.2390587926,-0.582046628,-0.3556825519,0.1131468043,0.0656422973,-0.0061616604,0.1265254766,-0.2457875162,0.3816057444,-0.2001672387,0.2035467923,-0.1050112024,-0.0678451806,-0.1877445281,0.0108466204,0.5554661751,-0.0447267443,0.4686457515,-0.4204111695,0.001695967,-0.3654823899,-0.4313524365,0.0518050753,-0.2234939486,0.1508376449,0.2496622056,0.1798228025,-0.1458245069,-0.3611705601,0.0605292395,0.0204255264,-0.4573238492,0.2512241304,-0.1501252502,-0.0803693831,-0.0255886894,-0.0521657877,-0.1506505907,-0.1471599787,0.2278306633,-0.0827528238,0.1523434669,0.057726454,-0.2247159332,0.4623630643,-0.2845320702,0.359039396,-0.202853784,-0.5756493211,0.4379122257,0.1339502037,-0.2917607725,0.32690081,0.0416171588,0.011513642,-0.112344563,-0.6560747027,-0.3126475811,-0.202562511,0.0411028527,0.1789817214,0.170477435,0.2729698718,0.0283344705,0.0772958323,-0.1937310547,-0.2915948927,0.0163943376,-0.2694795132,0.6347650886,-0.3804686964,0.3180988133,-0.0704906806,0.3545866907,0.5337086916,-0.0158611871,0.2080458701,0.087714605,0.5995046496,-0.1969528198,-0.292866528,0.0572097562,-0.3334006667,-0.114943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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"@rafikg I am sorry, but I can't reproduce your issue. For me it works OK for all languages. See: https:\/\/colab.research.google.com\/drive\/1yIcLw1it118-TYE3ZlFmV7gJcsF6UCsH?usp=sharing","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":20,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n @rafikg I am sorry, but I can't reproduce your issue. For me it works OK for all languages. See: https:\/\/colab.research.google.com\/drive\/1yIcLw1it118-TYE3ZlFmV7gJcsF6UCsH?usp=sharing","embeddings":[-0.3512474298,0.1659748405,-0.0933461338,0.268979609,0.399297893,-0.102183938,0.2309538126,0.5082911849,0.2222325206,0.097330682,-0.0228512436,0.1041470245,0.1113996878,0.2219661325,-0.1851458997,0.0578543469,0.2173554599,-0.0570550561,-0.2533855438,-0.153077662,-0.2149502784,0.0624584854,-0.0554581136,-0.2200378478,-0.1602759659,0.1394704729,0.0820946619,-0.1524900794,-0.183921352,-0.1459812671,0.3293203712,0.1178591549,0.0556145534,0.3236453235,-0.0001070226,0.2040041685,0.2990839779,-0.0768877044,-0.0912546366,-0.0336382762,-0.3112199008,-0.2986932099,-0.2680976987,-0.1773664504,-0.1828228533,0.1987105757,0.0724817812,-0.167153284,0.0687085316,0.386849016,0.296248436,0.2686999142,0.0334673487,0.0407550335,0.254124403,-0.1303069592,-0.0578428581,0.0844001025,-0.0567828156,0.0805412754,-0.293905288,0.2904757857,0.0570680127,-0.1725797951,-0.1215844899,-0.220440805,-0.0051831719,-0.2169273496,0.2734150887,0.4471406639,0.5685299039,-0.0763236508,-0.1707548052,0.1354046911,-0.1687248945,0.027112497,0.445066154,0.104661338,0.0166442022,0.0882177874,-0.1283191592,0.1812103391,-0.0128591573,0.2064723223,0.0011913655,0.4863916934,0.0275998581,-0.0272047184,0.0665921196,-0.1336713284,0.3506413996,-0.1676960438,-0.3299749792,0.213067323,-0.3384400904,0.0351562537,-0.0168431513,0.4636757076,0.3403212428,0.1304292381,-0.10141702,0.4662188292,-0.218862474,0.1009370908,-0.0590824522,0.0576631241,0.0901078656,0.2241656035,0.3509391844,0.1442184895,-0.1961075664,0.0732633844,-0.0143787535,-0.3945168555,0.254204154,0.0440176204,-0.1514145285,-0.2999998629,-0.0558682233,0.2624027431,-0.0080368333,-0.2529236376,0.1494958997,0.3489671052,-0.1697717458,0.2496064007,-0.2348296195,0.2139055878,-0.2030973136,-0.023256354,-0.2385923266,-0.0051278574,-0.2369240522,-0.1497152746,0.2340784967,-0.0105166901,0.3688211739,0.0160943046,0.1878849268,-0.1130303815,0.012296333,-0.1416330934,0.0113737602,0.1866099387,0.0047238232,0.1234261766,0.1573504955,-0.1416787207,0.029663546,0.1287870407,-0.285148263,-0.0296829138,-0.1501578689,0.2882274687,-0.349113524,-0.0838891193,-0.0797335654,-0.0562766418,0.2969731987,-0.3960301876,0.0868876725,-0.1847556233,-0.2750479281,-0.1467104256,0.1294103414,0.088183865,-0.3259649873,0.0213203114,0.0726870969,-0.3432592452,0.2998966575,0.1429444253,-0.0627998263,0.0922020674,-0.2962133288,0.2469485253,0.2433599383,-0.2807215452,-0.6659144759,-0.0469716936,0.1795022935,0.1618735492,0.1357581168,0.0910639763,0.0216402058,0.1293227524,-0.2026772797,0.0493909121,0.3155243397,0.1469931155,-0.3158887923,-0.006674903,0.3165896535,0.2735822499,0.2446402609,-0.2335270196,0.3639141619,0.0499181449,0.3457918465,-0.3562970757,0.0806927457,0.1447737813,0.4330528378,-0.0159328952,0.138646841,-0.1745616794,-0.1354894787,0.3069610894,-0.0697309151,0.1822150648,-0.0300461333,-0.0434212908,-0.1732576638,-0.2805732191,-0.3978433013,-0.032017082,0.2236767709,0.2563427091,-0.0205892958,0.1739768833,0.1427452713,0.0905583426,-0.4222576022,-0.1155385822,0.0050962404,0.336824894,-0.2231057286,0.0037713088,0.004788361,0.1873339266,-0.0271902941,-0.0443051383,-0.2419209927,0.3134897351,0.4701086581,0.0484211333,0.0198172256,0.167140007,0.0906082168,-0.3428725898,-0.1127596721,0.2516058683,0.0504373796,-0.1518440247,-0.297169745,0.4999100268,-0.0330227911,-0.0115667926,-0.0218061022,-0.0190687254,0.5172951221,-0.052553311,-0.1331133246,-0.2569864094,0.2441075444,-0.0408793539,0.0003900711,0.1944840848,-0.1625532508,-0.1632014662,0.4641732872,0.0092914449,0.1717578322,0.0180268977,0.1514775455,0.0339361876,0.1181821004,0.2950573862,0.1390046328,0.1081276387,0.1418115199,0.0777812898,-0.1272169203,-0.1647812426,-0.0170148164,-0.2341514379,0.3712726831,0.447524786,-0.0482041128,-0.2246752232,-0.6316118836,0.1337641627,0.01672538,0.2425728887,-0.2643508017,-0.1346791983,-0.2212461084,-0.1324246079,-0.2962605357,-0.0639371723,-0.4264190197,-0.3862217665,-0.0160005502,-0.0804329962,0.0175884292,0.378372997,-0.2358358353,-0.1345534623,0.0276106205,-0.0995627195,0.1409177631,-0.1615411192,-0.1490496248,0.1604714245,0.5094619393,0.1349325031,0.220164597,-0.2914759517,-0.0170371495,-0.2919807434,-0.5304228663,0.0413189158,-0.1313241571,0.1775806248,0.0782503486,0.1492722481,-0.3588340878,-0.2695961893,0.1675072163,0.0906556323,-0.3385855854,0.2954136729,-0.1829354018,-0.1626638025,0.0411284678,-0.2949366271,-0.0921771601,-0.1731689572,-0.1102222502,0.1876828969,0.1447623223,-0.0025333627,0.0840766802,0.149570331,-0.0073387958,0.1797210276,-0.351832509,-0.5127817988,0.5873093009,-0.1157962307,-0.4425738752,0.0956531093,-0.0046684025,0.3887705207,0.0631879568,-0.5137246251,-0.1367838532,-0.2080623806,0.2949262857,0.3126192093,0.0638087913,0.1734394282,0.0216411501,0.0019407967,-0.3571845591,-0.0086979661,0.155868575,-0.2304262221,0.4562925994,-0.2781353295,0.2814540565,-0.0098447716,0.4831856191,0.2707049847,0.1565785408,0.2876697481,-0.022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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"@kwchurch the PR #3787 fixes this issue (generated by a change in Google Drive service) for ALL datasets with this issue. Once we make our next library release (in a couple of days), the fix will be accessible to all users that update our library from PyPI.","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":47,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n @kwchurch the PR #3787 fixes this issue (generated by a change in Google Drive service) for ALL datasets with this issue. Once we make our next library release (in a couple of days), the fix will be accessible to all users that update our library from PyPI.","embeddings":[-0.2429647148,0.3284004033,-0.0522095747,0.2091574371,0.2420457304,0.0454644598,0.4633972943,0.3967266083,0.1045801565,0.163781926,0.0637604147,0.0762737691,0.1462747604,0.2466354221,0.0161855686,0.0372886658,0.3186243474,-0.0139126293,0.0529285371,-0.0484249778,-0.2717502117,0.0992430076,0.0516766086,-0.2813887,-0.1775577217,0.0915837735,-0.0154971182,-0.1474158168,-0.2145942748,-0.2976877391,0.3630701303,0.2711386085,-0.0292168874,0.2746335864,-0.0001117598,0.1542521268,0.3934959471,-0.0631986484,-0.2276700288,-0.0399156287,-0.1614489257,-0.2265233248,-0.1312558949,-0.2220349461,-0.0333574973,-0.0030996972,0.0003840826,-0.0174434818,-0.09864223,0.2395968586,0.2454054952,0.2541171312,-0.0100394338,0.0687777027,0.3356581032,-0.1450100839,-0.0270137973,0.2756662965,0.0205961335,-0.0195358768,-0.1914079189,0.1357617825,-0.0793000534,-0.1961645037,0.0235945359,-0.0310252644,-0.1670642495,-0.2379554808,0.277762562,0.4272859991,0.5865120888,-0.093872942,-0.2626327276,-0.0272866003,-0.0849753618,0.0190163068,0.5566830039,0.2118432075,0.0474564768,-0.0024932197,-0.1924237907,0.0940830559,0.0057476563,0.1238720417,-0.1686460674,0.428140223,0.1914733052,0.0343229286,0.1235837415,0.0175501872,0.328808248,-0.1180142388,-0.425691843,0.1470744908,-0.1740647703,-0.068479687,0.1330757141,0.3431574404,0.2252778262,0.2224048525,-0.1730957627,0.2877366543,-0.2697229981,0.1241290867,0.1302493066,0.1874374151,-0.011409062,0.2733399868,0.3673291504,0.1271051764,0.0959177613,0.0223360825,0.1017738283,-0.2587534189,0.2614052594,-0.0202154461,-0.005079919,-0.2922827005,-0.2627997696,0.2016365826,-0.1291125119,-0.3730564415,0.0261177365,0.2576850355,-0.0466408767,0.0911147818,-0.2363235056,0.2688863575,-0.204957515,0.1649820656,-0.253308773,0.1524679065,-0.1731967181,-0.0278696101,0.2140692621,-0.2453378141,0.271061033,0.0421767309,0.0942254514,-0.1241503358,-0.0150731979,-0.0861123875,-0.1308228523,0.2949570417,0.0143122291,0.1004755124,0.0959801823,-0.202389434,-0.1175343618,0.2469297051,-0.3652034402,-0.0767875314,-0.2146942317,0.259601295,-0.4683569968,-0.1977325231,-0.2112932056,-0.0883974954,0.2403289676,-0.4681393206,0.1306769848,-0.1794721484,-0.1310583353,-0.2804326713,0.0372473486,0.259054631,-0.3779575825,0.0179075263,-0.1263480932,-0.2976597548,0.1981602609,0.1736905277,-0.0948909223,0.1142621636,-0.3566371202,0.2794339657,0.0968638361,-0.2543406188,-0.6746219397,-0.196468696,0.0540131442,0.1163597703,0.1670746654,0.0946897119,0.1174434498,0.0269520096,-0.1056534499,0.0656075701,0.166638419,0.1300346404,-0.248226881,-0.1982771903,0.2942418456,0.302827388,0.2473233342,-0.1520588696,0.5033192635,-0.0608472489,0.2863866091,-0.25827384,0.0216892082,0.2310230285,0.5973029733,-0.0587751158,0.1351689547,-0.0366655998,-0.2938265502,0.3135802746,-0.0038314532,0.2642474771,-0.2495038509,-0.0982116982,-0.1613921821,-0.2126327753,-0.4853449166,-0.1398939788,0.1694221497,0.2567936182,-0.0541563146,0.2334055156,0.1797378361,-0.0606841594,-0.3010220826,-0.0939206704,-0.0425496846,0.4692881405,-0.2007826269,-0.0214751419,0.0689440891,0.2037726939,0.0624882914,-0.117439203,-0.242819488,0.2975325584,0.3107622266,0.3527574837,0.1219469756,0.3336082995,0.177507773,-0.4689955115,0.1406995356,0.2660351396,0.0525828898,-0.1993561387,-0.4282051921,0.5752825737,-0.0218879152,-0.0322550721,-0.1171925291,0.0647785962,0.4082843363,0.0116083585,-0.0581577905,-0.2659296393,0.1639698148,0.2486079186,0.2323319465,0.1841914058,-0.027765926,0.0080593247,0.6215816736,-0.1458328366,0.0326088592,0.1931882799,0.0849180892,-0.1133327633,0.1761584282,0.4557320178,0.1880515218,0.1165076271,0.1425455362,0.0567684472,-0.101770997,-0.2119744867,0.0529528819,-0.139218837,0.1236706674,0.3288817108,0.1639997959,-0.117956847,-0.6834475398,0.1666319817,0.075319238,0.2911661267,-0.2444658577,0.0078933127,-0.1305849701,-0.1106940508,-0.2580085397,-0.1042099446,-0.4947213829,-0.4992268085,0.0201150309,0.0542027242,0.0456992909,0.1817440093,-0.1264663935,-0.0398936309,-0.018853439,-0.359270364,-0.041404739,-0.2184320241,-0.138200596,0.1234443858,0.5310223699,0.0714368895,0.3775666952,-0.1441090256,0.0108478274,-0.6092322469,-0.5516393781,0.0206904951,-0.3562901616,0.2166653723,0.1002516374,0.2772900164,-0.1364315152,-0.3254091442,0.0813087225,0.0069433777,-0.3522288203,0.2524473369,-0.2324387878,-0.0828956366,-0.0077516572,-0.3539751172,-0.0762419254,-0.0621806383,0.0417254418,0.2200011164,0.1578659266,-0.0196705908,0.0875236169,0.1205254793,0.1330589056,-0.0840203762,-0.3750453889,-0.46932289,0.5122591853,-0.0465380996,-0.3430694938,0.1174870208,-0.043429777,0.1772421896,0.0321112461,-0.6218957901,-0.3068764508,-0.2643946707,0.4678970575,0.3035593331,0.0458447114,0.3787958324,0.1303197592,0.0149707785,-0.2127065808,-0.2036736161,0.0849602595,0.0001441793,0.4262845814,-0.2100153714,0.320987016,-0.1780598462,0.7085469365,0.1165665537,0.2349943519,0.3198896348,-0.0115551669,0.446334213,-0.2534203529,-0.1288089603,0.279412061,-0.1991914958,-0.2012407184,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3792","title":"Checksums didn't match for dataset source","comments":"By the way, @rafikg, I discovered the URL for Spanish was wrong. I've created a PR to fix it:\r\n- #3806 ","body":"## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":21,"text":"Checksums didn't match for dataset source \n ## Dataset viewer issue for 'wiki_lingua*'\r\n\r\n**Link:** *link to the dataset viewer page*\r\n\r\n`data = datasets.load_dataset(\"wiki_lingua\", name=language, split=\"train[:2000]\")\r\n`\r\n*short description of the issue*\r\n```\r\n[NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]()\r\n```\r\nAm I the one who added this dataset ? No\r\n \n By the way, @rafikg, I discovered the URL for Spanish was wrong. I've created a PR to fix it:\r\n- #3806 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3788","title":"Only-data dataset loaded unexpectedly as validation split","comments":"I see two options:\r\n1. drop the \"dev\" keyword since it can be considered too generic\r\n2. improve the pattern to something more reasonable, e.g. asking for a separator before and after \"dev\"\r\n```python\r\n[\"*[ ._-]dev[ ._-]*\", \"dev[ ._-]*\"]\r\n```\r\n\r\nI think 2. is nice. If we agree on this one we can even decide to require the separation for the other split keywords \"train\", \"test\" etc.","body":"## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`.","comment_length":67,"text":"Only-data dataset loaded unexpectedly as validation split \n ## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`. \n I see two options:\r\n1. drop the \"dev\" keyword since it can be considered too generic\r\n2. improve the pattern to something more reasonable, e.g. asking for a separator before and after \"dev\"\r\n```python\r\n[\"*[ ._-]dev[ ._-]*\", \"dev[ ._-]*\"]\r\n```\r\n\r\nI think 2. is nice. If we agree on this one we can even decide to require the separation for the other split keywords \"train\", \"test\" 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3788","title":"Only-data dataset loaded unexpectedly as validation split","comments":"Yes, I had something like that on mind: \"dev\" not being part of a word.\r\n```\r\n\"[^a-zA-Z]dev[^a-zA-Z]\"","body":"## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`.","comment_length":17,"text":"Only-data dataset loaded unexpectedly as validation split \n ## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`. \n Yes, I had something like that on mind: \"dev\" not being part of a word.\r\n```\r\n\"[^a-zA-Z]dev[^a-zA-Z]\"","embeddings":[-0.088002272,0.0279188883,-0.027738357,0.1770325303,0.1313004345,-0.2075339556,0.1929708421,0.5046429634,-0.2156713754,-0.0228310376,0.0592501983,0.2383531928,-0.0268459637,0.1029324234,0.0529118851,0.3532879055,0.1087843254,0.3332921267,0.0507116765,-0.1112702787,-0.0983700454,0.1819355339,-0.1226709336,0.192711249,-0.2429807782,-0.0095782503,0.3027471006,0.2253686339,-0.2820610106,-0.3011007011,0.3285196126,-0.1348655522,-0.3200425208,0.2108654827,-0.0001127307,0.0867364332,0.3100730777,-0.2425787151,-0.229948923,-0.1618557274,-0.4661335051,0.1224447563,0.002922406,0.0030957989,-0.0387964249,-0.180422917,0.0942153856,-0.2950849533,0.5840403438,0.4550178051,0.1698147207,-0.139985919,-0.1834507883,0.0923474357,-0.0159523711,0.5846722126,-0.0180578306,-0.1214122549,0.4448790252,-0.0925546438,0.0198147539,0.2332059443,-0.0860273167,-0.0585179739,0.2079603672,-0.0459024385,-0.1993392706,-0.3405713141,0.1373529434,0.5796712637,0.4373649359,-0.0347630754,-0.0532055125,-0.0414835848,-0.177277267,0.0094452538,0.2168860883,0.4902154505,-0.1794683039,0.2891905904,-0.1078630164,0.027884189,-0.1414067149,-0.0385294259,-0.317674607,0.4120477438,-0.1128458381,0.097885102,-0.0814171433,0.1471854448,-0.1225546673,-0.2777609527,-0.6254977584,0.1018268093,-0.0069130082,-0.1105401739,-0.1370843947,-0.2107051164,0.4692320526,-0.0514995791,-0.0020662213,0.1048962325,-0.2957263589,-0.0634072646,0.4367685616,0.0084864954,0.4937232137,0.1586397886,0.2276130617,-0.3168098629,0.1089285761,0.0097506437,0.2091460228,0.1643746346,-0.1396287233,-0.0602679513,0.1203873828,-0.3668869138,-0.3771348,0.1149882525,-0.3664397001,-0.0535864793,-0.0346776769,0.2477166057,0.2028772235,0.1697876751,-0.1891988218,0.136162281,0.1427058131,-0.4739903212,-0.151757732,0.0036459786,-0.3302995265,-0.1633767039,0.2045799196,0.0305273049,-0.0034879057,0.2374812812,-0.3042357862,-0.3216203153,-0.0680929348,0.0701562613,0.3976744115,0.4252328873,-0.2087522596,0.1733352393,-0.013689152,-0.4104897082,-0.1033686399,0.2410847545,-0.2091011703,-0.2204131931,-0.1063975766,0.2213609964,-0.1615235358,0.3692370653,0.0733218864,0.2204330415,-0.0304393768,-0.2488638163,-0.0416626744,-0.0866958722,0.1992295831,-0.0768144801,-0.0028756724,0.1567742079,-0.6828734875,0.1623236686,-0.2088780254,-0.3590155244,0.1570587605,0.0809791833,-0.2639681995,0.2363692522,-0.3801106215,0.3654722869,0.3223423064,0.1786479205,-0.2217073888,0.6720138788,-0.3073046505,0.1076053381,0.08283066,-0.2031462342,0.1064691991,0.1434769332,-0.0469472408,0.3483634293,0.0094061531,0.0012760579,-0.1520809084,0.0238956902,0.0193945076,-0.064857699,0.1979693621,0.1135746688,0.196904242,0.121175386,0.5547032356,-0.1058443561,-0.0213078614,0.0898152292,0.1741833389,-0.3107308149,0.2624230385,-0.3220580518,-0.4222828746,-0.0238068495,0.0456324033,-0.2013204992,0.0430447683,-0.0739948303,-0.2752792537,-0.0269032624,0.0299977921,-0.0017736211,0.1412227303,0.1926028281,-0.0607842878,-0.0888655409,-0.3319377005,0.3588097095,0.1489365399,0.0609434061,-0.191907689,0.106209293,0.0521465316,0.032199949,-0.2016760409,-0.0580115132,-0.020358922,-0.2957912087,0.0934733078,0.3168710172,0.1482022554,-0.0514777526,-0.1993944794,-0.1019803435,0.117214933,-0.0518899783,-0.1795444489,0.0764934421,0.2067740858,-0.434096694,-0.093253985,0.3510423005,-0.1695165187,0.1100158915,-0.1375530362,-0.0136168906,0.364903897,-0.3598239422,0.0313142128,-0.3072941601,0.3389900625,0.0035054472,0.090729028,0.1931265891,-0.2302774787,-0.0402164459,0.3529292643,-0.2396653295,0.0377871208,0.0580864176,-0.1738014072,-0.043129921,-0.1323294193,0.4132307768,0.6813955307,0.1722759753,-0.1811075211,0.0807034671,0.0889225006,-0.2809720039,0.2968563139,0.0638199672,-0.1491852403,0.4041067362,-0.1759461164,-0.1459420323,-0.6729195118,0.0133935679,0.246894449,-0.1380820423,-0.4242042005,0.1180314273,-0.3977299035,0.2719244957,-0.1743952334,-0.1022727787,-0.3019556701,-0.2779656649,0.2254227251,-0.0439367034,-0.1054992899,0.0677053779,0.0033567965,0.2840410471,-0.2790229023,-0.1072456166,-0.4919563532,-0.1060436219,-0.2632524669,0.053761106,0.277231276,0.3794971108,0.2353537232,0.011201242,-0.1672843695,-0.0751402006,-0.2810338438,0.1681727469,-0.1843732446,0.1646609157,0.2467135936,0.2105955034,0.3654249609,-0.0909525603,-0.1676936001,-0.0394184291,-0.3000725806,0.1439758837,-0.0147053469,-0.0832973421,-0.4492007494,-0.4140071273,0.046480041,-0.0965226218,0.1044434905,-0.2364017665,-0.0139992218,-0.0258046594,-0.2590017915,-0.0477615707,-0.2779610157,-0.043184422,-0.345400691,0.1615664065,0.1862956285,-0.0166870337,-0.1711655557,0.0426372625,-0.0052397735,-0.072040379,0.0483889766,-0.5085455179,0.0424858741,-0.1498475969,-0.2727142274,-0.2583830059,-0.3506739736,0.2235365957,0.014701956,0.0100878477,-0.3156614006,0.0077383332,0.2337886989,-0.1452381611,0.2493915856,-0.1206203923,0.3022750914,0.1928419471,0.4983901978,-0.2180531323,-0.1188885048,0.0009898224,0.0539673045,0.4351734519,-0.1222121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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3788","title":"Only-data dataset loaded unexpectedly as validation split","comments":"Is there a reason why we want that regex? It feels like something that'll still be an issue for some weird case. \"my_dataset_dev\" doesn't match your regex, \"my_dataset_validation\" doesn't either ... Why not always \"train\" unless specified?","body":"## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`.","comment_length":37,"text":"Only-data dataset loaded unexpectedly as validation split \n ## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`. \n Is there a reason why we want that regex? It feels like something that'll still be an issue for some weird case. \"my_dataset_dev\" doesn't match your regex, \"my_dataset_validation\" doesn't either ... Why not always \"train\" unless specified?","embeddings":[-0.1274332255,0.0463365652,0.0139261931,-0.0671942234,0.1907559931,-0.0963911489,0.5693850517,0.5711490512,-0.2740080357,-0.0007296972,0.0903317034,0.0587168112,-0.179105252,0.1223729551,-0.005380176,0.2239865214,0.0502592064,0.4511589408,0.2187023759,-0.0683338344,-0.0636768416,0.0432171337,0.0228769332,0.1736979336,-0.4844001234,0.208133772,0.3272176981,0.1623620242,-0.2965735793,-0.3022829592,0.3425771892,-0.3363748491,-0.0769948289,-0.0556685068,-0.000122981,0.1867652088,0.2002071142,-0.1363002062,-0.1785706878,-0.3270830214,-0.4942641854,0.1222937927,-0.0109435199,0.0674773082,-0.0173856448,-0.0483549647,0.3441960216,-0.2737605274,0.530836463,0.5715952516,0.0979135111,0.0065607284,-0.3564877808,0.0939724594,0.0976552442,0.3291975558,-0.0897977352,-0.1599944234,0.4199915528,-0.1325363964,-0.0978729501,0.1251478046,-0.0446917191,-0.1680592,0.0469343588,-0.0101857828,-0.0825900063,-0.3096586466,0.2554816604,0.6954454184,0.4477854669,-0.0251664147,0.0107357539,0.0254825111,-0.14846766,-0.0823857933,0.0225954987,0.4315354824,0.0543198362,0.4797018766,-0.2492552102,0.0409529172,-0.1632233709,-0.023087237,-0.2802360058,0.5173515677,-0.0831860155,0.2711013854,0.0402002074,0.4279142618,-0.2724554837,-0.2230664939,-0.3578901589,-0.050299082,-0.0016082736,-0.1989905238,-0.3146204352,-0.2628298104,0.3226871192,0.432733655,-0.0674809292,0.0758017004,-0.1262875646,-0.0972768292,0.2755651772,0.0212222524,0.3894793689,0.2469431162,0.2787753344,-0.371048063,0.0140632847,-0.1107735485,0.1411891282,0.2016811818,0.081483297,0.1657750905,-0.0132221533,-0.2512494028,-0.3512887061,-0.0178463254,-0.4045107365,-0.1001957878,0.0055158548,0.1085740775,0.2407179773,0.2439896613,-0.4038712084,0.1656776518,0.2356005907,-0.5061634779,-0.0921806917,0.0377103202,-0.3894563019,-0.0583244525,0.2534429729,-0.0866115987,0.1899886578,0.0742765814,-0.3072029948,-0.0023419724,0.0201724991,-0.2132630944,0.3155401647,0.5904126167,-0.4135338962,0.1819062978,0.0500132293,-0.3237427473,-0.1929771304,0.2625342309,-0.2036288083,-0.5489265323,-0.0106971646,0.1438617557,-0.0768964738,0.2821521461,-0.1801211983,0.1490896791,-0.0982888266,-0.3013285398,0.0149889002,-0.2428861409,0.2346042991,-0.1030109599,-0.0002065082,-0.0826100484,-0.6040170789,0.2746362984,-0.3007231057,-0.3635173738,0.2171871513,0.1278237998,-0.3864672482,0.2402827591,-0.3109819591,0.3648309112,0.3597526252,0.135112226,-0.1976697892,0.4856895208,-0.3041079342,0.0908471048,0.2601341605,-0.0522634797,0.1489023864,0.0610068142,-0.1732938141,0.2933680415,-0.100937672,-0.1435781866,0.0748902261,-0.0431973711,0.1050146893,0.0040324507,0.2468315363,0.1898636669,0.1936397254,0.1477013677,0.3714068532,-0.1822835058,0.015783418,-0.1406560242,0.2806784511,-0.2896481752,0.1834214181,-0.4289604127,-0.4562346935,0.0528905801,0.0901559368,-0.2520072758,0.1585744321,-0.1793937981,-0.2259932905,-0.1937545836,0.1052117199,-0.0799261257,0.0397095308,0.1125276983,-0.0310979504,-0.0483870953,-0.4218491912,0.2814528346,-0.0034771445,0.0888525844,0.0186825301,0.1923717707,0.0825931728,0.1141179502,-0.2103026807,-0.0090361303,-0.1777080745,-0.2034908682,0.2072505057,0.2492368519,0.2657283843,-0.180302158,-0.127754584,-0.0213789605,-0.0074299751,-0.1148177013,-0.1796225607,0.0451897085,0.0589263029,-0.5449883938,0.003994491,0.1283180118,0.0965889394,0.1933585554,-0.1384186894,-0.1197092012,0.0559017621,-0.3582339287,-0.1664898992,-0.2436105013,0.0453907773,-0.0530718453,0.128959164,0.3347526491,-0.2809301317,-0.2990193069,0.4270187616,-0.2714177072,-0.0572745502,0.1468416154,-0.1437078863,-0.0842588842,-0.1225657761,0.3349521458,0.7469993234,0.1922905594,-0.0878652781,0.1136658937,-0.030504588,-0.294841677,0.1681534052,0.0732657164,0.0639810786,0.1091219112,-0.2290091068,0.074455373,-0.6714305282,-0.2981657386,0.122945562,-0.1983818859,-0.2662791312,0.0842712,-0.4775272012,0.2308640182,-0.1554441005,-0.0118726995,-0.1630339473,-0.1460319906,0.1756571978,-0.169139877,-0.1561113894,0.0287745893,-0.2136192471,0.2595208883,-0.2454552054,-0.2197814733,-0.462198168,0.0571587123,-0.3422289193,0.02861581,0.0417512581,0.1492390037,0.1469502002,-0.0068022069,-0.1180597916,-0.1357982904,-0.3937138319,-0.0629358441,-0.1668349206,0.1260945797,0.2520232797,0.3180125952,0.1872455627,-0.1950896978,-0.2079909444,0.1797131598,-0.1451697797,0.0714099929,0.0943468064,0.0263677314,-0.3377201557,-0.4132433534,0.1484668851,-0.1175496504,0.0593731217,-0.360272944,-0.0952740163,0.1752778143,-0.2760939002,-0.0869190767,-0.4541954398,-0.0000819869,-0.1735533625,0.0805504099,0.2838127017,0.1558986604,-0.0717750788,0.0006086223,-0.1479669213,-0.1115326434,0.0642512664,-0.5772457719,0.0380982906,-0.1802288592,-0.3835252821,-0.0880153403,-0.2557346821,0.1719509661,-0.1636442691,0.0942872167,-0.1173135117,-0.0057910834,0.1879886687,-0.0684871078,0.1785638481,-0.0064169699,0.2433362752,0.364970535,0.5561192036,-0.1479273438,-0.1108958796,0.0841813087,0.0577650331,0.4436391294,-0.2950551808,-0.1336354464,0.0756597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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3788","title":"Only-data dataset loaded unexpectedly as validation split","comments":"The regex is needed as part of our effort to make datasets configurable without code. In particular we define some generic dataset repository structures that users can follow\r\n\r\n> ```\r\n> \"[^a-zA-Z]*dev[^a-zA-Z]*\"\r\n> ```\r\n\r\nunfortunately our glob doesn't support \"^\": \r\n\r\nhttps:\/\/github.com\/fsspec\/filesystem_spec\/blob\/3e739db7e53f5b408319dcc9d11e92bc1f938902\/fsspec\/spec.py#L465-L479","body":"## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`.","comment_length":41,"text":"Only-data dataset loaded unexpectedly as validation split \n ## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`. \n The regex is needed as part of our effort to make datasets configurable without code. In particular we define some generic dataset repository structures that users can follow\r\n\r\n> ```\r\n> \"[^a-zA-Z]*dev[^a-zA-Z]*\"\r\n> ```\r\n\r\nunfortunately our glob doesn't support \"^\": \r\n\r\nhttps:\/\/github.com\/fsspec\/filesystem_spec\/blob\/3e739db7e53f5b408319dcc9d11e92bc1f938902\/fsspec\/spec.py#L465-L479","embeddings":[-0.2896311283,0.1827503443,0.0006292716,0.1775887609,0.1653616428,-0.2184230238,0.2060531825,0.5261226892,-0.1824198812,-0.0074537867,-0.0446323007,0.0348759815,-0.0555939078,0.2794864774,-0.0553953983,0.3991408944,-0.0021835312,0.399159044,-0.0350155793,-0.0411930606,-0.1496796161,0.2117254883,-0.1078799069,0.1184669212,-0.3444529176,0.1370176226,0.2455454618,0.3243270814,-0.3588831127,-0.3046653569,0.458640933,-0.1565334946,-0.2863329649,0.1113541424,-0.0001159994,0.1259217113,0.1525174528,-0.1789465696,-0.2675485313,-0.1815841049,-0.4554390311,0.0686190873,-0.0056656972,0.0900882185,-0.1110185534,-0.22697258,0.0586023852,-0.3423709273,0.606983006,0.5616223216,0.1341303736,-0.1063446775,-0.2288362682,0.0253247693,0.0242944453,0.4447612464,-0.0440387651,-0.2380091697,0.2624004781,-0.1406498998,0.0810167938,0.0811199695,-0.0909405053,-0.0375018008,0.0959652513,-0.0784856528,-0.1244974211,-0.401990056,0.1006685421,0.6224045753,0.4138922989,-0.0100060105,-0.0931238979,-0.0069616404,-0.1539283395,0.0546344742,0.0720095709,0.4565141201,-0.0896625221,0.3684673309,-0.1097532213,0.0637997091,-0.0861231014,0.049601227,-0.3203403056,0.2602611184,-0.2232464701,0.0937945172,0.0839725956,0.146091789,-0.3984855115,-0.3803638816,-0.6075879335,0.1195205748,-0.0979220867,-0.167776078,-0.093318522,-0.2156542838,0.4105136395,0.1522500068,-0.1073725075,0.1691991687,-0.3459667265,-0.0103987809,0.2671326697,-0.0171794835,0.4530314803,0.2401887923,0.2560346127,-0.3048669994,0.0462497771,-0.0413677804,0.2935551405,0.0395997167,-0.2011382729,0.0629076064,0.1799268872,-0.3945200741,-0.4580667317,0.0937925875,-0.2040708959,-0.0116819991,0.0661965758,0.1777328551,0.2486944795,0.3133428097,-0.2401290536,0.1938104033,0.0562792793,-0.4795052111,-0.1773531586,-0.0548037961,-0.3577198088,0.0084045595,0.2416388541,-0.0237179399,-0.0289414059,0.1417959929,-0.3976960182,-0.1964898407,-0.1908515543,-0.1300801486,0.3800012767,0.596575141,-0.3666496277,0.096715413,0.1393505186,-0.3041128516,-0.1386105865,0.1361202002,-0.056830205,-0.2704534531,-0.1532317698,0.1874801517,-0.1741658002,0.3538158238,-0.2573140264,0.1827445477,-0.1472261101,-0.2873314023,0.0108797178,-0.1127265245,0.0776915103,-0.0659433007,0.0746821761,0.2974649668,-0.6433681846,0.0913626701,-0.2301869094,-0.3990930617,0.1192218736,0.2021074891,-0.2143281996,0.1765886694,-0.4728200436,0.4565638602,0.3657466769,0.0958788693,-0.3046408594,0.6667104959,-0.27398628,0.0467534661,0.0876990557,-0.1908031255,0.2351697981,0.0659251139,-0.2597238421,0.3934434056,-0.0541124493,0.0020925635,0.0015397897,-0.1680369675,-0.0467901677,-0.0394720212,0.2865822315,0.1542033702,0.2903695405,0.0084358416,0.5125174522,-0.1320218146,-0.0309150685,0.1209080964,0.3462734222,-0.1700336635,0.1344719827,-0.5364054441,-0.5545496345,0.1330640763,0.0967682078,-0.2239298671,0.0410261527,-0.0488659143,-0.273118645,-0.1434882879,-0.0393720567,-0.1070448682,0.0742943436,0.1293675005,-0.0219041631,-0.0710807219,-0.1807537675,0.4108897448,0.2079589218,0.1866017878,-0.1964506656,0.30971995,0.1424997002,-0.041645661,-0.099257648,0.0603027642,0.1433231682,-0.3311033547,0.0731175467,0.3588106036,0.3407734036,-0.0719216764,-0.1949144155,0.1283366084,0.0496067442,-0.1754577607,-0.1504823565,0.0469728708,0.1415186971,-0.431209892,-0.1089035943,0.3113250136,0.064641811,0.1727057397,-0.087426573,0.0332277827,0.2560132444,-0.3702300191,0.054222811,-0.3421584964,0.1724660099,0.1347082555,0.1809304506,0.411840111,-0.1587641388,-0.0542279892,0.3659169972,-0.2151606828,-0.0668394864,0.1150425971,-0.2638653219,-0.0381768979,-0.0424658991,0.3490414321,0.7468740344,0.1998914927,-0.2205108553,0.095773004,0.0970174,-0.2122473717,0.2961141467,-0.0244683027,0.0312596336,0.2977830768,-0.1570771635,-0.0926321372,-0.6620435715,-0.0309585668,0.1602119654,-0.161706537,-0.4178562462,0.1038517281,-0.4899833202,0.1920449883,-0.1793439686,0.033889465,-0.1955180168,-0.2178632468,0.0142099475,0.0655782223,-0.0433268547,0.0714674741,-0.043802578,0.1893530786,-0.2004003972,-0.2836703062,-0.3610703647,0.1382301003,-0.2255534232,0.0117990263,0.2605451345,0.3977605402,0.1363845766,0.1419319808,-0.1315458268,-0.2591775954,-0.2653755844,0.1579371691,-0.1939015388,0.1424243152,0.2616325915,0.1834741384,0.4881882966,0.005905489,-0.1424968094,-0.0226339214,-0.2403039783,0.2094313949,-0.0267576668,0.0122926403,-0.3627326488,-0.329472065,0.04043952,-0.0505187437,0.18354173,-0.3664740324,-0.0644116476,0.0799135417,-0.1437742412,-0.1214527786,-0.3125747442,-0.164902553,-0.2733038962,0.0664720908,0.2407579273,0.0618508346,-0.2340226322,-0.0364892371,-0.0670503676,-0.2482155561,0.0449329652,-0.445525229,0.0528757237,-0.3688613474,-0.1697103083,-0.0979591385,-0.266294688,0.3213313818,-0.0186872296,-0.0217785053,-0.2376883775,-0.010692995,0.215975374,-0.0389969051,0.0920840278,-0.2024389654,0.2377744466,0.2574343383,0.543055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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3788","title":"Only-data dataset loaded unexpectedly as validation split","comments":"> \"my_dataset_dev\" doesn't match your regex, \"my_dataset_validation\" doesn't either ... Why not always \"train\" unless specified?\r\n\r\nAnd `my_dataset_dev.foo` would match the pattern, and we also have the same pattern but for the \"validation\" keyword so `my_dataset_validation.foo` would work too","body":"## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`.","comment_length":39,"text":"Only-data dataset loaded unexpectedly as validation split \n ## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`. \n > \"my_dataset_dev\" doesn't match your regex, \"my_dataset_validation\" doesn't either ... Why not always \"train\" unless specified?\r\n\r\nAnd `my_dataset_dev.foo` would match the pattern, and we also have the same pattern but for the \"validation\" keyword so `my_dataset_validation.foo` would work too","embeddings":[-0.2208984345,0.0857266337,-0.0347688533,0.1135792211,0.1809141785,-0.0996530801,0.3154064119,0.5672202706,-0.1893932521,-0.0838952214,0.1645902246,0.2088008225,-0.129832983,0.1652335972,-0.0917873085,0.2014836669,-0.0027637859,0.3495736122,0.0560588017,-0.0532924831,-0.076896973,0.1015236676,-0.122002162,0.1134168431,-0.3804502785,-0.0129387006,0.2597783208,0.163485229,-0.362043798,-0.3314374089,0.3813689947,-0.2071760595,-0.0868865699,0.1233195513,-0.000104576,0.1588644087,0.2500627935,-0.1314888299,-0.1876455396,-0.2426013649,-0.4930363595,0.0607703589,0.0096519198,0.066970177,-0.1385424435,-0.2664804459,0.1241428405,-0.2396095246,0.5834530592,0.5452648401,0.2499004751,-0.0196580701,-0.2805877328,0.0571712777,0.072776638,0.2485498041,-0.0073453262,-0.0920424089,0.2138445675,-0.1290038377,-0.0983039737,0.3159591556,-0.0179419871,-0.1643265486,0.1405121386,-0.019314846,-0.1826287657,-0.2872433066,0.0834219232,0.5308599472,0.4345598221,-0.0452844799,-0.0788735226,-0.0015872834,-0.0702615157,-0.1049455553,0.1382253319,0.3547205031,0.0351525769,0.3261364698,-0.2698931992,0.0514911525,-0.1947723627,-0.0395853631,-0.283971101,0.456830889,-0.0912599266,0.1102964282,0.0835558549,0.1523317546,-0.2732423246,-0.2356929034,-0.5262628198,0.0965177491,-0.1425984502,-0.1510580778,-0.1848590076,-0.3051794171,0.3874665797,0.2818527222,-0.0299469084,0.1446030736,-0.2508957386,0.0090976385,0.3614221215,0.1660152674,0.3554050922,0.2600718439,0.1994612366,-0.2923181355,-0.0613251552,-0.0507889912,0.0948836207,0.0503208116,0.0841492638,0.0584196411,0.0275167041,-0.2168027908,-0.3912944794,0.152366519,-0.3639258444,-0.0782737136,0.0787516236,0.2389550805,0.1147419959,0.1175180823,-0.2474587262,0.1262148917,0.0434964858,-0.4197576344,-0.2397579402,0.1341084242,-0.3907539248,-0.1928761452,0.2594442964,-0.0486620255,0.1317231506,0.1608012319,-0.3307393789,-0.1238425374,0.0974707082,-0.0635736883,0.3475725055,0.4044531286,-0.2226878256,0.1330803931,-0.0225310903,-0.2255931944,-0.1716924608,0.1379488707,-0.2153739631,-0.3211957812,0.04470158,0.3423939645,-0.1430352777,0.1791964173,-0.0643164665,0.2351074219,-0.0738046393,-0.2133551389,-0.0663408041,-0.2143987566,0.1607054174,-0.1522091627,0.1527089924,0.0536452644,-0.5616633296,0.1415903717,-0.1821114123,-0.4047579765,0.1426196247,0.0634795651,-0.3252721727,0.260184586,-0.2569127679,0.4105698168,0.3593784273,0.047448989,-0.217916131,0.5959793925,-0.2907435,0.1160454601,-0.0133617437,-0.1479091942,0.1549328417,0.2354369015,-0.0599444248,0.4456369579,0.0063779182,-0.0317706801,-0.0838368535,-0.0573592931,-0.0183690842,0.0314054899,0.2037584186,0.0251165349,0.1158453822,0.1267816126,0.4252818227,-0.0975789875,0.0182106085,0.0657183081,0.2228586376,-0.3852426112,0.174911201,-0.3782429695,-0.455494374,0.0621349961,0.1014204696,-0.1375290602,0.1158957183,-0.0965654105,-0.351544261,-0.1073810905,-0.112665005,-0.1669163555,0.2667331398,0.0847346857,0.0104706809,-0.1284503341,-0.349945426,0.3109073043,0.1858596057,0.0998648256,-0.1760520488,0.1197582483,-0.014788569,0.0132744964,-0.1002954245,-0.0243149847,-0.0675534979,-0.2621866167,0.1230676323,0.3927683234,0.217102915,-0.0299398825,-0.1052883416,-0.1021350324,0.1064500213,-0.0435717516,-0.2622540891,0.0379270874,0.0455438942,-0.2813709974,-0.108936131,0.2900725305,-0.0013889121,0.1250045151,0.0022899567,-0.0796226785,0.2583753467,-0.3891922534,-0.0761532262,-0.3440887332,0.0553216301,0.0281457677,0.0582247861,0.2622612715,-0.2873220146,-0.0727630034,0.4256911278,-0.1903771162,-0.1109368354,0.0080456147,-0.1654184759,-0.0901099816,-0.0371381603,0.4648385942,0.5463998914,0.2240348905,-0.0557415597,0.0853977799,-0.1035086662,-0.2827940583,0.1874669194,-0.0605968796,0.0694380999,0.3435251713,-0.1227880493,-0.0765340328,-0.5940359235,-0.1997366995,0.2439713627,0.0213103537,-0.2915415466,0.1252110153,-0.5876446366,0.1531880498,-0.180186227,-0.034741275,-0.1548663527,-0.2169409394,0.2054713219,-0.1039911136,-0.1894842088,0.1027425677,-0.1585293263,0.0663044453,-0.1614370048,-0.139614284,-0.314532578,-0.0340427123,-0.3989769816,0.1783142388,0.1830153614,0.202048555,0.1871420294,0.0390509404,-0.142593503,-0.093196854,-0.2514522672,0.0934621021,-0.1117909774,0.1537796706,0.2714635432,0.2392383814,0.1676792502,-0.2009637505,-0.0879252329,0.0900718272,-0.2527702451,0.1226183847,0.03595341,0.0414168127,-0.369928658,-0.6525768638,0.0557039715,-0.1968078613,0.0148074236,-0.2343952209,-0.0492217503,0.1713948548,-0.0538853705,-0.0140293073,-0.27529338,0.0155106103,-0.2643646598,0.0334713608,0.2760209441,-0.0172580425,-0.138799578,0.018417839,-0.1217751801,0.0324588977,0.1251866072,-0.454574883,0.0104655465,-0.137670368,-0.2267862111,-0.1906207949,-0.3300799727,0.1948112547,0.0004654508,-0.0723876581,-0.1768394262,0.0356079414,0.2506485581,-0.0810753629,0.2056279927,-0.1632078439,0.279627502,0.2377657741,0.48543185,-0.1465912312,-0.2193388641,0.1091303676,-0.0153392535,0.3091852963,-0.1717417091,-0.1708227098,-0.051398024,-0.0194155499,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3788","title":"Only-data dataset loaded unexpectedly as validation split","comments":"> The regex is needed as part of our effort to make datasets configurable without code\r\n\r\nThis feels like coding with the filename ^^'","body":"## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`.","comment_length":24,"text":"Only-data dataset loaded unexpectedly as validation split \n ## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`. \n > The regex is needed as part of our effort to make datasets configurable without code\r\n\r\nThis feels like coding with the filename ^^'","embeddings":[-0.2534264028,0.1374669373,-0.0633535236,0.1325033009,0.1538759619,-0.1122744679,0.2060324252,0.5831243992,-0.1359724104,-0.0397470482,0.0174304284,0.1966339648,-0.0144110005,0.1911667883,-0.0082557062,0.3433128595,-0.0202850308,0.3917876184,0.1030328497,-0.0527235866,-0.1903064847,0.1377196014,-0.1135969833,0.1488873065,-0.2576709688,-0.0880301371,0.3335208595,0.2319152504,-0.2738495171,-0.3377774954,0.3790849745,-0.1357727945,-0.2470934987,0.1103559285,-0.000109626,0.0997929052,0.2316362858,-0.1707499325,-0.2282269448,-0.2254210263,-0.4662268758,0.0920559466,0.0786789432,0.0741876289,-0.0905462652,-0.2103116661,0.0876301229,-0.3661448658,0.6112679839,0.4726113379,0.1914151758,-0.0424764231,-0.2418504506,0.0434882306,0.0062210271,0.4266785383,-0.0177231692,-0.144967109,0.4151012003,-0.0726131201,-0.0263072271,0.2632802427,-0.0692825541,0.0331850573,0.1274673343,-0.0785608739,-0.2832398415,-0.3884375691,0.208441779,0.6335547566,0.4379570782,-0.0504162423,-0.0060889004,-0.009395468,-0.141963169,-0.0266385023,0.1768463999,0.490552783,-0.1171717048,0.3152581155,-0.2586171627,0.0299108829,-0.1499055624,-0.0153249614,-0.3329435587,0.3917550147,-0.1881999373,0.1212029308,0.0742554963,0.1509062052,-0.2433812022,-0.2829331458,-0.602609396,0.0193454456,-0.0603382587,-0.113021493,-0.2505188584,-0.1746103913,0.4233032763,0.1407607347,-0.0121223545,0.1704205871,-0.2575063407,-0.0422898382,0.3435744345,-0.0172562189,0.4756169319,0.2419594377,0.2894833386,-0.2996279597,0.1191264018,-0.0307964794,0.2374696285,0.0704793483,-0.0795251504,0.0504429266,0.1379532814,-0.3435830772,-0.4305416346,0.112812452,-0.2376895547,-0.0341486372,0.0450685397,0.1607361287,0.241193682,0.1232140437,-0.2608866394,0.1577786058,0.1206922233,-0.4907970726,-0.1601374745,-0.0061083254,-0.3168104887,-0.0982416868,0.2807225883,-0.0627444535,0.0568843633,0.234199509,-0.3388654888,-0.1769966334,0.0385920554,-0.0132178171,0.3620590568,0.5385186076,-0.1930283904,0.1522208005,0.0324255638,-0.416113764,-0.1281830072,0.1447970569,-0.1648785919,-0.2333536893,-0.089215003,0.2771678269,-0.1500632763,0.3300786614,-0.0673269928,0.1619232446,-0.1253925264,-0.2339958549,0.0062609701,-0.1406200975,0.294343859,-0.1050925851,0.0605702698,0.2052351087,-0.7030361891,0.1865289807,-0.2564065456,-0.4325766265,0.11583592,0.1193080395,-0.3030804694,0.1798395514,-0.3579505384,0.345751375,0.381065011,0.1369225383,-0.2833895683,0.6882148981,-0.3251454234,0.0281323735,0.0390314609,-0.0916057974,0.2069053501,0.2147555947,-0.1465684772,0.37396276,-0.0274132863,-0.001882941,-0.0861037448,-0.0619510114,-0.04601321,-0.0353171229,0.2768217623,0.0590764508,0.2491989285,0.0737738535,0.4694327414,-0.1444509923,0.0342189968,0.0812132508,0.2003496438,-0.3073643148,0.1330463141,-0.5007241368,-0.5176320672,0.0246730577,0.1073090434,-0.244692862,0.006626437,-0.0949314758,-0.3481309116,-0.0917699486,-0.0536602363,-0.0790446699,0.1834316105,0.2141593248,-0.0031710919,-0.0842809156,-0.2729726136,0.3970384002,0.177832365,0.1381016225,-0.205852434,0.2008201778,0.1437508017,-0.0335223638,-0.1138031036,-0.09790802,0.0074373186,-0.3075511754,0.0949085876,0.3141040802,0.202597782,-0.028511053,-0.1019746363,-0.0770140439,0.1247524694,-0.0890922397,-0.1983442903,0.0812766477,0.1366221756,-0.3862431347,-0.1405498832,0.3571501076,-0.1814738959,0.1216457114,-0.1029760912,0.0359729789,0.2368496507,-0.3651053607,0.0488837548,-0.3083798587,0.1828193218,0.002790764,0.1543618739,0.2497233152,-0.2458806485,-0.1465782672,0.3283917308,-0.2128027231,-0.0066975001,0.0520050377,-0.2213094831,-0.0352246389,-0.0412175506,0.4420039058,0.6184745431,0.2260458767,-0.1499776691,0.0490547717,0.0355686396,-0.3064547479,0.2550814152,0.0559418648,-0.0558010675,0.322193414,-0.1605852693,-0.1092708558,-0.6805307269,-0.042768307,0.2070407122,-0.106444262,-0.3759014606,0.0882658735,-0.5233182311,0.2416744977,-0.1156824306,-0.0798479021,-0.226300016,-0.2453113496,0.0903636217,-0.0444913208,-0.0965109915,0.091901876,-0.0945709497,0.1535678059,-0.1892304122,-0.2116939873,-0.3781189322,-0.0392394736,-0.2461030036,0.1237175837,0.2165718228,0.3587954938,0.2338155955,0.0606098883,-0.0607012846,-0.1768612564,-0.2637051642,0.1290323436,-0.1794264913,0.1678744406,0.2429053932,0.2681182027,0.3348748386,-0.0951916724,-0.1792085171,0.0440236777,-0.1547350436,0.1360843182,-0.033570949,-0.0308867749,-0.3797041178,-0.4908772707,0.0832906738,-0.1405000091,0.1521504968,-0.2634127736,-0.0613117851,0.029612774,-0.1183201671,-0.0717010126,-0.2554940581,-0.0650648326,-0.2728635073,0.0778047144,0.2598479986,0.0274454001,-0.1897049248,0.0276054684,-0.024532361,-0.0578681752,0.1157943159,-0.490701288,0.0456411839,-0.233008489,-0.1679980755,-0.173129037,-0.318623513,0.1718342453,0.0008054888,-0.0167593043,-0.204791829,0.0448749736,0.196048066,-0.1827642322,0.149135381,-0.1627620608,0.3199967444,0.1503566206,0.4808170497,-0.1954451799,-0.1431332082,0.0212288816,0.0393120274,0.467815429,-0.1227462068,-0.1801377535,0.0205203351,0.0546174981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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3788","title":"Only-data dataset loaded unexpectedly as validation split","comments":"This is still much easier than having to write a full dataset script right ? :p","body":"## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`.","comment_length":16,"text":"Only-data dataset loaded unexpectedly as validation split \n ## Describe the bug\r\nAs reported by @thomasw21 and @lhoestq, a dataset containing only a data file whose name matches the pattern `*dev*` will be returned as VALIDATION split, even if this is not the desired behavior, e.g. a file named `datosdevision.jsonl.gz`. \n This is still much easier than having to write a full dataset script right ? :p","embeddings":[-0.3309618235,0.0703853667,-0.1060946956,0.1277532279,0.1929706037,-0.1053082272,0.2801699936,0.5553888083,-0.0073977467,0.1058118939,0.1400861293,0.269392401,0.0448249131,0.2297164351,0.014963055,0.2836962342,-0.0368918069,0.3354949951,-0.1030773073,-0.09599711,-0.2033821344,0.1345839798,-0.1522018015,0.0484116487,-0.1609863639,0.016534159,0.2368632108,0.269030422,-0.2907958031,-0.3309462965,0.3171502352,-0.0525796376,-0.1299057007,0.1333444566,-0.0001025964,0.1290192604,0.3580307662,-0.1623654962,-0.2039929479,-0.0962607563,-0.320009172,0.0364690572,0.0859638825,-0.0730933845,-0.1251759678,-0.21713157,0.0332992747,-0.3119279146,0.5330464244,0.4383553267,0.2959493697,0.0110101681,-0.2266827375,0.0062906635,-0.0227616187,0.3966067731,-0.0897800773,-0.0501822531,0.4204058349,-0.0969832242,-0.0299730711,0.2262676358,-0.0589034557,-0.0883779377,0.2656701505,-0.085040465,-0.2587170601,-0.3214088082,0.1843139827,0.4006026983,0.5153904557,-0.0708760023,-0.0535391122,0.0299404282,-0.1233320907,-0.0614001378,0.1370181441,0.4438123405,-0.0304279346,0.2717040181,-0.2638072968,0.0759126842,-0.1946531981,-0.10418237,-0.2198858708,0.2486580908,-0.1339425892,0.1810288876,-0.0043835756,0.1467388868,-0.057096079,-0.3050841987,-0.5694639683,0.0469871499,-0.0783913657,-0.2236005068,-0.041422341,-0.135150075,0.4133839905,0.1020520627,0.0336495042,0.1835558116,-0.2357759178,-0.0478005819,0.4572341442,0.0603533946,0.4621773362,0.1949074566,0.2651198208,-0.3177362084,0.1303928196,0.0276962742,0.1811256558,0.0358846039,0.0494072139,0.0593120083,0.1709582508,-0.3450156748,-0.4236601889,0.0924023837,-0.1499183029,-0.0277376976,-0.0221761744,0.3059894443,0.234290868,0.1008734927,-0.1832647473,0.1357486397,-0.0148036405,-0.2968326509,-0.1944213957,0.0995212048,-0.3053974509,-0.184695974,0.25779441,-0.031973172,0.0830242112,0.1337875426,-0.2741501033,-0.1819568425,0.1403174847,0.092035234,0.3487985432,0.4546729326,0.0629423335,0.0790196583,-0.0136108659,-0.2489106506,-0.126571551,0.2455446422,-0.1639719158,-0.2543496788,-0.0395719744,0.3508048058,-0.1112662554,0.3070956767,0.0153619749,0.1108809188,-0.0867564529,-0.2571776509,-0.0146993697,-0.1921212673,0.0596702546,-0.1718803644,0.0233740862,0.2221717685,-0.6134169102,0.224649027,-0.1803044379,-0.4256346524,0.0383532904,0.0574864857,-0.3252331614,0.2121919543,-0.3945580125,0.2904384136,0.2569005191,-0.0476122648,-0.2074224353,0.6224556565,-0.2729626596,0.035676647,-0.0653124154,-0.0407639705,0.2238396853,0.0948804095,-0.0760903135,0.3104021549,-0.0230581332,0.1568607688,-0.0974213704,0.0015762929,0.0303130206,0.0178600308,0.2396214306,0.0430070199,0.2412914038,0.0426512994,0.4705911577,-0.1650230139,-0.005880428,0.106220834,0.1197737083,-0.4056812823,0.209761247,-0.2766773403,-0.5367823243,0.1016043127,0.1252337843,-0.1748456657,0.0362333842,-0.1146454662,-0.2598356009,0.0195982195,-0.0993899703,-0.1079750806,0.247024104,0.1720020026,0.0438214503,-0.2324726433,-0.3408408165,0.2903355062,0.1341687441,0.0367854983,-0.2781133056,0.1410366595,-0.0235650893,-0.0273387972,-0.0241337195,-0.1196674034,0.0237569455,-0.401874572,0.0536528826,0.3820715845,0.2330934256,-0.0220878739,-0.0743713826,-0.1029623672,0.0936062708,-0.0495205671,-0.1067845002,0.0393970162,0.1669900119,-0.3210772276,-0.2054782659,0.4737871885,-0.3001962602,0.0834620669,-0.1006128863,0.0436830595,0.2282475829,-0.2986279726,-0.0677999407,-0.3276397288,0.1605600119,0.1201535091,0.0870151371,0.1472115815,-0.2548534572,0.1517410576,0.3924946785,-0.2390089333,-0.0388407521,-0.0250018239,-0.074557513,-0.05546543,0.0043308833,0.4093601406,0.6356885433,0.1957814842,-0.2091552168,0.0672509745,-0.0043142177,-0.2182164639,0.3147996068,-0.0402938128,-0.0695201159,0.2633120418,-0.150373891,-0.2045220584,-0.5881255269,0.0355357639,0.1857346445,-0.0336123966,-0.3687416017,0.070055455,-0.4303840101,0.2599395514,-0.1211548746,-0.0621433929,-0.1135295704,-0.3575889468,0.1901266873,0.0248225536,-0.2035489529,0.1178438365,-0.0631943122,0.2219468802,-0.1046076417,0.0040152725,-0.3240401149,-0.1843868941,-0.1938624382,0.1700474173,0.2974514067,0.3323097229,0.3181928396,-0.0789499879,-0.0920943543,-0.2156677991,-0.2310534567,0.1469627619,-0.2359436005,0.1516396552,0.2616999447,0.270355463,0.3450802565,0.0401287936,-0.1634697616,-0.0505615175,-0.3344352245,0.2128011286,0.0424893647,-0.1869575381,-0.4820566773,-0.5956631303,0.1210985854,-0.1672099978,0.110036917,-0.1614882946,0.0499348789,-0.0446172617,0.0140938358,-0.0892276615,-0.1848934144,-0.0763213784,-0.318759948,0.0118212188,0.2288335115,-0.1857832968,-0.3805100024,0.0666419789,0.0005037754,-0.0105252275,0.0721074119,-0.4886790514,-0.0804499313,-0.126209721,-0.1103010178,-0.2704428732,-0.2867329121,0.2812088132,0.0448404215,-0.045391161,-0.3163352013,-0.042626638,0.1111585721,-0.2324247807,0.1200018153,-0.2147038728,0.3928374946,0.0402386785,0.4024089873,-0.1719452888,-0.1974951476,0.007307373,-0.0087511996,0.5414096117,-0.1752636433,-0.2332715392,0.0762638003,0.0175549798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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3786","title":"Bug downloading Virus scan warning page from Google Drive URLs","comments":"Once the PR merged into master and until our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","body":"## Describe the bug\r\nRecently, some issues were reported with URLs from Google Drive, where we were downloading the Virus scan warning page instead of the data file itself.\r\n\r\nSee:\r\n- #3758 \r\n- #3773\r\n- #3784\r\n","comment_length":77,"text":"Bug downloading Virus scan warning page from Google Drive URLs \n ## Describe the bug\r\nRecently, some issues were reported with URLs from Google Drive, where we were downloading the Virus scan warning page instead of the data file itself.\r\n\r\nSee:\r\n- #3758 \r\n- #3773\r\n- #3784\r\n \n Once the PR merged into master and until our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```","embeddings":[-0.1411156058,0.0148486532,-0.0489373654,0.2452072799,0.2049023658,0.0718999282,0.1173322499,0.2077260166,0.3714020848,0.1962255388,-0.0660764724,-0.1588386297,0.104025051,0.3147510886,0.0350952819,0.0102439476,-0.0000054037,-0.0964626148,0.1057199463,0.1173004806,-0.2640749812,0.201939851,-0.2606954873,-0.1197838709,0.1266730875,0.1938182712,0.0479765534,0.4057701528,-0.0455455445,-0.5470001698,0.0979442894,-0.0288391784,0.1665577143,0.4387001395,-0.000105521,0.0932274386,0.4261350334,-0.0705712736,-0.229837656,-0.0256256536,0.0284126587,0.1088639051,-0.1331687123,-0.2744812071,0.0558062643,-0.1216232926,-0.0469942987,0.1427108347,-0.1093326956,0.3487898111,0.2664717734,0.3460310996,0.2609410584,-0.1395503432,0.4716891348,-0.3454206884,0.0722027794,0.4545418322,0.2437393814,0.2246534377,-0.0612285398,0.3008182347,-0.0691065937,0.0059738215,0.212224111,0.1345112473,0.0208714735,-0.0127962288,0.0688561127,0.0526135899,0.0994581804,-0.156439051,-0.061013259,-0.0715019926,-0.0349402726,-0.1882204711,0.649463892,0.0312937461,0.0032204397,0.1033518612,-0.3511294723,0.01796549,0.0921679884,-0.0893078744,-0.1395701319,0.1011239663,-0.1798358858,-0.0679376125,0.2816278338,-0.0691835508,-0.093321912,-0.0337703489,-0.258007735,-0.0028570211,-0.2382014245,-0.0533648059,0.1677808762,0.0807977095,0.5323064923,0.2279168963,-0.1638192534,-0.0986694843,-0.3140091598,0.0359787904,0.2839816809,-0.0021044561,-0.2400714159,0.0674065128,0.4143233895,0.4904738665,0.2008998245,0.042259708,0.2429191619,-0.2140466571,0.3048649728,0.2450359464,0.0269734841,-0.4260000587,-0.2499926388,0.248838082,0.1782770455,-0.1074755639,0.1877805144,0.5385905504,-0.0415342823,-0.2541701496,0.1174651086,-0.0703487173,-0.0696782321,-0.0205841474,-0.1755223572,0.0456695892,-0.1864278018,-0.1048852429,0.2850907147,-0.3417072892,0.1433994323,-0.1886739582,0.1331669688,-0.0671033636,-0.225158602,-0.16440171,0.1132122278,0.5180908442,0.2539154589,0.0525331087,0.1002860665,0.0111270947,-0.0629471391,0.1020745263,-0.3318580687,-0.057261169,0.0155876894,0.2508224547,-0.3642735481,-0.1520965546,0.0001252547,-0.4304556847,-0.0960754454,-0.3096965551,-0.2252601534,-0.0796271637,-0.4299356639,-0.1845958084,0.2973344624,0.4193333983,0.0436217412,0.062744014,0.2072813064,-0.4957469106,-0.008084476,0.3710868359,0.0224661138,0.1027266085,-0.5113990903,0.1581877917,-0.2095684856,-0.1584495157,-0.4040753841,0.1070315912,-0.0688273013,-0.039564386,-0.2004256397,-0.0368008912,0.0204702578,-0.0742181093,0.0463339426,0.1971134096,0.1581498832,0.3225027621,-0.3161915541,-0.3007692099,0.1325165331,0.1309725195,0.0280478857,0.202662617,0.3800817728,-0.4896267354,0.2948115766,-0.1838342547,-0.0126760835,0.2333611697,0.4142033458,0.046560239,0.2728084922,-0.0874114037,-0.5222423077,0.1474086046,-0.103031449,-0.1354793906,-0.162946403,-0.1389006972,-0.4593801796,-0.222884953,-0.0038260005,-0.0200291667,0.1903982162,0.0931580737,0.0707147345,0.1501856893,-0.1012768149,-0.0550321788,-0.3797578514,0.1076888442,-0.0326090567,0.5057687759,-0.1920590997,-0.1180347279,0.2849501073,-0.1826711595,0.1946674287,0.0164196417,-0.1669659466,0.3766498268,-0.0092167724,0.1796727628,0.2724867463,0.0673896447,0.2016797513,-0.2801963091,0.0162838008,0.2619098723,0.2721871734,0.0945554823,-0.4390363991,0.1029567644,-0.4102964699,-0.0278459974,0.049799379,0.113503702,0.4112527668,-0.2343508303,0.1967646182,-0.1767484546,0.1824292243,0.4177321792,-0.0642830506,-0.1989487559,-0.0279152077,-0.2037508935,0.214560926,-0.0589481555,0.1069566011,0.0947261974,0.0375451371,-0.0340294354,0.0089380853,0.3956564963,0.1348544508,0.1172015667,0.1249493062,0.2098889351,-0.1070622429,-0.1224382818,0.0744077489,-0.088980034,0.0719648376,0.4981100261,-0.2086020857,-0.0012238215,-0.4620520473,-0.1181023419,0.2076952457,0.280133903,-0.4730307758,-0.137568444,0.0012108081,-0.1913802922,0.0162040927,0.3779181838,-0.2940392494,-0.3818683922,-0.0571150221,0.2835643888,-0.5213341713,-0.1323717684,-0.2573880255,0.3980229199,0.135119006,-0.1951166242,-0.0227596667,0.0310062468,-0.1024027243,0.1453348845,0.3720977604,0.1530350149,0.3674635291,-0.2584267557,0.1262212396,-0.6510292888,-0.1510399431,0.2374789119,-0.1302608848,0.2817849815,0.3016923964,0.190407604,-0.0992133766,-0.0805264786,0.1420813948,-0.3116321862,-0.3183635771,-0.0112691531,0.0776180699,-0.0566032603,-0.3090428114,-0.2224117815,0.2272860706,-0.2162471861,-0.2205207199,-0.022261491,0.1703645736,-0.0300876331,-0.081971705,0.140329212,-0.1297443807,0.1374970675,-0.422139436,-0.402267009,0.3589147031,-0.1814653277,-0.530308485,0.2876936495,0.0901424587,-0.2078543454,0.1261117607,-0.5394994617,-0.2977412343,-0.2696743011,-0.1497476697,0.2215961963,0.0601897091,-0.0434450172,-0.1375572681,-0.1823477,-0.1027503461,-0.2300942093,-0.1189079434,-0.0748331994,0.3211814463,0.0206298716,0.0398426652,0.2014838755,0.217903927,0.1966948062,0.0459550843,0.2559400797,-0.039816767,0.4510171711,0.053782817,-0.0471305922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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3784","title":"Unable to Download CNN-Dailymail Dataset ","comments":"Glad to help @albertvillanova! Just fine-tuning the PR, will comment once I am able to get it up and running \ud83d\ude00","body":"## Describe the bug\r\nI am unable to download the CNN-Dailymail dataset. Upon closer investigation, I realised why this was happening:\r\n- The dataset sits in Google Drive, and both the CNN and DM datasets are large.\r\n- Google is unable to scan the folder for viruses, **so the link which would originally download the dataset, now downloads the source code of this web page:**\r\n![image](https:\/\/user-images.githubusercontent.com\/58678541\/155658435-c2f497d7-7601-4332-94b1-18a62dd96422.png)\r\n- **This leads to the following error**:\r\n```python\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n```\r\n\r\n## Expected results\r\nThat the dataset is downloaded and processed just like other datasets.\r\n\r\n## Actual results\r\nHit with this error:\r\n```python\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n","comment_length":21,"text":"Unable to Download CNN-Dailymail Dataset  \n ## Describe the bug\r\nI am unable to download the CNN-Dailymail dataset. Upon closer investigation, I realised why this was happening:\r\n- The dataset sits in Google Drive, and both the CNN and DM datasets are large.\r\n- Google is unable to scan the folder for viruses, **so the link which would originally download the dataset, now downloads the source code of this web page:**\r\n![image](https:\/\/user-images.githubusercontent.com\/58678541\/155658435-c2f497d7-7601-4332-94b1-18a62dd96422.png)\r\n- **This leads to the following error**:\r\n```python\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\r\n```\r\n\r\n## Expected results\r\nThat the dataset is downloaded and processed just like other datasets.\r\n\r\n## Actual results\r\nHit with this error:\r\n```python\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n \n Glad to help @albertvillanova! Just fine-tuning the PR, will comment once I am able to get it up and running 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3778","title":"Not be able to download dataset - \"Newsroom\"","comments":"Hi @Darshan2104, thanks for reporting.\r\n\r\nPlease note that at Hugging Face we do not host the data of this dataset, but just a loading script pointing to the host of the data owners.\r\n\r\nApparently the data owners changed their data host server. After googling it, I found their new website at: https:\/\/lil.nlp.cornell.edu\/newsroom\/index.html\r\n- Download page: https:\/\/lil.nlp.cornell.edu\/newsroom\/download\/index.html\r\n\r\nI'm fixing the link in our Datasets library.","body":"Hello,\r\n\r\nI tried to download the **newsroom** dataset but it didn't work out for me. it said me to **download it manually**!\r\n\r\nFor manually, Link is also didn't work! It is sawing some ad or something!\r\n\r\nIf anybody has solved this issue please help me out or if somebody has this dataset please share your google drive link, it would be a great help!\r\n\r\nThanks\r\nDarshan Tank","comment_length":64,"text":"Not be able to download dataset - \"Newsroom\" \n Hello,\r\n\r\nI tried to download the **newsroom** dataset but it didn't work out for me. it said me to **download it manually**!\r\n\r\nFor manually, Link is also didn't work! It is sawing some ad or something!\r\n\r\nIf anybody has solved this issue please help me out or if somebody has this dataset please share your google drive link, it would be a great help!\r\n\r\nThanks\r\nDarshan Tank \n Hi @Darshan2104, thanks for reporting.\r\n\r\nPlease note that at Hugging Face we do not host the data of this dataset, but just a loading script pointing to the host of the data owners.\r\n\r\nApparently the data owners changed their data host server. After googling it, I found their new website at: https:\/\/lil.nlp.cornell.edu\/newsroom\/index.html\r\n- Download page: https:\/\/lil.nlp.cornell.edu\/newsroom\/download\/index.html\r\n\r\nI'm fixing the link in our Datasets library.","embeddings":[-0.2675008774,0.3221220076,-0.0054876413,0.2053162754,0.1594460458,0.2996758521,0.0258156843,0.3109137714,0.1214274913,-0.073516041,-0.0882344469,-0.1992520988,-0.1055619121,0.2716179788,0.2644298375,-0.1924167126,-0.0738763437,-0.0039601857,0.3711564243,0.0322975926,-0.2235563695,0.1841428727,-0.2225543857,-0.0244407933,-0.1433093101,-0.2122727185,-0.1043519899,-0.100704737,-0.2001161128,-0.2502781451,0.3740208745,0.1349195242,0.1478975564,0.3529644907,-0.0001080288,-0.0810016096,0.2260466516,-0.0850661322,-0.3888166547,-0.3082153797,-0.1518844217,-0.1692034602,-0.1396277696,-0.2477680594,-0.217744112,-0.1414769143,0.1752343178,-0.073332265,0.2109329402,0.4661034048,0.2523401082,0.1950782984,0.3654140234,-0.2798071206,0.1339748204,0.0832642466,0.0876240134,0.3295874,0.0307582859,0.0928991809,0.3254492283,0.0568843037,-0.0021516019,0.0981693566,-0.1656405181,-0.0354878977,-0.05275736,-0.4916574061,0.3031512797,0.1887556612,0.37373209,-0.1202301532,0.0108404709,0.0534062125,0.2579651475,0.1529447734,0.1388570368,0.5117775798,-0.1628190428,0.1885869801,-0.4082055688,-0.2983433902,-0.2047052383,0.3400616348,-0.1082670838,0.1917594075,-0.2550647557,0.2336904407,0.1912591308,0.1265420616,0.0560293347,-0.0133917173,-0.2932968438,0.0759386048,-0.0349328071,-0.0515171997,-0.1390096098,0.4175426066,0.2816632092,-0.012915289,-0.4827950895,-0.1988606155,-0.0480276868,-0.055913575,0.1929905266,0.025858948,0.0632655099,0.2449608594,0.3160052299,0.3875321746,-0.0424227044,-0.0717140064,-0.0816185698,-0.1962844878,-0.2995400727,-0.2143014073,0.252913475,-0.3735087514,-0.3413817883,0.182779789,0.009553995,0.1210278273,-0.007626778,0.1538823843,0.2112742066,0.0097083729,0.1942097992,0.0417562388,0.1261757612,-0.3743090332,-0.0941717029,-0.0008858741,-0.2028099895,-0.0105184466,0.2263931632,-0.1674083173,0.2545631826,0.1190228984,0.010428709,-0.1176538691,-0.1752435118,-0.1018648893,-0.0713659748,0.2941143811,0.168540597,0.3754449785,-0.0614590533,-0.3697327673,-0.0495161526,0.1469881833,-0.2383281142,-0.2084790021,-0.2601289451,0.175352335,-0.3115735054,-0.0367950909,-0.1764308661,0.2182430178,-0.1693558693,0.191899091,-0.0008441595,0.1052790657,0.038738355,-0.0450994633,0.2566155493,0.5027397275,-0.5429788232,-0.0026735007,-0.1395549178,-0.2276098281,0.0402759723,0.210998565,-0.278778851,0.1349637508,-0.3671524227,-0.1432964355,0.5410512686,-0.1919691414,-0.7831023932,0.1556905508,-0.2381466031,-0.2968292236,-0.1245845258,0.128755793,0.0594461262,0.1293927729,-0.3315303624,0.4958166778,0.1205862463,-0.0548402257,-0.2818962634,-0.0199820176,0.0227240939,0.3163263798,0.1345930696,0.1323402226,0.1280531734,0.0393356606,0.2706606984,0.163755253,0.2925259173,0.3316911459,0.3519579172,0.1669935286,0.0638929307,-0.126431793,-0.3052884936,0.0652485117,0.0645305291,0.003676041,-0.0559924394,-0.1078101397,-0.6091347337,-0.0753096789,-0.2668991089,-0.2143023163,0.121964246,0.07273525,0.2372113913,0.2794639468,-0.1052530259,0.1721559763,-0.1146164909,-0.0047820075,-0.1825932413,0.3883746862,-0.1526741087,0.0806682855,0.0560805835,-0.1768415868,0.2523116171,0.0801874623,-0.1371442229,0.1969293654,-0.1244659424,0.1013314873,0.4563266337,-0.0809354335,0.1727071106,-0.5965457559,0.3060429096,0.1133920252,0.012151082,0.1026804373,-0.0871264935,0.0346090682,-0.1303884387,-0.0115673319,-0.2377209812,0.3756661415,0.3626182675,-0.2186976969,0.3080255985,0.0325950496,0.2872394919,0.1462464482,0.2500495315,-0.066201441,-0.1653925627,0.2156213969,0.3702352643,-0.0106317559,-0.0865172967,0.2145854235,-0.4376338422,-0.0131273707,-0.1728681326,0.4402172565,0.1750272661,0.0950863585,0.0056579825,0.1965543926,0.256141305,-0.2743582726,0.2170638442,-0.165165931,0.1283911318,0.0799951777,0.0638694391,0.0296212863,-0.2397199869,0.1111707315,0.2891844213,0.2821584344,-0.1470350921,-0.114627257,-0.0545983091,-0.574526608,-0.1614574343,0.2620957494,-0.2643229365,-0.3387516141,-0.1577908248,0.0829982609,0.1675217599,-0.1195576638,-0.0729654878,0.3826088905,0.0108727682,0.0563611239,-0.1629780531,-0.2033117712,-0.2791087627,0.1436116099,0.2377176434,0.1657489538,0.2411340028,-0.2983354032,0.0134607917,-0.8658280969,-0.1199951023,0.0553305522,0.0472047701,0.7588219047,0.0161843188,0.5726412535,-0.3704835176,-0.1018382683,0.0819101483,-0.1701071113,0.0124751609,-0.0339832976,-0.095474489,-0.1059751883,0.0939494297,-0.2061119378,-0.4901126921,-0.0944386199,0.2325196862,-0.1120441109,-0.0076307803,0.0149957826,-0.2653831542,-0.0740806162,0.0305231865,-0.2437018752,-0.2850532532,-0.3150360286,0.423991859,-0.415907681,-0.6227352619,0.2858628631,0.2171749473,0.097028181,-0.0252534617,-0.6097105742,0.1715103388,-0.2230416238,0.0767574087,0.2796695828,-0.053533081,0.079979457,-0.3987588584,0.0386280082,-0.0231110435,-0.1213335469,-0.0269809924,0.1628937274,0.4298493564,0.1660517901,0.3656005859,0.0631685182,0.5530520082,0.347122699,-0.0891399756,0.4183832705,-0.1566711962,0.245966047,0.0387535766,-0.43630445,0.2317789495,0.0810431391,-0.0956093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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3776","title":"Allow download only some files from the Wikipedia dataset","comments":"Hi @jvanz, thank you for your proposal.\r\n\r\nIn fact, we are aware that it is very common the problem you mention. Because of that, we are currently working in implementing a new version of wikipedia on the Hub, with all data preprocessed (no need to use Apache Beam), from where you will be able to use `data_files` to load only a specific subset of the data files.\r\n\r\nSee:\r\n- #3401  ","body":"**Is your feature request related to a problem? Please describe.**\r\nThe Wikipedia dataset can be really big. This is a problem if you want to use it locally in a laptop with the Apache Beam `DirectRunner`. Even if your laptop have a considerable amount of memory (e.g. 32gb).\r\n\r\n\r\n**Describe the solution you'd like**\r\nI would like to use the `data_files` argument in the `load_dataset` function to define which file in the wikipedia dataset I would like to download. Thus, I can work with the dataset in a smaller machine using the Apache Beam `DirectRunner`.\r\n\r\n**Describe alternatives you've considered**\r\nI've tried to use the `simple` Wikipedia dataset. But it's in English and I would like to use Portuguese texts in my model. \r\n","comment_length":70,"text":"Allow download only some files from the Wikipedia dataset \n **Is your feature request related to a problem? Please describe.**\r\nThe Wikipedia dataset can be really big. This is a problem if you want to use it locally in a laptop with the Apache Beam `DirectRunner`. Even if your laptop have a considerable amount of memory (e.g. 32gb).\r\n\r\n\r\n**Describe the solution you'd like**\r\nI would like to use the `data_files` argument in the `load_dataset` function to define which file in the wikipedia dataset I would like to download. Thus, I can work with the dataset in a smaller machine using the Apache Beam `DirectRunner`.\r\n\r\n**Describe alternatives you've considered**\r\nI've tried to use the `simple` Wikipedia dataset. But it's in English and I would like to use Portuguese texts in my model. \r\n \n Hi @jvanz, thank you for your proposal.\r\n\r\nIn fact, we are aware that it is very common the problem you mention. Because of that, we are currently working in implementing a new version of wikipedia on the Hub, with all data preprocessed (no need to use Apache Beam), from where you will be able to use `data_files` to load only a specific subset of the data files.\r\n\r\nSee:\r\n- #3401  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3773","title":"Checksum mismatch for the reddit_tifu dataset","comments":"@albertvillanova Thank you for the fast response! However I am still getting the same error:\r\n\r\nDownloading: 2.23kB [00:00, ?B\/s]\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\Anna\\PycharmProjects\\summarization\\main.py\", line 17, in <module>\r\n    dataset = load_dataset('reddit_tifu', 'long')\r\n  File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\utils\\info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\nI have cleaned the cache\/huggingface\/datasets & cache\/huggingface\/modules files and also tried on another machine with a fresh installation of trasnformers & datasets. \r\nThe reddit_tifu.py that gets downloaded still has the previous url on line 51, _URL = \"https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF\" ","body":"## Describe the bug\r\nA checksum occurs when downloading the reddit_tifu data (both long & short).\r\n\r\n## Steps to reproduce the bug\r\nreddit_tifu_dataset = load_dataset('reddit_tifu', 'long')\r\n\r\n## Expected results\r\nThe expected result is for the dataset to be downloaded and cached locally.\r\n\r\n## Actual results\r\n  File \"\/...\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 7.0.0\r\n","comment_length":112,"text":"Checksum mismatch for the reddit_tifu dataset \n ## Describe the bug\r\nA checksum occurs when downloading the reddit_tifu data (both long & short).\r\n\r\n## Steps to reproduce the bug\r\nreddit_tifu_dataset = load_dataset('reddit_tifu', 'long')\r\n\r\n## Expected results\r\nThe expected result is for the dataset to be downloaded and cached locally.\r\n\r\n## Actual results\r\n  File \"\/...\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 7.0.0\r\n \n @albertvillanova Thank you for the fast response! However I am still getting the same error:\r\n\r\nDownloading: 2.23kB [00:00, ?B\/s]\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\Anna\\PycharmProjects\\summarization\\main.py\", line 17, in <module>\r\n    dataset = load_dataset('reddit_tifu', 'long')\r\n  File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\utils\\info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\nI have cleaned the cache\/huggingface\/datasets & cache\/huggingface\/modules files and also tried on another machine with a fresh installation of trasnformers & datasets. \r\nThe reddit_tifu.py that gets downloaded still has the previous url on line 51, _URL = \"https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF\" 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3773","title":"Checksum mismatch for the reddit_tifu dataset","comments":"Hi @anna-kay, I'm sorry I didn't clearly explain the details to you:\r\n- the error has been fixed in our `master` branch on GitHub: https:\/\/github.com\/huggingface\/datasets\/commit\/8ae21bf6a77175dc803ce2f1b93d18b8fbf45586\r\n- the fix will not be accessible to users in PyPI until our next release of the `datasets` library\r\n  - our latest release (version 1.18.3) was made 23 days ago: https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.18.3\r\n- in the meantime, you can get the fix if you install datasets from our GitHub `master` branch:\r\n  ```\r\n  pip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n  ```","body":"## Describe the bug\r\nA checksum occurs when downloading the reddit_tifu data (both long & short).\r\n\r\n## Steps to reproduce the bug\r\nreddit_tifu_dataset = load_dataset('reddit_tifu', 'long')\r\n\r\n## Expected results\r\nThe expected result is for the dataset to be downloaded and cached locally.\r\n\r\n## Actual results\r\n  File \"\/...\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 7.0.0\r\n","comment_length":79,"text":"Checksum mismatch for the reddit_tifu dataset \n ## Describe the bug\r\nA checksum occurs when downloading the reddit_tifu data (both long & short).\r\n\r\n## Steps to reproduce the bug\r\nreddit_tifu_dataset = load_dataset('reddit_tifu', 'long')\r\n\r\n## Expected results\r\nThe expected result is for the dataset to be downloaded and cached locally.\r\n\r\n## Actual results\r\n  File \"\/...\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 7.0.0\r\n \n Hi @anna-kay, I'm sorry I didn't clearly explain the details to you:\r\n- the error has been fixed in our `master` branch on GitHub: https:\/\/github.com\/huggingface\/datasets\/commit\/8ae21bf6a77175dc803ce2f1b93d18b8fbf45586\r\n- the fix will not be accessible to users in PyPI until our next release of the `datasets` library\r\n  - our latest release (version 1.18.3) was made 23 days ago: https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.18.3\r\n- in the meantime, you can get the fix if you install datasets from our GitHub `master` branch:\r\n  ```\r\n  pip install git+https:\/\/github.com\/huggingface\/datasets#egg=datasets\r\n  ```","embeddings":[-0.1900521815,0.1128555834,-0.0797177777,0.196441859,0.3247573972,-0.0811686292,0.1428151578,0.4333870709,-0.0206154529,0.0688304082,-0.0649133399,0.1989684403,0.4393799007,0.1943292618,0.0490712263,0.0611956455,0.133478269,-0.0215586368,-0.0648427233,0.0326624848,-0.1154862046,0.1041744351,-0.1048084721,-0.2022411227,0.0166334193,0.1674809605,-0.0203009117,0.1483326256,-0.1234824285,-0.2369653285,0.3213939071,0.1848241389,-0.2820000052,0.5908808112,-0.0001072045,0.0223760977,0.3191757798,-0.083121933,-0.1561593711,-0.1123308092,-0.3493349254,-0.0809210241,-0.2770732045,-0.2004156858,0.086354807,0.2190300673,-0.0081517249,0.0516385436,-0.12305253,0.2096395493,0.2611836195,0.3993742168,0.173152253,0.0657081306,0.274464339,-0.0072053168,0.045706749,0.3484501839,0.0166959967,-0.0543671772,-0.1045602933,0.1447267383,-0.2458269,0.060738612,-0.0518971756,0.1324625164,-0.0749662071,-0.09726955,0.0297746025,0.3908531964,0.29327932,-0.2951296866,-0.4336782098,0.0002976328,-0.1404084265,-0.1784240454,0.4304537177,0.1810365766,-0.1601701975,0.0978985131,-0.2294465005,0.2026464641,0.1290161312,0.1222629845,0.1238708422,-0.1714086831,0.1207337305,-0.0176681932,-0.1323726326,-0.0206042212,0.1186266094,-0.1007885858,-0.0816676691,-0.0806247517,-0.3440829813,-0.1756420732,0.0081650708,0.0481287055,0.5169190168,0.352896452,0.1957685053,0.2363931537,-0.1294255108,0.2041101158,-0.0787816793,0.1537344009,-0.0301914904,0.186013639,0.3256675303,0.2757438123,-0.0776731074,0.0798188522,0.2315088362,-0.1360028684,0.3752283752,0.1980141401,0.1843112409,-0.4330933094,-0.3279226124,0.2412915081,0.1221460849,-0.1825948507,0.1401900202,0.2640247643,-0.2861633599,0.0954353884,-0.2421660572,0.1299962848,-0.1998590231,-0.0081822155,-0.2965956926,0.0658655167,-0.1602591723,0.0352553949,0.0992699414,-0.4089719355,0.5231050849,0.0299857296,0.3165205717,-0.1344500035,-0.0665293559,-0.2424529791,0.0297520459,0.412263155,-0.2284212261,0.0456630588,0.1462377459,0.1088723093,-0.1864545643,-0.025832776,-0.3109516799,-0.1719523072,0.0050527188,0.2971459329,-0.3963709474,-0.1924602091,0.0918117166,-0.4222532809,0.3521613479,-0.2544281185,0.1407964826,-0.1962087452,-0.2611275911,-0.3696995974,0.1097751185,0.1826737672,-0.2477601022,0.1508902758,0.0754026398,-0.1965850443,0.2714075744,0.2489169836,0.0108074332,-0.0797698274,-0.2370494753,0.1663478464,0.1132355928,-0.5046200752,-0.755987525,-0.1171245798,-0.0347920656,0.152717486,0.1203250363,0.2016509026,0.1000864953,-0.2326379865,0.2696674764,0.2050117701,0.0211327337,0.2172419727,-0.4487831295,-0.3921631873,0.0476241335,0.0449912883,0.0848116428,0.0004526726,0.2512328625,-0.2366859466,0.3878806233,0.096217826,-0.2050976753,0.0489341654,0.6228857636,0.1208895594,0.026378857,-0.2893865108,-0.2855338454,0.2649043798,-0.0496696159,0.2823435962,-0.089461118,-0.218816027,-0.4124773145,-0.0784593746,0.0033865783,-0.0624950565,0.1973959506,0.282882452,0.4548982382,0.0034748325,-0.0819331557,0.2327015847,-0.3240798712,0.0754799098,-0.0735908225,0.4671474993,-0.1748411655,-0.1043951362,0.2091561407,0.01483793,0.181918934,-0.0745657086,-0.1118322089,0.6493151188,0.2096166164,-0.010231806,0.1306544989,0.1869522631,0.1141154543,-0.3123774529,-0.1495935023,0.4982485175,0.0764218345,0.0816584527,-0.090833202,0.4276823401,-0.0718391463,-0.0361983962,-0.0441985242,0.0134355323,0.3792238832,-0.2235130668,-0.098149538,-0.3026881814,0.331146121,0.0549338646,-0.2260263562,0.0051604561,-0.1235533506,0.1147535741,0.4327100515,-0.0999964476,-0.0571467541,0.1013757437,0.1241979524,-0.1487919092,0.0486647934,0.4523023367,0.3454919755,0.224787578,0.0643827915,0.0892541632,-0.32139346,-0.153971523,0.0174308103,0.0458892174,0.1105460748,0.2371440083,0.1888468117,0.047771737,-0.2897650898,-0.1348036081,-0.0195506532,0.2800768316,-0.3876809478,-0.1071541384,-0.2627955079,-0.2101280093,-0.0690895915,-0.0354891829,-0.2628886104,-0.3803192377,0.0741935447,0.14797993,-0.1453528702,0.146857515,-0.3750653267,-0.1486241817,0.2107025683,-0.4727038145,0.0203626305,-0.1480471194,-0.0376015343,0.1181348786,0.3940758109,0.1269145459,0.4453445971,-0.3863299489,-0.2040854841,-0.4251219928,-0.4446247518,0.1203537509,-0.1808071136,0.2683765888,0.1481494308,0.2438798994,0.1742381752,-0.1616061926,0.2343334258,-0.1609544158,-0.459362179,0.2563982904,0.0555076003,-0.0548775494,-0.0429597571,-0.1084837168,0.0763258487,-0.2454841584,-0.1008840948,0.1502738595,0.2498251945,0.081325382,0.1859627962,-0.019110078,-0.1265299469,0.1893495172,-0.3376818299,-0.6981480718,0.3856304288,-0.0169410463,-0.3967518806,-0.1206033155,0.0159215257,-0.0357479602,0.315492779,-0.5722042918,-0.6082021594,-0.2810340822,0.2543127537,0.1847759783,-0.0343190022,0.1415259838,-0.0988332704,-0.2230892032,-0.2056070268,-0.0796211734,0.0574314073,-0.0101950048,0.4035979211,-0.105673112,0.1560165435,0.0198890306,0.2135324329,0.3623521626,-0.1258280873,0.0133258216,0.044184912,0.4059491456,-0.0478409566,-0.2120320946,0.0195331834,-0.1336002052,-0.0367622226,0.04429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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3769","title":"`dataset = dataset.map()` causes faiss index lost","comments":"Hi ! Indeed `map` is dropping the index right now, because one can create a dataset with more or fewer rows using `map` (and therefore the index might not be relevant anymore)\r\n\r\nI guess we could check the resulting dataset length, and if the user hasn't changed the dataset size we could keep the index, what do you think ?","body":"## Describe the bug\r\nassigning the resulted dataset to original dataset causes lost of the faiss index\r\n\r\n## Steps to reproduce the bug\r\n`my_dataset` is a regular loaded dataset. It's a part of a customed dataset structure\r\n```python\r\nself.dataset.add_faiss_index('embeddings')\r\nself.dataset.list_indexes()\r\n# ['embeddings']\r\n\r\ndataset2 = my_dataset.map(\r\n    lambda x: self._get_nearest_examples_batch(x['text']), batch=True\r\n)\r\n\r\n# the unexpected result:\r\ndataset2.list_indexes()\r\n# []\r\n\r\nself.dataset.list_indexes()\r\n# ['embeddings']\r\n```\r\n\r\nin case something wrong with my `_get_nearest_examples_batch()`, it's like this\r\n```python\r\ndef _get_nearest_examples_batch(self, examples, k=5):\r\n    queries = embed(examples)\r\n    scores_batch, retrievals_batch = self.dataset.get_nearest_examples_batch(self.faiss_column, queries, k)\r\n    return {\r\n        'neighbors': [batch['text'] for batch in retrievals_batch],\r\n        'scores': scores_batch\r\n    }\r\n```\r\n\r\n## Expected results\r\n`map` shouldn't drop the indexes, in another word, indexes should be carried to the generated dataset\r\n\r\n## Actual results\r\nmap drops the indexes\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Ubuntu 20.04.3 LTS\r\n- Python version: 3.8.12\r\n- PyArrow version: 7.0.0\r\n","comment_length":60,"text":"`dataset = dataset.map()` causes faiss index lost \n ## Describe the bug\r\nassigning the resulted dataset to original dataset causes lost of the faiss index\r\n\r\n## Steps to reproduce the bug\r\n`my_dataset` is a regular loaded dataset. It's a part of a customed dataset structure\r\n```python\r\nself.dataset.add_faiss_index('embeddings')\r\nself.dataset.list_indexes()\r\n# ['embeddings']\r\n\r\ndataset2 = my_dataset.map(\r\n    lambda x: self._get_nearest_examples_batch(x['text']), batch=True\r\n)\r\n\r\n# the unexpected result:\r\ndataset2.list_indexes()\r\n# []\r\n\r\nself.dataset.list_indexes()\r\n# ['embeddings']\r\n```\r\n\r\nin case something wrong with my `_get_nearest_examples_batch()`, it's like this\r\n```python\r\ndef _get_nearest_examples_batch(self, examples, k=5):\r\n    queries = embed(examples)\r\n    scores_batch, retrievals_batch = self.dataset.get_nearest_examples_batch(self.faiss_column, queries, k)\r\n    return {\r\n        'neighbors': [batch['text'] for batch in retrievals_batch],\r\n        'scores': scores_batch\r\n    }\r\n```\r\n\r\n## Expected results\r\n`map` shouldn't drop the indexes, in another word, indexes should be carried to the generated dataset\r\n\r\n## Actual results\r\nmap drops the indexes\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Ubuntu 20.04.3 LTS\r\n- Python version: 3.8.12\r\n- PyArrow version: 7.0.0\r\n \n Hi ! Indeed `map` is dropping the index right now, because one can create a dataset with more or fewer rows using `map` (and therefore the index might not be relevant anymore)\r\n\r\nI guess we could check the resulting dataset length, and if the user hasn't changed the dataset size we could keep the index, what do you think ?","embeddings":[-0.1911486089,-0.0692249089,-0.0801809505,0.2704691887,0.0758193508,0.3400026262,0.2190925479,0.2129373401,0.7335775495,0.2396205515,-0.04684541,0.4691385627,0.0690719932,-0.3312780857,-0.0963514596,0.1843682528,0.2518339455,0.1134472042,0.0620867088,-0.146529749,-0.4394628406,0.0760563686,-0.140169248,-0.0207162797,0.0901545286,0.0839502737,-0.0263562463,-0.0259226412,0.0192383174,-0.3719553351,0.2027835101,-0.1621653289,0.0135402381,0.2944709063,-0.0001150066,0.0753501728,0.2552717626,0.0976214707,-0.1818515807,0.0022913457,-0.3680090308,-0.1619146317,0.0330053195,-0.2573132813,0.0360365771,-0.2040443569,0.0101934383,-0.714844346,0.0745701864,0.1525011957,0.1970359534,0.079609111,0.011719618,-0.1608531475,0.3800722957,-0.241572395,0.032672625,0.2320608497,0.1937450022,0.0476858057,0.0387998298,0.0913485512,-0.256644994,-0.0771811604,0.0332195722,0.2068942338,0.4984325767,-0.2534565032,0.0315788127,0.0668564811,0.2079063058,-0.2423778921,-0.3016092181,-0.1637865454,0.0828491226,-0.1101468951,-0.1753850579,-0.2801824808,0.0913514867,0.1508374959,-0.0752540678,0.0240797941,0.0282745566,0.3262602091,-0.0830581412,0.1297165751,0.0275328159,0.1942843944,-0.1280674785,-0.1322176456,-0.1335902959,-0.2621436119,-0.0397355966,0.1735881865,-0.2167930305,0.013539088,0.5819121003,-0.185916096,0.2752067149,-0.4270854592,-0.191792652,0.1537507623,-0.0851717368,0.1216877177,0.1536150724,0.1291370839,-0.0546056703,0.0720758066,-0.0678115264,-0.0005283147,-0.2035431117,-0.0045629619,0.3902345598,-0.010725854,0.1460058093,-0.1397516876,0.3604791164,-0.184867844,-0.5118567944,0.4449152946,-0.4484354258,0.1102553383,-0.0885753855,0.1751728505,0.099226363,-0.1783083826,-0.0682126135,0.1619322896,-0.1765494794,0.2468927205,-0.2875252366,-0.3388490081,-0.0726551488,0.0518817417,-0.047451023,-0.1595358253,0.2329659611,0.0948834941,-0.0972637087,-0.0149044385,-0.2687085271,-0.0719302893,0.4188791513,0.0744464695,-0.089137502,0.3233932853,0.0497746803,-0.2202224433,-0.3680206835,0.2061845511,-0.3416214585,-0.1874527782,-0.0579411611,0.1887406856,0.0784799084,0.0133480579,-0.0335922502,0.3451971412,0.1154943109,-0.0896610841,0.1665623933,-0.3244566321,-0.3158201277,-0.3441810012,0.2195607871,0.4310138524,-0.3843838573,-0.1360039711,-0.1208726838,0.126479283,-0.0914997533,0.4754203558,-0.0996300355,0.3783807158,-0.2927538753,0.3297795057,0.2567464411,-0.0122927856,-0.6850582361,0.0759187415,-0.2346158922,0.0578922108,-0.2608588338,0.2387983799,0.4400718212,-0.0670734793,0.4054531753,0.2582812011,-0.0252120737,0.0416099802,-0.3333949447,-0.2749070227,0.1446979195,0.0427328274,-0.1129922792,0.1568305939,0.3974310458,-0.6001852155,0.217614606,-0.0971522108,0.1204736382,0.3154964745,0.2285405546,0.0421189554,0.1756845862,-0.0813207328,-0.2526892126,0.2269020081,0.0174413119,-0.2902833819,-0.2356605679,-0.1651174575,-0.1349028349,0.2119572759,-0.158993721,0.1016762927,0.046050787,-0.263897866,-0.1985471845,0.0784490779,-0.1023062766,-0.2009828687,-0.0598515905,-0.0007715742,-0.3925261199,0.2268337309,0.0819785818,0.0133820586,-0.2800276577,0.1946807653,0.2043576241,-0.1171621755,-0.0921395347,0.2677072883,0.0032275245,0.0414220281,0.0929442495,-0.1823689938,0.12763004,-0.250461638,0.2400085032,0.2230004966,0.0528375581,0.0046788887,-0.0558107533,0.1438117474,0.1643575132,0.2769144475,-0.2316286117,-0.1539463997,0.0702025592,-0.1395825297,-0.0016561355,-0.4454217553,-0.2246779352,0.2032606155,0.2192825973,0.0935154334,0.0216958113,0.4387492239,0.2224783748,-0.2813401222,0.0016355923,0.2541955411,-0.4458291829,-0.0646480247,-0.1990847141,-0.1652481258,0.5625893474,0.2078017741,0.1347596496,-0.1119442061,0.0033267997,-0.1590523124,0.13341555,0.1164067835,0.0274027605,0.2662467957,0.2823493183,0.0835092142,-0.2206876427,-0.0628904477,-0.1036375985,0.1861101389,-0.4410587549,0.0513400026,-0.1415988207,0.139485687,-0.0972197279,-0.3726759255,-0.0916082114,-0.3762195408,0.1005137265,0.0597093515,-0.2121706903,0.11060559,-0.1432983875,-0.0722136497,0.1967636943,-0.3656094968,0.0554706194,-0.2949757874,-0.2165376246,-0.017518416,-0.0773144364,-0.0541970246,0.1641048342,0.1493090689,-0.3259568214,-0.4412869811,-0.0963822454,-0.0051218034,-0.0871293917,0.4307576716,-0.0135687226,0.0915113911,-0.0775088072,-0.0620937198,0.0835977569,-0.0739264116,-0.1428130567,-0.0695846677,-0.1674863845,-0.2469978333,-0.0291821491,-0.3706071377,-0.0360091329,-0.2245023996,0.0830209479,0.0329144783,0.0728297904,0.0014258722,0.2640031576,0.0654884279,0.0283075068,-0.1898321658,-0.4016532004,-0.2703045309,0.2342381924,-0.0719780251,-0.2165070474,-0.1616550684,-0.1890924424,-0.0203435365,0.3600479662,-0.3398641646,0.1785095632,-0.0085988538,0.5397697687,0.0032354426,0.2203159928,0.3667458594,0.1073556989,-0.0333750844,-0.1183572486,-0.1693040431,0.171335116,0.3060128391,0.1123872623,-0.0084124533,0.3930235803,-0.2250884026,0.8033993244,0.304426074,-0.3347319961,0.411819458,-0.172155261,0.4298173785,-0.1641591787,-0.2937368155,0.0265028886,0.0737311542,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3763","title":"It's not possible download `20200501.pt` dataset","comments":"Hi @jvanz, thanks for reporting.\r\n\r\nPlease note that Wikimedia website does not longer host Wikipedia dumps for so old dates.\r\n\r\nFor a list of accessible dump dates of `pt` Wikipedia, please see: https:\/\/dumps.wikimedia.org\/ptwiki\/\r\n\r\nYou can load for example `20220220` `pt` Wikipedia:\r\n```python\r\ndataset = load_dataset(\"wikipedia\", language=\"pt\", date=\"20220220\", beam_runner=\"DirectRunner\")\r\n```","body":"## Describe the bug\r\nThe dataset `20200501.pt` is broken. \r\n\r\nThe available datasets: https:\/\/dumps.wikimedia.org\/ptwiki\/\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wikipedia\", \"20200501.pt\", beam_runner='DirectRunner')\r\n```\r\n\r\n## Expected results\r\nI expect to download the dataset locally. \r\n\r\n## Actual results\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wikipedia\", \"20200501.pt\", beam_runner='DirectRunner')\r\nDownloading and preparing dataset wikipedia\/20200501.pt to \/home\/jvanz\/.cache\/huggingface\/datasets\/wikipedia\/20200501.pt\/1.0.0\/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475...\r\n\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/apache_beam\/__init__.py:79: UserWarning: This version of Apache Beam has not been sufficiently tested on Python 3.9. You may encounter bugs or missing features.\r\n  warnings.warn(\r\n  0%|                                                                                                                                                                                                                   | 0\/1 [00:00<?, ?it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1245, in _download_and_prepare\r\n    super()._download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 661, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/jvanz\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikipedia\/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475\/wikipedia.py\", line 420, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 307, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 195, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 260, in map_nested\r\n    mapped = [\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 261, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 196, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 216, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 298, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 612, in get_from_cache\r\n    raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/ptwiki\/20200501\/dumpstatus.json\r\n\r\n```\r\n\r\n## Environment info\r\n```\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.3.18-150300.59.49-default-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n```","comment_length":49,"text":"It's not possible download `20200501.pt` dataset \n ## Describe the bug\r\nThe dataset `20200501.pt` is broken. \r\n\r\nThe available datasets: https:\/\/dumps.wikimedia.org\/ptwiki\/\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wikipedia\", \"20200501.pt\", beam_runner='DirectRunner')\r\n```\r\n\r\n## Expected results\r\nI expect to download the dataset locally. \r\n\r\n## Actual results\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wikipedia\", \"20200501.pt\", beam_runner='DirectRunner')\r\nDownloading and preparing dataset wikipedia\/20200501.pt to \/home\/jvanz\/.cache\/huggingface\/datasets\/wikipedia\/20200501.pt\/1.0.0\/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475...\r\n\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/apache_beam\/__init__.py:79: UserWarning: This version of Apache Beam has not been sufficiently tested on Python 3.9. You may encounter bugs or missing features.\r\n  warnings.warn(\r\n  0%|                                                                                                                                                                                                                   | 0\/1 [00:00<?, ?it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1245, in _download_and_prepare\r\n    super()._download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 661, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/jvanz\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikipedia\/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475\/wikipedia.py\", line 420, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 307, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 195, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 260, in map_nested\r\n    mapped = [\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 261, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 196, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 216, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 298, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 612, in get_from_cache\r\n    raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/ptwiki\/20200501\/dumpstatus.json\r\n\r\n```\r\n\r\n## Environment info\r\n```\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.3.18-150300.59.49-default-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n``` \n Hi @jvanz, thanks for reporting.\r\n\r\nPlease note that Wikimedia website does not longer host Wikipedia dumps for so old dates.\r\n\r\nFor a list of accessible dump dates of `pt` Wikipedia, please see: https:\/\/dumps.wikimedia.org\/ptwiki\/\r\n\r\nYou can load for example `20220220` `pt` Wikipedia:\r\n```python\r\ndataset = load_dataset(\"wikipedia\", language=\"pt\", date=\"20220220\", beam_runner=\"DirectRunner\")\r\n```","embeddings":[-0.3223346174,-0.1315499395,-0.0983523577,0.3211584687,0.2504533827,0.1500658691,0.0497237779,0.4589625299,0.0925198868,0.1612989157,0.1003179476,0.354709655,0.017580511,0.1351880133,-0.0279239938,-0.4124290049,0.0741506442,0.0216097347,-0.1954786032,0.0282272212,-0.2157017291,0.1640369296,-0.1948072165,-0.1926063299,-0.0209509991,-0.0578748696,0.143527016,-0.0421197824,-0.2080964595,-0.2229808122,0.1963418722,0.0195764005,0.1284812987,0.4782943726,-0.0001053136,0.1754009426,0.4400368631,-0.0290658046,-0.3724507391,-0.117692396,-0.1738838255,-0.1003989503,0.1145689264,-0.2962558866,-0.1716281921,-0.2144687325,0.0091438489,-0.2390604764,0.5132158995,0.2274693549,0.3231376112,0.4897455573,0.3873954415,-0.2229724824,0.4347153008,-0.1045805588,-0.1649615765,0.0650641918,-0.0769872293,0.2244307548,0.1632049978,0.2718215585,-0.0501564592,-0.0482423045,0.2512191534,-0.3109748065,0.0844580904,-0.426661253,0.2108005434,0.265067637,0.8665359616,-0.1871121824,-0.1075497344,0.0424290672,-0.05383211,0.0176125783,0.2213540375,0.1038815081,-0.2137550414,0.0224770084,0.0820365846,-0.1347355396,-0.2055648416,0.2402864099,-0.1167198047,0.2269532084,0.0299348682,0.1449246258,-0.1973686516,-0.0164674502,-0.0633138493,0.0475632809,0.1337091625,0.0893888324,-0.2678207457,-0.0422388315,-0.1053919941,0.1117191389,0.1169947609,0.1454482228,0.0958065465,0.2617616057,-0.0914402455,-0.0378101543,0.0341945626,0.2339129001,-0.1935859174,-0.0507200547,0.189233765,0.2287673056,-0.069193542,0.0472603068,0.0020275081,-0.2916690111,-0.263990581,0.1476930678,0.1127631813,-0.0786185339,-0.0183903091,0.0541397929,-0.2202125639,-0.0048255604,-0.2431868613,0.2852347493,-0.0487261303,0.3993789852,0.2344824523,0.1287545711,-0.2242077887,-0.4861928523,-0.1402329952,0.1473304033,-0.1106380224,0.0434008539,0.1618039012,-0.2273458838,0.257929951,-0.0197151937,-0.0530695394,-0.0251200981,-0.008738908,0.1091203019,-0.1057044566,0.2762655616,0.2591024935,0.3945358098,0.0405692793,0.2590233684,0.0018428388,-0.021217417,-0.2165844589,-0.2285818756,-0.2206411064,0.2521075606,-0.0131891994,-0.0047372812,-0.2291074544,0.0599272922,0.0986430123,-0.0805990919,0.0532944649,-0.142176643,-0.3216168582,-0.3087879717,0.4316569567,0.4920247197,-0.4255306721,0.1396030933,-0.0567286685,-0.0116971219,-0.0810582116,-0.0570174605,-0.1123933941,0.3496849835,-0.2192973793,-0.1809511036,0.2068288773,-0.3976510465,-0.2395835966,0.0906252861,-0.0634689108,-0.1056231409,0.1840570569,-0.0871479213,0.1540351808,0.0485327728,-0.2306942195,0.2110485584,0.137154609,-0.0479360111,-0.3818024695,-0.2028018981,-0.0171982832,0.0189915635,0.3237563372,0.1146467328,0.1676122993,0.5691867471,0.4555253088,0.0832124352,0.1777917743,0.4013090432,-0.2085609287,-0.0413433872,0.2671727836,-0.2288277596,-0.2487842739,0.2187516987,-0.0757088885,0.0348802097,0.1100947037,-0.0199408978,-0.6193234324,0.0314692631,-0.1231240258,-0.2005749643,0.2724452913,-0.044091668,0.1745682508,0.2743963897,0.0039639845,0.1924756169,-0.0867020339,0.0039485786,-0.5343642235,0.4087268412,-0.1935027838,-0.0015835753,0.065760985,0.0260758027,0.4088334739,-0.170558542,-0.1354473084,0.2723590434,-0.0304472353,0.2194167376,-0.1594223678,0.17986992,0.2396046668,-0.4609465599,0.0548013933,0.4481234252,0.1931599826,0.0678291395,0.0223196074,0.1240772158,0.138874799,0.1711906344,0.0337423719,0.2331715375,0.1935805678,0.0328348987,0.0641198978,-0.1908414513,0.3381064534,0.4348169863,0.1477684379,-0.2877872288,0.1254891008,0.1717788726,0.1579682678,0.0004035448,-0.14410083,-0.0412019566,-0.4308423102,0.0323522426,0.2122803479,-0.0631793216,-0.0784077719,0.2154160589,0.1682796627,0.0785733163,0.0418810323,-0.2345798165,0.2350731343,0.2690863311,0.296972692,0.1277826726,0.0447369702,0.1964007169,-0.1010829508,-0.2479411513,-0.002607428,0.3132402599,-0.3036606908,-0.0806536451,-0.4067722857,-0.4931701124,-0.3470222354,0.3208346963,-0.0742621496,-0.4287638664,-0.0380097479,0.2559501827,-0.0731308907,0.2184461206,0.0031276429,-0.0283503961,0.1946926117,0.0330018252,-0.4107174575,-0.1592382044,-0.2357391268,0.1306862235,0.1672376692,-0.0801034644,0.353747189,-0.1118827388,-0.1493054479,-0.4041166902,-0.2278541774,0.3301378787,-0.0719805136,-0.0573167279,-0.0529668294,0.4375869036,0.0946542248,-0.0434463881,0.1403905749,-0.3532881439,-0.2544414699,-0.0294814315,-0.1358548403,0.0752243847,-0.141345039,-0.4217614532,-0.1067153662,-0.4200991392,0.0635845289,0.4178258479,0.0400255397,0.1874130964,0.1365332901,-0.0267466102,0.1471457183,0.1477039009,-0.1003999263,-0.2613899112,0.2261000872,-0.2935187221,-0.5474257469,0.1274439394,-0.0443643034,0.1247173324,0.124836646,-0.3924241662,-0.2617845833,0.0056216181,0.0976368114,-0.1082213148,0.0930217654,0.3413145542,-0.2386584431,-0.0822928175,-0.1694237441,0.1190399975,-0.1367995888,-0.3998409212,0.0524377711,0.1804439723,0.1382700503,0.1225860119,0.7687302232,0.3579521477,0.0507154167,0.3671432734,-0.1381763071,0.169045344,-0.2479787171,-0.14265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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3763","title":"It's not possible download `20200501.pt` dataset","comments":"> ```python\r\n> dataset = load_dataset(\"wikipedia\", language=\"pt\", date=\"20220220\", beam_runner=\"DirectRunner\")\r\n> ```\r\n\r\nThank you! I did not know that I can do this. I was following the example in the error message when I do not define which language dataset I'm trying to download.\r\n\r\nI've tried something similar changing the date in the `load_dataset` call that I've shared in the bug description. Obviously, it did not work. I need to read the docs more carefully next time. My bad!\r\n\r\nThanks again and sorry for the noise.\r\n\r\n","body":"## Describe the bug\r\nThe dataset `20200501.pt` is broken. \r\n\r\nThe available datasets: https:\/\/dumps.wikimedia.org\/ptwiki\/\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wikipedia\", \"20200501.pt\", beam_runner='DirectRunner')\r\n```\r\n\r\n## Expected results\r\nI expect to download the dataset locally. \r\n\r\n## Actual results\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wikipedia\", \"20200501.pt\", beam_runner='DirectRunner')\r\nDownloading and preparing dataset wikipedia\/20200501.pt to \/home\/jvanz\/.cache\/huggingface\/datasets\/wikipedia\/20200501.pt\/1.0.0\/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475...\r\n\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/apache_beam\/__init__.py:79: UserWarning: This version of Apache Beam has not been sufficiently tested on Python 3.9. You may encounter bugs or missing features.\r\n  warnings.warn(\r\n  0%|                                                                                                                                                                                                                   | 0\/1 [00:00<?, ?it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1245, in _download_and_prepare\r\n    super()._download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 661, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/jvanz\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikipedia\/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475\/wikipedia.py\", line 420, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 307, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 195, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 260, in map_nested\r\n    mapped = [\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 261, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 196, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 216, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 298, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 612, in get_from_cache\r\n    raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/ptwiki\/20200501\/dumpstatus.json\r\n\r\n```\r\n\r\n## Environment info\r\n```\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.3.18-150300.59.49-default-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n```","comment_length":85,"text":"It's not possible download `20200501.pt` dataset \n ## Describe the bug\r\nThe dataset `20200501.pt` is broken. \r\n\r\nThe available datasets: https:\/\/dumps.wikimedia.org\/ptwiki\/\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wikipedia\", \"20200501.pt\", beam_runner='DirectRunner')\r\n```\r\n\r\n## Expected results\r\nI expect to download the dataset locally. \r\n\r\n## Actual results\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wikipedia\", \"20200501.pt\", beam_runner='DirectRunner')\r\nDownloading and preparing dataset wikipedia\/20200501.pt to \/home\/jvanz\/.cache\/huggingface\/datasets\/wikipedia\/20200501.pt\/1.0.0\/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475...\r\n\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/apache_beam\/__init__.py:79: UserWarning: This version of Apache Beam has not been sufficiently tested on Python 3.9. You may encounter bugs or missing features.\r\n  warnings.warn(\r\n  0%|                                                                                                                                                                                                                   | 0\/1 [00:00<?, ?it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1702, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1245, in _download_and_prepare\r\n    super()._download_and_prepare(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 661, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/jvanz\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikipedia\/009f923d9b6dd00c00c8cdc7f408f2b47f45dd4f5fb7982a21f9448f4afbe475\/wikipedia.py\", line 420, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 307, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 195, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 260, in map_nested\r\n    mapped = [\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 261, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 196, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 216, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 298, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/jvanz\/anaconda3\/envs\/tf-gpu\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 612, in get_from_cache\r\n    raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/ptwiki\/20200501\/dumpstatus.json\r\n\r\n```\r\n\r\n## Environment info\r\n```\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.3.18-150300.59.49-default-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n``` \n > ```python\r\n> dataset = load_dataset(\"wikipedia\", language=\"pt\", date=\"20220220\", beam_runner=\"DirectRunner\")\r\n> ```\r\n\r\nThank you! I did not know that I can do this. I was following the example in the error message when I do not define which language dataset I'm trying to download.\r\n\r\nI've tried something similar changing the date in the `load_dataset` call that I've shared in the bug description. Obviously, it did not work. I need to read the docs more carefully next time. My bad!\r\n\r\nThanks again and sorry for the noise.\r\n\r\n","embeddings":[-0.3223346174,-0.1315499395,-0.0983523577,0.3211584687,0.2504533827,0.1500658691,0.0497237779,0.4589625299,0.0925198868,0.1612989157,0.1003179476,0.354709655,0.017580511,0.1351880133,-0.0279239938,-0.4124290049,0.0741506442,0.0216097347,-0.1954786032,0.0282272212,-0.2157017291,0.1640369296,-0.1948072165,-0.1926063299,-0.0209509991,-0.0578748696,0.143527016,-0.0421197824,-0.2080964595,-0.2229808122,0.1963418722,0.0195764005,0.1284812987,0.4782943726,-0.0001053136,0.1754009426,0.4400368631,-0.0290658046,-0.3724507391,-0.117692396,-0.1738838255,-0.1003989503,0.1145689264,-0.2962558866,-0.1716281921,-0.2144687325,0.0091438489,-0.2390604764,0.5132158995,0.2274693549,0.3231376112,0.4897455573,0.3873954415,-0.2229724824,0.4347153008,-0.1045805588,-0.1649615765,0.0650641918,-0.0769872293,0.2244307548,0.1632049978,0.2718215585,-0.0501564592,-0.0482423045,0.2512191534,-0.3109748065,0.0844580904,-0.426661253,0.2108005434,0.265067637,0.8665359616,-0.1871121824,-0.1075497344,0.0424290672,-0.05383211,0.0176125783,0.2213540375,0.1038815081,-0.2137550414,0.0224770084,0.0820365846,-0.1347355396,-0.2055648416,0.2402864099,-0.1167198047,0.2269532084,0.0299348682,0.1449246258,-0.1973686516,-0.0164674502,-0.0633138493,0.0475632809,0.1337091625,0.0893888324,-0.2678207457,-0.0422388315,-0.1053919941,0.1117191389,0.1169947609,0.1454482228,0.0958065465,0.2617616057,-0.0914402455,-0.0378101543,0.0341945626,0.2339129001,-0.1935859174,-0.0507200547,0.189233765,0.2287673056,-0.069193542,0.0472603068,0.0020275081,-0.2916690111,-0.263990581,0.1476930678,0.1127631813,-0.0786185339,-0.0183903091,0.0541397929,-0.2202125639,-0.0048255604,-0.2431868613,0.2852347493,-0.0487261303,0.3993789852,0.2344824523,0.1287545711,-0.2242077887,-0.4861928523,-0.1402329952,0.1473304033,-0.1106380224,0.0434008539,0.1618039012,-0.2273458838,0.257929951,-0.0197151937,-0.0530695394,-0.0251200981,-0.008738908,0.1091203019,-0.1057044566,0.2762655616,0.2591024935,0.3945358098,0.0405692793,0.2590233684,0.0018428388,-0.021217417,-0.2165844589,-0.2285818756,-0.2206411064,0.2521075606,-0.0131891994,-0.0047372812,-0.2291074544,0.0599272922,0.0986430123,-0.0805990919,0.0532944649,-0.142176643,-0.3216168582,-0.3087879717,0.4316569567,0.4920247197,-0.4255306721,0.1396030933,-0.0567286685,-0.0116971219,-0.0810582116,-0.0570174605,-0.1123933941,0.3496849835,-0.2192973793,-0.1809511036,0.2068288773,-0.3976510465,-0.2395835966,0.0906252861,-0.0634689108,-0.1056231409,0.1840570569,-0.0871479213,0.1540351808,0.0485327728,-0.2306942195,0.2110485584,0.137154609,-0.0479360111,-0.3818024695,-0.2028018981,-0.0171982832,0.0189915635,0.3237563372,0.1146467328,0.1676122993,0.5691867471,0.4555253088,0.0832124352,0.1777917743,0.4013090432,-0.2085609287,-0.0413433872,0.2671727836,-0.2288277596,-0.2487842739,0.2187516987,-0.0757088885,0.0348802097,0.1100947037,-0.0199408978,-0.6193234324,0.0314692631,-0.1231240258,-0.2005749643,0.2724452913,-0.044091668,0.1745682508,0.2743963897,0.0039639845,0.1924756169,-0.0867020339,0.0039485786,-0.5343642235,0.4087268412,-0.1935027838,-0.0015835753,0.065760985,0.0260758027,0.4088334739,-0.170558542,-0.1354473084,0.2723590434,-0.0304472353,0.2194167376,-0.1594223678,0.17986992,0.2396046668,-0.4609465599,0.0548013933,0.4481234252,0.1931599826,0.0678291395,0.0223196074,0.1240772158,0.138874799,0.1711906344,0.0337423719,0.2331715375,0.1935805678,0.0328348987,0.0641198978,-0.1908414513,0.3381064534,0.4348169863,0.1477684379,-0.2877872288,0.1254891008,0.1717788726,0.1579682678,0.0004035448,-0.14410083,-0.0412019566,-0.4308423102,0.0323522426,0.2122803479,-0.0631793216,-0.0784077719,0.2154160589,0.1682796627,0.0785733163,0.0418810323,-0.2345798165,0.2350731343,0.2690863311,0.296972692,0.1277826726,0.0447369702,0.1964007169,-0.1010829508,-0.2479411513,-0.002607428,0.3132402599,-0.3036606908,-0.0806536451,-0.4067722857,-0.4931701124,-0.3470222354,0.3208346963,-0.0742621496,-0.4287638664,-0.0380097479,0.2559501827,-0.0731308907,0.2184461206,0.0031276429,-0.0283503961,0.1946926117,0.0330018252,-0.4107174575,-0.1592382044,-0.2357391268,0.1306862235,0.1672376692,-0.0801034644,0.353747189,-0.1118827388,-0.1493054479,-0.4041166902,-0.2278541774,0.3301378787,-0.0719805136,-0.0573167279,-0.0529668294,0.4375869036,0.0946542248,-0.0434463881,0.1403905749,-0.3532881439,-0.2544414699,-0.0294814315,-0.1358548403,0.0752243847,-0.141345039,-0.4217614532,-0.1067153662,-0.4200991392,0.0635845289,0.4178258479,0.0400255397,0.1874130964,0.1365332901,-0.0267466102,0.1471457183,0.1477039009,-0.1003999263,-0.2613899112,0.2261000872,-0.2935187221,-0.5474257469,0.1274439394,-0.0443643034,0.1247173324,0.124836646,-0.3924241662,-0.2617845833,0.0056216181,0.0976368114,-0.1082213148,0.0930217654,0.3413145542,-0.2386584431,-0.0822928175,-0.1694237441,0.1190399975,-0.1367995888,-0.3998409212,0.0524377711,0.1804439723,0.1382700503,0.1225860119,0.7687302232,0.3579521477,0.0507154167,0.3671432734,-0.1381763071,0.169045344,-0.2479787171,-0.1426578462,0.0712148622,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3762","title":"`Dataset.class_encode` should support custom class names","comments":"Hi @Dref360, thanks a lot for your proposal.\r\n\r\nIt totally makes sense to have more flexibility when class encoding, I agree.\r\n\r\nYou could even further customize the class encoding by passing an instance of `ClassLabel` itself (instead of replicating `ClassLabel` instantiation arguments as `Dataset.class_encode_column` arguments).\r\n\r\nAnd the latter made me think of `Dataset.cast_column`...\r\n\r\nMaybe better to have some others' opinions @lhoestq @mariosasko ","body":"I can make a PR, just wanted approval before starting.\r\n\r\n**Is your feature request related to a problem? Please describe.**\r\n\r\nIt is often the case that classes are not ordered in alphabetical order. Current `class_encode_column` sort the classes before indexing.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L1235\r\n\r\n**Describe the solution you'd like**\r\nI would like to add a **optional** parameter `class_names` to `class_encode_column` that would  be used for the mapping instead of sorting the unique values.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nOne can use map instead. I find it harder to read.\r\n```python\r\nCLASS_NAMES = ['apple', 'orange', 'potato']\r\nds = ds.map(lambda item: CLASS_NAMES.index(item[label_column]))\r\n\r\n# Proposition\r\nds = ds.class_encode_column(label_column, CLASS_NAMES)\r\n```\r\n\r\n**Additional context**\r\n\r\nI can make the PR if this feature is accepted.\r\n","comment_length":62,"text":"`Dataset.class_encode` should support custom class names \n I can make a PR, just wanted approval before starting.\r\n\r\n**Is your feature request related to a problem? Please describe.**\r\n\r\nIt is often the case that classes are not ordered in alphabetical order. Current `class_encode_column` sort the classes before indexing.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L1235\r\n\r\n**Describe the solution you'd like**\r\nI would like to add a **optional** parameter `class_names` to `class_encode_column` that would  be used for the mapping instead of sorting the unique values.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nOne can use map instead. I find it harder to read.\r\n```python\r\nCLASS_NAMES = ['apple', 'orange', 'potato']\r\nds = ds.map(lambda item: CLASS_NAMES.index(item[label_column]))\r\n\r\n# Proposition\r\nds = ds.class_encode_column(label_column, CLASS_NAMES)\r\n```\r\n\r\n**Additional context**\r\n\r\nI can make the PR if this feature is accepted.\r\n \n Hi @Dref360, thanks a lot for your proposal.\r\n\r\nIt totally makes sense to have more flexibility when class encoding, I agree.\r\n\r\nYou could even further customize the class encoding by passing an instance of `ClassLabel` itself (instead of replicating `ClassLabel` instantiation arguments as `Dataset.class_encode_column` arguments).\r\n\r\nAnd the latter made me think of `Dataset.cast_column`...\r\n\r\nMaybe better to have some others' opinions @lhoestq @mariosasko 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3762","title":"`Dataset.class_encode` should support custom class names","comments":"Hi @Dref360! You can use [`Dataset.align_labels_with_mapping`](https:\/\/huggingface.co\/docs\/datasets\/master\/package_reference\/main_classes.html#datasets.Dataset.align_labels_with_mapping) after `Dataset.class_encode_column` to assign a different mapping of labels to ids.\r\n\r\n@albertvillanova I'd like to avoid adding more complexity to the API where it's not (absolutely) needed, so I don't think introducing a new param in `Dataset.class_encode_column` is a good idea.\r\n\r\n","body":"I can make a PR, just wanted approval before starting.\r\n\r\n**Is your feature request related to a problem? Please describe.**\r\n\r\nIt is often the case that classes are not ordered in alphabetical order. Current `class_encode_column` sort the classes before indexing.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L1235\r\n\r\n**Describe the solution you'd like**\r\nI would like to add a **optional** parameter `class_names` to `class_encode_column` that would  be used for the mapping instead of sorting the unique values.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nOne can use map instead. I find it harder to read.\r\n```python\r\nCLASS_NAMES = ['apple', 'orange', 'potato']\r\nds = ds.map(lambda item: CLASS_NAMES.index(item[label_column]))\r\n\r\n# Proposition\r\nds = ds.class_encode_column(label_column, CLASS_NAMES)\r\n```\r\n\r\n**Additional context**\r\n\r\nI can make the PR if this feature is accepted.\r\n","comment_length":47,"text":"`Dataset.class_encode` should support custom class names \n I can make a PR, just wanted approval before starting.\r\n\r\n**Is your feature request related to a problem? Please describe.**\r\n\r\nIt is often the case that classes are not ordered in alphabetical order. Current `class_encode_column` sort the classes before indexing.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L1235\r\n\r\n**Describe the solution you'd like**\r\nI would like to add a **optional** parameter `class_names` to `class_encode_column` that would  be used for the mapping instead of sorting the unique values.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nOne can use map instead. I find it harder to read.\r\n```python\r\nCLASS_NAMES = ['apple', 'orange', 'potato']\r\nds = ds.map(lambda item: CLASS_NAMES.index(item[label_column]))\r\n\r\n# Proposition\r\nds = ds.class_encode_column(label_column, CLASS_NAMES)\r\n```\r\n\r\n**Additional context**\r\n\r\nI can make the PR if this feature is accepted.\r\n \n Hi @Dref360! You can use [`Dataset.align_labels_with_mapping`](https:\/\/huggingface.co\/docs\/datasets\/master\/package_reference\/main_classes.html#datasets.Dataset.align_labels_with_mapping) after `Dataset.class_encode_column` to assign a different mapping of labels to ids.\r\n\r\n@albertvillanova I'd like to avoid adding more complexity to the API where it's not (absolutely) needed, so I don't think introducing a new param in `Dataset.class_encode_column` is a good idea.\r\n\r\n","embeddings":[0.1994742155,-0.0115085812,0.0501547903,0.1014925539,0.3611441851,0.2609221637,0.2167878002,0.0255504679,0.0818393007,0.117306523,0.0086967517,0.6595109701,-0.1692374051,0.1270327121,0.047164131,-0.3817210793,-0.0801747292,0.0698391125,0.1967080832,-0.0349072404,-0.6529199481,0.154260084,0.0237760097,-0.0882008597,0.2193388343,0.0926609337,-0.1380752027,0.0285093728,-0.0251034331,-0.5012537241,0.2955214679,0.3677632213,-0.030214902,0.1586484462,-0.0001164297,-0.1592454165,-0.0390073024,-0.1422561407,-0.2330772281,-0.2812616825,-0.2510818243,-0.061314512,-0.0078858109,-0.374058187,-0.385158211,-0.2750989199,-0.0512297861,-0.3772494793,0.1344210505,-0.0315010808,0.1564963162,-0.1278666109,0.0498935729,-0.1175782159,0.1249241158,0.1101077572,-0.1956817806,0.1239766032,0.151665464,0.3846846819,0.1421044022,0.3117631078,0.1245168,-0.166425094,0.4047255516,0.0738305375,0.0522125848,-0.0629907697,-0.0451525375,0.1754328609,0.4312139153,-0.3508844078,-0.5304557085,-0.2970797718,0.3410781622,-0.6122488976,0.0023101354,-0.1541728526,0.1518126577,0.1289786249,0.0822417513,0.0607070774,-0.1696063578,0.2751836181,0.1457087696,0.3369252384,0.0023261341,0.0657428727,0.0213727299,-0.4121970832,-0.0621266738,0.0281977374,0.1231577173,0.4307447076,-0.1275954098,-0.3845306635,-0.207728222,0.0550974533,-0.0074022077,-0.0131239891,-0.208295837,0.1597569883,0.0843080953,-0.164991796,-0.0553761125,0.3089486063,0.4909807444,0.5153467059,-0.0111446511,-0.0544977263,-0.0215957146,0.0445815995,-0.1205638051,0.0353207737,0.043433886,0.0468758903,-0.1335154325,-0.0627337769,-0.0273758397,0.0273602251,-0.1142203957,-0.0404045247,0.2779928446,0.3559280038,0.2354698181,-0.1715613604,0.1534448415,0.035332296,0.2233296931,0.0280964151,0.0010065357,-0.2548128963,0.0087282537,0.418813616,0.1724846512,0.1152442172,0.2061409652,0.0345044136,0.0313948691,0.0539997965,0.0785695538,0.3354120851,0.3290554583,0.0157281458,-0.4670566022,0.168841362,0.0923547223,-0.2490223944,-0.3754129112,-0.0442442484,-0.3989104629,-0.3458833098,-0.1658120006,0.1205369607,0.091527991,-0.2843419015,0.0025381278,0.2375995666,0.1715294272,-0.198417604,0.2595560849,-0.017419178,-0.1553354114,-0.1106023639,-0.1657951772,0.1600832641,-0.0977771953,-0.427236855,0.0385400653,0.3133600354,-0.1017804369,0.0106945485,-0.0737820715,-0.2740506828,0.0066263266,0.0605073981,0.2585880756,-0.6207507253,-0.3050141037,0.1470887065,-0.040456187,-0.1262495816,0.3811635673,0.3994442523,0.2366111875,-0.1276562959,0.2627563179,-0.103215687,0.1515901983,-0.0436866991,0.0909376293,-0.0666124672,0.0202768426,0.3606598377,-0.4370360374,-0.0547469035,-0.1316605359,-0.3559798002,0.0620946884,-0.458658725,-0.1240823716,0.1442602873,0.1717093587,0.2610217035,0.2127273232,-0.2261274159,-0.5414165258,0.2412896007,0.0403054915,0.0238805171,-0.2919275761,-0.5915957689,0.1253410131,-0.2035527825,-0.217508167,0.1247740239,0.0706054494,-0.2701051235,0.1429553628,-0.2614474595,-0.2889050245,0.141530931,-0.4437044561,0.2632511258,-0.2415961474,-0.1054376513,0.1996423304,0.1404084265,-0.2319159806,0.2162286937,0.2609140277,-0.1734912544,-0.0140670249,0.2107859701,0.1290743202,-0.3676989675,0.065619871,0.1517666727,0.2081337124,-0.1174646392,-0.0170766003,0.0618753508,-0.1151871681,0.0476103388,-0.4791373909,0.8631471992,0.0483663268,0.4055610299,0.0307701323,-0.094713226,0.1682963967,-0.131353274,-0.4411602616,-0.4517971873,0.0224076547,-0.129775703,0.3244477212,-0.0481363423,-0.4404683113,0.0403982736,0.3754479289,-0.0665903986,0.106836848,-0.035965573,-0.015875902,0.4167144597,0.1897064149,0.4448103011,0.3876829445,0.343678087,-0.1918571442,-0.0934871063,-0.1758313328,0.0206551403,0.1779718995,0.0826763511,-0.1529404074,-0.0511572361,0.1316677332,0.009440056,-0.382545948,-0.2453683913,0.1037902534,-0.2218341529,-0.336273402,-0.007099296,-0.1039493605,-0.1925694346,-0.1904396862,-0.4223113358,-0.1461534798,-0.0669543594,0.2642043829,-0.3178998232,-0.1915753931,0.3298573494,-0.1969032884,0.2837178111,-0.0512496829,-0.7253999114,0.0953960195,-0.0241139773,0.0929552913,-0.0121495854,-0.224856317,-0.1700678468,0.4998141825,-0.0160038341,0.1314118207,-0.1698040366,-0.6717211008,-0.0676539615,-0.234688893,0.024961751,0.2994855642,-0.1668728292,-0.1296280026,-0.2076132149,0.2870743871,0.1730613708,-0.0870910808,-0.2752997279,0.0281673316,-0.1120926663,-0.2392704934,-0.3698710203,-0.1595619321,-0.2344538718,0.2363277078,0.1610906273,0.2554937303,-0.0221754983,-0.1338851452,0.0559524707,0.1440361142,0.0058546555,-0.2290513813,-0.1098808348,0.4167484641,-0.4332853258,0.1441312581,-0.1687251031,-0.2919520736,0.0854342505,-0.0820281506,0.1217223927,-0.452183336,-0.1092749462,0.331948638,0.0719299167,0.1048816815,0.3265850246,0.3692308664,-0.0220492259,-0.1383684874,-0.3641899228,0.0708298236,0.3147133291,0.1764398068,0.1937414706,0.5414764881,-0.1310500652,0.8178832531,0.1473552436,-0.2775659859,0.2606447041,0.0499271527,0.2039223611,0.0331509635,-0.2426369339,-0.4365832806,0.1202870831,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3761","title":"Know your data for HF hub ","comments":"Hi @Muhtasham you should take a look at https:\/\/huggingface.co\/blog\/data-measurements-tool and accompanying demo app at https:\/\/huggingface.co\/spaces\/huggingface\/data-measurements-tool\r\n\r\nWe would be interested in your feedback. cc @meg-huggingface @sashavor @yjernite ","body":"**Is your feature request related to a problem? Please describe.**\r\nWould be great to see be able to understand datasets with the goal of improving data quality, and helping mitigate fairness and bias issues.\r\n\r\n**Describe the solution you'd like**\r\nSomething like https:\/\/knowyourdata.withgoogle.com\/ for HF hub","comment_length":26,"text":"Know your data for HF hub  \n **Is your feature request related to a problem? Please describe.**\r\nWould be great to see be able to understand datasets with the goal of improving data quality, and helping mitigate fairness and bias issues.\r\n\r\n**Describe the solution you'd like**\r\nSomething like https:\/\/knowyourdata.withgoogle.com\/ for HF hub \n Hi @Muhtasham you should take a look at https:\/\/huggingface.co\/blog\/data-measurements-tool and accompanying demo app at https:\/\/huggingface.co\/spaces\/huggingface\/data-measurements-tool\r\n\r\nWe would be interested in your feedback. cc @meg-huggingface @sashavor @yjernite 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3760","title":"Unable to view the Gradio flagged call back dataset","comments":"Hi @kingabzpro.\r\n\r\nI think you need to create a loading script that creates the dataset from the CSV file and the image paths.\r\n\r\nAs example, you could have a look at the Food-101 dataset: https:\/\/huggingface.co\/datasets\/food101\r\n- Loading script: https:\/\/huggingface.co\/datasets\/food101\/blob\/main\/food101.py\r\n\r\nOnce the loading script is created, the viewer will show a previsualization of your dataset. ","body":"## Dataset viewer issue for '*savtadepth-flags*'\r\n\r\n**Link:** *[savtadepth-flags](https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags)*\r\n\r\n*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https:\/\/huggingface.co\/spaces\/kingabzpro\/savtadepth.*\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":54,"text":"Unable to view the Gradio flagged call back dataset \n ## Dataset viewer issue for '*savtadepth-flags*'\r\n\r\n**Link:** *[savtadepth-flags](https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags)*\r\n\r\n*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https:\/\/huggingface.co\/spaces\/kingabzpro\/savtadepth.*\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n Hi @kingabzpro.\r\n\r\nI think you need to create a loading script that creates the dataset from the CSV file and the image paths.\r\n\r\nAs example, you could have a look at the Food-101 dataset: https:\/\/huggingface.co\/datasets\/food101\r\n- Loading script: https:\/\/huggingface.co\/datasets\/food101\/blob\/main\/food101.py\r\n\r\nOnce the loading script is created, the viewer will show a previsualization of your dataset. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3760","title":"Unable to view the Gradio flagged call back dataset","comments":"@albertvillanova I don't think this is the issue. I have created another dataset with similar files and format and it works. https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags-V2","body":"## Dataset viewer issue for '*savtadepth-flags*'\r\n\r\n**Link:** *[savtadepth-flags](https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags)*\r\n\r\n*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https:\/\/huggingface.co\/spaces\/kingabzpro\/savtadepth.*\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":22,"text":"Unable to view the Gradio flagged call back dataset \n ## Dataset viewer issue for '*savtadepth-flags*'\r\n\r\n**Link:** *[savtadepth-flags](https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags)*\r\n\r\n*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https:\/\/huggingface.co\/spaces\/kingabzpro\/savtadepth.*\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n @albertvillanova I don't think this is the issue. I have created another dataset with similar files and format and it works. https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags-V2","embeddings":[-0.2301908433,0.2327468395,0.017911939,0.258522898,0.3205559552,0.0420702845,0.2842935026,0.1822576374,0.1492126435,-0.0256713778,-0.261585772,-0.0426337942,-0.0275213551,0.2540270686,0.2023281008,0.1306374818,-0.1105963439,0.0396924503,0.4807678163,-0.0408442989,-0.2699695826,0.2151141614,-0.2170586437,0.1796571314,-0.1426707655,0.1199335307,-0.1433013231,0.1607236564,0.0692560077,-0.7044485807,0.3042168915,0.1342078745,0.1566878259,0.2719092667,-0.000125643,0.1056998968,0.3615066409,0.0437329486,-0.1210339591,-0.0461626723,-0.293605864,0.1221995652,-0.1436011791,-0.0530823767,-0.2181891203,-0.1527149975,-0.0472905152,-0.4126582146,0.302930057,0.4316344559,0.065476425,-0.1427522153,0.359626025,0.0041668541,0.008274843,0.2960895896,-0.1556775123,0.6021966338,0.2040904611,0.3086089194,0.0858717337,0.2976689935,-0.0923878178,-0.2209959924,-0.1152254716,-0.1514522135,-0.2680233121,-0.0633779764,0.3323370516,0.2542966008,0.2575377524,-0.1446802616,-0.2291829884,-0.1874720901,0.0283187199,-0.1558132619,0.1971019357,0.1401869953,0.0795743242,0.2590258121,0.0682763308,-0.3026799262,-0.3113855124,0.1596668065,0.0800451413,0.1994847357,-0.3224802017,0.2667939067,0.1054663882,0.0448210835,-0.0200012084,0.0192226991,-0.1947365403,0.376008451,-0.2039401978,0.0839917064,0.0140829599,0.0548819862,0.2495447099,0.2131458521,-0.0205786526,0.5517393351,-0.4834856689,-0.0172386188,0.0305627882,0.3248462379,0.005318501,0.3393243253,0.4062658846,0.2059786916,0.0193209462,-0.2409460098,-0.3740726411,-0.1834504306,-0.3507684767,-0.3531448841,0.458507508,-0.4231191278,-0.1991099417,0.0746865869,-0.3559271693,0.0053743804,0.241997838,0.3633016944,-0.1878536791,-0.0538439192,-0.0007647076,0.1617081016,0.031834539,-0.3609313667,0.0071369857,-0.2517217696,-0.2464188039,0.0134617938,0.2027644217,-0.7676009536,0.103705667,-0.0188642051,0.0143019101,-0.2383394986,0.1046034843,-0.2684713006,0.1870520115,0.0732034445,0.1412306577,0.1195873842,0.2688913047,-0.0866194963,-0.0039310092,0.0781372488,-0.2682118118,-0.2991442084,-0.1390845329,-0.0230092034,-0.309284091,0.0268064756,-0.3886808157,0.0642444789,-0.1104101613,-0.1740878671,-0.1506350189,0.1095388085,-0.1677576602,-0.0550867058,0.1103230342,0.293977648,-0.4045161307,-0.0181215648,-0.2454753071,-0.1778713167,0.2332534045,0.2163479477,-0.1368346512,-0.1522499919,-0.6082149148,-0.1547182649,0.6634247899,-0.3806282878,-0.4640959501,0.3786874413,0.2173662782,-0.1903721094,0.0012161572,-0.1104003489,0.1005601138,-0.0004536893,-0.2230275422,0.0166698266,0.0837990493,-0.0208500791,-0.1720507145,0.0790693015,-0.1607104987,0.284390986,0.0763029307,0.3679303825,0.074707523,-0.482417345,0.4230321944,0.2345462739,0.2965774238,0.0760795027,0.4369642138,0.0191400535,0.2462519258,0.1508789808,0.0609879643,-0.0414882302,0.0746421814,-0.1893358082,0.0400738232,-0.0430748649,-0.6470255256,0.0296090543,-0.6404581666,-0.0452385843,-0.0330900028,0.3000833392,0.1316984147,0.2117789388,-0.0608997196,0.3245082498,-0.0510309637,0.1312728822,0.3497632146,0.3004626334,0.1317352355,-0.209056899,0.3057383299,0.3575719595,0.4467677474,-0.0966736451,-0.0940518826,0.2466455996,0.0382884853,0.0657623708,0.2254912853,0.0792981908,0.3033358157,-0.4428430498,0.3665838242,0.3891192973,0.0960701555,-0.0967116728,-0.2103396356,0.3410865068,-0.1538408697,0.2720107138,0.0189813245,0.3951554894,0.2804583609,0.020946933,-0.3029544055,-0.2818605304,0.0755142272,-0.1347603649,0.5949738026,-0.2405678928,-0.5125947595,0.1466995776,0.350964576,0.0828469694,-0.0213230848,0.0620462596,-0.3352376521,-0.1644592136,0.2630271614,0.2677890956,0.2637200654,0.1049181148,-0.0624941736,0.2175122201,-0.1960276067,-0.0307708755,0.0444398485,0.1150743589,-0.0591448583,0.0807290152,0.1693603098,-0.1580168158,-0.3951410949,-0.3067112267,0.1099814698,0.1832209527,-0.140287742,0.0803198293,0.1215571538,-0.1558333337,-0.3028432131,-0.8024637699,-0.3347619176,-0.6785559654,-0.1997913271,0.0291439556,0.2530490458,0.1932246685,-0.2996147275,0.1092124507,0.0338339135,0.4119323194,-0.1836684197,-0.0128913643,-0.3078899384,0.0108185699,0.0508936122,-0.3181147575,0.4198613465,0.1081127897,0.1772096902,-0.4429500997,-0.3084259331,0.1286514103,0.101909332,0.0606671385,0.1558887661,-0.0040987204,-0.0706737116,0.1187530905,0.1130500436,0.1221626103,0.0860653669,-0.1135013774,-0.1362852603,0.2783604264,0.2194603831,-0.0894463211,-0.0292012077,-0.1599810421,0.0891624689,0.0825963244,0.2569646537,0.2782411575,-0.0565396659,0.3231970072,-0.0870493874,-0.0828353316,-0.4470670521,-0.5315869451,0.3002490997,-0.5356848836,-0.425970763,0.5518063903,0.0687901825,0.6693540812,-0.3916644156,-0.291829139,0.1372448802,-0.0801689848,-0.0261226837,-0.2005767524,-0.3534429371,-0.0663855448,-0.0611053444,0.1349651217,-0.0233639851,0.0054049902,0.0422511585,-0.2040325403,0.2795239687,0.1382728517,0.2946818769,-0.0903955922,0.5105327964,0.275998354,0.1339413822,0.3849802017,-0.1876622587,0.365687489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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3760","title":"Unable to view the Gradio flagged call back dataset","comments":"Yes, you are right, that was not the issue.\r\n\r\nJust take into account that sometimes the viewer can take some time until it shows the preview of the dataset.\r\nAfter some time, yours is finally properly shown: https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags","body":"## Dataset viewer issue for '*savtadepth-flags*'\r\n\r\n**Link:** *[savtadepth-flags](https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags)*\r\n\r\n*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https:\/\/huggingface.co\/spaces\/kingabzpro\/savtadepth.*\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":38,"text":"Unable to view the Gradio flagged call back dataset \n ## Dataset viewer issue for '*savtadepth-flags*'\r\n\r\n**Link:** *[savtadepth-flags](https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags)*\r\n\r\n*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https:\/\/huggingface.co\/spaces\/kingabzpro\/savtadepth.*\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n Yes, you are right, that was not the issue.\r\n\r\nJust take into account that sometimes the viewer can take some time until it shows the preview of the dataset.\r\nAfter some time, yours is finally properly shown: https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags","embeddings":[-0.2451808006,0.2172696739,-0.0100675272,0.1485991776,0.3047354519,-0.0232700948,0.3388378918,0.1904136986,0.1421711892,-0.0403717533,-0.2485891879,-0.0416517481,0.042475719,0.3279090226,0.1481872052,0.0801420957,-0.164989084,-0.0025604393,0.3318656385,-0.0744965151,-0.2404005677,0.2184651196,-0.198389098,0.1400346905,-0.1509701461,0.0896854848,-0.133410722,0.1845109463,-0.0405380316,-0.7055110931,0.3206534982,0.0452000238,0.1075466797,0.2702994645,-0.0001136452,0.0386099815,0.3141260445,0.0404058024,-0.1319923103,-0.0751442313,-0.1840314269,0.0731597468,-0.0892675072,-0.0789771006,-0.2417403907,-0.1663530767,-0.0268228184,-0.294911474,0.3331057429,0.4128551185,0.1912207752,-0.0998846367,0.3347681761,-0.1035261229,-0.0383945107,0.2325206846,-0.1898033321,0.5654545426,0.1187502444,0.3276019394,0.0341665931,0.3455224335,-0.0766709745,-0.1780973524,-0.132671386,-0.0711127892,-0.263368696,-0.1440364569,0.2922636569,0.2429383844,0.273799777,-0.1256709099,-0.2565204501,-0.1663526297,0.0120453853,-0.1154874861,0.165918529,0.160893485,0.0107825082,0.2521769404,0.0313204192,-0.1360774189,-0.1639926285,0.1014855728,-0.0482920446,0.2336518019,-0.3677152693,0.2267353237,0.0437236615,0.0488433465,-0.0728852376,-0.02018141,-0.2109933048,0.3867950439,-0.2435383499,0.0971878916,0.0932847783,0.007321124,0.2355001569,0.2621013522,0.024441421,0.4998616278,-0.4895963967,-0.0520591103,0.0569255166,0.3217858672,-0.0229947269,0.3728580475,0.3568581641,0.2021455318,0.1030584425,-0.2617143691,-0.2814794481,-0.1517260075,-0.3366512954,-0.2829221785,0.4993107021,-0.4490833879,-0.2210935503,0.0354423709,-0.2777849138,0.0328471996,0.1488755494,0.3706707358,-0.2444616556,-0.0934086666,0.0038257858,0.1387331337,-0.0224194452,-0.2844638228,-0.074510783,-0.2638781071,-0.3180971444,0.0517820343,0.2399510592,-0.6690097451,0.1662805527,0.0029279799,0.0475525856,-0.1179763079,0.1175004989,-0.1703399271,0.230619356,0.144775033,0.1944641769,0.1791832596,0.3607034087,-0.0737095773,-0.0234337654,0.1071121916,-0.1737023741,-0.3332186639,-0.0809696168,0.1016179398,-0.3303343654,0.0140734399,-0.3311188221,0.0622687526,-0.0951945409,-0.1434297264,-0.1134761199,0.1348218471,-0.1691880077,-0.0859245583,0.1822631359,0.2671454549,-0.3748564422,-0.0584972911,-0.2394499332,-0.2535743117,0.2536352277,0.2038361132,-0.1087668538,-0.1137461811,-0.6575048566,-0.0568478592,0.5600674152,-0.2918376029,-0.4499616325,0.3592647612,0.11003609,-0.1312812865,-0.0553718992,-0.0997985303,0.1187340319,-0.0087949689,-0.211595282,0.0909647867,0.0620164722,0.0960457623,-0.2528326809,0.0998264551,-0.1409150958,0.3061097562,0.0631770715,0.3449298441,-0.0110453963,-0.4977178276,0.437556833,0.2316793203,0.2282626182,0.0150982011,0.4481454194,-0.0143927401,0.1796612591,0.2262547016,0.0460630283,0.0175636653,0.0439093187,-0.1050593555,0.0871689245,-0.0839161575,-0.6136056781,0.0107951863,-0.6057508588,-0.1108261123,0.1012012288,0.291844964,0.1272338778,0.1692130864,-0.0099812225,0.3123264611,-0.0669713318,0.1068720818,0.3282106817,0.3394792676,0.0678558052,-0.206504494,0.3360212147,0.2148016989,0.3736755848,-0.0337485895,-0.1024147868,0.2295364887,0.00572161,0.0734737143,0.1690776795,0.1092143729,0.2542159557,-0.3873763084,0.3619598448,0.3177455068,0.0735027939,-0.1207741201,-0.239745155,0.3932703435,-0.131900683,0.2443479449,-0.0189540833,0.3749752343,0.3120695353,0.0187704507,-0.3583385348,-0.2295577377,0.028885603,-0.0910003036,0.6233767271,-0.2685593069,-0.5436406136,0.1922470033,0.3649598658,0.1083697006,-0.0533765033,0.040777795,-0.3436514139,-0.1961502582,0.2522169054,0.2797298133,0.299099952,0.1709516793,-0.047258269,0.1939994246,-0.1774495691,-0.1112802178,0.0450228788,0.0822586119,-0.0524503812,0.0886277407,0.1192335039,-0.1313448846,-0.4325873256,-0.1834266782,0.0954782069,0.0843199044,-0.124270767,0.0275848731,0.1451120526,-0.166449666,-0.2629115283,-0.8655324578,-0.2931800783,-0.7084571719,-0.1865780503,-0.0089949984,0.1429086477,0.3001388907,-0.2834837735,0.0737200081,0.0296540912,0.3884524107,-0.1322602779,0.0337761417,-0.3404634297,0.1230536252,0.0567376949,-0.2851123214,0.5187379718,0.0800520852,0.2168355882,-0.510543108,-0.3771116138,0.1435988694,0.0408356152,0.0627850667,0.1728163511,0.0262714922,-0.0249104798,0.0687860176,0.1717730463,0.1278869957,0.0217741877,-0.1006158069,-0.1586984694,0.2541661263,0.2138211727,-0.1491189152,0.0173093583,-0.3095295727,0.0862616301,0.0884378105,0.2447342277,0.2466074079,-0.0228861142,0.3376174867,-0.1081707403,-0.0795187578,-0.4211046994,-0.5648121834,0.3028729856,-0.5843526125,-0.4841679633,0.557575047,0.0592098348,0.689265728,-0.4229899943,-0.3481506705,0.0321079455,-0.0970746502,0.0624680854,-0.1584663689,-0.3396703601,0.0310910083,-0.0876697376,0.033543501,-0.0256673358,-0.068437323,0.0367828906,-0.2469372898,0.2829134762,0.0062640868,0.2530349493,-0.0841878802,0.5879269242,0.2910130322,0.111770533,0.3015129268,-0.1480162144,0.3247597516,-0.25243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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3760","title":"Unable to view the Gradio flagged call back dataset","comments":"The problem was resolved by deleted the dataset and creating new one with similar name and then clicking on flag button.","body":"## Dataset viewer issue for '*savtadepth-flags*'\r\n\r\n**Link:** *[savtadepth-flags](https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags)*\r\n\r\n*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https:\/\/huggingface.co\/spaces\/kingabzpro\/savtadepth.*\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":21,"text":"Unable to view the Gradio flagged call back dataset \n ## Dataset viewer issue for '*savtadepth-flags*'\r\n\r\n**Link:** *[savtadepth-flags](https:\/\/huggingface.co\/datasets\/kingabzpro\/savtadepth-flags)*\r\n\r\n*with the Gradio 2.8.1 the dataset viers stopped working. I tried to add values manually but its not working. The dataset is also not showing the link with the app https:\/\/huggingface.co\/spaces\/kingabzpro\/savtadepth.*\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n The problem was resolved by deleted the dataset and creating new one with similar name and then clicking on flag button.","embeddings":[-0.2355169356,0.2943934202,0.0060479157,0.2653228641,0.2650960386,0.0637758002,0.3267032802,0.2095134705,0.1700184494,-0.0364795253,-0.2569231689,0.0024182224,-0.0134499995,0.2589096427,0.2008361518,0.1542169452,-0.1398296654,0.0278554633,0.4430777729,-0.0308097247,-0.2674981356,0.1858101189,-0.2205571383,0.1695207506,-0.1131373718,0.0894504413,-0.126984939,0.1336180717,0.0613197722,-0.7181728482,0.3228184283,0.1041280404,0.1078045815,0.269320488,-0.0001230333,0.1024647653,0.3216154277,0.0709967092,-0.0966292471,-0.0176631678,-0.3023273945,0.1287791878,-0.1250493824,-0.0833500028,-0.1453164667,-0.162962839,-0.071638599,-0.3818092346,0.2716278732,0.4424566627,0.0941039026,-0.1220946908,0.3669966161,0.0063405363,-0.0045827636,0.2895882428,-0.1679652929,0.5839022994,0.1528354287,0.2808048427,0.0497864895,0.3091456592,-0.1120201126,-0.1797435731,-0.1535256803,-0.1376198679,-0.193189472,-0.0987945125,0.3484725952,0.233225137,0.2464078218,-0.1871655434,-0.1738734543,-0.1676462442,0.0552687719,-0.1802206039,0.1909429282,0.15212062,0.0725113451,0.2408001572,0.096195519,-0.2789200246,-0.2954922616,0.123179689,0.0382094719,0.2181276828,-0.3497818708,0.2460631579,0.0527099967,0.0393779501,0.031619817,0.0254940223,-0.2002449036,0.4070971012,-0.2663309276,0.0743209496,0.0306531023,-0.004214942,0.2765722871,0.2275020182,-0.0096445596,0.5251384974,-0.4718078375,-0.0305526201,0.0009575749,0.3196538091,0.0253720544,0.377440989,0.3796113729,0.1447895169,0.0012619204,-0.2135660648,-0.3300433159,-0.1585326344,-0.2723031342,-0.3502053022,0.5405042768,-0.4369174242,-0.2295495123,0.0724423006,-0.3770100772,-0.0231481418,0.2539897263,0.343473047,-0.1893346161,-0.0912961215,-0.00786677,0.1212212518,0.026816519,-0.317510277,-0.0015563073,-0.2654656768,-0.2453669161,-0.0011583411,0.2099779844,-0.7443686724,0.0974939093,0.036758583,-0.0206257887,-0.2245161533,0.1439558119,-0.2703608274,0.1730954647,0.0898646712,0.1649185568,0.0838625431,0.2818487883,-0.0586606897,-0.0042053154,0.0958619341,-0.2273251563,-0.3144424558,-0.1681329608,0.0166417956,-0.2781189382,-0.0227773488,-0.366530925,0.1069607958,-0.1156488657,-0.1436082423,-0.1401287317,0.1209289059,-0.1966751665,-0.0824266076,0.0682376102,0.318569243,-0.3932817578,-0.0395694636,-0.3099460006,-0.2163460851,0.2583622634,0.191096127,-0.1390059143,-0.1378192455,-0.6348692775,-0.1955416501,0.6662368178,-0.3661037385,-0.4506864548,0.3709712327,0.1801438034,-0.1938591152,0.0048030484,-0.1392714232,0.0835179463,-0.0080185253,-0.2260647416,0.0342665911,0.0644764826,-0.0136743719,-0.15491198,0.0659880489,-0.1293749511,0.2898727357,0.1254978031,0.3889501691,0.0901969448,-0.4979568422,0.4055314064,0.2317888737,0.2797596455,0.0762324631,0.4652943015,-0.0365556851,0.2497079819,0.1094037443,0.0196035858,-0.0406338163,0.0540224984,-0.1946742982,0.0456498936,-0.0391497016,-0.657694757,0.0489208288,-0.6480051875,-0.0222524311,0.0078741666,0.2978199422,0.1016684622,0.1889357865,-0.0869732276,0.3560329676,-0.1364875883,0.1632444561,0.4099937379,0.2784736753,0.153249383,-0.2057267576,0.2984183431,0.3030783534,0.4388907254,-0.0828579515,-0.1270913333,0.2037903368,0.0142717473,0.0554179996,0.2572813034,0.0843546018,0.2908248007,-0.4364844859,0.3947046995,0.357763499,0.1132674515,-0.1012058854,-0.1653984636,0.3060288429,-0.1939430237,0.261163801,-0.0357532129,0.3655903637,0.2947801352,-0.007263036,-0.2714254856,-0.2940517962,0.068665646,-0.0881245807,0.6414015293,-0.1815391779,-0.5248585939,0.1637569219,0.3295204639,0.1051241159,-0.0365306474,0.094472155,-0.2989908159,-0.1683603376,0.2295453995,0.2968539596,0.289164573,0.1093241498,-0.0719477311,0.2378695011,-0.201574862,-0.0437268466,0.0647822991,0.1092842296,-0.0196290798,0.1216607466,0.1799588054,-0.1312857568,-0.4589933753,-0.3170966208,0.1568867564,0.1720401943,-0.1236340553,0.0606089458,0.1284347624,-0.1886540651,-0.2988693416,-0.8101158142,-0.3045811057,-0.6304618716,-0.1999519765,-0.0276911557,0.2441981882,0.2051532567,-0.3122504354,0.1036904976,0.0591522194,0.4035730362,-0.1591193974,0.0410583392,-0.3044067025,0.0344008282,0.0248467624,-0.3071833849,0.4491609931,0.117031984,0.2117261738,-0.4828074872,-0.3043327034,0.1444804668,0.1116009057,0.0725444257,0.1432306021,-0.0366106927,-0.0705390349,0.0960182846,0.0870503932,0.106836684,0.0944505408,-0.1127742603,-0.1771877706,0.2874805033,0.2588608563,-0.1209997833,-0.0616152249,-0.1782978326,0.0489784218,0.1207455918,0.3033418655,0.26563555,-0.1088736653,0.3372550607,-0.0457355492,-0.0712015331,-0.4468245208,-0.5630888939,0.2624328434,-0.5182621479,-0.382681191,0.5426203609,0.0691808984,0.6903467178,-0.3819341958,-0.2891637981,0.1169923469,-0.1135613918,0.0204586759,-0.24390167,-0.3445113599,-0.0790595487,-0.0318171941,0.115491271,-0.0127004273,-0.0131962486,0.020780772,-0.2383499593,0.2789647281,0.1356903315,0.2775102556,-0.1056695804,0.5239006877,0.272729367,0.1360677034,0.3735147715,-0.1812072396,0.3228047192,-0.1324310303,-0.5331724286,0.3188865781,0.1867165864,0.2228451669,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3758","title":"head_qa file missing","comments":"We usually find issues with files hosted at Google Drive...\r\n\r\nIn this case we download the Google Drive Virus scan warning instead of the data file.","body":"## Describe the bug\r\n\r\nA file for the `head_qa` dataset is missing (https:\/\/drive.google.com\/u\/0\/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t\/HEAD_EN\/train_HEAD_EN.json)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> load_dataset(\"head_qa\", name=\"en\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe dataset should be loaded\r\n\r\n## Actual results\r\n\r\n```\r\nDownloading and preparing dataset head_qa\/en (download: 75.69 MiB, generated: 2.69 MiB, post-processed: Unknown size, total: 78.38 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/head_qa\/en\/1.1.0\/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9...\r\nDownloading data: 2.21kB [00:00, 2.05MB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1729, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.4.dev0\r\n- Platform: Linux-5.11.0-1028-aws-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 6.0.1\r\n","comment_length":26,"text":"head_qa file missing \n ## Describe the bug\r\n\r\nA file for the `head_qa` dataset is missing (https:\/\/drive.google.com\/u\/0\/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t\/HEAD_EN\/train_HEAD_EN.json)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> load_dataset(\"head_qa\", name=\"en\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe dataset should be loaded\r\n\r\n## Actual results\r\n\r\n```\r\nDownloading and preparing dataset head_qa\/en (download: 75.69 MiB, generated: 2.69 MiB, post-processed: Unknown size, total: 78.38 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/head_qa\/en\/1.1.0\/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9...\r\nDownloading data: 2.21kB [00:00, 2.05MB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1729, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 665, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.4.dev0\r\n- Platform: Linux-5.11.0-1028-aws-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 6.0.1\r\n \n We usually find issues with files hosted at Google Drive...\r\n\r\nIn this case we download the Google Drive Virus scan warning instead of the data file.","embeddings":[-0.1781005412,-0.0522136129,-0.0929787531,0.2617272437,0.3136710525,0.3797077537,0.1941574067,0.4621256292,0.1194368303,0.2630708516,0.1950953156,0.089244023,0.1379041225,-0.0750945881,0.3748984635,-0.288326472,0.0637705326,0.2063560635,-0.2365849912,-0.0722539201,-0.0815820023,0.2823314667,-0.1268199086,-0.042919565,-0.1497624069,0.0839746967,-0.0494592078,0.2454205453,-0.0519793034,-0.2814292908,0.1577526033,-0.2553601563,-0.0261587854,0.5781394839,-0.0001083226,0.1217147782,0.3071921766,-0.077296108,-0.2486902028,-0.1200581267,-0.5962380171,-0.3827218413,-0.1269704551,-0.1652567834,-0.0927703232,-0.0494528115,0.1256548464,-0.169276759,0.2128854841,0.3607211113,0.2594930828,-0.037888594,0.1839154959,-0.3124138713,0.3138035238,-0.0157612041,-0.0189745836,0.1501330733,-0.0193485264,-0.0125344107,0.1696982086,0.2352372259,0.3057818711,-0.3524092734,0.1210108697,0.0725672245,-0.0762186646,-0.0558482371,0.2318865955,0.2219490856,0.4309580326,-0.2813855708,-0.407289058,-0.2959379554,0.1384273022,-0.3634186387,0.050970789,0.2841399908,-0.0608852245,0.1433836818,0.0308860652,-0.1031794026,0.0656633675,-0.1198840737,-0.1365810782,0.0531249344,-0.0306437351,-0.1332874745,-0.2372119725,0.1387248784,-0.1991673559,0.0401350781,-0.2711879313,0.2066572756,-0.3898538351,-0.0447587408,0.3738014996,-0.2273965031,0.2003787309,-0.1287407577,0.1878515929,-0.1501271129,-0.0971608162,0.0249728095,0.2773096859,0.2147299647,-0.2483074516,0.0381846614,0.3954850435,0.34258762,-0.057591591,-0.1785352379,-0.009931419,-0.3473189175,0.1506473124,0.323925525,0.2956176698,-0.2372678071,-0.2689134181,0.1695413142,-0.0916024297,-0.1291337311,-0.2349870056,0.2160566598,-0.117342487,0.1246858537,0.086211279,0.2211381197,-0.3679081202,-0.0901468247,-0.4726134241,0.0898958817,-0.0841303021,-0.16226919,-0.0017907622,-0.0751026869,0.4925451577,-0.0764918849,0.0606031045,-0.076016441,0.0387465879,-0.1111577451,-0.1832618117,0.3794213235,0.0532246977,-0.0017889427,0.0278756376,0.0125846388,-0.1098529845,0.2380626649,-0.2724591196,-0.249000594,-0.2275829464,0.2995327711,-0.0934609026,0.0368655734,-0.3352677226,0.0954057947,0.1204542965,-0.1083368361,0.2097889036,-0.1720744669,-0.3457913399,-0.2408235818,0.2790066898,0.5628089309,-0.6022145152,-0.26238662,0.0257754065,-0.1114988178,-0.0880621821,0.1725333184,-0.0530743748,0.3926216662,-0.509848237,-0.1033496484,0.1773672551,-0.6048900485,-0.4241235852,-0.0828880146,-0.2371413261,0.0798543319,0.0654831976,-0.1432080418,-0.1500678062,-0.206875667,0.1966032386,0.3135958314,-0.1718961298,0.0326320566,-0.1459295601,-0.193372339,-0.2199163884,0.1729965955,0.1683736891,0.0540756583,0.2002934068,-0.2644297779,0.2710745335,0.0588716604,0.1616553962,0.485560149,0.30781582,-0.0472922213,0.0383572206,-0.0340454169,-0.4830217957,0.3231064379,-0.1221726164,0.0135705536,-0.4478161931,-0.1928005815,-0.2828296423,-0.1223417595,-0.1356036067,-0.2164429873,0.1936095804,0.1955885291,-0.0687731951,0.364885956,0.0161877349,0.1988381594,0.0358416028,0.0201410465,-0.4260579646,0.22824049,-0.0181415211,0.0410639383,0.2048836648,0.3158150017,0.1445923746,-0.0560989492,-0.1904136837,0.2051935345,0.0202962197,0.1472800821,0.290933162,0.0512311272,0.2097542733,-0.1103218123,0.0590200648,0.0438116267,0.187703535,-0.2392660975,-0.3201234639,0.1621075571,0.0230256468,0.0267578904,0.0235378705,-0.1539444327,0.3034171462,-0.0570767261,0.0706312209,-0.1211710349,-0.0213522781,0.2515400648,0.0741993636,-0.064856194,-0.2706858516,0.1984400302,0.916235745,-0.0789517835,-0.0420373119,0.2007583678,-0.1530686319,-0.1349940598,-0.0044500451,0.1884175837,0.5589416623,0.1392500401,0.1722442359,0.1139334291,0.2804502249,-0.2631017566,0.1287359595,0.0402716435,0.0656528845,0.3121936321,0.2291074246,0.0764595121,0.0500464328,-0.1635766029,-0.1456474364,0.0651100501,-0.2851305008,0.0070123854,-0.2634484172,0.0384880565,-0.1969512403,-0.0114664249,-0.0691030249,-0.2198833674,0.0491611473,-0.1714583486,-0.2018367648,0.1048512757,-0.1117667779,0.1865814775,0.2237732708,-0.4059130251,-0.0094849998,-0.0265706517,-0.2808388174,0.1108914763,0.0020679836,-0.01974543,0.0659367666,-0.2687683702,-0.009210676,-0.2990801632,-0.0908634067,0.0481345132,-0.0860827565,0.5289391279,0.2424651682,0.1589205563,-0.0792538151,0.1636834145,0.2528226078,0.1577307731,-0.3642362058,0.1889013499,0.0264358725,0.0927174985,-0.0902484879,-0.322055161,-0.1461977512,-0.4906452298,0.0670775771,-0.0544975586,0.1123727784,0.1730561554,0.1348293573,0.0463718623,0.0969432518,0.0860611126,-0.1491784304,-0.4366996288,0.2259852737,-0.3727569878,-0.4575226307,0.1848910749,0.0403981023,0.4931563139,-0.0068369955,-0.7194020152,-0.1545271724,-0.1968225092,0.3019233644,-0.3056768775,0.2418660671,0.3853589296,-0.1316006184,-0.1218441427,-0.250064671,-0.3013921678,0.0445986614,-0.0286546201,0.200306803,-0.2908668518,0.5006809235,-0.084117718,0.7236378789,0.400036931,0.0831564218,0.2048232704,0.0170118641,0.3331622481,-0.4954121113,-0.1358254999,0.1706604511,0.1788479388,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3756","title":"Images get decoded when using `map()` with `input_columns` argument on a dataset","comments":"Hi! If I'm not mistaken, this behavior is intentional, but I agree it could be more intuitive.\r\n\r\n@albertvillanova Do you remember why you decided not to decode columns in the `Audio` feature PR when `input_columns` is not `None`? IMO we should decode those columns, and we don't even have to use lazy structures here because the user explicitly requires them in the map transform. \r\n\r\ncc @lhoestq for visibility","body":"## Describe the bug\r\n\r\nThe `datasets.features.Image` feature class decodes image data by default. Expectedly, when indexing a dataset or using the `map()` method, images are returned as PIL Image instances.\r\n\r\nHowever, when calling `map()` and setting a specific data column with the `input_columns` argument, the image data is passed as raw byte representation to the mapping function.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchvision import transforms\r\nfrom PIL.Image import Image\r\n\r\ndataset = load_dataset('mnist', split='train')\r\n\r\ndef transform_all_columns(example):\r\n    # example['image'] is encoded as PIL Image\r\n    assert isinstance(example['image'], Image)\r\n    return example\r\n\r\ndef transform_image_column(image):\r\n    # image is decoded here and represented as raw bytes\r\n    assert isinstance(image, Image)\r\n    return image\r\n\r\n# single-sample dataset for debugging purposes\r\ndev = dataset.select([0])\r\n\r\ndev.map(transform_all_columns)\r\ndev.map(transform_image_column, input_columns='image')\r\n```\r\n\r\n## Expected results\r\n\r\nImage data should be passed in decoded form, i.e. as PIL Image objects to the mapping function unless the `decode` attribute on the image feature is set to `False`.\r\n\r\n## Actual results\r\n\r\nThe mapping function receives images as raw byte data.\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.11.0-49-generic-x86_64-with-glibc2.32\r\n- Python version: 3.8.0b4\r\n- PyArrow version: 7.0.0\r\n","comment_length":68,"text":"Images get decoded when using `map()` with `input_columns` argument on a dataset \n ## Describe the bug\r\n\r\nThe `datasets.features.Image` feature class decodes image data by default. Expectedly, when indexing a dataset or using the `map()` method, images are returned as PIL Image instances.\r\n\r\nHowever, when calling `map()` and setting a specific data column with the `input_columns` argument, the image data is passed as raw byte representation to the mapping function.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchvision import transforms\r\nfrom PIL.Image import Image\r\n\r\ndataset = load_dataset('mnist', split='train')\r\n\r\ndef transform_all_columns(example):\r\n    # example['image'] is encoded as PIL Image\r\n    assert isinstance(example['image'], Image)\r\n    return example\r\n\r\ndef transform_image_column(image):\r\n    # image is decoded here and represented as raw bytes\r\n    assert isinstance(image, Image)\r\n    return image\r\n\r\n# single-sample dataset for debugging purposes\r\ndev = dataset.select([0])\r\n\r\ndev.map(transform_all_columns)\r\ndev.map(transform_image_column, input_columns='image')\r\n```\r\n\r\n## Expected results\r\n\r\nImage data should be passed in decoded form, i.e. as PIL Image objects to the mapping function unless the `decode` attribute on the image feature is set to `False`.\r\n\r\n## Actual results\r\n\r\nThe mapping function receives images as raw byte data.\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.11.0-49-generic-x86_64-with-glibc2.32\r\n- Python version: 3.8.0b4\r\n- PyArrow version: 7.0.0\r\n \n Hi! If I'm not mistaken, this behavior is intentional, but I agree it could be more intuitive.\r\n\r\n@albertvillanova Do you remember why you decided not to decode columns in the `Audio` feature PR when `input_columns` is not `None`? IMO we should decode those columns, and we don't even have to use lazy structures here because the user explicitly requires them in the map transform. \r\n\r\ncc @lhoestq for 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3756","title":"Images get decoded when using `map()` with `input_columns` argument on a dataset","comments":"I think I excluded to decorate the function when `input_columns` were passed as a quick fix for some non-passing tests: \r\n- https:\/\/github.com\/huggingface\/datasets\/pull\/2324\/commits\/9d7c3e8fa53e23ec636859b4407eeec904b1b3f9\r\n\r\nThat PR was quite complex and I decided to focus on the main feature requests, leaving refinements for subsequent PRs.\r\n\r\nNote that when `input_columns` are passed, the signature of the function is effectively changed, while the decorated function expects an item (whether an example or a batch) as first arg (which is not the case when passing `input_columns`.\r\n\r\nI agree we should consider supporting the case when `input_columns` are passed.","body":"## Describe the bug\r\n\r\nThe `datasets.features.Image` feature class decodes image data by default. Expectedly, when indexing a dataset or using the `map()` method, images are returned as PIL Image instances.\r\n\r\nHowever, when calling `map()` and setting a specific data column with the `input_columns` argument, the image data is passed as raw byte representation to the mapping function.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchvision import transforms\r\nfrom PIL.Image import Image\r\n\r\ndataset = load_dataset('mnist', split='train')\r\n\r\ndef transform_all_columns(example):\r\n    # example['image'] is encoded as PIL Image\r\n    assert isinstance(example['image'], Image)\r\n    return example\r\n\r\ndef transform_image_column(image):\r\n    # image is decoded here and represented as raw bytes\r\n    assert isinstance(image, Image)\r\n    return image\r\n\r\n# single-sample dataset for debugging purposes\r\ndev = dataset.select([0])\r\n\r\ndev.map(transform_all_columns)\r\ndev.map(transform_image_column, input_columns='image')\r\n```\r\n\r\n## Expected results\r\n\r\nImage data should be passed in decoded form, i.e. as PIL Image objects to the mapping function unless the `decode` attribute on the image feature is set to `False`.\r\n\r\n## Actual results\r\n\r\nThe mapping function receives images as raw byte data.\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.11.0-49-generic-x86_64-with-glibc2.32\r\n- Python version: 3.8.0b4\r\n- PyArrow version: 7.0.0\r\n","comment_length":92,"text":"Images get decoded when using `map()` with `input_columns` argument on a dataset \n ## Describe the bug\r\n\r\nThe `datasets.features.Image` feature class decodes image data by default. Expectedly, when indexing a dataset or using the `map()` method, images are returned as PIL Image instances.\r\n\r\nHowever, when calling `map()` and setting a specific data column with the `input_columns` argument, the image data is passed as raw byte representation to the mapping function.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchvision import transforms\r\nfrom PIL.Image import Image\r\n\r\ndataset = load_dataset('mnist', split='train')\r\n\r\ndef transform_all_columns(example):\r\n    # example['image'] is encoded as PIL Image\r\n    assert isinstance(example['image'], Image)\r\n    return example\r\n\r\ndef transform_image_column(image):\r\n    # image is decoded here and represented as raw bytes\r\n    assert isinstance(image, Image)\r\n    return image\r\n\r\n# single-sample dataset for debugging purposes\r\ndev = dataset.select([0])\r\n\r\ndev.map(transform_all_columns)\r\ndev.map(transform_image_column, input_columns='image')\r\n```\r\n\r\n## Expected results\r\n\r\nImage data should be passed in decoded form, i.e. as PIL Image objects to the mapping function unless the `decode` attribute on the image feature is set to `False`.\r\n\r\n## Actual results\r\n\r\nThe mapping function receives images as raw byte data.\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.11.0-49-generic-x86_64-with-glibc2.32\r\n- Python version: 3.8.0b4\r\n- PyArrow version: 7.0.0\r\n \n I think I excluded to decorate the function when `input_columns` were passed as a quick fix for some non-passing tests: \r\n- https:\/\/github.com\/huggingface\/datasets\/pull\/2324\/commits\/9d7c3e8fa53e23ec636859b4407eeec904b1b3f9\r\n\r\nThat PR was quite complex and I decided to focus on the main feature requests, leaving refinements for subsequent PRs.\r\n\r\nNote that when `input_columns` are passed, the signature of the function is effectively changed, while the decorated function expects an item (whether an example or a batch) as first arg (which is not the case when passing `input_columns`.\r\n\r\nI agree we should consider supporting the case when `input_columns` are passed.","embeddings":[-0.0805239826,-0.2047774494,-0.0520250127,0.4860448837,0.5501019955,0.1830330342,0.336760968,0.3066670895,0.1687328666,-0.024067495,-0.018218169,0.652554214,0.0815849975,-0.3871566355,-0.2242290527,-0.2102148533,0.1314525157,0.278316468,-0.2537818551,-0.1469145268,-0.5218716264,0.1328319013,-0.3421211839,0.0935613289,-0.041862797,-0.0370032229,-0.1045034006,-0.1714456081,-0.2953556478,-0.0098646181,-0.1141357422,-0.141582787,-0.0357877985,0.3574280441,-0.0001185327,0.1455430537,0.3700042963,-0.0184392259,-0.1025739759,-0.080845803,-0.3826579154,-0.2473088503,-0.053069409,-0.3855968714,-0.0409871489,-0.4449501932,0.0547002181,-0.1687892675,0.2449920326,0.0911477804,0.1796796173,-0.0401930101,0.0524375513,0.5353929996,0.2489610165,0.5667456985,-0.2239691764,-0.2175085694,0.0238900408,-0.1527421027,-0.1924559325,0.5349344015,-0.3647031784,0.1426444203,0.2278841883,0.2759280503,-0.1226093173,-0.3315133154,0.0998427719,-0.0878683329,0.0413872004,-0.0788856149,-0.223345235,-0.2782940567,-0.1096885875,-0.2015570253,0.2365045547,0.185339123,-0.0922359005,0.0961780548,-0.5135345459,0.0411225297,0.0177253075,0.1587932408,-0.3180483282,-0.1790480167,-0.1422786713,0.2815212309,0.0234969798,-0.0626856163,0.1062866151,-0.2838018537,0.2161496729,0.1180982143,-0.118221119,-0.0460715331,0.0055635422,-0.0802453384,0.1058755741,-0.4866411984,-0.0483475029,-0.0430211276,-0.155370459,0.3395785987,0.3423370123,0.0310032144,-0.1538052559,0.6405078173,0.1163729727,-0.0945387855,-0.1479616016,-0.1497254521,0.1647619605,0.0776437446,0.1795057207,0.0466406904,0.3288537264,-0.0585621446,-0.0110128559,0.0388678014,-0.2332906425,0.1129131913,-0.0102521256,0.1457612962,0.2159517407,0.4574359953,0.3417314887,0.1523993462,-0.2216602117,-0.1858636439,-0.1097259745,-0.1907668114,-0.3000618219,-0.0322694592,-0.1817915589,0.0311250947,-0.0522434339,0.3152363896,-0.0897360817,-0.1395656615,0.1635999829,-0.3777251542,0.7308545113,0.2060258687,-0.1899973005,0.097268559,0.1131734028,0.1827281415,-0.1404879838,0.4627766311,-0.7486118674,0.1253462285,-0.0835589394,0.0375720002,-0.0254713856,0.1388578564,-0.1368380189,0.1480100006,0.2884498537,-0.3117554784,0.108610034,-0.2977656126,-0.2261789888,-0.2173134089,0.1425655484,0.2022576034,-0.0316197276,0.2336303741,-0.0524735264,-0.175699681,0.2208767533,0.156020537,-0.1337201893,0.0391394272,-0.0936416686,0.1853840947,0.0850935206,-0.243705973,-0.4039486051,0.4293061495,0.3069880009,0.2466655523,-0.2713567913,0.1373978108,0.3571941555,0.0666496828,-0.0516413935,0.3794569373,-0.0895813182,0.4424227178,-0.0661436319,-0.0518434644,0.2827427685,0.0762036443,-0.050388135,0.2312168926,0.1454647183,-0.1996564418,0.0609376654,-0.1110975817,0.0129208732,0.1178748831,0.0380132981,-0.0397473127,0.1769058257,0.0293698683,-0.0654311702,-0.0339818485,0.473695755,-0.1166508049,-0.6160824299,-0.2499492019,0.0372945108,0.0519869775,-0.2332948446,0.1643583179,0.1201334894,0.021035431,-0.3022561967,-0.074817501,-0.0775363371,0.142087236,-0.1867563128,0.0646439716,-0.1727352887,0.1767625362,0.1024178416,-0.0579526015,-0.2732099593,0.0513227955,-0.0215209164,-0.0597707257,-0.2079659551,0.4187328815,0.3242230415,0.2379254252,-0.3922466338,0.0647418872,0.2951210737,-0.3202978969,-0.0308453962,0.0231985953,0.1876065284,0.1397132576,-0.0628727749,0.1670365483,0.081845209,0.2202779204,-0.1893865019,0.098590225,-0.0479674861,0.0010367705,-0.3557887375,-0.3803116679,-0.3871732354,-0.1124995202,-0.1942474395,0.1929647774,-0.186690852,-0.1941452026,0.5954807401,-0.0139603969,0.1995778978,0.1764183193,-0.1109143794,0.109525837,0.4734024405,0.0205659755,0.3944807649,-0.1263993233,-0.1475019753,0.0469796397,-0.1674609333,0.2834454179,0.2340676337,0.252653867,-0.0141706923,-0.0246090069,0.1994217634,0.0258057751,-0.3169675469,-0.0107124913,0.1144643724,-0.0314842723,-0.3084050119,0.2026359886,-0.6300112009,-0.2670508325,-0.0890297294,0.1548762321,0.1239508986,-0.1821493655,-0.1225756258,-0.110798195,-0.0299266651,0.1432515383,-0.2486198545,0.0744278729,0.2605251074,-0.3705513179,-0.1540307701,0.0104883993,0.03098174,-0.0154460156,0.0093633924,-0.2178519815,0.1667638868,-0.0885823071,0.0499963462,-0.1883879304,-0.3770008981,0.3553508222,-0.4507011771,0.0552496612,0.256129384,0.1347603202,-0.1260889024,-0.0328004733,0.3196588457,0.0123099238,-0.157507807,0.1478203535,0.1540007144,-0.2938903272,-0.457805723,-0.0256871209,0.1582940668,-0.1518784314,-0.0876802579,-0.0199854393,0.1471639872,0.0905196294,0.4343443513,-0.0354434773,0.0690843984,0.1083260551,-0.1920334548,-0.075639382,0.4212609529,-0.0677339658,-0.2054030448,-0.1327290386,-0.0372554101,-0.1403137594,0.6791891456,-0.5022761226,-0.379638046,-0.2140233815,0.0566240661,0.0029990065,0.1300110519,-0.1545590311,0.2865641415,0.1296948344,-0.2044921666,-0.2508614659,-0.204169035,0.2723428011,0.3598075211,0.3556019962,0.4608394802,0.1625314504,0.2441470921,0.0196174644,-0.379503876,0.3852626979,-0.2935680747,0.464076221,-0.1162,-0.165448904,0.0271080434,-0.08521569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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3755","title":"Cannot preview dataset","comments":"Thanks for reporting. The dataset viewer depends on some backend treatments, and for now, they might take some hours to get processed. We're working on improving it.","body":"## Dataset viewer issue for '*rubrix\/news*'\r\n\r\n**Link:https:\/\/huggingface.co\/datasets\/rubrix\/news** *link to the dataset viewer page*\r\n\r\nCannot see the dataset preview:\r\n\r\n```\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Cache is waiting to be refreshed.\r\n```\r\nAm I the one who added this dataset ? No\r\n","comment_length":27,"text":"Cannot preview dataset \n ## Dataset viewer issue for '*rubrix\/news*'\r\n\r\n**Link:https:\/\/huggingface.co\/datasets\/rubrix\/news** *link to the dataset viewer page*\r\n\r\nCannot see the dataset preview:\r\n\r\n```\r\nStatus code:   400\r\nException:     Status400Error\r\nMessage:       Not found. Cache is waiting to be refreshed.\r\n```\r\nAm I the one who added this dataset ? No\r\n \n Thanks for reporting. The dataset viewer depends on some backend treatments, and for now, they might take some hours to get processed. We're working on improving it.","embeddings":[-0.3031063974,-0.2469096482,-0.0138341747,0.3150565326,0.1146640629,0.3597427607,0.10625422,0.4012050033,0.0818886906,0.0675458685,-0.1898427606,0.1830478311,0.027365122,-0.1792658567,0.1113656461,-0.0800136253,0.0722587928,0.1015292257,-0.2903141081,0.1101938039,-0.3038566709,0.1340917051,-0.0727705806,-0.0307444222,-0.3440261781,0.005121999,-0.0414851159,-0.0443541519,-0.2155594975,-0.3400560319,0.3452973068,0.1297949255,0.1788191199,0.5764945745,-0.0001148881,0.1152957976,0.5214242935,0.0857577398,-0.2970721722,-0.0525722466,-0.0793219954,-0.2776618302,0.1518081427,-0.0411452502,-0.2971932888,-0.3426694572,0.0350876413,-0.4472393095,0.1395913213,0.0333697088,0.2217714041,0.4787703156,0.1533151418,-0.3157825172,0.1812812239,-0.2041362971,-0.3004303873,0.2167396098,0.1075814217,0.1818378717,-0.0498641022,0.4378060699,0.0723853484,-0.0474453829,0.2929780185,-0.1317194849,0.0770136043,-0.3073500395,0.2123434246,0.29484424,0.7607720494,0.0163568631,-0.3007167578,0.2011523247,0.1899546385,-0.284159869,0.2184312493,0.1334729791,0.0964105427,0.1444517225,-0.4584880173,-0.3266382813,0.0428427272,0.0236167293,-0.0685049221,0.3771346807,-0.05980606,0.1526465118,0.0207309313,-0.0161075238,0.7436220646,-0.0909657329,-0.2877439559,0.1765889972,-0.2088386267,0.0976700485,-0.1030722633,0.1049954221,0.0069636987,0.0515904278,-0.0381050892,0.0872409269,0.0710760728,0.1841839552,0.0643164441,0.0183098186,0.0276355576,0.0866606385,0.2724919319,0.062006522,0.4102101326,-0.0550557263,-0.133567974,0.1348682195,0.0044742441,-0.2949164212,0.0467365645,-0.0619684793,-0.1389712542,0.2272564471,-0.1065584049,-0.1032016054,0.0251023881,0.3528427482,-0.2811786532,-0.006623928,0.0424459465,0.0126562575,-0.0872921944,-0.3030167222,-0.2253340632,-0.0388087146,-0.2319059074,0.0270863995,0.2133615613,-0.1782913804,0.1489046067,-0.1526591033,0.2184905559,-0.0681911558,-0.0708881915,-0.006972217,-0.0743942857,0.3557970822,0.0219508782,0.2329134643,0.1407278031,-0.0268313568,0.1266434044,0.2500464916,-0.1985143125,-0.28376472,-0.3578680456,0.1046193987,-0.39611727,-0.1118237078,-0.0810637325,0.0638688728,-0.0231306292,-0.1665537059,-0.1567779034,0.1844399571,-0.210921213,-0.0658260509,0.1571345031,0.4731239974,-0.5292838216,-0.065525651,-0.3157709837,-0.2098798156,0.0597181395,0.1292472929,-0.230198741,0.0606703386,-0.336655587,-0.1415604651,0.3583886623,0.0093771815,-0.7506267428,-0.0910729244,-0.2110627592,0.0107350564,0.1389247775,0.2723368406,0.0893839374,-0.1648658067,-0.1650716066,-0.0137703819,0.1788918227,-0.0756429732,-0.2180538774,-0.2598811388,0.3801323175,0.1521912813,0.3552782536,-0.0113475788,-0.0530875623,-0.3625586331,0.2047605515,0.1935480833,0.0482156463,0.0831795558,0.2257868201,0.1725882739,0.1371124834,-0.160100013,-0.2841464281,0.1310467422,0.0433724485,-0.1328805834,0.0190177076,-0.0446760617,-0.5528768301,0.1506550908,-0.2162678242,-0.3191357255,0.0770359188,0.1650235951,-0.0840659887,0.1580578536,-0.2327490151,0.24846977,-0.1549502164,0.0157305524,-0.1965380758,0.2908469141,0.0828861669,0.0247996133,-0.014135235,0.0564565733,0.1414887309,-0.1443529725,-0.2023290098,0.3482679129,-0.0500484556,-0.1026743576,0.5013907552,-0.2201072723,0.0196614396,-0.4857968986,0.1385163814,-0.0543545671,0.1668610275,0.0885619074,-0.1872053891,0.0387889408,0.0103598023,-0.0005545895,-0.2318773121,0.2010591775,0.3338687718,0.1270988733,0.0651098266,-0.0028890388,0.1453486085,-0.1077753007,0.1607382596,-0.1591635793,-0.2257271707,-0.0057067568,0.0605554245,-0.0934211835,-0.059224125,0.1349645555,-0.2389499843,-0.0425516963,-0.0274919737,0.2311882228,0.1628988385,0.1814316809,-0.0390326381,-0.0169968344,0.1069781706,0.0088426406,-0.0676754266,0.1603197604,0.1420854777,0.1269440502,0.0567875765,-0.1643930227,-0.3521049023,0.1756481528,0.4050614536,0.1985215843,-0.1135703921,-0.0154464636,-0.1402332485,-0.149924472,0.0513425358,-0.1629918963,-0.1920261681,-0.5623269081,0.1332443506,0.2849773765,0.2947365642,0.3221453726,-0.3208365142,0.3276288509,0.0667458773,0.2956089973,-0.1848796308,0.0387149751,0.0275302175,0.0629283562,0.1711112857,-0.0593340024,0.3967885077,-0.120049119,0.4145958722,-0.7059033513,-0.2785641551,0.1479918808,-0.0773858652,0.4803585112,-0.0949949548,0.2023557723,-0.1265613288,-0.0169788655,0.1099246144,-0.1324225962,-0.1173787415,0.105916217,-0.2020782083,-0.0806980729,0.2512028515,-0.2343082577,-0.194901228,-0.3405519724,-0.1509379596,-0.294739306,-0.0842303261,-0.3334482014,0.0178973656,0.0371114276,-0.2764880061,-0.2262631953,-0.3225293458,-0.5905178785,0.4719985127,-0.3383314312,-0.4295725226,0.2452738583,0.0727440715,0.1636823565,0.0177655816,-0.6611583829,-0.0944538936,-0.2139285207,0.1389569491,0.0842715949,-0.2294623852,0.416449368,-0.13182199,-0.0300295874,-0.0818800852,-0.2471175939,-0.3121103346,-0.3986546695,0.525796175,-0.186829865,0.359349072,-0.0195211824,0.8361702561,0.3613685369,0.0860609934,0.2556226254,-0.0601257719,0.6425043941,-0.2647356689,-0.3709156811,-0.2761953175,-0.1602952331,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3753","title":"Expanding streaming capabilities","comments":"Cool ! `filter` will be very useful. There can be a filter that you can apply on a streaming dataset:\r\n```python\r\nload_dataset(..., streaming=True).filter(lambda x: x[\"lang\"] == \"sw\")\r\n```\r\n\r\nOtherwise if you want to apply a filter on the source files that are going to be used for streaming, the logic has to be impIemented directly in the dataset script, or if there's no dataset script this can be done with pattern matching\r\n```python\r\nload_dataset(..., lang=\"sw\")                    # if the dataset script supports this parameter\r\nload_dataset(..., data_files=\"data\/lang=sw\/*\")  # if there's no dataset script, but only data files\r\n```\r\n\r\n--------------\r\n\r\nHere are also some additional ideas of API to convert from iterable to map-style dataset:\r\n```python\r\non_disk_dataset = streaming_dataset.to_disk()\r\non_disk_dataset = streaming_dataset.to_disk(path=\"path\/to\/my\/dataset\/dir\")\r\n\r\nin_memory_dataset = streaming_dataset.take(100).to_memory()  # to experiment without having to write files\r\n```\r\n--------------\r\n\r\nFinally regarding `push_to_hub`, we can replace `batch_size` by `shard_size` (same API as for on-disk datasets). The default is 500MB per file\r\n\r\nLet me know what you think !","body":"Some ideas for a few features that could be useful when working with large datasets in streaming mode. \r\n\r\n## `filter` for `IterableDataset`\r\nAdding filtering to streaming datasets would be useful in several scenarios:\r\n- filter a dataset with many languages for a subset of languages\r\n- filter a dataset for specific licenses\r\n- other custom logic to get a subset\r\nThe only way to achieve this at the moment is I think through writing a custom loading script and implementing filters there.\r\n\r\n## `IterableDataset` to `Dataset` conversion\r\nIn combination with the above filter a functionality to \"play\" the whole stream would be useful. The motivation is that often one might filter the dataset to get a manageable size for experimentation. In that case streaming mode is no longer necessary as the filtered dataset is small enough and it would be useful to be able to play through the whole stream to create a normal `Dataset` with all its benefits.\r\n\r\n```python\r\nds = load_dataset(\"some_large_dataset\", streaming=True) \r\nds_filter = ds.filter(lambda x: x[\"lang\"]=\"fr\")\r\nds_filter = ds_filter.stream() # here the `IterableDataset` is converted to a `Dataset`\r\n```\r\nNaturally, this could be expanded with `stream(n=1000)` which creates a `Dataset` with the first `n` elements similar to `take`.\r\n\r\n## Stream to the Hub\r\nWhile streaming allows to use a dataset as is without saving the whole dataset on the local machine it is currently not possible to process a dataset and add it to the hub. The only way to do this is by downloading the full dataset and saving the processed dataset again before pushing them to the hub. The API could looks something like:\r\n\r\n```python\r\nds = load_dataset(\"some_large_dataset\", streaming=True)\r\nds_filter = ds.filter(some_filter_func)\r\nds_processed = ds_filter.map(some_processing_func)\r\nds_processed.push_to_hub(\"new_better_dataset\", batch_size=100_000)\r\n```\r\n\r\nUnder the hood this could be done by processing and aggregating `batch_size` elements and then pushing that batch as a single file to the hub. With this functionality one could process and create TB scale datasets while only requiring size of `batch_size` local disk space.\r\n\r\ncc @lhoestq @albertvillanova ","comment_length":160,"text":"Expanding streaming capabilities \n Some ideas for a few features that could be useful when working with large datasets in streaming mode. \r\n\r\n## `filter` for `IterableDataset`\r\nAdding filtering to streaming datasets would be useful in several scenarios:\r\n- filter a dataset with many languages for a subset of languages\r\n- filter a dataset for specific licenses\r\n- other custom logic to get a subset\r\nThe only way to achieve this at the moment is I think through writing a custom loading script and implementing filters there.\r\n\r\n## `IterableDataset` to `Dataset` conversion\r\nIn combination with the above filter a functionality to \"play\" the whole stream would be useful. The motivation is that often one might filter the dataset to get a manageable size for experimentation. In that case streaming mode is no longer necessary as the filtered dataset is small enough and it would be useful to be able to play through the whole stream to create a normal `Dataset` with all its benefits.\r\n\r\n```python\r\nds = load_dataset(\"some_large_dataset\", streaming=True) \r\nds_filter = ds.filter(lambda x: x[\"lang\"]=\"fr\")\r\nds_filter = ds_filter.stream() # here the `IterableDataset` is converted to a `Dataset`\r\n```\r\nNaturally, this could be expanded with `stream(n=1000)` which creates a `Dataset` with the first `n` elements similar to `take`.\r\n\r\n## Stream to the Hub\r\nWhile streaming allows to use a dataset as is without saving the whole dataset on the local machine it is currently not possible to process a dataset and add it to the hub. The only way to do this is by downloading the full dataset and saving the processed dataset again before pushing them to the hub. The API could looks something like:\r\n\r\n```python\r\nds = load_dataset(\"some_large_dataset\", streaming=True)\r\nds_filter = ds.filter(some_filter_func)\r\nds_processed = ds_filter.map(some_processing_func)\r\nds_processed.push_to_hub(\"new_better_dataset\", batch_size=100_000)\r\n```\r\n\r\nUnder the hood this could be done by processing and aggregating `batch_size` elements and then pushing that batch as a single file to the hub. With this functionality one could process and create TB scale datasets while only requiring size of `batch_size` local disk space.\r\n\r\ncc @lhoestq @albertvillanova  \n Cool ! `filter` will be very useful. There can be a filter that you can apply on a streaming dataset:\r\n```python\r\nload_dataset(..., streaming=True).filter(lambda x: x[\"lang\"] == \"sw\")\r\n```\r\n\r\nOtherwise if you want to apply a filter on the source files that are going to be used for streaming, the logic has to be impIemented directly in the dataset script, or if there's no dataset script this can be done with pattern matching\r\n```python\r\nload_dataset(..., lang=\"sw\")                    # if the dataset script supports this parameter\r\nload_dataset(..., data_files=\"data\/lang=sw\/*\")  # if there's no dataset script, but only data files\r\n```\r\n\r\n--------------\r\n\r\nHere are also some additional ideas of API to convert from iterable to map-style dataset:\r\n```python\r\non_disk_dataset = streaming_dataset.to_disk()\r\non_disk_dataset = streaming_dataset.to_disk(path=\"path\/to\/my\/dataset\/dir\")\r\n\r\nin_memory_dataset = streaming_dataset.take(100).to_memory()  # to experiment without having to write files\r\n```\r\n--------------\r\n\r\nFinally regarding `push_to_hub`, we can replace `batch_size` by `shard_size` (same API as for on-disk datasets). The default is 500MB per file\r\n\r\nLet me know what you think 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3739","title":"Pubmed dataset does not work in streaming mode","comments":"Thanks for reporting, @abhi-mosaic (related to #3655).\r\n\r\nPlease note that `xml.etree.ElementTree.parse` already supports streaming:\r\n- #3476\r\n\r\nNo need to refactor to use `open`\/`xopen`. Is is enough with importing the package `as ET` (instead of `as etree`).","body":"## Describe the bug\r\nTrying to use the `pubmed` dataset with `streaming=True` fails.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\npubmed_train = datasets.load_dataset('pubmed', split='train', streaming=True)\r\nprint (next(iter(pubmed_train)))\r\n```\r\n\r\n## Expected results\r\nI would expect to see the first training sample from the pubmed dataset. \r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/abhinav\/Documents\/mosaicml\/mosaicml_venv\/lib\/python3.8\/site-packages\/datasets\/iterable_dataset.py\", line 367, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/Users\/abhinav\/Documents\/mosaicml\/mosaicml_venv\/lib\/python3.8\/site-packages\/datasets\/iterable_dataset.py\", line 364, in _iter\r\n    yield from ex_iterable\r\n  File \"\/Users\/abhinav\/Documents\/mosaicml\/mosaicml_venv\/lib\/python3.8\/site-packages\/datasets\/iterable_dataset.py\", line 79, in __iter__\r\n    for key, example in self.generate_examples_fn(**self.kwargs):\r\n  File \"\/Users\/abhinav\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/pubmed\/9715addf10c42a7877a2149ae0c5f2fddabefc775cd1bd9b03ac3f012b86ce46\/pubmed.py\", line 373, in _generate_examples\r\n    tree = etree.parse(filename)\r\n  File \"\/Library\/Developer\/CommandLineTools\/Library\/Frameworks\/Python3.framework\/Versions\/3.8\/lib\/python3.8\/xml\/etree\/ElementTree.py\", line 1202, in parse\r\n    tree.parse(source, parser)\r\n  File \"\/Library\/Developer\/CommandLineTools\/Library\/Frameworks\/Python3.framework\/Versions\/3.8\/lib\/python3.8\/xml\/etree\/ElementTree.py\", line 584, in parse\r\n    source = open(source, \"rb\")\r\nFileNotFoundError: [Errno 2] No such file or directory: 'gzip:\/\/pubmed21n0001.xml::ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0001.xml.gz'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.2\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.2\r\n- PyArrow version: 6.0.0\r\n\r\n## Comments\r\nThe error looks like an issue with `open` vs. `xopen` inside the `xml` package. It looks like it's trying to open the remote source URL, which has been edited with prefix `gzip:\/\/...`. \r\n\r\nMaybe there can be an explicit `xopen` before passing the raw data to `etree`, something like:\r\n\r\n```python\r\n# Before\r\ntree = etree.parse(filename)\r\nroot = tree.getroot()\r\n\r\n# After\r\nwith xopen(filename) as f:\r\n  data_str = f.read()\r\nroot = etree.fromstring(data_str)\r\n```","comment_length":36,"text":"Pubmed dataset does not work in streaming mode \n ## Describe the bug\r\nTrying to use the `pubmed` dataset with `streaming=True` fails.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\npubmed_train = datasets.load_dataset('pubmed', split='train', streaming=True)\r\nprint (next(iter(pubmed_train)))\r\n```\r\n\r\n## Expected results\r\nI would expect to see the first training sample from the pubmed dataset. \r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/abhinav\/Documents\/mosaicml\/mosaicml_venv\/lib\/python3.8\/site-packages\/datasets\/iterable_dataset.py\", line 367, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/Users\/abhinav\/Documents\/mosaicml\/mosaicml_venv\/lib\/python3.8\/site-packages\/datasets\/iterable_dataset.py\", line 364, in _iter\r\n    yield from ex_iterable\r\n  File \"\/Users\/abhinav\/Documents\/mosaicml\/mosaicml_venv\/lib\/python3.8\/site-packages\/datasets\/iterable_dataset.py\", line 79, in __iter__\r\n    for key, example in self.generate_examples_fn(**self.kwargs):\r\n  File \"\/Users\/abhinav\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/pubmed\/9715addf10c42a7877a2149ae0c5f2fddabefc775cd1bd9b03ac3f012b86ce46\/pubmed.py\", line 373, in _generate_examples\r\n    tree = etree.parse(filename)\r\n  File \"\/Library\/Developer\/CommandLineTools\/Library\/Frameworks\/Python3.framework\/Versions\/3.8\/lib\/python3.8\/xml\/etree\/ElementTree.py\", line 1202, in parse\r\n    tree.parse(source, parser)\r\n  File \"\/Library\/Developer\/CommandLineTools\/Library\/Frameworks\/Python3.framework\/Versions\/3.8\/lib\/python3.8\/xml\/etree\/ElementTree.py\", line 584, in parse\r\n    source = open(source, \"rb\")\r\nFileNotFoundError: [Errno 2] No such file or directory: 'gzip:\/\/pubmed21n0001.xml::ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0001.xml.gz'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.2\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.2\r\n- PyArrow version: 6.0.0\r\n\r\n## Comments\r\nThe error looks like an issue with `open` vs. `xopen` inside the `xml` package. It looks like it's trying to open the remote source URL, which has been edited with prefix `gzip:\/\/...`. \r\n\r\nMaybe there can be an explicit `xopen` before passing the raw data to `etree`, something like:\r\n\r\n```python\r\n# Before\r\ntree = etree.parse(filename)\r\nroot = tree.getroot()\r\n\r\n# After\r\nwith xopen(filename) as f:\r\n  data_str = f.read()\r\nroot = etree.fromstring(data_str)\r\n``` \n Thanks for reporting, @abhi-mosaic (related to #3655).\r\n\r\nPlease note that `xml.etree.ElementTree.parse` already supports streaming:\r\n- #3476\r\n\r\nNo need to refactor to use `open`\/`xopen`. Is is enough with importing the package `as ET` (instead of `as etree`).","embeddings":[-0.2567092776,-0.1076773703,0.0392323025,0.0182671975,0.3081786036,-0.0294964164,0.3082023561,0.3113961816,-0.0702121928,0.0274792463,0.103150472,0.2790321112,-0.1545047462,0.1329768449,0.0741169751,-0.1817835718,0.1080831587,0.1717888117,0.0063671404,-0.006676998,-0.0284887739,0.0646730587,-0.3442023993,-0.2530309856,0.1164922193,-0.0241161119,0.0450961366,0.2479396611,-0.2276089936,-0.3835966587,0.3262422681,0.1716510355,0.1358906627,0.4910625219,-0.000109901,0.0947196484,0.4737319052,-0.0826142058,-0.2528693676,-0.2126826793,-0.2600460052,-0.0678214133,0.1563938111,-0.2775077224,-0.3232384026,-0.2747630179,-0.0918047875,-0.5758281946,0.1725554615,0.4786944091,0.2556690574,0.3239405453,-0.1142405793,0.0177668743,0.2219899148,0.0383818336,-0.2422207147,0.2945570946,0.318459481,0.2381202579,0.0152795082,0.4170446098,-0.0533431694,0.0943459719,0.117291078,0.10967803,0.0057976875,-0.1186605245,0.0245236661,0.18770805,0.399777323,-0.2930714488,-0.2114300132,-0.1289615035,-0.0425555035,-0.373703897,0.2545690835,0.0627751276,-0.1751846075,0.0901269615,-0.162687853,-0.06405209,-0.2869330943,0.1759809852,0.0041734995,0.2013476491,-0.1265219003,-0.0192544907,0.0320601277,0.0240150671,0.3089472055,-0.049511835,-0.0265547372,0.0013797182,-0.4330780804,-0.0757589489,-0.0301168449,0.0056554116,0.104438819,0.4206589162,0.3070503771,0.0617160462,0.2427207381,0.0215658788,0.1631121784,-0.1736769229,-0.1854246408,0.199168846,0.2021305561,0.4485298395,-0.0805672109,-0.2398638576,0.1579828858,0.0512198471,0.1015314832,-0.2145983428,0.3575849533,-0.2054946423,-0.0932685211,0.1342087835,-0.3329713047,0.0340251587,-0.0157568399,0.268414557,0.0053137997,0.2361925691,0.1295848042,0.2464135736,-0.2494116575,-0.1446945965,-0.1988382787,0.0070773046,-0.1113636494,-0.1015238762,0.170947969,-0.6453522444,0.2290001959,0.0312116835,0.1912696064,-0.1172291115,0.1226246059,-0.2710644603,0.2153065354,0.2155139893,0.0479179695,0.2775526345,0.2315822542,-0.0613537543,0.0251174644,-0.0653202087,-0.0045889965,-0.2500825524,-0.088588424,0.2499218136,-0.20765616,-0.1161581054,-0.0396695137,0.1532844454,0.0820932686,-0.3422112763,-0.0487960242,-0.2345176339,-0.2153350711,-0.1746809036,0.5263875723,0.2595040202,-0.0297874175,0.0031153271,-0.2655655742,0.1034295559,0.4895380139,0.1062486544,-0.2078961581,0.0615046062,-0.071936585,0.0055886125,0.5491595864,-0.2789798081,-0.605547905,0.247622028,0.0217596572,0.4248399436,0.2428156435,-0.0845872015,0.4106715918,-0.0588969588,-0.1106088385,0.4604533613,-0.1615543962,0.2281895727,-0.3953381479,-0.0913783982,0.2286241055,0.1831508279,0.2996662259,0.035207469,0.2294350266,0.4935002625,0.3309438229,0.0139923645,0.1894841045,0.1048112214,0.1170776114,0.0577164032,0.1869655401,-0.3412793875,0.1178865135,0.1162867621,0.327604115,-0.0738728344,-0.0275928155,0.0188310444,-0.2481947094,-0.1453095973,-0.4472605884,-0.5021520257,0.1962643415,0.3404534161,0.0739082918,0.0986950547,-0.338598907,0.1652060151,-0.293766886,-0.0398225784,-0.3618009686,0.4139595032,0.0006833686,-0.4323040843,0.1104596481,-0.0249517113,0.0367982648,-0.0204774216,-0.2419360578,0.472753942,-0.198114872,0.4407780766,-0.1245451495,0.0421546362,0.2229147851,-0.5197710395,0.0858584344,0.4703594148,0.1940230578,0.132062912,-0.3360251486,0.0004724096,-0.0182303861,0.1594507992,0.0995403528,0.0774794817,0.1812310666,-0.028282823,-0.3531385064,-0.1100846156,0.3463565111,-0.276534915,-0.0167934019,-0.3498179615,-0.3807040751,0.0403996222,0.373018533,0.2005860955,0.0158084296,-0.1137693226,-0.3420221508,-0.091422528,0.0439159647,0.2532890141,0.3440485895,0.1239366233,0.2185879201,0.203226313,0.0296729244,-0.1889369339,0.1715429574,0.1127976999,0.1730987281,0.2443699986,0.1746104509,-0.0491900332,-0.3332817852,-0.1475109905,0.0527635477,0.2331732512,-0.3762113154,0.1099106967,-0.3760869801,-0.1457890719,-0.3291432559,-0.1573858857,0.1855613887,-0.2492584437,-0.1569315493,0.5577376485,0.029674381,0.1043916047,-0.2339250594,-0.0097405426,0.2429134101,-0.0180190783,-0.1752682626,0.0709251761,-0.1638562828,0.1846531332,-0.0010315337,0.0670370832,0.3432892263,-0.1362132728,-0.1671034694,-0.3904285729,-0.0850560218,0.2374866307,-0.0341368951,0.2739309967,0.0089407498,0.159245342,0.0506968349,-0.0163435899,0.3310104609,-0.5872151852,0.0456778593,0.186678201,0.0758794174,0.2648508549,-0.0226379931,-0.444293499,-0.1811420619,-0.4436195493,-0.2413235158,0.0215986464,0.0059732641,0.2187627554,0.0535367839,0.1104795784,0.0478863642,0.17420277,-0.1298673898,-0.4568093717,0.3374615312,-0.1690227389,-0.430536896,-0.0430452302,0.1691167057,-0.1200278848,0.4949428439,-0.3422319293,-0.0125477184,0.0362850018,-0.1296218038,0.0388699658,-0.0243544374,0.3013808429,-0.2012639493,-0.0284992922,-0.2972625196,0.1321712583,-0.1158604026,0.0957514495,0.2818239629,0.2037264556,0.5223630667,0.1498862356,0.688475728,0.5986143351,0.0404105,0.3990858495,-0.1403531283,0.2263091803,-0.0012399991,-0.3859663308,0.2330579311,-0.0951732919,0.0048557702,0.1561087668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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3738","title":"For data-only datasets, streaming and non-streaming don't behave the same","comments":"Note that we might change the heuristic and create a different config per file, at least in that case.","body":"See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n```","comment_length":19,"text":"For data-only datasets, streaming and non-streaming don't behave the same \n See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n``` \n Note that we might change the heuristic and create a different config per file, at least in that case.","embeddings":[-0.1471763551,-0.6080195904,0.0020139604,0.2507557869,0.359465152,0.0139178112,0.1687213182,0.2110685557,0.083505623,0.0817445442,-0.3420369327,0.1702123731,-0.1189063266,0.3036907017,0.0937546343,-0.3056588471,0.159532398,0.1109990701,-0.2961656153,-0.0981278196,-0.0076988777,0.2240802497,-0.1810887009,-0.3297926188,-0.1043010801,0.1737027764,0.1395613253,-0.0809820518,0.1045855284,-0.1263988167,0.1994576156,0.0767974779,0.1242218614,0.5881059766,-0.0001102195,0.285846293,0.1854329556,-0.2264382988,-0.19272165,-0.3488371372,-0.0819243416,0.2038207948,0.1665898263,-0.207558915,-0.339366436,-0.1604879051,-0.0232692957,-0.3243314326,0.572083354,0.2510288954,0.2104201317,0.6628690958,-0.2209248841,-0.0766758174,0.0247655269,0.2161830068,-0.2035769671,0.2541887462,-0.0944820046,0.4356520176,0.1061514318,0.3564093411,-0.2065912187,0.0382632054,0.4028326571,0.1423471719,-0.2964428067,-0.2077165544,0.1926320791,0.5481566191,0.32585603,-0.2693969309,-0.339376539,-0.3271180391,-0.0275781713,-0.2468357533,0.1115852147,0.2851887047,-0.0978090838,0.2190041542,-0.2835857868,0.1774062663,-0.0958502144,0.0244583916,-0.1553760618,0.3722753227,-0.1558926105,0.0788626522,0.0476611592,-0.1520877481,-0.0142812002,-0.3164885938,0.0027518647,-0.0033955979,-0.3223057687,-0.0757033527,0.0392436944,-0.1551247239,0.0288946256,0.1674398035,0.4002329409,0.0758837312,0.1345700324,0.1468308568,0.0595702343,0.0147227636,0.0802069455,-0.0645243376,0.2079160661,0.0997114852,-0.1072927341,-0.244160533,0.3200443089,0.1965501904,-0.0847769752,-0.4600597322,0.3851961493,-0.0461812466,-0.2785801589,-0.1200082526,-0.3110646307,-0.0223008655,0.1738017648,0.4814924598,-0.0511951558,0.0560052693,-0.0579139926,0.3288652301,-0.0116389804,-0.0010351856,-0.3238562942,-0.0864569694,0.0807698518,0.1027583033,0.1851246506,-0.385935843,0.4762923419,0.0858666375,0.0902238116,0.0813824311,-0.0838058665,-0.0285714809,0.2093238831,0.4957208335,0.1952600926,0.1616440564,0.2296439111,-0.2187334001,-0.2407370657,-0.0816208646,0.0373845585,-0.076227732,0.2936624885,0.2281078398,-0.2673906088,-0.2086371034,-0.2747941613,0.2466879189,-0.1379573643,-0.2960714698,0.0976708904,-0.0006242363,-0.0069695711,-0.2529863417,0.1640166789,0.2054248303,-0.1967445761,-0.3590604067,0.4238373935,-0.0696852878,0.2468966693,0.5191632509,-0.2162554115,-0.083255969,-0.086601004,0.1841408312,0.3234291971,-0.236716494,-0.2932063937,0.4450637996,-0.0214542951,0.3800020218,0.4525405765,0.1133752167,0.387149632,-0.0253302865,0.0821200162,0.1877122521,0.0891930312,0.1383299828,0.0005307122,-0.0818210095,0.0557394996,-0.0177538302,-0.2198273391,0.1864511371,-0.0542111173,0.2378252596,0.3866393864,-0.1227634922,0.0955921933,0.2492309362,0.0278107058,0.0841381028,-0.1017539501,-0.2666337192,-0.5663552284,0.1156954318,0.1961968988,-0.0105722062,-0.0373767279,-0.3354552686,-0.1627111286,-0.1486293674,-0.2381812185,-0.1918063462,0.191869691,0.2160943449,0.2294142544,-0.0579051226,-0.3425168395,0.2329841405,-0.1294587702,0.1588121057,-0.516131103,0.2736102045,0.0633980334,-0.2143644243,-0.0530003309,-0.097419709,0.0606903248,-0.089718014,-0.0100368867,0.3504856527,-0.1089573205,0.3134915829,-0.2344281375,0.4961461425,0.2668770254,-0.1883774251,-0.0466293804,0.4751684964,0.2154039294,-0.1246935502,-0.4435529411,0.4490568042,-0.1326920837,0.3501239419,0.2671548724,-0.1016992554,0.3507598042,0.1320291907,-0.5745479465,-0.1943862587,0.0862337947,-0.183827579,0.1034906358,0.0160139482,-0.6025926471,-0.0544088706,0.5669587851,-0.0349860452,0.0080634123,-0.0719246268,-0.4338728487,0.0712547898,0.0869189426,0.1950968206,0.4976267815,0.25206092,0.3521637917,0.2105461657,-0.074851349,-0.3685138226,0.2423160374,0.1320714206,0.0249587987,0.1030122116,0.0288465153,-0.0757123455,-0.4879135191,-0.0766509473,-0.0729150921,-0.0556565821,-0.283082515,0.2335002124,-0.4161814451,-0.1997805983,-0.3649851978,-0.4089732766,-0.2736245692,-0.2414459586,-0.0969617367,0.3924412727,-0.166088894,0.0761197284,-0.0038575393,-0.0949353278,-0.0361225232,-0.3969391286,-0.3365665972,-0.0419173501,0.0321512893,0.0477412976,0.0145558286,-0.0395411327,0.3013984263,-0.1977808774,-0.2450960577,-0.2017071247,-0.194547087,0.2379913479,-0.2833317518,-0.0750529841,0.0793332458,0.4024925232,0.2286729366,-0.2038232237,0.3570702374,-0.217123419,-0.0460917205,0.2414536923,0.0980227366,0.0460055098,-0.1519765407,-0.3295801282,-0.0179752391,-0.4542370439,0.3621647358,-0.0174758583,0.0178103596,0.3286877871,0.1383274645,0.1779690534,-0.1150079668,0.3019878268,-0.1503393203,-0.2376279831,0.3211557567,-0.2065510899,-0.3664948642,0.1392052174,0.1990440339,0.1663504839,0.0670399219,-0.2930223346,-0.049434144,-0.326526463,0.0385052077,0.1132010445,0.1051503867,0.3006159663,-0.141127035,-0.0828169808,-0.2275162339,-0.0557435229,-0.0070718075,0.061091464,0.1330196112,0.0770829171,0.4788240194,-0.0393618643,0.4121434689,0.3855175972,-0.0889042169,0.320083648,-0.2393969744,0.3067497313,-0.234220773,-0.1552781761,-0.0757322013,-0.020383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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3738","title":"For data-only datasets, streaming and non-streaming don't behave the same","comments":"Hi @severo, thanks for reporting.\r\n\r\nYes, this happens because when non-streaming, a cast of all data is done in order to \"concatenate\" it all into a single dataset (thus the error), while this casting is not done while yielding item by item in streaming mode.\r\n\r\nMaybe in streaming mode we should keep the schema (inferred from the first item) and throw an exception if a subsequent item does not conform to the inferred schema?","body":"See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n```","comment_length":74,"text":"For data-only datasets, streaming and non-streaming don't behave the same \n See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n``` \n Hi @severo, thanks for reporting.\r\n\r\nYes, this happens because when non-streaming, a cast of all data is done in order to \"concatenate\" it all into a single dataset (thus the error), while this casting is not done while yielding item by item in streaming mode.\r\n\r\nMaybe in streaming mode we should keep the schema (inferred from the first item) and throw an exception if a subsequent item does not conform to the inferred schema?","embeddings":[-0.1483691335,-0.5971015096,0.0425092764,0.3026602864,0.4449430704,0.0343072265,0.0938618928,0.2274340093,-0.0198623743,0.1207608804,-0.2812955379,0.163243562,-0.1369573921,0.2652867436,0.0114958389,-0.3093685806,0.1662293971,0.1124370918,-0.3150340319,-0.0867637098,-0.034930449,0.1955483407,-0.1453727186,-0.211995706,-0.083390601,0.1361234933,0.1163567975,-0.0861025304,0.1048669219,-0.1392381042,0.1678072363,-0.1296817362,0.1045156568,0.6797522902,-0.0001127699,0.2066969275,0.2664151192,-0.2004862875,-0.2330942154,-0.3872595429,-0.1826975495,0.1999833286,0.1647102535,-0.1955533326,-0.2638483644,-0.113550812,-0.0205750559,-0.3217554986,0.5125505924,0.1771340072,0.1821899563,0.6846286058,-0.1682326347,-0.0475427173,-0.0061915019,0.1716743112,-0.2508841753,0.2004660666,-0.0314934254,0.4735362232,0.1523118764,0.4614452422,-0.2017080337,0.021424599,0.3477565944,0.0653764382,-0.2426325232,-0.2455516011,0.1682893634,0.5889729857,0.3167929947,-0.2784743011,-0.4343252778,-0.3266445696,0.0259043314,-0.236583665,0.037401095,0.3559144437,-0.1176273078,0.2137734294,-0.4141128957,0.2120926678,-0.1228156686,-0.0317869112,-0.1808419824,0.4034492373,-0.1403856575,0.1256176978,-0.1262842268,-0.1155175194,-0.0125927087,-0.3752647936,-0.0438918918,-0.0303763933,-0.354049176,-0.1260910779,0.0400970131,-0.0460770316,0.0673868433,0.1470156014,0.3766259551,0.0953385308,0.1109039634,0.1008864343,0.243907094,-0.0490461104,0.0529390909,-0.1530080736,0.2018466294,0.0482556149,-0.0081612142,-0.2411839217,0.3945322633,0.2717736363,-0.0767909214,-0.3022184372,0.4603638053,-0.0663588643,-0.3291620016,-0.0974652246,-0.325458765,0.106469743,0.1170788407,0.3950734437,-0.0151084466,0.1753035337,-0.0723732188,0.3374946713,0.0536676273,-0.0761574358,-0.3149593472,-0.1557171047,0.0644872338,0.0807957649,0.0659840703,-0.415288955,0.3459503651,0.0320689008,0.0205753874,0.0528704263,-0.0758730546,-0.1088523716,0.1628322601,0.5072857141,0.1707226634,0.1415912211,0.2164114118,-0.1423628479,-0.1859295964,0.0184457377,0.0073601454,-0.2466872633,0.3043279648,0.2124235481,-0.2852187455,-0.1053194702,-0.2324451953,0.2000662535,-0.0778062791,-0.2584817111,0.1093929335,0.05725592,-0.0549519658,-0.1658333689,0.100419119,0.212473318,-0.166137442,-0.2669169605,0.3579495549,-0.1481362581,0.1928768158,0.5332435369,-0.227382943,-0.0541699752,-0.1709254235,0.1774095297,0.248906374,-0.1442927122,-0.2420775592,0.4730577171,-0.0911309943,0.4531012177,0.3604582846,0.0879457518,0.3624897301,-0.0883616954,-0.0115475962,0.1585402191,-0.0652664229,0.2012977749,-0.0599410348,-0.1532737017,0.176965341,-0.0701861456,-0.2099511474,0.2512781322,-0.0002245715,0.2780895829,0.3053920567,-0.2139311135,0.0681057498,0.2172363997,0.0519030206,0.0169870947,-0.0610051975,-0.3112374246,-0.6572113037,0.147321701,0.24592565,0.0594442748,-0.1182880029,-0.3471957445,-0.1130159274,-0.0662179142,-0.1728887558,-0.0584129281,0.1696875244,0.1475952715,0.1351737529,-0.0992500484,-0.2190576196,0.2426240593,-0.2021968216,0.1962522715,-0.5070541501,0.2261355072,0.0646441355,-0.2289232612,-0.1498266011,-0.0736146867,0.0392672606,-0.1265672147,-0.0423650853,0.370344758,-0.1095660999,0.370074302,-0.3650715351,0.4260793626,0.2276661545,-0.1879204959,-0.132422179,0.4703009725,0.2765606642,-0.1769826859,-0.4317968786,0.4542832971,-0.0810319856,0.3805332184,0.2623717785,-0.0433867872,0.2821673751,0.0999449641,-0.5687912107,-0.1889935136,0.1466084868,-0.1980088949,0.0821161792,-0.0275003966,-0.6260436177,-0.0818668976,0.5218032598,-0.097513862,0.0567462109,-0.0565612614,-0.4084969759,0.1513520181,0.1556981355,0.2319199443,0.5203043222,0.2339862883,0.2847252786,0.2387353331,0.0155132739,-0.3605205715,0.2217682898,0.2253194302,0.0581981279,0.2065259516,0.0888970122,0.0387953222,-0.472650677,-0.0398563333,0.0439219065,-0.0497876182,-0.3616195619,0.1897777617,-0.2793539464,-0.1332273334,-0.4320119023,-0.3525982201,-0.2684051991,-0.3052372336,-0.1526872218,0.426174134,-0.2619640529,0.0982126743,0.0409308411,-0.0758339465,-0.0635670498,-0.2504404783,-0.3685217202,-0.1148799434,0.1055748463,0.0027033961,0.0208573584,-0.0408429354,0.3102529943,-0.1758226901,-0.1799994409,-0.2013841718,-0.3425168693,0.2545910776,-0.3312719166,-0.0712436289,0.1103568226,0.3671300411,0.2287732959,-0.28891927,0.3052082658,-0.2019626051,-0.1596291214,0.3864353299,0.049790632,0.069575429,-0.2025364041,-0.3087229133,-0.0438043699,-0.4455457032,0.3741573989,-0.1118274704,0.0830386952,0.3282185793,0.0529964343,0.1399500221,-0.1393480599,0.3081430495,-0.1440440118,-0.1767004877,0.2722373903,-0.1522265971,-0.3596999943,0.130432561,0.1467941999,0.1458731443,0.1673517823,-0.3535766304,-0.1181002855,-0.2114096284,-0.0131252836,0.0931494981,-0.0203246661,0.3762073219,-0.1236343086,-0.055235222,-0.1663290113,0.0650939196,-0.0791722238,0.0375267453,0.2067644894,0.0092071118,0.5708674788,0.0954432189,0.425822705,0.34739995,-0.031817548,0.2382348627,-0.1142010912,0.2976043224,-0.2585869431,-0.103523694,-0.1044901162,-0.1095463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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3738","title":"For data-only datasets, streaming and non-streaming don't behave the same","comments":"Why do we want to concatenate the files? Is it the expected behavior for most datasets that lack a script and dataset info?","body":"See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n```","comment_length":23,"text":"For data-only datasets, streaming and non-streaming don't behave the same \n See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n``` \n Why do we want to concatenate the files? Is it the expected behavior for most datasets that lack a script and dataset info?","embeddings":[-0.1206137985,-0.5383890867,0.0515678041,0.1705870032,0.3425415456,0.1174005792,0.2200132012,0.1988752633,-0.0872355402,0.1894494146,-0.3170532584,0.1043425724,-0.0130866282,0.287632972,0.0533946119,-0.3094181418,0.1563249677,0.242549479,-0.1608393192,-0.1402353644,-0.1171626747,0.0883864537,-0.1769117266,-0.3052862883,-0.2488360703,0.2742664218,-0.0004401306,0.0479022078,0.0451704487,-0.158680588,0.1905763596,-0.051653225,0.0450898036,0.4641680717,-0.0001260868,0.1651509851,0.3315838277,-0.2629677653,-0.326312989,-0.4357123375,-0.2210155874,0.0910022631,0.2275753766,-0.1439338773,-0.0935243741,-0.3121585846,0.2295220494,-0.4739470184,0.362049073,0.1561794132,0.0569125265,0.4719947278,-0.2614101171,-0.0374588445,-0.0714825019,0.246928975,-0.1371471286,0.1699278057,0.0441574492,0.3927860856,0.1172641963,0.2284319252,-0.1788067073,-0.12388096,0.3190617561,0.1430529505,-0.3147367239,-0.3722531199,0.2304693609,0.6345444918,0.4502031505,-0.2759081125,-0.3170225024,-0.3609164655,0.0357790515,-0.1240996048,-0.0504160486,0.4053917825,0.1146335155,0.2875302732,-0.339294374,0.2703860402,-0.1358238012,0.007942576,-0.1250429153,0.3707238734,-0.0693035275,0.2123728395,-0.1973436177,-0.0016888164,0.0456526428,-0.7146862149,-0.1331838369,-0.0319290534,-0.2938157618,0.0334014408,-0.0527391843,-0.0386386439,0.0730897486,0.2539539337,0.2587566972,-0.0264061186,0.1871838272,0.0472787321,0.1794299483,-0.099832274,0.2185737342,-0.1231092066,0.1755918562,-0.0907197818,0.068380855,-0.2554058731,0.238729611,0.2820042074,-0.0902247429,-0.3584251106,0.4758055806,-0.051250305,-0.2333011329,-0.2166467309,-0.3566566408,0.0142179988,0.0343090929,0.1182818636,-0.037228439,0.3369821012,-0.0545538925,0.3823513091,0.1265539378,0.0723581389,-0.1891515702,-0.236061573,0.1706501544,-0.1201571152,0.0944780782,-0.4429014623,0.3692002594,0.0935581326,0.1600293964,0.1969265044,0.0246499311,-0.0827273354,0.1580802351,0.5991453528,0.346226126,0.2426948696,0.2238832861,-0.3454335034,-0.2060003877,0.0550701879,-0.2059081644,-0.2673391402,0.2012937963,0.1064644605,-0.1980838925,-0.1151012853,-0.2377961129,0.191083923,-0.1379400939,-0.4120372534,0.0313940719,-0.0346222259,0.0950652212,-0.2269511819,0.0092849731,0.2143708467,-0.2531340718,-0.1588894725,0.3497660458,-0.1069996804,0.416932106,0.5071638227,-0.1920313239,-0.0555663817,-0.3016809225,0.1530561,0.4405658543,-0.2316012532,-0.1370073706,0.4704860449,-0.1330840886,0.448867768,0.6467944384,0.0495264232,0.3584112525,-0.0752495751,0.1598003507,0.2209355384,-0.0892417356,0.0646051168,0.0578346848,-0.0406460203,0.2336542159,-0.117351383,-0.4235688448,0.2780710459,0.0663305223,0.1573579311,0.2995962799,-0.2910202444,0.1142154932,0.1107535809,-0.0099462159,0.0119099664,-0.0379451476,-0.1028422788,-0.7063177824,0.0943267047,0.4044200182,-0.0977683291,-0.0939113796,-0.529673636,0.0321806408,-0.2442175746,-0.1816018373,-0.046938289,-0.0392738283,0.2735718191,0.07667467,-0.1087134331,-0.414594084,0.2806007862,-0.2821247876,0.140953809,-0.4363392889,0.216329217,0.1213287041,-0.0579908416,-0.1616949737,-0.0323638581,-0.0964170694,-0.0615766905,-0.0085034352,0.3986407518,-0.0889096633,0.274852246,-0.1533254832,0.4624287486,0.2268507034,-0.0426930152,-0.0561780445,0.2750268877,0.2703582346,-0.3998810351,-0.4423682988,0.4256439805,0.0241020173,0.5342103243,0.2320563942,-0.0769135058,0.2442184538,0.2045807391,-0.6032145023,-0.1794254184,0.1391974986,-0.1462787092,0.1807547063,0.165222615,-0.7006341815,-0.0129130911,0.604262054,-0.0899340287,0.0132059511,-0.0768339261,-0.4499608278,0.0134813478,0.0245706383,0.1704323739,0.5974843502,0.2367908508,0.3337240815,0.247694239,-0.0369154923,-0.4216633141,0.2245985717,0.228532061,-0.0034418297,0.2199080288,0.0226728059,-0.02379274,-0.5395421386,-0.1443346441,-0.0934030563,-0.3698193133,-0.2651266456,0.2416110635,-0.3046420813,-0.2337327898,-0.4013023376,-0.2750680149,-0.2501999736,-0.3082203865,-0.1630567312,0.3631367087,-0.1598504335,0.0633502528,0.0912528634,0.0336483642,-0.0307105165,-0.4054464996,-0.3958795965,0.0042987564,0.025374461,-0.0422302149,-0.1651964784,-0.2015535831,0.182580784,-0.4123965502,0.0063052541,-0.2825549841,-0.3813865185,0.0325040892,-0.2630619109,-0.1116022915,0.2347976565,0.3812502921,0.2482414544,-0.2475867271,0.1716652513,0.1116193756,-0.1019527242,0.3831915259,0.029674856,0.0323894881,-0.2926638424,-0.3173146844,0.0382862017,-0.3346970677,0.3487883806,-0.0776589587,0.0321659148,0.2424954176,0.0478258394,0.0919375047,-0.1546943039,0.3376653194,-0.0312274285,-0.1764204949,0.4250022769,-0.0422013327,-0.3252126873,0.1854332536,0.1537780762,-0.0034096758,0.1530099809,-0.3026395738,-0.1137079224,-0.1473021954,0.0350677371,0.1714673787,0.2818560898,0.3204518259,-0.0375649519,0.0727507994,-0.2154933512,0.0713815093,-0.1273212433,0.1471746862,0.2405139357,-0.0062564327,0.5801610351,-0.0633708313,0.3206469119,0.3542489409,0.0691772327,0.2221060842,-0.1533844769,0.5626493692,-0.3361137509,-0.0056873355,-0.0921634138,0.0592761785,0.07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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3738","title":"For data-only datasets, streaming and non-streaming don't behave the same","comments":"These files are two different dataset configurations since they don't share the same schema.\r\n\r\nIMO the streaming mode should fail in this case, as @albertvillanova said.\r\n\r\nThere is one challenge though: inferring the schema from the first example is not robust enough in the general case - especially if some fields are nullable. I guess we can at least make sure that no new columns are added","body":"See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n```","comment_length":67,"text":"For data-only datasets, streaming and non-streaming don't behave the same \n See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n``` \n These files are two different dataset configurations since they don't share the same schema.\r\n\r\nIMO the streaming mode should fail in this case, as @albertvillanova said.\r\n\r\nThere is one challenge though: inferring the schema from the first example is not robust enough in the general case - especially if some fields are nullable. I guess we can at least make sure that no new columns are added","embeddings":[-0.2412600368,-0.5383829474,0.0270498935,0.2054670304,0.2709468007,0.0090809157,0.1926283985,0.0634072796,0.0702457502,-0.0795699134,-0.3011707664,0.0683554783,-0.2525920272,0.367969811,0.1206432208,-0.3124427497,0.1600003541,-0.0171230901,-0.3173449934,-0.0600102693,0.0236264262,0.0415329225,-0.119423978,-0.2680511177,-0.0171647165,0.1346462071,0.1771923751,-0.0150241572,-0.0233534556,-0.1186598614,0.2169085592,0.0839459822,0.1419481188,0.5696662068,-0.0001130326,0.3498701751,0.2884454429,-0.268484503,-0.0491359793,-0.2941718102,-0.2325997502,0.1380950958,0.0730933174,-0.2454400361,-0.2997662127,-0.0947210789,-0.0831086338,-0.3203808963,0.5762209892,0.1769592762,0.178630054,0.7188823819,-0.2709390521,0.0224907659,0.0166963357,0.2370266914,-0.1771011353,0.2138018012,-0.2069440931,0.498513788,0.1654556245,0.4527532458,-0.2617514133,0.0235097259,0.4668934345,0.1084107384,-0.2373250425,-0.1582691967,0.1063453108,0.5543656945,0.2933368683,-0.2589655519,-0.4447121024,-0.3128412068,-0.0269896369,-0.2924083471,0.2358603626,0.1726540625,-0.1088013053,0.130877465,-0.3318739831,0.0303078499,-0.0416413024,-0.0528649539,-0.2697367668,0.3944042027,-0.0677920505,0.1143538207,-0.1904087365,-0.0274692103,-0.0071353293,-0.3840796351,-0.007273715,0.04714166,-0.3793286383,-0.2316426486,0.0027467788,-0.0532374419,0.1123973355,0.1178570539,0.4273262322,0.0602819659,-0.0723845363,0.140473932,0.1907036304,-0.0825646371,-0.0241856799,-0.2436479628,0.1753753722,0.0949274674,-0.010706149,-0.1704713404,0.3926061094,0.1824493557,-0.0159892384,-0.467068553,0.4041879773,-0.0142755844,-0.2607741952,0.0188599192,-0.3494086266,0.0307434611,0.1268546581,0.3991905749,0.0414135084,0.1681549996,-0.14023,0.3956479728,0.1418871731,-0.0374520458,-0.264254272,-0.120438613,0.1167609915,0.1273502111,0.1767224669,-0.4076744616,0.3004650474,0.1078393087,-0.0075977165,0.1687792391,-0.039817512,-0.0759401992,0.2319807261,0.5176733732,0.0889779255,0.0854825452,0.1971511245,-0.1376226991,-0.2030010521,-0.0175296105,0.0899649039,-0.1348858923,0.3645585179,0.2006076425,-0.2983201742,-0.1265799254,-0.3280696869,0.2738249302,-0.1051444635,-0.3546569943,0.2421523482,0.1028455794,0.0045166085,-0.0654183179,0.068353273,0.3014217615,-0.1371734142,-0.3517813087,0.3745602071,-0.1420833319,0.0633811653,0.396494925,-0.2604461312,-0.1726981103,-0.0568641797,0.1192348003,0.2587145567,-0.1990503669,-0.140650019,0.3671530485,0.0010986706,0.4389566481,0.4631586075,0.1225180775,0.2834223211,-0.1474774331,0.0420365036,0.0183014423,0.0288813449,0.0901533961,-0.1237566844,-0.2582390308,0.1698456407,-0.0312430784,-0.0054634917,0.1839994788,-0.1600430757,0.3309911788,0.2204710543,-0.0324786082,-0.0528689586,0.1152787581,0.0216851998,-0.1074825972,-0.1553170383,-0.3237203956,-0.5997964144,0.1413893551,0.2730637789,-0.0368723944,0.088363409,-0.3782525957,-0.0194603205,-0.1390698403,-0.2150016278,-0.1137282625,0.1706187725,0.1236941293,0.213312,-0.1254091263,-0.5201584101,0.1785679609,-0.1668058336,0.138796851,-0.4079779685,0.2503011227,0.1898098886,-0.0590522699,-0.1335945129,-0.2008558959,-0.005852174,-0.0765873119,0.0963444337,0.3401153386,-0.1523290128,0.4398685098,-0.4019547105,0.4905747473,0.2948010862,-0.0606754906,-0.1908798516,0.4449426234,0.2299092561,-0.0032155744,-0.3805764616,0.3664931357,-0.0894454867,0.2351005375,0.3135695755,-0.1813832819,0.3096239269,0.1704879254,-0.6693195701,-0.2104649693,0.0517602563,-0.0757537261,0.0045575132,0.0277973469,-0.6246353984,-0.1120519936,0.4159115851,-0.0644247532,0.057408344,-0.1032906994,-0.4192698002,0.0712433457,0.1934913248,0.2231160104,0.5707005262,0.1808987707,0.2908955514,0.3163960278,-0.1112846062,-0.3006909192,0.2768771648,0.146091342,0.103781864,0.2976565063,0.0326187573,-0.0036206017,-0.5076035261,-0.0076567493,0.0867892951,-0.027964877,-0.3640411198,0.1154484823,-0.1998693198,-0.1810983121,-0.3621348739,-0.2940898836,-0.2320670336,-0.2695859671,-0.0961318314,0.4844964445,-0.2314226925,0.1133762524,-0.0595863014,-0.0268608965,-0.1088873073,-0.3794322908,-0.3866936564,-0.0586980879,0.1725725085,0.0410127044,0.1160050854,-0.1162459403,0.2189069539,-0.1684300303,-0.3869819641,-0.1694254577,-0.2790864408,0.2802112699,-0.2125308961,-0.1039927825,0.0521492548,0.3394423127,0.3401866257,-0.124317117,0.262678504,-0.2192281634,-0.0771614835,0.2447466105,0.1261286587,0.1458190531,-0.1980738789,-0.2231805325,-0.0156066166,-0.3393798172,0.2648115754,0.0321860798,-0.0423578843,0.3082715869,-0.0486999154,0.1208217144,-0.1000450179,0.187632218,-0.2353551388,-0.2016167492,0.2444131523,-0.0715884343,-0.2608214021,0.1559014618,0.1568452865,0.2009707987,0.2289337367,-0.2054036558,-0.103902109,-0.1416472793,0.0192032289,0.1109671071,-0.0144844754,0.3887906969,-0.1071483716,-0.0862451568,-0.0885353461,0.1787834466,-0.0450153016,0.1269846261,0.0323128812,-0.0253024828,0.4796411395,-0.053677097,0.4142831266,0.3394565284,-0.0783004165,0.2857117057,-0.1527602226,0.3537485301,-0.2245797515,-0.0089292927,-0.1485145092,-0.04257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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3738","title":"For data-only datasets, streaming and non-streaming don't behave the same","comments":"OK. So, if we make the streaming also fail, the dataset https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata will never be [viewable](https:\/\/github.com\/huggingface\/datasets-preview-backend\/issues\/144) (be it using streaming or fallback to downloading the files), right?\r\n","body":"See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n```","comment_length":27,"text":"For data-only datasets, streaming and non-streaming don't behave the same \n See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n``` \n OK. So, if we make the streaming also fail, the dataset https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata will never be [viewable](https:\/\/github.com\/huggingface\/datasets-preview-backend\/issues\/144) (be it using streaming or fallback to downloading the files), right?\r\n","embeddings":[-0.1478589773,-0.4494978786,-0.0741418898,0.2103791833,0.2105886936,0.0522013605,0.0881880447,0.0913034528,-0.0407917276,0.0463269353,-0.3037819266,0.057506144,-0.2236424536,0.2118000239,0.1254729182,-0.2901278436,0.1195497736,-0.006280364,-0.2550479472,-0.0152238254,-0.0845370367,0.0715108216,-0.2137810141,-0.2269221395,0.0557990931,0.1627507806,0.1999137104,-0.0221497323,-0.0602399819,-0.2695470452,0.1121931747,0.0508085862,0.0711018667,0.4732878506,-0.000114504,0.2984876335,0.3541761935,-0.19663243,-0.3506260216,-0.3316375613,-0.280587852,0.178619653,0.0345859379,-0.0440269634,-0.1648897231,-0.1403223425,0.1589135677,-0.4487288892,0.5669639111,0.2355094105,0.1699257344,0.7521890998,-0.0781834126,0.031679675,0.0616398565,0.2577153742,-0.2764289975,0.2800395191,-0.132273823,0.5018449426,0.1466065943,0.3891105652,-0.1332185268,-0.0203837119,0.282731384,0.0527035482,-0.2738933563,-0.1839812994,0.0445820652,0.4300328195,0.3452637196,-0.2495512068,-0.5196864605,-0.4067823589,-0.0212010685,-0.2482258528,0.0938519016,0.2507465482,-0.1045276299,0.2265966386,-0.3885076344,0.0504990928,-0.1674192101,0.0698013157,-0.1540216208,0.3471154869,-0.1843487471,0.1641264558,-0.1460231245,0.0652457625,-0.0569268689,-0.3374098241,0.0646076128,-0.0415964872,-0.2174106538,-0.1439347565,0.0302347206,-0.1196117997,0.1587115228,0.1104602963,0.367770195,0.0842090175,-0.0384728238,0.1386219263,0.1714997888,-0.1038200557,0.0015082725,-0.0755343437,0.3441009521,0.0816942975,0.1218969747,-0.2314760089,0.4176133871,0.2150393128,-0.1922094673,-0.3458209038,0.5612107515,-0.1313292533,-0.3419469297,0.0510893501,-0.3104566336,0.018766379,0.0592657849,0.3595071733,-0.0974364355,0.0945085734,-0.1205455661,0.3131225407,0.0831520259,-0.0408157483,-0.25315997,-0.2495080084,-0.0133017749,0.1549571306,0.2395796478,-0.4230980575,0.4493789971,-0.0575857349,0.0557439364,0.1444497108,0.0567086488,-0.0846264213,0.2500111163,0.5757033825,0.1072356328,0.1575231999,0.2004322708,-0.1346221268,-0.1370329857,-0.0490462966,0.1110513583,-0.1836498529,0.3953109682,0.2129563689,-0.3245653808,0.0415194519,-0.4206823409,0.2050958872,-0.1665044278,-0.3570684791,0.2908055186,0.1365611553,0.0879633129,-0.1595434994,0.1356865764,0.321456641,-0.1425380856,-0.3362163007,0.3625541627,-0.2854034007,0.1037593111,0.3970133364,-0.3117203712,-0.1761909127,-0.2166483551,0.0811070725,0.329651773,-0.21603553,-0.3010597527,0.4250743389,-0.127288267,0.478531301,0.4156757891,0.055660408,0.3472301662,-0.1588106751,-0.0744066611,0.0221447535,0.0448644757,0.010186309,-0.1735445112,-0.1559885293,0.1891141981,-0.0770808458,-0.0115096727,0.1550904065,0.0625823215,0.3453904986,0.2423582375,0.0374716036,0.0774490759,0.1184183359,-0.063665919,-0.0546214208,-0.1414439678,-0.2345508933,-0.5934894681,0.1811052114,0.1395464391,-0.1571350396,-0.0810493901,-0.3562892675,-0.0253439005,-0.1521451473,-0.2009490132,-0.1941036284,0.1768863946,0.2061495632,0.1608347446,0.047454305,-0.4427681863,0.2811455727,-0.3165417314,0.1827634871,-0.4249800742,0.3045063913,0.0428058654,-0.0469346419,-0.1951591372,-0.1901529878,-0.0417339727,-0.1025008932,0.0651435405,0.4362589121,-0.1296312511,0.4226964712,-0.2239351571,0.4042709768,0.3643576801,-0.3247521222,-0.0475283787,0.4844664037,0.2341374457,-0.0880954266,-0.3704104424,0.3383549154,-0.1165036932,0.2937100232,0.3216595352,-0.2183492184,0.2179898769,0.1387622803,-0.8032082915,-0.222622931,0.0210442226,-0.250426501,0.2169848531,-0.0362792127,-0.5673174262,-0.1815538853,0.3675608635,-0.0562360324,-0.0141896298,-0.0687232316,-0.4802674651,0.0184339006,0.1745596975,0.1824487895,0.6112623215,0.2233077586,0.4058092535,0.1818708181,-0.0727017298,-0.4136187434,0.1187613755,0.2484194487,0.0101091638,0.2940137088,-0.0207186677,0.0519989654,-0.4973077476,-0.0646241754,0.0406991839,-0.1690495908,-0.2849884927,0.08290454,-0.1555962116,-0.3039013147,-0.276902914,-0.3366936445,-0.216584444,-0.2800288498,-0.04796841,0.4411713779,-0.2072842568,0.0094299633,-0.0370832942,0.0054215132,-0.2275667191,-0.1900614649,-0.404473573,0.0462872125,0.1255261451,0.0892993212,-0.021840699,-0.1006690189,0.3789830208,-0.2708913088,-0.2332958877,-0.3128159642,-0.1897356212,0.2238960564,-0.1400125176,-0.1001706719,0.069892101,0.378011018,0.2731811404,-0.1160480306,0.2384052128,-0.2263177186,-0.0307008382,0.0880981311,0.1333524585,0.1980137974,-0.2199648619,-0.2042320222,-0.1417229027,-0.4381321073,0.3865692317,0.0013490552,-0.0357230902,0.3208018541,-0.0327087082,0.2613034248,-0.1153158545,0.2593611777,-0.2156671137,-0.310033083,0.1202344373,-0.1789932847,-0.2674042284,0.1363770962,0.2697364986,0.2327836752,0.1749540269,-0.3859589994,-0.0656912625,-0.1062248349,-0.0162394885,0.0416450836,-0.0047236145,0.4160581231,-0.1245764643,-0.0906173661,-0.1798332036,0.1996015608,-0.2654953599,0.0294161551,0.180599004,-0.0255471393,0.4012048244,0.0580002367,0.6073742509,0.3520385325,-0.0236240402,0.2837017775,-0.056931071,0.5842094421,-0.190333873,-0.0209226832,-0.0828511119,-0.045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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3738","title":"For data-only datasets, streaming and non-streaming don't behave the same","comments":"Yes, until we have a way for the user to specify explicitly that those two files are different configurations.\r\n\r\nWe can maybe have some rule to detect this automatically, maybe checking the first line of each file ? That would mean that for dataset of 10,000+ files we would have to verify every single one of them just to know if there is one ore more configurations, so I'm not sure if this is a good idea","body":"See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n```","comment_length":77,"text":"For data-only datasets, streaming and non-streaming don't behave the same \n See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n``` \n Yes, until we have a way for the user to specify explicitly that those two files are different configurations.\r\n\r\nWe can maybe have some rule to detect this automatically, maybe checking the first line of each file ? That would mean that for dataset of 10,000+ files we would have to verify every single one of them just to know if there is one ore more configurations, so I'm not sure if this is a good idea","embeddings":[-0.3082470298,-0.5184178948,-0.0382427573,0.3086873293,0.2399062961,-0.1777454466,0.1599006355,0.2285964638,0.0310773607,0.1484983712,-0.313431114,0.0760296509,-0.2314167917,0.3043786883,0.068923071,-0.1778727174,0.1001174673,0.0215645749,-0.2695713043,0.0381325744,0.0504423678,0.095039241,-0.0176803209,-0.3359543085,-0.032602407,0.1079818606,0.1726800948,-0.2192111313,0.0931721553,-0.2083723843,0.1254274249,0.1907020956,0.0392258391,0.61921525,-0.0001151269,0.2951510847,0.1569999009,-0.2101192921,-0.3404222727,-0.213882491,-0.1957309097,0.0939738974,0.1236779988,-0.2578983605,-0.2444018573,-0.1383280307,0.0739902034,-0.4624170959,0.5808234811,0.0603096858,0.1370110065,0.6894252896,-0.2578959167,0.0712567046,0.0306536928,0.3770949841,-0.1869815439,0.1760165989,0.025905963,0.2774808407,0.0802437142,0.4781523943,-0.1905562729,0.0735663176,0.309250474,0.1008996516,-0.2998847365,-0.2868616879,0.0354861766,0.8005132079,0.2309515923,-0.2933905721,-0.4663556218,-0.5412455201,0.0539690033,-0.2137937397,0.1546986848,0.2513723075,-0.2412419021,0.1695293486,-0.3637163043,0.0759337693,0.0083804643,-0.0932379067,-0.1666170508,0.3539155424,-0.0315733328,0.1098036095,-0.0803376734,-0.0854479596,-0.0577355064,-0.4206847847,-0.0075137015,-0.1145325303,-0.4488012493,-0.2463471889,-0.0593581498,-0.2017485201,0.1266729981,0.2455490083,0.5271072388,0.1324909031,0.0106547615,0.1107345894,0.2580032945,-0.0294538029,-0.0617306978,-0.1695369929,0.3040086627,-0.004547887,-0.0648989528,-0.0372927599,0.4171834588,0.0040256917,-0.0598904714,-0.354870975,0.4889145494,0.0001403529,-0.2520433962,-0.1559586078,-0.3276767433,-0.0688031316,0.2073491812,0.3143391907,-0.026323963,0.1008651257,-0.121259883,0.2417666465,-0.0401653238,-0.00879924,-0.1646929085,-0.1421793699,0.1788171977,0.1264715344,0.1763153225,-0.4270465076,0.2643744648,0.0971698239,0.0909998491,0.1272325069,0.0093350234,-0.1152369455,0.1951934844,0.4526906312,0.0865887031,0.0758654922,0.1422007382,-0.2127012908,-0.3397787809,-0.0930605158,-0.0112618543,-0.1402258426,0.3173361123,0.168801412,-0.2894005477,-0.149137035,-0.3385223746,0.3359251618,-0.0985588655,-0.2993284166,0.2146558911,0.0775701255,0.0175711773,-0.1925462186,0.0049019209,0.3442749679,-0.0303209554,-0.3749287426,0.5006689429,-0.184865579,0.0457566567,0.5155394077,-0.3070793748,-0.1609099954,-0.137338683,0.0582233109,0.4330542088,-0.2312273681,-0.1373900324,0.5077056885,-0.0704240575,0.4548470378,0.6455833316,0.1640830636,0.3551745713,-0.19747895,0.0658475608,0.0985688269,0.1416191161,0.190860942,-0.0559805967,-0.3606121242,0.0942009464,0.001828499,-0.1350393146,0.0314707384,-0.0424820669,0.1803073883,0.3546951413,-0.1303757131,0.0293989815,0.0829997808,0.0465204567,0.0757796168,-0.2130817771,-0.3276477754,-0.5854145288,0.1535120159,0.2168979943,0.004278894,0.0568963625,-0.4053398967,0.1346978992,-0.2715455592,-0.1700777709,-0.0998237729,0.1528692544,0.2993738353,0.1630601436,-0.0837266967,-0.4470394254,0.2474564761,-0.1530035287,0.1013561189,-0.4527001977,0.2161575556,0.1555100977,-0.0762308389,-0.0405517593,-0.1665045172,-0.1035157368,-0.1231833175,0.0994054675,0.3341627419,-0.077566959,0.2892237306,-0.218040958,0.6023272276,0.3007739484,-0.0369709134,-0.1589267105,0.4493798912,0.1325514019,-0.0013669361,-0.4237198234,0.5684637427,-0.0678805113,0.3472043872,0.324439764,-0.2264806628,0.3786475956,0.1276921779,-0.6654533744,-0.2073666751,0.0650617853,-0.1591666639,0.1062871143,0.1037975624,-0.6663360596,-0.1235016957,0.455761224,0.0568862483,0.0714938939,-0.1016871259,-0.303022325,0.0687078685,0.0218744203,0.2617726028,0.5722913742,0.3148631454,0.3061863482,0.1352056116,-0.1071497425,-0.3137720525,0.240411222,0.1419292092,0.0027955635,0.2051744163,0.0618368983,-0.0137480078,-0.5397719741,-0.2897809744,0.0310899932,-0.1206422225,-0.3535257876,0.1591437757,-0.2993676364,-0.2763223946,-0.4542533159,-0.3219878674,-0.1844394803,-0.1120561212,0.1210274324,0.351144284,-0.198326081,0.1909813583,0.0536422692,0.1235101297,-0.1515331715,-0.4265708029,-0.2402628511,-0.0479239449,0.15617989,0.0349606462,0.2396463901,-0.1196395606,0.324118048,-0.1323101372,-0.3408622742,-0.3773535192,-0.3103275895,0.2406273782,-0.160707444,-0.0364998132,0.0514434651,0.2772845328,0.3637998402,-0.2292243689,0.1798206419,-0.0247686505,-0.12553069,0.1724477559,0.0604174845,0.0770396516,-0.202959761,-0.3052752912,-0.0881336778,-0.4390097857,0.2991521358,0.024567008,0.1481760442,0.3179356754,0.1261687875,0.0431302711,-0.1087724641,0.322976619,-0.1644598693,-0.2167707831,0.2085405439,-0.1286997497,-0.1363783926,0.0606045537,0.0922262222,0.0181185957,0.3068186343,-0.3468239307,-0.1716689467,-0.288708955,0.0851617008,0.0487040281,0.045338653,0.3745139539,-0.0252406895,-0.0478086956,-0.0154314023,0.2031715065,-0.0855525956,0.1397381723,0.1278606057,-0.0333208628,0.3332683444,-0.0292576682,0.388877511,0.3233383,-0.0658911616,0.309296608,-0.3051552176,0.4001675546,-0.1866798103,0.036472857,-0.0887251794,0.0373310857,-0.0029517757,0.2814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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3738","title":"For data-only datasets, streaming and non-streaming don't behave the same","comments":"i think requiring the user to specify that those two files are different configurations is in that case perfectly reasonable.\r\n\r\n(Maybe at some point we could however detect this type of case and prompt them to define a config mapping etc)","body":"See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n```","comment_length":41,"text":"For data-only datasets, streaming and non-streaming don't behave the same \n See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n``` \n i think requiring the user to specify that those two files are different configurations is in that case perfectly reasonable.\r\n\r\n(Maybe at some point we could however detect this type of case and prompt them to define a config mapping etc)","embeddings":[-0.2233444899,-0.4214344025,-0.0021985066,0.1339172572,0.2301208824,-0.0035511507,0.2808357775,0.1670944542,0.0998134986,0.2093741298,-0.2885890305,0.1102919579,-0.134337604,0.2366630733,-0.0533177964,-0.2150022686,-0.0486763902,0.2093241811,-0.2974537611,0.0923037678,-0.0150778266,0.1008050665,0.0148498956,-0.1711389869,-0.0644591376,0.0812812075,0.3112645447,-0.1727838516,0.0719248354,-0.2626925707,0.2579387724,0.1870133728,0.0134156533,0.4857348502,-0.0001140483,0.3494027853,0.1203366518,-0.24823834,-0.292308569,-0.2568771243,-0.1734357774,0.186154604,0.2433245629,-0.2470901012,-0.4046140909,-0.1318022758,0.0890057683,-0.393643111,0.6443093419,0.0630068555,0.1556930691,0.7103543878,-0.2697224021,0.0305283591,0.0546427183,0.2719465196,-0.316255182,0.2769480348,0.0577504411,0.3399302065,0.0659920871,0.4977875352,-0.1718471944,-0.0012798178,0.3757724464,0.0779244155,-0.1816138029,-0.2621951401,0.0787375495,0.7078379989,0.3204835951,-0.1622799784,-0.3552647531,-0.5171586275,-0.0036760522,-0.1877159327,0.1801579446,0.2560243607,-0.2569729984,0.3108401895,-0.3341355026,0.0131141702,-0.0346930362,-0.0515208393,-0.2428777516,0.3296746016,-0.0066503263,0.1443000138,-0.0998986289,-0.2065779269,-0.0923952386,-0.3043286204,0.0266736895,-0.1119085923,-0.3613395095,-0.2805916667,-0.0295421369,-0.2571385801,-0.0897052884,0.1564699411,0.4581819475,0.0564380102,-0.0106025897,0.1604639143,0.1385127753,0.0659664795,0.0640547946,-0.044842463,0.3021835089,-0.0085794125,0.0675042123,-0.1044121534,0.4839114249,-0.0476918966,-0.0754826963,-0.3381004333,0.4743090272,-0.0716399997,-0.3297521472,-0.0686332881,-0.3235562146,0.0057913205,0.1588217467,0.2853727937,-0.0002027233,0.0196432527,0.0095635336,0.3699380457,0.0088274358,0.0721587986,-0.1508601606,-0.1577475816,0.0697180703,0.1356395632,0.1870667338,-0.4004435539,0.3187720776,0.0437930748,0.0831924081,0.2587075233,0.0757673979,-0.0013553961,0.2292717248,0.5308381915,0.1751175076,0.1515413672,0.2264197618,-0.249749884,-0.3881147504,0.0322919935,0.0001293128,-0.1751824468,0.320258528,0.1938901395,-0.3001348376,-0.1503284276,-0.2139661461,0.225260675,-0.0942801312,-0.3379848301,0.1302672923,0.0201160107,-0.0779615268,-0.2426917702,-0.0492095537,0.2737179101,-0.1224832237,-0.3772878349,0.4058139026,-0.1283729076,0.0790349841,0.4491136372,-0.4801844656,-0.096743919,-0.0575956404,0.0891516805,0.4695510864,-0.2234834582,-0.1699417681,0.3957287669,-0.2613094747,0.5019287467,0.5931545496,0.1157466397,0.2998907864,-0.2311172783,0.0885314345,0.0993356556,0.1891452521,0.1826224029,-0.0103472415,-0.3363390863,0.1451185942,0.0053771641,-0.1126777232,0.1585474014,-0.028075831,0.2152702808,0.2873319089,-0.1937039942,0.0725756213,0.1046166196,0.0392703228,0.0914306715,-0.2542392612,-0.22053276,-0.4814638197,0.2149932086,0.2357083708,0.1400369406,0.0296995118,-0.464717567,0.0089968536,-0.257266134,-0.1318085939,-0.1140838116,0.1487285346,0.1871564239,0.1608484089,-0.1147601455,-0.3443019986,0.1959440261,-0.1698569506,0.1663301885,-0.4277072549,0.1092417389,0.0479908772,-0.0323615298,-0.0679692402,-0.1699734032,-0.0197880268,-0.069106631,0.1015127748,0.3154256344,-0.0605662502,0.2407154739,-0.2765024006,0.5690934658,0.2229238898,-0.0733589306,-0.0122297397,0.395280093,0.1093034893,-0.042492047,-0.322866559,0.6292223334,-0.0549246334,0.266956985,0.2618725598,-0.1065781564,0.300725311,0.1250817031,-0.6318336129,-0.2399519831,-0.0238251965,-0.1484910995,0.0051317788,0.1287684441,-0.7282102704,-0.1167424172,0.4667280018,0.1132080033,0.0433376953,-0.1175897866,-0.2033468783,0.0880210325,0.0098267635,0.2119669616,0.6163747311,0.3373116255,0.2256931961,0.1405231804,-0.0855836794,-0.3304407895,0.2760782838,0.0465020575,0.0341564231,0.077802144,-0.0360328145,-0.0366829596,-0.4618175924,-0.1227256358,0.0495085157,-0.203084603,-0.4038203359,0.0349425413,-0.2809727788,-0.264908433,-0.5144850612,-0.4316544831,-0.213835761,-0.2425701022,0.1333639324,0.4218397439,-0.2024148703,0.2690264285,-0.014870774,0.1326134503,-0.212408796,-0.4550639987,-0.2142619938,-0.0925905257,0.0106433155,0.0565816984,0.0858507752,-0.2404251248,0.3347844183,-0.1648717821,-0.3064383864,-0.3532366157,-0.3113577366,0.2051136792,-0.1830603033,-0.0296692066,0.1357299089,0.445258528,0.4038859308,-0.2956257463,0.260356307,0.1029172465,-0.0334229693,0.0809658691,0.0746112615,0.0205763169,-0.1932881325,-0.2945740223,-0.0463340394,-0.3966225088,0.2930373549,-0.0878909528,0.1034263968,0.3333612382,0.0703628287,-0.0248072259,-0.1085736528,0.3241487443,-0.1844288707,-0.1565229744,0.3420799077,-0.1564460993,-0.1999052912,0.1084253415,0.2732634842,0.0875594467,0.2661105692,-0.2692555189,-0.190672949,-0.3617763519,0.0657585636,0.0011339491,0.0094710877,0.3337347806,0.0162861217,-0.0858311579,-0.0606516972,-0.0483641848,0.0096079838,0.1152011603,0.1574111134,-0.034230385,0.3647552431,-0.0302230902,0.5202605724,0.2624782324,-0.1141642034,0.2729769051,-0.2735076547,0.4291053116,-0.2663354576,-0.0560796484,-0.0988261998,0.1617914289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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3738","title":"For data-only datasets, streaming and non-streaming don't behave the same","comments":"OK, so, before closing the issue, what do you think should be done?\r\n\r\n> Maybe in streaming mode we should keep the schema (inferred from the first item) and throw an exception if a subsequent item does not conform to the inferred schema?\r\n\r\nor nothing?","body":"See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n```","comment_length":45,"text":"For data-only datasets, streaming and non-streaming don't behave the same \n See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n``` \n OK, so, before closing the issue, what do you think should be done?\r\n\r\n> Maybe in streaming mode we should keep the schema (inferred from the first item) and throw an exception if a subsequent item does not conform to the inferred schema?\r\n\r\nor nothing?","embeddings":[-0.1384043097,-0.4757501781,0.0538570173,0.2831660807,0.3996578455,0.011318367,0.0607538223,0.2039568275,0.0111170504,0.0105743734,-0.1794573963,0.1295819432,-0.2606135011,0.3045921624,-0.0265068561,-0.315857321,0.1480508596,0.0406329371,-0.2192368656,-0.0878200307,-0.0394786671,0.0910753086,-0.0791350305,-0.2313048244,-0.0387764238,0.0049671452,0.1821740866,-0.1045945287,0.1114064753,-0.2526325285,0.1560154557,0.0111595998,0.1723188162,0.6449152231,-0.0001166101,0.1720432937,0.239015907,-0.2452839911,-0.1525820047,-0.2940411866,-0.2368701249,0.1929946542,0.089620024,-0.1756906956,-0.2489248663,-0.1791533679,-0.07038147,-0.3795278072,0.4697889388,0.1450497061,0.157779485,0.53310287,-0.1480898857,0.0737177804,0.0648686588,0.1707909554,-0.1961707473,0.2670665085,0.0107471338,0.4634708166,0.1574567854,0.4963762462,-0.2784911394,-0.0616097078,0.3872331083,-0.0059145093,-0.2312195301,-0.2153811604,0.0852113217,0.5853293538,0.3723184168,-0.2653102279,-0.455850035,-0.363478899,0.0001444591,-0.2805524468,0.090734832,0.227043733,-0.0900170133,0.2664522231,-0.3730814457,-0.0070202756,-0.1562043428,0.0257311258,-0.2012628615,0.4241470098,-0.0323982611,0.0980253145,-0.2046120316,-0.1268505752,0.0624218993,-0.3583699167,-0.1186869368,-0.0361497439,-0.3660165966,-0.1641637087,0.0364807732,-0.0721646473,0.0980826467,0.1569355577,0.3908222616,0.1253406703,0.103219822,0.1354748309,0.2480282485,-0.0193738174,0.0794759244,-0.2062425464,0.2347849905,0.1678290963,-0.0364889912,-0.1581432521,0.3737772703,0.2563280165,-0.0319133103,-0.2835431695,0.5091555715,-0.092363812,-0.2972975671,0.0661675707,-0.4334412217,0.0598022155,0.1989015937,0.3959702253,-0.032011468,0.2635872364,-0.0375319421,0.4147938192,0.1436358392,-0.0583584532,-0.248323366,-0.1523912996,0.1305465102,0.0656387731,0.0656205416,-0.3326264322,0.2727421224,0.0368952081,-0.0413580239,0.008802996,-0.0513650142,-0.1158857644,0.1237192303,0.4952463508,0.0597333051,0.188842088,0.1750625819,-0.2029357404,-0.1999236196,0.0261147916,0.0171522405,-0.18759045,0.2937581241,0.2057072967,-0.3395512402,-0.1309090704,-0.2683582306,0.2002242953,-0.1184669808,-0.3252471089,0.1017666608,0.0690891147,-0.0170334745,-0.213491112,0.0865111202,0.2236722559,-0.1875329167,-0.1680973023,0.3439197242,-0.0575879924,0.2115917802,0.4903813004,-0.2222221345,-0.0055594188,-0.1617922783,0.0901486948,0.2683251202,-0.1665117592,-0.255740881,0.4413866699,-0.0797711164,0.4888782501,0.4631508291,0.1008192748,0.3615371287,-0.1864865571,0.0535750091,0.2137326449,-0.1209115237,0.1937320828,-0.1662084162,-0.2514895797,0.1576710045,-0.0051059541,-0.1342413276,0.2109761238,-0.0550853275,0.2924557328,0.3326236308,-0.1482198387,0.020922849,0.1659037471,0.1744710952,0.0146670733,-0.1589974016,-0.3718556464,-0.6552301645,0.1502920836,0.3122111559,-0.0102286292,-0.1320592165,-0.4446816146,-0.1374317706,-0.1001189947,-0.2082691193,-0.0232667197,0.1577446908,0.1339889765,0.0564014837,-0.0211272761,-0.2195064276,0.1453534663,-0.1472361237,0.1839986742,-0.4333924949,0.2262506783,0.0856888518,-0.2298184335,-0.0883730352,-0.0675660521,0.0621819906,-0.1665123403,-0.0200576615,0.39148435,-0.10875801,0.4678963125,-0.3575282395,0.4541223347,0.2068493962,-0.1654783189,-0.1530075967,0.4843896329,0.2628300786,-0.1075013578,-0.4356392324,0.4680840373,-0.124163352,0.312068522,0.1955604255,-0.0529448725,0.347807467,0.1101052016,-0.562784493,-0.135911122,0.1150519475,-0.2120832354,-0.0361356661,0.0177273974,-0.6258201599,-0.0674272925,0.5084958673,-0.0499413125,0.102793552,-0.0867765471,-0.293030113,0.2070996612,0.2730525434,0.2333793044,0.4691301882,0.2374659181,0.2312435061,0.2272900343,0.0074687451,-0.3010634184,0.2865703702,0.268476516,0.1270989329,0.2459010929,0.0946770161,-0.0089465976,-0.406062454,-0.0909177437,0.024968043,0.0062700696,-0.3920211792,0.1626303792,-0.286470741,-0.1533374339,-0.4370515943,-0.3609807491,-0.3027062118,-0.2793719769,-0.0920124501,0.5347334743,-0.3396762908,0.1333919019,-0.0283348579,-0.0619236156,-0.09519694,-0.361523658,-0.357324183,-0.150213033,0.1324289292,-0.0039817002,0.1512797028,-0.0791263208,0.3454875946,-0.2117743641,-0.243947342,-0.1492229253,-0.346211642,0.215366438,-0.2426479459,-0.0200150125,0.114805676,0.4525537193,0.2756755948,-0.3828173578,0.2423650771,-0.1443707496,-0.0147035336,0.3436447382,0.1136183962,0.1243655756,-0.1157789379,-0.3505538106,-0.0652959645,-0.4942249358,0.304843545,-0.1510709822,0.0556184575,0.3016626239,0.0113258976,0.0820855498,-0.1336285621,0.2981750965,-0.1424662769,-0.1771238744,0.2772522867,-0.0260187797,-0.2704297602,0.1601638794,0.0643045679,0.1410566568,0.2269678265,-0.3162599802,-0.1364210695,-0.1527213305,-0.0019172631,0.0658750758,-0.0907379612,0.4019165337,-0.0173831992,-0.0031701124,-0.1127659976,0.1024564207,-0.0645992607,0.0949962661,0.267010361,0.101451546,0.6615737081,0.0180401765,0.4182753563,0.345218569,0.0452565625,0.3259258568,-0.0673598945,0.207765609,-0.1190987602,-0.1323026717,-0.0986136645,-0.0440654643,-0.121178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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3738","title":"For data-only datasets, streaming and non-streaming don't behave the same","comments":"We should at least raise an error if a new sample has column names that are missing, or if it has extra columns. No need to check for the type for now.\r\n\r\nI'm in favor of having an error especially because we want to avoid silent issues as much as possible - i.e. when something goes wrong (when schemas don't match or some data are missing) and no errors\/warnings are raised.\r\n\r\nConsistency between streaming and non-streaming is also important.","body":"See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n```","comment_length":79,"text":"For data-only datasets, streaming and non-streaming don't behave the same \n See https:\/\/huggingface.co\/datasets\/huggingface\/transformers-metadata: it only contains two JSON files.\r\n\r\nIn streaming mode, the files are concatenated, and thus the rows might be dictionaries with different keys:\r\n\r\n```python\r\nimport datasets as ds\r\niterable_dataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\", streaming=True);\r\nrows = list(iterable_dataset.take(100))\r\nrows[0]\r\n# {'model_type': 'albert', 'pytorch': True, 'tensorflow': True, 'flax': True, 'processor': 'AutoTokenizer'}\r\nrows[99]\r\n# {'model_class': 'BartModel', 'pipeline_tag': 'feature-extraction', 'auto_class': 'AutoModel'}\r\n```\r\n\r\nIn normal mode, an exception is thrown:\r\n\r\n```python\r\nimport datasets as ds\r\ndataset = ds.load_dataset(\"huggingface\/transformers-metadata\", split=\"train\");\r\n```\r\n\r\n```\r\nValueError: Couldn't cast\r\nmodel_class: string\r\npipeline_tag: string\r\nauto_class: string\r\nto\r\n{'model_type': Value(dtype='string', id=None), 'pytorch': Value(dtype='bool', id=None), 'tensorflow': Value(dtype='bool', id=None), 'flax': Value(dtype='bool', id=None), 'processor': Value(dtype='string', id=None)}\r\nbecause column names don't match\r\n``` \n We should at least raise an error if a new sample has column names that are missing, or if it has extra columns. No need to check for the type for now.\r\n\r\nI'm in favor of having an error especially because we want to avoid silent issues as much as possible - i.e. when something goes wrong (when schemas don't match or some data are missing) and no errors\/warnings are raised.\r\n\r\nConsistency between streaming and non-streaming is also important.","embeddings":[-0.1809254438,-0.5316244364,0.0316508971,0.0601782575,0.3263519406,0.0084531,0.1868890375,0.2202479392,0.0926466286,0.2087869495,-0.1634202152,0.0506506637,-0.2015397251,0.2456848472,0.00248496,-0.3584954441,0.1400905102,0.1418414414,-0.2897824049,-0.1043144614,-0.0991665497,0.1500131041,-0.0996572971,-0.2278494388,-0.1240105554,0.1046606824,0.0549462996,-0.1187525764,0.0941162035,-0.2843291759,0.1743442714,0.0110145984,0.1439231336,0.5653604269,-0.0001124764,0.2100822031,0.332670629,-0.2171497494,-0.2414312363,-0.2853049338,-0.089140296,-0.000679109,0.1708539277,-0.2200297862,-0.302431941,-0.1820758581,0.0683061257,-0.3380937576,0.4719050825,0.1834677458,0.1546743661,0.7683611512,-0.1194892228,-0.0654175133,0.0043026162,0.2759346962,-0.2086884826,0.1730810404,-0.0528462082,0.4428835809,0.0624860451,0.4583586156,-0.1876227558,0.042534411,0.2273248881,0.0870983005,-0.2493346035,-0.2186230868,0.0255597457,0.7112854123,0.4405924082,-0.2733048797,-0.4906795323,-0.4351958632,-0.0027571328,-0.2412810028,0.2228451073,0.1724825948,-0.032311786,0.1935937107,-0.2959655523,0.1439522803,-0.049766507,-0.0339107662,-0.1086932942,0.3696992397,-0.0117507242,0.08924146,-0.1619715989,-0.0738065317,-0.0763138086,-0.3120171428,-0.0202116501,0.0216209851,-0.3522692919,-0.3687588871,-0.0130682448,-0.2255485803,0.0162821226,0.1780807078,0.3441541195,0.0041321139,0.1229706034,0.1702754647,0.1917194575,-0.085538812,-0.005185883,-0.1060075089,0.3469257653,-0.0023193769,0.1454007477,-0.20573695,0.3595810831,0.2549842894,0.0143059446,-0.1758189499,0.465121001,-0.1359065026,-0.2297186553,-0.0665329993,-0.2516765594,-0.0061476734,0.1033188179,0.3582749665,-0.1015755758,0.2497339249,-0.1106884554,0.3302401304,0.0066350908,-0.1575054675,-0.2702985108,-0.1359008551,0.119320333,0.0688028708,0.1707773954,-0.2567985654,0.3642003536,0.0212642383,-0.0249249879,0.2668775618,-0.0192116164,-0.0825823843,0.1678825766,0.6142479777,0.1086318418,0.1345243752,0.1604276001,-0.0728851929,-0.2917256355,-0.000315882,0.0687761977,-0.2879528105,0.1081879586,0.2175955027,-0.2916425765,-0.1755245477,-0.2418343276,0.2868228257,-0.0845408291,-0.4276534319,0.1285772622,0.0665956885,0.0450933166,-0.2367299646,0.0642180741,0.2498354316,-0.0903388858,-0.3491172493,0.2711890042,-0.1556811631,0.2300503105,0.4288411736,-0.2143104821,-0.1565318704,-0.1237404794,0.107480064,0.2705134749,-0.1940407902,-0.1048836932,0.3202501237,0.0353840292,0.3805830777,0.5008402467,0.1066312119,0.3570744097,-0.2333212346,-0.0313909352,0.1873152703,-0.0679953545,0.2008288205,-0.1010952219,-0.1705612242,0.0730369911,-0.0168960672,-0.103701435,0.1658029407,-0.0346838236,0.270749867,0.1679513752,-0.1953003556,-0.1120365039,0.0686326921,0.2227205038,0.0530154221,-0.1274278611,-0.1860773265,-0.6895400882,0.1518601626,0.2706539631,0.0681749284,0.0211604945,-0.3538506329,0.0108141378,-0.1593010575,-0.1088307351,-0.019957874,0.1349103898,0.1542232633,0.2531899512,0.054344248,-0.3653137982,0.1085956246,-0.2718577683,0.2544926107,-0.4513849616,0.2082997859,0.1270127147,-0.2515734732,-0.0393429622,-0.0920103714,0.0192950424,-0.0756306946,-0.0185305867,0.4355844259,-0.0529905669,0.3708617091,-0.3376258612,0.5024017692,0.2975319326,-0.1018832996,-0.0988905355,0.4604573548,0.1362500638,-0.036076244,-0.3573494852,0.4440053701,-0.0385405049,0.164325729,0.2139505446,-0.1646823734,0.3277751207,0.1552814245,-0.6544130445,-0.1415665299,0.014559743,-0.1600479186,0.017597612,-0.0359999985,-0.6513873935,-0.2358172536,0.5664861202,-0.1466791332,0.1615258902,-0.073614575,-0.3359392583,0.0786063895,0.2073836178,0.2439175546,0.5239241123,0.2860292494,0.2210257351,0.0833529979,-0.1684782803,-0.338260293,0.1921916455,0.14827393,0.0064193429,0.12321724,0.003984875,0.0513013639,-0.573407352,-0.0654829219,-0.0676363856,-0.024994975,-0.3253073692,-0.0019202528,-0.1464582682,-0.1565923095,-0.3758758008,-0.3173003793,-0.2045220733,-0.35354352,0.0600942299,0.4066990316,-0.3574149907,0.1820973903,0.0108153578,0.0417943113,-0.1494998187,-0.3323454559,-0.3616426587,-0.0899482891,0.0060116006,0.0653043091,-0.0267355517,-0.1624450535,0.3940886259,-0.2592197359,-0.168600738,-0.1509453356,-0.4553832114,0.1833593845,-0.2325514704,-0.1021052673,0.1635888517,0.3673364222,0.4110051095,-0.3287941217,0.2939399481,-0.2167088091,-0.1160314009,0.301726073,0.0567394234,0.1071306989,-0.2175086737,-0.3035912812,-0.0908851773,-0.4036374986,0.1903245896,-0.1907985806,-0.0125002358,0.3122229576,0.0654907301,0.0617477,-0.1447427869,0.3119392991,-0.2076838613,-0.204869017,0.3407564759,-0.091923587,-0.2838462293,0.1660424471,0.0524718985,0.1205389872,0.1091195568,-0.3190398216,-0.2355264276,-0.2528862655,0.0394021906,0.1928677261,0.0237987936,0.3759891391,-0.0302170832,-0.1088920236,-0.1131593287,0.0867637172,-0.1315190196,0.1397638172,0.2134161592,-0.0108424602,0.5859719515,-0.0533883609,0.4006889462,0.3169788122,-0.2010911703,0.2741609514,-0.1745304763,0.409181416,-0.1664254665,-0.000614851,-0.0377880707,-0.0753729939,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3735","title":"Performance of `datasets` at scale","comments":"The most surprising part to me is the saving time. Wondering if it could be due to compression (`ParquetWriter` uses SNAPPY  compression by default; it can be turned off with `to_parquet(..., compression=None)`).   ","body":"# Performance of `datasets` at 1TB scale\r\n\r\n## What is this?\r\nDuring the processing of a large dataset I monitored the performance of the `datasets` library to see if there are any bottlenecks. The insights of this analysis could guide the decision making to improve the performance of the library.\r\n\r\n## Dataset\r\nThe dataset is a 1.1TB extract from GitHub with 120M code files and is stored as 5000 `.json.gz` files. The goal of the preprocessing is to remove duplicates and filter files based on their stats. While the calculating of the hashes for deduplication and stats for filtering can be parallelized the filtering itself is run with a single process. After processing the files are pushed to the hub.\r\n\r\n## Machine\r\nThe experiment was run on a `m1` machine on GCP with 96 CPU cores and 1.3TB RAM.\r\n\r\n## Performance breakdown\r\n- Loading the data **3.5h** (_30sec_ from cache)\r\n    - **1h57min** single core loading (not sure what is going on here, corresponds to second progress bar)\r\n    - **1h10min** multi core json reading\r\n    - **20min** remaining time before and after the two main processes mentioned above\r\n- Process the data **2h** (_20min_ from cache)\r\n    - **20min** Getting reading for processing \r\n    - **40min** Hashing and files stats (96 workers)\r\n    - **58min** Deduplication filtering (single worker)\r\n- Save parquet files **5h**\r\n    -  Saving 1000 parquet files (16 workers)\r\n- Push to hub **37min**\r\n    - **34min** git add\r\n    - **3min** git push (several hours with `Repository.git_push()`)\r\n\r\n## Conclusion\r\nIt appears that loading and saving the data is the main bottleneck at that scale (**8.5h**) whereas processing (**2h**) and pushing the data to the hub (**0.5h**) is relatively fast. To optimize the performance at this scale it would make sense to consider such an end-to-end example and target the bottlenecks which seem to be loading from and saving to disk. The processing itself seems to run relatively fast.\r\n\r\n## Notes\r\n- map operation on a 1TB dataset with 96 workers requires >1TB RAM\r\n- map operation does not maintain 100% CPU utilization with 96 workers\r\n- sometimes when the script crashes all the data files have a corresponding `*.lock` file in the data folder (or multiple e.g. `*.lock.lock` when it happened a several times). This causes the cache **not** to be triggered (which is significant at that scale) - i guess because there are new data files\r\n- parallelizing `to_parquet` decreased the saving time from 17h to 5h, however adding more workers at this point had almost no effect. not sure if this is:\r\n    a) a bug in my parallelization logic, \r\n    b) i\/o limit to load data form disk to memory or \r\n    c) i\/o limit to write from memory to disk.\r\n- Using `Repository.git_push()` was much slower than using command line `git-lfs` - 10-20MB\/s vs. 300MB\/s! The `Dataset.push_to_hub()` function is even slower as it only uploads one file at a time with only a few MB\/s, whereas `Repository.git_push()` pushes files in parallel (each at a similar speed).\r\n\r\ncc @lhoestq @julien-c @LysandreJik @SBrandeis\r\n","comment_length":32,"text":"Performance of `datasets` at scale \n # Performance of `datasets` at 1TB scale\r\n\r\n## What is this?\r\nDuring the processing of a large dataset I monitored the performance of the `datasets` library to see if there are any bottlenecks. The insights of this analysis could guide the decision making to improve the performance of the library.\r\n\r\n## Dataset\r\nThe dataset is a 1.1TB extract from GitHub with 120M code files and is stored as 5000 `.json.gz` files. The goal of the preprocessing is to remove duplicates and filter files based on their stats. While the calculating of the hashes for deduplication and stats for filtering can be parallelized the filtering itself is run with a single process. After processing the files are pushed to the hub.\r\n\r\n## Machine\r\nThe experiment was run on a `m1` machine on GCP with 96 CPU cores and 1.3TB RAM.\r\n\r\n## Performance breakdown\r\n- Loading the data **3.5h** (_30sec_ from cache)\r\n    - **1h57min** single core loading (not sure what is going on here, corresponds to second progress bar)\r\n    - **1h10min** multi core json reading\r\n    - **20min** remaining time before and after the two main processes mentioned above\r\n- Process the data **2h** (_20min_ from cache)\r\n    - **20min** Getting reading for processing \r\n    - **40min** Hashing and files stats (96 workers)\r\n    - **58min** Deduplication filtering (single worker)\r\n- Save parquet files **5h**\r\n    -  Saving 1000 parquet files (16 workers)\r\n- Push to hub **37min**\r\n    - **34min** git add\r\n    - **3min** git push (several hours with `Repository.git_push()`)\r\n\r\n## Conclusion\r\nIt appears that loading and saving the data is the main bottleneck at that scale (**8.5h**) whereas processing (**2h**) and pushing the data to the hub (**0.5h**) is relatively fast. To optimize the performance at this scale it would make sense to consider such an end-to-end example and target the bottlenecks which seem to be loading from and saving to disk. The processing itself seems to run relatively fast.\r\n\r\n## Notes\r\n- map operation on a 1TB dataset with 96 workers requires >1TB RAM\r\n- map operation does not maintain 100% CPU utilization with 96 workers\r\n- sometimes when the script crashes all the data files have a corresponding `*.lock` file in the data folder (or multiple e.g. `*.lock.lock` when it happened a several times). This causes the cache **not** to be triggered (which is significant at that scale) - i guess because there are new data files\r\n- parallelizing `to_parquet` decreased the saving time from 17h to 5h, however adding more workers at this point had almost no effect. not sure if this is:\r\n    a) a bug in my parallelization logic, \r\n    b) i\/o limit to load data form disk to memory or \r\n    c) i\/o limit to write from memory to disk.\r\n- Using `Repository.git_push()` was much slower than using command line `git-lfs` - 10-20MB\/s vs. 300MB\/s! The `Dataset.push_to_hub()` function is even slower as it only uploads one file at a time with only a few MB\/s, whereas `Repository.git_push()` pushes files in parallel (each at a similar speed).\r\n\r\ncc @lhoestq @julien-c @LysandreJik @SBrandeis\r\n \n The most surprising part to me is the saving time. Wondering if it could be due to compression (`ParquetWriter` uses SNAPPY  compression by default; it can be turned off with `to_parquet(..., compression=None)`).   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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3735","title":"Performance of `datasets` at scale","comments":"+1 to what @mariosasko mentioned. Also, @lvwerra did you parallelize `to_parquet` using similar approach in #2747? (we used multiprocessing at the shard level). I'm working on a similar PR to add multi_proc in `to_parquet` which might give you further speed up. \r\nStas benchmarked his approach and mine in this [gist](https:\/\/gist.github.com\/stas00\/dc1597a1e245c5915cfeefa0eee6902c) for `lama` dataset when we were working on adding multi_proc support for `to_json`.","body":"# Performance of `datasets` at 1TB scale\r\n\r\n## What is this?\r\nDuring the processing of a large dataset I monitored the performance of the `datasets` library to see if there are any bottlenecks. The insights of this analysis could guide the decision making to improve the performance of the library.\r\n\r\n## Dataset\r\nThe dataset is a 1.1TB extract from GitHub with 120M code files and is stored as 5000 `.json.gz` files. The goal of the preprocessing is to remove duplicates and filter files based on their stats. While the calculating of the hashes for deduplication and stats for filtering can be parallelized the filtering itself is run with a single process. After processing the files are pushed to the hub.\r\n\r\n## Machine\r\nThe experiment was run on a `m1` machine on GCP with 96 CPU cores and 1.3TB RAM.\r\n\r\n## Performance breakdown\r\n- Loading the data **3.5h** (_30sec_ from cache)\r\n    - **1h57min** single core loading (not sure what is going on here, corresponds to second progress bar)\r\n    - **1h10min** multi core json reading\r\n    - **20min** remaining time before and after the two main processes mentioned above\r\n- Process the data **2h** (_20min_ from cache)\r\n    - **20min** Getting reading for processing \r\n    - **40min** Hashing and files stats (96 workers)\r\n    - **58min** Deduplication filtering (single worker)\r\n- Save parquet files **5h**\r\n    -  Saving 1000 parquet files (16 workers)\r\n- Push to hub **37min**\r\n    - **34min** git add\r\n    - **3min** git push (several hours with `Repository.git_push()`)\r\n\r\n## Conclusion\r\nIt appears that loading and saving the data is the main bottleneck at that scale (**8.5h**) whereas processing (**2h**) and pushing the data to the hub (**0.5h**) is relatively fast. To optimize the performance at this scale it would make sense to consider such an end-to-end example and target the bottlenecks which seem to be loading from and saving to disk. The processing itself seems to run relatively fast.\r\n\r\n## Notes\r\n- map operation on a 1TB dataset with 96 workers requires >1TB RAM\r\n- map operation does not maintain 100% CPU utilization with 96 workers\r\n- sometimes when the script crashes all the data files have a corresponding `*.lock` file in the data folder (or multiple e.g. `*.lock.lock` when it happened a several times). This causes the cache **not** to be triggered (which is significant at that scale) - i guess because there are new data files\r\n- parallelizing `to_parquet` decreased the saving time from 17h to 5h, however adding more workers at this point had almost no effect. not sure if this is:\r\n    a) a bug in my parallelization logic, \r\n    b) i\/o limit to load data form disk to memory or \r\n    c) i\/o limit to write from memory to disk.\r\n- Using `Repository.git_push()` was much slower than using command line `git-lfs` - 10-20MB\/s vs. 300MB\/s! The `Dataset.push_to_hub()` function is even slower as it only uploads one file at a time with only a few MB\/s, whereas `Repository.git_push()` pushes files in parallel (each at a similar speed).\r\n\r\ncc @lhoestq @julien-c @LysandreJik @SBrandeis\r\n","comment_length":63,"text":"Performance of `datasets` at scale \n # Performance of `datasets` at 1TB scale\r\n\r\n## What is this?\r\nDuring the processing of a large dataset I monitored the performance of the `datasets` library to see if there are any bottlenecks. The insights of this analysis could guide the decision making to improve the performance of the library.\r\n\r\n## Dataset\r\nThe dataset is a 1.1TB extract from GitHub with 120M code files and is stored as 5000 `.json.gz` files. The goal of the preprocessing is to remove duplicates and filter files based on their stats. While the calculating of the hashes for deduplication and stats for filtering can be parallelized the filtering itself is run with a single process. After processing the files are pushed to the hub.\r\n\r\n## Machine\r\nThe experiment was run on a `m1` machine on GCP with 96 CPU cores and 1.3TB RAM.\r\n\r\n## Performance breakdown\r\n- Loading the data **3.5h** (_30sec_ from cache)\r\n    - **1h57min** single core loading (not sure what is going on here, corresponds to second progress bar)\r\n    - **1h10min** multi core json reading\r\n    - **20min** remaining time before and after the two main processes mentioned above\r\n- Process the data **2h** (_20min_ from cache)\r\n    - **20min** Getting reading for processing \r\n    - **40min** Hashing and files stats (96 workers)\r\n    - **58min** Deduplication filtering (single worker)\r\n- Save parquet files **5h**\r\n    -  Saving 1000 parquet files (16 workers)\r\n- Push to hub **37min**\r\n    - **34min** git add\r\n    - **3min** git push (several hours with `Repository.git_push()`)\r\n\r\n## Conclusion\r\nIt appears that loading and saving the data is the main bottleneck at that scale (**8.5h**) whereas processing (**2h**) and pushing the data to the hub (**0.5h**) is relatively fast. To optimize the performance at this scale it would make sense to consider such an end-to-end example and target the bottlenecks which seem to be loading from and saving to disk. The processing itself seems to run relatively fast.\r\n\r\n## Notes\r\n- map operation on a 1TB dataset with 96 workers requires >1TB RAM\r\n- map operation does not maintain 100% CPU utilization with 96 workers\r\n- sometimes when the script crashes all the data files have a corresponding `*.lock` file in the data folder (or multiple e.g. `*.lock.lock` when it happened a several times). This causes the cache **not** to be triggered (which is significant at that scale) - i guess because there are new data files\r\n- parallelizing `to_parquet` decreased the saving time from 17h to 5h, however adding more workers at this point had almost no effect. not sure if this is:\r\n    a) a bug in my parallelization logic, \r\n    b) i\/o limit to load data form disk to memory or \r\n    c) i\/o limit to write from memory to disk.\r\n- Using `Repository.git_push()` was much slower than using command line `git-lfs` - 10-20MB\/s vs. 300MB\/s! The `Dataset.push_to_hub()` function is even slower as it only uploads one file at a time with only a few MB\/s, whereas `Repository.git_push()` pushes files in parallel (each at a similar speed).\r\n\r\ncc @lhoestq @julien-c @LysandreJik @SBrandeis\r\n \n +1 to what @mariosasko mentioned. Also, @lvwerra did you parallelize `to_parquet` using similar approach in #2747? (we used multiprocessing at the shard level). I'm working on a similar PR to add multi_proc in `to_parquet` which might give you further speed up. \r\nStas benchmarked his approach and mine in this [gist](https:\/\/gist.github.com\/stas00\/dc1597a1e245c5915cfeefa0eee6902c) for `lama` dataset when we were working on adding multi_proc support for `to_json`.","embeddings":[-0.4659070969,-0.0334514417,-0.1042618528,0.1815132946,0.09017279,0.0411181934,0.1751108766,0.3004228473,0.1423732936,-0.020190781,0.0761042908,0.1921414435,-0.0361627303,0.5749627352,-0.0226485524,-0.0665424913,-0.024128383,0.00486258,-0.2170311362,-0.0295536667,0.1303034127,0.0838657022,0.0580883734,-0.1897372007,-0.1032667011,-0.4335455,-0.0547460876,0.1031981483,-0.093588233,-0.1723837554,0.0365678854,0.0232662838,0.0603459477,0.5355906487,-0.0001064028,-0.0037981691,0.0693409592,-0.017813405,-0.3276855648,-0.0306420363,-0.0807494149,-0.3819633722,-0.1816390157,-0.2334250212,0.1224205419,-0.0380455256,-0.0416168794,-0.7578882575,0.0272068605,-0.1709458232,0.2347729951,-0.066122584,-0.5441614389,-0.0660221353,-0.0510834642,0.0841042846,-0.2505224943,0.1660184562,0.4419589639,0.198574543,-0.2908447087,0.2806810737,0.0501781516,-0.1402428597,0.2424619198,0.0107200872,0.0189891197,-0.1265322566,0.1494351774,0.1591048986,0.4943113029,-0.2639029622,-0.5152831674,-0.5910289288,-0.2188973725,-0.1239592135,-0.0640999228,0.07239943,-0.0242735874,0.03951554,-0.5453306437,-0.3364058435,0.0755238011,-0.1015030965,0.0527676307,-0.1372779608,-0.1122473404,0.1690036207,0.1036126167,0.1288138181,-0.0780985057,-0.5316543579,0.1825611591,0.0113407131,-0.5560039878,-0.1962097287,0.2384477854,0.0797650293,0.1639602929,0.2785152793,0.3226137459,-0.0000825339,0.2032580972,0.0491072014,0.4091487527,0.4878739119,-0.042939648,-0.3635562956,0.3549003601,-0.0826552808,-0.0781438649,0.1740575135,0.1084833145,-0.0235748067,-0.2178195566,-0.0065718079,-0.34451437,-0.15207991,-0.1128423139,0.0765136853,0.2610681951,-0.2637384236,0.0884713307,0.1096983254,-0.0019572303,0.3133580089,-0.1893426925,-0.1612520367,-0.1549726129,0.0153179644,-0.2031328678,0.2025257945,-0.3175885677,0.1466596425,0.3268586695,-0.196361959,0.2054068744,0.2217589766,0.1263010949,0.1356928647,0.2359555364,-0.4846547842,-0.1140155718,0.1967720687,-0.083263427,0.1256706715,0.0401114449,0.4170620143,-0.4650570452,0.1545623839,-0.1673641652,-0.2159665674,0.3965204954,0.1520241052,-0.3574827611,0.0312667973,-0.7180786729,0.5014957786,0.0722377598,0.0608451106,-0.1499890238,-0.0582933165,-0.202276051,-0.2382455319,0.1433064491,0.1004444584,-0.5952253342,0.039811641,-0.0827574432,0.2274797559,0.2801483572,0.5949133635,-0.2786278427,0.3154120445,-0.132608071,0.1690835506,0.2502268255,-0.2021712214,-0.1818022281,0.4072179496,-0.3574754596,0.2338113785,-0.04718538,0.0384914726,0.5381559134,-0.1066406593,0.1942964792,0.3177718818,0.0592700914,0.1060627475,-0.3885086477,-0.2834320068,-0.0630052909,0.1715500653,-0.0240550842,-0.0384415388,-0.091830872,-0.2808524072,0.1350597888,-0.1157413498,0.1523383856,0.1152733788,-0.1168165579,-0.442094177,0.1646611094,0.0767284855,-0.0253387522,0.4415579438,0.0068021943,0.0272431672,0.2186858952,-0.0061798901,-0.1469609737,-0.3828402758,0.1626692712,0.0055914181,0.1083489582,0.0548757985,-0.0770878717,-0.1671313792,-0.3033909202,0.3395930827,-0.0862190947,-0.2440734208,-0.148452282,0.2126960903,0.2101798356,-0.1810200512,0.0671825707,-0.1383659095,-0.2575671077,-0.1958304495,-0.1110695153,0.2122611254,0.0124501064,0.4286311567,0.673610568,0.5321832895,0.0686089844,0.3288456202,0.2076271176,-0.1058699787,0.174623698,-0.0656665042,-0.3201049566,0.5834710598,0.0327148698,0.2420437485,0.2527612746,-0.2054068893,0.0596441887,-0.0177116338,0.0971731246,0.3638671339,0.4805283546,0.1781892627,0.2831573486,0.1749209613,-0.228337571,0.317473352,0.5138953328,-0.158185333,-0.1741689742,0.1986161768,-0.0249111932,-0.3234164417,0.3138000369,-0.0173349436,0.5131072402,0.1840822548,0.2191129923,-0.0053399326,0.0708386526,0.0031996318,0.2711157501,0.2290483266,0.2012622356,0.0647584498,0.22803545,0.0614945292,-0.2042477131,-0.4103016853,-0.0825143531,0.0016986865,-0.2799901664,0.3875601292,0.0128300097,0.1938333809,0.2101430297,0.1622980237,-0.2429269552,-0.1681760252,0.0066168699,0.120685026,-0.1304167062,-0.1741104722,-0.0482589863,0.5270912051,-0.1620855033,-0.2291511297,-0.1468924284,-0.2157183886,-0.1359124631,0.1499738097,0.1895511597,0.1133727133,0.3135982156,0.0937918276,0.0555540547,0.1082075387,-0.3052628934,0.1156726778,-0.2094689608,0.2779457271,-0.0894600973,0.0519102663,-0.4050691128,0.0505255237,-0.2382140011,-0.1042054594,0.0455068648,-0.2716208398,0.0572412685,-0.0281857569,-0.0867699981,-0.3160498738,-0.2466942966,-0.3988619447,0.3420243859,-0.0930549726,0.2090013772,-0.1098384708,-0.1668050885,0.0083634425,0.233869493,0.2750072479,-0.2227626741,-0.7221102118,0.0866067708,-0.0280634854,-0.057057634,-0.2671347558,0.0368492715,0.298640281,0.3308610022,-0.4447726607,0.2482071221,-0.15401344,-0.0340872444,-0.019734066,0.1724804342,0.1778370887,-0.1636390835,-0.2523724735,-0.1536010653,-0.225924626,0.1461166888,0.2001769692,0.0234918222,-0.1889057308,0.1153807789,0.2759388983,0.5526593924,0.0340610668,-0.1499445587,0.1128969714,0.2286046147,-0.0454021432,-0.2618842423,-0.1879077703,0.2005143911,-0.1850178689,-0.3225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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3735","title":"Performance of `datasets` at scale","comments":"@mariosasko I did not turn it off but I can try the next time - I have to run the pipeline again, anyway. \r\n\r\n@bhavitvyamalik Yes, I also sharded the dataset and used multiprocessing to save each shard. I'll have a closer look at your approach, too.","body":"# Performance of `datasets` at 1TB scale\r\n\r\n## What is this?\r\nDuring the processing of a large dataset I monitored the performance of the `datasets` library to see if there are any bottlenecks. The insights of this analysis could guide the decision making to improve the performance of the library.\r\n\r\n## Dataset\r\nThe dataset is a 1.1TB extract from GitHub with 120M code files and is stored as 5000 `.json.gz` files. The goal of the preprocessing is to remove duplicates and filter files based on their stats. While the calculating of the hashes for deduplication and stats for filtering can be parallelized the filtering itself is run with a single process. After processing the files are pushed to the hub.\r\n\r\n## Machine\r\nThe experiment was run on a `m1` machine on GCP with 96 CPU cores and 1.3TB RAM.\r\n\r\n## Performance breakdown\r\n- Loading the data **3.5h** (_30sec_ from cache)\r\n    - **1h57min** single core loading (not sure what is going on here, corresponds to second progress bar)\r\n    - **1h10min** multi core json reading\r\n    - **20min** remaining time before and after the two main processes mentioned above\r\n- Process the data **2h** (_20min_ from cache)\r\n    - **20min** Getting reading for processing \r\n    - **40min** Hashing and files stats (96 workers)\r\n    - **58min** Deduplication filtering (single worker)\r\n- Save parquet files **5h**\r\n    -  Saving 1000 parquet files (16 workers)\r\n- Push to hub **37min**\r\n    - **34min** git add\r\n    - **3min** git push (several hours with `Repository.git_push()`)\r\n\r\n## Conclusion\r\nIt appears that loading and saving the data is the main bottleneck at that scale (**8.5h**) whereas processing (**2h**) and pushing the data to the hub (**0.5h**) is relatively fast. To optimize the performance at this scale it would make sense to consider such an end-to-end example and target the bottlenecks which seem to be loading from and saving to disk. The processing itself seems to run relatively fast.\r\n\r\n## Notes\r\n- map operation on a 1TB dataset with 96 workers requires >1TB RAM\r\n- map operation does not maintain 100% CPU utilization with 96 workers\r\n- sometimes when the script crashes all the data files have a corresponding `*.lock` file in the data folder (or multiple e.g. `*.lock.lock` when it happened a several times). This causes the cache **not** to be triggered (which is significant at that scale) - i guess because there are new data files\r\n- parallelizing `to_parquet` decreased the saving time from 17h to 5h, however adding more workers at this point had almost no effect. not sure if this is:\r\n    a) a bug in my parallelization logic, \r\n    b) i\/o limit to load data form disk to memory or \r\n    c) i\/o limit to write from memory to disk.\r\n- Using `Repository.git_push()` was much slower than using command line `git-lfs` - 10-20MB\/s vs. 300MB\/s! The `Dataset.push_to_hub()` function is even slower as it only uploads one file at a time with only a few MB\/s, whereas `Repository.git_push()` pushes files in parallel (each at a similar speed).\r\n\r\ncc @lhoestq @julien-c @LysandreJik @SBrandeis\r\n","comment_length":46,"text":"Performance of `datasets` at scale \n # Performance of `datasets` at 1TB scale\r\n\r\n## What is this?\r\nDuring the processing of a large dataset I monitored the performance of the `datasets` library to see if there are any bottlenecks. The insights of this analysis could guide the decision making to improve the performance of the library.\r\n\r\n## Dataset\r\nThe dataset is a 1.1TB extract from GitHub with 120M code files and is stored as 5000 `.json.gz` files. The goal of the preprocessing is to remove duplicates and filter files based on their stats. While the calculating of the hashes for deduplication and stats for filtering can be parallelized the filtering itself is run with a single process. After processing the files are pushed to the hub.\r\n\r\n## Machine\r\nThe experiment was run on a `m1` machine on GCP with 96 CPU cores and 1.3TB RAM.\r\n\r\n## Performance breakdown\r\n- Loading the data **3.5h** (_30sec_ from cache)\r\n    - **1h57min** single core loading (not sure what is going on here, corresponds to second progress bar)\r\n    - **1h10min** multi core json reading\r\n    - **20min** remaining time before and after the two main processes mentioned above\r\n- Process the data **2h** (_20min_ from cache)\r\n    - **20min** Getting reading for processing \r\n    - **40min** Hashing and files stats (96 workers)\r\n    - **58min** Deduplication filtering (single worker)\r\n- Save parquet files **5h**\r\n    -  Saving 1000 parquet files (16 workers)\r\n- Push to hub **37min**\r\n    - **34min** git add\r\n    - **3min** git push (several hours with `Repository.git_push()`)\r\n\r\n## Conclusion\r\nIt appears that loading and saving the data is the main bottleneck at that scale (**8.5h**) whereas processing (**2h**) and pushing the data to the hub (**0.5h**) is relatively fast. To optimize the performance at this scale it would make sense to consider such an end-to-end example and target the bottlenecks which seem to be loading from and saving to disk. The processing itself seems to run relatively fast.\r\n\r\n## Notes\r\n- map operation on a 1TB dataset with 96 workers requires >1TB RAM\r\n- map operation does not maintain 100% CPU utilization with 96 workers\r\n- sometimes when the script crashes all the data files have a corresponding `*.lock` file in the data folder (or multiple e.g. `*.lock.lock` when it happened a several times). This causes the cache **not** to be triggered (which is significant at that scale) - i guess because there are new data files\r\n- parallelizing `to_parquet` decreased the saving time from 17h to 5h, however adding more workers at this point had almost no effect. not sure if this is:\r\n    a) a bug in my parallelization logic, \r\n    b) i\/o limit to load data form disk to memory or \r\n    c) i\/o limit to write from memory to disk.\r\n- Using `Repository.git_push()` was much slower than using command line `git-lfs` - 10-20MB\/s vs. 300MB\/s! The `Dataset.push_to_hub()` function is even slower as it only uploads one file at a time with only a few MB\/s, whereas `Repository.git_push()` pushes files in parallel (each at a similar speed).\r\n\r\ncc @lhoestq @julien-c @LysandreJik @SBrandeis\r\n \n @mariosasko I did not turn it off but I can try the next time - I have to run the pipeline again, anyway. \r\n\r\n@bhavitvyamalik Yes, I also sharded the dataset and used multiprocessing to save each shard. I'll have a closer look at your approach, too.","embeddings":[-0.4659070969,-0.0334514417,-0.1042618528,0.1815132946,0.09017279,0.0411181934,0.1751108766,0.3004228473,0.1423732936,-0.020190781,0.0761042908,0.1921414435,-0.0361627303,0.5749627352,-0.0226485524,-0.0665424913,-0.024128383,0.00486258,-0.2170311362,-0.0295536667,0.1303034127,0.0838657022,0.0580883734,-0.1897372007,-0.1032667011,-0.4335455,-0.0547460876,0.1031981483,-0.093588233,-0.1723837554,0.0365678854,0.0232662838,0.0603459477,0.5355906487,-0.0001064028,-0.0037981691,0.0693409592,-0.017813405,-0.3276855648,-0.0306420363,-0.0807494149,-0.3819633722,-0.1816390157,-0.2334250212,0.1224205419,-0.0380455256,-0.0416168794,-0.7578882575,0.0272068605,-0.1709458232,0.2347729951,-0.066122584,-0.5441614389,-0.0660221353,-0.0510834642,0.0841042846,-0.2505224943,0.1660184562,0.4419589639,0.198574543,-0.2908447087,0.2806810737,0.0501781516,-0.1402428597,0.2424619198,0.0107200872,0.0189891197,-0.1265322566,0.1494351774,0.1591048986,0.4943113029,-0.2639029622,-0.5152831674,-0.5910289288,-0.2188973725,-0.1239592135,-0.0640999228,0.07239943,-0.0242735874,0.03951554,-0.5453306437,-0.3364058435,0.0755238011,-0.1015030965,0.0527676307,-0.1372779608,-0.1122473404,0.1690036207,0.1036126167,0.1288138181,-0.0780985057,-0.5316543579,0.1825611591,0.0113407131,-0.5560039878,-0.1962097287,0.2384477854,0.0797650293,0.1639602929,0.2785152793,0.3226137459,-0.0000825339,0.2032580972,0.0491072014,0.4091487527,0.4878739119,-0.042939648,-0.3635562956,0.3549003601,-0.0826552808,-0.0781438649,0.1740575135,0.1084833145,-0.0235748067,-0.2178195566,-0.0065718079,-0.34451437,-0.15207991,-0.1128423139,0.0765136853,0.2610681951,-0.2637384236,0.0884713307,0.1096983254,-0.0019572303,0.3133580089,-0.1893426925,-0.1612520367,-0.1549726129,0.0153179644,-0.2031328678,0.2025257945,-0.3175885677,0.1466596425,0.3268586695,-0.196361959,0.2054068744,0.2217589766,0.1263010949,0.1356928647,0.2359555364,-0.4846547842,-0.1140155718,0.1967720687,-0.083263427,0.1256706715,0.0401114449,0.4170620143,-0.4650570452,0.1545623839,-0.1673641652,-0.2159665674,0.3965204954,0.1520241052,-0.3574827611,0.0312667973,-0.7180786729,0.5014957786,0.0722377598,0.0608451106,-0.1499890238,-0.0582933165,-0.202276051,-0.2382455319,0.1433064491,0.1004444584,-0.5952253342,0.039811641,-0.0827574432,0.2274797559,0.2801483572,0.5949133635,-0.2786278427,0.3154120445,-0.132608071,0.1690835506,0.2502268255,-0.2021712214,-0.1818022281,0.4072179496,-0.3574754596,0.2338113785,-0.04718538,0.0384914726,0.5381559134,-0.1066406593,0.1942964792,0.3177718818,0.0592700914,0.1060627475,-0.3885086477,-0.2834320068,-0.0630052909,0.1715500653,-0.0240550842,-0.0384415388,-0.091830872,-0.2808524072,0.1350597888,-0.1157413498,0.1523383856,0.1152733788,-0.1168165579,-0.442094177,0.1646611094,0.0767284855,-0.0253387522,0.4415579438,0.0068021943,0.0272431672,0.2186858952,-0.0061798901,-0.1469609737,-0.3828402758,0.1626692712,0.0055914181,0.1083489582,0.0548757985,-0.0770878717,-0.1671313792,-0.3033909202,0.3395930827,-0.0862190947,-0.2440734208,-0.148452282,0.2126960903,0.2101798356,-0.1810200512,0.0671825707,-0.1383659095,-0.2575671077,-0.1958304495,-0.1110695153,0.2122611254,0.0124501064,0.4286311567,0.673610568,0.5321832895,0.0686089844,0.3288456202,0.2076271176,-0.1058699787,0.174623698,-0.0656665042,-0.3201049566,0.5834710598,0.0327148698,0.2420437485,0.2527612746,-0.2054068893,0.0596441887,-0.0177116338,0.0971731246,0.3638671339,0.4805283546,0.1781892627,0.2831573486,0.1749209613,-0.228337571,0.317473352,0.5138953328,-0.158185333,-0.1741689742,0.1986161768,-0.0249111932,-0.3234164417,0.3138000369,-0.0173349436,0.5131072402,0.1840822548,0.2191129923,-0.0053399326,0.0708386526,0.0031996318,0.2711157501,0.2290483266,0.2012622356,0.0647584498,0.22803545,0.0614945292,-0.2042477131,-0.4103016853,-0.0825143531,0.0016986865,-0.2799901664,0.3875601292,0.0128300097,0.1938333809,0.2101430297,0.1622980237,-0.2429269552,-0.1681760252,0.0066168699,0.120685026,-0.1304167062,-0.1741104722,-0.0482589863,0.5270912051,-0.1620855033,-0.2291511297,-0.1468924284,-0.2157183886,-0.1359124631,0.1499738097,0.1895511597,0.1133727133,0.3135982156,0.0937918276,0.0555540547,0.1082075387,-0.3052628934,0.1156726778,-0.2094689608,0.2779457271,-0.0894600973,0.0519102663,-0.4050691128,0.0505255237,-0.2382140011,-0.1042054594,0.0455068648,-0.2716208398,0.0572412685,-0.0281857569,-0.0867699981,-0.3160498738,-0.2466942966,-0.3988619447,0.3420243859,-0.0930549726,0.2090013772,-0.1098384708,-0.1668050885,0.0083634425,0.233869493,0.2750072479,-0.2227626741,-0.7221102118,0.0866067708,-0.0280634854,-0.057057634,-0.2671347558,0.0368492715,0.298640281,0.3308610022,-0.4447726607,0.2482071221,-0.15401344,-0.0340872444,-0.019734066,0.1724804342,0.1778370887,-0.1636390835,-0.2523724735,-0.1536010653,-0.225924626,0.1461166888,0.2001769692,0.0234918222,-0.1889057308,0.1153807789,0.2759388983,0.5526593924,0.0340610668,-0.1499445587,0.1128969714,0.2286046147,-0.0454021432,-0.2618842423,-0.1879077703,0.2005143911,-0.1850178689,-0.3225645721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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3730","title":"Checksum Error when loading multi-news dataset","comments":"Thanks for reporting @byw2.\r\nWe are fixing it.\r\nIn the meantime, you can load the dataset by passing `ignore_verifications=True`:\r\n ```python\r\ndataset = load_dataset(\"multi_news\", ignore_verifications=True)","body":"## Describe the bug\r\nWhen using the load_dataset function from datasets module to load the Multi-News dataset, does not load the dataset but throws Checksum Error instead.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"multi_news\")\r\n```\r\n\r\n\r\n## Expected results\r\nShould download and load Multi-News dataset.\r\n\r\n## Actual results\r\nThrows the following error and cannot load data successfully:\r\n\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C']\r\n```\r\n\r\nCould this issue please be looked at? Thanks!","comment_length":24,"text":"Checksum Error when loading multi-news dataset \n ## Describe the bug\r\nWhen using the load_dataset function from datasets module to load the Multi-News dataset, does not load the dataset but throws Checksum Error instead.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"multi_news\")\r\n```\r\n\r\n\r\n## Expected results\r\nShould download and load Multi-News dataset.\r\n\r\n## Actual results\r\nThrows the following error and cannot load data successfully:\r\n\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C']\r\n```\r\n\r\nCould this issue please be looked at? Thanks! \n Thanks for reporting @byw2.\r\nWe are fixing it.\r\nIn the meantime, you can load the dataset by passing `ignore_verifications=True`:\r\n ```python\r\ndataset = load_dataset(\"multi_news\", ignore_verifications=True)","embeddings":[-0.205984652,0.1197468042,-0.0531001166,0.3288652599,0.1786070168,0.0489471368,0.3487609923,0.249125123,0.2209575623,0.0042161313,-0.0201206785,0.1327265203,0.0797100663,0.2458918244,-0.1772806644,0.0411300734,0.2067430317,-0.1955719888,0.1274430603,-0.0312859342,-0.3297872841,0.1181754619,-0.2370896637,-0.2267685533,-0.1624123752,0.2662163973,0.0150024202,0.3836467564,-0.033635553,-0.2803119421,0.1913193315,0.1104997918,0.0778091624,0.5794479251,-0.0001141592,0.0379749462,0.3875361383,-0.0912785754,-0.0958946869,-0.1308540404,-0.2874310017,-0.4877913892,-0.0445630923,-0.2293666005,0.0709106773,0.1538929492,-0.2482254654,-0.2432665825,0.0975644439,0.3267342746,0.2390640825,0.4230077267,0.2544765174,0.0237647761,0.0177409519,-0.1576568782,0.1026651189,0.3537888825,0.1848806441,-0.1061005518,0.0285235755,0.3856686354,-0.1653269082,0.2086877972,-0.1128539741,-0.0362413824,0.1587055922,-0.0918634161,-0.0014377176,0.2864731252,0.3175829053,-0.1208011955,-0.3408112228,-0.1007710844,-0.0210788157,0.1227956265,0.3523018956,0.3814173341,-0.2119971514,-0.0906626582,-0.3286902606,-0.0127555039,-0.0122859431,0.2351042926,-0.1029999927,0.1998426765,0.0248898398,0.1845498979,-0.0121075138,-0.080306828,0.3272580504,-0.2888536453,-0.027100483,0.1070201695,-0.5055981874,0.1140574813,-0.0597519428,0.2061194479,0.2526604533,0.1809902191,-0.1982086599,0.2865148783,-0.2422312945,0.209447369,0.4146184921,-0.3547204435,-0.1554227024,0.3807002604,0.320533067,0.2539538145,-0.0263377652,0.003689782,0.0080676014,-0.1700704396,0.3988575637,0.0998637155,0.233240962,-0.4311794937,-0.1960068494,0.1478628963,0.0758363903,-0.1639168262,0.1042856798,0.2698504031,-0.1860772967,0.4284563065,0.0185914878,0.245677188,-0.128015548,-0.1969038993,-0.1876535118,-0.0634163246,-0.1969166547,0.0901124105,0.394657284,-0.1753546298,0.176764071,0.1101383939,0.3065106869,-0.3300118446,0.0941002518,-0.1473979652,-0.2201703787,0.452706039,0.0217790604,0.1940246373,0.2682870924,0.0326529518,-0.0634646192,0.3517195284,-0.3919578791,-0.132844761,-0.2729199231,0.1887739897,-0.3750145137,0.040950913,-0.1376643926,-0.2400496304,0.3433099985,-0.2988632917,-0.0369987674,-0.1588506699,-0.2394757867,-0.2472772896,0.1288065612,0.2410427183,-0.1015245095,0.0481026284,-0.1139600426,-0.3490525186,0.1987658739,0.1052022874,-0.1751963496,0.081065841,-0.1232426614,-0.2742056847,0.1238146797,-0.3778420091,-0.5636994839,0.0855170041,0.050888788,0.4084915817,-0.0900019407,0.2368323356,-0.2711334527,-0.0103786616,0.1018014774,0.4118106663,-0.0501482412,0.3596552312,-0.2052662522,-0.0876458213,0.4425911903,0.385683924,0.2671597898,-0.0200616885,0.1352394074,-0.0543112494,0.359923631,0.0068930946,-0.0988720655,0.2040783465,0.2804013491,-0.0457123891,0.2103063911,-0.3516051769,-0.4273765385,0.3540055156,0.0251618586,-0.0667076483,-0.0828051046,-0.0068572741,-0.2578179538,-0.1616541147,-0.1995265633,0.139430806,0.106914185,0.2792451978,-0.0357974172,0.0425244085,-0.1936643571,0.5159775019,-0.3331541717,0.0681523606,-0.2833895087,0.4532343149,-0.024955133,-0.0112781357,0.0518072173,0.0292618554,0.1269611418,0.0310455877,-0.2810219824,0.4598624706,0.3538020551,0.0352371074,-0.2034104466,0.1769073009,0.0475854985,-0.4247560501,-0.0163478069,0.5475433469,0.2332070321,-0.220475778,-0.2518158257,0.4091760218,-0.1917810887,0.0708731636,-0.1337246895,0.1087096483,0.2027146518,-0.1766235381,-0.0823989213,-0.1281023622,0.1501429826,0.0283641797,-0.1294992417,-0.057719931,-0.0266560372,-0.2217739671,0.2212229073,0.0438423529,0.0890536532,0.0922325477,0.0198466741,-0.0520069599,0.0115037942,0.4045679569,0.5191167593,-0.0450794697,-0.0932355374,0.2312475145,0.1098312214,0.0255235806,0.0302232504,-0.0017675869,0.3400188386,0.5167150497,-0.0111028394,0.0565057918,-0.0879474133,0.1519751698,0.2303335518,0.2021666616,-0.5246728063,0.0134520717,-0.1401617974,-0.1605640948,-0.2632805109,-0.3154574335,-0.3706279099,-0.3993639052,-0.1928209811,0.2324373424,-0.0030813585,0.0628831536,-0.3529969752,-0.0348244458,0.1316118538,-0.0827024058,0.2738521397,-0.0441923775,0.0383094363,0.0347636938,0.5645970702,0.3133824468,0.2080513984,-0.1265526712,-0.1257926971,-0.4407030046,-0.2139967382,-0.0067534591,-0.0471464247,0.3020974696,0.1182477921,0.1396203637,0.0202749614,-0.123494938,0.2714424431,-0.0965453684,-0.1482515931,0.4831484556,-0.0230153389,0.1436275393,0.1176366881,-0.0995940119,-0.2871211469,-0.2008910477,-0.1995051056,-0.1272387207,0.2332015634,-0.1180138215,-0.031302195,-0.0770440176,0.0497257933,0.2218975723,-0.2487400621,-0.3856969178,0.333288312,-0.1293684393,-0.424038291,0.0446727239,0.1770867109,-0.1063227281,0.0630918965,-0.3612331152,-0.1998114139,-0.136247769,-0.1235540807,0.0437163636,0.0665183812,0.0494046137,-0.1878027469,0.0073497137,-0.2340153754,-0.0430096723,-0.2207806259,-0.1006944776,0.4510292113,0.0109230541,-0.0275037996,-0.1743351966,0.2283869833,0.557951808,0.0253044441,0.1140629873,0.2196660787,0.2866417468,0.0349732339,-0.3662172556,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3729","title":"Wrong number of examples when loading a text dataset","comments":"Hi @kg-nlp, thanks for reporting.\r\n\r\nThat is weird... I guess we would need some sample data file where this behavior appears to reproduce the bug for further investigation... ","body":"## Describe the bug\r\nwhen I use load_dataset to read a txt file  I find  that the number of the samples is incorrect\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfr = open('train.txt','r',encoding='utf-8').readlines()\r\nprint(len(fr))  # 1199637\r\n\r\ndatasets = load_dataset('text', data_files={'train': ['train.txt']}, streaming=False)\r\nprint(len(datasets['train']))  # 1199649\r\n```\r\nI also use command line operation to verify it\r\n```\r\n$ wc -l train.txt \r\n1199637 train.txt\r\n```\r\n\r\n## Expected results\r\nplease fix that issue \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.3\r\n- Platform:windows&linux\r\n- Python version:3.7\r\n- PyArrow version:6.0.1\r\n","comment_length":28,"text":"Wrong number of examples when loading a text dataset \n ## Describe the bug\r\nwhen I use load_dataset to read a txt file  I find  that the number of the samples is incorrect\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfr = open('train.txt','r',encoding='utf-8').readlines()\r\nprint(len(fr))  # 1199637\r\n\r\ndatasets = load_dataset('text', data_files={'train': ['train.txt']}, streaming=False)\r\nprint(len(datasets['train']))  # 1199649\r\n```\r\nI also use command line operation to verify it\r\n```\r\n$ wc -l train.txt \r\n1199637 train.txt\r\n```\r\n\r\n## Expected results\r\nplease fix that issue \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.3\r\n- Platform:windows&linux\r\n- Python version:3.7\r\n- PyArrow version:6.0.1\r\n \n Hi @kg-nlp, thanks for reporting.\r\n\r\nThat is weird... I guess we would need some sample data file where this behavior appears to reproduce the bug for further investigation... 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3729","title":"Wrong number of examples when loading a text dataset","comments":"ok, I found the reason why that two results are not same.\r\nthere is \/u2029 in the text, the datasets will split sentence according to the \/u2029,but when I use open function will not do that .\r\nso I want to know which function shell do that\r\nthanks","body":"## Describe the bug\r\nwhen I use load_dataset to read a txt file  I find  that the number of the samples is incorrect\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfr = open('train.txt','r',encoding='utf-8').readlines()\r\nprint(len(fr))  # 1199637\r\n\r\ndatasets = load_dataset('text', data_files={'train': ['train.txt']}, streaming=False)\r\nprint(len(datasets['train']))  # 1199649\r\n```\r\nI also use command line operation to verify it\r\n```\r\n$ wc -l train.txt \r\n1199637 train.txt\r\n```\r\n\r\n## Expected results\r\nplease fix that issue \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.3\r\n- Platform:windows&linux\r\n- Python version:3.7\r\n- PyArrow version:6.0.1\r\n","comment_length":48,"text":"Wrong number of examples when loading a text dataset \n ## Describe the bug\r\nwhen I use load_dataset to read a txt file  I find  that the number of the samples is incorrect\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfr = open('train.txt','r',encoding='utf-8').readlines()\r\nprint(len(fr))  # 1199637\r\n\r\ndatasets = load_dataset('text', data_files={'train': ['train.txt']}, streaming=False)\r\nprint(len(datasets['train']))  # 1199649\r\n```\r\nI also use command line operation to verify it\r\n```\r\n$ wc -l train.txt \r\n1199637 train.txt\r\n```\r\n\r\n## Expected results\r\nplease fix that issue \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.3\r\n- Platform:windows&linux\r\n- Python version:3.7\r\n- PyArrow version:6.0.1\r\n \n ok, I found the reason why that two results are not same.\r\nthere is \/u2029 in the text, the datasets will split sentence according to the \/u2029,but when I use open function will not do that .\r\nso I want to know which function shell do that\r\nthanks","embeddings":[-0.1179774404,-0.3109660447,-0.0979497954,0.5308281779,-0.2185823023,-0.2745322585,0.4456656277,-0.0981145799,-0.0977493301,0.2998818755,0.1997928619,0.1997050345,0.225201726,0.0837187022,0.4043925703,-0.2532300353,0.3243588507,0.1103492081,-0.3028644621,-0.3011627495,-0.3266862631,0.3298743963,-0.5788975954,-0.0395577811,0.0238313861,0.0146071827,-0.1144157797,0.0756705552,0.1382208318,-0.22233814,-0.0298007596,-0.2255301923,0.0456645451,0.607475996,-0.000130784,-0.147770077,0.1718780994,-0.1714130342,-0.1760737002,-0.3555422425,-0.0849578828,-0.1413165033,0.1185136363,-0.3289649487,0.202903986,-0.0044347197,0.1191058904,-0.37954247,0.347482264,0.5153579116,-0.0114118354,0.0882377774,-0.0923803076,0.1461806446,0.602393508,0.3524336517,0.2270669341,0.0482018217,0.1238716096,0.196059376,0.0146993445,0.2157525122,-0.1909078658,0.0472142361,-0.1410325319,0.4023495018,-0.1741722524,-0.2830734551,0.1244287267,0.4505331814,0.6050252914,-0.094947271,-0.0804180354,-0.2066157013,-0.3524691164,-0.3300081789,0.1667869091,0.4850310087,-0.1726277322,0.1443881392,-0.328218013,0.3519707024,-0.0271787513,0.3540976048,-0.1804660857,0.2304325551,-0.1614184827,0.243256107,0.1364526749,0.0161352139,0.2176029235,-0.3214571774,-0.0727094635,-0.0103913462,-0.0522845574,0.3033769131,-0.0654666498,0.0162328426,0.1928154826,0.203519851,0.0815624893,-0.0094222696,-0.1377618462,0.0800897926,0.0209738277,-0.1104165912,0.2055837214,0.2387871891,-0.0276683196,0.0234441105,-0.4286907911,-0.0773418918,-0.0260543432,-0.2956408858,0.1035572067,-0.0855715275,0.0797018856,-0.193084836,-0.1866650581,0.0388844423,-0.2883912623,-0.27052176,-0.1455364972,0.379922092,0.1151069999,0.1452505291,0.0572792105,0.1869571358,-0.3512643278,-0.4397099316,-0.1162078232,-0.2128758281,-0.176807344,0.0471192226,-0.033647541,-0.0555749722,0.4136425257,0.4178874791,-0.1136131287,-0.6000419855,0.1632476449,-0.5801773071,0.3332073987,-0.0133021064,-0.0402729139,0.0535646081,0.1336361468,0.003110545,-0.103650175,0.4238190949,-0.387358278,-0.0977011397,0.1182241142,0.023600826,0.0239742845,-0.0903761163,-0.237234816,0.3357466161,0.0899010077,-0.3980391026,0.0845457613,-0.2845927179,-0.3863131702,-0.1820073575,0.2264471352,0.457732141,-0.1748025864,-0.0080591058,-0.1657974869,0.0918713734,0.5408601761,0.2855290473,-0.3539678752,0.4130348265,-0.3112204075,0.2326115668,0.6476521492,-0.5296447277,-0.3016530871,0.3802607656,0.2477472425,-0.0128274299,0.2804383934,-0.2228319496,0.394390583,0.0597079061,-0.0878883526,0.0827520192,0.1988788098,0.2281417847,0.0157488193,-0.1948557049,0.437441498,-0.0059044645,-0.0342353322,-0.1829513758,0.3372790515,-0.2156374007,0.6224686503,-0.161188826,-0.1549241692,0.0970160589,0.0192659888,0.0920785218,0.0941646621,-0.3533728123,-0.1384357959,-0.3016625047,0.0651525036,-0.0992824659,0.0801748782,-0.0195097774,-0.2794989944,-0.122394219,0.0485204458,0.0048936037,-0.0828178972,0.3303269148,-0.0735149011,0.0554201342,0.0234530885,0.1369936913,-0.1265589744,0.2502350211,-0.0190043226,0.1574949026,0.2170175612,-0.2406488508,-0.160000667,-0.1849151552,0.26040712,-0.0886494741,-0.2802876532,0.628708899,0.4948029816,0.2514179647,-0.3509379923,-0.2203659266,0.1316302866,-0.2598638535,0.0800960958,0.344081521,0.2187232673,0.0515555404,-0.340247035,-0.0899327546,0.0811681002,0.0318858959,0.0334710144,-0.3678994179,-0.0572378226,0.0437051542,0.0236840546,-0.1447802782,0.2991198301,-0.0625827312,0.478051722,0.1178395748,-0.2506072521,0.0625656247,0.7225699425,-0.1877245009,0.2956129611,0.2894875705,-0.0924279764,-0.2422515601,0.025101779,0.0669632107,0.7136521935,0.2103144825,0.0693169981,0.0461837351,0.3399066627,-0.1624480188,0.5214267969,0.1967126727,0.0312174652,0.2458702922,0.1130538136,-0.1504305899,-0.1462441385,0.0517664962,-0.0507737733,-0.0085264249,-0.3657481968,0.2603031993,-0.3202599585,0.0515094362,-0.507753849,0.1223207936,-0.0171770733,0.0694095641,-0.1940997988,-0.2421718091,0.0004148647,-0.1459674984,-0.002453086,0.1587038785,-0.0487401523,0.0985020399,0.2204530835,0.1804155707,-0.442495048,-0.1255320162,0.0308403913,0.3287263811,0.0263577811,-0.6156694889,-0.1904802173,-0.0395940579,-0.2047424018,0.168582201,0.3877078593,0.2889833152,0.1711494476,-0.1493317932,-0.1549554169,-0.2233451605,-0.0263030156,-0.2066444606,-0.1486172527,-0.0290879253,0.0786640942,0.0665004775,-0.0611181259,-0.620878756,-0.2773664296,-0.167854026,0.0873198211,0.0534876026,0.3857952952,-0.0131866848,-0.2140890658,0.0183316153,-0.0039184187,0.231465891,-0.101076223,-0.4292242825,-0.0390235633,-0.2115380168,-0.1620844305,-0.3111856282,-0.047689274,-0.4347507954,0.3173328638,-0.4377599359,0.0629331768,-0.2569020092,-0.223460108,-0.3540326059,0.3713594973,0.3437672853,-0.044655554,0.1385413855,-0.4754344225,-0.1362384707,0.416122824,-0.0540861897,0.201827541,0.1551679075,0.2988247871,-0.0514894649,0.101719223,0.6196196079,-0.0690758452,0.0438832901,-0.3399069607,-0.0504338667,-0.0757123902,0.0440043099,0.1508846432,0.2129272372,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3720","title":"Builder Configuration Update Required on Common Voice Dataset","comments":"Hi @aasem, thanks for reporting.\r\n\r\nPlease note that currently Commom Voice is hosted on our Hub as a community dataset by the Mozilla Foundation. See all Common Voice versions here: https:\/\/huggingface.co\/mozilla-foundation\r\n\r\nMaybe we should add an explaining note in our \"legacy\" Common Voice canonical script? What do you think @lhoestq @mariosasko ?","body":"Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n","comment_length":52,"text":"Builder Configuration Update Required on Common Voice Dataset \n Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n \n Hi @aasem, thanks for reporting.\r\n\r\nPlease note that currently Commom Voice is hosted on our Hub as a community dataset by the Mozilla Foundation. See all Common Voice versions here: https:\/\/huggingface.co\/mozilla-foundation\r\n\r\nMaybe we should add an explaining note in our \"legacy\" Common Voice canonical script? What do you think @lhoestq @mariosasko 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3720","title":"Builder Configuration Update Required on Common Voice Dataset","comments":"Thank you, @albertvillanova, for the quick response. I am not sure about the exact flow but I guess adding the following lines under the `_Languages` dictionary definition in [common_voice.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py) might resolve the issue. I guess the dataset is recently made available so the file needs updating.\r\n\r\n```\r\n\"ur\": {\r\n        \"Language\": \"Urdu\",\r\n        \"Date\": \"2022-01-19\",\r\n        \"Size\": \"68 MB\",\r\n        \"Version\": \"ur_3h_2022-01-19\",\r\n        \"Validated_Hr_Total\": 1,\r\n        \"Overall_Hr_Total\": 3,\r\n        \"Number_Of_Voice\": 48,\r\n    },\r\n```\r\n","body":"Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n","comment_length":66,"text":"Builder Configuration Update Required on Common Voice Dataset \n Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n \n Thank you, @albertvillanova, for the quick response. I am not sure about the exact flow but I guess adding the following lines under the `_Languages` dictionary definition in [common_voice.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py) might resolve the issue. I guess the dataset is recently made available so the file needs updating.\r\n\r\n```\r\n\"ur\": {\r\n        \"Language\": \"Urdu\",\r\n        \"Date\": \"2022-01-19\",\r\n        \"Size\": \"68 MB\",\r\n        \"Version\": \"ur_3h_2022-01-19\",\r\n        \"Validated_Hr_Total\": 1,\r\n        \"Overall_Hr_Total\": 3,\r\n        \"Number_Of_Voice\": 48,\r\n    },\r\n```\r\n","embeddings":[-0.2908357978,0.0055583888,0.0204208307,-0.0029066796,0.2136823237,0.1312914938,0.00759451,0.2225900441,-0.3800755739,0.2097705007,0.1213132739,-0.0567614511,-0.1200532243,-0.0659271926,0.2116896212,-0.2299367189,0.0533254743,0.1275776625,0.1963357031,-0.1596669555,-0.1043495387,0.4816318154,-0.2682038546,0.0548327938,-0.5411224365,0.2194282562,-0.2755522728,0.1162296906,0.0283807926,-0.4112052321,0.4951475561,0.1081569567,0.0152038718,0.2059914023,-0.0001207059,0.0509980842,0.3045148551,-0.205505386,-0.3528477252,-0.3148371875,-0.1785151958,-0.0814388543,-0.1536946297,0.0608404391,-0.3236380816,-0.0678614154,0.0856132209,-0.0477417596,0.2400249243,0.3028701842,0.1308620423,0.2231404632,0.260674715,-0.0982350186,-0.3236755431,0.1243111789,-0.0068564988,0.3932752311,0.4783205688,0.1142107099,-0.1197663471,0.2875882983,0.229286775,-0.5864346623,-0.1881359965,-0.1359365433,0.0500608645,-0.3521125317,0.4854003191,0.1274149269,0.2738968134,-0.1607760042,-0.2502791584,-0.1866691262,0.1965872496,0.0551397242,0.3249399364,0.1374180615,-0.2768469751,0.3665007353,0.1062302291,-0.5239907503,0.0420545451,0.1942790151,0.0106531642,0.2046847045,0.0444206148,-0.0162738543,0.0008806473,-0.0729729012,-0.1095108613,0.0687877163,-0.0547695085,0.5032042265,-0.3537587225,-0.0752144605,0.2071716189,0.0987238213,0.0255004857,-0.3048518002,-0.1943498552,0.1398406029,-0.1433329731,0.1913865656,-0.0001593525,0.0534840375,0.4506546557,0.12182796,-0.0353944078,0.1661688536,0.1274307668,0.1321332157,-0.2592797875,-0.1841912717,-0.126510933,0.03266792,0.0492249988,0.0621249229,0.0073343096,0.0276757926,-0.035328228,-0.225335449,0.0686640218,0.2400593013,0.1713148504,0.0080029238,-0.1175936908,0.1644600481,-0.0587222241,-0.5961043239,-0.1625127792,-0.0481886268,-0.2239510566,0.1129948497,0.132775709,-0.4981382191,0.2381600291,0.1638474613,-0.0964469016,0.0023041714,-0.256516248,0.1399298459,-0.3158945143,0.0921259373,-0.1321972758,0.1310485005,0.1479326636,0.0518304743,-0.125641644,0.2587088346,-0.2624058127,-0.5020983219,-0.3278324604,0.1025468111,-0.008100356,-0.0146109443,0.2924259305,0.3559291661,0.316840142,-0.5310708284,-0.0048706727,-0.0426159538,-0.1158704758,0.0609903149,0.3232062459,0.090705432,-0.2899176478,-0.4400933385,-0.1525494903,-0.0586867854,0.0913045332,-0.113895759,-0.2159978896,0.1150377914,-0.3318367004,0.0013817041,0.6829844713,-0.0729053617,-0.3918665349,-0.0429814681,0.0748173669,0.1223854423,-0.0154881729,-0.1865920722,-0.2165825367,0.0497472398,0.4554485083,0.1353193372,0.0058324183,-0.0249127448,-0.0595965236,-0.1532550156,0.3179484904,0.2440789193,0.2161670625,0.1347364932,0.0354998559,0.1000854224,0.5115041137,-0.0635273904,0.2611421049,0.2704722285,0.0522532612,-0.0277348515,0.0519514531,-0.0380625799,-0.1583540142,0.3301851153,-0.0426886678,0.5204116106,-0.0313188285,0.0962377638,-0.2429840267,-0.1410793364,-0.3780166805,-0.263261497,0.0155051025,0.0911145061,0.0601452589,0.0790660679,-0.3656291962,0.2121502161,-0.1801400185,0.0800599828,-0.254830122,0.1921251565,-0.2450394183,-0.2929905653,0.0880619958,0.2539528012,-0.1268752217,-0.1011014283,-0.216665253,0.0425210483,-0.1541674435,0.2036916018,-0.1122358441,-0.0286116544,-0.0530483201,-0.1551287919,0.2785306871,-0.1960288286,-0.2524026036,-0.0396076366,0.5442428589,0.2946858108,0.3803505003,0.1705548912,0.1527050287,0.1800675094,0.1939085424,0.1287762672,-0.2886508405,-0.3227873445,-0.0630123317,-0.3511415124,0.7412947416,0.2975058258,-0.2861213982,-0.1357183307,0.7640948892,0.17993927,0.0632342324,0.3372727036,-0.1844738424,0.0431176387,0.1031614468,-0.0100013651,0.2679766417,0.122369267,-0.0895852149,-0.0323412828,0.1979377717,-0.0439110361,0.0720987171,0.0062572011,0.3182021379,0.1142530814,0.1719795316,-0.0024461881,-0.6014638543,0.1344894022,0.1564836949,0.0418429226,-0.0216865651,0.0393718369,-0.1422621012,-0.0170454253,-0.3513841331,-0.4398271441,-0.7331687212,-0.1579493433,0.1362893432,-0.4609130621,0.2316339612,0.4726679921,0.0289521646,-0.0738370568,-0.0497973301,-0.2922382951,-0.1302630752,0.0338374153,-0.4032023847,-0.0207481198,0.1720300615,-0.2918146551,-0.0019616906,-0.4050537944,-0.1169297695,-0.0324550346,-0.4024910927,0.1904634684,-0.2526712418,-0.1155858487,0.2304838896,0.0862880647,-0.0617379099,-0.053085383,0.4484821558,0.1948496103,-0.1862523407,0.1089532748,-0.1408803612,0.1284983009,0.0729340762,-0.2374997437,-0.4063602388,-0.2058403343,-0.2098096162,-0.3025349379,0.127933234,0.021029612,0.065828234,0.0875855014,-0.4724543393,0.2766239047,-0.2141955495,-0.3921625018,0.1490669399,-0.0467985347,-0.1201998964,0.065131247,-0.1032300293,0.8525460958,-0.3133350313,-0.1819258779,0.0967062637,-0.0234473646,0.4156477749,-0.091055803,-0.0863822848,0.4217534959,0.1038711965,0.0985857397,0.0556862205,-0.2424025089,-0.0249069054,0.3907266557,0.4810016155,0.0429190807,0.0942979753,0.0484870784,0.5028449297,0.2166108787,-0.0654479787,0.3135363758,-0.1708972156,0.3219956458,-0.3096116483,-0.4273965955,0.1447331607,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3720","title":"Builder Configuration Update Required on Common Voice Dataset","comments":"@aasem for compliance reasons, we are no longer updating the `common_voice.py` script.\r\n\r\nWe agreed with Mozilla Foundation to use their community datasets instead, which will ask you to accept their terms of use:\r\n```\r\nYou need to share your contact information to access this dataset.\r\n\r\nThis repository is publicly accessible, but you have to register to access its content \u2014 don't worry, it's just one click!\r\n\r\nBy clicking on \u201cAccess repository\u201d below, you accept that your contact information (email address and username) can be shared with the repository authors. This will let the authors get in touch for instance if some parts of the repository's contents need to be taken down for licensing reasons.\r\n\r\nBy clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset.\r\n\r\nYou will immediately be granted access to the contents of the dataset. \r\n```\r\n\r\nIn order to use e.g. their Common Voice dataset version 8.0, please:\r\n- First visit their dataset page: https:\/\/huggingface.co\/datasets\/mozilla-foundation\/common_voice_8_0\r\n- Accept their term of use by clicking \"Access repository\"\r\n- You can then load their dataset with:\r\n  ```python\r\n  load_dataset(\"mozilla-foundation\/common_voice_8_0\", \"ur\", split=\"train+validation\")\r\n  ```","body":"Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n","comment_length":192,"text":"Builder Configuration Update Required on Common Voice Dataset \n Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n \n @aasem for compliance reasons, we are no longer updating the `common_voice.py` script.\r\n\r\nWe agreed with Mozilla Foundation to use their community datasets instead, which will ask you to accept their terms of use:\r\n```\r\nYou need to share your contact information to access this dataset.\r\n\r\nThis repository is publicly accessible, but you have to register to access its content \u2014 don't worry, it's just one click!\r\n\r\nBy clicking on \u201cAccess repository\u201d below, you accept that your contact information (email address and username) can be shared with the repository authors. This will let the authors get in touch for instance if some parts of the repository's contents need to be taken down for licensing reasons.\r\n\r\nBy clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset.\r\n\r\nYou will immediately be granted access to the contents of the dataset. \r\n```\r\n\r\nIn order to use e.g. their Common Voice dataset version 8.0, please:\r\n- First visit their dataset page: https:\/\/huggingface.co\/datasets\/mozilla-foundation\/common_voice_8_0\r\n- Accept their term of use by clicking \"Access repository\"\r\n- You can then load their dataset with:\r\n  ```python\r\n  load_dataset(\"mozilla-foundation\/common_voice_8_0\", \"ur\", split=\"train+validation\")\r\n  ```","embeddings":[-0.2701787055,0.1846491694,0.0216178801,-0.0467146896,0.2218097895,0.1341914386,0.0782254934,0.2611939609,-0.3444997072,0.2042433619,0.0835880041,0.0007254302,-0.0550362691,-0.2131643742,0.1940620989,-0.1723413616,-0.0371394642,0.0623359904,0.2901751399,-0.1792626083,-0.0461893231,0.4205768108,-0.2547436357,0.0333571099,-0.3832223415,0.1970173717,-0.2731724679,0.1139354482,0.0257302281,-0.4072321951,0.4312179387,0.1667819023,0.0128112733,0.1948578358,-0.0001181784,-0.0078281499,0.3218308687,-0.2202407569,-0.3348544538,-0.3261708915,-0.1984146982,-0.0119284345,-0.2250855267,0.0890562758,-0.2201439738,-0.1073720902,0.0465694293,-0.2661781013,0.1648060828,0.3095470965,0.1522983164,0.0347199515,0.263515532,0.057834696,-0.1443324089,0.1634119004,0.0025876004,0.2975453138,0.5539869666,0.1154576167,-0.2062479854,0.2904123664,0.1208199933,-0.6270701289,-0.1615087688,-0.1102427989,0.090880461,-0.4446860552,0.5423778296,0.1663790941,0.4070019424,-0.1846757382,-0.1752522588,-0.0736778453,0.2179977894,0.1522438228,0.2730460167,0.201620847,-0.3175541162,0.3733955622,0.2031228989,-0.5383445024,0.013119203,0.2084814012,-0.0386966243,0.4087766707,0.0831440091,-0.0567096509,-0.0589036793,-0.0570171513,0.0246045981,0.0027872627,0.0079797683,0.5240368247,-0.2532660067,-0.0004916727,0.2331455946,-0.0269643702,0.1498920321,-0.3110576272,-0.0913015306,0.0880023241,-0.1986840665,0.0707447901,-0.0478515215,0.0656330958,0.3881886899,0.1461245567,0.0343257226,0.1700264066,0.1322287917,0.157012254,-0.213456288,-0.1482331902,-0.1146488935,0.1446651518,0.1541460454,-0.0021013031,-0.049362123,0.1061394513,-0.0931190625,-0.3329341114,0.0623409115,0.2222325206,0.184960112,0.1899880171,-0.1388737112,0.1858421415,-0.0232726559,-0.5851811171,-0.1002886668,-0.0459463224,-0.187526077,0.1297103316,0.1856243908,-0.4974770546,0.2223251462,0.1274029911,-0.0756349787,-0.0561649315,-0.2984444201,0.1965829581,-0.2330908477,0.0710607246,-0.1092418656,0.1736520529,0.0701738149,0.0289636124,-0.1365977824,0.3192185163,-0.2599220276,-0.4506541491,-0.4090306759,0.1251230091,0.1300950497,-0.0826435015,0.4988136292,0.2763231099,0.3077235222,-0.5006359816,0.0001363128,0.0173578486,-0.0570213757,0.0276080668,0.3253281713,0.1677109897,-0.3012040854,-0.28025648,-0.3135439456,0.0707103088,0.1603237242,-0.1808367074,-0.1201149225,0.0733416528,-0.3116239011,0.0333976671,0.7790825963,-0.1677157581,-0.4790555835,0.0219257809,0.1396418512,0.0827079937,0.0068513947,-0.2239320576,-0.1812756211,0.0624463558,0.5157653093,0.1951883882,-0.0058534979,0.0144459987,-0.058883369,-0.1541263014,0.3881707788,0.2112069428,0.1639789641,0.1040608287,0.0684293658,-0.0459152274,0.5399805903,-0.0277860984,0.2757627368,0.2738893032,0.108500883,-0.0466114171,0.0746449903,-0.0312595926,-0.1613155901,0.2428162694,-0.0170538072,0.4701360464,-0.1238397732,0.0616240241,-0.3362824321,-0.096640408,-0.3751532435,-0.2890616655,0.0395230614,0.1789444685,0.0809023529,0.0252897292,-0.3092114925,0.2298463434,-0.0470100567,0.0803396478,-0.1949589401,0.2104521394,-0.2957889736,-0.2708081007,0.1547638625,0.3057383001,-0.0598567575,-0.0881541893,-0.255305022,0.0534491986,-0.1175608933,0.1522875726,0.0137148127,-0.0209453888,-0.0489345267,-0.2585636675,0.4145895243,-0.1406833231,-0.2215216607,0.0046337745,0.5660431981,0.2579684854,0.4205475152,0.1149616167,0.1141372547,0.2213538438,0.2426531315,0.1855402738,-0.2900332808,-0.3073269129,-0.1142549366,-0.2446368784,0.7858130336,0.2615171373,-0.2450515479,-0.0727098808,0.8122878075,0.114256829,0.0403654724,0.2558017075,-0.2505368888,0.0055996636,0.0872640759,-0.0161918532,0.1447207034,0.1427836418,-0.080839783,0.0160636064,0.1528205425,0.0041056415,0.0600124709,-0.0047915974,0.2600280344,0.0837673992,0.2187531143,0.0710710287,-0.6178215742,0.0019851627,0.0918332338,0.0263705738,-0.0544144139,-0.0161712077,-0.1516764164,0.0161386076,-0.2732307315,-0.3675224483,-0.7375062108,-0.2391146868,0.1164959595,-0.4565888345,0.2874049246,0.4714638293,-0.0943844467,-0.0552378818,-0.0109570082,-0.2012848407,-0.1926843077,0.0161535311,-0.3973553181,0.0029585506,0.1795271337,-0.3157176673,-0.1000271812,-0.4443764091,-0.0707024634,-0.0137052657,-0.3867810369,0.1555360556,-0.2500522733,-0.2778266966,0.1421440244,0.0186665691,0.0311007183,-0.042438738,0.3672082424,0.1479574591,-0.1506014317,0.0966041759,-0.1220324561,0.0927821398,0.0666201785,-0.2759468853,-0.3619393706,-0.2397970408,-0.340131551,-0.2560638189,0.0957272649,-0.0321244039,-0.0083610434,0.0293099955,-0.3886837363,0.315416038,-0.2329733223,-0.4565511942,0.2022611946,-0.0168596655,-0.1263232082,0.0590076596,-0.139678508,0.7859899998,-0.2366304845,-0.2034480423,0.1628461927,-0.0339234136,0.4637684226,-0.1535221934,-0.0558347739,0.3551008999,0.2209562212,0.0700015798,0.0591993108,-0.2784328461,-0.1981468052,0.4603914917,0.4658780694,-0.0179031231,0.0148281911,0.0439717695,0.5942344666,0.0956219509,-0.0124640893,0.3695394397,-0.1865829527,0.3260105252,-0.2591225505,-0.3907227814,0.2221300155,0.2226483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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3720","title":"Builder Configuration Update Required on Common Voice Dataset","comments":"@albertvillanova \r\n>Maybe we should add an explaining note in our \"legacy\" Common Voice canonical script?\r\n\r\nYes, I agree we should have a deprecation notice in the canonical script to redirect users to the new script.","body":"Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n","comment_length":35,"text":"Builder Configuration Update Required on Common Voice Dataset \n Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n \n @albertvillanova \r\n>Maybe we should add an explaining note in our \"legacy\" Common Voice canonical script?\r\n\r\nYes, I agree we should have a deprecation notice in the canonical script to redirect users to the new script.","embeddings":[-0.3522529304,0.1033181399,0.0201226864,-0.1326443851,0.1710537374,0.1720188856,0.0630823448,0.2731882036,-0.3063314855,0.1558270007,0.1680758744,-0.0426010415,-0.1602722555,-0.0915837884,0.1674865037,-0.1476795822,0.1083246469,0.1433460265,0.1850376576,-0.1774392277,-0.1586069316,0.4307896197,-0.2323101014,0.0798870251,-0.5154443383,0.1996849626,-0.2547980547,0.0714853406,0.0414717011,-0.4977306724,0.4699044824,0.1408040076,0.0474974029,0.2106499374,-0.0001205559,-0.0275100954,0.4122455716,-0.1631011665,-0.3418973088,-0.2194465101,-0.1893410385,0.029380938,-0.137470454,0.0710207373,-0.2607863545,-0.0753603131,0.1074650437,-0.1601910293,0.1273377687,0.3107064068,0.1518803835,0.1562838405,0.157583192,-0.111400038,-0.2922447622,0.1330053359,0.0179440957,0.3620038033,0.5607208014,0.0299778674,-0.2190153301,0.313519448,0.1459033489,-0.6504855156,-0.1770207584,-0.0640640035,0.0636484548,-0.4134812653,0.4211590588,0.1227198914,0.3281157613,-0.2612512708,-0.1868748367,-0.1350414157,0.2155850828,0.0354578756,0.2786013782,0.1158040687,-0.3055977523,0.3909783363,0.1230018511,-0.5547813177,0.0415645689,0.1430408806,0.0599004477,0.2126104534,0.1155144125,-0.0007496771,-0.0182148311,0.0080176964,-0.0215055719,0.0763103664,-0.017177403,0.448864162,-0.2820701897,-0.1697417051,0.2253741324,0.0549496487,0.0765018091,-0.2639242113,-0.1445338279,0.0897393972,-0.2120158076,0.208256498,-0.0226097368,0.0366048887,0.4093725085,0.0847765356,0.0894412398,0.1711655557,0.1919761151,0.1599317044,-0.1971687973,-0.1689052582,-0.0541952364,0.1333771944,0.1946796179,0.0496794209,-0.0971828476,0.0965298489,0.0363513827,-0.2179555893,-0.0123256622,0.1881616712,0.189172551,0.0914142206,-0.1017654464,0.1625272185,-0.043805968,-0.5497660041,-0.126335144,-0.0330637619,-0.2330600619,0.1613046974,0.1677865684,-0.3842532933,0.1722162068,0.1856464893,-0.0325245559,0.0028044081,-0.3224741518,0.1512543112,-0.3368621469,0.0588827059,-0.1526080668,0.1063080132,0.138372466,0.0625889301,-0.1938138008,0.3695892692,-0.264777869,-0.519144237,-0.3021205962,0.1160351261,0.0615407825,-0.0589137599,0.2977272868,0.3547103703,0.2194709927,-0.5543857813,0.0027838482,-0.0766697973,-0.143862471,0.0217171535,0.2281663716,0.1890581548,-0.3330281377,-0.3413621187,-0.1724366397,-0.0545593649,0.0844632834,-0.0810491294,-0.2136669606,0.0548971482,-0.4318243265,0.0072485521,0.6999953389,-0.2034419626,-0.3517695069,0.0053138253,0.0402579308,0.066513598,-0.0127290143,-0.1480647624,-0.1117341444,-0.0838305801,0.4318807423,0.0798334703,-0.0191551391,-0.0034835632,-0.096763812,-0.2404426634,0.3646824956,0.1745160669,0.1716967374,0.1579771936,0.0487060361,0.0813601688,0.592592597,-0.0252112821,0.2746746838,0.2419187129,0.127744481,-0.113015987,0.1053567156,0.0020211323,-0.1872264892,0.2731019258,0.0265036132,0.4738446474,-0.0230797026,-0.0621073544,-0.2702676058,-0.1638099402,-0.3491888642,-0.1983696073,0.0347898863,0.0425757654,0.0712900311,0.1206812114,-0.4054278135,0.2449346483,-0.1585190445,0.0524175055,-0.1996538937,0.1893429011,-0.2333540767,-0.2744240463,0.1315122247,0.3227104247,-0.1120817587,-0.081221126,-0.2127457112,0.0845538378,-0.1309115142,0.1799828857,-0.0972993746,0.0375116803,-0.0619282238,-0.2297191173,0.3419252336,-0.1807065606,-0.2753421664,-0.0356878713,0.548804462,0.2774992883,0.382612437,0.1381535381,0.1161886379,0.1411132365,0.1699013561,0.0662648678,-0.2976711988,-0.3034600019,-0.1949524134,-0.30363065,0.763879776,0.240592137,-0.2667411864,-0.0513104387,0.8287902474,0.0899934694,0.0059307688,0.3505577445,-0.1516144723,0.0439736396,0.1340977848,0.0188102368,0.3038089573,0.1121364236,-0.1509730518,0.0838019401,0.1654784977,-0.0272245482,0.0632450581,0.0317024589,0.283039242,0.076768592,0.1947282851,0.0401474498,-0.5750323534,0.11406295,0.09186095,0.0403341353,-0.0588757806,0.0085433163,-0.1425372809,-0.0042914473,-0.3665978312,-0.300914824,-0.6628491282,-0.1825281382,0.2117183357,-0.4257106483,0.225866273,0.4649564326,-0.0006895869,-0.0032473628,-0.0888683721,-0.2238200754,-0.2192706168,0.042339243,-0.4212856889,-0.0025178662,0.1711834371,-0.3296618164,-0.0022590079,-0.4184907675,-0.1295863241,-0.1344056576,-0.4120208025,0.1870002598,-0.3147923946,-0.2051991522,0.1813228279,0.0400609635,0.0124537591,-0.1029042974,0.2958241105,0.1188433617,-0.1922781616,0.052221626,-0.1300125122,0.1090009362,0.0215958077,-0.2990380526,-0.4190744758,-0.2645123899,-0.2634142339,-0.3295086026,0.1203806624,-0.1019971445,0.0341812782,-0.0228999369,-0.4415484965,0.2304666936,-0.1923547089,-0.2914888561,0.1356487721,-0.0061654504,-0.107308276,0.1539044827,-0.130885914,0.8600862026,-0.3415636718,-0.2619377673,0.0489256047,0.0178915393,0.465108335,-0.0929954872,-0.0898270458,0.4485110939,0.1806672364,0.0807382762,0.1086427569,-0.2655322552,-0.1260876656,0.3750096262,0.428450197,0.0873354673,0.1688395888,0.031597279,0.494187057,0.2320779413,-0.0199742764,0.3100401759,-0.1812588274,0.392999053,-0.3141340315,-0.3339924216,0.1423079669,0.1778759509,0.1753397137,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3720","title":"Builder Configuration Update Required on Common Voice Dataset","comments":"@albertvillanova, \r\nI now get the following error after downloading my access token from the huggingface and passing it to `load_dataset` call:\r\n\r\n`AttributeError: 'DownloadManager' object has no attribute 'download_config'`\r\n\r\nAny quick pointer on how it might be resolved?","body":"Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n","comment_length":37,"text":"Builder Configuration Update Required on Common Voice Dataset \n Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n \n @albertvillanova, \r\nI now get the following error after downloading my access token from the huggingface and passing it to `load_dataset` call:\r\n\r\n`AttributeError: 'DownloadManager' object has no attribute 'download_config'`\r\n\r\nAny quick pointer on how it might be resolved?","embeddings":[-0.3459944725,0.1322568655,0.0547214448,0.1285538077,0.2149555087,0.1292429119,-0.050948102,0.1270666867,-0.2447927892,0.239728272,0.0264984984,-0.1509607732,-0.1511384696,-0.0213905275,0.2012618184,-0.1719040126,0.0843131393,0.1090749726,0.2482062876,-0.1460992992,-0.1487039626,0.4359525144,-0.1951358765,0.0031196494,-0.4305735528,0.1956361532,-0.2621565759,0.0956976637,-0.0180060156,-0.4505707026,0.4051676989,0.1467573047,-0.0111129787,0.2010225207,-0.00013073,0.0514123961,0.3157663047,-0.1669122279,-0.3394399583,-0.2167919129,-0.2754828036,-0.080290623,-0.067471385,0.129833132,-0.0944667384,-0.0287288968,0.1289856881,-0.0151488632,0.2119413614,0.2737083435,0.0471936651,0.2926717103,0.2737058103,-0.1149177849,-0.1837889701,0.0682927743,0.0603457727,0.3665644825,0.5087208152,0.2313554734,-0.1313098669,0.2878470123,0.1894003153,-0.5187128782,0.0025705139,-0.1216044426,0.1075331047,-0.3963472247,0.3933460414,0.046633482,0.473048538,-0.2256487161,-0.328261584,-0.2741532028,0.3110173643,0.0719106868,0.3789778948,0.1132092774,-0.318133533,0.2667765617,-0.0314835273,-0.7084383368,0.0040666717,0.1845715344,-0.1300723106,0.3106956184,-0.045945812,-0.0134524805,-0.0176109355,0.0031470503,-0.1011075824,0.007552579,-0.1200268045,0.5215550065,-0.296988368,-0.0908477306,0.0824799687,0.0866270512,0.0159240086,-0.0431475043,-0.3242815435,0.051368203,-0.1382447928,0.0941380486,0.0654749498,0.1463479549,0.3159985244,0.1114454567,0.0481319763,0.1623015702,0.2790052295,0.0941597521,-0.2870368361,-0.0735440999,-0.2128575295,-0.0328904502,0.1201368123,0.0390154049,-0.0502134152,0.0255488008,0.0087746521,-0.2560299039,0.0316209868,0.2587611377,0.0677770451,-0.0969455987,-0.1000860035,0.2151847333,-0.0433287248,-0.5586462617,-0.1484946012,-0.0758477896,-0.2051550299,0.0942450836,0.0559494719,-0.4402641952,0.1883941889,0.1614669412,-0.0359868519,0.0041479343,-0.2430931032,0.1549849212,-0.3503910005,0.1944819093,-0.082490027,0.2097103,0.049299255,-0.1035560519,-0.1698820293,0.1819810122,-0.2879370153,-0.5845227838,-0.2888302207,0.0180786457,-0.1182521358,-0.0511720777,0.2197323889,0.301100105,0.2204618305,-0.4286470413,-0.0032679054,0.0211775694,-0.0581588596,0.0526599735,0.3025656343,0.3268556595,-0.1503112465,-0.581803143,-0.1521652639,-0.1925572455,0.1198282838,-0.1565515697,-0.1648659408,0.1883983165,-0.3244934976,0.0962791815,0.7419460416,-0.2670149207,-0.5895029306,0.0191324223,-0.02607264,0.0672478229,0.0920737311,-0.141222924,-0.0183920022,0.0830589905,0.4719425738,0.1333376169,0.0077872491,-0.0592413843,0.0489115752,-0.2698408961,0.2462919354,0.2564917207,0.275850594,0.1663590819,0.058385659,0.0392930135,0.4597682059,0.0278313439,0.2805572748,0.2652473748,-0.0355958715,0.0243018828,-0.0858358368,-0.0133454679,-0.2749377191,0.3007694483,-0.0379622728,0.4958792925,-0.1729555428,0.0749291107,-0.315063417,-0.0842017606,-0.4081351757,-0.2310946137,-0.0832492411,0.2009050697,0.0702998862,0.0580726601,-0.3710614443,0.2923161983,-0.141686365,0.2561649978,-0.2008577734,0.0903370529,-0.1440974772,-0.2279278338,0.0949095264,0.208450824,-0.0583846755,-0.073642835,-0.1922822893,0.0595068,-0.2084594369,0.2588720918,-0.1123358384,0.0383945741,0.0370801575,-0.0130473729,0.2324933559,-0.2837127149,-0.1833860278,-0.0873674452,0.6223797202,0.1952591985,0.4754658639,0.2340109497,0.1319372058,0.2217617333,0.1280464977,0.1959969699,-0.2401198298,-0.3358668089,-0.1056143641,-0.3081219792,0.8003216982,0.1450218856,-0.2361577302,-0.0715679973,0.5892788768,0.0744197667,-0.000312559,0.2823847234,-0.2704826593,0.0986725539,0.1660287976,0.1198500767,0.3411705196,0.0539292432,-0.0874961391,0.0570279509,0.2011035532,-0.0255671013,0.1320526302,0.0862529874,0.2132443488,0.18284145,0.0223923232,0.0503909364,-0.5178416371,0.0300018713,0.1381241679,0.1762107164,-0.2290557474,-0.0498489775,-0.0961775109,-0.1003635451,-0.2881371677,-0.4622676373,-0.7433071136,-0.2276617587,0.0364783816,-0.3437884748,0.2294596136,0.413341701,-0.125344187,-0.0118296333,-0.1396936178,-0.2652039826,-0.1442063153,0.1362252831,-0.2719215155,-0.1130816862,0.2263657898,-0.3603705764,-0.0167714115,-0.4548215568,0.0004947613,-0.0313259736,-0.2938322425,0.2791158557,-0.1685983539,-0.0674608201,0.2285807282,0.0369791053,-0.0187183432,-0.0688517466,0.3479686677,0.2690822482,-0.2615562975,-0.0019574168,-0.088408187,0.0442515947,0.1192081496,-0.1728069633,-0.4756915867,-0.1432586163,-0.1227962673,-0.2545155287,0.1118453667,-0.0245715529,0.0163910184,0.1449401528,-0.6059529185,0.3170272112,-0.2923720777,-0.4364978671,0.1844406873,0.0142381378,-0.0613025837,0.1279161423,-0.0810472071,0.7980654836,-0.2032328546,-0.2470098585,0.1916583776,-0.1477986872,0.318446517,-0.1474574506,0.126217559,0.492262125,0.150077045,0.1536325663,0.1460922807,-0.1736527234,-0.0064746593,0.2409919053,0.4019601941,-0.0152604738,0.0075167851,-0.0610461347,0.642342329,0.1556293666,0.1800457835,0.2433714569,-0.1453859508,0.3894176781,-0.1154493541,-0.3765098453,0.2146733701,0.1646865159,0.163776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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3720","title":"Builder Configuration Update Required on Common Voice Dataset","comments":"@aasem What version of `datasets` are you using? We renamed that attribute from `_download_config` to `download_conig` fairly recently, so updating to the newest version should resolve the issue:\r\n```\r\npip install -U datasets\r\n```","body":"Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n","comment_length":34,"text":"Builder Configuration Update Required on Common Voice Dataset \n Missing language in Common Voice dataset\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/common_voice\r\n\r\nI tried to call the Urdu dataset using `load_dataset(\"common_voice\", \"ur\", split=\"train+validation\")` but couldn't due to builder configuration not found. I checked the source file here for the languages support:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nand Urdu isn't included there. I assume a quick update will fix the issue as Urdu speech is now available at the Common Voice dataset.\r\n\r\nAm I the one who added this dataset? No\r\n \n @aasem What version of `datasets` are you using? We renamed that attribute from `_download_config` to `download_conig` fairly recently, so updating to the newest version should resolve the issue:\r\n```\r\npip install -U datasets\r\n```","embeddings":[-0.430241853,0.014459704,-0.0066323709,-0.0191776622,0.299969852,0.1326848119,-0.0474837683,0.253323406,-0.2436420768,0.2547215819,0.1182379574,0.0407667495,-0.1010223478,-0.0704342425,0.0853906125,-0.2976475358,0.0260498654,0.1262316406,0.1972049624,-0.1836623549,-0.0590083301,0.4645923078,-0.2173829526,0.0484866835,-0.4478036761,0.2026701123,-0.173303023,0.0576153323,-0.053107217,-0.4284586608,0.4685158134,0.0730503574,0.0750908628,0.1988165379,-0.0001178258,0.0252811108,0.3525792658,-0.1808712929,-0.3558821678,-0.394590795,-0.1374940127,-0.1325046718,-0.0831025094,0.0698650926,-0.2989428639,-0.0481339358,0.1246012524,0.0368366167,0.2491158098,0.3504155874,0.1645643711,0.2123973668,0.2337152958,-0.1399895251,-0.2383018285,0.0019431073,-0.0396253876,0.3316527903,0.5330460072,0.1292244345,-0.0991815999,0.3053898513,0.126862824,-0.492310673,-0.2446545362,-0.1049236953,0.1643680781,-0.4463748336,0.4554313719,0.1322494596,0.3071188629,-0.193952769,-0.2695966661,-0.1739220619,0.1920871884,0.0727552548,0.3341265321,0.1522068083,-0.3438274562,0.3605360985,0.135794729,-0.4982654452,0.0861575752,0.1837193668,-0.015098501,0.3006568849,0.0800275654,-0.0379560106,0.0602839403,-0.0499058329,0.0051762066,0.1440896094,-0.0382803567,0.5270379186,-0.3212834299,-0.0322094746,0.1927325428,0.0975177139,-0.0036038593,-0.2393683195,-0.1578174233,0.1184804663,-0.1213577762,0.2642939389,-0.0033445172,0.0583825782,0.3653529882,0.1924238503,0.0098784855,0.1484612077,0.0744134337,0.0869006366,-0.2648194432,-0.1784101129,-0.1157240197,0.0250694137,0.0379021727,0.0447950959,-0.0358810984,0.0450708345,0.0365925431,-0.2083063722,0.0723790899,0.2659603059,0.1655614823,0.018012004,-0.151270479,0.2249604464,-0.0614561103,-0.4480246902,-0.1584913284,-0.1164213791,-0.2179161161,0.0249327291,0.174451977,-0.4064832926,0.3047653437,0.2249563336,-0.0740548372,0.0412230045,-0.174843356,0.1224006489,-0.3673940003,0.1946695,-0.075898625,0.1537748277,0.2108789384,-0.0550709553,-0.1423316747,0.2665357292,-0.2683547735,-0.4749639332,-0.2863515615,0.1389381737,0.0345315933,-0.082045801,0.2621566355,0.2751950324,0.2880734205,-0.5289615393,0.0175562892,-0.1161568612,-0.0421902053,0.0542563312,0.277764976,0.0283041336,-0.2035741508,-0.4509720802,-0.1763399541,-0.0694375783,0.1541028917,-0.0844346657,-0.1567052454,0.0683315471,-0.3061808944,0.0725273415,0.7816514373,-0.2015039176,-0.4458963275,0.081365481,0.0785729885,0.0811705291,-0.0015984869,-0.205158636,-0.1545177996,0.0968463421,0.4227564037,0.1443090737,-0.0098059615,0.0206735693,-0.0781973228,-0.1641698182,0.2926579416,0.2259029895,0.1810735613,0.1135153249,-0.0034120877,0.030100435,0.4710220397,-0.0492616184,0.2537801266,0.2711366713,0.1351173073,-0.0133658154,0.0297244843,-0.110571675,-0.0474264175,0.28189224,-0.0827611908,0.5258169174,-0.1203861833,0.0301374532,-0.2779195905,-0.121950306,-0.3947939873,-0.2336574644,0.0609983131,0.0485641956,0.0566051863,0.0704083145,-0.3358747959,0.2351654023,-0.142861411,0.1314790547,-0.1734749824,0.1759935468,-0.2958936095,-0.3439694047,0.0557349399,0.2669535279,-0.1488748938,-0.0131695038,-0.2313038558,0.0984795839,-0.1399256587,0.190984726,-0.0370399989,-0.0331040919,-0.043539349,-0.1558599174,0.2483003139,-0.1634674668,-0.2660379112,0.0280952752,0.5118798614,0.2140195221,0.3687297404,0.2318595201,0.1579793245,0.2520909607,0.3096698821,0.0929633304,-0.3026858866,-0.3266765773,-0.0300249122,-0.4179950356,0.7162793279,0.2432138324,-0.2914030254,-0.1405866593,0.6921341419,0.1950389147,0.0645955428,0.2852546275,-0.1757946759,0.1210226715,0.1094024926,0.0820676014,0.2834104896,0.1302866787,-0.0639221668,0.0007563418,0.1214846596,-0.0213843919,0.0959709436,0.005385811,0.1982962787,0.1612799466,0.1391904205,-0.0391373001,-0.5715380311,0.1680821478,0.1029373482,0.0882116407,-0.0197620038,0.0856187493,-0.1219703481,0.0032507277,-0.3465492427,-0.4842865467,-0.7132410407,-0.1681497544,0.1704498082,-0.4119645357,0.2219446152,0.5161858797,0.0192335807,-0.1805452555,-0.0377747044,-0.3778408468,-0.1725972146,0.0431780703,-0.3814512491,0.0074906931,0.1543233395,-0.2271493077,0.0275917463,-0.4460037053,-0.1395495236,-0.0202385262,-0.4089605808,0.1705434918,-0.2762296498,-0.0271863714,0.2743650973,0.103151232,-0.1229165122,-0.0722079873,0.5153434873,0.1699982136,-0.1084272191,0.1031263024,-0.0992493853,0.0792511776,0.0439126939,-0.2424269617,-0.4811848998,-0.2693168521,-0.2213365883,-0.2476161271,0.0940041468,-0.0753199235,0.1323987395,0.0591645464,-0.4360085428,0.304472506,-0.1880509704,-0.3422819972,0.1162997857,-0.0717407167,-0.108463265,0.1110917553,-0.074544169,0.9142205715,-0.2555788159,-0.2188795954,0.0413994491,-0.0517704524,0.4083396792,-0.0831034854,0.0116490182,0.3796218932,0.1616257429,0.0561213307,0.0315365344,-0.1404851675,-0.0480565019,0.3334973156,0.4881172478,0.0000466289,0.1188738495,0.0288747102,0.5200865269,0.2635193467,0.0424093455,0.3136353791,-0.1941027343,0.2989246845,-0.3263640702,-0.4151137173,0.0951229632,0.2101225257,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3717","title":"wrong condition in `Features ClassLabel encode_example`","comments":"Hi @Tudyx, \r\n\r\nPlease note that in Python, the boolean NOT operator (`not`) has lower precedence than comparison operators (`<=`, `<`), thus the expression you mention is equivalent to:\r\n```python\r\n not (-1 <= example_data < self.num_classes)\r\n```\r\n\r\nAlso note that as expected, the exception is raised if:\r\n- `example_data < -1`\r\n- or `example_data >= self.num_classes`\r\n\r\nThe raise of the exception is expected when `example_data` equals 4 and `self.num_classes` equals 4 too.","body":"## Describe the bug\r\n\r\nThe `encode_example` function in *features.py* seems to have a wrong condition.\r\n\r\n```python\r\nif not -1 <= example_data < self.num_classes:\r\n    raise ValueError(f\"Class label {example_data:d} greater than configured num_classes {self.num_classes}\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe `not - 1` condition change the result of the condition. For instance, if  `example_data`  equals 4 and ` self.num_classes` equals 4 too, `example_data < self.num_classes` will give `False` as expected  . But if i add the  `not - 1`  condition, `not -1 <= example_data < self.num_classes` will give `True` and raise an exception.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.18.3\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.00\r\n","comment_length":71,"text":"wrong condition in `Features ClassLabel encode_example` \n ## Describe the bug\r\n\r\nThe `encode_example` function in *features.py* seems to have a wrong condition.\r\n\r\n```python\r\nif not -1 <= example_data < self.num_classes:\r\n    raise ValueError(f\"Class label {example_data:d} greater than configured num_classes {self.num_classes}\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe `not - 1` condition change the result of the condition. For instance, if  `example_data`  equals 4 and ` self.num_classes` equals 4 too, `example_data < self.num_classes` will give `False` as expected  . But if i add the  `not - 1`  condition, `not -1 <= example_data < self.num_classes` will give `True` and raise an exception.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.18.3\r\n- Python version: 3.8.10\r\n- PyArrow version: 7.00\r\n \n Hi @Tudyx, \r\n\r\nPlease note that in Python, the boolean NOT operator (`not`) has lower precedence than comparison operators (`<=`, `<`), thus the expression you mention is equivalent to:\r\n```python\r\n not (-1 <= example_data < self.num_classes)\r\n```\r\n\r\nAlso note that as expected, the exception is raised if:\r\n- `example_data < -1`\r\n- or `example_data >= self.num_classes`\r\n\r\nThe raise of the exception is expected when `example_data` equals 4 and `self.num_classes` equals 4 too.","embeddings":[0.139200151,-0.1168352813,-0.0464339256,0.2833452821,0.2257677168,-0.0928369761,0.227546528,0.2446246743,-0.185413152,0.0163679607,0.3163578808,0.3322764337,-0.2180690765,0.4177814126,-0.1956312954,0.0690541044,0.1742625386,0.2923478186,0.3052041531,-0.1466154456,-0.4508392215,0.2037653476,-0.4939356148,0.1102814302,-0.2552688122,0.4114963412,0.0251681935,-0.0455012694,-0.1442919225,-0.5386763215,0.2943563759,-0.1257145405,-0.0223188438,0.1085255221,-0.0001155188,-0.2337384969,0.0351587906,-0.1259315461,-0.1464302391,-0.0845981985,-0.3166151047,-0.3450344205,-0.2740906775,-0.2822527885,-0.4162560701,-0.113664113,-0.2484330684,-0.0567566045,-0.0092579201,0.2645043433,0.1412788182,-0.0087071927,0.3859907985,0.2960890234,0.4332487285,0.0621005371,-0.0305478144,0.0906672552,0.0906483456,-0.0267994162,0.1395627856,0.5557590723,0.206762746,-0.1378126591,0.2980459332,0.0263043419,-0.0054103965,-0.5017408133,-0.1302710027,-0.1441257745,0.176970005,-0.2568836212,-0.4789208174,-0.2553236485,-0.0822703913,-0.5255109072,0.4755953252,-0.0404295698,0.0459742174,0.3716600537,-0.1482358873,0.1275645196,-0.2422151119,0.2733958662,-0.0819878429,0.4597249329,-0.0691973493,0.0792695209,-0.1412950605,-0.0432581007,0.0453193188,0.1638562977,-0.0295121018,0.2085459977,-0.3918803036,-0.3410562277,0.0350318588,0.1097199321,0.0242842752,0.046168834,-0.4930081367,-0.0027145792,-0.0488184839,0.0537305847,-0.0529153794,0.2443073541,0.5898000598,0.4207377136,-0.173484832,-0.0677786246,-0.1285391301,0.1263169944,-0.0867505074,-0.2123233527,0.5468603969,0.3610424995,0.2732152641,-0.3242684305,-0.4655994773,0.3997124434,-0.5603884459,0.0490846634,0.2750928402,0.1231047958,0.1508033723,-0.3179329038,0.1169207394,0.3124153614,-0.1949822903,-0.2579091489,-0.1024820432,-0.0393960923,-0.1714466512,-0.0894586518,-0.0380274802,0.0733321458,0.1866294742,0.2102657408,0.1509996504,-0.4848146141,0.0480602421,0.0679795519,0.3838150501,0.1255258918,-0.4460720122,0.0358643532,0.5511580706,-0.1780006886,0.0333387703,0.1903916001,-0.2268249989,-0.1817424744,0.1347750574,0.1236685067,-0.0285709258,0.0999003574,0.1855861247,0.1691887081,0.3559328914,-0.3832543194,0.4201492071,-0.3239125311,-0.1336054355,-0.0457997546,0.2818570435,0.2962965071,-0.267450273,0.1642813236,0.2953378856,0.0765828937,-0.0379399732,-0.2096305639,-0.2275160998,-0.3374303281,-0.2817925513,-0.1247417703,0.3352416754,-0.4339080155,-0.5221586823,0.3354225457,-0.189548105,-0.005483239,0.0176249128,0.1959249377,-0.3554052114,-0.1807686985,-0.0187197123,-0.0622593947,0.0743444413,-0.1393534094,-0.1728699654,0.2232574075,0.6270376444,-0.1913530678,-0.1197631061,-0.1236337051,-0.1349328309,0.0391862914,0.3433813453,-0.1599814147,-0.2196530849,-0.0029995218,0.181755811,-0.0588947311,0.0031940823,-0.2197805792,0.1552835703,0.3490315676,-0.3802885711,0.0445894301,0.0694104359,-0.2287765741,-0.143418625,-0.1392132491,-0.1747957319,-0.2996671796,0.1030180901,-0.0691107213,0.134009853,-0.1166168004,-0.1360384077,0.2705122828,-0.2698817849,0.0449472629,-0.0127265202,-0.084203884,-0.0970905423,0.0397103056,-0.3252114654,0.0066997511,0.4033418,0.1268467307,-0.4386605322,0.3150492907,0.2799206972,-0.5763798356,-0.5585458279,-0.3521613181,-0.0446841232,-0.4395969808,-0.1003700346,0.482234478,0.1199846938,0.1341769546,-0.2119083256,0.5699879527,0.319770366,0.1774186492,-0.1692922711,-0.1416973621,-0.0437605977,-0.0079108914,-0.3227877319,-0.3363434672,0.1867052615,-0.2696790993,0.0046729194,-0.0307627823,-0.2352392972,0.2736084759,0.3889759481,-0.0387140699,0.1974908262,-0.0140854781,0.0666559115,0.1342966259,0.0894715264,-0.0002912714,0.3074724972,0.0759201795,-0.3748263121,0.0910888687,-0.2203162462,0.0230643079,0.198962599,0.0375445038,0.1901022196,0.0841649696,0.2700104117,-0.0403777696,-0.0115933688,-0.3272846639,-0.1198654473,-0.1470396221,-0.556766212,0.4093423188,-0.1889249235,-0.0802325234,-0.1687009484,-0.1943313181,-0.3196844757,-0.1189848781,0.3236733973,-0.1016162559,0.0434697829,0.1441488713,-0.2157455236,-0.2658471763,-0.1436488926,-0.3155213296,0.3337629437,-0.0584303513,-0.3982538283,-0.0785928816,-0.2352460176,0.0643085539,0.070890896,-0.1228165329,-0.101136297,-0.0326530337,-0.4829127192,-0.1346971691,-0.3647966087,0.3755168021,0.008773135,-0.3207112849,-0.3118802011,0.1044228524,0.2565691173,0.2373335212,-0.2071880847,0.1402395517,0.2906802595,-0.1010305658,-0.2006433308,-0.2576774359,-0.1223507449,-0.0988362357,-0.2935706377,0.167758733,0.2438693494,0.1809227616,0.0539492518,0.1337608844,0.1513590068,0.0500659533,-0.2198760659,-0.0471493006,0.1546441913,0.0910965875,-0.1779745966,-0.241979152,-0.076970689,-0.1677571833,-0.0693529099,0.0512930937,-0.1822077036,-0.061405424,-0.0063495981,-0.1771753579,-0.1230566576,0.2021297663,0.2072768211,-0.0045968946,-0.3378387988,-0.1271737516,0.1342794299,-0.0422475636,0.1681434512,-0.0687878951,0.2639659941,-0.1583139747,0.2243696302,0.4413802922,-0.0175038446,0.2131908238,-0.1227184907,0.0766612664,0.207280606,-0.2901151776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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3708","title":"Loading JSON gets stuck with many workers\/threads","comments":"Hi ! Note that it does `block_size *= 2` until `block_size > len(batch)`, so it doesn't loop indefinitely. What do you mean by \"get stuck indefinitely\" then ? Is this the actual call to `paj.read_json` that hangs ?\r\n\r\n> increasing the `chunksize` argument decreases the chance of getting stuck\r\n\r\nCould you share the values of chunksize that you're using to observe this ? And maybe the order of magnitude of number of bytes per line of JSON ?","body":"## Describe the bug\r\nLoading a JSON dataset with `load_dataset` can get stuck when running on a machine with many CPUs. This is especially an issue when loading a large dataset on a large machine. \r\n\r\n\r\n## Steps to reproduce the bug\r\nI originally created the following script to reproduce the issue:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom multiprocessing import Process\r\nfrom tqdm import tqdm\r\nimport datasets\r\nfrom transformers import set_seed\r\n\r\ndef run_tasks_in_parallel(tasks, ds_list):\r\n    for _ in tqdm(range(1000)):\r\n        print('new batch')\r\n        running_tasks = [Process(target=task, args=(ds, i)) for i, (task, ds) in enumerate(zip(tasks, ds_list))]\r\n        for running_task in running_tasks:\r\n            running_task.start()\r\n        for running_task in running_tasks:\r\n            running_task.join()\r\n\r\ndef get_dataset():\r\n    dataset_name = 'transformersbook\/codeparrot'\r\n    ds = load_dataset(dataset_name+'-train', split=\"train\", streaming=True)\r\n    ds = ds.shuffle(buffer_size=1000, seed=1)\r\n    return iter(ds)\r\n\r\ndef get_next_element(ds, process_id,  N=10000):\r\n    for _ in range(N):\r\n        _ = next(ds)['content']\r\n    print(f'process {process_id} done')\r\n    return\r\n\r\nset_seed(1)\r\ndatasets.utils.logging.set_verbosity_debug()\r\n\r\nn_processes = 8\r\ntasks = [get_next_element for _ in range(n_processes)]\r\nargs = [get_dataset() for _ in range(n_processes)]\r\nrun_tasks_in_parallel(tasks, args)\r\n```\r\n\r\nToday I noticed that it can happen when running it on a single process on a machine with many cores without streaming. So just `load_dataset(\"transformersbook\/codeparrot-train\")` alone might cause the issue after waiting long enough or trying many times. It's a slightly random process which makes it especially hard to track down. When I encountered it today it had already processed 17GB of data (the size of the cache folder when it got stuck) before getting stuck.\r\n\r\nHere's my current understanding of the error. As far as I can tell it happens in the following block: https:\/\/github.com\/huggingface\/datasets\/blob\/be701e9e89ab38022612c7263edc015bc7feaff9\/src\/datasets\/packaged_modules\/json\/json.py#L119-L139\r\n\r\nWhen the try on line 121 fails and the `block_size` is increased it can happen that it can't read the JSON again and gets stuck indefinitely. A hint that points in that direction is that increasing the `chunksize` argument decreases the chance of getting stuck and vice versa. Maybe it is an issue with a lock on the file that is not properly released.\r\n\r\n## Expected results\r\nRead a JSON before the end of the universe.\r\n\r\n## Actual results\r\nRead a JSON not before the end of the universe.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-glibc2.28\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n@lhoestq we dicsussed this a while ago. @albertvillanova we discussed this today :) \r\n","comment_length":78,"text":"Loading JSON gets stuck with many workers\/threads \n ## Describe the bug\r\nLoading a JSON dataset with `load_dataset` can get stuck when running on a machine with many CPUs. This is especially an issue when loading a large dataset on a large machine. \r\n\r\n\r\n## Steps to reproduce the bug\r\nI originally created the following script to reproduce the issue:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom multiprocessing import Process\r\nfrom tqdm import tqdm\r\nimport datasets\r\nfrom transformers import set_seed\r\n\r\ndef run_tasks_in_parallel(tasks, ds_list):\r\n    for _ in tqdm(range(1000)):\r\n        print('new batch')\r\n        running_tasks = [Process(target=task, args=(ds, i)) for i, (task, ds) in enumerate(zip(tasks, ds_list))]\r\n        for running_task in running_tasks:\r\n            running_task.start()\r\n        for running_task in running_tasks:\r\n            running_task.join()\r\n\r\ndef get_dataset():\r\n    dataset_name = 'transformersbook\/codeparrot'\r\n    ds = load_dataset(dataset_name+'-train', split=\"train\", streaming=True)\r\n    ds = ds.shuffle(buffer_size=1000, seed=1)\r\n    return iter(ds)\r\n\r\ndef get_next_element(ds, process_id,  N=10000):\r\n    for _ in range(N):\r\n        _ = next(ds)['content']\r\n    print(f'process {process_id} done')\r\n    return\r\n\r\nset_seed(1)\r\ndatasets.utils.logging.set_verbosity_debug()\r\n\r\nn_processes = 8\r\ntasks = [get_next_element for _ in range(n_processes)]\r\nargs = [get_dataset() for _ in range(n_processes)]\r\nrun_tasks_in_parallel(tasks, args)\r\n```\r\n\r\nToday I noticed that it can happen when running it on a single process on a machine with many cores without streaming. So just `load_dataset(\"transformersbook\/codeparrot-train\")` alone might cause the issue after waiting long enough or trying many times. It's a slightly random process which makes it especially hard to track down. When I encountered it today it had already processed 17GB of data (the size of the cache folder when it got stuck) before getting stuck.\r\n\r\nHere's my current understanding of the error. As far as I can tell it happens in the following block: https:\/\/github.com\/huggingface\/datasets\/blob\/be701e9e89ab38022612c7263edc015bc7feaff9\/src\/datasets\/packaged_modules\/json\/json.py#L119-L139\r\n\r\nWhen the try on line 121 fails and the `block_size` is increased it can happen that it can't read the JSON again and gets stuck indefinitely. A hint that points in that direction is that increasing the `chunksize` argument decreases the chance of getting stuck and vice versa. Maybe it is an issue with a lock on the file that is not properly released.\r\n\r\n## Expected results\r\nRead a JSON before the end of the universe.\r\n\r\n## Actual results\r\nRead a JSON not before the end of the universe.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-glibc2.28\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n@lhoestq we dicsussed this a while ago. @albertvillanova we discussed this today :) \r\n \n Hi ! Note that it does `block_size *= 2` until `block_size > len(batch)`, so it doesn't loop indefinitely. What do you mean by \"get stuck indefinitely\" then ? Is this the actual call to `paj.read_json` that hangs ?\r\n\r\n> increasing the `chunksize` argument decreases the chance of getting stuck\r\n\r\nCould you share the values of chunksize that you're using to observe this ? And maybe the order of magnitude of number of bytes per line of JSON 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3708","title":"Loading JSON gets stuck with many workers\/threads","comments":"To clarify, I don't think it loops indefinitely but the `paj.read_json` gets stuck after the first try. That's why I think it could be an issue with a lock somewhere. \r\n\r\nUsing `load_dataset(..., chunksize=40<<20)` worked without errors.","body":"## Describe the bug\r\nLoading a JSON dataset with `load_dataset` can get stuck when running on a machine with many CPUs. This is especially an issue when loading a large dataset on a large machine. \r\n\r\n\r\n## Steps to reproduce the bug\r\nI originally created the following script to reproduce the issue:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom multiprocessing import Process\r\nfrom tqdm import tqdm\r\nimport datasets\r\nfrom transformers import set_seed\r\n\r\ndef run_tasks_in_parallel(tasks, ds_list):\r\n    for _ in tqdm(range(1000)):\r\n        print('new batch')\r\n        running_tasks = [Process(target=task, args=(ds, i)) for i, (task, ds) in enumerate(zip(tasks, ds_list))]\r\n        for running_task in running_tasks:\r\n            running_task.start()\r\n        for running_task in running_tasks:\r\n            running_task.join()\r\n\r\ndef get_dataset():\r\n    dataset_name = 'transformersbook\/codeparrot'\r\n    ds = load_dataset(dataset_name+'-train', split=\"train\", streaming=True)\r\n    ds = ds.shuffle(buffer_size=1000, seed=1)\r\n    return iter(ds)\r\n\r\ndef get_next_element(ds, process_id,  N=10000):\r\n    for _ in range(N):\r\n        _ = next(ds)['content']\r\n    print(f'process {process_id} done')\r\n    return\r\n\r\nset_seed(1)\r\ndatasets.utils.logging.set_verbosity_debug()\r\n\r\nn_processes = 8\r\ntasks = [get_next_element for _ in range(n_processes)]\r\nargs = [get_dataset() for _ in range(n_processes)]\r\nrun_tasks_in_parallel(tasks, args)\r\n```\r\n\r\nToday I noticed that it can happen when running it on a single process on a machine with many cores without streaming. So just `load_dataset(\"transformersbook\/codeparrot-train\")` alone might cause the issue after waiting long enough or trying many times. It's a slightly random process which makes it especially hard to track down. When I encountered it today it had already processed 17GB of data (the size of the cache folder when it got stuck) before getting stuck.\r\n\r\nHere's my current understanding of the error. As far as I can tell it happens in the following block: https:\/\/github.com\/huggingface\/datasets\/blob\/be701e9e89ab38022612c7263edc015bc7feaff9\/src\/datasets\/packaged_modules\/json\/json.py#L119-L139\r\n\r\nWhen the try on line 121 fails and the `block_size` is increased it can happen that it can't read the JSON again and gets stuck indefinitely. A hint that points in that direction is that increasing the `chunksize` argument decreases the chance of getting stuck and vice versa. Maybe it is an issue with a lock on the file that is not properly released.\r\n\r\n## Expected results\r\nRead a JSON before the end of the universe.\r\n\r\n## Actual results\r\nRead a JSON not before the end of the universe.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-glibc2.28\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n@lhoestq we dicsussed this a while ago. @albertvillanova we discussed this today :) \r\n","comment_length":36,"text":"Loading JSON gets stuck with many workers\/threads \n ## Describe the bug\r\nLoading a JSON dataset with `load_dataset` can get stuck when running on a machine with many CPUs. This is especially an issue when loading a large dataset on a large machine. \r\n\r\n\r\n## Steps to reproduce the bug\r\nI originally created the following script to reproduce the issue:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom multiprocessing import Process\r\nfrom tqdm import tqdm\r\nimport datasets\r\nfrom transformers import set_seed\r\n\r\ndef run_tasks_in_parallel(tasks, ds_list):\r\n    for _ in tqdm(range(1000)):\r\n        print('new batch')\r\n        running_tasks = [Process(target=task, args=(ds, i)) for i, (task, ds) in enumerate(zip(tasks, ds_list))]\r\n        for running_task in running_tasks:\r\n            running_task.start()\r\n        for running_task in running_tasks:\r\n            running_task.join()\r\n\r\ndef get_dataset():\r\n    dataset_name = 'transformersbook\/codeparrot'\r\n    ds = load_dataset(dataset_name+'-train', split=\"train\", streaming=True)\r\n    ds = ds.shuffle(buffer_size=1000, seed=1)\r\n    return iter(ds)\r\n\r\ndef get_next_element(ds, process_id,  N=10000):\r\n    for _ in range(N):\r\n        _ = next(ds)['content']\r\n    print(f'process {process_id} done')\r\n    return\r\n\r\nset_seed(1)\r\ndatasets.utils.logging.set_verbosity_debug()\r\n\r\nn_processes = 8\r\ntasks = [get_next_element for _ in range(n_processes)]\r\nargs = [get_dataset() for _ in range(n_processes)]\r\nrun_tasks_in_parallel(tasks, args)\r\n```\r\n\r\nToday I noticed that it can happen when running it on a single process on a machine with many cores without streaming. So just `load_dataset(\"transformersbook\/codeparrot-train\")` alone might cause the issue after waiting long enough or trying many times. It's a slightly random process which makes it especially hard to track down. When I encountered it today it had already processed 17GB of data (the size of the cache folder when it got stuck) before getting stuck.\r\n\r\nHere's my current understanding of the error. As far as I can tell it happens in the following block: https:\/\/github.com\/huggingface\/datasets\/blob\/be701e9e89ab38022612c7263edc015bc7feaff9\/src\/datasets\/packaged_modules\/json\/json.py#L119-L139\r\n\r\nWhen the try on line 121 fails and the `block_size` is increased it can happen that it can't read the JSON again and gets stuck indefinitely. A hint that points in that direction is that increasing the `chunksize` argument decreases the chance of getting stuck and vice versa. Maybe it is an issue with a lock on the file that is not properly released.\r\n\r\n## Expected results\r\nRead a JSON before the end of the universe.\r\n\r\n## Actual results\r\nRead a JSON not before the end of the universe.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-glibc2.28\r\n- Python version: 3.9.10\r\n- PyArrow version: 7.0.0\r\n\r\n@lhoestq we dicsussed this a while ago. @albertvillanova we discussed this today :) \r\n \n To clarify, I don't think it loops indefinitely but the `paj.read_json` gets stuck after the first try. That's why I think it could be an issue with a lock somewhere. \r\n\r\nUsing `load_dataset(..., chunksize=40<<20)` worked without 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3707","title":"`.select`: unexpected behavior with `indices`","comments":"Hi! Currently, we compute the final index as `index % len(dset)`. I agree this behavior is somewhat unexpected and that it would be more appropriate to raise an error instead (this is what `df.iloc` in Pandas does, for instance).\r\n\r\n@albertvillanova @lhoestq wdyt?","body":"## Describe the bug\r\nThe `.select` method will not throw when sending `indices` bigger than the dataset length; `indices` will be wrapped instead. This behavior is not documented anywhere, and is not intuitive. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset\r\nds = Dataset.from_dict({\"text\": [\"d\", \"e\", \"f\"], \"label\": [4, 5, 6]})\r\nres1 = ds.select([1, 2, 3])['text']\r\nres2 = ds.select([1000])['text']\r\n```\r\n\r\n## Expected results\r\nBoth results should throw an `Error`.\r\n\r\n## Actual results\r\n`res1` will give `['e', 'f', 'd']`\r\n`res2` will give `['e']`\r\n\r\n## Environment info\r\nBug found from this environment:\r\n- `datasets` version: 1.16.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.7\r\n- PyArrow version: 6.0.1\r\n\r\nIt was also replicated on `master`.\r\n","comment_length":42,"text":"`.select`: unexpected behavior with `indices` \n ## Describe the bug\r\nThe `.select` method will not throw when sending `indices` bigger than the dataset length; `indices` will be wrapped instead. This behavior is not documented anywhere, and is not intuitive. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset\r\nds = Dataset.from_dict({\"text\": [\"d\", \"e\", \"f\"], \"label\": [4, 5, 6]})\r\nres1 = ds.select([1, 2, 3])['text']\r\nres2 = ds.select([1000])['text']\r\n```\r\n\r\n## Expected results\r\nBoth results should throw an `Error`.\r\n\r\n## Actual results\r\n`res1` will give `['e', 'f', 'd']`\r\n`res2` will give `['e']`\r\n\r\n## Environment info\r\nBug found from this environment:\r\n- `datasets` version: 1.16.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.7\r\n- PyArrow version: 6.0.1\r\n\r\nIt was also replicated on `master`.\r\n \n Hi! Currently, we compute the final index as `index % len(dset)`. I agree this behavior is somewhat unexpected and that it would be more appropriate to raise an error instead (this is what `df.iloc` in Pandas does, for instance).\r\n\r\n@albertvillanova @lhoestq wdyt?","embeddings":[-0.1406792104,-0.2200403959,-0.0164007172,0.421248585,0.080513306,-0.0664444268,0.3318190575,0.1800905764,0.2260116488,0.3523018658,0.0550060123,0.5035145283,0.2490186989,0.0551461205,-0.3621675074,-0.0069084591,-0.0928680897,-0.0007303704,-0.10786625,-0.2236005813,-0.4042106867,-0.0127164656,-0.5059925318,0.1052561253,-0.2274845243,-0.1286627203,-0.1505575478,0.1837270111,0.0608895719,-0.3698925078,0.4700392783,-0.2398736775,-0.0116807371,0.5768731833,-0.000117251,-0.0000100979,0.4072289467,-0.0571493357,-0.2262028754,-0.3893957734,-0.3538131118,-0.1014072523,0.1910930127,-0.4258198738,0.1446349323,-0.2120478302,-0.4215377569,-0.3713423908,-0.1704817712,0.2791962326,0.1976974159,0.2803103328,-0.0636637583,-0.1430146694,0.0545460396,0.0742818937,-0.0318183489,0.1006104052,0.186353147,0.2882220447,0.4073314071,-0.0879016668,-0.1697832346,-0.2392816991,-0.1605294943,0.2728234828,0.3839997053,-0.1437534541,0.1467793584,0.2069348395,0.5882375836,0.0347016081,-0.4236955941,-0.2585166991,0.0047375024,-0.1325268,0.0409921296,0.1795067042,0.1269292086,0.1420362741,-0.19757393,0.4535567164,-0.1213855669,0.1923929006,-0.2001330405,0.3081309795,-0.0841496512,0.2973252237,-0.0069496664,-0.0915156975,0.3177823126,-0.257424593,0.0210591741,0.0172656532,-0.0955389366,-0.1888456494,0.1159358472,-0.4559608996,0.0175983887,-0.0015730292,-0.0342508852,-0.0877790973,0.0013096202,0.0715716928,0.4374556839,0.187211737,0.111560531,0.3882369995,0.0179197639,-0.1415736377,0.0266174488,0.0657143369,0.3102414608,-0.0653989166,0.0024623042,-0.1324131787,-0.016409805,-0.1190335229,-0.613470614,0.2751408815,-0.3014095426,-0.2049233913,0.1722830087,0.0545777008,-0.1257159859,0.0208926406,0.0039961208,0.3223001659,-0.0795451179,0.4327450693,-0.3277006745,-0.0175781641,0.2219952196,-0.2650450766,-0.1784340888,-0.2099618167,0.0197413135,0.2539813221,0.3586366773,-0.0524978898,-0.0857994035,-0.2851755023,0.5566039681,0.3535552323,-0.202890709,0.227529645,0.1678236276,-0.4895456731,-0.1562260836,0.5654066205,-0.2627522945,-0.2346047759,0.0407493412,0.1487716138,0.0501507707,0.0407920443,0.1119234264,0.3035483658,0.1255395561,0.1713759303,0.3463031054,-0.2435470968,-0.2847441137,-0.3063895106,0.3163887858,0.2331206053,-0.6451547742,0.2126570344,0.0731361434,0.2095740885,0.206109941,0.3387117386,-0.2034549564,-0.2656462491,-0.251714617,0.2881545424,-0.0724768713,-0.0644291416,-0.4600592256,0.1637969017,-0.1817776263,0.2048294395,0.0079366425,0.0252934601,0.0528505817,-0.0623388216,0.4831158519,0.3251284063,-0.152016893,-0.1633284539,-0.3221902847,-0.1596337557,0.4073847532,-0.1211804748,0.0817912668,0.107478708,-0.003295636,-0.3215925097,0.3833304644,0.1885981411,-0.1792615801,-0.0726812407,0.2672541738,0.15860416,0.2021000236,-0.264311105,0.1059717536,0.2465495616,0.1788619757,-0.1756860912,-0.1619627476,-0.1848074198,-0.4245097935,0.2057427913,-0.0349120013,0.0958378837,-0.0137717286,-0.3494173586,0.1015063599,0.2955604196,-0.0313343629,0.0604575127,-0.196331948,-0.0226164851,-0.0098727709,0.2168610841,0.0369852409,-0.1837093681,-0.3887676001,0.0045741294,0.3141160309,-0.165643096,-0.1975696385,0.3373259008,-0.0231241211,-0.4515972733,-0.2398637533,-0.231638819,0.0226027686,-0.2589595616,-0.0396937281,0.2254926562,0.2618507147,-0.1210512519,-0.1213427112,0.412964493,-0.6382671595,0.3640055954,-0.2253091931,-0.2046100497,-0.1111485213,0.134890303,0.0060240631,-0.3106340468,-0.3526574373,0.0558508113,0.0497126244,0.0222496279,-0.379060775,0.0914185047,0.3135495484,-0.0000956207,0.1344731003,0.2956870794,0.3159418702,0.1969125867,-0.2756559551,-0.2701026797,0.4334504902,0.2364560366,0.2707704008,-0.180892095,-0.0999135599,-0.220523268,0.1433384717,0.1996341646,-0.2018342465,0.2124926001,0.243986547,-0.0296467654,-0.0777524263,0.3047036231,0.1134745926,-0.2394504696,-0.0807252675,0.4629401267,-0.4288699627,-0.2504285872,-0.0297218952,-0.4137918353,0.0128640663,-0.1941554695,-0.0541438684,0.0078532398,-0.2706688344,-0.0048624771,-0.2557487786,0.0399167612,0.2598944008,-0.1274520159,0.1738856584,0.1228147671,-0.2395069003,-0.0311576109,-0.0946858674,0.0588430315,0.0419894382,0.4630435109,-0.1721559614,-0.0045067109,-0.3105078042,-0.0237794816,-0.1769631952,0.3952775002,-0.0594080798,-0.0412364379,-0.0095516052,-0.0268989075,0.2449279875,0.2960557342,0.0740320235,0.1530686617,0.3230585754,0.0436708964,-0.1370000243,-0.3280561268,-0.3072470725,-0.1861270815,0.484946996,-0.1287981868,0.1233998314,0.2900773883,0.2636614442,-0.0639258102,0.3103580773,0.0173926093,-0.3168874383,0.058911033,0.3274109364,0.0793978125,-0.2324830443,-0.3417497873,-0.1378064603,-0.2633539438,-0.0358989649,-0.2961303592,0.139822796,-0.3899917305,0.2122469544,0.0217778143,0.3343012631,0.3400411606,-0.1570483744,-0.1163877994,-0.1058411747,-0.0759940818,0.1048949063,0.2671169639,-0.1453283727,0.0445297509,0.5216935873,0.0945316181,0.1453209072,0.2059812695,0.0934904218,0.3175841272,0.0268480331,0.232798025,-0.3673285842,-0.3574232161,-0.0302303005,0.05845784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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3707","title":"`.select`: unexpected behavior with `indices`","comments":"I agree. I think `index % len(dset)` was used to support negative indices.\r\n\r\nI think this needs to be fixed in `datasets.formatting.formatting._check_valid_index_key` if I'm not mistaken","body":"## Describe the bug\r\nThe `.select` method will not throw when sending `indices` bigger than the dataset length; `indices` will be wrapped instead. This behavior is not documented anywhere, and is not intuitive. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset\r\nds = Dataset.from_dict({\"text\": [\"d\", \"e\", \"f\"], \"label\": [4, 5, 6]})\r\nres1 = ds.select([1, 2, 3])['text']\r\nres2 = ds.select([1000])['text']\r\n```\r\n\r\n## Expected results\r\nBoth results should throw an `Error`.\r\n\r\n## Actual results\r\n`res1` will give `['e', 'f', 'd']`\r\n`res2` will give `['e']`\r\n\r\n## Environment info\r\nBug found from this environment:\r\n- `datasets` version: 1.16.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.7\r\n- PyArrow version: 6.0.1\r\n\r\nIt was also replicated on `master`.\r\n","comment_length":26,"text":"`.select`: unexpected behavior with `indices` \n ## Describe the bug\r\nThe `.select` method will not throw when sending `indices` bigger than the dataset length; `indices` will be wrapped instead. This behavior is not documented anywhere, and is not intuitive. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import Dataset\r\nds = Dataset.from_dict({\"text\": [\"d\", \"e\", \"f\"], \"label\": [4, 5, 6]})\r\nres1 = ds.select([1, 2, 3])['text']\r\nres2 = ds.select([1000])['text']\r\n```\r\n\r\n## Expected results\r\nBoth results should throw an `Error`.\r\n\r\n## Actual results\r\n`res1` will give `['e', 'f', 'd']`\r\n`res2` will give `['e']`\r\n\r\n## Environment info\r\nBug found from this environment:\r\n- `datasets` version: 1.16.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.7\r\n- PyArrow version: 6.0.1\r\n\r\nIt was also replicated on `master`.\r\n \n I agree. I think `index % len(dset)` was used to support negative indices.\r\n\r\nI think this needs to be fixed in `datasets.formatting.formatting._check_valid_index_key` if I'm not mistaken","embeddings":[-0.1581118256,-0.2566547096,-0.0318493024,0.3439424038,0.0620097965,-0.1153480783,0.2147521526,0.1504351199,0.0474208631,0.1787701249,0.023442585,0.4659062922,0.1104497388,0.2063291669,-0.2355959415,-0.0393907763,-0.0478052124,0.1092762873,-0.0718752667,-0.2068987042,-0.3366354704,-0.0356889702,-0.5757123232,0.0837555453,-0.1641563773,-0.0182927679,-0.0621083155,0.1235099211,-0.1070536599,-0.5298037529,0.3057125211,-0.2158895433,-0.0967368782,0.5451513529,-0.0001169744,0.0017412991,0.3811472058,-0.0632788986,-0.3474035859,-0.4936479628,-0.3702419996,-0.1828750819,0.2301588655,-0.3887124956,-0.0179448333,-0.2861152887,-0.4122709036,-0.1727458537,-0.0123755522,0.4159559011,0.2099024951,0.3488657475,-0.0404945575,-0.1349040717,0.1497021914,0.1727237999,-0.0592641495,0.1571769863,0.2399825156,0.3473305702,0.4776534438,0.0112758158,-0.2240045667,-0.2448197752,0.0015204323,0.1492775381,0.2655278742,-0.174933359,0.122537151,0.2352573276,0.6761217117,-0.0804949328,-0.4270874262,-0.2495288551,-0.0066865785,-0.2439664453,0.0939667299,0.2789232433,0.1014233232,0.2211145759,-0.3897400498,0.4294858575,-0.1921057552,0.1596384346,-0.1189881191,0.4052827954,-0.0483344086,0.2288538665,-0.0422389619,-0.0313987881,0.2003205717,-0.2215491533,0.0208235569,0.0096748471,-0.0515562519,-0.159435153,0.0599508807,-0.3780201972,0.1880676895,-0.029572783,-0.0581267327,-0.0670441538,-0.0183467306,0.0469102412,0.449355334,0.1533828527,0.2756320536,0.401892066,0.00294133,-0.1943481863,0.1055427641,0.0045518684,0.274443239,-0.1081838459,0.0777407885,-0.0083325719,0.1049730256,-0.1630516499,-0.708044827,0.2806645334,-0.2781647146,-0.2454129159,0.1855260283,0.0245916229,-0.0857666954,-0.18375884,-0.0526713952,0.3886145949,-0.1980821788,0.3978111744,-0.2580804229,-0.0326536484,0.0735572502,-0.3227912784,-0.1204452887,-0.1943402588,0.0572789013,0.3581944108,0.2798860073,-0.049946636,-0.0663669482,-0.1996941119,0.5739776492,0.429505229,-0.3382675052,0.3253776133,0.1038052142,-0.5640048981,-0.1135744527,0.5444083214,-0.2942146361,-0.1887836158,-0.0125475042,0.1450985223,-0.0688818991,0.029112909,0.064765662,0.2302652597,0.1515112668,0.0503363535,0.3318602145,-0.2615749538,-0.2329056859,-0.3144554794,0.3786612749,0.193001017,-0.7006777525,0.215425238,0.035812851,0.1456594616,0.2246702462,0.3364183009,-0.2049576342,-0.2759767473,-0.171154961,0.2950776815,-0.0663600564,-0.0869260356,-0.4678588808,0.1264016777,-0.1955089867,0.1037254184,-0.0640202388,-0.0420602076,0.0678206682,-0.0075868587,0.4078871608,0.1847688705,0.0147881033,-0.1353029162,-0.3584272265,-0.0568569563,0.4799253941,-0.0868812203,0.0836014748,-0.0784630328,-0.1028895453,-0.3444367051,0.351819396,0.1150488555,-0.1615842283,-0.0586439073,0.2709873617,0.0350194313,0.1700571179,-0.2695848644,0.03007273,0.2179637253,0.1442894787,-0.0557779111,-0.2543896139,-0.1448678672,-0.4641844332,0.1933853924,-0.0843385011,0.0407844745,0.0337512456,-0.2736566961,0.1564758122,0.3396391273,-0.0660395771,-0.0590916649,-0.0052248733,0.0466036424,-0.0231138878,0.2207495421,0.0115701668,-0.1267050356,-0.4134861827,0.0874484181,0.3883929253,-0.1485215425,-0.1563797891,0.400362879,0.0342124403,-0.4754242599,-0.4042356908,-0.175787285,0.1814812124,-0.3511995971,-0.0058648782,0.3633110225,0.2903061211,-0.0599071011,-0.2198096514,0.4935309887,-0.6418589354,0.2648921907,-0.3211603463,-0.1800986677,-0.0920274034,0.0240429472,-0.1156705096,-0.3568778038,-0.2840332985,-0.0293388069,0.0372815244,0.0875625685,-0.4061092734,0.1337350905,0.2475755215,-0.0095978882,0.2073511481,0.2014664561,0.1676612347,0.2067384273,-0.1180419475,-0.1693270355,0.4711149633,0.2670166492,0.1800027788,-0.0531017445,-0.1691519469,-0.3011798263,0.1585734487,0.1537411362,-0.2510235012,0.2746016085,0.2456737608,-0.0064884056,-0.1427149326,0.3626933098,0.0325508043,-0.1702232957,-0.1921850592,0.4608003199,-0.5085989237,-0.2869627476,-0.058348611,-0.3509469628,0.0529568009,-0.2076448202,0.1111565158,-0.11582499,-0.2791835666,0.0692230389,-0.1236802116,-0.1001536995,0.187332958,-0.2519260943,0.0819239169,0.0548425801,-0.1672561169,-0.0330089331,-0.0253001824,0.1886255294,0.106813848,0.3479966819,-0.2152612805,-0.0165053643,-0.4353421032,-0.0057928786,-0.2445293367,0.4042944014,0.0701480582,0.0422775932,-0.0154042132,0.0650909021,0.3432338834,0.2335931212,0.0114489282,0.1327450275,0.3150341213,-0.0373269096,-0.2916518152,-0.2902848423,-0.3030243814,-0.2122081816,0.4115201235,-0.0895920396,0.0884501636,0.4013747275,0.3077822924,-0.1048998907,0.1910101324,0.1407678723,-0.2120799869,0.1132866219,0.4186188579,0.0470172949,-0.2938953936,-0.2615655363,-0.0383792929,-0.2677470744,0.0062107309,-0.2143516093,0.1324631125,-0.2953748405,0.1502756625,0.1504857987,0.3297008276,0.257725656,-0.0727986321,-0.1031320095,-0.0988803804,-0.1179579049,-0.0469378345,0.1458900273,-0.0731736049,0.0986143947,0.3962852359,0.0123126172,0.1597640663,0.2040556222,0.0937812701,0.2832610011,-0.096777983,0.3331476152,-0.282263875,-0.3551945388,0.001750648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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3706","title":"Unable to load dataset 'big_patent'","comments":"Hi @ankitk2109,\r\n\r\nHave you tried passing the split name with the keyword `split=`? See e.g. an example in our Quick Start docs: https:\/\/huggingface.co\/docs\/datasets\/quickstart.html#load-the-dataset-and-model\r\n```python\r\n ds = load_dataset(\"big_patent\", \"d\", split=\"validation\")","body":"## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n","comment_length":29,"text":"Unable to load dataset 'big_patent' \n ## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n \n Hi @ankitk2109,\r\n\r\nHave you tried passing the split name with the keyword `split=`? See e.g. an example in our Quick Start docs: https:\/\/huggingface.co\/docs\/datasets\/quickstart.html#load-the-dataset-and-model\r\n```python\r\n ds = load_dataset(\"big_patent\", \"d\", split=\"validation\")","embeddings":[-0.2909578085,-0.44364205,-0.0362718143,0.4938032925,0.3471500278,0.1680755168,0.1101175994,0.2599285841,0.123266302,0.0131379589,-0.3273754418,0.0412846059,-0.0739787519,0.4042493999,0.2557622492,-0.318164736,0.0480562486,0.0218536705,-0.0038347607,0.0419148728,-0.0644505024,0.2344736904,-0.208348155,0.1764118522,-0.2814987302,-0.0952213556,-0.0727218837,0.4192191064,-0.3228100538,-0.3388760686,0.1521950662,-0.1212521642,0.2596234381,0.529330194,-0.0001148636,0.0244610719,0.2767922878,-0.187952593,-0.1013577059,-0.1539053321,-0.2347305566,-0.2031393647,-0.059402898,-0.1751500517,-0.0745704174,-0.2342299223,-0.1639535278,0.0179501623,0.5242285132,0.2934007943,0.2369320393,0.4707919061,0.2136581838,-0.3216651082,0.0181708019,0.0262763295,-0.1019209996,0.4350251555,0.0529353395,-0.3723367453,0.0968300328,0.2172873467,-0.0176996887,-0.003792454,0.393597126,0.0606079362,-0.0950684622,-0.2639677823,0.0590642244,0.5966840386,0.2717687488,-0.0618392453,-0.4950475991,-0.3204409182,-0.0488474332,-0.326005429,0.1925781667,0.3023129404,0.0324415341,0.2788945436,-0.1315791756,-0.1124486029,-0.1943399012,0.1674503088,-0.0741977245,0.0530179553,-0.179146558,-0.0153050525,0.3501121402,-0.0100653609,-0.404658258,-0.0514153726,-0.0997564867,0.2872427702,-0.2861612141,-0.0864909887,0.0252728015,0.4249366224,0.5065218806,0.115453206,-0.0678641126,-0.1744180471,-0.0453033634,0.1938708127,0.2006927729,0.268291831,-0.052797474,0.1033293903,0.2763952613,0.3429692388,0.0919481665,-0.121598132,-0.1864951998,-0.2666000724,-0.07180392,-0.0970724523,0.285251379,-0.1995940804,-0.4697966576,0.2665925324,-0.2123701423,-0.0105020655,0.1111721098,0.5680711269,-0.2093000859,-0.0777122006,-0.0942931026,0.315357089,-0.1798445433,-0.1612043232,-0.2451562434,0.1743841022,-0.2039025873,0.0577221811,0.1472332329,-0.4627150595,0.589443326,-0.0887099504,0.1616475135,-0.1669939756,-0.3689382374,-0.031877432,-0.2539183199,0.2555697858,-0.0759890974,0.0002495767,0.2092807144,-0.1665753871,-0.1523058563,0.0325098112,-0.0887487605,-0.3188486099,-0.192386508,0.1912493259,-0.4124349654,0.0181362014,-0.316375792,0.0077414354,0.0979570672,-0.0280898269,-0.0131845847,0.0393363684,-0.1955822408,-0.2080342174,0.4114389122,0.5706096888,-0.3228745162,-0.1559971869,0.0030290037,-0.2494296581,-0.2029290497,0.2870931029,-0.2368375808,0.4035827816,-0.3486957848,0.3338913918,0.4181254506,-0.2629847527,-0.4035798013,0.2390343845,-0.365172267,0.112225607,0.1041674912,-0.2092903703,-0.0445176139,-0.0147860302,0.1727684587,0.337143302,-0.0864036009,0.0006531463,-0.2518474162,-0.2371661365,-0.0473306924,0.2649086416,0.1543023288,0.1263296604,0.0728127882,0.1140632182,0.4183441103,0.1006924883,-0.1012757048,0.2235410959,0.2335946858,0.1602322459,0.0199706256,-0.3511474431,-0.3536374867,0.3134797812,-0.0480204932,-0.1751310378,0.036445912,-0.1340466887,-0.6375268698,0.1087989435,-0.2396834195,-0.3147512376,0.0723594874,-0.0229374208,0.3659430444,0.0453941487,-0.3204306662,0.4340825379,0.0401625782,0.2399395704,-0.373611629,0.2210837752,-0.1569570899,-0.0406738669,-0.0623741187,0.0868031085,0.1759711206,-0.235268414,-0.0548081957,0.497830987,-0.0805376098,-0.0629274771,-0.1056549251,0.0620635785,0.1347397566,-0.2888620198,-0.0040490576,-0.0082370862,0.1666671336,0.0626885965,-0.174999848,0.4085611701,-0.2471392751,0.0140142897,0.0118559543,0.0320317894,0.2807125151,-0.350015372,-0.039045807,-0.1619247794,0.4750182033,-0.0244542025,0.1674619168,-0.0612960272,-0.3079705238,-0.2400860488,0.3339612484,-0.1617390364,-0.1153143868,0.0595946573,-0.265471369,0.2189455926,0.0151332496,0.0353749134,0.6804064512,0.2215873897,-0.199349314,0.174061358,0.138422817,-0.1805367768,0.2058750391,0.0734763443,0.0932248309,0.2970503867,-0.0252154917,-0.0167847238,-0.3723252416,-0.1138726473,0.1259740889,0.1397290081,-0.4085558057,0.1293452829,-0.1672914028,-0.1413541138,-0.2781369686,-0.037217956,-0.2275803834,-0.0807277784,0.0363981538,0.3805219233,0.0279494841,0.105541423,-0.2249004245,0.0588473417,-0.0316982083,-0.0766158104,-0.2205594778,-0.0543790124,-0.1792729199,0.0848843604,0.3072938323,-0.0865305215,0.3997852802,-0.1781784594,-0.1971810013,-0.1459721923,-0.2057650387,0.0350717381,-0.1505807936,0.4368481636,0.1399733275,0.3322175443,0.0083740931,-0.1025540307,0.4151856005,-0.0330931321,-0.2525492311,0.0267353784,0.2783600986,-0.1495124996,-0.0831844285,-0.3199345171,-0.2588192523,-0.3746390045,0.3599083126,-0.2315447032,-0.100081645,0.1623061299,0.0548077524,0.1598539799,-0.2708934546,0.0745476186,-0.2919233441,-0.1851168871,0.311188668,-0.2600325346,-0.3558492661,0.006025217,-0.0150407497,-0.1144625172,-0.0508814603,-0.401224345,-0.1163474619,-0.1760311127,0.1871656924,-0.1198232993,0.1779903173,0.220196858,-0.200708434,0.0407258943,-0.1618645936,-0.1475538015,-0.0683341175,-0.0719266236,0.3799078763,-0.1347138584,0.4567934573,-0.0645060688,0.6228042841,0.2473705262,0.079592742,0.356331408,-0.1909286827,0.3116853237,-0.1614795923,-0.5098656416,0.1164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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3706","title":"Unable to load dataset 'big_patent'","comments":"Hi @albertvillanova,\r\n\r\nThanks for your response.\r\n\r\nYes, I tried the `split='validation'` as well. But getting the same issue. ","body":"## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n","comment_length":18,"text":"Unable to load dataset 'big_patent' \n ## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n \n Hi @albertvillanova,\r\n\r\nThanks for your response.\r\n\r\nYes, I tried the `split='validation'` as well. But getting the same issue. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3706","title":"Unable to load dataset 'big_patent'","comments":"I'm sorry, but I can't reproduce your problem:\r\n```python\r\nIn [5]: ds = load_dataset(\"big_patent\", \"d\", split=\"validation\")\r\nDownloading and preparing dataset big_patent\/d (download: 6.01 GiB, generated: 169.61 MiB, post-processed: Unknown size, total: 6.17 GiB) to ...\/.cache\/big_patent\/d\/1.0.0\/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c...\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6.45G\/6.45G [27:36<00:00, 3.89MB\/s]\r\nExtracting data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [03:18<00:00, 66.08s\/it]\r\nDataset big_patent downloaded and prepared to ...\/.cache\/big_patent\/d\/1.0.0\/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c. Subsequent calls will reuse this data.    \r\n\r\nIn [6]: ds\r\nOut[6]: \r\nDataset({\r\n    features: ['description', 'abstract'],\r\n    num_rows: 565\r\n})\r\n","body":"## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n","comment_length":72,"text":"Unable to load dataset 'big_patent' \n ## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n \n I'm sorry, but I can't reproduce your problem:\r\n```python\r\nIn [5]: ds = load_dataset(\"big_patent\", \"d\", split=\"validation\")\r\nDownloading and preparing dataset big_patent\/d (download: 6.01 GiB, generated: 169.61 MiB, post-processed: Unknown size, total: 6.17 GiB) to ...\/.cache\/big_patent\/d\/1.0.0\/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c...\r\nDownloading data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6.45G\/6.45G [27:36<00:00, 3.89MB\/s]\r\nExtracting data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [03:18<00:00, 66.08s\/it]\r\nDataset big_patent downloaded and prepared to ...\/.cache\/big_patent\/d\/1.0.0\/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c. Subsequent calls will reuse this data.    \r\n\r\nIn [6]: ds\r\nOut[6]: \r\nDataset({\r\n    features: ['description', 'abstract'],\r\n    num_rows: 565\r\n})\r\n","embeddings":[-0.3682879508,-0.0730187818,-0.0981642529,0.4866808355,0.2612976134,0.1087047607,0.1902629137,0.4533426464,0.1328500062,0.0507910624,-0.1489646435,0.0408351123,-0.0469962768,0.1007057205,0.2328070104,-0.2097253501,-0.0155002773,0.0232882351,0.0051833256,0.0060296245,-0.0987461507,0.129907608,-0.2687000334,0.1135137528,-0.368141681,-0.0815721452,-0.0396402664,0.3741674721,-0.338365078,-0.4146147668,0.1638189554,-0.2265547961,0.3576312661,0.5622879863,-0.000109203,0.0047574551,0.3318439126,-0.1561055928,-0.0908021703,-0.112390928,-0.2417168766,-0.3539105952,-0.17382662,-0.1823471785,-0.0461618826,-0.3202545047,-0.0948261842,-0.0314359926,0.4347105324,0.4495943785,0.2745093703,0.1924174577,0.2096430808,-0.2114297301,0.1761598736,-0.0313269831,-0.1019821763,0.4435688257,0.1698704809,-0.3919801712,0.0784895495,0.0916571096,-0.0897388533,-0.0348170772,0.1716111302,0.0303962864,0.015523619,-0.2665235102,0.0745535865,0.4592535496,0.5383386016,-0.1158987358,-0.5281611681,-0.1376314908,-0.0229906514,-0.4335496724,0.2548339963,0.3805737495,0.065277651,0.2594262362,-0.1121233255,0.0174307022,-0.2773415446,0.1851326823,-0.1887109876,0.0820152164,-0.0088843433,-0.0442150086,0.1800851226,0.0482837446,-0.0997322053,-0.1995606124,-0.0826144964,0.2931369841,-0.4373385012,0.0057924893,-0.0829809308,0.2763950229,0.3701728582,0.223444581,0.0901962444,-0.1932974905,0.006016029,0.1660112739,0.0932414159,0.392392695,-0.2494475245,0.117295444,0.2309470177,0.1474417448,-0.0217086878,-0.0513451472,-0.340130955,-0.3856710196,0.1329058856,0.0421642475,0.2450291514,-0.2601640821,-0.4902772903,0.3435234129,-0.3494569063,0.0326005854,-0.0159837194,0.4445595741,-0.3029431999,-0.0181968287,-0.038357418,0.2081557661,-0.1018218994,-0.2429646552,-0.2469913363,0.1992968023,-0.2560521662,-0.0101011796,0.289175421,-0.3413996994,0.4819457233,-0.0411853939,0.1472111344,-0.0950166956,-0.1882989705,-0.16976583,-0.1626415551,0.3517328203,-0.0833038092,-0.0850974917,0.0991726145,-0.0794533566,-0.0423816703,0.2965618372,-0.0518526025,-0.2915807962,-0.2040316314,0.2417541444,-0.3858859241,0.0449703671,-0.3335243165,-0.0680684075,0.1863616407,-0.0831084698,-0.0334098265,-0.1858043373,0.000597592,-0.293923676,0.3789961636,0.3985200226,-0.4452841282,0.0998215824,-0.0410423428,-0.1348038614,0.009615181,0.3066475689,-0.2457949966,0.4283972085,-0.2598330975,0.3813353181,0.4965125024,-0.2858560085,-0.6028748155,0.2717935741,-0.3184297085,0.1841652542,0.0993255526,-0.1399062425,0.0144271115,0.041505117,0.2125439495,0.3780831099,-0.0898913667,-0.0400790907,-0.3875922561,-0.136859715,0.1167166308,0.3031254709,0.2204812914,0.0105653685,0.1338800192,0.0862748772,0.3662292659,0.0936404541,-0.0801283047,0.2044468075,0.321567893,0.014045639,-0.0041480227,-0.2569757998,-0.2751962543,0.3451512754,-0.1342795044,-0.1274589598,0.0611125454,-0.1276059598,-0.5625544786,-0.0039153583,-0.236484617,-0.4119276106,0.1345894188,-0.0561242849,0.3075603545,-0.0052132942,-0.23379004,0.3282643259,0.0816293806,0.0870508254,-0.2435968816,0.2091226429,-0.1828648001,-0.0927363932,-0.0305683818,0.0262119044,0.2136001736,-0.2828761041,-0.0948189124,0.3661102951,-0.0614993908,-0.1923974752,-0.1102206931,0.0760068223,0.0969966799,-0.2232278138,0.0849786103,0.0815982148,0.1878359318,0.0591500998,-0.0880457088,0.2846266329,-0.1370981187,-0.0719402134,-0.0875009224,-0.030096706,0.3015582263,-0.4204992056,0.0413951166,0.0255561359,0.4404284358,0.0460680053,0.1947162002,-0.0206526648,-0.2072464675,-0.2084558308,0.3347267807,-0.2246733755,-0.0893204287,-0.0203356817,-0.2575101256,0.1129042357,-0.0100261215,0.1053454205,0.7213094234,0.2547222078,-0.131949693,0.1307661235,0.1519484818,-0.18834126,0.2060360014,-0.068954505,0.0771053135,0.4563933611,0.057711836,0.0569651276,-0.3148258626,-0.2311693728,0.2920331657,0.2929759026,-0.2381234467,0.1529401243,-0.2461232543,-0.0572576039,-0.2684027255,0.0258297194,-0.1595387757,-0.0763938949,0.0536499433,0.4005663097,0.0726148412,0.1665126979,-0.3220763803,-0.0175275095,0.0549022742,-0.2531180084,-0.1342835724,-0.1346611232,-0.2753431201,0.1478000581,0.3450895846,-0.1414323002,0.3372323513,-0.1169438139,-0.2276068032,-0.1874548644,-0.1681834906,-0.0491427816,-0.0146639589,0.3486142755,-0.0633018538,0.2377136648,0.1413642168,-0.0832337812,0.3019162118,-0.0436513498,-0.2937874496,0.0724476874,0.192750141,-0.1884534806,-0.0800882205,-0.5354232192,-0.3623871207,-0.3503447473,0.172847569,-0.17707856,-0.0157680213,0.1954183429,0.0660064295,0.0286154877,-0.0051231845,-0.0381248407,-0.2999990284,-0.1246536449,0.3988994062,-0.2351685613,-0.3870456517,-0.0070947106,-0.0668070391,-0.0139365681,0.2069350183,-0.476431191,0.0656005219,-0.1482663602,0.2200092822,-0.1942003071,0.0203497633,0.2404978573,-0.1508293301,-0.0282536428,-0.1403853893,0.0345757976,-0.1071346924,0.1019218788,0.4392844141,-0.1323355734,0.3339024484,0.0453205556,0.6579955816,0.1545328498,-0.0078453058,0.4131689072,-0.0669066608,0.2580496967,-0.2004615813,-0.4123344719,0.1697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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3706","title":"Unable to load dataset 'big_patent'","comments":"Maybe you had a connection issue while downloading the file and this was corrupted?\r\nOur cache system uses the file you downloaded first time.\r\nIf so, you could try forcing redownload of the file with:\r\n```python\r\nds = load_dataset(\"big_patent\", \"d\", split=\"validation\", download_mode=\"force_redownload\")","body":"## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n","comment_length":42,"text":"Unable to load dataset 'big_patent' \n ## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n \n Maybe you had a connection issue while downloading the file and this was corrupted?\r\nOur cache system uses the file you downloaded first time.\r\nIf so, you could try forcing redownload of the file with:\r\n```python\r\nds = load_dataset(\"big_patent\", \"d\", split=\"validation\", download_mode=\"force_redownload\")","embeddings":[-0.3243026733,-0.0634642616,-0.091080308,0.4624115527,0.2718910277,0.1327950805,0.1778509617,0.3625105917,0.211462453,0.0136449933,-0.1294032186,0.0310822893,-0.0108540365,0.0591870472,0.2250687778,-0.192853868,-0.0490768179,0.0843680501,0.0163286887,0.0109159583,-0.0123168044,0.0591580682,-0.1977817714,0.1106584296,-0.3402142227,-0.0736958608,-0.044906944,0.299146533,-0.2972818911,-0.5419922471,0.1852142662,-0.1503287554,0.3482485414,0.5717642307,-0.000107195,-0.0292219799,0.2905396223,-0.1144514307,-0.076279439,-0.0482268743,-0.1866549253,-0.2939330041,-0.1440649331,-0.1638743281,-0.0943338946,-0.2520746291,-0.0719059482,-0.0246763341,0.5176610351,0.4919293821,0.2957119346,0.1897821426,0.2192977816,-0.2458821237,0.2262987196,-0.1256299764,-0.0711132139,0.4544146657,0.1350763291,-0.3757109642,0.060201332,0.1330727041,-0.0980936885,0.0250314903,0.2223377079,0.0723636299,0.003093312,-0.2420899719,0.0719982907,0.4272085726,0.5574073792,-0.1127725095,-0.4370565414,-0.1075201109,-0.0166957527,-0.4655809999,0.2414692044,0.3366621733,0.0591367856,0.2617325485,-0.0604626387,-0.0655900016,-0.1916041821,0.1513997167,-0.2231407166,0.0489345044,0.007790924,-0.0555403903,0.2374736369,0.0582835339,-0.0123020923,-0.0694663525,-0.1352882087,0.2155119181,-0.3910676539,0.0244844928,-0.0025069481,0.2967518866,0.3203063607,0.1771893352,0.1319128275,-0.1782511324,0.0175039973,0.178771615,0.0905295089,0.392814815,-0.2204331309,0.0851108506,0.286680162,0.1055884734,-0.0768859312,-0.0683945939,-0.2790606618,-0.3359855413,0.2583765388,0.0425597243,0.2092514336,-0.2910451591,-0.412679553,0.3022851646,-0.2766718864,-0.0274525899,-0.0777544454,0.4447163641,-0.3319360912,-0.0402841754,-0.0602872707,0.147028327,-0.1023993492,-0.2291830927,-0.2446246594,0.1194993556,-0.2428130507,0.0555394776,0.3281067014,-0.3929649591,0.50782305,-0.0639049783,0.1198013127,-0.1008846089,-0.1366113126,-0.11568854,-0.1333393753,0.3366374075,-0.1022295579,-0.0265726857,0.1297363043,-0.1566541493,-0.0554902926,0.2164727002,-0.2023093253,-0.3174774051,-0.1730093956,0.2816120386,-0.380253464,-0.0488849357,-0.2695587575,-0.1087642759,0.2363456488,-0.138542071,-0.0110950265,-0.2146476954,0.0919430032,-0.3260649145,0.3403561115,0.3661621809,-0.4667423368,0.0819877759,-0.0704268813,-0.1347352862,-0.0442781299,0.2992789745,-0.33039096,0.4181740284,-0.2750754356,0.3968857229,0.5123382211,-0.3021091819,-0.6816371679,0.1778944284,-0.3507903218,0.107715793,0.0360146426,-0.0618215762,0.0831353143,0.0922444984,0.196787864,0.3217496574,-0.0268769581,0.0362024158,-0.3494177163,-0.130664587,0.1429083198,0.227196455,0.2000953257,0.0204344261,0.0530698225,0.077321738,0.359380722,0.0898349881,-0.1258370876,0.2392069697,0.279654175,0.1026581973,0.0166553035,-0.2277117372,-0.1800855696,0.2920030355,-0.1472926438,-0.1182714254,0.0482380129,-0.1780235916,-0.5588321686,-0.058097735,-0.2762337923,-0.4082072973,0.1732597351,0.0726273134,0.3352765143,0.0104417065,-0.2582805753,0.3409456313,0.1264092028,0.0959098637,-0.2306629121,0.1850986332,-0.2506646514,-0.1188307181,0.0193346571,-0.0312438533,0.2203681469,-0.2212936431,-0.0797730833,0.4032303691,-0.0821371973,-0.1896848381,-0.1210330576,0.0764887482,0.0655593425,-0.2793646157,0.0790907368,0.1217743009,0.1629796922,0.0521816239,-0.0955395326,0.2277709842,-0.0718451589,-0.1557538211,-0.098663196,-0.0239338484,0.3139716387,-0.4008608758,0.0237509441,0.0446406379,0.3557163775,-0.0690018982,0.1074241549,-0.0118766874,-0.2305865139,-0.1248118579,0.4804252982,-0.1425725371,-0.0000345461,-0.0826224014,-0.3145105839,0.1329973042,-0.026739439,0.0822155774,0.660972774,0.252843231,-0.1144786477,0.122339882,0.0645644441,-0.1867901832,0.1505004168,-0.1246921122,0.0413972922,0.3728896379,0.1470932811,0.0481893793,-0.3953385353,-0.1462314874,0.2132281959,0.3074476719,-0.2286889553,0.1710043103,-0.2927307785,-0.0429802984,-0.3281579912,0.0031411583,-0.1743630916,-0.0726773217,0.0954926983,0.3861579299,0.1364307851,0.1847362816,-0.2805730999,0.0419796966,0.0612311661,-0.2250143886,-0.1260474473,-0.1670670956,-0.299759388,0.1476459205,0.2431309223,-0.0989760682,0.3927187324,-0.1154504195,-0.2068323344,-0.1952327043,-0.1783994585,-0.1264044642,-0.0416505598,0.2738985121,-0.0541381575,0.3426373303,0.115601778,-0.1209443435,0.3319731057,-0.1226029992,-0.2567352653,0.0401423164,0.2377329767,-0.1732020825,-0.1162685752,-0.5754587054,-0.2668613791,-0.3083139062,0.1720191836,-0.1370240748,-0.0284260623,0.0961335599,0.0974492207,0.0644323528,-0.0973830074,0.047364492,-0.3309240937,-0.1606755853,0.426592648,-0.2678788304,-0.4273442626,0.0102593452,-0.0296288878,-0.0415553339,0.1333106607,-0.5410686731,0.0149183199,-0.1611468196,0.1985553056,-0.1386070698,0.0682027489,0.3129092157,-0.0891513824,-0.0552661717,-0.0720626861,-0.032141652,-0.1337406933,0.0926932618,0.5221737027,-0.1390981376,0.2959141731,-0.03000791,0.6365861297,0.1386316866,-0.0699681416,0.3978078365,-0.1474407911,0.2591441274,-0.1688461602,-0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3706","title":"Unable to load dataset 'big_patent'","comments":"I am able to download the dataset with ``` download_mode=\"force_redownload\"```. As you mentioned it was an issue with the cached version which was failed earlier due to a network issue. I am closing the issue now, once again thank you.","body":"## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n","comment_length":40,"text":"Unable to load dataset 'big_patent' \n ## Describe the bug\r\nUnable to load the \"big_patent\" dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('big_patent', 'd', 'validation')\r\n```\r\n\r\n## Expected results\r\nDownload big_patents' validation split from the 'd' subset\r\n\r\n## Getting an error saying:\r\n{FileNotFoundError}Local file ..\\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.18.3\r\n- Platform: Windows\r\n- Python version:3.8\r\n- PyArrow version:7.0.0\r\n \n I am able to download the dataset with ``` download_mode=\"force_redownload\"```. As you mentioned it was an issue with the cached version which was failed earlier due to a network issue. I am closing the issue now, once again thank 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3704","title":"OSCAR-2109 datasets are misaligned and truncated","comments":"Hi @adrianeboyd, thanks for reporting.\r\n\r\nThere is indeed a bug in that community dataset:\r\nLine:\r\n```python\r\nmetadata_and_text_files = list(zip(metadata_files, text_files))\r\n``` \r\nshould be replaced with\r\n```python\r\nmetadata_and_text_files = list(zip(sorted(metadata_files), sorted(text_files)))\r\n```\r\n\r\nI am going to contact their owners (https:\/\/huggingface.co\/oscar-corpus) in order to inform them about the bug.\r\n\r\nI keep you informed.","body":"## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.","comment_length":51,"text":"OSCAR-2109 datasets are misaligned and truncated \n ## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data. \n Hi @adrianeboyd, thanks for reporting.\r\n\r\nThere is indeed a bug in that community dataset:\r\nLine:\r\n```python\r\nmetadata_and_text_files = list(zip(metadata_files, text_files))\r\n``` \r\nshould be replaced with\r\n```python\r\nmetadata_and_text_files = list(zip(sorted(metadata_files), sorted(text_files)))\r\n```\r\n\r\nI am going to contact their owners (https:\/\/huggingface.co\/oscar-corpus) in order to inform them about the bug.\r\n\r\nI keep you 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3704","title":"OSCAR-2109 datasets are misaligned and truncated","comments":"That fix is part of it, but it's clearly not the only issue.\r\n\r\nI also already contacted the OSCAR creators, but I reported it here because it looked like huggingface members were the main authors in the git history. Is there a better place to have reported this?","body":"## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.","comment_length":48,"text":"OSCAR-2109 datasets are misaligned and truncated \n ## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data. \n That fix is part of it, but it's clearly not the only issue.\r\n\r\nI also already contacted the OSCAR creators, but I reported it here because it looked like huggingface members were the main authors in the git history. Is there a better place to have reported 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3704","title":"OSCAR-2109 datasets are misaligned and truncated","comments":"Hello,\r\n\r\nWe've had an issue that could be linked to this one here: https:\/\/github.com\/oscar-corpus\/corpus\/issues\/15.\r\n\r\nI have been spot checking the source (`.txt`\/`.jsonl`) files for a while, and have not found issues, especially in the start\/end of corpora (but I conceed that more integration testing would be necessary on our side).\r\n\r\nThe text and metadata files are designed to be used in sync (with `lang_part_n.txt` and `lang_meta_part_n.jsonl` working together), while staying independent from part to part, so that anyone could randomly choose a part and work with it.\r\n\r\nThe fix @albertvillanova proposed should fix the problem, as the parts will be in sync again.\r\n\r\nLet me know if you need help or more details, I'd be glad to help!","body":"## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.","comment_length":118,"text":"OSCAR-2109 datasets are misaligned and truncated \n ## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data. \n Hello,\r\n\r\nWe've had an issue that could be linked to this one here: https:\/\/github.com\/oscar-corpus\/corpus\/issues\/15.\r\n\r\nI have been spot checking the source (`.txt`\/`.jsonl`) files for a while, and have not found issues, especially in the start\/end of corpora (but I conceed that more integration testing would be necessary on our side).\r\n\r\nThe text and metadata files are designed to be used in sync (with `lang_part_n.txt` and `lang_meta_part_n.jsonl` working together), while staying independent from part to part, so that anyone could randomly choose a part and work with it.\r\n\r\nThe fix @albertvillanova proposed should fix the problem, as the parts will be in sync again.\r\n\r\nLet me know if you need help or more details, I'd be glad to help!","embeddings":[-0.3790076673,0.3509356081,0.0164458845,0.5407813191,-0.1386850923,0.0147257438,0.2296266258,0.3700638413,-0.3047657311,-0.0990706384,0.0361928307,-0.0641968846,0.1635020226,-0.0632027015,0.0641614646,-0.2708619833,0.1433135867,0.0770916864,0.0587199628,-0.0915282145,-0.1945028901,0.2278799266,-0.2512326241,-0.1715530157,-0.4618670344,0.0205017552,-0.1564037949,0.1160182133,-0.1815607995,-0.2947756052,0.0523528866,-0.118448168,-0.1783859283,0.4717750549,-0.0001191647,-0.332468003,0.2028630525,0.0530874468,-0.2554106712,-0.0556327626,-0.1461407393,-0.3505928516,-0.1262875795,-0.0434630439,-0.0790544152,-0.174564153,-0.1623656452,-0.2141503096,0.3381189704,0.3711010218,0.1265106946,0.0639129728,0.1273596883,-0.1510596573,0.2542283833,0.1348651648,-0.1388940066,-0.3037272096,0.3203901947,0.1566210538,-0.0350660197,0.158431381,-0.0297336411,-0.1473465264,0.1379244626,0.0902881175,-0.3786524236,-0.2571187615,0.2216887027,0.2976016104,0.5767251253,-0.2120313942,-0.4630664885,-0.5475748181,0.1078173667,-0.3117791712,0.4378286004,0.4162479341,0.1882850379,-0.013812691,-0.247304827,0.2649748325,-0.0803462714,0.1719249338,-0.5789376497,0.4632933438,0.0772491619,0.0338957831,-0.4923261702,-0.0914476663,0.0862304866,-0.3761126101,-0.1873320937,0.0571788177,-0.2277076989,-0.1457071155,-0.1969701499,-0.1923042089,0.1047644243,0.3106939793,-0.0733263493,-0.0888947397,0.1091103405,-0.2119215131,0.6761890054,0.1046889424,-0.0652909055,0.2076425254,0.0943629593,0.17147699,0.0160182938,0.0890522599,0.1051053628,-0.2882238328,0.1120735779,0.0195242129,0.1282585412,-0.1831296086,-0.230723843,0.2883025706,-0.1379312575,0.1082547083,-0.0461649038,-0.0705253258,-0.5019134879,0.1079166904,-0.0414200723,0.1176008806,-0.1549671888,-0.1128472686,-0.1286266446,-0.0596448295,-0.1675335765,-0.1926116198,0.1582930982,-0.2758381069,0.1505256295,0.2324216664,0.3471394181,-0.1846704632,-0.2321317345,-0.2329635024,0.0747359395,0.1991908997,0.2065338641,0.1880712658,-0.2373024374,0.063236095,-0.254432857,0.1781011969,-0.3618549109,-0.1559672356,-0.1872980893,0.1418364048,-0.2844474316,0.0614295267,0.0626612157,0.2264574617,0.4596114457,0.0929124728,0.019462144,-0.3186949492,-0.2900915444,-0.0254702903,0.1912834197,0.4387021363,-0.2970393598,0.2012897134,-0.2967318296,0.3992405832,0.3132759929,0.2712260485,0.1522270143,0.2627609968,-0.198289752,0.3230545521,0.2010234147,-0.1185871139,-0.2857627869,0.0821363777,0.0881256983,0.3660310209,-0.1722454429,-0.2275576144,0.4341608584,-0.0403528064,0.3588856757,0.5894742608,0.0106053073,0.1733696759,-0.2774277925,-0.119371891,-0.0689152703,0.0889845565,0.3800786734,-0.4034855962,-0.1223550364,-0.0077362754,0.1980925649,0.176506117,0.2641663253,0.0949202999,-0.1784048826,0.2107343376,0.1653486639,-0.3377285302,-0.3772593439,0.109922573,0.0756269321,0.1544352174,0.0418069437,0.0334683582,-0.258023113,-0.066181004,-0.0596408024,-0.0737458318,0.1073911563,0.0354172811,0.0974579304,0.0181609318,-0.1704438478,0.052124545,0.2261193097,0.2308353931,-0.3561495543,0.2964910269,0.0610093474,-0.1259621233,0.0262813885,0.4283260107,0.1682807058,-0.041395247,-0.151622802,0.4449450076,0.2975042164,0.1925300658,-0.4956775904,0.1724902391,0.4749038517,-0.006226053,0.112322703,0.3238494396,0.00992209,-0.0976219997,-0.1129284203,0.521246314,0.0101971626,0.2618319392,-0.0283670016,0.2055058926,0.1805193722,-0.1423949152,-0.0875572562,-0.3197295666,0.2787263989,-0.0231911503,0.0142284557,0.2514231503,-0.3420141637,0.2411614805,0.4854954779,-0.025714485,-0.0791651234,0.1931245476,-0.316960752,-0.1326340437,0.0258187354,0.3226028085,0.1576765478,0.1619859487,0.0172816683,0.266533494,-0.1853713542,-0.3316446245,0.3278588355,0.0851676613,-0.0567742549,0.5718154311,0.2162770778,0.3166656792,-0.1537766159,0.0617133938,-0.1516015083,0.0504466668,-0.4918235838,0.0950645804,-0.2991832793,-0.1825205684,-0.5129337907,-0.1602847427,-0.2074745893,-0.2467242926,0.260092169,0.0254211333,-0.2985143363,0.2057433128,-0.1014931425,0.269506216,-0.1845807433,0.2096012533,-0.146751523,0.0695597678,-0.2421948165,0.0120203532,0.2340933084,0.1246491149,0.1499788165,-0.3004074693,-0.2243103981,-0.3780749142,-0.457000643,0.1865401417,0.0078502307,0.2183189392,0.2282629311,-0.3605905771,-0.0303027183,-0.3667495549,0.1411125064,0.154933989,-0.1107989848,0.1694106609,-0.1644711196,-0.1822965741,-0.102645345,-0.3200548291,-0.0187836364,-0.2177804708,0.0803834349,0.2193825096,0.270870626,0.4646671414,-0.0715001449,-0.5104335546,-0.087614961,0.0743571594,-0.2903060615,-0.207360819,0.3214320242,-0.1233307794,-0.5085403323,-0.2286387682,-0.05329201,-0.1258547604,0.344055444,-0.3664493859,0.0525037684,-0.2484286278,-0.2093068361,-0.0410553925,-0.0645167381,0.1437440664,-0.0874940604,0.0579901338,-0.1850070059,-0.0350375921,0.1543656886,0.0635980144,0.2712463737,-0.3381327093,0.0320667475,-0.0634473711,0.2967006862,0.387837708,-0.0097248359,-0.063828215,0.0608416758,0.0690403804,-0.2515520453,-0.1362679303,0.4068369269,0.0143531412,-0.0109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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3704","title":"OSCAR-2109 datasets are misaligned and truncated","comments":"I'm happy to move the discussion to the other repo!\r\n\r\nMerely sorting the files only **maybe** fixes the processing of the first part. If the first part contains non-unix newlines, it will still be misaligned\/truncated, and all the following parts will be truncated with incorrect text offsets and metadata due the offset and newline bugs.","body":"## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.","comment_length":55,"text":"OSCAR-2109 datasets are misaligned and truncated \n ## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data. \n I'm happy to move the discussion to the other repo!\r\n\r\nMerely sorting the files only **maybe** fixes the processing of the first part. If the first part contains non-unix newlines, it will still be misaligned\/truncated, and all the following parts will be truncated with incorrect text offsets and metadata due the offset and newline 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3704","title":"OSCAR-2109 datasets are misaligned and truncated","comments":"Hi @Uinelj, This is a total noobs question but how can I integrate that bugfix into my code? I reinstalled the datasets library this time from source. Should that have fixed the issue? I am still facing the misalignment issue. Do I need to download the dataset from scratch?","body":"## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.","comment_length":49,"text":"OSCAR-2109 datasets are misaligned and truncated \n ## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data. \n Hi @Uinelj, This is a total noobs question but how can I integrate that bugfix into my code? I reinstalled the datasets library this time from source. Should that have fixed the issue? I am still facing the misalignment issue. Do I need to download the dataset from scratch?","embeddings":[-0.3790076673,0.3509356081,0.0164458845,0.5407813191,-0.1386850923,0.0147257438,0.2296266258,0.3700638413,-0.3047657311,-0.0990706384,0.0361928307,-0.0641968846,0.1635020226,-0.0632027015,0.0641614646,-0.2708619833,0.1433135867,0.0770916864,0.0587199628,-0.0915282145,-0.1945028901,0.2278799266,-0.2512326241,-0.1715530157,-0.4618670344,0.0205017552,-0.1564037949,0.1160182133,-0.1815607995,-0.2947756052,0.0523528866,-0.118448168,-0.1783859283,0.4717750549,-0.0001191647,-0.332468003,0.2028630525,0.0530874468,-0.2554106712,-0.0556327626,-0.1461407393,-0.3505928516,-0.1262875795,-0.0434630439,-0.0790544152,-0.174564153,-0.1623656452,-0.2141503096,0.3381189704,0.3711010218,0.1265106946,0.0639129728,0.1273596883,-0.1510596573,0.2542283833,0.1348651648,-0.1388940066,-0.3037272096,0.3203901947,0.1566210538,-0.0350660197,0.158431381,-0.0297336411,-0.1473465264,0.1379244626,0.0902881175,-0.3786524236,-0.2571187615,0.2216887027,0.2976016104,0.5767251253,-0.2120313942,-0.4630664885,-0.5475748181,0.1078173667,-0.3117791712,0.4378286004,0.4162479341,0.1882850379,-0.013812691,-0.247304827,0.2649748325,-0.0803462714,0.1719249338,-0.5789376497,0.4632933438,0.0772491619,0.0338957831,-0.4923261702,-0.0914476663,0.0862304866,-0.3761126101,-0.1873320937,0.0571788177,-0.2277076989,-0.1457071155,-0.1969701499,-0.1923042089,0.1047644243,0.3106939793,-0.0733263493,-0.0888947397,0.1091103405,-0.2119215131,0.6761890054,0.1046889424,-0.0652909055,0.2076425254,0.0943629593,0.17147699,0.0160182938,0.0890522599,0.1051053628,-0.2882238328,0.1120735779,0.0195242129,0.1282585412,-0.1831296086,-0.230723843,0.2883025706,-0.1379312575,0.1082547083,-0.0461649038,-0.0705253258,-0.5019134879,0.1079166904,-0.0414200723,0.1176008806,-0.1549671888,-0.1128472686,-0.1286266446,-0.0596448295,-0.1675335765,-0.1926116198,0.1582930982,-0.2758381069,0.1505256295,0.2324216664,0.3471394181,-0.1846704632,-0.2321317345,-0.2329635024,0.0747359395,0.1991908997,0.2065338641,0.1880712658,-0.2373024374,0.063236095,-0.254432857,0.1781011969,-0.3618549109,-0.1559672356,-0.1872980893,0.1418364048,-0.2844474316,0.0614295267,0.0626612157,0.2264574617,0.4596114457,0.0929124728,0.019462144,-0.3186949492,-0.2900915444,-0.0254702903,0.1912834197,0.4387021363,-0.2970393598,0.2012897134,-0.2967318296,0.3992405832,0.3132759929,0.2712260485,0.1522270143,0.2627609968,-0.198289752,0.3230545521,0.2010234147,-0.1185871139,-0.2857627869,0.0821363777,0.0881256983,0.3660310209,-0.1722454429,-0.2275576144,0.4341608584,-0.0403528064,0.3588856757,0.5894742608,0.0106053073,0.1733696759,-0.2774277925,-0.119371891,-0.0689152703,0.0889845565,0.3800786734,-0.4034855962,-0.1223550364,-0.0077362754,0.1980925649,0.176506117,0.2641663253,0.0949202999,-0.1784048826,0.2107343376,0.1653486639,-0.3377285302,-0.3772593439,0.109922573,0.0756269321,0.1544352174,0.0418069437,0.0334683582,-0.258023113,-0.066181004,-0.0596408024,-0.0737458318,0.1073911563,0.0354172811,0.0974579304,0.0181609318,-0.1704438478,0.052124545,0.2261193097,0.2308353931,-0.3561495543,0.2964910269,0.0610093474,-0.1259621233,0.0262813885,0.4283260107,0.1682807058,-0.041395247,-0.151622802,0.4449450076,0.2975042164,0.1925300658,-0.4956775904,0.1724902391,0.4749038517,-0.006226053,0.112322703,0.3238494396,0.00992209,-0.0976219997,-0.1129284203,0.521246314,0.0101971626,0.2618319392,-0.0283670016,0.2055058926,0.1805193722,-0.1423949152,-0.0875572562,-0.3197295666,0.2787263989,-0.0231911503,0.0142284557,0.2514231503,-0.3420141637,0.2411614805,0.4854954779,-0.025714485,-0.0791651234,0.1931245476,-0.316960752,-0.1326340437,0.0258187354,0.3226028085,0.1576765478,0.1619859487,0.0172816683,0.266533494,-0.1853713542,-0.3316446245,0.3278588355,0.0851676613,-0.0567742549,0.5718154311,0.2162770778,0.3166656792,-0.1537766159,0.0617133938,-0.1516015083,0.0504466668,-0.4918235838,0.0950645804,-0.2991832793,-0.1825205684,-0.5129337907,-0.1602847427,-0.2074745893,-0.2467242926,0.260092169,0.0254211333,-0.2985143363,0.2057433128,-0.1014931425,0.269506216,-0.1845807433,0.2096012533,-0.146751523,0.0695597678,-0.2421948165,0.0120203532,0.2340933084,0.1246491149,0.1499788165,-0.3004074693,-0.2243103981,-0.3780749142,-0.457000643,0.1865401417,0.0078502307,0.2183189392,0.2282629311,-0.3605905771,-0.0303027183,-0.3667495549,0.1411125064,0.154933989,-0.1107989848,0.1694106609,-0.1644711196,-0.1822965741,-0.102645345,-0.3200548291,-0.0187836364,-0.2177804708,0.0803834349,0.2193825096,0.270870626,0.4646671414,-0.0715001449,-0.5104335546,-0.087614961,0.0743571594,-0.2903060615,-0.207360819,0.3214320242,-0.1233307794,-0.5085403323,-0.2286387682,-0.05329201,-0.1258547604,0.344055444,-0.3664493859,0.0525037684,-0.2484286278,-0.2093068361,-0.0410553925,-0.0645167381,0.1437440664,-0.0874940604,0.0579901338,-0.1850070059,-0.0350375921,0.1543656886,0.0635980144,0.2712463737,-0.3381327093,0.0320667475,-0.0634473711,0.2967006862,0.387837708,-0.0097248359,-0.063828215,0.0608416758,0.0690403804,-0.2515520453,-0.1362679303,0.4068369269,0.0143531412,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3704","title":"OSCAR-2109 datasets are misaligned and truncated","comments":"Sorry @norakassner for the late reply.\r\n\r\nThere are indeed several issues creating the misalignment, as @adrianeboyd cleverly pointed out:\r\n-  https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/commit\/3cd7e95aa1799b73c5ea8afc3989635f3e19b86b fixed one of them\r\n- but there are still others to be fixed","body":"## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.","comment_length":34,"text":"OSCAR-2109 datasets are misaligned and truncated \n ## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data. \n Sorry @norakassner for the late reply.\r\n\r\nThere are indeed several issues creating the misalignment, as @adrianeboyd cleverly pointed out:\r\n-  https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/commit\/3cd7e95aa1799b73c5ea8afc3989635f3e19b86b fixed one of them\r\n- but there are still others to be 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3704","title":"OSCAR-2109 datasets are misaligned and truncated","comments":"Normally, the issues should be fixed now:\r\n- Fix offset initialization for each file: https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/commit\/1ad9b7bfe00798a9258a923b887bb1c8d732b833\r\n- Disable default universal newline support: https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/commit\/0c2f307d3167f03632f502af361ac6c3c393f510\r\n\r\nFeel free to reopen if you find additional misalignments\/truncations.\r\n\r\nCC: @adrianeboyd @norakassner @Uinelj ","body":"## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.","comment_length":35,"text":"OSCAR-2109 datasets are misaligned and truncated \n ## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data. \n Normally, the issues should be fixed now:\r\n- Fix offset initialization for each file: https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/commit\/1ad9b7bfe00798a9258a923b887bb1c8d732b833\r\n- Disable default universal newline support: https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/commit\/0c2f307d3167f03632f502af361ac6c3c393f510\r\n\r\nFeel free to reopen if you find additional misalignments\/truncations.\r\n\r\nCC: @adrianeboyd @norakassner @Uinelj 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3704","title":"OSCAR-2109 datasets are misaligned and truncated","comments":"Thanks for the updates!\r\n\r\nThe purist in me would still like to have the rstrip not strip additional characters from the original text (unicode whitespace mainly in practice, I think), but the differences are extremely small in practice and it doesn't actually matter for my current task:\r\n\r\n```python\r\ntext = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip(\"\\n\")\r\n```","body":"## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data.","comment_length":56,"text":"OSCAR-2109 datasets are misaligned and truncated \n ## Describe the bug\r\n\r\nThe `oscar-corpus\/OSCAR-2109` data appears to be misaligned and truncated by the dataset builder for subsets that contain more than one part and for cases where the texts contain non-unix newlines.\r\n\r\n## Steps to reproduce the bug\r\n\r\nA few examples, although I'm not sure how deterministic the particular (mis)alignment is in various configurations:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_fi\", split=\"train\", use_auth_token=True)\r\nentry = dataset[0]\r\n# entry[\"text\"] is from fi_part_3.txt.gz\r\n# entry[\"meta\"] is from fi_meta_part_2.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_no\", split=\"train\", use_auth_token=True)\r\nentry = dataset[900000]\r\n# entry[\"text\"] is from no_part_3.txt.gz and contains a blank line\r\n# entry[\"meta\"] is from no_meta_part_1.jsonl.gz\r\n\r\ndataset = load_dataset(\"oscar-corpus\/OSCAR-2109\", \"deduplicated_mk\", split=\"train\", streaming=True, use_auth_token=True)\r\n# 9088 texts in the dataset are empty\r\n```\r\n\r\nFor `deduplicated_fi`, all exported raw texts from the dataset are 17GB rather than 20GB as reported in the data splits overview table. The token count with `wc -w` for the raw texts is 2,067,556,874 rather than the expected 2,357,264,196 from the data splits table.\r\n\r\nFor `deduplicated_no` all exported raw texts contain 624,040,887 rather than the expected 776,354,517 tokens.\r\n\r\nFor `deduplicated_mk` it is 122,236,936 rather than 134,544,934 tokens. \r\n\r\nI'm not expecting the `wc -w` counts to line up exactly with the data splits table, but for comparison the `wc -w` count for `deduplicated_mk` on the raw texts is 134,545,424.\r\n\r\n## Issues\r\n\r\n* The meta \/ text files are not paired correctly when loading, so the extracted texts do not have the right offsets, the metadata is not associated with the correct text, and the text files may not be processed to the end or may be processed beyond the end (empty texts).\r\n* The line count offset is not reset per file so the texts aren't aligned to the right offsets in any parts beyond the first part, leading to truncation when in effect blank lines are not skipped.\r\n* Non-unix newline characters are treated as newlines when reading the text files while the metadata only counts unix newlines for its line offsets, leading to further misalignments between the metadata and the extracted texts, and which also results in truncation.\r\n\r\n## Expected results\r\n\r\nAll texts from the OSCAR release are extracted according to the metadata and aligned with the correct metadata.\r\n\r\n## Fixes\r\n\r\nNot necessarily the exact fixes\/checks you may want to use (I didn't test all languages or do any cross-platform testing, I'm not sure all the details are compatible with streaming), however to highlight the issues:\r\n\r\n```diff\r\ndiff --git a\/OSCAR-2109.py b\/OSCAR-2109.py\r\nindex bbac1076..5eee8de7 100644\r\n--- a\/OSCAR-2109.py\r\n+++ b\/OSCAR-2109.py\r\n@@ -20,6 +20,7 @@\r\n import collections\r\n import gzip\r\n import json\r\n+import os\r\n \r\n import datasets\r\n \r\n@@ -387,9 +388,20 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n         with open(checksum_file, encoding=\"utf-8\") as f:\r\n             data_filenames = [line.split()[1] for line in f if line]\r\n             data_urls = [self.config.base_data_path + data_filename for data_filename in data_filenames]\r\n-        text_files = dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")])\r\n-        metadata_files = dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")])\r\n+        # sort filenames so corresponding parts are aligned\r\n+        text_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".txt.gz\")]))\r\n+        metadata_files = sorted(dl_manager.download([url for url in data_urls if url.endswith(\".jsonl.gz\")]))\r\n+        assert len(text_files) == len(metadata_files)\r\n         metadata_and_text_files = list(zip(metadata_files, text_files))\r\n+        for meta_path, text_path in metadata_and_text_files:\r\n+            # check that meta\/text part numbers are the same\r\n+            if \"part\" in os.path.basename(text_path):\r\n+                assert (\r\n+                    os.path.basename(text_path).replace(\".txt.gz\", \"\").split(\"_\")[-1]\r\n+                    == os.path.basename(meta_path).replace(\".jsonl.gz\", \"\").split(\"_\")[-1]\r\n+                )\r\n+            else:\r\n+                assert len(metadata_and_text_files) == 1\r\n         return [\r\n             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"metadata_and_text_files\": metadata_and_text_files}),\r\n         ]\r\n@@ -397,10 +409,14 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n     def _generate_examples(self, metadata_and_text_files):\r\n         \"\"\"This function returns the examples in the raw (text) form by iterating on all the files.\"\"\"\r\n         id_ = 0\r\n-        offset = 0\r\n         for meta_path, text_path in metadata_and_text_files:\r\n+            # line offsets are per text file\r\n+            offset = 0\r\n             logger.info(\"generating examples from = %s\", text_path)\r\n-            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\") as text_f:\r\n+            # some texts contain non-Unix newlines that should not be\r\n+            # interpreted as line breaks for the line counts in the metadata\r\n+            # with readline()\r\n+            with gzip.open(open(text_path, \"rb\"), \"rt\", encoding=\"utf-8\", newline=\"\\n\") as text_f:\r\n                 with gzip.open(open(meta_path, \"rb\"), \"rt\", encoding=\"utf-8\") as meta_f:\r\n                     for line in meta_f:\r\n                         # read meta\r\n@@ -411,7 +427,12 @@ class Oscar2109(datasets.GeneratorBasedBuilder):\r\n                             offset += 1\r\n                             text_f.readline()\r\n                         # read text\r\n-                        text = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip()\r\n+                        text_lines = [text_f.readline() for _ in range(meta[\"nb_sentences\"])]\r\n+                        # all lines contain text (no blank lines or EOF)\r\n+                        assert all(text_lines)\r\n+                        assert \"\\n\" not in text_lines\r\n                         offset += meta[\"nb_sentences\"]\r\n+                        # only strip the trailing newline\r\n+                        text = \"\".join(text_lines).rstrip(\"\\n\")\r\n                         yield id_, {\"id\": id_, \"text\": text, \"meta\": meta}\r\n                         id_ += 1\r\n```\r\n\r\nI've tested this with a number of smaller deduplicated languages with 1-20 parts and the resulting datasets looked correct in terms of word count and size when compared to the data splits table and raw texts, and the text\/metadata alignments were correct in all my spot checks. However, there are many many languages I didn't test and I'm not sure that there aren't any texts containing blank lines in the corpus, for instance. For the cases I tested, the assertions related to blank lines and EOF made it easier to verify that the text and metadata were aligned as intended, since there would be little chance of spurious alignments of variable-length texts across so much data. \n Thanks for the updates!\r\n\r\nThe purist in me would still like to have the rstrip not strip additional characters from the original text (unicode whitespace mainly in practice, I think), but the differences are extremely small in practice and it doesn't actually matter for my current task:\r\n\r\n```python\r\ntext = \"\".join([text_f.readline() for _ in range(meta[\"nb_sentences\"])]).rstrip(\"\\n\")\r\n```","embeddings":[-0.3790076673,0.3509356081,0.0164458845,0.5407813191,-0.1386850923,0.0147257438,0.2296266258,0.3700638413,-0.3047657311,-0.0990706384,0.0361928307,-0.0641968846,0.1635020226,-0.0632027015,0.0641614646,-0.2708619833,0.1433135867,0.0770916864,0.0587199628,-0.0915282145,-0.1945028901,0.2278799266,-0.2512326241,-0.1715530157,-0.4618670344,0.0205017552,-0.1564037949,0.1160182133,-0.1815607995,-0.2947756052,0.0523528866,-0.118448168,-0.1783859283,0.4717750549,-0.0001191647,-0.332468003,0.2028630525,0.0530874468,-0.2554106712,-0.0556327626,-0.1461407393,-0.3505928516,-0.1262875795,-0.0434630439,-0.0790544152,-0.174564153,-0.1623656452,-0.2141503096,0.3381189704,0.3711010218,0.1265106946,0.0639129728,0.1273596883,-0.1510596573,0.2542283833,0.1348651648,-0.1388940066,-0.3037272096,0.3203901947,0.1566210538,-0.0350660197,0.158431381,-0.0297336411,-0.1473465264,0.1379244626,0.0902881175,-0.3786524236,-0.2571187615,0.2216887027,0.2976016104,0.5767251253,-0.2120313942,-0.4630664885,-0.5475748181,0.1078173667,-0.3117791712,0.4378286004,0.4162479341,0.1882850379,-0.013812691,-0.247304827,0.2649748325,-0.0803462714,0.1719249338,-0.5789376497,0.4632933438,0.0772491619,0.0338957831,-0.4923261702,-0.0914476663,0.0862304866,-0.3761126101,-0.1873320937,0.0571788177,-0.2277076989,-0.1457071155,-0.1969701499,-0.1923042089,0.1047644243,0.3106939793,-0.0733263493,-0.0888947397,0.1091103405,-0.2119215131,0.6761890054,0.1046889424,-0.0652909055,0.2076425254,0.0943629593,0.17147699,0.0160182938,0.0890522599,0.1051053628,-0.2882238328,0.1120735779,0.0195242129,0.1282585412,-0.1831296086,-0.230723843,0.2883025706,-0.1379312575,0.1082547083,-0.0461649038,-0.0705253258,-0.5019134879,0.1079166904,-0.0414200723,0.1176008806,-0.1549671888,-0.1128472686,-0.1286266446,-0.0596448295,-0.1675335765,-0.1926116198,0.1582930982,-0.2758381069,0.1505256295,0.2324216664,0.3471394181,-0.1846704632,-0.2321317345,-0.2329635024,0.0747359395,0.1991908997,0.2065338641,0.1880712658,-0.2373024374,0.063236095,-0.254432857,0.1781011969,-0.3618549109,-0.1559672356,-0.1872980893,0.1418364048,-0.2844474316,0.0614295267,0.0626612157,0.2264574617,0.4596114457,0.0929124728,0.019462144,-0.3186949492,-0.2900915444,-0.0254702903,0.1912834197,0.4387021363,-0.2970393598,0.2012897134,-0.2967318296,0.3992405832,0.3132759929,0.2712260485,0.1522270143,0.2627609968,-0.198289752,0.3230545521,0.2010234147,-0.1185871139,-0.2857627869,0.0821363777,0.0881256983,0.3660310209,-0.1722454429,-0.2275576144,0.4341608584,-0.0403528064,0.3588856757,0.5894742608,0.0106053073,0.1733696759,-0.2774277925,-0.119371891,-0.0689152703,0.0889845565,0.3800786734,-0.4034855962,-0.1223550364,-0.0077362754,0.1980925649,0.176506117,0.2641663253,0.0949202999,-0.1784048826,0.2107343376,0.1653486639,-0.3377285302,-0.3772593439,0.109922573,0.0756269321,0.1544352174,0.0418069437,0.0334683582,-0.258023113,-0.066181004,-0.0596408024,-0.0737458318,0.1073911563,0.0354172811,0.0974579304,0.0181609318,-0.1704438478,0.052124545,0.2261193097,0.2308353931,-0.3561495543,0.2964910269,0.0610093474,-0.1259621233,0.0262813885,0.4283260107,0.1682807058,-0.041395247,-0.151622802,0.4449450076,0.2975042164,0.1925300658,-0.4956775904,0.1724902391,0.4749038517,-0.006226053,0.112322703,0.3238494396,0.00992209,-0.0976219997,-0.1129284203,0.521246314,0.0101971626,0.2618319392,-0.0283670016,0.2055058926,0.1805193722,-0.1423949152,-0.0875572562,-0.3197295666,0.2787263989,-0.0231911503,0.0142284557,0.2514231503,-0.3420141637,0.2411614805,0.4854954779,-0.025714485,-0.0791651234,0.1931245476,-0.316960752,-0.1326340437,0.0258187354,0.3226028085,0.1576765478,0.1619859487,0.0172816683,0.266533494,-0.1853713542,-0.3316446245,0.3278588355,0.0851676613,-0.0567742549,0.5718154311,0.2162770778,0.3166656792,-0.1537766159,0.0617133938,-0.1516015083,0.0504466668,-0.4918235838,0.0950645804,-0.2991832793,-0.1825205684,-0.5129337907,-0.1602847427,-0.2074745893,-0.2467242926,0.260092169,0.0254211333,-0.2985143363,0.2057433128,-0.1014931425,0.269506216,-0.1845807433,0.2096012533,-0.146751523,0.0695597678,-0.2421948165,0.0120203532,0.2340933084,0.1246491149,0.1499788165,-0.3004074693,-0.2243103981,-0.3780749142,-0.457000643,0.1865401417,0.0078502307,0.2183189392,0.2282629311,-0.3605905771,-0.0303027183,-0.3667495549,0.1411125064,0.154933989,-0.1107989848,0.1694106609,-0.1644711196,-0.1822965741,-0.102645345,-0.3200548291,-0.0187836364,-0.2177804708,0.0803834349,0.2193825096,0.270870626,0.4646671414,-0.0715001449,-0.5104335546,-0.087614961,0.0743571594,-0.2903060615,-0.207360819,0.3214320242,-0.1233307794,-0.5085403323,-0.2286387682,-0.05329201,-0.1258547604,0.344055444,-0.3664493859,0.0525037684,-0.2484286278,-0.2093068361,-0.0410553925,-0.0645167381,0.1437440664,-0.0874940604,0.0579901338,-0.1850070059,-0.0350375921,0.1543656886,0.0635980144,0.2712463737,-0.3381327093,0.0320667475,-0.0634473711,0.2967006862,0.387837708,-0.0097248359,-0.063828215,0.0608416758,0.0690403804,-0.251552045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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3703","title":"ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'","comments":"Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.","body":"hi :\r\nI want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric\/seqeval\/seqeval.py')\r\n\r\nBut tips:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 604, in <module>\r\n    main()\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 481, in main\r\n    metric = load_metric(path='mymetric\/seqeval\/seqeval.py')\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 450, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\nImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'\r\n\r\n\r\n**What should I do? Please help me, thank you**\r\n\r\n","comment_length":26,"text":"ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance' \n hi :\r\nI want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric\/seqeval\/seqeval.py')\r\n\r\nBut tips:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 604, in <module>\r\n    main()\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 481, in main\r\n    metric = load_metric(path='mymetric\/seqeval\/seqeval.py')\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 450, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\nImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'\r\n\r\n\r\n**What should I do? Please help me, thank you**\r\n\r\n \n Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.","embeddings":[-0.1979479194,-0.0261321086,-0.0460703038,0.0487141907,0.2330713272,-0.096679844,0.3034621179,-0.0408187322,0.1501881033,0.2724967599,-0.0265024006,0.4663180113,-0.1179601625,-0.0697475597,0.0039565177,0.0554591827,-0.2216039747,0.2347219586,0.0374865755,-0.0331859216,-0.3001255691,0.2145698965,-0.2344894856,0.2681357563,-0.0791216642,0.0428807698,0.1921438128,0.1060338914,-0.1730915457,-0.4851666093,0.3963502347,0.2527001202,0.2361014485,0.4080011845,-0.0001140326,-0.0214428641,0.2709655166,-0.0814151913,-0.0926528871,-0.4800740182,-0.2137541026,-0.5905323625,0.4979463816,-0.3348412216,0.0933581889,0.0121970503,-0.1398535818,0.020907402,0.1554713249,0.327530086,0.2050293386,0.4454393685,0.0971884876,-0.1384479553,-0.0632550716,-0.0564685576,0.008883547,0.7511857748,0.2068042904,0.1642239392,-0.0643612146,0.0962777436,0.0718856826,0.0620078072,0.2578077614,-0.0638942197,0.5207052827,-0.2371596992,-0.1974184364,0.0036946007,0.1224608719,-0.2489218414,-0.0865392312,0.0901481509,-0.0320516117,-0.4814990461,-0.0559668913,-0.1059812382,0.0111764297,-0.0716191754,-0.0177829955,-0.2118641734,-0.4469778836,0.2658893764,-0.3425782621,0.4370118082,-0.1072766334,0.1368531734,0.2247858047,0.0208489783,0.2723988891,0.2029409707,0.1898023933,0.057978522,-0.4520132244,-0.0288933702,-0.0880793557,-0.0658389553,-0.0305744857,-0.0587966107,-0.1339221299,0.098715134,0.3098281324,0.2916836441,-0.091382049,0.6517192125,0.3377406895,0.4321175814,0.2035247087,0.2704062164,0.1807589978,-0.109682247,-0.1727244407,-0.450296998,0.1957330853,0.0636366159,0.1645015031,-0.3404764533,-0.3436180949,-0.1505814046,-0.2326823622,-0.0095633194,0.3662144244,0.1507045329,-0.0954392254,0.0000508257,0.1900399625,0.5599957705,-0.0843653083,-0.0691097677,-0.0304037817,0.1791378856,0.0727979168,0.0143829081,0.0391145237,-0.0694535598,0.239059791,-0.2586400807,0.286447674,-0.0223094523,0.2096647769,0.1377358884,-0.4173983037,0.351806581,-0.0094321566,0.0917100534,0.3532958925,0.0192349162,-0.3062001467,0.0976874679,-0.2374823093,-0.5792042017,0.0754895061,0.1995245814,-0.3711233735,-0.114400357,0.3118022084,-0.0638349205,-0.1937606484,-0.2773612142,-0.1155235842,-0.2225832045,-0.3335003257,-0.0043511968,0.320143044,0.1791336387,-0.4487842023,-0.2989313304,-0.3232614994,-0.2704151273,0.1070110351,-0.0197555814,-0.2131884992,0.4746305346,0.0325462185,-0.1217713431,1.0754005909,-0.7784476876,-0.3757666647,0.2723841667,-0.0754533336,-0.251606822,-0.0646240264,-0.0506270453,0.1729064137,0.029583592,0.0770192742,0.3225595355,0.0343495421,-0.4245265424,-0.2225569934,-0.2319390029,0.0283640493,0.4272222519,0.3709185719,0.1490344256,0.2347436845,0.1208439544,-0.1481283009,0.0273190532,0.0445012525,0.1245270297,0.2708447278,-0.0495967939,-0.0150113078,0.0189241432,0.1745639145,0.2018603534,-0.5225266814,0.1313835233,-0.4019147456,0.0632581785,-0.7659033537,-0.0594446436,-0.4247314334,0.0007480033,0.1244969219,-0.1193387955,0.240754962,0.1856294721,-0.0417070836,-0.029217463,-0.0159980655,0.0710891336,-0.1137419194,-0.0868964568,-0.1799122244,0.0160867535,0.0041742218,0.2460654527,0.2122626007,-0.0848975033,-0.1142229736,0.4146362841,-0.2632613182,0.0829232931,-0.0900867432,0.0788449049,0.1463386118,-0.1019574925,0.1673912853,0.0266927332,0.0544099361,-0.0489997454,0.3102546036,0.2317424417,-0.0537059717,0.3437825739,-0.2282939106,0.2108148634,0.2397156954,-0.0163375679,-0.4618647397,-0.1367999762,0.3128174841,-0.0918126926,0.245095402,-0.2623735964,0.2108664066,-0.2797982395,-0.0316974744,-0.1158661321,0.2651967704,0.0664712936,-0.1013796628,0.28501755,-0.0615778305,-0.1800721288,0.3577702641,0.0727574602,-0.2627810538,0.0549711175,-0.1299016178,-0.1805511415,0.002099371,0.2140284926,0.1735487133,0.1697609276,-0.0379609093,-0.1251806617,-0.1962415278,-0.2717216611,-0.1510292888,0.1419923753,-0.1622516811,0.294279933,-0.0980271176,0.1694356501,-0.0129253455,-0.4523292184,-0.2327128649,-0.3811557293,0.0839164332,-0.0099743921,0.2784684598,0.3743315339,0.1494904459,0.1578245908,0.1660917699,-0.4609966874,-0.1026237383,-0.463152647,-0.1960980445,0.0721980408,-0.1099208072,0.3196079731,0.1307651401,-0.2258260399,0.3233151734,0.0724735186,-0.2795782387,0.0031714002,-0.0075614345,0.4564611912,0.1657129079,-0.1211819947,0.230553031,-0.1008356661,0.4136296809,-0.3056054413,0.0831575245,0.3960273266,-0.3513771296,0.0929083452,-0.1298003048,-0.2726880908,-0.5074702501,-0.5389553308,-0.1200405508,0.2248357236,0.0944209397,0.2269604355,0.0628648996,0.2085781395,0.1162578985,0.2902165353,-0.0363203883,0.0084942393,0.4001490474,-0.1158268601,-0.3115709722,0.085397087,-0.2145757675,0.5719311237,-0.1507521868,-0.2420794517,-0.3464058936,0.039930813,0.1482756287,-0.0536372997,-0.0993200094,0.3595161736,0.0237187594,0.0447530709,0.0573061891,0.0195230339,0.0349811204,0.0608805045,0.1680472642,-0.0083995666,0.5144190788,-0.116491653,0.4497143626,-0.2406818718,0.0199907701,0.1410911679,0.1020832434,0.3284988701,0.0814132765,-0.4152861238,-0.1343125403,0.15092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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3703","title":"ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'","comments":"> Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.\r\nI installed seqeval, but still reported the same error. That's too bad.\r\n","body":"hi :\r\nI want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric\/seqeval\/seqeval.py')\r\n\r\nBut tips:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 604, in <module>\r\n    main()\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 481, in main\r\n    metric = load_metric(path='mymetric\/seqeval\/seqeval.py')\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 450, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\nImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'\r\n\r\n\r\n**What should I do? Please help me, thank you**\r\n\r\n","comment_length":39,"text":"ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance' \n hi :\r\nI want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric\/seqeval\/seqeval.py')\r\n\r\nBut tips:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 604, in <module>\r\n    main()\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 481, in main\r\n    metric = load_metric(path='mymetric\/seqeval\/seqeval.py')\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 450, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\nImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'\r\n\r\n\r\n**What should I do? Please help me, thank you**\r\n\r\n \n > Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.\r\nI installed seqeval, but still reported the same error. That's too bad.\r\n","embeddings":[-0.2025145888,-0.0279518254,-0.0466900282,0.0476440899,0.2293506116,-0.1002444848,0.3064733148,-0.0407145023,0.146865353,0.276491046,-0.0252317674,0.4661179185,-0.1189257652,-0.0669721663,-0.0000807318,0.0616329238,-0.2240399569,0.2324857414,0.0310467277,-0.0296035241,-0.3011247218,0.2146048397,-0.2374896705,0.2736974359,-0.0739125088,0.0465195328,0.2010021061,0.103712745,-0.1756857634,-0.4891562462,0.4008096159,0.2491996437,0.2313049585,0.4036441147,-0.0001138707,-0.0150956335,0.2780589163,-0.0787833184,-0.0996870175,-0.4829646945,-0.210483402,-0.5888839364,0.4955228865,-0.3405120075,0.0931681618,0.0149684912,-0.1330665499,0.0193495173,0.1577629149,0.3294856548,0.205463931,0.4512306154,0.0945245922,-0.1398309618,-0.0673576593,-0.0595150329,0.0107719051,0.7501040101,0.2036802173,0.1629694253,-0.0659887269,0.0995291173,0.0690616965,0.0638605207,0.2584679425,-0.0671810284,0.5169752836,-0.2330883145,-0.1939142793,0.0083851665,0.1228221282,-0.2496388406,-0.085781157,0.0923526809,-0.0340215303,-0.4787332118,-0.0583675317,-0.1057141274,0.0123411277,-0.0680129156,-0.0200048555,-0.201941371,-0.4495805502,0.2637662888,-0.3446573317,0.439693749,-0.1082586646,0.1417422742,0.2188713402,0.0211461931,0.2701703012,0.2068824023,0.1849045008,0.0622404553,-0.455462575,-0.0276470073,-0.0894450322,-0.0685219392,-0.0267668255,-0.0591471903,-0.1268630028,0.0950334296,0.3147328496,0.2969961166,-0.085688591,0.6534166336,0.3433457315,0.4243914783,0.2029866129,0.2708715498,0.184736684,-0.1047305092,-0.1704871505,-0.4511906505,0.1975441724,0.0585411638,0.1663205326,-0.3356545269,-0.3485317826,-0.1539675295,-0.2325417995,-0.0061114253,0.3664210439,0.1509002149,-0.0930547342,-0.0018767525,0.1888307035,0.5547141433,-0.083308503,-0.0657913461,-0.0320013426,0.1802445054,0.0706020594,0.0086104758,0.0335523337,-0.068814218,0.2342293561,-0.2625126541,0.289103061,-0.0210766047,0.2090938687,0.1346366853,-0.4188351035,0.3546420038,-0.012009332,0.0891442895,0.3532588482,0.0181913208,-0.3046501577,0.0956390575,-0.2342186719,-0.5816629529,0.0723597109,0.2021177411,-0.3697007895,-0.1130423918,0.306620568,-0.0647407919,-0.1939974576,-0.2735031843,-0.1166081429,-0.2249218076,-0.3362628818,-0.0021972486,0.3209306002,0.1816615611,-0.4488910437,-0.3034177721,-0.3197414279,-0.2704491317,0.1056644469,-0.0132055283,-0.2172609717,0.4705058038,0.0340935737,-0.115488708,1.0733263493,-0.7779160142,-0.3742954731,0.2764210105,-0.0778986514,-0.2530370355,-0.0605131537,-0.0562203713,0.177163735,0.0283984598,0.0809494704,0.3228155673,0.0379435718,-0.4226367474,-0.2223681211,-0.2325319797,0.0265782885,0.4268845618,0.3628680706,0.1468880624,0.2428524345,0.1247010604,-0.1466401219,0.029068809,0.0449117124,0.1211653054,0.2668244839,-0.0527574793,-0.0131501602,0.0217598211,0.1764865816,0.2063652426,-0.5209376216,0.1368269473,-0.4022715092,0.0599339269,-0.7653227448,-0.0500035509,-0.422064513,0.0015953189,0.1265860945,-0.119885847,0.2348815054,0.1899112165,-0.0401926637,-0.0238805823,-0.0214935541,0.0719727278,-0.1200214177,-0.0934689865,-0.1774323136,0.0183060244,0.0003539902,0.2439840734,0.2076898068,-0.0864808112,-0.1143552288,0.4139468372,-0.2625236511,0.0849559456,-0.093378976,0.0817183107,0.1442589313,-0.1050831303,0.1645179093,0.0294669271,0.0587419495,-0.0449657217,0.3105558455,0.231676355,-0.0511753671,0.3431288898,-0.2287068367,0.2081099004,0.2438541204,-0.013966131,-0.4618021548,-0.1357342899,0.3137227297,-0.091659151,0.2473900765,-0.2618757188,0.207165122,-0.2856674492,-0.0341616049,-0.1177029312,0.2632401288,0.0703012347,-0.1028881297,0.2809599638,-0.0608638376,-0.179324463,0.3559464216,0.0716165602,-0.2572084069,0.0568579882,-0.132095024,-0.177612707,0.0063939509,0.2167281657,0.1746207029,0.1693098843,-0.0366194323,-0.1254641563,-0.1988752037,-0.2741218507,-0.1510288417,0.1416069716,-0.1613252461,0.3016014099,-0.1014346182,0.1650681049,-0.0150497993,-0.4460833669,-0.2284573317,-0.3802943528,0.0838763937,-0.0049690017,0.2765415013,0.3717143834,0.1528256685,0.1572869718,0.1693787873,-0.457094878,-0.102105774,-0.4605821669,-0.1936948895,0.0737299398,-0.1095635742,0.3194565773,0.1323557049,-0.2245699763,0.3179763854,0.0715384558,-0.2771766782,0.0033464173,-0.0091480399,0.4581056535,0.1684639752,-0.1221236363,0.2235534489,-0.0974083319,0.4119965136,-0.3103607595,0.0832100436,0.4007310271,-0.3566398919,0.0941532701,-0.1290638149,-0.2731547058,-0.5141029358,-0.5375084281,-0.1152686551,0.221185267,0.0957870558,0.2296150774,0.0585885979,0.2081615776,0.118830502,0.2825506628,-0.0374037027,0.0084913466,0.3960719705,-0.1140788049,-0.3100214005,0.0858973041,-0.2141456157,0.5709905624,-0.1516765207,-0.2393828928,-0.3460558653,0.0428029262,0.1458270997,-0.054017242,-0.1048948243,0.3562494814,0.0233128108,0.0405108742,0.058331646,0.02399607,0.0345662907,0.0599281602,0.1606605053,-0.0005709465,0.5134320855,-0.1092297286,0.4509761035,-0.2403599918,0.0187897068,0.1452676505,0.1005170196,0.3275944293,0.0763406456,-0.4108572602,-0.1358181387,0.1507793963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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3703","title":"ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'","comments":"> > Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.\r\n> > I installed seqeval, but still reported the same error. That's too bad.\r\n\r\nSame issue here. What should I do to fix this error? Please help! Thank you.","body":"hi :\r\nI want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric\/seqeval\/seqeval.py')\r\n\r\nBut tips:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 604, in <module>\r\n    main()\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 481, in main\r\n    metric = load_metric(path='mymetric\/seqeval\/seqeval.py')\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 450, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\nImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'\r\n\r\n\r\n**What should I do? Please help me, thank you**\r\n\r\n","comment_length":57,"text":"ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance' \n hi :\r\nI want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric\/seqeval\/seqeval.py')\r\n\r\nBut tips:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 604, in <module>\r\n    main()\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 481, in main\r\n    metric = load_metric(path='mymetric\/seqeval\/seqeval.py')\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 450, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\nImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'\r\n\r\n\r\n**What should I do? Please help me, thank you**\r\n\r\n \n > > Hi! Some of our metrics require additional dependencies to work. In your case, simply installing the `seqeval` package with `pip install seqeval` should resolve the issue.\r\n> > I installed seqeval, but still reported the same error. That's too bad.\r\n\r\nSame issue here. What should I do to fix this error? Please help! Thank 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3703","title":"ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'","comments":"I tried to install **seqeval** package through anaconda instead of pip:\r\n`conda install -c conda-forge seqeval`\r\nIt worked for me!","body":"hi :\r\nI want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric\/seqeval\/seqeval.py')\r\n\r\nBut tips:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 604, in <module>\r\n    main()\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 481, in main\r\n    metric = load_metric(path='mymetric\/seqeval\/seqeval.py')\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 450, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\nImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'\r\n\r\n\r\n**What should I do? Please help me, thank you**\r\n\r\n","comment_length":20,"text":"ImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance' \n hi :\r\nI want to use the seqeval indicator because of direct load_ When metric ('seqeval '), it will prompt that the network connection fails. So I downloaded the seqeval Py to load locally. Loading code: metric = load_ metric(path='mymetric\/seqeval\/seqeval.py')\r\n\r\nBut tips:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 604, in <module>\r\n    main()\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers\/examples\/pytorch\/token-classification\/run_ner.py\", line 481, in main\r\n    metric = load_metric(path='mymetric\/seqeval\/seqeval.py')\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/ubuntu\/Python3.6_project\/zyf_project\/transformers_venv_0209\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 450, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\nImportError: To be able to use this metric, you need to install the following dependencies['seqeval'] using 'pip install seqeval' for instance'\r\n\r\n\r\n**What should I do? Please help me, thank you**\r\n\r\n \n I tried to install **seqeval** package through anaconda instead of pip:\r\n`conda install -c conda-forge seqeval`\r\nIt worked for me!","embeddings":[-0.2087468654,0.0372916944,-0.0465948693,0.072329022,0.2412149757,-0.0558812059,0.2564868927,-0.0605248734,0.1419684291,0.2192428708,-0.0591692775,0.4464016557,-0.0956758112,-0.0059286691,-0.0359139144,0.148316741,-0.2069617063,0.2406512052,-0.0371863432,0.0028509647,-0.2867521644,0.2352458686,-0.2564758062,0.2505311072,-0.0475933477,0.0571909137,0.1954377741,0.0072608669,-0.1976895928,-0.5025829673,0.4850375652,0.217438966,0.2079081833,0.3762910664,-0.0001174601,0.0319709592,0.3690811396,-0.0952921957,-0.1718562394,-0.4783449769,-0.3205077648,-0.5130681992,0.5269656777,-0.3058232069,0.0885111094,0.0292177275,-0.1107499078,-0.0240599625,0.1778381169,0.3361066878,0.1669583917,0.2630862296,0.1258268803,-0.1309136599,-0.2301620096,0.0019779156,0.0428356975,0.7595732212,0.146020636,0.1746554077,0.0758227482,0.0784208328,0.0285460316,0.0452049635,0.2011564225,-0.0874915794,0.5028619766,-0.2602458894,-0.1526450068,-0.0486267656,0.1614626497,-0.2761489153,-0.1230822355,0.0994085521,-0.0127738146,-0.5130904913,-0.1063023582,-0.1056440324,-0.0279390179,0.0080144415,-0.0359038301,-0.1492491513,-0.4326949418,0.2709537148,-0.3117968738,0.4153107703,-0.1283206195,0.1606809646,0.2233530283,0.0203232057,0.2715520859,0.2447936237,0.1489202827,0.0869956911,-0.4877147973,-0.0131967478,-0.1484341025,-0.0562127866,0.0023839921,-0.0441812016,-0.1809227616,0.0393875353,0.3930700719,0.2967618108,-0.0981344804,0.6219246387,0.357318908,0.371014595,0.1836043447,0.3313843906,0.2147048265,-0.0878243521,-0.2239682823,-0.4504406452,0.0893149152,0.0634945855,0.125414297,-0.3780108392,-0.4266169667,-0.1975742131,-0.2155860215,0.0496610776,0.3364881873,0.1405483484,-0.1469281614,-0.0380986258,0.2102144212,0.6005765796,-0.0277173091,-0.0281964354,0.0267463587,0.128245011,0.0804976076,-0.0085678538,0.027742615,-0.0596645698,0.208461076,-0.2660515904,0.3032333553,-0.0390845425,0.1763700843,0.0943297073,-0.50192523,0.3969790041,-0.0380750522,0.1400240511,0.3283745646,-0.016388135,-0.3016519845,0.0767175779,-0.1898449808,-0.5450773835,0.0411780439,0.1507419199,-0.3135043979,-0.0753008649,0.2629317939,-0.1297386438,-0.1532688737,-0.176383391,-0.1472283602,-0.136627838,-0.3156131208,0.0208940767,0.3549022377,0.2638795376,-0.4305792153,-0.2555378675,-0.2794272602,-0.2352385819,0.1007767618,-0.0666622967,-0.1949931681,0.4748153389,0.0382667966,-0.2277066112,1.0826610327,-0.8446878195,-0.3576920331,0.3303817213,-0.0335762836,-0.2673645318,-0.0154450154,-0.1473251879,0.193748191,0.0438117795,0.2184532732,0.3422571123,0.0937621593,-0.4303322136,-0.2170404196,-0.3466625214,-0.0433618538,0.4328075647,0.2767166495,0.229407981,0.2343654335,-0.0056399386,-0.111153841,0.0780345201,0.0129445707,0.1070327684,0.2436043918,-0.037523564,-0.0177335888,0.1152105853,0.2096263915,0.1915220022,-0.4299561977,0.1195736006,-0.515812993,0.065843001,-0.8217948079,-0.0121173132,-0.4042294919,0.0048277029,0.100560315,-0.1015288383,0.2891888022,0.2664214671,0.0009355889,0.2050703466,0.0195371099,0.1353588402,-0.142003879,-0.1853110939,-0.1479314566,0.0091055213,0.0229866412,0.2738297284,0.2164185792,-0.12087176,-0.0773565918,0.3575807512,-0.2354070395,0.0691946372,-0.1806841642,-0.024016194,0.1213499084,-0.073001951,0.1323382556,-0.0072703487,0.0418684371,0.0326995254,0.3497380614,0.1568348557,-0.1342119128,0.3638946414,-0.2474763542,0.2457410395,0.3569285274,-0.0464366898,-0.4436225891,-0.1681466997,0.2591181099,0.0388967395,0.3319955468,-0.2851253152,0.241350174,-0.2690143287,-0.0936235115,-0.0962520018,0.2296790034,0.0945069268,-0.0749114975,0.3712141514,0.0130852209,-0.1242352426,0.3567167521,0.0221968926,-0.296770066,0.0617671572,-0.1314371526,-0.1539867669,0.0446400903,0.2240011245,0.2344970703,0.1390019059,-0.0423551649,-0.119042784,-0.1133167148,-0.2999303639,-0.1465968341,0.1381365508,-0.1517492682,0.3342899382,-0.1514784694,0.0865624771,-0.0370966941,-0.468291223,-0.1171993986,-0.3040896356,0.0316551924,-0.0032057296,0.2797944844,0.3605888784,0.0702879503,0.056447573,0.1500882208,-0.5112866759,-0.0843136236,-0.3852345943,-0.1657407731,0.0269255359,-0.0485734232,0.3134684861,0.1123059914,-0.2898101211,0.3296618462,0.2706934214,-0.2095049322,0.0187386125,0.0223723017,0.5347397327,0.0880515501,-0.1208777577,0.243203342,-0.149808988,0.3346036375,-0.1884743422,0.0862308294,0.3794455528,-0.3161566556,0.1385695338,-0.1367622018,-0.2149270177,-0.6087607145,-0.5496641397,-0.0692064688,0.2232232392,0.096339196,0.2610893846,0.137235105,0.2133788168,0.117143333,0.2923634946,-0.0060261134,0.0236068368,0.3615864515,-0.0735616088,-0.2507145405,0.0473729707,-0.2083182484,0.6679053307,-0.1210902929,-0.2205058485,-0.2373547405,0.0951577947,0.1191139519,-0.0363542922,-0.2319513112,0.3904613256,-0.0103085302,0.0835047662,0.1086978614,0.0928333625,0.0345463268,-0.0119025316,0.1510070413,0.0553152561,0.5769953132,-0.1147254854,0.394967109,-0.1582970023,0.012737575,0.1522153169,0.1688733846,0.3301058114,0.077810742,-0.4182884395,-0.1480140835,0.1563309133,0.1103331074,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3700","title":"Unable to load a dataset","comments":"Hi! `load_dataset` is intended to be used to load a canonical dataset (`wikipedia`), a packaged dataset (`csv`, `json`, ...) or a dataset hosted on the Hub. For local datasets saved with `save_to_disk(\"path\/to\/dataset\")`, use `load_from_disk(\"path\/to\/dataset\")`.","body":"## Describe the bug\r\nUnable to load a dataset from Huggingface that I have just saved.\r\n\r\n\r\n## Steps to reproduce the bug\r\nOn Google colab\r\n`! pip install datasets `\r\n`from datasets import load_dataset`\r\n`my_path = \"wiki_dataset\"`\r\n`dataset = load_dataset('wikipedia', \"20200501.fr\")`\r\n`dataset.save_to_disk(my_path)`\r\n`dataset = load_dataset(my_path)`\r\n\r\n\r\n## Expected results\r\nLoading the dataset\r\n\r\n## Actual results\r\nValueError: Couldn't cast\r\n_data_files: list<item: struct<filename: string>>\r\n  child 0, item: struct<filename: string>\r\n      child 0, filename: string\r\n_fingerprint: string\r\n_format_columns: null\r\n_format_kwargs: struct<>\r\n_format_type: null\r\n_indexes: struct<>\r\n_output_all_columns: bool\r\n_split: string\r\nto\r\n{'builder_name': Value(dtype='string', id=None), 'citation': Value(dtype='string', id=None), 'config_name': Value(dtype='string', id=None), 'dataset_size': Value(dtype='int64', id=None), 'description': Value(dtype='string', id=None), 'download_checksums': {}, 'download_size': Value(dtype='int64', id=None), 'features': {'title': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}, 'text': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'post_processed': Value(dtype='null', id=None), 'post_processing_size': Value(dtype='null', id=None), 'size_in_bytes': Value(dtype='int64', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='string', id=None)}}, 'supervised_keys': Value(dtype='null', id=None), 'task_templates': Value(dtype='null', id=None), 'version': {'version_str': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'major': Value(dtype='int64', id=None), 'minor': Value(dtype='int64', id=None), 'patch': Value(dtype='int64', id=None)}}\r\nbecause column names don't match\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n\r\n","comment_length":34,"text":"Unable to load a dataset \n ## Describe the bug\r\nUnable to load a dataset from Huggingface that I have just saved.\r\n\r\n\r\n## Steps to reproduce the bug\r\nOn Google colab\r\n`! pip install datasets `\r\n`from datasets import load_dataset`\r\n`my_path = \"wiki_dataset\"`\r\n`dataset = load_dataset('wikipedia', \"20200501.fr\")`\r\n`dataset.save_to_disk(my_path)`\r\n`dataset = load_dataset(my_path)`\r\n\r\n\r\n## Expected results\r\nLoading the dataset\r\n\r\n## Actual results\r\nValueError: Couldn't cast\r\n_data_files: list<item: struct<filename: string>>\r\n  child 0, item: struct<filename: string>\r\n      child 0, filename: string\r\n_fingerprint: string\r\n_format_columns: null\r\n_format_kwargs: struct<>\r\n_format_type: null\r\n_indexes: struct<>\r\n_output_all_columns: bool\r\n_split: string\r\nto\r\n{'builder_name': Value(dtype='string', id=None), 'citation': Value(dtype='string', id=None), 'config_name': Value(dtype='string', id=None), 'dataset_size': Value(dtype='int64', id=None), 'description': Value(dtype='string', id=None), 'download_checksums': {}, 'download_size': Value(dtype='int64', id=None), 'features': {'title': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}, 'text': {'dtype': Value(dtype='string', id=None), 'id': Value(dtype='null', id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'post_processed': Value(dtype='null', id=None), 'post_processing_size': Value(dtype='null', id=None), 'size_in_bytes': Value(dtype='int64', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='string', id=None)}}, 'supervised_keys': Value(dtype='null', id=None), 'task_templates': Value(dtype='null', id=None), 'version': {'version_str': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'major': Value(dtype='int64', id=None), 'minor': Value(dtype='int64', id=None), 'patch': Value(dtype='int64', id=None)}}\r\nbecause column names don't match\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n\r\n \n Hi! `load_dataset` is intended to be used to load a canonical dataset (`wikipedia`), a packaged dataset (`csv`, `json`, ...) or a dataset hosted on the Hub. For local datasets saved with `save_to_disk(\"path\/to\/dataset\")`, use `load_from_disk(\"path\/to\/dataset\")`.","embeddings":[-0.2790257037,-0.2967555225,0.0411759764,0.626776576,0.33209759,0.128716588,0.2145057619,-0.0036901305,0.315826416,0.1009259224,-0.2397876829,0.3568310738,-0.0890317857,0.312253654,0.0596991181,-0.2918172181,0.1328978688,-0.1646208614,-0.1491224468,0.0217258669,-0.2166098207,0.3478344679,-0.2553952634,-0.1431855857,-0.1593204886,-0.0799565986,0.0542275123,0.2413376421,-0.3208477497,-0.3172348142,0.491853714,-0.0856288075,0.2403718382,0.4735710025,-0.0001178982,0.0541064143,0.4414445162,-0.0260321461,-0.4630026817,-0.4212158322,-0.2768461108,-0.2748171985,0.3068687618,-0.2042381763,-0.2726684511,0.207951948,0.0005594997,-0.187108025,0.3531118035,0.1420472711,0.20813559,0.2181011438,0.1239820421,-0.176191777,0.264074266,0.0115974229,-0.2796831131,0.366476506,-0.00943131,0.0553174354,0.2334932834,0.2208238095,-0.2653164864,-0.0940435901,0.2155617625,0.069542475,-0.3269442022,-0.4095650017,0.1819069982,0.1847801954,0.7262665629,-0.3082105219,-0.2309250981,-0.1196193025,0.0265469551,-0.1159922704,0.2761306763,0.2231067866,-0.0455215834,0.1178396642,-0.1529319286,-0.0102034342,-0.0254091378,0.0900819078,-0.2004641294,0.1854040325,-0.2293183208,0.0862049907,0.1486412436,-0.0173760094,0.0627895519,-0.0377143174,-0.2233910412,0.2334854156,-0.1637939811,0.2246186584,0.1037934199,0.5545833707,0.2443953454,-0.0387923978,-0.0213436,0.0191301946,-0.0907519981,0.2846336961,0.3972859383,0.0441105478,-0.0346481018,0.0974783003,0.3152963817,0.294090867,-0.1481926739,-0.1608844548,0.0105106821,-0.1142581776,0.007621034,0.0199362356,0.492467165,-0.2829960585,-0.3456639647,0.1083750725,-0.0368055664,0.0426988192,0.1695314944,0.5610018373,-0.2880218923,-0.1240937859,0.1401197463,0.1376232952,-0.1313970983,0.0319439657,-0.203322202,0.0313463956,0.0007534899,-0.0336141773,0.2387810946,-0.2969408333,0.2756969929,0.0935666412,0.0753140748,-0.1427744031,-0.1730637401,-0.250770539,-0.0359527953,0.4230418801,0.2505062222,0.220064804,0.2615420818,-0.3838598728,-0.1701788157,0.1385278553,-0.3024224937,-0.0780862197,-0.2305008322,0.1496246755,-0.4394129217,0.1100264639,-0.936480701,0.1408713609,0.0625239164,-0.2118988484,-0.0304552317,-0.2150778472,-0.3149793744,-0.2498346269,0.3911840618,0.5392605662,-0.1923070103,-0.0356377438,-0.1667948067,-0.1944304705,0.0793640018,0.2717033625,-0.059428364,0.3193778992,-0.2436948419,0.0205405578,0.264164865,-0.2467814237,-0.5420710444,-0.0695564002,0.0067820265,0.2555262148,-0.1206035912,-0.0639017299,0.3289166391,-0.0689248741,0.0829748288,0.4101097286,0.0460794307,0.1715820879,-0.3047131598,-0.1869523078,0.0728418976,0.1463370919,-0.0179423019,0.0667720512,0.2346282154,0.0862701237,0.1040430367,-0.1401870549,-0.061751239,0.2394611537,0.3891818821,0.0165388919,0.1209256724,-0.2260227501,-0.8237968087,0.2682746351,0.1548864841,0.1085006818,-0.2648231387,-0.1783606261,-0.2970389426,0.1333643645,-0.2987667024,0.0049045193,0.0648605153,-0.0249908771,-0.0434722491,0.2176035941,-0.0844284967,0.2986529171,0.1077024266,0.1154139116,-0.2680626512,0.4216537178,-0.1781187654,-0.1878094673,0.0577211753,0.0635186359,0.22338067,-0.2274817079,-0.0107064601,0.0960584581,0.125733465,0.2404047996,-0.1465985328,-0.1809124798,0.2255290449,-0.4678215683,0.0015987379,0.1265387982,0.2321948111,0.0047568763,-0.2391504347,0.1532386392,-0.2210099697,0.0950137973,-0.0478524417,0.0023000063,0.3110736012,-0.0307076853,0.0195823498,-0.1690815538,0.3321277499,0.1837939471,0.2391187251,0.059884239,-0.3443986177,0.1939164996,0.2064486891,0.0768959895,0.1003798246,0.1280243248,-0.1547597498,-0.0011511284,0.1200007051,0.1531922817,0.2261005193,0.1154718995,0.0344447792,0.1097908914,0.0470997021,-0.0364268981,0.213872835,0.1201431677,0.0979410112,0.2117496431,0.2166278064,0.1195410043,-0.2979359329,0.0585308373,-0.0077522574,0.3398452401,-0.4212239981,0.1853006929,-0.2622367144,-0.1929824948,-0.0617371202,-0.1428423077,-0.2671996355,-0.2536307871,-0.2375868261,0.5170041323,0.0329120681,-0.0015957551,0.11986617,0.1558510512,-0.0500015095,-0.2120888084,-0.3231782913,-0.2004773766,0.0348403975,0.0060451855,0.4275411367,0.2321515083,0.3703943193,-0.1881028414,-0.0665643662,-0.3638424575,-0.2226314694,0.1898567826,-0.2442073524,0.6976232529,0.128633678,0.3730340302,-0.3154392838,-0.175389573,0.3589410782,0.0899787471,-0.1815504879,0.0390026271,-0.0971304402,-0.1191555783,-0.0464336313,-0.0819386467,-0.2936699688,-0.2921175957,0.2495022267,0.0184916854,0.1123023555,0.1112414375,0.0714355111,0.1453770548,0.0881624445,0.0959173739,-0.2088637501,-0.2817971408,0.2937942147,-0.2878470123,-0.3239341378,0.0957493111,0.1173667088,0.0920970291,0.074381128,-0.5608040094,-0.2206557393,-0.1234502792,0.1988714784,-0.0228530392,-0.0030096269,0.2323394567,0.0710071996,0.1360606551,-0.1279760748,-0.3654650152,-0.283908993,0.0910209119,0.237716943,0.0153617114,0.3684545755,-0.2187125832,0.621593833,0.2808212042,0.1129685491,0.3524125516,-0.1651170701,0.2774626911,-0.2685063481,-0.6332156658,0.051839843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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3688","title":"Pyarrow version error","comments":"Hi @Zaker237, thanks for reporting.\r\n\r\nThis is weird: the error you get is only thrown if the installed pyarrow version is less than 3.0.0.\r\n\r\nCould you please check that you install pyarrow in the same Python virtual environment where you installed datasets?\r\n\r\nFrom the Python command line (or terminal) where you get the error, please type:\r\n```\r\nimport pyarrow\r\nprint(pyarrow.__version__)\r\nimport datasets\r\nprint(datasets.__version__)\r\n``` ","body":"## Describe the bug\r\nI installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error:\r\n`To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`.\r\ni tryed with all version of pyarrow execpt `4.0.0` but still get the same error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-19-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py in <module>\r\n     26 \r\n     27 \r\n---> 28 if _version.parse(pyarrow.__version__).major < 3:\r\n     29     raise ImportWarning(\r\n     30         \"To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\\n\"\r\n\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n## Environment info\r\nTraceback (most recent call last):\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"C:\\Users\\Alex\\AppData\\Local\\Continuum\\anaconda3\\Scripts\\datasets-cli.exe\\__main__.py\", line 5, in <module>\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py\", line 28, in <module>\r\n    if _version.parse(pyarrow.__version__).major < 3:\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n- `datasets` version:\r\n- Platform: Linux(Ubuntu) and Windows: conda on the both\r\n- Python version: 3.7\r\n- PyArrow version: 7.0.0\r\n","comment_length":64,"text":"Pyarrow version error \n ## Describe the bug\r\nI installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error:\r\n`To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`.\r\ni tryed with all version of pyarrow execpt `4.0.0` but still get the same error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-19-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py in <module>\r\n     26 \r\n     27 \r\n---> 28 if _version.parse(pyarrow.__version__).major < 3:\r\n     29     raise ImportWarning(\r\n     30         \"To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\\n\"\r\n\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n## Environment info\r\nTraceback (most recent call last):\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"C:\\Users\\Alex\\AppData\\Local\\Continuum\\anaconda3\\Scripts\\datasets-cli.exe\\__main__.py\", line 5, in <module>\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py\", line 28, in <module>\r\n    if _version.parse(pyarrow.__version__).major < 3:\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n- `datasets` version:\r\n- Platform: Linux(Ubuntu) and Windows: conda on the both\r\n- Python version: 3.7\r\n- PyArrow version: 7.0.0\r\n \n Hi @Zaker237, thanks for reporting.\r\n\r\nThis is weird: the error you get is only thrown if the installed pyarrow version is less than 3.0.0.\r\n\r\nCould you please check that you install pyarrow in the same Python virtual environment where you installed datasets?\r\n\r\nFrom the Python command line (or terminal) where you get the error, please type:\r\n```\r\nimport pyarrow\r\nprint(pyarrow.__version__)\r\nimport datasets\r\nprint(datasets.__version__)\r\n``` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3688","title":"Pyarrow version error","comments":"hi @albertvillanova  i try yesterday to create a new python environement with python 7 and try it on the environement and it worked. so i think that the error was not the package but may be jupyter notebook on conda. still yet i'm not yet sure but it worked in an environment created with venv","body":"## Describe the bug\r\nI installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error:\r\n`To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`.\r\ni tryed with all version of pyarrow execpt `4.0.0` but still get the same error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-19-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py in <module>\r\n     26 \r\n     27 \r\n---> 28 if _version.parse(pyarrow.__version__).major < 3:\r\n     29     raise ImportWarning(\r\n     30         \"To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\\n\"\r\n\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n## Environment info\r\nTraceback (most recent call last):\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"C:\\Users\\Alex\\AppData\\Local\\Continuum\\anaconda3\\Scripts\\datasets-cli.exe\\__main__.py\", line 5, in <module>\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py\", line 28, in <module>\r\n    if _version.parse(pyarrow.__version__).major < 3:\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n- `datasets` version:\r\n- Platform: Linux(Ubuntu) and Windows: conda on the both\r\n- Python version: 3.7\r\n- PyArrow version: 7.0.0\r\n","comment_length":55,"text":"Pyarrow version error \n ## Describe the bug\r\nI installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error:\r\n`To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`.\r\ni tryed with all version of pyarrow execpt `4.0.0` but still get the same error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-19-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py in <module>\r\n     26 \r\n     27 \r\n---> 28 if _version.parse(pyarrow.__version__).major < 3:\r\n     29     raise ImportWarning(\r\n     30         \"To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\\n\"\r\n\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n## Environment info\r\nTraceback (most recent call last):\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"C:\\Users\\Alex\\AppData\\Local\\Continuum\\anaconda3\\Scripts\\datasets-cli.exe\\__main__.py\", line 5, in <module>\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py\", line 28, in <module>\r\n    if _version.parse(pyarrow.__version__).major < 3:\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n- `datasets` version:\r\n- Platform: Linux(Ubuntu) and Windows: conda on the both\r\n- Python version: 3.7\r\n- PyArrow version: 7.0.0\r\n \n hi @albertvillanova  i try yesterday to create a new python environement with python 7 and try it on the environement and it worked. so i think that the error was not the package but may be jupyter notebook on conda. still yet i'm not yet sure but it worked in an environment created with 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3688","title":"Pyarrow version error","comments":"OK, thanks @Zaker237 for your feedback.\r\n\r\nI close this issue then. Please, feel free to reopen it if the problem arises again.","body":"## Describe the bug\r\nI installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error:\r\n`To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`.\r\ni tryed with all version of pyarrow execpt `4.0.0` but still get the same error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-19-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py in <module>\r\n     26 \r\n     27 \r\n---> 28 if _version.parse(pyarrow.__version__).major < 3:\r\n     29     raise ImportWarning(\r\n     30         \"To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\\n\"\r\n\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n## Environment info\r\nTraceback (most recent call last):\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"C:\\Users\\Alex\\AppData\\Local\\Continuum\\anaconda3\\Scripts\\datasets-cli.exe\\__main__.py\", line 5, in <module>\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py\", line 28, in <module>\r\n    if _version.parse(pyarrow.__version__).major < 3:\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n- `datasets` version:\r\n- Platform: Linux(Ubuntu) and Windows: conda on the both\r\n- Python version: 3.7\r\n- PyArrow version: 7.0.0\r\n","comment_length":22,"text":"Pyarrow version error \n ## Describe the bug\r\nI installed datasets(version 1.17.0, 1.18.0, 1.18.3) but i'm right now nor able to import it because of pyarrow. when i try to import it, i get the following error:\r\n`To use datasets, the module pyarrow>=3.0.0 is required, and the current version of pyarrow doesn't match this condition`.\r\ni tryed with all version of pyarrow execpt `4.0.0` but still get the same error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-19-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py in <module>\r\n     26 \r\n     27 \r\n---> 28 if _version.parse(pyarrow.__version__).major < 3:\r\n     29     raise ImportWarning(\r\n     30         \"To use `datasets`, the module `pyarrow>=3.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\\n\"\r\n\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n## Environment info\r\nTraceback (most recent call last):\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"C:\\Users\\Alex\\AppData\\Local\\Continuum\\anaconda3\\Scripts\\datasets-cli.exe\\__main__.py\", line 5, in <module>\r\n  File \"c:\\users\\alex\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages\\datasets\\__init__.py\", line 28, in <module>\r\n    if _version.parse(pyarrow.__version__).major < 3:\r\nAttributeError: 'Version' object has no attribute 'major'\r\n\r\n- `datasets` version:\r\n- Platform: Linux(Ubuntu) and Windows: conda on the both\r\n- Python version: 3.7\r\n- PyArrow version: 7.0.0\r\n \n OK, thanks @Zaker237 for your feedback.\r\n\r\nI close this issue then. Please, feel free to reopen it if the problem arises again.","embeddings":[-0.4256338775,0.2237605006,-0.0069540613,0.120792754,0.0590525046,0.0466510989,0.187753275,0.3738027513,-0.2632450759,-0.0977325812,0.1039726064,0.3018874526,-0.1692363173,-0.0959771648,-0.0230370406,-0.1642787606,0.3303414881,0.1971315593,-0.0938644856,0.0507560223,-0.2155962735,0.0681534186,-0.3759562969,0.2657382786,-0.1499122679,0.0329050422,-0.0793449506,0.0197755359,-0.4686665237,-0.6579994559,0.351922065,-0.1885745078,0.5027190447,0.5248025656,-0.0001199248,-0.0250754021,0.4257618189,0.0778006613,-0.2366793156,-0.4816680253,-0.0414818302,-0.2526873648,0.227885887,-0.0137280636,0.2511608601,-0.5172687173,-0.0017127048,0.1481793523,0.2187119126,0.3957417607,0.1560764164,0.2217472792,0.5808710456,0.0216015819,0.4553936124,0.1229482442,-0.1378252953,0.5206586123,0.3371717334,-0.1061920598,0.2225818783,-0.0251360722,-0.0958754122,-0.0065742875,0.3165675104,-0.0353865325,0.4572519064,-0.1524517685,-0.0650992021,0.0830968469,0.4442392588,-0.5779130459,-0.5222275257,-0.1147045419,0.0815890133,-0.4627070427,0.2076316625,0.0770755038,-0.1474930346,0.1955500096,-0.089472197,-0.0478082672,-0.2814340591,0.3370066583,-0.2295808494,0.3015702963,0.1001517996,0.163579002,0.0377671719,-0.2392084002,0.5784666538,-0.0897761434,0.0215655658,0.2040641606,-0.2897427976,0.1885193437,0.1013834253,0.3034085035,0.2356242537,-0.0945531502,-0.3734942675,-0.2429316342,0.1146432236,0.1572794765,0.1790711731,-0.027114816,0.1008858457,0.0930599496,0.0493216589,-0.0303272698,0.3163028061,0.0503880978,-0.2608648837,-0.3740573525,0.3064880371,0.159373641,0.5250378847,-0.2553820908,-0.0571703352,-0.1621272713,-0.4975509048,0.020547485,-0.1359618753,0.0637722313,-0.1657112539,0.0599818863,0.0673895776,0.2620628774,-0.1604866683,-0.1504558772,0.0655753613,-0.0002583512,-0.3126213551,-0.1297592521,0.0460585393,-0.3897313476,0.1263863444,0.1315647811,-0.133840546,0.1845247746,-0.1251455992,-0.1898612976,-0.1141616628,0.2454486638,-0.1061748564,0.2373944819,-0.0563796684,-0.330776453,-0.2896636426,0.2206621617,-0.1686180085,-0.2780706286,-0.4139544666,0.0963478237,-0.2700883746,-0.1037639901,-0.1477412879,-0.1551325768,0.0499453098,-0.2569126487,-0.0584211014,-0.5352342129,0.0338338092,-0.2344973236,0.1830926836,0.22536017,-0.7069930434,0.0321647115,-0.1856891066,-0.3143295348,-0.1900149733,-0.1181127205,-0.205831334,0.0666367933,-0.0152995251,0.004005468,0.3231740594,-0.4396960437,-0.3771212101,-0.2159758657,0.0328749157,-0.0645018965,-0.0051206923,-0.1237517223,0.1629498452,0.0130216349,0.0470774397,0.2014013529,0.0301624425,-0.0821903795,-0.0734788328,-0.3571386337,0.1178167015,0.2205913663,0.0694898218,-0.2226176858,0.1635835022,-0.2469979674,0.2454330325,0.0376326069,0.114397414,0.1391561329,0.4252536893,0.1180331185,-0.0496882908,-0.3738448918,-0.3749521077,0.1986272484,-0.0051661339,-0.1343676448,-0.3107326925,-0.0507822037,-0.254552573,0.2034230679,-0.0549698472,0.1911174059,0.038367521,-0.0535939485,0.2257213742,0.1538194567,-0.1565220058,0.1284491271,0.0784923211,0.1799109131,-0.1609285623,0.288677454,-0.4102800488,-0.3990303576,0.1236132681,0.1087660342,0.2497483492,-0.0024512603,0.0336179249,0.1711705178,-0.0290237665,0.073181361,-0.5439940691,0.0422203057,-0.028033359,-0.188724488,0.109936446,-0.0792583227,-0.0055925646,0.2512382269,0.400061667,0.0403769948,0.3314853609,0.1997803599,-0.036818359,0.1393982172,-0.0421819128,0.0299923401,-0.0073009059,-0.0525082797,0.1012384817,0.1977418363,0.1318674386,-0.046660699,-0.1234453246,-0.0060504172,0.450350374,0.0597987995,0.1299467534,0.0699021444,-0.4403264523,0.2006116211,0.1465410739,-0.0481150448,0.3530407846,0.2147040218,-0.2447317243,0.2174173743,-0.0168927535,-0.0232428703,0.1836981624,0.2458224893,0.4465551972,0.0699395761,0.1596866399,0.1036066338,0.0162799843,-0.2183869481,-0.0841363445,0.302415669,-0.439075321,0.1989969909,-0.3718813956,-0.1365318596,-0.2467175126,-0.4241203666,-0.2708543539,0.0065218876,-0.1436679363,0.403737247,0.2039154172,0.3272770643,-0.0200914275,-0.242337659,-0.0955376625,-0.2552652955,-0.1128731221,-0.1971100867,-0.5011848211,-0.0184555147,0.2928414047,-0.1414897591,0.192752257,-0.2991769612,0.1098538339,-0.2075841129,-0.1496831179,-0.0542582907,-0.1105448827,0.4164358675,0.2712006569,0.1033282876,0.0461991206,-0.0376312137,0.2260366529,-0.1213335842,-0.3179676235,-0.0437602587,-0.2126750648,-0.122909762,0.0941246748,-0.1485286504,-0.4478538632,-0.2514327168,0.0359965079,0.1118308976,0.1820019335,0.2117303759,0.2903399467,0.1522826254,-0.2607866228,-0.1108699888,0.2044766247,-0.0256367512,0.3808658719,-0.0713749304,-0.315841049,0.1266172081,0.0319360942,-0.1778588593,0.244502753,-0.1457462311,-0.1388029754,0.0840640888,0.6056478024,-0.0723337829,-0.0586514287,0.5412829518,0.1113644019,0.0854084864,-0.0420831814,-0.2987922728,-0.3469508886,-0.0470992588,0.2168199867,0.148236692,0.4185532331,0.0609443113,0.6380971074,0.0985398963,-0.3517868221,0.1823668182,-0.2339906693,0.1723241359,0.0241684206,-0.3478276432,-0.1777208,-0.0466810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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3687","title":"Can't get the text data when calling to_tf_dataset","comments":"You are correct that `to_tf_dataset` only handles numerical columns right now, yes, though this is a limitation we might remove in future! The main reason we do this is that our models mostly do not include the tokenizer as a model layer, because it's very difficult to compile some of them in TF. So the \"normal\" Huggingface workflow is to first tokenize your dataset, and then pass tokenized tensors to the model.\r\n\r\nFor your use case, would you prefer to pass strings to the model, and use some text processing layers instead of the built-in tokenizers?","body":"I am working with the SST2 dataset, and am using TensorFlow 2.5\r\nI'd like to convert it to a `tf.data.Dataset` by calling the `to_tf_dataset` method.\r\nThe following snippet is what I am using to achieve this:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\")\r\n\r\ntrain_dataset = dataset[\"train\"].to_tf_dataset(columns=['sentence'], label_cols=\"label\", shuffle=True, batch_size=8,collate_fn=data_collator)\r\n```\r\nHowever, this only gets me the labels; the text--the most important part--is missing:\r\n\r\n```\r\nfor s in train_dataset.take(1):\r\n  print(s) #prints something like: ({}, <tf.Tensor: shape=(8,), ...>)\r\n```\r\n\r\nAs you can see, it only returns the label part, not the data, as indicated by the empty dictionary, `{}`. So far, I've played with various settings of the method arguments, but to no avail; I do not want to perform any text processing at this time. On my quest to achieve what I want ( a `tf.data.Dataset`), I've consulted these resources:\r\n\r\n[https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf](https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf)\r\n[https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow](https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow)\r\n\r\nI was surprised to not find more extensive examples on how to transform a Hugginface dataset to one compatible with TensorFlow.\r\n\r\nIf you could point me to where I am going wrong, please do so.\r\nThanks in advance for your support.\r\n\r\n---\r\nEdit: In the [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html#datasets.Dataset.to_tf_dataset), I found the following description:\r\n\r\n\r\n_In general, only columns that the model can use as input should be included here (numeric data only)._\r\n\r\nDoes this imply that no textual, i.e., `string` data can be loaded?\r\n","comment_length":96,"text":"Can't get the text data when calling to_tf_dataset \n I am working with the SST2 dataset, and am using TensorFlow 2.5\r\nI'd like to convert it to a `tf.data.Dataset` by calling the `to_tf_dataset` method.\r\nThe following snippet is what I am using to achieve this:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\")\r\n\r\ntrain_dataset = dataset[\"train\"].to_tf_dataset(columns=['sentence'], label_cols=\"label\", shuffle=True, batch_size=8,collate_fn=data_collator)\r\n```\r\nHowever, this only gets me the labels; the text--the most important part--is missing:\r\n\r\n```\r\nfor s in train_dataset.take(1):\r\n  print(s) #prints something like: ({}, <tf.Tensor: shape=(8,), ...>)\r\n```\r\n\r\nAs you can see, it only returns the label part, not the data, as indicated by the empty dictionary, `{}`. So far, I've played with various settings of the method arguments, but to no avail; I do not want to perform any text processing at this time. On my quest to achieve what I want ( a `tf.data.Dataset`), I've consulted these resources:\r\n\r\n[https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf](https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf)\r\n[https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow](https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow)\r\n\r\nI was surprised to not find more extensive examples on how to transform a Hugginface dataset to one compatible with TensorFlow.\r\n\r\nIf you could point me to where I am going wrong, please do so.\r\nThanks in advance for your support.\r\n\r\n---\r\nEdit: In the [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html#datasets.Dataset.to_tf_dataset), I found the following description:\r\n\r\n\r\n_In general, only columns that the model can use as input should be included here (numeric data only)._\r\n\r\nDoes this imply that no textual, i.e., `string` data can be loaded?\r\n \n You are correct that `to_tf_dataset` only handles numerical columns right now, yes, though this is a limitation we might remove in future! The main reason we do this is that our models mostly do not include the tokenizer as a model layer, because it's very difficult to compile some of them in TF. So the \"normal\" Huggingface workflow is to first tokenize your dataset, and then pass tokenized tensors to the model.\r\n\r\nFor your use case, would you prefer to pass strings to the model, and use some text processing layers instead of the built-in tokenizers?","embeddings":[0.3068760633,-0.2420512438,0.0682802349,0.3844342828,0.31130445,0.12069235,0.3427681029,0.4319317639,-0.1418879479,0.1407024711,-0.0737772435,-0.0379528925,0.037617296,0.3645376265,0.1628636867,-0.2193425149,0.0983589962,0.1495363265,-0.3627761006,-0.2187286764,0.01730044,0.3332818747,-0.0504235514,0.0537579805,-0.4780084193,0.2818849385,0.0408288129,0.158696115,0.069420211,-0.0129830614,0.2851800621,-0.0297352746,-0.0361352563,0.4447243214,-0.0001269763,0.2168246806,-0.0782130212,-0.2287095636,-0.0787931606,-0.375374943,0.1657571793,-0.1196120232,0.2105719149,-0.1184052974,-0.4093673825,0.0496716239,0.0003640366,0.2153626382,0.4314881265,0.3190482259,0.0026417929,0.1317079514,0.2536615431,0.1724730581,0.0878237635,0.2073566765,-0.189788267,-0.1200593635,0.0869093463,0.1084912866,0.1736397892,0.4555377364,-0.0397023112,-0.062423788,0.2187524438,0.3472046554,-0.3327510357,-0.3331519663,0.2526435554,0.305082649,0.7274180055,-0.307133615,-0.2625457346,-0.3518846631,-0.0724802539,0.1643013805,0.0191324223,-0.1753756851,-0.403369993,0.1200897023,-0.3463733494,-0.2728510201,-0.3162670135,0.0927409381,-0.1567460895,0.1692517698,-0.1593461931,0.0951363891,0.1082484052,-0.0758512542,-0.0393438116,-0.0519248433,0.1710885912,0.255925566,-0.4065845013,-0.1237830892,0.0695492625,-0.3654457331,-0.0545939356,-0.1781465113,-0.2268883586,-0.0380902812,-0.258120954,-0.1024651825,0.1855781972,-0.1286499947,0.1217678264,0.0859715268,-0.0603484698,-0.3307785988,-0.0305460617,-0.2414521426,-0.1433438659,0.0335247666,-0.1037828848,-0.0848988518,0.3044998944,-0.162492916,-0.009046955,-0.2130165547,-0.2945986688,0.0611808412,-0.127007395,0.1291798502,-0.1001915857,0.2671489418,0.3854938745,0.0259527825,-0.148401171,-0.1834006608,-0.2004361004,-0.0546362288,-0.0778055787,0.0031802298,0.2949286997,-0.3224166334,0.0423114039,-0.0327102579,0.0516933613,0.1380060762,-0.2193129659,0.0182657931,0.2279606462,0.1675924808,-0.0387042686,0.3050489128,0.4531749785,-0.2246551216,0.0751103759,0.0095812976,-0.3496600688,-0.1288196892,0.0160775371,-0.0277956463,-0.0529087558,-0.0923146978,-0.0857612714,0.5933643579,0.0874927342,0.0932561606,-0.053214334,-0.0705876127,-0.2894848883,0.0111589953,0.417999208,0.4417740405,-0.5384357572,-0.0934698284,0.4582495987,-0.1314123869,0.0455541536,0.0349639282,0.0961590111,0.4265984893,0.0452962853,0.1076438501,0.3030736744,-0.2581470609,-0.1372737139,0.0972367674,-0.1001725644,0.1263935864,-0.1133091301,-0.0041706879,-0.0834794417,0.1800476909,0.0399306603,0.016067639,-0.2212524712,-0.0129080992,0.0517025031,0.0045962483,0.2721183002,0.1233567521,0.0386747569,0.4092639685,-0.2847221792,0.1754700989,0.1245854422,-0.0830927938,0.1481680423,0.3648364842,0.0426922366,0.4389078319,0.0915864483,0.051411733,-0.2232377231,0.1708628982,0.4331250787,0.3410030901,-0.0596916676,0.0445501767,-0.1836937815,-0.0102933217,0.1771234572,-0.088745594,-0.048087161,0.1078803763,0.1881380528,0.2541702986,-0.2177129686,0.2977586687,-0.314917326,0.2967324853,-0.4221659899,0.3177092373,0.4513082206,-0.4089156389,-0.0213722549,0.2392359972,0.0285096411,0.0101136314,-0.0527249351,0.2196440101,-0.0609774925,0.2196020782,-0.4841223061,0.4686743319,-0.0419075973,-0.1384867877,-0.1132741794,0.0693923384,0.1648575664,-0.1746756434,-0.1859621704,-0.0609724,-0.1006936431,0.4127697051,0.0567202494,0.0290704835,-0.1753455698,-0.0432899185,-0.2258906364,0.2440308928,-0.1540449858,-0.5480883718,0.1783436239,0.0011796411,-0.5144265294,0.1967716217,0.920312345,0.0196158905,-0.2100317776,0.2219884098,-0.3204725087,-0.0235058591,-0.0061306152,-0.0590544268,0.0996938646,0.1117013246,0.0055733309,-0.0348898359,0.1547805816,0.0101945838,0.2375042737,0.0553521104,0.2460256368,-0.0327990353,-0.0866182074,0.1529930383,-0.1689173281,0.0955349505,-0.2502052486,0.1820400059,-0.4058982432,0.0608409271,0.0380398743,-0.550000906,-0.0876850337,-0.0396951288,-0.0125937294,-0.3134301305,-0.2270384431,0.1717083305,0.0608857982,-0.19399333,0.0135574108,0.1357903033,0.1343172044,-0.1471980065,-0.3681708276,-0.2097782493,-0.2816808522,-0.043782901,-0.1732278019,-0.1021050066,0.0011739869,-0.1489526033,0.1834532022,-0.0831883922,-0.2622401416,0.2362401783,-0.5525916815,-0.1185733676,0.1789247841,0.5591651201,-0.097927615,-0.1169765592,0.1653730422,-0.2041675597,-0.2265608609,0.2745604217,-0.0766028836,-0.1125941575,-0.1618115604,-0.4125032127,-0.1092282236,-0.1121641472,0.4364111125,-0.0792573318,-0.055777967,0.6051892042,0.1745734513,0.3289149404,0.199198097,0.1743386239,-0.0873185769,-0.4112897515,0.3432025909,0.028600648,-0.3271426558,-0.0232793931,0.1330792606,0.0609620735,-0.0996873006,-0.3912669718,-0.0022722702,-0.2983817756,-0.0272810012,-0.0920319408,0.1692035198,0.4847096503,-0.1173144132,0.0581597798,-0.2168235481,-0.3283683658,0.4047162533,0.1540789306,0.0784091353,0.1407799572,0.4283557236,0.3226523399,0.5716152787,0.039291434,-0.1771520227,-0.1622580141,-0.3019980192,0.1810318679,-0.1703669131,-0.4717082381,0.2076875418,-0.151299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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3687","title":"Can't get the text data when calling to_tf_dataset","comments":"Thanks for the quick follow-up to my issue.\r\n\r\nFor my use-case, instead of the built-in tokenizers I wanted to use the `TextVectorization` layer to map from strings to integers. To achieve this, I came up with the following solution:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\nimport tensorflow as tf\r\nimport string\r\nimport re\r\nfrom tensorflow.keras.layers.experimental.preprocessing import TextVectorization\r\n\r\n#some hyper-parameters for the text-to-integer mapping\r\nmax_features = 20000\r\nembedding_dim = 128\r\nsequence_length = 210\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\", \"default\")\r\n\r\n#adapt the vectorization layer on train data only\r\nvectorize_layer.adapt(dataset[\"train\"].to_dict(batched=False)[\"sentence\"])\r\n\r\ndef prepare_features(text, label):\r\n    text = tf.expand_dims(text, -1)\r\n    return {\"vectorized_text\": vectorize_layer(text)[0], \"label\": tf.expand_dims(label, axis=-1)}\r\n\r\nencoded_dataset = dataset.map(lambda example: prepare_features(example[\"sentence\"], example[\"label\"]), batched=False)\r\n\r\n\r\ndef custom_standardization(input_data):\r\n    lowercase = tf.strings.lower(input_data)\r\n    return tf.strings.regex_replace(\r\n        lowercase, f\"[{re.escape(string.punctuation)}]\", \"\"\r\n    )\r\n\r\nvectorize_layer = TextVectorization(\r\n    standardize=custom_standardization,\r\n    max_tokens=max_features,\r\n    output_mode=\"int\",\r\n    output_sequence_length=sequence_length,\r\n)\r\n\r\ntrain_dataset = encoded_dataset[\"train\"].to_tf_dataset(columns=['vectorized_text'], label_cols=[\"label\"],\r\n                                                       shuffle=True, batch_size=1, collate_fn=data_collator).unbatch()\r\n#similar for the other sub-sets\r\n\r\n```\r\n\r\nSince the strings would have been mapped to integers or floats at some point, it's no drawback that this mapping is done early in the process. \r\n\r\nFor the future, however, it'd be more convenient to get the string data, since I am also inspecting the dataset (longest sentence, shortest sentence), which is more challenging when working with integer or float. For now, this can be done by calling `to_dict`.","body":"I am working with the SST2 dataset, and am using TensorFlow 2.5\r\nI'd like to convert it to a `tf.data.Dataset` by calling the `to_tf_dataset` method.\r\nThe following snippet is what I am using to achieve this:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\")\r\n\r\ntrain_dataset = dataset[\"train\"].to_tf_dataset(columns=['sentence'], label_cols=\"label\", shuffle=True, batch_size=8,collate_fn=data_collator)\r\n```\r\nHowever, this only gets me the labels; the text--the most important part--is missing:\r\n\r\n```\r\nfor s in train_dataset.take(1):\r\n  print(s) #prints something like: ({}, <tf.Tensor: shape=(8,), ...>)\r\n```\r\n\r\nAs you can see, it only returns the label part, not the data, as indicated by the empty dictionary, `{}`. So far, I've played with various settings of the method arguments, but to no avail; I do not want to perform any text processing at this time. On my quest to achieve what I want ( a `tf.data.Dataset`), I've consulted these resources:\r\n\r\n[https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf](https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf)\r\n[https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow](https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow)\r\n\r\nI was surprised to not find more extensive examples on how to transform a Hugginface dataset to one compatible with TensorFlow.\r\n\r\nIf you could point me to where I am going wrong, please do so.\r\nThanks in advance for your support.\r\n\r\n---\r\nEdit: In the [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html#datasets.Dataset.to_tf_dataset), I found the following description:\r\n\r\n\r\n_In general, only columns that the model can use as input should be included here (numeric data only)._\r\n\r\nDoes this imply that no textual, i.e., `string` data can be loaded?\r\n","comment_length":212,"text":"Can't get the text data when calling to_tf_dataset \n I am working with the SST2 dataset, and am using TensorFlow 2.5\r\nI'd like to convert it to a `tf.data.Dataset` by calling the `to_tf_dataset` method.\r\nThe following snippet is what I am using to achieve this:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\")\r\n\r\ntrain_dataset = dataset[\"train\"].to_tf_dataset(columns=['sentence'], label_cols=\"label\", shuffle=True, batch_size=8,collate_fn=data_collator)\r\n```\r\nHowever, this only gets me the labels; the text--the most important part--is missing:\r\n\r\n```\r\nfor s in train_dataset.take(1):\r\n  print(s) #prints something like: ({}, <tf.Tensor: shape=(8,), ...>)\r\n```\r\n\r\nAs you can see, it only returns the label part, not the data, as indicated by the empty dictionary, `{}`. So far, I've played with various settings of the method arguments, but to no avail; I do not want to perform any text processing at this time. On my quest to achieve what I want ( a `tf.data.Dataset`), I've consulted these resources:\r\n\r\n[https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf](https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf)\r\n[https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow](https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow)\r\n\r\nI was surprised to not find more extensive examples on how to transform a Hugginface dataset to one compatible with TensorFlow.\r\n\r\nIf you could point me to where I am going wrong, please do so.\r\nThanks in advance for your support.\r\n\r\n---\r\nEdit: In the [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html#datasets.Dataset.to_tf_dataset), I found the following description:\r\n\r\n\r\n_In general, only columns that the model can use as input should be included here (numeric data only)._\r\n\r\nDoes this imply that no textual, i.e., `string` data can be loaded?\r\n \n Thanks for the quick follow-up to my issue.\r\n\r\nFor my use-case, instead of the built-in tokenizers I wanted to use the `TextVectorization` layer to map from strings to integers. To achieve this, I came up with the following solution:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\nimport tensorflow as tf\r\nimport string\r\nimport re\r\nfrom tensorflow.keras.layers.experimental.preprocessing import TextVectorization\r\n\r\n#some hyper-parameters for the text-to-integer mapping\r\nmax_features = 20000\r\nembedding_dim = 128\r\nsequence_length = 210\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\", \"default\")\r\n\r\n#adapt the vectorization layer on train data only\r\nvectorize_layer.adapt(dataset[\"train\"].to_dict(batched=False)[\"sentence\"])\r\n\r\ndef prepare_features(text, label):\r\n    text = tf.expand_dims(text, -1)\r\n    return {\"vectorized_text\": vectorize_layer(text)[0], \"label\": tf.expand_dims(label, axis=-1)}\r\n\r\nencoded_dataset = dataset.map(lambda example: prepare_features(example[\"sentence\"], example[\"label\"]), batched=False)\r\n\r\n\r\ndef custom_standardization(input_data):\r\n    lowercase = tf.strings.lower(input_data)\r\n    return tf.strings.regex_replace(\r\n        lowercase, f\"[{re.escape(string.punctuation)}]\", \"\"\r\n    )\r\n\r\nvectorize_layer = TextVectorization(\r\n    standardize=custom_standardization,\r\n    max_tokens=max_features,\r\n    output_mode=\"int\",\r\n    output_sequence_length=sequence_length,\r\n)\r\n\r\ntrain_dataset = encoded_dataset[\"train\"].to_tf_dataset(columns=['vectorized_text'], label_cols=[\"label\"],\r\n                                                       shuffle=True, batch_size=1, collate_fn=data_collator).unbatch()\r\n#similar for the other sub-sets\r\n\r\n```\r\n\r\nSince the strings would have been mapped to integers or floats at some point, it's no drawback that this mapping is done early in the process. \r\n\r\nFor the future, however, it'd be more convenient to get the string data, since I am also inspecting the dataset (longest sentence, shortest sentence), which is more challenging when working with integer or float. For now, this can be done by calling 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3687","title":"Can't get the text data when calling to_tf_dataset","comments":"> For the future, however, it'd be more convenient to get the string data, since I am also inspecting the dataset (longest sentence, shortest sentence), which is more challenging when working with integer or float.\r\n\r\nYes, I agree, so let's keep this issue open.","body":"I am working with the SST2 dataset, and am using TensorFlow 2.5\r\nI'd like to convert it to a `tf.data.Dataset` by calling the `to_tf_dataset` method.\r\nThe following snippet is what I am using to achieve this:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\")\r\n\r\ntrain_dataset = dataset[\"train\"].to_tf_dataset(columns=['sentence'], label_cols=\"label\", shuffle=True, batch_size=8,collate_fn=data_collator)\r\n```\r\nHowever, this only gets me the labels; the text--the most important part--is missing:\r\n\r\n```\r\nfor s in train_dataset.take(1):\r\n  print(s) #prints something like: ({}, <tf.Tensor: shape=(8,), ...>)\r\n```\r\n\r\nAs you can see, it only returns the label part, not the data, as indicated by the empty dictionary, `{}`. So far, I've played with various settings of the method arguments, but to no avail; I do not want to perform any text processing at this time. On my quest to achieve what I want ( a `tf.data.Dataset`), I've consulted these resources:\r\n\r\n[https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf](https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf)\r\n[https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow](https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow)\r\n\r\nI was surprised to not find more extensive examples on how to transform a Hugginface dataset to one compatible with TensorFlow.\r\n\r\nIf you could point me to where I am going wrong, please do so.\r\nThanks in advance for your support.\r\n\r\n---\r\nEdit: In the [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html#datasets.Dataset.to_tf_dataset), I found the following description:\r\n\r\n\r\n_In general, only columns that the model can use as input should be included here (numeric data only)._\r\n\r\nDoes this imply that no textual, i.e., `string` data can be loaded?\r\n","comment_length":44,"text":"Can't get the text data when calling to_tf_dataset \n I am working with the SST2 dataset, and am using TensorFlow 2.5\r\nI'd like to convert it to a `tf.data.Dataset` by calling the `to_tf_dataset` method.\r\nThe following snippet is what I am using to achieve this:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import DefaultDataCollator\r\n\r\ndata_collator = DefaultDataCollator(return_tensors=\"tf\")\r\ndataset = load_dataset(\"sst\")\r\n\r\ntrain_dataset = dataset[\"train\"].to_tf_dataset(columns=['sentence'], label_cols=\"label\", shuffle=True, batch_size=8,collate_fn=data_collator)\r\n```\r\nHowever, this only gets me the labels; the text--the most important part--is missing:\r\n\r\n```\r\nfor s in train_dataset.take(1):\r\n  print(s) #prints something like: ({}, <tf.Tensor: shape=(8,), ...>)\r\n```\r\n\r\nAs you can see, it only returns the label part, not the data, as indicated by the empty dictionary, `{}`. So far, I've played with various settings of the method arguments, but to no avail; I do not want to perform any text processing at this time. On my quest to achieve what I want ( a `tf.data.Dataset`), I've consulted these resources:\r\n\r\n[https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf](https:\/\/www.philschmid.de\/huggingface-transformers-keras-tf)\r\n[https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow](https:\/\/huggingface.co\/docs\/datasets\/use_dataset.html?highlight=tensorflow)\r\n\r\nI was surprised to not find more extensive examples on how to transform a Hugginface dataset to one compatible with TensorFlow.\r\n\r\nIf you could point me to where I am going wrong, please do so.\r\nThanks in advance for your support.\r\n\r\n---\r\nEdit: In the [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html#datasets.Dataset.to_tf_dataset), I found the following description:\r\n\r\n\r\n_In general, only columns that the model can use as input should be included here (numeric data only)._\r\n\r\nDoes this imply that no textual, i.e., `string` data can be loaded?\r\n \n > For the future, however, it'd be more convenient to get the string data, since I am also inspecting the dataset (longest sentence, shortest sentence), which is more challenging when working with integer or float.\r\n\r\nYes, I agree, so let's keep this issue open.","embeddings":[0.3068760633,-0.2420512438,0.0682802349,0.3844342828,0.31130445,0.12069235,0.3427681029,0.4319317639,-0.1418879479,0.1407024711,-0.0737772435,-0.0379528925,0.037617296,0.3645376265,0.1628636867,-0.2193425149,0.0983589962,0.1495363265,-0.3627761006,-0.2187286764,0.01730044,0.3332818747,-0.0504235514,0.0537579805,-0.4780084193,0.2818849385,0.0408288129,0.158696115,0.069420211,-0.0129830614,0.2851800621,-0.0297352746,-0.0361352563,0.4447243214,-0.0001269763,0.2168246806,-0.0782130212,-0.2287095636,-0.0787931606,-0.375374943,0.1657571793,-0.1196120232,0.2105719149,-0.1184052974,-0.4093673825,0.0496716239,0.0003640366,0.2153626382,0.4314881265,0.3190482259,0.0026417929,0.1317079514,0.2536615431,0.1724730581,0.0878237635,0.2073566765,-0.189788267,-0.1200593635,0.0869093463,0.1084912866,0.1736397892,0.4555377364,-0.0397023112,-0.062423788,0.2187524438,0.3472046554,-0.3327510357,-0.3331519663,0.2526435554,0.305082649,0.7274180055,-0.307133615,-0.2625457346,-0.3518846631,-0.0724802539,0.1643013805,0.0191324223,-0.1753756851,-0.403369993,0.1200897023,-0.3463733494,-0.2728510201,-0.3162670135,0.0927409381,-0.1567460895,0.1692517698,-0.1593461931,0.0951363891,0.1082484052,-0.0758512542,-0.0393438116,-0.0519248433,0.1710885912,0.255925566,-0.4065845013,-0.1237830892,0.0695492625,-0.3654457331,-0.0545939356,-0.1781465113,-0.2268883586,-0.0380902812,-0.258120954,-0.1024651825,0.1855781972,-0.1286499947,0.1217678264,0.0859715268,-0.0603484698,-0.3307785988,-0.0305460617,-0.2414521426,-0.1433438659,0.0335247666,-0.1037828848,-0.0848988518,0.3044998944,-0.162492916,-0.009046955,-0.2130165547,-0.2945986688,0.0611808412,-0.127007395,0.1291798502,-0.1001915857,0.2671489418,0.3854938745,0.0259527825,-0.148401171,-0.1834006608,-0.2004361004,-0.0546362288,-0.0778055787,0.0031802298,0.2949286997,-0.3224166334,0.0423114039,-0.0327102579,0.0516933613,0.1380060762,-0.2193129659,0.0182657931,0.2279606462,0.1675924808,-0.0387042686,0.3050489128,0.4531749785,-0.2246551216,0.0751103759,0.0095812976,-0.3496600688,-0.1288196892,0.0160775371,-0.0277956463,-0.0529087558,-0.0923146978,-0.0857612714,0.5933643579,0.0874927342,0.0932561606,-0.053214334,-0.0705876127,-0.2894848883,0.0111589953,0.417999208,0.4417740405,-0.5384357572,-0.0934698284,0.4582495987,-0.1314123869,0.0455541536,0.0349639282,0.0961590111,0.4265984893,0.0452962853,0.1076438501,0.3030736744,-0.2581470609,-0.1372737139,0.0972367674,-0.1001725644,0.1263935864,-0.1133091301,-0.0041706879,-0.0834794417,0.1800476909,0.0399306603,0.016067639,-0.2212524712,-0.0129080992,0.0517025031,0.0045962483,0.2721183002,0.1233567521,0.0386747569,0.4092639685,-0.2847221792,0.1754700989,0.1245854422,-0.0830927938,0.1481680423,0.3648364842,0.0426922366,0.4389078319,0.0915864483,0.051411733,-0.2232377231,0.1708628982,0.4331250787,0.3410030901,-0.0596916676,0.0445501767,-0.1836937815,-0.0102933217,0.1771234572,-0.088745594,-0.048087161,0.1078803763,0.1881380528,0.2541702986,-0.2177129686,0.2977586687,-0.314917326,0.2967324853,-0.4221659899,0.3177092373,0.4513082206,-0.4089156389,-0.0213722549,0.2392359972,0.0285096411,0.0101136314,-0.0527249351,0.2196440101,-0.0609774925,0.2196020782,-0.4841223061,0.4686743319,-0.0419075973,-0.1384867877,-0.1132741794,0.0693923384,0.1648575664,-0.1746756434,-0.1859621704,-0.0609724,-0.1006936431,0.4127697051,0.0567202494,0.0290704835,-0.1753455698,-0.0432899185,-0.2258906364,0.2440308928,-0.1540449858,-0.5480883718,0.1783436239,0.0011796411,-0.5144265294,0.1967716217,0.920312345,0.0196158905,-0.2100317776,0.2219884098,-0.3204725087,-0.0235058591,-0.0061306152,-0.0590544268,0.0996938646,0.1117013246,0.0055733309,-0.0348898359,0.1547805816,0.0101945838,0.2375042737,0.0553521104,0.2460256368,-0.0327990353,-0.0866182074,0.1529930383,-0.1689173281,0.0955349505,-0.2502052486,0.1820400059,-0.4058982432,0.0608409271,0.0380398743,-0.550000906,-0.0876850337,-0.0396951288,-0.0125937294,-0.3134301305,-0.2270384431,0.1717083305,0.0608857982,-0.19399333,0.0135574108,0.1357903033,0.1343172044,-0.1471980065,-0.3681708276,-0.2097782493,-0.2816808522,-0.043782901,-0.1732278019,-0.1021050066,0.0011739869,-0.1489526033,0.1834532022,-0.0831883922,-0.2622401416,0.2362401783,-0.5525916815,-0.1185733676,0.1789247841,0.5591651201,-0.097927615,-0.1169765592,0.1653730422,-0.2041675597,-0.2265608609,0.2745604217,-0.0766028836,-0.1125941575,-0.1618115604,-0.4125032127,-0.1092282236,-0.1121641472,0.4364111125,-0.0792573318,-0.055777967,0.6051892042,0.1745734513,0.3289149404,0.199198097,0.1743386239,-0.0873185769,-0.4112897515,0.3432025909,0.028600648,-0.3271426558,-0.0232793931,0.1330792606,0.0609620735,-0.0996873006,-0.3912669718,-0.0022722702,-0.2983817756,-0.0272810012,-0.0920319408,0.1692035198,0.4847096503,-0.1173144132,0.0581597798,-0.2168235481,-0.3283683658,0.4047162533,0.1540789306,0.0784091353,0.1407799572,0.4283557236,0.3226523399,0.5716152787,0.039291434,-0.1771520227,-0.1622580141,-0.3019980192,0.1810318679,-0.1703669131,-0.4717082381,0.2076875418,-0.1512997597,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3686","title":"`Translation` features cannot be `flatten`ed","comments":"Thanks for reporting, @SBrandeis! Some additional feature types that don't behave as expected when flattened: `Audio`, `Image` and `TranslationVariableLanguages`","body":"## Describe the bug\r\n\r\n(`Dataset.flatten`)[https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L1265] fails for columns with feature (`Translation`)[https:\/\/github.com\/huggingface\/datasets\/blob\/3edbeb0ec6519b79f1119adc251a1a6b379a2c12\/src\/datasets\/features\/translation.py#L8]\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"europa_ecdc_tm\", \"en2fr\", split=\"train[:10]\")\r\nprint(dataset.features)\r\n# {'translation': Translation(languages=['en', 'fr'], id=None)}\r\nprint(dataset[0])\r\n# {'translation': {'en': 'Vaccination against hepatitis C is not yet available.', 'fr': 'Aucune vaccination contre l\u2019h\u00e9patite C n\u2019est encore disponible.'}}\r\n\r\ndataset.flatten()\r\n```\r\n\r\n## Expected results\r\n\r\n`dataset.flatten` should flatten the `Translation` column as if it were a dict of `Value(\"string\")`\r\n\r\n```python\r\ndataset[0]\r\n# {'translation.en':  'Vaccination against hepatitis C is not yet available.', 'translation.fr': 'Aucune vaccination contre l\u2019h\u00e9patite C n\u2019est encore disponible.' }\r\ndataset.features\r\n# {'translation.en': Value(\"string\"), 'translation.fr': Value(\"string\")}\r\n```\r\n\r\n## Actual results\r\n\r\n```python\r\nIn [31]: dset.flatten()\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-31-bb88eb5276ee> in <module>\r\n----> 1 dset.flatten()\r\n\r\n[...]\\site-packages\\datasets\\fingerprint.py in wrapper(*args, **kwargs)\r\n    411             # Call actual function\r\n    412\r\n--> 413             out = func(self, *args, **kwargs)\r\n    414\r\n    415             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n[...]\\site-packages\\datasets\\arrow_dataset.py in flatten(self, new_fingerprint, max_depth)\r\n   1294                 break\r\n   1295         dataset.info.features = self.features.flatten(max_depth=max_depth)\r\n-> 1296         dataset._data = update_metadata_with_features(dataset._data, dataset.features)\r\n   1297         logger.info(f'Flattened dataset from depth {depth} to depth {1 if depth + 1 < max_depth else \"unknown\"}.')\r\n   1298         dataset._fingerprint = new_fingerprint\r\n\r\n[...]\\site-packages\\datasets\\arrow_dataset.py in update_metadata_with_features(table, features)\r\n    534 def update_metadata_with_features(table: Table, features: Features):\r\n    535     \"\"\"To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema.\"\"\"\r\n--> 536     features = Features({col_name: features[col_name] for col_name in table.column_names})\r\n    537     if table.schema.metadata is None or b\"huggingface\" not in table.schema.metadata:\r\n    538         pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features))\r\n\r\n[...]\\site-packages\\datasets\\arrow_dataset.py in <dictcomp>(.0)\r\n    534 def update_metadata_with_features(table: Table, features: Features):\r\n    535     \"\"\"To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema.\"\"\"\r\n--> 536     features = Features({col_name: features[col_name] for col_name in table.column_names})\r\n    537     if table.schema.metadata is None or b\"huggingface\" not in table.schema.metadata:\r\n    538         pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features))\r\n\r\nKeyError: 'translation.en'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.18.3\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":19,"text":"`Translation` features cannot be `flatten`ed \n ## Describe the bug\r\n\r\n(`Dataset.flatten`)[https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L1265] fails for columns with feature (`Translation`)[https:\/\/github.com\/huggingface\/datasets\/blob\/3edbeb0ec6519b79f1119adc251a1a6b379a2c12\/src\/datasets\/features\/translation.py#L8]\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"europa_ecdc_tm\", \"en2fr\", split=\"train[:10]\")\r\nprint(dataset.features)\r\n# {'translation': Translation(languages=['en', 'fr'], id=None)}\r\nprint(dataset[0])\r\n# {'translation': {'en': 'Vaccination against hepatitis C is not yet available.', 'fr': 'Aucune vaccination contre l\u2019h\u00e9patite C n\u2019est encore disponible.'}}\r\n\r\ndataset.flatten()\r\n```\r\n\r\n## Expected results\r\n\r\n`dataset.flatten` should flatten the `Translation` column as if it were a dict of `Value(\"string\")`\r\n\r\n```python\r\ndataset[0]\r\n# {'translation.en':  'Vaccination against hepatitis C is not yet available.', 'translation.fr': 'Aucune vaccination contre l\u2019h\u00e9patite C n\u2019est encore disponible.' }\r\ndataset.features\r\n# {'translation.en': Value(\"string\"), 'translation.fr': Value(\"string\")}\r\n```\r\n\r\n## Actual results\r\n\r\n```python\r\nIn [31]: dset.flatten()\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-31-bb88eb5276ee> in <module>\r\n----> 1 dset.flatten()\r\n\r\n[...]\\site-packages\\datasets\\fingerprint.py in wrapper(*args, **kwargs)\r\n    411             # Call actual function\r\n    412\r\n--> 413             out = func(self, *args, **kwargs)\r\n    414\r\n    415             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n[...]\\site-packages\\datasets\\arrow_dataset.py in flatten(self, new_fingerprint, max_depth)\r\n   1294                 break\r\n   1295         dataset.info.features = self.features.flatten(max_depth=max_depth)\r\n-> 1296         dataset._data = update_metadata_with_features(dataset._data, dataset.features)\r\n   1297         logger.info(f'Flattened dataset from depth {depth} to depth {1 if depth + 1 < max_depth else \"unknown\"}.')\r\n   1298         dataset._fingerprint = new_fingerprint\r\n\r\n[...]\\site-packages\\datasets\\arrow_dataset.py in update_metadata_with_features(table, features)\r\n    534 def update_metadata_with_features(table: Table, features: Features):\r\n    535     \"\"\"To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema.\"\"\"\r\n--> 536     features = Features({col_name: features[col_name] for col_name in table.column_names})\r\n    537     if table.schema.metadata is None or b\"huggingface\" not in table.schema.metadata:\r\n    538         pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features))\r\n\r\n[...]\\site-packages\\datasets\\arrow_dataset.py in <dictcomp>(.0)\r\n    534 def update_metadata_with_features(table: Table, features: Features):\r\n    535     \"\"\"To be used in dataset transforms that modify the features of the dataset, in order to update the features stored in the metadata of its schema.\"\"\"\r\n--> 536     features = Features({col_name: features[col_name] for col_name in table.column_names})\r\n    537     if table.schema.metadata is None or b\"huggingface\" not in table.schema.metadata:\r\n    538         pa_metadata = ArrowWriter._build_metadata(DatasetInfo(features=features))\r\n\r\nKeyError: 'translation.en'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.18.3\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n \n Thanks for reporting, @SBrandeis! Some additional feature types that don't behave as expected when flattened: `Audio`, `Image` and 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3679","title":"Download datasets from a private hub","comments":"Hi ! For information one can set the environment variable `HF_ENDPOINT` (default is `https:\/\/huggingface.co`) if they want to use a private hub.\r\n\r\nWe may need to coordinate with the other libraries to have a consistent way of changing the hub endpoint","body":"In the context of a private hub deployment, customers would like to use load_dataset() to load datasets from their hub, not from the public hub. This doesn't seem to be configurable at the moment and it would be nice to add this feature.\r\n\r\nThe obvious workaround is to clone the repo first and then load it from local storage, but this adds an extra step. It'd be great to have the same experience regardless of where the hub is hosted.\r\n\r\nThe same issue exists with the transformers library and the CLI. I'm going to create issues there as well, and I'll reference them below.","comment_length":41,"text":"Download datasets from a private hub \n In the context of a private hub deployment, customers would like to use load_dataset() to load datasets from their hub, not from the public hub. This doesn't seem to be configurable at the moment and it would be nice to add this feature.\r\n\r\nThe obvious workaround is to clone the repo first and then load it from local storage, but this adds an extra step. It'd be great to have the same experience regardless of where the hub is hosted.\r\n\r\nThe same issue exists with the transformers library and the CLI. I'm going to create issues there as well, and I'll reference them below. \n Hi ! For information one can set the environment variable `HF_ENDPOINT` (default is `https:\/\/huggingface.co`) if they want to use a private hub.\r\n\r\nWe may need to coordinate with the other libraries to have a consistent way of changing the hub endpoint","embeddings":[-0.4606106281,0.077151902,0.0306205191,0.1708157659,-0.2113159597,-0.1057052836,0.5121508241,0.254435569,0.4819353819,0.229382515,-0.5677486658,0.197628215,0.2814870775,0.2804977,0.2473967671,0.0667532831,0.0141827278,0.3287021816,-0.157313928,-0.2259972394,-0.2019055486,-0.0153824091,0.0357885323,0.1148333848,-0.0167252459,0.1250416487,0.0873746276,0.258682847,-0.0032846278,-0.1124771461,0.7012623549,0.3309569359,0.2634236515,0.2206950188,-0.0001173288,0.0782248527,0.1846950501,-0.1501341909,-0.2869737744,-0.2156019509,-0.5422084928,0.1053060293,0.174824506,-0.2338173836,0.0741154924,0.3850899637,0.0220593661,-0.1799146235,0.0405562967,0.0074074743,0.1242415458,0.3469274342,-0.4818258584,-0.3673527837,-0.0539213978,0.3104167283,-0.1304547489,0.1608943194,0.2384341657,0.2073912919,-0.0496547893,-0.1712598056,-0.2431141138,0.1949545294,0.3729144037,0.0783449486,-0.0179728791,0.105729267,0.0534580201,0.2981562614,0.8518051505,-0.3786970973,-0.2266943902,-0.1440212578,-0.0169376321,-0.094388403,0.1561396122,0.215498507,-0.1578997821,0.4063869119,-0.4301889539,-0.5188640356,-0.4010586441,0.1023630723,-0.0409404375,0.25390625,0.1194047555,0.148870483,0.120751746,0.1879173815,0.4051358104,-0.3276927173,-0.0032679194,0.1982744485,-0.013758122,-0.1306460202,-0.0659028664,0.1791389883,0.1517208815,0.4757666588,0.0064764996,0.2185502201,-0.2427648157,0.1406053901,0.1059106216,0.0134543544,0.0198532082,0.3916558027,0.4304110408,0.1142833978,0.1609475017,0.0663404539,-0.055380743,0.300455451,-0.2873865366,-0.1527588367,-0.0396356322,-0.3218610585,0.0101729417,-0.1305256188,0.2281173766,0.0044305772,0.2125696987,0.22000359,0.0749740973,0.2030180991,-0.0155126648,0.0942863077,-0.2598352134,-0.1695177108,-0.1833509058,-0.3025673926,-0.0486377403,0.3249515593,0.2974440157,-0.0022805694,0.0372614488,-0.035430707,0.1655647755,0.1332723051,-0.0271353424,0.0167681947,-0.1424580067,0.3885997236,0.0843465254,0.217609331,0.0706371069,-0.122064285,-0.416855514,0.0543429591,-0.3959577382,-0.5899045467,-0.0118023613,0.0526952781,-0.414262861,-0.1492138058,-0.4891627431,-0.204249531,-0.2330688685,-0.2012724727,0.0879687518,0.2556909025,-0.204776004,-0.0738914609,0.183108598,0.5701056719,-0.3575924635,-0.1896174699,-0.189272657,-0.348062098,-0.0830664188,-0.1610019654,-0.4374441504,0.2461931258,-0.2313014567,0.0601781718,0.3368880451,-0.693554163,-0.6610252261,0.4053163528,-0.2744194269,-0.1722327918,0.0133770583,0.4048632681,0.2205314338,0.0094809625,-0.0993287712,0.7679859996,0.1683380008,0.0660624728,0.0361030623,-0.4059855938,-0.1756847054,0.3331786692,0.1884182245,0.3093059063,0.2663765848,0.5553732514,0.240478456,-0.0030223774,0.2052251399,-0.0261370428,0.2178154439,-0.1563191712,-0.0648681894,0.0236354358,-0.4469411671,0.369831413,-0.2957054675,-0.0154287769,0.2806516588,0.0031958369,-0.2762033045,-0.1124327555,-0.2047534585,0.1250622422,-0.0603424087,0.0972506925,0.0955529436,-0.1550374776,-0.2105949819,0.5933259726,0.0344231389,0.1599042118,-0.1224833503,0.6645694971,0.1909587234,0.1365923136,-0.2671105266,-0.1332684308,0.0777899623,-0.0873769671,-0.173739776,0.475360781,-0.141158402,0.5385559797,0.1802531034,0.3142926693,0.28155303,-0.1260307282,0.3932147324,0.087323226,0.0609069318,-0.0348435044,-0.2127943188,0.3069990277,0.0799903646,0.2328882217,-0.0507858396,0.0524903201,0.0840385184,-0.2287947237,-0.1035668924,-0.1562733799,-0.0988606811,0.1769856662,0.2057546079,0.0399690941,0.1431002468,-0.0163147189,-0.0140989944,-0.0145354867,0.3997894526,0.0547895581,-0.1993051618,-0.0060337265,0.2888625562,0.3572155833,0.5837570429,0.0199733563,-0.1035339981,0.0529429279,-0.0397455096,-0.2033809125,-0.098756142,-0.0455574617,0.1489832997,0.0470508821,-0.0487173498,-0.2741113007,-0.080174163,-0.0415869094,0.3290340006,0.0590120926,-0.2965599597,-0.0419023931,-0.413348943,0.1738165319,-0.1675053686,-0.2295525223,-0.278793931,-0.0373551622,0.1019997895,0.3437865973,-0.0036719569,-0.1576555371,-0.1835863888,0.6080454588,-0.2708085775,-0.8044729233,-0.2451330721,-0.019904349,-0.1843577027,-0.0158788245,0.1322624683,0.2592703104,0.1241802871,-0.1935488135,0.157778576,-0.4334999621,0.1370563805,0.1979359835,0.2389259785,-0.1431655735,0.0913649872,0.0160163902,0.1788418442,0.0199276786,-0.0382889621,-0.2922511399,0.0999925658,0.0147384154,-0.1472788751,0.1005743816,-0.1375174075,-0.1449124366,-0.404632479,-0.4434989393,0.0824141055,0.051853288,0.1493697464,-0.0475371704,0.0884435847,0.0120147411,0.1572942138,-0.3404614627,0.0094686728,-0.5264298916,0.2581546605,-0.3550395668,-0.0528605059,0.1072625369,0.1384609044,0.2152216285,0.094362326,-0.5477355123,-0.3747791648,0.0769704506,0.2743516862,0.0274880882,0.0798747316,0.2381301522,-0.3053147495,0.1370469332,-0.1440700591,-0.0812444016,0.0999427438,0.1170558855,0.0570293777,0.0773769692,-0.2974720895,-0.0470838547,0.4045564234,-0.1168202534,0.1627387404,0.1575261801,0.1458578408,0.2726506889,-0.1791665405,0.1059321314,-0.0432018116,0.2316610068,-0.2293784767,0.328812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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3677","title":"Discovery cannot be streamed anymore","comments":"Seems like a regression from https:\/\/github.com\/huggingface\/datasets\/pull\/2843\r\n\r\nOr maybe it's an issue with the hosting. I don't think so, though, because https:\/\/www.dropbox.com\/s\/aox84z90nyyuikz\/discovery.zip seems to work as expected\r\n\r\n","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\niterable_dataset = load_dataset(\"discovery\", name=\"discovery\", split=\"train\", streaming=True)\r\nlist(iterable_dataset.take(1))\r\n```\r\n\r\n## Expected results\r\n\r\nThe first row of the train split.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 365, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 362, in _iter\r\n    yield from ex_iterable\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 272, in __iter__\r\n    yield from islice(self.ex_iterable, self.n)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 79, in __iter__\r\n    yield from self.generate_examples_fn(**self.kwargs)\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/discovery\/542fab7a9ddc1d9726160355f7baa06a1ccc44c40bc8e12c09e9bc743aca43a2\/discovery.py\", line 333, in _generate_examples\r\n    with open(data_file, encoding=\"utf8\") as f:\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/streaming.py\", line 64, in wrapper\r\n    return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/streaming_download_manager.py\", line 369, in xopen\r\n    file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 456, in open\r\n    return open_files(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 288, in open_files\r\n    fs, fs_token, paths = get_fs_token_paths(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 611, in get_fs_token_paths\r\n    fs = filesystem(protocol, **inkwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/registry.py\", line 253, in filesystem\r\n    return cls(**storage_options)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/spec.py\", line 68, in __call__\r\n    obj = super().__call__(*args, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/implementations\/zip.py\", line 57, in __init__\r\n    self.zip = zipfile.ZipFile(self.fo)\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 1257, in __init__\r\n    self._RealGetContents()\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 1320, in _RealGetContents\r\n    endrec = _EndRecData(fp)\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 263, in _EndRecData\r\n    fpin.seek(0, 2)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/implementations\/http.py\", line 676, in seek\r\n    raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.11.0-1027-aws-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 6.0.1\r\n","comment_length":26,"text":"Discovery cannot be streamed anymore \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\niterable_dataset = load_dataset(\"discovery\", name=\"discovery\", split=\"train\", streaming=True)\r\nlist(iterable_dataset.take(1))\r\n```\r\n\r\n## Expected results\r\n\r\nThe first row of the train split.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 365, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 362, in _iter\r\n    yield from ex_iterable\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 272, in __iter__\r\n    yield from islice(self.ex_iterable, self.n)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 79, in __iter__\r\n    yield from self.generate_examples_fn(**self.kwargs)\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/discovery\/542fab7a9ddc1d9726160355f7baa06a1ccc44c40bc8e12c09e9bc743aca43a2\/discovery.py\", line 333, in _generate_examples\r\n    with open(data_file, encoding=\"utf8\") as f:\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/streaming.py\", line 64, in wrapper\r\n    return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/streaming_download_manager.py\", line 369, in xopen\r\n    file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 456, in open\r\n    return open_files(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 288, in open_files\r\n    fs, fs_token, paths = get_fs_token_paths(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 611, in get_fs_token_paths\r\n    fs = filesystem(protocol, **inkwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/registry.py\", line 253, in filesystem\r\n    return cls(**storage_options)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/spec.py\", line 68, in __call__\r\n    obj = super().__call__(*args, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/implementations\/zip.py\", line 57, in __init__\r\n    self.zip = zipfile.ZipFile(self.fo)\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 1257, in __init__\r\n    self._RealGetContents()\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 1320, in _RealGetContents\r\n    endrec = _EndRecData(fp)\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 263, in _EndRecData\r\n    fpin.seek(0, 2)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/implementations\/http.py\", line 676, in seek\r\n    raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.11.0-1027-aws-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 6.0.1\r\n \n Seems like a regression from https:\/\/github.com\/huggingface\/datasets\/pull\/2843\r\n\r\nOr maybe it's an issue with the hosting. I don't think so, though, because https:\/\/www.dropbox.com\/s\/aox84z90nyyuikz\/discovery.zip seems to work as expected\r\n\r\n","embeddings":[-0.3655181229,-0.0733558238,-0.0322828889,-0.0026657826,0.1506629884,-0.1250083745,0.1500283927,0.4487423301,-0.0735849217,0.1144564599,-0.2661014497,0.3279666007,-0.2113558501,0.1654206514,0.2201968133,-0.1466773599,-0.0193211623,0.0676082373,-0.0802753791,-0.0611311458,-0.04561067,0.0137647074,-0.2612091005,-0.1815779209,-0.0674661025,-0.0553677455,0.0805339217,0.0255971644,-0.0362329371,-0.4952043891,0.1570274979,0.0273017846,0.1457659006,0.4627971649,-0.0001073918,0.1399014443,0.3732203543,-0.0144802993,-0.3286697268,-0.0405059569,-0.2410593778,0.1502100825,0.0388289392,-0.1267970949,-0.2326932102,-0.0685260445,-0.1491400003,-0.4148999155,0.2411292195,0.443582505,0.2929446697,0.3381202519,-0.0174716339,-0.1770827323,0.3175454736,-0.2050677836,-0.1426631808,0.0684444159,0.0238523073,-0.1575667858,-0.1311049759,0.3466460705,0.0661508441,-0.2222542763,-0.0414890945,-0.0066253557,-0.1704084277,-0.3858448863,-0.0200187434,0.2398868352,0.394941479,0.0216273945,-0.3675682843,-0.2044910192,-0.0180231128,-0.5792702436,0.0662688464,0.2279865593,-0.2301936299,0.0261121932,-0.0430576801,0.0986327603,-0.1845314652,0.1198256388,-0.0831183195,0.6132110953,-0.0369642414,0.0155068897,0.1575488895,0.0949755833,0.3449843228,0.1531576365,-0.0483798794,-0.1554875076,-0.496357739,-0.091540277,0.2399741262,-0.1496647149,-0.1597028673,0.3246928751,0.2294613272,0.1440875232,0.1422826201,-0.030525919,0.1839694232,0.0865141675,-0.1847870946,0.3468577862,0.1241810322,0.4574786425,-0.0402349085,-0.1212793142,0.0694402307,0.0025339648,0.0402074046,-0.1070178002,0.2652300298,-0.0227407329,-0.2888263762,0.0607719831,-0.2824479938,-0.0629401654,0.1563700289,0.3946096003,0.0825806633,0.1382832229,0.0548028983,0.0633943304,-0.072381556,-0.2076873481,-0.3778655529,-0.1231824681,-0.1255366802,0.0082085943,0.1243944541,-0.2748549283,0.3354786634,-0.1124870479,0.0797690302,-0.0189752281,0.0437268168,-0.1286178082,0.2410275936,0.3164691031,-0.0132679325,0.1176530495,0.0504577905,-0.0352395885,0.0420743935,-0.0168374404,-0.0018335331,-0.3706124425,-0.0562730283,0.316478312,-0.0041420232,0.0702529475,0.0418373123,0.2326110452,-0.0019213901,0.009486936,0.1705028415,-0.1969427168,-0.1321279556,-0.1367802918,0.4181419611,0.1831611842,-0.1884697676,-0.0623594262,-0.2430456728,0.0505174547,0.3339782357,0.2448001802,-0.3343691826,0.2202281058,-0.2439107001,0.2019973695,0.6632182002,-0.2325638086,-0.5201236606,0.1316173524,-0.2665511072,0.1451597363,0.0708992258,0.0305053666,0.2873786688,-0.0678786263,0.2496754974,0.1719874293,-0.1347944885,-0.0249076858,-0.347674042,-0.062958315,0.1759052724,0.0885532275,0.327758044,0.1153419167,0.1228571907,0.3209313154,0.5222423077,0.1631488204,0.053209655,0.1534581035,0.4586402178,0.0571262687,0.1529089063,-0.2580870092,0.0002558465,0.2420105338,0.1480098665,-0.153403908,0.0301033687,-0.1335408539,-0.4321954846,0.0344405994,-0.2557267547,-0.3085109591,0.307171464,0.2907875776,0.0726319477,0.0971803665,-0.2941869497,-0.0702503398,-0.1453288794,-0.0858336315,-0.3783612549,0.377543062,-0.0017773334,-0.2106154561,0.0199042708,-0.055738084,0.4009231329,0.0892233849,-0.1815021783,0.4799835384,-0.0836792886,0.3450047672,0.0857529119,0.0558992103,0.0657662302,-0.5181099176,0.0554526187,0.4718398154,0.0826459527,0.2006545812,0.0280446541,0.0276488904,-0.1197893173,0.1238503009,0.2442461848,0.0405694097,0.2707550526,-0.1172509715,-0.1554363072,-0.1469689906,0.1729419529,-0.3715301752,0.0206622295,-0.3022451997,-0.3878966868,0.3544343114,0.4254882932,0.0101477355,0.0921592936,0.0044533401,-0.2849496007,-0.1990662664,0.0103372587,0.2932668328,0.3448343873,0.1043083444,0.5300987363,0.0799769983,0.0522657447,-0.2445144206,0.387491852,0.1605038792,-0.0097349901,0.4622862637,0.2542254329,-0.0601327121,-0.4062525332,-0.1220689788,-0.0541902743,0.0997653008,-0.0729285628,0.2660607696,-0.2713739872,-0.0848574862,-0.2629807889,-0.3238993585,0.0808342546,-0.4061427414,0.076105006,0.4596718252,-0.117719695,0.127713874,-0.3112235963,0.0090846587,0.3156096637,0.26539886,-0.1225517467,-0.0473881327,-0.1243388355,0.2088092268,-0.1281842142,-0.0147234034,0.4145877361,0.0971134752,-0.3717166781,-0.3613494039,-0.3589254022,0.0793943107,0.0694754869,0.1035853326,0.1533423662,0.0005287164,0.0467470735,-0.3286145627,0.0230124965,-0.4246071875,-0.0527565628,0.0103157805,-0.0433804654,0.3366119862,-0.1891632974,-0.4676441848,-0.1016475484,-0.4884351194,0.180591315,-0.0878153071,-0.0457960479,0.1478096247,-0.1659241319,0.103567116,0.1446549147,-0.1002356261,-0.3520441949,-0.2001599669,0.2735273242,-0.342954874,-0.2923032343,0.0197354089,-0.0557800718,0.0644235015,0.3301330507,-0.4856591225,0.1330917329,-0.1786145121,0.0406093523,-0.0941746086,-0.1551759243,0.1108929068,-0.0206145979,-0.1679807752,-0.2543427348,0.0456002988,-0.232011646,-0.0259408914,0.2490832806,-0.122173354,0.4156986773,0.1722928435,0.5875689983,0.3306453824,0.1060581952,0.3238016963,-0.1396832466,0.2029517144,-0.212248683,-0.2961773276,0.1182102039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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3677","title":"Discovery cannot be streamed anymore","comments":"Hi @severo, thanks for reporting.\r\n\r\nSome servers do not support HTTP range requests, and those are required to stream some file formats (like ZIP in this case).\r\n\r\nLet me try to propose a workaround. ","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\niterable_dataset = load_dataset(\"discovery\", name=\"discovery\", split=\"train\", streaming=True)\r\nlist(iterable_dataset.take(1))\r\n```\r\n\r\n## Expected results\r\n\r\nThe first row of the train split.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 365, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 362, in _iter\r\n    yield from ex_iterable\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 272, in __iter__\r\n    yield from islice(self.ex_iterable, self.n)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 79, in __iter__\r\n    yield from self.generate_examples_fn(**self.kwargs)\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/discovery\/542fab7a9ddc1d9726160355f7baa06a1ccc44c40bc8e12c09e9bc743aca43a2\/discovery.py\", line 333, in _generate_examples\r\n    with open(data_file, encoding=\"utf8\") as f:\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/streaming.py\", line 64, in wrapper\r\n    return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/streaming_download_manager.py\", line 369, in xopen\r\n    file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 456, in open\r\n    return open_files(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 288, in open_files\r\n    fs, fs_token, paths = get_fs_token_paths(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 611, in get_fs_token_paths\r\n    fs = filesystem(protocol, **inkwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/registry.py\", line 253, in filesystem\r\n    return cls(**storage_options)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/spec.py\", line 68, in __call__\r\n    obj = super().__call__(*args, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/implementations\/zip.py\", line 57, in __init__\r\n    self.zip = zipfile.ZipFile(self.fo)\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 1257, in __init__\r\n    self._RealGetContents()\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 1320, in _RealGetContents\r\n    endrec = _EndRecData(fp)\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 263, in _EndRecData\r\n    fpin.seek(0, 2)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/implementations\/http.py\", line 676, in seek\r\n    raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.11.0-1027-aws-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 6.0.1\r\n","comment_length":34,"text":"Discovery cannot be streamed anymore \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\niterable_dataset = load_dataset(\"discovery\", name=\"discovery\", split=\"train\", streaming=True)\r\nlist(iterable_dataset.take(1))\r\n```\r\n\r\n## Expected results\r\n\r\nThe first row of the train split.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 365, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 362, in _iter\r\n    yield from ex_iterable\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 272, in __iter__\r\n    yield from islice(self.ex_iterable, self.n)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/iterable_dataset.py\", line 79, in __iter__\r\n    yield from self.generate_examples_fn(**self.kwargs)\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/discovery\/542fab7a9ddc1d9726160355f7baa06a1ccc44c40bc8e12c09e9bc743aca43a2\/discovery.py\", line 333, in _generate_examples\r\n    with open(data_file, encoding=\"utf8\") as f:\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/streaming.py\", line 64, in wrapper\r\n    return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/streaming_download_manager.py\", line 369, in xopen\r\n    file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 456, in open\r\n    return open_files(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 288, in open_files\r\n    fs, fs_token, paths = get_fs_token_paths(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/core.py\", line 611, in get_fs_token_paths\r\n    fs = filesystem(protocol, **inkwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/registry.py\", line 253, in filesystem\r\n    return cls(**storage_options)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/spec.py\", line 68, in __call__\r\n    obj = super().__call__(*args, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/implementations\/zip.py\", line 57, in __init__\r\n    self.zip = zipfile.ZipFile(self.fo)\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 1257, in __init__\r\n    self._RealGetContents()\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 1320, in _RealGetContents\r\n    endrec = _EndRecData(fp)\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/zipfile.py\", line 263, in _EndRecData\r\n    fpin.seek(0, 2)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/fsspec\/implementations\/http.py\", line 676, in seek\r\n    raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.18.3\r\n- Platform: Linux-5.11.0-1027-aws-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 6.0.1\r\n \n Hi @severo, thanks for reporting.\r\n\r\nSome servers do not support HTTP range requests, and those are required to stream some file formats (like ZIP in this case).\r\n\r\nLet me try to propose a workaround. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3676","title":"`None` replaced by `[]` after first batch in map","comments":"It looks like this is because of this behavior in pyarrow:\r\n```python\r\nimport pyarrow as pa\r\n\r\narr = pa.array([None, [0]])\r\nreconstructed_arr = pa.ListArray.from_arrays(arr.offsets, arr.values)\r\nprint(reconstructed_arr.to_pylist())\r\n# [[], [0]]\r\n```\r\n\r\nIt seems that `arr.offsets` can reconstruct the array properly, but an offsets array with null values can:\r\n```python\r\nfixed_offsets = pa.array([None, 0, 1])\r\nfixed_arr = pa.ListArray.from_arrays(fixed_offsets, arr.values)\r\nprint(fixed_arr.to_pylist())\r\n# [None, [0]]\r\n\r\nprint(arr.offsets.to_pylist())\r\n# [0, 0, 1]\r\nprint(fixed_offsets.to_pylist())\r\n# [None, 0, 1]\r\n```\r\nEDIT: this is because `arr.offsets` is not enough to reconstruct the array, we also need the validity bitmap","body":"Sometimes `None` can be replaced by `[]` when running map:\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict({\"a\": range(4)})\r\nds = ds.map(lambda x: {\"b\": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=[\"a\"])\r\nprint(ds.to_pandas())\r\n#              b\r\n# 0  [None, [0]]\r\n# 1    [[], [0]]\r\n# 2    [[], [0]]\r\n# 3    [[], [0]]\r\n```\r\n\r\nThis issue has been experienced when running the `run_qa.py` example from `transformers` (see issue https:\/\/github.com\/huggingface\/transformers\/issues\/15401)\r\n\r\nThis can be due to a bug in when casting `None` in nested lists. Casting only happens after the first batch, since the first batch is used to infer the feature types.\r\n\r\ncc @sgugger ","comment_length":89,"text":"`None` replaced by `[]` after first batch in map \n Sometimes `None` can be replaced by `[]` when running map:\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict({\"a\": range(4)})\r\nds = ds.map(lambda x: {\"b\": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=[\"a\"])\r\nprint(ds.to_pandas())\r\n#              b\r\n# 0  [None, [0]]\r\n# 1    [[], [0]]\r\n# 2    [[], [0]]\r\n# 3    [[], [0]]\r\n```\r\n\r\nThis issue has been experienced when running the `run_qa.py` example from `transformers` (see issue https:\/\/github.com\/huggingface\/transformers\/issues\/15401)\r\n\r\nThis can be due to a bug in when casting `None` in nested lists. Casting only happens after the first batch, since the first batch is used to infer the feature types.\r\n\r\ncc @sgugger  \n It looks like this is because of this behavior in pyarrow:\r\n```python\r\nimport pyarrow as pa\r\n\r\narr = pa.array([None, [0]])\r\nreconstructed_arr = pa.ListArray.from_arrays(arr.offsets, arr.values)\r\nprint(reconstructed_arr.to_pylist())\r\n# [[], [0]]\r\n```\r\n\r\nIt seems that `arr.offsets` can reconstruct the array properly, but an offsets array with null values can:\r\n```python\r\nfixed_offsets = pa.array([None, 0, 1])\r\nfixed_arr = pa.ListArray.from_arrays(fixed_offsets, arr.values)\r\nprint(fixed_arr.to_pylist())\r\n# [None, [0]]\r\n\r\nprint(arr.offsets.to_pylist())\r\n# [0, 0, 1]\r\nprint(fixed_offsets.to_pylist())\r\n# [None, 0, 1]\r\n```\r\nEDIT: this is because `arr.offsets` is not enough to reconstruct the array, we also need the validity bitmap","embeddings":[-0.1136858612,-0.1914151758,-0.0299210995,0.0782204345,0.4104253948,-0.041607786,0.6156226993,0.0721310154,-0.0982020944,0.1066265106,-0.1036106199,0.3490238786,0.1736715436,-0.4175108969,-0.1059338525,0.0281312298,0.1095848009,0.4044574499,-0.1162981093,-0.0610310584,-0.363171488,0.0291944984,-0.4508618414,-0.0044109519,-0.0383445062,-0.2794699371,-0.1703367978,-0.0299744941,0.3555466533,-0.3370648324,0.2752998471,-0.1644163579,-0.2598068118,0.4517090023,-0.0001180397,0.1161156893,0.4948911071,0.09446197,-0.0385051183,-0.2042392343,-0.1074039266,-0.1060822457,0.0216501523,-0.3108520806,0.1176609397,-0.2486673743,0.0988076255,-0.0674875304,0.0463690907,-0.1521207839,0.1607283652,0.6752725244,-0.3454923332,0.2374082655,0.1843590736,0.2315490395,0.1015149131,-0.2455725968,0.2220403403,-0.0251977257,0.0037821038,0.2245958447,-0.4723358452,-0.2222720385,0.1614747047,0.3266750276,0.2942175865,-0.3357074857,-0.1645574421,0.186095193,-0.1059205011,-0.3328682482,-0.2743718326,-0.2719662488,0.0120385243,-0.2584404349,-0.0980973691,0.1100277081,-0.0324839167,-0.0474627316,-0.228134349,0.2472334355,0.0023163552,0.1282662898,-0.1855329424,0.5647096038,0.1944187135,0.1518034339,-0.2083527297,-0.1812647581,0.2597408295,-0.4138785303,0.0002537176,0.2169197351,0.0794252604,0.1442216039,0.2279381752,-0.1117103025,0.0767247304,-0.4133430123,0.1676977724,0.0835939422,0.0830256045,0.2192093879,0.2564801276,0.1980356574,0.1500729769,0.0478826575,0.0975196809,0.125900358,0.0915902555,0.192165792,0.3487790227,0.044217512,0.2483372241,0.1749462634,0.3657806218,0.1389840543,-0.3104493916,-0.0177059174,-0.4436845481,0.0444509014,-0.0758401006,0.0320021063,-0.0025457554,0.477139622,0.1450270712,0.0541452654,-0.1647205353,0.0112522319,-0.2977714837,-0.3720106483,-0.2227880657,-0.2762266695,-0.0420933776,0.2459785491,0.4295325577,0.0685646906,0.193970412,0.064739123,-0.276730001,-0.141887024,0.1427678019,0.5722836256,-0.2077617347,0.2064817399,0.1323058903,-0.1504810601,0.0542026833,0.6829974055,0.0192456506,0.1881313175,0.1355339438,0.1033454761,-0.3265880048,0.2844194174,-0.26924178,-0.0124509176,0.2757714391,0.0092389118,0.0413483977,-0.1511960626,-0.1561624855,-0.3828741312,-0.00353787,0.4367256761,-0.2100595683,-0.153105408,0.0013394501,-0.0728502721,0.078769967,0.3008505404,-0.1130751371,0.1507680565,-0.2027452737,0.3325435817,0.160482198,-0.0534385145,-0.127980262,0.1578907222,-0.1256591976,0.1675651073,-0.0825351179,-0.0035244646,0.1729017049,-0.261049509,-0.0114653213,0.126920253,-0.0333529003,0.2157396823,-0.1717881709,-0.1635111123,0.2521702647,-0.1142703369,0.0039051312,0.1684462726,-0.2463826835,-0.1736783236,0.1850472987,-0.2736115158,0.2109913975,0.244278729,0.1794595867,-0.1733781099,0.1152084023,0.0427686647,-0.6502732635,0.1223556921,-0.6959511042,-0.1219315082,-0.4734947979,-0.2714660466,0.2242428213,0.1979165971,0.0808426589,0.0925417021,0.0757282376,-0.0065362314,-0.1661702543,-0.0782745853,0.0455839708,-0.4183980525,0.2513837516,0.2005099803,-0.3207902908,0.1644658595,0.0354470499,-0.1798900962,-0.3142128587,0.3077944517,0.272423178,-0.0710328072,0.0767238438,0.3899551034,-0.1496379673,0.047393892,-0.4747366309,0.0972447172,0.2383894175,-0.1766356528,-0.0580647811,0.4464641511,0.2297992259,-0.1800653338,-0.0384390987,0.4239417315,-0.1619359851,0.2554994822,-0.2961225212,0.0092263976,-0.047649961,-0.0767677501,-0.2644304931,-0.0923463181,-0.2155182958,0.4756233394,-0.0021397492,0.3292104006,-0.1941259801,0.135448128,0.1829587221,-0.4292539358,0.0479331017,-0.0844499916,-0.2436487079,0.0168351345,0.0634908229,-0.4740845859,0.3701242805,0.1676205546,-0.0861648694,-0.009387834,-0.2501011789,-0.0987988338,0.3511000574,0.3058516383,0.1763895452,0.0772427842,0.3679610193,-0.182210952,-0.1511120945,-0.1170734987,0.170238927,0.0741008669,-0.2723942101,0.2146526724,-0.1279246509,0.0822261125,-0.1373439133,-0.097398527,0.0493923537,-0.3497159481,-0.1653445661,0.1768893301,-0.3595085442,0.3532183468,0.179917261,-0.2910923064,0.1733736992,-0.0595185086,-0.3359766901,-0.0634483919,-0.1325952262,-0.0270628501,0.0060472162,-0.2603049874,-0.2582324147,0.1783900261,-0.2984941006,0.0135215893,-0.4891208708,-0.0667979196,-0.191023007,0.1911986172,0.2302284837,0.4031796753,-0.1681745648,-0.2837309539,0.2406082898,-0.0800225213,-0.3188225031,0.1251222193,-0.168178618,-0.0981488526,-0.4091344476,-0.0567135029,0.1292605251,-0.0878525004,-0.0343967229,-0.1499433368,0.1301620007,0.2691165209,0.2494009882,-0.0361294076,-0.1868569255,-0.2630517781,0.0737899095,0.168149516,0.3989340365,0.070441179,0.0710280165,-0.1253670901,-0.5094419122,0.0027503646,0.1458189189,-0.1409470588,-0.0694830939,0.0905807093,0.3053820133,0.164391309,0.0594411753,0.2200065255,0.3359684944,-0.0715721324,-0.0656621903,-0.1947592944,-0.0741556734,0.1235051379,0.0313881002,0.2277810723,0.1980286837,-0.0178699009,0.5833066702,0.1532241404,-0.3027835488,0.0903297737,-0.0439593531,0.0727568343,-0.0161349773,-0.0125382002,-0.3586632609,0.2314854115,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3676","title":"`None` replaced by `[]` after first batch in map","comments":"The offsets don't have nulls because they don't include the validity bitmap from `arr.buffers()[0]`, which is used to say which values are null and which values are non-null.\r\n\r\nThough the validity bitmap also seems to be wrong:\r\n```python\r\nbin(int(arr.buffers()[0].hex(), 16))\r\n# '0b10'\r\n# it should be 0b110 - 1 corresponds to non-null and 0 corresponds to null, if you take the bits in reverse order\r\n```\r\n\r\nSo apparently I can't even create the fixed offsets array using this.\r\n\r\nIf I understand correctly it's always missing the 1 on the left, so I can add it manually as a hack to fix the issue until this is fixed in pyarrow EDIT: actually it may be more complicated than that\r\n\r\nEDIT2: actuall it's right, it corresponds to the validity bitmap of the array of logical length 2. So if we use the offsets array, the values array, and this validity bitmap it should be possible to reconstruct the array properly","body":"Sometimes `None` can be replaced by `[]` when running map:\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict({\"a\": range(4)})\r\nds = ds.map(lambda x: {\"b\": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=[\"a\"])\r\nprint(ds.to_pandas())\r\n#              b\r\n# 0  [None, [0]]\r\n# 1    [[], [0]]\r\n# 2    [[], [0]]\r\n# 3    [[], [0]]\r\n```\r\n\r\nThis issue has been experienced when running the `run_qa.py` example from `transformers` (see issue https:\/\/github.com\/huggingface\/transformers\/issues\/15401)\r\n\r\nThis can be due to a bug in when casting `None` in nested lists. Casting only happens after the first batch, since the first batch is used to infer the feature types.\r\n\r\ncc @sgugger ","comment_length":158,"text":"`None` replaced by `[]` after first batch in map \n Sometimes `None` can be replaced by `[]` when running map:\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict({\"a\": range(4)})\r\nds = ds.map(lambda x: {\"b\": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=[\"a\"])\r\nprint(ds.to_pandas())\r\n#              b\r\n# 0  [None, [0]]\r\n# 1    [[], [0]]\r\n# 2    [[], [0]]\r\n# 3    [[], [0]]\r\n```\r\n\r\nThis issue has been experienced when running the `run_qa.py` example from `transformers` (see issue https:\/\/github.com\/huggingface\/transformers\/issues\/15401)\r\n\r\nThis can be due to a bug in when casting `None` in nested lists. Casting only happens after the first batch, since the first batch is used to infer the feature types.\r\n\r\ncc @sgugger  \n The offsets don't have nulls because they don't include the validity bitmap from `arr.buffers()[0]`, which is used to say which values are null and which values are non-null.\r\n\r\nThough the validity bitmap also seems to be wrong:\r\n```python\r\nbin(int(arr.buffers()[0].hex(), 16))\r\n# '0b10'\r\n# it should be 0b110 - 1 corresponds to non-null and 0 corresponds to null, if you take the bits in reverse order\r\n```\r\n\r\nSo apparently I can't even create the fixed offsets array using this.\r\n\r\nIf I understand correctly it's always missing the 1 on the left, so I can add it manually as a hack to fix the issue until this is fixed in pyarrow EDIT: actually it may be more complicated than that\r\n\r\nEDIT2: actuall it's right, it corresponds to the validity bitmap of the array of logical length 2. So if we use the offsets array, the values array, and this validity bitmap it should be possible to reconstruct the array 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3673","title":"`load_dataset(\"snli\")` is different from dataset viewer","comments":"Yes, we decided to replace the encoded label with the corresponding label when possible in the dataset viewer. But\r\n1. maybe it's the wrong default\r\n2. we could find a way to show both (with a switch, or showing both ie. `0 (neutral)`).\r\n","body":"## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n","comment_length":43,"text":"`load_dataset(\"snli\")` is different from dataset viewer \n ## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n \n Yes, we decided to replace the encoded label with the corresponding label when possible in the dataset viewer. But\r\n1. maybe it's the wrong default\r\n2. we could find a way to show both (with a switch, or showing both ie. `0 (neutral)`).\r\n","embeddings":[-0.0393052846,-0.1823725402,-0.0017422192,0.4216582775,0.0142074348,-0.0808522627,0.5498262048,0.0203978624,0.28027004,0.2657595277,-0.4218336046,0.5688481331,0.1886246353,0.1806400716,-0.0699714795,0.1307980269,0.3021640182,0.3575283885,0.1494968534,-0.3441230655,-0.2443670332,0.1038501561,-0.2927233279,0.0603623278,-0.2949047983,0.2377304882,-0.0728700086,-0.1414373368,-0.1077354476,-0.5973829031,0.2867645323,0.2467381656,0.3105250895,0.1238456592,-0.0001189849,-0.122718811,0.4192235172,-0.1069659814,-0.4806711078,-0.0223493055,-0.276792407,-0.2519828677,0.2108837366,-0.3821427226,-0.0309112668,-0.2255168557,0.183480382,-0.2305024266,-0.0995423719,0.1888523698,0.1516296715,0.3421573937,-0.2830817401,0.1237546727,0.3154720068,-0.0811308175,0.0031825372,0.2634034753,-0.0125440536,0.5375497937,-0.023656575,0.4749746621,-0.1274907291,0.1780159771,0.2586636245,0.2430608124,0.2091540694,-0.3036938012,-0.0055261697,0.4061287344,0.7524923086,-0.2359340191,-0.3463690877,-0.2032067478,-0.0099539654,-0.0967002064,0.2401991189,0.0429824591,0.0744324327,0.2797005475,-0.0488678105,-0.1360646933,-0.0016470748,0.18072927,-0.5825662017,0.547950685,-0.008625919,0.1378822327,-0.2341581434,0.1181453392,0.4963300228,-0.3542025685,-0.0860183686,0.0311791673,-0.3174740374,0.0663033351,-0.191843912,0.1354210228,-0.1128525063,0.3866560757,0.0627205372,0.0627111122,-0.2378096431,0.2152927518,0.2273729742,0.0540045165,0.397195071,0.2915125787,0.1245158315,0.0253057946,0.0207189247,0.0034588212,-0.2560732365,-0.1619827151,0.0899844766,-0.2003275901,0.2030232996,-0.2271879017,-0.460423857,0.0263027791,-0.3311215341,-0.1634784639,0.0435803048,0.0234662015,-0.1691533774,0.3740774989,-0.0686000586,0.1701313704,-0.2520403266,-0.494523257,-0.1730837673,-0.1753836721,-0.0420437492,-0.1013919562,0.0922513008,-0.3794521987,0.1974752098,0.0234835073,0.0522876605,-0.2141188681,-0.1060055643,-0.0649420097,0.2972066104,0.0161574222,0.1903555244,0.4385063648,-0.1000266522,-0.3550155163,-0.0557562299,0.3639518321,-0.3408920467,0.0310428124,-0.000243245,0.0847769305,-0.3165571094,-0.1592758447,-0.076220952,-0.0202156529,-0.2182889432,-0.3039499223,0.0150272781,-0.1457468718,-0.0333116502,-0.223195985,0.2502690256,0.362062782,-0.4974029064,-0.1168495417,-0.0490334556,-0.2972284257,0.0486157537,0.1499000788,-0.0158461034,0.1237448081,-0.2667315602,0.119591549,0.4398123026,-0.6635593772,-0.7674961686,0.2276609689,0.1235307902,0.1709460169,0.0090373969,0.2479587495,0.0823269561,0.0004525536,-0.0605190136,0.0695319995,0.3001027703,-0.0346424952,-0.3003418148,-0.213285923,0.4024129808,0.2598534822,0.0633788109,0.1851261407,0.073245801,0.2444664836,0.4175949991,0.0295889378,0.0724696144,0.2555890381,0.1293899417,0.0725759193,0.0414516814,0.0796422437,-0.1787542403,0.1635587662,-0.3506641686,0.2243194133,0.0505266562,-0.1303341985,-0.2418947071,-0.147519514,-0.4594031572,-0.0953191891,0.1001927108,0.3123216927,-0.2245428711,0.0642555058,0.1004965827,0.5050897002,-0.2674719095,0.0721832886,-0.2040071189,0.3950837553,0.114063099,0.1878951788,-0.2337498069,-0.0423937403,0.2396884561,0.0514222272,-0.2432212979,0.3180273771,0.0278168786,0.2195767015,-0.1316160411,-0.0923356935,0.2200835645,-0.3748424947,0.2315675467,0.2238556296,0.121420294,0.1152175143,-0.2812599838,0.1881208122,-0.2238842398,0.0913913324,-0.1717155874,0.0181067977,0.2258476466,-0.0671512932,-0.4102564156,-0.288276881,-0.0303952731,0.0037784281,0.4395770431,0.044815857,-0.1358726025,0.0511885509,0.5669069886,-0.0813413113,-0.0007749613,-0.1179648563,-0.4432290196,-0.0915846974,0.2960561514,0.2000529021,0.323591888,0.1304726303,0.0581022128,0.1680087894,-0.0505372621,-0.0343749113,0.2078824043,0.3095169961,0.2065257132,0.1654354036,0.0785008371,-0.0645288676,-0.2435599715,-0.0810785294,0.1279997528,0.0924521759,-0.3535680771,-0.0132887708,-0.2948646247,-0.122152634,-0.3024202585,-0.1383730173,-0.212683782,-0.440186739,-0.0433646552,0.130277589,-0.0139039075,0.2458388209,-0.0109979315,0.1535804719,-0.1779358685,0.0441901088,0.0116826389,-0.0896141306,-0.4452424645,-0.0037488558,-0.042886544,0.1339955032,0.1209492087,-0.6430805922,0.3100214601,-0.3107770085,-0.2739502788,0.4029090106,0.1473855823,0.0902989283,-0.1432068944,0.0015064511,-0.074766688,0.1928428411,0.1429047287,-0.0330291465,0.0349119678,-0.0027324574,-0.1485040337,-0.2840753198,-0.0050747939,-0.487152487,-0.5198711753,-0.2189658731,-0.2955598235,0.1132139489,0.1818957329,-0.0608626939,-0.1297598779,-0.058021713,0.2238664925,-0.0458628275,-0.2928392291,-0.2710165679,0.3757045567,-0.2392656505,-0.2880704999,0.026649233,-0.0191102382,0.1195075288,-0.0748373419,-0.4133297801,0.1799559444,0.06729763,0.1960434914,-0.0499513522,-0.0789793506,0.0701729953,0.1727333814,0.1419012994,-0.2500665784,-0.0824663043,0.094549574,0.0011372666,0.2540899813,0.1110708863,-0.0969623849,-0.1376870275,-0.142279461,0.5799586177,0.0655579418,0.2824213803,-0.188670069,0.3398887813,-0.0634837747,0.0564724952,-0.0379451551,0.2731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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3673","title":"`load_dataset(\"snli\")` is different from dataset viewer","comments":"Hi @severo,\r\n\r\nThanks for clarifying. \r\n\r\nI think this default is a bit counterintuitive for the user. However, this is a personal opinion that might not be general. I think it is nice to have the actual (non-encoded) labels in the viewer. On the other hand, it would be nice to match what the user sees with what they get when they download a dataset. I don't know - I can see the difficulty of choosing a default :)\r\nMaybe having non-encoded labels as a default can be useful?\r\n\r\nAnyway, I think the issue has been addressed. Thanks a lot for your super-quick answer!\r\n\r\n ","body":"## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n","comment_length":103,"text":"`load_dataset(\"snli\")` is different from dataset viewer \n ## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n \n Hi @severo,\r\n\r\nThanks for clarifying. \r\n\r\nI think this default is a bit counterintuitive for the user. However, this is a personal opinion that might not be general. I think it is nice to have the actual (non-encoded) labels in the viewer. On the other hand, it would be nice to match what the user sees with what they get when they download a dataset. I don't know - I can see the difficulty of choosing a default :)\r\nMaybe having non-encoded labels as a default can be useful?\r\n\r\nAnyway, I think the issue has been addressed. Thanks a lot for your super-quick answer!\r\n\r\n 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3673","title":"`load_dataset(\"snli\")` is different from dataset viewer","comments":"Thanks for the \ud83d\udc4d  in https:\/\/github.com\/huggingface\/datasets\/issues\/3673#issuecomment-1029008349 @mariosasko @gary149 @pietrolesci, but as I proposed various solutions, it's not clear to me which you prefer. Could you write your preferences as a comment?\r\n\r\n_(note for myself: one idea per comment in the future)_","body":"## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n","comment_length":41,"text":"`load_dataset(\"snli\")` is different from dataset viewer \n ## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n \n Thanks for the \ud83d\udc4d  in https:\/\/github.com\/huggingface\/datasets\/issues\/3673#issuecomment-1029008349 @mariosasko @gary149 @pietrolesci, but as I proposed various solutions, it's not clear to me which you prefer. Could you write your preferences as a comment?\r\n\r\n_(note for myself: one idea per comment in the future)_","embeddings":[-0.1536539793,-0.2408142388,-0.0024182391,0.4605814815,0.0789477602,-0.0453555919,0.5275520086,0.0667690188,0.3200161755,0.2510014474,-0.4140201211,0.5160109401,0.1262120157,0.3489976823,-0.0521481298,-0.0057208012,0.2405709177,0.2476890087,0.1201812699,-0.2395076603,-0.2063920647,0.1942078918,-0.2625923753,0.0766495094,-0.4142891765,0.2262769789,-0.0675373003,-0.042598933,-0.137813285,-0.5563794971,0.3142529726,0.2368284911,0.3090595007,0.2012597769,-0.0001104161,-0.1342341602,0.3411118984,-0.0684205219,-0.4755108058,-0.0335832909,-0.1448299736,-0.2815862596,0.1631133407,-0.3128740489,-0.0806512758,-0.2087664157,0.1077431813,-0.1702707112,0.0886612236,0.1217454076,0.2370366901,0.3791616559,-0.1561652273,0.0422914326,0.2877450883,-0.0863603503,-0.105497174,0.2572644353,-0.0851978585,0.5013123155,-0.0081750425,0.5028520226,-0.1326138675,0.1911470443,0.2579504848,0.171175465,0.0947141424,-0.2595364749,-0.0205896571,0.3962481022,0.7565456629,-0.2963085175,-0.4253669977,-0.2853422463,-0.0551752336,-0.1184899658,0.303216666,0.0520961471,0.1269689053,0.2844517231,-0.0579974391,-0.067742914,-0.0570057966,0.1353558749,-0.499081254,0.3664663732,-0.1161034033,0.0746837407,-0.1482645124,0.0392924137,0.2596974969,-0.2563042343,-0.0601060353,0.0683998391,-0.4251800776,0.0497147851,-0.1005899012,0.1501078308,0.0365465358,0.4559513927,0.0318133123,0.1460573524,-0.283387959,0.2235760689,0.2910857201,0.0815613791,0.2992412448,0.1246430129,0.2671198249,0.0308154784,0.0851052105,-0.0956528261,-0.2028865069,-0.2131142467,0.0253666658,-0.191825971,0.1956291944,-0.2592724264,-0.4385325015,0.0546682552,-0.2682840824,-0.150679782,0.1086552218,0.1420286447,-0.177135244,0.3838928938,0.0136454813,0.2114047259,-0.3451212943,-0.4207561314,-0.2193928808,-0.1221117377,-0.0269526187,0.0259566773,0.1836032867,-0.416572839,0.2626661956,0.0255663972,0.0724917427,-0.1747067273,-0.1608399749,-0.054964792,0.176310122,0.031422507,0.228286922,0.3765416145,-0.1894267052,-0.1816333532,-0.0372277386,0.173370868,-0.2991354167,-0.0508178696,-0.0715993196,0.1497798115,-0.3032228649,-0.190608263,-0.1385210901,-0.0409615152,-0.1969936788,-0.2119736075,-0.0362060256,-0.1100635976,-0.057784982,-0.212735042,0.2889381945,0.3870347738,-0.4113298953,-0.1182435006,0.0130036063,-0.3465321362,-0.0533451065,0.2268649936,0.0640283078,0.1763776392,-0.2721455693,0.270399183,0.3493606448,-0.565829277,-0.6599961519,0.1502437741,0.0993637145,0.1258420199,-0.0039063189,0.2091213763,0.104084529,0.0061196121,-0.0144014927,0.1046314314,0.3242759109,0.0420323499,-0.2992992997,-0.2213838398,0.2345039994,0.3144501746,0.0295399204,0.0516186655,0.0720297694,0.1636671722,0.3721935451,-0.0959483609,0.07388556,0.269660145,0.1864665151,0.1153040081,0.0190057997,-0.0893073976,-0.2332547009,0.1654383242,-0.2666759789,0.1964389682,0.0669019148,-0.1512573361,-0.2312560678,-0.0741171241,-0.4327299297,-0.0624545254,0.1655536592,0.2050961703,-0.1105702966,0.0584048331,0.053848289,0.5442249775,-0.2314736247,0.1092880592,-0.2709345222,0.3831186295,0.0469948985,0.1190891638,-0.0739143565,0.0773185939,0.2330120057,-0.0075265998,-0.1831045151,0.3162035048,0.0364247672,0.203010723,-0.0304695908,-0.0570004396,0.2329339981,-0.3588128388,0.2485113144,0.1359971911,0.0960476547,0.1156125292,-0.3073271513,0.3106613159,-0.1555312276,0.0244043488,-0.115406394,0.0784633234,0.2443965524,-0.0929604545,-0.2919707894,-0.2549028397,0.1133521497,-0.0939635336,0.3893780112,0.0046144254,-0.1336565465,0.0682587326,0.5573861599,-0.0867458731,-0.0010192797,-0.0055088792,-0.3846860826,-0.0830098987,0.239064455,0.104402706,0.291747123,0.2101118863,0.0052647805,0.150821805,-0.0127610266,-0.0646786764,0.1738671809,0.2301417142,0.0848668665,0.1765855998,0.0665887967,0.0038058872,-0.3357537389,-0.025119273,0.0657738745,0.0541800261,-0.4010172486,-0.0324535444,-0.2555370331,-0.1369889528,-0.2213863432,-0.1332629323,-0.3424389064,-0.4149467349,0.002043088,0.2513472736,-0.0031538792,0.1905710697,0.0303755123,0.2361038476,-0.1242448762,0.0353583321,-0.0656436756,-0.0969125628,-0.4196287394,0.0603499189,0.0087282136,0.200642705,0.2770235538,-0.5506370068,0.3622714281,-0.4168204367,-0.2781745195,0.3639362454,0.0373139381,0.1529791653,-0.0718928874,0.0200253483,-0.0845519528,0.1587203443,0.2181200534,-0.1389855593,-0.0202186946,0.0539872348,-0.1407928616,-0.3831698596,-0.0128747476,-0.4853124619,-0.4209367335,-0.3240780532,-0.0733959675,0.1408304423,0.1533467025,0.1094475612,-0.1539347917,-0.0441389941,0.1937931478,0.0673353001,-0.4034281969,-0.2972574234,0.3123317659,-0.326087594,-0.4099208415,0.0464503914,0.0759471953,0.0780939832,-0.0725510269,-0.4286692441,-0.0059618033,0.0221696571,0.2001076341,0.0083193211,-0.0080889156,0.1022173986,0.1269720048,0.0637082681,-0.241180107,-0.1944603175,0.1592594236,0.0192123875,0.1945749819,0.0971550271,-0.0125800539,-0.1018390805,-0.0141483815,0.6688722372,0.0758124813,0.3925870359,-0.2286921889,0.3190315068,-0.1512658,-0.0309984535,0.0694304034,0.1133654565,0.0457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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3673","title":"`load_dataset(\"snli\")` is different from dataset viewer","comments":"As I am working with seq2seq, I prefer having the label in string form rather than numeric. So the viewer is fine and the underlying dataset should be \"decoded\" (from int to str). In this way, the user does not have to search for a mapping `int -> original name` (even though is trivial to find, I reckon). Also, encoding labels is rather easy.\r\n\r\nI hope this is useful","body":"## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n","comment_length":69,"text":"`load_dataset(\"snli\")` is different from dataset viewer \n ## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n \n As I am working with seq2seq, I prefer having the label in string form rather than numeric. So the viewer is fine and the underlying dataset should be \"decoded\" (from int to str). In this way, the user does not have to search for a mapping `int -> original name` (even though is trivial to find, I reckon). Also, encoding labels is rather easy.\r\n\r\nI hope this is 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3673","title":"`load_dataset(\"snli\")` is different from dataset viewer","comments":"I like the idea of \"0 (neutral)\". The label name can even be greyed to make it clear that it's not part of the actual item in the dataset, it's just the meaning.","body":"## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n","comment_length":33,"text":"`load_dataset(\"snli\")` is different from dataset viewer \n ## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n \n I like the idea of \"0 (neutral)\". The label name can even be greyed to make it clear that it's not part of the actual item in the dataset, it's just the meaning.","embeddings":[-0.0656781048,-0.2434975058,-0.001020272,0.4355773032,0.0055619539,-0.0518787019,0.5364515185,0.0190294348,0.2789101601,0.216972068,-0.3383782804,0.5619639754,0.0860196427,0.1822404414,-0.1028692424,0.0764549077,0.2609827518,0.3385199308,0.2346189022,-0.3860845268,-0.2393456697,0.1215369627,-0.3120495081,0.0825821012,-0.3442725539,0.148551926,-0.1055334881,-0.0657419339,-0.1381711215,-0.5484629273,0.2691628933,0.1737175286,0.2668170631,0.2401495874,-0.0001179326,-0.1663757861,0.3859063983,-0.135799244,-0.4375489056,-0.0680478886,-0.2510532141,-0.2467142045,0.1539749205,-0.3979912698,-0.018404115,-0.1849783063,0.1802860647,-0.2312404215,-0.0275699738,0.2057957053,0.1772117019,0.3529951274,-0.2069793642,0.1253229827,0.3524623513,-0.0830637589,-0.0485139675,0.3050325513,-0.016743606,0.5132563114,0.0233142301,0.5064599514,-0.2202558964,0.2045859993,0.3415904939,0.2061543167,0.1994347125,-0.3415551782,0.0500706881,0.456279397,0.7916783094,-0.1771454662,-0.4208519459,-0.16991584,-0.0314037092,-0.083154209,0.2051162124,0.121020861,0.0698665902,0.2498155087,-0.0031237022,-0.0925411731,-0.0902721435,0.1829577386,-0.566660881,0.6245370507,0.0216111448,0.1263610274,-0.2595534623,0.0710972399,0.4512704611,-0.3504821658,-0.0940799266,0.1435051113,-0.3535329103,0.0594663024,-0.1931166798,0.2289914936,-0.1058071926,0.3744428754,0.0622722283,0.0415745042,-0.2166689485,0.2069184482,0.2813459039,0.0206670929,0.3269980252,0.3155979514,0.1356548965,-0.0603022911,-0.0045593563,-0.0313413627,-0.2938717604,-0.1688507199,0.1086523607,-0.2128355354,0.2349847555,-0.2419356853,-0.4386314452,0.0678495914,-0.3043436706,-0.1030683368,0.0000312867,0.0490751415,-0.0945826471,0.3033585846,-0.0410945788,0.1749679595,-0.3162452281,-0.438554436,-0.147429347,-0.1003238931,-0.0110079721,-0.1448344141,0.0119111231,-0.3490406871,0.2512908578,-0.0913206637,0.0328094848,-0.2201829851,-0.05208949,-0.0392390676,0.2495287508,0.0852336958,0.1381466687,0.4305205643,-0.0855335519,-0.3655709326,-0.0652772412,0.3710043728,-0.3505884409,0.0233339611,-0.0789754167,0.1014539003,-0.3480347097,-0.1934705824,-0.0040002633,-0.0920058861,-0.1536670178,-0.2391409278,0.0497179776,-0.1287529767,-0.0061768345,-0.2537837625,0.1860856563,0.425966084,-0.5881800652,-0.1175808534,-0.1182160601,-0.274066925,0.0369137861,0.0763363168,0.046669554,0.1934598684,-0.2197036296,0.1776691228,0.398719877,-0.6455846429,-0.7292135358,0.2705031037,-0.0097210975,0.1124598458,0.0317988507,0.3079766333,0.026729349,0.0456790105,-0.0073995651,0.0106257489,0.23810184,-0.0213321913,-0.3089089096,-0.1642981172,0.3965671957,0.2898480594,0.0882836282,0.0971039236,0.0789990053,0.2431212664,0.3537748754,-0.0425306708,0.0827226117,0.2844006419,0.2458204478,0.078399621,0.0406247899,0.0117001506,-0.2381602973,0.1609676927,-0.3007526696,0.2645718157,0.0610168278,-0.1498270333,-0.2624793649,-0.1350352019,-0.4891640246,-0.143779546,0.1380053312,0.2761728466,-0.2956489027,-0.000766239,0.0710242242,0.552115202,-0.1823372394,0.071582973,-0.1888379604,0.3368681073,0.0346835069,0.1486327499,-0.1914846897,0.018303642,0.2668607235,0.0182568301,-0.2184643447,0.3330881,0.1122114509,0.1791117638,-0.0462740734,-0.0604735762,0.1869297177,-0.332816422,0.2987859845,0.2307973355,0.1330578029,0.0820560977,-0.1975228041,0.3374079764,-0.2103105038,0.0753463879,-0.2033384591,0.0465900674,0.2199327648,-0.0868552178,-0.3834569752,-0.1844842434,0.1339513958,-0.0060252994,0.5090209246,0.0246291962,-0.1317417473,0.0183259677,0.4903703928,-0.1039162949,0.0112268878,-0.0806967542,-0.3799371719,-0.0537472963,0.2659404576,0.196892038,0.327121228,0.1352377832,0.010421114,0.1851283759,-0.0359456055,-0.0392550528,0.1766056567,0.3242972195,0.1956926733,0.1355526596,-0.0412940867,-0.0395545177,-0.1957881153,-0.1047532931,0.1233531758,0.120035775,-0.3331170678,0.0303602293,-0.2051906139,-0.1222019047,-0.2636516988,-0.1694049686,-0.2711653113,-0.4559597075,-0.0838898271,0.053742189,0.0200193245,0.2069679499,-0.0727652311,0.2113105655,-0.1184750274,0.0249347296,0.0052898373,-0.1571695358,-0.4511204958,-0.0088537708,-0.0174494628,0.1641462594,0.2242801338,-0.5873143673,0.358183831,-0.2557605207,-0.282561481,0.3788734972,0.0895975903,0.1147175729,-0.08198376,0.0209376439,-0.109927848,0.2018232495,0.1287567168,-0.0802040622,0.0581792854,0.0137800891,-0.1278193295,-0.3789302111,-0.0244191606,-0.4566992223,-0.4868037999,-0.2119251639,-0.2468610853,0.0929170027,0.1921365559,0.0121888174,-0.1417455226,0.0117351552,0.1944310069,-0.058898557,-0.2702171803,-0.2222687155,0.3685072064,-0.2343417853,-0.3159908652,0.1320606321,-0.0013562948,0.1343502104,-0.0744212419,-0.371771574,0.1328444034,0.1321620941,0.1218714789,-0.0088408273,-0.0676275641,0.1156630889,0.1279091239,0.1326593459,-0.2925481498,-0.1315274537,0.0137321679,-0.0302434806,0.2567032576,0.0845402554,-0.1026656032,-0.1795812845,-0.1389961839,0.5846102834,0.0942111015,0.2548664808,-0.2014132589,0.284373045,-0.0531347916,0.0246518087,-0.0120046102,0.2396105677,-0.1250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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3673","title":"`load_dataset(\"snli\")` is different from dataset viewer","comments":"Proposals by @gary149. Which one do you prefer? Please vote with the thumbs\r\n\r\n- \ud83d\udc4d \r\n\r\n    ![image](https:\/\/user-images.githubusercontent.com\/1676121\/152387949-883c7d7e-a9f3-48aa-bff9-11a691555e6e.png)\r\n\r\n- \ud83d\udc4e \r\n\r\n    ![image (1)](https:\/\/user-images.githubusercontent.com\/1676121\/152388061-32d95e42-cade-4ae4-9a77-7365e7b72b8f.png)\r\n\r\n","body":"## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n","comment_length":20,"text":"`load_dataset(\"snli\")` is different from dataset viewer \n ## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n \n Proposals by @gary149. Which one do you prefer? Please vote with the thumbs\r\n\r\n- \ud83d\udc4d \r\n\r\n    ![image](https:\/\/user-images.githubusercontent.com\/1676121\/152387949-883c7d7e-a9f3-48aa-bff9-11a691555e6e.png)\r\n\r\n- \ud83d\udc4e \r\n\r\n    ![image (1)](https:\/\/user-images.githubusercontent.com\/1676121\/152388061-32d95e42-cade-4ae4-9a77-7365e7b72b8f.png)\r\n\r\n","embeddings":[-0.1302458346,-0.1830817759,-0.0252131522,0.5175071955,-0.0003677709,-0.0492844768,0.5257968903,0.088770248,0.2461691648,0.1868776381,-0.3800661862,0.5411989093,0.1030182689,0.2068625689,-0.009695,0.0477101728,0.3122695982,0.2211067379,0.1662655026,-0.3286719918,-0.2333319187,0.1108598188,-0.2700231671,0.0521049574,-0.3184041679,0.2129747272,-0.1296790838,-0.0314587094,-0.1585333347,-0.5702453852,0.2953884602,0.2271755785,0.3646424413,0.2328637689,-0.0001160943,-0.1757742316,0.357105881,-0.0760125965,-0.413184464,0.0310790204,-0.277556926,-0.3120056391,0.1387517005,-0.3376826942,-0.0070620808,-0.2117976099,0.1117269844,-0.2638785541,0.0288608633,0.1669032574,0.2006939352,0.2764907181,-0.197258994,0.0954348147,0.3219870329,-0.0806755126,-0.0817381144,0.2694889903,0.0138932057,0.5074266195,-0.0106030656,0.4912211597,-0.1759994924,0.126805976,0.3263944685,0.1634107828,0.1539624482,-0.2635478675,-0.0375672951,0.3608476222,0.8421895504,-0.2335493565,-0.3751461804,-0.1788934171,-0.1119445264,-0.0716715902,0.2897381186,0.0736044049,0.0939680412,0.2379724979,-0.1132481545,-0.1297464818,-0.074417524,0.1679014415,-0.5436638594,0.5042737126,-0.0097448565,0.0973642394,-0.2441214472,0.0617228299,0.4981323481,-0.321230799,-0.0950260535,0.0621997677,-0.4461980164,0.0426117517,-0.1309319139,0.1792846173,-0.0859359279,0.5154831409,0.0429145731,0.0664329976,-0.2258679867,0.1949572712,0.2736305296,0.0333601125,0.3164565265,0.2935307324,0.1515432298,-0.0662219822,0.0039359527,-0.0327631347,-0.2629520297,-0.2115567625,0.0618562773,-0.1954626292,0.1165583581,-0.2436255962,-0.370669812,0.1196877435,-0.3336246312,-0.157270804,0.0697948262,0.0912210792,-0.158438012,0.3883584142,-0.0334117301,0.2105955333,-0.2833561301,-0.440867871,-0.1853911877,-0.0918250531,-0.0333656929,-0.0620152056,0.1067915186,-0.3423282802,0.2142176181,-0.0073192315,0.0273266174,-0.2377880067,-0.1151122153,-0.0585019998,0.2352929711,-0.0172974672,0.1795917153,0.3484696448,-0.1749310642,-0.2635014653,-0.0481985286,0.2942179143,-0.3281772733,-0.0057527288,-0.0392924994,0.1326673031,-0.3020253778,-0.2167897075,-0.0504712723,-0.0626256093,-0.1919828802,-0.2750220895,0.03871084,-0.158850342,-0.0396864787,-0.2756965756,0.2245642543,0.3557268679,-0.5565042496,-0.0399159379,-0.082149826,-0.2681942582,-0.0169902705,0.1646520048,0.0517786555,0.2010850161,-0.2576365173,0.2370967418,0.415329963,-0.675663352,-0.739320159,0.2193741947,0.1237431169,0.1234052479,0.0070967493,0.2927246094,0.1010914892,-0.0062861112,-0.0431368537,0.0861505046,0.2468004525,-0.0323387086,-0.2773888409,-0.1933324784,0.3083760142,0.3084543943,0.1077744439,0.0536785498,0.0943631157,0.2351257652,0.4132530391,-0.0008930735,0.0216132533,0.289155066,0.1579268426,0.0772184879,0.0050024348,-0.0093468605,-0.2063665241,0.160885483,-0.2835281491,0.1475498229,0.0880732313,-0.0837212279,-0.2655626535,-0.0527438261,-0.4388690591,-0.0853888318,0.1355678737,0.2220741361,-0.1798535436,0.0139685674,0.0765245482,0.5658473372,-0.2579488158,0.0782684237,-0.2157822102,0.4106417894,0.0435993634,0.1344022304,-0.1521106213,0.0016428032,0.24293603,-0.0374348797,-0.2410736382,0.3443699181,0.1046380028,0.2203443795,-0.0623237267,-0.0572160892,0.2155727297,-0.390011549,0.2825292051,0.1854536235,0.1090377346,0.0690499395,-0.2776863873,0.2148415893,-0.1723305434,0.0370157249,-0.123891525,0.0613356456,0.1761069745,-0.0649711937,-0.2728857398,-0.2325164527,0.1108125076,-0.0074168053,0.4646260142,-0.0048777112,-0.0809639841,0.0867805481,0.5205998421,-0.082183674,-0.0322078764,-0.0810937807,-0.3475959599,-0.082494311,0.2854994833,0.1949962676,0.3468109071,0.1448757499,-0.0222066939,0.1778828949,0.0155296344,-0.0895762369,0.1864084452,0.2915520072,0.1548502445,0.1626945138,0.0182210915,-0.0137029476,-0.2022682279,-0.031384971,0.0575049296,0.0520101264,-0.3265249133,-0.0037633155,-0.2562427521,-0.1583680809,-0.2697996795,-0.1332142204,-0.2925403416,-0.4346982539,-0.0333956555,0.1305039376,-0.0046350495,0.1491975635,-0.0476757474,0.1977948844,-0.1663302928,0.0402129292,-0.046824716,-0.118636027,-0.4238847494,0.0313463844,0.0572809689,0.1570297182,0.2074751705,-0.55401057,0.4437426925,-0.3893872797,-0.2818351686,0.3616261184,0.1248365194,0.1123461649,-0.1293566823,-0.0334925279,-0.131842047,0.1597810537,0.1564844549,-0.0944186896,0.044517301,0.023168087,-0.1009450853,-0.4044086635,0.0265853703,-0.4813500941,-0.4914270937,-0.2584356368,-0.2358166873,0.1773008853,0.2016792893,-0.0044236854,-0.130648315,-0.0383957438,0.2848192155,0.0065141721,-0.3078127801,-0.2632705569,0.3119410276,-0.2979295552,-0.3914735019,-0.0101747382,0.0600698814,0.1315884739,-0.0323226526,-0.4796514809,0.1741033643,0.0857605785,0.1512227207,-0.0584346503,-0.1087495461,0.1186127067,0.1322487295,0.114641726,-0.2835550606,-0.1016855836,0.1150616631,-0.0037464306,0.2599984407,0.0640323088,-0.0012702192,-0.1483075768,-0.0923098549,0.638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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3673","title":"`load_dataset(\"snli\")` is different from dataset viewer","comments":"It's [live](https:\/\/huggingface.co\/datasets\/glue\/viewer\/cola\/train):\r\n\r\n<img width=\"1126\" alt=\"Capture d\u2019e\u0301cran 2022-02-14 a\u0300 10 26 03\" src=\"https:\/\/user-images.githubusercontent.com\/1676121\/153836716-25f6205b-96af-42d8-880a-7c09cb24c420.png\">\r\n\r\nThanks all for the help to improve the UI!","body":"## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n","comment_length":21,"text":"`load_dataset(\"snli\")` is different from dataset viewer \n ## Describe the bug\r\nThe dataset that is downloaded from the Hub via `load_dataset(\"snli\")` is different from what is available in the dataset viewer. In the viewer the labels are not encoded (i.e., \"neutral\", \"entailment\", \"contradiction\"), while the downloaded dataset shows the encoded labels (i.e., 0, 1, 2).\r\n\r\nIs this expected? \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Ubuntu 20.4\r\n- Python version: 3.7\r\n \n It's [live](https:\/\/huggingface.co\/datasets\/glue\/viewer\/cola\/train):\r\n\r\n<img width=\"1126\" alt=\"Capture d\u2019e\u0301cran 2022-02-14 a\u0300 10 26 03\" src=\"https:\/\/user-images.githubusercontent.com\/1676121\/153836716-25f6205b-96af-42d8-880a-7c09cb24c420.png\">\r\n\r\nThanks all for the help to improve the UI!","embeddings":[-0.0064430838,-0.2428969145,0.019707948,0.6077647805,0.0732068643,0.005316454,0.5198220611,0.0131178014,0.260227263,0.2141699493,-0.4702808261,0.5367407203,0.1597671509,0.2426189631,0.005344979,-0.0563698709,0.2554400861,0.1189136133,0.0553388335,-0.3076821864,-0.1341778338,0.2016994208,-0.3041015863,0.0489220396,-0.4574280083,0.1967655271,-0.1557752192,-0.0450349301,-0.2171373814,-0.4867064059,0.2861916423,0.2543797791,0.3570179343,0.2457081378,-0.0001199943,-0.1218545362,0.3823718429,-0.0823237523,-0.3099080324,0.0156192295,-0.3204242587,-0.2331589758,0.1828343123,-0.3198301792,0.0447577834,-0.0239727478,0.1342258453,-0.1597485393,0.1379564703,0.0814063475,0.1734392345,0.3165448308,-0.1558661163,0.1665886492,0.297049731,0.0042951829,-0.153384611,0.4528729022,0.0146898469,0.4162859619,0.0019901651,0.5269786119,-0.174363479,0.1054651961,0.2336627245,0.2451968342,-0.0024979664,-0.4208703935,-0.010572887,0.4031848013,0.6379487514,-0.2363778055,-0.4284302294,-0.2658585608,-0.0474705212,0.0768921748,0.3423919976,0.0917761996,0.0979076773,0.2828370631,-0.2296976596,-0.1794125438,0.0147559689,0.1685112864,-0.3707300425,0.3597637415,-0.0873529091,0.1049415097,-0.0628791302,0.0859986395,0.3671888113,-0.2328381091,-0.1253893226,0.107100457,-0.3575327992,0.0683060586,-0.2168015242,0.2777096629,-0.0483165383,0.3269683123,0.0479310602,0.0424393639,-0.3024329841,0.2242122144,0.2542800307,0.131485343,0.2289593667,0.2808257639,0.1406529844,0.1185765713,-0.0936499014,-0.0717796907,-0.2732815444,-0.1814824194,-0.0561150722,-0.2559432983,0.130381614,-0.2022069246,-0.3471795321,-0.0435422547,-0.4037630558,-0.0610711873,0.0796926692,0.0150466524,-0.2588484585,0.3387650847,-0.020119302,0.2879836559,-0.2885898054,-0.4588458538,-0.1847869903,-0.0191836022,-0.0180670023,0.1093283817,0.005171374,-0.5658550858,0.1750073135,0.005836647,0.0063140956,-0.2658625543,-0.2674105763,-0.0961157382,0.218933776,-0.0323035158,0.237627238,0.3088070154,-0.1074486524,-0.4657440782,0.0240984708,0.2295045704,-0.4289417267,-0.0280945431,-0.0938758105,0.0421130955,-0.3580291569,-0.1640860587,-0.2401587814,-0.0007249335,-0.1781112999,-0.0869936869,0.1171965525,-0.0613590144,-0.0627825111,-0.2711819708,0.2352330536,0.4916338921,-0.5553283095,-0.1713428646,-0.1097294018,-0.2680667639,-0.0097768689,0.2294110805,0.1243728325,0.1817121208,-0.3251162171,0.2751686275,0.5087054372,-0.6290182471,-0.8031197786,0.1996575445,0.1498866528,0.2038627863,-0.0457518995,0.2930973172,0.1666163653,0.0438572429,-0.094296813,0.0948546529,0.1817951202,-0.0031496107,-0.2699453235,-0.2824936509,0.1953805536,0.2711076438,-0.0078497194,0.123377867,0.1416295618,0.048769474,0.4454262257,0.1373722553,0.0487676524,0.2027635574,0.2380326688,0.1254663169,-0.0510737896,0.0447634235,-0.2976768911,0.171191901,-0.088979207,0.2229278684,0.0774532706,-0.1230306625,-0.2787851989,-0.0550847724,-0.5727106333,-0.1209381223,0.0550086871,0.1671701521,-0.1735084057,0.072083205,0.1146734133,0.6974750757,-0.2431124598,0.1904909164,-0.325527668,0.4629066586,0.0573193841,0.102362439,-0.2000759095,-0.0168366041,0.2057282627,-0.1499302983,-0.2440530807,0.5067718625,0.1337088645,0.2545658052,-0.0345230661,-0.1128376126,0.270460248,-0.4081318378,0.2452062815,0.0531966053,0.0330627896,0.1422913074,-0.262824297,0.2239102274,-0.1802345067,0.1084277406,-0.1082451865,0.083913669,0.0733629316,-0.0065826825,-0.271051079,-0.3414189219,0.0175486244,-0.0750002936,0.5755707026,-0.0074094068,-0.0986014605,0.1326058507,0.4964179099,-0.0037743927,-0.0723921731,0.0175790135,-0.3878917396,-0.0570076294,0.2678984404,0.2528986037,0.2944895625,0.0670434609,-0.0180785041,0.2009304464,0.1127235219,-0.0566724986,0.1442560703,0.26245597,0.0923343003,0.0417676792,0.0917626396,0.0547442958,-0.1945657581,0.0536950454,-0.0098648248,0.0300244801,-0.30582425,0.0220927522,-0.3420466781,-0.4310947955,-0.3352242708,-0.1222179532,-0.3557984531,-0.3766319454,-0.0430049412,0.1260156184,0.1620614529,0.2090047449,-0.0101290569,0.1777987182,-0.2111268789,0.04125043,-0.0697411522,-0.0393238887,-0.4309803545,-0.0384542532,0.0529600419,0.2138834149,0.1975914538,-0.577457726,0.43517223,-0.3846112192,-0.3941919506,0.4876919687,0.0535332672,0.2390837371,-0.0962126181,-0.0821378902,-0.1810078919,0.2311857641,0.2454943508,-0.0591487177,-0.0264287256,-0.0893799365,-0.0880284086,-0.3288740814,0.0276066586,-0.2835839093,-0.4954476953,-0.1997601539,-0.0786144808,0.0911392868,0.2049662322,0.0419941247,-0.0059423819,-0.0846273527,0.2007293701,-0.0428385846,-0.3427959681,-0.4128999412,0.2978184819,-0.2654390335,-0.3559142649,-0.0764790326,-0.0150099117,0.1212586313,-0.0816464424,-0.5331026912,-0.0413177013,-0.0153356493,0.1175168157,0.0247076526,-0.0406532884,0.0617223568,0.1050779894,0.2086007893,-0.2513926029,-0.2253181338,0.0148549546,0.1259605885,0.2696135044,0.1449317634,0.147592932,-0.1418752223,0.0007798555,0.5889146924,0.0517504364,0.4254845977,-0.3365862966,0.4682641923,-0.1051919088,-0.1181264371,0.055861596,0.1322781593,0.05885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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3668","title":" Couldn't cast array of type string error with cast_column","comments":"Hi ! I wasn't able to reproduce the error, are you still experiencing this ? I tried calling `cast_column` on a string column containing paths.\r\n\r\nIf you manage to share a reproducible code example that would be perfect","body":"## Describe the bug\r\n\r\n\r\nIn OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column(\"audio\",Audio(sampling_rate=16_000))\r\nmethod I get error\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214027-9c42a71a-dd24-463c-a346-57e0287e5a8f.png)\r\n\r\nThis was working with datasets version 1.17.1.dev0\r\nbut now with version 1.18.3 produces the error above.\r\n\r\n## Steps to reproduce the bug\r\n\r\nload dataset:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152216145-159553b6-cddc-4f0b-8607-7e76b600e22a.png)\r\n\r\n\r\nremove columns:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214707-7c7e89d1-87d8-4b4f-8cfc-5d7223d35644.png)\r\n\r\nrun my fix_path function.\r\nThis also creates the audio column that is referring to the absolute file path of the audio\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214773-51f71ccf-d31b-4449-b63a-1af56436e49f.png)\r\n\r\nThen I concatenate few other datasets and finally try the cast_column method\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215032-f341ec86-9d6d-48c9-943b-e2efe37a4d98.png)\r\n\r\nbut get error:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215073-b85bd057-98e8-413c-9b05-51e9805f2c24.png)\r\n\r\n\r\n\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: \r\nOVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn\/ovh_huggingface)\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215161-b4ff7bfb-2736-4afb-9223-761a3338d23c.png)\r\n\r\n- Python version: 3.8.8\r\n- PyArrow version:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215936-4d365760-557e-456b-b5eb-ad1d15cf5073.png)\r\n\r\n","comment_length":38,"text":" Couldn't cast array of type string error with cast_column \n ## Describe the bug\r\n\r\n\r\nIn OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column(\"audio\",Audio(sampling_rate=16_000))\r\nmethod I get error\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214027-9c42a71a-dd24-463c-a346-57e0287e5a8f.png)\r\n\r\nThis was working with datasets version 1.17.1.dev0\r\nbut now with version 1.18.3 produces the error above.\r\n\r\n## Steps to reproduce the bug\r\n\r\nload dataset:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152216145-159553b6-cddc-4f0b-8607-7e76b600e22a.png)\r\n\r\n\r\nremove columns:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214707-7c7e89d1-87d8-4b4f-8cfc-5d7223d35644.png)\r\n\r\nrun my fix_path function.\r\nThis also creates the audio column that is referring to the absolute file path of the audio\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214773-51f71ccf-d31b-4449-b63a-1af56436e49f.png)\r\n\r\nThen I concatenate few other datasets and finally try the cast_column method\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215032-f341ec86-9d6d-48c9-943b-e2efe37a4d98.png)\r\n\r\nbut get error:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215073-b85bd057-98e8-413c-9b05-51e9805f2c24.png)\r\n\r\n\r\n\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: \r\nOVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn\/ovh_huggingface)\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215161-b4ff7bfb-2736-4afb-9223-761a3338d23c.png)\r\n\r\n- Python version: 3.8.8\r\n- PyArrow version:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215936-4d365760-557e-456b-b5eb-ad1d15cf5073.png)\r\n\r\n \n Hi ! I wasn't able to reproduce the error, are you still experiencing this ? I tried calling `cast_column` on a string column containing paths.\r\n\r\nIf you manage to share a reproducible code example that would be perfect","embeddings":[-0.213360101,-0.2731950879,0.080202207,0.0617940612,0.5655034184,-0.1390102059,0.4365329444,0.4131574035,-0.0390901454,0.0874475837,-0.22466591,0.2103301734,-0.1993310899,0.2030957937,0.024687944,-0.5783652663,0.253187269,0.0518444851,-0.2568010688,-0.1719430834,-0.2746168077,0.3036409616,-0.3599356711,0.2213895917,0.1377553195,0.0049380115,0.0553626232,-0.0702402741,-0.1641900986,-0.1888268888,0.0753125921,-0.3757588863,0.1674401164,0.2532269359,-0.0001248445,0.1116929352,0.0656985939,-0.015858328,-0.1642207205,-0.2465245724,0.0230894908,0.235326454,-0.0695690885,-0.1248801425,0.0433230139,0.0539879464,-0.0982668549,-0.2530709803,0.0450857803,0.5102421045,0.0695312098,0.5947118402,0.2233441621,0.1099638268,0.2402129471,0.1065690741,-0.3727177978,0.0274083912,0.4154843986,0.3787813485,0.0795512795,0.0436021462,-0.1400680989,-0.1295959204,0.2024482787,0.1417589486,0.1904175729,0.072505638,-0.0305672772,0.3137119412,0.5575301051,-0.0832538009,-0.1054438651,-0.0016731395,0.1952837706,-0.029130714,0.1627634317,0.2137503475,0.079700768,-0.025400551,-0.3943604231,0.4975740016,-0.1921615601,0.2805648446,0.2574909031,0.0990851149,-0.027264297,0.1010169312,0.0290123019,-0.3984086215,0.1183696911,-0.0509815663,-0.0506147146,0.2156897634,-0.3465467393,-0.068200238,-0.1920299679,-0.157300368,-0.0667866468,-0.3893394768,0.1655427963,0.1253930628,0.12501432,0.132836923,0.2112060189,0.0903086662,-0.0121415304,0.3836182952,0.163301751,0.384473443,-0.0922043622,0.1130320877,-0.0040797209,-0.0317202695,-0.1368385404,-0.0548108555,0.0696608499,-0.1232032627,-0.3120765984,-0.0278152376,-0.241014868,0.2306060344,0.3971683681,0.2632124424,0.3164305985,0.071095854,0.3672198355,0.4072531462,0.0369771868,-0.1261038929,-0.0582660697,0.2525618374,0.1814486682,-0.1198399141,-0.0812543407,0.309004575,0.0708551779,-0.1275916398,0.2796129584,-0.2084433883,-0.3605033159,-0.0103300558,-0.0427326038,0.2645478845,-0.1667206436,-0.2278493196,0.1601871997,-0.0398541503,-0.1113232225,0.0356515534,-0.2170597166,-0.4115760922,-0.3182275593,0.0259096473,0.0175582543,0.0753640831,-0.2861239612,-0.2729766965,0.1845409721,-0.4192551076,0.1737340242,-0.4156750739,-0.2206310779,-0.0852495432,0.1827265769,-0.3094207942,-0.2433081567,-0.0337529406,-0.2149109393,0.1407401413,0.0670400262,0.3245406449,-0.0841784105,0.2360176742,-0.4323160648,0.3454588652,0.0912274271,-0.3836919665,-0.0418183655,-0.0048414678,0.2209973484,0.0149566643,0.1976410747,-0.0497397259,0.3395648897,-0.2759539485,0.0013346609,0.3817938566,0.0029886672,0.278393954,-0.2870312035,-0.0710108802,-0.3581401706,0.1650443971,-0.0806663483,0.2003004104,-0.025511425,0.101809375,0.1612164676,-0.3681296408,0.2079146057,0.1987414509,0.4860338569,-0.1210977808,0.1061719656,-0.0960633829,-0.1745609492,-0.0156167829,0.352029115,-0.0621768497,-0.2556126416,-0.2406722456,-0.2895670831,0.0541290268,-0.0264180731,0.2775422633,-0.0542114526,-0.2085884064,0.2278104573,-0.0851818025,-0.1333454996,0.0488645919,-0.3263505697,0.0013793885,-0.1776771843,0.3220871389,-0.1347952932,-0.2418136746,-0.0673988238,0.2046933919,0.0814084858,-0.1595137715,-0.2156642973,0.4346250296,-0.2800219655,0.0893726274,-0.5544112325,0.2313667387,-0.0628366023,-0.1974608302,0.0383266993,-0.0506478995,0.2144628763,0.0636443868,0.0497483015,0.3993142843,-0.2742010951,0.2318888903,-0.0310341436,-0.0682755411,0.0125358524,-0.0495106466,0.0930720121,-0.2565164268,0.1734829992,-0.2881476879,0.1949261129,-0.0186584387,-0.4366870224,0.0327893831,0.3783967793,-0.0503224172,0.2094929963,0.276208967,0.0257325619,0.1380871981,-0.0601655878,0.0271871332,0.5409525633,-0.0039274599,-0.0564075932,0.1203479692,0.088614963,-0.1275627315,0.44832322,0.122709237,0.3373070955,0.238755852,0.1159460768,0.090908207,-0.3121602237,-0.0579294227,0.2197689861,0.234469831,-0.5730794072,0.0136301564,-0.3402596116,-0.2581060529,-0.2038135827,0.2291615009,0.1666056812,-0.1177441552,0.0109236846,0.4787477851,-0.3867030442,0.2668394446,0.0314132087,0.1098644808,0.1411491483,-0.1017154604,-0.1751498431,0.4787212014,0.1221293509,-0.0929697752,0.1589726955,-0.1454294324,-0.2769682705,-0.0100004617,-0.0862902403,-0.397012651,-0.2658312619,0.150063917,-0.2063833028,0.0530829094,0.374722451,0.3308995962,-0.352833271,-0.5159816146,0.2131866962,-0.3082813323,-0.3622294366,0.3476728499,-0.017044574,-0.2698612511,-0.237981528,-0.0806296691,-0.1293210983,-0.3928149343,0.0890307501,-0.4820475876,0.110342443,-0.1285010278,0.2354521602,0.100457713,-0.0886384994,0.1653348207,0.0408368148,-0.0449891873,0.2868727148,0.05135905,-0.2491475493,-0.2694913149,-0.0803469941,0.1373990625,-0.1930763572,-0.3739376366,-0.3176926374,-0.1232185364,0.1554573327,-0.021277437,-0.1856123507,0.3933194578,-0.2318658233,0.1075941622,-0.1304226816,-0.0113792541,0.1863670647,0.3536132574,0.2719209194,0.2089813948,0.6763507724,0.2076230943,-0.0196056589,0.1672382951,-0.0947562158,0.4936757982,-0.254583776,-0.0529151484,-0.3124418259,0.0040425649,0.2231858075,0.0312301815,-0.24120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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3668","title":" Couldn't cast array of type string error with cast_column","comments":"Hi,\r\n\r\nI think my team mate got this solved. Clolsing it for now and will reopen if I experience this again.\r\nThanks :) ","body":"## Describe the bug\r\n\r\n\r\nIn OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column(\"audio\",Audio(sampling_rate=16_000))\r\nmethod I get error\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214027-9c42a71a-dd24-463c-a346-57e0287e5a8f.png)\r\n\r\nThis was working with datasets version 1.17.1.dev0\r\nbut now with version 1.18.3 produces the error above.\r\n\r\n## Steps to reproduce the bug\r\n\r\nload dataset:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152216145-159553b6-cddc-4f0b-8607-7e76b600e22a.png)\r\n\r\n\r\nremove columns:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214707-7c7e89d1-87d8-4b4f-8cfc-5d7223d35644.png)\r\n\r\nrun my fix_path function.\r\nThis also creates the audio column that is referring to the absolute file path of the audio\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214773-51f71ccf-d31b-4449-b63a-1af56436e49f.png)\r\n\r\nThen I concatenate few other datasets and finally try the cast_column method\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215032-f341ec86-9d6d-48c9-943b-e2efe37a4d98.png)\r\n\r\nbut get error:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215073-b85bd057-98e8-413c-9b05-51e9805f2c24.png)\r\n\r\n\r\n\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: \r\nOVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn\/ovh_huggingface)\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215161-b4ff7bfb-2736-4afb-9223-761a3338d23c.png)\r\n\r\n- Python version: 3.8.8\r\n- PyArrow version:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215936-4d365760-557e-456b-b5eb-ad1d15cf5073.png)\r\n\r\n","comment_length":23,"text":" Couldn't cast array of type string error with cast_column \n ## Describe the bug\r\n\r\n\r\nIn OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column(\"audio\",Audio(sampling_rate=16_000))\r\nmethod I get error\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214027-9c42a71a-dd24-463c-a346-57e0287e5a8f.png)\r\n\r\nThis was working with datasets version 1.17.1.dev0\r\nbut now with version 1.18.3 produces the error above.\r\n\r\n## Steps to reproduce the bug\r\n\r\nload dataset:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152216145-159553b6-cddc-4f0b-8607-7e76b600e22a.png)\r\n\r\n\r\nremove columns:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214707-7c7e89d1-87d8-4b4f-8cfc-5d7223d35644.png)\r\n\r\nrun my fix_path function.\r\nThis also creates the audio column that is referring to the absolute file path of the audio\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214773-51f71ccf-d31b-4449-b63a-1af56436e49f.png)\r\n\r\nThen I concatenate few other datasets and finally try the cast_column method\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215032-f341ec86-9d6d-48c9-943b-e2efe37a4d98.png)\r\n\r\nbut get error:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215073-b85bd057-98e8-413c-9b05-51e9805f2c24.png)\r\n\r\n\r\n\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: \r\nOVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn\/ovh_huggingface)\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215161-b4ff7bfb-2736-4afb-9223-761a3338d23c.png)\r\n\r\n- Python version: 3.8.8\r\n- PyArrow version:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215936-4d365760-557e-456b-b5eb-ad1d15cf5073.png)\r\n\r\n \n Hi,\r\n\r\nI think my team mate got this solved. Clolsing it for now and will reopen if I experience this again.\r\nThanks :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3668","title":" Couldn't cast array of type string error with cast_column","comments":"Hi @R4ZZ3,\r\n\r\nIf it is not too much of a bother, can you please help me how to resolve this error? I am exactly getting the same error where I am going as per the documentation guideline:\r\n\r\n`my_audio_dataset = my_audio_dataset.cast_column(\"audio_paths\", Audio())`\r\n\r\nwhere `\"audio_paths\"` is a dataset column (feature) having strings of absolute paths to mp3 files of the dataset.\r\n\r\n","body":"## Describe the bug\r\n\r\n\r\nIn OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column(\"audio\",Audio(sampling_rate=16_000))\r\nmethod I get error\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214027-9c42a71a-dd24-463c-a346-57e0287e5a8f.png)\r\n\r\nThis was working with datasets version 1.17.1.dev0\r\nbut now with version 1.18.3 produces the error above.\r\n\r\n## Steps to reproduce the bug\r\n\r\nload dataset:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152216145-159553b6-cddc-4f0b-8607-7e76b600e22a.png)\r\n\r\n\r\nremove columns:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214707-7c7e89d1-87d8-4b4f-8cfc-5d7223d35644.png)\r\n\r\nrun my fix_path function.\r\nThis also creates the audio column that is referring to the absolute file path of the audio\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214773-51f71ccf-d31b-4449-b63a-1af56436e49f.png)\r\n\r\nThen I concatenate few other datasets and finally try the cast_column method\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215032-f341ec86-9d6d-48c9-943b-e2efe37a4d98.png)\r\n\r\nbut get error:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215073-b85bd057-98e8-413c-9b05-51e9805f2c24.png)\r\n\r\n\r\n\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: \r\nOVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn\/ovh_huggingface)\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215161-b4ff7bfb-2736-4afb-9223-761a3338d23c.png)\r\n\r\n- Python version: 3.8.8\r\n- PyArrow version:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215936-4d365760-557e-456b-b5eb-ad1d15cf5073.png)\r\n\r\n","comment_length":59,"text":" Couldn't cast array of type string error with cast_column \n ## Describe the bug\r\n\r\n\r\nIn OVH cloud during Huggingface Robust-speech-recognition event on a AI training notebook instance using jupyter lab and running jupyter notebook When using the dataset.cast_column(\"audio\",Audio(sampling_rate=16_000))\r\nmethod I get error\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214027-9c42a71a-dd24-463c-a346-57e0287e5a8f.png)\r\n\r\nThis was working with datasets version 1.17.1.dev0\r\nbut now with version 1.18.3 produces the error above.\r\n\r\n## Steps to reproduce the bug\r\n\r\nload dataset:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152216145-159553b6-cddc-4f0b-8607-7e76b600e22a.png)\r\n\r\n\r\nremove columns:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214707-7c7e89d1-87d8-4b4f-8cfc-5d7223d35644.png)\r\n\r\nrun my fix_path function.\r\nThis also creates the audio column that is referring to the absolute file path of the audio\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152214773-51f71ccf-d31b-4449-b63a-1af56436e49f.png)\r\n\r\nThen I concatenate few other datasets and finally try the cast_column method\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215032-f341ec86-9d6d-48c9-943b-e2efe37a4d98.png)\r\n\r\nbut get error:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215073-b85bd057-98e8-413c-9b05-51e9805f2c24.png)\r\n\r\n\r\n\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3\r\n- Platform: \r\nOVH Cloud, AI Training section, container for Huggingface Robust Speech Recognition event image(baaastijn\/ovh_huggingface)\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215161-b4ff7bfb-2736-4afb-9223-761a3338d23c.png)\r\n\r\n- Python version: 3.8.8\r\n- PyArrow version:\r\n![image](https:\/\/user-images.githubusercontent.com\/25264037\/152215936-4d365760-557e-456b-b5eb-ad1d15cf5073.png)\r\n\r\n \n Hi @R4ZZ3,\r\n\r\nIf it is not too much of a bother, can you please help me how to resolve this error? I am exactly getting the same error where I am going as per the documentation guideline:\r\n\r\n`my_audio_dataset = my_audio_dataset.cast_column(\"audio_paths\", Audio())`\r\n\r\nwhere `\"audio_paths\"` is a dataset column (feature) having strings of absolute paths to mp3 files of the dataset.\r\n\r\n","embeddings":[-0.213360101,-0.2731950879,0.080202207,0.0617940612,0.5655034184,-0.1390102059,0.4365329444,0.4131574035,-0.0390901454,0.0874475837,-0.22466591,0.2103301734,-0.1993310899,0.2030957937,0.024687944,-0.5783652663,0.253187269,0.0518444851,-0.2568010688,-0.1719430834,-0.2746168077,0.3036409616,-0.3599356711,0.2213895917,0.1377553195,0.0049380115,0.0553626232,-0.0702402741,-0.1641900986,-0.1888268888,0.0753125921,-0.3757588863,0.1674401164,0.2532269359,-0.0001248445,0.1116929352,0.0656985939,-0.015858328,-0.1642207205,-0.2465245724,0.0230894908,0.235326454,-0.0695690885,-0.1248801425,0.0433230139,0.0539879464,-0.0982668549,-0.2530709803,0.0450857803,0.5102421045,0.0695312098,0.5947118402,0.2233441621,0.1099638268,0.2402129471,0.1065690741,-0.3727177978,0.0274083912,0.4154843986,0.3787813485,0.0795512795,0.0436021462,-0.1400680989,-0.1295959204,0.2024482787,0.1417589486,0.1904175729,0.072505638,-0.0305672772,0.3137119412,0.5575301051,-0.0832538009,-0.1054438651,-0.0016731395,0.1952837706,-0.029130714,0.1627634317,0.2137503475,0.079700768,-0.025400551,-0.3943604231,0.4975740016,-0.1921615601,0.2805648446,0.2574909031,0.0990851149,-0.027264297,0.1010169312,0.0290123019,-0.3984086215,0.1183696911,-0.0509815663,-0.0506147146,0.2156897634,-0.3465467393,-0.068200238,-0.1920299679,-0.157300368,-0.0667866468,-0.3893394768,0.1655427963,0.1253930628,0.12501432,0.132836923,0.2112060189,0.0903086662,-0.0121415304,0.3836182952,0.163301751,0.384473443,-0.0922043622,0.1130320877,-0.0040797209,-0.0317202695,-0.1368385404,-0.0548108555,0.0696608499,-0.1232032627,-0.3120765984,-0.0278152376,-0.241014868,0.2306060344,0.3971683681,0.2632124424,0.3164305985,0.071095854,0.3672198355,0.4072531462,0.0369771868,-0.1261038929,-0.0582660697,0.2525618374,0.1814486682,-0.1198399141,-0.0812543407,0.309004575,0.0708551779,-0.1275916398,0.2796129584,-0.2084433883,-0.3605033159,-0.0103300558,-0.0427326038,0.2645478845,-0.1667206436,-0.2278493196,0.1601871997,-0.0398541503,-0.1113232225,0.0356515534,-0.2170597166,-0.4115760922,-0.3182275593,0.0259096473,0.0175582543,0.0753640831,-0.2861239612,-0.2729766965,0.1845409721,-0.4192551076,0.1737340242,-0.4156750739,-0.2206310779,-0.0852495432,0.1827265769,-0.3094207942,-0.2433081567,-0.0337529406,-0.2149109393,0.1407401413,0.0670400262,0.3245406449,-0.0841784105,0.2360176742,-0.4323160648,0.3454588652,0.0912274271,-0.3836919665,-0.0418183655,-0.0048414678,0.2209973484,0.0149566643,0.1976410747,-0.0497397259,0.3395648897,-0.2759539485,0.0013346609,0.3817938566,0.0029886672,0.278393954,-0.2870312035,-0.0710108802,-0.3581401706,0.1650443971,-0.0806663483,0.2003004104,-0.025511425,0.101809375,0.1612164676,-0.3681296408,0.2079146057,0.1987414509,0.4860338569,-0.1210977808,0.1061719656,-0.0960633829,-0.1745609492,-0.0156167829,0.352029115,-0.0621768497,-0.2556126416,-0.2406722456,-0.2895670831,0.0541290268,-0.0264180731,0.2775422633,-0.0542114526,-0.2085884064,0.2278104573,-0.0851818025,-0.1333454996,0.0488645919,-0.3263505697,0.0013793885,-0.1776771843,0.3220871389,-0.1347952932,-0.2418136746,-0.0673988238,0.2046933919,0.0814084858,-0.1595137715,-0.2156642973,0.4346250296,-0.2800219655,0.0893726274,-0.5544112325,0.2313667387,-0.0628366023,-0.1974608302,0.0383266993,-0.0506478995,0.2144628763,0.0636443868,0.0497483015,0.3993142843,-0.2742010951,0.2318888903,-0.0310341436,-0.0682755411,0.0125358524,-0.0495106466,0.0930720121,-0.2565164268,0.1734829992,-0.2881476879,0.1949261129,-0.0186584387,-0.4366870224,0.0327893831,0.3783967793,-0.0503224172,0.2094929963,0.276208967,0.0257325619,0.1380871981,-0.0601655878,0.0271871332,0.5409525633,-0.0039274599,-0.0564075932,0.1203479692,0.088614963,-0.1275627315,0.44832322,0.122709237,0.3373070955,0.238755852,0.1159460768,0.090908207,-0.3121602237,-0.0579294227,0.2197689861,0.234469831,-0.5730794072,0.0136301564,-0.3402596116,-0.2581060529,-0.2038135827,0.2291615009,0.1666056812,-0.1177441552,0.0109236846,0.4787477851,-0.3867030442,0.2668394446,0.0314132087,0.1098644808,0.1411491483,-0.1017154604,-0.1751498431,0.4787212014,0.1221293509,-0.0929697752,0.1589726955,-0.1454294324,-0.2769682705,-0.0100004617,-0.0862902403,-0.397012651,-0.2658312619,0.150063917,-0.2063833028,0.0530829094,0.374722451,0.3308995962,-0.352833271,-0.5159816146,0.2131866962,-0.3082813323,-0.3622294366,0.3476728499,-0.017044574,-0.2698612511,-0.237981528,-0.0806296691,-0.1293210983,-0.3928149343,0.0890307501,-0.4820475876,0.110342443,-0.1285010278,0.2354521602,0.100457713,-0.0886384994,0.1653348207,0.0408368148,-0.0449891873,0.2868727148,0.05135905,-0.2491475493,-0.2694913149,-0.0803469941,0.1373990625,-0.1930763572,-0.3739376366,-0.3176926374,-0.1232185364,0.1554573327,-0.021277437,-0.1856123507,0.3933194578,-0.2318658233,0.1075941622,-0.1304226816,-0.0113792541,0.1863670647,0.3536132574,0.2719209194,0.2089813948,0.6763507724,0.2076230943,-0.0196056589,0.1672382951,-0.0947562158,0.4936757982,-0.254583776,-0.0529151484,-0.3124418259,0.0040425649,0.2231858075,0.0312301815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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3663","title":"[Audio] Path of Common Voice cannot be used for audio loading anymore","comments":"Having talked to @lhoestq, I see that this feature is no longer supported. \r\n\r\nI really don't think this was a good idea. It is a major breaking change and one for which we don't even have a working solution at the moment, which is bad for PyTorch as we don't want to force people to have `datasets` decode audio files automatically, but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files - e.g. `common_voice` doesn't work anymore in a TF training script. Note this worked perfectly fine before making the change (think it was done [here](https:\/\/github.com\/huggingface\/datasets\/pull\/3290) no?)\r\n\r\nIMO, it's really important to think about a solution here and I strongly favor to make a difference here between loading a dataset in streaming mode and in non-streaming mode, so that in non-streaming mode the actual downloaded file is displayed. It's really crucial for people to be able to analyse the original files IMO when the dataset is not downloaded in streaming mode. \r\n\r\nThere are the following reasons why it is paramount to have access to the **original** audio file in my opinion (in non-streaming mode):\r\n- There are a wide variety of different libraries to load audio data with varying support on different platforms. For me it was quite clear that there is simply to single good library to load audio files for all platforms - so we have to leave the option to the user to decide which loading to use.\r\n- We had support for audio datasets a long time before streaming audio was possible. There were quite some versions where  we advertised **everywhere** to load the audio from the path name (and there are many places where we still do even though it's not possible anymore). To give some examples:\r\n     - Official example of TF Wav2Vec2: https:\/\/github.com\/huggingface\/transformers\/blob\/f427e750490b486944cc9be3c99834ad5cf78b57\/src\/transformers\/models\/wav2vec2\/modeling_tf_wav2vec2.py#L1423 Wav2Vec2 is as important for speech as BERT is for NLP - so it's **very** important. The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment. Same goes for Flax.\r\n     - The most downloaded non-nlp checkpoint: https:\/\/huggingface.co\/facebook\/wav2vec2-base-960h#usage has a usage example which doesn't work anymore with the current datasets implementation. I'll update this now, but we have >1000 wav2vec2 checkpoints on the Hub and we can't update all the model cards.\r\n    => This is a big breaking change with no current solution. For `transformers` breaking changes are one of the biggest complaints.\r\n- Similar to this we also shouldn't assume that there is only one resampling method for Audio. I think it's good to have one offered automatically by `datasets`, but we have to leave the user the freedom to choose her\/his own resampling as well. Resampling can take very different filtering windows and other parameters which are currently somewhat hardcoded in `datasets`, which users might very well want to change.\r\n\r\n\r\n=> IMO, it's a **very** big priority to again have the correct absolute path in non-streaming mode. The other solution of providing a path-like object derived from the bytes stocked in the `.array` file is not nearly as user-friendly, but better than nothing. ","body":"## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":522,"text":"[Audio] Path of Common Voice cannot be used for audio loading anymore \n ## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n \n Having talked to @lhoestq, I see that this feature is no longer supported. \r\n\r\nI really don't think this was a good idea. It is a major breaking change and one for which we don't even have a working solution at the moment, which is bad for PyTorch as we don't want to force people to have `datasets` decode audio files automatically, but **really** bad for Tensorflow and Flax where we **currently cannot** even use `datasets` to load `.mp3` files - e.g. `common_voice` doesn't work anymore in a TF training script. Note this worked perfectly fine before making the change (think it was done [here](https:\/\/github.com\/huggingface\/datasets\/pull\/3290) no?)\r\n\r\nIMO, it's really important to think about a solution here and I strongly favor to make a difference here between loading a dataset in streaming mode and in non-streaming mode, so that in non-streaming mode the actual downloaded file is displayed. It's really crucial for people to be able to analyse the original files IMO when the dataset is not downloaded in streaming mode. \r\n\r\nThere are the following reasons why it is paramount to have access to the **original** audio file in my opinion (in non-streaming mode):\r\n- There are a wide variety of different libraries to load audio data with varying support on different platforms. For me it was quite clear that there is simply to single good library to load audio files for all platforms - so we have to leave the option to the user to decide which loading to use.\r\n- We had support for audio datasets a long time before streaming audio was possible. There were quite some versions where  we advertised **everywhere** to load the audio from the path name (and there are many places where we still do even though it's not possible anymore). To give some examples:\r\n     - Official example of TF Wav2Vec2: https:\/\/github.com\/huggingface\/transformers\/blob\/f427e750490b486944cc9be3c99834ad5cf78b57\/src\/transformers\/models\/wav2vec2\/modeling_tf_wav2vec2.py#L1423 Wav2Vec2 is as important for speech as BERT is for NLP - so it's **very** important. The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment. Same goes for Flax.\r\n     - The most downloaded non-nlp checkpoint: https:\/\/huggingface.co\/facebook\/wav2vec2-base-960h#usage has a usage example which doesn't work anymore with the current datasets implementation. I'll update this now, but we have >1000 wav2vec2 checkpoints on the Hub and we can't update all the model cards.\r\n    => This is a big breaking change with no current solution. For `transformers` breaking changes are one of the biggest complaints.\r\n- Similar to this we also shouldn't assume that there is only one resampling method for Audio. I think it's good to have one offered automatically by `datasets`, but we have to leave the user the freedom to choose her\/his own resampling as well. Resampling can take very different filtering windows and other parameters which are currently somewhat hardcoded in `datasets`, which users might very well want to change.\r\n\r\n\r\n=> IMO, it's a **very** big priority to again have the correct absolute path in non-streaming mode. The other solution of providing a path-like object derived from the bytes stocked in the `.array` file is not nearly as user-friendly, but better than nothing. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3663","title":"[Audio] Path of Common Voice cannot be used for audio loading anymore","comments":"Agree that we need to have access to the original sound files. Few days ago I was looking for these original files because I suspected there is bug in the audio resampling (confirmed in https:\/\/github.com\/huggingface\/datasets\/issues\/3662) and I want to do my own resampling to workaround the bug, which is now not possible anymore due to the unavailability of the original files.","body":"## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":61,"text":"[Audio] Path of Common Voice cannot be used for audio loading anymore \n ## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n \n Agree that we need to have access to the original sound files. Few days ago I was looking for these original files because I suspected there is bug in the audio resampling (confirmed in https:\/\/github.com\/huggingface\/datasets\/issues\/3662) and I want to do my own resampling to workaround the bug, which is now not possible anymore due to the unavailability of the original files.","embeddings":[-0.2235464454,-0.2508069873,0.058177378,0.3293782771,0.1734078377,-0.210495159,0.2099271119,0.1210177094,-0.1521231979,0.3673801422,-0.6560899019,0.3635474443,-0.1372642815,-0.4980185926,0.0886321366,-0.1008791849,-0.110535562,0.1237621456,-0.3077791035,-0.199335143,-0.2052755356,0.3687650859,-0.1148762256,0.3093585372,-0.1245567203,0.1159779355,0.1676184684,0.4149178267,0.1158688739,-0.2309928834,-0.0826062933,-0.1745959967,0.0724799335,0.6048516035,-0.0001101102,-0.0286049843,0.2055482566,-0.0999350771,-0.3643501997,-0.4191497564,-0.0692866668,0.2924094498,-0.2301339358,-0.024117332,-0.0311295036,-0.0969377607,-0.0884031355,-0.5478186607,0.5029036403,0.3794579804,0.2484062612,0.2115845531,-0.0293187089,-0.0384982526,0.4414693415,0.0015787898,0.1894886643,0.3692121506,0.4471860528,0.067873396,0.1744174212,0.3428400755,-0.2924934924,-0.3575713933,-0.0356633663,-0.0551099516,-0.3787844777,-0.1849421114,0.2713564634,0.2820595503,0.2759120464,-0.1342865974,-0.4718924165,-0.1051788703,-0.0500947386,-0.2102209181,0.2397290617,0.1575888246,-0.2833800912,0.1570705175,-0.1872570366,0.0917167366,0.1873201579,0.1229324639,0.2312909812,-0.0606006756,-0.0879521966,0.0724333748,0.4026638269,-0.141367957,-0.1740721464,0.0810490921,0.0969442427,0.3780001998,-0.1745997071,0.2402158231,-0.00908616,-0.1359924674,0.1275611818,-0.0878913477,0.3138513267,0.0054719625,-0.2422680408,0.0009420236,0.0176979136,0.2126087546,-0.2627485394,0.1851064861,0.2996043265,0.3170666695,0.0981552228,-0.0062704426,-0.2567254901,-0.2634004951,-0.196849376,0.2120463848,0.0757382438,-0.2214101255,-0.3402504325,-0.1287789941,-0.0801436901,0.0245100856,0.3346521854,0.4186262786,0.0249187909,0.1850333214,0.4110202491,0.0950763226,-0.1469951719,0.0096325912,-0.0472654253,0.0384198762,-0.2576952279,0.0497570895,0.2392605394,-0.5327596664,0.4773782194,0.1040800437,0.2390337884,-0.236659795,-0.3121516407,0.0719774589,-0.0303936917,0.0169959404,-0.1404670328,0.0817334726,-0.1211216971,0.4287886024,-0.1201233417,0.2123818398,-0.3835169673,-0.3191388845,-0.0386544466,0.1882132739,0.1165881902,0.147714138,0.0313835889,-0.1966530085,-0.0228218641,-0.4181768894,0.1886573136,-0.0551925488,-0.3482072949,-0.0175439864,0.575517416,0.2618656158,-0.2857509553,-0.0680570826,-0.0392274633,0.1123637632,0.0255708545,0.4370591044,-0.2572608292,-0.0529892482,-0.4929912984,0.0699927285,0.331286639,-0.5099335909,-0.3319928348,-0.1324737817,-0.172866866,0.2829922438,0.076280117,-0.2292779833,0.0964037776,-0.1004235521,0.5451257229,0.200269565,0.2241450995,0.0035730046,-0.1326708347,-0.2573148012,-0.2135896981,-0.0093972916,-0.0934992954,0.0356982537,0.0028849428,-0.1048701555,0.298099339,-0.2021240145,-0.0109928697,0.2920259833,-0.0430826358,-0.0670606121,0.0866296515,-0.2228401154,0.0996656641,0.1524570882,-0.3136264384,-0.2470068783,-0.1274873763,-0.1918285489,-0.4758899808,-0.2438409179,-0.2321186513,-0.1560033262,0.0522339717,0.4020837843,-0.2089049071,-0.0757313669,-0.2552022636,0.09051238,-0.1367674768,-0.0767297223,-0.2711431682,0.1984200627,-0.0986899137,0.0210870486,-0.0667712092,0.4917852581,0.2398004234,0.0792957544,-0.1916426271,0.4648348093,-0.0930332914,0.4767717421,-0.3690917194,-0.0836227983,0.149631083,-0.5944237709,0.2219480127,0.2715825737,0.2395667434,0.0414247699,-0.0145127028,0.17845954,-0.057129398,0.1797384024,0.3046351075,-0.2391348928,0.1257806718,0.18972601,-0.1901748329,0.186802879,0.3000095785,0.0067869159,0.5505467057,-0.0417082123,-0.5312463641,-0.1972385943,0.4029199183,-0.1463142186,0.022711752,0.2689433992,-0.0661131814,-0.0044564959,0.0994518399,-0.2597194612,0.3085303903,0.1697524339,0.1947172582,0.1196402386,0.1255621761,0.0228143651,0.2604046464,0.0499075465,-0.141015619,0.5100840926,0.1563070863,0.2774419188,-0.3016078472,-0.2175031453,-0.3143009245,-0.1754392833,-0.5606178045,-0.0849811882,-0.2145432085,0.137810424,-0.3821189106,-0.1320073307,-0.1630068272,0.1208607256,-0.0214181282,0.2565780878,-0.1005272344,0.2896355093,0.1622851491,0.1791319847,0.0134691736,-0.1964534819,-0.0657655373,0.2176677883,-0.330956459,0.0169457924,0.1020021364,-0.2239239365,-0.1831480116,-0.096820794,-0.1114963219,-0.2599877417,0.0076843277,0.1228017136,0.2384566963,-0.0542362817,0.1759352237,-0.0389667153,0.0399885364,-0.5635157228,0.1167161167,-0.051801499,-0.106416814,0.061481487,0.1555188596,0.259151876,-0.1117804796,-0.5187001824,-0.2876343131,-0.5462143421,0.2667261958,-0.4297226965,0.1619964689,0.2694040239,0.0591366254,0.1814007759,-0.115193814,0.493171066,-0.2489960641,-0.1397405267,0.2414619774,-0.1417268962,-0.3955577612,-0.2539163232,-0.035679169,0.3887385726,0.1803068519,-0.1708768904,0.0672760829,0.1570505798,-0.0303559788,-0.0229838155,0.2241770774,0.1567875445,-0.0013685955,-0.0507988147,0.0669092238,-0.2451994568,-0.1624171585,0.1575557292,0.3722218275,0.2120011598,0.3317290843,-0.1729264706,0.9316864014,0.1263894737,0.0822793096,0.2540717721,-0.3123831749,0.3371025026,-0.4183792174,-0.4320434034,-0.1200345159,-0.01209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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3663","title":"[Audio] Path of Common Voice cannot be used for audio loading anymore","comments":"@patrickvonplaten \r\n> The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n\r\nJust to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\n> The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n\r\nI'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 (https:\/\/github.com\/huggingface\/datasets\/pull\/3667#issuecomment-1030090627).\r\n\r\nYour concern is reasonable, but there are situations where we can only serve bytes (see https:\/\/github.com\/huggingface\/datasets\/pull\/3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path\/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n","body":"## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":180,"text":"[Audio] Path of Common Voice cannot be used for audio loading anymore \n ## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n \n @patrickvonplaten \r\n> The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n\r\nJust to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\n> The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n\r\nI'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 (https:\/\/github.com\/huggingface\/datasets\/pull\/3667#issuecomment-1030090627).\r\n\r\nYour concern is reasonable, but there are situations where we can only serve bytes (see https:\/\/github.com\/huggingface\/datasets\/pull\/3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path\/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n","embeddings":[-0.2384493649,-0.249923408,-0.0043615019,0.2015873641,0.3130870759,-0.2325707227,0.3949717879,0.1109661385,-0.0644507557,0.4705464244,-0.3877641559,0.6395120621,-0.213687405,-0.4975003004,-0.0062276511,-0.0945751965,-0.0755985156,0.1740211844,-0.1145132333,-0.1541432738,-0.2636338472,0.2086414546,-0.1397751421,0.1795027703,0.0483521633,0.0622179694,0.212690115,0.38262254,0.0168558471,-0.2787776291,-0.0479773432,-0.1511484832,-0.040366821,0.5485746861,-0.000103644,-0.0686596781,0.4149723053,-0.1180224493,-0.420774579,-0.4706493914,-0.257561028,0.201532498,-0.1253193766,-0.0557803884,0.1043771207,-0.0386890434,0.0438051857,-0.4763795137,0.2947276235,0.4227175713,0.2718324363,0.1440465301,-0.0124979168,-0.0557483882,0.4092701077,0.0327963457,0.0247894228,0.2707351744,0.3393032849,0.1090590283,0.0179207567,0.2124940157,-0.4085491002,-0.0401802659,0.2035200894,0.057842657,-0.2551391721,-0.1210077927,0.2579274774,0.3314177692,0.5015078783,-0.2147116065,-0.1904911399,0.0125537999,-0.0749672502,-0.2999319136,0.2629273832,0.16203475,-0.319586277,0.1104764044,-0.1589100361,0.2322375476,0.0126692653,0.1977043748,0.2113834172,-0.0039224522,-0.0148470085,0.0562498383,0.481030345,-0.1545008719,0.0647260919,-0.0867429972,0.1724595129,0.3594917953,-0.0357575491,0.1891441047,0.0436302088,-0.3162017763,0.2241269648,-0.2670716643,0.4459472299,0.1396840364,-0.3244559467,0.3695639074,0.0312428027,0.1005757675,-0.2271658182,-0.0428416431,0.2930122316,0.2135304809,0.095833689,-0.0111696618,-0.4092290401,-0.2222895026,-0.089971967,0.1320044547,0.0674660429,-0.1199484244,-0.5384296179,-0.0588040873,-0.156704545,0.0195115041,0.2785852551,0.5244032145,0.2296361625,0.2259866744,0.2518940866,0.0986378193,0.0198624991,0.142004773,0.0030089938,-0.0090987096,-0.2314496338,-0.0936208963,0.1506336629,-0.2194308639,0.4100604355,0.0117680468,0.3438124061,-0.2208497524,-0.2051681429,-0.0484843254,0.0772341713,-0.0241616555,0.0015768798,0.1301334947,-0.1770187616,0.4373728037,-0.2404656261,0.3217303753,-0.2469156682,-0.3003751636,0.0586414449,0.249071449,0.181675896,0.0342714265,-0.0007962319,-0.0726203918,-0.1169106513,-0.5039426088,0.0473231673,-0.2089494616,-0.3850365877,-0.1320982873,0.4230646789,0.1201388016,-0.1088225469,-0.0585329682,-0.1071400791,0.0235489234,0.0718948171,0.3258564174,-0.3318327069,-0.0158628114,-0.3536455035,0.0137809953,0.371769309,-0.5226552486,-0.3260345757,0.1277082115,-0.1386220753,0.2258429974,0.079417564,-0.0460036546,0.0333876386,-0.0976119936,0.4942391217,0.5363734365,0.1854413897,0.1339287609,-0.1693513244,-0.3888673186,0.1101832837,0.1094646081,-0.1019731909,-0.0972361416,0.0581837147,0.0207817312,0.3428899646,-0.1975990683,-0.0653522015,0.2130442858,0.1968108416,-0.1765433401,0.0579001792,-0.1368452162,0.0434618779,0.0751973018,0.1156436652,-0.2570050359,-0.0264140349,-0.2239675075,-0.2934289575,-0.1855719835,-0.34913975,0.102959998,0.1244208217,0.3708513379,-0.3051375151,-0.1745317727,-0.2071672529,0.0544803031,0.0324607976,-0.159088254,-0.0746376887,0.2268785238,-0.1935987324,0.08058016,0.0660802647,0.4514498413,0.1673912257,0.0963071957,-0.1907900572,0.4728240967,-0.04283702,0.5302453041,-0.4532773197,0.0200611707,0.1795412004,-0.4725144804,0.2682035267,0.415506959,0.3760184944,0.0117206257,-0.0781175345,0.1243077591,0.0813554004,0.2206584364,0.2310439497,-0.1863463819,0.2294005156,0.1461855769,-0.2119591683,0.202373445,0.2494200617,-0.1787680835,0.4883844554,-0.0032736701,-0.5486312509,-0.0751020387,0.4278264344,-0.0583511479,0.1101359054,0.260705322,-0.1297413856,-0.1277982146,0.0214449782,-0.2432692498,0.2710770667,0.2451897115,0.1167077199,0.0213018451,-0.0039210627,-0.0285791084,0.2961062491,0.1920725852,0.1866515875,0.4540010095,0.0910265297,0.2121794075,-0.2806640863,-0.3479629457,-0.1728038788,-0.0819181725,-0.4462837279,-0.0688102767,-0.2905993164,0.0785999671,-0.3974082768,-0.1609701961,-0.0367223248,0.1226320788,0.0621648803,0.3044417799,-0.2404888719,0.257514149,0.1167644039,0.1699346006,0.1114157513,-0.2203891575,-0.1058726162,0.0821516439,-0.2396018803,0.0996970907,0.2822874784,-0.0810508505,-0.1006473452,0.070941858,-0.047056701,-0.1973879784,0.0485938787,0.1215293109,0.1731450409,-0.086364463,0.1029153615,-0.0719196871,0.1740368009,-0.6421675682,0.1133203655,-0.1736422479,-0.0771260783,0.1729803532,0.1620006263,-0.0474815145,-0.1556614637,-0.586589694,-0.2913160026,-0.5993866324,0.0963748321,-0.4013847411,0.2808516324,0.2140429914,0.0965802222,0.2181031853,0.0082206698,0.428022325,-0.2296697646,0.0572000034,0.2130559981,-0.2096718997,-0.3551926613,-0.1110898331,0.0121481828,0.3766715527,0.1949148923,-0.1259349585,0.0651615784,0.1679739952,-0.1563054472,-0.0364753753,0.0198593549,0.100357011,0.2297852784,-0.0921055377,-0.065677233,-0.2044433057,-0.1743673235,0.1345050335,0.3170948327,0.2954878807,0.4867725372,-0.0348165855,0.698030889,0.0648173466,-0.0015775,0.2130765617,-0.2088526934,0.2108625472,-0.4066536725,-0.2474605441,0.1337408572,0.0025835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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3663","title":"[Audio] Path of Common Voice cannot be used for audio loading anymore","comments":"Related to this discussion: in https:\/\/github.com\/huggingface\/datasets\/pull\/3664#issuecomment-1031866858 I propose how we could change `iter_archive` to work for streaming and also return local paths (as it used too !). I'd love your opinions on this","body":"## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":33,"text":"[Audio] Path of Common Voice cannot be used for audio loading anymore \n ## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n \n Related to this discussion: in https:\/\/github.com\/huggingface\/datasets\/pull\/3664#issuecomment-1031866858 I propose how we could change `iter_archive` to work for streaming and also return local paths (as it used too !). I'd love your opinions on this","embeddings":[-0.4006461799,-0.1878708154,0.1284466833,0.302398026,0.1465085894,-0.3337066174,0.3811359704,0.1097430438,0.0484994873,0.2912321985,-0.472140342,0.4858807325,-0.2812138498,-0.240128696,-0.0488462746,-0.1721187234,-0.1430081576,0.2351566702,-0.2137912363,-0.1515817344,-0.2294130921,0.3312096894,-0.0033002743,0.2660254538,0.10715051,0.079700321,0.2556148767,0.3459969759,-0.0634130985,-0.284401536,-0.0158162173,-0.1862930208,0.1649888009,0.7877656221,-0.0001180319,0.0322492048,0.3535940647,-0.0902248695,-0.4361501336,-0.6335359216,-0.2511949539,0.4254493713,0.0100178989,0.0470020995,0.1018609926,-0.0832428187,-0.0432129167,-0.4303299785,0.1326353997,0.5224964619,0.1566489935,0.3561066687,-0.2794969082,-0.0236564316,0.380272001,0.2182952613,-0.0150249954,0.3261241913,0.2605858445,0.0326177366,0.0273776166,0.2976111174,-0.3224210143,-0.0741953105,0.2155826986,0.0533485524,-0.3381350935,-0.0237012319,0.1344766915,0.25378865,0.5289695263,-0.2242388129,-0.333793819,-0.0965834707,-0.1653074771,-0.3621305823,0.2945831418,0.1191871241,-0.4217838943,0.0696958527,-0.1380294561,0.0867150724,-0.0885024145,0.1281371415,0.1479112655,0.1008287221,-0.055714272,0.1125375405,0.5342321992,0.0335103162,0.1168000922,0.0697644055,0.1773038805,0.1946253479,-0.1423113495,0.1155939251,0.1252596676,-0.0142899612,0.0544435196,-0.0746446401,0.585834682,-0.0228076726,-0.3341450989,0.1945168972,0.1521347612,0.1617316306,-0.1684007347,0.0091683418,0.3793669343,0.3705484569,0.1848657727,-0.178485617,-0.2010128051,-0.0897018909,-0.2588822246,-0.0213362332,0.0532174148,-0.2474553734,-0.3844672441,-0.0949992836,-0.1815351248,-0.1698553562,0.37135607,0.3952997625,0.0980468169,0.2902674973,0.1314212829,0.1773455143,0.0613860786,0.1077280641,0.0342872441,-0.0343237445,-0.2112903744,0.0429772176,0.1265135109,-0.5959817767,0.3969786465,-0.0431193262,0.2971084416,-0.0018686585,-0.2784637511,-0.1119150221,0.2507126927,-0.0680221841,-0.0797815248,0.1994601488,-0.0355540179,0.2385418713,-0.264542222,0.1596010327,-0.1735498458,-0.5409842134,0.0790498555,0.0625752136,0.0205381941,-0.005110411,-0.0212135315,-0.1635636538,-0.1186205447,-0.3908739984,0.167038992,-0.1701210737,-0.5051621199,-0.0413736403,0.4781266153,0.3975670934,-0.2229637057,-0.1678232253,-0.0131551996,0.0309897847,0.0991887748,0.6340601444,-0.3268609047,0.0988404825,-0.4532506466,-0.0229349714,0.3022870421,-0.6025639772,-0.3053236604,0.3402985334,-0.157788679,0.359644711,0.2253461331,-0.150071308,0.0278482214,-0.0839387178,0.3503541648,0.3449782729,0.1597215235,0.0969668701,-0.1604896635,-0.3048454225,0.1578611881,0.0805269331,-0.0010254937,0.1597709954,-0.0463537499,0.0096432492,0.259411186,-0.1821974814,0.0229606647,0.0902386159,0.2802380025,-0.0343505479,0.0986611247,-0.0671851784,0.1066148356,0.0899458677,-0.0022194784,-0.1501861811,-0.0255635679,-0.1076982319,-0.2531234026,-0.0781994611,-0.3794100881,-0.1634327322,-0.0454558916,0.4580377042,-0.1344090104,-0.0216927156,-0.4133028686,0.2251215875,-0.0655682608,-0.1673961431,-0.2994363606,0.3385186791,0.0186602529,0.0853181332,-0.0633473918,0.4890001416,0.1180751845,0.0491427481,-0.0849454328,0.4422448277,-0.318135947,0.5754509568,-0.3141672313,0.1270013005,0.3242458105,-0.4045220315,0.2913877964,0.3321124017,0.252012074,0.0662067533,-0.0745445937,-0.0179696325,-0.0275906213,0.4462329447,0.2376484871,-0.2948025167,0.0699294135,0.1673601121,-0.3602975309,0.2601936162,0.2367823124,-0.1683507413,0.5140911937,0.009198701,-0.5761753917,-0.1246908605,0.3260204494,0.0726037771,-0.0024443578,0.4693034589,-0.103914164,-0.1473156214,0.1469024867,-0.1762161404,0.5176908374,0.0764417872,0.2347855419,0.0893286988,0.0907959044,-0.0649850219,0.3605656624,0.1415732503,-0.0473703556,0.471139282,0.1047656909,0.2150641531,-0.4118264019,-0.2450986207,-0.2518109381,-0.0054932926,-0.4787896276,-0.0440913104,-0.319835484,-0.1430862993,-0.3836460412,-0.1065486595,-0.1685740948,0.0674423203,-0.0853024125,0.4848079085,-0.1093896255,0.1342101991,0.1485206485,0.238375023,-0.0102710994,-0.3050440848,-0.1532896459,0.0360830501,-0.2072962373,-0.0316595249,0.1668955386,-0.1771318763,-0.0907463953,-0.0414614901,-0.2578228116,-0.4607104659,0.0871961191,0.30162251,0.1926195323,-0.1320621222,0.2688616514,0.2151851803,0.1254661083,-0.7165542841,0.1891671121,-0.1316910088,-0.0774944872,0.0265057199,-0.0043882085,0.2947843075,-0.0484204255,-0.437682271,-0.3294979036,-0.6286438704,0.5104750991,-0.3834416568,0.2687637508,0.2755350471,0.0252912175,0.0513894856,-0.1692019999,0.2230201066,-0.2421351373,-0.2965969741,0.275572747,-0.242907241,-0.2840861976,-0.2683320045,0.1857891977,0.4265767038,0.2128242701,-0.3133063018,-0.0500522405,0.1479149014,-0.0198531896,0.1155532375,0.1928610206,0.1763996631,0.1310831457,0.0253811553,0.0520107634,-0.2257397622,0.0078966506,0.2001636475,0.3833730221,0.5774782896,0.6338347793,-0.0171852838,0.6961476803,0.2213217318,0.0566398576,0.4172232449,-0.217632696,0.369367063,-0.456327498,-0.3855545521,0.1795154363,-0.1470762193,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3663","title":"[Audio] Path of Common Voice cannot be used for audio loading anymore","comments":"> @patrickvonplaten\r\n> \r\n> > The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n> \r\n> Just to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\nYes! \r\n\r\n> \r\n> > The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n> \r\n> I'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 ([#3667 (comment)](https:\/\/github.com\/huggingface\/datasets\/pull\/3667#issuecomment-1030090627)). \r\n> Your concern is reasonable, but there are situations where we can only serve bytes (see #3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path\/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n\r\nYes this might be, but I highly doubt that `soundfile` is the go-to library for audio then. @anton-l and I have tried out a bunch of different audio loading libraries (`soundfile`, `librosa`, `torchaudio`, pure `ffmpeg`, `audioread`, ...). One thing that was pretty clear  to me is that there is just no \"de-facto standard\" library and they all have pros and cons. None of the libraries really supports \"batch\"-ed audio loading. Some depend on PyTorch. `torchaudio` is 100x faster (really!) than `librosa's` fallback on MP3. `torchaudio` often has problems with multi-proessing, ... Also we should keep in mind that resampling is similarly not as simple as reading a text file. It's a pretty complex signal processing transform and people very well might want to use special filters, etc...at the moment we just hard-code `torchaudio's` or `librosa's` default filter when doing resampling.\r\n\r\n=> All this to say that we **should definitely** care about whether we rely on local paths or bytes IMO. We don't want to loose all users that are forced to use `datasets` decoding or resampling or have to built a very much not intuitive way of loading bytes into a numpy array. It's much more intuitive to be able to inspect a local file. I feel pretty strongly about this and am happy to also jump on a call. Keeping libraries flexible and lean as well as exposing internals is very important IMO (this philosophy has worked quite well so far with Transformers).\r\n\r\n","body":"## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":436,"text":"[Audio] Path of Common Voice cannot be used for audio loading anymore \n ## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n \n > @patrickvonplaten\r\n> \r\n> > The other solution of providing a path-like object derived from the bytes stocked in the .array file is not nearly as user-friendly, but better than nothing\r\n> \r\n> Just to clarify, here you describe the approach that uses the `Audio.decode` attribute to access the underlying bytes?\r\n\r\nYes! \r\n\r\n> \r\n> > The official example currently doesn't work and we don't even have a workaround for it for MP3 files at the moment\r\n> \r\n> I'd assume this is because we use `sox_io` as a backend for decoding. However, soon we should be able to use `soundfile`, which supports path-like objects, for MP3 ([#3667 (comment)](https:\/\/github.com\/huggingface\/datasets\/pull\/3667#issuecomment-1030090627)). \r\n> Your concern is reasonable, but there are situations where we can only serve bytes (see #3685 for instance). IMO it makes sense to fix the affected datasets for now, but I don't think we should care too much whether we rely on local paths or bytes after soundfile adds support for MP3 as long as our examples work (shouldn't be too hard to update the `map_to_array` functions) and we properly document how to access the underlying path\/bytes for custom decoding (via `ds.cast_column(\"audio\", Audio(decode=False))`).\r\n\r\nYes this might be, but I highly doubt that `soundfile` is the go-to library for audio then. @anton-l and I have tried out a bunch of different audio loading libraries (`soundfile`, `librosa`, `torchaudio`, pure `ffmpeg`, `audioread`, ...). One thing that was pretty clear  to me is that there is just no \"de-facto standard\" library and they all have pros and cons. None of the libraries really supports \"batch\"-ed audio loading. Some depend on PyTorch. `torchaudio` is 100x faster (really!) than `librosa's` fallback on MP3. `torchaudio` often has problems with multi-proessing, ... Also we should keep in mind that resampling is similarly not as simple as reading a text file. It's a pretty complex signal processing transform and people very well might want to use special filters, etc...at the moment we just hard-code `torchaudio's` or `librosa's` default filter when doing resampling.\r\n\r\n=> All this to say that we **should definitely** care about whether we rely on local paths or bytes IMO. We don't want to loose all users that are forced to use `datasets` decoding or resampling or have to built a very much not intuitive way of loading bytes into a numpy array. It's much more intuitive to be able to inspect a local file. I feel pretty strongly about this and am happy to also jump on a call. Keeping libraries flexible and lean as well as exposing internals is very important IMO (this philosophy has worked quite well so far with Transformers).\r\n\r\n","embeddings":[-0.2332063317,-0.2523467243,-0.0081091681,0.1981706321,0.3064034283,-0.2232054174,0.3953648508,0.1080420092,-0.0817282572,0.4623277783,-0.3875496089,0.6493381262,-0.1950623095,-0.5039451122,-0.0033024726,-0.0967950821,-0.0767634735,0.1829169691,-0.1241675466,-0.1620041281,-0.255618304,0.2182803154,-0.1498633921,0.1779648215,0.0606934242,0.0731833875,0.2102116048,0.3769886494,0.0155763468,-0.2737391293,-0.0325760841,-0.1696504205,-0.0501783453,0.5492194891,-0.000102947,-0.0711952671,0.4199832976,-0.1069015861,-0.4190986454,-0.4592362642,-0.2433914691,0.1933731139,-0.1274232864,-0.0624613725,0.107632786,-0.0429241881,0.0352803133,-0.4575997293,0.2951762974,0.4341416657,0.2828570902,0.1593734473,-0.0116190268,-0.0623735785,0.4252146184,0.0194384158,0.0196369793,0.2805384994,0.3094103634,0.1214956567,0.0322004259,0.2039184272,-0.4030435681,-0.0344908163,0.2013940066,0.0558443405,-0.2570273876,-0.1351146996,0.2717606723,0.3273460567,0.4937537313,-0.2101838291,-0.1724919677,0.0195490085,-0.0634949803,-0.2980490923,0.2648139298,0.1622038484,-0.3270856142,0.1016217545,-0.1550291777,0.2354328781,0.034391664,0.1880381256,0.1914430559,0.0063869813,-0.0321788341,0.0687457249,0.4809293449,-0.1478382945,0.0613534413,-0.0840324536,0.1551690251,0.3596088886,-0.0459604599,0.1954321265,0.045850493,-0.3089722991,0.2129941583,-0.2694235444,0.4437986016,0.1241611764,-0.3166840374,0.3567178845,0.0469849184,0.0970346704,-0.2392352074,-0.0284290072,0.2798290849,0.1935779601,0.1047534347,-0.0049257842,-0.3901763856,-0.2358288467,-0.0798973888,0.1270931363,0.0727103278,-0.1239435226,-0.5352805257,-0.0615271591,-0.1607872695,0.0266580861,0.2590009272,0.5112503171,0.2303373218,0.1993984729,0.2404585183,0.1037222296,0.0038277472,0.1496823132,-0.0031402183,-0.0091569414,-0.2300004959,-0.0833584219,0.1358402222,-0.2407841831,0.4071699381,0.0208899044,0.3405945599,-0.2206906676,-0.2005772293,-0.042035833,0.0759554356,-0.0141533064,-0.0008510126,0.1519627273,-0.1758684963,0.4300580621,-0.233249709,0.32510373,-0.2420154959,-0.2868523002,0.0572222099,0.2612059414,0.1787887067,0.0383339636,-0.0024682777,-0.0886950195,-0.0931306183,-0.5085016489,0.066113919,-0.2201270908,-0.3695695102,-0.1272946298,0.4137049317,0.1191348284,-0.1089372858,-0.0529684238,-0.1034791917,0.0307374652,0.0647366196,0.332189858,-0.3417332768,-0.0161696002,-0.3643704355,0.0237231497,0.3838517368,-0.5176237226,-0.3185038865,0.1338870972,-0.14607054,0.2377097011,0.047758881,-0.0535725281,0.0332278162,-0.0934997946,0.4831914604,0.5096592903,0.1974703819,0.129513666,-0.172898531,-0.3674100041,0.1053883508,0.0972316489,-0.1109044403,-0.0929355249,0.0562472939,0.0225115735,0.3351052999,-0.1976041496,-0.0713027641,0.2292013168,0.1764217615,-0.1795431823,0.0685912669,-0.1313091218,0.0564122535,0.0722801685,0.1131141484,-0.2393149137,-0.0135942325,-0.2218701541,-0.2980783284,-0.2016931921,-0.3582271636,0.0800753832,0.1287958175,0.3750946522,-0.3106052279,-0.1692768186,-0.2045575231,0.0626534075,0.0357504673,-0.1651894152,-0.0939096212,0.2139829248,-0.2039276063,0.0876017287,0.0668954626,0.4386648536,0.1709470153,0.0891104937,-0.2033072859,0.4777558148,-0.0428954624,0.5221608877,-0.4595141113,-0.0045928848,0.1743981987,-0.4920390248,0.2599830925,0.4164294302,0.3752251565,0.0055609788,-0.0543057956,0.117128022,0.0848346651,0.2167362869,0.2330079824,-0.1886286288,0.2402204573,0.1532745212,-0.2146715075,0.2161380351,0.2481144369,-0.1733598709,0.4994978011,-0.0090128286,-0.5419604778,-0.0852596685,0.4390204251,-0.0645920187,0.1074316129,0.2585014999,-0.1426354349,-0.1180185229,0.0097076818,-0.2403352112,0.3030273616,0.2378910184,0.1315611154,0.0317249186,-0.011697053,-0.037945576,0.2940672934,0.1876923889,0.1675206721,0.4522863328,0.0989464968,0.2154770941,-0.2905780673,-0.3468568325,-0.1596834362,-0.0866616815,-0.4483430386,-0.0599709488,-0.2873392701,0.082463935,-0.3851674199,-0.1599200815,-0.0396751277,0.1227376983,0.0547221117,0.2932718396,-0.2265932858,0.2587745488,0.1315230578,0.1622203887,0.1189630926,-0.2205778956,-0.1051521301,0.0917067826,-0.2580105066,0.1074119136,0.2655197978,-0.0630938336,-0.1037217453,0.0578721799,-0.0599769503,-0.2157029212,0.0519847758,0.1341681182,0.1616624147,-0.0976136252,0.1276303083,-0.0667769909,0.1713850647,-0.6292068958,0.1084224805,-0.1696010083,-0.0850704312,0.1677167267,0.157895118,-0.0414085165,-0.1794236451,-0.5857207179,-0.2744980752,-0.5987807512,0.1051800027,-0.3950908482,0.273224771,0.2236685902,0.0949149355,0.238594532,0.0064045265,0.4196072221,-0.2400586307,0.063111864,0.2147583663,-0.2214963585,-0.365607053,-0.098613672,0.0124517102,0.3927568495,0.1714221388,-0.134542495,0.0709095076,0.1681662947,-0.1668137908,-0.0557291284,0.0233465005,0.0954780951,0.2192516774,-0.097895205,-0.0656045005,-0.2093141526,-0.1739774197,0.131853953,0.3108267486,0.2685288489,0.4785527885,-0.0600596182,0.6965278387,0.0665715933,0.0028680002,0.2108596116,-0.2164646983,0.2235576212,-0.4187255502,-0.2443578839,0.1261030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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3663","title":"[Audio] Path of Common Voice cannot be used for audio loading anymore","comments":"From https:\/\/github.com\/huggingface\/datasets\/pull\/3736 the Common Voice dataset now gives access to the local audio files as before","body":"## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":16,"text":"[Audio] Path of Common Voice cannot be used for audio loading anymore \n ## Describe the bug\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchaudio import load\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\n# both of the following commands fail at the moment\r\nload(ds[0][\"audio\"][\"path\"])\r\nload(ds[0][\"path\"])\r\n```\r\n\r\n## Expected results\r\n\r\nThe path should be the complete absolute path to the downloaded audio file not some relative path.\r\n\r\n\r\n## Actual results\r\n\r\n```bash\r\n~\/hugging_face\/venv_3.9\/lib\/python3.9\/site-packages\/torchaudio\/backend\/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    150                 filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    151         filepath = os.fspath(filepath)\r\n--> 152     return torch.ops.torchaudio.sox_io_load_audio_file(\r\n    153         filepath, frame_offset, num_frames, normalize, channels_first, format)\r\n    154\r\n\r\nRuntimeError: Error loading audio file: failed to open file cv-corpus-6.1-2020-12-11\/ab\/clips\/common_voice_ab_19904194.mp3\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.3.dev0\r\n- Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.1\r\n- PyArrow version: 3.0.0\r\n\r\n \n From https:\/\/github.com\/huggingface\/datasets\/pull\/3736 the Common Voice dataset now gives access to the local audio files as 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3662","title":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates","comments":"Thanks @lhoestq for finding the reason of incorrect resampling. This issue affects all languages which have sound files with different sampling rates such as Turkish and Luganda.","body":"The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180","comment_length":27,"text":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates \n The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180 \n Thanks @lhoestq for finding the reason of incorrect resampling. This issue affects all languages which have sound files with different sampling rates such as Turkish and Luganda.","embeddings":[-0.1460941732,0.0788261071,-0.0020536396,0.2036605328,0.1585872471,-0.1734847575,-0.1209071651,0.0837721154,-0.3985756934,0.2145069093,-0.4509893656,0.3006876111,0.1822164506,-0.4004628658,-0.622135818,-0.1660756618,-0.0155579252,0.1286703199,-0.2838031054,-0.0721553564,-0.2328843474,0.258531481,-0.2964225411,-0.1001581475,-0.0362958349,0.4119768441,0.1832629889,-0.2388220876,-0.0896096677,-0.1828368753,0.022568671,0.1056201756,0.1876092702,0.3837061822,-0.0001115617,-0.0865773633,0.1827040762,-0.1413338482,-0.0855505168,-0.2254209965,-0.1185976267,-0.1173252314,-0.4114627242,0.0647974163,-0.2249769866,-0.3062074482,-0.297401011,-0.4533960819,0.2612646818,0.1298217773,0.2027457803,0.1092380509,-0.3005739152,0.0564740188,0.2202183306,-0.0268500187,0.0419951193,0.2339995354,0.4132256508,0.2830609381,-0.3060887754,0.1806107312,-0.3838153481,0.2093024999,0.131767422,-0.2394376546,-0.0508891866,-0.3169384599,0.2317393273,0.2749604285,0.453391403,-0.0501742326,-0.3614310026,-0.1169934198,-0.1323623955,-0.2589998543,0.2301852256,0.1991679519,-0.0810558051,0.048190698,-0.0801890939,0.2605890036,0.2387397587,0.1177090779,-0.1058367491,0.2852226198,-0.2396839112,0.2262996733,0.2805168033,-0.1046322435,0.0298784152,-0.0125860563,0.0387350097,0.267775774,-0.3461430669,-0.1364609897,-0.1750180572,-0.1891794205,0.1515023708,0.3177064359,0.0006362318,0.0798467323,0.1731051654,0.0363282524,0.2438846081,-0.3282821476,-0.1916962713,-0.061297778,0.2681612074,0.1488363296,-0.0764216706,-0.0112151001,0.4847250581,-0.6192810535,-0.1885357499,0.2291862667,0.0610712953,-0.2351725698,-0.4075015783,0.0935173556,-0.3637566864,-0.0894750655,-0.048844099,-0.1574319899,0.1243287101,0.4777147472,-0.0492312536,0.5173670053,-0.3142126799,-0.1295961887,0.0802226961,-0.3812759221,0.0756463408,0.146761924,0.1043199599,-0.021064451,0.38318187,0.7153710127,0.088660121,-0.3186173141,0.0258031674,-0.1003806815,-0.1569891572,0.0509377755,-0.0314881913,0.5028655529,-0.2180861086,0.3937728405,-0.1839671135,0.2689741254,-0.3671022654,0.3006893992,0.071424678,0.1599383801,-0.1294512749,-0.0140234493,0.1251783669,0.1479975134,0.1298280656,-0.4347710311,0.0947998092,-0.2129634172,-0.4563727379,-0.1075830758,0.2338129729,0.1505531371,-0.1144355685,-0.0234337822,0.0930787399,0.1790628433,0.4453189969,0.2048745006,-0.0703657046,-0.2897978127,-0.2515300512,-0.0347417481,0.2534614205,-0.180833444,-0.5505566001,-0.0580801703,0.1901846379,0.2057768852,0.0117612882,0.1280459315,0.2748425901,-0.2938296199,-0.3297542036,0.6382164955,-0.1201267466,0.1216582507,-0.1949240863,-0.1624276042,0.1361429393,-0.0936813727,-0.0299504399,0.105959788,-0.1979262978,-0.1944111288,0.3539228141,0.0381709039,0.001933297,0.1785877198,-0.1918264776,0.0499293581,0.1376502961,-0.2023392171,0.407315731,0.1041942239,-0.2863277197,0.0086529832,0.0034862692,0.145270437,-0.1031722724,-0.4828282893,-0.2956618667,-0.0365349762,0.0698616728,0.387211591,-0.1795106083,0.066280432,0.1159474775,-0.1755527407,0.2229754776,0.0803622305,-0.1613960266,0.1296748817,0.1960100681,-0.0775408521,0.0834837109,-0.0000226977,0.2714771926,0.1604035646,-0.4371697903,0.3239318132,0.2681857646,0.3521743119,-0.4519734979,-0.0659163594,0.1981453598,-0.0265590362,-0.0086656325,0.1851561219,0.1349373907,-0.1394756883,0.3535216749,0.5703448653,0.1618211418,0.3104351461,0.007683794,-0.01212731,0.2110380828,0.029355906,-0.2627797723,0.0303133633,0.0032012807,0.1242183447,0.1997937858,-0.0261691101,0.0377093591,0.0056896396,0.2928415537,-0.0485121533,-0.1703531742,-0.0230347179,-0.1730762422,0.0501041599,-0.1326348931,0.3048569262,0.4331973791,0.067299664,0.2554117739,-0.1042873859,0.0014171388,-0.0915935859,-0.0000976337,0.2825237513,-0.1580266804,0.3164250553,-0.0284761339,0.0422196239,-0.1200731024,0.0117761623,-0.0273053609,0.0009411707,-0.1936518848,0.0489429124,-0.4558529854,0.0396497771,-0.5908758044,-0.597895503,-0.3436766267,-0.1736394763,-0.0826289281,0.4724974334,-0.3936939538,0.2770261765,0.1918892115,-0.0742349252,-0.1328110993,0.118445836,0.1960740238,0.2347589433,-0.2547200024,-0.0279964339,0.334748596,-0.0996989161,0.1346578896,-0.177738741,-0.2194124609,0.0103004631,-0.015392852,0.0347982273,0.1908388734,0.2926647663,-0.3275520205,-0.0327284634,-0.0138635868,-0.3070710599,0.270888716,0.1644405574,0.1271026433,0.0566697828,0.1360233426,0.3126129508,0.0129454266,-0.2508625388,-0.0889688358,-0.3013151586,-0.4376185536,-0.1687079668,-0.0113104163,0.0368866809,0.052912008,-0.2219525278,0.0304224454,0.3742418289,-0.4413391948,-0.1902925819,0.2083319277,-0.0685583353,-0.2716111243,-0.2280297726,0.1437895298,0.1209190637,0.3398850858,-0.0414449759,0.0580531582,-0.2839697599,-0.100126259,-0.1656562835,0.2310043871,-0.0249808412,-0.0702847615,-0.0085627064,-0.1171208322,0.0337181203,-0.0777354017,0.033463113,0.3657099009,-0.0960723311,0.088313587,-0.1777734458,0.5676959157,0.2875253856,0.0004212977,0.2202279866,0.0448365845,-0.0388974287,-0.157064274,0.0999374762,0.1248698607,-0.082896009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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3662","title":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates","comments":"@cahya-wirawan - do you know how many languages have different sampling rates in Common Voice? I'm quite surprised to see this for multiple languages actually","body":"The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180","comment_length":25,"text":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates \n The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180 \n @cahya-wirawan - do you know how many languages have different sampling rates in Common Voice? I'm quite surprised to see this for multiple languages actually","embeddings":[-0.1460941732,0.0788261071,-0.0020536396,0.2036605328,0.1585872471,-0.1734847575,-0.1209071651,0.0837721154,-0.3985756934,0.2145069093,-0.4509893656,0.3006876111,0.1822164506,-0.4004628658,-0.622135818,-0.1660756618,-0.0155579252,0.1286703199,-0.2838031054,-0.0721553564,-0.2328843474,0.258531481,-0.2964225411,-0.1001581475,-0.0362958349,0.4119768441,0.1832629889,-0.2388220876,-0.0896096677,-0.1828368753,0.022568671,0.1056201756,0.1876092702,0.3837061822,-0.0001115617,-0.0865773633,0.1827040762,-0.1413338482,-0.0855505168,-0.2254209965,-0.1185976267,-0.1173252314,-0.4114627242,0.0647974163,-0.2249769866,-0.3062074482,-0.297401011,-0.4533960819,0.2612646818,0.1298217773,0.2027457803,0.1092380509,-0.3005739152,0.0564740188,0.2202183306,-0.0268500187,0.0419951193,0.2339995354,0.4132256508,0.2830609381,-0.3060887754,0.1806107312,-0.3838153481,0.2093024999,0.131767422,-0.2394376546,-0.0508891866,-0.3169384599,0.2317393273,0.2749604285,0.453391403,-0.0501742326,-0.3614310026,-0.1169934198,-0.1323623955,-0.2589998543,0.2301852256,0.1991679519,-0.0810558051,0.048190698,-0.0801890939,0.2605890036,0.2387397587,0.1177090779,-0.1058367491,0.2852226198,-0.2396839112,0.2262996733,0.2805168033,-0.1046322435,0.0298784152,-0.0125860563,0.0387350097,0.267775774,-0.3461430669,-0.1364609897,-0.1750180572,-0.1891794205,0.1515023708,0.3177064359,0.0006362318,0.0798467323,0.1731051654,0.0363282524,0.2438846081,-0.3282821476,-0.1916962713,-0.061297778,0.2681612074,0.1488363296,-0.0764216706,-0.0112151001,0.4847250581,-0.6192810535,-0.1885357499,0.2291862667,0.0610712953,-0.2351725698,-0.4075015783,0.0935173556,-0.3637566864,-0.0894750655,-0.048844099,-0.1574319899,0.1243287101,0.4777147472,-0.0492312536,0.5173670053,-0.3142126799,-0.1295961887,0.0802226961,-0.3812759221,0.0756463408,0.146761924,0.1043199599,-0.021064451,0.38318187,0.7153710127,0.088660121,-0.3186173141,0.0258031674,-0.1003806815,-0.1569891572,0.0509377755,-0.0314881913,0.5028655529,-0.2180861086,0.3937728405,-0.1839671135,0.2689741254,-0.3671022654,0.3006893992,0.071424678,0.1599383801,-0.1294512749,-0.0140234493,0.1251783669,0.1479975134,0.1298280656,-0.4347710311,0.0947998092,-0.2129634172,-0.4563727379,-0.1075830758,0.2338129729,0.1505531371,-0.1144355685,-0.0234337822,0.0930787399,0.1790628433,0.4453189969,0.2048745006,-0.0703657046,-0.2897978127,-0.2515300512,-0.0347417481,0.2534614205,-0.180833444,-0.5505566001,-0.0580801703,0.1901846379,0.2057768852,0.0117612882,0.1280459315,0.2748425901,-0.2938296199,-0.3297542036,0.6382164955,-0.1201267466,0.1216582507,-0.1949240863,-0.1624276042,0.1361429393,-0.0936813727,-0.0299504399,0.105959788,-0.1979262978,-0.1944111288,0.3539228141,0.0381709039,0.001933297,0.1785877198,-0.1918264776,0.0499293581,0.1376502961,-0.2023392171,0.407315731,0.1041942239,-0.2863277197,0.0086529832,0.0034862692,0.145270437,-0.1031722724,-0.4828282893,-0.2956618667,-0.0365349762,0.0698616728,0.387211591,-0.1795106083,0.066280432,0.1159474775,-0.1755527407,0.2229754776,0.0803622305,-0.1613960266,0.1296748817,0.1960100681,-0.0775408521,0.0834837109,-0.0000226977,0.2714771926,0.1604035646,-0.4371697903,0.3239318132,0.2681857646,0.3521743119,-0.4519734979,-0.0659163594,0.1981453598,-0.0265590362,-0.0086656325,0.1851561219,0.1349373907,-0.1394756883,0.3535216749,0.5703448653,0.1618211418,0.3104351461,0.007683794,-0.01212731,0.2110380828,0.029355906,-0.2627797723,0.0303133633,0.0032012807,0.1242183447,0.1997937858,-0.0261691101,0.0377093591,0.0056896396,0.2928415537,-0.0485121533,-0.1703531742,-0.0230347179,-0.1730762422,0.0501041599,-0.1326348931,0.3048569262,0.4331973791,0.067299664,0.2554117739,-0.1042873859,0.0014171388,-0.0915935859,-0.0000976337,0.2825237513,-0.1580266804,0.3164250553,-0.0284761339,0.0422196239,-0.1200731024,0.0117761623,-0.0273053609,0.0009411707,-0.1936518848,0.0489429124,-0.4558529854,0.0396497771,-0.5908758044,-0.597895503,-0.3436766267,-0.1736394763,-0.0826289281,0.4724974334,-0.3936939538,0.2770261765,0.1918892115,-0.0742349252,-0.1328110993,0.118445836,0.1960740238,0.2347589433,-0.2547200024,-0.0279964339,0.334748596,-0.0996989161,0.1346578896,-0.177738741,-0.2194124609,0.0103004631,-0.015392852,0.0347982273,0.1908388734,0.2926647663,-0.3275520205,-0.0327284634,-0.0138635868,-0.3070710599,0.270888716,0.1644405574,0.1271026433,0.0566697828,0.1360233426,0.3126129508,0.0129454266,-0.2508625388,-0.0889688358,-0.3013151586,-0.4376185536,-0.1687079668,-0.0113104163,0.0368866809,0.052912008,-0.2219525278,0.0304224454,0.3742418289,-0.4413391948,-0.1902925819,0.2083319277,-0.0685583353,-0.2716111243,-0.2280297726,0.1437895298,0.1209190637,0.3398850858,-0.0414449759,0.0580531582,-0.2839697599,-0.100126259,-0.1656562835,0.2310043871,-0.0249808412,-0.0702847615,-0.0085627064,-0.1171208322,0.0337181203,-0.0777354017,0.033463113,0.3657099009,-0.0960723311,0.088313587,-0.1777734458,0.5676959157,0.2875253856,0.0004212977,0.2202279866,0.0448365845,-0.0388974287,-0.157064274,0.0999374762,0.1248698607,-0.082896009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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3662","title":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates","comments":"@cahya-wirawan, I can reproduce the problem for Common Voice 7 for Turkish. Here a script you can use:\r\n\r\n\r\n```python\r\n#!\/usr\/bin\/env python3\r\nfrom datasets import load_dataset\r\nimport torchaudio\r\nfrom io import BytesIO\r\nfrom datasets import Audio\r\nfrom collections import Counter\r\nimport sys\r\n\r\nds_name = str(sys.argv[1])\r\nlang = str(sys.argv[2])\r\n\r\nds = load_dataset(ds_name, lang, split=\"train\", use_auth_token=True)\r\nds = ds.cast_column(\"audio\", Audio(decode=False))\r\n\r\nall_sampling_rates = []\r\n\r\n\r\ndef print_sampling_rate(x):\r\n    x, sr = torchaudio.load(BytesIO(x[\"audio\"][\"bytes\"]), format=\"mp3\")\r\n    all_sampling_rates.append(sr)\r\n\r\nds.map(print_sampling_rate)\r\n\r\n\r\nprint(Counter(all_sampling_rates))\r\n```\r\n\r\ncan be run with:\r\n\r\n```bash\r\npython run.py mozilla-foundation\/common_voice_7_0 tr\r\n```\r\n\r\nFor CV 6.1 all samples seem to have the same audio","body":"The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180","comment_length":92,"text":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates \n The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180 \n @cahya-wirawan, I can reproduce the problem for Common Voice 7 for Turkish. Here a script you can use:\r\n\r\n\r\n```python\r\n#!\/usr\/bin\/env python3\r\nfrom datasets import load_dataset\r\nimport torchaudio\r\nfrom io import BytesIO\r\nfrom datasets import Audio\r\nfrom collections import Counter\r\nimport sys\r\n\r\nds_name = str(sys.argv[1])\r\nlang = str(sys.argv[2])\r\n\r\nds = load_dataset(ds_name, lang, split=\"train\", use_auth_token=True)\r\nds = ds.cast_column(\"audio\", Audio(decode=False))\r\n\r\nall_sampling_rates = []\r\n\r\n\r\ndef print_sampling_rate(x):\r\n    x, sr = torchaudio.load(BytesIO(x[\"audio\"][\"bytes\"]), format=\"mp3\")\r\n    all_sampling_rates.append(sr)\r\n\r\nds.map(print_sampling_rate)\r\n\r\n\r\nprint(Counter(all_sampling_rates))\r\n```\r\n\r\ncan be run with:\r\n\r\n```bash\r\npython run.py mozilla-foundation\/common_voice_7_0 tr\r\n```\r\n\r\nFor CV 6.1 all samples seem to have the same 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3662","title":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates","comments":"It actually shows that many more samples are in 32kHz format than it 48kHz which is unexpected. Thanks a lot for flagging! Will contact Common Voice about this as well","body":"The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180","comment_length":30,"text":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates \n The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180 \n It actually shows that many more samples are in 32kHz format than it 48kHz which is unexpected. Thanks a lot for flagging! Will contact Common Voice about this as well","embeddings":[-0.1460941732,0.0788261071,-0.0020536396,0.2036605328,0.1585872471,-0.1734847575,-0.1209071651,0.0837721154,-0.3985756934,0.2145069093,-0.4509893656,0.3006876111,0.1822164506,-0.4004628658,-0.622135818,-0.1660756618,-0.0155579252,0.1286703199,-0.2838031054,-0.0721553564,-0.2328843474,0.258531481,-0.2964225411,-0.1001581475,-0.0362958349,0.4119768441,0.1832629889,-0.2388220876,-0.0896096677,-0.1828368753,0.022568671,0.1056201756,0.1876092702,0.3837061822,-0.0001115617,-0.0865773633,0.1827040762,-0.1413338482,-0.0855505168,-0.2254209965,-0.1185976267,-0.1173252314,-0.4114627242,0.0647974163,-0.2249769866,-0.3062074482,-0.297401011,-0.4533960819,0.2612646818,0.1298217773,0.2027457803,0.1092380509,-0.3005739152,0.0564740188,0.2202183306,-0.0268500187,0.0419951193,0.2339995354,0.4132256508,0.2830609381,-0.3060887754,0.1806107312,-0.3838153481,0.2093024999,0.131767422,-0.2394376546,-0.0508891866,-0.3169384599,0.2317393273,0.2749604285,0.453391403,-0.0501742326,-0.3614310026,-0.1169934198,-0.1323623955,-0.2589998543,0.2301852256,0.1991679519,-0.0810558051,0.048190698,-0.0801890939,0.2605890036,0.2387397587,0.1177090779,-0.1058367491,0.2852226198,-0.2396839112,0.2262996733,0.2805168033,-0.1046322435,0.0298784152,-0.0125860563,0.0387350097,0.267775774,-0.3461430669,-0.1364609897,-0.1750180572,-0.1891794205,0.1515023708,0.3177064359,0.0006362318,0.0798467323,0.1731051654,0.0363282524,0.2438846081,-0.3282821476,-0.1916962713,-0.061297778,0.2681612074,0.1488363296,-0.0764216706,-0.0112151001,0.4847250581,-0.6192810535,-0.1885357499,0.2291862667,0.0610712953,-0.2351725698,-0.4075015783,0.0935173556,-0.3637566864,-0.0894750655,-0.048844099,-0.1574319899,0.1243287101,0.4777147472,-0.0492312536,0.5173670053,-0.3142126799,-0.1295961887,0.0802226961,-0.3812759221,0.0756463408,0.146761924,0.1043199599,-0.021064451,0.38318187,0.7153710127,0.088660121,-0.3186173141,0.0258031674,-0.1003806815,-0.1569891572,0.0509377755,-0.0314881913,0.5028655529,-0.2180861086,0.3937728405,-0.1839671135,0.2689741254,-0.3671022654,0.3006893992,0.071424678,0.1599383801,-0.1294512749,-0.0140234493,0.1251783669,0.1479975134,0.1298280656,-0.4347710311,0.0947998092,-0.2129634172,-0.4563727379,-0.1075830758,0.2338129729,0.1505531371,-0.1144355685,-0.0234337822,0.0930787399,0.1790628433,0.4453189969,0.2048745006,-0.0703657046,-0.2897978127,-0.2515300512,-0.0347417481,0.2534614205,-0.180833444,-0.5505566001,-0.0580801703,0.1901846379,0.2057768852,0.0117612882,0.1280459315,0.2748425901,-0.2938296199,-0.3297542036,0.6382164955,-0.1201267466,0.1216582507,-0.1949240863,-0.1624276042,0.1361429393,-0.0936813727,-0.0299504399,0.105959788,-0.1979262978,-0.1944111288,0.3539228141,0.0381709039,0.001933297,0.1785877198,-0.1918264776,0.0499293581,0.1376502961,-0.2023392171,0.407315731,0.1041942239,-0.2863277197,0.0086529832,0.0034862692,0.145270437,-0.1031722724,-0.4828282893,-0.2956618667,-0.0365349762,0.0698616728,0.387211591,-0.1795106083,0.066280432,0.1159474775,-0.1755527407,0.2229754776,0.0803622305,-0.1613960266,0.1296748817,0.1960100681,-0.0775408521,0.0834837109,-0.0000226977,0.2714771926,0.1604035646,-0.4371697903,0.3239318132,0.2681857646,0.3521743119,-0.4519734979,-0.0659163594,0.1981453598,-0.0265590362,-0.0086656325,0.1851561219,0.1349373907,-0.1394756883,0.3535216749,0.5703448653,0.1618211418,0.3104351461,0.007683794,-0.01212731,0.2110380828,0.029355906,-0.2627797723,0.0303133633,0.0032012807,0.1242183447,0.1997937858,-0.0261691101,0.0377093591,0.0056896396,0.2928415537,-0.0485121533,-0.1703531742,-0.0230347179,-0.1730762422,0.0501041599,-0.1326348931,0.3048569262,0.4331973791,0.067299664,0.2554117739,-0.1042873859,0.0014171388,-0.0915935859,-0.0000976337,0.2825237513,-0.1580266804,0.3164250553,-0.0284761339,0.0422196239,-0.1200731024,0.0117761623,-0.0273053609,0.0009411707,-0.1936518848,0.0489429124,-0.4558529854,0.0396497771,-0.5908758044,-0.597895503,-0.3436766267,-0.1736394763,-0.0826289281,0.4724974334,-0.3936939538,0.2770261765,0.1918892115,-0.0742349252,-0.1328110993,0.118445836,0.1960740238,0.2347589433,-0.2547200024,-0.0279964339,0.334748596,-0.0996989161,0.1346578896,-0.177738741,-0.2194124609,0.0103004631,-0.015392852,0.0347982273,0.1908388734,0.2926647663,-0.3275520205,-0.0327284634,-0.0138635868,-0.3070710599,0.270888716,0.1644405574,0.1271026433,0.0566697828,0.1360233426,0.3126129508,0.0129454266,-0.2508625388,-0.0889688358,-0.3013151586,-0.4376185536,-0.1687079668,-0.0113104163,0.0368866809,0.052912008,-0.2219525278,0.0304224454,0.3742418289,-0.4413391948,-0.1902925819,0.2083319277,-0.0685583353,-0.2716111243,-0.2280297726,0.1437895298,0.1209190637,0.3398850858,-0.0414449759,0.0580531582,-0.2839697599,-0.100126259,-0.1656562835,0.2310043871,-0.0249808412,-0.0702847615,-0.0085627064,-0.1171208322,0.0337181203,-0.0777354017,0.033463113,0.3657099009,-0.0960723311,0.088313587,-0.1777734458,0.5676959157,0.2875253856,0.0004212977,0.2202279866,0.0448365845,-0.0388974287,-0.157064274,0.0999374762,0.1248698607,-0.0828960091,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3662","title":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates","comments":"I only checked the CV 7.0 for Turkish, Luganda and Indonesian, they have audio files with difference sampling rates, and all of them are affected by this issue. Percentage of incorrect resampling as follow, Turkish: 9.1%, Luganda: 88.2% and Indonesian: 64.1%.\r\nI checked it using the original CV files. I check the original sampling rates and the length of audio array of each files and compare it with the length of audio array (and the sampling rate which is always 48kHz) from mozilla-foundation\/common_voice_7_0 datasets. if the length of audio array from dataset is not equal to 48kHz\/original sampling rate * length of audio array of the original audio file then it is affected,","body":"The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180","comment_length":113,"text":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates \n The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180 \n I only checked the CV 7.0 for Turkish, Luganda and Indonesian, they have audio files with difference sampling rates, and all of them are affected by this issue. Percentage of incorrect resampling as follow, Turkish: 9.1%, Luganda: 88.2% and Indonesian: 64.1%.\r\nI checked it using the original CV files. I check the original sampling rates and the length of audio array of each files and compare it with the length of audio array (and the sampling rate which is always 48kHz) from mozilla-foundation\/common_voice_7_0 datasets. if the length of audio array from dataset is not equal to 48kHz\/original sampling rate * length of audio array of the original audio file then it is affected,","embeddings":[-0.1460941732,0.0788261071,-0.0020536396,0.2036605328,0.1585872471,-0.1734847575,-0.1209071651,0.0837721154,-0.3985756934,0.2145069093,-0.4509893656,0.3006876111,0.1822164506,-0.4004628658,-0.622135818,-0.1660756618,-0.0155579252,0.1286703199,-0.2838031054,-0.0721553564,-0.2328843474,0.258531481,-0.2964225411,-0.1001581475,-0.0362958349,0.4119768441,0.1832629889,-0.2388220876,-0.0896096677,-0.1828368753,0.022568671,0.1056201756,0.1876092702,0.3837061822,-0.0001115617,-0.0865773633,0.1827040762,-0.1413338482,-0.0855505168,-0.2254209965,-0.1185976267,-0.1173252314,-0.4114627242,0.0647974163,-0.2249769866,-0.3062074482,-0.297401011,-0.4533960819,0.2612646818,0.1298217773,0.2027457803,0.1092380509,-0.3005739152,0.0564740188,0.2202183306,-0.0268500187,0.0419951193,0.2339995354,0.4132256508,0.2830609381,-0.3060887754,0.1806107312,-0.3838153481,0.2093024999,0.131767422,-0.2394376546,-0.0508891866,-0.3169384599,0.2317393273,0.2749604285,0.453391403,-0.0501742326,-0.3614310026,-0.1169934198,-0.1323623955,-0.2589998543,0.2301852256,0.1991679519,-0.0810558051,0.048190698,-0.0801890939,0.2605890036,0.2387397587,0.1177090779,-0.1058367491,0.2852226198,-0.2396839112,0.2262996733,0.2805168033,-0.1046322435,0.0298784152,-0.0125860563,0.0387350097,0.267775774,-0.3461430669,-0.1364609897,-0.1750180572,-0.1891794205,0.1515023708,0.3177064359,0.0006362318,0.0798467323,0.1731051654,0.0363282524,0.2438846081,-0.3282821476,-0.1916962713,-0.061297778,0.2681612074,0.1488363296,-0.0764216706,-0.0112151001,0.4847250581,-0.6192810535,-0.1885357499,0.2291862667,0.0610712953,-0.2351725698,-0.4075015783,0.0935173556,-0.3637566864,-0.0894750655,-0.048844099,-0.1574319899,0.1243287101,0.4777147472,-0.0492312536,0.5173670053,-0.3142126799,-0.1295961887,0.0802226961,-0.3812759221,0.0756463408,0.146761924,0.1043199599,-0.021064451,0.38318187,0.7153710127,0.088660121,-0.3186173141,0.0258031674,-0.1003806815,-0.1569891572,0.0509377755,-0.0314881913,0.5028655529,-0.2180861086,0.3937728405,-0.1839671135,0.2689741254,-0.3671022654,0.3006893992,0.071424678,0.1599383801,-0.1294512749,-0.0140234493,0.1251783669,0.1479975134,0.1298280656,-0.4347710311,0.0947998092,-0.2129634172,-0.4563727379,-0.1075830758,0.2338129729,0.1505531371,-0.1144355685,-0.0234337822,0.0930787399,0.1790628433,0.4453189969,0.2048745006,-0.0703657046,-0.2897978127,-0.2515300512,-0.0347417481,0.2534614205,-0.180833444,-0.5505566001,-0.0580801703,0.1901846379,0.2057768852,0.0117612882,0.1280459315,0.2748425901,-0.2938296199,-0.3297542036,0.6382164955,-0.1201267466,0.1216582507,-0.1949240863,-0.1624276042,0.1361429393,-0.0936813727,-0.0299504399,0.105959788,-0.1979262978,-0.1944111288,0.3539228141,0.0381709039,0.001933297,0.1785877198,-0.1918264776,0.0499293581,0.1376502961,-0.2023392171,0.407315731,0.1041942239,-0.2863277197,0.0086529832,0.0034862692,0.145270437,-0.1031722724,-0.4828282893,-0.2956618667,-0.0365349762,0.0698616728,0.387211591,-0.1795106083,0.066280432,0.1159474775,-0.1755527407,0.2229754776,0.0803622305,-0.1613960266,0.1296748817,0.1960100681,-0.0775408521,0.0834837109,-0.0000226977,0.2714771926,0.1604035646,-0.4371697903,0.3239318132,0.2681857646,0.3521743119,-0.4519734979,-0.0659163594,0.1981453598,-0.0265590362,-0.0086656325,0.1851561219,0.1349373907,-0.1394756883,0.3535216749,0.5703448653,0.1618211418,0.3104351461,0.007683794,-0.01212731,0.2110380828,0.029355906,-0.2627797723,0.0303133633,0.0032012807,0.1242183447,0.1997937858,-0.0261691101,0.0377093591,0.0056896396,0.2928415537,-0.0485121533,-0.1703531742,-0.0230347179,-0.1730762422,0.0501041599,-0.1326348931,0.3048569262,0.4331973791,0.067299664,0.2554117739,-0.1042873859,0.0014171388,-0.0915935859,-0.0000976337,0.2825237513,-0.1580266804,0.3164250553,-0.0284761339,0.0422196239,-0.1200731024,0.0117761623,-0.0273053609,0.0009411707,-0.1936518848,0.0489429124,-0.4558529854,0.0396497771,-0.5908758044,-0.597895503,-0.3436766267,-0.1736394763,-0.0826289281,0.4724974334,-0.3936939538,0.2770261765,0.1918892115,-0.0742349252,-0.1328110993,0.118445836,0.1960740238,0.2347589433,-0.2547200024,-0.0279964339,0.334748596,-0.0996989161,0.1346578896,-0.177738741,-0.2194124609,0.0103004631,-0.015392852,0.0347982273,0.1908388734,0.2926647663,-0.3275520205,-0.0327284634,-0.0138635868,-0.3070710599,0.270888716,0.1644405574,0.1271026433,0.0566697828,0.1360233426,0.3126129508,0.0129454266,-0.2508625388,-0.0889688358,-0.3013151586,-0.4376185536,-0.1687079668,-0.0113104163,0.0368866809,0.052912008,-0.2219525278,0.0304224454,0.3742418289,-0.4413391948,-0.1902925819,0.2083319277,-0.0685583353,-0.2716111243,-0.2280297726,0.1437895298,0.1209190637,0.3398850858,-0.0414449759,0.0580531582,-0.2839697599,-0.100126259,-0.1656562835,0.2310043871,-0.0249808412,-0.0702847615,-0.0085627064,-0.1171208322,0.0337181203,-0.0777354017,0.033463113,0.3657099009,-0.0960723311,0.088313587,-0.1777734458,0.5676959157,0.2875253856,0.0004212977,0.2202279866,0.0448365845,-0.0388974287,-0.157064274,0.0999374762,0.1248698607,-0.08289600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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3662","title":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates","comments":"Ok wow, thanks a lot for checking this - you've found a pretty big bug :sweat_smile: It seems like **a lot** more datasets are actually affected than I original thought. We'll try to solve this as soon as possible and make an announcement tomorrow.","body":"The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180","comment_length":44,"text":"[Audio] MP3 resampling is incorrect when dataset's audio files have different sampling rates \n The Audio feature resampler for MP3 gets stuck with the first original frequencies it meets, which leads to subsequent decoding to be incorrect.\r\n\r\nHere is a code to reproduce the issue:\r\n\r\nLet's first consider two audio files with different sampling rates 32000 and 16000:\r\n```python\r\n# first download a mp3 file with sampling_rate=32000\r\n!wget https:\/\/file-examples-com.github.io\/uploads\/2017\/11\/file_example_MP3_700KB.mp3\r\n\r\nimport torchaudio\r\n\r\naudio_path = \"file_example_MP3_700KB.mp3\"\r\naudio_path2 = audio_path.replace(\".mp3\", \"_resampled.mp3\")\r\nresample = torchaudio.transforms.Resample(32000, 16000)  # create a new file with sampling_rate=16000\r\ntorchaudio.save(audio_path2, resample(torchaudio.load(audio_path)[0]), 16000)\r\n```\r\n\r\nThen we can see an issue here when decoding:\r\n```python\r\nfrom datasets import Dataset, Audio\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[0]  # decode the first audio file sets the resampler orig_freq to 32000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 32000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding is fine\r\n# (1308096,)\r\n\r\ndataset = Dataset.from_dict({\"audio\": [audio_path, audio_path2]}).cast_column(\"audio\", Audio(48000))\r\ndataset[1]  # decode the second audio file sets the resampler orig_freq to 16000\r\nprint(dataset .features[\"audio\"]._resampler.orig_freq)\r\n# 16000\r\nprint(dataset[0][\"audio\"][\"array\"].shape)  # here decoding uses orig_freq=16000 instead of 32000\r\n# (2616192,)\r\n```\r\n\r\nThe value of `orig_freq` doesn't change no matter what file needs to be decoded\r\n\r\ncc @patrickvonplaten @anton-l @cahya-wirawan @albertvillanova \r\n\r\nThe issue seems to be here in `Audio.decode_mp3`:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/features\/audio.py#L176-L180 \n Ok wow, thanks a lot for checking this - you've found a pretty big bug :sweat_smile: It seems like **a lot** more datasets are actually affected than I original thought. We'll try to solve this as soon as possible and make an announcement tomorrow.","embeddings":[-0.1460941732,0.0788261071,-0.0020536396,0.2036605328,0.1585872471,-0.1734847575,-0.1209071651,0.0837721154,-0.3985756934,0.2145069093,-0.4509893656,0.3006876111,0.1822164506,-0.4004628658,-0.622135818,-0.1660756618,-0.0155579252,0.1286703199,-0.2838031054,-0.0721553564,-0.2328843474,0.258531481,-0.2964225411,-0.1001581475,-0.0362958349,0.4119768441,0.1832629889,-0.2388220876,-0.0896096677,-0.1828368753,0.022568671,0.1056201756,0.1876092702,0.3837061822,-0.0001115617,-0.0865773633,0.1827040762,-0.1413338482,-0.0855505168,-0.2254209965,-0.1185976267,-0.1173252314,-0.4114627242,0.0647974163,-0.2249769866,-0.3062074482,-0.297401011,-0.4533960819,0.2612646818,0.1298217773,0.2027457803,0.1092380509,-0.3005739152,0.0564740188,0.2202183306,-0.0268500187,0.0419951193,0.2339995354,0.4132256508,0.2830609381,-0.3060887754,0.1806107312,-0.3838153481,0.2093024999,0.131767422,-0.2394376546,-0.0508891866,-0.3169384599,0.2317393273,0.2749604285,0.453391403,-0.0501742326,-0.3614310026,-0.1169934198,-0.1323623955,-0.2589998543,0.2301852256,0.1991679519,-0.0810558051,0.048190698,-0.0801890939,0.2605890036,0.2387397587,0.1177090779,-0.1058367491,0.2852226198,-0.2396839112,0.2262996733,0.2805168033,-0.1046322435,0.0298784152,-0.0125860563,0.0387350097,0.267775774,-0.3461430669,-0.1364609897,-0.1750180572,-0.1891794205,0.1515023708,0.3177064359,0.0006362318,0.0798467323,0.1731051654,0.0363282524,0.2438846081,-0.3282821476,-0.1916962713,-0.061297778,0.2681612074,0.1488363296,-0.0764216706,-0.0112151001,0.4847250581,-0.6192810535,-0.1885357499,0.2291862667,0.0610712953,-0.2351725698,-0.4075015783,0.0935173556,-0.3637566864,-0.0894750655,-0.048844099,-0.1574319899,0.1243287101,0.4777147472,-0.0492312536,0.5173670053,-0.3142126799,-0.1295961887,0.0802226961,-0.3812759221,0.0756463408,0.146761924,0.1043199599,-0.021064451,0.38318187,0.7153710127,0.088660121,-0.3186173141,0.0258031674,-0.1003806815,-0.1569891572,0.0509377755,-0.0314881913,0.5028655529,-0.2180861086,0.3937728405,-0.1839671135,0.2689741254,-0.3671022654,0.3006893992,0.071424678,0.1599383801,-0.1294512749,-0.0140234493,0.1251783669,0.1479975134,0.1298280656,-0.4347710311,0.0947998092,-0.2129634172,-0.4563727379,-0.1075830758,0.2338129729,0.1505531371,-0.1144355685,-0.0234337822,0.0930787399,0.1790628433,0.4453189969,0.2048745006,-0.0703657046,-0.2897978127,-0.2515300512,-0.0347417481,0.2534614205,-0.180833444,-0.5505566001,-0.0580801703,0.1901846379,0.2057768852,0.0117612882,0.1280459315,0.2748425901,-0.2938296199,-0.3297542036,0.6382164955,-0.1201267466,0.1216582507,-0.1949240863,-0.1624276042,0.1361429393,-0.0936813727,-0.0299504399,0.105959788,-0.1979262978,-0.1944111288,0.3539228141,0.0381709039,0.001933297,0.1785877198,-0.1918264776,0.0499293581,0.1376502961,-0.2023392171,0.407315731,0.1041942239,-0.2863277197,0.0086529832,0.0034862692,0.145270437,-0.1031722724,-0.4828282893,-0.2956618667,-0.0365349762,0.0698616728,0.387211591,-0.1795106083,0.066280432,0.1159474775,-0.1755527407,0.2229754776,0.0803622305,-0.1613960266,0.1296748817,0.1960100681,-0.0775408521,0.0834837109,-0.0000226977,0.2714771926,0.1604035646,-0.4371697903,0.3239318132,0.2681857646,0.3521743119,-0.4519734979,-0.0659163594,0.1981453598,-0.0265590362,-0.0086656325,0.1851561219,0.1349373907,-0.1394756883,0.3535216749,0.5703448653,0.1618211418,0.3104351461,0.007683794,-0.01212731,0.2110380828,0.029355906,-0.2627797723,0.0303133633,0.0032012807,0.1242183447,0.1997937858,-0.0261691101,0.0377093591,0.0056896396,0.2928415537,-0.0485121533,-0.1703531742,-0.0230347179,-0.1730762422,0.0501041599,-0.1326348931,0.3048569262,0.4331973791,0.067299664,0.2554117739,-0.1042873859,0.0014171388,-0.0915935859,-0.0000976337,0.2825237513,-0.1580266804,0.3164250553,-0.0284761339,0.0422196239,-0.1200731024,0.0117761623,-0.0273053609,0.0009411707,-0.1936518848,0.0489429124,-0.4558529854,0.0396497771,-0.5908758044,-0.597895503,-0.3436766267,-0.1736394763,-0.0826289281,0.4724974334,-0.3936939538,0.2770261765,0.1918892115,-0.0742349252,-0.1328110993,0.118445836,0.1960740238,0.2347589433,-0.2547200024,-0.0279964339,0.334748596,-0.0996989161,0.1346578896,-0.177738741,-0.2194124609,0.0103004631,-0.015392852,0.0347982273,0.1908388734,0.2926647663,-0.3275520205,-0.0327284634,-0.0138635868,-0.3070710599,0.270888716,0.1644405574,0.1271026433,0.0566697828,0.1360233426,0.3126129508,0.0129454266,-0.2508625388,-0.0889688358,-0.3013151586,-0.4376185536,-0.1687079668,-0.0113104163,0.0368866809,0.052912008,-0.2219525278,0.0304224454,0.3742418289,-0.4413391948,-0.1902925819,0.2083319277,-0.0685583353,-0.2716111243,-0.2280297726,0.1437895298,0.1209190637,0.3398850858,-0.0414449759,0.0580531582,-0.2839697599,-0.100126259,-0.1656562835,0.2310043871,-0.0249808412,-0.0702847615,-0.0085627064,-0.1171208322,0.0337181203,-0.0777354017,0.033463113,0.3657099009,-0.0960723311,0.088313587,-0.1777734458,0.5676959157,0.2875253856,0.0004212977,0.2202279866,0.0448365845,-0.0388974287,-0.157064274,0.0999374762,0.1248698607,-0.08289600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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3659","title":"push_to_hub but preview not working","comments":"Hi @thomas-happify, please note that the preview may take some time before rendering the data.\r\n\r\nI've seen it is already working.\r\n\r\nI close this issue. Please feel free to reopen it if the problem arises again.","body":"## Dataset viewer issue for '*happifyhealth\/twitter_pnn*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/happifyhealth\/twitter_pnn)*\r\n\r\nI used \r\n```\r\ndataset.push_to_hub(\"happifyhealth\/twitter_pnn\")\r\n```\r\nbut the preview is not working.\r\n\r\nAm I the one who added this dataset ? Yes\r\n\r\n\r\n","comment_length":36,"text":"push_to_hub but preview not working \n ## Dataset viewer issue for '*happifyhealth\/twitter_pnn*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/happifyhealth\/twitter_pnn)*\r\n\r\nI used \r\n```\r\ndataset.push_to_hub(\"happifyhealth\/twitter_pnn\")\r\n```\r\nbut the preview is not working.\r\n\r\nAm I the one who added this dataset ? Yes\r\n\r\n\r\n \n Hi @thomas-happify, please note that the preview may take some time before rendering the data.\r\n\r\nI've seen it is already working.\r\n\r\nI close this issue. Please feel free to reopen it if the problem arises again.","embeddings":[-0.1996080428,-0.6430017948,0.0241142828,0.0323764868,-0.0506664924,0.0509806983,0.2666333318,0.1995358914,0.286125809,0.1242962405,-0.0812876374,0.1542449296,-0.104072541,0.2985956967,0.3564331234,0.1371709257,0.3512626588,0.1522513628,0.1213511154,-0.0610035732,-0.2736179829,0.0867041498,-0.0252886508,0.1587693542,-0.4548517466,0.1195807457,-0.1797019541,0.0636524484,-0.0898282826,-0.1301985383,0.2034426183,0.3370772004,-0.0331451744,0.3389542103,-0.000126329,0.1300115734,0.4350093305,-0.025424717,-0.0471561849,-0.2566837966,-0.1695996523,-0.2281656265,0.0145647768,-0.0726464018,-0.2266685367,0.2217547297,-0.0159262791,-0.0715133548,0.1927194595,0.124112539,0.0946573019,0.2940319479,0.1467675418,-0.3985036612,0.4763542414,0.2259108573,-0.324465245,-0.0620655268,0.1950268149,0.280970633,0.0534807518,0.1656684875,0.185685873,0.0205496717,0.2138178349,-0.0779749975,0.1042336002,-0.2485862225,0.0795354545,0.1218548939,0.3920807838,-0.3683127761,-0.167075187,-0.1148997471,-0.1890948564,-0.0457863659,0.218816027,-0.1009973437,-0.0721763745,-0.0447538532,-0.2727781236,-0.4939103127,0.0349737965,0.3324866593,0.0529485568,0.2413692921,-0.2147942632,0.0637090057,0.0517253019,0.244258523,0.3602962494,-0.3133049309,-0.1115485132,0.0547933541,-0.1854119301,-0.1815115362,0.0931779966,-0.0400004424,-0.0561401173,0.0351290852,-0.2001997083,0.3473917842,-0.3266626894,0.0186208542,0.3579784036,-0.0036332209,0.3321229219,0.3082150221,0.3472621441,0.3464228511,0.3747460544,-0.0364539661,0.0131829102,-0.0103173302,-0.2103850842,-0.2713846266,0.1595955789,-0.1171215549,-0.2959374487,-0.0617118403,0.0004852744,-0.0498336032,-0.0410911664,0.4142235219,0.0670558289,0.1079732105,0.0313469358,0.1647640765,-0.1326647699,-0.0751263872,-0.3315626681,0.1325677782,-0.0154438131,0.3932078183,0.103897281,-0.4226351976,0.004105682,-0.2443215549,0.122948505,-0.2090463191,-0.2662138045,0.1143807918,0.022300072,0.1229307204,0.2305394858,0.3009183407,0.0854069367,-0.2293229699,-0.1285656393,0.3374109566,-0.2036113143,-0.4558889568,-0.060146708,0.051312916,-0.5186673403,0.0421213321,-0.6294175386,0.2414449602,0.080201827,0.1275150627,-0.0205212701,0.3132723868,-0.0050444175,-0.0301088467,0.3158892095,0.2718809247,-0.1452226043,-0.2953098118,-0.4801647663,-0.0971942395,-0.0391819663,0.2470337451,-0.0916187093,0.3915237188,-0.2847918272,0.0708137378,0.5884613991,-0.5741123557,-0.3668738008,-0.1666906029,-0.2916245759,-0.1299312711,-0.0819734111,0.1781610399,0.0171025265,-0.1373046786,-0.2871249914,-0.1593411565,0.1764450669,0.0877068937,-0.1814440787,-0.19176431,0.0971284807,0.3094496727,0.0585194454,0.2650600672,0.2443223298,0.2729723752,0.3900858462,0.2920959592,0.4229649305,-0.0128733451,0.4756320715,-0.1778804511,-0.0501300097,0.0127403503,-0.0091606975,-0.077231884,0.0176291708,0.0502182096,-0.013309232,-0.1422254592,-0.5514951944,0.0798953846,-0.2820866406,-0.2366642207,-0.0544513352,-0.0083301542,-0.0189052839,-0.0258368738,-0.104391031,0.3032747209,-0.2626594007,-0.0880483165,-0.4953666329,0.4944090247,0.0654404089,0.0004872533,-0.0285549089,0.1431253254,0.373106122,-0.2590089738,0.0230976548,0.1941913962,-0.028643569,0.3015009165,0.220449239,-0.3083807528,0.239260301,-0.52581352,0.2689436376,-0.0792033523,0.050900612,-0.2264017761,-0.3775633276,0.1666277945,0.2810717225,-0.093602486,-0.2408490777,-0.0027160712,-0.0402571522,0.0801157057,0.1402060837,-0.2364748567,0.0978675708,-0.0397230238,0.1675955504,-0.3682876825,0.2101956606,0.1449370831,0.3790762126,-0.0174031742,0.0739324093,0.1266037822,-0.3684618771,-0.1600973606,0.0245025009,-0.3089169264,0.0346603058,-0.0634479001,-0.072087042,0.3619308472,0.2812840641,-0.1696235836,-0.0197171457,0.0743042976,-0.0017971906,0.0281606652,0.0091243023,-0.1988501102,-0.3848394752,0.3511669934,0.3228273094,0.3217178881,-0.3995072246,-0.1929266602,-0.0651409924,-0.1784514934,0.3327738345,-0.3844303489,-0.0832832158,-0.5352111459,0.024735745,0.3858912587,0.245245114,0.2197469771,0.1070715711,0.1574906707,-0.0862317234,0.0397233143,-0.4330979884,0.0209380779,-0.0687213913,0.0491049886,0.0069860257,-0.0444694459,0.3022416234,-0.1605462134,0.2578216493,-0.3668964207,-0.160751462,0.061627157,-0.1101819649,0.1044939533,-0.1309171617,0.2072824091,-0.007788721,0.2026596963,0.2977568209,-0.2892795801,-0.384943217,-0.0110613173,-0.1574531198,-0.2388631254,0.1479904205,-0.2679211199,-0.2433319092,-0.2984473109,0.0807511806,0.0077935103,-0.0874004886,-0.097713396,-0.1012777463,0.0059797643,0.0152742248,-0.5348470211,-0.3619309962,-0.4816968143,0.6310399771,-0.3261607885,-0.2388715744,0.2841972113,0.3792698085,-0.1432460696,-0.204056859,-0.5507110357,-0.1929485798,-0.0648831725,0.2071166635,0.3631443977,-0.2077852339,0.2256682068,-0.160438627,0.0610290132,-0.0925905481,-0.3023544848,-0.1133916155,-0.0222947039,0.1760034263,-0.101233013,0.2026545107,-0.016849,0.6545849442,0.4570469856,-0.2175309211,0.5210587978,-0.1902040392,0.3716276288,-0.1912721545,-0.2094690949,0.0515961461,0.0826707259,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3656","title":"checksum error subjqa dataset","comments":"Hi @RensDimmendaal, \r\n\r\nI'm sorry but I can't reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n   ...: ds = load_dataset(\"subjqa\", \"electronics\")\r\nDownloading builder script: 9.15kB [00:00, 4.10MB\/s]                                                                                                                                                       \r\nDownloading metadata: 17.7kB [00:00, 8.51MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset subjqa\/electronics (download: 10.86 MiB, generated: 3.01 MiB, post-processed: Unknown size, total: 13.86 MiB) to ...\/.cache\/huggingface\/datasets\/subjqa\/electronics\/1.1.0\/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd...\r\nDownloading data: 11.4MB [00:03, 3.50MB\/s]\r\nDataset subjqa downloaded and prepared to ...\/.cache\/huggingface\/datasets\/subjqa\/electronics\/1.1.0\/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 605.09it\/s]\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n        num_rows: 1295\r\n    })\r\n    test: Dataset({\r\n        features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n        num_rows: 358\r\n    })\r\n    validation: Dataset({\r\n        features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n        num_rows: 255\r\n    })\r\n})\r\n```\r\n\r\nCould you please try again and see if the problem persists?\r\n\r\nIf that is the case, you can circumvent the issue by passing `ignore_verifications`:\r\n```python\r\nds = load_dataset(\"subjqa\", \"electronics\", ignore_verifications=True)","body":"## Describe the bug\r\n\r\nI get a checksum error when loading the `subjqa` dataset (used in the transformers book).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nsubjqa = load_dataset(\"subjqa\",\"electronics\")\r\n```\r\n\r\n## Expected results\r\nLoading the dataset\r\n\r\n## Actual results\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\r\n<ipython-input-2-d2857d460155> in <module>()\r\n      2 from datasets import load_dataset\r\n      3 \r\n----> 4 subjqa = load_dataset(\"subjqa\",\"electronics\")\r\n\r\n3 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/lewtun\/SubjQA\/archive\/refs\/heads\/master.zip']\r\n```\r\n\r\n## Environment info\r\n\r\nGoogle colab\r\n\r\n- `datasets` version: 1.18.2\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0","comment_length":174,"text":"checksum error subjqa dataset \n ## Describe the bug\r\n\r\nI get a checksum error when loading the `subjqa` dataset (used in the transformers book).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nsubjqa = load_dataset(\"subjqa\",\"electronics\")\r\n```\r\n\r\n## Expected results\r\nLoading the dataset\r\n\r\n## Actual results\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\r\n<ipython-input-2-d2857d460155> in <module>()\r\n      2 from datasets import load_dataset\r\n      3 \r\n----> 4 subjqa = load_dataset(\"subjqa\",\"electronics\")\r\n\r\n3 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/lewtun\/SubjQA\/archive\/refs\/heads\/master.zip']\r\n```\r\n\r\n## Environment info\r\n\r\nGoogle colab\r\n\r\n- `datasets` version: 1.18.2\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0 \n Hi @RensDimmendaal, \r\n\r\nI'm sorry but I can't reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n   ...: ds = load_dataset(\"subjqa\", \"electronics\")\r\nDownloading builder script: 9.15kB [00:00, 4.10MB\/s]                                                                                                                                                       \r\nDownloading metadata: 17.7kB [00:00, 8.51MB\/s]                                                                                                                                                             \r\nDownloading and preparing dataset subjqa\/electronics (download: 10.86 MiB, generated: 3.01 MiB, post-processed: Unknown size, total: 13.86 MiB) to ...\/.cache\/huggingface\/datasets\/subjqa\/electronics\/1.1.0\/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd...\r\nDownloading data: 11.4MB [00:03, 3.50MB\/s]\r\nDataset subjqa downloaded and prepared to ...\/.cache\/huggingface\/datasets\/subjqa\/electronics\/1.1.0\/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 605.09it\/s]\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n        num_rows: 1295\r\n    })\r\n    test: Dataset({\r\n        features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n        num_rows: 358\r\n    })\r\n    validation: Dataset({\r\n        features: ['domain', 'nn_mod', 'nn_asp', 'query_mod', 'query_asp', 'q_reviews_id', 'question_subj_level', 'ques_subj_score', 'is_ques_subjective', 'review_id', 'id', 'title', 'context', 'question', 'answers'],\r\n        num_rows: 255\r\n    })\r\n})\r\n```\r\n\r\nCould you please try again and see if the problem persists?\r\n\r\nIf that is the case, you can circumvent the issue by passing `ignore_verifications`:\r\n```python\r\nds = load_dataset(\"subjqa\", \"electronics\", ignore_verifications=True)","embeddings":[-0.1055612192,0.197503224,0.043293424,0.2761411965,0.1469845772,0.0702440068,0.4774221778,0.2563087344,0.0278038736,0.1766105294,-0.1053531244,0.1167669222,0.0757053122,0.0232626535,-0.0491605848,-0.032239113,0.1780380458,0.0984127298,-0.1423471421,0.0862167999,-0.1492410004,0.2593261898,-0.290684849,-0.180845663,-0.1593652964,0.08498732,0.1761405766,-0.0925293267,-0.0815195143,-0.1610922962,0.487635076,0.0168163925,0.0499083139,0.4608284533,-0.0001180156,0.2763191462,0.4594174623,-0.074077256,-0.5092215538,0.0488461033,-0.6486703157,-0.1169950068,-0.1160537824,-0.3099162579,-0.0631145984,0.288374573,-0.0167556461,-0.0028222965,0.094787702,-0.0292485524,0.1560578346,0.5503484607,-0.0047769579,0.1039344147,0.3004462123,-0.2122612298,-0.0576517209,0.5142282844,0.1362509131,-0.0457470827,-0.00161365,0.1320661455,-0.2748381793,0.0609102175,0.1297538728,0.1008607894,0.0483074784,-0.0577567853,0.1158231199,0.2416591793,0.3301006258,-0.4332376122,-0.4309455454,-0.1048870087,-0.249003306,-0.0860560238,0.4025180042,0.190497309,-0.0425609238,-0.0275450479,-0.3256329596,0.4020820558,0.0978190824,-0.0330282301,-0.0089524342,0.3445588052,0.1207488179,0.0274399947,-0.1075103655,-0.0738759041,0.223623842,-0.2697113752,-0.3600434363,0.0754866749,-0.6078194976,-0.2199159563,-0.0403614007,0.1668658406,0.0982246697,0.3332298994,0.2282094806,0.0647065192,0.0866718888,0.2326618433,0.2238360047,0.184594214,-0.1669264287,0.3724652231,0.2124345154,0.3052088618,0.0041095167,0.0426437743,0.1238755584,-0.0860023275,0.3985908329,0.2974323332,0.2850406766,-0.4628419578,-0.2799482942,0.1709050238,-0.0945018753,-0.1961514354,0.2418668866,0.2211742401,-0.2287855297,-0.0712491497,-0.0199169889,-0.1047546342,-0.2528156638,-0.0512422845,-0.2089841068,0.0044908035,-0.161588341,0.0355527923,0.1621334255,-0.0018747196,0.1471467316,-0.139164865,0.3671690226,-0.0894849077,0.1941008121,-0.3023131192,-0.2374657393,0.6131480932,-0.2233698815,-0.0017424395,0.1296951771,-0.1130552664,-0.1656946689,0.1569492221,-0.0928339064,-0.1011396945,-0.0120737934,0.1284634918,-0.5168784857,0.0026980531,-0.1925522238,-0.4474129975,0.439897418,-0.3833447695,-0.0374387912,-0.0503482223,-0.2483354211,-0.1595823318,0.2312986851,0.279712528,-0.1162057966,-0.1004024148,-0.1294553578,-0.1281601787,0.2277418524,0.2448114753,-0.0545616709,0.3032378852,-0.2222343087,-0.3238142133,-0.1002146751,-0.0908018351,-0.837700069,0.0687940866,0.1619603932,0.2998065054,-0.0007948275,0.2254322916,0.1021479219,-0.3155489564,0.321065098,0.0783243328,-0.1435024589,0.2473397553,-0.3333222866,-0.2833250165,0.1734079421,0.2346214056,0.0870844796,0.1611444801,0.0468581915,-0.0628376529,0.1581547856,-0.055644054,-0.2238786221,0.1655486375,0.5274860263,-0.1994600445,0.1837357879,-0.1718287468,-0.3441474438,0.3888059855,-0.3148507774,0.0820729211,-0.2380364537,-0.0304870754,-0.2756675184,-0.1168963984,-0.1471779346,0.0225196071,0.058489807,0.186751768,0.0427099839,-0.1417255849,-0.0062132133,0.2465833277,-0.0584371388,0.0751520023,-0.4644465148,0.5822559595,-0.1435305029,0.0292447899,0.0815623775,0.1108222306,0.1314820051,-0.0426130891,-0.3091084957,0.3432969451,0.2593013346,-0.1270147115,-0.0514571592,0.3191727698,0.1039252728,-0.4373101294,-0.0387337804,0.5726418495,0.1860397458,0.0874466151,-0.1791335344,0.380854696,-0.2561936378,0.0257655848,-0.2217787057,0.1319688857,0.2334974259,-0.2358869612,-0.3486128449,-0.1906292737,0.0650547296,0.2713133991,-0.1507870257,0.1799456626,0.265034914,-0.2875052989,0.2757571042,-0.1381177008,0.0937936679,0.0377962105,0.0016643526,0.1492726654,0.0817632675,0.6540036798,0.4007970989,0.0187710281,-0.147091493,0.0437576324,-0.1327946186,0.0764559507,0.0545801371,0.2476278841,0.0775718763,0.2612143159,-0.0032680968,0.120274663,-0.1841925383,-0.0329188593,0.0794075802,0.1833722591,-0.3159413934,0.3497982919,-0.1573604345,-0.1215969026,-0.2612386048,-0.3103192151,-0.2022434771,-0.3067605495,-0.2024319768,0.1387527287,-0.273504287,0.355222255,-0.1964807361,0.059055075,0.1557092518,-0.360624671,0.1332791448,-0.1198549941,-0.0393044055,0.0080050994,0.5770179033,-0.0079771997,0.2582215667,-0.1894374341,-0.206114307,-0.2787080705,-0.3340940177,0.1109157056,-0.1308805197,0.5320440531,0.2335092872,0.0134968553,-0.028347645,-0.4525052309,0.152689606,0.2494196445,-0.2384338826,0.3622530103,-0.1824526638,-0.0574315786,-0.0050854282,0.0094690667,0.0947414264,-0.1693330109,-0.2236790359,-0.1186031178,0.2572151721,0.4086362123,-0.1020390168,-0.0119336639,0.2608864307,0.2026724964,-0.0535287857,-0.4881094396,0.5018008351,0.1785417199,-0.3580312133,-0.0692054853,-0.4843327403,0.2130857855,0.0614961646,-0.3966418505,-0.0732122213,-0.3415591717,-0.1576867998,0.4262276888,-0.0642952546,0.0694607422,-0.0383737274,0.0684704557,-0.1199797392,-0.4470398128,-0.1586635709,0.0393148437,0.6030381918,-0.0949882492,0.2788450718,-0.0618812293,0.2587199509,0.4451218545,-0.0130184358,-0.0132232113,0.3949538469,0.1592634767,-0.1122619957,-0.3926002979,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3656","title":"checksum error subjqa dataset","comments":"Thanks checking!\r\n\r\nYou're totally right. I don't know what's changed, but I'm glad it's working now!\r\n\r\n","body":"## Describe the bug\r\n\r\nI get a checksum error when loading the `subjqa` dataset (used in the transformers book).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nsubjqa = load_dataset(\"subjqa\",\"electronics\")\r\n```\r\n\r\n## Expected results\r\nLoading the dataset\r\n\r\n## Actual results\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\r\n<ipython-input-2-d2857d460155> in <module>()\r\n      2 from datasets import load_dataset\r\n      3 \r\n----> 4 subjqa = load_dataset(\"subjqa\",\"electronics\")\r\n\r\n3 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/lewtun\/SubjQA\/archive\/refs\/heads\/master.zip']\r\n```\r\n\r\n## Environment info\r\n\r\nGoogle colab\r\n\r\n- `datasets` version: 1.18.2\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0","comment_length":16,"text":"checksum error subjqa dataset \n ## Describe the bug\r\n\r\nI get a checksum error when loading the `subjqa` dataset (used in the transformers book).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nsubjqa = load_dataset(\"subjqa\",\"electronics\")\r\n```\r\n\r\n## Expected results\r\nLoading the dataset\r\n\r\n## Actual results\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\r\n<ipython-input-2-d2857d460155> in <module>()\r\n      2 from datasets import load_dataset\r\n      3 \r\n----> 4 subjqa = load_dataset(\"subjqa\",\"electronics\")\r\n\r\n3 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/lewtun\/SubjQA\/archive\/refs\/heads\/master.zip']\r\n```\r\n\r\n## Environment info\r\n\r\nGoogle colab\r\n\r\n- `datasets` version: 1.18.2\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0 \n Thanks checking!\r\n\r\nYou're totally right. I don't know what's changed, but I'm glad it's working now!\r\n\r\n","embeddings":[-0.1055612192,0.197503224,0.043293424,0.2761411965,0.1469845772,0.0702440068,0.4774221778,0.2563087344,0.0278038736,0.1766105294,-0.1053531244,0.1167669222,0.0757053122,0.0232626535,-0.0491605848,-0.032239113,0.1780380458,0.0984127298,-0.1423471421,0.0862167999,-0.1492410004,0.2593261898,-0.290684849,-0.180845663,-0.1593652964,0.08498732,0.1761405766,-0.0925293267,-0.0815195143,-0.1610922962,0.487635076,0.0168163925,0.0499083139,0.4608284533,-0.0001180156,0.2763191462,0.4594174623,-0.074077256,-0.5092215538,0.0488461033,-0.6486703157,-0.1169950068,-0.1160537824,-0.3099162579,-0.0631145984,0.288374573,-0.0167556461,-0.0028222965,0.094787702,-0.0292485524,0.1560578346,0.5503484607,-0.0047769579,0.1039344147,0.3004462123,-0.2122612298,-0.0576517209,0.5142282844,0.1362509131,-0.0457470827,-0.00161365,0.1320661455,-0.2748381793,0.0609102175,0.1297538728,0.1008607894,0.0483074784,-0.0577567853,0.1158231199,0.2416591793,0.3301006258,-0.4332376122,-0.4309455454,-0.1048870087,-0.249003306,-0.0860560238,0.4025180042,0.190497309,-0.0425609238,-0.0275450479,-0.3256329596,0.4020820558,0.0978190824,-0.0330282301,-0.0089524342,0.3445588052,0.1207488179,0.0274399947,-0.1075103655,-0.0738759041,0.223623842,-0.2697113752,-0.3600434363,0.0754866749,-0.6078194976,-0.2199159563,-0.0403614007,0.1668658406,0.0982246697,0.3332298994,0.2282094806,0.0647065192,0.0866718888,0.2326618433,0.2238360047,0.184594214,-0.1669264287,0.3724652231,0.2124345154,0.3052088618,0.0041095167,0.0426437743,0.1238755584,-0.0860023275,0.3985908329,0.2974323332,0.2850406766,-0.4628419578,-0.2799482942,0.1709050238,-0.0945018753,-0.1961514354,0.2418668866,0.2211742401,-0.2287855297,-0.0712491497,-0.0199169889,-0.1047546342,-0.2528156638,-0.0512422845,-0.2089841068,0.0044908035,-0.161588341,0.0355527923,0.1621334255,-0.0018747196,0.1471467316,-0.139164865,0.3671690226,-0.0894849077,0.1941008121,-0.3023131192,-0.2374657393,0.6131480932,-0.2233698815,-0.0017424395,0.1296951771,-0.1130552664,-0.1656946689,0.1569492221,-0.0928339064,-0.1011396945,-0.0120737934,0.1284634918,-0.5168784857,0.0026980531,-0.1925522238,-0.4474129975,0.439897418,-0.3833447695,-0.0374387912,-0.0503482223,-0.2483354211,-0.1595823318,0.2312986851,0.279712528,-0.1162057966,-0.1004024148,-0.1294553578,-0.1281601787,0.2277418524,0.2448114753,-0.0545616709,0.3032378852,-0.2222343087,-0.3238142133,-0.1002146751,-0.0908018351,-0.837700069,0.0687940866,0.1619603932,0.2998065054,-0.0007948275,0.2254322916,0.1021479219,-0.3155489564,0.321065098,0.0783243328,-0.1435024589,0.2473397553,-0.3333222866,-0.2833250165,0.1734079421,0.2346214056,0.0870844796,0.1611444801,0.0468581915,-0.0628376529,0.1581547856,-0.055644054,-0.2238786221,0.1655486375,0.5274860263,-0.1994600445,0.1837357879,-0.1718287468,-0.3441474438,0.3888059855,-0.3148507774,0.0820729211,-0.2380364537,-0.0304870754,-0.2756675184,-0.1168963984,-0.1471779346,0.0225196071,0.058489807,0.186751768,0.0427099839,-0.1417255849,-0.0062132133,0.2465833277,-0.0584371388,0.0751520023,-0.4644465148,0.5822559595,-0.1435305029,0.0292447899,0.0815623775,0.1108222306,0.1314820051,-0.0426130891,-0.3091084957,0.3432969451,0.2593013346,-0.1270147115,-0.0514571592,0.3191727698,0.1039252728,-0.4373101294,-0.0387337804,0.5726418495,0.1860397458,0.0874466151,-0.1791335344,0.380854696,-0.2561936378,0.0257655848,-0.2217787057,0.1319688857,0.2334974259,-0.2358869612,-0.3486128449,-0.1906292737,0.0650547296,0.2713133991,-0.1507870257,0.1799456626,0.265034914,-0.2875052989,0.2757571042,-0.1381177008,0.0937936679,0.0377962105,0.0016643526,0.1492726654,0.0817632675,0.6540036798,0.4007970989,0.0187710281,-0.147091493,0.0437576324,-0.1327946186,0.0764559507,0.0545801371,0.2476278841,0.0775718763,0.2612143159,-0.0032680968,0.120274663,-0.1841925383,-0.0329188593,0.0794075802,0.1833722591,-0.3159413934,0.3497982919,-0.1573604345,-0.1215969026,-0.2612386048,-0.3103192151,-0.2022434771,-0.3067605495,-0.2024319768,0.1387527287,-0.273504287,0.355222255,-0.1964807361,0.059055075,0.1557092518,-0.360624671,0.1332791448,-0.1198549941,-0.0393044055,0.0080050994,0.5770179033,-0.0079771997,0.2582215667,-0.1894374341,-0.206114307,-0.2787080705,-0.3340940177,0.1109157056,-0.1308805197,0.5320440531,0.2335092872,0.0134968553,-0.028347645,-0.4525052309,0.152689606,0.2494196445,-0.2384338826,0.3622530103,-0.1824526638,-0.0574315786,-0.0050854282,0.0094690667,0.0947414264,-0.1693330109,-0.2236790359,-0.1186031178,0.2572151721,0.4086362123,-0.1020390168,-0.0119336639,0.2608864307,0.2026724964,-0.0535287857,-0.4881094396,0.5018008351,0.1785417199,-0.3580312133,-0.0692054853,-0.4843327403,0.2130857855,0.0614961646,-0.3966418505,-0.0732122213,-0.3415591717,-0.1576867998,0.4262276888,-0.0642952546,0.0694607422,-0.0383737274,0.0684704557,-0.1199797392,-0.4470398128,-0.1586635709,0.0393148437,0.6030381918,-0.0949882492,0.2788450718,-0.0618812293,0.2587199509,0.4451218545,-0.0130184358,-0.0132232113,0.3949538469,0.1592634767,-0.1122619957,-0.3926002979,-0.0002650109,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3655","title":"Pubmed dataset not reachable","comments":"Hey @albertvillanova, sorry to reopen this... I can confirm that on `master` branch the dataset is downloadable now but it is still broken in streaming mode:\r\n\r\n```python\r\n >>> import datasets\r\n >>> pubmed_train = datasets.load_dataset('pubmed', split='train', streaming=True)\r\n >>> next(iter(pubmed_train))\r\n```\r\n```\r\n No such file or directory: 'gzip:\/\/pubmed22n0001.xml::ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed22n0001.xml.gz'\r\n```\r\n","body":"## Describe the bug\r\nTrying to use the `pubmed` dataset fails to reach \/ download the source files.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\npubmed_train = datasets.load_dataset('pubmed', split='train')\r\n```\r\n\r\n## Expected results\r\nShould begin downloading the pubmed dataset.\r\n\r\n## Actual results\r\n```\r\nConnectionError: Couldn't reach ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0865.xml.gz (InvalidSchema(\"No connection adapters were found for 'ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0865.xml.gz'\"))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.2\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.2\r\n- PyArrow version: 6.0.0\r\n","comment_length":47,"text":"Pubmed dataset not reachable \n ## Describe the bug\r\nTrying to use the `pubmed` dataset fails to reach \/ download the source files.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\npubmed_train = datasets.load_dataset('pubmed', split='train')\r\n```\r\n\r\n## Expected results\r\nShould begin downloading the pubmed dataset.\r\n\r\n## Actual results\r\n```\r\nConnectionError: Couldn't reach ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0865.xml.gz (InvalidSchema(\"No connection adapters were found for 'ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0865.xml.gz'\"))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.2\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.2\r\n- PyArrow version: 6.0.0\r\n \n Hey @albertvillanova, sorry to reopen this... I can confirm that on `master` branch the dataset is downloadable now but it is still broken in streaming mode:\r\n\r\n```python\r\n >>> import datasets\r\n >>> pubmed_train = datasets.load_dataset('pubmed', split='train', streaming=True)\r\n >>> next(iter(pubmed_train))\r\n```\r\n```\r\n No such file or directory: 'gzip:\/\/pubmed22n0001.xml::ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed22n0001.xml.gz'\r\n```\r\n","embeddings":[-0.2074101716,0.1169078648,0.0022659171,0.0530289598,0.3004154563,0.0551191159,0.2131826133,0.3712816536,-0.0309724826,0.1522596329,0.0972941965,0.1235700175,-0.0315115601,-0.0394262411,0.0356216505,-0.2128071636,0.0310949516,0.1027331948,-0.1332084239,-0.0257836543,-0.069714956,0.259172231,-0.2140829563,0.0081866672,0.1457503736,-0.1570599228,-0.0020102814,0.2512613535,-0.441809684,-0.3751014471,0.2805963755,-0.0588955991,0.1327445805,0.5370138884,-0.0001120987,0.0758022964,0.3092150986,-0.0953914821,-0.3105969131,-0.4847258031,-0.1918494999,-0.3352661729,0.0200632568,-0.1861431748,-0.1333480477,-0.4152390063,0.0801856518,-0.4675629437,0.4051360786,0.5354921222,0.2427059114,0.1706363112,0.2623288929,-0.0978868827,0.4108558297,-0.1886476576,-0.1491772681,0.4597066641,0.3495509923,0.1145345867,0.0158821698,0.2598161101,0.026281219,0.0370019563,0.1488333791,0.0797103271,0.1044788137,-0.2746823132,-0.1309557259,0.2076090872,0.5238941312,-0.2744813263,-0.346144557,-0.0922665,0.0026451855,-0.0162163358,0.2899870872,0.108153753,-0.3194653094,0.1532728076,-0.1041900665,-0.2942540348,-0.3251927197,0.2770183682,-0.0521295592,0.1807235926,-0.0904814154,0.0580533333,-0.0037828779,0.1087585166,0.1281471848,0.0285129901,0.0846842825,0.0838307738,-0.2679138482,0.0046322793,-0.0512808934,-0.2405997664,0.1810916215,0.1317794323,0.3189258277,-0.1668960601,0.1076069027,0.1053491905,0.2569641471,-0.1839040071,-0.4049089253,0.2753127217,0.3423760533,0.296002984,-0.1295993477,-0.1195120513,0.0182661787,-0.214714393,0.0449953377,-0.0450315401,0.3379419148,-0.2916997075,-0.1847312003,0.2044089139,-0.2187030911,0.0092780711,-0.2457177639,0.2761384249,-0.0838845,0.1519627422,0.1724804193,0.1339210272,-0.1324976981,-0.1976551414,-0.1083239689,-0.0383685492,-0.2295671552,-0.0815073922,0.2227923721,-0.5893378258,0.2056229115,-0.092292577,0.0533492751,-0.1055807173,0.124785468,-0.1889833808,0.0131783467,0.2741267383,0.1041555926,0.3164862692,0.0737647489,-0.0194671154,-0.1081908196,0.0609870441,-0.176723212,-0.259157896,-0.3004733622,0.2372605652,-0.3559769988,-0.0325090922,-0.0414643139,-0.0698878616,0.0422659107,-0.3797501624,-0.1300459057,-0.2116526514,-0.1017305106,-0.3129887283,0.4999181926,0.2197969258,-0.1099301949,0.0291012824,-0.3089393377,0.0125896893,0.3131345212,0.22346358,-0.1297043115,0.175226301,-0.2135012895,0.071422033,0.7844866514,-0.202313751,-0.6946220398,0.1790052652,-0.210074231,0.2515136302,0.0992877632,-0.1804223359,0.2692959309,0.027792979,0.0223408286,0.4429989457,-0.1295907497,0.0954080224,-0.376855284,-0.1809780151,0.2645015121,0.0707189068,0.3329279125,0.0273346566,0.3396524191,0.2625985742,0.3900891244,0.129174158,0.1292333901,0.2788785398,0.2124441862,-0.1029383317,0.2082561851,-0.3375148475,-0.0219140369,0.1995317936,0.0236695111,0.0915007889,-0.3370118141,0.079873383,-0.365908891,-0.0706142709,-0.1436413378,-0.0732963756,0.1209138706,0.1546758711,0.1348096579,0.0056899497,-0.2445527017,0.102639176,-0.2573539913,0.1799162924,-0.4767989516,0.5247051716,-0.2753245234,-0.2639074326,0.1493821591,-0.0594662242,0.0782517195,-0.2473704368,-0.1884663254,0.3262284398,-0.0898365229,0.4042282999,0.1509567201,0.0784255192,0.173112765,-0.5773721337,0.146088317,0.5344434977,0.1401947141,0.1441710293,-0.1815096289,-0.0379135162,-0.0041192002,0.1211461052,-0.0577970631,0.2662403584,0.2846449614,0.1196311191,-0.215016067,-0.0612593405,0.3638119996,-0.1122060046,-0.1805666685,-0.2813312411,-0.2493005097,0.2196279317,0.2933458984,0.0224763174,-0.0956715345,0.1811170876,-0.2379931509,-0.2285688519,0.0689897537,0.28716591,0.3896379173,0.1346143037,0.3929100335,0.1794806868,-0.055135455,-0.191031605,0.2068504244,0.0187318437,0.0939030126,0.3714283407,0.1320660114,0.2326649278,-0.0009329626,-0.08836402,0.0631619766,0.3008036911,-0.5093200207,0.0719853118,-0.3441044688,-0.203448832,-0.3238011003,-0.080546461,-0.011336159,-0.2965337932,-0.1144556105,0.4158811271,0.1187455952,0.057648465,-0.2862133384,0.0979970992,0.0412500985,-0.0590831488,-0.1356944144,0.025734311,-0.0119499145,0.1719957292,0.1817404032,-0.0129595567,0.3175698817,-0.2071629465,-0.2024195045,-0.4215452969,-0.2381792367,0.1386705935,-0.1386390924,0.4023841918,0.2673825026,0.3287020922,0.1759059429,0.0683912039,0.2962345481,-0.3507906199,-0.0565809757,0.1255339831,-0.0725815073,-0.0010704338,0.0264497269,-0.4655545056,-0.3567475975,-0.4699304402,-0.0676892847,0.0006597048,0.1761454642,0.2064632624,0.0795064643,0.0598783977,0.0790317059,0.1797581762,0.0144304512,-0.4593195617,0.4347861409,-0.2116261274,-0.4152799249,0.111478582,0.1945479065,0.0129109863,0.564507544,-0.5898390412,0.1728848815,0.0587651394,0.0619570501,0.0565222427,-0.0121315178,0.1423960328,-0.1212666929,-0.0476036146,-0.2015986592,0.1058443263,-0.218041122,0.148960337,0.4977503419,0.1290280968,0.2202204615,0.1761851609,0.7410674691,0.4272934794,0.1407305002,0.333545208,-0.047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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3655","title":"Pubmed dataset not reachable","comments":"Hi @abhi-mosaic, would you mind opening another issue for this new problem?\r\n\r\nFirst issue (already solved) was a ConnectionError due to the yearly update release of PubMed: we fixed it by updating the URLs from year 2021 to year 2022.\r\n\r\nHowever this is another problem: to make pubmed streamable. Please note that NOT all our datastes are streamable: we are making streamable more and more of them... but this is an on-going process...\r\n\r\nThanks.","body":"## Describe the bug\r\nTrying to use the `pubmed` dataset fails to reach \/ download the source files.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\npubmed_train = datasets.load_dataset('pubmed', split='train')\r\n```\r\n\r\n## Expected results\r\nShould begin downloading the pubmed dataset.\r\n\r\n## Actual results\r\n```\r\nConnectionError: Couldn't reach ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0865.xml.gz (InvalidSchema(\"No connection adapters were found for 'ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0865.xml.gz'\"))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.2\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.2\r\n- PyArrow version: 6.0.0\r\n","comment_length":74,"text":"Pubmed dataset not reachable \n ## Describe the bug\r\nTrying to use the `pubmed` dataset fails to reach \/ download the source files.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\npubmed_train = datasets.load_dataset('pubmed', split='train')\r\n```\r\n\r\n## Expected results\r\nShould begin downloading the pubmed dataset.\r\n\r\n## Actual results\r\n```\r\nConnectionError: Couldn't reach ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0865.xml.gz (InvalidSchema(\"No connection adapters were found for 'ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed21n0865.xml.gz'\"))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.2\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.2\r\n- PyArrow version: 6.0.0\r\n \n Hi @abhi-mosaic, would you mind opening another issue for this new problem?\r\n\r\nFirst issue (already solved) was a ConnectionError due to the yearly update release of PubMed: we fixed it by updating the URLs from year 2021 to year 2022.\r\n\r\nHowever this is another problem: to make pubmed streamable. Please note that NOT all our datastes are streamable: we are making streamable more and more of them... but this is an on-going process...\r\n\r\nThanks.","embeddings":[-0.1536322981,0.2288035899,0.0192875117,-0.0351098441,0.3287486136,-0.034097936,0.2292305976,0.3855732679,-0.0587704927,0.1195992604,0.1655031145,0.1151800603,0.0343649909,-0.0607218221,0.092541486,-0.2345781475,0.1303251535,-0.0077874959,-0.131456852,-0.0398080349,-0.1331569701,0.1829691231,-0.2059070468,0.012546584,0.0963737369,-0.1207454652,-0.1008842587,0.2554300427,-0.4888890386,-0.3429449797,0.4349650145,-0.0066636424,0.1188803166,0.4767967761,-0.0001137823,0.0372959375,0.3024642766,-0.0412893444,-0.2731783986,-0.4829633534,-0.126378268,-0.353993386,0.0908342153,-0.1152822822,-0.104884699,-0.3191659749,0.1325920075,-0.3982731104,0.411269635,0.5273427963,0.2060405016,0.1299105585,0.4054101706,-0.0669362172,0.4596109688,-0.168161422,-0.2087436467,0.4768769145,0.3874637485,0.0255629811,-0.0342698544,0.1861446351,-0.0185042359,0.0559639297,0.1550273299,0.1287112683,0.1827865094,-0.2571589053,-0.1405653805,0.1962733865,0.4982114434,-0.2780264914,-0.3657625616,-0.1016749814,-0.0080263317,0.0315263979,0.2816640437,0.0926376358,-0.2986725867,0.2231842726,-0.1800581366,-0.3696814477,-0.3471407592,0.357829839,0.0599553287,0.1090635136,-0.046910096,0.1065667272,0.0125335613,0.0717444494,0.2354697436,0.0063892445,0.2038969994,0.1701916456,-0.2666940391,0.0157208107,-0.1009419635,-0.1999602169,0.2203825414,0.1718008369,0.2540204525,-0.1701925397,0.0187910181,0.1352211982,0.2155107111,-0.3423014283,-0.4341131151,0.3169974387,0.3170790076,0.202366963,-0.0833871737,-0.0545181558,-0.027703071,-0.1462919414,-0.0703823119,-0.0761185363,0.3756221235,-0.344355911,-0.1212954521,0.1587523371,-0.2531454861,-0.0406129025,-0.2996461689,0.2285579592,-0.0818176344,0.1356580406,0.1716534495,0.1700463444,-0.146557048,-0.1548162997,-0.0865210742,0.0304633807,-0.2700049877,-0.0090755848,0.2843510807,-0.5180811882,0.0770328268,-0.0465759784,-0.06485039,-0.1438421458,0.1465452015,-0.1821359247,-0.0640359521,0.2336350381,0.0432524458,0.348634392,0.0472463928,-0.0645093471,-0.0813017935,0.0913021788,-0.2508470416,-0.2281762809,-0.3079614639,0.2044465244,-0.3491154015,-0.0399956182,-0.0560619421,-0.0203500427,0.0315541886,-0.3301474154,-0.2809568346,-0.2346003801,-0.0823865458,-0.3095888495,0.5355237722,0.2268613577,-0.0718440935,0.0339852013,-0.2521806359,0.04565477,0.2855406106,0.1933409423,-0.0942687467,0.1401101798,-0.1919133663,-0.0128566781,0.7585021853,-0.2116169333,-0.6730872393,0.0645007342,-0.2271052152,0.1599464715,0.0370842181,-0.1385752857,0.3150069118,-0.0182200894,0.0563519336,0.4303773046,-0.1550802737,0.1412943155,-0.4008304775,-0.2360298038,0.2001671493,0.074340567,0.3574759662,0.0154962502,0.4077563286,0.327000469,0.3927328587,0.1679984033,0.142789349,0.2196515799,0.2368974388,-0.1608407944,0.2012227029,-0.3131212592,0.0183076877,0.1848868579,0.0564795025,0.1150164157,-0.2172202021,0.0933895409,-0.449463129,-0.0603316054,-0.1310330778,0.0237972457,0.0863050297,0.0884409472,0.1011081189,0.0946955383,-0.2191182077,0.1421429515,-0.3052558899,0.2574293315,-0.5002524853,0.632982254,-0.2867296934,-0.2463166565,0.1921056062,-0.0057930066,0.1238153353,-0.1894498318,-0.1701843441,0.217118606,-0.0529207252,0.3776287735,0.3162323236,0.1634304821,0.1281465143,-0.5693015456,0.1762686074,0.3330846131,0.1443209052,0.1333983839,-0.0679721609,-0.1229306757,0.0392000601,0.1172608435,-0.0580312088,0.2483664304,0.2854869962,0.0466155224,-0.1644997597,-0.0458753593,0.3026342988,-0.0387338959,-0.2577056587,-0.2287078798,-0.1847186983,0.2489582896,0.2644144297,0.0288573001,-0.1116351709,0.2241343409,-0.164406687,-0.2947533727,-0.0229937565,0.3401166201,0.3119670749,0.0956040546,0.2926183641,0.1952847689,-0.1087037325,-0.1532805562,0.0703450814,0.0970802084,0.1511957794,0.3180322051,0.1704834402,0.3209736943,0.0060397279,-0.0707035288,0.0720515102,0.2829959691,-0.5137298107,0.063562043,-0.377413094,-0.0806334391,-0.3667496443,-0.0871893093,-0.0962104872,-0.3335883915,-0.118546918,0.4173598289,0.0842662752,0.0606198348,-0.3701123297,0.1396126598,-0.0337922499,-0.0143040624,-0.0952551961,-0.0048407414,-0.0156625286,0.1413173378,0.2028031945,0.0572989732,0.3370110989,-0.208752811,-0.0905027688,-0.4573096335,-0.2746303678,0.1235747486,-0.0699725896,0.4650313258,0.2010865211,0.3462782502,0.0456272624,0.0825175792,0.2799485326,-0.3221946955,0.0030812407,0.0571989603,-0.0819747597,0.0024592273,0.1578942984,-0.4227117598,-0.3572372794,-0.406270951,-0.1373934746,-0.024445476,0.1316272616,0.1221956611,0.0417288765,-0.0011843013,-0.0950568542,0.1673695445,0.0694722906,-0.4614995718,0.4097439647,-0.127400279,-0.4121168852,0.1863327324,0.0827883556,-0.0878424197,0.5742648244,-0.5836915374,0.0695991963,0.1074242517,-0.0521035083,0.1742429733,-0.0801162273,0.169632256,-0.1723901033,-0.0045615272,-0.1613446176,0.0206887312,-0.2215697467,0.1861089468,0.5380587578,0.1758100539,0.1226126105,0.1578364074,0.8509505391,0.3988356292,0.1541863382,0.3370580375,0.012300536,0.1471323073,-0.0405625962,-0.4720502496,0.2158017009,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3644","title":"Add a GROUP BY operator","comments":"Hi ! At the moment you can use `to_pandas()` to get a pandas DataFrame that supports `group_by` operations (make sure your dataset fits in memory though)\r\n\r\nWe use Arrow as a back-end for `datasets` and it doesn't have native group by (see https:\/\/github.com\/apache\/arrow\/issues\/2189) unfortunately.\r\n\r\nI just drafted what it could look like to have `group_by` in `datasets`:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n\r\ndef group_by(d, col, join): \r\n    \"\"\"from: https:\/\/github.com\/huggingface\/datasets\/issues\/3644\"\"\"\r\n    # Get the indices of each group\r\n    groups = {key: [] for key in d.unique(col)} \r\n    def create_groups_indices(key, i): \r\n        groups[key].append(i) \r\n    d.map(create_groups_indices, with_indices=True, input_columns=col) \r\n    # Get one dataset object per group\r\n    groups = {key: d.select(indices) for key, indices in groups.items()} \r\n    # Apply join function\r\n    groups = {\r\n        key: dataset_group.map(join, batched=True, batch_size=len(dataset_group), remove_columns=d.column_names)\r\n        for key, dataset_group in groups.items()\r\n    } \r\n    # Return concatenation of all the joined groups\r\n    return concatenate_datasets(groups.values())\r\n```\r\n\r\nexample of usage:\r\n```python\r\n\r\ndef join(batch): \r\n    # take the batch of all the examples of a group, and return a batch with one aggregated example\r\n    # (we could aggregate examples into several rows instead of one, if you want)\r\n    return {\"total\": [batch[\"i\"]]} \r\n\r\nd = Dataset.from_dict({\r\n    \"i\": [i for i in range(50)],\r\n    \"group_key\": [i % 4 for i in range(50)],\r\n})\r\nprint(group_by(d, \"group_key\", join))\r\n#                                                total\r\n# 0  [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48]\r\n# 1  [1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41, 45, 49]\r\n# 2     [2, 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46]\r\n# 3     [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47]\r\n```\r\n\r\nLet me know if that helps !\r\n\r\ncc @albertvillanova @mariosasko for visibility","body":"**Is your feature request related to a problem? Please describe.**\r\nUsing batch mapping, we can easily split examples. However, we lack an appropriate option for merging them back together by some key. Consider this example:\r\n\r\n```python\r\n# features:\r\n# {\r\n#    \"example_id\": datasets.Value(\"int32\"),\r\n#    \"text\": datasets.Value(\"string\")\r\n# }\r\n\r\nds = datasets.Dataset()\r\n\r\n\r\ndef split(examples):\r\n    sentences = [text.split(\".\") for text in examples[\"text\"]]\r\n    return {\r\n        \"example_id\": [\r\n            example_id\r\n            for example_id, sents in zip(examples[\"example_id\"], sentences)\r\n            for _ in sents\r\n        ],\r\n        \"sentence\": [sent for sents in sentences for sent in sents],\r\n        \"sentence_id\": [i for sents in sentences for i in range(len(sents))],\r\n    }\r\n\r\n\r\nsplit_ds = ds.map(split, batched=True)\r\n\r\n\r\ndef process(examples):\r\n    outputs = some_neural_network_that_works_on_sentences(examples[\"sentence\"])\r\n    return {\"outputs\": outputs}\r\n\r\n\r\nsplit_ds = split_ds.map(process, batched=True)\r\n```\r\n\r\nI have a dataset consisting of texts that I would like to process sentence by sentence in a batched way. Afterwards, I would like to put it back together as it was, merging the outputs together.\r\n\r\n**Describe the solution you'd like**\r\nIdeally, it would look something like this:\r\n\r\n```python\r\ndef join(examples):\r\n    order = np.argsort(examples[\"sentence_id\"])\r\n    text = \".\".join(examples[\"text\"][i] for i in order)\r\n    outputs = [examples[\"outputs\"][i] for i in order]\r\n    return {\"text\": text, \"outputs\": outputs}\r\n\r\n\r\nds = split_ds.group_by(\"example_id\", join)\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nRight now, we can do this:\r\n```python\r\ndef merge(example):\r\n    meeting_id = example[\"example_id\"]\r\n    parts = split_ds.filter(lambda x: x[\"example_id\"] == meeting_id).sort(\"segment_no\")\r\n    return {\"outputs\": list(parts[\"outputs\"])}\r\n\r\nds = ds.map(merge)\r\n```\r\n\r\nOf course, we could process the dataset like this:\r\n\r\n```python\r\ndef process(example):\r\n    outputs = some_neural_network_that_works_on_sentences(example[\"text\"].split(\".\"))\r\n    return {\"outputs\": outputs}\r\n\r\nds = ds.map(process, batched=True)\r\n```\r\n\r\nHowever, that does not allow using an arbitrary batch size and may lead to very inefficient use of resources if the batch size is much larger than the number of sentences in one example.\r\n\r\nI would very much appreciate some kind of group by operator to merge examples based on the value of one column.\r\n","comment_length":271,"text":"Add a GROUP BY operator \n **Is your feature request related to a problem? Please describe.**\r\nUsing batch mapping, we can easily split examples. However, we lack an appropriate option for merging them back together by some key. Consider this example:\r\n\r\n```python\r\n# features:\r\n# {\r\n#    \"example_id\": datasets.Value(\"int32\"),\r\n#    \"text\": datasets.Value(\"string\")\r\n# }\r\n\r\nds = datasets.Dataset()\r\n\r\n\r\ndef split(examples):\r\n    sentences = [text.split(\".\") for text in examples[\"text\"]]\r\n    return {\r\n        \"example_id\": [\r\n            example_id\r\n            for example_id, sents in zip(examples[\"example_id\"], sentences)\r\n            for _ in sents\r\n        ],\r\n        \"sentence\": [sent for sents in sentences for sent in sents],\r\n        \"sentence_id\": [i for sents in sentences for i in range(len(sents))],\r\n    }\r\n\r\n\r\nsplit_ds = ds.map(split, batched=True)\r\n\r\n\r\ndef process(examples):\r\n    outputs = some_neural_network_that_works_on_sentences(examples[\"sentence\"])\r\n    return {\"outputs\": outputs}\r\n\r\n\r\nsplit_ds = split_ds.map(process, batched=True)\r\n```\r\n\r\nI have a dataset consisting of texts that I would like to process sentence by sentence in a batched way. Afterwards, I would like to put it back together as it was, merging the outputs together.\r\n\r\n**Describe the solution you'd like**\r\nIdeally, it would look something like this:\r\n\r\n```python\r\ndef join(examples):\r\n    order = np.argsort(examples[\"sentence_id\"])\r\n    text = \".\".join(examples[\"text\"][i] for i in order)\r\n    outputs = [examples[\"outputs\"][i] for i in order]\r\n    return {\"text\": text, \"outputs\": outputs}\r\n\r\n\r\nds = split_ds.group_by(\"example_id\", join)\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nRight now, we can do this:\r\n```python\r\ndef merge(example):\r\n    meeting_id = example[\"example_id\"]\r\n    parts = split_ds.filter(lambda x: x[\"example_id\"] == meeting_id).sort(\"segment_no\")\r\n    return {\"outputs\": list(parts[\"outputs\"])}\r\n\r\nds = ds.map(merge)\r\n```\r\n\r\nOf course, we could process the dataset like this:\r\n\r\n```python\r\ndef process(example):\r\n    outputs = some_neural_network_that_works_on_sentences(example[\"text\"].split(\".\"))\r\n    return {\"outputs\": outputs}\r\n\r\nds = ds.map(process, batched=True)\r\n```\r\n\r\nHowever, that does not allow using an arbitrary batch size and may lead to very inefficient use of resources if the batch size is much larger than the number of sentences in one example.\r\n\r\nI would very much appreciate some kind of group by operator to merge examples based on the value of one column.\r\n \n Hi ! At the moment you can use `to_pandas()` to get a pandas DataFrame that supports `group_by` operations (make sure your dataset fits in memory though)\r\n\r\nWe use Arrow as a back-end for `datasets` and it doesn't have native group by (see https:\/\/github.com\/apache\/arrow\/issues\/2189) unfortunately.\r\n\r\nI just drafted what it could look like to have `group_by` in `datasets`:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n\r\ndef group_by(d, col, join): \r\n    \"\"\"from: https:\/\/github.com\/huggingface\/datasets\/issues\/3644\"\"\"\r\n    # Get the indices of each group\r\n    groups = {key: [] for key in d.unique(col)} \r\n    def create_groups_indices(key, i): \r\n        groups[key].append(i) \r\n    d.map(create_groups_indices, with_indices=True, input_columns=col) \r\n    # Get one dataset object per group\r\n    groups = {key: d.select(indices) for key, indices in groups.items()} \r\n    # Apply join function\r\n    groups = {\r\n        key: dataset_group.map(join, batched=True, batch_size=len(dataset_group), remove_columns=d.column_names)\r\n        for key, dataset_group in groups.items()\r\n    } \r\n    # Return concatenation of all the joined groups\r\n    return concatenate_datasets(groups.values())\r\n```\r\n\r\nexample of usage:\r\n```python\r\n\r\ndef join(batch): \r\n    # take the batch of all the examples of a group, and return a batch with one aggregated example\r\n    # (we could aggregate examples into several rows instead of one, if you want)\r\n    return {\"total\": [batch[\"i\"]]} \r\n\r\nd = Dataset.from_dict({\r\n    \"i\": [i for i in range(50)],\r\n    \"group_key\": [i % 4 for i in range(50)],\r\n})\r\nprint(group_by(d, \"group_key\", join))\r\n#                                                total\r\n# 0  [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48]\r\n# 1  [1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41, 45, 49]\r\n# 2     [2, 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46]\r\n# 3     [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47]\r\n```\r\n\r\nLet me know if that helps !\r\n\r\ncc @albertvillanova @mariosasko for 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3644","title":"Add a GROUP BY operator","comments":"@lhoestq As of PyArrow 7.0.0, `pa.Table` has the [`group_by` method](https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.Table.html#pyarrow.Table.group_by), so we should also consider using that function for grouping. ","body":"**Is your feature request related to a problem? Please describe.**\r\nUsing batch mapping, we can easily split examples. However, we lack an appropriate option for merging them back together by some key. Consider this example:\r\n\r\n```python\r\n# features:\r\n# {\r\n#    \"example_id\": datasets.Value(\"int32\"),\r\n#    \"text\": datasets.Value(\"string\")\r\n# }\r\n\r\nds = datasets.Dataset()\r\n\r\n\r\ndef split(examples):\r\n    sentences = [text.split(\".\") for text in examples[\"text\"]]\r\n    return {\r\n        \"example_id\": [\r\n            example_id\r\n            for example_id, sents in zip(examples[\"example_id\"], sentences)\r\n            for _ in sents\r\n        ],\r\n        \"sentence\": [sent for sents in sentences for sent in sents],\r\n        \"sentence_id\": [i for sents in sentences for i in range(len(sents))],\r\n    }\r\n\r\n\r\nsplit_ds = ds.map(split, batched=True)\r\n\r\n\r\ndef process(examples):\r\n    outputs = some_neural_network_that_works_on_sentences(examples[\"sentence\"])\r\n    return {\"outputs\": outputs}\r\n\r\n\r\nsplit_ds = split_ds.map(process, batched=True)\r\n```\r\n\r\nI have a dataset consisting of texts that I would like to process sentence by sentence in a batched way. Afterwards, I would like to put it back together as it was, merging the outputs together.\r\n\r\n**Describe the solution you'd like**\r\nIdeally, it would look something like this:\r\n\r\n```python\r\ndef join(examples):\r\n    order = np.argsort(examples[\"sentence_id\"])\r\n    text = \".\".join(examples[\"text\"][i] for i in order)\r\n    outputs = [examples[\"outputs\"][i] for i in order]\r\n    return {\"text\": text, \"outputs\": outputs}\r\n\r\n\r\nds = split_ds.group_by(\"example_id\", join)\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nRight now, we can do this:\r\n```python\r\ndef merge(example):\r\n    meeting_id = example[\"example_id\"]\r\n    parts = split_ds.filter(lambda x: x[\"example_id\"] == meeting_id).sort(\"segment_no\")\r\n    return {\"outputs\": list(parts[\"outputs\"])}\r\n\r\nds = ds.map(merge)\r\n```\r\n\r\nOf course, we could process the dataset like this:\r\n\r\n```python\r\ndef process(example):\r\n    outputs = some_neural_network_that_works_on_sentences(example[\"text\"].split(\".\"))\r\n    return {\"outputs\": outputs}\r\n\r\nds = ds.map(process, batched=True)\r\n```\r\n\r\nHowever, that does not allow using an arbitrary batch size and may lead to very inefficient use of resources if the batch size is much larger than the number of sentences in one example.\r\n\r\nI would very much appreciate some kind of group by operator to merge examples based on the value of one column.\r\n","comment_length":20,"text":"Add a GROUP BY operator \n **Is your feature request related to a problem? Please describe.**\r\nUsing batch mapping, we can easily split examples. However, we lack an appropriate option for merging them back together by some key. Consider this example:\r\n\r\n```python\r\n# features:\r\n# {\r\n#    \"example_id\": datasets.Value(\"int32\"),\r\n#    \"text\": datasets.Value(\"string\")\r\n# }\r\n\r\nds = datasets.Dataset()\r\n\r\n\r\ndef split(examples):\r\n    sentences = [text.split(\".\") for text in examples[\"text\"]]\r\n    return {\r\n        \"example_id\": [\r\n            example_id\r\n            for example_id, sents in zip(examples[\"example_id\"], sentences)\r\n            for _ in sents\r\n        ],\r\n        \"sentence\": [sent for sents in sentences for sent in sents],\r\n        \"sentence_id\": [i for sents in sentences for i in range(len(sents))],\r\n    }\r\n\r\n\r\nsplit_ds = ds.map(split, batched=True)\r\n\r\n\r\ndef process(examples):\r\n    outputs = some_neural_network_that_works_on_sentences(examples[\"sentence\"])\r\n    return {\"outputs\": outputs}\r\n\r\n\r\nsplit_ds = split_ds.map(process, batched=True)\r\n```\r\n\r\nI have a dataset consisting of texts that I would like to process sentence by sentence in a batched way. Afterwards, I would like to put it back together as it was, merging the outputs together.\r\n\r\n**Describe the solution you'd like**\r\nIdeally, it would look something like this:\r\n\r\n```python\r\ndef join(examples):\r\n    order = np.argsort(examples[\"sentence_id\"])\r\n    text = \".\".join(examples[\"text\"][i] for i in order)\r\n    outputs = [examples[\"outputs\"][i] for i in order]\r\n    return {\"text\": text, \"outputs\": outputs}\r\n\r\n\r\nds = split_ds.group_by(\"example_id\", join)\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nRight now, we can do this:\r\n```python\r\ndef merge(example):\r\n    meeting_id = example[\"example_id\"]\r\n    parts = split_ds.filter(lambda x: x[\"example_id\"] == meeting_id).sort(\"segment_no\")\r\n    return {\"outputs\": list(parts[\"outputs\"])}\r\n\r\nds = ds.map(merge)\r\n```\r\n\r\nOf course, we could process the dataset like this:\r\n\r\n```python\r\ndef process(example):\r\n    outputs = some_neural_network_that_works_on_sentences(example[\"text\"].split(\".\"))\r\n    return {\"outputs\": outputs}\r\n\r\nds = ds.map(process, batched=True)\r\n```\r\n\r\nHowever, that does not allow using an arbitrary batch size and may lead to very inefficient use of resources if the batch size is much larger than the number of sentences in one example.\r\n\r\nI would very much appreciate some kind of group by operator to merge examples based on the value of one column.\r\n \n @lhoestq As of PyArrow 7.0.0, `pa.Table` has the [`group_by` method](https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.Table.html#pyarrow.Table.group_by), so we should also consider using that function for grouping. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3639","title":"same value of precision, recall, f1 score at each epoch for classification task. ","comments":"Hi @Dhanachandra, \r\n\r\nWe have tests for all our metrics and they work as expected: under the hood, we use scikit-learn implementations.\r\n\r\nMaybe the cause is somewhere else. For example:\r\n- Is it a binary or a multiclass or a multilabel classification? Default computation of these metrics is for binary classification; if you would like multiclass or multilabel, you should pass the corresponding parameters; see their documentation (e.g.: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.precision_score.html) or code below:\r\n\r\nhttps:\/\/huggingface.co\/docs\/datasets\/using_metrics.html#computing-the-metric-scores\r\n\r\n```python\r\nIn [1]: from datasets import load_metric\r\n\r\nIn [2]: precision = load_metric(\"precision\")\r\n\r\nIn [3]: print(precision.inputs_description)\r\n\r\nArgs:\r\n    predictions: Predicted labels, as returned by a model.\r\n    references: Ground truth labels.\r\n    labels: The set of labels to include when average != 'binary', and\r\n        their order if average is None. Labels present in the data can\r\n        be excluded, for example to calculate a multiclass average ignoring\r\n        a majority negative class, while labels not present in the data will\r\n        result in 0 components in a macro average. For multilabel targets,\r\n        labels are column indices. By default, all labels in y_true and\r\n        y_pred are used in sorted order.\r\n    average: This parameter is required for multiclass\/multilabel targets.\r\n        If None, the scores for each class are returned. Otherwise, this\r\n        determines the type of averaging performed on the data:\r\n            binary: Only report results for the class specified by pos_label.\r\n                This is applicable only if targets (y_{true,pred}) are binary.\r\n            micro: Calculate metrics globally by counting the total true positives,\r\n                false negatives and false positives.\r\n            macro: Calculate metrics for each label, and find their unweighted mean.\r\n                This does not take label imbalance into account.\r\n            weighted: Calculate metrics for each label, and find their average\r\n                weighted by support (the number of true instances for each label).\r\n                This alters \u2018macro\u2019 to account for label imbalance; it can result\r\n                in an F-score that is not between precision and recall.\r\n            samples: Calculate metrics for each instance, and find their average\r\n                (only meaningful for multilabel classification).\r\n    sample_weight: Sample weights.\r\n\r\nReturns:\r\n    precision: Precision score.\r\n\r\nExamples:\r\n\r\n    >>> precision_metric = datasets.load_metric(\"precision\")\r\n    >>> results = precision_metric.compute(references=[0, 1], predictions=[0, 1])\r\n    >>> print(results)\r\n    {'precision': 1.0}\r\n\r\n    >>> predictions = [0, 2, 1, 0, 0, 1]\r\n    >>> references = [0, 1, 2, 0, 1, 2]\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')\r\n    >>> print(results)\r\n    {'precision': 0.2222222222222222}\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')\r\n    >>> print(results)\r\n    {'precision': 0.3333333333333333}\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')\r\n    >>> print(results)\r\n    {'precision': 0.2222222222222222}\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\r\n    >>> print(results)\r\n    {'precision': array([0.66666667, 0.        , 0.        ])}\r\n```\r\n","body":"**1st Epoch:** \r\n1\/27\/2022 09:30:48 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/f1\/default\/default_experiment-1-0.arrow.59it\/s]\r\n01\/27\/2022 09:30:48 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/precision\/default\/default_experiment-1-0.arrow\r\n01\/27\/2022 09:30:49 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/recall\/default\/default_experiment-1-0.arrow\r\nPRECISION:  {'precision': 0.7612903225806451}\r\nRECALL:  {'recall': 0.7612903225806451}\r\nF1:  {'f1': 0.7612903225806451}\r\n{'eval_loss': 1.4658324718475342, 'eval_accuracy': 0.7612903118133545, 'eval_runtime': 30.0054, 'eval_samples_per_second': 46.492, 'eval_steps_per_second': 46.492, 'epoch': 3.0} \r\n**4th Epoch:**\r\n1\/27\/2022 09:56:55 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/f1\/default\/default_experiment-1-0.arrow.92it\/s]\r\n01\/27\/2022 09:56:56 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/precision\/default\/default_experiment-1-0.arrow\r\n01\/27\/2022 09:56:56 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/recall\/default\/default_experiment-1-0.arrow\r\nPRECISION:  {'precision': 0.7698924731182796}\r\nRECALL:  {'recall': 0.7698924731182796}\r\nF1:  {'f1': 0.7698924731182796}\r\n\r\n\r\n## Environment info\r\n!git clone https:\/\/github.com\/huggingface\/transformers\r\n%cd transformers\r\n!pip install .\r\n!pip install -r \/content\/transformers\/examples\/pytorch\/token-classification\/requirements.txt\r\n!pip install datasets","comment_length":398,"text":"same value of precision, recall, f1 score at each epoch for classification task.  \n **1st Epoch:** \r\n1\/27\/2022 09:30:48 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/f1\/default\/default_experiment-1-0.arrow.59it\/s]\r\n01\/27\/2022 09:30:48 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/precision\/default\/default_experiment-1-0.arrow\r\n01\/27\/2022 09:30:49 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/recall\/default\/default_experiment-1-0.arrow\r\nPRECISION:  {'precision': 0.7612903225806451}\r\nRECALL:  {'recall': 0.7612903225806451}\r\nF1:  {'f1': 0.7612903225806451}\r\n{'eval_loss': 1.4658324718475342, 'eval_accuracy': 0.7612903118133545, 'eval_runtime': 30.0054, 'eval_samples_per_second': 46.492, 'eval_steps_per_second': 46.492, 'epoch': 3.0} \r\n**4th Epoch:**\r\n1\/27\/2022 09:56:55 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/f1\/default\/default_experiment-1-0.arrow.92it\/s]\r\n01\/27\/2022 09:56:56 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/precision\/default\/default_experiment-1-0.arrow\r\n01\/27\/2022 09:56:56 - INFO - datasets.metric - Removing \/home\/ubuntu\/.cache\/huggingface\/metrics\/recall\/default\/default_experiment-1-0.arrow\r\nPRECISION:  {'precision': 0.7698924731182796}\r\nRECALL:  {'recall': 0.7698924731182796}\r\nF1:  {'f1': 0.7698924731182796}\r\n\r\n\r\n## Environment info\r\n!git clone https:\/\/github.com\/huggingface\/transformers\r\n%cd transformers\r\n!pip install .\r\n!pip install -r \/content\/transformers\/examples\/pytorch\/token-classification\/requirements.txt\r\n!pip install datasets \n Hi @Dhanachandra, \r\n\r\nWe have tests for all our metrics and they work as expected: under the hood, we use scikit-learn implementations.\r\n\r\nMaybe the cause is somewhere else. For example:\r\n- Is it a binary or a multiclass or a multilabel classification? Default computation of these metrics is for binary classification; if you would like multiclass or multilabel, you should pass the corresponding parameters; see their documentation (e.g.: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.precision_score.html) or code below:\r\n\r\nhttps:\/\/huggingface.co\/docs\/datasets\/using_metrics.html#computing-the-metric-scores\r\n\r\n```python\r\nIn [1]: from datasets import load_metric\r\n\r\nIn [2]: precision = load_metric(\"precision\")\r\n\r\nIn [3]: print(precision.inputs_description)\r\n\r\nArgs:\r\n    predictions: Predicted labels, as returned by a model.\r\n    references: Ground truth labels.\r\n    labels: The set of labels to include when average != 'binary', and\r\n        their order if average is None. Labels present in the data can\r\n        be excluded, for example to calculate a multiclass average ignoring\r\n        a majority negative class, while labels not present in the data will\r\n        result in 0 components in a macro average. For multilabel targets,\r\n        labels are column indices. By default, all labels in y_true and\r\n        y_pred are used in sorted order.\r\n    average: This parameter is required for multiclass\/multilabel targets.\r\n        If None, the scores for each class are returned. Otherwise, this\r\n        determines the type of averaging performed on the data:\r\n            binary: Only report results for the class specified by pos_label.\r\n                This is applicable only if targets (y_{true,pred}) are binary.\r\n            micro: Calculate metrics globally by counting the total true positives,\r\n                false negatives and false positives.\r\n            macro: Calculate metrics for each label, and find their unweighted mean.\r\n                This does not take label imbalance into account.\r\n            weighted: Calculate metrics for each label, and find their average\r\n                weighted by support (the number of true instances for each label).\r\n                This alters \u2018macro\u2019 to account for label imbalance; it can result\r\n                in an F-score that is not between precision and recall.\r\n            samples: Calculate metrics for each instance, and find their average\r\n                (only meaningful for multilabel classification).\r\n    sample_weight: Sample weights.\r\n\r\nReturns:\r\n    precision: Precision score.\r\n\r\nExamples:\r\n\r\n    >>> precision_metric = datasets.load_metric(\"precision\")\r\n    >>> results = precision_metric.compute(references=[0, 1], predictions=[0, 1])\r\n    >>> print(results)\r\n    {'precision': 1.0}\r\n\r\n    >>> predictions = [0, 2, 1, 0, 0, 1]\r\n    >>> references = [0, 1, 2, 0, 1, 2]\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')\r\n    >>> print(results)\r\n    {'precision': 0.2222222222222222}\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')\r\n    >>> print(results)\r\n    {'precision': 0.3333333333333333}\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')\r\n    >>> print(results)\r\n    {'precision': 0.2222222222222222}\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\r\n    >>> print(results)\r\n    {'precision': array([0.66666667, 0.        , 0.        ])}\r\n```\r\n","embeddings":[-0.363080591,-0.5349164009,-0.1348887533,0.3408069611,0.3478322327,-0.1159789339,0.0651122332,0.0968725905,0.113320969,0.3607126474,-0.2553243339,0.0797416568,-0.0859256685,0.1978700757,-0.346848011,-0.0864400715,-0.1220594868,0.0709547326,-0.172645241,-0.3670738339,-0.1983228028,0.0767815784,-0.1609514207,-0.189760685,-0.1447436064,-0.0671929345,0.0870443806,-0.2550611496,0.105969891,-0.2330175489,0.0666293427,0.1758299172,-0.0731638893,0.4503523111,-0.0001074554,-0.166098401,0.0875954255,0.0527953468,-0.0412163623,0.1953305453,-0.2632068396,0.1074929014,0.037423376,-0.3219937384,-0.3362590373,-0.0808990747,-0.0956826136,-0.3925581276,0.4333877861,0.0644742176,0.2285597324,0.243032366,-0.1337979287,-0.180608511,-0.2185883522,0.0840309039,-0.2104355395,0.2881695926,-0.3420954049,0.2329456955,-0.0997925326,0.49025774,0.2781395316,0.3199638128,0.3722534776,-0.0029810783,0.2627192438,-0.2027817219,0.0568232238,0.0494337641,0.1166930422,-0.0278458241,-0.4261944592,0.0181684531,0.0286212564,-0.6254861951,-0.205535382,0.1510664225,-0.0350375697,0.0511781015,-0.2637854815,0.2046987712,-0.4194371104,-0.1944323629,-0.0300671812,0.2777183354,-0.1411836892,0.1148991436,0.1639353782,-0.0428727306,-0.1417563856,0.0387254618,0.0698472261,0.0513348021,-0.5178251266,-0.0590772294,-0.1521456987,0.32431674,0.1087499037,0.281958282,0.1727223098,0.2318883836,0.2882585824,-0.077139914,-0.2691071928,0.6583000422,0.4242798984,0.1062558964,0.053670004,0.1117003411,-0.2849034071,-0.0181805901,0.2381246537,-0.0070892279,0.4110021293,0.0358718708,-0.1370849162,-0.2375721037,-0.3669945002,0.135076791,-0.2749371529,-0.1440572441,-0.1149682328,0.3504874706,-0.317185998,0.0682933182,-0.0967221782,-0.034878958,-0.0457173549,-0.0484945178,-0.3673635721,0.030618703,-0.2563114166,0.171435833,0.0532185249,-0.1677173674,0.4094257355,0.1684539765,-0.1156652868,-0.316947788,0.1067087501,-0.2530526519,0.1909887493,0.0792977065,-0.1858451217,0.1231450066,0.038523145,-0.0459869653,-0.0854625553,-0.2121753246,0.04494204,0.0948242471,0.2319287956,0.3102006018,-0.0546044335,0.007472713,-0.1611372083,0.2893581986,-0.1400333643,0.3458944857,0.2522304356,-0.0974023268,-0.1922782362,-0.1453226507,0.1850777566,0.2195886523,0.0342715122,-0.3888315558,0.2732219696,-0.053858377,0.1948771924,0.5466176271,0.1284097284,-0.1622762978,-0.0158605445,0.0869971365,0.1575419754,-0.4299239516,-0.3581172824,-0.0113836937,-0.060492754,0.0299574994,0.1884784549,0.0488247685,0.2150959224,-0.0287658833,0.1264807433,-0.2156106979,0.1475554705,-0.0990040153,-0.3290174901,-0.1591979265,-0.0695819408,-0.0295128915,0.109730348,-0.2451648712,0.0366931446,-0.144506231,0.0437550098,-0.1329924762,0.0442499556,0.1855325848,0.1693263948,-0.3450786471,-0.0029760182,-0.1303439885,-0.2754619122,0.2310290337,-0.2273091525,0.0924515203,0.4162734747,-0.3824858367,-0.2648028731,-0.1191323698,-0.3082906008,-0.298554033,0.2295023799,0.0987996459,0.2453044057,0.0367562436,-0.0685974807,0.4368590117,-0.1745294183,0.0935337394,-0.0276749972,-0.2173832655,-0.1588425487,-0.0637661591,0.0214602482,0.0675125197,0.4013673365,-0.0255446881,0.0553029925,0.2208456099,-0.0675332919,0.3289401829,0.0140578113,0.30615592,0.3109261096,-0.1223383471,-0.0173461922,-0.0480885208,-0.0849514231,0.0231523905,-0.0548474044,0.468870461,0.3409278095,0.0205646064,0.1307309121,-0.23797822,-0.1399385929,-0.2294663787,-0.1207031235,-0.3321445286,0.3471297026,-0.1810747981,0.4134418368,-0.0318479277,-0.4896856844,0.1922650337,0.3381737471,-0.057285022,0.1750863642,-0.0869852602,-0.2449291348,-0.0728246719,-0.0094225593,0.0009693991,0.3785572946,0.2256960869,0.2842047811,0.2803961337,-0.1041428372,-0.1674134433,0.1510547251,-0.0491268374,0.1766267568,0.2332819104,0.1903264076,0.1768172979,-0.4134241641,-0.0196296945,-0.2118805349,0.0465414599,-0.1503682882,-0.063728638,-0.284915179,0.0693872198,-0.3819268048,-0.1036624685,-0.1178488582,-0.0633047074,-0.0507847518,-0.0903640464,0.0722126514,0.2306317091,0.1457494348,0.4624323845,0.0365798697,0.4673592746,-0.2767843008,-0.1236720756,-0.0956571549,0.1075766981,-0.1657754183,0.0970071703,0.4396247566,-0.234695375,0.0380775556,-0.0868306458,-0.4365196228,0.0723414049,0.1113230288,0.2992518842,-0.0367067978,0.017403774,-0.2437954992,-0.1270801425,0.1441874057,-0.4181399345,-0.2632300258,-0.517360568,-0.1276029199,0.0799000412,-0.2181191742,-0.1043053046,-0.0332680792,-0.1472749412,0.5340277553,0.0377776548,0.0784479156,0.2887876034,-0.3131586313,0.095566541,0.1972413957,0.2237526476,-0.6303911805,-0.2110383064,0.1364924908,-0.3628213704,-0.3143248856,-0.1594119072,-0.077983506,0.3637910783,0.1062545925,-0.2663221061,-0.4045942724,-0.072131969,-0.1223437563,-0.1737003028,0.0328135379,0.1293150485,-0.195535332,-0.1182528287,-0.0394818671,-0.3033211827,0.3744620085,-0.082440123,0.0170600638,-0.3322844505,0.3049956858,0.1172725484,0.664300859,0.3694365025,-0.1278337538,0.0607330352,0.0903304294,0.2415251881,-0.1015394181,-0.0590534322,0.4608382285,0.1742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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3638","title":"AutoTokenizer hash value got change after datasets.map","comments":"This issue was original reported at https:\/\/github.com\/huggingface\/transformers\/issues\/14931 and It seems like this issue also occur with other AutoClass like AutoFeatureExtractor.","body":"## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n","comment_length":20,"text":"AutoTokenizer hash value got change after datasets.map \n ## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n \n This issue was original reported at https:\/\/github.com\/huggingface\/transformers\/issues\/14931 and It seems like this issue also occur with other AutoClass like AutoFeatureExtractor.","embeddings":[-0.1290389895,-0.2237777412,0.0150257908,0.3089294136,0.1342905015,-0.1396988332,0.2722429335,-0.0213704128,0.1214969531,0.1917948574,-0.1113677323,0.4286390245,-0.0865156427,-0.1164643839,-0.093268536,0.2705232799,0.1173472106,0.2221373171,-0.1098648459,-0.115385063,-0.0433059596,0.1107288972,-0.160367012,-0.1080356836,-0.3463075161,0.0401016995,0.0112999491,-0.3782944381,0.0619351976,-0.4243637621,0.1362453401,0.1397164613,0.1863562167,0.2984294295,-0.0001171864,0.0206879359,0.2634544671,0.0814768672,0.2142258734,0.0227778312,-0.2633487284,0.2867546976,-0.0680297837,-0.2092536986,-0.1148338914,0.0637526661,-0.2346738726,-0.4112156332,0.4010907114,0.1370520145,0.1676609218,0.265053153,-0.0295746904,0.1561013162,0.1950607598,-0.2246259749,-0.0078154467,-0.1290911138,-0.1391805112,-0.1560953259,-0.260060966,0.4577957392,0.0652744919,0.0406456515,0.2493761182,0.1124404669,-0.0790539756,-0.1640958488,0.2876113653,0.0402858108,0.1305542141,-0.3388209939,-0.1817626804,-0.0899926275,-0.0903692394,-0.0305115972,0.3779542744,-0.1524048746,0.4101909697,0.2683367431,-0.4246233404,0.1250701398,0.0212593544,0.0642474368,-0.1634887308,0.2503798008,-0.2617929876,0.1431821436,0.0500419587,-0.1286612451,-0.2013938129,0.0033287928,-0.0929486975,0.1595355719,-0.0953543186,-0.2332700491,0.0361989811,0.2379884571,0.088070035,0.2050545663,-0.0059785983,0.2491226643,-0.3129657209,-0.0035656653,-0.2320785522,0.2290501297,0.2229257375,0.0678976402,0.0071965102,-0.0135630518,-0.3116592765,-0.1306877285,0.1639947891,0.0017765176,0.6122227311,0.026759211,0.000423697,-0.0426872261,0.1696216762,0.0610469505,-0.176026985,0.0386033691,0.0206214357,0.2798212469,-0.2673545778,0.0977701321,-0.4449682236,0.0592899173,-0.0858648419,-0.0716235638,-0.2777846754,-0.2363528162,-0.1498707533,0.2740612626,0.018678667,-0.138920784,0.4285882711,0.224912867,-0.2019874156,0.0052161384,0.2339090705,-0.1477645785,0.3670988679,0.0141577981,-0.1002950519,0.3968282938,-0.0625139251,0.1093774289,-0.1585982442,0.0867584869,-0.4202547073,-0.1149113253,0.1427502483,0.1810493916,-0.2359759063,-0.1470829099,-0.0123531604,-0.0943361074,0.5517305732,0.1885139793,0.2053229511,-0.1906185001,-0.3774396479,-0.1670985967,0.1371552646,0.429928273,0.1415239871,-0.3465373516,-0.0069267899,0.1113580242,0.144919619,0.1601531506,-0.1594061852,0.1631575823,-0.2525579035,0.4392732978,-0.1229568645,-0.3600201309,-0.8233261108,-0.0825346261,-0.0226957891,0.2045874745,0.0318595804,-0.0521903224,0.1389169693,0.0396046005,0.1750185341,0.0284431465,0.1858418584,0.1434478164,-0.20178096,-0.3123971224,0.1794166863,-0.1200787425,0.1925228834,-0.0996570513,0.1055537686,0.2651252151,0.0480860732,0.1697579175,0.1798374951,0.096684508,0.228670314,-0.0949888751,0.0694238245,-0.1184869334,-0.2058321387,0.2039507776,-0.2798772454,-0.1156386882,-0.2133646756,-0.2392555773,-0.1242336705,-0.1246243417,-0.2508599758,-0.3404219151,0.0978192687,0.2711330652,0.2151311785,-0.0283064283,-0.0373322517,0.2418347895,0.2313210368,0.1175898463,-0.408470124,0.0859987885,0.1761122644,-0.0131876478,-0.3359203637,0.0784226656,0.2836026549,-0.1195491701,-0.2199673206,0.2823270857,0.0105302343,0.2286410481,0.0030768341,0.1484373808,0.3184492886,-0.2953836322,0.1541731358,0.1367931217,0.010563571,-0.0349391177,0.2280777693,0.2604185045,0.1618883014,-0.1064367145,-0.2310633957,-0.1776387095,0.143085286,-0.2034890205,-0.0414632,-0.6310230494,-0.2869870663,-0.0399209447,0.4664593637,0.3052936494,0.3841997385,0.2170520872,0.4824305773,0.0446157716,-0.0634299219,0.0985600948,-0.6580629349,-0.2892426252,-0.0690553859,0.0885554031,0.3110248744,0.1416464448,0.0175059997,-0.0136347124,0.1301017851,-0.0981583148,0.0765925273,-0.0881243497,-0.0408726595,0.1614612192,0.2415743023,0.10956949,-0.0566446483,0.2523660362,0.092473425,0.1726307571,-0.2688057125,-0.1953448504,-0.4462532699,0.2175259888,-0.1089922935,-0.3932588398,-0.2174677104,-0.2994203866,-0.0486758724,-0.0266145077,0.0640528649,0.2963679731,-0.0371520817,0.121508792,0.1712560058,0.010051894,-0.1866554618,-0.1086469442,-0.1959078759,-0.0628710017,-0.1262022704,-0.2984254658,0.246213451,-0.322111547,-0.0503055155,-0.4367668331,-0.4157623053,0.039737165,-0.0142618464,0.3031576872,0.0843426362,-0.3237101436,-0.0513597131,0.0596320257,0.1031402797,-0.3965801001,-0.1878370047,0.0169542767,-0.0608519241,0.1163916439,0.1418776065,-0.1144008189,-0.2012399882,0.1851786822,0.0206523668,0.1596389413,-0.0030977547,0.232596606,-0.1871893406,0.0373546146,-0.3481857479,-0.1306055486,-0.4223102629,-0.5347287059,0.440870434,-0.2120868266,-0.0973363519,-0.224679336,-0.0642814189,0.1365156472,0.1383520067,-0.3518812656,-0.3388182223,-0.4628320932,0.2229212224,-0.0906214267,0.0774424151,0.1990380585,0.0000101998,0.0213525761,-0.1795017719,-0.0962906554,0.4276157022,-0.2056139261,0.1141735315,0.1681474447,0.2394112498,0.1121957004,0.6299887896,0.2836839557,-0.4267061055,0.3919728696,-0.0654886514,0.08812657,-0.1985778213,-0.2495500892,-0.1474831849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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3638","title":"AutoTokenizer hash value got change after datasets.map","comments":"Thanks for moving the issue here !\r\n\r\nI wasn't able to reproduce the issue on my env (the hashes stay the same):\r\n```\r\n- `transformers` version: 1.15.0\r\n- `tokenizers` version: 0.10.3\r\n- `datasets` version: 1.18.1\r\n- `dill` version: 0.3.4\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11\r\n- Python version: 3.7.10\r\n- PyArrow version: 6.0.1\r\n```\r\nHowever I was able to reproduce it on Google Colab (the hashes end up different):\r\n```\r\n- `transformers` version: 1.15.0\r\n- `tokenizers` version: 0.10.3\r\n- `datasets` version: 1.18.1\r\n- `dill` version: 0.3.4\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n```\r\nI'll investigate why it doesn't work properly on Google Colab :)","body":"## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n","comment_length":106,"text":"AutoTokenizer hash value got change after datasets.map \n ## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n \n Thanks for moving the issue here !\r\n\r\nI wasn't able to reproduce the issue on my env (the hashes stay the same):\r\n```\r\n- `transformers` version: 1.15.0\r\n- `tokenizers` version: 0.10.3\r\n- `datasets` version: 1.18.1\r\n- `dill` version: 0.3.4\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11\r\n- Python version: 3.7.10\r\n- PyArrow version: 6.0.1\r\n```\r\nHowever I was able to reproduce it on Google Colab (the hashes end up different):\r\n```\r\n- `transformers` version: 1.15.0\r\n- `tokenizers` version: 0.10.3\r\n- `datasets` version: 1.18.1\r\n- `dill` version: 0.3.4\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n```\r\nI'll investigate why it doesn't work properly on Google Colab :)","embeddings":[-0.1290389895,-0.2237777412,0.0150257908,0.3089294136,0.1342905015,-0.1396988332,0.2722429335,-0.0213704128,0.1214969531,0.1917948574,-0.1113677323,0.4286390245,-0.0865156427,-0.1164643839,-0.093268536,0.2705232799,0.1173472106,0.2221373171,-0.1098648459,-0.115385063,-0.0433059596,0.1107288972,-0.160367012,-0.1080356836,-0.3463075161,0.0401016995,0.0112999491,-0.3782944381,0.0619351976,-0.4243637621,0.1362453401,0.1397164613,0.1863562167,0.2984294295,-0.0001171864,0.0206879359,0.2634544671,0.0814768672,0.2142258734,0.0227778312,-0.2633487284,0.2867546976,-0.0680297837,-0.2092536986,-0.1148338914,0.0637526661,-0.2346738726,-0.4112156332,0.4010907114,0.1370520145,0.1676609218,0.265053153,-0.0295746904,0.1561013162,0.1950607598,-0.2246259749,-0.0078154467,-0.1290911138,-0.1391805112,-0.1560953259,-0.260060966,0.4577957392,0.0652744919,0.0406456515,0.2493761182,0.1124404669,-0.0790539756,-0.1640958488,0.2876113653,0.0402858108,0.1305542141,-0.3388209939,-0.1817626804,-0.0899926275,-0.0903692394,-0.0305115972,0.3779542744,-0.1524048746,0.4101909697,0.2683367431,-0.4246233404,0.1250701398,0.0212593544,0.0642474368,-0.1634887308,0.2503798008,-0.2617929876,0.1431821436,0.0500419587,-0.1286612451,-0.2013938129,0.0033287928,-0.0929486975,0.1595355719,-0.0953543186,-0.2332700491,0.0361989811,0.2379884571,0.088070035,0.2050545663,-0.0059785983,0.2491226643,-0.3129657209,-0.0035656653,-0.2320785522,0.2290501297,0.2229257375,0.0678976402,0.0071965102,-0.0135630518,-0.3116592765,-0.1306877285,0.1639947891,0.0017765176,0.6122227311,0.026759211,0.000423697,-0.0426872261,0.1696216762,0.0610469505,-0.176026985,0.0386033691,0.0206214357,0.2798212469,-0.2673545778,0.0977701321,-0.4449682236,0.0592899173,-0.0858648419,-0.0716235638,-0.2777846754,-0.2363528162,-0.1498707533,0.2740612626,0.018678667,-0.138920784,0.4285882711,0.224912867,-0.2019874156,0.0052161384,0.2339090705,-0.1477645785,0.3670988679,0.0141577981,-0.1002950519,0.3968282938,-0.0625139251,0.1093774289,-0.1585982442,0.0867584869,-0.4202547073,-0.1149113253,0.1427502483,0.1810493916,-0.2359759063,-0.1470829099,-0.0123531604,-0.0943361074,0.5517305732,0.1885139793,0.2053229511,-0.1906185001,-0.3774396479,-0.1670985967,0.1371552646,0.429928273,0.1415239871,-0.3465373516,-0.0069267899,0.1113580242,0.144919619,0.1601531506,-0.1594061852,0.1631575823,-0.2525579035,0.4392732978,-0.1229568645,-0.3600201309,-0.8233261108,-0.0825346261,-0.0226957891,0.2045874745,0.0318595804,-0.0521903224,0.1389169693,0.0396046005,0.1750185341,0.0284431465,0.1858418584,0.1434478164,-0.20178096,-0.3123971224,0.1794166863,-0.1200787425,0.1925228834,-0.0996570513,0.1055537686,0.2651252151,0.0480860732,0.1697579175,0.1798374951,0.096684508,0.228670314,-0.0949888751,0.0694238245,-0.1184869334,-0.2058321387,0.2039507776,-0.2798772454,-0.1156386882,-0.2133646756,-0.2392555773,-0.1242336705,-0.1246243417,-0.2508599758,-0.3404219151,0.0978192687,0.2711330652,0.2151311785,-0.0283064283,-0.0373322517,0.2418347895,0.2313210368,0.1175898463,-0.408470124,0.0859987885,0.1761122644,-0.0131876478,-0.3359203637,0.0784226656,0.2836026549,-0.1195491701,-0.2199673206,0.2823270857,0.0105302343,0.2286410481,0.0030768341,0.1484373808,0.3184492886,-0.2953836322,0.1541731358,0.1367931217,0.010563571,-0.0349391177,0.2280777693,0.2604185045,0.1618883014,-0.1064367145,-0.2310633957,-0.1776387095,0.143085286,-0.2034890205,-0.0414632,-0.6310230494,-0.2869870663,-0.0399209447,0.4664593637,0.3052936494,0.3841997385,0.2170520872,0.4824305773,0.0446157716,-0.0634299219,0.0985600948,-0.6580629349,-0.2892426252,-0.0690553859,0.0885554031,0.3110248744,0.1416464448,0.0175059997,-0.0136347124,0.1301017851,-0.0981583148,0.0765925273,-0.0881243497,-0.0408726595,0.1614612192,0.2415743023,0.10956949,-0.0566446483,0.2523660362,0.092473425,0.1726307571,-0.2688057125,-0.1953448504,-0.4462532699,0.2175259888,-0.1089922935,-0.3932588398,-0.2174677104,-0.2994203866,-0.0486758724,-0.0266145077,0.0640528649,0.2963679731,-0.0371520817,0.121508792,0.1712560058,0.010051894,-0.1866554618,-0.1086469442,-0.1959078759,-0.0628710017,-0.1262022704,-0.2984254658,0.246213451,-0.322111547,-0.0503055155,-0.4367668331,-0.4157623053,0.039737165,-0.0142618464,0.3031576872,0.0843426362,-0.3237101436,-0.0513597131,0.0596320257,0.1031402797,-0.3965801001,-0.1878370047,0.0169542767,-0.0608519241,0.1163916439,0.1418776065,-0.1144008189,-0.2012399882,0.1851786822,0.0206523668,0.1596389413,-0.0030977547,0.232596606,-0.1871893406,0.0373546146,-0.3481857479,-0.1306055486,-0.4223102629,-0.5347287059,0.440870434,-0.2120868266,-0.0973363519,-0.224679336,-0.0642814189,0.1365156472,0.1383520067,-0.3518812656,-0.3388182223,-0.4628320932,0.2229212224,-0.0906214267,0.0774424151,0.1990380585,0.0000101998,0.0213525761,-0.1795017719,-0.0962906554,0.4276157022,-0.2056139261,0.1141735315,0.1681474447,0.2394112498,0.1121957004,0.6299887896,0.2836839557,-0.4267061055,0.3919728696,-0.0654886514,0.08812657,-0.1985778213,-0.2495500892,-0.1474831849,0.1235787943,0.297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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3638","title":"AutoTokenizer hash value got change after datasets.map","comments":"I found the issue: the tokenizer has something inside it that changes.\r\n\r\nBefore the call, `tokenizer._tokenizer.truncation` is None, and after the call it changes to this for some reason:\r\n```\r\n{'max_length': 512, 'strategy': 'longest_first', 'stride': 0}\r\n```\r\n\r\nDoes anybody know why calling the tokenizer would change its state this way ? cc @Narsil @SaulLu maybe ?","body":"## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n","comment_length":56,"text":"AutoTokenizer hash value got change after datasets.map \n ## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  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1\/1 [00:00<00:00, 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2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n \n I found the issue: the tokenizer has something inside it that changes.\r\n\r\nBefore the call, `tokenizer._tokenizer.truncation` is None, and after the call it changes to this for some reason:\r\n```\r\n{'max_length': 512, 'strategy': 'longest_first', 'stride': 0}\r\n```\r\n\r\nDoes anybody know why calling the tokenizer would change its state this way ? cc @Narsil @SaulLu maybe 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3638","title":"AutoTokenizer hash value got change after datasets.map","comments":"`tokenizer.encode(..)` does not accept argument like max_length, strategy or stride.\r\n\r\nIn `tokenizers` you have to modify the tokenizer state by setting various `TruncationParams` (and\/or `PaddingParams`).\r\nHowever, since this is modifying the state, you need to mutably borrow the tokenizer (a rust concept). The key principle is that there can ever be only 1 mutable borrow at a time during the span of the tokenizer lifecycle.\r\n\r\nBecause of this, if `transformers` blindly set  `TruncationParams` and `PaddingParams` on every call, it would cause the tokenizer to crash (or make the various threads accessing it hang, which is not necessarily better).\r\n\r\nIn order to avoid that, we decided to handle it this way : https:\/\/github.com\/huggingface\/transformers\/pull\/12550 . \r\n\r\nWhich should explain the state of the tokenizer being modified (hence its hash).\r\n\r\nNow for a temporary solution, simply encoding once with the tokenizer should give it it's proper hash (since by default the tokenizer doesn't have this state, looks at the first encoding call, and creates it).\r\n\r\nWe could try and set these 2 dicts at initialization time, but it wouldn't work if a user modified the tokenizer state later\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(..)\r\ntokenizer.truncation_side = \"left\"\r\n# Now we have a difference between `tokenizer._tokenizer.truncation` and `tokenizer.truncation_side`\r\n```\r\nIf we wanted to fix it correctly it would mean mapping every assignation to it's proper location on `tokenizer.{padding\/truncation}`\r\n\r\nI think it's important to note that we cannot guarantee a tokenizer' hash remains the same if *any* of those parameters are modified through the `.map` function.\r\n\r\nEdit: Another option would be to override the default __hash__ function, but I don't know if there's  a sound implementation that could fit.","body":"## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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4\/4 [00:00<00:00,  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1\/1 [00:00<00:00, 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2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n","comment_length":271,"text":"AutoTokenizer hash value got change after datasets.map \n ## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n \n `tokenizer.encode(..)` does not accept argument like max_length, strategy or stride.\r\n\r\nIn `tokenizers` you have to modify the tokenizer state by setting various `TruncationParams` (and\/or `PaddingParams`).\r\nHowever, since this is modifying the state, you need to mutably borrow the tokenizer (a rust concept). The key principle is that there can ever be only 1 mutable borrow at a time during the span of the tokenizer lifecycle.\r\n\r\nBecause of this, if `transformers` blindly set  `TruncationParams` and `PaddingParams` on every call, it would cause the tokenizer to crash (or make the various threads accessing it hang, which is not necessarily better).\r\n\r\nIn order to avoid that, we decided to handle it this way : https:\/\/github.com\/huggingface\/transformers\/pull\/12550 . \r\n\r\nWhich should explain the state of the tokenizer being modified (hence its hash).\r\n\r\nNow for a temporary solution, simply encoding once with the tokenizer should give it it's proper hash (since by default the tokenizer doesn't have this state, looks at the first encoding call, and creates it).\r\n\r\nWe could try and set these 2 dicts at initialization time, but it wouldn't work if a user modified the tokenizer state later\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(..)\r\ntokenizer.truncation_side = \"left\"\r\n# Now we have a difference between `tokenizer._tokenizer.truncation` and `tokenizer.truncation_side`\r\n```\r\nIf we wanted to fix it correctly it would mean mapping every assignation to it's proper location on `tokenizer.{padding\/truncation}`\r\n\r\nI think it's important to note that we cannot guarantee a tokenizer' hash remains the same if *any* of those parameters are modified through the `.map` function.\r\n\r\nEdit: Another option would be to override the default __hash__ function, but I don't know if there's  a sound implementation that could 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3638","title":"AutoTokenizer hash value got change after datasets.map","comments":"Thanks a lot for the explanation !\r\nI think if we set these 2 dicts at initialization time it would be amazing already\r\n\r\nShall we open an issue in `transformers` to ask for these dictionaries to be set when the tokenizer is instantiated ?\r\n\r\n> Edit: Another option would be to override the default hash function, but I don't know if there's a sound implementation that could fit.\r\n\r\nIn `datasets` we can easily have custom hashing for objects of the other HF libraries if we want. For example we ignore the cache some tokenizers have. However in this specific case it touches parameters that may change the behavior of the tokenizer itself. I'm not sure the logic that determines how a tokenizer behaves should be in `datasets`","body":"## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n","comment_length":127,"text":"AutoTokenizer hash value got change after datasets.map \n ## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n \n Thanks a lot for the explanation !\r\nI think if we set these 2 dicts at initialization time it would be amazing already\r\n\r\nShall we open an issue in `transformers` to ask for these dictionaries to be set when the tokenizer is instantiated ?\r\n\r\n> Edit: Another option would be to override the default hash function, but I don't know if there's a sound implementation that could fit.\r\n\r\nIn `datasets` we can easily have custom hashing for objects of the other HF libraries if we want. For example we ignore the cache some tokenizers have. However in this specific case it touches parameters that may change the behavior of the tokenizer itself. I'm not sure the logic that determines how a tokenizer behaves should be in 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3638","title":"AutoTokenizer hash value got change after datasets.map","comments":"A hack we could have in the `datasets` lib would be to call the tokenizer before hashing it in order to set all its parameters correctly - but it sounds a lot like a hack and I'm not sure this can work in the long run","body":"## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n","comment_length":46,"text":"AutoTokenizer hash value got change after datasets.map \n ## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n \n A hack we could have in the `datasets` lib would be to call the tokenizer before hashing it in order to set all its parameters correctly - but it sounds a lot like a hack and I'm not sure this can work in the long run","embeddings":[-0.1290389895,-0.2237777412,0.0150257908,0.3089294136,0.1342905015,-0.1396988332,0.2722429335,-0.0213704128,0.1214969531,0.1917948574,-0.1113677323,0.4286390245,-0.0865156427,-0.1164643839,-0.093268536,0.2705232799,0.1173472106,0.2221373171,-0.1098648459,-0.115385063,-0.0433059596,0.1107288972,-0.160367012,-0.1080356836,-0.3463075161,0.0401016995,0.0112999491,-0.3782944381,0.0619351976,-0.4243637621,0.1362453401,0.1397164613,0.1863562167,0.2984294295,-0.0001171864,0.0206879359,0.2634544671,0.0814768672,0.2142258734,0.0227778312,-0.2633487284,0.2867546976,-0.0680297837,-0.2092536986,-0.1148338914,0.0637526661,-0.2346738726,-0.4112156332,0.4010907114,0.1370520145,0.1676609218,0.265053153,-0.0295746904,0.1561013162,0.1950607598,-0.2246259749,-0.0078154467,-0.1290911138,-0.1391805112,-0.1560953259,-0.260060966,0.4577957392,0.0652744919,0.0406456515,0.2493761182,0.1124404669,-0.0790539756,-0.1640958488,0.2876113653,0.0402858108,0.1305542141,-0.3388209939,-0.1817626804,-0.0899926275,-0.0903692394,-0.0305115972,0.3779542744,-0.1524048746,0.4101909697,0.2683367431,-0.4246233404,0.1250701398,0.0212593544,0.0642474368,-0.1634887308,0.2503798008,-0.2617929876,0.1431821436,0.0500419587,-0.1286612451,-0.2013938129,0.0033287928,-0.0929486975,0.1595355719,-0.0953543186,-0.2332700491,0.0361989811,0.2379884571,0.088070035,0.2050545663,-0.0059785983,0.2491226643,-0.3129657209,-0.0035656653,-0.2320785522,0.2290501297,0.2229257375,0.0678976402,0.0071965102,-0.0135630518,-0.3116592765,-0.1306877285,0.1639947891,0.0017765176,0.6122227311,0.026759211,0.000423697,-0.0426872261,0.1696216762,0.0610469505,-0.176026985,0.0386033691,0.0206214357,0.2798212469,-0.2673545778,0.0977701321,-0.4449682236,0.0592899173,-0.0858648419,-0.0716235638,-0.2777846754,-0.2363528162,-0.1498707533,0.2740612626,0.018678667,-0.138920784,0.4285882711,0.224912867,-0.2019874156,0.0052161384,0.2339090705,-0.1477645785,0.3670988679,0.0141577981,-0.1002950519,0.3968282938,-0.0625139251,0.1093774289,-0.1585982442,0.0867584869,-0.4202547073,-0.1149113253,0.1427502483,0.1810493916,-0.2359759063,-0.1470829099,-0.0123531604,-0.0943361074,0.5517305732,0.1885139793,0.2053229511,-0.1906185001,-0.3774396479,-0.1670985967,0.1371552646,0.429928273,0.1415239871,-0.3465373516,-0.0069267899,0.1113580242,0.144919619,0.1601531506,-0.1594061852,0.1631575823,-0.2525579035,0.4392732978,-0.1229568645,-0.3600201309,-0.8233261108,-0.0825346261,-0.0226957891,0.2045874745,0.0318595804,-0.0521903224,0.1389169693,0.0396046005,0.1750185341,0.0284431465,0.1858418584,0.1434478164,-0.20178096,-0.3123971224,0.1794166863,-0.1200787425,0.1925228834,-0.0996570513,0.1055537686,0.2651252151,0.0480860732,0.1697579175,0.1798374951,0.096684508,0.228670314,-0.0949888751,0.0694238245,-0.1184869334,-0.2058321387,0.2039507776,-0.2798772454,-0.1156386882,-0.2133646756,-0.2392555773,-0.1242336705,-0.1246243417,-0.2508599758,-0.3404219151,0.0978192687,0.2711330652,0.2151311785,-0.0283064283,-0.0373322517,0.2418347895,0.2313210368,0.1175898463,-0.408470124,0.0859987885,0.1761122644,-0.0131876478,-0.3359203637,0.0784226656,0.2836026549,-0.1195491701,-0.2199673206,0.2823270857,0.0105302343,0.2286410481,0.0030768341,0.1484373808,0.3184492886,-0.2953836322,0.1541731358,0.1367931217,0.010563571,-0.0349391177,0.2280777693,0.2604185045,0.1618883014,-0.1064367145,-0.2310633957,-0.1776387095,0.143085286,-0.2034890205,-0.0414632,-0.6310230494,-0.2869870663,-0.0399209447,0.4664593637,0.3052936494,0.3841997385,0.2170520872,0.4824305773,0.0446157716,-0.0634299219,0.0985600948,-0.6580629349,-0.2892426252,-0.0690553859,0.0885554031,0.3110248744,0.1416464448,0.0175059997,-0.0136347124,0.1301017851,-0.0981583148,0.0765925273,-0.0881243497,-0.0408726595,0.1614612192,0.2415743023,0.10956949,-0.0566446483,0.2523660362,0.092473425,0.1726307571,-0.2688057125,-0.1953448504,-0.4462532699,0.2175259888,-0.1089922935,-0.3932588398,-0.2174677104,-0.2994203866,-0.0486758724,-0.0266145077,0.0640528649,0.2963679731,-0.0371520817,0.121508792,0.1712560058,0.010051894,-0.1866554618,-0.1086469442,-0.1959078759,-0.0628710017,-0.1262022704,-0.2984254658,0.246213451,-0.322111547,-0.0503055155,-0.4367668331,-0.4157623053,0.039737165,-0.0142618464,0.3031576872,0.0843426362,-0.3237101436,-0.0513597131,0.0596320257,0.1031402797,-0.3965801001,-0.1878370047,0.0169542767,-0.0608519241,0.1163916439,0.1418776065,-0.1144008189,-0.2012399882,0.1851786822,0.0206523668,0.1596389413,-0.0030977547,0.232596606,-0.1871893406,0.0373546146,-0.3481857479,-0.1306055486,-0.4223102629,-0.5347287059,0.440870434,-0.2120868266,-0.0973363519,-0.224679336,-0.0642814189,0.1365156472,0.1383520067,-0.3518812656,-0.3388182223,-0.4628320932,0.2229212224,-0.0906214267,0.0774424151,0.1990380585,0.0000101998,0.0213525761,-0.1795017719,-0.0962906554,0.4276157022,-0.2056139261,0.1141735315,0.1681474447,0.2394112498,0.1121957004,0.6299887896,0.2836839557,-0.4267061055,0.3919728696,-0.0654886514,0.08812657,-0.1985778213,-0.2495500892,-0.1474831849,0.1235787943,0.29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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3638","title":"AutoTokenizer hash value got change after datasets.map","comments":"Fully agree with everything you said. \r\n\r\nI think the best course of action is creating an issue in `transformers`. I can start the work on this.\r\nI think the code changes are fairly simple. Making a sound test + not breaking other stuff might be different :D","body":"## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n","comment_length":47,"text":"AutoTokenizer hash value got change after datasets.map \n ## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n \n Fully agree with everything you said. \r\n\r\nI think the best course of action is creating an issue in `transformers`. I can start the work on this.\r\nI think the code changes are fairly simple. Making a sound test + not breaking other stuff might be different 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3638","title":"AutoTokenizer hash value got change after datasets.map","comments":"It should be noted that this problem also occurs in other AutoClasses, such as AutoFeatureExtractor, so I don't think handling it in Datasets is a long-term practice either.","body":"## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n","comment_length":28,"text":"AutoTokenizer hash value got change after datasets.map \n ## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n \n It should be noted that this problem also occurs in other AutoClasses, such as AutoFeatureExtractor, so I don't think handling it in Datasets is a long-term practice either.","embeddings":[-0.1290389895,-0.2237777412,0.0150257908,0.3089294136,0.1342905015,-0.1396988332,0.2722429335,-0.0213704128,0.1214969531,0.1917948574,-0.1113677323,0.4286390245,-0.0865156427,-0.1164643839,-0.093268536,0.2705232799,0.1173472106,0.2221373171,-0.1098648459,-0.115385063,-0.0433059596,0.1107288972,-0.160367012,-0.1080356836,-0.3463075161,0.0401016995,0.0112999491,-0.3782944381,0.0619351976,-0.4243637621,0.1362453401,0.1397164613,0.1863562167,0.2984294295,-0.0001171864,0.0206879359,0.2634544671,0.0814768672,0.2142258734,0.0227778312,-0.2633487284,0.2867546976,-0.0680297837,-0.2092536986,-0.1148338914,0.0637526661,-0.2346738726,-0.4112156332,0.4010907114,0.1370520145,0.1676609218,0.265053153,-0.0295746904,0.1561013162,0.1950607598,-0.2246259749,-0.0078154467,-0.1290911138,-0.1391805112,-0.1560953259,-0.260060966,0.4577957392,0.0652744919,0.0406456515,0.2493761182,0.1124404669,-0.0790539756,-0.1640958488,0.2876113653,0.0402858108,0.1305542141,-0.3388209939,-0.1817626804,-0.0899926275,-0.0903692394,-0.0305115972,0.3779542744,-0.1524048746,0.4101909697,0.2683367431,-0.4246233404,0.1250701398,0.0212593544,0.0642474368,-0.1634887308,0.2503798008,-0.2617929876,0.1431821436,0.0500419587,-0.1286612451,-0.2013938129,0.0033287928,-0.0929486975,0.1595355719,-0.0953543186,-0.2332700491,0.0361989811,0.2379884571,0.088070035,0.2050545663,-0.0059785983,0.2491226643,-0.3129657209,-0.0035656653,-0.2320785522,0.2290501297,0.2229257375,0.0678976402,0.0071965102,-0.0135630518,-0.3116592765,-0.1306877285,0.1639947891,0.0017765176,0.6122227311,0.026759211,0.000423697,-0.0426872261,0.1696216762,0.0610469505,-0.176026985,0.0386033691,0.0206214357,0.2798212469,-0.2673545778,0.0977701321,-0.4449682236,0.0592899173,-0.0858648419,-0.0716235638,-0.2777846754,-0.2363528162,-0.1498707533,0.2740612626,0.018678667,-0.138920784,0.4285882711,0.224912867,-0.2019874156,0.0052161384,0.2339090705,-0.1477645785,0.3670988679,0.0141577981,-0.1002950519,0.3968282938,-0.0625139251,0.1093774289,-0.1585982442,0.0867584869,-0.4202547073,-0.1149113253,0.1427502483,0.1810493916,-0.2359759063,-0.1470829099,-0.0123531604,-0.0943361074,0.5517305732,0.1885139793,0.2053229511,-0.1906185001,-0.3774396479,-0.1670985967,0.1371552646,0.429928273,0.1415239871,-0.3465373516,-0.0069267899,0.1113580242,0.144919619,0.1601531506,-0.1594061852,0.1631575823,-0.2525579035,0.4392732978,-0.1229568645,-0.3600201309,-0.8233261108,-0.0825346261,-0.0226957891,0.2045874745,0.0318595804,-0.0521903224,0.1389169693,0.0396046005,0.1750185341,0.0284431465,0.1858418584,0.1434478164,-0.20178096,-0.3123971224,0.1794166863,-0.1200787425,0.1925228834,-0.0996570513,0.1055537686,0.2651252151,0.0480860732,0.1697579175,0.1798374951,0.096684508,0.228670314,-0.0949888751,0.0694238245,-0.1184869334,-0.2058321387,0.2039507776,-0.2798772454,-0.1156386882,-0.2133646756,-0.2392555773,-0.1242336705,-0.1246243417,-0.2508599758,-0.3404219151,0.0978192687,0.2711330652,0.2151311785,-0.0283064283,-0.0373322517,0.2418347895,0.2313210368,0.1175898463,-0.408470124,0.0859987885,0.1761122644,-0.0131876478,-0.3359203637,0.0784226656,0.2836026549,-0.1195491701,-0.2199673206,0.2823270857,0.0105302343,0.2286410481,0.0030768341,0.1484373808,0.3184492886,-0.2953836322,0.1541731358,0.1367931217,0.010563571,-0.0349391177,0.2280777693,0.2604185045,0.1618883014,-0.1064367145,-0.2310633957,-0.1776387095,0.143085286,-0.2034890205,-0.0414632,-0.6310230494,-0.2869870663,-0.0399209447,0.4664593637,0.3052936494,0.3841997385,0.2170520872,0.4824305773,0.0446157716,-0.0634299219,0.0985600948,-0.6580629349,-0.2892426252,-0.0690553859,0.0885554031,0.3110248744,0.1416464448,0.0175059997,-0.0136347124,0.1301017851,-0.0981583148,0.0765925273,-0.0881243497,-0.0408726595,0.1614612192,0.2415743023,0.10956949,-0.0566446483,0.2523660362,0.092473425,0.1726307571,-0.2688057125,-0.1953448504,-0.4462532699,0.2175259888,-0.1089922935,-0.3932588398,-0.2174677104,-0.2994203866,-0.0486758724,-0.0266145077,0.0640528649,0.2963679731,-0.0371520817,0.121508792,0.1712560058,0.010051894,-0.1866554618,-0.1086469442,-0.1959078759,-0.0628710017,-0.1262022704,-0.2984254658,0.246213451,-0.322111547,-0.0503055155,-0.4367668331,-0.4157623053,0.039737165,-0.0142618464,0.3031576872,0.0843426362,-0.3237101436,-0.0513597131,0.0596320257,0.1031402797,-0.3965801001,-0.1878370047,0.0169542767,-0.0608519241,0.1163916439,0.1418776065,-0.1144008189,-0.2012399882,0.1851786822,0.0206523668,0.1596389413,-0.0030977547,0.232596606,-0.1871893406,0.0373546146,-0.3481857479,-0.1306055486,-0.4223102629,-0.5347287059,0.440870434,-0.2120868266,-0.0973363519,-0.224679336,-0.0642814189,0.1365156472,0.1383520067,-0.3518812656,-0.3388182223,-0.4628320932,0.2229212224,-0.0906214267,0.0774424151,0.1990380585,0.0000101998,0.0213525761,-0.1795017719,-0.0962906554,0.4276157022,-0.2056139261,0.1141735315,0.1681474447,0.2394112498,0.1121957004,0.6299887896,0.2836839557,-0.4267061055,0.3919728696,-0.0654886514,0.08812657,-0.1985778213,-0.2495500892,-0.1474831849,0.1235787943,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3638","title":"AutoTokenizer hash value got change after datasets.map","comments":"> I think the best course of action is creating an issue in `transformers`. I can start the work on this.\r\n\r\n@Narsil Hi, I reopen this issue in `transformers` https:\/\/github.com\/huggingface\/transformers\/issues\/14931","body":"## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n","comment_length":30,"text":"AutoTokenizer hash value got change after datasets.map \n ## Describe the bug\r\nAutoTokenizer hash value got change after datasets.map\r\n\r\n## Steps to reproduce the bug\r\n1. trash huggingface datasets cache\r\n2. run the following code:\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\ngot\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1112.35it\/s]\r\nf4976bb4694ebc51\r\n3fca35a1fd4a1251\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4\/4 [00:00<00:00,  6.96ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 15.25ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  5.81ba\/s]\r\nd32837619b7d7d01\r\n5fd925c82edd62b6\r\n```\r\n3. run raw_datasets.map(tokenize_function, batched=True) again and see some dataset are not using cache.\r\n\r\n## Expected results\r\n`AutoTokenizer` work like specific Tokenizer (The hash value don't change after map):\r\n```python\r\nfrom transformers import AutoTokenizer, BertTokenizer\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\ntokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\r\n\r\ndef tokenize_function(example):\r\n    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\r\n\r\nraw_datasets = load_dataset(\"glue\", \"mrpc\")\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n\r\ntokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\nprint(Hasher.hash(tokenize_function))\r\nprint(Hasher.hash(tokenizer))\r\n```\r\n\r\n```\r\nReusing dataset glue (\/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1091.22it\/s]\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-6b07ff82ae9d5c51.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-af738a6d84f3864b.arrow\r\nLoading cached processed dataset at \/home1\/wts\/.cache\/huggingface\/datasets\/glue\/mrpc\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\/cache-531d2a603ba713c1.arrow\r\n46d4b31f54153fc7\r\n5b8771afd8d43888\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.4.0-91-generic-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n \n > I think the best course of action is creating an issue in `transformers`. I can start the work on this.\r\n\r\n@Narsil Hi, I reopen this issue in `transformers` https:\/\/github.com\/huggingface\/transformers\/issues\/14931","embeddings":[-0.1290389895,-0.2237777412,0.0150257908,0.3089294136,0.1342905015,-0.1396988332,0.2722429335,-0.0213704128,0.1214969531,0.1917948574,-0.1113677323,0.4286390245,-0.0865156427,-0.1164643839,-0.093268536,0.2705232799,0.1173472106,0.2221373171,-0.1098648459,-0.115385063,-0.0433059596,0.1107288972,-0.160367012,-0.1080356836,-0.3463075161,0.0401016995,0.0112999491,-0.3782944381,0.0619351976,-0.4243637621,0.1362453401,0.1397164613,0.1863562167,0.2984294295,-0.0001171864,0.0206879359,0.2634544671,0.0814768672,0.2142258734,0.0227778312,-0.2633487284,0.2867546976,-0.0680297837,-0.2092536986,-0.1148338914,0.0637526661,-0.2346738726,-0.4112156332,0.4010907114,0.1370520145,0.1676609218,0.265053153,-0.0295746904,0.1561013162,0.1950607598,-0.2246259749,-0.0078154467,-0.1290911138,-0.1391805112,-0.1560953259,-0.260060966,0.4577957392,0.0652744919,0.0406456515,0.2493761182,0.1124404669,-0.0790539756,-0.1640958488,0.2876113653,0.0402858108,0.1305542141,-0.3388209939,-0.1817626804,-0.0899926275,-0.0903692394,-0.0305115972,0.3779542744,-0.1524048746,0.4101909697,0.2683367431,-0.4246233404,0.1250701398,0.0212593544,0.0642474368,-0.1634887308,0.2503798008,-0.2617929876,0.1431821436,0.0500419587,-0.1286612451,-0.2013938129,0.0033287928,-0.0929486975,0.1595355719,-0.0953543186,-0.2332700491,0.0361989811,0.2379884571,0.088070035,0.2050545663,-0.0059785983,0.2491226643,-0.3129657209,-0.0035656653,-0.2320785522,0.2290501297,0.2229257375,0.0678976402,0.0071965102,-0.0135630518,-0.3116592765,-0.1306877285,0.1639947891,0.0017765176,0.6122227311,0.026759211,0.000423697,-0.0426872261,0.1696216762,0.0610469505,-0.176026985,0.0386033691,0.0206214357,0.2798212469,-0.2673545778,0.0977701321,-0.4449682236,0.0592899173,-0.0858648419,-0.0716235638,-0.2777846754,-0.2363528162,-0.1498707533,0.2740612626,0.018678667,-0.138920784,0.4285882711,0.224912867,-0.2019874156,0.0052161384,0.2339090705,-0.1477645785,0.3670988679,0.0141577981,-0.1002950519,0.3968282938,-0.0625139251,0.1093774289,-0.1585982442,0.0867584869,-0.4202547073,-0.1149113253,0.1427502483,0.1810493916,-0.2359759063,-0.1470829099,-0.0123531604,-0.0943361074,0.5517305732,0.1885139793,0.2053229511,-0.1906185001,-0.3774396479,-0.1670985967,0.1371552646,0.429928273,0.1415239871,-0.3465373516,-0.0069267899,0.1113580242,0.144919619,0.1601531506,-0.1594061852,0.1631575823,-0.2525579035,0.4392732978,-0.1229568645,-0.3600201309,-0.8233261108,-0.0825346261,-0.0226957891,0.2045874745,0.0318595804,-0.0521903224,0.1389169693,0.0396046005,0.1750185341,0.0284431465,0.1858418584,0.1434478164,-0.20178096,-0.3123971224,0.1794166863,-0.1200787425,0.1925228834,-0.0996570513,0.1055537686,0.2651252151,0.0480860732,0.1697579175,0.1798374951,0.096684508,0.228670314,-0.0949888751,0.0694238245,-0.1184869334,-0.2058321387,0.2039507776,-0.2798772454,-0.1156386882,-0.2133646756,-0.2392555773,-0.1242336705,-0.1246243417,-0.2508599758,-0.3404219151,0.0978192687,0.2711330652,0.2151311785,-0.0283064283,-0.0373322517,0.2418347895,0.2313210368,0.1175898463,-0.408470124,0.0859987885,0.1761122644,-0.0131876478,-0.3359203637,0.0784226656,0.2836026549,-0.1195491701,-0.2199673206,0.2823270857,0.0105302343,0.2286410481,0.0030768341,0.1484373808,0.3184492886,-0.2953836322,0.1541731358,0.1367931217,0.010563571,-0.0349391177,0.2280777693,0.2604185045,0.1618883014,-0.1064367145,-0.2310633957,-0.1776387095,0.143085286,-0.2034890205,-0.0414632,-0.6310230494,-0.2869870663,-0.0399209447,0.4664593637,0.3052936494,0.3841997385,0.2170520872,0.4824305773,0.0446157716,-0.0634299219,0.0985600948,-0.6580629349,-0.2892426252,-0.0690553859,0.0885554031,0.3110248744,0.1416464448,0.0175059997,-0.0136347124,0.1301017851,-0.0981583148,0.0765925273,-0.0881243497,-0.0408726595,0.1614612192,0.2415743023,0.10956949,-0.0566446483,0.2523660362,0.092473425,0.1726307571,-0.2688057125,-0.1953448504,-0.4462532699,0.2175259888,-0.1089922935,-0.3932588398,-0.2174677104,-0.2994203866,-0.0486758724,-0.0266145077,0.0640528649,0.2963679731,-0.0371520817,0.121508792,0.1712560058,0.010051894,-0.1866554618,-0.1086469442,-0.1959078759,-0.0628710017,-0.1262022704,-0.2984254658,0.246213451,-0.322111547,-0.0503055155,-0.4367668331,-0.4157623053,0.039737165,-0.0142618464,0.3031576872,0.0843426362,-0.3237101436,-0.0513597131,0.0596320257,0.1031402797,-0.3965801001,-0.1878370047,0.0169542767,-0.0608519241,0.1163916439,0.1418776065,-0.1144008189,-0.2012399882,0.1851786822,0.0206523668,0.1596389413,-0.0030977547,0.232596606,-0.1871893406,0.0373546146,-0.3481857479,-0.1306055486,-0.4223102629,-0.5347287059,0.440870434,-0.2120868266,-0.0973363519,-0.224679336,-0.0642814189,0.1365156472,0.1383520067,-0.3518812656,-0.3388182223,-0.4628320932,0.2229212224,-0.0906214267,0.0774424151,0.1990380585,0.0000101998,0.0213525761,-0.1795017719,-0.0962906554,0.4276157022,-0.2056139261,0.1141735315,0.1681474447,0.2394112498,0.1121957004,0.6299887896,0.2836839557,-0.4267061055,0.3919728696,-0.0654886514,0.08812657,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3637","title":"[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18","comments":"Hi @lewtun!\r\n \r\nThis one was tricky to debug. Initially, I tought there is a bug in the recently-added (by @lhoestq ) `cast_array_to_feature` function because `git bisect` points to the https:\/\/github.com\/huggingface\/datasets\/commit\/6ca96c707502e0689f9b58d94f46d871fa5a3c9c commit. Then, I noticed that the feature tpye of the `dialogue` field is `list`, which explains why you didn't get an error in earlier versions. Is there a specific reason why you use `list` instead of `Sequence` in the script? Maybe to avoid turning list of dicts to dicts of lists as it's done by `Sequence` for compatibility with TFDS or for performance reasons? If the field was `Sequence`, you would get an error in `encode_nested_example` because **the scripts yields some additional (nested) columns which are not specified in the `features` dictionary**. Previously, these additional columns would've been ignored by PyArrow (1), but now we have a check for them (2).\r\n(1) See PyArrow behavior:\r\n```\r\n>>> pa.array([{\"a\": 2, \"b\": 3}], type=pa.struct({\"a\": pa.int32()})) # pyarrow ignores the extra column\r\n-- is_valid: all not null\r\n-- child 0 type: int32\r\n  [\r\n    2\r\n  ]\r\n ```\r\n\r\n(2) Check:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/table.py#L1059\r\n\r\nThe fix is very simple: just add the missing columns to the _EMPTY_BELIEF_STATE list:\r\n```python\r\n_EMPTY_BELIEF_STATE.extend(['\u901a\u7528-\u4ea7\u54c1\u7c7b\u522b', '\u706b\u8f66-\u8231\u4f4d\u6863\u6b21', '\u901a\u7528-\u7cfb\u5217', '\u901a\u7528-\u4ef7\u683c\u533a\u95f4', '\u901a\u7528-\u54c1\u724c'])\r\n```","body":"## Describe the bug\r\nI am trying to load the [`GEM\/RiSAWOZ` dataset](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too.\r\n\r\nAs far as I can tell, the dataset loading script is correct and the problematic features [here](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ\/blob\/main\/RiSAWOZ.py#L237) also look fine to me.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"GEM\/RiSAWOZ\")\r\n```\r\n\r\n## Expected results\r\nI can load the dataset without error.\r\n\r\n## Actual results\r\n\r\n<details><summary>Traceback<\/summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1083                     example = self.info.features.encode_example(record)\r\n-> 1084                     writer.write(example, key)\r\n   1085             finally:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    445 \r\n--> 446             self.write_examples_on_file()\r\n    447 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\/var\/folders\/28\/k4cy5q7s2hs92xq7_h89_vgm0000gn\/T\/ipykernel_44306\/2896005239.py in <module>\r\n----> 1 dset = load_dataset(\"GEM\/RiSAWOZ\")\r\n      2 dset\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1692 \r\n   1693     # Download and prepare data\r\n-> 1694     builder_instance.download_and_prepare(\r\n   1695         download_config=download_config,\r\n   1696         download_mode=download_mode,\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    594                     if not downloaded_from_gcs:\r\n--> 595                         self._download_and_prepare(\r\n    596                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    597                         )\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    682             try:\r\n    683                 # Prepare split will record examples associated to the split\r\n--> 684                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    685             except OSError as e:\r\n    686                 raise OSError(\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1084                     writer.write(example, key)\r\n   1085             finally:\r\n-> 1086                 num_examples, num_bytes = writer.finalize()\r\n   1087 \r\n   1088         split_generator.split_info.num_examples = num_examples\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in finalize(self, close_stream)\r\n    525             # Re-intializing to empty list for next batch\r\n    526             self.hkey_record = []\r\n--> 527         self.write_examples_on_file()\r\n    528         if self.pa_writer is None:\r\n    529             if self.schema:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    402             # Since current_examples contains (example, key) tuples\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n    406 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    495             col_try_type = try_features[col] if try_features is not None and col in try_features else None\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n    499         schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    203                 # Also, when trying type \"string\", we don't want to convert integers or floats to \"string\".\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n    207         except (TypeError, pa.lib.ArrowInvalid) as e:  # handle type errors and overflows\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1063         # feature must be either [subfeature] or Sequence(subfeature)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n   1067             if feature.length > -1:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1085     elif not isinstance(feature, (Sequence, dict, list, tuple)):\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n   1089 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n```\r\n\r\n<\/details>\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":197,"text":"[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18 \n ## Describe the bug\r\nI am trying to load the [`GEM\/RiSAWOZ` dataset](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too.\r\n\r\nAs far as I can tell, the dataset loading script is correct and the problematic features [here](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ\/blob\/main\/RiSAWOZ.py#L237) also look fine to me.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"GEM\/RiSAWOZ\")\r\n```\r\n\r\n## Expected results\r\nI can load the dataset without error.\r\n\r\n## Actual results\r\n\r\n<details><summary>Traceback<\/summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1083                     example = self.info.features.encode_example(record)\r\n-> 1084                     writer.write(example, key)\r\n   1085             finally:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    445 \r\n--> 446             self.write_examples_on_file()\r\n    447 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\/var\/folders\/28\/k4cy5q7s2hs92xq7_h89_vgm0000gn\/T\/ipykernel_44306\/2896005239.py in <module>\r\n----> 1 dset = load_dataset(\"GEM\/RiSAWOZ\")\r\n      2 dset\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1692 \r\n   1693     # Download and prepare data\r\n-> 1694     builder_instance.download_and_prepare(\r\n   1695         download_config=download_config,\r\n   1696         download_mode=download_mode,\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    594                     if not downloaded_from_gcs:\r\n--> 595                         self._download_and_prepare(\r\n    596                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    597                         )\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    682             try:\r\n    683                 # Prepare split will record examples associated to the split\r\n--> 684                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    685             except OSError as e:\r\n    686                 raise OSError(\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1084                     writer.write(example, key)\r\n   1085             finally:\r\n-> 1086                 num_examples, num_bytes = writer.finalize()\r\n   1087 \r\n   1088         split_generator.split_info.num_examples = num_examples\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in finalize(self, close_stream)\r\n    525             # Re-intializing to empty list for next batch\r\n    526             self.hkey_record = []\r\n--> 527         self.write_examples_on_file()\r\n    528         if self.pa_writer is None:\r\n    529             if self.schema:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    402             # Since current_examples contains (example, key) tuples\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n    406 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    495             col_try_type = try_features[col] if try_features is not None and col in try_features else None\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n    499         schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    203                 # Also, when trying type \"string\", we don't want to convert integers or floats to \"string\".\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n    207         except (TypeError, pa.lib.ArrowInvalid) as e:  # handle type errors and overflows\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1063         # feature must be either [subfeature] or Sequence(subfeature)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n   1067             if feature.length > -1:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1085     elif not isinstance(feature, (Sequence, dict, list, tuple)):\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n   1089 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n```\r\n\r\n<\/details>\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n Hi @lewtun!\r\n \r\nThis one was tricky to debug. Initially, I tought there is a bug in the recently-added (by @lhoestq ) `cast_array_to_feature` function because `git bisect` points to the https:\/\/github.com\/huggingface\/datasets\/commit\/6ca96c707502e0689f9b58d94f46d871fa5a3c9c commit. Then, I noticed that the feature tpye of the `dialogue` field is `list`, which explains why you didn't get an error in earlier versions. Is there a specific reason why you use `list` instead of `Sequence` in the script? Maybe to avoid turning list of dicts to dicts of lists as it's done by `Sequence` for compatibility with TFDS or for performance reasons? If the field was `Sequence`, you would get an error in `encode_nested_example` because **the scripts yields some additional (nested) columns which are not specified in the `features` dictionary**. Previously, these additional columns would've been ignored by PyArrow (1), but now we have a check for them (2).\r\n(1) See PyArrow behavior:\r\n```\r\n>>> pa.array([{\"a\": 2, \"b\": 3}], type=pa.struct({\"a\": pa.int32()})) # pyarrow ignores the extra column\r\n-- is_valid: all not null\r\n-- child 0 type: int32\r\n  [\r\n    2\r\n  ]\r\n ```\r\n\r\n(2) Check:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c417d52def6e20359ca16c6723e0a2855e5c3fd\/src\/datasets\/table.py#L1059\r\n\r\nThe fix is very simple: just add the missing columns to the _EMPTY_BELIEF_STATE list:\r\n```python\r\n_EMPTY_BELIEF_STATE.extend(['\u901a\u7528-\u4ea7\u54c1\u7c7b\u522b', '\u706b\u8f66-\u8231\u4f4d\u6863\u6b21', '\u901a\u7528-\u7cfb\u5217', '\u901a\u7528-\u4ef7\u683c\u533a\u95f4', '\u901a\u7528-\u54c1\u724c'])\r\n```","embeddings":[-0.1942696571,-0.4909287691,0.0600802898,0.5314732194,0.4639725685,0.1549371183,0.2992590368,0.3703403473,0.1878763437,-0.0143708531,0.1101714894,0.5128638744,-0.3276836276,0.1034816429,0.0563642532,-0.2771357596,0.2133807838,0.0304361284,-0.1236653253,0.0571421757,0.1015927196,0.2774196267,-0.1580940485,0.3223943114,-0.177803576,0.1896527559,0.1909873784,0.3998605907,-0.1899659485,-0.493301928,0.6088303328,-0.3547360003,0.3529712558,0.2716292739,-0.0001241634,0.2320222259,0.4078266621,0.012609574,-0.2205002159,-0.3889898062,-0.2872396708,-0.3370229304,0.3555959165,0.0433429405,0.0666344985,-0.1898171902,-0.0262948573,-0.1967689395,0.0074145594,0.0752304718,0.1018247232,0.5603157878,0.3825861514,-0.1824073493,0.1919645816,0.0826466903,-0.3385210037,0.1542516947,0.138455376,0.6070917845,0.268075943,0.1615090072,-0.0491786189,-0.0906088725,0.5576862097,-0.103054814,-0.0564994216,-0.1598872095,-0.1619885713,0.2661495507,0.4195816815,-0.0913306549,-0.4803362191,-0.2445016205,-0.1568792313,0.1568656415,0.21170488,-0.0096013695,0.0259571541,0.2101078331,-0.3084632158,-0.0033286109,0.0759534165,-0.0759758502,0.1593330055,-0.0776601657,-0.1189897731,0.1543727517,0.1549899429,-0.2964865267,0.0986974239,-0.1423982829,-0.2271053046,0.0975215435,-0.153576687,0.1252183765,0.1266498268,0.0366891697,0.0160740055,-0.0651022345,0.0298234988,0.1507367939,-0.35328269,0.2018560767,0.4332413673,0.2174664587,0.178684324,0.0913464949,-0.1988650262,0.1686339229,0.0395610929,0.0975488499,-0.0670284331,-0.1301365942,0.247482717,-0.0393823422,0.3550628424,-0.2471610755,-0.0883404016,-0.1057405993,0.0680669472,0.1440200359,0.2797644138,0.2924188375,0.0017775551,0.3551595509,0.1253634989,0.3235913217,-0.0397814848,-0.2188540995,-0.167372793,-0.0234555844,0.0130565492,-0.4220882952,-0.0089158015,-0.3365992308,0.1036493182,-0.1182179973,-0.0210645758,-0.2249391079,-0.2367394418,0.2362743616,-0.0747250915,0.3233361244,-0.052383028,0.2356864512,0.4166336954,-0.0531071387,-0.1469531804,0.0487975702,-0.3159548044,-0.339076966,-0.0866677538,0.119581826,-0.254712522,-0.1372659802,-0.2474257648,-0.0462892763,0.2874094248,-0.511916399,-0.2409537435,-0.2571010888,-0.1087409034,-0.1277848482,0.0484971777,0.1353400201,-0.362275064,-0.1674032658,-0.3637704849,-0.4549883306,0.1102411598,0.240791589,-0.0935699344,-0.0527783372,-0.3162724376,0.3000915051,0.4084950984,0.0981535167,-0.2104138136,0.2250009328,0.0723604783,0.0590207018,0.1104202867,0.1961289644,0.2790901363,-0.1740741581,-0.1117369086,0.0848923028,-0.1996245533,0.040106371,-0.3926500678,-0.2444086969,-0.0278117117,0.0508838259,-0.0308870096,0.3185037076,0.0314625166,0.0595753789,0.2317246348,-0.0186059419,0.1118688136,-0.0566809028,0.1279017776,0.1090304554,-0.003881942,-0.4135949612,-0.3670502603,0.4241425097,0.1472156495,-0.1322740465,0.0726334974,0.0550310165,-0.3470769823,0.2225904763,-0.1151379272,0.348615706,-0.0512186475,0.0203841757,-0.1503184885,-0.1448521614,-0.2338885665,-0.2151050568,0.1371222585,-0.0441854708,-0.5224093795,-0.0429971516,-0.1325335801,-0.2706715167,-0.2429058999,-0.0835895538,0.1356125623,-0.1731237769,-0.3182046115,0.2236567289,-0.0784818828,0.2531950474,-0.1508158743,-0.0962944329,-0.0786364079,-0.2554769814,-0.0008580303,0.1905666739,0.0915428251,-0.0011686352,0.0820051283,0.4654118717,0.1451885104,0.3244556487,-0.0720716119,0.3501816988,0.291541934,0.1136104465,0.1384634674,-0.5138630867,0.1396218836,-0.3997299075,-0.1792991757,0.1241501868,-0.1873838007,0.1845445931,0.1627548933,-0.0541169494,0.2096038908,0.1043134481,0.0326686688,0.0236979518,-0.2005431056,0.2297652364,0.2854864597,-0.0370812155,-0.0129693318,-0.0510888174,0.1672250628,-0.012757509,0.4683577418,0.0660867319,0.1984333694,0.0047486234,-0.0378314555,-0.0329948552,-0.192838192,-0.0420875102,0.059436392,0.2085192651,-0.4780389369,0.1811769903,-0.3215309083,0.3393013775,-0.2576460838,-0.2467057556,-0.0748819858,-0.3460410833,-0.1982435733,0.2163226902,-0.2268827558,0.2133338004,-0.177493766,-0.0097445808,-0.0959311947,-0.0358729064,-0.1884303242,0.1360535175,-0.1761716455,-0.1525814086,0.3079068065,-0.1383276582,0.0199490301,-0.067627497,-0.0861045718,-0.0740520209,-0.2235280275,0.0977003276,-0.0130775189,0.3453876376,0.3630618155,0.2607116997,0.1488767564,-0.2942780554,0.4073486328,-0.3938877881,-0.3691505492,0.4227531254,-0.0391222909,-0.1764794439,-0.3195714951,-0.2263647765,-0.1529022604,-0.437604934,-0.0075782747,-0.13399297,0.0972464681,-0.0186711866,0.4262812436,0.0709741414,-0.1276955754,0.2040297985,-0.0204139948,-0.0096580507,0.1895896196,0.1194485277,-0.2456169128,0.0500036664,-0.0100701302,0.1323182136,-0.0020673785,-0.3501155078,0.13245745,-0.128473863,-0.0216742679,0.174705103,-0.2467836142,0.2356105149,0.0210880004,0.0295159183,-0.1336812228,-0.1877533197,-0.020620171,-0.3842800558,0.0637407824,0.0186779927,0.5528272986,-0.0107114604,0.5343385339,0.3602442741,-0.1410549134,0.3175684214,0.0225362461,-0.0584072582,-0.3318219781,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3637","title":"[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18","comments":"Hey @mariosasko, thank you so much for figuring this one out - it certainly looks like a tricky bug \ud83d\ude31 ! I don't think there's a specific reason to use `list` instead of `Sequence` with the script, but I'll let the dataset creators know to see if your suggestion is acceptable.\r\n\r\nThank you again!","body":"## Describe the bug\r\nI am trying to load the [`GEM\/RiSAWOZ` dataset](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too.\r\n\r\nAs far as I can tell, the dataset loading script is correct and the problematic features [here](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ\/blob\/main\/RiSAWOZ.py#L237) also look fine to me.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"GEM\/RiSAWOZ\")\r\n```\r\n\r\n## Expected results\r\nI can load the dataset without error.\r\n\r\n## Actual results\r\n\r\n<details><summary>Traceback<\/summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1083                     example = self.info.features.encode_example(record)\r\n-> 1084                     writer.write(example, key)\r\n   1085             finally:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    445 \r\n--> 446             self.write_examples_on_file()\r\n    447 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\/var\/folders\/28\/k4cy5q7s2hs92xq7_h89_vgm0000gn\/T\/ipykernel_44306\/2896005239.py in <module>\r\n----> 1 dset = load_dataset(\"GEM\/RiSAWOZ\")\r\n      2 dset\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1692 \r\n   1693     # Download and prepare data\r\n-> 1694     builder_instance.download_and_prepare(\r\n   1695         download_config=download_config,\r\n   1696         download_mode=download_mode,\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    594                     if not downloaded_from_gcs:\r\n--> 595                         self._download_and_prepare(\r\n    596                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    597                         )\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    682             try:\r\n    683                 # Prepare split will record examples associated to the split\r\n--> 684                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    685             except OSError as e:\r\n    686                 raise OSError(\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1084                     writer.write(example, key)\r\n   1085             finally:\r\n-> 1086                 num_examples, num_bytes = writer.finalize()\r\n   1087 \r\n   1088         split_generator.split_info.num_examples = num_examples\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in finalize(self, close_stream)\r\n    525             # Re-intializing to empty list for next batch\r\n    526             self.hkey_record = []\r\n--> 527         self.write_examples_on_file()\r\n    528         if self.pa_writer is None:\r\n    529             if self.schema:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    402             # Since current_examples contains (example, key) tuples\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n    406 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    495             col_try_type = try_features[col] if try_features is not None and col in try_features else None\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n    499         schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    203                 # Also, when trying type \"string\", we don't want to convert integers or floats to \"string\".\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n    207         except (TypeError, pa.lib.ArrowInvalid) as e:  # handle type errors and overflows\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1063         # feature must be either [subfeature] or Sequence(subfeature)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n   1067             if feature.length > -1:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1085     elif not isinstance(feature, (Sequence, dict, list, tuple)):\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n   1089 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n```\r\n\r\n<\/details>\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":54,"text":"[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18 \n ## Describe the bug\r\nI am trying to load the [`GEM\/RiSAWOZ` dataset](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too.\r\n\r\nAs far as I can tell, the dataset loading script is correct and the problematic features [here](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ\/blob\/main\/RiSAWOZ.py#L237) also look fine to me.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"GEM\/RiSAWOZ\")\r\n```\r\n\r\n## Expected results\r\nI can load the dataset without error.\r\n\r\n## Actual results\r\n\r\n<details><summary>Traceback<\/summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1083                     example = self.info.features.encode_example(record)\r\n-> 1084                     writer.write(example, key)\r\n   1085             finally:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    445 \r\n--> 446             self.write_examples_on_file()\r\n    447 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\/var\/folders\/28\/k4cy5q7s2hs92xq7_h89_vgm0000gn\/T\/ipykernel_44306\/2896005239.py in <module>\r\n----> 1 dset = load_dataset(\"GEM\/RiSAWOZ\")\r\n      2 dset\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1692 \r\n   1693     # Download and prepare data\r\n-> 1694     builder_instance.download_and_prepare(\r\n   1695         download_config=download_config,\r\n   1696         download_mode=download_mode,\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    594                     if not downloaded_from_gcs:\r\n--> 595                         self._download_and_prepare(\r\n    596                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    597                         )\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    682             try:\r\n    683                 # Prepare split will record examples associated to the split\r\n--> 684                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    685             except OSError as e:\r\n    686                 raise OSError(\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1084                     writer.write(example, key)\r\n   1085             finally:\r\n-> 1086                 num_examples, num_bytes = writer.finalize()\r\n   1087 \r\n   1088         split_generator.split_info.num_examples = num_examples\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in finalize(self, close_stream)\r\n    525             # Re-intializing to empty list for next batch\r\n    526             self.hkey_record = []\r\n--> 527         self.write_examples_on_file()\r\n    528         if self.pa_writer is None:\r\n    529             if self.schema:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    402             # Since current_examples contains (example, key) tuples\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n    406 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    495             col_try_type = try_features[col] if try_features is not None and col in try_features else None\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n    499         schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    203                 # Also, when trying type \"string\", we don't want to convert integers or floats to \"string\".\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n    207         except (TypeError, pa.lib.ArrowInvalid) as e:  # handle type errors and overflows\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1063         # feature must be either [subfeature] or Sequence(subfeature)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n   1067             if feature.length > -1:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1085     elif not isinstance(feature, (Sequence, dict, list, tuple)):\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n   1089 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n```\r\n\r\n<\/details>\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n Hey @mariosasko, thank you so much for figuring this one out - it certainly looks like a tricky bug \ud83d\ude31 ! I don't think there's a specific reason to use `list` instead of `Sequence` with the script, but I'll let the dataset creators know to see if your suggestion is acceptable.\r\n\r\nThank you again!","embeddings":[-0.1942696571,-0.4909287691,0.0600802898,0.5314732194,0.4639725685,0.1549371183,0.2992590368,0.3703403473,0.1878763437,-0.0143708531,0.1101714894,0.5128638744,-0.3276836276,0.1034816429,0.0563642532,-0.2771357596,0.2133807838,0.0304361284,-0.1236653253,0.0571421757,0.1015927196,0.2774196267,-0.1580940485,0.3223943114,-0.177803576,0.1896527559,0.1909873784,0.3998605907,-0.1899659485,-0.493301928,0.6088303328,-0.3547360003,0.3529712558,0.2716292739,-0.0001241634,0.2320222259,0.4078266621,0.012609574,-0.2205002159,-0.3889898062,-0.2872396708,-0.3370229304,0.3555959165,0.0433429405,0.0666344985,-0.1898171902,-0.0262948573,-0.1967689395,0.0074145594,0.0752304718,0.1018247232,0.5603157878,0.3825861514,-0.1824073493,0.1919645816,0.0826466903,-0.3385210037,0.1542516947,0.138455376,0.6070917845,0.268075943,0.1615090072,-0.0491786189,-0.0906088725,0.5576862097,-0.103054814,-0.0564994216,-0.1598872095,-0.1619885713,0.2661495507,0.4195816815,-0.0913306549,-0.4803362191,-0.2445016205,-0.1568792313,0.1568656415,0.21170488,-0.0096013695,0.0259571541,0.2101078331,-0.3084632158,-0.0033286109,0.0759534165,-0.0759758502,0.1593330055,-0.0776601657,-0.1189897731,0.1543727517,0.1549899429,-0.2964865267,0.0986974239,-0.1423982829,-0.2271053046,0.0975215435,-0.153576687,0.1252183765,0.1266498268,0.0366891697,0.0160740055,-0.0651022345,0.0298234988,0.1507367939,-0.35328269,0.2018560767,0.4332413673,0.2174664587,0.178684324,0.0913464949,-0.1988650262,0.1686339229,0.0395610929,0.0975488499,-0.0670284331,-0.1301365942,0.247482717,-0.0393823422,0.3550628424,-0.2471610755,-0.0883404016,-0.1057405993,0.0680669472,0.1440200359,0.2797644138,0.2924188375,0.0017775551,0.3551595509,0.1253634989,0.3235913217,-0.0397814848,-0.2188540995,-0.167372793,-0.0234555844,0.0130565492,-0.4220882952,-0.0089158015,-0.3365992308,0.1036493182,-0.1182179973,-0.0210645758,-0.2249391079,-0.2367394418,0.2362743616,-0.0747250915,0.3233361244,-0.052383028,0.2356864512,0.4166336954,-0.0531071387,-0.1469531804,0.0487975702,-0.3159548044,-0.339076966,-0.0866677538,0.119581826,-0.254712522,-0.1372659802,-0.2474257648,-0.0462892763,0.2874094248,-0.511916399,-0.2409537435,-0.2571010888,-0.1087409034,-0.1277848482,0.0484971777,0.1353400201,-0.362275064,-0.1674032658,-0.3637704849,-0.4549883306,0.1102411598,0.240791589,-0.0935699344,-0.0527783372,-0.3162724376,0.3000915051,0.4084950984,0.0981535167,-0.2104138136,0.2250009328,0.0723604783,0.0590207018,0.1104202867,0.1961289644,0.2790901363,-0.1740741581,-0.1117369086,0.0848923028,-0.1996245533,0.040106371,-0.3926500678,-0.2444086969,-0.0278117117,0.0508838259,-0.0308870096,0.3185037076,0.0314625166,0.0595753789,0.2317246348,-0.0186059419,0.1118688136,-0.0566809028,0.1279017776,0.1090304554,-0.003881942,-0.4135949612,-0.3670502603,0.4241425097,0.1472156495,-0.1322740465,0.0726334974,0.0550310165,-0.3470769823,0.2225904763,-0.1151379272,0.348615706,-0.0512186475,0.0203841757,-0.1503184885,-0.1448521614,-0.2338885665,-0.2151050568,0.1371222585,-0.0441854708,-0.5224093795,-0.0429971516,-0.1325335801,-0.2706715167,-0.2429058999,-0.0835895538,0.1356125623,-0.1731237769,-0.3182046115,0.2236567289,-0.0784818828,0.2531950474,-0.1508158743,-0.0962944329,-0.0786364079,-0.2554769814,-0.0008580303,0.1905666739,0.0915428251,-0.0011686352,0.0820051283,0.4654118717,0.1451885104,0.3244556487,-0.0720716119,0.3501816988,0.291541934,0.1136104465,0.1384634674,-0.5138630867,0.1396218836,-0.3997299075,-0.1792991757,0.1241501868,-0.1873838007,0.1845445931,0.1627548933,-0.0541169494,0.2096038908,0.1043134481,0.0326686688,0.0236979518,-0.2005431056,0.2297652364,0.2854864597,-0.0370812155,-0.0129693318,-0.0510888174,0.1672250628,-0.012757509,0.4683577418,0.0660867319,0.1984333694,0.0047486234,-0.0378314555,-0.0329948552,-0.192838192,-0.0420875102,0.059436392,0.2085192651,-0.4780389369,0.1811769903,-0.3215309083,0.3393013775,-0.2576460838,-0.2467057556,-0.0748819858,-0.3460410833,-0.1982435733,0.2163226902,-0.2268827558,0.2133338004,-0.177493766,-0.0097445808,-0.0959311947,-0.0358729064,-0.1884303242,0.1360535175,-0.1761716455,-0.1525814086,0.3079068065,-0.1383276582,0.0199490301,-0.067627497,-0.0861045718,-0.0740520209,-0.2235280275,0.0977003276,-0.0130775189,0.3453876376,0.3630618155,0.2607116997,0.1488767564,-0.2942780554,0.4073486328,-0.3938877881,-0.3691505492,0.4227531254,-0.0391222909,-0.1764794439,-0.3195714951,-0.2263647765,-0.1529022604,-0.437604934,-0.0075782747,-0.13399297,0.0972464681,-0.0186711866,0.4262812436,0.0709741414,-0.1276955754,0.2040297985,-0.0204139948,-0.0096580507,0.1895896196,0.1194485277,-0.2456169128,0.0500036664,-0.0100701302,0.1323182136,-0.0020673785,-0.3501155078,0.13245745,-0.128473863,-0.0216742679,0.174705103,-0.2467836142,0.2356105149,0.0210880004,0.0295159183,-0.1336812228,-0.1877533197,-0.020620171,-0.3842800558,0.0637407824,0.0186779927,0.5528272986,-0.0107114604,0.5343385339,0.3602442741,-0.1410549134,0.3175684214,0.0225362461,-0.0584072582,-0.3318219781,-0.4552114904,-0.1578633934,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3637","title":"[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18","comments":"Thanks, this was indeed the fix! Would it make sense to produce a more informative error message in such cases? \r\n\r\nThe issue can be closed. \r\n\r\n","body":"## Describe the bug\r\nI am trying to load the [`GEM\/RiSAWOZ` dataset](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too.\r\n\r\nAs far as I can tell, the dataset loading script is correct and the problematic features [here](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ\/blob\/main\/RiSAWOZ.py#L237) also look fine to me.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"GEM\/RiSAWOZ\")\r\n```\r\n\r\n## Expected results\r\nI can load the dataset without error.\r\n\r\n## Actual results\r\n\r\n<details><summary>Traceback<\/summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1083                     example = self.info.features.encode_example(record)\r\n-> 1084                     writer.write(example, key)\r\n   1085             finally:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    445 \r\n--> 446             self.write_examples_on_file()\r\n    447 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\/var\/folders\/28\/k4cy5q7s2hs92xq7_h89_vgm0000gn\/T\/ipykernel_44306\/2896005239.py in <module>\r\n----> 1 dset = load_dataset(\"GEM\/RiSAWOZ\")\r\n      2 dset\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1692 \r\n   1693     # Download and prepare data\r\n-> 1694     builder_instance.download_and_prepare(\r\n   1695         download_config=download_config,\r\n   1696         download_mode=download_mode,\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    594                     if not downloaded_from_gcs:\r\n--> 595                         self._download_and_prepare(\r\n    596                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    597                         )\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    682             try:\r\n    683                 # Prepare split will record examples associated to the split\r\n--> 684                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    685             except OSError as e:\r\n    686                 raise OSError(\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1084                     writer.write(example, key)\r\n   1085             finally:\r\n-> 1086                 num_examples, num_bytes = writer.finalize()\r\n   1087 \r\n   1088         split_generator.split_info.num_examples = num_examples\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in finalize(self, close_stream)\r\n    525             # Re-intializing to empty list for next batch\r\n    526             self.hkey_record = []\r\n--> 527         self.write_examples_on_file()\r\n    528         if self.pa_writer is None:\r\n    529             if self.schema:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    402             # Since current_examples contains (example, key) tuples\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n    406 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    495             col_try_type = try_features[col] if try_features is not None and col in try_features else None\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n    499         schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    203                 # Also, when trying type \"string\", we don't want to convert integers or floats to \"string\".\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n    207         except (TypeError, pa.lib.ArrowInvalid) as e:  # handle type errors and overflows\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1063         # feature must be either [subfeature] or Sequence(subfeature)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n   1067             if feature.length > -1:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1085     elif not isinstance(feature, (Sequence, dict, list, tuple)):\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n   1089 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n```\r\n\r\n<\/details>\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":25,"text":"[TypeError: Couldn't cast array of type] Cannot load dataset in v1.18 \n ## Describe the bug\r\nI am trying to load the [`GEM\/RiSAWOZ` dataset](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ) in `datasets` v1.18.1 and am running into a type error when casting the features. The strange thing is that I can load the dataset with v1.17.0. Note that the error is also present if I install from `master` too.\r\n\r\nAs far as I can tell, the dataset loading script is correct and the problematic features [here](https:\/\/huggingface.co\/datasets\/GEM\/RiSAWOZ\/blob\/main\/RiSAWOZ.py#L237) also look fine to me.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"GEM\/RiSAWOZ\")\r\n```\r\n\r\n## Expected results\r\nI can load the dataset without error.\r\n\r\n## Actual results\r\n\r\n<details><summary>Traceback<\/summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1083                     example = self.info.features.encode_example(record)\r\n-> 1084                     writer.write(example, key)\r\n   1085             finally:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    445 \r\n--> 446             self.write_examples_on_file()\r\n    447 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\/var\/folders\/28\/k4cy5q7s2hs92xq7_h89_vgm0000gn\/T\/ipykernel_44306\/2896005239.py in <module>\r\n----> 1 dset = load_dataset(\"GEM\/RiSAWOZ\")\r\n      2 dset\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1692 \r\n   1693     # Download and prepare data\r\n-> 1694     builder_instance.download_and_prepare(\r\n   1695         download_config=download_config,\r\n   1696         download_mode=download_mode,\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    593                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    594                     if not downloaded_from_gcs:\r\n--> 595                         self._download_and_prepare(\r\n    596                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    597                         )\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    682             try:\r\n    683                 # Prepare split will record examples associated to the split\r\n--> 684                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    685             except OSError as e:\r\n    686                 raise OSError(\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1084                     writer.write(example, key)\r\n   1085             finally:\r\n-> 1086                 num_examples, num_bytes = writer.finalize()\r\n   1087 \r\n   1088         split_generator.split_info.num_examples = num_examples\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in finalize(self, close_stream)\r\n    525             # Re-intializing to empty list for next batch\r\n    526             self.hkey_record = []\r\n--> 527         self.write_examples_on_file()\r\n    528         if self.pa_writer is None:\r\n    529             if self.schema:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_examples_on_file(self)\r\n    402             # Since current_examples contains (example, key) tuples\r\n    403             batch_examples[col] = [row[0][col] for row in self.current_examples]\r\n--> 404         self.write_batch(batch_examples=batch_examples)\r\n    405         self.current_examples = []\r\n    406 \r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    495             col_try_type = try_features[col] if try_features is not None and col in try_features else None\r\n    496             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n--> 497             arrays.append(pa.array(typed_sequence))\r\n    498             inferred_features[col] = typed_sequence.get_inferred_type()\r\n    499         schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    203                 # Also, when trying type \"string\", we don't want to convert integers or floats to \"string\".\r\n    204                 # We only do it if trying_type is False - since this is what the user asks for.\r\n--> 205                 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)\r\n    206             return out\r\n    207         except (TypeError, pa.lib.ArrowInvalid) as e:  # handle type errors and overflows\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1063         # feature must be either [subfeature] or Sequence(subfeature)\r\n   1064         if isinstance(feature, list):\r\n-> 1065             return pa.ListArray.from_arrays(array.offsets, _c(array.values, feature[0]))\r\n   1066         elif isinstance(feature, Sequence):\r\n   1067             if feature.length > -1:\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in <listcomp>(.0)\r\n   1058             }\r\n   1059         if isinstance(feature, dict) and set(field.name for field in array.type) == set(feature):\r\n-> 1060             arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n   1061             return pa.StructArray.from_arrays(arrays, names=list(feature))\r\n   1062     elif pa.types.is_list(array.type):\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    942         if pa.types.is_list(array.type) and config.PYARROW_VERSION < version.parse(\"4.0.0\"):\r\n    943             array = _sanitize(array)\r\n--> 944         return func(array, *args, **kwargs)\r\n    945 \r\n    946     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in wrapper(array, *args, **kwargs)\r\n    918             return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n    919         else:\r\n--> 920             return func(array, *args, **kwargs)\r\n    921 \r\n    922     return wrapper\r\n\r\n~\/miniconda3\/envs\/huggingface\/lib\/python3.8\/site-packages\/datasets\/table.py in cast_array_to_feature(array, feature, allow_number_to_str)\r\n   1085     elif not isinstance(feature, (Sequence, dict, list, tuple)):\r\n   1086         return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n-> 1087     raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\n   1088 \r\n   1089 \r\n\r\nTypeError: Couldn't cast array of type\r\nstruct<\u533b\u9662-3.0T MRI: string, \u533b\u9662-CT: string, \u533b\u9662-DSA: string, \u533b\u9662-\u516c\u4ea4\u7ebf\u8def: string, \u533b\u9662-\u533a\u57df: string, \u533b\u9662-\u540d\u79f0: string, \u533b\u9662-\u5730\u5740: string, \u533b\u9662-\u5730\u94c1\u53ef\u8fbe: string, \u533b\u9662-\u5730\u94c1\u7ebf\u8def: string, \u533b\u9662-\u6027\u8d28: string, \u533b\u9662-\u6302\u53f7\u65f6\u95f4: string, \u533b\u9662-\u7535\u8bdd: string, \u533b\u9662-\u7b49\u7ea7: string, \u533b\u9662-\u7c7b\u522b: string, \u533b\u9662-\u91cd\u70b9\u79d1\u5ba4: string, \u533b\u9662-\u95e8\u8bca\u65f6\u95f4: string, \u5929\u6c14-\u57ce\u5e02: string, \u5929\u6c14-\u5929\u6c14: string, \u5929\u6c14-\u65e5\u671f: string, \u5929\u6c14-\u6e29\u5ea6: string, \u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6: string, \u5929\u6c14-\u98ce\u529b\u98ce\u5411: string, \u65c5\u6e38\u666f\u70b9-\u533a\u57df: string, \u65c5\u6e38\u666f\u70b9-\u540d\u79f0: string, \u65c5\u6e38\u666f\u70b9-\u5730\u5740: string, \u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4: string, \u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b: string, \u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4: string, \u65c5\u6e38\u666f\u70b9-\u6d88\u8d39: string, \u65c5\u6e38\u666f\u70b9-\u7279\u70b9: string, \u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801: string, \u65c5\u6e38\u666f\u70b9-\u8bc4\u5206: string, \u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c: string, \u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143): string, \u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf: string, \u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73: string, \u6c7d\u8f66-\u5382\u5546: string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L): string, \u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps): string, \u6c7d\u8f66-\u540d\u79f0: string, \u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a: string, \u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf: string, \u6c7d\u8f66-\u5ea7\u4f4d\u6570: string, \u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed: string, \u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce: string, \u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4: string, \u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73: string, \u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6: string, \u6c7d\u8f66-\u7ea7\u522b: string, \u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km): string, \u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b: string, \u6c7d\u8f66-\u8f66\u578b: string, \u6c7d\u8f66-\u8f66\u7cfb: string, \u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm): string, \u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f: string, \u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf: string, \u706b\u8f66-\u51fa\u53d1\u5730: string, \u706b\u8f66-\u51fa\u53d1\u65f6\u95f4: string, \u706b\u8f66-\u5230\u8fbe\u65f6\u95f4: string, \u706b\u8f66-\u5750\u5e2d: string, \u706b\u8f66-\u65e5\u671f: string, \u706b\u8f66-\u65f6\u957f: string, \u706b\u8f66-\u76ee\u7684\u5730: string, \u706b\u8f66-\u7968\u4ef7: string, \u706b\u8f66-\u8231\u4f4d\u6863\u6b21: string, \u706b\u8f66-\u8f66\u578b: string, \u706b\u8f66-\u8f66\u6b21\u4fe1\u606f: string, \u7535\u5f71-\u4e3b\u6f14: string, \u7535\u5f71-\u4e3b\u6f14\u540d\u5355: string, \u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4: string, \u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u5f71-\u5bfc\u6f14: string, \u7535\u5f71-\u5e74\u4ee3: string, \u7535\u5f71-\u7247\u540d: string, \u7535\u5f71-\u7247\u957f: string, \u7535\u5f71-\u7c7b\u578b: string, \u7535\u5f71-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u8111-CPU: string, \u7535\u8111-CPU\u578b\u53f7: string, \u7535\u8111-\u4ea7\u54c1\u7c7b\u522b: string, \u7535\u8111-\u4ef7\u683c: string, \u7535\u8111-\u4ef7\u683c\u533a\u95f4: string, \u7535\u8111-\u5185\u5b58\u5bb9\u91cf: string, \u7535\u8111-\u5206\u7c7b: string, \u7535\u8111-\u54c1\u724c: string, \u7535\u8111-\u5546\u54c1\u540d\u79f0: string, \u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8: string, \u7535\u8111-\u5f85\u673a\u65f6\u957f: string, \u7535\u8111-\u663e\u5361\u578b\u53f7: string, \u7535\u8111-\u663e\u5361\u7c7b\u522b: string, \u7535\u8111-\u6e38\u620f\u6027\u80fd: string, \u7535\u8111-\u7279\u6027: string, \u7535\u8111-\u786c\u76d8\u5bb9\u91cf: string, \u7535\u8111-\u7cfb\u5217: string, \u7535\u8111-\u7cfb\u7edf: string, \u7535\u8111-\u8272\u7cfb: string, \u7535\u8111-\u88f8\u673a\u91cd\u91cf: string, \u7535\u89c6\u5267-\u4e3b\u6f14: string, \u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355: string, \u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a: string, \u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f: string, \u7535\u89c6\u5267-\u5bfc\u6f14: string, \u7535\u89c6\u5267-\u5e74\u4ee3: string, \u7535\u89c6\u5267-\u7247\u540d: string, \u7535\u89c6\u5267-\u7c7b\u578b: string, \u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206: string, \u7535\u89c6\u5267-\u96c6\u6570: string, \u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f: string, \u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4: string, \u8f85\u5bfc\u73ed-\u4ef7\u683c: string, \u8f85\u5bfc\u73ed-\u533a\u57df: string, \u8f85\u5bfc\u73ed-\u5e74\u7ea7: string, \u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9: string, \u8f85\u5bfc\u73ed-\u6559\u5e08: string, \u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u65f6\u6bb5: string, \u8f85\u5bfc\u73ed-\u6821\u533a: string, \u8f85\u5bfc\u73ed-\u6bcf\u5468: string, \u8f85\u5bfc\u73ed-\u73ed\u53f7: string, \u8f85\u5bfc\u73ed-\u79d1\u76ee: string, \u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f: string, \u8f85\u5bfc\u73ed-\u8bfe\u65f6: string, \u8f85\u5bfc\u73ed-\u8bfe\u6b21: string, \u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740: string, \u8f85\u5bfc\u73ed-\u96be\u5ea6: string, \u901a\u7528-\u4ea7\u54c1\u7c7b\u522b: string, \u901a\u7528-\u4ef7\u683c\u533a\u95f4: string, \u901a\u7528-\u54c1\u724c: string, \u901a\u7528-\u7cfb\u5217: string, \u9152\u5e97-\u4ef7\u4f4d: string, \u9152\u5e97-\u505c\u8f66\u573a: string, \u9152\u5e97-\u533a\u57df: string, \u9152\u5e97-\u540d\u79f0: string, \u9152\u5e97-\u5730\u5740: string, \u9152\u5e97-\u623f\u578b: string, \u9152\u5e97-\u623f\u8d39: string, \u9152\u5e97-\u661f\u7ea7: string, \u9152\u5e97-\u7535\u8bdd\u53f7\u7801: string, \u9152\u5e97-\u8bc4\u5206: string, \u9152\u5e97-\u9152\u5e97\u7c7b\u578b: string, \u98de\u673a-\u51c6\u70b9\u7387: string, \u98de\u673a-\u51fa\u53d1\u5730: string, \u98de\u673a-\u5230\u8fbe\u65f6\u95f4: string, \u98de\u673a-\u65e5\u671f: string, \u98de\u673a-\u76ee\u7684\u5730: string, \u98de\u673a-\u7968\u4ef7: string, \u98de\u673a-\u822a\u73ed\u4fe1\u606f: string, \u98de\u673a-\u8231\u4f4d\u6863\u6b21: string, \u98de\u673a-\u8d77\u98de\u65f6\u95f4: string, \u9910\u5385-\u4eba\u5747\u6d88\u8d39: string, \u9910\u5385-\u4ef7\u4f4d: string, \u9910\u5385-\u533a\u57df: string, \u9910\u5385-\u540d\u79f0: string, \u9910\u5385-\u5730\u5740: string, \u9910\u5385-\u63a8\u8350\u83dc: string, \u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe: string, \u9910\u5385-\u7535\u8bdd\u53f7\u7801: string, \u9910\u5385-\u83dc\u7cfb: string, \u9910\u5385-\u8425\u4e1a\u65f6\u95f4: string, \u9910\u5385-\u8bc4\u5206: string>\r\nto\r\n{'\u65c5\u6e38\u666f\u70b9-\u540d\u79f0': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u533a\u57df': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u666f\u70b9\u7c7b\u578b': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6700\u9002\u5408\u4eba\u7fa4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u6d88\u8d39': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u95e8\u7968\u4ef7\u683c': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5730\u5740': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u8bc4\u5206': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u5f00\u653e\u65f6\u95f4': Value(dtype='string', id=None), '\u65c5\u6e38\u666f\u70b9-\u7279\u70b9': Value(dtype='string', id=None), '\u9910\u5385-\u540d\u79f0': Value(dtype='string', id=None), '\u9910\u5385-\u533a\u57df': Value(dtype='string', id=None), '\u9910\u5385-\u83dc\u7cfb': Value(dtype='string', id=None), '\u9910\u5385-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9910\u5385-\u662f\u5426\u5730\u94c1\u76f4\u8fbe': Value(dtype='string', id=None), '\u9910\u5385-\u4eba\u5747\u6d88\u8d39': Value(dtype='string', id=None), '\u9910\u5385-\u5730\u5740': Value(dtype='string', id=None), '\u9910\u5385-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9910\u5385-\u8bc4\u5206': Value(dtype='string', id=None), '\u9910\u5385-\u8425\u4e1a\u65f6\u95f4': Value(dtype='string', id=None), '\u9910\u5385-\u63a8\u8350\u83dc': Value(dtype='string', id=None), '\u9152\u5e97-\u540d\u79f0': Value(dtype='string', id=None), '\u9152\u5e97-\u533a\u57df': Value(dtype='string', id=None), '\u9152\u5e97-\u661f\u7ea7': Value(dtype='string', id=None), '\u9152\u5e97-\u4ef7\u4f4d': Value(dtype='string', id=None), '\u9152\u5e97-\u9152\u5e97\u7c7b\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u578b': Value(dtype='string', id=None), '\u9152\u5e97-\u505c\u8f66\u573a': Value(dtype='string', id=None), '\u9152\u5e97-\u623f\u8d39': Value(dtype='string', id=None), '\u9152\u5e97-\u5730\u5740': Value(dtype='string', id=None), '\u9152\u5e97-\u7535\u8bdd\u53f7\u7801': Value(dtype='string', id=None), '\u9152\u5e97-\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u8111-\u54c1\u724c': Value(dtype='string', id=None), '\u7535\u8111-\u4ea7\u54c1\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u5206\u7c7b': Value(dtype='string', id=None), '\u7535\u8111-\u5185\u5b58\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u5c4f\u5e55\u5c3a\u5bf8': Value(dtype='string', id=None), '\u7535\u8111-CPU': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u5217': Value(dtype='string', id=None), '\u7535\u8111-\u5546\u54c1\u540d\u79f0': Value(dtype='string', id=None), '\u7535\u8111-\u7cfb\u7edf': Value(dtype='string', id=None), '\u7535\u8111-\u6e38\u620f\u6027\u80fd': Value(dtype='string', id=None), '\u7535\u8111-CPU\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u88f8\u673a\u91cd\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u7c7b\u522b': Value(dtype='string', id=None), '\u7535\u8111-\u663e\u5361\u578b\u53f7': Value(dtype='string', id=None), '\u7535\u8111-\u7279\u6027': Value(dtype='string', id=None), '\u7535\u8111-\u8272\u7cfb': Value(dtype='string', id=None), '\u7535\u8111-\u5f85\u673a\u65f6\u957f': Value(dtype='string', id=None), '\u7535\u8111-\u786c\u76d8\u5bb9\u91cf': Value(dtype='string', id=None), '\u7535\u8111-\u4ef7\u683c': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u706b\u8f66-\u65e5\u671f': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u706b\u8f66-\u5750\u5e2d': Value(dtype='string', id=None), '\u706b\u8f66-\u8f66\u6b21\u4fe1\u606f': Value(dtype='string', id=None), '\u706b\u8f66-\u65f6\u957f': Value(dtype='string', id=None), '\u706b\u8f66-\u51fa\u53d1\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u706b\u8f66-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51fa\u53d1\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u76ee\u7684\u5730': Value(dtype='string', id=None), '\u98de\u673a-\u65e5\u671f': Value(dtype='string', id=None), '\u98de\u673a-\u8231\u4f4d\u6863\u6b21': Value(dtype='string', id=None), '\u98de\u673a-\u822a\u73ed\u4fe1\u606f': Value(dtype='string', id=None), '\u98de\u673a-\u8d77\u98de\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u5230\u8fbe\u65f6\u95f4': Value(dtype='string', id=None), '\u98de\u673a-\u7968\u4ef7': Value(dtype='string', id=None), '\u98de\u673a-\u51c6\u70b9\u7387': Value(dtype='string', id=None), '\u5929\u6c14-\u57ce\u5e02': Value(dtype='string', id=None), '\u5929\u6c14-\u65e5\u671f': Value(dtype='string', id=None), '\u5929\u6c14-\u5929\u6c14': Value(dtype='string', id=None), '\u5929\u6c14-\u6e29\u5ea6': Value(dtype='string', id=None), '\u5929\u6c14-\u98ce\u529b\u98ce\u5411': Value(dtype='string', id=None), '\u5929\u6c14-\u7d2b\u5916\u7ebf\u5f3a\u5ea6': Value(dtype='string', id=None), '\u7535\u5f71-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u5f71-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u5f71-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u5f71-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u5f71-\u5177\u4f53\u4e0a\u6620\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u5f71-\u7247\u957f': Value(dtype='string', id=None), '\u7535\u5f71-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5236\u7247\u56fd\u5bb6\/\u5730\u533a': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7c7b\u578b': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5e74\u4ee3': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5bfc\u6f14': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u7247\u540d': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u4e3b\u6f14\u540d\u5355': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u9996\u64ad\u65f6\u95f4': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u96c6\u6570': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u5355\u96c6\u7247\u957f': Value(dtype='string', id=None), '\u7535\u89c6\u5267-\u8c46\u74e3\u8bc4\u5206': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u73ed\u53f7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u96be\u5ea6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u79d1\u76ee': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5e74\u7ea7': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u533a\u57df': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6821\u533a': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65b9\u5f0f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u5f00\u59cb\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u7ed3\u675f\u65e5\u671f': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6bcf\u5468': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0a\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4e0b\u8bfe\u65f6\u95f4': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u65f6\u6bb5': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u6b21': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u65f6': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5ba4\u5730\u70b9': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u4ef7\u683c': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u8bfe\u7a0b\u7f51\u5740': Value(dtype='string', id=None), '\u8f85\u5bfc\u73ed-\u6559\u5e08\u7f51\u5740': Value(dtype='string', id=None), '\u6c7d\u8f66-\u540d\u79f0': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7ea7\u522b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u4f4d\u6570': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u8eab\u5c3a\u5bf8(mm)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5382\u5546': Value(dtype='string', id=None), '\u6c7d\u8f66-\u80fd\u6e90\u7c7b\u578b': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u6392\u91cf(L)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u53d1\u52a8\u673a\u9a6c\u529b(Ps)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a71\u52a8\u65b9\u5f0f': Value(dtype='string', id=None), '\u6c7d\u8f66-\u7efc\u5408\u6cb9\u8017(L\/100km)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u73af\u4fdd\u6807\u51c6': Value(dtype='string', id=None), '\u6c7d\u8f66-\u9a7e\u9a76\u8f85\u52a9\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5de1\u822a\u7cfb\u7edf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u4ef7\u683c(\u4e07\u5143)': Value(dtype='string', id=None), '\u6c7d\u8f66-\u8f66\u7cfb': Value(dtype='string', id=None), '\u6c7d\u8f66-\u52a8\u529b\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6cb9\u8017\u6c34\u5e73': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5012\u8f66\u5f71\u50cf': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5b9a\u901f\u5de1\u822a': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u52a0\u70ed': Value(dtype='string', id=None), '\u6c7d\u8f66-\u5ea7\u6905\u901a\u98ce': Value(dtype='string', id=None), '\u6c7d\u8f66-\u6240\u5c5e\u4ef7\u683c\u533a\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u540d\u79f0': Value(dtype='string', id=None), '\u533b\u9662-\u7b49\u7ea7': Value(dtype='string', id=None), '\u533b\u9662-\u7c7b\u522b': Value(dtype='string', id=None), '\u533b\u9662-\u6027\u8d28': Value(dtype='string', id=None), '\u533b\u9662-\u533a\u57df': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u5740': Value(dtype='string', id=None), '\u533b\u9662-\u7535\u8bdd': Value(dtype='string', id=None), '\u533b\u9662-\u6302\u53f7\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u95e8\u8bca\u65f6\u95f4': Value(dtype='string', id=None), '\u533b\u9662-\u516c\u4ea4\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u53ef\u8fbe': Value(dtype='string', id=None), '\u533b\u9662-\u5730\u94c1\u7ebf\u8def': Value(dtype='string', id=None), '\u533b\u9662-\u91cd\u70b9\u79d1\u5ba4': Value(dtype='string', id=None), '\u533b\u9662-CT': Value(dtype='string', id=None), '\u533b\u9662-3.0T MRI': Value(dtype='string', id=None), '\u533b\u9662-DSA': Value(dtype='string', id=None)}\r\n```\r\n\r\n<\/details>\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n Thanks, this was indeed the fix! Would it make sense to produce a more informative error message in such cases? \r\n\r\nThe issue can be closed. \r\n\r\n","embeddings":[-0.1942696571,-0.4909287691,0.0600802898,0.5314732194,0.4639725685,0.1549371183,0.2992590368,0.3703403473,0.1878763437,-0.0143708531,0.1101714894,0.5128638744,-0.3276836276,0.1034816429,0.0563642532,-0.2771357596,0.2133807838,0.0304361284,-0.1236653253,0.0571421757,0.1015927196,0.2774196267,-0.1580940485,0.3223943114,-0.177803576,0.1896527559,0.1909873784,0.3998605907,-0.1899659485,-0.493301928,0.6088303328,-0.3547360003,0.3529712558,0.2716292739,-0.0001241634,0.2320222259,0.4078266621,0.012609574,-0.2205002159,-0.3889898062,-0.2872396708,-0.3370229304,0.3555959165,0.0433429405,0.0666344985,-0.1898171902,-0.0262948573,-0.1967689395,0.0074145594,0.0752304718,0.1018247232,0.5603157878,0.3825861514,-0.1824073493,0.1919645816,0.0826466903,-0.3385210037,0.1542516947,0.138455376,0.6070917845,0.268075943,0.1615090072,-0.0491786189,-0.0906088725,0.5576862097,-0.103054814,-0.0564994216,-0.1598872095,-0.1619885713,0.2661495507,0.4195816815,-0.0913306549,-0.4803362191,-0.2445016205,-0.1568792313,0.1568656415,0.21170488,-0.0096013695,0.0259571541,0.2101078331,-0.3084632158,-0.0033286109,0.0759534165,-0.0759758502,0.1593330055,-0.0776601657,-0.1189897731,0.1543727517,0.1549899429,-0.2964865267,0.0986974239,-0.1423982829,-0.2271053046,0.0975215435,-0.153576687,0.1252183765,0.1266498268,0.0366891697,0.0160740055,-0.0651022345,0.0298234988,0.1507367939,-0.35328269,0.2018560767,0.4332413673,0.2174664587,0.178684324,0.0913464949,-0.1988650262,0.1686339229,0.0395610929,0.0975488499,-0.0670284331,-0.1301365942,0.247482717,-0.0393823422,0.3550628424,-0.2471610755,-0.0883404016,-0.1057405993,0.0680669472,0.1440200359,0.2797644138,0.2924188375,0.0017775551,0.3551595509,0.1253634989,0.3235913217,-0.0397814848,-0.2188540995,-0.167372793,-0.0234555844,0.0130565492,-0.4220882952,-0.0089158015,-0.3365992308,0.1036493182,-0.1182179973,-0.0210645758,-0.2249391079,-0.2367394418,0.2362743616,-0.0747250915,0.3233361244,-0.052383028,0.2356864512,0.4166336954,-0.0531071387,-0.1469531804,0.0487975702,-0.3159548044,-0.339076966,-0.0866677538,0.119581826,-0.254712522,-0.1372659802,-0.2474257648,-0.0462892763,0.2874094248,-0.511916399,-0.2409537435,-0.2571010888,-0.1087409034,-0.1277848482,0.0484971777,0.1353400201,-0.362275064,-0.1674032658,-0.3637704849,-0.4549883306,0.1102411598,0.240791589,-0.0935699344,-0.0527783372,-0.3162724376,0.3000915051,0.4084950984,0.0981535167,-0.2104138136,0.2250009328,0.0723604783,0.0590207018,0.1104202867,0.1961289644,0.2790901363,-0.1740741581,-0.1117369086,0.0848923028,-0.1996245533,0.040106371,-0.3926500678,-0.2444086969,-0.0278117117,0.0508838259,-0.0308870096,0.3185037076,0.0314625166,0.0595753789,0.2317246348,-0.0186059419,0.1118688136,-0.0566809028,0.1279017776,0.1090304554,-0.003881942,-0.4135949612,-0.3670502603,0.4241425097,0.1472156495,-0.1322740465,0.0726334974,0.0550310165,-0.3470769823,0.2225904763,-0.1151379272,0.348615706,-0.0512186475,0.0203841757,-0.1503184885,-0.1448521614,-0.2338885665,-0.2151050568,0.1371222585,-0.0441854708,-0.5224093795,-0.0429971516,-0.1325335801,-0.2706715167,-0.2429058999,-0.0835895538,0.1356125623,-0.1731237769,-0.3182046115,0.2236567289,-0.0784818828,0.2531950474,-0.1508158743,-0.0962944329,-0.0786364079,-0.2554769814,-0.0008580303,0.1905666739,0.0915428251,-0.0011686352,0.0820051283,0.4654118717,0.1451885104,0.3244556487,-0.0720716119,0.3501816988,0.291541934,0.1136104465,0.1384634674,-0.5138630867,0.1396218836,-0.3997299075,-0.1792991757,0.1241501868,-0.1873838007,0.1845445931,0.1627548933,-0.0541169494,0.2096038908,0.1043134481,0.0326686688,0.0236979518,-0.2005431056,0.2297652364,0.2854864597,-0.0370812155,-0.0129693318,-0.0510888174,0.1672250628,-0.012757509,0.4683577418,0.0660867319,0.1984333694,0.0047486234,-0.0378314555,-0.0329948552,-0.192838192,-0.0420875102,0.059436392,0.2085192651,-0.4780389369,0.1811769903,-0.3215309083,0.3393013775,-0.2576460838,-0.2467057556,-0.0748819858,-0.3460410833,-0.1982435733,0.2163226902,-0.2268827558,0.2133338004,-0.177493766,-0.0097445808,-0.0959311947,-0.0358729064,-0.1884303242,0.1360535175,-0.1761716455,-0.1525814086,0.3079068065,-0.1383276582,0.0199490301,-0.067627497,-0.0861045718,-0.0740520209,-0.2235280275,0.0977003276,-0.0130775189,0.3453876376,0.3630618155,0.2607116997,0.1488767564,-0.2942780554,0.4073486328,-0.3938877881,-0.3691505492,0.4227531254,-0.0391222909,-0.1764794439,-0.3195714951,-0.2263647765,-0.1529022604,-0.437604934,-0.0075782747,-0.13399297,0.0972464681,-0.0186711866,0.4262812436,0.0709741414,-0.1276955754,0.2040297985,-0.0204139948,-0.0096580507,0.1895896196,0.1194485277,-0.2456169128,0.0500036664,-0.0100701302,0.1323182136,-0.0020673785,-0.3501155078,0.13245745,-0.128473863,-0.0216742679,0.174705103,-0.2467836142,0.2356105149,0.0210880004,0.0295159183,-0.1336812228,-0.1877533197,-0.020620171,-0.3842800558,0.0637407824,0.0186779927,0.5528272986,-0.0107114604,0.5343385339,0.3602442741,-0.1410549134,0.3175684214,0.0225362461,-0.0584072582,-0.3318219781,-0.4552114904,-0.1578633934,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3634","title":"Dataset.shuffle(seed=None) gives fixed row permutation","comments":"I'm not sure if this is expected behavior.\r\n\r\nAm I supposed to work with a copy of the dataset, i.e. `shuffled_dataset = data.shuffle(seed=None)`?\r\n\r\n```diff\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n    {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+shuffled_data = data.shuffle(seed=None)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+    shuffled_data = shuffled_data.shuffle(seed=None)\r\n+    print(shuffled_data[:])\r\n-    print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n    df = data.to_pandas().sample(frac=1.0)\r\n    print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\nor provide a `generator` instead?\r\n\r\n```diff\r\nimport datasets\r\n+from numpy.random import default_rng\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n    {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+rng = default_rng()\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+   print(data.shuffle(generator=rng)[:])\r\n-   print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n    df = data.to_pandas().sample(frac=1.0)\r\n    print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```","body":"## Describe the bug\r\nRepeated attempts to `shuffle` a dataset without specifying a seed give the same results.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n    {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n    print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n    df = data.to_pandas().sample(frac=1.0)\r\n    print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\n## Expected results\r\nI assumed that the default setting would initialize a new\/random state of a `np.random.BitGenerator` (see [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)).\r\n\r\nWouldn't that reshuffle the rows each time I call `data.shuffle()`?\r\n\r\n## Actual results\r\n\r\n```bash\r\nShuffle dataset\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n\r\nShuffle via pandas\r\n{'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']}\r\n{'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']}\r\n{'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']}\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.1\r\n","comment_length":158,"text":"Dataset.shuffle(seed=None) gives fixed row permutation \n ## Describe the bug\r\nRepeated attempts to `shuffle` a dataset without specifying a seed give the same results.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n    {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n    print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n    df = data.to_pandas().sample(frac=1.0)\r\n    print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\n## Expected results\r\nI assumed that the default setting would initialize a new\/random state of a `np.random.BitGenerator` (see [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)).\r\n\r\nWouldn't that reshuffle the rows each time I call `data.shuffle()`?\r\n\r\n## Actual results\r\n\r\n```bash\r\nShuffle dataset\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n\r\nShuffle via pandas\r\n{'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']}\r\n{'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']}\r\n{'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']}\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.1\r\n \n I'm not sure if this is expected behavior.\r\n\r\nAm I supposed to work with a copy of the dataset, i.e. `shuffled_dataset = data.shuffle(seed=None)`?\r\n\r\n```diff\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n    {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+shuffled_data = data.shuffle(seed=None)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+    shuffled_data = shuffled_data.shuffle(seed=None)\r\n+    print(shuffled_data[:])\r\n-    print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n    df = data.to_pandas().sample(frac=1.0)\r\n    print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\nor provide a `generator` instead?\r\n\r\n```diff\r\nimport datasets\r\n+from numpy.random import default_rng\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n    {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+rng = default_rng()\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+   print(data.shuffle(generator=rng)[:])\r\n-   print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n    df = data.to_pandas().sample(frac=1.0)\r\n    print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```","embeddings":[0.2256515324,-0.2673812211,0.0401448272,0.1096486524,0.2171937972,0.0051689916,0.5835940242,-0.0988350213,-0.2688477635,0.4067517817,0.0916139334,0.448505491,-0.1216276214,0.3065915108,0.1507817656,0.15466097,0.3101199269,0.0387727134,-0.0948879197,-0.2629058957,-0.2420204431,-0.234519586,-0.5006458759,-0.2106885612,-0.4018545151,0.1617213041,-0.2888137698,0.2026994526,0.0705021098,-0.0197257996,0.2843486965,0.3294395208,-0.182694912,0.4684556425,-0.0001162541,-0.1587899774,0.2686457634,-0.1192180738,-0.1626604199,-0.1475037932,-0.1593791991,0.2459887564,-0.1424250156,-0.0334041156,-0.0766226724,-0.2888720334,-0.0247700401,-0.1218587607,0.001615891,-0.1474996358,0.1500995308,0.0871204808,0.048824355,0.1291518062,0.4296832383,0.2574065924,-0.0081770672,0.2552580535,-0.0817478672,0.0750361905,-0.0653486326,-0.0377783477,-0.0579670183,0.2558073401,0.3295450509,0.0968593881,-0.0336233303,-0.2755067647,0.1060640141,0.4645741582,0.195292905,-0.2900371552,-0.2904609442,-0.4516665936,0.0601981692,-0.2949527204,0.0565521792,0.187667951,-0.2025703937,-0.284272939,-0.0451226346,0.3667741418,0.2454072535,0.0015646274,-0.0354937315,0.300910145,-0.051007133,0.1184059456,0.167954579,-0.2017057985,0.3343931735,-0.2941753268,0.0219167583,0.1606019437,-0.3496526182,-0.010953282,0.266964674,0.2966360748,0.1985847354,0.0852917358,0.0021539063,0.1383581907,-0.0180992708,-0.020286981,0.2764521241,0.1235436276,0.0311447009,0.2717364132,0.1070865318,0.265622437,0.1255067736,0.2547366321,0.3363692462,0.0085410504,0.1369514763,-0.2322992384,0.2124376893,0.0484589934,-0.2943051755,-0.059516944,-0.2466785163,-0.1283288896,-0.0556636713,0.1537535936,0.073738113,0.0665647611,-0.2489829957,-0.0331139751,-0.0881608874,0.119541131,-0.2969888151,-0.1325567067,-0.1701522768,0.064477846,0.0462292433,-0.1106834486,0.229282245,0.4473640919,0.1004277542,-0.0769886523,-0.1968511343,0.0402094834,0.1274353415,0.0879498199,-0.0164086912,0.1226735264,0.1982973218,0.0803192854,-0.0240994077,0.2995972037,-0.135333702,0.1544157118,0.1475070566,0.1874859631,-0.3464987874,0.0641237721,-0.3623846173,0.1767256856,-0.0652635694,-0.0037054229,0.1555008441,-0.3015502095,-0.1249377877,-0.2656255364,0.0616981313,0.1461602151,-0.0251625013,-0.1438874304,0.3342524171,0.0184092075,0.4784055948,0.3257240653,-0.2096535265,-0.0154031487,-0.2255904824,-0.1495620757,-0.1083063781,0.4567145407,-0.404740572,-0.4431342483,-0.2153984755,0.4331863821,0.0081781792,0.0819744691,0.0179577619,-0.0385788977,0.3817168176,0.2444965392,-0.1899465173,0.3296740651,-0.3144621253,-0.1855110228,0.2505238056,0.2462030649,-0.2222328037,0.0585850663,0.0733148307,-0.0889952928,0.3848060369,-0.1289527863,-0.0884655863,-0.1433282197,0.0179358907,0.4039312005,0.1823563427,-0.1657503396,-0.2599182427,0.1171400473,0.2018537223,0.0029514164,-0.0604130849,-0.2741460502,0.0338523686,-0.2631422877,-0.2504562438,-0.2856299877,0.0440495387,0.1757374853,0.0869551674,0.1491073668,-0.2291628271,-0.0802078247,-0.1484195441,0.0645995066,-0.5968744755,0.2440189868,0.085352622,-0.3012914658,-0.1596513689,0.1743728518,0.0097556319,-0.0758560896,0.0311991293,0.3636751175,0.2924382389,-0.3252248168,-0.3238766789,-0.1303281337,0.1343487352,0.1681209803,-0.1399608254,0.1470192671,0.1779777408,-0.0804239288,-0.1213926524,0.5381622314,-0.5788695216,0.032266885,-0.0726493374,-0.1201386377,0.116070807,0.1493435353,-0.1945491731,0.0822782218,0.219588697,-0.0059956829,-0.0465641096,0.3415364921,-0.4768418372,0.0323311687,0.3071290553,-0.2322489768,-0.0269540306,-0.0837393552,0.0188734327,-0.0999819934,-0.048683472,0.2807813883,0.3230981529,0.2688831985,-0.0718416721,0.1516860873,-0.143819347,-0.2490876317,-0.114500761,-0.0580040216,0.1040322408,0.0853907168,0.1769119352,0.0389101654,-0.3168679178,-0.2389862686,0.0818918273,0.324721843,-0.1803409755,-0.051445175,-0.4431859255,-0.0387666635,-0.0093169846,-0.3071050644,-0.2708665729,-0.104381226,0.0101056304,0.4893619716,-0.1115540788,0.3053559959,0.1564190984,0.2078217566,0.1381389201,0.0748212785,-0.127433598,0.143788144,-0.2818396091,0.015092222,-0.0390199386,0.1349907666,0.2054224908,-0.2569692433,-0.3116918206,-0.1203716025,-0.0179515574,-0.1420621574,-0.2211238891,0.401306957,0.0253586937,-0.0233792849,-0.2860898376,-0.2271323204,0.1904279143,-0.0451351106,-0.0648901239,0.3365612924,0.1105495319,-0.1068386286,-0.1458196491,0.0136300707,-0.0034219052,-0.1048861668,0.2351644188,-0.0386774726,-0.0458830632,0.0987713411,0.1670247018,-0.1384063214,-0.3173841238,-0.1414354891,-0.398733139,-0.3888043165,0.1470938027,0.0624408163,-0.2756631076,-0.1270302832,-0.2158236504,-0.3860142529,0.4554981291,-0.1000796556,0.2293786407,-0.3016572595,0.2923062742,0.1860692054,0.3463490903,0.4538743198,-0.1194277108,0.1996245831,0.0213341042,-0.3375160098,-0.1280880868,0.0289199036,0.231497407,0.1576372534,0.2687918842,-0.0216471236,0.3854142427,-0.0398481339,-0.0098091448,0.0762818828,-0.1578669548,0.5059734583,-0.3897587359,-0.11300974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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3634","title":"Dataset.shuffle(seed=None) gives fixed row permutation","comments":"Hi! Thanks for reporting! Yes, this is not expected behavior. I've opened a PR with the fix.","body":"## Describe the bug\r\nRepeated attempts to `shuffle` a dataset without specifying a seed give the same results.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n    {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n    print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n    df = data.to_pandas().sample(frac=1.0)\r\n    print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\n## Expected results\r\nI assumed that the default setting would initialize a new\/random state of a `np.random.BitGenerator` (see [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)).\r\n\r\nWouldn't that reshuffle the rows each time I call `data.shuffle()`?\r\n\r\n## Actual results\r\n\r\n```bash\r\nShuffle dataset\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n\r\nShuffle via pandas\r\n{'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']}\r\n{'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']}\r\n{'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']}\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.1\r\n","comment_length":17,"text":"Dataset.shuffle(seed=None) gives fixed row permutation \n ## Describe the bug\r\nRepeated attempts to `shuffle` a dataset without specifying a seed give the same results.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n    {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n    print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n    df = data.to_pandas().sample(frac=1.0)\r\n    print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\n## Expected results\r\nI assumed that the default setting would initialize a new\/random state of a `np.random.BitGenerator` (see [docs](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=shuffle#datasets.Dataset.shuffle)).\r\n\r\nWouldn't that reshuffle the rows each time I call `data.shuffle()`?\r\n\r\n## Actual results\r\n\r\n```bash\r\nShuffle dataset\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n{'feature': [5, 1, 3, 2, 4], 'label': ['e', 'a', 'c', 'b', 'd']}\r\n\r\nShuffle via pandas\r\n{'feature': [4, 2, 3, 1, 5], 'label': ['d', 'b', 'c', 'a', 'e']}\r\n{'feature': [2, 5, 3, 4, 1], 'label': ['b', 'e', 'c', 'd', 'a']}\r\n{'feature': [5, 2, 3, 1, 4], 'label': ['e', 'b', 'c', 'a', 'd']}\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-5.13.0-27-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.1\r\n \n Hi! Thanks for reporting! Yes, this is not expected behavior. I've opened a PR with the fix.","embeddings":[0.2256515324,-0.2673812211,0.0401448272,0.1096486524,0.2171937972,0.0051689916,0.5835940242,-0.0988350213,-0.2688477635,0.4067517817,0.0916139334,0.448505491,-0.1216276214,0.3065915108,0.1507817656,0.15466097,0.3101199269,0.0387727134,-0.0948879197,-0.2629058957,-0.2420204431,-0.234519586,-0.5006458759,-0.2106885612,-0.4018545151,0.1617213041,-0.2888137698,0.2026994526,0.0705021098,-0.0197257996,0.2843486965,0.3294395208,-0.182694912,0.4684556425,-0.0001162541,-0.1587899774,0.2686457634,-0.1192180738,-0.1626604199,-0.1475037932,-0.1593791991,0.2459887564,-0.1424250156,-0.0334041156,-0.0766226724,-0.2888720334,-0.0247700401,-0.1218587607,0.001615891,-0.1474996358,0.1500995308,0.0871204808,0.048824355,0.1291518062,0.4296832383,0.2574065924,-0.0081770672,0.2552580535,-0.0817478672,0.0750361905,-0.0653486326,-0.0377783477,-0.0579670183,0.2558073401,0.3295450509,0.0968593881,-0.0336233303,-0.2755067647,0.1060640141,0.4645741582,0.195292905,-0.2900371552,-0.2904609442,-0.4516665936,0.0601981692,-0.2949527204,0.0565521792,0.187667951,-0.2025703937,-0.284272939,-0.0451226346,0.3667741418,0.2454072535,0.0015646274,-0.0354937315,0.300910145,-0.051007133,0.1184059456,0.167954579,-0.2017057985,0.3343931735,-0.2941753268,0.0219167583,0.1606019437,-0.3496526182,-0.010953282,0.266964674,0.2966360748,0.1985847354,0.0852917358,0.0021539063,0.1383581907,-0.0180992708,-0.020286981,0.2764521241,0.1235436276,0.0311447009,0.2717364132,0.1070865318,0.265622437,0.1255067736,0.2547366321,0.3363692462,0.0085410504,0.1369514763,-0.2322992384,0.2124376893,0.0484589934,-0.2943051755,-0.059516944,-0.2466785163,-0.1283288896,-0.0556636713,0.1537535936,0.073738113,0.0665647611,-0.2489829957,-0.0331139751,-0.0881608874,0.119541131,-0.2969888151,-0.1325567067,-0.1701522768,0.064477846,0.0462292433,-0.1106834486,0.229282245,0.4473640919,0.1004277542,-0.0769886523,-0.1968511343,0.0402094834,0.1274353415,0.0879498199,-0.0164086912,0.1226735264,0.1982973218,0.0803192854,-0.0240994077,0.2995972037,-0.135333702,0.1544157118,0.1475070566,0.1874859631,-0.3464987874,0.0641237721,-0.3623846173,0.1767256856,-0.0652635694,-0.0037054229,0.1555008441,-0.3015502095,-0.1249377877,-0.2656255364,0.0616981313,0.1461602151,-0.0251625013,-0.1438874304,0.3342524171,0.0184092075,0.4784055948,0.3257240653,-0.2096535265,-0.0154031487,-0.2255904824,-0.1495620757,-0.1083063781,0.4567145407,-0.404740572,-0.4431342483,-0.2153984755,0.4331863821,0.0081781792,0.0819744691,0.0179577619,-0.0385788977,0.3817168176,0.2444965392,-0.1899465173,0.3296740651,-0.3144621253,-0.1855110228,0.2505238056,0.2462030649,-0.2222328037,0.0585850663,0.0733148307,-0.0889952928,0.3848060369,-0.1289527863,-0.0884655863,-0.1433282197,0.0179358907,0.4039312005,0.1823563427,-0.1657503396,-0.2599182427,0.1171400473,0.2018537223,0.0029514164,-0.0604130849,-0.2741460502,0.0338523686,-0.2631422877,-0.2504562438,-0.2856299877,0.0440495387,0.1757374853,0.0869551674,0.1491073668,-0.2291628271,-0.0802078247,-0.1484195441,0.0645995066,-0.5968744755,0.2440189868,0.085352622,-0.3012914658,-0.1596513689,0.1743728518,0.0097556319,-0.0758560896,0.0311991293,0.3636751175,0.2924382389,-0.3252248168,-0.3238766789,-0.1303281337,0.1343487352,0.1681209803,-0.1399608254,0.1470192671,0.1779777408,-0.0804239288,-0.1213926524,0.5381622314,-0.5788695216,0.032266885,-0.0726493374,-0.1201386377,0.116070807,0.1493435353,-0.1945491731,0.0822782218,0.219588697,-0.0059956829,-0.0465641096,0.3415364921,-0.4768418372,0.0323311687,0.3071290553,-0.2322489768,-0.0269540306,-0.0837393552,0.0188734327,-0.0999819934,-0.048683472,0.2807813883,0.3230981529,0.2688831985,-0.0718416721,0.1516860873,-0.143819347,-0.2490876317,-0.114500761,-0.0580040216,0.1040322408,0.0853907168,0.1769119352,0.0389101654,-0.3168679178,-0.2389862686,0.0818918273,0.324721843,-0.1803409755,-0.051445175,-0.4431859255,-0.0387666635,-0.0093169846,-0.3071050644,-0.2708665729,-0.104381226,0.0101056304,0.4893619716,-0.1115540788,0.3053559959,0.1564190984,0.2078217566,0.1381389201,0.0748212785,-0.127433598,0.143788144,-0.2818396091,0.015092222,-0.0390199386,0.1349907666,0.2054224908,-0.2569692433,-0.3116918206,-0.1203716025,-0.0179515574,-0.1420621574,-0.2211238891,0.401306957,0.0253586937,-0.0233792849,-0.2860898376,-0.2271323204,0.1904279143,-0.0451351106,-0.0648901239,0.3365612924,0.1105495319,-0.1068386286,-0.1458196491,0.0136300707,-0.0034219052,-0.1048861668,0.2351644188,-0.0386774726,-0.0458830632,0.0987713411,0.1670247018,-0.1384063214,-0.3173841238,-0.1414354891,-0.398733139,-0.3888043165,0.1470938027,0.0624408163,-0.2756631076,-0.1270302832,-0.2158236504,-0.3860142529,0.4554981291,-0.1000796556,0.2293786407,-0.3016572595,0.2923062742,0.1860692054,0.3463490903,0.4538743198,-0.1194277108,0.1996245831,0.0213341042,-0.3375160098,-0.1280880868,0.0289199036,0.231497407,0.1576372534,0.2687918842,-0.0216471236,0.3854142427,-0.0398481339,-0.0098091448,0.0762818828,-0.1578669548,0.5059734583,-0.3897587359,-0.1130097434,0.223213315,0.4898449779,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3632","title":"Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid)","comments":"Hi @AnzorGozalishvili,\r\n\r\nMaybe their site was temporarily down, but it seems to work fine now.\r\n\r\nCould you please try again and confirm if the problem persists? ","body":"## Describe the bug\r\nThe dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable. \r\nCheck out the dataset [homepage](http:\/\/data.statmt.org\/cc-100\/)  which isn't accessible.\r\nAlso the URLs for dataset file per language isn't accessible: http:\/\/data.statmt.org\/cc-100\/<language code here>.txt.xz (language codes: am, sr, ka, etc.)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"cc100\", \"ka\")\r\n```\r\nIt throws 503 error.\r\n\r\n## Expected results\r\nIt should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible.\r\n\r\n\r\n## Environment info\r\nRun from google colab. Just installed the library using pip:\r\n```!pip install -U datasets```\r\n","comment_length":26,"text":"Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid) \n ## Describe the bug\r\nThe dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable. \r\nCheck out the dataset [homepage](http:\/\/data.statmt.org\/cc-100\/)  which isn't accessible.\r\nAlso the URLs for dataset file per language isn't accessible: http:\/\/data.statmt.org\/cc-100\/<language code here>.txt.xz (language codes: am, sr, ka, etc.)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"cc100\", \"ka\")\r\n```\r\nIt throws 503 error.\r\n\r\n## Expected results\r\nIt should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible.\r\n\r\n\r\n## Environment info\r\nRun from google colab. Just installed the library using pip:\r\n```!pip install -U datasets```\r\n \n Hi @AnzorGozalishvili,\r\n\r\nMaybe their site was temporarily down, but it seems to work fine now.\r\n\r\nCould you please try again and confirm if the problem persists? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3632","title":"Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid)","comments":"Hi @albertvillanova \r\nI checked and it works. \r\nIt seems that it was really temporarily down.\r\nThanks!","body":"## Describe the bug\r\nThe dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable. \r\nCheck out the dataset [homepage](http:\/\/data.statmt.org\/cc-100\/)  which isn't accessible.\r\nAlso the URLs for dataset file per language isn't accessible: http:\/\/data.statmt.org\/cc-100\/<language code here>.txt.xz (language codes: am, sr, ka, etc.)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"cc100\", \"ka\")\r\n```\r\nIt throws 503 error.\r\n\r\n## Expected results\r\nIt should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible.\r\n\r\n\r\n## Environment info\r\nRun from google colab. Just installed the library using pip:\r\n```!pip install -U datasets```\r\n","comment_length":16,"text":"Adding CC-100: Monolingual Datasets from Web Crawl Data (Datasets links are invalid) \n ## Describe the bug\r\nThe dataset links are no longer valid for CC-100. It seems that the website which was keeping these files are no longer accessible and therefore this dataset became unusable. \r\nCheck out the dataset [homepage](http:\/\/data.statmt.org\/cc-100\/)  which isn't accessible.\r\nAlso the URLs for dataset file per language isn't accessible: http:\/\/data.statmt.org\/cc-100\/<language code here>.txt.xz (language codes: am, sr, ka, etc.)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"cc100\", \"ka\")\r\n```\r\nIt throws 503 error.\r\n\r\n## Expected results\r\nIt should successfully download and load dataset but it throws an exception because the dataset files are no longer accessible.\r\n\r\n\r\n## Environment info\r\nRun from google colab. Just installed the library using pip:\r\n```!pip install -U datasets```\r\n \n Hi @albertvillanova \r\nI checked and it works. \r\nIt seems that it was really temporarily down.\r\nThanks!","embeddings":[-0.1686967611,0.0409710743,0.0132527687,0.2089209855,0.1232640594,0.0467352197,0.2052617073,0.1223097593,0.090792276,0.0844411775,-0.2521445453,0.2533139288,0.0570646003,0.1296911091,0.3611765504,0.0015484138,0.0005128022,-0.1116828844,-0.2077601403,0.0492008515,-0.2357863188,0.0165771525,-0.1149304062,-0.1767584085,-0.1167561337,0.059359353,-0.1564053446,0.1667041779,-0.2282366902,-0.4358338416,0.2157018781,0.132277742,0.4082997739,0.1873313189,-0.0001197625,0.0349855945,0.5446320772,0.0662514716,-0.6426510811,-0.3356354237,-0.6715113521,-0.3007978201,0.0123793874,-0.0274393763,0.0167441647,0.3618825078,-0.0277006309,-0.4767542779,0.2271572351,0.6008827686,0.1476815045,0.0868870914,0.1727495342,-0.273933202,0.2489446104,-0.1708149612,-0.0690145344,0.7275503874,0.4946788847,0.1295213401,0.4125565886,0.1845821291,0.0076128491,-0.3968623877,-0.1176399663,-0.2100417763,-0.1340990216,-0.3567731678,0.3307417035,0.2912730575,0.6424117088,0.0039947438,-0.3784964383,-0.0929805189,-0.04989383,-0.4816658199,0.250785321,0.2574690878,0.3046650589,0.3628766835,-0.1393032372,-0.2735240757,-0.0908594429,0.332493037,0.0453152731,0.3748703003,-0.1621333361,0.0785315558,0.0413401425,0.0042316224,0.4125800133,0.0716830492,-0.0358617865,0.0789627209,-0.1519688219,0.1507550478,0.1264357418,0.3536153734,0.1471926421,0.0660248399,0.2507809401,0.1017672569,-0.311288178,0.169849962,0.0381552912,0.1067219228,-0.115070045,0.0738558173,0.36889413,0.1592101902,0.0677630901,-0.1182435304,-0.0613989197,-0.2674590647,-0.2283688188,0.2556322813,0.1531332433,-0.1462880522,-0.4231629372,0.1360474229,-0.3301637173,-0.2110749781,0.1639319062,0.311186552,0.0336261131,0.0575815365,-0.0706889704,0.2372721285,-0.1245018318,-0.2089276165,0.0322139561,0.1241169348,-0.2433841527,-0.123011902,0.1392674595,-0.2505142391,0.0031761823,0.0438435152,-0.0865866616,-0.15705733,0.0317896046,-0.2140890658,-0.1386997551,0.1562520862,0.114407368,0.1867271811,-0.0522822291,-0.3724611402,-0.0087178759,0.0832167417,-0.5701639056,-0.2169689089,-0.2791009545,0.1283304691,-0.2695639431,-0.1489639133,-0.2853392959,0.2058371603,0.0143828038,-0.145001471,-0.0216260888,-0.0961254761,-0.2967612743,-0.1305194497,0.0792146921,0.7386155128,-0.4342745543,-0.0487582944,-0.2769090831,0.1364398897,0.0921775773,0.0569845177,-0.2276147753,0.060472142,-0.3234635592,-0.1970670223,0.1078342795,-0.2446378022,-0.4979788065,0.1224724203,-0.0324271023,0.0064146454,-0.2172319442,-0.2011305988,-0.0924139917,-0.0511519238,0.0176741108,0.0383276679,0.1035180688,-0.0806716979,-0.2991198301,-0.1938670725,0.4211417735,-0.1555362195,0.3791495562,-0.0043476517,0.5662850142,-0.3644711077,0.2176109999,-0.2659943104,0.1939075738,0.2963501513,0.4399976432,0.104909271,0.1654064953,-0.1256374419,-0.2411819249,0.3201892376,-0.0381001122,0.2452437133,-0.327829957,0.0344119817,-0.4981052577,0.0252895933,-0.2098160088,-0.0962168202,0.0309322122,0.1802653968,-0.1229469776,0.4207363129,-0.0275431424,0.284327507,0.0063904673,0.0278623253,-0.0266463794,0.4727975428,-0.1960923672,0.0982606038,-0.0017491744,0.2903722823,0.3947387338,-0.1779213101,-0.2541545033,0.2662622631,-0.1262878627,0.28142488,0.2548257709,0.0063220873,0.6262040138,-0.5681077838,0.0841665491,0.0707007796,0.1762450039,0.1867062896,0.0240749493,0.0313535333,-0.1332453936,0.0684031174,0.0738880634,0.1566822976,0.4207104743,0.0130586019,-0.095451422,-0.0991427451,0.3143623173,0.201690048,0.4600761533,-0.0670760199,-0.1045030653,0.3120795488,0.1087697074,-0.0742047653,0.1983181089,0.2902634442,-0.1534668952,-0.0399734415,0.2858678401,0.3327562213,0.1055980399,-0.1059690416,0.1433264613,-0.0740237534,-0.1304115802,-0.1692166924,0.1089303344,0.0656104386,0.1571389288,0.0382815599,0.2888274193,0.2285306156,-0.3155480623,-0.1867598593,0.0503456332,0.1155015454,-0.3573441803,0.3317319751,-0.4286235571,-0.36168167,-0.1568653584,-0.1484414339,-0.1551262587,-0.2817638516,-0.2330395132,-0.0495344773,-0.1388604641,-0.0122660529,-0.1106764823,0.0758868679,-0.0893936902,0.080806464,0.039159365,-0.051743485,-0.0502603203,0.010099669,0.2640382648,-0.1724706143,0.1706154197,-0.2192882895,0.0068141492,-0.3051438034,-0.4764650464,0.2179599255,-0.0245563462,0.316522181,-0.1215496138,0.0458607897,-0.4109133482,-0.0262237899,0.1984729171,0.1690068245,0.2612793148,0.0858153328,0.0416367948,-0.0913753211,-0.1101654395,-0.1233545095,-0.5252873898,-0.183202073,-0.0632142723,0.1652841866,0.1549461633,0.3366315961,0.1474257112,0.0757809058,0.2183714062,0.149412334,-0.2455479056,-0.1805853397,0.3179423809,-0.2896233201,-0.4237010479,0.0131103965,-0.1682003886,0.3746481538,0.0923853964,-0.1329201311,0.0456717126,-0.2685450315,-0.0893445387,0.0802462175,0.0379066207,0.2551349699,0.0053009707,0.1122633591,-0.1591553092,-0.0158406701,-0.3392065763,0.3350474536,0.378221482,0.1334944814,0.0154628167,0.1000001803,0.4393829703,0.1920236349,0.2206997871,0.633807838,-0.0688003004,0.5687462687,0.0546479374,-0.3938676417,0.0556845404,-0.2478004545,-0.249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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3625","title":"Add a metadata field for when source data was produced ","comments":"A question to the datasets maintainers: is there a policy about how the set of allowed metadata fields is maintained and expanded?\r\n\r\nMetadata are very important, but defining the standard is always a struggle between allowing exhaustivity without being too complex. Archivists have Dublin Core, open data has https:\/\/frictionlessdata.io\/, geo has ISO 19139 and INSPIRE, etc. and it's always a mess! I'm not sure we want to dig too much into it, but I'm curious to know if there has been some work on the metadata standard.","body":"**Is your feature request related to a problem? Please describe.**\r\nThe current problem is that information about when source data was produced is not easily visible. Though there are a variety of metadata fields available in the dataset viewer, time period information is not included. This feature request suggests making metadata relating to the time that the underlying *source* data was produced more prominent and outlines why this specific information is of particular importance, both in domain-specific historic research and more broadly.\r\n\r\n**Describe the solution you'd like**\r\n\r\nThere are a variety of metadata fields exposed in the dataset viewer (license, task categories, etc.) These fields make this metadata more prominent both for human users and as potentially machine-actionable information (for example, through the API). I would propose to add a metadata field that says when some underlying data was produced. For example, a dataset would be labelled as being produced between `1800-1900`. \r\n\r\n**Describe alternatives you've considered**\r\nThis information is sometimes available in the Datacard or a paper describing the dataset. However, it's often not that easy to identify or extract this information, particularly if you want to use this field as a filter to identify relevant datasets. \r\n\r\n**Additional context**\r\n\r\nI believe this feature is  relevant for a number of reasons: \r\n- Increasingly, there is an interest in using historical data for training language models (for example, https:\/\/huggingface.co\/dbmdz\/bert-base-historic-dutch-cased), and datasets to support this task (for example, https:\/\/huggingface.co\/datasets\/bnl_newspapers). For these datasets, indicating the time periods covered is particularly relevant. \r\n- More broadly, time is likely a common source of domain drift. Datasets of movie reviews from the 90s may not work well for recent movie reviews. As the documentation and long-term management of ML data become more of a priority, quickly understanding the time when the underlying text (or other data types) is arguably more important. \r\n- time-series data: datasets are adding more support for time series data. Again, the periods covered might be particularly relevant here.\r\n\r\n**open questions**\r\n\r\n- I think some of my points above apply not only to the underlying data but also to annotations. As a result, there could also be an argument for encoding this information somewhere. However, I would argue (but could be persuaded otherwise) that this is probably less important for filtering. This type of context is already addressed in the datasheets template and often requires more narrative to discuss. \r\n- what level of granularity would make sense for this? e.g. assigning a decade, century or year?\r\n- how to encode this information? What formatting makes sense \r\n- what specific time to encode; a data range? (mean, modal, min, max value?) \r\n\r\nThis is a slightly amorphous feature request - I would be happy to discuss further\/try and propose a more concrete solution if this seems like something that could be worth considering. I realise this might also touch on other parts of the \ud83e\udd17 hubs ecosystem. ","comment_length":87,"text":"Add a metadata field for when source data was produced  \n **Is your feature request related to a problem? Please describe.**\r\nThe current problem is that information about when source data was produced is not easily visible. Though there are a variety of metadata fields available in the dataset viewer, time period information is not included. This feature request suggests making metadata relating to the time that the underlying *source* data was produced more prominent and outlines why this specific information is of particular importance, both in domain-specific historic research and more broadly.\r\n\r\n**Describe the solution you'd like**\r\n\r\nThere are a variety of metadata fields exposed in the dataset viewer (license, task categories, etc.) These fields make this metadata more prominent both for human users and as potentially machine-actionable information (for example, through the API). I would propose to add a metadata field that says when some underlying data was produced. For example, a dataset would be labelled as being produced between `1800-1900`. \r\n\r\n**Describe alternatives you've considered**\r\nThis information is sometimes available in the Datacard or a paper describing the dataset. However, it's often not that easy to identify or extract this information, particularly if you want to use this field as a filter to identify relevant datasets. \r\n\r\n**Additional context**\r\n\r\nI believe this feature is  relevant for a number of reasons: \r\n- Increasingly, there is an interest in using historical data for training language models (for example, https:\/\/huggingface.co\/dbmdz\/bert-base-historic-dutch-cased), and datasets to support this task (for example, https:\/\/huggingface.co\/datasets\/bnl_newspapers). For these datasets, indicating the time periods covered is particularly relevant. \r\n- More broadly, time is likely a common source of domain drift. Datasets of movie reviews from the 90s may not work well for recent movie reviews. As the documentation and long-term management of ML data become more of a priority, quickly understanding the time when the underlying text (or other data types) is arguably more important. \r\n- time-series data: datasets are adding more support for time series data. Again, the periods covered might be particularly relevant here.\r\n\r\n**open questions**\r\n\r\n- I think some of my points above apply not only to the underlying data but also to annotations. As a result, there could also be an argument for encoding this information somewhere. However, I would argue (but could be persuaded otherwise) that this is probably less important for filtering. This type of context is already addressed in the datasheets template and often requires more narrative to discuss. \r\n- what level of granularity would make sense for this? e.g. assigning a decade, century or year?\r\n- how to encode this information? What formatting makes sense \r\n- what specific time to encode; a data range? (mean, modal, min, max value?) \r\n\r\nThis is a slightly amorphous feature request - I would be happy to discuss further\/try and propose a more concrete solution if this seems like something that could be worth considering. I realise this might also touch on other parts of the \ud83e\udd17 hubs ecosystem.  \n A question to the datasets maintainers: is there a policy about how the set of allowed metadata fields is maintained and expanded?\r\n\r\nMetadata are very important, but defining the standard is always a struggle between allowing exhaustivity without being too complex. Archivists have Dublin Core, open data has https:\/\/frictionlessdata.io\/, geo has ISO 19139 and INSPIRE, etc. and it's always a mess! I'm not sure we want to dig too much into it, but I'm curious to know if there has been some work on the metadata standard.","embeddings":[-0.3327316046,0.242878288,-0.0236787926,-0.1035891846,-0.2707236707,-0.161868304,0.3315868378,0.2197171301,-0.5503543019,-0.0032401765,0.4462814033,0.369513303,-0.1991122067,0.0012923117,-0.3244534135,-0.0545658618,-0.0300791133,0.1974954307,0.0388661698,0.2795035839,-0.1341541559,-0.364916265,0.0155880023,-0.1106297672,-0.6584821343,-0.0131683247,-0.0097074443,-0.2457628846,-0.2800125778,-0.347343713,-0.1150522679,0.1789954305,0.3024338782,0.0823434368,-0.0001082704,-0.2205022722,0.1021556482,-0.0304494202,-0.3979971111,0.2353868634,-0.3093628287,0.0089428062,-0.0106915412,-0.1935678273,-0.2694323957,0.0382288694,-0.201572597,-0.2302813977,-0.0226498004,0.2096547484,0.2180386633,0.1013942435,0.0353039093,0.0034383256,0.2110026032,0.4970020354,-0.3136864305,0.1491892189,0.4633832276,0.225606665,-0.079034172,0.530323565,-0.1318507344,-0.1563623548,0.4803422689,0.0794337541,-0.0196660198,0.0221894868,0.0381955616,0.0657822043,1.049754262,-0.3005072176,-0.6400737166,-0.3131266534,-0.0287612863,-0.2899562716,-0.0206243843,0.046960257,0.1359191239,0.2862557769,0.1529170722,-0.000678813,-0.2413852364,0.2581864893,0.1906585544,0.5964329243,-0.0935956016,-0.0045740223,-0.0229586288,-0.1731840521,0.3466400802,-0.0437574238,0.0845183879,0.3244001865,0.2550894022,-0.4106376767,-0.0201488528,-0.2211661488,-0.268504858,0.3116215467,-0.1554580927,-0.2152313739,-0.3488799036,0.0143809384,0.1823634207,-0.052183751,-0.0654093027,0.0571547523,0.3324834406,-0.2685377598,0.0818619356,-0.2084450722,0.0705763474,0.1232950389,0.2235993445,0.1682754457,0.2489261329,-0.113694191,-0.017906636,-0.0670531839,-0.3448579013,-0.3703078628,0.0319025554,-0.1758317947,-0.0820303783,0.1649256498,-0.2577328086,-0.0813993141,0.3373109698,-0.4842534363,0.0376184173,0.0848376229,-0.2134590596,0.1989759356,0.197170198,-0.1040382758,-0.0543208383,0.3667907715,-0.4674564898,-0.2671705484,0.1362527907,-0.0502733663,0.1213929504,0.2728666067,-0.6708783507,-0.0359789915,-0.032806024,0.0680196211,-0.4065240026,0.1614418924,-0.1267834008,-0.228515327,-0.2913940847,0.2165710181,0.039569743,-0.0169440955,-0.0348333269,0.2622374892,-0.0513778888,-0.1128492579,0.3826444149,0.3345524669,-0.2886178493,-0.1055599898,-0.082405977,0.3132627904,-0.3706119657,0.1262744963,-0.4333333671,0.0289445184,-0.0300284512,-0.19344154,-0.1165626124,0.3691637218,0.0785310119,-0.0796539262,0.1349880844,-0.1545498222,0.1034118831,-0.0115289651,-0.1743309051,0.2645852268,0.0896005109,0.3461870551,0.1644789279,-0.4002454281,-0.0855921283,0.0776793137,-0.1693274081,0.0163295921,0.017023256,-0.1683268845,-0.0003568447,0.3097575605,-0.0556346662,0.2323745489,0.0646072328,0.202599436,-0.1027858108,0.0701776519,0.2355365008,0.1136039123,0.3630999327,0.0911595225,-0.0845476463,-0.4591390193,-0.1014471129,0.0275457483,0.3418161869,0.2621898055,0.0094870869,-0.4824475944,0.1077303812,0.233178854,0.1329491436,-0.007528815,0.171253249,-0.2108646929,-0.0024532888,-0.0255560912,-0.0476236902,-0.0279383957,0.0315505564,0.1658472568,-0.0290506687,-0.1027569622,0.0742767677,-0.0956065208,-0.1544671506,0.4426935911,0.1273771375,0.1511203349,-0.0580422953,0.0653107017,0.296631366,0.4081923068,-0.1175224036,0.8323480487,0.0305611696,-0.2687147558,0.287121594,-0.0672875866,-0.204389438,-0.1289660931,-0.2116232812,0.1538204253,0.1308294386,-0.0287393201,-0.3559423089,-0.1117937937,0.0012397374,-0.0301073305,-0.1111632362,-0.2542960644,-0.362264514,0.134841904,-0.0443804674,0.2592229545,-0.1999973208,0.6437974572,0.5077074766,-0.0199635625,0.0618594699,0.4252171814,-0.2763418853,-0.3513448834,0.120551914,0.0618071556,0.1337195933,0.200529933,-0.0377341993,-0.0462110937,0.0852499232,0.0601457469,0.1491706073,0.1479827315,0.0735947639,-0.1713436544,-0.0110751214,0.4501060843,0.3422296047,-0.3069055676,-0.1561525911,0.2666021883,-0.0435724705,-0.0174240097,0.0462701954,-0.0571401082,-0.0237586703,-0.328551054,-0.15485695,-0.1723489463,0.001621644,0.391100049,-0.0763695836,0.0266892798,0.0899001732,0.571431756,-0.1188944653,0.3691207767,-0.0877683461,-0.3721864522,0.1481584013,0.12639229,-0.1003205106,-0.0094333449,0.6776945591,0.1245403066,0.5420809984,-0.4314917922,-0.5420833826,-0.0654086471,0.0156484116,0.1042211577,-0.1033383682,0.1693786681,0.0319062807,-0.2418942004,-0.1628422439,0.2095567584,0.0186936818,-0.2436220646,-0.2564201653,0.2242481261,0.0179338679,0.1551483274,-0.2873895168,-0.3234743774,0.0730577484,-0.2449717671,0.1440972388,0.5653583407,-0.3769894838,-0.2217641324,0.0785501674,0.4951767623,-0.2336641401,-0.2602891922,-0.0161653031,-0.3853640556,0.1395075619,0.1315381825,-0.0312081538,-0.2742132545,0.1159913763,0.0107761184,-0.2133482993,0.1616202742,0.2240667492,-0.0755981654,-0.3857015967,0.3822264075,0.1567512751,-0.0942954198,0.1475506574,-0.183683157,-0.2372683883,0.0148465429,-0.0601211563,0.1527030915,0.1147020906,-0.0181364305,0.5780406594,0.0567739233,-0.3313297331,-0.036548093,0.0358875617,0.0517444909,-0.1286507845,0.0938071385,0.1434611976,-0.00958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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3625","title":"Add a metadata field for when source data was produced ","comments":"> Metadata are very important, but defining the standard is always a struggle between allowing exhaustivity without being too complex. Archivists have Dublin Core, open data has [frictionlessdata.io](https:\/\/frictionlessdata.io\/), geo has ISO 19139 and INSPIRE, etc. and it's always a mess! I'm not sure we want to dig too much into it, but I'm curious to know if there has been some work on the metadata standard.\r\n\r\n\r\nI thought this is a potential issue with adding this field since it might be hard to define what is general enough to be useful for most data vs what becomes very domain-specific. Potentially adding one extra field leads to more and more fields in the future. \r\n\r\nAnother issue is that there are some metadata standards around data i.e. [datacite](https:\/\/schema.datacite.org\/meta\/kernel-4.4\/), but not many aimed explicitly at ML data afaik. Some of the discussions around metadata for ML are also more focused on versioning\/managing data in production environments. My thinking is that here, some reference to the time of production would also often be tracked\/relevant, i.e. for triggering model training, so having this information available in the hub would also help address this use case. ","body":"**Is your feature request related to a problem? Please describe.**\r\nThe current problem is that information about when source data was produced is not easily visible. Though there are a variety of metadata fields available in the dataset viewer, time period information is not included. This feature request suggests making metadata relating to the time that the underlying *source* data was produced more prominent and outlines why this specific information is of particular importance, both in domain-specific historic research and more broadly.\r\n\r\n**Describe the solution you'd like**\r\n\r\nThere are a variety of metadata fields exposed in the dataset viewer (license, task categories, etc.) These fields make this metadata more prominent both for human users and as potentially machine-actionable information (for example, through the API). I would propose to add a metadata field that says when some underlying data was produced. For example, a dataset would be labelled as being produced between `1800-1900`. \r\n\r\n**Describe alternatives you've considered**\r\nThis information is sometimes available in the Datacard or a paper describing the dataset. However, it's often not that easy to identify or extract this information, particularly if you want to use this field as a filter to identify relevant datasets. \r\n\r\n**Additional context**\r\n\r\nI believe this feature is  relevant for a number of reasons: \r\n- Increasingly, there is an interest in using historical data for training language models (for example, https:\/\/huggingface.co\/dbmdz\/bert-base-historic-dutch-cased), and datasets to support this task (for example, https:\/\/huggingface.co\/datasets\/bnl_newspapers). For these datasets, indicating the time periods covered is particularly relevant. \r\n- More broadly, time is likely a common source of domain drift. Datasets of movie reviews from the 90s may not work well for recent movie reviews. As the documentation and long-term management of ML data become more of a priority, quickly understanding the time when the underlying text (or other data types) is arguably more important. \r\n- time-series data: datasets are adding more support for time series data. Again, the periods covered might be particularly relevant here.\r\n\r\n**open questions**\r\n\r\n- I think some of my points above apply not only to the underlying data but also to annotations. As a result, there could also be an argument for encoding this information somewhere. However, I would argue (but could be persuaded otherwise) that this is probably less important for filtering. This type of context is already addressed in the datasheets template and often requires more narrative to discuss. \r\n- what level of granularity would make sense for this? e.g. assigning a decade, century or year?\r\n- how to encode this information? What formatting makes sense \r\n- what specific time to encode; a data range? (mean, modal, min, max value?) \r\n\r\nThis is a slightly amorphous feature request - I would be happy to discuss further\/try and propose a more concrete solution if this seems like something that could be worth considering. I realise this might also touch on other parts of the \ud83e\udd17 hubs ecosystem. ","comment_length":190,"text":"Add a metadata field for when source data was produced  \n **Is your feature request related to a problem? Please describe.**\r\nThe current problem is that information about when source data was produced is not easily visible. Though there are a variety of metadata fields available in the dataset viewer, time period information is not included. This feature request suggests making metadata relating to the time that the underlying *source* data was produced more prominent and outlines why this specific information is of particular importance, both in domain-specific historic research and more broadly.\r\n\r\n**Describe the solution you'd like**\r\n\r\nThere are a variety of metadata fields exposed in the dataset viewer (license, task categories, etc.) These fields make this metadata more prominent both for human users and as potentially machine-actionable information (for example, through the API). I would propose to add a metadata field that says when some underlying data was produced. For example, a dataset would be labelled as being produced between `1800-1900`. \r\n\r\n**Describe alternatives you've considered**\r\nThis information is sometimes available in the Datacard or a paper describing the dataset. However, it's often not that easy to identify or extract this information, particularly if you want to use this field as a filter to identify relevant datasets. \r\n\r\n**Additional context**\r\n\r\nI believe this feature is  relevant for a number of reasons: \r\n- Increasingly, there is an interest in using historical data for training language models (for example, https:\/\/huggingface.co\/dbmdz\/bert-base-historic-dutch-cased), and datasets to support this task (for example, https:\/\/huggingface.co\/datasets\/bnl_newspapers). For these datasets, indicating the time periods covered is particularly relevant. \r\n- More broadly, time is likely a common source of domain drift. Datasets of movie reviews from the 90s may not work well for recent movie reviews. As the documentation and long-term management of ML data become more of a priority, quickly understanding the time when the underlying text (or other data types) is arguably more important. \r\n- time-series data: datasets are adding more support for time series data. Again, the periods covered might be particularly relevant here.\r\n\r\n**open questions**\r\n\r\n- I think some of my points above apply not only to the underlying data but also to annotations. As a result, there could also be an argument for encoding this information somewhere. However, I would argue (but could be persuaded otherwise) that this is probably less important for filtering. This type of context is already addressed in the datasheets template and often requires more narrative to discuss. \r\n- what level of granularity would make sense for this? e.g. assigning a decade, century or year?\r\n- how to encode this information? What formatting makes sense \r\n- what specific time to encode; a data range? (mean, modal, min, max value?) \r\n\r\nThis is a slightly amorphous feature request - I would be happy to discuss further\/try and propose a more concrete solution if this seems like something that could be worth considering. I realise this might also touch on other parts of the \ud83e\udd17 hubs ecosystem.  \n > Metadata are very important, but defining the standard is always a struggle between allowing exhaustivity without being too complex. Archivists have Dublin Core, open data has [frictionlessdata.io](https:\/\/frictionlessdata.io\/), geo has ISO 19139 and INSPIRE, etc. and it's always a mess! I'm not sure we want to dig too much into it, but I'm curious to know if there has been some work on the metadata standard.\r\n\r\n\r\nI thought this is a potential issue with adding this field since it might be hard to define what is general enough to be useful for most data vs what becomes very domain-specific. Potentially adding one extra field leads to more and more fields in the future. \r\n\r\nAnother issue is that there are some metadata standards around data i.e. [datacite](https:\/\/schema.datacite.org\/meta\/kernel-4.4\/), but not many aimed explicitly at ML data afaik. Some of the discussions around metadata for ML are also more focused on versioning\/managing data in production environments. My thinking is that here, some reference to the time of production would also often be tracked\/relevant, i.e. for triggering model training, so having this information available in the hub would also help address this use case. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3621","title":"Consider adding `ipywidgets` as a dependency.","comments":"Hi! We use `tqdm` to display progress bars, so I suggest you open this issue in their repo.","body":"When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. \r\n\r\n```\r\nImportError: IProgress not found. Please update jupyter and ipywidgets. See https:\/\/ipywidgets.readthedocs.io\/en\/stable\/user_install.html\r\n```\r\n\r\nIt's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here? ","comment_length":18,"text":"Consider adding `ipywidgets` as a dependency. \n When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. \r\n\r\n```\r\nImportError: IProgress not found. Please update jupyter and ipywidgets. See https:\/\/ipywidgets.readthedocs.io\/en\/stable\/user_install.html\r\n```\r\n\r\nIt's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here?  \n Hi! We use `tqdm` to display progress bars, so I suggest you open this issue in their 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3621","title":"Consider adding `ipywidgets` as a dependency.","comments":"It depends on how you use `tqdm`, no? \r\n\r\nDoesn't this library import via; \r\n\r\n```\r\nfrom tqdm.notebook import tqdm\r\n```","body":"When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. \r\n\r\n```\r\nImportError: IProgress not found. Please update jupyter and ipywidgets. See https:\/\/ipywidgets.readthedocs.io\/en\/stable\/user_install.html\r\n```\r\n\r\nIt's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here? ","comment_length":19,"text":"Consider adding `ipywidgets` as a dependency. \n When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. \r\n\r\n```\r\nImportError: IProgress not found. Please update jupyter and ipywidgets. See https:\/\/ipywidgets.readthedocs.io\/en\/stable\/user_install.html\r\n```\r\n\r\nIt's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here?  \n It depends on how you use `tqdm`, no? \r\n\r\nDoesn't this library import via; \r\n\r\n```\r\nfrom tqdm.notebook import tqdm\r\n```","embeddings":[-0.2867106795,0.4942071736,-0.047291249,-0.2324493229,0.1650669724,-0.2206283063,0.6369203329,0.1494793296,-0.0111660035,0.1839484423,-0.2802361548,0.0793602914,-0.170582056,0.2784912586,0.1941229552,0.2538712621,0.0409015715,0.388545692,-0.5920869708,-0.0226150136,-0.0274323765,0.0317737125,-0.2519717515,0.0497721843,-0.3118755817,-0.00637097,0.0501383394,0.2135571837,-0.0480112173,-0.5016096234,0.3662218153,0.3646164834,0.1196338683,0.3131043315,-0.0001131121,0.2414921671,0.2094498724,-0.0503104217,-0.1127117276,0.3710414469,0.0699549541,-0.4585804045,-0.0724108294,-0.3041639924,-0.00058453,-0.0945899412,-0.3035489917,-0.3308672309,0.1647031009,0.0278553348,0.1689834297,0.6418876648,0.0572979935,0.0200646613,0.3541466892,0.0491874218,-0.1691835076,0.0541394837,0.392421186,-0.1361269951,0.0147135733,0.2750822902,-0.0642510951,0.1276462674,0.0923620015,0.127877295,0.121081844,-0.0701377094,-0.2905291319,0.2758178115,0.4292106628,-0.2694457173,-0.241892904,0.0631709844,0.2139132917,-0.0991105512,0.3131178916,-0.4497647285,-0.2148748785,0.3068560362,-0.2315535396,-0.3455029428,-0.2333667576,0.3053714633,-0.178516075,0.2559023499,0.1142109111,0.1173414961,-0.020838365,-0.2946541607,0.4433988929,0.2211328,0.0886547714,-0.186753273,-0.0137836486,-0.3005487621,0.1260389537,-0.2102058679,0.0498821773,0.2383190989,-0.251087904,-0.0774682611,-0.0204875823,0.1061850637,0.0319511965,-0.1739576608,0.0392494723,0.1870372742,0.6599034071,-0.2035989016,-0.0168552902,0.0832774192,0.22527951,-0.1722165644,0.2229706794,-0.1814571172,0.468480289,-0.1900544316,-0.2219352424,0.0188303161,-0.1649876684,0.1439587921,0.1685570925,0.1279684454,-0.0480058752,0.1102581918,0.1568909883,0.1513887495,-0.2572240531,-0.2877492607,-0.040915519,0.0528841987,0.2196617723,0.043338567,0.2291922122,0.2989785969,0.195372656,-0.0508207306,0.5787413716,0.4522234797,0.3816139102,0.063462317,0.0333807506,0.485406816,-0.1315650642,-0.1026447564,0.0249156374,-0.3536833227,-0.1086811274,0.0737250149,-0.06332919,-0.2351402342,0.1102351695,0.1308979094,-0.2046388686,-0.2079652995,0.110362798,-0.2341775745,-0.3072833121,-0.4954727292,-0.1940033883,-0.225695163,0.4243276715,-0.1131459922,0.2249849886,0.2653319538,-0.4601117074,-0.206294775,-0.1605770439,-0.1564612091,-0.0049215225,0.0961389914,-0.2040979564,0.3270099163,0.0292879567,-0.0830506906,0.3702295423,-0.6408061981,-0.6399726272,0.0296219978,0.0197184887,0.0846647918,-0.0857841671,0.5241470933,0.3068738878,0.0858612806,-0.2093664259,0.4806626737,-0.1483370066,-0.1971708983,-0.0576341413,-0.1359053999,0.037328124,0.3130689561,0.3392161727,-0.0800921544,0.1497282535,-0.0417397656,-0.0673754811,-0.0009295359,-0.0244379956,0.3128733337,0.1665967107,0.0542193651,-0.1264471412,-0.1959999949,-0.1107273027,0.1192788035,-0.0579270981,0.1257328242,0.1042965055,-0.1754799932,-0.1352234036,0.0954681188,-0.1298621893,-0.1013115793,0.0930080563,0.1596420258,-0.1232720912,-0.0036806255,-0.0725574046,0.1472810954,0.2529512942,0.1762088835,-0.0238665938,0.2613352537,-0.0677271932,-0.3586936593,0.1090484038,0.1701628566,-0.3352772892,-0.1319350153,-0.090010561,0.3407942653,0.4082674384,-0.2380343378,0.3246031106,0.052015502,0.1756595522,-0.1937439889,0.0732379481,0.2521193922,-0.0690041929,0.0098223789,0.0256880112,0.3500206769,-0.2590278983,0.0103162555,-0.1288803816,0.4146057069,0.292673111,0.030755274,-0.6084951758,-0.261746794,0.0162529368,0.060585957,0.0955780149,-0.1752193272,-0.2543353438,0.0159982406,0.0334494859,0.0887204185,0.3246415555,-0.2100156099,-0.2137272954,0.4359301031,0.2404468805,-0.0372973382,0.4492695928,0.2806437612,-0.0783175975,-0.0772553384,0.0278904196,-0.0997195616,-0.0492741987,0.1808351576,0.0051845736,-0.0872966647,-0.0871735439,0.0076958952,-0.219966799,-0.0928663313,-0.24161838,-0.0027122186,-0.1748854071,0.1118109301,-0.4491063654,-0.2293030471,0.2030508071,-0.5212423205,-0.2160508484,-0.2445704192,0.1619918495,-0.0168201011,-0.093066968,0.5576912165,0.0955446959,0.4702445269,0.2033366263,-0.2983323932,0.1565522552,-0.1312997937,-0.2992711663,0.105443731,0.2421674132,0.1363967061,0.5393559933,-0.0316917785,0.2466255873,-0.4845532179,-0.4732038975,0.0545136295,0.1022535488,0.6899997592,0.3237273097,0.0621517524,-0.1387086809,0.0440892391,0.1663338542,-0.0385055654,-0.0729079843,0.0181349721,0.0235917605,-0.2008430809,-0.0341473669,-0.3360387087,-0.2986097634,-0.2950071394,-0.1769661903,0.3486222327,-0.0829797387,-0.0927814394,0.0161242541,0.0588536486,0.3180839717,-0.0206322167,0.0898869634,-0.3707610369,0.5029928088,-0.3979713023,-0.1910589337,-0.0333998315,-0.1291874796,0.1801941991,0.0777427852,-0.551129818,-0.1410762966,0.0654744357,0.7284299731,-0.0402193889,0.27640903,0.3267892599,0.1705258042,0.0306240302,-0.1317822486,-0.1115472391,0.0896473452,-0.1533233672,0.0859258771,0.130498752,0.357206732,-0.2334507555,0.671611011,-0.1177755222,0.1416477561,0.2552243471,-0.1352647841,0.5681773424,-0.0814024881,-0.4260882139,0.0525081828,0.2062787116,0.18034131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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3621","title":"Consider adding `ipywidgets` as a dependency.","comments":"Hi! Sorry for the late reply. We import `tqdm` as `from tqdm.auto import tqdm`, which should be equal to `from tqdm.notebook import tqdm` in Jupyter.","body":"When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. \r\n\r\n```\r\nImportError: IProgress not found. Please update jupyter and ipywidgets. See https:\/\/ipywidgets.readthedocs.io\/en\/stable\/user_install.html\r\n```\r\n\r\nIt's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here? ","comment_length":25,"text":"Consider adding `ipywidgets` as a dependency. \n When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. \r\n\r\n```\r\nImportError: IProgress not found. Please update jupyter and ipywidgets. See https:\/\/ipywidgets.readthedocs.io\/en\/stable\/user_install.html\r\n```\r\n\r\nIt's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here?  \n Hi! Sorry for the late reply. We import `tqdm` as `from tqdm.auto import tqdm`, which should be equal to `from tqdm.notebook import tqdm` in Jupyter.","embeddings":[-0.1685504317,0.50844419,-0.0195758715,-0.2097098976,0.1149031818,-0.1665547341,0.5911026597,0.0885968357,0.1410357505,0.0943322331,-0.1721211821,0.0448134691,-0.170433715,0.2847236693,0.2028012425,0.2025008798,0.1609583199,0.4420512915,-0.4774926305,-0.0616520382,-0.0198265035,0.0675859526,-0.2546599805,0.0595068,-0.3997332454,0.0885454193,0.0606174581,0.1451113522,-0.1114646718,-0.4789669812,0.4817399085,0.3293169439,0.2032408267,0.2878236175,-0.0001234447,0.1922899634,0.2596254051,-0.0520715304,-0.0932222083,0.3647446036,-0.0823448524,-0.4693398476,-0.1016970575,-0.2261384726,0.0594943911,-0.1099924147,-0.2557118237,-0.336371243,0.1219858825,0.0253571086,0.0898779631,0.5811770558,0.2138712555,0.0789188743,0.3351934254,0.128590256,-0.1139082387,0.171223551,0.3407630324,-0.2107190341,0.0859559253,0.2119522542,-0.0273709819,0.0556847788,0.1549502164,0.0684654936,0.2630328536,-0.0927390978,-0.3842209578,0.2570611238,0.3445683122,-0.2310515195,-0.2545744479,0.0407447852,0.2522335649,-0.0393471643,0.2569672763,-0.4015806913,-0.1233980432,0.3272014558,-0.056185104,-0.4476970434,-0.3259234428,0.3417302966,-0.181694597,0.331874907,0.2103931457,0.1498581767,-0.0527483821,-0.2122429907,0.4515530467,0.1603628993,0.1194889396,-0.232340157,-0.0090403268,-0.2996988893,0.0513653867,0.0165519491,-0.0393716581,0.1774909198,-0.3965403438,-0.2101783007,-0.0295280181,0.1045373678,0.0671353936,-0.224643752,0.0579538196,0.2437630296,0.6480407119,-0.2174522877,-0.0064567658,0.1251282841,0.2001015693,-0.1195754334,0.2362049073,-0.243898809,0.5231437683,-0.2179198563,-0.2972778678,-0.0013594086,-0.3092194498,0.0915072784,0.197770521,-0.0522266664,-0.0317385346,0.1582835764,0.2382341474,0.1970879138,-0.2010266483,-0.2705866396,-0.0113629084,0.1114515513,0.3091169,0.1525520682,0.1853639483,0.2360281944,0.2103490978,-0.0696178526,0.6064560413,0.4185260534,0.3196578026,0.0612754822,-0.1102308333,0.5214223266,-0.2120833099,-0.0693131238,0.0779702067,-0.3890131414,-0.069693312,0.187078014,-0.0529217273,-0.2552993894,0.1179900542,0.0280969851,-0.310980022,-0.2678518593,0.0074774404,-0.2682482898,-0.2972452343,-0.4722965062,-0.1850695014,-0.2891263664,0.4349457622,-0.0765816942,0.1708253771,0.3357801735,-0.5350365639,-0.318212986,-0.1197821274,-0.170345515,-0.0202378258,0.0612188466,-0.1838838756,0.3589406013,0.0496852435,-0.1337317228,0.2330092192,-0.6073670387,-0.6372490525,-0.0384174101,0.0309043266,0.0821943134,-0.0322204232,0.5464903712,0.2199385017,0.1073566228,-0.2905369699,0.5305736065,-0.1707075387,-0.3148157597,0.000233418,-0.1572038978,0.0525842495,0.3821460307,0.3017145991,-0.0155985532,0.0419664457,-0.0272891391,-0.1193347275,0.0279182922,-0.0456451327,0.3243476152,0.2819452882,0.181511879,-0.0668634772,-0.1824529767,0.0377735198,0.0850263685,-0.1041104347,0.1425857544,0.0677598417,-0.1662837714,-0.164384678,0.0914453417,-0.1221525148,-0.0299371537,-0.0123716453,0.1472651809,-0.0960382149,-0.029169146,-0.0489733405,0.2585155368,0.1359170973,0.1807267368,-0.0606971122,0.3430267274,-0.0771039724,-0.293377459,0.1164945588,0.1331450343,-0.3250312209,-0.0987025648,-0.0574167147,0.2371311337,0.3736011684,-0.2221815735,0.2751277387,0.0536928177,0.1722169518,-0.2344397753,0.1207380518,0.283395499,-0.0861429647,-0.0340739749,0.1598800272,0.3205682635,-0.1950031966,0.0895878151,-0.2047543973,0.4380503297,0.2292641401,0.0682305768,-0.7125282884,-0.2089517713,-0.0303567499,-0.0262819305,0.078798838,-0.1719080061,-0.1718355268,-0.0435793921,-0.0596734025,0.1309544295,0.2944906354,-0.1481168419,-0.2126215994,0.4972481728,0.2546371818,-0.0878406838,0.5374804735,0.2056119591,-0.223343879,-0.1307638288,0.1174082309,-0.0253011882,-0.0672526583,0.1921796054,0.1046351716,-0.1248905659,-0.1162488833,-0.0341325514,-0.1350661963,-0.0997405797,-0.2580318153,-0.0173016489,-0.2075005621,0.0964481905,-0.432273984,-0.2243959457,0.133968994,-0.646040976,-0.1793083996,-0.2263546288,0.0625813305,0.0232425313,0.0181032587,0.6019340754,0.0819001496,0.4491597712,0.1590191126,-0.3954349458,0.1814783663,-0.0910184905,-0.2453006804,0.0259521101,0.1940068156,0.0941623971,0.4677790105,-0.0489937663,0.2114511281,-0.4771712422,-0.4866349995,-0.0355444998,0.0848359838,0.6208817363,0.3454454243,0.1769768596,-0.0360726081,-0.0165864993,0.1064069867,-0.0493480898,-0.0330524668,-0.0774542913,-0.0204655137,-0.1203218177,0.0899635851,-0.257106185,-0.4227822423,-0.1934100389,-0.1419569999,0.389373064,-0.137485221,-0.1464570761,0.0899869353,0.0038533648,0.3946998715,-0.0849063769,0.1783850193,-0.3254538178,0.5075464249,-0.2760146558,-0.0805082545,-0.0292313118,-0.1853514016,0.1358507574,0.0001397798,-0.5549381971,-0.0791399777,0.1207562909,0.7490760088,-0.0230759028,0.2369900197,0.395862937,0.2010243237,0.1365619302,-0.147538662,-0.1586487293,0.0217812397,-0.0687815472,0.1309904456,0.2934628427,0.411888361,-0.2914858162,0.6725053787,-0.0392446853,0.1736196876,0.2743342519,-0.017120257,0.4721428752,-0.0264176689,-0.5013676882,-0.0639459416,0.183232232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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3621","title":"Consider adding `ipywidgets` as a dependency.","comments":"Any objection if I make a PR that checks if the widgets library is installed beforehand? ","body":"When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. \r\n\r\n```\r\nImportError: IProgress not found. Please update jupyter and ipywidgets. See https:\/\/ipywidgets.readthedocs.io\/en\/stable\/user_install.html\r\n```\r\n\r\nIt's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here? ","comment_length":16,"text":"Consider adding `ipywidgets` as a dependency. \n When I install `datasets` in a fresh virtualenv with jupyterlab I always see this error. \r\n\r\n```\r\nImportError: IProgress not found. Please update jupyter and ipywidgets. See https:\/\/ipywidgets.readthedocs.io\/en\/stable\/user_install.html\r\n```\r\n\r\nIt's a bit of a nuisance, because I need to run shut down the jupyterlab server in order to install the required dependency. Might it be an option to just include it as a dependency here?  \n Any objection if I make a PR that checks if the widgets library is installed beforehand? ","embeddings":[-0.1698442698,0.6253151298,-0.0337271504,-0.1724755466,0.0254311822,-0.0749922842,0.5609043241,0.091315791,0.3140835762,0.1015947312,0.0781524107,-0.0286186375,-0.1514092684,0.2816160917,0.2125277817,0.2335123718,0.2751716673,0.5633584857,-0.3676189184,-0.0988628194,-0.1116576642,-0.0347697064,-0.1519545317,0.054806821,-0.3511427045,0.0238980148,0.0634740889,0.1016883925,-0.1363855302,-0.4607231617,0.5777135491,0.3291802108,0.1829310954,0.0741047785,-0.0001255787,0.1500867754,0.2985599935,-0.0007330668,-0.1851280034,0.313207984,-0.1297178715,-0.4040048718,-0.0787598118,-0.217444852,0.074321419,-0.1031622216,-0.2274563611,-0.3523018658,0.0122304391,-0.0648630559,0.0581169836,0.4925007522,0.1856990606,0.1952482462,0.540325582,0.228028968,-0.1165828258,0.0856620371,0.2041208893,-0.2684325576,0.117027469,0.219079569,0.1306940913,0.0630072057,0.3097292185,0.020999942,0.2759649754,0.0660604239,-0.4033079445,0.2106679082,0.3250305951,-0.1496429294,-0.3562248647,-0.0704772472,0.2550877035,-0.0583491139,0.3422490358,-0.343024075,-0.0263727885,0.2894653082,0.1078769639,-0.5781266689,-0.2664656937,0.3693397045,-0.0700972304,0.4795940816,0.3161057532,0.1985240579,-0.096745573,-0.118320778,0.3611533046,0.0562217347,0.0338833705,-0.1796239465,0.2060660571,-0.2017767131,0.0711063519,0.2309264839,-0.0618221164,0.1645115018,-0.4302865863,-0.2952086031,-0.0416543968,0.0496840887,0.1817873865,-0.2933804989,-0.0076885424,0.3316508234,0.6616971493,-0.2163463086,0.096936889,0.0619819313,0.245998472,0.0888934955,0.1565702856,-0.2701086104,0.5052210093,-0.3423947096,-0.2367963046,-0.0201018341,-0.3855848014,-0.0303693227,0.2329205126,-0.149579823,-0.0513507091,0.2823865712,0.2630005777,0.1474102736,-0.0590522811,0.0385921597,-0.0033856637,0.1570970118,0.1823120117,0.2168343365,0.2251617312,0.136355564,0.2969412804,-0.0861796066,0.5005742311,0.4187027216,0.3313661218,0.0448663719,-0.1637666076,0.5464607477,-0.2956809998,-0.0783812031,-0.006352569,-0.389311105,-0.0754316524,0.1939589232,-0.010092278,-0.2734134793,0.0666242987,0.0123201273,-0.4461486638,-0.1366171241,0.0074669723,-0.3248819709,-0.1796102673,-0.5992833376,-0.1514532268,-0.2283673286,0.3973806798,-0.0664260387,0.1021310464,0.3476396799,-0.5715787411,-0.3402833641,-0.0733755603,-0.248094514,-0.1069971994,-0.0280985758,-0.2908342481,0.2364388555,0.0351662971,-0.1487950683,0.1314183027,-0.5397216082,-0.5965554714,-0.2122627199,0.0612486191,0.1992186457,-0.0264243521,0.630325973,0.0169660114,-0.0413509421,-0.4201816916,0.5909957886,-0.1248455271,-0.3550853133,-0.0117166284,-0.2473416477,0.0927202404,0.3950352669,0.3215246797,0.0194064118,-0.0159378499,0.1638846397,-0.0895746425,-0.0454516038,-0.0500956513,0.1150229424,0.374553293,0.2908500731,0.0085010184,-0.1669322848,0.100447759,0.1137760133,-0.1868174374,0.080026336,0.0953593478,-0.2668139637,0.0079602478,0.0991381109,-0.0951654539,0.0167217217,-0.0533156879,0.2961575091,-0.0656139255,-0.0399345271,-0.171814993,0.1526818126,-0.0902493,0.1530538797,-0.0607449487,0.3729671538,-0.0645931065,-0.213549301,0.0417710021,0.0943675488,-0.3669329286,-0.0591212474,-0.0483365841,0.1481357068,0.327796936,-0.17300345,0.1973952949,0.049253609,0.194995746,-0.0542365462,0.1898228973,0.2790264785,-0.021626465,-0.0084807863,0.0441554412,0.3051654398,-0.0862031281,0.1973567903,-0.1512655318,0.2281395346,0.1152425036,0.1136171594,-0.6816268563,-0.1844781935,0.0491011776,-0.0551538803,0.1662989855,-0.1312473565,-0.1032835096,-0.1892936677,0.0557085611,0.1774373055,0.2608678639,-0.0827178508,-0.1090378314,0.3614251912,0.348364085,-0.1498748511,0.5598439574,0.171015963,-0.3067475259,-0.2627647221,0.159425199,-0.0286735781,-0.1001953781,0.1697976738,0.2052291632,-0.1520933956,-0.1030023769,-0.0541317947,-0.16005826,-0.0652012378,-0.3704786897,-0.0605898425,-0.1716332436,0.0416574478,-0.2319144905,-0.165748179,0.2021162808,-0.5451384187,-0.1748807877,-0.1765933484,0.109300673,0.0422493704,-0.0613354929,0.4978554547,0.0863556787,0.4895181358,0.0004231014,-0.4752272964,0.1618616432,-0.0845327005,-0.1753151715,0.0304615367,0.2084637582,0.1494943798,0.4531145096,0.062623024,0.1433312595,-0.437212646,-0.5477261543,-0.0479024202,0.0771344379,0.4420427382,0.3594392836,0.1661392599,0.0904601514,-0.0872828737,0.0847364366,-0.0206912998,0.0135197463,-0.2606229484,-0.0162112806,-0.0813413635,-0.0012296337,-0.1995185912,-0.4544087946,-0.1254741848,-0.0532973148,0.5242174268,-0.1751287729,-0.2322378457,0.1581639796,-0.0572875626,0.4474237561,-0.1707735211,0.1811971515,-0.2363148034,0.3916352391,-0.1750195771,0.0277962442,-0.0649271235,-0.2297561765,0.0183800422,0.1602411419,-0.6476020217,-0.037026491,0.0292277578,0.6905674934,-0.024008438,0.2126170844,0.4880790114,0.2428169847,0.1215016097,-0.1361619234,-0.1325758249,0.1461812407,-0.1052389964,0.1317664981,0.3715669513,0.3233343661,-0.4095641971,0.7123070359,-0.1857303679,0.087515153,0.3322164118,0.0178529061,0.4932100773,0.0050757942,-0.3528508842,-0.0024240899,0.2259401381,0.0705666095,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3618","title":"TIMIT Dataset not working with GPU","comments":"Hi ! I think you should avoid calling `timit_train['audio']`. Indeed by doing so you're **loading all the audio column in memory**. This is problematic in your case because the TIMIT dataset is huge.\r\n\r\nIf you want to access the audio data of some samples, you should do this instead `timit_train[:10][\"train\"]` for example.\r\n\r\nOther than that, I'm not sure why you get a `TypeError: string indices must be integers`, do you have a code snippet that reproduces the issue that you can share here ?","body":"## Describe the bug\r\nI am working trying to use the TIMIT dataset in order to fine-tune Wav2Vec2 model and I am unable to load the \"audio\" column from the dataset when working with a GPU. \r\n\r\nI am working on Amazon Sagemaker Studio, on the Python 3 (PyTorch 1.8 Python 3.6 GPU Optimized) environment, with a single ml.g4dn.xlarge instance (corresponds to a Tesla T4 GPU). \r\n\r\nI don't know if the issue is GPU related or Python environment related because everything works when I work off of the CPU Optimized environment with a non-GPU instance. My code also works on Google Colab with a GPU instance. \r\n\r\nThis issue is blocking because I cannot get the 'audio' column in any way due to this error, which means that I can't pass it to any functions. I later use the dataset.map function and that is where I originally noticed this error. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train['audio'])\r\n```\r\n\r\n## Expected results\r\nExpected to see inside the 'audio' column, which contains an 'array' nested field with the array data I actually need.\r\n\r\n## Actual results\r\n\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-ceeac555e921> in <module>\r\n----> 1 timit_train['audio']\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1917         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1918         return self._getitem(\r\n-> 1919             key,\r\n   1920         )\r\n   1921 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1902         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1903         formatted_output = format_table(\r\n-> 1904             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1905         )\r\n   1906         return formatted_output\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    529     python_formatter = PythonFormatter(features=None)\r\n    530     if format_columns is None:\r\n--> 531         return formatter(pa_table, query_type=query_type)\r\n    532     elif query_type == \"column\":\r\n    533         if key in format_columns:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    280             return self.format_row(pa_table)\r\n    281         elif query_type == \"column\":\r\n--> 282             return self.format_column(pa_table)\r\n    283         elif query_type == \"batch\":\r\n    284             return self.format_batch(pa_table)\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_column(self, pa_table)\r\n    315         column = self.python_arrow_extractor().extract_column(pa_table)\r\n    316         if self.decoded:\r\n--> 317             column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])\r\n    318         return column\r\n    319 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_column(self, column, column_name)\r\n    221 \r\n    222     def decode_column(self, column: list, column_name: str) -> list:\r\n--> 223         return self.features.decode_column(column, column_name) if self.features else column\r\n    224 \r\n    225     def decode_batch(self, batch: dict) -> dict:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_column(self, column, column_name)\r\n   1337         return (\r\n   1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n-> 1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n   1341         )\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <listcomp>(.0)\r\n   1336         \"\"\"\r\n   1337         return (\r\n-> 1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n   1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n     85             dict\r\n     86         \"\"\"\r\n---> 87         path, file = (value[\"path\"], BytesIO(value[\"bytes\"])) if value[\"bytes\"] is not None else (value[\"path\"], None)\r\n     88         if path is None and file is None:\r\n     89             raise ValueError(f\"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.\")\r\n\r\nTypeError: string indices must be integers\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-4.14.256-197.484.amzn2.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n","comment_length":84,"text":"TIMIT Dataset not working with GPU \n ## Describe the bug\r\nI am working trying to use the TIMIT dataset in order to fine-tune Wav2Vec2 model and I am unable to load the \"audio\" column from the dataset when working with a GPU. \r\n\r\nI am working on Amazon Sagemaker Studio, on the Python 3 (PyTorch 1.8 Python 3.6 GPU Optimized) environment, with a single ml.g4dn.xlarge instance (corresponds to a Tesla T4 GPU). \r\n\r\nI don't know if the issue is GPU related or Python environment related because everything works when I work off of the CPU Optimized environment with a non-GPU instance. My code also works on Google Colab with a GPU instance. \r\n\r\nThis issue is blocking because I cannot get the 'audio' column in any way due to this error, which means that I can't pass it to any functions. I later use the dataset.map function and that is where I originally noticed this error. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train['audio'])\r\n```\r\n\r\n## Expected results\r\nExpected to see inside the 'audio' column, which contains an 'array' nested field with the array data I actually need.\r\n\r\n## Actual results\r\n\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-ceeac555e921> in <module>\r\n----> 1 timit_train['audio']\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1917         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1918         return self._getitem(\r\n-> 1919             key,\r\n   1920         )\r\n   1921 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1902         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1903         formatted_output = format_table(\r\n-> 1904             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1905         )\r\n   1906         return formatted_output\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    529     python_formatter = PythonFormatter(features=None)\r\n    530     if format_columns is None:\r\n--> 531         return formatter(pa_table, query_type=query_type)\r\n    532     elif query_type == \"column\":\r\n    533         if key in format_columns:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    280             return self.format_row(pa_table)\r\n    281         elif query_type == \"column\":\r\n--> 282             return self.format_column(pa_table)\r\n    283         elif query_type == \"batch\":\r\n    284             return self.format_batch(pa_table)\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_column(self, pa_table)\r\n    315         column = self.python_arrow_extractor().extract_column(pa_table)\r\n    316         if self.decoded:\r\n--> 317             column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])\r\n    318         return column\r\n    319 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_column(self, column, column_name)\r\n    221 \r\n    222     def decode_column(self, column: list, column_name: str) -> list:\r\n--> 223         return self.features.decode_column(column, column_name) if self.features else column\r\n    224 \r\n    225     def decode_batch(self, batch: dict) -> dict:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_column(self, column, column_name)\r\n   1337         return (\r\n   1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n-> 1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n   1341         )\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <listcomp>(.0)\r\n   1336         \"\"\"\r\n   1337         return (\r\n-> 1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n   1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n     85             dict\r\n     86         \"\"\"\r\n---> 87         path, file = (value[\"path\"], BytesIO(value[\"bytes\"])) if value[\"bytes\"] is not None else (value[\"path\"], None)\r\n     88         if path is None and file is None:\r\n     89             raise ValueError(f\"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.\")\r\n\r\nTypeError: string indices must be integers\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-4.14.256-197.484.amzn2.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n \n Hi ! I think you should avoid calling `timit_train['audio']`. Indeed by doing so you're **loading all the audio column in memory**. This is problematic in your case because the TIMIT dataset is huge.\r\n\r\nIf you want to access the audio data of some samples, you should do this instead `timit_train[:10][\"train\"]` for example.\r\n\r\nOther than that, I'm not sure why you get a `TypeError: string indices must be integers`, do you have a code snippet that reproduces the issue that you can share here 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3618","title":"TIMIT Dataset not working with GPU","comments":"I get the same error when I try to do `timit_train[0]` or really any indexing into the whole thing. \r\n\r\nReally, that IS the code snippet that reproduces the issue. If you index into other fields like 'file' or whatever, it works. As soon as one of the fields you're looking into is 'audio', you get that issue. It's a weird issue and I suspect it's Sagemaker\/environment related, maybe the mix of libraries and dependencies are not good. \r\n\r\n\r\nExample code snippet with issue. \r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train[0])\r\n```","body":"## Describe the bug\r\nI am working trying to use the TIMIT dataset in order to fine-tune Wav2Vec2 model and I am unable to load the \"audio\" column from the dataset when working with a GPU. \r\n\r\nI am working on Amazon Sagemaker Studio, on the Python 3 (PyTorch 1.8 Python 3.6 GPU Optimized) environment, with a single ml.g4dn.xlarge instance (corresponds to a Tesla T4 GPU). \r\n\r\nI don't know if the issue is GPU related or Python environment related because everything works when I work off of the CPU Optimized environment with a non-GPU instance. My code also works on Google Colab with a GPU instance. \r\n\r\nThis issue is blocking because I cannot get the 'audio' column in any way due to this error, which means that I can't pass it to any functions. I later use the dataset.map function and that is where I originally noticed this error. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train['audio'])\r\n```\r\n\r\n## Expected results\r\nExpected to see inside the 'audio' column, which contains an 'array' nested field with the array data I actually need.\r\n\r\n## Actual results\r\n\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-ceeac555e921> in <module>\r\n----> 1 timit_train['audio']\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1917         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1918         return self._getitem(\r\n-> 1919             key,\r\n   1920         )\r\n   1921 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1902         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1903         formatted_output = format_table(\r\n-> 1904             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1905         )\r\n   1906         return formatted_output\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    529     python_formatter = PythonFormatter(features=None)\r\n    530     if format_columns is None:\r\n--> 531         return formatter(pa_table, query_type=query_type)\r\n    532     elif query_type == \"column\":\r\n    533         if key in format_columns:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    280             return self.format_row(pa_table)\r\n    281         elif query_type == \"column\":\r\n--> 282             return self.format_column(pa_table)\r\n    283         elif query_type == \"batch\":\r\n    284             return self.format_batch(pa_table)\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_column(self, pa_table)\r\n    315         column = self.python_arrow_extractor().extract_column(pa_table)\r\n    316         if self.decoded:\r\n--> 317             column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])\r\n    318         return column\r\n    319 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_column(self, column, column_name)\r\n    221 \r\n    222     def decode_column(self, column: list, column_name: str) -> list:\r\n--> 223         return self.features.decode_column(column, column_name) if self.features else column\r\n    224 \r\n    225     def decode_batch(self, batch: dict) -> dict:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_column(self, column, column_name)\r\n   1337         return (\r\n   1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n-> 1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n   1341         )\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <listcomp>(.0)\r\n   1336         \"\"\"\r\n   1337         return (\r\n-> 1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n   1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n     85             dict\r\n     86         \"\"\"\r\n---> 87         path, file = (value[\"path\"], BytesIO(value[\"bytes\"])) if value[\"bytes\"] is not None else (value[\"path\"], None)\r\n     88         if path is None and file is None:\r\n     89             raise ValueError(f\"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.\")\r\n\r\nTypeError: string indices must be integers\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-4.14.256-197.484.amzn2.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n","comment_length":93,"text":"TIMIT Dataset not working with GPU \n ## Describe the bug\r\nI am working trying to use the TIMIT dataset in order to fine-tune Wav2Vec2 model and I am unable to load the \"audio\" column from the dataset when working with a GPU. \r\n\r\nI am working on Amazon Sagemaker Studio, on the Python 3 (PyTorch 1.8 Python 3.6 GPU Optimized) environment, with a single ml.g4dn.xlarge instance (corresponds to a Tesla T4 GPU). \r\n\r\nI don't know if the issue is GPU related or Python environment related because everything works when I work off of the CPU Optimized environment with a non-GPU instance. My code also works on Google Colab with a GPU instance. \r\n\r\nThis issue is blocking because I cannot get the 'audio' column in any way due to this error, which means that I can't pass it to any functions. I later use the dataset.map function and that is where I originally noticed this error. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train['audio'])\r\n```\r\n\r\n## Expected results\r\nExpected to see inside the 'audio' column, which contains an 'array' nested field with the array data I actually need.\r\n\r\n## Actual results\r\n\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-ceeac555e921> in <module>\r\n----> 1 timit_train['audio']\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1917         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1918         return self._getitem(\r\n-> 1919             key,\r\n   1920         )\r\n   1921 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1902         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1903         formatted_output = format_table(\r\n-> 1904             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1905         )\r\n   1906         return formatted_output\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    529     python_formatter = PythonFormatter(features=None)\r\n    530     if format_columns is None:\r\n--> 531         return formatter(pa_table, query_type=query_type)\r\n    532     elif query_type == \"column\":\r\n    533         if key in format_columns:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    280             return self.format_row(pa_table)\r\n    281         elif query_type == \"column\":\r\n--> 282             return self.format_column(pa_table)\r\n    283         elif query_type == \"batch\":\r\n    284             return self.format_batch(pa_table)\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_column(self, pa_table)\r\n    315         column = self.python_arrow_extractor().extract_column(pa_table)\r\n    316         if self.decoded:\r\n--> 317             column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])\r\n    318         return column\r\n    319 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_column(self, column, column_name)\r\n    221 \r\n    222     def decode_column(self, column: list, column_name: str) -> list:\r\n--> 223         return self.features.decode_column(column, column_name) if self.features else column\r\n    224 \r\n    225     def decode_batch(self, batch: dict) -> dict:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_column(self, column, column_name)\r\n   1337         return (\r\n   1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n-> 1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n   1341         )\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <listcomp>(.0)\r\n   1336         \"\"\"\r\n   1337         return (\r\n-> 1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n   1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n     85             dict\r\n     86         \"\"\"\r\n---> 87         path, file = (value[\"path\"], BytesIO(value[\"bytes\"])) if value[\"bytes\"] is not None else (value[\"path\"], None)\r\n     88         if path is None and file is None:\r\n     89             raise ValueError(f\"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.\")\r\n\r\nTypeError: string indices must be integers\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-4.14.256-197.484.amzn2.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n \n I get the same error when I try to do `timit_train[0]` or really any indexing into the whole thing. \r\n\r\nReally, that IS the code snippet that reproduces the issue. If you index into other fields like 'file' or whatever, it works. As soon as one of the fields you're looking into is 'audio', you get that issue. It's a weird issue and I suspect it's Sagemaker\/environment related, maybe the mix of libraries and dependencies are not good. \r\n\r\n\r\nExample code snippet with issue. \r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train[0])\r\n```","embeddings":[-0.1453546584,-0.104081817,-0.0035637582,0.4680363536,0.4012949169,-0.0252674911,0.4242494702,0.2931958735,-0.1032612175,0.0186552573,-0.0983695909,0.5495100021,-0.0063606654,0.2732049227,0.1169300601,-0.2103835046,-0.1081267521,-0.1027194262,-0.1941502988,0.2106096148,-0.2738253772,0.1691141278,-0.296458602,0.4507962167,-0.014375479,0.0297155194,0.4842514396,0.1759416759,0.0774407163,-0.208142817,0.2863997221,-0.3206768036,0.1216829047,0.4999122322,-0.0001201344,0.2589619756,0.2124398053,-0.1648833901,-0.1748983115,-0.4557399452,-0.0997843221,-0.0070486125,-0.1593946368,0.0279697627,-0.3590205014,-0.0961345285,-0.0872092023,-0.3805216849,-0.0607861094,0.3169794381,0.091583848,0.0138005652,0.1753880829,0.0358862281,0.1158403531,-0.0338133201,-0.1987026781,0.1178979948,0.0329384506,-0.2579992414,0.1998914331,0.2917821705,-0.2212016881,0.274148941,0.283529371,0.0944070667,-0.3582466841,-0.3364072442,0.0866873935,-0.0088151637,0.6263715029,-0.5339729786,-0.3961464465,-0.0125040486,0.143409729,-0.2794909179,0.2155765146,0.1725507975,-0.3856131434,0.0611052215,-0.2525270283,0.1584769487,-0.2741960287,0.1384822875,0.0527276024,0.1286486685,-0.0021206259,0.3445750475,0.0177487712,-0.127738148,-0.2029892355,0.2075668722,0.0490635484,0.1695920527,-0.5074941516,0.1561606824,-0.059011247,-0.2784886956,0.0213649739,0.4442002475,0.3085011542,0.0684167445,0.23127909,0.2866385281,0.1607069224,-0.2133553028,-0.1315346956,-0.0774342641,0.3800646365,-0.1579200923,-0.0312989093,-0.2791537344,-0.0948119909,-0.0583202727,-0.0575705469,-0.0962945521,0.3323002458,-0.2600176632,-0.6997206211,-0.0417490453,-0.5195009112,0.1506727338,0.1491725147,0.3462822437,0.2316844314,0.1574573815,0.1515972912,0.2129493356,-0.0948882475,-0.0798667073,0.0896477923,-0.1056541204,0.0886520818,0.1058684662,0.2811169028,-0.2602176964,0.2803219855,0.0191752277,0.4490257502,0.0192876738,0.2292634249,-0.2954483628,-0.0156822279,0.4971877337,0.1166474149,0.2651301622,0.1039259136,0.0101515679,0.019681206,0.2307701111,-0.1679602712,-0.2292965204,0.1407343447,0.0016076138,0.0669177696,0.1103815734,-0.2389707267,0.0578474589,0.3206511438,-0.4441979527,-0.0643071011,-0.1949266493,-0.4316872656,-0.2013162673,0.3722192943,-0.1084438562,-0.6513620615,0.1045408025,0.0236543603,0.1632914096,0.0088314852,0.3961001039,-0.0112046422,0.2315008193,-0.0613735989,-0.0440554693,-0.0140292821,-0.2861174941,-0.5760608315,0.0165105835,-0.0763746873,0.1896140277,0.0881204754,0.078960605,-0.1311474144,0.0413256101,0.3340962231,0.152549684,-0.0303562693,-0.024043845,-0.0345951803,-0.0441510379,0.4431475401,0.2284798026,0.0528265201,0.0194367524,-0.070595555,0.0870610923,0.0799353719,0.0270778444,0.0522463657,0.1466532052,0.4021408558,-0.3157339394,0.1598216593,-0.3271747828,-0.1129191592,0.1966174692,0.169944644,0.1953425854,-0.2066404223,0.1386379898,-0.190658316,0.1201774552,-0.0267531388,-0.0696803629,-0.0604868345,0.2993454933,-0.2808762789,-0.0124049559,-0.1343550235,0.3729431331,-0.1461433172,-0.0864052847,-0.187531516,-0.0000643681,0.1419466585,-0.2393694967,0.0002675102,0.0797235146,0.0870929137,0.0045570638,-0.1264182925,0.2659954131,0.1864773184,-0.012377847,-0.5099662542,0.1358452141,0.2132548839,-0.2505379319,-0.1092746928,0.4693290889,0.3796738386,-0.1245099008,-0.0790436044,0.3849307299,-0.1193043366,0.3330422342,-0.1116545722,0.1812598854,0.2093194127,0.2744412422,0.0068642171,0.1506504565,0.0211222898,-0.2048847228,0.2734593153,-0.1904793531,-0.2818722725,0.2286007255,0.3271782994,0.209379375,-0.0514547676,0.1076724827,-0.2721427977,0.1516777873,0.1378263682,-0.2153422832,0.3620251417,0.1566127986,-0.2942176759,-0.1039869562,-0.3839656413,-0.0999749228,0.1770616174,0.1884637624,-0.07377287,0.013778029,-0.0414108299,-0.1179173514,-0.0008465609,-0.2245992422,0.0906270668,0.4030400515,-0.5930880308,0.1000925973,0.0191031341,-0.267700702,0.1207419783,-0.1434216648,-0.0740686804,-0.0160417967,-0.1190148741,0.6340527534,-0.1469588876,0.2620181143,-0.0815269873,0.141478017,0.357057929,-0.1296302229,-0.0988034233,-0.0885025337,-0.0854306966,0.0011890752,0.401635915,-0.0439859852,0.2023258954,0.2336533517,0.1412505656,-0.2749038935,-0.0897909328,0.0508538112,-0.3653043807,0.7204539776,-0.164005518,0.1258496791,-0.146631822,-0.1920343786,0.2292629927,-0.0488662086,-0.146155417,0.2437191308,0.0446082503,0.0003845509,-0.115956597,-0.4202421904,0.0132127041,-0.3999997377,0.3586186171,0.1072512046,0.1895511001,0.1659380943,0.1549631506,0.1562078744,0.5091471672,0.1099719256,-0.2041820735,-0.2590503693,0.2574759126,-0.0697098821,-0.4649181366,-0.1124719456,0.4360385239,0.3336766064,0.2588377893,-0.1185894012,0.2212994248,0.1288627237,-0.0324079879,-0.0766127557,-0.2388685346,0.1317046434,-0.072827287,0.0494288467,-0.2745557427,-0.3032081723,0.1397332996,0.0226834603,0.1132419184,0.392868638,0.5157334805,0.0421098284,0.5403509736,0.2728717923,-0.0509799272,0.130217433,-0.2260807157,0.1423340589,-0.0559348762,-0.3948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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3618","title":"TIMIT Dataset not working with GPU","comments":"Ok I see ! From the error you got, it looks like the `value` encoded in the arrow file of the TIMIT dataset you loaded is a string instead of a dictionary with keys \"path\" and \"bytes\" but we don't support this since 1.18\r\n\r\nCan you try regenerating the dataset with `load_dataset('timit_asr', download_mode=\"force_redownload\")` please ? I think it should fix the issue.","body":"## Describe the bug\r\nI am working trying to use the TIMIT dataset in order to fine-tune Wav2Vec2 model and I am unable to load the \"audio\" column from the dataset when working with a GPU. \r\n\r\nI am working on Amazon Sagemaker Studio, on the Python 3 (PyTorch 1.8 Python 3.6 GPU Optimized) environment, with a single ml.g4dn.xlarge instance (corresponds to a Tesla T4 GPU). \r\n\r\nI don't know if the issue is GPU related or Python environment related because everything works when I work off of the CPU Optimized environment with a non-GPU instance. My code also works on Google Colab with a GPU instance. \r\n\r\nThis issue is blocking because I cannot get the 'audio' column in any way due to this error, which means that I can't pass it to any functions. I later use the dataset.map function and that is where I originally noticed this error. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train['audio'])\r\n```\r\n\r\n## Expected results\r\nExpected to see inside the 'audio' column, which contains an 'array' nested field with the array data I actually need.\r\n\r\n## Actual results\r\n\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-ceeac555e921> in <module>\r\n----> 1 timit_train['audio']\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1917         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1918         return self._getitem(\r\n-> 1919             key,\r\n   1920         )\r\n   1921 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1902         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1903         formatted_output = format_table(\r\n-> 1904             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1905         )\r\n   1906         return formatted_output\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    529     python_formatter = PythonFormatter(features=None)\r\n    530     if format_columns is None:\r\n--> 531         return formatter(pa_table, query_type=query_type)\r\n    532     elif query_type == \"column\":\r\n    533         if key in format_columns:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    280             return self.format_row(pa_table)\r\n    281         elif query_type == \"column\":\r\n--> 282             return self.format_column(pa_table)\r\n    283         elif query_type == \"batch\":\r\n    284             return self.format_batch(pa_table)\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_column(self, pa_table)\r\n    315         column = self.python_arrow_extractor().extract_column(pa_table)\r\n    316         if self.decoded:\r\n--> 317             column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])\r\n    318         return column\r\n    319 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_column(self, column, column_name)\r\n    221 \r\n    222     def decode_column(self, column: list, column_name: str) -> list:\r\n--> 223         return self.features.decode_column(column, column_name) if self.features else column\r\n    224 \r\n    225     def decode_batch(self, batch: dict) -> dict:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_column(self, column, column_name)\r\n   1337         return (\r\n   1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n-> 1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n   1341         )\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <listcomp>(.0)\r\n   1336         \"\"\"\r\n   1337         return (\r\n-> 1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n   1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n     85             dict\r\n     86         \"\"\"\r\n---> 87         path, file = (value[\"path\"], BytesIO(value[\"bytes\"])) if value[\"bytes\"] is not None else (value[\"path\"], None)\r\n     88         if path is None and file is None:\r\n     89             raise ValueError(f\"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.\")\r\n\r\nTypeError: string indices must be integers\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-4.14.256-197.484.amzn2.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n","comment_length":62,"text":"TIMIT Dataset not working with GPU \n ## Describe the bug\r\nI am working trying to use the TIMIT dataset in order to fine-tune Wav2Vec2 model and I am unable to load the \"audio\" column from the dataset when working with a GPU. \r\n\r\nI am working on Amazon Sagemaker Studio, on the Python 3 (PyTorch 1.8 Python 3.6 GPU Optimized) environment, with a single ml.g4dn.xlarge instance (corresponds to a Tesla T4 GPU). \r\n\r\nI don't know if the issue is GPU related or Python environment related because everything works when I work off of the CPU Optimized environment with a non-GPU instance. My code also works on Google Colab with a GPU instance. \r\n\r\nThis issue is blocking because I cannot get the 'audio' column in any way due to this error, which means that I can't pass it to any functions. I later use the dataset.map function and that is where I originally noticed this error. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ntimit_train = load_dataset('timit_asr', split='train')\r\nprint(timit_train['audio'])\r\n```\r\n\r\n## Expected results\r\nExpected to see inside the 'audio' column, which contains an 'array' nested field with the array data I actually need.\r\n\r\n## Actual results\r\n\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-ceeac555e921> in <module>\r\n----> 1 timit_train['audio']\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1917         \"\"\"Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).\"\"\"\r\n   1918         return self._getitem(\r\n-> 1919             key,\r\n   1920         )\r\n   1921 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _getitem(self, key, decoded, **kwargs)\r\n   1902         pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n   1903         formatted_output = format_table(\r\n-> 1904             pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\r\n   1905         )\r\n   1906         return formatted_output\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns)\r\n    529     python_formatter = PythonFormatter(features=None)\r\n    530     if format_columns is None:\r\n--> 531         return formatter(pa_table, query_type=query_type)\r\n    532     elif query_type == \"column\":\r\n    533         if key in format_columns:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in __call__(self, pa_table, query_type)\r\n    280             return self.format_row(pa_table)\r\n    281         elif query_type == \"column\":\r\n--> 282             return self.format_column(pa_table)\r\n    283         elif query_type == \"batch\":\r\n    284             return self.format_batch(pa_table)\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in format_column(self, pa_table)\r\n    315         column = self.python_arrow_extractor().extract_column(pa_table)\r\n    316         if self.decoded:\r\n--> 317             column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])\r\n    318         return column\r\n    319 \r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/formatting.py in decode_column(self, column, column_name)\r\n    221 \r\n    222     def decode_column(self, column: list, column_name: str) -> list:\r\n--> 223         return self.features.decode_column(column, column_name) if self.features else column\r\n    224 \r\n    225     def decode_batch(self, batch: dict) -> dict:\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in decode_column(self, column, column_name)\r\n   1337         return (\r\n   1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n-> 1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n   1341         )\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/features.py in <listcomp>(.0)\r\n   1336         \"\"\"\r\n   1337         return (\r\n-> 1338             [self[column_name].decode_example(value) if value is not None else None for value in column]\r\n   1339             if self._column_requires_decoding[column_name]\r\n   1340             else column\r\n\r\n\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in decode_example(self, value)\r\n     85             dict\r\n     86         \"\"\"\r\n---> 87         path, file = (value[\"path\"], BytesIO(value[\"bytes\"])) if value[\"bytes\"] is not None else (value[\"path\"], None)\r\n     88         if path is None and file is None:\r\n     89             raise ValueError(f\"An audio sample should have one of 'path' or 'bytes' but both are None in {value}.\")\r\n\r\nTypeError: string indices must be integers\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: Linux-4.14.256-197.484.amzn2.x86_64-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n \n Ok I see ! From the error you got, it looks like the `value` encoded in the arrow file of the TIMIT dataset you loaded is a string instead of a dictionary with keys \"path\" and \"bytes\" but we don't support this since 1.18\r\n\r\nCan you try regenerating the dataset with `load_dataset('timit_asr', download_mode=\"force_redownload\")` please ? I think it should fix the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3615","title":"Dataset BnL Historical Newspapers does not work in streaming mode","comments":"@albertvillanova let me know if there is anything I can do to help with this. I had a quick look at the code again and though I could try the following changes:\r\n- use `download` instead of `download_and_extract`\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/d3d339fb86d378f4cb3c5d1de423315c07a466c6\/datasets\/bnl_newspapers\/bnl_newspapers.py#L136\r\n- swith to using `iter_archive` to loop through downloaded data to replace\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/d3d339fb86d378f4cb3c5d1de423315c07a466c6\/datasets\/bnl_newspapers\/bnl_newspapers.py#L159\r\n\r\nLet me know if it's useful for me to try and make those changes. ","body":"## Describe the bug\r\nWhen trying to load in streaming mode, it \"hangs\"...\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds = load_dataset(\"bnl_newspapers\", split=\"train\", streaming=True)\r\n```\r\n\r\n## Expected results\r\nThe code should be optimized, so that it works fast in streaming mode.\r\n\r\nCC: @davanstrien \r\n","comment_length":66,"text":"Dataset BnL Historical Newspapers does not work in streaming mode \n ## Describe the bug\r\nWhen trying to load in streaming mode, it \"hangs\"...\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds = load_dataset(\"bnl_newspapers\", split=\"train\", streaming=True)\r\n```\r\n\r\n## Expected results\r\nThe code should be optimized, so that it works fast in streaming mode.\r\n\r\nCC: @davanstrien \r\n \n @albertvillanova let me know if there is anything I can do to help with this. I had a quick look at the code again and though I could try the following changes:\r\n- use `download` instead of `download_and_extract`\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/d3d339fb86d378f4cb3c5d1de423315c07a466c6\/datasets\/bnl_newspapers\/bnl_newspapers.py#L136\r\n- swith to using `iter_archive` to loop through downloaded data to replace\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/d3d339fb86d378f4cb3c5d1de423315c07a466c6\/datasets\/bnl_newspapers\/bnl_newspapers.py#L159\r\n\r\nLet me know if it's useful for me to try and make those changes. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3615","title":"Dataset BnL Historical Newspapers does not work in streaming mode","comments":"Thanks @davanstrien.\r\n\r\nI have already been working on it so that it can be used in the BigScience workshop.\r\n\r\nI agree that the `rglob()` is not efficient in this case.\r\n\r\nI tried different solutions without success:\r\n- `iter_archive` cannot be used in this case because it does not support ZIP files yet\r\n\r\nFinally I have used `iter_files()`.","body":"## Describe the bug\r\nWhen trying to load in streaming mode, it \"hangs\"...\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds = load_dataset(\"bnl_newspapers\", split=\"train\", streaming=True)\r\n```\r\n\r\n## Expected results\r\nThe code should be optimized, so that it works fast in streaming mode.\r\n\r\nCC: @davanstrien \r\n","comment_length":57,"text":"Dataset BnL Historical Newspapers does not work in streaming mode \n ## Describe the bug\r\nWhen trying to load in streaming mode, it \"hangs\"...\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds = load_dataset(\"bnl_newspapers\", split=\"train\", streaming=True)\r\n```\r\n\r\n## Expected results\r\nThe code should be optimized, so that it works fast in streaming mode.\r\n\r\nCC: @davanstrien \r\n \n Thanks @davanstrien.\r\n\r\nI have already been working on it so that it can be used in the BigScience workshop.\r\n\r\nI agree that the `rglob()` is not efficient in this case.\r\n\r\nI tried different solutions without success:\r\n- `iter_archive` cannot be used in this case because it does not support ZIP files yet\r\n\r\nFinally I have used `iter_files()`.","embeddings":[-0.5628229976,-0.1082760319,0.066400148,0.3091987371,0.1017119363,0.0588343218,0.2123794109,0.4981284142,0.1771590412,-0.0876948014,-0.0313862897,0.1764089316,-0.2093245834,-0.03907343,-0.2260355651,-0.2108297795,0.2899227142,0.2498152554,0.0469629057,0.0502229072,-0.247550264,0.1315081716,-0.5979421735,-0.4282113016,-0.151083678,0.0072059557,0.0507255085,0.1879526228,-0.0633820072,-0.0157369692,0.1460561901,0.092070967,0.539573431,0.5176458955,-0.0001239608,-0.0058312141,0.2569395602,-0.0951874033,-0.2906940579,-0.2585138381,-0.1020105034,-0.0654195398,-0.1160979941,-0.1392216682,-0.3070298135,-0.2799561322,0.0730518103,-0.372559458,0.3655770123,0.3863728642,0.0761841983,0.1409159601,-0.2004665583,0.0304286927,-0.0278573427,0.0884688944,-0.1991853267,0.1636557281,0.0284786895,0.2199260592,-0.1214351282,0.5261397362,-0.0443482697,-0.1346151829,0.150292486,-0.0292994268,0.0342360288,-0.0540623218,-0.1070085391,0.1110854745,0.3655198812,-0.0679475665,-0.0724586919,-0.0942812562,0.0151458587,-0.379784584,0.223927483,0.2556236982,-0.2337908596,0.1618899405,-0.0715164989,-0.1619419754,-0.2990466058,0.1293321699,0.1243598685,0.3584263921,-0.0392775498,0.0846694857,0.219649747,0.0624307767,0.4606912732,-0.0810480788,-0.0387357697,0.0168541241,-0.6160313487,0.1716556847,-0.054936178,0.4378824234,-0.2609729469,0.2320350707,0.1889012605,-0.0447734147,0.3474468887,0.0766767412,0.1760540009,0.0455833077,-0.1449991316,-0.075901486,0.3708620369,0.2841505706,-0.2485094965,-0.0292164627,-0.1745184362,0.0778652728,0.0627373457,-0.3138671219,0.1779146194,-0.1268665791,0.1037227586,-0.0083748931,-0.4026608467,-0.0410087556,0.0827723518,0.4458233118,-0.2637748122,0.2303815782,0.0432289802,-0.1934979409,-0.0636027753,-0.1737868786,-0.0581782758,-0.0795029476,-0.1065658778,-0.325121671,0.0170537904,-0.4737388194,-0.163561061,0.0957130343,-0.2170664519,0.0028374281,0.0662010387,-0.3951210082,0.3001797795,0.2269988656,0.105883956,0.080353938,0.0909439772,0.069856748,-0.315231204,0.2838226855,0.0892565325,-0.2813375294,0.0133516872,0.0403753743,-0.1535685658,0.1038670316,0.0692967027,0.3817712367,0.0241086241,-0.5601127148,-0.0512308702,0.1000353694,-0.2327526361,-0.0708361864,0.3535822928,0.1926698089,-0.2860065699,0.0130664473,-0.2482088357,0.0597468354,0.6290706396,0.1090383306,-0.0327578746,-0.059740819,-0.0215753969,-0.3601935506,0.663829565,0.1785086542,-0.622603476,0.3290167451,-0.0440114848,0.3789473772,0.2568139136,0.4254401028,0.1411831528,-0.1134290919,-0.0331385769,0.7479308844,0.0334100872,0.1972770244,-0.3132410944,0.027229866,0.623860538,0.4029535651,-0.0365589373,0.0945411325,-0.2269355059,0.2794879675,0.2694794238,0.1740864515,0.0402032323,0.0617434047,0.1293718517,-0.1117051616,0.2114737779,-0.2633317411,-0.1505530775,0.0874705836,0.2182415873,0.0771940649,0.1195280328,-0.029401293,-0.1356998086,-0.0990679488,-0.2282642722,-0.0811941475,-0.0774864405,0.2016582489,0.2784943581,0.1968649179,-0.229596436,0.8236356378,-0.1420962811,-0.1451895982,-0.537983954,0.0282994341,-0.0312607437,-0.216249451,0.1084005758,0.0711532533,-0.1476658583,-0.1075340286,-0.2726504803,0.2455589622,-0.3096320033,0.1885534227,-0.1253382117,-0.0732260421,0.0132537698,-0.2688418031,0.2107096016,0.5545505881,0.0169930607,-0.0410040319,-0.3205723763,0.134883672,0.1117879897,0.3895522654,-0.0641787201,0.0346044339,-0.0769980475,0.0227531735,0.0520085655,0.188433975,0.4336002171,-0.1703287661,0.1715059578,0.2094156891,-0.3681907356,-0.0367128141,0.4160188735,0.2357079685,-0.0367852636,0.4611195326,-0.2587347031,-0.1360315979,0.2198809832,-0.0219249818,0.3283163309,0.107834354,0.1342516989,0.0524982698,0.2249078006,-0.1705567241,0.2240938991,-0.0902677327,0.2084422857,0.0002341991,0.2020129412,-0.0453307405,-0.1905066371,-0.1370838434,0.0800946504,0.3698249459,-0.0507295094,0.2315987647,-0.114080973,-0.2091335207,-0.1722792238,-0.205121696,-0.0704996958,-0.1947006583,-0.19900617,0.4103382528,0.1228985935,0.2779926062,-0.0066672629,0.2831016779,0.3669303358,-0.246125415,-0.2704752386,-0.0430125631,-0.2304379791,0.0233424902,0.1838711798,0.028867485,0.2801909745,-0.0030248547,-0.0659067258,-0.1101367995,-0.0394902788,0.0402787067,-0.141075477,0.372110337,-0.0289673433,0.2113324702,-0.3266677558,0.036768876,0.2304907292,-0.1415741444,0.1516413987,0.2878417373,-0.0702594072,0.3904882669,0.0940603614,-0.3694126904,-0.4928657115,-0.505716145,0.2641811073,-0.305236429,0.2377545089,0.0303493161,0.1730629206,-0.2884064019,0.3283095062,0.0890200362,-0.1140572801,-0.5250397921,0.6210094094,-0.0405421183,-0.3506958187,-0.2335722744,0.1969624162,-0.4144710004,0.2828270793,-0.4940618277,0.1258619875,-0.0825575069,-0.0796834603,0.0279836152,-0.1077235267,0.0426410176,-0.3474772573,0.0832949728,0.0351568237,0.1976484507,-0.0235355459,-0.2384924442,0.5426084995,0.4358035922,0.1139423102,-0.0220930763,0.010853596,0.5969015956,0.0477664433,0.3033671379,0.0328852944,0.2285105139,0.0305124763,-0.3502994478,0.0602583103,-0.035133291,-0.16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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3615","title":"Dataset BnL Historical Newspapers does not work in streaming mode","comments":"I see this is fixed now \ud83d\ude42.  I also picked up a few other tips from your redactors so hopefully my next attempts will support streaming from the start. ","body":"## Describe the bug\r\nWhen trying to load in streaming mode, it \"hangs\"...\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds = load_dataset(\"bnl_newspapers\", split=\"train\", streaming=True)\r\n```\r\n\r\n## Expected results\r\nThe code should be optimized, so that it works fast in streaming mode.\r\n\r\nCC: @davanstrien \r\n","comment_length":29,"text":"Dataset BnL Historical Newspapers does not work in streaming mode \n ## Describe the bug\r\nWhen trying to load in streaming mode, it \"hangs\"...\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds = load_dataset(\"bnl_newspapers\", split=\"train\", streaming=True)\r\n```\r\n\r\n## Expected results\r\nThe code should be optimized, so that it works fast in streaming mode.\r\n\r\nCC: @davanstrien \r\n \n I see this is fixed now \ud83d\ude42.  I also picked up a few other tips from your redactors so hopefully my next attempts will support streaming from the start. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3613","title":"Files not updating in dataset viewer","comments":"Yes. The jobs queue is full right now, following an upgrade... Back to normality in the next hours hopefully. I'll look at your datasets to be sure the dataset viewer works as expected on them.","body":"## Dataset viewer issue for '*name of the dataset*'\r\n\r\n**Link:**\r\nSome examples:\r\n* https:\/\/huggingface.co\/datasets\/abidlabs\/crowdsourced-speech4\r\n* https:\/\/huggingface.co\/datasets\/abidlabs\/test-audio-13\r\n\r\n*short description of the issue*\r\nIt seems that the dataset viewer is reading a cached version of the dataset and it is not updating to reflect new files that are added to the dataset. I get this error:\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/1778297\/150566660-30dc0dcd-18fd-4471-b70c-7c4bdc6a23c6.png)\r\n\r\n\r\nAm I the one who added this dataset? Yes","comment_length":35,"text":"Files not updating in dataset viewer \n ## Dataset viewer issue for '*name of the dataset*'\r\n\r\n**Link:**\r\nSome examples:\r\n* https:\/\/huggingface.co\/datasets\/abidlabs\/crowdsourced-speech4\r\n* https:\/\/huggingface.co\/datasets\/abidlabs\/test-audio-13\r\n\r\n*short description of the issue*\r\nIt seems that the dataset viewer is reading a cached version of the dataset and it is not updating to reflect new files that are added to the dataset. I get this error:\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/1778297\/150566660-30dc0dcd-18fd-4471-b70c-7c4bdc6a23c6.png)\r\n\r\n\r\nAm I the one who added this dataset? Yes \n Yes. The jobs queue is full right now, following an upgrade... Back to normality in the next hours hopefully. I'll look at your datasets to be sure the dataset viewer works as expected on them.","embeddings":[-0.3797993958,0.1163963303,-0.1014506444,0.1263844371,0.069827497,0.0949609205,0.0576606318,0.2934433222,0.0349193849,0.1519840062,0.0154691096,0.0699020997,-0.0732653961,-0.0716505945,-0.1866238117,0.1329110116,0.1891684532,-0.0158928875,-0.1370636672,-0.0425216816,-0.1500421464,0.1812663823,-0.1953300536,-0.1917188168,-0.5102657676,-0.05862749,-0.1823950559,-0.063470155,-0.2453574985,-0.2994509935,0.1661976576,0.2411733866,0.1041127443,0.6502740979,-0.0000981088,-0.0521728806,0.5114244223,0.0228085462,-0.3603644073,-0.1221653074,-0.3154078424,-0.3242826462,-0.1659524143,0.1284027994,-0.0797580928,-0.2443586588,0.3302291334,-0.1247742698,0.4149988592,0.1566211581,0.366379559,0.0402798876,0.2756925523,-0.2505027652,-0.0003958576,0.1777418256,0.1342049688,0.0211697258,0.2491110712,0.1365860105,-0.1409418434,0.4649128318,-0.0911551714,0.0061408873,0.3645097315,-0.1182491109,-0.0793156847,-0.4502667189,0.4696727395,0.1723979414,0.8310869336,-0.0073154466,-0.3534178138,-0.190024808,0.1084379032,-0.0592287779,0.3401077688,0.1133539751,0.0294576176,0.0112022469,-0.1891117543,-0.2167092115,-0.001943567,0.178586036,-0.130814597,-0.3422622681,-0.1936006844,-0.0548494346,0.0226331856,-0.3203732967,0.2890894413,-0.0595045164,-0.123982124,0.1351860166,-0.336764127,0.1458028555,-0.0029467302,0.234618172,-0.12516886,-0.0203898139,-0.2012650967,0.0026021535,-0.3285803795,0.297085911,0.2398402393,0.0495491177,0.0486230701,0.0686090663,0.3268591464,0.0951788276,-0.1518556774,-0.1375863254,-0.2464445084,-0.3035639226,-0.1140851527,-0.2341127843,0.1832088232,-0.1488665342,-0.1870077103,0.1036555916,0.1306025982,-0.0729805306,-0.1247226894,0.3120933771,-0.0310750529,0.0982407779,0.0921261013,0.0831993744,-0.189279303,-0.2492618859,-0.2011504173,-0.1229143143,-0.0896774679,0.0741677508,0.2296394557,-0.1843435317,0.35012725,-0.0180093162,0.0174463782,-0.1835936457,-0.0630436167,-0.1805619597,-0.0519403443,0.3123163879,0.0376806743,0.3051740527,-0.1387082487,-0.1716875434,0.0986444503,0.3894823194,-0.3012876213,-0.0557833426,-0.2668680549,0.3298264444,-0.1172416657,-0.1984176487,-0.2858681679,0.1178671569,0.0420729667,-0.2683655918,-0.1846080571,-0.1499709636,-0.0406783856,-0.036065422,0.1038323194,0.4076505303,-0.3388268352,0.0032922095,-0.2602191269,0.0138331139,-0.1028774604,0.086937122,-0.0069887326,0.0097886687,-0.268363893,0.0062850341,0.1270243973,-0.3847046494,-0.5280434489,-0.0297441259,-0.0330849029,-0.0157594513,0.0240339618,0.1500901133,0.259921968,-0.3060280979,-0.2477757633,-0.1611901671,0.0495824255,0.0272007547,-0.221625343,-0.0826704875,-0.0679654479,0.1417823434,0.0818750858,0.0294099897,0.1362805963,-0.0552844591,0.21134983,0.1385192871,0.2052317262,0.2716358304,0.1960362792,0.3052735031,0.0563507676,0.099306114,-0.3562804461,0.1298313141,0.0376449153,0.0418946594,0.0157968104,-0.0851344988,-0.2506103218,-0.2104936093,-0.3710706234,-0.2747805119,0.2186831385,0.3672999442,-0.2285307199,0.1147597209,-0.188118726,0.2092079967,-0.0540912077,-0.0054566972,-0.1929949969,0.2282471508,-0.0378693417,-0.1162830219,0.1645760685,-0.0926979557,0.1265499592,-0.0108347535,-0.1853955984,0.382879138,0.1736241132,0.4428767562,0.2326337397,0.0549138039,0.1642238796,-0.1678904444,0.1736439615,0.0216006339,-0.0783279985,-0.1034857407,-0.2347921878,0.0039347173,-0.0645064339,-0.0159601364,-0.061405126,0.0463142022,0.2971681356,-0.0066355164,0.10979864,-0.0107095474,-0.0891543701,0.0736524016,0.2277671248,0.0412106551,-0.2464620024,0.0067996625,0.5360502005,0.1395554841,-0.1999922097,0.1967850029,-0.3689282835,0.0143630719,0.1373363733,0.3390746117,0.0664704368,0.2723039985,-0.0071910154,0.0493992716,0.251170963,-0.2523833811,-0.0046108454,0.065507926,0.0597896464,0.3073239625,0.2224246711,-0.0185986254,-0.475104332,0.1727584898,0.1905309856,0.1737126261,-0.1331215501,-0.2849432528,-0.1486572027,-0.4039020836,-0.2709473968,0.0022586579,-0.3288231492,-0.3557167649,0.1184472516,0.3749230206,0.0303154141,0.3267637193,0.0496012755,0.1472487152,-0.1504375339,0.1302461475,-0.334297955,0.0329131521,-0.2647185922,0.200499326,0.5254769325,0.0717940256,0.2383502722,-0.1200599447,0.2712791562,-0.5074763298,-0.2029633671,-0.0330373496,-0.1062900871,0.2703757584,-0.2450430989,0.1793392748,-0.1538425982,-0.1656550169,0.0279559977,-0.1590454876,-0.0996900424,-0.1304417998,0.1363810897,-0.0856307521,0.0222110525,-0.3074048758,-0.1948359162,-0.4157477915,0.0974736735,0.024686167,0.0921779424,0.24353607,-0.0127847362,-0.1117708832,-0.1671362072,-0.0716496482,-0.3129086494,-0.5299854875,0.2602340877,-0.2684812844,-0.4490498602,0.301053226,0.2041321546,0.2233510315,-0.1852356642,-0.5290555954,-0.2735422254,-0.0840139911,0.1781710833,0.1819737256,-0.2361415476,0.2275514603,-0.0622923523,-0.1516861469,-0.2720486522,-0.1614667028,-0.097547017,0.1197665334,0.3388823569,-0.0882906392,0.1707779765,0.010773058,0.572522819,0.4264969826,-0.0531297885,0.2424162477,-0.0738277286,0.4359691739,-0.2324405313,-0.1901980191,0.1824272275,-0.12276399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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3608","title":"Add support for continuous metrics (RMSE, MAE)","comments":"Hey @ck37 \r\n\r\nYou can always use a custom metric as explained [in this guide from HF](https:\/\/huggingface.co\/docs\/datasets\/master\/loading_metrics.html#using-a-custom-metric-script).\r\n\r\nIf this issue needs to be contributed to (for enhancing the metric API) I think [this link](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.mean_absolute_error.html) would be helpful for the `MAE` metric.","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nI am uploading our dataset and models for the \"Constructing interval measures\" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https:\/\/huggingface.co\/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome.\r\n\r\n**Describe the solution you'd like**\r\nI would like to be able to tag our models on the Hub with the following metrics:\r\n- RMSE\r\n- MAE\r\n\r\n**Describe alternatives you've considered**\r\n\r\nI don't know if there are any alternatives.\r\n\r\n**Additional context**\r\nOur preprint is available here: https:\/\/arxiv.org\/abs\/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https:\/\/www.kaggle.com\/c\/jigsaw-toxic-severity-rating\/overview . I have our first model uploaded to the Hub at https:\/\/huggingface.co\/ucberkeley-dlab\/hate-measure-roberta-large\r\n\r\nThanks,\r\nChris\r\n","comment_length":40,"text":"Add support for continuous metrics (RMSE, MAE) \n **Is your feature request related to a problem? Please describe.**\r\n\r\nI am uploading our dataset and models for the \"Constructing interval measures\" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https:\/\/huggingface.co\/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome.\r\n\r\n**Describe the solution you'd like**\r\nI would like to be able to tag our models on the Hub with the following metrics:\r\n- RMSE\r\n- MAE\r\n\r\n**Describe alternatives you've considered**\r\n\r\nI don't know if there are any alternatives.\r\n\r\n**Additional context**\r\nOur preprint is available here: https:\/\/arxiv.org\/abs\/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https:\/\/www.kaggle.com\/c\/jigsaw-toxic-severity-rating\/overview . I have our first model uploaded to the Hub at https:\/\/huggingface.co\/ucberkeley-dlab\/hate-measure-roberta-large\r\n\r\nThanks,\r\nChris\r\n \n Hey @ck37 \r\n\r\nYou can always use a custom metric as explained [in this guide from HF](https:\/\/huggingface.co\/docs\/datasets\/master\/loading_metrics.html#using-a-custom-metric-script).\r\n\r\nIf this issue needs to be contributed to (for enhancing the metric API) I think [this link](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.mean_absolute_error.html) would be helpful for the `MAE` metric.","embeddings":[-0.2287477851,-0.3802618682,-0.088135317,-0.1381486058,0.2096349597,0.1603593677,-0.2304011285,0.074478507,0.3844652772,0.0689124539,-0.0625768229,0.2589018047,-0.3647345603,0.2241400182,-0.1389570534,-0.3047390878,-0.0666410401,-0.0653640032,0.2313117832,0.1159719825,-0.1732453853,-0.0680030733,-0.0164643694,0.1343035698,-0.7210459709,-0.1397714615,-0.2097362578,-0.1337979585,-0.2628184855,-0.5267782807,0.4249002934,0.1194113716,0.2145846039,0.0191094596,-0.0001098693,-0.2125865072,0.4086971581,-0.1118723154,-0.1955540925,-0.2383985817,-0.2119683623,-0.1390225291,0.0620325916,-0.0058831726,0.0255751256,0.0992672965,-0.0716490299,-0.1151267141,0.3077863455,0.0666044429,0.1250884682,0.1492088139,-0.0785386413,-0.3562817872,-0.0456497706,0.398799926,-0.2262850851,0.5990537405,0.3708301485,-0.1175738052,-0.1327048391,0.2112252712,0.0902322158,-0.0303791855,0.5033262372,0.0305044223,0.5993275642,-0.2023895681,-0.1125273332,0.2646527588,0.105479978,-0.1550527662,-0.4436764419,-0.2344265133,0.1696690619,-0.7406082153,-0.1525864601,-0.0890197083,-0.1796445251,0.1205665469,-0.2004231662,-0.4116162956,-0.3185279369,0.3033254743,0.0710002109,0.4423882663,0.1523084342,-0.032279171,0.06217058,0.0615549684,-0.4483602047,-0.0091257887,-0.1529836059,0.1338813901,-0.2948444188,-0.2533435225,-0.2381610721,0.2369192094,0.1067314968,-0.1393347532,-0.0097083086,0.0525809079,0.0642017722,0.129260391,0.1616495997,0.4878112972,0.296404779,-0.178545922,0.2578564882,0.2839888632,0.2687698603,0.0549481809,0.1810737252,0.0796786696,-0.0503876731,0.1647811979,-0.3738879859,-0.0063289376,-0.2888810635,-0.0101282038,-0.0246944409,-0.0490854159,0.2627682686,0.1690077186,-0.0061840178,0.1144955605,-0.097409904,0.3574237823,-0.0103092128,0.1019780561,-0.0343285836,0.0388281792,-0.4484539628,0.2361994535,0.3590000272,-0.1089955196,0.1792716533,-0.1816836596,0.0461185053,-0.0456314795,-0.2682065666,0.0972971618,0.1034374759,-0.0122650489,-0.3942844272,-0.0428360067,-0.0231149476,0.050794851,-0.1293402165,-0.1373204887,-0.2313486636,-0.1679449826,-0.1630494893,0.1302888989,-0.1684511751,-0.2467959225,0.2545478046,0.5239136219,-0.2566770911,-0.1997871995,0.2260095328,0.2134888768,-0.5124636292,0.0600663498,0.1064770222,-0.0775361285,-0.1531212479,-0.4292040169,-0.3077002466,-0.0376505814,-0.2522058487,0.0427437536,0.0822166055,0.1780913025,-0.0038403929,0.0582961664,0.3913878798,-0.466309011,-0.0054434603,-0.3925218284,-0.182129994,-0.0614300258,-0.0603374802,0.2335403562,0.5110326409,-0.0497086383,0.3263564706,-0.0459445305,0.0193698108,-0.2409967482,-0.2493760437,-0.4313952923,-0.0823184252,0.2958958745,0.1868514568,-0.2786138952,0.1266662478,0.031363558,0.2986094356,0.0065555153,0.2278689593,-0.2079782486,0.1812901646,-0.2778404653,-0.0548440032,-0.5259387493,-0.0942337438,0.1031795964,-0.1752367914,0.1800033152,0.1926979423,-0.0357822776,-0.346062988,0.1245192587,0.0004862054,-0.1637719274,0.0911450759,-0.4256184399,0.0388936736,0.1444264054,-0.1104743481,0.2048959136,-0.1441166699,0.1538081318,0.1629245728,0.1132121831,0.0236019138,-0.1897604764,0.2439981401,0.6977482438,0.0872690454,0.2143075913,-0.1046384051,0.0769985244,-0.0621943772,0.2635698915,0.3075040579,0.453373462,0.4856910706,0.1056728885,0.102947019,-0.0953767747,0.0611083508,-0.0390506089,-0.2208702564,0.3565107286,-0.0580852591,0.129761517,-0.2425393015,0.2348065674,-0.1005065218,-0.0866031945,-0.2559162378,-0.2933890522,0.2091530412,-0.2724311054,-0.3040788174,-0.3226968348,0.1636137515,-0.1600514054,0.1464779675,0.1321895421,0.3413190544,0.1510983407,-0.3630064428,-0.2305338979,0.1637821198,-0.2527413666,0.4033881724,0.2015531063,-0.0580276325,0.0719824955,-0.1630466729,-0.1764489263,0.2561104298,0.4411120117,-0.138394624,0.377998203,0.3139275312,-0.1255758703,-0.1279610991,-0.063177973,-0.1858561337,-0.2833300233,-0.0166015159,-0.2359152734,-0.1772122681,-0.0081593692,0.1184459031,-0.4975194633,-0.2889413238,-0.4255946875,0.4767525196,0.0423912928,-0.0951995179,0.3244538009,0.2221829891,0.7471811771,-0.1823209375,0.3336438835,-0.1747672856,-0.5080200434,0.0377229489,0.0895160511,-0.2720042467,0.0020234755,0.4994557202,0.1263137758,0.5515380502,0.0860147774,-0.4121525586,0.2125064731,-0.0417410322,0.3485884368,0.1580930054,-0.1372122616,0.174109146,-0.0794685408,0.1983856708,-0.2601165175,0.1103423238,-0.0636943728,-0.1129318103,-0.0138784787,-0.1818122715,-0.0030481701,-0.4230081141,-0.4251227975,-0.1153667271,-0.0624901466,-0.1476397216,0.0856332704,-0.037371289,0.137946859,-0.3875272274,0.1089602932,-0.0998844653,-0.3127792776,0.3412471414,-0.3482740521,0.1003847644,0.233918801,-0.3252345324,0.1239626333,-0.0992721617,-0.2007179856,-0.8155383468,0.2333425581,0.1369035393,0.0359540544,0.0123302834,0.0937313214,-0.0580421053,-0.1589034796,-0.0109972982,-0.2629624307,0.2283291221,0.297824055,0.3886986673,-0.1719253957,0.0413080826,0.1339334995,0.4497789741,0.4648592472,-0.1560313106,0.2555316091,0.3287071586,0.141823411,0.0876368582,-0.2169346958,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3608","title":"Add support for continuous metrics (RMSE, MAE)","comments":"You can use a local metric script just by providing its path instead of the usual shortcut name ","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nI am uploading our dataset and models for the \"Constructing interval measures\" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https:\/\/huggingface.co\/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome.\r\n\r\n**Describe the solution you'd like**\r\nI would like to be able to tag our models on the Hub with the following metrics:\r\n- RMSE\r\n- MAE\r\n\r\n**Describe alternatives you've considered**\r\n\r\nI don't know if there are any alternatives.\r\n\r\n**Additional context**\r\nOur preprint is available here: https:\/\/arxiv.org\/abs\/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https:\/\/www.kaggle.com\/c\/jigsaw-toxic-severity-rating\/overview . I have our first model uploaded to the Hub at https:\/\/huggingface.co\/ucberkeley-dlab\/hate-measure-roberta-large\r\n\r\nThanks,\r\nChris\r\n","comment_length":18,"text":"Add support for continuous metrics (RMSE, MAE) \n **Is your feature request related to a problem? Please describe.**\r\n\r\nI am uploading our dataset and models for the \"Constructing interval measures\" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not relevant. The only continuous metrics available at https:\/\/huggingface.co\/metrics are pearson & spearman correlation, which don't ensure that the prediction is on the same scale as the outcome.\r\n\r\n**Describe the solution you'd like**\r\nI would like to be able to tag our models on the Hub with the following metrics:\r\n- RMSE\r\n- MAE\r\n\r\n**Describe alternatives you've considered**\r\n\r\nI don't know if there are any alternatives.\r\n\r\n**Additional context**\r\nOur preprint is available here: https:\/\/arxiv.org\/abs\/2009.10277 . We are making it available for use in Jigsaw's Toxic Severity Rating Kaggle competition: https:\/\/www.kaggle.com\/c\/jigsaw-toxic-severity-rating\/overview . I have our first model uploaded to the Hub at https:\/\/huggingface.co\/ucberkeley-dlab\/hate-measure-roberta-large\r\n\r\nThanks,\r\nChris\r\n \n You can use a local metric script just by providing its path instead of the usual shortcut name 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3606","title":"audio column not saved correctly after resampling","comments":"Hi ! We just released a new version of `datasets` that should fix this.\r\n\r\nI tested resampling and using save\/load_from_disk afterwards and it seems to be fixed now","body":"## Describe the bug\r\nAfter resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type. \r\n\r\n## Steps to reproduce the bug\r\n- load a subset of common voice dataset (48Khz)\r\n- resample audio column to 16Khz\r\n- save with save_to_disk()\r\n- load with load_from_disk()\r\n\r\n## Expected results\r\nI expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it)\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None),\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Actual results\r\nAudio column does not have the right type\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': {'bytes': Value(dtype='binary', id=None),\r\n  'path': Value(dtype='string', id=None)},\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: linux\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":28,"text":"audio column not saved correctly after resampling \n ## Describe the bug\r\nAfter resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type. \r\n\r\n## Steps to reproduce the bug\r\n- load a subset of common voice dataset (48Khz)\r\n- resample audio column to 16Khz\r\n- save with save_to_disk()\r\n- load with load_from_disk()\r\n\r\n## Expected results\r\nI expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it)\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None),\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Actual results\r\nAudio column does not have the right type\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': {'bytes': Value(dtype='binary', id=None),\r\n  'path': Value(dtype='string', id=None)},\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: linux\r\n- Python version:\r\n- PyArrow version:\r\n \n Hi ! We just released a new version of `datasets` that should fix this.\r\n\r\nI tested resampling and using save\/load_from_disk afterwards and it seems to be fixed now","embeddings":[-0.2528468668,0.1135271788,0.0847011656,0.2255779654,0.4603187442,-0.0732036904,0.1811231524,0.3404786885,-0.100949131,0.072595045,-0.4832226634,0.3691771924,-0.0401458442,-0.0950343609,-0.016474843,-0.1994142979,0.253772378,0.0790266693,-0.1742043197,-0.2164461762,-0.2962217629,0.3525260389,-0.1891235858,0.0692899674,-0.0691816956,0.0024943524,0.1407295614,0.2073658109,0.1072040424,0.0623662844,0.0481992289,-0.3001407981,0.3213793933,0.1699996442,-0.0001128995,-0.3090110123,0.0154149253,-0.26169312,0.0699161887,0.0612795316,-0.0414638184,0.063449569,-0.4947632551,-0.0165033415,-0.0087051159,0.0780238435,-0.3153741062,-0.3832596838,0.075151749,0.1381967366,0.2147356719,-0.0492028892,-0.0012087126,0.2371677905,-0.0120170452,0.0725444704,-0.1989045143,0.0233739614,-0.0382523313,0.084967196,0.1009249091,0.3572224975,-0.0879144967,-0.3120154142,-0.1956322938,-0.0470526703,-0.2996374667,-0.016086489,0.2595231235,0.1041530743,0.5175701976,-0.0048607402,-0.2894320786,0.1914596111,-0.0201264378,-0.2386482358,0.2378665209,0.11559508,0.0820131972,0.2807939351,-0.114289932,0.0266375449,0.0994041413,-0.0470104069,0.0836498663,-0.1439673007,-0.2032047808,0.1181911677,0.1636584848,-0.2784495652,-0.2374081314,-0.0678003356,-0.0963646099,0.3195744157,-0.5439091921,-0.3499386311,-0.2866734266,0.0055186409,-0.02169176,0.0298028085,0.0182989221,0.1406375915,0.1998255998,-0.0217386596,0.3478654027,0.25250718,0.1130222082,0.1037762538,0.3521870971,-0.0746134296,0.0223373044,-0.1477273405,0.0836910233,0.2690508962,0.5099980831,0.0197620876,-0.0672589615,-0.3000497222,-0.215644598,0.2878972888,0.0345071591,0.0496333316,-0.1710567921,0.1251995265,-0.0764239952,0.4590423107,0.0327584445,0.4337727129,-0.0936871469,-0.0147611778,-0.0085533923,-0.285579741,0.2067206651,0.0529183745,0.2645802796,0.0313418433,0.149652496,0.3465572894,-0.3265753388,-0.3838713467,-0.2842288315,0.0488256775,-0.1309251785,0.1274591386,-0.0423228294,0.2578017414,-0.169645071,0.0020383908,-0.0619815588,0.4289250076,-0.2903953791,-0.1125708371,-0.2923180461,0.1907127947,0.0864497423,0.01311481,-0.2744628787,0.0089156907,0.4834215045,-0.2748688161,0.0773145184,-0.0806785971,-0.156592086,-0.0632597134,0.1286389828,-0.4940532446,-0.4053566456,0.1426913589,-0.1175569221,0.0030383833,0.3993891776,0.2996939719,0.3102984428,-0.1869683117,-0.0549759865,0.4295400083,0.3371576071,-0.2676149309,-0.5990151167,-0.1776275784,0.1468712091,0.195140481,-0.0752951652,0.0491231158,0.4992274046,-0.1595564336,0.2319821417,0.4562256634,0.0454690419,0.2549941838,-0.2811857164,-0.0572666712,-0.1447132677,-0.0621904135,-0.2464320511,0.3153680563,-0.0039912653,0.0241305418,0.3176392317,0.0370048583,0.1516219378,0.049002897,0.3597035408,-0.2816777527,0.086169593,-0.2143613845,-0.1178093031,-0.0486440957,-0.1503629386,-0.2163766623,0.2488511354,-0.0914003253,-0.3676003516,-0.3645982444,-0.211054951,-0.0076363389,0.0876459703,0.070729591,-0.3114383519,0.0439621173,-0.1159251928,0.0142311594,0.0953133553,-0.048462607,-0.1316908747,0.0089559956,-0.0677897111,-0.1861932129,0.0149719333,0.3123132288,0.3450390399,0.1589996666,-0.2119969726,0.5700929761,0.0687832236,0.3879567385,-0.6059723496,-0.1892716289,0.1249326766,-0.0319604836,-0.02657976,-0.1580916941,0.2721366882,-0.087607421,-0.0798756778,0.3454011977,0.3811722994,0.03451702,-0.0312260184,0.0697629899,0.134630233,0.0370552838,0.0012721029,-0.0233151317,-0.387467742,-0.1099682376,-0.0968809202,-0.1654323637,-0.3453846276,0.1170536578,0.5308033824,-0.1850415915,0.2281680107,0.0075674364,-0.1470188349,-0.1087351218,0.11249277,0.2404013425,0.4666194022,0.0254021157,0.1515969634,-0.1779877692,-0.0336271785,-0.1533637196,0.0971812382,-0.0775929689,-0.0901784897,0.4509015679,0.1687517762,-0.1973530054,-0.3663101494,0.6564012766,-0.1237365156,0.0765913874,-0.5308884382,-0.2410463095,-0.2315033376,0.3585713506,-0.0324926376,-0.2053437233,-0.0422544293,-0.1393603832,0.0086700097,0.5790909529,-0.2330861241,0.2047368735,0.1589430124,0.1110529304,0.0579952449,0.0984031782,0.0009991904,0.2495824099,-0.3836546838,0.0910881162,-0.0382781513,-0.1425764114,-0.067705065,-0.0772963837,-0.0807073638,-0.142843321,-0.0605803467,-0.0367148854,-0.0281044133,0.3250164092,-0.2733569741,0.2014377564,-0.1686044186,-0.2538139224,0.1812571585,0.0491873287,-0.2323763222,0.3382057846,0.0537913442,0.3048371375,0.0122893471,-0.3900026977,-0.1645228267,-0.2841766179,0.0562601946,-0.4697789252,0.063871257,-0.0945688263,-0.0432002954,-0.332893312,-0.0877643824,0.0427139029,-0.6847988963,-0.0959495604,0.5248351693,-0.1414026916,-0.4598069191,-0.2473507077,0.1240168288,0.1540137529,-0.136568442,0.0365164578,0.233084619,0.0508523956,-0.0311741699,-0.0240800381,0.1157595441,0.3267199397,-0.1417649835,0.0993379876,0.0251767728,-0.1981301755,0.0282436907,0.536411047,0.5549006462,-0.1644926816,0.1786877662,-0.1952566653,0.2202575058,0.0919293314,0.1740239859,0.16861476,0.0544732399,0.0890806541,-0.3747354448,-0.1621648222,0.1152465269,-0.1892510206,0.0707589909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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3606","title":"audio column not saved correctly after resampling","comments":"Hi @lhoestq, \r\n\r\nJust tested the latest datasets version, and confirming that this is fixed for me. \r\n\r\nThanks!","body":"## Describe the bug\r\nAfter resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type. \r\n\r\n## Steps to reproduce the bug\r\n- load a subset of common voice dataset (48Khz)\r\n- resample audio column to 16Khz\r\n- save with save_to_disk()\r\n- load with load_from_disk()\r\n\r\n## Expected results\r\nI expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it)\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None),\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Actual results\r\nAudio column does not have the right type\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': {'bytes': Value(dtype='binary', id=None),\r\n  'path': Value(dtype='string', id=None)},\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: linux\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":17,"text":"audio column not saved correctly after resampling \n ## Describe the bug\r\nAfter resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type. \r\n\r\n## Steps to reproduce the bug\r\n- load a subset of common voice dataset (48Khz)\r\n- resample audio column to 16Khz\r\n- save with save_to_disk()\r\n- load with load_from_disk()\r\n\r\n## Expected results\r\nI expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it)\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None),\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Actual results\r\nAudio column does not have the right type\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': {'bytes': Value(dtype='binary', id=None),\r\n  'path': Value(dtype='string', id=None)},\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: linux\r\n- Python version:\r\n- PyArrow version:\r\n \n Hi @lhoestq, \r\n\r\nJust tested the latest datasets version, and confirming that this is fixed for me. \r\n\r\nThanks!","embeddings":[-0.2528468668,0.1135271788,0.0847011656,0.2255779654,0.4603187442,-0.0732036904,0.1811231524,0.3404786885,-0.100949131,0.072595045,-0.4832226634,0.3691771924,-0.0401458442,-0.0950343609,-0.016474843,-0.1994142979,0.253772378,0.0790266693,-0.1742043197,-0.2164461762,-0.2962217629,0.3525260389,-0.1891235858,0.0692899674,-0.0691816956,0.0024943524,0.1407295614,0.2073658109,0.1072040424,0.0623662844,0.0481992289,-0.3001407981,0.3213793933,0.1699996442,-0.0001128995,-0.3090110123,0.0154149253,-0.26169312,0.0699161887,0.0612795316,-0.0414638184,0.063449569,-0.4947632551,-0.0165033415,-0.0087051159,0.0780238435,-0.3153741062,-0.3832596838,0.075151749,0.1381967366,0.2147356719,-0.0492028892,-0.0012087126,0.2371677905,-0.0120170452,0.0725444704,-0.1989045143,0.0233739614,-0.0382523313,0.084967196,0.1009249091,0.3572224975,-0.0879144967,-0.3120154142,-0.1956322938,-0.0470526703,-0.2996374667,-0.016086489,0.2595231235,0.1041530743,0.5175701976,-0.0048607402,-0.2894320786,0.1914596111,-0.0201264378,-0.2386482358,0.2378665209,0.11559508,0.0820131972,0.2807939351,-0.114289932,0.0266375449,0.0994041413,-0.0470104069,0.0836498663,-0.1439673007,-0.2032047808,0.1181911677,0.1636584848,-0.2784495652,-0.2374081314,-0.0678003356,-0.0963646099,0.3195744157,-0.5439091921,-0.3499386311,-0.2866734266,0.0055186409,-0.02169176,0.0298028085,0.0182989221,0.1406375915,0.1998255998,-0.0217386596,0.3478654027,0.25250718,0.1130222082,0.1037762538,0.3521870971,-0.0746134296,0.0223373044,-0.1477273405,0.0836910233,0.2690508962,0.5099980831,0.0197620876,-0.0672589615,-0.3000497222,-0.215644598,0.2878972888,0.0345071591,0.0496333316,-0.1710567921,0.1251995265,-0.0764239952,0.4590423107,0.0327584445,0.4337727129,-0.0936871469,-0.0147611778,-0.0085533923,-0.285579741,0.2067206651,0.0529183745,0.2645802796,0.0313418433,0.149652496,0.3465572894,-0.3265753388,-0.3838713467,-0.2842288315,0.0488256775,-0.1309251785,0.1274591386,-0.0423228294,0.2578017414,-0.169645071,0.0020383908,-0.0619815588,0.4289250076,-0.2903953791,-0.1125708371,-0.2923180461,0.1907127947,0.0864497423,0.01311481,-0.2744628787,0.0089156907,0.4834215045,-0.2748688161,0.0773145184,-0.0806785971,-0.156592086,-0.0632597134,0.1286389828,-0.4940532446,-0.4053566456,0.1426913589,-0.1175569221,0.0030383833,0.3993891776,0.2996939719,0.3102984428,-0.1869683117,-0.0549759865,0.4295400083,0.3371576071,-0.2676149309,-0.5990151167,-0.1776275784,0.1468712091,0.195140481,-0.0752951652,0.0491231158,0.4992274046,-0.1595564336,0.2319821417,0.4562256634,0.0454690419,0.2549941838,-0.2811857164,-0.0572666712,-0.1447132677,-0.0621904135,-0.2464320511,0.3153680563,-0.0039912653,0.0241305418,0.3176392317,0.0370048583,0.1516219378,0.049002897,0.3597035408,-0.2816777527,0.086169593,-0.2143613845,-0.1178093031,-0.0486440957,-0.1503629386,-0.2163766623,0.2488511354,-0.0914003253,-0.3676003516,-0.3645982444,-0.211054951,-0.0076363389,0.0876459703,0.070729591,-0.3114383519,0.0439621173,-0.1159251928,0.0142311594,0.0953133553,-0.048462607,-0.1316908747,0.0089559956,-0.0677897111,-0.1861932129,0.0149719333,0.3123132288,0.3450390399,0.1589996666,-0.2119969726,0.5700929761,0.0687832236,0.3879567385,-0.6059723496,-0.1892716289,0.1249326766,-0.0319604836,-0.02657976,-0.1580916941,0.2721366882,-0.087607421,-0.0798756778,0.3454011977,0.3811722994,0.03451702,-0.0312260184,0.0697629899,0.134630233,0.0370552838,0.0012721029,-0.0233151317,-0.387467742,-0.1099682376,-0.0968809202,-0.1654323637,-0.3453846276,0.1170536578,0.5308033824,-0.1850415915,0.2281680107,0.0075674364,-0.1470188349,-0.1087351218,0.11249277,0.2404013425,0.4666194022,0.0254021157,0.1515969634,-0.1779877692,-0.0336271785,-0.1533637196,0.0971812382,-0.0775929689,-0.0901784897,0.4509015679,0.1687517762,-0.1973530054,-0.3663101494,0.6564012766,-0.1237365156,0.0765913874,-0.5308884382,-0.2410463095,-0.2315033376,0.3585713506,-0.0324926376,-0.2053437233,-0.0422544293,-0.1393603832,0.0086700097,0.5790909529,-0.2330861241,0.2047368735,0.1589430124,0.1110529304,0.0579952449,0.0984031782,0.0009991904,0.2495824099,-0.3836546838,0.0910881162,-0.0382781513,-0.1425764114,-0.067705065,-0.0772963837,-0.0807073638,-0.142843321,-0.0605803467,-0.0367148854,-0.0281044133,0.3250164092,-0.2733569741,0.2014377564,-0.1686044186,-0.2538139224,0.1812571585,0.0491873287,-0.2323763222,0.3382057846,0.0537913442,0.3048371375,0.0122893471,-0.3900026977,-0.1645228267,-0.2841766179,0.0562601946,-0.4697789252,0.063871257,-0.0945688263,-0.0432002954,-0.332893312,-0.0877643824,0.0427139029,-0.6847988963,-0.0959495604,0.5248351693,-0.1414026916,-0.4598069191,-0.2473507077,0.1240168288,0.1540137529,-0.136568442,0.0365164578,0.233084619,0.0508523956,-0.0311741699,-0.0240800381,0.1157595441,0.3267199397,-0.1417649835,0.0993379876,0.0251767728,-0.1981301755,0.0282436907,0.536411047,0.5549006462,-0.1644926816,0.1786877662,-0.1952566653,0.2202575058,0.0919293314,0.1740239859,0.16861476,0.0544732399,0.0890806541,-0.3747354448,-0.1621648222,0.1152465269,-0.1892510206,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3606","title":"audio column not saved correctly after resampling","comments":"Also, just an FYI, data that I had saved (with save_to_disk) previously from common voice using datasets==1.17.0 now give the error below when loading (with load_from disk) using datasets==1.18.0. \r\n\r\nHowever, when starting fresh using load_dataset, then doing the resampling, the save\/load_from disk worked fine. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n   1747         return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n   1748     elif fs.isfile(Path(dest_dataset_path, config.DATASETDICT_JSON_FILENAME).as_posix()):\r\n-> 1749         return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n   1750     else:\r\n   1751         raise FileNotFoundError(\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in load_from_disk(dataset_dict_path, fs, keep_in_memory)\r\n    769                 else Path(dest_dataset_dict_path, k).as_posix()\r\n    770             )\r\n--> 771             dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n    772         return dataset_dict\r\n    773 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n   1118             info=dataset_info,\r\n   1119             split=split,\r\n-> 1120             fingerprint=state[\"_fingerprint\"],\r\n   1121         )\r\n   1122 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    655         if self.info.features.type != inferred_features.type:\r\n    656             raise ValueError(\r\n--> 657                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    658             )\r\n    659 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=48000, mono=True, id=None), 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<bytes: binary, path: string>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64>\r\n\r\nbut expected something like\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<path: string, bytes: binary>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64> \r\n```","body":"## Describe the bug\r\nAfter resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type. \r\n\r\n## Steps to reproduce the bug\r\n- load a subset of common voice dataset (48Khz)\r\n- resample audio column to 16Khz\r\n- save with save_to_disk()\r\n- load with load_from_disk()\r\n\r\n## Expected results\r\nI expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it)\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None),\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Actual results\r\nAudio column does not have the right type\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': {'bytes': Value(dtype='binary', id=None),\r\n  'path': Value(dtype='string', id=None)},\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: linux\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":290,"text":"audio column not saved correctly after resampling \n ## Describe the bug\r\nAfter resampling the audio column, saving with save_to_disk doesn't seem to save with the correct type. \r\n\r\n## Steps to reproduce the bug\r\n- load a subset of common voice dataset (48Khz)\r\n- resample audio column to 16Khz\r\n- save with save_to_disk()\r\n- load with load_from_disk()\r\n\r\n## Expected results\r\nI expected that after saving the data, and then loading it back in, the audio column has the correct dataset.Audio type (i.e. same as before saving it)\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None),\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Actual results\r\nAudio column does not have the right type\r\n{'accent': Value(dtype='string', id=None),\r\n 'age': Value(dtype='string', id=None),\r\n 'audio': {'bytes': Value(dtype='binary', id=None),\r\n  'path': Value(dtype='string', id=None)},\r\n 'client_id': Value(dtype='string', id=None),\r\n 'down_votes': Value(dtype='int64', id=None),\r\n 'gender': Value(dtype='string', id=None),\r\n 'locale': Value(dtype='string', id=None),\r\n 'path': Value(dtype='string', id=None),\r\n 'segment': Value(dtype='string', id=None),\r\n 'sentence': Value(dtype='string', id=None),\r\n 'up_votes': Value(dtype='int64', id=None)}\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.0\r\n- Platform: linux\r\n- Python version:\r\n- PyArrow version:\r\n \n Also, just an FYI, data that I had saved (with save_to_disk) previously from common voice using datasets==1.17.0 now give the error below when loading (with load_from disk) using datasets==1.18.0. \r\n\r\nHowever, when starting fresh using load_dataset, then doing the resampling, the save\/load_from disk worked fine. \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n   1747         return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n   1748     elif fs.isfile(Path(dest_dataset_path, config.DATASETDICT_JSON_FILENAME).as_posix()):\r\n-> 1749         return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n   1750     else:\r\n   1751         raise FileNotFoundError(\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in load_from_disk(dataset_dict_path, fs, keep_in_memory)\r\n    769                 else Path(dest_dataset_dict_path, k).as_posix()\r\n    770             )\r\n--> 771             dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n    772         return dataset_dict\r\n    773 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in load_from_disk(dataset_path, fs, keep_in_memory)\r\n   1118             info=dataset_info,\r\n   1119             split=split,\r\n-> 1120             fingerprint=state[\"_fingerprint\"],\r\n   1121         )\r\n   1122 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    655         if self.info.features.type != inferred_features.type:\r\n    656             raise ValueError(\r\n--> 657                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    658             )\r\n    659 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=48000, mono=True, id=None), 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<bytes: binary, path: string>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64>\r\n\r\nbut expected something like\r\n{'accent': Value(dtype='string', id=None), 'age': Value(dtype='string', id=None), 'audio': {'path': Value(dtype='string', id=None), 'bytes': Value(dtype='binary', id=None)}, 'client_id': Value(dtype='string', id=None), 'down_votes': Value(dtype='int64', id=None), 'gender': Value(dtype='string', id=None), 'locale': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'segment': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'up_votes': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<accent: string, age: string, audio: struct<path: string, bytes: binary>, client_id: string, down_votes: int64, gender: string, locale: string, path: string, segment: string, sentence: string, up_votes: int64> \r\n```","embeddings":[-0.2528468668,0.1135271788,0.0847011656,0.2255779654,0.4603187442,-0.0732036904,0.1811231524,0.3404786885,-0.100949131,0.072595045,-0.4832226634,0.3691771924,-0.0401458442,-0.0950343609,-0.016474843,-0.1994142979,0.253772378,0.0790266693,-0.1742043197,-0.2164461762,-0.2962217629,0.3525260389,-0.1891235858,0.0692899674,-0.0691816956,0.0024943524,0.1407295614,0.2073658109,0.1072040424,0.0623662844,0.0481992289,-0.3001407981,0.3213793933,0.1699996442,-0.0001128995,-0.3090110123,0.0154149253,-0.26169312,0.0699161887,0.0612795316,-0.0414638184,0.063449569,-0.4947632551,-0.0165033415,-0.0087051159,0.0780238435,-0.3153741062,-0.3832596838,0.075151749,0.1381967366,0.2147356719,-0.0492028892,-0.0012087126,0.2371677905,-0.0120170452,0.0725444704,-0.1989045143,0.0233739614,-0.0382523313,0.084967196,0.1009249091,0.3572224975,-0.0879144967,-0.3120154142,-0.1956322938,-0.0470526703,-0.2996374667,-0.016086489,0.2595231235,0.1041530743,0.5175701976,-0.0048607402,-0.2894320786,0.1914596111,-0.0201264378,-0.2386482358,0.2378665209,0.11559508,0.0820131972,0.2807939351,-0.114289932,0.0266375449,0.0994041413,-0.0470104069,0.0836498663,-0.1439673007,-0.2032047808,0.1181911677,0.1636584848,-0.2784495652,-0.2374081314,-0.0678003356,-0.0963646099,0.3195744157,-0.5439091921,-0.3499386311,-0.2866734266,0.0055186409,-0.02169176,0.0298028085,0.0182989221,0.1406375915,0.1998255998,-0.0217386596,0.3478654027,0.25250718,0.1130222082,0.1037762538,0.3521870971,-0.0746134296,0.0223373044,-0.1477273405,0.0836910233,0.2690508962,0.5099980831,0.0197620876,-0.0672589615,-0.3000497222,-0.215644598,0.2878972888,0.0345071591,0.0496333316,-0.1710567921,0.1251995265,-0.0764239952,0.4590423107,0.0327584445,0.4337727129,-0.0936871469,-0.0147611778,-0.0085533923,-0.285579741,0.2067206651,0.0529183745,0.2645802796,0.0313418433,0.149652496,0.3465572894,-0.3265753388,-0.3838713467,-0.2842288315,0.0488256775,-0.1309251785,0.1274591386,-0.0423228294,0.2578017414,-0.169645071,0.0020383908,-0.0619815588,0.4289250076,-0.2903953791,-0.1125708371,-0.2923180461,0.1907127947,0.0864497423,0.01311481,-0.2744628787,0.0089156907,0.4834215045,-0.2748688161,0.0773145184,-0.0806785971,-0.156592086,-0.0632597134,0.1286389828,-0.4940532446,-0.4053566456,0.1426913589,-0.1175569221,0.0030383833,0.3993891776,0.2996939719,0.3102984428,-0.1869683117,-0.0549759865,0.4295400083,0.3371576071,-0.2676149309,-0.5990151167,-0.1776275784,0.1468712091,0.195140481,-0.0752951652,0.0491231158,0.4992274046,-0.1595564336,0.2319821417,0.4562256634,0.0454690419,0.2549941838,-0.2811857164,-0.0572666712,-0.1447132677,-0.0621904135,-0.2464320511,0.3153680563,-0.0039912653,0.0241305418,0.3176392317,0.0370048583,0.1516219378,0.049002897,0.3597035408,-0.2816777527,0.086169593,-0.2143613845,-0.1178093031,-0.0486440957,-0.1503629386,-0.2163766623,0.2488511354,-0.0914003253,-0.3676003516,-0.3645982444,-0.211054951,-0.0076363389,0.0876459703,0.070729591,-0.3114383519,0.0439621173,-0.1159251928,0.0142311594,0.0953133553,-0.048462607,-0.1316908747,0.0089559956,-0.0677897111,-0.1861932129,0.0149719333,0.3123132288,0.3450390399,0.1589996666,-0.2119969726,0.5700929761,0.0687832236,0.3879567385,-0.6059723496,-0.1892716289,0.1249326766,-0.0319604836,-0.02657976,-0.1580916941,0.2721366882,-0.087607421,-0.0798756778,0.3454011977,0.3811722994,0.03451702,-0.0312260184,0.0697629899,0.134630233,0.0370552838,0.0012721029,-0.0233151317,-0.387467742,-0.1099682376,-0.0968809202,-0.1654323637,-0.3453846276,0.1170536578,0.5308033824,-0.1850415915,0.2281680107,0.0075674364,-0.1470188349,-0.1087351218,0.11249277,0.2404013425,0.4666194022,0.0254021157,0.1515969634,-0.1779877692,-0.0336271785,-0.1533637196,0.0971812382,-0.0775929689,-0.0901784897,0.4509015679,0.1687517762,-0.1973530054,-0.3663101494,0.6564012766,-0.1237365156,0.0765913874,-0.5308884382,-0.2410463095,-0.2315033376,0.3585713506,-0.0324926376,-0.2053437233,-0.0422544293,-0.1393603832,0.0086700097,0.5790909529,-0.2330861241,0.2047368735,0.1589430124,0.1110529304,0.0579952449,0.0984031782,0.0009991904,0.2495824099,-0.3836546838,0.0910881162,-0.0382781513,-0.1425764114,-0.067705065,-0.0772963837,-0.0807073638,-0.142843321,-0.0605803467,-0.0367148854,-0.0281044133,0.3250164092,-0.2733569741,0.2014377564,-0.1686044186,-0.2538139224,0.1812571585,0.0491873287,-0.2323763222,0.3382057846,0.0537913442,0.3048371375,0.0122893471,-0.3900026977,-0.1645228267,-0.2841766179,0.0562601946,-0.4697789252,0.063871257,-0.0945688263,-0.0432002954,-0.332893312,-0.0877643824,0.0427139029,-0.6847988963,-0.0959495604,0.5248351693,-0.1414026916,-0.4598069191,-0.2473507077,0.1240168288,0.1540137529,-0.136568442,0.0365164578,0.233084619,0.0508523956,-0.0311741699,-0.0240800381,0.1157595441,0.3267199397,-0.1417649835,0.0993379876,0.0251767728,-0.1981301755,0.0282436907,0.536411047,0.5549006462,-0.1644926816,0.1786877662,-0.1952566653,0.2202575058,0.0919293314,0.1740239859,0.16861476,0.0544732399,0.0890806541,-0.3747354448,-0.1621648222,0.1152465269,-0.1892510206,0.070758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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3598","title":"Readme info not being parsed to show on Dataset card page","comments":"i suspect a markdown parsing error, @severo do you want to take a quick look at it when you have some time?","body":"## Describe the bug\r\nThe info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.\r\n\r\n## Steps to reproduce the bug\r\n\r\n# Sample code to reproduce the bug\r\nThe README file is this one: https:\/\/huggingface.co\/datasets\/softcatala\/Tilde-MODEL-Catalan\/blob\/main\/README.md\r\n\r\n## Expected results\r\nREADME info should appear in the Dataset card page.\r\n\r\n## Actual results\r\nNothing is shown. However, labels are parsed and shown successfully.\r\n","comment_length":22,"text":"Readme info not being parsed to show on Dataset card page \n ## Describe the bug\r\nThe info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.\r\n\r\n## Steps to reproduce the bug\r\n\r\n# Sample code to reproduce the bug\r\nThe README file is this one: https:\/\/huggingface.co\/datasets\/softcatala\/Tilde-MODEL-Catalan\/blob\/main\/README.md\r\n\r\n## Expected results\r\nREADME info should appear in the Dataset card page.\r\n\r\n## Actual results\r\nNothing is shown. However, labels are parsed and shown successfully.\r\n \n i suspect a markdown parsing error, @severo do you want to take a quick look at it when you have some time?","embeddings":[-0.3140525222,-0.4610443115,-0.0429991037,0.4984802604,0.3827379048,0.3550933301,0.1448011547,0.2580853701,-0.0173243023,0.1981521547,0.2267408073,0.4555902183,0.2250602841,0.2512837946,0.1963026524,0.0808808804,0.0349486656,-0.0920211747,0.1977231652,-0.1067305729,-0.2473593056,0.1543774903,-0.3241177499,-0.0942046717,-0.092370905,0.0133497072,0.0029535424,0.0504695736,-0.1747718155,-0.5224248767,0.1767552644,-0.1511462033,0.3618125618,0.1607668102,-0.0001106336,-0.1824619919,0.315841645,-0.0843574777,-0.0419781432,-0.1817768067,-0.3962010443,-0.2806947231,0.079050988,-0.1203188747,-0.2523438334,-0.3274749815,0.0376302451,-0.1252006292,0.193965286,0.1411038637,0.1637543887,-0.3640953302,0.1856638789,-0.0204802323,0.4237763286,0.2685471177,-0.0164377727,0.1987747699,0.4201975763,0.1997928023,0.0914720446,0.7301710248,0.1059241518,0.2021210343,0.3100326657,0.3551767766,0.8252458572,-0.2795642316,0.3831157386,0.3101479709,0.7847022414,-0.2429856509,-0.1057442576,-0.4970705211,-0.0879466459,-0.010688521,0.2108477652,0.3280597329,-0.0365953632,0.3784502745,-0.0506302007,-0.0048048468,0.0547890365,-0.0950311571,0.001096649,0.5242741704,-0.2832426727,-0.0571611151,0.0024037233,-0.1717625558,0.2931788862,-0.1215355471,-0.3141620755,0.2157039046,0.3242368698,0.058315888,-0.1071250737,-0.1317786574,0.3472331762,-0.2782696187,-0.3365406394,0.3707974255,0.0575271063,0.14726004,0.5468642712,-0.2543666065,0.1581031829,0.2046253532,-0.0022324137,0.1814304739,0.1220025793,-0.1703629792,-0.1377391219,0.201900363,-0.1469798833,0.0566577166,0.0945180133,-0.4469933808,-0.279191643,-0.004604511,0.0414181501,-0.2688416243,0.0359695777,0.4885918498,-0.1089301035,0.0790118873,0.2505738437,-0.1276200116,-0.1221649423,-0.3481455743,-0.0595718957,-0.0568646789,-0.287978977,-0.0682622567,0.0366633832,-0.038611766,0.2776674628,0.0461958311,-0.2116167992,-0.4243303835,0.0117332097,-0.0448232666,0.4117027819,0.0550144166,0.1049844623,0.1771049798,0.1194894612,-0.2118742913,-0.1826071739,0.2271167934,-0.6256475449,0.0573028922,-0.1749230623,0.1694274843,0.0295734443,0.2186888456,-0.2645472586,0.3057063222,0.4815330505,-0.175322786,0.080898501,0.0571028739,-0.3383783102,-0.0774890184,0.5665248036,0.4267726243,-0.1739796251,-0.1010266915,0.1803540438,-0.0164754502,0.3285779357,0.0081308344,-0.2300945967,0.297978133,-0.1857609451,0.151367709,0.2442270666,-0.391161859,-0.2191675901,0.3132398129,-0.129792437,-0.1477809101,-0.1479326487,-0.1642772853,0.2048344016,0.1196009666,0.2258936465,0.0621139966,0.0555352718,0.1468570381,-0.3387607634,-0.119141303,-0.0221695006,0.0334401876,-0.2367823273,-0.056135118,0.1682124287,-0.0008387056,0.5972523689,0.1099428982,0.3113249242,0.2032069564,0.2289343327,0.0031351997,0.3492143452,-0.0516331792,-0.0982161611,-0.1104554385,-0.10147544,-0.0574768484,-0.1456391215,-0.2206108868,-0.3791940212,-0.0683441684,-0.2924550176,-0.3935261965,0.1354883015,0.1103968322,-0.1304017901,0.2179115713,-0.1108496413,0.3211989105,-0.2584501505,0.049006857,-0.2663177252,0.2394272685,-0.0226246789,-0.3214514852,0.0235513244,0.2702671885,0.1650603861,-0.0122348331,-0.0328592435,0.3955875337,0.0825235844,0.0278643649,-0.0983169377,-0.2100536674,0.353407681,-0.4291702211,0.0019597567,-0.0336271524,0.0594785102,0.0155169275,-0.5142490268,0.0112308906,0.1586810499,0.1273284703,-0.0412214845,-0.0456395298,0.1857715547,0.0385266207,0.2067752928,-0.4706867933,0.0561495237,-0.0752895772,0.0460689999,-0.3609573543,-0.10088256,0.4318500161,0.6484763622,0.1841639429,0.1198147386,-0.1421729773,-0.3876870573,-0.0295820087,0.3624075353,0.1356722713,-0.092834644,0.2949030101,0.1990677714,0.2486388683,0.0539165214,0.0687130764,0.1215727776,0.0555083044,-0.1196460649,-0.0079050884,-0.1613430679,0.1032402143,0.0631111786,0.249031499,0.0193064474,0.2722133696,-0.4646293223,0.0543612577,-0.2053163052,-0.1657657623,-0.2891041934,0.1311848313,-0.1366363168,-0.364495188,-0.1536030024,0.0063861553,-0.0336506516,-0.0129036969,-0.1106476113,-0.1212176979,-0.1357174665,0.4625689387,-0.2494232059,-0.1519388855,-0.2723652124,0.0307775717,0.2503507435,0.4859339297,-0.0686034039,-0.5785263181,0.144014895,-0.1576886177,-0.3148590922,0.38094908,-0.109805949,0.3597176373,-0.069326818,0.1775024384,-0.0557520539,-0.005092781,0.2561485171,-0.0919518769,-0.1512398869,-0.2151206881,0.1624249667,-0.2490853965,-0.3209443688,-0.5362594128,-0.0324574597,-0.1570575237,0.0679239705,-0.3295710385,0.0348044299,0.0708702058,-0.048952058,0.1805886775,-0.5282492042,0.0450087227,-0.1945773214,-0.3357584476,0.1956544369,-0.3485111892,-0.3333944082,0.4135296643,0.0910211429,0.0141175352,-0.1585070044,-0.3295392096,0.3269414008,-0.0637490377,-0.2254870832,-0.2577459216,0.1639323235,0.1462712437,0.053981889,0.0731114373,-0.1758440435,-0.2493490875,-0.1927275062,-0.235117957,0.3732219934,-0.0625705421,0.3638647199,0.0520195588,-0.0630182773,0.0311336946,-0.0135563398,-0.0025253994,-0.1480239481,0.3016509414,0.0541081131,-0.1037655622,0.0485160239,0.0446427613,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3598","title":"Readme info not being parsed to show on Dataset card page","comments":"# Problem\r\nThe issue seems to coming from the front matter of the README\r\n```---\r\nannotations_creators:\r\n- no-annotation\r\nlanguage_creators:\r\n- machine-generated\r\nlanguages:\r\n- 'ca'\r\n- 'de'\r\nlicenses:\r\n- cc-by-4.0\r\nmultilinguality:\r\n- translation\r\npretty_name: Catalan-German aligned corpora to train NMT systems.\r\nsize_categories:\r\n- \"1M<n<10M\"            \r\nsource_datasets:\r\n- extended|tilde_model\r\ntask_categories:\r\n- machine-translation\r\ntask_ids:\r\n- machine-translation\r\n---\r\n``` \r\n# Solution\r\nThe fix is to correctly style the README as explained [here](https:\/\/huggingface.co\/docs\/datasets\/v1.12.0\/dataset_card.html). I have also correctly parsed the font matter as shown below:\r\n```\r\n---\r\nannotations_creators: []\r\nlanguage_creators: [machine-generated]\r\nlanguages: ['ca', 'de']\r\nlicenses: []\r\nmultilinguality:\r\n- multilingual\r\npretty_name: 'Catalan-German aligned corpora to train NMT systems.'\r\nsize_categories: \r\n- 1M<n<10M\r\nsource_datasets: ['extended|tilde_model']\r\ntask_categories: ['machine-translation']\r\ntask_ids: ['machine-translation']\r\n---\r\n```\r\nYou can find the README for a sample dataset [here](https:\/\/huggingface.co\/datasets\/ritwikraha\/Test)","body":"## Describe the bug\r\nThe info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.\r\n\r\n## Steps to reproduce the bug\r\n\r\n# Sample code to reproduce the bug\r\nThe README file is this one: https:\/\/huggingface.co\/datasets\/softcatala\/Tilde-MODEL-Catalan\/blob\/main\/README.md\r\n\r\n## Expected results\r\nREADME info should appear in the Dataset card page.\r\n\r\n## Actual results\r\nNothing is shown. However, labels are parsed and shown successfully.\r\n","comment_length":121,"text":"Readme info not being parsed to show on Dataset card page \n ## Describe the bug\r\nThe info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.\r\n\r\n## Steps to reproduce the bug\r\n\r\n# Sample code to reproduce the bug\r\nThe README file is this one: https:\/\/huggingface.co\/datasets\/softcatala\/Tilde-MODEL-Catalan\/blob\/main\/README.md\r\n\r\n## Expected results\r\nREADME info should appear in the Dataset card page.\r\n\r\n## Actual results\r\nNothing is shown. However, labels are parsed and shown successfully.\r\n \n # Problem\r\nThe issue seems to coming from the front matter of the README\r\n```---\r\nannotations_creators:\r\n- no-annotation\r\nlanguage_creators:\r\n- machine-generated\r\nlanguages:\r\n- 'ca'\r\n- 'de'\r\nlicenses:\r\n- cc-by-4.0\r\nmultilinguality:\r\n- translation\r\npretty_name: Catalan-German aligned corpora to train NMT systems.\r\nsize_categories:\r\n- \"1M<n<10M\"            \r\nsource_datasets:\r\n- extended|tilde_model\r\ntask_categories:\r\n- machine-translation\r\ntask_ids:\r\n- machine-translation\r\n---\r\n``` \r\n# Solution\r\nThe fix is to correctly style the README as explained [here](https:\/\/huggingface.co\/docs\/datasets\/v1.12.0\/dataset_card.html). I have also correctly parsed the font matter as shown below:\r\n```\r\n---\r\nannotations_creators: []\r\nlanguage_creators: [machine-generated]\r\nlanguages: ['ca', 'de']\r\nlicenses: []\r\nmultilinguality:\r\n- multilingual\r\npretty_name: 'Catalan-German aligned corpora to train NMT systems.'\r\nsize_categories: \r\n- 1M<n<10M\r\nsource_datasets: ['extended|tilde_model']\r\ntask_categories: ['machine-translation']\r\ntask_ids: ['machine-translation']\r\n---\r\n```\r\nYou can find the README for a sample dataset [here](https:\/\/huggingface.co\/datasets\/ritwikraha\/Test)","embeddings":[-0.2200217396,-0.4916218817,0.0016025736,0.6471504569,0.4602582753,0.2593291402,0.0698466375,0.2069073617,-0.0885193571,0.1984074563,0.1808968484,0.4812804759,0.3071287274,0.1263435483,0.2062164843,0.1296864748,0.0109635564,-0.2193576694,0.2972040176,-0.1837093532,-0.1439480633,0.2011325061,-0.3098044097,-0.1654568762,-0.1763849258,0.0367293842,-0.0149178738,0.1407997757,-0.0395962074,-0.4099974632,0.2198219746,-0.0546951108,0.2331006527,0.1394225508,-0.0001224593,-0.2016873211,0.2720484734,-0.1059329659,0.0271567814,-0.277594775,-0.2358171195,-0.3690283597,0.0241333283,-0.1159167737,-0.265307337,-0.3223181069,0.0130804302,-0.0951957852,0.2967308462,0.0833316892,0.0112181073,-0.3431652188,0.3340057433,0.1666260511,0.314751178,0.228454262,-0.0764061436,0.1927694082,0.3905033767,0.3298907578,0.0615567714,0.7368040681,0.0566944256,0.1706761718,0.3015003204,0.311144799,0.661806643,-0.3187084794,0.3193925321,0.3462354243,0.5792184472,-0.1925496757,-0.2490107864,-0.6705213189,-0.1588783711,-0.072995089,0.206533283,0.2302052975,0.0188995544,0.4041961133,-0.0478040352,-0.0856638998,-0.0154267885,0.0643145293,-0.0612409264,0.5336286426,-0.2550933361,0.0221612956,0.0456475392,-0.1984039545,0.3620317876,-0.1382163167,-0.3320044875,0.3631872237,0.2892424762,0.0327182636,-0.10965202,-0.0188815314,0.3416900039,-0.307213366,-0.364213407,0.5142814517,-0.1544071734,0.0700612292,0.5688092113,-0.2267081141,0.1909000129,0.212839976,0.0043802662,0.018324405,0.2145942152,-0.2169317752,-0.2651573122,0.1064608917,-0.1800326556,0.0966790915,-0.0324208289,-0.3713850081,-0.1376959831,-0.0249638297,-0.0180799607,-0.2377972603,0.030807741,0.3496031761,-0.0356996171,0.2786740959,0.2141122073,-0.0835793465,-0.1369649768,-0.314748913,-0.0073665483,-0.1454797238,-0.2935535312,-0.0458968543,0.1289874613,-0.152117461,0.2966819406,0.0111681717,-0.0710970759,-0.3637539446,-0.0983516648,-0.1493258476,0.4819292426,0.0504457578,0.0323742814,0.2647614777,0.0884489119,-0.1135186255,-0.1472811401,0.2246918529,-0.660140872,0.1659624875,-0.0931804925,0.0119398925,-0.1122485772,0.3220578432,-0.1952007264,0.3939468563,0.4933974147,-0.1226415634,0.1171121746,0.0132390056,-0.4490611553,0.031590987,0.5345925093,0.5175892711,-0.0191764142,-0.1500317007,0.0433778614,0.0453377962,0.2968921661,0.1055672616,-0.0843717232,0.2660788,-0.182061255,0.0725701079,0.2067914605,-0.5112656355,-0.2876424491,0.4181278646,-0.0814819187,0.0240491796,-0.0763799772,-0.145050332,0.0861300156,0.1976022869,0.2893286347,0.1065545008,-0.0097625135,0.124452278,-0.2996032536,-0.0828289017,-0.0780795813,-0.060599722,-0.2559676468,-0.0189651549,0.0678975284,0.0876924843,0.6415072083,0.1592071354,0.3426516056,0.2400611937,0.1294636577,0.0388038196,0.3489439189,0.0347970426,-0.2334389985,-0.141740784,0.0071315933,0.0050626793,-0.1755857766,-0.2213160694,-0.368893832,-0.2104979157,-0.2735341787,-0.3503691554,-0.0326992869,0.0850990266,-0.1345097572,0.1608942002,0.1098886654,0.4407609403,-0.1406396031,0.0747978613,-0.2838712335,0.1897787154,0.07891348,-0.249405697,0.0156786256,0.3605496883,0.2897311449,-0.1674573123,0.0273279697,0.4320842028,0.2573441267,0.1604863852,-0.2457062304,-0.1195011288,0.4136089087,-0.303552717,-0.0924386382,-0.0895196646,0.005263865,-0.0865937397,-0.5360169411,0.0700217336,0.0720070451,0.2437548488,-0.0466417,0.0421463586,0.218111515,-0.0074603911,0.1341972202,-0.5990081429,0.0009454045,-0.1469187289,0.1196893677,-0.2651996911,-0.1308966428,0.3781145513,0.6157801747,0.3441339135,-0.0017508746,-0.1363657713,-0.3082754612,-0.0475328639,0.3687645793,0.0751972497,-0.1158885732,0.2100578845,0.1813776493,0.2782336771,0.2667924762,0.0488146916,0.1567844003,0.1185052842,-0.0539852418,0.0360426791,-0.1191061586,0.1525870264,0.0747639984,0.3228670657,0.0587910526,0.2806970179,-0.5309557319,0.1269135475,-0.2121856213,-0.1828490943,-0.2588044107,-0.0178992897,-0.1895601898,-0.3917068541,-0.2296689004,0.0879253373,0.0218373146,-0.0510382243,0.0192051437,-0.2082371563,-0.1454439759,0.3556164205,-0.3444797397,-0.1351249069,-0.1976812631,-0.0719252899,0.1750896871,0.3769292533,-0.1716256738,-0.5501978397,0.1170760095,-0.1349294782,-0.3997960985,0.3919311762,-0.14774701,0.4442909062,-0.1158072725,0.0522706173,-0.1137929186,0.1412654221,0.2442421019,0.0253494512,-0.1359860301,-0.1871385276,0.1339742243,-0.1681199819,-0.3780758381,-0.4673047066,0.01212986,-0.1548791975,0.1075094193,-0.4019791484,0.0539048985,0.2490124106,-0.1050416976,-0.0115223192,-0.555118382,0.0301932413,-0.2000283301,-0.2905510962,0.2050391138,-0.2631584704,-0.2743886411,0.3221085072,0.0607115179,0.1317209899,-0.0409211852,-0.2908808887,0.2714009881,-0.0409259684,-0.1415193379,-0.1713069677,0.1517988294,0.1436208636,0.0111232484,0.1596801877,-0.2228906304,-0.1862038821,-0.1926728189,-0.1989809722,0.3240866661,-0.0085085016,0.2160847634,0.0914148167,0.053264685,0.0781093836,0.0772315189,0.031910941,-0.0051479507,0.2994758189,0.0622322001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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3598","title":"Readme info not being parsed to show on Dataset card page","comments":"Thank you. It finally worked implementing your changes and leaving a white line between title and text in the description.","body":"## Describe the bug\r\nThe info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.\r\n\r\n## Steps to reproduce the bug\r\n\r\n# Sample code to reproduce the bug\r\nThe README file is this one: https:\/\/huggingface.co\/datasets\/softcatala\/Tilde-MODEL-Catalan\/blob\/main\/README.md\r\n\r\n## Expected results\r\nREADME info should appear in the Dataset card page.\r\n\r\n## Actual results\r\nNothing is shown. However, labels are parsed and shown successfully.\r\n","comment_length":20,"text":"Readme info not being parsed to show on Dataset card page \n ## Describe the bug\r\nThe info contained in the README.md file is not being shown in the dataset main page. Basic info and table of contents are properly formatted in the README.\r\n\r\n## Steps to reproduce the bug\r\n\r\n# Sample code to reproduce the bug\r\nThe README file is this one: https:\/\/huggingface.co\/datasets\/softcatala\/Tilde-MODEL-Catalan\/blob\/main\/README.md\r\n\r\n## Expected results\r\nREADME info should appear in the Dataset card page.\r\n\r\n## Actual results\r\nNothing is shown. However, labels are parsed and shown successfully.\r\n \n Thank you. It finally worked implementing your changes and leaving a white line between title and text in the description.","embeddings":[-0.1167041659,-0.1736112982,-0.045795016,0.1973757744,0.4050146639,0.1493382454,0.2383347899,0.0577398203,0.0127059491,0.2358467877,0.1479030102,0.3144129217,0.342817843,0.3346490562,0.2078314424,-0.0082403338,0.0039031005,0.0413329862,0.157225728,-0.0487844273,-0.1416903883,0.1252379268,-0.2226826847,-0.0539462641,-0.1764365435,0.0658417493,0.0260807052,0.0239636078,-0.1715485752,-0.6609655619,0.2419894189,0.2248468846,0.1234376803,0.1978035569,-0.0001096589,-0.2465655953,0.2606937885,-0.0628371835,0.1081163287,-0.3238119185,-0.1792507619,-0.3169767261,0.1996181607,-0.0515887327,-0.2192113847,-0.1626137346,0.0221041739,0.0332952589,0.0174520891,0.1025004089,0.1428238004,-0.3840937614,0.1205927506,-0.0681634396,0.2673474252,0.237911731,0.09867879,0.1189631671,0.3710790277,0.156893,0.0120217223,0.6945773363,0.1935561001,0.0935233757,0.3485500515,0.2467933148,0.525326252,-0.2600476444,0.2548160553,0.4280278087,0.7338127494,-0.326938808,-0.1892324686,-0.2952084541,-0.0518417209,-0.219751969,0.2260278165,0.2936688662,-0.0492241532,0.233053416,-0.0306199733,0.0457347184,-0.0377038568,-0.1501912326,-0.0286397282,0.4264786541,-0.0951976851,-0.0185207725,0.1318847835,-0.2931891978,0.1836218238,-0.0418338105,-0.3696623445,0.1697614193,0.1388695389,-0.1519876122,-0.0133475289,-0.2065726519,0.409288466,-0.3734413087,-0.2098618597,0.3157309592,-0.0412172116,0.0843014047,0.3842053711,-0.0995577276,0.238143906,0.0624584109,-0.0696683824,0.1682773978,0.1996625811,-0.2419347614,-0.0418432541,0.0899284407,-0.1403366923,0.0143131586,0.0904046223,-0.4951444566,-0.290997386,-0.0221231952,-0.1017787308,-0.0996878371,-0.1458853334,0.5022286177,-0.0363998935,0.0327931307,0.3763754666,-0.1542336494,-0.0652275234,-0.2554433942,-0.097528547,-0.1170436889,-0.3137434125,0.0676602274,0.0598172061,0.0639893413,0.2639661431,0.0601179712,-0.0946284384,-0.2830135226,-0.034194909,0.0750627071,0.3472568393,0.08005777,0.0867493972,0.1188200861,0.0982324779,-0.2993614376,-0.1225876361,0.224022761,-0.4750011265,0.0360566266,-0.3489007056,0.1764496267,0.096068643,0.2405107021,-0.1320963055,0.2510787845,0.309209913,-0.0240782462,0.1507257372,0.205146119,-0.2778104246,0.1954750717,0.2915138602,0.4414319396,-0.1886434853,-0.1386417598,0.1899214238,-0.021959668,0.2691300213,0.0708117113,-0.1837423444,0.2673613429,-0.0602176413,0.2468871176,0.1938892007,-0.4319396019,-0.265245378,0.3445347846,-0.0991085842,-0.174123913,-0.0179960858,-0.3433903456,0.2579950094,0.2148468345,0.2524544001,-0.1661844552,0.2207473367,0.1385655105,-0.4266414344,-0.136676088,0.0357451923,-0.0256464612,-0.3049356043,-0.1253447831,0.0962802842,0.1158161163,0.6959297657,0.1533014923,0.2612820566,0.3033487201,0.3522498608,-0.0241079852,0.1763312072,0.1081263572,-0.1885164976,-0.205165118,-0.1070253998,0.3355359733,0.1078419983,-0.3675116897,-0.3377491832,-0.1526023746,-0.3350577056,-0.6366425157,0.1587340534,-0.026014762,-0.0797026828,0.1886144429,0.142946735,0.2323897332,-0.4032110274,-0.0069445577,0.0257401746,0.1990541518,-0.0228636544,-0.3178856671,0.0417094976,0.285197854,0.1359803081,0.1975289583,-0.2380350679,0.3778533638,-0.0871393457,0.1901870519,-0.1460529715,-0.1313732713,0.3406006694,-0.3864449859,0.0468952134,0.0530255958,0.0787706524,0.1119849533,-0.5889363885,-0.0511996932,0.0092698513,0.2011033893,-0.0133767007,-0.086116828,0.3998263776,-0.0015351331,-0.0550966449,-0.5001233816,0.0018205596,0.0782649443,0.0798629597,-0.2310591787,-0.2024347633,0.4564603269,0.7715742588,0.2994486094,0.1313943118,-0.0570428036,-0.3332345188,-0.1103474796,0.4654714465,-0.0301005952,-0.0333724916,0.4190673232,0.1118241623,0.3922007382,0.0271021277,-0.0690485835,0.2271161377,0.0179217942,-0.2430725545,0.0053284839,-0.0871078074,-0.0019775869,0.0216249749,0.1614951789,-0.0621345267,0.1773824096,-0.4573901594,-0.0209666733,-0.059481848,-0.1763333976,-0.3098483086,0.0728614554,-0.226456508,-0.2746067941,-0.0652500615,0.0985697061,-0.1899863631,0.1901051104,0.0619632825,-0.2433644682,0.0687262788,0.5073621273,-0.1716789752,-0.1616760343,-0.0801507533,0.0512221493,0.0832858905,0.3237129748,-0.0136595191,-0.4906448722,0.1861931533,-0.183313325,-0.3186184466,0.3141609132,-0.0434810929,0.3247363269,-0.0012802701,0.2342228591,-0.1370958686,0.0556825101,0.1851817667,-0.0226868782,-0.1859363467,-0.3348245621,0.0282390658,-0.0656819269,-0.3817827106,-0.4774839878,-0.0519074611,-0.3157139122,0.1555022001,-0.4246220291,0.2464950383,0.1970513612,0.0351669155,0.292681396,-0.5459514856,0.0388873406,-0.1668567061,-0.3823118806,0.0558052883,-0.4048288167,-0.3618627489,0.3949918449,0.1631520689,0.0267085936,-0.3341090679,-0.2514848709,0.281521827,-0.014467366,-0.1334931254,-0.0572424009,0.1144765168,0.3793832064,0.157716915,-0.0697497651,-0.1339190155,-0.3119757771,-0.0438641012,-0.1155861169,0.2979969978,-0.1389149725,0.3886365294,0.0120682651,-0.0624466874,0.0627971739,0.048076421,0.030457627,-0.0962059423,0.3052570224,-0.0440442637,-0.2031846195,0.0139322206,0.1166826412,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3597","title":"ERROR: File \"setup.py\" or \"setup.cfg\" not found. Directory cannot be installed in editable mode: \/content","comments":"Hi! The `cd` command in Jupyer\/Colab needs to start with `%`, so this should work:\r\n```\r\n!git clone https:\/\/github.com\/huggingface\/datasets.git\r\n%cd datasets\r\n!pip install -e \".[streaming]\"\r\n```","body":"## Bug\r\nThe install of streaming dataset is giving following error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n! git clone https:\/\/github.com\/huggingface\/datasets.git\r\n! cd datasets\r\n! pip install -e \".[streaming]\"\r\n```\r\n\r\n## Actual results\r\nCloning into 'datasets'...\r\nremote: Enumerating objects: 50816, done.\r\nremote: Counting objects: 100% (2356\/2356), done.\r\nremote: Compressing objects: 100% (1606\/1606), done.\r\nremote: Total 50816 (delta 834), reused 1741 (delta 525), pack-reused 48460\r\nReceiving objects: 100% (50816\/50816), 72.47 MiB | 27.68 MiB\/s, done.\r\nResolving deltas: 100% (22541\/22541), done.\r\nChecking out files: 100% (6722\/6722), done.\r\nERROR: File \"setup.py\" or \"setup.cfg\" not found. Directory cannot be installed in editable mode: \/content\r\n\r\n","comment_length":26,"text":"ERROR: File \"setup.py\" or \"setup.cfg\" not found. Directory cannot be installed in editable mode: \/content \n ## Bug\r\nThe install of streaming dataset is giving following error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n! git clone https:\/\/github.com\/huggingface\/datasets.git\r\n! cd datasets\r\n! pip install -e \".[streaming]\"\r\n```\r\n\r\n## Actual results\r\nCloning into 'datasets'...\r\nremote: Enumerating objects: 50816, done.\r\nremote: Counting objects: 100% (2356\/2356), done.\r\nremote: Compressing objects: 100% (1606\/1606), done.\r\nremote: Total 50816 (delta 834), reused 1741 (delta 525), pack-reused 48460\r\nReceiving objects: 100% (50816\/50816), 72.47 MiB | 27.68 MiB\/s, done.\r\nResolving deltas: 100% (22541\/22541), done.\r\nChecking out files: 100% (6722\/6722), done.\r\nERROR: File \"setup.py\" or \"setup.cfg\" not found. Directory cannot be installed in editable mode: \/content\r\n\r\n \n Hi! The `cd` command in Jupyer\/Colab needs to start with `%`, so this should work:\r\n```\r\n!git clone https:\/\/github.com\/huggingface\/datasets.git\r\n%cd datasets\r\n!pip install -e \".[streaming]\"\r\n```","embeddings":[-0.2805957198,0.0059206383,0.0532628857,0.0867150575,0.123359479,0.1511144489,-0.0670932904,0.1101511866,-0.2740466893,0.1692893207,-0.2694025636,0.3483413756,-0.2103758454,0.270406872,-0.0117878318,-0.1742319614,0.0239561982,0.3310485184,-0.2299902737,0.1471987516,-0.0600807667,0.2225801349,-0.2159906924,-0.3008274436,-0.2476941943,-0.1016362831,-0.0691180378,0.1205927953,-0.5584033728,-0.2806479633,0.1041319817,0.1633412689,-0.0252924133,0.9435852766,-0.0001159732,-0.0086410046,0.5131060481,-0.1744882762,-0.6947641373,-0.1360992789,-0.0912804976,-0.014951474,0.0127460314,-0.0473671928,0.1082392558,0.0562896393,0.0263047833,-0.068740502,0.309050858,0.143361479,0.2015933841,0.1568181664,0.1444752514,-0.0507714748,-0.0703359097,0.2792480588,-0.2552321851,0.5715397596,0.220463112,0.120934777,0.2613908052,0.3037084639,-0.0702393129,-0.0765445605,0.1885153353,-0.1269201785,-0.200649932,-0.4825867116,0.0409781188,0.0563263632,0.4074153304,-0.5007212758,-0.4555950165,-0.2031183988,0.4305533171,-0.3596735895,0.0891724378,0.1970397681,-0.4193040431,0.1830880195,-0.4821976125,-0.2693838179,-0.1416005939,-0.1220119968,0.3392283916,0.1236027852,-0.125980407,0.0074230037,0.1137878224,0.0922003165,0.295617342,-0.2294700593,-0.1589822769,0.021280231,-0.011537387,-0.1348154396,-0.1258449852,0.3401717544,-0.0969651118,0.0303784739,0.1030370593,-0.4377256036,-0.0378597565,0.2999757826,0.0572614297,0.5299224854,-0.0511162058,0.3056846857,0.349390775,0.4275912344,0.1947135925,0.0665071011,-0.044716958,-0.0888031423,-0.2629457414,0.0052211881,0.123506397,-0.0624485724,-0.3710983098,-0.2866512537,0.0056777531,0.0480568632,0.1651791185,0.3149047494,-0.047017131,-0.0867424682,-0.0290107634,0.2301737964,-0.0108509259,-0.1146075055,-0.0847799778,0.3135746121,-0.0676297843,-0.0123688169,0.0703540072,-0.4096912444,0.4586766064,-0.0203489549,0.3117511868,0.1368588954,-0.1506400704,-0.0384646729,-0.1762401313,0.6022830009,0.0088949017,0.0615576543,0.1057889909,-0.2156726718,-0.1500876993,0.269641906,-0.0501857251,-0.3334853947,0.1827374995,0.1373783499,-0.2166858166,0.0537721403,-0.6689689755,-0.1430408657,-0.0639956743,-0.3073045909,0.043407958,-0.1194344759,0.2065997869,-0.2860464752,0.3842343688,0.6091390848,-0.3521528542,-0.0660741106,-0.3087066114,0.020290371,0.4488038123,0.2872610986,0.07764934,0.0572032072,-0.2471998483,-0.2377770394,0.3621507287,-0.3017600179,-0.4505339861,0.2287169993,-0.1284441352,0.1532518864,0.2038684189,-0.0014137191,0.2820830941,-0.132711947,0.1330847591,0.0195775274,-0.1596546322,0.0412293412,-0.2415658534,-0.5157351494,-0.1326236874,-0.0147449309,0.0274023116,0.2429527044,0.0229475033,-0.1610869318,0.0148685845,0.0839121938,-0.0202167705,0.2453862429,0.4260365367,-0.0667825565,-0.1125691533,-0.1231413335,-0.1107688323,0.262385577,0.322597146,0.1744645685,-0.2464390248,-0.0643576682,-0.4895506501,0.2226728201,-0.2624047399,-0.4788955152,0.0845860839,0.251054734,0.32389763,-0.0351609215,-0.2020380944,0.4624599516,0.1771710515,0.150537312,-0.2489418834,0.2391053736,-0.4088406265,-0.3820581734,0.1196340919,0.0330663584,0.1249103248,-0.2154666334,-0.1925525218,0.4968467355,-0.1947487295,0.2320080996,0.1174687967,-0.1313884407,0.2224768102,-0.2169948816,0.0650008097,0.2646797001,0.1529495269,0.1158818826,0.020526439,-0.136520803,-0.3016507328,0.0320014283,0.2034619153,0.0685080811,0.1900989115,0.4223009646,-0.0897941366,-0.1778442264,0.2964971364,0.1260839701,0.0989721715,-0.211625725,-0.0077148401,0.0923992619,0.021646088,0.080589436,-0.0283261929,0.2159596682,-0.1818532199,-0.0117291557,0.1398031265,0.3961995244,0.5896663666,0.2376558632,0.0708565041,0.1218008325,0.1118363962,-0.2394696623,0.4201748073,-0.000641141,0.0830243528,0.1387091875,0.0675139278,-0.010495469,-0.1372612566,-0.3381998837,-0.079672575,0.2591256201,-0.2590177655,-0.0463667065,-0.1750999987,-0.3868788183,-0.2604928017,-0.1891011447,-0.0300498586,-0.1601986736,0.1231332645,0.4293966293,-0.0015687189,0.2788415849,0.0870347768,0.1031847969,0.0460295826,-0.0098648081,-0.0913040265,0.1508735567,0.0699023977,0.0282456465,0.1626147777,-0.0318284146,0.4001652002,-0.1747565567,-0.0209874865,-0.218155846,-0.1677189767,0.241368711,-0.1114946604,0.6180770993,0.2661001682,0.4592854679,0.0041472027,-0.3879849315,0.1446588784,0.0463061742,-0.1369533986,0.0333752632,-0.0002430995,-0.2438081801,-0.1226320863,-0.2254492044,-0.3785442412,-0.4328781962,0.254336834,0.2345899343,0.0537234619,0.1100647673,0.166373089,0.0636156648,0.2898416519,0.122811757,-0.0541149937,-0.5589814782,0.252083391,-0.3775458932,-0.0720543787,0.0205587614,0.0416783169,0.0512192063,-0.1223204732,-0.4090929329,0.0361611992,-0.2404900044,0.1619968712,0.1167777926,0.124184452,0.1887364239,0.1009562314,0.015083327,-0.110156171,-0.1925556809,-0.26914078,0.1991300285,0.3631562293,0.0030858717,0.3257459104,-0.0962239355,0.5873413086,0.5794035196,0.3977865577,0.1987003982,-0.0593294576,0.5639400482,-0.3945723176,-0.344612509,0.2578229308,-0.0594799146,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3596","title":"Loss of cast `Image` feature on certain dataset method","comments":"Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`\/`Audio` feature.","body":"## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0","comment_length":30,"text":"Loss of cast `Image` feature on certain dataset method \n ## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0 \n Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`\/`Audio` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3596","title":"Loss of cast `Image` feature on certain dataset method","comments":"> Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`\/`Audio` feature.\r\n\r\nThanks, I'll keep an eye out for #3575 getting merged. I managed to use `push_to_hub` sucesfully with images when they were loaded via `map` - something like `ds.map(lambda example: {\"img\": load_image_function(example['fname']})`, this only pushed  the images to the hub if the `load_image_function` return a PIL Image without the filename attribute though. I guess this might often be the prefered behaviour though. \r\n","body":"## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0","comment_length":92,"text":"Loss of cast `Image` feature on certain dataset method \n ## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0 \n > Hi! Thanks for reporting! The issue with `cast_column` should be fixed by #3575 and after we merge that PR I'll start working on the `push_to_hub` support for the `Image`\/`Audio` feature.\r\n\r\nThanks, I'll keep an eye out for #3575 getting merged. I managed to use `push_to_hub` sucesfully with images when they were loaded via `map` - something like `ds.map(lambda example: {\"img\": load_image_function(example['fname']})`, this only pushed  the images to the hub if the `load_image_function` return a PIL Image without the filename attribute though. I guess this might often be the prefered behaviour though. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3596","title":"Loss of cast `Image` feature on certain dataset method","comments":"Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?","body":"## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0","comment_length":25,"text":"Loss of cast `Image` feature on certain dataset method \n ## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0 \n Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3596","title":"Loss of cast `Image` feature on certain dataset method","comments":"> Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?\r\n\r\nThanks for checking. There is no longer an error when calling `select` but it appears the cast value isn't preserved. Before `select`\r\n\r\n```python\r\ndataset.features\r\n{'url': Image(id=None)}\r\n```\r\n\r\nafter select:\r\n```\r\n{'url': Value(dtype='string', id=None)}\r\n```\r\n\r\nUpdated Colab example [here](https:\/\/colab.research.google.com\/gist\/davanstrien\/4e88f55a3675c279b5c2f64299ae5c6f\/potential_casting_bug.ipynb) ","body":"## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0","comment_length":64,"text":"Loss of cast `Image` feature on certain dataset method \n ## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0 \n > Hi ! We merged the PR and did a release of `datasets` that includes the changes. Can you try updating `datasets` and try again ?\r\n\r\nThanks for checking. There is no longer an error when calling `select` but it appears the cast value isn't preserved. Before `select`\r\n\r\n```python\r\ndataset.features\r\n{'url': Image(id=None)}\r\n```\r\n\r\nafter select:\r\n```\r\n{'url': Value(dtype='string', id=None)}\r\n```\r\n\r\nUpdated Colab example [here](https:\/\/colab.research.google.com\/gist\/davanstrien\/4e88f55a3675c279b5c2f64299ae5c6f\/potential_casting_bug.ipynb) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3596","title":"Loss of cast `Image` feature on certain dataset method","comments":"Hmmm, if I re-run your google colab I'm getting the right type at the end:\r\n```\r\nsample.features\r\n# {'url': Image(id=None)}\r\n```","body":"## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0","comment_length":21,"text":"Loss of cast `Image` feature on certain dataset method \n ## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0 \n Hmmm, if I re-run your google colab I'm getting the right type at the end:\r\n```\r\nsample.features\r\n# {'url': Image(id=None)}\r\n```","embeddings":[-0.2129750401,0.0474176332,0.0389729962,0.0052658911,0.6166042686,0.1256067306,0.5568073988,0.3356979489,-0.0696555749,0.0527434275,-0.0588103943,0.4363155365,0.0470173843,-0.1245156005,0.2604292035,-0.2225400656,0.1796382517,-0.088664569,-0.2497291565,-0.0093806554,-0.3457134068,-0.0661690533,-0.1893476248,0.237794891,-0.1622090042,-0.0066236057,0.1640383452,-0.0999357551,0.1083052233,-0.02997908,0.1094763204,-0.2759464383,-0.0227155872,0.2365704477,-0.0001221718,-0.0650704503,0.3803016245,0.1371917278,-0.2269872427,-0.0291916002,-0.5200994611,-0.0374795273,0.1395735145,-0.3502290249,0.2283566892,0.258901149,0.138547793,-0.0057900003,-0.1646184623,0.2796777785,0.1743338555,0.4169071317,-0.1179317683,0.0851160064,0.0722785518,0.462868154,-0.3308898807,0.0254451185,0.1116626561,0.3031894267,0.2586580813,0.3366073072,-0.1592242867,-0.3194156587,0.0250834078,0.2142708451,-0.2843176425,-0.0781230032,0.0946592912,-0.149554804,0.3191038966,-0.3005331159,-0.3253417909,-0.1712527722,0.1816153824,0.0329558849,0.1259126365,0.2044390738,0.1956205815,-0.1157480553,-0.5924866199,0.0183527041,-0.0085015027,0.1172127351,-0.3056593537,-0.1439818591,0.0070282724,0.2836532593,-0.2083894759,0.0262531694,0.0473851897,-0.2720200717,0.0605402887,-0.073223494,-0.0166722592,-0.0155033469,0.1188419461,0.0479596145,-0.1330493838,0.0619520321,-0.0283177681,0.0416062102,-0.284496516,0.1649549901,0.4856223166,-0.071655713,-0.1864895076,0.3215299547,0.3532755971,0.4134801924,-0.1369770169,-0.18048352,0.3006671965,0.1830180883,0.5300553441,-0.0874374881,0.4510234892,-0.1081015468,-0.3315900564,-0.0002492832,0.0127328932,0.3132005334,0.0374834836,-0.080360882,0.0762053654,0.1434401423,0.5463795662,0.3438687623,-0.0879273638,-0.2247524858,-0.2489573658,0.0367450304,-0.0654137284,-0.2301191092,-0.1365180314,0.1686651558,-0.1438099146,0.0565820299,0.1042663306,-0.0901758447,-0.170759365,-0.1447998136,0.0503545068,0.2149537057,-0.2269266248,0.0025323171,0.0520610958,-0.0884701088,-0.1506866813,0.1472952217,-0.3751041591,-0.0410818197,-0.0657969415,0.1167187542,-0.1498431563,0.0185176115,-0.4390596449,0.0777312368,0.2409621924,0.0095929485,0.2663366497,-0.4195596874,-0.2266319692,-0.158867836,0.0716339648,0.2041888684,-0.1159173623,-0.1556359082,-0.2473703176,-0.009397625,0.1166885346,0.2143127471,-0.0851544291,0.220188573,-0.1553695053,0.1473579109,0.3139224052,-0.1477224827,-0.3504661024,-0.0881445706,0.0493957028,0.1589130014,-0.393587023,0.2253856808,0.4084370136,-0.4220543206,-0.2208257765,0.4992130995,-0.3667493761,0.287787199,-0.0515157692,-0.0572100766,-0.2323425114,0.2307857275,0.2822721303,0.2687316537,0.2102840245,0.3427780867,-0.0379124507,-0.0262793582,0.2914376259,-0.0867514685,0.3882529736,-0.0889564678,-0.0678304806,-0.4541125596,-0.1059577242,0.028253302,0.4255813956,-0.0085924733,-0.5174813271,-0.1013184339,-0.0854577944,0.1925174594,0.0403112881,0.2541805208,0.0197453797,-0.2305017114,-0.3230268061,0.2358158678,-0.0565976985,0.0063596629,-0.116468139,0.1345171481,-0.2400365174,0.456056565,-0.0052539916,-0.3721536696,-0.4887198508,0.1276485622,0.0498059131,0.0649493933,-0.2820021212,0.3803838491,-0.0080364672,0.2810367346,-0.1981515139,0.2927629054,0.2661064267,-0.3616521955,0.0815318078,0.2056104243,0.3214098513,0.1190567315,-0.2546125054,0.1512320489,-0.0619870611,0.0886867568,-0.0484200008,0.2666267455,-0.0770879835,-0.0554106198,-0.0165515244,-0.2249839902,-0.1481883973,-0.0495920852,-0.3853006363,-0.0742675513,-0.4956923127,0.160868451,0.5034552217,-0.064394258,0.3723058701,0.3330649436,-0.0202448275,0.0904543847,0.0312909186,0.0792247355,0.465877682,-0.1677254438,0.1057599634,-0.0002362067,-0.1788220555,0.0161224175,0.2243048996,0.22327061,-0.1491248608,-0.078996025,0.3754320443,0.1582886875,-0.1314380467,0.1041737199,0.2087130398,0.1085172519,-0.4319802523,0.2590956986,-0.4952986836,-0.000392321,-0.2448255122,0.4045078158,0.0609066784,-0.5520868301,-0.2235296071,0.205739975,-0.2956372201,0.1914073378,-0.6933467984,0.0808954835,0.0826316401,-0.223979637,-0.1248702779,0.1186760366,-0.1262242049,-0.0981422663,0.1419001222,-0.020754423,0.2755362988,-0.0254655052,-0.1842598468,-0.5889195204,-0.3886367381,-0.0630069226,-0.0756904334,0.3410921991,0.365183264,0.1305098236,0.0006975764,-0.0128048332,0.4348779321,-0.1086291298,-0.2362839729,0.3483401537,-0.0918570608,-0.1570043713,-0.2010462284,0.1228516847,0.0980474353,-0.1359257847,0.097685203,-0.2406579107,0.0018191669,0.1143695265,0.0268062763,-0.3181385696,0.0508754179,-0.0115876812,-0.2531526089,0.1562105268,0.2456842065,0.0552294105,-0.1961913109,-0.2346479893,0.0089848544,-0.0035512014,0.3924398124,-0.4369997084,-0.211905688,-0.2768316567,0.6420891285,0.0023066944,-0.1714525968,0.1048808172,0.0654591769,0.0926557183,-0.1656760275,-0.2180747837,-0.0064853714,0.4205619693,0.2507826984,0.231713891,0.3459702134,0.2101274878,-0.0322271772,-0.2190695852,-0.3280091584,0.5038994551,-0.0784509107,0.1444236785,-0.2922509015,-0.3914310932,0.355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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3596","title":"Loss of cast `Image` feature on certain dataset method","comments":"Appolgies - I've just run again and also got this output. I have also sucesfully used the `push_to_hub` method. I think this is fixed now so will close this issue. ","body":"## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0","comment_length":30,"text":"Loss of cast `Image` feature on certain dataset method \n ## Describe the bug\r\n\r\nWhen an a column is cast to an `Image` feature, the cast type appears to be lost during certain operations. I first noticed this when using the `push_to_hub` method on a dataset that contained urls pointing to images which had been cast to an `image`. This also happens when using select on a dataset which has had a column cast to an `Image`.\r\n\r\nI suspect this might be related to https:\/\/github.com\/huggingface\/datasets\/pull\/3556 but I don't believe that pull request fixes this issue.  \r\n\r\n## Steps to reproduce the bug\r\n\r\nAn example of casting a url to an image followed by using the `select` method:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets import features\r\nurl = \"https:\/\/cf.ltkcdn.net\/cats\/images\/std-lg\/246866-1200x816-grey-white-kitten.webp\"\r\ndata_dict = {\"url\": [url]*2}\r\ndataset = Dataset.from_dict(data_dict)\r\ndataset =  dataset.cast_column('url',features.Image())\r\nsample = dataset.select([1])\r\n```\r\n\r\n[example notebook](https:\/\/gist.github.com\/davanstrien\/06e53f4383c28ae77ce1b30d0eaf0d70#file-potential_casting_bug-ipynb)\r\n\r\n## Expected results\r\nThe cast value is maintained when further methods are applied to the dataset. \r\n\r\n## Actual results\r\n```python\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-12-47f393bc2d0d> in <module>()\r\n----> 1 sample = dataset.select([1])\r\n\r\n4 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    487         }\r\n    488         # apply actual function\r\n--> 489         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    490         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    491         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    409             # Call actual function\r\n    410 \r\n--> 411             out = func(self, *args, **kwargs)\r\n    412 \r\n    413             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)\r\n   2772             )\r\n   2773         else:\r\n-> 2774             return self._new_dataset_with_indices(indices_buffer=buf_writer.getvalue(), fingerprint=new_fingerprint)\r\n   2775 \r\n   2776     @transmit_format\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _new_dataset_with_indices(self, indices_cache_file_name, indices_buffer, fingerprint)\r\n   2688             split=self.split,\r\n   2689             indices_table=indices_table,\r\n-> 2690             fingerprint=fingerprint,\r\n   2691         )\r\n   2692 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)\r\n    664         if self.info.features.type != inferred_features.type:\r\n    665             raise ValueError(\r\n--> 666                 f\"External features info don't match the dataset:\\nGot\\n{self.info.features}\\nwith type\\n{self.info.features.type}\\n\\nbut expected something like\\n{inferred_features}\\nwith type\\n{inferred_features.type}\"\r\n    667             )\r\n    668 \r\n\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'url': Image(id=None)}\r\nwith type\r\nstruct<url: extension<arrow.py_extension_type<ImageExtensionType>>>\r\n\r\nbut expected something like\r\n{'url': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<url: string>\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0 \n Appolgies - I've just run again and also got this output. I have also sucesfully used the `push_to_hub` method. I think this is fixed now so will close this issue. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3583","title":"Add The Medical Segmentation Decathlon Dataset","comments":"Hello! I have recently been involved with a medical image segmentation project myself and was going through the `The Medical Segmentation Decathlon Dataset` as well. \r\nI haven't yet had experience adding datasets to this repository yet but would love to get started. Should I take this issue?\r\nIf yes, I've got two questions -\r\n1. There are 10 different datasets available, so are all datasets to be added in a single PR, or one at a time? \r\n2. Since it's a competition, masks for the test-set are not available. How is that to be tackled? Sorry if it's a silly question, I have recently started exploring `datasets`.","body":"## Adding a Dataset\r\n- **Name:** *The Medical Segmentation Decathlon Dataset*\r\n- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. \r\n- **Paper:** [link to the dataset paper if available](https:\/\/arxiv.org\/abs\/2106.05735)\r\n- **Data:** http:\/\/medicaldecathlon.com\/\r\n- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.\r\n\r\n(cc @osanseviero @abidlabs )\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":107,"text":"Add The Medical Segmentation Decathlon Dataset \n ## Adding a Dataset\r\n- **Name:** *The Medical Segmentation Decathlon Dataset*\r\n- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. \r\n- **Paper:** [link to the dataset paper if available](https:\/\/arxiv.org\/abs\/2106.05735)\r\n- **Data:** http:\/\/medicaldecathlon.com\/\r\n- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.\r\n\r\n(cc @osanseviero @abidlabs )\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hello! I have recently been involved with a medical image segmentation project myself and was going through the `The Medical Segmentation Decathlon Dataset` as well. \r\nI haven't yet had experience adding datasets to this repository yet but would love to get started. Should I take this issue?\r\nIf yes, I've got two questions -\r\n1. There are 10 different datasets available, so are all datasets to be added in a single PR, or one at a time? \r\n2. Since it's a competition, masks for the test-set are not available. How is that to be tackled? Sorry if it's a silly question, I have recently started exploring 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3583","title":"Add The Medical Segmentation Decathlon Dataset","comments":"Hi! Sure, feel free to take this issue. You can self-assign the issue by commenting `#self-assign`.\r\n\r\nTo answer your questions:\r\n1. It makes the most sense to add each one as a separate config, so one dataset script with 10 configs in a single PR.\r\n2. Just set masks in the test set to `None`.\r\n\r\nNote that the images\/masks in this dataset are in NIfTI format, which our `Image` feature currently doesn't support, so I think it's best to yield the paths to the images\/masks in the script and add a preprocessing section to the card where we explain how to load\/process the images\/masks with `nibabel` (I can help with that).   \r\n\r\n","body":"## Adding a Dataset\r\n- **Name:** *The Medical Segmentation Decathlon Dataset*\r\n- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. \r\n- **Paper:** [link to the dataset paper if available](https:\/\/arxiv.org\/abs\/2106.05735)\r\n- **Data:** http:\/\/medicaldecathlon.com\/\r\n- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.\r\n\r\n(cc @osanseviero @abidlabs )\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":111,"text":"Add The Medical Segmentation Decathlon Dataset \n ## Adding a Dataset\r\n- **Name:** *The Medical Segmentation Decathlon Dataset*\r\n- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. \r\n- **Paper:** [link to the dataset paper if available](https:\/\/arxiv.org\/abs\/2106.05735)\r\n- **Data:** http:\/\/medicaldecathlon.com\/\r\n- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.\r\n\r\n(cc @osanseviero @abidlabs )\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hi! Sure, feel free to take this issue. You can self-assign the issue by commenting `#self-assign`.\r\n\r\nTo answer your questions:\r\n1. It makes the most sense to add each one as a separate config, so one dataset script with 10 configs in a single PR.\r\n2. Just set masks in the test set to `None`.\r\n\r\nNote that the images\/masks in this dataset are in NIfTI format, which our `Image` feature currently doesn't support, so I think it's best to yield the paths to the images\/masks in the script and add a preprocessing section to the card where we explain how to load\/process the images\/masks with `nibabel` (I can help with that).   \r\n\r\n","embeddings":[-0.0416806489,-0.2808953524,-0.0886710212,-0.0931892544,0.0039943364,0.089877978,0.5937296748,0.3467412293,0.2115156204,0.249948442,0.0095478697,-0.1307510585,-0.2073961198,0.4167206883,0.264959991,-0.1821739078,-0.059342403,0.0177173689,-0.0619513728,0.0080960114,-0.0619861074,-0.0537799969,-0.0030613139,-0.1445761472,-0.0732167512,-0.3286903203,-0.1275140196,0.3754027486,-0.0769618899,-0.5650207996,-0.1318928152,0.1798969805,-0.093209669,0.2871100008,-0.0001096797,-0.0723100826,-0.0663739294,-0.1481020004,-0.1413078755,-0.0628374442,-0.0498100743,-0.2816704214,-0.0691193044,-0.1301915348,-0.2603259087,-0.2475028634,0.0319499522,-0.1964936256,0.4506407082,0.1420233548,0.2190000117,0.260242939,-0.4598580301,-0.0309112184,0.0021774773,0.5844779611,-0.1091321483,-0.022447953,0.1217595339,-0.2706949413,0.0222313963,0.6395750642,-0.0325265899,-0.0019522667,0.4639188051,0.2181223035,0.1870066971,-0.602383852,-0.0803778619,0.2695087194,-0.2177195698,-0.264087528,-0.2790741324,-0.4205762446,0.1000538915,-0.2172895521,-0.0527027547,0.2446800619,-0.1116107479,-0.0154201398,-0.4999107718,0.1070367396,-0.152899906,-0.0794555992,0.1330478042,-0.1302419007,0.1425839216,0.0294545274,0.3343709111,-0.1861781776,-0.3729122579,-0.1629238427,0.0230471008,0.1523552239,-0.1720882803,-0.2654804885,-0.1640525013,-0.0532294996,0.3697679639,0.4832857549,0.0638967231,-0.0363192409,0.1824027151,-0.1211501658,-0.0438338853,-0.0496315695,-0.0577207915,0.1234183535,0.0168576427,0.2397851199,0.1633352339,0.0896857083,0.1217905581,0.0572430044,-0.3308767378,-0.225578025,-0.0811458975,-0.0002389076,-0.1609500647,0.1005170122,0.1293538511,-0.0600307956,-0.0789091587,0.3934301436,0.1934274882,-0.2706232071,0.1464315653,0.0803373456,-0.2459008843,0.0893398002,-0.167099461,0.2625873089,-0.1636089832,0.3475334048,0.2404977679,-0.1024779528,0.2453824133,0.0274734125,0.3050078154,0.1664649695,0.1687847525,-0.1899652183,0.5680445433,0.390955776,0.0693667829,-0.1719575822,0.0636894777,-0.1183826923,-0.2308974266,-0.0526297465,0.0715566501,0.2025759369,0.0479910709,0.1618745178,-0.1220277548,0.2159880698,0.197296977,0.4100221097,-0.20480223,-0.278372407,0.0180773567,-0.1484359652,-0.0630647987,-0.1960710734,0.4046357274,0.7366446257,-0.1881806403,-0.3006503582,0.1651222408,0.1611491144,-0.1473592073,0.1625330001,-0.0792371929,-0.1011228487,-0.2083938122,0.2506602108,-0.0208885167,-0.3614989519,-0.1380157471,-0.1473024935,-0.1980968118,0.2477645725,0.1343169361,0.2642163038,0.0702365339,-0.1379286945,-0.3474314213,0.2342158258,-0.3819925785,-0.0169453491,-0.1304654032,-0.2278578877,0.0961102694,0.3411928415,0.0058606551,-0.1600929797,0.0409064218,-0.194865942,0.1363843232,-0.2909425199,0.0167646445,-0.1834193766,0.094512783,-0.0776235834,0.0195889361,0.1464338154,-0.4670471847,0.0032581349,0.0051509654,0.3907476366,-0.196441859,-0.2885179222,0.0712867901,-0.2742973566,-0.1524673104,-0.3264074028,0.086625725,0.3682676256,-0.0044148467,-0.2439429313,-0.367813766,0.3020978272,0.281229943,0.4132826626,-0.3112316728,0.1932676136,-0.0672894269,-0.1260512769,0.0285760369,0.1363999397,0.0372302532,-0.2934664786,0.3263398707,0.2597668171,0.0967223644,-0.0245333184,0.2332860976,0.2276165634,0.3397419453,-0.3128359318,0.2440294921,-0.0033105474,-0.0279782284,-0.001368053,-0.3182863295,0.4781301916,-0.142115891,0.1490639746,0.0055699218,0.0834042504,0.0616829507,-0.1932919472,-0.3222839236,-0.2899094224,0.1068332419,0.2562481165,0.1443831921,-0.1298871934,-0.3940067291,0.2061266601,-0.0347848944,0.0050314884,-0.1869213432,-0.0628742203,-0.0344698653,0.2116027325,0.1730648726,0.0503274426,0.234424457,0.3359961212,-0.2914762199,0.1896426529,-0.1102050468,-0.201731652,-0.0119462572,0.1649126559,0.1288300157,0.0313235372,0.0574738197,0.02916779,-0.0659528524,-0.3705381155,-0.159819752,-0.2319275141,-0.2561617196,0.1771766841,0.2117701769,-0.5314975381,-0.137127623,0.0214565936,-0.360874325,0.1332493126,0.176723823,-0.4479264021,-0.2611065805,0.3589024246,-0.0130380401,0.7541037202,-0.3076581657,-0.1590916514,-0.0614667237,0.1022902206,0.0414564498,0.1183601618,0.2024322152,0.0564917549,0.5010102987,-0.2838726342,-0.0768436417,0.055888027,-0.4825155139,0.2617583573,-0.2779964209,0.368784517,0.0168919079,0.1516841054,-0.0750751793,-0.3401265144,0.1577025354,-0.0175424162,-0.0202640276,-0.088282451,-0.010910227,0.1026632562,-0.2867509723,-0.2638291121,-0.2939733863,-0.1479651779,0.2171963304,0.4306926429,0.2903030813,0.5444642901,0.0694083273,-0.2203624845,-0.2823677659,0.0057777655,-0.0489183441,-0.2748466134,0.0509400405,-0.3986024559,-0.2244811058,-0.1483401358,-0.0381620377,0.1743812859,0.3041326404,-0.5840750337,-0.3773758411,0.0433274023,0.0417979918,-0.0177986622,0.093382962,0.2026418597,0.2028740644,-0.1489675194,-0.1933598816,-0.1448374093,0.4020903707,-0.0905529857,-0.0050732414,-0.084618561,0.0886423439,-0.0112927267,0.3520188034,0.5585624576,-0.041352652,0.0970612988,-0.0215927456,0.3215947449,0.1582598537,-0.1642064899,0.4019832015,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3583","title":"Add The Medical Segmentation Decathlon Dataset","comments":"> Note that the images\/masks in this dataset are in NIfTI format, which our `Image` feature currently doesn't support, so I think it's best to yield the paths to the images\/masks in the script and add a preprocessing section to the card where we explain how to load\/process the images\/masks with `nibabel` (I can help with that).\r\n\r\nGotcha, thanks. Will start working on the issue and let you know in case of any doubt.","body":"## Adding a Dataset\r\n- **Name:** *The Medical Segmentation Decathlon Dataset*\r\n- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. \r\n- **Paper:** [link to the dataset paper if available](https:\/\/arxiv.org\/abs\/2106.05735)\r\n- **Data:** http:\/\/medicaldecathlon.com\/\r\n- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.\r\n\r\n(cc @osanseviero @abidlabs )\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":74,"text":"Add The Medical Segmentation Decathlon Dataset \n ## Adding a Dataset\r\n- **Name:** *The Medical Segmentation Decathlon Dataset*\r\n- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. \r\n- **Paper:** [link to the dataset paper if available](https:\/\/arxiv.org\/abs\/2106.05735)\r\n- **Data:** http:\/\/medicaldecathlon.com\/\r\n- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.\r\n\r\n(cc @osanseviero @abidlabs )\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n > Note that the images\/masks in this dataset are in NIfTI format, which our `Image` feature currently doesn't support, so I think it's best to yield the paths to the images\/masks in the script and add a preprocessing section to the card where we explain how to load\/process the images\/masks with `nibabel` (I can help with that).\r\n\r\nGotcha, thanks. Will start working on the issue and let you know in case of any doubt.","embeddings":[-0.1030202135,-0.3495713174,-0.0923558027,-0.0719333515,0.0702072755,0.0257594418,0.4989381135,0.3989433348,0.1786016673,0.2884393632,0.1217409521,-0.0976136774,-0.0700701624,0.4220618606,0.2009042948,-0.2264310271,-0.0912130699,0.1593823284,-0.0431685559,-0.0614765175,-0.0576389544,0.1652671248,-0.0458687618,-0.1220146045,-0.1154585183,-0.0751406103,-0.013816908,0.3508155346,-0.1860343367,-0.3985083103,-0.1568481773,-0.0365778953,-0.109599933,0.3065210581,-0.0001017393,-0.1228337064,0.0520878397,-0.1671818495,-0.2173714787,-0.1566441059,0.1018691808,-0.3571836948,-0.1813537627,-0.1000310779,-0.4260004461,-0.1772131622,0.1006781235,-0.0702310503,0.4301293492,0.1017090306,0.3303818405,0.1865781695,-0.1618561298,-0.1682281792,0.015509136,0.5975186229,-0.1346626878,0.0038828391,0.0789613724,-0.2860840559,0.0279936474,0.6627330184,-0.0137067595,-0.0004612914,0.3518675864,0.1845760643,0.1710548401,-0.3687853515,0.1277834475,0.2065193504,-0.2927893698,-0.2038034946,-0.2195631266,-0.346567601,0.0120053869,-0.2399956435,-0.0746804401,0.2082212418,-0.171630919,0.0506629758,-0.3471395969,0.1454146355,-0.1587759256,0.0378057696,0.0466733761,-0.1031343415,-0.0391310789,-0.0072927997,0.3734031022,-0.15823026,-0.4397559464,0.2064626664,-0.0199090112,0.1089145243,-0.1775828004,-0.1042389721,-0.0578127913,0.043304082,0.3505148292,0.3669303358,0.0963280872,-0.0388357826,0.1680968255,0.1169865802,-0.0710839555,-0.1212878227,-0.1142615303,0.0235786866,-0.0780525208,0.3236264884,0.0309008919,-0.0408689864,-0.014735112,0.0200572722,-0.2039210498,-0.1326318979,-0.1654777974,0.0096092522,-0.2538626492,0.1039358675,0.1575081348,0.1713993996,-0.1493287832,0.5126617551,0.0655447245,-0.1321479231,0.2309193313,0.1385965049,-0.2645122111,0.0066369553,-0.1842860878,0.2103703916,-0.2848117054,0.1985583752,0.1731203347,-0.0511563681,0.2402776331,-0.173782602,0.2975144684,0.1361084729,0.0312635005,-0.08669433,0.526409924,0.3225569725,0.1600692868,-0.2040838152,0.1604797989,-0.0623490661,-0.0688920245,-0.089480415,-0.0593640208,0.2253618836,-0.281190753,0.2381012887,0.0801582411,0.1716098785,0.2271069139,0.246744588,-0.0949017927,-0.3116090596,0.0225813296,-0.0911156759,-0.136276871,-0.1589854807,0.32635355,0.4502588511,-0.0986896381,-0.2812121212,0.017793823,0.2184369117,-0.0353616625,0.2320460826,-0.0064206677,-0.1730822623,-0.199639976,0.2454140633,-0.1474399865,-0.2844959199,-0.2687811553,-0.1102238894,-0.2984247208,0.1337788403,0.0797773674,0.3462888896,0.1245170012,0.0003100123,-0.2823770046,0.3074983656,-0.2769683897,0.0840137079,-0.1103841886,-0.2314400077,0.0025395458,0.3742994964,0.0878807753,-0.1769256741,0.1187493429,-0.0335077196,0.2108140141,-0.1848245114,0.030841127,-0.0654139593,0.1507299691,-0.0261888951,0.0666893125,0.0022620461,-0.2938583195,-0.1004928425,-0.028510429,0.4164271057,-0.2574871182,-0.243259564,-0.1509407908,-0.1516591758,-0.1075237021,-0.2267517,0.2221160233,0.2534936666,-0.0279906839,-0.0795490071,-0.1298573762,0.2711097896,0.2940106988,0.241349861,-0.2526028454,0.2824271917,-0.1938882023,-0.2229467183,0.0674194843,0.1713175476,0.0972198099,-0.0894352123,0.2696671784,0.2727972865,0.0370184928,0.0491338,0.1355922669,0.2062860131,0.180480212,-0.3791785836,0.1152164862,0.163655147,0.0125960419,-0.0068360991,-0.2400848716,0.4280413091,-0.008307524,0.0280842967,0.1207099557,0.1585116088,0.0811806694,-0.2246307433,-0.1912694126,-0.3248205185,0.1022740006,0.0940316468,0.0015318271,-0.3088214099,-0.3144609034,0.1649955064,0.290145576,0.1850467175,0.0723982751,0.0586167499,-0.2483713329,0.1427526474,0.0880658776,-0.075601384,-0.0677286834,0.3389711678,-0.3149442077,0.1856880039,-0.0018356493,-0.1616711766,0.0356717445,-0.0047954023,0.1220822185,0.0652422383,0.1664774418,-0.0203938708,-0.2233642042,-0.1988184005,-0.3101239204,-0.1766803116,-0.1563244462,0.2144066393,0.1364801228,-0.4693802297,-0.0305713564,-0.0748900101,-0.2373307794,0.012379365,0.1337807626,-0.1066100374,-0.2218174934,0.2172345966,-0.0423557535,0.5310469866,-0.0479443781,-0.143222481,-0.1638324857,0.0106237587,0.0400116369,0.1945754886,-0.0069634933,0.0347914957,0.4289876819,-0.2992619276,0.1743645668,-0.0445251502,-0.5828444958,0.2217331231,-0.2203927785,0.2422935963,0.07401108,0.2284936458,-0.1184982806,-0.2228923738,0.2280266136,-0.3005107641,-0.1120791361,0.0363688283,-0.0790260583,0.1151515916,-0.338614285,-0.0625389516,-0.1808620095,-0.3114027977,0.2943761945,0.4752098322,0.2629404068,0.4756630957,0.2121477425,-0.1434404552,-0.2634915709,0.0103329578,-0.2075832039,-0.2373756617,0.1086454317,-0.4552199244,-0.3003670573,-0.2068035901,-0.0596461333,0.2468778789,0.133082062,-0.4675873518,-0.2864726484,0.0841128677,-0.0240524933,-0.0049016671,0.0134569975,0.2011291981,0.0760352835,-0.2163457125,-0.2062519193,-0.1512937993,0.2134851962,-0.1237591654,0.0138029354,-0.1844760925,0.1674087793,0.1657561213,0.2354913801,0.185270071,-0.157365486,0.3289034665,-0.1415564567,0.4081221819,-0.0237260871,-0.2871920168,0.3021250367,0.2362466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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3583","title":"Add The Medical Segmentation Decathlon Dataset","comments":"This is great! There is a first model on the HUb that uses this dataset! https:\/\/huggingface.co\/MONAI\/example_spleen_segmentation","body":"## Adding a Dataset\r\n- **Name:** *The Medical Segmentation Decathlon Dataset*\r\n- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. \r\n- **Paper:** [link to the dataset paper if available](https:\/\/arxiv.org\/abs\/2106.05735)\r\n- **Data:** http:\/\/medicaldecathlon.com\/\r\n- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.\r\n\r\n(cc @osanseviero @abidlabs )\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":16,"text":"Add The Medical Segmentation Decathlon Dataset \n ## Adding a Dataset\r\n- **Name:** *The Medical Segmentation Decathlon Dataset*\r\n- **Description:** The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data, and small objects. \r\n- **Paper:** [link to the dataset paper if available](https:\/\/arxiv.org\/abs\/2106.05735)\r\n- **Data:** http:\/\/medicaldecathlon.com\/\r\n- **Motivation:** Hugging Face seeks to democratize ML for society. One of the growing niches within ML is the ML + Medicine community. Key data sets will help increase the supply of HF resources for starting an initial community.\r\n\r\n(cc @osanseviero @abidlabs )\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n This is great! There is a first model on the HUb that uses this dataset! https:\/\/huggingface.co\/MONAI\/example_spleen_segmentation","embeddings":[-0.0270240251,-0.3896330595,-0.1645667106,-0.2371070683,0.007202866,0.0695009828,0.4940696955,0.3347842097,0.0314946361,0.3018217385,-0.0489490069,-0.1498310119,-0.1921689957,0.4657768607,0.2673729062,-0.3335251212,-0.0200640671,0.189046666,0.1262889653,-0.1176435426,0.1101025194,0.0684565678,-0.0155895362,-0.0797500163,-0.2350757718,-0.0479806475,-0.0456711762,0.3668196499,-0.1265965998,-0.4435362816,-0.2004517466,-0.0710551068,0.0146811204,0.3904241025,-0.0001022133,-0.1528246254,-0.09777271,-0.1257195026,-0.2877317071,-0.030125957,0.1588530988,-0.4097624123,-0.0699474663,-0.1407022029,-0.2770802677,-0.0044869217,0.037684273,0.1013040468,0.3316261768,0.0976642817,0.3041471243,0.2825219333,-0.0126219606,-0.2623268068,0.0228024777,0.5798238516,-0.2012302577,0.1847318113,0.2622629404,-0.0992613435,-0.152466774,0.5862280726,0.0421996266,0.159469679,0.2711848319,0.0433811694,0.2923082113,-0.4727606475,-0.0124950558,0.4039778113,-0.1382982284,-0.1511771381,-0.2232174575,-0.4442086816,0.1072370857,-0.2762315869,-0.2574434578,0.289886564,-0.051466424,0.1522119641,-0.2577718198,0.0036277296,-0.2377881557,-0.0909333304,-0.0291007031,-0.0461924225,-0.0407049321,-0.1594787538,0.2633348405,-0.0717205629,-0.3847168088,0.2813397348,0.1984395385,0.1305493414,-0.3410107791,-0.3583397567,-0.2088497728,0.2040734589,0.3273281753,0.2604530156,0.1606565714,-0.0675938651,0.1017331704,0.0421061292,0.0347271636,-0.2112629414,-0.2997442782,0.0877122059,-0.0418335386,0.1646373868,-0.067671977,0.0748420134,0.0317487866,0.2372334749,-0.1756162494,-0.3039319217,-0.3474838734,-0.0090217358,-0.2521408796,0.0530918837,-0.0348399244,0.2632689178,-0.0276465267,0.421682179,-0.0331667364,0.0262604561,-0.0091025205,0.1190422848,-0.1878586113,-0.1435456574,-0.2009522766,0.106552735,-0.2127273828,0.2158328891,0.2102496475,-0.1496812403,0.1415399313,-0.158275798,0.3442231417,0.0292333439,-0.0430992842,-0.0598432384,0.3995527923,0.2475836277,0.0398639366,-0.2233409286,0.0359926261,-0.0616043992,-0.0626193956,-0.3629679978,-0.2360110432,-0.0290030744,-0.4206961393,0.2049833685,-0.0648422018,0.1476444155,0.3341108561,0.4652749002,-0.2485466301,-0.2124276757,0.0083085317,0.1336000413,-0.0218613837,-0.2038933784,0.326679647,0.2934150696,-0.080998145,-0.3146287501,0.0319045819,0.3607744575,-0.1202193722,0.3692783415,0.1106318831,0.0218539126,-0.0454110913,0.382189393,-0.0475707203,-0.2366626114,-0.3699792325,-0.1487261653,-0.2932542562,0.0697970316,0.0203042645,0.3585277498,0.1651853025,-0.0837104842,-0.2864162624,0.3109349012,-0.2615262866,0.0935187563,-0.1753572226,-0.2441123873,-0.125976935,0.3686763346,0.1090934873,-0.1824784875,0.2339745015,0.156520471,0.2729777396,-0.2223870158,-0.0548267215,-0.0983998403,0.1403229088,0.0401861556,0.1376828253,-0.2686367929,-0.293541193,-0.1880060881,0.0772233978,0.288477689,0.0011855633,-0.2121804953,-0.1492410004,-0.0908191875,0.0613224171,-0.0511973202,0.2838754952,0.0176527631,0.0253170468,0.0366011895,-0.228490293,0.1887470186,0.1027790457,0.2885996401,-0.2223873883,0.5107651353,-0.0773623586,-0.2251158059,0.056394998,0.3128339052,-0.0781033263,-0.0574518517,0.3977352381,0.2373290211,-0.0813108683,0.1435075402,0.0811458528,0.4554278553,0.2488145977,-0.3409591317,0.0328012593,-0.1235306561,-0.0202112608,0.131902501,-0.2997781634,0.3614705503,0.0998407602,-0.0618681721,0.0857859403,0.2117730528,0.0081057064,-0.2116843909,-0.1753280312,-0.1873120517,0.2966581583,-0.0891013816,-0.0060290908,-0.2188634425,-0.4155858159,0.1656572074,0.2492468059,-0.0998106003,0.2614775896,-0.0337023884,-0.3641694784,-0.0865608081,0.110018529,-0.1091312617,-0.1419516355,0.3325551152,-0.1870556921,-0.0073413118,-0.0332106836,-0.1935309023,-0.0487366728,0.0025242381,0.1727673262,0.1774921715,0.2582595646,0.1353942007,-0.2072937489,-0.0874211192,-0.3345640302,-0.168932572,-0.0903283134,0.1120992973,0.1643742472,-0.1769794822,0.1059033945,-0.0945914313,-0.1321154088,-0.046644967,0.2646431625,-0.0785985589,-0.098271057,0.015169614,-0.0858989507,0.7049549222,-0.1361885816,0.0419225991,-0.133105725,0.0939274728,0.233752653,0.2394781858,-0.0764289871,0.1191804782,0.3059718013,-0.2420288622,0.2000966221,0.036666397,-0.7282127738,0.2268716395,-0.1291047037,0.2942335606,0.2232903689,-0.0075599425,-0.1949145496,-0.1630249619,0.0128745837,-0.3004370928,-0.0198667403,0.0949900374,-0.0219527539,0.2965886891,-0.1570224464,0.0541690886,-0.260984987,-0.2736380398,0.4029827118,0.3724204302,0.1196592748,0.1512292325,0.1189253032,-0.0150116021,-0.2391367406,0.0704123974,-0.1903069466,-0.123623468,0.1052781418,-0.4946622849,-0.2025734335,-0.430796504,-0.2452955246,0.2248695344,0.0104960036,-0.4720847607,-0.222979486,0.0702598244,-0.0961693674,-0.0956355408,-0.0690864772,0.1743999571,-0.1138960496,-0.1574192792,-0.1773956567,0.0359616652,0.1424243003,0.0274224207,-0.0615113266,-0.3750939667,0.0887202546,0.291652292,0.3221223354,0.1217202917,-0.0926461592,0.185972929,-0.060981191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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3582","title":"conll 2003 dataset source url is no longer valid","comments":"Thanks for reporting !\r\n\r\nI pushed a temporary fix on `master` that uses an URL from a previous commit to access the dataset for now, until we have a better solution","body":"## Describe the bug\r\nLoading `conll2003` dataset fails because it was removed (just yesterday 1\/14\/2022) from the location it is looking for.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n\r\nload_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should load.\r\n\r\n## Actual results\r\nIt is looking for the dataset at `https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt` but it was removed from there yesterday (see [commit](https:\/\/github.com\/davidsbatista\/NER-datasets\/commit\/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https:\/\/github.com\/davidsbatista\/NER-datasets\/issues\/8)). \r\n\r\n- We should replace this with an alternate valid location.\r\n- this is being referenced in the huggingface course chapter 7 [colab notebook](https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/course\/chapter7\/section2_pt.ipynb), which is also broken.\r\n\r\n```python\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-27c956bec93c> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 raw_datasets = load_dataset(\"conll2003\")\r\n\r\n11 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params)\r\n    610             )\r\n    611         elif response is not None and response.status_code == 404:\r\n--> 612             raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\n    613         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    614         if head_error is not None:\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":31,"text":"conll 2003 dataset source url is no longer valid \n ## Describe the bug\r\nLoading `conll2003` dataset fails because it was removed (just yesterday 1\/14\/2022) from the location it is looking for.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n\r\nload_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should load.\r\n\r\n## Actual results\r\nIt is looking for the dataset at `https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt` but it was removed from there yesterday (see [commit](https:\/\/github.com\/davidsbatista\/NER-datasets\/commit\/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https:\/\/github.com\/davidsbatista\/NER-datasets\/issues\/8)). \r\n\r\n- We should replace this with an alternate valid location.\r\n- this is being referenced in the huggingface course chapter 7 [colab notebook](https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/course\/chapter7\/section2_pt.ipynb), which is also broken.\r\n\r\n```python\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-27c956bec93c> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 raw_datasets = load_dataset(\"conll2003\")\r\n\r\n11 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params)\r\n    610             )\r\n    611         elif response is not None and response.status_code == 404:\r\n--> 612             raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\n    613         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    614         if head_error is not None:\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n Thanks for reporting !\r\n\r\nI pushed a temporary fix on `master` that uses an URL from a previous commit to access the dataset for now, until we have a better solution","embeddings":[0.0253508743,0.0967391506,0.0659234151,0.2801700532,-0.0838981122,-0.0983771309,0.3944683671,0.1143220738,-0.3661048412,0.0100173401,-0.1598329097,0.1530811787,-0.1152236015,-0.1167481393,0.0637082607,0.1321541518,0.0121242665,0.0008361613,0.0810595006,0.0310798343,-0.3898718655,0.4209730327,-0.436529696,0.0629989505,-0.3902914524,0.0804226995,-0.1699384004,0.4743028283,-0.2052620351,-0.1979974657,0.4889354706,-0.1144519746,0.1795954108,0.2775034308,-0.000112998,0.103252247,0.2143999636,-0.0595820621,-0.5245830417,-0.3750483692,-0.5292636752,-0.0421056226,0.0451600514,-0.0714642256,-0.094487682,0.4565857053,-0.0474239513,0.031945195,0.3811579049,0.7521836758,0.1968506873,0.4414004683,0.1743049026,-0.1015697047,0.5255883336,0.0766246617,-0.1110145152,0.611988306,-0.0278865136,-0.0886688828,0.0172361434,0.2796197236,-0.1529473364,-0.1965388507,0.2908532321,0.1870019734,-0.2838505805,-0.1528361142,-0.1646656096,0.1080818847,0.2675343156,-0.2151463777,-0.4405225813,-0.2066777945,-0.0290534366,-0.4647011161,0.3244928718,-0.0935686156,-0.0408924371,0.3328480721,-0.0077435365,-0.3877592087,-0.2175440788,0.2450853139,-0.2362277955,0.4616678059,-0.1982468516,-0.066113621,-0.0414223596,0.0715459362,0.37297225,0.0184816308,0.1353750527,0.1575662941,-0.1948742121,0.0408189744,0.082762152,0.0212453082,0.0772701502,0.0586710423,0.0275252722,-0.3540599942,-0.2466814369,0.0627681091,-0.0127242059,0.1849476844,-0.1086140051,0.2846390009,0.2959340215,0.4536518157,-0.0691285804,-0.1370474845,-0.0618425086,-0.1590445787,-0.307711035,0.1159121767,0.433087796,-0.2834415436,-0.4536597133,0.126570791,-0.0754554421,-0.0376194939,0.1437015235,0.2931141853,-0.241908282,0.1813094169,0.3210597038,0.094743222,-0.06653063,-0.2068404257,-0.1123541221,0.0930936411,-0.1624079943,-0.1120410785,0.0973364189,-0.1617785543,0.2950328887,0.0580092371,-0.0498440079,-0.0270657185,-0.1186182648,-0.0825597271,0.0584098026,0.0959895104,-0.2559085786,-0.0700639412,0.1872965693,-0.1893984377,-0.0909802467,-0.0214243028,-0.4910676479,-0.3637754917,-0.5540169477,0.1681317836,-0.134054184,-0.080256097,0.2238660753,-0.1684781611,-0.1284168959,-0.2272424251,0.004145198,-0.1627315879,-0.4808427691,-0.2206245214,0.3789054155,0.6022602916,-0.0749853924,-0.1445361525,-0.0296848919,-0.0125886295,-0.0113349473,0.0722647831,-0.2435076386,0.1706334352,-0.4337827563,-0.2722162604,0.3397963941,-0.3444936872,-0.3977215886,0.2569458187,-0.2445739359,0.1040635929,-0.1270396113,0.0098683322,0.0058274218,-0.130029723,0.4136150479,0.2132978588,0.2842938304,-0.1912845969,-0.3426695168,-0.077121377,-0.3532919884,0.0929971263,0.1055356786,0.1955367774,0.0883222446,0.0729057789,0.3487870693,-0.0456364006,-0.0111034336,0.478099674,0.5230430365,0.2254862636,-0.1831437349,-0.1006929576,-0.5838643312,0.213248536,-0.1837218106,0.1683391929,-0.1405273378,-0.0316183679,-0.3568355441,0.035863772,-0.2229214013,-0.2888863385,0.0769764483,0.2807328701,0.1073600575,0.3723207116,-0.285992533,0.5278151631,-0.0764865503,0.1875048876,-0.4465022683,0.2189900875,-0.1892785728,-0.0683519617,-0.0328541324,0.2968818545,0.27581653,-0.0469952896,-0.2104010433,0.4799608886,0.283393383,-0.0339315869,0.0530573204,-0.1412366629,0.3879187107,0.0216604602,0.0175174735,-0.123481527,-0.0369033143,0.2466683388,0.0356053747,0.408716321,0.1581158042,-0.1722097695,0.1716519892,0.0450473167,0.226972878,-0.244221136,0.0487260036,-0.2441655099,0.2858726382,0.3254209757,0.1884651333,0.2218257189,-0.4042294323,-0.0688452721,0.2046667337,-0.0681114346,-0.1472202986,0.400452286,-0.2549818158,0.0204700064,0.0422203541,0.4559444189,0.369723469,-0.001817177,-0.0816523284,0.1717090905,-0.1631028801,-0.1395670325,0.3055935204,-0.133567661,0.3088124096,0.1874018312,0.1382217705,0.2676287889,-0.139377445,-0.3593532145,-0.1998706311,0.1674644798,-0.4105623662,0.1584680378,-0.2326515913,-0.1283443272,-0.328533709,-0.2339738756,-0.3903584182,-0.2872918546,-0.0921417847,0.2891158164,0.0806996226,0.0992857963,-0.3818290532,0.0520648472,-0.0832891613,-0.2070006281,-0.0555493385,-0.1511660814,-0.242262885,0.0415615775,0.1475660503,-0.1073286235,0.0884688199,-0.5785441399,-0.1117588878,-0.2843217254,-0.2335873991,0.1857416779,-0.0151786525,0.3821646571,0.2664926648,-0.0697068051,-0.2937789261,-0.1993516088,0.1901033819,-0.1194521561,-0.1287555993,0.1649289876,-0.0462373532,0.1242082641,-0.0826411396,-0.261094898,-0.2372127175,-0.2120794654,-0.0767431706,-0.1635101438,0.0332957283,0.5627381206,0.0108379964,0.2882924378,-0.1466232836,0.3226822615,-0.1974567324,-0.4903647602,0.2691137791,-0.1067606434,-0.2987867296,-0.0245029572,-0.1014020368,0.0484302975,0.0408869982,-0.6074543595,-0.3333548307,0.0715179667,0.243982181,-0.0836486891,-0.0916203707,0.1310889274,0.0044972026,0.0169883296,-0.1084388867,-0.1526850015,-0.0279960949,-0.0123940697,0.4001575708,0.1530859917,0.2856673598,0.0420149229,0.5507603884,0.0928633809,-0.2339056581,0.475905031,-0.2186861485,0.4636931717,-0.0296103004,-0.4504034817,0.0066044629,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3582","title":"conll 2003 dataset source url is no longer valid","comments":"I changed the URL again to use another host, the fix is available on `master` and we'll probably do a new release of `datasets` tomorrow.\r\n\r\nIn the meantime, feel free to do `load_dataset(..., revision=\"master\")` to use the fixed script","body":"## Describe the bug\r\nLoading `conll2003` dataset fails because it was removed (just yesterday 1\/14\/2022) from the location it is looking for.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n\r\nload_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should load.\r\n\r\n## Actual results\r\nIt is looking for the dataset at `https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt` but it was removed from there yesterday (see [commit](https:\/\/github.com\/davidsbatista\/NER-datasets\/commit\/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https:\/\/github.com\/davidsbatista\/NER-datasets\/issues\/8)). \r\n\r\n- We should replace this with an alternate valid location.\r\n- this is being referenced in the huggingface course chapter 7 [colab notebook](https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/course\/chapter7\/section2_pt.ipynb), which is also broken.\r\n\r\n```python\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-27c956bec93c> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 raw_datasets = load_dataset(\"conll2003\")\r\n\r\n11 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params)\r\n    610             )\r\n    611         elif response is not None and response.status_code == 404:\r\n--> 612             raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\n    613         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    614         if head_error is not None:\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":39,"text":"conll 2003 dataset source url is no longer valid \n ## Describe the bug\r\nLoading `conll2003` dataset fails because it was removed (just yesterday 1\/14\/2022) from the location it is looking for.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n\r\nload_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should load.\r\n\r\n## Actual results\r\nIt is looking for the dataset at `https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt` but it was removed from there yesterday (see [commit](https:\/\/github.com\/davidsbatista\/NER-datasets\/commit\/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https:\/\/github.com\/davidsbatista\/NER-datasets\/issues\/8)). \r\n\r\n- We should replace this with an alternate valid location.\r\n- this is being referenced in the huggingface course chapter 7 [colab notebook](https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/course\/chapter7\/section2_pt.ipynb), which is also broken.\r\n\r\n```python\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-27c956bec93c> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 raw_datasets = load_dataset(\"conll2003\")\r\n\r\n11 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params)\r\n    610             )\r\n    611         elif response is not None and response.status_code == 404:\r\n--> 612             raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\n    613         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    614         if head_error is not None:\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n I changed the URL again to use another host, the fix is available on `master` and we'll probably do a new release of `datasets` tomorrow.\r\n\r\nIn the meantime, feel free to do `load_dataset(..., revision=\"master\")` to use the fixed script","embeddings":[0.0253508743,0.0967391506,0.0659234151,0.2801700532,-0.0838981122,-0.0983771309,0.3944683671,0.1143220738,-0.3661048412,0.0100173401,-0.1598329097,0.1530811787,-0.1152236015,-0.1167481393,0.0637082607,0.1321541518,0.0121242665,0.0008361613,0.0810595006,0.0310798343,-0.3898718655,0.4209730327,-0.436529696,0.0629989505,-0.3902914524,0.0804226995,-0.1699384004,0.4743028283,-0.2052620351,-0.1979974657,0.4889354706,-0.1144519746,0.1795954108,0.2775034308,-0.000112998,0.103252247,0.2143999636,-0.0595820621,-0.5245830417,-0.3750483692,-0.5292636752,-0.0421056226,0.0451600514,-0.0714642256,-0.094487682,0.4565857053,-0.0474239513,0.031945195,0.3811579049,0.7521836758,0.1968506873,0.4414004683,0.1743049026,-0.1015697047,0.5255883336,0.0766246617,-0.1110145152,0.611988306,-0.0278865136,-0.0886688828,0.0172361434,0.2796197236,-0.1529473364,-0.1965388507,0.2908532321,0.1870019734,-0.2838505805,-0.1528361142,-0.1646656096,0.1080818847,0.2675343156,-0.2151463777,-0.4405225813,-0.2066777945,-0.0290534366,-0.4647011161,0.3244928718,-0.0935686156,-0.0408924371,0.3328480721,-0.0077435365,-0.3877592087,-0.2175440788,0.2450853139,-0.2362277955,0.4616678059,-0.1982468516,-0.066113621,-0.0414223596,0.0715459362,0.37297225,0.0184816308,0.1353750527,0.1575662941,-0.1948742121,0.0408189744,0.082762152,0.0212453082,0.0772701502,0.0586710423,0.0275252722,-0.3540599942,-0.2466814369,0.0627681091,-0.0127242059,0.1849476844,-0.1086140051,0.2846390009,0.2959340215,0.4536518157,-0.0691285804,-0.1370474845,-0.0618425086,-0.1590445787,-0.307711035,0.1159121767,0.433087796,-0.2834415436,-0.4536597133,0.126570791,-0.0754554421,-0.0376194939,0.1437015235,0.2931141853,-0.241908282,0.1813094169,0.3210597038,0.094743222,-0.06653063,-0.2068404257,-0.1123541221,0.0930936411,-0.1624079943,-0.1120410785,0.0973364189,-0.1617785543,0.2950328887,0.0580092371,-0.0498440079,-0.0270657185,-0.1186182648,-0.0825597271,0.0584098026,0.0959895104,-0.2559085786,-0.0700639412,0.1872965693,-0.1893984377,-0.0909802467,-0.0214243028,-0.4910676479,-0.3637754917,-0.5540169477,0.1681317836,-0.134054184,-0.080256097,0.2238660753,-0.1684781611,-0.1284168959,-0.2272424251,0.004145198,-0.1627315879,-0.4808427691,-0.2206245214,0.3789054155,0.6022602916,-0.0749853924,-0.1445361525,-0.0296848919,-0.0125886295,-0.0113349473,0.0722647831,-0.2435076386,0.1706334352,-0.4337827563,-0.2722162604,0.3397963941,-0.3444936872,-0.3977215886,0.2569458187,-0.2445739359,0.1040635929,-0.1270396113,0.0098683322,0.0058274218,-0.130029723,0.4136150479,0.2132978588,0.2842938304,-0.1912845969,-0.3426695168,-0.077121377,-0.3532919884,0.0929971263,0.1055356786,0.1955367774,0.0883222446,0.0729057789,0.3487870693,-0.0456364006,-0.0111034336,0.478099674,0.5230430365,0.2254862636,-0.1831437349,-0.1006929576,-0.5838643312,0.213248536,-0.1837218106,0.1683391929,-0.1405273378,-0.0316183679,-0.3568355441,0.035863772,-0.2229214013,-0.2888863385,0.0769764483,0.2807328701,0.1073600575,0.3723207116,-0.285992533,0.5278151631,-0.0764865503,0.1875048876,-0.4465022683,0.2189900875,-0.1892785728,-0.0683519617,-0.0328541324,0.2968818545,0.27581653,-0.0469952896,-0.2104010433,0.4799608886,0.283393383,-0.0339315869,0.0530573204,-0.1412366629,0.3879187107,0.0216604602,0.0175174735,-0.123481527,-0.0369033143,0.2466683388,0.0356053747,0.408716321,0.1581158042,-0.1722097695,0.1716519892,0.0450473167,0.226972878,-0.244221136,0.0487260036,-0.2441655099,0.2858726382,0.3254209757,0.1884651333,0.2218257189,-0.4042294323,-0.0688452721,0.2046667337,-0.0681114346,-0.1472202986,0.400452286,-0.2549818158,0.0204700064,0.0422203541,0.4559444189,0.369723469,-0.001817177,-0.0816523284,0.1717090905,-0.1631028801,-0.1395670325,0.3055935204,-0.133567661,0.3088124096,0.1874018312,0.1382217705,0.2676287889,-0.139377445,-0.3593532145,-0.1998706311,0.1674644798,-0.4105623662,0.1584680378,-0.2326515913,-0.1283443272,-0.328533709,-0.2339738756,-0.3903584182,-0.2872918546,-0.0921417847,0.2891158164,0.0806996226,0.0992857963,-0.3818290532,0.0520648472,-0.0832891613,-0.2070006281,-0.0555493385,-0.1511660814,-0.242262885,0.0415615775,0.1475660503,-0.1073286235,0.0884688199,-0.5785441399,-0.1117588878,-0.2843217254,-0.2335873991,0.1857416779,-0.0151786525,0.3821646571,0.2664926648,-0.0697068051,-0.2937789261,-0.1993516088,0.1901033819,-0.1194521561,-0.1287555993,0.1649289876,-0.0462373532,0.1242082641,-0.0826411396,-0.261094898,-0.2372127175,-0.2120794654,-0.0767431706,-0.1635101438,0.0332957283,0.5627381206,0.0108379964,0.2882924378,-0.1466232836,0.3226822615,-0.1974567324,-0.4903647602,0.2691137791,-0.1067606434,-0.2987867296,-0.0245029572,-0.1014020368,0.0484302975,0.0408869982,-0.6074543595,-0.3333548307,0.0715179667,0.243982181,-0.0836486891,-0.0916203707,0.1310889274,0.0044972026,0.0169883296,-0.1084388867,-0.1526850015,-0.0279960949,-0.0123940697,0.4001575708,0.1530859917,0.2856673598,0.0420149229,0.5507603884,0.0928633809,-0.2339056581,0.475905031,-0.2186861485,0.4636931717,-0.0296103004,-0.4504034817,0.0066044629,-0.03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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3582","title":"conll 2003 dataset source url is no longer valid","comments":"We just released a new version of `datasets` with a working URL. Feel free to update `datasets` and try again :)","body":"## Describe the bug\r\nLoading `conll2003` dataset fails because it was removed (just yesterday 1\/14\/2022) from the location it is looking for.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n\r\nload_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should load.\r\n\r\n## Actual results\r\nIt is looking for the dataset at `https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt` but it was removed from there yesterday (see [commit](https:\/\/github.com\/davidsbatista\/NER-datasets\/commit\/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https:\/\/github.com\/davidsbatista\/NER-datasets\/issues\/8)). \r\n\r\n- We should replace this with an alternate valid location.\r\n- this is being referenced in the huggingface course chapter 7 [colab notebook](https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/course\/chapter7\/section2_pt.ipynb), which is also broken.\r\n\r\n```python\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-27c956bec93c> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 raw_datasets = load_dataset(\"conll2003\")\r\n\r\n11 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params)\r\n    610             )\r\n    611         elif response is not None and response.status_code == 404:\r\n--> 612             raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\n    613         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    614         if head_error is not None:\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":21,"text":"conll 2003 dataset source url is no longer valid \n ## Describe the bug\r\nLoading `conll2003` dataset fails because it was removed (just yesterday 1\/14\/2022) from the location it is looking for.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n\r\nload_dataset(\"conll2003\")\r\n```\r\n\r\n## Expected results\r\nThe dataset should load.\r\n\r\n## Actual results\r\nIt is looking for the dataset at `https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt` but it was removed from there yesterday (see [commit](https:\/\/github.com\/davidsbatista\/NER-datasets\/commit\/9d8f45cc7331569af8eb3422bbe1c97cbebd5690) that removed the file and related [issue](https:\/\/github.com\/davidsbatista\/NER-datasets\/issues\/8)). \r\n\r\n- We should replace this with an alternate valid location.\r\n- this is being referenced in the huggingface course chapter 7 [colab notebook](https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/course\/chapter7\/section2_pt.ipynb), which is also broken.\r\n\r\n```python\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-27c956bec93c> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 raw_datasets = load_dataset(\"conll2003\")\r\n\r\n11 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params)\r\n    610             )\r\n    611         elif response is not None and response.status_code == 404:\r\n--> 612             raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\n    613         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    614         if head_error is not None:\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/davidsbatista\/NER-datasets\/raw\/master\/CONLL2003\/train.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n We just released a new version of `datasets` with a working URL. Feel free to update `datasets` and try again :)","embeddings":[0.0253508743,0.0967391506,0.0659234151,0.2801700532,-0.0838981122,-0.0983771309,0.3944683671,0.1143220738,-0.3661048412,0.0100173401,-0.1598329097,0.1530811787,-0.1152236015,-0.1167481393,0.0637082607,0.1321541518,0.0121242665,0.0008361613,0.0810595006,0.0310798343,-0.3898718655,0.4209730327,-0.436529696,0.0629989505,-0.3902914524,0.0804226995,-0.1699384004,0.4743028283,-0.2052620351,-0.1979974657,0.4889354706,-0.1144519746,0.1795954108,0.2775034308,-0.000112998,0.103252247,0.2143999636,-0.0595820621,-0.5245830417,-0.3750483692,-0.5292636752,-0.0421056226,0.0451600514,-0.0714642256,-0.094487682,0.4565857053,-0.0474239513,0.031945195,0.3811579049,0.7521836758,0.1968506873,0.4414004683,0.1743049026,-0.1015697047,0.5255883336,0.0766246617,-0.1110145152,0.611988306,-0.0278865136,-0.0886688828,0.0172361434,0.2796197236,-0.1529473364,-0.1965388507,0.2908532321,0.1870019734,-0.2838505805,-0.1528361142,-0.1646656096,0.1080818847,0.2675343156,-0.2151463777,-0.4405225813,-0.2066777945,-0.0290534366,-0.4647011161,0.3244928718,-0.0935686156,-0.0408924371,0.3328480721,-0.0077435365,-0.3877592087,-0.2175440788,0.2450853139,-0.2362277955,0.4616678059,-0.1982468516,-0.066113621,-0.0414223596,0.0715459362,0.37297225,0.0184816308,0.1353750527,0.1575662941,-0.1948742121,0.0408189744,0.082762152,0.0212453082,0.0772701502,0.0586710423,0.0275252722,-0.3540599942,-0.2466814369,0.0627681091,-0.0127242059,0.1849476844,-0.1086140051,0.2846390009,0.2959340215,0.4536518157,-0.0691285804,-0.1370474845,-0.0618425086,-0.1590445787,-0.307711035,0.1159121767,0.433087796,-0.2834415436,-0.4536597133,0.126570791,-0.0754554421,-0.0376194939,0.1437015235,0.2931141853,-0.241908282,0.1813094169,0.3210597038,0.094743222,-0.06653063,-0.2068404257,-0.1123541221,0.0930936411,-0.1624079943,-0.1120410785,0.0973364189,-0.1617785543,0.2950328887,0.0580092371,-0.0498440079,-0.0270657185,-0.1186182648,-0.0825597271,0.0584098026,0.0959895104,-0.2559085786,-0.0700639412,0.1872965693,-0.1893984377,-0.0909802467,-0.0214243028,-0.4910676479,-0.3637754917,-0.5540169477,0.1681317836,-0.134054184,-0.080256097,0.2238660753,-0.1684781611,-0.1284168959,-0.2272424251,0.004145198,-0.1627315879,-0.4808427691,-0.2206245214,0.3789054155,0.6022602916,-0.0749853924,-0.1445361525,-0.0296848919,-0.0125886295,-0.0113349473,0.0722647831,-0.2435076386,0.1706334352,-0.4337827563,-0.2722162604,0.3397963941,-0.3444936872,-0.3977215886,0.2569458187,-0.2445739359,0.1040635929,-0.1270396113,0.0098683322,0.0058274218,-0.130029723,0.4136150479,0.2132978588,0.2842938304,-0.1912845969,-0.3426695168,-0.077121377,-0.3532919884,0.0929971263,0.1055356786,0.1955367774,0.0883222446,0.0729057789,0.3487870693,-0.0456364006,-0.0111034336,0.478099674,0.5230430365,0.2254862636,-0.1831437349,-0.1006929576,-0.5838643312,0.213248536,-0.1837218106,0.1683391929,-0.1405273378,-0.0316183679,-0.3568355441,0.035863772,-0.2229214013,-0.2888863385,0.0769764483,0.2807328701,0.1073600575,0.3723207116,-0.285992533,0.5278151631,-0.0764865503,0.1875048876,-0.4465022683,0.2189900875,-0.1892785728,-0.0683519617,-0.0328541324,0.2968818545,0.27581653,-0.0469952896,-0.2104010433,0.4799608886,0.283393383,-0.0339315869,0.0530573204,-0.1412366629,0.3879187107,0.0216604602,0.0175174735,-0.123481527,-0.0369033143,0.2466683388,0.0356053747,0.408716321,0.1581158042,-0.1722097695,0.1716519892,0.0450473167,0.226972878,-0.244221136,0.0487260036,-0.2441655099,0.2858726382,0.3254209757,0.1884651333,0.2218257189,-0.4042294323,-0.0688452721,0.2046667337,-0.0681114346,-0.1472202986,0.400452286,-0.2549818158,0.0204700064,0.0422203541,0.4559444189,0.369723469,-0.001817177,-0.0816523284,0.1717090905,-0.1631028801,-0.1395670325,0.3055935204,-0.133567661,0.3088124096,0.1874018312,0.1382217705,0.2676287889,-0.139377445,-0.3593532145,-0.1998706311,0.1674644798,-0.4105623662,0.1584680378,-0.2326515913,-0.1283443272,-0.328533709,-0.2339738756,-0.3903584182,-0.2872918546,-0.0921417847,0.2891158164,0.0806996226,0.0992857963,-0.3818290532,0.0520648472,-0.0832891613,-0.2070006281,-0.0555493385,-0.1511660814,-0.242262885,0.0415615775,0.1475660503,-0.1073286235,0.0884688199,-0.5785441399,-0.1117588878,-0.2843217254,-0.2335873991,0.1857416779,-0.0151786525,0.3821646571,0.2664926648,-0.0697068051,-0.2937789261,-0.1993516088,0.1901033819,-0.1194521561,-0.1287555993,0.1649289876,-0.0462373532,0.1242082641,-0.0826411396,-0.261094898,-0.2372127175,-0.2120794654,-0.0767431706,-0.1635101438,0.0332957283,0.5627381206,0.0108379964,0.2882924378,-0.1466232836,0.3226822615,-0.1974567324,-0.4903647602,0.2691137791,-0.1067606434,-0.2987867296,-0.0245029572,-0.1014020368,0.0484302975,0.0408869982,-0.6074543595,-0.3333548307,0.0715179667,0.243982181,-0.0836486891,-0.0916203707,0.1310889274,0.0044972026,0.0169883296,-0.1084388867,-0.1526850015,-0.0279960949,-0.0123940697,0.4001575708,0.1530859917,0.2856673598,0.0420149229,0.5507603884,0.0928633809,-0.2339056581,0.475905031,-0.2186861485,0.4636931717,-0.0296103004,-0.4504034817,0.0066044629,-0.037001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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3580","title":"Bug in wiki bio load","comments":"+1, here's the error I got: \r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>>\r\n>>> load_dataset(\"wiki_bio\")\r\nDownloading: 7.58kB [00:00, 4.42MB\/s]\r\nDownloading: 2.71kB [00:00, 1.30MB\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to \/home\/jxm3\/.cache\/huggingface\/datasets\/wiki_bio\/default\/1.1.0\/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1694, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 595, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 662, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/jxm3\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_bio\/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9\/wiki_bio.py\", line 125, in _split_generators\r\n    data_dir = dl_manager.download_and_extract(my_urls)\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 308, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 251, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 298, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 612, in get_from_cache\r\n    raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https:\/\/drive.google.com\/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil\r\n>>>\r\n```\r\n","body":"\r\nwiki_bio is failing to load because of a failing drive link . Can someone fix this ?\r\n\r\n\r\n![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https:\/\/user-images.githubusercontent.com\/3104771\/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)\r\n\r\n\r\n![653C1C76-C725-4A04-A0D8-084373BA612F](https:\/\/user-images.githubusercontent.com\/3104771\/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)\r\na","comment_length":154,"text":"Bug in wiki bio load \n \r\nwiki_bio is failing to load because of a failing drive link . Can someone fix this ?\r\n\r\n\r\n![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https:\/\/user-images.githubusercontent.com\/3104771\/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)\r\n\r\n\r\n![653C1C76-C725-4A04-A0D8-084373BA612F](https:\/\/user-images.githubusercontent.com\/3104771\/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)\r\na \n +1, here's the error I got: \r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>>\r\n>>> load_dataset(\"wiki_bio\")\r\nDownloading: 7.58kB [00:00, 4.42MB\/s]\r\nDownloading: 2.71kB [00:00, 1.30MB\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to \/home\/jxm3\/.cache\/huggingface\/datasets\/wiki_bio\/default\/1.1.0\/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1694, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 595, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 662, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/jxm3\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_bio\/5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9\/wiki_bio.py\", line 125, in _split_generators\r\n    data_dir = dl_manager.download_and_extract(my_urls)\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 308, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 251, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 298, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/jxm3\/.conda\/envs\/torch\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 612, in get_from_cache\r\n    raise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https:\/\/drive.google.com\/uc?export=download&id=1L7aoUXzHPzyzQ0ns4ApBbYepsjFOtXil\r\n>>>\r\n```\r\n","embeddings":[-0.0613214895,0.0982688144,-0.030225493,0.126051113,0.0026653463,0.2975157201,0.6769424677,0.3991512656,0.4630135894,0.0595791563,0.1997942626,-0.242229566,0.3955298066,0.3415492475,0.2029342949,-0.0963702425,0.0754146203,0.1798695028,0.1641553789,0.0234626364,-0.4353509247,0.2718142271,-0.3163642287,-0.0709683597,-0.002290132,0.22046794,-0.0486068018,0.1739232838,0.1275298744,-0.3497761786,0.1432876289,-0.0045093447,0.1551928073,0.0697783306,-0.0001120384,-0.0412580706,0.5240585208,0.069204092,-0.3178609014,-0.1597370356,-0.2016114891,-0.0385247841,-0.1497911811,-0.2445720285,0.2229894102,0.2346726805,-0.2139970809,0.1252019405,-0.0390264094,0.0953148827,0.1987968534,-0.0583579652,0.264619559,0.0181411169,0.3732730746,0.015579571,0.0040919543,0.1633307785,0.0842430964,-0.0787589848,0.1086568534,0.2383841425,-0.0801680982,0.0364523605,0.2402081639,0.123717241,0.469260633,0.0567198433,0.3530778289,0.1969899088,0.5903743505,-0.0294337086,-0.0415295511,-0.1280737221,0.1252167672,0.0674242452,0.5954214931,0.0832501501,-0.2610494196,0.0010719476,-0.0759623349,-0.1854422092,0.0309086554,0.1090274006,-0.2020791322,0.4105585515,-0.0226749089,0.1136513874,0.1786898822,-0.0503635034,0.4395202696,-0.1361653358,-0.2339849472,0.18967852,-0.2070202976,-0.0135004651,-0.1201119721,0.41560179,0.3326581717,-0.3259741664,0.0862748548,-0.1449963599,-0.0840853676,0.1567333043,0.3609651625,0.1589263678,-0.3195385039,-0.2217243314,0.189144358,0.5125040412,-0.130596295,-0.0907886773,0.1653167307,-0.2762562931,0.0100307371,-0.0455007143,-0.0257763099,-0.1979433447,-0.2321029902,0.2980462909,-0.0614853874,-0.0023867898,-0.1458631605,0.2161226571,-0.1866012216,0.2515212595,0.3574251235,0.0559015572,-0.0824438408,0.2357937843,-0.0064378078,0.1222191602,-0.1600075066,0.0751022398,0.0410184935,0.2605436742,0.1595253944,-0.0307617113,0.0222867522,-0.3969288468,-0.2733926177,-0.0234412905,-0.1531258523,0.4796097875,0.2142433673,0.2036482394,0.0983550176,-0.2911838293,-0.0044181887,0.1289331764,-0.4867872596,-0.0358149558,-0.1450826377,0.2033907622,-0.0839760005,0.0512783639,-0.3130125403,-0.2335721552,-0.1537126005,-0.5989356637,-0.1933736801,0.1386006176,-0.2395391464,-0.102435112,0.403942138,0.4156989753,-0.0000541941,0.0006991071,-0.1018153951,0.2216100246,-0.1046177,-0.0115218479,-0.0665410459,0.2728336155,-0.4923345149,0.0681296363,0.1394853443,-0.5229017735,-0.3849204183,0.3950119317,-0.0558391213,-0.0748338923,-0.2254000306,0.0224610306,-0.1718604565,-0.2614791989,0.1190689057,0.4715368152,-0.0372190624,0.0370563418,-0.3601984978,-0.4255829155,-0.2468298227,0.0589730665,0.1532993168,0.1000132933,0.3745215237,0.3775415421,0.3174523115,-0.0526693128,0.0075055971,0.3026787639,-0.0005600263,-0.2805960178,0.0717741922,0.1443171352,-0.6967496872,0.2145917565,0.1342657655,-0.1452382654,0.0655705631,-0.0180689245,-0.3974491358,-0.0226091743,-0.3040260375,0.0289520267,0.1877515912,-0.1294851303,-0.3595653176,0.3108113706,0.0705135763,-0.4047595561,0.0033295471,-0.0048869266,-0.1110967696,0.5477662683,-0.2119179666,0.150764972,0.1548175663,0.1962379962,0.2885978222,0.0184415374,-0.2963323891,0.392665565,0.0202850606,0.5912394524,-0.1091182679,0.0454167128,0.2439493984,-0.1134022325,-0.0318651982,0.2581079304,0.1675770432,-0.3847643733,-0.3260569274,0.0671096519,-0.472224772,0.2535614967,-0.0317482315,0.1512221098,-0.0039379271,-0.1320747435,0.2686768174,-0.1653392315,0.2369306087,0.3005069792,0.1409517378,-0.1240868568,0.0487931184,-0.0583147891,-0.0739744604,0.0053917342,-0.0375068039,0.0606845543,-0.2345571369,-0.2763439715,0.2204357088,0.6737368703,0.2600640953,-0.0056435373,0.008669883,0.1155956611,0.0886212215,-0.18024607,0.1946679652,-0.1819587648,0.3414661884,0.5268102884,-0.0419185348,0.1309008896,-0.020058075,0.1966886371,-0.0317105018,0.1356153786,-0.3464455605,0.0159595404,0.1411153823,0.0895185992,-0.157001555,-0.1600889862,-0.1566586941,-0.5299163461,-0.15299505,0.3101745844,0.0519989915,-0.0020080714,-0.2153823525,0.0633930117,-0.0450393856,-0.3012972772,-0.2802171707,-0.1662390977,-0.1843262762,0.0100740036,0.3071173728,0.396907419,-0.0935427621,-0.2608627975,-0.0544393994,-0.5180279016,-0.2140032053,0.0895876512,0.009760295,0.2636322677,0.3468266129,0.4414674342,-0.0027953975,-0.3451707363,0.3658565581,-0.2022987157,0.0751834065,0.1757012159,0.1088137031,0.2546120584,0.0045825029,-0.0621655285,0.0434251055,-0.1362870783,-0.0995253548,0.0989913121,0.0213517193,0.1648164392,0.219696328,-0.1433319747,0.3730157614,-0.3456761539,-0.2219168544,-0.5155210495,0.1750331521,0.0200924855,-0.2819946408,0.09420757,0.0491555631,0.3897114396,-0.1497111768,-0.776252687,-0.1750955135,0.1764869839,0.1341585517,-0.271033287,0.1375262737,-0.0636330247,0.1490376294,-0.042676542,-0.1238147616,0.0575469844,-0.1657859683,-0.1460288167,0.4222706258,0.1180638373,0.3106063008,-0.101060234,0.4293082654,-0.2367261946,0.1414282918,0.3598569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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3580","title":"Bug in wiki bio load","comments":"@alejandrocros and @lhoestq - you added the wiki_bio dataset in #1173. It doesn't work anymore. Can you take a look at this?","body":"\r\nwiki_bio is failing to load because of a failing drive link . Can someone fix this ?\r\n\r\n\r\n![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https:\/\/user-images.githubusercontent.com\/3104771\/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)\r\n\r\n\r\n![653C1C76-C725-4A04-A0D8-084373BA612F](https:\/\/user-images.githubusercontent.com\/3104771\/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)\r\na","comment_length":22,"text":"Bug in wiki bio load \n \r\nwiki_bio is failing to load because of a failing drive link . Can someone fix this ?\r\n\r\n\r\n![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https:\/\/user-images.githubusercontent.com\/3104771\/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)\r\n\r\n\r\n![653C1C76-C725-4A04-A0D8-084373BA612F](https:\/\/user-images.githubusercontent.com\/3104771\/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)\r\na \n @alejandrocros and @lhoestq - you added the wiki_bio dataset in #1173. It doesn't work anymore. Can you take a look at this?","embeddings":[0.0321894959,0.0049940669,-0.0749883726,0.1220265478,0.0161778145,0.290413022,0.6395819783,0.2936856747,0.4558517337,0.0400672667,0.0808754042,-0.1471674889,0.4145272374,0.3167216778,0.263781786,-0.0694373772,0.1417486966,0.16800116,0.0526713766,-0.0401645377,-0.4097276926,0.2011000067,-0.3452149034,-0.0935672149,0.0569347478,0.3452291489,-0.0383207351,0.2691899538,0.199358359,-0.29567945,0.1510447562,-0.0349285901,0.1660062373,-0.0131755034,-0.0001058121,-0.0514534153,0.5034427047,0.1031742096,-0.2838110328,-0.0819240287,-0.1528717577,0.0106591536,-0.1329655796,-0.2454868108,0.2331488878,0.4125382602,-0.1100320593,0.1383206546,-0.0737389997,-0.0282732304,0.2804980874,-0.1134442016,0.2686680257,-0.0070366072,0.3582028151,0.06121042,-0.0504147783,0.1302831173,0.1840687841,-0.1097537354,0.1090710983,0.3015873134,-0.0726225078,-0.0958294347,0.1213851571,0.1309723556,0.558611095,0.0022678364,0.2993284464,0.1721433848,0.5308620334,0.0990452021,0.1447457075,0.0643848553,0.034838032,0.1001195386,0.6453248262,0.0554173514,-0.1523694694,-0.018427901,0.0134239849,-0.2109502852,0.0009021644,0.0461047441,-0.1298765093,0.515730679,0.0441597998,0.0524289533,0.1457583159,-0.0095196962,0.5015140772,0.0094792545,-0.3065364957,0.2240989655,-0.1354564279,-0.0738135576,-0.1615032703,0.5286126733,0.384470284,-0.2776507139,0.0381725691,-0.0262186285,-0.0645926595,0.0504532307,0.3267804086,-0.030116925,-0.2475217432,-0.2702341378,0.2053951919,0.379085511,-0.2248223275,-0.0329669937,0.188525036,-0.2643594146,0.0336488038,-0.0834994912,-0.0327563733,-0.2932049036,-0.0934328288,0.2882064581,-0.1196328327,-0.087282598,-0.1877694875,0.2108185291,-0.1012838855,0.1958021075,0.3764629066,-0.0337201767,-0.0617507696,0.2167543769,-0.0570984744,0.0615501516,-0.1889374852,0.1595327258,0.0608033203,0.2485337108,0.1584461182,0.0628738776,-0.0486482345,-0.3765259981,-0.3045425117,0.0580807477,-0.1916730106,0.4507753551,0.255925566,0.1502248943,-0.0096712867,-0.2717125416,0.0544998348,0.2107950002,-0.554646492,-0.0510775484,-0.2441646755,0.2522595525,-0.0867981613,0.0537909679,-0.3021355867,-0.2135031372,-0.1333463043,-0.6531453729,-0.1036270186,0.2123245895,-0.1859178543,-0.0474093966,0.3338044882,0.3355461955,0.0466080084,0.0594822876,-0.1239496693,0.2168782502,-0.0644382909,-0.0583729558,-0.0706964806,0.2424899787,-0.4760739803,0.1222538278,0.1051838249,-0.3855217993,-0.2761519253,0.223329857,-0.0169867836,-0.0117251743,-0.2174095809,0.0348907709,-0.23602359,-0.2666628361,0.1021768227,0.4103855491,0.0582663864,0.022033114,-0.4388059974,-0.3693470061,-0.1575177759,0.013841263,0.2064611167,0.1542690694,0.4071463645,0.4161362648,0.3813930154,-0.1062851697,-0.0628228486,0.2918653786,0.1130944639,-0.2300512046,0.1492866725,0.158733815,-0.6886051297,0.1638334841,-0.0348459557,-0.0678870007,0.2805159688,0.0246762577,-0.3883666098,-0.0936737731,-0.2190939188,0.015217986,0.2497886866,-0.1104150489,-0.3157586157,0.3361888826,0.0844962001,-0.4631015956,-0.0451097973,-0.0395954102,-0.1290201396,0.7025783658,-0.2560609579,0.0583577342,0.0436949953,0.1731843501,0.2751400769,0.0257321559,-0.2385995984,0.2625119984,-0.0397055447,0.4564499855,-0.036448855,0.1105245799,0.2029577047,-0.1760912687,-0.1875555515,0.2225958407,0.2034550607,-0.3999117911,-0.2713213265,-0.0182986055,-0.4392457008,0.2484749407,-0.1601634026,0.1663233042,0.0198344924,-0.1942344457,0.2979615927,-0.0596637465,0.2741820514,0.2583999932,0.0189835373,-0.1534977406,0.0143327499,-0.0110604605,-0.068617925,-0.0169748776,-0.0032374479,0.0752656609,-0.2245486826,-0.2744238675,0.2297228724,0.391695261,0.2010614425,0.0326472335,-0.0148468148,0.0639988035,0.0839594379,-0.1966770142,0.2214975059,-0.2267283648,0.3052073121,0.3712220788,-0.0168706328,0.1292235255,-0.0936925858,0.2555835247,-0.0397827998,0.1310715228,-0.3490861952,-0.0623319298,0.243987605,0.1217945516,-0.0369529985,-0.2234916389,-0.1594158858,-0.5010502934,-0.0478355736,0.2554010451,0.0774664134,0.0915174261,-0.096352689,0.1500690579,-0.0995004997,-0.0543693937,-0.4265757501,-0.2093021274,-0.1642440557,0.0969957486,0.3460110724,0.4251787364,-0.14961721,-0.1537747085,-0.0506019257,-0.5013497472,-0.336368531,0.0525864772,-0.0587711036,0.2904320359,0.1100968793,0.4559998512,-0.1108579636,-0.3694721162,0.2613790333,-0.227362752,0.0814247057,0.2397348434,0.0877933949,0.2054670453,-0.0210456643,-0.1237465069,0.1157535017,-0.0277383383,-0.1201118752,0.0837673992,0.0372009389,0.1328160316,0.127431944,-0.2187091112,0.3348774016,-0.3814541101,-0.2775143981,-0.4310785234,0.170397386,-0.0256064702,-0.3397460282,0.1047017351,0.1897272021,0.2834199369,-0.2781020105,-0.7148829699,-0.0752819255,0.1562079638,-0.0082503175,-0.2018227279,0.1012285501,0.0073724552,0.1456773579,-0.0707076788,0.0154481977,0.0533302315,-0.0948344469,-0.1259067953,0.3674834967,0.0575857833,0.1990105659,-0.1684051603,0.3298319876,-0.2656654119,0.2251413912,0.2511430681,0.1473822892,0.1543538123,-0.0118273301,-0.105031468,0.2092683017,-0.1489278525,-0.2076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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3580","title":"Bug in wiki bio load","comments":"And if something is wrong with Google Drive, you could try to download (and collate and unzip) from here: https:\/\/github.com\/DavidGrangier\/wikipedia-biography-dataset","body":"\r\nwiki_bio is failing to load because of a failing drive link . Can someone fix this ?\r\n\r\n\r\n![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https:\/\/user-images.githubusercontent.com\/3104771\/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)\r\n\r\n\r\n![653C1C76-C725-4A04-A0D8-084373BA612F](https:\/\/user-images.githubusercontent.com\/3104771\/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)\r\na","comment_length":20,"text":"Bug in wiki bio load \n \r\nwiki_bio is failing to load because of a failing drive link . Can someone fix this ?\r\n\r\n\r\n![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https:\/\/user-images.githubusercontent.com\/3104771\/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)\r\n\r\n\r\n![653C1C76-C725-4A04-A0D8-084373BA612F](https:\/\/user-images.githubusercontent.com\/3104771\/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)\r\na \n And if something is wrong with Google Drive, you could try to download (and collate and unzip) from here: https:\/\/github.com\/DavidGrangier\/wikipedia-biography-dataset","embeddings":[0.1476311684,0.1652757525,-0.0648237541,0.1482932419,-0.0046192119,0.3781236708,0.5248034,0.3122825027,0.4007312953,0.1247581318,0.1112694517,-0.2811302543,0.4056454003,0.1641616225,0.0635543838,-0.2315276116,0.0691661686,0.2504969537,0.1303052753,-0.0925100595,-0.3381927609,0.2806797326,-0.2111637145,-0.0135703441,0.0083447173,0.2383122891,0.0313065201,0.2887836099,0.1030191854,-0.1287060976,-0.0786870122,0.0231960267,0.0263998043,0.0089260684,-0.0001106031,-0.1400737911,0.6025159955,0.052697029,-0.3154246211,-0.2390892804,0.0291462932,0.1459306777,-0.140184328,-0.3149750531,0.2412002087,0.3346362412,-0.0330021456,0.2944811583,-0.0024353229,-0.0741562098,0.2819491327,-0.2865889668,0.2999055684,-0.0900394544,0.5521534681,0.047972437,0.0854459479,-0.0248486437,-0.1103062779,-0.2025004923,-0.061501652,0.3241799772,-0.0459266603,-0.0522672571,0.1982011348,-0.0003633196,0.4951778352,-0.091888696,0.5286716819,0.3288867176,0.6026454568,0.2896836102,0.0688242465,0.1307013184,0.087647818,0.277474612,0.6808757186,0.2711421251,-0.2731283903,-0.1245028228,-0.0315353125,-0.2270620614,-0.0384169221,0.0302629471,-0.2816871405,0.3649848402,0.0537003875,0.0290116519,0.2578218579,-0.1464340389,0.4291780293,-0.049177967,-0.1617682725,0.1931588501,0.0498032421,-0.0632263646,-0.106891498,0.5294387937,0.3183227181,-0.3183695078,-0.092073366,-0.0989704654,0.0116630718,-0.0430302285,0.3947785795,-0.0325762853,-0.1733271331,-0.2937980592,0.3552398682,0.4326059818,-0.267837137,0.0278599598,0.1568792164,-0.2157912999,-0.0477299988,-0.0801468417,-0.147211954,-0.3518073261,-0.0683414191,0.4189556539,-0.2193312347,-0.1522508711,-0.3031925857,0.2753006518,-0.2899969816,0.1507226527,0.3255280852,-0.0813533589,-0.0319961496,0.2373151183,-0.053475678,0.0967513248,-0.1567658782,0.1748630852,0.0246847663,0.180820331,0.1945071816,0.0573883019,-0.0745503455,-0.3791556358,-0.2652486265,-0.0634309351,-0.1310120225,0.4032914937,0.3503064513,-0.0286419298,-0.0434618257,-0.4216338098,0.0673842877,0.1931349635,-0.4179632068,-0.0768504143,-0.099868834,0.2259990126,-0.1221128926,0.0876889676,-0.2450100332,-0.1560429633,-0.1189746633,-0.5366658568,-0.172839582,0.3096041679,-0.1135781035,-0.0983851254,0.2896368504,0.3288465738,0.119189471,0.0542803966,-0.1864719838,0.3301957548,-0.0803645924,0.0754858479,0.0009153791,0.3409098089,-0.398330003,0.1914965957,0.0873281285,-0.2572926879,-0.1586746573,0.1543870121,0.1517822295,0.019803619,-0.2539971173,-0.0857005045,-0.0694400668,-0.124756664,0.0888382345,0.5150842667,0.0330690071,0.1140450463,-0.3237347007,-0.461507678,-0.0707880184,0.0185788833,0.3067045808,0.2249738127,0.3534302115,0.3639578819,0.3170072436,-0.2588341236,-0.0846608207,0.3196497858,-0.1151655391,-0.0066366461,0.1749435812,0.2577255666,-0.5381885171,0.0511623584,-0.0965017751,-0.1074640676,0.0248862226,-0.1538000256,-0.2821564674,-0.175778985,-0.1969385445,0.1293459535,0.2652692497,-0.0238354653,-0.1398825794,0.4027508795,0.3327243626,-0.5813550353,-0.1140065119,-0.0918761492,-0.1384447366,0.6158581972,-0.2570338249,0.0733020827,0.2064156979,0.1187893897,0.201456517,0.0233771205,-0.2099895328,0.1154979467,0.0024182466,0.5367825627,-0.0206125099,-0.0006908859,0.118585825,-0.3087593615,-0.1930518597,0.3786311448,0.1443347931,-0.4185045958,-0.4837063253,0.0024553577,-0.3105711639,0.2553342879,-0.050780464,0.2323589027,0.0361547582,-0.1437794119,0.294944942,-0.1071149707,0.2535235882,0.3147041202,0.0164735932,-0.1692744046,-0.0515078567,-0.0192513708,0.0018677523,0.0000743257,0.1068892404,0.1163860783,-0.13639763,-0.3284756541,0.2177049071,0.2683370411,-0.0311978199,0.0314473622,0.1093386039,-0.0251781661,0.2100269198,-0.1764999628,0.2032684684,-0.1921776235,0.2915428877,0.3162910342,-0.071731247,0.1508385837,-0.1266915947,0.3200337887,-0.0586953536,0.1391146034,-0.24954772,-0.113797605,0.2444885075,0.160320878,0.1817503273,-0.2265075147,-0.2478237748,-0.4206968546,-0.0778102353,0.3690401018,-0.0216372982,0.0125859259,0.0439046137,0.1388626546,0.0350753404,-0.1111846194,-0.2949465215,-0.3050589263,0.0300741885,0.1029798537,0.3381418288,0.2427385896,-0.0986951888,-0.2393284738,-0.1810600907,-0.5997213125,-0.387819767,0.0467533804,-0.1382029057,0.1142626107,0.0705007166,0.428663075,-0.0997114852,-0.3338389397,0.1581416875,-0.1870561242,-0.002859361,0.1710867286,0.141608119,0.126643464,0.146779418,-0.0744590089,0.1002205983,-0.0318405181,0.0235466436,0.1643733233,0.091539152,0.1473246664,0.0878676623,-0.2603486478,0.5300244689,-0.1505686939,-0.2649846375,-0.2927205861,0.2718267143,-0.1536803097,-0.4416193366,0.1319747567,0.1079612449,0.1279838532,-0.103409484,-0.6762399673,0.0038941917,0.0321394354,0.1004091129,-0.2191498876,0.0200204793,0.0420841984,0.1559020579,-0.0808535367,0.027901683,0.0143694757,-0.1561449468,-0.1501433998,0.293420285,0.0609126911,0.1581365764,-0.1721152365,0.4349756837,-0.1250733137,0.4221833646,0.1456225961,0.167959407,0.0533022843,0.0075705759,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3580","title":"Bug in wiki bio load","comments":"Hi ! Thanks for reporting. I've downloaded the data and concatenated them into a zip file available here: https:\/\/huggingface.co\/datasets\/wiki_bio\/tree\/main\/data\r\n\r\nI guess we can update the dataset script to use this zip file now :)","body":"\r\nwiki_bio is failing to load because of a failing drive link . Can someone fix this ?\r\n\r\n\r\n![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https:\/\/user-images.githubusercontent.com\/3104771\/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)\r\n\r\n\r\n![653C1C76-C725-4A04-A0D8-084373BA612F](https:\/\/user-images.githubusercontent.com\/3104771\/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)\r\na","comment_length":34,"text":"Bug in wiki bio load \n \r\nwiki_bio is failing to load because of a failing drive link . Can someone fix this ?\r\n\r\n\r\n![7E90023B-A3B1-4930-BA25-45CCCB4E1710](https:\/\/user-images.githubusercontent.com\/3104771\/149617870-5a32a2da-2c78-483b-bff6-d7534215a423.png)\r\n\r\n\r\n![653C1C76-C725-4A04-A0D8-084373BA612F](https:\/\/user-images.githubusercontent.com\/3104771\/149617875-ef0e30b0-b76e-48cf-b3eb-93ba8e6e5465.png)\r\na \n Hi ! Thanks for reporting. I've downloaded the data and concatenated them into a zip file available here: https:\/\/huggingface.co\/datasets\/wiki_bio\/tree\/main\/data\r\n\r\nI guess we can update the dataset script to use this zip file now :)","embeddings":[0.0163166486,-0.0223633386,-0.035045851,0.2036606222,0.0102496166,0.2816864252,0.5000675321,0.3356050849,0.4480066001,0.0293328557,0.1067658663,-0.1336870641,0.4862635732,0.4575187564,0.2189482152,-0.1626470089,0.1658854634,0.1360335052,0.0206577852,-0.0127585912,-0.4223105609,0.3174726665,-0.3087361753,-0.080560267,-0.0701327473,0.2891200781,-0.0966143236,0.3522364795,0.2119254619,-0.2622824311,0.1463516951,0.0123300729,0.2225925922,0.0451565459,-0.0001142207,-0.0872222781,0.5115489364,0.0817741752,-0.3094241619,-0.1039692685,-0.0552549362,0.0436297357,-0.1129244864,-0.2454094142,0.1677948236,0.4410642385,-0.11817687,0.1103781834,0.0343428925,0.0156643316,0.1986288726,-0.038846802,0.2776686251,0.0248492286,0.423270762,0.0927720368,0.0471867435,0.1105430946,0.1847807318,-0.181220904,-0.0168623962,0.236293599,-0.0238042548,-0.1795315593,0.231816873,0.1941763908,0.5102156401,-0.0582506768,0.3229575157,0.2204763591,0.5006221533,0.0508592837,0.0754977763,-0.033306554,0.0439198501,0.0792961642,0.7035596371,0.1308577359,-0.2140887082,0.0253026225,0.0133073432,-0.1730102599,-0.0536160879,0.0528652556,-0.098962523,0.5030226111,0.0335703604,0.0401484743,0.169645384,-0.0740402192,0.4577164948,-0.0825601891,-0.3223028779,0.178813085,-0.1841688901,-0.0673980713,-0.2229752541,0.6477211118,0.421598196,-0.2287415713,0.0180988554,-0.0839565173,-0.0503723808,0.0274769459,0.3889910579,0.1034048572,-0.2191027701,-0.2277691364,0.2454682291,0.5021097064,-0.2373131514,-0.0823961869,0.1406003535,-0.2782981694,-0.0137903839,-0.0821334571,-0.0528982617,-0.3208425939,-0.1444659531,0.2822514772,-0.0319880396,-0.0207032841,-0.123153314,0.2447157502,-0.0772334114,0.2802614868,0.4166567028,0.0252137762,-0.0861539617,0.2686971426,0.0033230328,0.0933245942,-0.1366189271,0.17429851,0.0598139241,0.2234099209,0.1708332449,0.0521260537,0.109444961,-0.4258496761,-0.4251036346,0.1126538813,-0.1809799075,0.4825682342,0.2418424636,0.0462234095,0.0476133525,-0.2950499058,0.0346721038,0.2166050822,-0.5040418506,-0.0648913532,-0.1656537652,0.1601506472,-0.1602068394,0.0543649457,-0.3685587645,-0.151417613,-0.1464851052,-0.5909231901,-0.0911266953,0.2752743661,-0.3323353529,-0.0877261087,0.3463121951,0.4308664799,0.1311336458,0.0535060912,-0.0616659746,0.2564442158,-0.1859455556,-0.0330473483,-0.0105595002,0.1490720809,-0.5086305141,0.1755836457,0.0474641286,-0.4497593343,-0.241566658,0.2776597738,-0.0650148243,0.0063104685,-0.1730714887,0.0477581546,-0.2678454816,-0.3215220869,0.0282281991,0.4580121636,0.075582549,0.0927440077,-0.3564406633,-0.4244353473,-0.1727635115,0.0715245306,0.1371546835,0.1501121372,0.3231941164,0.2641462684,0.4316619039,-0.163586691,-0.0249233097,0.291757673,0.0311746392,-0.1670077741,0.1149960831,0.1067847088,-0.7576182485,0.1634249091,0.0245542377,-0.155562669,0.0889120251,-0.1153747514,-0.3525182903,-0.0263049249,-0.2581290603,0.0376675688,0.171874702,-0.1229643598,-0.2498288453,0.3445883095,0.0595841445,-0.3671065569,-0.0183920152,0.0434394926,-0.1466162056,0.7144907713,-0.2179838419,0.1095630601,0.1508633792,0.1751336306,0.3475314379,-0.0247249659,-0.2469007373,0.3569745421,-0.0182961896,0.5679570436,-0.0617845282,0.0830129385,0.1912268996,-0.0979649425,-0.1297141761,0.282970041,0.1211860925,-0.4112236798,-0.3974450529,0.0937187672,-0.4727893174,0.2498744726,-0.0892329738,0.1837416887,-0.0783595443,-0.1845320016,0.2632255256,-0.0776803121,0.273235321,0.2743215263,0.0389599912,-0.1385318935,-0.0483199023,0.0108665265,-0.0011139787,-0.1248547137,-0.0608884655,0.0935028121,-0.1190752089,-0.2704334557,0.3091192544,0.4944938719,0.1492561102,0.0214002281,0.0008212459,0.1634782404,0.0953967199,-0.1863516569,0.2361178994,-0.19609043,0.3373146951,0.4056652188,-0.043456953,0.0751205906,-0.1484210193,0.2891167998,-0.0704092681,0.0346911587,-0.3788948357,0.0087851994,0.2322016507,-0.0303037297,-0.0797390416,-0.1847614348,-0.1976346374,-0.5136330128,-0.026324423,0.3334523439,0.0388101675,0.0274479147,-0.0908408612,0.1354119331,-0.0795901567,-0.1568049937,-0.4128514528,-0.1758208126,-0.1676538438,0.0146720428,0.4229948819,0.4709059894,-0.1670358777,-0.293158412,-0.0649555624,-0.554220736,-0.2947585881,0.0758207738,-0.0699502453,0.2634213567,0.2078248262,0.5022150874,-0.1125208288,-0.363111645,0.3053625822,-0.2141825259,0.0333321318,0.230119884,0.1985619068,0.1840203702,-0.0355810411,-0.1173482835,0.1574911177,-0.0501382835,-0.0340980403,0.0385613032,0.0945262015,0.0467357524,0.1288246512,-0.2173050344,0.2867928445,-0.3447936177,-0.233449325,-0.4904560447,0.214574948,0.006671031,-0.3832451403,0.0675609857,0.1523984075,0.2815793753,-0.2775557935,-0.7139182687,-0.1733462363,0.1079545841,0.0977711976,-0.2688208818,0.1178396568,-0.0084023364,0.134930104,0.0173086226,-0.0559611395,0.0684446543,-0.1140020415,-0.1843150258,0.3281839788,0.1176253632,0.3382871151,-0.1907029301,0.3177236915,-0.1615430117,0.2783433795,0.2923175097,0.0245499164,0.2091881335,0.0535020009,-0.0265628491,0.1622712612,-0.217931658,-0.2155956924,0.401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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3578","title":"label information get lost after parquet serialization","comments":"Hi ! We did a release of `datasets` today that may fix this issue. Can you try updating `datasets` and trying again ?\r\n\r\nEDIT: the issue is still there actually\r\n\r\nI think we can fix that by storing the Features in the parquet schema metadata, and then reload them when loading the parquet file","body":"## Describe the bug\r\nIn *dataset_info.json* file, information about the label get lost after the dataset serialization.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# normal save\r\ndataset = load_dataset('glue', 'sst2', split='train')\r\ndataset.save_to_disk(\"normal_save\")\r\n\r\n\r\n# save after parquet serialization\r\ndataset.to_parquet(\"glue-sst2-train.parquet\")\r\ndataset = load_dataset(\"parquet\", data_files='glue-sst2-train.parquet')\r\ndataset.save_to_disk(\"save_after_parquet\")\r\n```\r\n\r\n## Expected results\r\nI expected to keep label information in *dataset_info.json* file even after parquet serialization\r\n\r\n## Actual results\r\nIn the normal serialization i got\r\n```json\r\n\"label\": {\r\n      \"num_classes\": 2,\r\n      \"names\": [\r\n        \"negative\",\r\n        \"positive\"\r\n      ],\r\n      \"names_file\": null,\r\n      \"id\": null,\r\n      \"_type\": \"ClassLabel\"\r\n    },\r\n```\r\nAnd after parquet serialization i got\r\n```json\r\n\"label\": {\r\n      \"dtype\": \"int64\",\r\n      \"id\": null,\r\n      \"_type\": \"Value\"\r\n    },\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: ubuntu 20.04\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.1\r\n","comment_length":54,"text":"label information get lost after parquet serialization \n ## Describe the bug\r\nIn *dataset_info.json* file, information about the label get lost after the dataset serialization.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# normal save\r\ndataset = load_dataset('glue', 'sst2', split='train')\r\ndataset.save_to_disk(\"normal_save\")\r\n\r\n\r\n# save after parquet serialization\r\ndataset.to_parquet(\"glue-sst2-train.parquet\")\r\ndataset = load_dataset(\"parquet\", data_files='glue-sst2-train.parquet')\r\ndataset.save_to_disk(\"save_after_parquet\")\r\n```\r\n\r\n## Expected results\r\nI expected to keep label information in *dataset_info.json* file even after parquet serialization\r\n\r\n## Actual results\r\nIn the normal serialization i got\r\n```json\r\n\"label\": {\r\n      \"num_classes\": 2,\r\n      \"names\": [\r\n        \"negative\",\r\n        \"positive\"\r\n      ],\r\n      \"names_file\": null,\r\n      \"id\": null,\r\n      \"_type\": \"ClassLabel\"\r\n    },\r\n```\r\nAnd after parquet serialization i got\r\n```json\r\n\"label\": {\r\n      \"dtype\": \"int64\",\r\n      \"id\": null,\r\n      \"_type\": \"Value\"\r\n    },\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.18.0\r\n- Platform: ubuntu 20.04\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.1\r\n \n Hi ! We did a release of `datasets` today that may fix this issue. Can you try updating `datasets` and trying again ?\r\n\r\nEDIT: the issue is still there actually\r\n\r\nI think we can fix that by storing the Features in the parquet schema metadata, and then reload them when loading the parquet 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3572","title":"ConnectionError in IndicGLUE dataset","comments":"@sahoodib, thanks for reporting.\r\n\r\nIndeed, none of the data links appearing in the IndicGLUE website are working, e.g.: https:\/\/storage.googleapis.com\/ai4bharat-public-indic-nlp-corpora\/evaluations\/soham-articles.tar.gz\r\n```\r\n<Error>\r\n<Code>UserProjectAccountProblem<\/Code>\r\n<Message>User project billing account not in good standing.<\/Message>\r\n<Details>\r\nThe billing account for the owning project is disabled in state delinquent\r\n<\/Details>\r\n<\/Error>\r\n```\r\n\r\nWe have contacted the data owners to inform them about their issue and ask them if they plan to fix it.","body":"While I am trying to load IndicGLUE dataset (https:\/\/huggingface.co\/datasets\/indic_glue) it is giving me with the error:\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/ai4bharat-public-indic-nlp-corpora\/evaluations\/wikiann-ner.tar.gz (error 403)","comment_length":67,"text":"ConnectionError in IndicGLUE dataset \n While I am trying to load IndicGLUE dataset (https:\/\/huggingface.co\/datasets\/indic_glue) it is giving me with the error:\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/ai4bharat-public-indic-nlp-corpora\/evaluations\/wikiann-ner.tar.gz (error 403) \n @sahoodib, thanks for reporting.\r\n\r\nIndeed, none of the data links appearing in the IndicGLUE website are working, e.g.: https:\/\/storage.googleapis.com\/ai4bharat-public-indic-nlp-corpora\/evaluations\/soham-articles.tar.gz\r\n```\r\n<Error>\r\n<Code>UserProjectAccountProblem<\/Code>\r\n<Message>User project billing account not in good standing.<\/Message>\r\n<Details>\r\nThe billing account for the owning project is disabled in state delinquent\r\n<\/Details>\r\n<\/Error>\r\n```\r\n\r\nWe have contacted the data owners to inform them about their issue and ask them if they plan to fix it.","embeddings":[-0.4043238163,0.387838304,-0.0267184712,0.6261591911,0.0222608168,0.0886497349,0.1807949394,0.0415820628,0.0253699459,0.0412169956,-0.0606036671,-0.2099456191,0.3280408084,0.0549297705,0.1314407438,0.0362573415,-0.2428229153,0.1725625694,-0.0705661103,0.2514489889,-0.2611514628,0.4301236868,-0.276168257,0.5252304077,-0.0091512064,-0.0537977256,0.0108432937,0.1877591759,-0.0104837222,-0.4994136095,0.4097232223,0.0057260222,0.2434547544,0.2724930048,-0.0001148371,-0.0497000851,0.6451971531,-0.0762541965,-0.178613767,-0.1532892287,-0.6412775517,-0.2427854985,0.1779623926,0.0917276219,-0.213596642,-0.0609541498,0.119334504,-0.153008461,0.1720350385,0.1226513907,0.1434804052,-0.2940347791,0.4689921141,-0.0948362276,-0.1054938287,-0.3243202865,0.1159839258,0.652716279,-0.1453702003,-0.5358205438,0.2206300348,0.1204415411,0.0583425872,-0.0585747473,0.2120805085,-0.0209000949,-0.0717201531,-0.3748863041,0.2222804278,0.3638872504,0.8399502635,-0.0058807987,-0.3516870737,0.0886770636,0.3189181089,-0.0978785679,0.4786069095,0.3489349484,-0.0027945389,0.3746259809,-0.0251937062,-0.3879663646,-0.2738530338,0.2120631188,-0.1646928638,-0.0210491326,-0.1610714346,0.0917803347,0.1783414632,-0.1919796318,-0.0398931578,0.0685461611,-0.3091009855,0.1744864583,-0.3250119388,0.1233158335,-0.350332886,0.0742144212,0.1767469347,0.2901988328,0.2617743909,0.1712960303,-0.0327749141,0.222211048,0.1798158288,0.1033342108,0.10824617,0.1028319523,0.5134886503,0.1314285696,0.2461077422,-0.2020864934,-0.1810677052,-0.0711729154,-0.3360684514,-0.0832634568,0.2495873123,-0.2327741534,-0.5119813681,0.2193897218,-0.3385059834,0.0359851122,-0.0610215366,0.1666566581,-0.1722097844,-0.1876680404,0.1876834929,0.1526194513,-0.0753739029,-0.1564220339,-0.0393907614,-0.0923379064,-0.0072491057,0.2800862193,0.032679297,-0.0812889785,0.0462285504,-0.2177617401,-0.0794775262,-0.0255289208,0.3496100307,-0.2297019064,-0.3227311969,0.3794977367,0.1459615231,-0.0265808683,0.0602169894,-0.56702739,0.0025045658,-0.1946541071,-0.4127659798,-0.2171999663,-0.3056875467,0.1236249506,-0.1164994016,0.0521680266,-0.0839988068,-0.0021004106,0.1473970711,-0.2747706473,-0.1525275856,-0.207008943,0.0727130324,-0.135136351,0.162454918,0.5512099862,-0.3507907093,-0.1561379731,-0.0442161635,-0.1689479649,0.0834233388,0.5338199139,-0.1540870667,-0.1225421503,-0.2926576734,0.2910323739,0.4141795635,-0.1171645671,-0.6818208694,0.4594955146,-0.1853525192,-0.0949610248,0.019374229,-0.0100036766,0.3676594496,0.0912883431,0.3919124007,0.3410646915,0.0082314787,-0.3407230079,-0.0037188001,-0.2206572294,0.1422779411,-0.1007763371,-0.1310127825,0.0886129141,0.1986708492,-0.3302692771,0.3150429428,0.1557753235,0.2241776139,0.2674646378,0.2597386837,0.1525428742,-0.0645681471,0.1860861331,-0.2782550156,0.1297168732,-0.3208766878,0.359400183,-0.2253600806,0.0618743934,-0.0764995292,-0.0053297742,-0.1715795696,0.1813429743,0.0618218482,0.1955775917,-0.0632374808,0.2962318957,-0.0213240962,0.8180757761,0.0439379252,0.1541223377,-0.3403177857,0.6038760543,-0.1925953925,-0.0187008251,0.3814825416,0.1033407077,0.2193464935,-0.2051445097,-0.018639382,0.2784308195,-0.2629795074,0.1583879739,0.5227828622,0.2214931101,0.2187639028,-0.6946052313,0.1649155766,0.0423456244,0.322745353,0.1464090347,0.429700464,0.2449942976,0.4545120597,0.2388554215,0.0176411886,0.3985767961,0.3640809953,0.0775695071,-0.1980287135,-0.2063798457,0.1067024693,-0.2293257266,0.0395724922,-0.0973092839,-0.3466549218,-0.1869159937,-0.1025797576,-0.0822357535,0.0431497656,0.1083366126,-0.2157338113,-0.1440313607,0.1325029731,0.1115585864,0.1195609123,-0.0112293763,-0.2147521675,-0.0698565021,0.1115629151,-0.1424110681,0.0944968313,0.0730740204,0.0463057868,0.232512936,-0.0618937053,0.0358949713,-0.0667574555,-0.3982254267,0.1079233587,0.3428257406,-0.1860565096,0.1115395352,-0.031291265,-0.4492977262,0.1326304674,-0.2871261239,-0.4464939833,-0.3150816858,-0.2452969253,0.0396403335,0.1951527745,0.0948884636,-0.280588001,0.0238431599,0.1031718999,-0.0439376272,-0.3232941926,-0.0632631406,-0.2854241729,0.0290876813,0.3187896609,0.0566738024,0.1126113608,-0.0444393344,0.1536144763,-0.4827733934,-0.0449292362,0.0977782235,0.0310119782,0.1911696345,0.1024855673,0.3012567759,0.2151899636,0.0681737661,0.2945026457,-0.0669876039,0.1450516135,0.2086138278,-0.0022625125,0.0922969133,0.2886859179,0.0403538682,-0.5257275105,-0.2788963318,0.0938425362,0.1821525693,0.0894120261,0.0784528628,-0.0442047231,0.2979562283,0.1778970361,-0.0852061883,-0.1618532985,0.001210936,0.1440295577,-0.1092908382,-0.2463215888,0.1381119192,0.0955154896,-0.0830287188,0.0091527868,-0.5455816388,0.0350878127,-0.152095437,-0.0795238987,-0.0262155663,-0.3101910055,0.0676446781,-0.1388937831,0.0482567213,0.3072764874,-0.2789993882,-0.0609411821,0.3940562904,0.2243275791,0.4074164629,0.4497856796,-0.188877061,0.2701703012,0.4175566435,0.1496576965,0.3208696544,0.0086926445,0.4890313447,0.0152723826,-0.1860278845,-0.2715027034,0.0705168173,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3568","title":"Downloading Hugging Face Medical Dialog Dataset NonMatchingSplitsSizesError","comments":"Hi @fabianslife, thanks for reporting.\r\n\r\nI think you were using an old version of `datasets` because this bug was already fixed in version `1.13.0` (13 Oct 2021):\r\n- Fix: 55fd140a63b8f03a0e72985647e498f1fc799d3f\r\n- PR: #3046\r\n- Issue: #2969 \r\n\r\nPlease, feel free to update the library: `pip install -U datasets`.","body":"I wanted to download the Nedical Dialog Dataset from huggingface, using this github link:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/medical_dialog\r\n\r\nAfter downloading the raw datasets from google drive, i unpacked everything and put it in the same folder as the medical_dialog.py which is:\r\n\r\n```\r\nimport copy\r\nimport os\r\nimport re\r\n\r\nimport datasets\r\n\r\n\r\n_CITATION = \"\"\"\\\r\n@article{chen2020meddiag,\r\n  title={MedDialog: a large-scale medical dialogue dataset},\r\n  author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},\r\n  journal={arXiv preprint arXiv:2004.03329},\r\n  year={2020}\r\n}\r\n\"\"\"\r\n\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThe MedDialog dataset (English) contains conversations (in English) between doctors and patients.\\\r\nIt has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. \\\r\nThe raw dialogues are from healthcaremagic.com and icliniq.com.\\\r\n\r\nAll copyrights of the data belong to healthcaremagic.com and icliniq.com.\r\n\"\"\"\r\n\r\n_HOMEPAGE = \"https:\/\/github.com\/UCSD-AI4H\/Medical-Dialogue-System\"\r\n\r\n_LICENSE = \"\"\r\n\r\n\r\nclass MedicalDialog(datasets.GeneratorBasedBuilder):\r\n    VERSION = datasets.Version(\"1.0.0\")\r\n\r\n    BUILDER_CONFIGS = [\r\n        datasets.BuilderConfig(name=\"en\", description=\"The dataset of medical dialogs in English.\", version=VERSION),\r\n        datasets.BuilderConfig(name=\"zh\", description=\"The dataset of medical dialogs in Chinese.\", version=VERSION),\r\n    ]\r\n\r\n    @property\r\n    def manual_download_instructions(self):\r\n        return \"\"\"\\\r\n    \\n  For English:\\nYou need to go to https:\/\/drive.google.com\/drive\/folders\/1g29ssimdZ6JzTST6Y8g6h-ogUNReBtJD?usp=sharing,\\\r\n    and manually download the dataset from Google Drive. Once it is completed,\r\n    a file named Medical-Dialogue-Dataset-English-<timestamp-info>.zip will appear in your Downloads folder(\r\n    or whichever folder your browser chooses to save files to). Unzip the folder to obtain\r\n    a folder named \"Medical-Dialogue-Dataset-English\" several text files.\r\n\r\n    Now, you can specify the path to this folder for the data_dir argument in the\r\n    datasets.load_dataset(...) option.\r\n    The <path\/to\/folder> can e.g. be \"\/Downloads\/Medical-Dialogue-Dataset-English\".\r\n    The data can then be loaded using the below command:\\\r\n         datasets.load_dataset(\"medical_dialog\", name=\"en\", data_dir=\"\/Downloads\/Medical-Dialogue-Dataset-English\")`.\r\n\r\n    \\n  For Chinese:\\nFollow the above process. Change the 'name' to 'zh'.The download link is https:\/\/drive.google.com\/drive\/folders\/1r09_i8nJ9c1nliXVGXwSqRYqklcHd9e2\r\n\r\n    **NOTE**\r\n    - A caution while downloading from drive. It is better to download single files since creating a zip might not include files <500 MB. This has been observed mutiple times.\r\n    - After downloading the files and adding them to the appropriate folder, the path of the folder can be given as input tu the data_dir path.\r\n    \"\"\"\r\n\r\n    datasets.load_dataset(\"medical_dialog\", name=\"en\", data_dir=\"Medical-Dialogue-Dataset-English\")\r\n\r\n    def _info(self):\r\n        if self.config.name == \"zh\":\r\n            features = datasets.Features(\r\n                {\r\n                    \"file_name\": datasets.Value(\"string\"),\r\n                    \"dialogue_id\": datasets.Value(\"int32\"),\r\n                    \"dialogue_url\": datasets.Value(\"string\"),\r\n                    \"dialogue_turns\": datasets.Sequence(\r\n                        {\r\n                            \"speaker\": datasets.ClassLabel(names=[\"\u75c5\u4eba\", \"\u533b\u751f\"]),\r\n                            \"utterance\": datasets.Value(\"string\"),\r\n                        }\r\n                    ),\r\n                }\r\n            )\r\n\r\n        if self.config.name == \"en\":\r\n            features = datasets.Features(\r\n                {\r\n                    \"file_name\": datasets.Value(\"string\"),\r\n                    \"dialogue_id\": datasets.Value(\"int32\"),\r\n                    \"dialogue_url\": datasets.Value(\"string\"),\r\n                    \"dialogue_turns\": datasets.Sequence(\r\n                        {\r\n                            \"speaker\": datasets.ClassLabel(names=[\"Patient\", \"Doctor\"]),\r\n                            \"utterance\": datasets.Value(\"string\"),\r\n                        }\r\n                    ),\r\n                }\r\n            )\r\n\r\n        return datasets.DatasetInfo(\r\n            # This is the description that will appear on the datasets page.\r\n            description=_DESCRIPTION,\r\n            features=features,\r\n            supervised_keys=None,\r\n            # Homepage of the dataset for documentation\r\n            homepage=_HOMEPAGE,\r\n            # License for the dataset if available\r\n            license=_LICENSE,\r\n            # Citation for the dataset\r\n            citation=_CITATION,\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n        path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))\r\n        if not os.path.exists(path_to_manual_file):\r\n            raise FileNotFoundError(\r\n                f\"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('medical_dialog', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})\"\r\n            )\r\n\r\n        filepaths = [\r\n            os.path.join(path_to_manual_file, txt_file_name)\r\n            for txt_file_name in sorted(os.listdir(path_to_manual_file))\r\n            if txt_file_name.endswith(\"txt\")\r\n        ]\r\n\r\n        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepaths\": filepaths})]\r\n\r\n    def _generate_examples(self, filepaths):\r\n        \"\"\"Yields examples. Iterates over each file and give the creates the corresponding features.\r\n\r\n        NOTE:\r\n        - The code makes some assumption on the structure of the raw .txt file.\r\n        - There are some checks to separate different id's. Hopefully, should not cause further issues later when more txt files are added.\r\n        \"\"\"\r\n        data_lang = self.config.name\r\n        id_ = -1\r\n        for filepath in filepaths:\r\n            with open(filepath, encoding=\"utf-8\") as f_in:\r\n                # Parameters to just \"sectionize\" the raw data\r\n                last_part = \"\"\r\n                last_dialog = {}\r\n                last_list = []\r\n                last_user = \"\"\r\n                check_list = []\r\n\r\n                # These flags are present to have a single function address both chinese and english data\r\n                # English data is a little hahazard (i.e. the sentences spans multiple different lines),\r\n                # Chinese is compact with one line for doctor and patient.\r\n                conv_flag = False\r\n                des_flag = False\r\n\r\n                while True:\r\n                    line = f_in.readline()\r\n                    if not line:\r\n                        break\r\n\r\n                    # Extracting the dialog id\r\n                    if line[:2] == \"id\":  # Hardcode alert!\r\n                        # Handling ID references that may come in the description\r\n                        # These were observed in the Chinese dataset and were not\r\n                        # followed by numbers\r\n                        try:\r\n                            dialogue_id = int(re.findall(r\"\\d+\", line)[0])\r\n                        except IndexError:\r\n                            continue\r\n\r\n                    # Extracting the url\r\n                    if line[:4] == \"http\":  # Hardcode alert!\r\n                        dialogue_url = line.rstrip()\r\n\r\n                    # Extracting the patient info from description.\r\n                    if line[:11] == \"Description\":  # Hardcode alert!\r\n                        last_part = \"description\"\r\n                        last_dialog = {}\r\n                        last_list = []\r\n                        last_user = \"\"\r\n                        last_conv = {\"speaker\": \"\", \"utterance\": \"\"}\r\n                        while True:\r\n                            line = f_in.readline()\r\n                            if (not line) or (line in [\"\\n\", \"\\n\\r\"]):\r\n                                break\r\n                            else:\r\n                                if data_lang == \"zh\":  # Condition in chinese\r\n                                    if line[:5] == \"\u75c5\u60c5\u63cf\u8ff0\uff1a\":  # Hardcode alert!\r\n                                        last_user = \"\u75c5\u4eba\"\r\n                                        sen = f_in.readline().rstrip()\r\n                                        des_flag = True\r\n\r\n                                if data_lang == \"en\":\r\n                                    last_user = \"Patient\"\r\n                                    sen = line.rstrip()\r\n                                    des_flag = True\r\n\r\n                                if des_flag:\r\n                                    if sen == \"\":\r\n                                        continue\r\n                                    if sen in check_list:\r\n                                        last_conv[\"speaker\"] = \"\"\r\n                                        last_conv[\"utterance\"] = \"\"\r\n                                    else:\r\n                                        last_conv[\"speaker\"] = last_user\r\n                                        last_conv[\"utterance\"] = sen\r\n                                        check_list.append(sen)\r\n                                    des_flag = False\r\n                                    break\r\n                    # Extracting the conversation info from dialogue.\r\n                    elif line[:8] == \"Dialogue\":  # Hardcode alert!\r\n                        if last_part == \"description\" and len(last_conv[\"utterance\"]) > 0:\r\n                            last_part = \"dialogue\"\r\n                            if data_lang == \"zh\":\r\n                                last_user = \"\u75c5\u4eba\"\r\n\r\n                            if data_lang == \"en\":\r\n                                last_user = \"Patient\"\r\n\r\n                            while True:\r\n                                line = f_in.readline()\r\n                                if (not line) or (line in [\"\\n\", \"\\n\\r\"]):\r\n                                    conv_flag = False\r\n                                    last_user = \"\"\r\n                                    last_list.append(copy.deepcopy(last_conv))\r\n                                    # To ensure close of conversation, only even number of sentences\r\n                                    # are extracted\r\n                                    last_turn = len(last_list)\r\n                                    if int(last_turn \/ 2) > 0:\r\n                                        temp = int(last_turn \/ 2)\r\n                                        id_ += 1\r\n                                        last_dialog[\"file_name\"] = filepath\r\n                                        last_dialog[\"dialogue_id\"] = dialogue_id\r\n                                        last_dialog[\"dialogue_url\"] = dialogue_url\r\n                                        last_dialog[\"dialogue_turns\"] = last_list[: temp * 2]\r\n                                        yield id_, last_dialog\r\n                                    break\r\n\r\n                                if data_lang == \"zh\":\r\n                                    if line[:3] == \"\u75c5\u4eba\uff1a\" or line[:3] == \"\u533b\u751f\uff1a\":  # Hardcode alert!\r\n                                        user = line[:2]  # Hardcode alert!\r\n                                        line = f_in.readline()\r\n                                        conv_flag = True\r\n\r\n                                # The elif block is to ensure that multi-line sentences are captured.\r\n                                # This has been observed only in english.\r\n                                if data_lang == \"en\":\r\n                                    if line.strip() == \"Patient:\" or line.strip() == \"Doctor:\":  # Hardcode alert!\r\n                                        user = line.replace(\":\", \"\").rstrip()\r\n                                        line = f_in.readline()\r\n                                        conv_flag = True\r\n                                    elif line[:2] != \"id\":  # Hardcode alert!\r\n                                        conv_flag = True\r\n\r\n                                # Continues till the next ID is parsed\r\n                                if conv_flag:\r\n                                    sen = line.rstrip()\r\n                                    if sen == \"\":\r\n                                        continue\r\n\r\n                                    if user == last_user:\r\n                                        last_conv[\"utterance\"] = last_conv[\"utterance\"] + sen\r\n                                    else:\r\n                                        last_user = user\r\n                                        last_list.append(copy.deepcopy(last_conv))\r\n                                        last_conv[\"utterance\"] = sen\r\n                                        last_conv[\"speaker\"] = user\r\n```\r\n\r\nrunning this code gives me the error:\r\n\r\n```\r\n  File \"C:\\Users\\Fabia\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\utils\\info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=292801173, num_examples=229674, dataset_name='medical_dialog')}]\r\n```","comment_length":47,"text":"Downloading Hugging Face Medical Dialog Dataset NonMatchingSplitsSizesError \n I wanted to download the Nedical Dialog Dataset from huggingface, using this github link:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/medical_dialog\r\n\r\nAfter downloading the raw datasets from google drive, i unpacked everything and put it in the same folder as the medical_dialog.py which is:\r\n\r\n```\r\nimport copy\r\nimport os\r\nimport re\r\n\r\nimport datasets\r\n\r\n\r\n_CITATION = \"\"\"\\\r\n@article{chen2020meddiag,\r\n  title={MedDialog: a large-scale medical dialogue dataset},\r\n  author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},\r\n  journal={arXiv preprint arXiv:2004.03329},\r\n  year={2020}\r\n}\r\n\"\"\"\r\n\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThe MedDialog dataset (English) contains conversations (in English) between doctors and patients.\\\r\nIt has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. \\\r\nThe raw dialogues are from healthcaremagic.com and icliniq.com.\\\r\n\r\nAll copyrights of the data belong to healthcaremagic.com and icliniq.com.\r\n\"\"\"\r\n\r\n_HOMEPAGE = \"https:\/\/github.com\/UCSD-AI4H\/Medical-Dialogue-System\"\r\n\r\n_LICENSE = \"\"\r\n\r\n\r\nclass MedicalDialog(datasets.GeneratorBasedBuilder):\r\n    VERSION = datasets.Version(\"1.0.0\")\r\n\r\n    BUILDER_CONFIGS = [\r\n        datasets.BuilderConfig(name=\"en\", description=\"The dataset of medical dialogs in English.\", version=VERSION),\r\n        datasets.BuilderConfig(name=\"zh\", description=\"The dataset of medical dialogs in Chinese.\", version=VERSION),\r\n    ]\r\n\r\n    @property\r\n    def manual_download_instructions(self):\r\n        return \"\"\"\\\r\n    \\n  For English:\\nYou need to go to https:\/\/drive.google.com\/drive\/folders\/1g29ssimdZ6JzTST6Y8g6h-ogUNReBtJD?usp=sharing,\\\r\n    and manually download the dataset from Google Drive. Once it is completed,\r\n    a file named Medical-Dialogue-Dataset-English-<timestamp-info>.zip will appear in your Downloads folder(\r\n    or whichever folder your browser chooses to save files to). Unzip the folder to obtain\r\n    a folder named \"Medical-Dialogue-Dataset-English\" several text files.\r\n\r\n    Now, you can specify the path to this folder for the data_dir argument in the\r\n    datasets.load_dataset(...) option.\r\n    The <path\/to\/folder> can e.g. be \"\/Downloads\/Medical-Dialogue-Dataset-English\".\r\n    The data can then be loaded using the below command:\\\r\n         datasets.load_dataset(\"medical_dialog\", name=\"en\", data_dir=\"\/Downloads\/Medical-Dialogue-Dataset-English\")`.\r\n\r\n    \\n  For Chinese:\\nFollow the above process. Change the 'name' to 'zh'.The download link is https:\/\/drive.google.com\/drive\/folders\/1r09_i8nJ9c1nliXVGXwSqRYqklcHd9e2\r\n\r\n    **NOTE**\r\n    - A caution while downloading from drive. It is better to download single files since creating a zip might not include files <500 MB. This has been observed mutiple times.\r\n    - After downloading the files and adding them to the appropriate folder, the path of the folder can be given as input tu the data_dir path.\r\n    \"\"\"\r\n\r\n    datasets.load_dataset(\"medical_dialog\", name=\"en\", data_dir=\"Medical-Dialogue-Dataset-English\")\r\n\r\n    def _info(self):\r\n        if self.config.name == \"zh\":\r\n            features = datasets.Features(\r\n                {\r\n                    \"file_name\": datasets.Value(\"string\"),\r\n                    \"dialogue_id\": datasets.Value(\"int32\"),\r\n                    \"dialogue_url\": datasets.Value(\"string\"),\r\n                    \"dialogue_turns\": datasets.Sequence(\r\n                        {\r\n                            \"speaker\": datasets.ClassLabel(names=[\"\u75c5\u4eba\", \"\u533b\u751f\"]),\r\n                            \"utterance\": datasets.Value(\"string\"),\r\n                        }\r\n                    ),\r\n                }\r\n            )\r\n\r\n        if self.config.name == \"en\":\r\n            features = datasets.Features(\r\n                {\r\n                    \"file_name\": datasets.Value(\"string\"),\r\n                    \"dialogue_id\": datasets.Value(\"int32\"),\r\n                    \"dialogue_url\": datasets.Value(\"string\"),\r\n                    \"dialogue_turns\": datasets.Sequence(\r\n                        {\r\n                            \"speaker\": datasets.ClassLabel(names=[\"Patient\", \"Doctor\"]),\r\n                            \"utterance\": datasets.Value(\"string\"),\r\n                        }\r\n                    ),\r\n                }\r\n            )\r\n\r\n        return datasets.DatasetInfo(\r\n            # This is the description that will appear on the datasets page.\r\n            description=_DESCRIPTION,\r\n            features=features,\r\n            supervised_keys=None,\r\n            # Homepage of the dataset for documentation\r\n            homepage=_HOMEPAGE,\r\n            # License for the dataset if available\r\n            license=_LICENSE,\r\n            # Citation for the dataset\r\n            citation=_CITATION,\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n        path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))\r\n        if not os.path.exists(path_to_manual_file):\r\n            raise FileNotFoundError(\r\n                f\"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('medical_dialog', data_dir=...)`. Manual download instructions: {self.manual_download_instructions})\"\r\n            )\r\n\r\n        filepaths = [\r\n            os.path.join(path_to_manual_file, txt_file_name)\r\n            for txt_file_name in sorted(os.listdir(path_to_manual_file))\r\n            if txt_file_name.endswith(\"txt\")\r\n        ]\r\n\r\n        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepaths\": filepaths})]\r\n\r\n    def _generate_examples(self, filepaths):\r\n        \"\"\"Yields examples. Iterates over each file and give the creates the corresponding features.\r\n\r\n        NOTE:\r\n        - The code makes some assumption on the structure of the raw .txt file.\r\n        - There are some checks to separate different id's. Hopefully, should not cause further issues later when more txt files are added.\r\n        \"\"\"\r\n        data_lang = self.config.name\r\n        id_ = -1\r\n        for filepath in filepaths:\r\n            with open(filepath, encoding=\"utf-8\") as f_in:\r\n                # Parameters to just \"sectionize\" the raw data\r\n                last_part = \"\"\r\n                last_dialog = {}\r\n                last_list = []\r\n                last_user = \"\"\r\n                check_list = []\r\n\r\n                # These flags are present to have a single function address both chinese and english data\r\n                # English data is a little hahazard (i.e. the sentences spans multiple different lines),\r\n                # Chinese is compact with one line for doctor and patient.\r\n                conv_flag = False\r\n                des_flag = False\r\n\r\n                while True:\r\n                    line = f_in.readline()\r\n                    if not line:\r\n                        break\r\n\r\n                    # Extracting the dialog id\r\n                    if line[:2] == \"id\":  # Hardcode alert!\r\n                        # Handling ID references that may come in the description\r\n                        # These were observed in the Chinese dataset and were not\r\n                        # followed by numbers\r\n                        try:\r\n                            dialogue_id = int(re.findall(r\"\\d+\", line)[0])\r\n                        except IndexError:\r\n                            continue\r\n\r\n                    # Extracting the url\r\n                    if line[:4] == \"http\":  # Hardcode alert!\r\n                        dialogue_url = line.rstrip()\r\n\r\n                    # Extracting the patient info from description.\r\n                    if line[:11] == \"Description\":  # Hardcode alert!\r\n                        last_part = \"description\"\r\n                        last_dialog = {}\r\n                        last_list = []\r\n                        last_user = \"\"\r\n                        last_conv = {\"speaker\": \"\", \"utterance\": \"\"}\r\n                        while True:\r\n                            line = f_in.readline()\r\n                            if (not line) or (line in [\"\\n\", \"\\n\\r\"]):\r\n                                break\r\n                            else:\r\n                                if data_lang == \"zh\":  # Condition in chinese\r\n                                    if line[:5] == \"\u75c5\u60c5\u63cf\u8ff0\uff1a\":  # Hardcode alert!\r\n                                        last_user = \"\u75c5\u4eba\"\r\n                                        sen = f_in.readline().rstrip()\r\n                                        des_flag = True\r\n\r\n                                if data_lang == \"en\":\r\n                                    last_user = \"Patient\"\r\n                                    sen = line.rstrip()\r\n                                    des_flag = True\r\n\r\n                                if des_flag:\r\n                                    if sen == \"\":\r\n                                        continue\r\n                                    if sen in check_list:\r\n                                        last_conv[\"speaker\"] = \"\"\r\n                                        last_conv[\"utterance\"] = \"\"\r\n                                    else:\r\n                                        last_conv[\"speaker\"] = last_user\r\n                                        last_conv[\"utterance\"] = sen\r\n                                        check_list.append(sen)\r\n                                    des_flag = False\r\n                                    break\r\n                    # Extracting the conversation info from dialogue.\r\n                    elif line[:8] == \"Dialogue\":  # Hardcode alert!\r\n                        if last_part == \"description\" and len(last_conv[\"utterance\"]) > 0:\r\n                            last_part = \"dialogue\"\r\n                            if data_lang == \"zh\":\r\n                                last_user = \"\u75c5\u4eba\"\r\n\r\n                            if data_lang == \"en\":\r\n                                last_user = \"Patient\"\r\n\r\n                            while True:\r\n                                line = f_in.readline()\r\n                                if (not line) or (line in [\"\\n\", \"\\n\\r\"]):\r\n                                    conv_flag = False\r\n                                    last_user = \"\"\r\n                                    last_list.append(copy.deepcopy(last_conv))\r\n                                    # To ensure close of conversation, only even number of sentences\r\n                                    # are extracted\r\n                                    last_turn = len(last_list)\r\n                                    if int(last_turn \/ 2) > 0:\r\n                                        temp = int(last_turn \/ 2)\r\n                                        id_ += 1\r\n                                        last_dialog[\"file_name\"] = filepath\r\n                                        last_dialog[\"dialogue_id\"] = dialogue_id\r\n                                        last_dialog[\"dialogue_url\"] = dialogue_url\r\n                                        last_dialog[\"dialogue_turns\"] = last_list[: temp * 2]\r\n                                        yield id_, last_dialog\r\n                                    break\r\n\r\n                                if data_lang == \"zh\":\r\n                                    if line[:3] == \"\u75c5\u4eba\uff1a\" or line[:3] == \"\u533b\u751f\uff1a\":  # Hardcode alert!\r\n                                        user = line[:2]  # Hardcode alert!\r\n                                        line = f_in.readline()\r\n                                        conv_flag = True\r\n\r\n                                # The elif block is to ensure that multi-line sentences are captured.\r\n                                # This has been observed only in english.\r\n                                if data_lang == \"en\":\r\n                                    if line.strip() == \"Patient:\" or line.strip() == \"Doctor:\":  # Hardcode alert!\r\n                                        user = line.replace(\":\", \"\").rstrip()\r\n                                        line = f_in.readline()\r\n                                        conv_flag = True\r\n                                    elif line[:2] != \"id\":  # Hardcode alert!\r\n                                        conv_flag = True\r\n\r\n                                # Continues till the next ID is parsed\r\n                                if conv_flag:\r\n                                    sen = line.rstrip()\r\n                                    if sen == \"\":\r\n                                        continue\r\n\r\n                                    if user == last_user:\r\n                                        last_conv[\"utterance\"] = last_conv[\"utterance\"] + sen\r\n                                    else:\r\n                                        last_user = user\r\n                                        last_list.append(copy.deepcopy(last_conv))\r\n                                        last_conv[\"utterance\"] = sen\r\n                                        last_conv[\"speaker\"] = user\r\n```\r\n\r\nrunning this code gives me the error:\r\n\r\n```\r\n  File \"C:\\Users\\Fabia\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\utils\\info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=292801173, num_examples=229674, dataset_name='medical_dialog')}]\r\n``` \n Hi @fabianslife, thanks for reporting.\r\n\r\nI think you were using an old version of `datasets` because this bug was already fixed in version `1.13.0` (13 Oct 2021):\r\n- Fix: 55fd140a63b8f03a0e72985647e498f1fc799d3f\r\n- PR: #3046\r\n- Issue: #2969 \r\n\r\nPlease, feel free to update the library: `pip install -U datasets`.","embeddings":[-0.0822347552,-0.1012544557,0.0506370962,0.2017837614,0.2633806169,0.1685770005,0.0695232898,0.3702644706,0.0356434956,0.1093901098,-0.3243410885,-0.167857185,-0.2540262341,0.2069634944,-0.0428665206,-0.0932666361,-0.1223634034,0.1837751418,-0.0260314215,-0.0639086515,-0.2897328138,0.3934534192,-0.1954780817,0.1877860725,-0.1856004894,-0.1499009877,-0.0101950476,0.2556079626,-0.1152212992,-0.4220693409,0.0924977735,-0.0495883413,-0.1492705494,0.2886666656,-0.0001166153,-0.0065964512,0.4009492397,-0.071384266,-0.4339607656,-0.2540564835,0.1313821822,-0.320515722,0.1037529111,-0.0638358295,-0.1562529653,-0.195309639,0.1677977294,-0.0769336596,0.3271376193,0.4283125997,0.2095063925,0.484965235,0.1842076182,-0.1346450597,-0.0405901857,-0.2048740238,-0.1212995872,0.2380761355,0.1828806251,0.0269928686,0.0527462102,0.3027258515,-0.1575199068,0.0641235337,0.1269064099,-0.0285634343,0.1781821251,-0.2157180011,0.1740413308,0.4907418191,0.3824261129,-0.2173603326,-0.1474620402,-0.485942632,-0.1597753614,-0.0888685733,0.16857256,0.2050094157,-0.1217920706,0.1508705914,-0.2983013093,-0.2131405771,-0.1136630699,0.1146658882,0.2418625653,-0.2215080112,-0.1397751123,0.0445083231,0.3040449619,-0.0977019966,0.0613327324,-0.0643990487,0.148147732,0.2524747252,-0.3526425064,0.0232417993,0.06867975,0.5975241065,0.4845497012,0.0587841757,-0.026376823,-0.1818014383,0.1276576668,-0.0384420976,0.0964785367,0.0655281767,-0.0612437464,0.138753444,-0.0170429628,0.327691406,0.111850664,0.1094721854,0.1075877249,-0.3062863648,-0.0333932042,-0.0190596953,0.345741719,-0.1425897628,-0.2742963731,0.2366682142,-0.0440537743,0.0637440979,-0.0333527587,0.2590544224,-0.1769034415,0.0435286276,0.1166358814,0.1159578562,-0.3451220393,-0.1613075882,-0.2608228326,0.1643092334,-0.0332896002,0.1689850092,0.0347326025,-0.1255004406,0.2564378679,-0.1071617082,0.2307156622,0.072280474,0.0567064211,0.0726835132,-0.2184642553,0.261107564,-0.0535263605,0.2364993691,0.2328539491,-0.2591096759,-0.13875705,-0.1284897029,0.0172981191,-0.3381438553,-0.0802353323,0.1900099665,-0.2638617456,0.0143085998,0.0297068972,0.1307978481,0.0930802077,-0.1983010471,0.0333299115,-0.0617579482,0.0044347458,-0.2744118273,0.1232729778,0.7426923513,-0.1395770907,-0.2830492258,0.0191568267,-0.1410677582,0.1419605315,0.5420596004,0.0058149956,0.0687235147,-0.411934644,0.3345681727,-0.2171801031,-0.3580029905,-0.6660628915,0.2153518796,-0.3179678917,0.3620891571,0.2330599725,0.0182178654,0.0280610584,0.0030949546,0.0458009057,0.1369432062,-0.0440055467,-0.0849106312,-0.3701815903,-0.4301192462,0.2411246002,-0.0232842956,0.1990385503,0.1035283804,0.0802483559,0.0383671522,0.3199335933,-0.1821790636,0.0209116731,0.2389181256,0.1267651916,0.2265432626,-0.0901512653,-0.1460616589,-0.5814971328,0.2160627991,-0.1932527423,0.2225378901,-0.3028105497,-0.1779702902,-0.2360212207,-0.1964425594,-0.2772652507,-0.2810019851,0.1227609366,0.2595586777,0.3135051429,0.2352919877,0.0345582291,0.7752690911,-0.0233306028,0.2522841394,-0.7721101046,0.3411808312,-0.1991529614,-0.1179219931,0.221140489,0.0363147371,0.2060836107,-0.064401716,0.0028669583,0.484965384,-0.1496256739,0.41090253,-0.0291125905,0.1369061917,0.2067424059,-0.3650001884,-0.0245873928,0.0684621558,0.0142565211,0.0281601101,-0.0001098991,0.1398437023,-0.1336727291,0.168501541,0.1113565341,0.1893994659,0.1078232303,-0.0124495616,-0.3713844717,-0.2351922989,0.4695648253,0.245634824,0.2423610687,0.062389005,-0.4302945137,-0.0021306393,0.3643639982,-0.1484611928,0.1277198046,-0.0148428082,-0.1894427091,-0.0664966702,-0.0950460657,-0.1106681824,0.121865049,0.2724353075,-0.0799275041,0.3874335587,-0.066577889,-0.2628052235,0.0560828857,0.0531188995,0.1897227019,0.2704373598,-0.1357045621,-0.0093827099,-0.482129693,0.1189675853,-0.1958283931,0.3352092505,-0.4910240769,-0.3174894154,-0.2799964845,-0.0921366364,0.0074218088,-0.1840839982,-0.2012170851,-0.1917652786,-0.165681228,-0.0163899958,0.040547464,0.2200471163,-0.1257200241,0.1210513487,0.0384350941,-0.1624421924,-0.215819031,-0.079565689,-0.1359449923,0.0479290783,-0.0171363577,-0.0904392377,0.4363782704,-0.5416260362,0.0498075821,-0.2271078378,-0.3000355363,0.0101097627,-0.205334723,0.2798484266,0.3179301322,0.434451133,-0.0014324194,0.0614344776,0.2266158611,-0.2231183946,-0.1952257454,-0.0322888605,0.0966494828,0.0942689329,0.0933218598,-0.2693897486,-0.3744224012,-0.4122770131,0.3693107367,-0.1089500636,0.0985020995,0.5286793709,0.0807345808,0.3109911382,-0.1672538817,0.2240255028,-0.2821725309,-0.123208046,0.2557339072,-0.082004942,-0.3442485929,-0.1020711735,0.0245835837,0.1317225397,-0.0023874964,-0.4030704796,-0.292111963,-0.2679082453,0.048352886,0.2299390733,-0.0519907735,0.5416551232,-0.0149333943,-0.0770050734,-0.0996069759,-0.3142591119,0.0239279829,0.043260742,0.0952379033,-0.0779519975,0.6070737839,0.021416625,0.5544836521,0.4699343145,0.2559576929,0.2294146121,-0.1516991109,0.5467892885,-0.0128668398,-0.5121337771,0.2927035093,0.0441056415,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3563","title":"Dataset.from_pandas preserves useless index","comments":"Hi! That makes sense. Sure, feel free to open a PR! Just a small suggestion: let's make `preserve_index` a parameter of `Dataset.from_pandas` (which we then pass to `InMemoryTable.from_pandas`) with `None` as a default value to not have this as a breaking change. ","body":"## Describe the bug\r\nLet's say that you want to create a Dataset object from pandas dataframe. Most likely you will write something like this:\r\n\r\n```\r\nimport pandas as pd\r\nfrom datasets import Dataset\r\n\r\n\r\ndf = pd.read_csv('some_dataset.csv')\r\n# Some DataFrame preprocessing code...\r\ndataset = Dataset.from_pandas(df)\r\n```\r\nIf your preprocessing code contain indexing operations like this:\r\n```\r\ndf = df[df.col1 == some_value]\r\n```\r\nthen your df.index can be changed from (default) ```RangeIndex(start=0, stop=16590, step=1)``` to something like this ```Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,\r\n                9,\r\n            ...\r\n            83979, 83980, 83981, 83982, 83983, 83984, 83985, 83986, 83987,\r\n            83988],\r\n           dtype='int64', length=16590)```\r\n\r\nIn this case, PyArrow (by default) will preserve this non-standard index. In the result, your dataset object will have the extra field that you likely don't want to have: '__index_level_0__'.\r\n\r\nYou can easily fix this by just adding extra argument ```preserve_index=False``` to call of ```InMemoryTable.from_pandas``` in ```arrow_dataset.py```.\r\n\r\nIf you approve that this isn't desirable behavior, I can make a PR fixing that.\r\n\r\n## Environment info\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.11.0-44-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n\r\n","comment_length":42,"text":"Dataset.from_pandas preserves useless index \n ## Describe the bug\r\nLet's say that you want to create a Dataset object from pandas dataframe. Most likely you will write something like this:\r\n\r\n```\r\nimport pandas as pd\r\nfrom datasets import Dataset\r\n\r\n\r\ndf = pd.read_csv('some_dataset.csv')\r\n# Some DataFrame preprocessing code...\r\ndataset = Dataset.from_pandas(df)\r\n```\r\nIf your preprocessing code contain indexing operations like this:\r\n```\r\ndf = df[df.col1 == some_value]\r\n```\r\nthen your df.index can be changed from (default) ```RangeIndex(start=0, stop=16590, step=1)``` to something like this ```Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,\r\n                9,\r\n            ...\r\n            83979, 83980, 83981, 83982, 83983, 83984, 83985, 83986, 83987,\r\n            83988],\r\n           dtype='int64', length=16590)```\r\n\r\nIn this case, PyArrow (by default) will preserve this non-standard index. In the result, your dataset object will have the extra field that you likely don't want to have: '__index_level_0__'.\r\n\r\nYou can easily fix this by just adding extra argument ```preserve_index=False``` to call of ```InMemoryTable.from_pandas``` in ```arrow_dataset.py```.\r\n\r\nIf you approve that this isn't desirable behavior, I can make a PR fixing that.\r\n\r\n## Environment info\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.11.0-44-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n\r\n \n Hi! That makes sense. Sure, feel free to open a PR! Just a small suggestion: let's make `preserve_index` a parameter of `Dataset.from_pandas` (which we then pass to `InMemoryTable.from_pandas`) with `None` as a default value to not have this as a breaking change. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3561","title":"Cannot load \u2018bookcorpusopen\u2019","comments":"The host of this copy of the dataset (https:\/\/the-eye.eu) is down and has been down for a good amount of time ([potentially months](https:\/\/www.reddit.com\/r\/Roms\/comments\/q82s15\/theeye_downdied\/))\r\n\r\nFinding this dataset is a little esoteric, as the original authors took down the official BookCorpus dataset some time ago.\r\n\r\nThere are community-created versions of BookCorpus, such as the files hosted in the link below.\r\nhttps:\/\/battle.shawwn.com\/sdb\/bookcorpus\/\r\n\r\nAnd more discussion here:\r\nhttps:\/\/github.com\/soskek\/bookcorpus\r\n\r\nDo we want to remove this dataset entirely?  There's a fair argument for this, given that the official BookCorpus dataset was taken down by the authors.   If not, perhaps can open a PR with the link to the community-created tar above and updated dataset description.","body":"## Describe the bug\r\nCannot load 'bookcorpusopen'\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('bookcorpusopen')\r\n```\r\nor\r\n```python\r\ndataset = load_dataset('bookcorpusopen',script_version='master')\r\n```\r\n\r\n## Actual results\r\nConnectionError: Couldn't reach https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/books1.tar.gz\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64\r\n- Python version:  3.6.13\r\n- PyArrow version: 6.0.1\r\n","comment_length":109,"text":"Cannot load \u2018bookcorpusopen\u2019 \n ## Describe the bug\r\nCannot load 'bookcorpusopen'\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('bookcorpusopen')\r\n```\r\nor\r\n```python\r\ndataset = load_dataset('bookcorpusopen',script_version='master')\r\n```\r\n\r\n## Actual results\r\nConnectionError: Couldn't reach https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/books1.tar.gz\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64\r\n- Python version:  3.6.13\r\n- PyArrow version: 6.0.1\r\n \n The host of this copy of the dataset (https:\/\/the-eye.eu) is down and has been down for a good amount of time ([potentially months](https:\/\/www.reddit.com\/r\/Roms\/comments\/q82s15\/theeye_downdied\/))\r\n\r\nFinding this dataset is a little esoteric, as the original authors took down the official BookCorpus dataset some time ago.\r\n\r\nThere are community-created versions of BookCorpus, such as the files hosted in the link below.\r\nhttps:\/\/battle.shawwn.com\/sdb\/bookcorpus\/\r\n\r\nAnd more discussion here:\r\nhttps:\/\/github.com\/soskek\/bookcorpus\r\n\r\nDo we want to remove this dataset entirely?  There's a fair argument for this, given that the official BookCorpus dataset was taken down by the authors.   If not, perhaps can open a PR with the link to the community-created tar above and updated dataset 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3561","title":"Cannot load \u2018bookcorpusopen\u2019","comments":"Hi! The `bookcorpusopen` dataset is not working for the same reason as explained in this comment: https:\/\/github.com\/huggingface\/datasets\/issues\/3504#issuecomment-1004564980","body":"## Describe the bug\r\nCannot load 'bookcorpusopen'\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('bookcorpusopen')\r\n```\r\nor\r\n```python\r\ndataset = load_dataset('bookcorpusopen',script_version='master')\r\n```\r\n\r\n## Actual results\r\nConnectionError: Couldn't reach https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/books1.tar.gz\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64\r\n- Python version:  3.6.13\r\n- PyArrow version: 6.0.1\r\n","comment_length":17,"text":"Cannot load \u2018bookcorpusopen\u2019 \n ## Describe the bug\r\nCannot load 'bookcorpusopen'\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('bookcorpusopen')\r\n```\r\nor\r\n```python\r\ndataset = load_dataset('bookcorpusopen',script_version='master')\r\n```\r\n\r\n## Actual results\r\nConnectionError: Couldn't reach https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/books1.tar.gz\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64\r\n- Python version:  3.6.13\r\n- PyArrow version: 6.0.1\r\n \n Hi! The `bookcorpusopen` dataset is not working for the same reason as explained in this comment: https:\/\/github.com\/huggingface\/datasets\/issues\/3504#issuecomment-1004564980","embeddings":[-0.3040995002,-0.0313266627,-0.0976267681,0.3863771856,0.1762520969,-0.0748728663,0.3626804352,0.1297378093,0.0686655417,-0.0113906423,-0.3443720341,0.2929642797,0.4512728751,0.4727549255,0.2462408543,-0.2257456928,0.0581256412,0.0326240137,-0.1498840749,-0.0128785754,-0.274191469,0.2433874011,-0.2439013273,0.1151217818,0.0347777382,-0.0482542962,-0.1153986976,0.207637623,-0.1773623079,-0.2931484282,0.3920192719,0.0796982348,0.3414254785,0.6839101911,-0.0001057868,0.0988885239,0.217961967,-0.1007972881,-0.2508341074,-0.3339309096,0.0136698093,-0.2229678035,0.1835235208,-0.3404555619,-0.1066095904,-0.0038230938,0.0682203174,-0.0778976977,0.21577169,0.2731751204,0.3140532076,0.4865564704,0.5041320324,-0.1894099861,0.2589673102,-0.1930471063,-0.305526346,0.3859064877,0.1879624575,-0.022761602,0.0069263834,0.2931952775,0.0704253837,0.0184212811,0.296484828,0.1651797742,0.2890575826,-0.2286773026,0.1311086863,0.2888595164,0.5893779397,-0.3430301249,-0.4288451374,0.1980902106,0.0353052728,-0.0482676513,0.2403666824,0.2741202712,0.0024975631,0.0978773162,-0.0529075786,-0.1914308965,-0.2008307874,0.3476568758,-0.0746483207,0.1899848878,-0.1406449825,0.0944450945,0.4421225488,-0.0651950687,-0.0740436539,0.0313826203,-0.2132891268,0.2532491982,-0.268321991,0.0807927027,-0.1002193466,0.3529554307,0.1015539095,0.0460744239,0.1917613149,0.0786438212,-0.0968853608,0.2581847906,0.3640650511,0.1725259721,-0.0227867626,0.0971098244,0.2282193601,0.7007204294,-0.0137575874,-0.1636498272,-0.1453305334,-0.1738512218,0.086859189,-0.2776423693,0.3585456908,-0.3601553142,-0.3709005117,0.3883834183,-0.0415780358,-0.0881089643,0.0041235317,0.5087109804,-0.2309177071,0.0699048266,0.3570576608,0.192726016,-0.1977942735,0.0101118628,-0.1891305149,0.1511403918,-0.2733268142,-0.0187430717,0.3191659451,-0.3257420361,0.4035120606,-0.0399335474,0.2166830748,-0.0889154822,0.1461163014,-0.224631384,-0.127489984,0.2353281528,0.2621248066,-0.0685862675,0.0656794533,-0.0196940266,-0.1300846934,0.0930283815,-0.1994357854,-0.0278199166,-0.3251883984,0.2825751007,-0.1520911604,-0.068627879,-0.4004472196,-0.0609164312,0.1089003906,-0.2294536084,0.0211115647,-0.1992716491,0.0245260429,-0.1094150022,0.3554528356,0.302875638,-0.3606639504,-0.143423155,-0.2361548692,-0.1805370599,-0.055502668,0.2575698495,-0.2320537269,-0.0333135687,-0.1370911151,0.0158603694,0.312315613,-0.2744396031,-0.5513470769,0.21504125,-0.0283901803,0.2342712879,-0.1087093949,0.112246111,-0.2205354273,0.0237915181,0.2164283842,0.2077672631,0.1260240972,-0.1710084081,-0.2055868357,-0.3486626446,0.0166730992,0.441149056,0.1020839587,-0.0726987571,0.0253831092,-0.0721803233,0.3531666398,0.0695233569,-0.0735368431,0.1227054894,0.1832720488,0.0771999285,0.2287885398,-0.145321399,-0.2660005689,0.2397564054,-0.3486400247,0.2044306099,-0.0615602694,-0.2220141739,-0.3950300813,0.0354565382,-0.1266085207,-0.0811913237,0.1774270535,0.0145139601,-0.1073365957,0.0187751148,-0.1582647115,0.1478346735,0.0688465759,0.1915236264,-0.5456565022,0.2845678031,-0.3376746476,-0.1068238616,-0.0281464066,0.0898925141,0.0734542683,-0.1532949656,0.0061350167,0.3480977714,-0.2124079913,-0.0243983883,0.1298401356,0.0591789335,0.0733825341,-0.4544088542,0.1580823809,0.2984005213,0.2761690915,0.0501611531,-0.1404435337,0.2039929181,0.234247297,0.2024296671,0.2034059167,-0.0016332247,0.2011037171,-0.0892284513,-0.0016705495,0.0262155514,0.3363716304,-0.2481397539,0.0602840446,-0.0251088962,-0.3129990697,-0.0402558073,0.1746577024,-0.0669003502,0.2029158622,0.1479573846,-0.1903835386,-0.0178290997,0.1150681078,-0.3738415539,0.2573303878,0.2263431251,-0.249754101,0.093097806,-0.1334784329,-0.0581297837,0.1939085722,0.156917572,-0.0053372532,0.0540845022,0.0828355104,0.041943498,-0.2955307364,-0.346786648,-0.1753730923,0.1976494193,-0.2493764162,0.1494531333,0.1006179303,-0.178540498,-0.0730084553,0.0395986997,-0.2517968416,-0.0576482452,-0.1782161295,0.5135449767,0.165368408,0.0944239944,-0.2523269653,0.1649957001,-0.1305601895,-0.008304568,-0.3104059398,-0.1165602058,-0.2800060213,0.1639331877,0.2169736326,-0.0081355497,0.1147061065,-0.379889667,-0.2331970036,-0.2768366039,-0.2770318985,0.0278612953,-0.0243407059,0.4442034066,-0.0164538361,0.2563557327,-0.2690221369,-0.2391490936,0.4104328156,-0.0252183229,-0.0551099442,0.0633900017,-0.1336033493,-0.3349413574,-0.0116803888,-0.3949994445,-0.2395922691,-0.4058280289,0.3706286252,0.2338161469,0.0733823478,0.1049863696,0.098870866,0.0991876721,0.2292345464,0.1874793619,-0.2214592397,-0.2830171287,0.2920249999,-0.1198168173,-0.4630991518,0.0656867102,0.1131291091,0.1902663261,-0.0264893956,-0.6776518822,-0.1692540944,-0.17520006,0.0313436985,-0.1260605156,0.1965571344,0.0402386896,-0.0052936105,-0.0928208381,0.0170208234,-0.4726465642,0.0850818828,0.1414824128,0.2064213753,-0.1225702986,0.1178360283,-0.1756323278,0.5118344426,0.1037818566,-0.0811485052,0.4418145716,0.109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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3561","title":"Cannot load \u2018bookcorpusopen\u2019","comments":"Hi @HUIYINXUE, it should work now that the data owners created a mirror server with all data, and we updated the URL in our library.","body":"## Describe the bug\r\nCannot load 'bookcorpusopen'\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('bookcorpusopen')\r\n```\r\nor\r\n```python\r\ndataset = load_dataset('bookcorpusopen',script_version='master')\r\n```\r\n\r\n## Actual results\r\nConnectionError: Couldn't reach https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/books1.tar.gz\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64\r\n- Python version:  3.6.13\r\n- PyArrow version: 6.0.1\r\n","comment_length":25,"text":"Cannot load \u2018bookcorpusopen\u2019 \n ## Describe the bug\r\nCannot load 'bookcorpusopen'\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('bookcorpusopen')\r\n```\r\nor\r\n```python\r\ndataset = load_dataset('bookcorpusopen',script_version='master')\r\n```\r\n\r\n## Actual results\r\nConnectionError: Couldn't reach https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/books1.tar.gz\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux version 3.10.0-1160.45.1.el7.x86_64\r\n- Python version:  3.6.13\r\n- PyArrow version: 6.0.1\r\n \n Hi @HUIYINXUE, it should work now that the data owners created a mirror server with all data, and we updated the URL in our library.","embeddings":[-0.2113384902,0.1578315496,-0.0710610002,0.3246245086,-0.0623679087,-0.140038237,0.3946605325,0.1990951896,0.0004560956,-0.0569876283,-0.3438050747,0.3186571896,0.4406040609,0.2468889952,0.1852917969,-0.1837171912,-0.0093767913,0.0977920741,-0.2471713871,-0.1362241656,-0.2226492912,0.2103239149,-0.2186716497,0.0564595014,0.0957674906,0.0557494424,-0.187488392,0.2291536778,-0.1800056547,-0.2768181562,0.3075956404,0.062044397,0.2802502513,0.7133696675,-0.0001043753,0.1147787347,0.2440024614,-0.1123206988,-0.199603647,-0.3130018413,0.0770697892,-0.2271604538,0.2209303975,-0.3804935515,-0.0671213865,-0.0349606276,0.0549525395,-0.0544916168,0.1544204801,0.3217821717,0.3033832312,0.403488785,0.4645877779,-0.1542148292,0.3528247774,-0.3595564067,-0.2430076003,0.4711845219,0.232970506,-0.0000270404,-0.0594385304,0.2717027366,0.0006132856,0.0236882605,0.1965855211,0.0888850838,0.2946029305,-0.1633601934,0.0341508314,0.2413433045,0.7398321629,-0.2725261748,-0.4196252525,0.2690437436,-0.0104719056,0.1753212959,0.1445173323,0.294185102,0.0955387652,0.1861481518,-0.0064806337,-0.2058640569,-0.2433583289,0.3780087531,-0.1200261787,0.3623871803,-0.0302216318,0.0756170899,0.3546874821,-0.0821364075,0.1572980881,0.1028212011,-0.1625429094,0.2287708223,-0.1837217808,0.0521075912,-0.1136939973,0.0814215168,0.0651358962,0.2042747736,0.2832838893,0.0479988158,-0.1897606701,0.3114686012,0.2890380621,0.2099096626,-0.099048093,0.2186518461,0.2040910721,0.5851657391,-0.0522702523,-0.113378197,-0.1831315756,-0.1301211864,-0.0175680239,-0.2839523852,0.327290833,-0.3732510805,-0.2428334951,0.33405599,-0.1135899425,0.0038518673,-0.0512588322,0.4544197023,-0.2428421229,0.1243252009,0.4575770199,0.1149265245,-0.1609885395,-0.0197773241,-0.1905477047,0.0718089789,-0.2310280204,-0.0921796262,0.3717740774,-0.2346972227,0.3886071146,-0.1324553192,0.2145487815,-0.0450345352,0.2261027247,-0.1107658371,-0.0937564373,0.2854638696,0.2430879176,-0.0938536972,0.0770736858,0.0442291014,-0.1203149781,0.1645058244,-0.3400858939,-0.1221260428,-0.3745462596,0.2861120999,-0.1825068444,-0.1074182317,-0.2950915694,-0.206216976,0.0992175713,-0.3185287416,0.0059393649,-0.0863392428,0.0790097415,-0.2045502216,0.3045512438,0.2460777462,-0.3608829081,-0.0126386313,-0.2692113817,-0.0628831014,-0.0544339567,0.2876454592,-0.2625780702,-0.1211231649,-0.188109085,0.0460060164,0.3114867508,-0.2631565034,-0.6067820191,0.2937272787,-0.2281534225,0.1462283581,-0.0250608101,0.1078272834,-0.0763035342,-0.0253907274,0.2408120334,0.2417179644,0.2699095607,-0.1109598801,-0.339435041,-0.3910665214,-0.0384813137,0.4875764549,0.1057464555,-0.0100299772,0.0949734375,-0.0540075637,0.4236530364,0.1404286921,-0.0630757511,0.1220274866,0.2237709016,0.1767886579,0.3094778061,-0.0424293242,-0.0907081366,0.2697152495,-0.3930765986,0.205322817,-0.0588648431,-0.2123311758,-0.4285577536,0.0650538728,-0.0899624527,-0.0569387451,0.2328094989,-0.0904606655,-0.2030474544,0.039656654,-0.065625146,0.1372672468,0.0822560638,0.1634832472,-0.464152962,0.3984283209,-0.3690484464,-0.022507919,-0.0202388763,-0.0086791366,0.1869437099,-0.1579684615,-0.0603019893,0.3134416938,-0.2789283693,0.1034663618,0.2607092261,0.0233395956,0.0776284933,-0.5886683464,0.3687871397,0.4780152738,0.296459496,0.0148393577,-0.0755921453,0.1813370436,0.2256781012,0.1825273633,0.1536370218,0.0372750387,0.1909890175,-0.1526694745,0.0254960097,0.02025792,0.334603101,-0.1322570294,-0.0063850624,0.0396117643,-0.2098359913,-0.0408608988,0.1187139302,-0.0673250183,0.2158240676,0.1518544555,-0.1765340269,-0.0545134097,0.114311792,-0.261731267,0.2376492471,0.2424408346,-0.1963001192,0.1205817759,-0.0530797727,-0.1583869159,0.2539414465,0.1262936741,0.0370220914,0.0898857415,0.1443256289,0.0009161456,-0.2410615832,-0.4116147161,-0.074695237,0.1623810083,-0.238294661,0.0791035891,0.0664237291,-0.0957225412,-0.1217726246,0.0451389626,-0.1997783035,-0.1133355424,-0.1184487864,0.4504687488,0.0264466312,0.0648226291,-0.3711137772,0.0416001864,-0.1645880491,-0.1014222056,-0.281121403,-0.1806521565,-0.2888853848,0.1768964827,0.1535650939,-0.0825318918,0.0570343472,-0.3095963001,-0.1843841076,-0.2965043485,-0.2994405329,-0.0456903838,0.0486637875,0.2570069134,0.0005066129,0.2092554122,-0.1285835505,-0.2552427948,0.3586301804,-0.0274177287,0.0589398481,0.1379740238,-0.1063657776,-0.2267007828,-0.0193145592,-0.5125080943,-0.3380695283,-0.3987953663,0.3147429824,0.1875486523,0.1704497486,-0.0023097964,0.1591467708,0.0435288027,0.3087789416,0.160881266,-0.1396674067,-0.2524963915,0.3737236559,-0.1478974968,-0.4355966449,0.0946127921,0.1519608349,0.0961538255,-0.0401359461,-0.6759764552,-0.1097933576,-0.137506038,-0.0273623299,-0.0781618506,0.1414001733,0.0561331175,0.0347310305,-0.1499190181,0.0273851901,-0.4032808542,0.0654855296,0.1590841115,0.1545920968,-0.0934003815,0.1210777313,-0.0830075592,0.6092501283,-0.0274671987,-0.0149574783,0.4369601309,0.1339591593,0.2517248094,-0.2581679225,-0.4237673283,-0.1597118229,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3558","title":"Integrate Milvus (pymilvus) library","comments":"Hi @mariosasko\uff0cJust search randomly and I found this issue~ I'm the tech lead of Milvus and we are looking forward to integrate milvus together with huggingface datasets.\r\n\r\nAny suggestion on how we could start?\r\n","body":"Milvus is a popular open-source vector database. We should add a new vector index to support this project.","comment_length":34,"text":"Integrate Milvus (pymilvus) library \n Milvus is a popular open-source vector database. We should add a new vector index to support this project. \n Hi @mariosasko\uff0cJust search randomly and I found this issue~ I'm the tech lead of Milvus and we are looking forward to integrate milvus together with huggingface datasets.\r\n\r\nAny suggestion on how we could start?\r\n","embeddings":[0.0640774816,-0.6056881547,-0.1171274707,0.3045173883,0.2704038322,0.0546635538,-0.0459694006,0.1624074578,0.089754574,0.32889691,-0.033493381,-0.2064778507,-0.213996619,0.0561559014,-0.0317879207,0.0591637976,-0.0616703033,0.2412929535,0.0012620946,-0.0326104611,-0.2023345083,0.2339383513,-0.1005807593,0.0185905267,0.2333412319,0.1426489651,0.1572431624,-0.116084747,-0.2688404322,-0.2826811373,0.1873019487,-0.235159114,-0.1514758617,0.5717047453,-0.0001188723,-0.065259181,0.232637167,-0.0549626872,0.1972396523,-0.1731403321,0.0347703137,-0.3233485222,-0.1281180084,-0.1074109152,-0.1706589013,-0.2926495373,-0.0710717365,-0.5964468122,-0.2635928988,0.1063942015,0.1442859024,0.4784964323,0.3292408586,-0.0758835226,0.3599385321,-0.1217438653,-0.1919480413,0.6081720591,0.5009283423,-0.0002410253,0.2405852228,0.2835017741,-0.0646191314,-0.1182208732,0.5529876947,0.118072629,0.8380258679,-0.1818927526,-0.0281102061,0.0272072796,0.1346935332,-0.0680356845,0.0575867593,0.136464566,0.1755418628,-0.4938670993,0.0569407083,0.1757733524,0.1318285316,0.2205092609,0.2655480802,-0.1725444496,-0.0752056912,0.2154688388,-0.0028356249,0.3418536782,0.0233716089,0.0758144259,0.3946983516,-0.1318679005,0.0224272572,0.5681365728,0.0185265467,-0.1319822967,-0.1189177036,0.0572713278,-0.0633484796,-0.370901525,0.4313020706,-0.0031254361,0.0226372518,-0.2082559019,0.0029616042,0.2597185373,-0.1714757234,-0.2013426423,-0.0861433223,-0.2329562157,0.2983798981,0.0723392889,0.1022780985,0.0376557484,0.2513903379,-0.1774544269,-0.2825944126,-0.3470976055,0.0432439037,0.1033102944,-0.2078979462,-0.1809220612,-0.2211031616,-0.0037084329,0.1467458159,0.0357606262,-0.2684468627,-0.2309141457,0.1364337653,0.012912483,-0.3062095046,0.0455557182,-0.0202398133,0.1122938022,-0.0688343868,-0.1838409454,0.0792477131,-0.387175113,0.0374007933,-0.0771065652,0.1869303882,0.0767988116,-0.0416094661,-0.0475880094,0.0578583367,0.3150106072,-0.1441960484,-0.1388157308,0.0892366767,-0.0176054947,-0.2436798513,0.0026869369,-0.3039500415,-0.3894797266,-0.3341700435,0.0803234875,0.4449620247,0.0663631409,0.4900227785,0.3132517636,-0.1803755611,-0.5042194128,-0.1224638522,0.2646353841,0.0156278443,-0.2413053215,0.4447286427,0.1417584121,-0.1247413307,0.2167758197,-0.0623413473,0.2353208363,0.3730480075,0.2841133475,-0.1614310592,-0.3372091949,-0.2997216582,0.12811248,-0.3179246187,-0.4486317039,-0.1208470762,-0.5448650718,-0.3067323267,-0.428493619,0.3326907456,0.4694445133,-0.1430888474,0.2331377119,0.1648110449,0.3336721361,-0.0445085913,-0.2517267168,-0.0597840622,-0.3283792138,0.6525873542,0.1713432521,0.0242095869,-0.1411654055,0.4616517425,-0.5795547962,-0.0121718589,0.0231889356,-0.0090962509,0.1459647268,0.3686927259,0.1586294025,0.3911118209,-0.0865119547,0.1163509935,0.1051461399,-0.3396741152,0.0361319147,-0.2508215606,-0.0356583409,-0.262917161,-0.1352828592,0.0364983156,0.0670113564,-0.0318697244,-0.2382505387,-0.3115351796,0.1542328,-0.1922541857,-0.0548176914,-0.1652855128,-0.0521132685,-0.4417099357,0.3776814044,-0.1195372939,-0.0210145917,0.0794625282,0.2951495051,0.0438770242,-0.1631699502,0.1528001577,0.221200794,-0.0852444768,0.0153189581,0.7192296386,0.2945525348,0.1079726294,-0.5347040296,0.0068729245,-0.004113887,0.152810216,-0.0925316438,0.2486053705,0.1572888047,-0.0147102671,0.1780791134,-0.1594856381,0.0065057292,0.2473971844,-0.0028935482,0.0434050374,-0.1572742313,0.0645032749,0.1076474786,0.0761686638,-0.1415784955,-0.3542029262,-0.1171538532,0.1643651426,-0.1013180688,0.1175804213,0.1627499461,-0.3167309761,0.0348996706,-0.0897209719,-0.5359889269,0.113502197,0.2461377978,-0.1771799922,-0.1272273213,0.2609112263,-0.0614463277,0.1805731952,0.2068294883,-0.0777910501,0.1472896785,0.4162645936,0.0431599356,-0.2616813779,0.2158873677,-0.0106772669,-0.0219984669,0.0007526871,0.0371475667,-0.1250479817,0.1806038916,0.1357902735,-0.0941898376,-0.6648200154,-0.21427387,0.0728989989,0.1275453866,0.1695735455,0.186993584,0.4651474655,0.4631187022,0.0528205521,-0.1447897106,0.0881837755,-0.1521667391,-0.0431034677,-0.0009560478,-0.0788458735,0.1492588669,0.5806851983,0.0815668553,0.1800957769,-0.4703770578,-0.6065315008,0.0823465586,-0.1861572564,0.1771574318,0.2125248462,-0.143645376,-0.2309378982,-0.1994223446,0.1557216048,-0.0082057295,-0.1297174245,0.049261976,-0.3353469074,0.0701403394,-0.1421249062,-0.2989518344,-0.2652880251,-0.3564405143,0.1097657457,0.0875867158,-0.0726087093,0.0388253443,-0.0016442416,0.192720741,0.069671236,-0.0211953688,-0.1786548346,0.0742830932,0.4342830479,-0.1867426485,-0.3065767884,0.1381774992,-0.106496118,0.0571724959,0.0850121528,-0.3523309827,0.1546783298,-0.0610073991,0.321857661,-0.1123160124,0.3270028234,0.4461300969,-0.0446480066,-0.0889688879,-0.1905187815,-0.1769396514,0.1037847474,0.2450828105,0.306088984,0.3555232584,-0.0756057352,-0.045141615,0.6219729781,-0.3386338353,-0.145699203,0.2428369075,-0.1936111152,0.4376241863,-0.013922493,-0.4716158509,0.0962177292,0.31425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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3558","title":"Integrate Milvus (pymilvus) library","comments":"Hi! For starters, I suggest you take a look at this file: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/search.py, which contains all the code for Faiss\/ElasticSearch support. We could set up a Slack channel for additional guidance. Let me know what you prefer.","body":"Milvus is a popular open-source vector database. We should add a new vector index to support this project.","comment_length":37,"text":"Integrate Milvus (pymilvus) library \n Milvus is a popular open-source vector database. We should add a new vector index to support this project. \n Hi! For starters, I suggest you take a look at this file: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/search.py, which contains all the code for Faiss\/ElasticSearch support. We could set up a Slack channel for additional guidance. Let me know what you prefer.","embeddings":[-0.1996865124,-0.2113726139,-0.2736817896,-0.1039367989,-0.0298222601,-0.0717025474,-0.0178256333,0.2599715889,0.2742890418,0.0905228779,-0.0979634225,-0.1096254885,-0.2341024578,-0.0411488228,0.0008004052,0.096957989,0.0917071998,0.1584693789,0.0501033813,-0.0148179624,-0.0903303251,0.1128597781,0.0499019325,0.0641860887,0.2216902971,0.1480342001,-0.1244576424,-0.0498956628,-0.2565742731,-0.2667877972,0.155899778,-0.0964860246,0.1288018674,0.2598873079,-0.0001064196,-0.0179433953,0.1433655918,0.0047418368,0.0997691751,-0.008428582,-0.1184489653,-0.2279232442,0.0532172918,-0.2662228644,-0.2144886106,-0.4859131873,-0.1341069639,-0.4452917874,0.2199272662,0.1467214376,0.2336974144,0.0746306702,0.3601367474,0.0071706832,0.5529497266,-0.2372093648,-0.2885304987,0.0051301681,0.4539782703,0.0610531643,0.147769317,0.3699939549,-0.1159967035,-0.1947923452,0.2221236676,0.1068704352,0.4270570874,-0.0072708521,-0.1538195014,0.180395633,0.1145691276,-0.2229722589,-0.069983542,0.1914348304,-0.0334239043,-0.2576312125,-0.1261110157,0.1187095344,0.0613940507,0.1803676486,0.234078452,-0.4090566635,-0.2084459215,0.37183249,-0.121609427,0.2715989947,-0.0212193448,-0.2059277445,0.1730968803,-0.3538994789,-0.1679363549,0.2785620987,0.2305601686,0.0571451746,-0.2942256331,-0.2377623171,-0.0811335668,-0.5706093907,0.3375135064,-0.2074989527,-0.1955699921,0.0900900885,0.2216227651,0.3228747547,-0.2101264149,-0.2097263783,-0.2001668811,-0.0291996654,0.1937060058,-0.3186358809,-0.1002790034,-0.0251536667,0.045113001,0.0220185108,-0.2475152761,-0.3446319401,-0.0511972979,-0.0935775265,0.0037609718,-0.0861360207,-0.1421067119,0.0033981514,0.1561703384,0.2810290158,-0.1028172895,-0.4162721634,0.1258645207,0.0582375713,-0.0031147806,0.1550138295,-0.1084359735,-0.0049561821,0.0129169133,0.0761945993,0.1336523592,-0.612026751,0.0612682179,-0.1838202327,0.0037607059,0.1686346382,0.2643792629,0.0274821762,0.2055301517,0.2035522461,-0.112096779,-0.5167549849,-0.1855094582,0.1699675024,-0.2262835652,-0.1139602885,-0.3778394163,-0.4942479432,-0.4481313527,0.223447144,0.179616645,-0.2228077501,0.4024080932,0.4126860499,-0.3548000157,-0.2963236272,-0.1058106348,0.3906877339,0.1571444124,-0.1351463199,0.2902104259,-0.1313346177,-0.0202642456,0.0118932091,-0.224899888,0.5048989058,-0.066206418,0.1392602921,-0.0347672887,-0.2790068388,-0.0036113148,0.3304454088,0.4822557867,-0.3000309169,-0.022747688,-0.2636089623,0.0796514526,-0.5524977446,0.0523917824,0.2052306533,0.0892669037,0.0531252697,0.0213130936,0.4072104394,-0.0258304328,-0.1311260462,-0.1655041873,-0.4313465953,0.0357989371,0.4187693,0.1334159523,-0.3452706337,0.4032148421,-0.5415089726,-0.1832793504,-0.1210523769,-0.0457675308,0.0926906839,0.5260559916,0.1576603502,0.309151113,-0.3850442469,0.1198112965,-0.0333884284,-0.2431650609,0.0870585367,-0.3523505926,-0.0732066557,-0.0951682776,-0.0982966349,0.0927227512,0.1428927183,0.2098098844,-0.3033082783,-0.1098267436,0.1945928037,-0.3140211701,-0.279889822,-0.1142740399,-0.018832894,-0.3054896891,0.3918406665,-0.092969276,0.0881129131,0.0749448538,0.1675078273,0.0752209947,-0.1479287595,0.1869114786,0.1549421549,-0.2739956081,-0.0091644293,0.897593677,0.4802392125,0.0175997149,-0.3842442036,0.1477982253,0.0055868775,0.020104982,0.0911381692,-0.1244325638,0.2941147387,0.30314821,0.2254726589,-0.0775811598,-0.0455071591,0.150298059,0.0024084388,-0.0476280227,-0.0599103905,-0.0640741065,-0.1232686117,0.1006904468,-0.1453697383,-0.1322485805,-0.0649666414,0.2379294634,-0.1393032521,0.3229658902,0.2683369219,-0.2578306496,0.016376067,0.0201774221,-0.3871349692,-0.0300799906,0.299926877,-0.0569418222,-0.2085567117,-0.0344196483,0.0972692817,0.2749275863,0.1304407567,0.0294433609,-0.0136726368,0.1956468076,-0.0211677868,-0.2328202128,-0.1826576442,-0.205739066,-0.0771109387,0.1428837776,0.2005376369,0.0110436771,0.3600652516,0.190353781,-0.0566259474,-0.3708267808,-0.0073947213,0.3997585177,-0.0783466026,-0.0328829736,0.1164171919,0.011711617,0.445202291,0.2204931527,-0.3487304151,-0.1669187099,-0.2349137813,0.0431053899,0.2400677949,-0.0015251637,0.1874573678,0.4249186516,0.2295061648,0.2107598633,-0.3933824003,-0.4839552343,0.2477773726,-0.1741295606,0.055748105,0.1956837475,-0.1962392032,-0.3780662715,-0.0483062752,0.0864711553,-0.1529412717,-0.0368567482,0.102177985,-0.1631674767,0.1023586318,0.0488767698,-0.3954357803,-0.391669333,-0.3595965207,-0.0274754893,-0.044620797,-0.0226063933,-0.1771224439,0.2990555167,0.1686669737,0.1279668659,0.1348585784,-0.1889818758,0.0649247617,0.4527845085,-0.2566149235,-0.1708542854,-0.0181466974,-0.0681993589,-0.0381558798,0.4663718343,-0.1299447119,0.124049224,0.0856280997,0.0353716537,0.1382257044,0.3004852831,0.2683667243,0.099178344,-0.2645448148,-0.0217294078,0.0738531947,0.0220988914,0.2893731892,0.2130494565,0.2308407724,-0.0029476578,-0.0548145473,0.6531763673,-0.2967818677,-0.263138175,0.269102633,-0.0120521896,0.1178177968,0.1618407071,-0.1622471362,-0.0150555195,0.19445596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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3558","title":"Integrate Milvus (pymilvus) library","comments":"> Hi! For starters, I suggest you take a look at this file: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/search.py, which contains all the code for Faiss\/ElasticSearch support. We could set up a Slack channel for additional guidance. Let me know what you prefer.\r\n\r\nSure, we take a look and do some research","body":"Milvus is a popular open-source vector database. We should add a new vector index to support this project.","comment_length":47,"text":"Integrate Milvus (pymilvus) library \n Milvus is a popular open-source vector database. We should add a new vector index to support this project. \n > Hi! For starters, I suggest you take a look at this file: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/search.py, which contains all the code for Faiss\/ElasticSearch support. We could set up a Slack channel for additional guidance. Let me know what you prefer.\r\n\r\nSure, we take a look and do some 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3555","title":"DuplicatedKeysError when loading tweet_qa dataset","comments":"Hi, we've just merged the PR with the fix. The fixed version of the dataset can be downloaded as follows:\r\n```python\r\nimport datasets\r\ndset = datasets.load_dataset(\"tweet_qa\", revision=\"master\")\r\n```","body":"When loading the tweet_qa dataset with `load_dataset('tweet_qa')`, the following error occurs: \r\n\r\n`DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 2a167f9e016ba338e1813fed275a6a1e\r\nKeys should be unique and deterministic in nature\r\n`\r\nMight be related to issues #2433 and #2333\r\n\r\n- `datasets` version: 1.17.0\r\n- Python version: 3.8.5\r\n","comment_length":28,"text":"DuplicatedKeysError when loading tweet_qa dataset \n When loading the tweet_qa dataset with `load_dataset('tweet_qa')`, the following error occurs: \r\n\r\n`DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 2a167f9e016ba338e1813fed275a6a1e\r\nKeys should be unique and deterministic in nature\r\n`\r\nMight be related to issues #2433 and #2333\r\n\r\n- `datasets` version: 1.17.0\r\n- Python version: 3.8.5\r\n \n Hi, we've just merged the PR with the fix. The fixed version of the dataset can be downloaded as follows:\r\n```python\r\nimport datasets\r\ndset = datasets.load_dataset(\"tweet_qa\", revision=\"master\")\r\n```","embeddings":[0.0209831372,-0.2672317028,-0.0047796932,0.2069364935,0.3006340861,0.0638987496,0.1432422101,0.3013507426,-0.1967462599,0.0905201063,0.0005052412,0.3443003297,-0.1993973702,0.2585405707,0.1344979554,-0.0611697175,-0.1748430729,-0.0619423836,-0.0889877677,0.0461023599,-0.1691439003,0.4314880669,-0.1449283808,0.1948376447,-0.1514071375,0.0726586431,-0.03861624,0.0718170702,0.0979665816,-0.3187615275,0.0485631712,0.0315148011,-0.1733046621,0.634721756,-0.0001032614,0.1497586519,0.1402187645,-0.0559784658,-0.0438543297,-0.2027411461,0.052895993,-0.0531772859,0.1835677177,-0.1234565973,-0.1724006236,-0.2952159643,0.0381583981,-0.2132155299,0.5270635486,0.1263758242,0.3108348846,0.2570207119,0.2260606736,-0.0647266731,0.1587929279,-0.2609911263,-0.0890782624,0.0479854085,0.1330113709,0.0808945447,0.3148095906,0.281791687,0.1307473779,0.0402647331,0.3670466244,-0.1551924497,0.1408164501,-0.3080179393,0.2870360315,0.3218346536,0.4155680835,-0.3932709992,-0.175862059,-0.1174163222,0.2359460294,-0.187917605,0.045060724,-0.1858867109,-0.0891250521,0.1174016744,0.0474096015,-0.1125850603,-0.0982176363,0.0356329493,-0.0093264543,0.0726037696,-0.0037691055,0.1117288917,-0.1335886866,-0.2727934718,-0.1170971915,-0.0241192784,0.053175997,0.1824921817,-0.4530871809,0.0190223679,0.0287690181,-0.1686785519,0.136867106,0.0670942366,0.0485959165,0.2350107431,0.1514670551,0.0192157459,0.3881796002,0.1467554718,0.0179717056,0.0523315109,0.4256108999,-0.3366816938,-0.4012551904,0.1335009336,0.0612709858,-0.1619870961,0.4245050251,-0.0933111534,0.3518025279,-0.0684880614,-0.1004280522,0.0112379743,-0.1678741425,-0.097198762,-0.367778331,0.1684320271,0.2764485478,0.1619240046,0.0642196611,0.1393605322,-0.2932191491,-0.0441524088,-0.2724289298,-0.1754116416,0.2297791541,0.0165113322,0.1141072661,-0.3037880659,0.2697277665,0.0232720505,-0.0162297543,-0.1190122366,0.2231529951,0.1861414313,-0.3691112995,0.2522197962,0.2159582376,0.3801512122,0.0444414876,-0.0657735094,-0.0184348207,0.1413260102,-0.2036395818,-0.5011164546,-0.2332925498,0.3129762411,-0.1472163945,-0.1054590344,0.0000288039,0.2654809654,0.3024115562,0.0329152346,-0.2014226317,-0.1434974819,-0.1591224223,-0.2269378453,0.0831914395,0.2825365961,-0.2837951481,-0.2192053795,-0.1994063854,-0.1903503388,-0.1688141823,0.3608957827,-0.1670725495,0.3379670978,-0.1507979482,0.1051340997,0.3109602034,-0.5079997778,-0.2172870934,0.2449388206,-0.3078500628,0.1172403246,-0.0237100236,0.1034532711,0.1113828048,-0.0154376486,0.166525811,0.1040709838,0.0611671284,-0.0051490082,-0.2918877304,-0.2327947319,0.1726959944,-0.0086264675,0.0913039446,-0.1310146749,0.0289897043,0.0485393777,0.1855810285,-0.1124879196,-0.0967187881,0.015643036,0.3962932229,0.1154067367,0.1158813015,-0.0207047332,-0.2441351861,0.221499145,0.0745184943,0.2376573086,-0.1278319657,-0.1703802049,-0.3137261271,-0.0227810629,-0.2901741862,-0.1469519585,0.2759569883,0.1269543469,0.0991338193,0.2253146023,-0.2245865613,0.0736839622,0.0904177353,-0.035306409,-0.1671426594,-0.0907323882,-0.1985816956,-0.0000151215,0.1321508586,0.1417249441,0.1675450802,-0.1575846821,-0.0930963382,0.3807486892,-0.1459652632,0.1834532917,-0.1173605919,0.0946479291,0.2701910138,-0.1927556396,-0.1976428181,0.2730550468,0.1179155111,-0.1717869639,0.2042406797,0.3086826205,0.1158495545,-0.086658299,0.0686480105,-0.1727933735,0.22210069,0.0525725707,0.060960792,-0.2166211456,0.013796825,-0.1454033405,-0.0733691975,-0.050934162,-0.188305527,0.3138227761,0.4415294528,-0.0362132415,0.2726063132,-0.057901457,-0.1167600378,-0.1369900405,-0.2282812595,0.4364471734,0.2698990107,0.1560087055,-0.2016587257,0.1695035547,0.044144962,-0.2727576792,0.3852111995,-0.110537976,0.085784778,0.3363998234,0.1037397534,0.0073589222,-0.4314967096,0.105317995,0.1750355363,0.3343110085,-0.3183242679,-0.1757626683,-0.3871370256,-0.1014154628,0.0267933253,0.136571601,0.1242056638,-0.4778547287,0.3482121229,0.1228084788,-0.004535777,0.1328110546,0.0152961006,0.0610447377,0.2126481533,0.0505138971,0.0225327071,-0.3593290746,-0.3428866863,0.1738720983,0.1837026924,-0.1618129611,0.3100452125,-0.2434146404,0.029103497,-0.2370080054,-0.5286772251,0.1101814434,-0.3338294625,0.3879237175,0.0459498912,0.2415130287,-0.0656213984,-0.1326458156,0.2585237324,-0.2900001407,-0.3225954175,0.1913190633,-0.0040636156,-0.1177861765,-0.2863712609,-0.6119118333,-0.23202084,-0.0067995857,0.0650994405,-0.0217828285,0.1029453576,0.1672924906,0.0034649211,0.0664110556,0.0913111418,-0.1194217205,-0.3855084479,-0.2991402447,0.3120849431,-0.291605711,-0.4581759572,0.1919594854,0.3693512976,0.2002984136,-0.4235447645,-0.6195805073,-0.3562765121,-0.2360387295,0.4172386825,0.0098468876,0.1735919267,-0.051483959,0.0889141411,-0.2018241137,-0.077507101,-0.1550941467,0.1873609871,-0.1811659336,0.0159242172,-0.028253587,0.4260447919,-0.1087984741,0.6520240307,0.2633993328,-0.1470683515,0.1576391459,-0.2643157542,0.1971449405,-0.1781636924,-0.1741813123,-0.038144037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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3554","title":"ImportError: cannot import name 'is_valid_waiter_error'","comments":"Hi! I can't reproduce this error in Colab, but I'm assuming you are using Amazon SageMaker Studio Notebooks (you mention the `conda_pytorch_p36` kernel), so maybe @philschmid knows more about what might be causing this issue? ","body":"Based on [SO post](https:\/\/stackoverflow.com\/q\/70606147\/17840900).\r\n\r\nI'm following along to this [Notebook][1], cell \"**Loading the dataset**\".\r\n\r\nKernel: `conda_pytorch_p36`.\r\n\r\nI run:\r\n```\r\n! pip install datasets transformers optimum[intel]\r\n```\r\n\r\nOutput:\r\n```\r\nRequirement already satisfied: datasets in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (1.17.0)\r\nRequirement already satisfied: transformers in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (4.15.0)\r\nRequirement already satisfied: optimum[intel] in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (0.1.3)\r\nRequirement already satisfied: numpy>=1.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: dill in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.3.4)\r\nRequirement already satisfied: tqdm>=4.62.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.62.3)\r\nRequirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.2.1)\r\nRequirement already satisfied: packaging in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (21.3)\r\nRequirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (6.0.1)\r\nRequirement already satisfied: pandas in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.1.5)\r\nRequirement already satisfied: xxhash in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.0.2)\r\nRequirement already satisfied: aiohttp in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (3.8.1)\r\nRequirement already satisfied: fsspec[http]>=2021.05.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2021.11.1)\r\nRequirement already satisfied: dataclasses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.8)\r\nRequirement already satisfied: multiprocess in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: importlib-metadata in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.5.0)\r\nRequirement already satisfied: requests>=2.19.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: pyyaml>=5.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (5.4.1)\r\nRequirement already satisfied: regex!=2019.12.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (2021.4.4)\r\nRequirement already satisfied: tokenizers<0.11,>=0.10.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.10.3)\r\nRequirement already satisfied: filelock in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (3.0.12)\r\nRequirement already satisfied: sacremoses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.0.46)\r\nRequirement already satisfied: torch>=1.9 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.10.1)\r\nRequirement already satisfied: sympy in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.8)\r\nRequirement already satisfied: coloredlogs in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (15.0.1)\r\nRequirement already satisfied: pycocotools in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (2.0.3)\r\nRequirement already satisfied: neural-compressor>=1.7 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.9)\r\nRequirement already satisfied: typing-extensions>=3.7.4.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.10.0.0)\r\nRequirement already satisfied: sigopt in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.2.0)\r\nRequirement already satisfied: 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satisfied: smmap<6,>=3.0.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from gitdb<5,>=4.0.1->GitPython>=2.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (5.0.0)\r\nRequirement already satisfied: pyasn1-modules>=0.2.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.8)\r\nRequirement already satisfied: cachetools<5.0,>=2.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (4.2.2)\r\nRequirement already satisfied: parso<0.8.0,>=0.7.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from jedi>=0.10->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.1)\r\nRequirement already satisfied: ipython-genutils in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from traitlets>=4.2->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0)\r\nRequirement already satisfied: ptyprocess>=0.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pexpect->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.0)\r\nRequirement already satisfied: oauthlib>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests-oauthlib->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.1)\r\n```\r\n\r\n---\r\n\r\n**Cell:**\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n```\r\nOR\r\n```python\r\nimport datasets\r\n```\r\n\r\n**Traceback:**\r\n```\r\n---------------------------------------------------------------------------\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-7-34fb7ba3338d> in <module>\r\n----> 1 from datasets import load_dataset, load_metric\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/__init__.py in <module>\r\n     32     )\r\n     33 \r\n---> 34 from .arrow_dataset import Dataset, concatenate_datasets\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     59 from . import config, utils\r\n     60 from .arrow_reader import ArrowReader\r\n---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence\r\n     62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper\r\n     63 from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in <module>\r\n     26 \r\n     27 from . import config, utils\r\n---> 28 from .features import (\r\n     29     Features,\r\n     30     ImageExtensionType,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/__init__.py in <module>\r\n      1 # flake8: noqa\r\n----> 2 from .audio import Audio\r\n      3 from .features import *\r\n      4 from .features import (\r\n      5     _ArrayXD,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in <module>\r\n      5 import pyarrow as pa\r\n      6 \r\n----> 7 from ..utils.streaming_download_manager import xopen\r\n      8 \r\n      9 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/utils\/streaming_download_manager.py in <module>\r\n     16 \r\n     17 from .. import config\r\n---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS\r\n     19 from .download_manager import DownloadConfig, map_nested\r\n     20 from .file_utils import (\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/__init__.py in <module>\r\n     11 \r\n     12 if _has_s3fs:\r\n---> 13     from .s3filesystem import S3FileSystem  # noqa: F401\r\n     14 \r\n     15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/s3filesystem.py in <module>\r\n----> 1 import s3fs\r\n      2 \r\n      3 \r\n      4 class S3FileSystem(s3fs.S3FileSystem):\r\n      5     \"\"\"\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/__init__.py in <module>\r\n----> 1 from .core import S3FileSystem, S3File\r\n      2 from .mapping import S3Map\r\n      3 \r\n      4 from ._version import get_versions\r\n      5 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/core.py in <module>\r\n     12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper\r\n     13 \r\n---> 14 import aiobotocore\r\n     15 import botocore\r\n     16 import aiobotocore.session\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/__init__.py in <module>\r\n----> 1 from .session import get_session, AioSession\r\n      2 \r\n      3 __all__ = ['get_session', 'AioSession']\r\n      4 __version__ = '1.3.0'\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/session.py in <module>\r\n      4 from botocore import retryhandler, translate\r\n      5 from botocore.exceptions import PartialCredentialsError\r\n----> 6 from .client import AioClientCreator, AioBaseClient\r\n      7 from .hooks import AioHierarchicalEmitter\r\n      8 from .parsers import AioResponseParserFactory\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/client.py in <module>\r\n     11 from .args import AioClientArgsCreator\r\n     12 from .utils import AioS3RegionRedirector\r\n---> 13 from . import waiter\r\n     14 \r\n     15 history_recorder = get_global_history_recorder()\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/waiter.py in <module>\r\n      4 from botocore.exceptions import ClientError\r\n      5 from botocore.waiter import WaiterModel  # noqa: F401, lgtm[py\/unused-import]\r\n----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \\\r\n      7     NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error\r\n      8 from botocore.docs.docstring import WaiterDocstring\r\n\r\nImportError: cannot import name 'is_valid_waiter_error'\r\n```\r\n\r\nPlease let me know if there's anything else I can add to post.\r\n\r\n  [1]: https:\/\/github.com\/huggingface\/notebooks\/blob\/master\/examples\/text_classification_quantization_inc.ipynb","comment_length":35,"text":"ImportError: cannot import name 'is_valid_waiter_error' \n Based on [SO post](https:\/\/stackoverflow.com\/q\/70606147\/17840900).\r\n\r\nI'm following along to this [Notebook][1], cell \"**Loading the dataset**\".\r\n\r\nKernel: `conda_pytorch_p36`.\r\n\r\nI run:\r\n```\r\n! pip install datasets transformers optimum[intel]\r\n```\r\n\r\nOutput:\r\n```\r\nRequirement already satisfied: datasets in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (1.17.0)\r\nRequirement already satisfied: transformers in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (4.15.0)\r\nRequirement already satisfied: optimum[intel] in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (0.1.3)\r\nRequirement already satisfied: numpy>=1.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: dill in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.3.4)\r\nRequirement already satisfied: tqdm>=4.62.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.62.3)\r\nRequirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.2.1)\r\nRequirement already satisfied: packaging in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (21.3)\r\nRequirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (6.0.1)\r\nRequirement already satisfied: pandas in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.1.5)\r\nRequirement already satisfied: xxhash in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.0.2)\r\nRequirement already satisfied: aiohttp in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (3.8.1)\r\nRequirement already satisfied: fsspec[http]>=2021.05.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2021.11.1)\r\nRequirement already satisfied: dataclasses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.8)\r\nRequirement already satisfied: multiprocess in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: importlib-metadata in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.5.0)\r\nRequirement already satisfied: requests>=2.19.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: pyyaml>=5.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (5.4.1)\r\nRequirement already satisfied: regex!=2019.12.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (2021.4.4)\r\nRequirement already satisfied: tokenizers<0.11,>=0.10.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.10.3)\r\nRequirement already satisfied: filelock in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (3.0.12)\r\nRequirement already satisfied: sacremoses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.0.46)\r\nRequirement already satisfied: torch>=1.9 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.10.1)\r\nRequirement already satisfied: sympy in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.8)\r\nRequirement already satisfied: coloredlogs in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (15.0.1)\r\nRequirement already satisfied: pycocotools in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (2.0.3)\r\nRequirement already satisfied: neural-compressor>=1.7 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.9)\r\nRequirement already satisfied: typing-extensions>=3.7.4.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.10.0.0)\r\nRequirement already satisfied: sigopt in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.2.0)\r\nRequirement already satisfied: opencv-python in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (4.5.1.48)\r\nRequirement already satisfied: cryptography in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.4.7)\r\nRequirement already satisfied: py-cpuinfo in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.0.0)\r\nRequirement already satisfied: gevent in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (21.1.2)\r\nRequirement already satisfied: schema in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.7.5)\r\nRequirement already satisfied: psutil in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.8.0)\r\nRequirement already satisfied: gevent-websocket in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.10.1)\r\nRequirement already satisfied: hyperopt in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.2.7)\r\nRequirement already satisfied: Flask in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.0.1)\r\nRequirement already satisfied: prettytable in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.5.0)\r\nRequirement already satisfied: Flask-SocketIO in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.1.1)\r\nRequirement already satisfied: scikit-learn in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.24.2)\r\nRequirement already satisfied: Pillow in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.4.0)\r\nRequirement already satisfied: Flask-Cors in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.0.10)\r\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: certifi>=2017.4.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: yarl<2.0,>=1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.6.3)\r\nRequirement already satisfied: charset-normalizer<3.0,>=2.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (2.0.9)\r\nRequirement already satisfied: attrs>=17.3.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (21.2.0)\r\nRequirement already satisfied: asynctest==0.13.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (0.13.0)\r\nRequirement already satisfied: idna-ssl>=1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.1.0)\r\nRequirement already satisfied: async-timeout<5.0,>=4.0.0a3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (4.0.1)\r\nRequirement already satisfied: aiosignal>=1.1.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.2.0)\r\nRequirement already satisfied: frozenlist>=1.1.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.2.0)\r\nRequirement already satisfied: multidict<7.0,>=4.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (5.1.0)\r\nRequirement already satisfied: humanfriendly>=9.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from coloredlogs->optimum[intel]) (10.0)\r\nRequirement already satisfied: zipp>=0.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: matplotlib>=2.1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pycocotools->optimum[intel]) (3.3.4)\r\nRequirement already satisfied: cython>=0.27.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pycocotools->optimum[intel]) (0.29.23)\r\nRequirement already satisfied: setuptools>=18.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pycocotools->optimum[intel]) (52.0.0.post20210125)\r\nRequirement already satisfied: joblib in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sacremoses->transformers) (1.0.1)\r\nRequirement already satisfied: click in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sacremoses->transformers) (8.0.1)\r\nRequirement already satisfied: six in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sacremoses->transformers) (1.16.0)\r\nRequirement already satisfied: mpmath>=0.19 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sympy->optimum[intel]) (1.2.1)\r\nRequirement already satisfied: kiwisolver>=1.0.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from 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hyperopt->neural-compressor>=1.7->optimum[intel]) (0.18.2)\r\nRequirement already satisfied: cloudpickle in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (1.6.0)\r\nRequirement already satisfied: networkx>=2.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (2.5)\r\nRequirement already satisfied: scipy in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (1.5.3)\r\nRequirement already satisfied: py4j in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from hyperopt->neural-compressor>=1.7->optimum[intel]) (0.10.7)\r\nRequirement already satisfied: wcwidth in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from prettytable->neural-compressor>=1.7->optimum[intel]) 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satisfied: smmap<6,>=3.0.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from gitdb<5,>=4.0.1->GitPython>=2.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (5.0.0)\r\nRequirement already satisfied: pyasn1-modules>=0.2.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.8)\r\nRequirement already satisfied: cachetools<5.0,>=2.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (4.2.2)\r\nRequirement already satisfied: parso<0.8.0,>=0.7.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from jedi>=0.10->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.1)\r\nRequirement already satisfied: ipython-genutils in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from traitlets>=4.2->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0)\r\nRequirement already satisfied: ptyprocess>=0.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pexpect->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.0)\r\nRequirement already satisfied: oauthlib>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests-oauthlib->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.1)\r\n```\r\n\r\n---\r\n\r\n**Cell:**\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n```\r\nOR\r\n```python\r\nimport datasets\r\n```\r\n\r\n**Traceback:**\r\n```\r\n---------------------------------------------------------------------------\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-7-34fb7ba3338d> in <module>\r\n----> 1 from datasets import load_dataset, load_metric\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/__init__.py in <module>\r\n     32     )\r\n     33 \r\n---> 34 from .arrow_dataset import Dataset, concatenate_datasets\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     59 from . import config, utils\r\n     60 from .arrow_reader import ArrowReader\r\n---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence\r\n     62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper\r\n     63 from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in <module>\r\n     26 \r\n     27 from . import config, utils\r\n---> 28 from .features import (\r\n     29     Features,\r\n     30     ImageExtensionType,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/__init__.py in <module>\r\n      1 # flake8: noqa\r\n----> 2 from .audio import Audio\r\n      3 from .features import *\r\n      4 from .features import (\r\n      5     _ArrayXD,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in <module>\r\n      5 import pyarrow as pa\r\n      6 \r\n----> 7 from ..utils.streaming_download_manager import xopen\r\n      8 \r\n      9 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/utils\/streaming_download_manager.py in <module>\r\n     16 \r\n     17 from .. import config\r\n---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS\r\n     19 from .download_manager import DownloadConfig, map_nested\r\n     20 from .file_utils import (\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/__init__.py in <module>\r\n     11 \r\n     12 if _has_s3fs:\r\n---> 13     from .s3filesystem import S3FileSystem  # noqa: F401\r\n     14 \r\n     15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/s3filesystem.py in <module>\r\n----> 1 import s3fs\r\n      2 \r\n      3 \r\n      4 class S3FileSystem(s3fs.S3FileSystem):\r\n      5     \"\"\"\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/__init__.py in <module>\r\n----> 1 from .core import S3FileSystem, S3File\r\n      2 from .mapping import S3Map\r\n      3 \r\n      4 from ._version import get_versions\r\n      5 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/core.py in <module>\r\n     12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper\r\n     13 \r\n---> 14 import aiobotocore\r\n     15 import botocore\r\n     16 import aiobotocore.session\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/__init__.py in <module>\r\n----> 1 from .session import get_session, AioSession\r\n      2 \r\n      3 __all__ = ['get_session', 'AioSession']\r\n      4 __version__ = '1.3.0'\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/session.py in <module>\r\n      4 from botocore import retryhandler, translate\r\n      5 from botocore.exceptions import PartialCredentialsError\r\n----> 6 from .client import AioClientCreator, AioBaseClient\r\n      7 from .hooks import AioHierarchicalEmitter\r\n      8 from .parsers import AioResponseParserFactory\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/client.py in <module>\r\n     11 from .args import AioClientArgsCreator\r\n     12 from .utils import AioS3RegionRedirector\r\n---> 13 from . import waiter\r\n     14 \r\n     15 history_recorder = get_global_history_recorder()\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/waiter.py in <module>\r\n      4 from botocore.exceptions import ClientError\r\n      5 from botocore.waiter import WaiterModel  # noqa: F401, lgtm[py\/unused-import]\r\n----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \\\r\n      7     NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error\r\n      8 from botocore.docs.docstring import WaiterDocstring\r\n\r\nImportError: cannot import name 'is_valid_waiter_error'\r\n```\r\n\r\nPlease let me know if there's anything else I can add to post.\r\n\r\n  [1]: https:\/\/github.com\/huggingface\/notebooks\/blob\/master\/examples\/text_classification_quantization_inc.ipynb \n Hi! I can't reproduce this error in Colab, but I'm assuming you are using Amazon SageMaker Studio Notebooks (you mention the `conda_pytorch_p36` kernel), so maybe @philschmid knows more about what might be causing this issue? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3554","title":"ImportError: cannot import name 'is_valid_waiter_error'","comments":"Hey @mariosasko. Yes, I am using **Amazon SageMaker Studio Jupyter Labs**. However, I no longer need this notebook; but it would be nice to have this problem solved for others. So don't stress too much if you two can't reproduce error.","body":"Based on [SO post](https:\/\/stackoverflow.com\/q\/70606147\/17840900).\r\n\r\nI'm following along to this [Notebook][1], cell \"**Loading the dataset**\".\r\n\r\nKernel: `conda_pytorch_p36`.\r\n\r\nI run:\r\n```\r\n! pip install datasets transformers optimum[intel]\r\n```\r\n\r\nOutput:\r\n```\r\nRequirement already satisfied: datasets in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (1.17.0)\r\nRequirement already satisfied: transformers in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (4.15.0)\r\nRequirement already satisfied: optimum[intel] in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (0.1.3)\r\nRequirement already satisfied: numpy>=1.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: dill in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.3.4)\r\nRequirement already satisfied: tqdm>=4.62.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.62.3)\r\nRequirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.2.1)\r\nRequirement already satisfied: packaging in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (21.3)\r\nRequirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (6.0.1)\r\nRequirement already satisfied: pandas in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.1.5)\r\nRequirement already satisfied: xxhash in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.0.2)\r\nRequirement already satisfied: aiohttp in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (3.8.1)\r\nRequirement already satisfied: fsspec[http]>=2021.05.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2021.11.1)\r\nRequirement already satisfied: dataclasses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.8)\r\nRequirement already satisfied: multiprocess in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: importlib-metadata in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.5.0)\r\nRequirement already satisfied: requests>=2.19.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: pyyaml>=5.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (5.4.1)\r\nRequirement already satisfied: regex!=2019.12.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (2021.4.4)\r\nRequirement already satisfied: tokenizers<0.11,>=0.10.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.10.3)\r\nRequirement already satisfied: filelock in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (3.0.12)\r\nRequirement already satisfied: sacremoses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.0.46)\r\nRequirement already satisfied: torch>=1.9 in 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\/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from jedi>=0.10->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.1)\r\nRequirement already satisfied: ipython-genutils in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from traitlets>=4.2->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0)\r\nRequirement already satisfied: ptyprocess>=0.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pexpect->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.0)\r\nRequirement already satisfied: oauthlib>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests-oauthlib->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.1)\r\n```\r\n\r\n---\r\n\r\n**Cell:**\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n```\r\nOR\r\n```python\r\nimport datasets\r\n```\r\n\r\n**Traceback:**\r\n```\r\n---------------------------------------------------------------------------\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-7-34fb7ba3338d> in <module>\r\n----> 1 from datasets import load_dataset, load_metric\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/__init__.py in <module>\r\n     32     )\r\n     33 \r\n---> 34 from .arrow_dataset import Dataset, concatenate_datasets\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     59 from . import config, utils\r\n     60 from .arrow_reader import ArrowReader\r\n---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence\r\n     62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper\r\n     63 from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in <module>\r\n     26 \r\n     27 from . import config, utils\r\n---> 28 from .features import (\r\n     29     Features,\r\n     30     ImageExtensionType,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/__init__.py in <module>\r\n      1 # flake8: noqa\r\n----> 2 from .audio import Audio\r\n      3 from .features import *\r\n      4 from .features import (\r\n      5     _ArrayXD,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in <module>\r\n      5 import pyarrow as pa\r\n      6 \r\n----> 7 from ..utils.streaming_download_manager import xopen\r\n      8 \r\n      9 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/utils\/streaming_download_manager.py in <module>\r\n     16 \r\n     17 from .. import config\r\n---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS\r\n     19 from .download_manager import DownloadConfig, map_nested\r\n     20 from .file_utils import (\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/__init__.py in <module>\r\n     11 \r\n     12 if _has_s3fs:\r\n---> 13     from .s3filesystem import S3FileSystem  # noqa: F401\r\n     14 \r\n     15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/s3filesystem.py in <module>\r\n----> 1 import s3fs\r\n      2 \r\n      3 \r\n      4 class S3FileSystem(s3fs.S3FileSystem):\r\n      5     \"\"\"\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/__init__.py in <module>\r\n----> 1 from .core import S3FileSystem, S3File\r\n      2 from .mapping import S3Map\r\n      3 \r\n      4 from ._version import get_versions\r\n      5 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/core.py in <module>\r\n     12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper\r\n     13 \r\n---> 14 import aiobotocore\r\n     15 import botocore\r\n     16 import aiobotocore.session\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/__init__.py in <module>\r\n----> 1 from .session import get_session, AioSession\r\n      2 \r\n      3 __all__ = ['get_session', 'AioSession']\r\n      4 __version__ = '1.3.0'\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/session.py in <module>\r\n      4 from botocore import retryhandler, translate\r\n      5 from botocore.exceptions import PartialCredentialsError\r\n----> 6 from .client import AioClientCreator, AioBaseClient\r\n      7 from .hooks import AioHierarchicalEmitter\r\n      8 from .parsers import AioResponseParserFactory\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/client.py in <module>\r\n     11 from .args import AioClientArgsCreator\r\n     12 from .utils import AioS3RegionRedirector\r\n---> 13 from . import waiter\r\n     14 \r\n     15 history_recorder = get_global_history_recorder()\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/waiter.py in <module>\r\n      4 from botocore.exceptions import ClientError\r\n      5 from botocore.waiter import WaiterModel  # noqa: F401, lgtm[py\/unused-import]\r\n----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \\\r\n      7     NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error\r\n      8 from botocore.docs.docstring import WaiterDocstring\r\n\r\nImportError: cannot import name 'is_valid_waiter_error'\r\n```\r\n\r\nPlease let me know if there's anything else I can add to post.\r\n\r\n  [1]: https:\/\/github.com\/huggingface\/notebooks\/blob\/master\/examples\/text_classification_quantization_inc.ipynb","comment_length":41,"text":"ImportError: cannot import name 'is_valid_waiter_error' \n Based on [SO post](https:\/\/stackoverflow.com\/q\/70606147\/17840900).\r\n\r\nI'm following along to this [Notebook][1], cell \"**Loading the dataset**\".\r\n\r\nKernel: `conda_pytorch_p36`.\r\n\r\nI run:\r\n```\r\n! pip install datasets transformers optimum[intel]\r\n```\r\n\r\nOutput:\r\n```\r\nRequirement already satisfied: datasets in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (1.17.0)\r\nRequirement already satisfied: transformers in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (4.15.0)\r\nRequirement already satisfied: optimum[intel] in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (0.1.3)\r\nRequirement already satisfied: numpy>=1.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: dill in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.3.4)\r\nRequirement already satisfied: tqdm>=4.62.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.62.3)\r\nRequirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.2.1)\r\nRequirement already satisfied: packaging in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (21.3)\r\nRequirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (6.0.1)\r\nRequirement already satisfied: pandas in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.1.5)\r\nRequirement already satisfied: xxhash in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.0.2)\r\nRequirement already satisfied: aiohttp in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (3.8.1)\r\nRequirement already satisfied: fsspec[http]>=2021.05.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2021.11.1)\r\nRequirement already satisfied: dataclasses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.8)\r\nRequirement already satisfied: multiprocess in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: importlib-metadata in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.5.0)\r\nRequirement already satisfied: requests>=2.19.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: pyyaml>=5.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (5.4.1)\r\nRequirement already satisfied: regex!=2019.12.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (2021.4.4)\r\nRequirement already satisfied: tokenizers<0.11,>=0.10.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.10.3)\r\nRequirement already satisfied: filelock in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (3.0.12)\r\nRequirement already satisfied: sacremoses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.0.46)\r\nRequirement already satisfied: torch>=1.9 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.10.1)\r\nRequirement already satisfied: sympy in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.8)\r\nRequirement already satisfied: coloredlogs in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (15.0.1)\r\nRequirement already satisfied: pycocotools in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (2.0.3)\r\nRequirement already satisfied: neural-compressor>=1.7 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.9)\r\nRequirement already satisfied: typing-extensions>=3.7.4.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.10.0.0)\r\nRequirement already satisfied: sigopt in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.2.0)\r\nRequirement already satisfied: opencv-python in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (4.5.1.48)\r\nRequirement already satisfied: cryptography in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.4.7)\r\nRequirement already satisfied: py-cpuinfo in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.0.0)\r\nRequirement already satisfied: gevent in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (21.1.2)\r\nRequirement already satisfied: schema in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.7.5)\r\nRequirement already satisfied: psutil in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.8.0)\r\nRequirement already satisfied: gevent-websocket in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.10.1)\r\nRequirement already satisfied: hyperopt in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.2.7)\r\nRequirement already satisfied: Flask in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.0.1)\r\nRequirement already satisfied: prettytable in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.5.0)\r\nRequirement already satisfied: Flask-SocketIO in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.1.1)\r\nRequirement already satisfied: scikit-learn in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.24.2)\r\nRequirement already satisfied: Pillow in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.4.0)\r\nRequirement already satisfied: Flask-Cors in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.0.10)\r\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: certifi>=2017.4.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: yarl<2.0,>=1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.6.3)\r\nRequirement already satisfied: charset-normalizer<3.0,>=2.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (2.0.9)\r\nRequirement already satisfied: attrs>=17.3.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (21.2.0)\r\nRequirement already satisfied: asynctest==0.13.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (0.13.0)\r\nRequirement already satisfied: idna-ssl>=1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.1.0)\r\nRequirement already satisfied: async-timeout<5.0,>=4.0.0a3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (4.0.1)\r\nRequirement already satisfied: aiosignal>=1.1.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.2.0)\r\nRequirement already satisfied: frozenlist>=1.1.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.2.0)\r\nRequirement already satisfied: multidict<7.0,>=4.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (5.1.0)\r\nRequirement already satisfied: humanfriendly>=9.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from coloredlogs->optimum[intel]) (10.0)\r\nRequirement already satisfied: zipp>=0.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: matplotlib>=2.1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pycocotools->optimum[intel]) (3.3.4)\r\nRequirement already satisfied: cython>=0.27.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pycocotools->optimum[intel]) (0.29.23)\r\nRequirement already satisfied: setuptools>=18.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pycocotools->optimum[intel]) (52.0.0.post20210125)\r\nRequirement already satisfied: joblib in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sacremoses->transformers) (1.0.1)\r\nRequirement already satisfied: click in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sacremoses->transformers) (8.0.1)\r\nRequirement already satisfied: six in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sacremoses->transformers) (1.16.0)\r\nRequirement already satisfied: mpmath>=0.19 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sympy->optimum[intel]) 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(1.30.2)\r\nRequirement already satisfied: requests-oauthlib in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (1.3.0)\r\nRequirement already satisfied: importlib-resources in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from Pint<0.17.0,>=0.16.0->sigopt->neural-compressor>=1.7->optimum[intel]) (5.4.0)\r\nRequirement already satisfied: python-engineio>=4.3.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from python-socketio>=5.0.2->Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (4.3.0)\r\nRequirement already satisfied: bidict>=0.21.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from python-socketio>=5.0.2->Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (0.21.4)\r\nRequirement already satisfied: pyasn1>=0.1.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from rsa<5.0.0,>=4.7->sigopt->neural-compressor>=1.7->optimum[intel]) (0.4.8)\r\nRequirement already satisfied: smmap<6,>=3.0.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from gitdb<5,>=4.0.1->GitPython>=2.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (5.0.0)\r\nRequirement already satisfied: pyasn1-modules>=0.2.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.8)\r\nRequirement already satisfied: cachetools<5.0,>=2.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (4.2.2)\r\nRequirement already satisfied: parso<0.8.0,>=0.7.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from jedi>=0.10->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.1)\r\nRequirement already satisfied: ipython-genutils in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from traitlets>=4.2->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0)\r\nRequirement already satisfied: ptyprocess>=0.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pexpect->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.0)\r\nRequirement already satisfied: oauthlib>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests-oauthlib->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.1)\r\n```\r\n\r\n---\r\n\r\n**Cell:**\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n```\r\nOR\r\n```python\r\nimport datasets\r\n```\r\n\r\n**Traceback:**\r\n```\r\n---------------------------------------------------------------------------\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-7-34fb7ba3338d> in <module>\r\n----> 1 from datasets import load_dataset, load_metric\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/__init__.py in <module>\r\n     32     )\r\n     33 \r\n---> 34 from .arrow_dataset import Dataset, concatenate_datasets\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     59 from . import config, utils\r\n     60 from .arrow_reader import ArrowReader\r\n---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence\r\n     62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper\r\n     63 from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in <module>\r\n     26 \r\n     27 from . import config, utils\r\n---> 28 from .features import (\r\n     29     Features,\r\n     30     ImageExtensionType,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/__init__.py in <module>\r\n      1 # flake8: noqa\r\n----> 2 from .audio import Audio\r\n      3 from .features import *\r\n      4 from .features import (\r\n      5     _ArrayXD,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in <module>\r\n      5 import pyarrow as pa\r\n      6 \r\n----> 7 from ..utils.streaming_download_manager import xopen\r\n      8 \r\n      9 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/utils\/streaming_download_manager.py in <module>\r\n     16 \r\n     17 from .. import config\r\n---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS\r\n     19 from .download_manager import DownloadConfig, map_nested\r\n     20 from .file_utils import (\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/__init__.py in <module>\r\n     11 \r\n     12 if _has_s3fs:\r\n---> 13     from .s3filesystem import S3FileSystem  # noqa: F401\r\n     14 \r\n     15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/s3filesystem.py in <module>\r\n----> 1 import s3fs\r\n      2 \r\n      3 \r\n      4 class S3FileSystem(s3fs.S3FileSystem):\r\n      5     \"\"\"\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/__init__.py in <module>\r\n----> 1 from .core import S3FileSystem, S3File\r\n      2 from .mapping import S3Map\r\n      3 \r\n      4 from ._version import get_versions\r\n      5 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/core.py in <module>\r\n     12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper\r\n     13 \r\n---> 14 import aiobotocore\r\n     15 import botocore\r\n     16 import aiobotocore.session\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/__init__.py in <module>\r\n----> 1 from .session import get_session, AioSession\r\n      2 \r\n      3 __all__ = ['get_session', 'AioSession']\r\n      4 __version__ = '1.3.0'\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/session.py in <module>\r\n      4 from botocore import retryhandler, translate\r\n      5 from botocore.exceptions import PartialCredentialsError\r\n----> 6 from .client import AioClientCreator, AioBaseClient\r\n      7 from .hooks import AioHierarchicalEmitter\r\n      8 from .parsers import AioResponseParserFactory\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/client.py in <module>\r\n     11 from .args import AioClientArgsCreator\r\n     12 from .utils import AioS3RegionRedirector\r\n---> 13 from . import waiter\r\n     14 \r\n     15 history_recorder = get_global_history_recorder()\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/waiter.py in <module>\r\n      4 from botocore.exceptions import ClientError\r\n      5 from botocore.waiter import WaiterModel  # noqa: F401, lgtm[py\/unused-import]\r\n----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \\\r\n      7     NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error\r\n      8 from botocore.docs.docstring import WaiterDocstring\r\n\r\nImportError: cannot import name 'is_valid_waiter_error'\r\n```\r\n\r\nPlease let me know if there's anything else I can add to post.\r\n\r\n  [1]: https:\/\/github.com\/huggingface\/notebooks\/blob\/master\/examples\/text_classification_quantization_inc.ipynb \n Hey @mariosasko. Yes, I am using **Amazon SageMaker Studio Jupyter Labs**. However, I no longer need this notebook; but it would be nice to have this problem solved for others. So don't stress too much if you two can't reproduce error.","embeddings":[-0.1478826404,-0.1469275355,-0.2213936746,0.1788337231,0.1569536626,-0.1981431842,0.3735091984,0.1772014052,-0.0016113664,0.0087772449,-0.1001607925,0.3891833425,0.0245104004,0.1154367626,-0.013237684,-0.0450457409,0.0596402362,0.2368053943,-0.1831150949,-0.0913483128,-0.04930925,0.1821940392,-0.0914353356,0.0991099402,-0.5285232663,0.0171162989,0.3036388755,-0.1104502529,-0.3566949666,-0.3713032901,0.282556802,0.0187198557,-0.1013864651,0.4599289894,-0.0001122417,0.2301795334,0.247648716,-0.0499859117,-0.3345486522,-0.2134088129,-0.0398929715,-0.2608366907,0.1104155555,-0.3822896183,-0.0143552506,-0.1368085891,-0.1702284366,-0.0528264381,0.4874280095,0.4079868495,0.2351165116,0.3357532322,0.1470539868,-0.0369232967,0.0279227626,-0.0211199634,-0.0040117516,-0.2910866141,-0.0123531129,-0.0471109003,0.0962781087,0.1077362299,-0.2222494781,0.1710375696,0.1241175085,-0.0654973909,-0.0056314445,-0.2618317008,-0.071614027,0.0502547026,0.3171563447,-0.0479387194,-0.2048241496,0.0131165227,-0.1751160622,-0.034117613,0.2309505343,-0.1560395807,-0.2949763834,0.1756315678,-0.0247068089,0.0081619257,-0.2785773575,0.1817537993,-0.4565280974,0.7678371072,-0.0489432514,0.0298464466,0.1483291984,-0.0376046188,0.3596741557,0.0164219253,0.252312988,0.0981852561,-0.2793003619,0.0397320949,0.2744542062,0.1319995373,-0.155529052,0.3421450257,-0.0452987552,-0.0104287956,0.1772433817,0.1695553064,-0.1268127114,0.1690245867,0.0345578492,0.4688956141,0.0148680145,0.2946377397,0.1175014451,0.0100929569,0.0981885716,-0.241864875,-0.2369599342,-0.0289265551,0.4705239832,-0.0926314965,-0.4008589685,0.2684024274,-0.0243986174,-0.0137886908,0.2722730637,0.5937827229,-0.2541587353,0.0152359381,0.2565144002,0.1324543804,-0.1600556523,-0.0037185543,-0.1664024293,0.0108998492,-0.0480646081,-0.2606745064,0.3093199432,-0.0377330258,0.3955484331,-0.0325192697,0.331504792,-0.0167365875,0.0469160713,-0.0624638572,0.0432012416,0.4161675274,-0.0650722757,-0.0360062681,0.2363214791,-0.22278364,-0.0510434434,0.1127198637,-0.1398519874,-0.204610154,-0.1627254337,0.2663304806,-0.0711949989,-0.2033764273,-0.0496625379,-0.3503129482,0.2936307788,-0.3419859409,-0.0596644096,-0.1041735411,0.3260512352,-0.0699320585,0.1980648488,0.1620360911,-0.0309997145,-0.2566133738,-0.0580762811,-0.0227418318,-0.0156978611,0.2671217918,-0.0691749677,0.2928949594,0.006995047,-0.0671879575,0.3016172051,-0.3634080589,-0.4850995839,0.0759641454,-0.0397107936,0.0359583423,0.2842036188,-0.0800512582,0.2552694082,0.0652229637,0.3026781678,0.1900785565,0.0718901977,-0.1407696605,-0.2025606185,0.0093414774,-0.0015717288,0.2212698609,0.3340733349,-0.1150452495,-0.0708577633,0.0863414705,0.0546513684,0.0711740553,0.024613183,0.2608941793,0.4277401268,0.1926631033,-0.1258417964,-0.1145479232,-0.1011542454,0.0480534025,-0.0098282751,0.268134743,-0.2944584787,-0.0841062665,-0.3610241711,0.1938845962,-0.2637549043,-0.0621224977,0.2436020076,0.0911015496,-0.0583872274,-0.0399753004,-0.2093599737,0.5022643209,-0.0612062216,0.317474395,-0.1920445114,0.1801327169,-0.2967030108,0.0811511353,-0.1548826545,0.033550512,0.1584484577,-0.160759598,0.001973341,0.0806027874,0.0995634198,-0.0911889002,-0.4025059938,-0.0693585202,0.0140238879,-0.0404616781,-0.0612957552,0.0771564245,0.0371593237,-0.0049723787,-0.1816972941,0.1851636618,-0.1059819832,0.1643475145,-0.0125468783,0.0808050707,0.3824872077,-0.1058043092,-0.1905660182,-0.1865923703,0.4284561872,0.044614099,0.4007039964,-0.0897713602,0.1700816154,-0.1535908729,0.1374217421,0.0062702345,0.1750783026,-0.114324525,0.0300715454,0.2086066008,0.2309206277,0.3923053741,0.4204880595,0.2654602528,-0.1815606952,-0.0590885207,-0.1567089111,-0.1053406298,0.2149128914,0.1123132557,0.1805267483,0.1572758406,-0.0655310526,-0.0514626242,-0.2755738795,-0.3691127896,0.0449733324,0.2246839404,-0.3862240911,0.1971429884,-0.3713847101,0.0378694683,-0.0700483099,-0.4746542573,-0.1963273436,-0.2479985952,0.0187220424,0.1868580431,0.0340275168,0.3932765424,0.2305352688,0.1749004275,0.0847125575,-0.5530105829,-0.3919090331,-0.1822863966,0.0130748367,0.1629482955,0.0899288878,0.03951478,0.2660743594,-0.4861732423,-0.042901963,-0.0474388711,-0.511628747,0.0688923299,-0.2954367399,0.3580017388,0.1606633365,0.2106071562,0.0412858054,-0.0698724315,0.1822589487,-0.1377104223,-0.1190349087,0.0367959142,0.1163153499,-0.3229210377,0.0954666138,-0.3759789765,-0.3106664419,-0.3536981344,0.0166444015,0.3028669357,0.1337806135,0.0542098358,0.1005365625,0.0942064002,0.2855480313,0.2112272233,-0.2116113603,0.0495011993,0.3355379403,-0.3503984213,-0.2486626506,-0.1395148486,0.0325690955,0.6050505638,-0.2025162131,-0.5017106533,-0.0703518763,0.0724209771,0.0871891156,-0.168718487,0.2250495255,0.381659925,0.2218536586,-0.0721164867,-0.1877246946,-0.2443275899,-0.2268707603,0.0523877554,0.1715290546,-0.1827576458,0.3797294796,0.0422425456,0.4859939516,0.0453510098,-0.1519201398,0.3058897555,-0.0000817663,0.2879120409,-0.1440901458,-0.4906707406,-0.2398596108,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3554","title":"ImportError: cannot import name 'is_valid_waiter_error'","comments":"Hey @danielbellhv, \r\n\r\nThis issue might be related to Studio probably not having an up to date `botocore` and `boto3` version. I ran into this as well a while back. My workaround was \r\n```python\r\n# using older dataset due to incompatibility of sagemaker notebook & aws-cli with > s3fs and fsspec to >= 2021.10\r\n!pip install  \"datasets==1.13\" --upgrade\r\n```\r\n\r\nIn `datasets` we use the latest `s3fs` and `fsspec` but aws-cli and notebook is not supporting this. You could also update the `aws-cli` and associated packages to get the latest `datasets` version\r\n","body":"Based on [SO post](https:\/\/stackoverflow.com\/q\/70606147\/17840900).\r\n\r\nI'm following along to this [Notebook][1], cell \"**Loading the dataset**\".\r\n\r\nKernel: `conda_pytorch_p36`.\r\n\r\nI run:\r\n```\r\n! pip install datasets transformers optimum[intel]\r\n```\r\n\r\nOutput:\r\n```\r\nRequirement already satisfied: datasets in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (1.17.0)\r\nRequirement already satisfied: transformers in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (4.15.0)\r\nRequirement already satisfied: optimum[intel] in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (0.1.3)\r\nRequirement already satisfied: numpy>=1.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: dill in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.3.4)\r\nRequirement already satisfied: tqdm>=4.62.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.62.3)\r\nRequirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.2.1)\r\nRequirement already satisfied: packaging in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (21.3)\r\nRequirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (6.0.1)\r\nRequirement already satisfied: pandas in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.1.5)\r\nRequirement already satisfied: xxhash in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.0.2)\r\nRequirement already satisfied: aiohttp in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (3.8.1)\r\nRequirement already satisfied: fsspec[http]>=2021.05.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2021.11.1)\r\nRequirement already satisfied: dataclasses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.8)\r\nRequirement already satisfied: multiprocess in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: importlib-metadata in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.5.0)\r\nRequirement already satisfied: requests>=2.19.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: pyyaml>=5.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (5.4.1)\r\nRequirement already satisfied: regex!=2019.12.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (2021.4.4)\r\nRequirement already satisfied: tokenizers<0.11,>=0.10.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.10.3)\r\nRequirement already satisfied: filelock in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (3.0.12)\r\nRequirement already satisfied: sacremoses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.0.46)\r\nRequirement already satisfied: torch>=1.9 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.10.1)\r\nRequirement already satisfied: sympy in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.8)\r\nRequirement already satisfied: coloredlogs in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (15.0.1)\r\nRequirement already satisfied: pycocotools in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (2.0.3)\r\nRequirement already satisfied: neural-compressor>=1.7 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.9)\r\nRequirement already satisfied: typing-extensions>=3.7.4.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.10.0.0)\r\nRequirement already satisfied: sigopt in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.2.0)\r\nRequirement already satisfied: opencv-python in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (4.5.1.48)\r\nRequirement already satisfied: cryptography in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.4.7)\r\nRequirement already satisfied: py-cpuinfo in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.0.0)\r\nRequirement already satisfied: gevent in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (21.1.2)\r\nRequirement already satisfied: schema in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from 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ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (4.3.3)\r\nRequirement already satisfied: jedi>=0.10 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.17.2)\r\nRequirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (3.0.19)\r\nRequirement already satisfied: backcall in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0)\r\nRequirement already satisfied: pygments in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (2.9.0)\r\nRequirement already satisfied: pexpect in 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(1.30.2)\r\nRequirement already satisfied: requests-oauthlib in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (1.3.0)\r\nRequirement already satisfied: importlib-resources in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from Pint<0.17.0,>=0.16.0->sigopt->neural-compressor>=1.7->optimum[intel]) (5.4.0)\r\nRequirement already satisfied: python-engineio>=4.3.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from python-socketio>=5.0.2->Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (4.3.0)\r\nRequirement already satisfied: bidict>=0.21.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from python-socketio>=5.0.2->Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (0.21.4)\r\nRequirement already satisfied: pyasn1>=0.1.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from rsa<5.0.0,>=4.7->sigopt->neural-compressor>=1.7->optimum[intel]) (0.4.8)\r\nRequirement already satisfied: smmap<6,>=3.0.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from gitdb<5,>=4.0.1->GitPython>=2.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (5.0.0)\r\nRequirement already satisfied: pyasn1-modules>=0.2.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.8)\r\nRequirement already satisfied: cachetools<5.0,>=2.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (4.2.2)\r\nRequirement already satisfied: parso<0.8.0,>=0.7.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from jedi>=0.10->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.1)\r\nRequirement already satisfied: ipython-genutils in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from traitlets>=4.2->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0)\r\nRequirement already satisfied: ptyprocess>=0.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pexpect->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.0)\r\nRequirement already satisfied: oauthlib>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests-oauthlib->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.1)\r\n```\r\n\r\n---\r\n\r\n**Cell:**\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n```\r\nOR\r\n```python\r\nimport datasets\r\n```\r\n\r\n**Traceback:**\r\n```\r\n---------------------------------------------------------------------------\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-7-34fb7ba3338d> in <module>\r\n----> 1 from datasets import load_dataset, load_metric\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/__init__.py in <module>\r\n     32     )\r\n     33 \r\n---> 34 from .arrow_dataset import Dataset, concatenate_datasets\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     59 from . import config, utils\r\n     60 from .arrow_reader import ArrowReader\r\n---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence\r\n     62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper\r\n     63 from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in <module>\r\n     26 \r\n     27 from . import config, utils\r\n---> 28 from .features import (\r\n     29     Features,\r\n     30     ImageExtensionType,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/__init__.py in <module>\r\n      1 # flake8: noqa\r\n----> 2 from .audio import Audio\r\n      3 from .features import *\r\n      4 from .features import (\r\n      5     _ArrayXD,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in <module>\r\n      5 import pyarrow as pa\r\n      6 \r\n----> 7 from ..utils.streaming_download_manager import xopen\r\n      8 \r\n      9 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/utils\/streaming_download_manager.py in <module>\r\n     16 \r\n     17 from .. import config\r\n---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS\r\n     19 from .download_manager import DownloadConfig, map_nested\r\n     20 from .file_utils import (\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/__init__.py in <module>\r\n     11 \r\n     12 if _has_s3fs:\r\n---> 13     from .s3filesystem import S3FileSystem  # noqa: F401\r\n     14 \r\n     15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/s3filesystem.py in <module>\r\n----> 1 import s3fs\r\n      2 \r\n      3 \r\n      4 class S3FileSystem(s3fs.S3FileSystem):\r\n      5     \"\"\"\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/__init__.py in <module>\r\n----> 1 from .core import S3FileSystem, S3File\r\n      2 from .mapping import S3Map\r\n      3 \r\n      4 from ._version import get_versions\r\n      5 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/core.py in <module>\r\n     12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper\r\n     13 \r\n---> 14 import aiobotocore\r\n     15 import botocore\r\n     16 import aiobotocore.session\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/__init__.py in <module>\r\n----> 1 from .session import get_session, AioSession\r\n      2 \r\n      3 __all__ = ['get_session', 'AioSession']\r\n      4 __version__ = '1.3.0'\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/session.py in <module>\r\n      4 from botocore import retryhandler, translate\r\n      5 from botocore.exceptions import PartialCredentialsError\r\n----> 6 from .client import AioClientCreator, AioBaseClient\r\n      7 from .hooks import AioHierarchicalEmitter\r\n      8 from .parsers import AioResponseParserFactory\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/client.py in <module>\r\n     11 from .args import AioClientArgsCreator\r\n     12 from .utils import AioS3RegionRedirector\r\n---> 13 from . import waiter\r\n     14 \r\n     15 history_recorder = get_global_history_recorder()\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/waiter.py in <module>\r\n      4 from botocore.exceptions import ClientError\r\n      5 from botocore.waiter import WaiterModel  # noqa: F401, lgtm[py\/unused-import]\r\n----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \\\r\n      7     NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error\r\n      8 from botocore.docs.docstring import WaiterDocstring\r\n\r\nImportError: cannot import name 'is_valid_waiter_error'\r\n```\r\n\r\nPlease let me know if there's anything else I can add to post.\r\n\r\n  [1]: https:\/\/github.com\/huggingface\/notebooks\/blob\/master\/examples\/text_classification_quantization_inc.ipynb","comment_length":90,"text":"ImportError: cannot import name 'is_valid_waiter_error' \n Based on [SO post](https:\/\/stackoverflow.com\/q\/70606147\/17840900).\r\n\r\nI'm following along to this [Notebook][1], cell \"**Loading the dataset**\".\r\n\r\nKernel: `conda_pytorch_p36`.\r\n\r\nI run:\r\n```\r\n! pip install datasets transformers optimum[intel]\r\n```\r\n\r\nOutput:\r\n```\r\nRequirement already satisfied: datasets in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (1.17.0)\r\nRequirement already satisfied: transformers in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (4.15.0)\r\nRequirement already satisfied: optimum[intel] in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (0.1.3)\r\nRequirement already satisfied: numpy>=1.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: dill in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.3.4)\r\nRequirement already satisfied: tqdm>=4.62.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.62.3)\r\nRequirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.2.1)\r\nRequirement already satisfied: packaging in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (21.3)\r\nRequirement already satisfied: pyarrow!=4.0.0,>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (6.0.1)\r\nRequirement already satisfied: pandas in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (1.1.5)\r\nRequirement already satisfied: xxhash in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.0.2)\r\nRequirement already satisfied: aiohttp in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (3.8.1)\r\nRequirement already satisfied: fsspec[http]>=2021.05.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2021.11.1)\r\nRequirement already satisfied: dataclasses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.8)\r\nRequirement already satisfied: multiprocess in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: importlib-metadata in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (4.5.0)\r\nRequirement already satisfied: requests>=2.19.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: pyyaml>=5.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (5.4.1)\r\nRequirement already satisfied: regex!=2019.12.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (2021.4.4)\r\nRequirement already satisfied: tokenizers<0.11,>=0.10.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.10.3)\r\nRequirement already satisfied: filelock in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (3.0.12)\r\nRequirement already satisfied: sacremoses in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from transformers) (0.0.46)\r\nRequirement already satisfied: torch>=1.9 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.10.1)\r\nRequirement already satisfied: sympy in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.8)\r\nRequirement already satisfied: coloredlogs in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (15.0.1)\r\nRequirement already satisfied: pycocotools in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (2.0.3)\r\nRequirement already satisfied: neural-compressor>=1.7 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from optimum[intel]) (1.9)\r\nRequirement already satisfied: typing-extensions>=3.7.4.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.10.0.0)\r\nRequirement already satisfied: sigopt in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.2.0)\r\nRequirement already satisfied: opencv-python in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (4.5.1.48)\r\nRequirement already satisfied: cryptography in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.4.7)\r\nRequirement already satisfied: py-cpuinfo in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.0.0)\r\nRequirement already satisfied: gevent in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (21.1.2)\r\nRequirement already satisfied: schema in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.7.5)\r\nRequirement already satisfied: psutil in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.8.0)\r\nRequirement already satisfied: gevent-websocket in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.10.1)\r\nRequirement already satisfied: hyperopt in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.2.7)\r\nRequirement already satisfied: Flask in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.0.1)\r\nRequirement already satisfied: prettytable in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (2.5.0)\r\nRequirement already satisfied: Flask-SocketIO in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (5.1.1)\r\nRequirement already satisfied: scikit-learn in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (0.24.2)\r\nRequirement already satisfied: Pillow in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (8.4.0)\r\nRequirement already satisfied: Flask-Cors in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from neural-compressor>=1.7->optimum[intel]) (3.0.10)\r\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: certifi>=2017.4.17 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: yarl<2.0,>=1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.6.3)\r\nRequirement already satisfied: charset-normalizer<3.0,>=2.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (2.0.9)\r\nRequirement already satisfied: attrs>=17.3.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (21.2.0)\r\nRequirement already satisfied: asynctest==0.13.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (0.13.0)\r\nRequirement already satisfied: idna-ssl>=1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.1.0)\r\nRequirement already satisfied: async-timeout<5.0,>=4.0.0a3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (4.0.1)\r\nRequirement already satisfied: aiosignal>=1.1.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.2.0)\r\nRequirement already satisfied: frozenlist>=1.1.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (1.2.0)\r\nRequirement already satisfied: multidict<7.0,>=4.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from aiohttp->datasets) (5.1.0)\r\nRequirement already satisfied: humanfriendly>=9.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from coloredlogs->optimum[intel]) (10.0)\r\nRequirement already satisfied: zipp>=0.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.2 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: matplotlib>=2.1.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pycocotools->optimum[intel]) (3.3.4)\r\nRequirement already satisfied: cython>=0.27.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pycocotools->optimum[intel]) (0.29.23)\r\nRequirement already satisfied: setuptools>=18.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pycocotools->optimum[intel]) (52.0.0.post20210125)\r\nRequirement already satisfied: joblib in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sacremoses->transformers) (1.0.1)\r\nRequirement already satisfied: click in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sacremoses->transformers) (8.0.1)\r\nRequirement already satisfied: six in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sacremoses->transformers) (1.16.0)\r\nRequirement already satisfied: mpmath>=0.19 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from sympy->optimum[intel]) (1.2.1)\r\nRequirement already satisfied: kiwisolver>=1.0.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from matplotlib>=2.1.0->pycocotools->optimum[intel]) (1.3.1)\r\nRequirement already satisfied: cycler>=0.10 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/cycler-0.10.0-py3.6.egg (from matplotlib>=2.1.0->pycocotools->optimum[intel]) (0.10.0)\r\nRequirement already satisfied: cffi>=1.12 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from cryptography->neural-compressor>=1.7->optimum[intel]) (1.14.5)\r\nRequirement already satisfied: Werkzeug>=2.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from Flask->neural-compressor>=1.7->optimum[intel]) (2.0.2)\r\nRequirement already satisfied: Jinja2>=3.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from Flask->neural-compressor>=1.7->optimum[intel]) 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(1.30.2)\r\nRequirement already satisfied: requests-oauthlib in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (1.3.0)\r\nRequirement already satisfied: importlib-resources in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from Pint<0.17.0,>=0.16.0->sigopt->neural-compressor>=1.7->optimum[intel]) (5.4.0)\r\nRequirement already satisfied: python-engineio>=4.3.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from python-socketio>=5.0.2->Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (4.3.0)\r\nRequirement already satisfied: bidict>=0.21.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from python-socketio>=5.0.2->Flask-SocketIO->neural-compressor>=1.7->optimum[intel]) (0.21.4)\r\nRequirement already satisfied: pyasn1>=0.1.3 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from rsa<5.0.0,>=4.7->sigopt->neural-compressor>=1.7->optimum[intel]) (0.4.8)\r\nRequirement already satisfied: smmap<6,>=3.0.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from gitdb<5,>=4.0.1->GitPython>=2.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (5.0.0)\r\nRequirement already satisfied: pyasn1-modules>=0.2.1 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.8)\r\nRequirement already satisfied: cachetools<5.0,>=2.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from google-auth>=1.0.1->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (4.2.2)\r\nRequirement already satisfied: parso<0.8.0,>=0.7.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from jedi>=0.10->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.1)\r\nRequirement already satisfied: ipython-genutils in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from traitlets>=4.2->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.2.0)\r\nRequirement already satisfied: ptyprocess>=0.5 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from pexpect->ipython>=5.0.0->sigopt->neural-compressor>=1.7->optimum[intel]) (0.7.0)\r\nRequirement already satisfied: oauthlib>=3.0.0 in \/home\/ec2-user\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages (from requests-oauthlib->kubernetes<13.0.0,>=12.0.1->sigopt->neural-compressor>=1.7->optimum[intel]) (3.1.1)\r\n```\r\n\r\n---\r\n\r\n**Cell:**\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n```\r\nOR\r\n```python\r\nimport datasets\r\n```\r\n\r\n**Traceback:**\r\n```\r\n---------------------------------------------------------------------------\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-7-34fb7ba3338d> in <module>\r\n----> 1 from datasets import load_dataset, load_metric\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/__init__.py in <module>\r\n     32     )\r\n     33 \r\n---> 34 from .arrow_dataset import Dataset, concatenate_datasets\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     59 from . import config, utils\r\n     60 from .arrow_reader import ArrowReader\r\n---> 61 from .arrow_writer import ArrowWriter, OptimizedTypedSequence\r\n     62 from .features import ClassLabel, Features, FeatureType, Sequence, Value, _ArrayXD, pandas_types_mapper\r\n     63 from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in <module>\r\n     26 \r\n     27 from . import config, utils\r\n---> 28 from .features import (\r\n     29     Features,\r\n     30     ImageExtensionType,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/__init__.py in <module>\r\n      1 # flake8: noqa\r\n----> 2 from .audio import Audio\r\n      3 from .features import *\r\n      4 from .features import (\r\n      5     _ArrayXD,\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/features\/audio.py in <module>\r\n      5 import pyarrow as pa\r\n      6 \r\n----> 7 from ..utils.streaming_download_manager import xopen\r\n      8 \r\n      9 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/utils\/streaming_download_manager.py in <module>\r\n     16 \r\n     17 from .. import config\r\n---> 18 from ..filesystems import COMPRESSION_FILESYSTEMS\r\n     19 from .download_manager import DownloadConfig, map_nested\r\n     20 from .file_utils import (\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/__init__.py in <module>\r\n     11 \r\n     12 if _has_s3fs:\r\n---> 13     from .s3filesystem import S3FileSystem  # noqa: F401\r\n     14 \r\n     15 COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/filesystems\/s3filesystem.py in <module>\r\n----> 1 import s3fs\r\n      2 \r\n      3 \r\n      4 class S3FileSystem(s3fs.S3FileSystem):\r\n      5     \"\"\"\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/__init__.py in <module>\r\n----> 1 from .core import S3FileSystem, S3File\r\n      2 from .mapping import S3Map\r\n      3 \r\n      4 from ._version import get_versions\r\n      5 \r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/s3fs\/core.py in <module>\r\n     12 from fsspec.asyn import AsyncFileSystem, sync, sync_wrapper\r\n     13 \r\n---> 14 import aiobotocore\r\n     15 import botocore\r\n     16 import aiobotocore.session\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/__init__.py in <module>\r\n----> 1 from .session import get_session, AioSession\r\n      2 \r\n      3 __all__ = ['get_session', 'AioSession']\r\n      4 __version__ = '1.3.0'\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/session.py in <module>\r\n      4 from botocore import retryhandler, translate\r\n      5 from botocore.exceptions import PartialCredentialsError\r\n----> 6 from .client import AioClientCreator, AioBaseClient\r\n      7 from .hooks import AioHierarchicalEmitter\r\n      8 from .parsers import AioResponseParserFactory\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/client.py in <module>\r\n     11 from .args import AioClientArgsCreator\r\n     12 from .utils import AioS3RegionRedirector\r\n---> 13 from . import waiter\r\n     14 \r\n     15 history_recorder = get_global_history_recorder()\r\n\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/aiobotocore\/waiter.py in <module>\r\n      4 from botocore.exceptions import ClientError\r\n      5 from botocore.waiter import WaiterModel  # noqa: F401, lgtm[py\/unused-import]\r\n----> 6 from botocore.waiter import Waiter, xform_name, logger, WaiterError, \\\r\n      7     NormalizedOperationMethod as _NormalizedOperationMethod, is_valid_waiter_error\r\n      8 from botocore.docs.docstring import WaiterDocstring\r\n\r\nImportError: cannot import name 'is_valid_waiter_error'\r\n```\r\n\r\nPlease let me know if there's anything else I can add to post.\r\n\r\n  [1]: https:\/\/github.com\/huggingface\/notebooks\/blob\/master\/examples\/text_classification_quantization_inc.ipynb \n Hey @danielbellhv, \r\n\r\nThis issue might be related to Studio probably not having an up to date `botocore` and `boto3` version. I ran into this as well a while back. My workaround was \r\n```python\r\n# using older dataset due to incompatibility of sagemaker notebook & aws-cli with > s3fs and fsspec to >= 2021.10\r\n!pip install  \"datasets==1.13\" --upgrade\r\n```\r\n\r\nIn `datasets` we use the latest `s3fs` and `fsspec` but aws-cli and notebook is not supporting this. You could also update the `aws-cli` and associated packages to get the latest `datasets` version\r\n","embeddings":[-0.1478826404,-0.1469275355,-0.2213936746,0.1788337231,0.1569536626,-0.1981431842,0.3735091984,0.1772014052,-0.0016113664,0.0087772449,-0.1001607925,0.3891833425,0.0245104004,0.1154367626,-0.013237684,-0.0450457409,0.0596402362,0.2368053943,-0.1831150949,-0.0913483128,-0.04930925,0.1821940392,-0.0914353356,0.0991099402,-0.5285232663,0.0171162989,0.3036388755,-0.1104502529,-0.3566949666,-0.3713032901,0.282556802,0.0187198557,-0.1013864651,0.4599289894,-0.0001122417,0.2301795334,0.247648716,-0.0499859117,-0.3345486522,-0.2134088129,-0.0398929715,-0.2608366907,0.1104155555,-0.3822896183,-0.0143552506,-0.1368085891,-0.1702284366,-0.0528264381,0.4874280095,0.4079868495,0.2351165116,0.3357532322,0.1470539868,-0.0369232967,0.0279227626,-0.0211199634,-0.0040117516,-0.2910866141,-0.0123531129,-0.0471109003,0.0962781087,0.1077362299,-0.2222494781,0.1710375696,0.1241175085,-0.0654973909,-0.0056314445,-0.2618317008,-0.071614027,0.0502547026,0.3171563447,-0.0479387194,-0.2048241496,0.0131165227,-0.1751160622,-0.034117613,0.2309505343,-0.1560395807,-0.2949763834,0.1756315678,-0.0247068089,0.0081619257,-0.2785773575,0.1817537993,-0.4565280974,0.7678371072,-0.0489432514,0.0298464466,0.1483291984,-0.0376046188,0.3596741557,0.0164219253,0.252312988,0.0981852561,-0.2793003619,0.0397320949,0.2744542062,0.1319995373,-0.155529052,0.3421450257,-0.0452987552,-0.0104287956,0.1772433817,0.1695553064,-0.1268127114,0.1690245867,0.0345578492,0.4688956141,0.0148680145,0.2946377397,0.1175014451,0.0100929569,0.0981885716,-0.241864875,-0.2369599342,-0.0289265551,0.4705239832,-0.0926314965,-0.4008589685,0.2684024274,-0.0243986174,-0.0137886908,0.2722730637,0.5937827229,-0.2541587353,0.0152359381,0.2565144002,0.1324543804,-0.1600556523,-0.0037185543,-0.1664024293,0.0108998492,-0.0480646081,-0.2606745064,0.3093199432,-0.0377330258,0.3955484331,-0.0325192697,0.331504792,-0.0167365875,0.0469160713,-0.0624638572,0.0432012416,0.4161675274,-0.0650722757,-0.0360062681,0.2363214791,-0.22278364,-0.0510434434,0.1127198637,-0.1398519874,-0.204610154,-0.1627254337,0.2663304806,-0.0711949989,-0.2033764273,-0.0496625379,-0.3503129482,0.2936307788,-0.3419859409,-0.0596644096,-0.1041735411,0.3260512352,-0.0699320585,0.1980648488,0.1620360911,-0.0309997145,-0.2566133738,-0.0580762811,-0.0227418318,-0.0156978611,0.2671217918,-0.0691749677,0.2928949594,0.006995047,-0.0671879575,0.3016172051,-0.3634080589,-0.4850995839,0.0759641454,-0.0397107936,0.0359583423,0.2842036188,-0.0800512582,0.2552694082,0.0652229637,0.3026781678,0.1900785565,0.0718901977,-0.1407696605,-0.2025606185,0.0093414774,-0.0015717288,0.2212698609,0.3340733349,-0.1150452495,-0.0708577633,0.0863414705,0.0546513684,0.0711740553,0.024613183,0.2608941793,0.4277401268,0.1926631033,-0.1258417964,-0.1145479232,-0.1011542454,0.0480534025,-0.0098282751,0.268134743,-0.2944584787,-0.0841062665,-0.3610241711,0.1938845962,-0.2637549043,-0.0621224977,0.2436020076,0.0911015496,-0.0583872274,-0.0399753004,-0.2093599737,0.5022643209,-0.0612062216,0.317474395,-0.1920445114,0.1801327169,-0.2967030108,0.0811511353,-0.1548826545,0.033550512,0.1584484577,-0.160759598,0.001973341,0.0806027874,0.0995634198,-0.0911889002,-0.4025059938,-0.0693585202,0.0140238879,-0.0404616781,-0.0612957552,0.0771564245,0.0371593237,-0.0049723787,-0.1816972941,0.1851636618,-0.1059819832,0.1643475145,-0.0125468783,0.0808050707,0.3824872077,-0.1058043092,-0.1905660182,-0.1865923703,0.4284561872,0.044614099,0.4007039964,-0.0897713602,0.1700816154,-0.1535908729,0.1374217421,0.0062702345,0.1750783026,-0.114324525,0.0300715454,0.2086066008,0.2309206277,0.3923053741,0.4204880595,0.2654602528,-0.1815606952,-0.0590885207,-0.1567089111,-0.1053406298,0.2149128914,0.1123132557,0.1805267483,0.1572758406,-0.0655310526,-0.0514626242,-0.2755738795,-0.3691127896,0.0449733324,0.2246839404,-0.3862240911,0.1971429884,-0.3713847101,0.0378694683,-0.0700483099,-0.4746542573,-0.1963273436,-0.2479985952,0.0187220424,0.1868580431,0.0340275168,0.3932765424,0.2305352688,0.1749004275,0.0847125575,-0.5530105829,-0.3919090331,-0.1822863966,0.0130748367,0.1629482955,0.0899288878,0.03951478,0.2660743594,-0.4861732423,-0.042901963,-0.0474388711,-0.511628747,0.0688923299,-0.2954367399,0.3580017388,0.1606633365,0.2106071562,0.0412858054,-0.0698724315,0.1822589487,-0.1377104223,-0.1190349087,0.0367959142,0.1163153499,-0.3229210377,0.0954666138,-0.3759789765,-0.3106664419,-0.3536981344,0.0166444015,0.3028669357,0.1337806135,0.0542098358,0.1005365625,0.0942064002,0.2855480313,0.2112272233,-0.2116113603,0.0495011993,0.3355379403,-0.3503984213,-0.2486626506,-0.1395148486,0.0325690955,0.6050505638,-0.2025162131,-0.5017106533,-0.0703518763,0.0724209771,0.0871891156,-0.168718487,0.2250495255,0.381659925,0.2218536586,-0.0721164867,-0.1877246946,-0.2443275899,-0.2268707603,0.0523877554,0.1715290546,-0.1827576458,0.3797294796,0.0422425456,0.4859939516,0.0453510098,-0.1519201398,0.3058897555,-0.0000817663,0.2879120409,-0.1440901458,-0.4906707406,-0.23985961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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3553","title":"set_format(\"np\") no longer works for Image data","comments":"This error also propagates to jax and is even trickier to fix, since `.with_format(type='jax')` will use numpy conversion internally (and fail). For a three line failure:\r\n\r\n```python\r\ndataset = datasets.load_dataset(\"mnist\")\r\ndataset.set_format(\"jax\")\r\nX_train = dataset[\"train\"][\"image\"]\r\n```","body":"## Describe the bug\r\n`dataset.set_format(\"np\")` no longer works for image data, previously you could load the MNIST like this:\r\n\r\n```python\r\ndataset = load_dataset(\"mnist\")\r\ndataset.set_format(\"np\")\r\nX_train = dataset[\"train\"][\"image\"][..., None] # <== No longer a numpy array\r\n```\r\nbut now it doesn't work, `set_format(\"np\")` seems to have no effect and the dataset just returns a list\/array of PIL images instead of numpy arrays as requested.\r\n","comment_length":35,"text":"set_format(\"np\") no longer works for Image data \n ## Describe the bug\r\n`dataset.set_format(\"np\")` no longer works for image data, previously you could load the MNIST like this:\r\n\r\n```python\r\ndataset = load_dataset(\"mnist\")\r\ndataset.set_format(\"np\")\r\nX_train = dataset[\"train\"][\"image\"][..., None] # <== No longer a numpy array\r\n```\r\nbut now it doesn't work, `set_format(\"np\")` seems to have no effect and the dataset just returns a list\/array of PIL images instead of numpy arrays as requested.\r\n \n This error also propagates to jax and is even trickier to fix, since `.with_format(type='jax')` will use numpy conversion internally (and fail). For a three line failure:\r\n\r\n```python\r\ndataset = datasets.load_dataset(\"mnist\")\r\ndataset.set_format(\"jax\")\r\nX_train = dataset[\"train\"][\"image\"]\r\n```","embeddings":[-0.0351042375,0.1116235554,-0.0163862556,-0.1090963036,0.5629802942,-0.0685692728,0.6217336059,0.4775541127,-0.174456045,0.1781868041,0.0658132136,0.5270745158,-0.0880533755,0.1173616797,0.0401968546,-0.360545665,0.0720745772,0.2647361159,0.0808507875,0.1596166193,-0.2727206945,0.0092618605,-0.1465445161,0.0034733359,-0.5034466386,-0.080928795,-0.1346642673,0.062474966,-0.2421914339,-0.4246686697,0.0983465612,-0.2450551838,0.1019948646,0.4534077048,-0.0001089365,0.0271534733,0.3976078629,-0.0286022685,-0.1434714645,0.1479641199,-0.2425326407,-0.6847038269,-0.0113291154,-0.389519155,-0.1181757078,-0.6137135029,0.1512786001,-0.1608414948,0.2759802341,0.3020053506,0.265676856,0.3735826313,0.2316918373,-0.0511063822,-0.2185554653,0.2524042428,-0.1683115065,0.1620824784,0.0784726962,0.1988572776,0.2597343922,0.5298147202,-0.0807198659,-0.0531505868,0.1742205471,0.1831924617,0.2833398283,0.0358217359,-0.0156707577,0.1402590275,0.5266160965,-0.0735962167,-0.1954973191,-0.2019924819,0.1465503871,-0.3878443539,0.1582660973,0.186277315,-0.187066406,-0.1155843884,-0.2630524337,0.0817793757,-0.1390256584,0.3541709185,-0.1157849208,-0.0676849112,0.0157392528,0.0803292543,-0.0788850561,-0.0602477789,0.1954143047,0.0539929867,0.0174034294,0.044251468,0.0084662661,-0.0733496621,-0.1420342177,-0.4403004646,-0.1133211628,-0.1448114514,0.1452974379,0.1026845798,-0.1827955395,0.219125092,0.4138929844,0.1623381823,-0.0417224281,0.067423515,0.2067485601,0.1377824545,0.1517888904,-0.1474220753,0.0383776166,-0.3996121287,0.2932494283,0.0515110008,0.3725039363,-0.062688455,0.062183205,-0.1082596406,-0.0444613323,0.008498339,-0.0774776712,0.0998676196,0.1481800228,0.3952062428,0.1595819443,0.2254127264,-0.0880635083,-0.2274916917,-0.1482561529,0.0865744054,-0.3660970926,-0.1981011182,0.0021030272,0.087773867,0.1401326656,-0.0305958968,0.0234381966,-0.0633087829,0.1319309324,-0.1421961039,0.3071516156,0.2525740266,-0.1399776042,0.0432522818,0.1336581707,0.0036237293,-0.1867277175,0.1242255047,-0.4695411026,0.0291407909,-0.222511217,0.1855416298,0.0852298737,0.0446871668,-0.154036209,-0.1068982482,0.2773965001,-0.1968356073,0.2839349806,-0.4382214248,-0.2772710323,-0.22404553,0.1149160266,0.0332334675,-0.1932681352,0.143932879,0.1071672067,-0.0074809873,0.101805985,0.1918512583,-0.0473606624,0.1452838629,-0.2476387322,-0.1362895668,0.2143566459,-0.1408878714,-0.092895247,-0.1015692353,0.1607040912,-0.0027221877,-0.1773070395,0.3334982991,-0.264177382,0.1362925172,0.0569125079,0.5629370809,0.01436844,0.1592327654,-0.118840389,-0.1784052998,0.0310414433,0.335862577,0.0138055095,0.0077814781,-0.0330259167,0.1882647574,0.2014693171,-0.0925346464,-0.0534339212,0.0755807087,0.1995651871,-0.2911316156,0.0185355544,-0.0256019011,-0.1572625339,0.0303243399,0.0272069387,0.0695616379,-0.1368524581,0.0173730887,-0.1804391146,-0.1368698478,-0.3472112119,-0.183655858,0.2206353098,0.1827758849,-0.1109232828,0.1879811585,-0.1583167613,0.0659511909,-0.0056471634,0.0051680137,-0.4220474362,0.3041942418,0.0441638306,-0.2824536562,-0.1916975975,0.2994112074,0.0614318363,0.0656114891,-0.2639605403,0.5730683804,0.4213165939,0.21778211,-0.4591380656,-0.1787801385,0.1666873842,-0.3457986712,-0.0895585343,0.5837769508,0.0480316728,0.0573355965,-0.4763412476,0.3855287731,-0.0087685175,0.0341812409,-0.1211704686,0.1601755619,0.1805398464,0.040736597,-0.1198853999,-0.4135543406,-0.0469768122,0.1916652471,-0.2490782142,-0.0563012995,-0.3349249065,-0.1348215342,0.4423457086,0.1429050267,0.1328818351,-0.1122381017,0.0106045529,-0.0336153097,0.141293183,0.0877160132,0.1900299042,0.1870515347,-0.1748009473,0.1700357795,-0.1333753169,-0.2049434781,0.211657986,0.0917286575,0.5073425174,-0.0030152374,0.4448339343,-0.1088944674,-0.186001569,-0.1464076042,-0.1816645861,0.1302128136,-0.0796161592,0.0216191262,-0.3496924341,-0.4065953493,-0.2717847228,0.2504838407,-0.0179770067,-0.3456211984,-0.110982351,-0.2161916941,-0.1071744561,0.0358983502,-0.341196835,0.0089064864,0.2165851891,0.0862340629,-0.1426629722,-0.1380441338,-0.1106988192,0.0731697604,0.2722600996,0.0089960545,0.2439144403,-0.0768207833,-0.0398930795,-0.1394505203,-0.2901536226,0.1382262558,0.2168733627,0.1040568203,0.4167672396,0.3230099082,-0.1401655972,0.131554991,0.4002191424,-0.1627411544,-0.0254810248,0.2691704333,0.0449874662,0.0960905701,-0.1788372844,-0.1522419304,-0.2751213312,-0.197709322,-0.1126001999,0.1163999364,0.1436607391,0.7054166794,0.2757717669,0.0490966961,0.0176464319,-0.0115007907,-0.0730227008,-0.0953914598,0.2717971206,-0.167538628,-0.268257767,-0.130321607,0.1279798746,-0.0629053786,0.1383193731,-0.1871500909,-0.4157292843,-0.0484753586,0.0385482274,0.036615517,-0.0263769217,-0.0091452412,0.0726405084,-0.1758125424,-0.1355222315,0.2338327318,-0.1310919523,0.1179280877,0.3204636872,0.1383843422,0.1337814778,0.2210441679,-0.2168316692,-0.0214186199,-0.4519105256,0.4663637877,-0.2218855023,0.4458685219,-0.2807243168,-0.2061612606,0.173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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3553","title":"set_format(\"np\") no longer works for Image data","comments":"Hi! We've recently introduced a new Image feature that yields PIL Images (and caches transforms on them) instead of arrays.\r\n\r\nHowever, this feature requires a custom transform to yield np arrays directly:\r\n```python\r\nddict = datasets.load_dataset(\"mnist\")\r\n\r\ndef pil_image_to_array(batch):\r\n     return {\"image\": [np.array(img) for img in batch[\"image\"]]}  # or jnp.array(img) for Jax\r\n\r\nddict.set_transform(pil_image_to_array, columns=\"image\", output_all_columns=True)\r\n```\r\n\r\n[Docs](https:\/\/huggingface.co\/docs\/datasets\/master\/process.html#format-transform) on `set_transform`.\r\n\r\nAlso, the approach proposed by @cgarciae is not the best because it loads the entire column in memory.\r\n\r\n@albertvillanova @lhoestq WDYT? The Audio and the Image feature currently don't support the TF\/Jax\/PT Formatters, but for the Numpy Formatter maybe it makes more sense to return np arrays (and not a dict in the case of the Audio feature or a PIL Image object in the case of the Image feature).","body":"## Describe the bug\r\n`dataset.set_format(\"np\")` no longer works for image data, previously you could load the MNIST like this:\r\n\r\n```python\r\ndataset = load_dataset(\"mnist\")\r\ndataset.set_format(\"np\")\r\nX_train = dataset[\"train\"][\"image\"][..., None] # <== No longer a numpy array\r\n```\r\nbut now it doesn't work, `set_format(\"np\")` seems to have no effect and the dataset just returns a list\/array of PIL images instead of numpy arrays as requested.\r\n","comment_length":127,"text":"set_format(\"np\") no longer works for Image data \n ## Describe the bug\r\n`dataset.set_format(\"np\")` no longer works for image data, previously you could load the MNIST like this:\r\n\r\n```python\r\ndataset = load_dataset(\"mnist\")\r\ndataset.set_format(\"np\")\r\nX_train = dataset[\"train\"][\"image\"][..., None] # <== No longer a numpy array\r\n```\r\nbut now it doesn't work, `set_format(\"np\")` seems to have no effect and the dataset just returns a list\/array of PIL images instead of numpy arrays as requested.\r\n \n Hi! We've recently introduced a new Image feature that yields PIL Images (and caches transforms on them) instead of arrays.\r\n\r\nHowever, this feature requires a custom transform to yield np arrays directly:\r\n```python\r\nddict = datasets.load_dataset(\"mnist\")\r\n\r\ndef pil_image_to_array(batch):\r\n     return {\"image\": [np.array(img) for img in batch[\"image\"]]}  # or jnp.array(img) for Jax\r\n\r\nddict.set_transform(pil_image_to_array, columns=\"image\", output_all_columns=True)\r\n```\r\n\r\n[Docs](https:\/\/huggingface.co\/docs\/datasets\/master\/process.html#format-transform) on `set_transform`.\r\n\r\nAlso, the approach proposed by @cgarciae is not the best because it loads the entire column in memory.\r\n\r\n@albertvillanova @lhoestq WDYT? The Audio and the Image feature currently don't support the TF\/Jax\/PT Formatters, but for the Numpy Formatter maybe it makes more sense to return np arrays (and not a dict in the case of the Audio feature or a PIL Image object in the case of the Image feature).","embeddings":[-0.1515492797,0.0228346623,0.0084200129,-0.0925695151,0.4749733806,-0.1852436215,0.6634010077,0.2563915849,-0.2602324486,0.1248803437,-0.1729414761,0.5843827128,-0.1788265407,0.1715170145,0.1118779108,-0.3430041075,0.037220411,0.3603931069,0.0618003458,0.1723250747,-0.3272289634,0.1344289184,-0.2634638846,0.0861904025,-0.4767289162,-0.0661723763,-0.1078891978,0.0066489414,-0.1984827816,-0.3672807217,0.0650554076,-0.0899897441,0.0130424621,0.4456020892,-0.0001152095,-0.0384522863,0.3978100121,-0.1642994881,-0.1810420305,0.0872644559,-0.1467793286,-0.6463106871,-0.1325520426,-0.2548480332,-0.085217528,-0.6860212088,0.0792469531,-0.0645475239,0.299092412,0.2482527047,0.2060498446,0.3390338719,0.2749555707,0.241182372,-0.2573979497,0.4382196069,-0.1105692461,0.121643208,0.14532727,0.0237083528,0.2539550662,0.5484273434,-0.2209919244,0.057382755,0.2404436767,0.2813939154,0.1152970865,-0.0220948458,0.0401747674,0.2176548839,0.4898160696,-0.194709152,-0.3166612387,-0.4122259319,0.1198855042,-0.4587461054,0.0406491272,0.1984524429,-0.2650132179,-0.0567154214,-0.3740573823,0.117111966,0.022289183,0.282320708,-0.0456483811,-0.1759704202,-0.102842696,0.0576643758,0.0419889204,-0.1210117713,0.1083854809,0.0596802644,0.0371035039,0.1796310097,0.049041599,-0.1666657329,-0.0945568308,-0.4323474467,-0.1529680789,-0.1894753873,0.0809905827,0.1916535497,-0.295122534,0.1840164065,0.2888409197,0.2164526582,-0.0821163431,-0.052544605,0.2852154076,0.1045385376,0.2400375009,-0.1601969302,0.1156529561,-0.2244512886,0.2787859142,-0.0385520905,0.4168109596,0.0301385615,0.0088888602,-0.1293613762,0.0642319247,-0.0609923787,-0.1282880902,0.1508503854,0.2486305982,0.5602433681,0.2349901795,0.2159015685,-0.161427334,-0.1925284863,-0.0822317302,-0.0066276472,-0.1989300251,-0.0362181589,0.0848497301,0.1765982509,0.0753409937,-0.0173358992,-0.0246204808,-0.0415076502,-0.0088925706,-0.1065941304,0.4241457283,0.2596993446,-0.213935405,0.070402272,0.0541282035,0.12676768,-0.2885316908,0.2092341036,-0.4288038313,0.0158637948,-0.150563404,0.1087000966,0.0312744863,0.1694938093,-0.3941247761,0.0987799987,0.2028861195,-0.3692638576,0.3841761053,-0.4700893164,-0.5587830544,-0.1695154309,0.219180584,0.10941609,-0.15333049,0.0761644468,0.0735369474,0.0013314129,0.0375324376,0.1380723864,0.1073155776,0.2133853287,-0.2286975384,-0.2122686952,0.1484706402,-0.2332204431,-0.2268927097,-0.1247232035,0.170171544,0.1617253721,-0.1016111523,0.2244248539,-0.0890861973,0.0674528331,0.0569264367,0.6075391173,-0.0263402946,0.3059260249,-0.0066907695,-0.2196368277,-0.0805483982,0.2984388471,-0.1166429967,0.0484249629,-0.1367314011,0.2734428942,0.2728921175,-0.0852852389,-0.0018212099,-0.053659454,0.2712172568,-0.2026076764,0.0456809253,-0.1647481769,-0.2059661597,-0.024968205,0.0778464824,0.0168578587,-0.2611259818,-0.0013882406,-0.11531616,-0.0032682864,-0.3520046473,-0.2371587008,0.1536419392,0.2365895808,-0.0054405127,0.1986441314,-0.224278003,0.2526259422,0.0625587776,-0.0131861325,-0.4114777148,0.2703095376,0.2271252126,-0.2620180249,-0.2473960221,0.4269947708,-0.0331896283,0.030218184,-0.2343096435,0.7249737978,0.4657435119,0.2926521599,-0.7064929008,0.005573702,0.2819223702,-0.4165596068,0.0745073333,0.5468757749,0.1683638394,-0.0033943981,-0.4245774448,0.487470001,-0.0482061282,0.1244054139,-0.076186657,0.0511436611,0.048612915,0.15201132,-0.2072227448,-0.3055911958,-0.1525851637,0.0037297937,-0.1452241987,0.0710786507,-0.410209775,-0.0925608203,0.4910864532,0.0218860954,0.1934860498,0.0064909826,-0.0543947816,-0.0897959471,0.1562196761,-0.0058005042,0.2562033236,0.1710118651,-0.346944958,0.1377921253,-0.078686595,-0.0972845405,0.2629502118,0.1237175316,0.3333344162,0.0847156793,0.5339019895,-0.1529109478,-0.3027718663,-0.1224266663,-0.2235995382,0.0744187161,-0.1552685052,-0.0308650266,-0.3485654891,-0.4398038685,-0.293605566,0.201510638,0.0674071684,-0.1315792352,-0.1878162622,-0.0391659029,-0.0384644344,0.1930157989,-0.2447387725,0.0952165946,0.1490460932,-0.0046018814,-0.1872458607,0.0307853408,-0.0364398733,-0.0075907512,0.1841243207,0.0098262532,0.0433920324,-0.0488695875,-0.1583243608,-0.1105450913,-0.2222775519,0.2108156234,0.1248641536,0.0866228789,0.4271631837,0.3520511687,-0.0414105356,0.0280100256,0.3629502654,-0.2032914162,-0.0452713147,0.2451814413,0.092248708,0.2228109539,-0.2316290885,-0.0856586993,-0.3287822902,-0.2470317334,0.1328262538,-0.0508760624,0.1495001018,0.7958388329,0.2256081253,0.0150555363,0.0143995667,0.158626467,-0.1078373119,0.0249553286,0.2690542042,-0.1032271311,-0.1912805885,-0.3754964471,0.0862703696,-0.1258632839,0.0828886777,-0.198238939,-0.6452749372,-0.0801532641,0.0068528322,0.0896690264,0.0765862465,-0.0839835033,0.0985774994,-0.0448857509,-0.119873412,0.068802245,-0.2342877984,0.1943042874,0.1935246587,0.3671119213,0.2396018058,0.1421524584,-0.103465952,-0.1070089564,-0.3542129099,0.3868313134,-0.2588150501,0.5685852766,-0.3324806988,-0.2524153292,0.1753918231,0.1222763136,-0.0575757362,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3553","title":"set_format(\"np\") no longer works for Image data","comments":"Yes I agree it should return arrays and not a PIL image (and possible an array instead of a dict for audio data).\r\nI'm currently finishing some code refactoring of the image and audio and opening a PR today. Maybe we can look into that after the refactoring","body":"## Describe the bug\r\n`dataset.set_format(\"np\")` no longer works for image data, previously you could load the MNIST like this:\r\n\r\n```python\r\ndataset = load_dataset(\"mnist\")\r\ndataset.set_format(\"np\")\r\nX_train = dataset[\"train\"][\"image\"][..., None] # <== No longer a numpy array\r\n```\r\nbut now it doesn't work, `set_format(\"np\")` seems to have no effect and the dataset just returns a list\/array of PIL images instead of numpy arrays as requested.\r\n","comment_length":48,"text":"set_format(\"np\") no longer works for Image data \n ## Describe the bug\r\n`dataset.set_format(\"np\")` no longer works for image data, previously you could load the MNIST like this:\r\n\r\n```python\r\ndataset = load_dataset(\"mnist\")\r\ndataset.set_format(\"np\")\r\nX_train = dataset[\"train\"][\"image\"][..., None] # <== No longer a numpy array\r\n```\r\nbut now it doesn't work, `set_format(\"np\")` seems to have no effect and the dataset just returns a list\/array of PIL images instead of numpy arrays as requested.\r\n \n Yes I agree it should return arrays and not a PIL image (and possible an array instead of a dict for audio data).\r\nI'm currently finishing some code refactoring of the image and audio and opening a PR today. Maybe we can look into that after the refactoring","embeddings":[-0.3260748982,0.0780692175,-0.0351973586,-0.01431459,0.3845539987,-0.326015383,0.5448748469,0.2585427165,-0.1670455486,0.1273834407,-0.1693264395,0.7061197162,-0.2626220286,0.0751626417,0.1509302706,-0.2775863707,0.1291606724,0.244228676,0.1163545027,0.0611656085,-0.3141691685,0.0769306347,-0.3639255762,-0.0002278433,-0.3928610384,-0.1809426397,-0.1425784975,0.063793391,-0.1997185051,-0.4782776237,-0.0001464207,-0.0778095424,0.0579646043,0.3881599009,-0.0001148768,-0.0996315852,0.4576435387,-0.0902772769,-0.0668746755,-0.0522191972,-0.1356754005,-0.4700755179,-0.0503630489,-0.1526646465,-0.0782119334,-0.6301774979,0.1184907705,-0.0943327323,0.2201139778,0.4080253243,0.2271854579,0.3000588715,0.2481983006,0.1179533824,-0.1870691031,0.4389388561,-0.041356381,0.190660134,0.2529710233,0.0918148756,0.2735998034,0.4729372263,-0.1231460944,-0.0009072702,0.1328152567,0.2702372372,0.0094384551,-0.1130516157,-0.1320375651,0.2401176244,0.4953453243,-0.0738330111,-0.216076687,-0.2380731702,0.1493586749,-0.3737766147,0.0778542683,0.1764614284,-0.2331383973,0.0255572274,-0.2819570005,-0.0041694748,-0.0645024553,0.4499406517,-0.0563824661,-0.0825753585,-0.0380411334,0.0953865647,0.1551286131,-0.0870804563,0.2446872741,0.1778549254,0.0208592732,0.0836102366,-0.0243989993,-0.1199119613,-0.1602737904,-0.3180035651,-0.2036099732,-0.0245923307,0.1058492586,0.1808284223,-0.3809664249,0.2120750546,0.417684108,0.0365846269,-0.101059936,0.0009745922,0.3168264627,-0.035009902,0.2243604064,-0.1512208879,-0.0043178336,-0.3199767768,0.3007965982,-0.0382321849,0.3924555182,-0.0902449414,-0.0413970537,-0.0497657433,-0.0424254127,-0.0787457451,-0.1247610003,0.1619146019,0.1877604425,0.3976829052,0.1805302352,0.309841603,-0.1259360015,-0.1194381788,-0.0477052741,0.0478594638,-0.2245724946,-0.136379227,0.1291354448,0.2282876819,0.078481704,0.0551731512,0.1070874557,-0.1345256418,0.102890119,-0.1820439398,0.4688177109,0.2575932443,-0.1899707317,0.0925097689,-0.0860690475,0.0348288342,-0.2492945343,0.2126648873,-0.5047066212,0.0371624827,-0.2815003395,0.1686287671,0.0570477843,0.1027853638,-0.1967705339,0.0829077363,0.1825657487,-0.421782136,0.3385518789,-0.5774265528,-0.378352195,-0.1803832948,0.2274566293,0.0411201455,-0.2426270843,0.0984145254,-0.0348911174,-0.0412097909,0.0697199926,0.1980859488,0.0794065073,0.0636220574,-0.2573801875,-0.0623532645,0.2354950905,-0.2996082902,-0.3437261283,-0.1225305125,0.1301441789,0.0225322656,-0.0898519754,0.3080223799,-0.1194547638,0.1168915257,0.1486276984,0.4580123127,0.0573697314,0.1408165842,-0.0979478434,-0.1830167621,0.0482524969,0.4184815288,-0.1028878763,0.0517015196,-0.0544676781,0.1859762222,0.3439672887,0.0267854314,-0.1110530347,0.0167449918,0.3398381174,-0.2197363526,0.0028658744,-0.2095488012,0.0431219339,-0.1478593349,0.0239294525,0.09074945,-0.0936709195,-0.0303695556,-0.1324692816,-0.1538934708,-0.3827353418,-0.227309674,0.1805304438,0.2178440839,-0.2259850055,0.2257787883,-0.1897241324,0.210985288,0.0285527855,-0.1189275533,-0.2801930606,0.207654044,0.0363392457,-0.3014754057,-0.1671212912,0.4065726697,-0.0117297713,0.0527628548,-0.0849065185,0.7142558694,0.4944222271,0.0282402094,-0.6157608628,-0.139495343,0.2729225457,-0.5527370572,0.0515605509,0.5118812919,0.029019203,-0.0603746362,-0.4614301324,0.3658854067,-0.0356744342,0.0439652652,-0.1680302173,0.1717938632,0.0797093362,0.1198758557,-0.2308692783,-0.2444691509,-0.0997757688,0.0320894308,0.0188924335,-0.0510597378,-0.559438169,-0.033658158,0.4645650089,0.1148756593,0.2590465844,0.030108083,-0.0271455254,-0.1495889276,0.145184204,0.0261468384,0.3400303721,0.1537375301,-0.2149109691,0.2407577932,-0.1196470484,-0.168277353,0.2154102027,0.0665376186,0.2591868639,-0.0151094822,0.4109228849,-0.1855473518,-0.3271982074,-0.2407163382,-0.2805555463,0.0690908879,-0.2462228239,-0.0605846718,-0.4072412252,-0.3430660367,-0.2578244507,0.2560182214,-0.0303829499,-0.0620953478,-0.1329571456,-0.0942464024,0.0122307483,0.2505583167,0.0444024056,0.0950258151,0.0890674889,0.1215653494,-0.0997853354,-0.0057609519,-0.1538457125,0.0515323989,0.1825868338,-0.0648511574,0.0681577772,-0.13333565,-0.2062539309,0.0428938866,-0.1157940328,0.16394943,0.2646991611,0.1813242882,0.4035841823,0.2773594856,-0.1286718696,0.0645195544,0.2330512851,-0.1497825086,-0.0170547031,0.2547568083,0.0568230189,0.2278751582,-0.2534988821,-0.2349434793,-0.5620393753,-0.1764635593,0.0791657642,0.045702707,0.1623631418,0.782941699,0.321175009,-0.1044586077,-0.1472107172,0.0812298059,-0.2245293409,-0.1039700657,0.2771010101,-0.1156859323,-0.2942650616,-0.3315516114,0.2496508062,-0.0640183762,0.1773401797,-0.204098925,-0.547647655,0.1387839168,-0.0398061499,-0.0032208213,0.0813934729,-0.0075422986,0.1137833595,-0.0838071182,-0.1142450795,0.2080407292,-0.1732748896,0.1508308649,0.3007612228,0.2344008386,0.122335501,0.0832255185,-0.1548364758,0.050878115,-0.2700662315,0.3505953252,-0.31752792,0.603567481,-0.3017684519,-0.1525532603,0.2335356921,0.13624431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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3548","title":"Specify the feature types of a dataset on the Hub without needing a dataset script","comments":"After looking into this, discovered that this is already supported if the `dataset_infos.json` file is configured correctly! Here is a working example: https:\/\/huggingface.co\/datasets\/abidlabs\/test-audio-13\r\n\r\nThis should be probably be documented, though. ","body":"**Is your feature request related to a problem? Please describe.**\r\nCurrently if I upload a CSV with paths to audio files, the column type is string instead of Audio.\r\n\r\n**Describe the solution you'd like**\r\nI'd like to be able to specify the types of the column, so that when loading the dataset I directly get the features types I want.\r\n\r\nThe feature types could read from the `dataset_infos.json` for example.\r\n\r\n**Describe alternatives you've considered**\r\nCreate a dataset script to specify the features, but that seems complicated for a simple thing.\r\n\r\ncc @abidlabs ","comment_length":30,"text":"Specify the feature types of a dataset on the Hub without needing a dataset script \n **Is your feature request related to a problem? Please describe.**\r\nCurrently if I upload a CSV with paths to audio files, the column type is string instead of Audio.\r\n\r\n**Describe the solution you'd like**\r\nI'd like to be able to specify the types of the column, so that when loading the dataset I directly get the features types I want.\r\n\r\nThe feature types could read from the `dataset_infos.json` for example.\r\n\r\n**Describe alternatives you've considered**\r\nCreate a dataset script to specify the features, but that seems complicated for a simple thing.\r\n\r\ncc @abidlabs  \n After looking into this, discovered that this is already supported if the `dataset_infos.json` file is configured correctly! Here is a working example: https:\/\/huggingface.co\/datasets\/abidlabs\/test-audio-13\r\n\r\nThis should be probably be documented, though. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3547","title":"Datasets created with `push_to_hub` can't be accessed in offline mode","comments":"Thanks for reporting. I think this can be fixed by improving the `CachedDatasetModuleFactory` and making it look into the `parquet` cache directory (datasets from push_to_hub are loaded with the parquet dataset builder). I'll look into it","body":"## Describe the bug\r\nIn offline mode, one can still access previously-cached datasets. This fails with datasets created with `push_to_hub`.\r\n\r\n## Steps to reproduce the bug\r\nin Python:\r\n```\r\nimport datasets\r\nmpwiki = datasets.load_dataset(\"teven\/matched_passages_wikidata\")\r\n```\r\nin bash:\r\n```\r\nexport HF_DATASETS_OFFLINE=1\r\n```\r\nin Python:\r\n```\r\nimport datasets\r\nmpwiki = datasets.load_dataset(\"teven\/matched_passages_wikidata\")\r\n```\r\n\r\n## Expected results\r\n`datasets` should find the previously-cached dataset.\r\n\r\n## Actual results\r\nConnectionError: Couln't reach the Hugging Face Hub for dataset 'teven\/matched_passages_wikidata': Offline mode is enabled\r\n\r\n## Environment info\r\n- `datasets` version: 1.16.2.dev0\r\n- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":36,"text":"Datasets created with `push_to_hub` can't be accessed in offline mode \n ## Describe the bug\r\nIn offline mode, one can still access previously-cached datasets. This fails with datasets created with `push_to_hub`.\r\n\r\n## Steps to reproduce the bug\r\nin Python:\r\n```\r\nimport datasets\r\nmpwiki = datasets.load_dataset(\"teven\/matched_passages_wikidata\")\r\n```\r\nin bash:\r\n```\r\nexport HF_DATASETS_OFFLINE=1\r\n```\r\nin Python:\r\n```\r\nimport datasets\r\nmpwiki = datasets.load_dataset(\"teven\/matched_passages_wikidata\")\r\n```\r\n\r\n## Expected results\r\n`datasets` should find the previously-cached dataset.\r\n\r\n## Actual results\r\nConnectionError: Couln't reach the Hugging Face Hub for dataset 'teven\/matched_passages_wikidata': Offline mode is enabled\r\n\r\n## Environment info\r\n- `datasets` version: 1.16.2.dev0\r\n- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n Thanks for reporting. I think this can be fixed by improving the `CachedDatasetModuleFactory` and making it look into the `parquet` cache directory (datasets from push_to_hub are loaded with the parquet dataset builder). I'll look into 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3543","title":"Allow loading community metrics from the hub, just like datasets","comments":"Hi ! Thanks for your message :) This is a great idea indeed. We haven't started working on this yet though. For now I guess you can host your metric on the Hub (either with your model or your dataset) and use `hf_hub_download` to download it (docs [here](https:\/\/github.com\/huggingface\/huggingface_hub\/blob\/main\/docs\/hub\/how-to-downstream.md#cached_download))","body":"**Is your feature request related to a problem? Please describe.**\r\nCurrently, I can load a metric implemented by me by providing the local path to the file in `load_metric`.\r\nHowever, there is no option to do it with the metric uploaded to the hub.\r\nThis means that if I want to allow other users to use it, they must download it first which makes the usage less smooth.\r\n\r\n**Describe the solution you'd like**\r\nLoad metrics from the hub just like datasets are loaded.\r\nIn order to not break stuff, the convention can be to put the metric file in a \"metrics\" folder in the hub.\r\n\r\n","comment_length":48,"text":"Allow loading community metrics from the hub, just like datasets \n **Is your feature request related to a problem? Please describe.**\r\nCurrently, I can load a metric implemented by me by providing the local path to the file in `load_metric`.\r\nHowever, there is no option to do it with the metric uploaded to the hub.\r\nThis means that if I want to allow other users to use it, they must download it first which makes the usage less smooth.\r\n\r\n**Describe the solution you'd like**\r\nLoad metrics from the hub just like datasets are loaded.\r\nIn order to not break stuff, the convention can be to put the metric file in a \"metrics\" folder in the hub.\r\n\r\n \n Hi ! Thanks for your message :) This is a great idea indeed. We haven't started working on this yet though. For now I guess you can host your metric on the Hub (either with your model or your dataset) and use `hf_hub_download` to download it (docs [here](https:\/\/github.com\/huggingface\/huggingface_hub\/blob\/main\/docs\/hub\/how-to-downstream.md#cached_download))","embeddings":[-0.5048285723,-0.1317419857,-0.0456833281,0.118879199,0.0301416479,-0.0557258725,0.1593100876,0.0130903004,0.4941082597,0.4180036783,-0.4741497934,0.2941922247,-0.030540878,0.3988614082,-0.0859557316,-0.0085316077,-0.2955569327,-0.0084172478,-0.1162704304,0.04746316,0.0530211106,-0.1613152325,0.2532279193,0.2135639787,-0.3227631748,0.0802231729,0.2006453276,0.1970959902,-0.2728241086,-0.271738708,0.4238562584,0.5630329251,0.1159579977,0.3305658996,-0.0001061543,-0.0786158741,0.2991358936,0.0714589879,-0.2334742248,-0.2987889647,-0.0998635367,-0.0467876196,0.1687865108,0.106479615,-0.2268282026,0.1906067282,0.0272015166,-0.0006079808,0.0478473306,-0.0264002271,0.2057810575,0.4432120025,-0.3400796354,-0.4115642607,-0.3352015913,0.5020110607,-0.1131511182,0.6779898405,0.2542400658,0.1181293577,-0.1706924289,0.0282250829,0.217123732,0.2356941402,0.5956735015,-0.0598754138,0.1636102349,0.0851399228,-0.0610250235,0.4085016847,0.3021130264,-0.1725061685,-0.1158095226,-0.1716305614,0.0671825856,-0.2395231277,-0.0074468618,-0.0297320317,-0.1379471719,0.2530710101,-0.2390896529,-0.3526847064,-0.239985615,-0.0570030846,-0.082609944,0.1024699956,-0.1462444514,0.0091399569,-0.0159791671,0.0984272137,-0.0335093625,0.0513619781,-0.0967073143,0.160479635,-0.0273148213,0.0006598272,0.0604100488,0.4617573321,0.4568035007,0.0989831313,0.223091796,0.2363085449,-0.2709524035,0.1536442637,0.1666388959,-0.0514117107,0.3012264371,0.0900226012,0.3651626408,0.3395864069,0.0630212054,-0.2943929136,0.0013646024,0.0950855985,-0.2591581345,-0.0282777306,-0.1462460458,-0.2027712315,-0.1327975392,-0.345040977,0.6931810379,0.0107112071,0.5417445898,0.3176425993,0.0588979907,0.1156138778,-0.2716206014,0.1921786219,-0.124152936,-0.0830546394,-0.2337159365,-0.1367152631,-0.0581922308,0.5368873477,0.2357898355,-0.4752216041,0.2796849608,-0.1052458435,0.4121448994,-0.1889229715,-0.1375778913,0.1757005751,-0.3105190396,-0.000621177,0.0973391533,-0.0083371904,0.0176485553,-0.0816879645,-0.3113299906,-0.1079660654,-0.4191294312,-0.4300983846,-0.1207527965,0.1663354337,-0.2481534481,0.0390246361,-0.3980375826,0.2604451478,-0.455578953,-0.0795760602,-0.1187220439,0.4053467512,-0.4844881594,-0.0446178615,0.1672071368,0.3117994666,-0.0116004646,-0.3317888379,0.0522931591,-0.1847352237,-0.3395117819,0.0058232406,-0.3431921899,0.2713147402,-0.0651893243,0.0612064525,0.4639922678,-0.7296112776,-0.3447312713,0.0470648706,-0.1977259517,-0.1224218607,-0.0847094804,0.0929160267,0.2078304589,-0.0274922047,0.1770866066,0.3326738775,0.2414581925,-0.0928222835,-0.183544457,-0.3459456265,-0.2056160122,0.2501347363,0.1853287965,0.2791804373,0.4789744914,0.1579925418,0.1434046626,0.016690718,0.024214698,0.0730512217,0.0910602435,-0.017386144,0.025993444,-0.3406645358,0.0581321046,0.3591681421,-0.3307289183,-0.2445982099,0.430156678,-0.2815641761,-0.598786056,0.0479112081,-0.1537450254,0.1859924197,0.0626046285,-0.245463118,-0.0970342085,-0.0847762376,-0.1952187866,0.676841557,-0.3861151636,0.0737623647,0.0159387719,0.3578001857,0.0738205388,0.1324582398,0.1614790112,0.0508238859,0.1817266494,0.2040105313,-0.1290761232,0.3390097022,-0.221795097,0.5933005214,0.190608561,0.3129599988,0.4003564417,0.0135593768,0.2407013178,-0.3326106369,0.0620366931,0.0203188434,-0.4599303603,0.4124231339,-0.0898021907,-0.1582268029,-0.1493534893,-0.0730365291,0.0381992906,-0.0086762356,-0.2438444644,-0.1727913618,0.1414290816,-0.1632406861,0.0497478731,-0.1695922762,0.2954288721,-0.0324655846,0.2090896517,0.2398213446,0.1131671146,0.159512192,-0.1599695235,0.073976092,0.0628263801,-0.3479548991,0.3871245086,0.2069304138,0.1549094319,0.0545405298,0.0346076228,-0.1390326321,-0.1454036534,-0.033110708,-0.0943500102,0.1842846274,0.2141081691,-0.074752748,-0.4123498201,0.1814618558,0.0693580881,-0.073162578,-0.2632795572,-0.1892239898,-0.0068978746,0.2533797026,0.3100186288,-0.2020059079,-0.2694756687,-0.1015035659,0.2656511366,0.4933464527,0.0952443331,-0.0005410491,0.1782482415,0.5631861687,-0.2715002894,0.1884184331,-0.2121149451,-0.1278713048,0.0593698956,0.020320829,-0.0393019952,0.195187062,0.2764162719,-0.0951631516,0.4720876813,-0.134356752,-0.2242958844,0.113420777,0.2601184547,0.3740410507,-0.1224247292,0.1001453251,-0.0113253249,0.0601618998,0.3156373501,-0.2842022479,-0.004988858,0.0033768881,-0.1428985447,0.0821564421,-0.1812296361,0.0957565829,-0.2408505529,-0.5417822599,0.1672073305,0.318084836,0.0454223491,0.0610462502,-0.0065479288,0.0177388098,-0.000151706,-0.1139754802,-0.3045319021,-0.5673322678,0.2799920738,-0.4677861929,-0.284417212,0.0358983316,0.2711373866,0.1072545871,-0.2286892235,-0.2398112118,-0.6675006747,0.1405434459,0.2934449613,0.0415147245,-0.032300923,0.1428237408,-0.127929166,0.0273346435,-0.0762030631,-0.1513911039,0.0574278608,0.21272479,-0.067813389,-0.1173641309,-0.1311901957,0.1090189889,0.3894680142,0.1722111702,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3543","title":"Allow loading community metrics from the hub, just like datasets","comments":"Here's the code I used, in case it can be of help to someone else:\r\n```python\r\nimport os, shutil\r\nfrom huggingface_hub import hf_hub_download\r\ndef download_metric(repo_id, file_path):\r\n    # repo_id: for models \"username\/model_name\", for datasets \"datasets\/username\/model_name\"\r\n    local_metric_path = hf_hub_download(repo_id=repo_id, filename=file_path)\r\n    updated_local_metric_path = (os.path.dirname(local_metric_path) + os.path.basename(local_metric_path).replace(\".\", \"_\") + \".py\")\r\n    shutil.copy(local_metric_path, updated_local_metric_path)\r\n    return updated_local_metric_path\r\n\r\nmetric = load_metric(download_metric(REPO_ID, FILE_PATH))\r\n```","body":"**Is your feature request related to a problem? Please describe.**\r\nCurrently, I can load a metric implemented by me by providing the local path to the file in `load_metric`.\r\nHowever, there is no option to do it with the metric uploaded to the hub.\r\nThis means that if I want to allow other users to use it, they must download it first which makes the usage less smooth.\r\n\r\n**Describe the solution you'd like**\r\nLoad metrics from the hub just like datasets are loaded.\r\nIn order to not break stuff, the convention can be to put the metric file in a \"metrics\" folder in the hub.\r\n\r\n","comment_length":55,"text":"Allow loading community metrics from the hub, just like datasets \n **Is your feature request related to a problem? Please describe.**\r\nCurrently, I can load a metric implemented by me by providing the local path to the file in `load_metric`.\r\nHowever, there is no option to do it with the metric uploaded to the hub.\r\nThis means that if I want to allow other users to use it, they must download it first which makes the usage less smooth.\r\n\r\n**Describe the solution you'd like**\r\nLoad metrics from the hub just like datasets are loaded.\r\nIn order to not break stuff, the convention can be to put the metric file in a \"metrics\" folder in the hub.\r\n\r\n \n Here's the code I used, in case it can be of help to someone else:\r\n```python\r\nimport os, shutil\r\nfrom huggingface_hub import hf_hub_download\r\ndef download_metric(repo_id, file_path):\r\n    # repo_id: for models \"username\/model_name\", for datasets \"datasets\/username\/model_name\"\r\n    local_metric_path = hf_hub_download(repo_id=repo_id, filename=file_path)\r\n    updated_local_metric_path = (os.path.dirname(local_metric_path) + os.path.basename(local_metric_path).replace(\".\", \"_\") + \".py\")\r\n    shutil.copy(local_metric_path, updated_local_metric_path)\r\n    return updated_local_metric_path\r\n\r\nmetric = load_metric(download_metric(REPO_ID, FILE_PATH))\r\n```","embeddings":[-0.4704200029,-0.1260264367,0.0011761843,0.147998184,0.0563809089,-0.0769123286,0.1348915398,0.0765774474,0.6092922688,0.4103240371,-0.5060345531,0.2940291762,-0.0671620667,0.2850588262,-0.073428534,-0.0907043517,-0.2695915997,-0.0278288368,-0.154440105,-0.0260264054,-0.0280563477,-0.1558345705,0.2080505788,0.1911709011,-0.1678884923,0.1476731449,0.1840712428,0.2838584185,-0.2053979933,-0.3304075301,0.4456528127,0.5503481627,0.188353017,0.3859688342,-0.0001135956,-0.0875580013,0.2550706267,0.110550046,-0.2337162197,-0.3525857627,-0.0754409358,-0.2017876059,0.201848954,0.0904764012,-0.1953085661,0.1337656975,-0.0052482048,-0.0347205512,0.1268303692,-0.04189796,0.1403914392,0.5090541244,-0.4325528741,-0.4037284255,-0.2709554732,0.4168080986,-0.1642153412,0.74679178,0.2396563888,-0.009979411,-0.1102264076,-0.0478150211,0.0756042004,0.2568552494,0.6512981653,-0.0675274134,0.233090058,0.1151658669,-0.1534958929,0.3534965515,0.2140933722,-0.2449527234,-0.1234981269,-0.176320076,0.0345768929,-0.3227671385,-0.0632178113,0.0085648037,-0.1887713224,0.2590174675,-0.1045303717,-0.4148365855,-0.2106392533,0.0558641106,-0.0368170515,0.204969123,-0.1062420905,0.04313742,-0.0101786647,0.1935498267,0.0538453758,-0.0365648307,0.0431547426,0.2609760463,-0.0955910459,0.0587807037,0.0312891528,0.4027686715,0.4302574098,0.2181361318,0.1376515329,0.190573737,-0.2361375242,0.0994971246,0.1102146283,-0.0431917682,0.2460267991,0.177760452,0.3514087498,0.4180748165,0.031708505,-0.2101016939,-0.0810457915,-0.0173753016,-0.1889169663,0.0157540105,-0.028880816,-0.153141126,-0.1056935117,-0.2846639752,0.5684268475,0.0382225662,0.5057634115,0.3517763317,0.023657551,0.2005731612,-0.2550623715,0.2763522565,-0.1900288165,0.0690013692,-0.2084003687,-0.0587523282,-0.0582622588,0.5887382627,0.2346771657,-0.5247984529,0.2368818521,-0.020049639,0.4915434718,-0.1969480664,-0.0732844621,0.1076072305,-0.2679136992,0.0220667049,0.0579362586,0.1327782571,0.1491751969,-0.0598001406,-0.3408454061,-0.0260326825,-0.4711173475,-0.4081018567,-0.1040825248,0.1098518074,-0.2859275639,0.0734653473,-0.4317522645,0.0775853917,-0.4821169674,0.0047324812,-0.1120352298,0.2686305642,-0.4315985441,-0.1315024197,0.2438562363,0.4339782894,0.0677799135,-0.2590990663,-0.0088185044,-0.1126210615,-0.2572835088,0.1092870608,-0.3610681891,0.2790734768,-0.0954107344,0.0389835499,0.5211935639,-0.9458454251,-0.3328950703,0.0644903779,-0.1664206237,0.0073257601,-0.0065960204,0.0809182227,0.2234162688,-0.0517423637,0.2329126,0.3964782357,0.3361796439,-0.048221536,-0.1591374278,-0.3762648702,-0.1921637058,0.2307858616,0.2786682844,0.3496337235,0.4163078368,0.168048501,0.2255159914,-0.0263604037,0.0003291946,0.0974824876,0.1049616188,0.1143808886,0.0129298279,-0.3890976012,0.0799957588,0.373683095,-0.2948543131,-0.2535492182,0.3295488954,-0.3071286082,-0.6497321129,0.071739614,-0.2174431682,0.2218298614,-0.0094778901,-0.1320744902,-0.1342636198,-0.0985235348,-0.1925451159,0.7412904501,-0.346699357,0.1802421808,0.1307886839,0.431215167,0.0543900914,0.1018288955,0.1308611482,0.0267635975,0.2703481913,0.1251391321,-0.0699022189,0.3287086785,-0.0880893767,0.4650452733,0.275870204,0.2898958623,0.4067501426,-0.0054783784,0.2856782377,-0.2366474271,0.105758816,0.024572419,-0.3323638439,0.4842099249,-0.0818310753,-0.1042988449,-0.1160514504,-0.061478015,-0.0328243189,-0.0258056726,-0.266711086,-0.1020414382,0.1413592547,-0.0467019565,-0.0725787804,-0.19819206,0.3134130538,-0.1237369478,0.2371419668,0.209175393,0.1050275713,0.0258193519,-0.1288862377,0.1993850321,0.0162910055,-0.3479979038,0.383109808,0.1440574378,0.1234130338,0.1185269952,-0.050628636,-0.0750529245,-0.180474177,0.0105946315,-0.1091977432,0.1926470697,0.1619760394,0.0011689542,-0.4022043645,0.1124768034,0.0063632303,-0.0325336605,-0.339060992,-0.1496531814,-0.0382805392,0.2645616233,0.2170615792,-0.2157888561,-0.2793001831,-0.1349743307,0.075092189,0.5538929701,0.1614897251,0.046863839,0.1447576284,0.5236113667,-0.2551171482,-0.0568953007,-0.1482296288,-0.0142176375,0.1439611763,-0.0725774243,-0.0454467088,0.1739036441,0.2413813621,-0.0912044421,0.4172996581,-0.2088555843,-0.1891923547,0.0768805519,0.2501609921,0.4611062706,-0.092969805,0.1304715276,0.0025873159,0.009027129,0.3372783363,-0.3128151,0.0546654575,-0.0676621571,-0.0604059212,0.1071833521,-0.1951394975,0.1111327037,-0.221358791,-0.5807493329,0.150195092,0.4398252368,0.0685188174,0.0794210061,0.0031908704,0.0320581682,0.0665732846,-0.0774655715,-0.2650559843,-0.4061390162,0.2982352972,-0.4582740068,-0.2638453841,0.0022567052,0.1692918092,0.0940052569,-0.1531164199,-0.2486801296,-0.7160105109,0.0647158474,0.4212590456,-0.0038632387,0.0864085779,0.1784058362,-0.139520824,0.0859609321,-0.0885400772,-0.1797664315,0.0611112677,0.3109692633,-0.1558004618,-0.0821072832,-0.1974602044,0.042480886,0.4971693456,0.1698794961,0.2713298798,0.3501393795,-0.0511983298,0.5025962591,-0.1764495075,-0.1557632983,0.0280106813,0.1081065461,0.149509549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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3518","title":"Add PubMed Central Open Access dataset","comments":"In the framework of BigScience:\r\n- bigscience-workshop\/data_tooling#121\r\n\r\nI have created this dataset as a community dataset: https:\/\/huggingface.co\/datasets\/albertvillanova\/pmc_open_access\r\n\r\nHowever, I was wondering that it may be more appropriate to move it under an org namespace: `pubmed_central` or `pmc`\r\nThis way, we could add other datasets I'm also working on: Author Manuscript Dataset, Historical OCR Dataset, LitArch Open Access Subset.\r\n\r\nWhat do you think? @lhoestq @mariosasko ","body":"## Adding a Dataset\r\n- **Name:** PubMed Central Open Access\r\n- **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse.\r\n- **Paper:** *link to the dataset paper if available*\r\n- **Data:** https:\/\/www.ncbi.nlm.nih.gov\/pmc\/tools\/openftlist\/\r\n- **Motivation:** *what are some good reasons to have this dataset*\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":64,"text":"Add PubMed Central Open Access dataset \n ## Adding a Dataset\r\n- **Name:** PubMed Central Open Access\r\n- **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse.\r\n- **Paper:** *link to the dataset paper if available*\r\n- **Data:** https:\/\/www.ncbi.nlm.nih.gov\/pmc\/tools\/openftlist\/\r\n- **Motivation:** *what are some good reasons to have this dataset*\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n In the framework of BigScience:\r\n- bigscience-workshop\/data_tooling#121\r\n\r\nI have created this dataset as a community dataset: https:\/\/huggingface.co\/datasets\/albertvillanova\/pmc_open_access\r\n\r\nHowever, I was wondering that it may be more appropriate to move it under an org namespace: `pubmed_central` or `pmc`\r\nThis way, we could add other datasets I'm also working on: Author Manuscript Dataset, Historical OCR Dataset, LitArch Open Access Subset.\r\n\r\nWhat do you think? @lhoestq @mariosasko 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3518","title":"Add PubMed Central Open Access dataset","comments":"Why not ! Having them under such namespaces would also help people searching for this kind of datasets.\r\nWe can also invite people from pubmed at one point","body":"## Adding a Dataset\r\n- **Name:** PubMed Central Open Access\r\n- **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse.\r\n- **Paper:** *link to the dataset paper if available*\r\n- **Data:** https:\/\/www.ncbi.nlm.nih.gov\/pmc\/tools\/openftlist\/\r\n- **Motivation:** *what are some good reasons to have this dataset*\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":28,"text":"Add PubMed Central Open Access dataset \n ## Adding a Dataset\r\n- **Name:** PubMed Central Open Access\r\n- **Description:** The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under license terms that allow reuse.\r\n- **Paper:** *link to the dataset paper if available*\r\n- **Data:** https:\/\/www.ncbi.nlm.nih.gov\/pmc\/tools\/openftlist\/\r\n- **Motivation:** *what are some good reasons to have this dataset*\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Why not ! Having them under such namespaces would also help people searching for this kind of datasets.\r\nWe can also invite people from pubmed at one point","embeddings":[-0.1280040443,0.0742645636,-0.211685732,-0.2449235022,-0.0705323368,0.0678360015,0.2654061317,0.3234461248,0.0099771908,0.2698275149,-0.0652088895,0.0095180478,-0.2703081071,0.0643097982,0.0232648458,-0.0676074699,0.0715511963,0.1311516017,0.2244762629,0.0050917328,-0.0993055776,0.0186134689,-0.1237551197,-0.2121479213,0.2558355927,-0.056514062,-0.0822058469,-0.0111050326,-0.2948715091,-0.45085904,-0.1412626058,0.238197282,0.1326746047,0.2251695395,-0.0000961674,-0.0759048834,-0.1182172075,0.0998770967,-0.1848814487,0.0235352665,-0.0201120283,-0.3003403544,0.0766225234,-0.2687698901,-0.2607449293,-0.5329335332,0.0978421345,-0.3341441453,0.1831433326,0.3041622043,0.369152844,0.3268483281,-0.1013851538,-0.3705291152,0.3279954791,0.0003663824,-0.2456732839,0.0822278485,0.6235818267,0.2256847024,0.0297966804,0.2237740308,-0.2458031774,-0.0331798792,0.1427438557,0.0285610687,0.0399103872,-0.1708510965,-0.0770372599,0.305006355,0.4682054222,-0.3800939918,-0.0309388041,-0.0965770707,-0.0522093102,0.138459757,-0.2950524688,0.3492048681,0.0841962174,0.2334497124,0.095820196,-0.0828865618,-0.1012832299,0.1053209305,0.2938145101,0.0699053034,-0.1859209836,-0.1211629882,0.0319175236,0.0587847717,-0.0202209372,0.0206593052,0.1724480093,-0.1314724237,0.1021204963,-0.3139236569,0.150930807,-0.1949461102,0.251391381,0.2167342752,0.0367946513,-0.1020486876,0.1162253693,0.0715754852,-0.0017002396,-0.5248041153,-0.3403421938,0.0942236111,0.3266788721,-0.1112598553,0.0006946524,0.0904249102,0.064463228,0.3547414243,0.0028256336,-0.2959750295,-0.12537238,-0.0632536635,0.0375000276,0.0870003998,0.3897683322,0.2141194493,-0.1490397304,0.2779925168,0.1109147519,-0.0033814476,0.1092120633,0.1779906899,0.0093522398,-0.1384045631,-0.1603924185,-0.0396468751,-0.1436195076,0.2475953251,0.3301928341,0.1167239696,-0.0025754441,-0.1237144545,0.1156003773,-0.0173105299,0.3514297307,0.2168145329,0.1712786108,0.2035544962,0.1046206951,-0.119913891,0.0734673515,-0.0285593774,-0.2501289248,-0.2050315589,-0.0585514456,-0.1613783538,-0.4806890488,0.2917519212,0.0094388369,0.0916549265,0.2390635908,0.2338013649,-0.0698597133,-0.3419358134,-0.0314960591,0.3233860135,-0.124267526,-0.27944085,0.1158930287,0.1990669817,0.4297747314,0.0273278747,-0.1970634758,0.2913464904,-0.1055470482,0.05849424,-0.0860311911,0.0614399835,-0.0484732725,0.0154493246,0.3792079091,-0.29401052,-0.2806819677,-0.1421525925,-0.2885065079,-0.4496105611,0.2844284177,0.1744529158,0.1436320692,-0.2541533411,-0.0067826086,0.0495911054,-0.2286540121,0.2805503905,-0.2419991642,-0.3191723228,-0.2077178359,0.2636912763,0.2522673607,-0.0437560193,0.4847251475,0.2123839259,0.3112641871,-0.2396378964,0.0599452145,-0.0230623018,0.0700477958,-0.1831100881,-0.0221620686,-0.4137480557,0.0988000184,-0.1156584397,-0.0067367158,0.1990428865,-0.0126902424,-0.3588165045,-0.1010544822,-0.0539925843,0.1405901313,-0.0249334611,0.3153842092,0.2400958538,0.1042278111,0.0658864975,-0.227862224,-0.2299523503,-0.2612198591,0.0464830212,-0.1635570526,0.1964289993,-0.0180061087,0.1414874941,0.322689712,0.349068433,-0.171723932,0.1529966146,0.2631435692,0.0218955092,-0.3503317535,0.2370727509,0.4939686656,0.5346071124,-0.0284743719,-0.5362479091,0.320954293,0.0228617322,-0.0566795543,0.1403742135,-0.2808603346,0.2318211943,0.1512131244,-0.0352704525,0.0491502434,0.0815448314,0.1778818518,-0.0945932195,0.0497105941,-0.2267061919,0.0445164777,0.4692377448,-0.1025191322,-0.081692636,-0.1940103322,0.3206011653,0.1220691875,0.0833857283,0.3152082562,-0.1234608516,-0.383497268,-0.0056539527,0.1156540588,0.0826121941,0.073839739,0.5113787651,0.1193058267,-0.0692156181,0.1874412149,-0.3105480671,0.0419050381,0.0340089276,-0.0976944044,0.144368723,0.2473182529,0.1933352351,-0.2472588867,0.0993439257,0.0365056768,0.0098756477,-0.0143781723,-0.0570934303,0.0025788308,0.1841159463,0.060987372,0.1431196928,0.1850916743,-0.0694454163,0.29393062,0.2412143946,-0.1766327918,-0.0854523927,-0.4922679961,0.5900940895,-0.2299290597,0.055498004,0.0186845027,-0.1614556015,0.1516496241,0.3181247115,0.2887971401,0.0664016455,0.6696482301,0.0313167796,0.3937740326,-0.4056630135,-0.3360238969,0.0261899251,-0.0007525544,-0.0379861072,0.0170341004,0.0970293656,0.0349382311,-0.1576403528,-0.0455048345,-0.2956984043,0.0575998835,0.0592252798,-0.0149510857,-0.1941445917,-0.1087011397,-0.4036976993,-0.266071558,-0.3916775286,0.0819501728,-0.0877956823,-0.0255381595,0.0684870705,-0.1992979646,0.0908353329,-0.2347147167,0.3864313066,-0.2032037377,-0.1316523701,0.1770554632,-0.4397175014,-0.2599435747,-0.1334656775,-0.162285015,-0.1684755236,0.0279065613,-0.4149580002,-0.0387340412,0.0514315702,-0.0503842495,0.428917706,-0.0090847025,0.382466048,0.0711326227,-0.2857226431,-0.0257630385,0.0079945931,-0.0673999712,-0.1470200121,0.0081280386,-0.0453075767,-0.3236503005,0.0221125968,0.5284542441,0.2726762593,0.0762905329,0.2002137005,-0.1006997675,0.2036378086,0.0538820364,-0.2337334007,0.2363712043,-0.0131719923,0.09947851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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3510","title":"`wiki_dpr` details for Open Domain Question Answering tasks","comments":"Hi ! According to the DPR paper, the wikipedia dump is the one from Dec. 20, 2018.\r\nEach instance contains a paragraph of at most 100 word, as well as the title of the wikipedia page it comes from and the DPR  embedding (a 768-d vector).","body":"Hey guys!\r\n\r\nThanks for creating the `wiki_dpr` dataset!\r\n\r\nI am currently trying to use the dataset for context retrieval using DPR on NQ questions and need details about what each of the files and data instances mean, which version of the Wikipedia dump it uses, etc. Please respond at your earliest convenience regarding the same! Thanks a ton!\r\n\r\nP.S.: (If one of @thomwolf @lewtun @lhoestq could respond, that would be even better since they have the first-hand details of the dataset. If anyone else has those, please reach out! Thanks!)","comment_length":46,"text":"`wiki_dpr` details for Open Domain Question Answering tasks \n Hey guys!\r\n\r\nThanks for creating the `wiki_dpr` dataset!\r\n\r\nI am currently trying to use the dataset for context retrieval using DPR on NQ questions and need details about what each of the files and data instances mean, which version of the Wikipedia dump it uses, etc. Please respond at your earliest convenience regarding the same! Thanks a ton!\r\n\r\nP.S.: (If one of @thomwolf @lewtun @lhoestq could respond, that would be even better since they have the first-hand details of the dataset. If anyone else has those, please reach out! Thanks!) \n Hi ! According to the DPR paper, the wikipedia dump is the one from Dec. 20, 2018.\r\nEach instance contains a paragraph of at most 100 word, as well as the title of the wikipedia page it comes from and the DPR  embedding (a 768-d vector).","embeddings":[0.1468071342,-0.359988749,-0.1104264185,0.5688869953,-0.2651134729,0.0071313758,0.1473308206,0.1168369725,-0.1801615059,-0.2350244373,-0.0067059365,-0.0107020382,0.0064400276,0.1478557885,0.1353963464,-0.5107957125,0.1008949801,0.0089568719,0.0648311153,-0.2404284477,-0.1580114067,0.1133476868,-0.503869772,0.1290733069,-0.1562557518,-0.2433903664,-0.2280952036,0.157140255,-0.3485134244,-0.4826010168,0.306692183,0.1999156475,0.5760837197,0.1271486431,-0.0001116685,-0.2763666809,0.2172320485,-0.028494522,-0.1582502872,0.4231159985,-0.5140339136,-0.1671158224,0.1726880819,-0.3551968932,0.3106271029,-0.5295951962,0.0004728137,0.3169938326,0.3888758719,0.1339208037,0.159408465,-0.1981488913,-0.0706540942,-0.1687020063,-0.0432024784,0.2109137475,0.0879055411,-0.2134168297,0.0548031181,0.0790384039,-0.1157179698,0.0932191387,-0.3038277626,0.0596618727,0.3489578962,-0.1674750149,-0.151779145,-0.4351554215,0.415456593,0.6235175729,1.0557755232,-0.0582362674,0.1653031707,0.0842050686,-0.3430510461,-0.2632784545,0.0724847093,0.2549051344,-0.0597159453,0.0844703168,0.0459232144,0.0988880247,-0.3525492549,-0.0660268441,-0.4859414101,0.3603990078,-0.0956521183,-0.1524918228,-0.133260265,-0.2201259136,-0.1197413206,-0.2582203448,-0.0758702531,0.1380987167,0.0813380331,-0.196039021,-0.01443935,0.2058966309,0.1039169356,0.1517531276,-0.0321798064,0.0553497933,0.1553788036,-0.067901887,0.5894582868,-0.1172456741,0.0824517459,0.2480858862,0.2179448009,-0.177153334,-0.093212679,0.2241453677,-0.0958791375,0.1698106378,-0.0952590257,-0.3475190401,-0.0256959386,-0.1765568703,0.339594841,0.5081170201,-0.2119091898,-0.2511981726,-0.5433133245,0.143961817,-0.0724347979,0.2263397872,0.1261410117,-0.1283296198,-0.0093738288,-0.0317851715,0.0312478412,-0.1439609379,-0.1165599898,-0.0909124538,-0.0995639563,0.2544190586,0.3007560074,-0.0132864481,-0.0120897451,-0.0628187954,0.2883186042,0.1435114145,0.4088805318,0.2431744933,0.026467124,0.0653223842,-0.1296442598,-0.2920664549,-0.329018712,0.1271612793,-0.014519034,-0.12554802,-0.3794350028,0.1818445474,-0.2204211056,-0.0311303679,-0.1434298754,0.3879498839,-0.1079710126,-0.225708276,0.2376204878,-0.1241787672,0.0422233492,-0.3442400992,0.0996990427,0.2531833351,-0.5339756012,0.215415284,0.0884593129,-0.017949678,0.0035743045,-0.0027887872,-0.0778324082,0.2600714862,0.0996522009,0.255810976,0.1653073877,-0.3504905403,-0.0240312926,0.2304502726,0.261932075,-0.0031490512,0.0400838777,-0.0888658911,0.4833315611,0.0871085003,-0.0310043674,0.3420556784,0.0983751193,-0.0745489225,-0.1251423955,-0.0581349358,-0.1222247332,-0.0268394724,-0.0604450591,-0.1612696797,0.2109293789,0.5080269575,0.2458340526,0.0620282553,0.0785709918,0.2437517047,-0.1031630263,0.0318601206,0.4806425571,-0.0707213581,-0.2980217338,-0.0254670475,-0.1643808186,0.2947773933,0.2091660649,-0.0569561198,-0.2462622821,0.0296608359,-0.0694214627,-0.6346977949,0.1825539321,-0.1347417086,0.2002725303,0.2829961777,0.0263551436,-0.417914629,0.1168470755,-0.1508008987,-0.3515052795,0.0402130522,-0.0528306589,0.2482932359,0.0282762274,0.1365651786,0.0012267125,0.2096632719,0.1662369967,-0.1990261972,0.3099917471,0.0575625859,0.3121471405,0.5474573374,0.1672431827,-0.4221031964,0.3135584593,0.0097276038,0.0506816246,-0.1355798841,-0.3305469453,0.4808566272,0.101553835,-0.0911633372,-0.1326792538,0.120954439,0.2047293782,-0.1241742447,0.2408509105,-0.1761586964,-0.0536061898,0.5355110168,-0.128827244,0.1641760021,-0.1978630573,0.2073498666,-0.0705109537,-0.6034587622,0.0323049836,-0.0261398666,-0.3675804734,-0.6090572476,0.3488849998,0.1280794889,-0.0511549488,0.1210689768,0.3134018183,0.0250292122,0.1054705083,-0.281401515,0.2922220528,0.1323705614,-0.2947298288,0.109288916,-0.1143744588,0.3988980949,-0.0341961421,0.0417791195,0.0217811074,0.2325127274,-0.0273009185,-0.0260130614,-0.2558702826,-0.1994346082,0.2162512392,0.430196166,-0.1592898071,-0.2968256474,0.1049419194,-0.2731721699,-0.0319016464,0.0428140759,-0.0398170054,0.4947299659,-0.0914441422,0.1142946035,-0.1254177094,-0.5702392459,-0.2216057479,0.1671451777,0.0043518869,0.4730897546,0.2006804645,0.0615445152,0.0279714148,-0.1948076338,-0.377112329,0.2749087512,0.0271191336,0.123520188,0.0299482159,0.2710005939,-0.2959020436,-0.1787261516,-0.2965994477,0.0112042557,-0.1558640897,0.0502531417,-0.1415265501,-0.0006119537,0.2380174994,-0.356146276,-0.213893503,-0.1005491465,0.1703161746,0.0556773432,0.041574385,0.019921327,-0.3117669523,0.1492575705,0.0666553527,0.0884122029,-0.3115164638,0.0268899426,0.1876999736,-0.3332182467,-0.3622553051,-0.0269673932,-0.2470218688,-0.0422626063,0.0147013431,-0.5535596609,-0.1292991936,0.0975081772,0.4674603045,0.458452493,0.1356951743,0.2166372836,0.0487003289,-0.0223429054,0.0183916744,0.0778308809,-0.1877858639,-0.0271365661,0.0465604775,0.1986153573,-0.1598826051,-0.1615210921,0.8978901505,0.328779757,0.0007101775,-0.1886298805,0.0542047992,0.0828764141,0.0812458172,0.3094263971,0.1461465657,-0.158357814,-0.12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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work), so that for datasets that have a working dataset preview, we can remove the dummy data IMO. On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data.\r\n\r\nI don't really have an opinion regarding the JSON metadata as I don't know enough about it.\r\n\r\n","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":105,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work), so that for datasets that have a working dataset preview, we can remove the dummy data IMO. On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data.\r\n\r\nI don't really have an opinion regarding the JSON metadata as I don't know enough about it.\r\n\r\n","embeddings":[-0.2486523837,0.0137669854,0.0282520037,0.1720923036,0.0348428302,0.2506078184,0.3021662533,0.1551799625,-0.1902871281,0.0683344081,0.2187202871,0.0646495,-0.0933551192,0.2443664968,-0.11610955,-0.0490054116,0.1391409039,0.0312337968,0.1161178648,0.0746645927,-0.320725441,-0.2926142216,-0.0709843785,-0.241312772,-0.25042665,0.1092141718,-0.1224096641,0.0149589712,-0.3816934228,-0.1647889763,0.1133756861,0.3793191612,0.0979223698,0.254429549,-0.0001264203,0.012803317,0.6090388894,-0.0712980032,-0.4011119306,-0.1539132744,-0.3898027539,0.3628113568,-0.0220422093,0.0321549736,-0.2279220521,-0.1073064804,-0.2318498045,-0.3765940368,-0.0290931258,0.1177050844,0.0256800652,0.0396150909,-0.4309017062,-0.0631438568,-0.0313715488,0.9255705476,-0.1825040579,0.0989713296,0.4341517389,0.1677660346,-0.0156634729,0.2434833497,0.0460387506,-0.073274225,0.0879202634,-0.0114353839,0.1045585498,-0.2269557267,0.3958575428,0.1985933185,1.1765061617,-0.2331520021,-0.195523113,-0.1157744452,0.192661345,-0.0146535924,0.2199673206,0.2478120029,0.050990697,0.1411825418,-0.4045705497,-0.3414753377,-0.0444598719,0.088233225,-0.1029475406,0.2359270751,0.0860851929,0.1686286777,-0.1502656788,0.0836121514,0.3949390948,-0.7694560885,-0.2043079734,-0.1173363253,0.4072949886,-0.3681434691,-0.1559506059,-0.1759455502,0.2297382504,0.3057636321,-0.0552043617,-0.0458590649,-0.1806497276,-0.1297556609,0.6822134852,-0.1319944263,0.2199286222,0.4627607465,0.284848243,0.1766171455,0.2574861944,-0.0315733403,0.1004958749,0.4986410737,-0.3643734157,0.1501466185,0.2979203463,-0.4887465835,-0.0696498826,0.0692838803,-0.1809287369,-0.2053922713,0.0799736753,0.0412657037,-0.0877052918,0.354286015,-0.2711997628,0.1464087665,0.3190538287,-0.0799843445,0.0080537796,-0.0237996522,-0.1003374234,0.2988204956,0.2166265696,0.219088316,0.3037216365,0.2398719639,0.4130532742,0.0553476252,0.2576729953,-0.2618197203,0.0307007562,0.2436750084,-0.3425038159,0.1409930587,-0.1227422804,-0.0024409604,-0.2252644897,0.4225278795,0.0982008874,-0.475749433,0.0818144307,0.0361490771,-0.3409238458,0.2439304292,-0.536916852,0.2835014164,-0.4571817517,-0.0821095631,0.143770203,0.0071927765,0.0578448549,-0.1192630231,-0.0832067132,0.2534864545,-0.2441457957,0.0986580104,0.2421218306,0.072187297,-0.0823988467,0.1026090905,-0.5668734312,-0.0173743311,-0.1907698363,-0.4189896882,0.2413858771,-0.8214488029,-0.0991703421,0.3857122362,0.1366876066,0.2780225575,0.1970302314,-0.0171156693,0.1563876569,-0.2286946028,-0.0189910326,0.0432743952,-0.2614361942,-0.0347660221,-0.2717988789,-0.2289516777,0.0505958349,0.3059806526,-0.0392987058,-0.0525125191,-0.1926520467,0.3646830022,0.1365926415,0.063433975,0.0849897191,0.1070234105,0.4106438756,-0.3799117208,0.0647511184,0.2652450204,-0.607475698,0.1401079893,0.2517114878,0.3863726556,0.1255481839,-0.5563474894,-0.0805995017,-0.1913725138,0.0351367146,-0.1770506352,-0.0404792055,0.0315022282,0.1153379828,-0.0994777754,-0.2010633349,0.1973277479,-0.2527266145,0.2045271844,0.0242694486,0.5208440423,0.0934555158,0.2553086877,0.1447548121,0.0518063158,-0.1822935641,-0.2388097048,0.0535623245,0.2125790864,0.1466684639,0.1059790775,0.3040186167,0.5328745246,0.504565537,-0.3572555482,0.1747084558,0.1949000955,0.0583741665,-0.2650447488,-0.6684811115,0.4018935561,-0.2051252425,-0.0164159518,-0.1205787137,-0.1986343712,0.0239946786,-0.0178094264,-0.5093137622,-0.1551640928,0.0300024804,0.3529705107,0.0941116214,0.2207177579,-0.2849732637,0.0801604018,0.2103642821,-0.0866306424,-0.2486231625,-0.0398216955,-0.2598922551,-0.2340337932,0.3855400681,-0.0764451921,0.7989638448,0.0883768797,0.1172174439,0.0155884093,0.0058107227,0.0148226218,0.2417990714,-0.1069393679,0.0657230467,-0.0059653255,-0.0123099508,-0.0430612415,0.0476202369,-0.0808848664,0.2607133985,0.2574305236,-0.0737557709,-0.2194237411,-0.0323582478,-0.3028434515,0.2093314677,-0.1242902428,-0.1464805007,-0.1831118017,0.1439923644,0.2891624272,-0.11528299,0.0884756744,0.0853968635,0.497772038,-0.2961884141,-0.2895886302,-0.2863369882,-0.2353239655,-0.052375596,0.0494159833,0.2383066565,0.0238177069,0.2719206512,0.0393214375,0.1430505812,-0.4720919728,-0.5405819416,0.2405348718,-0.0001880982,0.2240737826,-0.0566676967,0.0606758818,-0.0024813518,-0.0516297072,0.0781564936,0.0332841277,-0.0436775349,-0.2030100524,-0.1050015464,-0.0594243817,-0.1466126442,-0.2361753881,-0.0594711527,-0.2491319925,0.113178812,-0.0464330204,-0.1321882755,-0.0750780776,-0.1236278415,-0.4684521258,0.0574584566,-0.2236882001,-0.2692930698,-0.5981268287,-0.0194494948,0.0145444619,-0.1172961891,0.2431931645,0.1091459095,0.2024856508,0.2283025831,-0.6837747097,-0.436245054,0.2931388617,0.3793487251,-0.0636715963,-0.3816944361,0.2476785928,0.227576986,0.0922098085,0.1237607747,-0.2312771529,-0.2526858747,0.2111117989,0.2029976547,0.0622877739,0.0612444915,0.0631498545,0.3500649929,0.2516457438,-0.0819624215,-0.2772547305,0.2394866496,0.3754942119,-0.1660518646,-0.0558466502,0.0604625978,0.1601310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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"I don't know all the details, but generally I'd be in favor of unifying the metadata formats into YAML inside .md (and so deprecating the dataset_infos.json) \r\n\r\n(Ultimately the CI can run on \"HuggingFace Actions\" instead of on GitHub)","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":38,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n I don't know all the details, but generally I'd be in favor of unifying the metadata formats into YAML inside .md (and so deprecating the dataset_infos.json) \r\n\r\n(Ultimately the CI can run on \"HuggingFace Actions\" instead of on GitHub)","embeddings":[-0.256570369,-0.0132412249,0.0461185277,0.141600728,0.1393592507,0.2571754456,0.3560962081,0.1884698123,-0.232667014,0.0781046972,0.1527016908,0.1550486982,-0.1056064889,0.2942404449,-0.0938433334,0.0398535617,0.1528295577,-0.0513014495,0.0435608812,0.1166529879,-0.2324075848,-0.2528561354,-0.0571075715,-0.2305631042,-0.2598579824,0.1249527633,-0.156695202,0.063340053,-0.4106205702,-0.2137594521,0.1743178368,0.32470119,0.0896924958,0.2540900409,-0.0001246251,0.0060735089,0.6050924659,-0.0537861511,-0.2970484793,-0.1600689292,-0.3328147829,0.2906111181,0.0145549532,0.0346825719,-0.2549169064,-0.1105039492,-0.3020368516,-0.3809310496,-0.0573394373,0.1132747978,0.0441936143,-0.001626321,-0.4321269095,-0.1240691394,0.0176065098,0.9487275481,-0.2242538035,0.0832726359,0.440020293,0.1104187146,0.0055346005,0.251141727,-0.018303223,-0.1480806172,0.1482376456,0.0542647876,0.2769651115,-0.1803621501,0.3802469075,0.1691189259,1.1847327948,-0.2980667055,-0.1961340755,-0.085547626,0.1469855905,0.0561020449,0.1904314011,0.2749424875,0.0226563197,0.1590432078,-0.4943564832,-0.2969922125,-0.0018676138,0.0925922766,-0.1353558451,0.2303594649,0.0598984994,0.111999616,-0.0817824677,0.0569798723,0.4314073622,-0.6801913381,-0.2379593551,-0.1470595896,0.4681392014,-0.4178721905,-0.182286039,-0.1639823616,0.3154355586,0.3232321441,-0.132210806,-0.0676480979,-0.1182577163,-0.1468310058,0.7002789378,-0.1668504626,0.1949214488,0.4919017553,0.2789281011,0.1883300543,0.2716889083,-0.0199729186,0.0895086303,0.4109479487,-0.3386646509,0.1187298074,0.3129404485,-0.4845794141,-0.0528713949,-0.0052831518,-0.1292871535,-0.1126814038,0.0170138665,0.0350759886,-0.0387644023,0.3999949992,-0.2567218244,0.1766819805,0.3021097779,-0.0074772565,0.011240432,0.0128178978,-0.1566255987,0.2825138271,0.1905026287,0.3046737909,0.2801948786,0.3589211106,0.4855592847,0.0062277154,0.186615333,-0.175279364,0.0425025485,0.2534369528,-0.3218349814,0.0975918695,-0.0386157483,-0.0875001475,-0.263576597,0.3770352006,-0.0413468555,-0.4021497965,0.0433178917,0.0500483364,-0.2884599268,0.3348756135,-0.4575405121,0.220131427,-0.3668031394,0.0455244072,0.1497786492,0.0110588567,0.0818303823,-0.1558545977,-0.0595885813,0.2145081311,-0.2190612108,0.0998605639,0.1867482513,0.0997149274,-0.1493074894,0.116714552,-0.5950161815,-0.0193428528,-0.2265223861,-0.3761904538,0.2680710256,-0.9781396389,-0.0862034187,0.387542218,0.1509796381,0.2233335078,0.19288975,-0.028169699,0.2047894895,-0.2677517235,0.0752758905,0.0267115477,-0.3526499569,-0.040414352,-0.2413202226,-0.2611382008,0.002888049,0.249381423,-0.0650252774,-0.0704544932,-0.0812994838,0.3685341775,0.1718640178,0.0031156859,0.0648459718,0.1550646871,0.3700852096,-0.3747098446,0.1169399172,0.2551825941,-0.6471691728,0.1039180383,0.2409285456,0.3850513101,0.0610975027,-0.6422055364,-0.1037903652,-0.1907630712,0.0131256264,-0.231643185,-0.0295446236,-0.0320500582,0.0282447487,-0.0393639207,-0.1840425581,0.1557932645,-0.1785773039,0.225411281,0.0972929224,0.4212728739,0.0978087038,0.2518070042,0.1794213802,0.1216342151,-0.1627429128,-0.2107483894,0.0858389959,0.235506326,0.1420682818,0.1429115236,0.3499450684,0.553630054,0.4912445545,-0.3566739261,0.2160500735,0.0493652336,0.0186699722,-0.2800174057,-0.6802324653,0.4566258788,-0.268802166,0.0014736108,-0.1239590198,-0.1167333275,0.0358803421,-0.04860349,-0.5542075634,-0.2367333174,-0.0548825748,0.263278842,0.0951881707,0.2151771486,-0.1527880579,0.1387812346,0.2934572995,-0.0639307797,-0.219885841,-0.0632853955,-0.2505202293,-0.1648672968,0.2953703403,-0.0481904969,0.7303119898,0.1328808963,0.1025366411,0.0637681633,0.019003313,0.0166128408,0.2468033433,-0.0941285342,0.1161879152,0.0116854664,0.0773398355,-0.0375979133,0.1234449297,0.0776261166,0.1560184658,0.2009219676,-0.0647542551,-0.1422568262,-0.0912884399,-0.1877266616,0.1913492084,-0.1030500233,-0.2025361508,-0.15512833,0.0911140889,0.2413774729,-0.0629168525,0.110638462,0.0553514361,0.4378426373,-0.2798095644,-0.1776716858,-0.1645486206,-0.2106557935,-0.0440260023,0.0446693525,0.2849071622,0.0155349001,0.275665313,-0.0140441954,0.197857067,-0.5741674304,-0.5353813767,0.2564862669,0.0225439351,0.2368777841,0.0462734848,-0.0300052799,-0.0133645767,-0.0198775157,0.0942618325,0.1059285328,-0.0426516905,-0.1391507387,-0.1234074384,-0.0554157123,-0.2215985954,-0.2531766891,-0.0401937887,-0.2280889004,-0.0011410918,-0.0830315277,-0.1553629637,-0.0639993995,-0.1233598962,-0.5331252813,-0.0243522115,-0.2296661139,-0.2312086523,-0.5105111599,-0.0055031311,-0.0475646965,-0.0964686349,0.2881301641,0.1338597089,0.1658919007,0.2360759825,-0.6717325449,-0.4597176313,0.3057682514,0.3347573876,-0.005994021,-0.3518353999,0.244657591,0.2788265049,0.0932810009,0.1383455247,-0.2069217712,-0.2460865527,0.0792101026,0.1690744162,0.037590269,0.0475280881,0.1589485109,0.2641867101,0.2458786815,-0.0773316398,-0.3262373507,0.193205893,0.3924695253,-0.1080077589,-0.104701817,0.0172931533,0.1727538109,-0.0671515986,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"The dataset_infos.json file currently has these useful infos for each dataset configuration, that I think can be moved to the dataset tags:\r\n- Size of the dataset in MB: download size, arrow file size, and total size (sum of download + arrow)\r\n- Size of each split in MB and number of examples. Again this can be moved to the dataset tags\r\n- Feature type of each column\r\n- supported task templates (it defines what columns correspond to the features and labels for example)\r\n\r\nBut it also has this, which I'm not sure if it should be in the tags or not:\r\n- Checksums of the downloaded files for integrity verifications\r\n\r\nSo ultimately this file could probably be deprecated in favor of having the infos in the tags.\r\n\r\n> Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nTo get the exact number of examples and size in MB of the dataset, one needs to download and generate it completely. IMO these infos are very important when someone considers using a dataset. Though using streaming we could do some extrapolation to have approximate values instead.\r\n\r\nFor the integrity verifications we also need the number of examples and the checksums of the downloaded files, so it requires the dataset to be fully downloaded once. This can be optional though.\r\n\r\n> IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work)\r\n\r\nI agree with this. Usually if a dataset works in streaming mode, then it works in non-streaming mode (the other way around is not true though).\r\n\r\n> On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data.\r\n\r\nYes indeed, or at least make sure that it was tested on the true data.","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":355,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n The dataset_infos.json file currently has these useful infos for each dataset configuration, that I think can be moved to the dataset tags:\r\n- Size of the dataset in MB: download size, arrow file size, and total size (sum of download + arrow)\r\n- Size of each split in MB and number of examples. Again this can be moved to the dataset tags\r\n- Feature type of each column\r\n- supported task templates (it defines what columns correspond to the features and labels for example)\r\n\r\nBut it also has this, which I'm not sure if it should be in the tags or not:\r\n- Checksums of the downloaded files for integrity verifications\r\n\r\nSo ultimately this file could probably be deprecated in favor of having the infos in the tags.\r\n\r\n> Also note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nTo get the exact number of examples and size in MB of the dataset, one needs to download and generate it completely. IMO these infos are very important when someone considers using a dataset. Though using streaming we could do some extrapolation to have approximate values instead.\r\n\r\nFor the integrity verifications we also need the number of examples and the checksums of the downloaded files, so it requires the dataset to be fully downloaded once. This can be optional though.\r\n\r\n> IMO, the data streaming test is good enough of a test that the dataset works correctly (assuming that we can more or less ensure that if streaming works then the non-streaming case will also work)\r\n\r\nI agree with this. Usually if a dataset works in streaming mode, then it works in non-streaming mode (the other way around is not true though).\r\n\r\n> On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data.\r\n\r\nYes indeed, or at least make sure that it was tested on the true data.","embeddings":[-0.2210955322,0.006916299,0.0213331804,0.2000362724,0.1244291663,0.3079422414,0.2800019979,0.2172091752,-0.0784054175,0.096394375,0.2354323268,0.1877376884,-0.0900826305,0.3275887668,-0.0727664456,-0.015771484,0.1152650937,0.0301213879,0.1735402793,0.108022742,-0.2839276195,-0.2887781262,-0.0311908964,-0.2195353657,-0.3118304908,0.0708045363,-0.148216188,0.0251574703,-0.3925423324,-0.2551784813,0.1556084454,0.3005902767,0.1127887145,0.164299652,-0.0001224926,0.0712170824,0.5550849438,-0.0289685931,-0.3917882442,-0.163217023,-0.4925613105,0.2374786735,-0.0445607044,0.0124402344,-0.2017588764,-0.2084181756,-0.240285337,-0.2984036505,-0.0418401621,0.13405402,0.0343528502,-0.0409509726,-0.3865479231,-0.1780650169,0.0645647049,0.9078686833,-0.2102441937,0.1271470487,0.4289444685,0.1529577971,0.0245041102,0.326145798,0.0558364801,-0.1429644972,0.1884842366,-0.024475811,0.127888903,-0.20458211,0.389210999,0.2028096765,1.1805289984,-0.2984223068,-0.1752672642,-0.1384029984,0.1525873393,-0.0377219059,0.1832220852,0.2163560838,0.0798648223,0.1787981689,-0.4444426,-0.326357156,-0.0194117632,0.0566714443,-0.0700395554,0.2103638947,0.0666915104,0.1638323218,-0.1348730773,0.1070075333,0.2654390931,-0.6999871731,-0.25283584,-0.1038836539,0.4104929566,-0.3335484862,-0.127632618,-0.2637266219,0.3395620286,0.3031009436,-0.0606808364,-0.1517610997,-0.1202440113,-0.0731893629,0.7145395875,-0.0699144006,0.1902432442,0.4698488414,0.1969082952,0.2032787651,0.311781913,-0.0511935651,0.0088811154,0.411459446,-0.3733815551,0.1473640352,0.2769475281,-0.396017909,-0.0723867267,0.0367024466,-0.1326276958,-0.1609281003,0.0868936703,0.0169257615,-0.0804301426,0.3018757701,-0.2698969543,0.0985458121,0.3401651084,-0.0762263387,-0.001632203,0.0208019577,-0.1440438479,0.2887396514,0.129859373,0.2579699159,0.3302911818,0.2647242546,0.4206183851,0.068991296,0.2493017167,-0.2406682819,0.055718895,0.2864408791,-0.319488734,0.1364715099,-0.1285973191,-0.0684110522,-0.2857650518,0.4004405141,-0.0176003221,-0.4607973695,-0.0020796668,0.0538963974,-0.2711711228,0.2970173657,-0.5828508139,0.3256414235,-0.3997907937,-0.0412875302,0.1680087298,-0.022229936,0.018707877,-0.1763269156,-0.0427709334,0.2106144875,-0.3152956069,0.0616496988,0.2595338821,0.0927527472,-0.1336485893,0.0908618867,-0.6103591919,0.0816172585,-0.2089208364,-0.309278667,0.2543433607,-0.8403229713,-0.149604395,0.3902073503,0.208795473,0.2094253898,0.1515908688,0.0183181763,0.1785830706,-0.1954102665,-0.0026970641,0.0359922498,-0.3083601594,-0.0463451855,-0.2085132152,-0.2920589149,0.0910402536,0.2639701962,-0.0959585905,-0.1236294061,-0.1475804299,0.3946583569,0.0984619111,0.015823368,0.0525003634,0.1682745218,0.3374057412,-0.3927162886,0.1344363093,0.2572000325,-0.6598459482,0.1868916601,0.1654248238,0.3377948403,0.0868850052,-0.5778264403,-0.1670805663,-0.1954964846,0.026164379,-0.3015490174,-0.041592475,-0.0131726544,0.2018497139,-0.1454060376,-0.1893324405,0.1935058385,-0.2928801179,0.2157315314,0.0256238524,0.4264737666,0.0359659381,0.2122019082,0.1712299883,0.1127696633,-0.1798417568,-0.2050189823,0.0274981689,0.228337273,0.2143550068,-0.0057858932,0.3482722938,0.5337327719,0.4653946161,-0.2988368869,0.2050931007,0.0947857425,0.0471490175,-0.1710038781,-0.6474599242,0.3310769498,-0.2230480164,-0.0072270874,-0.1052765027,-0.1359325945,0.0464696623,0.0589717627,-0.4383323193,-0.1309178323,-0.0905921757,0.396209687,0.1611952037,0.2313714474,-0.2130068541,0.1161329076,0.3556317985,-0.0343427844,-0.2293509692,-0.03436004,-0.2794616818,-0.2061218172,0.4193127155,-0.0824765265,0.7531794906,0.1462221295,0.1103860438,-0.0258705653,-0.0501205362,0.059208326,0.19847776,-0.1164448336,0.0976670235,-0.0475071333,-0.0457681417,-0.0271971878,0.1161283925,-0.0686227977,0.2259610742,0.248383522,-0.0864614397,-0.1396695375,-0.0852167681,-0.3184207678,0.2284795195,-0.0491148569,-0.2026500553,-0.1847951561,0.1936953515,0.1265633106,-0.0995669514,0.1218189597,0.1299761981,0.4502471685,-0.2398066372,-0.2140857726,-0.2615545094,-0.249287039,-0.1126448587,0.0526733808,0.2719908357,0.0299424157,0.2362425327,-0.0195372328,0.1474666297,-0.4518902898,-0.5225102305,0.267347455,-0.0162876155,0.3029921055,0.0484133549,0.0412349142,-0.0516044311,-0.0323951095,0.062097095,0.1496198028,-0.0704204589,-0.236309886,-0.1133575514,-0.0633453652,-0.2317938209,-0.3378817439,-0.0925960094,-0.2846384645,0.1502203345,-0.0773870051,-0.1027369797,0.0193304326,0.0129555305,-0.4175916612,0.0257477928,-0.1537337154,-0.2391972244,-0.5657061338,0.0110342028,-0.0273890663,-0.088850297,0.2655244768,0.1111277342,0.2430260777,0.2342038453,-0.6086315513,-0.4187903404,0.213971898,0.3895591795,-0.0758737549,-0.3151684105,0.2793072164,0.2290166765,0.1067208424,0.1175944805,-0.2546659112,-0.2088501304,0.1025094166,0.1292864382,0.1272270679,-0.0053912695,0.1767268777,0.3566434383,0.2389940321,-0.1325138062,-0.2747151554,0.191247046,0.3884133995,-0.1418129355,-0.0829225257,0.1117803454,0.1565793455,-0.0695869029,0.3088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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"(note that if we wanted to display sizes, etc we could also pretty easily parse the `dataset_infos.json` on the hub side)","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":21,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n (note that if we wanted to display sizes, etc we could also pretty easily parse the `dataset_infos.json` on the hub side)","embeddings":[-0.2448804379,-0.0072729602,0.0365820117,0.1965514272,0.0787771568,0.2738457024,0.3356237411,0.1830959618,-0.1585743129,0.1159564778,0.1745053828,0.129195407,-0.038630981,0.2549438179,-0.1055925786,0.0186515749,0.1146854311,0.0495394282,0.0804450214,0.0655288771,-0.3198399246,-0.2816701531,-0.0632381588,-0.2555608451,-0.3255380392,0.1402469724,-0.1412353963,0.0127852624,-0.3754119873,-0.2028705776,0.1749619246,0.3253169954,0.1176563352,0.1910839677,-0.00012484,0.0245984495,0.5953423381,-0.0396741144,-0.3544955254,-0.1475136429,-0.3949455321,0.3029106855,-0.0091454107,0.0002633182,-0.2615972161,-0.1314409375,-0.2109635174,-0.3209058046,-0.0479369983,0.1222055554,0.0411138013,0.0386703946,-0.4607897103,-0.1866856366,-0.0347404853,0.9352336526,-0.2265194952,0.0461828001,0.3475133479,0.1384930164,-0.0224170778,0.2036411315,0.0573354624,-0.1447868198,0.0753309131,0.030237712,0.1645533442,-0.2002706379,0.4008167982,0.2043205053,1.152135253,-0.2731022537,-0.1573779583,-0.1098147482,0.1627563089,0.054084029,0.1839992404,0.2215022743,0.0325044058,0.1627646089,-0.4268219471,-0.3527646959,-0.0002798178,0.0587724634,-0.1230927631,0.1822795123,0.103362605,0.1533601284,-0.1324159056,0.0984300524,0.4020735025,-0.7522619367,-0.2735717893,-0.1007317677,0.4321744144,-0.4182077944,-0.1062970757,-0.2099195272,0.2524696887,0.3113320172,-0.0914527699,-0.071165204,-0.1720695943,-0.1221877337,0.7082363963,-0.1236141846,0.2212323099,0.5232919455,0.2780006528,0.1661794186,0.2873710692,-0.0057616737,0.0576483943,0.4986962974,-0.3875405788,0.1142004505,0.2498285472,-0.4602185786,-0.0922137052,0.0809109285,-0.1082363203,-0.1439626962,0.0528812036,0.0467311889,-0.0712866411,0.3402046561,-0.2294056416,0.119468987,0.2805403173,-0.0817140564,-0.0199744217,-0.00523121,-0.0966558978,0.3138819039,0.1771490872,0.3406615257,0.2998600602,0.2923063338,0.4300595522,0.0593834445,0.2132393569,-0.2793402076,0.0001081129,0.2182279527,-0.2386262417,0.1239299774,-0.0862811357,-0.0506768562,-0.248231411,0.4380425513,0.0574146137,-0.4373384416,0.0171915647,0.0438864306,-0.3926957548,0.3019969463,-0.5288165808,0.2820004225,-0.4432471097,-0.0249199308,0.1190091744,0.0195546113,0.0248714406,-0.1251828671,-0.0602716841,0.2222675383,-0.2909181118,0.0822683722,0.2265956849,0.0277493931,-0.1541759074,0.1189551279,-0.6049752235,0.0128296651,-0.248007372,-0.3945966065,0.2990493476,-0.8168703914,-0.1244232878,0.3451868892,0.1384906918,0.2133285999,0.134509027,-0.0079274317,0.1656026691,-0.2160921544,0.0251348428,0.0117832925,-0.2696979642,-0.0653972104,-0.2308374047,-0.2717416584,0.0172463153,0.2806309164,0.0060130307,-0.0614545159,-0.141182065,0.3822865188,0.187000975,0.0458829775,0.0840020403,0.1599844694,0.4090305865,-0.4020367861,0.1422463655,0.2636935115,-0.5938820839,0.192190513,0.1457270384,0.4153379798,0.1573153585,-0.5842548609,-0.1127416119,-0.1733083278,0.0505855456,-0.2129986137,-0.0308425631,-0.0095128724,0.1204679385,-0.048431661,-0.1656340361,0.1967916489,-0.2263900936,0.1808981597,0.0349747278,0.5117428303,0.1074042693,0.2380801886,0.1106771752,0.0937676951,-0.1670984775,-0.1756876707,0.051222153,0.1868213415,0.166206941,0.1008573249,0.3528006673,0.4733612835,0.4692263007,-0.3767587543,0.1899408996,0.1217423156,0.0405228212,-0.2540629804,-0.6696937084,0.4085489511,-0.16381374,-0.0549569689,-0.1621216685,-0.1577786356,0.006258497,-0.0241209455,-0.4573490024,-0.1596007049,-0.0181768686,0.4296535552,0.0940246433,0.211081475,-0.2111327946,0.083350949,0.2673321366,-0.0746683478,-0.2052908689,-0.0710528418,-0.2826610804,-0.1918040663,0.3640234768,-0.1359581947,0.7602382898,0.1191381067,0.1038705558,0.0466120169,-0.0236744173,0.057541009,0.2185347527,-0.1110881567,0.1060415283,-0.0385598838,0.0367091447,-0.0684517473,0.0923174098,-0.0268131103,0.2278692573,0.254339993,-0.0719151869,-0.2512279451,-0.0217592716,-0.2160615921,0.2402980179,-0.0480847508,-0.1620964408,-0.1591301262,0.1419719011,0.275701195,-0.0853084922,0.0841932818,0.0269442201,0.4895677567,-0.264459312,-0.2518651485,-0.2223692685,-0.2057266831,-0.0746065155,0.0647648796,0.2126504034,0.0384401046,0.2144055963,0.0102604805,0.169995755,-0.4834700227,-0.5269751549,0.2771587074,0.018449584,0.2274238914,0.004313367,0.0021068845,-0.0397794321,0.0353264771,0.088758029,0.0775971189,-0.0531272553,-0.2162503302,-0.118530117,-0.0581046231,-0.1817250103,-0.2194181681,-0.0004431568,-0.2431334853,0.0819654688,-0.0527376346,-0.1218453646,-0.1247655749,-0.0735534206,-0.4697289765,0.0690143481,-0.26545313,-0.274199158,-0.5547904372,0.0313585922,-0.0406834781,-0.1250791997,0.2537890971,0.0537317879,0.1624379456,0.1741422564,-0.7173498869,-0.4413898587,0.2426537722,0.3998214006,-0.0657702088,-0.3543562591,0.2726742625,0.2117991596,0.0958070308,0.1249481216,-0.2847732306,-0.2780542374,0.1635120213,0.2081743479,0.0431039706,-0.0001117498,0.1260637343,0.2677659392,0.2481461912,-0.0625018999,-0.2880840003,0.1741873175,0.4026474357,-0.1415920854,-0.0407556184,0.0074259806,0.1931579113,-0.12057296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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"I agree that we can move the relevant parts of `dataset_infos.json` to the YAML tags.\r\n\r\n> On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data. <\r\n> > Yes indeed, or at least make sure that it was tested on the true data.\r\n\r\nI like the idea of testing streaming and falling back to the dummy data test if streaming does not work. Generating dummy data can be very tedious, so this would be a nice incentive for the contributors to make their datasets streamable.  ","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":112,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n I agree that we can move the relevant parts of `dataset_infos.json` to the YAML tags.\r\n\r\n> On the other hand, it seems like not all datasets have streaming enabled yet and for those datasets (if they are used a lot), I think it would be nice to continue testing some dummy data. <\r\n> > Yes indeed, or at least make sure that it was tested on the true data.\r\n\r\nI like the idea of testing streaming and falling back to the dummy data test if streaming does not work. Generating dummy data can be very tedious, so this would be a nice incentive for the contributors to make their datasets streamable.  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"About dummy data, please see e.g. this PR: https:\/\/github.com\/huggingface\/datasets\/pull\/3692\/commits\/62368daac0672041524a471386d5e78005cf357a\r\n- I updated the previous dummy data: I just had to rename the file and its directory\r\n  - the dummy data zip contains only a single file: `pubmed22n0001.xml.gz`\r\n\r\nThen I discover it fails: https:\/\/app.circleci.com\/pipelines\/github\/huggingface\/datasets\/9800\/workflows\/173a4433-8feb-4fc6-ab9e-59762084e3e1\/jobs\/60437\r\n```\r\nNo such file or directory: '...\/dummy_data\/pubmed22n0002.xml.gz'\r\n```\r\n- it needs dummy data for all the 1114 files: \r\n  `_URLs = [f\"ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed22n{i:04d}.xml.gz\" for i in range(1, 1115)]`\r\n- this confirms me that it never passed the test: these dummy data files were not present before my PR\r\n- therefore, is it really useful the data test if we just ignore it when it does not pass?\r\n\r\nIn relation with JSON metadata, I'm generating the file for `pubmed` (see above) in a GCP instance: it's running for more than 3 hours and only 9 million examples generated so far (before my PR, it had 32 million, now it has more).","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":151,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n About dummy data, please see e.g. this PR: https:\/\/github.com\/huggingface\/datasets\/pull\/3692\/commits\/62368daac0672041524a471386d5e78005cf357a\r\n- I updated the previous dummy data: I just had to rename the file and its directory\r\n  - the dummy data zip contains only a single file: `pubmed22n0001.xml.gz`\r\n\r\nThen I discover it fails: https:\/\/app.circleci.com\/pipelines\/github\/huggingface\/datasets\/9800\/workflows\/173a4433-8feb-4fc6-ab9e-59762084e3e1\/jobs\/60437\r\n```\r\nNo such file or directory: '...\/dummy_data\/pubmed22n0002.xml.gz'\r\n```\r\n- it needs dummy data for all the 1114 files: \r\n  `_URLs = [f\"ftp:\/\/ftp.ncbi.nlm.nih.gov\/pubmed\/baseline\/pubmed22n{i:04d}.xml.gz\" for i in range(1, 1115)]`\r\n- this confirms me that it never passed the test: these dummy data files were not present before my PR\r\n- therefore, is it really useful the data test if we just ignore it when it does not pass?\r\n\r\nIn relation with JSON metadata, I'm generating the file for `pubmed` (see above) in a GCP instance: it's running for more than 3 hours and only 9 million examples generated so far (before my PR, it had 32 million, now it has more).","embeddings":[-0.1429852098,-0.0608485937,0.0550826378,0.2112829238,0.1136180535,0.2524832487,0.3205827475,0.1741959602,-0.1172097549,0.1219320074,0.119910799,0.0238966588,-0.0377397686,0.2567650974,-0.081749253,-0.0914016962,0.1276216209,0.0220602006,0.0700242221,0.0524605699,-0.3250976205,-0.1204105243,-0.0531781726,-0.2826391459,-0.3528701365,0.1647422016,-0.2057263553,0.1340597421,-0.3978284895,-0.1914656609,0.2212942839,0.3630504906,0.0465126,0.3235675693,-0.0001267659,0.0619500652,0.6010789275,-0.0551041178,-0.4333779812,-0.1551014185,-0.179158479,0.1921116859,-0.0500923656,0.0137453238,-0.2771270573,-0.0524925813,-0.2159398049,-0.3742837608,0.066442728,0.1287029386,0.0129018687,0.1784936041,-0.2623546124,-0.1545286328,-0.023074802,0.9064522982,-0.254262805,0.1952040344,0.3773668408,0.1876832843,0.0672511086,0.243038401,-0.0148470104,-0.1117309034,0.0829192251,0.067599766,0.1029968783,-0.1896290481,0.4011779726,0.2238108814,0.9586890936,-0.3036628067,-0.2907433808,-0.2157545835,0.1869855523,-0.018167438,0.1913446188,0.1785246879,0.0397410728,0.1960579306,-0.4012612402,-0.3143668771,0.0349098034,0.1434731632,-0.089387916,0.0873936862,-0.0288745146,0.1693081707,-0.0745380372,0.1521278173,0.1572715491,-0.7519180775,-0.1615585685,-0.0427117683,0.4198247492,-0.3990245163,-0.1570977271,-0.1758507341,0.3184007108,0.2953709066,-0.0797373205,-0.0783586949,-0.1785328686,-0.1203648821,0.6392642856,-0.1442229897,0.1338598579,0.4546445012,0.3168293536,0.2358445078,0.363696903,-0.0701958612,0.1284397095,0.50733459,-0.4522354901,0.0971080214,0.3184725344,-0.4132571816,-0.1428737491,0.0626272187,-0.1000593677,-0.1632749736,0.0307039618,0.0711588934,-0.1013697982,0.3538363576,-0.1644988805,0.1907867789,0.304149121,-0.1593931466,-0.0180036798,-0.0600864738,-0.0546600558,0.3600043952,0.2489937693,0.1196480691,0.4207497835,0.2822524011,0.3832467794,0.0626733154,0.2533537149,-0.2977337241,-0.0506058298,0.2508964539,-0.3156482279,0.1417945027,-0.0279275328,-0.0912313014,-0.2454261482,0.3223766088,-0.0054741018,-0.4390693903,-0.0475830063,-0.0138124516,-0.3721047342,0.3217371702,-0.5260320902,0.3213419616,-0.3927743733,-0.0628277287,0.0567727424,0.0744912848,-0.0998715311,-0.1265881956,0.042944368,0.3122408092,-0.2367040515,0.0174602829,0.1731427759,0.1049892902,-0.1668089479,0.1312972456,-0.5550536513,0.0419931114,-0.2621078491,-0.3298402131,0.1961668879,-0.8516141176,-0.0879922956,0.2924164832,0.1329149753,0.3353053927,0.1732609123,-0.0866570622,0.0540083311,-0.2318421453,-0.0112432204,-0.0167254657,-0.2163784206,-0.0797599033,-0.2545322478,-0.2757946551,-0.0368803479,0.286340028,-0.0640804023,-0.0497497283,-0.1633559465,0.3115152419,0.1990866214,0.0139964549,0.0923486874,0.2358526886,0.284874469,-0.3034650981,0.1365368813,0.2105217278,-0.6838443279,0.2064489871,0.078623794,0.4251703322,0.0726056769,-0.6026446819,-0.1549231261,-0.2257650346,0.0565143339,-0.2781121135,-0.0550547093,0.0764184594,0.2158071846,-0.002187822,-0.2200258374,0.2114330083,-0.2686792314,0.2725765109,-0.1222392395,0.5405551791,0.1225195453,0.2313307673,0.1497876197,0.1526961327,-0.1663904935,-0.2877867222,0.113422744,0.2495893985,0.1154926419,0.0987081006,0.3016177416,0.4256037772,0.4663090408,-0.2544305325,0.1710942984,0.1453971267,0.096828036,-0.2723918259,-0.6889027953,0.4246875048,-0.1205625087,-0.0665785596,-0.1160175204,-0.1899977475,0.0405497439,-0.0703768283,-0.4578503668,-0.1708367914,0.044185698,0.3524603844,0.215745613,0.1903335154,-0.2490666807,0.0777383968,0.2929936647,-0.1252759099,-0.2523808777,-0.0696252882,-0.3806866109,-0.1830219477,0.3261989653,-0.0722164139,0.8438600898,0.1063762829,0.0947174802,0.036222402,-0.0005897807,0.0315613113,0.2223544121,-0.1240880191,0.0020637487,0.033570502,-0.0288486332,0.0045728222,0.0244585238,0.0192387179,0.2222863883,0.2319801599,-0.1616860032,-0.2502903342,-0.0618762076,-0.278766185,0.1589181274,0.0535118543,-0.1840762049,-0.1880640388,0.1139781401,0.3379371464,-0.0659023374,0.0425532535,0.0684422925,0.5200089216,-0.332665056,-0.2546700537,-0.314461112,-0.1272608042,-0.0163928345,0.0325903632,0.2754479349,-0.0253780913,0.2311139703,-0.103265509,0.0704788417,-0.4760935307,-0.6030097604,0.2469695359,-0.0072483998,0.2965971231,0.0559286103,0.0940007493,-0.0368827358,0.0088452147,0.1728194356,0.0606235303,-0.1016817242,-0.1895773113,-0.0529670753,-0.0590511821,-0.1961001158,-0.2047655433,-0.021740485,-0.2118025869,0.1771488637,-0.0235654041,-0.1862995923,0.0400797725,-0.072180517,-0.3192133009,0.0288817827,-0.1649609208,-0.3288452625,-0.6179897785,0.0457111225,-0.0193849206,-0.1366125643,0.2020439357,0.038969215,0.2182576805,0.1936032921,-0.7374120355,-0.5587514043,0.2243494242,0.3620937765,-0.0357315168,-0.259903729,0.3653229177,0.1726313829,0.131198734,0.0838752538,-0.2743244171,-0.2635352612,0.0797692388,0.2031674981,0.1050876677,0.0785322785,0.1302153468,0.4728124142,0.3580767512,-0.0178326573,-0.1806871146,0.1935706884,0.5549227595,-0.1765861809,-0.1317026466,0.0003307606,0.0993885323,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"I mention in https:\/\/github.com\/huggingface\/datasets-server\/wiki\/Preliminary-design that the future \"datasets server\" could be in charge of generating both the dummy data and the dataset-info.json file if required (or their equivalent).","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":28,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n I mention in https:\/\/github.com\/huggingface\/datasets-server\/wiki\/Preliminary-design that the future \"datasets server\" could be in charge of generating both the dummy data and the dataset-info.json file if required (or their equivalent).","embeddings":[-0.1777145416,-0.0036464962,0.0536854677,0.2157487273,0.0634276718,0.2323121727,0.3523749411,0.1556677669,-0.1672703624,0.1026072949,0.1788234264,0.0926265195,-0.0486213565,0.3518209159,-0.0754188672,-0.0335627049,0.1359460801,0.0997844189,0.0033888777,0.0867451429,-0.3042047322,-0.2361476272,-0.0841136128,-0.2575914264,-0.2976867557,0.1526352465,-0.1894829124,0.0637387857,-0.423704803,-0.2219994664,0.2153356671,0.3534487486,0.0537121855,0.2138227522,-0.0001225653,0.0252202787,0.5969030857,-0.0178429186,-0.3981497884,-0.1710563302,-0.3274739087,0.2461636513,-0.018915318,-0.0014089404,-0.2814589143,-0.1528160572,-0.1891472191,-0.3273020983,0.0186736677,0.0812248811,0.0400922149,0.0874936879,-0.4291189313,-0.161233753,-0.0818699151,0.9571205378,-0.2585009933,0.0330736302,0.3326473534,0.1197417229,0.0107427612,0.2116229981,0.0401402414,-0.1141880006,0.0903176889,0.0753191859,0.0913881287,-0.2153702974,0.3814258873,0.2678993046,1.1522866488,-0.2762087584,-0.1973186582,-0.1446506083,0.1739800125,0.0381490551,0.2397034913,0.1658890843,0.0618104823,0.1640255004,-0.4449845552,-0.3431915939,0.019874217,0.1072299927,-0.0705582947,0.0952443555,0.0344449915,0.1379490495,-0.120149754,0.0939857736,0.2629705966,-0.7454292774,-0.2575989962,-0.0603594072,0.3870790005,-0.4218226373,-0.1135559976,-0.2115353048,0.3538294137,0.3345223069,-0.1111299917,-0.1067095548,-0.2213679999,-0.1537574232,0.7225149274,-0.1378650963,0.2329865843,0.4398677945,0.2958947122,0.2330438942,0.3381995261,-0.0201255586,0.0953224152,0.4814765453,-0.3909199238,0.0541624427,0.3049864173,-0.4372656643,-0.0209819153,0.0554892831,-0.0864917189,-0.1405843049,0.0117983148,0.0285985935,-0.0846017748,0.3715600669,-0.2032060176,0.1446044892,0.2942996025,-0.1359740347,-0.0157378614,-0.0665860996,-0.0642579645,0.3746604323,0.2032365352,0.2113078684,0.3452891707,0.3458712399,0.3903814852,0.0701777041,0.2428622544,-0.2930632234,-0.0394829959,0.2185766846,-0.305960238,0.1420075893,-0.0935559645,-0.0047493004,-0.2060715258,0.4009050429,0.068924129,-0.4504179657,-0.0180698074,0.0422205403,-0.3725984693,0.3105050325,-0.6302959919,0.3212887049,-0.4494965076,-0.0101763597,0.1009695679,0.0517377257,-0.0523767173,-0.1248102635,0.0181058832,0.3076165617,-0.2698203921,0.0732858479,0.1889258921,0.0005741452,-0.1943521202,0.1526818275,-0.6245530248,0.012281727,-0.2694575489,-0.3449342847,0.2291910201,-0.7686023116,-0.0897507146,0.3314984143,0.1622375548,0.2125796825,0.1180821732,-0.0541321933,0.169932574,-0.187785089,-0.0364271514,0.0258862376,-0.2505978346,-0.0701418296,-0.2595448792,-0.3064088821,-0.0002328469,0.3123075962,-0.0486242622,-0.036681816,-0.1954509467,0.2864949107,0.1775185764,0.0179921947,0.0949040204,0.1565399915,0.3916997612,-0.3014125228,0.1352308244,0.2334111035,-0.6444411874,0.1975171864,0.1951592118,0.4320062101,0.1485063732,-0.5930857658,-0.1029032618,-0.1735842079,0.0435142294,-0.2010965645,-0.0340521485,0.011558379,0.1211373731,-0.0452665351,-0.2242076546,0.2274678051,-0.2596687973,0.2351394445,-0.0245991088,0.5059051514,0.1014836207,0.2720294893,0.1597903073,0.080415152,-0.1777515858,-0.2350762486,0.0844995826,0.2283540815,0.1265163273,0.0839713961,0.3611894548,0.4594213665,0.4610917866,-0.316285342,0.1583163589,0.1003952846,0.0955330729,-0.2565865517,-0.5998068452,0.3915375471,-0.1737543792,-0.0438623168,-0.1641141325,-0.1432138681,0.013043642,-0.0482496992,-0.4275664389,-0.1415355951,-0.0097642224,0.3799641728,0.1296391338,0.205654338,-0.2946891785,0.0531005934,0.2750363648,-0.1206515208,-0.2213631272,-0.0399356522,-0.2835047245,-0.2131585777,0.3361598253,-0.1328588873,0.7713498473,0.1209634542,0.0436615795,0.0377164446,0.0255414452,0.0883209482,0.2186287344,-0.1258336157,0.1182078496,-0.0324634872,0.0457503088,-0.0494486615,0.0811763927,0.0021895999,0.2144863456,0.2055608481,-0.0961874351,-0.2264665365,-0.032739237,-0.2271749228,0.230019331,0.0140653988,-0.1798840165,-0.1528633684,0.1385829151,0.2403527051,-0.1137424111,0.0850012675,0.0343831107,0.4886502624,-0.3061149716,-0.2410278767,-0.2677022517,-0.1657676697,-0.0734802708,0.0618367456,0.1971720159,0.0236211531,0.2405918092,-0.0351364464,0.1009806767,-0.5213587284,-0.5410516262,0.2955498993,0.000111146,0.2679004669,0.0899562761,0.0666274726,-0.0487341136,0.0581960715,0.1107020304,0.0175703857,-0.0886334851,-0.2240762711,-0.0859874859,-0.035683956,-0.2029500157,-0.2795728445,0.0065375674,-0.2301285416,0.1363245398,-0.087768741,-0.1254767478,-0.0585361533,-0.0880343094,-0.4339042008,0.0181648135,-0.1985791326,-0.3128736019,-0.5986893177,0.0234376919,-0.0372062698,-0.1292189509,0.2439360023,0.0605121218,0.1218541563,0.1591914594,-0.7447003722,-0.503297925,0.2345252633,0.4234189689,-0.0477773175,-0.3173967898,0.3109992743,0.2278091758,0.0832133815,0.117054522,-0.3207497895,-0.2171185762,0.1447833776,0.1627760679,0.1277528107,0.0953727812,0.1252430379,0.3877673447,0.2440478653,-0.0326830074,-0.2251190543,0.1728609204,0.4601613283,-0.1436405033,-0.0553417541,-0.0205386169,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"Hi ! I think dummy data generation is out of scope for the datasets server, since it's about generating the original data files.\r\n\r\nThat would be amazing to have it generate the dataset_infos.json though !","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":35,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n Hi ! I think dummy data generation is out of scope for the datasets server, since it's about generating the original data files.\r\n\r\nThat would be amazing to have it generate the dataset_infos.json though 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"From some offline discussion with @mariosasko and especially for vision datasets, we'll probably not require dummy data anymore and use streaming instead :) This will make adding a new dataset much easier.\r\nThis should also make sure that streaming works as expected directly in the CI, without having to check the dataset viewer once the PR is merged","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":58,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n From some offline discussion with @mariosasko and especially for vision datasets, we'll probably not require dummy data anymore and use streaming instead :) This will make adding a new dataset much easier.\r\nThis should also make sure that streaming works as expected directly in the CI, without having to check the dataset viewer once the PR is merged","embeddings":[-0.324167341,0.0028328248,0.0530669689,0.1227907613,0.0889794827,0.2199121714,0.2552138865,0.181076169,-0.151201427,0.0983157679,0.1719059646,0.1244266182,-0.0338423066,0.3266555667,-0.1211212948,-0.0729698092,0.1732606739,0.0790969059,0.1076072305,0.1135490388,-0.2933543622,-0.2771551013,-0.0892890468,-0.2865457535,-0.2818804383,0.1704629809,-0.1468866467,0.0664553493,-0.4142172039,-0.1764491349,0.1573468298,0.3842192888,0.1258397251,0.2026726902,-0.0001244875,0.0609174818,0.5964473486,-0.0615681224,-0.3459528685,-0.1388453543,-0.3442916274,0.3251214027,-0.003727458,0.0563985817,-0.3120826483,-0.1080530286,-0.201258257,-0.3454532325,-0.0418565013,0.1187121943,0.0408904217,0.1177650392,-0.4162550867,-0.1850221008,-0.0828741193,0.8959911466,-0.2184733748,0.0396170802,0.3214275539,0.1252553314,-0.0214541443,0.2462197542,0.0636519492,-0.1543284357,0.1575545371,0.0029876065,0.0538955592,-0.2519349158,0.3336925507,0.1782874763,1.0410091877,-0.2155256718,-0.1378970593,-0.0828523561,0.1488085091,-0.0570683964,0.2091362178,0.2203895003,0.0047215954,0.1472854167,-0.431294769,-0.3242120147,-0.0675403848,0.0863318667,-0.0682986453,0.1963187754,0.1512874365,0.1827614307,-0.0327358618,0.1232687756,0.4678573012,-0.7272579074,-0.2923150957,-0.1763062775,0.3717646897,-0.3945515752,-0.1382314265,-0.0913938731,0.1544781774,0.3186751306,-0.0517111346,-0.029998906,-0.1204471663,-0.0908390805,0.6764491796,-0.0969469771,0.1591026634,0.5062063932,0.2608688772,0.186664775,0.2671683729,0.0150522729,0.0697799623,0.4231527746,-0.4522964954,0.0954696164,0.2877253592,-0.4338810146,-0.0992421582,-0.0105744312,-0.1103541926,-0.1238320619,0.0416929834,0.0783529803,-0.0860734433,0.4189316034,-0.172242403,0.1072689518,0.2390529662,-0.1500844657,-0.0111545613,0.0161892697,-0.0937850997,0.2817307413,0.1632752717,0.2352614701,0.2449205816,0.2655420005,0.5070669055,0.1084123403,0.2386462837,-0.2171188295,0.124587737,0.2789686918,-0.2482052743,0.0188659392,-0.0539843254,-0.0935710222,-0.2289473861,0.4336292744,0.0884446576,-0.4489964247,-0.025048973,0.0494367965,-0.3690259159,0.2312483341,-0.4969612956,0.3566550314,-0.5019924641,-0.0650997013,0.0835509449,0.0231483653,0.0555041991,-0.1173825338,-0.0324313343,0.2143105567,-0.2944134474,0.091299735,0.1851639003,0.0251273643,-0.1170075536,0.1568720192,-0.6129286289,-0.0500508882,-0.2285742164,-0.3935675323,0.308671236,-0.8477147222,-0.1294122934,0.2989561558,0.1432191581,0.1975875497,0.1881816834,0.0636190251,0.2252759486,-0.2162258923,-0.0111498795,0.1082947925,-0.3547193706,-0.0279187653,-0.236062333,-0.2612779737,-0.0100395214,0.301876843,-0.0409657992,-0.0554577969,-0.1049722806,0.3760010004,0.1688578725,-0.0092442241,0.0784527361,0.1436163187,0.3767305911,-0.4412901103,0.117150262,0.3038473129,-0.573350668,0.1398736089,0.2014058679,0.431647718,0.1817213744,-0.5865430236,-0.1029357314,-0.1961623579,-0.00735809,-0.2065261006,-0.0317926966,0.0277238581,0.0852416456,-0.1101697013,-0.1717994511,0.2060650885,-0.219273597,0.2135257572,-0.0054578595,0.5620456934,0.0850515291,0.2040946186,0.0904040858,0.0994197354,-0.1562521905,-0.2186807692,0.0306699369,0.2174743265,0.1191920489,0.1722205281,0.3574069738,0.4845868945,0.5241595507,-0.4383271337,0.1730112433,0.1681897193,-0.007097024,-0.2035503536,-0.6603571177,0.4108438194,-0.2032639533,-0.0603548959,-0.1086585075,-0.1631268263,0.0191797893,-0.0263504125,-0.5009217858,-0.1570665389,0.0206336956,0.3617022634,0.0346780829,0.2119878978,-0.2503418028,0.1653726846,0.2489848137,-0.0418820232,-0.2560426295,-0.0734014735,-0.3119319081,-0.2224857807,0.4121029079,-0.0615794584,0.7546881437,0.1410357505,0.0716554001,0.0559244789,-0.018389361,0.0470758937,0.2953934073,-0.0540556423,0.0979322866,-0.024065664,0.031623479,-0.0318357684,0.0031075308,-0.0114891399,0.2157937884,0.1867376864,-0.0456021167,-0.2046147436,0.0024964316,-0.3290114403,0.219188109,-0.0153650073,-0.1362142861,-0.1916926354,0.1173145697,0.3332619071,-0.1289616078,0.1369430423,0.081258513,0.5222626925,-0.2611395419,-0.2801066935,-0.2488164306,-0.1813661307,-0.0823927596,0.0644390061,0.2530829012,-0.0110609038,0.2992450893,0.042416025,0.2190316319,-0.5009664297,-0.6021681428,0.2781816721,-0.0115862666,0.2329910696,-0.0345762521,-0.0089707868,0.0068086493,-0.0550082698,0.0509786494,0.0610424578,-0.0705520362,-0.2175285071,-0.1650014222,-0.0931695774,-0.1749334931,-0.2989956439,-0.0271899812,-0.2966522574,0.0865369141,-0.0438253544,-0.1612772346,-0.1292584836,-0.0358278342,-0.5355433822,0.0194082502,-0.3132395148,-0.2721154094,-0.5159097314,-0.0053508626,-0.0551146157,-0.1502458602,0.2905944288,0.0149662336,0.1878290325,0.1801382899,-0.6355614066,-0.4519744813,0.2450995296,0.4014110863,-0.0096777286,-0.3944191337,0.3081797361,0.2317153066,0.078151159,0.1073630452,-0.2837238908,-0.2847268879,0.2288764566,0.2375812232,0.0354452915,0.0019912384,0.1353358179,0.4114744365,0.3028433025,-0.1114418358,-0.2220630646,0.1965157837,0.4624945819,-0.1647167504,-0.0858350545,0.0284273978,0.118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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"It seems that migration from dataset-info.json to dataset card YAML has been acted.\r\n\r\nProbably it's a good idea, but I didn't find the pros and cons of this decision, so I put some I could think of:\r\n\r\npros:\r\n- only one file to parse, share, sync\r\n- it gives a hint to the users that if you write your dataset card, you should also specify the metadata\r\n\r\ncons:\r\n- the metadata header might be very long, before reaching the start of the README\/dataset card. It might be surprising when you edit the file because the metadata is not shown on top when the dataset card is rendered (dataset page). It also somewhat prevents including large strings like the checksums\r\n- YAML vs JSON: not sure which one is easier for users to fill and maintain\r\n- two concepts are mixed in the same file (metadata and documentation). This means that if you're interested only in one of them, you still have to know how to parse the whole file.\r\n- [low priority] besides the JSON file, we might want to support yaml or toml file if the user prefers (as [prettier](https:\/\/prettier.io\/docs\/en\/configuration.html) and others do for their config files, for example). Inside the md, I understand that only YAML is allowed","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":210,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n It seems that migration from dataset-info.json to dataset card YAML has been acted.\r\n\r\nProbably it's a good idea, but I didn't find the pros and cons of this decision, so I put some I could think of:\r\n\r\npros:\r\n- only one file to parse, share, sync\r\n- it gives a hint to the users that if you write your dataset card, you should also specify the metadata\r\n\r\ncons:\r\n- the metadata header might be very long, before reaching the start of the README\/dataset card. It might be surprising when you edit the file because the metadata is not shown on top when the dataset card is rendered (dataset page). It also somewhat prevents including large strings like the checksums\r\n- YAML vs JSON: not sure which one is easier for users to fill and maintain\r\n- two concepts are mixed in the same file (metadata and documentation). This means that if you're interested only in one of them, you still have to know how to parse the whole file.\r\n- [low priority] besides the JSON file, we might want to support yaml or toml file if the user prefers (as [prettier](https:\/\/prettier.io\/docs\/en\/configuration.html) and others do for their config files, for example). Inside the md, I understand that only YAML is allowed","embeddings":[-0.2458126247,0.0725626871,0.0490857661,0.1374435127,0.0960528851,0.2946303487,0.3775530457,0.2325654477,-0.2260082364,0.0578870587,0.2044942081,0.1616476774,-0.0737447143,0.1169879138,-0.0357845686,0.0991990268,0.1957188696,-0.0824115798,0.0424897596,0.0821364671,-0.3140948713,-0.2128604352,-0.0029769661,-0.194262445,-0.2713184059,0.2255710959,-0.0595801584,0.1250095516,-0.3897125423,-0.2232363522,0.2344605476,0.2648131251,0.0101264725,0.2118583322,-0.0001254561,0.0219637845,0.5573883057,-0.0310817771,-0.2862559557,-0.1731314361,-0.396632731,0.2392092645,-0.0645182133,-0.0239438526,-0.164225027,-0.1230784655,-0.2316626459,-0.4236285686,-0.0904371142,0.1119435877,0.0209232923,-0.1281619668,-0.4025225937,-0.1342912912,0.057674557,0.8744248748,-0.1635181159,0.0866915509,0.415391624,0.0357445963,-0.0156164607,0.2925401926,-0.0131098935,-0.2143667787,0.1448258162,0.0217461642,0.3097138107,-0.1213153154,0.4607018828,0.2310020775,1.0232338905,-0.3339762986,-0.1650910825,-0.19203417,0.2018479258,0.0468973294,0.2180628926,0.2553749681,0.0349449031,0.1748922169,-0.3350853026,-0.3043083251,-0.0532127246,0.0867359638,-0.1037169173,0.1884146631,0.1187336147,0.1460499167,-0.1171578616,0.1146330312,0.5123534799,-0.6611407399,-0.2052417696,-0.0494499207,0.4328435361,-0.2740964293,-0.1120888889,-0.2723451257,0.3622401655,0.1502530724,-0.1056204587,-0.0845806077,-0.0515956357,-0.1613936126,0.6765850186,-0.1951910108,0.2709446847,0.4857600629,0.2068418115,0.1097978726,0.3558478355,-0.0169469956,0.0275986772,0.4236778617,-0.2334450185,0.1517972946,0.3061842322,-0.5264110565,-0.0700253993,-0.001095061,-0.1513603926,-0.2167411596,0.0434962101,0.0732601732,-0.0667539984,0.3365080357,-0.2944548428,0.1129323393,0.3871592283,-0.0272708535,0.0292915404,-0.0000673568,-0.132174775,0.2954184115,0.1795015186,0.2546354234,0.3569710255,0.2749106884,0.277842164,0.1254778355,0.2126247436,-0.2273497432,-0.0203181654,0.2221147865,-0.2674277127,0.1357553452,-0.0906985402,0.015498477,-0.24258627,0.3936828375,-0.0075669014,-0.4848937392,0.1711171716,0.0466446355,-0.1833802462,0.3787023127,-0.5226441026,0.3028146029,-0.26547575,-0.0639022365,0.0943751559,-0.0391243547,0.0758403167,-0.2001127601,-0.0780474767,0.20601134,-0.268923074,0.02656818,0.1821846217,0.183543101,-0.1128548086,0.1387722939,-0.6814335585,0.0086811986,-0.1930308938,-0.2238128483,0.2639654875,-0.966242671,-0.1644698381,0.3931189477,0.1148647368,0.2616600692,0.1859055012,-0.019324027,0.1546001136,-0.3390679955,0.1039417312,0.0225530136,-0.2659648657,-0.0311997551,-0.2678821683,-0.2672682405,0.0256132726,0.2164315432,-0.0166652929,-0.1552046835,-0.0920514241,0.4619902372,0.2085514367,0.0265066754,0.1126232594,0.1695785969,0.4537016153,-0.3635901511,0.0706777424,0.2630224526,-0.5882102251,0.0995649546,0.0996045619,0.363019973,0.0374168158,-0.5928767323,-0.0928263292,-0.226436004,-0.051916685,-0.2224386632,-0.0334075019,0.0161732621,0.1760018617,-0.0365160219,-0.1445961893,0.2631179094,-0.2954929769,0.2064348757,0.0821133554,0.4420949519,0.1300015301,0.2988011539,0.0418299064,0.0362826586,-0.1302722096,-0.2257068455,0.0472138748,0.2654964328,0.129019618,0.0593933165,0.222712785,0.5241359472,0.532487452,-0.3105399609,0.1798179001,0.0409290642,0.0641151294,-0.2755149901,-0.6814858317,0.3605363667,-0.2061824054,0.0529465899,-0.0831595585,-0.2165973186,-0.0067493669,0.0130301481,-0.5220207572,-0.2126064897,-0.0013488701,0.2512677014,0.1375462115,0.2171067894,-0.158551842,0.134575054,0.4089348018,-0.0532734245,-0.1820455492,-0.0487137623,-0.315318644,-0.2136713862,0.3184398413,0.0696081445,0.7673094869,0.0888233408,0.0289571583,0.027129285,-0.0342022777,0.0250274818,0.2386960685,-0.1692148894,0.2030093223,0.0309121311,-0.0013745782,0.0103522344,0.1021597609,-0.002522184,0.1919775307,0.1853081882,-0.0265789554,-0.2327497751,-0.0444626138,-0.2168558985,0.2352804989,-0.1599761099,-0.07205268,-0.2218924761,0.1295793951,0.1980031729,-0.0776146576,0.0246686824,0.0489620976,0.3080640137,-0.2680728734,-0.3100034893,-0.1774625778,-0.1845178157,-0.0389610715,0.0294553116,0.3014757931,0.0572616048,0.2272363901,-0.0219884887,0.0902074575,-0.5844402313,-0.5543049574,0.2199606001,-0.0114921462,0.2267918438,0.0125625487,-0.0125384955,-0.0493450314,-0.0357794128,0.1228653789,0.1481039971,-0.0678221807,-0.1665272564,-0.1013268232,-0.0693413615,-0.1386357248,-0.3014492989,-0.1190196201,-0.2797603607,0.0616527088,-0.0320064053,-0.1312158853,-0.1170180738,-0.1184770465,-0.4778541625,-0.0405216515,-0.1947007924,-0.2331459224,-0.4756765962,0.0033598703,-0.0417956337,-0.1865061969,0.2016943395,0.153142795,0.2526442707,0.2515683472,-0.6404397488,-0.3635880947,0.2204318941,0.3617004156,-0.078526549,-0.374837786,0.24095276,0.4136597216,0.1096958593,0.1314532459,-0.1525264233,-0.2058734,0.1164273843,0.173341319,0.0965744779,0.0631293356,0.2052791417,0.3014649153,0.2303938121,-0.0238438025,-0.3041137159,0.2195901871,0.3392546475,-0.1601367295,-0.1856108755,-0.010290294,0.10560368,0.0536897704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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"> the metadata header might be very long, before reaching the start of the README\/dataset card. It might be surprising when you edit the file because the metadata is not shown on top when the dataset card is rendered (dataset page). It also somewhat prevents including large strings like the checksums\r\n\r\nNote that we could simply not have the checksums in the YAML metadata at all, or maybe at one point have a pointer to another file instead.\r\n\r\nWe can also choose to hide (collapse) certain sections in the YAML by default when we open the dataset card editor.\r\n\r\n> two concepts are mixed in the same file (metadata and documentation). This means that if you're interested only in one of them, you still have to know how to parse the whole file.\r\n\r\nI think it's fine for now. Later if we really end up with too many YAML sections we can see if we need to tweak the API endpoints or the `datasets`\/`huggingface_hub` tools\r\n\r\n> YAML vs JSON: not sure which one is easier for users to fill and maintain\r\n\r\nRegarding YAML vs JSON: I think YAML is easier to write by hand, and I also think that it's better for consistency - i.e. we're using more and more YAML to configure models\/datasets\/spaces","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":214,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n > the metadata header might be very long, before reaching the start of the README\/dataset card. It might be surprising when you edit the file because the metadata is not shown on top when the dataset card is rendered (dataset page). It also somewhat prevents including large strings like the checksums\r\n\r\nNote that we could simply not have the checksums in the YAML metadata at all, or maybe at one point have a pointer to another file instead.\r\n\r\nWe can also choose to hide (collapse) certain sections in the YAML by default when we open the dataset card editor.\r\n\r\n> two concepts are mixed in the same file (metadata and documentation). This means that if you're interested only in one of them, you still have to know how to parse the whole file.\r\n\r\nI think it's fine for now. Later if we really end up with too many YAML sections we can see if we need to tweak the API endpoints or the `datasets`\/`huggingface_hub` tools\r\n\r\n> YAML vs JSON: not sure which one is easier for users to fill and maintain\r\n\r\nRegarding YAML vs JSON: I think YAML is easier to write by hand, and I also think that it's better for consistency - i.e. we're using more and more YAML to configure models\/datasets\/spaces","embeddings":[-0.2032295018,0.0275493022,0.0572874844,0.1954611838,0.1391557753,0.2496224493,0.4129856229,0.19449462,-0.1112280264,0.0788741782,0.191549778,0.1532636732,-0.0137921609,0.2418870777,-0.0354788154,0.0570902266,0.1467804462,-0.0240855869,0.0660682544,0.1013741419,-0.2296428531,-0.2435064316,-0.0125359781,-0.1627849489,-0.2779266536,0.1427884996,-0.0652330741,0.1634722203,-0.4329428375,-0.2414532602,0.3005446494,0.3051056564,0.0020639722,0.1965622753,-0.0001206806,0.0554364584,0.613301158,-0.0277128462,-0.2977305949,-0.1639792025,-0.3489322364,0.1645846814,-0.0341399461,-0.0296193548,-0.2175879478,-0.1416803002,-0.2961347997,-0.3454911411,0.0267088842,0.0341599323,0.055760514,-0.0105358595,-0.3974081278,-0.1319599301,0.0406824648,0.8578313589,-0.215408802,0.0073759565,0.4081346989,0.0433143973,-0.04104577,0.2915607393,0.0144681511,-0.1538504958,0.1862286329,0.0656514391,0.2155630291,-0.2193215936,0.4002033472,0.2973579764,1.0688923597,-0.3371957839,-0.2243124694,-0.2196121663,0.1188323721,0.0474281162,0.2191232443,0.2440916896,-0.0084527126,0.1859667152,-0.3491558135,-0.3049739003,-0.0282643344,0.1032609418,-0.0754990131,0.0973196849,0.097412236,0.1616744846,-0.0920737758,0.0742115751,0.4320083559,-0.6401070952,-0.2296019197,-0.0204195194,0.3946665823,-0.3357396126,-0.1088185087,-0.1853628606,0.4152907133,0.2259097397,-0.1066606045,-0.0294247102,-0.1542458683,-0.1099236831,0.6978176236,-0.1677553654,0.2600267231,0.4338560402,0.2486697882,0.2175522298,0.386487484,-0.0584408641,0.0749311075,0.390486151,-0.2591837943,0.1152345836,0.3168912232,-0.5075969696,-0.0931856856,0.0079694074,-0.1403206885,-0.1961096227,0.0536825955,0.0672760233,-0.0601142831,0.3517616689,-0.2512370348,0.1553713679,0.3039606214,-0.0421387441,-0.0015312273,0.0114317527,-0.0696379468,0.4269008338,0.2096896321,0.2208818197,0.3422110379,0.3063427508,0.3109740913,0.0957737118,0.2234769613,-0.2359330356,-0.0022663868,0.2112263292,-0.2616116703,0.1187112406,-0.1080663353,-0.0542081483,-0.2392328084,0.3686836064,-0.0343023539,-0.4156424403,0.1549537182,0.0680000186,-0.2514640093,0.3964616358,-0.5492043495,0.2827776074,-0.3300181925,-0.0136808725,0.1507816464,0.0278171543,0.06753093,-0.1844967902,-0.0198989995,0.1929878443,-0.2380095422,0.0165696591,0.1729349345,0.0911781117,-0.2271542251,0.115610905,-0.6257846355,0.0117681613,-0.2002187669,-0.2311410308,0.25095281,-0.9791876078,-0.1357682198,0.3369643986,0.1923931539,0.306812644,0.1007229388,-0.0269655325,0.204730764,-0.3195357323,0.0598338991,0.0448934063,-0.287368238,-0.0733108446,-0.2740700245,-0.2847240269,-0.0311870538,0.2559045851,-0.1018762589,-0.1768003106,-0.0819052756,0.4318945706,0.1764957756,0.0096044717,0.1389526576,0.1965236366,0.4517000616,-0.3156622946,0.1218510047,0.2339281589,-0.617705524,0.1692653894,0.1508003622,0.3814041018,0.0297961254,-0.5918344259,-0.0770729035,-0.2163894475,-0.0099973474,-0.2433729768,-0.0004900079,-0.0917037129,0.1173523068,-0.0609473474,-0.1818112284,0.293587774,-0.2397624552,0.252627939,0.0016179697,0.4414769113,0.1160955727,0.2773279548,0.0916013047,0.1166318879,-0.1280636787,-0.2069753855,0.0751188323,0.2555847764,0.1764248163,0.0543967821,0.2166914344,0.5889649987,0.5203462243,-0.3372323215,0.1648435891,0.0310957208,0.0868470594,-0.273045361,-0.6699860096,0.3655448854,-0.167994678,0.0393056609,-0.0999948084,-0.1275830269,0.0478587933,-0.0494696796,-0.5536085367,-0.2255428433,-0.0240113828,0.2392835319,0.1081806645,0.1632965952,-0.0988025442,0.1633233726,0.3604865968,-0.0616309345,-0.2134318203,0.0010252112,-0.3381297886,-0.2014231682,0.2981230915,0.0251020174,0.6484029293,0.1195752174,0.0219304133,0.0218614582,-0.0329280272,0.0507324114,0.1812932789,-0.0765976831,0.184025377,-0.0249003954,0.0772506148,-0.0204036534,0.1338985413,0.0042114644,0.1021790281,0.1913019866,-0.0672358498,-0.1753218323,-0.0205878075,-0.1959000528,0.2499360889,-0.1517713219,-0.1473194808,-0.192551285,0.1387666315,0.2298336029,-0.097950995,0.0825345889,0.0725630522,0.3959184289,-0.2813746333,-0.1204542741,-0.2327213436,-0.1651359499,-0.0439332016,0.0582945459,0.3040472269,0.0584442653,0.2808158696,0.012222887,0.1155137494,-0.5600783229,-0.5690749884,0.2730994225,-0.0361542888,0.3282233477,0.0467674769,0.0323229581,-0.0145600643,-0.0297625884,0.1656498164,0.0986706838,-0.0658978596,-0.1802344024,-0.092560865,-0.054456152,-0.1930689365,-0.2319607139,-0.1192612499,-0.2891087234,0.0945846662,-0.0122798784,-0.1299389303,-0.0698719174,-0.1466843486,-0.4840102792,0.016533412,-0.1905807257,-0.2415479273,-0.4816348255,0.018159274,-0.0676408485,-0.1654388905,0.2375149131,0.0746868625,0.2262073159,0.1856739819,-0.6290559769,-0.4427180588,0.2132830471,0.4102081656,-0.0428391434,-0.357946068,0.2518574893,0.3507940471,0.0924570858,0.1079109237,-0.2506565154,-0.2095091194,0.1034978032,0.1455168873,0.1050754189,0.0466659255,0.2024914771,0.2713688314,0.2672967315,-0.010602315,-0.2592625022,0.2496733367,0.3086403906,-0.147670269,-0.1051755846,-0.0706837848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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"> the metadata header might be very long, before reaching the start of the README\/dataset card. It might be surprising when you edit the file because the metadata is not shown on top when the dataset card is rendered (dataset page). It also somewhat prevents including large strings like the checksums\r\n\r\nWe can definitely work on this on the hub side to make the UX better","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":66,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n > the metadata header might be very long, before reaching the start of the README\/dataset card. It might be surprising when you edit the file because the metadata is not shown on top when the dataset card is rendered (dataset page). It also somewhat prevents including large strings like the checksums\r\n\r\nWe can definitely work on this on the hub side to make the UX better","embeddings":[-0.2525481284,-0.0045228945,0.0422584377,0.219287008,0.1078495607,0.2615558207,0.3254027963,0.2054434866,-0.1367201358,0.0993754193,0.1784401983,0.1867026985,-0.0159958955,0.2168875486,-0.1041496918,0.0664005429,0.1291634142,0.0403312966,0.0660876855,0.0407170728,-0.2711907327,-0.3084928393,-0.0883713663,-0.2363281548,-0.3418318033,0.1558412313,-0.1023289785,0.079493694,-0.4107551277,-0.2224003673,0.2102193087,0.3314020634,0.0823895559,0.2150482237,-0.0001231236,0.0152532496,0.6182843447,-0.0485327393,-0.3104630411,-0.1856080294,-0.450504005,0.3207578957,-0.0650574937,0.0100589385,-0.2107750177,-0.0936586782,-0.2375881374,-0.3037695587,-0.077387549,0.101300016,0.0444973707,0.0051483735,-0.4160040319,-0.1687513143,0.0330958441,0.8936148882,-0.198870182,0.0789231434,0.3599604964,0.1149144769,-0.0309540741,0.2300137728,0.00820175,-0.1346883327,0.1320376098,0.0445426777,0.16278705,-0.1996753514,0.391634196,0.2508244514,1.1065523624,-0.3127261996,-0.1738735288,-0.1500198096,0.1871939451,0.049596481,0.2381418645,0.2170524001,0.0106300041,0.198247686,-0.3822774887,-0.3232987225,0.009063377,0.0624960847,-0.0804189816,0.2336845994,0.1116821766,0.1541482955,-0.1175564453,0.0766077042,0.4607906938,-0.7217175961,-0.2561194599,-0.1176133081,0.4314759374,-0.3670303524,-0.1327340305,-0.1991815418,0.3130513728,0.2609933019,-0.0994268805,-0.0443601795,-0.203858763,-0.1046342403,0.6426250339,-0.1599444598,0.2338840067,0.5109956861,0.2782661021,0.143988356,0.270388633,-0.014881243,0.0604097992,0.4577284455,-0.3568858504,0.1483709067,0.2336447835,-0.5000591874,-0.0868633389,0.0218681842,-0.0850191489,-0.1798961312,0.0644370392,0.0528997406,-0.1026858836,0.3543018401,-0.2872564793,0.1227082387,0.2872100174,-0.0495818704,-0.0191137567,0.014601388,-0.0787925273,0.3330207765,0.1980875432,0.2689501345,0.2838200331,0.2883686125,0.3829467595,0.0430410393,0.2239151299,-0.272436738,0.0399016365,0.2089583129,-0.2341770083,0.1211272553,-0.0935063884,-0.0573227964,-0.2556301355,0.4163293242,-0.0476057529,-0.4204232991,0.0555746295,0.0483933128,-0.3420533836,0.305131346,-0.5157048106,0.2184375823,-0.4042597115,-0.0043596029,0.1280784905,0.0397926532,0.0289146993,-0.1501345336,-0.0568787307,0.2313000858,-0.2466068119,0.1109433621,0.2637714446,0.0867198408,-0.1723582,0.1362842619,-0.6118921041,0.0381445773,-0.249405086,-0.3352676034,0.3074497283,-0.9067507982,-0.149451986,0.3540629745,0.1372424215,0.1994910538,0.1451292336,-0.0056158621,0.1458416879,-0.2662973702,0.0621817671,0.0161732882,-0.2690818608,-0.0398891345,-0.2645443082,-0.30974406,0.0254723355,0.254365325,-0.0178794246,-0.1049051359,-0.1294215471,0.3828357756,0.2038938999,0.0218903478,0.0995198488,0.1730932444,0.4194046557,-0.4043859243,0.1228681207,0.2118182182,-0.5747005939,0.1965003759,0.1510179639,0.3921768367,0.0904664919,-0.5748603344,-0.1295395792,-0.2214390039,0.0229710378,-0.1632959694,-0.0176555589,-0.0039859088,0.1153920889,-0.0626371801,-0.1773683727,0.2601031065,-0.2354895324,0.2049115747,0.0070345285,0.5532863736,0.1208796948,0.2234710902,0.0995252654,0.0859158635,-0.1156590655,-0.1725365222,0.031197641,0.2328219414,0.1568130106,0.0949268416,0.320274055,0.5157935619,0.5151194334,-0.3858297765,0.189721331,0.0689926594,0.0579347461,-0.2653342783,-0.6614844203,0.376614362,-0.219334349,-0.036572665,-0.1329129487,-0.1853913367,-0.0119761033,-0.0271138269,-0.4595808983,-0.1980403364,-0.0551999174,0.3902985752,0.1035207063,0.2205667198,-0.1320796758,0.1005307958,0.3477063179,-0.0434069335,-0.169025287,-0.026548788,-0.2705810368,-0.1837723255,0.3264898062,-0.0888950527,0.7185097337,0.1131923124,0.0744254738,0.0407848582,-0.0585348383,0.0252180658,0.1874708682,-0.1067244411,0.1525640488,-0.0082076378,0.0102783609,-0.0359077789,0.1247929335,-0.0660185516,0.2201230824,0.2535126507,-0.0889558569,-0.2743715346,-0.0874541849,-0.2149306536,0.2183045447,-0.0880999267,-0.1652086526,-0.1622237861,0.1102952585,0.2573671341,-0.0689136386,0.0974062532,0.0755444914,0.4247818887,-0.2427297384,-0.2073098719,-0.2257247865,-0.2101557553,-0.0617535077,0.0435462296,0.2831186354,0.1004354432,0.2257657945,0.0065849014,0.1618759781,-0.4886365235,-0.5541843176,0.2728196979,0.0284700636,0.2284679264,0.0097369757,-0.0396349169,-0.0118208313,0.0202935226,0.0922368839,0.1108258963,-0.0412646048,-0.2017139941,-0.1290156692,-0.0792834982,-0.1783655882,-0.2368696034,-0.0142039983,-0.2797547877,0.0808191895,-0.0592837036,-0.1162352487,-0.1147874072,-0.0783710703,-0.4636089504,0.0083306739,-0.2500186861,-0.2498617619,-0.580327034,0.0595152862,-0.0640820563,-0.160915032,0.2584356964,0.0595842563,0.2080442905,0.1807608753,-0.664005518,-0.465025723,0.1876856983,0.3958704472,-0.0891816765,-0.3439868391,0.2664584219,0.2400678992,0.0967696905,0.1208003014,-0.2726140022,-0.242238313,0.1333175153,0.1515721381,0.0658952817,0.0056432313,0.1735859811,0.2237876952,0.2377055436,-0.0388107784,-0.3151886463,0.2118251622,0.369985044,-0.1415926665,-0.0502196327,0.0021606395,0.1966559291,-0.0544296131,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3507","title":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data","comments":"Tensorflow Datasets catalog includes a community catalog where you can find and use HF datasets (see [here](https:\/\/www.tensorflow.org\/datasets\/community_catalog\/huggingface)).\r\n\r\nFYI I noticed today that they are using the exported dataset_infos.json files from github to get the metadata (see their code [here](https:\/\/github.com\/tensorflow\/datasets\/blob\/a482f01c036a10496f5e22e69a2ef81b707cc418\/tensorflow_datasets\/scripts\/documentation\/build_community_catalog.py#L261))","body":"I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw ","comment_length":39,"text":"Discuss whether support canonical datasets w\/o dataset_infos.json and\/or dummy data \n I open this PR to have a public discussion about this topic and make a decision.\r\n\r\nAs previously discussed, once we have the metadata in the dataset card (README file, containing both Markdown info and YAML tags), what is the point of having also the JSON metadata (dataset_infos.json file)?\r\n\r\nOn the other hand, the dummy data is necessary for testing (in our CI suite) that the canonical dataset loads correctly. However:\r\n- the dataset preview feature is already an indirect test that the dataset loads correctly (it also tests it is streamable though)\r\n- we are migrating canonical datasets to the Hub\r\nDo we really need to continue testing them in out CI?\r\n\r\nAlso note that for generating both (dataset_infos.json file and dummy data), the entire dataset needs being downloaded. This can be an issue for huge datasets (like WIT, with 400 GB of data).\r\n\r\nFeel free to ping other people for the discussion.\r\n\r\nCC: @lhoestq @mariosasko @thomwolf @julien-c @patrickvonplaten @anton-l @LysandreJik @yjernite @nateraw  \n Tensorflow Datasets catalog includes a community catalog where you can find and use HF datasets (see [here](https:\/\/www.tensorflow.org\/datasets\/community_catalog\/huggingface)).\r\n\r\nFYI I noticed today that they are using the exported dataset_infos.json files from github to get the metadata (see their code [here](https:\/\/github.com\/tensorflow\/datasets\/blob\/a482f01c036a10496f5e22e69a2ef81b707cc418\/tensorflow_datasets\/scripts\/documentation\/build_community_catalog.py#L261))","embeddings":[-0.1930967867,-0.0408930182,0.0171368141,0.1483710855,0.0837190971,0.2352292389,0.4617842436,0.3679296374,-0.1482818872,0.1370882392,0.0799530894,0.0977992192,-0.0425132401,0.156961754,-0.0325321332,-0.1178672984,0.0716279745,0.084002845,0.0171170738,-0.1026259288,-0.2108213753,-0.3138793409,-0.0573205352,-0.2419691384,-0.240710035,0.2100915164,-0.0826085359,-0.026686538,-0.2046019286,-0.0941648707,0.2508805096,0.2647181153,0.1250740141,0.3568685055,-0.0001222372,0.1595200896,0.4303728044,-0.0048717242,-0.2071068138,-0.3389700651,-0.2597809434,0.2637265623,-0.1862554848,-0.007216075,-0.265596658,-0.0657427162,-0.174295336,-0.2730125189,0.0540486276,0.2508936226,0.0341714732,0.291720897,-0.3452797532,-0.0460385978,-0.0394303352,0.8040384054,-0.2310288697,0.0436120965,0.1752564162,-0.0227116104,0.0732167587,0.331781894,0.0840110481,-0.0748217553,0.1333269328,0.093022503,0.1514278501,-0.2897081971,0.3353879154,0.4625920057,0.9551768303,-0.2319923341,-0.1840748936,-0.1619186103,0.1765868813,-0.0823298246,0.1787538379,0.226660043,-0.0300183725,0.112180993,-0.2798975408,-0.269677788,-0.1554620564,0.1911628097,-0.2069740742,0.1905494779,0.0602022856,0.1731398404,-0.1506349146,0.1130907461,0.3398441672,-0.6478111744,-0.0235280655,0.0077918088,0.2210532874,-0.4013286233,-0.1160971895,-0.6053965092,0.2573472857,0.0512126163,-0.1545630842,0.0001488268,-0.3753089011,-0.0703495368,0.5297185183,-0.1863648891,0.1865562052,0.5259376764,0.2331216633,0.0960352495,0.2218007296,0.0031762279,0.0176595226,0.4236888289,-0.4282916486,0.0247692671,0.2190483809,-0.2993412316,-0.1377866417,0.1236956865,-0.2629763484,-0.1937132478,0.0605257079,0.138984412,-0.1903844774,0.3507860601,-0.2152276784,0.1436670423,0.1995143294,-0.0806107894,-0.0641855374,0.0483273454,0.0200268086,0.2545834482,0.1653652638,0.117798306,0.2901413441,0.2367284596,0.4559178948,0.068114154,0.2697553933,-0.2989701629,-0.0130412765,0.3570164144,-0.2557722926,0.061637979,-0.0130517669,-0.0606037341,-0.2182210088,0.3738618791,-0.0934308246,-0.4933842123,-0.0475795791,0.0746927559,-0.2213823795,0.2663300931,-0.5500370264,0.2959045172,-0.361807853,0.2120878696,0.1310445219,-0.0638472214,-0.1128613725,-0.1722283065,-0.0023029472,0.0976131782,-0.3554140627,0.0895124003,0.2415868342,0.1412079483,-0.2171725631,0.263635695,-0.5941981077,0.1387082934,-0.1592742205,-0.2609978616,0.3432138562,-0.871271193,-0.1100460365,0.2763182819,0.1221800074,0.2301328927,0.0018485665,-0.060817264,0.1014338583,-0.1660165191,0.024368709,0.0041151387,-0.1762381643,-0.187489897,-0.2242808193,-0.2462519556,0.0196793601,0.3388539851,0.159147948,-0.0542472079,-0.2054231614,0.447139889,0.1881514341,-0.0602033064,0.0240201298,0.246002391,0.4291908145,-0.1845162809,0.1296522319,0.2087480575,-0.5540621281,0.2240829915,0.1663803905,0.3638636172,0.2231105566,-0.5873089433,0.0096068541,-0.1301365644,0.1181784272,-0.1861403733,-0.0187666174,-0.0961214527,-0.0250549689,0.0001557443,-0.3404201567,0.2665949166,-0.4510496855,0.2357604653,0.0045176833,0.628601253,0.1515499949,0.2835291922,0.087544173,0.1167594641,-0.236701861,-0.2827849686,0.0671132207,0.1489972174,-0.0532049723,0.1615292579,0.0903540626,0.6129931808,0.5335848927,-0.2884148061,0.1019164175,0.0000825509,-0.0369455032,-0.2884541154,-0.5666662455,0.4422420263,-0.0559044033,-0.0029908968,-0.082581304,-0.2270180881,-0.011904465,-0.0730722174,-0.4256631732,-0.0976899341,-0.0030437128,0.2948181331,0.0984265134,0.1704134047,-0.4284432232,0.0354533195,0.5133139491,-0.0624064915,-0.2216023505,0.0383285433,-0.2280472219,-0.1407339275,0.3491388857,-0.1897028536,0.7110159993,0.0155705735,0.0787204355,0.1616213471,-0.1448249817,0.0144923069,0.1541190147,-0.2103215307,0.2163838297,-0.0064031463,-0.0052794465,0.0875518918,0.0183210652,-0.0013102505,0.1640996933,0.2196376175,-0.1486721188,-0.1519057006,-0.0950851589,-0.2850099504,0.1047462523,0.0338383615,-0.1715426147,-0.1407742649,0.0386831798,0.3406376839,-0.0305808634,0.0886437967,0.1808142066,0.4147265851,-0.1868278384,-0.2315230221,-0.1765961647,-0.0925497785,-0.0487230793,0.0531851575,0.1743244082,-0.0626507103,0.2001698911,0.075068973,0.0259980764,-0.4509513974,-0.6469026804,0.2413496971,-0.0558759756,0.0996505991,-0.0629610941,0.1520871222,-0.1433275491,-0.0910054147,0.130847156,0.0458131731,0.0052245893,-0.1650268435,-0.1705234498,-0.0297836922,-0.1498758942,-0.3198326826,-0.043808762,-0.2606500983,0.1365999132,-0.0259413458,-0.0753489882,0.1221207529,-0.0366969779,-0.2608627379,0.0517302118,-0.2346722335,-0.2944401205,-0.5804101825,0.0650938973,-0.0839869156,-0.2328498363,0.2278934121,0.004894549,0.2839174867,0.2167740911,-0.7857512832,-0.4269509017,0.2274852544,0.3932648301,-0.0797615722,-0.3337604403,0.2175691277,0.0189387072,0.1448348612,0.0404929295,-0.1132277995,-0.1262375414,0.2549744844,0.1166179478,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3505","title":"cast_column function not working with map function in streaming mode for Audio features","comments":"Hi! This is probably due to the fact that `IterableDataset.map` sets `features` to `None` before mapping examples. We can fix the issue by passing the old `features` dict to the map generator and performing encoding\/decoding there (before calling the map transform function).","body":"## Describe the bug\r\nI am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only.\r\nI am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset, Audio\r\nfrom transformers import Wav2Vec2Processor\r\n\r\ndef encode(batch, processor):\r\n    print(\"Audio: \",batch['audio'])\r\n    batch[\"input_values\"] = processor(batch[\"audio\"]['array'], sampling_rate=16000).input_values\r\n    return batch\r\n\r\ndef print_ds(ds):\r\n    iterator = iter(ds)\r\n    for d in iterator:\r\n        print(\"Data: \",d)\r\n        break\r\n\r\nprocessor = Wav2Vec2Processor.from_pretrained(pretrained_model_path)\r\n\r\ndataset = load_dataset(\"custom_dataset.py\",\"train\",data_files={'train':'train_path.txt'},\r\n                                data_dir=\"data\", streaming=True, split=\"train\")\r\n\r\nprint(\"Features: \",dataset.features)\r\n\r\nprint_ds(dataset)\r\n\r\ndataset = dataset.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n\r\nprint(\"Features: \",dataset.features)\r\n\r\nprint_ds(dataset)\r\n\r\ndataset = dataset.map(lambda x: encode(x,processor))\r\n\r\nprint(\"Features: \",dataset.features)\r\n\r\nprint_ds(dataset)\r\n\r\n\r\n\r\n```\r\n\r\n## Expected results\r\n\r\nmap function not printing Audio type features be used with processor function and getting error in processor call due to this.\r\n\r\n## Actual results\r\n\r\n# after load_dataset call\r\nFeatures:  {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)}\r\nData:  {'sentence': '\u0914\u0930 \u0905\u092a\u0928\u0947 \u092a\u0947\u091f \u0915\u094b \u092e\u093e\u0901 \u0915\u0940 \u0938\u094d\u0935\u093e\u0926\u093f\u0937\u094d\u091f \u0917\u0930\u092e\u0917\u0930\u092e \u091c\u0932\u0947\u092c\u093f\u092f\u093e\u0901 \u0939\u095c\u092a\u0924\u0947\\n', 'audio': 'data\/0116_003.wav'}\r\n\r\n# after cast_column call\r\nFeatures:  {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)}\r\nData:  {'sentence': '\u0914\u0930 \u0905\u092a\u0928\u0947 \u092a\u0947\u091f \u0915\u094b \u092e\u093e\u0901 \u0915\u0940 \u0938\u094d\u0935\u093e\u0926\u093f\u0937\u094d\u091f \u0917\u0930\u092e\u0917\u0930\u092e \u091c\u0932\u0947\u092c\u093f\u092f\u093e\u0901 \u0939\u095c\u092a\u0924\u0947\\n', 'audio': {'path': 'data\/0116_003.wav', 'array': array([ 1.2662281e-06,  1.0264218e-06, -1.3615092e-06, ...,\r\n        1.3017889e-02,  1.0085563e-02,  4.8155054e-03], dtype=float32), 'sampling_rate': 16000}}\r\n\r\n# after map call\r\nFeatures:  None\r\nAudio:  data\/0116_003.wav\r\n\r\nTraceback (most recent call last):\r\n  File \"demo2.py\", line 36, in <module>\r\n    print_ds(dataset)\r\n  File \"demo2.py\", line 11, in print_ds\r\n    for d in iterator:\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/iterable_dataset.py\", line 341, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/iterable_dataset.py\", line 338, in _iter\r\n    yield from ex_iterable\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/iterable_dataset.py\", line 192, in __iter__\r\n    yield key, self.function(example)\r\n  File \"demo2.py\", line 32, in <lambda>\r\n    dataset = dataset.map(lambda x: batch_encode(x,processor))\r\n  File \"demo2.py\", line 6, in batch_encode\r\n    batch[\"input_values\"] = processor(batch[\"audio\"]['array'], sampling_rate=16000).input_values\r\nTypeError: string indices must be integers\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-4.14.243 with-debian-bullseye-sid\r\n- Python version: 3.7.9\r\n- PyArrow version: 6.0.1\r\n","comment_length":42,"text":"cast_column function not working with map function in streaming mode for Audio features \n ## Describe the bug\r\nI am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only.\r\nI am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset, Audio\r\nfrom transformers import Wav2Vec2Processor\r\n\r\ndef encode(batch, processor):\r\n    print(\"Audio: \",batch['audio'])\r\n    batch[\"input_values\"] = processor(batch[\"audio\"]['array'], sampling_rate=16000).input_values\r\n    return batch\r\n\r\ndef print_ds(ds):\r\n    iterator = iter(ds)\r\n    for d in iterator:\r\n        print(\"Data: \",d)\r\n        break\r\n\r\nprocessor = Wav2Vec2Processor.from_pretrained(pretrained_model_path)\r\n\r\ndataset = load_dataset(\"custom_dataset.py\",\"train\",data_files={'train':'train_path.txt'},\r\n                                data_dir=\"data\", streaming=True, split=\"train\")\r\n\r\nprint(\"Features: \",dataset.features)\r\n\r\nprint_ds(dataset)\r\n\r\ndataset = dataset.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n\r\nprint(\"Features: \",dataset.features)\r\n\r\nprint_ds(dataset)\r\n\r\ndataset = dataset.map(lambda x: encode(x,processor))\r\n\r\nprint(\"Features: \",dataset.features)\r\n\r\nprint_ds(dataset)\r\n\r\n\r\n\r\n```\r\n\r\n## Expected results\r\n\r\nmap function not printing Audio type features be used with processor function and getting error in processor call due to this.\r\n\r\n## Actual results\r\n\r\n# after load_dataset call\r\nFeatures:  {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)}\r\nData:  {'sentence': '\u0914\u0930 \u0905\u092a\u0928\u0947 \u092a\u0947\u091f \u0915\u094b \u092e\u093e\u0901 \u0915\u0940 \u0938\u094d\u0935\u093e\u0926\u093f\u0937\u094d\u091f \u0917\u0930\u092e\u0917\u0930\u092e \u091c\u0932\u0947\u092c\u093f\u092f\u093e\u0901 \u0939\u095c\u092a\u0924\u0947\\n', 'audio': 'data\/0116_003.wav'}\r\n\r\n# after cast_column call\r\nFeatures:  {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)}\r\nData:  {'sentence': '\u0914\u0930 \u0905\u092a\u0928\u0947 \u092a\u0947\u091f \u0915\u094b \u092e\u093e\u0901 \u0915\u0940 \u0938\u094d\u0935\u093e\u0926\u093f\u0937\u094d\u091f \u0917\u0930\u092e\u0917\u0930\u092e \u091c\u0932\u0947\u092c\u093f\u092f\u093e\u0901 \u0939\u095c\u092a\u0924\u0947\\n', 'audio': {'path': 'data\/0116_003.wav', 'array': array([ 1.2662281e-06,  1.0264218e-06, -1.3615092e-06, ...,\r\n        1.3017889e-02,  1.0085563e-02,  4.8155054e-03], dtype=float32), 'sampling_rate': 16000}}\r\n\r\n# after map call\r\nFeatures:  None\r\nAudio:  data\/0116_003.wav\r\n\r\nTraceback (most recent call last):\r\n  File \"demo2.py\", line 36, in <module>\r\n    print_ds(dataset)\r\n  File \"demo2.py\", line 11, in print_ds\r\n    for d in iterator:\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/iterable_dataset.py\", line 341, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/iterable_dataset.py\", line 338, in _iter\r\n    yield from ex_iterable\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/iterable_dataset.py\", line 192, in __iter__\r\n    yield key, self.function(example)\r\n  File \"demo2.py\", line 32, in <lambda>\r\n    dataset = dataset.map(lambda x: batch_encode(x,processor))\r\n  File \"demo2.py\", line 6, in batch_encode\r\n    batch[\"input_values\"] = processor(batch[\"audio\"]['array'], sampling_rate=16000).input_values\r\nTypeError: string indices must be integers\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-4.14.243 with-debian-bullseye-sid\r\n- Python version: 3.7.9\r\n- PyArrow version: 6.0.1\r\n \n Hi! This is probably due to the fact that `IterableDataset.map` sets `features` to `None` before mapping examples. We can fix the issue by passing the old `features` dict to the map generator and performing encoding\/decoding there (before calling the map transform function).","embeddings":[-0.2622294724,-0.2025066316,-0.0266618002,0.35771662,0.6027311683,-0.2296939492,0.3419703543,0.3029460907,0.2976565361,-0.0616048537,-0.0520020351,0.7353788614,-0.170747906,0.1945960075,-0.0731457993,-0.2837215662,0.1454189718,0.1953485459,-0.2825011015,-0.1273447722,-0.4571538866,0.266276896,-0.5858387947,0.2607055902,0.1315929145,0.1269850135,0.3911371529,-0.0558034293,-0.0058511598,0.0119487047,0.0709327832,-0.3035010993,0.2639533281,0.3384836316,-0.0001273209,-0.01005759,0.2654284835,-0.1152285039,0.0421557687,-0.1628179401,-0.5065383315,0.0108539155,-0.1724243313,-0.2680978179,-0.2423131168,-0.0755802393,-0.2448245287,-0.3864841759,0.1261117607,0.4162013829,0.0513846949,-0.0125744846,0.0004481834,0.2920205891,-0.0410074592,0.2500533164,-0.1632404178,-0.2827578485,0.4039226174,0.2799262702,0.1716988832,0.4097907543,-0.3054201603,-0.189018622,0.2012020499,0.0870049745,0.0525163859,-0.3052863479,0.2437959611,0.0800205097,0.7388804555,-0.215737626,-0.0468573384,0.1816224307,-0.0824642032,-0.200829342,0.3370784521,0.0756121054,-0.0204869024,0.1056837887,-0.3921613097,0.4437986016,0.1107804254,0.2740789354,0.2294820696,0.1720707417,-0.1795602739,0.2363815308,-0.0695680603,-0.2040263116,-0.0221354272,-0.1341201067,0.0279268343,0.43709144,-0.3593988121,-0.2515300512,-0.0444706604,-0.0032966889,-0.3157380521,0.0519566946,0.0216356944,0.3925277889,0.1234937608,0.0938350409,0.228002131,-0.0510588475,0.0008998344,0.3334600627,0.1110179201,0.1315761507,-0.3697620034,-0.131785512,-0.0382160954,0.0798163265,0.0605839081,0.186251834,0.0260364972,-0.2497038245,0.0764498785,-0.1182755604,-0.1678248048,0.0070870626,0.2809309065,0.4877811372,0.183333084,0.4044602513,0.1203613654,0.2574437261,-0.003140216,-0.1776707023,0.0840895325,-0.1287069768,0.0117293764,-0.0060195918,0.0182060488,0.2866123915,0.2414563745,0.0980077684,0.1071999595,-0.2441667169,-0.1221688911,-0.3176814616,0.2119741142,0.0030413666,-0.2090205103,0.1638397872,-0.0101244655,0.2265064269,-0.0471812822,0.2381007224,-0.3662849963,-0.2675878108,-0.093396157,-0.0010761637,0.1161949039,0.322194308,-0.1536295712,0.0612343997,0.3623498082,-0.6997801661,0.0954071656,-0.4201662838,-0.435526371,-0.1637335122,0.254259944,-0.1953831762,-0.0231242795,-0.0938970521,-0.2415255755,0.0905785188,0.4225087166,0.1920286268,-0.1534341872,0.2236084044,-0.3049720228,0.1352205724,0.9009022117,-0.4058737755,-0.4501795173,0.2768709362,0.2986758351,0.167773813,0.0994963497,-0.0011042197,0.3990194201,-0.2763809264,-0.1823781133,0.4065686464,0.1563879102,0.3689537942,-0.0520184338,-0.0952974781,0.2213767171,0.0388221219,-0.0506980158,0.5168458223,0.0837466568,0.0967709869,0.1961031407,-0.183642149,0.3395857513,0.0826530084,0.1636096984,-0.0412129872,0.0213948768,-0.331161499,-0.0408198424,0.0523189865,0.1841138154,-0.1628913134,-0.1639954001,-0.0609971136,-0.0883297846,-0.1034896299,-0.2182547599,0.1538134813,-0.0608255677,-0.0276368298,0.16020675,-0.131727308,0.0014977517,0.287674427,-0.149968639,-0.2683257163,-0.0389655232,0.0992078334,0.3014132082,-0.1411533207,-0.1287005693,0.2912289202,0.3118904531,0.1603486985,-0.3191339374,0.2160568088,0.1409419328,0.158634305,-0.6849149466,0.2192506045,0.0656085312,-0.363858372,0.1252858341,0.3009476364,0.523863554,-0.1400036663,0.0592611842,0.2220312953,0.2513669431,0.2689090669,0.0396322012,-0.0774181336,0.141925782,-0.0120313354,-0.0247998945,-0.0389427766,-0.3056529462,-0.3279690742,0.0739354044,-0.0633990318,-0.2098125368,-0.0415339619,0.8125289679,0.0605159812,0.2783505917,0.0987929627,-0.2616200149,-0.0487156585,0.2482557595,-0.195268631,0.474860996,-0.0486866534,0.0303082112,-0.0334712192,0.1873235255,0.1295358092,0.2900334001,0.0046804338,-0.1638182849,0.1424697787,0.0798766539,-0.1637425572,0.0008391118,-0.0190562122,0.1437794119,0.0469194762,-0.4936681688,-0.227344498,-0.4648261964,-0.1153557599,-0.1666266471,-0.0599866509,0.5092506409,0.0893868804,-0.3088843822,0.4446440339,-0.0951270685,0.2908420861,-0.1469131857,-0.2637878954,0.1620958149,-0.3564774394,-0.0953711197,0.2510156631,-0.0679312795,-0.0724670142,0.0697362795,0.3131883442,-0.2888309956,0.1012984291,-0.1983561367,-0.2583791018,-0.0535521843,0.0664231181,0.0366905592,-0.1491824985,-0.0462143645,0.0849677697,-0.104456082,-0.2431717664,0.2912279367,-0.1066788509,-0.0193355326,0.3490876257,0.0152087482,-0.0196820796,-0.1917309463,-0.2694986463,-0.1783271581,-0.4100151658,-0.0557864048,-0.1864387989,0.1280480474,-0.4934854805,0.1497714967,0.0313416794,0.2044689357,0.0084125828,-0.348556608,0.0114279278,0.55524683,-0.0836592168,-0.2093534321,-0.1142520905,-0.0217916053,0.047701858,0.1209980249,-0.055112835,0.1179174706,-0.0448079407,0.1447479129,0.0450589694,-0.0214345697,0.348490417,0.0411443971,0.230314225,-0.1711704433,-0.1864885986,-0.073375456,0.3733538985,0.2533346117,0.6482356191,0.8357483149,-0.2835794091,0.2486017942,0.3465374708,-0.0985205844,0.2963235974,-0.2686643004,-0.2231538594,-0.2796331644,-0.1124286503,0.2684706151,-0.0886671916,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3504","title":"Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst","comments":"Hi @ToddMorrill, thanks for reporting.\r\n\r\nThree weeks ago I contacted the team who created the Pile dataset to report this issue with their data host server: https:\/\/the-eye.eu\r\n\r\nThey told me that unfortunately, the-eye was heavily affected by the recent tornado catastrophe in the US. They hope to have their data back online asap.","body":"## Describe the bug\r\nI am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https:\/\/huggingface.co\/course\/chapter5\/4?fw=pt).\r\n\r\nhttps:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\n\r\n# This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\ndata_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\npubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\npubmed_dataset\r\n```\r\n\r\nI also tried with `wget` as follows.\r\n```\r\nwget https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\r\n```\r\n\r\n## Expected results\r\nI expect to be able to download this file.\r\n\r\n## Actual results\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\ntimeout                                   Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    158         try:\r\n--> 159             conn = connection.create_connection(\r\n    160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\ntimeout: timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeoutError                       Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    664             # Make the request on the httplib connection object.\r\n--> 665             httplib_response = self._make_request(\r\n    666                 conn,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    375         try:\r\n--> 376             self._validate_conn(conn)\r\n    377         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    995         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 996             conn.connect()\r\n    997 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in connect(self)\r\n    313         # Add certificate verification\r\n--> 314         conn = self._new_conn()\r\n    315         hostname = self.host\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    163         except SocketTimeout:\r\n--> 164             raise ConnectTimeoutError(\r\n    165                 self,\r\n\r\nConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    438             if not chunked:\r\n--> 439                 resp = conn.urlopen(\r\n    440                     method=request.method,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    718 \r\n--> 719             retries = retries.increment(\r\n    720                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    435         if new_retry.is_exhausted():\r\n--> 436             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    437 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeout                            Traceback (most recent call last)\r\n\/tmp\/ipykernel_15104\/606583593.py in <module>\r\n      3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\n      4 data_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\n----> 5 pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\n      6 pubmed_dataset\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1655 \r\n   1656     # Create a dataset builder\r\n-> 1657     builder_instance = load_dataset_builder(\r\n   1658         path=path,\r\n   1659         name=name,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1492         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1493         download_config.use_auth_token = use_auth_token\r\n-> 1494     dataset_module = dataset_module_factory(\r\n   1495         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1496     )\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1116     # Try packaged\r\n   1117     if path in _PACKAGED_DATASETS_MODULES:\r\n-> 1118         return PackagedDatasetModuleFactory(\r\n   1119             path, data_files=data_files, download_config=download_config, download_mode=download_mode\r\n   1120         ).get_module()\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in get_module(self)\r\n    773             else get_patterns_locally(str(Path().resolve()))\r\n    774         )\r\n--> 775         data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)\r\n    776         module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]\r\n    777         builder_kwargs = {\"hash\": hash, \"data_files\": data_files}\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    576         for key, patterns_for_key in patterns.items():\r\n    577             out[key] = (\r\n--> 578                 DataFilesList.from_local_or_remote(\r\n    579                     patterns_for_key,\r\n    580                     base_path=base_path,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    545         base_path = base_path if base_path is not None else str(Path().resolve())\r\n    546         data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n--> 547         origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n    548         return cls(data_files, origin_metadata)\r\n    549 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)\r\n    492     data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None\r\n    493 ) -> Tuple[str]:\r\n--> 494     return thread_map(\r\n    495         partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),\r\n    496         data_files,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)\r\n     92     \"\"\"\r\n     93     from concurrent.futures import ThreadPoolExecutor\r\n---> 94     return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     95 \r\n     96 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     74             map_args.update(chunksize=chunksize)\r\n     75         with PoolExecutor(**pool_kwargs) as ex:\r\n---> 76             return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))\r\n     77 \r\n     78 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/notebook.py in __iter__(self)\r\n    252     def __iter__(self):\r\n    253         try:\r\n--> 254             for obj in super(tqdm_notebook, self).__iter__():\r\n    255                 # return super(tqdm...) will not catch exception\r\n    256                 yield obj\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1171         # (note: keep this check outside the loop for performance)\r\n   1172         if self.disable:\r\n-> 1173             for obj in iterable:\r\n   1174                 yield obj\r\n   1175             return\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result_iterator()\r\n    617                     # Careful not to keep a reference to the popped future\r\n    618                     if timeout is None:\r\n--> 619                         yield fs.pop().result()\r\n    620                     else:\r\n    621                         yield fs.pop().result(end_time - time.monotonic())\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    442                     raise CancelledError()\r\n    443                 elif self._state == FINISHED:\r\n--> 444                     return self.__get_result()\r\n    445                 else:\r\n    446                     raise TimeoutError()\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in __get_result(self)\r\n    387         if self._exception:\r\n    388             try:\r\n--> 389                 raise self._exception\r\n    390             finally:\r\n    391                 # Break a reference cycle with the exception in self._exception\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)\r\n    483     if isinstance(data_file, Url):\r\n    484         data_file = str(data_file)\r\n--> 485         return (request_etag(data_file, use_auth_token=use_auth_token),)\r\n    486     else:\r\n    487         data_file = str(data_file.resolve())\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in request_etag(url, use_auth_token)\r\n    489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:\r\n    490     headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)\r\n--> 491     response = http_head(url, headers=headers, max_retries=3)\r\n    492     response.raise_for_status()\r\n    493     etag = response.headers.get(\"ETag\") if response.ok else None\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)\r\n    474     headers = copy.deepcopy(headers) or {}\r\n    475     headers[\"user-agent\"] = get_datasets_user_agent(user_agent=headers.get(\"user-agent\"))\r\n--> 476     response = _request_with_retry(\r\n    477         method=\"HEAD\",\r\n    478         url=url,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n    408             if tries > max_retries:\r\n--> 409                 raise err\r\n    410             else:\r\n    411                 logger.info(f\"{method} request to {url} timed out, retrying... [{tries\/max_retries}]\")\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    403         tries += 1\r\n    404         try:\r\n--> 405             response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)\r\n    406             success = True\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     58     # cases, and look like a memory leak in others.\r\n     59     with sessions.Session() as session:\r\n---> 60         return session.request(method=method, url=url, **kwargs)\r\n     61 \r\n     62 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    531         }\r\n    532         send_kwargs.update(settings)\r\n--> 533         resp = self.send(prep, **send_kwargs)\r\n    534 \r\n    535         return resp\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    644 \r\n    645         # Send the request\r\n--> 646         r = adapter.send(request, **kwargs)\r\n    647 \r\n    648         # Total elapsed time of the request (approximately)\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    502                 # TODO: Remove this in 3.0.0: see #2811\r\n    503                 if not isinstance(e.reason, NewConnectionError):\r\n--> 504                     raise ConnectTimeout(e, request=request)\r\n    505 \r\n    506             if isinstance(e.reason, ResponseError):\r\n\r\nConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.1","comment_length":53,"text":"Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst \n ## Describe the bug\r\nI am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https:\/\/huggingface.co\/course\/chapter5\/4?fw=pt).\r\n\r\nhttps:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\n\r\n# This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\ndata_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\npubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\npubmed_dataset\r\n```\r\n\r\nI also tried with `wget` as follows.\r\n```\r\nwget https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\r\n```\r\n\r\n## Expected results\r\nI expect to be able to download this file.\r\n\r\n## Actual results\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\ntimeout                                   Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    158         try:\r\n--> 159             conn = connection.create_connection(\r\n    160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\ntimeout: timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeoutError                       Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    664             # Make the request on the httplib connection object.\r\n--> 665             httplib_response = self._make_request(\r\n    666                 conn,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    375         try:\r\n--> 376             self._validate_conn(conn)\r\n    377         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    995         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 996             conn.connect()\r\n    997 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in connect(self)\r\n    313         # Add certificate verification\r\n--> 314         conn = self._new_conn()\r\n    315         hostname = self.host\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    163         except SocketTimeout:\r\n--> 164             raise ConnectTimeoutError(\r\n    165                 self,\r\n\r\nConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    438             if not chunked:\r\n--> 439                 resp = conn.urlopen(\r\n    440                     method=request.method,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    718 \r\n--> 719             retries = retries.increment(\r\n    720                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    435         if new_retry.is_exhausted():\r\n--> 436             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    437 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeout                            Traceback (most recent call last)\r\n\/tmp\/ipykernel_15104\/606583593.py in <module>\r\n      3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\n      4 data_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\n----> 5 pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\n      6 pubmed_dataset\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1655 \r\n   1656     # Create a dataset builder\r\n-> 1657     builder_instance = load_dataset_builder(\r\n   1658         path=path,\r\n   1659         name=name,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1492         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1493         download_config.use_auth_token = use_auth_token\r\n-> 1494     dataset_module = dataset_module_factory(\r\n   1495         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1496     )\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1116     # Try packaged\r\n   1117     if path in _PACKAGED_DATASETS_MODULES:\r\n-> 1118         return PackagedDatasetModuleFactory(\r\n   1119             path, data_files=data_files, download_config=download_config, download_mode=download_mode\r\n   1120         ).get_module()\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in get_module(self)\r\n    773             else get_patterns_locally(str(Path().resolve()))\r\n    774         )\r\n--> 775         data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)\r\n    776         module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]\r\n    777         builder_kwargs = {\"hash\": hash, \"data_files\": data_files}\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    576         for key, patterns_for_key in patterns.items():\r\n    577             out[key] = (\r\n--> 578                 DataFilesList.from_local_or_remote(\r\n    579                     patterns_for_key,\r\n    580                     base_path=base_path,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    545         base_path = base_path if base_path is not None else str(Path().resolve())\r\n    546         data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n--> 547         origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n    548         return cls(data_files, origin_metadata)\r\n    549 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)\r\n    492     data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None\r\n    493 ) -> Tuple[str]:\r\n--> 494     return thread_map(\r\n    495         partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),\r\n    496         data_files,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)\r\n     92     \"\"\"\r\n     93     from concurrent.futures import ThreadPoolExecutor\r\n---> 94     return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     95 \r\n     96 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     74             map_args.update(chunksize=chunksize)\r\n     75         with PoolExecutor(**pool_kwargs) as ex:\r\n---> 76             return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))\r\n     77 \r\n     78 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/notebook.py in __iter__(self)\r\n    252     def __iter__(self):\r\n    253         try:\r\n--> 254             for obj in super(tqdm_notebook, self).__iter__():\r\n    255                 # return super(tqdm...) will not catch exception\r\n    256                 yield obj\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1171         # (note: keep this check outside the loop for performance)\r\n   1172         if self.disable:\r\n-> 1173             for obj in iterable:\r\n   1174                 yield obj\r\n   1175             return\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result_iterator()\r\n    617                     # Careful not to keep a reference to the popped future\r\n    618                     if timeout is None:\r\n--> 619                         yield fs.pop().result()\r\n    620                     else:\r\n    621                         yield fs.pop().result(end_time - time.monotonic())\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    442                     raise CancelledError()\r\n    443                 elif self._state == FINISHED:\r\n--> 444                     return self.__get_result()\r\n    445                 else:\r\n    446                     raise TimeoutError()\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in __get_result(self)\r\n    387         if self._exception:\r\n    388             try:\r\n--> 389                 raise self._exception\r\n    390             finally:\r\n    391                 # Break a reference cycle with the exception in self._exception\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)\r\n    483     if isinstance(data_file, Url):\r\n    484         data_file = str(data_file)\r\n--> 485         return (request_etag(data_file, use_auth_token=use_auth_token),)\r\n    486     else:\r\n    487         data_file = str(data_file.resolve())\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in request_etag(url, use_auth_token)\r\n    489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:\r\n    490     headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)\r\n--> 491     response = http_head(url, headers=headers, max_retries=3)\r\n    492     response.raise_for_status()\r\n    493     etag = response.headers.get(\"ETag\") if response.ok else None\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)\r\n    474     headers = copy.deepcopy(headers) or {}\r\n    475     headers[\"user-agent\"] = get_datasets_user_agent(user_agent=headers.get(\"user-agent\"))\r\n--> 476     response = _request_with_retry(\r\n    477         method=\"HEAD\",\r\n    478         url=url,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n    408             if tries > max_retries:\r\n--> 409                 raise err\r\n    410             else:\r\n    411                 logger.info(f\"{method} request to {url} timed out, retrying... [{tries\/max_retries}]\")\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    403         tries += 1\r\n    404         try:\r\n--> 405             response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)\r\n    406             success = True\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     58     # cases, and look like a memory leak in others.\r\n     59     with sessions.Session() as session:\r\n---> 60         return session.request(method=method, url=url, **kwargs)\r\n     61 \r\n     62 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    531         }\r\n    532         send_kwargs.update(settings)\r\n--> 533         resp = self.send(prep, **send_kwargs)\r\n    534 \r\n    535         return resp\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    644 \r\n    645         # Send the request\r\n--> 646         r = adapter.send(request, **kwargs)\r\n    647 \r\n    648         # Total elapsed time of the request (approximately)\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    502                 # TODO: Remove this in 3.0.0: see #2811\r\n    503                 if not isinstance(e.reason, NewConnectionError):\r\n--> 504                     raise ConnectTimeout(e, request=request)\r\n    505 \r\n    506             if isinstance(e.reason, ResponseError):\r\n\r\nConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.1 \n Hi @ToddMorrill, thanks for reporting.\r\n\r\nThree weeks ago I contacted the team who created the Pile dataset to report this issue with their data host server: https:\/\/the-eye.eu\r\n\r\nThey told me that unfortunately, the-eye was heavily affected by the recent tornado catastrophe in the US. They hope to have their data back online asap.","embeddings":[0.0793418959,-0.1990928948,-0.0149751296,0.3214185238,0.0415688939,0.0988509953,0.2786989808,0.3070807755,-0.0256318357,0.0241770521,0.0213189796,0.0649864301,0.2614777088,-0.009051688,0.0891451091,-0.1374546587,-0.1224959195,0.0288259462,0.1671681255,-0.0706991181,-0.1634531021,0.3103227615,-0.2114495188,0.0229474511,0.3344618082,-0.0487442054,-0.0180449206,0.0796105936,-0.259480238,-0.400164336,0.3467973471,0.0676352531,0.096873574,0.2832933366,-0.0001152371,-0.1814393252,0.3865606189,-0.106088683,-0.1463316381,-0.6200528145,-0.0925693586,-0.3742443621,0.0368294343,-0.2364805937,-0.0161443893,-0.2569465339,-0.0521956868,0.0574441589,0.5009845495,0.2733213902,0.1600577831,0.3386075497,0.4762297571,-0.1643427014,0.2631475627,-0.0007334378,-0.1128453687,0.3671372831,0.1977306157,0.1890682876,0.0809969008,0.1884224117,0.1872570515,0.021432722,0.0720425397,0.2159957588,-0.1662811637,-0.2893691063,0.2658520639,0.2006779909,0.5076361895,-0.1013349444,-0.3343558609,-0.1578326672,-0.0257836934,0.2880061567,0.125923261,0.3794694245,-0.2153221816,0.1394665837,0.0239908434,-0.07528238,-0.2904427648,0.1411405206,0.1545024216,-0.0535571761,-0.1805348247,0.2100210488,0.2253744155,-0.1191945896,0.115672946,-0.1589013785,0.0045637703,0.1201125383,-0.0652741641,-0.1301217973,0.1300206631,-0.2145696729,0.2953865826,0.1702559441,-0.0052002738,-0.1111152545,0.0564476922,-0.016203735,0.3816420734,-0.1233996078,-0.0841436759,-0.2060020864,0.3447534144,0.6683691144,0.2530405223,-0.001518407,0.1052560136,-0.0171635952,-0.1383760124,-0.1462052763,0.285618484,-0.3072584569,-0.229311049,0.2585442662,-0.3156474829,0.1121014878,-0.3288187683,0.2693771124,-0.1420352608,0.1492166221,0.4245757461,0.1484355479,-0.029062096,-0.2422586977,-0.1016428843,0.0527961552,0.1146823019,-0.1797043234,0.2691029012,-0.1405486614,0.328401953,0.0177316628,0.0434700251,-0.2795040905,-0.0850494206,0.1849165559,0.0152455885,0.2789573073,0.3548772037,0.350933522,-0.0322624482,0.0762057304,-0.1938822716,0.0723607466,-0.2473401129,-0.0533916466,-0.0830340758,0.1086283773,-0.3945971131,0.0259636771,-0.1337233633,-0.0649369508,-0.1995491683,0.1299387515,0.0005099957,-0.1368607879,-0.0169746205,-0.1217870638,0.53069067,0.4324537814,0.0124963019,-0.1217362508,-0.1118799746,0.0434905589,-0.1127401292,0.0006612553,-0.2803042829,0.0575219169,-0.1191475466,0.264998138,0.1093816087,-0.4711232483,-0.8733941317,0.0114978738,-0.3416739106,0.252620846,-0.0470301732,-0.0134672951,-0.0048580039,0.0233112834,-0.0141062178,0.4104496539,0.1495377868,-0.0713912174,-0.2368456423,-0.373826772,-0.0259877052,0.1753927171,0.0423936807,-0.0787834451,0.1425980926,0.3396872878,0.4201105237,0.0455283709,0.2494730651,0.2911367714,0.1919087023,-0.0834890008,0.17820099,-0.0896165222,-0.5133877993,0.1730192006,0.0517218523,0.2064817995,-0.4273390472,-0.1029789969,-0.3319749236,-0.0099718776,-0.2414238453,-0.2010108829,0.0930675864,0.0288966335,0.1742316931,0.2303346843,-0.0119322129,0.1851139963,-0.1602055728,0.1431324631,-0.5135131478,0.1428674012,-0.2051269412,0.0414538234,0.2167689949,-0.0474701859,0.0622037388,-0.0518584326,-0.069551535,0.1353901178,-0.1594231129,0.2733830214,0.0773170218,-0.0797431469,0.1603217125,-0.5517671108,0.0019696909,0.490470767,0.1188812703,0.0783354491,-0.1263228804,0.2736314535,0.0137049435,0.0661550611,0.1842766106,0.2874756157,0.3209612072,-0.1492517442,-0.3007631898,-0.0183587074,0.3522838354,0.2142374814,-0.1417354494,-0.2299476266,-0.4221257269,0.1657796204,0.4969475865,-0.262748301,-0.0531389602,0.2558321357,-0.1094783917,-0.0873026997,-0.0679852217,0.2343420386,0.1452105939,0.1128676832,0.0103099011,0.2695217133,0.0871118009,-0.144890517,0.1212059483,0.1995584667,0.0297716856,0.1385250837,0.0680976138,0.1382665038,-0.064184323,-0.1483417302,-0.0802401155,0.0919035822,-0.450083524,-0.1027803272,-0.0939685032,0.1880657822,-0.2880827188,-0.2205043733,-0.3286839128,-0.1170019954,-0.2444553971,0.3951078951,-0.0902109742,-0.1454010457,-0.300141871,0.3773148954,0.0136409337,-0.0375468619,-0.2003991306,-0.118748568,-0.3264170885,0.0232078396,0.0127662206,0.0733814389,0.1335761845,-0.3038693964,-0.0058854846,-0.2667237222,-0.2802242339,0.1317507476,-0.1951259524,0.3825179636,0.1503156573,0.5875208378,-0.1490400434,-0.0307233725,0.2851510942,-0.3901843429,-0.2450075895,-0.2378758341,-0.0231324416,-0.1235968918,-0.0828843266,-0.4791831374,-0.2473434061,-0.3006749153,0.4074532092,-0.1374833882,0.0367514007,0.1760338992,-0.1233249828,0.1073087677,0.1578091234,0.1812890172,-0.1553850025,-0.4383784831,0.3929957747,-0.2362470925,-0.7573886514,0.1659151614,0.0932070911,0.0847234204,0.1104102135,-0.5766034126,-0.0191007871,0.041887816,0.0486723669,-0.0347316265,0.0436797254,0.1139030531,-0.1765689105,0.0315663479,-0.154655993,-0.1199721321,0.1829023808,0.0851229653,0.3600035012,0.1991250962,0.2229770273,0.1364159733,0.5776369572,0.3293662667,0.0902140364,0.1597546786,0.1262653321,0.0691311806,0.053406477,-0.2993422747,-0.1624239087,-0.2021421343,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3504","title":"Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst","comments":"Hi @ToddMorrill, people from the Pile team have mirrored their data in a new host server: https:\/\/mystic.the-eye.eu\r\n\r\nSee:\r\n- #3627\r\n\r\nIt should work if you update your URL.\r\n\r\nWe should also update the URL in our course material.","body":"## Describe the bug\r\nI am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https:\/\/huggingface.co\/course\/chapter5\/4?fw=pt).\r\n\r\nhttps:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\n\r\n# This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\ndata_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\npubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\npubmed_dataset\r\n```\r\n\r\nI also tried with `wget` as follows.\r\n```\r\nwget https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\r\n```\r\n\r\n## Expected results\r\nI expect to be able to download this file.\r\n\r\n## Actual results\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\ntimeout                                   Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    158         try:\r\n--> 159             conn = connection.create_connection(\r\n    160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\ntimeout: timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeoutError                       Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    664             # Make the request on the httplib connection object.\r\n--> 665             httplib_response = self._make_request(\r\n    666                 conn,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    375         try:\r\n--> 376             self._validate_conn(conn)\r\n    377         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    995         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 996             conn.connect()\r\n    997 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in connect(self)\r\n    313         # Add certificate verification\r\n--> 314         conn = self._new_conn()\r\n    315         hostname = self.host\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    163         except SocketTimeout:\r\n--> 164             raise ConnectTimeoutError(\r\n    165                 self,\r\n\r\nConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    438             if not chunked:\r\n--> 439                 resp = conn.urlopen(\r\n    440                     method=request.method,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    718 \r\n--> 719             retries = retries.increment(\r\n    720                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    435         if new_retry.is_exhausted():\r\n--> 436             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    437 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeout                            Traceback (most recent call last)\r\n\/tmp\/ipykernel_15104\/606583593.py in <module>\r\n      3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\n      4 data_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\n----> 5 pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\n      6 pubmed_dataset\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1655 \r\n   1656     # Create a dataset builder\r\n-> 1657     builder_instance = load_dataset_builder(\r\n   1658         path=path,\r\n   1659         name=name,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1492         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1493         download_config.use_auth_token = use_auth_token\r\n-> 1494     dataset_module = dataset_module_factory(\r\n   1495         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1496     )\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1116     # Try packaged\r\n   1117     if path in _PACKAGED_DATASETS_MODULES:\r\n-> 1118         return PackagedDatasetModuleFactory(\r\n   1119             path, data_files=data_files, download_config=download_config, download_mode=download_mode\r\n   1120         ).get_module()\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in get_module(self)\r\n    773             else get_patterns_locally(str(Path().resolve()))\r\n    774         )\r\n--> 775         data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)\r\n    776         module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]\r\n    777         builder_kwargs = {\"hash\": hash, \"data_files\": data_files}\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    576         for key, patterns_for_key in patterns.items():\r\n    577             out[key] = (\r\n--> 578                 DataFilesList.from_local_or_remote(\r\n    579                     patterns_for_key,\r\n    580                     base_path=base_path,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    545         base_path = base_path if base_path is not None else str(Path().resolve())\r\n    546         data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n--> 547         origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n    548         return cls(data_files, origin_metadata)\r\n    549 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)\r\n    492     data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None\r\n    493 ) -> Tuple[str]:\r\n--> 494     return thread_map(\r\n    495         partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),\r\n    496         data_files,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)\r\n     92     \"\"\"\r\n     93     from concurrent.futures import ThreadPoolExecutor\r\n---> 94     return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     95 \r\n     96 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     74             map_args.update(chunksize=chunksize)\r\n     75         with PoolExecutor(**pool_kwargs) as ex:\r\n---> 76             return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))\r\n     77 \r\n     78 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/notebook.py in __iter__(self)\r\n    252     def __iter__(self):\r\n    253         try:\r\n--> 254             for obj in super(tqdm_notebook, self).__iter__():\r\n    255                 # return super(tqdm...) will not catch exception\r\n    256                 yield obj\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1171         # (note: keep this check outside the loop for performance)\r\n   1172         if self.disable:\r\n-> 1173             for obj in iterable:\r\n   1174                 yield obj\r\n   1175             return\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result_iterator()\r\n    617                     # Careful not to keep a reference to the popped future\r\n    618                     if timeout is None:\r\n--> 619                         yield fs.pop().result()\r\n    620                     else:\r\n    621                         yield fs.pop().result(end_time - time.monotonic())\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    442                     raise CancelledError()\r\n    443                 elif self._state == FINISHED:\r\n--> 444                     return self.__get_result()\r\n    445                 else:\r\n    446                     raise TimeoutError()\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in __get_result(self)\r\n    387         if self._exception:\r\n    388             try:\r\n--> 389                 raise self._exception\r\n    390             finally:\r\n    391                 # Break a reference cycle with the exception in self._exception\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)\r\n    483     if isinstance(data_file, Url):\r\n    484         data_file = str(data_file)\r\n--> 485         return (request_etag(data_file, use_auth_token=use_auth_token),)\r\n    486     else:\r\n    487         data_file = str(data_file.resolve())\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in request_etag(url, use_auth_token)\r\n    489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:\r\n    490     headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)\r\n--> 491     response = http_head(url, headers=headers, max_retries=3)\r\n    492     response.raise_for_status()\r\n    493     etag = response.headers.get(\"ETag\") if response.ok else None\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)\r\n    474     headers = copy.deepcopy(headers) or {}\r\n    475     headers[\"user-agent\"] = get_datasets_user_agent(user_agent=headers.get(\"user-agent\"))\r\n--> 476     response = _request_with_retry(\r\n    477         method=\"HEAD\",\r\n    478         url=url,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n    408             if tries > max_retries:\r\n--> 409                 raise err\r\n    410             else:\r\n    411                 logger.info(f\"{method} request to {url} timed out, retrying... [{tries\/max_retries}]\")\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    403         tries += 1\r\n    404         try:\r\n--> 405             response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)\r\n    406             success = True\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     58     # cases, and look like a memory leak in others.\r\n     59     with sessions.Session() as session:\r\n---> 60         return session.request(method=method, url=url, **kwargs)\r\n     61 \r\n     62 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    531         }\r\n    532         send_kwargs.update(settings)\r\n--> 533         resp = self.send(prep, **send_kwargs)\r\n    534 \r\n    535         return resp\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    644 \r\n    645         # Send the request\r\n--> 646         r = adapter.send(request, **kwargs)\r\n    647 \r\n    648         # Total elapsed time of the request (approximately)\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    502                 # TODO: Remove this in 3.0.0: see #2811\r\n    503                 if not isinstance(e.reason, NewConnectionError):\r\n--> 504                     raise ConnectTimeout(e, request=request)\r\n    505 \r\n    506             if isinstance(e.reason, ResponseError):\r\n\r\nConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.1","comment_length":38,"text":"Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst \n ## Describe the bug\r\nI am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https:\/\/huggingface.co\/course\/chapter5\/4?fw=pt).\r\n\r\nhttps:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\n\r\n# This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\ndata_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\npubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\npubmed_dataset\r\n```\r\n\r\nI also tried with `wget` as follows.\r\n```\r\nwget https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\r\n```\r\n\r\n## Expected results\r\nI expect to be able to download this file.\r\n\r\n## Actual results\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\ntimeout                                   Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    158         try:\r\n--> 159             conn = connection.create_connection(\r\n    160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\ntimeout: timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeoutError                       Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    664             # Make the request on the httplib connection object.\r\n--> 665             httplib_response = self._make_request(\r\n    666                 conn,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    375         try:\r\n--> 376             self._validate_conn(conn)\r\n    377         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    995         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 996             conn.connect()\r\n    997 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in connect(self)\r\n    313         # Add certificate verification\r\n--> 314         conn = self._new_conn()\r\n    315         hostname = self.host\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    163         except SocketTimeout:\r\n--> 164             raise ConnectTimeoutError(\r\n    165                 self,\r\n\r\nConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    438             if not chunked:\r\n--> 439                 resp = conn.urlopen(\r\n    440                     method=request.method,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    718 \r\n--> 719             retries = retries.increment(\r\n    720                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    435         if new_retry.is_exhausted():\r\n--> 436             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    437 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeout                            Traceback (most recent call last)\r\n\/tmp\/ipykernel_15104\/606583593.py in <module>\r\n      3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\n      4 data_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\n----> 5 pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\n      6 pubmed_dataset\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1655 \r\n   1656     # Create a dataset builder\r\n-> 1657     builder_instance = load_dataset_builder(\r\n   1658         path=path,\r\n   1659         name=name,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1492         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1493         download_config.use_auth_token = use_auth_token\r\n-> 1494     dataset_module = dataset_module_factory(\r\n   1495         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1496     )\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1116     # Try packaged\r\n   1117     if path in _PACKAGED_DATASETS_MODULES:\r\n-> 1118         return PackagedDatasetModuleFactory(\r\n   1119             path, data_files=data_files, download_config=download_config, download_mode=download_mode\r\n   1120         ).get_module()\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in get_module(self)\r\n    773             else get_patterns_locally(str(Path().resolve()))\r\n    774         )\r\n--> 775         data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)\r\n    776         module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]\r\n    777         builder_kwargs = {\"hash\": hash, \"data_files\": data_files}\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    576         for key, patterns_for_key in patterns.items():\r\n    577             out[key] = (\r\n--> 578                 DataFilesList.from_local_or_remote(\r\n    579                     patterns_for_key,\r\n    580                     base_path=base_path,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    545         base_path = base_path if base_path is not None else str(Path().resolve())\r\n    546         data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n--> 547         origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n    548         return cls(data_files, origin_metadata)\r\n    549 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)\r\n    492     data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None\r\n    493 ) -> Tuple[str]:\r\n--> 494     return thread_map(\r\n    495         partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),\r\n    496         data_files,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)\r\n     92     \"\"\"\r\n     93     from concurrent.futures import ThreadPoolExecutor\r\n---> 94     return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     95 \r\n     96 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     74             map_args.update(chunksize=chunksize)\r\n     75         with PoolExecutor(**pool_kwargs) as ex:\r\n---> 76             return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))\r\n     77 \r\n     78 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/notebook.py in __iter__(self)\r\n    252     def __iter__(self):\r\n    253         try:\r\n--> 254             for obj in super(tqdm_notebook, self).__iter__():\r\n    255                 # return super(tqdm...) will not catch exception\r\n    256                 yield obj\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1171         # (note: keep this check outside the loop for performance)\r\n   1172         if self.disable:\r\n-> 1173             for obj in iterable:\r\n   1174                 yield obj\r\n   1175             return\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result_iterator()\r\n    617                     # Careful not to keep a reference to the popped future\r\n    618                     if timeout is None:\r\n--> 619                         yield fs.pop().result()\r\n    620                     else:\r\n    621                         yield fs.pop().result(end_time - time.monotonic())\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    442                     raise CancelledError()\r\n    443                 elif self._state == FINISHED:\r\n--> 444                     return self.__get_result()\r\n    445                 else:\r\n    446                     raise TimeoutError()\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in __get_result(self)\r\n    387         if self._exception:\r\n    388             try:\r\n--> 389                 raise self._exception\r\n    390             finally:\r\n    391                 # Break a reference cycle with the exception in self._exception\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)\r\n    483     if isinstance(data_file, Url):\r\n    484         data_file = str(data_file)\r\n--> 485         return (request_etag(data_file, use_auth_token=use_auth_token),)\r\n    486     else:\r\n    487         data_file = str(data_file.resolve())\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in request_etag(url, use_auth_token)\r\n    489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:\r\n    490     headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)\r\n--> 491     response = http_head(url, headers=headers, max_retries=3)\r\n    492     response.raise_for_status()\r\n    493     etag = response.headers.get(\"ETag\") if response.ok else None\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)\r\n    474     headers = copy.deepcopy(headers) or {}\r\n    475     headers[\"user-agent\"] = get_datasets_user_agent(user_agent=headers.get(\"user-agent\"))\r\n--> 476     response = _request_with_retry(\r\n    477         method=\"HEAD\",\r\n    478         url=url,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n    408             if tries > max_retries:\r\n--> 409                 raise err\r\n    410             else:\r\n    411                 logger.info(f\"{method} request to {url} timed out, retrying... [{tries\/max_retries}]\")\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    403         tries += 1\r\n    404         try:\r\n--> 405             response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)\r\n    406             success = True\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     58     # cases, and look like a memory leak in others.\r\n     59     with sessions.Session() as session:\r\n---> 60         return session.request(method=method, url=url, **kwargs)\r\n     61 \r\n     62 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    531         }\r\n    532         send_kwargs.update(settings)\r\n--> 533         resp = self.send(prep, **send_kwargs)\r\n    534 \r\n    535         return resp\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    644 \r\n    645         # Send the request\r\n--> 646         r = adapter.send(request, **kwargs)\r\n    647 \r\n    648         # Total elapsed time of the request (approximately)\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    502                 # TODO: Remove this in 3.0.0: see #2811\r\n    503                 if not isinstance(e.reason, NewConnectionError):\r\n--> 504                     raise ConnectTimeout(e, request=request)\r\n    505 \r\n    506             if isinstance(e.reason, ResponseError):\r\n\r\nConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.1 \n Hi @ToddMorrill, people from the Pile team have mirrored their data in a new host server: https:\/\/mystic.the-eye.eu\r\n\r\nSee:\r\n- #3627\r\n\r\nIt should work if you update your URL.\r\n\r\nWe should also update the URL in our course material.","embeddings":[0.0793418959,-0.1990928948,-0.0149751296,0.3214185238,0.0415688939,0.0988509953,0.2786989808,0.3070807755,-0.0256318357,0.0241770521,0.0213189796,0.0649864301,0.2614777088,-0.009051688,0.0891451091,-0.1374546587,-0.1224959195,0.0288259462,0.1671681255,-0.0706991181,-0.1634531021,0.3103227615,-0.2114495188,0.0229474511,0.3344618082,-0.0487442054,-0.0180449206,0.0796105936,-0.259480238,-0.400164336,0.3467973471,0.0676352531,0.096873574,0.2832933366,-0.0001152371,-0.1814393252,0.3865606189,-0.106088683,-0.1463316381,-0.6200528145,-0.0925693586,-0.3742443621,0.0368294343,-0.2364805937,-0.0161443893,-0.2569465339,-0.0521956868,0.0574441589,0.5009845495,0.2733213902,0.1600577831,0.3386075497,0.4762297571,-0.1643427014,0.2631475627,-0.0007334378,-0.1128453687,0.3671372831,0.1977306157,0.1890682876,0.0809969008,0.1884224117,0.1872570515,0.021432722,0.0720425397,0.2159957588,-0.1662811637,-0.2893691063,0.2658520639,0.2006779909,0.5076361895,-0.1013349444,-0.3343558609,-0.1578326672,-0.0257836934,0.2880061567,0.125923261,0.3794694245,-0.2153221816,0.1394665837,0.0239908434,-0.07528238,-0.2904427648,0.1411405206,0.1545024216,-0.0535571761,-0.1805348247,0.2100210488,0.2253744155,-0.1191945896,0.115672946,-0.1589013785,0.0045637703,0.1201125383,-0.0652741641,-0.1301217973,0.1300206631,-0.2145696729,0.2953865826,0.1702559441,-0.0052002738,-0.1111152545,0.0564476922,-0.016203735,0.3816420734,-0.1233996078,-0.0841436759,-0.2060020864,0.3447534144,0.6683691144,0.2530405223,-0.001518407,0.1052560136,-0.0171635952,-0.1383760124,-0.1462052763,0.285618484,-0.3072584569,-0.229311049,0.2585442662,-0.3156474829,0.1121014878,-0.3288187683,0.2693771124,-0.1420352608,0.1492166221,0.4245757461,0.1484355479,-0.029062096,-0.2422586977,-0.1016428843,0.0527961552,0.1146823019,-0.1797043234,0.2691029012,-0.1405486614,0.328401953,0.0177316628,0.0434700251,-0.2795040905,-0.0850494206,0.1849165559,0.0152455885,0.2789573073,0.3548772037,0.350933522,-0.0322624482,0.0762057304,-0.1938822716,0.0723607466,-0.2473401129,-0.0533916466,-0.0830340758,0.1086283773,-0.3945971131,0.0259636771,-0.1337233633,-0.0649369508,-0.1995491683,0.1299387515,0.0005099957,-0.1368607879,-0.0169746205,-0.1217870638,0.53069067,0.4324537814,0.0124963019,-0.1217362508,-0.1118799746,0.0434905589,-0.1127401292,0.0006612553,-0.2803042829,0.0575219169,-0.1191475466,0.264998138,0.1093816087,-0.4711232483,-0.8733941317,0.0114978738,-0.3416739106,0.252620846,-0.0470301732,-0.0134672951,-0.0048580039,0.0233112834,-0.0141062178,0.4104496539,0.1495377868,-0.0713912174,-0.2368456423,-0.373826772,-0.0259877052,0.1753927171,0.0423936807,-0.0787834451,0.1425980926,0.3396872878,0.4201105237,0.0455283709,0.2494730651,0.2911367714,0.1919087023,-0.0834890008,0.17820099,-0.0896165222,-0.5133877993,0.1730192006,0.0517218523,0.2064817995,-0.4273390472,-0.1029789969,-0.3319749236,-0.0099718776,-0.2414238453,-0.2010108829,0.0930675864,0.0288966335,0.1742316931,0.2303346843,-0.0119322129,0.1851139963,-0.1602055728,0.1431324631,-0.5135131478,0.1428674012,-0.2051269412,0.0414538234,0.2167689949,-0.0474701859,0.0622037388,-0.0518584326,-0.069551535,0.1353901178,-0.1594231129,0.2733830214,0.0773170218,-0.0797431469,0.1603217125,-0.5517671108,0.0019696909,0.490470767,0.1188812703,0.0783354491,-0.1263228804,0.2736314535,0.0137049435,0.0661550611,0.1842766106,0.2874756157,0.3209612072,-0.1492517442,-0.3007631898,-0.0183587074,0.3522838354,0.2142374814,-0.1417354494,-0.2299476266,-0.4221257269,0.1657796204,0.4969475865,-0.262748301,-0.0531389602,0.2558321357,-0.1094783917,-0.0873026997,-0.0679852217,0.2343420386,0.1452105939,0.1128676832,0.0103099011,0.2695217133,0.0871118009,-0.144890517,0.1212059483,0.1995584667,0.0297716856,0.1385250837,0.0680976138,0.1382665038,-0.064184323,-0.1483417302,-0.0802401155,0.0919035822,-0.450083524,-0.1027803272,-0.0939685032,0.1880657822,-0.2880827188,-0.2205043733,-0.3286839128,-0.1170019954,-0.2444553971,0.3951078951,-0.0902109742,-0.1454010457,-0.300141871,0.3773148954,0.0136409337,-0.0375468619,-0.2003991306,-0.118748568,-0.3264170885,0.0232078396,0.0127662206,0.0733814389,0.1335761845,-0.3038693964,-0.0058854846,-0.2667237222,-0.2802242339,0.1317507476,-0.1951259524,0.3825179636,0.1503156573,0.5875208378,-0.1490400434,-0.0307233725,0.2851510942,-0.3901843429,-0.2450075895,-0.2378758341,-0.0231324416,-0.1235968918,-0.0828843266,-0.4791831374,-0.2473434061,-0.3006749153,0.4074532092,-0.1374833882,0.0367514007,0.1760338992,-0.1233249828,0.1073087677,0.1578091234,0.1812890172,-0.1553850025,-0.4383784831,0.3929957747,-0.2362470925,-0.7573886514,0.1659151614,0.0932070911,0.0847234204,0.1104102135,-0.5766034126,-0.0191007871,0.041887816,0.0486723669,-0.0347316265,0.0436797254,0.1139030531,-0.1765689105,0.0315663479,-0.154655993,-0.1199721321,0.1829023808,0.0851229653,0.3600035012,0.1991250962,0.2229770273,0.1364159733,0.5776369572,0.3293662667,0.0902140364,0.1597546786,0.1262653321,0.0691311806,0.053406477,-0.2993422747,-0.1624239087,-0.2021421343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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3504","title":"Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst","comments":"The old URL is still present in the HuggingFace course here: \r\nhttps:\/\/huggingface.co\/course\/chapter5\/4?fw=pt\r\n\r\nI have created a PR for the Notebook here: https:\/\/github.com\/huggingface\/notebooks\/pull\/148\r\nNot sure if the HTML is in a public repo. I wasn't able to find it. ","body":"## Describe the bug\r\nI am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https:\/\/huggingface.co\/course\/chapter5\/4?fw=pt).\r\n\r\nhttps:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\n\r\n# This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\ndata_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\npubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\npubmed_dataset\r\n```\r\n\r\nI also tried with `wget` as follows.\r\n```\r\nwget https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\r\n```\r\n\r\n## Expected results\r\nI expect to be able to download this file.\r\n\r\n## Actual results\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\ntimeout                                   Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    158         try:\r\n--> 159             conn = connection.create_connection(\r\n    160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\ntimeout: timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeoutError                       Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    664             # Make the request on the httplib connection object.\r\n--> 665             httplib_response = self._make_request(\r\n    666                 conn,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    375         try:\r\n--> 376             self._validate_conn(conn)\r\n    377         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    995         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 996             conn.connect()\r\n    997 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in connect(self)\r\n    313         # Add certificate verification\r\n--> 314         conn = self._new_conn()\r\n    315         hostname = self.host\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    163         except SocketTimeout:\r\n--> 164             raise ConnectTimeoutError(\r\n    165                 self,\r\n\r\nConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    438             if not chunked:\r\n--> 439                 resp = conn.urlopen(\r\n    440                     method=request.method,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    718 \r\n--> 719             retries = retries.increment(\r\n    720                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    435         if new_retry.is_exhausted():\r\n--> 436             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    437 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeout                            Traceback (most recent call last)\r\n\/tmp\/ipykernel_15104\/606583593.py in <module>\r\n      3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\n      4 data_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\n----> 5 pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\n      6 pubmed_dataset\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1655 \r\n   1656     # Create a dataset builder\r\n-> 1657     builder_instance = load_dataset_builder(\r\n   1658         path=path,\r\n   1659         name=name,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1492         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1493         download_config.use_auth_token = use_auth_token\r\n-> 1494     dataset_module = dataset_module_factory(\r\n   1495         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1496     )\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1116     # Try packaged\r\n   1117     if path in _PACKAGED_DATASETS_MODULES:\r\n-> 1118         return PackagedDatasetModuleFactory(\r\n   1119             path, data_files=data_files, download_config=download_config, download_mode=download_mode\r\n   1120         ).get_module()\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in get_module(self)\r\n    773             else get_patterns_locally(str(Path().resolve()))\r\n    774         )\r\n--> 775         data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)\r\n    776         module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]\r\n    777         builder_kwargs = {\"hash\": hash, \"data_files\": data_files}\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    576         for key, patterns_for_key in patterns.items():\r\n    577             out[key] = (\r\n--> 578                 DataFilesList.from_local_or_remote(\r\n    579                     patterns_for_key,\r\n    580                     base_path=base_path,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    545         base_path = base_path if base_path is not None else str(Path().resolve())\r\n    546         data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n--> 547         origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n    548         return cls(data_files, origin_metadata)\r\n    549 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)\r\n    492     data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None\r\n    493 ) -> Tuple[str]:\r\n--> 494     return thread_map(\r\n    495         partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),\r\n    496         data_files,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)\r\n     92     \"\"\"\r\n     93     from concurrent.futures import ThreadPoolExecutor\r\n---> 94     return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     95 \r\n     96 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     74             map_args.update(chunksize=chunksize)\r\n     75         with PoolExecutor(**pool_kwargs) as ex:\r\n---> 76             return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))\r\n     77 \r\n     78 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/notebook.py in __iter__(self)\r\n    252     def __iter__(self):\r\n    253         try:\r\n--> 254             for obj in super(tqdm_notebook, self).__iter__():\r\n    255                 # return super(tqdm...) will not catch exception\r\n    256                 yield obj\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1171         # (note: keep this check outside the loop for performance)\r\n   1172         if self.disable:\r\n-> 1173             for obj in iterable:\r\n   1174                 yield obj\r\n   1175             return\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result_iterator()\r\n    617                     # Careful not to keep a reference to the popped future\r\n    618                     if timeout is None:\r\n--> 619                         yield fs.pop().result()\r\n    620                     else:\r\n    621                         yield fs.pop().result(end_time - time.monotonic())\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    442                     raise CancelledError()\r\n    443                 elif self._state == FINISHED:\r\n--> 444                     return self.__get_result()\r\n    445                 else:\r\n    446                     raise TimeoutError()\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in __get_result(self)\r\n    387         if self._exception:\r\n    388             try:\r\n--> 389                 raise self._exception\r\n    390             finally:\r\n    391                 # Break a reference cycle with the exception in self._exception\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)\r\n    483     if isinstance(data_file, Url):\r\n    484         data_file = str(data_file)\r\n--> 485         return (request_etag(data_file, use_auth_token=use_auth_token),)\r\n    486     else:\r\n    487         data_file = str(data_file.resolve())\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in request_etag(url, use_auth_token)\r\n    489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:\r\n    490     headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)\r\n--> 491     response = http_head(url, headers=headers, max_retries=3)\r\n    492     response.raise_for_status()\r\n    493     etag = response.headers.get(\"ETag\") if response.ok else None\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)\r\n    474     headers = copy.deepcopy(headers) or {}\r\n    475     headers[\"user-agent\"] = get_datasets_user_agent(user_agent=headers.get(\"user-agent\"))\r\n--> 476     response = _request_with_retry(\r\n    477         method=\"HEAD\",\r\n    478         url=url,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n    408             if tries > max_retries:\r\n--> 409                 raise err\r\n    410             else:\r\n    411                 logger.info(f\"{method} request to {url} timed out, retrying... [{tries\/max_retries}]\")\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    403         tries += 1\r\n    404         try:\r\n--> 405             response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)\r\n    406             success = True\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     58     # cases, and look like a memory leak in others.\r\n     59     with sessions.Session() as session:\r\n---> 60         return session.request(method=method, url=url, **kwargs)\r\n     61 \r\n     62 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    531         }\r\n    532         send_kwargs.update(settings)\r\n--> 533         resp = self.send(prep, **send_kwargs)\r\n    534 \r\n    535         return resp\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    644 \r\n    645         # Send the request\r\n--> 646         r = adapter.send(request, **kwargs)\r\n    647 \r\n    648         # Total elapsed time of the request (approximately)\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    502                 # TODO: Remove this in 3.0.0: see #2811\r\n    503                 if not isinstance(e.reason, NewConnectionError):\r\n--> 504                     raise ConnectTimeout(e, request=request)\r\n    505 \r\n    506             if isinstance(e.reason, ResponseError):\r\n\r\nConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.1","comment_length":38,"text":"Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst \n ## Describe the bug\r\nI am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https:\/\/huggingface.co\/course\/chapter5\/4?fw=pt).\r\n\r\nhttps:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst \r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\n\r\n# This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\ndata_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\npubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\npubmed_dataset\r\n```\r\n\r\nI also tried with `wget` as follows.\r\n```\r\nwget https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\r\n```\r\n\r\n## Expected results\r\nI expect to be able to download this file.\r\n\r\n## Actual results\r\nTraceback\r\n```\r\n---------------------------------------------------------------------------\r\ntimeout                                   Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    158         try:\r\n--> 159             conn = connection.create_connection(\r\n    160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\ntimeout: timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeoutError                       Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    664             # Make the request on the httplib connection object.\r\n--> 665             httplib_response = self._make_request(\r\n    666                 conn,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    375         try:\r\n--> 376             self._validate_conn(conn)\r\n    377         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    995         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 996             conn.connect()\r\n    997 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in connect(self)\r\n    313         # Add certificate verification\r\n--> 314         conn = self._new_conn()\r\n    315         hostname = self.host\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connection.py in _new_conn(self)\r\n    163         except SocketTimeout:\r\n--> 164             raise ConnectTimeoutError(\r\n    165                 self,\r\n\r\nConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    438             if not chunked:\r\n--> 439                 resp = conn.urlopen(\r\n    440                     method=request.method,\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    718 \r\n--> 719             retries = retries.increment(\r\n    720                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n\r\n\/usr\/lib\/python3\/dist-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    435         if new_retry.is_exhausted():\r\n--> 436             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    437 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectTimeout                            Traceback (most recent call last)\r\n\/tmp\/ipykernel_15104\/606583593.py in <module>\r\n      3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)\r\n      4 data_files = \"https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\r\n----> 5 pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\n      6 pubmed_dataset\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)\r\n   1655 \r\n   1656     # Create a dataset builder\r\n-> 1657     builder_instance = load_dataset_builder(\r\n   1658         path=path,\r\n   1659         name=name,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)\r\n   1492         download_config = download_config.copy() if download_config else DownloadConfig()\r\n   1493         download_config.use_auth_token = use_auth_token\r\n-> 1494     dataset_module = dataset_module_factory(\r\n   1495         path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n   1496     )\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)\r\n   1116     # Try packaged\r\n   1117     if path in _PACKAGED_DATASETS_MODULES:\r\n-> 1118         return PackagedDatasetModuleFactory(\r\n   1119             path, data_files=data_files, download_config=download_config, download_mode=download_mode\r\n   1120         ).get_module()\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py in get_module(self)\r\n    773             else get_patterns_locally(str(Path().resolve()))\r\n    774         )\r\n--> 775         data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)\r\n    776         module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]\r\n    777         builder_kwargs = {\"hash\": hash, \"data_files\": data_files}\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    576         for key, patterns_for_key in patterns.items():\r\n    577             out[key] = (\r\n--> 578                 DataFilesList.from_local_or_remote(\r\n    579                     patterns_for_key,\r\n    580                     base_path=base_path,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)\r\n    545         base_path = base_path if base_path is not None else str(Path().resolve())\r\n    546         data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n--> 547         origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)\r\n    548         return cls(data_files, origin_metadata)\r\n    549 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)\r\n    492     data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None\r\n    493 ) -> Tuple[str]:\r\n--> 494     return thread_map(\r\n    495         partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),\r\n    496         data_files,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)\r\n     92     \"\"\"\r\n     93     from concurrent.futures import ThreadPoolExecutor\r\n---> 94     return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     95 \r\n     96 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/contrib\/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n     74             map_args.update(chunksize=chunksize)\r\n     75         with PoolExecutor(**pool_kwargs) as ex:\r\n---> 76             return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))\r\n     77 \r\n     78 \r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/notebook.py in __iter__(self)\r\n    252     def __iter__(self):\r\n    253         try:\r\n--> 254             for obj in super(tqdm_notebook, self).__iter__():\r\n    255                 # return super(tqdm...) will not catch exception\r\n    256                 yield obj\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1171         # (note: keep this check outside the loop for performance)\r\n   1172         if self.disable:\r\n-> 1173             for obj in iterable:\r\n   1174                 yield obj\r\n   1175             return\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result_iterator()\r\n    617                     # Careful not to keep a reference to the popped future\r\n    618                     if timeout is None:\r\n--> 619                         yield fs.pop().result()\r\n    620                     else:\r\n    621                         yield fs.pop().result(end_time - time.monotonic())\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    442                     raise CancelledError()\r\n    443                 elif self._state == FINISHED:\r\n--> 444                     return self.__get_result()\r\n    445                 else:\r\n    446                     raise TimeoutError()\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/_base.py in __get_result(self)\r\n    387         if self._exception:\r\n    388             try:\r\n--> 389                 raise self._exception\r\n    390             finally:\r\n    391                 # Break a reference cycle with the exception in self._exception\r\n\r\n\/usr\/lib\/python3.8\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)\r\n    483     if isinstance(data_file, Url):\r\n    484         data_file = str(data_file)\r\n--> 485         return (request_etag(data_file, use_auth_token=use_auth_token),)\r\n    486     else:\r\n    487         data_file = str(data_file.resolve())\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in request_etag(url, use_auth_token)\r\n    489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:\r\n    490     headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)\r\n--> 491     response = http_head(url, headers=headers, max_retries=3)\r\n    492     response.raise_for_status()\r\n    493     etag = response.headers.get(\"ETag\") if response.ok else None\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)\r\n    474     headers = copy.deepcopy(headers) or {}\r\n    475     headers[\"user-agent\"] = get_datasets_user_agent(user_agent=headers.get(\"user-agent\"))\r\n--> 476     response = _request_with_retry(\r\n    477         method=\"HEAD\",\r\n    478         url=url,\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n    408             if tries > max_retries:\r\n--> 409                 raise err\r\n    410             else:\r\n    411                 logger.info(f\"{method} request to {url} timed out, retrying... [{tries\/max_retries}]\")\r\n\r\n~\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\r\n    403         tries += 1\r\n    404         try:\r\n--> 405             response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)\r\n    406             success = True\r\n    407         except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     58     # cases, and look like a memory leak in others.\r\n     59     with sessions.Session() as session:\r\n---> 60         return session.request(method=method, url=url, **kwargs)\r\n     61 \r\n     62 \r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    531         }\r\n    532         send_kwargs.update(settings)\r\n--> 533         resp = self.send(prep, **send_kwargs)\r\n    534 \r\n    535         return resp\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    644 \r\n    645         # Send the request\r\n--> 646         r = adapter.send(request, **kwargs)\r\n    647 \r\n    648         # Total elapsed time of the request (approximately)\r\n\r\n\/usr\/lib\/python3\/dist-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    502                 # TODO: Remove this in 3.0.0: see #2811\r\n    503                 if not isinstance(e.reason, NewConnectionError):\r\n--> 504                     raise ConnectTimeout(e, request=request)\r\n    505 \r\n    506             if isinstance(e.reason, ResponseError):\r\n\r\nConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: \/public\/AI\/pile_preliminary_components\/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.17.0\r\n- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.1 \n The old URL is still present in the HuggingFace course here: \r\nhttps:\/\/huggingface.co\/course\/chapter5\/4?fw=pt\r\n\r\nI have created a PR for the Notebook here: https:\/\/github.com\/huggingface\/notebooks\/pull\/148\r\nNot sure if the HTML is in a public repo. I wasn't able to find it. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3499","title":"Adjusting chunk size for streaming datasets","comments":"Hi ! Data streaming uses `fsspec` to read the data files progressively. IIRC the block size for buffering is 5MiB by default. So every time you finish iterating over a block, it downloads the next one. You can still try to increase the `fsspec` block size for buffering if it can help. To do so you just need to increase `fsspec.spec.AbstractBufferedFile.DEFAULT_BLOCK_SIZE `\r\n\r\nCurrently this is unfortunately done in a single thread, so it blocks the processing to download and uncompress the next block. At one point it would be nice to be able to do that in parallel !","body":"**Is your feature request related to a problem? Please describe.**\r\nI want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing. \r\n\r\n**Describe the solution you'd like**\r\nI would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.\r\n\r\n","comment_length":99,"text":"Adjusting chunk size for streaming datasets \n **Is your feature request related to a problem? Please describe.**\r\nI want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing. \r\n\r\n**Describe the solution you'd like**\r\nI would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.\r\n\r\n \n Hi ! Data streaming uses `fsspec` to read the data files progressively. IIRC the block size for buffering is 5MiB by default. So every time you finish iterating over a block, it downloads the next one. You can still try to increase the `fsspec` block size for buffering if it can help. To do so you just need to increase `fsspec.spec.AbstractBufferedFile.DEFAULT_BLOCK_SIZE `\r\n\r\nCurrently this is unfortunately done in a single thread, so it blocks the processing to download and uncompress the next block. At one point it would be nice to be able to do that in parallel 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3490","title":"Does datasets support load text from HDFS?","comments":"Hi ! `datasets` currently supports reading local files or files over HTTP. We may add support for other filesystems (cloud storages, hdfs...) at one point though :)","body":"The raw text data is stored on HDFS due to the dataset's size is too large to store on my develop machine, \r\nso I wander does datasets support read data from hdfs?","comment_length":27,"text":"Does datasets support load text from HDFS? \n The raw text data is stored on HDFS due to the dataset's size is too large to store on my develop machine, \r\nso I wander does datasets support read data from hdfs? \n Hi ! `datasets` currently supports reading local files or files over HTTP. We may add support for other filesystems (cloud storages, hdfs...) at one point though :)","embeddings":[-0.3971247673,0.0771138594,-0.2187531739,0.3979652524,0.0818778425,-0.0516115054,0.3947127759,0.0203538649,0.3752116561,-0.0103761349,-0.4098941088,-0.1132297143,0.0458147265,0.4414331615,0.1317271143,0.1704084128,0.0876928791,-0.0880468041,-0.0780741572,-0.0664893538,-0.0723643899,0.0449704602,-0.1958407611,-0.0986035243,-0.1383970231,0.0206858944,-0.2444384098,0.1659332365,-0.1807905585,-0.17199637,0.2904438376,0.2946028709,0.2875447869,0.3207382858,-0.0001013837,0.013286191,0.2074205577,-0.0434822552,-0.2026391178,-0.3350112736,-0.0653473064,-0.2407704145,0.0035842261,-0.2166056633,-0.0226571411,-0.2916092277,0.1962841749,-0.3205721676,0.1705725044,0.0757761747,0.2325266302,-0.0088318344,-0.1013397574,0.0907874852,0.1396611631,0.5480781794,-0.1571068615,0.022093188,0.4860548079,0.2169069201,-0.1732485741,0.1997145265,-0.0902614146,0.1511503011,0.1779228747,0.1132758632,-0.1546010822,-0.290484935,0.0558003485,0.2423138767,0.9940255284,-0.3276417553,-0.4075832963,-0.3322303295,-0.0641545355,0.1604030877,0.3607281148,0.3255701363,-0.1696306467,0.425382942,-0.5047800541,-0.6933261156,-0.136362195,-0.0957370847,-0.0807734802,-0.0932077765,-0.3084053397,-0.1098844409,0.0643704012,0.0913825929,0.7180293798,-0.3602030575,-0.0597852729,0.2084618062,-0.256123513,-0.0960733518,-0.3207004666,0.0902550519,0.2026826441,-0.2118564844,0.0198484715,0.2277435809,-0.4566640556,0.0648562983,0.5252538919,-0.2089330852,0.3063522279,-0.0718113333,0.0628684983,-0.2984002829,0.0360976197,-0.3316276371,-0.2401827574,0.0921874791,-0.2810925543,-0.3829035461,0.1116236821,-0.225317046,0.0822216421,0.0337940343,0.1559274197,-0.1913526952,0.0619576126,0.3919928074,-0.1081591323,0.2064447552,-0.0714291036,0.0408842303,0.0963771865,-0.2708081603,-0.0522054471,-0.1336162984,0.0331963226,0.037285462,0.1115337014,-0.1255082786,-0.0152168917,0.0489356518,0.0978308693,0.0926067978,0.0981463343,-0.3789795637,0.2233297974,0.1619247347,0.0714052767,0.1607787609,0.0607538931,-0.2745662332,-0.0073825456,0.3100050092,-0.5396037698,-0.1091501191,-0.0649788752,0.2071307003,-0.0480634421,-0.0459501147,-0.5413884521,0.2662038803,-0.2232277393,-0.0022773675,0.0217980593,0.1019906551,-0.1735138148,-0.0805796459,0.1502744704,0.1084254235,-0.5315918326,0.0687938631,-0.1965751052,-0.310577333,-0.1554385275,0.1261643469,-0.0588436648,-0.0599662699,-0.1359012574,0.3645393252,0.3171965182,-0.2120140493,-0.0320078805,0.7275360227,0.3368384838,-0.1937543601,-0.3636115193,0.0429058783,0.2797918022,0.0767887011,0.0072787679,0.3445045948,0.1434052736,-0.0343244933,-0.0992922857,-0.2606255114,-0.1513897181,0.3098195195,0.1538524032,0.0209490247,0.2251601815,-0.1150299013,0.3242802322,-0.070314981,0.0500118583,0.3459143341,0.2236997932,0.3099581897,-0.067834422,-0.1959764808,-0.1566720903,0.2332297415,0.2586607933,-0.2221777737,0.1721016765,-0.1538184881,0.1086552441,0.1497265249,-0.0045293239,0.142799139,0.1545957625,0.0388088003,-0.2398286909,0.0632679686,-0.3949104846,0.4017183185,-0.3499346077,-0.0181994885,0.0926126167,0.4166501462,-0.0106172608,0.0808213353,-0.0876213834,-0.174996525,-0.1940192282,-0.0414365679,0.1010763645,0.4668955803,-0.0750200301,0.2455573976,0.2863835394,0.1798900813,0.2454586625,-0.1643291116,0.2677406669,-0.1628617048,0.2774422765,-0.0821917057,-0.3020057678,0.2089003026,-0.2663176656,-0.0514871962,0.0614554584,-0.0203881674,0.0925380141,-0.0201942753,0.0566268899,-0.0049063354,0.0394120626,-0.0558157042,0.2595349252,-0.0715441704,-0.3631034791,-0.0958029702,0.2407079935,-0.0317040458,0.0271428954,0.1611095816,-0.3337494731,-0.2464182973,0.3278942406,0.1170702726,0.2371632308,0.2530483305,0.2812438905,-0.0230466779,0.1505654603,-0.0941457599,0.1930572689,-0.1070410162,-0.1335049123,-0.04947583,-0.0451727808,-0.1455820352,-0.3172807097,0.1474345922,0.0526198186,-0.0921370387,-0.2168500274,0.0984408781,-0.1659574211,-0.1912459731,0.251908958,-0.1315381676,-0.1917570531,-0.0826798901,0.0158641487,0.1581752896,0.1226821244,-0.3144491613,0.0499446392,0.340634644,-0.132779792,0.0643338785,-0.4494998157,-0.1645043343,-0.0645798668,0.1781389117,0.4641900659,0.1733225882,0.1368003935,0.0078943968,-0.0287120454,-0.1798613966,0.1170417368,0.209057942,0.0459234938,0.1049794331,-0.3072612584,0.1867192239,-0.2108752578,0.2874262631,-0.0336629525,-0.0681386888,-0.2360317111,-0.246449396,0.3845226467,-0.1867474169,-0.1210920364,-0.0512889102,-0.3403867483,-0.1262528896,0.2414739877,0.0097173527,0.0269961637,0.1361623108,0.0651313961,0.2103681862,-0.0211638492,-0.0318976752,-0.1222801656,-0.7032638192,0.2029696256,0.0007148445,-0.3657356501,0.1774182618,0.3640442491,-0.4068465531,0.3074136674,-0.5222819448,0.1057425663,0.0909911916,0.0678119138,0.0349537954,0.1624972075,0.1927142739,0.0572923534,-0.0291604046,-0.1969799101,0.0112023614,-0.0817696303,0.0078595988,-0.1554274708,0.105401516,-0.0368551314,0.1991499066,0.2418667823,-0.1906700283,0.061072357,0.3159396648,0.1158397198,0.4249778986,0.0039967578,-0.1487399638,0.1157605425,-0.21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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3488","title":"URL query parameters are set as path in the compression hop for fsspec","comments":"I think the test passes because it simply ignore what's after `gzip:\/\/`.\r\n\r\nThe returned urlpath is expected to look like `gzip:\/\/filename::url`, and the filename is currently considered to be what's after the final `\/`, hence the result.\r\n\r\nWe can decide to change this and simply have `gzip:\/\/::url`, this way we don't need to guess the filename, what do you think ?","body":"## Describe the bug\r\nThere is an ssue with `StreamingDownloadManager._extract`.\r\n\r\nI don't know how the test `test_streaming_gg_drive_gzipped` passes:\r\n\r\nFor \r\n```python\r\nTEST_GG_DRIVE_GZIPPED_URL = \"https:\/\/drive.google.com\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz\"\r\nurlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL)\r\n```\r\ngives `urlpath`:\r\n```python \r\n'gzip:\/\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https:\/\/drive.google.com\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz'\r\n```\r\n\r\nThe gzip path makes no sense: `gzip:\/\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz`\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets.utils.streaming_download_manager import StreamingDownloadManager\r\n\r\ndl_manager = StreamingDownloadManager()\r\nurlpath = dl_manager.extract(\"https:\/\/drive.google.com\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz\")\r\nprint(urlpath)\r\n```\r\n\r\n## Expected results\r\nThe query parameters should not be set as part of the path.\r\n\r\n","comment_length":61,"text":"URL query parameters are set as path in the compression hop for fsspec \n ## Describe the bug\r\nThere is an ssue with `StreamingDownloadManager._extract`.\r\n\r\nI don't know how the test `test_streaming_gg_drive_gzipped` passes:\r\n\r\nFor \r\n```python\r\nTEST_GG_DRIVE_GZIPPED_URL = \"https:\/\/drive.google.com\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz\"\r\nurlpath = StreamingDownloadManager().download_and_extract(TEST_GG_DRIVE_GZIPPED_URL)\r\n```\r\ngives `urlpath`:\r\n```python \r\n'gzip:\/\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz::https:\/\/drive.google.com\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz'\r\n```\r\n\r\nThe gzip path makes no sense: `gzip:\/\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz`\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets.utils.streaming_download_manager import StreamingDownloadManager\r\n\r\ndl_manager = StreamingDownloadManager()\r\nurlpath = dl_manager.extract(\"https:\/\/drive.google.com\/uc?export=download&id=1Bt4Garpf0QLiwkJhHJzXaVa0I0H5Qhwz\")\r\nprint(urlpath)\r\n```\r\n\r\n## Expected results\r\nThe query parameters should not be set as part of the path.\r\n\r\n \n I think the test passes because it simply ignore what's after `gzip:\/\/`.\r\n\r\nThe returned urlpath is expected to look like `gzip:\/\/filename::url`, and the filename is currently considered to be what's after the final `\/`, hence the result.\r\n\r\nWe can decide to change this and simply have `gzip:\/\/::url`, this way we don't need to guess the filename, what do you think 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3485","title":"skip columns which cannot set to specific format when set_format","comments":"You can add columns that you wish to set into `torch` format using `dataset.set_format(\"torch\", ['id', 'abc'])` so that input batch of the transform only contains those columns","body":"**Is your feature request related to a problem? Please describe.**\r\nWhen using `dataset.set_format(\"torch\")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns.\r\n\r\n**Describe the solution you'd like**\r\nskip columns which cannot set to specific format when set_format instead of raise an error.\r\n","comment_length":27,"text":"skip columns which cannot set to specific format when set_format \n **Is your feature request related to a problem? Please describe.**\r\nWhen using `dataset.set_format(\"torch\")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns.\r\n\r\n**Describe the solution you'd like**\r\nskip columns which cannot set to specific format when set_format instead of raise an error.\r\n \n You can add columns that you wish to set into `torch` format using `dataset.set_format(\"torch\", ['id', 'abc'])` so that input batch of the transform only contains those 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3485","title":"skip columns which cannot set to specific format when set_format","comments":"Sorry, I miss `output_all_columns` args and thought after `dataset.set_format(\"torch\", columns=columns)` I can only get specific columns I assigned.","body":"**Is your feature request related to a problem? Please describe.**\r\nWhen using `dataset.set_format(\"torch\")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns.\r\n\r\n**Describe the solution you'd like**\r\nskip columns which cannot set to specific format when set_format instead of raise an error.\r\n","comment_length":18,"text":"skip columns which cannot set to specific format when set_format \n **Is your feature request related to a problem? Please describe.**\r\nWhen using `dataset.set_format(\"torch\")`, I must make sure every columns in datasets can convert to `torch`, however, sometimes I want to keep some string columns.\r\n\r\n**Describe the solution you'd like**\r\nskip columns which cannot set to specific format when set_format instead of raise an error.\r\n \n Sorry, I miss `output_all_columns` args and thought after `dataset.set_format(\"torch\", columns=columns)` I can only get specific columns I assigned.","embeddings":[-0.2914404571,-0.2245159596,-0.0692841485,-0.1242923662,0.477096349,0.3206254244,0.1285253763,0.52149266,-0.3331494331,0.2311048955,-0.0285508558,0.3594093025,-0.1113209352,0.3187874854,-0.2002548724,-0.0854489058,-0.0791168287,0.2076984495,-0.1604340225,0.014232724,-0.5035930276,-0.0673879832,-0.2356801033,-0.0830828398,-0.042203296,-0.0407308862,0.2280006856,-0.2339219451,-0.1043410376,-0.0715303496,0.1030836254,-0.0062280516,0.0117383106,0.074041687,-0.0001111193,-0.0483462512,0.0563386343,-0.1714338511,-0.3745975196,-0.0939023271,-0.2877213359,-0.1493564993,-0.0840597451,-0.3283057809,-0.1775550544,-0.0828023404,-0.0985556543,-0.5474972129,-0.0203521401,0.6072782874,0.2206605375,-0.0401345119,-0.2079625577,0.055497244,0.2789070606,0.2965490818,-0.2213106602,-0.0968408957,0.1535821706,0.3496623039,0.0949912295,0.4483106732,-0.4570739269,-0.0624537989,-0.1271032989,-0.2069598883,-0.1368163824,-0.1696696728,0.4456742108,0.2214466482,0.7606423497,-0.2737029195,-0.316372633,-0.226203084,0.1231352836,-0.4307572842,-0.152812019,0.2060290277,-0.081119217,0.3249386251,-0.4119481742,-0.2360967994,-0.3548166752,0.0206680708,0.0154922437,-0.1401573867,-0.2034671307,0.1073921248,-0.0197076108,0.2311379015,-0.0227133185,-0.1184492782,0.0166852083,0.1451244652,-0.3125087917,-0.2637701333,-0.2920744717,0.0391481929,0.1654727608,0.2100578099,-0.0946577564,0.1892053783,0.4185742438,0.1166027859,0.0142898383,-0.1966972947,0.3529673219,0.4129503071,0.2284555584,-0.2608051896,0.0902177542,-0.1787822098,0.2510503232,0.0122540621,0.1422172636,0.2125259042,0.4701575339,-0.3024906516,-0.195353359,0.0543044098,0.2018183619,-0.1699425727,-0.0415419489,0.385753572,0.1464445144,0.2023287714,-0.2144159973,0.2098639458,0.0900561512,-0.1094220877,-0.0882425532,-0.006340926,0.0488334708,-0.0225140341,0.277058363,-0.0209622979,0.1137561724,0.2638829052,-0.1532070488,0.2357264757,0.1655739695,-0.1551435441,0.4289333224,0.1166245416,-0.1073725745,0.3771649897,0.1887647063,0.4448906481,-0.2652189732,0.3001042306,0.0262689311,-0.4886758327,-0.1240728796,0.1897279173,-0.1745367944,0.1538805515,-0.2301210761,0.1860429347,0.2575902343,0.1702310145,0.065128237,-0.2447384894,-0.0263834782,-0.1958010942,0.1367727369,0.3595796227,-0.4913682044,0.0623077713,0.2883013785,-0.2364070565,0.3357134759,-0.2854501307,0.2273771763,-0.1654102802,-0.1578746587,-0.292296946,0.4903785288,0.0289669391,0.1280627251,-0.1101099476,0.0750920326,-0.0886223987,0.3851989806,-0.1272100061,-0.0153314751,0.0889120921,-0.1597905755,0.2394315004,-0.152988866,0.1102747768,0.1256935298,0.1100111976,0.5099291205,0.0754286051,-0.082608752,0.3482392132,-0.2346823066,-0.1466189325,0.0688330308,-0.1807711422,0.013040523,-0.0465665124,0.1079153642,-0.168072626,0.1345524192,-0.3423453569,-0.4078040719,-0.0723513067,0.7319090962,-0.2726657689,0.0789644495,-0.3352166414,-0.2049711347,-0.2567695975,0.1107120663,0.2908731997,0.0822041035,0.1966240853,0.0545496605,-0.106527999,-0.5920351744,0.0368179679,-0.3498799801,-0.0226362012,-0.2332909852,-0.2295648754,0.1478670985,-0.1000895202,0.3202294707,0.1154924631,0.15486902,0.0003392342,-0.0780381486,0.1400153488,0.0008422844,0.0635078996,-0.4399292171,0.2348705232,-0.1209630445,0.0051154136,-0.174255535,-0.1457698643,0.1381262541,0.113330327,-0.143717587,0.3182297647,-0.2004452795,0.5244541168,0.1569336951,0.2001060247,0.0668624565,-0.0913647786,-0.1017789766,-0.5089071393,0.1526879072,-0.0591256395,-0.0944917649,0.1287007928,-0.7387918234,0.0884019136,0.7176113725,0.1192103475,-0.0314771272,0.0900376514,0.2187543213,0.1777922362,0.3118282557,-0.0519140586,0.2368334681,0.5076602697,0.0116272774,-0.1381976008,-0.0922898948,-0.129025057,0.3106614649,0.1459738612,-0.0658758804,0.1146137491,0.0951663256,-0.2121423334,-0.2340369374,0.2081094533,0.1620759517,0.0343376994,-0.2693778276,-0.1163114533,-0.4660423994,-0.1862977743,-0.0989227593,-0.0114763854,0.1098720208,-0.1399483383,0.1891931295,0.0588927045,-0.2704147995,0.194331497,-0.5419806242,0.3021380305,0.0318913236,-0.186453253,-0.2978926003,-0.0602536015,-0.100456275,0.1462895572,0.0680489242,0.1089321896,0.5652695298,0.0802232996,-0.1786692739,-0.2160671502,0.159184888,0.1441479474,-0.2287181616,0.0423990823,0.3255177438,0.3930433691,0.1002423391,0.000831472,0.1468833834,0.0258595943,0.02243682,0.1841795444,0.0773941353,0.3059366643,0.0698511973,-0.4689540565,-0.1738312244,-0.4129554033,-0.006067527,-0.3026125729,0.3904267848,-0.064727217,0.0705012903,-0.2405887991,-0.096789524,0.2355311513,-0.161106497,-0.0985510722,0.4318622947,-0.1593332142,0.0272570848,-0.0217301752,0.2005007118,-0.226597935,0.3195030391,-0.3393158317,-0.3503687382,-0.4836186171,-0.2014925331,-0.2478859276,-0.0805845112,0.0872783735,0.2044842094,-0.1194624007,-0.0754452124,-0.3026875854,0.0276773367,0.0317813866,-0.1168134287,0.1787039042,0.2837756276,0.0403632596,0.1262183338,-0.0303592291,0.1901771128,0.1458006799,-0.154009372,0.3480650783,-0.0720340982,-0.4622347653,0.1703335196,0.0283041,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3484","title":"make shape verification to use ArrayXD instead of nested lists for map","comments":"Hi! \r\n\r\nYes, this makes sense for numeric values, but first I have to finish https:\/\/github.com\/huggingface\/datasets\/pull\/3336 because currently ArrayXD only allows the first dimension to be dynamic.","body":"As describe in https:\/\/github.com\/huggingface\/datasets\/issues\/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https:\/\/colab.research.google.com\/drive\/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added.","comment_length":26,"text":"make shape verification to use ArrayXD instead of nested lists for map \n As describe in https:\/\/github.com\/huggingface\/datasets\/issues\/2005#issuecomment-793716753 and mentioned by @mariosasko in [image feature example](https:\/\/colab.research.google.com\/drive\/1mIrTnqTVkWLJWoBzT1ABSe-LFelIep1c#scrollTo=ow3XHDvf2I0B&line=1&uniqifier=1), IMO make shape verifcaiton to use ArrayXD instead of nested lists for map can help user reduce unnecessary cast. I notice datasets have done something special for `input_ids` and `attention_mask` which is also unnecessary after this feature added. \n Hi! \r\n\r\nYes, this makes sense for numeric values, but first I have to finish https:\/\/github.com\/huggingface\/datasets\/pull\/3336 because currently ArrayXD only allows the first dimension to be dynamic.","embeddings":[-0.3617063463,-0.4997367859,-0.1934600025,0.2382973731,0.3340796232,-0.0900437534,0.3655256629,0.2702301145,0.4711222649,0.2727882266,-0.013780999,0.1076698527,-0.0560969822,0.2658046782,0.0668743849,-0.0237628389,0.1465830654,0.2901391387,-0.3523029685,-0.0038966481,-0.2070188522,-0.2951275706,-0.1612805873,-0.0156408828,0.0162876602,-0.0644532666,-0.0121286642,-0.1191867888,-0.0432091318,-0.4249670506,0.2761992514,0.1818973124,-0.3901961148,0.0035761518,-0.0001122029,-0.0866778716,0.1540628225,0.12015149,0.0822975859,0.2682411671,-0.6081886292,-0.1868353039,-0.137990132,-0.4825141728,0.0594834462,-0.1219725609,-0.0082458677,-0.326521188,0.2653705478,-0.2063877881,0.1219748482,0.2893180251,0.0690844804,0.3019159138,-0.0391511284,0.4708933532,-0.0422993042,0.0102592157,0.4459713399,0.5078372359,0.0667610094,0.4769306779,-0.209975183,-0.1104425788,0.1160406843,-0.175396353,-0.02899516,-0.3436536789,-0.0598426387,0.5457000136,0.2493317127,-0.1551606506,-0.0435638539,-0.5095642209,-0.0702764317,0.1996081769,0.2885709405,-0.0565811992,0.1449424475,0.0004638646,-0.6731485724,-0.0134156263,-0.0427379385,-0.009534454,0.1808354706,0.0090902895,0.0580534264,0.1509800702,0.1544749737,-0.351390034,0.2712717354,-0.3496264219,-0.0620278195,-0.1291017532,-0.1777101457,-0.3769586384,-0.1137966514,-0.240396902,0.2445563525,-0.1304438263,0.144474864,0.5333007574,-0.0204016566,-0.136216253,0.1289216131,0.1822893173,0.1266711056,-0.0040782299,0.2083386183,0.0161060654,0.1459575742,0.12321008,0.1385818571,0.1420345306,-0.066774711,0.2679992914,0.2094806135,-0.0993035808,0.0260702353,-0.169345215,-0.3022428453,-0.2353593707,0.3432766199,0.0645122081,0.0705999583,0.1304491311,-0.2737263441,0.0153090805,0.1663095057,0.1137114242,-0.0416167006,0.0025949543,0.06410487,-0.1667583883,0.0147058973,0.018425649,0.1924858987,-0.1477683187,0.3495835066,0.3496863246,-0.2062441856,-0.1072972864,0.4603831768,0.2156812847,-0.3630843163,-0.3477852345,-0.0060931477,0.2848420739,-0.1099114269,0.0992548391,-0.0453804024,-0.1174411401,0.3077042997,0.1186542809,-0.0798894614,-0.0182407051,-0.153168112,0.2311298102,0.1367105097,-0.0851466656,0.1168536693,-0.1048444584,-0.2077824175,-0.1332199425,0.0068224911,0.1125716642,0.0735047981,-0.2184477746,-0.2746071219,-0.1613022238,-0.2305235267,0.2406419963,-0.2212048024,0.3927025795,-0.2865469456,0.3523676991,0.273912698,-0.3217481375,-0.1695717722,0.1361937374,0.0505715311,0.0448396839,0.0840131864,0.1275388747,0.1957843751,-0.194086656,-0.3776833713,-0.2142427266,-0.0354144499,0.2020052224,-0.0703835115,-0.2931569815,0.1673988402,-0.0343309641,-0.0443432368,0.1609960049,0.2104109973,0.1372911334,0.1466521621,-0.6703623533,-0.145172298,-0.1677084714,0.5546861291,-0.7015405297,-0.16094473,-0.2866317928,-0.6092735529,0.0824236721,0.06030396,-0.0698151365,0.0113237612,-0.1889583319,0.0476957709,0.0229468029,-0.1473824382,0.3428663909,0.0431336313,-0.2642665505,0.1243319511,-0.4750567973,-0.1463641673,-0.1403355002,-0.3317645788,0.1511569172,-0.3418850899,-0.0740297139,0.0253905524,0.2492803931,-0.1347031742,0.2711458802,-0.0787974223,-0.3208595514,-0.0117366398,0.3361576498,-0.1260939091,-0.0565728247,-0.1134579182,0.668459475,0.3834910393,-0.4229672253,0.1849145591,-0.0341769122,0.2618166804,-0.2506638169,-0.0938554481,0.4037902653,0.0568220615,0.1163129732,-0.0970087349,-0.1143205836,-0.1189957336,-0.1221703142,-0.073232539,-0.4438503385,-0.1745235622,-0.1476112306,0.0368663818,0.3050388992,-0.3751012087,0.4575926661,0.4057354033,-0.0148899425,0.2459946424,0.2771879733,0.2664321959,-0.1492680609,-0.0251274612,0.0717759058,0.2972205579,0.1839858592,-0.008832328,0.2680280507,0.187516883,0.0329393819,0.551215291,0.0414168201,-0.3288394809,-0.0131557407,-0.0748040825,0.0142668011,-0.2691059113,-0.0270964801,0.0331057347,-0.0959292576,-0.2966939807,-0.2144793421,-0.3066112101,-0.5192969441,-0.0916707814,-0.0669237524,0.0043746298,-0.1169979945,0.0837557688,-0.028230777,-0.185175851,0.4575862288,-0.0076681683,0.1538325399,0.1248972639,-0.189335525,-0.352422744,0.2354794592,0.0815501586,0.0831785277,0.1823144704,0.0127552738,0.4032604098,-0.1303650141,0.3674851656,-0.4832504988,-0.5660079718,0.0021202459,-0.1256917119,-0.241487056,0.4407701492,0.056246344,-0.1681418419,-0.0673948005,-0.1520162672,-0.1861788929,-0.3604731262,-0.0515236072,-0.162151739,-0.2408919334,-0.3392470479,-0.1968208253,0.2265878916,-0.1269875914,0.2789385617,-0.0844125375,0.3010373116,-0.0038794128,0.2223734707,0.401378274,-0.2174049616,-0.0219461098,-0.0524546951,-0.1853907257,0.0138806198,0.2933528721,-0.1249920949,-0.353187561,-0.452619046,0.1065491363,0.1169504896,-0.2568136156,-0.3797925115,-0.4734036326,0.1929100603,0.0121280551,0.0545253344,0.3909544349,0.1894197315,-0.0597027615,-0.0522890128,-0.0866683125,0.1504241973,0.2260037512,0.0679908767,0.2974728942,0.2050196081,0.2974531949,0.4570002556,0.1714632958,-0.1454422027,0.2504066825,-0.0462025851,0.208090961,-0.1758260876,-0.5362504125,0.3528294861,0.0998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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3480","title":"the compression format requested when saving a dataset in json format is not respected","comments":"Thanks for reporting @SaulLu.\r\n\r\nAt first sight I think the problem is caused because `pandas` only takes into account the `compression` parameter if called with a non-null file path or buffer. And in our implementation, we call pandas `to_json` with `None` `path_or_buf`.\r\n\r\nWe should fix this:\r\n- either handling directly the `compression` parameter ourselves\r\n- or refactoring to pass non-null path or buffer to pandas\r\n\r\nCC: @lhoestq","body":"## Describe the bug\r\nIn the documentation of the `to_json` method, it is stated in the parameters that \r\n> **to_json_kwargs \u2013 Parameters to pass to pandas\u2019s pandas.DataFrame.to_json.\r\n\r\nhowever when we pass for example `compression=\"gzip\"`, the saved file is not compressed.\r\n\r\nWould you also have expected compression to be applied? :relaxed: \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nmy_dict = {\"a\": [1, 2, 3], \"b\": [1, 2, 3]}\r\n```\r\n### Result with datasets\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndataset = Dataset.from_dict(my_dict)\r\ndataset.to_json(\"dic_with_datasets.jsonl.gz\", compression=\"gzip\")\r\n!cat dic_with_datasets.jsonl.gz\r\n```\r\noutput\r\n```\r\n{\"a\":1,\"b\":1}\r\n{\"a\":2,\"b\":2}\r\n{\"a\":3,\"b\":3}\r\n```\r\nNote: I would expected to see binary data here\r\n\r\n### Result with pandas\r\n```python\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame(my_dict)\r\ndf.to_json(\"dic_with_pandas.jsonl.gz\", lines=True, orient=\"records\", compression=\"gzip\")\r\n!cat dic_with_pandas.jsonl.gz\r\n```\r\noutput\r\n```\r\n4\ufffd\ufffda\u0002\ufffddic_with_pandas.jsonl\ufffd\ufffdVJT\ufffd2\ufffdQJ\u0002\ufffd\ufffd\\\ufffd \ufffd\u0011\ufffdg\u0004\ufffd\u0019\ufffdy\u01b5\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u0003\ufffd\ufffd\u000e\ufffd)\ufffd\ufffd\ufffd\r\n```\r\nNote: It looks like binary data\r\n\r\n## Expected results\r\n\r\nI would have expected that the saved result with datasets would also be a binary file\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17\r\n- Python version: 3.8.11\r\n- PyArrow version: 5.0.0\r\n","comment_length":67,"text":"the compression format requested when saving a dataset in json format is not respected \n ## Describe the bug\r\nIn the documentation of the `to_json` method, it is stated in the parameters that \r\n> **to_json_kwargs \u2013 Parameters to pass to pandas\u2019s pandas.DataFrame.to_json.\r\n\r\nhowever when we pass for example `compression=\"gzip\"`, the saved file is not compressed.\r\n\r\nWould you also have expected compression to be applied? :relaxed: \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nmy_dict = {\"a\": [1, 2, 3], \"b\": [1, 2, 3]}\r\n```\r\n### Result with datasets\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndataset = Dataset.from_dict(my_dict)\r\ndataset.to_json(\"dic_with_datasets.jsonl.gz\", compression=\"gzip\")\r\n!cat dic_with_datasets.jsonl.gz\r\n```\r\noutput\r\n```\r\n{\"a\":1,\"b\":1}\r\n{\"a\":2,\"b\":2}\r\n{\"a\":3,\"b\":3}\r\n```\r\nNote: I would expected to see binary data here\r\n\r\n### Result with pandas\r\n```python\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame(my_dict)\r\ndf.to_json(\"dic_with_pandas.jsonl.gz\", lines=True, orient=\"records\", compression=\"gzip\")\r\n!cat dic_with_pandas.jsonl.gz\r\n```\r\noutput\r\n```\r\n4\ufffd\ufffda\u0002\ufffddic_with_pandas.jsonl\ufffd\ufffdVJT\ufffd2\ufffdQJ\u0002\ufffd\ufffd\\\ufffd \ufffd\u0011\ufffdg\u0004\ufffd\u0019\ufffdy\u01b5\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u0003\ufffd\ufffd\u000e\ufffd)\ufffd\ufffd\ufffd\r\n```\r\nNote: It looks like binary data\r\n\r\n## Expected results\r\n\r\nI would have expected that the saved result with datasets would also be a binary file\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17\r\n- Python version: 3.8.11\r\n- PyArrow version: 5.0.0\r\n \n Thanks for reporting @SaulLu.\r\n\r\nAt first sight I think the problem is caused because `pandas` only takes into account the `compression` parameter if called with a non-null file path or buffer. And in our implementation, we call pandas `to_json` with `None` `path_or_buf`.\r\n\r\nWe should fix this:\r\n- either handling directly the `compression` parameter ourselves\r\n- or refactoring to pass non-null path or buffer to pandas\r\n\r\nCC: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3480","title":"the compression format requested when saving a dataset in json format is not respected","comments":"I was thinking if we can handle the `compression` parameter by ourselves? Compression types will be similar to what `pandas` offer. Initially, we can try this with 2-3 compression types and see how good\/bad it is? Let me know if it sounds good, I can raise a PR for this next week","body":"## Describe the bug\r\nIn the documentation of the `to_json` method, it is stated in the parameters that \r\n> **to_json_kwargs \u2013 Parameters to pass to pandas\u2019s pandas.DataFrame.to_json.\r\n\r\nhowever when we pass for example `compression=\"gzip\"`, the saved file is not compressed.\r\n\r\nWould you also have expected compression to be applied? :relaxed: \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nmy_dict = {\"a\": [1, 2, 3], \"b\": [1, 2, 3]}\r\n```\r\n### Result with datasets\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndataset = Dataset.from_dict(my_dict)\r\ndataset.to_json(\"dic_with_datasets.jsonl.gz\", compression=\"gzip\")\r\n!cat dic_with_datasets.jsonl.gz\r\n```\r\noutput\r\n```\r\n{\"a\":1,\"b\":1}\r\n{\"a\":2,\"b\":2}\r\n{\"a\":3,\"b\":3}\r\n```\r\nNote: I would expected to see binary data here\r\n\r\n### Result with pandas\r\n```python\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame(my_dict)\r\ndf.to_json(\"dic_with_pandas.jsonl.gz\", lines=True, orient=\"records\", compression=\"gzip\")\r\n!cat dic_with_pandas.jsonl.gz\r\n```\r\noutput\r\n```\r\n4\ufffd\ufffda\u0002\ufffddic_with_pandas.jsonl\ufffd\ufffdVJT\ufffd2\ufffdQJ\u0002\ufffd\ufffd\\\ufffd \ufffd\u0011\ufffdg\u0004\ufffd\u0019\ufffdy\u01b5\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u0003\ufffd\ufffd\u000e\ufffd)\ufffd\ufffd\ufffd\r\n```\r\nNote: It looks like binary data\r\n\r\n## Expected results\r\n\r\nI would have expected that the saved result with datasets would also be a binary file\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17\r\n- Python version: 3.8.11\r\n- PyArrow version: 5.0.0\r\n","comment_length":52,"text":"the compression format requested when saving a dataset in json format is not respected \n ## Describe the bug\r\nIn the documentation of the `to_json` method, it is stated in the parameters that \r\n> **to_json_kwargs \u2013 Parameters to pass to pandas\u2019s pandas.DataFrame.to_json.\r\n\r\nhowever when we pass for example `compression=\"gzip\"`, the saved file is not compressed.\r\n\r\nWould you also have expected compression to be applied? :relaxed: \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nmy_dict = {\"a\": [1, 2, 3], \"b\": [1, 2, 3]}\r\n```\r\n### Result with datasets\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndataset = Dataset.from_dict(my_dict)\r\ndataset.to_json(\"dic_with_datasets.jsonl.gz\", compression=\"gzip\")\r\n!cat dic_with_datasets.jsonl.gz\r\n```\r\noutput\r\n```\r\n{\"a\":1,\"b\":1}\r\n{\"a\":2,\"b\":2}\r\n{\"a\":3,\"b\":3}\r\n```\r\nNote: I would expected to see binary data here\r\n\r\n### Result with pandas\r\n```python\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame(my_dict)\r\ndf.to_json(\"dic_with_pandas.jsonl.gz\", lines=True, orient=\"records\", compression=\"gzip\")\r\n!cat dic_with_pandas.jsonl.gz\r\n```\r\noutput\r\n```\r\n4\ufffd\ufffda\u0002\ufffddic_with_pandas.jsonl\ufffd\ufffdVJT\ufffd2\ufffdQJ\u0002\ufffd\ufffd\\\ufffd \ufffd\u0011\ufffdg\u0004\ufffd\u0019\ufffdy\u01b5\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u0003\ufffd\ufffd\u000e\ufffd)\ufffd\ufffd\ufffd\r\n```\r\nNote: It looks like binary data\r\n\r\n## Expected results\r\n\r\nI would have expected that the saved result with datasets would also be a binary file\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17\r\n- Python version: 3.8.11\r\n- PyArrow version: 5.0.0\r\n \n I was thinking if we can handle the `compression` parameter by ourselves? Compression types will be similar to what `pandas` offer. Initially, we can try this with 2-3 compression types and see how good\/bad it is? Let me know if it sounds good, I can raise a PR for this next week","embeddings":[0.0959548354,0.0843265951,0.0052431556,0.1249303147,0.2311230302,0.2284453958,0.1374156177,0.7355427742,-0.16820544,0.0322648659,0.0988726094,0.6285889745,0.1668548435,-0.1779369265,-0.0252586454,-0.1777681112,0.2336672097,0.2783780098,0.1768107712,-0.0489057936,-0.3310530484,0.2026296556,-0.0932226405,0.0754082054,-0.0871060193,-0.3380551636,-0.2930557132,0.0848299265,-0.2822119296,-0.3208672404,0.2543919981,-0.105954051,0.1139651313,0.273597002,-0.0001103283,-0.1187786534,0.5136426091,-0.1129107848,-0.363856703,-0.114303872,-0.5151296258,-0.508479178,-0.0209044665,-0.3923640251,-0.0042846925,-0.6715155244,-0.1304219216,-0.4284902513,0.5897341371,0.10642609,0.2530962825,-0.1132919416,0.217919156,-0.0776713192,-0.4429490268,0.3793645203,-0.1885321885,0.1870910078,-0.1671291292,0.2569932044,0.1956584156,0.0812296718,-0.0926643461,-0.0216841251,0.0097036641,0.0350486971,0.0197319202,-0.049836278,-0.0271457471,0.0761540458,0.4737274349,-0.3058116436,-0.6643253565,-0.0795003176,0.0658668801,-0.2094259858,0.0189698897,0.2863759398,-0.1166771054,0.0483192317,-0.1525430083,-0.0630100146,-0.2724609673,0.037153583,-0.0365027785,-0.0492574647,-0.2536219358,-0.0369627289,-0.0159973241,-0.0305202678,-0.0708623901,-0.5273459554,-0.1865663975,0.1457218826,-0.0719662979,-0.3064985275,0.0000979775,-0.2987372279,0.1936794817,0.0176803023,-0.0028411939,-0.1849239469,0.1466291547,-0.0029882861,0.4204558134,0.1502865106,0.0802358985,0.3146013319,0.0991527438,0.0850721523,0.1365204751,-0.1048124582,0.0079435827,-0.1041578054,0.3732403219,-0.1439913511,0.3312721252,-0.2030402571,-0.2168008238,0.0833558291,-0.2611645758,0.0630734563,-0.3124164939,-0.1507606655,-0.151478231,0.2332491726,0.0034765028,0.3078620732,0.1370254755,0.4168913364,-0.2586381137,0.0041816426,0.1273588836,-0.1432482302,0.037802007,-0.404655993,0.0895783454,0.5105071664,-0.0224778112,-0.0826416835,-0.0100104315,-0.0854525492,0.1955714971,0.2579299212,0.1437709928,0.2792631984,0.2089689076,-0.2113306075,-0.2986185849,0.2486827672,-0.4605109692,0.0292070527,-0.2560098767,0.1595024019,-0.0429275036,0.0551165156,-0.3348673582,0.2748680115,0.1966769993,-0.1801180393,0.0321298465,0.0267427415,0.0006638476,-0.4571328759,0.2918198109,0.1182112992,-0.5121605396,0.0213816706,0.0750145912,0.2308680564,0.6207122803,0.5247013569,-0.1191911474,-0.1412649304,-0.1423731297,0.1645470411,0.3763665557,-0.0468307249,-0.4045383632,0.3642501831,0.0242298953,0.484774828,0.1305292696,-0.2228394896,0.1550682038,-0.0363502875,0.1992651522,0.3693659008,-0.0557480641,0.061911758,-0.443051517,-0.0642922074,0.4678366482,-0.1699503064,-0.3883444071,0.2178977579,-0.0024532175,0.2428593636,0.2506137192,-0.0804761127,0.0638635233,0.3086355925,0.1198658124,-0.3146740794,0.1632295847,0.305210501,0.1472622752,-0.1145886481,0.0809543952,-0.1498436928,-0.3048726916,-0.1407957524,-0.2058815658,-0.1875877082,-0.1076930016,-0.1177968606,0.178043142,0.0128931319,-0.0120472396,0.3903467357,0.0264936518,0.4820532203,0.1005362496,-0.0010462644,-0.275995791,0.1158206686,-0.0379241854,-0.2311154604,0.3320188224,-0.0974183977,0.2295006216,-0.1428048462,-0.3001346588,0.1413774937,0.2170105577,0.225867644,-0.0660839453,-0.0925227404,0.1412862539,0.1051122099,0.150296554,0.5381323099,0.1808963269,0.0085955299,-0.5527151227,0.4099636674,0.0243916251,-0.0862066373,-0.1178914756,0.0540285036,0.3378255367,0.1083683297,-0.2041237205,-0.0977725014,-0.2750462592,-0.047234986,-0.1340637505,0.1668381542,-0.3097893,0.0225491617,0.6822719574,-0.0963479653,0.0762596428,0.2830695808,-0.0423355177,-0.415725857,0.1052125618,0.29757303,0.4616684616,0.0805430487,0.1279066801,-0.2288963348,-0.1549199969,-0.0906844959,0.1673585027,-0.0770629868,0.2545939386,0.1181304231,0.1110527962,0.2820705771,-0.02908957,0.302323401,-0.0234718323,0.0714600831,-0.230846256,0.2031538039,-0.4720599353,-0.0302268229,0.0331024304,-0.1164301112,-0.0778421462,-0.3131670654,-0.111989446,-0.076964736,0.0319995694,0.0924168378,-0.1024044082,0.2103663832,0.2172863185,-0.200513199,-0.2865603566,0.1968993992,-0.281617105,0.1433317512,-0.1378450245,-0.2001897246,0.1667106599,-0.0754663274,0.0600255914,0.0569988824,-0.1135573164,-0.0663515776,-0.2575481832,0.2288730443,0.0273011904,0.3378923237,-0.2068569809,-0.1598192155,0.1648992747,0.1095995307,-0.1696803421,0.1982071847,-0.0528215617,-0.0715170875,-0.0932695791,-0.0318812542,-0.1572362185,-0.1063423231,0.2704162598,-0.0565909073,0.1392194927,0.1973008364,0.2140405625,-0.1268416792,0.3513579071,0.0511153713,-0.2392889112,-0.5068238378,0.760248065,-0.2449452877,-0.3859737217,-0.1725374758,0.0795288906,0.1614610851,-0.0880033523,-0.1534081399,-0.0119472044,-0.2249779403,-0.0678158253,-0.1848438382,0.0198616926,0.0403063148,0.0518770069,-0.1263992637,-0.1105454788,-0.261053443,-0.0330028906,0.3372409642,0.4534072578,-0.019029323,0.4135375321,0.1546539366,-0.1793993413,0.5717155337,0.0476985015,0.309468478,0.1278040707,0.3763611615,-0.0722103193,-0.2975987792,-0.0612749644,-0.0679244846,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3480","title":"the compression format requested when saving a dataset in json format is not respected","comments":"Hi ! Thanks for your help @bhavitvyamalik :)\r\nMaybe let's start with `gzip` ? I think it's the most common use case, then if we're fine with it we can add other compression methods","body":"## Describe the bug\r\nIn the documentation of the `to_json` method, it is stated in the parameters that \r\n> **to_json_kwargs \u2013 Parameters to pass to pandas\u2019s pandas.DataFrame.to_json.\r\n\r\nhowever when we pass for example `compression=\"gzip\"`, the saved file is not compressed.\r\n\r\nWould you also have expected compression to be applied? :relaxed: \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nmy_dict = {\"a\": [1, 2, 3], \"b\": [1, 2, 3]}\r\n```\r\n### Result with datasets\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndataset = Dataset.from_dict(my_dict)\r\ndataset.to_json(\"dic_with_datasets.jsonl.gz\", compression=\"gzip\")\r\n!cat dic_with_datasets.jsonl.gz\r\n```\r\noutput\r\n```\r\n{\"a\":1,\"b\":1}\r\n{\"a\":2,\"b\":2}\r\n{\"a\":3,\"b\":3}\r\n```\r\nNote: I would expected to see binary data here\r\n\r\n### Result with pandas\r\n```python\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame(my_dict)\r\ndf.to_json(\"dic_with_pandas.jsonl.gz\", lines=True, orient=\"records\", compression=\"gzip\")\r\n!cat dic_with_pandas.jsonl.gz\r\n```\r\noutput\r\n```\r\n4\ufffd\ufffda\u0002\ufffddic_with_pandas.jsonl\ufffd\ufffdVJT\ufffd2\ufffdQJ\u0002\ufffd\ufffd\\\ufffd \ufffd\u0011\ufffdg\u0004\ufffd\u0019\ufffdy\u01b5\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u0003\ufffd\ufffd\u000e\ufffd)\ufffd\ufffd\ufffd\r\n```\r\nNote: It looks like binary data\r\n\r\n## Expected results\r\n\r\nI would have expected that the saved result with datasets would also be a binary file\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17\r\n- Python version: 3.8.11\r\n- PyArrow version: 5.0.0\r\n","comment_length":34,"text":"the compression format requested when saving a dataset in json format is not respected \n ## Describe the bug\r\nIn the documentation of the `to_json` method, it is stated in the parameters that \r\n> **to_json_kwargs \u2013 Parameters to pass to pandas\u2019s pandas.DataFrame.to_json.\r\n\r\nhowever when we pass for example `compression=\"gzip\"`, the saved file is not compressed.\r\n\r\nWould you also have expected compression to be applied? :relaxed: \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nmy_dict = {\"a\": [1, 2, 3], \"b\": [1, 2, 3]}\r\n```\r\n### Result with datasets\r\n```python\r\nfrom datasets import Dataset\r\n\r\ndataset = Dataset.from_dict(my_dict)\r\ndataset.to_json(\"dic_with_datasets.jsonl.gz\", compression=\"gzip\")\r\n!cat dic_with_datasets.jsonl.gz\r\n```\r\noutput\r\n```\r\n{\"a\":1,\"b\":1}\r\n{\"a\":2,\"b\":2}\r\n{\"a\":3,\"b\":3}\r\n```\r\nNote: I would expected to see binary data here\r\n\r\n### Result with pandas\r\n```python\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame(my_dict)\r\ndf.to_json(\"dic_with_pandas.jsonl.gz\", lines=True, orient=\"records\", compression=\"gzip\")\r\n!cat dic_with_pandas.jsonl.gz\r\n```\r\noutput\r\n```\r\n4\ufffd\ufffda\u0002\ufffddic_with_pandas.jsonl\ufffd\ufffdVJT\ufffd2\ufffdQJ\u0002\ufffd\ufffd\\\ufffd \ufffd\u0011\ufffdg\u0004\ufffd\u0019\ufffdy\u01b5\ufffd\ufffd\ufffd\ufffd\ufffd\ufffd\u0003\ufffd\ufffd\u000e\ufffd)\ufffd\ufffd\ufffd\r\n```\r\nNote: It looks like binary data\r\n\r\n## Expected results\r\n\r\nI would have expected that the saved result with datasets would also be a binary file\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.18.0-193.70.1.el8_2.x86_64-x86_64-with-glibc2.17\r\n- Python version: 3.8.11\r\n- PyArrow version: 5.0.0\r\n \n Hi ! Thanks for your help @bhavitvyamalik :)\r\nMaybe let's start with `gzip` ? I think it's the most common use case, then if we're fine with it we can add other compression 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3475","title":"The rotten_tomatoes dataset of movie reviews contains some reviews in Spanish ","comments":"Hi @puzzler10, thanks for reporting.\r\n\r\nPlease note this dataset is not hosted on Hugging Face Hub. See: \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c8f914473b041833fd47178fa4373cdcb56ac522\/datasets\/rotten_tomatoes\/rotten_tomatoes.py#L42\r\n\r\nIf there are issues with the source data of a dataset, you should contact the data owners\/creators instead. In the homepage associated with this dataset (http:\/\/www.cs.cornell.edu\/people\/pabo\/movie-review-data\/), you can find the authors of the dataset and how to contact them:\r\n> If you have any questions or comments regarding this site, please send email to Bo Pang or Lillian Lee.\r\n\r\nP.S.: Please also note that the example you gave of non-English review is in Portuguese (not Spanish). ;)","body":"## Describe the bug\r\nSee title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user. \r\n\r\n## Steps to reproduce the bug\r\nGo to the [dataset viewer](https:\/\/huggingface.co\/datasets\/viewer\/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that. \r\n\r\n## Expected results\r\nEnglish movie reviews only. \r\n\r\n## Actual results\r\nExample of a Spanish movie review (4173): \r\n> \"\u00c9 uma pena que , mais tarde , o pr\u00f3prio filme abandone o tom de par\u00f3dia e passe a utilizar os mesmos clich\u00eas que havia satirizado \"\r\n\r\n","comment_length":95,"text":"The rotten_tomatoes dataset of movie reviews contains some reviews in Spanish  \n ## Describe the bug\r\nSee title. I don't think this is intentional and they probably should be removed. If they stay the dataset description should be at least updated to make it clear to the user. \r\n\r\n## Steps to reproduce the bug\r\nGo to the [dataset viewer](https:\/\/huggingface.co\/datasets\/viewer\/?dataset=rotten_tomatoes) for the dataset, set the offset to 4160 for the train dataset, and scroll through the results. I found ones at index 4166 and 4173. There's others too (e.g. index 2888) but those two are easy to find like that. \r\n\r\n## Expected results\r\nEnglish movie reviews only. \r\n\r\n## Actual results\r\nExample of a Spanish movie review (4173): \r\n> \"\u00c9 uma pena que , mais tarde , o pr\u00f3prio filme abandone o tom de par\u00f3dia e passe a utilizar os mesmos clich\u00eas que havia satirizado \"\r\n\r\n \n Hi @puzzler10, thanks for reporting.\r\n\r\nPlease note this dataset is not hosted on Hugging Face Hub. See: \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c8f914473b041833fd47178fa4373cdcb56ac522\/datasets\/rotten_tomatoes\/rotten_tomatoes.py#L42\r\n\r\nIf there are issues with the source data of a dataset, you should contact the data owners\/creators instead. In the homepage associated with this dataset (http:\/\/www.cs.cornell.edu\/people\/pabo\/movie-review-data\/), you can find the authors of the dataset and how to contact them:\r\n> If you have any questions or comments regarding this site, please send email to Bo Pang or Lillian Lee.\r\n\r\nP.S.: Please also note that the example you gave of non-English review is in Portuguese (not Spanish). ;)","embeddings":[0.0775979161,0.338380754,-0.1574923545,0.2872701585,0.3433725238,0.2087221146,0.0929912329,0.2479000241,-0.1174309254,0.0967246294,-0.4815724194,0.0033951288,0.011461312,-0.1756011397,0.0343780629,0.0748330727,0.2239581943,-0.0237253495,-0.1734269857,-0.2515214384,-0.0800896287,0.2126247585,0.1339432895,0.0995080695,-0.6924958825,0.0753566325,0.1364068538,-0.1510757059,0.3064257801,-0.1701555103,0.2806670368,-0.1623495817,-0.1956360191,0.6200705171,-0.0001128261,0.0604964793,0.3802312315,-0.0089866528,0.0584997162,-0.2841436863,-0.0630171224,-0.1023702845,-0.1558225751,-0.0836035013,-0.0952554792,-0.2996973097,-0.1322908551,-0.4439215958,0.0886466354,0.372276783,0.2058450878,0.1275351495,-0.323587805,-0.190646261,0.468154192,0.5497838259,-0.1119370833,0.3121244311,0.166376695,0.0387279093,-0.222202599,0.2103516459,0.027932154,-0.331479162,-0.1717760861,-0.0002904417,-0.2278207541,-0.3157125413,0.1599531472,0.4272159934,0.072253257,-0.4220971167,-0.2358408272,-0.1337208301,0.0804924592,0.0871546119,0.1504492611,0.4234380424,0.0720908418,0.3903191686,-0.0943479165,0.1298758537,0.208190769,0.1347892582,0.3898299634,0.1491748095,0.0354839489,-0.0451403186,0.0731770098,0.0825666115,0.2751669884,-0.0486882553,-0.0266626775,-0.0222957842,0.0025260686,0.0048840512,-0.2662068009,0.1408528537,0.1187990084,-0.2384689152,-0.0836012065,0.0818534791,-0.1328640729,0.1475377232,0.3307208121,0.1451527923,0.0160523225,0.2547890246,0.1486599743,0.1915071011,0.0695658922,0.120708771,0.2936166227,-0.0904944912,0.0287149195,0.1910785139,0.1238923147,-0.0972818807,-0.3087182343,0.1970048994,0.0062069343,0.0614616536,0.0072942944,0.1471930444,-0.0519223362,0.2531540096,-0.1871700436,0.1709069312,-0.0393965319,-0.4547872245,-0.2431351244,0.1819987893,-0.3064079881,-0.015186673,-0.0156175569,-0.1073295847,0.3698096573,0.1516220272,0.1817557067,-0.3672723472,-0.4065672159,-0.5185276866,0.1344592869,0.2496619374,0.0738296509,0.19303222,-0.0148870563,-0.1174209565,-0.0984634906,0.541666925,-0.31225124,-0.0149293141,-0.3429646194,0.1529159844,-0.2386552244,-0.1685645878,-0.2737686634,0.2624275386,0.1455352008,0.0287512094,0.3838166893,-0.0201556757,-0.1142106801,0.2381730676,-0.1148680374,0.5075318813,-0.2212487012,-0.1801906079,-0.1266902089,-0.1786124557,0.3226434588,0.4618787766,-0.0044941166,0.1159671992,-0.4006375968,0.2212025225,-0.0666367263,0.0466276072,-0.2677904665,0.1455524564,-0.0366334319,0.1818751097,-0.0784796327,0.2202774584,0.0248968955,0.0529197603,-0.1381687522,0.1723628938,0.286757201,-0.0404152423,-0.3129346371,-0.161255464,0.2182195038,0.1112569571,0.0281855632,0.1776432395,-0.0300484058,-0.1221740991,0.3241310716,0.2765091062,0.0695828348,0.3186800778,0.2371929437,0.4378860891,0.1384546608,-0.0968614593,-0.2474743128,0.0149248512,-0.1557590216,0.0562712252,0.0447745658,-0.5113102794,-0.3602093756,-0.0132228676,-0.2501747906,-0.1949224174,0.1053244397,-0.0138034327,-0.3512333333,0.3576211929,-0.1997231394,0.1217272505,-0.4329378009,-0.0262240078,-0.0406560823,0.1214763448,-0.0825009272,0.0528572872,-0.1179282665,0.2896500528,-0.0748581886,-0.0824676454,-0.1300328821,0.3152928054,0.1967157871,0.3893191218,-0.0194513872,-0.0056010955,0.2285887599,-0.3850582838,-0.2388341576,0.4023305178,0.0154407937,0.0600294322,-0.123793982,0.3775286376,-0.0294926967,-0.0430825017,-0.1184948161,-0.0119607756,0.2532700896,-0.0337960944,-0.2571258843,-0.4835956693,0.3958641291,-0.0373440608,0.1882107258,0.4324666858,-0.1766366214,-0.165462181,0.6964841485,-0.0892054662,0.3233801425,0.3627530932,0.0811079368,-0.0337453149,0.0539155751,0.0769800618,0.2238269001,-0.0148434406,0.056882713,0.3743569553,0.2150232792,-0.2548015118,-0.0254938379,0.1791518927,0.0146043785,0.2527244687,0.0570069253,0.0871398076,-0.3475522399,0.0494978726,-0.2337252796,0.1289096028,-0.3740016222,0.151350528,-0.2788359821,-0.1856578887,-0.5338105559,-0.1826967746,-0.1371814013,-0.0083032344,0.3848928511,-0.5350186825,-0.420091182,0.4128506482,-0.0485074632,0.2151174098,-0.3225395083,-0.072700955,-0.0984830856,-0.2087969184,-0.2122240961,0.0624180771,0.2326645106,-0.1914136112,0.1400382519,-0.6252584457,-0.2342722714,-0.0519729592,-0.0677420348,0.0808719024,-0.0218153987,0.3384752274,-0.1872259825,-0.0543417111,-0.1486214548,-0.0664141551,0.0193077978,-0.045873668,-0.0664139763,0.0654736161,-0.1022132486,0.0451304205,-0.0477653891,-0.4743637443,-0.1710327715,-0.0067692506,-0.055004172,-0.3167714477,0.088954024,-0.010828346,0.1036850065,0.1132144332,-0.2468840778,0.1163725033,-0.353025347,-0.1623297036,0.2913242579,-0.0481733158,-0.3797511756,-0.0257846974,0.0602941252,-0.071515739,-0.1957808286,-0.658616364,0.0306738764,-0.232111603,0.0807388797,0.0919475034,-0.2128856927,0.0899068937,-0.3091146052,-0.0878180861,0.0255958624,0.0077335872,0.125587225,-0.1055699065,-0.00377391,-0.0514738485,0.2540181279,0.0256382488,0.2261019647,0.2845231891,0.0648220032,0.2778257728,-0.0692923814,0.5555639267,-0.1446276307,-0.2002168894,-0.0392271616,-0.1196576729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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3473","title":"Iterating over a vision dataset doesn't decode the images","comments":"As discussed, I remember I set `decoded=False` here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed.","body":"## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n","comment_length":32,"text":"Iterating over a vision dataset doesn't decode the images \n ## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n \n As discussed, I remember I set `decoded=False` here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed.","embeddings":[-0.1267589033,-0.3187015951,-0.0562809445,0.4169413149,0.2039800137,-0.0484368168,0.1617484391,0.0489288718,-0.0526035391,0.2663680017,0.0970083997,0.5622792244,-0.0772200599,-0.1984003484,-0.0889357999,-0.1298076957,0.0134137813,0.3308174014,0.0319914706,-0.0851626173,-0.221248135,0.0739016682,-0.3497111499,-0.2482757568,0.0404270999,-0.0333078876,-0.2307286412,-0.0113927219,-0.1831245422,-0.4801517725,-0.0190013722,0.0766223148,0.1080364436,0.3269090652,-0.0001233442,0.1580651999,0.4136051536,-0.0816947818,-0.2558897734,-0.0877094641,-0.1963955611,-0.1986256391,0.1231700405,-0.1644231826,-0.103052564,-0.5773655772,0.0963547006,-0.2540629208,0.2806848884,0.0971984863,0.0906033292,0.0578032285,0.0072702942,0.5657334328,0.1217624098,0.2430295944,-0.1889679879,0.3289595842,-0.0120504033,0.166863054,-0.2594265938,0.4326403439,-0.0639949143,0.1591316909,0.0229824707,0.2228319198,-0.1415267438,-0.4424693882,-0.1678468734,-0.0291417781,0.391911,-0.1493212134,-0.4843539298,-0.3056430519,-0.0892800689,-0.4744009674,0.3207057118,0.1855035126,-0.2700004876,0.0513191633,-0.0098778475,0.1063635126,-0.2212205231,0.1970554292,-0.3180182278,-0.1031935439,-0.1198609546,0.1506922543,0.3663077354,-0.0418141931,0.3552364409,-0.0610671639,-0.1626599133,-0.1827730536,-0.4076960087,-0.145699665,0.012552388,0.0227547828,-0.0596322976,0.0632374659,0.0618497059,0.0507345274,-0.213389948,0.1981836557,0.3155868351,0.0130282314,-0.185451299,0.5198147297,0.1808299422,0.1634860933,-0.1871843338,-0.1224340498,-0.0934877247,-0.066390641,0.1576224864,-0.0263313372,0.4403359592,-0.094866693,-0.2429992408,-0.24938187,-0.7110186219,-0.1815322638,-0.0041944971,0.0064308387,0.3910170496,0.2369331121,0.137049675,0.1956526637,-0.2314056307,-0.3425643444,-0.0648727342,-0.0609407015,-0.1817814261,0.0364419296,0.1330509037,0.2255575806,-0.0531563386,0.0031519001,-0.010381991,-0.3065256476,0.2088885158,-0.4075783491,0.6748942733,0.2751647532,-0.1785521358,-0.0045593106,0.0054930877,-0.1114238724,-0.1219115481,0.1804520041,-0.1693372428,0.1900360584,0.1028235406,-0.0086661605,-0.3492946327,-0.2689424455,-0.1031586826,0.3269144297,0.0283501614,-0.3455925882,0.2146221995,-0.2212673128,-0.3251113296,-0.1322068572,0.3171463311,0.3856445551,-0.542763412,0.2411512733,-0.088670671,-0.2690981925,0.2534230947,0.3627998829,-0.1090234742,0.0704136714,-0.4402825236,0.1128328666,0.1061736122,-0.4006195664,-0.5919460654,0.3357585073,0.3105616868,0.548802495,0.0985212997,0.134186089,0.048815839,-0.1111064702,-0.3746320009,0.2024700791,-0.2129583359,-0.1101818085,-0.1909808666,0.0850209296,0.4172558188,0.2780317962,0.1408447474,0.1567090601,0.111710228,-0.1480823606,0.4006379843,-0.0973088518,0.0915614739,-0.0285576768,0.1129539907,-0.1719705611,0.4677255452,-0.0812920406,0.0758643821,0.2467507571,0.1724957824,-0.0886350945,-0.4330663681,0.0197653491,0.2811436951,0.038954936,-0.6287848949,-0.1328056306,0.1098162532,0.4489210248,-0.1869977415,0.0757132322,-0.2966896594,0.1416507661,-0.2522657514,-0.0929448903,-0.113023527,0.281068027,0.1186057031,-0.1102878749,-0.3108250797,0.1958695054,-0.0428442955,-0.1964039207,-0.0123498822,0.2776182294,0.4771280885,0.2388500273,-0.1488774866,0.0481056608,0.3421061039,-0.8031753898,-0.0728758946,0.1556115896,0.078559868,-0.0426283702,0.1856788397,-0.0287770741,0.1905686557,0.4862320423,-0.0316706598,0.0139806792,0.0803483129,-0.0323069617,-0.4243849516,-0.1617768407,0.0389234349,-0.2887592018,0.0967056453,-0.062751554,-0.3451255262,0.0983114764,0.4214999676,-0.0603764467,0.0459925197,-0.0152915185,-0.15809232,0.1275508851,0.1995846182,0.1186507046,0.3133378625,-0.0434245281,0.1277444214,0.0672862753,-0.0749397278,0.1446155906,0.132930845,0.5702859163,0.4758957326,0.1545083076,0.1971397251,0.0762590989,-0.4968160689,-0.4346294999,0.0160891991,0.0094667459,-0.0373856872,0.4796384275,-0.4988850951,-0.6380172968,-0.1414712518,0.2508309186,-0.1091573611,-0.2609803081,-0.307438612,0.1694042832,0.0737185255,0.0461524203,-0.0924054235,0.0984591693,0.120571807,-0.0827531964,-0.4270429611,-0.0763798431,-0.1016987488,-0.0748348013,0.3222793937,-0.4541301131,0.2685772181,-0.101516813,-0.1747851372,-0.2843221128,-0.3780846596,0.3806130588,-0.2976788878,0.2670212686,0.0583527014,-0.0379896201,-0.1247358993,0.0768180937,0.2522687614,0.1157051697,-0.2459194362,0.0980882794,-0.1105843559,-0.0600565448,-0.1505394578,0.0118721528,-0.1089466363,-0.2478487492,0.2897525132,0.1167298034,0.1662427485,0.5830760598,0.0139597338,0.0977305174,-0.2325422764,0.0630619153,-0.0763918757,0.0724922419,0.1357617527,-0.0284344796,-0.2734294534,0.0716621354,-0.109353222,0.3095182478,0.2691143751,-0.4650784433,-0.0519452542,-0.1901526153,-0.0970966443,-0.2115965635,-0.1585824192,-0.0870614499,0.0328052565,0.0877343863,-0.3036300242,-0.1060898826,0.0277337171,0.2181598842,0.3797618747,0.2735289037,0.1846491992,0.385202229,0.3678389788,0.3901163638,-0.0676933676,0.5244944096,-0.3241094053,0.2545664012,0.0370949879,-0.2164426595,0.1448197812,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3473","title":"Iterating over a vision dataset doesn't decode the images","comments":"> I set decoded=False here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/3430 will add more control to decoding, so I think it's OK to enable decoding in `__iter__` for now. After we merge the linked PR, the user can easily disable it again.","body":"## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n","comment_length":61,"text":"Iterating over a vision dataset doesn't decode the images \n ## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n \n > I set decoded=False here to avoid decoding just by iterating over examples of dataset. We wanted to decode only if the \"audio\" field (for Audio feature) was accessed\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/3430 will add more control to decoding, so I think it's OK to enable decoding in `__iter__` for now. After we merge the linked PR, the user can easily disable it again.","embeddings":[-0.1568086445,-0.3336028159,-0.0613895133,0.445343703,0.235735625,-0.0553113371,0.1046505719,0.0181351136,-0.0682635531,0.2494533658,0.0704558715,0.5809364915,-0.1020292267,-0.1611502171,-0.0695885271,-0.1490104795,0.0153617989,0.3161056936,-0.0318766348,-0.0999816731,-0.2071295232,0.1023740768,-0.3060073555,-0.2811090052,0.0188561548,-0.0585902631,-0.2602617145,0.0054779612,-0.1705716699,-0.4633087218,-0.0435333326,0.0652808174,0.0898322612,0.355078131,-0.0001218025,0.1714568287,0.3926334977,-0.0766749084,-0.2516234815,-0.0872385725,-0.1548252702,-0.2000945956,0.1211210266,-0.1686757207,-0.156610623,-0.5621830225,0.1026415452,-0.2368814498,0.3039441407,0.0785366893,0.1132612377,0.1018178836,0.0205362309,0.5415076613,0.11716941,0.2465613484,-0.1867715567,0.3536757231,-0.0122043034,0.1801640689,-0.2669995427,0.4592628181,-0.0306760054,0.1353230774,0.0458476953,0.2410849631,-0.130769819,-0.4000463188,-0.1753910333,-0.0427971147,0.3311606348,-0.1504939348,-0.4958273172,-0.3084160089,-0.0932425112,-0.4708471894,0.3129250705,0.1953632683,-0.2901806533,0.0664149225,-0.0583338514,0.1247055307,-0.2093394548,0.2014327794,-0.3378785849,-0.1084322631,-0.1527800113,0.1447119266,0.4033125043,-0.0320575424,0.2779607773,-0.0420987271,-0.1421369016,-0.1954577416,-0.384766221,-0.123214148,0.0203070287,0.052448336,-0.0645507798,0.057800848,0.0794765949,0.0680558532,-0.2058541328,0.1781585366,0.2851572037,0.0202387404,-0.1997603476,0.4919993579,0.1737650931,0.1901934892,-0.1810987741,-0.1067950577,-0.0819827691,-0.0589187481,0.1030345261,-0.0223376118,0.4108563364,-0.0787447095,-0.2441378981,-0.2362947017,-0.6744170189,-0.1464812756,0.0183618348,0.0698095635,0.3414122164,0.2264322042,0.1556865871,0.1344257891,-0.226848945,-0.3217453659,-0.1096207872,-0.0446848013,-0.2074657381,0.0636196807,0.118553862,0.1841810644,-0.0217320323,-0.0202759225,-0.0256526247,-0.3012328446,0.159138456,-0.3774540722,0.6764338017,0.2588718534,-0.2081511915,-0.0501942672,-0.0108573427,-0.1215524301,-0.1060340181,0.1040910482,-0.1598773748,0.1993070394,0.1099520475,0.0229155626,-0.343024224,-0.2527408004,-0.1139446422,0.3253353834,0.0164483618,-0.2926619053,0.2120170742,-0.1828408092,-0.2859787941,-0.1214959323,0.3268653452,0.3754223883,-0.4916129708,0.2148304284,-0.0532012582,-0.2656234503,0.2252278775,0.3700034916,-0.0711033419,0.0508660935,-0.4439809322,0.0826460347,0.1004215702,-0.4002555013,-0.5635709167,0.3373476267,0.2992919981,0.5197437406,0.1139113158,0.1541145891,0.0362261198,-0.0740389079,-0.3475143313,0.2028939575,-0.1982478797,-0.0837427154,-0.208316654,0.0672504976,0.3516070843,0.303717047,0.1218970567,0.1487179101,0.1276997924,-0.1665146053,0.4245145023,-0.1215859503,0.0877042636,0.0038810095,0.1069944128,-0.1671827286,0.4273763299,-0.1038651094,0.0264869444,0.2240028381,0.1610207707,-0.0620159954,-0.4413610399,-0.0088367388,0.2648387551,0.0263024364,-0.6344653368,-0.1679090112,0.1348585337,0.4636657834,-0.120084241,0.0967944413,-0.2615162432,0.1347488165,-0.2638899386,-0.0599217042,-0.1450873762,0.3203407824,0.116304867,-0.1255962551,-0.2873788476,0.2208419889,-0.042670507,-0.2179711163,0.020701969,0.2638669312,0.4587855637,0.2756500244,-0.1257914007,0.0469119623,0.3517671824,-0.8030489683,-0.0910907686,0.1566009969,0.0756345838,-0.0306998231,0.1582782567,0.0143701965,0.1837855428,0.4665871859,-0.0111178774,0.0108780321,0.0697320104,-0.0202179924,-0.4101634324,-0.2020788491,0.0385808907,-0.2970333993,0.1312540174,-0.0589047708,-0.3312997818,0.0796960369,0.4144174457,-0.0640871301,0.0264880043,-0.043092709,-0.1900426,0.1377954334,0.2496273518,0.1111700833,0.2860055566,0.0026255406,0.1423756331,0.1045075879,-0.0816872641,0.1179993153,0.1393829435,0.5456994772,0.4626993537,0.1497631222,0.1879799068,0.0575522892,-0.5441728234,-0.4354055822,0.0043943105,0.0276715681,-0.0366460271,0.4652586579,-0.486556977,-0.6728243828,-0.1544693708,0.277394563,-0.1142084375,-0.2607966363,-0.3231728077,0.1858039498,0.0421830788,0.0228843335,-0.0827150568,0.0799110159,0.1354552954,-0.0803539753,-0.4561859369,-0.0923105478,-0.107468985,-0.0554934293,0.3315434754,-0.4613158703,0.3218897879,-0.1233356968,-0.1762063205,-0.3147499263,-0.4080669582,0.4133491516,-0.2908176184,0.2821210325,0.0902347639,-0.0402427875,-0.14261204,0.0689728931,0.2738063931,0.1060169712,-0.2722799778,0.0666863024,-0.1127244756,-0.0556656569,-0.1696951538,0.0006255064,-0.1130330712,-0.2717240155,0.3335731328,0.1064278483,0.155790627,0.6183410287,0.0436823815,0.1239534542,-0.2952923477,0.0915805921,-0.1106557995,0.0803732798,0.1076768339,-0.049384404,-0.2995259762,0.0594698675,-0.1269840002,0.343302995,0.2483128458,-0.4860584438,-0.1088283733,-0.2001978159,-0.0837468207,-0.1784470528,-0.1218656152,-0.08096046,0.0230099503,0.0630221143,-0.2706599534,-0.09869086,0.0159639027,0.1883414388,0.3758375645,0.2445726097,0.1805021614,0.4051983953,0.3571251631,0.4265909791,-0.1064085811,0.535115242,-0.3379611075,0.2825843096,0.0346545093,-0.2376668453,0.1590054929,-0.009301569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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3473","title":"Iterating over a vision dataset doesn't decode the images","comments":"@mariosasko I wonder why there is no issue in `Audio` feature with decoding disabled in `__iter__`, whereas there is in `Image` feature.\r\n\r\nEnabling decoding in `__iter__` will make fail Audio regressions tests: https:\/\/github.com\/huggingface\/datasets\/runs\/4608657230?check_suite_focus=true\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests\/features\/test_audio.py::test_dataset_with_audio_feature_map_is_not_decoded\r\nFAILED tests\/features\/test_audio.py::test_dataset_with_audio_feature_map_is_decoded\r\n========================= 2 failed, 15 passed in 8.37s =========================","body":"## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n","comment_length":52,"text":"Iterating over a vision dataset doesn't decode the images \n ## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n \n @mariosasko I wonder why there is no issue in `Audio` feature with decoding disabled in `__iter__`, whereas there is in `Image` feature.\r\n\r\nEnabling decoding in `__iter__` will make fail Audio regressions tests: https:\/\/github.com\/huggingface\/datasets\/runs\/4608657230?check_suite_focus=true\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests\/features\/test_audio.py::test_dataset_with_audio_feature_map_is_not_decoded\r\nFAILED tests\/features\/test_audio.py::test_dataset_with_audio_feature_map_is_decoded\r\n========================= 2 failed, 15 passed in 8.37s =========================","embeddings":[-0.1508926451,-0.2987541854,-0.1016990989,0.3872717619,0.1785423607,-0.0492618158,0.2464303076,-0.0049403724,-0.0694661289,0.2798423767,0.1267052591,0.5622637272,-0.0683700293,-0.1775304675,-0.0727773979,-0.1908055246,0.0280030798,0.3463977873,-0.0484353676,-0.1198532283,-0.2097039223,0.0610203631,-0.3540823758,-0.2867845595,0.0293848403,-0.0790162534,-0.1957996041,-0.0591776483,-0.2314470112,-0.4575715065,-0.0549452901,0.0637345687,0.0858783796,0.4071694016,-0.0001241098,0.1431870908,0.4660795927,-0.0811272636,-0.223749131,-0.1340426654,-0.1988790333,-0.203984201,0.1442229599,-0.1930418164,-0.0260292478,-0.5617832541,0.0845060796,-0.1878740191,0.2679088116,0.0799903497,0.0865791291,0.0903769881,-0.0407948829,0.5087563396,0.1601594687,0.2625190616,-0.2169996947,0.403536886,-0.0918390825,0.1491852105,-0.2212044597,0.4030221701,-0.0220493097,0.2219146639,0.0232551098,0.1872171909,-0.0879673436,-0.4516590834,-0.1846902072,-0.0002235874,0.3955826759,-0.1291419566,-0.5207871795,-0.249455139,-0.1179659441,-0.4843530059,0.3066271544,0.1991360039,-0.2482752651,0.0622346848,-0.0441440716,0.1085856333,-0.213307783,0.195518747,-0.3655472994,-0.1182959899,-0.1054923162,0.1603344679,0.4263389111,-0.0066058831,0.3257413805,-0.0765706524,-0.1238799021,-0.1968616098,-0.4099780917,-0.1517914534,-0.0081752054,0.0128235454,-0.0663985312,0.1275535524,0.0555604957,0.0338790081,-0.2205980718,0.227312386,0.2971059084,0.0926152915,-0.1889971793,0.4479317963,0.1977076083,0.2057056129,-0.1664725542,-0.1323336065,-0.003168036,-0.1106000841,0.1331940144,-0.0083534094,0.3838190734,-0.1342207044,-0.3089213669,-0.2744192481,-0.6928958297,-0.2151532769,-0.0326775126,0.0485569499,0.3954883218,0.2837198079,0.1071369648,0.1769823283,-0.2263320088,-0.2182202786,-0.0732737407,-0.0167380217,-0.2031639814,0.0650622696,0.0845030323,0.2156632841,0.01889221,-0.025048567,0.0733010769,-0.2795396149,0.1963832527,-0.4504353404,0.6664813161,0.2498281002,-0.1914384663,-0.0200239941,-0.0899545476,-0.0729600713,-0.1243763939,0.1066603288,-0.1641364545,0.2333995253,0.1615813375,-0.0080687236,-0.2998095453,-0.2181786448,-0.0976172909,0.2716174126,-0.0111481063,-0.3784265518,0.2249278277,-0.2431568801,-0.3054457605,-0.1291559786,0.3100272119,0.3128607571,-0.5085043907,0.2006617934,-0.0162927844,-0.2212713808,0.249969855,0.3959437013,-0.0986951366,0.0844645277,-0.4364677966,0.0621709749,0.1344302148,-0.4060561955,-0.5937789679,0.3419034779,0.2812226713,0.5021307468,0.1213834211,0.1274094135,0.0562572256,-0.0778070837,-0.3730583191,0.212302804,-0.1704393923,-0.1350432187,-0.1770518869,0.0763641894,0.4441019893,0.2274185419,0.085584484,0.1611724645,0.099484995,-0.1404563189,0.3779696226,-0.1352634579,0.0600024424,0.0175988432,0.1667942256,-0.2541065812,0.4518941939,-0.1109010056,0.0761640072,0.1810267419,0.1533889472,-0.0429987125,-0.3983070552,0.0401850045,0.2361035794,0.0147945797,-0.5782123208,-0.1555480957,0.1069725975,0.4082618952,-0.1363737136,0.1310918033,-0.2769382894,0.1275988966,-0.2673377693,-0.0611224845,-0.0884667635,0.3378523588,0.1147940382,-0.1228825152,-0.2844683528,0.1970627904,-0.0701019391,-0.2469532043,-0.0420146026,0.2363684028,0.4958340228,0.2482404858,-0.1738081127,0.0634526461,0.4048700929,-0.8184466958,-0.073153235,0.2047595084,0.1147230119,-0.0112547418,0.2148874998,0.0004396444,0.2084027082,0.5009201169,0.0192254931,0.0070004459,0.0656421259,-0.0250212736,-0.4626917839,-0.175869301,0.0846921355,-0.3450936675,0.1315338314,-0.0851583704,-0.281075567,0.0045752814,0.4192501307,-0.0414196849,0.0370095409,-0.0708709136,-0.1737422645,0.1137733236,0.2625331879,0.106069468,0.3817303777,-0.0307464879,0.1158301532,0.0387668088,-0.0853009596,0.1163396016,0.1622754931,0.5593478084,0.4519827962,0.1674319059,0.1739865839,0.0989616737,-0.4996618629,-0.4535168409,-0.0369942859,-0.0368289985,0.0005099152,0.4410974681,-0.5239853859,-0.6593930721,-0.1555223763,0.2634774745,-0.1059421673,-0.210880518,-0.3275830448,0.1243178621,0.0108220382,0.0531242043,-0.0784798861,0.131729573,0.1481854618,-0.0105120083,-0.4107992053,-0.0609148107,-0.1017963216,-0.0715701133,0.2680945992,-0.3913148046,0.2765231431,-0.1607375741,-0.2429051697,-0.277836591,-0.4230124354,0.4261128306,-0.211836189,0.3343224525,0.0761890486,-0.0550734922,-0.1300878078,0.0421040766,0.2627417743,0.070496358,-0.2798241973,0.0307927541,-0.1302014142,-0.061176192,-0.1811628938,-0.0208158214,-0.1162304059,-0.3061003387,0.3118715584,0.1193757281,0.152237922,0.5688929558,0.0269612335,0.0545581244,-0.2560636103,0.1370017678,-0.0859854668,0.1329922229,0.1052357405,-0.062963739,-0.3697578013,0.0258465931,-0.1197765544,0.324038744,0.2857977748,-0.4700036943,-0.1853572726,-0.1529528946,-0.0834465623,-0.1562948674,-0.0713528246,-0.0684403554,-0.0024921396,0.0872634053,-0.3065321743,-0.145703584,-0.0116434051,0.2517802417,0.3584548831,0.3331951499,0.2047285587,0.3619554639,0.3501825631,0.4554519057,-0.091901958,0.5750848651,-0.2717235684,0.2448507845,-0.0402139202,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3473","title":"Iterating over a vision dataset doesn't decode the images","comments":"Please also note that the regression tests were implemented in accordance with the specifications:\r\n- when doing a `map` (wich calls `__iter__`) of a function that doesn't access the audio field, the decoding should be disabled; this is why the decoding is disabled in `__iter__` (and only enabled in `__getitem__`).","body":"## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n","comment_length":50,"text":"Iterating over a vision dataset doesn't decode the images \n ## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n \n Please also note that the regression tests were implemented in accordance with the specifications:\r\n- when doing a `map` (wich calls `__iter__`) of a function that doesn't access the audio field, the decoding should be disabled; this is why the decoding is disabled in `__iter__` (and only enabled in `__getitem__`).","embeddings":[-0.1789717376,-0.2866587341,-0.0869732872,0.3713634908,0.1995099634,-0.053819146,0.190698117,0.0316141695,-0.014085101,0.2848648429,0.1102564707,0.6305352449,-0.0778807998,-0.2217878401,-0.0395909771,-0.1451561749,-0.033466246,0.3611388803,-0.095234476,-0.1042018086,-0.2602480054,0.0639253557,-0.3702395856,-0.2472733855,0.0826140493,-0.1064708903,-0.1789023578,0.0012990745,-0.2136855572,-0.419698298,-0.0781214461,0.0044329697,0.024822114,0.4059697092,-0.000121519,0.155496344,0.4242656827,-0.1094425321,-0.1894099563,-0.1349501759,-0.2027017176,-0.1602315307,0.066919826,-0.1810098588,-0.0649215952,-0.5232774019,0.0838992819,-0.2499025613,0.3002088368,0.1066237018,0.1142506674,0.0308190193,-0.0185435489,0.5315041542,0.1692824066,0.2468280047,-0.1708952039,0.4152217507,-0.0583793148,0.1219315827,-0.2273100168,0.4460647106,-0.0496105067,0.1681677997,0.0244364887,0.2050689906,-0.0472400263,-0.4605138004,-0.1653554142,-0.0530324169,0.3309443593,-0.1667299718,-0.4589380026,-0.2208451629,-0.1271262765,-0.483151108,0.3219931126,0.1420208663,-0.2906318009,0.0384123512,-0.0874651298,0.0926057696,-0.1853723973,0.233818531,-0.3556720316,-0.0948428735,-0.1044339612,0.1443348676,0.4409030378,-0.0498954095,0.273302108,-0.0700625554,-0.175009504,-0.1239951178,-0.387539953,-0.1693895757,0.0216692295,-0.0079218056,-0.0731508061,0.0553601719,0.0740679055,0.0431641079,-0.222076416,0.2388478518,0.2710496187,0.073825784,-0.2300851643,0.4705203474,0.1888391376,0.1522963941,-0.1840726733,-0.1155910119,0.0143344142,-0.0940683633,0.0885743126,-0.0208535865,0.333584547,-0.1228833944,-0.226377055,-0.2289358526,-0.7423387766,-0.1648159921,-0.0226203911,0.1202650592,0.4201435149,0.2499835938,0.160336569,0.1388780028,-0.2229293436,-0.2300181389,-0.0838883668,-0.0361916311,-0.2393380105,0.0666321069,0.0850357339,0.2303414494,0.0008738479,-0.0233197957,0.0254531372,-0.3203956783,0.1987048239,-0.4061117172,0.7475296855,0.266182512,-0.1966234297,-0.0207602829,-0.0584057122,-0.1047020108,-0.1098293588,0.1222004518,-0.1829855293,0.1975502372,0.1330412626,0.0161937997,-0.3011019826,-0.1963587999,-0.0891501233,0.2865545154,-0.019779481,-0.4216880202,0.2456891835,-0.2424384356,-0.3471265435,-0.1291727573,0.3110240996,0.3311357796,-0.4823746979,0.1953655928,-0.0488570333,-0.1908260882,0.2448620498,0.3664059937,-0.0892487317,0.1136384606,-0.4554418623,0.1003911346,0.1573898494,-0.3971651495,-0.6393212676,0.3502377272,0.231743291,0.494427681,0.0558656305,0.1349155754,0.0965901837,-0.057020966,-0.3248891532,0.2013661563,-0.1744830906,-0.0605685413,-0.1837569028,0.0833396316,0.4329297245,0.2383269668,0.0847174898,0.157225281,0.1294759661,-0.1424467564,0.4032502174,-0.1334685087,0.0418915451,0.0283043459,0.1365902722,-0.2799534202,0.414601028,-0.1249684989,0.0605203919,0.1535262614,0.1304369867,-0.0678590313,-0.390612185,0.0622259714,0.2232219279,0.024536008,-0.5923889875,-0.1557962596,0.1363750994,0.441811502,-0.1529629827,0.145750314,-0.2616074085,0.0797238648,-0.2535971403,-0.1095393002,-0.08532805,0.3213583827,0.1046991572,-0.1463036537,-0.3029435873,0.2016638964,-0.0656338185,-0.2406123132,-0.0405219235,0.2570308447,0.5106732249,0.2430373728,-0.1637868732,0.0927480683,0.3728280067,-0.8071994185,-0.0675373748,0.2072450966,0.1155464128,-0.047430452,0.207103774,-0.0210243035,0.2241636217,0.4733750224,-0.0394442379,0.0184530914,0.0707857758,0.0043259463,-0.4431526065,-0.1662729084,0.0379339717,-0.3015814424,0.1298765093,-0.0871949717,-0.2402890176,0.0040040016,0.459458679,-0.0768882856,0.0631589442,-0.0765895471,-0.1967228204,0.0913171172,0.2570132017,0.0713362619,0.3440681398,-0.0330947489,0.0938638076,0.0678784847,-0.0720629469,0.1591129005,0.1837814599,0.55155617,0.4681215286,0.1443288326,0.1762770563,0.0576140359,-0.5171973705,-0.4493567944,-0.0362451859,-0.0022028401,-0.0369969644,0.4303495288,-0.4798645973,-0.6042447686,-0.1478458643,0.244213745,-0.1147380993,-0.2363819033,-0.3204598129,0.1604547054,0.0097890478,0.0859222412,-0.0370288976,0.1484366059,0.1657845229,-0.0771361887,-0.4657946229,-0.1050615385,-0.1010615155,-0.0530971251,0.318828553,-0.4390096068,0.2606321871,-0.1121332869,-0.2220498323,-0.2575856447,-0.406434387,0.4390818477,-0.2303664535,0.2908095121,0.0675045028,-0.0366751887,-0.1556222141,0.0563264303,0.2675887346,0.0460659117,-0.2839901745,0.0758156553,-0.1616164744,-0.0519537516,-0.193350479,-0.0367530026,-0.1021111012,-0.3071850836,0.286872983,0.0870409161,0.1550758928,0.5662665367,0.0637728199,0.0994426236,-0.312209487,0.0703218877,-0.129359901,0.1263516545,0.1209410727,-0.0781080127,-0.3277115524,0.0548627563,-0.1014037654,0.3211347163,0.255636096,-0.4615962803,-0.139207989,-0.11394196,-0.1203628331,-0.2021487653,-0.082971707,-0.0418891348,0.0374264605,0.0702889115,-0.2928248048,-0.1629464775,0.0005132784,0.2870491445,0.374794811,0.3042930961,0.1930341423,0.3521549702,0.3680737317,0.4678539634,-0.1262819469,0.4818854034,-0.3723908365,0.2566928864,-0.039553795,-0.2187256962,0.1598908901,0.00166513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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3473","title":"Iterating over a vision dataset doesn't decode the images","comments":">  I wonder why there is no issue in Audio feature with decoding disabled in __iter__, whereas there is in Image feature.\r\n\r\n@albertvillanova Not sure if I understand this part. Currently, both the Image and the Audio feature don't decode data in `__iter__`, so their behavior is aligned there.\r\n","body":"## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n","comment_length":49,"text":"Iterating over a vision dataset doesn't decode the images \n ## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n \n >  I wonder why there is no issue in Audio feature with decoding disabled in __iter__, whereas there is in Image feature.\r\n\r\n@albertvillanova Not sure if I understand this part. Currently, both the Image and the Audio feature don't decode data in `__iter__`, so their behavior is aligned there.\r\n","embeddings":[-0.0908605754,-0.3369822502,-0.0806187317,0.4213090539,0.1601837277,-0.0303245746,0.2214545459,-0.0054396512,-0.0804102719,0.2857024074,0.0867206901,0.522911191,-0.0322172642,-0.1369965672,-0.1372658461,-0.2022333443,0.034654364,0.3320819139,0.0491904728,-0.094181098,-0.2120271772,0.0692790747,-0.3560756147,-0.2714835405,0.0287527945,-0.0240425058,-0.1912502646,-0.0384814218,-0.2333519012,-0.4596177936,-0.0735020339,0.0624065734,0.1069096625,0.3332907259,-0.0001234678,0.1775427163,0.4807682037,-0.0630617291,-0.2231529355,-0.1209533587,-0.2199769318,-0.2186955661,0.1461547613,-0.1675781161,-0.0430960059,-0.6059791446,0.1180562675,-0.1544657499,0.2774113417,0.0970702618,0.0833945349,0.0875461996,0.020761406,0.5472238064,0.1661500782,0.2713360488,-0.2325910181,0.351698339,-0.0323964432,0.166589275,-0.2491093278,0.4132460356,-0.0429001041,0.1907826364,0.0424254872,0.2177723646,-0.1191266626,-0.4638727903,-0.1571336687,-0.0455738828,0.4525871873,-0.0991377681,-0.4682760239,-0.2749092877,-0.1443212926,-0.4398695827,0.3522159159,0.1916549504,-0.2702871263,0.0498342887,-0.0388257876,0.0680792779,-0.2106287032,0.1847963482,-0.3528878987,-0.1452784836,-0.1344645917,0.160861671,0.3615965545,-0.013283872,0.3524276614,-0.070374079,-0.0752509534,-0.2126308233,-0.4061593413,-0.1271720827,0.0104732253,0.0428062528,-0.0998826623,0.1195170954,0.0283099711,0.0362606719,-0.2304583043,0.2502749562,0.2944628894,0.0682688728,-0.1703338921,0.4527851343,0.2027796358,0.2124351412,-0.1716159135,-0.173780784,-0.0208225604,-0.1203547344,0.1328115612,-0.0210483503,0.4478603601,-0.1283734888,-0.3054828346,-0.2959783375,-0.6404027939,-0.2040372044,-0.0053763897,0.0150067611,0.3795146942,0.2782295942,0.1363413632,0.2026945502,-0.2545624077,-0.2744057178,-0.044167988,-0.0293455292,-0.1664632261,0.0537853017,0.0836706534,0.216182068,-0.0224883724,-0.0046650511,0.0562215783,-0.2639275491,0.2102002054,-0.4909255505,0.6442561746,0.2474070638,-0.1822413057,0.0048419163,-0.0544548929,-0.036518354,-0.0994633436,0.1656705141,-0.2319951653,0.2143937796,0.1147632897,-0.0181505606,-0.2979396284,-0.2385148257,-0.06015414,0.2943538725,0.0188954026,-0.4275335371,0.2312263846,-0.2260757834,-0.338511169,-0.1789216846,0.300744921,0.3443672359,-0.520435214,0.2083214968,-0.0686152279,-0.2916470468,0.2639980316,0.3806130886,-0.0905225575,0.0582767986,-0.39754197,0.0879919454,0.1202373132,-0.41813007,-0.5851980448,0.3284204006,0.2917782068,0.543648541,0.0896535292,0.1287198812,0.0569567531,-0.1090874374,-0.3777758479,0.162675336,-0.1970510334,-0.1646244973,-0.1831424236,0.0381750725,0.4477258027,0.2457762957,0.0879469812,0.1824037582,0.0836636573,-0.1350914091,0.3082947731,-0.0842775553,0.1149117276,-0.0026675165,0.1280903518,-0.1605348587,0.4604099691,-0.0582015216,0.0567246862,0.2398647368,0.1586253196,-0.069979772,-0.4116464257,0.0340497978,0.2502873838,0.0053226724,-0.6026596427,-0.1840769798,0.100051716,0.3911402225,-0.1722599715,0.0750079006,-0.2879251242,0.1562561989,-0.2274364978,-0.070545122,-0.106201075,0.332337141,0.0990592912,-0.1156464666,-0.3004308045,0.1669392288,-0.0395339616,-0.179915309,-0.0364034064,0.2521410584,0.508816123,0.2554190755,-0.2290395051,0.0991925523,0.3801515996,-0.8088202477,-0.047642421,0.1818999648,0.1353771091,-0.0351324119,0.1607133001,-0.0209609922,0.1898577064,0.4863986969,0.0180055648,0.020946553,0.0272614285,-0.040973518,-0.4108512998,-0.1598880589,0.0555309877,-0.359834224,0.1163079068,-0.0849130601,-0.3463285863,0.0285160728,0.4301254153,-0.0078558419,0.0274858903,-0.0121027185,-0.1642508805,0.0956229046,0.2290307581,0.0368794352,0.3624595702,-0.0403189696,0.1253333539,0.0441467315,-0.0402598791,0.1075817645,0.1185019538,0.5623557568,0.4528129995,0.1497626305,0.2138261795,0.1046032831,-0.4651176631,-0.3948517442,0.0173310023,-0.0395511575,0.0022946319,0.4201058447,-0.4783698618,-0.6801810861,-0.1319321841,0.2919656038,-0.1373365819,-0.2480566949,-0.2933246791,0.16762124,0.0744961053,0.0011371329,-0.093898885,0.1231784746,0.1003368869,0.0233593266,-0.4160423279,-0.0200763568,-0.0839689746,-0.0615409017,0.2699318826,-0.3786158562,0.2343955934,-0.1289455146,-0.2306693792,-0.2783615589,-0.4092332423,0.4204208255,-0.2558790445,0.308785975,0.0481365658,-0.0239787586,-0.1335709542,0.0554684103,0.2966936827,0.0995071828,-0.2817155719,0.0105051082,-0.100234054,-0.0481166765,-0.1619300544,0.0149217332,-0.1258956343,-0.2914820313,0.3358874023,0.1118795201,0.1844101846,0.5449226499,0.0024032106,0.0694594979,-0.1748829931,0.1276399493,-0.0803179145,0.100536339,0.1013311073,-0.0648764595,-0.3380976021,0.0068744365,-0.0328442417,0.3177341521,0.2685554922,-0.4728650153,-0.1454167962,-0.2204590142,-0.0250391047,-0.1675822139,-0.1128591523,-0.065229781,0.0092151389,0.0880516917,-0.3293046951,-0.1398232728,-0.0065730973,0.2604362667,0.3264961839,0.3766280115,0.1959261,0.367395401,0.3560912013,0.4180588722,-0.0242102947,0.6217771769,-0.245354563,0.223540321,-0.046562288,-0.2100647092,0.1969669759,-0.0736183673,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3473","title":"Iterating over a vision dataset doesn't decode the images","comments":"Therefore, this is not an issue, neither for Audio nor Image feature.\r\n\r\nCould you please elaborate more on the expected use case? @lhoestq @NielsRogge \r\n\r\nThe expected use cases (in accordance with the specs: see #2324):\r\n- decoding should be enabled when accessing a specific item (`__getitem__`)\r\n- decoding should be disabled while iterating (`__iter__`) to allow preprocessing of non-audio\/image features (like label or text, for example) using `.map`\r\n-  decoding should be enabled in a `.map` only if the `.map` function accesses the audio\/image feature (implemented using `LazyDict`)","body":"## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n","comment_length":88,"text":"Iterating over a vision dataset doesn't decode the images \n ## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n \n Therefore, this is not an issue, neither for Audio nor Image feature.\r\n\r\nCould you please elaborate more on the expected use case? @lhoestq @NielsRogge \r\n\r\nThe expected use cases (in accordance with the specs: see #2324):\r\n- decoding should be enabled when accessing a specific item (`__getitem__`)\r\n- decoding should be disabled while iterating (`__iter__`) to allow preprocessing of non-audio\/image features (like label or text, for example) using `.map`\r\n-  decoding should be enabled in a `.map` only if the `.map` function accesses the audio\/image feature (implemented using `LazyDict`)","embeddings":[-0.130967021,-0.3112412691,-0.0679312572,0.3901096582,0.165642336,-0.0171954315,0.1670644283,0.007290029,-0.0455669165,0.2232475579,0.1539148688,0.5721439123,-0.070878908,-0.1515948027,-0.0927555412,-0.1920368373,0.002681307,0.3228374422,-0.009204465,-0.0985440761,-0.2209009677,0.0836700052,-0.3190724254,-0.2576848269,0.0163268466,-0.0802775845,-0.2616451681,0.0022411612,-0.1657000035,-0.4797743261,-0.046868179,0.0536954738,0.1137048826,0.3426578343,-0.000123371,0.1679347008,0.4204083681,-0.0609173328,-0.2382424921,-0.0931018218,-0.1300405115,-0.2252032906,0.1417699307,-0.206286788,-0.1086685881,-0.5756086707,0.1164940149,-0.1719064862,0.2990896404,0.0805667639,0.1003608555,0.0786870793,0.0268805698,0.5348858237,0.1268808395,0.262055248,-0.1915723234,0.3795755506,-0.0819906741,0.1523656398,-0.2265734226,0.4234169424,-0.0407795161,0.1625088304,0.0459188111,0.2566451728,-0.0618672892,-0.3925707042,-0.2108315974,-0.0704898983,0.3844064176,-0.1287846863,-0.4892503023,-0.294834435,-0.1134384647,-0.4816841483,0.3079325557,0.1710925996,-0.2560333014,0.0514432564,-0.0389752239,0.1137616709,-0.2321751714,0.1943756491,-0.3842063248,-0.1371913254,-0.1188229099,0.1888590455,0.383761555,0.0254175682,0.324244976,-0.0397754423,-0.1219604164,-0.2259707153,-0.4048022628,-0.1139254496,0.0141935032,0.0479030684,-0.1168747842,0.0735412091,0.0904510394,0.0253531244,-0.1905914992,0.1984432787,0.3154221177,0.0642930791,-0.2172318846,0.5264363289,0.1674220711,0.1824644506,-0.1531718224,-0.1240152791,-0.0570666976,-0.08416228,0.1249045283,-0.0331044905,0.3717365265,-0.0632437468,-0.2747363448,-0.2811986804,-0.6601768732,-0.1651630551,-0.017316984,0.0598408282,0.3070096672,0.2883628607,0.1867514253,0.1140825599,-0.2170606554,-0.2788389623,-0.0932619199,-0.0317226872,-0.2435943335,0.0493802503,0.0799775124,0.2068816125,-0.0438048579,-0.0709474832,-0.0334604904,-0.2442276329,0.1933225542,-0.4286789298,0.7123475075,0.266790688,-0.1924308836,-0.0362608284,-0.0194888227,-0.0708547235,-0.1043723002,0.1413101405,-0.2246702611,0.1645435691,0.0612062663,-0.0037571427,-0.313567996,-0.2626793683,-0.1021480784,0.2687808871,0.0264658052,-0.3095988631,0.2118606269,-0.2410896271,-0.3004998863,-0.1741159558,0.2916233242,0.3652232289,-0.5320481658,0.2255468071,-0.0586126894,-0.236671254,0.243771404,0.3749918044,-0.1029966995,0.0977406874,-0.4363642037,0.0781510547,0.0998174921,-0.3840516806,-0.56832546,0.3204511106,0.3181258738,0.509075284,0.1046629027,0.183335945,0.0871788412,-0.0827105045,-0.3418692052,0.2368037701,-0.1993415654,-0.150527373,-0.1837529987,0.0871360376,0.352743119,0.3418269753,0.1408895254,0.1522852778,0.118018724,-0.1581151634,0.3760344386,-0.1204307377,0.1040535495,-0.0025972244,0.1060805693,-0.1732270867,0.4980133176,0.0002642766,0.0268525481,0.2268901914,0.1645442843,-0.098103717,-0.416321665,0.0076780347,0.2511902452,0.0234160367,-0.6252770424,-0.1606754065,0.1129983962,0.4316333532,-0.1213449314,0.1060633063,-0.26882267,0.1635497659,-0.2945820093,-0.0449128896,-0.0863586813,0.3689501286,0.0981357098,-0.1386189759,-0.3041940629,0.1692073345,-0.0618771687,-0.2264762521,0.026779484,0.2421223968,0.4436725378,0.2180733979,-0.1374225169,0.0696389824,0.3748374283,-0.7750802636,-0.0550409742,0.1202523783,0.092034705,-0.0321789645,0.1888886094,-0.0244977903,0.1490598917,0.4148454368,-0.0626838654,0.0244243555,0.0490993224,-0.0507962182,-0.3914754391,-0.1951413453,0.053559456,-0.293174088,0.083867982,-0.1047246158,-0.3040669858,0.0470212959,0.3915954232,-0.0683519915,0.0310528204,-0.0119749466,-0.1522816271,0.154426828,0.2524829805,0.0959657952,0.3310517073,-0.0306925755,0.1360144168,0.0888770968,-0.0498919189,0.1334743649,0.1227091476,0.547316432,0.5113514662,0.1373404115,0.1559273899,0.0878323242,-0.4740809202,-0.4449009895,0.0098325135,0.0164757483,-0.0121462643,0.4929941893,-0.4329056144,-0.6520398259,-0.1479946524,0.3014078736,-0.1000873744,-0.2661985457,-0.3282777667,0.1593120396,0.0421013907,0.0042237318,-0.0764365271,0.128104642,0.1698386222,-0.06090324,-0.4357952178,-0.0834841058,-0.1008679494,-0.0573102348,0.2620513141,-0.472094208,0.296102494,-0.1642473638,-0.184308067,-0.3008644283,-0.4247019887,0.449849844,-0.2876509428,0.2851577103,0.1359593719,-0.0270002577,-0.1465896368,0.1057954878,0.2568151653,0.0721546039,-0.2531436086,0.0290682856,-0.1440201104,-0.0812732279,-0.1689151973,0.0218214393,-0.1387387514,-0.2649270594,0.327914685,0.1458526254,0.1670962274,0.5533841848,0.0363836028,0.1165100411,-0.3094137907,0.0389367603,-0.0631699488,0.093251586,0.0919588432,-0.0540023148,-0.3187607825,0.0787416473,-0.1316720247,0.3096338212,0.2529428005,-0.5129864812,-0.1434838623,-0.1662822515,-0.0325547382,-0.1769916266,-0.0939173996,-0.0785918236,-0.0139195668,0.0647692606,-0.2802662253,-0.1121049002,0.0199058689,0.216501236,0.3581593633,0.3518359363,0.1820649207,0.431790024,0.348210007,0.4034549594,-0.1325007677,0.5520018339,-0.3079079092,0.2670593262,0.0216831695,-0.2070575356,0.1616764665,-0.0652968064,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3473","title":"Iterating over a vision dataset doesn't decode the images","comments":"For me it's not an issue, actually. I just (mistakenly) tried to iterate over a PyTorch Dataset instead of a PyTorch DataLoader, \r\n\r\ni.e. I did this:\r\n\r\n`batch = next(iter(train_ds)) `\r\n\r\nwhereas I actually wanted to do\r\n\r\n`batch = next(iter(train_dataloader))`\r\n\r\nand then it turned out that in the first case, the image was a string of bytes rather than a Pillow image, hence Quentin opened an issue.","body":"## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n","comment_length":66,"text":"Iterating over a vision dataset doesn't decode the images \n ## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n \n For me it's not an issue, actually. I just (mistakenly) tried to iterate over a PyTorch Dataset instead of a PyTorch DataLoader, \r\n\r\ni.e. I did this:\r\n\r\n`batch = next(iter(train_ds)) `\r\n\r\nwhereas I actually wanted to do\r\n\r\n`batch = next(iter(train_dataloader))`\r\n\r\nand then it turned out that in the first case, the image was a string of bytes rather than a Pillow image, hence Quentin opened an issue.","embeddings":[-0.0878729224,-0.309632957,-0.0400018021,0.3918703794,0.133465752,-0.082113564,0.189643234,0.0339634158,0.0456693657,0.2231497616,0.1672265083,0.5336774588,-0.0977950096,-0.1827500612,-0.0427957214,-0.1867165715,-0.0286196563,0.2891233563,-0.0311484877,-0.0773727149,-0.1957762986,0.0315753259,-0.2635381818,-0.2707316577,0.0007089018,-0.0261415858,-0.2875036895,-0.0103439214,-0.1470340341,-0.4633405507,0.0448571704,0.0660748929,0.1828947812,0.3788698018,-0.0001244723,0.1929392517,0.4055196643,-0.0527809113,-0.2820524275,-0.0384098701,-0.019667821,-0.260805577,0.1749287397,-0.2284380645,-0.100838244,-0.6216950417,0.1086754352,-0.2213279009,0.2153074145,0.0831178501,0.0939756408,0.1570036858,-0.0289739873,0.5055196881,0.1235690266,0.2154964358,-0.1988954991,0.4084229171,0.0479845293,0.1016621366,-0.3335599005,0.3707704246,-0.0758280158,0.2041996419,0.1035409197,0.2142032832,-0.0377003327,-0.3779368997,-0.2750452459,-0.0637775213,0.273882091,-0.1113050878,-0.4553218782,-0.3406582475,-0.1492066532,-0.4248559773,0.2960886061,0.1072019115,-0.2506217659,0.0054067476,-0.0038840491,0.1186378524,-0.2856172919,0.2526076436,-0.3819330931,-0.0532801449,-0.0363026448,0.1724628955,0.3810614645,0.0611828305,0.472315073,-0.0358588211,-0.12146651,-0.2440557778,-0.3798344433,-0.0791010037,0.0279796459,-0.0230474547,-0.1205051765,0.0869494453,0.1496713459,0.0158301946,-0.0931315422,0.1723005474,0.3186183572,0.1097847223,-0.2536126673,0.5824272037,0.128835097,0.1366136521,-0.1118728593,-0.061452888,-0.0408869348,-0.1830238402,0.2101133317,0.0224941913,0.3571399152,-0.0294046719,-0.2095262706,-0.2825665176,-0.7051033378,-0.1227430478,0.0210521817,-0.001541107,0.2765548527,0.2614836693,0.236880064,0.1107281148,-0.2681060135,-0.2635682523,-0.0909578428,-0.0522143506,-0.2132708281,0.0430396087,0.1634559184,0.2075075358,-0.0834221616,-0.022496745,0.1048792899,-0.2499863803,0.1869221628,-0.3640169501,0.7249725461,0.2837795913,-0.2336752415,-0.0662014037,-0.0033204781,-0.0324585773,-0.0849179551,0.224093467,-0.250043869,0.1385765225,-0.0095634945,-0.0091605335,-0.3609267175,-0.305218637,-0.0690890625,0.2010338157,0.0618170723,-0.2876731455,0.229355514,-0.3440267146,-0.1905531585,-0.1361827552,0.2468261868,0.2973623872,-0.5268825889,0.1514051557,0.0009573395,-0.212798059,0.3428062201,0.4085386395,-0.1592548937,0.1403372288,-0.4379440248,0.0744839087,0.0676317066,-0.3187306523,-0.5857679844,0.3498162031,0.260748893,0.5002265573,0.1170068532,0.2477396429,0.1337599754,-0.0328990184,-0.2950569987,0.225838989,-0.1808554828,-0.1341839582,-0.2003586143,0.0760048702,0.4546192288,0.3528382182,0.2399783731,0.1707792729,0.0537507497,-0.1410190761,0.3842090666,-0.2008417398,0.0690263733,0.0195987318,0.0491267741,-0.1346079111,0.5177261233,-0.0173563808,-0.0205115397,0.2682201564,0.0748402178,-0.0413057841,-0.4080244005,0.0724145994,0.2838975489,0.0694516152,-0.6171793342,-0.1635385752,0.0882107615,0.3656786084,-0.1373343319,-0.0448759161,-0.2552236319,0.1736798882,-0.306989938,-0.0621544831,-0.0867500156,0.3566477299,0.0766239092,-0.1647554189,-0.3696189225,0.1613254398,-0.0722417608,-0.2446294874,0.0004944999,0.1747194082,0.4015968442,0.1404508948,-0.2013043165,0.0129954191,0.3658571541,-0.7169280648,-0.1114290506,0.1751357019,0.0030072923,-0.0588649064,0.2435585707,-0.0184274483,0.12754713,0.4802016616,-0.0975903347,0.0225526523,0.0579180792,-0.0747414157,-0.3947720528,-0.1594982147,0.1926435381,-0.2450582087,0.0673617274,-0.0886724368,-0.2355216444,0.0305501595,0.3042601347,-0.0796729699,0.0107243741,-0.0115538025,-0.1643076986,0.163854897,0.2252741754,0.0984577239,0.2718268335,-0.0431728102,0.1399523765,0.0406639799,-0.1195521578,0.1051957011,0.0382866003,0.5769978166,0.5054655075,0.1072191,0.1574972421,0.1048093811,-0.4390026331,-0.411706388,0.0112966932,0.0767565593,-0.0361306518,0.6062255502,-0.4511976242,-0.6237025261,-0.1048390269,0.2462697178,-0.1777424514,-0.2944360673,-0.3359276354,0.1851857305,0.049490273,0.0028169279,-0.1385549158,0.1041713208,0.1629293263,-0.1957796067,-0.3997068703,-0.1297776848,-0.1413852274,-0.0776636004,0.279494971,-0.5620763898,0.2890907526,-0.127920717,-0.1836529374,-0.364274472,-0.3823758066,0.4327214062,-0.2421467304,0.2252053022,0.0912266299,0.0143857356,-0.0683111846,0.1021727696,0.2394058257,0.0183779895,-0.2515060306,0.0955790281,-0.1501940638,-0.0447525457,-0.164932996,0.0547348224,-0.1141082868,-0.2045221925,0.2308684736,0.1901368648,0.1518034786,0.5521467328,0.0836066306,0.1140305027,-0.233496815,-0.0155710513,-0.0284495279,0.09177012,0.1402529627,-0.0764743686,-0.296959132,0.0077442066,-0.129349187,0.2714537978,0.3083683252,-0.5498194695,-0.1633278579,-0.1945910156,0.0177641995,-0.1889929622,-0.1816180348,-0.0237387661,0.0215862263,0.0888961479,-0.2221962959,-0.0718507171,0.0456670262,0.2324100137,0.3779267371,0.2438490987,0.1224699989,0.4003370106,0.4336366057,0.2888174653,-0.1816259921,0.4989641011,-0.3595416248,0.2170036286,0.0421869569,-0.2785230577,0.1677436978,-0.1488573998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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3473","title":"Iterating over a vision dataset doesn't decode the images","comments":"Thanks @NielsRogge for the context.\r\n\r\nSo IMO everything is working as expected.\r\n\r\nI'm closing this issue. Feel free to reopen it again if further changes of the specs should be addressed.","body":"## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n","comment_length":31,"text":"Iterating over a vision dataset doesn't decode the images \n ## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n \n Thanks @NielsRogge for the context.\r\n\r\nSo IMO everything is working as expected.\r\n\r\nI'm closing this issue. Feel free to reopen it again if further changes of the specs should be addressed.","embeddings":[-0.0911607221,-0.3595893085,-0.0508328304,0.4086554646,0.1412413716,-0.0136509771,0.1258955449,0.0593581237,-0.0061525367,0.1949331462,0.1792142391,0.5321471691,-0.0439155176,-0.123993434,-0.0646306276,-0.1569756866,0.0175929554,0.2997389734,0.025263695,-0.1067634597,-0.236745134,0.1048496217,-0.3097244203,-0.2923023701,0.0453215316,-0.0297624487,-0.2835356891,0.0016704382,-0.1749753356,-0.4527773559,0.0106984554,0.0525574833,0.1064740196,0.338737905,-0.0001223067,0.1524634212,0.3982882202,-0.0363617837,-0.2432936132,-0.0822823197,-0.1017013043,-0.2396047413,0.1475264579,-0.2043797374,-0.1358155906,-0.5493173599,0.0961470976,-0.1692905575,0.2917230129,0.049881883,0.105962269,0.0853294358,0.0628652796,0.5347548127,0.1060519367,0.2336911261,-0.1704912782,0.371229142,-0.110761255,0.1593020111,-0.1882724464,0.3761422038,-0.0737323686,0.1669455618,0.0354022123,0.2564545572,-0.0318813622,-0.4047648907,-0.2371650636,-0.052755706,0.350736171,-0.1313528419,-0.4559846818,-0.3025566936,-0.1228604317,-0.4613295197,0.3491023779,0.2087185532,-0.2571617365,0.0707955211,0.0135813551,0.0929708704,-0.2530208826,0.1994618773,-0.3653825223,-0.1408730447,-0.0832649544,0.1692869961,0.3491600454,0.0227989033,0.3840191662,-0.0553712174,-0.169682771,-0.2348558307,-0.405003041,-0.1098937616,-0.0418720767,0.0936924219,-0.0782495514,0.0987874195,0.0812913924,0.0185899641,-0.2000343353,0.2009766698,0.2716214955,0.0429743975,-0.2058274448,0.569375515,0.111940816,0.1324679255,-0.1616629511,-0.0997979641,-0.0698540211,-0.1237009689,0.1229671985,-0.0359726213,0.3391933143,-0.0593269393,-0.1868901402,-0.2733954191,-0.679695487,-0.1522933841,-0.026672462,0.0285304375,0.3164388537,0.2817386389,0.2265049666,0.1340816319,-0.2488736361,-0.3376652598,-0.1045629457,-0.0530943908,-0.2234989107,0.0431535877,0.0859729946,0.1988488585,-0.0849190876,-0.0349665284,-0.0414972827,-0.28442204,0.1692164689,-0.4114149213,0.727887392,0.2451030463,-0.2046801746,-0.0599257387,0.0205791127,-0.1148970202,-0.0864230618,0.1402481943,-0.2274932712,0.2030461282,0.0548212156,-0.0041932324,-0.3438708782,-0.3087536097,-0.0886718035,0.2518979907,0.0435001291,-0.2548530102,0.1877720952,-0.2359256744,-0.2585912943,-0.17446132,0.3156964183,0.3751049936,-0.5280704498,0.2404889315,-0.0253316853,-0.2566173077,0.2659897804,0.3764412105,-0.0824033394,0.0788738132,-0.4481162429,0.0594563447,0.0579567775,-0.3490834534,-0.5716887116,0.3678847551,0.3008437753,0.4581709504,0.0607156418,0.1693108976,0.0652856529,-0.0700075477,-0.394620657,0.2477511466,-0.1886060089,-0.153315112,-0.2217034101,0.1093066037,0.3931856453,0.3083885014,0.1736359447,0.1508388072,0.0663974434,-0.1880112737,0.4236241281,-0.0763439015,0.1011090055,-0.0191542078,0.131547913,-0.153348282,0.5154823661,-0.0131918052,0.0228842665,0.2377530634,0.1587820649,-0.1194875091,-0.4076041281,0.0624833144,0.2453977168,0.0540886112,-0.6149246693,-0.1257508099,0.1096824333,0.3666636646,-0.117337741,0.0955614001,-0.2539739609,0.174434796,-0.2745249271,-0.0256792177,-0.0797456577,0.3857542574,0.0923135355,-0.14750579,-0.2950839698,0.1444891244,-0.0473525189,-0.2182925791,0.0022063078,0.2441836298,0.4047057927,0.1732898802,-0.0832949579,0.0262283292,0.3780457675,-0.8003008962,-0.0965069309,0.1252137721,0.0924558565,-0.0296922736,0.1963568628,-0.0080180503,0.1075182259,0.4401964843,-0.0773388147,0.0582292564,0.0414632633,-0.0852360129,-0.3500137925,-0.1823795736,0.0994296074,-0.2844878435,0.0612335689,-0.1354648471,-0.3116195798,0.0598555729,0.3767957985,-0.0289666597,0.006114176,-0.0221030973,-0.1333801895,0.1847706139,0.2215431035,0.0749272108,0.285879612,-0.0357298292,0.1086832061,0.0946683809,-0.0629878715,0.1246674433,0.1014808416,0.5285307765,0.5080180168,0.0885710046,0.1533771753,0.0564639419,-0.4357745051,-0.4007245004,0.0216768626,0.0007946214,-0.0270494055,0.5111715794,-0.4568688571,-0.6644811034,-0.1626348197,0.2539759576,-0.1542433351,-0.3112695217,-0.3570478559,0.1398819983,0.0995430723,-0.0049346047,-0.1207466722,0.0916078761,0.1518118382,-0.1098245159,-0.4499150515,-0.1213232502,-0.0881967917,-0.0589500181,0.3093395829,-0.4779467285,0.3300900459,-0.1392738372,-0.1544647813,-0.2941292524,-0.3996735215,0.4428444207,-0.3422985673,0.2650536597,0.1150620133,-0.0115081863,-0.1615803987,0.1639096588,0.2919086814,-0.0104989028,-0.2537606657,0.0495128818,-0.1142214164,-0.1066487879,-0.1671772152,0.0319864154,-0.0970015749,-0.2107329518,0.2991684973,0.1794542223,0.1658940613,0.5234163404,0.044322893,0.1026446447,-0.2546553016,-0.0407918394,-0.0390506163,0.0732247606,0.0950058699,-0.0637252703,-0.3026874959,0.1027852371,-0.1231299713,0.2993479967,0.2342746854,-0.5083338618,-0.0858056992,-0.1998247355,-0.0131617235,-0.2161602229,-0.1364502758,-0.1070121229,-0.0125705916,0.0652891025,-0.2897032499,-0.1108064428,0.0539299287,0.199897483,0.3998770118,0.3275471628,0.1469457448,0.4360583425,0.3380973935,0.3946782947,-0.1318712085,0.518897295,-0.339130491,0.2213444263,0.0602631792,-0.2288505584,0.1625664085,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3473","title":"Iterating over a vision dataset doesn't decode the images","comments":"Thanks for the details :)\r\n\r\nI still think that it's unexpected to get different results when doing\r\n```python\r\nfor i in range(len(dataset)):\r\n    sample = dataset[i]\r\n```\r\nand\r\n```python\r\nfor sample in dataset:\r\n    pass\r\n```\r\neven though I understand that if you don't need to decode the data, then decoding image or audio data when iterating is a waste of time and resources.\r\n\r\nBut in this case users can still drop the column that need decoding to get the full speed back no ?","body":"## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n","comment_length":83,"text":"Iterating over a vision dataset doesn't decode the images \n ## Describe the bug\r\n\r\nIf I load `mnist` and I iterate over the dataset, the images are not decoded, and the dictionary with the bytes is returned.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport PIL\r\n\r\nmnist = load_dataset(\"mnist\", split=\"train\")\r\n\r\nfirst_image = mnist[0][\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # passes\r\nfirst_image = next(iter(mnist))[\"image\"]\r\nassert isinstance(first_image, PIL.PngImagePlugin.PngImageFile)  # fails\r\n```\r\n\r\n## Expected results\r\n\r\nThe image should be decoded, as a PIL Image\r\n\r\n## Actual results\r\n\r\nWe get a dictionary\r\n```\r\n{'bytes': b'\\x89PNG\\r\\n\\x1a\\n\\x00..., 'path': None}\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.17.1.dev0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.2\r\n- PyArrow version: 6.0.0\r\n\r\nThe bug also exists in 1.17.0\r\n\r\n## Investigation\r\n\r\nI think the issue is that decoding is disabled in `__iter__`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfe5b73387c5e27de6a16b0caeb39d3b9ded66d6\/src\/datasets\/arrow_dataset.py#L1651-L1661\r\n\r\nDo you remember why it was disabled in the first place @albertvillanova ?\r\nAlso cc @mariosasko @NielsRogge \r\n \n Thanks for the details :)\r\n\r\nI still think that it's unexpected to get different results when doing\r\n```python\r\nfor i in range(len(dataset)):\r\n    sample = dataset[i]\r\n```\r\nand\r\n```python\r\nfor sample in dataset:\r\n    pass\r\n```\r\neven though I understand that if you don't need to decode the data, then decoding image or audio data when iterating is a waste of time and resources.\r\n\r\nBut in this case users can still drop the column that need decoding to get the full speed back no ?","embeddings":[-0.0234935898,-0.3361242414,-0.1152201965,0.402048111,0.1095664278,-0.0673545077,0.1837364733,0.0540370606,-0.0070808232,0.2113575488,0.155963704,0.5565049052,-0.0202432517,-0.1345619857,-0.0524966344,-0.1830283403,0.057849329,0.2923587561,-0.002866291,-0.2041169554,-0.1988760978,0.0604527816,-0.3156460822,-0.2815858722,0.070692569,-0.0508689843,-0.2603250146,-0.0414551161,-0.1578272134,-0.4807849824,0.0064167315,0.0626600385,0.0977132171,0.3476984203,-0.0001239544,0.1797284037,0.3697611392,-0.0600921325,-0.2316689491,-0.0620642528,-0.0670433119,-0.2336890697,0.1844451576,-0.2357018888,-0.1078235656,-0.5691386461,0.0634719506,-0.2426801473,0.1982858032,0.0299339313,0.1044545397,0.1374366432,-0.0821859688,0.4861968458,0.0990635306,0.1997207403,-0.1434845328,0.3341662288,-0.0854219571,0.1146869734,-0.1739712805,0.4096756577,-0.041136492,0.1984057873,0.0472220816,0.2659731805,-0.0231733881,-0.4276508093,-0.229064256,-0.0319039337,0.2555895448,-0.0642956421,-0.4854050577,-0.2815858722,-0.1896435469,-0.4356891811,0.2507335544,0.2384613454,-0.2627663314,0.0587496869,-0.0392664373,0.166699484,-0.2242992371,0.2101664096,-0.4287174642,-0.077184774,-0.0839525163,0.2104033381,0.323154062,0.0519092008,0.3572217822,-0.1034021229,-0.1309675574,-0.2421122342,-0.3684843183,-0.1100837067,0.0147397937,0.0731804371,-0.1165348366,0.1041937619,0.1032307446,0.0007168294,-0.1779521704,0.1134223044,0.2612885535,0.0626054406,-0.205927968,0.5471674204,0.0654840693,0.1705539227,-0.2201611549,-0.0833314732,0.0185401514,-0.1567067802,0.136312753,0.0035689189,0.3504022658,-0.0872906744,-0.263184756,-0.2911523581,-0.7114155293,-0.1818782389,-0.0205356926,0.0244926456,0.2793687284,0.2959498167,0.1611348838,0.1303564012,-0.2619990408,-0.2456376702,-0.130381614,-0.0453058742,-0.2053301483,0.0287903659,0.0614897087,0.2032850534,-0.056729991,0.022016678,0.0002552875,-0.2620112002,0.1870384514,-0.3853565753,0.8205059171,0.2273032814,-0.1830672771,-0.0129614584,0.0031569363,-0.1114744097,-0.1290124804,0.142063573,-0.1933180988,0.2060521394,0.0750844628,0.01604544,-0.2894883752,-0.2520462871,-0.0894333944,0.2164532542,-0.0007522642,-0.1945303082,0.235179022,-0.2445408255,-0.2447902411,-0.1964253485,0.2565392256,0.3249502182,-0.529468596,0.2342914194,0.0103512388,-0.1889152378,0.2224580497,0.3856262267,-0.1493366659,0.1515871137,-0.5117515326,0.0323726386,0.1189810336,-0.4190916121,-0.5537783504,0.3360391855,0.3010096848,0.4762022197,0.136503458,0.1696668863,0.1061726809,-0.04487345,-0.3553396165,0.2338249534,-0.165522486,-0.1094727963,-0.1788869351,0.070970282,0.4124054909,0.2405241579,0.140434444,0.162998423,0.1313122809,-0.2226352841,0.4608110487,-0.1421099305,0.0387570187,-0.0044448413,0.0459867977,-0.1517646611,0.4703570008,0.0369949862,0.0640165955,0.2233190536,0.1435664147,-0.0603481457,-0.4233555198,0.0320126452,0.2202589512,0.0322570056,-0.5997841358,-0.1970522106,0.1258280277,0.4092452228,-0.0758432969,0.0994009301,-0.1898171455,0.1650001854,-0.2649019063,-0.0399742089,-0.1172801554,0.4144609869,0.142581135,-0.0906680897,-0.3539779484,0.1236365661,-0.0238613784,-0.2374575734,0.0008115401,0.1729074419,0.423926115,0.1968388259,-0.1127213836,0.0101171527,0.3677069843,-0.7532385588,-0.02116514,0.1671849042,0.1519617289,-0.0153462775,0.2026368529,0.0550212115,0.117096588,0.4938752651,-0.0361592025,-0.0038909419,0.02300331,-0.0382533781,-0.4000650048,-0.2207865864,0.0610157773,-0.1868431866,0.1420645118,-0.0844786242,-0.2543902695,0.0977450907,0.4000334442,-0.0576374643,-0.049693767,-0.0889267623,-0.090679504,0.1964712888,0.2205047607,0.0938544273,0.3501236737,-0.0284751877,0.1333800852,0.0484173968,-0.0739958212,0.0858779773,0.1334929913,0.5782439113,0.463059783,0.0481867716,0.1683953702,0.0435121022,-0.4422195554,-0.4519513845,-0.0332036614,-0.0213229768,0.0298746582,0.536924839,-0.4395083487,-0.6671127081,-0.2282710969,0.2201487571,-0.1040659323,-0.2639163435,-0.4156981111,0.0808053091,0.0840738118,-0.0574168563,-0.096753262,0.093280226,0.1806041002,-0.0732322037,-0.375774473,-0.0778238103,-0.1428257823,-0.0618656427,0.2412932962,-0.4702975452,0.3342758417,-0.1751321256,-0.2196707577,-0.3098741472,-0.4495579898,0.4461212456,-0.2912869453,0.2805336416,0.1335661262,-0.0281270947,-0.0904940441,0.1323565245,0.2416998893,0.0652611256,-0.2522411048,-0.0285710935,-0.1227765009,-0.0927897915,-0.1431409717,-0.031560827,-0.1643955261,-0.2141999751,0.2830533087,0.2059312314,0.1695607156,0.4932217002,0.0340701677,0.0465543531,-0.2204387486,0.0601630844,-0.0551540963,0.0768017024,0.083712928,-0.0949175209,-0.3210145533,0.0083416617,-0.1933167279,0.3068571687,0.2551883161,-0.5353495479,-0.1574672908,-0.2421328425,-0.0183112156,-0.1570772678,-0.0729087591,-0.0670793429,-0.0259784944,0.0689644516,-0.2773471475,-0.1267391592,0.0135353934,0.1845151335,0.2970393598,0.3197326064,0.1418925226,0.3920773566,0.3586404622,0.4529588521,-0.1417464316,0.5425730944,-0.305681318,0.2285239697,-0.0003578507,-0.2642243207,0.1242554337,0.0056967121,-0.2451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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3465","title":"Unable to load 'cnn_dailymail' dataset","comments":"Hi @talha1503, thanks for reporting.\r\n\r\nIt seems there is an issue with one of the data files hosted at Google Drive:\r\n```\r\nGoogle Drive - Quota exceeded\r\n\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nAs you probably know, Hugging Face does not host the data, and in this case the data owner decided to host their data at Google Drive, which has quota limits.\r\n\r\nIs there anything we could do, @lhoestq @mariosasko?","body":"## Describe the bug\r\nI wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)\r\n```\r\n\r\n## Expected results\r\nExpecting to load 'cnn_dailymail' dataset.\r\n\r\n## Actual results\r\n`NotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'`\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n","comment_length":141,"text":"Unable to load 'cnn_dailymail' dataset \n ## Describe the bug\r\nI wanted to load cnn_dailymail dataset from huggingface datasets on Google Colab, but I am getting an error while loading it.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('cnn_dailymail', '3.0.0', ignore_verifications = True)\r\n```\r\n\r\n## Expected results\r\nExpecting to load 'cnn_dailymail' dataset.\r\n\r\n## Actual results\r\n`NotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'`\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n \n Hi @talha1503, thanks for reporting.\r\n\r\nIt seems there is an issue with one of the data files hosted at Google Drive:\r\n```\r\nGoogle Drive - Quota exceeded\r\n\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nAs you probably know, Hugging Face does not host the data, and in this case the data owner decided to host their data at Google Drive, which has quota limits.\r\n\r\nIs there anything we could do, @lhoestq @mariosasko?","embeddings":[0.0230112262,-0.2250260711,-0.0139146466,0.5434480309,0.1939676851,0.1551864594,0.3652307689,-0.1235439703,0.1347780526,0.3054013848,-0.2102702409,-0.1074484289,-0.3181636035,0.3879692554,0.0041481084,-0.0061605773,-0.040717341,-0.1317533106,0.1692016274,0.0624724068,-0.2353699356,0.4150594175,-0.2294249237,-0.0838031545,-0.4770193398,-0.1755899042,0.1128524989,0.3310137093,-0.0377258472,-0.0648799911,0.2543583214,-0.1035663411,0.1995366663,0.5372697711,-0.0001254998,-0.0555218644,0.3967915773,-0.000592366,-0.5252463818,-0.3715400398,0.1726908833,-0.3304089308,0.1851206273,0.0218206793,-0.038799651,0.0655036047,0.1637996882,0.0114580551,0.4811926484,0.2550405562,0.1010259539,0.035241276,0.2433707118,0.0704189315,-0.0020349657,-0.0124462005,-0.0387875587,0.7515343428,-0.0503058322,-0.0454496965,0.3414474726,0.1512459368,-0.138803497,-0.0447141044,0.1979112774,0.1772916317,-0.2959625125,-0.5034223199,0.2351294756,0.1063976362,0.3145458996,-0.0675582588,-0.2313410938,-0.3294522166,0.0950320959,-0.1341526061,0.3958300352,0.3932416737,-0.2202928215,0.1434070021,-0.4104234576,-0.2003124505,-0.1568993628,0.2687765062,-0.2365629375,0.0405122712,-0.2253819853,0.0834423527,0.2206823677,-0.1398704946,0.3458634913,-0.0576565452,0.1683300883,0.1789548248,-0.1392944753,0.2394211441,0.0945101157,0.379823029,0.1144432798,-0.0593501143,0.0062105092,-0.1760039777,-0.2923934758,0.1631809473,0.2653346062,0.2462624758,-0.1383957714,0.1303697228,0.2692582905,0.4706641734,-0.0062941941,-0.114902541,-0.2332527339,-0.1110919788,-0.1398421526,-0.1631755978,0.4632206261,-0.1027583182,-0.3627831638,0.0974242389,-0.4801661074,0.1204212904,0.2591857314,0.4501115978,-0.1253009886,-0.3054507375,0.0464825928,0.0186545178,-0.0138650183,0.0258596838,-0.1992218792,0.1967486143,-0.0810763612,0.3058435917,0.291420579,-0.5062553883,0.1622043103,-0.0972943008,0.4246958792,-0.294390738,-0.1976758838,-0.1862356365,-0.2110640854,0.4570817649,0.0382618532,0.1559120119,0.1811485142,-0.1450174898,-0.1126012728,0.2071550041,-0.4280316532,-0.5033727884,0.2137936056,0.0317512937,-0.3086360991,-0.0129840337,-0.3929101229,0.1536570042,-0.0207641944,-0.2166941762,-0.1443376094,0.1148531288,-0.2740401328,-0.2169234008,0.317253232,0.742406249,-0.3934482634,-0.0715923235,-0.191004768,-0.0880803466,0.1371550113,0.4123043716,-0.1544852853,-0.0682026222,-0.3380497396,-0.2568278909,-0.1611906737,-0.1022495031,-0.6697843671,-0.0635763854,-0.1778502613,0.2048621029,-0.037986692,-0.0530771315,-0.1901344806,-0.0538890958,0.3706645072,0.360791415,-0.0271593593,0.0379079916,-0.2132962048,-0.2990059257,-0.0511327274,0.20179075,0.1830848455,0.1509733647,0.1484087259,-0.1326335967,0.0311852321,-0.0587783866,-0.1522222906,0.2634432614,0.3047017455,0.0443647318,-0.0147395572,0.1549899429,-0.5649030209,0.1647638232,0.0730357468,0.0641487241,-0.1525342911,-0.0473675989,-0.2791849375,0.1648056656,0.0489795394,-0.0516733043,-0.034166228,0.0705383196,-0.0238654874,0.1793438494,-0.080440335,0.5021486282,-0.1490390748,0.0923118293,-0.378734082,0.5213676095,-0.1040107757,-0.1225110218,0.0737370551,0.1268044114,0.2142617255,-0.1718752086,-0.0749877393,0.0761472732,-0.0529297665,0.3394695818,0.0592226535,0.118639268,0.2243252397,-0.1458507329,0.0773990005,0.2169710398,0.0589155927,0.1218707114,-0.1138600186,0.3146374226,-0.1091662496,0.1290401369,0.0641205832,-0.0476330966,0.04639275,0.039292451,0.1575433463,-0.0965403989,0.360591948,0.0205586776,0.367262423,0.0420411043,-0.2838922739,0.1290200949,0.1141856238,-0.1277030855,-0.0297754798,0.2732394636,-0.287592411,-0.1847156435,-0.0308263618,0.2713721693,0.2499836832,0.1059540883,0.0103192292,0.0804890618,0.3138588369,-0.1738964021,0.1642559618,0.2570932209,0.1052145958,0.1125633195,0.2179974318,0.0634157956,-0.3104628325,-0.2177761197,0.1112924963,0.4027701914,-0.392914772,0.1998316944,0.0058502732,-0.2730064392,0.0712341592,-0.2157375813,-0.3645640314,-0.1664581597,-0.0500367954,0.3437081873,0.1183920205,-0.1574749649,0.3576323092,0.476180017,0.0891631767,-0.1643454731,-0.2879029214,0.064449355,-0.1293938756,-0.0842646211,0.4242928624,-0.2705403566,0.185459733,-0.058853697,-0.1219884381,-0.2714028656,-0.1778675914,0.1126486287,0.0537536032,0.6729850173,-0.1541175693,0.3009418547,-0.3657962382,-0.0313789733,0.0215457622,0.0066658952,-0.0682364851,-0.0517066158,-0.0092734732,0.1856945753,-0.0113893608,0.0836857259,-0.5145084262,-0.297309339,0.2817970812,0.0171648692,0.1146258935,0.3267267346,0.0678987205,0.0973066315,0.1269856542,-0.0789712369,-0.3093142509,-0.4290426075,0.367590189,-0.2903364003,-0.3825683594,0.0803258345,0.1156314909,-0.1954658478,-0.102765888,-0.5431509018,-0.4884872139,-0.1517885625,-0.2819659114,-0.20134826,-0.1382389665,-0.0120681254,-0.1885020882,0.0793199688,0.1610139608,-0.4690049291,-0.2041218281,0.2485726774,-0.0002313131,0.1237239093,0.2695480883,-0.1540245414,0.4549063146,0.2180372179,0.1225162819,0.4726420045,-0.0433106162,0.3131794035,-0.0419954471,-0.4830948114,0.1025140584,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3464","title":"struct.error: 'i' format requires -2147483648 <= number <= 2147483647","comments":"Hi ! Can you try setting `datasets.config.MAX_TABLE_NBYTES_FOR_PICKLING` to a smaller value than `4 << 30` (4GiB), for example `500 << 20` (500MiB) ? It should reduce the maximum size of the arrow table being pickled during multiprocessing.\r\n\r\nIf it fixes the issue, we can consider lowering the default value for everyone.","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nusing latest datasets=datasets-1.16.1-py3-none-any.whl\r\nprocess my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:\r\n![image](https:\/\/user-images.githubusercontent.com\/30341159\/146865779-3d25d011-1f42-4026-9e1b-76f6e1d172e9.png)\r\n\r\nthen I get this error:\r\n![image](https:\/\/user-images.githubusercontent.com\/30341159\/146865844-e60a404c-5f3a-403c-b2f1-acd943b5cdb8.png)\r\n\r\nI have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: linux docker\r\n- Python version: 3.6\r\n\r\n","comment_length":51,"text":"struct.error: 'i' format requires -2147483648 <= number <= 2147483647 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nusing latest datasets=datasets-1.16.1-py3-none-any.whl\r\nprocess my own multilingual dataset by following codes, and the number of rows in all dataset is 306000, the max_length of each sentence is 256:\r\n![image](https:\/\/user-images.githubusercontent.com\/30341159\/146865779-3d25d011-1f42-4026-9e1b-76f6e1d172e9.png)\r\n\r\nthen I get this error:\r\n![image](https:\/\/user-images.githubusercontent.com\/30341159\/146865844-e60a404c-5f3a-403c-b2f1-acd943b5cdb8.png)\r\n\r\nI have seen the issue in #2134 and #2150, so I don't understand why latest repo still can't deal with big dataset.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: linux docker\r\n- Python version: 3.6\r\n\r\n \n Hi ! Can you try setting `datasets.config.MAX_TABLE_NBYTES_FOR_PICKLING` to a smaller value than `4 << 30` (4GiB), for example `500 << 20` (500MiB) ? It should reduce the maximum size of the arrow table being pickled during multiprocessing.\r\n\r\nIf it fixes the issue, we can consider lowering the default value for everyone.","embeddings":[-0.5384349823,-0.082264699,-0.0349174514,0.2288369387,0.1133693308,-0.1969541162,0.2010791004,0.4309727252,-0.0918043852,0.3907879293,0.1387390792,0.1428715438,0.1288887709,0.2428793311,-0.0235363971,-0.0707041696,0.1808977276,0.0859171152,-0.1746377647,0.1659537703,-0.2032332569,-0.0278887656,-0.163053304,-0.1536658704,-0.5480692983,0.0005016483,-0.0620197393,-0.010138833,-0.4876843095,-0.3528679609,0.2218099385,0.0886288658,0.0819906965,0.411305666,-0.0001241034,-0.0268359296,0.5084322691,-0.2209366709,-0.2780162692,-0.2772823572,-0.2406199127,-0.5049488544,-0.1133873761,-0.1737505049,0.0773790032,-0.0634905621,-0.2675313056,-0.0562673919,0.1371558905,0.2442339063,0.1534085721,0.2857002318,0.5780895948,-0.0958919749,0.2978576124,0.0326467939,-0.0568410642,0.3053095937,0.4750715196,0.0986062288,0.0511922501,0.3523120284,0.1782509089,0.1202766672,-0.2894182801,-0.2593256533,-0.094894737,-0.2375603318,0.3431322277,0.4392081499,0.600966692,-0.1090554744,-0.5246434212,0.0183104686,-0.0566375293,-0.3611823916,0.066355139,0.4359689653,0.0446294025,0.0774465874,-0.120000191,-0.1919209212,-0.3403612077,0.1099071056,-0.2807947099,0.5084503889,-0.0002369383,0.3241043389,0.269123584,-0.2954533994,-0.1417981833,0.3741218746,-0.1445263624,-0.008214524,-0.2889171541,-0.170432806,0.0287264399,-0.3917070627,0.2970862687,0.0318842568,-0.2691430151,0.2176343352,-0.0289138909,0.0299068131,0.3956596553,0.0280892272,-0.1726854891,0.2038862854,0.28403458,-0.0760360956,0.0302337427,-0.1068551764,0.065700829,-0.0927904174,-0.2750658095,-0.0823732764,0.2654267848,-0.0890703499,-0.1347198933,0.1500898451,-0.3328028023,0.0396791212,-0.0050684619,0.2159719318,0.146856904,0.5722494721,-0.2010575533,0.2948303521,-0.2154137343,-0.3623837531,-0.0753928199,-0.0884770155,-0.0378600843,-0.2421654463,-0.0638368353,0.1438666731,-0.1002694666,0.048114419,0.2022008002,-0.2782202959,0.0053424081,-0.5713713765,0.1368834525,0.4288010597,-0.0164542478,-0.0347853601,0.0232274067,-0.1983043998,-0.0827638507,0.2273742706,-0.4137197435,-0.55789572,-0.326672703,0.069264248,-0.1159463003,0.1039786935,-0.2318886667,0.0662799701,0.311406821,-0.0189804137,0.0655409247,-0.4752933085,-0.0135738365,-0.0178757161,0.0015026062,0.267879039,-0.3704155087,0.2438281775,-0.0039737839,0.0112629654,0.4659633636,0.3013346493,-0.1010151282,0.089778468,-0.027595818,0.1961847395,0.2273396552,-0.2005071491,-0.595026195,0.2643864751,-0.0811973587,-0.1140886545,0.1650772989,-0.1253833622,0.1965021342,-0.0783476606,-0.1940753907,0.2406806648,-0.1089026257,0.1000358611,-0.1870465279,-0.4390318692,-0.1266910732,0.0379241854,0.3180192709,-0.3492611945,0.1569744051,-0.0247756876,0.3511512578,-0.2750967145,0.3789663017,0.105978258,0.492457062,0.0885809287,0.0620699078,-0.3141270876,-0.0675399378,0.2233368307,0.2673791647,0.0586738661,-0.1972017437,-0.0379450582,0.0222491883,0.226236701,0.0138758942,0.0445030741,-0.0457620174,0.1241100729,-0.006402533,-0.2182148695,-0.0899342746,-0.1526758522,-0.4419281483,-0.0457666479,0.1152385697,0.2027310431,-0.2154668123,-0.2418031543,0.0766714513,0.1940673143,0.0593014881,-0.2730768323,-0.0282821134,0.12137115,0.258959651,-0.198790133,-0.3149816096,0.1099881381,-0.0317600183,-0.1137712151,0.0759524852,0.0197081938,0.0771822855,-0.0658860728,-0.0541401803,0.2741505206,-0.0583594292,0.4708822668,0.0479380228,-0.0793486759,0.5058752894,0.0772911012,0.1070793197,-0.4674157798,-0.0611864403,-0.2018247843,0.14494358,-0.1993955076,-0.5295602679,0.2134782672,0.6119214296,0.2000371516,0.2246787548,0.4339175224,0.1743690073,-0.076371558,0.1137666479,0.2270752639,0.2086993605,0.171458602,-0.0626738816,-0.2532310486,0.0923594162,-0.1275476664,0.1618090123,0.0539889596,0.3573842645,0.2873145044,0.3187533915,-0.1501573771,-0.2116693556,-0.1474033296,0.043704398,0.2525758147,-0.1025980413,0.3139878213,-0.3828183711,-0.2048487067,-0.3216044903,-0.4308438599,-0.0579514764,-0.1950390488,-0.2190297395,0.171360448,-0.1697769314,0.0281075332,0.1976944655,0.1917217076,0.1490100771,0.1287999749,0.0231590066,-0.1631801873,-0.1982567012,-0.001591278,0.3123299479,-0.2358704805,0.2853463292,0.0015744481,-0.0418017283,-0.1202196479,-0.5259135365,0.1528804004,-0.0606178455,0.0873548314,0.1366585344,0.2795856297,0.1262052655,0.1552208215,0.2027641535,-0.1644732505,-0.182262361,-0.0118766343,-0.2220060676,-0.1325571537,-0.1650478393,-0.0943104252,-0.2029580921,-0.4268335104,0.5911211967,0.1336915791,0.3321895301,-0.0247485563,0.5224895477,0.2492333949,0.1395522952,-0.076724872,0.0294438284,0.1449003965,0.1411738843,0.0349973515,-0.3560551405,-0.1640523523,0.1744730622,0.1618975848,0.222355783,-0.429369241,0.0939313397,-0.4254792631,0.2123131603,-0.2394701838,0.3795748353,0.2940493822,-0.0471876897,-0.0419976674,-0.1430192739,0.0739416853,-0.0580583923,0.2688370049,-0.0045849653,0.1756292731,0.5213539004,0.1682527959,0.6480833888,0.2791680098,0.085224852,0.3997257948,0.2305715978,0.330825597,-0.5880964398,-0.1565574855,0.1712594628,-0.171894446,0.198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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3457","title":"Add CMU Graphics Lab Motion Capture dataset","comments":"This dataset has files in ASF\/AMC format. [ The skeleton file is the ASF file (Acclaim Skeleton File). The motion file is the AMC file (Acclaim Motion Capture data). ]  \r\n\r\nSome questions : \r\n1. How do we go about representing these features using datasets.Features and generate examples ?\r\n2. The dataset download link for ASF\/AMC files does not have metadata information, for eg : category and subcategory information. We will need to crawl the website for this information. The authors mention \"Please don't crawl this database for all motions.\" Can we mail the authors for this information ?\r\nThe dataset structure is as follows : \r\n```\r\nsubjects\r\n\t- 01\r\n\t\t- 01_01.amc\r\n\t\t- 01_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 01.asf\r\n\t- 02\r\n\t\t- 02_01.amc\r\n\t\t- 02_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 02.asf\r\n```\r\nThere is no metadata regarding the category, sub-category and motion description.\r\n\r\nNeed your inputs. @mariosasko \/ @lhoestq  \r\nThank you.\r\n","body":"## Adding a Dataset\r\n- **Name:** CMU Graphics Lab Motion Capture database\r\n- **Description:** The database contains free motions which you can download and use. \r\n- **Data:** http:\/\/mocap.cs.cmu.edu\/\r\n- **Motivation:** Nice motion capture dataset\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).","comment_length":149,"text":"Add CMU Graphics Lab Motion Capture dataset \n ## Adding a Dataset\r\n- **Name:** CMU Graphics Lab Motion Capture database\r\n- **Description:** The database contains free motions which you can download and use. \r\n- **Data:** http:\/\/mocap.cs.cmu.edu\/\r\n- **Motivation:** Nice motion capture dataset\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md). \n This dataset has files in ASF\/AMC format. [ The skeleton file is the ASF file (Acclaim Skeleton File). The motion file is the AMC file (Acclaim Motion Capture data). ]  \r\n\r\nSome questions : \r\n1. How do we go about representing these features using datasets.Features and generate examples ?\r\n2. The dataset download link for ASF\/AMC files does not have metadata information, for eg : category and subcategory information. We will need to crawl the website for this information. The authors mention \"Please don't crawl this database for all motions.\" Can we mail the authors for this information ?\r\nThe dataset structure is as follows : \r\n```\r\nsubjects\r\n\t- 01\r\n\t\t- 01_01.amc\r\n\t\t- 01_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 01.asf\r\n\t- 02\r\n\t\t- 02_01.amc\r\n\t\t- 02_02.amc\r\n\t\t.\r\n\t\t.\r\n\t\t.\r\n\t\t- 02.asf\r\n```\r\nThere is no metadata regarding the category, sub-category and motion description.\r\n\r\nNeed your inputs. @mariosasko \/ @lhoestq  \r\nThank you.\r\n","embeddings":[-0.1400220543,-0.027625991,0.0362781323,0.1367382705,0.2119430453,0.2255600393,0.1493565887,0.0857909918,-0.4835157096,-0.156121999,-0.0873226747,0.0038938534,-0.2831747532,0.216411978,0.063206777,-0.3122254908,-0.0616575293,-0.1458099186,-0.0537848659,-0.2758547664,-0.0898385271,-0.1037114263,0.015996255,-0.0323652104,-0.6374867558,-0.0654065236,-0.3875576556,0.2147621065,-0.2260099351,-0.4087279141,-0.3373527825,0.2072522789,0.40685004,0.5765081644,-0.0001131307,-0.4016377032,0.0653209612,-0.2132929862,-0.1151432246,0.164180547,-0.2310396731,-0.1535726339,-0.0636354387,-0.2122583538,0.0311889704,-0.3918628991,-0.0085227638,-0.3635850549,-0.1860733032,0.2949467301,0.1244692355,-0.0005026138,-0.364122659,-0.1241854876,0.1355755776,0.5201157928,-0.2748973072,0.24899818,0.7377389073,0.1898404509,-0.2848301232,0.3149984479,-0.0378082767,-0.151132673,0.5103838444,0.1605923474,0.3241167068,-0.3630833328,0.1048902124,0.3407914639,0.8395394683,-0.2183963656,-0.2629059553,-0.1564950496,0.1774731129,0.1476420164,-0.1722437143,0.6073663831,-0.020943081,-0.0168476421,-0.3747269809,-0.0319343843,-0.4083564281,0.0605070516,0.0779446959,-0.0795107707,-0.2194832116,0.0350986086,-0.2649666071,-0.1569716036,0.2529105842,-0.2321330309,0.1644406319,0.1635801345,0.1147709265,-0.1625462323,-0.0378793143,0.2688343525,0.1383374333,0.276568085,0.1975000054,-0.1224118471,-0.1890544891,0.0904501602,0.0273204912,-0.0589001551,-0.0227347482,0.0728421509,0.1150961891,0.2436126769,-0.1930193305,-0.1006022021,-0.2753277421,0.1365774125,-0.1668396592,0.1390446424,0.3570899367,-0.1142525077,-0.1180441752,0.1934358329,-0.0019466404,-0.050126709,0.0684207529,0.4002502263,-0.1606645435,-0.1128859892,-0.1889220625,0.474883914,0.2344404459,-0.2148491591,-0.1953552365,0.1008570716,-0.3271576762,0.1681252271,0.3198526502,0.0254491977,0.2317337245,-0.0604693815,0.1867731959,0.100410156,0.1624730825,-0.111513257,0.4061921835,0.0452844873,0.0603469498,0.1594377607,0.0224956311,0.2701933384,-0.2322195023,-0.0118181752,0.0611282103,-0.0307678506,0.0289255064,0.122589618,-0.050324779,-0.0273965281,-0.1090965644,0.4404791296,-0.4700838327,0.346149087,0.2209822983,0.417213589,-0.0245637521,-0.4634299278,-0.0932590291,0.6238137484,-0.0927883238,-0.0393052064,0.0144301141,0.1332089156,0.130057469,0.1097676456,-0.0436870493,0.1013089567,-0.2751733065,-0.0506909043,0.0825120136,-0.2171290964,-0.029795127,0.0677353814,-0.1304371208,-0.1525132656,0.2268570662,0.224149555,0.1370590925,-0.3030342758,-0.2862597704,0.2341409624,-0.4007502496,-0.0824841186,0.1561965793,-0.2114047259,0.0604804903,0.1347588599,0.1436564773,0.1295098513,0.2639016211,-0.2452561557,0.1549334526,-0.0552621037,0.0129809333,-0.155333817,0.0980347171,0.1490193754,0.1041384563,-0.1278229654,-0.4800176322,0.2161590457,0.254750222,0.0406540968,-0.1503544152,-0.3730540872,-0.2745630145,-0.2620145679,-0.0372649767,-0.2534671724,0.1768480688,0.0720311925,-0.2099177241,-0.0346772037,-0.2814798951,0.1846440881,-0.1760546416,-0.0222290587,0.1052458808,0.3814592063,0.0925477967,-0.1060567871,0.0855630115,0.3382657766,0.1607357711,0.0136168301,0.036548432,0.3229469359,-0.1487431079,0.4442763031,0.3780811727,0.503313601,0.2942516506,-0.5858908892,0.5324748158,-0.3263148367,0.2316194475,-0.185338214,-0.3577288091,0.1231131926,-0.2889223695,0.0488829426,0.3499815762,0.1409685016,0.000664497,0.0613107719,-0.1616209745,-0.2185155302,0.0483859889,0.2592018545,-0.1210568249,-0.0613985546,-0.3294605911,0.2543341815,0.4596470296,-0.2020505965,0.1267012805,-0.1662961543,-0.6999562383,0.3093130589,0.2173116058,0.1033369824,0.1290134341,0.2238806337,0.096321933,0.1051132232,-0.0071490211,-0.0417909659,-0.0206434801,0.1765358001,-0.0778206065,0.0669111535,-0.1640239954,0.1029934436,-0.1681706905,-0.4113626778,0.1688838154,0.0531031042,-0.3054962456,-0.2473192364,-0.0100061717,-0.2244969755,-0.03979614,-0.093298845,0.0178053062,-0.0389634036,-0.1140248403,-0.0812717155,-0.0084119244,0.0522594005,-0.7297088504,0.3797699809,-0.2959038615,0.3477704227,0.1200914383,-0.0779194757,0.2251708806,0.1344409138,0.104979001,0.4389456213,0.3485681117,-0.1841533035,0.7457005382,-0.5460291505,-0.2688458562,0.2574372888,0.0516892374,-0.00506627,0.1130555421,-0.0624815784,-0.1427017301,0.0007245883,0.051383771,-0.2489318103,-0.0135420095,-0.0334204659,-0.1615210027,-0.3636419177,-0.0555207953,-0.0418729335,-0.0501854792,-0.2639074624,0.0857783929,0.1531990021,0.1020026952,0.2329886258,-0.1441215575,0.3608811498,0.0188699588,0.2020856589,-0.2765527964,-0.2311446667,0.4899625182,-0.3443776965,-0.3677471578,0.4210934937,-0.2119164467,-0.0454555936,0.1211021394,-0.0331721678,-0.1129573733,-0.2030473351,0.1302261055,0.2275913656,0.1409729868,0.1904391348,-0.1411192119,0.014639616,-0.324346453,-0.1377446502,0.157176286,0.5568598509,-0.0669746175,-0.1256689429,0.1755598336,0.0075396029,0.6380577087,-0.0577066652,-0.1499279588,0.083614558,-0.1510771364,0.1722671241,0.5456943512,-0.2156186402,0.2823483348,-0.00731342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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3457","title":"Add CMU Graphics Lab Motion Capture dataset","comments":"Hi @dnaveenr! Thanks for working on this!\r\n\r\n1. We can use the `Sequence(Value(\"string\"))` feature type for the subject's AMC files and `Value(\"string\")` for the subject's ASF file (`Value(\"string\")` represents the file paths) + the types for categories\/subcategories and descriptions.\r\n2. We can use this URL to download the motion descriptions: http:\/\/mocap.cs.cmu.edu\/search.php?subjectnumber=<subject_number>&motion=%%%&maincat=%&subcat=%&subtext=yes where `subject_number` is the number between 1 and 144. And to get categories\/subcategories, feel free to contact the authors (they state in the FAQ they are happy to help) and ask them if they can provide the mapping from categories\/subcategories to the AMC files to avoid crawling. You can also mention that your goal is to make their dataset more accessible by adding its loading script to the Hub.\r\n\r\nThe AMC files are also available in the tvd, c3d, mpg and avi formats (the links are in the [FAQ](http:\/\/mocap.cs.cmu.edu\/faqs.php) section), so it would be nice to have one config for each of these additional formats. \r\n\r\nAnd additionally, we can add a `Data Preprocessing` section to the card where we explain how to load\/process the files. I can help with that.","body":"## Adding a Dataset\r\n- **Name:** CMU Graphics Lab Motion Capture database\r\n- **Description:** The database contains free motions which you can download and use. \r\n- **Data:** http:\/\/mocap.cs.cmu.edu\/\r\n- **Motivation:** Nice motion capture dataset\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).","comment_length":181,"text":"Add CMU Graphics Lab Motion Capture dataset \n ## Adding a Dataset\r\n- **Name:** CMU Graphics Lab Motion Capture database\r\n- **Description:** The database contains free motions which you can download and use. \r\n- **Data:** http:\/\/mocap.cs.cmu.edu\/\r\n- **Motivation:** Nice motion capture dataset\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md). \n Hi @dnaveenr! Thanks for working on this!\r\n\r\n1. We can use the `Sequence(Value(\"string\"))` feature type for the subject's AMC files and `Value(\"string\")` for the subject's ASF file (`Value(\"string\")` represents the file paths) + the types for categories\/subcategories and descriptions.\r\n2. We can use this URL to download the motion descriptions: http:\/\/mocap.cs.cmu.edu\/search.php?subjectnumber=<subject_number>&motion=%%%&maincat=%&subcat=%&subtext=yes where `subject_number` is the number between 1 and 144. And to get categories\/subcategories, feel free to contact the authors (they state in the FAQ they are happy to help) and ask them if they can provide the mapping from categories\/subcategories to the AMC files to avoid crawling. You can also mention that your goal is to make their dataset more accessible by adding its loading script to the Hub.\r\n\r\nThe AMC files are also available in the tvd, c3d, mpg and avi formats (the links are in the [FAQ](http:\/\/mocap.cs.cmu.edu\/faqs.php) section), so it would be nice to have one config for each of these additional formats. \r\n\r\nAnd additionally, we can add a `Data Preprocessing` section to the card where we explain how to load\/process the files. I can help with that.","embeddings":[-0.4960852563,-0.1240226179,0.002748213,-0.1107789427,0.154397577,0.1139656529,0.1740248352,0.3626151085,-0.2517266273,0.0132644465,-0.0316170566,0.2011338323,-0.2203564495,0.2996659577,0.1233678386,-0.2626333237,-0.1171883345,0.0168652348,0.0039789276,0.0507579297,-0.0486088395,-0.0908001438,0.0642027631,-0.0518022291,-0.5443966389,-0.071310699,-0.1362726092,0.0050750077,-0.0411196686,-0.388261497,-0.2753128409,0.2596599162,0.3541995287,0.3954922259,-0.0000942395,-0.4407471418,0.1040877625,-0.2074260265,-0.0074811201,0.1970569342,-0.1303724647,0.1150807813,-0.0425809696,-0.0578825921,-0.0772628263,-0.0508242026,0.1742357016,-0.3936122954,-0.0886777714,0.1971293688,0.2805770338,0.0744215623,-0.4238091707,-0.1916279644,0.1947312355,0.3632388711,-0.2272345126,0.2694550753,0.6113545299,0.0367118791,-0.2882374823,0.4228581488,0.0240168516,-0.2793124616,0.3041776121,0.1647532582,0.3565840423,-0.221887961,0.0705246329,0.4010550976,0.3263351619,-0.2416431755,-0.1196281239,-0.2067329586,0.2400846183,0.0542622246,-0.1944368929,0.5154285431,-0.1349855363,-0.0479199812,-0.3454515636,0.1247222871,-0.3052943945,0.0254054461,0.1995281726,0.0214099903,-0.0483036414,-0.1748409271,-0.1150877476,-0.1398900151,0.2983126938,-0.0587012991,-0.0626482069,-0.0582958087,-0.0554747172,-0.2594682872,-0.1260073036,0.4815650284,-0.0119526377,0.1876192093,0.3063398004,-0.003526181,0.0295174159,0.1449312717,-0.0118336249,-0.1040926352,-0.1355749071,-0.1600268334,0.1189807057,0.3818285167,-0.1906790286,-0.007577078,-0.0787904188,0.1291138977,-0.4579196274,0.1056648195,-0.0048457114,-0.0107595511,-0.0761230811,-0.0570746921,0.0409741886,0.1874066889,0.1004199982,0.4281868935,-0.1500755847,0.1554812342,-0.1164333597,0.2229958922,0.1760844439,-0.1158044636,-0.2115875632,0.1410417259,-0.3126330972,0.1814247668,0.3142623901,0.06060737,0.0352777243,-0.1999575496,0.3241238594,0.2059709579,0.0892290473,0.0497038849,0.3995546103,-0.0072816354,0.1379188746,-0.0983865261,0.0284083057,0.1417240351,-0.2292376757,-0.1853696257,-0.0508881696,-0.06763044,-0.0504443087,0.3033755124,0.1704795063,-0.0975755379,0.0071804249,0.3797612786,-0.4973148704,0.0725156143,0.0464037694,0.4011538327,-0.1499674469,-0.2507193387,-0.147070691,0.3850391507,-0.1367049664,-0.0698678941,-0.0400624759,0.3181882799,-0.071922645,-0.0772346184,-0.1889240891,-0.0696231425,-0.2197985947,0.0342228226,0.0018689159,-0.4552355409,0.0243485719,0.1252510548,-0.219987303,-0.226607874,0.3096724153,0.3534067273,0.2409878373,-0.3401840329,-0.076569818,0.2426417768,-0.3257286251,0.0745481923,0.0188991632,-0.2514092624,-0.1175466031,0.2180514038,0.1328672469,-0.0833582208,0.1072252542,0.0052415198,0.3301070631,-0.0614774078,0.0904086828,-0.1720047593,0.1140380353,-0.0116568962,-0.1017305851,-0.0878665671,-0.2407265306,-0.1125450358,-0.0426259711,0.1458758414,0.0209237412,-0.3692901134,-0.0115989558,-0.3396850824,-0.13177751,-0.1868186295,0.3590085506,0.1751458943,-0.0809750184,-0.1010052785,-0.1108255386,0.2873199582,-0.1093781814,-0.1063148975,-0.0613691919,0.2973018885,-0.127139762,-0.1250881106,0.1705500484,0.1214656979,0.1366881132,0.1781122833,0.1175427809,0.3785557747,-0.2778995335,0.5389204025,0.1986455619,0.3418379128,0.3314183652,-0.4729326665,0.3213377297,-0.1990239024,0.1060935631,-0.0926861018,-0.3011716008,0.2251749337,-0.2329297811,0.0370853879,0.3080088198,0.0483624488,0.082250081,0.0542126596,-0.1415449679,-0.3039551079,0.0222147387,0.5489254594,-0.281976223,-0.107593745,-0.1715851873,0.3637143075,0.5353631973,-0.0161634274,0.0730184391,-0.0080765495,-0.5193972588,0.0882324949,0.1959294677,0.243604362,0.0430636182,0.3839612007,0.0149544869,0.0750847608,-0.0993361399,-0.2369475961,0.0534883291,0.0799020082,-0.0735700205,0.1495938599,-0.0494702719,-0.0258462392,-0.2619705796,-0.3784789443,0.0512227975,-0.169078961,-0.0857622847,-0.1907033622,-0.1058873534,-0.2423504293,-0.0114481701,-0.1548277438,0.089542225,-0.146191448,0.1556957215,0.0663291216,-0.1862788647,0.2463085055,-0.587269783,0.4227310121,-0.2755745053,0.2455538809,0.02982351,-0.3227598071,0.186506629,0.3210703135,0.1494893283,0.0533521324,0.3413957953,-0.1991027594,0.5412055254,-0.3576001823,-0.2640688419,0.3640882671,-0.0042332606,-0.1462613344,0.062944673,0.0328093469,-0.003904179,-0.2070294023,0.0099979136,-0.4275729954,0.0741153657,-0.0880544558,-0.1762161106,-0.2951569855,-0.2209257483,-0.1209931448,-0.0588036813,-0.4365814328,0.073749654,0.148136571,-0.0295701511,0.0888273194,-0.0626708344,0.1286013126,-0.270594418,0.2606357932,-0.2722079158,-0.3457015157,0.3824453652,-0.5332672596,-0.3621774614,0.2659261823,-0.1266623139,0.1888104975,-0.0256772172,-0.0414878726,-0.1940095425,-0.0452972092,-0.094729498,0.1875895709,-0.0183681604,0.3262373209,0.0046384889,-0.1873566955,-0.1508151144,-0.1254446954,0.1153445393,0.48689273,-0.1008879617,-0.0939440578,0.0585150309,0.1076357067,0.3617962003,0.0160429534,-0.1585391313,0.172108978,-0.1032317355,0.2155788243,0.2069042623,-0.022255348,0.1921558976,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3457","title":"Add CMU Graphics Lab Motion Capture dataset","comments":"Hi @mariosasko ,\r\n\r\n1. Thanks for this, so we can add the file paths.\r\n2. Yes, I had already mailed the authors a couple of days back actually, asking for the metadata details[ i.e category, sub-category and motion description] . They are yet to respond though, I will wait for a couple of days and try to follow up with them again. :) Else we can use the workaround solution.\r\n\r\nYes. Supporting all the formats would be helpful. \r\n\r\n> And additionally, we can add a Data Preprocessing section to the card where we explain how to load\/process the files. I can help with that.\r\n\r\nOkay. Got it.","body":"## Adding a Dataset\r\n- **Name:** CMU Graphics Lab Motion Capture database\r\n- **Description:** The database contains free motions which you can download and use. \r\n- **Data:** http:\/\/mocap.cs.cmu.edu\/\r\n- **Motivation:** Nice motion capture dataset\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).","comment_length":107,"text":"Add CMU Graphics Lab Motion Capture dataset \n ## Adding a Dataset\r\n- **Name:** CMU Graphics Lab Motion Capture database\r\n- **Description:** The database contains free motions which you can download and use. \r\n- **Data:** http:\/\/mocap.cs.cmu.edu\/\r\n- **Motivation:** Nice motion capture dataset\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md). \n Hi @mariosasko ,\r\n\r\n1. Thanks for this, so we can add the file paths.\r\n2. Yes, I had already mailed the authors a couple of days back actually, asking for the metadata details[ i.e category, sub-category and motion description] . They are yet to respond though, I will wait for a couple of days and try to follow up with them again. :) Else we can use the workaround solution.\r\n\r\nYes. Supporting all the formats would be helpful. \r\n\r\n> And additionally, we can add a Data Preprocessing section to the card where we explain how to load\/process the files. I can help with that.\r\n\r\nOkay. Got it.","embeddings":[-0.4981458783,-0.0221615788,-0.0469946153,0.0064928317,0.13370125,0.0644236803,0.1049064621,0.4042607844,-0.2334995419,-0.0190541372,0.0281624775,0.185005784,-0.1908475906,0.3392911255,0.1693347096,-0.3363627195,-0.0530753471,0.1396873444,-0.084388651,0.0431747176,-0.0702569187,-0.0249847081,0.0503050536,-0.0793321952,-0.5730770826,-0.036610771,-0.2418303192,0.0657718033,-0.2355055213,-0.3812151253,-0.1515893787,0.2230670452,0.2959674597,0.5824574828,-0.0000948434,-0.3243024051,0.0629595444,-0.258810252,0.0887473375,0.1055802181,-0.2039959729,0.0784146413,-0.1515657604,-0.0559247322,-0.1220610887,-0.2471212298,0.0467004478,-0.3461387455,-0.0075525222,0.2073260248,0.3116970658,-0.0035007284,-0.313914597,-0.1816879362,0.2378664911,0.4768871367,-0.2770292163,0.1410598606,0.5510949492,-0.1267867535,-0.1734276116,0.2997722328,0.0554611497,-0.1801768243,0.2177936435,0.1132786721,0.2498760521,-0.171500355,0.0992961228,0.3220065534,0.3295996487,-0.1652958095,-0.0957682878,-0.0678417385,0.225767687,-0.1749817878,-0.1236645803,0.4608753026,-0.0922929868,-0.0565486215,-0.2547587156,0.2015286982,-0.360598594,-0.0423740745,0.186917305,0.0455610417,-0.0124093397,-0.1918715984,-0.0284666754,0.0617090315,0.2081516534,0.1267126054,-0.1763555259,-0.1029744744,0.0568527095,-0.0996601731,-0.1523745358,0.3770132959,0.0109923724,0.1968974024,0.307092458,-0.0243735872,0.3053174019,0.162431404,-0.0006435739,-0.1145503893,-0.2630261779,-0.1508609056,0.1786684245,0.2597842515,-0.1624658108,-0.0230556037,-0.0135349352,0.0289584082,-0.4591377974,0.0789211616,0.0146382768,-0.095938094,-0.0621488616,-0.0679323524,0.1116767898,0.1956792176,0.0260222461,0.4270818532,-0.1568955928,0.0706194416,0.0227914397,0.2247108221,0.1465117484,-0.1015086919,-0.1933488399,0.1417063028,-0.3106881678,0.097677879,0.276574403,-0.0457049049,0.103815712,-0.1878251433,0.2486882955,0.2369477153,0.2115856558,0.090698339,0.4239697754,0.0147601785,0.2386053801,-0.1235827357,0.0555840991,0.1949058771,-0.1912536472,-0.0044817729,-0.1302088201,-0.1384671181,-0.2769141495,0.3093915582,0.156432122,-0.1223629713,-0.0473072305,0.3197385371,-0.5377843976,-0.042652104,0.09759783,0.3489634991,-0.0806644708,-0.2303364575,-0.0629603118,0.2824792862,-0.2477249801,-0.0679347664,-0.021243155,0.1074495539,0.1620387286,-0.0918610841,-0.1870147437,-0.0562135689,-0.3052001595,0.0679553002,0.1537091881,-0.3336899579,-0.0161790438,0.0015798787,-0.3141984046,-0.2627300918,0.3646116853,0.3498815298,0.1459096223,-0.2555578053,-0.0568005145,0.218999669,-0.3130106926,0.114803873,-0.056401886,-0.296385318,-0.1078089327,0.254426688,0.1566588879,-0.108258836,0.0742671117,0.0215129424,0.3067342043,-0.1496973485,0.0809151307,-0.0753701925,0.362077713,0.0892869085,-0.1412518173,0.0060097706,-0.1464473307,-0.0223637987,0.0065399855,0.0853067487,0.0643663928,-0.3977823257,0.0160998721,-0.2690815628,-0.2272782177,-0.1461433321,0.375395,0.1250147223,-0.058285933,-0.0789873526,-0.1650189012,0.2836883664,-0.1146516576,-0.1271134466,0.1180400029,0.2153396606,-0.2561337054,-0.2255680114,0.1270809174,0.0138942609,0.1521248817,0.036972601,0.1092166677,0.3439686596,-0.1316234022,0.4132355452,0.3029044569,0.2882444859,0.2750085294,-0.2853953242,0.1959349215,-0.2214489132,0.0465581864,-0.0797015354,-0.2311279774,0.2224486768,-0.3138033152,-0.1442582756,0.2498954087,-0.0051122401,0.1240687892,-0.0120050721,-0.0642857999,-0.2163313776,0.0958799124,0.4152866006,-0.2925167382,-0.1605742425,-0.2114545554,0.1853242666,0.5400575995,0.0621564426,0.1041005999,0.0423915908,-0.4080939889,0.0643935651,0.2109219432,0.2510005832,0.1540826261,0.4482453465,0.0528492741,0.0633643121,-0.0755683854,-0.2184040695,0.1686778963,0.0303386562,-0.1039144173,0.1220475882,-0.0763755962,-0.0978105888,-0.3262307048,-0.248040542,0.0914746821,-0.0281621423,-0.0665826052,-0.2283033431,-0.1500046104,-0.2582568228,0.0508247167,-0.2095878124,0.2133729309,-0.1467850804,0.1812415868,0.1492549181,-0.175729841,0.2535951734,-0.5214284062,0.4311153293,-0.1774576604,0.023469219,-0.0076991078,-0.2500818968,0.0944264978,0.3460312486,0.2047763467,-0.0006259814,0.4760358334,-0.1944999248,0.5204677582,-0.298723042,-0.3560855687,0.3346393108,-0.0788154453,-0.0897704214,0.0361751579,0.069680132,-0.0106489146,-0.1186572686,0.0638788491,-0.4273793697,0.0294523519,-0.1118755415,-0.2443003654,-0.241352424,-0.3961725831,-0.0959484801,-0.0197522677,-0.4302155972,0.2168921679,0.1528255939,0.023027353,-0.0164601449,0.1332615614,0.2773321569,-0.2705044448,0.1880264282,-0.2356775403,-0.4563959241,0.3951850832,-0.4901809692,-0.521402657,0.3873633146,0.0478592217,0.2386886179,0.0260635447,-0.2218921185,-0.4051901698,0.0296941809,0.0997681469,0.202660203,-0.0180965457,0.3315907717,0.0010501542,-0.2476575673,-0.1438535154,-0.1208211705,0.1242231354,0.4446294904,-0.0463530943,-0.1026803553,0.0793120787,0.1683973968,0.4410665929,-0.0320553035,-0.2071597576,0.3174209595,-0.1695325822,0.3594523072,0.1103770733,-0.0641025677,0.1890424341,-0.1180913895,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3455","title":"Easier information editing","comments":"Hi ! I guess you are talking about the dataset cards that are in this repository on github ?\r\n\r\nI think github allows to submit a PR even for 1 line though the `Edit file` button on the page of the dataset card.\r\n\r\nMaybe let's mention this in `CONTRIBUTING.md` ?","body":"**Is your feature request related to a problem? Please describe.**\r\nIt requires a lot of effort to improve a datasheet. \r\n\r\n**Describe the solution you'd like**\r\nUI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.)\r\n\r\n**Describe alternatives you've considered**\r\nThe current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.\r\n\r\n\r\n","comment_length":50,"text":"Easier information editing \n **Is your feature request related to a problem? Please describe.**\r\nIt requires a lot of effort to improve a datasheet. \r\n\r\n**Describe the solution you'd like**\r\nUI or at least a link to the place where the code that needs to be edited is (and an easy way to edit this code directly from the site, without cloning, branching, makefile etc.)\r\n\r\n**Describe alternatives you've considered**\r\nThe current Ux is to have the 8 steps for contribution while One just wishes to change a line a type etc.\r\n\r\n\r\n \n Hi ! I guess you are talking about the dataset cards that are in this repository on github ?\r\n\r\nI think github allows to submit a PR even for 1 line though the `Edit file` button on the page of the dataset card.\r\n\r\nMaybe let's mention this in `CONTRIBUTING.md` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3452","title":"why the stratify option is omitted from test_train_split function?","comments":"Hi ! It's simply not added yet :)\r\n\r\nIf someone wants to contribute to add the `stratify` parameter I'd be happy to give some pointers.\r\n\r\nIn the meantime, I guess you can use `sklearn` or other tools to do a stratified train\/test split over the **indices** of your dataset and then do\r\n```\r\ntrain_dataset = dataset.select(train_indices)\r\ntest_dataset = dataset.select(test_indices)\r\n```","body":"why the stratify option is omitted from test_train_split function?\r\n\r\nis there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset.","comment_length":60,"text":"why the stratify option is omitted from test_train_split function? \n why the stratify option is omitted from test_train_split function?\r\n\r\nis there any other way implement the stratify option while splitting the dataset? as it is important point to be considered while splitting the dataset. \n Hi ! It's simply not added yet :)\r\n\r\nIf someone wants to contribute to add the `stratify` parameter I'd be happy to give some pointers.\r\n\r\nIn the meantime, I guess you can use `sklearn` or other tools to do a stratified train\/test split over the **indices** of your dataset and then do\r\n```\r\ntrain_dataset = dataset.select(train_indices)\r\ntest_dataset = dataset.select(test_indices)\r\n```","embeddings":[-0.5293185711,-0.0548487976,-0.1174930632,-0.1046919376,0.2126726359,0.012887856,0.336581856,0.2544068098,-0.0457449295,0.3628115952,0.1107298359,0.3568530977,-0.0795329064,0.3337858617,-0.043533314,-0.3662009239,-0.1260601729,0.0729328096,-0.1467674077,0.1772595197,-0.1039990559,-0.0129164243,-0.3873732984,-0.3057707846,-0.2252361327,0.0702287853,0.0586887039,-0.0608846471,-0.1367442012,-0.0820587426,0.199954018,0.1456409991,0.1332621276,0.3081832528,-0.0001005132,-0.1260007024,0.0541954562,-0.3209929764,-0.0293754824,-0.3746545911,-0.2733127475,0.1714514792,-0.180419445,-0.0938221216,-0.3420349658,-0.0470603146,-0.2644602954,-0.3474151492,0.221185118,0.1745135933,0.2192722857,0.2382258177,-0.2318424284,-0.2352667153,-0.0395843871,-0.0449041314,-0.2666398585,0.0135265719,-0.280048281,0.0497715473,-0.0255399272,0.083329238,0.4014353752,-0.1324953735,0.0300218444,-0.0463330448,-0.3737834096,-0.1678961962,-0.3583140373,0.0288178865,0.2718638778,0.0783441439,-0.2228535265,0.2425990403,0.0505778082,-0.2832590938,0.0475941822,0.2006926388,0.0481877662,0.1128956899,-0.2429917008,0.13710244,-0.2634061575,0.1216252595,0.1645548195,0.4433889389,-0.1465304196,0.050229691,0.4911282063,0.2465078831,-0.4197573662,-0.0086899856,0.1950312406,0.1160286739,-0.4278466702,-0.2447155863,-0.2443812937,-0.4169799984,0.1869134903,0.281376183,0.0196660552,0.1705506891,0.3918431401,-0.0355120637,0.2392878085,0.1457157433,0.0624060594,0.1754171848,-0.1203636974,-0.091933094,-0.1698856056,-0.2005967498,0.1691756397,-0.2368946075,0.0099353837,0.000221185,-0.280325979,-0.0403936096,-0.2624587417,-0.5259945989,-0.3531084955,-0.1513389647,0.2167070657,0.2259501815,0.0681448206,-0.1666807085,-0.4671081603,0.1817965955,-0.3539069593,0.0087077441,-0.2596066296,0.2588580549,-0.1435713023,0.1466374248,0.042954769,-0.2523297668,-0.0371691249,0.1986064762,0.4258390963,-0.0077978796,0.0650171712,0.0208548512,0.3993742168,0.2053536773,0.1677502692,-0.2307276279,-0.0420140103,0.2585530579,-0.1453729719,0.1100435853,-0.0746974796,-0.7046906352,-0.1315713823,0.2467367202,0.18747437,-0.0157088023,-0.3309480846,0.3428332508,0.0199637599,-0.0772054344,0.0505352803,-0.1828574836,-0.261115551,-0.2019371092,0.5801535249,-0.0572152212,-0.1188031286,-0.0547438823,-0.1034315974,-0.0496894903,0.588847518,-0.0119191865,-0.2260581553,-0.2613184154,-0.0850056112,0.4826071858,0.4657333791,0.2611481249,-0.1008506864,0.0100430101,-0.0862527937,-0.2454692572,0.1231401935,-0.1468536109,0.1340683103,0.0542579405,-0.2181073576,0.3325821757,-0.2235338539,-0.1235538498,0.0050206953,-0.0130631132,0.5355623364,0.2754991353,0.0882402286,-0.0696561188,0.1616599709,0.2390507162,0.363923341,-0.1028911695,0.0738061816,-0.1507028192,0.1759150773,-0.1642567366,-0.0793270394,-0.486815244,0.093018502,-0.057297606,0.3632222414,0.009460235,0.2746326923,-0.199120909,-0.2743804455,-0.4468292594,-0.0030787867,0.0651063696,0.2027722001,-0.0169077478,0.0577214137,-0.2317452282,-0.1870107353,0.206637755,-0.2627967298,-0.0116387988,0.1986272782,0.2463790476,-0.1194040999,-0.1947696954,0.2462299019,0.2559918463,0.0608318634,-0.2980196774,-0.0715280175,0.4443039596,0.3153750598,-0.4252051711,-0.0025901396,-0.0906657502,0.1184684187,-0.2216585875,-0.2597106099,-0.0728717744,-0.0314477757,0.0598785989,-0.2320489883,0.4483129084,-0.5069956183,-0.0435905121,0.0904277414,0.0358660668,0.082172662,-0.1542258859,0.0194873828,-0.2882736623,-0.4150859416,-0.328564316,-0.055288434,0.0027182919,-0.4433059096,0.1119070277,0.1959341615,0.0526796244,0.010222923,-0.2524600327,0.3787611425,-0.0490855575,0.1296453178,0.1601631641,0.341904372,0.2244421244,0.2554574907,-0.2841293514,-0.1453517228,-0.0598684326,0.1061394438,0.1282236725,0.0661009103,0.0386329778,-0.2191491872,0.0215695743,-0.2911768258,-0.0077092866,-0.0074595409,-0.2004340142,-0.0601886734,0.021980701,-0.4383558929,-0.0876042023,-0.0536807217,-0.0878061503,-0.2174814492,-0.2263458073,0.139500007,-0.2363580167,-0.27331388,0.27245152,-0.2287923396,0.4479766786,0.1180225089,-0.0103186937,0.1868900061,0.1513221413,0.0810617357,0.2461502403,-0.1442613751,0.4279319048,0.4200902879,0.1108106747,-0.0860172734,0.0973855853,-0.1871665716,0.1492992043,-0.2975415289,0.1476678699,-0.0322473533,-0.1874532253,-0.3575737178,-0.2959253192,-0.1772409678,-0.2416424602,-0.0711901933,-0.1908291876,0.2216784209,0.0119765596,-0.4236190021,-0.7918320298,-0.1250245869,-0.283290565,-0.2390761673,-0.1878419966,-0.0486992225,-0.0906089693,0.0539685562,-0.0158189572,-0.0031704661,-0.0341046676,-0.2375297695,-0.170140788,0.3310906887,-0.464967519,-0.2550136447,0.0570112355,0.2148554027,-0.1260569394,-0.0304274354,0.0143970335,0.0782649294,0.0607779212,0.1845165491,-0.3242484331,0.0352262855,0.230334729,-0.1621305346,-0.0711570382,-0.2606828511,-0.1054630876,0.2171173841,0.4661805034,0.3706049621,-0.1051320285,0.1368004531,0.1467940658,0.7618216276,0.1414898634,-0.0374400094,0.1675718874,-0.2166176438,-0.0557148941,-0.0650105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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3450","title":"Unexpected behavior doing Split + Filter","comments":"Hi ! This is an issue with `datasets` 1.12. Sorry for the inconvenience. Can you update to `>=1.13` ?\r\nsee https:\/\/github.com\/huggingface\/datasets\/issues\/3190\r\n\r\nMaybe we should also backport the bug fix to `1.12` (in a new version `1.12.2`)","body":"## Describe the bug\r\nI observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter') \r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\ndic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']}\r\ndf = pd.DataFrame.from_dict(dic)\r\ndataset = Dataset.from_pandas(df)\r\nsplit_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42)\r\ntrain_dataset = split_dataset[\"train\"]\r\neval_dataset = split_dataset[\"test\"]\r\neval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0)\r\nprint( eval_dataset['x'])\r\nprint(eval_dataset_2['x'])\r\n```\r\nOne observes that elements in eval_dataset2 are actually coming from the training dataset...\r\n\r\n## Expected results\r\nThe expected results would be that the filtered eval dataset would only contain elements from the original eval dataset.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows 10\r\n- Python version: 3.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":36,"text":"Unexpected behavior doing Split + Filter \n ## Describe the bug\r\nI observed unexpected behavior when applying 'train_test_split' followed by 'filter' on dataset. Elements of the training dataset eventually end up in the test dataset (after applying the 'filter') \r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\ndic = {'x': [1,2,3,4,5,6,7,8,9], 'y':['q','w','e','r','t','y','u','i','o']}\r\ndf = pd.DataFrame.from_dict(dic)\r\ndataset = Dataset.from_pandas(df)\r\nsplit_dataset = dataset.train_test_split(test_size=0.5, shuffle=False, seed=42)\r\ntrain_dataset = split_dataset[\"train\"]\r\neval_dataset = split_dataset[\"test\"]\r\neval_dataset_2 = eval_dataset.filter(lambda example: example['x'] % 2 == 0)\r\nprint( eval_dataset['x'])\r\nprint(eval_dataset_2['x'])\r\n```\r\nOne observes that elements in eval_dataset2 are actually coming from the training dataset...\r\n\r\n## Expected results\r\nThe expected results would be that the filtered eval dataset would only contain elements from the original eval dataset.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows 10\r\n- Python version: 3.7\r\n- PyArrow version: 5.0.0\r\n \n Hi ! This is an issue with `datasets` 1.12. Sorry for the inconvenience. Can you update to `>=1.13` ?\r\nsee https:\/\/github.com\/huggingface\/datasets\/issues\/3190\r\n\r\nMaybe we should also backport the bug fix to `1.12` (in a new version `1.12.2`)","embeddings":[-0.138400957,-0.1338319629,-0.1100935116,0.0964839384,0.1088928729,0.0105677266,0.1856799871,0.2552304864,-0.1076798737,-0.0520350635,-0.1723313928,0.4741822779,0.0075506978,0.4032782316,-0.0472608022,-0.1393703371,0.0273552351,-0.0092401318,0.0107644228,-0.1231957003,-0.1186910123,0.1305603832,-0.3450925946,-0.0211687498,0.0160577334,-0.1023742333,-0.0240286961,0.117523998,0.2100033313,0.0490001515,0.3208969235,-0.1596713066,-0.1252474785,0.5050557852,-0.0001075718,-0.092717275,0.0806826651,0.0984010324,-0.038394209,-0.3049235046,-0.170486778,0.0601614788,0.1348724961,-0.051496353,-0.1158732921,-0.1224386245,-0.4196151197,-0.248365894,0.4687273204,0.3174343407,0.264963448,0.1299319267,-0.1650582105,0.1352073997,0.159216553,0.187307477,-0.0192102659,-0.1090774834,0.2359384447,-0.2113779932,0.155759424,0.3869305253,-0.1809796393,0.0119459229,-0.0923115835,0.2481513172,-0.1681575179,-0.4544041157,0.0596653149,0.0295422394,0.0430420898,-0.1502936184,-0.2797866762,-0.3362353444,-0.3209257722,-0.1481666267,0.1696151942,0.2549111247,-0.1336040497,0.2767778933,-0.3288031816,0.0778299049,0.1044429019,0.1273612976,-0.4663669467,0.135682106,-0.036578998,0.1197790578,-0.0690987259,0.2465391606,0.1135677621,-0.424918443,0.0696541518,0.2419555634,-0.1216738075,-0.1311838925,0.0682711154,-0.0786887631,-0.0386361852,0.2547769248,-0.2057004571,-0.0214397237,-0.0199756194,0.1225353703,0.2637890875,0.2001241148,0.0986893699,0.4429874718,-0.0844327137,-0.0767104179,-0.2536473572,-0.0309065413,0.4921280742,-0.0013514153,0.079854928,-0.242647931,0.169601202,-0.1942244023,-0.5532504916,0.0515480153,-0.5781364441,0.0407118388,0.3252858818,0.1709709316,-0.0841478407,0.0329661183,-0.0981778726,0.1854674071,-0.2953637838,0.0002502699,-0.2247842401,-0.0153385615,-0.2727735341,-0.1284759492,-0.1375158876,-0.4086945355,0.1086936966,0.3947978616,-0.0017876767,-0.1926835477,0.2269218713,-0.1517411768,0.2613779008,0.2852190435,0.0432117544,0.2685986757,0.104558982,-0.0864047632,-0.0256851297,0.3098201156,-0.2362314165,-0.1381268054,0.3043734133,0.3099155426,-0.2205150425,0.2333230674,-0.0567467846,0.2969113886,0.3151141107,0.0319144726,-0.0042019095,-0.3321929872,-0.4573896229,-0.1195784658,0.3700871766,0.1626729965,-0.4257055819,-0.0984007269,-0.1460276991,-0.0601887852,0.2116642743,0.1654639393,-0.1265265197,0.2050435841,-0.179526642,0.1669016182,0.3088752031,-0.0847353935,-0.4206391871,0.2875164151,-0.2150916755,0.5698436499,-0.3545037508,-0.1641797572,0.2067889422,-0.0042378237,0.0928821936,0.5877627134,-0.3927010894,-0.0434156395,-0.3075742424,0.0627626479,0.3789293468,-0.0720625296,0.2284971178,0.1420857906,0.2151849121,-0.0858755261,0.3948541582,0.0994535834,0.0170629397,-0.0925395191,0.3155041933,0.280619204,0.148449257,-0.3772067428,0.0583437122,0.0136493295,0.1497059464,-0.3050144315,-0.0617714301,-0.1205028668,-0.3843897879,0.0283851102,-0.3712405264,-0.3694946468,0.0561055206,0.0130806528,0.0259648375,-0.1087865159,-0.0178271662,0.5779597163,-0.1555447131,0.2821295261,-0.1433887184,0.4870605469,0.1040682793,-0.1162368804,-0.3136705756,0.166074574,0.1494644433,-0.1154004782,-0.0627291352,0.4116946459,0.2902377844,-0.0891071409,-0.1935036778,-0.3059326708,0.2332514524,-0.1775747836,-0.2204999626,0.2306405157,0.1639938802,0.0155658619,-0.0878172666,0.5444143414,-0.1215674281,0.1579540968,-0.0176631082,0.1376488358,0.2201114744,-0.1096430272,0.0691928118,-0.2117485255,0.0934024751,-0.1326968968,0.1794778705,0.1538206637,-0.2419984192,0.203008011,0.0552732795,-0.1786912829,-0.1528128088,0.0055647334,-0.0044174255,0.0313545056,-0.0550176129,0.3595907986,0.5270840526,0.1536070853,0.2043435872,0.1680779606,-0.1669703275,-0.1589186788,0.1792212576,0.3218391538,-0.0959981456,0.2224206924,0.3366275132,-0.0253335573,-0.0985156149,0.3425017893,0.0764830261,0.0098035671,-0.3634087145,0.4151411355,-0.2992745638,-0.0939565077,-0.4596648514,-0.1753466576,0.0002663155,-0.2886038721,0.1413304061,0.2473591268,-0.1031972244,0.0035897889,0.0711675882,0.2468176633,-0.0999923274,0.0397536457,0.1709495038,0.1394022107,-0.4091955125,0.1948802173,0.1409971565,0.2390760779,0.3816769123,0.1443630457,-0.3392957449,-0.0139478277,-0.6195431352,0.0732089207,-0.0653965622,0.7178720236,-0.2272519618,-0.3906619847,-0.0918108374,-0.3839026392,-0.0535623245,-0.107211262,-0.2624142766,0.0031697035,0.2909303308,0.3045808673,-0.2897278368,-0.692152679,-0.2217157036,-0.0192995481,-0.01899934,-0.1854163259,-0.0416104719,0.0970648751,-0.1159362495,-0.1389606744,-0.1850246638,-0.1831263155,-0.2674400806,0.1000059247,0.1397006214,-0.0625687689,-0.1067098901,-0.0872950852,-0.2687193453,-0.2576799393,0.5187577009,-0.2705380917,-0.1802950352,-0.2210891694,0.0512014776,0.0279260613,-0.1044697165,-0.0457028188,-0.1686640531,0.0926035866,-0.1920961738,0.0524801649,0.1140600517,-0.0407963023,0.1301473677,-0.201864779,0.468459636,0.2949859798,0.6497479081,0.2458693534,0.19669801,-0.0822288394,0.047391016,0.1746470034,-0.3168677986,-0.5449874401,-0.0238277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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3449","title":"Add `__add__()`, `__iadd__()` and similar to `Dataset` class","comments":"I was going through the codebase, and I believe the implementation of __add__() and __iadd__() will be similar to concatenate_datasets() after the elimination of code for arguments other than the list of datasets (info, split, axis). \r\n(Assuming elimination of axis means concatenating over axis 1.)","body":"**Is your feature request related to a problem? Please describe.**\r\nNo.\r\n\r\n**Describe the solution you'd like**\r\nI would like to be able to concatenate datasets as follows:\r\n```python\r\n>>> dataset[\"train\"] += dataset[\"validation\"]\r\n```\r\n\r\n... instead of using `concatenate_datasets()`:\r\n```python\r\n>>> raw_datasets[\"train\"] = concatenate_datasets([raw_datasets[\"train\"], raw_datasets[\"validation\"]])\r\n>>> del raw_datasets[\"validation\"]\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nWell, I have considered `concatenate_datasets()` \ud83d\ude00\r\n\r\n**Additional context**\r\nN.a.\r\n","comment_length":45,"text":"Add `__add__()`, `__iadd__()` and similar to `Dataset` class \n **Is your feature request related to a problem? Please describe.**\r\nNo.\r\n\r\n**Describe the solution you'd like**\r\nI would like to be able to concatenate datasets as follows:\r\n```python\r\n>>> dataset[\"train\"] += dataset[\"validation\"]\r\n```\r\n\r\n... instead of using `concatenate_datasets()`:\r\n```python\r\n>>> raw_datasets[\"train\"] = concatenate_datasets([raw_datasets[\"train\"], raw_datasets[\"validation\"]])\r\n>>> del raw_datasets[\"validation\"]\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nWell, I have considered `concatenate_datasets()` \ud83d\ude00\r\n\r\n**Additional context**\r\nN.a.\r\n \n I was going through the codebase, and I believe the implementation of __add__() and __iadd__() will be similar to concatenate_datasets() after the elimination of code for arguments other than the list of datasets (info, split, axis). \r\n(Assuming elimination of axis means concatenating over axis 1.)","embeddings":[-0.2595472932,0.017440863,-0.1195521951,-0.0258939266,0.2975655198,0.4441085458,0.4135000706,0.1891676486,-0.2233492434,0.1560039222,-0.1052791327,0.3455276191,-0.1738569587,0.2189388424,-0.0191162284,-0.2958265245,0.0940745771,0.1944159567,-0.2048506141,-0.0652213618,-0.2910801172,-0.1032396033,-0.2048806399,-0.1049068049,-0.3372943103,0.0320241973,-0.3964616954,-0.0228168219,-0.0235595983,-0.4783444703,0.0162491295,0.2818638384,0.0211481787,0.114586331,-0.0001064326,-0.1459656209,0.0214315727,0.0459088534,-0.3905855417,0.0986688957,-0.4882591069,-0.3452111781,0.1621003002,-0.3436639607,0.0043024537,-0.4625983238,-0.0599404164,-0.1710886508,0.1158836856,0.1376538575,0.2389483005,-0.1006015763,0.16874066,-0.3601846993,-0.2358101308,0.1798995584,-0.0714269504,0.039931614,0.1272180676,0.0999911875,0.2845571637,0.1375632584,-0.0227373,-0.3559100628,0.2602878213,0.219792068,0.0023154186,-0.1984972209,-0.0623920299,0.1377972215,0.5021820068,-0.4305051863,-0.4552251399,-0.2267746925,0.3570364118,-0.3564400077,-0.1249917448,-0.0528209545,0.0390644073,0.3308339119,0.0132600153,-0.1510402858,-0.180124715,0.3075237572,0.1479302198,0.3278825879,0.3167098761,0.0305330586,0.1927704513,-0.186455518,0.1302894056,-0.3030750155,0.0716370493,0.2941231132,-0.2609857321,-0.2485321015,-0.0200101808,-0.3020642996,0.1187974438,0.1461311281,-0.0259013828,0.0931192189,-0.0991990268,0.0924042538,0.0870944858,0.1135501415,0.1171283647,0.1999643296,-0.1956739128,-0.0901534185,0.1077657118,-0.1143561602,-0.1203609332,0.0804942697,0.3179930747,0.0622173958,0.0461498983,0.2882075608,-0.0154212443,-0.0783488303,-0.1242223531,-0.1957698464,0.0821542293,0.2476604432,0.0126504134,0.2873687148,0.0806153044,0.0664195493,0.2175951451,-0.0238049664,-0.1431884766,0.2489823401,-0.0514824055,0.0256059505,0.088479057,-0.1370185763,-0.0401636213,0.2343642861,0.2128252685,0.1655546874,0.0863460079,-0.104987897,0.3274391294,-0.0708492845,-0.1822507083,-0.2478383631,-0.0434374809,-0.2334573716,-0.1174745336,0.1437436342,-0.0900427401,-0.2977755964,-0.3816041052,0.3086342514,0.0085585862,-0.0770433322,0.0800645277,0.3208282888,-0.0361231714,-0.1532812566,-0.0161735788,-0.0387381725,0.2272024006,-0.1776875556,0.2414104044,0.3017528653,-0.2696482837,-0.1631047577,-0.1870983392,-0.1138330176,0.0013711622,0.0511680581,-0.3414821923,0.2404451966,-0.2598552704,0.1235634759,0.6342798471,-0.4105707407,-0.0774582624,-0.1083783433,0.0498293191,0.2510841489,0.2692233324,0.2428611219,0.12804313,0.0564053506,0.5487980843,0.0091146687,-0.0660882965,-0.3532037139,-0.0344152488,-0.1252344996,0.2653492689,0.090591453,-0.0344841592,0.2274063975,0.0365350842,-0.2643185556,0.4115911424,-0.4603284895,-0.0633027554,-0.0448084436,0.2832428813,-0.1797126681,-0.1395959705,-0.3004193306,-0.3836975694,0.0211358611,0.1063437685,0.1154492274,0.1375421137,-0.4216380119,-0.0343876891,0.1350981146,-0.1927197725,-0.0342205539,0.2293697596,-0.0604496263,0.0532009602,-0.3321825266,-0.3627728522,0.4948559701,-0.2531910241,0.0776117221,-0.0182157382,0.0577071607,0.1038971841,0.1035039574,-0.2378663868,0.3036180735,0.0029524905,-0.0260489359,0.0090546319,0.2687134743,-0.1234979481,-0.2876237631,0.1439641565,0.3037224412,0.1403281838,0.1625833958,-0.0676725507,-0.2867135108,0.1585084051,-0.1200354844,-0.0660985634,0.2533482313,0.2065893263,0.2272050977,0.1385451704,0.1820485592,0.2871912122,-0.1389209032,-0.2498131543,-0.4372169673,-0.0487495326,0.1636432409,0.1203076839,0.2665177584,-0.3791871071,0.0753268749,0.5721165538,-0.0962551311,0.1265950054,0.0343698971,-0.1833010763,0.220638603,-0.0368990563,0.1234714091,0.6229575872,0.2844320238,-0.0629395247,0.2930699587,0.0768877417,-0.0499128066,0.250004828,0.1103067994,0.1023188606,0.0187278725,0.2379681468,0.0872468278,0.0056170346,-0.3952101767,0.0734241381,-0.0100744199,-0.1223523989,-0.042232424,-0.1173972115,-0.0262960959,-0.1066768989,-0.157255426,-0.0746131167,-0.2773828804,0.0066972948,-0.1124100909,-0.2860072553,0.1339144856,-0.20895423,0.3594205379,-0.1691145599,-0.3589504063,0.0971403122,0.093126215,0.0098802485,0.1744168103,0.0070226323,-0.1525987536,0.4334675074,-0.035583403,0.1216016412,-0.0446452014,-0.3801866472,0.0136735756,0.0945760906,0.0386205092,0.2233310491,-0.1238015965,0.0659618601,-0.6741276979,0.07126908,0.3070908785,0.0575158782,-0.190081656,0.174355492,-0.2124805748,-0.1812720895,-0.5040622354,-0.4765726626,-0.4218771458,0.2393351495,-0.1172712445,0.1262838989,0.0730644166,0.0578221194,0.1897955388,0.079150103,0.1497885436,-0.1872174293,-0.0774409026,0.3482376933,-0.275832057,-0.0440897048,-0.1635343283,-0.5210453272,0.303881228,0.1452835351,-0.0172894001,-0.4382298291,-0.1404106617,0.33235237,-0.3144206703,0.0667773113,0.185616523,0.4337626696,-0.169280529,-0.0284734834,-0.0664868206,0.1687264293,0.0465638973,0.0033104157,-0.0176553782,0.1107644141,-0.0774482712,0.466039449,0.1832103431,-0.2601554692,0.0944430381,-0.0165760759,0.3457299173,-0.1759648174,-0.5632662177,-0.1874110699,-0.0321855024,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3448","title":"JSONDecodeError with HuggingFace dataset viewer","comments":"Hi ! I think the issue comes from the dataset_infos.json file: it has the \"flat\" field twice.\r\n\r\nCan you try deleting this file and regenerating it please ?","body":"## Dataset viewer issue for 'pubmed_neg'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/IGESML\/pubmed_neg\r\n\r\nI am getting the error: \r\nStatus code:   400\r\nException:     JSONDecodeError\r\nMessage:       Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)\r\n\r\nI have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":28,"text":"JSONDecodeError with HuggingFace dataset viewer \n ## Dataset viewer issue for 'pubmed_neg'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/IGESML\/pubmed_neg\r\n\r\nI am getting the error: \r\nStatus code:   400\r\nException:     JSONDecodeError\r\nMessage:       Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)\r\n\r\nI have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n Hi ! I think the issue comes from the dataset_infos.json file: it has the \"flat\" field twice.\r\n\r\nCan you try deleting this file and regenerating it please ?","embeddings":[0.2295412868,-0.1201279089,0.0490055345,0.2303446084,0.1695688665,0.0577237643,0.0852907375,0.1623661816,-0.0017492537,0.0962925926,0.1668468267,0.2956422865,-0.1245156527,0.0679478049,-0.1284821033,-0.2570445538,0.1415820718,0.2197291404,0.1280412525,-0.0773434117,-0.1991611123,0.5407519341,-0.1372190267,0.192583546,-0.0773457885,-0.1646020263,0.1248664558,0.0503626093,-0.2352313846,-0.6108036637,0.182527259,0.0068032858,0.0243877526,0.5318037271,-0.0001243129,0.0471875519,0.3346125782,-0.0701062232,-0.2907139063,-0.3544469178,-0.2027767301,-0.0309145674,0.3325050771,-0.182250604,-0.0991285294,-0.3767585158,-0.1806701571,-0.4984890819,0.5452632904,0.0639695972,0.0923635066,-0.0461195633,0.1396582276,-0.0715853944,-0.0621325597,0.3902522624,-0.0133272894,0.2967038751,0.2882288098,0.5704969764,0.1080626249,0.4412478209,-0.0178408418,0.1737408489,0.1619185656,0.1689561009,-0.1997101456,-0.1587149203,0.3424786329,0.2488835603,0.5452134609,-0.2241825908,-0.2792271972,-0.2669478059,0.0643336251,0.0024407001,0.3452028036,0.0083259875,-0.0145484935,0.1722726375,-0.3152355552,-0.3719947636,-0.1623046994,-0.1056018025,-0.0734335855,-0.0727036372,-0.2976114154,0.2091559172,0.089127481,-0.2091566771,-0.1303325891,-0.1438708156,-0.3724161685,0.1404169947,0.1052261144,-0.1883953065,-0.2302408814,-0.1910564899,0.3178777099,0.1374256462,-0.0221345443,-0.0583721586,-0.0210298859,0.0997097492,0.2559551597,0.0002932319,0.2522409856,0.0216255896,0.2153244764,0.202524066,0.168530494,-0.1484929174,0.0064702397,0.0565134063,-0.2069923431,-0.4021457434,0.504319489,-0.2971711457,-0.3226638138,0.2807429433,-0.1377946883,-0.2078129798,-0.1317892075,0.1296420544,0.250205338,-0.0802325755,0.1082564965,0.1659631282,0.0875335559,-0.4068311751,-0.025401568,-0.0262041055,0.1853227764,0.1277040094,-0.035123907,-0.2440193892,0.2320865095,0.0083876811,-0.0301685855,-0.1955079734,-0.1086891443,0.0756641254,-0.1501650512,0.1510702223,0.0881979465,0.2398092151,0.0988890976,-0.6970396042,0.0353300236,-0.0859774277,-0.2810891271,0.040967036,-0.7361623049,-0.0163425487,-0.4227284789,-0.1399766058,-0.395357281,0.1251387894,0.0999378785,-0.1930174381,-0.0031423592,0.3656954765,0.0307343472,-0.0423296019,0.2172966897,0.4921429455,-0.3275252283,-0.2051380426,0.0404642448,-0.1297537386,-0.0918976292,0.30103001,-0.1225112975,0.2531971633,-0.1277527809,0.2245864868,0.261170119,-0.3775946796,-0.3580396771,0.5831646919,-0.3189093173,0.1684471369,0.1616857946,-0.2265276909,0.0802990496,0.1343965977,0.1131761968,-0.1584900469,0.0598538034,-0.0979632437,-0.1174650863,-0.2006267458,0.327208519,0.0305780936,-0.2070436776,0.0288002845,0.1961562335,0.0922545195,0.3440177441,-0.129954949,0.1904044151,0.424005121,0.2430154681,0.0928819999,0.0623508431,-0.1569495499,-0.3644618392,-0.0922742337,-0.0718799233,0.0066992845,-0.5873866677,0.0104596531,-0.172500208,-0.0020521334,-0.4468910098,-0.2488488555,-0.00159277,0.2237251848,-0.0027161895,0.3182986975,-0.2253239751,0.208275795,0.1188766137,0.2265119702,-0.6014866233,0.1080584377,0.0201244559,-0.1411705017,-0.0721626207,0.0549031012,0.1241338775,-0.1473495066,-0.1096349284,0.2997171581,-0.0551036708,0.1626174003,0.3453701437,-0.2165054083,0.1825624853,-0.1181676239,-0.0140550723,0.0508094244,0.2017797083,-0.0062842686,-0.2202066928,0.2319242656,-0.0150048519,0.1570708901,-0.2471929491,0.1603632867,0.288400799,0.0039754007,-0.1542052478,-0.3582911193,0.1974277198,-0.0393344723,0.0235706214,-0.0899544358,-0.2563655078,-0.1705635041,0.4985347092,-0.0941681117,0.0101865008,0.1693344414,-0.2732705176,0.2383759469,0.0346935093,0.462946713,0.4355122745,0.1239129528,-0.3527151942,0.1744803935,0.2548743486,0.000483007,0.1870091259,0.1449763775,0.1398271769,0.0091768596,0.0285163485,0.1515179873,-0.2448882163,-0.1957334727,0.1217240319,0.2097321153,-0.4421113431,-0.0256041717,-0.4401094615,-0.2000644207,-0.1665914953,0.1970727593,-0.2758945227,-0.1708987057,-0.1555635482,0.0371114314,-0.0398121029,0.0619012192,-0.1538421959,0.1874183863,-0.0599620342,0.0014247972,-0.1998274326,-0.050304763,-0.2853178978,-0.0683406666,0.0891919583,-0.1387417614,0.2294259518,-0.1810251772,0.1941750199,-0.3740405142,-0.3094110489,0.2482059151,-0.3803494573,0.3117533326,0.1076398864,0.3278937638,-0.0475668907,-0.1454925239,0.2850399315,0.212923497,-0.2250965983,0.0322139561,0.1010831892,-0.2061994076,-0.1429094672,-0.0264665727,-0.0592397489,-0.0138073349,0.4695782363,-0.1580190808,-0.0514833592,0.0173544269,-0.1464054137,0.2308708876,-0.3388151824,0.190544799,-0.1678787172,-0.4876132309,0.4006462693,-0.219731763,-0.2718029022,0.1303027719,0.2467713058,0.051967252,-0.5086187124,-0.4440146685,-0.1026450396,-0.1463536769,-0.0265284497,-0.0213408694,-0.0924287736,0.1545895338,0.2491445094,0.0165530071,-0.1715128869,-0.2680092156,0.275687784,-0.1611708701,0.3910309672,0.2023322731,0.4182276428,0.0617465042,-0.0747497231,0.7103188634,-0.0398213714,0.3504880667,-0.2346097231,0.2757658362,0.1269503683,-0.3707773983,-0.1381982118,0.026598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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3448","title":"JSONDecodeError with HuggingFace dataset viewer","comments":"Thanks! That fixed that, but now I am getting:\r\nServer Error\r\nStatus code:   400\r\nException:     KeyError\r\nMessage:       'feature'\r\n\r\nI checked the dataset_infos.json and pubmed_neg.py script, I don't use 'feature' anywhere as a key. Is the dataset viewer expecting that I do?","body":"## Dataset viewer issue for 'pubmed_neg'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/IGESML\/pubmed_neg\r\n\r\nI am getting the error: \r\nStatus code:   400\r\nException:     JSONDecodeError\r\nMessage:       Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)\r\n\r\nI have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":41,"text":"JSONDecodeError with HuggingFace dataset viewer \n ## Dataset viewer issue for 'pubmed_neg'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/IGESML\/pubmed_neg\r\n\r\nI am getting the error: \r\nStatus code:   400\r\nException:     JSONDecodeError\r\nMessage:       Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)\r\n\r\nI have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n Thanks! That fixed that, but now I am getting:\r\nServer Error\r\nStatus code:   400\r\nException:     KeyError\r\nMessage:       'feature'\r\n\r\nI checked the dataset_infos.json and pubmed_neg.py script, I don't use 'feature' anywhere as a key. Is the dataset viewer expecting that I do?","embeddings":[0.3349671364,-0.1692849696,0.0579739735,0.1783906072,0.1159863025,0.0022667211,0.1658776551,0.0293856338,-0.0354908593,0.0884653255,0.2415662408,0.2807984948,-0.174342081,0.1949500293,-0.0547123179,-0.1336170733,0.1672873795,0.2802902162,0.1399284303,-0.0962147489,-0.3200158775,0.4835496545,-0.1113525704,0.2538942099,-0.1230857372,-0.0757378787,0.0703951642,0.0585577525,-0.1995698363,-0.7043631673,0.1773025692,-0.0072673741,-0.0351408236,0.4634981751,-0.0001290876,0.0687241107,0.3097587824,-0.1144309267,-0.278086096,-0.3072423339,-0.166217342,-0.1538925469,0.3584643006,-0.2714165747,-0.0792954192,-0.4530891776,-0.1439643204,-0.2645535469,0.4040037096,0.1243401915,0.0578721389,0.0514813438,0.1682052314,0.0169091709,-0.0030943968,0.4283492267,-0.081247665,0.1662643105,0.2754842937,0.4196508229,0.0199616849,0.3997250497,0.0405172445,0.1109438986,0.4023475647,0.1284209788,-0.2308472693,-0.3225635588,0.2879575789,0.2035833746,0.6325820684,-0.1054757088,-0.3470055163,-0.2554841638,-0.0125988973,-0.0197246112,0.3616762757,-0.0729537085,-0.0287491009,0.1142563596,-0.3089888692,-0.3980180323,-0.1286627352,-0.1236184686,-0.1246644109,0.0069030179,-0.2426863164,0.2447191924,-0.0167258419,-0.2082816511,-0.2184604704,-0.1835671961,-0.3062976897,0.0961383358,0.1263483614,-0.1399061233,-0.0396155827,-0.0413489826,0.2723996341,0.2609731555,-0.1559863687,-0.0331040621,-0.1381061673,0.0119647486,0.2366566062,0.0782929808,0.2441239655,0.0388283618,0.278298378,0.1848609298,0.121051617,-0.1172662973,0.0966968015,0.1035451442,-0.0511791296,-0.385137856,0.6089190841,-0.2565054893,-0.1650778502,0.2584144473,-0.0248461924,-0.2358985692,-0.2105111182,0.2018018216,0.2602513433,-0.0967715457,-0.0104964264,0.2346326411,0.003064459,-0.3710445166,-0.0109616742,-0.0362292677,0.2046205252,0.0623643436,-0.0496472307,-0.2730508149,0.2131715417,-0.1220353395,0.0371998772,-0.247858569,-0.1643042266,0.0415503532,-0.1394079924,0.159796685,0.0278211329,0.2956233323,0.1879885793,-0.6802110076,0.0230045263,-0.07349471,-0.2282997519,-0.1025695801,-0.5639523864,-0.0608194023,-0.5605066419,-0.1886560768,-0.3920433521,0.059317369,0.0812333748,-0.1287496388,0.0182899609,0.2890235484,-0.0394311734,-0.1684209853,0.2044117004,0.646289289,-0.3311199248,-0.2533486784,0.0695171729,-0.0825598538,-0.1533651054,0.2355886698,-0.1371327341,0.263792932,-0.1116498858,0.1958496869,0.308172226,-0.3805346191,-0.4767789841,0.5036274195,-0.3513451517,0.2147819102,0.2225226611,-0.1615451276,0.1954507977,0.126835987,0.0390141718,-0.2238811702,0.0654438436,-0.087347582,-0.106970422,-0.2826239467,0.4202961028,0.0063175298,-0.0479510278,0.1142773852,0.1569791287,-0.0610958785,0.3008697033,-0.1378504783,0.1971647739,0.344925046,0.2918770909,0.1919661462,0.1857849807,-0.0488329679,-0.2832795382,0.0313591994,0.0830611661,0.0926783755,-0.7186766267,-0.0113365436,-0.1608979255,0.0134001113,-0.5100708008,-0.2663248777,-0.0512595326,0.2040724754,-0.0340174101,0.3180084229,-0.2896168828,0.1976919174,0.2598674595,0.2952733338,-0.5851886868,0.0498288088,-0.076126121,-0.1188911051,-0.1132950559,0.0855460316,0.1669544131,-0.1262003779,-0.1453748345,0.2251471728,-0.0028869989,0.1757924408,0.317725122,-0.2263643891,0.2261291295,-0.2070198506,0.094389379,0.1115805656,0.2308668047,-0.0225616805,-0.1117334664,0.2900496125,0.0542627051,0.2631193697,-0.2631476223,0.2149174213,0.1809910089,0.0867472216,-0.1674720049,-0.2828970253,0.176640287,-0.183866784,-0.0252185855,-0.1010047272,-0.21936737,-0.134860009,0.5794929266,-0.0922637433,0.0689011887,0.1674258858,-0.3074680567,0.1980749965,0.1228733435,0.3350041211,0.4535338879,0.0840268657,-0.371968776,0.1350116283,0.2665401995,0.0463371351,0.1618740112,0.2451468855,-0.0212059468,-0.0629027188,0.0009349277,0.1120323986,-0.1706873327,-0.1634312123,0.0624065399,0.1588699818,-0.4913253486,0.0203472096,-0.4321138561,-0.2673591375,-0.0064394861,0.2229283601,-0.2938955426,-0.2348913699,-0.1914521754,-0.041168239,0.0865859389,0.0116413627,-0.3322995305,0.0579644926,-0.1234921515,-0.052305758,-0.1568173915,0.0066063413,-0.2464476675,-0.1342485547,-0.014982542,-0.1416885257,0.2038479298,-0.173459813,0.2748650908,-0.3760204911,-0.3403144181,0.1934227794,-0.4669493735,0.2915153503,0.1712654978,0.3249737322,0.0115099037,-0.0826754943,0.2971022427,0.14735201,-0.2755604684,0.0868833214,0.1278882176,-0.184796229,-0.147312969,-0.1283162832,-0.0837551206,-0.0611207038,0.5471817255,0.0458366759,-0.0073457137,0.0352044962,-0.1841142327,0.1741680205,-0.2019837052,0.1474637985,-0.1000351757,-0.2023385763,0.4952688813,-0.1158047467,-0.2570666671,0.2463963926,0.1946710944,-0.0627766028,-0.5214529634,-0.4580495656,-0.1125500724,-0.2222227752,0.1731907427,0.0361224636,-0.0211323313,0.2044447809,0.2828429043,0.0924885273,-0.1331234574,-0.3217517734,0.2168679684,-0.1149043813,0.3705759048,0.2295424044,0.5801634789,0.0267304871,0.1267062426,0.4855312407,0.0813874677,0.4397223294,-0.2448515445,0.2744467556,0.0405866541,-0.3967849612,-0.1443812102,-0.006742474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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3448","title":"JSONDecodeError with HuggingFace dataset viewer","comments":"It seems that the `feature` key is missing from some feature type definition in your dataset_infos.json:\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t}\r\n```\r\nThey should be\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n                                 \"feature\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\",\r\n                                 \"feature\": {\"num_classes\": 5, \"names\": [\"-\", \"S\", \"H\", \"N\", \"C\"], \"names_file\": null, \"id\": null, \"_type\": \"ClassLabel\"}\r\n\t\t\t}\r\n```\r\n\r\nNote that you can generate the dataset_infos.json automatically to avoid mistakes:\r\n```bash\r\ndatasets-cli test .\/path\/to\/dataset --save_infos\r\n```","body":"## Dataset viewer issue for 'pubmed_neg'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/IGESML\/pubmed_neg\r\n\r\nI am getting the error: \r\nStatus code:   400\r\nException:     JSONDecodeError\r\nMessage:       Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)\r\n\r\nI have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":98,"text":"JSONDecodeError with HuggingFace dataset viewer \n ## Dataset viewer issue for 'pubmed_neg'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/IGESML\/pubmed_neg\r\n\r\nI am getting the error: \r\nStatus code:   400\r\nException:     JSONDecodeError\r\nMessage:       Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)\r\n\r\nI have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n It seems that the `feature` key is missing from some feature type definition in your dataset_infos.json:\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t}\r\n```\r\nThey should be\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n                                 \"feature\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\",\r\n                                 \"feature\": {\"num_classes\": 5, \"names\": [\"-\", \"S\", \"H\", \"N\", \"C\"], \"names_file\": null, \"id\": null, \"_type\": \"ClassLabel\"}\r\n\t\t\t}\r\n```\r\n\r\nNote that you can generate the dataset_infos.json automatically to avoid mistakes:\r\n```bash\r\ndatasets-cli test .\/path\/to\/dataset --save_infos\r\n```","embeddings":[0.1093122661,-0.0348638669,0.0341624841,0.1241071299,0.1576343775,0.0917587876,0.0911142975,0.1819450259,-0.0062628654,0.1343885362,0.1603801847,0.3906162977,-0.2260686457,0.1779267639,-0.0963280722,-0.2384367734,0.1535855085,0.2733953595,0.1301556528,-0.0759228468,-0.2729405463,0.6004837751,-0.1502153426,0.1359083652,-0.0068322737,-0.0958882198,0.1005223319,0.0405355059,-0.2024142742,-0.7026222348,0.1683975905,-0.0063662156,0.0300413873,0.4620122313,-0.0001211502,0.0333364233,0.3822965324,-0.1279216409,-0.286842227,-0.5271570086,-0.108776249,-0.1505896449,0.3054638803,-0.2841655016,-0.1448465884,-0.4547342658,-0.1411083788,-0.5221767426,0.3412872255,0.133037284,0.1192946807,-0.0580138005,0.1341992319,-0.1666595042,0.0392422229,0.5257142186,-0.1785455346,0.1319947243,0.331159085,0.6049295664,0.162131384,0.4071726799,0.0375235491,0.0914948583,0.3848195374,0.1884721965,-0.2242306024,-0.1516788602,0.2209335268,0.1750059873,0.555213809,-0.2547833622,-0.305143714,-0.1874635965,0.0066121197,-0.1356923282,0.2798222899,-0.0489554293,-0.0145274773,0.1108416244,-0.2952205241,-0.3404343128,-0.1523089558,-0.102129221,0.0194405094,-0.0662155524,-0.2849166691,0.2092006356,0.0850688145,-0.3149309158,-0.3175573349,-0.1850054711,-0.3517676592,0.1706749797,0.1709850132,-0.1567334831,-0.0435671732,-0.2973996401,0.3636882901,0.3019363582,-0.1608979553,-0.0685373619,-0.0384029262,0.0727108568,0.2752904892,-0.0061229779,0.2997716963,-0.0665325522,0.2071032971,0.2547295392,0.175812602,-0.2130620331,0.079550311,0.1326754242,-0.1013409644,-0.2653522193,0.5658555031,-0.2766280472,-0.2066801041,0.3014304638,-0.0416287109,-0.1439402997,-0.2037470788,0.2117976844,0.3092153072,-0.1413501501,0.138833046,0.1595688313,0.0005356945,-0.298417449,-0.0462876447,0.0183112863,0.1826556772,-0.0296959411,-0.0485125668,-0.2004300803,0.2719325423,-0.1463876963,0.0536725,-0.1852938235,-0.1973432153,0.0847408399,-0.0669970214,0.1572293937,0.0809410885,0.2671144903,0.1706958413,-0.7364261746,-0.076339215,-0.0900569633,-0.1889546067,0.0143751018,-0.6508471966,0.0112123117,-0.4802261591,-0.0963125676,-0.4396257401,0.0782658085,-0.0237853341,-0.128268525,-0.127511248,0.284240514,-0.0606185459,-0.0887375772,0.2764641941,0.4429079294,-0.2907970846,-0.2434027195,0.0037530153,-0.0473262332,-0.0456876867,0.2244795412,-0.1125805378,0.2330550551,-0.1621324718,0.3425215483,0.2254609168,-0.3335537314,-0.2924158573,0.5230197906,-0.3223001361,0.1681215018,0.1430889666,-0.1122111976,0.1940627694,0.0901904181,0.0394078158,-0.1306493729,0.050568901,-0.0248873495,-0.0731722265,-0.2406373769,0.3468249738,-0.0318510421,-0.1912434995,-0.0145508312,0.2580163479,0.0916866064,0.2945631742,-0.1904601455,0.2009151578,0.4366209507,0.3877867758,0.1005310118,0.1008360758,-0.1720669568,-0.4046903551,-0.0164617766,0.0369936749,0.1107461527,-0.6803885698,-0.0313697867,-0.1524730176,0.0572902411,-0.3744131029,-0.2691678405,0.0226570778,0.2479569018,-0.00268661,0.3482218981,-0.2437298447,0.062520951,0.2246949375,0.2162645459,-0.5772847533,0.0481823273,-0.1265468448,-0.1679996252,-0.0439058654,0.0870394111,0.1563396007,-0.1797901392,-0.1244462952,0.3061775863,0.0051156161,0.1507056952,0.2925599515,-0.1950380653,0.2211877108,-0.2424278855,-0.0040767868,0.1290703863,0.2625069022,0.0031891731,-0.2692582905,0.2989778817,0.0648638755,0.2477887422,-0.1695524901,0.1751236916,0.2752639949,0.0367805921,-0.1249590516,-0.2753444016,0.1542330235,-0.0392179154,-0.1631763428,-0.1266905069,-0.2342574,-0.1162989289,0.5628195405,0.0472199693,0.0538324751,0.1096791551,-0.3088524342,0.2412755489,0.0871486366,0.3299828768,0.4539816976,0.1667144597,-0.3659740686,0.1616223007,0.2496373206,0.0431213714,0.1970296353,0.121995993,0.0971447602,-0.0060131289,0.0324105248,0.1219163314,-0.1761827022,-0.2821279466,0.0186545644,0.1560916454,-0.3983763456,-0.011126929,-0.4180438221,-0.1300456524,0.0653399676,0.1875070781,-0.2200119048,-0.2188098431,-0.1642107069,0.0319009274,-0.0648122132,0.0901922137,-0.2692290545,0.0862569883,-0.0667277575,-0.1745951623,-0.1348442882,-0.0743364617,-0.2887207568,-0.0621476248,0.0359552167,-0.0985461697,0.2402281314,-0.0918835774,0.2595294118,-0.4695132971,-0.4172474742,0.2439706922,-0.5047015548,0.3316902816,0.1762337089,0.2495268881,-0.0514501743,-0.2022715211,0.3402777612,0.1803774387,-0.2988286018,0.1188240349,0.1158012226,-0.2846002579,-0.2487902343,-0.2366877943,-0.0032470932,-0.1473990232,0.5743985176,0.0062005925,-0.0000805877,0.1568951011,-0.1111056581,0.1752441525,-0.2334509939,0.1923501641,-0.0976101905,-0.2441761792,0.4443972707,-0.2938814163,-0.354903549,0.2314734161,0.2514662743,-0.0253378227,-0.4378635585,-0.3998903632,0.0089924913,-0.1614861637,0.1909886599,0.0160929989,-0.1111535057,0.2367255092,0.2811399996,-0.0117877349,-0.2141019106,-0.3667080402,0.3498558402,-0.1862111539,0.4151290357,0.2623143494,0.4251691997,0.059358228,-0.0071866284,0.5740022063,-0.1012948006,0.4094875455,-0.2559713423,0.2831798792,-0.0050351885,-0.390117377,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3447","title":"HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading ","comments":"Hi ! Indeed it says \"downloading and preparing\" but in your case it didn't need to download anything since you used local files (it would have thrown an error otherwise). I think we can improve the logging to make it clearer in this case","body":"## Describe the bug\r\nAccording to https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to \"run in full offline mode\". It didn't work for me. At the very beginning, datasets still tried to download \"custom data configuration\" for JSON, despite I have run the program once and cached all data into the same --cache_dir. \r\n\r\n\"Downloading\" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers \"FileNotFound\" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nexport HF_DATASETS_OFFLINE=1\r\npython run_clm.py --model_name_or_path=models\/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache\/trainpy.v2\r\n```\r\n\r\n## Expected results\r\ndatasets should stop all \"downloading\" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, \"default-471372bed4b51b53\", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.\r\n\r\n## Actual results\r\nThe logging shows datasets are still downloading into \"datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426\". \r\n```\r\n12\/16\/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53\r\n12\/16\/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)\r\nDownloading and preparing dataset json\/default to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 17623.13it\/s]\r\n12\/16\/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1206.99it\/s]\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.\r\n12\/16\/2021 10:26:00 - INFO - datasets.builder - Generating split train\r\n12\/16\/2021 10:26:01 - INFO - datasets.builder - Generating split validation\r\n12\/16\/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.\r\nDataset json downloaded and prepared to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 53.54it\/s]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux\r\n- Python version:  3.8.10\r\n- PyArrow version: 6.0.1\r\n","comment_length":44,"text":"HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading  \n ## Describe the bug\r\nAccording to https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to \"run in full offline mode\". It didn't work for me. At the very beginning, datasets still tried to download \"custom data configuration\" for JSON, despite I have run the program once and cached all data into the same --cache_dir. \r\n\r\n\"Downloading\" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers \"FileNotFound\" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nexport HF_DATASETS_OFFLINE=1\r\npython run_clm.py --model_name_or_path=models\/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache\/trainpy.v2\r\n```\r\n\r\n## Expected results\r\ndatasets should stop all \"downloading\" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, \"default-471372bed4b51b53\", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.\r\n\r\n## Actual results\r\nThe logging shows datasets are still downloading into \"datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426\". \r\n```\r\n12\/16\/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53\r\n12\/16\/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)\r\nDownloading and preparing dataset json\/default to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 17623.13it\/s]\r\n12\/16\/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1206.99it\/s]\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.\r\n12\/16\/2021 10:26:00 - INFO - datasets.builder - Generating split train\r\n12\/16\/2021 10:26:01 - INFO - datasets.builder - Generating split validation\r\n12\/16\/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.\r\nDataset json downloaded and prepared to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 53.54it\/s]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux\r\n- Python version:  3.8.10\r\n- PyArrow version: 6.0.1\r\n \n Hi ! Indeed it says \"downloading and preparing\" but in your case it didn't need to download anything since you used local files (it would have thrown an error otherwise). I think we can improve the logging to make it clearer in this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3447","title":"HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading ","comments":"@lhoestq Thank you for explaining. I am sorry but I was not clear about my intention. I didn't want to kill internet traffic; I wanted to kill all write activity. In other words, you can imagine that my storage has only read access but crashes on write.\r\n\r\nWhen run_clm.py is invoked with the same parameters, the hash in the cache directory \"datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/...\" doesn't change, and my job can load cached data properly. This is great.\r\n\r\nUnfortunately, when params change (which happens sometimes), the hash changes and the old cache is invalid. datasets builder would create a new cache directory with the new hash and create JSON builder there, even though every JSON builder is the same. I didn't find a way to avoid such behavior.\r\n\r\nThis problem can be resolved when using datasets.map() for tokenizing and grouping text. This function allows me to specify output filenames with --cache_file_names, so that the cached files are always valid.\r\n\r\nThis is the code that I used to freeze cache filenames for tokenization. I wish I could do the same to datasets.load_dataset()\r\n```\r\n        tokenized_datasets = raw_datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=data_args.preprocessing_num_workers,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=not data_args.overwrite_cache,\r\n            desc=\"Running tokenizer on dataset\",\r\n            cache_file_names={k: os.path.join(model_args.cache_dir, f'{k}-tokenized') for k in raw_datasets},\r\n        )\r\n```","body":"## Describe the bug\r\nAccording to https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to \"run in full offline mode\". It didn't work for me. At the very beginning, datasets still tried to download \"custom data configuration\" for JSON, despite I have run the program once and cached all data into the same --cache_dir. \r\n\r\n\"Downloading\" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers \"FileNotFound\" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nexport HF_DATASETS_OFFLINE=1\r\npython run_clm.py --model_name_or_path=models\/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache\/trainpy.v2\r\n```\r\n\r\n## Expected results\r\ndatasets should stop all \"downloading\" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, \"default-471372bed4b51b53\", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.\r\n\r\n## Actual results\r\nThe logging shows datasets are still downloading into \"datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426\". \r\n```\r\n12\/16\/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53\r\n12\/16\/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)\r\nDownloading and preparing dataset json\/default to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 17623.13it\/s]\r\n12\/16\/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1206.99it\/s]\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.\r\n12\/16\/2021 10:26:00 - INFO - datasets.builder - Generating split train\r\n12\/16\/2021 10:26:01 - INFO - datasets.builder - Generating split validation\r\n12\/16\/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.\r\nDataset json downloaded and prepared to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 53.54it\/s]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux\r\n- Python version:  3.8.10\r\n- PyArrow version: 6.0.1\r\n","comment_length":201,"text":"HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading  \n ## Describe the bug\r\nAccording to https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to \"run in full offline mode\". It didn't work for me. At the very beginning, datasets still tried to download \"custom data configuration\" for JSON, despite I have run the program once and cached all data into the same --cache_dir. \r\n\r\n\"Downloading\" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers \"FileNotFound\" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nexport HF_DATASETS_OFFLINE=1\r\npython run_clm.py --model_name_or_path=models\/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache\/trainpy.v2\r\n```\r\n\r\n## Expected results\r\ndatasets should stop all \"downloading\" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, \"default-471372bed4b51b53\", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.\r\n\r\n## Actual results\r\nThe logging shows datasets are still downloading into \"datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426\". \r\n```\r\n12\/16\/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53\r\n12\/16\/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)\r\nDownloading and preparing dataset json\/default to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 17623.13it\/s]\r\n12\/16\/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1206.99it\/s]\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.\r\n12\/16\/2021 10:26:00 - INFO - datasets.builder - Generating split train\r\n12\/16\/2021 10:26:01 - INFO - datasets.builder - Generating split validation\r\n12\/16\/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.\r\nDataset json downloaded and prepared to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 53.54it\/s]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux\r\n- Python version:  3.8.10\r\n- PyArrow version: 6.0.1\r\n \n @lhoestq Thank you for explaining. I am sorry but I was not clear about my intention. I didn't want to kill internet traffic; I wanted to kill all write activity. In other words, you can imagine that my storage has only read access but crashes on write.\r\n\r\nWhen run_clm.py is invoked with the same parameters, the hash in the cache directory \"datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/...\" doesn't change, and my job can load cached data properly. This is great.\r\n\r\nUnfortunately, when params change (which happens sometimes), the hash changes and the old cache is invalid. datasets builder would create a new cache directory with the new hash and create JSON builder there, even though every JSON builder is the same. I didn't find a way to avoid such behavior.\r\n\r\nThis problem can be resolved when using datasets.map() for tokenizing and grouping text. This function allows me to specify output filenames with --cache_file_names, so that the cached files are always valid.\r\n\r\nThis is the code that I used to freeze cache filenames for tokenization. I wish I could do the same to datasets.load_dataset()\r\n```\r\n        tokenized_datasets = raw_datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=data_args.preprocessing_num_workers,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=not data_args.overwrite_cache,\r\n            desc=\"Running tokenizer on dataset\",\r\n            cache_file_names={k: os.path.join(model_args.cache_dir, f'{k}-tokenized') for k in raw_datasets},\r\n        )\r\n```","embeddings":[-0.3771052957,0.3645547628,0.0545567684,0.2978963852,0.2478348911,-0.0242794752,0.2448207587,0.1876752824,0.2710855305,0.0606378429,-0.0563663468,0.0012010495,-0.0477961414,0.2244567275,-0.0424795933,0.1671871692,0.0634076744,0.0005655012,-0.0805838779,-0.0723702237,-0.1495418847,0.3304847479,-0.0279875472,-0.061943002,-0.2950896025,-0.1792235076,0.0909410119,0.0981358811,-0.3247746527,-0.3094445169,0.4932436347,0.2930701673,0.1043173969,0.4861290455,-0.0001161499,0.1958226562,0.006450261,-0.041602429,-0.5929573178,-0.3239891529,-0.1176577881,-0.1286777407,0.1511260569,-0.0768131614,-0.0185801256,-0.1940190047,-0.0987970084,-0.2483436614,0.3681132793,0.2415207624,0.1489446163,0.3037275672,0.0386151746,0.0001062843,0.0733178779,0.2232067883,-0.3229056299,0.2753352821,-0.1661335975,-0.0935220644,-0.242661193,0.1819607466,-0.1729590297,0.3153599203,0.3019117713,0.0796649605,-0.1847041696,-0.4123038054,0.1690776795,0.3626731336,0.4065832794,-0.3111477196,-0.1943264455,-0.5541572571,-0.0637432858,-0.1435642987,0.3801208436,0.2441411018,-0.3796820045,0.2399763763,-0.3043856919,-0.3187648356,-0.0009626833,0.1685284674,-0.0111772893,-0.0554610789,-0.2092858255,-0.0167397447,0.226202175,0.1810107529,0.0339593068,-0.4548458457,-0.1745292395,0.0656251684,-0.3045862317,-0.1220215559,0.0826460496,0.0941202343,0.2019513547,0.1261329949,0.0708350241,0.0370872095,-0.0037308291,-0.0368725769,0.2631406784,0.2189545333,-0.156598419,0.0544232614,0.398475498,0.4138941765,-0.0910498127,0.1133311689,0.1390940398,-0.1481044739,0.1437506825,-0.3489007056,0.2990494967,-0.2262060642,-0.0919842497,0.273773551,0.1178047433,-0.0363216214,0.1147340313,0.3758813143,-0.1040927246,-0.1193517596,0.0399821103,-0.0158385299,-0.3484830558,-0.0775393099,-0.1470933706,-0.0114030791,-0.0828940347,0.1293116361,0.3246578276,-0.1211195141,0.2896915674,0.2500594556,0.0473053344,-0.0292508937,0.273914367,-0.205669269,0.0953872278,0.4357789159,-0.2006678432,0.224529922,0.2022900581,0.0142191835,-0.0627847314,-0.04609631,-0.2970682383,-0.3883806467,-0.1470984817,0.1297326237,-0.2992551625,-0.1291610003,-0.5435217023,0.0572485253,0.0918480977,0.0691200867,0.2140518129,-0.1235573962,-0.0203222558,-0.0991169736,0.2302666008,0.6482765079,-0.2848670781,-0.1725961566,0.1467903852,-0.2133754939,-0.0293805748,0.1418742687,-0.3825208247,0.524117887,-0.3629797995,-0.2708618045,0.2983470261,-0.0997628868,-0.3915809095,0.5430010557,-0.1321303248,0.3104219437,-0.0222129636,0.0559614487,0.100716725,0.1705105901,-0.132849291,0.295037508,0.0685305297,0.1895580739,-0.272243619,-0.4819199443,-0.1582439244,0.2285337448,0.1787435859,0.2692220807,0.0790867433,-0.0650703609,0.2074872404,0.0204033423,0.19366166,0.2614831328,0.2287385315,0.1060356051,-0.0761726946,-0.0113378223,-0.822819829,0.3931925595,-0.1229440495,-0.0384808928,-0.3631316125,-0.2203973681,0.013806046,-0.1179291308,-0.4330795109,-0.2106693387,0.0303962566,0.0356017426,0.2881068289,0.0516909659,-0.2993872762,0.433138907,-0.0202198885,0.3480179608,-0.1344725639,0.2775659859,0.2102475762,0.0078160902,-0.1034398377,-0.1379252821,0.028992502,-0.2600718737,-0.175044328,0.5280986428,0.0697494373,0.1832969189,0.0691075325,0.2608171701,0.153273657,0.493922621,0.1274274439,0.0821771845,-0.0323043689,0.0236229897,-0.1821878403,0.4888338745,0.0547962449,0.2446392179,0.0493415706,-0.1625960022,0.2766588032,0.0204469189,-0.1260582358,-0.190297544,0.1064880043,0.0430429727,0.380043298,0.1267900914,-0.2862273455,0.1810451299,0.5636476278,0.1511563212,-0.100763768,0.1134173423,-0.0285383482,-0.1053911224,0.2328044921,0.5286232829,0.4508613348,-0.0096197454,0.0356267951,0.184050709,-0.2254061699,-0.2421067804,0.1064272821,-0.0181591939,0.1540235728,0.2736233175,0.2255670279,-0.1165464669,-0.7022886276,-0.2253244221,0.0172580034,0.2141298056,-0.3369896412,0.1370541155,-0.5385786891,-0.0919950753,-0.1469149739,0.0953565165,-0.4603865147,-0.3161154985,0.0773422867,0.5820373893,-0.0759492889,0.1314274371,-0.2651425302,0.3445367813,-0.1593550146,-0.4816991091,-0.1539898217,0.0479734093,-0.2989278138,-0.072402142,0.2802780867,-0.2258649468,0.2112364471,-0.0219619889,-0.4363894165,-0.4036360979,-0.0402258001,0.2546170056,-0.0959333554,0.3412907124,0.2886512578,0.3193323016,-0.0477513261,0.1725632399,0.0733626783,-0.0397766419,0.0854187906,0.0028322535,-0.0329313129,0.1220457032,-0.2263487279,-0.2187577188,-0.1459910721,-0.3106691241,0.2668371201,0.1135435253,0.1194322482,0.1856219321,0.2772601545,0.1343689263,-0.1656145304,0.0657610223,-0.2607954144,-0.8752501607,0.3158273399,-0.1488036811,-0.205936417,0.2142065614,0.0660966635,0.0644003823,0.0058930726,-0.7921885848,-0.1587282717,-0.2364687324,0.4459841251,0.0821543708,0.0003216239,0.3384247124,-0.0375408046,0.0361631028,-0.0595459752,0.144040212,0.2835912108,-0.0054472829,0.2494149357,-0.0213511381,0.3766891956,0.0354930721,0.5628857017,0.105065316,-0.068455331,0.4087569714,0.0241689105,0.5009694695,-0.4342517257,-0.0742149353,0.0390258282,-0.1013528779,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3447","title":"HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading ","comments":"Hi ! `load_dataset` may re-generate your dataset if some parameters changed indeed. If you want to freeze a dataset loaded with `load_dataset`, I think the best solution is just to save it somewhere on your disk with `.save_to_disk(my_dataset_dir)` and reload it with `load_from_disk(my_dataset_dir)`. This way you will be able to reload the dataset without having to run `load_dataset`","body":"## Describe the bug\r\nAccording to https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to \"run in full offline mode\". It didn't work for me. At the very beginning, datasets still tried to download \"custom data configuration\" for JSON, despite I have run the program once and cached all data into the same --cache_dir. \r\n\r\n\"Downloading\" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers \"FileNotFound\" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nexport HF_DATASETS_OFFLINE=1\r\npython run_clm.py --model_name_or_path=models\/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache\/trainpy.v2\r\n```\r\n\r\n## Expected results\r\ndatasets should stop all \"downloading\" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, \"default-471372bed4b51b53\", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.\r\n\r\n## Actual results\r\nThe logging shows datasets are still downloading into \"datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426\". \r\n```\r\n12\/16\/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53\r\n12\/16\/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)\r\nDownloading and preparing dataset json\/default to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 17623.13it\/s]\r\n12\/16\/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1206.99it\/s]\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.\r\n12\/16\/2021 10:26:00 - INFO - datasets.builder - Generating split train\r\n12\/16\/2021 10:26:01 - INFO - datasets.builder - Generating split validation\r\n12\/16\/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.\r\nDataset json downloaded and prepared to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 53.54it\/s]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux\r\n- Python version:  3.8.10\r\n- PyArrow version: 6.0.1\r\n","comment_length":58,"text":"HF_DATASETS_OFFLINE=1 didn't stop datasets.builder from downloading  \n ## Describe the bug\r\nAccording to https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#loading-a-dataset-builder, setting HF_DATASETS_OFFLINE to 1 should make datasets to \"run in full offline mode\". It didn't work for me. At the very beginning, datasets still tried to download \"custom data configuration\" for JSON, despite I have run the program once and cached all data into the same --cache_dir. \r\n\r\n\"Downloading\" is not an issue when running with local disk, but crashes often with cloud storage because (1) multiply GPU processes try to access the same file, AND (2) FileLocker fails to synchronize all processes, due to storage throttling. 99% of times, when the main process releases FileLocker, the file is not actually ready for access in cloud storage and thus triggers \"FileNotFound\" errors for all other processes. Well, another way to resolve the problem is to investigate super reliable cloud storage, but that's out of scope here.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nexport HF_DATASETS_OFFLINE=1\r\npython run_clm.py --model_name_or_path=models\/gpt-j-6B --train_file=trainpy.v2.train.json --validation_file=trainpy.v2.eval.json --cache_dir=datacache\/trainpy.v2\r\n```\r\n\r\n## Expected results\r\ndatasets should stop all \"downloading\" behavior but reuse the cached JSON configuration. I think the problem here is part of the cache directory path, \"default-471372bed4b51b53\", is randomly generated, and it could change if some parameters changed. And I didn't find a way to use a fixed path to ensure datasets to reuse cached data every time.\r\n\r\n## Actual results\r\nThe logging shows datasets are still downloading into \"datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426\". \r\n```\r\n12\/16\/2021 10:25:59 - WARNING - datasets.builder - Using custom data configuration default-471372bed4b51b53\r\n12\/16\/2021 10:25:59 - INFO - datasets.builder - Generating dataset json (datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426)\r\nDownloading and preparing dataset json\/default to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 17623.13it\/s]\r\n12\/16\/2021 10:25:59 - INFO - datasets.utils.download_manager - Downloading took 0.0 min\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.download_manager - Checksum Computation took 0.0 min\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1206.99it\/s]\r\n12\/16\/2021 10:26:00 - INFO - datasets.utils.info_utils - Unable to verify checksums.\r\n12\/16\/2021 10:26:00 - INFO - datasets.builder - Generating split train\r\n12\/16\/2021 10:26:01 - INFO - datasets.builder - Generating split validation\r\n12\/16\/2021 10:26:02 - INFO - datasets.utils.info_utils - Unable to verify splits sizes.\r\nDataset json downloaded and prepared to  datacache\/trainpy.v2\/json\/default-471372bed4b51b53\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 53.54it\/s]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux\r\n- Python version:  3.8.10\r\n- PyArrow version: 6.0.1\r\n \n Hi ! `load_dataset` may re-generate your dataset if some parameters changed indeed. If you want to freeze a dataset loaded with `load_dataset`, I think the best solution is just to save it somewhere on your disk with `.save_to_disk(my_dataset_dir)` and reload it with `load_from_disk(my_dataset_dir)`. This way you will be able to reload the dataset without having to run 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3444","title":"Align the Dataset and IterableDataset processing API","comments":"Yes I agree, these should be as aligned as possible. Maybe we can also check the feedback in the survey at http:\/\/hf.co\/oss-survey and see if people mentioned related things on the API (in particular if we go the breaking change way, it would be good to be sure we are taking the right direction for the community).","body":"## Intro\r\n\r\nCurrently the two classes have two distinct API for processing:\r\n\r\n### The `.map()` method\r\n\r\nBoth have those parameters in common: function, batched, batch_size\r\n\r\n- IterableDataset is missing those parameters:\r\nwith_indices, with_rank, input_columns, drop_last_batch, remove_columns, features, disable_nullable, fn_kwargs, num_proc\r\n\r\n- Dataset also has additional parameters that are exclusive, due to caching:\r\nkeep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint\r\n\r\n- There is also an important difference in terms of behavior:\r\n**Dataset.map adds new columns** (with dict.update)\r\nBUT\r\n**IterableDataset discards previous columns** (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n- Dataset.map is eager while IterableDataset.map is lazy\r\n\r\n### The `.shuffle()` method\r\n\r\n- Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.\r\n\r\n- IterableDataset is missing the parameter generator\r\n\r\n- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint\r\n\r\n### The `.with_format()` method\r\n\r\n- IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n### Other methods\r\n\r\n- Both have the same `remove_columns` method\r\n- IterableDataset is missing: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform\r\n- And others don't really make sense for an iterable dataset: select, sort, add_column, add_item\r\n- Dataset is missing skip and take, that IterableDataset implements.\r\n\r\n## Questions\r\n\r\nI think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.\r\n\r\n1. What should be aligned and what shouldn't between those two APIs ?\r\n\r\nIMO the minimum is to align the main processing methods.\r\n\r\nIt would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters.\r\n\r\nIt would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n\r\n2. What are the breaking changes for IterableDataset ?\r\n\r\nThe main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them.\r\n\r\n3. Shall we also do some changes for regular datasets ?\r\n\r\nI agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:\r\n- keep the eager Dataset.map with caching\r\n- keep the with_transform method for lazy processing\r\n- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)\r\n\r\nWe could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\nFor information, TFDS does lazy map by default, and has an additional `.cache()` method.\r\n\r\n## Opinions ?\r\n\r\nI'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\ncc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger ","comment_length":57,"text":"Align the Dataset and IterableDataset processing API \n ## Intro\r\n\r\nCurrently the two classes have two distinct API for processing:\r\n\r\n### The `.map()` method\r\n\r\nBoth have those parameters in common: function, batched, batch_size\r\n\r\n- IterableDataset is missing those parameters:\r\nwith_indices, with_rank, input_columns, drop_last_batch, remove_columns, features, disable_nullable, fn_kwargs, num_proc\r\n\r\n- Dataset also has additional parameters that are exclusive, due to caching:\r\nkeep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint\r\n\r\n- There is also an important difference in terms of behavior:\r\n**Dataset.map adds new columns** (with dict.update)\r\nBUT\r\n**IterableDataset discards previous columns** (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n- Dataset.map is eager while IterableDataset.map is lazy\r\n\r\n### The `.shuffle()` method\r\n\r\n- Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.\r\n\r\n- IterableDataset is missing the parameter generator\r\n\r\n- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint\r\n\r\n### The `.with_format()` method\r\n\r\n- IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n### Other methods\r\n\r\n- Both have the same `remove_columns` method\r\n- IterableDataset is missing: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform\r\n- And others don't really make sense for an iterable dataset: select, sort, add_column, add_item\r\n- Dataset is missing skip and take, that IterableDataset implements.\r\n\r\n## Questions\r\n\r\nI think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.\r\n\r\n1. What should be aligned and what shouldn't between those two APIs ?\r\n\r\nIMO the minimum is to align the main processing methods.\r\n\r\nIt would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters.\r\n\r\nIt would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n\r\n2. What are the breaking changes for IterableDataset ?\r\n\r\nThe main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them.\r\n\r\n3. Shall we also do some changes for regular datasets ?\r\n\r\nI agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:\r\n- keep the eager Dataset.map with caching\r\n- keep the with_transform method for lazy processing\r\n- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)\r\n\r\nWe could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\nFor information, TFDS does lazy map by default, and has an additional `.cache()` method.\r\n\r\n## Opinions ?\r\n\r\nI'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\ncc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger  \n Yes I agree, these should be as aligned as possible. Maybe we can also check the feedback in the survey at http:\/\/hf.co\/oss-survey and see if people mentioned related things on the API (in particular if we go the breaking change way, it would be good to be sure we are taking the right direction for the community).","embeddings":[-0.4088525474,0.0703886002,-0.1826431006,0.1011598632,0.1535394192,0.1712657362,0.1793358624,0.4203015864,-0.1248599514,0.1267996281,-0.0965422243,0.4365522563,-0.0362225585,0.1045318767,-0.1478759348,-0.239873156,0.1861059219,0.2008081824,-0.2912281454,-0.1036827266,-0.3949563205,0.0552298687,-0.3762719333,-0.1587012708,0.20320867,-0.3815857172,-0.2470869273,-0.0237530768,-0.1631987989,-0.1764978021,0.2641444802,0.3600022495,-0.0030031686,0.440108031,-0.0001035961,-0.1517155468,0.1586819291,-0.0434100293,-0.1534931213,0.1260907054,-0.2308628112,-0.1146780476,-0.229084745,-0.43036291,-0.0673348084,-0.4406300187,0.0209379159,-0.3395364583,0.2980356812,0.1623904705,0.2427192181,-0.0922978446,-0.0949911773,-0.1588321477,-0.0522320345,0.1502773911,-0.1721200943,-0.2116830796,0.3446257412,0.2219965905,-0.1189257801,0.0991046354,-0.1147055477,-0.0338132791,0.2634684741,-0.0672511831,-0.2070111632,-0.2588835061,0.1595659703,0.117195718,0.4054489136,-0.3294045329,-0.3923185468,-0.4578973055,-0.0950731412,-0.4153778255,0.0426211618,-0.1662911922,0.1635380536,0.0496852659,-0.2528590262,0.1466912627,-0.0590290613,-0.02039578,0.20544222,0.3153709173,-0.0049070474,0.1218149886,-0.182278797,-0.0163886156,0.4185235798,-0.1868131757,0.1164721549,0.4013648629,-0.3323753178,-0.2703324854,0.2315940261,-0.1564336121,0.0558884405,0.4004893005,0.1718945503,0.1605055332,0.094121553,0.0242355783,0.2868935764,0.2347005606,-0.0592046566,0.3019541204,0.0505101755,0.0852525234,0.0523392931,0.0115288049,0.2380922586,-0.08236783,0.2259825766,0.1008024812,-0.0991849154,-0.0021463872,-0.2308947891,-0.0766684338,-0.0939526781,-0.2808576226,-0.0907835439,0.1681826115,-0.152246505,0.3265187144,-0.2387930602,0.011831318,0.118321918,-0.0064184554,-0.231280908,0.0621685907,0.0342375003,0.0637620911,0.1716178507,0.0573761053,0.2021390051,0.2352996916,-0.0402894467,0.3078820407,0.2633217871,-0.2779312134,0.5297861099,0.050035134,0.1125083417,0.0706157088,-0.0339871049,0.1646754593,-0.171982795,0.0933515504,-0.0374183841,-0.194755435,0.3522886038,0.314327389,-0.1063656285,-0.1467631012,-0.166037336,0.2969782948,0.0589153208,-0.3297497332,0.0917108208,-0.1845941395,-0.0215977617,-0.4717492759,0.1498201489,-0.0041785622,-0.3207444847,0.0841460451,-0.0463728346,0.0728655607,0.3883541822,0.1823013127,-0.1276461631,0.2361913621,-0.237191692,0.1614260226,0.3505791128,-0.1269297302,-0.2910232544,0.2580966055,0.2455105782,0.4343285859,0.0315224081,0.0257092975,0.3592629731,-0.1929415762,0.1338221878,0.2081800401,-0.4055424035,-0.0393661074,-0.1023251489,-0.1185180247,0.0896623731,-0.0253012516,0.2138373107,0.0043837125,-0.0270473994,-0.2997667789,0.3770951927,-0.2561500967,0.0849154368,0.1060997397,0.2420439124,-0.141662389,0.0986763537,-0.3688501418,-0.4162253439,0.1258704662,0.1059057787,-0.0542703457,0.2160719335,-0.2247670144,-0.1591992527,-0.1739040613,-0.2223182172,-0.2218108177,0.2399442345,-0.0028043378,0.1778603494,-0.3227798641,-0.3074143231,0.1102418453,-0.2998302579,-0.2147975117,-0.2052803189,-0.1565272063,0.0169330686,-0.1092813313,0.2515977323,0.3537291884,-0.0534845851,-0.227939412,0.0116484547,0.2254836112,0.1264863163,-0.0572768562,-0.0792503878,0.5078697205,0.1493995339,0.3317223489,-0.2037219107,-0.1142875329,0.136912182,-0.1654927135,-0.1757854819,0.4973132312,-0.335368067,0.2750992775,0.4102871418,-0.2995547652,0.4029887319,0.115784429,-0.143663168,-0.0574713685,-0.1752649993,-0.0967897698,0.2751607299,0.0606216118,-0.2556514144,0.3273020685,0.5726827979,0.0361370891,-0.1002059802,-0.1547125578,-0.0543651991,-0.2058887482,0.1508186162,0.4764367342,0.4796456993,0.256302923,0.1167387441,0.0036920914,-0.0489872769,0.0065353429,0.2420447469,0.2053320408,0.381901145,0.2473000884,-0.0560471714,0.2558583319,-0.143584609,-0.4367814064,-0.0775162354,0.2965328097,0.0304793976,-0.1230422184,-0.0937113315,-0.0882741213,0.0787152648,-0.1633800268,0.2057161927,-0.5248797536,-0.0057398831,0.1359853446,-0.2640916407,0.0778510943,-0.1357420534,0.1839352101,-0.1115652695,-0.0920116231,-0.2628382742,-0.1795540303,-0.1160064787,0.2207994312,0.1492943913,-0.2309354991,0.5525325537,0.0034910729,0.1104330197,-0.1831870973,-0.3148903847,0.1829965711,-0.2334032655,-0.0934313834,0.3089739084,0.0537207127,0.0617577434,-0.3398544192,0.0978550389,-0.2993351519,-0.1654749215,-0.1521718949,-0.0721656084,-0.4444735646,-0.1493290216,-0.50461483,0.1052791178,-0.3744410872,0.0652974173,0.0234543923,0.2462833077,0.1070563346,-0.0528685637,0.0851153508,0.2249361724,0.2035253644,-0.3054971993,-0.1719645709,0.2625108361,-0.2081556171,-0.2463432103,-0.0512267575,-0.178250879,0.5515277982,0.2740971148,0.0364471599,-0.083949551,-0.3048900366,0.2759169042,0.0605894476,-0.0258912947,0.4949573576,0.2188521028,-0.134488672,-0.1581901461,-0.2062716782,0.0789617226,0.1920719743,-0.0577512011,0.1474713236,0.4454344809,0.0465013161,0.789770782,0.1162522212,-0.0683413371,0.1553920507,-0.0496133603,0.1668015271,-0.3357062936,-0.2550676763,0.1658229083,-0.047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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3444","title":"Align the Dataset and IterableDataset processing API","comments":"I like this proposal.\r\n\r\n> There is also an important difference in terms of behavior:\r\nDataset.map adds new columns (with dict.update)\r\nBUT\r\nIterableDataset discards previous columns (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n> The main breaking change would be the change of behavior of IterableDataset.map, because currently it discards all the previous columns instead of keeping them.\r\n\r\nYes, this behavior of `IterableDataset.map` was surprising to me the first time I used it because I was expecting the same behavior as `Dataset.map`, so I'm OK with the breaking change here.\r\n\r\n> IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n\\+  it's also missing the actual formatting code (we return unformatted tensors)\r\n> We could have a completely aligned map method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\n> For information, TFDS does lazy map by default, and has an additional .cache() method.\r\n\r\nIf I understand this part correctly, the idea would be for `Dataset.map` to behave similarly to `Dataset.with_transform` (lazy processing) and to have an option to cache processed data (with `.cache()`). This idea is really nice because it can also be applied to `IterableDataset` to fix https:\/\/github.com\/huggingface\/datasets\/issues\/3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?) \r\n> If the two APIs are more aligned it would be awesome for the examples in transformers, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\nYes, it would be amazing to have an option to easily switch between these two modes.\r\n\r\nI agree with the rest.\r\n","body":"## Intro\r\n\r\nCurrently the two classes have two distinct API for processing:\r\n\r\n### The `.map()` method\r\n\r\nBoth have those parameters in common: function, batched, batch_size\r\n\r\n- IterableDataset is missing those parameters:\r\nwith_indices, with_rank, input_columns, drop_last_batch, remove_columns, features, disable_nullable, fn_kwargs, num_proc\r\n\r\n- Dataset also has additional parameters that are exclusive, due to caching:\r\nkeep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint\r\n\r\n- There is also an important difference in terms of behavior:\r\n**Dataset.map adds new columns** (with dict.update)\r\nBUT\r\n**IterableDataset discards previous columns** (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n- Dataset.map is eager while IterableDataset.map is lazy\r\n\r\n### The `.shuffle()` method\r\n\r\n- Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.\r\n\r\n- IterableDataset is missing the parameter generator\r\n\r\n- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint\r\n\r\n### The `.with_format()` method\r\n\r\n- IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n### Other methods\r\n\r\n- Both have the same `remove_columns` method\r\n- IterableDataset is missing: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform\r\n- And others don't really make sense for an iterable dataset: select, sort, add_column, add_item\r\n- Dataset is missing skip and take, that IterableDataset implements.\r\n\r\n## Questions\r\n\r\nI think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.\r\n\r\n1. What should be aligned and what shouldn't between those two APIs ?\r\n\r\nIMO the minimum is to align the main processing methods.\r\n\r\nIt would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters.\r\n\r\nIt would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n\r\n2. What are the breaking changes for IterableDataset ?\r\n\r\nThe main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them.\r\n\r\n3. Shall we also do some changes for regular datasets ?\r\n\r\nI agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:\r\n- keep the eager Dataset.map with caching\r\n- keep the with_transform method for lazy processing\r\n- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)\r\n\r\nWe could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\nFor information, TFDS does lazy map by default, and has an additional `.cache()` method.\r\n\r\n## Opinions ?\r\n\r\nI'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\ncc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger ","comment_length":322,"text":"Align the Dataset and IterableDataset processing API \n ## Intro\r\n\r\nCurrently the two classes have two distinct API for processing:\r\n\r\n### The `.map()` method\r\n\r\nBoth have those parameters in common: function, batched, batch_size\r\n\r\n- IterableDataset is missing those parameters:\r\nwith_indices, with_rank, input_columns, drop_last_batch, remove_columns, features, disable_nullable, fn_kwargs, num_proc\r\n\r\n- Dataset also has additional parameters that are exclusive, due to caching:\r\nkeep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint\r\n\r\n- There is also an important difference in terms of behavior:\r\n**Dataset.map adds new columns** (with dict.update)\r\nBUT\r\n**IterableDataset discards previous columns** (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n- Dataset.map is eager while IterableDataset.map is lazy\r\n\r\n### The `.shuffle()` method\r\n\r\n- Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.\r\n\r\n- IterableDataset is missing the parameter generator\r\n\r\n- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint\r\n\r\n### The `.with_format()` method\r\n\r\n- IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n### Other methods\r\n\r\n- Both have the same `remove_columns` method\r\n- IterableDataset is missing: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform\r\n- And others don't really make sense for an iterable dataset: select, sort, add_column, add_item\r\n- Dataset is missing skip and take, that IterableDataset implements.\r\n\r\n## Questions\r\n\r\nI think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.\r\n\r\n1. What should be aligned and what shouldn't between those two APIs ?\r\n\r\nIMO the minimum is to align the main processing methods.\r\n\r\nIt would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters.\r\n\r\nIt would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n\r\n2. What are the breaking changes for IterableDataset ?\r\n\r\nThe main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them.\r\n\r\n3. Shall we also do some changes for regular datasets ?\r\n\r\nI agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:\r\n- keep the eager Dataset.map with caching\r\n- keep the with_transform method for lazy processing\r\n- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)\r\n\r\nWe could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\nFor information, TFDS does lazy map by default, and has an additional `.cache()` method.\r\n\r\n## Opinions ?\r\n\r\nI'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\ncc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger  \n I like this proposal.\r\n\r\n> There is also an important difference in terms of behavior:\r\nDataset.map adds new columns (with dict.update)\r\nBUT\r\nIterableDataset discards previous columns (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n> The main breaking change would be the change of behavior of IterableDataset.map, because currently it discards all the previous columns instead of keeping them.\r\n\r\nYes, this behavior of `IterableDataset.map` was surprising to me the first time I used it because I was expecting the same behavior as `Dataset.map`, so I'm OK with the breaking change here.\r\n\r\n> IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n\\+  it's also missing the actual formatting code (we return unformatted tensors)\r\n> We could have a completely aligned map method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\n> For information, TFDS does lazy map by default, and has an additional .cache() method.\r\n\r\nIf I understand this part correctly, the idea would be for `Dataset.map` to behave similarly to `Dataset.with_transform` (lazy processing) and to have an option to cache processed data (with `.cache()`). This idea is really nice because it can also be applied to `IterableDataset` to fix https:\/\/github.com\/huggingface\/datasets\/issues\/3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?) \r\n> If the two APIs are more aligned it would be awesome for the examples in transformers, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\nYes, it would be amazing to have an option to easily switch between these two modes.\r\n\r\nI agree with the rest.\r\n","embeddings":[-0.4088525474,0.0703886002,-0.1826431006,0.1011598632,0.1535394192,0.1712657362,0.1793358624,0.4203015864,-0.1248599514,0.1267996281,-0.0965422243,0.4365522563,-0.0362225585,0.1045318767,-0.1478759348,-0.239873156,0.1861059219,0.2008081824,-0.2912281454,-0.1036827266,-0.3949563205,0.0552298687,-0.3762719333,-0.1587012708,0.20320867,-0.3815857172,-0.2470869273,-0.0237530768,-0.1631987989,-0.1764978021,0.2641444802,0.3600022495,-0.0030031686,0.440108031,-0.0001035961,-0.1517155468,0.1586819291,-0.0434100293,-0.1534931213,0.1260907054,-0.2308628112,-0.1146780476,-0.229084745,-0.43036291,-0.0673348084,-0.4406300187,0.0209379159,-0.3395364583,0.2980356812,0.1623904705,0.2427192181,-0.0922978446,-0.0949911773,-0.1588321477,-0.0522320345,0.1502773911,-0.1721200943,-0.2116830796,0.3446257412,0.2219965905,-0.1189257801,0.0991046354,-0.1147055477,-0.0338132791,0.2634684741,-0.0672511831,-0.2070111632,-0.2588835061,0.1595659703,0.117195718,0.4054489136,-0.3294045329,-0.3923185468,-0.4578973055,-0.0950731412,-0.4153778255,0.0426211618,-0.1662911922,0.1635380536,0.0496852659,-0.2528590262,0.1466912627,-0.0590290613,-0.02039578,0.20544222,0.3153709173,-0.0049070474,0.1218149886,-0.182278797,-0.0163886156,0.4185235798,-0.1868131757,0.1164721549,0.4013648629,-0.3323753178,-0.2703324854,0.2315940261,-0.1564336121,0.0558884405,0.4004893005,0.1718945503,0.1605055332,0.094121553,0.0242355783,0.2868935764,0.2347005606,-0.0592046566,0.3019541204,0.0505101755,0.0852525234,0.0523392931,0.0115288049,0.2380922586,-0.08236783,0.2259825766,0.1008024812,-0.0991849154,-0.0021463872,-0.2308947891,-0.0766684338,-0.0939526781,-0.2808576226,-0.0907835439,0.1681826115,-0.152246505,0.3265187144,-0.2387930602,0.011831318,0.118321918,-0.0064184554,-0.231280908,0.0621685907,0.0342375003,0.0637620911,0.1716178507,0.0573761053,0.2021390051,0.2352996916,-0.0402894467,0.3078820407,0.2633217871,-0.2779312134,0.5297861099,0.050035134,0.1125083417,0.0706157088,-0.0339871049,0.1646754593,-0.171982795,0.0933515504,-0.0374183841,-0.194755435,0.3522886038,0.314327389,-0.1063656285,-0.1467631012,-0.166037336,0.2969782948,0.0589153208,-0.3297497332,0.0917108208,-0.1845941395,-0.0215977617,-0.4717492759,0.1498201489,-0.0041785622,-0.3207444847,0.0841460451,-0.0463728346,0.0728655607,0.3883541822,0.1823013127,-0.1276461631,0.2361913621,-0.237191692,0.1614260226,0.3505791128,-0.1269297302,-0.2910232544,0.2580966055,0.2455105782,0.4343285859,0.0315224081,0.0257092975,0.3592629731,-0.1929415762,0.1338221878,0.2081800401,-0.4055424035,-0.0393661074,-0.1023251489,-0.1185180247,0.0896623731,-0.0253012516,0.2138373107,0.0043837125,-0.0270473994,-0.2997667789,0.3770951927,-0.2561500967,0.0849154368,0.1060997397,0.2420439124,-0.141662389,0.0986763537,-0.3688501418,-0.4162253439,0.1258704662,0.1059057787,-0.0542703457,0.2160719335,-0.2247670144,-0.1591992527,-0.1739040613,-0.2223182172,-0.2218108177,0.2399442345,-0.0028043378,0.1778603494,-0.3227798641,-0.3074143231,0.1102418453,-0.2998302579,-0.2147975117,-0.2052803189,-0.1565272063,0.0169330686,-0.1092813313,0.2515977323,0.3537291884,-0.0534845851,-0.227939412,0.0116484547,0.2254836112,0.1264863163,-0.0572768562,-0.0792503878,0.5078697205,0.1493995339,0.3317223489,-0.2037219107,-0.1142875329,0.136912182,-0.1654927135,-0.1757854819,0.4973132312,-0.335368067,0.2750992775,0.4102871418,-0.2995547652,0.4029887319,0.115784429,-0.143663168,-0.0574713685,-0.1752649993,-0.0967897698,0.2751607299,0.0606216118,-0.2556514144,0.3273020685,0.5726827979,0.0361370891,-0.1002059802,-0.1547125578,-0.0543651991,-0.2058887482,0.1508186162,0.4764367342,0.4796456993,0.256302923,0.1167387441,0.0036920914,-0.0489872769,0.0065353429,0.2420447469,0.2053320408,0.381901145,0.2473000884,-0.0560471714,0.2558583319,-0.143584609,-0.4367814064,-0.0775162354,0.2965328097,0.0304793976,-0.1230422184,-0.0937113315,-0.0882741213,0.0787152648,-0.1633800268,0.2057161927,-0.5248797536,-0.0057398831,0.1359853446,-0.2640916407,0.0778510943,-0.1357420534,0.1839352101,-0.1115652695,-0.0920116231,-0.2628382742,-0.1795540303,-0.1160064787,0.2207994312,0.1492943913,-0.2309354991,0.5525325537,0.0034910729,0.1104330197,-0.1831870973,-0.3148903847,0.1829965711,-0.2334032655,-0.0934313834,0.3089739084,0.0537207127,0.0617577434,-0.3398544192,0.0978550389,-0.2993351519,-0.1654749215,-0.1521718949,-0.0721656084,-0.4444735646,-0.1493290216,-0.50461483,0.1052791178,-0.3744410872,0.0652974173,0.0234543923,0.2462833077,0.1070563346,-0.0528685637,0.0851153508,0.2249361724,0.2035253644,-0.3054971993,-0.1719645709,0.2625108361,-0.2081556171,-0.2463432103,-0.0512267575,-0.178250879,0.5515277982,0.2740971148,0.0364471599,-0.083949551,-0.3048900366,0.2759169042,0.0605894476,-0.0258912947,0.4949573576,0.2188521028,-0.134488672,-0.1581901461,-0.2062716782,0.0789617226,0.1920719743,-0.0577512011,0.1474713236,0.4454344809,0.0465013161,0.789770782,0.1162522212,-0.0683413371,0.1553920507,-0.0496133603,0.1668015271,-0.3357062936,-0.2550676763,0.1658229083,-0.04773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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3444","title":"Align the Dataset and IterableDataset processing API","comments":"> If I understand this part correctly, the idea would be for Dataset.map to behave similarly to Dataset.with_transform (lazy processing) and to have an option to cache processed data (with .cache()). This idea is really nice because it can also be applied to IterableDataset to fix #3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?)\r\n\r\nYea this is too big of a change in my opinion. Anyway it's fine as it is right now with streaming=lazy and regular=eager.","body":"## Intro\r\n\r\nCurrently the two classes have two distinct API for processing:\r\n\r\n### The `.map()` method\r\n\r\nBoth have those parameters in common: function, batched, batch_size\r\n\r\n- IterableDataset is missing those parameters:\r\nwith_indices, with_rank, input_columns, drop_last_batch, remove_columns, features, disable_nullable, fn_kwargs, num_proc\r\n\r\n- Dataset also has additional parameters that are exclusive, due to caching:\r\nkeep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint\r\n\r\n- There is also an important difference in terms of behavior:\r\n**Dataset.map adds new columns** (with dict.update)\r\nBUT\r\n**IterableDataset discards previous columns** (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n- Dataset.map is eager while IterableDataset.map is lazy\r\n\r\n### The `.shuffle()` method\r\n\r\n- Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.\r\n\r\n- IterableDataset is missing the parameter generator\r\n\r\n- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint\r\n\r\n### The `.with_format()` method\r\n\r\n- IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n### Other methods\r\n\r\n- Both have the same `remove_columns` method\r\n- IterableDataset is missing: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform\r\n- And others don't really make sense for an iterable dataset: select, sort, add_column, add_item\r\n- Dataset is missing skip and take, that IterableDataset implements.\r\n\r\n## Questions\r\n\r\nI think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.\r\n\r\n1. What should be aligned and what shouldn't between those two APIs ?\r\n\r\nIMO the minimum is to align the main processing methods.\r\n\r\nIt would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters.\r\n\r\nIt would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n\r\n2. What are the breaking changes for IterableDataset ?\r\n\r\nThe main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them.\r\n\r\n3. Shall we also do some changes for regular datasets ?\r\n\r\nI agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:\r\n- keep the eager Dataset.map with caching\r\n- keep the with_transform method for lazy processing\r\n- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)\r\n\r\nWe could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\nFor information, TFDS does lazy map by default, and has an additional `.cache()` method.\r\n\r\n## Opinions ?\r\n\r\nI'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\ncc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger ","comment_length":105,"text":"Align the Dataset and IterableDataset processing API \n ## Intro\r\n\r\nCurrently the two classes have two distinct API for processing:\r\n\r\n### The `.map()` method\r\n\r\nBoth have those parameters in common: function, batched, batch_size\r\n\r\n- IterableDataset is missing those parameters:\r\nwith_indices, with_rank, input_columns, drop_last_batch, remove_columns, features, disable_nullable, fn_kwargs, num_proc\r\n\r\n- Dataset also has additional parameters that are exclusive, due to caching:\r\nkeep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, suffix_template, new_fingerprint\r\n\r\n- There is also an important difference in terms of behavior:\r\n**Dataset.map adds new columns** (with dict.update)\r\nBUT\r\n**IterableDataset discards previous columns** (it overwrites the dict)\r\nIMO the two methods should have the same behavior. This would be an important breaking change though.\r\n\r\n- Dataset.map is eager while IterableDataset.map is lazy\r\n\r\n### The `.shuffle()` method\r\n\r\n- Both have an optional seed parameter, but IterableDataset requires a mandatory parameter buffer_size to control the size of the local buffer used for approximate shuffling.\r\n\r\n- IterableDataset is missing the parameter generator\r\n\r\n- Also Dataset has exclusive parameters due to caching: keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint\r\n\r\n### The `.with_format()` method\r\n\r\n- IterableDataset only supports \"torch\" (it misses tf, jax, pandas, arrow) and is missing the parameters: columns, output_all_columns and format_kwargs\r\n\r\n### Other methods\r\n\r\n- Both have the same `remove_columns` method\r\n- IterableDataset is missing: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n- Some other methods are missing but we can discuss them: set_transform, formatted_as, with_transform\r\n- And others don't really make sense for an iterable dataset: select, sort, add_column, add_item\r\n- Dataset is missing skip and take, that IterableDataset implements.\r\n\r\n## Questions\r\n\r\nI think it would be nice to be able to switch between streaming and regular dataset easily, without changing the processing code significantly.\r\n\r\n1. What should be aligned and what shouldn't between those two APIs ?\r\n\r\nIMO the minimum is to align the main processing methods.\r\n\r\nIt would mean aligning breaking the current `Iterable.map` to have the same behavior as `Dataset.map` (add columns with dict.update), and add multiprocessing as well as the missing parameters.\r\n\r\nIt would also mean implementing the missing methods: cast, cast_column, filter, rename_column, rename_columns, class_encode_column, flatten, prepare_for_task, train_test_split, shard\r\n\r\n2. What are the breaking changes for IterableDataset ?\r\n\r\nThe main breaking change would be the change of behavior of `IterableDataset.map`, because currently it discards all the previous columns instead of keeping them.\r\n\r\n3. Shall we also do some changes for regular datasets ?\r\n\r\nI agree the simplest would be to have the exact same methods for both Dataset and IterableDataset. However this is probably not a good idea because it would prevent users from using the best benefits of them. That's why we can keep some aspects of regular datasets as they are:\r\n- keep the eager Dataset.map with caching\r\n- keep the with_transform method for lazy processing\r\n- keep Dataset.select (it could also be added to IterableDataset even though it's not recommended)\r\n\r\nWe could have a completely aligned `map` method if both methods were lazy by default, but this is a very big breaking change so I'm not sure we can consider doing that.\r\n\r\nFor information, TFDS does lazy map by default, and has an additional `.cache()` method.\r\n\r\n## Opinions ?\r\n\r\nI'd love to gather some opinions about this here. If the two APIs are more aligned it would be awesome for the examples in `transformers`, and it would create a satisfactory experience for users that want to switch from one mode to the other.\r\n\r\ncc @mariosasko @albertvillanova @thomwolf @patrickvonplaten @sgugger  \n > If I understand this part correctly, the idea would be for Dataset.map to behave similarly to Dataset.with_transform (lazy processing) and to have an option to cache processed data (with .cache()). This idea is really nice because it can also be applied to IterableDataset to fix #3142 (again we get the aligned APIs). However, this change would break a lot of things, so I'm still not sure if this is a step in the right direction (maybe it's OK for Datasets 2.0?)\r\n\r\nYea this is too big of a change in my opinion. Anyway it's fine as it is right now with streaming=lazy and regular=eager.","embeddings":[-0.4088525474,0.0703886002,-0.1826431006,0.1011598632,0.1535394192,0.1712657362,0.1793358624,0.4203015864,-0.1248599514,0.1267996281,-0.0965422243,0.4365522563,-0.0362225585,0.1045318767,-0.1478759348,-0.239873156,0.1861059219,0.2008081824,-0.2912281454,-0.1036827266,-0.3949563205,0.0552298687,-0.3762719333,-0.1587012708,0.20320867,-0.3815857172,-0.2470869273,-0.0237530768,-0.1631987989,-0.1764978021,0.2641444802,0.3600022495,-0.0030031686,0.440108031,-0.0001035961,-0.1517155468,0.1586819291,-0.0434100293,-0.1534931213,0.1260907054,-0.2308628112,-0.1146780476,-0.229084745,-0.43036291,-0.0673348084,-0.4406300187,0.0209379159,-0.3395364583,0.2980356812,0.1623904705,0.2427192181,-0.0922978446,-0.0949911773,-0.1588321477,-0.0522320345,0.1502773911,-0.1721200943,-0.2116830796,0.3446257412,0.2219965905,-0.1189257801,0.0991046354,-0.1147055477,-0.0338132791,0.2634684741,-0.0672511831,-0.2070111632,-0.2588835061,0.1595659703,0.117195718,0.4054489136,-0.3294045329,-0.3923185468,-0.4578973055,-0.0950731412,-0.4153778255,0.0426211618,-0.1662911922,0.1635380536,0.0496852659,-0.2528590262,0.1466912627,-0.0590290613,-0.02039578,0.20544222,0.3153709173,-0.0049070474,0.1218149886,-0.182278797,-0.0163886156,0.4185235798,-0.1868131757,0.1164721549,0.4013648629,-0.3323753178,-0.2703324854,0.2315940261,-0.1564336121,0.0558884405,0.4004893005,0.1718945503,0.1605055332,0.094121553,0.0242355783,0.2868935764,0.2347005606,-0.0592046566,0.3019541204,0.0505101755,0.0852525234,0.0523392931,0.0115288049,0.2380922586,-0.08236783,0.2259825766,0.1008024812,-0.0991849154,-0.0021463872,-0.2308947891,-0.0766684338,-0.0939526781,-0.2808576226,-0.0907835439,0.1681826115,-0.152246505,0.3265187144,-0.2387930602,0.011831318,0.118321918,-0.0064184554,-0.231280908,0.0621685907,0.0342375003,0.0637620911,0.1716178507,0.0573761053,0.2021390051,0.2352996916,-0.0402894467,0.3078820407,0.2633217871,-0.2779312134,0.5297861099,0.050035134,0.1125083417,0.0706157088,-0.0339871049,0.1646754593,-0.171982795,0.0933515504,-0.0374183841,-0.194755435,0.3522886038,0.314327389,-0.1063656285,-0.1467631012,-0.166037336,0.2969782948,0.0589153208,-0.3297497332,0.0917108208,-0.1845941395,-0.0215977617,-0.4717492759,0.1498201489,-0.0041785622,-0.3207444847,0.0841460451,-0.0463728346,0.0728655607,0.3883541822,0.1823013127,-0.1276461631,0.2361913621,-0.237191692,0.1614260226,0.3505791128,-0.1269297302,-0.2910232544,0.2580966055,0.2455105782,0.4343285859,0.0315224081,0.0257092975,0.3592629731,-0.1929415762,0.1338221878,0.2081800401,-0.4055424035,-0.0393661074,-0.1023251489,-0.1185180247,0.0896623731,-0.0253012516,0.2138373107,0.0043837125,-0.0270473994,-0.2997667789,0.3770951927,-0.2561500967,0.0849154368,0.1060997397,0.2420439124,-0.141662389,0.0986763537,-0.3688501418,-0.4162253439,0.1258704662,0.1059057787,-0.0542703457,0.2160719335,-0.2247670144,-0.1591992527,-0.1739040613,-0.2223182172,-0.2218108177,0.2399442345,-0.0028043378,0.1778603494,-0.3227798641,-0.3074143231,0.1102418453,-0.2998302579,-0.2147975117,-0.2052803189,-0.1565272063,0.0169330686,-0.1092813313,0.2515977323,0.3537291884,-0.0534845851,-0.227939412,0.0116484547,0.2254836112,0.1264863163,-0.0572768562,-0.0792503878,0.5078697205,0.1493995339,0.3317223489,-0.2037219107,-0.1142875329,0.136912182,-0.1654927135,-0.1757854819,0.4973132312,-0.335368067,0.2750992775,0.4102871418,-0.2995547652,0.4029887319,0.115784429,-0.143663168,-0.0574713685,-0.1752649993,-0.0967897698,0.2751607299,0.0606216118,-0.2556514144,0.3273020685,0.5726827979,0.0361370891,-0.1002059802,-0.1547125578,-0.0543651991,-0.2058887482,0.1508186162,0.4764367342,0.4796456993,0.256302923,0.1167387441,0.0036920914,-0.0489872769,0.0065353429,0.2420447469,0.2053320408,0.381901145,0.2473000884,-0.0560471714,0.2558583319,-0.143584609,-0.4367814064,-0.0775162354,0.2965328097,0.0304793976,-0.1230422184,-0.0937113315,-0.0882741213,0.0787152648,-0.1633800268,0.2057161927,-0.5248797536,-0.0057398831,0.1359853446,-0.2640916407,0.0778510943,-0.1357420534,0.1839352101,-0.1115652695,-0.0920116231,-0.2628382742,-0.1795540303,-0.1160064787,0.2207994312,0.1492943913,-0.2309354991,0.5525325537,0.0034910729,0.1104330197,-0.1831870973,-0.3148903847,0.1829965711,-0.2334032655,-0.0934313834,0.3089739084,0.0537207127,0.0617577434,-0.3398544192,0.0978550389,-0.2993351519,-0.1654749215,-0.1521718949,-0.0721656084,-0.4444735646,-0.1493290216,-0.50461483,0.1052791178,-0.3744410872,0.0652974173,0.0234543923,0.2462833077,0.1070563346,-0.0528685637,0.0851153508,0.2249361724,0.2035253644,-0.3054971993,-0.1719645709,0.2625108361,-0.2081556171,-0.2463432103,-0.0512267575,-0.178250879,0.5515277982,0.2740971148,0.0364471599,-0.083949551,-0.3048900366,0.2759169042,0.0605894476,-0.0258912947,0.4949573576,0.2188521028,-0.134488672,-0.1581901461,-0.2062716782,0.0789617226,0.1920719743,-0.0577512011,0.1474713236,0.4454344809,0.0465013161,0.789770782,0.1162522212,-0.0683413371,0.1553920507,-0.0496133603,0.1668015271,-0.3357062936,-0.2550676763,0.1658229083,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3440","title":"datasets keeps reading from cached files, although I disabled it","comments":"Hi ! What version of `datasets` are you using ? Can you also provide the logs you get before it raises the error ?","body":"## Describe the bug\r\nHi,\r\nI am trying to avoid dataset library using cached files, I get the following bug when this tried to read the cached files. I tried to do the followings:\r\n```\r\nfrom datasets import set_caching_enabled\r\nset_caching_enabled(False)\r\n```\r\nalso force redownlaod:\r\n```\r\n download_mode='force_redownload'\r\n```\r\nbut none worked so far, this is on a cluster and on some of the machines this reads from the cached files, I really appreciate any idea on how to fully remove caching @lhoestq \r\nmany thanks\r\n\r\n```\r\nFile \"run_clm.py\", line 496, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 419, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 943, in train\r\n    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/transformers\/trainer.py\", line 1445, in _maybe_log_save_evaluate\r\n    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 172, in evaluate\r\n    output = self.eval_loop(\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 241, in eval_loop\r\n    metrics = self.compute_pet_metrics(eval_datasets, model, self.extra_info[metric_key_prefix], task=task)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 268, in compute_pet_metrics\r\n    centroids = self._compute_per_token_train_centroids(model, task=task)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 353, in _compute_per_token_train_centroids\r\n    data = get_label_samples(self.get_per_task_train_dataset(task), label)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 350, in get_label_samples\r\n    return dataset.filter(lambda example: int(example['labels']) == label)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 470, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 406, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2519, in filter\r\n    indices = self.map(\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2036, in map\r\n    return self._map_single(\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 503, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 470, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 406, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2248, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 654, in from_file\r\n    return cls(\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 593, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/features\/features.py\", line 1092, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/features\/features.py\", line 1081, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'candidates_ids': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'labels': Value(dtype='int64', id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'extra_fields': {}, 'task': Value(dtype='string', id=None)}\r\n\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: linux \r\n- Python version: 3.8.12 \r\n- PyArrow version: 6.0.1\r\n","comment_length":24,"text":"datasets keeps reading from cached files, although I disabled it \n ## Describe the bug\r\nHi,\r\nI am trying to avoid dataset library using cached files, I get the following bug when this tried to read the cached files. I tried to do the followings:\r\n```\r\nfrom datasets import set_caching_enabled\r\nset_caching_enabled(False)\r\n```\r\nalso force redownlaod:\r\n```\r\n download_mode='force_redownload'\r\n```\r\nbut none worked so far, this is on a cluster and on some of the machines this reads from the cached files, I really appreciate any idea on how to fully remove caching @lhoestq \r\nmany thanks\r\n\r\n```\r\nFile \"run_clm.py\", line 496, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 419, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 943, in train\r\n    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/transformers\/trainer.py\", line 1445, in _maybe_log_save_evaluate\r\n    metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 172, in evaluate\r\n    output = self.eval_loop(\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 241, in eval_loop\r\n    metrics = self.compute_pet_metrics(eval_datasets, model, self.extra_info[metric_key_prefix], task=task)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 268, in compute_pet_metrics\r\n    centroids = self._compute_per_token_train_centroids(model, task=task)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 353, in _compute_per_token_train_centroids\r\n    data = get_label_samples(self.get_per_task_train_dataset(task), label)\r\n  File \"\/users\/dara\/codes\/fewshot\/debug\/fewshot\/third_party\/trainers\/trainer.py\", line 350, in get_label_samples\r\n    return dataset.filter(lambda example: int(example['labels']) == label)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 470, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 406, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2519, in filter\r\n    indices = self.map(\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2036, in map\r\n    return self._map_single(\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 503, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 470, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 406, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2248, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 654, in from_file\r\n    return cls(\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 593, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/features\/features.py\", line 1092, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \"\/users\/dara\/conda\/envs\/multisuccess\/lib\/python3.8\/site-packages\/datasets\/features\/features.py\", line 1081, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'candidates_ids': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'labels': Value(dtype='int64', id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'extra_fields': {}, 'task': Value(dtype='string', id=None)}\r\n\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: linux \r\n- Python version: 3.8.12 \r\n- PyArrow version: 6.0.1\r\n \n Hi ! What version of `datasets` are you using ? Can you also provide the logs you get before it raises the error ?","embeddings":[-0.1347910464,0.0252024401,0.0009026572,0.5154469609,0.4938991964,0.2646333873,0.0577917509,0.3259382546,0.0154797761,-0.0155143179,-0.2143101096,0.2182266116,-0.085602507,-0.3043364286,-0.1368988901,0.2771289051,0.1166640222,0.0681687742,0.0084787812,-0.0709792599,-0.3026719391,-0.0255557746,-0.2052818537,-0.1392595172,-0.3482949734,-0.3214122057,0.1381619424,0.0075004208,-0.2036438137,-0.2740698755,0.4375575781,0.0028206988,0.0893770605,0.5267432928,-0.0001160746,0.0314322896,0.1130925938,0.036463283,-0.0466691665,-0.0476419665,-0.0376654118,-0.0472618639,0.3255213797,-0.2387639433,-0.1338167042,-0.1992304623,-0.1100827008,-0.7465632558,0.4422792196,0.4422801137,0.2044254094,-0.0111770881,-0.253969878,0.3288721144,0.2135256231,-0.2099314183,-0.2228217721,0.1832697988,0.2086636871,-0.0339810438,-0.4124691784,0.4314967394,-0.458113879,0.2001528442,0.385848552,-0.0813777,-0.1843187809,-0.3547955453,0.1968844086,-0.0773097873,0.6554116011,-0.5104448795,-0.2234267145,-0.3071141541,-0.1280069947,-0.2817061543,0.322357595,0.2355410606,-0.0164910015,0.2743005455,-0.4476623535,-0.2035817206,-0.268255204,0.0064548692,0.1146933585,-0.1818071306,-0.0806559697,-0.0062085046,0.2816998363,0.140617311,0.7125240564,-0.1121703386,-0.1603088528,0.1727031469,-0.4422088861,0.0334234089,-0.0894000232,0.4821132123,0.0502711721,0.2645425797,-0.0537976213,0.0786590427,-0.1516376585,0.0134789506,0.172657758,0.3093884289,-0.1447681785,0.4031307101,0.1806671023,0.1639500558,-0.2777956426,-0.0618460402,0.2777055204,-0.1662115306,0.6028597951,-0.0185191799,0.1513296962,-0.4025511742,-0.0992430076,0.0622789152,-0.1391216815,0.0017983414,0.1857323498,0.261366725,-0.1333520412,0.2755849361,-0.0062638125,0.0802935362,-0.3829359412,-0.1316264719,-0.1894788891,0.1735136509,-0.1626890451,0.1001403853,0.3576357961,-0.3488933146,0.1726450622,0.3647532463,0.078015618,-0.2128515691,0.3054151237,-0.38930282,0.3533241451,0.1857806444,-0.2119143605,0.3226234019,0.3489100337,-0.2285857052,-0.0369241126,0.2101988345,-0.5204888582,-0.1280182898,0.1387249529,0.1730236411,-0.3193344772,0.0718142539,-0.2431098074,-0.0246388949,0.3993123174,-0.0198422801,0.1885061264,-0.1070012972,-0.2108370364,-0.2313872129,0.0495150611,0.4662143588,-0.2028484792,0.1330049187,-0.0383607782,-0.0798029155,-0.0053065801,0.1470537186,-0.1381859034,0.1418814808,-0.3478869796,-0.2191423327,0.2481331378,-0.5889911056,-0.8006486297,0.2653120458,0.2215212435,0.2436000258,0.0362364724,0.3255742788,-0.0917181,0.0562623926,-0.1296575516,0.0400861837,0.139033854,-0.0191247258,-0.1640001833,-0.3946735263,0.117661275,0.1274540573,0.1215949878,0.278989464,0.2014134973,0.0965597853,0.2482379526,-0.0630899072,0.0736225471,0.3326491117,0.1358741969,0.0082368655,0.2744855583,-0.2027184665,-0.4298446476,0.2138185203,-0.0000380107,-0.4653173685,-0.3672342002,-0.3241559863,-0.0096510956,-0.1919163465,-0.4852232933,-0.0810113475,0.0685174242,-0.1516575366,0.0228847712,0.0424296111,-0.2088167965,0.5369127989,-0.169410795,0.1113310233,-0.3570793271,-0.1020254269,0.0235208366,0.022709623,-0.2411291003,0.050535243,0.2807759941,0.0284304935,-0.2509106696,0.3418461978,0.2283074558,0.198672384,-0.1491061896,0.358851552,0.2382885814,-0.121366255,-0.066647552,0.0512814671,0.1486518085,-0.0570430197,-0.0710928515,0.1589010209,-0.1821681857,0.114935115,-0.1578965187,-0.0836702064,0.1419345737,-0.1440801322,0.1643738449,-0.0298305005,0.1915688813,-0.0289207157,0.4275228083,0.2735748291,-0.1063179225,-0.0610344298,0.4289514422,0.2639595568,-0.0853558183,-0.0585678369,-0.0546097755,-0.143546775,0.0495650917,0.5101735592,0.2027996629,0.0540242232,0.2070148289,0.329121083,0.1233243123,-0.0752892494,0.0654144883,0.1733806729,0.0314770117,0.1283113509,0.0793276057,-0.0276672915,-0.2090268433,0.1276447028,-0.025570292,0.3077681363,-0.3078732193,0.3045547903,-0.6040995717,-0.3373563588,-0.1494439691,0.2008710057,-0.1669395864,-0.1591370702,-0.129267022,0.4065452516,0.3137301505,0.0997060463,-0.3969267607,0.1712881774,0.02531025,-0.378103137,-0.1359472573,-0.0190089419,-0.2054713815,-0.0227103941,0.3963700235,-0.2685648501,0.1744906455,-0.0971136242,-0.0605771989,-0.3700219393,0.0511597134,-0.0430151597,0.1310772747,0.371923238,-0.0071007186,-0.0963818133,-0.3381528556,0.0884461477,-0.1145657152,-0.1268669069,0.0914133564,-0.0404216349,-0.023778636,0.3179363012,-0.1407576054,-0.4279044867,-0.1378009021,-0.1757675558,-0.0294475779,0.0090287495,0.0518949069,0.4343237877,-0.071657978,0.1872653812,0.1484451294,0.1082531065,-0.0833160058,-0.583583653,0.3847765028,0.0708788261,-0.2034755349,0.1745611131,0.0651163384,0.2045125216,0.3564770818,-0.6828066111,-0.3330172598,-0.1190030202,0.0513891391,-0.2413746715,-0.0784249827,0.1261018068,0.0544722117,0.1294624656,-0.2159832269,0.1969522089,0.1558251232,-0.3336179554,0.1633087248,0.0664343387,0.0745889023,0.2744601667,0.6522008777,0.2870256007,0.1414102763,0.3819145858,0.0016723403,0.5076673031,-0.027662551,-0.3993758857,-0.1943570524,-0.1096756384,-0.22546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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3431","title":"Unable to resolve any data file after loading once","comments":"Hi ! `load_dataset` accepts as input either a local dataset directory or a dataset name from the Hugging Face Hub.\r\n\r\nSo here you are getting this error the second time because it tries to load the local `wiki_dpr` directory, instead of `wiki_dpr` from the Hub. It doesn't work since it's a **cache** directory, not a **dataset** directory in itself.\r\n\r\nTo fix that you can use another cache directory like `cache_dir=\"\/data2\/whr\/lzy\/open_domain_data\/retrieval\/cache\"`","body":"when I rerun my program, it occurs this error\r\n\" Unable to resolve any data file that matches '['**train*']' at \/data2\/whr\/lzy\/open_domain_data\/retrieval\/wiki_dpr with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'zip']\", so how could i deal with this problem?\r\nthx.\r\nAnd below is my code .\r\n![image](https:\/\/user-images.githubusercontent.com\/84694183\/146023446-d75fdec8-65c1-484f-80d8-6c20ff5e994b.png)\r\n","comment_length":70,"text":"Unable to resolve any data file after loading once \n when I rerun my program, it occurs this error\r\n\" Unable to resolve any data file that matches '['**train*']' at \/data2\/whr\/lzy\/open_domain_data\/retrieval\/wiki_dpr with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'zip']\", so how could i deal with this problem?\r\nthx.\r\nAnd below is my code .\r\n![image](https:\/\/user-images.githubusercontent.com\/84694183\/146023446-d75fdec8-65c1-484f-80d8-6c20ff5e994b.png)\r\n \n Hi ! `load_dataset` accepts as input either a local dataset directory or a dataset name from the Hugging Face Hub.\r\n\r\nSo here you are getting this error the second time because it tries to load the local `wiki_dpr` directory, instead of `wiki_dpr` from the Hub. It doesn't work since it's a **cache** directory, not a **dataset** directory in itself.\r\n\r\nTo fix that you can use another cache directory like `cache_dir=\"\/data2\/whr\/lzy\/open_domain_data\/retrieval\/cache\"`","embeddings":[0.0235008635,-0.0923301652,-0.0853218436,0.5271118283,0.3798450232,0.1411335766,0.0799061283,0.5025752783,0.2032741457,0.075701341,0.1105006933,-0.0108610131,0.1759341806,0.0473018736,-0.0830670223,-0.1982480884,-0.1227090359,0.2124537528,0.1570385545,0.0565080158,-0.0941122398,0.2072376609,0.0278493315,-0.0109031927,-0.5511701107,0.011474126,-0.1955417693,0.0679086968,0.0216841437,-0.4020965099,0.5563516021,0.0417731777,0.4304460287,0.3836531937,-0.0001147879,0.1027839333,0.1191908792,-0.0145256007,-0.1031954885,-0.2323745787,0.2735654712,-0.1032646075,0.2602539062,-0.4209733903,-0.2330068052,0.0823136047,0.1298935264,-0.0588665828,0.8446519375,0.0390019342,0.1634757221,-0.3755128086,0.0628807396,-0.1028688923,0.0546225607,0.1275776923,0.1820348054,0.2474461347,0.2063876987,-0.1432091296,-0.0783759803,0.3664891422,-0.0231795628,0.21223028,0.2097848654,0.1975144148,0.0217422042,-0.2805303037,0.3199225068,0.0206745826,0.6778355241,0.012539031,-0.0483256765,-0.0385635942,-0.0159509014,-0.1628515124,0.3767315149,0.0805789977,0.0846255943,0.263956815,-0.0626924932,-0.275041014,-0.1078953221,-0.0027286841,-0.1556399763,0.2074198127,0.0134997759,0.2024932355,0.3164553046,-0.1212428734,0.0737415329,0.2488400489,-0.2387197167,0.2314715236,-0.5613316298,0.0437605903,-0.0487952977,0.0177736357,0.11971955,0.0875322595,0.1650348455,-0.0063528423,-0.0101503385,-0.0565263219,0.5378552079,0.4283067584,0.0773698688,0.0403711386,0.3489691019,0.0741118863,-0.3704510033,-0.0154923433,-0.3845213354,-0.1791652292,-0.104389511,-0.108219929,0.2127159834,-0.0949091315,0.0244928934,0.3061708808,-0.0241150502,-0.0525071993,0.033493042,0.5147625208,-0.2212890536,0.2455554456,0.1658208519,0.0363781974,0.0289339367,-0.0536870658,-0.0705097988,-0.0107553974,-0.2826848924,0.1171798408,0.3204649091,0.0291497298,0.233680293,0.0045102574,0.1812270582,-0.4299929738,0.0546167046,-0.1799017489,0.0267857052,0.2145901918,-0.0805901438,0.1579175889,-0.0025218897,-0.2529569566,0.1067553461,0.1903117001,-0.617818594,-0.2562895715,-0.0138408346,0.1138290018,-0.2807008028,0.3627818525,-0.0424153171,-0.0234448481,0.0297729522,-0.2394821346,0.0080752876,-0.0210881364,0.1281894445,-0.2538211346,0.48589468,0.3196288645,-0.0638929158,0.2794658542,-0.0143166715,0.0300565362,-0.2417261302,0.3479002714,-0.2619828582,0.2430753559,-0.4131560028,0.1957117021,0.2988141477,-0.6477742195,-0.6307656765,0.1833774,-0.2321539521,0.1225112081,-0.0445170328,0.1439868957,-0.0180675965,0.064765282,0.181521371,0.0626942217,-0.0347340591,-0.1994812936,-0.2252824157,-0.1114919037,0.0847897083,0.2439162284,0.1684807092,0.1059507355,0.111045517,0.197210297,0.172819674,-0.1011706442,0.1137946025,0.1980824172,0.186095044,0.2890424132,0.1525543183,0.1522701383,-0.5122994781,0.3835255504,-0.1815196276,-0.1087397784,-0.2200752497,-0.1297431737,-0.0147929005,-0.2311617881,-0.4051472545,-0.2030912638,0.0720169693,0.0274624228,0.1080283895,-0.0268759783,-0.1719650775,0.4957537949,-0.1741046757,-0.0086795483,-0.3582464755,0.3695960939,-0.1100534797,-0.1175261214,-0.0407610796,-0.0480816625,0.192012772,-0.2018754631,-0.0150943287,0.2767949104,-0.0455341302,0.2759907544,0.3504788578,-0.0303513557,0.0141139105,-0.4298632443,-0.1681644022,-0.0785901695,0.1163953468,-0.0835217312,0.0435049981,0.1105845943,-0.1786929369,0.0420909859,0.0390596166,-0.0749718025,0.3698667884,-0.2445075959,-0.0955384299,-0.1610662639,0.1946417838,0.2705614269,0.0809741244,-0.090046443,-0.1153574586,-0.1419992894,0.3758886755,-0.1289181709,0.0867355242,0.1408331543,-0.0298601706,-0.097160235,-0.0181996375,0.1824226975,0.1616166383,0.0739424154,-0.1047720462,0.3152353168,-0.0865571871,-0.0678830966,-0.0864325017,-0.1811197549,0.192516461,0.6114946008,0.047348924,0.0732480139,-0.2106780857,0.040263053,0.0511453599,0.2326361239,-0.0743700266,-0.0754744858,-0.3911803365,0.001901056,0.0698460937,0.06267979,-0.4266354144,-0.0436502695,-0.0567511581,0.0579970963,0.1082179025,-0.0630066842,0.1116991043,0.3665393889,-0.0765982792,-0.1037627459,-0.3823924661,-0.6609821916,-0.3258283734,-0.0303017404,0.0409820192,-0.0039785858,0.3425938785,-0.3290385008,0.1344327033,-0.4004616141,-0.451887846,0.0455463864,0.0460637398,0.3655441105,-0.0100122131,0.6576488614,-0.4055892527,-0.4164311886,0.3954068422,-0.2024630159,-0.0403900929,-0.1551208347,0.1684688777,0.0293728895,-0.193748042,-0.3351163864,-0.2538432479,-0.4043921232,0.3545826972,-0.1435310245,0.1343774498,0.1249050945,-0.1490846723,0.2145118266,-0.1962374896,-0.0387228429,-0.4604593217,-0.3923173547,0.3985952437,-0.1796930432,-0.215968743,0.2528777421,-0.1614364237,0.3047696948,0.1052085757,-0.6089670062,-0.335694164,0.0180502366,0.1981135756,-0.12249282,0.0663661361,0.1610474735,-0.1329281032,-0.0359090045,-0.0402983502,-0.0049992725,0.3628984392,0.36121732,0.3401915431,0.1938131601,0.1360828727,-0.1760372519,0.4638030827,0.0734676048,0.066348061,0.5312769413,0.040231403,0.1391575485,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3425","title":"Getting configs names takes too long","comments":"It looks like it's currently calling `HfFileSystem.ls()` ~8 times at the root and for each subdirectory:\r\n- \"\"\r\n- \"en.noblocklist\"\r\n- \"en.noclean\"\r\n- \"en\"\r\n- \"multilingual\"\r\n- \"realnewslike\"\r\n\r\nCurrently `ls` is slow because it iterates on all the files inside the repository.\r\n\r\nAn easy optimization would be to cache the result of each call to `ls`.\r\nWe can also optimize `ls` by using a tree structure per directory instead of a list of all the files.\r\n","body":"\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import get_dataset_config_names\r\nget_dataset_config_names(\"allenai\/c4\")\r\n```\r\n\r\n## Expected results\r\n\r\nI would expect to get the answer quickly, at least in less than 10s\r\n\r\n## Actual results\r\n\r\nIt takes about 45s on my environment\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.11.0-1022-aws-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 4.0.1","comment_length":76,"text":"Getting configs names takes too long \n \r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import get_dataset_config_names\r\nget_dataset_config_names(\"allenai\/c4\")\r\n```\r\n\r\n## Expected results\r\n\r\nI would expect to get the answer quickly, at least in less than 10s\r\n\r\n## Actual results\r\n\r\nIt takes about 45s on my environment\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.11.0-1022-aws-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 4.0.1 \n It looks like it's currently calling `HfFileSystem.ls()` ~8 times at the root and for each subdirectory:\r\n- \"\"\r\n- \"en.noblocklist\"\r\n- \"en.noclean\"\r\n- \"en\"\r\n- \"multilingual\"\r\n- \"realnewslike\"\r\n\r\nCurrently `ls` is slow because it iterates on all the files inside the repository.\r\n\r\nAn easy optimization would be to cache the result of each call to `ls`.\r\nWe can also optimize `ls` by using a tree structure per directory instead of a list of all the files.\r\n","embeddings":[-0.3283517361,0.1722301096,-0.1880416125,0.377034992,0.1999282688,-0.1003855541,0.1385928094,0.4915504456,-0.0290575009,0.3562120199,-0.1960750818,0.3529298604,0.1320978403,-0.2432374805,-0.2165947706,0.0623893961,-0.0295605697,0.1501357108,0.0637909696,-0.242928654,-0.2170454711,0.1156888083,-0.0441160016,-0.0471616425,-0.2419756055,0.0851693153,-0.1668527573,-0.0232328642,-0.1466598064,-0.3311946094,0.1002261117,0.0848367512,-0.1900842935,0.3920302093,-0.0001138624,-0.1775078923,0.2576200366,0.140865311,-0.3977589607,0.2370924652,-0.3137456477,-0.4717762768,0.0364426412,-0.3989422917,-0.10394416,-0.0872274786,-0.0072798226,-0.2398997545,-0.1956025213,0.1267162263,0.1788365543,0.1481159925,-0.2144662291,0.0868485123,0.4284647107,-0.1136829779,-0.1058955044,-0.1914025396,0.125366509,-0.1125434861,0.1715472043,0.4061120152,-0.2142761648,0.1782011986,0.0585699305,-0.0194620993,-0.1078312993,-0.2298285812,0.3125296831,0.3142497838,0.5726144314,-0.2321846038,-0.3419172466,-0.323548913,0.0541948378,-0.1749776304,0.2400868684,0.1122343466,-0.1550094932,0.0262181349,-0.0721315593,-0.1331977397,0.0129825221,0.1082114726,-0.048631344,0.4571968317,0.0467727482,-0.0054736496,0.1402049661,-0.221310243,0.0722059906,-0.4857116342,-0.0585106947,0.230164513,-0.5162575245,0.030086711,0.2911925912,0.3916608691,-0.0195694007,0.1962168515,-0.1529492885,0.1309498101,0.3077014983,-0.009258911,-0.1205130219,0.1298979521,0.155303061,0.4172442555,0.282774508,-0.0810803398,-0.0660672635,0.0803235769,-0.0750353858,-0.225893572,-0.0902184695,-0.1788239926,0.085382469,-0.3055813015,0.0705239251,0.0903451145,-0.2249866426,-0.2075741887,0.1587720811,0.2674562335,-0.1989422888,0.1211345494,-0.0228626467,-0.2458030879,-0.2259302288,0.1952972561,-0.2576244771,-0.0953390822,-0.1651114523,0.0572579019,0.019877512,-0.183991909,0.2660895884,-0.05403037,0.1658897698,0.0845018327,0.0274058301,-0.0110198967,0.0234090257,0.4828352928,-0.0930834785,0.1153513193,-0.0869711265,-0.4192585647,-0.3782443106,-0.1182523072,-0.1593545973,-0.4123914838,0.0327872932,0.1672682762,0.0334813818,0.2363192439,-0.0814567655,0.0307098906,0.1712090671,0.3955306113,0.0439533778,-0.0754915029,0.3969984949,-0.0836614519,0.2459640205,0.3607472479,-0.3711392581,0.1132621691,-0.1675348282,0.2238899767,0.1707318127,0.398134023,-0.1165165231,0.0575772673,-0.0783673301,0.1092142761,0.4840186238,-0.2567394972,-0.3556316793,0.2435783595,0.135758698,-0.171135366,0.2956235111,0.0885225907,-0.2591405213,0.0573478788,-0.1520088166,0.0588653162,0.0064534945,0.1101330519,-0.089774482,-0.1814336777,-0.4494473636,0.3117050529,-0.2495408952,-0.24444969,0.1137358695,-0.2190407068,0.3829838932,-0.2123192847,-0.2444945872,0.2426518202,0.2843569517,0.3706578612,0.0931758732,0.1730277389,-0.4357804954,0.3328464925,0.3461660445,0.1651809812,-0.3426589966,-0.2341165692,0.1669635922,0.2084057331,-0.0366159119,0.0010175717,0.0448976234,0.5023500323,0.1103987098,-0.2829916477,0.0368316732,0.1823266894,-0.237961933,0.0409009904,-0.5903840065,0.1523786783,0.066091679,0.1453374028,0.0285566114,-0.2185182422,0.184042424,-0.1441027373,-0.0535626113,0.1866043061,0.0244361479,0.3301219344,0.2091056108,0.5872895718,-0.1624205858,0.0245122835,0.508823514,-0.002259294,0.0817064792,-0.0719905794,-0.0889330357,0.4640775621,0.4207330048,0.5638931394,-0.011915124,-0.1841080636,0.2009587139,0.052886039,-0.0723398253,-0.0945340693,0.3010472059,0.193893373,0.3651150763,0.253334105,-0.25181517,0.1736258566,0.6453280449,-0.1584895998,-0.311929971,0.003440374,0.1898710281,-0.0122193685,0.2791705132,0.1414458603,0.0700717941,0.3760904372,0.0091779251,-0.006705144,-0.1947557032,-0.1270785928,0.4664418995,0.2177502215,0.1594700515,-0.0252337996,0.0531325527,-0.2167075425,-0.301372081,-0.6742097735,-0.2163198888,0.2862898111,-0.2078043818,0.3290455937,-0.4347781539,-0.111124374,0.129653424,0.0133911734,-0.0087593505,-0.2641426325,-0.0278264396,-0.087209411,-0.1944994777,0.3942289948,-0.0393069051,0.062185768,0.0958165228,-0.3419716358,-0.5869118571,0.0433401428,-0.2015624642,-0.0439750031,0.2102998793,-0.1791886091,0.3392644823,-0.2636739314,-0.1485764086,-0.5283122659,-0.25836429,0.1344671249,-0.240752697,0.2412718534,0.210521847,-0.0376081094,0.01849626,-0.1526322365,0.1601608694,0.3820944726,0.025650654,0.022284124,-0.1658669561,-0.3764614463,0.0940106511,-0.4570722282,-0.0807384402,-0.1377251446,0.1575509161,0.1508632898,0.3315750957,0.1174994856,0.0676116794,0.0896922052,-0.1037590206,-0.0773838162,0.016228728,-0.3667279184,0.496004343,0.0495689996,0.1045259684,-0.2792678773,0.112328425,0.1453101784,0.0782467052,-0.573797822,0.2637543678,-0.4898380637,0.5774407387,0.0308240671,0.2673079967,0.1702292264,0.2299755663,-0.1606563032,0.0416975431,-0.1787073016,-0.032975696,-0.0291739553,0.1730594039,0.0437913761,0.3468036652,-0.1811505854,0.3531592786,0.3370424211,-0.0873997509,-0.0238868278,-0.085077323,0.1680864692,-0.1783614457,-0.235203445,0.2234627455,0.2654000521,-0.1874074489,0.400691121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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3423","title":"data duplicate when setting num_works > 1 with streaming data","comments":"Hi ! Thanks for reporting :)\r\n\r\nWhen using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.\r\n\r\nWe can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.","body":"## Describe the bug\r\nThe data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport shutil\r\n\r\nNUM_OF_USER = 1000000\r\nNUM_OF_ACTION = 50000\r\nNUM_OF_SEQUENCE = 10000\r\nNUM_OF_FILES = 32\r\nNUM_OF_WORKERS = 16\r\n\r\nif __name__ == \"__main__\":\r\n    shutil.rmtree(\".\/dataset\")\r\n    for i in range(NUM_OF_FILES):\r\n        sequence_data = pd.DataFrame(\r\n            {\r\n                \"imei\": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),\r\n                \"sequence\": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)\r\n            }\r\n        )\r\n\r\n        if not os.path.exists(\".\/dataset\"):\r\n            os.makedirs(\".\/dataset\")\r\n\r\n        sequence_data.to_csv(f\".\/dataset\/sequence_data_{i}.csv\",\r\n                            \r\n index=False)\r\n\r\n    dataset = load_dataset(\"csv\",\r\n                           data_files=[os.path.join(\".\/dataset\",file) for file in os.listdir(\".\/dataset\") if file.endswith(\".csv\")],\r\n                           split=\"train\",\r\n                           streaming=True).with_format(\"torch\")\r\n    data_loader = DataLoader(dataset,\r\n                             batch_size=1024,\r\n                             num_workers=NUM_OF_WORKERS)\r\n    \r\n    result = pd.DataFrame()\r\n    for i, batch in tqdm(enumerate(data_loader)):\r\n        result = pd.concat([result, \r\n                           pd.DataFrame(batch)],\r\n                           axis=0)\r\n    result.to_csv(f\"num_work_{NUM_OF_WORKERS}.csv\", index=False)\r\n\r\n```\r\n\r\n## Expected results\r\ndata do not duplicate \r\n\r\n## Actual results\r\ndata duplicate NUM_OF_WORKERS = 16 \r\n![image](https:\/\/user-images.githubusercontent.com\/16486492\/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:datasets==1.14.0\r\n- Platform:transformers==4.11.3\r\n- Python version:3.8\r\n- PyArrow version:\r\n","comment_length":55,"text":"data duplicate when setting num_works > 1 with streaming data \n ## Describe the bug\r\nThe data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport shutil\r\n\r\nNUM_OF_USER = 1000000\r\nNUM_OF_ACTION = 50000\r\nNUM_OF_SEQUENCE = 10000\r\nNUM_OF_FILES = 32\r\nNUM_OF_WORKERS = 16\r\n\r\nif __name__ == \"__main__\":\r\n    shutil.rmtree(\".\/dataset\")\r\n    for i in range(NUM_OF_FILES):\r\n        sequence_data = pd.DataFrame(\r\n            {\r\n                \"imei\": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),\r\n                \"sequence\": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)\r\n            }\r\n        )\r\n\r\n        if not os.path.exists(\".\/dataset\"):\r\n            os.makedirs(\".\/dataset\")\r\n\r\n        sequence_data.to_csv(f\".\/dataset\/sequence_data_{i}.csv\",\r\n                            \r\n index=False)\r\n\r\n    dataset = load_dataset(\"csv\",\r\n                           data_files=[os.path.join(\".\/dataset\",file) for file in os.listdir(\".\/dataset\") if file.endswith(\".csv\")],\r\n                           split=\"train\",\r\n                           streaming=True).with_format(\"torch\")\r\n    data_loader = DataLoader(dataset,\r\n                             batch_size=1024,\r\n                             num_workers=NUM_OF_WORKERS)\r\n    \r\n    result = pd.DataFrame()\r\n    for i, batch in tqdm(enumerate(data_loader)):\r\n        result = pd.concat([result, \r\n                           pd.DataFrame(batch)],\r\n                           axis=0)\r\n    result.to_csv(f\"num_work_{NUM_OF_WORKERS}.csv\", index=False)\r\n\r\n```\r\n\r\n## Expected results\r\ndata do not duplicate \r\n\r\n## Actual results\r\ndata duplicate NUM_OF_WORKERS = 16 \r\n![image](https:\/\/user-images.githubusercontent.com\/16486492\/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:datasets==1.14.0\r\n- Platform:transformers==4.11.3\r\n- Python version:3.8\r\n- PyArrow version:\r\n \n Hi ! Thanks for reporting :)\r\n\r\nWhen using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.\r\n\r\nWe can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.","embeddings":[-0.2325759083,-0.3752959073,-0.0161685999,0.3272429705,0.2545717955,-0.1762159318,0.5691003799,0.3358854949,-0.2572703063,0.4211996794,0.0956361145,0.2009150088,-0.0080960095,0.1235202029,0.1447009742,0.0096041495,0.0426974446,0.1493969709,-0.2690352499,0.1793183833,-0.0729406402,0.0054131676,0.008901637,-0.122166492,-0.2812237144,0.0286027398,-0.0868536755,0.2736003995,0.1483580023,-0.2246375233,-0.1355644912,0.0167593323,0.2225168347,0.7733383775,-0.0001093749,0.0273634605,0.116353184,-0.0564244874,-0.264180541,-0.1926758587,0.1805039197,-0.1169928983,0.1388166547,-0.1363617927,-0.0714549795,-0.1025256515,-0.1689470559,-0.2384038419,0.5982191563,0.3020549715,0.2001245469,0.2040702105,-0.236864388,0.0807680264,-0.0227857046,-0.1564834118,-0.0560325012,0.3235714138,0.4014077783,0.2742700279,-0.2470954508,0.4327002466,0.0168397427,0.2134007961,0.046701815,-0.0043556453,0.1431002766,-0.3292376101,0.1463054717,0.2457196862,0.2474499792,-0.2629604638,-0.0524540432,-0.1481214464,0.0667590424,-0.4061611295,0.1443606764,-0.0307433121,-0.2216884941,0.0767681077,-0.0286441687,0.3717752099,-0.0570945069,-0.1216570511,0.0836961567,-0.0682971105,-0.0232122056,0.1365587562,0.111545451,-0.0734321326,0.4617797732,-0.1524797678,0.0924359858,-0.1511488855,-0.5506557226,-0.0842757523,-0.1262444556,-0.1812554896,-0.015259604,0.1713980585,0.486538738,0.0704091638,0.2151361108,0.0869304463,0.1190571859,0.1585400403,-0.2548934221,-0.2556326389,0.1860320717,0.2502898276,-0.3122906983,-0.0726551414,0.163232699,-0.2154925615,0.4953040779,0.1664380878,0.3587914109,0.0031169213,-0.2563691735,0.064285703,-0.0471297204,0.0858364627,-0.0064095547,0.1443857253,-0.0242643859,0.4580359757,-0.175456956,0.1291881949,-0.19054313,-0.4064303935,-0.2643485367,-0.1287214309,-0.0944205672,0.0119195199,0.1807195246,-0.3319128752,0.3005280197,0.427857697,0.1608971804,-0.3793382347,0.4736736715,-0.310723871,0.0393359959,0.0551888533,0.0177812129,0.2736406028,0.3146786988,0.1342144758,0.1491893381,0.2838482559,-0.1892234087,-0.2029174417,-0.0568243153,0.301050812,0.1310585886,0.1251802295,-0.0480440892,0.0954210982,0.0963971689,-0.1041650474,0.1092256904,-0.1158669665,-0.2004852444,-0.2430848777,-0.055028785,0.3452440798,0.0651237294,0.0787207782,0.0774830878,0.0190097988,0.4452487826,0.2374710888,-0.1243976429,0.1111590639,-0.2851854265,0.0325277522,0.5462093353,-0.2593693733,-0.4046396017,0.4371257126,-0.3668300509,0.1128631979,0.4238324463,0.3645490706,0.3320660591,-0.0692271292,0.2379937321,0.1600594521,-0.0987206474,0.1280480027,-0.3108533919,-0.0816035941,0.2053225338,-0.0725031868,-0.1776511222,-0.0163214188,0.1787858456,-0.3957609832,0.1258981973,-0.0926750079,0.01561769,-0.0106573384,0.0279165264,0.010069116,0.0942915976,-0.1388716996,-0.3768971562,0.127870217,-0.0430587344,-0.229995355,0.0710651875,-0.2208888084,-0.241495952,-0.2183575034,-0.3691315055,-0.2393311262,0.232052967,0.2318074703,-0.1677778512,-0.4088276029,-0.0711866394,0.3986447752,-0.2709224522,-0.119124569,-0.1939222068,0.1301829815,-0.1840546131,-0.2086043209,0.0535818227,0.1124008819,0.3394465148,0.0721246749,-0.1523097157,0.3804547489,0.1329358369,0.373552829,-0.0380914658,-0.0956406966,0.1595097184,-0.0778009519,-0.138357535,0.459884733,0.2602323294,-0.0261141695,-0.1658344716,0.2862228751,-0.3266163468,-0.1722668111,-0.0017226854,-0.0652851164,0.290743947,-0.0369180627,0.0375464931,-0.1470056921,0.2070088834,0.1391851455,0.0649995953,0.0727382079,-0.4059760273,0.2096565217,-0.1640548557,-0.0075465762,-0.0047583617,-0.2022501379,-0.168426007,-0.1741588712,0.1045868769,0.1734930426,0.5916928649,0.2543935478,0.0968851075,-0.0018729042,0.0082861381,-0.1786678284,0.1541018933,-0.1791673452,-0.1836656034,0.4155960083,0.2356172651,0.0200191811,-0.4517164826,-0.1200749129,0.1196532249,0.2558178902,-0.3397211134,0.0509375371,-0.2482747734,0.0186310112,-0.6064311862,-0.0510986298,-0.0000103841,-0.1501993686,-0.1013374999,0.5159274936,0.0118257208,0.1438350379,-0.0190672725,0.0992669985,0.2845371366,0.2305806428,-0.0683516413,0.1002187952,-0.1617562175,0.1221622601,0.0412559807,-0.0153145893,0.454814285,0.0532765687,0.1019117907,-0.566701293,-0.2578651607,-0.0815544575,0.1752304584,0.5197481513,0.0439644903,0.1636111885,-0.0249477997,-0.1216459945,-0.1182227805,-0.1421073824,-0.2364521325,0.00281301,-0.085693799,0.2417737991,-0.2683663666,-0.380556345,-0.0926019251,-0.2641489208,-0.0859281644,-0.0391500778,0.2593413591,0.0706426948,0.0145755401,0.1512508243,0.4747598469,0.1178862602,-0.4887931347,-0.4494748414,0.1610949486,-0.119093433,-0.2410864383,-0.1528855562,-0.2208818197,0.0288341213,0.1341529936,-0.4471950233,-0.2873138189,0.0889251381,0.1785939038,-0.2398266494,0.0668604299,0.2155981362,0.0780531913,-0.2084505707,-0.1242405176,0.1337822825,-0.1466189474,0.0573122762,0.1487949193,-0.0183943994,0.4291377664,0.0459717363,0.4843537807,0.4392160177,-0.1078841239,0.2628549337,-0.2216822505,0.1691561192,-0.0023895176,-0.2227887064,0.0420336947,-0.1378424019,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3423","title":"data duplicate when setting num_works > 1 with streaming data","comments":"> Hi ! Thanks for reporting :)\r\n> \r\n> When using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.\r\n> \r\n> We can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.\r\nHi ! Thanks for reply\r\n\r\nDo u have some plans to fix the problem?\r\n","body":"## Describe the bug\r\nThe data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport shutil\r\n\r\nNUM_OF_USER = 1000000\r\nNUM_OF_ACTION = 50000\r\nNUM_OF_SEQUENCE = 10000\r\nNUM_OF_FILES = 32\r\nNUM_OF_WORKERS = 16\r\n\r\nif __name__ == \"__main__\":\r\n    shutil.rmtree(\".\/dataset\")\r\n    for i in range(NUM_OF_FILES):\r\n        sequence_data = pd.DataFrame(\r\n            {\r\n                \"imei\": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),\r\n                \"sequence\": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)\r\n            }\r\n        )\r\n\r\n        if not os.path.exists(\".\/dataset\"):\r\n            os.makedirs(\".\/dataset\")\r\n\r\n        sequence_data.to_csv(f\".\/dataset\/sequence_data_{i}.csv\",\r\n                            \r\n index=False)\r\n\r\n    dataset = load_dataset(\"csv\",\r\n                           data_files=[os.path.join(\".\/dataset\",file) for file in os.listdir(\".\/dataset\") if file.endswith(\".csv\")],\r\n                           split=\"train\",\r\n                           streaming=True).with_format(\"torch\")\r\n    data_loader = DataLoader(dataset,\r\n                             batch_size=1024,\r\n                             num_workers=NUM_OF_WORKERS)\r\n    \r\n    result = pd.DataFrame()\r\n    for i, batch in tqdm(enumerate(data_loader)):\r\n        result = pd.concat([result, \r\n                           pd.DataFrame(batch)],\r\n                           axis=0)\r\n    result.to_csv(f\"num_work_{NUM_OF_WORKERS}.csv\", index=False)\r\n\r\n```\r\n\r\n## Expected results\r\ndata do not duplicate \r\n\r\n## Actual results\r\ndata duplicate NUM_OF_WORKERS = 16 \r\n![image](https:\/\/user-images.githubusercontent.com\/16486492\/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:datasets==1.14.0\r\n- Platform:transformers==4.11.3\r\n- Python version:3.8\r\n- PyArrow version:\r\n","comment_length":74,"text":"data duplicate when setting num_works > 1 with streaming data \n ## Describe the bug\r\nThe data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport shutil\r\n\r\nNUM_OF_USER = 1000000\r\nNUM_OF_ACTION = 50000\r\nNUM_OF_SEQUENCE = 10000\r\nNUM_OF_FILES = 32\r\nNUM_OF_WORKERS = 16\r\n\r\nif __name__ == \"__main__\":\r\n    shutil.rmtree(\".\/dataset\")\r\n    for i in range(NUM_OF_FILES):\r\n        sequence_data = pd.DataFrame(\r\n            {\r\n                \"imei\": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),\r\n                \"sequence\": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)\r\n            }\r\n        )\r\n\r\n        if not os.path.exists(\".\/dataset\"):\r\n            os.makedirs(\".\/dataset\")\r\n\r\n        sequence_data.to_csv(f\".\/dataset\/sequence_data_{i}.csv\",\r\n                            \r\n index=False)\r\n\r\n    dataset = load_dataset(\"csv\",\r\n                           data_files=[os.path.join(\".\/dataset\",file) for file in os.listdir(\".\/dataset\") if file.endswith(\".csv\")],\r\n                           split=\"train\",\r\n                           streaming=True).with_format(\"torch\")\r\n    data_loader = DataLoader(dataset,\r\n                             batch_size=1024,\r\n                             num_workers=NUM_OF_WORKERS)\r\n    \r\n    result = pd.DataFrame()\r\n    for i, batch in tqdm(enumerate(data_loader)):\r\n        result = pd.concat([result, \r\n                           pd.DataFrame(batch)],\r\n                           axis=0)\r\n    result.to_csv(f\"num_work_{NUM_OF_WORKERS}.csv\", index=False)\r\n\r\n```\r\n\r\n## Expected results\r\ndata do not duplicate \r\n\r\n## Actual results\r\ndata duplicate NUM_OF_WORKERS = 16 \r\n![image](https:\/\/user-images.githubusercontent.com\/16486492\/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:datasets==1.14.0\r\n- Platform:transformers==4.11.3\r\n- Python version:3.8\r\n- PyArrow version:\r\n \n > Hi ! Thanks for reporting :)\r\n> \r\n> When using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.\r\n> \r\n> We can probably fix this in `datasets` by checking `torch.utils.data.get_worker_info()` which gives the worker id if it happens.\r\nHi ! Thanks for reply\r\n\r\nDo u have some plans to fix the problem?\r\n","embeddings":[-0.2325759083,-0.3752959073,-0.0161685999,0.3272429705,0.2545717955,-0.1762159318,0.5691003799,0.3358854949,-0.2572703063,0.4211996794,0.0956361145,0.2009150088,-0.0080960095,0.1235202029,0.1447009742,0.0096041495,0.0426974446,0.1493969709,-0.2690352499,0.1793183833,-0.0729406402,0.0054131676,0.008901637,-0.122166492,-0.2812237144,0.0286027398,-0.0868536755,0.2736003995,0.1483580023,-0.2246375233,-0.1355644912,0.0167593323,0.2225168347,0.7733383775,-0.0001093749,0.0273634605,0.116353184,-0.0564244874,-0.264180541,-0.1926758587,0.1805039197,-0.1169928983,0.1388166547,-0.1363617927,-0.0714549795,-0.1025256515,-0.1689470559,-0.2384038419,0.5982191563,0.3020549715,0.2001245469,0.2040702105,-0.236864388,0.0807680264,-0.0227857046,-0.1564834118,-0.0560325012,0.3235714138,0.4014077783,0.2742700279,-0.2470954508,0.4327002466,0.0168397427,0.2134007961,0.046701815,-0.0043556453,0.1431002766,-0.3292376101,0.1463054717,0.2457196862,0.2474499792,-0.2629604638,-0.0524540432,-0.1481214464,0.0667590424,-0.4061611295,0.1443606764,-0.0307433121,-0.2216884941,0.0767681077,-0.0286441687,0.3717752099,-0.0570945069,-0.1216570511,0.0836961567,-0.0682971105,-0.0232122056,0.1365587562,0.111545451,-0.0734321326,0.4617797732,-0.1524797678,0.0924359858,-0.1511488855,-0.5506557226,-0.0842757523,-0.1262444556,-0.1812554896,-0.015259604,0.1713980585,0.486538738,0.0704091638,0.2151361108,0.0869304463,0.1190571859,0.1585400403,-0.2548934221,-0.2556326389,0.1860320717,0.2502898276,-0.3122906983,-0.0726551414,0.163232699,-0.2154925615,0.4953040779,0.1664380878,0.3587914109,0.0031169213,-0.2563691735,0.064285703,-0.0471297204,0.0858364627,-0.0064095547,0.1443857253,-0.0242643859,0.4580359757,-0.175456956,0.1291881949,-0.19054313,-0.4064303935,-0.2643485367,-0.1287214309,-0.0944205672,0.0119195199,0.1807195246,-0.3319128752,0.3005280197,0.427857697,0.1608971804,-0.3793382347,0.4736736715,-0.310723871,0.0393359959,0.0551888533,0.0177812129,0.2736406028,0.3146786988,0.1342144758,0.1491893381,0.2838482559,-0.1892234087,-0.2029174417,-0.0568243153,0.301050812,0.1310585886,0.1251802295,-0.0480440892,0.0954210982,0.0963971689,-0.1041650474,0.1092256904,-0.1158669665,-0.2004852444,-0.2430848777,-0.055028785,0.3452440798,0.0651237294,0.0787207782,0.0774830878,0.0190097988,0.4452487826,0.2374710888,-0.1243976429,0.1111590639,-0.2851854265,0.0325277522,0.5462093353,-0.2593693733,-0.4046396017,0.4371257126,-0.3668300509,0.1128631979,0.4238324463,0.3645490706,0.3320660591,-0.0692271292,0.2379937321,0.1600594521,-0.0987206474,0.1280480027,-0.3108533919,-0.0816035941,0.2053225338,-0.0725031868,-0.1776511222,-0.0163214188,0.1787858456,-0.3957609832,0.1258981973,-0.0926750079,0.01561769,-0.0106573384,0.0279165264,0.010069116,0.0942915976,-0.1388716996,-0.3768971562,0.127870217,-0.0430587344,-0.229995355,0.0710651875,-0.2208888084,-0.241495952,-0.2183575034,-0.3691315055,-0.2393311262,0.232052967,0.2318074703,-0.1677778512,-0.4088276029,-0.0711866394,0.3986447752,-0.2709224522,-0.119124569,-0.1939222068,0.1301829815,-0.1840546131,-0.2086043209,0.0535818227,0.1124008819,0.3394465148,0.0721246749,-0.1523097157,0.3804547489,0.1329358369,0.373552829,-0.0380914658,-0.0956406966,0.1595097184,-0.0778009519,-0.138357535,0.459884733,0.2602323294,-0.0261141695,-0.1658344716,0.2862228751,-0.3266163468,-0.1722668111,-0.0017226854,-0.0652851164,0.290743947,-0.0369180627,0.0375464931,-0.1470056921,0.2070088834,0.1391851455,0.0649995953,0.0727382079,-0.4059760273,0.2096565217,-0.1640548557,-0.0075465762,-0.0047583617,-0.2022501379,-0.168426007,-0.1741588712,0.1045868769,0.1734930426,0.5916928649,0.2543935478,0.0968851075,-0.0018729042,0.0082861381,-0.1786678284,0.1541018933,-0.1791673452,-0.1836656034,0.4155960083,0.2356172651,0.0200191811,-0.4517164826,-0.1200749129,0.1196532249,0.2558178902,-0.3397211134,0.0509375371,-0.2482747734,0.0186310112,-0.6064311862,-0.0510986298,-0.0000103841,-0.1501993686,-0.1013374999,0.5159274936,0.0118257208,0.1438350379,-0.0190672725,0.0992669985,0.2845371366,0.2305806428,-0.0683516413,0.1002187952,-0.1617562175,0.1221622601,0.0412559807,-0.0153145893,0.454814285,0.0532765687,0.1019117907,-0.566701293,-0.2578651607,-0.0815544575,0.1752304584,0.5197481513,0.0439644903,0.1636111885,-0.0249477997,-0.1216459945,-0.1182227805,-0.1421073824,-0.2364521325,0.00281301,-0.085693799,0.2417737991,-0.2683663666,-0.380556345,-0.0926019251,-0.2641489208,-0.0859281644,-0.0391500778,0.2593413591,0.0706426948,0.0145755401,0.1512508243,0.4747598469,0.1178862602,-0.4887931347,-0.4494748414,0.1610949486,-0.119093433,-0.2410864383,-0.1528855562,-0.2208818197,0.0288341213,0.1341529936,-0.4471950233,-0.2873138189,0.0889251381,0.1785939038,-0.2398266494,0.0668604299,0.2155981362,0.0780531913,-0.2084505707,-0.1242405176,0.1337822825,-0.1466189474,0.0573122762,0.1487949193,-0.0183943994,0.4291377664,0.0459717363,0.4843537807,0.4392160177,-0.1078841239,0.2628549337,-0.2216822505,0.1691561192,-0.0023895176,-0.2227887064,0.0420336947,-0.1378424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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3423","title":"data duplicate when setting num_works > 1 with streaming data","comments":"Isn\u2019t that somehow a bug on PyTorch side? (Just asking because this behavior seems quite general and maybe not what would be intended)","body":"## Describe the bug\r\nThe data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport shutil\r\n\r\nNUM_OF_USER = 1000000\r\nNUM_OF_ACTION = 50000\r\nNUM_OF_SEQUENCE = 10000\r\nNUM_OF_FILES = 32\r\nNUM_OF_WORKERS = 16\r\n\r\nif __name__ == \"__main__\":\r\n    shutil.rmtree(\".\/dataset\")\r\n    for i in range(NUM_OF_FILES):\r\n        sequence_data = pd.DataFrame(\r\n            {\r\n                \"imei\": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),\r\n                \"sequence\": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)\r\n            }\r\n        )\r\n\r\n        if not os.path.exists(\".\/dataset\"):\r\n            os.makedirs(\".\/dataset\")\r\n\r\n        sequence_data.to_csv(f\".\/dataset\/sequence_data_{i}.csv\",\r\n                            \r\n index=False)\r\n\r\n    dataset = load_dataset(\"csv\",\r\n                           data_files=[os.path.join(\".\/dataset\",file) for file in os.listdir(\".\/dataset\") if file.endswith(\".csv\")],\r\n                           split=\"train\",\r\n                           streaming=True).with_format(\"torch\")\r\n    data_loader = DataLoader(dataset,\r\n                             batch_size=1024,\r\n                             num_workers=NUM_OF_WORKERS)\r\n    \r\n    result = pd.DataFrame()\r\n    for i, batch in tqdm(enumerate(data_loader)):\r\n        result = pd.concat([result, \r\n                           pd.DataFrame(batch)],\r\n                           axis=0)\r\n    result.to_csv(f\"num_work_{NUM_OF_WORKERS}.csv\", index=False)\r\n\r\n```\r\n\r\n## Expected results\r\ndata do not duplicate \r\n\r\n## Actual results\r\ndata duplicate NUM_OF_WORKERS = 16 \r\n![image](https:\/\/user-images.githubusercontent.com\/16486492\/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:datasets==1.14.0\r\n- Platform:transformers==4.11.3\r\n- Python version:3.8\r\n- PyArrow version:\r\n","comment_length":23,"text":"data duplicate when setting num_works > 1 with streaming data \n ## Describe the bug\r\nThe data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport shutil\r\n\r\nNUM_OF_USER = 1000000\r\nNUM_OF_ACTION = 50000\r\nNUM_OF_SEQUENCE = 10000\r\nNUM_OF_FILES = 32\r\nNUM_OF_WORKERS = 16\r\n\r\nif __name__ == \"__main__\":\r\n    shutil.rmtree(\".\/dataset\")\r\n    for i in range(NUM_OF_FILES):\r\n        sequence_data = pd.DataFrame(\r\n            {\r\n                \"imei\": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),\r\n                \"sequence\": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)\r\n            }\r\n        )\r\n\r\n        if not os.path.exists(\".\/dataset\"):\r\n            os.makedirs(\".\/dataset\")\r\n\r\n        sequence_data.to_csv(f\".\/dataset\/sequence_data_{i}.csv\",\r\n                            \r\n index=False)\r\n\r\n    dataset = load_dataset(\"csv\",\r\n                           data_files=[os.path.join(\".\/dataset\",file) for file in os.listdir(\".\/dataset\") if file.endswith(\".csv\")],\r\n                           split=\"train\",\r\n                           streaming=True).with_format(\"torch\")\r\n    data_loader = DataLoader(dataset,\r\n                             batch_size=1024,\r\n                             num_workers=NUM_OF_WORKERS)\r\n    \r\n    result = pd.DataFrame()\r\n    for i, batch in tqdm(enumerate(data_loader)):\r\n        result = pd.concat([result, \r\n                           pd.DataFrame(batch)],\r\n                           axis=0)\r\n    result.to_csv(f\"num_work_{NUM_OF_WORKERS}.csv\", index=False)\r\n\r\n```\r\n\r\n## Expected results\r\ndata do not duplicate \r\n\r\n## Actual results\r\ndata duplicate NUM_OF_WORKERS = 16 \r\n![image](https:\/\/user-images.githubusercontent.com\/16486492\/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:datasets==1.14.0\r\n- Platform:transformers==4.11.3\r\n- Python version:3.8\r\n- PyArrow version:\r\n \n Isn\u2019t that somehow a bug on PyTorch side? (Just asking because this behavior seems quite general and maybe not what would be intended)","embeddings":[-0.2325759083,-0.3752959073,-0.0161685999,0.3272429705,0.2545717955,-0.1762159318,0.5691003799,0.3358854949,-0.2572703063,0.4211996794,0.0956361145,0.2009150088,-0.0080960095,0.1235202029,0.1447009742,0.0096041495,0.0426974446,0.1493969709,-0.2690352499,0.1793183833,-0.0729406402,0.0054131676,0.008901637,-0.122166492,-0.2812237144,0.0286027398,-0.0868536755,0.2736003995,0.1483580023,-0.2246375233,-0.1355644912,0.0167593323,0.2225168347,0.7733383775,-0.0001093749,0.0273634605,0.116353184,-0.0564244874,-0.264180541,-0.1926758587,0.1805039197,-0.1169928983,0.1388166547,-0.1363617927,-0.0714549795,-0.1025256515,-0.1689470559,-0.2384038419,0.5982191563,0.3020549715,0.2001245469,0.2040702105,-0.236864388,0.0807680264,-0.0227857046,-0.1564834118,-0.0560325012,0.3235714138,0.4014077783,0.2742700279,-0.2470954508,0.4327002466,0.0168397427,0.2134007961,0.046701815,-0.0043556453,0.1431002766,-0.3292376101,0.1463054717,0.2457196862,0.2474499792,-0.2629604638,-0.0524540432,-0.1481214464,0.0667590424,-0.4061611295,0.1443606764,-0.0307433121,-0.2216884941,0.0767681077,-0.0286441687,0.3717752099,-0.0570945069,-0.1216570511,0.0836961567,-0.0682971105,-0.0232122056,0.1365587562,0.111545451,-0.0734321326,0.4617797732,-0.1524797678,0.0924359858,-0.1511488855,-0.5506557226,-0.0842757523,-0.1262444556,-0.1812554896,-0.015259604,0.1713980585,0.486538738,0.0704091638,0.2151361108,0.0869304463,0.1190571859,0.1585400403,-0.2548934221,-0.2556326389,0.1860320717,0.2502898276,-0.3122906983,-0.0726551414,0.163232699,-0.2154925615,0.4953040779,0.1664380878,0.3587914109,0.0031169213,-0.2563691735,0.064285703,-0.0471297204,0.0858364627,-0.0064095547,0.1443857253,-0.0242643859,0.4580359757,-0.175456956,0.1291881949,-0.19054313,-0.4064303935,-0.2643485367,-0.1287214309,-0.0944205672,0.0119195199,0.1807195246,-0.3319128752,0.3005280197,0.427857697,0.1608971804,-0.3793382347,0.4736736715,-0.310723871,0.0393359959,0.0551888533,0.0177812129,0.2736406028,0.3146786988,0.1342144758,0.1491893381,0.2838482559,-0.1892234087,-0.2029174417,-0.0568243153,0.301050812,0.1310585886,0.1251802295,-0.0480440892,0.0954210982,0.0963971689,-0.1041650474,0.1092256904,-0.1158669665,-0.2004852444,-0.2430848777,-0.055028785,0.3452440798,0.0651237294,0.0787207782,0.0774830878,0.0190097988,0.4452487826,0.2374710888,-0.1243976429,0.1111590639,-0.2851854265,0.0325277522,0.5462093353,-0.2593693733,-0.4046396017,0.4371257126,-0.3668300509,0.1128631979,0.4238324463,0.3645490706,0.3320660591,-0.0692271292,0.2379937321,0.1600594521,-0.0987206474,0.1280480027,-0.3108533919,-0.0816035941,0.2053225338,-0.0725031868,-0.1776511222,-0.0163214188,0.1787858456,-0.3957609832,0.1258981973,-0.0926750079,0.01561769,-0.0106573384,0.0279165264,0.010069116,0.0942915976,-0.1388716996,-0.3768971562,0.127870217,-0.0430587344,-0.229995355,0.0710651875,-0.2208888084,-0.241495952,-0.2183575034,-0.3691315055,-0.2393311262,0.232052967,0.2318074703,-0.1677778512,-0.4088276029,-0.0711866394,0.3986447752,-0.2709224522,-0.119124569,-0.1939222068,0.1301829815,-0.1840546131,-0.2086043209,0.0535818227,0.1124008819,0.3394465148,0.0721246749,-0.1523097157,0.3804547489,0.1329358369,0.373552829,-0.0380914658,-0.0956406966,0.1595097184,-0.0778009519,-0.138357535,0.459884733,0.2602323294,-0.0261141695,-0.1658344716,0.2862228751,-0.3266163468,-0.1722668111,-0.0017226854,-0.0652851164,0.290743947,-0.0369180627,0.0375464931,-0.1470056921,0.2070088834,0.1391851455,0.0649995953,0.0727382079,-0.4059760273,0.2096565217,-0.1640548557,-0.0075465762,-0.0047583617,-0.2022501379,-0.168426007,-0.1741588712,0.1045868769,0.1734930426,0.5916928649,0.2543935478,0.0968851075,-0.0018729042,0.0082861381,-0.1786678284,0.1541018933,-0.1791673452,-0.1836656034,0.4155960083,0.2356172651,0.0200191811,-0.4517164826,-0.1200749129,0.1196532249,0.2558178902,-0.3397211134,0.0509375371,-0.2482747734,0.0186310112,-0.6064311862,-0.0510986298,-0.0000103841,-0.1501993686,-0.1013374999,0.5159274936,0.0118257208,0.1438350379,-0.0190672725,0.0992669985,0.2845371366,0.2305806428,-0.0683516413,0.1002187952,-0.1617562175,0.1221622601,0.0412559807,-0.0153145893,0.454814285,0.0532765687,0.1019117907,-0.566701293,-0.2578651607,-0.0815544575,0.1752304584,0.5197481513,0.0439644903,0.1636111885,-0.0249477997,-0.1216459945,-0.1182227805,-0.1421073824,-0.2364521325,0.00281301,-0.085693799,0.2417737991,-0.2683663666,-0.380556345,-0.0926019251,-0.2641489208,-0.0859281644,-0.0391500778,0.2593413591,0.0706426948,0.0145755401,0.1512508243,0.4747598469,0.1178862602,-0.4887931347,-0.4494748414,0.1610949486,-0.119093433,-0.2410864383,-0.1528855562,-0.2208818197,0.0288341213,0.1341529936,-0.4471950233,-0.2873138189,0.0889251381,0.1785939038,-0.2398266494,0.0668604299,0.2155981362,0.0780531913,-0.2084505707,-0.1242405176,0.1337822825,-0.1466189474,0.0573122762,0.1487949193,-0.0183943994,0.4291377664,0.0459717363,0.4843537807,0.4392160177,-0.1078841239,0.2628549337,-0.2216822505,0.1691561192,-0.0023895176,-0.2227887064,0.0420336947,-0.1378424019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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3423","title":"data duplicate when setting num_works > 1 with streaming data","comments":"From PyTorch's documentation [here](https:\/\/pytorch.org\/docs\/stable\/data.html#dataset-types):\r\n\r\n> When using an IterableDataset with multi-process data loading. The same dataset object is replicated on each worker process, and thus the replicas must be configured differently to avoid duplicated data. See [IterableDataset](https:\/\/pytorch.org\/docs\/stable\/data.html#torch.utils.data.IterableDataset) documentations for how to achieve this.\r\n\r\nIt looks like an intended behavior from PyTorch\r\n\r\nAs suggested in the [docstring of the IterableDataset class](https:\/\/pytorch.org\/docs\/stable\/data.html#torch.utils.data.IterableDataset), we could pass a `worker_init_fn` to the DataLoader to fix this. It could be called `streaming_worker_init_fn` for example.\r\n\r\nHowever, while this solution works, I'm worried that many users simply don't know about this parameter and just start their training with duplicate data without knowing it. That's why I'm more in favor of integrating the check on the worker id directly in `datasets` in our implementation of `IterableDataset.__iter__`.","body":"## Describe the bug\r\nThe data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport shutil\r\n\r\nNUM_OF_USER = 1000000\r\nNUM_OF_ACTION = 50000\r\nNUM_OF_SEQUENCE = 10000\r\nNUM_OF_FILES = 32\r\nNUM_OF_WORKERS = 16\r\n\r\nif __name__ == \"__main__\":\r\n    shutil.rmtree(\".\/dataset\")\r\n    for i in range(NUM_OF_FILES):\r\n        sequence_data = pd.DataFrame(\r\n            {\r\n                \"imei\": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),\r\n                \"sequence\": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)\r\n            }\r\n        )\r\n\r\n        if not os.path.exists(\".\/dataset\"):\r\n            os.makedirs(\".\/dataset\")\r\n\r\n        sequence_data.to_csv(f\".\/dataset\/sequence_data_{i}.csv\",\r\n                            \r\n index=False)\r\n\r\n    dataset = load_dataset(\"csv\",\r\n                           data_files=[os.path.join(\".\/dataset\",file) for file in os.listdir(\".\/dataset\") if file.endswith(\".csv\")],\r\n                           split=\"train\",\r\n                           streaming=True).with_format(\"torch\")\r\n    data_loader = DataLoader(dataset,\r\n                             batch_size=1024,\r\n                             num_workers=NUM_OF_WORKERS)\r\n    \r\n    result = pd.DataFrame()\r\n    for i, batch in tqdm(enumerate(data_loader)):\r\n        result = pd.concat([result, \r\n                           pd.DataFrame(batch)],\r\n                           axis=0)\r\n    result.to_csv(f\"num_work_{NUM_OF_WORKERS}.csv\", index=False)\r\n\r\n```\r\n\r\n## Expected results\r\ndata do not duplicate \r\n\r\n## Actual results\r\ndata duplicate NUM_OF_WORKERS = 16 \r\n![image](https:\/\/user-images.githubusercontent.com\/16486492\/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:datasets==1.14.0\r\n- Platform:transformers==4.11.3\r\n- Python version:3.8\r\n- PyArrow version:\r\n","comment_length":127,"text":"data duplicate when setting num_works > 1 with streaming data \n ## Describe the bug\r\nThe data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\nimport shutil\r\n\r\nNUM_OF_USER = 1000000\r\nNUM_OF_ACTION = 50000\r\nNUM_OF_SEQUENCE = 10000\r\nNUM_OF_FILES = 32\r\nNUM_OF_WORKERS = 16\r\n\r\nif __name__ == \"__main__\":\r\n    shutil.rmtree(\".\/dataset\")\r\n    for i in range(NUM_OF_FILES):\r\n        sequence_data = pd.DataFrame(\r\n            {\r\n                \"imei\": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),\r\n                \"sequence\": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)\r\n            }\r\n        )\r\n\r\n        if not os.path.exists(\".\/dataset\"):\r\n            os.makedirs(\".\/dataset\")\r\n\r\n        sequence_data.to_csv(f\".\/dataset\/sequence_data_{i}.csv\",\r\n                            \r\n index=False)\r\n\r\n    dataset = load_dataset(\"csv\",\r\n                           data_files=[os.path.join(\".\/dataset\",file) for file in os.listdir(\".\/dataset\") if file.endswith(\".csv\")],\r\n                           split=\"train\",\r\n                           streaming=True).with_format(\"torch\")\r\n    data_loader = DataLoader(dataset,\r\n                             batch_size=1024,\r\n                             num_workers=NUM_OF_WORKERS)\r\n    \r\n    result = pd.DataFrame()\r\n    for i, batch in tqdm(enumerate(data_loader)):\r\n        result = pd.concat([result, \r\n                           pd.DataFrame(batch)],\r\n                           axis=0)\r\n    result.to_csv(f\"num_work_{NUM_OF_WORKERS}.csv\", index=False)\r\n\r\n```\r\n\r\n## Expected results\r\ndata do not duplicate \r\n\r\n## Actual results\r\ndata duplicate NUM_OF_WORKERS = 16 \r\n![image](https:\/\/user-images.githubusercontent.com\/16486492\/145748707-9d2df25b-2f4f-4d7b-a83e-242be4fc8934.png)\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:datasets==1.14.0\r\n- Platform:transformers==4.11.3\r\n- Python version:3.8\r\n- PyArrow version:\r\n \n From PyTorch's documentation [here](https:\/\/pytorch.org\/docs\/stable\/data.html#dataset-types):\r\n\r\n> When using an IterableDataset with multi-process data loading. The same dataset object is replicated on each worker process, and thus the replicas must be configured differently to avoid duplicated data. See [IterableDataset](https:\/\/pytorch.org\/docs\/stable\/data.html#torch.utils.data.IterableDataset) documentations for how to achieve this.\r\n\r\nIt looks like an intended behavior from PyTorch\r\n\r\nAs suggested in the [docstring of the IterableDataset class](https:\/\/pytorch.org\/docs\/stable\/data.html#torch.utils.data.IterableDataset), we could pass a `worker_init_fn` to the DataLoader to fix this. It could be called `streaming_worker_init_fn` for example.\r\n\r\nHowever, while this solution works, I'm worried that many users simply don't know about this parameter and just start their training with duplicate data without knowing it. That's why I'm more in favor of integrating the check on the worker id directly in `datasets` in our implementation of `IterableDataset.__iter__`.","embeddings":[-0.2325759083,-0.3752959073,-0.0161685999,0.3272429705,0.2545717955,-0.1762159318,0.5691003799,0.3358854949,-0.2572703063,0.4211996794,0.0956361145,0.2009150088,-0.0080960095,0.1235202029,0.1447009742,0.0096041495,0.0426974446,0.1493969709,-0.2690352499,0.1793183833,-0.0729406402,0.0054131676,0.008901637,-0.122166492,-0.2812237144,0.0286027398,-0.0868536755,0.2736003995,0.1483580023,-0.2246375233,-0.1355644912,0.0167593323,0.2225168347,0.7733383775,-0.0001093749,0.0273634605,0.116353184,-0.0564244874,-0.264180541,-0.1926758587,0.1805039197,-0.1169928983,0.1388166547,-0.1363617927,-0.0714549795,-0.1025256515,-0.1689470559,-0.2384038419,0.5982191563,0.3020549715,0.2001245469,0.2040702105,-0.236864388,0.0807680264,-0.0227857046,-0.1564834118,-0.0560325012,0.3235714138,0.4014077783,0.2742700279,-0.2470954508,0.4327002466,0.0168397427,0.2134007961,0.046701815,-0.0043556453,0.1431002766,-0.3292376101,0.1463054717,0.2457196862,0.2474499792,-0.2629604638,-0.0524540432,-0.1481214464,0.0667590424,-0.4061611295,0.1443606764,-0.0307433121,-0.2216884941,0.0767681077,-0.0286441687,0.3717752099,-0.0570945069,-0.1216570511,0.0836961567,-0.0682971105,-0.0232122056,0.1365587562,0.111545451,-0.0734321326,0.4617797732,-0.1524797678,0.0924359858,-0.1511488855,-0.5506557226,-0.0842757523,-0.1262444556,-0.1812554896,-0.015259604,0.1713980585,0.486538738,0.0704091638,0.2151361108,0.0869304463,0.1190571859,0.1585400403,-0.2548934221,-0.2556326389,0.1860320717,0.2502898276,-0.3122906983,-0.0726551414,0.163232699,-0.2154925615,0.4953040779,0.1664380878,0.3587914109,0.0031169213,-0.2563691735,0.064285703,-0.0471297204,0.0858364627,-0.0064095547,0.1443857253,-0.0242643859,0.4580359757,-0.175456956,0.1291881949,-0.19054313,-0.4064303935,-0.2643485367,-0.1287214309,-0.0944205672,0.0119195199,0.1807195246,-0.3319128752,0.3005280197,0.427857697,0.1608971804,-0.3793382347,0.4736736715,-0.310723871,0.0393359959,0.0551888533,0.0177812129,0.2736406028,0.3146786988,0.1342144758,0.1491893381,0.2838482559,-0.1892234087,-0.2029174417,-0.0568243153,0.301050812,0.1310585886,0.1251802295,-0.0480440892,0.0954210982,0.0963971689,-0.1041650474,0.1092256904,-0.1158669665,-0.2004852444,-0.2430848777,-0.055028785,0.3452440798,0.0651237294,0.0787207782,0.0774830878,0.0190097988,0.4452487826,0.2374710888,-0.1243976429,0.1111590639,-0.2851854265,0.0325277522,0.5462093353,-0.2593693733,-0.4046396017,0.4371257126,-0.3668300509,0.1128631979,0.4238324463,0.3645490706,0.3320660591,-0.0692271292,0.2379937321,0.1600594521,-0.0987206474,0.1280480027,-0.3108533919,-0.0816035941,0.2053225338,-0.0725031868,-0.1776511222,-0.0163214188,0.1787858456,-0.3957609832,0.1258981973,-0.0926750079,0.01561769,-0.0106573384,0.0279165264,0.010069116,0.0942915976,-0.1388716996,-0.3768971562,0.127870217,-0.0430587344,-0.229995355,0.0710651875,-0.2208888084,-0.241495952,-0.2183575034,-0.3691315055,-0.2393311262,0.232052967,0.2318074703,-0.1677778512,-0.4088276029,-0.0711866394,0.3986447752,-0.2709224522,-0.119124569,-0.1939222068,0.1301829815,-0.1840546131,-0.2086043209,0.0535818227,0.1124008819,0.3394465148,0.0721246749,-0.1523097157,0.3804547489,0.1329358369,0.373552829,-0.0380914658,-0.0956406966,0.1595097184,-0.0778009519,-0.138357535,0.459884733,0.2602323294,-0.0261141695,-0.1658344716,0.2862228751,-0.3266163468,-0.1722668111,-0.0017226854,-0.0652851164,0.290743947,-0.0369180627,0.0375464931,-0.1470056921,0.2070088834,0.1391851455,0.0649995953,0.0727382079,-0.4059760273,0.2096565217,-0.1640548557,-0.0075465762,-0.0047583617,-0.2022501379,-0.168426007,-0.1741588712,0.1045868769,0.1734930426,0.5916928649,0.2543935478,0.0968851075,-0.0018729042,0.0082861381,-0.1786678284,0.1541018933,-0.1791673452,-0.1836656034,0.4155960083,0.2356172651,0.0200191811,-0.4517164826,-0.1200749129,0.1196532249,0.2558178902,-0.3397211134,0.0509375371,-0.2482747734,0.0186310112,-0.6064311862,-0.0510986298,-0.0000103841,-0.1501993686,-0.1013374999,0.5159274936,0.0118257208,0.1438350379,-0.0190672725,0.0992669985,0.2845371366,0.2305806428,-0.0683516413,0.1002187952,-0.1617562175,0.1221622601,0.0412559807,-0.0153145893,0.454814285,0.0532765687,0.1019117907,-0.566701293,-0.2578651607,-0.0815544575,0.1752304584,0.5197481513,0.0439644903,0.1636111885,-0.0249477997,-0.1216459945,-0.1182227805,-0.1421073824,-0.2364521325,0.00281301,-0.085693799,0.2417737991,-0.2683663666,-0.380556345,-0.0926019251,-0.2641489208,-0.0859281644,-0.0391500778,0.2593413591,0.0706426948,0.0145755401,0.1512508243,0.4747598469,0.1178862602,-0.4887931347,-0.4494748414,0.1610949486,-0.119093433,-0.2410864383,-0.1528855562,-0.2208818197,0.0288341213,0.1341529936,-0.4471950233,-0.2873138189,0.0889251381,0.1785939038,-0.2398266494,0.0668604299,0.2155981362,0.0780531913,-0.2084505707,-0.1242405176,0.1337822825,-0.1466189474,0.0573122762,0.1487949193,-0.0183943994,0.4291377664,0.0459717363,0.4843537807,0.4392160177,-0.1078841239,0.2628549337,-0.2216822505,0.1691561192,-0.0023895176,-0.2227887064,0.042033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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3422","title":"Error about load_metric","comments":"Hi ! I wasn't able to reproduce your error.\r\n\r\nCan you try to clear your cache at `~\/.cache\/huggingface\/modules` and try again ?","body":"## Describe the bug\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1371, in load_metric\r\n    metric = metric_cls(\r\nTypeError: 'NoneType' object is not callable\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nmetric = load_metric(\"glue\", \"sst2\")\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 6.0.1\r\n","comment_length":22,"text":"Error about load_metric \n ## Describe the bug\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1371, in load_metric\r\n    metric = metric_cls(\r\nTypeError: 'NoneType' object is not callable\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nmetric = load_metric(\"glue\", \"sst2\")\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 6.0.1\r\n \n Hi ! I wasn't able to reproduce your error.\r\n\r\nCan you try to clear your cache at `~\/.cache\/huggingface\/modules` and try again ?","embeddings":[-0.1543191969,-0.3634535968,0.0099820923,0.3949096203,0.478028059,-0.0076499404,0.182597369,0.1899203509,0.1398091167,0.1264667958,-0.2365790904,0.0703852475,0.0390484408,0.2697517872,0.2777210772,-0.1408572942,-0.2320579141,0.1285475492,-0.394849509,0.1379795223,-0.3345135748,0.3227806091,-0.2020760775,0.01516743,-0.3880251348,-0.0697292909,0.0320156366,0.3490539491,-0.054203447,-0.3160963058,0.4209107757,-0.122329995,0.2104737312,0.6237584949,-0.0001083054,0.001929114,0.4295632541,-0.0698551983,-0.1801166832,-0.2114720792,-0.08952301,-0.3079338372,0.2405197024,-0.3028723598,-0.1621544808,-0.0146059524,-0.2227859646,-0.1776644289,0.3323100805,0.3742239773,0.2997358441,0.6357430816,0.1827405095,-0.2761428356,-0.0700488463,-0.0462734811,-0.0110345753,0.5874062181,-0.0932068229,-0.0215858258,0.2237029076,0.1913189888,-0.1458403468,-0.0720235109,0.4881642461,0.0657689944,0.1218042672,-0.1812840402,0.0592669547,0.1686421186,0.3828804791,-0.4165065289,-0.1942468286,-0.0256043915,-0.0409783497,-0.3089539111,0.2429405749,-0.088457711,-0.0158922598,0.1669608951,-0.1686073244,-0.1738453507,-0.1004387587,0.1321236491,-0.1666910946,-0.0566617139,-0.1698295474,0.0470447578,0.3265686631,-0.0522196926,-0.2726073265,0.2022337914,-0.0763458982,0.2358358353,-0.4515022933,-0.0882411003,0.1740597934,-0.0404452533,0.1762193739,0.1709146202,-0.00004491,-0.0666070506,0.0723955184,0.2488779128,0.0324113145,0.5187012553,0.2003649026,0.1472767889,0.4208550453,0.1673791558,0.0964683071,-0.0402158462,0.024107663,-0.334397316,0.2084077448,0.0671968311,0.3447593153,-0.0841714963,-0.3775247335,0.0181287937,0.1573890001,0.0060320143,0.1943289787,0.364074111,-0.0849587694,0.0213761535,0.3098779023,0.2635484934,-0.1441980451,-0.0453131497,-0.3079618216,0.0855333135,-0.285023123,0.091869399,0.1223318949,-0.2411907464,0.2972179353,-0.0596995093,0.2320476621,0.0052755899,-0.1801827252,0.0002737579,-0.4559584558,0.3155317605,-0.0426329039,-0.0340475254,0.3438808322,-0.2498008162,-0.1758813858,-0.0667436197,-0.396586448,-0.3489952087,-0.2281557471,0.1906859428,-0.2567121685,-0.0366570838,-0.0905082375,-0.0524979942,0.081659086,-0.0341059081,-0.0221799593,0.0213688035,-0.2081436366,-0.1809950471,0.2654057443,0.4197615683,-0.1241385639,-0.4193939865,0.2475048155,-0.2722270489,0.1515624821,0.0735410824,0.0466711856,0.0418201424,-0.2164571285,-0.0529857799,0.5696073771,-0.4870827794,-0.305324167,-0.012945517,-0.0912947729,-0.0660426766,-0.0997282267,-0.1221842691,0.0712336674,0.1602921635,0.3691287339,0.1814728528,0.2460640967,-0.0673031211,-0.295971036,-0.2739145458,0.0151239391,0.1323706806,0.1343489289,0.2073107064,0.125881806,-0.2733312547,-0.019735828,0.009207774,-0.1091541052,0.3077543378,0.3138891459,0.0530329719,0.0884157047,-0.374016583,-0.4855465591,0.2208614498,-0.1038557589,0.2484366894,0.058520738,-0.0572746135,-0.5404746532,0.0557723753,0.1149239987,-0.0396869481,0.117755644,-0.0932686329,0.0560618378,0.2103019506,-0.217421636,0.5194115639,0.0435418636,0.2268600762,-0.0502142049,0.283323288,-0.0082025258,-0.2894024253,0.1883428544,0.3154477775,0.367877692,-0.1913115531,-0.1713062078,0.379566431,-0.0026116713,0.0007110174,-0.0385931693,0.0413059928,0.1458428949,-0.1447084695,-0.02530629,-0.128543362,0.10233742,-0.0015310281,0.3126978874,0.2648938,-0.02905735,0.1211369559,-0.053695444,0.2351401448,0.1111266315,-0.1546281576,0.008936801,-0.2567315996,0.2545923293,-0.1343357861,0.4579006732,0.0160899777,-0.0869358778,-0.1570410579,0.3953169882,-0.0673047379,0.1365798265,0.2145171463,-0.2131549716,0.0786093548,0.0189036895,-0.0976484269,0.4290671945,0.2625548542,-0.1425192505,0.1476904154,0.0134682627,-0.0187322088,0.1198476553,-0.1157620177,0.2183315009,0.0774159953,0.145182386,0.0439757556,-0.236202389,0.0713774264,-0.3299257755,0.1857252419,-0.3560778201,0.0890058205,-0.1118910164,0.1443869919,0.0165852401,-0.1988376677,-0.362349838,-0.1232983992,-0.0745541379,0.077243112,0.1588585526,0.2377408296,0.1133858338,0.2581502497,0.1740185469,-0.1417489648,-0.2986341417,-0.1178747043,-0.1444078684,-0.0114939492,0.1132480353,0.0490717702,0.2982063293,-0.3821719289,0.2066467553,-0.0268712398,-0.3316158056,0.1415923387,-0.085970588,0.5356303453,0.3830052912,0.222393617,0.0601645187,0.0308397003,0.4310182929,-0.2781937122,-0.1283781081,0.3072193563,-0.1938468367,-0.0050387303,-0.310732156,-0.0656445473,-0.3405122161,-0.4235738516,0.1724359244,0.142646715,0.1275133491,0.229676351,0.1658717394,0.2529863417,-0.0127115427,0.1873032898,-0.2911027372,-0.4766461253,0.1857629865,-0.1643527895,-0.4779871106,-0.0241932087,0.0987193882,0.3553822935,-0.1274407953,-0.3843420446,-0.4785290658,-0.1180266961,0.2772627175,-0.23944363,0.0787075683,0.2070347369,0.0424267799,-0.1203311235,-0.2294768095,-0.2517616749,0.1537403166,0.0673766732,-0.0223558173,-0.000650788,0.2750593126,-0.1600405723,0.3475376964,0.222756207,-0.2459636927,0.4428962767,-0.1437605619,0.5232295394,-0.1130053848,-0.41645661,-0.0518745668,0.1353650242,0.2595990598,0.1685199738,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3419","title":"`.to_json` is extremely slow after `.select`","comments":"Hi ! It's slower indeed because a datasets on which `select`\/`shard`\/`train_test_split`\/`shuffle` has been called has to do additional steps to retrieve the data of the dataset table in the right order.\r\n\r\nIndeed, if you call `dataset.select([0, 5, 10])`, the underlying table of the dataset is not altered to keep the examples at index 0, 5, and 10. Instead, an indices mapping is added on top of the table, that says that the first example is at index 0, the second at index 5 and the last one at index 10.\r\n\r\nTherefore accessing the examples of the dataset is slower because of the additional step that uses the indices mapping.\r\n\r\nThe step that takes the most time is to query the dataset table from a list of indices here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/047dc756ed20fbf06e6bcaf910464aba0e20610a\/src\/datasets\/formatting\/formatting.py#L61-L63\r\n\r\nIn your case it can be made significantly faster by checking if the indices are contiguous. If they're contiguous, we could pass a python `slice` or `range` instead of a list of integers to `_query_table`. This way `_query_table` will do only one lookup to get the queried batch instead of `batch_size` lookups.\r\n\r\nGiven that calling `select` with contiguous indices is a common use case I'm in favor of implementing such an optimization :)\r\nLet me know what you think","body":"## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":208,"text":"`.to_json` is extremely slow after `.select` \n ## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n Hi ! It's slower indeed because a datasets on which `select`\/`shard`\/`train_test_split`\/`shuffle` has been called has to do additional steps to retrieve the data of the dataset table in the right order.\r\n\r\nIndeed, if you call `dataset.select([0, 5, 10])`, the underlying table of the dataset is not altered to keep the examples at index 0, 5, and 10. Instead, an indices mapping is added on top of the table, that says that the first example is at index 0, the second at index 5 and the last one at index 10.\r\n\r\nTherefore accessing the examples of the dataset is slower because of the additional step that uses the indices mapping.\r\n\r\nThe step that takes the most time is to query the dataset table from a list of indices here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/047dc756ed20fbf06e6bcaf910464aba0e20610a\/src\/datasets\/formatting\/formatting.py#L61-L63\r\n\r\nIn your case it can be made significantly faster by checking if the indices are contiguous. If they're contiguous, we could pass a python `slice` or `range` instead of a list of integers to `_query_table`. This way `_query_table` will do only one lookup to get the queried batch instead of `batch_size` lookups.\r\n\r\nGiven that calling `select` with contiguous indices is a common use case I'm in favor of implementing such an optimization :)\r\nLet me know what you think","embeddings":[-0.313434422,0.1887500882,-0.0610729903,-0.0651992857,0.2008450478,0.0744216293,0.0842365548,0.4034847617,-0.2616817653,0.2137250453,0.0064627342,0.7067225575,-0.0072279763,-0.0352008827,0.0019336272,-0.0267546829,0.1643505692,-0.1066156179,0.0210075695,-0.1266007721,-0.1056945622,-0.0096035162,-0.2797526419,-0.0329513326,-0.1409995556,-0.278481245,0.1729202867,0.2560811043,-0.0184411537,-0.3377084732,-0.001387456,-0.09669175,-0.1162274331,0.240971595,-0.0001087118,0.073411718,0.2043657005,0.0226168856,-0.3878852725,-0.1577961743,-0.4634679556,-0.1034036428,0.0274383146,-0.2707283199,-0.0232565496,0.0306706168,-0.1517099738,-0.2918874919,0.3181185722,0.104088366,0.2415897697,0.3510611355,-0.2576994002,-0.1619841903,0.2756759822,0.1905020326,-0.1756992638,0.4026584029,0.0441578217,0.113614738,0.1366682649,0.0894813389,-0.1013909653,-0.1817278713,-0.1199253276,0.0945555046,-0.1025140435,0.0468741618,0.0227330029,0.1377214044,0.2284837812,-0.249694705,-0.3619492948,-0.3039984703,-0.0144988745,-0.0674319491,0.1812416762,-0.0457140692,0.0519656651,-0.0960386619,-0.2840663493,0.1411961466,0.1532616764,-0.0746637136,0.2828759253,0.0203245785,-0.0437165201,0.0648751184,0.3973839581,-0.2269379199,0.0284302216,-0.3199338317,0.0073034572,0.0225738399,-0.5987464786,-0.3852683008,0.0011238664,-0.5389380455,-0.1619405895,0.170269683,0.2211832702,0.1481893212,0.2020164877,0.0272193737,0.3664287329,0.1000211015,0.0174551103,0.2625764608,0.0871516615,0.2086936831,-0.0138086928,0.1481658816,0.1846824884,-0.2047813386,0.0859758332,-0.0904582664,0.0278282724,-0.0262623578,-0.4396527112,0.1123470664,-0.1807840019,-0.2214799523,0.0383389108,0.2987681329,-0.0489865616,-0.2342661321,-0.2089882493,0.0701904297,-0.3014138639,0.1154295653,-0.3093779087,0.2089119107,-0.054713361,0.1547599733,-0.1174713895,-0.090628013,0.0556414388,0.1574628651,0.0302594937,0.2194629759,0.2669264674,-0.1213572994,0.0269682128,0.2240351588,0.1139715314,0.1025405675,-0.1760900468,-0.4222274721,-0.3850272,0.2565736175,-0.2997810543,-0.197881341,0.2909232974,0.288102746,-0.1039831415,-0.1973569989,-0.7724059224,0.2375657409,0.0038515371,-0.0859799609,-0.0889124274,-0.1200824156,-0.0707586482,-0.3188278377,0.1193365827,0.1258348823,-0.4098061919,0.0476830043,0.1725779772,-0.0028526592,0.5176390409,0.3855898678,-0.2536501884,0.0334358849,0.0381848812,0.4675065279,0.3435556889,0.1652612835,-0.4090454578,0.0550912395,-0.2947689891,0.1766481698,0.0297031626,0.1767355204,0.2951000929,-0.1199591681,0.3080723584,0.4912303686,0.0926657617,0.2578487992,-0.4891909063,-0.1710205227,0.0482303165,0.2656106353,-0.4736604393,0.0205282345,0.0919034258,0.2612651885,0.2743983567,-0.1598334908,-0.1288863569,0.3128334582,0.2605656981,-0.3065893352,-0.1825327873,-0.1751386821,-0.0845702291,0.1775669754,0.0653960034,-0.2772377431,0.0805992186,-0.1494891793,-0.1239721924,-0.0368661806,-0.300757587,0.0498293042,0.1021756604,-0.0479757376,0.313644588,0.0571883433,0.011516043,-0.0163431503,-0.3065514266,-0.0460003875,-0.4206381142,0.3021364808,-0.0428696461,-0.497956723,0.1682612449,-0.0791453794,0.0463178083,-0.3157119751,-0.2837692797,0.2126906514,0.131946981,0.2195281684,0.1680983007,-0.0133237401,-0.0074640331,0.1460094154,0.1451130807,0.2135331035,0.0524985045,-0.0406725071,-0.5448127389,0.5830827355,-0.2446796,0.2318924665,0.1716699749,-0.3438756466,0.1707448363,0.2592818737,0.1353050321,0.0284491591,-0.0213536117,0.4851010144,-0.0954677314,0.0561239608,-0.4406299591,0.3064272702,0.7711958289,-0.1944100559,0.0032762608,0.293841064,-0.0097143771,-0.3446843922,-0.1106972918,0.2224391848,0.4611261189,0.0964711085,0.0803127363,-0.1639898717,-0.048783198,-0.1641253531,0.0692556798,-0.1358742714,0.1462063342,0.081366457,0.4279744029,-0.0467497483,-0.1886072308,0.0017234377,-0.0665479228,0.2904410064,-0.1108381748,0.230622232,-0.3287338912,0.3466447294,0.0706557259,0.0279639363,-0.0069142506,-0.3691058755,0.1192109585,0.2100225091,-0.2380548418,0.0761863664,0.0345140621,0.0194845237,0.1538158953,-0.3518487215,-0.2038659751,-0.0765817687,-0.2606610656,0.1363903731,0.073344633,0.2094242722,0.221929118,0.0080150273,-0.2296081185,-0.0430735946,-0.2065671682,-0.1438586414,-0.1672305018,0.4950193167,-0.0068699955,0.0881972313,-0.1601240486,-0.3883982599,0.2172718942,0.0743155554,-0.3592293859,0.1920381039,-0.1569349468,-0.0472035594,0.1851295829,-0.5259930491,-0.116099745,-0.1435838938,0.4302063584,0.0158865433,0.0419203229,0.0627425835,0.2521982193,0.016787529,-0.0299190991,0.0941867083,-0.3023793399,-0.3653062582,0.4596996605,0.010931096,-0.1705315262,-0.2121686637,-0.1080060378,-0.1047391519,-0.0167380031,-0.1640966982,-0.027894387,-0.5087156296,0.1799611598,-0.0483630002,0.0166078433,0.4398763478,-0.0021377211,-0.1723222733,0.1671260446,-0.3047691882,-0.0020591135,0.42144081,0.0138518056,-0.2207443714,0.3652326763,0.1683793664,0.6924611926,0.1479629427,-0.1337080002,0.0095420843,0.0462547615,-0.0364885181,-0.5109039545,-0.2004982531,0.0751402974,-0.1127563193,-0.11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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3419","title":"`.to_json` is extremely slow after `.select`","comments":"Hi, thanks for the response!\r\nI still don't understand why it is so much slower than iterating and saving:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 99 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 47 seconds\r\n\r\nselected_subset3 = original.select([i for i in range(len(original)) if i % 2 == 0])\r\nselected_subset3.to_json(\"from_select3.json\")  # Takes 49 seconds\r\n\r\nimport json\r\nimport time\r\ndef fast_to_json(dataset, path):\r\n    start = time.time()\r\n    with open(path, mode=\"w\") as f:\r\n        for example in dataset:\r\n            f.write(json.dumps(example, separators=(',', ':')) + \"\\n\")\r\n    end = time.time()\r\n    print(f\"Saved {len(dataset)} examples to {path} in {end - start} seconds.\")\r\n\r\nfast_to_json(original, \"from_original_fast.json\")\r\nfast_to_json(selected_subset1, \"from_select1_fast.json\")\r\nfast_to_json(selected_subset2, \"from_select2_fast.json\")\r\nfast_to_json(selected_subset3, \"from_select3_fast.json\")\r\n```\r\n```\r\nSaved 87599 examples to from_original_fast.json in 8 seconds.\r\nSaved 87599 examples to from_select1_fast.json in 10 seconds.\r\nSaved 43799 examples to from_select2_fast.json in 6 seconds.\r\nSaved 43800 examples to from_select3_fast.json in 5 seconds.\r\n```","body":"## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":157,"text":"`.to_json` is extremely slow after `.select` \n ## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n Hi, thanks for the response!\r\nI still don't understand why it is so much slower than iterating and saving:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 99 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 47 seconds\r\n\r\nselected_subset3 = original.select([i for i in range(len(original)) if i % 2 == 0])\r\nselected_subset3.to_json(\"from_select3.json\")  # Takes 49 seconds\r\n\r\nimport json\r\nimport time\r\ndef fast_to_json(dataset, path):\r\n    start = time.time()\r\n    with open(path, mode=\"w\") as f:\r\n        for example in dataset:\r\n            f.write(json.dumps(example, separators=(',', ':')) + \"\\n\")\r\n    end = time.time()\r\n    print(f\"Saved {len(dataset)} examples to {path} in {end - start} seconds.\")\r\n\r\nfast_to_json(original, \"from_original_fast.json\")\r\nfast_to_json(selected_subset1, \"from_select1_fast.json\")\r\nfast_to_json(selected_subset2, \"from_select2_fast.json\")\r\nfast_to_json(selected_subset3, \"from_select3_fast.json\")\r\n```\r\n```\r\nSaved 87599 examples to from_original_fast.json in 8 seconds.\r\nSaved 87599 examples to from_select1_fast.json in 10 seconds.\r\nSaved 43799 examples to from_select2_fast.json in 6 seconds.\r\nSaved 43800 examples to from_select3_fast.json in 5 seconds.\r\n```","embeddings":[-0.1415250599,0.0571958907,-0.0722180828,0.0681986436,0.1361246854,0.2194977701,0.0467468277,0.3875997961,-0.2274016142,0.0900384486,0.0242867824,0.5975726843,0.1035960317,-0.0051028533,-0.0279696044,-0.0860148296,0.2221941352,-0.090099141,0.1746015549,-0.1625321358,-0.0590379611,0.0549281389,-0.2299279869,-0.0594084337,-0.0879532695,-0.2191502601,0.2162452936,0.2001277208,-0.0479845479,-0.5275933146,0.1242539063,-0.2489387542,-0.0099345353,0.2772338092,-0.0001129507,-0.0150656961,0.2114278376,0.0991385281,-0.3368493319,-0.1462554634,-0.4151120782,-0.2256796956,0.0833851472,-0.2980417311,0.0663224682,-0.0846409872,-0.1769530624,-0.4959801435,0.2122639567,0.1154771224,0.2199397683,0.4510139823,-0.3033972681,-0.0790809765,-0.0552353896,0.1815313101,-0.2140922248,0.4801719785,0.210704267,0.0391824841,0.2521124184,-0.0031738391,-0.1437682658,-0.1535900831,0.1075889766,0.0181159396,-0.0976980776,-0.0757536963,-0.0088002393,-0.0124158859,0.5476435423,-0.3393138051,-0.338447839,-0.2084254026,-0.2480506748,-0.0675920323,0.0595411807,0.086067833,0.0672685429,-0.0964647606,-0.2494886667,0.0444303714,-0.0011115848,-0.133929342,0.2964820266,-0.0866539404,-0.1005112082,0.1777945161,0.2886149585,0.0143432617,0.0285983775,-0.4829730093,0.0172682069,0.118723534,-0.5491201878,-0.3798493147,0.0983630717,-0.3264731765,-0.2221335769,0.2354887724,0.0646224469,-0.0775679648,0.1080516651,-0.0336576514,0.4661752284,0.2574312389,0.0259038135,0.2694675326,0.1002528891,0.1381375194,-0.061342448,0.0192514621,0.2627741396,-0.1673509479,0.4899332821,-0.0496560596,0.2087734342,-0.2451496869,-0.5025871992,0.1798560768,-0.4054972231,-0.296925813,-0.0407052524,0.0846816376,-0.1106597781,-0.0833001584,-0.1026803255,0.2033504993,-0.2756373286,0.1512176991,-0.1914521009,0.1665674448,0.0792311355,0.1671354324,-0.1999266297,-0.2481621653,0.0595089905,0.2827673852,-0.0709184781,0.0619025752,0.3161606789,-0.175026238,-0.0531242304,0.0700414553,0.1081812009,0.3433859646,-0.0705209076,-0.5403032899,-0.3883313537,0.2814794481,-0.2215906233,-0.1794191152,0.1389424354,0.1950952709,-0.1272898018,-0.2638616562,-0.7340606451,0.2221503854,0.0634201244,-0.0135688474,0.1003855616,-0.0268680435,-0.1333955824,-0.384229362,-0.037619669,0.2866803706,-0.5492133498,0.1751156747,0.3376580775,-0.0216705147,0.4824243784,0.4459162354,-0.2432140857,0.2343834937,-0.0623710901,0.2931307256,0.1755411923,-0.0465444885,-0.3762187064,0.2605929375,-0.249162063,0.3272435069,-0.0022405547,0.242737636,-0.0398649722,-0.3347544372,0.1664063036,0.3895263076,0.1134123951,0.2121183127,-0.5212130547,-0.127552554,0.1446263045,0.073232919,-0.4954229891,0.1144247353,-0.0918686241,0.2167656124,0.4174663424,-0.2440636456,-0.1367348582,0.4039120376,0.378632158,-0.2235647291,-0.2100963742,-0.0380246788,-0.2238331288,0.2148566842,0.0691618398,-0.2901184857,0.1228446141,-0.1143846288,-0.1213709638,0.0359931476,-0.3633660972,0.0177804939,0.029991921,0.0938612521,0.3762406111,0.1257515997,0.0760857016,0.2111977339,-0.0117080901,-0.0949797258,-0.5944109559,0.2689835131,0.006283931,-0.2690674663,0.0431745388,-0.1246752143,0.2134533674,-0.2701281607,-0.3491314352,0.1790646762,0.081095688,0.2691417038,0.0847278386,0.0725810379,0.0372069627,-0.0228917468,0.2787644267,0.3253175318,0.249527052,-0.0865150541,-0.4419249296,0.3939855099,-0.098351039,0.2821744084,0.1420218647,-0.3718956709,-0.0028666293,0.1616998017,0.1463009566,0.0656281114,0.1483605206,0.4280653,0.1280821115,0.0643312186,-0.5504087806,0.2929074764,0.5612951517,-0.1986440271,-0.1426465213,0.1668505669,0.0247515887,-0.247676909,-0.0830123648,0.0780623332,0.4658495188,-0.0273767281,0.0384074412,-0.1275174916,0.0145459734,-0.1688537449,0.1621080637,-0.0564676002,0.3576236367,-0.0531663969,0.4698258638,0.0399378799,-0.0673468113,0.0076176184,-0.2238798738,0.1914984286,-0.1102311984,0.3662955463,-0.305578053,0.1687118113,-0.0470910259,-0.1412175894,-0.015767334,-0.3286733031,-0.1775774062,0.1054160222,-0.0817886293,-0.2565819323,-0.0798306763,-0.0772594512,0.057860259,-0.4655488431,-0.1963255107,-0.1133724153,-0.3123549223,0.0382497758,0.0750328675,0.0501147732,0.2391261458,0.0579664819,-0.2794501483,-0.0085643129,-0.2900518477,-0.2131363302,-0.2033651024,0.5882795453,-0.08576978,0.0599749908,-0.0326219611,-0.1904877722,0.1898450553,0.185442701,-0.2672302127,0.0668513775,-0.1086260006,-0.0711196512,0.3810196221,-0.3852272332,-0.1057122946,-0.1523788422,0.4267995954,0.0308882538,-0.0053049289,0.2086131424,0.1565080583,-0.0308350679,0.2568912506,0.1917402446,-0.2718600333,-0.2790311575,0.445302695,0.0398448482,-0.1928117126,-0.2465357631,0.0268601067,-0.0000369715,-0.2446015924,-0.4528475702,-0.0464095213,-0.6421845555,0.3299521506,-0.1069821641,0.0565498732,0.2111712247,-0.0208445657,-0.0986385792,0.0668166652,-0.4338129461,-0.1214081496,0.3728361726,0.0166761559,-0.0548191369,0.3770763278,0.1341181397,0.435967207,0.4670132101,-0.0132511519,0.2590842843,0.1184200644,-0.0744527727,-0.5639160275,-0.4056038857,-0.027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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3419","title":"`.to_json` is extremely slow after `.select`","comments":"There are slight differences between what you're doing and what `to_json` is actually doing.\r\nIn particular `to_json` currently converts batches of rows (as an arrow table) to a pandas dataframe, and then to JSON Lines. From your benchmark it looks like it's faster if we don't use pandas.\r\n\r\nThanks for investigating, I think we can optimize `to_json` significantly thanks to your test.","body":"## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":62,"text":"`.to_json` is extremely slow after `.select` \n ## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n There are slight differences between what you're doing and what `to_json` is actually doing.\r\nIn particular `to_json` currently converts batches of rows (as an arrow table) to a pandas dataframe, and then to JSON Lines. From your benchmark it looks like it's faster if we don't use pandas.\r\n\r\nThanks for investigating, I think we can optimize `to_json` significantly thanks to your test.","embeddings":[-0.2503956556,0.2378025949,-0.1249386668,-0.1713120937,0.1673326492,0.0435038805,0.0635808408,0.6106276512,-0.2218094766,0.0939174592,-0.0214228984,0.6983181834,0.0707777292,-0.0791962072,-0.052465111,0.0165270902,0.1349958628,-0.0152917653,0.0161850806,-0.137096405,-0.2098416686,0.0587011427,-0.1599251181,0.0364312902,-0.0972952917,-0.245674327,0.1418773979,0.1193135083,0.0031335631,-0.3770898581,-0.0082238233,-0.1620412171,-0.090942584,0.266423583,-0.0001016013,0.035038434,0.0929894447,0.0512076318,-0.3995313644,-0.0975438282,-0.21094428,-0.2653712034,0.1664621979,-0.3124704957,-0.0308484323,-0.0645478964,-0.1038406193,-0.2907900512,0.4291031361,0.1806687713,0.3017877936,0.3703415394,-0.183923468,-0.0814735964,-0.0025728918,0.1360290796,-0.1501732171,0.3968874812,0.084518306,0.0671150386,0.0540100336,0.0687456951,-0.1829894036,-0.166460216,0.0057012253,0.0333064981,-0.1071067825,-0.0879265517,0.0016004603,0.1248910278,0.3625845611,-0.3336620033,-0.3885551095,-0.2272557616,0.0169747733,0.0107999807,0.0573972054,0.0401426218,0.0153409662,-0.0245472305,-0.1420697719,0.0230329037,-0.0134239523,-0.1471849084,0.1463748068,-0.0191187374,-0.0518242978,0.0440156981,0.3721791804,-0.1001984105,-0.0347947218,-0.4278785288,0.0418715402,0.0599871799,-0.5377771258,-0.4182266891,-0.0032801367,-0.574554801,-0.2515133917,0.2420827597,0.2485871613,0.0951320678,0.2942125797,-0.0187993683,0.2998636365,0.066895701,-0.0129845375,0.18664442,0.1584454179,0.0646876693,-0.0923466682,0.1964017451,0.0509716719,-0.1453686655,0.2192655653,-0.1794421077,0.1002954766,-0.0394340642,-0.4513497651,0.0497816354,-0.3608951867,-0.2151991427,-0.0964339599,0.1281971335,-0.1647619754,-0.0625297129,-0.0768108293,0.0488204993,-0.1995474696,0.1734288931,-0.2556200624,0.2718817592,0.0244568698,0.0215946399,-0.0569456145,-0.0914172232,-0.044179149,0.0997319371,-0.0294541493,0.1936224401,0.2282822728,-0.0830627382,-0.2056282312,0.1060934365,0.1342857182,0.0724938288,-0.1086307615,-0.4018715918,-0.4661525488,0.2079288661,-0.1239951849,-0.2403931767,0.1721950471,0.306345433,-0.0494334362,-0.2604129612,-0.6070008874,0.3252218962,-0.0811797678,0.1348979324,-0.0353861377,0.0923537761,0.1217912063,-0.3821807206,0.046674557,0.2121394426,-0.4239342213,0.0754387826,0.4371509552,0.0275444761,0.4549981058,0.4105760753,-0.2795130908,0.1250847876,0.1757537574,0.2624897659,0.3259247839,-0.070393689,-0.3116870821,0.2136981189,-0.2763044238,0.0480348803,0.0502008833,0.1537259221,0.1320892274,-0.1800529063,0.2622278929,0.4670516849,0.1403416991,0.2515660822,-0.4862967134,-0.191004321,0.1443220079,0.2512946427,-0.452180773,0.0356111303,-0.0242468119,0.2695321441,0.2546751797,-0.2365940064,-0.0728919581,0.3078218102,0.3168504834,-0.3446027935,-0.2763066292,-0.0258470755,-0.1641949266,0.0958668739,0.0299474951,-0.1285357922,0.0658767968,-0.1606662273,-0.0096177086,0.0248193406,-0.1556762308,0.0577026755,0.1573221534,-0.0082071824,0.4396992922,0.0597882718,-0.0909114853,0.1265623868,-0.1795945317,0.0661458895,-0.2796420455,0.29029724,0.0203492679,-0.3482203186,0.2736332119,-0.089902997,0.0373933166,-0.2426604331,-0.2186572403,0.1230854243,-0.0649454668,0.2436866611,0.3069055676,-0.01190178,-0.0351588689,0.0836762264,0.1919575632,0.2085307986,0.1408371478,-0.0025740576,-0.511140883,0.488132894,-0.088151589,0.2310771644,0.1836253703,-0.3565558493,0.0818884671,0.152937755,0.0006004111,0.1818207949,-0.1522518694,0.5064851642,0.0720419288,0.2064108402,-0.5585559011,0.2828577459,0.6308927536,-0.2551844418,0.0175116993,0.2926717103,0.0494721644,-0.2787276804,-0.1366982013,0.0548409261,0.3175991178,0.1794975698,0.0788779482,-0.1997202635,-0.1232643649,-0.0835416391,0.1687604934,-0.0648908094,0.3700633645,-0.0799199119,0.3501679003,-0.0177040976,-0.1962514967,0.0022505573,-0.1674723774,0.2572056651,0.0623291656,0.2555964589,-0.2237697244,0.2412625849,0.1460203379,-0.0900496989,0.0742033049,-0.2842897475,0.0141887246,0.0799041167,-0.1959104985,0.0227383319,-0.0893857256,0.1119928807,0.056220904,-0.5037916303,-0.1990330219,-0.0855021551,-0.2611595988,0.2037778199,0.0390838571,0.1658779383,0.1911857575,0.0712452084,-0.0850706175,0.0408610255,-0.1997080296,-0.1211268827,-0.2610803843,0.3525977731,-0.0771377906,0.1473808736,-0.1672986895,-0.2616323233,0.1869076788,0.0753712952,-0.3148607314,0.0545536205,-0.1685239226,-0.1175377741,0.2826784551,-0.4324418902,-0.1714738458,-0.2707915306,0.4579292834,0.0450440943,0.0337205008,0.0417611413,0.2312013954,0.1090017781,0.1417304724,0.1279232502,-0.1969240159,-0.2280314267,0.5487471819,0.1174134836,-0.2402161211,-0.1919681281,-0.0614275113,0.0096163843,-0.2422952354,-0.253614217,-0.0845460296,-0.4037929475,0.2707931399,-0.0385647602,-0.003955509,0.2902638018,0.0601819716,-0.2616479099,0.2031909823,-0.2721652985,-0.0705965161,0.3222844303,-0.0459735468,-0.2378683686,0.2979865372,0.081571497,0.3511072993,0.2829776406,-0.1055573076,0.1088355035,0.0262575168,0.0281908177,-0.3876002133,-0.2867637575,0.0781808496,-0.1837572604,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3419","title":"`.to_json` is extremely slow after `.select`","comments":"Thanks for your observations, @eladsegal! I spent some time with this and tried different approaches. Turns out that https:\/\/github.com\/huggingface\/datasets\/blob\/bb13373637b1acc55f8a468a8927a56cf4732230\/src\/datasets\/io\/json.py#L100 is giving the problem when we use `to_json` after `select`. This is when `indices` parameter in `query_table` is not `None` (if it is `None` then `to_json` should work as expected)\r\n\r\nIn order to circumvent this problem, I found out instead of doing Arrow Table -> Pandas-> JSON we can directly go to JSON by using `to_pydict()` which is a little slower than the current approach but at least `select` works properly now. Lmk what you guys think of it @lhoestq, @eladsegal?","body":"## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":100,"text":"`.to_json` is extremely slow after `.select` \n ## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n Thanks for your observations, @eladsegal! I spent some time with this and tried different approaches. Turns out that https:\/\/github.com\/huggingface\/datasets\/blob\/bb13373637b1acc55f8a468a8927a56cf4732230\/src\/datasets\/io\/json.py#L100 is giving the problem when we use `to_json` after `select`. This is when `indices` parameter in `query_table` is not `None` (if it is `None` then `to_json` should work as expected)\r\n\r\nIn order to circumvent this problem, I found out instead of doing Arrow Table -> Pandas-> JSON we can directly go to JSON by using `to_pydict()` which is a little slower than the current approach but at least `select` works properly now. Lmk what you guys think of it @lhoestq, @eladsegal?","embeddings":[-0.2212162465,0.1027244925,-0.0186258778,0.1187598556,0.2550522685,0.1281237155,-0.0699111968,0.4468904138,-0.1404289752,0.074277766,-0.0600852445,0.748949945,0.1289311796,0.0481081605,0.0308407135,0.1059496254,0.0500634313,0.0200780164,-0.0194911193,-0.0684503913,-0.25148049,0.0447713472,-0.1163404658,-0.1094157025,-0.0245004892,-0.2224408686,0.301497519,0.0965212807,-0.0075460277,-0.5093442202,0.0734264031,-0.1758193523,-0.1354286969,0.2081252933,-0.0001137351,0.0679667294,0.2719681263,0.0535827093,-0.5365384817,-0.2311479896,-0.3573000133,-0.1388944536,0.2557536066,-0.2531038225,-0.1336450726,0.0310371052,-0.2982208133,-0.3060910702,0.3510068953,0.0293588992,0.2309678197,0.3628421426,-0.1646858156,-0.1156873405,0.2009745836,0.3561598957,-0.1560838372,0.3634009361,0.2044870257,0.062048085,0.1399969012,0.1037328914,-0.0372603536,-0.1979279965,0.0561097786,-0.0299405251,-0.2686740458,-0.0273577012,-0.091612637,0.1446886063,0.4100542367,-0.2249979228,-0.3544403315,-0.284142375,-0.0421535261,-0.2869589329,0.1476748884,-0.1135686636,0.0757285506,-0.0883660764,-0.3265121579,-0.0646919906,0.0433594622,-0.1626914144,0.1979072541,0.0272796098,-0.121052213,0.0473257154,0.3540072441,-0.1101484224,-0.2436466366,-0.1927635521,0.0111336773,0.0036387665,-0.6001594067,-0.3125503659,0.2634396553,-0.4776876271,-0.1184315532,0.1514357179,0.1553475857,0.0649714321,0.155753985,-0.1298450232,0.2791084647,0.2930161357,-0.0213466473,0.135121882,0.2560071945,0.4250746071,0.0322534852,0.1277359873,0.1885884553,-0.1201057509,0.1591016352,-0.0116672972,0.2295641005,-0.1727412343,-0.5613192916,0.2919115424,-0.2507844269,-0.1939925402,0.0901200473,0.1056155562,-0.0429243445,-0.1535503119,0.084748365,0.0801765472,-0.3961695135,0.175100252,-0.2035049051,0.0765612721,-0.0428599864,0.1174691021,-0.0187813137,-0.3109201193,-0.0811688825,0.2774204612,-0.0323448107,0.1001661792,0.2295548022,-0.0565466732,-0.0378033221,0.2724796832,-0.0369924307,0.2297145426,-0.0668641105,-0.4862953424,-0.4216569364,0.172247529,-0.2782519162,-0.2276366353,0.1720308214,0.2213503718,-0.1306201518,-0.1439815462,-0.6190981269,0.2602891028,0.019380955,0.1665795445,0.0303729065,0.0334030651,-0.2842151523,-0.2251516134,0.2023116052,0.4405194521,-0.4770878255,0.0386164822,0.2817363739,-0.009271252,0.3368906081,0.3594050407,-0.1530907303,0.1290905476,0.027323395,0.3048484027,0.0335331187,-0.1145821884,-0.3603202701,0.1636500806,-0.3505187035,0.2438640594,-0.0057157762,0.0610195324,0.151401788,-0.1962347329,0.4059719145,0.3996655643,0.1287598312,0.123558253,-0.4668517411,-0.256183207,0.0911667645,0.2694249451,-0.5443955064,-0.0205237158,-0.0250919815,0.0719312206,0.190844208,-0.2245122939,-0.0070600449,0.3932720125,0.4758758843,-0.2522065639,-0.0497876965,0.0791520178,-0.2843796015,0.1939019561,0.0884153992,-0.1630727202,-0.0640568957,-0.2232184708,-0.1110469997,0.0332391299,-0.2478414774,0.0461337566,0.0455023199,-0.1986781955,0.2824607193,0.1716527194,0.0209639147,0.035856735,-0.1062549874,-0.0303472653,-0.6539128423,0.0898554027,0.1172933728,-0.2634695768,0.2731594443,0.0470463037,0.1274698526,-0.2707420886,-0.3214109838,0.1371391565,0.0450294018,0.3204576969,0.135424912,0.1093328223,0.1170870587,0.0453337692,0.1104482189,0.2354531586,0.1805741787,-0.0813203081,-0.4657789171,0.4923704863,-0.2915321589,0.338735044,0.1077594459,-0.3615040481,0.1258699894,0.1946073472,0.0654672459,0.0324674323,0.1312692165,0.4772085547,0.212449491,0.1212142482,-0.5144758821,0.3360305429,0.4645707011,-0.146996066,0.028578924,0.3510458767,0.051995527,-0.2610159516,-0.0155810891,0.1390163004,0.2602511346,0.0281643029,-0.029363662,-0.1913943887,-0.0704117194,-0.2189589143,0.1376753896,-0.2415276021,0.2456596494,0.1483842134,0.4473212063,-0.0324297063,-0.2387772202,0.176796928,-0.1974191368,0.2349348664,-0.1809414029,0.2663921118,-0.3574253917,0.2530988157,0.1515167058,-0.0133972038,-0.0843179971,-0.3487616181,0.0585238226,0.1145432144,-0.2319311649,0.0241059046,-0.0544898584,0.1498364955,0.1685010642,-0.4382399023,-0.299387157,-0.1966487169,-0.1576527357,0.0336170867,0.0358425751,-0.0238323919,0.1410059929,0.0698705241,-0.1107529253,-0.0514250845,-0.3640058041,-0.1149779186,-0.2514734566,0.605520308,0.1485511065,0.1012539044,-0.1017710567,-0.073296994,0.268974036,0.181629777,-0.36371997,0.1602526307,-0.2575188279,0.1196004972,0.166021198,-0.4174368083,-0.0465087555,-0.205749929,0.519074142,-0.1302302331,-0.0128375208,0.2838122845,0.2633405328,0.0694026425,0.1586938351,0.1444570571,-0.2756016254,-0.1759734154,0.5513932109,0.0617153347,-0.1343177557,-0.2934929132,0.0372429788,-0.0353237651,-0.1132434905,-0.4752552211,-0.0632552952,-0.5432243943,0.1347203106,-0.2238169014,0.0941976756,0.2979942858,0.0702135414,-0.1411167383,0.1116132215,-0.4074321687,0.0897669569,0.3451041281,-0.0651409477,-0.22445409,0.5375289321,0.0739889666,0.5368629098,0.241573438,-0.0202099402,0.2776854932,0.1894548237,0.1000248715,-0.4498464465,-0.2339782566,-0.1182972342,-0.1132830754,-0.1261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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3419","title":"`.to_json` is extremely slow after `.select`","comments":"Posting it in @eladsegal's format:\r\n\r\nFor `squad`:\r\nSaving examples using current `to_json` in 3.63 secs\r\nSaving examples to `from_select1_fast.json` in 5.00 secs\r\nSaving examples to `from_select2_fast.json` in 2.45 secs\r\nSaving examples to `from_select3_fast.json` in 2.50 secs\r\n\r\nFor `squad_v2`:\r\nSaving examples using current `to_json` in 5.26 secs\r\nSaving examples to `from_select1_fast.json` in 7.54 secs\r\nSaving examples to `from_select2_fast.json` in 3.80 secs\r\nSaving examples to `from_select3_fast.json` in 3.67 secs","body":"## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":67,"text":"`.to_json` is extremely slow after `.select` \n ## Describe the bug\r\nSaving a dataset to JSON with `to_json` is extremely slow after using `.select` on the original dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal = load_dataset(\"squad\", split=\"train\")\r\noriginal.to_json(\"from_original.json\")  # Takes 0 seconds\r\n\r\nselected_subset1 = original.select([i for i in range(len(original))])\r\nselected_subset1.to_json(\"from_select1.json\")  # Takes 212 seconds\r\n\r\nselected_subset2 = original.select([i for i in range(int(len(original) \/ 2))])\r\nselected_subset2.to_json(\"from_select2.json\")  # Takes 90 seconds\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master (https:\/\/github.com\/huggingface\/datasets\/commit\/6090f3cfb5c819f441dd4a4bb635e037c875b044)\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n Posting it in @eladsegal's format:\r\n\r\nFor `squad`:\r\nSaving examples using current `to_json` in 3.63 secs\r\nSaving examples to `from_select1_fast.json` in 5.00 secs\r\nSaving examples to `from_select2_fast.json` in 2.45 secs\r\nSaving examples to `from_select3_fast.json` in 2.50 secs\r\n\r\nFor `squad_v2`:\r\nSaving examples using current `to_json` in 5.26 secs\r\nSaving examples to `from_select1_fast.json` in 7.54 secs\r\nSaving examples to `from_select2_fast.json` in 3.80 secs\r\nSaving examples to `from_select3_fast.json` in 3.67 secs","embeddings":[-0.3101398945,0.0925763249,-0.1085358113,-0.0349019356,0.182151258,0.1389459968,-0.0513328612,0.4654998779,-0.1702198833,0.0859453827,0.0617809854,0.6600286961,0.0525259338,0.0195463374,-0.1049224213,-0.0219950695,0.0947580636,-0.0787241012,0.1732741594,-0.0824128911,-0.132746309,0.1048035473,-0.195071578,-0.0190457739,-0.0556672141,-0.3036060631,0.2953051031,0.1781031638,0.0155086871,-0.4784871936,0.1058768108,-0.1730509251,-0.0840068832,0.2338904887,-0.0001066579,0.0368822627,0.1929184347,0.0825722888,-0.3625864387,-0.0895353183,-0.3908022344,-0.2717339396,0.1206814274,-0.284360677,-0.1011261046,-0.0439241938,-0.1937714815,-0.4481664002,0.3356669843,0.1652196199,0.2847042382,0.4140948057,-0.2547280192,-0.2032730132,0.0044360431,0.1908098161,-0.2182022482,0.4373441339,0.2347447127,0.0890788808,0.1562524885,0.0430552773,-0.0626534075,-0.1150782481,-0.0051134182,-0.0379588045,-0.0684244707,-0.0497368984,-0.0214931183,0.0146029405,0.4432532489,-0.2930603325,-0.3304581046,-0.2409869879,-0.1328951865,-0.0335187726,0.1038661152,-0.0092892377,0.0008332982,-0.0807042271,-0.242013827,0.0014889351,0.0538696162,-0.0926033035,0.307017684,-0.0700997561,-0.1199655607,0.0939499736,0.2816169858,-0.1315194964,-0.0952739045,-0.3491824865,-0.0710213631,0.0004509366,-0.5408169627,-0.4136012793,0.0465425067,-0.4677429795,-0.1650530696,0.1790829599,0.0914597809,0.0172321908,0.1170058623,-0.0221647937,0.3905233741,0.1335604489,0.010408245,0.2518072128,0.2083253413,0.1959742159,-0.0584820211,0.1454666704,0.1999350935,-0.2137701511,0.3569830954,-0.0210645497,0.1926976144,-0.1759212911,-0.5197473764,0.1698698252,-0.2654919922,-0.1962191612,0.0026948275,0.1522908956,-0.1220091805,-0.0925733075,0.0023498959,0.1002734452,-0.2956938148,0.0893375948,-0.2441126853,0.184056446,0.0275758933,0.102300331,-0.1210135892,-0.1915973276,0.0439514443,0.2082799524,-0.0890973508,0.0620743819,0.2927599847,-0.1573476344,-0.092764236,0.0887923464,0.0755944252,0.2409780324,-0.0868396536,-0.4670543969,-0.3881091774,0.1809863448,-0.2469850481,-0.1912647337,0.0847602338,0.3024466336,-0.1123494655,-0.2580761313,-0.6403449774,0.2589890361,0.0800568312,0.0033739535,0.046765469,-0.0327606387,-0.1095700487,-0.3089951575,0.0517954379,0.304823488,-0.4987023175,0.0709783435,0.2913199663,-0.011349244,0.4098978341,0.4345088303,-0.2302618474,0.1239538565,0.0286066234,0.2146447897,0.2414585948,-0.0525999404,-0.332241714,0.1941470206,-0.29217875,0.1924491376,-0.0204159301,0.1823188663,0.0735547245,-0.2719898224,0.2826765478,0.5098420978,0.1336228549,0.222611025,-0.5169451237,-0.2150835991,0.0380381122,0.2103845328,-0.4273740947,0.0262842346,-0.0402994864,0.2743596137,0.2963391542,-0.189492628,-0.0960929245,0.4337852895,0.395844996,-0.2876401842,-0.1960326284,-0.1190550327,-0.2049240172,0.1731495261,0.1013550535,-0.3316759467,0.1613973379,-0.1059202626,-0.1220137998,-0.0325641669,-0.3136019111,0.0467588566,0.1178763062,0.0696443766,0.3417298198,0.1253043413,0.0698955357,0.1657481045,-0.1250647604,-0.0441772267,-0.4724466205,0.2642064393,-0.0274436921,-0.367477715,0.2000818849,-0.0712567046,0.1593779325,-0.2454683185,-0.3436285853,0.1945635825,0.0625225827,0.2814647853,0.1846247166,0.0023094097,-0.020704506,-0.0149892,0.1427637637,0.3501737118,0.1804854721,-0.0246120691,-0.4810679555,0.4610844254,-0.0740692616,0.1711320132,0.1725001931,-0.3167748749,0.1461391151,0.1134841219,0.1797669381,0.0381495357,0.0388208367,0.4783597589,0.1270123869,0.0464911237,-0.5469593406,0.3104864955,0.6120458245,-0.1466079801,0.0353751294,0.2590615451,0.0279312227,-0.2505809665,-0.1480273455,0.1169471592,0.4037787616,0.0912788659,0.0221950263,-0.2011806518,0.0194044635,-0.1583772451,0.1431124806,-0.1335957497,0.3038618267,0.0587469637,0.4947928488,-0.0163551327,-0.1629910469,-0.0288125295,-0.1577610821,0.2393364757,-0.0893448293,0.2411554754,-0.2787166536,0.2055014819,0.0202218425,-0.0606365018,0.0610073768,-0.3003528416,-0.0214134715,0.1191721186,-0.1499899328,-0.024165662,-0.0948151052,0.0668287575,0.1080948859,-0.4636535645,-0.1929395944,-0.0924959779,-0.2734645903,0.1294699162,0.0319547914,0.0660178512,0.2641532123,0.1217958629,-0.2140788436,-0.0552292354,-0.2589120567,-0.1643758267,-0.1386522502,0.5349969864,0.0076821283,0.1347991973,0.014630449,-0.2734349668,0.2179102004,0.125284031,-0.2962169647,0.0642193258,-0.2177680582,-0.0689742342,0.2944498062,-0.4831016362,-0.1660628766,-0.2771814466,0.4062387943,0.0270057674,-0.0422264449,0.2060838491,0.1874758601,0.0468616188,0.1974523813,0.1790335774,-0.256066829,-0.2406071275,0.4510720372,0.0564361103,-0.1551891714,-0.1753032953,-0.0248839334,-0.0524927676,-0.2502911687,-0.3361753821,-0.0892985687,-0.6219489574,0.3222277164,-0.1140654162,-0.0353574529,0.2646648288,-0.0032363038,-0.1994390041,0.1122306958,-0.4300956726,-0.0224622227,0.342081666,-0.0131812394,-0.213052839,0.3529871106,0.1097690687,0.4554391503,0.4090585113,-0.1087109968,0.1908501089,0.1020549089,-0.03937288,-0.4100880027,-0.3472266793,0.0379795134,-0.1312115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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3416","title":"disaster_response_messages unavailable","comments":"Hi, thanks for reporting! This is a duplicate of https:\/\/github.com\/huggingface\/datasets\/issues\/3240. We are working on a fix.\r\n\r\n","body":"## Dataset viewer issue for '* disaster_response_messages*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/disaster_response_messages\r\n\r\nDataset unavailable. Link dead: https:\/\/datasets.appen.com\/appen_datasets\/disaster_response_data\/disaster_response_messages_training.csv\r\n\r\nAm I the one who added this dataset ?No\r\n","comment_length":16,"text":"disaster_response_messages unavailable \n ## Dataset viewer issue for '* disaster_response_messages*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/disaster_response_messages\r\n\r\nDataset unavailable. Link dead: https:\/\/datasets.appen.com\/appen_datasets\/disaster_response_data\/disaster_response_messages_training.csv\r\n\r\nAm I the one who added this dataset ?No\r\n \n Hi, thanks for reporting! This is a duplicate of https:\/\/github.com\/huggingface\/datasets\/issues\/3240. We are working on a fix.\r\n\r\n","embeddings":[-0.0299737286,-0.5003211498,-0.0671464652,0.3193087578,0.2122156322,0.2117060125,0.0487526841,-0.0459236726,0.0785183683,0.038044218,-0.1369878203,0.0033188548,-0.364807725,0.0728736892,0.3103381097,-0.1563291997,-0.0478128716,0.0277250241,-0.1398406029,0.1422196031,-0.1771476418,0.2890433073,-0.0540030859,-0.0990055352,-0.2285627872,0.1154409871,-0.1777836531,0.0107457712,-0.2866511345,-0.4454722404,0.2775404453,-0.0819219574,-0.0389213935,0.2899874449,-0.0001123664,0.2116796821,0.2893011272,-0.042307239,-0.3996542692,-0.3504158258,-0.1997318417,-0.3478319645,0.0716870055,0.2090807706,-0.2646025419,-0.1738754362,0.1395048946,-0.0506786034,0.3579210937,0.4835311174,0.2269794941,0.3641416132,0.389980793,-0.3576337397,-0.1157787144,0.3465768099,-0.1911729872,0.4458273351,0.1102845818,0.2318105102,0.1256678551,0.4026734233,0.2942788601,-0.0906164125,0.2226272225,-0.0136389639,-0.0924926624,-0.2220221609,0.1054359376,0.253585875,0.5293086171,0.1891980469,-0.1826913655,-0.2561541498,0.2347144037,0.0206320435,0.3474799395,0.0841168612,-0.0358588584,0.3678695858,-0.3810766637,-0.3276612759,-0.07575427,0.0987573788,-0.0779489428,-0.2321640104,-0.354375571,-0.0309593715,0.1163538098,0.0470855199,-0.1986650825,0.2614681423,-0.0426690765,0.1606514305,-0.2143050581,-0.0832213685,-0.034965571,-0.0596217513,0.0405698605,-0.1706077158,-0.1644185781,-0.127827853,-0.075824365,-0.0180738959,0.2007799,-0.145962432,-0.075873211,-0.0241293088,0.2275981307,0.3193720877,0.1655257195,-0.0225367825,-0.0340410136,-0.0566328242,-0.3551487327,-0.2178953439,0.264709264,-0.0856192484,-0.3815219402,0.1003755182,-0.1799608767,-0.0490685962,0.1058381647,0.28082937,-0.1419560462,-0.0330454744,0.2358370721,0.1186067015,-0.0580852479,-0.1859557331,-0.2163185626,0.0217992533,-0.2409333587,0.0482624657,0.1400180757,-0.124954693,0.2232749313,0.119290635,0.0134862671,-0.0801843554,-0.2735644877,0.0867689103,-0.2249712497,0.3265425265,-0.1742536277,0.2312970906,0.0811845809,0.0593383759,0.1854857057,-0.1720252931,-0.2649029195,-0.4155016541,-0.3194849491,0.1969664246,-0.2242165804,-0.138505891,-0.1974963695,0.348713845,-0.2947647274,-0.0058729737,0.0125221862,0.2461334467,-0.0732460991,-0.1544661075,0.2622744739,0.6182906628,-0.4583748877,-0.2095780224,-0.1083629653,-0.4166224897,-0.0548756495,0.1128678843,-0.1665382534,0.0583279468,-0.5202240944,-0.1781945825,0.1034247875,0.0787859261,-0.3780346811,0.2601467967,-0.2509767711,0.0353801884,0.0130675919,0.0732858032,-0.0152492952,0.0118924454,0.3443200588,-0.2326517552,0.21061261,-0.2434655726,-0.1978896707,-0.1473296434,-0.0800581425,0.0401177816,0.1391501874,0.1212517768,0.1472719461,-0.2042873055,0.2786703408,0.1320928484,0.1007717475,0.3994412422,0.0888512954,0.4095033109,-0.0345550589,-0.3016703427,-0.2823216319,0.052182626,0.1295682341,0.075566344,-0.161512211,-0.1168438122,-0.3499845564,0.0066689802,-0.2822821736,-0.232999295,0.0990226567,0.031710159,-0.0975138322,0.3505385816,-0.2743443549,0.2439557463,-0.1475837231,0.1908287108,-0.0857722908,0.2456445694,-0.0328312144,0.0786674395,0.1223068535,0.1445461363,0.2025675476,-0.2024893761,0.0609173663,0.1910790652,-0.2864559293,0.2929493189,0.1641233265,-0.1031976268,0.395349443,-0.2833126187,-0.0221462622,-0.0058773565,-0.0091231037,0.03591251,0.0497126691,0.1098951697,0.0361204669,-0.0505238734,0.0481615141,0.3905566335,0.2027193606,0.1255100071,0.0460466258,-0.2952642441,0.4156197906,-0.2552629709,0.2555008531,-0.123711288,-0.5482590795,0.121470958,0.155679509,-0.01223059,-0.0797785074,0.1661076993,-0.3135465384,0.1526514143,0.0863787755,0.2055056989,0.4743486941,0.1211688444,0.0944968238,0.1419577897,0.0833977982,-0.2654426396,0.1715260446,0.0955152586,0.0847584456,0.1264387369,0.2306046635,0.0627992824,-0.6860172153,0.1786149144,-0.0245525111,0.0704921484,-0.2868274748,0.089332059,-0.2168475389,-0.4501330554,-0.0417789742,-0.200338617,-0.2872135639,-0.3427044749,0.1639034897,-0.0844308957,0.0105023915,0.0023929367,0.0422871113,0.1444423944,-0.3048490882,0.3941405714,-0.2151815593,0.2305174023,-0.1490975022,0.1350276619,-0.0951351374,-0.4219671786,0.3490423262,-0.1376728714,0.1671481282,-0.4405506253,-0.3121163845,0.2409561872,-0.19032152,0.2534345686,0.4084072411,0.3524568379,-0.2246049792,-0.0788920671,0.2826424539,-0.3633129001,-0.2287949771,-0.2020217329,-0.0808707476,0.09998364,-0.1418733299,-0.3560785055,-0.4679156542,-0.3456310332,0.332372427,-0.1477753073,-0.1233576462,0.0576736294,-0.05909805,0.1531910896,-0.1439454108,-0.0050233048,-0.4409775436,-0.4946687818,0.2675391138,-0.3384297788,-0.3320698142,0.1973769367,0.124529548,0.1705575883,-0.0989008844,-0.6122781038,-0.2373950332,-0.0790803134,-0.1603680849,-0.0285303947,-0.0322212651,0.2173502892,-0.1074162051,-0.0968480855,-0.0529327281,-0.0699402392,-0.1652570069,-0.0885061994,0.4766077697,-0.2985799611,0.4627819061,0.2019155174,0.8320382833,0.2451500744,0.0158250984,0.3560374379,-0.2802177668,0.628675878,0.0547197461,-0.3551826179,0.1142252982,-0.062110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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3415","title":"Non-deterministic tests: CI tests randomly fail","comments":"I think it might come from two different issues:\r\n1. Google Drive is an unreliable host, mainly because of quota limitations\r\n2. the staging environment can sometimes raise some errors\r\n\r\nFor Google Drive tests we could set up some retries with backup URLs if necessary I guess.\r\nFor staging on the other hand, I guess we can investigate what causes this and discuss with the back-end team","body":"## Describe the bug\r\nSome CI tests fail randomly.\r\n\r\n1. In https:\/\/github.com\/huggingface\/datasets\/pull\/3375\/commits\/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:\r\n   ```\r\n   =========================== short test summary info ============================\r\n   FAILED tests\/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https:\/\/drive.google.com\/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]\r\n   FAILED tests\/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...\r\n   FAILED tests\/test_streaming_download_manager.py::test_streaming_gg_drive_zipped\r\n   = 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =\r\n   ```\r\n\r\n2. After re-running the CI (without any change in the code) in https:\/\/github.com\/huggingface\/datasets\/pull\/3375\/commits\/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test (one on Linux and a different one on Windows):\r\n   - On Linux:\r\n      ```\r\n      =========================== short test summary info ============================\r\n      FAILED tests\/test_streaming_download_manager.py::test_streaming_gg_drive_zipped\r\n      = 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =\r\n      ```\r\n   - On Windows:\r\n      ```\r\n      =========================== short test summary info ===========================\r\n      FAILED tests\/test_load.py::test_load_dataset_builder_for_community_dataset_without_script\r\n      = 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =\r\n      ```\r\n\r\n   The test `tests\/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.\r\n\r\n3. After re-running again the CI (without any change in the code) in https:\/\/github.com\/huggingface\/datasets\/pull\/3375\/commits\/39f32f2119cf91b86867216bb5c356c586503c6a, ALL the tests passed.\r\n\r\n","comment_length":67,"text":"Non-deterministic tests: CI tests randomly fail \n ## Describe the bug\r\nSome CI tests fail randomly.\r\n\r\n1. In https:\/\/github.com\/huggingface\/datasets\/pull\/3375\/commits\/c10275fe36085601cb7bdb9daee9a8f1fc734f48, there were 3 failing tests, only on Linux:\r\n   ```\r\n   =========================== short test summary info ============================\r\n   FAILED tests\/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol[https:\/\/drive.google.com\/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh-zip]\r\n   FAILED tests\/test_streaming_download_manager.py::test_streaming_gg_drive - Fi...\r\n   FAILED tests\/test_streaming_download_manager.py::test_streaming_gg_drive_zipped\r\n   = 3 failed, 3553 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 192.79s (0:03:12) =\r\n   ```\r\n\r\n2. After re-running the CI (without any change in the code) in https:\/\/github.com\/huggingface\/datasets\/pull\/3375\/commits\/57bfe1f342cd3c59d2510b992d5f06a0761eb147, there was only 1 failing test (one on Linux and a different one on Windows):\r\n   - On Linux:\r\n      ```\r\n      =========================== short test summary info ============================\r\n      FAILED tests\/test_streaming_download_manager.py::test_streaming_gg_drive_zipped\r\n      = 1 failed, 3555 passed, 2950 skipped, 2 xfailed, 1 xpassed, 125 warnings in 199.76s (0:03:19) =\r\n      ```\r\n   - On Windows:\r\n      ```\r\n      =========================== short test summary info ===========================\r\n      FAILED tests\/test_load.py::test_load_dataset_builder_for_community_dataset_without_script\r\n      = 1 failed, 3551 passed, 2954 skipped, 2 xfailed, 1 xpassed, 121 warnings in 478.58s (0:07:58) =\r\n      ```\r\n\r\n   The test `tests\/test_streaming_download_manager.py::test_streaming_gg_drive_zipped` passes locally.\r\n\r\n3. After re-running again the CI (without any change in the code) in https:\/\/github.com\/huggingface\/datasets\/pull\/3375\/commits\/39f32f2119cf91b86867216bb5c356c586503c6a, ALL the tests passed.\r\n\r\n \n I think it might come from two different issues:\r\n1. Google Drive is an unreliable host, mainly because of quota limitations\r\n2. the staging environment can sometimes raise some errors\r\n\r\nFor Google Drive tests we could set up some retries with backup URLs if necessary I guess.\r\nFor staging on the other hand, I guess we can investigate what causes this and discuss with the back-end 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3400","title":"Improve Wikipedia loading script","comments":"Thanks! See https:\/\/public.paws.wmcloud.org\/User:Isaac_(WMF)\/HuggingFace%20Wikipedia%20Processing.ipynb for more implementation details \/ some data around the overhead induced by adding the extra preprocessing steps (stripping link prefixes and magic words)","body":"As reported by @geohci, the \"wikipedia\" processing\/loading script could be improved by some additional small suggested processing functions:\r\n- _extract_content(filepath):\r\n  - Replace .startswith(\"#redirect\") with more structured approach: if elem.find(f\".\/{namespace}redirect\") is None: continue\r\n- _parse_and_clean_wikicode(raw_content, parser):\r\n  - Remove rm_template from cleaning -- this is redundant with .strip_code() from mwparserformhell\r\n  - Build a language-specific list of namespace prefixes to filter out per below get_namespace_prefixes\r\n  - Optional: strip prefixes like categories -- e.g., Category:Towns in Tianjin becomes Towns in Tianjin\r\n  - Optional: strip magic words\r\n\r\n\r\n\r\n","comment_length":26,"text":"Improve Wikipedia loading script \n As reported by @geohci, the \"wikipedia\" processing\/loading script could be improved by some additional small suggested processing functions:\r\n- _extract_content(filepath):\r\n  - Replace .startswith(\"#redirect\") with more structured approach: if elem.find(f\".\/{namespace}redirect\") is None: continue\r\n- _parse_and_clean_wikicode(raw_content, parser):\r\n  - Remove rm_template from cleaning -- this is redundant with .strip_code() from mwparserformhell\r\n  - Build a language-specific list of namespace prefixes to filter out per below get_namespace_prefixes\r\n  - Optional: strip prefixes like categories -- e.g., Category:Towns in Tianjin becomes Towns in Tianjin\r\n  - Optional: strip magic words\r\n\r\n\r\n\r\n \n Thanks! See https:\/\/public.paws.wmcloud.org\/User:Isaac_(WMF)\/HuggingFace%20Wikipedia%20Processing.ipynb for more implementation details \/ some data around the overhead induced by adding the extra preprocessing steps (stripping link prefixes and magic words)","embeddings":[0.193967253,0.0157280192,-0.1693702489,-0.0298058297,-0.1310297847,0.0113721862,0.1996667981,0.523822248,0.4024959207,-0.0102351895,0.028697513,0.0813337564,0.1722234488,-0.0693215057,0.0228343289,-0.1620895565,0.0589284487,0.0751872957,-0.119600445,-0.0207439661,-0.4444516897,0.1861189455,-0.4511621296,-0.0859893411,-0.0912466422,0.2421658933,0.1153257564,0.2092314661,0.1948690712,-0.2872225344,-0.0061049596,0.3577410877,-0.0401709378,0.1109702662,-0.000100273,-0.2469588816,0.5153377652,0.0351660624,-0.3312367499,0.1636210829,-0.0898449123,-0.2193905711,0.1844627708,-0.4156948924,-0.0574796051,0.1117336676,0.0641759932,-0.0384056978,0.2483652234,-0.1463218331,0.3120387197,-0.1311064065,-0.3388059437,0.0696730614,0.298871845,0.2239449769,0.0603198782,0.0594344214,0.0962077007,-0.3713482916,-0.171621114,0.5728351474,-0.4025574028,-0.2046320736,0.283133328,0.0182251371,-0.009749921,-0.3233197927,0.2650039494,0.3149866462,0.3173887432,0.0236398485,0.0248519592,-0.1276536435,-0.0964280814,-0.1156853139,0.2442684472,-0.0068534031,-0.2562580109,-0.0331519991,0.0890235379,-0.0106243929,-0.133870706,0.0926016197,0.1641381085,0.5520073175,0.0779267326,-0.0354280323,0.1138820946,-0.107110247,-0.228353247,-0.1192658991,0.2019224912,0.2766710222,0.1578531265,0.0767140165,0.1123177707,0.4077828228,0.1442709565,-0.1260867566,-0.2911911607,0.2781639993,0.1760199666,-0.0045608357,0.0566200837,-0.1745509952,0.0607301667,-0.4144009352,0.4771041572,0.3822630942,-0.1582810283,0.1549773961,0.0367836058,-0.1165253222,-0.0515702628,-0.1489293575,0.0711133555,0.0160701629,-0.0365724824,0.2464364171,0.1388548613,-0.1188737601,-0.2540472448,0.3988131881,-0.2095292658,0.428959161,0.3343982995,-0.0866677314,-0.3663809299,0.1412834823,0.0008143462,0.1519882977,-0.2420830578,0.3433485329,0.2891665399,0.3463391662,0.2299791127,0.1195135191,0.0228334963,-0.2649950683,-0.1235791296,0.1033720598,0.1624999046,0.1821767986,0.2008585185,0.199591592,-0.0202678293,-0.3072807491,-0.0665585846,0.0621545762,-0.190403074,0.0098703578,-0.1434508711,0.2966477871,0.1566785425,0.3270171285,-0.1444630027,0.1403221637,-0.062606059,-0.0610849857,-0.1680757701,0.2016084641,-0.1793375313,-0.1757236123,0.2882601619,0.5597960949,0.0883889273,0.0583681427,0.0320228785,0.3633699417,0.2103021294,0.1494206488,-0.1388691217,0.3393671811,-0.1479441971,0.0950756967,0.1117618904,-0.3317254186,0.1356148571,0.1435266435,-0.0265914667,0.3619920015,0.0789326802,0.0482244119,0.1417698562,-0.0312789641,0.1057107523,0.4501649737,0.2587348223,0.3759874701,-0.4333106279,-0.0674497783,0.1218119487,0.0629834086,0.0771722794,-0.1574197859,-0.2470975965,0.3719614446,0.4996143878,-0.2192957997,0.0603060722,0.0161312353,-0.2356400192,0.2280776054,0.246270135,0.0390634015,-0.3629969656,0.0018376014,0.1100868136,0.3641886115,-0.0224608649,-0.3322691917,-0.273465395,-0.1027374119,-0.2649635673,-0.1050421149,0.3653174639,0.1083378494,-0.1081008539,0.0767306164,0.0920750275,-0.153931722,-0.0843398869,-0.0225493368,-0.2017499804,-0.0264848955,-0.218571052,0.0780077875,0.0057834443,0.1952637583,0.0887192786,0.0484556295,0.0568522103,0.0124555165,0.1700160652,0.1198600829,0.2219557613,-0.0985064358,0.1282908767,-0.2813477218,-0.0226592403,0.3502528071,0.1358818263,-0.1962112933,-0.2686543167,0.3152805567,-0.1093212143,0.3799706995,-0.0246535465,-0.1635637879,0.2754855752,-0.1843495816,0.1146072,-0.2074881345,0.1068575233,0.4016964734,-0.0700614676,-0.0360371284,0.133471936,0.1356583983,0.0684077442,-0.1412667781,0.0886938646,0.2324621379,-0.2240824848,-0.2752403021,-0.1043944657,-0.3513838351,-0.2523005903,0.204717949,-0.020919729,0.0270533971,0.0858573392,-0.2293268293,0.4935016334,0.1044580042,0.1242622435,0.0732782558,0.1463689804,0.0798652694,-0.2584498823,-0.1309597641,-0.3803953528,0.0250889342,-0.1574728042,-0.0367807336,-0.0132050477,-0.2037907839,0.0287743434,-0.0379242189,-0.2146698684,-0.392367512,0.0190134346,0.3406577706,-0.2125298679,-0.0141022336,0.1600332409,0.1455851346,0.0288527422,-0.1303872764,-0.2512117922,-0.4596255124,-0.1194253713,0.1566368639,0.2660679221,0.1523712575,0.2854027152,-0.1775169075,-0.0688067302,-0.089013353,-0.409250468,0.0873957872,-0.0440469198,-0.0718678758,-0.0676469728,0.4663816988,0.0483357944,-0.3969710171,0.033520408,0.0154991224,0.1801412851,0.0213558879,-0.0510100871,-0.133361429,-0.1260954589,-0.2026037574,0.0398702696,-0.2294678241,0.0449720994,0.0135232406,0.2352086455,0.0966310129,-0.0794006661,-0.1473275572,0.0748510212,0.139011547,-0.1198072061,0.0573259927,0.2472428977,-0.1321106404,-0.219401896,-0.0100070294,-0.1814778447,0.1122782975,-0.1714845151,-0.2322449684,0.0118436292,0.259634465,0.3425201178,0.2679559588,-0.0650587678,0.2999715209,0.3202681541,-0.2191019356,0.0658632442,0.027941294,-0.0477507226,-0.3199148774,-0.0894196257,0.2240569592,-0.0051505202,-0.1894938201,0.5411359072,0.0464814454,0.236559689,0.249522686,0.0899610966,-0.060334228,-0.1252017319,-0.220252037,-0.1825008839,-0.1974010319,-0.0929219127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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3398","title":"Add URL field to Wikimedia dataset instances: wikipedia,...","comments":"@geohci, I think the field \"url\" does not appear in the Wikimedia dumps. Therefore I guess we should generate it, using the \"title\" field and making some transformation of it (replacing spaces with underscores) and prepending the domain (created using the language)?","body":"As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field \"url\"), so that we conform to proper attribution from license requirement. See, e.g.: https:\/\/fair-trec.github.io\/docs\/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2\r\n\r\nThis should be done for all pre-processed datasets under \"wikimedia\" org in the Hub: https:\/\/huggingface.co\/wikimedia\r\n","comment_length":42,"text":"Add URL field to Wikimedia dataset instances: wikipedia,... \n As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field \"url\"), so that we conform to proper attribution from license requirement. See, e.g.: https:\/\/fair-trec.github.io\/docs\/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2\r\n\r\nThis should be done for all pre-processed datasets under \"wikimedia\" org in the Hub: https:\/\/huggingface.co\/wikimedia\r\n \n @geohci, I think the field \"url\" does not appear in the Wikimedia dumps. Therefore I guess we should generate it, using the \"title\" field and making some transformation of it (replacing spaces with underscores) and prepending the domain (created using the language)?","embeddings":[0.1571044922,0.231396988,0.0553594083,-0.0971820951,-0.0335843898,0.1205125451,0.0998200178,-0.0271698199,0.1562785804,0.161117062,0.128759861,0.3992243111,0.2427761853,-0.016125774,0.1180698946,-0.0430260822,0.0546707436,-0.1464302987,0.079359442,-0.0267325062,-0.5429909825,-0.1044838503,-0.1917269677,0.1146845073,-0.3291085362,0.0800307617,-0.1599080563,0.0441328362,-0.0529719591,-0.5854725838,-0.0042662933,0.2980201542,0.1002144441,0.130604431,-0.0001132734,-0.132488519,0.724306345,-0.0478105471,-0.3696550727,-0.086521633,-0.3484997749,0.0716842636,-0.0329330228,-0.3411331177,-0.0347375087,0.0769961923,0.2073157281,0.2120357454,-0.1239781603,-0.0874495134,0.1283513606,-0.0816619769,0.0216713641,-0.1109744161,-0.0641791001,0.0164172482,-0.0062291906,-0.0231010355,-0.1283851862,0.0336464643,-0.0439920165,0.2164625376,0.1745321304,-0.1757094115,-0.0529074147,0.1840191633,-0.4282551706,-0.210112974,0.1052658334,0.2758535147,0.4850408137,-0.2248807549,-0.3822431564,0.0523294993,0.3067312539,0.0171261542,0.1801780313,0.2024202496,0.2740784287,0.113871336,0.0558751784,-0.2460248768,-0.3675602078,0.2832782567,0.012548686,0.7729819417,0.1766661108,0.2104034275,-0.4395722151,0.0830569565,0.0963478833,-0.267180413,0.2103016675,-0.029733669,0.2215019315,-0.2775943875,-0.0385479257,0.3087939918,0.0627626777,-0.2031495869,-0.1683690548,-0.0974444523,-0.0315201618,0.1059766635,0.0595838428,-0.3651390076,0.0287308283,0.1623824239,0.4684321582,0.0194594767,0.0763422847,-0.1140980721,-0.1072527841,0.2345822006,-0.4018836617,0.0570343919,0.0196053181,0.0645909086,-0.0794445798,0.1376130283,0.2561467588,0.0578246601,0.0104996618,0.2400204539,-0.1860045195,0.037508104,-0.1100718975,0.1483381391,-0.0181226637,-0.0486922339,-0.1246275157,0.2159365267,0.2020657957,0.4192013741,0.1302263141,0.0430967137,0.5221181512,-0.0742369071,0.111836724,-0.2072144449,0.0853823423,0.1027615145,0.0571578555,0.2295497358,0.2783561647,0.3453049064,0.0631174445,-0.1750156283,-0.0634327307,0.2873021662,-0.8060637712,-0.3427830637,-0.1192170903,0.0656811222,-0.1072659865,-0.1637141407,-0.069081232,0.1616758704,0.0532798022,-0.0507364795,0.0564290285,0.2502653599,-0.3048220575,-0.1007436439,0.1588172615,0.2112121731,0.0714336857,0.1531759351,-0.3376043737,0.3999429941,-0.0365651213,-0.3135677278,-0.1817648411,0.5109193325,0.0769782141,0.4451569319,0.0096157873,-0.5028415918,0.1002635062,-0.1535638571,-0.1584365666,-0.0804938376,-0.2627419233,0.0280170422,0.1922604591,-0.0362297818,0.1505689174,0.3364805877,-0.0768672302,0.1025324762,-0.1525750905,-0.0875772089,-0.1516565084,0.2858836651,0.2035781443,-0.1158205196,0.1859499216,0.2688694596,0.1455790251,-0.1816478372,0.0648293942,0.0687533244,0.1228660718,-0.0816272348,0.1302000284,-0.0538911261,0.1087942645,0.0118215857,-0.0961103439,0.3593488336,0.0140496334,-0.3419363499,-0.3029313684,0.2157124281,-0.1393555105,-0.1969225407,0.1480536014,-0.0733193308,-0.2070028931,0.2494328469,-0.0883369595,-0.3459621966,-0.2277374268,-0.0458799079,0.0200499278,0.1895114481,-0.0531902872,0.2605975568,-0.0001136615,0.3334261775,0.3131519854,0.1439888626,0.0837199762,0.3808574677,0.3683308065,0.4696272612,0.7099096179,-0.0142405229,0.369246304,-0.135806933,-0.1793780327,0.1773076504,0.0171841849,0.1411431581,-0.0648852512,0.5167850256,0.0271173324,0.1138551533,-0.0152509874,0.1059939191,0.288797915,0.0636154488,-0.0846141279,-0.2062882036,-0.3655456901,0.424862057,-0.4011357725,-0.1193473712,0.2904344797,0.1843611598,0.2467885464,-0.2662814856,0.2120951265,0.2430992275,-0.2634654343,-0.2581275105,0.0794075355,-0.0077674901,-0.0398137383,0.1474407017,-0.0038376851,0.089223206,-0.119008109,-0.1929742247,0.4178104699,0.394569844,0.0778944194,0.1824633181,0.0439283028,-0.0746510625,-0.1595222056,-0.2145778239,0.1168200076,0.1221205741,-0.2806233168,-0.259426564,-0.073969163,0.3685996532,0.1147523746,-0.1522684395,-0.2333462983,-0.4991745651,0.3300071359,0.2598216534,-0.3870491087,0.0638198406,-0.0760407373,0.4897830784,0.0043366603,0.0693052337,-0.1406706274,-0.5611676574,-0.0849700719,0.1216319054,0.0549024679,-0.0145730395,0.0793684572,0.2277263403,0.3706838489,-0.2828298807,-0.4933713675,-0.0375123881,-0.0887301415,0.2672691941,-0.1062394381,0.2783947289,-0.2394114882,-0.3541610539,0.05984338,0.1034299433,0.2085786015,-0.0580077954,-0.2077987641,-0.1127872989,0.1855123788,-0.047304593,-0.1030104682,-0.0299227014,-0.3002201915,0.0457697064,0.1811196059,-0.2830669582,-0.1259369701,0.0104339318,0.0368904322,-0.2636103034,-0.291146636,-0.5249606371,0.6405799389,-0.627412498,-0.4060340226,-0.0865539759,-0.2978346646,0.1969012469,0.0065608076,-0.1384172589,-0.4011151493,0.3541615307,-0.071669057,0.4516208768,0.1317014843,0.2808437943,0.1297415942,-0.1068558991,-0.0504303463,0.033198107,0.2216802835,0.6089900136,0.0844609067,0.180093497,-0.176105395,0.0348352194,0.818659544,0.1571892053,0.675640583,0.2751736939,-0.0693541095,-0.0109159155,0.0215186644,0.2668714225,0.0392020456,0.0589268804,-0.1293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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3398","title":"Add URL field to Wikimedia dataset instances: wikipedia,...","comments":"Indeed:\r\n\r\n> To re-distribute text on Wikipedia in any form, provide credit to the authors either by including a) a [hyperlink](https:\/\/en.wikipedia.org\/wiki\/Hyperlink) (where possible) or [URL](https:\/\/en.wikipedia.org\/wiki\/URL) to the page or pages you are re-using, b) a hyperlink (where possible) or URL to an alternative, stable online copy which is freely accessible, which conforms with the license, and which provides credit to the authors in a manner equivalent to the credit given on this website, or c) a list of all authors. (Any list of authors may be filtered to exclude very small or irrelevant contributions.) This applies to text developed by the Wikipedia community. Text from external sources may attach additional attribution requirements to the work, which should be indicated on an article's face or on its talk page. For example, a page may have a banner or other notation indicating that some or all of its content was originally published somewhere else. Where such notations are visible in the page itself, they should generally be preserved by re-users.\r\n\r\nsource: https:\/\/en.wikipedia.org\/wiki\/Wikipedia:Copyrights\r\n\r\nI guess it's fine to add the URL field - it can be constructed easily from the title page IIRC.","body":"As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field \"url\"), so that we conform to proper attribution from license requirement. See, e.g.: https:\/\/fair-trec.github.io\/docs\/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2\r\n\r\nThis should be done for all pre-processed datasets under \"wikimedia\" org in the Hub: https:\/\/huggingface.co\/wikimedia\r\n","comment_length":190,"text":"Add URL field to Wikimedia dataset instances: wikipedia,... \n As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field \"url\"), so that we conform to proper attribution from license requirement. See, e.g.: https:\/\/fair-trec.github.io\/docs\/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2\r\n\r\nThis should be done for all pre-processed datasets under \"wikimedia\" org in the Hub: https:\/\/huggingface.co\/wikimedia\r\n \n Indeed:\r\n\r\n> To re-distribute text on Wikipedia in any form, provide credit to the authors either by including a) a [hyperlink](https:\/\/en.wikipedia.org\/wiki\/Hyperlink) (where possible) or [URL](https:\/\/en.wikipedia.org\/wiki\/URL) to the page or pages you are re-using, b) a hyperlink (where possible) or URL to an alternative, stable online copy which is freely accessible, which conforms with the license, and which provides credit to the authors in a manner equivalent to the credit given on this website, or c) a list of all authors. (Any list of authors may be filtered to exclude very small or irrelevant contributions.) This applies to text developed by the Wikipedia community. Text from external sources may attach additional attribution requirements to the work, which should be indicated on an article's face or on its talk page. For example, a page may have a banner or other notation indicating that some or all of its content was originally published somewhere else. Where such notations are visible in the page itself, they should generally be preserved by re-users.\r\n\r\nsource: https:\/\/en.wikipedia.org\/wiki\/Wikipedia:Copyrights\r\n\r\nI guess it's fine to add the URL field - it can be constructed easily from the title page IIRC.","embeddings":[0.146903649,0.1563640684,0.0456908084,-0.0973034501,-0.0687778965,0.0882161558,0.2351890951,0.1012060642,0.2732244134,0.096547924,-0.000646503,0.5451562405,0.3293744326,-0.0347850733,0.1449323148,0.0359693021,-0.0703825653,-0.0078174472,0.0900945961,-0.1189330593,-0.2662050724,-0.1626251936,-0.0155345891,0.1017157435,-0.335657686,0.1159459725,-0.0399596877,0.000519811,0.1574195325,-0.5787082911,-0.0819804072,0.3225381076,0.1236573383,-0.0274403747,-0.0001021357,-0.0546460114,0.5468027592,-0.0475492887,-0.5382336378,-0.0233309586,-0.2566256821,0.0489836335,0.0568551049,-0.3973729312,-0.0301079359,0.1406404823,0.1811786294,0.2460213155,0.0732582659,-0.1679517478,0.2535535395,-0.078463465,0.1035996452,-0.0924300477,0.1822033972,0.0754530206,-0.0247870982,0.081605643,-0.1732114404,0.2063212991,-0.0600360632,0.394289881,-0.0004339365,-0.0701079443,0.1379075795,0.0638573766,-0.2373415828,-0.1481060982,0.0993542299,0.3771221042,0.4801135361,-0.2192427069,-0.3594067991,-0.170009926,0.2406635433,0.1399696022,0.1156473607,0.1680255979,0.2211130261,0.1134126782,0.1342476904,-0.3733397126,-0.3180849254,0.185287863,0.0048248647,0.5783543587,0.1496167332,0.1824681461,-0.3639406562,0.1655489355,0.0077460189,-0.2296756506,0.2507576346,-0.1130988076,0.3515103161,-0.2751329541,-0.0183911826,0.1524537653,0.1466957331,-0.2047617435,-0.2302435786,-0.129403308,0.005068575,0.0897691771,0.1719194651,-0.3878020942,-0.1513750851,0.1986067891,0.6666780114,0.093213439,-0.028370047,-0.1217510477,0.0849263668,0.266918391,-0.2247014195,-0.0882998854,0.0703122243,0.0077627366,-0.1085538268,0.0707323998,0.2000033408,0.1023821533,-0.0299217664,0.1968512982,-0.1519148797,0.0512222461,-0.0737477615,0.0330855548,0.0059441128,-0.067698583,-0.1098105758,0.1221285686,0.1795981377,0.4806160331,0.1750595868,-0.0541612096,0.3845685124,-0.077248089,0.1967746615,-0.2339741588,0.0770030692,0.105984129,0.0359319709,0.2334008217,0.2348480225,0.3434664905,-0.0305459965,-0.2094244808,-0.1508964002,0.2128831595,-0.6181073785,-0.2740844786,-0.0675012171,0.1999603957,-0.0985726193,-0.1737556458,0.0588583983,0.0555684119,0.0001099471,-0.1407596767,0.0249535013,0.3181324005,-0.2238849699,-0.1643950939,0.1900383234,0.3026750088,0.0978508145,0.1391932666,-0.2733478546,0.4062922001,-0.135760352,-0.1495834142,-0.2442492098,0.5067586303,0.0497436225,0.3871420324,0.1573559493,-0.4277001619,-0.0964093059,-0.1044158265,-0.1221537963,-0.023458384,-0.2543870509,0.2141285241,0.2637697756,-0.1073984504,-0.0499410778,0.2635251284,0.0718703046,0.2180797309,-0.2854137123,-0.1267760992,-0.0481805503,0.29873842,0.1856767535,-0.0288638007,0.2431949377,0.379671067,0.1560983062,-0.2787531614,0.147797212,-0.0194067843,0.1948796064,0.022983782,0.1043910384,-0.1474000812,0.1707333624,0.0292080045,-0.1088099703,0.3266704082,0.063906759,-0.4402584434,-0.3041575253,0.0790002942,-0.173571378,-0.2978614271,0.3113654256,-0.0526488088,-0.1099535674,0.24457331,0.0824493915,-0.2290253043,-0.2422149032,-0.1462170482,0.1103818789,0.2601084113,-0.1399697214,0.2263802439,-0.0266408082,0.3510403335,0.1955284327,0.2239446938,0.1063674167,0.2827289999,0.1464265138,0.3602927923,0.5668962002,-0.0803723782,0.3841746747,-0.2069436908,-0.056525562,0.1418414563,0.0722997263,0.1228994355,-0.2107604146,0.4323965609,-0.0234720781,-0.0054069073,0.0210635643,0.147802338,0.2514047325,0.0401968434,-0.082495302,-0.1763094217,-0.4588902593,0.4294438064,-0.4020911157,-0.073837392,0.1629471332,0.2735232115,0.2167516947,0.0060063987,0.3993231356,0.1910374463,-0.344912976,-0.1843084246,0.1536907852,0.0344006047,-0.2411941141,0.2756949663,0.0639947876,0.0136994412,-0.1985095739,-0.2328681499,0.3469600976,0.1388813108,0.0548968911,0.1657069474,0.1288610399,0.0168112386,-0.1835386455,-0.0398836881,0.0279997438,0.0197533406,-0.2920458019,-0.2925819755,-0.0371975563,0.1780955791,0.0230464712,-0.0646428764,-0.3090922832,-0.4628012776,0.3608567715,0.4439627826,-0.3925619125,0.0900406092,0.0259260759,0.3952256143,-0.1167472973,-0.022768775,-0.1975241899,-0.5979509354,-0.0163485575,0.2233111858,0.1192639843,0.0379118696,0.1763363779,0.3054111898,0.1970912516,-0.5195220709,-0.4121031165,-0.0221714191,-0.0835980922,0.1182542816,-0.1202159598,0.2942838669,-0.2619203925,-0.2266940624,0.0871878117,0.0447883643,0.1951005757,-0.0271187946,-0.0644074008,-0.0412842892,0.0593127757,-0.1258223951,0.0462755151,-0.1167904139,-0.1541951001,0.0671914667,0.0896048248,-0.2045487314,-0.0314996205,-0.0528336652,0.1352984011,-0.0001872442,-0.4103954136,-0.3000585437,0.6064271927,-0.5251866579,-0.444552213,-0.0520803221,-0.0892059058,0.0576481782,0.0781401023,-0.2847552896,-0.4102748632,0.3254010677,0.0466098972,0.2960566282,0.1602220237,0.3260317743,0.1192245334,-0.1974093765,-0.0528343655,0.0748987868,0.1374464035,0.3943043351,-0.0685464963,0.008586484,-0.2041344941,0.0334285758,0.7748719454,0.2701438367,0.5633050203,0.2508845031,-0.0253861919,0.1676225662,-0.0936691239,0.2571891546,0.0504775643,-0.0735867023,-0.0677668601,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3398","title":"Add URL field to Wikimedia dataset instances: wikipedia,...","comments":"yep, sorry forgot that that wasn't already in the dumps. specifically `f\"https:\/\/{language}.wikipedia.org\/wiki\/{title.replace(' ', '_')}` should do it","body":"As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field \"url\"), so that we conform to proper attribution from license requirement. See, e.g.: https:\/\/fair-trec.github.io\/docs\/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2\r\n\r\nThis should be done for all pre-processed datasets under \"wikimedia\" org in the Hub: https:\/\/huggingface.co\/wikimedia\r\n","comment_length":17,"text":"Add URL field to Wikimedia dataset instances: wikipedia,... \n As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field \"url\"), so that we conform to proper attribution from license requirement. See, e.g.: https:\/\/fair-trec.github.io\/docs\/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2\r\n\r\nThis should be done for all pre-processed datasets under \"wikimedia\" org in the Hub: https:\/\/huggingface.co\/wikimedia\r\n \n yep, sorry forgot that that wasn't already in the dumps. specifically `f\"https:\/\/{language}.wikipedia.org\/wiki\/{title.replace(' ', '_')}` should do it","embeddings":[0.1569754183,0.153305456,-0.0069040572,-0.1684149206,0.0327237174,0.131046325,0.0735661238,0.1554717571,0.2668462396,0.1516581774,0.0103206662,0.5502920151,0.3304093778,-0.0482818447,-0.0146247027,0.0482312553,0.0911027193,-0.0532020703,0.1225775853,-0.0800341368,-0.3383880258,0.0399760716,-0.1739843339,0.1279705167,-0.2993780375,0.0576869883,-0.0620991476,0.1107453555,0.0233533327,-0.5956231952,-0.0591642112,0.449372232,-0.0468075164,0.1134301201,-0.0001041698,-0.0922248885,0.5972483158,0.0153600872,-0.4168347716,-0.0108973635,-0.2418028861,0.0857032388,-0.0102304276,-0.2739644051,-0.1164697409,-0.0620010644,0.1152026951,0.1036485061,-0.0051356922,-0.0659435242,0.2283047438,-0.0157090612,-0.0432872586,-0.2180989683,0.0814112425,0.0542515479,-0.0174058378,-0.0268793479,-0.2068989575,0.0073999679,-0.0486091897,0.3843645751,-0.0591936447,-0.1436747611,-0.0722059011,0.0506037399,-0.3064975441,-0.2710343003,0.2315893173,0.3889625967,0.3913798928,-0.2141827941,-0.2705120742,-0.0320469402,0.1841512173,0.0692070574,0.1077938303,0.1772961169,0.2137195468,0.1401238292,0.1176073924,-0.2778815329,-0.2628441155,0.2081470937,0.0593264401,0.7477734089,0.0992077291,0.1046562865,-0.3063514829,0.0013275027,-0.0065040221,-0.1551403552,0.0683440715,-0.1263579875,0.1762769967,-0.221114248,-0.0165474545,0.1898050457,0.1757512093,-0.2128415853,-0.3463748991,0.1564345956,0.0020413846,0.1314543635,-0.0222510323,-0.3366542161,0.0733020455,0.2002923042,0.5897761583,0.1161626875,-0.0649857074,-0.06808386,0.0775445178,0.2019591928,-0.4392175078,0.0609339736,0.0611131229,0.0253421478,-0.1611120105,0.1997833848,0.2719551027,0.0255979132,-0.0776785016,0.4350876212,-0.1876143217,0.0759518072,-0.095814012,0.2157298923,-0.1640853286,-0.0480190106,-0.1787875444,0.1961251348,0.1571211666,0.4440368116,0.1950270683,0.0986280516,0.4554645419,-0.1042498127,0.2662776709,-0.2906147838,0.0502859876,0.0748111308,0.0504033118,0.2081161737,0.3091643155,0.3266215324,0.0816686749,-0.2749885321,-0.0483173504,0.2294975966,-0.6770358682,-0.107156232,-0.1320023686,0.1452887803,-0.0299426634,-0.1405929029,0.0341737159,0.213194415,0.0166889764,-0.0886399522,0.0196911786,0.3607058525,-0.3236530423,-0.0820653066,0.1527977139,0.2311656475,0.1102725938,0.0668202862,-0.1086337417,0.5067186952,-0.0940237939,-0.1313897073,-0.0900819823,0.3954958022,0.0751240849,0.5513405204,0.0588121787,-0.5358224511,-0.0112280902,-0.1759869754,-0.0632712618,-0.0336360969,-0.20804362,0.0010073996,0.2313655764,-0.0549408793,0.0194345284,0.3071814179,0.0838320777,0.198980689,-0.2382894158,-0.10379868,-0.1863385141,0.2628117502,0.1689277589,-0.164974153,0.0886096805,0.189281553,0.2113837302,-0.333399117,0.0810456574,0.0015996963,0.1511651576,0.0541750416,0.2056539208,-0.1873548478,0.0915029645,0.0036029937,-0.089099519,0.358943373,-0.0087103741,-0.4522177279,-0.3365608156,0.1887783706,-0.2233256251,-0.148583129,0.2698014677,-0.0196333267,-0.1806796044,0.3411155343,-0.0055567473,-0.2414970547,-0.1890343875,-0.0047178059,0.0684144646,0.2270139158,-0.0913907662,0.1470177025,-0.0206932742,0.2524677217,0.3554238379,0.148268804,0.0750713795,0.3871055841,0.3087367117,0.3763490021,0.5801576972,-0.1475729495,0.3004485667,-0.2147487104,-0.1176938117,0.3358063102,-0.0011503991,0.1813631058,-0.2873904109,0.5433686376,0.0684750304,0.1063629389,0.0035955023,0.0970967859,0.344598949,-0.0447314382,-0.0097642904,-0.2633306682,-0.2830386758,0.4216473103,-0.4752157629,-0.224409163,0.1995721757,0.1422834992,0.3035838902,-0.045527555,0.4068110883,0.2076283842,-0.3091475964,-0.1592333913,0.1513534933,-0.1203540564,-0.206881091,0.2328027785,-0.0513810776,0.0300954431,-0.0312644318,-0.2866463661,0.3756705225,0.2890815139,0.033649236,0.2156124562,0.2581700087,-0.1159081981,-0.3107538819,-0.0811902955,-0.0054294826,0.0590824001,-0.2748798728,-0.0983567908,-0.2297761589,0.2191711366,0.1991605908,-0.1090412661,-0.208992064,-0.4358665943,0.4411310554,0.3972389996,-0.3820638359,0.1943532974,0.063267678,0.4297339916,0.0222193003,0.1103924364,-0.1458755136,-0.5155969262,-0.0339897722,0.1834845692,0.1118652895,-0.0335785076,0.0889063403,0.2371499687,0.1371296495,-0.3750267625,-0.4796653092,0.0487209037,-0.1015856639,0.1976365596,-0.1153525412,0.250359714,-0.2794218659,-0.2048845589,0.1470649987,0.0026649779,0.1692276299,-0.0070869727,-0.1067988873,-0.036055956,0.0649576932,-0.1239402145,0.0766570121,-0.1253014505,-0.1711119413,0.0910630748,0.1607179791,-0.2412042171,-0.0384526476,-0.1718402058,0.050810162,-0.1151922718,-0.3779584169,-0.3547456861,0.5493466258,-0.6466532946,-0.3817312717,-0.0461788066,-0.1216836274,0.0445349552,-0.0780424625,-0.1581057757,-0.3938281238,0.2260212004,-0.0709947646,0.2668583393,0.1100560054,0.2851067185,0.1146323383,-0.2410285175,-0.1836720407,-0.0135470331,0.1003211737,0.3792585135,-0.1244217008,0.1099872068,-0.0746062696,0.1089314371,0.6937591434,0.1274300665,0.6646242738,0.3323841393,-0.1110615954,0.0163898636,-0.035928648,0.2144140303,-0.0401338674,-0.0764073208,0.007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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3398","title":"Add URL field to Wikimedia dataset instances: wikipedia,...","comments":"Thanks @geohci.\r\n\r\nI had already been looking for information about the conversion from title to URL and I found that apart from replacing blanks with underscores, some other special character must also be percent-encoded (e.g. `\"` to `%22`): https:\/\/meta.wikimedia.org\/wiki\/Help:URL\r\n\r\nTherefore, I have finally used `urllib.parse.quote` function. This additionally percent-encodes non-ASCII characters, but Wikimedia docs say these are equivalent:\r\n> For the other characters either the code or the character can be used in internal and external links, they are equivalent. The system does a conversion when needed.\r\n> [[%C3%80_propos_de_M%C3%A9ta]]\r\n> is rendered as [\u00c0_propos_de_M\u00e9ta](https:\/\/meta.wikimedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta), almost like [\u00c0 propos de M\u00e9ta](https:\/\/meta.wikimedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta), which leads to this page on Meta with in the address bar the URL\r\n> [http:\/\/meta.wikipedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta](https:\/\/meta.wikipedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta)\r\n> while [http:\/\/meta.wikipedia.org\/wiki\/\u00c0_propos_de_M\u00e9ta](https:\/\/meta.wikipedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta) leads to the same. ","body":"As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field \"url\"), so that we conform to proper attribution from license requirement. See, e.g.: https:\/\/fair-trec.github.io\/docs\/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2\r\n\r\nThis should be done for all pre-processed datasets under \"wikimedia\" org in the Hub: https:\/\/huggingface.co\/wikimedia\r\n","comment_length":123,"text":"Add URL field to Wikimedia dataset instances: wikipedia,... \n As reported by @geohci, in order to host pre-processed data in the Hub, we should add the full URL to data instances (new field \"url\"), so that we conform to proper attribution from license requirement. See, e.g.: https:\/\/fair-trec.github.io\/docs\/Fair_Ranking_2021_Participant_Instructions.pdf#subsection.3.2\r\n\r\nThis should be done for all pre-processed datasets under \"wikimedia\" org in the Hub: https:\/\/huggingface.co\/wikimedia\r\n \n Thanks @geohci.\r\n\r\nI had already been looking for information about the conversion from title to URL and I found that apart from replacing blanks with underscores, some other special character must also be percent-encoded (e.g. `\"` to `%22`): https:\/\/meta.wikimedia.org\/wiki\/Help:URL\r\n\r\nTherefore, I have finally used `urllib.parse.quote` function. This additionally percent-encodes non-ASCII characters, but Wikimedia docs say these are equivalent:\r\n> For the other characters either the code or the character can be used in internal and external links, they are equivalent. The system does a conversion when needed.\r\n> [[%C3%80_propos_de_M%C3%A9ta]]\r\n> is rendered as [\u00c0_propos_de_M\u00e9ta](https:\/\/meta.wikimedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta), almost like [\u00c0 propos de M\u00e9ta](https:\/\/meta.wikimedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta), which leads to this page on Meta with in the address bar the URL\r\n> [http:\/\/meta.wikipedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta](https:\/\/meta.wikipedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta)\r\n> while [http:\/\/meta.wikipedia.org\/wiki\/\u00c0_propos_de_M\u00e9ta](https:\/\/meta.wikipedia.org\/wiki\/%C3%80_propos_de_M%C3%A9ta) leads to the same. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3396","title":"Install Audio dependencies to support audio decoding","comments":"https:\/\/huggingface.co\/datasets\/projecte-aina\/parlament_parla -> works (but we still have to show an audio player)\r\n\r\nhttps:\/\/huggingface.co\/datasets\/openslr -> another issue: `Message:       [Errno 2] No such file or directory: '\/home\/hf\/datasets-preview-backend\/zip:\/asr_javanese\/data\/00\/00004fe6aa.flac'`","body":"## Dataset viewer issue for '*openslr*', '*projecte-aina\/parlament_parla*'\r\n\r\n**Link:** *https:\/\/huggingface.co\/datasets\/openslr*\r\n**Link:** *https:\/\/huggingface.co\/datasets\/projecte-aina\/parlament_parla*\r\n\r\nError:\r\n```\r\nStatus code:   400\r\nException:     ImportError\r\nMessage:       To support decoding audio files, please install 'librosa'.\r\n```\r\n\r\nAm I the one who added this dataset ? Yes-No\r\n- openslr: No\r\n- projecte-aina\/parlament_parla: Yes\r\n","comment_length":25,"text":"Install Audio dependencies to support audio decoding \n ## Dataset viewer issue for '*openslr*', '*projecte-aina\/parlament_parla*'\r\n\r\n**Link:** *https:\/\/huggingface.co\/datasets\/openslr*\r\n**Link:** *https:\/\/huggingface.co\/datasets\/projecte-aina\/parlament_parla*\r\n\r\nError:\r\n```\r\nStatus code:   400\r\nException:     ImportError\r\nMessage:       To support decoding audio files, please install 'librosa'.\r\n```\r\n\r\nAm I the one who added this dataset ? Yes-No\r\n- openslr: No\r\n- projecte-aina\/parlament_parla: Yes\r\n \n https:\/\/huggingface.co\/datasets\/projecte-aina\/parlament_parla -> works (but we still have to show an audio player)\r\n\r\nhttps:\/\/huggingface.co\/datasets\/openslr -> another issue: `Message:       [Errno 2] No such file or directory: '\/home\/hf\/datasets-preview-backend\/zip:\/asr_javanese\/data\/00\/00004fe6aa.flac'`","embeddings":[-0.2341923416,0.0829559043,-0.122821264,0.2838072479,0.1610809863,0.0163821112,-0.0711146519,-0.0265362151,-0.3266074955,0.2293246835,-0.3968029022,0.3755850494,-0.0406983159,0.0464401171,-0.1093377694,-0.2251802385,0.0919816718,0.3634328842,-0.1583889723,-0.0297107864,-0.2033931762,0.4243871272,-0.2201777995,0.0990480781,-0.1439190656,0.044713743,0.0281781964,-0.1364055276,-0.528677702,-0.3223713636,0.1723499596,-0.0390275195,0.0900084972,0.3624123931,-0.0001150156,-0.091618754,0.4367981553,-0.1400973797,-0.1307919174,-0.1518679112,-0.3172642887,-0.1341241449,0.1124356911,0.1376461089,0.110096395,-0.4963771105,-0.1170554534,-0.4504229426,0.4022116959,0.1258859187,0.2048760056,-0.0335429236,0.3752242327,0.0694859028,0.3817959428,0.0119473962,-0.1067782864,-0.0237843096,0.280141741,0.249543637,-0.0608517788,0.4002116024,-0.0564942621,0.0244186968,0.3291602433,-0.3017027378,-0.2653123736,-0.6008694172,0.1745326072,0.251907289,0.8086962104,-0.1358437687,-0.3358102739,0.1528605372,0.2076649219,-0.0824638307,0.2827852368,0.3051835895,-0.0887222514,0.3121015131,-0.0358692557,-0.2544262111,-0.0997515768,0.0654235482,-0.0356115475,0.3396914601,-0.2548053563,-0.1460393369,0.3748894632,-0.1155003458,0.1107476279,-0.0381479748,-0.2872827053,0.2715195715,-0.1036988497,0.0490625612,-0.0151686138,0.2508012652,-0.1090763435,0.2209175527,-0.3179308772,0.2702949345,-0.5252571702,0.1978554428,-0.0862125009,-0.0276250429,0.1423912644,0.0638514906,0.4108971357,0.1133724526,0.4606716931,-0.1374334544,-0.1745175123,-0.0782642365,-0.4310610294,0.0026512221,0.2989809215,-0.3825352192,-0.3316463232,0.1689644009,-0.515612781,-0.2463435531,0.1758948863,0.2025261372,-0.0357027128,0.2799893022,0.2141553462,0.3292528987,0.0440291986,-0.0873809084,0.0338462852,0.1558705419,-0.0241579376,0.0958414748,0.145536676,-0.1727420837,0.3236540258,0.0517025143,0.131014362,-0.0149723561,0.1575010121,-0.1141503602,-0.2443855703,0.4234372675,-0.0337987058,0.1163769141,-0.1253304332,0.0229568779,-0.0721585527,0.2676763833,-0.0922805369,-0.074315615,-0.1223398745,0.185437113,-0.2704920173,0.1822055578,-0.2250193059,0.0984551385,-0.3147136867,-0.4179733098,-0.1146482006,0.1290785521,-0.0591499209,0.0274897069,0.3090466261,0.1371216625,-0.6475445628,-0.1357955486,-0.2419781387,-0.4421837926,-0.0041040573,0.0454607308,-0.1231764406,0.0287720561,-0.3128088415,0.1355868131,0.5135677457,-0.3171833158,-0.4460082352,0.1856281906,0.157818526,0.1282500774,0.268031776,0.0952205509,0.1088121533,-0.1549203992,-0.3386913538,0.2537966669,0.1383818686,-0.1057019606,0.0372768193,-0.3360304236,-0.1785959899,0.2702171803,-0.0006194562,-0.0232060757,0.084705323,0.0132011492,0.1697147042,-0.1620840579,0.0488310382,0.2489348948,0.6218203306,0.147750482,0.0460199229,-0.3431352973,0.1593219042,-0.1608144045,-0.0088891145,0.2573930621,-0.4822890162,-0.1869457513,-0.2675108612,-0.1326587647,-0.4109540284,0.1133410335,0.1314917058,0.3642637432,-0.1769639105,0.0006497978,-0.1465877295,0.2756711841,0.2550823689,0.0214830115,-0.2809084058,0.1843811423,-0.1693040878,0.165086031,0.3021088839,0.2028157413,0.1107985452,-0.1228094026,-0.1675717682,0.3904953897,0.1124953553,0.3078901172,-0.0631735548,0.0798609853,0.2023760229,-0.6000860333,0.2217565477,-0.0316934474,0.1252828538,-0.191499576,0.0626561642,-0.0514456853,-0.0018213158,0.1445671022,0.1951113045,0.0218549464,0.3287648559,0.1392608881,-0.3494181633,-0.2493988574,0.035084907,-0.2263277173,0.5072664022,-0.0168626662,-0.1720818877,-0.1734227389,0.2258383632,-0.175607875,-0.0706792697,0.0417035595,-0.2512352765,0.1616012901,0.2226364762,0.0969344154,0.3722668886,0.297339946,-0.0336443,0.0266636033,0.1730284989,-0.2844958901,0.1765518934,0.227748394,0.0591930076,0.2298946977,-0.0593204461,-0.0016539326,-0.4046274424,-0.3306984901,-0.0946835205,0.2002406567,-0.1683919877,-0.2084956467,-0.4447605312,-0.2565888166,-0.270814389,-0.5788353682,-0.3302179873,-0.363263756,0.0036814904,0.0907743946,0.1078166813,0.384611398,0.0471163206,0.1418301016,-0.305633992,0.092219308,-0.2208845913,0.0711962357,-0.163400501,0.145783931,0.4816270173,0.2885896266,0.2275770307,-0.2130002826,0.3165298104,-0.2260604352,-0.239482075,0.2331434041,-0.0374882817,0.07991831,0.1769375652,0.0762788206,0.2532630861,-0.4635226727,0.1710245609,0.2487239838,-0.093171902,-0.0759452283,-0.0767557845,0.0502518788,-0.1054532677,-0.3222091794,-0.2128927559,-0.5416287184,0.1942805648,-0.0299149491,-0.0141887302,-0.0693673864,0.0704745427,0.3135614395,0.2139149457,0.2743998766,-0.1324430853,-0.0924821571,0.3485707641,-0.1475347579,-0.184292838,0.2961156964,0.2353955507,0.4261063039,-0.2425886542,-0.5917026401,-0.078682147,-0.0198497139,-0.2189587653,-0.1244477183,0.215487957,0.3251307905,0.2958337665,0.1003228053,-0.1010231376,-0.3052574694,-0.2448660135,-0.3995279372,0.3646704853,-0.1087181345,0.5343534946,-0.2807205915,0.5696766376,0.2660740018,0.1940642595,0.3537203074,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3394","title":"Preserve all feature types when saving a dataset on the Hub with `push_to_hub`","comments":"According to this [comment in the forum](https:\/\/discuss.huggingface.co\/t\/save-datasetdict-to-huggingface-hub\/12075\/8?u=lhoestq), using `push_to_hub` on a dataset with `ClassLabel` can also make the feature simply disappear when it's reloaded !","body":"Currently, if one of the dataset features is of type `ClassLabel`, saving the dataset with `push_to_hub` and reloading the dataset with `load_dataset` will return the feature of type `Value`. To fix this, we should do something similar to `save_to_disk` (which correctly preserves the types) and not only push the parquet files in `push_to_hub`, but also the dataset `info` (stored in a JSON file).","comment_length":25,"text":"Preserve all feature types when saving a dataset on the Hub with `push_to_hub` \n Currently, if one of the dataset features is of type `ClassLabel`, saving the dataset with `push_to_hub` and reloading the dataset with `load_dataset` will return the feature of type `Value`. To fix this, we should do something similar to `save_to_disk` (which correctly preserves the types) and not only push the parquet files in `push_to_hub`, but also the dataset `info` (stored in a JSON file). \n According to this [comment in the forum](https:\/\/discuss.huggingface.co\/t\/save-datasetdict-to-huggingface-hub\/12075\/8?u=lhoestq), using `push_to_hub` on a dataset with `ClassLabel` can also make the feature simply disappear when it's reloaded !","embeddings":[-0.2111828327,-0.421753943,0.0569308847,0.3006317616,0.2904071808,-0.012606862,0.1880393177,0.2776142359,0.0103308018,0.0954093561,-0.4370885789,0.4137706757,-0.1050542817,0.6337103248,-0.0238208994,0.10020037,0.2585050464,0.0915543959,0.1439445764,-0.1857993305,-0.3444071114,0.1544876397,0.2028089762,0.0133957742,-0.5050088763,-0.054549776,0.0639885589,0.1560144722,-0.0946276709,-0.2617856264,0.3016669154,0.1229141131,0.025128305,-0.0729009956,-0.000110251,-0.0156737752,0.1298502535,-0.2305691689,-0.1600672901,-0.1020419598,0.0002058071,0.0783444047,0.0938011706,-0.1145101711,-0.2116518766,0.2717472613,0.0144526977,0.1306255013,0.1541195065,0.0062580635,0.1556764841,0.0790164843,0.0498161763,-0.0331312381,0.3613734841,0.5582364202,-0.2765960395,-0.0294035543,-0.0095802275,0.5110067129,0.0887866542,0.233417958,0.0526150614,-0.3413919508,0.3785180748,0.1217030436,-0.0449218638,-0.0999422222,0.0799595788,0.0926999971,0.0750219971,-0.3629437685,-0.3133401275,-0.3963755965,-0.0397099629,-0.2836900353,0.2079851031,0.1384295523,0.1214550436,0.1115467548,-0.1508796364,-0.4572952092,-0.1571632773,-0.1476302296,0.0400412828,0.0916108042,-0.2346904725,0.0812396854,0.0121526569,-0.1263193339,-0.0720240474,-0.2739057243,-0.156880632,-0.0913951695,0.0116861146,-0.2402734458,-0.031894166,0.424956888,0.2972500324,0.1004036665,-0.0989396721,0.3380022943,-0.1870911568,0.2192956954,0.4268018007,-0.0188070219,0.5271793604,-0.1805874258,0.42024225,0.1471218318,0.0524932668,-0.0749884024,0.2663823962,0.5446487665,0.2874009907,-0.1537430882,0.4817509353,-0.3450792134,-0.060862422,0.0352336615,0.2810749412,0.1954682171,0.0321937688,0.3828085661,0.1109836102,0.2837486863,-0.1220058352,0.3229447603,0.0013725533,-0.1411076337,-0.3007627428,-0.0774794519,0.0995247811,-0.0327571183,0.2195303589,-0.3368292451,0.127617985,0.12511006,-0.0583160296,-0.0004967523,-0.3639057577,0.0839945972,0.0277556032,0.0992312729,-0.1218299121,0.1845408231,0.1087984741,-0.1937723458,-0.3874523342,-0.0881444737,-0.333060205,-0.1600575447,0.1413001418,0.1926597506,-0.0470275767,0.0849901512,-0.6471934319,0.2393123657,-0.0936850756,0.2038306743,0.0257515311,-0.0548233539,-0.1454126537,-0.2868355811,0.0360204466,0.073605828,-0.0433644168,-0.3077145219,0.4323259294,-0.2486533076,0.0101421094,0.1967361122,-0.1263927668,-0.1542363167,-0.1798282862,0.3444138765,0.2515203655,-0.5262074471,-0.2353115231,-0.2628826201,0.0922518447,0.0186674967,-0.0661896989,0.0914238617,0.3083098233,-0.2066019624,0.0431895815,0.1135391891,-0.1270747334,0.0599349327,-0.2486941665,-0.1129734963,0.0301579703,-0.0714983419,-0.1197437569,0.090457812,0.1212179288,0.3055332303,0.1151335165,-0.014730745,0.2339299619,0.0892550349,0.4966498017,0.2424628139,-0.0086092865,-0.1921979934,-0.6642532945,0.1567050219,-0.1047261804,-0.0424640551,0.1747421622,-0.4139216244,-0.0920232981,-0.0145477513,-0.2188376188,-0.1865826249,0.1649742424,-0.0974719226,-0.2838706374,0.2638101578,-0.2219925821,0.3030727804,-0.1150235385,0.2042959332,-0.4889190793,0.2745063901,0.0521300696,-0.2878074646,-0.0966311619,0.1534572393,0.2529257238,-0.1000866964,-0.0471871085,0.337811172,0.1415903717,0.4005408287,-0.0384719558,0.1847770363,0.2574726343,-0.0550115108,-0.1373236626,-0.157886073,0.2083815783,0.0544477664,-0.4241549075,0.393827647,0.269519031,0.0118456353,-0.036337819,0.0101912748,0.0239694174,-0.0016021844,-0.0232551415,-0.047967311,-0.3201803267,-0.0297734886,-0.0300833769,0.0825169981,-0.4610886276,0.3865580559,0.7573990822,0.0277054757,0.2959184647,0.0880670547,-0.3444045782,-0.0797882527,0.2980594933,-0.1290377975,-0.0097084129,0.102594927,0.0540905818,0.0746840313,-0.0199656226,0.0432243608,0.1364464462,0.1274911612,0.1322203875,0.0512946136,0.2477333099,-0.0496779382,-0.5481799841,0.3959191144,-0.1041584983,-0.1394161582,-0.2124568969,-0.1445792168,-0.1493777037,0.2394787967,0.0433822833,-0.0206777677,-0.4694598019,-0.304628253,0.1097649783,0.6526204944,-0.162480548,0.1188171431,0.032563284,0.4589157999,-0.065867722,-0.0868431479,-0.2195905298,-0.1028112546,-0.2089406699,0.0188839845,-0.0197737627,0.1329301149,0.4304415584,-0.0312740132,0.0483642705,-0.4149529636,-0.1042114645,0.1767737567,-0.3377329409,0.1289497018,0.1665407568,-0.0038129846,-0.1705454737,-0.0766853392,0.2470389605,-0.4182023108,-0.4161800146,0.0749557242,0.0910586789,0.0890929699,-0.0884128138,-0.2913160026,-0.0041478612,-0.4009437859,0.4675826132,0.0194863882,-0.0432234593,-0.0221576523,-0.2940726876,0.0625706688,-0.1647360921,-0.0370444395,-0.4541967213,-0.3463743627,0.2257574499,-0.0980386361,-0.3280847669,0.1089412123,0.0499332324,-0.0484684221,-0.1841909289,-0.111354433,-0.3577477038,-0.1003806219,0.4271505773,0.1114122644,0.0933503211,0.4188217223,0.070971638,-0.0560376719,-0.051734224,-0.3323302269,0.3198385835,0.4999794364,0.0792773888,-0.1264844537,-0.0916917175,-0.1294681579,0.0615199059,0.0050827563,0.0838054791,0.502581358,0.1114782989,0.4903522432,-0.3571724594,-0.1906967908,0.1450276077,-0.271084547,0.06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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3394","title":"Preserve all feature types when saving a dataset on the Hub with `push_to_hub`","comments":"Maybe we can also fix https:\/\/github.com\/huggingface\/datasets\/issues\/3035 while working on this because, as pointed out in my initial post, `save_to_disk` also saves the `dataset_info.json`  file.","body":"Currently, if one of the dataset features is of type `ClassLabel`, saving the dataset with `push_to_hub` and reloading the dataset with `load_dataset` will return the feature of type `Value`. To fix this, we should do something similar to `save_to_disk` (which correctly preserves the types) and not only push the parquet files in `push_to_hub`, but also the dataset `info` (stored in a JSON file).","comment_length":24,"text":"Preserve all feature types when saving a dataset on the Hub with `push_to_hub` \n Currently, if one of the dataset features is of type `ClassLabel`, saving the dataset with `push_to_hub` and reloading the dataset with `load_dataset` will return the feature of type `Value`. To fix this, we should do something similar to `save_to_disk` (which correctly preserves the types) and not only push the parquet files in `push_to_hub`, but also the dataset `info` (stored in a JSON file). \n Maybe we can also fix https:\/\/github.com\/huggingface\/datasets\/issues\/3035 while working on this because, as pointed out in my initial post, `save_to_disk` also saves the `dataset_info.json`  file.","embeddings":[-0.3021220863,-0.2699118257,0.0240608826,0.32323578,0.3119442165,-0.0222280566,0.2166179121,0.3837988377,0.0547649935,0.0118873622,-0.3860480785,0.4930748343,-0.0736067221,0.6211601496,0.0524837002,0.0989934877,0.2516204715,0.0786848143,0.1469024271,-0.1552141011,-0.2845312655,0.1313345283,0.1958055049,-0.0118042557,-0.460907191,-0.0900080204,0.1330183893,0.1588866562,-0.1387096792,-0.2946503162,0.2994625568,0.1221605986,0.02595919,-0.0027749662,-0.0001096402,0.0235099494,0.2058634013,-0.2454161346,-0.1425558627,-0.1004253477,-0.1000387445,-0.0144546516,0.0570117943,-0.1327543408,-0.2366562635,0.1689711958,0.0256154053,0.0871449411,0.1775970459,0.0167761352,0.1536736786,0.0686937198,0.0999163836,-0.069643639,0.4291981161,0.5489187837,-0.2761166692,-0.0771487057,0.0797064975,0.4552090764,0.086060971,0.2028851509,0.0745361596,-0.3039912879,0.3110577166,0.0976028889,-0.1224925369,-0.1364275664,0.0808181837,0.1017002314,0.2503564358,-0.4217153192,-0.3443127275,-0.3258582056,-0.1142173335,-0.3585391045,0.2208530009,0.1655047685,0.1118844375,0.0846943706,-0.0992589891,-0.4827113748,-0.1820761859,-0.0531473868,-0.0090017729,0.0856463462,-0.2151887417,0.0288310982,-0.0388921164,-0.1137350574,0.0321429372,-0.2388230711,-0.1958567053,-0.1685552299,-0.0711579025,-0.2829570174,-0.0570539013,0.2741913199,0.30139184,0.1405334026,-0.0969397873,0.39327088,-0.174527213,0.1807377189,0.4851614833,-0.0046934402,0.5186080933,-0.0842415765,0.4635519981,0.110045813,0.0031556024,-0.0843004212,0.2046651393,0.4838482141,0.3238300383,-0.0790379047,0.4993435442,-0.3543518484,0.0449049398,0.0598376095,0.304543227,0.1307562292,0.0445531681,0.3802148402,0.0740148127,0.316544652,-0.1378918439,0.383839041,0.004623326,-0.1476914138,-0.2837577164,-0.0596043989,0.0474168248,-0.032473281,0.2240760922,-0.2493433505,0.1294127107,0.1707199961,-0.0684116632,-0.0399486497,-0.2571166158,0.0558502227,0.0322645158,0.1268103272,-0.0857910961,0.2086875886,0.0428787991,-0.2128866464,-0.3665127456,-0.0683535784,-0.4375589192,-0.1985498816,0.1491827667,0.2027529031,-0.0357675143,0.0752004609,-0.7787155509,0.2497629076,-0.0412668176,0.1181547269,-0.0484818034,-0.0860253125,-0.044913251,-0.2948027849,0.0380637087,-0.0156915486,-0.116114907,-0.1900413036,0.3519598246,-0.1779084653,0.1000924334,0.2274990529,-0.1202969104,-0.1229248717,-0.1224252954,0.3776649237,0.3595583439,-0.4797188938,-0.2303798944,-0.2469902337,0.1811738014,-0.0597751625,-0.0600827225,0.0558528751,0.4553669095,-0.2050821781,0.040911261,0.1694822907,-0.1595251858,0.1472106874,-0.2395474166,-0.0752895623,0.007443375,0.0260310397,-0.0298953466,0.0602631494,0.1722241789,0.4010868669,0.0831671804,-0.0949695632,0.2542705238,0.0968860537,0.4945828617,0.1361558884,-0.0089747412,-0.2442577183,-0.6467323303,0.1479000151,-0.0866678655,-0.0816993415,0.1532782465,-0.3543923199,-0.103579767,-0.0401333347,-0.1867322475,-0.0497152209,0.1730326563,-0.0727642328,-0.3480592072,0.201560095,-0.2198510766,0.2052851915,-0.1116999537,0.1646290421,-0.4485347271,0.3771289289,0.0810900331,-0.3532527387,-0.0300225485,0.1372880638,0.200141266,-0.1606148481,-0.067513369,0.2811968029,0.2378727347,0.420571804,0.0416356362,0.2240206748,0.2690855265,-0.0991223902,-0.1164414138,-0.1270004213,0.1939643025,0.0318411961,-0.4412830472,0.36267519,0.2257050127,0.0075773452,-0.07602451,-0.0050440086,0.0949275196,0.0207554121,0.0312761851,0.0032508031,-0.3247913718,-0.0188617501,-0.1049163863,0.0669279471,-0.4537511468,0.4237674475,0.8299561739,0.0368752219,0.3571645021,0.0640423745,-0.3045715094,-0.0960534513,0.2837401927,-0.0841808021,-0.0296125617,0.130318433,0.0953419358,-0.0039496687,0.0241600238,0.0572877191,0.1295692176,0.0909015536,0.1678226441,0.151410982,0.2621446848,-0.0708298311,-0.534086287,0.3884509206,-0.0006557835,-0.0307143312,-0.2139373571,-0.1953597963,-0.237951681,0.2536944747,0.0986071154,0.0026648401,-0.4698371887,-0.2989413738,0.0820362344,0.6632597446,-0.2051056623,0.1634272933,0.0467886105,0.4383094907,-0.024068784,-0.1281100959,-0.2210642844,-0.1703967601,-0.1506046951,0.0293331593,0.1426886767,0.1662161648,0.4332895279,-0.0235262942,0.0634489059,-0.4498261213,-0.121590592,0.1468587369,-0.2993963957,0.1717862338,0.1365858465,0.0759358332,-0.1223195717,-0.0829485282,0.2224654406,-0.4190648496,-0.3879161477,0.1243366748,0.0651327074,0.0038590466,-0.0458292849,-0.3939792216,-0.0072224117,-0.4904204011,0.382124573,0.0202341489,0.0088171177,-0.0443277508,-0.2718417645,-0.0748301744,-0.1007511839,-0.0739381239,-0.4417406917,-0.3477036357,0.2454005182,-0.1362163574,-0.3047371507,0.0974603742,0.0962620229,-0.060989473,-0.0783886462,-0.0728864595,-0.3672956824,-0.0566795655,0.3749766052,0.1282467991,0.1047854349,0.4164512455,0.0444286913,-0.055431623,-0.051896546,-0.3737261891,0.2969402969,0.5758757591,0.1096875146,-0.0704133287,-0.05349252,-0.1432964206,0.158955276,0.0528846271,0.0844000205,0.5121845603,0.1273332834,0.4730950892,-0.3484995365,-0.1531778127,0.124942705,-0.2159369588,0.061158746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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3392","title":"Dataset viewer issue for `dansbecker\/hackernews_hiring_posts`","comments":"This issue was fixed by me calling `all_datasets.push_to_hub(\"hackernews_hiring_posts\")`.\r\n\r\nThe previous problems were from calling `all_datasets.save_to_disk` and then pushing with `my_repo.git_add` and `my_repo.push_to_hub`.\r\n","body":"## Dataset viewer issue for `dansbecker\/hackernews_hiring_posts`\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/dansbecker\/hackernews_hiring_posts\r\n\r\n*short description of the issue*\r\n\r\nDataset preview not showing for uploaded DatasetDict. See https:\/\/discuss.huggingface.co\/t\/dataset-preview-not-showing-for-uploaded-datasetdict\/12603\r\n\r\nAm I the one who added this dataset ?\r\n\r\nNo -> @dansbecker","comment_length":22,"text":"Dataset viewer issue for `dansbecker\/hackernews_hiring_posts` \n ## Dataset viewer issue for `dansbecker\/hackernews_hiring_posts`\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/dansbecker\/hackernews_hiring_posts\r\n\r\n*short description of the issue*\r\n\r\nDataset preview not showing for uploaded DatasetDict. See https:\/\/discuss.huggingface.co\/t\/dataset-preview-not-showing-for-uploaded-datasetdict\/12603\r\n\r\nAm I the one who added this dataset ?\r\n\r\nNo -> @dansbecker \n This issue was fixed by me calling `all_datasets.push_to_hub(\"hackernews_hiring_posts\")`.\r\n\r\nThe previous problems were from calling `all_datasets.save_to_disk` and then pushing with `my_repo.git_add` and `my_repo.push_to_hub`.\r\n","embeddings":[-0.5134435296,0.1532776952,0.0064594401,0.237880379,0.0082577514,0.0074704373,0.2240121663,0.3255405128,0.0523444638,0.0919942483,-0.0460805073,0.3024223745,-0.0253489111,0.0762420818,0.1139024422,0.2501679957,0.1825878024,0.0068214326,-0.168663919,0.0667676404,-0.0422956347,0.0518773161,-0.1205207556,-0.0830811337,-0.223053351,0.0443048142,-0.0318339281,0.018376641,-0.1017852724,-0.3942712247,0.3493478596,0.3863325417,0.2144290805,0.5339851379,-0.00011252,0.2463621348,0.3110193014,0.0796538815,-0.4716637433,-0.3088158667,-0.0915643424,-0.4195663929,0.1730877161,-0.0914185271,-0.3157420754,-0.164146319,0.196663186,-0.101711601,0.2552104592,0.1723325551,0.2406387031,0.3618878126,0.1469934583,-0.2920436859,-0.0442589261,0.4592669904,-0.3457597196,0.1152432486,0.2274986356,0.4340043366,-0.0704489872,0.3127879798,0.1528113931,0.1041295379,0.1301854551,-0.0767648742,-0.046088554,-0.2675954103,0.2284379154,0.0658422858,0.5412909389,-0.1817335337,-0.3430238366,-0.36268875,0.1792247742,0.0897166356,0.2097247094,0.0055898004,-0.1109886244,0.1277405471,-0.2710936069,-0.2650225163,-0.0846694633,0.1659498811,-0.0156149138,0.0970996022,-0.3134593964,0.0674097538,-0.0069681755,-0.1468907744,0.3378967643,-0.186106354,-0.2004930526,0.0779234096,-0.1873692572,-0.1042846367,0.1155033782,-0.1014246643,0.0415877141,0.1351523995,-0.0398216173,0.2689166665,-0.0568653196,-0.0608489029,0.3307768404,0.0487918705,0.3176579475,0.5985416174,0.4343684614,0.2248888165,0.2209426463,0.0125492802,-0.2219066173,-0.1415921301,-0.2046151459,-0.3607596755,0.6048564911,-0.2585216165,-0.3036930859,-0.0370559357,0.0910746008,-0.1715992689,-0.0463015735,0.352789402,-0.132284075,-0.2218681872,-0.101767078,0.4049353004,-0.2368851751,-0.1758909672,-0.2884770334,-0.2568658888,0.0648080334,0.0494938157,0.3303057551,-0.3322462738,0.1379345357,0.0180542283,0.0571631379,-0.1875557899,-0.1014573127,0.0952901319,-0.3499334753,0.1454809159,0.0908082202,0.3295710385,0.1161528528,-0.3071201444,0.1224789768,0.3039858937,-0.1540757418,-0.1981235445,-0.3435339928,0.1412562281,-0.5179543495,-0.0746822208,-0.5573969483,0.3335939348,-0.1253083646,0.0440361686,-0.1504084319,-0.0333738551,-0.0479944125,-0.037889827,0.303634882,0.5026234984,-0.2498507649,-0.2646941841,-0.2100682706,-0.3339929283,0.0981069133,0.0218382739,-0.1727958918,0.1070403755,-0.3671715856,-0.0634907037,0.2897714078,-0.2998129129,-0.5156721473,0.1981506348,-0.0819066986,0.3340158165,-0.0459680669,-0.0287430808,0.0637426004,-0.2635779381,-0.389438659,-0.1178529933,0.3101978302,0.0378597043,-0.2172090411,-0.007154054,0.0265504681,0.2831829786,0.1441987902,0.1149720401,0.0830327943,-0.069307521,0.2359765321,0.1113662347,0.1688345224,-0.040760465,0.1082908735,0.1981975734,0.0385612473,-0.1539904326,-0.4570316672,0.2164196223,0.0398208573,-0.0180413537,-0.136215359,-0.2080818117,-0.3114708066,0.0615369529,-0.4371369779,-0.3112858236,0.1740849465,0.1617032588,-0.2483762652,0.2363281697,0.0036447926,0.1294962615,-0.2237321287,0.135638997,-0.0726359263,0.3759023547,0.047735624,-0.0940212905,-0.0009150801,0.1136800572,0.2198604345,-0.1135169938,-0.0727111101,0.2322720587,-0.1917596906,0.4014608264,0.444604218,-0.2151560336,0.2804213464,-0.3628666699,0.0874271169,-0.0725969374,-0.0648500323,-0.1092314944,-0.295291394,-0.0419408157,-0.0588084646,0.0133461971,-0.2217290848,0.0103172101,0.1205928847,0.1086804196,0.0646629632,-0.0345038325,0.2263400704,0.1207539886,0.3179352582,-0.1235942617,-0.2778166533,0.2447001487,-0.037697766,-0.0087136421,0.0870496556,0.1905186921,-0.311627835,0.0887727514,0.0503791459,0.170416683,0.2224989086,0.0196829159,0.1643185765,0.0302186757,0.2023887932,-0.2176035643,0.0498605072,0.120232597,-0.1399103403,0.1383805275,0.1246851385,-0.052696947,-0.3597390056,0.2919618487,0.1288089156,0.2803254128,-0.303508997,-0.2007828653,-0.1788468808,-0.2863940895,-0.1101966277,-0.0878132731,-0.2583276331,-0.3144355714,0.0701716095,0.2318623364,-0.1136247665,0.2179687768,0.0179144498,0.3506783843,-0.1095707268,0.295851171,-0.15979743,0.1199637726,-0.1579505503,0.1464783996,0.4705760777,0.04087447,0.2913864255,-0.0129246628,0.2725556791,-0.6202504635,-0.2591621876,-0.048698388,-0.0308898576,0.5329672694,-0.2639756501,0.2136446983,-0.0627613142,0.0118313599,0.2037851512,0.01413019,-0.1589649171,-0.081525214,-0.0055822334,-0.0794516578,0.1093143001,-0.0731697679,-0.3034718335,-0.4439841509,0.364143163,0.1084314063,-0.0841876939,0.0759948716,-0.0388639495,-0.0368081816,0.1445423663,-0.0914552659,-0.2531934679,-0.4080031514,0.4086266458,-0.3934935331,-0.4622137249,0.2021647692,0.2661651671,-0.121863693,-0.2436640859,-0.7571804523,0.2925320864,-0.126176849,0.1556652635,0.2431791723,-0.2694512308,0.2703240514,-0.1115138829,0.0113426484,-0.0912011489,-0.2842431664,-0.0201308448,-0.2795218825,0.0539324619,-0.115115203,-0.0618401617,0.0329487659,0.7642993927,0.4535112679,0.175927788,0.0901761129,-0.080135487,0.5892638564,-0.010892747,-0.4424794018,0.1184158027,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3385","title":"None batched `with_transform`, `set_transform`","comments":"Hi ! Thanks for the suggestion :)\r\nIt makes sense to me, and it can surely be implemented by wrapping the user's function to make it a batched function. However I'm not a big fan of the inconsistency it would create with `map`: `with_transform` is batched by default while `map` isn't.\r\n\r\nIs there something you would like to contribute ? I can give you some pointers if you want","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nA `torch.utils.data.Dataset.__getitem__` operates on a single example.\r\nBut \ud83e\udd17 `Datasets.with_transform` doesn't seem to allow non-batched transform.\r\n\r\n**Describe the solution you'd like**\r\n\r\nHave a `batched=True` argument in `Datasets.with_transform`\r\n\r\n**Describe alternatives you've considered**\r\n\r\n* Convert a non-batched transform function to batched one myself. \r\n* Wrap a \ud83e\udd17 Dataset with torch Dataset, and add a `__getitem__`. \ud83d\ude44\r\n* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change.","comment_length":69,"text":"None batched `with_transform`, `set_transform` \n **Is your feature request related to a problem? Please describe.**\r\n\r\nA `torch.utils.data.Dataset.__getitem__` operates on a single example.\r\nBut \ud83e\udd17 `Datasets.with_transform` doesn't seem to allow non-batched transform.\r\n\r\n**Describe the solution you'd like**\r\n\r\nHave a `batched=True` argument in `Datasets.with_transform`\r\n\r\n**Describe alternatives you've considered**\r\n\r\n* Convert a non-batched transform function to batched one myself. \r\n* Wrap a \ud83e\udd17 Dataset with torch Dataset, and add a `__getitem__`. \ud83d\ude44\r\n* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change. \n Hi ! Thanks for the suggestion :)\r\nIt makes sense to me, and it can surely be implemented by wrapping the user's function to make it a batched function. However I'm not a big fan of the inconsistency it would create with `map`: `with_transform` is batched by default while `map` isn't.\r\n\r\nIs there something you would like to contribute ? I can give you some pointers if you 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3385","title":"None batched `with_transform`, `set_transform`","comments":"Hi @lhoestq ,\r\nSorry I missed your reply.\r\n\r\nI would love to contribute. But I don't know which solution would be the best for this repo.\r\n\r\n> However I'm not a big fan of the inconsistency it would create with map: with_transform is batched by default while map isn't.\r\n\r\nI agree. What do you think about the alternative solutions?\r\n\r\n> * Convert a non-batched transform function to batched one myself.\r\n\r\nThis won't be able to use torch loader multi-worker.\r\n\r\n> * Wrap a \ud83e\udd17 Dataset with torch Dataset, and add a __getitem__. \ud83d\ude44\r\n\r\nThis is actually pretty simple.\r\n\r\n```python\r\nimport torch\r\n\r\nclass LazyMapTorchDataset(torch.utils.data.Dataset):\r\n    def __init__(self, ds, fn):\r\n        self.ds = ds\r\n        self.fn = fn\r\n    def __getitem__(self, i):\r\n        return self.fn(self.ds[i])\r\n\r\nd = [{1:2, 2:3}, {1:3, 2:4}]\r\nds = LazyMapTorchDataset(d, lambda x:{k:v*2 for k,v in x.items()})\r\nfor i in range(2):\r\n    print(f'before {d[i]}')\r\n    print(f'after {ds[i]}')\r\n```\r\n```\r\nbefore {1: 2, 2: 3}\r\nafter {1: 4, 2: 6}\r\nbefore {1: 3, 2: 4}\r\nafter {1: 6, 2: 8}\r\n```\r\n\r\nBut this requires converting data to torch tensor myself. And this is really similar to `.map()`, why not just use it? So I have the next solution.\r\n\r\n> * Have lazy=False in Dataset.map, and returns a LazyDataset if lazy=True. This way the same map interface can be used, and existing code can be updated with one argument change.\r\n\r\nI think I like this solution best.  Because `.with_transform` is entangled with `.with_format`, so seems more flexible to modify the `.map` than to modify `.with_transform`.\r\n\r\nThe usage looks nice, too.\r\n```python\r\n# lazy, one to one, can be parallelized via torch loader, no need to set `num_worker` beforehand.\r\ndataset = dataset.map(fn, lazy=True, batched=False)\r\n# collate_fn\r\ndataloader = Dataloader(dataset.with_format('torch'), collate_fn=collate_fn, num_workers=...) \r\n```\r\n\r\nThere are some minor decisions like whether a lazy map should be allowed before another map, but I think we can work it out later. The implementation can probably borrow from `IterableDataset`.","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nA `torch.utils.data.Dataset.__getitem__` operates on a single example.\r\nBut \ud83e\udd17 `Datasets.with_transform` doesn't seem to allow non-batched transform.\r\n\r\n**Describe the solution you'd like**\r\n\r\nHave a `batched=True` argument in `Datasets.with_transform`\r\n\r\n**Describe alternatives you've considered**\r\n\r\n* Convert a non-batched transform function to batched one myself. \r\n* Wrap a \ud83e\udd17 Dataset with torch Dataset, and add a `__getitem__`. \ud83d\ude44\r\n* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change.","comment_length":315,"text":"None batched `with_transform`, `set_transform` \n **Is your feature request related to a problem? Please describe.**\r\n\r\nA `torch.utils.data.Dataset.__getitem__` operates on a single example.\r\nBut \ud83e\udd17 `Datasets.with_transform` doesn't seem to allow non-batched transform.\r\n\r\n**Describe the solution you'd like**\r\n\r\nHave a `batched=True` argument in `Datasets.with_transform`\r\n\r\n**Describe alternatives you've considered**\r\n\r\n* Convert a non-batched transform function to batched one myself. \r\n* Wrap a \ud83e\udd17 Dataset with torch Dataset, and add a `__getitem__`. \ud83d\ude44\r\n* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change. \n Hi @lhoestq ,\r\nSorry I missed your reply.\r\n\r\nI would love to contribute. But I don't know which solution would be the best for this repo.\r\n\r\n> However I'm not a big fan of the inconsistency it would create with map: with_transform is batched by default while map isn't.\r\n\r\nI agree. What do you think about the alternative solutions?\r\n\r\n> * Convert a non-batched transform function to batched one myself.\r\n\r\nThis won't be able to use torch loader multi-worker.\r\n\r\n> * Wrap a \ud83e\udd17 Dataset with torch Dataset, and add a __getitem__. \ud83d\ude44\r\n\r\nThis is actually pretty simple.\r\n\r\n```python\r\nimport torch\r\n\r\nclass LazyMapTorchDataset(torch.utils.data.Dataset):\r\n    def __init__(self, ds, fn):\r\n        self.ds = ds\r\n        self.fn = fn\r\n    def __getitem__(self, i):\r\n        return self.fn(self.ds[i])\r\n\r\nd = [{1:2, 2:3}, {1:3, 2:4}]\r\nds = LazyMapTorchDataset(d, lambda x:{k:v*2 for k,v in x.items()})\r\nfor i in range(2):\r\n    print(f'before {d[i]}')\r\n    print(f'after {ds[i]}')\r\n```\r\n```\r\nbefore {1: 2, 2: 3}\r\nafter {1: 4, 2: 6}\r\nbefore {1: 3, 2: 4}\r\nafter {1: 6, 2: 8}\r\n```\r\n\r\nBut this requires converting data to torch tensor myself. And this is really similar to `.map()`, why not just use it? So I have the next solution.\r\n\r\n> * Have lazy=False in Dataset.map, and returns a LazyDataset if lazy=True. This way the same map interface can be used, and existing code can be updated with one argument change.\r\n\r\nI think I like this solution best.  Because `.with_transform` is entangled with `.with_format`, so seems more flexible to modify the `.map` than to modify `.with_transform`.\r\n\r\nThe usage looks nice, too.\r\n```python\r\n# lazy, one to one, can be parallelized via torch loader, no need to set `num_worker` beforehand.\r\ndataset = dataset.map(fn, lazy=True, batched=False)\r\n# collate_fn\r\ndataloader = Dataloader(dataset.with_format('torch'), collate_fn=collate_fn, num_workers=...) \r\n```\r\n\r\nThere are some minor decisions like whether a lazy map should be allowed before another map, but I think we can work it out later. The implementation can probably borrow from 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3385","title":"None batched `with_transform`, `set_transform`","comments":"I like the idea of lazy map. On the other hand we should only have either lazy map or `with_transform` (not both). That's why I'd rather stick with `with_transform` for now (but maybe we can consider it for later major releases like `datasets` v2).\r\n\r\nI understand the issue with `with_transform` and `with_format` being exclusive, maybe we can separate them: first transform, them format.\r\n\r\nFinally I think what's also going to be important in the end will be the addition of multiprocessing to transforms","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nA `torch.utils.data.Dataset.__getitem__` operates on a single example.\r\nBut \ud83e\udd17 `Datasets.with_transform` doesn't seem to allow non-batched transform.\r\n\r\n**Describe the solution you'd like**\r\n\r\nHave a `batched=True` argument in `Datasets.with_transform`\r\n\r\n**Describe alternatives you've considered**\r\n\r\n* Convert a non-batched transform function to batched one myself. \r\n* Wrap a \ud83e\udd17 Dataset with torch Dataset, and add a `__getitem__`. \ud83d\ude44\r\n* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change.","comment_length":83,"text":"None batched `with_transform`, `set_transform` \n **Is your feature request related to a problem? Please describe.**\r\n\r\nA `torch.utils.data.Dataset.__getitem__` operates on a single example.\r\nBut \ud83e\udd17 `Datasets.with_transform` doesn't seem to allow non-batched transform.\r\n\r\n**Describe the solution you'd like**\r\n\r\nHave a `batched=True` argument in `Datasets.with_transform`\r\n\r\n**Describe alternatives you've considered**\r\n\r\n* Convert a non-batched transform function to batched one myself. \r\n* Wrap a \ud83e\udd17 Dataset with torch Dataset, and add a `__getitem__`. \ud83d\ude44\r\n* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change. \n I like the idea of lazy map. On the other hand we should only have either lazy map or `with_transform` (not both). That's why I'd rather stick with `with_transform` for now (but maybe we can consider it for later major releases like `datasets` v2).\r\n\r\nI understand the issue with `with_transform` and `with_format` being exclusive, maybe we can separate them: first transform, them format.\r\n\r\nFinally I think what's also going to be important in the end will be the addition of multiprocessing to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3381","title":"Unable to load audio_features from common_voice dataset","comments":"Hi ! Feel free to access `batch[\"audio\"][\"array\"]` and `batch[\"audio\"][\"sampling_rate\"]` instead\r\n\r\n`datasets` 1.16 introduced some changes in `common_voice` and now the `path` field is no longer a path to a local file (but rather the path to the file in the archive it's extracted from)","body":"## Describe the bug\r\nI am not able to load audio features from common_voice dataset\r\n\r\n## Steps to reproduce the bug\r\n\r\n```\r\nfrom datasets import load_dataset\r\nimport torchaudio\r\n\r\ntest_dataset = load_dataset(\"common_voice\", \"hi\", split=\"test[:2%]\")\r\nresampler = torchaudio.transforms.Resample(48_000, 16_000)\r\n\r\ndef speech_file_to_array_fn(batch):\r\n      speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\r\n      batch[\"speech\"] = resampler(speech_array).squeeze().numpy()\r\n      return batch\r\ntest_dataset = test_dataset.map(speech_file_to_array_fn)\r\n```\r\n## Expected results\r\n\r\nThis piece of code should return test_dataset after loading audio features.\r\n\r\n## Actual results\r\n\r\nReusing dataset common_voice (\/home\/jovyan\/.cache\/huggingface\/datasets\/common_voice\/hi\/6.1.0\/b879a355caa529b11f2249400b61cadd0d9433f334d5c60f8c7216ccedfecfe1)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/configuration_utils.py:341: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\r\n  \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\r\nSpecial tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\r\n  0%|                                                                                                                    | 0\/3 [00:00<?, ?ex\/s]formats: can't open input file `common_voice_hi_23795358.mp3': No such file or directory\r\n  0%|                                                                                                                    | 0\/3 [00:00<?, ?ex\/s]\r\nTraceback (most recent call last):\r\n  File \"demo_file.py\", line 23, in <module>\r\n    test_dataset = test_dataset.map(speech_file_to_array_fn)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 2036, in map\r\n    desc=desc,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 518, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 485, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 411, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 2368, in _map_single\r\n    example = apply_function_on_filtered_inputs(example, i, offset=offset)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 2277, in apply_function_on_filtered_inputs\r\n    processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1978, in decorated\r\n    result = f(decorated_item, *args, **kwargs)\r\n  File \"demo_file.py\", line 19, in speech_file_to_array_fn\r\n    speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/torchaudio\/backend\/sox_io_backend.py\", line 154, in load\r\n    filepath, frame_offset, num_frames, normalize, channels_first, format)\r\nRuntimeError: Error loading audio file: failed to open file common_voice_hi_23795358.mp3\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.14.243 with-debian-bullseye-sid\r\n- Python version: 3.7.9\r\n- PyArrow version: 6.0.1\r\n","comment_length":44,"text":"Unable to load audio_features from common_voice dataset \n ## Describe the bug\r\nI am not able to load audio features from common_voice dataset\r\n\r\n## Steps to reproduce the bug\r\n\r\n```\r\nfrom datasets import load_dataset\r\nimport torchaudio\r\n\r\ntest_dataset = load_dataset(\"common_voice\", \"hi\", split=\"test[:2%]\")\r\nresampler = torchaudio.transforms.Resample(48_000, 16_000)\r\n\r\ndef speech_file_to_array_fn(batch):\r\n      speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\r\n      batch[\"speech\"] = resampler(speech_array).squeeze().numpy()\r\n      return batch\r\ntest_dataset = test_dataset.map(speech_file_to_array_fn)\r\n```\r\n## Expected results\r\n\r\nThis piece of code should return test_dataset after loading audio features.\r\n\r\n## Actual results\r\n\r\nReusing dataset common_voice (\/home\/jovyan\/.cache\/huggingface\/datasets\/common_voice\/hi\/6.1.0\/b879a355caa529b11f2249400b61cadd0d9433f334d5c60f8c7216ccedfecfe1)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/configuration_utils.py:341: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\r\n  \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\r\nSpecial tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\r\n  0%|                                                                                                                    | 0\/3 [00:00<?, ?ex\/s]formats: can't open input file `common_voice_hi_23795358.mp3': No such file or directory\r\n  0%|                                                                                                                    | 0\/3 [00:00<?, ?ex\/s]\r\nTraceback (most recent call last):\r\n  File \"demo_file.py\", line 23, in <module>\r\n    test_dataset = test_dataset.map(speech_file_to_array_fn)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 2036, in map\r\n    desc=desc,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 518, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 485, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 411, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 2368, in _map_single\r\n    example = apply_function_on_filtered_inputs(example, i, offset=offset)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 2277, in apply_function_on_filtered_inputs\r\n    processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1978, in decorated\r\n    result = f(decorated_item, *args, **kwargs)\r\n  File \"demo_file.py\", line 19, in speech_file_to_array_fn\r\n    speech_array, sampling_rate = torchaudio.load(batch[\"path\"])\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/torchaudio\/backend\/sox_io_backend.py\", line 154, in load\r\n    filepath, frame_offset, num_frames, normalize, channels_first, format)\r\nRuntimeError: Error loading audio file: failed to open file common_voice_hi_23795358.mp3\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-4.14.243 with-debian-bullseye-sid\r\n- Python version: 3.7.9\r\n- PyArrow version: 6.0.1\r\n \n Hi ! Feel free to access `batch[\"audio\"][\"array\"]` and `batch[\"audio\"][\"sampling_rate\"]` instead\r\n\r\n`datasets` 1.16 introduced some changes in `common_voice` and now the `path` field is no longer a path to a local file (but rather the path to the file in the archive it's extracted from)","embeddings":[-0.2775653303,-0.377699405,-0.008247341,0.4291615188,0.3689377606,-0.1360183209,0.2537411451,0.2359365672,-0.0651015341,0.1566345841,-0.4660600722,0.408446759,-0.0926309079,-0.1993819773,-0.2011775076,-0.3714814484,-0.0002533231,0.0689452291,-0.0112082157,-0.3186630905,-0.164042294,0.1519653201,-0.2891056538,0.2532096803,-0.2725461721,-0.0168538708,0.3209072053,0.4006575644,-0.0706237704,-0.224872753,0.1625491232,-0.0675302371,0.2533233166,0.4339747131,-0.00010237,-0.0965338722,0.4900015891,-0.1168494225,-0.152507022,-0.3558962047,-0.1749282032,0.0059510139,-0.1245109066,0.0786946565,-0.0639635101,-0.1642854512,-0.1353449374,-0.3594505787,0.4507938325,0.3419066668,0.2822895646,0.034008693,0.0251495335,-0.0112968059,0.1772443354,-0.0688361749,-0.0507030822,0.1708286405,0.5350544453,-0.1520442963,0.0684930012,0.181519866,-0.3204141259,0.0101027573,-0.0052164393,-0.0850394294,-0.1120695397,-0.4039650559,0.1757857203,0.3050141037,0.5727843642,0.0104112551,-0.190910086,-0.02747095,-0.0009549915,-0.1399874538,0.2221495658,0.0908388048,-0.4333753884,0.1356173307,-0.1859804094,0.0185718089,0.0616510101,0.1024596319,0.128092736,-0.0766597986,-0.0983079076,0.0021526529,0.2859974802,-0.1944786012,-0.038241867,0.0539521948,0.0376476832,0.4715969861,-0.3221795857,0.1839037687,0.0707101971,-0.4383620918,0.2763041854,-0.079498291,0.1557024419,0.2056289017,-0.3275068998,0.1634942889,0.1494420171,0.1401921511,-0.0687095448,0.118610315,0.2255803198,0.0223583318,-0.1316255927,-0.1353686005,-0.2598273456,-0.0888940841,0.1838307232,0.0430761315,0.254758954,-0.3414801955,-0.4240113497,0.0397765823,-0.2352283299,0.0677125454,0.1790912598,0.352234304,0.076499559,0.2863867581,0.1509805322,0.3391672373,-0.108246237,0.0291215479,-0.0664998889,-0.0595572889,-0.2695510387,0.0288380142,0.3297540843,-0.1249560118,0.3503780067,0.0845228061,0.3011607528,-0.3452719748,-0.0260546729,0.0206901208,-0.2218712419,-0.0104035558,-0.0430311896,0.2822885215,0.0537848249,0.3718705475,-0.0474213399,0.3020956814,-0.2758889794,-0.2245312035,0.0279696342,0.2639414072,0.0790087357,0.2853378057,-0.1614023298,0.0973909646,0.0320809968,-0.3944593966,-0.0308326576,-0.2836869955,-0.2875131965,-0.0840971619,0.324491173,0.1483113468,-0.1427216828,-0.0767555982,0.104579255,-0.0893756598,0.0931411758,0.3479032218,-0.3023245335,-0.1048497632,-0.2285101414,0.3357654214,0.368268311,-0.4564194977,-0.4650276303,0.0562668219,-0.0527331568,0.3982046843,-0.1469584107,-0.0041053793,0.1404031217,0.0567930117,0.503677547,0.6017473936,0.1836447269,-0.0060184677,-0.1497829407,-0.1466087103,0.134218663,0.1616946459,-0.1128841713,0.1052242443,-0.0919595957,0.0295229852,0.2755798995,-0.0831507221,-0.1115476787,0.1827263385,0.1846423894,-0.0329647847,-0.007178471,-0.1592689455,-0.0524022877,0.2403719127,-0.1235485151,-0.107515499,0.1436256617,-0.0281221401,-0.3436089158,-0.17538625,-0.4537356198,-0.0588078722,0.1134343594,0.2467368096,-0.2134526223,-0.1574382037,-0.191502139,0.3806432486,0.0662559569,-0.014002258,-0.2688520253,0.348634094,-0.081700854,-0.0421664156,0.2161830515,0.397335887,0.2929587662,0.107388638,-0.3877897263,0.2837316394,-0.0288244467,0.3062722087,-0.5626254678,-0.1135611162,0.1104352921,-0.5467312932,0.2170504034,0.3450068533,0.2646120191,-0.2000190616,0.0419410802,0.3249724507,0.3049276471,0.0450546592,0.10005375,0.0238694549,0.3756213486,0.0265684742,0.0113283126,0.1897276044,0.2329136878,-0.1197350472,0.1864089221,-0.0041922433,-0.4241918623,-0.2908019722,0.2186315805,-0.0906432942,0.0691502988,0.0861336663,-0.2481804043,-0.091714099,0.0827763379,-0.489290297,0.4508749247,0.1614065319,0.0551870167,0.0061133048,0.0868340209,0.0113805449,0.0658006817,0.1446925104,-0.0056245639,0.4211513102,0.1876624227,0.1031544432,-0.2897184491,-0.1250754595,-0.039251525,0.0202461239,-0.3657715917,-0.0641545579,-0.1013482362,0.2084375322,-0.1297467649,-0.3509400785,-0.2030800134,0.0944955125,0.1675641537,0.5011947751,0.0834650397,0.2807448506,0.1111177281,0.1322643459,0.3202251792,-0.0848602206,-0.1125429869,0.2408455908,-0.3072722852,0.0831203237,0.2101168633,0.2146777064,-0.1189004034,0.2083834559,-0.0339248925,-0.1632777601,0.1330924034,0.0975156724,0.1069636643,0.2580434084,-0.1872930527,0.0731266811,0.0813565925,-0.3987401128,0.1598548442,-0.0720452294,-0.2160229981,0.2186235189,0.1455627829,0.1081680506,-0.0057018129,-0.5721533298,-0.3993852437,-0.6159629822,0.1075041741,-0.1923452169,0.0420979261,0.2383658886,0.0311726164,0.1163216978,0.3017820716,0.2430663258,-0.3142022789,0.1047222763,0.3863099217,-0.1617361605,-0.2655930221,-0.2917963862,-0.0094002066,0.1860472709,0.0848328024,-0.1888921559,0.0621366911,-0.0022719395,0.1255513728,-0.2125151455,-0.0585288778,0.0535390936,0.0995996371,-0.003310018,0.0606535524,-0.0625484586,-0.1536842734,0.2901517749,0.3029935658,0.083919175,0.3193400502,-0.3014785647,0.777985394,0.2231452167,-0.0067080823,0.1364192367,-0.122205697,0.0535373464,-0.314111799,-0.3926662207,0.0127626192,-0.2338947058,-0.1656208038,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3374","title":"NonMatchingChecksumError for the CLUE:cluewsc2020, chid, c3 and tnews","comments":"Seems like the issue still exists,:\r\n`Downloading and preparing dataset clue\/chid (download: 127.15 MiB, generated: 259.71 MiB, post-processed: Unknown size, total: 386.86 MiB) to \/mnt\/cache\/tanhaochen\/.cache\/huggingface\/datasets\/clue\/chid\/1.0.0\/e55b490cb7809dcd8db31b9a87119f2e2ec87cdc060da8a9ac070b070ca3e379...\r\nTraceback (most recent call last):\r\n  File \"\/mnt\/cache\/tanhaochen\/PromptCLUE\/test_datasets.py\", line 3, in <module>\r\n    cluewsc2020 = datasets.load_dataset(\"clue\",\"chid\")\r\n  File \"\/mnt\/cache\/tanhaochen\/dependencies\/datasets\/src\/datasets\/load.py\", line 1667, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/cache\/tanhaochen\/dependencies\/datasets\/src\/datasets\/builder.py\", line 593, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/cache\/tanhaochen\/dependencies\/datasets\/src\/datasets\/builder.py\", line 663, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/mnt\/cache\/tanhaochen\/dependencies\/datasets\/src\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/storage.googleapis.com\/cluebenchmark\/tasks\/chid_public.zip']\r\n`","body":"Hi, it seems like there are updates in cluewsc2020, chid, c3 and tnews, since i could not load them due to the checksum error.","comment_length":80,"text":"NonMatchingChecksumError for the CLUE:cluewsc2020, chid, c3 and tnews \n Hi, it seems like there are updates in cluewsc2020, chid, c3 and tnews, since i could not load them due to the checksum error. \n Seems like the issue still exists,:\r\n`Downloading and preparing dataset clue\/chid (download: 127.15 MiB, generated: 259.71 MiB, post-processed: Unknown size, total: 386.86 MiB) to \/mnt\/cache\/tanhaochen\/.cache\/huggingface\/datasets\/clue\/chid\/1.0.0\/e55b490cb7809dcd8db31b9a87119f2e2ec87cdc060da8a9ac070b070ca3e379...\r\nTraceback (most recent call last):\r\n  File \"\/mnt\/cache\/tanhaochen\/PromptCLUE\/test_datasets.py\", line 3, in <module>\r\n    cluewsc2020 = datasets.load_dataset(\"clue\",\"chid\")\r\n  File \"\/mnt\/cache\/tanhaochen\/dependencies\/datasets\/src\/datasets\/load.py\", line 1667, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/cache\/tanhaochen\/dependencies\/datasets\/src\/datasets\/builder.py\", line 593, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/cache\/tanhaochen\/dependencies\/datasets\/src\/datasets\/builder.py\", line 663, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/mnt\/cache\/tanhaochen\/dependencies\/datasets\/src\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/storage.googleapis.com\/cluebenchmark\/tasks\/chid_public.zip']\r\n`","embeddings":[0.0205238853,0.0295574758,-0.0395496227,0.0844477937,0.0949635655,-0.0198278036,0.0076567507,0.3946430981,-0.0262059793,0.2592134476,-0.1515903771,0.1983180642,0.0484178923,-0.0398280621,-0.282302767,0.4482048154,0.1787071824,0.0810303241,-0.2217121273,-0.0004590378,-0.2948582172,0.2038865238,-0.0992098972,-0.1695189029,-0.2627163827,0.07802701,0.0153831635,0.100141637,-0.1690970659,-0.626814127,0.3006024361,0.1955675483,-0.0522343963,0.2808867991,-0.0001166119,0.0365726948,0.372663945,-0.0730750859,-0.5593645573,-0.0717511252,-0.2208903134,-0.2990594506,-0.0081479903,-0.105982624,0.0529328026,0.432461381,0.2530667484,0.126370728,0.0239465926,0.2127453387,0.1870344579,0.542283535,0.2504796982,0.0214932691,0.3815031052,-0.310049355,-0.0011441268,0.356299758,0.4268056452,0.0999457464,-0.1088327169,0.2821003795,-0.0572429821,0.1274630874,0.1216992438,-0.130272463,0.4892978966,-0.2158914357,0.0929880589,0.3567408919,0.0699117109,0.0525951087,-0.3715976775,-0.1843430847,-0.277214855,-0.0401452594,0.4424699247,-0.0774373487,-0.300983876,-0.1931088269,-0.3348329067,0.2341246158,0.0404926576,0.1558519304,0.1404579431,0.2002375424,0.0396492258,-0.1774275601,-0.0274129137,-0.2429527342,0.0445172898,-0.1543333381,-0.126235798,0.0155007215,-0.5700606108,-0.1589836925,0.2233875543,0.5097836852,0.2235783786,0.3083149791,0.1559411436,0.4459997714,-0.126797691,0.1355055422,0.112879388,-0.0088923639,0.2417921573,-0.0652202815,0.2127415687,0.4899515212,0.0348530747,0.1085479334,-0.1093508825,-0.2123647481,0.0701454654,0.2057520449,0.3934559226,-0.5251977444,-0.2844413221,0.0923794806,0.2056829929,0.0305032711,0.0872600749,0.4240471125,-0.3059847057,0.3214543462,-0.0781190544,-0.1183025911,-0.0742103308,-0.1966417879,-0.2594106495,-0.1555034965,-0.1906234473,0.2014481276,0.1132296473,-0.1996067315,0.2181132287,-0.0456576869,0.5281758904,-0.0101786889,-0.0427708365,-0.0461044386,-0.0070130434,0.2603492737,-0.3032836318,0.0928973258,0.3188848794,-0.0124077667,-0.1309747994,0.0017804346,-0.1467471719,-0.1029062271,-0.0309227984,0.2080093026,-0.3245439231,-0.0507243723,0.0004419181,-0.3149618804,0.3503745496,-0.2040692419,0.0790616646,-0.3314083219,-0.4112185538,-0.3049841523,0.076990746,0.181963563,0.1176733598,0.0247491263,0.3202455044,-0.1532862782,-0.054933399,0.2173605263,-0.0079659941,-0.0670024976,-0.2816361487,-0.1776649207,0.1303806603,-0.3643544316,-0.3068808019,0.0444919728,0.2426497936,0.3270709515,-0.0078733051,-0.0896361619,-0.1705187261,-0.3500797749,0.1046051309,-0.1888491809,-0.1031404138,0.0409988686,-0.2448416352,-0.2807189524,0.2566521168,0.0839846432,0.022042254,-0.0129629867,0.1029782891,-0.3444349468,0.2949875891,-0.194067955,-0.0485758781,0.1569758803,0.4601086676,0.2263917476,0.161774382,-0.1967442036,-0.5187575817,0.3473887742,-0.1065846831,0.1470933408,0.0592956245,-0.2350488156,-0.1692436337,-0.2112558037,-0.1805664301,0.0260022711,0.1199902669,0.3110150695,0.2082963437,-0.0654688776,-0.2179190665,0.1659240574,-0.3537534177,0.1140283123,-0.4319522381,0.039079193,-0.1189014688,-0.1733825505,0.060783077,0.1820878685,0.2217690796,0.0144963376,-0.1520420909,0.5043876171,0.0016953639,-0.1385990828,-0.401376754,0.3150396049,-0.0464494042,-0.120220378,-0.1866140962,0.389103502,-0.0158160403,-0.1253188252,0.0248357262,0.3982477784,-0.192290619,0.2257643938,0.2527513206,-0.0866005868,0.1290866435,-0.1463698447,-0.1449083984,-0.1126567349,0.2302122563,-0.0087927114,-0.2811585665,0.0898895785,0.1059202254,0.0317413732,0.1645515114,-0.0207190495,0.0350659899,-0.0535593219,0.3409251571,-0.1307248324,-0.1482584327,0.5637940168,0.2120073587,0.1402406991,0.0405660793,0.1079324856,-0.0075810528,-0.0487695634,0.0493100025,0.0707458928,0.0628285259,0.6440423727,0.1324830651,-0.1079410017,-0.2843615413,0.1643864661,0.0573914349,0.2595639229,-0.2986547649,-0.0714283139,-0.4167108834,-0.0769734532,-0.0337848142,0.0185287539,-0.2412769347,-0.3044417202,0.0462785438,0.354865849,-0.1571391374,0.3552683294,-0.6084521413,-0.0246779248,-0.0805026889,0.0413973108,-0.2579580843,-0.2071685195,-0.1221651882,0.0074228272,0.3467196524,0.0039420892,0.2355703562,-0.4542526305,-0.0809827372,-0.3740085959,-0.4232891202,-0.0265773702,0.0168997217,0.0706500933,0.456272006,0.0762857795,-0.2394343317,-0.3309544921,0.3979717195,-0.4438554049,-0.4625356495,-0.0032548206,-0.071842581,0.0076438868,0.0243230052,-0.2051757425,0.0700262785,-0.2006365955,-0.0726415887,0.0031581172,0.141122669,0.4890061021,-0.1666831076,0.0116968267,-0.3243673742,0.3560529053,-0.2300129533,-0.6038921475,0.5373363495,0.0041273516,-0.2508692145,0.1404349953,-0.2338140309,0.1576086432,0.1112468988,-0.2945533693,-0.2656664252,-0.2629795372,0.1558066905,0.2234705091,-0.314483732,0.181542933,-0.0736283958,-0.167592749,-0.2052902877,-0.2486673295,0.1280166656,0.1054417938,0.6178351641,-0.107816346,0.4386000335,0.1062795445,0.0664525181,0.2654338479,-0.0730497763,0.1673177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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3369","title":"[Audio] Allow resampling for audio datasets in streaming mode","comments":"This requires implementing `cast_column` for iterable datasets, it could be a very nice addition !\r\n\r\n<s>It can also be useful to be able to disable the audio\/image decoding for the dataset viewer (see PR https:\/\/github.com\/huggingface\/datasets\/pull\/3430) cc @severo <\/s>\r\nEDIT: actually following https:\/\/github.com\/huggingface\/datasets\/issues\/3145 the dataset viewer might not need it anymore","body":"Many audio datasets like Common Voice always need to be resampled. This can very easily be done in non-streaming mode as follows:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"test\")\r\n\r\nds = ds.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nHowever in streaming mode it fails currently:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"test\", streaming=True)\r\n\r\nds = ds.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nwith the following error:\r\n\r\n```\r\nAttributeError: 'IterableDataset' object has no attribute 'cast_column'  \r\n```\r\n\r\nIt would be great if we could add such a feature (I'm not 100% sure though how complex this would be)","comment_length":50,"text":"[Audio] Allow resampling for audio datasets in streaming mode \n Many audio datasets like Common Voice always need to be resampled. This can very easily be done in non-streaming mode as follows:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"test\")\r\n\r\nds = ds.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nHowever in streaming mode it fails currently:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"test\", streaming=True)\r\n\r\nds = ds.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nwith the following error:\r\n\r\n```\r\nAttributeError: 'IterableDataset' object has no attribute 'cast_column'  \r\n```\r\n\r\nIt would be great if we could add such a feature (I'm not 100% sure though how complex this would be) \n This requires implementing `cast_column` for iterable datasets, it could be a very nice addition !\r\n\r\n<s>It can also be useful to be able to disable the audio\/image decoding for the dataset viewer (see PR https:\/\/github.com\/huggingface\/datasets\/pull\/3430) cc @severo <\/s>\r\nEDIT: actually following https:\/\/github.com\/huggingface\/datasets\/issues\/3145 the dataset viewer might not need it anymore","embeddings":[-0.5598962903,-0.0399099737,-0.0683552399,-0.1406607628,0.4281198382,0.0794272199,-0.0197004508,0.3021991551,-0.0330880992,0.3280914426,-0.4938261807,0.2927233279,-0.2910312116,0.2312468737,0.0701241121,-0.5171605945,0.1303919256,0.2717285752,-0.3455240726,0.1398267895,-0.2682445645,-0.0654539689,-0.262265265,-0.2252085805,0.1560017914,-0.0156700257,0.1735278368,-0.3102464378,-0.0653297976,-0.4529253542,-0.0891471729,0.3677962124,0.1712242067,0.19470644,-0.0001203669,-0.0321222618,0.2326771617,0.0323264673,-0.3381859064,0.0335201472,-0.4890121222,-0.0983662978,0.0262743663,0.0182973295,-0.087164335,-0.3974840641,-0.0674019605,-0.4369144142,0.293074578,0.1030170992,0.156940192,0.1917747855,-0.2427001595,0.1174414083,-0.0916097537,0.2473445535,-0.3325560689,-0.0419157818,0.4605769217,0.3699426651,0.0335211605,0.4571168125,-0.298576951,-0.0418987051,0.2328651398,-0.1949788779,-0.3876966238,-0.4129321873,0.1136499494,0.3623907566,0.8246940374,-0.4015907347,-0.6885802746,-0.3606873751,0.4200988412,-0.2896187901,0.0069403695,0.1210587472,0.0363536477,0.3133991063,-0.6834563017,-0.0354340002,-0.0789950043,0.09397614,0.2813484967,0.0568505079,-0.0956188738,0.1105108485,0.4091552198,-0.0617095642,0.0152615272,-0.046747908,0.110509038,0.2546931207,-0.4339013994,-0.3304509819,-0.1578676105,0.0685288161,0.1256451905,0.2860116661,0.2917950153,0.1781579405,0.1397669315,0.0354886949,0.2003244609,-0.0622387156,-0.1327770352,-0.1232488751,0.392663449,0.0761821121,0.1704780757,-0.1057646349,0.0450052433,0.0572169609,0.1658800095,0.1689322591,0.0899030641,0.0406295843,-0.0986579731,-0.1070596278,-0.2073839009,-0.1325087398,0.1452479213,-0.0369639024,0.1878559738,0.3012841046,-0.1913864017,0.4304111302,0.1752296537,-0.3052102625,0.0226301737,-0.1105510965,0.0853921175,0.2571446896,0.3357310295,-0.2983862162,0.0257938672,0.2341618091,0.1361136585,-0.0246290993,0.1538067311,-0.0182853099,-0.1262563467,0.3877310157,-0.180800423,0.0116172656,-0.0810279325,0.203710556,-0.1931931376,0.1173006445,0.0319551304,-0.2637951672,-0.1154088974,0.0591809899,-0.2955644429,-0.0894153863,-0.1874343753,0.4970225692,-0.14152053,-0.3647653759,0.3241899312,-0.0609255508,-0.2746299505,0.0272672102,0.4525734186,0.035437271,-0.3755993545,-0.2120317966,-0.2530660927,-0.2939559221,0.0297835711,0.082785517,-0.0162831079,-0.1302708834,0.0117435651,0.0667450652,0.5063066483,-0.2447977662,-0.4294309318,0.0430095419,0.1563761681,0.3383912444,0.3311876655,0.1683518738,0.3549143374,-0.0742812008,-0.2611412704,0.4684114158,-0.2120689005,-0.1101260632,0.0261671171,-0.2384483367,0.0417771973,0.3851336539,0.0402715206,0.4039864838,0.0888828039,-0.0697815493,0.0750930086,-0.2377980947,0.259714812,-0.2038574815,0.1237972677,-0.06960731,-0.0331982188,-0.6629144549,-0.1328342855,-0.0041190111,0.3424855769,-0.0795081109,-0.0707865506,-0.3367106318,0.0916640833,-0.1431745887,-0.1482680142,0.1533527374,-0.0225261506,0.0875026062,0.1096360087,-0.0746960714,-0.5553774834,0.207932964,-0.1297468245,-0.0466144606,-0.0533397272,0.1536362171,0.2858560979,0.0079611493,-0.0851956978,0.3947858512,-0.0918580592,0.0338209569,-0.043201685,0.3198718727,-0.1584313512,0.4796308577,-0.3276026249,0.4650476575,0.2236869186,-0.3226125538,0.1083132848,-0.1004635617,0.1354131699,-0.1560812443,0.0584063381,0.3667320609,-0.0449781865,0.3632372916,0.0312745608,0.0641759858,0.091388002,0.1403013766,-0.5115070939,-0.1760685295,-0.0995324105,-0.3794975281,0.2250062674,-0.2498199344,-0.5284563899,-0.1130747199,0.2394347489,-0.2140522599,0.0055780713,0.178390488,-0.1548204869,-0.0143347019,0.2795885801,0.2609135807,0.3595252633,0.2127412558,0.1786495447,0.1582676172,0.1146971956,0.0140506271,0.1314163357,0.2866766453,-0.0658219978,0.3254796267,0.01625132,0.1429256052,-0.3953850567,-0.203703478,-0.0033612191,0.0095851021,-0.1751020551,-0.2024122775,-0.2956289649,-0.2708905339,-0.0857386515,-0.1229956076,0.06458354,-0.0795370191,0.1414581835,0.4497592747,-0.3392570615,0.4384874105,-0.2527057827,0.3999791145,-0.1697011292,-0.203975141,-0.2398076802,0.2169283777,0.1976421624,0.0144114858,0.0715077445,-0.2192701548,0.2823613882,0.0047228155,0.1325936764,-0.1955920905,-0.11827223,0.1306115687,-0.13594006,0.1313455701,0.189599514,0.0327004381,-0.0404889323,-0.4407272637,0.2290777713,-0.0287530068,0.1128965095,0.1084503606,0.0070404778,0.2582620382,-0.0945801139,-0.0233603269,-0.3155883849,-0.6960618496,0.2818812132,-0.3225012422,0.0250322316,0.1615466177,0.0628368333,0.1684764475,-0.0432699844,0.3091671467,-0.1517888904,-0.2186586708,0.3302001953,-0.1230871975,-0.124576427,-0.2127516866,-0.0225111824,0.1818174273,0.4256777167,-0.3486183584,-0.0656612888,-0.1732284725,0.0241672248,-0.0624980591,0.0327276215,0.1499396563,0.144497484,0.0231247824,0.0023071754,-0.1343535185,-0.4355956018,0.2739707232,-0.0955993459,0.1293876916,0.5151429176,-0.1318723559,0.447442621,0.362991035,0.3133444488,0.2865398526,-0.0460871607,0.2860626578,-0.238708064,-0.3787442148,0.1737957597,-0.0590652488,0.0332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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3369","title":"[Audio] Allow resampling for audio datasets in streaming mode","comments":"Just to clarify a bit. This feature is **always** needed when using the common voice dataset in streaming mode. So I think it's quite important","body":"Many audio datasets like Common Voice always need to be resampled. This can very easily be done in non-streaming mode as follows:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"test\")\r\n\r\nds = ds.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nHowever in streaming mode it fails currently:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"test\", streaming=True)\r\n\r\nds = ds.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nwith the following error:\r\n\r\n```\r\nAttributeError: 'IterableDataset' object has no attribute 'cast_column'  \r\n```\r\n\r\nIt would be great if we could add such a feature (I'm not 100% sure though how complex this would be)","comment_length":25,"text":"[Audio] Allow resampling for audio datasets in streaming mode \n Many audio datasets like Common Voice always need to be resampled. This can very easily be done in non-streaming mode as follows:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"test\")\r\n\r\nds = ds.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nHowever in streaming mode it fails currently:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"test\", streaming=True)\r\n\r\nds = ds.cast_column(\"audio\", Audio(sampling_rate=16_000))\r\n```\r\n\r\nwith the following error:\r\n\r\n```\r\nAttributeError: 'IterableDataset' object has no attribute 'cast_column'  \r\n```\r\n\r\nIt would be great if we could add such a feature (I'm not 100% sure though how complex this would be) \n Just to clarify a bit. This feature is **always** needed when using the common voice dataset in streaming mode. So I think it's quite 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3358","title":"add new field, and get errors","comments":"Hi, \r\n\r\ncould you please post this question on our [Forum](https:\/\/discuss.huggingface.co\/) as we keep issues for bugs and feature requests? ","body":"after adding new field **tokenized_examples[\"example_id\"]**, and get errors below,\r\nI think it is due to changing data to tensor, and **tokenized_examples[\"example_id\"]** is string list \r\n**all fields**\r\n```\r\n***************** train_dataset 1: Dataset({\r\n    features: ['attention_mask', 'end_positions', 'example_id', 'input_ids', 'start_positions', 'token_type_ids'],\r\n    num_rows: 87714\r\n})\r\n```\r\n\r\n**Errors**\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 705, in convert_to_tensors\r\n    tensor = as_tensor(value)\r\nValueError: too many dimensions 'str'\r\n```","comment_length":19,"text":"add new field, and get errors \n after adding new field **tokenized_examples[\"example_id\"]**, and get errors below,\r\nI think it is due to changing data to tensor, and **tokenized_examples[\"example_id\"]** is string list \r\n**all fields**\r\n```\r\n***************** train_dataset 1: Dataset({\r\n    features: ['attention_mask', 'end_positions', 'example_id', 'input_ids', 'start_positions', 'token_type_ids'],\r\n    num_rows: 87714\r\n})\r\n```\r\n\r\n**Errors**\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 705, in convert_to_tensors\r\n    tensor = as_tensor(value)\r\nValueError: too many dimensions 'str'\r\n``` \n Hi, \r\n\r\ncould you please post this question on our [Forum](https:\/\/discuss.huggingface.co\/) as we keep issues for bugs and feature requests? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3358","title":"add new field, and get errors","comments":"> Hi,\r\n> \r\n> could you please post this question on our [Forum](https:\/\/discuss.huggingface.co\/) as we keep issues for bugs and feature requests?\r\n\r\nok.","body":"after adding new field **tokenized_examples[\"example_id\"]**, and get errors below,\r\nI think it is due to changing data to tensor, and **tokenized_examples[\"example_id\"]** is string list \r\n**all fields**\r\n```\r\n***************** train_dataset 1: Dataset({\r\n    features: ['attention_mask', 'end_positions', 'example_id', 'input_ids', 'start_positions', 'token_type_ids'],\r\n    num_rows: 87714\r\n})\r\n```\r\n\r\n**Errors**\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 705, in convert_to_tensors\r\n    tensor = as_tensor(value)\r\nValueError: too many dimensions 'str'\r\n```","comment_length":23,"text":"add new field, and get errors \n after adding new field **tokenized_examples[\"example_id\"]**, and get errors below,\r\nI think it is due to changing data to tensor, and **tokenized_examples[\"example_id\"]** is string list \r\n**all fields**\r\n```\r\n***************** train_dataset 1: Dataset({\r\n    features: ['attention_mask', 'end_positions', 'example_id', 'input_ids', 'start_positions', 'token_type_ids'],\r\n    num_rows: 87714\r\n})\r\n```\r\n\r\n**Errors**\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 705, in convert_to_tensors\r\n    tensor = as_tensor(value)\r\nValueError: too many dimensions 'str'\r\n``` \n > Hi,\r\n> \r\n> could you please post this question on our [Forum](https:\/\/discuss.huggingface.co\/) as we keep issues for bugs and feature requests?\r\n\r\nok.","embeddings":[0.0494642518,-0.4595836699,-0.041636657,0.2271217108,0.2988951206,0.2176525444,0.5312834978,0.3263184726,-0.1052218527,0.1934477985,0.3319586515,-0.0534845106,-0.095450379,0.1428465247,0.1321332157,-0.3765428364,0.2242112905,-0.0373566933,0.3674289584,0.0773196518,-0.2338248491,0.038177941,-0.0949909389,0.1430576444,-0.7133242488,-0.1978125572,-0.2818618417,-0.298021704,-0.1502490491,-0.4651329815,-0.0985193849,-0.197833553,0.2355629802,0.3881615698,-0.0001145084,-0.044521559,-0.0141636394,0.1363514662,0.0911902264,-0.3035225868,0.1415073425,-0.5094673038,0.1612447798,-0.2362727523,-0.0425677896,-0.3196815848,-0.2887626886,0.0572748557,0.2534811795,0.6204361916,0.207540378,0.2654423714,0.0896599069,-0.0128372796,0.2806482911,0.133343339,-0.158330977,-0.113585338,-0.0719459206,0.2066910863,0.3191041946,0.1022603214,-0.0878219455,-0.1461720318,-0.0177597571,0.2018309534,-0.011034064,-0.3475722969,-0.096915938,0.2371205389,0.0367233492,-0.0419366471,-0.2668240368,-0.2703268826,0.1498177052,-0.8005063534,-0.0065836366,-0.0847314894,0.151330635,-0.0944472924,0.1208642349,-0.183440268,-0.2301212102,0.2683455646,-0.0701834112,0.2840692401,0.0611521117,0.077147454,-0.0084742066,-0.2995910048,-0.2273299992,0.2170158774,0.083633028,0.0467170477,-0.3409608305,-0.3207493126,-0.2056909651,-0.8456074595,-0.029617846,-0.1311085969,0.1418230087,0.0743330345,-0.1018705592,0.1507778019,-0.1690877527,0.4221652448,-0.2767634094,0.3298920989,0.0317751877,-0.0018062063,0.091505155,-0.211987257,-0.0534382872,0.0746643767,-0.2136530578,-0.088164255,0.2710961998,0.1766838878,-0.2154186219,0.2351103574,-0.3669188917,-0.0839491412,0.3222770989,0.3090452254,-0.0106384251,-0.0633719489,-0.0220616534,0.3528705835,0.0900325254,0.0262558311,-0.1002127007,0.2345601618,0.0377739891,0.0894366875,-0.0110398717,0.2604201138,0.1594399661,0.1634726524,0.1630768776,-0.0813458487,0.0659970939,-0.3340250254,-0.0173028819,0.2424566746,-0.1721289158,0.1367064714,0.1150900647,-0.3379416466,-0.0994391069,0.137286216,-0.4385375082,-0.2794565558,-0.2625279129,0.2261706889,0.074576579,-0.2050843984,-0.1418493539,0.4258647859,0.1783704311,0.0411146656,0.0866138861,-0.1189154238,-0.2182130814,-0.2205146998,-0.0835604593,-0.0261223279,0.098756969,-0.0741422772,0.248852849,0.2132149786,0.2448011339,0.3794907033,-0.095520772,0.185325563,-0.1021115854,0.6436733603,0.5139328837,-0.1069780961,0.0839938819,0.0497982092,-0.1087113172,-0.0103986785,0.0872987136,-0.0677115247,0.3736271262,0.2547334433,0.0640832856,-0.049616985,-0.1789563894,0.0178275835,-0.2095883936,-0.0576371141,0.4878091812,-0.0416238047,0.0859870613,0.1384150386,-0.1223918945,0.3903976083,-0.1192833856,-0.2377116084,-0.0500201806,0.2092347592,0.4296057224,0.0549888536,0.0136455065,-0.271392256,-0.2155062556,-0.1048233435,-0.0285403654,0.3129197955,0.128751874,-0.1671168208,-0.0849140882,0.0855231881,0.0060462812,-0.0615012199,0.1639476418,-0.1279583275,-0.0668620691,-0.1708504856,-0.3525910676,-0.177763626,-0.2689225078,0.3091744184,-0.0066088545,0.091999568,-0.2017164826,-0.2708847821,-0.3419153094,0.1802118123,0.4479683042,-0.3205736876,-0.1500423849,0.2228434682,0.0955759808,-0.147021234,-0.17986314,0.5841492414,0.4731908739,0.0507566258,-0.049394343,0.105246976,-0.208009243,0.0479798056,0.298872143,0.2580320537,0.0668035746,0.0963130742,0.007230089,-0.0217527151,0.2727022767,-0.0496851057,-0.0714727417,-0.2484458536,0.0305007342,-0.0220686886,-0.2882011235,-0.196240738,-0.4023239613,0.0277787261,0.4156403244,0.0347076058,0.1178903878,0.348217696,0.0441492908,-0.0102664242,-0.1911491007,-0.135547176,0.1785566658,0.214329198,-0.0400324613,0.180518508,-0.0463352203,-0.0707816258,0.3125254512,0.0875535235,-0.0205632746,0.1875151545,0.2390920669,0.044914417,-0.0473155752,-0.1932897568,0.1837452948,0.4113520384,-0.3289888799,0.0441017374,-0.1352256685,0.1138922647,-0.3598469198,-0.5092570782,-0.1970317364,-0.0758054778,0.1417428702,0.1488669813,-0.0544113144,-0.0259814728,0.2484107018,0.1901666522,0.1533098668,0.2289119959,-0.2110155523,0.1808116883,-0.0996081606,0.08209344,0.1093159169,-0.367204845,0.1935218275,-0.0142109133,-0.073736459,-0.2033664882,-0.6990796328,0.0333571509,-0.1648033559,0.1501537561,0.0350849964,0.2067216039,-0.3207115829,-0.5492858887,0.3680838943,0.0411166213,-0.1737539619,0.1930916607,-0.1984650344,0.0747515038,-0.0703591481,-0.5334662795,-0.0048903637,-0.0804615989,0.3707931936,0.1231150106,0.2492713779,0.3679786921,0.119171761,0.1723250449,0.06138492,0.1761807948,-0.2066814899,-0.2650456429,0.1120147631,-0.3018296957,-0.2515174448,0.0293442886,-0.2135266215,0.0985988453,0.0209953468,-0.2288405001,-0.1682463139,-0.0633464977,0.088318646,-0.1324809492,-0.0277650468,0.4366385639,-0.0834533572,-0.0677977055,0.1670196652,-0.1521578431,0.0277898367,0.2006292492,0.3603654504,-0.0757441223,0.5954313278,0.1214720234,0.6986408234,-0.0174064562,-0.2562652826,0.4088597298,-0.5466686487,-0.0796640962,-0.1242734939,-0.5318894982,-0.0100131677,-0.163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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3353","title":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function","comments":"Hi ! Your function looks fine, I used to map `squad` locally and it indeed added the `example_id` field correctly.\r\n\r\nHowever I think that in the `compute_loss` method only a subset of the fields are available: the model inputs. Since `example_id` is not a model input (it's not passed as a parameter to the model), the data loader doesn't need to return it by default.\r\n\r\nHowever you can disable this behavior by setting `remove_unused_columns` to `False` to your training arguments. In this case in `compute_loss` you will get the full item with all the fields.\r\n\r\nNote that since the model doesn't take `example_id` as input, you will have to remove it from the inputs when `model(**inputs)` is called","body":"Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_","comment_length":118,"text":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function \n Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_ \n Hi ! Your function looks fine, I used to map `squad` locally and it indeed added the `example_id` field correctly.\r\n\r\nHowever I think that in the `compute_loss` method only a subset of the fields are available: the model inputs. Since `example_id` is not a model input (it's not passed as a parameter to the model), the data loader doesn't need to return it by default.\r\n\r\nHowever you can disable this behavior by setting `remove_unused_columns` to `False` to your training arguments. In this case in `compute_loss` you will get the full item with all the fields.\r\n\r\nNote that since the model doesn't take `example_id` as input, you will have to remove it from the inputs when `model(**inputs)` is called","embeddings":[-0.1465503126,-0.5799235106,-0.2740719914,0.1202488914,0.1545109749,0.0243581068,0.5186565518,0.1191994399,0.2118413597,0.4652204812,0.3532583714,0.3118035495,0.2009700239,-0.1237441674,0.3787088394,0.0022587166,-0.0379148163,0.2241287231,0.2628961802,-0.164249748,-0.1830895543,-0.0878599957,-0.2189251035,0.022623837,-0.2868743241,-0.2088925242,0.0530895814,-0.1255611479,-0.0868295953,-0.0424166285,0.1870988607,-0.1108928472,-0.2399866283,0.1997063309,-0.0001078606,0.1045565903,0.15331918,-0.0608258583,-0.0272861961,-0.154392451,-0.221708715,-0.2605640292,-0.0360181518,-0.3241761923,-0.0498110577,0.1234707162,-0.1091195419,-0.3235189021,0.0795218796,0.2246323079,0.2008236498,-0.1087318733,0.1274074763,-0.0000119168,0.2504413724,0.0550521351,0.0921007991,-0.2274123132,-0.2579442263,-0.0577591285,0.4909741282,0.4897178113,0.3399710357,-0.1195270941,0.012490157,0.2712907195,0.2939136624,-0.1918283999,0.0852967799,-0.1495797634,-0.2388663739,-0.087489076,-0.0544353239,0.1071322784,0.1659789681,-0.7740443349,-0.2672750056,-0.0151067059,0.0079791704,0.0360802077,0.259370774,0.0940451473,-0.3109925985,0.4295271635,0.0709782019,0.2999991477,0.0571780577,0.1011035368,0.1933594197,-0.0105595784,-0.0151524562,0.3169317544,0.3318520486,0.0747606829,-0.3491974771,0.0346169583,-0.087188594,-0.4820680022,-0.1534078866,-0.2185966372,0.3559983671,0.1997031868,0.2166691571,0.4776989222,-0.3577489555,0.2326746285,-0.1546849906,0.006708933,0.0488917753,-0.0942221656,0.016821079,-0.2492658794,-0.04760148,0.2071011961,0.0075181583,0.1878778338,0.0598699898,0.2564730942,-0.3819013536,-0.0711563528,-0.0432541296,-0.1892252415,0.2999638915,0.3324871063,0.1197901219,0.1190501899,0.1769400239,0.0945178494,-0.0657148808,-0.099536106,-0.3446669281,0.3517923951,-0.0081032198,-0.0312071051,0.2298301607,0.1371506751,0.2383448184,-0.1337925643,0.217964083,-0.0860010758,0.1516469121,-0.1166075617,0.4551611841,0.2630287111,-0.1637023538,0.0134522663,0.2568818033,-0.2118528187,0.0206136294,0.1065061092,-0.2221773863,-0.0247165244,-0.2702471018,0.1769709587,0.3174366355,-0.0659503415,0.1990665793,0.4340727031,0.0306911953,-0.1820393801,0.1591261178,0.2752167583,-0.4124224186,-0.2381925583,0.3947792649,-0.1607453674,-0.1401054263,-0.1417852044,-0.2604893148,0.1221225709,0.1909762919,-0.0842139199,-0.0475602075,-0.0282745399,0.0637513399,0.3494351208,0.8107305169,-0.3744963706,-0.2795383632,-0.0711329877,-0.2432865351,-0.3155625165,0.3230952024,0.1094323844,-0.0346454941,0.3508976698,0.3981731534,-0.2225912958,-0.058040753,-0.0951701403,-0.157324791,0.1440275609,0.2992533743,-0.0405859426,-0.0326449014,0.2290869951,0.1560373604,-0.0462948345,0.1761763543,0.0521368422,0.039058134,-0.0493439101,0.4451039732,-0.351529181,-0.0790179521,-0.3894445598,0.0563284457,0.0327257849,0.105559431,-0.1064713374,0.2491141409,-0.3219109774,-0.1566195935,-0.1171729341,0.265478313,0.1461735964,0.2600670159,0.1603274196,-0.1780052632,-0.2101497501,-0.1849680096,0.2014155835,0.108763285,-0.1175370291,-0.060579028,0.1088069901,-0.2590560019,-0.3654016852,-0.2350625992,0.1987410188,0.5106495023,-0.0705523863,0.1146340221,0.2025003135,0.057681296,-0.1379920244,-0.0804837197,0.2474099845,0.2388274521,0.1963553429,-0.0922088921,-0.0410734676,-0.0662459731,-0.0322652757,0.0253203772,0.1264402717,0.1489982307,-0.1023369357,-0.0388093889,-0.0366985127,0.0363959856,0.1048706844,-0.0081958948,-0.2124929279,-0.0292193331,-0.0271039866,-0.1880143732,-0.3396835029,-0.6948495507,0.2774123549,0.3753506839,0.2250717133,0.0887843072,-0.1543402076,0.1054507419,0.3096150756,0.1741133928,-0.0688998029,0.2824613154,0.2431524992,-0.0006570029,-0.192928493,-0.145914495,0.0332998782,-0.1217952222,0.0161185358,-0.0088924235,0.055340644,0.0460410826,-0.1220655441,-0.074140206,0.1098109782,-0.0425806828,0.1530543268,-0.1819597036,0.0745957792,-0.2065948546,-0.170156166,-0.017664196,-0.187885195,0.4881094396,-0.2357125431,0.1085633934,0.0685067698,-0.0312239602,0.1133598536,0.3010867238,0.3493436873,0.0032124466,0.4118722081,-0.5014946461,0.0754359066,0.0803718716,0.1347718239,-0.3237752616,0.2229704261,0.0899230987,0.1199859679,0.1634753197,0.1981914639,-0.5743003488,-0.0457440875,-0.1828482151,-0.108130075,-0.2321819514,0.3006093502,-0.4940449595,-0.4426553249,0.0445014201,-0.1192847192,-0.1709108502,-0.0220639743,-0.1097596586,0.061272461,-0.3739383817,-0.416738838,-0.2716558874,-0.2535756826,0.3533883691,0.140496403,0.1286893487,-0.0205968916,0.1233992577,0.0022617574,0.3900399208,0.3684776723,-0.2363979667,-0.3898071349,0.2644090652,-0.4682348073,-0.2753454745,0.1190087646,-0.2428464442,0.3059528768,-0.0551914088,-0.0429996289,-0.1326036155,0.1728415489,-0.139080599,-0.1978016198,0.2503290474,0.5233489275,-0.1263386607,-0.1503018737,-0.1779240817,-0.4433750212,0.1343267858,0.3658438623,0.2038743198,-0.3099859059,0.2279684544,-0.0131085021,0.5472480059,0.0150868604,-0.5900518894,0.1056578457,-0.3595626652,-0.0319622979,0.282009393,-0.3894617558,-0.0417870283,0.1235163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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3353","title":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function","comments":"Hi, I have set **args.remove_unused_columns=False** and  **training_args.remove_unused_columns=False**, but the field doesn't been contained yet.\r\n```\r\ndef main():\r\n    argp = HfArgumentParser(TrainingArguments)\r\n    # The HfArgumentParser object collects command-line arguments into an object (and provides default values for unspecified arguments).\r\n    # In particular, TrainingArguments has several keys that you'll need\/want to specify (when you call run.py from the command line):\r\n    # --do_train\r\n    #     When included, this argument tells the script to train a model.\r\n    #     See docstrings for \"--task\" and \"--dataset\" for how the training dataset is selected.\r\n    # --do_eval\r\n    #     When included, this argument tells the script to evaluate the trained\/loaded model on the validation split of the selected dataset.\r\n    # --per_device_train_batch_size <int, default=8>\r\n    #     This is the training batch size.\r\n    #     If you're running on GPU, you should try to make this as large as you can without getting CUDA out-of-memory errors.\r\n    #     For reference, with --max_length=128 and the default ELECTRA-small model, a batch size of 32 should fit in 4gb of GPU memory.\r\n    # --num_train_epochs <float, default=3.0>\r\n    #     How many passes to do through the training data.\r\n    # --output_dir <path>\r\n    #     Where to put the trained model checkpoint(s) and any eval predictions.\r\n    #     *This argument is required*.\r\n\r\n    argp.add_argument('--model', type=str,\r\n                      default='google\/electra-small-discriminator',\r\n                      help=\"\"\"This argument specifies the base model to fine-tune.\r\n        This should either be a HuggingFace model ID (see https:\/\/huggingface.co\/models)\r\n        or a path to a saved model checkpoint (a folder containing config.json and pytorch_model.bin).\"\"\")\r\n    argp.add_argument('--task', type=str, choices=['nli', 'qa'], required=True,\r\n                      help=\"\"\"This argument specifies which task to train\/evaluate on.\r\n        Pass \"nli\" for natural language inference or \"qa\" for question answering.\r\n        By default, \"nli\" will use the SNLI dataset, and \"qa\" will use the SQuAD dataset.\"\"\")\r\n    argp.add_argument('--dataset', type=str, default=None,\r\n                      help=\"\"\"This argument overrides the default dataset used for the specified task.\"\"\")\r\n    argp.add_argument('--max_length', type=int, default=128,\r\n                      help=\"\"\"This argument limits the maximum sequence length used during training\/evaluation.\r\n        Shorter sequence lengths need less memory and computation time, but some examples may end up getting truncated.\"\"\")\r\n    argp.add_argument('--max_train_samples', type=int, default=None,\r\n                      help='Limit the number of examples to train on.')\r\n    argp.add_argument('--max_eval_samples', type=int, default=None,\r\n                      help='Limit the number of examples to evaluate on.')\r\n\r\n    argp.remove_unused_columns = False\r\n    training_args, args = argp.parse_args_into_dataclasses()\r\n    args.remove_unused_columns=False\r\n    training_args.remove_unused_columns=False\r\n```\r\n\r\n\r\n```\r\n**************** train_dataset: Dataset({\r\n    features: ['id', 'title', 'context', 'question', 'answers'],\r\n    num_rows: 87599\r\n})\r\n\r\n\r\n**************** train_dataset_featurized: Dataset({\r\n    features: ['attention_mask', 'end_positions', 'input_ids', 'start_positions', 'token_type_ids'],\r\n    num_rows: 87714\r\n})\r\n```","body":"Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_","comment_length":373,"text":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function \n Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_ \n Hi, I have set **args.remove_unused_columns=False** and  **training_args.remove_unused_columns=False**, but the field doesn't been contained yet.\r\n```\r\ndef main():\r\n    argp = HfArgumentParser(TrainingArguments)\r\n    # The HfArgumentParser object collects command-line arguments into an object (and provides default values for unspecified arguments).\r\n    # In particular, TrainingArguments has several keys that you'll need\/want to specify (when you call run.py from the command line):\r\n    # --do_train\r\n    #     When included, this argument tells the script to train a model.\r\n    #     See docstrings for \"--task\" and \"--dataset\" for how the training dataset is selected.\r\n    # --do_eval\r\n    #     When included, this argument tells the script to evaluate the trained\/loaded model on the validation split of the selected dataset.\r\n    # --per_device_train_batch_size <int, default=8>\r\n    #     This is the training batch size.\r\n    #     If you're running on GPU, you should try to make this as large as you can without getting CUDA out-of-memory errors.\r\n    #     For reference, with --max_length=128 and the default ELECTRA-small model, a batch size of 32 should fit in 4gb of GPU memory.\r\n    # --num_train_epochs <float, default=3.0>\r\n    #     How many passes to do through the training data.\r\n    # --output_dir <path>\r\n    #     Where to put the trained model checkpoint(s) and any eval predictions.\r\n    #     *This argument is required*.\r\n\r\n    argp.add_argument('--model', type=str,\r\n                      default='google\/electra-small-discriminator',\r\n                      help=\"\"\"This argument specifies the base model to fine-tune.\r\n        This should either be a HuggingFace model ID (see https:\/\/huggingface.co\/models)\r\n        or a path to a saved model checkpoint (a folder containing config.json and pytorch_model.bin).\"\"\")\r\n    argp.add_argument('--task', type=str, choices=['nli', 'qa'], required=True,\r\n                      help=\"\"\"This argument specifies which task to train\/evaluate on.\r\n        Pass \"nli\" for natural language inference or \"qa\" for question answering.\r\n        By default, \"nli\" will use the SNLI dataset, and \"qa\" will use the SQuAD dataset.\"\"\")\r\n    argp.add_argument('--dataset', type=str, default=None,\r\n                      help=\"\"\"This argument overrides the default dataset used for the specified task.\"\"\")\r\n    argp.add_argument('--max_length', type=int, default=128,\r\n                      help=\"\"\"This argument limits the maximum sequence length used during training\/evaluation.\r\n        Shorter sequence lengths need less memory and computation time, but some examples may end up getting truncated.\"\"\")\r\n    argp.add_argument('--max_train_samples', type=int, default=None,\r\n                      help='Limit the number of examples to train on.')\r\n    argp.add_argument('--max_eval_samples', type=int, default=None,\r\n                      help='Limit the number of examples to evaluate on.')\r\n\r\n    argp.remove_unused_columns = False\r\n    training_args, args = argp.parse_args_into_dataclasses()\r\n    args.remove_unused_columns=False\r\n    training_args.remove_unused_columns=False\r\n```\r\n\r\n\r\n```\r\n**************** train_dataset: Dataset({\r\n    features: ['id', 'title', 'context', 'question', 'answers'],\r\n    num_rows: 87599\r\n})\r\n\r\n\r\n**************** train_dataset_featurized: Dataset({\r\n    features: ['attention_mask', 'end_positions', 'input_ids', 'start_positions', 'token_type_ids'],\r\n    num_rows: 87714\r\n})\r\n```","embeddings":[-0.1465503126,-0.5799235106,-0.2740719914,0.1202488914,0.1545109749,0.0243581068,0.5186565518,0.1191994399,0.2118413597,0.4652204812,0.3532583714,0.3118035495,0.2009700239,-0.1237441674,0.3787088394,0.0022587166,-0.0379148163,0.2241287231,0.2628961802,-0.164249748,-0.1830895543,-0.0878599957,-0.2189251035,0.022623837,-0.2868743241,-0.2088925242,0.0530895814,-0.1255611479,-0.0868295953,-0.0424166285,0.1870988607,-0.1108928472,-0.2399866283,0.1997063309,-0.0001078606,0.1045565903,0.15331918,-0.0608258583,-0.0272861961,-0.154392451,-0.221708715,-0.2605640292,-0.0360181518,-0.3241761923,-0.0498110577,0.1234707162,-0.1091195419,-0.3235189021,0.0795218796,0.2246323079,0.2008236498,-0.1087318733,0.1274074763,-0.0000119168,0.2504413724,0.0550521351,0.0921007991,-0.2274123132,-0.2579442263,-0.0577591285,0.4909741282,0.4897178113,0.3399710357,-0.1195270941,0.012490157,0.2712907195,0.2939136624,-0.1918283999,0.0852967799,-0.1495797634,-0.2388663739,-0.087489076,-0.0544353239,0.1071322784,0.1659789681,-0.7740443349,-0.2672750056,-0.0151067059,0.0079791704,0.0360802077,0.259370774,0.0940451473,-0.3109925985,0.4295271635,0.0709782019,0.2999991477,0.0571780577,0.1011035368,0.1933594197,-0.0105595784,-0.0151524562,0.3169317544,0.3318520486,0.0747606829,-0.3491974771,0.0346169583,-0.087188594,-0.4820680022,-0.1534078866,-0.2185966372,0.3559983671,0.1997031868,0.2166691571,0.4776989222,-0.3577489555,0.2326746285,-0.1546849906,0.006708933,0.0488917753,-0.0942221656,0.016821079,-0.2492658794,-0.04760148,0.2071011961,0.0075181583,0.1878778338,0.0598699898,0.2564730942,-0.3819013536,-0.0711563528,-0.0432541296,-0.1892252415,0.2999638915,0.3324871063,0.1197901219,0.1190501899,0.1769400239,0.0945178494,-0.0657148808,-0.099536106,-0.3446669281,0.3517923951,-0.0081032198,-0.0312071051,0.2298301607,0.1371506751,0.2383448184,-0.1337925643,0.217964083,-0.0860010758,0.1516469121,-0.1166075617,0.4551611841,0.2630287111,-0.1637023538,0.0134522663,0.2568818033,-0.2118528187,0.0206136294,0.1065061092,-0.2221773863,-0.0247165244,-0.2702471018,0.1769709587,0.3174366355,-0.0659503415,0.1990665793,0.4340727031,0.0306911953,-0.1820393801,0.1591261178,0.2752167583,-0.4124224186,-0.2381925583,0.3947792649,-0.1607453674,-0.1401054263,-0.1417852044,-0.2604893148,0.1221225709,0.1909762919,-0.0842139199,-0.0475602075,-0.0282745399,0.0637513399,0.3494351208,0.8107305169,-0.3744963706,-0.2795383632,-0.0711329877,-0.2432865351,-0.3155625165,0.3230952024,0.1094323844,-0.0346454941,0.3508976698,0.3981731534,-0.2225912958,-0.058040753,-0.0951701403,-0.157324791,0.1440275609,0.2992533743,-0.0405859426,-0.0326449014,0.2290869951,0.1560373604,-0.0462948345,0.1761763543,0.0521368422,0.039058134,-0.0493439101,0.4451039732,-0.351529181,-0.0790179521,-0.3894445598,0.0563284457,0.0327257849,0.105559431,-0.1064713374,0.2491141409,-0.3219109774,-0.1566195935,-0.1171729341,0.265478313,0.1461735964,0.2600670159,0.1603274196,-0.1780052632,-0.2101497501,-0.1849680096,0.2014155835,0.108763285,-0.1175370291,-0.060579028,0.1088069901,-0.2590560019,-0.3654016852,-0.2350625992,0.1987410188,0.5106495023,-0.0705523863,0.1146340221,0.2025003135,0.057681296,-0.1379920244,-0.0804837197,0.2474099845,0.2388274521,0.1963553429,-0.0922088921,-0.0410734676,-0.0662459731,-0.0322652757,0.0253203772,0.1264402717,0.1489982307,-0.1023369357,-0.0388093889,-0.0366985127,0.0363959856,0.1048706844,-0.0081958948,-0.2124929279,-0.0292193331,-0.0271039866,-0.1880143732,-0.3396835029,-0.6948495507,0.2774123549,0.3753506839,0.2250717133,0.0887843072,-0.1543402076,0.1054507419,0.3096150756,0.1741133928,-0.0688998029,0.2824613154,0.2431524992,-0.0006570029,-0.192928493,-0.145914495,0.0332998782,-0.1217952222,0.0161185358,-0.0088924235,0.055340644,0.0460410826,-0.1220655441,-0.074140206,0.1098109782,-0.0425806828,0.1530543268,-0.1819597036,0.0745957792,-0.2065948546,-0.170156166,-0.017664196,-0.187885195,0.4881094396,-0.2357125431,0.1085633934,0.0685067698,-0.0312239602,0.1133598536,0.3010867238,0.3493436873,0.0032124466,0.4118722081,-0.5014946461,0.0754359066,0.0803718716,0.1347718239,-0.3237752616,0.2229704261,0.0899230987,0.1199859679,0.1634753197,0.1981914639,-0.5743003488,-0.0457440875,-0.1828482151,-0.108130075,-0.2321819514,0.3006093502,-0.4940449595,-0.4426553249,0.0445014201,-0.1192847192,-0.1709108502,-0.0220639743,-0.1097596586,0.061272461,-0.3739383817,-0.416738838,-0.2716558874,-0.2535756826,0.3533883691,0.140496403,0.1286893487,-0.0205968916,0.1233992577,0.0022617574,0.3900399208,0.3684776723,-0.2363979667,-0.3898071349,0.2644090652,-0.4682348073,-0.2753454745,0.1190087646,-0.2428464442,0.3059528768,-0.0551914088,-0.0429996289,-0.1326036155,0.1728415489,-0.139080599,-0.1978016198,0.2503290474,0.5233489275,-0.1263386607,-0.1503018737,-0.1779240817,-0.4433750212,0.1343267858,0.3658438623,0.2038743198,-0.3099859059,0.2279684544,-0.0131085021,0.5472480059,0.0150868604,-0.5900518894,0.1056578457,-0.3595626652,-0.0319622979,0.282009393,-0.3894617558,-0.04178702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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3353","title":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function","comments":"Hi, I print the value, all are set to False, but don't work.\r\n```\r\n********************* training_args: TrainingArguments(\r\n_n_gpu=1,\r\nadafactor=False,\r\nadam_beta1=0.9,\r\nadam_beta2=0.999,\r\nadam_epsilon=1e-08,\r\ndataloader_drop_last=False,\r\ndataloader_num_workers=0,\r\ndataloader_pin_memory=True,\r\nddp_find_unused_parameters=None,\r\ndebug=[],\r\ndeepspeed=None,\r\ndisable_tqdm=False,\r\ndo_eval=False,\r\ndo_predict=False,\r\ndo_train=True,\r\neval_accumulation_steps=None,\r\neval_steps=None,\r\nevaluation_strategy=IntervalStrategy.NO,\r\nfp16=False,\r\nfp16_backend=auto,\r\nfp16_full_eval=False,\r\nfp16_opt_level=O1,\r\ngradient_accumulation_steps=1,\r\ngreater_is_better=None,\r\ngroup_by_length=False,\r\nignore_data_skip=False,\r\nlabel_names=None,\r\nlabel_smoothing_factor=0.0,\r\nlearning_rate=5e-05,\r\nlength_column_name=length,\r\nload_best_model_at_end=False,\r\nlocal_rank=-1,\r\nlog_level=-1,\r\nlog_level_replica=-1,\r\nlog_on_each_node=True,\r\nlogging_dir=.\/re_trained_model\/runs\/Dec01_14-15-08_399b9290604c,\r\nlogging_first_step=False,\r\nlogging_steps=500,\r\nlogging_strategy=IntervalStrategy.STEPS,\r\nlr_scheduler_type=SchedulerType.LINEAR,\r\nmax_grad_norm=1.0,\r\nmax_steps=-1,\r\nmetric_for_best_model=None,\r\nmp_parameters=,\r\nno_cuda=False,\r\nnum_train_epochs=3.0,\r\noutput_dir=.\/re_trained_model,\r\noverwrite_output_dir=False,\r\npast_index=-1,\r\nper_device_eval_batch_size=8,\r\nper_device_train_batch_size=8,\r\nprediction_loss_only=False,\r\npush_to_hub=False,\r\npush_to_hub_model_id=re_trained_model,\r\npush_to_hub_organization=None,\r\npush_to_hub_token=None,\r\nremove_unused_columns=False,\r\nreport_to=['tensorboard'],\r\nresume_from_checkpoint=None,\r\nrun_name=.\/re_trained_model,\r\nsave_on_each_node=False,\r\nsave_steps=500,\r\nsave_strategy=IntervalStrategy.STEPS,\r\nsave_total_limit=None,\r\nseed=42,\r\nsharded_ddp=[],\r\nskip_memory_metrics=True,\r\ntpu_metrics_debug=False,\r\ntpu_num_cores=None,\r\nuse_legacy_prediction_loop=False,\r\nwarmup_ratio=0.0,\r\nwarmup_steps=0,\r\nweight_decay=0.0,\r\n)\r\n```\r\n```\r\n********************* args: Namespace(dataset='squad', max_eval_samples=None, max_length=128, max_train_samples=None, model='google\/electra-small-discriminator', remove_unused_columns=False, task='qa')\r\n2021-12-01 14:15:10,048 - WARNING - datasets.builder - Reusing dataset squad (\/root\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\nSome weights of the model checkpoint at google\/electra-small-discriminator were not used when initializing ElectraForQuestionAnswering: ['discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']\r\n- This IS expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\nSome weights of ElectraForQuestionAnswering were not initialized from the model checkpoint at google\/electra-small-discriminator and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\nPreprocessing data... (this takes a little bit, should only happen once per dataset)\r\n```","body":"Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_","comment_length":247,"text":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function \n Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_ \n Hi, I print the value, all are set to False, but don't work.\r\n```\r\n********************* training_args: TrainingArguments(\r\n_n_gpu=1,\r\nadafactor=False,\r\nadam_beta1=0.9,\r\nadam_beta2=0.999,\r\nadam_epsilon=1e-08,\r\ndataloader_drop_last=False,\r\ndataloader_num_workers=0,\r\ndataloader_pin_memory=True,\r\nddp_find_unused_parameters=None,\r\ndebug=[],\r\ndeepspeed=None,\r\ndisable_tqdm=False,\r\ndo_eval=False,\r\ndo_predict=False,\r\ndo_train=True,\r\neval_accumulation_steps=None,\r\neval_steps=None,\r\nevaluation_strategy=IntervalStrategy.NO,\r\nfp16=False,\r\nfp16_backend=auto,\r\nfp16_full_eval=False,\r\nfp16_opt_level=O1,\r\ngradient_accumulation_steps=1,\r\ngreater_is_better=None,\r\ngroup_by_length=False,\r\nignore_data_skip=False,\r\nlabel_names=None,\r\nlabel_smoothing_factor=0.0,\r\nlearning_rate=5e-05,\r\nlength_column_name=length,\r\nload_best_model_at_end=False,\r\nlocal_rank=-1,\r\nlog_level=-1,\r\nlog_level_replica=-1,\r\nlog_on_each_node=True,\r\nlogging_dir=.\/re_trained_model\/runs\/Dec01_14-15-08_399b9290604c,\r\nlogging_first_step=False,\r\nlogging_steps=500,\r\nlogging_strategy=IntervalStrategy.STEPS,\r\nlr_scheduler_type=SchedulerType.LINEAR,\r\nmax_grad_norm=1.0,\r\nmax_steps=-1,\r\nmetric_for_best_model=None,\r\nmp_parameters=,\r\nno_cuda=False,\r\nnum_train_epochs=3.0,\r\noutput_dir=.\/re_trained_model,\r\noverwrite_output_dir=False,\r\npast_index=-1,\r\nper_device_eval_batch_size=8,\r\nper_device_train_batch_size=8,\r\nprediction_loss_only=False,\r\npush_to_hub=False,\r\npush_to_hub_model_id=re_trained_model,\r\npush_to_hub_organization=None,\r\npush_to_hub_token=None,\r\nremove_unused_columns=False,\r\nreport_to=['tensorboard'],\r\nresume_from_checkpoint=None,\r\nrun_name=.\/re_trained_model,\r\nsave_on_each_node=False,\r\nsave_steps=500,\r\nsave_strategy=IntervalStrategy.STEPS,\r\nsave_total_limit=None,\r\nseed=42,\r\nsharded_ddp=[],\r\nskip_memory_metrics=True,\r\ntpu_metrics_debug=False,\r\ntpu_num_cores=None,\r\nuse_legacy_prediction_loop=False,\r\nwarmup_ratio=0.0,\r\nwarmup_steps=0,\r\nweight_decay=0.0,\r\n)\r\n```\r\n```\r\n********************* args: Namespace(dataset='squad', max_eval_samples=None, max_length=128, max_train_samples=None, model='google\/electra-small-discriminator', remove_unused_columns=False, task='qa')\r\n2021-12-01 14:15:10,048 - WARNING - datasets.builder - Reusing dataset squad (\/root\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\nSome weights of the model checkpoint at google\/electra-small-discriminator were not used when initializing ElectraForQuestionAnswering: ['discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']\r\n- This IS expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\nSome weights of ElectraForQuestionAnswering were not initialized from the model checkpoint at google\/electra-small-discriminator and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\nPreprocessing data... (this takes a little bit, should only happen once per dataset)\r\n```","embeddings":[-0.1465503126,-0.5799235106,-0.2740719914,0.1202488914,0.1545109749,0.0243581068,0.5186565518,0.1191994399,0.2118413597,0.4652204812,0.3532583714,0.3118035495,0.2009700239,-0.1237441674,0.3787088394,0.0022587166,-0.0379148163,0.2241287231,0.2628961802,-0.164249748,-0.1830895543,-0.0878599957,-0.2189251035,0.022623837,-0.2868743241,-0.2088925242,0.0530895814,-0.1255611479,-0.0868295953,-0.0424166285,0.1870988607,-0.1108928472,-0.2399866283,0.1997063309,-0.0001078606,0.1045565903,0.15331918,-0.0608258583,-0.0272861961,-0.154392451,-0.221708715,-0.2605640292,-0.0360181518,-0.3241761923,-0.0498110577,0.1234707162,-0.1091195419,-0.3235189021,0.0795218796,0.2246323079,0.2008236498,-0.1087318733,0.1274074763,-0.0000119168,0.2504413724,0.0550521351,0.0921007991,-0.2274123132,-0.2579442263,-0.0577591285,0.4909741282,0.4897178113,0.3399710357,-0.1195270941,0.012490157,0.2712907195,0.2939136624,-0.1918283999,0.0852967799,-0.1495797634,-0.2388663739,-0.087489076,-0.0544353239,0.1071322784,0.1659789681,-0.7740443349,-0.2672750056,-0.0151067059,0.0079791704,0.0360802077,0.259370774,0.0940451473,-0.3109925985,0.4295271635,0.0709782019,0.2999991477,0.0571780577,0.1011035368,0.1933594197,-0.0105595784,-0.0151524562,0.3169317544,0.3318520486,0.0747606829,-0.3491974771,0.0346169583,-0.087188594,-0.4820680022,-0.1534078866,-0.2185966372,0.3559983671,0.1997031868,0.2166691571,0.4776989222,-0.3577489555,0.2326746285,-0.1546849906,0.006708933,0.0488917753,-0.0942221656,0.016821079,-0.2492658794,-0.04760148,0.2071011961,0.0075181583,0.1878778338,0.0598699898,0.2564730942,-0.3819013536,-0.0711563528,-0.0432541296,-0.1892252415,0.2999638915,0.3324871063,0.1197901219,0.1190501899,0.1769400239,0.0945178494,-0.0657148808,-0.099536106,-0.3446669281,0.3517923951,-0.0081032198,-0.0312071051,0.2298301607,0.1371506751,0.2383448184,-0.1337925643,0.217964083,-0.0860010758,0.1516469121,-0.1166075617,0.4551611841,0.2630287111,-0.1637023538,0.0134522663,0.2568818033,-0.2118528187,0.0206136294,0.1065061092,-0.2221773863,-0.0247165244,-0.2702471018,0.1769709587,0.3174366355,-0.0659503415,0.1990665793,0.4340727031,0.0306911953,-0.1820393801,0.1591261178,0.2752167583,-0.4124224186,-0.2381925583,0.3947792649,-0.1607453674,-0.1401054263,-0.1417852044,-0.2604893148,0.1221225709,0.1909762919,-0.0842139199,-0.0475602075,-0.0282745399,0.0637513399,0.3494351208,0.8107305169,-0.3744963706,-0.2795383632,-0.0711329877,-0.2432865351,-0.3155625165,0.3230952024,0.1094323844,-0.0346454941,0.3508976698,0.3981731534,-0.2225912958,-0.058040753,-0.0951701403,-0.157324791,0.1440275609,0.2992533743,-0.0405859426,-0.0326449014,0.2290869951,0.1560373604,-0.0462948345,0.1761763543,0.0521368422,0.039058134,-0.0493439101,0.4451039732,-0.351529181,-0.0790179521,-0.3894445598,0.0563284457,0.0327257849,0.105559431,-0.1064713374,0.2491141409,-0.3219109774,-0.1566195935,-0.1171729341,0.265478313,0.1461735964,0.2600670159,0.1603274196,-0.1780052632,-0.2101497501,-0.1849680096,0.2014155835,0.108763285,-0.1175370291,-0.060579028,0.1088069901,-0.2590560019,-0.3654016852,-0.2350625992,0.1987410188,0.5106495023,-0.0705523863,0.1146340221,0.2025003135,0.057681296,-0.1379920244,-0.0804837197,0.2474099845,0.2388274521,0.1963553429,-0.0922088921,-0.0410734676,-0.0662459731,-0.0322652757,0.0253203772,0.1264402717,0.1489982307,-0.1023369357,-0.0388093889,-0.0366985127,0.0363959856,0.1048706844,-0.0081958948,-0.2124929279,-0.0292193331,-0.0271039866,-0.1880143732,-0.3396835029,-0.6948495507,0.2774123549,0.3753506839,0.2250717133,0.0887843072,-0.1543402076,0.1054507419,0.3096150756,0.1741133928,-0.0688998029,0.2824613154,0.2431524992,-0.0006570029,-0.192928493,-0.145914495,0.0332998782,-0.1217952222,0.0161185358,-0.0088924235,0.055340644,0.0460410826,-0.1220655441,-0.074140206,0.1098109782,-0.0425806828,0.1530543268,-0.1819597036,0.0745957792,-0.2065948546,-0.170156166,-0.017664196,-0.187885195,0.4881094396,-0.2357125431,0.1085633934,0.0685067698,-0.0312239602,0.1133598536,0.3010867238,0.3493436873,0.0032124466,0.4118722081,-0.5014946461,0.0754359066,0.0803718716,0.1347718239,-0.3237752616,0.2229704261,0.0899230987,0.1199859679,0.1634753197,0.1981914639,-0.5743003488,-0.0457440875,-0.1828482151,-0.108130075,-0.2321819514,0.3006093502,-0.4940449595,-0.4426553249,0.0445014201,-0.1192847192,-0.1709108502,-0.0220639743,-0.1097596586,0.061272461,-0.3739383817,-0.416738838,-0.2716558874,-0.2535756826,0.3533883691,0.140496403,0.1286893487,-0.0205968916,0.1233992577,0.0022617574,0.3900399208,0.3684776723,-0.2363979667,-0.3898071349,0.2644090652,-0.4682348073,-0.2753454745,0.1190087646,-0.2428464442,0.3059528768,-0.0551914088,-0.0429996289,-0.1326036155,0.1728415489,-0.139080599,-0.1978016198,0.2503290474,0.5233489275,-0.1263386607,-0.1503018737,-0.1779240817,-0.4433750212,0.1343267858,0.3658438623,0.2038743198,-0.3099859059,0.2279684544,-0.0131085021,0.5472480059,0.0150868604,-0.5900518894,0.1056578457,-0.3595626652,-0.0319622979,0.282009393,-0.3894617558,-0.0417870283,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3353","title":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function","comments":"Hmmm, it might be because the default data collator removes all the fields with `string` type:\r\n\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/4c0dd199c8305903564c2edeae23d294edd4b321\/src\/transformers\/data\/data_collator.py#L107-L112\r\n\r\nI guess you also need a custom data collator that doesn't remove them.","body":"Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_","comment_length":30,"text":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function \n Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_ \n Hmmm, it might be because the default data collator removes all the fields with `string` type:\r\n\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/4c0dd199c8305903564c2edeae23d294edd4b321\/src\/transformers\/data\/data_collator.py#L107-L112\r\n\r\nI guess you also need a custom data collator that doesn't remove them.","embeddings":[-0.1465503126,-0.5799235106,-0.2740719914,0.1202488914,0.1545109749,0.0243581068,0.5186565518,0.1191994399,0.2118413597,0.4652204812,0.3532583714,0.3118035495,0.2009700239,-0.1237441674,0.3787088394,0.0022587166,-0.0379148163,0.2241287231,0.2628961802,-0.164249748,-0.1830895543,-0.0878599957,-0.2189251035,0.022623837,-0.2868743241,-0.2088925242,0.0530895814,-0.1255611479,-0.0868295953,-0.0424166285,0.1870988607,-0.1108928472,-0.2399866283,0.1997063309,-0.0001078606,0.1045565903,0.15331918,-0.0608258583,-0.0272861961,-0.154392451,-0.221708715,-0.2605640292,-0.0360181518,-0.3241761923,-0.0498110577,0.1234707162,-0.1091195419,-0.3235189021,0.0795218796,0.2246323079,0.2008236498,-0.1087318733,0.1274074763,-0.0000119168,0.2504413724,0.0550521351,0.0921007991,-0.2274123132,-0.2579442263,-0.0577591285,0.4909741282,0.4897178113,0.3399710357,-0.1195270941,0.012490157,0.2712907195,0.2939136624,-0.1918283999,0.0852967799,-0.1495797634,-0.2388663739,-0.087489076,-0.0544353239,0.1071322784,0.1659789681,-0.7740443349,-0.2672750056,-0.0151067059,0.0079791704,0.0360802077,0.259370774,0.0940451473,-0.3109925985,0.4295271635,0.0709782019,0.2999991477,0.0571780577,0.1011035368,0.1933594197,-0.0105595784,-0.0151524562,0.3169317544,0.3318520486,0.0747606829,-0.3491974771,0.0346169583,-0.087188594,-0.4820680022,-0.1534078866,-0.2185966372,0.3559983671,0.1997031868,0.2166691571,0.4776989222,-0.3577489555,0.2326746285,-0.1546849906,0.006708933,0.0488917753,-0.0942221656,0.016821079,-0.2492658794,-0.04760148,0.2071011961,0.0075181583,0.1878778338,0.0598699898,0.2564730942,-0.3819013536,-0.0711563528,-0.0432541296,-0.1892252415,0.2999638915,0.3324871063,0.1197901219,0.1190501899,0.1769400239,0.0945178494,-0.0657148808,-0.099536106,-0.3446669281,0.3517923951,-0.0081032198,-0.0312071051,0.2298301607,0.1371506751,0.2383448184,-0.1337925643,0.217964083,-0.0860010758,0.1516469121,-0.1166075617,0.4551611841,0.2630287111,-0.1637023538,0.0134522663,0.2568818033,-0.2118528187,0.0206136294,0.1065061092,-0.2221773863,-0.0247165244,-0.2702471018,0.1769709587,0.3174366355,-0.0659503415,0.1990665793,0.4340727031,0.0306911953,-0.1820393801,0.1591261178,0.2752167583,-0.4124224186,-0.2381925583,0.3947792649,-0.1607453674,-0.1401054263,-0.1417852044,-0.2604893148,0.1221225709,0.1909762919,-0.0842139199,-0.0475602075,-0.0282745399,0.0637513399,0.3494351208,0.8107305169,-0.3744963706,-0.2795383632,-0.0711329877,-0.2432865351,-0.3155625165,0.3230952024,0.1094323844,-0.0346454941,0.3508976698,0.3981731534,-0.2225912958,-0.058040753,-0.0951701403,-0.157324791,0.1440275609,0.2992533743,-0.0405859426,-0.0326449014,0.2290869951,0.1560373604,-0.0462948345,0.1761763543,0.0521368422,0.039058134,-0.0493439101,0.4451039732,-0.351529181,-0.0790179521,-0.3894445598,0.0563284457,0.0327257849,0.105559431,-0.1064713374,0.2491141409,-0.3219109774,-0.1566195935,-0.1171729341,0.265478313,0.1461735964,0.2600670159,0.1603274196,-0.1780052632,-0.2101497501,-0.1849680096,0.2014155835,0.108763285,-0.1175370291,-0.060579028,0.1088069901,-0.2590560019,-0.3654016852,-0.2350625992,0.1987410188,0.5106495023,-0.0705523863,0.1146340221,0.2025003135,0.057681296,-0.1379920244,-0.0804837197,0.2474099845,0.2388274521,0.1963553429,-0.0922088921,-0.0410734676,-0.0662459731,-0.0322652757,0.0253203772,0.1264402717,0.1489982307,-0.1023369357,-0.0388093889,-0.0366985127,0.0363959856,0.1048706844,-0.0081958948,-0.2124929279,-0.0292193331,-0.0271039866,-0.1880143732,-0.3396835029,-0.6948495507,0.2774123549,0.3753506839,0.2250717133,0.0887843072,-0.1543402076,0.1054507419,0.3096150756,0.1741133928,-0.0688998029,0.2824613154,0.2431524992,-0.0006570029,-0.192928493,-0.145914495,0.0332998782,-0.1217952222,0.0161185358,-0.0088924235,0.055340644,0.0460410826,-0.1220655441,-0.074140206,0.1098109782,-0.0425806828,0.1530543268,-0.1819597036,0.0745957792,-0.2065948546,-0.170156166,-0.017664196,-0.187885195,0.4881094396,-0.2357125431,0.1085633934,0.0685067698,-0.0312239602,0.1133598536,0.3010867238,0.3493436873,0.0032124466,0.4118722081,-0.5014946461,0.0754359066,0.0803718716,0.1347718239,-0.3237752616,0.2229704261,0.0899230987,0.1199859679,0.1634753197,0.1981914639,-0.5743003488,-0.0457440875,-0.1828482151,-0.108130075,-0.2321819514,0.3006093502,-0.4940449595,-0.4426553249,0.0445014201,-0.1192847192,-0.1709108502,-0.0220639743,-0.1097596586,0.061272461,-0.3739383817,-0.416738838,-0.2716558874,-0.2535756826,0.3533883691,0.140496403,0.1286893487,-0.0205968916,0.1233992577,0.0022617574,0.3900399208,0.3684776723,-0.2363979667,-0.3898071349,0.2644090652,-0.4682348073,-0.2753454745,0.1190087646,-0.2428464442,0.3059528768,-0.0551914088,-0.0429996289,-0.1326036155,0.1728415489,-0.139080599,-0.1978016198,0.2503290474,0.5233489275,-0.1263386607,-0.1503018737,-0.1779240817,-0.4433750212,0.1343267858,0.3658438623,0.2038743198,-0.3099859059,0.2279684544,-0.0131085021,0.5472480059,0.0150868604,-0.5900518894,0.1056578457,-0.3595626652,-0.0319622979,0.282009393,-0.3894617558,-0.0417870283,0.12351636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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3353","title":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function","comments":"I overwrite **get_train_dataloader**, and remove **_remove_unused_columns**, but it doesn't work.\r\n\r\n```\r\n    def get_train_dataloader(self) -> DataLoader:\r\n        \"\"\"\r\n        Returns the training :class:`~torch.utils.data.DataLoader`.\r\n\r\n        Will use no sampler if :obj:`self.train_dataset` does not implement :obj:`__len__`, a random sampler (adapted\r\n        to distributed training if necessary) otherwise.\r\n\r\n        Subclass and override this method if you want to inject some custom behavior.\r\n        \"\"\"\r\n        if self.train_dataset is None:\r\n            raise ValueError(\"Trainer: training requires a train_dataset.\")\r\n\r\n        train_dataset = self.train_dataset\r\n        # if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):\r\n        #     train_dataset = self._remove_unused_columns(train_dataset, description=\"training\")\r\n\r\n        if isinstance(train_dataset, torch.utils.data.IterableDataset):\r\n            if self.args.world_size > 1:\r\n                train_dataset = IterableDatasetShard(\r\n                    train_dataset,\r\n                    batch_size=self.args.train_batch_size,\r\n                    drop_last=self.args.dataloader_drop_last,\r\n                    num_processes=self.args.world_size,\r\n                    process_index=self.args.process_index,\r\n                )\r\n\r\n            return DataLoader(\r\n                train_dataset,\r\n                batch_size=self.args.train_batch_size,\r\n                collate_fn=self.data_collator,\r\n                num_workers=self.args.dataloader_num_workers,\r\n                pin_memory=self.args.dataloader_pin_memory,\r\n            )\r\n\r\n        train_sampler = self._get_train_sampler()\r\n\r\n        return DataLoader(\r\n            train_dataset,\r\n            batch_size=self.args.train_batch_size,\r\n            sampler=train_sampler,\r\n            collate_fn=self.data_collator,\r\n            drop_last=self.args.dataloader_drop_last,\r\n            num_workers=self.args.dataloader_num_workers,\r\n            pin_memory=self.args.dataloader_pin_memory,\r\n        )\r\n```","body":"Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_","comment_length":116,"text":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function \n Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_ \n I overwrite **get_train_dataloader**, and remove **_remove_unused_columns**, but it doesn't work.\r\n\r\n```\r\n    def get_train_dataloader(self) -> DataLoader:\r\n        \"\"\"\r\n        Returns the training :class:`~torch.utils.data.DataLoader`.\r\n\r\n        Will use no sampler if :obj:`self.train_dataset` does not implement :obj:`__len__`, a random sampler (adapted\r\n        to distributed training if necessary) otherwise.\r\n\r\n        Subclass and override this method if you want to inject some custom behavior.\r\n        \"\"\"\r\n        if self.train_dataset is None:\r\n            raise ValueError(\"Trainer: training requires a train_dataset.\")\r\n\r\n        train_dataset = self.train_dataset\r\n        # if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):\r\n        #     train_dataset = self._remove_unused_columns(train_dataset, description=\"training\")\r\n\r\n        if isinstance(train_dataset, torch.utils.data.IterableDataset):\r\n            if self.args.world_size > 1:\r\n                train_dataset = IterableDatasetShard(\r\n                    train_dataset,\r\n                    batch_size=self.args.train_batch_size,\r\n                    drop_last=self.args.dataloader_drop_last,\r\n                    num_processes=self.args.world_size,\r\n                    process_index=self.args.process_index,\r\n                )\r\n\r\n            return DataLoader(\r\n                train_dataset,\r\n                batch_size=self.args.train_batch_size,\r\n                collate_fn=self.data_collator,\r\n                num_workers=self.args.dataloader_num_workers,\r\n                pin_memory=self.args.dataloader_pin_memory,\r\n            )\r\n\r\n        train_sampler = self._get_train_sampler()\r\n\r\n        return DataLoader(\r\n            train_dataset,\r\n            batch_size=self.args.train_batch_size,\r\n            sampler=train_sampler,\r\n            collate_fn=self.data_collator,\r\n            drop_last=self.args.dataloader_drop_last,\r\n            num_workers=self.args.dataloader_num_workers,\r\n            pin_memory=self.args.dataloader_pin_memory,\r\n        )\r\n```","embeddings":[-0.1465503126,-0.5799235106,-0.2740719914,0.1202488914,0.1545109749,0.0243581068,0.5186565518,0.1191994399,0.2118413597,0.4652204812,0.3532583714,0.3118035495,0.2009700239,-0.1237441674,0.3787088394,0.0022587166,-0.0379148163,0.2241287231,0.2628961802,-0.164249748,-0.1830895543,-0.0878599957,-0.2189251035,0.022623837,-0.2868743241,-0.2088925242,0.0530895814,-0.1255611479,-0.0868295953,-0.0424166285,0.1870988607,-0.1108928472,-0.2399866283,0.1997063309,-0.0001078606,0.1045565903,0.15331918,-0.0608258583,-0.0272861961,-0.154392451,-0.221708715,-0.2605640292,-0.0360181518,-0.3241761923,-0.0498110577,0.1234707162,-0.1091195419,-0.3235189021,0.0795218796,0.2246323079,0.2008236498,-0.1087318733,0.1274074763,-0.0000119168,0.2504413724,0.0550521351,0.0921007991,-0.2274123132,-0.2579442263,-0.0577591285,0.4909741282,0.4897178113,0.3399710357,-0.1195270941,0.012490157,0.2712907195,0.2939136624,-0.1918283999,0.0852967799,-0.1495797634,-0.2388663739,-0.087489076,-0.0544353239,0.1071322784,0.1659789681,-0.7740443349,-0.2672750056,-0.0151067059,0.0079791704,0.0360802077,0.259370774,0.0940451473,-0.3109925985,0.4295271635,0.0709782019,0.2999991477,0.0571780577,0.1011035368,0.1933594197,-0.0105595784,-0.0151524562,0.3169317544,0.3318520486,0.0747606829,-0.3491974771,0.0346169583,-0.087188594,-0.4820680022,-0.1534078866,-0.2185966372,0.3559983671,0.1997031868,0.2166691571,0.4776989222,-0.3577489555,0.2326746285,-0.1546849906,0.006708933,0.0488917753,-0.0942221656,0.016821079,-0.2492658794,-0.04760148,0.2071011961,0.0075181583,0.1878778338,0.0598699898,0.2564730942,-0.3819013536,-0.0711563528,-0.0432541296,-0.1892252415,0.2999638915,0.3324871063,0.1197901219,0.1190501899,0.1769400239,0.0945178494,-0.0657148808,-0.099536106,-0.3446669281,0.3517923951,-0.0081032198,-0.0312071051,0.2298301607,0.1371506751,0.2383448184,-0.1337925643,0.217964083,-0.0860010758,0.1516469121,-0.1166075617,0.4551611841,0.2630287111,-0.1637023538,0.0134522663,0.2568818033,-0.2118528187,0.0206136294,0.1065061092,-0.2221773863,-0.0247165244,-0.2702471018,0.1769709587,0.3174366355,-0.0659503415,0.1990665793,0.4340727031,0.0306911953,-0.1820393801,0.1591261178,0.2752167583,-0.4124224186,-0.2381925583,0.3947792649,-0.1607453674,-0.1401054263,-0.1417852044,-0.2604893148,0.1221225709,0.1909762919,-0.0842139199,-0.0475602075,-0.0282745399,0.0637513399,0.3494351208,0.8107305169,-0.3744963706,-0.2795383632,-0.0711329877,-0.2432865351,-0.3155625165,0.3230952024,0.1094323844,-0.0346454941,0.3508976698,0.3981731534,-0.2225912958,-0.058040753,-0.0951701403,-0.157324791,0.1440275609,0.2992533743,-0.0405859426,-0.0326449014,0.2290869951,0.1560373604,-0.0462948345,0.1761763543,0.0521368422,0.039058134,-0.0493439101,0.4451039732,-0.351529181,-0.0790179521,-0.3894445598,0.0563284457,0.0327257849,0.105559431,-0.1064713374,0.2491141409,-0.3219109774,-0.1566195935,-0.1171729341,0.265478313,0.1461735964,0.2600670159,0.1603274196,-0.1780052632,-0.2101497501,-0.1849680096,0.2014155835,0.108763285,-0.1175370291,-0.060579028,0.1088069901,-0.2590560019,-0.3654016852,-0.2350625992,0.1987410188,0.5106495023,-0.0705523863,0.1146340221,0.2025003135,0.057681296,-0.1379920244,-0.0804837197,0.2474099845,0.2388274521,0.1963553429,-0.0922088921,-0.0410734676,-0.0662459731,-0.0322652757,0.0253203772,0.1264402717,0.1489982307,-0.1023369357,-0.0388093889,-0.0366985127,0.0363959856,0.1048706844,-0.0081958948,-0.2124929279,-0.0292193331,-0.0271039866,-0.1880143732,-0.3396835029,-0.6948495507,0.2774123549,0.3753506839,0.2250717133,0.0887843072,-0.1543402076,0.1054507419,0.3096150756,0.1741133928,-0.0688998029,0.2824613154,0.2431524992,-0.0006570029,-0.192928493,-0.145914495,0.0332998782,-0.1217952222,0.0161185358,-0.0088924235,0.055340644,0.0460410826,-0.1220655441,-0.074140206,0.1098109782,-0.0425806828,0.1530543268,-0.1819597036,0.0745957792,-0.2065948546,-0.170156166,-0.017664196,-0.187885195,0.4881094396,-0.2357125431,0.1085633934,0.0685067698,-0.0312239602,0.1133598536,0.3010867238,0.3493436873,0.0032124466,0.4118722081,-0.5014946461,0.0754359066,0.0803718716,0.1347718239,-0.3237752616,0.2229704261,0.0899230987,0.1199859679,0.1634753197,0.1981914639,-0.5743003488,-0.0457440875,-0.1828482151,-0.108130075,-0.2321819514,0.3006093502,-0.4940449595,-0.4426553249,0.0445014201,-0.1192847192,-0.1709108502,-0.0220639743,-0.1097596586,0.061272461,-0.3739383817,-0.416738838,-0.2716558874,-0.2535756826,0.3533883691,0.140496403,0.1286893487,-0.0205968916,0.1233992577,0.0022617574,0.3900399208,0.3684776723,-0.2363979667,-0.3898071349,0.2644090652,-0.4682348073,-0.2753454745,0.1190087646,-0.2428464442,0.3059528768,-0.0551914088,-0.0429996289,-0.1326036155,0.1728415489,-0.139080599,-0.1978016198,0.2503290474,0.5233489275,-0.1263386607,-0.1503018737,-0.1779240817,-0.4433750212,0.1343267858,0.3658438623,0.2038743198,-0.3099859059,0.2279684544,-0.0131085021,0.5472480059,0.0150868604,-0.5900518894,0.1056578457,-0.3595626652,-0.0319622979,0.282009393,-0.3894617558,-0.0417870283,0.12351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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3353","title":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function","comments":"Hi, it works now, thank you.\r\n1. **args.remove_unused_columns=False** and **training_args.remove_unused_columns=False**\r\n2. overwrite **get_train_dataloader**, and remove **_remove_unused_columns**\r\n3. add new fields, and can be got in **inputs**. ","body":"Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_","comment_length":26,"text":" add one field  \"example_id\", but I can't see it in the \"comput_loss\" function \n Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/3333#issuecomment-983457161_ \n Hi, it works now, thank you.\r\n1. **args.remove_unused_columns=False** and **training_args.remove_unused_columns=False**\r\n2. overwrite **get_train_dataloader**, and remove **_remove_unused_columns**\r\n3. add new fields, and can be got in **inputs**. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3346","title":"Failed to convert `string` with pyarrow for QED since 1.15.0","comments":"Actually, re-opening this issue cause the error persists\r\n\r\n```python\r\n>>> load_dataset(\"qed\")\r\nDownloading and preparing dataset qed\/qed (download: 13.43 MiB, generated: 9.70 MiB, post-processed: Unknown size, total: 23.14 MiB) to \/home\/victor_huggingface_co\/.cache\/huggingface\/datasets\/qed\/qed\/1.0.0\/47d8b6f033393aa520a8402d4baf2d6bdc1b2fbde3dc156e595d2ef34caf7d75...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 2228.64it\/s]\r\nTraceback (most recent call last):       \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1669, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 681, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1083, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 468, in finalize\r\n    self.write_examples_on_file()\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 339, in write_examples_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 229, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 125, in __arrow_array__\r\n    out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)\r\n  File \"pyarrow\/array.pxi\", line 315, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n  File \"pyarrow\/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Could not convert 'in' with type str: tried to convert to boolean\r\n```\r\n\r\nEnvironment (datasets and pyarrow):\r\n\r\n```bash\r\n(promptsource) victor_huggingface_co@victor-dev:~\/promptsource$ datasets-cli env\r\n\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.0.0-1020-gcp-x86_64-with-debian-buster-sid\r\n- Python version: 3.7.11\r\n- PyArrow version: 6.0.1\r\n```\r\n```bash\r\n(promptsource) victor_huggingface_co@victor-dev:~\/promptsource$ pip show pyarrow\r\nName: pyarrow\r\nVersion: 6.0.1\r\nSummary: Python library for Apache Arrow\r\nHome-page: https:\/\/arrow.apache.org\/\r\nAuthor: \r\nAuthor-email: \r\nLicense: Apache License, Version 2.0\r\nLocation: \/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\r\nRequires: numpy\r\nRequired-by: streamlit, datasets\r\n```","body":"## Describe the bug\r\nLoading QED was fine until 1.15.0.\r\nrelated: bigscience-workshop\/promptsource#659, bigscience-workshop\/promptsource#670\r\n\r\nNot sure where the root cause is, but here are some candidates:\r\n- #3158\r\n- #3120\r\n- #3196\r\n- #2891\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"qed\")\r\n```\r\n\r\n## Expected results\r\nLoading completed.\r\n\r\n## Actual results\r\n```shell\r\nArrowInvalid: Could not convert in with type str: tried to convert to boolean\r\nTraceback:\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/streamlit\/script_runner.py\", line 354, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/Users\/s0s0cr3\/Documents\/GitHub\/promptsource\/promptsource\/app.py\", line 260, in <module>\r\n    dataset = get_dataset(dataset_key, str(conf_option.name) if conf_option else None)\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/streamlit\/caching.py\", line 543, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/streamlit\/caching.py\", line 527, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/Users\/s0s0cr3\/Documents\/GitHub\/promptsource\/promptsource\/utils.py\", line 49, in get_dataset\r\n    builder_instance.download_and_prepare()\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/builder.py\", line 1106, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/arrow_writer.py\", line 456, in finalize\r\n    self.write_examples_on_file()\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/arrow_writer.py\", line 325, in write_examples_on_file\r\n    pa_array = pa.array(typed_sequence)\r\nFile \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\nFile \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/arrow_writer.py\", line 121, in __arrow_array__\r\n    out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)\r\nFile \"pyarrow\/array.pxi\", line 305, in pyarrow.lib.array\r\nFile \"pyarrow\/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\nFile \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\nFile \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.0, 1.16.1\r\n- Platform: macOS 1.15.7 or above\r\n- Python version: 3.7.12 and 3.9\r\n- PyArrow version: 3.0.0, 5.0.0, 6.0.1\r\n","comment_length":222,"text":"Failed to convert `string` with pyarrow for QED since 1.15.0 \n ## Describe the bug\r\nLoading QED was fine until 1.15.0.\r\nrelated: bigscience-workshop\/promptsource#659, bigscience-workshop\/promptsource#670\r\n\r\nNot sure where the root cause is, but here are some candidates:\r\n- #3158\r\n- #3120\r\n- #3196\r\n- #2891\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"qed\")\r\n```\r\n\r\n## Expected results\r\nLoading completed.\r\n\r\n## Actual results\r\n```shell\r\nArrowInvalid: Could not convert in with type str: tried to convert to boolean\r\nTraceback:\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/streamlit\/script_runner.py\", line 354, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/Users\/s0s0cr3\/Documents\/GitHub\/promptsource\/promptsource\/app.py\", line 260, in <module>\r\n    dataset = get_dataset(dataset_key, str(conf_option.name) if conf_option else None)\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/streamlit\/caching.py\", line 543, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/streamlit\/caching.py\", line 527, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/Users\/s0s0cr3\/Documents\/GitHub\/promptsource\/promptsource\/utils.py\", line 49, in get_dataset\r\n    builder_instance.download_and_prepare()\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/builder.py\", line 1106, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/arrow_writer.py\", line 456, in finalize\r\n    self.write_examples_on_file()\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/arrow_writer.py\", line 325, in write_examples_on_file\r\n    pa_array = pa.array(typed_sequence)\r\nFile \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\nFile \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\nFile \"\/Users\/s0s0cr3\/Library\/Python\/3.9\/lib\/python\/site-packages\/datasets\/arrow_writer.py\", line 121, in __arrow_array__\r\n    out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)\r\nFile \"pyarrow\/array.pxi\", line 305, in pyarrow.lib.array\r\nFile \"pyarrow\/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\nFile \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\nFile \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.0, 1.16.1\r\n- Platform: macOS 1.15.7 or above\r\n- Python version: 3.7.12 and 3.9\r\n- PyArrow version: 3.0.0, 5.0.0, 6.0.1\r\n \n Actually, re-opening this issue cause the error persists\r\n\r\n```python\r\n>>> load_dataset(\"qed\")\r\nDownloading and preparing dataset qed\/qed (download: 13.43 MiB, generated: 9.70 MiB, post-processed: Unknown size, total: 23.14 MiB) to \/home\/victor_huggingface_co\/.cache\/huggingface\/datasets\/qed\/qed\/1.0.0\/47d8b6f033393aa520a8402d4baf2d6bdc1b2fbde3dc156e595d2ef34caf7d75...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 2228.64it\/s]\r\nTraceback (most recent call last):       \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1669, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 594, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 681, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1083, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 468, in finalize\r\n    self.write_examples_on_file()\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 339, in write_examples_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 229, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 125, in __arrow_array__\r\n    out = pa.array(cast_to_python_objects(self.data, only_1d_for_numpy=True), type=type)\r\n  File \"pyarrow\/array.pxi\", line 315, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n  File \"pyarrow\/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Could not convert 'in' with type str: tried to convert to boolean\r\n```\r\n\r\nEnvironment (datasets and pyarrow):\r\n\r\n```bash\r\n(promptsource) victor_huggingface_co@victor-dev:~\/promptsource$ datasets-cli env\r\n\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.0.0-1020-gcp-x86_64-with-debian-buster-sid\r\n- Python version: 3.7.11\r\n- PyArrow version: 6.0.1\r\n```\r\n```bash\r\n(promptsource) victor_huggingface_co@victor-dev:~\/promptsource$ pip show pyarrow\r\nName: pyarrow\r\nVersion: 6.0.1\r\nSummary: Python library for Apache Arrow\r\nHome-page: https:\/\/arrow.apache.org\/\r\nAuthor: \r\nAuthor-email: \r\nLicense: Apache License, Version 2.0\r\nLocation: \/home\/victor_huggingface_co\/miniconda3\/envs\/promptsource\/lib\/python3.7\/site-packages\r\nRequires: numpy\r\nRequired-by: streamlit, datasets\r\n```","embeddings":[-0.3966261744,-0.0293864664,-0.010125475,-0.0253715906,0.43712762,-0.2806430459,0.3386760652,0.5561792254,-0.4660205543,0.315726459,0.043357823,0.4963359237,-0.0444318429,0.2531987131,-0.0515404381,-0.0324556865,0.3282586932,0.0855190903,-0.1907031983,0.1468899995,-0.290202558,0.4182667434,-0.3476002216,0.2766410112,-0.0359125286,0.0112020746,-0.0338214636,0.2789127827,-0.0952883512,-0.3773415983,0.0384189412,-0.2121876627,0.0456230789,0.0974894613,-0.0001167219,-0.1377231032,0.520044148,0.0448613353,-0.6164499521,-0.3640903831,-0.0084973499,-0.1379766166,0.275095582,0.0643836036,0.3526574671,-0.1664236486,0.1232780367,-0.0857829824,0.0488290526,0.377556324,0.1874644309,0.3224663734,0.0447001681,-0.1101855487,0.3798224926,0.2187081575,-0.1128101125,0.2455448508,0.5145852566,-0.0340350755,-0.2871376574,-0.0507837534,-0.0306306165,0.0147014055,0.1840144247,0.1609506905,0.2144821584,-0.0955492705,0.0505982824,0.199438557,-0.0757403076,-0.0409257151,-0.3641885519,-0.2433145195,-0.0481205359,-0.2821282744,0.0636922568,0.1457789838,-0.3126290739,0.0823410228,0.0190417357,-0.1535234898,-0.1970093846,-0.0278277546,-0.2780685723,0.4535437226,0.161357969,0.0663332492,-0.1642296463,-0.4638272226,-0.047277417,0.2076614946,-0.2762573361,-0.0243576914,-0.3566706181,0.1805441231,-0.166972965,0.2387904078,0.4651682675,0.0979776159,0.1155494079,0.1662839204,0.2505283058,-0.0958272666,0.0706760585,0.1754771918,0.0591556989,0.2348847687,0.2076435089,0.1650785655,-0.1005600169,0.1501938105,-0.3113728464,-0.1383319497,-0.0215176716,0.2842206359,0.4755910337,-0.1113110185,-0.1448830366,-0.0547553524,-0.3113928437,0.1721535474,-0.1202060059,0.4246998429,0.0681025237,0.2945428491,0.0428203717,0.2780848742,-0.0828723088,-0.34139359,-0.0051558372,-0.2315149009,-0.0717723519,-0.1258577257,0.0893809497,-0.4185526371,0.229604587,-0.0135392277,-0.0290313885,-0.0224327389,-0.3403320014,0.0309363659,0.1752632558,0.1766827404,-0.3072062433,0.1894589365,0.2229714543,0.0236804727,0.0641341284,0.5335205793,-0.1982446015,-0.3022823632,-0.2837785482,0.1756766289,-0.0722572505,-0.2957755923,-0.0797062665,0.086694181,0.020726148,-0.0144154113,-0.0071356907,-0.517038703,-0.0840360001,-0.1501352936,0.1374500692,0.0908866152,-0.6552962065,-0.0289990548,0.1703261286,0.0307616424,0.089212507,0.1232461557,-0.2585833967,0.0791954175,-0.2034782618,0.1814237982,0.3232539892,-0.5484134555,-0.3085800707,0.2000680119,-0.2893205881,-0.0078711947,0.1027714163,-0.2278964967,0.1128432229,-0.1517162174,0.0150395948,0.46476686,-0.0549881868,0.2338284552,-0.3825126588,-0.3578753471,0.0035761355,0.165876925,0.2508064806,0.0067905379,-0.0521403104,-0.0682171285,0.4362271428,-0.2637923062,0.1180776358,-0.026377365,0.3087871969,-0.2272905111,0.383990109,-0.0633906946,0.0071242121,0.1212887987,-0.6558685899,-0.0165953282,-0.3919725716,-0.0005628007,-0.3858846128,0.3725188971,-0.2480865866,0.0378617533,0.1214370579,0.0975750834,-0.08715415,0.1424744874,-0.2106298208,0.0392821394,-0.0430693366,0.275028497,0.0161682274,-0.2037834078,0.072890684,-0.4691082239,0.1234851331,-0.032508187,0.2151664346,0.207092002,-0.184193179,0.31522578,-0.0673098862,-0.0118664494,-0.2884595394,-0.3085126877,0.0796826407,-0.0351033919,0.0989230797,0.259639442,0.0966892987,0.0801631883,0.0345883295,0.3738891482,-0.1309763342,0.4032330513,0.1047166884,0.0102852732,0.0815374553,-0.023038853,0.1245807856,-0.0283414926,0.2938507199,0.3636017442,0.2539678812,-0.2051211894,-0.2609896362,0.3616354465,0.4327275753,-0.0231244229,0.0440303423,0.2438746542,-0.3290320039,-0.2158552855,0.0043329624,0.0351126008,0.2334616631,0.2212925851,0.0079243081,0.0766538754,0.1275396645,-0.2352773547,0.4961216152,0.1232409328,0.1517975032,0.2934261858,0.520468235,-0.28944543,-0.0357433744,-0.0686140954,0.1008448526,0.134122178,-0.5281241536,0.0634802133,-0.3993851542,-0.0690142363,-0.0223652646,-0.2730380297,0.0266030189,-0.4870314598,0.1566191763,0.3312990367,0.0970874503,0.2325144261,0.2916692793,0.0638570189,0.2807581723,-0.2097254694,-0.4015882611,-0.3157901764,-0.6300775409,0.0929317698,0.2213294655,-0.1620724797,-0.152388677,-0.1132861227,0.2214527875,-0.4510795772,-0.2491789758,0.156538859,-0.2109275907,0.6037584543,0.1827925444,0.0282292292,-0.2515258789,-0.0682961643,0.3746389449,-0.0295672435,-0.1904092729,0.3366345465,-0.1861748546,0.0957380086,-0.1751207262,-0.1736467332,-0.1435554475,-0.1459587961,0.1243361086,-0.2053036094,0.1318916082,0.2372525036,0.1952127516,0.1596050709,-0.1176489592,-0.1415264308,0.0838679448,-0.0995758325,0.4430107176,-0.1271874607,-0.4206482172,-0.0131347897,0.0995700061,-0.2086512893,0.1332320273,-0.1357427239,-0.0518978015,0.0065724025,0.3916836679,0.0625953227,-0.3102017045,0.1457018107,0.0947467461,-0.0758005604,-0.1309306771,-0.0202057306,0.2227570564,-0.1657493412,0.2335377783,0.0570845194,0.3902112544,0.1128182486,0.6373876333,-0.0524859093,0.2561361194,0.2227347195,-0.2994365096,-0.1658242941,-0.2255210727,-0.0485536791,-0.17992948,-0.226475045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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3345","title":"Failed to download species_800 from Google Drive zip file","comments":"Hi,\r\n\r\nthe dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?","body":"## Describe the bug\r\nOne can manually download the zip file on Google Drive, but `load_dataset()` cannot.\r\nrelated: #3248\r\n\r\n## Steps to reproduce the bug\r\n```shell\r\n> python\r\nPython 3.7.12 (default, Sep  5 2021, 08:34:29)\r\n[Clang 11.0.3 (clang-1103.0.32.62)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n```\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> s800 = load_dataset(\"species_800\")\r\n```\r\n\r\n## Expected results\r\nspecies_800 downloaded.\r\n\r\n## Actual results\r\n```shell\r\nDownloading: 5.68kB [00:00, 1.22MB\/s]\r\nDownloading: 2.70kB [00:00, 691kB\/s]\r\nDownloading and preparing dataset species800\/species_800 (download: 17.36 MiB, generated: 3.53 MiB, post-processed: Unknown size, total: 20.89 MiB) to \/Users\/mike\/.cache\/huggingface\/datasets\/species800\/species_800\/1.0.0\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...\r\n  0%|                                                                                             | 0\/1 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/mike\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/species_800\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976\/species_800.py\", line 104, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 197, in download\r\n    download_func, url_or_urls, map_tuple=True, num_proc=download_config.num_proc, disable_tqdm=False\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in map_nested\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in <listcomp>\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 305, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/u\/0\/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\/\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14,0 1.15.0, 1.16.1\r\n- Platform: macOS Catalina 10.15.7\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n","comment_length":24,"text":"Failed to download species_800 from Google Drive zip file \n ## Describe the bug\r\nOne can manually download the zip file on Google Drive, but `load_dataset()` cannot.\r\nrelated: #3248\r\n\r\n## Steps to reproduce the bug\r\n```shell\r\n> python\r\nPython 3.7.12 (default, Sep  5 2021, 08:34:29)\r\n[Clang 11.0.3 (clang-1103.0.32.62)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n```\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> s800 = load_dataset(\"species_800\")\r\n```\r\n\r\n## Expected results\r\nspecies_800 downloaded.\r\n\r\n## Actual results\r\n```shell\r\nDownloading: 5.68kB [00:00, 1.22MB\/s]\r\nDownloading: 2.70kB [00:00, 691kB\/s]\r\nDownloading and preparing dataset species800\/species_800 (download: 17.36 MiB, generated: 3.53 MiB, post-processed: Unknown size, total: 20.89 MiB) to \/Users\/mike\/.cache\/huggingface\/datasets\/species800\/species_800\/1.0.0\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...\r\n  0%|                                                                                             | 0\/1 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/mike\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/species_800\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976\/species_800.py\", line 104, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 197, in download\r\n    download_func, url_or_urls, map_tuple=True, num_proc=download_config.num_proc, disable_tqdm=False\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in map_nested\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in <listcomp>\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 305, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/u\/0\/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\/\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14,0 1.15.0, 1.16.1\r\n- Platform: macOS Catalina 10.15.7\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n \n Hi,\r\n\r\nthe dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?","embeddings":[-0.3141798377,-0.0384753048,-0.0492311642,0.202085644,0.1646690965,0.1617158055,0.2258746922,0.2475840002,0.1551795751,-0.0103479968,-0.1063768715,0.219359681,-0.018068675,0.2488592267,0.2162834108,-0.0912976936,-0.11937204,-0.0873612016,0.156401813,0.0459538028,-0.3997568488,0.3511322439,-0.149984926,-0.1370421648,0.1025243178,-0.0809275955,-0.0141765885,0.4140372574,-0.1233641282,-0.3596287966,0.2946695685,-0.0476930104,0.1257544011,0.3042761087,-0.0001109521,-0.0142271332,0.5148406625,0.0660413355,-0.5604019761,-0.2934930921,0.1227302551,-0.142518267,-0.0646062493,-0.2044623792,0.1588468105,-0.1592076719,0.1022413224,-0.395291537,0.3530472517,0.305456996,0.2533213198,0.1408453137,0.3898601532,-0.1664942354,0.4034858644,-0.0750307068,-0.0450003706,0.6451202035,0.0823443979,0.2718518078,0.0425241515,-0.0295168012,0.0881067887,0.0383990854,0.0006940892,0.0740868375,-0.2062595338,-0.329793185,0.2224157006,-0.0045586745,0.4395235181,-0.2389549315,-0.4470045865,-0.1738795638,0.1314546764,-0.4225738943,0.4308348298,0.2705192268,-0.1549898237,0.0667552426,-0.1866742373,0.0041199531,-0.0480872393,0.0420018099,-0.3033830822,0.1795376241,-0.159804523,0.1286745518,-0.0108466847,0.1046242341,0.0993989855,-0.2571163774,-0.1561887413,0.2032140791,-0.375210911,0.1464051604,0.1148999184,0.1149314642,0.3292047083,0.3075517416,-0.1292120665,-0.1239000559,-0.2372219414,0.1114825979,0.1988592744,0.4007109404,-0.1291653663,0.0336216576,0.2132976055,0.3256243765,0.0072204461,-0.0009816602,0.0766097978,-0.2363765091,0.2274569273,0.1388338357,0.4551579356,-0.0762318149,-0.4224096835,0.1605560184,-0.4209620655,-0.0498062558,-0.1374263167,0.1001049131,-0.1145346835,-0.0005757597,0.1056687534,0.2102585435,0.0547821969,-0.2062688917,-0.2014167309,0.1310749203,-0.0948302373,-0.0711650476,0.2246996164,-0.3604339957,0.2049075067,0.0555514991,-0.0399490595,-0.1359736472,-0.0607879162,-0.102394864,-0.3439169228,0.4209778309,0.2723088264,0.2099449337,0.0565685928,-0.0960333943,-0.0461224802,0.0073452811,-0.2212390304,-0.0100183785,-0.1647759825,0.2118284106,-0.2752346098,-0.0880684331,-0.3080993891,-0.0777449012,-0.111167036,0.0707626864,-0.0259702802,-0.0588426776,-0.282512337,-0.4713051915,0.1577072144,0.4734971523,-0.2767036557,0.1404646039,-0.4503751099,-0.0727284774,0.318918854,0.1745327115,-0.0535072014,0.2142745703,-0.4785755277,0.2197947353,0.2282406688,-0.1628596485,-0.5239958763,0.2947416902,-0.3254166245,0.3206423223,-0.0717687309,-0.0690489635,-0.0034135431,0.1464301944,0.1481631398,0.441503346,-0.0463455655,0.023559412,-0.2104189396,-0.3463624716,0.0896227211,0.3526123464,0.0403659381,0.1935517192,0.398881942,-0.4314285219,0.243265599,-0.0537187122,0.0368755609,0.3293349147,0.2900209725,0.0741829649,-0.0295131113,-0.113708429,-0.816111505,0.307670176,-0.1643370241,-0.0674314275,-0.3801316917,-0.0504241735,-0.6161765456,-0.0091839349,-0.0796073154,-0.0979318544,0.1177890599,0.2380363047,0.1952964365,0.2244137079,0.0221687201,0.4692757428,0.0749240443,0.2263446897,-0.2785496712,0.1771830469,-0.0874419659,-0.2783632874,0.241901353,-0.1988476962,0.1234326735,-0.108358264,-0.2305093855,0.3897268772,-0.0546176583,0.0481459796,-0.0299874488,-0.1138610169,0.1726949513,-0.1660011709,0.0772403255,0.2172894031,0.2041254938,0.060020417,-0.5396811962,0.1860113591,0.0372100919,-0.058715146,-0.1854602695,0.2006867826,0.1545968503,-0.1571739763,0.1069497541,0.2504679561,0.3380046785,0.0964783207,0.7787852287,-0.2652095556,-0.1462595314,0.0975311697,0.3151352704,-0.0279114358,0.1542777717,0.2826803327,-0.1724483967,-0.1511732936,0.0681391805,0.4547280371,0.5494402647,0.1251671165,0.3631508052,0.106113188,-0.0100026885,-0.1754011661,0.1537656933,0.0370990634,-0.0280965827,0.0848375782,0.268684715,0.0387534685,-0.2527609468,-0.0342041515,0.0901517272,0.1206784919,-0.3279278576,0.2239741832,-0.0853878781,0.0961751938,-0.0142295798,0.3047953844,-0.2716310322,-0.3072174788,-0.3262214065,0.3114138246,0.0292228013,-0.0398436226,0.0980325565,0.040501032,0.2946368158,-0.3845741153,-0.2616500556,0.2500508726,-0.278693527,0.0765002817,0.3521042466,-0.1339199692,0.4593882561,0.0032709118,0.0486196987,-0.5828672647,-0.3285715878,0.0445327275,-0.0769557953,0.4148810208,0.2503485084,0.2806974351,-0.1553094536,0.0032503069,0.2041028291,-0.1057121083,-0.2289656848,0.0168739911,-0.026518954,0.1165466383,-0.2804702818,-0.2161673009,-0.1667931378,-0.2883911431,0.4418578744,0.2707493305,0.0808252022,0.2550256848,-0.0025097039,0.1105970815,0.2118404955,-0.1537094712,-0.308970958,-0.3757978082,0.4339392781,-0.0978650525,-0.3770106435,0.0455545112,-0.0735116899,0.00285156,0.3280911744,-0.4320271313,-0.0983472988,-0.3764995933,0.5410790443,-0.3062827289,0.1946809739,0.0396609083,-0.0675373003,-0.0794733912,-0.0233076531,-0.0851500928,-0.0072274893,0.190859139,0.3523198068,0.1263815761,0.2312516123,-0.081024386,0.1155939698,0.1947387159,0.1424227804,0.420014292,-0.0924700499,0.2850713134,-0.1047576666,-0.0533394106,-0.0980363563,-0.1075425968,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3345","title":"Failed to download species_800 from Google Drive zip file","comments":"> Hi,\r\n> \r\n> the dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?\r\n\r\nI have tried that many times with both load_dataset() and a browser almost simultaneously. The browser always works for me while load_dataset() fails.","body":"## Describe the bug\r\nOne can manually download the zip file on Google Drive, but `load_dataset()` cannot.\r\nrelated: #3248\r\n\r\n## Steps to reproduce the bug\r\n```shell\r\n> python\r\nPython 3.7.12 (default, Sep  5 2021, 08:34:29)\r\n[Clang 11.0.3 (clang-1103.0.32.62)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n```\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> s800 = load_dataset(\"species_800\")\r\n```\r\n\r\n## Expected results\r\nspecies_800 downloaded.\r\n\r\n## Actual results\r\n```shell\r\nDownloading: 5.68kB [00:00, 1.22MB\/s]\r\nDownloading: 2.70kB [00:00, 691kB\/s]\r\nDownloading and preparing dataset species800\/species_800 (download: 17.36 MiB, generated: 3.53 MiB, post-processed: Unknown size, total: 20.89 MiB) to \/Users\/mike\/.cache\/huggingface\/datasets\/species800\/species_800\/1.0.0\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...\r\n  0%|                                                                                             | 0\/1 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/mike\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/species_800\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976\/species_800.py\", line 104, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 197, in download\r\n    download_func, url_or_urls, map_tuple=True, num_proc=download_config.num_proc, disable_tqdm=False\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in map_nested\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in <listcomp>\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 305, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/u\/0\/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\/\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14,0 1.15.0, 1.16.1\r\n- Platform: macOS Catalina 10.15.7\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n","comment_length":50,"text":"Failed to download species_800 from Google Drive zip file \n ## Describe the bug\r\nOne can manually download the zip file on Google Drive, but `load_dataset()` cannot.\r\nrelated: #3248\r\n\r\n## Steps to reproduce the bug\r\n```shell\r\n> python\r\nPython 3.7.12 (default, Sep  5 2021, 08:34:29)\r\n[Clang 11.0.3 (clang-1103.0.32.62)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n```\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> s800 = load_dataset(\"species_800\")\r\n```\r\n\r\n## Expected results\r\nspecies_800 downloaded.\r\n\r\n## Actual results\r\n```shell\r\nDownloading: 5.68kB [00:00, 1.22MB\/s]\r\nDownloading: 2.70kB [00:00, 691kB\/s]\r\nDownloading and preparing dataset species800\/species_800 (download: 17.36 MiB, generated: 3.53 MiB, post-processed: Unknown size, total: 20.89 MiB) to \/Users\/mike\/.cache\/huggingface\/datasets\/species800\/species_800\/1.0.0\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...\r\n  0%|                                                                                             | 0\/1 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/mike\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/species_800\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976\/species_800.py\", line 104, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 197, in download\r\n    download_func, url_or_urls, map_tuple=True, num_proc=download_config.num_proc, disable_tqdm=False\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in map_nested\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in <listcomp>\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 305, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/u\/0\/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\/\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14,0 1.15.0, 1.16.1\r\n- Platform: macOS Catalina 10.15.7\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n \n > Hi,\r\n> \r\n> the dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?\r\n\r\nI have tried that many times with both load_dataset() and a browser almost simultaneously. The browser always works for me while load_dataset() fails.","embeddings":[-0.3141798377,-0.0384753048,-0.0492311642,0.202085644,0.1646690965,0.1617158055,0.2258746922,0.2475840002,0.1551795751,-0.0103479968,-0.1063768715,0.219359681,-0.018068675,0.2488592267,0.2162834108,-0.0912976936,-0.11937204,-0.0873612016,0.156401813,0.0459538028,-0.3997568488,0.3511322439,-0.149984926,-0.1370421648,0.1025243178,-0.0809275955,-0.0141765885,0.4140372574,-0.1233641282,-0.3596287966,0.2946695685,-0.0476930104,0.1257544011,0.3042761087,-0.0001109521,-0.0142271332,0.5148406625,0.0660413355,-0.5604019761,-0.2934930921,0.1227302551,-0.142518267,-0.0646062493,-0.2044623792,0.1588468105,-0.1592076719,0.1022413224,-0.395291537,0.3530472517,0.305456996,0.2533213198,0.1408453137,0.3898601532,-0.1664942354,0.4034858644,-0.0750307068,-0.0450003706,0.6451202035,0.0823443979,0.2718518078,0.0425241515,-0.0295168012,0.0881067887,0.0383990854,0.0006940892,0.0740868375,-0.2062595338,-0.329793185,0.2224157006,-0.0045586745,0.4395235181,-0.2389549315,-0.4470045865,-0.1738795638,0.1314546764,-0.4225738943,0.4308348298,0.2705192268,-0.1549898237,0.0667552426,-0.1866742373,0.0041199531,-0.0480872393,0.0420018099,-0.3033830822,0.1795376241,-0.159804523,0.1286745518,-0.0108466847,0.1046242341,0.0993989855,-0.2571163774,-0.1561887413,0.2032140791,-0.375210911,0.1464051604,0.1148999184,0.1149314642,0.3292047083,0.3075517416,-0.1292120665,-0.1239000559,-0.2372219414,0.1114825979,0.1988592744,0.4007109404,-0.1291653663,0.0336216576,0.2132976055,0.3256243765,0.0072204461,-0.0009816602,0.0766097978,-0.2363765091,0.2274569273,0.1388338357,0.4551579356,-0.0762318149,-0.4224096835,0.1605560184,-0.4209620655,-0.0498062558,-0.1374263167,0.1001049131,-0.1145346835,-0.0005757597,0.1056687534,0.2102585435,0.0547821969,-0.2062688917,-0.2014167309,0.1310749203,-0.0948302373,-0.0711650476,0.2246996164,-0.3604339957,0.2049075067,0.0555514991,-0.0399490595,-0.1359736472,-0.0607879162,-0.102394864,-0.3439169228,0.4209778309,0.2723088264,0.2099449337,0.0565685928,-0.0960333943,-0.0461224802,0.0073452811,-0.2212390304,-0.0100183785,-0.1647759825,0.2118284106,-0.2752346098,-0.0880684331,-0.3080993891,-0.0777449012,-0.111167036,0.0707626864,-0.0259702802,-0.0588426776,-0.282512337,-0.4713051915,0.1577072144,0.4734971523,-0.2767036557,0.1404646039,-0.4503751099,-0.0727284774,0.318918854,0.1745327115,-0.0535072014,0.2142745703,-0.4785755277,0.2197947353,0.2282406688,-0.1628596485,-0.5239958763,0.2947416902,-0.3254166245,0.3206423223,-0.0717687309,-0.0690489635,-0.0034135431,0.1464301944,0.1481631398,0.441503346,-0.0463455655,0.023559412,-0.2104189396,-0.3463624716,0.0896227211,0.3526123464,0.0403659381,0.1935517192,0.398881942,-0.4314285219,0.243265599,-0.0537187122,0.0368755609,0.3293349147,0.2900209725,0.0741829649,-0.0295131113,-0.113708429,-0.816111505,0.307670176,-0.1643370241,-0.0674314275,-0.3801316917,-0.0504241735,-0.6161765456,-0.0091839349,-0.0796073154,-0.0979318544,0.1177890599,0.2380363047,0.1952964365,0.2244137079,0.0221687201,0.4692757428,0.0749240443,0.2263446897,-0.2785496712,0.1771830469,-0.0874419659,-0.2783632874,0.241901353,-0.1988476962,0.1234326735,-0.108358264,-0.2305093855,0.3897268772,-0.0546176583,0.0481459796,-0.0299874488,-0.1138610169,0.1726949513,-0.1660011709,0.0772403255,0.2172894031,0.2041254938,0.060020417,-0.5396811962,0.1860113591,0.0372100919,-0.058715146,-0.1854602695,0.2006867826,0.1545968503,-0.1571739763,0.1069497541,0.2504679561,0.3380046785,0.0964783207,0.7787852287,-0.2652095556,-0.1462595314,0.0975311697,0.3151352704,-0.0279114358,0.1542777717,0.2826803327,-0.1724483967,-0.1511732936,0.0681391805,0.4547280371,0.5494402647,0.1251671165,0.3631508052,0.106113188,-0.0100026885,-0.1754011661,0.1537656933,0.0370990634,-0.0280965827,0.0848375782,0.268684715,0.0387534685,-0.2527609468,-0.0342041515,0.0901517272,0.1206784919,-0.3279278576,0.2239741832,-0.0853878781,0.0961751938,-0.0142295798,0.3047953844,-0.2716310322,-0.3072174788,-0.3262214065,0.3114138246,0.0292228013,-0.0398436226,0.0980325565,0.040501032,0.2946368158,-0.3845741153,-0.2616500556,0.2500508726,-0.278693527,0.0765002817,0.3521042466,-0.1339199692,0.4593882561,0.0032709118,0.0486196987,-0.5828672647,-0.3285715878,0.0445327275,-0.0769557953,0.4148810208,0.2503485084,0.2806974351,-0.1553094536,0.0032503069,0.2041028291,-0.1057121083,-0.2289656848,0.0168739911,-0.026518954,0.1165466383,-0.2804702818,-0.2161673009,-0.1667931378,-0.2883911431,0.4418578744,0.2707493305,0.0808252022,0.2550256848,-0.0025097039,0.1105970815,0.2118404955,-0.1537094712,-0.308970958,-0.3757978082,0.4339392781,-0.0978650525,-0.3770106435,0.0455545112,-0.0735116899,0.00285156,0.3280911744,-0.4320271313,-0.0983472988,-0.3764995933,0.5410790443,-0.3062827289,0.1946809739,0.0396609083,-0.0675373003,-0.0794733912,-0.0233076531,-0.0851500928,-0.0072274893,0.190859139,0.3523198068,0.1263815761,0.2312516123,-0.081024386,0.1155939698,0.1947387159,0.1424227804,0.420014292,-0.0924700499,0.2850713134,-0.1047576666,-0.0533394106,-0.0980363563,-0.1075425968,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3345","title":"Failed to download species_800 from Google Drive zip file","comments":"@mariosasko \r\n> the dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?\r\n\r\nI've tried yet again just a moment ago. This time I realize that, the step `(... post-processed: Unknown size, total: 20.89 MiB) to \/Users\/mike\/.cache\/huggingface\/datasets\/species800\/species_800\/1.0.0\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...` and the one after seem unstable. If I want to retry, I will have to delete it (and probably other cache lock files). It **_sometimes_** works.\r\n\r\nBut I didn't try `download_mode=\"force_redownload\"` yet.\r\n\r\nAnyway, I suppose this isn't really a pressing issue for the time being, so I'm going to close this. Thank you.\r\n\r\n","body":"## Describe the bug\r\nOne can manually download the zip file on Google Drive, but `load_dataset()` cannot.\r\nrelated: #3248\r\n\r\n## Steps to reproduce the bug\r\n```shell\r\n> python\r\nPython 3.7.12 (default, Sep  5 2021, 08:34:29)\r\n[Clang 11.0.3 (clang-1103.0.32.62)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n```\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> s800 = load_dataset(\"species_800\")\r\n```\r\n\r\n## Expected results\r\nspecies_800 downloaded.\r\n\r\n## Actual results\r\n```shell\r\nDownloading: 5.68kB [00:00, 1.22MB\/s]\r\nDownloading: 2.70kB [00:00, 691kB\/s]\r\nDownloading and preparing dataset species800\/species_800 (download: 17.36 MiB, generated: 3.53 MiB, post-processed: Unknown size, total: 20.89 MiB) to \/Users\/mike\/.cache\/huggingface\/datasets\/species800\/species_800\/1.0.0\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...\r\n  0%|                                                                                             | 0\/1 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/mike\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/species_800\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976\/species_800.py\", line 104, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 197, in download\r\n    download_func, url_or_urls, map_tuple=True, num_proc=download_config.num_proc, disable_tqdm=False\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in map_nested\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in <listcomp>\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 305, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/u\/0\/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\/\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14,0 1.15.0, 1.16.1\r\n- Platform: macOS Catalina 10.15.7\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n","comment_length":102,"text":"Failed to download species_800 from Google Drive zip file \n ## Describe the bug\r\nOne can manually download the zip file on Google Drive, but `load_dataset()` cannot.\r\nrelated: #3248\r\n\r\n## Steps to reproduce the bug\r\n```shell\r\n> python\r\nPython 3.7.12 (default, Sep  5 2021, 08:34:29)\r\n[Clang 11.0.3 (clang-1103.0.32.62)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n```\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> s800 = load_dataset(\"species_800\")\r\n```\r\n\r\n## Expected results\r\nspecies_800 downloaded.\r\n\r\n## Actual results\r\n```shell\r\nDownloading: 5.68kB [00:00, 1.22MB\/s]\r\nDownloading: 2.70kB [00:00, 691kB\/s]\r\nDownloading and preparing dataset species800\/species_800 (download: 17.36 MiB, generated: 3.53 MiB, post-processed: Unknown size, total: 20.89 MiB) to \/Users\/mike\/.cache\/huggingface\/datasets\/species800\/species_800\/1.0.0\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...\r\n  0%|                                                                                             | 0\/1 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/mike\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/species_800\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976\/species_800.py\", line 104, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 197, in download\r\n    download_func, url_or_urls, map_tuple=True, num_proc=download_config.num_proc, disable_tqdm=False\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in map_nested\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 209, in <listcomp>\r\n    for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 305, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/Users\/mike\/Library\/Caches\/pypoetry\/virtualenvs\/promptsource-hsdAcWsQ-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/u\/0\/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\/\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14,0 1.15.0, 1.16.1\r\n- Platform: macOS Catalina 10.15.7\r\n- Python version: 3.7.12\r\n- PyArrow version: 6.0.1\r\n \n @mariosasko \r\n> the dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?\r\n\r\nI've tried yet again just a moment ago. This time I realize that, the step `(... post-processed: Unknown size, total: 20.89 MiB) to \/Users\/mike\/.cache\/huggingface\/datasets\/species800\/species_800\/1.0.0\/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...` and the one after seem unstable. If I want to retry, I will have to delete it (and probably other cache lock files). It **_sometimes_** works.\r\n\r\nBut I didn't try `download_mode=\"force_redownload\"` yet.\r\n\r\nAnyway, I suppose this isn't really a pressing issue for the time being, so I'm going to close this. Thank you.\r\n\r\n","embeddings":[-0.3141798377,-0.0384753048,-0.0492311642,0.202085644,0.1646690965,0.1617158055,0.2258746922,0.2475840002,0.1551795751,-0.0103479968,-0.1063768715,0.219359681,-0.018068675,0.2488592267,0.2162834108,-0.0912976936,-0.11937204,-0.0873612016,0.156401813,0.0459538028,-0.3997568488,0.3511322439,-0.149984926,-0.1370421648,0.1025243178,-0.0809275955,-0.0141765885,0.4140372574,-0.1233641282,-0.3596287966,0.2946695685,-0.0476930104,0.1257544011,0.3042761087,-0.0001109521,-0.0142271332,0.5148406625,0.0660413355,-0.5604019761,-0.2934930921,0.1227302551,-0.142518267,-0.0646062493,-0.2044623792,0.1588468105,-0.1592076719,0.1022413224,-0.395291537,0.3530472517,0.305456996,0.2533213198,0.1408453137,0.3898601532,-0.1664942354,0.4034858644,-0.0750307068,-0.0450003706,0.6451202035,0.0823443979,0.2718518078,0.0425241515,-0.0295168012,0.0881067887,0.0383990854,0.0006940892,0.0740868375,-0.2062595338,-0.329793185,0.2224157006,-0.0045586745,0.4395235181,-0.2389549315,-0.4470045865,-0.1738795638,0.1314546764,-0.4225738943,0.4308348298,0.2705192268,-0.1549898237,0.0667552426,-0.1866742373,0.0041199531,-0.0480872393,0.0420018099,-0.3033830822,0.1795376241,-0.159804523,0.1286745518,-0.0108466847,0.1046242341,0.0993989855,-0.2571163774,-0.1561887413,0.2032140791,-0.375210911,0.1464051604,0.1148999184,0.1149314642,0.3292047083,0.3075517416,-0.1292120665,-0.1239000559,-0.2372219414,0.1114825979,0.1988592744,0.4007109404,-0.1291653663,0.0336216576,0.2132976055,0.3256243765,0.0072204461,-0.0009816602,0.0766097978,-0.2363765091,0.2274569273,0.1388338357,0.4551579356,-0.0762318149,-0.4224096835,0.1605560184,-0.4209620655,-0.0498062558,-0.1374263167,0.1001049131,-0.1145346835,-0.0005757597,0.1056687534,0.2102585435,0.0547821969,-0.2062688917,-0.2014167309,0.1310749203,-0.0948302373,-0.0711650476,0.2246996164,-0.3604339957,0.2049075067,0.0555514991,-0.0399490595,-0.1359736472,-0.0607879162,-0.102394864,-0.3439169228,0.4209778309,0.2723088264,0.2099449337,0.0565685928,-0.0960333943,-0.0461224802,0.0073452811,-0.2212390304,-0.0100183785,-0.1647759825,0.2118284106,-0.2752346098,-0.0880684331,-0.3080993891,-0.0777449012,-0.111167036,0.0707626864,-0.0259702802,-0.0588426776,-0.282512337,-0.4713051915,0.1577072144,0.4734971523,-0.2767036557,0.1404646039,-0.4503751099,-0.0727284774,0.318918854,0.1745327115,-0.0535072014,0.2142745703,-0.4785755277,0.2197947353,0.2282406688,-0.1628596485,-0.5239958763,0.2947416902,-0.3254166245,0.3206423223,-0.0717687309,-0.0690489635,-0.0034135431,0.1464301944,0.1481631398,0.441503346,-0.0463455655,0.023559412,-0.2104189396,-0.3463624716,0.0896227211,0.3526123464,0.0403659381,0.1935517192,0.398881942,-0.4314285219,0.243265599,-0.0537187122,0.0368755609,0.3293349147,0.2900209725,0.0741829649,-0.0295131113,-0.113708429,-0.816111505,0.307670176,-0.1643370241,-0.0674314275,-0.3801316917,-0.0504241735,-0.6161765456,-0.0091839349,-0.0796073154,-0.0979318544,0.1177890599,0.2380363047,0.1952964365,0.2244137079,0.0221687201,0.4692757428,0.0749240443,0.2263446897,-0.2785496712,0.1771830469,-0.0874419659,-0.2783632874,0.241901353,-0.1988476962,0.1234326735,-0.108358264,-0.2305093855,0.3897268772,-0.0546176583,0.0481459796,-0.0299874488,-0.1138610169,0.1726949513,-0.1660011709,0.0772403255,0.2172894031,0.2041254938,0.060020417,-0.5396811962,0.1860113591,0.0372100919,-0.058715146,-0.1854602695,0.2006867826,0.1545968503,-0.1571739763,0.1069497541,0.2504679561,0.3380046785,0.0964783207,0.7787852287,-0.2652095556,-0.1462595314,0.0975311697,0.3151352704,-0.0279114358,0.1542777717,0.2826803327,-0.1724483967,-0.1511732936,0.0681391805,0.4547280371,0.5494402647,0.1251671165,0.3631508052,0.106113188,-0.0100026885,-0.1754011661,0.1537656933,0.0370990634,-0.0280965827,0.0848375782,0.268684715,0.0387534685,-0.2527609468,-0.0342041515,0.0901517272,0.1206784919,-0.3279278576,0.2239741832,-0.0853878781,0.0961751938,-0.0142295798,0.3047953844,-0.2716310322,-0.3072174788,-0.3262214065,0.3114138246,0.0292228013,-0.0398436226,0.0980325565,0.040501032,0.2946368158,-0.3845741153,-0.2616500556,0.2500508726,-0.278693527,0.0765002817,0.3521042466,-0.1339199692,0.4593882561,0.0032709118,0.0486196987,-0.5828672647,-0.3285715878,0.0445327275,-0.0769557953,0.4148810208,0.2503485084,0.2806974351,-0.1553094536,0.0032503069,0.2041028291,-0.1057121083,-0.2289656848,0.0168739911,-0.026518954,0.1165466383,-0.2804702818,-0.2161673009,-0.1667931378,-0.2883911431,0.4418578744,0.2707493305,0.0808252022,0.2550256848,-0.0025097039,0.1105970815,0.2118404955,-0.1537094712,-0.308970958,-0.3757978082,0.4339392781,-0.0978650525,-0.3770106435,0.0455545112,-0.0735116899,0.00285156,0.3280911744,-0.4320271313,-0.0983472988,-0.3764995933,0.5410790443,-0.3062827289,0.1946809739,0.0396609083,-0.0675373003,-0.0794733912,-0.0233076531,-0.0851500928,-0.0072274893,0.190859139,0.3523198068,0.1263815761,0.2312516123,-0.081024386,0.1155939698,0.1947387159,0.1424227804,0.420014292,-0.0924700499,0.2850713134,-0.1047576666,-0.0533394106,-0.0980363563,-0.10754259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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3341","title":"Mirror the canonical datasets to the Hugging Face Hub","comments":"I created a GitHub project to keep track of what needs to be done:\r\nhttps:\/\/github.com\/huggingface\/datasets\/projects\/3\r\n\r\nI also store my code in a (private for now) repository at https:\/\/github.com\/huggingface\/mirror_canonical_datasets_on_hub","body":"- [ ] create a repo on https:\/\/hf.co\/datasets for every canonical dataset\r\n- [ ] on every commit related to a dataset, update the hf.co repo\r\n\r\nSee https:\/\/github.com\/huggingface\/moon-landing\/pull\/1562\r\n\r\n@SBrandeis: I let you edit this description if needed to precise the intent.","comment_length":28,"text":"Mirror the canonical datasets to the Hugging Face Hub \n - [ ] create a repo on https:\/\/hf.co\/datasets for every canonical dataset\r\n- [ ] on every commit related to a dataset, update the hf.co repo\r\n\r\nSee https:\/\/github.com\/huggingface\/moon-landing\/pull\/1562\r\n\r\n@SBrandeis: I let you edit this description if needed to precise the intent. \n I created a GitHub project to keep track of what needs to be done:\r\nhttps:\/\/github.com\/huggingface\/datasets\/projects\/3\r\n\r\nI also store my code in a (private for now) repository at https:\/\/github.com\/huggingface\/mirror_canonical_datasets_on_hub","embeddings":[-0.1399022788,-0.228222698,-0.044737868,-0.0688018054,-0.0952058509,-0.0315396972,0.2022986412,0.3611529171,0.2504815459,0.247452274,-0.2225713879,-0.0466739275,-0.0990657881,0.4645624161,0.1602289081,0.1148871109,0.1948555857,0.1492442936,-0.3303128779,-0.1401979923,-0.1915598661,0.0548463389,0.2164637595,-0.1970640272,-0.1881078482,0.0603789836,-0.1895807385,0.1143198982,0.029500654,-0.1950644851,0.1445315033,0.5509652495,-0.0402591117,0.4087868929,-0.0001033234,-0.1346432418,-0.0946488008,0.036132317,-0.210001722,0.2130616903,0.019218836,0.014911864,-0.0886835754,-0.1156553999,-0.3502188325,0.0998075232,0.0849432647,-0.0077921986,0.6126039028,-0.1101512983,0.275631845,0.4718473256,-0.01167069,-0.2119522989,-0.0439751334,0.4008792341,-0.020286629,0.3190157712,0.0643485785,-0.0014482591,-0.2037810683,0.4754467607,0.1577785164,-0.0874233395,0.3538534641,-0.0237656198,-0.0397568308,-0.1200760677,-0.0564735122,0.0358122587,-0.0637291223,-0.4478743076,-0.3752831221,-0.2629747093,-0.0831640586,-0.1694616228,-0.1591474861,0.037935596,0.0586951338,-0.024418246,-0.2698394656,-0.4513088465,-0.0766888335,-0.0690581948,0.0347740166,0.0571260639,-0.0905396342,-0.1093541309,0.1155039966,-0.0496157408,-0.2794246376,-0.0523724519,0.1169923916,-0.0595501065,-0.0034447405,-0.3342265785,-0.0795747861,0.1460089982,0.4641376138,0.2129012644,-0.1246608049,0.1348954141,-0.1669301391,0.1390658617,0.0510976575,-0.0208211932,-0.0652807057,-0.1452500373,0.4058583081,0.2648107111,-0.044907406,0.1653911918,0.017110914,0.1916994154,-0.2810345888,-0.1849983484,0.1338231713,-0.3072553277,0.0896304995,0.0109797195,0.0926090702,0.0686039329,-0.1082748249,0.2600435615,0.0268181376,-0.0162639711,0.070961155,0.0085890386,-0.1388672143,-0.1898212731,-0.3074089289,-0.0752347261,-0.0117043946,0.4674014449,0.1131271496,-0.3167941272,0.2366050035,0.0132522443,0.5471705794,0.0196075682,-0.1153743118,0.2685265839,-0.1479641348,0.0516193025,-0.1125051379,-0.0726765618,0.095570311,-0.0253886022,-0.3246180415,-0.3927957118,0.0781563669,-0.2184799761,-0.0047747134,0.2594884634,-0.3289783597,-0.0521659367,-0.2298796475,0.1729379743,-0.2020565569,0.2269020826,0.0789085105,0.2411810905,0.1034419388,-0.0977746546,0.371244967,0.7181560397,0.2240888923,-0.2962023318,0.3089908063,0.0191900656,-0.1833186746,0.2745362818,-0.1124360859,-0.1290382594,-0.2643851638,-0.1095261797,-0.0685843825,-0.2103893459,-0.1687698364,-0.0536151975,-0.2934251428,0.0378598161,0.0605964214,0.1442467272,0.0733657181,-0.2597304583,-0.1364905387,0.1940589696,-0.0286556445,0.0727793127,-0.2650986612,-0.4695095122,-0.2597595155,0.3346839845,0.2301794738,0.1090824306,0.1431733072,0.1876089722,0.1667769551,-0.12215361,0.2466912717,0.1588569432,0.473677516,0.1701834351,-0.0724653676,-0.147092551,-0.2138132155,0.0245012324,-0.1168465316,0.0989032015,0.152474761,-0.6598361731,-0.2047573477,-0.0484351516,-0.082854785,-0.2082474977,0.2233777791,0.3050119579,0.1969971508,0.0063146967,-0.3243360519,0.4381789565,-0.1723386645,0.2119595259,-0.3341875076,0.4706754386,-0.0964090303,-0.0634112209,0.2946987748,0.3115993142,0.0264722891,-0.3461867273,0.1556392014,0.2419728786,-0.3025355935,0.3067388833,0.512162447,0.3694115579,0.2330253422,-0.2927227914,0.1160169691,0.0945655331,-0.2220087647,0.0829251632,-0.1982327551,0.5514415503,-0.0466557555,-0.0492706113,-0.0392461047,-0.0596488006,-0.02593017,-0.0939501449,-0.1644388139,-0.3177978694,0.0318958275,-0.1396772265,0.2996936738,0.0881456509,-0.4091952443,0.2628264725,0.3407301903,0.072377719,0.0364973545,0.0720487088,-0.117475532,0.0301621053,0.1883779764,-0.1477153748,-0.0670416281,0.3184131682,-0.012251568,0.3243702054,0.0394375212,-0.0790635794,-0.0652415454,-0.1389977336,-0.0882022232,-0.0367088318,0.0709698126,0.0203161221,-0.3535163403,-0.052728802,-0.2075939626,-0.099593617,-0.1265209466,-0.1090331674,-0.0529522933,-0.2740834951,-0.4831582308,-0.0451837406,-0.4293740392,-0.0575637147,0.225836277,0.1894544065,-0.0532367229,0.2586158514,0.043761801,0.4665670097,-0.1506016552,-0.0626188591,-0.2527849376,-0.0300519001,0.0647437349,0.1486184001,0.0087536126,-0.0962686464,0.5035105348,-0.1499568224,0.1034430191,-0.4643303454,-0.4791467488,0.0241392478,-0.1156018227,-0.2302286625,0.0803738013,-0.0009889991,-0.1144008189,-0.1072415933,-0.0019780349,-0.319139868,-0.0884397328,-0.2572429776,-0.1016824022,0.0148529215,-0.0970642939,0.013296227,-0.0992898494,-0.5056049228,0.308539778,0.0319683589,0.1280540824,-0.0779343396,-0.07019528,0.1173269302,-0.2701739669,-0.1916798055,-0.1832654923,-0.2178165615,0.0406898111,-0.3897850513,-0.1344104111,0.0628091171,-0.0778939202,0.0769377872,-0.0639377758,-0.5157498121,-0.7135971189,-0.008907496,0.3237415254,0.1254700124,0.207784757,0.1929588169,0.0059285881,-0.184705779,-0.0044502523,-0.2790122628,0.2138220519,0.1777514219,-0.0929935127,0.046087265,-0.0708607435,-0.0654195771,0.4560536742,0.6146107912,-0.096187748,0.2441712171,0.118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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3341","title":"Mirror the canonical datasets to the Hugging Face Hub","comments":"I understand that the datasets are mirrored on the Hub now, right? Might I close @lhoestq @SBrandeis?","body":"- [ ] create a repo on https:\/\/hf.co\/datasets for every canonical dataset\r\n- [ ] on every commit related to a dataset, update the hf.co repo\r\n\r\nSee https:\/\/github.com\/huggingface\/moon-landing\/pull\/1562\r\n\r\n@SBrandeis: I let you edit this description if needed to precise the intent.","comment_length":17,"text":"Mirror the canonical datasets to the Hugging Face Hub \n - [ ] create a repo on https:\/\/hf.co\/datasets for every canonical dataset\r\n- [ ] on every commit related to a dataset, update the hf.co repo\r\n\r\nSee https:\/\/github.com\/huggingface\/moon-landing\/pull\/1562\r\n\r\n@SBrandeis: I let you edit this description if needed to precise the intent. \n I understand that the datasets are mirrored on the Hub now, right? Might I close @lhoestq @SBrandeis?","embeddings":[-0.0218213238,-0.2926824689,-0.0275674351,-0.181780383,-0.1919669807,0.005772878,0.2162663639,0.2935407162,0.2686596513,0.1937050223,-0.3167092502,-0.1450393051,0.0045881332,0.3757868707,0.0550455675,0.1298287958,0.1976609677,0.1898721159,-0.2652084529,-0.2824350893,-0.2384762615,0.0123707708,0.1730353683,-0.1519567966,-0.0938288197,0.012294014,-0.1356128454,0.2138771266,-0.0551213399,-0.1670190543,0.1510974616,0.6337314248,-0.0726588741,0.3463303447,-0.0001053099,-0.0973950103,0.0144194718,0.0948006585,-0.2404113412,0.3264097273,0.0050095641,0.0500163473,-0.064599812,-0.1321754754,-0.2558149993,0.1050242409,0.1518678516,0.0905963257,0.5624607205,-0.1270121038,0.2543604374,0.4386521578,0.0299658701,-0.3103618622,-0.0414286032,0.4238120317,-0.0261314232,0.3576444685,0.0475738458,0.0977612138,-0.1371967345,0.4285319448,0.0065380554,-0.0360504575,0.2960019112,-0.0313616283,0.0644046068,-0.0579180792,-0.0268403348,-0.010230042,0.0404760502,-0.3807881176,-0.3590870202,-0.1884664893,-0.1839016229,-0.1905449331,-0.2730266452,0.0020519651,-0.0039425911,0.079539679,-0.300011605,-0.5658158064,-0.0108988965,-0.0649689734,-0.1422236413,0.1140117943,-0.0877540335,-0.0375647247,0.0703082681,0.0152051514,-0.3035496473,0.0074993931,0.111822404,-0.0976149142,-0.0322960578,-0.348542124,-0.063863799,0.1652544141,0.4128493667,0.3043963611,-0.2273901552,0.014606405,-0.1820363104,0.0990084931,-0.0426914245,0.115610607,-0.0380518511,-0.0659444109,0.2804884315,0.3567698002,0.0229426343,0.1754247695,0.0347152539,0.0708043352,-0.3577213883,-0.0768958628,0.3003357947,-0.2974942923,-0.0276858732,0.1635644734,0.0752163082,0.0721254572,-0.1698844284,0.2924946845,0.0735984296,0.019169597,0.1317875534,-0.0431978181,-0.2409163713,-0.1613029391,-0.3374612033,-0.0195217784,-0.0286914259,0.4527656436,-0.0279124398,-0.2202869207,0.2044316977,0.0341652296,0.5450916886,0.0470951721,-0.1392825246,0.2539679706,-0.1160715148,0.178258419,-0.1136862785,0.1272504479,0.0902973264,0.0773173124,-0.272674799,-0.1671628654,0.0160485227,-0.1571704149,-0.0402471386,0.2682977915,-0.429177165,-0.1025258899,-0.3170417249,0.0463065207,-0.3252808452,0.1584796011,0.2011333555,0.2662881315,0.1355053633,-0.1291640997,0.4017508626,0.6870622635,0.1591598094,-0.2488232702,0.2488106638,0.0117177814,0.0157250073,0.3531043828,-0.1227925271,-0.0872581154,-0.2512491047,-0.0900224373,-0.120700337,-0.1847310662,-0.1018737331,-0.0368503742,-0.3213782907,0.0844707489,0.0417049713,-0.0142349284,0.0449571796,-0.2735611498,-0.1126332358,0.0316139273,-0.0527457669,-0.042845346,-0.4392008483,-0.4809529781,-0.209805578,0.3541741967,0.2265408933,0.0620105155,0.0619760603,0.2830607593,0.2196986526,-0.0884097889,0.2387375385,0.1584238112,0.56321913,0.09104947,-0.1354202777,-0.0370655991,-0.2818971276,0.0016205895,-0.0459903702,0.1686668992,0.2280524373,-0.5953662992,-0.3070710003,-0.0784881413,-0.1321386695,-0.3140890002,0.1747406572,0.2362261713,0.107800886,-0.0034134134,-0.3685560226,0.4250989556,-0.2594373226,0.2197357863,-0.362952441,0.4800725877,-0.0724889114,0.0557169765,0.046425011,0.2748459578,0.0563068911,-0.3659336269,0.1982266158,0.1892866492,-0.3005183041,0.3458783925,0.5258868933,0.2588581443,0.2914502025,-0.3805143237,0.1299023181,0.0830931216,-0.2028670758,0.1108669266,-0.1950126886,0.4615956843,-0.0090223933,-0.1067107171,-0.0645100921,-0.0760197267,-0.0593149923,-0.1829432249,-0.2460247278,-0.2128323615,0.1173893213,-0.0294125713,0.183339864,0.1420517117,-0.339710921,0.2268883884,0.4386557937,0.0185123011,-0.0749196038,0.0695068017,-0.0537114665,0.0584687106,0.1515892148,-0.1466597468,-0.0823468119,0.2955560982,-0.0113158468,0.4402499497,0.0904371887,-0.0387270674,-0.0965716094,-0.1113956347,-0.0246221852,0.0118824951,0.0491143204,0.0301627219,-0.1877588779,-0.1010333374,-0.0862020254,-0.0662114844,-0.1249442771,-0.276900053,0.0682467818,-0.111569576,-0.4919810891,-0.0915016234,-0.4487615824,-0.1324346066,0.2169882357,0.1636694819,-0.0669253916,0.3111013472,0.0834344029,0.4831864834,-0.1601684839,0.0268932022,-0.2360664457,0.0496429577,0.0930503085,0.1226128712,-0.0973444879,-0.1443716139,0.3948913515,-0.1509771794,0.0610909387,-0.5150560737,-0.4390873313,0.0326615572,-0.1198547781,-0.2736372948,0.0401639789,0.1439704299,-0.1890550107,-0.0685720667,0.048936028,-0.2940531075,-0.0749467537,-0.2676689625,-0.1542602181,0.0336210728,-0.0516555198,-0.0469634421,-0.1099480614,-0.5106429458,0.2915961444,0.015018696,0.1118053719,-0.0178024862,-0.057717111,0.0614597611,-0.1838100404,-0.3469330668,-0.2580432594,-0.2924159467,-0.0466107205,-0.3319178224,-0.0370064303,0.1186370254,0.0211684536,0.1263955981,-0.1366081536,-0.4263136983,-0.8204240203,-0.0712867752,0.2312675864,0.0739469081,0.2300860584,0.1596162021,-0.1323050261,-0.1988427639,0.07144548,-0.3366409838,0.1716469228,0.2706880867,-0.1137542874,-0.0332691297,-0.0260626096,-0.1642011106,0.5518663526,0.6206747293,0.0372575149,0.0966650546,0.2313499004,0.3692430258,-0.1080568582,-0.0850096941,0.10232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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3339","title":"to_tf_dataset fails on TPU","comments":"This might be related to https:\/\/github.com\/tensorflow\/tensorflow\/issues\/38762 , what do you think @Rocketknight1 ?\r\n> Dataset.from_generator is expected to not work with TPUs as it uses py_function underneath which is incompatible with Cloud TPU 2VM setup. If you would like to read from large datasets, maybe try to materialize it on disk and use TFRecordDataest instead.","body":"Using `to_tf_dataset` to create a dataset and then putting it in `model.fit` results in an internal error on TPUs. I've only tried on Colab and Kaggle TPUs, not GCP TPUs.\r\n\r\n\r\n## Steps to reproduce the bug\r\nI made a colab to show the error. https:\/\/colab.research.google.com\/drive\/12x_PFKzGouFxqD4OuWfnycW_1TaT276z?usp=sharing\r\n\r\n## Expected results\r\ndataset from `to_tf_dataset` works in `model.fit`  \r\nRight below the first error in the colab I use `tf.data.Dataset.from_tensor_slices` and `model.fit` works just fine. This is the desired outcome.\r\n\r\n## Actual results\r\n```\r\nInternalError: 5 root error(s) found.\r\n  (0) INTERNAL: {{function_node __inference_train_function_30558}} failed to connect to all addresses\r\nAdditional GRPC error information from remote target \/job:localhost\/replica:0\/task:0\/device:CPU:0:\r\n:{\"created\":\"@1638231897.932218653\",\"description\":\"Failed to pick subchannel\",\"file\":\"third_party\/grpc\/src\/core\/ext\/filters\/client_channel\/client_channel.cc\",\"file_line\":3151,\"referenced_errors\":[{\"created\":\"@1638231897.932216754\",\"description\":\"failed to connect to all addresses\",\"file\":\"third_party\/grpc\/src\/core\/lib\/transport\/error_utils.cc\",\"file_line\":161,\"grpc_status\":14}]}\r\n\t [[{{node StatefulPartitionedCall}}]]\r\n\t [[MultiDeviceIteratorGetNextFromShard]]\r\nExecuting non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\n\t [[RemoteCall]]\r\n\t [[IteratorGetNextAsOptional]]\r\n\t [[tpu_compile_succeeded_assert\/_14023832043698465348\/_7\/_439]]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n- Tensorflow 2.7.0\r\n- `transformers` 4.12.5\r\n","comment_length":55,"text":"to_tf_dataset fails on TPU \n Using `to_tf_dataset` to create a dataset and then putting it in `model.fit` results in an internal error on TPUs. I've only tried on Colab and Kaggle TPUs, not GCP TPUs.\r\n\r\n\r\n## Steps to reproduce the bug\r\nI made a colab to show the error. https:\/\/colab.research.google.com\/drive\/12x_PFKzGouFxqD4OuWfnycW_1TaT276z?usp=sharing\r\n\r\n## Expected results\r\ndataset from `to_tf_dataset` works in `model.fit`  \r\nRight below the first error in the colab I use `tf.data.Dataset.from_tensor_slices` and `model.fit` works just fine. This is the desired outcome.\r\n\r\n## Actual results\r\n```\r\nInternalError: 5 root error(s) found.\r\n  (0) INTERNAL: {{function_node __inference_train_function_30558}} failed to connect to all addresses\r\nAdditional GRPC error information from remote target \/job:localhost\/replica:0\/task:0\/device:CPU:0:\r\n:{\"created\":\"@1638231897.932218653\",\"description\":\"Failed to pick subchannel\",\"file\":\"third_party\/grpc\/src\/core\/ext\/filters\/client_channel\/client_channel.cc\",\"file_line\":3151,\"referenced_errors\":[{\"created\":\"@1638231897.932216754\",\"description\":\"failed to connect to all addresses\",\"file\":\"third_party\/grpc\/src\/core\/lib\/transport\/error_utils.cc\",\"file_line\":161,\"grpc_status\":14}]}\r\n\t [[{{node StatefulPartitionedCall}}]]\r\n\t [[MultiDeviceIteratorGetNextFromShard]]\r\nExecuting non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\n\t [[RemoteCall]]\r\n\t [[IteratorGetNextAsOptional]]\r\n\t [[tpu_compile_succeeded_assert\/_14023832043698465348\/_7\/_439]]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n- Tensorflow 2.7.0\r\n- `transformers` 4.12.5\r\n \n This might be related to https:\/\/github.com\/tensorflow\/tensorflow\/issues\/38762 , what do you think @Rocketknight1 ?\r\n> Dataset.from_generator is expected to not work with TPUs as it uses py_function underneath which is incompatible with Cloud TPU 2VM setup. If you would like to read from large datasets, maybe try to materialize it on disk and use TFRecordDataest instead.","embeddings":[-0.2377156168,-0.1358831525,0.1592500061,0.2036830485,0.415363133,0.0100355344,0.3643488288,0.0257148035,-0.4569853544,0.1801204681,-0.0554242879,0.2911760509,0.2514829934,0.4929237664,0.1305927783,-0.0885012895,-0.1408301443,0.0494020469,-0.0877250358,-0.1319964677,-0.1496612281,0.4015712738,0.0868343338,0.0567880757,-0.5214211345,-0.1575675756,0.2263299525,-0.0939755291,0.120718129,0.1129215285,0.3648660481,-0.3170054555,-0.1816274971,0.7030661106,-0.0001187178,0.3797953427,-0.0694346726,-0.177759558,-0.1031216308,-0.0664451048,-0.3455982506,0.1405733675,0.022020923,-0.0226528235,-0.3122111559,0.3715746999,0.0919085592,0.3271478713,0.2484274507,0.4748540819,0.1062794626,0.5870956182,0.1409713477,-0.1173628196,-0.0748101175,-0.1198626682,-0.4575204551,0.2642862797,-0.2550958097,0.0084342845,0.2066527456,-0.2624977529,0.1700140685,-0.1378920227,0.0388896987,0.1986301839,0.2847880423,-0.5665248036,0.0617810115,0.3099963963,0.4110129178,-0.4460216761,-0.3044813871,-0.0078698881,-0.1459917128,-0.2574718297,0.2615571618,-0.1098161936,-0.2194311023,0.2189635336,-0.1486364603,0.0512507185,-0.1517457068,0.0437366813,-0.1780229956,0.2540244162,0.1723244935,0.1105854809,-0.0783795193,-0.0591197684,0.3977127075,0.0053141341,0.0010843383,-0.1233211979,-0.4784847498,-0.2640335858,-0.0352247283,-0.2578030527,-0.165463686,-0.3697738051,0.2542354763,-0.1878972054,0.0652356297,0.1911206394,0.2713412046,-0.0521753691,-0.4298442006,0.4683072865,-0.0494381897,-0.2337644249,-0.2361002266,0.008618447,0.2236470282,-0.211925447,-0.1250923574,-0.2815616131,0.068393074,-0.2035697699,-0.2338632792,0.0085221874,-0.2764796019,0.2113037407,-0.1459044814,0.0162973087,0.2459681332,0.1817941666,0.1868249029,0.0181064941,-0.3681665361,0.1552914828,-0.2345889807,-0.1149440035,-0.0510600768,0.0272474047,0.0482223034,-0.2405116111,-0.112962842,-0.1761602312,0.1284229755,0.1661120355,-0.0290868916,-0.2764335871,0.0377805717,0.6523357034,-0.0119724777,-0.1389116049,0.0409137234,-0.2477663904,-0.0044754762,0.1105827764,-0.3303799927,-0.3795881867,0.1784080863,0.0613013916,-0.0765636414,-0.1235885248,-0.1698188335,0.3062873781,0.1762576997,-0.3321532011,-0.137178421,-0.4028517604,-0.5160399675,-0.046557907,0.3421145976,0.229346633,-0.3999684453,0.1405467242,0.3465245664,-0.131147787,0.3667517006,0.2631673515,0.1796575636,0.2890564203,-0.0087556448,0.1207867265,0.3746035397,-0.053249009,-0.307603687,0.2929520905,-0.4511578083,-0.0498889834,-0.3144187629,-0.0748943985,-0.2482601404,-0.148232013,0.1959413588,0.3359013796,-0.4545333385,0.1155396774,-0.3559283614,-0.1035049111,0.094831191,0.0270725396,0.151578784,0.1515685916,0.1807979047,0.0927194133,0.1558755785,-0.0527642295,-0.1463715732,0.0524278283,0.6204758286,-0.3251643777,0.073489204,0.3552190661,-0.2454556972,0.227078855,0.0642022118,0.4079867303,0.1751741767,0.1910036355,0.0165358447,0.2132796943,0.2460157126,-0.069221884,0.0280522127,0.1479060948,-0.2696963251,-0.1228908896,-0.0124788238,0.4018410444,-0.253415823,-0.0039211768,-0.437643975,0.5116257668,0.0371225737,-0.3100266159,0.0485627726,0.1247181147,0.0814705566,0.013094373,-0.2894968092,0.2103201896,0.0579836108,0.2898690701,-0.1763057709,0.3690997362,0.0451592356,-0.3981795013,-0.4226454794,0.0784708709,0.153572768,0.0689358786,0.0463583283,0.0261190142,-0.0124219591,-0.0486858077,0.0855246335,-0.0038224079,0.0663370937,0.0859499499,0.1466538161,-0.0413411222,-0.1051916778,0.1442938745,-0.2223659605,0.1559813917,-0.4287628829,0.4230028391,0.3258288801,0.0899127498,-0.0879717097,0.129270345,0.2320271283,-0.2631539106,0.2595540881,0.4011161923,0.337250948,0.1074678227,-0.03128561,0.0652010068,-0.3169075251,-0.3241820633,0.0318318903,0.0588801429,0.1356630772,0.3109450936,0.2310612798,0.4301354587,-0.1165126413,-0.0366038904,0.0924784094,0.2334308773,-0.5189221501,0.0243093539,-0.1007749587,-0.1003216878,0.0185870733,0.240124017,-0.0033118387,-0.3447380364,-0.0645326376,0.1211464033,-0.2095198929,0.1038525105,-0.1429463774,0.1682709903,0.0045915716,-0.2821531892,-0.0071256105,-0.2306579202,-0.2735162377,0.0589575842,0.1348588914,0.3670635521,0.0600000024,-0.1300421506,-0.1335045546,0.1996669024,-0.4904161096,0.3301842511,-0.267329365,0.6307040453,0.0753396973,0.159629494,-0.3512656391,-0.3435926139,0.195498988,-0.2150818557,-0.2267365158,0.1991051286,-0.3109489977,-0.1440780759,-0.0591998845,-0.2922245562,-0.1498136818,-0.2958475351,0.1066618264,-0.0361902751,0.1189284846,0.5792866945,0.2318673134,0.087940447,0.2316231877,-0.1162114069,-0.0752675384,-0.017588947,0.209407866,-0.0206163675,-0.3054552078,0.0456145667,0.0830289423,0.1659790426,0.344001025,-0.5312437415,-0.1311127543,-0.2157510519,0.0798162073,0.0384740233,-0.1743490547,0.1771756113,0.0182618368,0.0340933762,-0.2266958058,-0.4738588929,0.2424236983,0.6612285376,0.128611058,0.0130050462,0.3885805905,-0.0767252371,0.3667707145,-0.0940792039,-0.3093855977,0.1707795709,-0.2439340949,0.1991695911,-0.2772428393,-0.131286785,0.459890455,-0.083759822,-0.3251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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3339","title":"to_tf_dataset fails on TPU","comments":"Hi @lhoestq @nbroad1881, I think it's very similar, yes. Unfortunately `to_tf_dataset` uses `tf.numpy_function` which can't be compiled - this is a necessary evil to load from the underlying Arrow dataset. We need to update the notebooks\/examples to clarify that this won't work, or to identify a workaround. You may be able to get it to work on an actual cloud TPU VM, but those are quite new and we haven't tested it yet. ","body":"Using `to_tf_dataset` to create a dataset and then putting it in `model.fit` results in an internal error on TPUs. I've only tried on Colab and Kaggle TPUs, not GCP TPUs.\r\n\r\n\r\n## Steps to reproduce the bug\r\nI made a colab to show the error. https:\/\/colab.research.google.com\/drive\/12x_PFKzGouFxqD4OuWfnycW_1TaT276z?usp=sharing\r\n\r\n## Expected results\r\ndataset from `to_tf_dataset` works in `model.fit`  \r\nRight below the first error in the colab I use `tf.data.Dataset.from_tensor_slices` and `model.fit` works just fine. This is the desired outcome.\r\n\r\n## Actual results\r\n```\r\nInternalError: 5 root error(s) found.\r\n  (0) INTERNAL: {{function_node __inference_train_function_30558}} failed to connect to all addresses\r\nAdditional GRPC error information from remote target \/job:localhost\/replica:0\/task:0\/device:CPU:0:\r\n:{\"created\":\"@1638231897.932218653\",\"description\":\"Failed to pick subchannel\",\"file\":\"third_party\/grpc\/src\/core\/ext\/filters\/client_channel\/client_channel.cc\",\"file_line\":3151,\"referenced_errors\":[{\"created\":\"@1638231897.932216754\",\"description\":\"failed to connect to all addresses\",\"file\":\"third_party\/grpc\/src\/core\/lib\/transport\/error_utils.cc\",\"file_line\":161,\"grpc_status\":14}]}\r\n\t [[{{node StatefulPartitionedCall}}]]\r\n\t [[MultiDeviceIteratorGetNextFromShard]]\r\nExecuting non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\n\t [[RemoteCall]]\r\n\t [[IteratorGetNextAsOptional]]\r\n\t [[tpu_compile_succeeded_assert\/_14023832043698465348\/_7\/_439]]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n- Tensorflow 2.7.0\r\n- `transformers` 4.12.5\r\n","comment_length":73,"text":"to_tf_dataset fails on TPU \n Using `to_tf_dataset` to create a dataset and then putting it in `model.fit` results in an internal error on TPUs. I've only tried on Colab and Kaggle TPUs, not GCP TPUs.\r\n\r\n\r\n## Steps to reproduce the bug\r\nI made a colab to show the error. https:\/\/colab.research.google.com\/drive\/12x_PFKzGouFxqD4OuWfnycW_1TaT276z?usp=sharing\r\n\r\n## Expected results\r\ndataset from `to_tf_dataset` works in `model.fit`  \r\nRight below the first error in the colab I use `tf.data.Dataset.from_tensor_slices` and `model.fit` works just fine. This is the desired outcome.\r\n\r\n## Actual results\r\n```\r\nInternalError: 5 root error(s) found.\r\n  (0) INTERNAL: {{function_node __inference_train_function_30558}} failed to connect to all addresses\r\nAdditional GRPC error information from remote target \/job:localhost\/replica:0\/task:0\/device:CPU:0:\r\n:{\"created\":\"@1638231897.932218653\",\"description\":\"Failed to pick subchannel\",\"file\":\"third_party\/grpc\/src\/core\/ext\/filters\/client_channel\/client_channel.cc\",\"file_line\":3151,\"referenced_errors\":[{\"created\":\"@1638231897.932216754\",\"description\":\"failed to connect to all addresses\",\"file\":\"third_party\/grpc\/src\/core\/lib\/transport\/error_utils.cc\",\"file_line\":161,\"grpc_status\":14}]}\r\n\t [[{{node StatefulPartitionedCall}}]]\r\n\t [[MultiDeviceIteratorGetNextFromShard]]\r\nExecuting non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\n\t [[RemoteCall]]\r\n\t [[IteratorGetNextAsOptional]]\r\n\t [[tpu_compile_succeeded_assert\/_14023832043698465348\/_7\/_439]]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n- Tensorflow 2.7.0\r\n- `transformers` 4.12.5\r\n \n Hi @lhoestq @nbroad1881, I think it's very similar, yes. Unfortunately `to_tf_dataset` uses `tf.numpy_function` which can't be compiled - this is a necessary evil to load from the underlying Arrow dataset. We need to update the notebooks\/examples to clarify that this won't work, or to identify a workaround. You may be able to get it to work on an actual cloud TPU VM, but those are quite new and we haven't tested it yet. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3339","title":"to_tf_dataset fails on TPU","comments":"Thank you for the explanation. I didn't realize the nuances of `tf.numpy_function`. In this scenario, would it be better to use `export(format='tfrecord')` ?  It's not quite the same, but for very large datasets that don't fit in memory it looks like it is the only option. I haven't used `export` before, but I do recall reading that there are suggestions for how big and how many tfrecords there should be to not bottleneck the TPU. It might be nice if there were a way for the `export` method to split the files up into appropriate chunk sizes depending on the size of the dataset and the number of devices. And if that is too much, it would be nice to be able to specify the number of files that would be created when using `export`. Well... maybe the user should just do the chunking themselves and call `export` a bunch of times.  Whatever the case, you have been helpful. Thanks Tensorflow boy ;-) ","body":"Using `to_tf_dataset` to create a dataset and then putting it in `model.fit` results in an internal error on TPUs. I've only tried on Colab and Kaggle TPUs, not GCP TPUs.\r\n\r\n\r\n## Steps to reproduce the bug\r\nI made a colab to show the error. https:\/\/colab.research.google.com\/drive\/12x_PFKzGouFxqD4OuWfnycW_1TaT276z?usp=sharing\r\n\r\n## Expected results\r\ndataset from `to_tf_dataset` works in `model.fit`  \r\nRight below the first error in the colab I use `tf.data.Dataset.from_tensor_slices` and `model.fit` works just fine. This is the desired outcome.\r\n\r\n## Actual results\r\n```\r\nInternalError: 5 root error(s) found.\r\n  (0) INTERNAL: {{function_node __inference_train_function_30558}} failed to connect to all addresses\r\nAdditional GRPC error information from remote target \/job:localhost\/replica:0\/task:0\/device:CPU:0:\r\n:{\"created\":\"@1638231897.932218653\",\"description\":\"Failed to pick subchannel\",\"file\":\"third_party\/grpc\/src\/core\/ext\/filters\/client_channel\/client_channel.cc\",\"file_line\":3151,\"referenced_errors\":[{\"created\":\"@1638231897.932216754\",\"description\":\"failed to connect to all addresses\",\"file\":\"third_party\/grpc\/src\/core\/lib\/transport\/error_utils.cc\",\"file_line\":161,\"grpc_status\":14}]}\r\n\t [[{{node StatefulPartitionedCall}}]]\r\n\t [[MultiDeviceIteratorGetNextFromShard]]\r\nExecuting non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\n\t [[RemoteCall]]\r\n\t [[IteratorGetNextAsOptional]]\r\n\t [[tpu_compile_succeeded_assert\/_14023832043698465348\/_7\/_439]]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n- Tensorflow 2.7.0\r\n- `transformers` 4.12.5\r\n","comment_length":163,"text":"to_tf_dataset fails on TPU \n Using `to_tf_dataset` to create a dataset and then putting it in `model.fit` results in an internal error on TPUs. I've only tried on Colab and Kaggle TPUs, not GCP TPUs.\r\n\r\n\r\n## Steps to reproduce the bug\r\nI made a colab to show the error. https:\/\/colab.research.google.com\/drive\/12x_PFKzGouFxqD4OuWfnycW_1TaT276z?usp=sharing\r\n\r\n## Expected results\r\ndataset from `to_tf_dataset` works in `model.fit`  \r\nRight below the first error in the colab I use `tf.data.Dataset.from_tensor_slices` and `model.fit` works just fine. This is the desired outcome.\r\n\r\n## Actual results\r\n```\r\nInternalError: 5 root error(s) found.\r\n  (0) INTERNAL: {{function_node __inference_train_function_30558}} failed to connect to all addresses\r\nAdditional GRPC error information from remote target \/job:localhost\/replica:0\/task:0\/device:CPU:0:\r\n:{\"created\":\"@1638231897.932218653\",\"description\":\"Failed to pick subchannel\",\"file\":\"third_party\/grpc\/src\/core\/ext\/filters\/client_channel\/client_channel.cc\",\"file_line\":3151,\"referenced_errors\":[{\"created\":\"@1638231897.932216754\",\"description\":\"failed to connect to all addresses\",\"file\":\"third_party\/grpc\/src\/core\/lib\/transport\/error_utils.cc\",\"file_line\":161,\"grpc_status\":14}]}\r\n\t [[{{node StatefulPartitionedCall}}]]\r\n\t [[MultiDeviceIteratorGetNextFromShard]]\r\nExecuting non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\n\t [[RemoteCall]]\r\n\t [[IteratorGetNextAsOptional]]\r\n\t [[tpu_compile_succeeded_assert\/_14023832043698465348\/_7\/_439]]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n- Tensorflow 2.7.0\r\n- `transformers` 4.12.5\r\n \n Thank you for the explanation. I didn't realize the nuances of `tf.numpy_function`. In this scenario, would it be better to use `export(format='tfrecord')` ?  It's not quite the same, but for very large datasets that don't fit in memory it looks like it is the only option. I haven't used `export` before, but I do recall reading that there are suggestions for how big and how many tfrecords there should be to not bottleneck the TPU. It might be nice if there were a way for the `export` method to split the files up into appropriate chunk sizes depending on the size of the dataset and the number of devices. And if that is too much, it would be nice to be able to specify the number of files that would be created when using `export`. Well... maybe the user should just do the chunking themselves and call `export` a bunch of times.  Whatever the case, you have been helpful. Thanks Tensorflow boy ;-) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3339","title":"to_tf_dataset fails on TPU","comments":"Yeah, this is something we really should have a proper guide on. I'll make a note to test some things and make a 'TF TPU best practices' notebook at some point, but in the meantime I think your solution of exporting TFRecords will probably work. ","body":"Using `to_tf_dataset` to create a dataset and then putting it in `model.fit` results in an internal error on TPUs. I've only tried on Colab and Kaggle TPUs, not GCP TPUs.\r\n\r\n\r\n## Steps to reproduce the bug\r\nI made a colab to show the error. https:\/\/colab.research.google.com\/drive\/12x_PFKzGouFxqD4OuWfnycW_1TaT276z?usp=sharing\r\n\r\n## Expected results\r\ndataset from `to_tf_dataset` works in `model.fit`  \r\nRight below the first error in the colab I use `tf.data.Dataset.from_tensor_slices` and `model.fit` works just fine. This is the desired outcome.\r\n\r\n## Actual results\r\n```\r\nInternalError: 5 root error(s) found.\r\n  (0) INTERNAL: {{function_node __inference_train_function_30558}} failed to connect to all addresses\r\nAdditional GRPC error information from remote target \/job:localhost\/replica:0\/task:0\/device:CPU:0:\r\n:{\"created\":\"@1638231897.932218653\",\"description\":\"Failed to pick subchannel\",\"file\":\"third_party\/grpc\/src\/core\/ext\/filters\/client_channel\/client_channel.cc\",\"file_line\":3151,\"referenced_errors\":[{\"created\":\"@1638231897.932216754\",\"description\":\"failed to connect to all addresses\",\"file\":\"third_party\/grpc\/src\/core\/lib\/transport\/error_utils.cc\",\"file_line\":161,\"grpc_status\":14}]}\r\n\t [[{{node StatefulPartitionedCall}}]]\r\n\t [[MultiDeviceIteratorGetNextFromShard]]\r\nExecuting non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\n\t [[RemoteCall]]\r\n\t [[IteratorGetNextAsOptional]]\r\n\t [[tpu_compile_succeeded_assert\/_14023832043698465348\/_7\/_439]]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n- Tensorflow 2.7.0\r\n- `transformers` 4.12.5\r\n","comment_length":45,"text":"to_tf_dataset fails on TPU \n Using `to_tf_dataset` to create a dataset and then putting it in `model.fit` results in an internal error on TPUs. I've only tried on Colab and Kaggle TPUs, not GCP TPUs.\r\n\r\n\r\n## Steps to reproduce the bug\r\nI made a colab to show the error. https:\/\/colab.research.google.com\/drive\/12x_PFKzGouFxqD4OuWfnycW_1TaT276z?usp=sharing\r\n\r\n## Expected results\r\ndataset from `to_tf_dataset` works in `model.fit`  \r\nRight below the first error in the colab I use `tf.data.Dataset.from_tensor_slices` and `model.fit` works just fine. This is the desired outcome.\r\n\r\n## Actual results\r\n```\r\nInternalError: 5 root error(s) found.\r\n  (0) INTERNAL: {{function_node __inference_train_function_30558}} failed to connect to all addresses\r\nAdditional GRPC error information from remote target \/job:localhost\/replica:0\/task:0\/device:CPU:0:\r\n:{\"created\":\"@1638231897.932218653\",\"description\":\"Failed to pick subchannel\",\"file\":\"third_party\/grpc\/src\/core\/ext\/filters\/client_channel\/client_channel.cc\",\"file_line\":3151,\"referenced_errors\":[{\"created\":\"@1638231897.932216754\",\"description\":\"failed to connect to all addresses\",\"file\":\"third_party\/grpc\/src\/core\/lib\/transport\/error_utils.cc\",\"file_line\":161,\"grpc_status\":14}]}\r\n\t [[{{node StatefulPartitionedCall}}]]\r\n\t [[MultiDeviceIteratorGetNextFromShard]]\r\nExecuting non-communication op <MultiDeviceIteratorGetNextFromShard> originally returned UnavailableError, and was replaced by InternalError to avoid invoking TF network error handling logic.\r\n\t [[RemoteCall]]\r\n\t [[IteratorGetNextAsOptional]]\r\n\t [[tpu_compile_succeeded_assert\/_14023832043698465348\/_7\/_439]]\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.16.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n- Tensorflow 2.7.0\r\n- `transformers` 4.12.5\r\n \n Yeah, this is something we really should have a proper guide on. I'll make a note to test some things and make a 'TF TPU best practices' notebook at some point, but in the meantime I think your solution of exporting TFRecords will probably work. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3337","title":"Typing of Dataset.__getitem__ could be improved.","comments":"Hi ! Thanks for the suggestion, I didn't know about this decorator.\r\n\r\nIf you are interesting in contributing, feel free to open a pull request to add the overload methods for each typing combination :) To assign you to this issue, you can comment `#self-assign` in this thread.\r\n\r\n`Dataset.__getitem__` is defined right here: https:\/\/github.com\/huggingface\/datasets\/blob\/e6f1352fe19679de897f3d962e616936a17094f5\/src\/datasets\/arrow_dataset.py#L1840","body":"## Describe the bug\r\n\r\nThe newly added typing for Dataset.__getitem__ is Union[Dict, List]. This makes tools like mypy a bit awkward to use as we need to check the type manually. We could use type overloading to make this easier. [Documentation](https:\/\/docs.python.org\/3\/library\/typing.html#typing.overload)\r\n\r\n## Steps to reproduce the bug\r\nLet's have a file `test.py`\r\n\r\n```python\r\nfrom typing import List, Dict, Any\r\n\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict({\r\n    'a': [1,2,3],\r\n    'b': [\"1\", \"2\", \"3\"]\r\n})\r\n\r\none_colum: List[str] = ds['a']\r\nsome_index: Dict[Any, Any] = ds[1]\r\n```\r\n\r\n## Expected results\r\n\r\nRunning `mypy test.py` should not give any error.\r\n\r\n\r\n## Actual results\r\n\r\n```\r\ntest.py:10: error: Incompatible types in assignment (expression has type \"Union[Dict[Any, Any], List[Any]]\", variable has type \"List[str]\")\r\ntest.py:11: error: Incompatible types in assignment (expression has type \"Union[Dict[Any, Any], List[Any]]\", variable has type \"Dict[Any, Any]\")\r\nFound 2 errors in 1 file (checked 1 source file)\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.1\r\n","comment_length":54,"text":"Typing of Dataset.__getitem__ could be improved. \n ## Describe the bug\r\n\r\nThe newly added typing for Dataset.__getitem__ is Union[Dict, List]. This makes tools like mypy a bit awkward to use as we need to check the type manually. We could use type overloading to make this easier. [Documentation](https:\/\/docs.python.org\/3\/library\/typing.html#typing.overload)\r\n\r\n## Steps to reproduce the bug\r\nLet's have a file `test.py`\r\n\r\n```python\r\nfrom typing import List, Dict, Any\r\n\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict({\r\n    'a': [1,2,3],\r\n    'b': [\"1\", \"2\", \"3\"]\r\n})\r\n\r\none_colum: List[str] = ds['a']\r\nsome_index: Dict[Any, Any] = ds[1]\r\n```\r\n\r\n## Expected results\r\n\r\nRunning `mypy test.py` should not give any error.\r\n\r\n\r\n## Actual results\r\n\r\n```\r\ntest.py:10: error: Incompatible types in assignment (expression has type \"Union[Dict[Any, Any], List[Any]]\", variable has type \"List[str]\")\r\ntest.py:11: error: Incompatible types in assignment (expression has type \"Union[Dict[Any, Any], List[Any]]\", variable has type \"Dict[Any, Any]\")\r\nFound 2 errors in 1 file (checked 1 source file)\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.1\r\n \n Hi ! Thanks for the suggestion, I didn't know about this decorator.\r\n\r\nIf you are interesting in contributing, feel free to open a pull request to add the overload methods for each typing combination :) To assign you to this issue, you can comment `#self-assign` in this thread.\r\n\r\n`Dataset.__getitem__` is defined right here: https:\/\/github.com\/huggingface\/datasets\/blob\/e6f1352fe19679de897f3d962e616936a17094f5\/src\/datasets\/arrow_dataset.py#L1840","embeddings":[-0.279602617,-0.0664093643,-0.0474866331,0.2626143396,0.2142493278,0.0547713414,0.2549815178,0.2996381819,-0.009178699,0.0392908528,0.0025680726,0.447822541,-0.0814645588,-0.0109511577,-0.1863643378,0.142916292,-0.052208934,-0.0558120161,-0.0211944208,-0.062976636,-0.4044335186,-0.0674046427,-0.3512154818,0.0151487207,0.012855486,-0.2228807062,-0.1409817189,0.1535911262,-0.1487654448,-0.3038251698,0.2481650859,0.3039274514,-0.0327401012,0.2038253993,-0.0001065116,-0.0136199761,0.3284392655,-0.0232824758,-0.4351055324,-0.3687729239,0.0240748357,-0.3340190053,0.177228272,-0.1938829869,-0.0166597702,-0.4151800573,-0.1775920242,-0.1853568703,-0.0326847062,0.145448491,0.2430755794,0.3607872128,0.0685980842,-0.1441198736,0.3199521005,0.3037069142,0.0098498138,0.0882957876,0.5188884139,0.0147874989,0.1696603745,-0.0611497834,-0.3055454493,0.071909681,0.0347741209,0.1830787957,0.007450907,0.0611500554,-0.0064469106,0.1542080194,0.4755381048,-0.2544184327,-0.373041898,-0.1602842361,0.0541948564,-0.1839574128,0.1542218179,-0.0073538362,-0.193593964,0.0478638485,0.1324910969,0.0566963516,0.0288913026,0.3768605292,-0.0007175534,0.2198072821,-0.1891786009,0.1520489007,0.1567249894,-0.1045779809,0.1624640822,-0.1003982648,-0.1079104766,0.1523643881,-0.1989557296,-0.1655276716,0.1633067727,0.1905065924,0.0704548508,0.1820976287,0.0495835245,0.1806571484,-0.2978616357,0.267137289,0.6184267998,0.052628953,0.0447509326,0.4063336253,0.0557262078,0.1749698967,-0.096790053,0.1385849565,0.1108841076,-0.2377769649,0.2419950813,0.1733097434,0.4390998185,-0.1246813163,-0.4228269458,-0.0432097726,-0.1312477291,-0.1781646013,0.1670832485,0.3955492973,-0.1682317555,0.0401575044,0.0233479217,0.3062456548,-0.0432143845,-0.1581338048,-0.2335505635,0.2383709401,0.0201739892,-0.2095246911,0.0974879041,0.1135697588,-0.1247687489,0.3159050643,0.2518859804,-0.0152660813,0.0434117801,-0.1543093324,0.2863591909,-0.0544586256,0.0041960287,0.0794224441,0.3952659965,-0.5772271156,-0.266746223,-0.1275113821,-0.2022865862,-0.1471032202,-0.0997006074,0.2397099286,0.0149679314,0.0599065311,-0.5099036098,0.0382080749,0.0895815194,-0.009013216,-0.0278485287,-0.1388056129,0.0365499035,-0.3465171158,0.0883145332,0.2977975905,-0.2803245783,0.0200109556,-0.2218660861,-0.0866009071,0.2857555747,0.2482022196,-0.163776204,0.08543735,-0.2965478003,-0.2305731624,0.4879127145,-0.1577480137,-0.3666643798,0.13203381,0.0957005098,0.2301547378,-0.0255684219,-0.0123710698,0.1269328892,-0.251419127,0.2697212994,0.0997303873,0.0339655578,-0.1119746268,-0.3627689481,-0.1752886325,0.2077658176,-0.1219014302,-0.2117953002,0.0882812962,-0.0303940736,-0.3006588817,0.2887588441,-0.1460958123,-0.1627717912,0.0552931353,0.3377376199,0.2344458252,0.0556503236,-0.0962404311,-0.4533878565,0.1052767113,0.107908994,0.2678934038,-0.4865047634,-0.3877128959,-0.0425435975,0.0735734552,-0.3656572998,0.0511286594,0.2038023174,0.1148199216,0.1551492214,-0.1428942978,0.0237296578,-0.0555866957,-0.1853061169,0.0318892971,-0.1272138655,0.0809114948,0.0534983836,-0.1363699436,-0.134128958,0.2929620743,0.0831591934,-0.0863516405,-0.0486012548,0.2139776945,0.2388863862,-0.1766703427,-0.3539280891,-0.0057835099,0.0110869026,-0.044007659,-0.1972834021,0.1135691404,0.0527292714,-0.0470343642,-0.1163835004,0.6227249503,-0.1970683634,0.1833121628,-0.1425448507,0.0899821967,0.1153504923,0.2138429135,-0.0477776416,-0.4353245199,-0.0472693592,0.3997893035,0.1384514421,0.0750222355,-0.3753085136,0.095993951,0.1255276948,-0.0023150032,0.4840841889,-0.0193728358,0.1247451529,0.0681102797,-0.1985039413,0.1477678567,0.5327914357,0.2021313608,-0.085671626,0.0717657879,-0.0283464734,-0.0357358158,0.1984359175,0.0653789788,-0.1585171521,-0.2034298778,0.1496973783,0.054660026,-0.0033879043,0.0678651109,-0.1354677528,0.403182447,-0.4138349295,-0.0116243353,-0.552486062,-0.125068754,-0.1896510869,0.0707700104,0.0613801889,-0.363235116,-0.214439556,-0.0272506624,-0.0964699313,0.0766006112,0.1910221577,0.0808413178,0.013317883,-0.481538415,0.25591892,-0.2066608518,-0.3399108648,0.1311069131,0.3597626686,-0.1295370013,0.379817903,0.0759914741,-0.188856408,-0.1901476532,-0.3298051357,0.1118969619,-0.1700222194,0.0926395357,0.6158170104,0.2519294322,-0.013300214,-0.4168689251,0.2064258754,0.0888151079,-0.1660304368,0.128006205,0.1207990348,-0.1967270374,-0.233197704,-0.1772842705,-0.3471639752,-0.3320070803,0.3797877729,0.0406689458,0.2066349238,-0.1206040978,-0.0271771383,0.017566938,0.010927706,0.2905508876,-0.2859282494,-0.1140552312,0.335837096,0.0643339753,-0.207926482,-0.2164973021,-0.3517494798,0.0502877384,-0.0333636068,-0.2998278439,-0.3190507889,-0.0641168803,0.4638198912,0.0675681457,0.2006419748,0.1210058779,0.3785345554,-0.1215684712,-0.1298146695,-0.0126095489,-0.1152308285,0.0932177156,0.0271598324,0.1062433347,0.0284796283,-0.030354172,0.3134742677,0.34959951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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"Hi ! The message you're getting is not an error. It simply says that your JSON dataset is being prepared to a location in `\/root\/.cache\/huggingface\/datasets`","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":25,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n Hi ! The message you're getting is not an error. It simply says that your JSON dataset is being prepared to a location in `\/root\/.cache\/huggingface\/datasets`","embeddings":[0.1340620369,-0.4356928766,-0.0010811903,0.4408901334,0.4953264892,0.1184779778,0.0806621313,0.3796994388,0.1051830649,-0.0702865943,-0.0353001133,0.1957520545,-0.0973736495,0.3267001212,-0.1525484174,-0.0933606103,0.0823301747,0.0740964636,-0.1414857954,0.079201676,-0.2150724679,0.3840417862,-0.0021207617,0.0098335668,-0.2920083106,-0.1203964874,0.0398099013,0.2432672679,-0.2985903621,-0.5010695457,0.2849071324,-0.0953261405,0.2465157658,0.4338198006,-0.0001125735,0.0708660856,0.2820902467,-0.1743132472,-0.386567831,-0.3857503831,-0.143133387,-0.3407208323,0.3662091196,-0.0762447491,-0.1111863628,-0.1300689876,-0.0994508192,-0.1398904622,0.8514152765,0.2543050945,0.272774756,0.3182742298,0.1154011339,-0.1199786514,0.036331661,0.3197357357,-0.0676280111,0.5441519618,0.0516467169,0.1807155758,0.194974497,0.2008494586,0.118613705,0.0050871503,0.3977425396,0.0041699121,-0.1350827813,-0.3455568254,-0.1897276342,0.0538934879,0.3800941706,-0.20956783,-0.6211257577,-0.5102400184,0.1506978124,-0.3400236964,0.3419012129,0.2085124105,-0.0516509749,-0.0215205215,-0.3525160849,-0.1947465688,-0.0643088594,-0.0507135615,0.3227282763,-0.1489931643,-0.3083499372,0.0252658259,0.1538552195,-0.2023451626,-0.1691514105,0.1035900787,-0.2362822741,0.3077508807,-0.260837853,-0.1753766388,-0.1506359279,-0.1923895478,0.0591602437,0.2445790619,-0.0059723533,-0.1902681887,-0.0174392257,0.1199742928,0.3295110166,0.4092621207,0.0092202015,-0.0294200331,0.0697398707,0.4435240924,0.0228274539,-0.2053289562,-0.0686553046,-0.1908247322,-0.136035338,-0.076735571,0.0681497976,-0.2350081503,-0.1345693767,0.1266384423,-0.214611277,-0.1963224411,-0.0224329438,0.308732152,-0.2215251923,0.1493363529,0.1597046256,0.3573338985,-0.1844123155,-0.0163934324,-0.0940117761,-0.0075662769,-0.2016065121,0.0842854828,0.2141086459,-0.0777736753,0.3825955391,0.007672633,0.1340125203,-0.3007404804,0.0501458533,-0.0096907346,-0.2060450464,0.248650983,0.047775764,0.0714094266,0.1079405546,-0.0929201469,-0.134129405,-0.1648007333,-0.3751425147,-0.2794168591,-0.016789116,0.2042695433,-0.1478004009,0.0965825096,-0.5965675712,0.0244278982,0.0365936868,-0.0513623543,0.1082153246,-0.0990752205,-0.0027628907,-0.2225879878,0.32801947,0.325101018,-0.1108144149,-0.322888732,0.2291617841,-0.2921516299,-0.0484710336,0.4269499779,-0.0420741402,0.2859465182,-0.3817110062,-0.015627984,0.03963615,-0.3100793064,-0.0865108147,0.1363205016,-0.069650732,0.1591800153,0.0549557544,-0.1607624441,-0.0790639371,0.0924433246,0.2820343673,0.2025058866,0.0589466244,0.0510977805,-0.2899795175,-0.3715030253,-0.174785912,0.1306510717,-0.281293124,0.1759981364,-0.0246574488,-0.0887699872,0.1605491936,-0.1388169676,0.0418549106,0.4429993927,0.1776805073,0.1887317896,-0.0643885881,0.0584285222,-0.6988535523,0.2129920125,-0.1795327365,0.036633648,-0.0559680723,-0.1083215252,-0.2770815194,-0.0816363394,-0.0650960505,-0.155367896,0.096467182,-0.0308582671,0.3887805641,0.2674198151,-0.2402361035,0.2569638193,-0.021355303,0.3883885145,-0.5748910308,0.1604111642,0.0129473926,-0.0904933065,0.1975717843,0.1584275216,-0.0000821222,-0.1781266481,-0.168141827,0.2582542002,-0.0293079745,0.2692436576,0.050637912,0.0325431302,0.0919509456,0.1784383208,-0.1880673021,0.0594680384,0.0560127497,-0.0459396914,-0.0248479992,0.4908506572,-0.147712782,0.1264652163,0.027234735,-0.1383651644,0.190455094,0.1250165552,-0.119729355,-0.1933061033,0.1668939143,0.1320362985,0.0804203525,-0.0231138468,-0.2956244051,-0.3768149614,0.4740639031,-0.0043988237,-0.099762179,0.4230203032,-0.0092096813,0.1029042676,-0.0497173741,0.1985415816,0.4678189754,0.0438467078,-0.2617486417,0.1859257519,0.1294295043,-0.1906598508,0.0476083457,-0.0608777069,0.3754659295,0.1994155794,0.2241749614,-0.0287387669,-0.296584934,0.0996707231,-0.1363717318,0.1992976964,-0.438029319,0.0708349645,-0.1407921612,-0.1407707185,-0.2005560994,-0.0041022766,-0.4785350561,-0.1014899239,-0.0444213636,0.2648246884,-0.0711329877,-0.0435952432,0.064300932,0.2944278419,0.0029918645,0.0631745905,-0.1944441944,0.0938577801,-0.2495764643,-0.0024779174,0.1381832063,-0.3324597478,0.2893588245,-0.4716011286,-0.0355372168,-0.0099507775,-0.1204724014,0.1316306889,-0.1494602412,0.4472638071,0.2032197863,0.339248389,-0.2161581218,-0.1259274185,0.4162805974,0.0124710687,-0.436858207,-0.1382227987,0.0863293633,0.2454118282,-0.2399867326,-0.3534657359,-0.1518462747,-0.3174444139,0.7419294715,0.1069433615,0.0040571811,0.1505650729,0.183223784,0.0978759825,-0.3299693763,0.2496654838,-0.2563899755,-0.7169758677,0.2397575378,-0.2577952445,-0.3208524585,0.0848456919,0.0991519764,0.2635003328,-0.3823283315,-0.5710347891,-0.4254975915,0.0067325439,-0.0262237005,-0.1655313224,0.1221013665,0.1177674606,-0.0972590074,-0.0105261561,-0.0508954152,-0.2957679927,0.0626534969,0.0256573912,0.2425006479,-0.0783230737,0.6527788043,-0.1599172503,0.58404392,0.3300723135,-0.0495455191,0.3927122951,-0.0520347208,0.3143526316,-0.2360168248,-0.1461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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"> \r\n\r\nbut I want to load local JSON file by command\r\n`python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\n**squad-retrain-data\/train-v2.0.json** is the local JSON file, how to load it and map it to a special structure?","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":37,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n > \r\n\r\nbut I want to load local JSON file by command\r\n`python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\n**squad-retrain-data\/train-v2.0.json** is the local JSON file, how to load it and map it to a special structure?","embeddings":[0.1912292391,-0.3675380945,-0.0029148916,0.5285807848,0.3912638426,0.0750027224,0.0855836719,0.4642628133,0.2954296768,-0.0933144167,-0.1045989022,0.223393634,0.0046624821,0.2920067012,-0.0838342831,-0.1504719257,0.0743772164,0.1011797935,-0.0999639928,0.0778005794,-0.3029290736,0.3927833438,0.0348863117,0.0766870901,-0.1023584008,0.0633738041,0.0872768909,0.2624526322,-0.2257563621,-0.5375313759,0.3394762874,-0.1491688639,0.2267315388,0.4444766939,-0.0001136894,0.2170349956,0.335390389,-0.2139928788,-0.2998431027,-0.4525947571,-0.2306821197,-0.2641102672,0.4249499142,-0.116370745,-0.119672209,-0.3074535728,-0.0818141475,-0.1540299207,0.8801879287,0.1835418642,0.2141236961,0.260722518,0.1658610702,-0.0820160732,0.0980498791,0.4361202121,-0.0139493011,0.7154096961,0.1889703721,0.1951339841,0.1510666162,0.0087780282,0.0577951483,0.078088671,0.2958555818,-0.0589196198,-0.0400353447,-0.1861749142,-0.1422232389,0.1445471048,0.4441285729,-0.2424722463,-0.5932990909,-0.3637834489,0.0628296733,-0.2452014089,0.3591327965,0.2384506911,-0.1228505522,0.0713817254,-0.2872864008,-0.2093147486,-0.1736642569,0.0911527649,0.3180138171,-0.0820699409,-0.3231346309,0.0564124398,0.1946074516,-0.2423978001,-0.1302840114,0.0313822702,-0.2208384275,0.3378192186,-0.1246524081,-0.0939464495,-0.2683625817,-0.2254652381,0.1195098013,0.0364677459,0.0275184438,-0.1408471316,-0.127202943,0.2469894886,0.313149929,0.380384475,0.0913556814,0.0493855253,-0.0073369918,0.4342573583,-0.0779131427,-0.1418356448,-0.123872824,-0.2145090401,-0.3287108541,-0.0635169595,0.0370172374,-0.2447982877,0.0240320936,0.0534050241,-0.3331878483,-0.1773035377,0.1416805238,0.3235252202,-0.1732915044,0.1130927503,0.121170111,0.2992584705,-0.108155638,0.0709112138,-0.0560327508,0.0868371055,-0.0942876562,0.0710741505,0.20123218,-0.1018952802,0.3712413013,-0.0131493267,-0.0123235844,-0.2611225843,0.0087421201,-0.006901057,-0.1912034303,0.2719696462,0.0515650064,0.1196876317,0.1977696419,-0.1753090173,-0.2226873189,-0.103270255,-0.4090574086,-0.3200065196,-0.0659148544,0.1496840566,-0.1829797924,0.1491928101,-0.6432971358,-0.1194921806,-0.0764723867,-0.1589272469,-0.0060215471,-0.0606163405,-0.1479971707,-0.1352481842,0.2835023403,0.3367237151,-0.2618966401,-0.2712306082,0.2087920308,-0.3465012014,-0.0459198691,0.475202322,-0.1166170612,0.3254611492,-0.4514953494,-0.0105209956,0.1742400676,-0.4279032648,-0.0977696106,0.339438051,-0.0401218943,0.148050949,0.0978164524,-0.2316547632,-0.206789881,0.0999717191,0.3258714974,0.174431622,0.2001135796,0.0621653832,-0.2348698527,-0.3146374822,-0.1985310912,0.2130947411,-0.3558551669,0.1898594648,0.060540691,-0.0163101573,0.1433537453,-0.1817374229,0.0742744058,0.4196127057,0.1361921132,0.0984620154,-0.0334889293,0.1216342077,-0.7362277508,0.1453267783,-0.2350452393,-0.0737103373,-0.0516415946,-0.0817970559,-0.3076819777,-0.0445506051,-0.1600235254,0.0536913425,0.0590226911,-0.0046988484,0.3805211186,0.2092677951,-0.2081823349,0.1797269434,-0.0701904446,0.2510400712,-0.5658289194,0.1460469663,0.0013766274,-0.0452428348,0.1973351538,0.1152990237,0.1028408855,-0.3353870213,-0.2073402405,0.1647704393,0.0589468889,0.307395488,-0.0591521673,-0.0197471902,0.067651011,0.1818955988,-0.2474107742,0.1101120263,0.106846422,-0.073008202,-0.1315912157,0.4609125853,-0.1507705897,0.1780730039,0.0676786304,-0.1789705902,0.3120943308,0.0886195078,-0.1725327671,-0.264362216,0.0998167321,0.2826200128,0.1882589012,0.0748639852,-0.2423676252,-0.478753984,0.47575894,-0.0576794185,-0.135611847,0.355050236,-0.0595937297,0.0680703446,-0.0408708416,0.1811363548,0.4471724629,-0.0281945262,-0.2612185776,0.2123829126,0.2633498013,-0.1909306645,0.0192346387,-0.061769262,0.2918362319,0.3174837232,0.1602708995,-0.129407391,-0.297681123,0.0273038242,-0.1031809822,0.1893872917,-0.4977816045,-0.0283130594,-0.2322118878,-0.2172591239,-0.3347572982,-0.0620168597,-0.2776678205,-0.0503215156,-0.1042948738,0.2689659297,-0.1751223505,-0.0872200653,0.1427212059,0.1581701934,-0.0402798653,-0.1668703407,-0.2561633587,0.0510841385,-0.2559107542,-0.0398731753,0.3317064047,-0.1843767017,0.1513108164,-0.5701091886,-0.172083348,0.0125816902,-0.0482002199,0.2196207941,-0.0286845751,0.4075647295,0.1101521552,0.3714239001,-0.2840047181,-0.125931263,0.460709393,0.0760357529,-0.4325689971,-0.14211604,-0.0211797692,0.2068887502,-0.187880367,-0.3427134752,-0.2549126148,-0.4415068626,0.876767993,0.1456833631,0.1659605205,0.0908496976,0.1034663022,0.1967276782,-0.2484889776,0.239603281,-0.1621099561,-0.8245722055,0.2767786086,-0.3162619472,-0.2588117123,0.0380942412,0.0502996817,0.2674008906,-0.3577706218,-0.5028178692,-0.3877109289,0.0689650774,-0.0732422844,-0.0802188292,0.0684036613,0.1067420617,-0.1099842787,0.0555104911,-0.0454006344,-0.2853200436,0.0675493255,0.1838707924,0.1419323981,0.1764858812,0.5833240151,-0.2115713656,0.5565189719,0.292714864,-0.042682834,0.4358937442,-0.0981550515,0.2216471136,-0.2796318531,-0.1350141615,-0.066992186,-0.081826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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"You can load it with `dataset = datasets.load_dataset('json', data_files=args.dataset)` as you said.\r\nThen if you need to apply additional processing to map it to a special structure, you can use rename columns or use `dataset.map`. For more information, you can check the documentation here: https:\/\/huggingface.co\/docs\/datasets\/process.html\r\n\r\nAlso feel free to share your `run.py` code so we can take a look","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":59,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n You can load it with `dataset = datasets.load_dataset('json', data_files=args.dataset)` as you said.\r\nThen if you need to apply additional processing to map it to a special structure, you can use rename columns or use `dataset.map`. For more information, you can check the documentation here: https:\/\/huggingface.co\/docs\/datasets\/process.html\r\n\r\nAlso feel free to share your `run.py` code so we can take a look","embeddings":[0.0703725219,-0.398591876,0.0252656285,0.4247377217,0.493349582,0.1074074805,0.049788259,0.4321736991,0.1036680862,-0.0652426928,-0.1320441365,0.3324907124,-0.0747635067,0.4676974714,-0.1136085689,-0.172107324,0.0741177499,0.0452482887,-0.2256744355,0.1473304927,-0.2754347622,0.316351831,0.0427873731,0.0132886823,-0.1858691573,-0.1375377923,-0.0373367965,0.2066912204,-0.3033361733,-0.4769499004,0.1730830818,-0.0827129409,0.2531701624,0.4677781761,-0.0001097229,0.057810206,0.1936288625,-0.182449609,-0.3609408438,-0.3822120428,-0.0739113763,-0.3502304554,0.3194873035,-0.1232311577,-0.1459575593,-0.1347859651,-0.1264757663,-0.0717577487,0.8670130968,0.2895652652,0.2806138098,0.3998851776,0.2383017689,-0.1640922129,0.0306960102,0.2805213034,-0.0770492852,0.5343003869,0.0823050588,0.1997465044,0.1642785817,0.1720381826,0.0796992332,-0.0098202247,0.3793884814,-0.024660591,-0.072572045,-0.3107660711,-0.1735550761,0.1293851733,0.3077872694,-0.2608633339,-0.5859309435,-0.4616162479,0.162549898,-0.3640613854,0.2680619359,0.2270878255,-0.008061341,-0.0377450213,-0.2957679927,-0.1404730976,-0.0817792118,0.0074223937,0.2751148939,-0.1715906858,-0.3041860163,0.0575622283,0.2095820308,-0.2520677745,-0.1877584904,0.1086133718,-0.1658951491,0.2826682031,-0.2700918913,-0.220571667,-0.172642827,-0.1785252094,0.0938299,0.1520285904,-0.0410982706,-0.1395681798,0.0223735441,0.0986385718,0.2394901067,0.3708792031,-0.008009308,0.0659445673,0.0075086704,0.4621219039,0.0178015009,-0.1932155639,-0.0753492862,-0.1608519852,-0.2629657984,-0.0912579149,0.0314072035,-0.1571424603,-0.1212543026,0.0525373518,-0.1963319927,-0.1172300503,0.0026977013,0.3732106984,-0.1924462169,0.0230272189,0.1408441812,0.3320847154,-0.1323201805,0.0303621478,-0.1105097979,0.0151418345,-0.1938459575,0.1033456028,0.1868413091,-0.1240080073,0.3627928495,0.0531600527,0.0111324964,-0.273945272,0.0649162233,-0.0123838019,-0.1874191612,0.2228441983,0.0853398368,0.0578292757,0.1612572819,-0.100003235,-0.1528233141,-0.1835716665,-0.3468767405,-0.2931381464,-0.0567968041,0.2192596942,-0.1986156702,0.0930022821,-0.5223016739,0.0227599796,-0.0002300822,-0.045959834,0.0232618842,-0.0975178778,-0.0395165123,-0.2022597939,0.3352073133,0.2626441121,-0.109416917,-0.2541512251,0.2200585753,-0.2962451875,-0.0611259416,0.3544145823,-0.0066234958,0.2606216371,-0.3168215454,-0.0324465781,0.0182415601,-0.2816700041,-0.0390796438,0.1208285987,-0.0786177069,0.1466306299,0.0105941901,-0.1567107737,0.0040436313,0.0603400245,0.192828849,0.1340052038,0.1056908816,0.0673350543,-0.2559539378,-0.3624229431,-0.1532948315,0.1213544905,-0.2730081081,0.2050553262,-0.0703261718,-0.094520323,0.1474420577,-0.2143447548,0.0708319247,0.3668997288,0.1404762864,0.1957300454,-0.0381482653,-0.0275046509,-0.6883352399,0.178634122,-0.1644841433,0.0874573439,-0.0424025804,-0.1518633068,-0.3249443471,-0.0952048972,-0.035874024,-0.143960923,0.1286502033,-0.0567194521,0.4008741379,0.1912907213,-0.2470028549,0.2000563741,-0.0704793409,0.3918793797,-0.4898602962,0.1099924594,-0.0237060785,-0.1076391563,0.2443419695,0.155842036,0.0273247231,-0.205937013,-0.090720892,0.2640830278,-0.0158812422,0.2190214694,-0.038067393,0.0164426658,0.0505055413,0.2449369133,-0.1663505137,0.0345552154,0.1137270033,-0.0455767028,-0.0712484121,0.5071192384,-0.1718536317,0.1630579084,0.0932126865,-0.0799175501,0.1827468127,0.0708496571,-0.1407684237,-0.2390193045,0.068965666,0.1030496582,0.1264151782,-0.0056608892,-0.3391359448,-0.3482997417,0.5498185158,-0.0310207829,-0.1417656541,0.4043051004,-0.071873121,0.1478957534,-0.0706516132,0.2480638176,0.3892067075,0.0948705897,-0.2737033367,0.2339655906,0.072496295,-0.1814327091,0.067800492,-0.0136297429,0.3392950892,0.1566801965,0.2276384681,-0.0488615185,-0.3238662183,0.1023098826,-0.1196427792,0.2325891256,-0.4538967311,-0.0564422496,-0.1094008833,-0.1673053056,-0.2384123355,-0.0456554592,-0.4120349586,-0.1298429221,-0.0302202292,0.244380787,-0.1469565928,0.0303666741,0.1395320445,0.2253275216,-0.0204017665,0.0702984408,-0.205742687,0.0112300627,-0.1855909824,0.0430763066,0.1673339456,-0.2190204412,0.3210679293,-0.4617026448,-0.0546444803,-0.0692963824,-0.1930748969,0.1542295516,-0.2678242326,0.4431122541,0.1877367496,0.3884458542,-0.1918159723,-0.1760345548,0.4210588038,0.0133259213,-0.3891232312,-0.0761326253,0.0368977636,0.198544085,-0.233791247,-0.4123493433,-0.1441267878,-0.3916701078,0.7527720928,0.0604848601,0.0834000781,0.1221925989,0.1933636665,0.1957612634,-0.3832631707,0.2597774565,-0.230611816,-0.6465620995,0.2809687853,-0.3070642948,-0.3586708009,0.0714103505,0.0961655974,0.2726450861,-0.3408721089,-0.491491884,-0.402502805,-0.0046539055,-0.062476635,-0.0595573075,0.1217768192,0.1352270544,-0.0624094568,-0.0427170582,-0.0431684256,-0.2507206202,0.0856824517,0.0805793405,0.2080333531,-0.0569236875,0.7160338759,-0.1528277844,0.557739377,0.4198946357,-0.1067894846,0.3364962637,-0.1727116257,0.2940631509,-0.2248264402,-0.1734181195,0.0171202105,-0.22102852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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"```\r\n# Dataset selection\r\n    if args.dataset.endswith('.json') or args.dataset.endswith('.jsonl'):\r\n        dataset_id = None\r\n        # Load from local json\/jsonl file\r\n        dataset = datasets.load_dataset('json', data_files=args.dataset)\r\n        # By default, the \"json\" dataset loader places all examples in the train split,\r\n        # so if we want to use a jsonl file for evaluation we need to get the \"train\" split\r\n        # from the loaded dataset\r\n        eval_split = 'train'\r\n    else:\r\n        default_datasets = {'qa': ('squad',), 'nli': ('snli',)}\r\n        dataset_id = tuple(args.dataset.split(':')) if args.dataset is not None else \\\r\n            default_datasets[args.task]\r\n        # MNLI has two validation splits (one with matched domains and one with mismatched domains). Most datasets just have one \"validation\" split\r\n        eval_split = 'validation_matched' if dataset_id == ('glue', 'mnli') else 'validation'\r\n        # Load the raw data\r\n        dataset = datasets.load_dataset(*dataset_id)\r\n```\r\n\r\nI want to load JSON squad dataset instead `dataset = datasets.load_dataset('squad')` to retrain the model. \r\n","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":136,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n ```\r\n# Dataset selection\r\n    if args.dataset.endswith('.json') or args.dataset.endswith('.jsonl'):\r\n        dataset_id = None\r\n        # Load from local json\/jsonl file\r\n        dataset = datasets.load_dataset('json', data_files=args.dataset)\r\n        # By default, the \"json\" dataset loader places all examples in the train split,\r\n        # so if we want to use a jsonl file for evaluation we need to get the \"train\" split\r\n        # from the loaded dataset\r\n        eval_split = 'train'\r\n    else:\r\n        default_datasets = {'qa': ('squad',), 'nli': ('snli',)}\r\n        dataset_id = tuple(args.dataset.split(':')) if args.dataset is not None else \\\r\n            default_datasets[args.task]\r\n        # MNLI has two validation splits (one with matched domains and one with mismatched domains). Most datasets just have one \"validation\" split\r\n        eval_split = 'validation_matched' if dataset_id == ('glue', 'mnli') else 'validation'\r\n        # Load the raw data\r\n        dataset = datasets.load_dataset(*dataset_id)\r\n```\r\n\r\nI want to load JSON squad dataset instead `dataset = datasets.load_dataset('squad')` to retrain the model. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"If your JSON has the same format as the SQuAD dataset, then you need to pass `field=\"data\"` to `load_dataset`, since the SQuAD format is one big JSON object in which the \"data\" field contains the list of questions and answers.\r\n```python\r\ndataset = datasets.load_dataset('json', data_files=args.dataset, field=\"data\")\r\n```\r\n\r\nLet me know if that helps :)\r\n\r\n","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":54,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n If your JSON has the same format as the SQuAD dataset, then you need to pass `field=\"data\"` to `load_dataset`, since the SQuAD format is one big JSON object in which the \"data\" field contains the list of questions and answers.\r\n```python\r\ndataset = datasets.load_dataset('json', data_files=args.dataset, field=\"data\")\r\n```\r\n\r\nLet me know if that helps :)\r\n\r\n","embeddings":[0.1509881914,-0.4040390849,0.0093087181,0.4606097937,0.5046645999,0.1076416671,0.1017882302,0.3800491095,0.0705014765,-0.0852148533,-0.0908838287,0.3143799603,-0.070169583,0.2513688207,-0.1658853143,-0.0779564232,0.0888327956,0.0130546885,-0.096167095,0.0574559532,-0.2108666897,0.3167820275,-0.0635553524,0.0575321727,-0.1894323975,-0.0899974182,0.0082351798,0.2288092971,-0.3139976859,-0.4765470922,0.2592283487,-0.143477574,0.2918536663,0.4062542319,-0.0001137175,0.0604161806,0.2810267508,-0.1821543723,-0.3842888176,-0.4302691519,-0.0858107135,-0.3285008371,0.4134623408,-0.0705338195,-0.102333717,-0.0738322064,-0.0984490216,-0.1354839951,0.9241043925,0.2639935017,0.2411632538,0.3660384715,0.1523807645,-0.1536723673,0.108902514,0.3462015092,-0.0603212789,0.5437940359,0.1788774133,0.1844476163,0.1970572472,0.1332710981,0.079227984,0.0119433692,0.3227821887,-0.0194672178,-0.0851571038,-0.3503003716,-0.2069041133,0.0802498162,0.3580498993,-0.2085027397,-0.5740363002,-0.4564509094,0.2037748694,-0.2330471277,0.3337394893,0.2167313546,-0.0390522182,-0.0063427971,-0.288998127,-0.1617595404,-0.1154531389,0.0545037687,0.3631143868,-0.0658698231,-0.2739937603,0.0649829581,0.1216587722,-0.2355513126,-0.2081924379,0.1158925518,-0.2066937238,0.3120721579,-0.2958756983,-0.2271510512,-0.1802414209,-0.3376178443,0.1299242079,0.2405913472,0.0149318939,-0.1965969801,0.0483104251,0.1080850288,0.4441877007,0.2970483601,0.0925309807,0.033890035,0.0369193666,0.4481206238,-0.0406341366,-0.1473990083,-0.1095407158,-0.1277868599,-0.1701505035,-0.0653031245,0.1303459257,-0.2256530076,-0.110364899,0.1687687635,-0.3327824473,-0.1991237998,-0.0055741374,0.3101699948,-0.2428603768,0.1404439211,0.136500448,0.3748108149,-0.1173205823,-0.0520130992,-0.0323099494,-0.0336034447,-0.1304422617,0.0716612786,0.2569180429,-0.0843585804,0.371645093,0.0733495653,0.1715624332,-0.4003707469,0.0115790265,-0.0490624011,-0.2556216121,0.1656665802,0.1443752199,0.0673416033,0.1018362194,-0.187698245,-0.1008505374,-0.0805240646,-0.362241298,-0.1897529662,-0.0696829185,0.1900392026,-0.1645857543,0.0173222981,-0.5817548037,0.0484974086,-0.020534968,-0.0679495037,0.1141839847,-0.1037723944,0.0274044406,-0.170732215,0.2817676663,0.3137200177,-0.1730689853,-0.315551728,0.278293401,-0.2604702711,-0.1300697029,0.4196869731,-0.0348122418,0.2817523181,-0.3295408487,-0.0256246105,0.1017980501,-0.3967046738,-0.0569440462,0.132817924,-0.0231278501,0.1753526032,0.0101531046,-0.1822166443,0.0111483606,0.1203568876,0.2536342144,0.2390412241,0.0537195392,0.0392456874,-0.3092142045,-0.3914029598,-0.195372209,0.2199299484,-0.2394935638,0.1532950252,-0.000697176,-0.0329473056,0.1821903735,-0.1564651281,0.0100044925,0.4263332784,0.1795449555,0.1940692812,-0.0503817387,-0.0042969515,-0.777326107,0.1245454103,-0.2235742211,0.0473219305,0.0327784456,-0.118803747,-0.2765946686,-0.1035673395,-0.0736735389,-0.0516598783,0.0903171524,-0.1092830151,0.3410097957,0.2877999544,-0.2390745133,0.1482574791,-0.083642453,0.4305508435,-0.5436589122,0.1818384975,-0.0285951924,-0.1262468696,0.2170761675,0.1718532145,0.0400868542,-0.1421134621,-0.1317711771,0.2209819406,0.0142018395,0.1882125139,0.0597626753,-0.0864123031,0.1529355645,0.1528063267,-0.2017223537,0.1650891602,0.1124667004,-0.0377750881,-0.0682961792,0.4642004967,-0.1274470985,0.1565675884,0.0093060695,-0.0555117987,0.1739489287,0.0896922797,-0.1511453688,-0.1389354616,0.162134245,0.1562861651,0.0686652586,-0.074266687,-0.2915640473,-0.403565377,0.5127906799,-0.0609379075,-0.0712404847,0.4394813478,-0.0242186598,0.1174090654,-0.1215933114,0.1456150413,0.4512653351,0.0260075368,-0.3486897945,0.1664756984,0.1350358725,-0.1467080563,0.0859859809,-0.005727035,0.4057777226,0.2141223997,0.258757025,-0.0067114779,-0.2772929072,0.0567463227,-0.1485979259,0.210983336,-0.4982673228,-0.0135876145,-0.0764117837,-0.1151042059,-0.1543153673,0.0320192166,-0.4799400568,-0.1024070531,-0.0040106378,0.2521391213,-0.0906398818,-0.0571439043,0.1593225896,0.3105989099,0.0443307944,0.0420960933,-0.1780399829,0.0037280503,-0.2537293732,-0.0164598972,0.1856947988,-0.3560531437,0.2357640117,-0.4365236461,-0.0895676017,-0.0325218141,-0.1421381682,0.1426030397,-0.1812764108,0.5109776855,0.1030100062,0.3385249972,-0.3648150265,-0.1404363066,0.4575711787,0.0080970172,-0.3988079131,-0.110497877,0.0646907911,0.2117900997,-0.2115662098,-0.4209469259,-0.2111952156,-0.3019480407,0.6834777594,0.1388973296,-0.0035204717,0.1959460527,0.1852080375,0.1516629308,-0.317052573,0.2268948108,-0.2718642056,-0.6679097414,0.2737722397,-0.2587821782,-0.3849787116,0.0282571539,0.1564607471,0.1358895898,-0.4363429844,-0.5586740971,-0.5158256292,0.0678529516,-0.0710685104,-0.2259964347,0.1294158697,0.0522618629,-0.1508230716,0.0223630946,-0.0461569317,-0.3275391459,0.0922733322,0.0741760433,0.2326544374,-0.1012455821,0.6246882677,-0.1615374088,0.6484114528,0.3887170851,0.0148317814,0.3194823861,-0.0399644524,0.2468175888,-0.1572778821,-0.175711289,0.083198674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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"Yes,  code works. but the format is not as expected.\r\n```\r\ndataset = datasets.load_dataset('json', data_files=args.dataset, field=\"data\")\r\n```\r\n```\r\npython3 run.py --do_train --task qa --dataset squad --output_dir .\/re_trained_model\/\r\n```\r\n************ train_dataset: Dataset({\r\n    features: ['id', 'title', 'context', 'question', 'answers'],\r\n    num_rows: 87599\r\n})\r\n\r\n\r\n```\r\npython3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/\r\n```\r\n************ train_dataset: Dataset({\r\n    features: ['title', 'paragraphs'],\r\n    num_rows: 442\r\n})\r\n\r\nI want the JSON to have the same format as before features. https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad_v2\/squad_v2.py is the script dealing with **squad** but how can I apply it by using JSON? ","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":88,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n Yes,  code works. but the format is not as expected.\r\n```\r\ndataset = datasets.load_dataset('json', data_files=args.dataset, field=\"data\")\r\n```\r\n```\r\npython3 run.py --do_train --task qa --dataset squad --output_dir .\/re_trained_model\/\r\n```\r\n************ train_dataset: Dataset({\r\n    features: ['id', 'title', 'context', 'question', 'answers'],\r\n    num_rows: 87599\r\n})\r\n\r\n\r\n```\r\npython3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/\r\n```\r\n************ train_dataset: Dataset({\r\n    features: ['title', 'paragraphs'],\r\n    num_rows: 442\r\n})\r\n\r\nI want the JSON to have the same format as before features. https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad_v2\/squad_v2.py is the script dealing with **squad** but how can I apply it by using JSON? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"Ok I see, you have the paragraphs so you just need to process them to extract the questions and answers. I think you can process the SQuAD-like data this way:\r\n```python\r\ndef process_squad(articles):\r\n    out = {\r\n        \"title\": [],\r\n        \"context\": [],\r\n        \"question\": [],\r\n        \"id\": [],\r\n        \"answers\": [],\r\n    }\r\n    for title, paragraphs in zip(articles[\"title\"], articles[\"paragraphs\"]):\r\n        for paragraph in paragraphs:\r\n            for qa in paragraph[\"qas\"]:\r\n                out[\"title\"].append(title)\r\n                out[\"context\"].append(paragraph[\"context\"])\r\n                out[\"question\"].append(qa[\"question\"])\r\n                out[\"id\"].append(qa[\"id\"])\r\n                out[\"answers\"].append({\r\n                    \"answer_start\": [answer[\"answer_start\"] for answer in qa[\"answers\"]],\r\n                    \"text\": [answer[\"text\"] for answer in qa[\"answers\"]],\r\n                })\r\n    return out\r\n\r\ndataset = dataset.map(process_squad, batched=True, remove_columns=[\"paragraphs\"])\r\n```\r\n\r\nI adapted the code from [squad.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/squad.py). The code takes as input a batch of articles (title + paragraphs) and gets all the questions and answers from the JSON structure.\r\n\r\nThe output is a dataset with `features: ['answers', 'context', 'id', 'question', 'title']`\r\n\r\nLet me know if that helps !\r\n","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":135,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n Ok I see, you have the paragraphs so you just need to process them to extract the questions and answers. I think you can process the SQuAD-like data this way:\r\n```python\r\ndef process_squad(articles):\r\n    out = {\r\n        \"title\": [],\r\n        \"context\": [],\r\n        \"question\": [],\r\n        \"id\": [],\r\n        \"answers\": [],\r\n    }\r\n    for title, paragraphs in zip(articles[\"title\"], articles[\"paragraphs\"]):\r\n        for paragraph in paragraphs:\r\n            for qa in paragraph[\"qas\"]:\r\n                out[\"title\"].append(title)\r\n                out[\"context\"].append(paragraph[\"context\"])\r\n                out[\"question\"].append(qa[\"question\"])\r\n                out[\"id\"].append(qa[\"id\"])\r\n                out[\"answers\"].append({\r\n                    \"answer_start\": [answer[\"answer_start\"] for answer in qa[\"answers\"]],\r\n                    \"text\": [answer[\"text\"] for answer in qa[\"answers\"]],\r\n                })\r\n    return out\r\n\r\ndataset = dataset.map(process_squad, batched=True, remove_columns=[\"paragraphs\"])\r\n```\r\n\r\nI adapted the code from [squad.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/squad.py). The code takes as input a batch of articles (title + paragraphs) and gets all the questions and answers from the JSON structure.\r\n\r\nThe output is a dataset with `features: ['answers', 'context', 'id', 'question', 'title']`\r\n\r\nLet me know if that helps !\r\n","embeddings":[0.2765028179,-0.3245534897,-0.0041766982,0.5035706758,0.4913226366,0.1213675812,0.1237286255,0.4133350253,-0.0751964301,-0.0259796828,-0.1301127821,0.3398238122,-0.0528951548,0.1487219036,-0.1727156639,-0.0546033494,0.101658076,-0.0482197069,-0.0415604636,0.0072898585,-0.2067223936,0.3235585988,-0.1091228351,0.0373797007,-0.2039662898,-0.1171853691,-0.0542484708,0.2437566966,-0.3019738197,-0.4565351307,0.1005720273,-0.0376291126,0.1664628386,0.4242761433,-0.000115848,0.023682829,0.1646315604,-0.2300631404,-0.3505630493,-0.3331951499,0.1268617511,-0.3761894107,0.4304664731,-0.2027411014,-0.1258757412,-0.1786093861,-0.0961151347,-0.1968050599,1.000897646,0.3400824964,0.2102464288,0.3470053077,0.1380794346,-0.0819271579,0.0969260782,0.254193157,-0.09332975,0.3230964243,0.2164286077,0.1647640616,0.1258269548,0.1750494242,-0.0443547592,0.0169819761,0.2572393417,0.0277701449,-0.0930708498,-0.3937787116,-0.1838332266,-0.0116599584,0.3137215972,-0.1443777084,-0.5413429141,-0.5017172694,0.173272565,-0.1106097773,0.2216003984,0.2464290708,-0.1395855695,0.0191115607,-0.3186024725,-0.1635534763,-0.1161368266,0.020361485,0.376231879,-0.0576741025,-0.2415270805,0.058829166,0.1568212062,-0.1417900771,-0.110510163,0.0353544839,-0.0728631318,0.3505918682,-0.2951955497,-0.271931231,-0.119678013,-0.3717408776,0.101887688,0.2420255691,0.165128991,-0.1785656959,0.0886187553,0.1241169348,0.4416555464,0.2034078091,0.1819427609,0.024594374,0.0192099009,0.439080447,-0.0542681143,-0.1794512719,-0.1601683944,-0.0393956788,-0.1938012093,-0.0268860981,0.1438042819,-0.2174710482,-0.1716743857,0.2964203358,-0.3927240968,-0.1637426317,-0.0833180547,0.2997610569,-0.2172032744,0.2244902849,0.1771828979,0.3254442215,-0.1757524312,-0.0753757954,-0.0627598166,0.0800185278,-0.1160259768,0.1105471402,0.3034598231,-0.0264823567,0.3090910912,0.0536966063,0.1259757876,-0.4635832608,-0.0287311338,-0.1711115241,-0.1550347358,0.0838222578,0.2033518404,0.0537917167,0.1703595519,-0.2115063667,-0.1047287509,-0.0145347901,-0.3091168404,-0.1507847756,0.0517889075,0.1621660143,-0.2003722489,0.0260235537,-0.5260037184,0.1792662144,0.0211688746,-0.0483966731,0.0394392349,-0.0462985039,-0.0367953591,-0.1936504394,0.2110569477,0.3823004365,-0.2816331685,-0.2164605558,0.2524059713,-0.2160268575,-0.1254362613,0.4440964162,0.0330643728,0.3277865946,-0.2648509145,0.0872756392,0.0183766447,-0.4131516814,-0.0350490585,0.1448048204,-0.0338211209,0.2014669329,0.0676671043,-0.1952917427,0.0580909178,0.1379051507,0.2548840642,0.2368625402,0.0818797275,0.0482616313,-0.2766848803,-0.3185343444,-0.2028414458,0.2648012042,-0.1877454519,0.1534411162,-0.0259627812,-0.0539906882,0.2166587561,-0.1516254842,0.0617932193,0.4533103704,0.1685115695,0.2387798429,-0.0217915308,-0.0480427593,-0.7455335855,0.0684665591,-0.0689239278,0.1211678833,-0.0239553154,-0.1425931752,-0.2511226833,-0.1089350432,-0.0805367827,-0.1743042618,0.1121965572,-0.1363128275,0.2241228074,0.2155306637,-0.1689866483,0.196333006,-0.0395984352,0.4472266138,-0.5573827028,0.2424890697,-0.0983350053,-0.1988380998,0.2147200555,0.2011444867,-0.03413295,-0.1126389652,-0.0747613311,0.1906284094,0.0068868394,0.213962391,0.1331874132,-0.1052476764,0.0838830769,0.1024441347,-0.1973106712,0.1743390113,0.174431771,0.0047704186,-0.1193331853,0.5076290965,-0.1010067314,0.1887680143,0.0628635213,-0.0756585747,0.1690589339,0.0063971109,-0.1002161652,-0.1184989661,0.1551008373,0.2553539872,0.0458386987,-0.0835980847,-0.3061404228,-0.2918842435,0.5586369038,-0.0590064935,-0.0678212643,0.36985147,0.0355377235,0.1182520837,-0.104399845,0.0434553027,0.4589352608,0.0911525488,-0.3391879201,0.1972616613,0.1484523863,-0.1437976807,0.102415897,0.0957410559,0.416686058,0.3183396161,0.3000156879,0.0597621724,-0.3067358136,0.1420869678,-0.2083359212,0.2186394632,-0.4925419688,0.0445043445,-0.1151591912,-0.1068992689,-0.2027998865,0.1134910136,-0.38274014,-0.1413226873,0.1015919819,0.1646257192,-0.1746531725,-0.0745295286,0.1970209777,0.3646427393,0.0864869505,0.1021096557,-0.1532651335,-0.0959886611,-0.1803574115,-0.0108701335,0.2143462598,-0.2645216286,0.1915950775,-0.386600107,-0.1071972921,-0.0969468504,-0.0862775967,0.2008049935,-0.2895598412,0.5592091084,-0.0038714898,0.3736162484,-0.2889699042,-0.2385130674,0.4713871181,-0.1099323854,-0.4625406861,-0.056993477,0.1825454831,0.123187989,-0.2012751848,-0.5368167162,-0.2449886352,-0.3387766182,0.6739483476,0.1798685193,0.0916329622,0.1191709712,0.1584135294,0.1084538251,-0.2553287148,0.2411592156,-0.2630098462,-0.5265831351,0.264023155,-0.2365655601,-0.4236216545,-0.0311488342,0.1406435966,0.051655259,-0.4131502211,-0.5645362735,-0.4872902036,0.1527820081,-0.133413434,-0.2397793084,0.1840979755,0.1705302298,-0.1347413361,-0.0131087303,-0.0866544023,-0.3071376383,0.1541531831,0.0357811414,0.1699274033,-0.1635740399,0.6400697231,-0.0140814558,0.6470273733,0.4268475473,0.1050798073,0.3005036116,0.0220353194,0.1812216789,-0.1015106216,-0.2334778458,0.1251755655,-0.31261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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"Yes, this works. But how to get the training output during training the squad by **Trainer** \r\nfor example https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/trainer_qa.py \r\nI want the training inputs, labels, outputs for every epoch and step to produce the training dynamic graph","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":37,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n Yes, this works. But how to get the training output during training the squad by **Trainer** \r\nfor example https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/trainer_qa.py \r\nI want the training inputs, labels, outputs for every epoch and step to produce the training dynamic graph","embeddings":[0.1636870503,-0.5511727333,0.0138231916,0.5805276632,0.5453875661,0.0155080343,0.1837681383,0.3460627496,-0.1402782202,-0.0444165058,-0.1037513837,0.4005067945,-0.0872829482,0.3356798887,-0.0048746532,-0.1373547316,-0.0322655663,0.0883196592,-0.2120641768,-0.0435362309,-0.0784141049,0.3408171833,-0.0657849535,0.2159004509,-0.3866486847,-0.074901782,0.0160652772,0.0193888005,-0.2331287414,-0.4273637235,0.1822326332,-0.1904924512,0.2277104855,0.3537916243,-0.0001214562,0.1815780997,0.1696080863,-0.2737134695,-0.4236272573,-0.3394161761,0.0149789313,-0.2824373245,0.4665519595,-0.1831331104,-0.1698022783,-0.1088880971,-0.0712159947,-0.1378267258,0.9953346252,0.246719107,0.1447521448,0.4332351983,-0.1022329926,-0.0538499206,-0.0200035293,0.4325115979,-0.0042137071,0.4011106789,0.2766084969,0.1857433319,0.092122443,0.1577474028,0.20493716,-0.0969196856,0.2966972589,0.0850413144,-0.0399078429,-0.2561959922,-0.0860436931,0.0621356443,0.3645497262,-0.1956049353,-0.617922008,-0.511834681,0.2931277156,-0.233016327,0.2232115269,0.2963487506,-0.1467442065,-0.0116617791,-0.4289112389,-0.055182904,-0.2553489208,-0.0105776172,0.395170182,-0.1006111503,-0.2855523527,0.0074419202,0.0503955223,0.0152527662,-0.1069205254,0.2221351266,-0.1559015661,0.3383010328,-0.239580974,-0.2506221831,-0.2232426107,-0.4823240936,0.0316428505,0.1609170884,0.1620073467,-0.209685117,0.0228484012,0.1549598426,0.3680728078,0.3324814141,0.1612847447,0.1982011199,-0.0182246529,0.4383017123,-0.1205411926,-0.1782140881,-0.1792849898,0.0185289606,-0.0369250961,0.0477470607,-0.0190944131,-0.1795270443,-0.0774950534,0.0609941185,-0.4027308524,-0.2068971992,0.0330593064,0.3342287838,-0.277715832,0.1090009511,0.19622311,0.2586772442,-0.0926334187,-0.1126774177,-0.0327680856,0.041943267,-0.2768532038,0.1217758879,0.2523882091,-0.0295948312,0.413005352,-0.0032395273,0.1995089501,-0.5130035877,0.0108767087,-0.0379751995,0.0250344612,0.1041550338,0.065761596,-0.1694674045,0.0324691683,-0.1195273101,-0.1300617605,-0.0932940841,-0.2749902308,-0.2965886593,-0.0377994999,0.10967879,-0.1234785616,0.1525318772,-0.5942756534,0.0849405825,-0.0305688344,-0.027647512,0.1082996055,-0.0998898149,0.0504887477,-0.1010691077,0.3797979653,0.3399546444,-0.2790542841,-0.5189014077,0.486404568,-0.2972993851,-0.1337172836,0.3298202455,-0.1263598651,0.457165122,-0.3179572225,-0.0723781809,0.2749302983,-0.4058860838,0.0231481902,0.1057100445,-0.0259380173,0.081194371,0.2001484931,-0.1397449821,0.1450316757,0.2414658964,0.2133336216,0.2179438621,0.0645201206,-0.0375544354,-0.104074277,-0.3301292658,-0.2853244841,0.2621779442,-0.1091356725,0.220884189,0.0041820845,0.0738994181,0.2792874575,-0.1829613596,0.0873229057,0.3323085606,-0.0028612332,0.218458876,-0.0164189618,0.0409829989,-0.7167807817,0.1077469364,-0.3189305067,-0.013390895,-0.06206524,-0.178390801,-0.077495262,-0.0896683708,-0.1359357834,-0.19507882,0.012871298,0.0002347632,0.4143766761,0.1440632641,-0.2236944437,0.2790149748,-0.2901728153,0.2560231984,-0.6140162349,0.2614722252,0.0539313406,-0.1541178226,0.1246306002,0.2394158542,-0.0194178149,-0.1818140298,-0.1298753768,0.1217082366,0.0132023497,0.2702406049,0.0339036509,-0.0618833043,0.1169374436,0.1872710139,-0.159676671,-0.0039735842,0.023343619,-0.073323153,0.0556844547,0.4446072578,-0.0728012472,0.183798939,0.1037344038,-0.1126605645,0.0906702131,0.1267679334,-0.3286932707,-0.0811034665,0.1002114937,0.158680588,0.1836833805,-0.170450151,-0.4569683671,-0.3868115246,0.6421719193,-0.161944285,-0.1573622376,0.3460471928,-0.0100870049,0.1197533682,-0.0686174631,0.1186641455,0.3632771969,0.0395905562,-0.2718096673,0.1451485008,0.1324161887,-0.1334781498,0.1245178357,0.0942167789,0.4557117224,0.2681281567,0.1112348959,0.0002301696,-0.228317216,-0.0366318934,-0.1264467537,0.1129832715,-0.4793060124,0.1654187888,-0.0006630208,-0.1219562814,-0.2450583428,0.0580629371,-0.1922247261,-0.065972276,-0.0722729936,0.1515493095,-0.0727946237,-0.0135539593,0.181331411,0.3344590068,0.0216826256,0.0280091092,-0.1873615086,-0.0256395731,-0.2674269676,-0.0728775188,0.0704697147,-0.350069046,0.1615641415,-0.4621557891,-0.1528127342,-0.0528874807,-0.0716813207,0.2898319662,-0.1096307561,0.3271858394,0.1288937628,0.251594007,-0.3873902261,-0.1634925902,0.3807101846,-0.0979527161,-0.5009236336,-0.1924830675,0.0161119383,0.2765606344,-0.1665629447,-0.40259552,-0.4009650648,-0.2580572665,0.7676854134,0.0252743047,0.0870430097,0.3073763251,0.1251283586,0.2890155315,-0.3125262558,0.3152787983,-0.2609527707,-0.7380498648,0.2494656891,-0.2390489727,-0.2945712805,0.0000297499,0.0465879925,0.2449500412,-0.3088509142,-0.6073960662,-0.569760263,0.2936202884,-0.0875559747,-0.3498703837,0.1662604511,0.1067950949,-0.2254952043,0.0926647633,0.0268217437,-0.1911950111,0.1900504082,0.0409537591,0.0953689814,-0.1435952336,0.565541923,-0.0583003461,0.7821050882,0.3943069577,-0.1291201264,0.0915575698,-0.1416793615,0.2546265721,-0.1605505496,-0.1398887336,-0.0556349941,-0.2983150482,0.0431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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"I think you may need to implement your own Trainer, from the `QuestionAnsweringTrainer` for example.\r\nThis way you can have the flexibility of saving all the inputs\/output used at each step","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":31,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n I think you may need to implement your own Trainer, from the `QuestionAnsweringTrainer` for example.\r\nThis way you can have the flexibility of saving all the inputs\/output used at each step","embeddings":[0.1112691984,-0.3947232962,0.0247267447,0.4891629219,0.5235648751,0.06859117,0.1418222636,0.4170044065,0.0509560779,0.0277665872,-0.0371599756,0.2889543474,-0.2010879219,0.4082464874,-0.1074437797,-0.1509507746,0.0424838401,0.1274575144,-0.2073004991,0.0787482113,-0.1535552293,0.2987521589,0.007998772,0.0157941487,-0.2861711979,-0.1163590029,0.020643184,0.1426507384,-0.2493416369,-0.4953288734,0.2064926773,-0.0817671493,0.2906377017,0.4381710291,-0.0001140089,0.1213441119,0.2173856646,-0.2367406636,-0.4340903759,-0.3380363882,-0.1292427927,-0.292935729,0.4220705032,-0.1002356037,-0.1599605083,-0.0974551216,-0.070611544,-0.0566470213,0.9737880826,0.3138096333,0.2417191267,0.3806009889,0.0472547747,-0.1344993114,0.0463556647,0.339757055,-0.0675403252,0.4890833199,0.1401900202,0.2162853181,0.1372416019,0.1405707151,0.1628670543,-0.0756560341,0.3369670808,-0.0443941951,-0.0372623168,-0.315504998,-0.158041358,0.0994194523,0.3612464666,-0.1237346679,-0.5696437359,-0.5445702076,0.2410784513,-0.2802480757,0.2999517918,0.1510308683,-0.0831008852,-0.0310340319,-0.3698155284,-0.1634981781,-0.1596574336,0.0137224887,0.3107425272,-0.195881635,-0.3178329766,0.0343277305,0.1845927387,-0.1384472996,-0.0885757059,0.154993251,-0.152704522,0.2772503793,-0.2310870737,-0.2488449514,-0.1680766195,-0.3051984012,0.0344780236,0.1920016855,0.0658599809,-0.1832513064,0.030198982,0.0876913741,0.3901366591,0.4024857283,-0.00580956,0.0245385095,0.0436934605,0.475687474,-0.0620944686,-0.1859446317,-0.132134825,-0.102084659,-0.1212420091,-0.0402432829,0.0006113923,-0.2094421685,-0.1407860518,0.125213027,-0.2952102721,-0.1797420084,0.0456118286,0.2827427685,-0.3114418983,0.1617396772,0.1454574019,0.340524137,-0.150945425,-0.0836318582,-0.0851378366,-0.0374473706,-0.2042680979,0.1064239368,0.2197372615,-0.0977961719,0.3945546746,-0.0018307191,0.1681854874,-0.3759332001,0.013161608,-0.0411350168,-0.1607893258,0.1360068172,0.0880338624,-0.0644876137,0.0387782678,-0.1442570239,-0.1159308702,-0.1377571523,-0.355843097,-0.230553031,0.0153523944,0.1844424009,-0.1429661363,0.0989888832,-0.606356442,0.1125724241,-0.0346191563,-0.0240641162,0.0614720173,-0.1473660618,-0.012183045,-0.1890545189,0.337826699,0.2633911371,-0.1507256925,-0.3629552722,0.2698024213,-0.2522869706,-0.1143054217,0.3930954933,-0.1404329538,0.4453256726,-0.3221615851,0.0198063646,0.1262174249,-0.3180969954,-0.0329830833,0.0996754318,-0.0393250622,0.1576235294,0.0374604724,-0.1705541015,0.0875929743,0.0572276413,0.2188617587,0.2307671756,0.0333887711,0.0083977031,-0.2527442575,-0.3746705949,-0.1956221908,0.1944528371,-0.1616234332,0.1630298644,-0.0483216494,-0.0267106835,0.1724979281,-0.2421633601,0.0542713478,0.4215505123,0.099500075,0.1931759566,-0.1181515902,0.0241059158,-0.639385283,0.1503076553,-0.131937623,-0.0262992997,0.0404377095,-0.1306869686,-0.2583486438,-0.1017685533,-0.1022324711,-0.1450546682,0.09521088,-0.0303215608,0.3669010997,0.2741235495,-0.2381111383,0.2908387184,-0.1309528351,0.3355651498,-0.5414313674,0.1578900218,0.0276835766,-0.1312685907,0.2576348484,0.2335923463,-0.0008013918,-0.1693964303,-0.1265757829,0.2199383378,-0.0653502271,0.1718067229,-0.005536004,-0.0469495058,0.0682795197,0.2018437237,-0.2160984576,0.0874310806,0.0818206742,-0.0454029292,-0.0198064838,0.5255049467,-0.0763702169,0.1000805944,0.0927751213,-0.1207757965,0.0670848936,0.0784141347,-0.1685927212,-0.1624522358,0.1375778466,0.116883859,0.0989115015,-0.1006552726,-0.4091237187,-0.4027702808,0.5495021939,-0.0622506924,-0.1097563282,0.4458475113,-0.0054175523,0.1669323891,-0.1080565825,0.211567685,0.4656704068,0.0383254699,-0.2399950325,0.1940265,0.0928833559,-0.1633372456,0.0570117384,-0.0006794676,0.3799537122,0.2334215939,0.2277351767,-0.0459856763,-0.3135321736,0.1105366796,-0.1193028092,0.2023747712,-0.4381201863,0.0814213157,-0.1607338488,-0.1157340631,-0.2392386049,0.0753254294,-0.3798130453,-0.0818894431,0.0178924613,0.2812099457,-0.1103716344,-0.0334956907,0.0980312452,0.2951040268,-0.0325162597,0.0820783749,-0.1111275852,0.0646067932,-0.3308162987,-0.0052523566,0.1982298791,-0.3733587265,0.2822651863,-0.5003091693,-0.0820920095,-0.028585339,-0.1098650768,0.1649475247,-0.1672258824,0.4625253081,0.1636489332,0.3259758949,-0.2975553274,-0.1575575918,0.4046215415,-0.0387069806,-0.4468819797,-0.1739544868,0.1131208837,0.2645466924,-0.2008871138,-0.411442697,-0.1932490617,-0.3115308881,0.740242362,0.0900957361,0.031014679,0.1369763166,0.2474390566,0.1774308681,-0.3284714818,0.2432313412,-0.2326196879,-0.7112438083,0.254041791,-0.2500843704,-0.3740943074,0.0360029601,0.0750103816,0.2772985101,-0.3498449922,-0.6203480363,-0.4395759702,0.1251158267,-0.0509175733,-0.2532443106,0.1953413188,0.1258715391,-0.1450640857,0.0215558745,0.010856282,-0.2534979582,0.105573684,0.0873022601,0.2464202344,-0.1551401168,0.6245081425,-0.1394654214,0.6633348465,0.2958070338,-0.0927939415,0.2997834384,-0.0751258731,0.2868361175,-0.2451336533,-0.1252555102,0.0751409978,-0.2318368256,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"> does there have any function to be overwritten to do this?\r\n\r\nok, I overwrote the compute_loss, thank you.","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":19,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n > does there have any function to be overwritten to do this?\r\n\r\nok, I overwrote the compute_loss, thank you.","embeddings":[0.0815108418,-0.3851129711,-0.0033685451,0.5281998515,0.4719855189,0.0685857832,0.0534329377,0.3249771595,0.1042511314,-0.0913520008,-0.0549961962,0.2146300673,-0.0638783723,0.3335620165,-0.1426371634,-0.1661888957,0.1353711188,0.0852737576,-0.1390957683,0.0327934846,-0.2544144988,0.3445377648,-0.0886775553,0.0139053836,-0.1697126478,-0.1576099843,0.0579531416,0.1895862371,-0.318105489,-0.5252275467,0.2614779472,-0.1253007054,0.275136441,0.4035142362,-0.0001189909,0.1029702052,0.2764685154,-0.2563679516,-0.3061412573,-0.2564256787,-0.1659692377,-0.3197919726,0.36217013,-0.1538837701,-0.0046014418,-0.2374949902,-0.0991263017,-0.1814701408,0.9331385493,0.3268528879,0.1891917288,0.3957656324,0.1144277677,-0.101488851,0.1067221165,0.309815377,-0.0428237021,0.6010365486,0.0418399498,0.1507999301,0.1916322708,0.1488131732,0.1326557845,-0.0158127882,0.3911005259,-0.0250442401,-0.0712352842,-0.2651063204,-0.2939341962,0.110524945,0.3862318099,-0.1940551102,-0.6282964945,-0.4510044456,0.185674116,-0.4028352797,0.3375015557,0.1678339988,-0.0935885981,-0.0623690896,-0.3490314782,-0.1916536391,-0.1163635403,0.0280560236,0.3780636489,-0.1326919347,-0.2840746343,0.045869559,0.2118028253,-0.1245931908,-0.179070428,0.1955013722,-0.2361700982,0.2319700718,-0.2474390566,-0.1852716953,-0.240761131,-0.2753387988,0.0421592817,0.1925025582,0.0940268487,-0.1818367988,-0.0823091343,0.1585895419,0.3452528715,0.4016541541,-0.0060671503,-0.0667783469,0.0953677818,0.5004611015,-0.0045942576,-0.2279393226,-0.0114183929,-0.1728967279,-0.1625816971,-0.0523582175,0.0447968133,-0.2103935778,-0.2214602083,0.0671898276,-0.2621159554,-0.2439329475,-0.0079634069,0.2383313775,-0.2687316239,0.1308762729,0.2595640719,0.3389331698,-0.2051233947,-0.0985279605,-0.0696197972,0.0056667575,-0.1862444282,0.1244389862,0.197019577,0.0095495284,0.3680535257,-0.0101780193,0.131933555,-0.3380235136,0.0026693768,-0.0390202403,-0.1869772375,0.1893362403,-0.0753843412,-0.0686884001,0.1103457659,-0.1576666832,-0.1184557304,-0.1389157325,-0.3716673553,-0.2018668354,-0.0052355058,0.1412645578,-0.1360032111,0.1149919331,-0.664745152,0.0902222022,-0.0484752469,-0.1550230831,0.0744199008,-0.1414824426,-0.0934651121,-0.1760892421,0.3488271534,0.3373160064,-0.2466780245,-0.3991613388,0.2335602343,-0.2829958498,0.0203807969,0.4823089838,-0.0682212785,0.3412544429,-0.3668683171,-0.0523325615,0.150322929,-0.289458096,-0.1069348082,0.0522857122,0.0113378717,0.1207267791,0.0726620927,-0.1980825961,-0.0639796555,0.0795122832,0.2676844001,0.1009567529,0.0686301365,-0.0445024334,-0.3383386433,-0.3744318187,-0.0976619497,0.1635705084,-0.2251802534,0.2285776585,0.0223910119,-0.0699916705,0.2082215995,-0.1502608806,0.0128793456,0.4201382101,0.1454185098,0.0403274633,-0.0470444262,0.0478819013,-0.712630868,0.1205745116,-0.205023393,0.0226247702,0.0416319519,-0.1089427546,-0.2674765289,-0.095523186,0.0063595949,-0.0513184555,0.0416355543,-0.0330027156,0.3359564841,0.3123084009,-0.245406881,0.2139024436,-0.1700245738,0.3526010215,-0.5210603476,0.175812155,0.0569436513,-0.1561980397,0.1790102571,0.1453888416,0.0613232441,-0.1435943395,-0.1849975288,0.2256602794,-0.0623873696,0.2801205516,0.0218439475,0.0008466918,0.1380520016,0.1400418878,-0.1709463894,0.1188804507,0.092126973,-0.0821863636,-0.0376459844,0.4579936266,-0.1763322055,0.0831286237,0.0901158154,-0.140857771,0.1441391408,0.0919496939,-0.1875928193,-0.14227283,0.1741372645,0.120757021,0.0467606299,-0.0530443415,-0.3467595875,-0.4706706405,0.5727598071,-0.0565965511,-0.0813036337,0.4223535359,-0.032758031,0.0782274008,-0.0316268355,0.2191238403,0.5283237696,-0.0227325894,-0.2587023377,0.2275752723,0.1293732226,-0.1822178513,0.0481142662,0.0408616774,0.4017906189,0.2405886501,0.2302875221,-0.0444369726,-0.3656556904,0.092527315,-0.0660875663,0.2045455277,-0.530713439,0.083786644,-0.12270163,-0.1303823441,-0.2359470576,0.1148532704,-0.4097819626,-0.161831677,-0.0387159437,0.2947398424,-0.1924370825,-0.0186648183,0.1006588265,0.3383847177,-0.0263525695,0.0937038139,-0.2746060789,0.0553267896,-0.3184817135,-0.0438077711,0.1332923174,-0.3197987378,0.173798427,-0.5357191563,-0.1540899873,0.0140071856,-0.1190657839,0.1248662248,-0.0569643117,0.5266489983,0.1695620418,0.3168803155,-0.348710686,-0.1526945084,0.4511867166,-0.0272287503,-0.3706933558,-0.1629493386,0.0641991273,0.3350775242,-0.1933667511,-0.4463437796,-0.2355517,-0.3437550962,0.7967392802,0.1176362708,0.0241354872,0.1712927818,0.1302338392,0.0810915008,-0.2753818929,0.2693115771,-0.247235015,-0.773139894,0.2217733711,-0.197582677,-0.3153229058,-0.0018347115,0.1121476367,0.2743991613,-0.2884964645,-0.6243683696,-0.3899911344,0.1217105985,-0.0982479826,-0.2565956712,0.1559383869,0.0720926076,-0.0986560658,0.0304498114,-0.0180851426,-0.2652378678,0.1116953045,0.0686237961,0.2659153938,-0.0159346163,0.6574980617,-0.1699451953,0.6400873065,0.3261754215,-0.0681585521,0.4542732835,-0.0662720352,0.3172608018,-0.1267099977,-0.1092664003,0.0392791219,-0.201726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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3333","title":" load JSON files, get the errors","comments":"Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```","body":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_","comment_length":576,"text":" load JSON files, get the errors \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`\r\n\r\n_Originally posted by @yanllearnn in https:\/\/github.com\/huggingface\/datasets\/issues\/730#issuecomment-981095050_ \n Hi,  I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs\r\n\r\n```\r\n*********************** inputs: {'attention_mask': tensor([[1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        ...,\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0],\r\n        [1, 1, 1,  ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25,  97,  93,  44,  25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2106,  ...,    0,    0,    0],\r\n        ...,\r\n        [ 101, 2339, 2001,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2515,  ...,    0,    0,    0],\r\n        [ 101, 2054, 2003,  ...,    0,    0,    0]], device='cuda:0'), 'start_positions': tensor([ 20,  90,  89,  41,  25,  96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        ...,\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0],\r\n        [0, 0, 0,  ..., 0, 0, 0]], device='cuda:0')} \r\n```\r\n\r\n```\r\n# This function preprocesses a question answering dataset, tokenizing the question and context text\r\n# and finding the right offsets for the answer spans in the tokenized context (to use as labels).\r\n# Adapted from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/question-answering\/run_qa.py\r\ndef prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):\r\n    questions = [q.lstrip() for q in examples[\"question\"]]\r\n    max_seq_length = tokenizer.model_max_length\r\n    # tokenize both questions and the corresponding context\r\n    # if the context length is longer than max_length, we split it to several\r\n    # chunks of max_length\r\n    tokenized_examples = tokenizer(\r\n        questions,\r\n        examples[\"context\"],\r\n        truncation=\"only_second\",\r\n        max_length=max_seq_length,\r\n        stride=min(max_seq_length \/\/ 2, 128),\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\"\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context,\r\n    # we need a map from a feature to its corresponding example.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position\r\n    # in the original context. This will help us compute the start_positions\r\n    # and end_positions to get the final answer string.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        # We will label features not containing the answer the index of the CLS token.\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        # from the feature idx to sample idx\r\n        sample_index = sample_mapping[i]\r\n        # get the answer for a feature\r\n        answers = examples[\"answers\"][sample_index]\r\n\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != 1:\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != 1:\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and\r\n                    offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and \\\r\n                        offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(\r\n                    token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n```","embeddings":[0.0734393597,-0.4256199598,-0.0255494919,0.5651887059,0.4744012356,0.2006402165,0.1329942346,0.3065039814,0.2205163985,0.0062530721,0.0286498182,0.266972661,0.0389591344,0.1274508089,-0.0082311379,-0.111008361,0.0925865248,0.1436216086,-0.0355725884,0.0444258004,-0.222628206,0.3515388668,-0.0905133933,0.0953718498,-0.1538115144,-0.1879830211,0.1928616911,0.0973474681,-0.2844776511,-0.4531756043,0.1854985505,-0.1204211116,0.1869038194,0.3397418559,-0.0001187606,0.1675628275,0.2767539024,-0.2527773678,-0.3382385969,-0.4102113545,-0.1842403859,-0.3959103227,0.3921874762,-0.2321057618,-0.0164642502,-0.091470331,-0.0949386731,-0.2592940629,0.7659286857,0.3204529583,0.1627116352,0.3860197067,0.0789503753,-0.0791747719,0.132180661,0.3757913411,-0.0550886393,0.4611625969,-0.0343110561,0.1833150983,0.239232108,0.2369401604,0.2409222275,-0.050943546,0.3252277672,0.1166691706,-0.0642910525,-0.2380516976,-0.2407401651,-0.0734935179,0.2316922247,-0.2415281087,-0.5960056186,-0.4347801507,0.2232940644,-0.628565073,0.2418408692,0.1741683483,-0.1212806329,0.0066747651,-0.2380976379,-0.0175327417,-0.1596454233,0.1040052623,0.3331530392,-0.1571329683,-0.2528928816,0.0471741073,0.1946138144,-0.0795100033,-0.2403193563,0.2129569799,-0.0740981847,0.2242096961,-0.2987619638,-0.1769327521,-0.1814175695,-0.5325203538,-0.0188633204,0.0768459886,0.177087605,-0.1347454488,-0.0793474093,0.250505656,0.1819874197,0.4582924545,-0.0386061594,-0.0935453698,0.1149275824,0.4940602779,0.0499887317,-0.292378366,-0.0328951962,-0.0665678531,-0.1765023172,0.0034102439,0.0344030745,-0.0937634856,-0.387406975,0.1212292314,-0.2318055332,-0.2967303991,0.1235930473,0.3598923385,-0.1907471716,0.0916771069,0.279718101,0.2974717915,-0.1028389409,-0.0707518086,-0.1148177832,0.0889629796,-0.1894780546,0.1372623295,0.2890866697,0.0291687492,0.3907815218,-0.0597167164,0.1494922638,-0.282687217,0.0416586772,-0.0097008394,-0.0377556793,0.2885141075,-0.0581160039,0.0021447982,0.2393065393,-0.2837919295,-0.1367074102,-0.1845345348,-0.362735033,-0.1417939365,-0.0831723586,0.092573598,-0.068552807,0.124018617,-0.5399175882,0.191012308,-0.1105796099,-0.1242899299,0.1232275441,-0.0056981873,-0.1576618999,-0.1617899388,0.4235161245,0.3245742917,-0.2805080712,-0.4246628284,0.188057214,-0.2004912198,0.0336076096,0.3087864816,-0.0638915002,0.2178486437,-0.3114291131,0.0604704358,0.3733687699,-0.3340502381,-0.146248281,0.0040434836,-0.1107432991,0.0755166709,0.2136193365,-0.1694552451,-0.1256664544,0.2007128447,0.406019628,-0.053680703,0.0734873116,-0.0922683179,-0.2177326083,-0.3475722075,-0.0645837858,0.1500949711,-0.3166697025,0.2475449443,0.0707268789,-0.052721519,0.2370484471,-0.1561662704,0.0238066092,0.3291124403,0.2108857036,0.0295296907,-0.1335942,0.012021306,-0.6663116217,0.1488202661,-0.0559489727,-0.0009939168,-0.099806726,-0.1514708996,-0.2187210321,-0.0801246911,0.0380750261,-0.024443496,0.0414480716,0.0157844331,0.241167441,0.2353658229,-0.2886298597,0.2078384608,-0.1041836366,0.2989165187,-0.5492715836,0.1278223097,0.024835648,-0.1582475901,0.0840444639,0.2374865562,0.1258109957,-0.1494405121,-0.1179857031,0.1566883028,0.0252649356,0.2322098464,-0.000311496,0.0346413441,0.1326908022,0.2516835034,-0.2333735973,0.0421810374,0.0467843525,0.0403080545,-0.0738918185,0.4846026599,-0.1913472414,0.0641470999,0.0935697109,-0.2057142854,0.2349876016,0.1714646965,-0.1424398869,-0.2587037385,0.1169799641,0.2050013095,0.0689831078,-0.1299411058,-0.4739685953,-0.3284455538,0.5703596473,-0.0124351233,-0.1488333791,0.3756956458,-0.0743944347,0.1801794916,0.0327497646,0.1573245823,0.4894227982,0.076194033,-0.2493573576,0.1498815417,0.0920983404,-0.1665639132,0.0879571065,0.0694682524,0.3957992792,0.2393935323,0.2204293907,-0.0630617514,-0.3578233719,0.04617697,-0.1008007452,0.2288715392,-0.5406150222,0.1027878821,-0.1649194807,-0.1895325482,-0.2369483858,0.053788837,-0.1459279209,-0.1558592767,-0.0540861301,0.2857053578,-0.1443484575,-0.0767287016,0.0797315165,0.4088663161,-0.0956559777,0.087185517,-0.4628477395,0.0338214785,-0.1774129421,-0.0359383412,0.0941320956,-0.2826230824,0.2023630738,-0.4245277941,-0.0510493368,0.0838934705,-0.2563055456,0.114542678,-0.1513549834,0.4081276059,0.0721612871,0.3661668599,-0.3820369244,-0.3003065288,0.4887295663,0.0281571951,-0.4268243015,-0.1026192158,0.0249014199,0.2586899102,-0.3081687093,-0.4536309242,-0.2857468426,-0.3502284884,0.8539360762,0.1615090519,0.0780880004,0.1736646891,0.1145954728,0.1828162372,-0.0969464034,0.3941983879,-0.2449298799,-0.8151064515,0.259580642,-0.3140243292,-0.3234488964,0.0767098442,0.0374799855,0.3545620441,-0.3839530945,-0.5797405243,-0.3934489787,0.1687809825,-0.0965950787,-0.202187717,0.2128165662,0.2249819487,-0.0814480931,0.0024337282,-0.0427055322,-0.4610921443,0.0501396693,0.1897545904,0.2239283025,-0.1387491077,0.6910790801,-0.1712830812,0.7668347955,0.3957063258,-0.1553558707,0.4311776161,-0.2050275058,0.2339503318,-0.0759227499,-0.1769961268,-0.0680524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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3331","title":"AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'","comments":"Hi,\r\n\r\nthe fix was merged and will be available in the next release of `datasets`.\r\nIn the meantime, you can use it by installing `datasets` directly from master as follows:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```","body":"## Describe the bug\r\nI add a new question answering dataset to huggingface datasets manually. Here is the link: [luozhouyang\/question-answering-datasets](https:\/\/huggingface.co\/datasets\/luozhouyang\/question-answering-datasets)\r\n\r\nBut when I load the dataset, an error raised: \r\n\r\n```bash\r\nAttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"luozhouyang\/question-answering-datasets\", data_files=[\"dureader_robust.train.json\"])\r\n```\r\n\r\n## Expected results\r\nLoad dataset successfully without any error.\r\n\r\n## Actual results\r\n```bash\r\nTraceback (most recent call last):\r\n  File \"\/mnt\/home\/zhouyang.lzy\/github\/naivenlp\/naivenlp\/tests\/question_answering_tests\/dataset_test.py\", line 89, in test_load_dataset_with_hf\r\n    data_files=[\"dureader_robust.train.json\"],\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1616, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1443, in load_dataset_builder\r\n    path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1157, in dataset_module_factory\r\n    raise e1 from None\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1144, in dataset_module_factory\r\n    download_mode=download_mode,\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 798, in get_module\r\n    raise FileNotFoundError(f\"No data files or dataset script found in {self.path}\")\r\nAttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: linux\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n","comment_length":35,"text":"AttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path' \n ## Describe the bug\r\nI add a new question answering dataset to huggingface datasets manually. Here is the link: [luozhouyang\/question-answering-datasets](https:\/\/huggingface.co\/datasets\/luozhouyang\/question-answering-datasets)\r\n\r\nBut when I load the dataset, an error raised: \r\n\r\n```bash\r\nAttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"luozhouyang\/question-answering-datasets\", data_files=[\"dureader_robust.train.json\"])\r\n```\r\n\r\n## Expected results\r\nLoad dataset successfully without any error.\r\n\r\n## Actual results\r\n```bash\r\nTraceback (most recent call last):\r\n  File \"\/mnt\/home\/zhouyang.lzy\/github\/naivenlp\/naivenlp\/tests\/question_answering_tests\/dataset_test.py\", line 89, in test_load_dataset_with_hf\r\n    data_files=[\"dureader_robust.train.json\"],\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1616, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1443, in load_dataset_builder\r\n    path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1157, in dataset_module_factory\r\n    raise e1 from None\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1144, in dataset_module_factory\r\n    download_mode=download_mode,\r\n  File \"\/mnt\/home\/zhouyang.lzy\/.conda\/envs\/naivenlp\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 798, in get_module\r\n    raise FileNotFoundError(f\"No data files or dataset script found in {self.path}\")\r\nAttributeError: 'CommunityDatasetModuleFactoryWithoutScript' object has no attribute 'path'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: linux\r\n- Python version: 3.6.13\r\n- PyArrow version: 6.0.1\r\n \n Hi,\r\n\r\nthe fix was merged and will be available in the next release of `datasets`.\r\nIn the meantime, you can use it by installing `datasets` directly from master as follows:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```","embeddings":[-0.2787339687,0.0727965534,0.112833567,0.4117526114,0.4317401052,-0.0501598269,0.1598853618,0.0534963086,0.1838313043,0.1735431254,-0.0451259688,0.5018625259,-0.2404234558,-0.0016097334,0.0799399689,-0.0443504825,-0.0731380209,0.0257307086,-0.331546098,-0.1445059478,-0.2158101797,0.3164508343,0.0473862104,0.070617348,-0.2744128406,-0.0099943839,-0.1308162212,0.3300017715,0.1829965264,-0.3933320642,0.4219392836,-0.1911423802,-0.07607916,0.8302136064,-0.0001241489,-0.0581912473,0.3435770869,-0.0268954132,-0.5387427211,-0.4684946239,-0.1969976127,0.1613379419,0.2675485015,-0.0240532793,-0.1951376349,0.1050663367,-0.0505824387,-0.5198591352,0.3432203829,0.3468331397,0.089557454,0.3002501428,-0.0527706258,-0.3051675558,0.037514668,0.6560080051,-0.1784095466,0.4218840897,0.0784151703,-0.0533555634,0.1238526031,0.0478966124,-0.0214636326,0.0967849344,0.2469697744,-0.037489567,-0.0424581729,-0.0216723662,0.2258145809,0.4112513959,0.4895048738,-0.3064202964,-0.4390739202,-0.340195775,0.1119012609,0.0570532568,0.2115943283,-0.0131656509,-0.0775136724,0.1357526034,-0.1250486523,-0.2432110459,-0.1925594211,0.3040333986,0.0901346132,0.0279234573,-0.2569789588,0.1546835899,-0.1775619984,-0.0338035077,-0.0682722628,-0.1177261248,-0.0370071419,0.2089711577,0.1536240876,0.0224299803,0.2688308358,0.2026349902,0.3649176359,0.3900002539,0.0802994668,-0.1274307221,-0.2093174607,-0.0985161811,0.1546527594,0.1029684097,0.1492053866,0.0320043154,0.1339885145,0.3810565472,-0.2272236496,-0.1392212808,-0.1970356256,0.0203951448,-0.0878249332,-0.1841398478,0.6454577446,-0.1766877919,-0.2396270484,-0.0721829981,0.0869949609,-0.0129657453,0.1286661327,0.5554531217,0.0107953977,0.2388472259,-0.041821029,0.2287647128,-0.121212855,-0.3004402518,-0.207490325,0.1726966947,-0.1161857769,0.1238116845,-0.0393626504,-0.1715916097,0.512400806,-0.2206803709,0.1060515121,-0.2476589084,-0.1734733731,0.0296182428,-0.0510952286,0.1313425899,-0.148239702,0.4764260352,0.2352474034,-0.3461237848,-0.0996148661,-0.0419311747,-0.2728951871,-0.5491750836,-0.1891883016,0.0548043065,-0.3041189015,-0.1654404402,-0.2867376506,-0.01312136,-0.0510400049,-0.112973541,-0.0372952186,0.0382951871,-0.2160524726,-0.1827798188,0.3061754405,0.9799981117,-0.2187826782,-0.1699777246,-0.188865155,-0.013910206,0.0263885371,-0.001692432,-0.2395437807,0.5026777983,-0.4195884466,-0.3035007417,0.340518117,-0.4972645044,-0.4756468832,0.4267511666,-0.1162379235,0.3746602833,0.2642252445,0.0560590178,-0.1689719707,0.0794365108,0.0365970656,0.352073878,0.0496668555,0.0240143351,-0.1700484008,-0.1904864311,0.2972666919,0.0707357824,0.0665839091,0.2133273333,0.0521329604,0.112254113,0.1602212787,-0.018420672,-0.0797747597,0.1052496359,0.273548156,0.2352924645,0.025420161,-0.2940059602,-0.4970040619,0.1151012331,0.0078881821,-0.1258898228,-0.2119793594,-0.2105855346,-0.5217484236,0.1241697297,-0.4467488229,-0.4100629985,-0.0837992802,0.4493433237,0.0561482199,0.121387206,-0.3306224346,0.3420642316,-0.1266608685,0.20761168,-0.2868251204,0.2391595244,-0.0861550868,-0.0626597777,-0.2252286375,0.1879992485,0.1581409127,-0.3349345922,-0.0869564787,0.4237733483,0.0375111848,0.3082801104,0.1722631156,-0.0638060421,0.2396488786,-0.1734756976,-0.2159062922,0.3953196108,0.3319018781,0.0851047114,-0.0501277111,0.2219134569,-0.1718685329,0.3179703653,0.0922976062,0.032530833,0.1901166141,0.0424596146,-0.0509308167,-0.097338669,0.2777732313,-0.0241433177,0.4420976639,-0.1178543642,-0.2573302388,-0.001931434,0.018810207,0.1338551342,0.1450869441,0.017266525,-0.3486052155,0.1133734956,-0.0146005731,0.2081747055,0.5339444876,0.1364122778,-0.1541600674,0.229839325,0.0203442462,-0.0414221585,0.1381049901,0.0853454843,0.3150325418,0.2272893786,0.0788992941,0.3679118454,-0.3229429424,-0.4072822034,-0.0201158039,0.2820461094,-0.6839030385,0.0197677426,-0.0987124443,-0.0736313462,-0.1289503276,-0.2198317498,-0.1033926755,-0.2003269643,-0.1801488996,0.2355478555,-0.138864547,0.11189197,0.0479045436,0.3571163416,0.0190723706,-0.3948113918,-0.1595096588,-0.077209726,-0.1405282915,-0.125569135,0.1964597255,-0.1358729899,0.1873035729,-0.2480552197,0.0094127161,-0.2849459648,-0.2837100625,0.0206098892,0.0822126791,0.5673214197,0.1559117883,0.1901232898,0.0540245324,-0.3113275468,0.3967212439,-0.1938399971,-0.0980173126,0.0654715896,0.0663815662,-0.1001559123,-0.1059629172,-0.4710609317,-0.0091848429,-0.3304134607,0.129252255,0.1273687482,0.0266624708,0.4325394332,0.1056141332,0.2433223724,-0.1540924609,0.2641762495,-0.1927035004,-0.2663599551,0.3511534333,-0.344897002,-0.4108614624,-0.1246335357,-0.1710551828,0.1554292291,-0.0010215756,-0.4866920114,-0.5058951378,-0.2046462744,0.304764837,-0.1422679722,-0.0035876527,0.5561560392,0.042022828,0.0306224301,-0.1046186164,-0.2572964132,0.2449798733,-0.0316588283,0.1902484298,-0.0641970113,0.3993234634,0.002709111,0.4635832608,0.1666262448,0.1492880434,0.2998658717,-0.4329093397,0.5911364555,-0.27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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3329","title":"Map function: Type error on iter #999","comments":"Hi, thanks for reporting.\r\n\r\nIt would be really helpful if you could provide the actual code of the `text_numbers_to_int` function so we can reproduce the error.","body":"## Describe the bug\r\nUsing the map function, it throws a type error on iter #999\r\n\r\nHere is the code I am calling:\r\n```\r\ndataset = datasets.load_dataset('squad')\r\ndataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})\r\n``` \r\ntext_numbers_to_int returns the input text with numbers replaced in the format {'context': text}\r\n\r\nIt happens at \r\n`\r\nFile \"C:\\Users\\lonek\\anaconda3\\envs\\ai\\Lib\\site-packages\\datasets\\arrow_writer.py\", line 289, in <listcomp>\r\n    [row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col\r\n`\r\n\r\nThe issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)\r\n\r\nHere is an example of what self.current_examples should be\r\n({'context': 'Super Bowl 50 was an...merals 50.'}, '')\r\n\r\nHere is an example of what self.current_examples are when it throws the error:\r\n('The Panthers used th... Marriott.', '')\r\n","comment_length":26,"text":"Map function: Type error on iter #999 \n ## Describe the bug\r\nUsing the map function, it throws a type error on iter #999\r\n\r\nHere is the code I am calling:\r\n```\r\ndataset = datasets.load_dataset('squad')\r\ndataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})\r\n``` \r\ntext_numbers_to_int returns the input text with numbers replaced in the format {'context': text}\r\n\r\nIt happens at \r\n`\r\nFile \"C:\\Users\\lonek\\anaconda3\\envs\\ai\\Lib\\site-packages\\datasets\\arrow_writer.py\", line 289, in <listcomp>\r\n    [row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col\r\n`\r\n\r\nThe issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)\r\n\r\nHere is an example of what self.current_examples should be\r\n({'context': 'Super Bowl 50 was an...merals 50.'}, '')\r\n\r\nHere is an example of what self.current_examples are when it throws the error:\r\n('The Panthers used th... Marriott.', '')\r\n \n Hi, thanks for reporting.\r\n\r\nIt would be really helpful if you could provide the actual code of the `text_numbers_to_int` function so we can reproduce the error.","embeddings":[-0.0695653185,-0.2543756664,-0.0294095688,0.3593808115,0.1476681828,0.0972300991,0.3781248629,0.3375344276,0.4425683618,-0.09956678,0.10787265,0.6538005471,0.1616185457,0.1190469563,-0.0926598608,0.0629926473,0.0863812268,0.008872957,-0.170688346,0.1339479536,-0.6150161624,0.1419069916,-0.6294468641,0.3102441132,0.0164315235,-0.164296627,0.2603218257,-0.0844830722,-0.140377298,-0.2403956205,0.2635409236,-0.3593070805,0.1922349185,0.4690084457,-0.0001219239,-0.0597826056,0.3256959617,-0.1646695137,-0.1911890209,-0.3309637606,-0.1952892989,0.1504079401,0.1113400757,-0.0666821748,0.1873725355,0.2067001313,-0.1081770658,-0.153156653,0.1125665158,0.3918088377,0.1293401271,0.0745214075,0.1760327518,-0.0504307449,0.2677646577,0.3218046129,-0.123700738,0.1542295665,0.2386264056,-0.1731003225,0.0830602944,0.3859218657,-0.1136509702,-0.0934611633,0.1458535194,0.0005077823,0.152147606,-0.4237283766,-0.1612037718,0.0075207185,0.3200412989,-0.1895659268,-0.235442549,0.1293602884,-0.3576364815,-0.289873004,0.1887789071,-0.204463616,-0.1991844028,-0.0867753401,0.0241219867,0.2110590786,-0.169650197,0.2874500751,-0.2640114427,0.1455147415,-0.2205433547,0.2866029441,0.208903268,-0.465451926,0.0121781938,0.098624289,0.0768381134,0.3226657212,-0.562923193,-0.3143519461,-0.0953401923,-0.334566474,-0.025059307,0.0041652662,0.2986057699,0.1036874428,-0.1280681044,0.4851785898,0.5331825018,0.2682121098,0.2365900427,0.5884683132,-0.0240519345,0.0094613964,0.0360066369,-0.048156254,0.0248188227,-0.0838494152,0.110644415,-0.0415437967,0.0830139667,-0.2129337192,-0.3643575907,0.3378989398,-0.6286007166,0.0229483899,0.0560774244,0.2339651436,0.1419189274,0.3150422871,0.2316492349,0.260264039,-0.0119976653,0.0174155142,-0.0570918545,0.0642896071,-0.1081487685,-0.2285646498,-0.0442168117,-0.0479407273,0.1371885389,0.0305918343,-0.2859510481,-0.2212137282,-0.0144990971,-0.2416336089,0.2745876908,0.1652420014,-0.2677915692,0.1656958163,0.4336304069,-0.2443249822,0.0087352097,0.3310343325,-0.4402856529,0.1173354015,-0.3285543025,0.0324859209,0.264561832,-0.0817037523,-0.0967928916,0.0765111372,0.2883660197,-0.492903918,0.1815505624,-0.3865415454,-0.2560729682,-0.3211383224,-0.0873301402,0.2857618332,-0.5808777213,-0.0517575778,-0.0230426136,0.0191442985,0.2398418486,0.4893757999,-0.0137978895,0.1687560678,-0.1019254699,0.6126767993,0.655757606,-0.172244072,-0.1647561342,0.4104525447,0.2205374837,-0.1191084087,-0.2869555056,0.0795079023,0.2351704687,-0.1696297526,0.3588592112,0.0405382141,-0.0572187342,0.0102603417,-0.2569832504,-0.0254549254,0.5739942789,0.0108687235,-0.0007416953,0.2869466841,0.1285882741,-0.2854795158,0.1132628992,-0.0428411998,0.0273094848,0.0206922293,0.2873142064,0.1878450066,0.1069874018,-0.430870086,-0.090972282,-0.2478297502,0.0114268567,-0.086992085,-0.0840532631,0.2179153115,-0.3674281836,0.2436568588,-0.1255195588,0.041434899,0.1213184446,-0.1705060899,-0.0791581422,-0.1233143061,-0.0251196809,-0.2009356022,-0.1189173758,0.1258534491,-0.2395033687,-0.0054368721,-0.0917650685,-0.3534821868,-0.2883020937,0.1287066489,0.2094672769,0.0962301418,-0.2935906053,0.1596086174,0.1707471162,-0.2016816139,-0.2244101614,0.08131475,0.0810285956,0.1332652122,-0.1151973084,0.5474795103,0.2505155802,-0.1415660083,0.046474088,0.1887075007,0.1749875098,0.424839586,-0.3136245906,0.4457046986,-0.0536945164,0.3130356967,-0.0287437197,-0.1417190135,-0.0828224868,0.1759933829,0.3023210466,-0.088560462,-0.0076478668,0.0008891552,0.1666963547,-0.1240794212,0.3597768843,0.2566112578,-0.2081843913,0.0375714861,-0.008095121,-0.1759697944,0.2340526134,-0.2820879817,-0.1806078702,-0.1485557556,-0.0424759239,0.2266539186,0.1624135673,0.3013562262,0.3988274634,0.0224944502,-0.056133572,-0.0535033159,-0.0582627952,-0.0715883896,-0.0936179981,0.3342617154,-0.5203601718,0.0864692554,-0.1320670396,-0.1436020434,-0.0006680304,-0.0683456957,-0.0070219841,-0.3734587729,-0.2227047831,0.2830422521,-0.1167346165,-0.0051346892,0.1645364314,-0.1163396165,0.5080772638,0.111896202,-0.109713763,-0.2054325044,-0.0711422414,0.0339057408,0.2402527928,-0.2218946517,-0.0922778174,0.2009496689,0.0038181117,-0.2439978719,-0.3361806273,0.2398133278,-0.2234873474,0.1015042588,0.3957751989,0.3984486461,-0.254617393,-0.1787180454,0.2923998833,-0.0411956608,-0.2352619171,0.3793849647,-0.0237308852,0.1017164141,-0.117744565,-0.2642734349,-0.1338201314,-0.2897910774,0.4506815374,0.1078318954,0.3529079556,-0.0236946922,0.1627840996,0.2006506175,0.0495787002,-0.2298311293,-0.3171624839,0.1308652461,0.1968705356,-0.1634985209,-0.111922957,-0.1869517714,0.0558604747,-0.0007266797,-0.0305926446,-0.1211786196,-0.3105867207,-0.1756079197,-0.0063938359,-0.2528829575,0.1740204692,0.4018419087,0.120680809,0.0169332977,-0.1384335309,-0.3979094923,0.1340526342,0.2687708735,0.2067524791,0.3104080856,0.3809592724,0.0319610313,0.2549996972,0.5371595621,-0.0497086383,0.3001978695,0.0078807734,-0.0893401951,-0.0461443625,-0.0571135581,0.0432770364,-0.3924381733,-0.141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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3329","title":"Map function: Type error on iter #999","comments":"```\r\ndef text_numbers_to_int(text, column=\"\"):\r\n    \"\"\"\r\n    Convert text numbers to int.\r\n\r\n    :param text: text numbers\r\n    :return: int\r\n    \"\"\"\r\n    try:\r\n        numbers = find_numbers(text)\r\n        if not numbers:\r\n            return text\r\n        result = \"\"\r\n        i, j = 0, 0\r\n        while i < len(text):\r\n            if j < len(numbers) and i == numbers[j][1]:\r\n                n = int(numbers[j][0]) if numbers[j][0] % 1 == 0 else float(numbers[j][0])\r\n                result += str(n)\r\n                i = numbers[j][2] #end\r\n                j += 1\r\n            else:\r\n                result += text[i]\r\n                i += 1\r\n        if column:\r\n            return{column: result}\r\n        else:\r\n            return {column: result}\r\n    except Exception as e:\r\n        print(e)\r\n        return {column: result}\r\n```","body":"## Describe the bug\r\nUsing the map function, it throws a type error on iter #999\r\n\r\nHere is the code I am calling:\r\n```\r\ndataset = datasets.load_dataset('squad')\r\ndataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})\r\n``` \r\ntext_numbers_to_int returns the input text with numbers replaced in the format {'context': text}\r\n\r\nIt happens at \r\n`\r\nFile \"C:\\Users\\lonek\\anaconda3\\envs\\ai\\Lib\\site-packages\\datasets\\arrow_writer.py\", line 289, in <listcomp>\r\n    [row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col\r\n`\r\n\r\nThe issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)\r\n\r\nHere is an example of what self.current_examples should be\r\n({'context': 'Super Bowl 50 was an...merals 50.'}, '')\r\n\r\nHere is an example of what self.current_examples are when it throws the error:\r\n('The Panthers used th... Marriott.', '')\r\n","comment_length":91,"text":"Map function: Type error on iter #999 \n ## Describe the bug\r\nUsing the map function, it throws a type error on iter #999\r\n\r\nHere is the code I am calling:\r\n```\r\ndataset = datasets.load_dataset('squad')\r\ndataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})\r\n``` \r\ntext_numbers_to_int returns the input text with numbers replaced in the format {'context': text}\r\n\r\nIt happens at \r\n`\r\nFile \"C:\\Users\\lonek\\anaconda3\\envs\\ai\\Lib\\site-packages\\datasets\\arrow_writer.py\", line 289, in <listcomp>\r\n    [row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col\r\n`\r\n\r\nThe issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)\r\n\r\nHere is an example of what self.current_examples should be\r\n({'context': 'Super Bowl 50 was an...merals 50.'}, '')\r\n\r\nHere is an example of what self.current_examples are when it throws the error:\r\n('The Panthers used th... Marriott.', '')\r\n \n ```\r\ndef text_numbers_to_int(text, column=\"\"):\r\n    \"\"\"\r\n    Convert text numbers to int.\r\n\r\n    :param text: text numbers\r\n    :return: int\r\n    \"\"\"\r\n    try:\r\n        numbers = find_numbers(text)\r\n        if not numbers:\r\n            return text\r\n        result = \"\"\r\n        i, j = 0, 0\r\n        while i < len(text):\r\n            if j < len(numbers) and i == numbers[j][1]:\r\n                n = int(numbers[j][0]) if numbers[j][0] % 1 == 0 else float(numbers[j][0])\r\n                result += str(n)\r\n                i = numbers[j][2] #end\r\n                j += 1\r\n            else:\r\n                result += text[i]\r\n                i += 1\r\n        if column:\r\n            return{column: result}\r\n        else:\r\n            return {column: result}\r\n    except Exception as e:\r\n        print(e)\r\n        return {column: result}\r\n```","embeddings":[-0.1012620628,-0.2614866197,-0.0429167412,0.360131681,0.1728991568,0.0626527742,0.3584762216,0.3256571591,0.5001986027,-0.0529095419,0.0537558831,0.6227619052,0.1381005794,0.0644424558,-0.1447208226,0.0169320758,0.0577132702,-0.0018927145,-0.107498832,0.1114242673,-0.5638147593,0.1433551461,-0.5702192783,0.2570309341,0.0048127598,-0.21654284,0.2775192559,-0.0957792401,-0.1651197672,-0.218123734,0.2940833867,-0.2994794846,0.1954320222,0.4470337927,-0.0001191251,0.014243627,0.2757125497,-0.15612562,-0.202074945,-0.3411087394,-0.1604282558,0.1284938306,0.0932468623,-0.0646709725,0.1391968727,0.2439178675,-0.1118630841,-0.1573573649,0.0779472664,0.3774295449,0.1603942364,0.1574549526,0.1495594233,-0.0785643011,0.2873196006,0.2529651523,-0.1549123526,0.0769886822,0.2113080621,-0.1963033676,0.0052301763,0.4048805535,-0.1831413358,-0.0796902776,0.0490305685,0.0325159468,0.2132160962,-0.4257724881,-0.1276637018,-0.0060885572,0.2319151312,-0.0629010871,-0.2234014571,0.1061327308,-0.4001218379,-0.2767655849,0.1499403268,-0.1544396728,-0.212350294,-0.0588717051,0.0398607142,0.2911484838,-0.1103022397,0.2908606827,-0.2672030032,0.2287457734,-0.210437566,0.2669139802,0.2014146149,-0.4097221494,-0.0574663766,0.1127537489,0.0565508977,0.3152278662,-0.5426421165,-0.2525951564,-0.0590487197,-0.3645429611,-0.0041886736,0.0139538245,0.3200315833,0.1058876961,-0.1163674295,0.4758039415,0.4637616575,0.3127885759,0.1498236805,0.6079452038,-0.0720362514,0.0112714972,0.0230532065,-0.0254175197,0.0730162039,-0.0537024848,0.1277382225,-0.0068752421,0.1092823595,-0.1468176097,-0.3932798803,0.3896169662,-0.6453598142,-0.0215465426,0.1180635095,0.2313045412,0.186202094,0.3875602782,0.2029906064,0.2032432407,-0.013562832,0.0693083704,-0.0998436883,0.0166542344,-0.0535442345,-0.2298493683,-0.0852423981,-0.026748769,0.1363468021,-0.007065502,-0.2677707672,-0.2192017883,-0.0185923222,-0.3035116792,0.2954966724,0.1844575256,-0.2750501335,0.1259485334,0.4909405708,-0.3181443214,0.0296580028,0.2716628909,-0.3922418952,0.1617648304,-0.2809867263,0.0891974345,0.362657249,-0.0899573192,-0.1513996124,0.0848012939,0.2686974406,-0.360709995,0.1525705606,-0.3809400499,-0.2665953636,-0.3156227767,-0.0845554546,0.246631965,-0.4842207432,-0.0631032661,0.0042872517,0.0539711118,0.2019751817,0.5303297043,-0.0069201929,0.2024489343,-0.1474529952,0.5987243056,0.6202795506,-0.213285774,-0.2050493211,0.4125594497,0.2524668276,-0.1173524186,-0.25888744,0.0438404307,0.2241847962,-0.1479713172,0.3191025555,0.0392677188,-0.0391228832,-0.0080852546,-0.299705416,-0.0363405384,0.5437585115,-0.0242023598,0.1037486047,0.2407695949,0.122788772,-0.2245328277,0.0773346275,-0.0260762032,-0.0452484861,0.019889649,0.3008202612,0.1016819552,0.1086485684,-0.4381996393,-0.1298133135,-0.2238330394,0.049968224,-0.0735048205,-0.082329385,0.1805239469,-0.3587851524,0.2458986044,-0.1618628949,0.0253473669,0.194043681,-0.1040551588,0.0092525715,-0.152556479,0.0393344946,-0.2895752788,-0.0878154561,0.0923506841,-0.1941938847,0.0466594212,-0.1315993071,-0.302249074,-0.3391085267,0.1269531399,0.2076937556,0.104577288,-0.3300054967,0.1553386301,0.1557224691,-0.1805660725,-0.2013393044,0.0896031186,0.0279376935,0.1424519122,-0.2360295504,0.5354239345,0.2369871885,-0.1263153553,0.1285619438,0.1471895427,0.1911658645,0.4348578453,-0.2396906167,0.4494385421,0.0178343784,0.3127276599,-0.0168229975,-0.1778780073,-0.0848632157,0.2212905437,0.3229800761,-0.0723192245,0.0034057966,-0.0496544242,0.212694943,-0.10403312,0.3284198046,0.2107952982,-0.1632687896,0.0855950415,-0.052865427,-0.1505675018,0.1977936774,-0.2186238021,-0.1296831667,-0.2180379033,-0.0643806234,0.2065324932,0.1744024307,0.3573567271,0.3666289449,0.0445515513,-0.0570892617,-0.0575516485,-0.1251293719,-0.0708370656,-0.0927406177,0.3160715401,-0.4278383553,0.141161859,-0.0838345364,-0.0875332132,0.0196734723,-0.0955071151,-0.0089005781,-0.3626332581,-0.2025992423,0.2698566318,-0.1165343523,-0.0283367727,0.1702144742,-0.1639162153,0.5541729927,0.0742105842,-0.1538384408,-0.2122323513,-0.0665378049,0.0640589967,0.2733142972,-0.3290651143,-0.0375967361,0.1813057512,-0.0722209439,-0.3015748262,-0.3599546552,0.2461034805,-0.2555811405,0.0714497715,0.4253471196,0.338393271,-0.3542421758,-0.2016049176,0.3357477486,-0.0218679309,-0.2430837005,0.380438149,0.0100058317,0.0402906425,-0.1529442221,-0.2635673285,-0.1432647258,-0.3321788609,0.432654649,0.1367597729,0.3517341912,-0.0060987812,0.1067303717,0.2188462913,0.0693674013,-0.210756734,-0.3670693636,0.0845735446,0.1728573889,-0.1350939125,-0.1024474725,-0.2162275761,-0.0902398825,0.0726041123,-0.0005010828,-0.1267652363,-0.3413905799,-0.2321779281,0.0299264695,-0.2277280539,0.1553225815,0.4101734161,0.1415114701,-0.0225103218,-0.1417881995,-0.3753780127,0.0969076082,0.2652604878,0.1715658307,0.2340039015,0.3867050111,0.095208928,0.3064419627,0.4467324317,-0.0570645779,0.3160679638,0.045934841,-0.0914881751,-0.1114186123,-0.1228703633,-0.007829546,-0.39091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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3329","title":"Map function: Type error on iter #999","comments":"Maybe this is because of the `return text` line ? I think it should return a dictionary rather than a string","body":"## Describe the bug\r\nUsing the map function, it throws a type error on iter #999\r\n\r\nHere is the code I am calling:\r\n```\r\ndataset = datasets.load_dataset('squad')\r\ndataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})\r\n``` \r\ntext_numbers_to_int returns the input text with numbers replaced in the format {'context': text}\r\n\r\nIt happens at \r\n`\r\nFile \"C:\\Users\\lonek\\anaconda3\\envs\\ai\\Lib\\site-packages\\datasets\\arrow_writer.py\", line 289, in <listcomp>\r\n    [row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col\r\n`\r\n\r\nThe issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)\r\n\r\nHere is an example of what self.current_examples should be\r\n({'context': 'Super Bowl 50 was an...merals 50.'}, '')\r\n\r\nHere is an example of what self.current_examples are when it throws the error:\r\n('The Panthers used th... Marriott.', '')\r\n","comment_length":21,"text":"Map function: Type error on iter #999 \n ## Describe the bug\r\nUsing the map function, it throws a type error on iter #999\r\n\r\nHere is the code I am calling:\r\n```\r\ndataset = datasets.load_dataset('squad')\r\ndataset['validation'].map(text_numbers_to_int, input_columns=['context'], fn_kwargs={'column': 'context'})\r\n``` \r\ntext_numbers_to_int returns the input text with numbers replaced in the format {'context': text}\r\n\r\nIt happens at \r\n`\r\nFile \"C:\\Users\\lonek\\anaconda3\\envs\\ai\\Lib\\site-packages\\datasets\\arrow_writer.py\", line 289, in <listcomp>\r\n    [row[0][col] for row in self.current_examples], type=col_type, try_type=col_try_type, col=col\r\n`\r\n\r\nThe issue is that the list comprehension expects self.current_examples to be type tuple(dict, str), but for some reason 26 out of 1000 of the sefl.current_examples are type tuple(str, str)\r\n\r\nHere is an example of what self.current_examples should be\r\n({'context': 'Super Bowl 50 was an...merals 50.'}, '')\r\n\r\nHere is an example of what self.current_examples are when it throws the error:\r\n('The Panthers used th... Marriott.', '')\r\n \n Maybe this is because of the `return text` line ? I think it should return a dictionary rather than a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3317","title":"Add desc parameter to Dataset filter method","comments":"Hi,\r\n\r\n`Dataset.map` allows more generic transforms compared to `Dataset.filter`, which purpose is very specific (to filter examples based on a condition). That's why I don't think we need the `desc` parameter there for consistency. #3196 has added descriptions to the  `Dataset` methods that call `.map` internally, but not for the `filter` method, so we should do that.\r\n\r\nDo you have a description in mind? Maybe `\"Filtering the dataset\"` or `\"Filtering the indices\"`? If yes, feel free to open a PR.","body":"**Is your feature request related to a problem? Please describe.**\r\nAs I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?\r\n\r\n**Describe the solution you'd like**\r\nAdd desc parameter to Dataset filter method\r\n\r\n**Describe alternatives you've considered**\r\nN\/A\r\n\r\n**Additional context**\r\nN\/A\r\n","comment_length":80,"text":"Add desc parameter to Dataset filter method \n **Is your feature request related to a problem? Please describe.**\r\nAs I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?\r\n\r\n**Describe the solution you'd like**\r\nAdd desc parameter to Dataset filter method\r\n\r\n**Describe alternatives you've considered**\r\nN\/A\r\n\r\n**Additional context**\r\nN\/A\r\n \n Hi,\r\n\r\n`Dataset.map` allows more generic transforms compared to `Dataset.filter`, which purpose is very specific (to filter examples based on a condition). That's why I don't think we need the `desc` parameter there for consistency. #3196 has added descriptions to the  `Dataset` methods that call `.map` internally, but not for the `filter` method, so we should do that.\r\n\r\nDo you have a description in mind? Maybe `\"Filtering the dataset\"` or `\"Filtering the indices\"`? If yes, feel free to open a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3317","title":"Add desc parameter to Dataset filter method","comments":"I'm personally ok with adding the `desc` parameter actually. Let's say you have different filters, it can be nice to differentiate between the different filters when they're running no ?","body":"**Is your feature request related to a problem? Please describe.**\r\nAs I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?\r\n\r\n**Describe the solution you'd like**\r\nAdd desc parameter to Dataset filter method\r\n\r\n**Describe alternatives you've considered**\r\nN\/A\r\n\r\n**Additional context**\r\nN\/A\r\n","comment_length":30,"text":"Add desc parameter to Dataset filter method \n **Is your feature request related to a problem? Please describe.**\r\nAs I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?\r\n\r\n**Describe the solution you'd like**\r\nAdd desc parameter to Dataset filter method\r\n\r\n**Describe alternatives you've considered**\r\nN\/A\r\n\r\n**Additional context**\r\nN\/A\r\n \n I'm personally ok with adding the `desc` parameter actually. Let's say you have different filters, it can be nice to differentiate between the different filters when they're running no 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3317","title":"Add desc parameter to Dataset filter method","comments":"@mariosasko the use case is filtering of a dataset prior to tokenization and subsequent training. As the dataset is huge it's just a matter of giving a user (model trainer) some feedback on what's going on. Otherwise, feedback is given for all steps in training preparation and not for filtering and the filtering in my use case lasts about 4-5 minutes. And yes, if there are more filtering stages, as @lhoestq pointed out, it would be nice to give some feedback. I thought desc is there already and got confused when I got the script error. ","body":"**Is your feature request related to a problem? Please describe.**\r\nAs I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?\r\n\r\n**Describe the solution you'd like**\r\nAdd desc parameter to Dataset filter method\r\n\r\n**Describe alternatives you've considered**\r\nN\/A\r\n\r\n**Additional context**\r\nN\/A\r\n","comment_length":96,"text":"Add desc parameter to Dataset filter method \n **Is your feature request related to a problem? Please describe.**\r\nAs I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?\r\n\r\n**Describe the solution you'd like**\r\nAdd desc parameter to Dataset filter method\r\n\r\n**Describe alternatives you've considered**\r\nN\/A\r\n\r\n**Additional context**\r\nN\/A\r\n \n @mariosasko the use case is filtering of a dataset prior to tokenization and subsequent training. As the dataset is huge it's just a matter of giving a user (model trainer) some feedback on what's going on. Otherwise, feedback is given for all steps in training preparation and not for filtering and the filtering in my use case lasts about 4-5 minutes. And yes, if there are more filtering stages, as @lhoestq pointed out, it would be nice to give some feedback. I thought desc is there already and got confused when I got the script error. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3317","title":"Add desc parameter to Dataset filter method","comments":"I don't have a strong opinion on that, so having `desc` as a parameter is also OK.","body":"**Is your feature request related to a problem? Please describe.**\r\nAs I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?\r\n\r\n**Describe the solution you'd like**\r\nAdd desc parameter to Dataset filter method\r\n\r\n**Describe alternatives you've considered**\r\nN\/A\r\n\r\n**Additional context**\r\nN\/A\r\n","comment_length":17,"text":"Add desc parameter to Dataset filter method \n **Is your feature request related to a problem? Please describe.**\r\nAs I was filtering very large datasets I noticed the filter method doesn't have the desc parameter which is available in the map method. Why don't we add a desc parameter to the filter method both for consistency and it's nice to give some feedback to users during long operations on Datasets?\r\n\r\n**Describe the solution you'd like**\r\nAdd desc parameter to Dataset filter method\r\n\r\n**Describe alternatives you've considered**\r\nN\/A\r\n\r\n**Additional context**\r\nN\/A\r\n \n I don't have a strong opinion on that, so having `desc` as a parameter is also OK.","embeddings":[-0.1287521571,-0.0055357679,-0.1885539144,-0.3054769039,0.1359179467,-0.2726878822,0.0132564893,0.1285358518,0.0402685069,0.3643822968,0.2256278396,0.3220198452,-0.0392239504,0.1355442107,-0.1702379435,0.0217238609,-0.1972775757,0.0151648326,0.0741905794,0.0693149716,-0.2096756548,-0.3124373555,0.0733717382,-0.4518630505,0.0692628697,0.159355402,-0.0564045124,-0.0860453248,-0.1909403503,-0.4803184867,0.3382697105,0.5113639235,0.0274061374,0.269156158,-0.0001110195,-0.131485492,0.4167279005,-0.1514617503,-0.0184803419,0.0308145285,-0.5436112881,-0.3175927103,-0.0747535825,-0.3342750669,-0.0418783091,0.5702056289,-0.2019378543,-0.2746688426,-0.0828934684,0.167656377,0.2455274761,-0.2117782086,-0.4498574436,0.0243599378,-0.0119331926,0.5251312256,-0.1594770551,0.0165402219,0.8244937658,0.0532150194,0.0179518331,0.0283069052,0.0402735844,-0.3217774928,0.2456885427,-0.3030413687,0.226633355,-0.2962318659,0.2843643427,0.042770341,0.5393816233,0.0807196274,-0.3334542513,0.0757230148,0.0411839485,-0.2079883367,-0.2928177714,-0.1330277026,-0.1520844549,0.1316225827,-0.2559988797,-0.3349279165,-0.1926720738,-0.0056798067,-0.1067241654,0.0528876819,-0.0819456875,0.0411779583,-0.117882587,0.066227451,0.0997806564,-0.3683362305,0.1188317239,0.1366898715,0.0344910845,-0.4208340347,0.3402895927,0.2126468867,0.1263082772,0.2201442719,-0.1895570457,-0.0591911823,0.1077513024,0.1045068279,0.0544102788,-0.0729838908,0.4107039869,0.1562047303,0.0119657647,-0.4749979079,-0.2605370879,-0.1007082537,0.4362113476,0.0824323073,0.2419471443,0.180065915,0.0199138373,-0.3728676438,-0.0129736532,-0.0322273485,0.1131752431,-0.1897702813,0.1466772854,0.0682641864,0.2862051129,0.3820786178,-0.0427900366,-0.1686585248,0.2208833545,-0.1069811285,-0.0753963068,-0.0509280302,-0.1237652153,0.1133807749,0.084700577,-0.2822649777,0.0671133474,0.0357482731,-0.1018685997,0.0763449222,-0.2094499022,-0.0998825431,0.0755472258,0.2059881836,-0.5056266785,-0.0645281821,-0.0070201871,0.0384668671,-0.2391226441,0.4176943898,-0.3596378565,-0.2161432207,0.0148800388,0.1997654885,0.239050895,-0.0177935418,0.1691495776,0.4102724493,-0.1721388251,-0.3328126967,0.1070846915,0.0338705778,-0.5663178563,-0.35524562,-0.1128410101,0.1622377187,-0.603117466,-0.0929781869,-0.56763345,0.2308330387,0.3311686516,0.1112223342,-0.383942306,0.0913689435,0.170358032,0.2217049599,0.4890659153,0.2458693236,-0.3940962255,0.4266363978,-0.1562192738,-0.0137705961,0.1715186536,0.1499586403,0.452552855,-0.3060207367,0.2749730945,0.0151882414,-0.5393587947,-0.1103123203,-0.2583931088,0.0375956409,0.3604873121,0.0510364771,0.0446579047,0.2472183704,0.4602890611,-0.370673418,0.0908037126,0.0866461545,-0.1442292929,0.0682015866,0.6660206914,0.0532021821,-0.1853626519,-0.3084341884,-0.0676258057,0.079167515,0.1055144668,-0.4428321123,0.314476043,-0.3552502692,-0.1836411804,0.1070220694,0.3641653061,0.0407598764,0.0208311863,-0.1476225555,-0.0598237514,-0.0227543525,-0.0907499939,-0.0726237446,-0.143704474,-0.3230597079,0.4114656746,0.2403992563,0.0316667892,0.0136051076,-0.1742653698,0.0117330281,0.1625001878,0.0937466174,-0.1911048889,0.0832299441,0.192266047,-0.2899171412,0.152896136,0.3417750001,0.1883061528,0.0872470811,0.0184585769,0.2720623314,0.2273686081,-0.2164483517,-0.2581890225,0.283338815,0.0210177675,0.1738794744,-0.0127355838,0.0805026442,0.2221186757,-0.1084845439,-0.1003873125,0.0037165,0.0939627141,0.290142417,-0.0659789219,0.2828141153,-0.3060240448,0.078481622,0.3969104886,-0.0691004097,0.0519242883,0.244584471,0.3198194802,-0.0073958724,0.3463861346,0.1448623389,0.6124714613,0.3837260604,-0.3019725084,-0.1306225359,0.1720867455,0.0014061844,0.1737763435,0.0292202216,0.1452766508,0.0231332108,0.1675800681,0.1570334435,-0.0564169809,-0.2233837545,0.1651679873,-0.1175369397,-0.0096402941,-0.1279949993,-0.0719111562,-0.2148825377,0.1146356538,0.0361819901,-0.0700053945,-0.2321895361,0.1442828178,0.0344556011,-0.2381450534,0.1514210701,-0.548517108,0.7579571009,-0.0205989759,-0.0584274419,-0.1803290248,-0.0607662909,-0.0373247117,0.0309453011,-0.0025642368,-0.1050069481,0.471275568,0.5372363925,0.434427768,-0.3387266695,-0.6350770593,-0.2494313866,0.1550836861,0.3155660331,-0.0093124108,0.1266282499,-0.067635417,-0.2034593821,-0.0156236924,0.1211184189,0.0107367588,-0.205773294,0.1300531179,-0.0159114897,-0.2365407646,-0.0454699136,-0.3073336482,-0.2994707227,-0.0487221144,-0.5578938723,0.1778644025,0.1480371207,-0.1125917658,-0.2583470941,-0.1448792368,-0.0974744484,-0.1533334255,-0.2219058573,0.2215546966,-0.1939791143,-0.109760724,-0.1562926322,-0.1061304808,-0.0577558391,0.2361232936,-0.1239206046,-0.2410775125,-0.2176619321,0.3387377262,0.035614334,-0.0940506309,0.4774368703,-0.1137401909,-0.054623384,-0.0364579335,-0.6701379418,0.0191901494,0.5955819488,0.3161893189,0.2058784515,0.7391363382,0.0796401128,0.5320225954,-0.0961750746,-0.0275445655,0.3238454163,0.2484630197,0.2115286887,-0.0154696247,-0.5373234153,0.2622416914,0.1052495614,-0.243362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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3313","title":"TriviaQA License Mismatch","comments":"Hi ! You're completely right, this must be mentioned in the dataset card.\r\nIf you're interesting in contributing, feel free to open a pull request to mention this in the `trivia_qa` dataset card in the \"Licensing Information\" section at https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/trivia_qa\/README.md","body":"## Describe the bug\r\n\r\nTriviaQA Webpage at http:\/\/nlp.cs.washington.edu\/triviaqa\/ says they do not own the copyright to the data. However, Huggingface datasets at https:\/\/huggingface.co\/datasets\/trivia_qa mentions that the dataset is released under Apache License\r\n\r\nIs the License Information on HuggingFace correct?","comment_length":40,"text":"TriviaQA License Mismatch \n ## Describe the bug\r\n\r\nTriviaQA Webpage at http:\/\/nlp.cs.washington.edu\/triviaqa\/ says they do not own the copyright to the data. However, Huggingface datasets at https:\/\/huggingface.co\/datasets\/trivia_qa mentions that the dataset is released under Apache License\r\n\r\nIs the License Information on HuggingFace correct? \n Hi ! You're completely right, this must be mentioned in the dataset card.\r\nIf you're interesting in contributing, feel free to open a pull request to mention this in the `trivia_qa` dataset card in the \"Licensing Information\" section at https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/trivia_qa\/README.md","embeddings":[0.0982676074,-0.0661502108,-0.0082701556,0.3446458876,-0.2029529214,0.1115980074,0.0691819489,0.2062564343,-0.2013846785,-0.2456236631,0.0544378795,0.1735268831,0.2805499434,0.0263400786,0.0973246023,-0.1761311889,0.1311206371,-0.2523021102,-0.1003348455,-0.0584066175,-0.1580771208,0.6378138661,-0.3034308255,0.1950032711,-0.0054561612,-0.2435030639,0.0956025347,0.2089476287,0.109580338,-0.3004067242,0.1129481941,0.0791692585,0.2103009224,0.2633871734,-0.0001164213,-0.2355644405,0.0323727205,-0.1809699684,0.0065182694,0.1887629479,-0.2239293158,0.2284472585,-0.237815544,0.0372400172,-0.1909450442,-0.014970636,0.0405406393,-0.3948632479,0.3168411255,-0.021381991,0.1625247896,0.4073354602,0.1648592949,-0.2726810575,-0.0559970178,-0.0839521736,0.0543358475,0.325270623,0.0765437931,0.0059296493,-0.0621766709,0.6628661156,0.2018465996,0.0564207621,0.3287846446,-0.0409243405,-0.079030931,-0.0520538762,0.3920587301,0.2581497431,0.3475054204,-0.3202480972,-0.2843126953,-0.0153520852,0.2668384314,0.1731363535,0.2527678311,0.4681387842,-0.059409555,0.1995477974,0.0915275812,-0.2464184761,-0.1186861023,-0.1801553965,-0.1933630556,-0.3333242536,-0.1785851717,0.1079196632,-0.1340970993,-0.2351819724,-0.0325930454,-0.0480555072,-0.3627616167,0.1113527417,-0.1278088242,-0.0623127408,-0.3473715782,0.2034244239,0.3467228711,0.2513729632,0.0211530477,-0.2071264684,-0.2882444859,-0.0219240822,0.2336892635,0.03407095,0.0312903784,-0.1500074267,0.1825237423,0.1349679977,0.1546158344,-0.0497613177,0.0570404008,0.0637900233,-0.5137333274,-0.1749687493,0.2890419662,-0.3127985001,-0.4315354526,0.1230657995,-0.4250369072,-0.1188841984,-0.0729733184,0.2571023703,-0.0263893884,-0.1087147072,0.048230052,0.1638773233,-0.0114664044,-0.3528983891,-0.2162830085,-0.0085973898,0.2353985012,0.17121692,-0.0224259179,-0.1113158017,0.2167388648,0.0653849617,0.2641149163,0.0081949746,0.0068486966,0.0588393621,0.0297918916,0.1998723,-0.1602960378,0.1519751102,0.1276954412,0.1275744885,-0.1997880191,0.2368298918,-0.1535185724,-0.1020948887,-0.1100896671,0.1193017289,-0.1113856062,0.0008593547,0.3842645884,0.0959584713,0.1184831336,-0.2811124325,0.1789159775,0.2497958839,-0.0940194502,-0.2125769556,0.0404393822,0.5094495416,-0.2031282932,-0.1393208355,0.1703476459,0.1144620404,-0.1324543804,0.398349911,-0.0499402471,-0.0762255937,-0.2951640189,0.103493385,0.0718349963,-0.5371121764,-0.3362713754,-0.1298372746,0.0526041053,-0.2414611876,0.1215051934,0.0380789302,-0.0224723723,-0.0101280529,-0.0922153518,0.1798488498,0.0700128973,-0.047816176,-0.1351598203,-0.4078400731,-0.0569580719,-0.0921722949,-0.220163554,0.2361742407,-0.0584167726,0.2819454968,0.279122591,0.2093563974,-0.2146569192,0.312736541,0.332090646,0.2268919051,-0.1486965865,-0.0433584787,-0.221115157,-0.0628567934,-0.4411792755,0.2157280892,0.1194764599,-0.3030934036,-0.3414130509,0.1544648409,0.1291077286,-0.4463903606,0.1253960431,0.119560048,0.2391420603,0.1584253311,-0.0925616696,0.1919390708,0.2253408581,0.1947154254,-0.5948241949,-0.3355887234,-0.350343138,0.2558816075,0.2043541819,0.2738722563,0.1982195824,0.0667234659,0.1623606533,0.2713441253,-0.1226905882,0.2962314188,0.2821547091,0.1007658616,0.3997849822,-0.1936398149,0.3042711616,0.1142858788,0.0453291051,0.1157384664,-0.3623536825,0.2523503602,-0.1091457084,-0.0218237881,-0.1075310931,0.2421563417,0.0663755164,0.2437746972,-0.045253437,-0.2596333027,-0.0599173754,0.4937546551,0.0730096772,0.1215104014,-0.0979150385,0.2074551731,0.3340547085,-0.0673609674,-0.0132222278,-0.25217098,-0.4714125395,0.0534116924,0.508145988,0.2275181711,0.3568394482,0.1614774019,0.0365093127,-0.225473389,0.1256321371,-0.2738893628,0.1143298298,-0.0202106331,-0.3390914798,-0.2217120826,-0.0544086061,-0.1218222454,-0.0832766369,0.0963117853,-0.1113499328,0.0204613786,-0.2970559895,0.0926957503,-0.0834461227,-0.4383682311,-0.4319065213,-0.1564169228,-0.6080540419,-0.2894659042,0.1919782907,0.2717407644,-0.0539796613,0.1983703971,-0.1836488545,0.1835733503,-0.300727576,-0.0828086734,-0.0654498711,-0.0405448563,-0.2884946764,0.089102909,0.0273139719,-0.1439325064,0.1594153792,-0.2892870009,0.0530896075,-0.3835473657,-0.3301992714,0.110988602,-0.1406344473,0.1410847902,0.1348466873,0.3785056472,-0.1204556152,0.1281545162,0.1143551022,0.0578619987,-0.1405495405,-0.183776632,0.0905355513,0.2597708702,-0.0311801247,-0.2863790989,0.1019085199,-0.2073943615,0.4722591639,0.0210572165,0.0202004947,0.4913277626,-0.2894729376,-0.1025670469,-0.375321418,0.1960310489,-0.4038912952,-0.2164246142,0.1279277951,-0.1228209659,-0.3673182726,0.1141561717,0.2792816758,0.3836864531,-0.2321309596,-0.1021641344,-0.2337140739,-0.1720328629,0.4539609253,-0.1314742416,0.0874481052,0.2207809687,-0.0437010713,-0.0453988761,-0.1504903883,-0.0785345584,0.0877419412,0.0845301822,0.1660193354,0.3420419097,-0.0526616275,-0.1555116773,0.7847992778,0.5099940896,0.2360464483,0.2462857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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3310","title":"Fatal error condition occurred in aws-c-io","comments":"Hi ! Are you having this issue only with this specific dataset, or it also happens with other ones like `squad` ?","body":"## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":22,"text":"Fatal error condition occurred in aws-c-io \n ## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n Hi ! Are you having this issue only with this specific dataset, or it also happens with other ones like `squad` ?","embeddings":[-0.482326597,-0.0941872224,-0.1207199544,0.0980171114,0.0666416734,-0.0746589527,0.2302932888,0.0807167739,0.0498343892,0.2464564741,0.0314399004,0.6151480675,0.0799871683,0.0899444073,-0.2150684595,0.0799752325,0.1774078906,0.2226742357,-0.3837793171,0.1506045908,-0.3002246618,0.3312653303,-0.328581959,0.1297631115,-0.1777175218,-0.2442936748,-0.0252515189,0.1826004684,0.0460977368,-0.3293400407,0.3206733465,-0.2650406063,0.2438400984,0.4518451691,-0.0001093318,-0.1118508577,0.3101602495,0.0945520848,-0.3214781284,-0.0317830555,-0.1824334711,-0.0217732396,0.1722509563,-0.3631685078,0.3220714331,0.2951670289,0.0071721771,-0.2874428332,0.1973875016,0.4085331857,0.2418861836,0.7135310769,0.1465580463,0.0556877889,0.184849292,-0.1402463615,-0.3562589884,0.1377412677,0.3027017713,-0.3610855639,0.215211466,0.1781431288,-0.1646543741,0.1518883109,0.0844428837,-0.0406350382,0.0620382056,-0.3105751872,0.22381033,-0.0043023298,0.372600913,-0.4975511432,-0.2278135717,0.1983138919,0.1189710572,-0.2930726111,0.2673714757,0.2600486279,-0.1133086085,0.1540763527,0.1476511359,0.33458215,-0.2547403574,0.1963027865,-0.1133234948,0.2745116353,-0.1227360591,0.094432883,-0.0969922096,0.0161617007,0.0290687941,-0.1192911118,-0.1253114641,0.1685812324,-0.7996679544,-0.0870911404,-0.0505601875,0.21392712,0.1183023974,-0.0086707696,0.0792443827,-0.1322982609,0.3381571472,0.2955602109,0.0983084589,-0.1588628441,0.0681325793,0.1116920114,0.3717543185,-0.0034810838,-0.1405858099,-0.1129026711,0.0967273265,-0.2409911454,0.2786039114,0.0161671638,0.4272921383,-0.1937427968,-0.4776920676,0.5653191805,-0.3984053135,0.1799086928,-0.0649989545,0.4042621255,0.0797654837,-0.1638060212,0.1143710911,0.1963441819,-0.3679198623,0.1306263804,-0.1418006867,0.0005099687,-0.1412115395,0.0195323396,0.1512918621,-0.233204633,0.1894149482,0.2659635246,0.2191423923,-0.4016888142,0.1106703207,-0.0974879339,-0.3781301677,0.250413388,0.3266514838,0.1434190869,-0.0666370913,-0.0205636155,0.1484071761,0.2709489465,-0.1687715799,-0.188902542,-0.3929622173,0.2797447145,-0.1146853119,0.0338507667,-0.1040300056,-0.2441022098,0.2317208052,-0.1020844579,0.1924037039,-0.2675975263,0.1456110626,-0.1156612039,0.0236121044,0.158810854,-0.1695824265,0.1416719556,-0.1734040827,-0.1573115438,0.1927895099,0.2721315026,-0.1239449307,-0.0391013138,-0.3496800959,0.0942633972,0.1699193418,-0.104384467,-0.3306805193,0.3258503079,-0.0885309279,-0.14471744,-0.105617106,-0.0206365883,0.0421575308,-0.0850138888,-0.1802265942,-0.215126276,-0.0075932932,0.1542020291,-0.3081534505,-0.3051500618,0.0597065501,0.1991615742,0.2608785927,-0.0395018905,0.3754089773,-0.0356416367,0.3499753475,-0.1175407171,-0.0922470912,0.1783539802,0.3556674421,-0.2298665047,0.052974917,-0.1591700315,-0.6702606082,0.1807160527,0.1656576246,0.2113174498,-0.0744269863,-0.1161605865,-0.0688246191,0.3155626655,-0.052259434,-0.0038405748,0.175831899,-0.1146855503,-0.0599473231,-0.1472005546,-0.2478774637,0.2320477664,-0.7155185938,0.1314234585,-0.1407735944,0.3005839288,-0.1876196265,-0.3593220115,0.120700866,-0.1952480823,0.2406069636,0.05937846,-0.3034169972,0.2202456594,-0.140111044,0.5234091878,-0.1214925125,0.2303862423,-0.0527440496,-0.3421447873,0.0352786519,0.0569428392,0.1885982752,-0.0019983344,0.1705243289,0.1777385473,-0.1596399993,0.2327786982,-0.1285790652,-0.0722865015,0.0722213984,-0.1394798607,-0.0093201147,0.1415272504,0.3039069772,0.1842691451,0.3013447523,-0.1546597332,-0.4457635581,-0.1569492966,0.2590029538,0.0071463129,0.2256677151,0.0899616331,-0.0806361735,0.0596683137,0.3690815866,-0.0267703384,0.7966685891,0.1177372932,-0.0753354281,0.1351558864,-0.0905155614,-0.2169246525,0.4426444769,0.2501828671,0.1745978147,0.4361056983,0.2811740041,0.034252461,-0.4044071436,-0.3952739239,-0.0197328273,0.2163792998,-0.2601400912,0.2554369271,-0.0373548009,0.1822557747,-0.0639198422,0.2400010526,0.0171624403,-0.0705006793,-0.0469140224,0.3004809022,-0.0830543265,0.2009395212,-0.1433956474,-0.0361895189,0.0629046932,0.0652644262,-0.3282933831,-0.1639317721,-0.1558138579,0.0677443668,0.2645582557,-0.1575245857,0.2829580009,0.0665981099,-0.1457821876,-0.3569094837,-0.0929068848,0.053434968,0.0123087978,0.3559880257,0.1835330576,0.286585778,-0.2607322931,-0.482654959,0.0965462252,-0.3337279856,0.0578402057,-0.0025460974,-0.2966887951,-0.0408783853,-0.2729908526,-0.3428073227,-0.1935271025,-0.3901371658,-0.0561674945,-0.1907971501,0.0778793097,-0.1475288868,0.2897515297,0.0984356925,0.3401771784,-0.2686767876,-0.2965058684,-0.3120572567,0.183034271,-0.1028924882,-0.2111272663,0.2261357456,0.2418118417,0.519775331,0.0329782814,-0.4396682084,0.2005505115,-0.0733786747,0.3120963573,-0.2664199173,0.148984611,0.1087667048,-0.1928352118,-0.0266905054,-0.1430124491,-0.0080376845,-0.2255253941,0.017436821,0.3101727664,-0.2037786096,0.4758344889,0.1304406524,0.6477096081,0.1487699151,-0.3631492555,0.3281261325,-0.0603640862,-0.0706748888,-0.1189915165,-0.1572399288,0.1850177199,-0.1347288191,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3310","title":"Fatal error condition occurred in aws-c-io","comments":"@lhoestq It happens also on `squad`. It successfully downloads the whole dataset and then crashes on: \r\n\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\nI tested it on Ubuntu and its working OK. Didn't test on non-preview version of Windows 11, `Windows-10-10.0.22504-SP0` is a preview version, not sure if this is causing it.","body":"## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":61,"text":"Fatal error condition occurred in aws-c-io \n ## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n @lhoestq It happens also on `squad`. It successfully downloads the whole dataset and then crashes on: \r\n\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\nI tested it on Ubuntu and its working OK. Didn't test on non-preview version of Windows 11, `Windows-10-10.0.22504-SP0` is a preview version, not sure if this is causing 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3310","title":"Fatal error condition occurred in aws-c-io","comments":"I see the same error in Windows-10.0.19042 as of a few days ago:\r\n\r\n`Fatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS`\r\n\r\npython                    3.8.12          h7840368_2_cpython    conda-forge\r\nboto3                     1.20.11            pyhd8ed1ab_0    conda-forge\r\nbotocore                  1.23.11            pyhd8ed1ab_0    conda-forge\r\n\r\n...but I am not using `datasets` (although I might take a look now that I know about it!)\r\n\r\nThe error has occurred a few times over the last two days, but not consistently enough for me to get it with DEBUG. If there is any interest I can report back here, but it seems not unique to `datasets`.","body":"## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":95,"text":"Fatal error condition occurred in aws-c-io \n ## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n I see the same error in Windows-10.0.19042 as of a few days ago:\r\n\r\n`Fatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS`\r\n\r\npython                    3.8.12          h7840368_2_cpython    conda-forge\r\nboto3                     1.20.11            pyhd8ed1ab_0    conda-forge\r\nbotocore                  1.23.11            pyhd8ed1ab_0    conda-forge\r\n\r\n...but I am not using `datasets` (although I might take a look now that I know about it!)\r\n\r\nThe error has occurred a few times over the last two days, but not consistently enough for me to get it with DEBUG. If there is any interest I can report back here, but it seems not unique to `datasets`.","embeddings":[-0.5499543548,-0.0519864187,-0.0950805396,0.0924664736,0.0359088965,-0.0551967956,0.2764566243,0.1207167059,0.0653903782,0.1175086349,-0.0487391911,0.6524449587,0.0359027721,0.1338519454,-0.2451501638,0.0767986253,0.2522390783,0.2151822895,-0.4482086599,0.2373423278,-0.2854837477,0.3709986806,-0.2946825027,0.1192941368,-0.1002907604,-0.2287340909,-0.1983651519,0.2068596035,0.1315042078,-0.3093938231,0.3265756965,-0.2388524115,0.260709554,0.3707391024,-0.0001157607,-0.0917856917,0.4071691632,0.1486902237,-0.3117602766,0.0608842522,-0.3279656768,0.0986503735,0.0363712534,-0.2565174997,0.4048961103,0.2050230801,-0.0249987468,-0.2850678265,0.1051981077,0.5059043169,0.1646059752,0.7699396014,0.1943997741,-0.0498260446,-0.0817570388,0.0031183595,-0.2729866207,0.3502187431,0.2688423693,-0.369284153,0.1845428646,0.1368412226,-0.1802813113,0.0859845355,-0.0347465687,-0.093160525,0.0909114182,-0.2314381748,0.2387269139,-0.0126068806,0.3991873562,-0.487446934,-0.271063447,0.1433965117,0.0923841298,-0.2043912411,0.3033299744,0.2395811081,-0.1512047648,0.2067984939,0.1790197492,0.3022569716,-0.2204627693,0.146666497,-0.0315936133,0.1247437149,-0.1796292812,0.1160246804,-0.0641091317,0.0285728201,0.0609194003,-0.2030083835,-0.2205758691,0.024106523,-0.7777591348,0.0045288946,-0.0353003927,0.3059518933,-0.0020858147,-0.0201336574,0.0530151017,-0.2604514062,0.3260268867,0.1583530605,0.0854598284,-0.174895823,0.0649420992,0.0954027548,0.2698765993,0.0368406586,0.0433923006,-0.0771570802,-0.1067199782,-0.4406810403,0.2347748131,0.0963895246,0.4120787382,-0.1730630547,-0.589012146,0.4901894331,-0.4142447114,0.2129436135,-0.0865227357,0.3402362466,0.0241558291,-0.0961448923,0.1311383992,0.25503847,-0.3748916686,0.2042334229,-0.0502572805,0.090601705,-0.1479992718,0.0254565403,0.2121167481,-0.3565871716,0.1356441677,0.273517251,0.1095330566,-0.3662677109,0.0266193692,0.0036830513,-0.3870200217,0.3093945384,0.1323302984,0.2463441491,-0.0133627253,0.2117122263,0.1005880609,0.3257830441,-0.0256925393,-0.2929842472,-0.4018700123,0.2093170732,-0.0628747791,0.0969868377,-0.0543976761,-0.2722679377,0.2890055478,-0.0486121699,0.2352813035,-0.2277192622,0.0832826868,-0.1051631048,0.0331252888,0.2624882758,-0.165119186,0.130304262,-0.241162926,-0.1495911777,0.1135120168,0.160461247,-0.1007713452,0.0089794155,-0.4193948805,-0.0071923919,0.0395267904,-0.0793138742,-0.2452780604,0.3874911368,-0.0355616845,-0.01865975,-0.062049605,-0.1626514494,-0.0758560225,-0.110447593,-0.0178956818,-0.4756789804,0.0559362955,-0.0080138389,-0.2914662957,-0.3126812279,0.1069756672,0.1609320045,0.2130126059,-0.0402935594,0.2886612713,-0.0392492674,0.3231606781,-0.051369518,-0.066037342,0.2187791318,0.3184036613,-0.3267127872,0.0563051179,-0.144039914,-0.5493645668,0.2409831285,0.2527566254,0.1963088661,-0.0165657867,-0.0629065856,-0.0942296535,0.3367317617,0.0385846235,-0.1820252389,0.0945251659,-0.1042870209,-0.0103584174,-0.1380599439,-0.2619074583,0.4413397014,-0.6677671671,0.1126746237,-0.1301283389,0.1367959082,-0.2374382913,-0.3212203681,0.0351993293,-0.1402149945,0.1950994879,0.1580634564,-0.258245945,0.1766111255,-0.1578155756,0.4828460515,-0.1985139847,0.3389188945,0.0324966535,-0.3154918849,-0.0294281151,0.1426783204,0.1578569263,0.0499503203,0.2831950486,0.2175726146,-0.2182431072,0.2862241268,-0.1284542233,-0.0120511083,0.0301051252,-0.0502830818,-0.0200290717,0.1148649827,0.4177231193,0.1449840814,0.2610106766,-0.1689374,-0.4338126779,-0.1010694802,0.1344828457,-0.0662300065,0.1614035964,0.0377268679,-0.0388793312,0.0679811016,0.4430504739,-0.10253416,0.853271246,-0.0847852305,-0.0818998441,0.2397218049,-0.1352391839,-0.2134366482,0.5190565586,0.2231746018,0.0854314417,0.5037112236,0.1369891614,0.1201840714,-0.2697924078,-0.5076423287,0.0977120996,0.2017125636,-0.3069103956,0.2155214101,-0.0066144834,0.1608610749,-0.137680456,0.1963378191,0.0677706748,-0.0010174013,-0.0749843642,0.343577981,-0.1497753561,0.2012768388,-0.2218019813,0.0472315103,-0.0674041733,0.2412846386,-0.3252986073,-0.0944621786,-0.2167848349,0.0150364088,0.108233206,-0.1634305865,0.296561569,0.0087109115,-0.0587374158,-0.2329644561,-0.0927876979,0.0088474415,0.1232949197,0.3535715044,0.2354681194,0.2022420764,-0.1104891002,-0.5874230862,0.0417113416,-0.2271300852,-0.0078617129,-0.063849248,-0.2713923454,0.005524456,-0.2999743819,-0.205431059,-0.0234898515,-0.4739430845,0.0252039228,-0.4458583891,0.0390360653,-0.0838099942,0.1785551906,0.149081856,0.3993597925,-0.1303739995,-0.2798657715,-0.2293706834,0.0465249978,-0.0348296501,-0.1626736671,0.1588022113,0.2172312289,0.4926842451,0.0797185674,-0.3393376172,0.2580157518,-0.1195010692,0.1867918968,-0.1811490953,0.0491637699,0.1079762354,-0.0956415758,0.0338958055,-0.1273284107,0.0297842454,-0.194727838,-0.0628012717,0.2559399605,-0.0705881789,0.4808875322,0.1483960003,0.4612820148,0.3384514153,-0.3334413171,0.2188146114,0.0769829601,0.1375112534,-0.0855768025,-0.2624497414,0.1904401481,-0.1460716128,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3310","title":"Fatal error condition occurred in aws-c-io","comments":"I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?","body":"## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":25,"text":"Fatal error condition occurred in aws-c-io \n ## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?","embeddings":[-0.5580899119,-0.0866052285,-0.1800105274,0.233155638,0.1390396059,-0.0578072816,0.1020179242,0.1604059339,0.1959946156,0.2191377878,0.0472118072,0.5035429001,-0.0814870149,0.1524396539,-0.0512856767,-0.0705786645,0.2270470262,0.1326991469,-0.3635616302,0.2618990839,-0.2235796303,0.2627207041,-0.2026831955,0.1686506867,-0.22370556,-0.19313173,-0.1214234382,0.2672085464,-0.1306808591,-0.3011425436,0.3730950356,-0.2657856941,0.2278282195,0.451644212,-0.0001039624,-0.0457099788,0.2753757536,0.1424202323,-0.3168179691,-0.0783031657,-0.3594657481,-0.0492862612,0.0719807819,-0.3418899775,0.3768461347,0.136936754,-0.049751427,-0.2243692726,0.1877654493,0.4523934722,0.2853610814,0.6975249648,0.2334341705,0.0338981412,0.2406613529,-0.1576882452,-0.2585333288,0.2231757492,0.217384696,-0.3406144083,0.2064781934,0.0588249452,-0.2616662383,0.0580960363,0.11309167,-0.0689571351,0.0112828864,-0.1876522303,0.0907397866,-0.0828919709,0.3635474145,-0.4973260462,-0.1244628355,0.2313821167,0.0128852315,-0.3166198134,0.1591460854,0.1135460585,-0.1026040018,0.133354634,0.0853157565,0.1846503168,-0.2870873511,0.2660007775,-0.1935051233,0.3399456739,-0.1535816044,0.0781577155,-0.091625452,0.0350055471,0.0082822787,-0.0728227273,-0.2277339548,0.1047399789,-0.6485286951,-0.066129297,0.0728498772,0.2460377514,0.099136211,0.0072351932,0.0361944698,-0.0946311876,0.1987974942,0.2689354122,0.075153023,0.0318723693,-0.0267764963,0.0699839517,0.3378072381,0.1059997976,-0.0680556744,-0.1602002531,0.0794105083,-0.2515023947,0.2491411269,0.0540216379,0.466520071,-0.2341666222,-0.4691081345,0.408459574,-0.3015960157,0.1269177943,-0.0450673327,0.4228080809,0.0739518255,-0.132183224,0.1611850709,0.1047218665,-0.2570319176,0.1472050697,-0.1638287008,0.0397831015,-0.2234100699,0.0382363163,0.1219640374,-0.223940894,0.1807436645,0.1838332862,0.1145102605,-0.3768922389,0.1691162437,0.019232342,-0.3441358805,0.3384668529,0.187603116,0.1574772745,0.0718117207,-0.0348829515,0.1204969585,0.226181671,-0.2306179702,-0.3109401762,-0.3090092242,0.3398908675,-0.0365162119,0.0697595254,-0.1349172741,-0.2245218754,0.145390451,-0.1713083386,0.1508699805,-0.2407563478,0.206962198,-0.1823586673,0.1344608963,0.2501844466,-0.3147757053,0.1470018923,-0.3205111027,-0.1503469199,0.0913679227,0.2561937571,-0.1589738876,0.0352723897,-0.3089305758,0.0305992793,0.1551471204,0.0464306846,-0.3182401061,0.3583157063,-0.0958063304,-0.1559319496,-0.0942559391,0.0234850496,-0.0946473032,-0.0589139275,-0.0174801406,-0.2600272894,-0.0592311285,0.1145751923,-0.2355815321,-0.2842610776,0.0446613096,0.1944518685,0.2549016476,-0.071017459,0.3657622635,-0.1082936153,0.3347533047,-0.1403303146,-0.1671600789,0.3004722893,0.4261004627,-0.156661585,0.0489574783,-0.3304820955,-0.4977252483,0.2141330987,0.2226576507,0.2173025608,-0.1248248592,-0.1662078202,-0.1753573418,0.3738298416,0.0586372949,-0.0506445728,0.2119262666,-0.0497814864,-0.0168523192,-0.0617943443,-0.2481017858,0.2332936823,-0.5981218815,0.0709532872,-0.2811796665,0.2732843161,-0.1892388612,-0.3173205853,0.0160034057,-0.2112745643,0.1684763134,0.0340317115,-0.21804519,0.26575014,-0.2044205219,0.4686380029,-0.2001602799,0.2894155383,-0.0485164821,-0.3430214822,0.1722514778,0.0255460348,0.17710118,-0.0134095289,0.3564876616,0.2049088925,-0.2383441329,0.2057049125,-0.0585470945,-0.0367575586,-0.0100899031,-0.1041613817,0.0792974457,0.0572305918,0.3227185905,0.027743319,0.1451968998,-0.2174709141,-0.3156684041,-0.1250321418,0.2692941129,-0.0032227326,0.2474181205,-0.0181315932,-0.1586838067,0.0984704867,0.4700219631,0.1214291528,0.7873452306,0.1902834028,-0.1476653218,0.1761024594,-0.18489483,-0.1504384726,0.411647588,0.1937536746,0.1181440204,0.2728657126,0.1356789619,0.0356617272,-0.4417082071,-0.4641523957,-0.1209664792,0.2546760738,-0.29793787,0.2971012294,-0.0888507888,0.0958377495,-0.0707038268,0.1168236732,0.0200050008,-0.1598554403,-0.0914934799,0.3216972947,-0.1792332828,0.0909569114,-0.1593285203,-0.092128925,0.0093543222,0.0950328037,-0.3576503992,-0.1598637253,-0.1707615405,0.1308873147,0.3484070897,0.0027621565,0.4173465371,0.0619248226,-0.1146875471,-0.2451522648,-0.1573433429,-0.0206214804,-0.1067988053,0.3480917513,0.2014884353,0.246744886,-0.2452280074,-0.4284566343,0.0656437948,-0.206974715,0.0409327336,0.000834041,-0.1801218241,-0.1447228491,-0.2529622614,-0.3234040737,-0.2212115228,-0.4034318924,-0.0665817484,-0.1826650798,0.1247239336,-0.1730831414,0.2974699736,0.1841197312,0.3028045297,-0.2465657741,-0.1778046787,-0.2766482532,0.0815851614,-0.0800498351,-0.1049397662,0.1979304999,0.2295288444,0.4112249911,0.104614906,-0.4618683457,0.2610212564,-0.1656036228,0.257293582,-0.1780205667,0.1114292592,0.1481206417,-0.0206375308,-0.0547719225,-0.0662049428,0.048046682,-0.3267090321,-0.0158718918,0.2883496881,-0.1356639713,0.5551948547,0.017856406,0.6567829251,0.1280540973,-0.2948003709,0.3476786613,-0.0864529163,0.0429034121,-0.1535404325,-0.1987636387,0.0465231612,-0.0679713637,-0.4468875825,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3310","title":"Fatal error condition occurred in aws-c-io","comments":"> I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?\r\n\r\nAgreed, this issue is not likely a bug in datasets, since I get the identical error without datasets installed.","body":"## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":45,"text":"Fatal error condition occurred in aws-c-io \n ## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n > I'm not sure what `datasets` has to do with a crash that seems related to `aws-c-io`, could it be an issue with your environment ?\r\n\r\nAgreed, this issue is not likely a bug in datasets, since I get the identical error without datasets installed.","embeddings":[-0.5877729654,-0.0985602587,-0.1441146284,0.2184793502,0.0923989713,-0.0479466133,0.1061486974,0.1226171628,0.2112214118,0.1895916313,0.0708782151,0.5784415603,-0.0558516607,0.1429107934,-0.1018205211,-0.0602276288,0.2509336174,0.1484443992,-0.3987596035,0.2907881439,-0.2431584746,0.2944349349,-0.2579341233,0.1939394474,-0.2135604471,-0.1925464869,-0.144696936,0.2619648576,-0.0919336453,-0.3031488359,0.3964144886,-0.2560611367,0.2435160279,0.4746379554,-0.0001061082,-0.0802018568,0.2947808206,0.1537581384,-0.2961405516,-0.0700709447,-0.3931898773,-0.0043014698,0.0910912901,-0.3130131662,0.4095440209,0.1187763885,-0.0355343372,-0.25431934,0.1728733182,0.4458432794,0.2461789995,0.6981562376,0.2192260027,0.027631335,0.1673302501,-0.137104243,-0.2794895172,0.2411759049,0.2327432483,-0.3658467531,0.2037448883,0.0629404113,-0.272555232,0.0917168036,0.1506555229,-0.0715620294,0.0003568027,-0.2031454891,0.1082318127,-0.0729651377,0.3915719986,-0.5057066679,-0.1164457276,0.2129105628,0.0425734818,-0.3092786968,0.1988964826,0.127735287,-0.139655903,0.1549288034,0.0949405953,0.1978705674,-0.3011851907,0.2730351985,-0.1823951155,0.275369525,-0.1561724991,0.0866137519,-0.0651198626,0.0592302643,0.0309847482,-0.131320402,-0.2017468214,0.0839840397,-0.6823880672,-0.0654324964,0.0735922828,0.2899926901,0.0798469558,-0.0321773738,0.0161203183,-0.1202557385,0.2045454085,0.2596762478,0.0885532126,-0.0285941307,0.0108804414,0.0731568038,0.32603845,0.0965156779,-0.0180213787,-0.1428620666,0.0893203542,-0.2459668964,0.2204299122,0.0406367928,0.471663326,-0.2280564457,-0.4993340373,0.4374729693,-0.3630462587,0.1760657877,-0.0628790483,0.4015411437,0.0552389883,-0.1487731636,0.1606702209,0.1476567537,-0.2831787765,0.1800121665,-0.1238824427,0.0437336452,-0.2326861173,0.0404608808,0.1293591112,-0.226401791,0.1680916995,0.2273063213,0.1051030755,-0.4078169763,0.1837703139,0.0392883122,-0.3672154546,0.3187618256,0.1861786097,0.1617942452,0.0614122227,0.028575588,0.1389396936,0.2625074685,-0.1644236445,-0.3175087273,-0.2961941361,0.313583076,-0.0716398582,0.0767013431,-0.1445116848,-0.2378244102,0.1425104737,-0.140611425,0.1529326141,-0.2565338314,0.1812167615,-0.1505510658,0.0858530179,0.2588173151,-0.2899450958,0.1360146999,-0.347525388,-0.1644179821,0.0763155445,0.2417829186,-0.1393268853,0.0218467135,-0.3356440961,0.030200921,0.1445575804,0.0923061967,-0.3059608042,0.388974607,-0.0927611142,-0.1098951101,-0.1311434358,0.0132037122,-0.1080991477,-0.0750204474,-0.028872177,-0.3261514306,-0.0640230924,0.0589595363,-0.2137888968,-0.3036156595,0.0653344691,0.2127976418,0.2309681475,-0.0334369168,0.3607060313,-0.083127737,0.3458636701,-0.1214040965,-0.1659340262,0.2928099334,0.4134373367,-0.1757052541,0.0350885987,-0.3258332312,-0.515524447,0.2304554731,0.2795439363,0.222651124,-0.1216916591,-0.1652115732,-0.1621968597,0.3786700964,0.0494515412,-0.0510930382,0.1771179289,-0.063086085,-0.059063673,-0.0888910368,-0.2687593102,0.2841090262,-0.647769928,0.0853074789,-0.2740427554,0.209120065,-0.1813038588,-0.3070008159,0.036824204,-0.2117384076,0.1424227655,0.061641138,-0.2239398062,0.2642253637,-0.1875464767,0.4725883901,-0.2061443627,0.3058314621,-0.0477043092,-0.377743721,0.1214581132,0.0293555651,0.1648995876,-0.0026105994,0.3278176486,0.1968502998,-0.2226947099,0.2401116043,-0.0987496376,-0.00536793,-0.0346283019,-0.0988172293,0.0412729643,0.0846100897,0.3563315868,0.0248862356,0.1307502091,-0.2181731462,-0.3548564613,-0.1304548383,0.2157484591,0.0052127508,0.2641460001,-0.0064031179,-0.1238355711,0.1349169165,0.4550622106,0.0737372786,0.8468420506,0.1617290676,-0.1289498657,0.2258804888,-0.1658566445,-0.1615800261,0.4344459176,0.1932590455,0.1356626749,0.2720466256,0.1133173034,0.0397038199,-0.4209651053,-0.487921834,-0.0897921398,0.2629649639,-0.3018692136,0.2855698466,-0.0599563532,0.0885579735,-0.072458446,0.1629515886,0.0512417331,-0.1267940551,-0.0727090463,0.3433230221,-0.1785356551,0.1132278591,-0.164522782,-0.0806008056,-0.0116327759,0.1421697885,-0.3507028222,-0.1870554984,-0.1730582118,0.1033710092,0.3211432993,0.0019080397,0.4160145521,0.0493783318,-0.126894936,-0.2298738956,-0.1113520414,-0.0115338368,-0.0860465467,0.3279513121,0.2397269756,0.2800233364,-0.2422124743,-0.3903715611,0.0365051515,-0.2309512049,0.0416380689,-0.0069022342,-0.1970526278,-0.1180887297,-0.2494740933,-0.3486529291,-0.1644391418,-0.4070203602,-0.0493023917,-0.244229272,0.0923831761,-0.1876922548,0.2940323055,0.1812491566,0.3194428682,-0.2857073843,-0.1931092739,-0.2581537962,0.0681869835,-0.0474565588,-0.106008023,0.2231672406,0.2655582428,0.4115251899,0.1044436768,-0.4842069149,0.2805033922,-0.1529534757,0.25570485,-0.1771907657,0.1008089483,0.1254772842,-0.0445096679,-0.0258188136,-0.0750812888,0.0647050142,-0.2931645513,-0.03490353,0.3022507727,-0.1162873507,0.5645014048,0.061568372,0.6580145955,0.1730603278,-0.2855513096,0.2953934073,-0.0488661826,0.05118661,-0.1462793797,-0.2019894868,0.0491037332,-0.0766164362,-0.4318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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3310","title":"Fatal error condition occurred in aws-c-io","comments":"Will close this issue. Bug in `aws-c-io` shouldn't be in `datasets` repo. Nevertheless, it can be useful to know that it happens. Thanks @leehaust @lhoestq ","body":"## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":25,"text":"Fatal error condition occurred in aws-c-io \n ## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n Will close this issue. Bug in `aws-c-io` shouldn't be in `datasets` repo. Nevertheless, it can be useful to know that it happens. Thanks @leehaust @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3310","title":"Fatal error condition occurred in aws-c-io","comments":"I have also had this issue since a few days, when running scripts using PyCharm in particular, but it does not seem to affect the script from running, only reporting this error at the end of the run.","body":"## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":38,"text":"Fatal error condition occurred in aws-c-io \n ## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n I have also had this issue since a few days, when running scripts using PyCharm in particular, but it does not seem to affect the script from running, only reporting this error at the end of the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3310","title":"Fatal error condition occurred in aws-c-io","comments":"I also get this issue, It appears after my script has finished running. I get the following error message\r\n```\r\nFatal error condition occurred in \/home\/conda\/feedstock_root\/build_artifacts\/aws-c-io_1637179816120\/work\/source\/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n################################################################################\r\nStack trace:\r\n################################################################################\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-c-common.so.1(aws_backtrace_print+0x59) [0x2aabe0479579]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-c-common.so.1(aws_fatal_assert+0x48) [0x2aabe04696c8]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/.\/libaws-c-io.so.1.0.0(+0x13ad3) [0x2aabe0624ad3]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/.\/libaws-c-io.so.1.0.0(+0x113ca) [0x2aabe06223ca]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-crt-cpp.so(_ZN3Aws3Crt2Io15ClientBootstrapD1Ev+0x3a) [0x2aabe041cf5a]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/libaws-cpp-sdk-core.so(+0x5f570) [0x2aabe00eb570]\r\n\/lib64\/libc.so.6(+0x39ce9) [0x2aaaab835ce9]\r\n\/lib64\/libc.so.6(+0x39d37) [0x2aaaab835d37]\r\n\/lib64\/libc.so.6(__libc_start_main+0xfc) [0x2aaaab81e55c]\r\npython(+0x1c721d) [0x55555571b21d]\r\nAborted\r\n```\r\nI don't get this issue when running my code in a container, and it seems more relevant to PyArrow but thought a more complete stack trace might be helpful to someone\r\n","body":"## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":95,"text":"Fatal error condition occurred in aws-c-io \n ## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n I also get this issue, It appears after my script has finished running. I get the following error message\r\n```\r\nFatal error condition occurred in \/home\/conda\/feedstock_root\/build_artifacts\/aws-c-io_1637179816120\/work\/source\/event_loop.c:72: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n################################################################################\r\nStack trace:\r\n################################################################################\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-c-common.so.1(aws_backtrace_print+0x59) [0x2aabe0479579]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-c-common.so.1(aws_fatal_assert+0x48) [0x2aabe04696c8]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/.\/libaws-c-io.so.1.0.0(+0x13ad3) [0x2aabe0624ad3]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/.\/libaws-c-io.so.1.0.0(+0x113ca) [0x2aabe06223ca]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-c-common.so.1(aws_ref_count_release+0x1d) [0x2aabe047b60d]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/.\/libaws-crt-cpp.so(_ZN3Aws3Crt2Io15ClientBootstrapD1Ev+0x3a) [0x2aabe041cf5a]\r\n\/home\/user_name\/conda_envs\/env_name\/lib\/python3.7\/site-packages\/pyarrow\/..\/..\/..\/.\/libaws-cpp-sdk-core.so(+0x5f570) [0x2aabe00eb570]\r\n\/lib64\/libc.so.6(+0x39ce9) [0x2aaaab835ce9]\r\n\/lib64\/libc.so.6(+0x39d37) [0x2aaaab835d37]\r\n\/lib64\/libc.so.6(__libc_start_main+0xfc) [0x2aaaab81e55c]\r\npython(+0x1c721d) [0x55555571b21d]\r\nAborted\r\n```\r\nI don't get this issue when running my code in a container, and it seems more relevant to PyArrow but thought a more complete stack trace might be helpful to someone\r\n","embeddings":[-0.5238194466,0.0124544147,-0.151010707,0.0529182814,0.1022879109,-0.0562829711,0.2720829844,0.1647469103,0.1394755989,0.2164279222,0.1449791491,0.6392529607,0.027232049,0.2679426968,-0.1522212178,0.0486654751,0.0849769935,0.224835813,-0.518291235,0.2776283324,-0.3364136815,0.4034168124,-0.2084615529,0.0623176508,-0.1796566397,-0.1809760481,-0.0179907065,0.2800240815,0.0241864342,-0.2930750847,0.2808550894,-0.1740760058,0.1775286645,0.3455489874,-0.0001122971,-0.1695673615,0.3209398687,0.0954146981,-0.2818976343,-0.0759116188,-0.2237413526,0.1225464195,-0.018413946,-0.3855493069,0.2665086091,0.2228139788,-0.0351273045,-0.3130322099,0.0639874414,0.3623966575,0.2311107516,0.6086918712,0.2390036583,0.1065835655,0.1769790798,0.0201971717,-0.2600747049,0.3164567351,0.2072057873,-0.3317071497,0.2380837649,0.2161259502,-0.2167508006,0.1238164008,0.1480984092,-0.1799426079,0.1517985165,-0.2383641154,0.2252559662,-0.0177667309,0.3117286861,-0.5552003384,-0.2122281343,0.2023416162,0.0684067458,-0.289975822,0.2288647741,0.2369190603,-0.0690649152,0.2507274151,0.1910125613,0.2601273954,-0.2863741219,0.1847884357,0.0689177066,0.1911074519,-0.1828379035,0.1061038226,-0.0358495899,0.0187331941,0.0074175433,-0.1829016358,-0.1297427714,0.0905333757,-0.6190882921,-0.1297151744,0.0512392782,0.3003859818,0.0847876891,0.0357260965,-0.0893477798,-0.1897670776,0.2961357534,0.1864766926,-0.0159307159,-0.0019253801,-0.0480254777,0.1399167478,0.2084679902,0.2280146033,-0.0154904742,-0.0808263719,0.0770037249,-0.3536376357,0.17915757,0.2183007002,0.5821425319,-0.1923940629,-0.5256128311,0.558688879,-0.272764504,0.118133463,-0.0232024863,0.3889777064,0.0861148834,-0.0477016382,0.1652632207,0.2172616273,-0.3164328635,0.1561413109,-0.1200020462,0.1590818167,-0.1640794724,0.0092636859,0.184533596,-0.2929286659,0.234120056,0.2619328797,0.0779030845,-0.4948627651,0.1453879923,-0.0477273948,-0.3259894848,0.3115953505,0.18208009,0.128849566,-0.0247574784,0.1924547702,0.1136576012,0.3079918325,-0.0596537068,-0.3040207326,-0.3930382431,0.265378207,-0.0842298493,0.107464999,-0.1293657422,-0.3998485506,0.374222517,0.0534043424,0.2311758697,-0.2634414434,0.0432297736,-0.1469577253,-0.0443529338,0.3126033843,-0.196622774,0.1726597548,-0.2365850359,-0.1387275308,0.263228178,0.1787834018,-0.1095556989,0.1297786981,-0.325376749,0.0368281715,0.0568409041,-0.0487653539,-0.2840595543,0.4315977395,-0.1971073449,0.0049776989,0.0662252232,-0.1383138299,-0.0531929433,-0.2159402072,-0.041555129,-0.3213523626,0.0290034469,0.1009669751,-0.2742828429,-0.297449261,0.0292206351,0.074093692,0.2205691636,-0.0281637181,0.3067128062,-0.0809546784,0.3037678003,-0.0793870017,-0.0203767773,0.2715915143,0.3118673563,-0.2359285653,0.0105238156,-0.1352689862,-0.4974723458,0.1576951742,0.1554072201,0.0694633871,-0.1817341298,-0.2130592763,-0.2614330649,0.3662571013,0.0462982059,-0.1079462022,0.1434023082,-0.0239270162,-0.0093898382,-0.1723456979,-0.3210767806,0.2475385517,-0.7090710402,0.0266252104,-0.280089438,0.1926078498,-0.2058549672,-0.3479899168,0.0967651084,-0.027067963,0.2817362845,0.0489110015,-0.3139442801,0.2076885551,-0.1238794699,0.4942332208,-0.2492685169,0.294922322,-0.1248090863,-0.2893086672,0.0072770338,0.1047294587,0.1936049014,0.0469898582,0.3869611025,0.3012582362,-0.2169072181,0.1706787646,-0.1525349617,-0.1586353928,0.0530840941,-0.1401323974,0.042161271,-0.0549660921,0.37405774,0.1470421106,0.193387717,-0.1575617343,-0.3210006952,-0.0652742162,0.216031462,-0.0248103645,0.1133152694,-0.032746926,0.0655438825,0.0147378501,0.3507847786,0.15206559,0.9047679305,0.0021477044,-0.2390841395,0.1623970121,-0.018826168,-0.2555149496,0.408932209,0.085513182,0.2295681387,0.3951897323,0.0730719939,0.0430452079,-0.2154822946,-0.5383497477,-0.0007863113,0.2184128016,-0.2915556133,0.2386303395,-0.0260642376,-0.0706566423,-0.0331003815,0.2069049329,0.2976565659,0.0203175414,-0.0556263328,0.3965717256,-0.2665339112,0.0989785045,-0.144050464,-0.0852193087,-0.0112185627,0.1877100319,-0.4556871057,-0.1737346351,-0.0300918669,0.0486782603,0.1671336442,-0.1494405121,0.2685695589,-0.0221605748,-0.2396876216,-0.3495423794,-0.1085358858,-0.07728488,0.005394516,0.3346959949,0.2625851333,0.3028533459,-0.1726220548,-0.494515568,0.0347615592,-0.2460217923,0.0378758088,-0.0304781124,-0.2570068836,-0.1170073301,-0.3578953147,-0.2480024695,-0.0715726092,-0.4674212933,-0.1076240689,-0.2539976835,0.1292697191,-0.1985675246,0.2198301256,0.0672132373,0.3614298105,-0.1912659407,-0.2563484609,-0.3309599459,0.0219521187,-0.1302226782,-0.1844267845,0.1995252073,0.1468322575,0.5444603562,-0.0247786827,-0.3840246201,0.2265354246,-0.1091861576,0.1617455333,-0.2423909903,0.0887831748,0.0484631285,-0.0183697082,0.0102663804,-0.1978460848,0.0946343914,-0.1706688404,-0.0472108684,0.2006496936,0.0310063045,0.4837890863,0.058215823,0.582547605,0.1878430992,-0.3849367797,0.3777856231,0.0994062349,0.0670489147,-0.0965986252,-0.1637932509,0.062348254,0.0077484082,-0.291842252,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3310","title":"Fatal error condition occurred in aws-c-io","comments":"@CallumMcMahon Do you have a small reproducer for this problem on Linux? I can reproduce this on Windows but sadly not with linux.","body":"## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":23,"text":"Fatal error condition occurred in aws-c-io \n ## Describe the bug\r\nFatal error when using the library\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikiann', 'en')\r\n```\r\n\r\n## Expected results\r\nNo fatal errors\r\n\r\n## Actual results\r\n```\r\nFatal error condition occurred in D:\\bld\\aws-c-io_1633633258269\\work\\source\\event_loop.c:74: aws_thread_launch(&cleanup_thread, s_event_loop_destroy_async_thread_fn, el_group, &thread_options) == AWS_OP_SUCCESS\r\nExiting Application\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.15.2.dev0\r\n- Platform: Windows-10-10.0.22504-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n @CallumMcMahon Do you have a small reproducer for this problem on Linux? I can reproduce this on Windows but sadly not with linux.","embeddings":[-0.4971728027,-0.2210785151,-0.0642463192,0.1765674204,0.0318540297,-0.1087293923,0.1770698428,0.0373218581,0.2282173634,0.2250352055,0.1644738764,0.6636958122,-0.0692412853,0.127204746,-0.1631729752,-0.1083406657,0.1769800335,0.1298452169,-0.6801725626,0.0820551887,-0.174765259,0.4391154945,-0.2951258719,0.0238216911,-0.2007175982,-0.217460826,-0.1243986636,0.1705572605,0.1157133803,-0.1533324718,0.3042349219,-0.3391991854,0.1236087084,0.5622186661,-0.0001124262,-0.1677456945,0.359116137,0.1087240726,-0.2666860223,-0.1745546311,-0.2416810691,0.0075407312,0.0885300636,-0.3842111826,0.3537594676,0.115463987,-0.0108280191,-0.2205198556,0.0820008367,0.2766173482,0.2125620693,0.5830558538,0.1947467029,0.0748724341,0.1517057717,0.0618792921,-0.3208602071,0.1528845131,0.2221178114,-0.3108467758,0.2576707602,0.2405781597,-0.1925660521,0.0092518851,-0.0281223152,-0.1240074337,-0.0798906386,-0.2105618268,0.1713142544,0.0140736597,0.3097489774,-0.4213241935,-0.1427878588,0.2894179523,0.1346809715,-0.1947109252,0.3737763166,0.2496002018,-0.1357899457,0.1493736953,0.0880448818,0.2445411235,-0.3302844763,0.3437497914,-0.063707605,0.2953143716,-0.1070979312,0.0868818313,-0.0091125946,-0.0671784729,-0.0411530286,-0.2508850694,-0.1044514403,0.0821552128,-0.728479147,-0.0583709143,-0.0703176856,0.3240495622,0.0685374811,-0.0896083191,0.0269974191,-0.2046242058,0.2174306214,0.2488621622,0.0102805812,-0.1458491087,-0.1105769128,0.2551917434,0.3134558499,-0.0184554551,-0.0818060264,-0.1087912321,0.0389893502,-0.2712669671,0.1726638526,0.0814342573,0.4826302528,-0.1829048991,-0.3514203131,0.4767962396,-0.486379087,0.2622300386,0.0846956,0.3751203418,0.0065925908,-0.1616888493,0.1712464988,0.1952331364,-0.3567648232,0.2068656832,-0.1200270131,0.0619109087,-0.1951188892,-0.0454051793,0.1687473804,-0.2796514034,0.2228827775,0.3305127919,0.2694880366,-0.4463101923,0.1737725437,-0.1254402399,-0.3136378527,0.3841664195,0.0921322852,0.1243513227,-0.0247930996,0.2494847327,0.1426856369,0.3251042068,-0.1971881837,-0.1820934117,-0.1820646077,0.2366505414,-0.0437932573,0.0327573717,-0.2009120733,-0.2346858382,0.2000063956,-0.0514407046,0.2320448607,-0.3304471076,0.1143444255,-0.064789027,0.045549009,0.1454087794,-0.0468237773,0.091573678,-0.2804612219,-0.0152140716,0.3730466664,0.3103266358,-0.0066643013,0.0152873741,-0.3445258439,0.0394351706,0.1324070841,0.0090872627,-0.3413743377,0.3993127048,-0.005295,-0.070301868,0.0062809661,0.0240060519,-0.0728095472,-0.1616175473,-0.1525369436,-0.2635859251,0.0767781809,0.055745054,-0.2545362413,-0.3842113912,-0.0124042584,0.1528314799,0.240497008,0.0370744728,0.4161718786,-0.0958683416,0.3665204346,-0.1395183206,-0.1105783358,0.2166212052,0.4201025963,-0.2477776706,0.0819138661,-0.2198245078,-0.3767223954,0.1796052605,0.2147606462,0.1382902712,-0.0472027734,-0.0727746114,-0.0797362849,0.2930425704,0.104553774,0.0451180413,0.1034785211,-0.1232922375,-0.0792543888,-0.1165885478,-0.2137743831,0.2280023098,-0.7132330537,0.0826667026,-0.1329379678,0.2858532071,-0.1397489011,-0.2968964577,0.0373142324,-0.149709329,0.2280991226,0.0576491356,-0.309460938,0.1808059961,-0.0139497612,0.6140978336,-0.1400344968,0.2267439216,-0.0744841024,-0.2919125557,0.0402402021,0.2479781657,0.1806978881,-0.0238757674,0.2635377645,0.1343580037,-0.1518611163,0.1887818277,-0.1832184643,-0.0121166604,0.0273240581,-0.0725386366,0.0366685055,0.113491036,0.5826764107,0.041599188,0.2341855913,-0.202329576,-0.542560041,-0.2884298563,0.1802105755,0.0044132462,0.3463574052,0.1237447262,-0.0377861001,0.167944476,0.3706900477,0.1230577007,0.784738183,0.0742017329,-0.0909934416,0.2033575177,0.0691717342,-0.2102230191,0.4836141467,0.1596344113,0.1099689454,0.4181239605,0.1432646662,0.0634283498,-0.3402434886,-0.4502233565,-0.0034488577,0.2070603371,-0.2114510685,0.3344227672,-0.1320061982,0.215218842,0.0160257798,0.3020496666,0.1067893803,0.0307484139,-0.093560271,0.3137027919,-0.159721598,0.2479713261,-0.0257422756,-0.0777897462,0.0986595079,0.0154434359,-0.4391576946,-0.1824282557,-0.1310597211,0.0309769027,0.2549408078,-0.0392394848,0.3637784421,0.0442694873,-0.2046811134,-0.2991777062,-0.0884102359,-0.0253146943,0.0517745353,0.2400110662,0.2649961412,0.2318099588,-0.2738067508,-0.3301585317,0.0673338249,-0.3510271013,-0.0421218053,0.0385896936,-0.211455062,0.0541915521,-0.2427961677,-0.2259412408,-0.1548725218,-0.4721360803,0.0159844495,-0.1986342967,0.1453644186,-0.0071303165,0.3135768175,0.0503641479,0.3863610923,-0.2150816023,-0.230047062,-0.2559607923,0.1507545561,-0.0343344472,-0.1940060556,0.2101307064,0.262491405,0.3747653663,0.2373035401,-0.5420317054,0.3050324321,-0.1387978643,0.1483830363,-0.289732635,0.2168990374,0.031779103,-0.1607585996,0.0539983101,-0.1752790809,0.151914224,-0.1951247454,0.0295254122,0.2537396252,0.0401963554,0.4799320698,0.0313796178,0.5219047666,0.1732301861,-0.2272823751,0.2573488057,0.0497614294,-0.0352587849,-0.1655822545,-0.1993865967,0.1364508569,0.0255601406,-0.4722708166,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3308","title":"\"dataset_infos.json\" missing for chr_en and mc4","comments":"Hi ! Thanks for reporting :) \r\nWe can easily add the metadata for `chr_en` IMO, but for mC4 it will take more time, since it requires to count the number of examples in each language","body":"## Describe the bug\r\nIn the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.\r\n\r\n## Steps to reproduce the bug\r\nCheck [chr_en](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/chr_en) and [mc4](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/mc4)","comment_length":35,"text":"\"dataset_infos.json\" missing for chr_en and mc4 \n ## Describe the bug\r\nIn the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.\r\n\r\n## Steps to reproduce the bug\r\nCheck [chr_en](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/chr_en) and [mc4](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/mc4) \n Hi ! Thanks for reporting :) \r\nWe can easily add the metadata for `chr_en` IMO, but for mC4 it will take more time, since it requires to count the number of examples in each 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3308","title":"\"dataset_infos.json\" missing for chr_en and mc4","comments":"No problem. I am trying to do some analysis on the metadata of all available datasets. Is reading `metadata_infos.json` for each dataset the correct way to go? \r\n\r\nI noticed that the same information is also available as special variables inside .py file of each dataset. So, I was wondering if `metadata_infos.json` has been deprecated?\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/8587189\/142914413-a95a1abf-6f3e-4fbe-96e5-16d3ca39c831.png)\r\n","body":"## Describe the bug\r\nIn the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.\r\n\r\n## Steps to reproduce the bug\r\nCheck [chr_en](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/chr_en) and [mc4](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/mc4)","comment_length":55,"text":"\"dataset_infos.json\" missing for chr_en and mc4 \n ## Describe the bug\r\nIn the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.\r\n\r\n## Steps to reproduce the bug\r\nCheck [chr_en](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/chr_en) and [mc4](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/mc4) \n No problem. I am trying to do some analysis on the metadata of all available datasets. Is reading `metadata_infos.json` for each dataset the correct way to go? \r\n\r\nI noticed that the same information is also available as special variables inside .py file of each dataset. So, I was wondering if `metadata_infos.json` has been deprecated?\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/8587189\/142914413-a95a1abf-6f3e-4fbe-96e5-16d3ca39c831.png)\r\n","embeddings":[0.2620972395,-0.1249724478,-0.1153001189,0.468054086,0.0923606381,0.1910525113,0.0018949726,0.2054129541,-0.4593904912,-0.0279046223,-0.0281210262,0.21106264,-0.1727293432,-0.1559250355,-0.1255880296,0.0337350704,0.2851159573,0.0156848636,0.1599000692,-0.0855103582,-0.2001864612,0.3976737559,-0.1283580065,0.0855577737,-0.0611436628,0.1830404848,-0.2063984424,-0.0442914404,-0.1590603441,-0.4312533736,0.2353590131,0.0398006178,-0.1587904245,0.2825770676,-0.0001251127,0.0179118067,0.53104496,-0.1131992489,-0.3600983918,-0.1971810013,-0.1917228103,-0.151606828,0.1934333742,-0.1082562208,-0.0064324779,-0.2723469138,-0.0451866984,-0.3405558467,-0.1059950292,0.1045565605,0.1024094224,0.2527138889,0.1498939842,0.0529036261,0.3177395165,0.6667821407,-0.0130154463,0.329939276,0.2802713513,0.1628184915,0.169150576,0.524482429,0.1133878827,0.0098667769,0.4061584771,0.0413057506,0.0279080421,-0.161144942,0.0385943577,0.1137315854,0.6281850338,-0.3180726767,-0.6205512285,-0.5171126127,0.0421819389,-0.1521086097,0.128065303,0.2289855033,-0.2200255692,0.2265117764,-0.1356838197,-0.4401713908,-0.081716314,0.17151393,-0.0654917434,0.4547342658,-0.3320720196,-0.0106923115,0.0212473758,-0.2276614904,-0.2065890282,-0.2141699344,-0.2002338767,0.0740266517,0.0194048397,-0.1642444134,0.1506361663,-0.1574799269,0.3973388672,0.2982677817,-0.1552033871,0.0142412707,-0.4449211359,0.1157401726,0.516900897,-0.3618490994,0.2781918347,0.1576844752,0.4728693068,-0.0293373056,0.241396457,-0.0765029043,-0.0284861643,0.1892191917,-0.2269596159,0.1307040453,0.6962186098,-0.3675422072,-0.1355881393,-0.0761683062,-0.2798680961,-0.4119046628,-0.062791869,0.1697809696,-0.1782142073,0.1502872854,-0.1597202867,0.1952351183,0.0888101161,-0.1005082056,-0.0876298025,0.0836941898,-0.0328964777,0.062537007,0.0426957496,-0.2426290959,0.4524251819,-0.0822954923,0.234055832,-0.2049576044,-0.2488939017,-0.0214336924,0.0001372371,0.4044772387,-0.400195241,0.1185118333,-0.0291414857,0.1280535907,-0.3230257034,-0.08377143,-0.4856918454,-0.3798880279,-0.1427205205,0.0162609965,-0.1413747817,0.2141557038,-0.4166173637,0.2911400199,0.0668532178,-0.132133171,0.303827405,0.1542172134,0.1118590534,-0.3002676666,0.0085799377,0.5118293166,-0.0537623428,-0.2376080602,0.1803430915,-0.2361474782,-0.079779245,0.1556486338,-0.4153841436,-0.1338288337,-0.441745162,-0.122756727,0.0690642372,-0.6692339778,0.1083243787,0.3726207316,0.250839293,0.1618162096,0.1560867429,-0.1157507524,-0.2510974109,-0.0705221817,0.0475723445,-0.1593424827,0.1504765898,-0.1640530229,0.0610949472,-0.2129558772,0.0974798575,0.1523430943,0.0643074811,-0.0347950719,0.2287399769,0.1414575577,0.4197342098,0.0584723279,0.0298486184,0.7357289791,0.2928808331,0.1050535664,0.1197198033,-0.2456133068,-0.4680986702,0.1146323606,0.1614962518,-0.0197069831,-0.3958986402,-0.3824353218,0.2775464356,0.039454516,-0.0877647996,-0.1406674385,0.0110388352,0.2245682031,0.2000224441,0.2311415672,-0.3343210518,0.1732726991,-0.1360770017,0.2504481971,-0.3837603927,0.2933644652,0.0711526573,0.1327929795,0.1786892265,0.2089080364,0.0978739858,-0.2458703518,0.0504463688,0.3593584299,0.0906973407,0.1442937851,0.0086992523,0.5004242659,0.437851578,-0.2424939126,0.1204492375,0.058166828,0.1423221678,-0.1385436952,-0.3602181971,0.3074885905,0.2514772713,0.1070790738,0.3296175897,-0.2098495513,0.105343245,0.2566896677,-0.4987874031,-0.1778766513,-0.0268712882,-0.1746728569,0.2450788617,-0.0216691811,-0.4503360987,0.0492837764,0.4989840388,-0.0814204887,0.0144133782,0.1777870506,-0.1786666512,-0.0681176335,0.0934234113,-0.0152560975,0.6174108386,0.0934220999,0.2012541592,0.0468402691,0.2973654866,-0.1294168085,0.1444325596,0.3667339087,0.1319393665,0.0561485253,0.2394863963,-0.0173350666,-0.1348867118,-0.2219813019,-0.1347410977,0.1604165584,-0.2291048616,0.2417922169,-0.4512477815,-0.3341360092,-0.0356086046,-0.3057706952,-0.5319724083,-0.2984521091,-0.2369563431,-0.1162810922,0.0021459996,0.0409864411,-0.1628410667,0.1497615427,-0.1716261059,0.0342641585,-0.3090724945,-0.023085285,-0.0023275148,-0.1156634167,0.067986168,-0.0645961389,0.0593400933,-0.2022435814,0.0673342198,-0.4856492877,-0.4524100721,0.2845436037,-0.0867384002,0.0310211834,0.3519079983,-0.109947145,0.1494416296,-0.0035047198,0.0994459912,-0.1475162655,-0.2540699244,-0.0845157057,-0.0062658717,-0.110463582,-0.2938987315,-0.1346004903,-0.4714292288,-0.1523196995,0.4943174422,-0.0069418061,-0.0166137349,0.4826880991,-0.299529165,-0.0523027219,-0.3712849915,0.1141370013,-0.0537950434,-0.4564822614,0.09996894,-0.1567221731,-0.0857933536,0.2020003647,0.3132757843,0.2445069104,-0.1075054035,-0.3546913564,-0.2441356927,0.111877948,0.2759058774,-0.3212593198,0.0514584817,0.2771966457,0.0263222288,0.2069663107,-0.0632968992,-0.2209658772,-0.1002330109,-0.1999513954,0.1400153339,0.058214739,0.2952195704,0.1928819418,0.2937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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3308","title":"\"dataset_infos.json\" missing for chr_en and mc4","comments":"The `dataset_infos.json` files have more information and are made to be used to analyze the datasets without having to run\/parse the python scripts. Moreover some datasets on the Hugging face don't even have a python script, and for those ones we'll make tools to generate the JSON file automatically :)","body":"## Describe the bug\r\nIn the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.\r\n\r\n## Steps to reproduce the bug\r\nCheck [chr_en](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/chr_en) and [mc4](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/mc4)","comment_length":50,"text":"\"dataset_infos.json\" missing for chr_en and mc4 \n ## Describe the bug\r\nIn the repository, every dataset has its metadata in a file called`dataset_infos.json`. But, this file is missing for two datasets: `chr_en` and `mc4`.\r\n\r\n## Steps to reproduce the bug\r\nCheck [chr_en](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/chr_en) and [mc4](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/mc4) \n The `dataset_infos.json` files have more information and are made to be used to analyze the datasets without having to run\/parse the python scripts. Moreover some datasets on the Hugging face don't even have a python script, and for those ones we'll make tools to generate the JSON file automatically :)","embeddings":[0.1424959004,-0.3361805379,-0.0471008718,0.2993094325,0.1907496601,0.1461187005,-0.0039601303,0.2632820904,-0.1192547679,0.2591180205,0.0356875546,0.2333019227,-0.0015293584,0.1996407658,0.0464105867,-0.196825251,0.0915271342,0.1346551031,-0.0754049793,-0.1017005891,-0.1049375758,0.4699754715,0.0858605877,-0.1786589772,-0.3154147863,0.0590284467,-0.2107362747,0.336191982,-0.0846944973,-0.4393536448,0.2027551532,0.0034777739,-0.1666439623,0.2816444635,-0.000106106,-0.0060727051,0.2696446776,-0.0739201009,-0.2553723454,-0.2630660832,0.0844724551,-0.2256420851,0.1437512338,-0.1313944012,-0.3203607202,-0.1809978485,0.0131730698,-0.099370487,0.3814764619,0.1023474634,0.281965524,0.4294534624,0.1331004798,-0.2865588963,0.1538785547,0.4924044013,-0.1093614101,0.1926050186,0.0343285017,0.0349541195,0.058328528,0.5477881432,0.3045518696,-0.1641929001,0.4170654416,0.1234893948,-0.1169155836,-0.3034311533,0.1149613708,0.2183613479,0.315705061,-0.1759996861,-0.3507823944,-0.3925176561,-0.0190139785,-0.039829988,0.2799794674,0.2793488801,-0.0127171706,0.1991525292,-0.2606709599,-0.0810175017,-0.0259157848,0.087221697,0.1355640739,0.018053744,-0.2172510773,-0.0278372839,0.1450437903,-0.256357193,-0.4451672733,0.0202324782,-0.16961281,0.2132694125,-0.0400007553,-0.0967123881,0.2374953926,0.0845439136,0.225019604,0.2796195149,-0.166989699,-0.132193327,-0.1913733035,0.0772249252,0.2441521287,-0.0601670519,0.1747711152,0.0596222244,0.3752610087,0.229058817,0.1380757093,-0.1329683661,0.13916412,0.1297982931,-0.1109472588,-0.008815581,0.3034057617,-0.1638901532,-0.1649661809,0.1461031139,-0.0308197457,-0.085224472,-0.0393372141,0.4535025656,-0.1288314313,-0.0464147255,0.0771618485,0.1292577088,-0.0297958013,-0.1345253885,-0.3170726001,0.218215242,-0.1258866042,0.0233715754,0.2030951232,-0.2486214191,0.615391016,0.0417253785,0.2700640559,-0.0830803216,-0.1232196167,0.2275478095,-0.0449870676,0.4646538794,0.0088393837,0.1197038144,0.0400103144,-0.004755178,-0.155844003,-0.1206337363,-0.3666443825,-0.1110193431,-0.117615968,0.2285210341,-0.1689119339,0.0989283547,-0.2454912066,0.0814547241,-0.1145157814,-0.0856053755,0.1747503132,0.1252717525,-0.1111769602,-0.2524935305,0.2494178414,0.4359768331,0.1716013104,-0.3153614998,0.1363251209,-0.2063259333,-0.0503524244,0.2774619758,-0.1221927479,-0.165611431,-0.4960058033,0.0561903752,-0.0617441721,-0.4715712965,-0.1198732331,0.1386394799,-0.1301436126,0.2527423203,0.1399473548,-0.0771145299,-0.3010393381,0.0806059688,0.0142832547,0.0412416458,0.0510037839,0.1577791125,-0.1199484318,-0.0448388495,-0.0532648452,0.127627939,-0.0439839251,0.0474157929,0.2018303424,-0.1060440242,0.2538097203,-0.1004453301,0.0638585985,0.5631516576,0.3067393303,0.0713964701,0.1317301393,-0.1302056015,-0.5370301604,0.1643483937,-0.0479244888,0.2292906344,-0.270537734,-0.3090182841,-0.1379106641,-0.079163976,-0.0730597079,-0.519867003,0.1604948789,0.1695910245,0.1093210131,0.2328281254,-0.1283526421,0.3243951201,-0.0686606616,0.2717542648,-0.5003327727,0.3355050683,-0.0482044518,-0.0248575304,0.2090596855,0.2360998541,0.1704810858,-0.2055140436,-0.0627889335,0.3651061952,-0.0526081622,0.3440015912,-0.0035843516,0.1942667812,0.2356310189,-0.2958717644,-0.1164619103,0.0781712607,0.0481295623,-0.0431900062,-0.3058302104,0.3658819199,0.0232937466,0.0796917155,0.2937783897,0.0950078741,0.1761128604,-0.0257317312,-0.223358348,-0.2430231869,0.2614963651,-0.0503082722,0.3055380583,-0.035399694,-0.6104288101,0.2276848555,0.5597065091,-0.0000485869,0.0330543146,0.1441433281,-0.1216791794,-0.0502264351,0.0947208777,0.0130167082,0.3824594021,0.2203695029,0.0513974205,0.3452106416,0.1740458608,-0.1263323128,0.1750158221,-0.016313538,0.0321521759,-0.0357767977,0.3045380116,0.0294900034,-0.321934104,-0.1235157102,-0.3826145828,0.0731777623,-0.3074985445,0.0887139291,-0.3216537833,-0.1796949655,-0.1393231153,-0.2217841744,-0.491348654,-0.2273418307,0.0481726006,-0.0019813967,-0.2723231912,0.0200437754,-0.0047661071,0.1845979989,-0.1147755459,0.2305113524,-0.3864361644,-0.0345268697,-0.0390563905,0.0528206713,0.0423911773,-0.0209299754,0.2206416279,-0.5669711232,0.1162264794,-0.3870073557,-0.5942266583,0.2708292603,-0.2351271957,0.0948633626,0.373696655,0.1556429267,-0.0747887194,-0.2025936991,0.2638014257,-0.2510450184,-0.4778466821,-0.0682634786,0.0389676839,-0.1756723672,-0.3727265,-0.3033907413,-0.1065033153,-0.2798736691,0.6854405403,-0.0130736409,0.0814104825,0.3911243379,-0.2414183766,0.1032520831,-0.4235672951,0.2506527305,-0.1690109372,-0.5865362883,0.1989886165,-0.5061445236,-0.4614357054,0.1831164658,0.1565694362,0.1493281573,-0.3004015088,-0.3457940221,-0.3192750812,-0.2514828444,0.2404763401,-0.2481626719,0.1734036505,0.2676467001,-0.0704103112,-0.0813821256,-0.3022873402,-0.2336363792,0.0820615441,-0.0327843651,0.0322816893,-0.3306878507,0.3886478543,-0.0050533689,0.2981330454,0.2393923849,0.0501122847,0.2477269024,-0.1638700068,0.6179242134,-0.1866845042,-0.3309558928,0.169597581,0.1775670946,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3304","title":"Dataset object has no attribute `to_tf_dataset`","comments":"The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n```\r\n# upgrade transformers and datasets to latest versions\r\n!pip install --upgrade transformers\r\n!pip install --upgrade datasets\r\n```\r\n\r\nRegards!","body":"I am following HuggingFace Course. I am at Fine-tuning a model. \r\nLink: https:\/\/huggingface.co\/course\/chapter3\/2?fw=tf\r\n\r\nI use tokenize_function and `map` as mentioned in the course to process data.\r\n\r\n`# define a tokenize function`\r\n`def Tokenize_function(example):`\r\n`    return tokenizer(example['sentence'], truncation=True)`\r\n\r\n`# tokenize entire data`\r\n`tokenized_data = raw_data.map(Tokenize_function, batched=True)`\r\n\r\nI get Dataset object at this point. When I try converting this to a TF dataset object as mentioned in the course, it throws the following error.\r\n\r\n`# convert to TF dataset`\r\n`train_data = tokenized_data[\"train\"].to_tf_dataset( `\r\n`   columns = ['attention_mask', 'input_ids', 'token_type_ids'], `\r\n`    label_cols = ['label'], `\r\n`    shuffle = True, `\r\n`    collate_fn = data_collator, `\r\n`    batch_size = 8 `\r\n`)`\r\n\r\nOutput:\r\n\r\n`---------------------------------------------------------------------------`\r\n`AttributeError                            Traceback (most recent call last)`\r\n`\/tmp\/ipykernel_42\/103099799.py in <module>`\r\n`      1 # convert to TF dataset`\r\n`----> 2 train_data = tokenized_data[\"train\"].to_tf_dataset( \\`\r\n`      3     columns = ['attention_mask', 'input_ids', 'token_type_ids'], \\`\r\n`      4     label_cols = ['label'], \\`\r\n`      5     shuffle = True, \\`\r\n`AttributeError: 'Dataset' object has no attribute 'to_tf_dataset'`\r\n\r\nWhen I look for `dir(tokenized_data[\"train\"])`, there is no method or attribute in the name of `to_tf_dataset`.\r\n\r\nWhy do I get this error? And how to clear this?\r\n\r\nPlease help me.","comment_length":39,"text":"Dataset object has no attribute `to_tf_dataset` \n I am following HuggingFace Course. I am at Fine-tuning a model. \r\nLink: https:\/\/huggingface.co\/course\/chapter3\/2?fw=tf\r\n\r\nI use tokenize_function and `map` as mentioned in the course to process data.\r\n\r\n`# define a tokenize function`\r\n`def Tokenize_function(example):`\r\n`    return tokenizer(example['sentence'], truncation=True)`\r\n\r\n`# tokenize entire data`\r\n`tokenized_data = raw_data.map(Tokenize_function, batched=True)`\r\n\r\nI get Dataset object at this point. When I try converting this to a TF dataset object as mentioned in the course, it throws the following error.\r\n\r\n`# convert to TF dataset`\r\n`train_data = tokenized_data[\"train\"].to_tf_dataset( `\r\n`   columns = ['attention_mask', 'input_ids', 'token_type_ids'], `\r\n`    label_cols = ['label'], `\r\n`    shuffle = True, `\r\n`    collate_fn = data_collator, `\r\n`    batch_size = 8 `\r\n`)`\r\n\r\nOutput:\r\n\r\n`---------------------------------------------------------------------------`\r\n`AttributeError                            Traceback (most recent call last)`\r\n`\/tmp\/ipykernel_42\/103099799.py in <module>`\r\n`      1 # convert to TF dataset`\r\n`----> 2 train_data = tokenized_data[\"train\"].to_tf_dataset( \\`\r\n`      3     columns = ['attention_mask', 'input_ids', 'token_type_ids'], \\`\r\n`      4     label_cols = ['label'], \\`\r\n`      5     shuffle = True, \\`\r\n`AttributeError: 'Dataset' object has no attribute 'to_tf_dataset'`\r\n\r\nWhen I look for `dir(tokenized_data[\"train\"])`, there is no method or attribute in the name of `to_tf_dataset`.\r\n\r\nWhy do I get this error? And how to clear this?\r\n\r\nPlease help me. \n The issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n```\r\n# upgrade transformers and datasets to latest versions\r\n!pip install --upgrade transformers\r\n!pip install --upgrade datasets\r\n```\r\n\r\nRegards!","embeddings":[-0.2293258607,-0.0800563693,0.0744439811,0.1120374277,0.6455808878,0.2319770902,0.1128886938,0.2750467062,-0.1273373663,-0.0271430481,-0.0409888886,0.3272252381,-0.3555475473,0.3133005202,0.1737270206,-0.23456797,0.1084549874,0.0091278683,-0.0873259604,-0.0865572542,-0.1162518486,0.2614684999,-0.0682839081,0.1360445917,-0.4334418476,-0.099201113,-0.0160555225,-0.3088770807,-0.1251862049,-0.1724084914,-0.0023630224,-0.0347179621,0.0345964469,0.5329075456,-0.0001182142,0.184536919,-0.1499791145,-0.0263902582,-0.0873747841,-0.3173581958,0.0251591895,0.026748633,-0.1025345549,-0.0619107671,-0.1931450218,-0.1696669012,0.0341835581,-0.0892672986,0.2019150853,0.4334086776,0.1723873168,0.3354493082,0.3696624935,0.0544138961,-0.0428958982,0.2692399919,-0.1672405452,0.1209967062,-0.2285322696,-0.2061608136,0.3004026413,0.1148081571,0.051963307,-0.0057908497,0.5629860759,0.0233251844,-0.1906661242,-0.2989127934,0.0094885379,0.1032067165,0.2847785652,-0.3382242322,-0.4468051791,-0.1033148244,0.0883327797,-0.0842733383,-0.1897837371,-0.1489567012,0.0705454499,0.1788675189,-0.3684657514,-0.2368637025,0.0074898051,0.1028252095,-0.2789250612,0.1730366498,-0.1957015842,0.1920984238,-0.1020734012,-0.204942748,-0.5331298709,0.0139094647,0.0900312886,0.3128385544,-0.2858850658,-0.2121660709,0.1300504208,-0.0943845436,0.2533922791,0.155984953,-0.1198950037,-0.1171697527,0.1376195401,0.0272276495,-0.0451919883,0.2059896886,0.0173400417,0.3120791316,-0.2308121771,-0.2683052123,-0.3433369398,-0.2611767948,-0.1681505889,0.017013777,0.0185818486,0.0384050608,0.4159691632,0.0772091374,-0.1363941133,0.0688805655,-0.2693951428,0.0515275709,-0.0109576024,0.2210065573,0.1345484108,-0.0888342708,-0.0228531305,0.0503997989,-0.0957751498,-0.1939894408,-0.2568036914,0.0496061444,-0.0163116772,-0.0801968053,-0.1525160223,-0.118092984,0.0851842314,-0.0456728376,0.1774120182,0.0609521903,0.0888316855,-0.215587303,0.2996960878,0.2359925658,-0.1855132282,0.2814008296,0.4470250905,-0.2328771353,-0.2273545265,-0.095908545,-0.4306271374,-0.3704139292,-0.2810092568,0.1334066838,-0.1303566694,-0.1787532717,-0.3799800277,0.2853480875,0.1515900344,-0.1230424941,0.0168734957,-0.2900517285,-0.4458047152,-0.1477543861,0.2956813872,0.3791250288,-0.2593316436,-0.140234977,0.2020433992,-0.1679741591,0.0248435568,0.0932274386,0.1293510795,0.4940908253,-0.2435204387,0.3543474674,0.121271342,-0.2747250497,-0.2544974387,0.1811427176,-0.2912672162,0.0536267646,-0.4289420247,-0.1962472647,0.226823017,-0.0404199362,0.2778682113,-0.0186852776,-0.1386889666,-0.0216598641,-0.1110368893,0.0307735857,0.4620264173,-0.2987313867,-0.1664425582,0.2338291854,-0.1596330255,0.0317204781,-0.1366698891,0.0800409839,-0.0015582583,0.1126620546,0.4320297241,0.1128697544,-0.1210551336,-0.197258383,-0.1744860858,0.0523061603,0.6149033308,0.2625882626,-0.0553571507,-0.1569518149,-0.2786991596,0.1726816595,-0.1202712953,-0.2238596976,0.0722861588,-0.0434457548,-0.0271864794,0.2130496651,-0.2445941567,0.0355144627,-0.1892379373,0.1691903621,-0.0280132331,0.1719612777,0.0871774778,-0.1274137646,-0.0312051848,0.1592688262,0.0877858251,-0.0205101464,-0.0890095904,0.2926640213,0.0208746735,-0.1042533442,-0.138314575,0.4566976428,0.1143922806,-0.1799503714,-0.5014650822,-0.0513194948,0.1898581535,0.0168277994,0.2308357805,0.2521333396,0.3509410024,0.2248948812,0.1154345945,0.2282953858,-0.0370225199,0.1305235922,-0.0511289015,-0.2564208806,-0.0242880266,-0.2934699655,0.0516374707,-0.0266230255,-0.157728374,0.2391720414,0.5992745161,-0.1008337289,0.0263007469,0.1606061012,-0.2859294116,0.0416292101,0.0707364157,0.2018861324,0.3395397365,0.1078307256,-0.0743491873,-0.016098449,0.0363270342,0.3117970824,0.1702318192,-0.1002091616,0.1712637842,0.0786944106,-0.1019992009,0.2615330815,-0.0969510749,-0.3979200423,0.0376945958,0.538155973,-0.6530122161,-0.1409333944,-0.1507139802,0.0415223427,0.1054502353,-0.0661404356,-0.2123907208,-0.4202547669,-0.1619809568,0.0063579641,0.1500335783,0.0670665056,-0.3246256113,0.0240374077,0.1650752872,-0.1674224883,-0.170380637,-0.1963184327,-0.2080313563,-0.006750796,0.171116814,-0.1235585958,0.1453474909,-0.3283054829,0.1608041525,-0.1800063401,-0.4412211776,0.3033456802,-0.3880034685,0.3335456848,0.2092026025,0.3714908361,-0.4037844241,-0.1171282604,0.4053278565,-0.3179014921,-0.3682287633,0.2427647561,0.0025506017,-0.3134343326,-0.0145392092,-0.0550511666,-0.168171674,-0.0528058298,0.2039964348,-0.1619161069,0.0684747249,0.4156090915,0.1613464057,0.2640888989,-0.2871160507,-0.0478683934,-0.1791815758,-0.4011687338,0.4648635983,-0.3192687035,-0.4173936546,0.0063627558,0.1448965669,0.0808489919,0.0198488273,-0.2945604324,-0.2246269733,-0.1476174444,0.1118725613,-0.1932275742,-0.0793002844,0.2335637063,0.1140257344,-0.0753255486,-0.0915052965,-0.3336330652,0.1286232471,-0.0078598112,0.0266864263,-0.1200470999,0.3507541418,0.2267935872,0.6952537894,0.0209529586,-0.277600795,0.1097612679,-0.6389496326,0.1539803743,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3303","title":"DataCollatorWithPadding: TypeError","comments":"\r\n> \r\n> Input:\r\n> \r\n> ```\r\n> tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n> ```\r\n> \r\n> Output:\r\n> \r\n> ```\r\n> TypeError                                 Traceback (most recent call last)\r\n> \/tmp\/ipykernel_42\/1563280798.py in <module>\r\n>       1 checkpoint = 'bert-base-uncased'\r\n>       2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\n> TypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n> ```\r\n> \r\n\r\nThe issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n`# upgrade transformers and datasets to latest versions`\r\n`!pip install --upgrade transformers`\r\n`!pip install --upgrade datasets`\r\n\r\nCheers!","body":"Hi,\r\nI am following the HuggingFace course. I am now at Fine-tuning [https:\/\/huggingface.co\/course\/chapter3\/3?fw=tf](https:\/\/huggingface.co\/course\/chapter3\/3?fw=tf).  When I set up `DataCollatorWithPadding` as following I got an error while trying to reproduce the course code in Kaggle. This error occurs with either a CPU-only-device or a GPU-device.\r\n\r\nInput:\r\n```checkpoint = 'bert-base-uncased'\r\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\ndata_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n```\r\n\r\nOutput:\r\n```---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n\/tmp\/ipykernel_42\/1563280798.py in <module>\r\n      1 checkpoint = 'bert-base-uncased'\r\n      2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\nTypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n```\r\n\r\nWhen I call `help` method, it too confirms that there is no argument `return_tensors`.\r\nInput:\r\n```\r\nhelp(DataCollatorWithPadding.__init__)\r\n```\r\nOutput:\r\n```\r\nHelp on function __init__ in module transformers.data.data_collator:\r\n\r\n__init__(self, tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = True, max_length: Union[int, NoneType] = None, pad_to_multiple_of: Union[int, NoneType] = None) -> None\r\n```\r\n\r\nBut, the source file *[Data Collator - docs](https:\/\/huggingface.co\/transformers\/main_classes\/data_collator.html#datacollatorwithpadding)* says that there is such an argument. By default, it returns Pytorch tensors while I need TF tensors.\r\n\r\nWhere do I miss?\r\nPlease help me.","comment_length":100,"text":"DataCollatorWithPadding: TypeError \n Hi,\r\nI am following the HuggingFace course. I am now at Fine-tuning [https:\/\/huggingface.co\/course\/chapter3\/3?fw=tf](https:\/\/huggingface.co\/course\/chapter3\/3?fw=tf).  When I set up `DataCollatorWithPadding` as following I got an error while trying to reproduce the course code in Kaggle. This error occurs with either a CPU-only-device or a GPU-device.\r\n\r\nInput:\r\n```checkpoint = 'bert-base-uncased'\r\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\ndata_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n```\r\n\r\nOutput:\r\n```---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n\/tmp\/ipykernel_42\/1563280798.py in <module>\r\n      1 checkpoint = 'bert-base-uncased'\r\n      2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\nTypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n```\r\n\r\nWhen I call `help` method, it too confirms that there is no argument `return_tensors`.\r\nInput:\r\n```\r\nhelp(DataCollatorWithPadding.__init__)\r\n```\r\nOutput:\r\n```\r\nHelp on function __init__ in module transformers.data.data_collator:\r\n\r\n__init__(self, tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase, padding: Union[bool, str, transformers.file_utils.PaddingStrategy] = True, max_length: Union[int, NoneType] = None, pad_to_multiple_of: Union[int, NoneType] = None) -> None\r\n```\r\n\r\nBut, the source file *[Data Collator - docs](https:\/\/huggingface.co\/transformers\/main_classes\/data_collator.html#datacollatorwithpadding)* says that there is such an argument. By default, it returns Pytorch tensors while I need TF tensors.\r\n\r\nWhere do I miss?\r\nPlease help me. \n \r\n> \r\n> Input:\r\n> \r\n> ```\r\n> tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n> ```\r\n> \r\n> Output:\r\n> \r\n> ```\r\n> TypeError                                 Traceback (most recent call last)\r\n> \/tmp\/ipykernel_42\/1563280798.py in <module>\r\n>       1 checkpoint = 'bert-base-uncased'\r\n>       2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\n> TypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n> ```\r\n> \r\n\r\nThe issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n`# upgrade transformers and datasets to latest versions`\r\n`!pip install --upgrade transformers`\r\n`!pip install --upgrade datasets`\r\n\r\nCheers!","embeddings":[0.0907471329,-0.4077562988,0.1217752993,0.270572722,0.3994098604,-0.1075447872,0.3337477446,0.2486715466,-0.1562586725,0.2049703598,0.2183446437,0.3773722053,-0.1649316996,0.1004559919,-0.1292612255,-0.2466284484,0.0724817663,0.0992895737,0.106700398,0.0274556298,-0.1963098794,0.2782025337,-0.2563391328,0.2940039337,-0.4043155611,-0.2832674384,0.0291374531,-0.0872014537,-0.0918924138,-0.3554833233,0.6174438,-0.1880169213,0.1952746958,0.4655008316,-0.0001232006,0.0219991058,0.1548153013,-0.0510115102,-0.0005345448,0.0242753886,0.4408678412,-0.0512312539,0.0462745316,-0.1565998346,-0.1620859057,-0.0035306991,-0.2143932879,0.06042137,0.5589393973,0.0414034873,0.0911951065,0.4949116707,0.396214962,-0.0242931005,-0.2705122828,0.003456363,-0.1884239465,0.2029719204,0.1705468148,-0.1687114984,-0.090814054,0.1691513658,0.0730620548,-0.2814804018,0.2705873251,0.0964557305,-0.3587110043,-0.176866591,0.0322367996,0.1703581661,0.3395695686,-0.2029431015,-0.2085593194,-0.4422971904,0.0018389891,0.0259851702,0.330740869,-0.2059244066,-0.4334168136,-0.1712003201,0.3541635573,-0.1798382849,-0.1218740568,0.046755787,-0.1096840948,0.3732096851,-0.1727919132,0.2091674656,0.5126460195,-0.1786212921,-0.0360492729,0.0573123619,0.3166143,0.3954916894,-0.422314018,-0.3555787206,0.0694389641,-0.1636395752,0.2097501904,-0.1103911623,0.1213409454,-0.128814891,-0.0467259996,0.1158407778,0.1694356054,0.3452320397,0.202491492,0.1099790484,0.1299539804,0.1982381344,0.2782259881,-0.2000216097,-0.1766925901,-0.1543037295,-0.0320663303,-0.0421528816,0.37797153,0.1100237668,-0.1728263497,-0.0064494428,-0.1040926799,0.0047323504,-0.0478628576,0.0970588475,0.1122909412,0.1333645135,0.0384091139,0.2623334825,-0.0415546149,-0.436365068,-0.0969944894,0.1700253934,-0.10818097,0.0220606737,-0.0003293996,-0.1697544605,0.0228497721,0.2122539878,0.3435997665,0.0270419698,-0.0242441949,-0.0410791263,-0.1517511904,0.0189107526,-0.0570111573,0.2212548703,0.4395179152,-0.304487735,-0.1533144116,-0.170779258,-0.1489654034,-0.2225074619,-0.0520108417,0.0528020784,0.0311574638,-0.089025043,-0.4236093462,-0.0007559821,0.4312827289,-0.0400346257,0.2274610996,-0.0489530191,0.1177733392,-0.1909470856,0.3168319166,0.06914673,-0.2420619279,-0.3666192591,-0.1178287119,0.0137534607,0.1744192541,0.4926896095,-0.0867659822,0.3146859407,-0.3299522698,0.4854142964,0.1965162307,-0.4147772491,-0.2897129357,-0.0101730153,-0.2051526904,0.3168308735,0.1806298792,-0.3117735982,-0.0127946874,0.1428427696,0.189041242,0.0790220946,-0.1904080361,-0.041206792,-0.2551420927,-0.1776952147,0.6569884419,0.0506029017,0.1034613773,0.0953819975,-0.3824129403,-0.0275840834,0.1264632046,-0.2209610343,0.0087065613,0.0319964774,0.5663701296,-0.1201404184,0.0381578207,0.1109017357,-0.2424649,0.203646794,-0.1018574238,0.296562463,-0.2458744198,-0.267074883,-0.084283106,-0.0200712718,-0.0400599018,-0.3060688972,0.0539815761,-0.1549272239,-0.109836705,0.0587449335,-0.2268825173,0.2349150628,-0.0377191231,0.3077705801,-0.5516865849,0.2547190189,-0.1629534811,-0.2400726229,-0.1153528392,0.141348511,0.2757762372,-0.1646277606,0.1821272075,0.2024705261,0.1198838502,-0.0909276679,-0.1581204385,0.0244048703,0.211248666,0.0059129107,-0.4189528525,0.0884565413,0.0644652992,-0.138727963,0.4610749483,0.6577061415,0.1968316287,0.2693302631,-0.0706149712,0.1529616565,-0.0222529229,0.0845628306,-0.0093535008,-0.0112244273,0.1697565466,-0.343208611,0.3537117839,-0.19918181,-0.2465914339,-0.0964340791,0.1527036726,0.1610875726,0.1404456645,0.1483604908,-0.0512008965,-0.022599224,0.0682333261,-0.3446058631,0.234653309,-0.1055287942,-0.2142851502,0.1483990848,-0.0047882735,-0.0758217573,0.1342958361,-0.0190486666,-0.0054084514,0.0477563553,-0.1936972439,0.2035297453,-0.1643396914,-0.1636389047,-0.1068778038,0.0816196129,-0.3217681944,-0.0965908468,-0.0752198696,-0.3869907856,-0.1305364519,-0.1132662892,-0.4660359323,-0.3192454875,-0.1947768182,0.1532653123,0.0426863842,0.2449619621,0.2691893876,0.6432507038,0.1048961803,0.0732062683,-0.3248144388,-0.249285683,-0.0805521086,0.0139379147,-0.1453000605,-0.4239328504,0.1055447683,0.0519247875,0.1372314692,-0.0792296901,-0.5207657814,0.1967500895,-0.2193227112,-0.0830801949,0.4242627919,0.461063683,-0.079744257,-0.6736230254,0.223354876,-0.1556907743,-0.4157527983,0.0293010753,0.0810560286,0.1730968058,-0.0889955908,0.2385457605,-0.0645737126,-0.2157787532,0.3046574891,-0.0546251684,-0.2164394706,0.0252494235,0.0244265068,0.3068295419,-0.0687965974,0.0303842649,-0.2571360171,-0.1018219143,0.2019558549,0.0502390228,-0.1549562961,-0.167727679,-0.0627885982,0.6279937029,-0.2523974776,-0.2657257617,-0.3213005364,-0.2551430166,0.6211448312,-0.2957867682,0.2346430421,0.2185512185,0.0044611697,0.1288349479,-0.0113764042,-0.2497559041,0.0866261274,-0.0520949848,0.1164823398,-0.0024958025,0.2689454556,0.2244747281,0.7780629992,0.2794655561,-0.1199751794,0.1822376102,0.0604962036,0.2687675059,-0.048940599,-0.3533353508,0.0842081383,-0.06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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3300","title":"\u2753 Dataset loading script from Hugging Face Hub","comments":"Hi ! In the next version of `datasets`, your train and test splits will be correctly separated (changes from #3027) if you create a dataset repository with only your CSV files.\r\n\r\nAlso it seems that you overwrite the `data_files` and `data_dir` arguments in your code, when you instantiate the AGNewsConfig objects. Those parameters are not necessary since you already know which files you want to load.\r\n\r\nYou can find an example on how to specify which file the dataset has to download in this [example script](https:\/\/huggingface.co\/datasets\/lhoestq\/custom_squad\/blob\/main\/custom_squad.py#L101-L107):\r\n```python\r\n_URLS = {\r\n    \"train\": \"train-v1.1.json\",  # you can use a URL or a relative path from the python script to your file in the repository\r\n    \"dev\": \"dev-v1.1.json\",\r\n}\r\n```\r\n```python\r\n    def _split_generators(self, dl_manager):\r\n        downloaded_files = dl_manager.download_and_extract(_URLS)\r\n\r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": downloaded_files[\"train\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": downloaded_files[\"dev\"]}),\r\n        ]\r\n```","body":"Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :)","comment_length":133,"text":"\u2753 Dataset loading script from Hugging Face Hub \n Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :) \n Hi ! In the next version of `datasets`, your train and test splits will be correctly separated (changes from #3027) if you create a dataset repository with only your CSV files.\r\n\r\nAlso it seems that you overwrite the `data_files` and `data_dir` arguments in your code, when you instantiate the AGNewsConfig objects. Those parameters are not necessary since you already know which files you want to load.\r\n\r\nYou can find an example on how to specify which file the dataset has to download in this [example script](https:\/\/huggingface.co\/datasets\/lhoestq\/custom_squad\/blob\/main\/custom_squad.py#L101-L107):\r\n```python\r\n_URLS = {\r\n    \"train\": \"train-v1.1.json\",  # you can use a URL or a relative path from the python script to your file in the repository\r\n    \"dev\": \"dev-v1.1.json\",\r\n}\r\n```\r\n```python\r\n    def _split_generators(self, dl_manager):\r\n        downloaded_files = dl_manager.download_and_extract(_URLS)\r\n\r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": downloaded_files[\"train\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": downloaded_files[\"dev\"]}),\r\n        ]\r\n```","embeddings":[-0.1013944596,-0.2682318389,0.0481378734,0.2958104014,0.0050044255,0.2030408978,0.318576932,0.0778238401,0.6256750226,0.1679822505,-0.2501534224,-0.009564762,-0.0821525753,0.4151841402,0.3813699484,0.0784426183,-0.0206500404,0.0400118828,0.1887957603,0.0250974558,-0.1375532448,0.3837665021,0.0763824433,0.0077810669,-0.493314147,0.2128802836,-0.2857845426,0.4582763314,0.0740133896,-0.3762182593,0.2354922444,0.4300831258,0.2528781593,0.6403617263,-0.0001249451,0.1098107547,0.1591600776,-0.1138905883,-0.0956899002,-0.2014762908,0.0161561482,-0.161892429,0.130920589,-0.0172267761,-0.2424184978,0.0468512923,0.00325807,-0.0802225769,0.3473279774,0.2357402146,0.0563454702,0.2728609145,-0.1312603652,-0.443547219,0.0174689591,0.1517464668,0.0274561942,0.4189985096,0.1316792816,-0.1114429682,-0.165600419,-0.0508331694,0.1466549337,-0.2918341756,0.3668185472,0.137062341,0.0178307965,-0.2287504077,0.0040627285,0.0872906819,0.2422103435,-0.415892005,-0.2001891583,-0.5779345036,0.1618516296,-0.063502878,0.1972288936,0.050199911,-0.2031318694,0.337412864,-0.2349115014,-0.459569633,-0.1967563629,-0.06045793,0.0083966469,-0.021248959,-0.0155867459,0.0808270127,0.2030259967,0.1483365744,0.0811843276,-0.2024038136,-0.0706204176,0.2287310362,-0.3177692294,-0.0517973788,0.0448424965,0.3959931135,0.0897563249,0.4525457025,0.250562489,0.1112954542,-0.2970133424,-0.0461823046,0.3673808277,0.3223735094,0.1733671427,0.0917073637,0.0806957707,0.4579350948,0.1037679464,-0.0396954753,-0.3316157758,-0.2362685502,-0.1628956348,-0.1623139828,0.2077281773,-0.1047008485,0.004119562,0.0015738982,0.3940437138,0.0291357394,0.2894953489,0.5255852342,-0.0274133123,0.047953628,-0.1072339416,0.2523892224,-0.2990715504,0.0768011808,-0.2405168563,0.1202780232,-0.1497951299,0.4809415042,0.358384639,-0.1696155816,0.2153215259,-0.0652229339,0.3285728097,-0.1149156019,-0.4599314332,0.1085850075,-0.0644741505,0.2236444354,0.3786461949,0.1802084744,0.0079359934,-0.2189952284,-0.2539786696,0.021460114,-0.0544977114,-0.4728634655,0.184788093,0.0050882446,-0.756069839,-0.0629069582,-0.2755767703,0.1090348139,-0.2921161354,-0.0744951144,-0.100741066,0.1818783581,-0.059988983,-0.1431609988,0.4176405072,0.805380404,-0.2034861594,-0.2937726974,-0.0042489977,-0.3307194114,-0.2163159847,0.0972279087,-0.1506862193,0.3602952361,-0.3286343813,0.0546265244,0.0492216572,-0.521525085,-0.3752057254,0.2202548385,-0.3353924155,0.371417731,0.1096294001,0.1960270256,-0.1123589203,-0.0703784376,-0.0425673835,0.1427777261,-0.0366843902,-0.1353934407,0.0645267069,-0.1079784483,-0.1532408893,0.3892443478,0.0270757191,0.2135211676,-0.0074452134,-0.1093640402,0.3000406921,0.0594717003,0.0122370012,0.1202410832,0.1604836136,0.2635266185,0.027280407,0.0868218839,-0.7402223349,0.4633337259,-0.1300240159,-0.0450160652,0.0104142409,-0.0262139421,-0.5219828486,-0.087406069,-0.2716314793,-0.1216759682,-0.1100197732,0.0981338024,0.1123671681,-0.1416285932,-0.3877284527,0.7349778414,-0.0626962483,0.2087917477,-0.7360531688,0.4157345295,0.1426883191,-0.0849803016,0.0858417675,0.1710783839,0.204046756,-0.1098989993,0.1043979153,0.4523719549,0.2277484387,0.5190566182,0.2757499218,-0.0180162881,0.2021626085,-0.1637680084,0.159212634,-0.2898055017,-0.0302266516,-0.0812680796,-0.3099774718,0.3510702848,-0.5239570737,0.2236639857,0.0652351826,-0.1092894748,0.0808994547,-0.0886811614,-0.3720907271,-0.2482620478,-0.0348806307,0.0981177911,0.2039618492,-0.0610644855,-0.0636430681,-0.0630121231,0.0198129658,-0.0643987358,-0.1382506937,0.0250936225,-0.1255679876,0.1586586237,-0.0531276502,0.134367913,0.3320257664,0.0388714187,-0.1407305002,0.4559983909,0.1119205952,-0.3129827082,0.0189028271,0.0208031274,0.136020124,0.2509736121,-0.2676664889,-0.089969404,-0.1539710164,-0.2001538426,-0.0408271365,0.0058497605,-0.4867648482,-0.1751788706,-0.0555203743,-0.0197868757,-0.333634764,-0.4218894541,-0.1755073369,-0.1395921856,-0.0412912592,0.2082535177,0.019442508,-0.0283025578,-0.0407070927,0.4492769837,-0.232856214,-0.1839451641,-0.1139629334,-0.1410126388,0.0106395567,-0.0916096717,0.0907450318,0.3076320291,0.0864062309,-0.3328333795,0.1278092563,-0.1610480994,0.0393866338,0.1631690562,-0.055835776,0.2211683393,0.1213877052,0.1339350045,0.1461731195,-0.1343851089,0.2532779276,-0.3192071915,-0.0004726235,-0.1416942477,0.1233274266,-0.0906312168,-0.0301186964,-0.2680341303,-0.159387961,-0.3183102906,0.2426226139,0.2899741232,0.0265031625,0.1644316912,0.0738087669,0.1179948896,-0.054422237,-0.2988057733,-0.1095833629,-0.4293092787,0.3634619117,-0.2794691324,-0.2455605119,0.2353779674,0.0157812443,0.2005967647,-0.2198474407,-0.4327170849,-0.2253528237,-0.0251241066,0.0810181722,0.1960662454,0.013775982,0.1373704374,-0.1330471039,0.1859571338,-0.1363255531,-0.4264879823,0.1130927131,0.1787567288,-0.0578229427,0.0464628264,-0.0031341107,-0.41860798,0.3957619369,0.4464557171,0.0968577862,0.2280035168,-0.2569909394,0.503986001,-0.1541680396,-0.3445741832,-0.4087285995,0.0733668134,-0.121464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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3300","title":"\u2753 Dataset loading script from Hugging Face Hub","comments":"Also I think the viewer will be updated when you fix the dataset script, let me know if it doesn't","body":"Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :)","comment_length":20,"text":"\u2753 Dataset loading script from Hugging Face Hub \n Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :) \n Also I think the viewer will be updated when you fix the dataset script, let me know if it 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3300","title":"\u2753 Dataset loading script from Hugging Face Hub","comments":"Hi @lhoestq,\r\n\r\nThanks a lot for the super quick answer!\r\n\r\nYour suggestion solves my issue. I am now able to load the dataset properly \ud83d\ude80 \r\nHowever, the dataviewer is not working yet.\r\n\r\nReally, thanks a lot for your help and consideration!\r\n\r\nBest,\r\nPietro","body":"Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :)","comment_length":43,"text":"\u2753 Dataset loading script from Hugging Face Hub \n Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :) \n Hi @lhoestq,\r\n\r\nThanks a lot for the super quick answer!\r\n\r\nYour suggestion solves my issue. I am now able to load the dataset properly \ud83d\ude80 \r\nHowever, the dataviewer is not working yet.\r\n\r\nReally, thanks a lot for your help and consideration!\r\n\r\nBest,\r\nPietro","embeddings":[-0.0676699206,-0.3583371341,0.022483794,0.289378792,-0.0218641758,0.1763964891,0.3361328244,0.0578270368,0.6890066266,0.1715690643,-0.2108740956,-0.0025719835,-0.1206178665,0.4642139673,0.3857719898,0.07062307,-0.0077105262,0.0814240649,0.2180107832,0.0330683999,-0.1217046157,0.2958473265,0.0779973641,0.0059165987,-0.4545527697,0.2256288081,-0.2406781167,0.4514046013,0.077364184,-0.3909235299,0.2707720101,0.4224498272,0.2928935885,0.6608330011,-0.000124707,0.116788052,0.1840605438,-0.1401636451,-0.0547034033,-0.2289549708,0.0721201897,-0.1354360729,0.1629096419,-0.0342043228,-0.2319945395,0.0687705502,-0.0081099337,-0.0467204116,0.2967296541,0.1950973868,0.069453977,0.3234808743,-0.1156888604,-0.3778458834,0.0269155856,0.1853325814,0.0754594132,0.4489681423,0.2222300321,-0.085924536,-0.1707752943,-0.0768978074,0.1575178802,-0.2441685349,0.3451807797,0.0982100368,0.000403022,-0.2306092381,0.0148605416,0.0838885903,0.3289597929,-0.4393268526,-0.19492957,-0.5515592694,0.1428820789,-0.0239776522,0.221517846,0.0286877118,-0.2171388417,0.281696111,-0.1541318148,-0.4457311928,-0.2036883831,-0.10846553,0.0049268538,-0.064986214,-0.0133749377,0.0760054365,0.1268198639,0.1091105714,0.2025818974,-0.1513466835,-0.0202549268,0.2291079909,-0.3277091682,-0.0614544339,0.0289704055,0.4854887128,0.0128040649,0.4018633366,0.2886451483,0.1415427178,-0.3395422101,-0.0251341537,0.278210938,0.3289190829,0.1996109784,0.0359056853,0.0595811605,0.4861668348,0.098402001,-0.0221721902,-0.3461022973,-0.2172963023,-0.1777408421,-0.1887255162,0.2020712495,-0.1042879596,-0.0285836738,-0.0162689649,0.473821491,0.0185869057,0.3117926121,0.588327229,-0.05107807,0.0562600717,-0.0607068278,0.1780005097,-0.2679527402,0.0540552624,-0.2189709693,0.2042044103,-0.148832202,0.4224178493,0.337639451,-0.1997383386,0.2639411688,-0.0630716011,0.3042192161,-0.0252396241,-0.4591215551,0.1007452682,-0.0707600862,0.1876244992,0.3827137947,0.1503549516,0.0167855863,-0.189352721,-0.2088868171,0.058944352,-0.0952133089,-0.4064995944,0.1760636568,0.0109711196,-0.7764009237,-0.0851726755,-0.2627549767,0.0407808721,-0.2931140661,-0.1369091123,-0.0386254936,0.1463119984,-0.0523250215,-0.1374107152,0.3844179809,0.7989910841,-0.2229913771,-0.3160703778,0.0598382466,-0.3673123717,-0.2459044904,0.0468451306,-0.1561316103,0.3962367773,-0.3363684416,-0.0147802699,0.0471944436,-0.5521427989,-0.406899929,0.1895688623,-0.355910033,0.3716001213,0.1696505696,0.2191878706,-0.1106923148,-0.0708040521,-0.0010000371,0.0986279696,-0.0060325731,-0.1531995088,0.0585989803,-0.0934271067,-0.1137411445,0.4101991653,0.0555170998,0.1733879745,0.0011318439,-0.0878123567,0.3085513115,0.0320446715,0.0386057757,0.1170250326,0.109042652,0.247579664,0.0517301559,0.0632563829,-0.7033709288,0.4801929891,-0.1473146379,-0.0593092702,-0.0095147993,0.0074688368,-0.4680337906,-0.0515196398,-0.3113443255,-0.1127344966,-0.0994274914,0.1251733601,0.100009203,-0.1572841704,-0.4241781533,0.7118449211,-0.1108605415,0.1794266552,-0.7325808406,0.4206786454,0.2018687874,-0.0721356943,0.0353345685,0.1582743675,0.182593435,-0.0931844935,0.1407068968,0.3817425668,0.2349176854,0.5389650464,0.2856177092,-0.0600034036,0.1902742386,-0.1519630551,0.144274354,-0.2465511411,0.0301570203,-0.0951823741,-0.2917487919,0.330177784,-0.5647222996,0.2332216799,0.0844825581,-0.1560740173,0.0608531646,-0.050256636,-0.3495590389,-0.2012443244,0.0698112398,0.1546072364,0.2765923738,-0.0764915347,-0.0984979495,-0.0695611909,-0.0397843495,-0.0536551178,-0.1087138206,0.0770691484,-0.1327333897,0.1685021222,-0.0325624757,0.0681510866,0.3095421493,0.0385343395,-0.1871993542,0.4441900551,0.1546762735,-0.301017195,-0.0293306913,0.0129762646,0.0764783174,0.2625555992,-0.2623699009,-0.0868182704,-0.1497544199,-0.2547953725,-0.0215539783,0.0191285517,-0.5106791854,-0.21906735,-0.0431133248,-0.0062742592,-0.3114545047,-0.447068572,-0.1862244159,-0.1135447994,-0.0854096413,0.2471442521,0.0727056563,-0.066217199,-0.0143945031,0.414820075,-0.2172588408,-0.2401588708,-0.109467648,-0.1774359494,0.0662659705,-0.0701447651,0.0587624982,0.2919691205,0.0826995894,-0.4009363949,0.1427520961,-0.1736676395,0.0133431386,0.1661788672,-0.0152305141,0.200214237,0.1358052641,0.1690766215,0.1442177147,-0.100114204,0.1916960627,-0.334847182,-0.0356958136,-0.1661961973,0.0811718106,-0.0486715026,0.0171814132,-0.1980267912,-0.2105765492,-0.3260781765,0.2839104235,0.3313007057,0.048574876,0.0561216399,0.0245115589,0.1342152208,-0.0738427341,-0.2391318381,-0.0987844542,-0.5038406253,0.3298659921,-0.2541193366,-0.2692652643,0.2224830687,0.036290288,0.2439689785,-0.2477840334,-0.4332645833,-0.2700277269,-0.004311162,0.1112935245,0.1868390292,0.0812629461,0.1534653008,-0.1233952194,0.1856253147,-0.1118250638,-0.4020266533,0.0771548748,0.1385293007,-0.0392024666,0.1073002592,0.0087828645,-0.4347752929,0.3514700234,0.3803658485,0.082137011,0.2682644725,-0.247282207,0.5148451328,-0.1642823964,-0.3210815787,-0.4268911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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3300","title":"\u2753 Dataset loading script from Hugging Face Hub","comments":"@lhoestq  I think I am having a related problem.\r\nMy call to load_dataset() looks like this:\r\n\r\n```\r\n    datasets = load_dataset(\r\n        os.path.abspath(layoutlmft.data.datasets.xfun.__file__),\r\n        f\"xfun.{data_args.lang}\",\r\n        additional_langs=data_args.additional_langs,\r\n        keep_in_memory=True,\r\n    )\r\n\r\n```\r\n\r\nMy _split_generation code is:\r\n\r\n```\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n\r\n        downloaded_file = dl_manager.download_and_extract(\"https:\/\/guillaumejaume.github.io\/FUNSD\/dataset.zip\")\r\n        return [\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": f\"{downloaded_file}\/dataset\/training_data\/\"}\r\n            ),\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TEST, gen_kwargs={\"filepath\": f\"{downloaded_file}\/dataset\/testing_data\/\"}\r\n            ),\r\n        ]\r\n\r\n```\r\nHowever I get the error \"TypeError: _generate_examples() got an unexpected keyword argument 'filepath'\"\r\nThe path looks right and I see the data in the path so I think the only problem I have is that it doesn't like the key \"filepath\".  However, the documentation (example [here](https:\/\/huggingface.co\/datasets\/lhoestq\/custom_squad\/blob\/main\/custom_squad.py#L101-L107)) seems to show that this is the correct parameter. \r\n\r\nHere is the full stack trace:\r\n\r\n```\r\nDownloading and preparing dataset xfun\/xfun.en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/caseygre\/.cache\/huggingface\/datasets\/xfun\/xfun.en\/0.0.0\/96b8cb7c57f6f822f0ab37ae3be7b82d84ac57062e774c9361ccf0a4b9ef61cc...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/caseygre\/PycharmProjects\/aegis-ml-new\/unilm\/venv-LayoutLM\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/caseygre\/PycharmProjects\/aegis-ml-new\/unilm\/venv-LayoutLM\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/caseygre\/PycharmProjects\/aegis-ml-new\/unilm\/venv-LayoutLM\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 975, in _prepare_split\r\n    generator = self._generate_examples(**split_generator.gen_kwargs)\r\nTypeError: _generate_examples() got an unexpected keyword argument 'filepath'\r\npython-BaseException\r\n```","body":"Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :)","comment_length":173,"text":"\u2753 Dataset loading script from Hugging Face Hub \n Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :) \n @lhoestq  I think I am having a related problem.\r\nMy call to load_dataset() looks like this:\r\n\r\n```\r\n    datasets = load_dataset(\r\n        os.path.abspath(layoutlmft.data.datasets.xfun.__file__),\r\n        f\"xfun.{data_args.lang}\",\r\n        additional_langs=data_args.additional_langs,\r\n        keep_in_memory=True,\r\n    )\r\n\r\n```\r\n\r\nMy _split_generation code is:\r\n\r\n```\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n\r\n        downloaded_file = dl_manager.download_and_extract(\"https:\/\/guillaumejaume.github.io\/FUNSD\/dataset.zip\")\r\n        return [\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": f\"{downloaded_file}\/dataset\/training_data\/\"}\r\n            ),\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TEST, gen_kwargs={\"filepath\": f\"{downloaded_file}\/dataset\/testing_data\/\"}\r\n            ),\r\n        ]\r\n\r\n```\r\nHowever I get the error \"TypeError: _generate_examples() got an unexpected keyword argument 'filepath'\"\r\nThe path looks right and I see the data in the path so I think the only problem I have is that it doesn't like the key \"filepath\".  However, the documentation (example [here](https:\/\/huggingface.co\/datasets\/lhoestq\/custom_squad\/blob\/main\/custom_squad.py#L101-L107)) seems to show that this is the correct parameter. \r\n\r\nHere is the full stack trace:\r\n\r\n```\r\nDownloading and preparing dataset xfun\/xfun.en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/caseygre\/.cache\/huggingface\/datasets\/xfun\/xfun.en\/0.0.0\/96b8cb7c57f6f822f0ab37ae3be7b82d84ac57062e774c9361ccf0a4b9ef61cc...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/caseygre\/PycharmProjects\/aegis-ml-new\/unilm\/venv-LayoutLM\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/caseygre\/PycharmProjects\/aegis-ml-new\/unilm\/venv-LayoutLM\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/caseygre\/PycharmProjects\/aegis-ml-new\/unilm\/venv-LayoutLM\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 975, in _prepare_split\r\n    generator = self._generate_examples(**split_generator.gen_kwargs)\r\nTypeError: _generate_examples() got an unexpected keyword argument 'filepath'\r\npython-BaseException\r\n```","embeddings":[-0.0687072426,-0.3811147809,0.0466551706,0.3227540255,0.0099625885,0.1686693728,0.3243077695,0.0713836551,0.6673384905,0.2038326561,-0.2203584164,-0.0036799437,-0.0676354915,0.410430342,0.4078680277,0.0784859285,0.0209659766,0.0293767322,0.1985024214,-0.0212111417,-0.1326785684,0.3249163926,0.1005780622,0.0507369824,-0.5055558681,0.2073848248,-0.2630449235,0.5008372068,0.0945408344,-0.3425169289,0.2188705802,0.4161888659,0.2778623402,0.6348177791,-0.0001247538,0.1073704511,0.1700045764,-0.1355928034,-0.0933155194,-0.2565156519,0.0836204141,-0.1391156167,0.1484513581,-0.0517434031,-0.2248863727,0.0747364908,-0.0258333981,-0.095879294,0.2998907566,0.1945005506,0.0733893737,0.3699014187,-0.1588223279,-0.3921982646,0.0161066372,0.2067050934,0.0424426012,0.390694648,0.2207685411,-0.0905672982,-0.1425688565,-0.0576215163,0.1596904248,-0.274394989,0.3410018086,0.1152086556,-0.0136569384,-0.2120606452,0.0398122035,0.1138231307,0.3056765497,-0.4165116549,-0.2361233085,-0.5492111444,0.1644784957,-0.0490297563,0.2420242876,0.0641079023,-0.2225864381,0.2782555819,-0.1492542624,-0.4457449615,-0.1467928141,-0.099940829,0.0134422109,-0.0400020927,-0.005396829,0.0824934393,0.1311320066,0.0941430926,0.1438910961,-0.1851318926,-0.0053941612,0.2223143429,-0.3184432983,0.0097607607,0.0696814507,0.4588823318,0.0442937985,0.3833853304,0.2576038837,0.1442041397,-0.36689502,-0.0231065992,0.2869673669,0.3376137912,0.1861396432,0.0535355024,0.0572032817,0.4321702123,0.0973461345,-0.0312683545,-0.3652653694,-0.2201036364,-0.1773609817,-0.1647165567,0.2137078941,-0.1119702682,-0.0263847876,0.0206160024,0.4543177485,0.0254196636,0.3166469038,0.5609063506,-0.0086844731,0.0562908351,-0.0617437959,0.2057056427,-0.3324807882,0.0553647093,-0.2353460342,0.1793930978,-0.1580763757,0.4365591109,0.3157144785,-0.1924028546,0.243403405,-0.0899509788,0.3111638129,-0.0863919035,-0.4890427887,0.1210113391,-0.0627134517,0.1773448884,0.3434853554,0.1568647325,0.0252493955,-0.167712301,-0.2320307791,0.047536958,-0.0993756428,-0.4261003435,0.1799601316,0.0040102075,-0.784383595,-0.070493415,-0.2382360697,0.051880084,-0.3201153278,-0.0828371197,-0.0346922055,0.1233801022,-0.0810020491,-0.1204082966,0.3979248106,0.8179820776,-0.2055467516,-0.3096859157,0.0982946754,-0.3511706293,-0.244221583,0.0860233456,-0.1583053172,0.3768188655,-0.3357265592,0.0240458008,0.0935792923,-0.5804439783,-0.3814990819,0.1585713029,-0.3816027343,0.3934432566,0.1514457762,0.2054952383,-0.1499152333,-0.0748277605,0.0192688499,0.1186423078,0.008561315,-0.1407986134,0.0447790809,-0.0917118192,-0.130762592,0.3938096762,0.0397870988,0.1520829052,0.0141234063,-0.093262136,0.2983929813,0.0242912546,0.0215008445,0.1544199139,0.0933707133,0.2889426947,0.0212396178,0.0950856581,-0.6947937012,0.4801172614,-0.1414615363,-0.0458275639,-0.0326080695,-0.0263054036,-0.4431809485,-0.0453924127,-0.3001222908,-0.0841115713,-0.0962145627,0.1292233765,0.1106693819,-0.1350785494,-0.4004233479,0.6751947403,-0.0943127871,0.1713161469,-0.7451930642,0.3974257112,0.1947119236,-0.0868465677,0.051202625,0.2066507339,0.1903985143,-0.096210137,0.1047344208,0.4121129215,0.2110675424,0.558755219,0.2877980769,-0.0335307606,0.2027516961,-0.1274246424,0.1423566639,-0.2659287751,-0.0002728419,-0.0658709183,-0.2937306166,0.3341748714,-0.5461947322,0.2396398187,0.083155416,-0.1435398608,0.0649274737,-0.0296046287,-0.3804564774,-0.2112141401,0.0880397484,0.1811085641,0.2727791667,-0.0523219965,-0.0888386965,-0.0532315597,0.0161194783,-0.0561436303,-0.0951902121,0.0359996706,-0.1056363583,0.1768858582,-0.0211715121,0.0434831008,0.30265975,0.0415336154,-0.1395219713,0.4386952817,0.0922248214,-0.314229995,-0.0668745041,0.0141318198,0.0545442142,0.238941595,-0.232749626,-0.0735130012,-0.185055539,-0.2353789061,-0.0154882912,-0.008120127,-0.4920174778,-0.1923986077,-0.0970680416,0.0108809983,-0.3327924311,-0.3969658017,-0.1954507381,-0.1448617578,-0.0755326077,0.2021211237,0.0469116494,-0.0488798469,-0.0252997428,0.4038213789,-0.2398245782,-0.2427616119,-0.109361425,-0.2060203552,0.0052175322,-0.0992936641,0.0874170512,0.3245896101,0.1181857586,-0.3871161938,0.1491816491,-0.2018143982,0.0174569022,0.1730303764,-0.0197433494,0.1845593154,0.0942517146,0.1213229895,0.1275694966,-0.0826068446,0.257271111,-0.3273714781,-0.0090720728,-0.161056906,0.1111128554,-0.0915481821,-0.0079701198,-0.2030676454,-0.2048926651,-0.3402987123,0.2810354233,0.2639711499,0.0411633588,0.0953518376,0.0282962359,0.1365263164,-0.0915093049,-0.2379805595,-0.1322862655,-0.4996322691,0.3203159273,-0.2762660086,-0.2830872238,0.2194439769,0.0461464189,0.2353377938,-0.1763916165,-0.4108128846,-0.2771072984,0.0160698891,0.1432332397,0.1844496876,0.0899492949,0.1665911973,-0.122530289,0.193791762,-0.1271424443,-0.4265615344,0.0765505731,0.1886963993,-0.0391561016,0.0599328987,-0.0137687037,-0.4099251628,0.2823261619,0.3782165051,0.1052175984,0.2834402323,-0.2523252964,0.5351218581,-0.1772910058,-0.287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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3300","title":"\u2753 Dataset loading script from Hugging Face Hub","comments":"Hi ! The `gen_kwargs` dictionary is passed to `_generate_examples`, so in your case it must be defined this way:\r\n```python\r\ndef _generate_examples(self, filepath):\r\n    ...\r\n```\r\n\r\nAnd here is an additional tip: you can use `os.path.join(downloaded_file, \"dataset\/testing_data\")` instead of `f\"downloaded_file}\/dataset\/testing_data\/\"` to get compatibility with Windows and streaming.\r\n\r\nIndeed Windows uses a backslash separator, not a slash, and streaming uses chained URLs (like `zip:\/\/dataset\/testing_data::https:\/\/https:\/\/guillaumejaume.github.io\/FUNSD\/dataset.zip` for example)","body":"Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :)","comment_length":64,"text":"\u2753 Dataset loading script from Hugging Face Hub \n Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :) \n Hi ! The `gen_kwargs` dictionary is passed to `_generate_examples`, so in your case it must be defined this way:\r\n```python\r\ndef _generate_examples(self, filepath):\r\n    ...\r\n```\r\n\r\nAnd here is an additional tip: you can use `os.path.join(downloaded_file, \"dataset\/testing_data\")` instead of `f\"downloaded_file}\/dataset\/testing_data\/\"` to get compatibility with Windows and streaming.\r\n\r\nIndeed Windows uses a backslash separator, not a slash, and streaming uses chained URLs (like `zip:\/\/dataset\/testing_data::https:\/\/https:\/\/guillaumejaume.github.io\/FUNSD\/dataset.zip` for example)","embeddings":[-0.0499407575,-0.3830173612,0.040472839,0.2923935354,0.0143708531,0.2027331442,0.2845094204,0.0854050815,0.6453070045,0.2024908811,-0.1862067878,0.0076826499,-0.1077617109,0.4451583326,0.4212678373,0.0472498871,0.0251378398,0.0623592697,0.1722055078,0.0322783068,-0.1175382286,0.3261955976,0.0835317075,0.0298315771,-0.4897895157,0.2181455493,-0.2584883273,0.4616767764,0.078107357,-0.3994250298,0.2592480183,0.4347685575,0.2646137178,0.6532836556,-0.0001256706,0.0983416811,0.1831788421,-0.1268395036,-0.0995917767,-0.2488794774,0.1088699028,-0.1258211881,0.1295743287,-0.0412329845,-0.2352456301,0.0514735579,0.0071591679,-0.0854752883,0.3432638049,0.1919130534,0.0557213947,0.3130877018,-0.1096156612,-0.4004022181,-0.0044502858,0.220582366,0.0413920358,0.4126243293,0.15282619,-0.1070879176,-0.1383880824,-0.0515688658,0.1415690035,-0.2861371636,0.4048371613,0.1253982335,0.0047103376,-0.247871384,0.0267849695,0.1079024673,0.3163538873,-0.4413034022,-0.2125831544,-0.5557181835,0.1657112837,-0.0668986514,0.1956508011,0.0687860921,-0.2638602555,0.2915757298,-0.2055279464,-0.4065828621,-0.2026362121,-0.0919548199,0.0366615206,-0.0210413318,0.0121212034,0.0790689141,0.1677331924,0.1014057398,0.1810568273,-0.1635070443,-0.0307979956,0.2440332323,-0.2827417552,-0.0422430895,0.073772043,0.4556825757,0.0851602033,0.4001600444,0.2814920545,0.12816149,-0.3147527575,-0.0243007001,0.2842279673,0.2892523706,0.2115805745,0.0681536868,0.0441821441,0.4761965573,0.08373826,-0.0383365788,-0.360742718,-0.2064829469,-0.145932436,-0.1868604273,0.1739719212,-0.0880522877,0.0058414335,0.0174906217,0.4186502695,0.0419838093,0.2834314108,0.5610792041,-0.0514255688,0.0488817319,-0.0580547042,0.2198458463,-0.2644787133,0.0968303829,-0.2174403965,0.1693614274,-0.1314844042,0.4672183096,0.3219363689,-0.1758646369,0.2732131183,-0.0757857338,0.3092551231,-0.0647306889,-0.4515860677,0.1390289515,-0.0642548203,0.1981265694,0.3595638573,0.1519027352,0.0077245575,-0.1802498102,-0.2480637431,0.0554383062,-0.0489046425,-0.42665416,0.178117916,-0.0076069622,-0.7926713228,-0.0813597664,-0.258761704,0.0854088143,-0.3137857914,-0.1358244419,-0.0824056119,0.1593907028,-0.0859359652,-0.1413918138,0.435566932,0.8009178638,-0.1905507892,-0.291629225,0.045253031,-0.2992218435,-0.2481770664,0.0575755462,-0.1315385401,0.3868306577,-0.328461647,0.0025862174,0.0877192095,-0.5646018982,-0.3841979802,0.1859819889,-0.327898711,0.3879547119,0.1763522923,0.1955648214,-0.1890699565,-0.0676634237,-0.0095249387,0.122491397,-0.0103408722,-0.1283683479,0.0492966771,-0.1105651706,-0.1392370909,0.3866754174,0.0338392928,0.1787731498,-0.0216509644,-0.0977855474,0.3324010074,0.0123426039,0.0146469362,0.1088444069,0.1146210134,0.2684595287,0.0176603217,0.0964808315,-0.6864127517,0.4811427295,-0.1703355312,-0.0456201769,-0.0413913317,-0.0487146638,-0.5014395714,-0.0474925041,-0.2889679372,-0.1028535068,-0.0976988226,0.0823430344,0.1514166296,-0.1251446754,-0.3874194026,0.6531110406,-0.0577682182,0.1640732884,-0.7522265315,0.4222580194,0.1910494417,-0.0732269958,0.0355237909,0.1866282523,0.1886085272,-0.0724656358,0.135627225,0.383003056,0.2247294486,0.5510323644,0.283606559,-0.0467480049,0.1749431193,-0.1294296235,0.1229723692,-0.2638170421,0.0029359756,-0.0517253913,-0.3186348975,0.3298032582,-0.5255208015,0.2054549456,0.0796156898,-0.124832131,0.055220589,-0.0334817059,-0.3449566364,-0.2087480575,0.0328058451,0.1656783074,0.2314485759,-0.0868926719,-0.0847054273,-0.052073244,-0.017488813,-0.0966448933,-0.122309655,0.0658023134,-0.1083947942,0.1423855573,-0.0437817015,0.0464913547,0.3151605725,0.046990376,-0.1237518638,0.4232679307,0.1169126332,-0.2850213349,-0.0329382531,0.0131738847,0.1024232879,0.2590565085,-0.2402907014,-0.0890000165,-0.1493281722,-0.2349274457,-0.034879107,0.0356959552,-0.4850831032,-0.1717493832,-0.0559365675,0.0290644001,-0.2937555611,-0.4097068906,-0.192296505,-0.1449240595,-0.0701438636,0.2697346509,0.0485946238,-0.0841283053,-0.0067872764,0.4119844139,-0.2064613998,-0.2137084901,-0.1478281319,-0.2036683708,0.0264418554,-0.090914458,0.1037978083,0.3092047274,0.1128839329,-0.3686627746,0.1403096467,-0.1770173609,0.0093976706,0.1316219866,-0.0418955721,0.259668529,0.1331252605,0.1750511527,0.0824615583,-0.1286527514,0.2352562249,-0.3252561688,0.0001296912,-0.1463747621,0.086516276,-0.0457573496,0.0063666101,-0.1511627138,-0.2069813162,-0.3272563815,0.3049988747,0.3199051917,0.0471818894,0.1079927683,0.0587993041,0.1121711656,-0.05975198,-0.2430334538,-0.1123932004,-0.5100323558,0.3396886885,-0.2841669619,-0.2764908671,0.2026758343,-0.0071477438,0.2625916302,-0.2137931436,-0.4428613484,-0.2881091833,0.0161232743,0.1254367083,0.1917485744,0.0595666058,0.1441066563,-0.1118825376,0.18966721,-0.1201139465,-0.3906848133,0.0981816649,0.1758269966,-0.0466290563,0.0906883404,-0.040537402,-0.4048850238,0.3249151707,0.4236224592,0.1023937613,0.2594901323,-0.273727864,0.5077310205,-0.1687914878,-0.3000270128,-0.4541245997,0.057140395,-0.1497907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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3300","title":"\u2753 Dataset loading script from Hugging Face Hub","comments":"Thanks for you quick reply @lhoestq and so sorry for my very delayed response.\r\nWe have gotten around the error another way but I will try to duplicate this when I can.  We may have had \"filepaths\" instead of \"filepath\" in our def of _generate_examples() and not noticed the difference.  If I find a more useful answer for others I will add to this ticket so they know what the issue was.\r\nNote: we do have our own _generate_examples() defined with the same def as Quentin has.  (But one version does have \"filepaths\".)\r\n","body":"Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :)","comment_length":93,"text":"\u2753 Dataset loading script from Hugging Face Hub \n Hi there,\r\n\r\nI am trying to add my custom `ag_news` with its own loading script on the Hugging Face datasets hub. In particular, I would like to test the addition of a second configuration to the existing `ag_news` dataset. Once it works in my hub, I plan to make a PR to the original dataset. However, in trying to do so I have encountered certain problems as detailed below.\r\n\r\nIssues I have encountered:\r\n- Without a loading script, the train and test files are loaded together into a unique `dataset.Dataset` -> so I wrote a loading script. Also, I need a loading script otherwise I cannot specify multiple configurations\r\n- Once my loading script is working locally, I do not manage to make it work on the hub. In particular, I would like to be able to load the dataset like this\r\n```python\r\nload_dataset(\"pietrolesci\/ag_news\", name=\"my_configuration\")\r\n```\r\n\r\nApparently, the `load_dataset` is able to pick up the loading script from the hub and run it. However, it errors because it is unable to find the files. The structure of my hub repo is the following\r\n```\r\nag_news.py\r\ntrain.csv\r\ntest.csv\r\n```\r\nand the loading script I specify  `data_dir=Path(__file__).parent` and `data_files=DataFilesDict({\"train\": \"train.csv\", \"test\": \"test.csv\"})`. In the documentation I could not find info regarding loading a dataset from the hub using a loading script present on the hub.\r\n\r\nAny suggestion is very much appreciated.\r\n\r\nBest,\r\nPietro\r\n\r\nLink to the hub repo: https:\/\/huggingface.co\/datasets\/pietrolesci\/ag_news\r\n\r\nBONUS: how can I make the data viewer work in this specific case? :) \n Thanks for you quick reply @lhoestq and so sorry for my very delayed response.\r\nWe have gotten around the error another way but I will try to duplicate this when I can.  We may have had \"filepaths\" instead of \"filepath\" in our def of _generate_examples() and not noticed the difference.  If I find a more useful answer for others I will add to this ticket so they know what the issue was.\r\nNote: we do have our own _generate_examples() defined with the same def as Quentin has.  (But one version does have \"filepaths\".)\r\n","embeddings":[-0.0717483833,-0.350597322,0.0383089893,0.3280479908,0.0319311619,0.1988089383,0.3233025074,0.1017353758,0.6908875108,0.2111232877,-0.1766379923,-0.0175195616,-0.1154561639,0.4286898077,0.4041133225,0.0324172974,-0.000102351,0.0363322459,0.1870842725,0.0189165883,-0.1229627579,0.3319467008,0.1023662686,0.0518042408,-0.5313972831,0.2066438496,-0.2494579107,0.4917796552,0.0616673604,-0.3541394174,0.2583398521,0.3971169591,0.3198552728,0.6629227996,-0.0001243056,0.1237238869,0.174620524,-0.1385752112,-0.0852004364,-0.2658960223,0.0827089325,-0.1128916517,0.1628904194,-0.0383761637,-0.2247487754,0.0760410801,0.0034781417,-0.0861069635,0.3065937161,0.2088813037,0.0691947266,0.3475869,-0.13244012,-0.4452581704,0.0347778425,0.1857174337,0.0447160527,0.416231364,0.2043058723,-0.1209830642,-0.1419692934,-0.0762255415,0.1272263676,-0.2640419602,0.390352428,0.1036336571,-0.0398119316,-0.2254951894,0.0415374935,0.1174842492,0.3514603078,-0.4162601233,-0.2069401294,-0.5414783359,0.1421429515,-0.0105673196,0.2338220924,0.0686782002,-0.2433250993,0.2881402671,-0.1738774925,-0.4040131271,-0.1957552731,-0.0995676294,0.0653301179,-0.1251260936,-0.0037573127,0.0759402215,0.1369052231,0.1348493248,0.1721021533,-0.1542004198,0.0211611018,0.2174008638,-0.2800221741,-0.017925784,0.0775528923,0.4689208567,0.028784642,0.3927243054,0.2778259516,0.1054236442,-0.3393219411,-0.0097077619,0.2878977358,0.3157760203,0.1617419273,0.0355033651,0.0366152711,0.4773265421,0.0790035129,-0.0593249351,-0.3967250586,-0.2549376786,-0.1834294796,-0.1747007519,0.1804809421,-0.0930661038,-0.048625797,-0.0042534331,0.4456155896,0.0379284918,0.321187079,0.583265543,0.0006398734,0.0418339185,-0.0633491427,0.2077432871,-0.2767314315,0.0921915695,-0.2363739312,0.2126915604,-0.1577061564,0.4422350526,0.3562923372,-0.184442848,0.2725764215,-0.1091451347,0.2959358394,-0.0594245307,-0.443180263,0.1322154552,-0.0518549681,0.2071539462,0.3730879724,0.1425227821,0.0339815989,-0.1856351644,-0.2256069332,0.0387307592,-0.0983552933,-0.4446667135,0.1678170711,-0.0013213217,-0.7579586506,-0.0677509084,-0.2606960833,0.0237950217,-0.32919842,-0.129270494,-0.0697280765,0.1572115272,-0.0443513244,-0.1366103142,0.3869754076,0.8359965086,-0.219754681,-0.280749172,0.0474631079,-0.3041669726,-0.240934819,0.1006198302,-0.1625528336,0.4139586091,-0.3665864766,-0.0024956656,0.047009103,-0.5897997618,-0.3620820045,0.1924866438,-0.3666898012,0.3581030965,0.1930384338,0.1897536814,-0.1515366137,-0.12133459,0.0543468036,0.0484385863,-0.0082070529,-0.1575734317,0.0457859412,-0.1029075608,-0.1410526484,0.3700055778,0.0390709899,0.1953780651,-0.0022860568,-0.0881346688,0.2912038565,0.0215734094,0.0149186067,0.1352943331,0.1052440554,0.2769114375,0.0327706374,0.0820803791,-0.673173964,0.4726196826,-0.1451392621,-0.0747926831,-0.0208550151,-0.0187929273,-0.484464407,-0.0757580996,-0.2675195336,-0.0824899301,-0.0886698365,0.0719372481,0.1065607145,-0.1648091227,-0.4099764824,0.6543111801,-0.1103368178,0.1522451937,-0.7318333983,0.4408029914,0.1761381775,-0.0567425676,0.0699295178,0.1813008785,0.2032765597,-0.0794853121,0.1408376843,0.3896514475,0.20425345,0.5383118391,0.3462734222,-0.0189682208,0.1657625884,-0.1739305258,0.1629267633,-0.2703404427,0.0204527173,-0.0473536849,-0.2975184917,0.2979249656,-0.5891471505,0.2173954993,0.1241814643,-0.1201958433,0.0532173999,-0.0355485864,-0.3529348969,-0.187060982,0.0825930536,0.1770011038,0.2578851283,-0.0689383224,-0.0841571316,-0.0622260682,0.0058356505,-0.0748085007,-0.1027878746,0.0715800971,-0.1163080782,0.1551165283,-0.0510940067,0.0907108635,0.3250445127,0.054609511,-0.1420341581,0.4131690264,0.1107391641,-0.3293683231,-0.0107841771,0.0333986878,0.0636412874,0.2469012886,-0.2849285603,-0.0572533719,-0.1804656833,-0.2401590347,-0.0568773933,0.0020768328,-0.4922873676,-0.1937588751,-0.0686610937,0.0338697657,-0.3145113587,-0.4024429619,-0.1901722103,-0.1208491996,-0.054041028,0.2346516997,0.0385150127,-0.0889807791,-0.0102238385,0.4278486073,-0.2084942311,-0.2107398212,-0.1379931122,-0.1947520077,0.0420859382,-0.0867425948,0.0940366089,0.3118621409,0.0848641545,-0.3916740417,0.145407781,-0.221144259,0.0490456969,0.156221211,-0.0250065904,0.2202173173,0.1585192978,0.1961461902,0.1072447449,-0.1115808934,0.2155299932,-0.3049222529,0.0129109677,-0.1252350211,0.0895580128,-0.0824310631,0.0129771493,-0.2128297985,-0.180269599,-0.3353612125,0.2832258344,0.298679322,0.0404625535,0.0873345807,0.0606222786,0.1331112832,-0.0727473423,-0.2273640633,-0.1172126681,-0.54374367,0.3262854815,-0.2810087502,-0.285527885,0.2395117879,0.0166910738,0.2565175295,-0.2250957042,-0.3971222043,-0.2715876102,0.0171123873,0.0989719629,0.2026391923,0.073101759,0.1833965778,-0.1082958058,0.1687832773,-0.15059717,-0.3926455975,0.1044907793,0.1429697275,-0.0393519104,0.0741490275,-0.0225892644,-0.3959669769,0.332829088,0.3965252638,0.1105422154,0.2867386043,-0.251783967,0.5248039365,-0.201289013,-0.3014825881,-0.4009758234,0.0358765721,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3298","title":"Agnews dataset viewer is not working","comments":"Hi ! Thanks for reporting\r\nWe've already fixed the code that generates the preview for this dataset, we'll release the fix soon :)","body":"## Dataset viewer issue for '*name of the dataset*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/ag_news\r\n\r\nHi there, the `ag_news` dataset viewer is not working.\r\n\r\nAm I the one who added this dataset? No\r\n","comment_length":23,"text":"Agnews dataset viewer is not working \n ## Dataset viewer issue for '*name of the dataset*'\r\n\r\n**Link:** https:\/\/huggingface.co\/datasets\/ag_news\r\n\r\nHi there, the `ag_news` dataset viewer is not working.\r\n\r\nAm I the one who added this dataset? No\r\n \n Hi ! Thanks for reporting\r\nWe've already fixed the code that generates the preview for this dataset, we'll release the fix soon :)","embeddings":[-0.4043059945,-0.0264240131,0.0003686886,0.1476445347,0.0435508601,0.1333726496,0.3327429295,0.3144618273,0.0698371008,0.1123042852,-0.1420612633,0.091188997,0.0224873945,0.2090032101,-0.0568056963,0.0026446364,0.212228477,0.0268219542,-0.0084450906,0.0673368275,-0.368013382,0.2244417965,-0.2016298473,0.1170305833,-0.1285111159,0.1274000108,-0.0965365767,-0.1762243658,-0.1552433521,-0.2899169326,-0.0025816951,0.0842715949,0.0311420672,0.5217631459,-0.000103283,0.1010403186,0.4334298372,0.0796466544,-0.0054651927,-0.0565683059,-0.3273562193,-0.2871347666,-0.1110616177,-0.0053097019,-0.346671313,-0.5621010065,0.2363569885,-0.3918738067,0.2673858702,0.330062449,0.3060542047,0.1413725168,0.3151664734,-0.4238250554,-0.0901523829,0.0550323837,-0.3888697624,0.000962583,-0.2080656439,0.1043650359,-0.0600090027,0.5543718934,0.1012794524,0.0907469392,0.0087246196,0.0239835437,0.2227649689,-0.2278691977,0.224193126,0.170764327,0.5400006771,-0.077060774,-0.1940050423,0.0249307007,0.0738201886,0.0740612745,0.2213905156,0.1932424456,-0.0386480018,0.3256552517,-0.1635756642,-0.1579020321,-0.2056147754,0.1984758675,-0.1429051608,0.4284205139,-0.3121683598,0.135169208,0.2287361324,-0.0011086189,0.1863489151,-0.0161247049,-0.3225675523,0.1072124317,-0.3390473127,0.046821706,0.023440741,0.1258302033,0.0295709725,0.1440169513,-0.0462772399,0.1701610684,0.0971869975,-0.0106011461,0.1618151367,-0.1307197362,0.2196853906,0.2602853775,0.4180873334,0.1968259364,0.2015919238,-0.1580488533,-0.1890345514,-0.2863471806,-0.2726505399,-0.4086321592,0.4276471138,-0.1937891394,-0.1975777447,0.1207834482,-0.1489069611,-0.0789350793,-0.1222297996,0.3080991209,0.0239815451,0.0102177132,0.032397598,0.3321218491,0.0197958257,-0.344232291,-0.1951651871,-0.2015168071,-0.2283239812,0.1892244965,0.2031849027,0.0160019994,0.0388627686,-0.1406556219,-0.05393048,-0.0944408774,-0.0554334931,-0.0203660131,-0.2511414289,0.2523833811,0.1773937345,0.2751958966,0.0841302127,-0.1411178708,0.0988958254,0.2247790247,0.0928968713,-0.1489784867,-0.3725584447,0.2376057357,-0.2715436816,-0.0183713753,-0.1430879086,0.4308350384,-0.0658243671,-0.0525754206,-0.1204632074,0.3070560694,-0.1352981925,-0.0461306907,0.2532790601,0.3063926399,-0.4178473651,-0.1399855018,-0.6139634252,-0.4744591415,-0.0395287424,0.0777434781,-0.0348404646,-0.0204775184,-0.2424951047,0.0542789809,0.2566102743,0.0221411344,-0.5047342777,0.1082394496,-0.013732194,-0.082442008,0.0848850682,0.0803005993,-0.1122739092,0.0075486028,-0.5248638391,0.1487564743,0.1957436502,-0.0322808623,-0.0347444601,-0.1055510566,0.0936891958,0.2343299091,0.1948361546,0.1672339588,0.1836379766,-0.1863853782,0.2712078989,0.2072738558,0.0781853199,0.0582350641,0.3617716134,-0.1008936465,0.1430462301,0.0856675729,-0.4692252576,0.0409461297,-0.1643395722,-0.0112381866,0.0514375493,-0.117546916,-0.5166199803,0.0127550699,-0.2683888078,-0.3667939305,0.1741594821,0.2442962825,-0.2183361948,0.2154183537,-0.0874722302,0.3231905699,-0.131071806,-0.0249398425,-0.2170347273,0.2167326212,-0.1353055537,0.0215540212,0.1239939481,-0.1843408942,0.2089676708,-0.0563595891,-0.1415092349,0.3823750317,-0.0211073551,0.3034712672,0.24749659,0.0092635844,0.0198998954,-0.5866423249,0.1919255257,0.1496186107,-0.0304874051,-0.0956990868,-0.2983906567,-0.0102490028,0.2460943609,-0.0889162347,-0.2147474736,0.2473534942,0.1991444826,-0.1084260494,-0.0379108302,-0.0808601603,0.0237829313,-0.2579249144,0.0284739882,-0.0743407458,-0.2515739501,-0.0562781133,0.0670825988,0.0209966525,-0.0790641233,0.0668586865,-0.3679118752,0.0745223761,-0.0012826133,0.1331476271,0.3399636149,0.111462526,-0.0005933801,0.2851763964,0.2826437056,-0.221022144,0.1102269217,0.1244457141,0.2289277762,0.0548571795,0.0124198766,-0.1819652021,-0.4193022251,0.2969458699,0.3087801933,0.2405601889,-0.2614530623,0.0141709577,-0.0026945292,-0.6008552313,-0.1600804776,-0.146211043,-0.1507454664,-0.4011120498,-0.0058832145,0.1939716339,-0.0246092714,0.5094067454,-0.1957161725,0.3517083228,0.034594398,0.4228999317,-0.3400968313,0.1087518856,-0.0853860378,0.2367570698,0.2185897529,0.0701107681,0.2005781829,-0.0160074849,0.2493840307,-0.5792793036,-0.3162872493,0.0914030746,-0.0838796869,0.4887744188,-0.0628257319,-0.0546306148,-0.1994388998,-0.0360897779,0.2228236496,-0.3146907687,-0.1118105948,-0.1585338563,-0.0148681561,-0.1253866255,0.0550101735,-0.2410700172,-0.0911597088,-0.3326108754,0.005324461,-0.1112737432,0.033380527,0.1499274969,-0.052950155,0.1028434634,-0.1093770713,-0.2059914023,-0.3528789282,-0.0459887162,0.5451158285,-0.3700009584,-0.4866624773,0.1486115009,0.2420635372,0.0680943653,-0.2212518603,-0.5363965034,0.045992557,-0.1624048054,-0.1443773657,0.3565907776,-0.255038172,-0.046399422,-0.2074223459,-0.1452995837,-0.2372574508,-0.1098631173,-0.0609682612,-0.325206548,0.3312585652,0.120362252,0.161659345,-0.075302422,0.4965876937,0.4347252846,-0.0178265348,0.2590395212,-0.3346518576,0.4300278723,-0.0242608991,-0.4015444517,0.0663296878,0.1221525371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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3297","title":".map() cache is wrongfully reused - only happens when the mapping function is imported","comments":"Hi ! Thanks for reporting. Indeed this is a current limitation of the usage we have of `dill` in `datasets`. I'd suggest you use your workaround for now until we find a way to fix this. Maybe functions that are not coming from a module not installed with pip should be dumped completely, rather than only taking their locations into account","body":"## Describe the bug\r\nWhen `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified.\r\nThe reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https:\/\/stackoverflow.com\/a\/67851411).\r\n\r\nI guess it is not a widespread case, but it can still lead to unwanted results unnoticeably. \r\n\r\n## Steps to reproduce the bug\r\nCreate files `a.py` and `b.py`:\r\n```python\r\n# a.py\r\nfrom datasets import load_dataset\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(mapping_func, batched=True)\r\n\r\ndef mapping_func(examples):\r\n    ID_LENGTH = 4\r\n    examples[\"id\"] = [id_[:ID_LENGTH] for id_ in examples[\"id\"]]\r\n    return examples\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n```python\r\n# b.py\r\nfrom datasets import load_dataset\r\nfrom a import mapping_func\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(mapping_func, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\nRun `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see \"Loading cached processed dataset at...\".\r\nNow change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function.\r\n\r\n## Expected results\r\nRun `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see \"Loading cached processed dataset at...\".\r\nNow change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result.\r\n\r\n## Workaround\r\nPut the mapping function inside a dummy class as a static method:\r\n```python\r\n# a.py\r\nclass MappingFuncClass:\r\n    @staticmethod\r\n    def mapping_func(examples):\r\n        ID_LENGTH = 4\r\n        examples[\"id\"] = [id_[:ID_LENGTH] for id_ in examples[\"id\"]]\r\n        return examples\r\n```\r\n```python\r\n# b.py\r\nfrom datasets import load_dataset\r\nfrom a import MappingFuncClass\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(MappingFuncClass.mapping_func, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":61,"text":".map() cache is wrongfully reused - only happens when the mapping function is imported \n ## Describe the bug\r\nWhen `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified.\r\nThe reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https:\/\/stackoverflow.com\/a\/67851411).\r\n\r\nI guess it is not a widespread case, but it can still lead to unwanted results unnoticeably. \r\n\r\n## Steps to reproduce the bug\r\nCreate files `a.py` and `b.py`:\r\n```python\r\n# a.py\r\nfrom datasets import load_dataset\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(mapping_func, batched=True)\r\n\r\ndef mapping_func(examples):\r\n    ID_LENGTH = 4\r\n    examples[\"id\"] = [id_[:ID_LENGTH] for id_ in examples[\"id\"]]\r\n    return examples\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n```python\r\n# b.py\r\nfrom datasets import load_dataset\r\nfrom a import mapping_func\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(mapping_func, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\nRun `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see \"Loading cached processed dataset at...\".\r\nNow change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function.\r\n\r\n## Expected results\r\nRun `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see \"Loading cached processed dataset at...\".\r\nNow change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result.\r\n\r\n## Workaround\r\nPut the mapping function inside a dummy class as a static method:\r\n```python\r\n# a.py\r\nclass MappingFuncClass:\r\n    @staticmethod\r\n    def mapping_func(examples):\r\n        ID_LENGTH = 4\r\n        examples[\"id\"] = [id_[:ID_LENGTH] for id_ in examples[\"id\"]]\r\n        return examples\r\n```\r\n```python\r\n# b.py\r\nfrom datasets import load_dataset\r\nfrom a import MappingFuncClass\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(MappingFuncClass.mapping_func, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Hi ! Thanks for reporting. Indeed this is a current limitation of the usage we have of `dill` in `datasets`. I'd suggest you use your workaround for now until we find a way to fix this. Maybe functions that are not coming from a module not installed with pip should be dumped completely, rather than only taking their locations into 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3297","title":".map() cache is wrongfully reused - only happens when the mapping function is imported","comments":"I agree. Sounds like a solution for it would be pretty dirty, even [cloudpickle](https:\/\/stackoverflow.com\/a\/16891169) doesn't help in this case.\r\nIn the meanwhile I think that adding a warning and the workaround somewhere in the documentation can be helpful.","body":"## Describe the bug\r\nWhen `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified.\r\nThe reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https:\/\/stackoverflow.com\/a\/67851411).\r\n\r\nI guess it is not a widespread case, but it can still lead to unwanted results unnoticeably. \r\n\r\n## Steps to reproduce the bug\r\nCreate files `a.py` and `b.py`:\r\n```python\r\n# a.py\r\nfrom datasets import load_dataset\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(mapping_func, batched=True)\r\n\r\ndef mapping_func(examples):\r\n    ID_LENGTH = 4\r\n    examples[\"id\"] = [id_[:ID_LENGTH] for id_ in examples[\"id\"]]\r\n    return examples\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n```python\r\n# b.py\r\nfrom datasets import load_dataset\r\nfrom a import mapping_func\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(mapping_func, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\nRun `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see \"Loading cached processed dataset at...\".\r\nNow change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function.\r\n\r\n## Expected results\r\nRun `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see \"Loading cached processed dataset at...\".\r\nNow change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result.\r\n\r\n## Workaround\r\nPut the mapping function inside a dummy class as a static method:\r\n```python\r\n# a.py\r\nclass MappingFuncClass:\r\n    @staticmethod\r\n    def mapping_func(examples):\r\n        ID_LENGTH = 4\r\n        examples[\"id\"] = [id_[:ID_LENGTH] for id_ in examples[\"id\"]]\r\n        return examples\r\n```\r\n```python\r\n# b.py\r\nfrom datasets import load_dataset\r\nfrom a import MappingFuncClass\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(MappingFuncClass.mapping_func, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":38,"text":".map() cache is wrongfully reused - only happens when the mapping function is imported \n ## Describe the bug\r\nWhen `.map` is used with a mapping function that is imported, the cache is reused even if the mapping function has been modified.\r\nThe reason for this is that `dill` that is used for creating the fingerprint [pickles imported functions by reference](https:\/\/stackoverflow.com\/a\/67851411).\r\n\r\nI guess it is not a widespread case, but it can still lead to unwanted results unnoticeably. \r\n\r\n## Steps to reproduce the bug\r\nCreate files `a.py` and `b.py`:\r\n```python\r\n# a.py\r\nfrom datasets import load_dataset\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(mapping_func, batched=True)\r\n\r\ndef mapping_func(examples):\r\n    ID_LENGTH = 4\r\n    examples[\"id\"] = [id_[:ID_LENGTH] for id_ in examples[\"id\"]]\r\n    return examples\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n```python\r\n# b.py\r\nfrom datasets import load_dataset\r\nfrom a import mapping_func\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(mapping_func, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\nRun `python b.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see \"Loading cached processed dataset at...\".\r\nNow change `ID_LENGTH` to another number in order to change the mapping function, and run `python b.py` again. You'll see that `.map` loads from the cache the result of the previous mapping function.\r\n\r\n## Expected results\r\nRun `python a.py` twice: In the first run you will see tqdm bars showing that the data is processed, and in the second run you will see \"Loading cached processed dataset at...\".\r\nNow change `ID_LENGTH` to another number in order to change the mapping function, and run `python a.py` again. You'll see that the dataset is being processed and that there's no reuse of the previous mapping function result.\r\n\r\n## Workaround\r\nPut the mapping function inside a dummy class as a static method:\r\n```python\r\n# a.py\r\nclass MappingFuncClass:\r\n    @staticmethod\r\n    def mapping_func(examples):\r\n        ID_LENGTH = 4\r\n        examples[\"id\"] = [id_[:ID_LENGTH] for id_ in examples[\"id\"]]\r\n        return examples\r\n```\r\n```python\r\n# b.py\r\nfrom datasets import load_dataset\r\nfrom a import MappingFuncClass\r\n\r\ndef main():\r\n    squad = load_dataset(\"squad\")\r\n    squad.map(MappingFuncClass.mapping_func, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n I agree. Sounds like a solution for it would be pretty dirty, even [cloudpickle](https:\/\/stackoverflow.com\/a\/16891169) doesn't help in this case.\r\nIn the meanwhile I think that adding a warning and the workaround somewhere in the documentation can be helpful.","embeddings":[0.1023012027,-0.1066755876,0.0231204201,0.0879038572,0.1407015771,0.0473644473,0.3940168619,0.3197050393,0.2703644633,0.0267432835,-0.2999673188,0.4735357761,0.2265993506,-0.3539206982,0.0825728774,0.2903340459,0.0108859418,-0.0799179822,-0.2749250233,-0.1475318968,-0.1313757747,0.2969118953,-0.1247686297,-0.0275311358,-0.376439184,-0.0019556126,-0.0825277418,0.2113381177,0.1549104452,-0.4368238151,0.2179662287,-0.0768809319,-0.0357417911,0.5390560627,-0.0001100551,0.0545330681,0.037948627,-0.0895744637,0.138772741,0.067065306,0.1007925719,0.0094174976,0.1730540395,-0.3614327312,0.052352149,0.2571273446,-0.1392584741,-0.5689488053,0.4473128617,-0.0037706527,0.2838268876,0.1656335741,-0.2150286436,0.0622951165,0.138141036,-0.2155904174,-0.0000414765,0.2292877883,0.3375443518,-0.2384904176,-0.2451906204,0.4039097726,-0.1638004631,0.191047281,0.1889219284,0.1947400421,0.4208261967,-0.308712095,0.2491790205,-0.004849989,-0.041127719,-0.3833579421,-0.0732465163,-0.2187645584,-0.1860321462,-0.0557427183,0.1161883399,-0.1445250213,0.0536536649,0.1453247517,-0.2832436562,0.0408813916,0.3707052171,-0.039730452,-0.0063563762,-0.0215353575,-0.0381649956,0.1183546111,0.0615455136,-0.1776630878,0.037040066,-0.17607449,-0.0996762589,0.2941996753,-0.2233273834,-0.0435088575,0.4221668839,-0.0841908604,0.1055063903,-0.097593002,0.1274204552,0.061025206,-0.0292265005,0.177526474,0.1327807307,0.3556653261,0.1741507798,0.3096067607,0.0534566939,-0.2720618248,-0.7355598211,0.1215485483,0.2828048766,-0.1556486487,0.7647553682,0.0435125194,0.1100470051,-0.0121013056,0.142991513,-0.0049048974,-0.040430963,-0.0808287784,0.0796089172,0.4066725373,0.0131170312,0.0054617352,-0.0661450401,-0.0505252331,-0.1652629524,0.0507579036,-0.2683415115,-0.0831432417,-0.2520213127,0.152064845,0.0607501417,-0.2434803694,0.3540925086,0.1689268202,0.1843659133,-0.2418224663,0.3760319352,-0.1699978113,0.5994768739,-0.0556994341,-0.1549906433,0.1392389387,0.407317102,-0.2025329769,-0.2129080147,0.0729139671,-0.5269342065,-0.1555873901,0.2404723465,0.2054583281,-0.189992398,0.0706652328,-0.0521674156,0.0144049814,0.341101259,-0.2802275419,0.1219002381,-0.1810951829,-0.3057302833,-0.4941648543,0.0235718079,0.7083752155,-0.0251598768,-0.2736561,0.1357262284,0.1535450816,-0.0367138013,0.2652257681,-0.3264649212,0.0614330918,-0.2804628313,0.2015713304,0.2636335492,-0.7171671391,-0.4548882842,0.1668405086,-0.0651698783,0.2845681012,-0.2433567494,0.0279370099,0.3499952257,-0.1811998487,0.204941079,0.2732794583,0.0507874973,0.1532680094,-0.4007441103,-0.0890882909,0.2792309523,-0.2028530389,0.0015269063,0.0550455898,0.1965015382,-0.325309962,0.0988278091,-0.0005315433,0.06897863,0.2172530293,0.1347641945,-0.0804610625,0.06436342,-0.1296326965,-0.4603275359,0.311249584,-0.3420245647,-0.3884298801,-0.0887216777,-0.2269691974,-0.0902559683,-0.105846025,-0.3727751672,-0.1762912869,0.1960372925,0.0713565201,0.2777426839,-0.1135747656,0.0695931762,0.0447206758,0.1683793217,-0.045299992,-0.1710415184,0.0201622918,-0.0644973963,-0.1186156422,-0.3411668837,0.0243052244,0.3964194953,-0.0268973038,-0.1344560683,0.2936363816,0.192590341,0.2072054446,0.0827482343,0.1628079712,0.0171598196,-0.0137480944,0.071888864,0.2202794105,0.1209201068,-0.1912017167,0.035031151,0.3627556562,-0.0067527881,0.1249562129,-0.1224873066,-0.0963469967,0.0098020881,-0.0292775873,-0.0996891707,-0.2967141867,-0.2278401703,0.074853465,0.3514564633,0.2422679365,-0.0494530387,0.3744911551,0.567898035,0.1662015021,0.2612208128,-0.1951845288,-0.173195377,-0.1958512962,-0.142663911,0.049183581,0.4420598149,0.0696123391,0.2038456351,0.0566760078,0.1787696183,-0.0142814517,0.080853641,-0.2015763819,0.1448410302,0.0276963208,0.3325086832,0.0253816918,-0.1214539185,0.0924009308,0.0325668342,-0.0607057698,-0.168235302,0.1196252778,-0.3712759316,0.2542580962,-0.2549405098,-0.2912468016,-0.2036878169,-0.3541094065,0.0248077586,0.0125803333,-0.0154635282,0.197420612,-0.0588531569,-0.03239353,0.0216251239,-0.4866385162,-0.0040563177,-0.4126639962,-0.0527538434,0.0113446033,0.0515435934,-0.1608328819,0.0378163829,-0.1455169618,-0.0390153676,-0.2864365578,-0.387530297,0.0413322151,-0.0202065054,0.3479377031,-0.1107772589,-0.0229981374,-0.2151874751,-0.0272025224,0.2387231737,-0.47873649,-0.0853155479,-0.1691823304,-0.153203249,-0.0796973333,-0.0748866126,-0.2895049453,-0.2095374614,-0.2024311125,-0.0054603093,0.0684697703,0.0879269466,0.2781627178,-0.1255795062,0.0414715186,-0.0748351812,0.1358408779,-0.44123438,-0.5766789317,0.2957417667,-0.3659024835,-0.1257682741,0.039718654,-0.0344123133,0.196620822,-0.0813050568,-0.3888836801,-0.5012034178,-0.0530582592,0.5198504925,-0.0083583919,0.2223881334,0.2656992376,0.1025977433,-0.0717999637,-0.1600393951,-0.3034080267,-0.0744127184,0.1303097457,0.1474382579,0.0244801007,-0.1155145839,0.0212923642,0.9486801624,0.434263289,-0.0702304468,0.2231628895,-0.0248619765,0.2981388867,-0.2454480529,-0.3688840568,-0.147698015,-0.29279989,0.0067813285,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3295","title":"Temporary dataset_path for remote fs URIs not built properly in arrow_dataset.py::load_from_disk","comments":"Hi ! Good catch and thanks for opening a PR :)\r\n\r\nI just responded in your PR","body":"## Describe the bug\r\nWhen trying to build a temporary dataset path from a remote URI in this block of code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/42f6b1d18a4a1b6009b6e62d115491be16dfca22\/src\/datasets\/arrow_dataset.py#L1038-L1042\r\n\r\nthe result is not the expected when passing an absolute path in an URI like `hdfs:\/\/\/absolute\/path`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset_path = \"hdfs:\/\/\/absolute\/path\"\r\nsrc_dataset_path = extract_path_from_uri(dataset_path)\r\ntmp_dir = get_temporary_cache_files_directory()\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\nprint(dataset_path)\r\n```\r\n\r\n## Expected results\r\nWith the code above, we would expect a value in `dataset_path` similar to:\r\n`\/tmp\/tmpnwxyvao5\/absolute\/path`\r\n\r\n## Actual results\r\nHowever, we get a `dataset_path` value like:\r\n`\/absolute\/path`\r\n\r\nThis is because this line here: https:\/\/github.com\/huggingface\/datasets\/blob\/42f6b1d18a4a1b6009b6e62d115491be16dfca22\/src\/datasets\/arrow_dataset.py#L1041\r\nreturns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments.\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.3\r\n- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":17,"text":"Temporary dataset_path for remote fs URIs not built properly in arrow_dataset.py::load_from_disk \n ## Describe the bug\r\nWhen trying to build a temporary dataset path from a remote URI in this block of code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/42f6b1d18a4a1b6009b6e62d115491be16dfca22\/src\/datasets\/arrow_dataset.py#L1038-L1042\r\n\r\nthe result is not the expected when passing an absolute path in an URI like `hdfs:\/\/\/absolute\/path`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset_path = \"hdfs:\/\/\/absolute\/path\"\r\nsrc_dataset_path = extract_path_from_uri(dataset_path)\r\ntmp_dir = get_temporary_cache_files_directory()\r\ndataset_path = Path(tmp_dir, src_dataset_path)\r\nprint(dataset_path)\r\n```\r\n\r\n## Expected results\r\nWith the code above, we would expect a value in `dataset_path` similar to:\r\n`\/tmp\/tmpnwxyvao5\/absolute\/path`\r\n\r\n## Actual results\r\nHowever, we get a `dataset_path` value like:\r\n`\/absolute\/path`\r\n\r\nThis is because this line here: https:\/\/github.com\/huggingface\/datasets\/blob\/42f6b1d18a4a1b6009b6e62d115491be16dfca22\/src\/datasets\/arrow_dataset.py#L1041\r\nreturns the last absolute path when two absolute paths (the one in `tmp_dir` and the one extracted from the URI in `src_dataset_path`) are passed as arguments.\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.3\r\n- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Hi ! Good catch and thanks for opening a PR :)\r\n\r\nI just responded in your PR","embeddings":[0.0369565487,-0.0552979372,0.0234631188,0.2166191638,0.232825622,-0.3071340621,0.2497996241,-0.0407906845,-0.1296833903,-0.0436535887,0.1355843693,0.1818684638,0.0551712587,-0.5092622638,-0.016656097,-0.0110698557,-0.0112730348,-0.0280553866,-0.1703254282,-0.193806991,-0.2978547812,0.3347130418,-0.0946578905,0.0225326773,-0.291944176,0.0611582398,-0.1683160067,0.1893920451,-0.0131181004,-0.2459224164,0.5834468007,0.0147810373,0.2270726264,0.3600237072,-0.0001227418,0.0625079498,0.3336173296,0.0004610022,-0.5637117624,-0.236770913,-0.3018205464,0.1294189692,-0.0014599742,-0.3753423393,0.10645549,-0.1972129196,-0.1457764953,-0.2187371999,0.0841312855,0.2057501525,0.088906996,0.3448927104,-0.3692678511,0.114416644,0.2438661754,0.2082259804,-0.0852706805,0.1335490495,-0.1734138727,0.0561628751,0.3708869517,0.1312756687,0.013537677,-0.1936226189,0.2899789214,0.2601412833,-0.1697858125,-0.0562079139,-0.0245982986,0.1895163655,0.2580575347,-0.2403489649,-0.4915512204,-0.00778822,-0.2396404147,0.1951585412,0.2667708099,0.2356148809,0.0226290189,0.1711234748,0.0542176217,0.0049750586,-0.2089629173,0.4400535226,0.0884061679,-0.0375311971,-0.0677341372,0.180317238,0.0270085093,-0.0706962198,0.1200051084,-0.4672776163,0.0200760812,0.4478993714,-0.0029306624,0.1765288115,-0.0494576879,0.1900863349,0.2520090342,0.0337677337,0.1486412883,-0.1374367028,-0.2142024934,0.0503963679,-0.0780339986,0.2546053231,0.0045732697,0.3935505152,0.0523616299,0.1109185815,-0.1036580279,-0.1537831724,0.0100211147,-0.2739598453,0.138782531,-0.1417103708,0.2718000412,0.0443902835,-0.1289559156,-0.0058015664,0.0528482161,0.1289093345,0.2091084719,0.1645995229,0.1895274818,0.1211814731,0.0173153225,0.2567891181,-0.0901088044,0.1672222167,-0.089304775,-0.0424951762,-0.1979617178,0.0674479753,-0.1602752209,-0.1096052676,0.3737947643,-0.024371013,-0.3875215054,0.0111441175,-0.0399082042,-0.2159235477,0.5142444372,0.2119117379,-0.0040561664,0.2605877817,-0.0347549021,0.0922369882,0.0739147589,0.12628977,-0.2505221665,-0.3963761032,-0.1564150602,0.0417642668,-0.1469452977,0.1908761561,-0.1882109642,-0.1214519367,0.2155371159,0.1596518904,0.2525575757,0.0014511655,-0.3045212328,-0.0750214607,0.3280437887,0.6654714346,-0.1814121902,0.0188633595,0.0941210017,-0.1371848285,0.0879538059,0.5107910633,-0.1098560393,0.0905418023,-0.4615572691,-0.0275875069,0.3696823716,-0.2799306214,-0.2352554351,0.5572881103,-0.233968839,0.0618090853,0.2341711968,0.1114201024,0.0965805352,-0.0272921436,0.0260075275,0.5655300021,0.0950023532,-0.0088536469,-0.4758117497,-0.2010188997,-0.0543933772,-0.1776195765,0.1362142414,0.0394256264,0.1685272455,-0.2147458941,0.6231133938,0.1159626916,0.0605211779,0.3416168988,0.321698606,-0.0339137539,0.1338920891,-0.0237494577,-0.4830392003,0.2293231189,0.0235859547,-0.1841332167,-0.3704819977,-0.0649403781,-0.236416176,0.2524692416,-0.2318084091,0.0479819253,-0.0089506041,0.1475865543,0.3812814057,0.1038767993,-0.1018514261,0.0984432697,-0.3267228603,0.1440318078,-0.1447340697,0.5861161351,0.1950219423,0.0661329851,-0.4032772183,0.1983178258,0.3066443205,-0.2730869353,-0.1848003268,0.6214783192,0.2610348165,0.2618401647,0.3044312894,0.002834982,0.2820240557,-0.0595909581,-0.0444000699,0.2906208038,0.2024151832,0.2073192894,-0.0966880023,0.4392247498,-0.326459378,0.4047853649,0.1110223979,0.1273143142,0.0892010331,0.0859241039,-0.1033521667,-0.5248622298,0.1138007343,0.2804963887,0.1325848848,0.0345444866,-0.1546172947,0.2714355886,0.1556960791,0.0191604774,-0.1639266759,0.1322447211,-0.1035462394,-0.0634357184,0.1040896103,0.5406012535,0.2467710227,0.0963678434,0.102173239,-0.2334133983,-0.0207310822,-0.1131187305,0.6549542546,0.0795148164,-0.2248226404,-0.1068968773,0.0565102771,-0.0619484708,-0.0794579014,-0.1383128464,0.1292449832,-0.0073965564,-0.2870837748,0.1309346408,-0.2684372067,-0.2801212966,-0.3753238022,-0.2892329991,0.0683641285,-0.2865796685,-0.147555694,-0.1664267927,-0.1738110781,0.0462355763,-0.1583985984,-0.1879720688,0.1065219343,-0.6734468937,-0.152839914,-0.0627656579,-0.2968199551,-0.0939883515,0.141708836,0.014688205,-0.0066528399,-0.1725390702,-0.0115720183,-0.756629169,-0.0303260293,0.0893976465,0.048715882,0.3711921275,0.2645783722,-0.0139244627,-0.0054185148,0.1107804254,0.2007708699,-0.1442755461,0.0902220309,0.2392406464,-0.0803697929,-0.0881446674,-0.1216318011,-0.0663775206,-0.2332560569,0.0812887028,0.0134561658,-0.0644520223,0.0940156281,0.1859406531,0.2846472561,-0.0859629363,-0.2038815916,-0.3004164696,-0.0166550893,-0.5483576059,0.4213199317,-0.210153982,-0.2728803158,-0.2175566554,-0.1899788976,0.090827696,0.3819717765,-0.5044758916,-0.234208256,-0.1366593987,0.6991258264,-0.1179358661,0.137232691,-0.0100591779,-0.1950983405,0.0905817896,-0.1995670497,0.0626807362,0.3449755907,-0.1166488826,0.2005590349,0.2459793538,0.3429844379,0.0392787866,0.8522260189,-0.0265619271,-0.0542071201,0.4391296804,-0.4285368919,0.4026318192,0.0163512882,-0.1890099496,-0.1155807078,0.2197943181,-0.30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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3292","title":"Not able to load 'wikipedia' dataset","comments":"Hi ! Indeed it looks like the code snippet on the Hugging face Hub doesn't show the second parameter\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/42851186\/142649237-45ba55c5-1a64-4c30-8692-2c8120572f92.png)\r\n\r\nThanks for reporting, I'm taking a look\r\n","body":"## Describe the bug\r\nI am following the instruction for loading the wikipedia dataset using datasets. However getting the below error.\r\n\r\n## Steps to reproduce the bug\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wikipedia\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)\r\n    339                         \"Config name is missing.\"\r\n    340                         \"\\nPlease pick one among the available configs: %s\" % list(self.builder_configs.keys())\r\n--> 341                         + \"\\nExample of usage:\\n\\t`{}`\".format(example_of_usage)\r\n    342                     )\r\n    343                 builder_config = self.BUILDER_CONFIGS[0]\r\n\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']\r\nExample of usage:\r\n\t`load_dataset('wikipedia', '20200501.aa')`\r\n\r\nI think the other parameter is missing in the load_dataset function that is not shown in the instruction.","comment_length":27,"text":"Not able to load 'wikipedia' dataset \n ## Describe the bug\r\nI am following the instruction for loading the wikipedia dataset using datasets. However getting the below error.\r\n\r\n## Steps to reproduce the bug\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wikipedia\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\n~\/anaconda3\/envs\/pytorch_p36\/lib\/python3.6\/site-packages\/datasets\/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)\r\n    339                         \"Config name is missing.\"\r\n    340                         \"\\nPlease pick one among the available configs: %s\" % list(self.builder_configs.keys())\r\n--> 341                         + \"\\nExample of usage:\\n\\t`{}`\".format(example_of_usage)\r\n    342                     )\r\n    343                 builder_config = self.BUILDER_CONFIGS[0]\r\n\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']\r\nExample of usage:\r\n\t`load_dataset('wikipedia', '20200501.aa')`\r\n\r\nI think the other parameter is missing in the load_dataset function that is not shown in the instruction. \n Hi ! Indeed it looks like the code snippet on the Hugging face Hub doesn't show the second parameter\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/42851186\/142649237-45ba55c5-1a64-4c30-8692-2c8120572f92.png)\r\n\r\nThanks for reporting, I'm taking a look\r\n","embeddings":[-0.0762499049,0.1433967799,-0.0506566018,0.3614869118,0.1556534469,0.3728309274,0.3982862234,0.2425006926,0.1862863898,0.0674072579,0.1126713827,0.3431459665,0.0850700513,-0.0809757039,0.1471761614,-0.1971465051,-0.0270588584,0.048061274,0.0294923857,0.1081224307,-0.1571225226,0.1489008069,-0.285777539,0.0999415889,-0.3015489578,0.1277658492,-0.0241985694,0.2004405111,0.027553726,-0.5392950177,0.3177032769,-0.0027131201,0.139542073,0.3887555897,-0.0001088778,-0.0040132371,0.660882175,0.001152668,-0.4263211489,-0.1657408923,-0.3333563805,-0.3038099706,0.1459583342,-0.4603992105,-0.0372549482,-0.1757346094,0.083918795,-0.1995670795,-0.0116897319,0.1413779259,0.2452874482,-0.1219422668,0.3861866295,-0.1507184654,0.0982669964,-0.0181874577,-0.015235126,0.2158151269,-0.1008501351,-0.0736299828,0.0440181568,0.1875631958,-0.208275184,0.0532173626,0.508793354,-0.0500105768,-0.0143478317,-0.3330169022,0.3587854207,0.3138601184,0.897426784,-0.2420722097,-0.117436491,-0.1413225234,0.1418507993,0.1661690325,0.2689995468,0.1020408049,-0.3792632222,-0.133372426,-0.043799825,-0.4811691344,-0.2208628654,0.4064056277,-0.1830914766,0.2739242315,-0.0501314476,0.1974300742,-0.2229762673,-0.1009689569,0.1023296118,-0.3811616302,0.1440517902,0.2503111959,-0.2573221028,0.1561060399,0.1723742038,-0.0082619349,0.1390543282,-0.1924439818,-0.3186344206,-0.1789719015,0.1592131853,0.1971671581,0.2543841004,-0.0453177132,0.0899199769,0.3081810176,0.2290574163,0.3004586101,-0.2279665321,-0.0432622954,-0.0444212779,-0.2414569408,0.0622906983,-0.0914030075,0.3935388923,-0.1665319055,-0.0644514486,0.2788004279,-0.15986754,-0.1446147114,0.0326212198,0.2899544835,-0.3346845806,-0.0866430178,0.2921962738,0.0758263692,-0.0972867012,0.0425463356,-0.205603227,0.0275313258,-0.1764673293,0.0646430701,0.2770912051,-0.3871306777,0.4450863898,0.2117186934,0.1712420732,-0.1367539316,-0.1352373511,0.0778246373,-0.0894153267,0.2698750794,0.1132350937,0.2365856469,0.0263854042,-0.2652977705,-0.0908163637,0.2578475177,-0.197195515,-0.2262065709,-0.303940773,0.2417937219,-0.1847670376,-0.0876879692,-0.0882266015,0.0224573016,0.2084349841,-0.3131102026,-0.1747239232,-0.0411927216,0.0336948559,-0.2673460841,0.3951310813,0.4149212539,-0.2076660991,-0.1178127676,-0.0589745343,0.146647647,0.0736879036,-0.053240139,-0.270588994,0.3825062513,-0.1123157218,-0.069378078,0.4443959892,-0.1020067707,-0.3050428331,0.1014274135,0.1648798436,0.1072032452,-0.0898692831,-0.0586829931,0.1451797783,0.1420227289,0.2261270732,0.3157556653,-0.001349353,-0.0606459789,-0.149749428,-0.2048335522,0.2640187144,0.2016724795,0.2600149512,0.0389702469,0.0659278259,0.1352381259,0.2274321169,-0.1065765321,-0.0679879487,0.3541468382,-0.1541171074,0.3320243955,0.2752763629,-0.1638210714,-0.5081701279,0.3589696586,0.1145787463,0.1903652102,-0.1336481869,0.0004892608,-0.3860522807,0.0331839919,-0.3197973967,-0.047953587,0.2004967332,0.1718931496,-0.1269348264,0.0897178277,-0.0490691252,-0.0309709609,-0.1140536368,0.003124211,-0.402071327,0.2683751881,-0.2581436038,0.0515189804,-0.0388799496,0.145632416,0.2769026756,0.0678526312,-0.1277253926,0.1147895679,0.1085089594,-0.1217944026,-0.0070833066,-0.2365659624,0.0957314894,-0.3781541884,0.1957129538,0.4140834212,0.1699285805,-0.1158525199,-0.1152519584,-0.0282832533,0.0518139079,0.2994949222,-0.1055549532,0.1787907481,0.3160393238,-0.0401727296,0.0400573574,-0.087614283,0.2812755108,0.0862448961,0.1163826734,-0.0680867732,-0.3055630922,-0.2052349746,0.1828150749,-0.0501879826,-0.0358584672,0.1251667589,-0.2739661038,-0.0369618125,0.038526129,-0.0518316329,0.1107295528,0.1819209456,-0.1637377441,-0.1179532632,-0.1568707675,0.0091814622,0.3547697365,0.2460718602,0.2839431763,0.2029428333,0.0543918088,0.1607450843,-0.1579761207,-0.2648550272,0.0597534962,0.1771062762,-0.4073270261,0.0732984617,-0.0970770866,-0.2272067517,0.057941217,-0.2466100603,-0.3912613988,-0.3991885483,-0.1147289351,0.0344350338,0.0470111482,0.292337805,-0.099790737,-0.0256753694,0.1458300948,-0.3234954178,-0.2285158932,-0.3758782148,-0.2756742239,0.0454219095,0.2205769867,-0.0539897121,0.2392622828,-0.0990083888,-0.1166928336,-0.4501042962,-0.3113396764,0.1635793746,-0.0780371428,0.4584934115,0.1675860584,0.3674376607,0.2156600505,-0.3337644935,0.3570527434,0.0802817643,0.0244859029,0.2931556106,-0.0788694099,-0.2447586805,0.1158897057,-0.2963204384,-0.1140128374,-0.2727452517,-0.2193789035,0.2467515022,0.0532577373,0.4353683889,-0.0012493704,0.0029924095,0.112112321,0.2858229578,-0.2634221613,-0.1440224349,0.4427886903,-0.293515712,-0.3429139555,0.0663545504,0.0269070435,0.1517423838,-0.0191973355,-0.4672423601,0.3088131249,0.0550603271,0.3868795633,-0.2447832525,0.1319734901,0.0719866678,0.1632777452,0.0522079729,-0.1593940556,-0.2360760719,-0.1644304246,-0.1190431714,0.4106622636,0.0564579628,0.3606626987,-0.2596132755,0.7011081576,0.2332719862,0.0301163681,0.1273600012,-0.1630524546,0.2219768316,-0.1452785283,-0.5021957159,0.0508690141,0.003918569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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3285","title":"Add IEMOCAP dataset","comments":"The IEMOCAP dataset is private and available only on request.\r\n```\r\nTo obtain the IEMOCAP data you just need to fill out an electronic release form below.\r\n```\r\n\r\n- [Request form](https:\/\/sail.usc.edu\/iemocap\/release_form.php)\r\n- [License ](https:\/\/sail.usc.edu\/iemocap\/Data_Release_Form_IEMOCAP.pdf)\r\n\r\n\r\n> We do not share the dataset for commercial purposes due to privacy concerns surrounding the participants of the research. The login details will only be emailed to the given academic email address.\r\n\r\nI think it won't be possible to add this dataset to \ud83e\udd17 datasets.","body":"## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":80,"text":"Add IEMOCAP dataset \n ## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n The IEMOCAP dataset is private and available only on request.\r\n```\r\nTo obtain the IEMOCAP data you just need to fill out an electronic release form below.\r\n```\r\n\r\n- [Request form](https:\/\/sail.usc.edu\/iemocap\/release_form.php)\r\n- [License ](https:\/\/sail.usc.edu\/iemocap\/Data_Release_Form_IEMOCAP.pdf)\r\n\r\n\r\n> We do not share the dataset for commercial purposes due to privacy concerns surrounding the participants of the research. The login details will only be emailed to the given academic email address.\r\n\r\nI think it won't be possible to add this dataset to \ud83e\udd17 datasets.","embeddings":[-0.3974730074,-0.1045243144,-0.1626392305,0.0194150582,-0.0553385913,-0.1147545204,0.5808410645,0.0518380366,0.0989709198,0.3002895117,-0.479585588,0.095807679,-0.1967374533,0.4536912739,0.1594684124,-0.0247065537,-0.0657190979,0.1287434399,0.0163776949,0.0037711014,0.0428196788,0.0552563816,-0.0223840456,-0.0564065836,-0.190722242,0.0491648652,-0.1851544678,0.041753646,-0.2844049335,-0.1643349677,0.0281801969,0.3415521681,-0.176335603,0.2900148034,-0.0000974872,-0.0472249836,-0.121965535,-0.3168735504,-0.0934416577,0.0588606633,-0.3996445239,0.0938746184,-0.3133712113,-0.1003325209,-0.3224113882,-0.0918992087,-0.2276781946,-0.2974072695,0.1359327585,0.3855352998,0.3168557286,0.0318122804,0.1325971037,-0.1405479014,0.2937948406,0.301514864,-0.2291444242,-0.0079625444,0.0914376825,0.1443847865,-0.0584492274,0.3583851457,-0.1107892841,0.0481697544,-0.103790544,0.0228634272,0.1141306907,-0.1054381803,0.1625217199,0.4214643538,0.5761349797,-0.3468238413,-0.2958821356,0.0910817683,0.295096606,-0.1853448898,0.1303765625,0.1522127837,0.2014203519,0.1935745776,-0.0518917181,-0.0593594387,-0.1860744953,0.114519231,-0.0618066788,0.2257768065,-0.3401396275,-0.1609715819,0.0187835675,-0.2647108436,-0.0855722129,0.231949538,-0.0095133176,-0.0812551901,-0.124015905,-0.1955129057,-0.0465836301,0.1206808612,0.0745654181,0.1428407729,0.188073054,0.0600981675,0.1922099143,0.1587520242,0.0740783438,-0.4278232455,-0.0281659197,-0.2811885178,0.4038633704,0.1260294467,-0.081957832,0.0422123931,0.1543979496,0.1268291622,-0.1551386714,-0.094344683,0.1372542083,-0.1460511535,-0.310397476,-0.1310610622,-0.087147221,-0.131588161,0.0602670237,0.2727729082,-0.0770408735,0.2141499221,-0.0198290609,0.3079814613,-0.0520647839,-0.3060114384,-0.1031976491,0.0786527395,-0.1309861988,0.2466424108,0.3833066821,0.2544534504,0.0175816212,-0.0562822074,0.0671899468,-0.0585954264,0.2251609713,-0.149390161,0.1864887327,0.0956897885,0.2446402758,-0.100544177,-0.1965136975,-0.2140137404,-0.2752705216,-0.333868593,-0.009057886,-0.1298046708,-0.3706524968,0.3281071186,0.0080053089,-0.1325461864,-0.0423209295,0.3735109866,-0.017597029,-0.2558067143,0.0362615138,0.2573404312,-0.4331171811,-0.0616893843,0.1306970268,0.2101192027,-0.1018630862,-0.2151276022,-0.2385180444,-0.0504704118,-0.2295742333,0.1759165227,-0.0021071469,0.1390889287,-0.2102532387,0.1659363508,0.0612418279,-0.3258379996,-0.3701393008,-0.2355850488,-0.1705555916,-0.1734068096,0.4858986139,0.3162851334,0.3446541727,-0.3364911377,0.2164064795,0.3085622787,-0.0907006413,0.0742432997,0.0914181992,-0.2873871028,-0.2052147537,0.2619989812,-0.0933020338,-0.2307524383,0.2690959275,0.0112947235,0.1067377031,-0.1691335887,0.3663845062,0.0592354015,0.3073548079,0.1239268109,-0.0555030592,-0.5151285529,0.0551023595,-0.1244319603,-0.1155045703,0.2578992248,0.053544499,-0.4508406222,-0.2166952342,-0.1120781749,-0.2036851197,-0.045556616,0.3301034868,0.358392477,-0.0219898634,0.0502927303,-0.1526480615,-0.0272607803,0.1640068442,-0.1499969959,-0.1567133069,0.1836344898,-0.1597310603,-0.0583025217,0.167095378,0.3672920763,-0.2413955331,0.1169637814,0.1638266742,0.2458032221,-0.0837597474,0.3472056687,0.2530812323,0.4615988731,0.0365937501,-0.6401762962,0.3632581234,-0.1111804172,0.0596844181,-0.0718179569,0.0502502136,0.4042708576,0.4181040227,-0.1626324654,0.0752158016,0.0587885343,0.4181021154,-0.1553660333,-0.1422188431,-0.4897952378,-0.2529436052,0.1109011471,-0.0389247909,-0.0332860351,-0.2375079393,0.1526479274,0.4643154442,0.1970067173,0.502689898,-0.080441609,-0.5265238881,-0.1293876469,0.0387695245,-0.1580598652,0.3450323045,0.3945532739,0.1465809643,-0.1527085006,0.1250337809,-0.1553648263,0.270286262,-0.1272284538,-0.1730488092,0.3177972138,0.237714693,-0.2155912369,-0.5438374877,-0.2140279561,-0.1496039033,-0.2476682365,-0.2814972699,-0.1900595427,-0.0985492766,-0.2753351927,0.0664279386,-0.3589451015,0.0022892724,0.0430160016,0.2472322285,0.1728020012,-0.2325554937,0.0980263427,-0.0712128058,0.4156302512,0.0834743008,0.1059082821,0.1688450128,-0.0483945347,-0.0500971302,0.3064126372,0.2266083658,0.023606712,0.37684232,-0.1279218197,0.1451693922,-0.3564573228,-0.2891181707,0.2807348669,-0.0852066278,0.0286561307,0.1189406961,-0.1386023164,0.1237154305,-0.152282685,0.2665056884,-0.148810178,-0.0797716081,0.0300229806,-0.1951825619,0.1086240411,-0.1118051633,-0.4838263392,-0.3841780424,-0.4040660858,0.2613053918,-0.0251937155,0.015451991,0.0193768181,-0.0764100105,0.1895790845,-0.111211285,0.3517568409,-0.4914855659,-0.2356438935,0.3176610172,-0.4468017519,-0.1779695153,0.0228047092,-0.2146840096,0.1400446892,-0.0900753364,-0.2333060056,-0.1168569922,0.0215702727,-0.0592535064,0.1374396086,0.1405300647,0.2056447864,0.1048659012,-0.2610454559,-0.1487955749,-0.1936950535,0.1677963436,0.2412878424,0.0453050025,-0.1000494659,0.0484149083,0.1213097423,0.2675737441,-0.0691100806,0.0277773887,0.4328352213,-0.2018036395,0.2844485641,-0.0886185318,-0.117259264,0.2999522984,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3285","title":"Add IEMOCAP dataset","comments":"Hi @dnaveenr ! We can contact the authors to see if they are interested in hosting the dataset on the Hub. In the meantime, feel free to work on a script with manual download.","body":"## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":34,"text":"Add IEMOCAP dataset \n ## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hi @dnaveenr ! We can contact the authors to see if they are interested in hosting the dataset on the Hub. In the meantime, feel free to work on a script with manual download.","embeddings":[-0.4055469036,-0.2296992838,-0.1807560474,0.0004847769,-0.0748523772,-0.0578592084,0.5305745006,0.1825385392,0.1652227193,0.3068115115,-0.384626925,0.0953106582,-0.2732805312,0.4907408059,0.2245352566,0.0488990434,0.0969656408,0.1993620396,0.0471553579,-0.000703578,0.0354212821,0.0814213902,-0.0095014703,-0.0823370442,-0.351668328,0.1987455785,-0.2206881791,0.1411189735,-0.2584029138,-0.1662731618,0.0248800237,0.354321301,-0.0716322288,0.2505130768,-0.0000988241,-0.1349209696,-0.1685384661,-0.2723159492,-0.0688481033,0.1564231664,-0.4463662207,0.1763774604,-0.2025402784,-0.1563003361,-0.2891265154,0.0724959299,-0.1692511439,-0.0866120458,0.115377821,0.3311148882,0.297701627,0.0832865685,0.0230067279,-0.2019423693,0.2292447835,0.3164521158,-0.1690038443,0.1264692247,0.3072814345,0.031493187,-0.2148004919,0.3775689602,-0.1979766041,0.0314840749,-0.0854486153,-0.0065766885,0.0647674277,-0.0030065938,0.1851745099,0.3269096613,0.4735693336,-0.3763047159,-0.1249215901,0.0618431307,0.2475082278,-0.1194206029,0.0768833011,0.1236088052,0.2185783237,0.1993244439,0.0220281463,-0.1295692921,-0.1449380219,-0.0387142301,-0.0749347433,0.1915435791,-0.2646912038,-0.247910738,-0.125350818,-0.1869175285,0.0348470733,0.2196126729,-0.0538337752,-0.1081794649,-0.0485489629,-0.2560434341,-0.0764608234,0.2724727988,-0.0114276214,0.0949857682,0.1375023872,0.1363196373,0.1234644428,0.1205244958,0.078936547,-0.3586461842,0.123868458,-0.3090033829,0.3752689958,0.2353308946,-0.0860026404,0.1102104634,-0.0306681711,0.1233010441,-0.0785324723,0.0222086757,0.0225742515,-0.0593842492,-0.3436057866,-0.1713915318,0.0988274366,-0.1552717239,0.1637502164,0.2435850203,-0.1434522122,0.2902776897,-0.0691640452,0.2632553279,-0.1991917938,-0.40346843,-0.1167277098,0.2067021877,-0.1226041839,0.2311908752,0.3377816975,0.2325381488,-0.0481078178,-0.1232386827,0.1114702746,-0.1520932019,0.1018455923,-0.0232566949,0.0188166928,-0.0068926923,0.3992336988,-0.1092396826,-0.1855024397,-0.2013188303,-0.2476266921,-0.3594683409,0.0011560742,-0.1896997541,-0.3214370906,0.3434807062,0.034021847,-0.1965645105,-0.0513799079,0.25321123,-0.1513297409,-0.2526369989,-0.0342845842,0.3833511174,-0.3333943784,-0.1175921634,0.0370694324,0.3737783134,0.0398960225,-0.1014182866,-0.095713757,0.0644334108,-0.3426504433,-0.0412885845,0.0872089341,0.232552737,-0.1589198709,0.1980449259,0.0807802975,-0.4544118643,-0.1634683162,-0.2395983487,-0.1323942989,-0.1751972437,0.3849944174,0.3145807385,0.4655407369,-0.3195223212,0.206750378,0.4071229696,-0.0781277493,0.2416629642,0.1486248225,-0.2205414325,-0.3788624704,0.4202786088,0.0171060767,-0.2494969517,0.1035420448,0.176557079,0.210217461,-0.1119004041,0.4049964547,0.0213758331,0.2068487704,0.0869240835,-0.0814821944,-0.6223570108,-0.0077463519,-0.1190185919,-0.0644428581,0.221939221,0.0643606856,-0.4770545363,-0.3433797956,-0.1354423016,-0.2023056149,0.0162553471,0.3522593975,0.2408307195,-0.0839425027,-0.074454546,-0.1803764999,0.0165675394,0.2430271059,-0.0577489175,-0.1978215426,0.2661644518,-0.1099383011,-0.1103334799,0.217375353,0.2831489146,-0.2027196139,0.104998216,0.1774764955,0.3006603718,-0.0133059816,0.4332779348,0.3335843086,0.4334613979,0.1204366311,-0.469778657,0.424459219,-0.1389223188,0.0034256841,-0.182739526,0.0233676843,0.3209548295,0.2265749723,-0.1985548288,0.0035384391,0.1829186231,0.2498593628,-0.197456494,-0.0816853791,-0.3488302529,-0.2815339565,0.1729870737,-0.1304216236,-0.0942937285,-0.0800543502,0.1761649698,0.3686724603,0.062793389,0.4839060903,-0.0252965111,-0.4922381043,-0.1405319124,0.0065092719,-0.2567342818,0.1706578285,0.3487956226,0.1394318342,-0.1907063425,0.1086371318,-0.1629848033,0.2993198633,-0.2170395851,-0.1901564896,0.2443532348,0.1448310018,-0.2748009264,-0.5054821968,-0.3829376101,-0.1342465878,-0.2504177392,-0.1790875494,-0.2289924324,-0.0524402894,-0.1857727617,0.1580083668,-0.4776255786,0.0828110576,0.0718998834,0.3356721699,0.3286881149,-0.1485678852,-0.0269506685,0.0581034236,0.4890366197,0.0734345019,0.1486870944,0.1484089941,-0.1455917954,-0.0494683348,0.2992688417,0.2804485559,0.1579984874,0.2720437944,-0.3263391256,0.2556183338,-0.2333499044,-0.0603987798,0.2359979302,-0.0429897271,-0.1228566542,0.1706241816,-0.0005423788,0.0975896493,-0.0702244341,0.2617724538,-0.0764029473,0.0817031264,0.1148875132,-0.0936243534,0.0169035587,0.0057232897,-0.3566914797,-0.328553617,-0.4351165295,0.0995483845,-0.0229423717,0.0495452508,-0.1849094629,0.0080050882,0.0404212363,-0.1627042741,0.3002125323,-0.2769973278,-0.206409201,0.2458483726,-0.4126310945,-0.1300030202,-0.0228780098,-0.2272145748,0.217292428,-0.1698155701,-0.1654430628,-0.1853236854,0.0882230029,-0.0150336083,0.1486928761,0.2058737725,0.2689308822,0.2658361495,-0.2360596657,-0.097531721,-0.0793205574,0.0726427063,0.2509938478,-0.1508813202,-0.0396373905,-0.1338961422,0.0452424362,0.0664419532,-0.0035576716,0.080888465,0.4724988937,-0.1506200433,0.1827837527,-0.0455332622,-0.0320664383,0.1512924731,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3285","title":"Add IEMOCAP dataset","comments":"Hi @mariosasko . Thanks for your response. Sure, I will mail them and find out if they're open to this.\r\n\r\nWork on a script with manual download ? This is new to me, any guidelines would be helpful here.\r\n","body":"## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":39,"text":"Add IEMOCAP dataset \n ## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hi @mariosasko . Thanks for your response. Sure, I will mail them and find out if they're open to this.\r\n\r\nWork on a script with manual download ? This is new to me, any guidelines would be helpful here.\r\n","embeddings":[-0.3969157636,-0.0930214822,-0.1669155657,-0.0267276522,-0.0511002094,-0.0638293251,0.417733103,0.099010855,0.1840799451,0.2257883251,-0.3469263613,0.0741996616,-0.286162138,0.5310558081,0.2502625287,0.0206210781,0.028656492,0.2210894078,-0.0193939228,-0.034224879,-0.1075728685,0.0823396295,0.0388759971,-0.1391589642,-0.241622299,0.105212912,-0.2594056129,0.1583882421,-0.3712256253,-0.2218901068,0.0002949498,0.3921109736,0.0019221654,0.2867297828,-0.0001013714,-0.2679996192,-0.2115185261,-0.3301728964,-0.0016908556,0.1131812781,-0.4438702762,0.0502610952,-0.1627598256,-0.2590406835,-0.2212345898,0.0757987425,-0.1932435781,-0.2303819656,0.2120333314,0.3563755751,0.2751033008,-0.0283764824,0.0547821522,-0.1752996743,0.3194935024,0.326785177,-0.0809821486,0.1051704437,0.2375777513,-0.0333679877,-0.0797811598,0.3542123735,-0.1636051983,0.0446819216,0.0378148593,0.0831312016,0.094419919,-0.2007470727,0.1544988006,0.3409003615,0.6211659312,-0.4425309002,-0.1508389115,0.1440994889,0.3110228777,-0.2312684208,0.0633675978,0.1811442822,0.2127952725,0.1580660045,-0.0083903596,-0.1627752334,-0.2060209215,-0.0524160117,-0.0099362824,0.0917360783,-0.2849499285,-0.229107514,0.0140378764,-0.1749322712,-0.1798778772,0.1817779243,-0.0399347246,0.0643591508,0.0617102049,-0.2692063749,-0.1133362725,0.1971508861,0.083974801,0.1854438335,0.1026690751,0.0334876589,0.181124866,0.0780184194,0.0601676926,-0.3585984707,0.0626527146,-0.3617118895,0.3205792308,0.215643391,-0.0352201089,0.1219041646,-0.0380078182,0.0002508443,0.0060103019,0.0138848061,0.1657730937,-0.2307111323,-0.3142762482,-0.1014427245,0.0878853276,-0.2844668031,0.0194303375,0.2533008456,-0.1349624544,0.2052133381,0.0263763946,0.3483769298,-0.1978463531,-0.375140518,-0.0196998157,0.2373479754,-0.1189154536,0.2136596888,0.3241894543,0.345512718,-0.0774529725,-0.0858003944,0.0108395535,-0.1446062773,0.1212506369,-0.0648861453,0.0285654645,0.0225103311,0.3870973587,-0.0346974097,-0.2153731734,-0.1501205117,-0.2511584759,-0.1744601578,0.1059412137,-0.1492915154,-0.3103212416,0.3467289209,0.1038118005,-0.2688969374,-0.0035400866,0.308649987,-0.1000683382,-0.2026960105,-0.0017694114,0.3477312922,-0.38610816,-0.1741273254,0.0709751397,0.5280352831,-0.1576309204,-0.0910873488,-0.203136608,-0.1035577655,-0.2131926864,-0.016615225,0.0796671659,0.2250288427,-0.1604059935,0.1076002941,0.1533395052,-0.302341938,-0.1417750269,-0.1151913702,-0.2263759077,-0.1752487272,0.499674499,0.316855967,0.3891353309,-0.3660928607,0.0730743855,0.4515549242,-0.1232969239,0.1418284625,0.0547943562,-0.2170426399,-0.26804021,0.3671307266,0.1057841033,-0.1512222886,0.1167727709,-0.0384706706,0.1187118813,-0.0733989924,0.3817498386,0.0097718965,0.2801240087,0.122645244,-0.1301310658,-0.5039895773,-0.1474750787,-0.1142157614,-0.0969293714,0.2268447131,-0.0677017123,-0.5169658661,-0.3581554592,-0.2025207579,-0.2271115184,0.0173870232,0.2786127925,0.3275581598,-0.0516226217,-0.0557221808,-0.1906371862,-0.1371147633,0.2103085518,0.011042661,-0.1770860404,0.179251954,-0.1561464071,-0.0744950697,0.1433050632,0.1806947887,-0.1690898985,-0.0199781917,0.1638213247,0.3702623844,-0.0164477509,0.3401270807,0.2754351497,0.3599399924,0.0830550045,-0.4316740036,0.4920523763,-0.1361781955,0.0803344101,-0.2606784999,-0.0067649125,0.3448170125,0.1452866048,-0.2278196365,0.0596056283,0.0807917565,0.1998204142,-0.2511593103,-0.0842585489,-0.3644350171,-0.331029892,0.1865649223,-0.1105685309,-0.126783967,-0.1775618345,0.1934749335,0.4001107812,0.0579451546,0.3549283147,0.0493723489,-0.331698209,-0.015928695,-0.1474427879,-0.1226791441,0.3348829746,0.3721970618,0.1259940416,-0.0813440904,0.1178440377,-0.1657516956,0.2891418636,-0.1715040654,-0.2135217488,0.2059936374,-0.048715312,-0.1479040086,-0.4152382314,-0.3969702721,-0.15248698,-0.2210519761,-0.2618440986,-0.273845464,0.0493089594,-0.2815859318,-0.0019775042,-0.442551136,0.1242284998,0.1259913594,0.2245976478,0.2045460641,-0.1970846057,0.0436259545,0.0185134094,0.4539781511,0.0051022391,0.1179192215,0.2281089723,-0.0731733069,-0.0658913478,0.2811152637,0.3851018846,0.1261566877,0.4242807627,-0.4298039675,0.1776681393,-0.2471362203,-0.0836122856,0.1644077003,-0.0686488226,-0.1025951579,0.1379189491,-0.008129132,0.2118372023,-0.1292063147,0.2387506366,-0.0955432132,0.0649232864,-0.0093465922,-0.1082935855,-0.1007668301,-0.0822717547,-0.5480563641,-0.3692064881,-0.4141217172,0.1445551962,0.0880295709,0.1281338334,-0.1560533196,0.0377066545,0.0219886284,-0.1194253117,0.4207760394,-0.2095882893,-0.1502071321,0.2653983831,-0.3291861713,-0.1932361722,0.138526082,-0.2451796085,0.1451018006,-0.044746343,-0.1438537389,-0.1328066289,0.0837299898,0.0281756781,0.0513853692,0.3161882162,0.3464291692,0.2921845317,-0.2010066211,-0.0467650667,-0.0365492553,0.1596928388,0.1346779466,-0.0575573966,-0.0824514478,0.1843449771,-0.0701732337,0.1292096525,-0.0198149271,0.0310265906,0.4420990944,-0.1871062666,0.299017489,-0.0483288616,-0.1102647558,0.1050124168,-0.01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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3285","title":"Add IEMOCAP dataset","comments":"> Thanks for your response. Sure, I will mail them and find out if they're open to this.\r\n\r\nIt's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.\r\n\r\n> Work on a script with manual download ? This is new to me, any guidelines would be helpful here.\r\n\r\nFor instance, this is one of the scripts with manual download: https:\/\/huggingface.co\/datasets\/arxiv_dataset. Compared to the standard dataset, it has the `manual_download_instructions` attribute and uses `dl_manager.manual_dir` (derived from `load_dataset(..., data_dir=\"path\/to\/data\")`) to access the dataset's data files.","body":"## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":100,"text":"Add IEMOCAP dataset \n ## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n > Thanks for your response. Sure, I will mail them and find out if they're open to this.\r\n\r\nIt's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.\r\n\r\n> Work on a script with manual download ? This is new to me, any guidelines would be helpful here.\r\n\r\nFor instance, this is one of the scripts with manual download: https:\/\/huggingface.co\/datasets\/arxiv_dataset. Compared to the standard dataset, it has the `manual_download_instructions` attribute and uses `dl_manager.manual_dir` (derived from `load_dataset(..., data_dir=\"path\/to\/data\")`) to access the dataset's data files.","embeddings":[-0.1976326704,-0.1684259176,-0.0636767894,0.0175230745,0.0493130758,-0.0771758854,0.3879879713,-0.0209072512,0.2226246148,0.1589049399,-0.3142954409,0.1245033294,-0.2589575052,0.493137747,0.3352166414,0.0632350519,-0.069049485,-0.0771615952,0.0377372243,-0.0911585838,-0.0632698908,-0.0694264919,-0.08410009,-0.0880699977,-0.0140769742,0.0246876515,-0.2020876706,0.352049619,-0.264557153,-0.3052986562,0.0457612909,0.5268149972,0.1364200115,0.3807054758,-0.0001032241,-0.2566708922,-0.0528977476,-0.3228548765,-0.118539989,-0.1616392136,-0.5238995552,-0.0021766317,-0.0208172128,-0.3468749821,-0.1160990596,-0.0025782939,-0.0293833762,-0.2435601652,0.2817175686,0.2164752632,0.2382119447,0.0875723064,0.0708474889,-0.1171519607,0.2240647674,0.3444556594,-0.0566493124,0.3198432028,0.320972681,0.0510379821,-0.0246015731,0.2910276651,-0.1474360973,0.0117050121,0.275740236,0.137694031,-0.1202403381,-0.3202723861,0.0580489971,0.4468721747,0.5915087461,-0.5359812379,-0.2713772655,0.1146239266,0.2571250796,-0.0438383073,0.1080763489,0.2229526639,0.1174224839,0.1256692261,-0.1580245942,-0.2050727904,-0.2792188525,0.0261230338,0.0203745551,-0.0571516007,-0.1541012675,-0.1578651369,0.1787210554,-0.1125942916,-0.2770731747,0.1454010308,0.1116763726,0.1842920482,0.1946740448,-0.1869062036,-0.0544463061,0.2351772934,0.1994969845,0.3310263753,0.0735797957,0.0475190058,-0.0525383018,0.1295692474,-0.0317281261,-0.2770075798,0.0054754713,-0.3249996603,0.431899786,0.4309085906,-0.0160613693,0.1148208082,-0.128174752,0.0058694729,0.0145841949,-0.0023721163,0.0822765604,-0.2254634798,-0.0044428459,-0.0616452843,0.148981899,-0.1874994487,-0.0764350519,0.209806487,-0.1395396292,0.0460446849,0.1335282922,0.4149430394,-0.1095603928,-0.2989899814,0.024263382,0.2379428148,-0.1652137637,0.2074671537,0.3909167647,0.2631652057,-0.049650833,-0.1334328353,0.1173486784,-0.0929875672,0.2444828451,-0.0918957815,0.1462017894,0.1194767505,0.401645422,-0.0025138771,-0.1967028975,-0.2368952632,-0.2346497327,-0.1512665898,0.1929617673,-0.2349763066,-0.0922970399,0.2787574232,-0.0158048812,-0.2831824422,-0.0890623257,0.0811953098,-0.1701705754,-0.1720317304,-0.1312028468,0.2764290571,-0.244125694,-0.2544768751,0.1483357698,0.6781426072,-0.17885378,0.0549135804,-0.1871155649,-0.1312099248,-0.177309528,0.0692283362,0.0180258248,0.1553894281,-0.1149086431,0.0754489973,0.110974744,-0.592789948,-0.1559046805,-0.0659447014,-0.1140162349,-0.0809073448,0.4672363997,0.2093616724,0.3543652594,-0.4699805379,-0.0231811088,0.3799631596,-0.0976534113,0.1353046,-0.1210099533,-0.3332985342,-0.2529212236,0.3960064948,0.1864428669,-0.3389748931,0.1525848508,-0.1967846751,0.1818382591,-0.0723359734,0.29125458,-0.1170283705,0.1545640379,0.1037477553,-0.0992515981,-0.2711988389,-0.2485113293,0.0632821172,-0.0818891898,0.3185341656,-0.1511423886,-0.4841241241,-0.2892690003,-0.2207234055,-0.1733456999,-0.0098066349,0.2109929472,0.3452808857,-0.0068032476,0.1196291298,-0.2417905182,-0.0344565399,-0.0105192047,0.1031965017,-0.4642116725,0.1071705967,-0.1113417223,0.0719308332,-0.0121845743,0.2176240683,-0.2186333984,-0.1566464454,0.2098682672,0.4024134874,0.0569474995,0.3882934451,0.3090573847,0.5236164927,0.1915487796,-0.4006142914,0.4709195793,-0.1676809937,0.1722318232,-0.2133637518,-0.1886011213,0.3207132816,-0.0606705397,-0.1985425055,0.2029253393,0.1023432687,0.23619017,-0.2597979009,-0.2368282378,-0.3330625594,-0.2395280004,0.1701111943,-0.1659462005,-0.2244155556,-0.3015864789,0.1305152923,0.2815518379,0.0890996158,0.2475627363,-0.0320413299,-0.3158792257,0.0958526209,-0.0640588552,0.050253965,0.2628042102,0.3320060074,0.1519377381,0.1185598746,0.0228425395,-0.2155296355,0.3094302118,-0.115677014,-0.2972514033,0.1328623295,-0.1466003358,0.0515926555,-0.2338109314,-0.3478033245,-0.131323576,-0.2176968902,-0.4285707474,-0.3046224713,0.0136490976,-0.3276863396,-0.1497454792,-0.4673312604,0.055481676,0.0102018425,0.0981535465,0.1984419078,-0.2346380502,-0.0153378602,-0.0587678067,0.4139855802,-0.1392839402,-0.0367141291,0.1432079524,-0.1316236705,0.0231349524,0.1889702082,0.5069559216,0.1217351705,0.5541547537,-0.4221610427,0.1453385651,-0.2083282322,-0.1573009789,0.117391251,-0.095117256,-0.0710598603,0.1300983578,-0.0297604613,0.3754975796,-0.0097616399,0.177973479,-0.0828721449,0.0994585976,-0.0176484268,-0.1161002517,-0.2043646574,-0.1599428058,-0.627009213,-0.4028511941,-0.3722584546,0.1227520928,0.1398556083,0.1771445274,-0.0097330175,-0.0149315987,0.1728881001,-0.0559042878,0.3880942464,-0.1865793765,-0.1519858092,0.2264164239,-0.3039666414,-0.2754429877,0.2018830627,-0.1584550738,0.2468005121,0.1102688387,-0.2547765672,-0.3259903491,0.0648168847,0.0175089743,0.0779066756,0.339780122,0.4312230051,0.2177170366,-0.1546649635,0.0840407908,-0.1067394912,0.201796636,0.1737605929,-0.09871874,0.0177986491,0.2479843497,0.0148396641,0.3313102424,-0.0007976942,0.1295551658,0.2882395387,-0.1439803839,0.2774880826,0.058606185,-0.1274117082,0.1410816014,-0.0344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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3285","title":"Add IEMOCAP dataset","comments":"> It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.\r\n\r\nYes. That would be perfect. Thanks.\r\n\r\n----\r\nOkay. Thanks for giving a reference. This is helpful. I will go through it.\r\n\r\n","body":"## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":49,"text":"Add IEMOCAP dataset \n ## Adding a Dataset\r\n- **Name:** IEMOCAP\r\n- **Description:** acted, multimodal and multispeaker database\r\n- **Paper:** https:\/\/sail.usc.edu\/iemocap\/Busso_2008_iemocap.pdf\r\n- **Data:** https:\/\/sail.usc.edu\/iemocap\/index.html\r\n- **Motivation:** Useful multimodal dataset\r\n\r\ncc @anton-l \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n > It's best to leave this part to us because we have to explain how login would work and (potentially) set up a custom verification for the dataset.\r\n\r\nYes. That would be perfect. Thanks.\r\n\r\n----\r\nOkay. Thanks for giving a reference. This is helpful. I will go through it.\r\n\r\n","embeddings":[-0.2882096767,-0.1497910321,-0.1636940688,-0.0070336056,-0.0361262672,-0.1127435043,0.5733911991,-0.0719574019,0.1140569299,0.2705541551,-0.304204613,-0.0156047177,-0.1448671967,0.4219711423,0.2603903711,-0.0183760989,0.0353236981,0.1186277419,0.0649689361,-0.0254527461,0.0358379707,-0.0189924296,-0.030200379,-0.0551065914,-0.2478062063,0.0683165342,-0.22352238,0.1611368209,-0.3675456643,-0.2540760338,0.0062407535,0.3478370607,-0.2025491893,0.3346051574,-0.0000977415,-0.1544601023,-0.1393565685,-0.1816324592,-0.1915020049,0.1342620403,-0.6082988977,0.1351191252,-0.1683496982,-0.1468512118,-0.3847377598,-0.0861251801,-0.0933551192,-0.2191459984,0.308878243,0.4141244888,0.3099032938,-0.1327072233,-0.0133764036,-0.0536600836,0.1432593465,0.3244230747,0.0137346433,0.0778457299,0.1851029545,-0.0441329107,-0.0989493728,0.2853444815,-0.0236775968,0.0567798018,-0.0097979121,0.0435179137,0.0298299547,0.0043543484,0.1107900739,0.3291456103,0.508518517,-0.3150939047,-0.1635603309,0.1282860488,0.1657789946,-0.1438340098,0.2948936522,0.0841270536,0.2297073454,0.181680724,-0.1292935759,-0.01189595,-0.2328401953,0.0517768823,-0.0956785604,0.0695670769,-0.1730481684,-0.1964158714,-0.0338484533,-0.216769591,-0.1398918331,0.2435197532,-0.0419785343,0.0498392098,-0.0646489561,-0.227574572,-0.0320412442,0.2772717178,0.0363166258,0.2403930277,0.0335585624,0.1055351794,0.1564272791,0.1254790872,0.0063320622,-0.2708207071,0.1027681157,-0.340652436,0.3184687793,0.138139382,-0.1255491823,0.1262865961,0.0330946967,0.0008070276,0.0091761453,0.0723665655,0.0634137914,-0.0886368081,-0.1429929137,-0.0709041953,-0.0530226529,-0.2351010442,0.0772470757,0.2391884625,-0.1884252131,0.0226843972,-0.0084874937,0.2388090044,-0.2531652749,-0.4216924906,-0.0978393182,0.1543857008,-0.1757078916,0.2367406189,0.3281206489,0.2206699699,-0.0210896097,-0.0338118449,-0.0309192818,-0.1417395622,0.382745266,-0.0616501942,0.1402963251,-0.0583948493,0.260181278,-0.205655545,-0.150328815,-0.2748587132,-0.1788001508,-0.4043699503,0.226541996,-0.1874750704,-0.4028677344,0.3480199277,0.1406851262,-0.2190283239,0.1273551732,0.233231321,-0.0640014336,-0.2096195519,0.0001490135,0.3871970773,-0.2499527633,-0.0645977557,-0.0101774707,0.2845591307,-0.0442464389,-0.1048956439,-0.1699341238,-0.2330086976,-0.3442476988,-0.0049630506,-0.0098833162,0.1982433945,-0.0756674036,0.1759763211,0.0668413937,-0.4050112963,-0.2201717496,-0.2489611506,-0.1223609671,-0.1540900916,0.4291630685,0.2761203051,0.4018418491,-0.2846635282,0.1940401196,0.4086144865,-0.121582754,0.2875221968,0.0100083742,-0.1953142434,-0.230449453,0.3554644287,0.0047769672,-0.3505119085,0.2136561126,0.0153555498,0.1452625692,-0.1712920368,0.2785537243,-0.0674742386,0.3075233102,0.0915323943,-0.1462178081,-0.3634193242,0.1100037396,-0.0997060239,0.0561709702,0.3417157531,0.1355079263,-0.4364667535,-0.2947011292,-0.14994663,-0.2873441875,-0.0692365021,0.3364546299,0.4657511115,-0.02009039,-0.0316325575,-0.1509985179,0.0388974138,0.2145886421,-0.0683060288,-0.1832308173,0.0911951959,-0.1963228136,0.0051647038,0.0257429387,0.2145414501,-0.1361344308,0.0848371461,0.1608144641,0.3604383767,0.0450685024,0.3119687736,0.2387313396,0.4614472985,0.2378631234,-0.502425313,0.3647435009,-0.1102838814,0.0368289687,-0.1080627516,0.0051392713,0.3000054359,0.3516964018,-0.3198416829,0.0706373826,0.0714700371,0.2570792139,-0.3253780305,-0.1075726449,-0.3050060272,-0.1871709973,-0.0281626508,-0.132802695,-0.1283819377,-0.2122341394,0.0797682181,0.3497877419,0.1250827312,0.4317652881,-0.1993883848,-0.4101662934,-0.154166311,0.1080862209,-0.0980940759,0.1881846488,0.3141822219,0.2338557243,-0.0406820886,0.0454966985,-0.0357840918,0.3200833499,-0.3611379266,-0.2839458287,0.1470887065,0.094184421,-0.2224730104,-0.4201084077,-0.3293828368,-0.1024086624,-0.2618298233,-0.3442914188,-0.1544607282,-0.0167977549,-0.2940986454,0.150707975,-0.5270518064,0.0667925626,0.0568581186,0.305334866,0.2213299721,-0.117912665,0.0655164644,0.0143478541,0.4581185281,0.0873766765,0.0583148189,0.2077387273,-0.1609340757,-0.0374357365,0.3066592813,0.367180407,0.1229112148,0.4014610648,-0.2959194183,0.1255176067,-0.2206701934,-0.2753942907,0.2873980105,-0.0307644345,0.0849211365,0.175487265,-0.0595259592,0.1625632793,-0.0345279314,0.2216857672,-0.020104453,-0.1226553023,0.0728488043,-0.0904850885,0.050097391,-0.0744889826,-0.4146085083,-0.3219703138,-0.2661839724,0.1902814358,0.0650254339,0.1008638591,-0.0274536461,-0.0246708021,0.1759124249,-0.1400707215,0.4756183624,-0.4204111099,-0.3041452765,0.1427919716,-0.379679352,-0.1819183826,0.0162853561,-0.2622572184,0.1984728277,-0.1203272864,-0.28566131,-0.2798056304,0.0266182311,-0.0054040155,0.0334951133,0.124636434,0.360006094,0.1803607643,-0.2287749201,-0.0183101129,-0.2371970862,0.1233060807,0.2464033663,0.0650302395,-0.1319220811,0.0399058945,0.0913204253,0.2903524041,-0.0705655962,0.0075758356,0.3430302143,-0.1922488064,0.1660251915,-0.0231214799,-0.1139984652,0.240501225,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3282","title":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py","comments":"Hi ! Thanks for reporting :)\r\nI think this is because the dataset is behind an access page. We can fix the dataset viewer\r\n\r\nIf you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.","body":"## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library.","comment_length":55,"text":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py \n ## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library. \n Hi ! Thanks for reporting :)\r\nI think this is because the dataset is behind an access page. We can fix the dataset viewer\r\n\r\nIf you also have this error when you use the `datasets` library in python, you should probably pass `use_auth_token=True` to the `load_dataset()` function to use your account to access the dataset.","embeddings":[-0.2027655542,0.1993871033,-0.017374428,0.3850066662,0.2765264809,0.1723119318,-0.0053111659,0.2269322872,-0.1176275387,0.1920672655,-0.2589604557,-0.0070930575,0.1979573816,-0.0506276265,0.0628777072,-0.2161214501,-0.108423546,-0.0997953713,-0.0893967822,-0.0184963308,-0.2720648348,0.001220902,-0.0487649627,0.2938479185,-0.0965312719,-0.0145583199,-0.0792539716,0.0217267759,-0.2849617898,-0.4174495041,0.2633956969,-0.0164406672,0.120861426,0.3305671215,-0.0001076563,-0.0294822901,0.5196359754,0.0876934901,-0.3862409294,-0.6431906819,-0.0600777939,-0.2329303473,0.218219474,-0.0073609622,-0.0681548268,-0.2332942337,0.2048517168,-0.2722421288,0.1073730215,0.2153255343,0.2902387679,0.2496889681,0.4348212481,-0.3205542266,0.1367881745,-0.1673882455,-0.1047596186,0.2307295501,0.161273554,0.1161321625,0.1450680941,0.1856269836,0.149871245,-0.0734690949,0.3786955476,-0.0934253559,-0.3029827476,-0.4241183996,0.279787153,0.1503997892,0.7434456348,-0.1431634724,-0.5065808296,0.072490789,0.1532173306,-0.0758514851,0.3953257501,0.4010617435,-0.0802650377,0.0391813479,-0.1685732901,-0.2570518851,-0.2772717774,0.5193194747,-0.1465916187,0.1637357324,-0.1218129024,0.1744399667,0.198920235,-0.0688541308,0.1120318025,-0.1013833284,0.0424797907,0.2691730857,-0.0772495344,0.289909482,-0.0484632812,0.3560121655,0.1361858696,0.0476825573,-0.0292357802,0.1458165944,-0.3487764299,0.0662998855,0.3788773417,0.0657486096,-0.1425423026,0.0131474957,0.3362303078,0.3394884169,0.1115785465,-0.1672976166,-0.1309718341,-0.3140822947,-0.1623685211,-0.1545755714,0.4265622199,-0.1780370474,-0.0994529352,0.1284577399,-0.2840757668,-0.1018088087,0.010945484,0.4008491337,-0.3027250469,0.1324792355,0.0159462914,0.1856576949,-0.0733617842,-0.1071999744,-0.1907057911,0.1186689436,-0.301815331,-0.0835120454,0.1537479162,-0.4672885239,0.1833637655,-0.0959873423,0.2318228334,-0.1408188045,0.1524848491,-0.1871028095,-0.130019471,0.1926506162,0.2402694374,0.4431701303,0.144887656,-0.2493508458,-0.0350493044,-0.017777076,-0.2433142811,-0.2388844639,-0.1557490379,0.2146773934,-0.0391366631,-0.0189708471,-0.1627596021,-0.2201969922,-0.1724713147,0.0299976822,-0.0218560807,-0.0686088651,-0.0802082941,-0.1935223192,0.2517106235,0.5226727724,-0.2972839773,0.040227104,-0.2712684274,-0.2423307002,-0.0353195593,0.1291242391,-0.2708793581,0.004239284,-0.2984908223,0.075006187,0.3271589875,-0.4252152741,-0.6005567312,0.2239187509,-0.0815637559,0.0328174755,-0.1335955113,0.0962633044,0.1569684297,0.0009915301,0.0051671662,0.1270408779,0.1342210025,-0.0878774449,-0.0318728499,-0.195467785,0.037680883,0.1891186684,0.1146788076,0.0800285712,0.2441413403,0.0552352257,0.2985984385,-0.0280875564,0.2217484713,0.1158690676,0.1207096502,0.4673009813,0.0116882604,-0.1403725445,-0.0985390544,0.156444326,0.0340971202,0.3388133049,-0.3569272757,-0.0902035683,-0.3856535256,-0.0072015696,-0.3222621083,0.0039491272,0.137561366,0.1365202218,0.1370213926,0.3564330935,-0.1535233259,0.2270907462,0.0697529912,0.2000745535,-0.4717859924,0.1559896022,-0.1643627435,0.1504879743,0.1082098335,0.0283033364,0.1852131784,-0.1798798293,-0.1612940133,0.2995283306,-0.0070307297,0.2834108174,0.0524220765,0.1394962966,0.3677929044,-0.4319247007,0.2029719502,0.2147857845,0.2503561378,0.1023976877,0.2297943681,0.1634100527,-0.0048998119,0.2317609936,0.2110593021,0.2715300918,0.4035413265,0.1901432723,-0.1751831323,-0.0753100365,0.2559177279,-0.0607591979,0.2961596847,-0.2085100859,-0.3017112613,-0.032553304,0.1828528494,-0.030301597,-0.028257966,0.2370916456,-0.30728966,0.1307977885,-0.0027038094,0.263373524,0.2102098316,0.0919415206,0.0311956499,0.3542659581,0.0155072194,-0.2901537716,0.165825665,0.1785586029,0.006477904,-0.0664737225,0.0198150538,0.0218312312,-0.2774796486,-0.0073401164,-0.2010952681,0.2857619822,-0.1946769357,0.0868147612,-0.3310256302,-0.5319350958,-0.1914975345,-0.2879303396,-0.2990102768,-0.397826761,-0.2203556746,0.1988133937,0.0171070714,0.0763075352,-0.469442606,-0.0245688017,-0.1042864025,0.0703786984,-0.0844596028,-0.1499598771,-0.1766346246,0.1138734668,0.2630017996,-0.0152411005,0.3576879799,-0.1284128577,0.1335319728,-0.4239311218,-0.1696913987,0.0647769123,0.0356579684,0.0985870361,0.124266021,0.3087802529,-0.0252569951,-0.1692080349,0.3450174928,-0.0598764941,-0.0684720725,-0.1151290089,0.0727193356,-0.0330433287,0.1429796815,-0.4839432836,-0.3743897974,-0.329521507,0.24061203,0.1000043005,0.1643775851,0.0726402774,-0.1213571057,0.2420024127,-0.1916273087,0.1328502297,-0.2671850622,-0.1641096026,0.3513081372,-0.3238592744,-0.593962431,0.3540237844,0.1044508293,0.2606615424,-0.0510714389,-0.3970609307,-0.2901276052,-0.2543046176,-0.0388174392,0.0765248984,0.1076825634,0.2036736757,-0.176022023,-0.0530065373,0.1342122555,-0.119803682,-0.1831570119,-0.2781971991,0.2680689096,-0.100048244,0.4044790566,-0.0658355951,0.4081807137,0.2941123247,0.1921271533,0.5276876688,0.0618735477,0.2531735599,-0.2463558316,-0.5655336976,0.0860580802,-0.1419036686,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3282","title":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py","comments":"Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.\r\n\r\nRegards!","body":"## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library.","comment_length":19,"text":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py \n ## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library. \n Ah ok, I didn't realise about the login page. I'll try `use_auth_token=True` and see if that solves it.\r\n\r\nRegards!","embeddings":[-0.1426576376,0.1558706015,0.0060982276,0.3392141759,0.2724204063,0.125944078,-0.0063692443,0.1940263957,-0.1445151865,0.2094281316,-0.2114293873,-0.1045741141,0.2127497196,0.0003536343,0.176192835,-0.1613225788,-0.0735766292,-0.1723281741,-0.0665443614,-0.0726506636,-0.2437771708,0.0468156002,-0.0527268723,0.2419153452,-0.1103380248,0.0628059208,-0.0879902765,0.1248419136,-0.3088072538,-0.420009464,0.2564610839,0.0710411072,0.0663240924,0.2381950319,-0.0001099139,-0.0292435754,0.5209181309,0.0833898708,-0.388916254,-0.5973929763,-0.0298813377,-0.1741570085,0.2516763508,0.03089495,-0.1596904099,-0.2020646632,0.2909854949,-0.1747122705,0.1350809783,0.1857192963,0.2642937303,0.1752738357,0.4002283514,-0.3170187473,0.1665190011,-0.0419938415,-0.1010097116,0.193336159,0.1918205321,0.0853466764,0.1221674606,0.1666827351,0.2154125869,-0.0940857679,0.4747639298,-0.100879401,-0.3567557633,-0.4271948934,0.2975759208,0.1990765929,0.6796488166,-0.0299578011,-0.4646299779,0.1472701579,0.1338990182,-0.0795759037,0.4736991525,0.2981299758,-0.0606892854,-0.0013068268,-0.1826720536,-0.3463903069,-0.343947947,0.541682601,-0.1120353416,0.2589780986,-0.037694063,0.1946148574,0.2165976912,-0.121859327,0.0505138822,0.0301652681,-0.0043818397,0.2684002519,-0.0627135932,0.2647522986,-0.0557427108,0.3592349887,0.2194002271,0.1044476554,-0.0506289974,0.2112493366,-0.3843449056,0.031320814,0.3504373729,0.128226921,-0.0700056925,-0.0563917644,0.412691772,0.3094669282,0.1102416813,-0.1214645132,-0.0997962654,-0.3400831819,-0.164928019,-0.1442169249,0.3900933862,-0.2699673474,0.0616042987,0.1003798917,-0.2255777717,-0.1480901539,-0.0893239602,0.4557418525,-0.2500990927,0.0686874762,0.0305611342,0.1907440424,-0.1334604919,-0.1558463126,-0.1560446918,0.1916098446,-0.2711634338,-0.1190526411,0.1629334688,-0.4172597528,0.1404407769,-0.1092695892,0.2058041096,-0.1701170951,0.1836716682,-0.0419157632,-0.0397577323,0.1667185873,0.2055386007,0.4178037941,0.0657206029,-0.2627524436,0.0155914854,-0.0149271125,-0.2235049456,-0.2401398569,-0.2342891097,0.1712268293,-0.0171804,-0.0324050672,-0.0970735997,-0.2013052702,-0.1855209619,0.0730488077,-0.0739560649,-0.0755740106,-0.0004046226,-0.2123306245,0.2407439649,0.5114324093,-0.2561294138,0.0139989471,-0.2016023844,-0.2845811844,-0.046655912,0.1912219375,-0.2767921686,-0.0645689592,-0.2945926785,0.1345972717,0.2801430821,-0.5271686316,-0.5403925776,0.1749270558,-0.1016477197,-0.0045392108,-0.0452519394,0.135179773,0.2007405162,-0.0012250146,-0.0463122912,0.1220155656,0.2215703726,-0.0852616653,-0.0657257587,-0.2004217803,0.037671715,0.218952775,0.1647018492,0.013656904,0.2783850133,0.0049807178,0.3799569011,-0.1239579096,0.2251577973,0.0996680185,0.1351427585,0.4891670048,0.0668975711,0.0573307201,-0.0893363431,0.118238464,0.0292587765,0.4636808336,-0.3552644849,-0.1291739643,-0.3622660339,-0.0888171941,-0.3391819894,-0.0633014515,0.0917970911,0.1674780995,0.1115445793,0.3894376457,-0.1492940933,0.2069069296,0.0764920861,0.2768447697,-0.5142536759,0.0994087607,-0.1682472825,0.1659316421,0.1055120453,-0.0185493287,0.2656729221,-0.3082680702,-0.1576258987,0.3220606148,-0.0502144396,0.3229683638,0.0850550607,0.0709248036,0.4094831944,-0.4273970127,0.1099206284,0.1484960467,0.1885130703,0.0437891558,0.1856258065,0.1640914232,0.0701140612,0.2006612867,0.2201159447,0.2539494634,0.4390580058,0.1340613961,-0.1533955485,-0.035190843,0.2800710797,-0.0538713932,0.2288250625,-0.3223938346,-0.3359993994,0.0001393973,0.1727751791,-0.0946434364,-0.0417837687,0.2228862196,-0.2919363678,0.1159747094,0.147044614,0.2446125001,0.1220352352,0.0647739917,0.0381595753,0.3802860677,-0.0631826818,-0.3046351373,0.1787006408,0.0222811196,-0.1093919724,-0.086854957,-0.004593946,-0.0907619447,-0.3596236408,0.0786579475,-0.2090318799,0.2652599514,-0.2834217846,0.0253336225,-0.3236965835,-0.5549081564,-0.1282782257,-0.258719027,-0.3667630255,-0.4716671705,-0.1990816444,0.2129578739,0.0010951955,0.0430765748,-0.3035564721,0.0255413651,-0.1155347899,0.1017439142,-0.1069767326,-0.1666280776,-0.1623939276,0.0849532261,0.2841731608,0.0504956655,0.3274031878,-0.2320938706,0.1414063424,-0.4280905426,-0.2585859001,0.1104029939,-0.0014176673,0.1184030548,0.1160944402,0.3185745478,0.0074924421,-0.1616096944,0.3599187136,-0.039754793,-0.1039863899,-0.153184846,0.0511819981,-0.0478067696,0.0527870581,-0.5101011395,-0.3066567481,-0.2633395195,0.3358568847,0.1852743477,0.2290140539,0.050445281,-0.1327347308,0.2589993179,-0.2343078256,0.1424285769,-0.3232118487,-0.2181006074,0.3821180165,-0.3514840007,-0.6084119081,0.4241505861,0.136922881,0.2261754125,-0.0885327384,-0.4297252893,-0.3331384361,-0.1811379492,-0.0792056844,0.0319261588,0.0875471979,0.2444503158,-0.2791543305,-0.0636888891,0.1463399529,-0.1846924424,-0.1403799653,-0.209050402,0.3378819227,-0.157899633,0.3982004523,-0.0570846759,0.4537926912,0.1372724324,0.3040841222,0.4874172211,0.0346873701,0.2897946239,-0.2126442939,-0.5094739199,0.1383038461,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3282","title":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py","comments":"Hi, \r\n\r\nUsing `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface\/token) solved the issue.\r\n\r\nShould I leave the issue open until you fix the Dataset viewer issue?","body":"## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library.","comment_length":29,"text":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py \n ## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library. \n Hi, \r\n\r\nUsing `use_auth_token=True` and downloading the credentials with `huggingface-cli login` (stored in .huggingface\/token) solved the issue.\r\n\r\nShould I leave the issue open until you fix the Dataset viewer issue?","embeddings":[-0.1648680121,0.1433242708,0.0291343257,0.3350149095,0.2738420665,0.074923858,-0.0048631728,0.1728967726,-0.1965864599,0.1607671678,-0.2475938052,-0.0661746189,0.0811843798,0.0479793847,0.1127686426,-0.1378086507,-0.0630303398,-0.1504307687,-0.0512392707,-0.0639650673,-0.2606893778,0.1049293876,0.077536352,0.2319314033,-0.1920366883,-0.0487604924,-0.0160141792,0.0920423344,-0.315001756,-0.4188505113,0.3254647553,0.0952016562,0.0847479701,0.2827459872,-0.0001113929,0.0213915035,0.4820181131,0.0182661302,-0.416356355,-0.5646927357,0.0629136637,-0.1423284113,0.1999108791,0.0616931766,-0.1711199284,-0.1302494705,0.2096135616,-0.2390466928,0.148641631,0.1638736129,0.2620609105,0.2970890701,0.442420423,-0.2402564734,0.0660261884,-0.0278597251,-0.1177856997,0.2988566756,0.1977045089,0.1660828292,0.0511000156,0.2248272747,0.2431872338,-0.1086461246,0.4299423993,-0.1444196701,-0.3956692815,-0.378906101,0.2582371533,0.1838949621,0.5891254544,-0.102121301,-0.523786366,0.0432993919,0.1704944819,-0.091513291,0.4428962171,0.2691284418,-0.0693952218,0.0183863938,-0.1929961443,-0.3573024869,-0.281239599,0.4102180004,-0.1340915561,0.1731859893,-0.1093139052,0.1661273539,0.2565060854,-0.1814710647,-0.052681528,0.0150121804,-0.0245618727,0.2357648015,-0.0509741083,0.253664732,0.0364052467,0.4300181568,0.1778436005,0.0742609948,-0.084923394,0.1818883717,-0.3433173895,0.0361677147,0.3593756855,0.0639172792,-0.0379124954,-0.0684419125,0.3963212073,0.354139924,0.1739614904,-0.1359802186,-0.0653798133,-0.2637338638,-0.2566165924,-0.2029314041,0.3955718577,-0.2229736596,-0.0703075975,0.1011715829,-0.2426477522,-0.128651157,-0.0032870802,0.4331555963,-0.2515621483,0.124963209,0.0209775288,0.2003933191,-0.1402842253,-0.1055204719,-0.1681615412,0.1077100933,-0.1562545449,-0.0732607692,0.1581320316,-0.5202474594,0.1302140951,-0.1410981268,0.2747987211,-0.085331887,0.0253729895,-0.1109708548,-0.1175153852,0.1356688589,0.1278952509,0.4154253602,0.0484779067,-0.304825753,-0.0221458785,-0.1340862811,-0.1579248607,-0.2419449836,-0.1772806346,0.1835121959,-0.1052705497,0.0505563691,-0.1933359355,-0.115852572,-0.2452076226,0.0524519645,-0.0804940239,-0.0147240888,-0.0942072645,-0.195319891,0.3287204504,0.5413911343,-0.2280702889,-0.0735980794,-0.1545091122,-0.2726242244,-0.1404061317,0.2142549306,-0.3014266491,-0.0241508354,-0.3289579451,0.0972642526,0.1634200215,-0.5110561252,-0.5605508089,0.119850263,-0.0942696556,0.1299544871,-0.0422296897,0.0560756028,0.2056980431,-0.0923220664,0.0145886475,0.0799283981,0.1618835628,-0.0954705775,-0.1197210327,-0.2709765136,-0.0339862891,0.1920759529,0.1689333469,-0.0296092201,0.1959730983,0.0348333977,0.3289233446,-0.0548351258,0.2571035922,0.072470814,0.1943337023,0.5006564856,-0.0227249898,-0.0636662096,-0.2460805923,0.1789496839,0.0314433686,0.3865858912,-0.4239183068,-0.1686963439,-0.294809103,-0.0511887223,-0.3298174739,-0.0533014797,0.1088368818,0.1261517107,0.1406559199,0.4854875803,-0.1768912375,0.2256992757,0.1023496613,0.2652810812,-0.5332070589,0.1512331516,-0.1307079643,0.1265871823,0.1184342802,0.0518193543,0.1566710621,-0.3374659121,-0.1054838598,0.3403726816,-0.107541725,0.3886392713,-0.0329751596,0.065895088,0.4032427669,-0.3652528524,0.0743092075,0.1194778979,0.1768293977,0.0560137667,0.1215043738,0.2094954997,0.063937746,0.1965591311,0.2126538306,0.2024215162,0.3590582013,0.1498666257,-0.2471624464,-0.081711866,0.2315029949,-0.0910443589,0.2907905281,-0.2016972601,-0.3573428094,-0.0615453385,0.2457259446,-0.0093013551,-0.1367065907,0.2730892599,-0.2404755503,0.1842944622,0.1136498004,0.1854681373,0.1792411655,0.0677397773,-0.036554873,0.2788806856,-0.0027008052,-0.2560558617,0.1319421828,0.1012536064,-0.0470342897,-0.0443528444,0.0117603922,-0.0714907944,-0.3670043349,0.0622925013,-0.2273091972,0.2873935103,-0.295081526,0.0524024107,-0.3244498074,-0.52840662,-0.1895990223,-0.3248274922,-0.4328795969,-0.3448533714,-0.2005723715,0.2038096488,-0.0174265951,0.0689126849,-0.2604407966,0.0276765563,-0.1879904717,0.1026783139,-0.143775031,-0.1602577865,-0.1744538993,0.0850467905,0.309897244,-0.0226449668,0.4051017165,-0.2517159581,0.0962352604,-0.3601129353,-0.2911450267,0.0663733855,0.0347974971,0.1243555099,0.1953638047,0.2821223736,-0.0836772025,-0.2490986437,0.4150030017,-0.0329315625,-0.1018265635,-0.2055191398,0.0695863515,0.0299716406,0.0890868828,-0.4010099769,-0.3569520414,-0.3483039439,0.49686867,0.1176532507,0.1508712322,0.0544914789,-0.1268778294,0.2905275822,-0.2951739728,0.1024101451,-0.272483021,-0.2414031923,0.3005036116,-0.2692341208,-0.5744300485,0.3569945395,0.1341259032,0.256046325,-0.093482241,-0.3751751184,-0.2904052436,-0.171301201,0.0619401745,0.0733307227,0.0434216149,0.2663610876,-0.1995150447,-0.024161607,0.1544011235,-0.1280870289,-0.0804491639,-0.2449535877,0.3054356873,-0.0863481089,0.395896852,-0.0426116362,0.4725375473,0.1841078848,0.1671898365,0.4509145021,0.0675524101,0.3779693246,-0.1688823849,-0.5807825327,0.1957687736,-0.1397457123,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3282","title":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py","comments":"Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks","body":"## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library.","comment_length":22,"text":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py \n ## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library. \n Cool ! Yes let's keep this issue open until the viewer is fixed - I'll close it when this is fixed. Thanks","embeddings":[-0.229873836,0.2996898293,0.0159760807,0.3480393589,0.2428968996,0.0843784884,0.0289746542,0.2151711136,-0.1925182343,0.1596947312,-0.2004185319,-0.0455984063,0.0961812213,-0.0956303105,0.0457464717,-0.1347220987,-0.0746335611,-0.0802193508,0.010940657,-0.0307847653,-0.3309085369,0.0190729648,0.0264009386,0.1898507178,-0.1415976435,-0.002422435,-0.0332421251,-0.0317968018,-0.3335893452,-0.4994384646,0.2556448281,0.1001761258,0.0713792369,0.3133493364,-0.000114235,-0.0024445618,0.5902069807,0.0585044101,-0.3949885368,-0.5379759073,0.0001062083,-0.1511713564,0.2318071574,0.0361641385,-0.0230173655,-0.1827933639,0.2449370772,-0.2556039989,0.0441402234,0.0928487852,0.2452827543,0.1593263,0.4805449545,-0.2739220858,0.1688618362,-0.0716561526,-0.1480239183,0.311268419,0.1860909611,0.1541007161,-0.0023194884,0.2536826432,0.1597193331,-0.1254213303,0.4019663036,-0.1866608262,-0.3489053845,-0.3790026307,0.2711659968,0.17643103,0.7827457786,-0.0749664977,-0.529553473,0.0869785547,0.1829093099,-0.0347934477,0.4602304995,0.3449670374,-0.0739646256,0.0632548109,-0.0966625214,-0.4122804403,-0.2963724136,0.4830139875,-0.2478574663,0.2111051679,-0.1058857366,0.1706064492,0.2466009706,-0.0315437503,0.1761511415,-0.0914617777,0.0048041698,0.1260887086,-0.0610686839,0.2419225872,-0.018010566,0.3624855876,0.0970337316,0.0227402598,-0.0540757701,0.1294177473,-0.3970662653,0.0786776021,0.4425960183,0.0277096964,-0.1189466342,-0.0337579511,0.4811334908,0.3868338764,0.1491969228,-0.1156435087,-0.0798192099,-0.358430177,-0.2400613427,-0.1581088901,0.5349317193,-0.1813724935,-0.1162183732,0.1087943017,-0.2916103303,-0.1362817734,-0.0274033658,0.3910418749,-0.3049879372,0.2576603591,-0.0204777289,0.1822950691,-0.0843114331,-0.1671702862,-0.1410032511,0.1125732064,-0.1958552003,-0.1458219439,0.1423449218,-0.4720421433,0.0342334844,-0.1146539077,0.2350501418,-0.1282939464,-0.0013345592,-0.1956862658,-0.1687623709,0.2050844431,0.1475646794,0.4707229137,0.0762194917,-0.3385763764,0.0200675651,0.0948889032,-0.178973496,-0.1761452854,-0.2293180823,0.160192281,-0.1160966977,0.0063542714,-0.1413848251,-0.1167223603,-0.1953812689,-0.0544833206,-0.0528623126,-0.0422725901,-0.0538646765,-0.2697580457,0.2780174911,0.5737357736,-0.430310756,0.0481966771,-0.2930599153,-0.2490433604,-0.0953378752,0.1703751236,-0.2750424743,-0.0231061932,-0.3633948565,-0.0040178197,0.3504539728,-0.4137138426,-0.6100943089,0.2769262493,-0.102873154,0.1056137457,-0.0046199607,0.1128518954,0.189327389,-0.1758648008,-0.0677834079,-0.0145004625,0.0849081054,-0.155244872,-0.0951093808,-0.2506184876,0.008833129,0.1377210915,0.1832074821,0.0703220516,0.2293515801,0.0483992249,0.3863138258,-0.013891045,0.3011806905,0.0596946254,0.2079655677,0.4459077716,0.0142939333,-0.0650164932,-0.2222712785,0.1323156655,0.0450821146,0.2480804473,-0.442140311,-0.1549325287,-0.3679063618,-0.0146225458,-0.3030097485,0.0011281993,0.0833160728,0.1101339906,-0.0796725005,0.4337307215,-0.1911389381,0.1123136505,0.1220113412,0.2302694321,-0.4320509732,0.1930648237,-0.1497543603,0.1702525467,0.1020201072,-0.0213246141,0.1584690511,-0.2534427345,-0.1866258085,0.3259759545,-0.0055881264,0.4241669476,0.0524661206,0.0903123021,0.3939425349,-0.4461978078,0.1471376419,0.2177410871,0.1981223226,0.0223085284,0.1740836203,0.1357348263,0.0480479524,0.1777253747,0.167674467,0.2750813663,0.3247309327,0.1291671693,-0.2120862454,-0.0665141568,0.2800680101,0.0043931156,0.2415407598,-0.1920406073,-0.3815710843,-0.0298312027,0.2416715175,-0.0348478667,-0.0620618463,0.3564066291,-0.2237990648,0.1400131583,0.004708346,0.2431880236,0.1736767888,0.0483181812,-0.0122868856,0.3337801099,0.0207949542,-0.3311938941,0.1385886073,0.2239211947,0.0236388687,0.0283532534,-0.0003623999,-0.0073172147,-0.3158526719,0.0369075648,-0.1662094891,0.2568278909,-0.2655573785,0.0659249872,-0.3369619846,-0.5760096312,-0.2453425825,-0.2708005309,-0.3468826115,-0.4530679286,-0.2076203823,0.2880374789,0.0124354083,0.1232611984,-0.4658845067,0.0824336112,-0.187606737,0.1517978907,-0.1346859187,-0.2102780938,-0.2040729672,0.1001465097,0.3019147217,-0.1046401933,0.3230867982,-0.1504737288,0.170094341,-0.3949621618,-0.2683959305,0.0396357253,0.109101899,0.1169106364,0.2513172626,0.2735126913,-0.0496764965,-0.2334198505,0.3328440785,-0.0249561295,-0.0152824307,-0.2254999429,0.0455313176,0.076345019,0.1926180869,-0.4399911165,-0.3111251593,-0.3987299204,0.3509678841,0.036338672,0.1771814525,-0.041016113,-0.1389437914,0.1653311849,-0.1535322368,0.0197794009,-0.2172439843,-0.1484054923,0.3588033319,-0.2915968895,-0.5922046304,0.3649150431,0.0981983989,0.1955282837,-0.0963245109,-0.4827671647,-0.2340155095,-0.1194745079,-0.0825557858,0.1086972132,-0.0253646523,0.152897343,-0.1199378371,-0.0326635726,0.1164936572,-0.08011242,-0.1925845742,-0.2677545846,0.3589240313,-0.035412997,0.4343391359,0.0076780859,0.3766166866,0.2034994066,0.2767508626,0.5129262209,0.0934117138,0.3596447408,-0.1912159175,-0.5392753482,0.1764730066,-0.257650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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3282","title":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py","comments":"The error I get when trying to load OSCAR 21.09 is this\r\n```\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nThe URL I get in the browser is this\r\n```\r\nhttps:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/blob\/main\/OSCAR-2109.py\r\n```\r\n\r\nMaybe URL is the issue? (resolve vs blob)","body":"## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library.","comment_length":38,"text":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py \n ## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library. \n The error I get when trying to load OSCAR 21.09 is this\r\n```\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nThe URL I get in the browser is this\r\n```\r\nhttps:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/blob\/main\/OSCAR-2109.py\r\n```\r\n\r\nMaybe URL is the issue? (resolve vs blob)","embeddings":[-0.1634293646,0.2305827588,0.0244139247,0.3770047724,0.0909550115,0.0307263155,0.0639222339,0.2214715034,-0.2426070422,0.2597851455,-0.2140439451,-0.1690793931,0.1428036392,-0.0072287335,0.1171834841,-0.2361695766,-0.0139903435,0.0460934602,0.1386416107,-0.0634480268,-0.4308363795,0.1030632034,-0.1162414402,0.1626313627,-0.227251485,0.1512606293,-0.1723881215,0.1730082184,-0.3458401263,-0.3519819677,0.3061034977,0.046717152,0.2580540478,0.2614613771,-0.0001221176,-0.1663710177,0.6389612556,0.0021849391,-0.3595401645,-0.5516812205,-0.162994504,-0.1420708299,0.2215951979,0.0352186374,0.0664145425,-0.3831170201,0.2749204636,-0.0009585669,-0.0898235366,0.0673161894,0.1336798668,0.1013796479,0.4491110444,-0.228814289,0.3703538477,-0.2455617338,-0.1604395807,0.3844380677,0.3922196031,0.0476539358,-0.0890206546,0.1451844424,0.2911841273,0.066065684,0.5376976728,0.067947194,-0.24009417,-0.277482003,0.1975746006,0.1158797368,0.6147933602,0.1687297076,-0.5776154995,0.0284370296,0.0173433851,-0.1774737835,0.513348043,0.2289515883,0.0670123696,-0.0167660844,-0.2055868804,-0.2974624932,-0.281974256,0.6054016352,-0.3230804205,0.3527911007,-0.1962362081,0.2045266628,0.2549601197,0.0984536484,0.3903214037,-0.2230046093,0.141242519,0.1254834682,0.01432628,0.3140039742,0.0219134577,0.3218464255,0.0568755604,0.0144735323,-0.0821170881,0.0553202964,-0.4408516884,0.0847862363,0.4427165985,0.296276778,-0.1541591883,-0.1357322782,0.3526934087,0.493868351,0.1289404631,-0.1192161888,-0.0292484276,-0.3672228754,-0.1897695363,-0.180099979,0.3398678601,-0.2949216366,-0.1871259511,-0.0269258898,-0.4334288836,-0.2115353346,0.0075664474,0.3699985445,-0.4262366295,0.2869897783,-0.1006203964,0.1194731593,-0.0286208335,0.0535573103,-0.1387276798,0.0904178396,-0.2627215981,-0.0804966018,0.1040205881,-0.4537070692,0.1608713716,-0.2464471012,0.3025624752,-0.1223393455,-0.0843087956,-0.2380046248,0.0702586994,0.1164896563,0.2463528663,0.3947826326,-0.0582395308,-0.3425529301,-0.0584462062,0.0900559053,-0.1847832501,-0.2601082921,-0.194811523,0.0644980744,-0.0511364304,0.0380486324,-0.0965151116,-0.2384216189,-0.2706737518,-0.0451815873,-0.0852116942,-0.0166378468,-0.0839581564,-0.2407632917,0.3611593843,0.3513658047,-0.3612565696,0.0548720583,-0.2463522255,-0.111935325,0.0418573245,0.1452336162,-0.2280138135,-0.0969439,-0.3065859377,0.0702105761,0.2733356059,-0.424045682,-0.6276173592,0.3681553006,-0.0900831595,0.063697286,-0.0478731431,0.2561643422,0.3231486082,-0.1684669107,0.2948794961,0.1055915728,0.1991183311,-0.1824542731,-0.0499974042,-0.2315442115,-0.2791441977,0.1502659023,0.1715565175,0.0536252446,0.1141769364,-0.1755530983,0.3752937913,-0.0181816891,0.1934530884,0.0279348455,0.1829760969,0.4601310492,-0.0410462916,0.017700756,-0.2865719497,0.2255953997,-0.0069108759,0.1592564732,-0.3726583719,-0.0450392962,-0.4383390248,-0.0795212835,-0.3449828625,0.0699360669,-0.0109431501,0.2669698894,0.1186762154,0.228226319,-0.2791146934,0.0939928442,0.0386987664,0.1661512852,-0.3229205012,0.2021329403,-0.2237226218,0.089278698,0.2197040915,-0.0687501207,0.2049534172,-0.3210240901,-0.1630332917,0.3772005737,-0.1500515342,0.4110321701,0.0095679304,0.1068941653,0.3600299358,-0.2728177905,0.2592155337,0.0368321389,0.1584140211,0.0051788772,0.2963704765,0.1289950758,0.1352246702,0.3052998483,0.3560337722,0.1303756386,0.2206304669,0.1318727732,-0.1680015773,-0.2125700414,0.1344110668,-0.0140379108,0.0748892128,-0.0695353001,-0.2341629714,-0.0483891107,0.4954429865,-0.1184334606,-0.1240243092,0.2452607453,-0.2931432128,0.019113943,0.1294873208,0.4569978416,0.3177912533,0.0797534436,0.0447039008,0.2724913955,-0.102917932,-0.3030645549,0.1119556651,0.178874597,0.0705454051,-0.0568288863,-0.1352138817,0.0036985097,-0.2112627327,0.1166179627,-0.0405154862,0.1121945977,-0.2197899669,0.075847432,-0.257671684,-0.3753141165,-0.2693045735,-0.3558127284,-0.207073316,-0.5857596993,-0.2034310997,0.3858914375,0.0385829881,0.0061853942,-0.4256000221,-0.0345757753,-0.2524451017,0.0116222706,0.0302860066,-0.1720416695,-0.366859287,0.009870383,0.2004255056,-0.0594959743,0.2204941958,-0.3487722278,0.2347176373,-0.3876611888,-0.2753637135,0.1577804685,0.1169600412,0.0811182931,0.3173652291,0.2408784181,-0.0086915763,-0.1354575902,0.4267875552,-0.0896478072,-0.028803261,-0.301363349,0.1015484259,-0.01898502,0.188381359,-0.4756125808,-0.2231463045,-0.2780424654,0.1963515282,0.0548832007,0.207589522,-0.0635755658,-0.0256148539,0.2208752036,-0.1638639271,0.1120077744,-0.1684299558,-0.1227882579,0.3501001596,-0.2236998677,-0.5654827356,0.3075140119,0.0532507077,0.1541446298,0.1375073344,-0.3897571564,-0.2891306579,-0.2945584953,-0.2117604315,0.1363909245,0.0315995403,0.1459656358,-0.0935766175,-0.0704415888,0.1336491704,-0.209416613,-0.164050132,-0.1400980502,0.5212142467,0.11635869,0.3665330112,-0.0525098108,0.5527784228,-0.0427238606,0.1107382476,0.591760397,0.1305331141,0.2883308828,-0.1749942601,-0.4224924445,0.2311102599,-0.2629131377,0.071862869,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3282","title":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py","comments":"> The error I get when trying to load OSCAR 21.09 is this\r\n> \r\n> ```\r\n> ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n> ```\r\n> \r\n> The URL I get in the browser is this\r\n> \r\n> ```\r\n> https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/blob\/main\/OSCAR-2109.py\r\n> ```\r\n> \r\n> Maybe URL is the issue? (resolve vs blob)\r\n\r\nYou need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:\r\n`\r\nload_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)`","body":"## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library.","comment_length":75,"text":"ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py \n ## Dataset viewer issue for '*oscar-corpus\/OSCAR-2109*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109)*\r\n\r\n*The dataset library cannot download any language from the oscar-corpus\/OSCAR-2109 dataset. By entering the URL in your browser I can access the file.*\r\n\r\n```\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n```\r\n\r\nAm I the one who added this dataset ? No\r\n\r\nUsing the older version of [OSCAR](https:\/\/huggingface.co\/datasets\/oscar) I don't have any issues downloading languages with the dataset library. \n > The error I get when trying to load OSCAR 21.09 is this\r\n> \r\n> ```\r\n> ConnectionError: Couldn't reach https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/resolve\/main\/OSCAR-2109.py\r\n> ```\r\n> \r\n> The URL I get in the browser is this\r\n> \r\n> ```\r\n> https:\/\/huggingface.co\/datasets\/oscar-corpus\/OSCAR-2109\/blob\/main\/OSCAR-2109.py\r\n> ```\r\n> \r\n> Maybe URL is the issue? (resolve vs blob)\r\n\r\nYou need to download your login credentials. See `huggingface-cli login` documentation and when loading the dataset use `use_auth_token=True`:\r\n`\r\nload_dataset(corpus, language, split=None, use_auth_token=True, cache_dir=cache_folder)`","embeddings":[-0.1638733894,0.1031890586,0.055886481,0.4317853153,0.1487078369,0.0859394968,-0.0256005283,0.2220762968,-0.2109852582,0.227425158,-0.2897389531,-0.1631971449,0.1137608588,0.0779964179,0.1040884927,-0.3276956081,-0.0717860907,-0.0296838731,0.0155160343,-0.0434336849,-0.3552161753,0.1934071332,-0.1150132045,0.1709185839,-0.1509546489,0.0495018065,-0.1499510258,0.1361217052,-0.3352977037,-0.3501966894,0.2912426591,0.0222268272,0.2644071877,0.2784636617,-0.0001186652,-0.1524905562,0.5552985072,-0.0016188255,-0.3989953697,-0.6619231701,-0.0655460581,-0.1310498565,0.2420238853,0.1166834757,-0.0942816958,-0.3715892732,0.225326553,-0.0853222758,0.0703280345,0.1241560206,0.1639692187,0.1869798452,0.4523052871,-0.296671927,0.1996554732,-0.206419602,-0.1360336691,0.3594049513,0.3711142838,0.0397752523,-0.0244579986,0.1873431355,0.2935543358,0.0187482666,0.5615558028,-0.0365997963,-0.2769493461,-0.3839036524,0.2437504381,0.1617873758,0.5894245505,0.0410233364,-0.531688571,0.0120509714,0.0930266529,-0.2431800663,0.4912880659,0.2625539303,-0.0021323562,-0.0444886684,-0.2387972623,-0.2721301615,-0.3040898144,0.5619142652,-0.1553154439,0.2651641667,-0.1763467789,0.1811925173,0.3177571297,-0.0435289294,0.1631722897,-0.1085590124,0.0544792712,0.2445486486,0.0142365852,0.2707107663,-0.0259170551,0.4211362004,0.1431509405,0.0676782504,-0.1729705781,0.0970864147,-0.3271921873,0.0430440977,0.4541617036,0.289175272,-0.0779119879,-0.08420863,0.2599658072,0.4737870395,0.1451326311,-0.1357613057,-0.0365358032,-0.3163921535,-0.2014557123,-0.2010498047,0.3045390248,-0.289273262,-0.0983846486,-0.0228189472,-0.3946243227,-0.1880671084,0.0390623324,0.4054936171,-0.3853670359,0.1950222403,-0.0636261627,0.1621128321,-0.0369289741,0.0097284382,-0.1503058523,0.1138707548,-0.2700103521,0.0570525564,0.1357753873,-0.5649883747,0.1742141843,-0.2156408131,0.4063630998,-0.1137597412,-0.0300886873,-0.1594503224,0.0670004115,0.094154723,0.2535082698,0.3764217198,0.0069881231,-0.3268054724,-0.093306914,-0.0754029751,-0.1621752679,-0.2903394699,-0.179129824,0.0825052485,-0.0334583074,0.0411549918,-0.2018692046,-0.1711429358,-0.2660002708,0.0378688686,-0.0441482812,-0.0165261421,-0.118014507,-0.1374870092,0.3825652301,0.4791223109,-0.2760547698,-0.0286441967,-0.1624875963,-0.1619932353,-0.0689478293,0.1087108031,-0.2244349271,-0.1267282963,-0.341414839,0.0469035655,0.1414640844,-0.4778728485,-0.5335482955,0.2529320121,-0.0220859125,0.1279724687,-0.0600140356,0.1213072091,0.3195920289,-0.1123642996,0.2323299944,0.0619165562,0.2045116425,-0.1535807699,-0.0603133291,-0.2424408197,-0.2765575349,0.1993158013,0.1609696448,0.0337738767,0.0843530968,-0.1612841189,0.3363697827,-0.0219703969,0.2061427534,0.0260773506,0.1616763324,0.5748981833,-0.0294567589,-0.0085738562,-0.3116137385,0.2600069642,0.0041435049,0.2780257761,-0.2965087295,-0.060976401,-0.3611241579,-0.1246617064,-0.3472208679,0.0224047843,0.0306390077,0.1922318637,0.2306405455,0.2870872915,-0.2578282058,0.2457544655,0.082661733,0.1794151813,-0.4501202404,0.1599956304,-0.1998930871,0.0957449079,0.1899534762,-0.0172961615,0.1881591678,-0.3220213056,-0.1128204837,0.4024185836,-0.1760047525,0.377614677,-0.0596355163,0.1169614717,0.3719926775,-0.3467190564,0.1667098403,-0.0219065789,0.1395221502,0.0601879992,0.2389681041,0.1789377481,0.1229723319,0.2920712233,0.338495791,0.2025623918,0.2432583719,0.1267199665,-0.256013155,-0.2201182544,0.1954761744,-0.1696827263,0.1095983684,-0.1609076113,-0.275262475,-0.0527810752,0.3305516243,-0.0293475576,-0.1515724361,0.2198265046,-0.3700796068,0.0941968784,0.1169456914,0.3748257458,0.2833869159,0.0712121055,-0.0068219807,0.3066985607,-0.0546738915,-0.25043872,0.1439349055,0.1520754248,0.096540533,-0.1203970611,-0.1467905641,0.0069654374,-0.2197115868,0.1224720776,-0.1018742621,0.1518095285,-0.2213490754,0.0616705231,-0.2506421804,-0.4344055653,-0.2312843949,-0.3254249394,-0.2749613822,-0.4291351438,-0.218476966,0.3423595428,0.1013740376,0.0370923616,-0.3319618404,-0.0006290245,-0.2469502389,0.0236600377,-0.0688267425,-0.156885162,-0.2824934125,0.0185846984,0.2548473775,-0.0629083589,0.3189648986,-0.3134774864,0.1675518006,-0.379977107,-0.2709624171,0.1830042303,0.0381708369,0.0448586158,0.2339812517,0.2578589618,-0.0928997919,-0.1637968272,0.4195308983,-0.1043775305,-0.0419291966,-0.2291504443,0.0845362619,-0.0058037546,0.1331637949,-0.457854569,-0.2821192741,-0.2767311931,0.3250186741,0.1260527521,0.1727057993,0.0877134204,-0.0060204496,0.2521425486,-0.326965481,0.1249875352,-0.2305599749,-0.1944032311,0.3160630763,-0.2967842519,-0.588547349,0.3663974404,0.0906330794,0.2255312204,0.0109060602,-0.3837221563,-0.3466086388,-0.2625720501,-0.1392285079,0.1117813066,0.026320098,0.244918853,-0.1679566354,-0.0444674119,0.1994948238,-0.2043111324,-0.1254384518,-0.1883742362,0.428342104,0.0235781111,0.3803780973,-0.0415243022,0.6155346036,0.114715524,0.1107644439,0.561516881,0.1457322538,0.3035130501,-0.1391305774,-0.5145660043,0.205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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3272","title":"Make iter_archive work with ZIP files","comments":"Hi ! Sure this is open for any contributor. If you're interested feel free to self-assign this issue to you by commenting `#self-assign`. Then if you have any question or if I can help, feel free to ping me.\r\n\r\nTo begin with, feel free to take a look at both implementations of `iter_archive` for local downloads and for data streaming:\r\n\r\nIn the `DownloadManager` for local dowloads:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f\/src\/datasets\/utils\/download_manager.py#L218-L242\r\n\r\nIn the `StreamingDownloadManager` to stream the content of the archive directly from the remote file:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f\/src\/datasets\/utils\/streaming_download_manager.py#L502-L526\r\n\r\nNotice the call to `xopen` that opens and streams a file given either an URL or a local path :)","body":"Currently users can use `dl_manager.iter_archive` in their dataset script to iterate over all the files of a TAR archive.\r\nIt would be nice if it could work with ZIP files too !","comment_length":103,"text":"Make iter_archive work with ZIP files \n Currently users can use `dl_manager.iter_archive` in their dataset script to iterate over all the files of a TAR archive.\r\nIt would be nice if it could work with ZIP files too ! \n Hi ! Sure this is open for any contributor. If you're interested feel free to self-assign this issue to you by commenting `#self-assign`. Then if you have any question or if I can help, feel free to ping me.\r\n\r\nTo begin with, feel free to take a look at both implementations of `iter_archive` for local downloads and for data streaming:\r\n\r\nIn the `DownloadManager` for local dowloads:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f\/src\/datasets\/utils\/download_manager.py#L218-L242\r\n\r\nIn the `StreamingDownloadManager` to stream the content of the archive directly from the remote file:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/dfa334bd8dc6cbc854b170379c7d2cb7e3d3fe4f\/src\/datasets\/utils\/streaming_download_manager.py#L502-L526\r\n\r\nNotice the call to `xopen` that opens and streams a file given either an URL or a local path :)","embeddings":[-0.5271627307,0.1634652019,-0.088229239,0.1586407423,-0.0910507292,-0.0960064009,0.1364820153,0.4259110689,-0.0266859233,0.0931344405,-0.0515032299,0.6554415226,-0.1210884601,0.4705848396,-0.03709637,0.049775552,-0.2543889284,0.3523833156,-0.4338119626,0.1170511618,-0.1553303897,0.1399048269,0.0251187515,-0.1207089573,0.319601804,0.147603035,-0.0903766975,0.2149929404,-0.2577503026,-0.4663317204,0.2648332715,0.183905825,0.4860049188,0.380725652,-0.0001018898,-0.0132329054,0.2184490412,-0.0306418557,-0.3586070538,-0.5234248638,-0.191772297,-0.1938414425,0.0293164048,-0.2377526015,0.175567925,-0.1182793826,0.1328226179,-0.1687230021,0.3739841878,0.1940903068,0.2256556153,0.39354074,0.0342692956,-0.0655986816,0.393124193,0.2336294353,-0.2398004979,0.3011564016,0.2305230647,-0.0569456331,-0.2225747705,0.1532352567,-0.0256221425,0.2302401215,0.2761284411,-0.0466438495,-0.2669267952,-0.1487555653,-0.2510719001,0.1707334071,0.6428155303,-0.4028434753,-0.3676697016,-0.0833093375,-0.2868480384,-0.3921358287,0.2826227248,0.2115217298,-0.2021242082,0.2111468166,-0.0168208592,0.067892924,-0.2679748535,0.0913361683,-0.1561860889,0.5876095891,-0.156952247,-0.1187316999,0.5183103085,-0.0751709342,0.6211431026,0.0702008232,-0.116140984,-0.1386107206,-0.1741613746,-0.3171595037,-0.0261461865,0.0057807965,0.1836361885,0.4942642748,0.2009918839,0.1524987221,-0.0054410319,0.1234273612,0.3155825436,0.2565484345,0.2426476926,0.1778856218,0.2892776728,0.3290776014,0.259023428,-0.241389364,-0.0546818338,0.0373719111,-0.1341748089,0.0991798714,0.0225422233,-0.0470219441,0.1330256611,-0.2533317506,0.0639221966,-0.4127046764,0.0058117234,-0.015988512,-0.1320154071,0.2903730273,-0.3919465244,0.0872249454,0.2396383137,-0.1170321852,0.072751604,0.0799738765,-0.103542462,0.083103478,0.3216746449,-0.220729053,0.110091269,-0.2038956881,-0.0091081057,0.372792542,0.35987854,-0.1966829598,0.3351735771,0.0514222793,0.0667118505,0.1031912342,-0.0155374659,-0.0532330833,-0.2440444976,-0.0414965786,-0.1204270795,-0.2244498879,-0.0018875451,0.1911209375,-0.2609449327,-0.1237304434,-0.0020650795,-0.1235101297,-0.2806330621,-0.402718097,-0.0579928197,0.2240043133,-0.2275245935,-0.1100085899,0.1459629834,0.4979763031,-0.6566015482,0.2179647088,-0.3574213684,0.0438682213,0.1641238034,-0.0355741829,-0.2093661278,0.1113675684,-0.3522228599,0.1473270804,0.3078065813,-0.2412119806,-0.3783233464,0.6909531951,-0.243099615,0.2401985377,0.1695813686,0.0003238217,0.3103085458,-0.318667531,-0.2762656808,0.3737614155,-0.0640217736,0.0319527052,-0.070401296,-0.2144974917,0.1358138621,0.2773148417,-0.0240746811,0.20106107,-0.1014485657,-0.3788157701,0.1642207354,-0.011355184,0.2451963723,0.0633152574,0.0693061203,0.2129595727,-0.0214895438,-0.0337447189,-0.1767006367,0.1574033797,-0.1406188458,-0.1823747009,-0.2724433541,-0.0777292848,-0.0888857394,0.0532152392,-0.2255094498,-0.1187285036,0.2174458802,0.4845650196,0.3232109845,0.0037964049,-0.3451392353,0.173753649,0.3744060695,0.06776613,-0.3528168499,0.0665207952,-0.0544546396,0.0075445138,0.1576262563,-0.2225645483,-0.0550995395,-0.3260387778,-0.0743695274,0.1854186505,-0.4222798645,-0.052383244,0.306764096,0.1259945184,0.2551638484,-0.1726116985,0.233445242,0.2393031269,0.1463115215,-0.1086448953,-0.3874176443,0.2007955313,-0.1367949247,0.4228151143,-0.0114525519,0.0907883048,0.1067225933,-0.1880362332,-0.1766637713,0.1731513143,-0.1320403963,0.0467729829,0.4383491874,-0.0839764923,0.1748831272,0.1226316094,0.1639257669,0.0592885986,-0.2266845554,0.3861336708,-0.17077218,0.1364413798,0.2209423929,0.6206468344,0.3971896768,0.1972800642,0.3054278493,-0.0511694811,0.3163579404,-0.214116618,-0.0649571642,0.1159182638,-0.134512946,0.1104798168,-0.1745249927,0.0046917852,-0.1926424652,-0.139615342,-0.0830713958,0.1154237241,0.1555056423,0.1730685681,-0.3750019372,-0.5810014009,0.1314027756,0.0819250196,-0.0748570785,-0.2305571139,-0.0509772487,0.4028926492,-0.1065446436,-0.0109515656,0.420886755,0.183842808,-0.0063161305,-0.126039356,-0.1868384928,-0.2244649231,-0.0986496061,0.2292301059,0.3748164475,-0.1534871161,0.3059163094,-0.1933226883,0.0605880655,-0.5222424865,0.2288917452,0.1726194024,0.0354888029,0.0150934868,0.0846079141,0.2433120608,0.0013855133,-0.1439164728,0.0658167824,0.0214116313,0.0632370785,-0.022269886,0.0591135509,0.0913818106,-0.1593388617,-0.0729871243,-0.3485808372,-0.6411200762,0.5931686163,0.0152189229,0.2974222302,-0.1092364639,0.1253543347,-0.1252326369,-0.0665415227,-0.192431286,0.0858522356,-0.6903446913,0.4563929439,-0.3439544737,-0.3164690733,0.0854330957,0.1286399215,0.1272040159,0.0629167408,-0.3114649951,-0.1554630548,-0.1371448636,0.273755908,0.0500022992,0.3798842728,0.260494858,0.0478868634,-0.0883072019,0.1361581385,-0.1443154663,0.2660086751,-0.1457962543,0.1602845192,0.6300449371,0.2263750285,0.0876348391,0.1180236787,0.0999111608,0.1801591963,0.450547725,-0.1196081191,0.2838406563,0.1534224898,-0.2456636578,-0.0859763622,-0.1731602252,0.0266098231,0.14035329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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"Hi @ZhaofengWu, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.91MB\/s]\r\nDownloading: 1.79kB [00:00, 1.79MB\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to .cache\\coqa\\default\\1.0.0\\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49.0M\/49.0M [00:06<00:00, 7.17MB\/s]\r\nDownloading: 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9.09M\/9.09M [00:01<00:00, 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2\/2 [00:00<00:00, 333.26it\/s]\r\nDataset coqa downloaded and prepared to .cache\\coqa\\default\\1.0.0\\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 285.49it\/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['source', 'story', 'questions', 'answers'],\r\n        num_rows: 7199\r\n    })\r\n    validation: Dataset({\r\n        features: ['source', 'story', 'questions', 'answers'],\r\n        num_rows: 500\r\n    })\r\n})\r\n```\r\n\r\nCould you please give more details about your development environment? You can run the command `datasets-cli env` and copy-and-paste its output:\r\n```\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```\r\nIt might be because you are using an old version of `datasets`. Could you please update it (`pip install -U datasets`) and confirm if the problem parsists? ","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":180,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 222\/222 [00:00<00:00, 1.38MB\/s]\r\nDownloading: 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2\/2 [00:01<00:00,  1.91it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n Hi @ZhaofengWu, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.91MB\/s]\r\nDownloading: 1.79kB [00:00, 1.79MB\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to .cache\\coqa\\default\\1.0.0\\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49.0M\/49.0M [00:06<00:00, 7.17MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 9.09M\/9.09M [00:01<00:00, 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2\/2 [00:12<00:00,  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2\/2 [00:00<00:00, 333.26it\/s]\r\nDataset coqa downloaded and prepared to .cache\\coqa\\default\\1.0.0\\553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 285.49it\/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['source', 'story', 'questions', 'answers'],\r\n        num_rows: 7199\r\n    })\r\n    validation: Dataset({\r\n        features: ['source', 'story', 'questions', 'answers'],\r\n        num_rows: 500\r\n    })\r\n})\r\n```\r\n\r\nCould you please give more details about your development environment? You can run the command `datasets-cli env` and copy-and-paste its output:\r\n```\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n```\r\nIt might be because you are using an old version of `datasets`. Could you please update it (`pip install -U datasets`) and confirm if the problem parsists? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"I'm getting the same error in two separate environments:\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.0-84-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.11\r\n- PyArrow version: 6.0.0\r\n```\r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.9.5\r\n- PyArrow version: 6.0.0\r\n```","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":43,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n I'm getting the same error in two separate environments:\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.0-84-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.11\r\n- PyArrow version: 6.0.0\r\n```\r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.9.5\r\n- PyArrow version: 6.0.0\r\n```","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"I'm sorry, but don't get to reproduce the error in the Linux environment.\r\n\r\n@mariosasko @lhoestq can you reproduce it?","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":19,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n I'm sorry, but don't get to reproduce the error in the Linux environment.\r\n\r\n@mariosasko @lhoestq can you reproduce it?","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0.01012039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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"I also can't reproduce the error on Windows\/Linux (tested both the master and the `1.15.1` version). ","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":16,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n I also can't reproduce the error on Windows\/Linux (tested both the master and the `1.15.1` version). 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"Maybe the file had issues during the download ? Could you try to delete your cache and try again ?\r\nBy default the downloads cache is at `~\/.cache\/huggingface\/datasets\/downloads`\r\n\r\nAlso can you check if you have a proxy that could prevent the download to succeed ? Are you able to download those files via your browser ?","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":56,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n Maybe the file had issues during the download ? Could you try to delete your cache and try again ?\r\nBy default the downloads cache is at `~\/.cache\/huggingface\/datasets\/downloads`\r\n\r\nAlso can you check if you have a proxy that could prevent the download to succeed ? Are you able to download those files via your browser ?","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0.01012039,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"I got the same error in a third environment (google cloud) as well. The internet for these three environments are all different so I don't think that's the reason.\r\n```\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-1022-gcp-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n```\r\nI deleted the entire `~\/.cache\/huggingface\/datasets` on my local mac, and got a different first time error.\r\n```\r\nPython 3.9.5 (default, May 18 2021, 12:31:01) \r\n[Clang 10.0.0 ] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.19MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 712kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 222\/222 [00:00<00:00, 1.36MB\/s]\r\n 50%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c                                                                                                            | 1\/2 [00:00<00:00,  2.47it\/s]Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/zhaofengw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/coqa\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0\/coqa.py\", line 70, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json\r\n>>> dataset = load_dataset(\"coqa\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1087.45it\/s]\r\n 50%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c                                                                                                            | 1\/2 [00:45<00:45, 45.60s\/it]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```\r\nI can access the URL using my browser, though I did notice a redirection -- could that have something to do with it?","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  1.91it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":380,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 222\/222 [00:00<00:00, 1.38MB\/s]\r\nDownloading: 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222\/222 [00:00<00:00, 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n I got the same error in a third environment (google cloud) as well. The internet for these three environments are all different so I don't think that's the reason.\r\n```\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-1022-gcp-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n```\r\nI deleted the entire `~\/.cache\/huggingface\/datasets` on my local mac, and got a different first time error.\r\n```\r\nPython 3.9.5 (default, May 18 2021, 12:31:01) \r\n[Clang 10.0.0 ] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.19MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 712kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 222\/222 [00:00<00:00, 1.36MB\/s]\r\n 50%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c                                                                                                            | 1\/2 [00:00<00:00,  2.47it\/s]Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/zhaofengw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/coqa\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0\/coqa.py\", line 70, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json\r\n>>> dataset = load_dataset(\"coqa\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:00<00:00,  6.26it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1087.45it\/s]\r\n 50%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c                                                                                                            | 1\/2 [00:45<00:45, 45.60s\/it]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```\r\nI can access the URL using my browser, though I did notice a redirection -- could that have something to do with it?","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0.01012039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0.2919954956,0.3922101855,-0.3292050958,-0.2399606854,-0.1682348996,0.4680682719,-0.2205517143,-0.0836519748,-0.4472205937,0.235364005,0.3340348601,0.0017696641,-0.1009070501,0.3955877423,-0.2101521641,0.0460619442,-0.140016064,0.2575282753,0.1515218318,-0.0527011901,-0.0923731923,-0.2995964885]}
{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"I can run your notebook fine, but if I create one myself, it has that error: https:\/\/colab.research.google.com\/drive\/107GIdhrauPO6ZiFDY7G9S74in4qqI2Kx?usp=sharing.\r\n\r\nIt's so funny -- it's like whenever you guys run it it's fine but whenever I run it it fails, whatever the environment is.","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":41,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n I can run your notebook fine, but if I create one myself, it has that error: https:\/\/colab.research.google.com\/drive\/107GIdhrauPO6ZiFDY7G9S74in4qqI2Kx?usp=sharing.\r\n\r\nIt's so funny -- it's like whenever you guys run it it's fine but whenever I run it it fails, whatever the environment is.","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0.01012039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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"I guess it must be some connection issue: the data owner may be blocking requests coming from your country or IP range...","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":22,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n I guess it must be some connection issue: the data owner may be blocking requests coming from your country or IP range...","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0.010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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"I mean, I don't think google colab sends the connection from my IP. Same applies to google cloud.","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":18,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n I mean, I don't think google colab sends the connection from my IP. Same applies to google 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"Hello, I am having the same error with @ZhaofengWu first with \"social bias frames\" dataset. As I found this report, I tried also \"coqa\" and it fails as well. \r\n\r\nI test this on Google Colab. \r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n```\r\n\r\nThen another environment\r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: macOS-12.0.1-arm64-arm-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n```\r\n\r\nI tried the notebook @albertvillanova provided earlier, and it fails...\r\n","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":82,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n Hello, I am having the same error with @ZhaofengWu first with \"social bias frames\" dataset. As I found this report, I tried also \"coqa\" and it fails as well. \r\n\r\nI test this on Google Colab. \r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n```\r\n\r\nThen another environment\r\n\r\n```\r\n- `datasets` version: 1.15.1\r\n- Platform: macOS-12.0.1-arm64-arm-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.1\r\n```\r\n\r\nI tried the notebook @albertvillanova provided earlier, and it fails...\r\n","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"Hi, still not able to reproduce the issue with `coqa`. If you still have this issue, could you please run these additional commands ?\r\n```python\r\n>>> import os\r\n>>> from hashlib import md5\r\n>>> from datasets.utils import DownloadManager, DownloadConfig\r\n>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download(\"https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json\")  # it returns the cached file\r\n>>> os.path.getsize(path)\r\n9090845\r\n>>> m = md5()\r\n>>> m.update(open(path, \"rb\").read())\r\n>>> m.hexdigest()\r\n`95d427588e3733e4ebec55f6938dbba6`\r\n>>> open(path).read(500)\r\n'{\\n  \"version\": \"1.0\",\\n  \"data\": [\\n    {\\n      \"source\": \"mctest\",\\n      \"id\": \"3dr23u6we5exclen4th8uq9rb42tel\",\\n      \"filename\": \"mc160.test.41\",\\n      \"story\": \"Once upon a time, in a barn near a farm house, there lived a little white kitten named Cotton. Cotton lived high up in a nice warm place above the barn where all of the farmer\\'s horses slept. But Cotton wasn\\'t alone in her little home above the barn, oh no. She shared her hay bed with her mommy and 5 other sisters. All of her sisters w'\r\n```\r\n\r\nThis way we can know whether you downloaded a corrupted file or an error file that could cause the `NonMatchingChecksumError` error to happen","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":169,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 222\/222 [00:00<00:00, 1.38MB\/s]\r\nDownloading: 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n Hi, still not able to reproduce the issue with `coqa`. If you still have this issue, could you please run these additional commands ?\r\n```python\r\n>>> import os\r\n>>> from hashlib import md5\r\n>>> from datasets.utils import DownloadManager, DownloadConfig\r\n>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download(\"https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json\")  # it returns the cached file\r\n>>> os.path.getsize(path)\r\n9090845\r\n>>> m = md5()\r\n>>> m.update(open(path, \"rb\").read())\r\n>>> m.hexdigest()\r\n`95d427588e3733e4ebec55f6938dbba6`\r\n>>> open(path).read(500)\r\n'{\\n  \"version\": \"1.0\",\\n  \"data\": [\\n    {\\n      \"source\": \"mctest\",\\n      \"id\": \"3dr23u6we5exclen4th8uq9rb42tel\",\\n      \"filename\": \"mc160.test.41\",\\n      \"story\": \"Once upon a time, in a barn near a farm house, there lived a little white kitten named Cotton. Cotton lived high up in a nice warm place above the barn where all of the farmer\\'s horses slept. But Cotton wasn\\'t alone in her little home above the barn, oh no. She shared her hay bed with her mommy and 5 other sisters. All of her sisters w'\r\n```\r\n\r\nThis way we can know whether you downloaded a corrupted file or an error file that could cause the `NonMatchingChecksumError` error to happen","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0.01012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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"```\r\n>>> import os\r\n>>> from hashlib import md5\r\n>>> from datasets.utils import DownloadManager, DownloadConfig\r\n>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download(\"https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json\")  # it returns the cached file\r\n>>> os.path.getsize(path)\r\n222\r\n>>> m = md5()\r\n>>> m.update(open(path, \"rb\").read())\r\n>>> m.hexdigest()\r\n'1195812a37c01a4481a4748c85d0c6a9'\r\n>>> open(path).read(500)\r\n'<html>\\n<head><title>503 Service Temporarily Unavailable<\/title><\/head>\\n<body bgcolor=\"white\">\\n<center><h1>503 Service Temporarily Unavailable<\/h1><\/center>\\n<hr><center>nginx\/1.10.3 (Ubuntu)<\/center>\\n<\/body>\\n<\/html>\\n'\r\n```\r\nLooks like there was a server-side error when downloading the dataset? But I don't believe this is a transient error given (a) deleting the cache and re-downloading gives the same error; (b) it happens on multiple platforms with different network configurations; (c) other people are getting this error too, see above. So I'm not sure why it works for some people but not others.","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":114,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n ```\r\n>>> import os\r\n>>> from hashlib import md5\r\n>>> from datasets.utils import DownloadManager, DownloadConfig\r\n>>> path = DownloadManager(download_config=DownloadConfig(use_etag=False)).download(\"https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json\")  # it returns the cached file\r\n>>> os.path.getsize(path)\r\n222\r\n>>> m = md5()\r\n>>> m.update(open(path, \"rb\").read())\r\n>>> m.hexdigest()\r\n'1195812a37c01a4481a4748c85d0c6a9'\r\n>>> open(path).read(500)\r\n'<html>\\n<head><title>503 Service Temporarily Unavailable<\/title><\/head>\\n<body bgcolor=\"white\">\\n<center><h1>503 Service Temporarily Unavailable<\/h1><\/center>\\n<hr><center>nginx\/1.10.3 (Ubuntu)<\/center>\\n<\/body>\\n<\/html>\\n'\r\n```\r\nLooks like there was a server-side error when downloading the dataset? But I don't believe this is a transient error given (a) deleting the cache and re-downloading gives the same error; (b) it happens on multiple platforms with different network configurations; (c) other people are getting this error too, see above. So I'm not sure why it works for some people but not others.","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0.0101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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"`wget https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json` does work. So I suspect there might be some problem in `datasets`' networking code? Can you give me some snippet that simulates how `datasets` requests the resource which I can run on my end?","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":36,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  1.91it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n `wget https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json` does work. So I suspect there might be some problem in `datasets`' networking code? Can you give me some snippet that simulates how `datasets` requests the resource which I can run on my end?","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0.0101203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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"Ok This is an issue with the server that hosts the data at `https:\/\/nlp.stanford.edu\/nlp\/data` that randomly returns 503 (by trying several times it also happens on my side), hopefully it can be fixed soon. I'll try to reach the people in charge of hosting the data","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":46,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:01<00:00,  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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n Ok This is an issue with the server that hosts the data at `https:\/\/nlp.stanford.edu\/nlp\/data` that randomly returns 503 (by trying several times it also happens on my side), hopefully it can be fixed soon. I'll try to reach the people in charge of hosting the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3269","title":"coqa NonMatchingChecksumError","comments":"You're right. I just opened a PR that would show this error if it happens again:\r\n```python\r\nConnectionError: Couldn't reach https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json (error 503)\r\n```","body":"```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n```","comment_length":24,"text":"coqa NonMatchingChecksumError \n ```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"coqa\")\r\nDownloading: 3.82kB [00:00, 1.26MB\/s]                                                                                                                                                                                                                       \r\nDownloading: 1.79kB [00:00, 733kB\/s]                                                                                                                                                                                                                        \r\nUsing custom data configuration default\r\nDownloading and preparing dataset coqa\/default (download: 55.40 MiB, generated: 18.35 MiB, post-processed: Unknown size, total: 73.75 MiB) to \/Users\/zhaofengw\/.cache\/huggingface\/datasets\/coqa\/default\/1.0.0\/553ce70bfdcd15ff4b5f4abc4fc2f37137139cde1f58f4f60384a53a327716f0...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:00<00:00, 1117.44it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/zhaofengw\/miniconda3\/lib\/python3.9\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-train-v1.0.json', 'https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json']\r\n``` \n You're right. I just opened a PR that would show this error if it happens again:\r\n```python\r\nConnectionError: Couldn't reach https:\/\/nlp.stanford.edu\/data\/coqa\/coqa-dev-v1.0.json (error 503)\r\n```","embeddings":[-0.2934427857,-0.1696565449,-0.2377286255,0.1443960071,0.1753029078,-0.0322032608,0.1085320041,0.3469724059,0.161299184,0.3450759053,-0.1242237315,0.0163034368,0.1327070892,0.3179677725,-0.240015015,0.4108852744,-0.0373037681,0.1308016926,-0.1365237683,-0.0846398696,-0.1620349735,0.1659812182,-0.0158253107,-0.2111383229,0.0286174845,-0.1091403142,0.1012178138,0.1480057836,-0.2446723729,-0.2835563123,0.0949826613,0.137284264,-0.2691482008,0.3023281991,-0.0001078333,-0.0027387773,0.3951042891,-0.0393311828,-0.3947451115,0.1491571814,-0.5660771132,-0.3265201449,-0.0497068502,-0.1700811982,-0.0475762337,0.236893788,-0.0658082142,-0.0895460248,0.2255399823,0.306049794,0.3302008808,0.5339953303,-0.1040195897,-0.1637518853,0.0723609775,0.0312907211,-0.1243560761,0.200153023,0.2160929441,0.1913791448,-0.2725894749,0.3140355945,-0.0051485323,0.094843924,-0.1797520518,-0.1449495703,0.1295254678,-0.1041831225,0.5087624788,0.2842761278,0.2749402821,-0.1762846559,-0.2551176846,-0.0769868866,-0.2005284429,-0.3510763347,0.2458525449,0.1496863663,-0.3402376473,-0.1736997664,-0.2658527792,0.3822206557,0.0357885845,-0.1117658615,-0.2776518166,-0.0641215667,-0.1459255219,-0.1076810509,0.198018834,-0.18681027,0.1026506498,-0.1099825576,-0.2069358379,0.0390763581,-0.5156915188,-0.1497013122,0.3126574159,0.3797361851,0.3413913548,0.2298102081,0.1898244917,0.2144635767,0.0262917895,-0.0213103984,-0.1556115746,0.3892468512,0.0111365616,-0.2469295859,0.2272471189,0.3603816628,-0.0843029842,0.0355638079,0.0979839116,-0.3103549182,0.1675388515,0.2534083128,0.0696227625,-0.4281233549,-0.4341353774,0.1496002823,0.1567326635,0.0682205558,0.1817064136,0.5912107825,-0.0307601076,-0.0638995767,-0.1661641747,0.0048914305,-0.306659013,-0.1340270191,-0.3710196316,0.0318624303,-0.1445314884,-0.0073270453,0.1801054776,-0.112124145,0.2391831577,-0.1675235927,0.3385009766,-0.0212339424,0.021790348,-0.2919424772,0.0382423401,0.4026381373,-0.0166557003,0.0475685783,0.2840785384,-0.0009176124,-0.1435100734,-0.0387288295,-0.0324046314,-0.2684517801,0.1098732501,0.3479250073,-0.0760672912,-0.0021593524,0.2647092938,-0.2092478424,0.3453938961,-0.0610553436,0.1354072392,0.0132579328,-0.1695465595,-0.2886041105,0.1636697203,0.4162479937,-0.0739360452,0.0326918028,0.0389647633,-0.1275648475,0.1730930358,0.1494193375,0.0685002729,0.099211514,-0.2140761465,-0.1019610763,0.2160824388,-0.3309719265,-0.6776990891,0.0996278971,0.0450314134,0.1138061956,0.155923754,0.021511944,0.0979758203,-0.0356189795,0.073812671,-0.0027296073,0.0310515054,0.1135115176,-0.3921761811,-0.3178527057,-0.0096485149,0.1058152989,0.163040176,-0.0572240017,0.2945693135,-0.4578173161,0.2040492445,-0.0922110826,-0.0113181602,0.1265555322,0.183243826,-0.203273803,-0.0444230959,0.0047910651,-0.2321656197,0.273280561,-0.4459483027,0.0604500584,-0.1888539344,-0.2607712746,-0.3654578924,-0.0270662811,-0.2108933777,-0.1442324966,0.307566911,0.2736818194,0.2748444378,0.0727439299,0.1172345728,0.4088438153,-0.1587978452,-0.0446122512,-0.3123234808,0.0431374125,-0.0987407267,-0.0775052086,-0.0335515998,0.3281102479,0.1500928998,-0.0227374341,-0.0789466128,0.3007961214,0.174903512,0.1836465895,-0.089492783,0.1273787618,-0.0025923445,0.0491319746,0.0637911186,0.1695269495,0.0761687607,-0.0143559426,-0.2190444767,0.4914734662,-0.1414714158,0.1785102487,0.1221492589,-0.1497725397,0.2457295507,-0.1566765904,-0.1108925417,-0.1186991557,0.2318888158,0.1775552481,0.0445964672,0.1038220301,-0.1184021384,0.1138405427,0.3319093585,-0.0649761334,0.0596818253,0.1184131429,0.0995199755,-0.0922825485,0.0749603957,0.3611769378,0.4361035824,0.1948367953,0.1704948545,-0.0578175634,-0.0070519205,-0.1699601561,0.1295239031,0.1215302944,-0.0382295586,0.4889440238,0.2384730726,-0.194740206,-0.4842346311,-0.1763319969,-0.0725151673,0.2050755024,-0.2900423706,0.0486944728,-0.1766154319,-0.2451492995,0.0188163351,-0.2312792987,0.0636469573,-0.4028047323,0.1215568781,0.1883776337,-0.0681356266,0.2474041432,-0.3666553199,0.1707922816,0.2022457868,-0.232103914,-0.1817018241,0.2013236135,-0.1045292765,0.2084092945,0.1824285835,0.0490029976,0.500837028,-0.337161839,0.0265548434,-0.2955658138,-0.2231432945,-0.0428610668,-0.0206169039,0.0258957129,0.2395589054,0.1678854674,-0.0973242223,-0.1612111479,0.1724000275,-0.0325508676,-0.4242854118,0.075664185,-0.2023723423,0.0788614228,-0.1848689616,-0.0412909351,-0.2166950405,-0.4261758626,0.1051655263,0.1664565951,0.2115243971,0.2834844589,0.0309406798,0.1837019771,0.0693146214,0.2948220074,-0.355112195,-0.6185642481,0.3038219512,-0.1185102314,-0.394877851,-0.0802288279,-0.2994073927,0.5251648426,-0.0534642711,-0.2560552359,-0.2709777951,-0.43960765,0.1916181594,0.0583119616,-0.0937016159,0.4126140773,-0.0196930356,-0.3062776625,-0.0540688261,-0.1069870889,0.101408571,-0.0774891078,0.2577379942,-0.2263450176,0.4307516217,0.0870746821,0.3716139495,0.3967954814,-0.0432463624,0.2767594755,-0.0012933231,0.3667164743,-0.2927216589,-0.345110327,0.1104861721,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3268","title":"Dataset viewer issue for 'liweili\/c4_200m'","comments":"Hi ! I think the issue comes from this [line](https:\/\/huggingface.co\/datasets\/liweili\/c4_200m\/blob\/main\/c4_200m.py#L87):\r\n```python\r\npath = filepath + \"\/*.tsv*\"\r\n```\r\n\r\nYou can fix this by doing this instead:\r\n```python\r\npath = os.path.join(filepath, \"\/*.tsv*\")\r\n```\r\n\r\nHere is why:\r\n\r\nLocally you can append `\"\/*.tsv*\"` to your local path, however it doesn't work in streaming mode, and the dataset viewer does use the streaming mode.\r\nIn streaming mode, the download and extract part is done lazily. It means that instead of using local paths, it's still passing around URLs and [chained URLs](https:\/\/filesystem-spec.readthedocs.io\/en\/latest\/features.html#url-chaining)\r\n\r\nTherefore in streaming mode, `filepath` is not a local path, but instead is equal to\r\n```python\r\nzip:\/\/::https:\/\/huggingface.co\/datasets\/liweili\/c4_200m\/resolve\/main\/data.zip\r\n```\r\nThe `zip:\/\/` part means that we navigate inside the remote ZIP file.\r\n\r\nYou must use `os.path.join` to navigate inside it and get your TSV files:\r\n```python\r\n>>> os.path.join(filepath, \"\/*.tsv*\")\r\nzip:\/\/*.tsv*::https:\/\/huggingface.co\/datasets\/liweili\/c4_200m\/resolve\/main\/data.zip\r\n```\r\n\r\n`datasets` extends `os.path.join`, `glob.glob`, etc. in your dataset scripts to work with remote files.","body":"## Dataset viewer issue for '*liweili\/c4_200m*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/liweili\/c4_200m)*\r\n\r\n*Server Error*\r\n```\r\nStatus code:   404\r\nException:     Status404Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the ressource does not exist.\r\n```\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":149,"text":"Dataset viewer issue for 'liweili\/c4_200m' \n ## Dataset viewer issue for '*liweili\/c4_200m*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/liweili\/c4_200m)*\r\n\r\n*Server Error*\r\n```\r\nStatus code:   404\r\nException:     Status404Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the ressource does not exist.\r\n```\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n Hi ! I think the issue comes from this [line](https:\/\/huggingface.co\/datasets\/liweili\/c4_200m\/blob\/main\/c4_200m.py#L87):\r\n```python\r\npath = filepath + \"\/*.tsv*\"\r\n```\r\n\r\nYou can fix this by doing this instead:\r\n```python\r\npath = os.path.join(filepath, \"\/*.tsv*\")\r\n```\r\n\r\nHere is why:\r\n\r\nLocally you can append `\"\/*.tsv*\"` to your local path, however it doesn't work in streaming mode, and the dataset viewer does use the streaming mode.\r\nIn streaming mode, the download and extract part is done lazily. It means that instead of using local paths, it's still passing around URLs and [chained URLs](https:\/\/filesystem-spec.readthedocs.io\/en\/latest\/features.html#url-chaining)\r\n\r\nTherefore in streaming mode, `filepath` is not a local path, but instead is equal to\r\n```python\r\nzip:\/\/::https:\/\/huggingface.co\/datasets\/liweili\/c4_200m\/resolve\/main\/data.zip\r\n```\r\nThe `zip:\/\/` part means that we navigate inside the remote ZIP file.\r\n\r\nYou must use `os.path.join` to navigate inside it and get your TSV files:\r\n```python\r\n>>> os.path.join(filepath, \"\/*.tsv*\")\r\nzip:\/\/*.tsv*::https:\/\/huggingface.co\/datasets\/liweili\/c4_200m\/resolve\/main\/data.zip\r\n```\r\n\r\n`datasets` extends `os.path.join`, `glob.glob`, etc. in your dataset scripts to work with remote 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3268","title":"Dataset viewer issue for 'liweili\/c4_200m'","comments":"hi @lhoestq ! thanks for the tip! i've updated the line of code but it's still not working. am i doing something else wrong? thank you!","body":"## Dataset viewer issue for '*liweili\/c4_200m*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/liweili\/c4_200m)*\r\n\r\n*Server Error*\r\n```\r\nStatus code:   404\r\nException:     Status404Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the ressource does not exist.\r\n```\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":26,"text":"Dataset viewer issue for 'liweili\/c4_200m' \n ## Dataset viewer issue for '*liweili\/c4_200m*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/liweili\/c4_200m)*\r\n\r\n*Server Error*\r\n```\r\nStatus code:   404\r\nException:     Status404Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the ressource does not exist.\r\n```\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n hi @lhoestq ! thanks for the tip! i've updated the line of code but it's still not working. am i doing something else wrong? thank you!","embeddings":[-0.2048721462,0.3397705853,-0.0439794846,0.4539771676,-0.0323564522,0.1852272749,0.2257332653,0.1327366084,-0.1553964466,0.1312171817,-0.1871982813,0.0624000877,-0.0724005327,0.0393495411,0.2863669991,0.0551074632,0.0001893561,0.3673499227,-0.0825174078,0.0408879369,-0.1322361827,0.1274661422,0.1093329415,-0.1181485653,-0.3941236436,0.1422859728,-0.249303028,0.1846636385,-0.1072236374,-0.5770610571,0.4048385918,0.0409668386,0.1327851862,0.5260570049,-0.0001129135,0.0550981462,0.3158532083,-0.062861979,-0.273853898,-0.0322619863,-0.3334214091,-0.1577420682,0.0199063867,-0.0058855144,-0.2317918986,-0.0223552268,0.0295373388,-0.4109069109,-0.1773696542,0.2109527588,0.2552067339,0.0374189951,0.2777439952,-0.0880989134,0.2361394167,-0.159309417,-0.136650607,0.1717811972,0.125050053,0.3404647708,0.1657673717,0.3363201618,0.0685637817,0.1015633792,0.1613705903,0.0147446664,-0.0567290708,-0.3110951185,0.3234444261,0.4259087145,1.0086327791,-0.1596551389,-0.182197094,0.157779634,0.0264525656,-0.0356676579,0.3250226676,0.0174005292,0.1640668213,0.2543435693,-0.1935992092,-0.3147679567,-0.1341320425,0.1571154594,-0.0777185708,0.0233902801,-0.0591348447,0.0415538736,0.1453623772,0.0051327143,0.1077366322,-0.1008594856,-0.1756092608,0.1881384701,-0.1503513306,0.026202675,0.0316473059,0.3358705044,0.0130893113,0.1098906994,-0.1315606534,-0.0250517074,-0.3255392015,-0.0005613684,-0.08522816,0.2204771638,0.0166280624,-0.1995026022,0.2707875073,-0.1139377356,0.2405614108,-0.0439554267,-0.0537728667,-0.2539819479,-0.2263048738,-0.172232002,0.2297488451,-0.2043187767,-0.3384668231,0.0619576126,-0.4313420355,-0.3143392801,0.1425851732,0.4864598513,-0.0231622849,0.0346580781,0.0651973486,-0.0570586585,-0.1482507139,-0.425801456,-0.2372843027,0.1039377376,0.0467431508,0.1587614715,0.2824326456,-0.4163477421,0.1914355755,-0.1234558523,0.0803627521,0.0883765668,0.0003862055,-0.0385149233,-0.1677870452,0.1041902676,0.0192432031,0.0991432369,0.0690491349,-0.2787953615,0.0832877681,0.3198287189,-0.0508652143,-0.3625472784,-0.2402844429,0.1697338969,-0.2351994365,-0.1519474238,0.052606672,0.0715865269,0.0877996311,-0.1823082119,0.0104640601,0.191397652,0.1962027252,-0.2433095872,0.2753798664,0.6847062707,-0.8575754762,-0.0645467639,-0.3746862411,-0.2956611514,-0.1132886708,-0.026285788,-0.0792923495,0.1603044719,-0.2466031611,0.0961682349,0.3950939775,-0.1686411649,-0.7296184897,-0.0896689519,-0.1179730073,0.0382092334,0.0388404503,0.2587750852,-0.0439007767,-0.0245998669,-0.3138964772,-0.2207815647,0.0821027085,-0.2556655407,-0.2414046377,-0.402444154,0.2327148914,0.1665413529,0.2135138065,-0.0416937508,0.3270434141,-0.1655866802,0.3146351278,-0.1481228173,0.2848622203,0.1528060138,0.4937233925,0.4150800705,0.2384996116,0.0726805702,-0.2617341876,0.2346223742,0.0227718521,0.0939582884,0.1159989312,-0.0149985515,-0.3226870596,-0.1462880075,-0.623902142,-0.3042360544,0.1195197627,0.2918022275,-0.0782617107,0.199516952,-0.1679522693,0.2367848754,-0.0580740348,0.0001180851,-0.0089897979,0.3300353885,-0.2128496915,0.02694574,-0.2829592526,0.1242420524,0.3206024468,-0.2916026115,-0.097470127,0.3694753051,-0.2169196159,0.080015786,0.2317297608,0.0138281668,0.3024756014,-0.5033234358,0.2809161544,0.1099462882,0.1187469289,-0.1578794569,0.0920366198,-0.109109886,-0.098198615,0.0000262573,-0.0787724629,0.1155532897,0.3489156663,0.036068175,0.1823004037,-0.2661878169,0.3603407443,-0.4597929418,0.3849895597,-0.2043879181,-0.1554352641,0.187618494,0.0977061838,-0.0256984252,0.0553841069,0.0270380713,-0.144201234,0.0466018803,0.2425389737,0.2145337909,0.3274315894,0.2337872982,-0.0986174494,0.0172702037,0.2810690105,-0.1537164152,0.1213996559,-0.1242411658,0.024914816,-0.00246132,0.0330068283,-0.0848755985,-0.4058820605,-0.1250520051,0.1864902079,0.2319677621,-0.1977392584,-0.0924724117,-0.377179563,-0.3416564763,-0.0381678641,-0.3886728287,-0.2931241393,-0.2251173854,0.040928714,0.0242749676,0.1285751909,0.1746923327,-0.3331778347,0.3201796114,-0.075171493,0.0246695373,-0.1758150905,0.0476693995,-0.3795057833,0.1338643879,0.2005193532,-0.2732555866,0.2606116533,-0.3598282635,0.4085065424,-0.6811468005,-0.4405822158,0.2074215263,0.0757717192,0.1575275362,-0.0284917559,0.1345665604,0.043119926,-0.1280988753,0.1332272142,0.0137637481,-0.0514026247,-0.1474151313,-0.0625314936,0.0202066489,0.0768695325,-0.4956459701,-0.4501398802,-0.2082397342,0.1185904369,0.0841676891,0.0203951932,-0.0850857049,0.02957353,0.2879749835,0.1091539487,-0.0643559247,-0.3191490173,-0.2505702972,0.3353726864,-0.3928938508,-0.3237980902,0.3501984775,0.0587564483,0.2683012784,-0.1648422331,-0.679756701,-0.0057594818,-0.0360540524,0.179038927,0.0517870896,-0.1050385609,0.3662234843,0.0355203897,0.0622180365,-0.181702897,-0.2415734828,0.0817042515,-0.1403156966,0.6115553975,-0.1261703074,0.4719209373,0.0504383184,0.7220000625,0.1288030446,0.0362597369,0.5672439337,-0.0392664783,0.5134875774,-0.1630890965,-0.3909627497,0.2895599604,-0.0259106457,0.0995257497,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3268","title":"Dataset viewer issue for 'liweili\/c4_200m'","comments":"Hi ! Your dataset code is all good now :)\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: d = load_dataset(\"liweili\/c4_200m\", streaming=True)\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.79k\/2.79k [00:00<00:00, 4.83MB\/s]\r\nUsing custom data configuration default\r\n\r\nIn [3]: next(iter(d[\"train\"]))\r\nOut[3]: \r\n{'input': 'Bitcoin is for $7,094 this morning, which CoinDesk says.',\r\n 'output': 'Bitcoin goes for $7,094 this morning, according to CoinDesk.'}\r\n```\r\nThough the viewer doesn't seem to be updated, I'll take a look at what's wrong","body":"## Dataset viewer issue for '*liweili\/c4_200m*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/liweili\/c4_200m)*\r\n\r\n*Server Error*\r\n```\r\nStatus code:   404\r\nException:     Status404Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the ressource does not exist.\r\n```\r\n\r\nAm I the one who added this dataset ? Yes\r\n","comment_length":73,"text":"Dataset viewer issue for 'liweili\/c4_200m' \n ## Dataset viewer issue for '*liweili\/c4_200m*'\r\n\r\n**Link:** *[link to the dataset viewer page](https:\/\/huggingface.co\/datasets\/liweili\/c4_200m)*\r\n\r\n*Server Error*\r\n```\r\nStatus code:   404\r\nException:     Status404Error\r\nMessage:       Not found. Maybe the cache is missing, or maybe the ressource does not exist.\r\n```\r\n\r\nAm I the one who added this dataset ? Yes\r\n \n Hi ! Your dataset code is all good now :)\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: d = load_dataset(\"liweili\/c4_200m\", streaming=True)\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.79k\/2.79k [00:00<00:00, 4.83MB\/s]\r\nUsing custom data configuration default\r\n\r\nIn [3]: next(iter(d[\"train\"]))\r\nOut[3]: \r\n{'input': 'Bitcoin is for $7,094 this morning, which CoinDesk says.',\r\n 'output': 'Bitcoin goes for $7,094 this morning, according to CoinDesk.'}\r\n```\r\nThough the viewer doesn't seem to be updated, I'll take a look at what's wrong","embeddings":[-0.4007692933,0.5141009092,-0.0849970281,0.3988130987,0.0900289789,0.1364436746,0.1792555451,0.3091340363,-0.2108910382,0.0321357362,-0.1187348962,0.0765217394,-0.150267452,0.140732035,0.0565302446,0.0477422141,-0.0537078716,0.2665193975,0.0039402097,-0.0002117278,-0.0424510241,-0.0146692321,-0.1056322679,-0.2330267578,-0.3087046742,0.0646813884,-0.0496255904,0.1228487268,-0.2014476955,-0.7416373491,0.4144558907,0.1476267427,0.2129897624,0.6299410462,-0.0001087017,-0.0401063338,0.3618809283,-0.0846242458,-0.3333467841,-0.2459673882,-0.1924147755,-0.3108967841,0.1801447272,-0.0852204636,-0.1757211983,-0.0382544696,0.0203838702,-0.3884213269,-0.0526738241,0.3199624419,0.2671203911,-0.0494043157,0.1281650364,0.0025561114,0.0641007796,-0.3388640583,-0.04876047,0.2593752742,-0.0729216561,0.3572562039,0.007752033,0.3772913516,-0.087031737,0.0585581139,0.0065956684,-0.0363972858,-0.0399336703,-0.3320505619,0.2127070576,0.3526161611,0.8941672444,-0.3157359362,-0.2543495595,0.0053007607,0.0572919622,-0.2602582276,0.2728718519,0.1214188188,0.0847510919,0.2360838205,-0.2256268412,-0.0489894487,-0.1151345745,0.2003809661,-0.3122188747,0.0122790029,-0.0654953271,-0.0101911183,0.1154809594,0.014477971,0.3160974979,-0.1034334674,-0.0007663476,0.1107822359,-0.3483014107,0.1237276495,0.0599022508,0.2145539671,-0.2226830423,0.0713195726,-0.0795922279,0.0493311137,-0.2725149393,0.0363457985,-0.0339742079,0.232973367,-0.0809715837,-0.0758843273,0.1555358768,0.0057218219,0.1435496658,-0.0044119437,-0.1303432733,-0.3305183947,-0.2120186687,-0.0507208221,0.22559084,-0.1655938625,-0.3701299727,0.1691506952,-0.5444761515,-0.2087195665,0.1050818861,0.4254771173,-0.1124471426,0.3648327887,0.0641033724,0.0098192254,-0.0722440779,-0.3663849235,-0.2442452461,0.0218718629,-0.0235073324,0.0375765301,0.3109258413,-0.4175085425,0.2665305734,-0.0317554846,0.1457663029,-0.0396661796,-0.0007463953,-0.1232738197,-0.0859994963,0.1506646872,-0.0320211612,0.1858263612,0.1180948466,-0.2619410753,-0.0054858313,0.2602054775,-0.0338704698,-0.2370221615,-0.1045707315,0.2064981014,-0.2634815574,-0.233758077,0.0500811711,0.1319770515,0.1192131937,-0.1345994622,-0.012352827,-0.0737965107,0.1800498068,-0.3232539296,0.3480347395,0.6132743955,-0.9262835383,-0.0187642351,-0.2667135596,-0.2094072104,0.0923389047,-0.0024526066,-0.1684658229,0.0984991789,-0.0833009258,0.0779534206,0.3039768338,-0.3614037633,-0.8733240366,0.045414634,-0.0198437292,0.2030908912,0.0372783802,0.2059206218,0.049311325,0.0079668965,-0.217185989,-0.1717077643,-0.0293435641,-0.182920754,-0.3870013952,-0.4867547154,0.3363811076,0.1442978084,0.2828352451,-0.2033629417,0.082462512,-0.0863004401,0.4526154101,0.0043520471,0.1636688113,-0.0335737057,0.4336581528,0.3732982576,0.1016987711,0.0855094269,-0.2266172469,0.2366595119,0.1657370925,0.1417648494,0.1294611841,0.0505280606,-0.3766458929,-0.1410570741,-0.5314278603,-0.2501836121,0.1991771013,0.2147833705,-0.225697875,0.282404691,-0.2276833951,0.3112389445,-0.1312550306,0.0564798862,0.1150321364,0.315230608,-0.1867691725,-0.020151915,-0.184332788,-0.0064433869,0.3707364798,-0.1074997559,-0.2069389373,0.4541055262,-0.0903069228,0.1619392782,0.0338728391,0.1695690006,0.3195780516,-0.293766439,0.2485746443,0.3134578764,0.1119017228,-0.0909818783,0.0667258129,-0.0103042945,-0.0411591716,-0.0123735582,-0.1136777326,0.0776782185,0.4457227886,-0.0351028144,0.0739103705,-0.2375185192,0.2543021441,-0.6323581934,0.4947152138,-0.2080086619,-0.1823916733,0.1969216019,0.1253861189,0.0055404273,-0.0213776287,0.0606497265,-0.2208241224,-0.005280178,0.2202162892,0.3471495211,0.1560810655,0.1406862289,-0.0528998896,0.1107911021,0.2280639559,-0.2104670703,0.1399739832,0.0020636083,0.0098753711,0.084460929,0.0909619033,0.0426733494,-0.2264441103,-0.1706041098,0.1036263406,0.2200416476,-0.1866649836,-0.1593715101,-0.504242003,-0.2797981501,-0.0793124437,-0.4231538773,-0.0841440335,-0.3479716778,-0.1076211557,0.0762192085,-0.0129182022,0.2375874221,-0.4154026806,0.0808465332,0.0732966661,-0.0230669528,-0.1814445853,-0.0418188274,-0.2727791369,0.1365589499,0.2468079329,-0.2233268321,0.242694512,-0.3069503009,0.1823765188,-0.3909946978,-0.4228442013,0.1924049407,0.0669285059,0.0989279076,-0.0644481108,0.0297013912,0.0723498389,-0.1181464195,0.0610013306,0.0426202677,0.1655790508,-0.0921205357,0.0266345236,0.0430305563,0.1588583589,-0.5491921306,-0.5089229941,-0.3816439211,-0.0231666099,0.096776396,0.0261078645,-0.0010656356,0.1853343546,0.2213654369,0.2258446664,-0.0989125967,-0.2020876259,-0.2103348523,0.2865548432,-0.2234373242,-0.2747684717,0.2888682485,0.0271667615,0.1455505937,-0.0059721088,-0.7311400771,0.2397739887,-0.0806099549,0.2536820769,0.0644018203,-0.2422815412,0.1995658129,0.1901161522,0.0283396039,-0.1819543839,-0.1400394589,0.011308996,-0.0808284059,0.4545102119,-0.1162305102,0.5022854805,0.2070989311,0.6270979643,0.2700489759,-0.0923425481,0.4450853765,0.0806220695,0.4189709127,-0.1422596127,-0.2870389223,0.1567460448,0.045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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3265","title":"Checksum error for kilt_task_wow","comments":"Using `dataset = load_dataset(\"kilt_tasks\", \"wow\", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution.","body":"## Describe the bug\r\nChecksum failed when downloads kilt_tasks_wow. See error output for details.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndatasets.load_datasets('kilt_tasks','wow')\r\n```\r\n\r\n## Expected results\r\nDownload successful\r\n\r\n## Actual results\r\n```\r\nDownloading and preparing dataset kilt_tasks\/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to \/root\/.cache\/huggingface\/datasets\/kilt_tasks\/wow\/1.0.0\/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 5121.25it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1527.42it\/s]\r\nTraceback (most recent call last):\r\n  File \"kilt_wow.py\", line 30, in <module>\r\n    main()\r\n  File \"kilt_wow.py\", line 27, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"kilt_wow.py\", line 21, in load_dataset\r\n    return datasets.load_dataset('kilt_tasks','wow')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/dl.fbaipublicfiles.com\/KILT\/wow-train-kilt.jsonl', 'http:\/\/dl.fbaipublicfiles.com\/KILT\/wow-dev-kilt.jsonl']\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 4.0.1\r\n","comment_length":19,"text":"Checksum error for kilt_task_wow \n ## Describe the bug\r\nChecksum failed when downloads kilt_tasks_wow. See error output for details.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndatasets.load_datasets('kilt_tasks','wow')\r\n```\r\n\r\n## Expected results\r\nDownload successful\r\n\r\n## Actual results\r\n```\r\nDownloading and preparing dataset kilt_tasks\/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to \/root\/.cache\/huggingface\/datasets\/kilt_tasks\/wow\/1.0.0\/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 5121.25it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1527.42it\/s]\r\nTraceback (most recent call last):\r\n  File \"kilt_wow.py\", line 30, in <module>\r\n    main()\r\n  File \"kilt_wow.py\", line 27, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"kilt_wow.py\", line 21, in load_dataset\r\n    return datasets.load_dataset('kilt_tasks','wow')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/dl.fbaipublicfiles.com\/KILT\/wow-train-kilt.jsonl', 'http:\/\/dl.fbaipublicfiles.com\/KILT\/wow-dev-kilt.jsonl']\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 4.0.1\r\n \n Using `dataset = load_dataset(\"kilt_tasks\", \"wow\", ignore_verifications=True)` may fix it, but I do not think it is a elegant solution.","embeddings":[-0.012826114,-0.2781215012,-0.0930910334,0.2878218889,0.39975968,-0.082880348,0.2399519533,0.5319927931,0.3643352985,0.1832445711,0.0476305559,0.3454277217,0.0678307712,0.3870454133,-0.0282223374,0.1116022989,0.0441306047,-0.1036071926,-0.0944885314,0.075940825,-0.2435697913,0.006694111,-0.2141817957,-0.1075400934,-0.1100084111,-0.1466596127,0.1140921637,0.2089682966,-0.1722585559,-0.3939291835,0.3610934615,-0.1468098015,-0.2470579594,0.4836378992,-0.0001109908,0.0874135569,0.4979125261,-0.0946588293,-0.3257265389,0.2187439203,-0.2208337337,-0.392575711,-0.1766952425,-0.5591237545,0.1095202044,0.3231536746,-0.151269421,0.0166531559,0.0793444291,0.1912074238,0.2628452778,0.5959451795,0.2259002626,-0.1648680419,0.334274739,-0.2491681725,-0.2119430453,0.4761389196,0.0798153505,0.0839698985,-0.080619663,0.2674737871,-0.0862880722,0.0745857731,-0.0623313263,-0.2316990644,0.2357163131,-0.1239100248,0.2631689906,0.211638391,0.0131389247,-0.3023893237,-0.3866113126,-0.1624423414,0.1638469547,-0.024262324,0.2983599305,0.270129472,-0.2362077236,-0.264085561,-0.294213295,0.2515365481,-0.0140555752,-0.0136045069,-0.0416360162,0.1932386607,0.0531325005,0.0917289928,0.1218991131,0.1144480035,-0.0156162456,0.050172139,-0.1965694278,0.0878199637,-0.6071638465,-0.2512291074,0.0340561755,0.1496302783,0.3839893341,0.1059774607,0.0410786346,0.0374564268,0.1584222466,0.0566388927,-0.0171032622,0.2904166281,0.0940265432,-0.1082717851,0.1896018535,0.4522922337,-0.2431432009,0.0651865527,-0.2118527889,-0.2496044338,0.1051946729,0.131954059,0.0374174081,-0.0672407821,-0.3496276736,0.2294101715,0.1588986814,0.2358551174,0.2828549147,0.2968425453,-0.3891031146,0.0931927711,-0.0465098619,0.3047631085,0.0400673188,0.0398539454,-0.2344688326,0.1480164826,-0.2909498513,-0.1988754719,0.2057028115,-0.4085449278,0.4943645298,-0.0837451369,0.2511699796,-0.3185094893,-0.0275853872,-0.1374467164,-0.1532082856,0.4017927647,-0.0500313416,0.0725839585,0.2177205086,0.3343732655,-0.0546421967,0.082197316,-0.442062974,-0.0571910888,-0.1371770799,0.2104993165,-0.2309618294,0.0125405593,-0.2689124346,-0.4179107547,0.4113922417,-0.2735811472,0.1810570061,-0.0960634053,-0.2016112655,-0.4305164218,0.261064291,0.0877299756,-0.1757511646,0.0467114449,-0.2709529996,-0.2940069735,0.1010699794,0.1193902567,-0.1870777011,0.5738449097,-0.1130400598,-0.0081109228,0.0014032435,-0.504488647,-0.6426463723,-0.0658599287,-0.0756244957,0.1515776515,-0.0899427831,0.0706603155,0.044053629,-0.0837417468,0.0144667877,0.0935859531,-0.0298684686,0.2259467095,-0.3437545896,-0.3017332852,0.0772289038,0.1929337829,0.2797109783,-0.175848484,0.3025607467,0.3612274528,0.1699944139,0.0790376961,0.0787121207,0.0994151756,0.2066200227,-0.2813226581,0.0432327352,-0.1722417176,-0.2846139669,0.4127875268,-0.2276907265,0.0094581526,-0.0272183474,-0.135557279,-0.4488742054,0.0707697496,-0.1877658963,-0.2652893364,0.2193028331,-0.0864298344,0.2091260403,-0.0262703523,-0.0770979822,0.078558147,-0.4619821906,0.0101320185,-0.0109650604,0.2134284228,0.0142190512,-0.1702802926,-0.1117766425,0.2550694644,0.0391685665,0.0254471898,-0.1915009022,0.457801044,0.0657750368,0.0850697607,0.3547760248,-0.2345830351,0.0772711188,-0.0705753565,-0.053846769,0.2073998153,0.146293655,-0.0788003951,0.0117744347,0.4415225983,-0.0940738022,-0.0080812741,-0.1512880325,-0.0672112629,0.0073434883,-0.2070638686,-0.0780209973,0.180580914,0.181524694,-0.1155864149,-0.0723165721,-0.1447075307,0.0641328469,-0.13563779,0.45214957,0.0771962851,0.1171919107,-0.0103695933,0.1338866204,0.0546178184,-0.0911714435,0.3222328722,0.4426181018,0.0551941618,0.1465139836,0.0251175631,-0.1807836443,-0.0666826218,0.0458556712,0.1736732125,0.2500114143,0.4820309579,0.1924389899,-0.023953665,-0.1193851456,0.1691374928,0.2175245434,0.213106975,-0.0167399272,-0.2323632389,-0.0784279481,-0.1616693735,-0.0877586007,-0.0425417013,-0.1546128392,-0.259536773,-0.0062627806,0.1712182164,-0.1001774594,0.2672737241,-0.3696242869,0.0906299278,0.3246646821,-0.4286694825,0.0408194885,-0.0976967067,0.0449514017,0.0947017074,0.4179500639,-0.2572253942,0.5363554955,-0.0860767514,-0.060641475,-0.2047609091,-0.2904702127,-0.0598202497,-0.1043633148,0.29212901,0.1008935422,0.4968917966,-0.295788765,-0.2212334871,0.4083017409,-0.2789632976,-0.2948337793,0.0946154296,0.1188264936,0.0558084026,-0.3278343678,-0.1349571943,-0.0236363318,-0.33616817,-0.1312640607,-0.0823907629,0.1631360352,0.2060041428,0.2733001113,0.3915460706,0.0997624397,0.0926080495,-0.1446234882,-0.6678726673,0.243363902,-0.0873005614,-0.4521773756,0.0486312136,-0.1096478701,0.4888756275,0.0911076292,-0.4867105186,-0.2733826637,-0.0066133575,0.2153505087,0.0214936715,-0.0024957762,0.093963258,-0.2705868781,-0.1402052492,-0.2706995904,0.1098224968,0.1001070887,-0.0493201613,0.5106634498,-0.0548375957,0.0889036283,0.1814402193,0.5321941972,0.3698294461,0.1716741771,0.3536885381,0.0106152296,0.1630486548,-0.0863210931,-0.3648349345,0.2911824286,-0.1914529651,0.058145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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3265","title":"Checksum error for kilt_task_wow","comments":"Hi @slyviacassell, thanks for reporting.\r\n\r\nYes, there is an issue with the checksum verification. I'm fixing it.\r\n\r\nAnd as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`. ","body":"## Describe the bug\r\nChecksum failed when downloads kilt_tasks_wow. See error output for details.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndatasets.load_datasets('kilt_tasks','wow')\r\n```\r\n\r\n## Expected results\r\nDownload successful\r\n\r\n## Actual results\r\n```\r\nDownloading and preparing dataset kilt_tasks\/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to \/root\/.cache\/huggingface\/datasets\/kilt_tasks\/wow\/1.0.0\/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 5121.25it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1527.42it\/s]\r\nTraceback (most recent call last):\r\n  File \"kilt_wow.py\", line 30, in <module>\r\n    main()\r\n  File \"kilt_wow.py\", line 27, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"kilt_wow.py\", line 21, in load_dataset\r\n    return datasets.load_dataset('kilt_tasks','wow')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/dl.fbaipublicfiles.com\/KILT\/wow-train-kilt.jsonl', 'http:\/\/dl.fbaipublicfiles.com\/KILT\/wow-dev-kilt.jsonl']\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 4.0.1\r\n","comment_length":33,"text":"Checksum error for kilt_task_wow \n ## Describe the bug\r\nChecksum failed when downloads kilt_tasks_wow. See error output for details.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndatasets.load_datasets('kilt_tasks','wow')\r\n```\r\n\r\n## Expected results\r\nDownload successful\r\n\r\n## Actual results\r\n```\r\nDownloading and preparing dataset kilt_tasks\/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to \/root\/.cache\/huggingface\/datasets\/kilt_tasks\/wow\/1.0.0\/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 5121.25it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 1527.42it\/s]\r\nTraceback (most recent call last):\r\n  File \"kilt_wow.py\", line 30, in <module>\r\n    main()\r\n  File \"kilt_wow.py\", line 27, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"kilt_wow.py\", line 21, in load_dataset\r\n    return datasets.load_dataset('kilt_tasks','wow')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 679, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/dl.fbaipublicfiles.com\/KILT\/wow-train-kilt.jsonl', 'http:\/\/dl.fbaipublicfiles.com\/KILT\/wow-dev-kilt.jsonl']\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 4.0.1\r\n \n Hi @slyviacassell, thanks for reporting.\r\n\r\nYes, there is an issue with the checksum verification. I'm fixing it.\r\n\r\nAnd as you pointed out, in the meantime, you can circumvent the problem by passing `ignore_verifications=True`. ","embeddings":[-0.012826114,-0.2781215012,-0.0930910334,0.2878218889,0.39975968,-0.082880348,0.2399519533,0.5319927931,0.3643352985,0.1832445711,0.0476305559,0.3454277217,0.0678307712,0.3870454133,-0.0282223374,0.1116022989,0.0441306047,-0.1036071926,-0.0944885314,0.075940825,-0.2435697913,0.006694111,-0.2141817957,-0.1075400934,-0.1100084111,-0.1466596127,0.1140921637,0.2089682966,-0.1722585559,-0.3939291835,0.3610934615,-0.1468098015,-0.2470579594,0.4836378992,-0.0001109908,0.0874135569,0.4979125261,-0.0946588293,-0.3257265389,0.2187439203,-0.2208337337,-0.392575711,-0.1766952425,-0.5591237545,0.1095202044,0.3231536746,-0.151269421,0.0166531559,0.0793444291,0.1912074238,0.2628452778,0.5959451795,0.2259002626,-0.1648680419,0.334274739,-0.2491681725,-0.2119430453,0.4761389196,0.0798153505,0.0839698985,-0.080619663,0.2674737871,-0.0862880722,0.0745857731,-0.0623313263,-0.2316990644,0.2357163131,-0.1239100248,0.2631689906,0.211638391,0.0131389247,-0.3023893237,-0.3866113126,-0.1624423414,0.1638469547,-0.024262324,0.2983599305,0.270129472,-0.2362077236,-0.264085561,-0.294213295,0.2515365481,-0.0140555752,-0.0136045069,-0.0416360162,0.1932386607,0.0531325005,0.0917289928,0.1218991131,0.1144480035,-0.0156162456,0.050172139,-0.1965694278,0.0878199637,-0.6071638465,-0.2512291074,0.0340561755,0.1496302783,0.3839893341,0.1059774607,0.0410786346,0.0374564268,0.1584222466,0.0566388927,-0.0171032622,0.2904166281,0.0940265432,-0.1082717851,0.1896018535,0.4522922337,-0.2431432009,0.0651865527,-0.2118527889,-0.2496044338,0.1051946729,0.131954059,0.0374174081,-0.0672407821,-0.3496276736,0.2294101715,0.1588986814,0.2358551174,0.2828549147,0.2968425453,-0.3891031146,0.0931927711,-0.0465098619,0.3047631085,0.0400673188,0.0398539454,-0.2344688326,0.1480164826,-0.2909498513,-0.1988754719,0.2057028115,-0.4085449278,0.4943645298,-0.0837451369,0.2511699796,-0.3185094893,-0.0275853872,-0.1374467164,-0.1532082856,0.4017927647,-0.0500313416,0.0725839585,0.2177205086,0.3343732655,-0.0546421967,0.082197316,-0.442062974,-0.0571910888,-0.1371770799,0.2104993165,-0.2309618294,0.0125405593,-0.2689124346,-0.4179107547,0.4113922417,-0.2735811472,0.1810570061,-0.0960634053,-0.2016112655,-0.4305164218,0.261064291,0.0877299756,-0.1757511646,0.0467114449,-0.2709529996,-0.2940069735,0.1010699794,0.1193902567,-0.1870777011,0.5738449097,-0.1130400598,-0.0081109228,0.0014032435,-0.504488647,-0.6426463723,-0.0658599287,-0.0756244957,0.1515776515,-0.0899427831,0.0706603155,0.044053629,-0.0837417468,0.0144667877,0.0935859531,-0.0298684686,0.2259467095,-0.3437545896,-0.3017332852,0.0772289038,0.1929337829,0.2797109783,-0.175848484,0.3025607467,0.3612274528,0.1699944139,0.0790376961,0.0787121207,0.0994151756,0.2066200227,-0.2813226581,0.0432327352,-0.1722417176,-0.2846139669,0.4127875268,-0.2276907265,0.0094581526,-0.0272183474,-0.135557279,-0.4488742054,0.0707697496,-0.1877658963,-0.2652893364,0.2193028331,-0.0864298344,0.2091260403,-0.0262703523,-0.0770979822,0.078558147,-0.4619821906,0.0101320185,-0.0109650604,0.2134284228,0.0142190512,-0.1702802926,-0.1117766425,0.2550694644,0.0391685665,0.0254471898,-0.1915009022,0.457801044,0.0657750368,0.0850697607,0.3547760248,-0.2345830351,0.0772711188,-0.0705753565,-0.053846769,0.2073998153,0.146293655,-0.0788003951,0.0117744347,0.4415225983,-0.0940738022,-0.0080812741,-0.1512880325,-0.0672112629,0.0073434883,-0.2070638686,-0.0780209973,0.180580914,0.181524694,-0.1155864149,-0.0723165721,-0.1447075307,0.0641328469,-0.13563779,0.45214957,0.0771962851,0.1171919107,-0.0103695933,0.1338866204,0.0546178184,-0.0911714435,0.3222328722,0.4426181018,0.0551941618,0.1465139836,0.0251175631,-0.1807836443,-0.0666826218,0.0458556712,0.1736732125,0.2500114143,0.4820309579,0.1924389899,-0.023953665,-0.1193851456,0.1691374928,0.2175245434,0.213106975,-0.0167399272,-0.2323632389,-0.0784279481,-0.1616693735,-0.0877586007,-0.0425417013,-0.1546128392,-0.259536773,-0.0062627806,0.1712182164,-0.1001774594,0.2672737241,-0.3696242869,0.0906299278,0.3246646821,-0.4286694825,0.0408194885,-0.0976967067,0.0449514017,0.0947017074,0.4179500639,-0.2572253942,0.5363554955,-0.0860767514,-0.060641475,-0.2047609091,-0.2904702127,-0.0598202497,-0.1043633148,0.29212901,0.1008935422,0.4968917966,-0.295788765,-0.2212334871,0.4083017409,-0.2789632976,-0.2948337793,0.0946154296,0.1188264936,0.0558084026,-0.3278343678,-0.1349571943,-0.0236363318,-0.33616817,-0.1312640607,-0.0823907629,0.1631360352,0.2060041428,0.2733001113,0.3915460706,0.0997624397,0.0926080495,-0.1446234882,-0.6678726673,0.243363902,-0.0873005614,-0.4521773756,0.0486312136,-0.1096478701,0.4888756275,0.0911076292,-0.4867105186,-0.2733826637,-0.0066133575,0.2153505087,0.0214936715,-0.0024957762,0.093963258,-0.2705868781,-0.1402052492,-0.2706995904,0.1098224968,0.1001070887,-0.0493201613,0.5106634498,-0.0548375957,0.0889036283,0.1814402193,0.5321941972,0.3698294461,0.1716741771,0.3536885381,0.0106152296,0.1630486548,-0.0863210931,-0.3648349345,0.2911824286,-0.1914529651,0.0581450425,0.13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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3264","title":"Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution","comments":"#take\r\nI am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy with new ones provided by authors.\r\n\r\nAs for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. I can include them in the dataset folder as the files are <1MB in size total.","body":"## Describe the bug\r\n- WikiAuto Manual  \r\nThe original manual datasets with the following downloading URL in this [repository](https:\/\/github.com\/chaojiang06\/wiki-auto) was [deleted](https:\/\/github.com\/chaojiang06\/wiki-auto\/commit\/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.  \r\n```\r\nhttps:\/\/github.com\/chaojiang06\/wiki-auto\/raw\/master\/wiki-manual\/train.tsv\r\n```\r\n\r\n- jeopardy  \r\nThe downloading URL for jeopardy may move from \r\n```\r\nhttp:\/\/skeeto.s3.amazonaws.com\/share\/JEOPARDY_QUESTIONS1.json.gz\r\n```\r\n to \r\n```\r\nhttps:\/\/drive.google.com\/file\/d\/0BwT5wj_P7BKXb2hfM3d2RHU1ckE\/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg\r\n```\r\n\r\n- definite_pronoun_resolution\r\nThe following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.\r\n```\r\nhttp:\/\/www.hlt.utdallas.edu\/~vince\/data\/emnlp12\/train.c.txt\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndatasets.load_datasets('wiki_auto','manual')\r\ndatasets.load_datasets('jeopardy')\r\ndatasets.load_datasets('definite_pronoun_resolution')\r\n```\r\n\r\n## Expected results\r\nDownload successfully \r\n\r\n## Actual results\r\n- WikiAuto Manual  \r\n```\r\nDownloading and preparing dataset wiki_auto\/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to \/root\/.cache\/huggingface\/datasets\/wiki_auto\/manual\/1.0.0\/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...\r\n  0%|                                                                                                                                                                                                                      | 0\/3 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"wiki_auto.py\", line 43, in <module>\r\n    main()\r\n  File \"wiki_auto.py\", line 40, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 24, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"wiki_auto.py\", line 34, in load_dataset\r\n    return datasets.load_dataset('wiki_auto', 'manual')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_auto\/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8\/wiki_auto.py\", line 193, in _split_generators\r\n    data_dir = dl_manager.download_and_extract(my_urls)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 592, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/chaojiang06\/wiki-auto\/raw\/master\/wiki-manual\/train.tsv\r\n```\r\n- jeopardy\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset jeopardy\/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to \/root\/.cache\/huggingface\/datasets\/jeopardy\/default\/0.1.0\/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...\r\nTraceback (most recent call last):\r\n  File \"jeopardy.py\", line 45, in <module>\r\n    main()\r\n  File \"jeopardy.py\", line 42, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"jeopardy.py\", line 36, in load_dataset\r\n    return datasets.load_dataset(\"jeopardy\")\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/jeopardy\/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810\/jeopardy.py\", line 72, in _split_generators\r\n    filepath = dl_manager.download_and_extract(_DATA_URL)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 206, in map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/skeeto.s3.amazonaws.com\/share\/JEOPARDY_QUESTIONS1.json.gz\r\n```\r\n- definite_pronoun_resolution\r\n```\r\nDownloading and preparing dataset definite_pronoun_resolution\/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to \/root\/.cache\/huggingface\/datasets\/definite_pronoun_resolution\/plain_text\/1.0.0\/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...\r\n  0%|                                                                                                                                                                                                                      | 0\/2 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"definite_pronoun_resolution.py\", line 37, in <module>\r\n    main()\r\n  File \"definite_pronoun_resolution.py\", line 34, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"definite_pronoun_resolution.py\", line 28, in load_dataset\r\n    return datasets.load_dataset('definite_pronoun_resolution')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/definite_pronoun_resolution\/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff\/definite_pronoun_resolution.py\", line 76, in _split_generators\r\n    files = dl_manager.download_and_extract(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/www.hlt.utdallas.edu\/~vince\/data\/emnlp12\/train.c.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 4.0.1\r\n","comment_length":60,"text":"Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution \n ## Describe the bug\r\n- WikiAuto Manual  \r\nThe original manual datasets with the following downloading URL in this [repository](https:\/\/github.com\/chaojiang06\/wiki-auto) was [deleted](https:\/\/github.com\/chaojiang06\/wiki-auto\/commit\/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.  \r\n```\r\nhttps:\/\/github.com\/chaojiang06\/wiki-auto\/raw\/master\/wiki-manual\/train.tsv\r\n```\r\n\r\n- jeopardy  \r\nThe downloading URL for jeopardy may move from \r\n```\r\nhttp:\/\/skeeto.s3.amazonaws.com\/share\/JEOPARDY_QUESTIONS1.json.gz\r\n```\r\n to \r\n```\r\nhttps:\/\/drive.google.com\/file\/d\/0BwT5wj_P7BKXb2hfM3d2RHU1ckE\/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg\r\n```\r\n\r\n- definite_pronoun_resolution\r\nThe following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.\r\n```\r\nhttp:\/\/www.hlt.utdallas.edu\/~vince\/data\/emnlp12\/train.c.txt\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndatasets.load_datasets('wiki_auto','manual')\r\ndatasets.load_datasets('jeopardy')\r\ndatasets.load_datasets('definite_pronoun_resolution')\r\n```\r\n\r\n## Expected results\r\nDownload successfully \r\n\r\n## Actual results\r\n- WikiAuto Manual  \r\n```\r\nDownloading and preparing dataset wiki_auto\/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to \/root\/.cache\/huggingface\/datasets\/wiki_auto\/manual\/1.0.0\/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...\r\n  0%|                                                                                                                                                                                                                      | 0\/3 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"wiki_auto.py\", line 43, in <module>\r\n    main()\r\n  File \"wiki_auto.py\", line 40, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 24, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"wiki_auto.py\", line 34, in load_dataset\r\n    return datasets.load_dataset('wiki_auto', 'manual')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_auto\/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8\/wiki_auto.py\", line 193, in _split_generators\r\n    data_dir = dl_manager.download_and_extract(my_urls)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 592, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/chaojiang06\/wiki-auto\/raw\/master\/wiki-manual\/train.tsv\r\n```\r\n- jeopardy\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset jeopardy\/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to \/root\/.cache\/huggingface\/datasets\/jeopardy\/default\/0.1.0\/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...\r\nTraceback (most recent call last):\r\n  File \"jeopardy.py\", line 45, in <module>\r\n    main()\r\n  File \"jeopardy.py\", line 42, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"jeopardy.py\", line 36, in load_dataset\r\n    return datasets.load_dataset(\"jeopardy\")\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/jeopardy\/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810\/jeopardy.py\", line 72, in _split_generators\r\n    filepath = dl_manager.download_and_extract(_DATA_URL)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 206, in map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/skeeto.s3.amazonaws.com\/share\/JEOPARDY_QUESTIONS1.json.gz\r\n```\r\n- definite_pronoun_resolution\r\n```\r\nDownloading and preparing dataset definite_pronoun_resolution\/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to \/root\/.cache\/huggingface\/datasets\/definite_pronoun_resolution\/plain_text\/1.0.0\/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...\r\n  0%|                                                                                                                                                                                                                      | 0\/2 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"definite_pronoun_resolution.py\", line 37, in <module>\r\n    main()\r\n  File \"definite_pronoun_resolution.py\", line 34, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"definite_pronoun_resolution.py\", line 28, in load_dataset\r\n    return datasets.load_dataset('definite_pronoun_resolution')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/definite_pronoun_resolution\/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff\/definite_pronoun_resolution.py\", line 76, in _split_generators\r\n    files = dl_manager.download_and_extract(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/www.hlt.utdallas.edu\/~vince\/data\/emnlp12\/train.c.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 4.0.1\r\n \n #take\r\nI am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy with new ones provided by authors.\r\n\r\nAs for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. I can include them in the dataset folder as the files are <1MB in size total.","embeddings":[0.1274086535,-0.1285838932,0.0336008258,0.0951998457,0.1693071872,0.0601078197,0.2048468888,0.2117164135,-0.2019647509,0.3956474364,-0.0465765782,0.0693234876,0.1639291495,-0.1324873716,0.0800742432,-0.3333905339,-0.0439455099,-0.129307583,0.189574182,-0.0911257714,-0.3678211868,0.0870707408,-0.361455828,-0.0264681652,-0.0438278653,0.105112344,-0.0500893891,0.0153138693,-0.1008494571,-0.4323172867,0.1448832154,0.114343062,0.1059086695,0.3385549784,-0.0001115378,-0.0094134388,0.2700737119,-0.0819867179,-0.5365573764,-0.1244863048,-0.5981214046,0.0144764883,-0.0473802127,-0.3203182817,-0.0003080522,0.4028949738,0.0933874995,-0.1322327256,0.1947433054,0.6222036481,0.203265518,0.0447213277,0.0913914144,-0.0178734045,0.1893317699,-0.1646449417,-0.1972949207,0.2965210378,-0.140024662,0.1177109256,0.0533954091,0.4021210372,0.1708796322,-0.0917849839,-0.0564775579,0.0954063311,0.1515652835,-0.1696895659,0.4046265483,0.326916486,0.5513573885,-0.096358344,-0.3186918795,-0.0678908452,0.3135268986,-0.1409622878,0.4568139613,0.2807403803,-0.0899092481,0.0021865277,-0.1422172785,-0.2625918984,-0.2591567039,0.1594658494,-0.1103814691,0.691344738,0.0393010527,0.025860969,-0.2234939486,-0.1198866069,0.0794393271,-0.1248971894,0.167090416,0.0989005268,-0.1130947098,-0.0177112762,-0.0376865678,0.0473587252,0.1010986343,-0.3097601533,0.4962767363,-0.0611918606,-0.219254002,-0.1327753514,0.0298338197,0.5464056134,0.0487203598,0.0949229524,0.1561982632,0.7578017116,0.0920691192,0.1227595061,-0.0266494527,-0.2039470673,-0.1632310152,0.1810674667,0.2267388105,-0.1090889946,-0.2119569033,0.1016965881,-0.3092087507,0.1155110672,-0.2900319695,0.1739516407,-0.4681166708,0.3308090866,0.0365073346,0.1312980801,-0.3229940832,0.1867274642,-0.106015265,0.1031576544,-0.0824854672,0.1901243329,0.108665742,0.1292644441,0.3938520253,0.1119662896,0.0305506419,-0.1637239456,-0.3638500869,0.0598980263,-0.1780915409,0.0081053236,-0.0594822429,0.0193167292,-0.1101831496,-0.4052199423,0.067399174,0.2459143251,-0.1449958235,-0.1565332711,0.0782275423,0.2217916697,0.1491992474,-0.3199025393,-0.215295583,0.1093323752,-0.0603173301,-0.1255055964,0.1506298631,-0.1182941794,-0.2259959579,-0.1791525632,0.265207082,0.2022221088,-0.2052334547,-0.0848978311,0.052528128,0.1963839978,0.2605561912,0.2015848756,-0.0450689681,-0.0392939299,-0.0994711444,0.3200992644,-0.0356214195,-0.6613448858,-0.2944655716,-0.0810039416,-0.2047011703,0.2935251296,0.0467961915,-0.3464063108,-0.1910433173,0.0992492288,0.2076385766,0.2389194965,0.1993886977,0.0739801526,-0.4987148345,-0.3489390314,-0.2077021748,-0.1053579524,0.1842177063,-0.0404999293,-0.0500637628,0.0307934321,0.6682904959,-0.022808766,0.1097543463,0.1725937128,0.2791417539,0.0660692304,0.0038900631,0.0336859487,-0.3063521385,0.161487475,-0.244459942,0.3172652721,-0.2633830309,-0.2884165049,-0.4617548585,-0.1467323452,-0.3577142954,-0.3360911608,0.1774288714,0.3326925039,0.2330954075,0.1839559823,0.1679841578,-0.1568388045,-0.2305636257,0.0160496719,-0.2550227344,0.0253910869,-0.2314936966,0.1949636787,-0.0984317288,0.1117226928,0.2032301575,0.0645613,-0.3432500958,0.4482427239,-0.0400165543,0.4258109033,0.2569474876,0.0094796419,0.3121987879,-0.1555703878,0.0253663417,0.2017523348,0.2093171924,0.2155269384,-0.321267873,0.3201157451,-0.2278438061,0.2955271602,0.2124639899,0.0061415709,0.4060687423,-0.1340261996,-0.027041154,-0.1601205021,-0.1623489708,0.5956903696,-0.3482043445,-0.162464276,-0.4324273765,-0.0166552756,0.1807424873,-0.0621191449,0.2663137019,0.2878283858,-0.2269503325,-0.3052477241,0.147170946,0.3281190693,0.1712152064,0.1615499109,0.2940356731,0.2110916674,-0.0548054352,-0.3781977892,0.4756670296,0.2027464956,-0.0630633533,0.3314947486,0.2529734969,0.0753821954,-0.1732913852,0.104192093,-0.1466297507,0.0015546173,-0.2683822811,-0.0435175449,-0.3165047467,0.1406209767,-0.3399684131,-0.0564709939,-0.095371522,-0.4581068754,0.3413339555,0.0953323394,-0.2963668406,0.1810443252,-0.1188158318,0.0406524204,-0.1452873647,-0.0211257767,-0.224706009,-0.4174306989,-0.4545572698,0.1591987014,-0.0793911815,-0.1410232186,-0.0416883491,-0.3193743527,-0.0390764326,-0.3910268247,-0.362236321,-0.2352760136,0.0022659581,0.0839124545,0.0299542658,0.3136538565,-0.238576293,-0.3720279038,0.1207821146,0.0243624654,-0.0321703665,-0.1460863352,0.0056191683,0.3455110192,0.1182345524,-0.6243346334,-0.1572629213,-0.2604809403,0.20511356,0.2315014452,0.0082740346,0.5019302368,0.1165750474,0.0651928112,0.0638767108,0.3580876291,-0.2012389898,-0.2027282417,0.1608871371,-0.2689364552,-0.3275578022,-0.1447928995,-0.1142005175,0.5140980482,0.207713455,-0.4951125085,0.0321055688,-0.1570852846,0.2840752304,0.0344164558,0.0387400761,0.2489308119,0.2695596814,-0.1904114336,-0.0844819099,0.0566938929,-0.0853246227,0.2235332429,0.2495162338,-0.0362066254,0.1373954266,0.270683676,0.9664540887,-0.1156795546,0.307765156,0.3761497438,-0.0739281848,0.0019606599,-0.133990705,-0.0082329195,0.1862277091,-0.1524351686,0.0489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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3264","title":"Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution","comments":"> #take I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy.\r\n> \r\n> As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. Anyone has opinions on whether it is preferable for me to host them somewhere (e.g. personal GDrive account) or upload them to the dataset folder directly and use github raw URLs? The files are <1MB in size.\r\n\r\nI am planning to fix it next few days. But my to-do list is full and I do not have the cache of definite_pronoun_resolution. I am glad that you can take this. Thanks a lot!","body":"## Describe the bug\r\n- WikiAuto Manual  \r\nThe original manual datasets with the following downloading URL in this [repository](https:\/\/github.com\/chaojiang06\/wiki-auto) was [deleted](https:\/\/github.com\/chaojiang06\/wiki-auto\/commit\/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.  \r\n```\r\nhttps:\/\/github.com\/chaojiang06\/wiki-auto\/raw\/master\/wiki-manual\/train.tsv\r\n```\r\n\r\n- jeopardy  \r\nThe downloading URL for jeopardy may move from \r\n```\r\nhttp:\/\/skeeto.s3.amazonaws.com\/share\/JEOPARDY_QUESTIONS1.json.gz\r\n```\r\n to \r\n```\r\nhttps:\/\/drive.google.com\/file\/d\/0BwT5wj_P7BKXb2hfM3d2RHU1ckE\/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg\r\n```\r\n\r\n- definite_pronoun_resolution\r\nThe following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.\r\n```\r\nhttp:\/\/www.hlt.utdallas.edu\/~vince\/data\/emnlp12\/train.c.txt\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndatasets.load_datasets('wiki_auto','manual')\r\ndatasets.load_datasets('jeopardy')\r\ndatasets.load_datasets('definite_pronoun_resolution')\r\n```\r\n\r\n## Expected results\r\nDownload successfully \r\n\r\n## Actual results\r\n- WikiAuto Manual  \r\n```\r\nDownloading and preparing dataset wiki_auto\/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to \/root\/.cache\/huggingface\/datasets\/wiki_auto\/manual\/1.0.0\/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...\r\n  0%|                                                                                                                                                                                                                      | 0\/3 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"wiki_auto.py\", line 43, in <module>\r\n    main()\r\n  File \"wiki_auto.py\", line 40, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 24, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"wiki_auto.py\", line 34, in load_dataset\r\n    return datasets.load_dataset('wiki_auto', 'manual')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_auto\/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8\/wiki_auto.py\", line 193, in _split_generators\r\n    data_dir = dl_manager.download_and_extract(my_urls)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 592, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/chaojiang06\/wiki-auto\/raw\/master\/wiki-manual\/train.tsv\r\n```\r\n- jeopardy\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset jeopardy\/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to \/root\/.cache\/huggingface\/datasets\/jeopardy\/default\/0.1.0\/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...\r\nTraceback (most recent call last):\r\n  File \"jeopardy.py\", line 45, in <module>\r\n    main()\r\n  File \"jeopardy.py\", line 42, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"jeopardy.py\", line 36, in load_dataset\r\n    return datasets.load_dataset(\"jeopardy\")\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/jeopardy\/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810\/jeopardy.py\", line 72, in _split_generators\r\n    filepath = dl_manager.download_and_extract(_DATA_URL)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 206, in map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/skeeto.s3.amazonaws.com\/share\/JEOPARDY_QUESTIONS1.json.gz\r\n```\r\n- definite_pronoun_resolution\r\n```\r\nDownloading and preparing dataset definite_pronoun_resolution\/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to \/root\/.cache\/huggingface\/datasets\/definite_pronoun_resolution\/plain_text\/1.0.0\/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...\r\n  0%|                                                                                                                                                                                                                      | 0\/2 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"definite_pronoun_resolution.py\", line 37, in <module>\r\n    main()\r\n  File \"definite_pronoun_resolution.py\", line 34, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"definite_pronoun_resolution.py\", line 28, in load_dataset\r\n    return datasets.load_dataset('definite_pronoun_resolution')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/definite_pronoun_resolution\/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff\/definite_pronoun_resolution.py\", line 76, in _split_generators\r\n    files = dl_manager.download_and_extract(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/www.hlt.utdallas.edu\/~vince\/data\/emnlp12\/train.c.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 4.0.1\r\n","comment_length":113,"text":"Downloading URL change for WikiAuto Manual, jeopardy and definite_pronoun_resolution \n ## Describe the bug\r\n- WikiAuto Manual  \r\nThe original manual datasets with the following downloading URL in this [repository](https:\/\/github.com\/chaojiang06\/wiki-auto) was [deleted](https:\/\/github.com\/chaojiang06\/wiki-auto\/commit\/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.  \r\n```\r\nhttps:\/\/github.com\/chaojiang06\/wiki-auto\/raw\/master\/wiki-manual\/train.tsv\r\n```\r\n\r\n- jeopardy  \r\nThe downloading URL for jeopardy may move from \r\n```\r\nhttp:\/\/skeeto.s3.amazonaws.com\/share\/JEOPARDY_QUESTIONS1.json.gz\r\n```\r\n to \r\n```\r\nhttps:\/\/drive.google.com\/file\/d\/0BwT5wj_P7BKXb2hfM3d2RHU1ckE\/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg\r\n```\r\n\r\n- definite_pronoun_resolution\r\nThe following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.\r\n```\r\nhttp:\/\/www.hlt.utdallas.edu\/~vince\/data\/emnlp12\/train.c.txt\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\ndatasets.load_datasets('wiki_auto','manual')\r\ndatasets.load_datasets('jeopardy')\r\ndatasets.load_datasets('definite_pronoun_resolution')\r\n```\r\n\r\n## Expected results\r\nDownload successfully \r\n\r\n## Actual results\r\n- WikiAuto Manual  \r\n```\r\nDownloading and preparing dataset wiki_auto\/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to \/root\/.cache\/huggingface\/datasets\/wiki_auto\/manual\/1.0.0\/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...\r\n  0%|                                                                                                                                                                                                                      | 0\/3 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"wiki_auto.py\", line 43, in <module>\r\n    main()\r\n  File \"wiki_auto.py\", line 40, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 24, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"wiki_auto.py\", line 34, in load_dataset\r\n    return datasets.load_dataset('wiki_auto', 'manual')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_auto\/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8\/wiki_auto.py\", line 193, in _split_generators\r\n    data_dir = dl_manager.download_and_extract(my_urls)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 592, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/github.com\/chaojiang06\/wiki-auto\/raw\/master\/wiki-manual\/train.tsv\r\n```\r\n- jeopardy\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset jeopardy\/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to \/root\/.cache\/huggingface\/datasets\/jeopardy\/default\/0.1.0\/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...\r\nTraceback (most recent call last):\r\n  File \"jeopardy.py\", line 45, in <module>\r\n    main()\r\n  File \"jeopardy.py\", line 42, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"jeopardy.py\", line 36, in load_dataset\r\n    return datasets.load_dataset(\"jeopardy\")\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/jeopardy\/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810\/jeopardy.py\", line 72, in _split_generators\r\n    filepath = dl_manager.download_and_extract(_DATA_URL)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 206, in map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/skeeto.s3.amazonaws.com\/share\/JEOPARDY_QUESTIONS1.json.gz\r\n```\r\n- definite_pronoun_resolution\r\n```\r\nDownloading and preparing dataset definite_pronoun_resolution\/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to \/root\/.cache\/huggingface\/datasets\/definite_pronoun_resolution\/plain_text\/1.0.0\/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...\r\n  0%|                                                                                                                                                                                                                      | 0\/2 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"definite_pronoun_resolution.py\", line 37, in <module>\r\n    main()\r\n  File \"definite_pronoun_resolution.py\", line 34, in main\r\n    train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path=\"..\/data\/\")\r\n  File \"\/workspace\/projects\/CrossFit\/tasks\/fewshot_gym_dataset.py\", line 79, in generate_k_shot_data\r\n    dataset = self.load_dataset()\r\n  File \"definite_pronoun_resolution.py\", line 28, in load_dataset\r\n    return datasets.load_dataset('definite_pronoun_resolution')\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/definite_pronoun_resolution\/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff\/definite_pronoun_resolution.py\", line 76, in _split_generators\r\n    files = dl_manager.download_and_extract(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 196, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    mapped = [\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 217, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 152, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/download_manager.py\", line 217, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 295, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 594, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/www.hlt.utdallas.edu\/~vince\/data\/emnlp12\/train.c.txt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.3\r\n- PyArrow version: 4.0.1\r\n \n > #take I am willing to fix this. Links can be replaced for WikiAuto Manual and jeopardy.\r\n> \r\n> As for the definite_pronoun_resolution URL, a certificate error seems to be preventing a download. I have the files on my local machine. Anyone has opinions on whether it is preferable for me to host them somewhere (e.g. personal GDrive account) or upload them to the dataset folder directly and use github raw URLs? The files are <1MB in size.\r\n\r\nI am planning to fix it next few days. But my to-do list is full and I do not have the cache of definite_pronoun_resolution. I am glad that you can take this. Thanks a lot!","embeddings":[0.1274086535,-0.1285838932,0.0336008258,0.0951998457,0.1693071872,0.0601078197,0.2048468888,0.2117164135,-0.2019647509,0.3956474364,-0.0465765782,0.0693234876,0.1639291495,-0.1324873716,0.0800742432,-0.3333905339,-0.0439455099,-0.129307583,0.189574182,-0.0911257714,-0.3678211868,0.0870707408,-0.361455828,-0.0264681652,-0.0438278653,0.105112344,-0.0500893891,0.0153138693,-0.1008494571,-0.4323172867,0.1448832154,0.114343062,0.1059086695,0.3385549784,-0.0001115378,-0.0094134388,0.2700737119,-0.0819867179,-0.5365573764,-0.1244863048,-0.5981214046,0.0144764883,-0.0473802127,-0.3203182817,-0.0003080522,0.4028949738,0.0933874995,-0.1322327256,0.1947433054,0.6222036481,0.203265518,0.0447213277,0.0913914144,-0.0178734045,0.1893317699,-0.1646449417,-0.1972949207,0.2965210378,-0.140024662,0.1177109256,0.0533954091,0.4021210372,0.1708796322,-0.0917849839,-0.0564775579,0.0954063311,0.1515652835,-0.1696895659,0.4046265483,0.326916486,0.5513573885,-0.096358344,-0.3186918795,-0.0678908452,0.3135268986,-0.1409622878,0.4568139613,0.2807403803,-0.0899092481,0.0021865277,-0.1422172785,-0.2625918984,-0.2591567039,0.1594658494,-0.1103814691,0.691344738,0.0393010527,0.025860969,-0.2234939486,-0.1198866069,0.0794393271,-0.1248971894,0.167090416,0.0989005268,-0.1130947098,-0.0177112762,-0.0376865678,0.0473587252,0.1010986343,-0.3097601533,0.4962767363,-0.0611918606,-0.219254002,-0.1327753514,0.0298338197,0.5464056134,0.0487203598,0.0949229524,0.1561982632,0.7578017116,0.0920691192,0.1227595061,-0.0266494527,-0.2039470673,-0.1632310152,0.1810674667,0.2267388105,-0.1090889946,-0.2119569033,0.1016965881,-0.3092087507,0.1155110672,-0.2900319695,0.1739516407,-0.4681166708,0.3308090866,0.0365073346,0.1312980801,-0.3229940832,0.1867274642,-0.106015265,0.1031576544,-0.0824854672,0.1901243329,0.108665742,0.1292644441,0.3938520253,0.1119662896,0.0305506419,-0.1637239456,-0.3638500869,0.0598980263,-0.1780915409,0.0081053236,-0.0594822429,0.0193167292,-0.1101831496,-0.4052199423,0.067399174,0.2459143251,-0.1449958235,-0.1565332711,0.0782275423,0.2217916697,0.1491992474,-0.3199025393,-0.215295583,0.1093323752,-0.0603173301,-0.1255055964,0.1506298631,-0.1182941794,-0.2259959579,-0.1791525632,0.265207082,0.2022221088,-0.2052334547,-0.0848978311,0.052528128,0.1963839978,0.2605561912,0.2015848756,-0.0450689681,-0.0392939299,-0.0994711444,0.3200992644,-0.0356214195,-0.6613448858,-0.2944655716,-0.0810039416,-0.2047011703,0.2935251296,0.0467961915,-0.3464063108,-0.1910433173,0.0992492288,0.2076385766,0.2389194965,0.1993886977,0.0739801526,-0.4987148345,-0.3489390314,-0.2077021748,-0.1053579524,0.1842177063,-0.0404999293,-0.0500637628,0.0307934321,0.6682904959,-0.022808766,0.1097543463,0.1725937128,0.2791417539,0.0660692304,0.0038900631,0.0336859487,-0.3063521385,0.161487475,-0.244459942,0.3172652721,-0.2633830309,-0.2884165049,-0.4617548585,-0.1467323452,-0.3577142954,-0.3360911608,0.1774288714,0.3326925039,0.2330954075,0.1839559823,0.1679841578,-0.1568388045,-0.2305636257,0.0160496719,-0.2550227344,0.0253910869,-0.2314936966,0.1949636787,-0.0984317288,0.1117226928,0.2032301575,0.0645613,-0.3432500958,0.4482427239,-0.0400165543,0.4258109033,0.2569474876,0.0094796419,0.3121987879,-0.1555703878,0.0253663417,0.2017523348,0.2093171924,0.2155269384,-0.321267873,0.3201157451,-0.2278438061,0.2955271602,0.2124639899,0.0061415709,0.4060687423,-0.1340261996,-0.027041154,-0.1601205021,-0.1623489708,0.5956903696,-0.3482043445,-0.162464276,-0.4324273765,-0.0166552756,0.1807424873,-0.0621191449,0.2663137019,0.2878283858,-0.2269503325,-0.3052477241,0.147170946,0.3281190693,0.1712152064,0.1615499109,0.2940356731,0.2110916674,-0.0548054352,-0.3781977892,0.4756670296,0.2027464956,-0.0630633533,0.3314947486,0.2529734969,0.0753821954,-0.1732913852,0.104192093,-0.1466297507,0.0015546173,-0.2683822811,-0.0435175449,-0.3165047467,0.1406209767,-0.3399684131,-0.0564709939,-0.095371522,-0.4581068754,0.3413339555,0.0953323394,-0.2963668406,0.1810443252,-0.1188158318,0.0406524204,-0.1452873647,-0.0211257767,-0.224706009,-0.4174306989,-0.4545572698,0.1591987014,-0.0793911815,-0.1410232186,-0.0416883491,-0.3193743527,-0.0390764326,-0.3910268247,-0.362236321,-0.2352760136,0.0022659581,0.0839124545,0.0299542658,0.3136538565,-0.238576293,-0.3720279038,0.1207821146,0.0243624654,-0.0321703665,-0.1460863352,0.0056191683,0.3455110192,0.1182345524,-0.6243346334,-0.1572629213,-0.2604809403,0.20511356,0.2315014452,0.0082740346,0.5019302368,0.1165750474,0.0651928112,0.0638767108,0.3580876291,-0.2012389898,-0.2027282417,0.1608871371,-0.2689364552,-0.3275578022,-0.1447928995,-0.1142005175,0.5140980482,0.207713455,-0.4951125085,0.0321055688,-0.1570852846,0.2840752304,0.0344164558,0.0387400761,0.2489308119,0.2695596814,-0.1904114336,-0.0844819099,0.0566938929,-0.0853246227,0.2235332429,0.2495162338,-0.0362066254,0.1373954266,0.270683676,0.9664540887,-0.1156795546,0.307765156,0.3761497438,-0.0739281848,0.0019606599,-0.133990705,-0.0082329195,0.1862277091,-0.1524351686,0.048951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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3261","title":"Scifi_TV_Shows: Having trouble getting viewer to find appropriate files","comments":"Hi ! I think this is because `iter_archive` doesn't support ZIP files yet. See https:\/\/github.com\/huggingface\/datasets\/issues\/3272\r\n\r\nYou can navigate into the archive this way instead:\r\n```python\r\n# in split_generators\r\ndata_dir = dl_manager.download_and_extract(url)\r\ntrain_filepath = os.path.join(data_dir, \"all-sci-fi-data-train.txt\")\r\nreturn [\r\n        datasets.SplitGenerator(\r\n            name=datasets.Split.TRAIN,\r\n            gen_kwargs={\r\n                \"filepath\": train_filepath,\r\n            },\r\n        ),\r\n...\r\n])\r\n\r\n# in generate_examples\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    ...\r\n```","body":"## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus  (Scifi_TV_Shows)*'\r\n\r\n**Link:** [link](https:\/\/huggingface.co\/datasets\/lara-martin\/Scifi_TV_Shows)\r\n\r\nI tried adding both a script (https:\/\/huggingface.co\/datasets\/lara-martin\/Scifi_TV_Shows\/blob\/main\/Scifi_TV_Shows.py) and some dummy examples (https:\/\/huggingface.co\/datasets\/lara-martin\/Scifi_TV_Shows\/tree\/main\/dummy), but the viewer still has a 404 error (\"Not found. Maybe the cache is missing, or maybe the ressource does not exist.\"). I'm not sure what to try next. Thanks in advance!\r\n\r\nAm I the one who added this dataset? Yes\r\n","comment_length":56,"text":"Scifi_TV_Shows: Having trouble getting viewer to find appropriate files \n ## Dataset viewer issue for '*Science Fiction TV Show Plots Corpus  (Scifi_TV_Shows)*'\r\n\r\n**Link:** [link](https:\/\/huggingface.co\/datasets\/lara-martin\/Scifi_TV_Shows)\r\n\r\nI tried adding both a script (https:\/\/huggingface.co\/datasets\/lara-martin\/Scifi_TV_Shows\/blob\/main\/Scifi_TV_Shows.py) and some dummy examples (https:\/\/huggingface.co\/datasets\/lara-martin\/Scifi_TV_Shows\/tree\/main\/dummy), but the viewer still has a 404 error (\"Not found. Maybe the cache is missing, or maybe the ressource does not exist.\"). I'm not sure what to try next. Thanks in advance!\r\n\r\nAm I the one who added this dataset? Yes\r\n \n Hi ! I think this is because `iter_archive` doesn't support ZIP files yet. See https:\/\/github.com\/huggingface\/datasets\/issues\/3272\r\n\r\nYou can navigate into the archive this way instead:\r\n```python\r\n# in split_generators\r\ndata_dir = dl_manager.download_and_extract(url)\r\ntrain_filepath = os.path.join(data_dir, \"all-sci-fi-data-train.txt\")\r\nreturn [\r\n        datasets.SplitGenerator(\r\n            name=datasets.Split.TRAIN,\r\n            gen_kwargs={\r\n                \"filepath\": train_filepath,\r\n            },\r\n        ),\r\n...\r\n])\r\n\r\n# in generate_examples\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    ...\r\n```","embeddings":[-0.2578956485,0.1072367802,0.0246644448,0.3973903656,0.0099124359,0.1714858711,-0.094960615,0.4377739727,0.1972423494,-0.0376985297,-0.243750155,0.0637549758,-0.3711230159,0.2452100515,-0.0400270633,-0.0794994384,0.0521555692,0.2073266208,0.0781618729,0.2033524811,-0.20797427,0.3911236823,-0.0313332528,-0.2373573333,-0.1876861006,-0.0280493535,0.0155400364,0.2262308747,0.0238837115,-0.3913226128,0.2353390753,0.0427778326,0.4197853506,0.7335675955,-0.0001276379,0.0809558854,0.3195190132,-0.0181425717,-0.466324985,-0.1888995916,0.1702178121,0.0487780683,0.0425786711,-0.0538721941,-0.1028237715,0.0525001772,0.2677486539,-0.1538628787,0.0061226953,0.3192885518,0.0828133672,0.2735298276,0.0104637807,-0.2287344933,0.4890917242,0.3731172383,-0.2077362835,0.0871032923,0.1566842645,0.1850289255,-0.0160036534,0.4463412166,-0.0170692764,-0.1017790437,0.3687829673,-0.0672902241,-0.3932147622,-0.4466490149,0.0255655628,0.3387854099,0.5310291052,-0.1485631913,-0.422660172,-0.2255581021,-0.2095415294,-0.1006524935,0.2175366879,0.0890913382,-0.366466701,0.2260035276,0.067697309,-0.3486118019,-0.2834869325,0.2729132175,0.0370484255,0.3228660226,0.0548537299,0.0028791018,0.2106059194,0.0226812717,0.2822701931,-0.0072304974,0.1758869737,-0.0097873909,-0.1510290653,-0.0945622921,-0.1204457805,0.2064314485,-0.125657782,0.2221329212,-0.0332030244,0.0494687594,0.0776137561,0.1084152833,0.2282467186,0.1460652798,-0.1346295625,0.1688771546,0.2716706395,0.4088217914,0.0020297382,-0.1103706583,-0.317283839,-0.249658227,-0.3006452918,-0.3927915394,0.0207530595,-0.1035971865,-0.1257263571,-0.0598447099,-0.2303457558,-0.2292767614,0.2490150481,0.4678494036,-0.1048478559,0.4186840951,0.0057775304,0.1184509695,-0.1403540522,-0.145643279,-0.085643746,0.1607934088,-0.0309084337,0.2858817577,0.392617017,-0.5056767464,0.1785641611,-0.3372624218,0.0751553699,0.030409487,0.2440626621,0.0060965074,0.0162492581,0.3095267117,0.1846514642,0.3829264343,0.2068053037,-0.2023781389,-0.3182447255,0.2405107021,-0.1215669215,-0.4125750065,-0.0523170382,-0.0041410867,-0.4686554968,-0.0510481074,-0.0552973598,-0.1254818738,-0.082793355,-0.4818350673,-0.1165750921,0.0382916927,0.1049360782,-0.1364407688,0.3249078989,0.7214387059,-0.4447275996,0.1429489702,-0.4849502742,0.050002683,0.088602446,0.1371067911,0.0801325813,0.0836389363,-0.5120591521,0.2723554969,0.5785639286,-0.3217739761,-0.2816600204,0.3629772067,-0.076872386,0.4380810857,0.5256972313,0.3733145297,0.0634116754,-0.3842640221,-0.2940008342,-0.1326645762,0.1311861873,-0.1941579282,-0.2203650177,-0.0585282221,0.0960332379,0.1801026165,0.3626593649,0.0302792042,0.126455754,-0.2705586255,0.320987165,0.0356333293,0.2168770283,0.2308591902,0.2646617293,0.5334528685,0.0931185335,-0.120827511,-0.1746967137,0.1955044717,0.0105835153,-0.0557916313,0.0375229791,-0.0863702297,-0.3083826602,-0.0417570435,-0.2223613709,-0.226576075,-0.077438876,0.2192373723,-0.0481105261,-0.0653527156,-0.2463285476,-0.0946356282,0.2513392568,0.4335049391,-0.6260385513,0.565438211,-0.1402245462,-0.1495989114,0.100029856,0.0948540643,0.016932074,-0.1590840816,0.0600765944,0.3398341835,0.0335991979,0.0985726267,0.2573944628,0.3039028645,0.143992722,-0.3934435546,0.0688290447,0.1676243544,0.0541577339,0.0827469677,-0.2595922053,-0.072502397,-0.0516384915,0.2287967652,0.1747172177,0.2206408381,0.1303989291,0.0633636117,-0.062992692,-0.2146698534,0.3059155047,0.0933828875,0.0489954874,0.0552855991,-0.404553771,0.2208796591,0.1372692436,-0.1543724686,0.0057920888,0.4339848459,-0.4639262259,0.017767461,0.2727708519,0.3250296414,0.2613107264,0.1279112101,-0.0060099303,-0.0001881585,0.0813319683,-0.449426204,0.1320924014,0.0515070371,0.0224737879,0.1612873077,-0.175695464,0.075063169,-0.1646340191,-0.224338457,-0.1672371179,0.0204787031,-0.3165845275,-0.0761036873,-0.1802721918,-0.3412854075,-0.3080722392,-0.0824609026,-0.117926307,-0.3886751235,0.1323527396,0.3958556354,-0.1133839265,0.1494421661,-0.0731746182,0.0592283159,-0.0628884137,-0.0890973806,-0.1962451041,-0.2040670365,-0.2020328045,-0.0344628096,0.344250381,-0.0252169091,0.1098708734,-0.3777253032,0.2430653423,-0.5242528915,-0.3552805483,0.1425971985,-0.0017212149,0.3345701993,0.1826107651,0.36375916,-0.1438212842,-0.3509579301,0.1178064197,0.0961274132,0.2360788733,0.0384031385,-0.0696412399,-0.1848304123,0.098109059,-0.3498830199,-0.1904246956,-0.507861197,0.2289803922,0.1920325309,0.1417486966,-0.0592130125,0.2662878931,-0.113559477,-0.0601436868,-0.0795984939,0.0331128873,-0.4984274805,0.9225259423,-0.4772047698,-0.3955017626,0.3990314603,-0.0850762948,0.2232207507,0.0329405926,-0.6755886078,-0.061745178,-0.0890464485,-0.1240516752,0.1886919588,-0.0126877027,0.2287772596,0.0191510618,0.1004242003,0.0250534788,-0.1733041704,0.061840985,-0.3782382309,0.1799627542,0.1180032417,0.1025742069,0.1669879109,0.5997533798,0.3769128323,0.2149371654,0.426528126,-0.0667656437,0.4888866842,0.106267035,-0.2140329033,0.3920517862,-0.1471905857,-0.0915777534,0.39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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3257","title":"Use f-strings for string formatting ","comments":"Hi, I would be glad to help with this. Is there anyone else working on it?","body":"f-strings offer better readability\/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.\r\n\r\n> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets\/*`.","comment_length":16,"text":"Use f-strings for string formatting  \n f-strings offer better readability\/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.\r\n\r\n> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets\/*`. \n Hi, I would be glad to help with this. Is there anyone else working on it?","embeddings":[-0.2523802817,-0.0726951137,-0.2729312479,-0.1937381029,0.3180894256,-0.2330753952,-0.0774312839,0.4338002205,-0.040199127,0.1741473228,-0.0738548487,0.2662825584,-0.1548373401,0.3264699876,-0.1182607561,0.0868056491,0.1632621735,0.3807905316,-0.2161477059,0.1693058312,-0.1103711277,0.0779970139,0.0519919246,0.134897083,-0.2487015277,0.0858768746,0.1192131937,0.1374290735,-0.1395835429,-0.434790194,-0.1817258745,0.3069978654,0.0703026056,0.2222204953,-0.0000949828,0.0271606091,0.2311797887,0.0436979756,-0.3314346373,0.1232458204,-0.2109092772,-0.0344887152,0.1456174403,-0.0889314264,0.0142958779,-0.1392343491,0.0569381788,-0.5099323988,0.1787339747,0.2311833352,0.4003539383,0.0390699878,-0.0411496088,-0.0085345404,0.342530787,0.4041211605,0.0323723331,-0.0085922368,0.3203770816,0.1296603233,-0.1589414924,0.4138066769,-0.1006368026,-0.155190289,0.3147909939,0.0559468456,-0.0841064155,-0.0789852291,0.1109193712,-0.0525074974,0.366766125,-0.3560483754,-0.2523085475,0.0077942624,-0.3357444406,-0.4071685076,0.1172589734,0.1212311834,-0.0416347533,0.3417412043,-0.0652644634,-0.239343524,-0.2610688806,-0.0572884828,0.2017262727,0.260660708,-0.1484228522,-0.1953216791,0.0315601155,-0.1053341776,-0.1411300451,-0.101876013,-0.0661114976,-0.2071034163,0.2120877951,-0.13007164,-0.2775655985,-0.0484674722,0.2807569504,0.0879694372,-0.0832312852,0.3163672984,0.0217471235,-0.077866517,-0.1153597981,-0.051789701,0.4173861444,-0.0149955871,0.6906054616,-0.2916191816,-0.0370156094,-0.1014230773,-0.0344508179,-0.0819745138,-0.130641982,0.048167374,-0.1955225617,-0.3250896037,0.1298604608,-0.0903259069,0.2885595858,-0.1552812457,0.0800227672,0.2853538096,0.1700581312,0.237720117,-0.1029121131,0.0018694079,0.078958258,-0.2446168661,-0.14125745,0.1635717303,-0.3244724274,-0.2156784981,0.0995389372,-0.1933213174,-0.0725328028,0.0278778542,0.015213198,0.1314663291,0.2343837023,0.0066516921,0.1630242914,0.2241340131,-0.2724485695,-0.053895738,-0.2228243351,-0.0302415192,-0.1230868846,0.1548333168,-0.0935558081,-0.1604486406,-0.3816612065,0.3397414684,0.0367383026,-0.1087882668,-0.041967988,0.1809029281,-0.0041748756,-0.2614741325,-0.0202423222,0.1684081107,0.0291900691,-0.2304304242,0.0795516297,0.1248810887,-0.2709435523,0.0624705702,0.3029824495,-0.2159647793,0.3210729063,0.2360389233,-0.0507489145,0.0081343725,-0.0407543182,0.0845086724,0.4870296717,-0.2552320063,0.1137886792,0.454489857,0.0785808191,-0.5273439884,0.2474670857,-0.1118507683,-0.0055812658,-0.122764945,0.0083142538,0.2160338014,0.0163005162,0.2410146743,-0.3616276085,-0.2695988119,-0.0348247513,0.2020904869,0.0282681212,-0.2691055238,0.2428238243,0.007292069,0.2419552505,-0.3977914155,0.046723973,-0.0060117641,0.2769952714,0.2032007575,-0.1717050076,-0.4671597779,-0.2583189309,-0.3357979357,-0.0382921696,-0.1996407807,-0.034207236,-0.1837275624,0.0809224993,-0.1600524485,0.1284912229,0.2244881541,0.2927680314,0.2583976984,0.0515114367,0.0482318699,-0.4219654799,0.2521479428,-0.0330409743,0.1054755151,0.1087584123,0.0594390519,-0.1089082435,-0.1827624887,0.0322765857,0.2674096823,-0.0017352557,-0.0496786125,0.0314774364,0.4911626875,0.1431986392,-0.0489951782,0.2378768176,0.3370943069,0.1942798197,0.0329167433,0.1932029277,0.0149543425,-0.0270448662,0.2025068104,-0.4407249391,0.3134257793,0.0566794537,-0.0144986687,0.2287248671,-0.0862162486,0.0165614542,0.0005646666,-0.1443877518,-0.3633645475,0.2022381276,-0.0165702943,0.0054343645,0.3159427941,-0.50208956,-0.0126333665,0.4482558668,-0.0608075336,0.3093239665,0.244748041,-0.0109685315,-0.195802629,0.0208227951,-0.0678592026,-0.0140534937,0.3974092603,-0.0730312839,-0.1436099857,0.1030321494,-0.1678659022,0.415982157,0.1735012084,-0.1123429015,0.0796339735,0.0042996947,0.1630784273,-0.2459085137,-0.3674094975,-0.3713025749,-0.053289976,-0.2728943229,-0.0523958243,-0.3514192104,-0.1832724214,-0.0892419219,-0.0387918912,-0.0084332172,-0.1786413193,0.095398359,-0.216544807,-0.3625355363,0.2765781879,-0.1983664185,0.3856353462,-0.1718426794,0.1374523938,-0.2796770334,-0.0118132355,-0.1352493763,0.2184859961,0.4806122184,0.1296229959,0.3343781531,-0.149080947,0.1792383194,-0.1480832845,-0.498409152,0.1922451705,-0.0153187644,-0.0083463024,0.3416820765,-0.0738732666,-0.0095987357,-0.3237278759,0.0131801143,-0.2174911499,-0.13684614,0.0699635297,-0.0918374807,0.0036709236,-0.4460922778,-0.1731782407,-0.2620763481,-0.3655594885,0.1358654797,-0.0839881822,0.2033451051,0.2190557569,-0.2323928326,0.0362013876,-0.2326081842,0.19773224,-0.0213630535,-0.3789437413,0.3082562089,-0.2724911869,-0.2259394228,-0.0975699052,0.0620756559,-0.4477419853,0.077005595,0.0325416774,-0.228652373,-0.3055326045,0.0094231237,0.0313180499,0.1510059237,-0.0738126412,0.2571246624,-0.3282734156,0.0424607582,0.0689308494,0.0979539976,-0.2458872944,-0.1237249225,0.1354771256,0.0077509885,0.1225875616,0.0309302267,0.009610381,0.0722230002,-0.0939763859,-0.0320576578,0.3903194964,-0.1885406971,-0.0032373432,0.3276165128,0.0433639735,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3257","title":"Use f-strings for string formatting ","comments":"Hi @Carlosbogo,\r\n\r\nwould you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has opened a PR that does that for all the other directories?","body":"f-strings offer better readability\/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.\r\n\r\n> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets\/*`.","comment_length":36,"text":"Use f-strings for string formatting  \n f-strings offer better readability\/performance than `str.format` and `%`, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.\r\n\r\n> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets\/*`. \n Hi @Carlosbogo,\r\n\r\nwould you be interested in replacing the `.format` and `%` syntax with f-strings in the modules in the `datasets` directory since @Mehdi2402 has opened a PR that does that for all the other directories?","embeddings":[-0.2773170471,-0.0062523698,-0.2638720274,-0.2012524158,0.3526017368,-0.2338514328,-0.0026534074,0.3380846083,-0.1398147494,0.1589684635,-0.0092815133,0.3141450286,-0.1093439758,0.2844198346,-0.1453710049,0.0645591766,0.0962566733,0.3454551697,-0.2286488563,0.1621789336,-0.0647564605,0.0216483399,0.016488988,0.1344257593,-0.3462028503,0.1120242476,0.1308508217,0.1923981756,-0.0933236778,-0.4705908895,-0.1409297585,0.2727006078,0.1589169651,0.2255077809,-0.0000964029,0.0431054458,0.2230277508,0.0064997687,-0.3922266066,0.1654273421,-0.2178720832,-0.1118051708,0.1537132114,-0.110700652,-0.0160886552,-0.1601373702,0.0221894253,-0.5188580751,0.0598465055,0.1956133246,0.3933539391,0.1359616667,-0.0390291028,0.031965401,0.2628338039,0.4316640794,0.0034726034,0.171420157,0.303840071,0.2500908673,-0.0075214985,0.3953907192,-0.1569811851,-0.0825043023,0.4277824759,0.0713804662,-0.034315668,-0.0823003724,0.1197505519,-0.103573285,0.4359422922,-0.3023333848,-0.2618739009,-0.1082615554,-0.3387669623,-0.3906792402,0.1259232163,0.0985449776,0.0273799207,0.3594382703,-0.0776867718,-0.269572854,-0.1576694548,-0.0389501341,0.104817979,0.2621433437,-0.1781180501,-0.1184933558,-0.1107012331,-0.196802333,-0.1003940254,-0.1562153697,-0.1192774698,-0.2484004796,0.2824899256,-0.070169501,-0.2967848778,-0.0178714953,0.1715266407,0.078138493,-0.1455417424,0.3011859655,0.0250594821,-0.0728645325,-0.0917058513,-0.0374990515,0.4903725088,0.1400831491,0.6557164788,-0.2786903083,-0.074092865,-0.0848158523,-0.0106661115,-0.131860137,-0.1168141365,0.0820507482,-0.151268959,-0.2880859375,0.0652328953,-0.0630733967,0.2093476355,-0.2144214511,0.0392836295,0.2863619328,0.2291798592,0.3211303055,-0.0682085752,-0.0825759098,0.0996076539,-0.219079271,-0.1353489459,0.1241706461,-0.3585174382,-0.2125968486,0.0490645356,-0.3000220358,-0.0886074603,0.0267010387,0.0519631542,0.1181792095,0.1938051879,-0.0028180839,0.1527672112,0.2664488852,-0.3139646649,0.0443456434,-0.2262586951,0.0200710502,-0.0269164313,0.249844417,-0.2054644376,-0.1035781205,-0.2537426949,0.3349479735,-0.0598157309,-0.1121722087,-0.0624526292,0.0766742229,0.0292071048,-0.2434670627,0.0739049688,0.0855388567,0.1538301855,-0.2729386389,0.0348980166,0.2200524956,-0.3064562976,0.0600602739,0.3117335439,-0.2062147111,0.3134835958,0.2618447244,-0.0385057405,0.0660821795,-0.0431492329,0.0167031009,0.5649341345,-0.2786110938,0.133518979,0.5079830885,0.1005510315,-0.498282671,0.1840938777,-0.165018931,-0.0026297227,-0.1093123555,0.0998900309,0.1563738137,0.1060720533,0.2727990746,-0.3800598979,-0.2395649701,-0.0147014018,0.2018724978,-0.0185175519,-0.1883575618,0.1622098833,0.0341533571,0.2665067017,-0.3893025517,0.0523173995,-0.0077972752,0.261492312,0.2361306548,-0.200963065,-0.5001106858,-0.2550662756,-0.2584882081,-0.0568840094,-0.2633844614,0.0005156219,-0.1382173747,0.1211203486,-0.2116448581,0.1195984632,0.1687565595,0.2641733885,0.2271766216,0.0209673513,0.0768584535,-0.4421519935,0.2264453769,0.0155638531,0.1343679726,0.151091218,0.1145249829,-0.1076780558,-0.2151968181,-0.0888351649,0.2626713812,0.0449291505,-0.1022863016,-0.015426374,0.5806491971,0.1256977618,-0.0301898904,0.1626925617,0.2532297969,0.1305971742,0.1287718117,0.1698157787,-0.0448721237,-0.0129762553,0.2402293682,-0.3975745738,0.2945055068,0.0873997137,-0.0190996304,0.1848156452,-0.0861101076,-0.046322383,0.0485286079,-0.0793328881,-0.4739487767,0.1322865337,0.0805754364,0.0233358629,0.3324823678,-0.4395854175,-0.0524033271,0.4432688653,-0.068990238,0.2728595138,0.1660463959,-0.0529638231,-0.1304603815,0.0676124692,-0.0293058269,0.1037501469,0.3986209333,-0.0356226675,-0.1042348221,0.1733064502,-0.1819036007,0.4123673439,0.2072544396,-0.1929921061,0.0844465196,0.0582437702,0.1501815468,-0.1483561248,-0.3337896764,-0.3622508347,-0.0641165003,-0.3206168413,-0.0197058544,-0.3620525002,-0.1367677599,-0.141217038,-0.0819029361,-0.0593476929,-0.2100986838,0.0444878787,-0.3224185407,-0.3296025097,0.2354355305,-0.1352745295,0.2653686404,-0.2315732241,0.0919557065,-0.3378700614,0.0629966781,-0.1751649976,0.1965325326,0.441149652,0.1879894584,0.3359060287,-0.1202881709,0.1120058,-0.24917458,-0.3545553088,0.2150991559,-0.0458376668,-0.0219890457,0.4210176766,-0.0598674528,0.1241617203,-0.2448571771,0.1431280673,-0.1505609751,-0.0186475124,0.0030731466,-0.077750884,-0.0142846219,-0.4594922066,-0.1522586346,-0.2369543016,-0.3785195053,0.0921911895,-0.0601499043,0.1050594822,0.1752321124,-0.2197510004,-0.0180319082,-0.2431263328,0.1235660389,-0.0468817428,-0.3524087667,0.2901868522,-0.2394014895,-0.1191399544,-0.0713963583,0.1501865834,-0.4855616093,0.1078350544,0.0681966692,-0.2905304432,-0.2971719205,-0.0600972362,0.0517931618,0.2384483367,-0.1359241605,0.2513189912,-0.3263928294,0.072666727,0.0167836286,0.039526742,-0.1422213614,-0.1281166226,0.1391811371,-0.0336904898,0.0430018082,0.0499735549,0.0926779807,-0.0852156952,-0.0853641555,-0.039473433,0.4183882475,-0.1670799553,-0.0077998256,0.3025262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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3253","title":"`GeneratorBasedBuilder` does not support `None` values","comments":"Hi,\r\n\r\nthanks for reporting and providing a minimal reproducible example. \r\n\r\nThis line of the PR I've linked in our discussion on the Forum will add support for `None` values:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/a53de01842aac65c66a49b2439e18fa93ff73ceb\/src\/datasets\/features\/features.py#L835\r\n\r\nI expect that PR to be merged soon.","body":"## Describe the bug\r\n`GeneratorBasedBuilder` does not support `None` values.\r\n\r\n## Steps to reproduce the bug\r\nSee [this repository](https:\/\/github.com\/pavel-lexyr\/huggingface-datasets-bug-reproduction) for minimal reproduction.\r\n\r\n## Expected results\r\nDataset is initialized with a `None` value in the `value` column.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"main.py\", line 3, in <module>\r\n    datasets.load_dataset(\".\/bad-data\")\r\n  File \"...\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"...\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"...\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"...\/datasets\/builder.py\", line 1103, in _prepare_split\r\n    example = self.info.features.encode_example(record)\r\n  File \"...\/datasets\/features\/features.py\", line 1033, in encode_example\r\n    return encode_nested_example(self, example)\r\n  File \"...\/datasets\/features\/features.py\", line 808, in encode_nested_example\r\n    return {\r\n  File \"...\/datasets\/features\/features.py\", line 809, in <dictcomp>\r\n    k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n  File \"...\/datasets\/features\/features.py\", line 855, in encode_nested_example\r\n    return schema.encode_example(obj)\r\n  File \"...\/datasets\/features\/features.py\", line 299, in encode_example\r\n    return float(value)\r\nTypeError: float() argument must be a string or a number, not 'NoneType'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.0","comment_length":38,"text":"`GeneratorBasedBuilder` does not support `None` values \n ## Describe the bug\r\n`GeneratorBasedBuilder` does not support `None` values.\r\n\r\n## Steps to reproduce the bug\r\nSee [this repository](https:\/\/github.com\/pavel-lexyr\/huggingface-datasets-bug-reproduction) for minimal reproduction.\r\n\r\n## Expected results\r\nDataset is initialized with a `None` value in the `value` column.\r\n\r\n## Actual results\r\n```\r\nTraceback (most recent call last):\r\n  File \"main.py\", line 3, in <module>\r\n    datasets.load_dataset(\".\/bad-data\")\r\n  File \"...\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"...\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"...\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"...\/datasets\/builder.py\", line 1103, in _prepare_split\r\n    example = self.info.features.encode_example(record)\r\n  File \"...\/datasets\/features\/features.py\", line 1033, in encode_example\r\n    return encode_nested_example(self, example)\r\n  File \"...\/datasets\/features\/features.py\", line 808, in encode_nested_example\r\n    return {\r\n  File \"...\/datasets\/features\/features.py\", line 809, in <dictcomp>\r\n    k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n  File \"...\/datasets\/features\/features.py\", line 855, in encode_nested_example\r\n    return schema.encode_example(obj)\r\n  File \"...\/datasets\/features\/features.py\", line 299, in encode_example\r\n    return float(value)\r\nTypeError: float() argument must be a string or a number, not 'NoneType'\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 6.0.0 \n Hi,\r\n\r\nthanks for reporting and providing a minimal reproducible example. \r\n\r\nThis line of the PR I've linked in our discussion on the Forum will add support for `None` values:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/a53de01842aac65c66a49b2439e18fa93ff73ceb\/src\/datasets\/features\/features.py#L835\r\n\r\nI expect that PR to be merged soon.","embeddings":[-0.3755476475,-0.0556601994,0.0175144374,0.3620695472,0.2084513903,-0.0467279814,0.2878646255,0.319686085,-0.0551298521,0.4114519954,-0.0919493362,0.2093869746,-0.0303598754,0.1589512974,-0.0881234556,-0.1058661565,-0.0093902946,0.4736216962,-0.1599651128,-0.1481885761,-0.2816653848,0.3852757514,-0.1525927037,0.0162906293,-0.365691036,-0.0074277781,-0.0209987219,0.2703851163,-0.1603848785,-0.4201865792,0.1774466634,0.165619418,-0.3027564883,0.3090296388,-0.0001119799,0.1557040066,0.4087575972,0.0456008092,-0.3536461294,-0.2457538843,0.0721886978,-0.1757731587,-0.2164087147,-0.2013084888,-0.3425670564,-0.2993764579,-0.2032786459,-0.0639350191,0.3508855402,0.0354738683,0.2294411361,0.3646340072,0.0870417729,-0.1591673344,0.3845463395,0.3267623186,-0.1977265626,-0.0396502428,-0.0824358985,0.0106162531,0.0736943781,0.3534073532,-0.2314940989,-0.1012741625,0.31397295,0.2191484123,0.1395631135,-0.2853599191,0.3522201478,0.3000060618,0.0422232263,-0.1063705161,-0.4223477542,-0.2927532196,0.018792823,-0.4310068488,0.1601584256,0.0639995858,-0.3690937757,0.1437055618,-0.1686571538,-0.11832688,-0.0970865861,-0.095818311,-0.2280778736,0.1279595941,-0.1591736227,-0.0342780724,0.2492367476,0.0524248295,-0.1444491148,-0.2468263954,-0.2079753131,0.1133106351,-0.1860731393,-0.1647473276,0.2346261442,0.247459814,0.3685048521,0.0273490697,-0.0617428236,-0.0931946561,-0.0168579295,0.1942133754,0.0464160889,-0.1052771509,0.3198723793,-0.1273213774,0.3373362124,0.2841594219,0.2452005744,-0.0162832383,0.1121449992,-0.1476888955,0.0471508317,0.0960346162,0.4349986315,-0.0542627387,-0.2283267379,0.1049944907,0.1429228932,0.1430878639,0.0955245048,0.424346745,-0.0563373715,-0.110740073,-0.0271075573,0.1991810203,-0.1170895025,-0.5678461194,-0.4084659815,-0.1203008518,-0.0415419117,-0.0713087171,0.1508459151,-0.4424683452,0.3402926028,0.3902488947,-0.0468724892,0.0752530769,0.0555638634,-0.2253489047,0.1543743312,0.3932977021,-0.1248079911,0.090613097,0.1714506298,-0.2468483895,0.0950002298,0.2942008972,-0.0158199184,-0.2071364671,-0.1477658153,0.1986858994,-0.2223283798,0.3990356624,-0.2986352444,0.2751629949,0.0705725402,-0.093156375,-0.1180658489,0.0505602397,-0.3167052269,-0.2622911036,0.6293163896,0.7424446344,-0.2216489762,-0.2276982665,-0.0847899616,-0.2480284125,0.1308169663,-0.0773386955,0.0004021906,0.0801476613,-0.288395077,0.0804913864,0.250095576,0.0589094795,-0.3071282208,0.2837047577,-0.1025520042,0.0465809926,0.1905659586,-0.1080015972,-0.0924423635,-0.0004113151,0.0871850923,0.0975351036,-0.0293313414,0.1038308814,-0.3773463666,-0.2787609398,0.0872239321,0.0262915324,0.0524037033,0.0965092331,-0.1343646795,-0.1027720049,0.1811463833,-0.230443418,0.0601641126,0.2059480697,0.1822027415,-0.1162640005,0.0575176924,-0.164809823,-0.6513995528,0.3667992949,-0.407523483,0.047634609,-0.2009479105,-0.2698302567,-0.0974812731,0.1023582518,-0.2258042991,-0.2962966561,0.1696782261,0.087904647,0.0374819003,-0.0975193307,-0.3238751888,0.0562144406,0.1103867367,0.2662498951,-0.4766644835,0.3119669855,-0.0222587157,-0.0636572167,-0.0740656778,0.430532068,0.2377886325,-0.0195402969,0.1481744498,0.3820676208,-0.0104705477,0.0823353603,-0.304931432,-0.3026287556,0.1902381927,0.0920059234,0.1444031447,0.0003466127,0.1574743837,0.0395606868,0.1896133274,0.3644085526,-0.0244385228,0.143626973,0.0049168728,-0.1079016775,0.0771589428,-0.0247099623,0.0319671854,-0.3617337346,0.0808835775,0.217824623,0.282589674,0.2107715011,-0.2946120203,0.1965197474,0.2486256063,-0.1464689672,0.0722484142,-0.0655768812,-0.2734433413,-0.1348510087,0.1162293032,0.1953472197,0.3297394812,0.2199229747,-0.0564793199,0.1154173762,0.0587956421,-0.1067689806,0.3183569014,-0.0983769521,0.0008356895,0.4370103478,0.0407288149,-0.095449917,-0.4368795156,0.0067376718,0.0139789069,0.2513408661,-0.2729531229,-0.0329150222,-0.1499967128,0.0271408372,0.0986128971,-0.0159707665,-0.4622295499,-0.2922784686,0.0987339839,0.1764291376,-0.2557471693,0.158558771,0.066698201,0.1675741524,-0.0395483896,0.1131353602,-0.4298528433,0.0131983096,-0.279448837,0.0465926267,-0.0070641744,0.02126332,0.1754879504,-0.172785908,-0.308101505,-0.1617891043,-0.1477693468,0.2050220668,-0.2430808097,0.3718704581,0.5029798746,0.1906328201,0.1493023485,-0.0795187652,0.3690253496,-0.2231031507,-0.2848534882,0.1857447624,0.0545427576,0.2075450122,-0.4394902289,-0.0951007679,-0.0041939537,-0.3338496685,0.1456007957,0.1017209738,0.2750333846,0.4047108889,0.2043797672,0.1245006025,-0.2244343013,0.0804507732,-0.1407114714,-0.3035990298,0.3421367705,-0.1803032607,-0.2238970101,0.0293073077,-0.3016879261,0.2244320959,-0.1195724905,-0.186769858,-0.0532187708,-0.0649959221,0.2919774055,-0.0516755767,0.2561918199,0.0521015748,0.3433226645,-0.1422456354,-0.0862030983,0.0194364656,0.182675764,-0.3563956916,-0.0096685793,0.1934410036,0.3018575013,-0.1547958851,0.4621321261,0.323541522,0.1011503339,0.2983311415,-0.1317043751,0.4643150568,-0.2260095477,-0.3710764647,-0.1370820403,0.4127301276,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3247","title":"Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError","comments":"Hi,\r\n\r\nthis issue is similar to https:\/\/github.com\/huggingface\/datasets\/issues\/3093, so you can either use the solution provided there or try to load the data in one chunk (you can control the chunk size by specifying the `chunksize` parameter (`int`) in `load_dataset`).\r\n\r\n@lhoestq Is this worth opening an issue on Jira? Basically, PyArrow doesn't allow casts that change the order of the struct fields because they treat `pa.struct` as an ordered sequence. Reordering fields manually in Python is probably too slow, so I think this needs to be fixed by them to be usable on our side.","body":"## Describe the bug\r\nWhen trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`\r\n\r\nSplitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.\r\n\r\nCreating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"json\", data_files=\"test.json\")\r\n```\r\n\r\ntest.json ~25MB\r\n```json\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n...\r\n```\r\n\r\nworking.json ~160bytes\r\n```json\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n```\r\n\r\n## Expected results\r\nIt should load the dataset from the json file without error.\r\n\r\n## Actual results\r\nIt raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/Users\/m\/workspace\/xxx\/project\/main.py\", line 60, in <module>\r\n    dataset = load_dataset(\"json\", data_files=\"result.json\")\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1627, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1159, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1685, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/table.pxi\", line 630, in pyarrow.lib._sanitize_arrays\r\n  File \"pyarrow\/array.pxi\", line 338, in pyarrow.lib.asarray\r\n  File \"pyarrow\/table.pxi\", line 304, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/pyarrow\/compute.py\", line 309, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 528, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 327, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 120, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-12.0.1-arm64-arm-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":94,"text":"Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError \n ## Describe the bug\r\nWhen trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`\r\n\r\nSplitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.\r\n\r\nCreating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"json\", data_files=\"test.json\")\r\n```\r\n\r\ntest.json ~25MB\r\n```json\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n...\r\n```\r\n\r\nworking.json ~160bytes\r\n```json\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n```\r\n\r\n## Expected results\r\nIt should load the dataset from the json file without error.\r\n\r\n## Actual results\r\nIt raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/Users\/m\/workspace\/xxx\/project\/main.py\", line 60, in <module>\r\n    dataset = load_dataset(\"json\", data_files=\"result.json\")\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1627, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1159, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1685, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/table.pxi\", line 630, in pyarrow.lib._sanitize_arrays\r\n  File \"pyarrow\/array.pxi\", line 338, in pyarrow.lib.asarray\r\n  File \"pyarrow\/table.pxi\", line 304, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/pyarrow\/compute.py\", line 309, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 528, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 327, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 120, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-12.0.1-arm64-arm-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n\r\n \n Hi,\r\n\r\nthis issue is similar to https:\/\/github.com\/huggingface\/datasets\/issues\/3093, so you can either use the solution provided there or try to load the data in one chunk (you can control the chunk size by specifying the `chunksize` parameter (`int`) in `load_dataset`).\r\n\r\n@lhoestq Is this worth opening an issue on Jira? Basically, PyArrow doesn't allow casts that change the order of the struct fields because they treat `pa.struct` as an ordered sequence. Reordering fields manually in Python is probably too slow, so I think this needs to be fixed by them to be usable on our 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3247","title":"Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError","comments":"I agree I would expect PyArrow to be able to handle this, do you want to open the issue @mariosasko ?\r\nAlthough maybe it's possible to fix struct casting on our side without hurting performance too much, if it's simply a matter of reordering the arrays in the StructArray","body":"## Describe the bug\r\nWhen trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`\r\n\r\nSplitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.\r\n\r\nCreating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"json\", data_files=\"test.json\")\r\n```\r\n\r\ntest.json ~25MB\r\n```json\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n...\r\n```\r\n\r\nworking.json ~160bytes\r\n```json\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n```\r\n\r\n## Expected results\r\nIt should load the dataset from the json file without error.\r\n\r\n## Actual results\r\nIt raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/Users\/m\/workspace\/xxx\/project\/main.py\", line 60, in <module>\r\n    dataset = load_dataset(\"json\", data_files=\"result.json\")\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1627, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1159, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1685, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/table.pxi\", line 630, in pyarrow.lib._sanitize_arrays\r\n  File \"pyarrow\/array.pxi\", line 338, in pyarrow.lib.asarray\r\n  File \"pyarrow\/table.pxi\", line 304, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/pyarrow\/compute.py\", line 309, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 528, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 327, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 120, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-12.0.1-arm64-arm-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":49,"text":"Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError \n ## Describe the bug\r\nWhen trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`\r\n\r\nSplitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.\r\n\r\nCreating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"json\", data_files=\"test.json\")\r\n```\r\n\r\ntest.json ~25MB\r\n```json\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n...\r\n```\r\n\r\nworking.json ~160bytes\r\n```json\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n```\r\n\r\n## Expected results\r\nIt should load the dataset from the json file without error.\r\n\r\n## Actual results\r\nIt raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/Users\/m\/workspace\/xxx\/project\/main.py\", line 60, in <module>\r\n    dataset = load_dataset(\"json\", data_files=\"result.json\")\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1627, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1159, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1685, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/table.pxi\", line 630, in pyarrow.lib._sanitize_arrays\r\n  File \"pyarrow\/array.pxi\", line 338, in pyarrow.lib.asarray\r\n  File \"pyarrow\/table.pxi\", line 304, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/opt\/homebrew\/Caskroom\/miniforge\/base\/envs\/xxx\/lib\/python3.9\/site-packages\/pyarrow\/compute.py\", line 309, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 528, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 327, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 120, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-12.0.1-arm64-arm-64bit\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n\r\n \n I agree I would expect PyArrow to be able to handle this, do you want to open the issue @mariosasko ?\r\nAlthough maybe it's possible to fix struct casting on our side without hurting performance too much, if it's simply a matter of reordering the arrays in the StructArray","embeddings":[-0.2685216069,0.2580779791,0.0205110498,0.422852695,0.4146390855,-0.0655424297,0.1011446416,0.4812645614,-0.1803750098,0.0088070678,0.0041480102,0.5188930631,-0.0924274847,-0.1117402688,-0.1435553133,-0.2028795034,0.0869871378,0.2631748617,0.0699901432,0.1292941123,-0.2368171811,0.0460878126,-0.0061396337,0.071987763,0.0432654619,-0.0293615367,0.0836515725,0.2532157302,-0.3475858271,-0.3850036263,0.0830017924,-0.4241952598,0.1509580612,0.5695892572,-0.0001192965,0.1421800554,0.5012943745,0.0393688753,-0.0830964074,-0.1483767629,-0.0636545047,-0.4736344814,0.3391488194,-0.128860563,0.0356659889,-0.5867011547,-0.1891465783,0.1355916262,0.2464377582,0.2395013869,0.134613052,0.1101537719,0.2780793011,0.164909482,0.3936537802,0.1557918638,-0.1783215404,0.3058731854,0.1480370015,0.2375492305,-0.3036314845,-0.2557924688,-0.0930501744,0.0695973337,0.0903164074,-0.1165802553,-0.1900126338,-0.1901444495,0.0098574106,0.3190166354,0.4243136048,-0.3391803801,-0.3169640303,-0.1863129884,0.0034616545,-0.0595803373,0.1094963923,0.4479415417,-0.1134480536,-0.0377000384,-0.1303182989,0.0987618938,-0.2722642124,0.2214359939,-0.0550525673,-0.2019573301,-0.0511674732,0.127133593,0.2577283382,-0.2757378817,-0.1883998811,-0.1501567364,-0.2618077993,0.0223376304,-0.1313218325,0.0113663208,0.0361468159,-0.2236246467,0.3101901412,0.0913928747,0.0724495947,0.0932882354,0.158274278,0.0947152451,0.3063271046,0.0885875598,-0.5824143887,-0.2200246304,0.2057945579,0.0697413534,-0.1005161852,0.039475359,-0.0648138523,-0.2751741111,0.0000687526,-0.2256437689,0.2499528974,-0.2675617635,-0.1892115325,0.0899534225,-0.5890737772,-0.0335568637,0.0088577308,0.3720727563,-0.0945331976,0.3057661355,-0.1382990927,0.3054494858,-0.142054677,-0.019770436,-0.1285653263,0.101566419,-0.0892658755,-0.1492343694,0.2270131707,-0.0150331352,0.0213337336,0.033289548,0.1203241572,-0.261033088,0.0920688584,-0.2314286232,-0.0581743121,0.3493137956,-0.0013036475,0.1033121049,-0.0734435394,-0.0910251662,-0.2422917038,0.4289920926,-0.233378157,-0.2951591909,-0.2863193452,0.0855252966,-0.5299463272,0.237646848,-0.4791770875,0.0042559882,0.0619006157,-0.0818742067,0.0763116702,-0.2806207538,0.1774022132,-0.4298685789,0.1278256774,0.1132826656,-0.4788976908,0.0926825628,-0.1563921273,-0.1699275821,0.1558201313,0.3822106421,-0.3199703991,0.259344548,-0.1228204668,0.3051092029,0.4267429411,0.0423607938,-0.6494880915,0.4930005372,-0.1894931644,0.2284263968,0.1868215799,0.0262260884,-0.0644258037,0.0438665785,-0.1537663192,0.4341382086,-0.1161027923,0.2806210518,-0.1995604187,-0.4987270236,0.139173016,0.2696709931,0.0380239822,-0.0301611349,0.2997680306,-0.0839595944,0.2386295944,-0.3696405292,0.176329419,0.2626356184,0.204784736,-0.4225416183,0.0546293855,0.0338283665,-0.5099052787,0.1065413058,0.0370161347,0.0688613206,-0.4520092607,0.1035877019,-0.0059705568,0.0643659979,0.0028593468,0.319899261,-0.0224402677,0.1289114207,0.1393742263,-0.0421876498,-0.0068830033,-0.1958381981,-0.0877289996,0.1557195485,-0.1760001034,0.4071504772,0.1976325065,-0.4245958924,0.0399934947,0.0539632142,-0.0806507543,-0.2285001576,-0.1663184315,0.0074162949,0.0412054583,0.0505228639,-0.6076710224,0.0562125668,-0.041743543,-0.2169116139,0.0704313666,0.1755395681,0.1755519211,-0.1702139676,0.2436993122,0.5796764493,-0.0815126449,0.2617235184,0.1455420107,-0.2649807632,0.2262677103,-0.0666742772,0.1393548101,-0.0700979307,0.092659615,0.2939119637,0.1974774599,0.2254297584,-0.3671551049,-0.1319163889,0.4628178179,-0.230550617,0.0238516629,0.3291141689,-0.2515145838,-0.0697555989,0.082508415,-0.0062668878,0.3583765328,0.1827448756,-0.1263873428,-0.0665090904,0.224285692,-0.0881001949,-0.0009289388,0.2303709388,0.4838810861,0.2976937294,0.4089714587,-0.0894894004,-0.2215610296,-0.2169477791,0.1238438562,0.3499284387,-0.3754760325,0.1301850379,-0.1577124894,0.0471372195,-0.1864225864,-0.3461136222,0.0273021907,-0.1249876469,-0.1927533895,0.3371633291,-0.16641137,0.0543793514,-0.3019798398,-0.119935587,-0.0022656778,-0.540653348,-0.0226875097,-0.045886267,-0.1391049027,0.0380125046,0.4958112836,0.046425581,-0.1688036472,0.1020649001,0.1502203494,0.0358201154,-0.2770982683,0.1178665385,-0.1363417804,0.2586500347,-0.0819692984,-0.0651819408,-0.1262767017,-0.3243674636,0.1793093681,0.0636392981,-0.4108586609,0.5533518791,-0.3100537658,0.0883808956,0.0823093876,-0.1188375428,0.0022026142,-0.3583166897,0.5482571125,0.2384316623,0.1114581749,-0.221085906,0.4352264404,0.2901580036,0.1765004843,0.1455474645,0.1661312729,0.0287315417,0.4048545659,-0.0072116754,-0.3473061621,-0.0111625046,-0.2408522815,-0.0148010049,0.1576986313,-0.596051693,0.1868940592,-0.2525784373,0.3005269766,-0.1920442432,-0.2490919679,0.1181921661,0.0705575198,0.0570304878,0.0594881475,-0.0551493727,-0.1332007945,0.1077125296,0.0573402271,-0.1079279706,0.6231406927,-0.0720253736,0.4573734105,0.1389309615,-0.2987989485,0.3068775833,0.0290632099,0.0592466854,-0.1996177584,-0.0690101236,0.1345566213,-0.0236684047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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3240","title":"Couldn't reach data file for disaster_response_messages","comments":"It looks like the dataset isn't available anymore on appen.com\r\n\r\nThe CSV files appear to still be available at https:\/\/www.kaggle.com\/landlord\/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ?","body":"## Describe the bug\r\nFollowing command gives an ConnectionError.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndisaster = load_dataset('disaster_response_messages')\r\n```\r\n\r\n## Error\r\n```\r\nConnectionError: Couldn't reach https:\/\/datasets.appen.com\/appen_datasets\/disaster_response_data\/disaster_response_messages_training.csv\r\n```\r\n## Expected results\r\nIt should load dataset without an error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Google Colab\r\n- Python version: 3.7\r\n- PyArrow version: \r\n","comment_length":42,"text":"Couldn't reach data file for disaster_response_messages \n ## Describe the bug\r\nFollowing command gives an ConnectionError.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndisaster = load_dataset('disaster_response_messages')\r\n```\r\n\r\n## Error\r\n```\r\nConnectionError: Couldn't reach https:\/\/datasets.appen.com\/appen_datasets\/disaster_response_data\/disaster_response_messages_training.csv\r\n```\r\n## Expected results\r\nIt should load dataset without an error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Google Colab\r\n- Python version: 3.7\r\n- PyArrow version: \r\n \n It looks like the dataset isn't available anymore on appen.com\r\n\r\nThe CSV files appear to still be available at https:\/\/www.kaggle.com\/landlord\/multilingual-disaster-response-messages though. It says that the data are under the CC0 license so I guess we can host the dataset elsewhere instead ?","embeddings":[-0.3969840407,0.0855659768,-0.1614023894,0.2481055707,0.2457592189,-0.0362629481,0.3150370717,0.0319769494,-0.1832846105,0.1035934761,-0.0032426342,0.1029786766,-0.003767122,0.0743466243,0.049531389,0.1760583073,-0.023667261,0.0551096275,-0.1005651876,0.2073476762,-0.0311281551,0.1435261071,-0.2613773346,0.1049190164,-0.4657374024,-0.0897200182,-0.2657594979,0.1460027695,-0.2090643197,-0.2735756338,0.4770918489,-0.0291446615,0.4401470423,0.3828821778,-0.0001136343,0.0864352062,0.2913171947,-0.1843004525,-0.5066476464,-0.4366844594,-0.1350796819,-0.5417945385,-0.0692779198,-0.1985249519,-0.1617289186,0.1612301916,0.1290531009,-0.2801404595,0.3676281869,0.6152136922,0.1603479087,-0.0746326298,0.3446936011,0.0257721115,-0.0369327031,-0.1262844652,0.0556785129,0.6446588039,0.350510776,-0.1698980331,0.11622224,0.2092891783,0.0821243152,-0.0421475954,-0.0400285646,0.1540919691,-0.1005355492,-0.2874974608,0.2255030423,0.1140584722,0.7865206599,-0.0920133665,-0.3918149173,-0.1222604886,0.2095555365,-0.144535616,0.4598049223,0.2943405807,-0.2272253782,0.294611156,-0.1799398512,-0.4011435807,-0.3395638764,0.1426121145,-0.2829220295,0.4165712893,-0.2716349959,0.0198649652,-0.0987794027,0.0252311248,0.0796059221,0.2791192234,0.129611969,0.1530275792,-0.2926832736,-0.0458487421,-0.2898765802,-0.1960427612,0.0110718217,-0.2231872529,0.1080094427,-0.1294674426,-0.2137910873,0.1712618172,0.3047849834,-0.013760888,-0.017327074,0.1351263076,0.4712276161,0.2899517119,-0.1058380008,-0.0755115896,-0.2736606896,-0.1352693886,-0.1774819046,0.2709310651,0.1913164854,-0.1279797405,-0.3298487067,0.1545625925,-0.3456397355,0.0073889014,-0.0531443842,0.3513726592,-0.3044222295,0.1348911971,0.1039430946,0.0318186954,0.0209730584,-0.2327300757,0.0203706995,-0.0169589482,-0.2097128481,-0.0610272437,0.1255645156,-0.0504112877,0.3136098981,-0.061348848,0.1693468243,-0.0218066163,0.0498381257,-0.0992942303,-0.2048039734,0.2159474939,-0.0125201894,0.1011493802,0.1282794774,-0.295563817,0.0537690558,0.0304893609,-0.6815571189,-0.4345814288,-0.1859687418,0.161190182,-0.0862061977,0.077183947,-0.2162399441,0.036694821,0.0581787266,-0.2446703911,0.1209076345,-0.085407272,-0.0060464027,-0.3039547205,0.3129096031,0.4996984601,-0.6247629523,0.1270640343,-0.0822661221,0.0940878168,0.0622255616,0.0689083859,-0.2570067346,-0.0045620799,-0.442751646,-0.1511285007,0.1291125119,-0.205365181,-0.6268686652,0.1775317937,-0.3933196366,-0.1377159357,-0.0637383983,-0.1185447052,-0.0299062505,0.0616241768,0.5025733709,0.0845749602,0.0724536628,-0.0913853645,-0.115148142,-0.1563278288,0.010013327,0.0004068212,0.0078599555,-0.19129619,0.1803660095,-0.186917901,0.3292578459,-0.1001000032,0.1806672215,0.2512605786,0.1726934612,0.1122404933,-0.0024886148,-0.0918303952,-0.1936529279,0.2481031418,-0.002301574,0.1520297676,-0.4120642841,-0.0448385365,-0.1322605908,0.1081578061,-0.1654274464,-0.1388155967,0.1474210024,0.0599275008,-0.0271300338,0.3393194675,-0.2179453224,0.6206422448,-0.2224138826,0.0454232134,0.0339102037,0.3739579022,-0.1882545799,0.0009399295,0.0664854124,0.0960180014,0.0577742048,-0.054325588,-0.0524769612,0.1427239478,-0.1477662474,0.3935874999,0.2423828989,-0.0400857925,0.3926169574,-0.20065099,0.2065882683,0.0522902422,0.1102463454,0.1842590719,0.2461006492,0.4160404205,0.2379682511,0.1410599351,0.1834272593,0.3020607829,0.270850718,0.1953156888,-0.0302409232,-0.0420211144,0.3442131877,-0.1360755563,0.2595064044,-0.1702784151,-0.2516419888,0.2048107684,0.2726486623,-0.1345017403,0.1478710473,0.16491431,-0.1811381131,-0.2199193388,0.331487596,0.1230210885,0.5469083786,-0.0323454551,0.0465808399,0.140773192,0.0283672269,-0.1654484719,0.3097757101,-0.0113720847,0.3063210845,0.1316851825,0.2388042808,0.1282463968,-0.2026645988,-0.4531943798,-0.0696528554,0.1392038316,-0.248469919,0.4003358483,-0.3243657947,-0.2086058706,-0.0948542207,-0.2028504312,-0.3212023377,-0.2442270517,-0.0390906855,-0.0417079851,0.092975758,-0.1112232581,-0.0876155272,0.1124147475,0.0152197061,0.0343894884,-0.1330260932,-0.2246639431,-0.2934444249,0.0975280106,0.091102086,-0.3301056623,0.2161059678,-0.2374528497,-0.0797938108,-0.2181642503,-0.290541172,0.2048645318,-0.0575633496,0.2340251058,0.1696407348,0.3792233169,-0.5597911477,-0.039876081,0.2884008586,-0.184771955,-0.1365689486,-0.05607301,-0.1952249706,0.0948193446,0.2103653252,-0.4817616642,-0.8588474393,-0.2892596126,0.0359247737,-0.2112618238,0.1312991977,0.0853810608,0.0323057733,0.2935266495,0.0263442099,0.037600223,-0.1422603875,-0.4129863977,0.1830306798,-0.3079848289,-0.2545878589,0.2075002939,0.1153316125,0.2488138825,0.0800672024,-0.3850058019,-0.2653521299,0.1503932625,0.0396990851,0.1027660668,-0.0878084749,0.1532350332,-0.1515508443,0.0291787051,0.0976520032,-0.0763662606,-0.1703296602,0.3905872107,0.3727982938,0.1578366607,0.2019816488,0.0592675097,0.8012974262,0.2782273591,0.0791759342,0.3385922909,-0.0428922772,0.3107747436,0.0497347042,-0.3727558553,0.2035189569,-0.1720288992,-0.31193110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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3236","title":"Loading of datasets changed in #3110 returns no examples ","comments":"Hi @eladsegal, thanks for reporting.\r\n\r\nI am sorry, but I can't reproduce the bug:\r\n```\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"qasper\")\r\nDownloading: 5.11kB [00:00, ?B\/s]\r\nDownloading and preparing dataset qasper\/qasper (download: 9.88 MiB, generated: 35.11 MiB, post-processed: Unknown size, total: 44.99 MiB) to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8...\r\nDataset qasper downloaded and prepared to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<?, ?it\/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n``` \r\n\r\nThis makes me suspect that the origin of the problem might be the cache: I didn't have this dataset in my cache, although I guess you already had it, before the code change introduced by #3110.\r\n\r\n@lhoestq might it be possible that the code change introduced by #3110 makes \"inaccessible\" all previously cached TAR-based datasets?\r\n- Before the caching system downloaded and extracted the tar dataset\r\n- Now it only downloads the tar dataset (no extraction is done)","body":"## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":173,"text":"Loading of datasets changed in #3110 returns no examples  \n ## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n Hi @eladsegal, thanks for reporting.\r\n\r\nI am sorry, but I can't reproduce the bug:\r\n```\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"qasper\")\r\nDownloading: 5.11kB [00:00, ?B\/s]\r\nDownloading and preparing dataset qasper\/qasper (download: 9.88 MiB, generated: 35.11 MiB, post-processed: Unknown size, total: 44.99 MiB) to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8...\r\nDataset qasper downloaded and prepared to .cache\\qasper\\qasper\\0.1.0\\b99154d2a15aa54bfc669f82b2eda715a2e342e81023d39613b0e2920fdb3ad8. Subsequent calls will reuse this data.\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<?, ?it\/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n``` \r\n\r\nThis makes me suspect that the origin of the problem might be the cache: I didn't have this dataset in my cache, although I guess you already had it, before the code change introduced by #3110.\r\n\r\n@lhoestq might it be possible that the code change introduced by #3110 makes \"inaccessible\" all previously cached TAR-based datasets?\r\n- Before the caching system downloaded and extracted the tar dataset\r\n- Now it only downloads the tar dataset (no extraction is done)","embeddings":[0.048198577,-0.086997591,-0.0242290739,0.2818654776,0.3240283132,-0.0273512322,0.2742412686,0.2374755442,0.1709855795,0.2015547305,-0.0715671331,0.3162328601,-0.0423282161,0.1720468551,0.1071112677,-0.2534903884,0.1101509109,0.1358650625,-0.3096776307,-0.0174040273,-0.0312140584,0.3098744452,-0.2977305949,-0.0926481485,-0.2869768739,0.2120054066,-0.3142177463,0.1809691787,-0.1463450193,-0.3778190315,0.3804140091,0.085701853,0.142021969,0.6262403131,-0.0001108685,0.1710154116,0.1643774956,-0.1009755954,-0.2533084452,-0.4094513357,-0.0717469379,-0.4076807499,0.1379683018,-0.0840175077,-0.3382484913,-0.3250941634,-0.2067220956,-0.1029313877,0.4555373788,0.3552956581,0.2413025498,0.4862979949,0.0441145748,-0.3515444994,0.0134598184,-0.1009903997,-0.0108462907,0.0179695357,0.16588597,0.1214358136,0.0917913541,0.1937301904,0.116671063,-0.1438790709,0.1437003464,-0.1031026468,-0.1645518392,-0.1611695588,0.0325739831,0.3317503333,0.417380631,-0.2056090087,-0.5523958206,-0.3343687952,-0.0657657832,-0.3471269906,0.1069067046,0.1082367301,0.0765277743,0.2580421865,-0.1972239763,0.1033521593,-0.0616276972,0.0491028912,-0.2557568848,0.0730325952,-0.1191484109,-0.1283471733,-0.0014372604,-0.0417609885,-0.104892008,0.0154960593,-0.0036515433,0.1501370221,-0.4318967462,0.0554302186,0.2375551462,0.0125552332,-0.0146855712,0.2383978665,0.2823120058,0.2580576241,-0.101495184,0.0426573679,0.0553129092,0.4307951629,0.0989541337,-0.0112816533,0.3555488884,0.390316695,-0.0860644132,-0.0921302959,-0.0349754281,-0.1879227608,0.1101076379,0.0507209487,0.2734403908,-0.2016668916,-0.2322237045,0.2300951928,-0.237139523,-0.075645335,0.0285553057,0.5070336461,-0.0779915079,0.0867880434,0.0345647335,0.1759792566,-0.1502693743,-0.3006089926,-0.4464168251,-0.0316895582,-0.3254978955,-0.0998986512,0.2723429501,-0.1960319728,0.4132769406,0.0805031285,-0.1448232681,-0.065148145,0.0624425486,0.1992406845,0.1993405223,0.2849488258,-0.0595135838,-0.010659649,0.2917140424,-0.0583441183,0.0071224286,0.0480793193,-0.2429093719,-0.2783383727,-0.1120141596,0.2665142119,-0.46140185,-0.0228120293,-0.0224667434,0.1883602142,0.0015456185,-0.0524675138,0.0370660163,-0.1295340359,-0.0360488184,-0.0366940498,0.4575083852,0.4533716142,-0.1228636354,-0.3646816611,0.2155804187,-0.0941884518,0.0080199577,0.0213705692,-0.0062804492,0.0874585435,-0.2322475314,-0.1220047846,0.2858053744,-0.2465898395,-0.4048399627,-0.0766043141,-0.0211875122,0.4242127836,0.2083310336,-0.252592802,0.0246394183,0.0277146883,0.127171725,-0.0023792374,0.2605972886,0.0864496827,-0.1792174131,-0.1496765465,0.0313811228,-0.0087175984,0.0463235825,0.2363815457,-0.0460510887,-0.1425695419,0.3198085725,-0.2629787028,-0.1114745289,0.2994379103,0.3906707764,0.0334933326,0.2075452358,-0.2101996988,-0.5983006358,0.4261895716,0.0083908746,0.1736962497,0.106466569,-0.1905425638,-0.3390614688,-0.0319938213,-0.2173839808,-0.4623180032,0.1443421841,0.1712841243,0.3113585711,0.1133099794,-0.2209654897,0.4782707989,-0.1391460001,0.2466123849,-0.4244332314,0.3553302288,0.0494857654,-0.1585938931,0.0004654837,0.3790495396,0.0895046145,-0.2791749835,-0.0319348909,0.4426724911,0.0848303661,0.1269914657,-0.1665710956,0.034147013,0.3217851818,0.1063585281,-0.0502242409,0.189823553,-0.0628749505,0.1435203552,-0.3038612306,0.4575380683,0.0085147955,0.1595537513,0.2568788826,-0.0430398583,0.1683444381,-0.1168816239,-0.347930342,-0.2479158044,0.2199457735,-0.2961525917,0.2113555521,0.1242557913,-0.4070572257,0.0541583113,0.3790639341,0.0197461639,0.0462705381,-0.0233377554,-0.2102935314,0.0364365652,0.3277456164,0.131628722,0.2561745048,0.275821656,-0.0519169867,0.1211834699,-0.054418195,-0.201366365,0.1555202454,-0.0264057089,-0.1224704683,0.2315487415,0.1616037339,0.093663767,-0.434733361,0.0915910378,-0.0664715022,0.1618293524,-0.4015597105,-0.0091291834,-0.3828177452,-0.0557134636,-0.1776841134,-0.2573938966,0.0134491287,-0.327496767,0.0290937871,0.1418606937,-0.1621785015,0.2784962654,-0.0668853819,0.0994898677,-0.0259306077,0.1342011988,-0.2214832902,-0.0414212942,-0.2053791881,0.0815152079,-0.0784958005,-0.0192770455,0.1802254319,-0.321257025,-0.0408071466,-0.2744917274,-0.5607581139,0.0327716433,-0.0596641563,0.3699053228,0.1861217767,0.0378416777,-0.0787617713,-0.028003674,0.3117524087,-0.0995522514,-0.3088869452,0.1620036811,0.0437144563,0.0506080873,-0.3222824335,-0.6019359231,-0.0519946478,-0.3921587169,0.2188689262,-0.0151086105,-0.0129247503,0.3306975067,0.1765703112,0.2407143861,-0.0358890295,0.1884777844,-0.1856399924,-0.2366809547,0.2083814889,-0.322465241,-0.517580092,-0.0995494649,-0.0682133213,0.1749484837,-0.0706238896,-0.4018831253,-0.4492530525,-0.2838637829,0.2869089842,0.0883646384,0.196379602,0.3195345998,-0.0170480739,-0.082762666,-0.2365194708,-0.2441498786,0.0866854787,-0.0396270901,-0.0130333714,-0.138891384,0.3085326254,-0.0408935174,0.5791708231,0.249665007,-0.0168729816,0.5041943789,-0.1128073931,0.4734928608,-0.2517380416,-0.3542965651,0.0175450891,-0.07911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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3236","title":"Loading of datasets changed in #3110 returns no examples ","comments":"I can't reproduce either in my environment (macos, python 3.7).\r\n\r\nIn your case it generates zero examples. This can only happen if the extraction of the TAR archive doesn't output the right filenames. Indeed if the `qasper` script can't find the right file to load, it's currently ignored and it returns zero examples. This case was not even considered when #3110 was developed since we considered the file names to be deterministic - and not depend on your environment.\r\n\r\nTherefore here is my hypothesis:\r\n- either the cache is corrupted somehow with an empty TAR archive\r\n- OR I suspect that the issue comes from python 3.8\r\n","body":"## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":107,"text":"Loading of datasets changed in #3110 returns no examples  \n ## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n I can't reproduce either in my environment (macos, python 3.7).\r\n\r\nIn your case it generates zero examples. This can only happen if the extraction of the TAR archive doesn't output the right filenames. Indeed if the `qasper` script can't find the right file to load, it's currently ignored and it returns zero examples. This case was not even considered when #3110 was developed since we considered the file names to be deterministic - and not depend on your environment.\r\n\r\nTherefore here is my hypothesis:\r\n- either the cache is corrupted somehow with an empty TAR archive\r\n- OR I suspect that the issue comes from python 3.8\r\n","embeddings":[0.048198577,-0.086997591,-0.0242290739,0.2818654776,0.3240283132,-0.0273512322,0.2742412686,0.2374755442,0.1709855795,0.2015547305,-0.0715671331,0.3162328601,-0.0423282161,0.1720468551,0.1071112677,-0.2534903884,0.1101509109,0.1358650625,-0.3096776307,-0.0174040273,-0.0312140584,0.3098744452,-0.2977305949,-0.0926481485,-0.2869768739,0.2120054066,-0.3142177463,0.1809691787,-0.1463450193,-0.3778190315,0.3804140091,0.085701853,0.142021969,0.6262403131,-0.0001108685,0.1710154116,0.1643774956,-0.1009755954,-0.2533084452,-0.4094513357,-0.0717469379,-0.4076807499,0.1379683018,-0.0840175077,-0.3382484913,-0.3250941634,-0.2067220956,-0.1029313877,0.4555373788,0.3552956581,0.2413025498,0.4862979949,0.0441145748,-0.3515444994,0.0134598184,-0.1009903997,-0.0108462907,0.0179695357,0.16588597,0.1214358136,0.0917913541,0.1937301904,0.116671063,-0.1438790709,0.1437003464,-0.1031026468,-0.1645518392,-0.1611695588,0.0325739831,0.3317503333,0.417380631,-0.2056090087,-0.5523958206,-0.3343687952,-0.0657657832,-0.3471269906,0.1069067046,0.1082367301,0.0765277743,0.2580421865,-0.1972239763,0.1033521593,-0.0616276972,0.0491028912,-0.2557568848,0.0730325952,-0.1191484109,-0.1283471733,-0.0014372604,-0.0417609885,-0.104892008,0.0154960593,-0.0036515433,0.1501370221,-0.4318967462,0.0554302186,0.2375551462,0.0125552332,-0.0146855712,0.2383978665,0.2823120058,0.2580576241,-0.101495184,0.0426573679,0.0553129092,0.4307951629,0.0989541337,-0.0112816533,0.3555488884,0.390316695,-0.0860644132,-0.0921302959,-0.0349754281,-0.1879227608,0.1101076379,0.0507209487,0.2734403908,-0.2016668916,-0.2322237045,0.2300951928,-0.237139523,-0.075645335,0.0285553057,0.5070336461,-0.0779915079,0.0867880434,0.0345647335,0.1759792566,-0.1502693743,-0.3006089926,-0.4464168251,-0.0316895582,-0.3254978955,-0.0998986512,0.2723429501,-0.1960319728,0.4132769406,0.0805031285,-0.1448232681,-0.065148145,0.0624425486,0.1992406845,0.1993405223,0.2849488258,-0.0595135838,-0.010659649,0.2917140424,-0.0583441183,0.0071224286,0.0480793193,-0.2429093719,-0.2783383727,-0.1120141596,0.2665142119,-0.46140185,-0.0228120293,-0.0224667434,0.1883602142,0.0015456185,-0.0524675138,0.0370660163,-0.1295340359,-0.0360488184,-0.0366940498,0.4575083852,0.4533716142,-0.1228636354,-0.3646816611,0.2155804187,-0.0941884518,0.0080199577,0.0213705692,-0.0062804492,0.0874585435,-0.2322475314,-0.1220047846,0.2858053744,-0.2465898395,-0.4048399627,-0.0766043141,-0.0211875122,0.4242127836,0.2083310336,-0.252592802,0.0246394183,0.0277146883,0.127171725,-0.0023792374,0.2605972886,0.0864496827,-0.1792174131,-0.1496765465,0.0313811228,-0.0087175984,0.0463235825,0.2363815457,-0.0460510887,-0.1425695419,0.3198085725,-0.2629787028,-0.1114745289,0.2994379103,0.3906707764,0.0334933326,0.2075452358,-0.2101996988,-0.5983006358,0.4261895716,0.0083908746,0.1736962497,0.106466569,-0.1905425638,-0.3390614688,-0.0319938213,-0.2173839808,-0.4623180032,0.1443421841,0.1712841243,0.3113585711,0.1133099794,-0.2209654897,0.4782707989,-0.1391460001,0.2466123849,-0.4244332314,0.3553302288,0.0494857654,-0.1585938931,0.0004654837,0.3790495396,0.0895046145,-0.2791749835,-0.0319348909,0.4426724911,0.0848303661,0.1269914657,-0.1665710956,0.034147013,0.3217851818,0.1063585281,-0.0502242409,0.189823553,-0.0628749505,0.1435203552,-0.3038612306,0.4575380683,0.0085147955,0.1595537513,0.2568788826,-0.0430398583,0.1683444381,-0.1168816239,-0.347930342,-0.2479158044,0.2199457735,-0.2961525917,0.2113555521,0.1242557913,-0.4070572257,0.0541583113,0.3790639341,0.0197461639,0.0462705381,-0.0233377554,-0.2102935314,0.0364365652,0.3277456164,0.131628722,0.2561745048,0.275821656,-0.0519169867,0.1211834699,-0.054418195,-0.201366365,0.1555202454,-0.0264057089,-0.1224704683,0.2315487415,0.1616037339,0.093663767,-0.434733361,0.0915910378,-0.0664715022,0.1618293524,-0.4015597105,-0.0091291834,-0.3828177452,-0.0557134636,-0.1776841134,-0.2573938966,0.0134491287,-0.327496767,0.0290937871,0.1418606937,-0.1621785015,0.2784962654,-0.0668853819,0.0994898677,-0.0259306077,0.1342011988,-0.2214832902,-0.0414212942,-0.2053791881,0.0815152079,-0.0784958005,-0.0192770455,0.1802254319,-0.321257025,-0.0408071466,-0.2744917274,-0.5607581139,0.0327716433,-0.0596641563,0.3699053228,0.1861217767,0.0378416777,-0.0787617713,-0.028003674,0.3117524087,-0.0995522514,-0.3088869452,0.1620036811,0.0437144563,0.0506080873,-0.3222824335,-0.6019359231,-0.0519946478,-0.3921587169,0.2188689262,-0.0151086105,-0.0129247503,0.3306975067,0.1765703112,0.2407143861,-0.0358890295,0.1884777844,-0.1856399924,-0.2366809547,0.2083814889,-0.322465241,-0.517580092,-0.0995494649,-0.0682133213,0.1749484837,-0.0706238896,-0.4018831253,-0.4492530525,-0.2838637829,0.2869089842,0.0883646384,0.196379602,0.3195345998,-0.0170480739,-0.082762666,-0.2365194708,-0.2441498786,0.0866854787,-0.0396270901,-0.0130333714,-0.138891384,0.3085326254,-0.0408935174,0.5791708231,0.249665007,-0.0168729816,0.5041943789,-0.1128073931,0.4734928608,-0.2517380416,-0.3542965651,0.0175450891,-0.079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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3236","title":"Loading of datasets changed in #3110 returns no examples ","comments":"I just tried again on python 3.8 and I was able to reproduce the issue. Let me work on a fix","body":"## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":21,"text":"Loading of datasets changed in #3110 returns no examples  \n ## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n I just tried again on python 3.8 and I was able to reproduce the issue. Let me work on a fix","embeddings":[0.048198577,-0.086997591,-0.0242290739,0.2818654776,0.3240283132,-0.0273512322,0.2742412686,0.2374755442,0.1709855795,0.2015547305,-0.0715671331,0.3162328601,-0.0423282161,0.1720468551,0.1071112677,-0.2534903884,0.1101509109,0.1358650625,-0.3096776307,-0.0174040273,-0.0312140584,0.3098744452,-0.2977305949,-0.0926481485,-0.2869768739,0.2120054066,-0.3142177463,0.1809691787,-0.1463450193,-0.3778190315,0.3804140091,0.085701853,0.142021969,0.6262403131,-0.0001108685,0.1710154116,0.1643774956,-0.1009755954,-0.2533084452,-0.4094513357,-0.0717469379,-0.4076807499,0.1379683018,-0.0840175077,-0.3382484913,-0.3250941634,-0.2067220956,-0.1029313877,0.4555373788,0.3552956581,0.2413025498,0.4862979949,0.0441145748,-0.3515444994,0.0134598184,-0.1009903997,-0.0108462907,0.0179695357,0.16588597,0.1214358136,0.0917913541,0.1937301904,0.116671063,-0.1438790709,0.1437003464,-0.1031026468,-0.1645518392,-0.1611695588,0.0325739831,0.3317503333,0.417380631,-0.2056090087,-0.5523958206,-0.3343687952,-0.0657657832,-0.3471269906,0.1069067046,0.1082367301,0.0765277743,0.2580421865,-0.1972239763,0.1033521593,-0.0616276972,0.0491028912,-0.2557568848,0.0730325952,-0.1191484109,-0.1283471733,-0.0014372604,-0.0417609885,-0.104892008,0.0154960593,-0.0036515433,0.1501370221,-0.4318967462,0.0554302186,0.2375551462,0.0125552332,-0.0146855712,0.2383978665,0.2823120058,0.2580576241,-0.101495184,0.0426573679,0.0553129092,0.4307951629,0.0989541337,-0.0112816533,0.3555488884,0.390316695,-0.0860644132,-0.0921302959,-0.0349754281,-0.1879227608,0.1101076379,0.0507209487,0.2734403908,-0.2016668916,-0.2322237045,0.2300951928,-0.237139523,-0.075645335,0.0285553057,0.5070336461,-0.0779915079,0.0867880434,0.0345647335,0.1759792566,-0.1502693743,-0.3006089926,-0.4464168251,-0.0316895582,-0.3254978955,-0.0998986512,0.2723429501,-0.1960319728,0.4132769406,0.0805031285,-0.1448232681,-0.065148145,0.0624425486,0.1992406845,0.1993405223,0.2849488258,-0.0595135838,-0.010659649,0.2917140424,-0.0583441183,0.0071224286,0.0480793193,-0.2429093719,-0.2783383727,-0.1120141596,0.2665142119,-0.46140185,-0.0228120293,-0.0224667434,0.1883602142,0.0015456185,-0.0524675138,0.0370660163,-0.1295340359,-0.0360488184,-0.0366940498,0.4575083852,0.4533716142,-0.1228636354,-0.3646816611,0.2155804187,-0.0941884518,0.0080199577,0.0213705692,-0.0062804492,0.0874585435,-0.2322475314,-0.1220047846,0.2858053744,-0.2465898395,-0.4048399627,-0.0766043141,-0.0211875122,0.4242127836,0.2083310336,-0.252592802,0.0246394183,0.0277146883,0.127171725,-0.0023792374,0.2605972886,0.0864496827,-0.1792174131,-0.1496765465,0.0313811228,-0.0087175984,0.0463235825,0.2363815457,-0.0460510887,-0.1425695419,0.3198085725,-0.2629787028,-0.1114745289,0.2994379103,0.3906707764,0.0334933326,0.2075452358,-0.2101996988,-0.5983006358,0.4261895716,0.0083908746,0.1736962497,0.106466569,-0.1905425638,-0.3390614688,-0.0319938213,-0.2173839808,-0.4623180032,0.1443421841,0.1712841243,0.3113585711,0.1133099794,-0.2209654897,0.4782707989,-0.1391460001,0.2466123849,-0.4244332314,0.3553302288,0.0494857654,-0.1585938931,0.0004654837,0.3790495396,0.0895046145,-0.2791749835,-0.0319348909,0.4426724911,0.0848303661,0.1269914657,-0.1665710956,0.034147013,0.3217851818,0.1063585281,-0.0502242409,0.189823553,-0.0628749505,0.1435203552,-0.3038612306,0.4575380683,0.0085147955,0.1595537513,0.2568788826,-0.0430398583,0.1683444381,-0.1168816239,-0.347930342,-0.2479158044,0.2199457735,-0.2961525917,0.2113555521,0.1242557913,-0.4070572257,0.0541583113,0.3790639341,0.0197461639,0.0462705381,-0.0233377554,-0.2102935314,0.0364365652,0.3277456164,0.131628722,0.2561745048,0.275821656,-0.0519169867,0.1211834699,-0.054418195,-0.201366365,0.1555202454,-0.0264057089,-0.1224704683,0.2315487415,0.1616037339,0.093663767,-0.434733361,0.0915910378,-0.0664715022,0.1618293524,-0.4015597105,-0.0091291834,-0.3828177452,-0.0557134636,-0.1776841134,-0.2573938966,0.0134491287,-0.327496767,0.0290937871,0.1418606937,-0.1621785015,0.2784962654,-0.0668853819,0.0994898677,-0.0259306077,0.1342011988,-0.2214832902,-0.0414212942,-0.2053791881,0.0815152079,-0.0784958005,-0.0192770455,0.1802254319,-0.321257025,-0.0408071466,-0.2744917274,-0.5607581139,0.0327716433,-0.0596641563,0.3699053228,0.1861217767,0.0378416777,-0.0787617713,-0.028003674,0.3117524087,-0.0995522514,-0.3088869452,0.1620036811,0.0437144563,0.0506080873,-0.3222824335,-0.6019359231,-0.0519946478,-0.3921587169,0.2188689262,-0.0151086105,-0.0129247503,0.3306975067,0.1765703112,0.2407143861,-0.0358890295,0.1884777844,-0.1856399924,-0.2366809547,0.2083814889,-0.322465241,-0.517580092,-0.0995494649,-0.0682133213,0.1749484837,-0.0706238896,-0.4018831253,-0.4492530525,-0.2838637829,0.2869089842,0.0883646384,0.196379602,0.3195345998,-0.0170480739,-0.082762666,-0.2365194708,-0.2441498786,0.0866854787,-0.0396270901,-0.0130333714,-0.138891384,0.3085326254,-0.0408935174,0.5791708231,0.249665007,-0.0168729816,0.5041943789,-0.1128073931,0.4734928608,-0.2517380416,-0.3542965651,0.0175450891,-0.0791126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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3236","title":"Loading of datasets changed in #3110 returns no examples ","comments":"Ok I found the issue. It's not related to python 3.8 in itself though. This issue happens because your local installation of `datasets` is outdated compared to the changes to datasets in #3110\r\n\r\nTo fix this you just have to pull the latest changes from `master` :)\r\n\r\nLet me know if that helps !\r\n\r\n--------------\r\n\r\nHere are more details about my investigation:\r\n\r\nIt's possible to reproduce this issue if you use `datasets<=1.15.1` or before b6469baa22c174b3906c631802a7016fedea6780 and if you load the dataset after revision b6469baa22c174b3906c631802a7016fedea6780. This is because `dl_manager.iter_archive` had issues at that time (and it was not used anywhere anyway).\r\n\r\nIn particular it was returning the absolute path to extracted files instead of the relative path of the file inside the archive. This was an issue because `dl_manager.iter_archive` isn't supposed to extract the TAR archive. Instead, it iterates over all the files inside the archive, without creating a directory with the extracted content.\r\n\r\nTherefore if you want to use the datasets on `master`, make sure that you have an up-to-date local installation of `datasets` as well, or you may face incompatibilities like this.","body":"## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":183,"text":"Loading of datasets changed in #3110 returns no examples  \n ## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n Ok I found the issue. It's not related to python 3.8 in itself though. This issue happens because your local installation of `datasets` is outdated compared to the changes to datasets in #3110\r\n\r\nTo fix this you just have to pull the latest changes from `master` :)\r\n\r\nLet me know if that helps !\r\n\r\n--------------\r\n\r\nHere are more details about my investigation:\r\n\r\nIt's possible to reproduce this issue if you use `datasets<=1.15.1` or before b6469baa22c174b3906c631802a7016fedea6780 and if you load the dataset after revision b6469baa22c174b3906c631802a7016fedea6780. This is because `dl_manager.iter_archive` had issues at that time (and it was not used anywhere anyway).\r\n\r\nIn particular it was returning the absolute path to extracted files instead of the relative path of the file inside the archive. This was an issue because `dl_manager.iter_archive` isn't supposed to extract the TAR archive. Instead, it iterates over all the files inside the archive, without creating a directory with the extracted content.\r\n\r\nTherefore if you want to use the datasets on `master`, make sure that you have an up-to-date local installation of `datasets` as well, or you may face incompatibilities like 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3236","title":"Loading of datasets changed in #3110 returns no examples ","comments":"Thanks!\r\nBut what about code that is already using older version of datasets? \r\nThe reason I encountered this issue was that suddenly one of my repos with version 1.12.1 started getting 0 examples.\r\nI handled it by adding `revision` to `load_dataset`, but I guess it would still be an issue for other users who doesn't know this.","body":"## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":57,"text":"Loading of datasets changed in #3110 returns no examples  \n ## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n Thanks!\r\nBut what about code that is already using older version of datasets? \r\nThe reason I encountered this issue was that suddenly one of my repos with version 1.12.1 started getting 0 examples.\r\nI handled it by adding `revision` to `load_dataset`, but I guess it would still be an issue for other users who doesn't know this.","embeddings":[0.048198577,-0.086997591,-0.0242290739,0.2818654776,0.3240283132,-0.0273512322,0.2742412686,0.2374755442,0.1709855795,0.2015547305,-0.0715671331,0.3162328601,-0.0423282161,0.1720468551,0.1071112677,-0.2534903884,0.1101509109,0.1358650625,-0.3096776307,-0.0174040273,-0.0312140584,0.3098744452,-0.2977305949,-0.0926481485,-0.2869768739,0.2120054066,-0.3142177463,0.1809691787,-0.1463450193,-0.3778190315,0.3804140091,0.085701853,0.142021969,0.6262403131,-0.0001108685,0.1710154116,0.1643774956,-0.1009755954,-0.2533084452,-0.4094513357,-0.0717469379,-0.4076807499,0.1379683018,-0.0840175077,-0.3382484913,-0.3250941634,-0.2067220956,-0.1029313877,0.4555373788,0.3552956581,0.2413025498,0.4862979949,0.0441145748,-0.3515444994,0.0134598184,-0.1009903997,-0.0108462907,0.0179695357,0.16588597,0.1214358136,0.0917913541,0.1937301904,0.116671063,-0.1438790709,0.1437003464,-0.1031026468,-0.1645518392,-0.1611695588,0.0325739831,0.3317503333,0.417380631,-0.2056090087,-0.5523958206,-0.3343687952,-0.0657657832,-0.3471269906,0.1069067046,0.1082367301,0.0765277743,0.2580421865,-0.1972239763,0.1033521593,-0.0616276972,0.0491028912,-0.2557568848,0.0730325952,-0.1191484109,-0.1283471733,-0.0014372604,-0.0417609885,-0.104892008,0.0154960593,-0.0036515433,0.1501370221,-0.4318967462,0.0554302186,0.2375551462,0.0125552332,-0.0146855712,0.2383978665,0.2823120058,0.2580576241,-0.101495184,0.0426573679,0.0553129092,0.4307951629,0.0989541337,-0.0112816533,0.3555488884,0.390316695,-0.0860644132,-0.0921302959,-0.0349754281,-0.1879227608,0.1101076379,0.0507209487,0.2734403908,-0.2016668916,-0.2322237045,0.2300951928,-0.237139523,-0.075645335,0.0285553057,0.5070336461,-0.0779915079,0.0867880434,0.0345647335,0.1759792566,-0.1502693743,-0.3006089926,-0.4464168251,-0.0316895582,-0.3254978955,-0.0998986512,0.2723429501,-0.1960319728,0.4132769406,0.0805031285,-0.1448232681,-0.065148145,0.0624425486,0.1992406845,0.1993405223,0.2849488258,-0.0595135838,-0.010659649,0.2917140424,-0.0583441183,0.0071224286,0.0480793193,-0.2429093719,-0.2783383727,-0.1120141596,0.2665142119,-0.46140185,-0.0228120293,-0.0224667434,0.1883602142,0.0015456185,-0.0524675138,0.0370660163,-0.1295340359,-0.0360488184,-0.0366940498,0.4575083852,0.4533716142,-0.1228636354,-0.3646816611,0.2155804187,-0.0941884518,0.0080199577,0.0213705692,-0.0062804492,0.0874585435,-0.2322475314,-0.1220047846,0.2858053744,-0.2465898395,-0.4048399627,-0.0766043141,-0.0211875122,0.4242127836,0.2083310336,-0.252592802,0.0246394183,0.0277146883,0.127171725,-0.0023792374,0.2605972886,0.0864496827,-0.1792174131,-0.1496765465,0.0313811228,-0.0087175984,0.0463235825,0.2363815457,-0.0460510887,-0.1425695419,0.3198085725,-0.2629787028,-0.1114745289,0.2994379103,0.3906707764,0.0334933326,0.2075452358,-0.2101996988,-0.5983006358,0.4261895716,0.0083908746,0.1736962497,0.106466569,-0.1905425638,-0.3390614688,-0.0319938213,-0.2173839808,-0.4623180032,0.1443421841,0.1712841243,0.3113585711,0.1133099794,-0.2209654897,0.4782707989,-0.1391460001,0.2466123849,-0.4244332314,0.3553302288,0.0494857654,-0.1585938931,0.0004654837,0.3790495396,0.0895046145,-0.2791749835,-0.0319348909,0.4426724911,0.0848303661,0.1269914657,-0.1665710956,0.034147013,0.3217851818,0.1063585281,-0.0502242409,0.189823553,-0.0628749505,0.1435203552,-0.3038612306,0.4575380683,0.0085147955,0.1595537513,0.2568788826,-0.0430398583,0.1683444381,-0.1168816239,-0.347930342,-0.2479158044,0.2199457735,-0.2961525917,0.2113555521,0.1242557913,-0.4070572257,0.0541583113,0.3790639341,0.0197461639,0.0462705381,-0.0233377554,-0.2102935314,0.0364365652,0.3277456164,0.131628722,0.2561745048,0.275821656,-0.0519169867,0.1211834699,-0.054418195,-0.201366365,0.1555202454,-0.0264057089,-0.1224704683,0.2315487415,0.1616037339,0.093663767,-0.434733361,0.0915910378,-0.0664715022,0.1618293524,-0.4015597105,-0.0091291834,-0.3828177452,-0.0557134636,-0.1776841134,-0.2573938966,0.0134491287,-0.327496767,0.0290937871,0.1418606937,-0.1621785015,0.2784962654,-0.0668853819,0.0994898677,-0.0259306077,0.1342011988,-0.2214832902,-0.0414212942,-0.2053791881,0.0815152079,-0.0784958005,-0.0192770455,0.1802254319,-0.321257025,-0.0408071466,-0.2744917274,-0.5607581139,0.0327716433,-0.0596641563,0.3699053228,0.1861217767,0.0378416777,-0.0787617713,-0.028003674,0.3117524087,-0.0995522514,-0.3088869452,0.1620036811,0.0437144563,0.0506080873,-0.3222824335,-0.6019359231,-0.0519946478,-0.3921587169,0.2188689262,-0.0151086105,-0.0129247503,0.3306975067,0.1765703112,0.2407143861,-0.0358890295,0.1884777844,-0.1856399924,-0.2366809547,0.2083814889,-0.322465241,-0.517580092,-0.0995494649,-0.0682133213,0.1749484837,-0.0706238896,-0.4018831253,-0.4492530525,-0.2838637829,0.2869089842,0.0883646384,0.196379602,0.3195345998,-0.0170480739,-0.082762666,-0.2365194708,-0.2441498786,0.0866854787,-0.0396270901,-0.0130333714,-0.138891384,0.3085326254,-0.0408935174,0.5791708231,0.249665007,-0.0168729816,0.5041943789,-0.1128073931,0.4734928608,-0.2517380416,-0.3542965651,0.0175450891,-0.07911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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3236","title":"Loading of datasets changed in #3110 returns no examples ","comments":"Hi, in 1.12.1 it uses the dataset scripts from that time, not the one on master.\r\n\r\nIt only uses the datasets from master if you installed `datasets` from source, or if the dataset isn't available in your local version (in this case it shows a warning and it loads from master).\r\n","body":"## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":51,"text":"Loading of datasets changed in #3110 returns no examples  \n ## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n Hi, in 1.12.1 it uses the dataset scripts from that time, not the one on master.\r\n\r\nIt only uses the datasets from master if you installed `datasets` from source, or if the dataset isn't available in your local version (in this case it shows a warning and it loads from master).\r\n","embeddings":[0.048198577,-0.086997591,-0.0242290739,0.2818654776,0.3240283132,-0.0273512322,0.2742412686,0.2374755442,0.1709855795,0.2015547305,-0.0715671331,0.3162328601,-0.0423282161,0.1720468551,0.1071112677,-0.2534903884,0.1101509109,0.1358650625,-0.3096776307,-0.0174040273,-0.0312140584,0.3098744452,-0.2977305949,-0.0926481485,-0.2869768739,0.2120054066,-0.3142177463,0.1809691787,-0.1463450193,-0.3778190315,0.3804140091,0.085701853,0.142021969,0.6262403131,-0.0001108685,0.1710154116,0.1643774956,-0.1009755954,-0.2533084452,-0.4094513357,-0.0717469379,-0.4076807499,0.1379683018,-0.0840175077,-0.3382484913,-0.3250941634,-0.2067220956,-0.1029313877,0.4555373788,0.3552956581,0.2413025498,0.4862979949,0.0441145748,-0.3515444994,0.0134598184,-0.1009903997,-0.0108462907,0.0179695357,0.16588597,0.1214358136,0.0917913541,0.1937301904,0.116671063,-0.1438790709,0.1437003464,-0.1031026468,-0.1645518392,-0.1611695588,0.0325739831,0.3317503333,0.417380631,-0.2056090087,-0.5523958206,-0.3343687952,-0.0657657832,-0.3471269906,0.1069067046,0.1082367301,0.0765277743,0.2580421865,-0.1972239763,0.1033521593,-0.0616276972,0.0491028912,-0.2557568848,0.0730325952,-0.1191484109,-0.1283471733,-0.0014372604,-0.0417609885,-0.104892008,0.0154960593,-0.0036515433,0.1501370221,-0.4318967462,0.0554302186,0.2375551462,0.0125552332,-0.0146855712,0.2383978665,0.2823120058,0.2580576241,-0.101495184,0.0426573679,0.0553129092,0.4307951629,0.0989541337,-0.0112816533,0.3555488884,0.390316695,-0.0860644132,-0.0921302959,-0.0349754281,-0.1879227608,0.1101076379,0.0507209487,0.2734403908,-0.2016668916,-0.2322237045,0.2300951928,-0.237139523,-0.075645335,0.0285553057,0.5070336461,-0.0779915079,0.0867880434,0.0345647335,0.1759792566,-0.1502693743,-0.3006089926,-0.4464168251,-0.0316895582,-0.3254978955,-0.0998986512,0.2723429501,-0.1960319728,0.4132769406,0.0805031285,-0.1448232681,-0.065148145,0.0624425486,0.1992406845,0.1993405223,0.2849488258,-0.0595135838,-0.010659649,0.2917140424,-0.0583441183,0.0071224286,0.0480793193,-0.2429093719,-0.2783383727,-0.1120141596,0.2665142119,-0.46140185,-0.0228120293,-0.0224667434,0.1883602142,0.0015456185,-0.0524675138,0.0370660163,-0.1295340359,-0.0360488184,-0.0366940498,0.4575083852,0.4533716142,-0.1228636354,-0.3646816611,0.2155804187,-0.0941884518,0.0080199577,0.0213705692,-0.0062804492,0.0874585435,-0.2322475314,-0.1220047846,0.2858053744,-0.2465898395,-0.4048399627,-0.0766043141,-0.0211875122,0.4242127836,0.2083310336,-0.252592802,0.0246394183,0.0277146883,0.127171725,-0.0023792374,0.2605972886,0.0864496827,-0.1792174131,-0.1496765465,0.0313811228,-0.0087175984,0.0463235825,0.2363815457,-0.0460510887,-0.1425695419,0.3198085725,-0.2629787028,-0.1114745289,0.2994379103,0.3906707764,0.0334933326,0.2075452358,-0.2101996988,-0.5983006358,0.4261895716,0.0083908746,0.1736962497,0.106466569,-0.1905425638,-0.3390614688,-0.0319938213,-0.2173839808,-0.4623180032,0.1443421841,0.1712841243,0.3113585711,0.1133099794,-0.2209654897,0.4782707989,-0.1391460001,0.2466123849,-0.4244332314,0.3553302288,0.0494857654,-0.1585938931,0.0004654837,0.3790495396,0.0895046145,-0.2791749835,-0.0319348909,0.4426724911,0.0848303661,0.1269914657,-0.1665710956,0.034147013,0.3217851818,0.1063585281,-0.0502242409,0.189823553,-0.0628749505,0.1435203552,-0.3038612306,0.4575380683,0.0085147955,0.1595537513,0.2568788826,-0.0430398583,0.1683444381,-0.1168816239,-0.347930342,-0.2479158044,0.2199457735,-0.2961525917,0.2113555521,0.1242557913,-0.4070572257,0.0541583113,0.3790639341,0.0197461639,0.0462705381,-0.0233377554,-0.2102935314,0.0364365652,0.3277456164,0.131628722,0.2561745048,0.275821656,-0.0519169867,0.1211834699,-0.054418195,-0.201366365,0.1555202454,-0.0264057089,-0.1224704683,0.2315487415,0.1616037339,0.093663767,-0.434733361,0.0915910378,-0.0664715022,0.1618293524,-0.4015597105,-0.0091291834,-0.3828177452,-0.0557134636,-0.1776841134,-0.2573938966,0.0134491287,-0.327496767,0.0290937871,0.1418606937,-0.1621785015,0.2784962654,-0.0668853819,0.0994898677,-0.0259306077,0.1342011988,-0.2214832902,-0.0414212942,-0.2053791881,0.0815152079,-0.0784958005,-0.0192770455,0.1802254319,-0.321257025,-0.0408071466,-0.2744917274,-0.5607581139,0.0327716433,-0.0596641563,0.3699053228,0.1861217767,0.0378416777,-0.0787617713,-0.028003674,0.3117524087,-0.0995522514,-0.3088869452,0.1620036811,0.0437144563,0.0506080873,-0.3222824335,-0.6019359231,-0.0519946478,-0.3921587169,0.2188689262,-0.0151086105,-0.0129247503,0.3306975067,0.1765703112,0.2407143861,-0.0358890295,0.1884777844,-0.1856399924,-0.2366809547,0.2083814889,-0.322465241,-0.517580092,-0.0995494649,-0.0682133213,0.1749484837,-0.0706238896,-0.4018831253,-0.4492530525,-0.2838637829,0.2869089842,0.0883646384,0.196379602,0.3195345998,-0.0170480739,-0.082762666,-0.2365194708,-0.2441498786,0.0866854787,-0.0396270901,-0.0130333714,-0.138891384,0.3085326254,-0.0408935174,0.5791708231,0.249665007,-0.0168729816,0.5041943789,-0.1128073931,0.4734928608,-0.2517380416,-0.3542965651,0.0175450891,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3236","title":"Loading of datasets changed in #3110 returns no examples ","comments":"OK, I understand the issue a bit better now.\r\nI see I wasn't on 1.12.1, but on 1.12.1.dev0 and since it is a dev version it uses master.\r\nSo users that use an old dev version must specify revision or else they'll encounter this problem.\r\n\r\nBTW, when I opened the issue I installed the latest master version with\r\n```\r\npip install git+git:\/\/github.com\/huggingface\/datasets@master#egg=datasets\r\n```\r\nand also used `download_mode=\"force_redownload\"`, and it still returned 0 examples.\r\nNow I deleted all of the cache and ran the code again, and it worked.\r\nI'm not sure what exactly happened here, but looks like it was due to a mix of an unofficial version and its cache.\r\n\r\nThanks again!","body":"## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":113,"text":"Loading of datasets changed in #3110 returns no examples  \n ## Describe the bug\r\nLoading of datasets changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110 returns no examples:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Steps to reproduce the bug\r\nLoad any of the datasets that were changed in https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\")\r\n\r\n# The problem only started with the commit of #3110\r\nload_dataset(\"qasper\", revision=\"b6469baa22c174b3906c631802a7016fedea6780\")\r\n```\r\n\r\n## Expected results\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 888\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'title', 'abstract', 'full_text', 'qas'],\r\n        num_rows: 281\r\n    })\r\n})\r\n```\r\nWhich can be received when specifying revision of the commit before https:\/\/github.com\/huggingface\/datasets\/pull\/3110:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"qasper\", revision=\"acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d\")\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.2.dev0 (master)\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n OK, I understand the issue a bit better now.\r\nI see I wasn't on 1.12.1, but on 1.12.1.dev0 and since it is a dev version it uses master.\r\nSo users that use an old dev version must specify revision or else they'll encounter this problem.\r\n\r\nBTW, when I opened the issue I installed the latest master version with\r\n```\r\npip install git+git:\/\/github.com\/huggingface\/datasets@master#egg=datasets\r\n```\r\nand also used `download_mode=\"force_redownload\"`, and it still returned 0 examples.\r\nNow I deleted all of the cache and ran the code again, and it worked.\r\nI'm not sure what exactly happened here, but looks like it was due to a mix of an unofficial version and its cache.\r\n\r\nThanks again!","embeddings":[0.048198577,-0.086997591,-0.0242290739,0.2818654776,0.3240283132,-0.0273512322,0.2742412686,0.2374755442,0.1709855795,0.2015547305,-0.0715671331,0.3162328601,-0.0423282161,0.1720468551,0.1071112677,-0.2534903884,0.1101509109,0.1358650625,-0.3096776307,-0.0174040273,-0.0312140584,0.3098744452,-0.2977305949,-0.0926481485,-0.2869768739,0.2120054066,-0.3142177463,0.1809691787,-0.1463450193,-0.3778190315,0.3804140091,0.085701853,0.142021969,0.6262403131,-0.0001108685,0.1710154116,0.1643774956,-0.1009755954,-0.2533084452,-0.4094513357,-0.0717469379,-0.4076807499,0.1379683018,-0.0840175077,-0.3382484913,-0.3250941634,-0.2067220956,-0.1029313877,0.4555373788,0.3552956581,0.2413025498,0.4862979949,0.0441145748,-0.3515444994,0.0134598184,-0.1009903997,-0.0108462907,0.0179695357,0.16588597,0.1214358136,0.0917913541,0.1937301904,0.116671063,-0.1438790709,0.1437003464,-0.1031026468,-0.1645518392,-0.1611695588,0.0325739831,0.3317503333,0.417380631,-0.2056090087,-0.5523958206,-0.3343687952,-0.0657657832,-0.3471269906,0.1069067046,0.1082367301,0.0765277743,0.2580421865,-0.1972239763,0.1033521593,-0.0616276972,0.0491028912,-0.2557568848,0.0730325952,-0.1191484109,-0.1283471733,-0.0014372604,-0.0417609885,-0.104892008,0.0154960593,-0.0036515433,0.1501370221,-0.4318967462,0.0554302186,0.2375551462,0.0125552332,-0.0146855712,0.2383978665,0.2823120058,0.2580576241,-0.101495184,0.0426573679,0.0553129092,0.4307951629,0.0989541337,-0.0112816533,0.3555488884,0.390316695,-0.0860644132,-0.0921302959,-0.0349754281,-0.1879227608,0.1101076379,0.0507209487,0.2734403908,-0.2016668916,-0.2322237045,0.2300951928,-0.237139523,-0.075645335,0.0285553057,0.5070336461,-0.0779915079,0.0867880434,0.0345647335,0.1759792566,-0.1502693743,-0.3006089926,-0.4464168251,-0.0316895582,-0.3254978955,-0.0998986512,0.2723429501,-0.1960319728,0.4132769406,0.0805031285,-0.1448232681,-0.065148145,0.0624425486,0.1992406845,0.1993405223,0.2849488258,-0.0595135838,-0.010659649,0.2917140424,-0.0583441183,0.0071224286,0.0480793193,-0.2429093719,-0.2783383727,-0.1120141596,0.2665142119,-0.46140185,-0.0228120293,-0.0224667434,0.1883602142,0.0015456185,-0.0524675138,0.0370660163,-0.1295340359,-0.0360488184,-0.0366940498,0.4575083852,0.4533716142,-0.1228636354,-0.3646816611,0.2155804187,-0.0941884518,0.0080199577,0.0213705692,-0.0062804492,0.0874585435,-0.2322475314,-0.1220047846,0.2858053744,-0.2465898395,-0.4048399627,-0.0766043141,-0.0211875122,0.4242127836,0.2083310336,-0.252592802,0.0246394183,0.0277146883,0.127171725,-0.0023792374,0.2605972886,0.0864496827,-0.1792174131,-0.1496765465,0.0313811228,-0.0087175984,0.0463235825,0.2363815457,-0.0460510887,-0.1425695419,0.3198085725,-0.2629787028,-0.1114745289,0.2994379103,0.3906707764,0.0334933326,0.2075452358,-0.2101996988,-0.5983006358,0.4261895716,0.0083908746,0.1736962497,0.106466569,-0.1905425638,-0.3390614688,-0.0319938213,-0.2173839808,-0.4623180032,0.1443421841,0.1712841243,0.3113585711,0.1133099794,-0.2209654897,0.4782707989,-0.1391460001,0.2466123849,-0.4244332314,0.3553302288,0.0494857654,-0.1585938931,0.0004654837,0.3790495396,0.0895046145,-0.2791749835,-0.0319348909,0.4426724911,0.0848303661,0.1269914657,-0.1665710956,0.034147013,0.3217851818,0.1063585281,-0.0502242409,0.189823553,-0.0628749505,0.1435203552,-0.3038612306,0.4575380683,0.0085147955,0.1595537513,0.2568788826,-0.0430398583,0.1683444381,-0.1168816239,-0.347930342,-0.2479158044,0.2199457735,-0.2961525917,0.2113555521,0.1242557913,-0.4070572257,0.0541583113,0.3790639341,0.0197461639,0.0462705381,-0.0233377554,-0.2102935314,0.0364365652,0.3277456164,0.131628722,0.2561745048,0.275821656,-0.0519169867,0.1211834699,-0.054418195,-0.201366365,0.1555202454,-0.0264057089,-0.1224704683,0.2315487415,0.1616037339,0.093663767,-0.434733361,0.0915910378,-0.0664715022,0.1618293524,-0.4015597105,-0.0091291834,-0.3828177452,-0.0557134636,-0.1776841134,-0.2573938966,0.0134491287,-0.327496767,0.0290937871,0.1418606937,-0.1621785015,0.2784962654,-0.0668853819,0.0994898677,-0.0259306077,0.1342011988,-0.2214832902,-0.0414212942,-0.2053791881,0.0815152079,-0.0784958005,-0.0192770455,0.1802254319,-0.321257025,-0.0408071466,-0.2744917274,-0.5607581139,0.0327716433,-0.0596641563,0.3699053228,0.1861217767,0.0378416777,-0.0787617713,-0.028003674,0.3117524087,-0.0995522514,-0.3088869452,0.1620036811,0.0437144563,0.0506080873,-0.3222824335,-0.6019359231,-0.0519946478,-0.3921587169,0.2188689262,-0.0151086105,-0.0129247503,0.3306975067,0.1765703112,0.2407143861,-0.0358890295,0.1884777844,-0.1856399924,-0.2366809547,0.2083814889,-0.322465241,-0.517580092,-0.0995494649,-0.0682133213,0.1749484837,-0.0706238896,-0.4018831253,-0.4492530525,-0.2838637829,0.2869089842,0.0883646384,0.196379602,0.3195345998,-0.0170480739,-0.082762666,-0.2365194708,-0.2441498786,0.0866854787,-0.0396270901,-0.0130333714,-0.138891384,0.3085326254,-0.0408935174,0.5791708231,0.249665007,-0.0168729816,0.5041943789,-0.1128073931,0.4734928608,-0.2517380416,-0.3542965651,0.0175450891,-0.0791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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3232","title":"The Xsum datasets seems not able to download.","comments":"> Hi ! On my side the URL is working fine, could you try again ?\r\n\r\nI try it again and cannot download the file (might because of my location). Could you please provide another download link(such as google drive)?   :>","body":"## Describe the bug\r\nThe download Link of the Xsum dataset provided in the repository is [Link](http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('xsum')\r\n```\r\n\r\n\r\n## Actual results\r\n``` python\r\nraise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz\r\n```\r\n\r\n","comment_length":41,"text":"The Xsum datasets seems not able to download. \n ## Describe the bug\r\nThe download Link of the Xsum dataset provided in the repository is [Link](http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('xsum')\r\n```\r\n\r\n\r\n## Actual results\r\n``` python\r\nraise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz\r\n```\r\n\r\n \n > Hi ! On my side the URL is working fine, could you try again ?\r\n\r\nI try it again and cannot download the file (might because of my location). Could you please provide another download link(such as google drive)?   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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3232","title":"The Xsum datasets seems not able to download.","comments":"I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.","body":"## Describe the bug\r\nThe download Link of the Xsum dataset provided in the repository is [Link](http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('xsum')\r\n```\r\n\r\n\r\n## Actual results\r\n``` python\r\nraise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz\r\n```\r\n\r\n","comment_length":41,"text":"The Xsum datasets seems not able to download. \n ## Describe the bug\r\nThe download Link of the Xsum dataset provided in the repository is [Link](http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('xsum')\r\n```\r\n\r\n\r\n## Actual results\r\n``` python\r\nraise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz\r\n```\r\n\r\n \n I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.","embeddings":[-0.331423223,-0.36743325,-0.0869149119,0.245414868,0.263882041,-0.0272144061,-0.1818688512,0.2482023984,0.4178061783,0.442614913,-0.2948056757,0.2520471513,0.2052701563,0.229403466,0.2333917171,-0.0991051942,-0.0418624319,-0.0463893972,-0.2623506784,-0.2412198782,0.0164785013,0.0969274938,-0.110728547,-0.203780517,-0.0685424209,0.192750603,-0.0489620231,0.1361096501,-0.0732006207,-0.2233384103,0.3807663023,0.011630916,-0.0209447723,0.2757839561,-0.0001023413,0.0666199028,0.0997403637,-0.0300284605,-0.1159865037,-0.1261527687,-0.0542520732,-0.1340835989,-0.0089215524,-0.2322687209,-0.0214335565,0.0928849727,0.1285055429,-0.2837916911,0.2894505858,0.1835973859,0.2622745931,0.3545215726,0.2521253228,-0.2354627848,0.0421027541,-0.6111720204,-0.0650988445,0.4002920389,0.5349779129,0.3604325652,0.2698487043,0.1387429982,-0.018430924,0.1159685105,0.0994656458,-0.0088067781,0.4251706898,-0.4580980837,-0.037701346,0.2458869368,0.6778416634,-0.0534331687,-0.3044258356,0.1442497075,0.1381594092,-0.0378242619,0.2553845048,0.1585571468,-0.0625750348,0.0369443893,-0.0324653871,0.0412391797,-0.3176603019,0.3286129832,-0.0225297641,0.1661618054,-0.1188479066,-0.0041794796,-0.1271898597,-0.0149068069,0.0586925298,0.0965282097,0.1769686341,0.1558577716,-0.2846116126,0.1427178681,-0.2149123698,0.0313394479,0.2618596256,0.058333423,0.1738475263,0.1864015907,-0.3274872601,0.2107256055,0.0806822404,-0.0660432503,0.0477895513,-0.4225457609,0.2691687644,0.0077106883,0.2094689459,0.0479163677,-0.255985707,-0.0337117016,-0.1369732916,0.0787271708,-0.0700795576,-0.4541297555,-0.4220673144,0.1466764659,0.1119578332,0.1790447831,-0.1028530002,0.075275369,-0.300473094,0.3445781767,0.1079167798,0.1784030646,-0.3236013949,-0.1927637756,-0.2283516973,0.1298200786,-0.0567768179,-0.1783258766,0.3263810575,-0.2338004559,0.375208348,-0.1128862724,0.1937286854,-0.0682487115,0.0858494863,0.1670882851,-0.3433820307,0.405330956,0.3277973533,0.0602793358,-0.1937612295,0.1055346429,-0.0933678895,-0.0158052165,-0.351914227,-0.1391734332,0.0196880717,0.3134671748,-0.0988511741,-0.3062335849,-0.1384419799,-0.1869195849,-0.1414750963,-0.0133790383,-0.3595939577,-0.0048309634,0.0094555821,-0.1606391966,0.4395252168,-0.0008216295,-0.4742822647,-0.0334128328,-0.068700932,-0.2187205255,0.066105634,0.1490438133,0.0694584697,-0.000257537,-0.0460173972,0.2085278332,-0.0542589575,-0.6585507393,-0.7203484178,-0.1148055047,-0.2071468979,-0.309538424,0.2289353758,0.2369522005,0.0606124587,0.094492957,0.2532227337,0.4002322853,0.2122631669,-0.0413894467,-0.2462932914,-0.2152741402,-0.1055723354,0.2010481507,0.0782496035,-0.1077929437,0.0202923547,0.2904139161,0.2692915797,0.0853498653,-0.0242516771,0.224437803,0.2133315504,-0.149274677,-0.0098860655,-0.3526052535,-0.0454413556,0.2160345465,-0.08785896,0.0697327256,0.1170552224,-0.1054366082,-0.5075933337,-0.1614287496,0.0519934893,0.0862977505,0.195231989,-0.0068011889,0.2040419281,0.1111040264,-0.1116588712,0.2036058605,-0.2161929309,-0.0081466744,-0.3005204201,0.3975138664,-0.1589350998,0.0453344136,0.2281156778,-0.004980552,0.0389041081,-0.3009449244,-0.0968311056,0.4578692913,-0.1779160053,0.180445835,0.2107467353,-0.1117250323,0.3272364438,-0.4625796378,0.1479894668,0.2682755589,0.0193960108,0.1460946351,-0.2089169323,0.0453074537,0.1333443075,-0.0372369178,0.1365720928,0.3394097388,0.3295834661,-0.1203344464,0.072463125,-0.1513463259,0.3328157067,0.2785775959,-0.0722141936,-0.23406142,-0.214117974,0.2760508955,0.0674101263,-0.0464743972,0.0974131972,-0.1003876925,-0.2719398439,-0.0075921691,0.0214757547,0.0423119962,0.2178730369,0.1396817118,0.1051537916,0.2042915374,-0.0685053095,-0.2312179506,0.0088404426,0.1198672056,-0.1966030151,0.2068317831,-0.2707982659,0.0285450127,-0.2991267145,-0.0654523596,0.0676434115,0.1342997849,-0.2118971199,-0.2524081171,-0.3269950151,-0.1748661399,0.155398041,0.0671173856,0.0526751205,-0.4180086255,0.0610684194,0.2545206249,-0.1621089876,0.0188998431,-0.4152399004,0.1980765909,0.1441424638,0.0169227049,-0.1083494201,-0.0798249468,-0.1220171154,0.1788119823,-0.011446693,0.2766009271,0.4290831983,-0.4553027153,0.0234288815,-0.3072319329,-0.1882995963,0.062737748,-0.0347595476,0.2030469924,-0.1330610216,0.2127378881,0.1495727003,-0.0523156188,0.1953900754,-0.3000569642,0.0232133511,0.121837981,-0.0415609814,-0.287565887,-0.0881008133,-0.665279448,-0.4408215284,-0.2259967923,-0.0622253828,0.156518355,0.1648942381,-0.0825173482,-0.0867541358,0.1618593037,0.1216490567,0.0609675758,-0.2026760876,-0.5381009579,0.4709047377,-0.1571447849,-0.735914588,0.3345752358,0.242462486,0.1541785151,0.2247953266,-0.412384063,-0.1649051607,-0.1363075674,0.0084265405,0.2673925459,0.2237539589,0.0059315031,-0.2515756786,-0.2184523344,-0.0748409629,0.1052635387,-0.0527499318,-0.0618761219,0.3117338717,-0.1575290263,-0.0436740071,0.2190057635,-0.2400135398,0.0570750237,0.0851279199,0.2238144279,-0.0094173681,0.12605232,-0.0303493999,-0.1730283648,0.3252556324,-0.0957668275,0.11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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3232","title":"The Xsum datasets seems not able to download.","comments":"> I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.\r\n\r\n:> ok. Thanks for your reply.","body":"## Describe the bug\r\nThe download Link of the Xsum dataset provided in the repository is [Link](http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('xsum')\r\n```\r\n\r\n\r\n## Actual results\r\n``` python\r\nraise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz\r\n```\r\n\r\n","comment_length":48,"text":"The Xsum datasets seems not able to download. \n ## Describe the bug\r\nThe download Link of the Xsum dataset provided in the repository is [Link](http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('xsum')\r\n```\r\n\r\n\r\n## Actual results\r\n``` python\r\nraise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/bollin.inf.ed.ac.uk\/public\/direct\/XSUM-EMNLP18-Summary-Data-Original.tar.gz\r\n```\r\n\r\n \n > I don't know other download links - this is the one provided by the authors of the dataset. Maybe you can try downloading from another location ? There are several solutions: a VPN, a remote VM or Google Colab for example.\r\n\r\n:> ok. Thanks for your reply.","embeddings":[-0.3331865668,-0.3943816423,-0.0960557684,0.2603081167,0.2701830864,-0.0308500063,-0.186469987,0.2266126275,0.4172308445,0.4343666434,-0.2952314913,0.2692231238,0.189033404,0.262875706,0.2242892087,-0.1081008017,-0.0503328629,-0.0338191502,-0.2698957324,-0.2509640455,0.0220208913,0.1142221913,-0.1153063625,-0.208722055,-0.0738673806,0.1842096001,-0.0624814071,0.1441022307,-0.0863123834,-0.2241960764,0.3790409863,0.0111865764,-0.0025083809,0.2709028125,-0.0001025416,0.0500127077,0.0930595845,-0.0314620435,-0.0893708467,-0.0961984769,-0.0602210537,-0.1364179254,-0.0071803066,-0.2456210107,-0.0231101159,0.098954238,0.1164865345,-0.2910426259,0.2828278542,0.1932455897,0.2635887563,0.3580075204,0.2694314718,-0.2109714299,0.0550954007,-0.6059842706,-0.0645666942,0.3859772086,0.5517980456,0.3631405234,0.2569544315,0.1440762281,-0.0156300627,0.1086799577,0.1041479185,-0.0181406438,0.4408896267,-0.483600229,-0.0649552867,0.2480995506,0.6997225881,-0.0777743459,-0.2917541862,0.149677068,0.1368191987,-0.0587952733,0.2467177659,0.1566776484,-0.0469718352,0.0305358823,-0.0306998547,0.0550833642,-0.3251155615,0.3186474741,-0.0180723723,0.1701724231,-0.1344882548,-0.0090969522,-0.133523941,-0.0315350853,0.0691634566,0.0903753638,0.1619882286,0.1564096063,-0.30048576,0.1194333956,-0.2136291414,0.0422641747,0.2669881284,0.0710889697,0.1824440062,0.1868773699,-0.3374997377,0.2084648907,0.0790748745,-0.0536056794,0.0885345861,-0.4192291498,0.2647752762,-0.0072427839,0.2187036276,0.0540563837,-0.254784137,-0.0272203255,-0.1209921166,0.0727730021,-0.0755157396,-0.4470128417,-0.4298918843,0.1524614692,0.1182738245,0.1659666896,-0.0920752957,0.0822654217,-0.3017962277,0.3540788591,0.0929235965,0.177167356,-0.3262222409,-0.1831181645,-0.2286143303,0.1500980258,-0.0580756031,-0.1754900366,0.3294424415,-0.2223073691,0.364708811,-0.1087289676,0.2077610344,-0.0637173206,0.0953832045,0.1464600861,-0.3404742479,0.4049582779,0.3226242959,0.0609436631,-0.2005066872,0.1112795323,-0.0874064639,0.0118849808,-0.3542284966,-0.1406759471,0.0084005706,0.3203359842,-0.0911239088,-0.3050469458,-0.1504710317,-0.172352761,-0.1186178923,0.001249584,-0.3344177008,-0.0240629297,0.0093656555,-0.1694460511,0.4229625165,-0.0313948579,-0.4814020097,-0.0307761636,-0.0579439327,-0.2459220439,0.0806736797,0.1470635831,0.0817775205,0.0028050048,-0.0612330288,0.2216237485,-0.0577327386,-0.6693435907,-0.7009033561,-0.1264821291,-0.1849506348,-0.3061267138,0.2229799926,0.2429105788,0.0561570898,0.1074318439,0.2482323349,0.3893900216,0.2002228498,-0.022889439,-0.2408042401,-0.2066565305,-0.0903406516,0.2036618441,0.0816978291,-0.1064247936,0.00734122,0.2834556401,0.2793328166,0.073008135,-0.0252430867,0.2351289243,0.2337058485,-0.1677936018,0.0090211183,-0.3701272905,-0.0504720323,0.2122426629,-0.0963126123,0.0734435245,0.1302365363,-0.1064960733,-0.5121939182,-0.1756363213,0.0503723696,0.0709370598,0.1982219517,-0.0177571438,0.1817720979,0.0957743749,-0.1306692511,0.2211687118,-0.2278567702,-0.018343756,-0.3073250055,0.3943319321,-0.1694321781,0.0370999947,0.2341395468,-0.0025968906,0.033505328,-0.3117394745,-0.0916783363,0.4463251829,-0.1743684858,0.1729971021,0.2096288055,-0.1184648052,0.336144954,-0.4633334577,0.1452406347,0.2489535958,0.0181869138,0.1221171767,-0.1947705895,0.0483615324,0.1212476939,-0.0609806366,0.1258871257,0.3105275631,0.3302658498,-0.1301493496,0.0604834147,-0.1618536264,0.3265810609,0.2836847603,-0.0802444294,-0.2443373799,-0.1977044791,0.2765052021,0.0629313588,-0.0472008139,0.1069274023,-0.092245467,-0.2746290565,-0.0048713274,0.0335232541,0.0454430319,0.2405519336,0.1509466469,0.0957200676,0.1986464411,-0.0543196537,-0.2271696925,0.0195513628,0.1166499779,-0.1848202795,0.2262802571,-0.2694711089,0.0242141988,-0.3155525923,-0.0623307079,0.0771784708,0.1465237886,-0.2164068818,-0.2711897492,-0.3040674627,-0.179685384,0.1709600836,0.0525439307,0.0523619018,-0.4137834013,0.0648385286,0.2429764718,-0.1489023119,0.0086251181,-0.4072483182,0.2058001608,0.16251643,0.0313275196,-0.1041900963,-0.0741677433,-0.0968105048,0.1858601123,-0.0260725506,0.2868191302,0.442976445,-0.4577338696,0.030682886,-0.2998049855,-0.1961786896,0.0768799931,-0.0458111018,0.2087686807,-0.1356749535,0.213495478,0.1636312902,-0.0589803346,0.1889709234,-0.2825785577,0.0080507509,0.1149460077,-0.0468084998,-0.27629897,-0.0977352187,-0.6756281853,-0.4522063136,-0.2092453092,-0.0566403717,0.167341724,0.1765293777,-0.0768651366,-0.1121861264,0.1709619164,0.1206318066,0.06004107,-0.2225435972,-0.5547968745,0.4554392695,-0.1665535569,-0.7234405875,0.3154025078,0.2330358028,0.1606282592,0.2140375227,-0.3980076313,-0.1741479039,-0.1349670589,0.000580424,0.2583885193,0.2056427151,0.0101450114,-0.2489761412,-0.2149875313,-0.0869335085,0.1063394919,-0.0512536131,-0.0466464125,0.3067921698,-0.1511773169,-0.0431981608,0.1992016882,-0.2496548742,0.053378161,0.0849669501,0.2279258519,-0.0276673436,0.1130746454,-0.0285103712,-0.1913942993,0.3319619894,-0.10807262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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3227","title":"Error in `Json(datasets.ArrowBasedBuilder)` class","comments":"I have additionally identified the source of the error, being that [this condition](https:\/\/github.com\/huggingface\/datasets\/blob\/fc46bba66ba4f432cc10501c16a677112e13984c\/src\/datasets\/packaged_modules\/json\/json.py#L124-L126) in the file\r\n`python3.8\/site-packages\/datasets\/packaged_modules\/json\/json.py` is not being entered correctly:\r\n```python\r\n                                    if (\r\n                                        isinstance(e, pa.ArrowInvalid)\r\n                                        and \"straddling\" not in str(e)\r\n                                        or block_size > len(batch)\r\n                                    ):\r\n```\r\n\r\nFrom what I can tell, in my case the block_size simply needs to be increased, but the error message does not contain \"straddling\" so the condition does trigger correctly and we fail to reach [the line to increase block_size](https:\/\/github.com\/huggingface\/datasets\/blob\/fc46bba66ba4f432cc10501c16a677112e13984c\/src\/datasets\/packaged_modules\/json\/json.py#L135).\r\n\r\nChanging the condition above to simply\r\n```python\r\n                                    if (\r\n                                        block_size > len(batch)\r\n                                    ):\r\n```\r\n\r\nFixes the error for me. I'm happy to create a PR containing this fix if the developers deem the other conditions unnecessary.","body":"## Describe the bug\r\nWhen a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.\r\n\r\n## Steps to reproduce the bug\r\nCreate a folder that contains the following:\r\n```\r\n.\r\n\u251c\u2500\u2500 testdata\r\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 mydata.json\r\n\u2514\u2500\u2500 test.py\r\n```\r\n\r\nPlease download [this file](https:\/\/github.com\/huggingface\/datasets\/files\/7491797\/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)\r\n:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!\r\n\r\n`test.py` simply contains:\r\n```python\r\nfrom datasets import load_dataset\r\nmy_dataset = load_dataset(\"testdata\")\r\n```\r\n\r\nTo reproduce the error, simply run\r\n```\r\npython test.py\r\n```\r\n\r\n## Expected results\r\nThe data should load correctly without error.\r\n\r\n## Actual results\r\nThe dataset builder fails with:\r\n```\r\nUsing custom data configuration testdata-d490389b8ab4fd82\r\nDownloading and preparing dataset json\/testdata to \/home\/junshern.chan\/.cache\/huggingface\/datasets\/json\/testdata-d490389b8ab4fd82\/0.0.0\/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 2264.74it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 447.01it\/s]\r\nFailed to read file '\/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 28, in <module>\r\n    my_dataset = load_dataset(\"testdata\")\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1156, in _prepare_split\r\n    for key, table in utils.tqdm(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1168, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/packaged_modules\/json\/json.py\", line 146, in _generate_tables\r\n    raise ValueError(\r\nValueError: Not able to read records in the JSON file at \/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. \r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n","comment_length":113,"text":"Error in `Json(datasets.ArrowBasedBuilder)` class \n ## Describe the bug\r\nWhen a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.\r\n\r\n## Steps to reproduce the bug\r\nCreate a folder that contains the following:\r\n```\r\n.\r\n\u251c\u2500\u2500 testdata\r\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 mydata.json\r\n\u2514\u2500\u2500 test.py\r\n```\r\n\r\nPlease download [this file](https:\/\/github.com\/huggingface\/datasets\/files\/7491797\/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)\r\n:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!\r\n\r\n`test.py` simply contains:\r\n```python\r\nfrom datasets import load_dataset\r\nmy_dataset = load_dataset(\"testdata\")\r\n```\r\n\r\nTo reproduce the error, simply run\r\n```\r\npython test.py\r\n```\r\n\r\n## Expected results\r\nThe data should load correctly without error.\r\n\r\n## Actual results\r\nThe dataset builder fails with:\r\n```\r\nUsing custom data configuration testdata-d490389b8ab4fd82\r\nDownloading and preparing dataset json\/testdata to \/home\/junshern.chan\/.cache\/huggingface\/datasets\/json\/testdata-d490389b8ab4fd82\/0.0.0\/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 2264.74it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 447.01it\/s]\r\nFailed to read file '\/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 28, in <module>\r\n    my_dataset = load_dataset(\"testdata\")\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1156, in _prepare_split\r\n    for key, table in utils.tqdm(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1168, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/packaged_modules\/json\/json.py\", line 146, in _generate_tables\r\n    raise ValueError(\r\nValueError: Not able to read records in the JSON file at \/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. \r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n \n I have additionally identified the source of the error, being that [this condition](https:\/\/github.com\/huggingface\/datasets\/blob\/fc46bba66ba4f432cc10501c16a677112e13984c\/src\/datasets\/packaged_modules\/json\/json.py#L124-L126) in the file\r\n`python3.8\/site-packages\/datasets\/packaged_modules\/json\/json.py` is not being entered correctly:\r\n```python\r\n                                    if (\r\n                                        isinstance(e, pa.ArrowInvalid)\r\n                                        and \"straddling\" not in str(e)\r\n                                        or block_size > len(batch)\r\n                                    ):\r\n```\r\n\r\nFrom what I can tell, in my case the block_size simply needs to be increased, but the error message does not contain \"straddling\" so the condition does trigger correctly and we fail to reach [the line to increase block_size](https:\/\/github.com\/huggingface\/datasets\/blob\/fc46bba66ba4f432cc10501c16a677112e13984c\/src\/datasets\/packaged_modules\/json\/json.py#L135).\r\n\r\nChanging the condition above to simply\r\n```python\r\n                                    if (\r\n                                        block_size > len(batch)\r\n                                    ):\r\n```\r\n\r\nFixes the error for me. I'm happy to create a PR containing this fix if the developers deem the other conditions unnecessary.","embeddings":[-0.2403125167,0.1650990993,-0.1146983206,0.482280165,0.0954774618,0.1549098492,0.1611647755,0.4468062818,0.1629000753,0.0544315688,0.2402579933,0.2136397958,-0.0641992092,0.0986968949,-0.0887032002,-0.0632352307,-0.0958852246,0.2196532637,0.05548108,0.2388131768,-0.1375442743,0.2395700365,-0.0216476265,0.0441664867,-0.1683245748,-0.0880119279,0.2261146754,0.1981148124,-0.3999561667,-0.6317936182,0.2991529107,-0.2521597445,-0.0838633999,0.4933881462,-0.0001105037,0.1315781325,0.5007123351,0.0465631559,-0.4148839414,-0.2232687771,-0.1364113241,-0.4095890224,0.1372785121,-0.1202351674,0.0772152022,-0.5005882382,-0.1944182962,-0.2052553296,0.6144830585,0.3781800568,0.2444537133,-0.0172362123,0.439291656,-0.0124509446,0.2353533208,0.2149992436,-0.1381492168,0.3187203407,0.18728517,0.423430562,-0.1916898489,0.0232486948,0.132730484,0.1254615337,0.2729103267,-0.0767261162,0.0167172328,-0.0212382264,0.2320043594,0.0730813667,0.5951886177,-0.375543952,-0.431763351,-0.2903711498,0.0442765206,-0.0678209066,0.388124913,0.1591873169,-0.1912016869,0.1276559085,-0.1218238547,-0.1066585705,-0.1995977312,0.0545986891,0.0055705006,0.0142568611,-0.2931369841,0.042768497,-0.0168282352,-0.3169675469,0.0828653201,-0.1454019845,-0.3469511867,0.2273458093,-0.1418411285,-0.1531399786,-0.103425473,-0.3367689848,0.331312567,0.1526703984,0.1026710272,-0.0181600768,-0.1312152743,0.1427518129,0.3137509525,0.149308756,0.1275745034,0.0306877419,0.0981538221,0.3543131351,-0.1037020609,-0.0901145488,-0.0526893586,-0.3660354018,0.028083222,-0.3211253285,0.2621266246,-0.2186490148,-0.1130315736,0.3243969083,-0.2721689045,-0.0167551339,0.0370390564,0.2277623713,0.0052966536,-0.1787994057,0.0841014087,0.2368547767,0.1973741651,-0.1256801635,-0.1394316405,-0.0941007435,-0.0140535943,-0.0533157848,0.1990813315,-0.1054171845,0.4438874125,0.1304032356,0.1367830634,-0.2728513479,0.0169609319,-0.0018022087,-0.1420742571,0.3386525214,-0.0260521062,0.0750593618,0.0869665071,-0.3207398355,-0.1433800757,0.1236498356,-0.1157213226,-0.1883138269,-0.290738225,0.2257093936,-0.0367966332,0.0658994466,-0.5976707339,-0.0011099846,0.0638555512,-0.1855823994,0.2316445112,-0.1494339854,0.1938385069,-0.1521389782,0.1029574499,0.1464119703,-0.4950017035,0.0221774504,-0.1589584202,-0.0176939517,-0.0424075797,0.1898302883,-0.3118867278,0.2282846421,-0.2827685177,0.1994856149,0.3716087639,-0.1947320402,-0.5814797282,0.4290313721,-0.0603754595,0.1802990586,0.0617141016,-0.1903425306,-0.0216886047,0.0542630926,0.177885294,0.1786771566,-0.0923178717,0.0467419252,-0.1771633029,-0.2370402217,-0.1145110205,0.1815148145,-0.1182245091,-0.1742785126,0.1272883862,0.0504545942,0.0662610531,-0.0625444427,0.1244463325,0.3878649771,0.0354947224,-0.1943142414,0.0840654969,-0.1906257123,-0.5504314303,0.1895055473,-0.1231901497,-0.0653060898,-0.3439671099,0.0051857266,-0.3859595656,0.1546897143,-0.2247076333,-0.0849858746,0.1455646157,-0.0632971302,0.1666700244,-0.0886188671,-0.1392122805,0.0785615742,-0.3218708038,0.2977219522,-0.2379867136,0.0187073015,-0.059816543,-0.1872344464,0.0309129804,-0.0353584215,-0.096554257,-0.0168781672,-0.3940271139,0.245917201,0.1489115059,-0.0034364259,0.0167317502,-0.0127036646,0.020289598,-0.1318188608,0.2086426765,0.3051687479,0.0084877135,-0.0966418833,-0.0439682193,0.3174681664,-0.0848724023,0.2753054798,0.0308774374,-0.2157543451,0.3587179482,0.176627025,-0.1378899068,-0.0662189573,0.0552623458,0.0341625996,0.3627393544,0.0012815826,-0.2344884574,-0.1374022067,0.5148671269,0.0055709057,0.0353187621,0.323432982,-0.3288779855,-0.0550703034,0.0166629814,0.3429915607,0.3857359588,0.1958513409,-0.1432737857,0.1436224729,0.1142028421,-0.1619254649,0.294993937,-0.0753320828,0.333345592,0.3271282315,0.3447594345,-0.0654993728,-0.351238519,-0.3344878852,-0.1525239348,0.1580728143,-0.2348374724,0.0627109259,-0.1558992267,-0.0391673632,-0.220437035,-0.1645056307,-0.2174471021,-0.1441415697,-0.019337099,0.0334845297,-0.1674218178,-0.0444168448,-0.0390951149,0.2795860469,0.1392436326,-0.2436903119,-0.282361567,0.0607379116,-0.3482090831,0.0674667805,0.4192628264,-0.0003479357,0.0694916174,-0.0711842105,-0.0388312414,-0.1816263795,-0.1992989033,0.0969154313,-0.0967315659,0.4307474494,0.2579059005,0.2820337713,-0.1104064509,-0.1597536802,0.2159285545,0.0003317513,-0.4779896736,0.0716935843,0.0592678189,0.1459024251,-0.0427414514,-0.186588347,0.0989596695,-0.5119397044,0.6149398685,0.2794421911,0.0852368698,0.1733131558,0.1801074296,0.3740797341,0.0623701774,0.2490324378,-0.0762317628,-0.3669802845,0.2387926877,-0.268437624,-0.2240346968,0.0622463711,0.0489933081,0.3258873522,-0.2646791935,-0.6064217687,0.2254449427,-0.2119208872,0.1771799326,-0.2865157723,-0.0056458907,0.0579444095,0.0400157422,-0.0808884352,-0.116959177,-0.2159638256,-0.0321979076,-0.0289163589,0.2511908114,-0.096083045,0.5281828046,-0.1473544836,0.4271023273,0.3951170444,-0.0573067628,0.2552519441,-0.0346699767,0.1327648461,-0.4422535002,-0.1322472095,-0.0498869643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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3227","title":"Error in `Json(datasets.ArrowBasedBuilder)` class","comments":"Hi ! I think the issue comes from the fact that your JSON file is not a valid JSON Lines file.\r\nEach example should be on one single line.\r\n\r\nCan you try fixing the format to have one line per example and try again ?","body":"## Describe the bug\r\nWhen a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.\r\n\r\n## Steps to reproduce the bug\r\nCreate a folder that contains the following:\r\n```\r\n.\r\n\u251c\u2500\u2500 testdata\r\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 mydata.json\r\n\u2514\u2500\u2500 test.py\r\n```\r\n\r\nPlease download [this file](https:\/\/github.com\/huggingface\/datasets\/files\/7491797\/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)\r\n:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!\r\n\r\n`test.py` simply contains:\r\n```python\r\nfrom datasets import load_dataset\r\nmy_dataset = load_dataset(\"testdata\")\r\n```\r\n\r\nTo reproduce the error, simply run\r\n```\r\npython test.py\r\n```\r\n\r\n## Expected results\r\nThe data should load correctly without error.\r\n\r\n## Actual results\r\nThe dataset builder fails with:\r\n```\r\nUsing custom data configuration testdata-d490389b8ab4fd82\r\nDownloading and preparing dataset json\/testdata to \/home\/junshern.chan\/.cache\/huggingface\/datasets\/json\/testdata-d490389b8ab4fd82\/0.0.0\/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 2264.74it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 447.01it\/s]\r\nFailed to read file '\/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 28, in <module>\r\n    my_dataset = load_dataset(\"testdata\")\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1156, in _prepare_split\r\n    for key, table in utils.tqdm(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1168, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/packaged_modules\/json\/json.py\", line 146, in _generate_tables\r\n    raise ValueError(\r\nValueError: Not able to read records in the JSON file at \/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. \r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n","comment_length":45,"text":"Error in `Json(datasets.ArrowBasedBuilder)` class \n ## Describe the bug\r\nWhen a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.\r\n\r\n## Steps to reproduce the bug\r\nCreate a folder that contains the following:\r\n```\r\n.\r\n\u251c\u2500\u2500 testdata\r\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 mydata.json\r\n\u2514\u2500\u2500 test.py\r\n```\r\n\r\nPlease download [this file](https:\/\/github.com\/huggingface\/datasets\/files\/7491797\/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)\r\n:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!\r\n\r\n`test.py` simply contains:\r\n```python\r\nfrom datasets import load_dataset\r\nmy_dataset = load_dataset(\"testdata\")\r\n```\r\n\r\nTo reproduce the error, simply run\r\n```\r\npython test.py\r\n```\r\n\r\n## Expected results\r\nThe data should load correctly without error.\r\n\r\n## Actual results\r\nThe dataset builder fails with:\r\n```\r\nUsing custom data configuration testdata-d490389b8ab4fd82\r\nDownloading and preparing dataset json\/testdata to \/home\/junshern.chan\/.cache\/huggingface\/datasets\/json\/testdata-d490389b8ab4fd82\/0.0.0\/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 2264.74it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 447.01it\/s]\r\nFailed to read file '\/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 28, in <module>\r\n    my_dataset = load_dataset(\"testdata\")\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1156, in _prepare_split\r\n    for key, table in utils.tqdm(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1168, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/packaged_modules\/json\/json.py\", line 146, in _generate_tables\r\n    raise ValueError(\r\nValueError: Not able to read records in the JSON file at \/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. \r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n \n Hi ! I think the issue comes from the fact that your JSON file is not a valid JSON Lines file.\r\nEach example should be on one single line.\r\n\r\nCan you try fixing the format to have one line per example and try again ?","embeddings":[-0.2403125167,0.1650990993,-0.1146983206,0.482280165,0.0954774618,0.1549098492,0.1611647755,0.4468062818,0.1629000753,0.0544315688,0.2402579933,0.2136397958,-0.0641992092,0.0986968949,-0.0887032002,-0.0632352307,-0.0958852246,0.2196532637,0.05548108,0.2388131768,-0.1375442743,0.2395700365,-0.0216476265,0.0441664867,-0.1683245748,-0.0880119279,0.2261146754,0.1981148124,-0.3999561667,-0.6317936182,0.2991529107,-0.2521597445,-0.0838633999,0.4933881462,-0.0001105037,0.1315781325,0.5007123351,0.0465631559,-0.4148839414,-0.2232687771,-0.1364113241,-0.4095890224,0.1372785121,-0.1202351674,0.0772152022,-0.5005882382,-0.1944182962,-0.2052553296,0.6144830585,0.3781800568,0.2444537133,-0.0172362123,0.439291656,-0.0124509446,0.2353533208,0.2149992436,-0.1381492168,0.3187203407,0.18728517,0.423430562,-0.1916898489,0.0232486948,0.132730484,0.1254615337,0.2729103267,-0.0767261162,0.0167172328,-0.0212382264,0.2320043594,0.0730813667,0.5951886177,-0.375543952,-0.431763351,-0.2903711498,0.0442765206,-0.0678209066,0.388124913,0.1591873169,-0.1912016869,0.1276559085,-0.1218238547,-0.1066585705,-0.1995977312,0.0545986891,0.0055705006,0.0142568611,-0.2931369841,0.042768497,-0.0168282352,-0.3169675469,0.0828653201,-0.1454019845,-0.3469511867,0.2273458093,-0.1418411285,-0.1531399786,-0.103425473,-0.3367689848,0.331312567,0.1526703984,0.1026710272,-0.0181600768,-0.1312152743,0.1427518129,0.3137509525,0.149308756,0.1275745034,0.0306877419,0.0981538221,0.3543131351,-0.1037020609,-0.0901145488,-0.0526893586,-0.3660354018,0.028083222,-0.3211253285,0.2621266246,-0.2186490148,-0.1130315736,0.3243969083,-0.2721689045,-0.0167551339,0.0370390564,0.2277623713,0.0052966536,-0.1787994057,0.0841014087,0.2368547767,0.1973741651,-0.1256801635,-0.1394316405,-0.0941007435,-0.0140535943,-0.0533157848,0.1990813315,-0.1054171845,0.4438874125,0.1304032356,0.1367830634,-0.2728513479,0.0169609319,-0.0018022087,-0.1420742571,0.3386525214,-0.0260521062,0.0750593618,0.0869665071,-0.3207398355,-0.1433800757,0.1236498356,-0.1157213226,-0.1883138269,-0.290738225,0.2257093936,-0.0367966332,0.0658994466,-0.5976707339,-0.0011099846,0.0638555512,-0.1855823994,0.2316445112,-0.1494339854,0.1938385069,-0.1521389782,0.1029574499,0.1464119703,-0.4950017035,0.0221774504,-0.1589584202,-0.0176939517,-0.0424075797,0.1898302883,-0.3118867278,0.2282846421,-0.2827685177,0.1994856149,0.3716087639,-0.1947320402,-0.5814797282,0.4290313721,-0.0603754595,0.1802990586,0.0617141016,-0.1903425306,-0.0216886047,0.0542630926,0.177885294,0.1786771566,-0.0923178717,0.0467419252,-0.1771633029,-0.2370402217,-0.1145110205,0.1815148145,-0.1182245091,-0.1742785126,0.1272883862,0.0504545942,0.0662610531,-0.0625444427,0.1244463325,0.3878649771,0.0354947224,-0.1943142414,0.0840654969,-0.1906257123,-0.5504314303,0.1895055473,-0.1231901497,-0.0653060898,-0.3439671099,0.0051857266,-0.3859595656,0.1546897143,-0.2247076333,-0.0849858746,0.1455646157,-0.0632971302,0.1666700244,-0.0886188671,-0.1392122805,0.0785615742,-0.3218708038,0.2977219522,-0.2379867136,0.0187073015,-0.059816543,-0.1872344464,0.0309129804,-0.0353584215,-0.096554257,-0.0168781672,-0.3940271139,0.245917201,0.1489115059,-0.0034364259,0.0167317502,-0.0127036646,0.020289598,-0.1318188608,0.2086426765,0.3051687479,0.0084877135,-0.0966418833,-0.0439682193,0.3174681664,-0.0848724023,0.2753054798,0.0308774374,-0.2157543451,0.3587179482,0.176627025,-0.1378899068,-0.0662189573,0.0552623458,0.0341625996,0.3627393544,0.0012815826,-0.2344884574,-0.1374022067,0.5148671269,0.0055709057,0.0353187621,0.323432982,-0.3288779855,-0.0550703034,0.0166629814,0.3429915607,0.3857359588,0.1958513409,-0.1432737857,0.1436224729,0.1142028421,-0.1619254649,0.294993937,-0.0753320828,0.333345592,0.3271282315,0.3447594345,-0.0654993728,-0.351238519,-0.3344878852,-0.1525239348,0.1580728143,-0.2348374724,0.0627109259,-0.1558992267,-0.0391673632,-0.220437035,-0.1645056307,-0.2174471021,-0.1441415697,-0.019337099,0.0334845297,-0.1674218178,-0.0444168448,-0.0390951149,0.2795860469,0.1392436326,-0.2436903119,-0.282361567,0.0607379116,-0.3482090831,0.0674667805,0.4192628264,-0.0003479357,0.0694916174,-0.0711842105,-0.0388312414,-0.1816263795,-0.1992989033,0.0969154313,-0.0967315659,0.4307474494,0.2579059005,0.2820337713,-0.1104064509,-0.1597536802,0.2159285545,0.0003317513,-0.4779896736,0.0716935843,0.0592678189,0.1459024251,-0.0427414514,-0.186588347,0.0989596695,-0.5119397044,0.6149398685,0.2794421911,0.0852368698,0.1733131558,0.1801074296,0.3740797341,0.0623701774,0.2490324378,-0.0762317628,-0.3669802845,0.2387926877,-0.268437624,-0.2240346968,0.0622463711,0.0489933081,0.3258873522,-0.2646791935,-0.6064217687,0.2254449427,-0.2119208872,0.1771799326,-0.2865157723,-0.0056458907,0.0579444095,0.0400157422,-0.0808884352,-0.116959177,-0.2159638256,-0.0321979076,-0.0289163589,0.2511908114,-0.096083045,0.5281828046,-0.1473544836,0.4271023273,0.3951170444,-0.0573067628,0.2552519441,-0.0346699767,0.1327648461,-0.4422535002,-0.1322472095,-0.0498869643,0.00547962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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3227","title":"Error in `Json(datasets.ArrowBasedBuilder)` class","comments":":open_mouth: you're right, that did it! I just put everything on a single line (my file only has a single example) and that fixed the error. Thank you so much!","body":"## Describe the bug\r\nWhen a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.\r\n\r\n## Steps to reproduce the bug\r\nCreate a folder that contains the following:\r\n```\r\n.\r\n\u251c\u2500\u2500 testdata\r\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 mydata.json\r\n\u2514\u2500\u2500 test.py\r\n```\r\n\r\nPlease download [this file](https:\/\/github.com\/huggingface\/datasets\/files\/7491797\/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)\r\n:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!\r\n\r\n`test.py` simply contains:\r\n```python\r\nfrom datasets import load_dataset\r\nmy_dataset = load_dataset(\"testdata\")\r\n```\r\n\r\nTo reproduce the error, simply run\r\n```\r\npython test.py\r\n```\r\n\r\n## Expected results\r\nThe data should load correctly without error.\r\n\r\n## Actual results\r\nThe dataset builder fails with:\r\n```\r\nUsing custom data configuration testdata-d490389b8ab4fd82\r\nDownloading and preparing dataset json\/testdata to \/home\/junshern.chan\/.cache\/huggingface\/datasets\/json\/testdata-d490389b8ab4fd82\/0.0.0\/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 2264.74it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 447.01it\/s]\r\nFailed to read file '\/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 28, in <module>\r\n    my_dataset = load_dataset(\"testdata\")\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1156, in _prepare_split\r\n    for key, table in utils.tqdm(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1168, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/packaged_modules\/json\/json.py\", line 146, in _generate_tables\r\n    raise ValueError(\r\nValueError: Not able to read records in the JSON file at \/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. \r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n","comment_length":30,"text":"Error in `Json(datasets.ArrowBasedBuilder)` class \n ## Describe the bug\r\nWhen a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.\r\n\r\n## Steps to reproduce the bug\r\nCreate a folder that contains the following:\r\n```\r\n.\r\n\u251c\u2500\u2500 testdata\r\n\u2502\u00a0\u00a0 \u2514\u2500\u2500 mydata.json\r\n\u2514\u2500\u2500 test.py\r\n```\r\n\r\nPlease download [this file](https:\/\/github.com\/huggingface\/datasets\/files\/7491797\/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)\r\n:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!\r\n\r\n`test.py` simply contains:\r\n```python\r\nfrom datasets import load_dataset\r\nmy_dataset = load_dataset(\"testdata\")\r\n```\r\n\r\nTo reproduce the error, simply run\r\n```\r\npython test.py\r\n```\r\n\r\n## Expected results\r\nThe data should load correctly without error.\r\n\r\n## Actual results\r\nThe dataset builder fails with:\r\n```\r\nUsing custom data configuration testdata-d490389b8ab4fd82\r\nDownloading and preparing dataset json\/testdata to \/home\/junshern.chan\/.cache\/huggingface\/datasets\/json\/testdata-d490389b8ab4fd82\/0.0.0\/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 2264.74it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 447.01it\/s]\r\nFailed to read file '\/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 28, in <module>\r\n    my_dataset = load_dataset(\"testdata\")\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 607, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1156, in _prepare_split\r\n    for key, table in utils.tqdm(\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1168, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/junshern.chan\/.casio\/miniconda\/envs\/hf-json-bug\/lib\/python3.8\/site-packages\/datasets\/packaged_modules\/json\/json.py\", line 146, in _generate_tables\r\n    raise ValueError(\r\nValueError: Not able to read records in the JSON file at \/home\/junshern.chan\/hf-json-bug\/testdata\/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. \r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.15.1\r\n- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyArrow version: 6.0.0\r\n \n :open_mouth: you're right, that did it! I just put everything on a single line (my file only has a single example) and that fixed the error. Thank you so much!","embeddings":[-0.2403125167,0.1650990993,-0.1146983206,0.482280165,0.0954774618,0.1549098492,0.1611647755,0.4468062818,0.1629000753,0.0544315688,0.2402579933,0.2136397958,-0.0641992092,0.0986968949,-0.0887032002,-0.0632352307,-0.0958852246,0.2196532637,0.05548108,0.2388131768,-0.1375442743,0.2395700365,-0.0216476265,0.0441664867,-0.1683245748,-0.0880119279,0.2261146754,0.1981148124,-0.3999561667,-0.6317936182,0.2991529107,-0.2521597445,-0.0838633999,0.4933881462,-0.0001105037,0.1315781325,0.5007123351,0.0465631559,-0.4148839414,-0.2232687771,-0.1364113241,-0.4095890224,0.1372785121,-0.1202351674,0.0772152022,-0.5005882382,-0.1944182962,-0.2052553296,0.6144830585,0.3781800568,0.2444537133,-0.0172362123,0.439291656,-0.0124509446,0.2353533208,0.2149992436,-0.1381492168,0.3187203407,0.18728517,0.423430562,-0.1916898489,0.0232486948,0.132730484,0.1254615337,0.2729103267,-0.0767261162,0.0167172328,-0.0212382264,0.2320043594,0.0730813667,0.5951886177,-0.375543952,-0.431763351,-0.2903711498,0.0442765206,-0.0678209066,0.388124913,0.1591873169,-0.1912016869,0.1276559085,-0.1218238547,-0.1066585705,-0.1995977312,0.0545986891,0.0055705006,0.0142568611,-0.2931369841,0.042768497,-0.0168282352,-0.3169675469,0.0828653201,-0.1454019845,-0.3469511867,0.2273458093,-0.1418411285,-0.1531399786,-0.103425473,-0.3367689848,0.331312567,0.1526703984,0.1026710272,-0.0181600768,-0.1312152743,0.1427518129,0.3137509525,0.149308756,0.1275745034,0.0306877419,0.0981538221,0.3543131351,-0.1037020609,-0.0901145488,-0.0526893586,-0.3660354018,0.028083222,-0.3211253285,0.2621266246,-0.2186490148,-0.1130315736,0.3243969083,-0.2721689045,-0.0167551339,0.0370390564,0.2277623713,0.0052966536,-0.1787994057,0.0841014087,0.2368547767,0.1973741651,-0.1256801635,-0.1394316405,-0.0941007435,-0.0140535943,-0.0533157848,0.1990813315,-0.1054171845,0.4438874125,0.1304032356,0.1367830634,-0.2728513479,0.0169609319,-0.0018022087,-0.1420742571,0.3386525214,-0.0260521062,0.0750593618,0.0869665071,-0.3207398355,-0.1433800757,0.1236498356,-0.1157213226,-0.1883138269,-0.290738225,0.2257093936,-0.0367966332,0.0658994466,-0.5976707339,-0.0011099846,0.0638555512,-0.1855823994,0.2316445112,-0.1494339854,0.1938385069,-0.1521389782,0.1029574499,0.1464119703,-0.4950017035,0.0221774504,-0.1589584202,-0.0176939517,-0.0424075797,0.1898302883,-0.3118867278,0.2282846421,-0.2827685177,0.1994856149,0.3716087639,-0.1947320402,-0.5814797282,0.4290313721,-0.0603754595,0.1802990586,0.0617141016,-0.1903425306,-0.0216886047,0.0542630926,0.177885294,0.1786771566,-0.0923178717,0.0467419252,-0.1771633029,-0.2370402217,-0.1145110205,0.1815148145,-0.1182245091,-0.1742785126,0.1272883862,0.0504545942,0.0662610531,-0.0625444427,0.1244463325,0.3878649771,0.0354947224,-0.1943142414,0.0840654969,-0.1906257123,-0.5504314303,0.1895055473,-0.1231901497,-0.0653060898,-0.3439671099,0.0051857266,-0.3859595656,0.1546897143,-0.2247076333,-0.0849858746,0.1455646157,-0.0632971302,0.1666700244,-0.0886188671,-0.1392122805,0.0785615742,-0.3218708038,0.2977219522,-0.2379867136,0.0187073015,-0.059816543,-0.1872344464,0.0309129804,-0.0353584215,-0.096554257,-0.0168781672,-0.3940271139,0.245917201,0.1489115059,-0.0034364259,0.0167317502,-0.0127036646,0.020289598,-0.1318188608,0.2086426765,0.3051687479,0.0084877135,-0.0966418833,-0.0439682193,0.3174681664,-0.0848724023,0.2753054798,0.0308774374,-0.2157543451,0.3587179482,0.176627025,-0.1378899068,-0.0662189573,0.0552623458,0.0341625996,0.3627393544,0.0012815826,-0.2344884574,-0.1374022067,0.5148671269,0.0055709057,0.0353187621,0.323432982,-0.3288779855,-0.0550703034,0.0166629814,0.3429915607,0.3857359588,0.1958513409,-0.1432737857,0.1436224729,0.1142028421,-0.1619254649,0.294993937,-0.0753320828,0.333345592,0.3271282315,0.3447594345,-0.0654993728,-0.351238519,-0.3344878852,-0.1525239348,0.1580728143,-0.2348374724,0.0627109259,-0.1558992267,-0.0391673632,-0.220437035,-0.1645056307,-0.2174471021,-0.1441415697,-0.019337099,0.0334845297,-0.1674218178,-0.0444168448,-0.0390951149,0.2795860469,0.1392436326,-0.2436903119,-0.282361567,0.0607379116,-0.3482090831,0.0674667805,0.4192628264,-0.0003479357,0.0694916174,-0.0711842105,-0.0388312414,-0.1816263795,-0.1992989033,0.0969154313,-0.0967315659,0.4307474494,0.2579059005,0.2820337713,-0.1104064509,-0.1597536802,0.2159285545,0.0003317513,-0.4779896736,0.0716935843,0.0592678189,0.1459024251,-0.0427414514,-0.186588347,0.0989596695,-0.5119397044,0.6149398685,0.2794421911,0.0852368698,0.1733131558,0.1801074296,0.3740797341,0.0623701774,0.2490324378,-0.0762317628,-0.3669802845,0.2387926877,-0.268437624,-0.2240346968,0.0622463711,0.0489933081,0.3258873522,-0.2646791935,-0.6064217687,0.2254449427,-0.2119208872,0.1771799326,-0.2865157723,-0.0056458907,0.0579444095,0.0400157422,-0.0808884352,-0.116959177,-0.2159638256,-0.0321979076,-0.0289163589,0.2511908114,-0.096083045,0.5281828046,-0.1473544836,0.4271023273,0.3951170444,-0.0573067628,0.2552519441,-0.0346699767,0.1327648461,-0.4422535002,-0.1322472095,-0.0498869643,0.0054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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3210","title":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py","comments":"Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?\r\n\r\nMaybe you're having this error because you don't have access to this URL from python ?","body":"when I use python examples\/pytorch\/translation\/run_translation.py     --model_name_or_path examples\/pytorch\/translation\/opus-mt-en-ro     --do_train     --do_eval     --source_lang en     --target_lang ro     --dataset_name wmt16     --dataset_config_name ro-en     --output_dir \/tmp\/tst-translation     --per_device_train_batch_size=4     --per_device_eval_batch_size=4     --overwrite_output_dir     --predict_with_generate to finetune translation model on huggingface, I get the issue\"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py\".But I can open the https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py by using website. What should I do to solve the issue?","comment_length":35,"text":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py \n when I use python examples\/pytorch\/translation\/run_translation.py     --model_name_or_path examples\/pytorch\/translation\/opus-mt-en-ro     --do_train     --do_eval     --source_lang en     --target_lang ro     --dataset_name wmt16     --dataset_config_name ro-en     --output_dir \/tmp\/tst-translation     --per_device_train_batch_size=4     --per_device_eval_batch_size=4     --overwrite_output_dir     --predict_with_generate to finetune translation model on huggingface, I get the issue\"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py\".But I can open the https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py by using website. What should I do to solve the issue? \n Hi ! Do you have some kind of proxy in your browser that gives you access to internet ?\r\n\r\nMaybe you're having this error because you don't have access to this URL from python ?","embeddings":[-0.0930820033,-0.3882340491,-0.0091214394,0.1529586613,0.3836990893,-0.0650625378,-0.0014787095,0.0271892436,-0.1174210086,0.4333743751,-0.1092182174,-0.1595528722,0.3426676393,0.1349743903,0.3313347697,-0.4141206741,-0.0376143903,-0.2521337569,-0.3091399372,0.1163745299,-0.1904375106,0.006159463,-0.1553151011,0.4519802332,-0.1227999851,0.0230165944,-0.1161986962,0.1812493503,-0.1616168618,-0.090040043,0.2339698076,-0.1223879531,0.086016193,0.633104682,-0.0001152875,0.291282773,0.31692788,0.01080235,-0.2478015572,-0.7171230316,0.1798837036,-0.0893040746,0.0302980486,-0.0283104535,-0.1110544503,0.0980466753,0.2487851977,-0.0181566104,0.3253205717,0.4141796529,0.1969481856,0.5055872798,0.4007528424,-0.1350556016,-0.0272531267,-0.4293341935,-0.0464920029,0.5597358346,0.090552181,0.0389654711,0.2032524198,0.1462634802,0.0367597677,-0.0222074185,0.0919772983,0.043982733,0.0431750789,-0.5436480045,0.2048203349,0.199747622,0.0197085813,-0.0259728096,-0.325432092,0.1636294723,-0.1599232405,-0.2409714013,0.3313125372,0.2471271604,-0.2648833692,0.2114829868,-0.2447139621,-0.2864344716,-0.4151312411,0.4957443178,-0.1166132241,0.2830678225,-0.1330638528,0.2165968418,0.1948328167,-0.0284111593,-0.2247250527,0.1118656099,0.303180486,0.1750667989,0.0457665361,0.2069664448,-0.0033285674,0.0472443476,0.1303165257,-0.0556804724,-0.0740627125,0.1128433272,0.1266449839,0.1408409029,-0.0392814539,0.1614819765,-0.2678851783,0.0755395442,0.4573183656,0.4898054302,-0.047989957,-0.0447215475,-0.2004100978,-0.4534274638,-0.3639001548,-0.0522321723,0.3964823782,0.1150523648,-0.1000093892,0.1669711918,-0.1783579141,-0.0562650636,0.0275582373,0.5089324713,-0.3785561323,-0.1174978837,0.2066662163,0.2035696208,-0.1725941002,-0.047136873,-0.1565240473,0.0717657208,-0.1782705784,0.1823792756,-0.0010480835,-0.0436635539,0.3234508038,-0.2906609476,0.3437750936,-0.4356211126,-0.025491029,-0.0871844888,0.0295301173,0.1696953923,0.268865943,0.0262316652,0.2548701465,-0.0393710099,-0.0677136853,-0.0173218455,-0.3118954897,-0.4043568671,0.1877011806,0.1553711891,-0.0176793672,0.0394031331,-0.0016794605,-0.124901101,-0.2162704617,0.1609058529,-0.1856099516,0.0256434791,-0.1794659793,-0.1012506634,0.4366515875,0.3248753846,0.3367983401,-0.1667286158,0.283641845,0.0960740298,0.219066903,0.3371478915,-0.1930499822,0.2371588796,-0.1781346202,-0.0113841286,0.3669598699,-0.2649623752,-0.4248211384,0.3282744288,-0.4896480739,-0.0246959236,-0.1093868464,-0.1062208489,0.0056144753,0.1419382542,0.2943850458,0.1707984805,-0.0237153787,-0.0647690594,-0.0331637897,-0.332354188,0.2970778644,-0.1107780337,0.2159586847,0.1153436974,0.143479079,-0.1525841355,0.2073067874,0.0322757624,0.0327009447,0.3215776384,0.0806670636,0.1966357976,-0.1086844131,-0.0895334035,0.0768306404,0.0489276536,-0.1248530373,0.3590252101,-0.2957674265,0.079556793,-0.2639226019,0.1496614367,-0.214020595,-0.1008936614,0.0697133318,0.0061231856,0.3395231664,0.265637219,0.0608465038,-0.1770690382,-0.2813945711,0.2078834921,-0.2546048164,0.2838094532,-0.4139495492,0.1367822886,-0.0549941324,0.1522858292,0.2467970252,-0.2308077365,-0.0873150751,0.2708224654,-0.3780185282,0.2507039309,0.24344486,0.3402449191,0.2136473209,-0.665450871,-0.0560660735,0.1964088827,0.0405313373,0.2639284432,0.4616982341,0.2858487964,0.0417159386,0.4953610897,0.4974422753,0.194770664,0.3821514845,0.1174808517,-0.3545909226,0.0510103144,0.3267090619,-0.0320452899,-0.0215776321,-0.2606867254,-0.1111810207,-0.1915271729,0.2415689081,-0.0752139986,0.2466730624,0.2822245359,-0.064012818,-0.0562332459,-0.3478586376,0.0246864054,0.0753175467,0.1111519933,0.2015426308,0.3978770375,-0.106102027,-0.13854222,0.3545995057,0.2399470657,0.0534446575,0.038748458,-0.1607277691,-0.0037658743,-0.0331528857,-0.0558577925,-0.2289720327,0.0483111851,-0.205395624,0.203879863,-0.2627325058,-0.3990846872,-0.3664082289,-0.4666282833,-0.5186995864,-0.2288581878,-0.2277515084,-0.0249777585,-0.1403739452,0.2179193199,-0.0952566117,0.0245211553,-0.0709113404,-0.0143745821,0.0962658226,-0.2077765465,-0.2750039399,0.0047861221,0.0331317373,-0.4152544737,0.2100702971,-0.3572312891,-0.2126265913,-0.2742788792,-0.2928537726,0.1296206862,0.1037698835,0.3902914822,0.2535191178,0.5234680772,0.1580985785,-0.0777634159,0.6147894263,-0.0551340953,-0.0011081162,-0.05852421,-0.0608426966,-0.0676351041,0.1368138343,-0.3228598237,-0.3177286088,-0.2845435441,0.1970181614,-0.2229854465,0.315084368,0.2369022965,0.1169968322,0.2412586957,-0.0924535543,0.3972175419,-0.1185106933,-0.3637586236,0.2181180567,-0.2219630927,-0.5753452778,0.1965656728,0.1742939949,0.2536409795,0.0196589921,-0.3724506497,-0.139095217,-0.1651719958,0.1573393345,0.225602746,0.2018876076,0.1807179749,-0.2321415544,-0.1040268168,-0.0118516171,-0.2092623115,0.0137852421,-0.1657152027,0.3661257327,0.2193436623,0.6551265717,-0.0125985211,0.4479271472,0.1187106892,0.1391967833,0.2367835641,-0.2566568851,0.1440774202,-0.1453217268,-0.3303592503,-0.0360445604,-0.2917890549,-0.03443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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3210","title":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py","comments":"You don't need authentication to access those github hosted files\r\nPlease check that you can access this URL from your browser and also from your terminal","body":"when I use python examples\/pytorch\/translation\/run_translation.py     --model_name_or_path examples\/pytorch\/translation\/opus-mt-en-ro     --do_train     --do_eval     --source_lang en     --target_lang ro     --dataset_name wmt16     --dataset_config_name ro-en     --output_dir \/tmp\/tst-translation     --per_device_train_batch_size=4     --per_device_eval_batch_size=4     --overwrite_output_dir     --predict_with_generate to finetune translation model on huggingface, I get the issue\"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py\".But I can open the https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py by using website. What should I do to solve the issue?","comment_length":26,"text":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py \n when I use python examples\/pytorch\/translation\/run_translation.py     --model_name_or_path examples\/pytorch\/translation\/opus-mt-en-ro     --do_train     --do_eval     --source_lang en     --target_lang ro     --dataset_name wmt16     --dataset_config_name ro-en     --output_dir \/tmp\/tst-translation     --per_device_train_batch_size=4     --per_device_eval_batch_size=4     --overwrite_output_dir     --predict_with_generate to finetune translation model on huggingface, I get the issue\"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py\".But I can open the https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.15.1\/datasets\/wmt16\/wmt16.py by using website. What should I do to solve the issue? \n You don't need authentication to access those github hosted files\r\nPlease check that you can access this URL from your browser and also from your terminal","embeddings":[-0.1509537101,-0.2567588985,-0.0155017553,0.2449313849,0.2433852702,-0.0078566838,0.0931585357,0.0966032222,-0.0795091242,0.4065466225,-0.2241298705,-0.2348442078,0.2964991629,0.0799348503,0.1829091012,-0.1545806378,-0.0657936707,-0.2579960823,-0.1552280635,0.041349832,-0.1073371619,0.0781104192,-0.1754534543,0.4801565707,-0.1020730138,-0.0386789516,0.0757803544,0.2212059796,-0.0804941058,-0.1503170133,0.2274384797,-0.0404367596,0.0538209416,0.6712306142,-0.0001167883,0.3110307455,0.2887931764,-0.0040878537,-0.2500893176,-0.7219985127,0.2048880458,-0.1496480852,-0.0655591339,-0.0031145695,-0.2526448071,0.008718119,0.2720429301,0.032589104,0.4103091359,0.381067872,0.1532221437,0.4907596707,0.4328309894,-0.1538493633,0.0431250408,-0.2989080548,0.0449850112,0.6197916269,0.216872558,0.0398653448,0.087013945,0.0937867314,0.0758692175,-0.0151608707,0.1010149196,-0.0392705388,-0.0506086312,-0.5751081109,0.1343626529,0.0881321952,-0.1322869509,0.0430220291,-0.2824388742,0.0252175163,-0.1981960833,-0.1705623865,0.3904671073,0.4674403667,-0.3060926795,0.2090854198,-0.4006349742,-0.2584942281,-0.4434069395,0.4437513947,0.0724640638,0.2254310846,-0.1580845863,0.2360318303,0.0569881238,-0.1667419821,-0.3735985458,0.0291130152,0.1840316057,0.1995318234,0.1505902261,0.1832542866,-0.0769736171,-0.0751188174,0.1593694687,0.0938971788,-0.2375121564,0.0562711284,-0.0507320091,0.1013597623,-0.0683070719,0.1583533436,-0.1253854036,0.1740636826,0.5152621269,0.5846592188,-0.1252694428,0.1206225976,-0.3041924834,-0.4383408129,-0.4256124794,-0.106776908,0.3761438131,0.0572141111,0.0132881533,0.2025860548,-0.1654171944,-0.1040329114,-0.0432499424,0.4347074032,-0.2848944962,-0.3164162636,0.1927833259,0.2856719494,-0.1028242782,-0.1809801906,-0.0980987027,-0.0042514456,-0.0021172801,0.1445174515,0.0263524838,-0.2774706483,0.4154528379,-0.1292771399,0.3802054822,-0.4737125039,-0.0118640112,-0.0269532744,0.0589635894,0.16522488,0.3468657732,0.0411014892,0.1586212069,-0.2671115398,-0.0290401019,-0.0536433011,-0.3161173761,-0.4234744608,0.083175227,0.1121805012,-0.019844804,0.1421327293,-0.1061693281,-0.1659054607,-0.1899522096,0.1598163098,-0.2226679623,-0.0498925932,0.096651651,-0.0787430853,0.4216910601,0.1995509267,0.5896773338,-0.0993665457,0.4339527786,0.154915601,0.225445807,0.3589088917,-0.2934634984,0.1978481859,-0.255417496,0.0747845098,0.2270254195,-0.4901200235,-0.4238408208,0.2611234188,-0.3190603256,-0.0048740953,-0.0475577302,-0.2100144774,0.0969178975,-0.0161679517,0.2846215367,0.1588593274,0.144785732,-0.0841969326,-0.0883201361,-0.3599423468,0.1821309924,-0.0904229954,0.1467757672,0.0510393195,0.2549144924,-0.1828037798,0.2434675843,-0.0353890136,0.0385040604,0.2445309311,-0.045738019,0.0467512012,-0.1550025642,0.1156839803,0.1714536101,0.043938946,-0.0122489305,0.3452628851,-0.3026330471,0.0610340126,-0.1600098312,0.1215137392,-0.2935656309,-0.184599638,0.0711800456,0.0382617563,0.2955882847,0.2374918908,0.1626801193,-0.1968389452,-0.3393874168,0.1901948452,-0.1469220072,0.2603962123,-0.3194339275,0.1302462816,0.090090014,0.1359282732,0.2819173634,-0.2827288806,-0.0708343685,0.1968520135,-0.3384465873,0.2623388767,0.3759232163,0.3197783232,0.164809674,-0.5091866851,-0.1029468179,0.1433856487,-0.0348112397,0.3124517798,0.4339692593,0.3601879179,0.101384446,0.4083537459,0.4650038779,0.1988862604,0.4267331958,0.1958149821,-0.40732795,0.0521472357,0.279915005,0.0484456792,-0.0073248791,-0.3184629679,-0.1923347116,-0.1135285571,0.2975920737,-0.1172287241,0.1333855838,0.22927095,-0.0620213374,-0.0172479022,-0.2373180538,0.1415507346,0.0221920311,0.0486582182,0.1789349467,0.3313022554,-0.1190859303,-0.1941947937,0.3829255998,0.1874775887,-0.0406341292,-0.0060001053,-0.0738094226,-0.0103239473,0.0111361025,-0.0602389127,-0.2321395129,0.0327709801,-0.2287322581,0.0743933991,-0.1901481897,-0.2741993964,-0.3249979317,-0.4724561274,-0.5843862891,-0.0630090684,-0.1577684581,0.0080216313,-0.0888915211,0.1642741263,0.2872470617,0.0684977546,-0.1314931363,0.0128187528,0.1722902507,-0.1771663427,-0.2594164312,-0.0485395752,0.2516717911,-0.5049004555,0.2356877923,-0.4138042331,-0.1826026142,-0.3557458222,-0.178110078,0.0635760576,-0.0287131555,0.4513370097,0.0762986317,0.4599321187,0.2691437602,-0.0110516567,0.5343033075,0.0867780223,-0.0108972769,-0.1471390873,-0.025689058,-0.1915903538,0.0835790709,-0.1646740735,-0.2484330237,-0.3005219102,0.2432477176,0.0089481305,0.3799396455,0.2487595379,0.0768191144,0.3025740087,-0.0264972243,0.3924579024,-0.0980942994,-0.5529931188,0.2889695466,-0.31413427,-0.5499428511,0.2781276405,0.355209291,0.3885726929,-0.0425271504,-0.4367412627,-0.0994295329,-0.1166774854,0.2813898027,0.1866792738,0.1754858345,0.0769267753,-0.1707226187,-0.0175652727,0.0526335351,-0.2943707407,0.1367958933,-0.0869853273,0.150767982,0.2118659765,0.4436598718,-0.037338715,0.6914635301,0.2878741026,0.1941111088,0.0688829124,-0.1906650662,0.1609854996,-0.0692016706,-0.2283073962,-0.0231293831,-0.13669300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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3204","title":"FileNotFoundError for TupleIE dataste","comments":"@mariosasko @lhoestq Could you give me an update on how to load the dataset after the fix?\r\nThanks.","body":"Hi,\r\n`dataset = datasets.load_dataset('tuple_ie', 'all')`\r\n\r\nreturns a FileNotFound error. Is the data not available? \r\n\r\nMany thanks.\r\n","comment_length":18,"text":"FileNotFoundError for TupleIE dataste \n Hi,\r\n`dataset = datasets.load_dataset('tuple_ie', 'all')`\r\n\r\nreturns a FileNotFound error. Is the data not available? \r\n\r\nMany thanks.\r\n \n @mariosasko @lhoestq Could you give me an update on how to load the dataset after the fix?\r\nThanks.","embeddings":[-0.1196369529,0.0451600589,-0.0531532243,0.4988981783,0.2058224231,0.156067282,0.2879101634,0.2625411451,0.2662692666,0.0063051092,0.0560929216,-0.0138805732,-0.1284624785,0.2602334321,-0.0655831099,-0.1212602928,-0.165182367,0.2249730527,0.0427779295,-0.0181837436,-0.2478189915,0.1665060222,-0.4162748158,0.0138770901,-0.2687963545,0.1630674154,-0.1331157684,0.4549990594,-0.1606255025,-0.1381944716,0.4359325171,-0.0432396866,0.1213596612,0.1598352939,-0.0001055389,0.0429350063,0.2902178466,-0.2782854438,-0.2363066375,-0.3211078942,-0.4993294179,-0.0028152731,-0.0334908888,-0.3217594624,-0.2159611285,0.1052285656,-0.0783581585,-0.2142108381,0.4745201766,0.3432846963,0.2274400145,0.0681694821,0.2238767743,-0.1401960254,0.3253952861,0.2632128894,-0.0426927172,-0.068682231,0.2652723789,-0.3149090111,0.2360957414,0.1896709502,-0.0427055769,-0.0513556339,-0.2809863389,-0.0185317509,0.0585492291,-0.250090152,0.0166018419,0.2242083549,0.7670741081,0.1049741432,-0.4100658298,0.1957039684,0.027835628,-0.2411700338,0.1856892109,-0.0117404796,0.0368023552,0.1393930465,0.2362641245,-0.2493599951,-0.3036237359,0.3177767396,-0.2781789303,0.0607926697,-0.1479185075,-0.0377877913,0.0007715671,-0.232272014,-0.2851449251,0.2375765592,-0.0983739942,0.1464173943,-0.1673810631,-0.2420621216,-0.0216600467,0.0058330493,0.1528690457,0.1066984534,0.0557867065,0.087097317,-0.2716069818,0.0542178936,0.3948206007,0.0479261577,0.0415563993,0.0564139895,0.31082654,0.1417484581,-0.2081707865,-0.056349501,-0.034201704,-0.1904169917,-0.1305862069,0.1542568207,0.7042602301,-0.4075497687,-0.4645266533,-0.0542949513,0.0225551073,-0.2060077637,0.1383613646,0.3462499082,-0.2172000259,0.1663615108,0.1262941062,0.1765700579,-0.1918983757,-0.4662945867,-0.2245907187,0.0529375449,-0.173620671,0.0080336314,0.265127331,-0.0620952249,0.2930434346,-0.07593178,0.0483385883,-0.0554839559,0.1442049742,-0.2144052982,0.0028127606,0.2280306816,0.0143881487,-0.0178065225,0.2582375109,-0.4266127944,0.1093884185,0.2520402074,-0.7108904123,-0.3892115355,-0.3980081975,0.2220368832,-0.2670136392,-0.0859945789,-0.1949270517,-0.0258687157,-0.069807753,-0.3809635937,-0.215407446,-0.1465828866,0.1312548518,-0.3905726671,0.2349525243,0.7793862224,-0.6047232151,0.0630533844,-0.1707817167,-0.3291160166,0.298070848,0.2991489172,-0.0834798962,0.1166760996,-0.3701768517,0.0261537023,0.5290744305,-0.0547900721,-0.4205896556,0.0172252059,0.1035530493,0.1493461281,-0.0227771159,-0.0009835596,-0.0231249277,-0.1245148852,0.3176123798,-0.0684465989,-0.1200854182,-0.0403890014,-0.2750503719,0.182437703,0.1693339646,0.4288368523,0.1472091973,0.1463044137,0.0873228908,-0.0037171238,0.3148342669,-0.0974449366,-0.0003572066,0.2578189969,0.3997285962,0.3802450299,0.0587822497,0.1154491976,-0.4067999721,0.1287438422,0.0439476408,-0.1356131434,-0.2752274871,-0.0916800648,-0.1009087935,-0.1266297251,-0.3329412937,-0.2522030175,0.1475646496,0.0758832544,0.0224290751,0.0146361887,-0.3561009765,0.0325502641,-0.0790786743,-0.0292849056,-0.2311645299,0.4723606408,0.0316236652,-0.2124637812,-0.0139164384,0.1160055101,-0.0746324956,-0.3244273961,-0.1959884763,0.2334078848,0.3944271505,-0.2003938258,0.2282147706,-0.149519071,0.2854166329,-0.2191949189,0.1478712261,0.1408933401,0.0824731365,-0.0399811156,-0.137414664,0.4697605073,-0.2172608227,-0.1942568719,0.1352632344,0.0442938283,0.2962692082,-0.1099108458,-0.1511108875,-0.1523764879,0.2788491249,0.1356874555,0.0607851706,-0.1238841712,-0.4841679037,-0.0447353758,0.3859453499,0.1631643474,0.0716298372,0.0363268778,-0.1887632608,0.1289861202,-0.1986592263,0.0625279173,0.6189296246,0.1692722589,-0.1732684821,-0.1133763939,0.2879259884,-0.09834598,0.0162394904,-0.0976404399,0.2170813382,0.2551521063,-0.0722794831,-0.0119094029,-0.3030003607,0.0227400064,-0.1139480025,0.3090086281,-0.2982609868,-0.1029420048,-0.2547317445,-0.4612659216,0.2989509404,-0.1692093313,-0.0804211199,-0.1060044616,0.145273149,0.0624583252,-0.1109116152,-0.0218471829,-0.116797097,0.1271489263,0.1075467765,-0.2023765445,-0.2182414234,-0.2721255422,-0.0950284898,0.2190290987,0.3342819512,-0.4088043571,0.2276571244,-0.1557149291,-0.0041112942,-0.1746983081,-0.3698335886,-0.1049311757,-0.0187025927,0.3566993475,0.0194474906,0.3945502341,-0.1435581297,0.1182210296,0.3385028243,-0.0618158393,-0.1711033583,0.2297807932,0.1394510269,0.0451823622,-0.0136903524,-0.6663831472,-0.4856442809,-0.2101214975,0.1815127581,0.2866955996,0.0148972059,-0.0410963893,0.0232721139,-0.1345594078,0.1266447455,-0.0851707458,-0.3077641428,-0.3702037632,0.3344349563,-0.3319247663,-0.4869295359,0.300722301,0.014079079,0.1728632003,0.1197764501,-0.4143418968,-0.1273867339,0.2798112631,0.1125086173,-0.1955720186,-0.2870689034,0.3970383406,-0.0345781669,-0.0906862468,-0.1133900657,0.0633179471,0.0193444546,0.2534275949,0.3832389116,-0.1979486197,0.2404646277,-0.1117616296,0.3203624785,0.1616945863,0.1415376961,0.5082679391,-0.14825207,0.1420863718,-0.1062607542,-0.241730094,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3204","title":"FileNotFoundError for TupleIE dataste","comments":"Hi @arda-vianai,\r\n\r\nfirst, you can try:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('tuple_ie', 'all', revision=\"master\")\r\n```\r\nIf this doesn't work, your version of `datasets` is missing some features that are required to run the dataset script, so install the master version with the following command:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```\r\nand then:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('tuple_ie', 'all')\r\n```\r\nshould work (even without `revision`).","body":"Hi,\r\n`dataset = datasets.load_dataset('tuple_ie', 'all')`\r\n\r\nreturns a FileNotFound error. Is the data not available? \r\n\r\nMany thanks.\r\n","comment_length":64,"text":"FileNotFoundError for TupleIE dataste \n Hi,\r\n`dataset = datasets.load_dataset('tuple_ie', 'all')`\r\n\r\nreturns a FileNotFound error. Is the data not available? \r\n\r\nMany thanks.\r\n \n Hi @arda-vianai,\r\n\r\nfirst, you can try:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('tuple_ie', 'all', revision=\"master\")\r\n```\r\nIf this doesn't work, your version of `datasets` is missing some features that are required to run the dataset script, so install the master version with the following command:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```\r\nand then:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('tuple_ie', 'all')\r\n```\r\nshould work (even without `revision`).","embeddings":[-0.1884028465,0.0600258149,-0.1039324477,0.2780112028,0.2167989165,0.2223355621,0.2928452194,0.4254568219,0.3324117959,0.1265628636,0.0710726008,0.0770501122,-0.0805492252,0.2199549228,-0.1739464253,0.0059488164,-0.0722435117,0.3442076743,-0.0204527583,-0.0128169982,-0.2555243969,0.1422875673,-0.3287061453,-0.0161633138,-0.2225392759,0.1903196722,-0.1256611049,0.2449322492,-0.2099729478,-0.2294143885,0.3297737241,0.0290527139,0.1780368537,0.4961600304,-0.0001017226,0.0659029931,0.2072933316,-0.2327689976,-0.262620002,-0.3174369633,-0.3245595098,-0.0629335493,0.098747693,-0.4337272346,-0.3259531558,-0.0727600381,-0.10350997,-0.1992860436,0.4231489897,0.30749318,0.3073568642,0.1573150754,0.2867789567,-0.1966301799,0.3415281475,0.1747042686,-0.068604894,-0.1339993924,0.0964973122,-0.1806237549,0.2836656272,0.2474169731,-0.1010740027,0.0716649592,-0.1066520214,0.0513509847,0.1918147057,-0.2570739686,0.0701638758,0.0985399187,0.6515603662,-0.2882898152,-0.504809916,0.2837334871,-0.065184541,-0.3108632863,0.0793355256,0.0909761637,0.0732144043,0.0581426919,0.1751354933,-0.0826160535,-0.2140293866,0.3825112879,-0.2152145058,0.054928299,-0.1153437048,-0.1092781872,0.0751917437,-0.2541840374,-0.1710431278,0.1419704258,-0.0423025265,0.1426503211,-0.0182557274,-0.2105207145,0.1233491451,-0.0755295902,0.159755677,0.1876920462,0.0576031581,0.04396227,-0.1729820222,0.246454522,0.3219005764,0.0942501947,0.0632248744,0.0979460329,0.3280446231,0.1251166314,-0.000156559,-0.0693565235,0.0165506788,-0.2103380859,0.0425511263,0.1938447803,0.6039740443,-0.2909435928,-0.34163782,-0.0090896469,-0.0324498266,-0.134377569,-0.0705743134,0.2835499644,-0.2364408523,0.1205775812,0.1510027647,0.2034811825,-0.2899571657,-0.2150042206,-0.2702139914,0.1067750603,-0.0646072552,-0.0987371057,0.2457273304,-0.1502390802,0.3038089275,-0.0909928828,0.0371890813,0.0320884213,0.1586518139,-0.1779493392,-0.0274964701,0.2439035624,0.2635677457,-0.0833066031,0.3418835104,-0.5316170454,-0.0566886216,0.1274126768,-0.4508863688,-0.3061481714,-0.2911940515,0.3058851361,-0.2022772282,-0.0607546195,-0.1749678105,-0.1501983851,0.1236895621,-0.2420856357,-0.2290040851,-0.1792710871,0.0722755417,-0.3932963312,0.2523382306,0.7262337208,-0.4364094436,0.0998231322,-0.0990035385,-0.2419247776,0.0450068153,0.2323188782,0.0348328985,0.0387278013,-0.194876492,-0.0605843961,0.4206044078,-0.1948913336,-0.3661433756,-0.0112042641,0.1280029863,0.007572996,-0.0302640013,0.0191785581,-0.0135409385,-0.1191022918,0.261872977,0.0193879213,-0.0426748991,0.0865644962,-0.0557381473,-0.11836043,0.056709487,0.2457703501,0.0525201,0.1381392479,0.1051095501,-0.1205463335,0.2974276841,-0.1351120919,0.1010173187,0.2827281654,0.4195114672,0.3884800673,0.0325545967,-0.0936233848,-0.2778609395,0.1546180397,0.1414123923,0.0345972478,-0.4303129911,-0.2123671174,-0.1282416135,-0.0379055329,-0.3256772459,-0.1976947635,0.2589091063,0.1400584579,0.1751213074,0.0352987871,-0.2834664285,-0.0914449543,0.0981215537,0.0547523312,-0.228539288,0.3920652568,-0.1500268281,-0.2166557759,0.1667317897,0.1542202383,-0.0270917658,-0.258993715,-0.1081852466,0.1903571337,0.3302900195,-0.1633556038,0.1002362967,-0.0644912794,0.1075603291,-0.2725981772,0.2172839493,0.2884937823,0.0794820487,0.0111039989,-0.1340310276,0.3363799155,-0.1052835286,-0.0714108646,0.0870400965,0.1211894974,0.2319198251,-0.1071924046,-0.134426415,-0.424192965,0.0902359188,0.1607980579,0.0368762948,-0.1865573823,-0.214329049,-0.0993712321,0.4551886916,0.2090732008,0.1186347976,0.0440044254,-0.2059645504,0.2168522775,-0.1860711724,-0.0005349828,0.4378248453,0.2825300992,-0.0516903996,-0.0300912093,0.1439564675,-0.0315019414,0.0054869652,-0.0079511041,0.057246957,0.0754397213,-0.0141705591,0.0190322716,-0.2870547771,-0.0607301034,-0.2124999762,0.3524796367,-0.1315884292,-0.0123045594,-0.2842728794,-0.2926873565,0.2449625582,-0.3191083372,-0.0582490042,-0.0915227979,0.0917971507,0.1309310347,-0.0566581786,0.0496346056,-0.0069833728,0.0131343594,0.22738792,-0.2872689366,-0.0278030615,-0.3809598684,-0.1570046097,0.2463087291,0.2401649952,-0.2751896679,0.42379722,-0.2361138761,-0.0219629724,-0.2930737436,-0.357707262,-0.1021803021,-0.1963368952,0.2687685192,0.0601025149,0.3273431361,0.0803576484,0.136785537,0.4028469324,-0.0481802188,-0.1761002988,0.175944671,0.1016500816,-0.1415935755,-0.1103651002,-0.5738961101,-0.4431554675,-0.3519484401,0.1656282246,0.4396013319,0.1674074978,-0.0087385522,0.2039482445,-0.1030218676,0.266883105,0.079615891,-0.2182001323,-0.2121763825,0.355414331,-0.4575640559,-0.5526000261,0.3649142981,-0.0317207985,0.182656765,0.2007889748,-0.3886916935,-0.2947537005,0.0452412926,0.1140660569,-0.0271809194,0.0950095505,0.3707847595,0.1734091043,-0.105787538,-0.0873529315,-0.0400123522,0.0031005377,0.0562795475,0.1349626333,-0.073542431,0.3813496828,-0.0567122847,0.2670510113,0.3404131532,0.0368356109,0.4256038666,-0.3057474196,0.2107226253,-0.196995616,-0.2787997425,-0.216582045,-0.1129824817,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3204","title":"FileNotFoundError for TupleIE dataste","comments":"@mariosasko \r\nThanks, it is working now. I actually did that before but I didn't restart the kernel. I restarted it and it works now. My bad!!!\r\nMany thanks and great job!\r\n-arda","body":"Hi,\r\n`dataset = datasets.load_dataset('tuple_ie', 'all')`\r\n\r\nreturns a FileNotFound error. Is the data not available? \r\n\r\nMany thanks.\r\n","comment_length":32,"text":"FileNotFoundError for TupleIE dataste \n Hi,\r\n`dataset = datasets.load_dataset('tuple_ie', 'all')`\r\n\r\nreturns a FileNotFound error. Is the data not available? \r\n\r\nMany thanks.\r\n \n @mariosasko \r\nThanks, it is working now. I actually did that before but I didn't restart the kernel. I restarted it and it works now. My bad!!!\r\nMany thanks and great job!\r\n-arda","embeddings":[-0.1555853486,-0.1022435129,-0.0895033851,0.444070369,0.342571497,0.0214985516,0.3312878311,0.261819452,0.350579977,0.1980799884,0.0239384416,0.1315802932,-0.1134960651,0.2030683756,-0.1134342998,-0.0368215293,-0.1141065061,0.2126443088,-0.0038388988,-0.0412513241,-0.182284236,0.2010690719,-0.4084221125,-0.0326406732,-0.3142307997,0.3056253493,-0.156740576,0.3229051828,-0.2302997857,-0.0565417446,0.4023867548,-0.0385134183,0.0575138293,0.1912997514,-0.0001010782,0.1850826591,0.3513436913,-0.2275903374,-0.1876301765,-0.2483350784,-0.5233640671,-0.0166133363,-0.0400866866,-0.3495929837,-0.3842514157,0.1457787305,0.02794878,-0.1965603381,0.3357736468,0.2159553915,0.2711255252,0.0848693252,0.2601053119,-0.2019708753,0.3969857991,0.2788959444,-0.076694563,-0.1469247192,0.282050252,-0.2643637657,0.2917733192,0.2338208109,0.0000155827,0.0159155,-0.2946620286,0.1134914309,-0.0116306134,-0.2059930265,0.1083311364,0.2397343069,0.6398406029,-0.0075675976,-0.2169286013,0.2511546314,-0.1126849651,-0.336256057,0.1879565269,-0.0366002172,-0.0484021865,0.1838766187,0.2606612146,-0.0676723942,-0.2701373398,0.4147526026,-0.1908260882,0.2408737838,-0.1331818849,-0.0498241186,0.0278939381,-0.2168534547,-0.2786678672,0.2261049896,-0.1212229282,0.0806939453,-0.1122208536,-0.2103828341,0.0043349043,-0.0168774482,0.1281472743,0.1091859043,0.0901346281,0.2075965852,-0.1647049785,0.1776722819,0.2434362024,-0.0546063706,0.0417262241,0.0774973854,0.3534740508,0.077897124,-0.1372027546,-0.1035810411,-0.0201605689,-0.1974708736,-0.1450389177,0.1483155489,0.646214366,-0.3472892046,-0.3221111,-0.2097121179,0.021515023,-0.0876323134,0.1184787378,0.3852744997,-0.1264348328,0.0080974381,0.0784479082,0.2222266644,-0.1940240264,-0.3543459475,-0.2578373551,0.1129918396,-0.1917887628,-0.0473625921,0.3513789773,0.0728213266,0.3801014721,-0.1551336944,0.1337356716,0.0585082844,0.2309383452,-0.2075114995,0.1405503005,0.2548700571,0.0403926335,-0.0982129574,0.276199609,-0.4181117713,0.1165138707,0.2705120146,-0.4841723442,-0.3089700043,-0.1903050244,0.2740747035,-0.0819774345,-0.0855904371,-0.0669302791,-0.006287212,0.0610757247,-0.3648465872,-0.1633922458,-0.036112465,0.1763242036,-0.3314662576,0.236863032,0.5967102647,-0.5580583215,0.1087647155,-0.1054831222,-0.3206526041,0.302013129,0.2507618666,-0.1043357998,-0.0054741646,-0.3114132881,0.193197161,0.4861353934,-0.1085037738,-0.3766987622,0.1116410941,0.1255702674,-0.041659683,0.0346211158,0.0643023923,-0.0274976213,0.045789808,0.3140434623,-0.065530628,-0.0352650099,0.0327445045,-0.2794345319,0.1056586727,0.0928149074,0.3560787141,0.1762642264,0.1162704229,0.1522967964,0.0178247951,0.3799046874,-0.1750828177,0.167086944,0.2092120349,0.4645079374,0.3158397675,-0.0227134116,0.1562071592,-0.2341100276,0.1402873546,0.0016283601,-0.0016813948,-0.2314149737,-0.1004714519,-0.0105927419,-0.1322559118,-0.2940573692,-0.3018004596,0.2339484096,0.1258558631,0.0314312838,-0.0527809635,-0.3654084504,-0.0665463954,0.0276324265,-0.024900591,-0.2347264886,0.4196669757,0.0043980391,-0.3000314236,-0.1051859185,-0.0120370341,-0.0035745949,-0.2609051764,-0.0696659386,0.2125303745,0.449796319,-0.2679314613,0.1171897501,-0.1597216278,0.2499149591,-0.2228108943,0.1169926971,0.2342107892,0.1023969427,-0.1034697145,-0.164756462,0.4544533491,-0.1616309434,-0.1740442663,0.1411165297,0.002681219,0.2857819796,-0.1418536305,-0.2254434973,-0.1879280806,0.2402953953,0.0315815397,-0.0228279661,-0.1682371497,-0.4714055955,-0.0636019558,0.3899435401,0.246280089,0.1224694401,-0.0776299238,-0.1570627242,0.1403824985,-0.0433857627,-0.0277141463,0.6090922952,0.1932736784,-0.0692721829,-0.0770122409,0.2295640409,-0.1137904823,0.0005058866,-0.1897443086,0.1589143127,0.1919807047,-0.144662261,-0.1308232546,-0.3667840958,0.0038945854,-0.0958980769,0.1913163364,-0.2329117805,-0.1253341883,-0.2497987896,-0.3942240775,0.4181593359,-0.2769227028,-0.0214174911,-0.1405730247,0.1398301423,0.0931814611,-0.1133082137,0.0549860299,0.0089866333,0.0813593939,0.1826504022,-0.2563973665,-0.2027769089,-0.2740492225,-0.0754793733,0.2264489383,0.2298571318,-0.2317928672,0.2090879381,-0.1007862911,-0.0561278984,-0.1495262086,-0.3461074233,-0.0374969617,-0.0816662908,0.1803698987,-0.0410263389,0.3408694565,-0.1705493182,0.1142181531,0.3435974121,-0.0732179061,-0.2421217561,0.2988082767,0.1412383616,-0.0858995691,-0.2227187157,-0.5324503779,-0.4597740769,-0.2680584788,0.2389387637,0.3374065757,0.0273892023,0.0951578841,0.1758738011,-0.0653207377,0.2038963735,-0.1053297222,-0.3393774033,-0.3179098368,0.3509102166,-0.4749841392,-0.5626799464,0.3396552205,0.0490766168,0.2503975928,0.1253388077,-0.3830838799,-0.2107987851,0.1779928058,0.069148533,-0.0760222599,-0.1578883827,0.4259518683,-0.0742481276,-0.171479404,-0.1015763953,0.0584198125,0.101330325,0.0961943418,0.2866736352,-0.2105667591,0.2831875384,-0.0120517015,0.2113296837,0.0509462878,0.2343187332,0.5367360711,-0.2305352837,0.2599921525,-0.1251459718,-0.1499682218,-0.09538360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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3190","title":"combination of shuffle and filter results in a bug","comments":"Hi ! There was a regression in `datasets` 1.12 that introduced this bug. It has been fixed in #3019 in 1.13\r\n\r\nCan you try to update `datasets` and try again ?","body":"## Describe the bug\r\nHi,\r\nI would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq.\r\n\r\nThanks.\r\n Best regards\r\nRabeeh \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport numpy as np\r\nimport datasets \r\n\r\ndatasets = datasets.load_dataset('super_glue', 'rte', script_version=\"master\")\r\nshuffled_data = datasets[\"train\"].shuffle(seed=42)\r\nfor label in range(2):\r\n    print(\"label \", label)\r\n    data = shuffled_data.filter(lambda example: int(example['label']) == label)\r\n    print(\"length \", len(data), np.unique(data['label']))\r\n```\r\n\r\n## Expected results\r\nFiltering per label, should only return the data with that specific label.\r\n\r\n## Actual results\r\nAs you can see, filtered data per label, has still two labels of [0, 1]\r\n```\r\nlabel  0\r\nlength  1249 [0 1]\r\nlabel  1\r\nlength  1241 [0 1]\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1 \r\n- Platform: linux \r\n- Python version: 3.7.11 \r\n- PyArrow version: 5.0.0 \r\n","comment_length":31,"text":"combination of shuffle and filter results in a bug \n ## Describe the bug\r\nHi,\r\nI would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq.\r\n\r\nThanks.\r\n Best regards\r\nRabeeh \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport numpy as np\r\nimport datasets \r\n\r\ndatasets = datasets.load_dataset('super_glue', 'rte', script_version=\"master\")\r\nshuffled_data = datasets[\"train\"].shuffle(seed=42)\r\nfor label in range(2):\r\n    print(\"label \", label)\r\n    data = shuffled_data.filter(lambda example: int(example['label']) == label)\r\n    print(\"length \", len(data), np.unique(data['label']))\r\n```\r\n\r\n## Expected results\r\nFiltering per label, should only return the data with that specific label.\r\n\r\n## Actual results\r\nAs you can see, filtered data per label, has still two labels of [0, 1]\r\n```\r\nlabel  0\r\nlength  1249 [0 1]\r\nlabel  1\r\nlength  1241 [0 1]\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1 \r\n- Platform: linux \r\n- Python version: 3.7.11 \r\n- PyArrow version: 5.0.0 \r\n \n Hi ! There was a regression in `datasets` 1.12 that introduced this bug. It has been fixed in #3019 in 1.13\r\n\r\nCan you try to update `datasets` and try again ?","embeddings":[0.2327433825,-0.1531250775,0.0333002433,0.1521297544,0.1432729214,-0.1116957814,0.2651192546,0.0427485295,0.0969828442,0.1914816499,-0.3320685029,0.5272731185,-0.2565167248,0.0196747351,-0.0846298561,0.1135222167,0.2200110257,-0.0801774487,0.2401883751,-0.1659165174,-0.219248578,-0.1141701415,-0.4065219462,-0.0924774781,-0.2146786749,0.0291705281,0.0631911308,-0.0524970964,-0.0867243707,0.1217783019,0.1559275985,0.4608099163,-0.2759886682,0.5565043092,-0.0001183147,0.0382911973,0.1996719092,-0.0183847696,-0.1324395537,-0.3922781348,0.0601483807,0.4741756916,-0.2535991073,0.1168471873,-0.0724522918,-0.1436346173,-0.1966367811,-0.1189216003,0.2382802665,-0.1578133702,0.1323052794,-0.2810742855,-0.076119639,0.1742739826,0.5770320296,0.2081137598,0.0954027399,0.2048232406,0.5185901523,-0.1017637774,0.3113615215,0.3658145368,-0.1308680326,0.0224588979,0.0901455656,-0.068250604,0.1000212207,-0.3251619637,0.0738758445,0.2581748068,0.1028039232,-0.2037578374,-0.5805407166,-0.349355787,0.0759444982,-0.126940459,0.0061658598,-0.0816938058,-0.2365731001,-0.2266127765,-0.2365185767,0.2060770541,0.3590961099,0.0977123082,0.124874182,0.7664955854,0.037104696,0.2190895677,0.1671866775,-0.192096591,0.478944391,-0.1907530725,0.1095514596,0.2778611481,-0.2346525937,-0.0540863462,0.0993441567,0.1610270441,0.1597518772,-0.07787285,0.0877231732,0.0769836083,0.0701523423,0.1450563818,0.3376092315,0.0649943128,0.199561283,0.4244193435,0.0503006876,0.1027287245,-0.2460009754,0.0169648789,0.1755650342,0.1925913692,0.0690482855,-0.1303409934,0.0514381118,-0.0682469457,-0.2193243802,-0.2236939669,-0.4936768115,-0.1910721213,0.3337350488,0.1223907769,0.0123931123,0.0576876402,0.0336319171,0.0318105333,-0.0680809468,0.0653800741,-0.0652051568,-0.0727993846,-0.1691485345,-0.1568198949,0.1608433723,-0.2737526,0.1697304994,0.3562220633,-0.0336130708,-0.1412303895,-0.0535368845,-0.012967254,0.0694112629,0.1782014817,0.1553823352,0.0191024262,-0.017489491,-0.4109329879,-0.0497786291,0.1562905759,-0.3928752244,0.0985228494,0.2194407433,0.1351189911,-0.0172255524,0.1422982514,-0.1957585514,0.3078100979,0.1663377136,-0.2295234799,-0.0491457321,-0.2953111231,-0.3190174401,-0.1430812925,0.0544605255,-0.001726403,-0.1188587844,-0.2188054323,-0.2674613297,-0.01940866,0.2560309172,0.3982991278,-0.0459841304,-0.0935226455,-0.0177392475,-0.2558947206,0.3043141961,0.0190914385,-0.3315225244,-0.3089678288,-0.0131875928,0.5003296733,-0.1409425735,0.1233195439,0.2147375196,-0.1443973929,0.5124357939,0.1342266947,-0.2772077918,-0.090241462,-0.2718447745,0.1479554176,0.1295047998,0.2747759521,-0.1212169454,0.0715622008,0.2366961688,-0.1491497904,0.2676641941,0.107929334,0.0886426121,-0.1058222204,0.2013274729,0.3708954453,0.2490317523,-0.2558938563,-0.0085980399,-0.0287082158,0.0097777788,-0.1937861443,0.2227869034,-0.2622683644,-0.0476703234,-0.1672424823,-0.2005528212,-0.2793495953,0.0516311564,0.0491122529,0.0453244485,-0.1953032315,-0.0220628753,0.2034123093,-0.2524298429,-0.0239355937,-0.2200563401,0.418543756,0.1629656851,-0.0847896338,-0.2434992045,0.1709434986,0.3113965392,0.2097327411,0.0617980696,0.34430632,0.233126089,-0.4874202609,-0.3277005255,-0.1153787896,0.1543931961,0.0829174891,-0.1514283419,-0.0371805727,0.4300502837,-0.0938733742,-0.2666361034,0.4246529639,-0.4405524731,0.2116705775,0.0977721661,-0.0342827551,0.2694040835,-0.0164908506,-0.0705310404,-0.0100555187,0.3263965249,-0.2569960952,-0.1679274291,0.0914242119,-0.3633231819,0.1060907245,0.0623549335,0.023657443,-0.136492759,-0.0401437692,0.1545794755,0.1019935161,-0.1994842887,0.4961659014,0.3946733177,0.2688271105,-0.0930597186,0.1775924414,0.0247131549,-0.1630890965,-0.0125515368,0.1275859773,-0.1545579135,0.0877254531,0.3712874353,0.102846548,-0.0923874155,-0.1513437182,0.1118582934,0.0079012364,-0.2249830067,-0.1102057621,-0.2821179926,-0.4204500616,-0.1595219225,-0.1196514368,-0.1167689562,-0.0501836836,0.2796971202,0.618277967,-0.1292990446,0.2404133976,0.416035533,0.1910700053,-0.2006070167,0.0195869952,0.06349051,-0.1221381649,-0.265019238,0.0282655153,-0.0209045839,0.1614724398,0.2580633461,0.1257250458,-0.3055665791,-0.1446702331,-0.5009296536,-0.0467412472,0.0569294803,0.3373673558,-0.2218541056,-0.3558818102,-0.2647213042,-0.2781486809,0.2641724348,-0.1862185001,-0.0523187406,0.213130191,0.0688502267,-0.0127511518,-0.3923531771,-0.1300048083,-0.4266842008,0.0097923027,-0.1690360904,-0.2022756636,0.0769852549,0.1330344081,-0.1541850567,-0.5149540901,-0.4621246755,-0.1876673847,-0.3447586894,-0.3383408487,0.0694998652,0.0225993861,-0.1424719542,-0.2377741337,-0.2404919118,-0.3220582306,0.6282309294,0.1158387214,-0.0284560788,0.0149550773,-0.2569147348,0.2097931504,0.2310784906,0.2792002857,-0.0780279264,0.1442008764,-0.2609615326,-0.4398974478,0.0112102684,0.0160538219,0.4219787121,0.0965665653,0.391569376,0.0624363385,0.4202925563,0.0583636761,0.2165850103,0.1393898129,0.0044640619,0.2680754066,-0.047608979,-0.4091447294,0.081712395,0.2430670559,-0.1532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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3189","title":"conll2003 incorrect label explanation","comments":"Hi @BramVanroy,\r\n\r\nsince these fields are of type `ClassLabel` (you can check this with `dset.features`), you can inspect the possible values with:\r\n```python\r\ndset.features[field_name].feature.names  # .feature because it's a sequence of labels\r\n```\r\n\r\nand to find the mapping between names and integers, use: \r\n```python\r\ndset.features[field_name].feature.int2str(value_or_values_list)  # map integer value to string value\r\n# or\r\ndset.features[field_name].feature.str2int(value_or_values_list)  # map string value to integer value\r\n```\r\n\r\n","body":"In the [conll2003](https:\/\/huggingface.co\/datasets\/conll2003#data-fields) README, the labels are described as follows\r\n\r\n> - `id`: a `string` feature.\r\n> - `tokens`: a `list` of `string` features.\r\n> - `pos_tags`: a `list` of classification labels, with possible values including `\"` (0), `''` (1), `#` (2), `$` (3), `(` (4).\r\n> - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).\r\n> - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8).\r\n\r\nFirst of all, it would be great if we can get a list of ALL possible pos_tags.\r\n\r\nSecond, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0.\r\n\r\nEDIT: not really a bug, sorry for mistagging.","comment_length":63,"text":"conll2003 incorrect label explanation \n In the [conll2003](https:\/\/huggingface.co\/datasets\/conll2003#data-fields) README, the labels are described as follows\r\n\r\n> - `id`: a `string` feature.\r\n> - `tokens`: a `list` of `string` features.\r\n> - `pos_tags`: a `list` of classification labels, with possible values including `\"` (0), `''` (1), `#` (2), `$` (3), `(` (4).\r\n> - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).\r\n> - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8).\r\n\r\nFirst of all, it would be great if we can get a list of ALL possible pos_tags.\r\n\r\nSecond, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0.\r\n\r\nEDIT: not really a bug, sorry for mistagging. \n Hi @BramVanroy,\r\n\r\nsince these fields are of type `ClassLabel` (you can check this with `dset.features`), you can inspect the possible values with:\r\n```python\r\ndset.features[field_name].feature.names  # .feature because it's a sequence of labels\r\n```\r\n\r\nand to find the mapping between names and integers, use: \r\n```python\r\ndset.features[field_name].feature.int2str(value_or_values_list)  # map integer value to string value\r\n# or\r\ndset.features[field_name].feature.str2int(value_or_values_list)  # map string value to integer value\r\n```\r\n\r\n","embeddings":[0.2851009965,-0.3064155281,0.0021601634,0.6274859309,0.138807252,-0.0540367551,0.3389790356,-0.0062683476,-0.3376251459,0.0564626977,-0.243972823,0.1636955738,0.1464697719,0.5847407579,-0.1180653423,-0.0284845196,0.1054671705,-0.1323460937,0.0885564312,-0.2658523917,-0.3584337831,0.3185120821,-0.3178260922,0.3235012591,-0.3402500451,0.1149109378,0.089439407,-0.1222399324,-0.2363581508,-0.4159289896,0.2158706486,-0.0454830676,0.0306407697,0.0467072837,-0.0001092901,-0.2089489996,0.2177192122,-0.1634123027,-0.1643763781,0.0702831894,0.0744194537,-0.0184868276,-0.0108463019,-0.2458385676,-0.1300026178,0.2500394285,-0.309022218,0.2144730091,-0.1296649128,0.1268018931,0.1981870383,-0.1270835847,0.2946297526,0.1672953069,0.7190062404,0.1951051801,-0.2482355237,0.2634314001,0.1332893968,0.0631875247,0.1029883847,0.4718458354,0.147969082,-0.1407456547,0.341547817,0.2648704648,-0.0865933299,-0.4870592952,0.1138140857,0.2865568697,0.3525372148,-0.1194517389,-0.5183684826,-0.3287561536,-0.3517352045,-0.8694476485,0.030075036,0.0188047439,0.0791608244,-0.0563649274,-0.113201052,0.1972649395,-0.0402200185,0.0086073102,-0.4291677475,0.76406461,-0.2855479717,0.0975607857,0.0131238289,-0.2834881544,-0.0157411657,0.233555913,0.1902821511,0.2765470445,-0.1552698761,-0.167014569,-0.1673572063,-0.0939000249,0.0363166034,0.0362172984,-0.2481510192,-0.1341647953,-0.2336934358,-0.0159782451,-0.0358924195,0.125579074,0.5320594907,0.2138878107,0.0805108547,0.1901343912,0.1588185728,-0.0690250024,-0.0093490267,0.036007762,-0.1324306428,-0.1077971086,-0.0216657445,-0.1841274649,-0.5320380926,0.2540157735,-0.1025378406,0.0224815402,0.3392115235,0.2050039023,-0.1003527641,0.0822477639,0.0652108714,-0.0293054096,0.1273849756,-0.3924905062,-0.1050417274,-0.1590064019,-0.1474582255,-0.1017765179,0.1262219548,-0.0364966653,0.2315730602,0.2265166938,-0.0180225838,-0.0399520844,-0.297141403,-0.07661663,0.2811763585,0.2230492234,-0.5597733855,0.1669770926,0.3262919486,-0.286981076,-0.2807250917,-0.0837034062,-0.3925254345,0.0927753225,-0.3060405254,0.1714826822,0.1339773387,-0.1493106633,0.1399024129,0.0398205593,0.1074564233,0.1445256025,0.2587771416,-0.2148466706,-0.3548719585,-0.1722031981,0.2426027209,0.1651032865,-0.3832881451,-0.2260065228,0.346096307,0.0908312723,0.2119127065,0.0624024235,0.0783535019,-0.2197865248,-0.0753638819,0.07828141,0.0760479718,-0.5613459349,0.0245216656,0.0786415115,0.3894347847,-0.2436551154,-0.0202113185,-0.0361077935,0.2743964493,-0.0423206687,0.2826616168,-0.2810056508,0.1414441168,-0.1015735567,-0.086844258,-0.0023457967,0.1303286701,-0.0249067508,-0.1470541209,-0.1918939948,-0.1206640527,-0.1859137118,0.0363602899,0.051152274,0.1570645422,0.2141456157,0.2024595439,0.1930658966,0.0446865521,0.0286564082,-0.685832262,0.1884559542,-0.0175902434,0.2507897317,0.2137129754,-0.0998714641,-0.3215646744,0.0234159492,0.0630553514,-0.2212471217,0.1767177284,0.0120012546,0.0279146414,-0.1300463527,-0.3887979984,0.3154907227,-0.0447043553,0.317828238,-0.0036801824,-0.2390901595,-0.0728585273,0.0269973539,-0.3017162979,0.2238755226,0.5495324731,0.0922204778,0.0158647187,0.1871215552,0.4664863646,-0.4734282792,-0.0990091488,0.2327627242,0.3855877519,0.0642509535,0.0296530183,-0.2188660204,0.0328743495,0.0663149059,0.0670220926,0.3510097861,0.1801719368,0.2783183455,0.0480626598,0.0654558092,0.2451350838,-0.1561864465,-0.2088573724,-0.2553439438,-0.0849123001,-0.021703871,0.3265167773,0.38284567,-0.5492247939,0.383698076,0.3301254809,-0.2069081962,-0.1873385161,0.1563941538,-0.2381500155,0.1375563741,-0.0608683266,0.2748166919,-0.0590092614,0.0850465447,0.1693341285,0.0388422608,-0.3113284409,-0.0090040835,0.1955499202,0.069335863,0.0220064875,-0.0917656496,-0.0093910648,0.0629244521,0.059263669,-0.2352235615,0.0422186516,-0.0252315719,-0.5072000027,0.1685226411,-0.0351463966,-0.1818320155,-0.1453436166,-0.8314294219,-0.6038691998,-0.3849696517,0.1011502966,-0.1323697865,-0.0716154054,0.0472136475,-0.1634567827,0.1540655196,-0.0411743894,0.1879826635,-0.1887130886,-0.141618982,-0.5278465152,-0.0016035219,-0.1938647628,-0.0800427347,0.0431739651,-0.5166995525,-0.0108147161,0.1199379265,-0.6379429102,0.4130997658,-0.3872377574,0.0750732645,0.2127246857,-0.1447097957,-0.222424984,-0.0632378384,0.1840276271,0.0922848955,-0.3630594909,0.014124793,0.0280065536,-0.0347873308,-0.306379199,-0.23331891,-0.2260965109,-0.0795982257,0.2334807962,0.2110396773,0.3160144389,0.5354116559,-0.3125356436,0.1899468452,0.0480461866,0.5105807185,-0.456340313,-0.3199643791,0.1999557018,-0.1148549989,-0.2553812265,0.1449966133,0.0164078232,0.1160964742,-0.2853352726,-0.0852599367,-0.0917524025,-0.2469113469,0.0788883865,0.0376698077,0.1497698575,-0.0246531069,0.1192756519,-0.0121505931,-0.1760865152,-0.4674036503,0.2674117386,-0.0321763717,0.3326590955,-0.0735888407,0.0280291811,-0.026869148,0.4407500029,0.1834047884,-0.189360857,0.1565894783,-0.0864723623,0.2751612067,0.0772886649,-0.0013064691,0.1445007324,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3188","title":"conll2002 issues","comments":"Hi ! Thanks for reporting :)\r\n\r\nThis is related to https:\/\/github.com\/huggingface\/datasets\/issues\/2742, I'm working on it. It should fix the viewer for around 80 datasets.\r\n","body":"**Link:** https:\/\/huggingface.co\/datasets\/conll2002\r\n\r\nThe dataset viewer throws a server error when trying to preview the dataset. \r\n\r\n```\r\nMessage:       Extraction protocol 'train' for file at 'https:\/\/raw.githubusercontent.com\/teropa\/nlp\/master\/resources\/corpora\/conll2002\/esp.train' is not implemented yet\r\n```\r\n\r\nIn addition, the \"point of contact\" has encoding issues and does not work when clicked.\r\n\r\nAm I the one who added this dataset ? No, @lhoestq  did","comment_length":24,"text":"conll2002 issues \n **Link:** https:\/\/huggingface.co\/datasets\/conll2002\r\n\r\nThe dataset viewer throws a server error when trying to preview the dataset. \r\n\r\n```\r\nMessage:       Extraction protocol 'train' for file at 'https:\/\/raw.githubusercontent.com\/teropa\/nlp\/master\/resources\/corpora\/conll2002\/esp.train' is not implemented yet\r\n```\r\n\r\nIn addition, the \"point of contact\" has encoding issues and does not work when clicked.\r\n\r\nAm I the one who added this dataset ? No, @lhoestq  did \n Hi ! Thanks for reporting :)\r\n\r\nThis is related to https:\/\/github.com\/huggingface\/datasets\/issues\/2742, I'm working on it. It should fix the viewer for around 80 datasets.\r\n","embeddings":[-0.0604766943,0.211621657,0.0638269857,0.3273206651,0.0287141427,0.0626273081,0.164267689,0.2000190467,-0.409727931,0.0621844493,-0.1819026917,0.174138844,-0.1920644939,0.3275818229,0.1100303903,-0.1469971091,0.0231316723,0.266028285,-0.2740889192,-0.1340391338,-0.3390942514,0.2005032301,-0.2740516067,-0.0482313521,-0.727024138,0.1928573847,-0.0466797687,-0.0308767371,-0.2678306103,-0.5034124851,0.4060788751,0.0744817406,0.2015138865,0.1235801354,-0.0001097693,0.1082848012,0.2916755378,0.0115172639,-0.2740279734,0.0959790722,0.196436286,-0.0662722513,0.1915773153,-0.1560251415,-0.0833941922,-0.2332997024,0.1194826886,-0.0019251389,0.4159096181,0.1150890514,0.1832318455,0.2205580026,0.3862595558,-0.0954334736,0.102334924,0.013929477,-0.3255589604,0.0413848348,0.0048778797,0.0235422328,0.0470967256,0.1393455714,0.0796047002,-0.3240928352,0.1127597541,0.0838970169,-0.3835463524,-0.4807264805,-0.0051252772,0.2730845809,0.5093571544,0.0125036193,-0.2451609522,-0.1163247898,0.1188783869,-0.3101844788,0.4459341168,-0.1029434726,-0.0037875385,0.3573113978,-0.3634050786,-0.3721711636,-0.2950364649,0.2421708107,-0.2546897233,0.4009061456,-0.2461625785,0.1009297892,0.0798904598,0.0482624806,0.2246441096,0.0745574534,0.0007234821,0.0788973644,-0.2045525014,-0.0431838967,0.2018828243,0.0075333016,-0.0427140519,0.2589985728,-0.2569617331,0.025572069,-0.2804476917,-0.0116944155,0.1149013638,0.0033461258,0.0003409129,0.2692270875,0.4567243457,-0.0879905671,0.266810745,0.0349759348,-0.3861987591,-0.0279029161,-0.473616451,-0.2452172637,0.3544571698,-0.3187205791,-0.2371295244,0.1554271579,-0.4954219162,-0.1723837107,0.2557730079,0.3164547086,-0.162829265,0.3507394493,0.0651408583,0.1100764871,-0.0113435034,-0.5758861303,-0.0718560889,-0.1858232319,-0.2005198598,0.0780185536,0.4062039852,-0.1903265268,0.1007787436,0.044077944,0.1186461374,0.1786971837,-0.0101753781,0.0135188457,-0.0318979323,-0.0916897655,-0.0901670828,0.0255444944,-0.0025170173,-0.0991936103,-0.0217501484,-0.2058881968,-0.2722259462,-0.1523723006,-0.5047148466,0.1113686636,-0.1424396634,-0.2190875411,0.3044292629,0.179440558,0.0420679338,-0.2135602683,-0.0503756963,0.1721233279,-0.3735018373,0.0343773887,0.4732883275,0.4559055865,-0.5309052467,-0.0837451592,0.0887627751,-0.3923244178,0.2993723154,0.1086857542,-0.2758282125,-0.0112868575,-0.2042329013,0.0760681853,0.2514146566,-0.1904543489,-0.44599545,0.2302018553,-0.1087493747,0.1484962404,-0.0041550919,-0.085996516,0.2952946126,-0.1698544174,0.0069266167,0.0112765506,0.2018580288,-0.3145305514,-0.2308240831,-0.1041077077,0.1820280403,0.2498575598,-0.1322971135,0.0457299538,-0.031733796,0.0057443613,0.2550143898,-0.0370040536,0.2567820251,0.1298138797,0.3040722311,0.1008115411,0.0092633804,0.0367733352,-0.2846894264,-0.0196127556,0.0753391534,0.2944586575,0.1420824528,-0.0992420837,-0.1052739322,-0.0214708652,-0.3832301497,-0.1859110743,0.0781563893,-0.0181558728,-0.0227918681,0.2437912226,-0.2173215896,0.4562471509,0.0390655585,0.0110054454,-0.0520488396,0.2618991733,-0.0812899545,-0.1060315669,0.010105148,0.2695683539,0.0795257688,-0.3282864094,-0.0133623267,0.3319521844,0.009035321,0.0814037621,0.2177319229,-0.0711475164,0.2347407937,-0.3518629372,-0.0039357771,0.0066357125,-0.1196680441,0.1001286432,-0.0926913396,0.210918963,0.4528231621,-0.1758219749,-0.0397162773,0.3095357418,0.2423325479,-0.1370932758,-0.0204647835,-0.346968323,0.2896919847,-0.1088019013,0.3238945305,-0.0186185837,-0.5903195739,0.0247441791,0.1721667796,0.0369192511,-0.147150591,0.3328795433,-0.0862082914,-0.0055503868,0.0295324065,0.2986822426,0.1924856305,0.1893727481,-0.0254396982,0.3722364008,-0.1151569188,-0.1280697733,0.2079276145,-0.0372046269,0.0461617224,0.083448723,-0.0594334006,0.2952935398,-0.256221205,-0.1725567132,-0.0671973154,0.1901200712,-0.2193949372,-0.1150080711,-0.5052608252,-0.5219963789,-0.0666919947,0.2210561782,-0.2068070322,-0.2692710757,0.1140239909,-0.0418878868,-0.0073721916,0.1941462755,-0.6668402553,0.3618406355,-0.157323271,0.168918848,-0.0536919907,-0.1055641323,-0.3456702232,0.1136632785,0.3822421432,-0.1444272846,0.3707620203,-0.2100592107,0.2090734094,-0.0062074261,-0.3846946657,0.4352638721,-0.0274452977,-0.0737024471,0.1023814231,-0.1167348474,-0.1396486312,-0.3272044361,0.1469643414,0.0912023336,-0.2869467437,0.0924001783,-0.0974902883,-0.3539519906,-0.225463137,-0.4965952039,-0.1170781925,-0.3316110969,0.230777055,-0.041275017,0.0750741363,0.2398607433,-0.1778217703,0.2026772201,-0.1415187418,-0.0580136813,-0.2804275751,-0.4968509674,0.4743338227,-0.4380083382,-0.3862813711,0.0958209336,0.0647472814,0.0988313332,-0.0009253967,-0.5417266488,0.0090426812,-0.1516052186,-0.0617965013,0.0016597108,-0.1957082301,0.2064771652,0.1753276885,-0.0509471372,-0.0733032972,-0.1269958317,0.3448425829,-0.0611828901,0.4398794174,-0.172625348,0.2040960044,0.1112029701,0.2575529218,0.352196008,-0.0128595829,0.6458727121,-0.0273445621,0.4406487048,-0.0451335497,-0.3324152827,0.4859947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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3188","title":"conll2002 issues","comments":"Ah, hadn't seen that sorry.\r\n\r\nThe scrambled \"point of contact\" is a separate issue though, I think.","body":"**Link:** https:\/\/huggingface.co\/datasets\/conll2002\r\n\r\nThe dataset viewer throws a server error when trying to preview the dataset. \r\n\r\n```\r\nMessage:       Extraction protocol 'train' for file at 'https:\/\/raw.githubusercontent.com\/teropa\/nlp\/master\/resources\/corpora\/conll2002\/esp.train' is not implemented yet\r\n```\r\n\r\nIn addition, the \"point of contact\" has encoding issues and does not work when clicked.\r\n\r\nAm I the one who added this dataset ? No, @lhoestq  did","comment_length":17,"text":"conll2002 issues \n **Link:** https:\/\/huggingface.co\/datasets\/conll2002\r\n\r\nThe dataset viewer throws a server error when trying to preview the dataset. \r\n\r\n```\r\nMessage:       Extraction protocol 'train' for file at 'https:\/\/raw.githubusercontent.com\/teropa\/nlp\/master\/resources\/corpora\/conll2002\/esp.train' is not implemented yet\r\n```\r\n\r\nIn addition, the \"point of contact\" has encoding issues and does not work when clicked.\r\n\r\nAm I the one who added this dataset ? No, @lhoestq  did \n Ah, hadn't seen that sorry.\r\n\r\nThe scrambled \"point of contact\" is a separate issue though, I think.","embeddings":[-0.0255834982,0.2223011553,0.0360254422,0.3023375571,-0.0530182011,0.0704935566,0.1777097136,0.2525493801,-0.4730506837,0.0556402616,-0.0888659954,0.1852440834,-0.1238658279,0.2847290933,0.0831431374,-0.1410982013,0.0679345429,0.2765767574,-0.194240272,-0.1755082905,-0.3190150857,0.107239902,-0.3307636678,0.0323951729,-0.745370388,0.1410535723,-0.0756684616,0.0298315138,-0.2923620343,-0.4572661817,0.3295481801,0.0830067545,0.2388349026,0.1145396456,-0.0001048182,0.0317851715,0.2909710407,-0.0187263582,-0.3222729862,0.112403594,0.1623233408,-0.0212006662,0.0538196974,-0.2109829485,-0.100859791,-0.1970284134,0.117445983,-0.010038754,0.401581943,0.1562746763,0.1898513585,0.1661803871,0.3150574863,-0.1531661451,0.1251710355,-0.043981377,-0.3512521386,0.0151580088,0.0717661679,-0.0102521796,0.1528759003,0.0863507092,0.0861906111,-0.2942908108,0.0136621762,0.1189877391,-0.3139154911,-0.4552235603,0.0088824071,0.331346482,0.4684495032,0.0302093606,-0.2183307409,0.0011294329,0.0839304253,-0.32002303,0.4287408292,-0.1131439209,-0.0484738201,0.3734037876,-0.2825210392,-0.3752900958,-0.3205248713,0.2398643047,-0.2284517884,0.4634186327,-0.195642069,0.0621738546,0.0213170201,0.0258059166,0.2577507496,0.1156050339,0.0113314381,0.1425051093,-0.1581910104,-0.0090643,0.1266121715,-0.0229662806,-0.0179696027,0.2247864008,-0.2342104763,0.0238987822,-0.2447517812,0.0332025513,0.0932120234,0.0197590645,-0.0064497036,0.2714125514,0.4648371041,-0.084789671,0.258548975,0.0637663603,-0.5472632051,0.0283949785,-0.4674302936,-0.1840285063,0.3195192218,-0.3606589735,-0.2772890329,0.1446952522,-0.5227145553,-0.1943805814,0.242690295,0.3051872551,-0.2026010603,0.2993974388,0.1138266698,0.1331765503,-0.0039769043,-0.6496364474,-0.0586918257,-0.2221461087,-0.2510863543,0.0722980127,0.3717161715,-0.011799749,0.1221055388,0.0322754011,0.0681980774,0.2260420024,-0.0021023422,0.0094385929,0.0475183129,-0.1731337905,-0.0988026559,-0.0119099934,-0.0707164332,-0.1126337126,-0.0202824324,-0.1083816513,-0.3170609176,-0.0694153011,-0.53078264,0.1484407187,-0.1638917625,-0.2786678374,0.4323653579,0.1897499263,0.1249657646,-0.2096893638,-0.1140678972,0.174415499,-0.3766957819,0.0049878736,0.4477428496,0.2891181409,-0.5736320019,-0.0165647306,0.1494041681,-0.2833333015,0.3776992559,0.1524633318,-0.2772666812,0.0369919874,-0.1723196357,0.1902697235,0.3014379442,-0.1795706749,-0.3490684927,0.1629271358,-0.1084823087,0.048765704,-0.0671879202,-0.1156786531,0.3509986699,-0.2021624297,0.0693803653,0.2298527509,0.2138390094,-0.2738339603,-0.258528322,-0.1119305268,0.2753084302,0.1277496368,-0.2449403703,0.0372537076,-0.0985112861,0.0524649918,0.2298534513,-0.012377914,0.2990776002,0.0797000527,0.296417892,0.0160843302,0.0213472825,0.0868555605,-0.2351915836,-0.0673595741,0.0563856214,0.3358801007,0.1476847976,-0.0753245056,-0.1496428698,-0.0350614041,-0.3468383849,-0.1237723231,0.1135059521,0.0048070434,0.0328648724,0.2038208991,-0.2011988014,0.3853740096,0.0157563277,-0.0840202048,0.081081517,0.2673097551,-0.1406974941,-0.1727505475,0.0084514134,0.2942276299,0.0769877806,-0.2891771793,0.0114988703,0.3392845094,-0.007437631,0.0221805833,0.1904236227,-0.0427174494,0.2125288844,-0.300095588,0.0035161232,0.0076064123,-0.0990460813,0.0953340679,-0.1440848559,0.2048449665,0.5047032833,-0.1373968869,0.0473927706,0.2636732161,0.2573362291,-0.2108141333,0.0254507065,-0.2913089991,0.2391400039,-0.1031013057,0.2774254978,-0.0331646837,-0.5680327415,0.132532537,0.2854784429,0.0257945322,-0.0779461786,0.2098442167,-0.0746407956,-0.1057670563,-0.0036390238,0.3433365226,0.122858271,0.2000249624,0.0238059703,0.3386673033,-0.246494934,-0.0810601488,0.171156764,-0.1191500202,0.1148935184,0.0632472187,-0.0295215659,0.2756746709,-0.142653361,-0.1570489258,0.0022477147,0.1881345063,-0.1799978763,-0.07660193,-0.5199654102,-0.4399763048,-0.0564520285,0.2141037583,-0.2182890177,-0.2891891897,0.1372157186,-0.0976617783,0.0045175683,0.1972752661,-0.5980059505,0.3731265068,-0.1431250125,0.156840831,0.0023198891,-0.1449828893,-0.3898048103,0.1318850368,0.3430997729,-0.0722456127,0.3741560578,-0.2818676531,0.1906012148,0.0650057867,-0.3531951606,0.4935385287,-0.0705274642,-0.0499783531,-0.0218495261,-0.0542343222,-0.1133912355,-0.3237288594,0.1582659036,0.046430137,-0.3249568045,0.0538042337,-0.0610868633,-0.4485621154,-0.2724867761,-0.5893459916,-0.1338881999,-0.3022079468,0.1409725994,0.0015231977,0.0657207444,0.2974038124,-0.1412080228,0.1829865873,-0.0440973081,-0.010388284,-0.4029562771,-0.5000158548,0.5217850208,-0.4438215494,-0.4245094657,0.0562521219,0.0589260124,0.0431521833,0.0395861976,-0.389990598,0.0820903108,-0.1570211947,-0.0408955403,-0.016912777,-0.1708807945,0.1832007319,0.1631731987,-0.0816108584,-0.1451477855,-0.1490674466,0.410040915,0.076944679,0.5598500371,-0.1415383071,0.0721328631,0.1380168647,0.255545944,0.2663234472,-0.11603304,0.5567478538,-0.0765694305,0.4077329636,-0.0308908224,-0.2096999139,0.4806019366,-0.0884988382,0.0481817387,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3181","title":"`None` converted to `\"None\"` when loading a dataset","comments":"Hi @eladsegal, thanks for reporting.\r\n\r\n@mariosasko I saw you are already working on this, but maybe my comment will be useful to you.\r\n\r\nAll values are casted to their corresponding feature type (including `None` values). For example if the feature type is `Value(\"bool\")`, `None` is casted to `False`.\r\n\r\nIt is true that strings were an exception, but this was recently fixed by @lhoestq (see #3158).","body":"## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":65,"text":"`None` converted to `\"None\"` when loading a dataset \n ## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Hi @eladsegal, thanks for reporting.\r\n\r\n@mariosasko I saw you are already working on this, but maybe my comment will be useful to you.\r\n\r\nAll values are casted to their corresponding feature type (including `None` values). For example if the feature type is `Value(\"bool\")`, `None` is casted to `False`.\r\n\r\nIt is true that strings were an exception, but this was recently fixed by @lhoestq (see #3158).","embeddings":[-0.074364692,-0.0289289225,0.0202890821,0.2942247093,0.4671348631,-0.0404260345,0.3494566679,0.2275470048,0.0246012975,0.5165578127,-0.0883265361,0.6322941184,-0.0893218815,0.0885337293,0.0443154909,-0.0311299935,0.1287148446,0.3545818925,-0.1534890234,0.0358554497,-0.0559385382,0.1567758471,-0.2015898079,0.0554703064,-0.204255864,0.0001950151,-0.0153095536,0.0064997156,0.0632325113,-0.5500470996,-0.0285557527,-0.2574139237,-0.1568410546,0.5352155566,-0.0001153082,0.0118074436,0.3876371086,0.2343209088,-0.4045577347,-0.5279954672,-0.2006205916,-0.2227911502,0.2552909553,-0.0441058688,-0.1661451459,-0.2024698853,-0.1053212583,-0.3687560856,-0.1631985158,0.2866930664,0.2213474512,0.4613040388,-0.4211884141,0.045616366,0.3438512087,0.0372860655,-0.0115573341,-0.0727401972,0.0320073925,0.4089702964,-0.0737864822,0.2828014791,-0.2029777616,-0.2242763042,0.2152375579,0.2015526444,0.0580068901,-0.2878353596,0.2259051651,0.3812761009,0.3539008796,-0.3959644139,-0.3045984507,-0.1388225853,0.2330368161,-0.2949928641,0.1394142509,0.2389477193,-0.135810703,0.1090045422,0.0278783161,-0.0374843813,-0.028482154,-0.0596601255,-0.5930216908,0.2717579305,-0.155123204,-0.0330960974,0.0518795177,-0.1880702674,-0.09524028,-0.2759627104,-0.2160402983,0.1376178563,-0.1395830959,0.0580853485,0.00493808,0.0938741639,-0.0923859477,-0.145164296,0.1963671893,0.0857735351,0.0210536625,0.2455893904,0.1635621339,0.319198072,0.2024616003,0.0092120171,0.3557839692,-0.0527962632,0.0881524384,0.1451586187,0.1016990468,0.0289619304,0.2892723382,0.0265311915,0.5120080709,-0.2998183966,-0.4277203381,0.1300507486,-0.3588064313,0.1893883646,-0.0233919118,0.188065812,-0.1585968882,0.4057354927,0.1063361987,0.1306806505,-0.1923540384,-0.2775872052,-0.3321047425,-0.0261636581,-0.052912958,-0.2081578374,0.086246863,-0.3544608057,0.0390301086,0.3406713009,0.075636819,-0.0633615255,-0.2309406698,-0.1289337575,-0.0175752155,0.4982245564,-0.0806755498,0.1513722986,0.234106198,-0.4056797028,-0.0607131571,0.8011903763,-0.1993332058,-0.0395228751,-0.1734526306,0.19518888,-0.3936984539,-0.0369189866,-0.1676321328,0.281921804,0.016933063,0.1303345561,0.0649848431,-0.3498057425,0.0037096357,-0.2259187251,0.0673156604,0.3076143563,-0.6774599552,-0.2717005014,-0.0362396501,-0.466730386,0.1612826288,-0.0774912983,0.1069210842,0.0266481694,-0.1703740656,0.153841123,0.3121855557,-0.0921310335,-0.2813615203,0.3656146228,-0.286762625,0.1350312531,-0.0275694709,-0.0748573467,0.0707632527,0.0934653655,0.0895049274,0.0181792937,0.0867445096,0.1591299325,-0.4913858473,-0.1291599274,0.4623967707,-0.0374182239,0.0841175318,0.1950540096,0.0064074374,-0.3511815369,0.2285252362,-0.129220292,0.1493531466,0.0745738968,0.3956736326,0.0381346457,0.0452309102,-0.3023313582,-0.4701995254,0.1431848258,-0.1787090898,0.1932463199,-0.2289486676,-0.2901903093,-0.00544442,0.0231542997,0.0146971168,0.1065347344,0.1563829482,0.2238950878,-0.3153887093,0.0381585918,-0.0946298689,0.0892270058,-0.0369667858,0.191085428,-0.1285594106,0.0402744748,0.1280012429,-0.2025585473,-0.2348784655,0.3516681194,0.2134971023,0.1185355112,-0.2256277204,0.466357857,-0.1201644465,0.0272628423,-0.4631201029,-0.1329893619,0.1694585085,-0.1776142716,0.1492184848,0.4430870712,0.2242839783,0.1036120132,-0.2592028975,0.4345674515,0.0383081138,0.3234424889,-0.0090559609,0.1112888753,0.0209530089,-0.0063032862,-0.2586576939,-0.3572495282,-0.0938061476,0.14143233,0.1503594667,0.3741160333,-0.4108514488,0.2392971218,0.1364663392,-0.4166621864,0.1086372659,0.1810409427,-0.3985330164,-0.0374068134,0.1068882942,0.2095808238,0.3684911728,0.2685147822,0.0769462287,0.081849061,-0.1579062641,-0.0449230038,0.3587135375,-0.0223294348,-0.0204385258,0.2448802292,0.2682704329,-0.0669097826,-0.2068700045,0.0818061158,0.0289013498,0.2273088843,-0.7267827392,0.0302578583,-0.5401936173,0.1630096436,-0.0831560865,-0.1632225811,-0.1344045401,-0.377050966,-0.0608964004,-0.1072860807,-0.1884910613,0.1150780693,-0.2849167585,0.0592130274,-0.2410746813,-0.0934475139,-0.1032881588,0.1292213202,-0.455177933,0.0457121916,0.0436944664,0.1446139216,0.1195406914,0.063152954,-0.0977505744,-0.1728764474,-0.3168606758,-0.0308952983,-0.2922932208,0.5581783056,0.1433067918,0.109754093,-0.0786030069,0.0260056946,0.212203458,-0.1062171012,-0.2325672358,0.2592372,-0.1210722178,0.141858086,-0.4369953573,-0.4309963286,-0.2294882238,-0.1611856073,-0.1150347516,-0.0192739796,0.2807175219,0.2254796773,0.1342697293,0.2584433556,0.0241432302,-0.0208887793,-0.1369646341,-0.1831407696,0.4126768112,-0.1314134598,-0.1789134145,0.2381022424,-0.0034113198,0.0171548016,0.0946745053,-0.3835977614,0.2659083903,-0.1073353067,0.3501320779,0.0700939447,-0.0062085371,0.1970041394,0.3374521434,-0.1416848302,-0.0443927906,-0.1426856369,0.0234024525,-0.1173374504,0.0683048517,0.0634143725,0.1028755829,0.0130356718,0.3595697582,-0.0317456089,-0.0253050998,0.0954656303,-0.2188646048,0.4289612472,-0.1835111827,-0.0405463539,-0.1812498719,0.0953609422,-0.007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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3181","title":"`None` converted to `\"None\"` when loading a dataset","comments":"Thanks for reporting.\r\n\r\nThis is actually a breaking change that I think can cause issues when users preprocess their data. String columns used to be nullable. Maybe we can correct https:\/\/github.com\/huggingface\/datasets\/pull\/3158 to keep the None values and avoid this breaking change ?\r\n\r\nEDIT: the other types (bool, int, etc) can also become nullable IMO","body":"## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":54,"text":"`None` converted to `\"None\"` when loading a dataset \n ## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Thanks for reporting.\r\n\r\nThis is actually a breaking change that I think can cause issues when users preprocess their data. String columns used to be nullable. Maybe we can correct https:\/\/github.com\/huggingface\/datasets\/pull\/3158 to keep the None values and avoid this breaking change ?\r\n\r\nEDIT: the other types (bool, int, etc) can also become nullable IMO","embeddings":[-0.0925697535,0.0141063323,0.0318010263,0.3350732327,0.5061743855,0.0844624788,0.4042359889,0.1468732804,0.0264111031,0.4644885063,-0.0690367371,0.4651447237,-0.0235636495,0.0580343306,0.0141549669,-0.1078599468,0.1389273554,0.346016407,-0.2450709045,0.1766272336,-0.1167035028,0.2629555166,-0.2267190516,0.0657160878,-0.2064883262,0.006207875,-0.0746437907,0.059632957,-0.0242877118,-0.5001731515,0.0517673008,-0.2451472431,-0.1269176304,0.5791054368,-0.0001208361,-0.0412970074,0.4041319788,0.2556312978,-0.4339710176,-0.4409274161,-0.1690383404,-0.243132025,0.2595787048,-0.0536118634,-0.0550301932,-0.2037643641,-0.1639199853,-0.4882366359,-0.1923491955,0.3460761011,0.188592881,0.4674662054,-0.3177024722,0.0804945976,0.2992608845,0.0863136053,-0.0305651445,-0.1044958904,0.0564730987,0.281167388,0.1069604754,0.1737932414,-0.2837574482,-0.2602707744,0.2038851082,0.28770715,0.0291301217,-0.2398698032,0.2964887619,0.3595554829,0.2978081405,-0.3920552135,-0.2797029912,-0.2091314048,0.3052699268,-0.3615011871,0.1830775142,0.2338898033,-0.0326639786,0.1523962468,-0.0202067252,-0.1131433025,-0.0767651126,-0.0578930303,-0.5974441171,0.1144129261,-0.2311328351,-0.0395778306,0.0025661548,-0.1645366549,-0.170690611,-0.3382855058,-0.2224289477,0.1699279845,-0.1779351532,0.0486815721,-0.1312073767,0.1459275037,0.0390099473,-0.197078526,0.1111878082,-0.012837748,0.0027538403,0.1595490873,0.2065309733,0.2574148178,0.2473937869,0.0031147585,0.3443643749,-0.0548961721,0.2380196303,0.1103178486,0.161287725,0.1115374714,0.2388521731,0.0010500816,0.5135545135,-0.2973032892,-0.3329550028,0.2025707215,-0.3289471269,0.1842172444,-0.0795115829,0.039072033,-0.1184933931,0.5361998677,0.1446799487,0.2098577917,-0.1342795044,-0.2786277235,-0.2672537863,-0.1559936404,0.0178578552,-0.1179832071,0.0074170376,-0.4730784595,-0.0175565332,0.372633189,-0.0143556576,-0.0484061912,-0.2207683176,-0.1837187558,-0.1471928209,0.4730444849,-0.1016089693,0.1996736974,0.2124488354,-0.3510929942,-0.048603598,0.733166039,-0.2416217327,-0.0045014848,-0.2779672444,0.1253989339,-0.3253654838,0.0574431941,-0.2498323619,0.2698930204,0.0761951581,0.1143139675,0.0696475953,-0.3928349316,-0.0912579075,-0.2012573928,-0.0478905886,0.4467959106,-0.6931033731,-0.1780738086,-0.0022079358,-0.4130167365,0.1215184331,-0.0878432989,0.1393419355,0.0258682091,-0.1643548161,0.1067159548,0.1845289916,0.0203624684,-0.2249414027,0.3387204707,-0.272323221,0.146842286,-0.0364809334,-0.1123026833,0.0156871118,0.0228156541,0.0611236989,-0.0380126461,0.0056858547,0.1418935657,-0.4294868708,-0.1946583986,0.4210460484,-0.093313545,0.0575591587,0.3305713832,-0.0739545524,-0.2981686294,0.1521409899,-0.1743438691,0.2368209511,0.1598234028,0.2988969386,0.1115308702,0.0041720769,-0.3726209104,-0.6002713442,0.0219647419,-0.0939645991,0.1801317483,-0.2249450088,-0.2469424158,-0.0244356059,0.0261534955,0.0998709872,0.2598115206,0.0632569566,0.1639031023,-0.2833773494,0.1378242373,-0.1888092607,0.1674937457,-0.1904387474,0.2553904355,-0.2758713663,0.0419886671,0.2011882514,-0.0957883075,-0.2440818697,0.3246859312,0.1857877672,0.0188870691,-0.1740731746,0.4714582264,-0.0506100543,0.0628385097,-0.4059763253,-0.0364156179,0.2064483464,-0.1051705778,0.1313941926,0.3298893273,0.218701154,0.0600045286,-0.2549962103,0.4667553008,-0.1259753555,0.3167218268,-0.0975206345,0.1255903989,-0.0273458008,0.045577962,-0.2536076605,-0.3578542173,-0.0447339416,0.127570197,0.1547270715,0.375305742,-0.4400852025,0.1694969386,0.0702292994,-0.4307414293,0.0788140893,0.2739160955,-0.3712773323,-0.0420086123,0.079442732,0.2453400195,0.428774327,0.1703190655,0.0790107846,0.1073719934,-0.1641753614,-0.084889695,0.3751640618,0.0416988768,0.024335878,0.2593086362,0.2786157727,0.0003787666,-0.1593972743,0.1922775209,0.0015843918,0.2867144644,-0.7476947308,0.0660025179,-0.4945851862,0.1803055555,-0.1113060936,-0.1293589473,-0.0935828388,-0.4681963027,-0.2054168582,-0.070024699,-0.0972488299,-0.0377714336,-0.3114909828,0.1449471265,-0.1971795857,-0.0349924751,-0.2296810895,0.1037679389,-0.4430875182,0.0093425037,0.1142371371,0.0212430153,-0.0110164527,-0.1004714146,-0.1658878624,-0.2410218269,-0.2905340195,-0.0224507079,-0.3732768297,0.4957594275,0.2103521377,0.2159153521,-0.048533123,0.1548432261,0.2097968012,-0.0304253548,-0.2236600518,0.2784524858,-0.1508896351,0.1539647132,-0.3582233787,-0.3771109283,-0.1389286071,-0.0485518873,0.0010643047,-0.1114203855,0.2199182957,0.2147963643,0.1145830229,0.2423040122,-0.0263045542,-0.0214713085,-0.1379004568,-0.2705386281,0.4563384354,-0.0827284306,-0.2439508736,0.2422796786,0.0957864821,0.0226462446,0.070783712,-0.3880716562,0.1668209881,-0.0506014749,0.2875539958,-0.0281956829,-0.0325896665,0.0860668123,0.3021438122,-0.0281829517,0.0014262024,-0.1887271851,-0.1705160141,-0.1870358586,0.056809485,0.1416852623,0.1931550354,-0.1057237834,0.3625071943,0.0679906383,0.0275852755,0.0944436044,-0.2436357141,0.4361570477,-0.2267601192,-0.1062297672,-0.2557299435,0.04756106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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3181","title":"`None` converted to `\"None\"` when loading a dataset","comments":"So what would be the best way to handle a feature that can have a null value in some of the instances? So far I used `None`.\r\nUsing the empty string won't be a good option, as it can be an actual value in the data and is not the same as not having a value at all.","body":"## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":58,"text":"`None` converted to `\"None\"` when loading a dataset \n ## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n So what would be the best way to handle a feature that can have a null value in some of the instances? So far I used `None`.\r\nUsing the empty string won't be a good option, as it can be an actual value in the data and is not the same as not having a value at all.","embeddings":[0.0052766432,-0.0747644752,0.0570606254,0.3098500669,0.1798289418,0.0002795036,0.4044811428,0.1222435459,-0.0889378861,0.4486833513,0.2950798869,0.3863019645,-0.1925675422,0.1190018952,-0.0907517225,0.09096542,0.1764330268,0.4477598965,0.0782448947,0.0410379358,-0.1360656917,0.076015532,-0.1882273704,0.0911550671,-0.3578633964,-0.1168849096,0.0977273583,-0.0800803304,0.2879969478,-0.4692052901,-0.0594014116,-0.1033081189,-0.2546673119,0.4687261581,-0.000126619,0.0444998965,0.5570528507,0.1430192888,-0.4269385636,-0.3813508749,-0.2314835191,-0.3043973446,0.1865386814,-0.1271432489,-0.1492880732,-0.024563143,-0.0808003992,-0.4146336019,-0.4143237174,0.1533219516,0.0985203087,0.4720102251,-0.5315294862,0.175133884,0.3666889966,0.0809057206,0.0356243178,-0.1752463281,0.0785779357,0.3048976362,0.0310232881,0.3250702024,-0.1950265318,-0.2513515949,0.4226878285,0.2374102473,0.1617750674,-0.1195502356,0.0962228402,0.2393037826,0.3973616362,-0.1452640742,-0.3584757447,-0.2358646989,0.1254953891,-0.4328288734,0.1608404666,0.1340261102,-0.0634757131,0.1368529201,0.0298910346,-0.1022547558,-0.0794483051,0.0594027974,-0.8688824773,0.1934468597,0.0155554088,0.0415434428,-0.1896171719,-0.3825513422,-0.2090657502,-0.3143939376,-0.00942217,0.1599766016,0.0671230629,0.0412595868,0.1621508896,0.063158907,0.015826147,-0.2179715484,0.0761623979,0.062298838,-0.0429130457,0.3731674254,0.1922516525,0.2337046266,0.421610117,-0.1353509873,0.6016111374,0.0283080786,0.1431593597,0.2019733638,0.0583037212,0.1271743178,0.271784395,0.0483824275,0.7048224211,-0.3317322731,-0.3835399151,0.3324647546,-0.3704832196,0.222323209,-0.0461367369,0.0861053616,-0.1115740389,0.5990819335,0.2542337179,0.098600395,-0.1660553217,-0.3170439601,-0.2582539916,-0.3268263638,0.118278034,-0.1568398923,0.0247787554,-0.2988595665,0.1749487966,0.1153726503,0.0383166,-0.03242217,-0.1229846403,-0.2014870644,-0.2004284561,0.4103600681,-0.1164957657,0.1337821782,0.0880195275,-0.3038028777,-0.0600879937,0.775924027,0.0075772307,-0.228005752,-0.1460437477,0.0643530637,-0.5251387358,0.0584546775,-0.0432200953,0.4700005054,-0.0096711786,-0.0638751239,-0.0463315696,-0.1369732618,-0.0905409977,-0.2392628491,-0.0016136327,0.5341787934,-0.7298114896,-0.139588207,-0.1797449142,-0.3280589581,0.0451995134,-0.1848615855,0.0697984323,0.0019272362,-0.0319765918,0.1766752005,0.2246389836,-0.0696314722,-0.2860862017,0.2071077675,-0.3047316074,0.1104117036,-0.0211097728,0.1966665238,0.0430544578,-0.069110401,0.0491733365,0.0290840492,0.0308920946,0.1456039995,-0.4907612801,-0.1103941053,0.5216987729,-0.0465309434,0.1093515381,0.0806447864,-0.068086274,-0.1621979922,0.0218979586,-0.0084300116,0.2404968888,0.0609774068,0.4414910078,0.2434708923,-0.1540139019,-0.1405384988,-0.6653048396,0.2005898356,-0.1546669006,0.0394710377,-0.1835226715,-0.4091540277,0.0370097309,0.0824955851,0.1448739618,0.1595186144,-0.0062754634,0.2451797575,-0.3078084886,0.127814129,-0.2210236788,-0.0327342711,-0.0250009485,0.2186092734,-0.2116209269,0.1118602902,0.2545877993,-0.1314558089,-0.302221626,0.1893352419,0.2839818597,0.1301604211,-0.0762681365,0.398716867,-0.0922458768,0.1341902018,-0.4483004808,-0.0576366112,0.3796609342,0.0031061387,0.1720391214,0.4482932687,0.2809912562,0.1092370003,-0.5051977634,0.5239848495,0.1982153952,0.2406323403,-0.061232321,0.0188942403,-0.0197964888,0.0670286268,-0.2754276693,-0.3605764806,-0.1200105175,0.1125276685,0.1867107898,0.469543159,-0.5353844166,0.1674604714,-0.023561893,-0.4500308633,0.2033385187,0.1099345237,-0.3153220415,0.0647957176,0.3532356024,-0.0719847679,0.3230209053,0.2383095026,-0.0007330772,0.028333487,-0.036356084,0.1017807201,0.3486508131,0.1213220879,-0.0373960398,0.2905023396,0.271410048,-0.1900577247,0.0731537119,0.075275898,0.236811325,0.3264053762,-0.6416156292,-0.1415083408,-0.4613527954,0.1695278436,-0.1044526845,-0.0669712648,-0.2251286209,-0.3396617472,-0.0734308138,0.0304610301,-0.3658464551,0.0752382502,-0.3795759082,0.0681328103,-0.2285345197,-0.2263945043,-0.0052646482,0.0603655726,-0.4304728508,-0.096665591,0.0705835745,0.225850895,0.0468607321,0.0673926026,-0.2984251082,-0.1514244229,-0.2645202279,-0.0347755477,-0.1552939862,0.4667359591,0.0827750713,0.2185355127,-0.0483035967,0.1004244313,0.2149032205,0.0920617431,-0.2238402367,0.2418260127,-0.0157554131,0.3053979576,-0.2746045589,-0.2883380353,-0.1341988146,-0.2280351967,-0.0841100141,-0.1444774419,0.4153591692,0.020952262,-0.0075352346,0.207981661,0.2962358296,-0.2801599205,0.0531675778,-0.1560534239,0.4223109484,0.0025750659,-0.0990326554,0.2433500886,-0.0596641004,-0.1800011545,0.2424950898,-0.4049969614,0.0352220833,0.1385174096,0.4844274521,0.1061697826,-0.2149813473,0.2519590259,0.3648093939,0.0010497946,-0.0293134134,-0.243229717,0.1559978276,-0.1029721349,0.0354995504,0.3438739777,0.1021162197,0.0648596659,0.3889149129,0.1395799667,0.0192503538,0.0792010799,-0.1046053171,0.0983701497,0.0985652655,0.0579497926,-0.3433536589,0.2516409457,-0.1628453285,0.3270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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3181","title":"`None` converted to `\"None\"` when loading a dataset","comments":"Hi @eladsegal,\r\n\r\nUse `None`. As @albertvillanova correctly pointed out, this change in conversion was introduced (by mistake) in #3158. To avoid it, install the earlier revision with:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git@8107844ec0e7add005db0585c772ee20adc01a5e\r\n```\r\n\r\nI'm making all the feature types nullable as we speak, and the fix will be merged probably early next week.","body":"## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":52,"text":"`None` converted to `\"None\"` when loading a dataset \n ## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Hi @eladsegal,\r\n\r\nUse `None`. As @albertvillanova correctly pointed out, this change in conversion was introduced (by mistake) in #3158. To avoid it, install the earlier revision with:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git@8107844ec0e7add005db0585c772ee20adc01a5e\r\n```\r\n\r\nI'm making all the feature types nullable as we speak, and the fix will be merged probably early next week.","embeddings":[-0.171092689,-0.0119526703,0.0143992305,0.2961724699,0.43068102,0.0251631346,0.3792949021,0.2542135119,-0.0140081206,0.4866128862,-0.0783689842,0.5426307321,-0.0509569906,0.1476789266,0.0132165076,-0.0821947828,0.1226768941,0.3703929186,-0.2146400958,0.0617908239,-0.0963982269,0.2255148888,-0.1402019113,0.0674669221,-0.2287077457,0.0866092443,0.0323510319,-0.0400422178,-0.0447052792,-0.5660223961,0.0235966314,-0.2083915621,-0.1109047085,0.5356736779,-0.0001116147,0.0529084504,0.4322141409,0.2323066145,-0.332746774,-0.47617805,-0.166637376,-0.2859125733,0.2685653269,-0.0458771326,-0.106201008,-0.2190005779,-0.0831997171,-0.4019645154,-0.2507114112,0.3426715732,0.2578689456,0.4955489635,-0.3780506551,-0.0329088382,0.3404314518,0.0659533367,-0.0897972137,-0.121790655,0.0322991721,0.3072835803,-0.0473988503,0.2761014402,-0.2294470519,-0.2025157362,0.2580201328,0.2006513029,0.1071209908,-0.2685667872,0.2597986162,0.397672236,0.3670590222,-0.3031966686,-0.2636419833,-0.1467573643,0.1452924758,-0.3436309993,0.1324823797,0.1910241097,-0.0941171348,0.0685333163,0.0357554667,-0.0011443932,-0.0239593275,-0.0902939364,-0.6481676102,0.1719691008,-0.1995633543,-0.0421913676,0.1078097373,-0.2202782482,-0.1949972957,-0.2522360981,-0.2234056294,0.0801572502,-0.1065792516,0.1003684402,0.0845007822,0.004697375,0.0115766581,-0.0951292291,0.1015336737,0.0424099416,-0.0822677463,0.1687573344,0.170925349,0.3348125517,0.2328552753,-0.0728574544,0.3799546957,0.0193400308,0.1769856364,0.1070192307,0.2127702087,0.0343354121,0.1755049378,-0.0000450868,0.5437867641,-0.3101889193,-0.3896652758,0.1163265333,-0.2785485983,0.2316963077,-0.0284226984,0.1327222288,-0.0441335663,0.5136786103,0.1059886664,0.1423939914,-0.2512943745,-0.231328994,-0.3500553668,-0.092775017,-0.0683213398,-0.22664994,0.0656907707,-0.324749738,0.0508021526,0.2728202343,0.0716435537,0.005544506,-0.2475884408,-0.1382153034,-0.0585426614,0.4336970747,-0.1161393449,0.1596887112,0.2403079867,-0.342874676,-0.1022366062,0.6999922395,-0.2216656059,-0.0532755181,-0.2450820953,0.2223022133,-0.2760620415,-0.020124102,-0.2484144121,0.3693502843,-0.0279349722,0.077321358,0.0874128938,-0.3635734618,-0.0147844823,-0.1839386523,0.0209497977,0.3797963858,-0.6199555397,-0.2599905431,-0.0703686699,-0.504216969,0.1629291773,-0.0413955897,0.0530338511,-0.067306757,-0.1998141259,0.1769290119,0.2583929896,-0.0766337663,-0.2854508162,0.2802451551,-0.2618688643,0.101308465,-0.0213798862,-0.0898157284,0.060262356,0.0344470404,0.0802695751,-0.0696555153,0.0330555364,0.1684586555,-0.4463491142,-0.1742885113,0.4488964379,-0.0532704629,0.1189142242,0.1514573097,-0.0131312609,-0.3364873528,0.156673342,-0.0996240601,0.1135944426,0.1214237511,0.3474076092,0.0477840714,0.0446909666,-0.2841199338,-0.5288832784,0.1509966254,-0.1891069263,0.2401483506,-0.1580392718,-0.2939599156,-0.0306463782,-0.0047346223,0.015625976,0.0619904213,0.1947253495,0.173867017,-0.3227350414,0.1073520854,-0.1037438884,0.0853953883,-0.0294980742,0.1388478726,-0.2094201148,0.0488263071,0.1489574015,-0.1194536015,-0.2431014925,0.3520189524,0.2271415144,0.1242949516,-0.2448139787,0.4155716598,-0.0631174743,0.0845967755,-0.3108740449,-0.0033377362,0.2012369782,-0.2009038329,0.1446829885,0.433712244,0.2067083716,0.1068297103,-0.2725558877,0.4156304896,0.0620116256,0.3115216494,-0.0363130271,0.1447064579,0.0924078375,-0.0093405396,-0.233885169,-0.3483479023,-0.0623013973,0.2405408472,0.0831786245,0.3555119634,-0.4104103148,0.2481977493,0.2190868109,-0.3608699739,0.0919088796,0.1771574169,-0.4412681162,-0.0692244619,0.1451369226,0.1348848641,0.3439298272,0.2493994385,0.1540547311,0.0727082863,-0.1303773224,-0.056442447,0.3508002758,0.0216917749,-0.0542482473,0.1896622777,0.2745889425,-0.1013241112,-0.2222869545,0.1033036783,-0.0222288277,0.2475629747,-0.6477204561,0.0214120597,-0.5448576212,0.1577532589,-0.0513008758,-0.101199694,-0.1735530347,-0.4598994255,-0.0596310087,-0.0637275353,-0.1688531339,0.0606418326,-0.2946921289,0.0790323913,-0.1894397438,-0.0448192842,-0.0921838507,0.1211065426,-0.4602267146,0.0721276999,0.0793971717,0.1120033115,0.0942427367,-0.0211445875,-0.1400137395,-0.2468906045,-0.3335292935,-0.0407770835,-0.2937829494,0.4520369172,0.1857922077,0.1359868795,-0.0852918997,0.1245296597,0.2048654258,-0.1200823411,-0.2296515703,0.2370333821,-0.2326395214,0.1798279881,-0.435779959,-0.4690159857,-0.1908026338,-0.1922897696,-0.0238063503,-0.036903996,0.2424751073,0.2144143134,0.1368720829,0.2860684991,0.0342019498,-0.0677920058,-0.1841774285,-0.1419622451,0.4167297482,-0.1353093088,-0.2124438435,0.2918638587,0.0325958282,0.0221098792,0.0316136591,-0.3978864253,0.1595614105,-0.1317650527,0.4551573694,0.0719641447,-0.004756114,0.1841751039,0.3115221858,-0.1610429585,-0.03254411,-0.1116780117,-0.0031236841,-0.1589923501,0.0680799782,0.0545915999,0.1455592215,-0.0275701787,0.4293257594,0.0561835244,0.0138204806,0.0943828598,-0.1955546737,0.4180707037,-0.2736783922,-0.0683417693,-0.1494675428,0.07352441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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3181","title":"`None` converted to `\"None\"` when loading a dataset","comments":"https:\/\/github.com\/huggingface\/datasets\/pull\/3195 fixed it, we'll do a new release soon :)\r\n\r\nFor now feel free to install `datasets` from the master branch","body":"## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":21,"text":"`None` converted to `\"None\"` when loading a dataset \n ## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n https:\/\/github.com\/huggingface\/datasets\/pull\/3195 fixed it, we'll do a new release soon :)\r\n\r\nFor now feel free to install `datasets` from the master branch","embeddings":[-0.1464286894,-0.0612440482,0.0032046074,0.34257254,0.4700648487,0.0306642093,0.334800303,0.2211404294,0.0670462772,0.5334501266,-0.0878189877,0.5156677365,-0.0535896681,0.0836548731,0.0218493324,-0.0698359832,0.067249544,0.3368621469,-0.2149792761,0.0690565631,-0.0510460362,0.2278888226,-0.1687968522,0.0543104857,-0.1880399287,0.027158061,-0.043887157,-0.0020170847,0.0024068477,-0.5169743299,0.0471328832,-0.2155148685,-0.0738767311,0.6061301827,-0.0001111699,0.0452013761,0.3825384378,0.2205606848,-0.3372118175,-0.5307696462,-0.1704455316,-0.2074138224,0.2169650793,-0.050287094,-0.1858266145,-0.2876165807,-0.0798146054,-0.4031275213,-0.09037707,0.3316808045,0.2655036747,0.5151591897,-0.4043794274,-0.0385680161,0.2803567946,0.0307318624,0.0271575563,-0.0554390773,0.0053836158,0.313554585,-0.04910025,0.2781364024,-0.2296465933,-0.205423072,0.2045810819,0.2261075526,0.0747924447,-0.272007376,0.2240144163,0.3992187083,0.3231036067,-0.3918199539,-0.2766830027,-0.1522118896,0.1909399778,-0.3002001047,0.1350709796,0.1665227115,-0.0800633132,0.1199470237,0.0296222307,-0.0127183637,-0.0447182059,-0.0767631605,-0.5624727607,0.1738100648,-0.2073180825,-0.0460129827,0.1074341089,-0.1538490951,-0.1882901639,-0.2707335055,-0.2653435469,0.0933801383,-0.1691854149,0.1125136614,0.0445411615,0.0673968792,-0.0342658833,-0.1328983605,0.183909893,0.001427021,-0.0319623165,0.255869478,0.22757411,0.3134540915,0.2292572111,-0.0074047744,0.3832809925,0.0290098637,0.139861539,0.0664294884,0.170465976,0.0197122116,0.2445342243,-0.0367630944,0.4530421495,-0.2622944415,-0.4035242796,0.1484562159,-0.3096363246,0.1542907506,0.0053459513,0.1733410954,-0.134373948,0.4529518187,0.1688604504,0.14541471,-0.2296205461,-0.2072658241,-0.3875866532,-0.0516239628,-0.0540093593,-0.1725946367,0.041647654,-0.3691145182,0.0462655164,0.2953909636,0.1190989241,-0.047181692,-0.2332774252,-0.1458194703,-0.0711246282,0.4725527763,-0.0256095808,0.1662109345,0.1731166095,-0.3628256619,-0.0479792692,0.6715440154,-0.2385471016,-0.0016715205,-0.2328092158,0.2333145589,-0.3128711581,-0.0128531316,-0.291703105,0.2969140708,0.0389242694,0.0586586073,0.1103760302,-0.3613496721,-0.0263249688,-0.2005540729,0.0823695883,0.3260118961,-0.5988991857,-0.2802537978,-0.0197203588,-0.478159219,0.1590226591,0.0030354427,0.0811264738,0.02981144,-0.1895202398,0.162214011,0.2640683949,-0.1565073878,-0.3092396259,0.2861739099,-0.2655008137,0.1096290946,0.0039899996,-0.1216491312,0.0459208228,0.0993604511,0.1124358326,-0.0055910768,0.070598051,0.1660817415,-0.4398126304,-0.1569270343,0.3803024888,0.0008757661,0.0925072879,0.1377413273,-0.0324139632,-0.3787893951,0.2111176103,-0.1360213459,0.1469317079,0.1233910024,0.3447324932,0.070498392,0.0487093776,-0.3260641694,-0.4675877392,0.1111263484,-0.1682384163,0.2240984142,-0.2030492276,-0.2516444027,-0.0113908416,-0.0358905084,0.0115017584,0.0721544474,0.1911522597,0.1706003845,-0.2488903999,0.1067962348,-0.0728913397,0.1359638423,-0.0008910046,0.1770516485,-0.268539995,0.0771034732,0.1397308409,-0.158851102,-0.201322034,0.3541151881,0.19433406,0.098550722,-0.1789046526,0.452691555,-0.1001664773,0.0614931472,-0.3214010596,-0.0819908679,0.1620353758,-0.1917992234,0.096396178,0.3754052222,0.1808876991,0.0819437802,-0.2591477334,0.409809351,-0.0000183072,0.3266770244,-0.0588273518,0.1414732039,0.0595218875,-0.0081907669,-0.1907538027,-0.296654582,-0.0191169605,0.1431607753,0.1661077142,0.3045220971,-0.3817097843,0.1830173433,0.1662718207,-0.3843568861,0.0652404875,0.1776399314,-0.4099298716,-0.0634118915,0.0703240559,0.2304132879,0.3671705723,0.2751632035,0.1044529825,0.0759237409,-0.0960692912,-0.0713571981,0.3210592866,0.0281882659,0.0088171363,0.2297412306,0.2763282061,-0.0390315913,-0.2238443941,0.0587113053,-0.0708165914,0.2546351552,-0.6769945025,0.0568106547,-0.5281854868,0.1612529904,-0.05044806,-0.1600017399,-0.1808292419,-0.409606427,-0.091640994,-0.0529155843,-0.1558789909,0.0441979319,-0.2121656388,0.0913759246,-0.1819241047,-0.0580979362,-0.1374528259,0.0806463808,-0.4434924424,0.0818186998,0.0417373776,0.1148706377,0.1215856895,-0.0194693077,-0.0950031504,-0.1863658577,-0.2749503255,-0.021837743,-0.3252559304,0.5287588239,0.177967459,0.1645759493,-0.1283813566,0.0893056318,0.2270675153,-0.1248831078,-0.2321186215,0.2204389721,-0.1805830002,0.1472769827,-0.4056718051,-0.4252120554,-0.2348914891,-0.1911501437,-0.0707008317,-0.0540197976,0.223173812,0.2177827656,0.1466342211,0.2807642221,-0.0243899412,-0.031467434,-0.2213175893,-0.2754343748,0.4149610102,-0.1593691558,-0.2384494543,0.2242481112,0.0444364324,0.1326303929,0.0389939658,-0.4489899874,0.1232664436,-0.0975598767,0.3615841269,0.0424863175,0.0741743222,0.1854760349,0.2464903146,-0.1728146225,-0.061452575,-0.138642326,0.0087373443,-0.1641625613,0.0785600096,0.0327794142,0.1865615845,-0.0454007573,0.426094681,0.0671161041,-0.0009067012,0.0853033289,-0.228286311,0.4659278691,-0.225803256,-0.0884863809,-0.1874990016,0.1110296398,0.035892155,0.2392123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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3181","title":"`None` converted to `\"None\"` when loading a dataset","comments":"Thanks, but unfortunately looks like it isn't fixed yet \ud83d\ude22 \r\n[notebook for 1.14.0](https:\/\/colab.research.google.com\/drive\/1SV3sFXPJMWSQgbm4pr9Y1Q8OJ4JYKcDo?usp=sharing)\r\n[notebook for master](https:\/\/colab.research.google.com\/drive\/145wDpuO74MmsuI0SVLcI1IswG6aHpyhi?usp=sharing)","body":"## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":16,"text":"`None` converted to `\"None\"` when loading a dataset \n ## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Thanks, but unfortunately looks like it isn't fixed yet \ud83d\ude22 \r\n[notebook for 1.14.0](https:\/\/colab.research.google.com\/drive\/1SV3sFXPJMWSQgbm4pr9Y1Q8OJ4JYKcDo?usp=sharing)\r\n[notebook for master](https:\/\/colab.research.google.com\/drive\/145wDpuO74MmsuI0SVLcI1IswG6aHpyhi?usp=sharing)","embeddings":[-0.128923431,-0.0297710281,0.02035469,0.35103181,0.4544923604,0.0599752069,0.3477735817,0.2126248628,0.0653543472,0.5317575336,-0.0835782811,0.5394251347,-0.0758214965,0.0443203673,0.0360326283,-0.03917614,0.1098452657,0.3058930337,-0.2327348739,0.0301555023,-0.0654738396,0.2169408202,-0.1848359257,0.0347997844,-0.1775140762,0.0175757594,-0.057943061,0.0098583791,0.045812346,-0.534976244,0.0258295238,-0.2391976565,-0.0401083529,0.6087322831,-0.0001171895,0.02992828,0.44172436,0.2407182455,-0.4080582559,-0.5608811378,-0.2539386153,-0.2232549936,0.2598147392,-0.0287615228,-0.1494943649,-0.2960254848,-0.1072960123,-0.476067692,-0.1811434776,0.3268410563,0.2138039768,0.4328982532,-0.4331311882,-0.0038451138,0.2327245027,-0.0078904489,0.0582489371,-0.0631876439,0.0291847661,0.363696754,-0.0124256983,0.245854035,-0.2055969536,-0.2169153392,0.181395486,0.1980961114,0.1801862419,-0.2631107569,0.2297756523,0.3462202251,0.3852308095,-0.3827926517,-0.2755905986,-0.1117306426,0.2184463143,-0.2605068684,0.1121400967,0.2242871672,-0.0859988555,0.1268851906,-0.0359044857,-0.0217208657,-0.0321160853,-0.0674208775,-0.631426692,0.2334798276,-0.1463194937,-0.0216916241,0.0760588199,-0.1283751726,-0.1068805233,-0.2957783937,-0.2900698781,0.1206524596,-0.1651546657,0.0994569287,-0.0099383388,0.1336245239,-0.1078205407,-0.1506924927,0.1966739893,-0.0358866341,0.0048524011,0.2598774731,0.2605917454,0.3496499062,0.185023874,0.037833456,0.3577560782,-0.039699506,0.1036746874,0.1086417064,0.1424263269,0.0150276041,0.3130607903,0.0321267992,0.4534280896,-0.2656858265,-0.3642272651,0.1588844657,-0.4115264416,0.1716500074,-0.0167900734,0.1071938202,-0.1126839891,0.535595417,0.1935284138,0.1580688953,-0.1806242466,-0.2229047567,-0.3278614581,-0.0866206512,-0.0178431496,-0.2004689425,0.0508044325,-0.3684325218,0.0092293499,0.3504809141,0.0973187238,-0.0982295945,-0.2199798226,-0.1456735283,-0.0415933654,0.5034333467,-0.0381135345,0.1795743406,0.1843678355,-0.400413841,-0.0518927872,0.7935638428,-0.2319362611,-0.0194414258,-0.2299182713,0.1799780577,-0.3036557138,0.0025173428,-0.284981221,0.2809558809,0.0508082323,0.0877895951,0.1277857572,-0.4189588428,-0.0227395743,-0.2008952945,0.0304852985,0.3171203136,-0.6810722351,-0.2386657596,-0.087989524,-0.4317515492,0.1914569139,-0.0554008074,0.1130524203,0.0536723845,-0.2213324904,0.0907136127,0.2469018847,-0.1666344851,-0.3231775761,0.3620012701,-0.3147909641,0.1502885818,0.032126788,-0.1075566858,0.0688928366,0.0852675885,0.0827468932,0.0233983453,0.025848588,0.1803908795,-0.5188949704,-0.1349263787,0.3875522316,-0.0475643836,0.1005733758,0.2346361727,-0.0226266868,-0.3393294215,0.2567855716,-0.1196704656,0.1831967831,0.1247735918,0.3949888349,0.0872471631,0.051353354,-0.2349777967,-0.4538817704,0.0913564041,-0.1634679437,0.2037071139,-0.2402661741,-0.23599419,-0.0169436373,-0.024354063,0.0336653776,0.1205275059,0.1353310794,0.2047242522,-0.2963230908,0.0677815005,-0.0048217978,0.1337612271,0.0007174189,0.1994582564,-0.2202946544,0.0572067723,0.1594148278,-0.1293933541,-0.2498660833,0.3166414201,0.2133391052,0.0847746655,-0.2230152637,0.4591525197,-0.0814393088,0.0737406015,-0.3628711998,-0.0893215165,0.1859741509,-0.181210041,0.1417658925,0.4692144394,0.2086610645,0.0826499835,-0.2143686265,0.4020468295,-0.0163875204,0.3272393346,-0.0654393137,0.1558644921,0.0240641665,0.0102797886,-0.1887962371,-0.3583347797,-0.0770161822,0.1532948911,0.2208812833,0.3200656176,-0.3268501163,0.2021985352,0.0462655462,-0.4436893463,0.0528730378,0.1689406931,-0.4144892097,-0.0823322758,0.1095923483,0.3036741018,0.4046466947,0.2341792434,0.1367325485,0.107895188,-0.0821500942,-0.0643893331,0.3734174967,0.0130021358,-0.0123999054,0.2255874574,0.3018727899,-0.0801869854,-0.158117801,0.0953850001,-0.0162457488,0.287032336,-0.7219932675,0.050181035,-0.5528531075,0.1895661503,-0.1131668761,-0.1703343689,-0.1266744286,-0.4181542993,-0.1487699747,-0.0748803988,-0.1465845108,-0.0039066821,-0.2512751818,0.0252591856,-0.2233772874,-0.0686951578,-0.1109179482,0.0964452177,-0.4671443999,0.0337994583,0.0560671017,0.0797724277,0.0963246524,-0.0060962024,-0.1476422548,-0.1791709512,-0.2660902143,-0.035391584,-0.25337556,0.5185126662,0.1440327913,0.1302251816,-0.11019402,0.0738864318,0.1878613234,-0.0874077827,-0.2062253654,0.2476879209,-0.1945811063,0.1796300411,-0.3906600773,-0.4096363187,-0.2703451216,-0.1353099644,-0.111777693,-0.0621848851,0.2372724861,0.1944610626,0.1335325837,0.2045364827,0.0737999901,-0.0507278629,-0.1338633001,-0.2230641842,0.3791624606,-0.1239220202,-0.1833256185,0.2124636769,0.0700249821,0.1049542427,0.0772855207,-0.4346069396,0.2538853288,-0.1176484004,0.3721303046,0.0260198265,0.0178925619,0.1655243188,0.3136645257,-0.1161840484,-0.0367876254,-0.0826196745,-0.0098471092,-0.1737647206,0.0671473444,0.0731651485,0.1864269823,-0.0426817201,0.405642122,0.0613596141,-0.0137413237,0.06632299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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3181","title":"`None` converted to `\"None\"` when loading a dataset","comments":"Oh, sorry. I deleted the fix by accident when I was resolving a merge conflict. Let me fix this real quick.","body":"## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":21,"text":"`None` converted to `\"None\"` when loading a dataset \n ## Describe the bug\r\nWhen loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nqasper = load_dataset(\"qasper\", split=\"train\", download_mode=\"reuse_cache_if_exists\")\r\nprint(qasper[60][\"full_text\"][\"section_name\"])\r\n```\r\n\r\nWhen installing version 1.1.40, the output is\r\n`[None, 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nWhen installing from the master branch, the output is\r\n`['None', 'Introduction', 'Benchmark Datasets', ...]`\r\n\r\nNotice how the first element was changed from `NoneType` to `str`.\r\n\r\n## Expected results\r\n`None` should stay as is.\r\n\r\n## Actual results\r\n`None` is converted to a string.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: master\r\n- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Oh, sorry. I deleted the fix by accident when I was resolving a merge conflict. Let me fix this real quick.","embeddings":[-0.1147126555,0.0136743085,0.0202211011,0.3419383764,0.4503497779,0.0526508801,0.2959988415,0.219535172,0.0499796122,0.5178978443,-0.105266735,0.5846870542,-0.0678575709,-0.0207228437,0.0296908971,-0.0621311478,0.1152243763,0.334970206,-0.2030991316,0.0398527086,-0.0570103414,0.2160631567,-0.1514354944,0.0501665734,-0.2026712298,0.0485046692,-0.0486548804,0.0483689792,-0.0047975089,-0.5570877194,0.0088143917,-0.2539268136,-0.0620393753,0.6050813794,-0.0001157383,0.0479933545,0.4315148294,0.2505500913,-0.4212139845,-0.5519666076,-0.2199693024,-0.2015581578,0.2262358069,-0.0297792815,-0.1138478667,-0.2914707661,-0.0855813771,-0.3873486221,-0.1464907527,0.3179005384,0.2245864868,0.4525856078,-0.4035227895,-0.0200540163,0.2998401821,0.0119617889,0.0620974265,-0.0044698115,0.0230282079,0.3179568946,-0.0509989709,0.2502599657,-0.2320387959,-0.2159140557,0.2059410512,0.2145478278,0.2079014331,-0.239127636,0.2007955611,0.3331368566,0.3371536732,-0.4109536409,-0.2448705584,-0.1131495014,0.2025986612,-0.3050120771,0.1224776879,0.2190345675,-0.0976096168,0.1202470884,0.0268630777,-0.0363824703,-0.0421758741,-0.063588798,-0.6279235482,0.2784633338,-0.1100542024,-0.0245730411,0.0902420506,-0.1088500619,-0.1291361153,-0.2736847699,-0.2926332653,0.0921353102,-0.104885295,0.0988696814,0.0111007513,0.0942206606,-0.080860056,-0.1399230957,0.1558122188,-0.0523684099,0.0143230977,0.2719790637,0.1973437369,0.333381772,0.1830679923,0.042783536,0.3367649913,-0.0477339439,0.1381703764,0.1245058998,0.1013796404,0.0104032913,0.2856212258,0.0354040936,0.4912146628,-0.2636115551,-0.3900060654,0.1111015752,-0.3835565746,0.12710841,-0.0717791095,0.1379645467,-0.1365853995,0.4354638755,0.1322076321,0.1562484205,-0.1918228567,-0.2227645218,-0.3445286453,-0.051021561,-0.064523302,-0.2091605216,0.0423188172,-0.4008868635,0.0251716599,0.3265525103,0.0950695947,-0.0555601865,-0.2315492183,-0.1210821867,-0.0499058478,0.5236973166,-0.0462858863,0.1692642123,0.2177025527,-0.4019732475,-0.0615469627,0.7788647413,-0.2436653078,-0.0696816668,-0.2847962976,0.1897511482,-0.3129331768,0.0067273905,-0.2576360106,0.2460449487,0.0616837144,0.0679815635,0.1111834794,-0.3899156749,0.0269548707,-0.2300127596,0.0409426503,0.2821367979,-0.6615744233,-0.2588751018,-0.0729546323,-0.4559813738,0.2041563541,-0.0068376842,0.1046151444,0.0472204573,-0.1996590942,0.1338536739,0.2810949981,-0.1671688557,-0.3505240083,0.3034850359,-0.3332687914,0.1373402178,0.0140646119,-0.1182672381,0.0621016175,0.0828006864,0.1067940742,0.0010093593,0.0501020998,0.1913452148,-0.491556406,-0.1665972322,0.391361177,-0.0292363316,0.0832762495,0.1713206172,-0.0148971155,-0.346955359,0.2699846327,-0.0874365419,0.1533454806,0.1224732101,0.3786240816,0.0647770241,0.046861764,-0.2618806958,-0.450871259,0.126230523,-0.192903325,0.2382202744,-0.2372428626,-0.2448140085,-0.0517165922,-0.0108542284,0.029871976,0.1173878983,0.1567519009,0.2081984133,-0.2882652581,0.067429319,-0.0367036797,0.1005243137,0.0026712862,0.2117916644,-0.2216126621,0.0758095384,0.1084673628,-0.154458046,-0.2445387691,0.3457500041,0.2168449163,0.1089643165,-0.1872242987,0.4525820017,-0.0980249271,0.0897333622,-0.3890820742,-0.0972481668,0.1509337425,-0.182301417,0.1338848323,0.4232851863,0.1643429846,0.0870149955,-0.2332681865,0.3427321911,0.0370602272,0.3159338236,-0.0744974688,0.1298269033,0.002738669,0.0030956138,-0.2146288604,-0.3411803842,-0.0542869121,0.1683574617,0.1871845275,0.3444029987,-0.29099226,0.1830552518,0.1498093009,-0.4413324893,0.0380146801,0.1700332612,-0.4569516778,-0.0696574748,0.1049283817,0.2288631052,0.3769007921,0.2537060082,0.120595403,0.0907925069,-0.1290889531,-0.0565488711,0.3581959009,0.0172254629,0.0495939627,0.2201281339,0.2890397906,-0.0385311618,-0.1697393954,0.0768074095,-0.0242943075,0.3015410304,-0.7321715355,0.0363961197,-0.5188020468,0.1717751324,-0.0952540785,-0.2316323519,-0.1702776998,-0.4145472646,-0.1080272645,-0.06350559,-0.1373106092,0.0545857549,-0.1709756702,0.0222916696,-0.228955999,-0.078565143,-0.1221843734,0.0858006477,-0.4540356398,0.047349453,0.0601979829,0.0882903785,0.1142756268,-0.0037200311,-0.1359753609,-0.1744892895,-0.2806692421,-0.0401971415,-0.3133094013,0.5257245898,0.162463218,0.1519570947,-0.0960412994,0.0501732565,0.1939437389,-0.0811987743,-0.2163532525,0.2314611226,-0.1884563714,0.1616545916,-0.3891935945,-0.4299276471,-0.2559581399,-0.1704466939,-0.0848031566,-0.0481542572,0.2447379529,0.1816386133,0.1346267611,0.2280836701,0.0221652333,-0.0513077006,-0.1619959623,-0.2243853658,0.3939982951,-0.1365154535,-0.1832748652,0.2623387873,0.0775028169,0.1046056226,0.0580065772,-0.4282045364,0.2791337073,-0.0760888755,0.3650729954,0.0353091508,0.0097000049,0.2040940225,0.3331024945,-0.128146559,-0.0360778272,-0.1162696108,-0.0320402831,-0.1283910722,0.1050490439,0.028685607,0.154630661,-0.0466301329,0.4541005492,0.0457416214,0.0172405206,0.0228588991,-0.2077738494,0.4054095149,-0.2046213895,-0.0793900192,-0.2077354342,0.0960012376,-0.0104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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"After some digging, I found that this is caused by `dill` and using `recurse=True)` when trying to dump the object. The problem also occurs without multiprocessing. I can only find [the following information](https:\/\/dill.readthedocs.io\/en\/latest\/dill.html#dill._dill.dumps) about this:\r\n\r\n> If recurse=True, then objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. This is needed for functions defined via exec().\r\n\r\nIn the utils, this is explicitly enabled\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/df63614223bf1dd1feb267d39d741bada613352c\/src\/datasets\/utils\/py_utils.py#L327-L330\r\n\r\nIs this really necessary? Is there a way around it? Also pinging the spaCy team in case this is easy to solve on their end. (I hope so.)","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":108,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n After some digging, I found that this is caused by `dill` and using `recurse=True)` when trying to dump the object. The problem also occurs without multiprocessing. I can only find [the following information](https:\/\/dill.readthedocs.io\/en\/latest\/dill.html#dill._dill.dumps) about this:\r\n\r\n> If recurse=True, then objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. This is needed for functions defined via exec().\r\n\r\nIn the utils, this is explicitly enabled\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/df63614223bf1dd1feb267d39d741bada613352c\/src\/datasets\/utils\/py_utils.py#L327-L330\r\n\r\nIs this really necessary? Is there a way around it? Also pinging the spaCy team in case this is easy to solve on their end. (I hope so.)","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764,0.036733076,-0.0528672971,-0.5302314758,-0.1007551402,0.048996672,-0.1999999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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"Hi ! Thanks for reporting\r\n\r\nYes `recurse=True` is necessary to be able to hash all the objects that are passed to the `map` function\r\n\r\nEDIT: hopefully this object can be serializable soon, but otherwise we can consider adding more control to the user on how to hash objects that are not serializable (as mentioned in https:\/\/github.com\/huggingface\/datasets\/issues\/3044#issuecomment-948818210)","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":56,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n Hi ! Thanks for reporting\r\n\r\nYes `recurse=True` is necessary to be able to hash all the objects that are passed to the `map` function\r\n\r\nEDIT: hopefully this object can be serializable soon, but otherwise we can consider adding more control to the user on how to hash objects that are not serializable (as mentioned in https:\/\/github.com\/huggingface\/datasets\/issues\/3044#issuecomment-948818210)","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"I submitted a PR to spacy that should fix this issue (linked above). I'll leave this open until that PR is merged. ","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":22,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n I submitted a PR to spacy that should fix this issue (linked above). I'll leave this open until that PR is merged. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"@lhoestq After some testing I find that even with the updated spaCy, no cache files are used. I do not get any warnings though, but I can see that map is run every time I run the code. Do you have thoughts about why? If you want to try the tests below, make sure to install spaCy from [here](https:\/\/github.com\/BramVanroy\/spaCy) and installing the base model with `python -m spacy download en_core_web_sm`.\r\n\r\n```python\r\nfrom functools import partial\r\nfrom pathlib import Path\r\n\r\nimport spacy\r\nfrom datasets import Dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, l):\r\n    return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\ndef main():\r\n    fin = r\"some\/file\/with\/many\/lines\"\r\n    lines = Path(fin).read_text(encoding=\"utf-8\").splitlines()\r\n    nlp = spacy.load(\"en_core_web_sm\")\r\n    ds = Dataset.from_dict({\"text\": lines, \"text_id\": list(range(len(lines)))})\r\n    tok = partial(tokenize, nlp)\r\n    ds = ds.map(tok, load_from_cache_file=True)\r\n    print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n```\r\n\r\n... or with load_dataset (here I get the message that `load_dataset` can reuse the dataset, but still I see all samples being processed via the tqdm progressbar):\r\n\r\n```python\r\nfrom functools import partial\r\n\r\nimport spacy\r\nfrom datasets import load_dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, sample):\r\n    return {\"tok\": [t.text for t in nlp(sample[\"text\"])]}\r\n\r\ndef main():\r\n    fin = r\"some\/file\/with\/many\/lines\"\r\n    nlp = spacy.load(\"en_core_web_sm\")\r\n    tok_func = partial(tokenize, nlp)\r\n    ds = load_dataset('text', data_files=fin)\r\n    ds = ds[\"train\"].map(tok_func)\r\n    print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n```","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":207,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n @lhoestq After some testing I find that even with the updated spaCy, no cache files are used. I do not get any warnings though, but I can see that map is run every time I run the code. Do you have thoughts about why? If you want to try the tests below, make sure to install spaCy from [here](https:\/\/github.com\/BramVanroy\/spaCy) and installing the base model with `python -m spacy download en_core_web_sm`.\r\n\r\n```python\r\nfrom functools import partial\r\nfrom pathlib import Path\r\n\r\nimport spacy\r\nfrom datasets import Dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, l):\r\n    return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\ndef main():\r\n    fin = r\"some\/file\/with\/many\/lines\"\r\n    lines = Path(fin).read_text(encoding=\"utf-8\").splitlines()\r\n    nlp = spacy.load(\"en_core_web_sm\")\r\n    ds = Dataset.from_dict({\"text\": lines, \"text_id\": list(range(len(lines)))})\r\n    tok = partial(tokenize, nlp)\r\n    ds = ds.map(tok, load_from_cache_file=True)\r\n    print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n```\r\n\r\n... or with load_dataset (here I get the message that `load_dataset` can reuse the dataset, but still I see all samples being processed via the tqdm progressbar):\r\n\r\n```python\r\nfrom functools import partial\r\n\r\nimport spacy\r\nfrom datasets import load_dataset\r\nimport datasets\r\ndatasets.logging.set_verbosity_debug()\r\n\r\ndef tokenize(nlp, sample):\r\n    return {\"tok\": [t.text for t in nlp(sample[\"text\"])]}\r\n\r\ndef main():\r\n    fin = r\"some\/file\/with\/many\/lines\"\r\n    nlp = spacy.load(\"en_core_web_sm\")\r\n    tok_func = partial(tokenize, nlp)\r\n    ds = load_dataset('text', data_files=fin)\r\n    ds = ds[\"train\"].map(tok_func)\r\n    print(ds[0:2])\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n```","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764,0.036733076,-0.0528672971,-0.5302314758,-0.1007551402,0.048996672,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"It looks like every time you load `en_core_web_sm` you get a different python object:\r\n```python\r\nimport spacy\r\nfrom datasets.fingerprint import Hasher\r\n\r\nnlp1 = spacy.load(\"en_core_web_sm\")\r\nnlp2 = spacy.load(\"en_core_web_sm\")\r\nHasher.hash(nlp1), Hasher.hash(nlp2)\r\n# ('f6196a33882fea3b', 'a4c676a071f266ff')\r\n```\r\nHere is a list of attributes that have different hashes for `nlp1` and `nlp2`:\r\n- tagger\r\n- parser\r\n- entity\r\n- pipeline (it's the list of the three attributes above)\r\n\r\nI just took a look at the tagger for example and I found subtle differences (there may be other differences though):\r\n```python\r\nnlp1.tagger.model.tok2vec.embed.id, nlp2.tagger.model.tok2vec.embed.id\r\n# (1721, 2243)\r\n```\r\n\r\nWe can try to find all the differences and find the best way to hash those objects properly","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":109,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n It looks like every time you load `en_core_web_sm` you get a different python object:\r\n```python\r\nimport spacy\r\nfrom datasets.fingerprint import Hasher\r\n\r\nnlp1 = spacy.load(\"en_core_web_sm\")\r\nnlp2 = spacy.load(\"en_core_web_sm\")\r\nHasher.hash(nlp1), Hasher.hash(nlp2)\r\n# ('f6196a33882fea3b', 'a4c676a071f266ff')\r\n```\r\nHere is a list of attributes that have different hashes for `nlp1` and `nlp2`:\r\n- tagger\r\n- parser\r\n- entity\r\n- pipeline (it's the list of the three attributes above)\r\n\r\nI just took a look at the tagger for example and I found subtle differences (there may be other differences though):\r\n```python\r\nnlp1.tagger.model.tok2vec.embed.id, nlp2.tagger.model.tok2vec.embed.id\r\n# (1721, 2243)\r\n```\r\n\r\nWe can try to find all the differences and find the best way to hash those objects properly","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764,0.036733076,-0.0528672971,-0.5302314758,-0.1007551402,0.048996672,-0.199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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"Thanks for searching! I went looking, and found that this is an implementation detail of thinc\r\n\r\nhttps:\/\/github.com\/explosion\/thinc\/blob\/68691e303ae68cae4bc803299016f1fc064328bf\/thinc\/model.py#L96-L98\r\n\r\nPresumably (?) exactly to distinguish between different parts in memory when multiple models are loaded. Do not think that this can be changed on their end - but I will ask what exactly it is for (I'm curious).\r\n\r\nDo you think it is overkill to write something into the hasher explicitly to deal with spaCy models? It seems like something that is beneficial to many, but I do not know if you are open to adding third-party-specific ways to deal with this. If you are, I can have a look for this specific case how we can ignore `thinc.Model.id` from the hasher.","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":119,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n Thanks for searching! I went looking, and found that this is an implementation detail of thinc\r\n\r\nhttps:\/\/github.com\/explosion\/thinc\/blob\/68691e303ae68cae4bc803299016f1fc064328bf\/thinc\/model.py#L96-L98\r\n\r\nPresumably (?) exactly to distinguish between different parts in memory when multiple models are loaded. Do not think that this can be changed on their end - but I will ask what exactly it is for (I'm curious).\r\n\r\nDo you think it is overkill to write something into the hasher explicitly to deal with spaCy models? It seems like something that is beneficial to many, but I do not know if you are open to adding third-party-specific ways to deal with this. If you are, I can have a look for this specific case how we can ignore `thinc.Model.id` from the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"It can be even simpler to hash the bytes of the pipeline instead\r\n```python\r\nnlp1.to_bytes() == nlp2.to_bytes()  # True\r\n```\r\n\r\nIMO we should integrate the custom hashing for spacy models into `datasets` (we use a custom Pickler for that).\r\nWhat could be done on Spacy's side instead (if they think it's nice to have) is to implement a custom pickling for these classes using `to_bytes`\/`from_bytes` to have deterministic pickle dumps.\r\n\r\nFinally I think it would be nice in the future to add an API to let `datasets` users control this kind of things. Something like being able to define your own hashing if you use complex objects.\r\n```python\r\n@datasets.register_hash(spacy.language.Language)\r\ndef hash_spacy_language(nlp):\r\n    return Hasher.hash(nlp.to_bytes())\r\n```","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":114,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n It can be even simpler to hash the bytes of the pipeline instead\r\n```python\r\nnlp1.to_bytes() == nlp2.to_bytes()  # True\r\n```\r\n\r\nIMO we should integrate the custom hashing for spacy models into `datasets` (we use a custom Pickler for that).\r\nWhat could be done on Spacy's side instead (if they think it's nice to have) is to implement a custom pickling for these classes using `to_bytes`\/`from_bytes` to have deterministic pickle dumps.\r\n\r\nFinally I think it would be nice in the future to add an API to let `datasets` users control this kind of things. Something like being able to define your own hashing if you use complex objects.\r\n```python\r\n@datasets.register_hash(spacy.language.Language)\r\ndef hash_spacy_language(nlp):\r\n    return Hasher.hash(nlp.to_bytes())\r\n```","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764,0.036733076,-0.0528672971,-0.5302314758,-0.100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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"I do not quite understand what you mean. as far as I can tell, using `to_bytes` does a pickle dump behind the scene (with `srsly`), recursively using `to_bytes` on the required objects. Therefore, the result of `to_bytes` is a deterministic pickle dump AFAICT. Or do you mean that you wish that using your own pickler and running `dumps(nlp)` should also be deterministic? I guess that would require `__setstate__` and `__getstate__` methods on all the objects that have to\/from_bytes. I'll have a listen over at spaCy what they think, and if that would solve the issue. I'll try this locally first, if I find the time.\r\n\r\nI agree that having the option to use a custom hasher would be useful. I like your suggestion!\r\n\r\nEDIT: after trying some things and reading through their API, it seems that they explicitly do not want this. https:\/\/spacy.io\/usage\/saving-loading#pipeline\r\n\r\n> When serializing the pipeline, keep in mind that this will only save out the binary data for the individual components to allow spaCy to restore them \u2013 not the entire objects. This is a good thing, because it makes serialization safe. But it also means that you have to take care of storing the config, which contains the pipeline configuration and all the relevant settings.\r\n\r\nBest way forward therefore seems to implement the ability to specify a hasher depending on the objects that are pickled, as you suggested. I can work on this if that is useful. I could use some pointers as to how you would like to implement the `register_hash` functionality though. I assume using `catalogue` over at Explosion might be a good starting point.\r\n\r\n","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":271,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n I do not quite understand what you mean. as far as I can tell, using `to_bytes` does a pickle dump behind the scene (with `srsly`), recursively using `to_bytes` on the required objects. Therefore, the result of `to_bytes` is a deterministic pickle dump AFAICT. Or do you mean that you wish that using your own pickler and running `dumps(nlp)` should also be deterministic? I guess that would require `__setstate__` and `__getstate__` methods on all the objects that have to\/from_bytes. I'll have a listen over at spaCy what they think, and if that would solve the issue. I'll try this locally first, if I find the time.\r\n\r\nI agree that having the option to use a custom hasher would be useful. I like your suggestion!\r\n\r\nEDIT: after trying some things and reading through their API, it seems that they explicitly do not want this. https:\/\/spacy.io\/usage\/saving-loading#pipeline\r\n\r\n> When serializing the pipeline, keep in mind that this will only save out the binary data for the individual components to allow spaCy to restore them \u2013 not the entire objects. This is a good thing, because it makes serialization safe. But it also means that you have to take care of storing the config, which contains the pipeline configuration and all the relevant settings.\r\n\r\nBest way forward therefore seems to implement the ability to specify a hasher depending on the objects that are pickled, as you suggested. I can work on this if that is useful. I could use some pointers as to how you would like to implement the `register_hash` functionality though. I assume using `catalogue` over at Explosion might be a good starting point.\r\n\r\n","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764,0.036733076,-0.0528672971,-0.5302314758,-0.1007551402,0.048996672,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"Interestingly, my PR does not solve the issue discussed above. The `tokenize` function hash is different on every run, because for some reason `nlp.__call__` has a different hash every time. The issue therefore seems to run much deeper than I thought. If you have any ideas, I'm all ears.\r\n\r\n```shell\r\ngit clone https:\/\/github.com\/explosion\/spaCy.git\r\ncd spaCy\/\r\ngit checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf\r\ncd ..\r\n\r\ngit clone https:\/\/github.com\/BramVanroy\/datasets.git\r\ncd datasets\r\ngit checkout registry\r\npip install -e .\r\npip install ..\/spaCy\r\nspacy download en_core_web_sm\r\n```\r\n\r\n```python\r\nimport spacy\r\n\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\nfrom datasets.utils.registry import hashers\r\n\r\n@hashers.register(spacy.Language)\r\ndef hash_spacy_language(nlp):\r\n    return Hasher.hash(nlp.to_bytes())\r\n\r\ndef main():\r\n    fin = r\"your\/large\/file\"\r\n    nlp = spacy.load(\"en_core_web_sm\")\r\n    # This is now always the same yay!\r\n    print(Hasher.hash(nlp))\r\n\r\n    def tokenize(l):\r\n        return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\n    ds =  load_dataset(\"text\", data_files=fin)\r\n    # But this is not...\r\n    print(Hasher.hash(tokenize))\r\n    # ... because of this\r\n    print(Hasher.hash(nlp.__call__))\r\n    ds = ds[\"train\"].map(tokenize)\r\n    print(ds[0:2])\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n```","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":151,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n Interestingly, my PR does not solve the issue discussed above. The `tokenize` function hash is different on every run, because for some reason `nlp.__call__` has a different hash every time. The issue therefore seems to run much deeper than I thought. If you have any ideas, I'm all ears.\r\n\r\n```shell\r\ngit clone https:\/\/github.com\/explosion\/spaCy.git\r\ncd spaCy\/\r\ngit checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf\r\ncd ..\r\n\r\ngit clone https:\/\/github.com\/BramVanroy\/datasets.git\r\ncd datasets\r\ngit checkout registry\r\npip install -e .\r\npip install ..\/spaCy\r\nspacy download en_core_web_sm\r\n```\r\n\r\n```python\r\nimport spacy\r\n\r\nfrom datasets import load_dataset\r\nfrom datasets.fingerprint import Hasher\r\nfrom datasets.utils.registry import hashers\r\n\r\n@hashers.register(spacy.Language)\r\ndef hash_spacy_language(nlp):\r\n    return Hasher.hash(nlp.to_bytes())\r\n\r\ndef main():\r\n    fin = r\"your\/large\/file\"\r\n    nlp = spacy.load(\"en_core_web_sm\")\r\n    # This is now always the same yay!\r\n    print(Hasher.hash(nlp))\r\n\r\n    def tokenize(l):\r\n        return {\"tok\": [t.text for t in nlp(l[\"text\"])]}\r\n\r\n    ds =  load_dataset(\"text\", data_files=fin)\r\n    # But this is not...\r\n    print(Hasher.hash(tokenize))\r\n    # ... because of this\r\n    print(Hasher.hash(nlp.__call__))\r\n    ds = ds[\"train\"].map(tokenize)\r\n    print(ds[0:2])\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n```","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764,0.036733076,-0.0528672971,-0.5302314758,-0.1007551402,0.048996672,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"Hi ! I just answered in your PR :) In order for your custom hashing to be used for nested objects, you must integrate it into our recursive pickler that we use for hashing.","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":34,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n Hi ! I just answered in your PR :) In order for your custom hashing to be used for nested objects, you must integrate it into our recursive pickler that we use for hashing.","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764,0.036733076,-0.0528672971,-0.5302314758,-0.1007551402,0.048996672,-0.199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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"I don't quite understand the design constraints of `datasets` or the script that you're running, but my usual advice is to avoid using pickle unless you _absolutely_ have to. So for instance instead of doing your `partial` over the `nlp` object itself, can you just pass the string `en_core_web_sm` in? This will mean calling `spacy.load()` inside the work function, but this is no worse than having to call `pickle.load()` on the contents of the NLP object anyway -- in fact you'll generally find `spacy.load()` faster, apart from the disk read.\r\n\r\nIf you need to pass in the bytes data and don't want to read from disk, you could do something like this:\r\n\r\n```\r\nmsg = (nlp.lang, nlp.to_bytes())\r\n\r\ndef unpack(lang, bytes_data):\r\n    return spacy.blank(lang).from_bytes(bytes_data)\r\n```\r\n\r\nI think that should probably work: the Thinc `model.to_dict()` method (which is used by the `model.to_bytes()` method) doesn't pack the model's ID into the message, so the `nlp.to_bytes()` that you get shouldn't be affected by the global IDs. So you should get a clean message from `nlp.to_bytes()` that doesn't depend on the global state.","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":177,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n I don't quite understand the design constraints of `datasets` or the script that you're running, but my usual advice is to avoid using pickle unless you _absolutely_ have to. So for instance instead of doing your `partial` over the `nlp` object itself, can you just pass the string `en_core_web_sm` in? This will mean calling `spacy.load()` inside the work function, but this is no worse than having to call `pickle.load()` on the contents of the NLP object anyway -- in fact you'll generally find `spacy.load()` faster, apart from the disk read.\r\n\r\nIf you need to pass in the bytes data and don't want to read from disk, you could do something like this:\r\n\r\n```\r\nmsg = (nlp.lang, nlp.to_bytes())\r\n\r\ndef unpack(lang, bytes_data):\r\n    return spacy.blank(lang).from_bytes(bytes_data)\r\n```\r\n\r\nI think that should probably work: the Thinc `model.to_dict()` method (which is used by the `model.to_bytes()` method) doesn't pack the model's ID into the message, so the `nlp.to_bytes()` that you get shouldn't be affected by the global IDs. So you should get a clean message from `nlp.to_bytes()` that doesn't depend on the global state.","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764,0.036733076,-0.0528672971,-0.5302314758,-0.1007551402,0.048996672,-0.19999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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3178","title":"\"Property couldn't be hashed properly\" even though fully picklable","comments":"Hi Matthew, thanks for chiming in! We are currently implementing exactly what you suggest: `to_bytes()` as a default before pickling - but we may prefer `to_dict` to avoid double dumping.\r\n\r\n`datasets` uses pickle dumps (actually dill) to get unique representations of processing steps (a \"fingerprint\" or hash). So it never needs to re-load that dump - it just needs its value to create a hash. If a fingerprint is identical to a cached fingerprint, then the result can be retrieved from the on-disk cache. (@lhoestq or @mariosasko can correct me if I'm wrong.)\r\n\r\nI was experiencing the issue that parsing with spaCy gave me a different fingerprint on every run of the script and thus it could never load the processed dataset from cache. At first I thought the reason was that spaCy Language objects were not picklable with recursive dill, but even after [adjusting for that](https:\/\/github.com\/explosion\/spaCy\/pull\/9593) the issue persisted. @lhoestq found that this is due to the changing `id`, which you discussed [here](https:\/\/github.com\/explosion\/spaCy\/discussions\/9609#discussioncomment-1661081). So yes, you are right. On the surface there simply seems to be an incompatibility between `datasets` default caching functionality as it is currently implemented and `spacy.Language`.\r\n\r\nThe [linked PR](https:\/\/github.com\/huggingface\/datasets\/pull\/3224) aims to remedy that, though. Up to now I have put some effort into making it easier to define your own \"pickling\" function for a given type (and optionally any of its subclasses). That allows us to tell `datasets` that instead of doing `dill.save(nlp)` (non-deterministic), to use `dill.save(nlp.to_bytes())` (deterministic). When I find some more time, the PR [will be expanded](https:\/\/github.com\/huggingface\/datasets\/pull\/3224#issuecomment-968958528) to improve the user-experience a bit and add a built-in function to pickle `spacy.Language` as one of the defaults (using `to_bytes()`).","body":"## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n","comment_length":275,"text":"\"Property couldn't be hashed properly\" even though fully picklable \n ## Describe the bug\r\nI am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.\r\n\r\n## Steps to reproduce the bug\r\n\r\nHere is a [colab](https:\/\/colab.research.google.com\/drive\/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging\/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).\r\n\r\n```python\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10%]\")\r\n        ds = ds.map(self.parse, batched=True, num_proc=6)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled!\")\r\n\r\n    pr.process()\r\n\r\n```\r\n\r\n---\r\n\r\nHere is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.\r\n\r\n```python\r\n\r\nfrom datasets.fingerprint import Hasher\r\nimport pickle\r\n\r\nfrom datasets import load_dataset\r\nimport spacy\r\n\r\n\r\nclass Processor:\r\n    def __init__(self):\r\n        self.nlp = spacy.load(\"en_core_web_sm\", disable=[\"tagger\", \"parser\", \"ner\", \"lemmatizer\"])\r\n\r\n    @staticmethod\r\n    def collate(batch):\r\n        return [d[\"en\"] for d in batch]\r\n\r\n    def parse(self, batch):\r\n        batch = batch[\"translation\"]\r\n        return {\"translation_tok\": [{\"en_tok\": \" \".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}\r\n\r\n    def process(self):\r\n        ds = load_dataset(\"wmt16\", \"de-en\", split=\"train[:10]\")\r\n        return ds.map(self.parse, batched=True)\r\n\r\n\r\nif __name__ == '__main__':\r\n    pr = Processor()\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr, f)\r\n    print(\"Successfully pickled class instance!\")\r\n\r\n    # succeeds\r\n    with open(\"temp.pkl\", \"wb\") as f:\r\n        pickle.dump(pr.nlp, f)\r\n    print(\"Successfully pickled nlp!\")\r\n\r\n    # fails\r\n    print(Hasher.hash(pr.nlp))\r\n    pr.process()\r\n```\r\n\r\n## Expected results\r\nThis to be picklable, working (fingerprinted), and no warning.\r\n\r\n## Actual results\r\nIn the first snippet, I get this warning \r\nParameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\nIn the second, I get this traceback which directs to the `Hasher.hash` line.\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 918, in save_global\r\n    obj2, parent = _getattribute(module, name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 266, in _getattribute\r\n    .format(name, obj))\r\nAttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \" scratch_4.py\", line 40, in <module>\r\n    print(Hasher.hash(pr.nlp))\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 191, in hash\r\n    return cls.hash_default(value)\r\n  File \" \\lib\\site-packages\\datasets\\fingerprint.py\", line 184, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 345, in dumps\r\n    dump(obj, file)\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 320, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 498, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 781, in save_list\r\n    self._batch_appends(obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 805, in _batch_appends\r\n    save(x)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 634, in save_reduce\r\n    save(state)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1176, in save_instancemethod0\r\n    pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 523, in save_function\r\n    obj=obj,\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 990, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \" \\lib\\site-packages\\dill\\_dill.py\", line 1439, in save_type\r\n    StockPickler.save_global(pickler, obj, name=name)\r\n  File \" \\Python\\Python36\\lib\\pickle.py\", line 922, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes\r\n```\r\n\r\n## Environment info\r\nTried on both Linux and Windows\r\n\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12\r\n- PyArrow version: 6.0.0\r\n\r\n \n Hi Matthew, thanks for chiming in! We are currently implementing exactly what you suggest: `to_bytes()` as a default before pickling - but we may prefer `to_dict` to avoid double dumping.\r\n\r\n`datasets` uses pickle dumps (actually dill) to get unique representations of processing steps (a \"fingerprint\" or hash). So it never needs to re-load that dump - it just needs its value to create a hash. If a fingerprint is identical to a cached fingerprint, then the result can be retrieved from the on-disk cache. (@lhoestq or @mariosasko can correct me if I'm wrong.)\r\n\r\nI was experiencing the issue that parsing with spaCy gave me a different fingerprint on every run of the script and thus it could never load the processed dataset from cache. At first I thought the reason was that spaCy Language objects were not picklable with recursive dill, but even after [adjusting for that](https:\/\/github.com\/explosion\/spaCy\/pull\/9593) the issue persisted. @lhoestq found that this is due to the changing `id`, which you discussed [here](https:\/\/github.com\/explosion\/spaCy\/discussions\/9609#discussioncomment-1661081). So yes, you are right. On the surface there simply seems to be an incompatibility between `datasets` default caching functionality as it is currently implemented and `spacy.Language`.\r\n\r\nThe [linked PR](https:\/\/github.com\/huggingface\/datasets\/pull\/3224) aims to remedy that, though. Up to now I have put some effort into making it easier to define your own \"pickling\" function for a given type (and optionally any of its subclasses). That allows us to tell `datasets` that instead of doing `dill.save(nlp)` (non-deterministic), to use `dill.save(nlp.to_bytes())` (deterministic). When I find some more time, the PR [will be expanded](https:\/\/github.com\/huggingface\/datasets\/pull\/3224#issuecomment-968958528) to improve the user-experience a bit and add a built-in function to pickle `spacy.Language` as one of the defaults (using `to_bytes()`).","embeddings":[-0.0547036938,-0.0296594072,0.1383617818,0.1674574167,0.2572078407,-0.188639313,0.33454898,0.0546138696,0.0525934994,0.1248955727,0.0389523357,0.5095515847,-0.2360347807,0.3779695034,-0.1779328436,0.0818780139,0.0879111588,-0.0570350029,0.0878297389,-0.1197969168,-0.0697437897,-0.0256731529,-0.3639790118,0.01503482,-0.5814359188,-0.2471633106,0.1233719438,-0.0043507125,-0.1769181937,-0.3019956648,0.1507754028,-0.0842577592,-0.0198121648,0.0914171338,-0.0001276944,-0.0400402956,0.2713608146,-0.0208547153,-0.0257949624,-0.4120484591,0.1907013357,-0.3608407676,0.0740301609,-0.2113474011,-0.16362001,0.3611477315,-0.2467539757,-0.214310497,0.2606074214,0.0114906598,0.0392241031,0.3941231668,0.0432313085,0.2574500144,-0.0588376112,-0.0148632545,-0.4733216465,0.1091146693,0.118690528,0.1557984799,-0.0484532043,-0.019749172,0.0093571767,-0.1011297852,-0.0401526354,0.1538346261,-0.23730281,-0.0516982935,0.1653802991,0.0052832738,0.0333906002,-0.3204621673,-0.3859627247,-0.1491619647,0.0474648774,-0.5314660072,0.4600657523,0.0664498806,0.1255585402,0.1659143716,-0.084816806,0.0914705098,0.1008480862,0.1648702174,0.2551170886,0.4194832146,-0.0190645009,0.2278354764,-0.0405593291,-0.1689596474,0.2658731639,0.0705321431,0.1795147359,0.0836894214,-0.0195624307,-0.0307153463,-0.167005524,0.247721076,0.2644009292,-0.0484202914,0.0071611051,0.1911678463,-0.3302521408,0.0856913105,0.1315111369,-0.265206933,0.2935677171,0.4055650532,-0.0374958441,0.0583079532,-0.4280022979,0.035611812,0.0313390084,-0.0029137377,0.1146609187,0.1762256175,0.2027963698,-0.1554872841,-0.1113360152,0.2510735393,-0.4037125111,-0.0514082201,0.1103125736,0.1127122343,-0.1159863546,-0.2407060564,-0.1923094988,0.2413772345,-0.3373745084,-0.3447399437,-0.0766617358,0.0834254995,-0.4073738456,-0.0222927537,0.0333841741,0.1963321567,0.3777971566,0.1117830276,-0.1122462749,0.1048940644,-0.0382493846,-0.2463393658,0.1190529391,0.0087577198,-0.159172982,0.1909404695,0.1781329364,-0.4805569649,-0.2565740049,0.0611920618,-0.2101026177,0.0271804016,-0.02233487,-0.0561760291,-0.3674877584,-0.1039264277,-0.0106293187,0.2302386612,0.3314213753,0.0433114581,0.0690918937,-0.360896796,-0.3603521287,-0.2394990623,0.0475332774,0.2380012125,-0.2766398787,-0.2168495655,-0.2440193743,0.0189823508,0.9000561833,0.2183278203,-0.0556712337,0.0234985203,-0.234384656,0.7594916224,0.0905849487,-0.1478675902,-0.5776649714,-0.1676310003,-0.0203919671,0.1384981573,0.022997383,0.022761615,0.3518771231,0.0264443848,0.3190078139,0.302678436,0.1961116791,-0.3028723598,-0.3790920973,-0.1425586641,0.3757954538,-0.313248843,0.1351571828,0.1308344156,0.3068952262,0.4246307909,0.2254490703,0.2583011985,0.1783138067,-0.0941027626,0.3221184611,-0.0918223262,-0.1540009528,-0.1793642044,-0.1915611178,0.2066819221,0.0237353481,0.2731806636,-0.3610517383,-0.1430861652,0.052444011,0.1768563837,-0.1652959734,-0.3081740141,-0.0823016539,0.036754664,-0.1271739006,0.0828387663,-0.1993959248,-0.0555443093,-0.1569061577,0.0780032873,0.1198150963,0.0761380643,-0.1625960618,-0.4827049673,-0.1736913025,0.2461586148,0.301432848,0.0034809613,-0.2449759394,-0.0888495743,0.1731424183,-0.3360348344,-0.1316602528,0.2585936785,0.3164642751,0.0204325281,0.0327870101,0.010144149,-0.0102994051,-0.0306295734,0.286368072,0.2844121158,-0.1854794919,0.001376048,-0.1170925796,0.1922468096,0.4385980964,-0.0604098253,0.0050208797,-0.4889422059,0.155336082,-0.0615306497,0.4590322673,0.3527908325,-0.3520227969,0.31373927,0.7528018355,0.1944849938,0.3439008296,-0.037836425,0.0523135699,0.0474327095,0.1562966406,-0.0160854664,0.1846251339,-0.0083611915,0.1808766872,0.1700416654,-0.2105774581,-0.1544240862,0.0091516357,-0.0678448603,-0.1412785053,0.2443775982,0.0432101935,0.125393495,-0.1704931706,-0.1573506147,0.0509856939,0.3068309426,-0.3004427552,-0.0909456685,-0.7436488271,0.3857065141,-0.0108450754,-0.0645972863,-0.1444188207,-0.1175103188,0.0300630666,0.1875724941,-0.1544269472,0.2837773561,-0.3315265775,0.1442706138,-0.0231078155,-0.1037352905,-0.0666977614,0.0035622269,-0.3922694921,-0.0960427225,0.1824146956,0.043741174,0.1582038105,0.0307289753,-0.1245739013,-0.1211785227,-0.1619984359,0.2076599896,-0.1014190987,0.659953475,0.0151883299,-0.0401166789,-0.2227497995,-0.0424414054,0.3229541481,0.0481724255,-0.1957855225,0.0668940693,0.1534007192,-0.2971332073,-0.1280110925,-0.0810015723,-0.3987239897,-0.2029241472,-0.0015432611,0.0409981273,0.1024123207,0.1651544422,-0.0439475961,0.0399931893,-0.1768000275,0.1112605184,-0.1959138662,-0.5463485718,0.1524966061,-0.022918338,-0.2902197242,-0.1396176517,-0.0799594223,0.1385860294,0.2206831127,-0.0128684556,0.1158865914,-0.2101252079,0.4909319282,0.0909304097,-0.0949426144,0.155754894,0.1762559861,0.138419643,-0.2511659861,-0.1176665723,0.2310352474,-0.3556731343,0.2830182314,0.2095107287,-0.1625985503,0.3103232682,0.5404534936,0.0646589175,-0.448571831,0.6580184698,-0.0632328764,0.036733076,-0.0528672971,-0.5302314758,-0.1007551402,0.048996672,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3177","title":"More control over TQDM when using map\/filter with multiple processes","comments":"Hi,\r\n\r\nIt's hard to provide an API that would cover all use-cases with tqdm in this project.\r\n\r\nHowever, you can make it work by defining a custom decorator (a bit hacky tho) as follows:\r\n```python\r\nimport datasets\r\n\r\ndef progress_only_on_rank_0(func):\r\n    def wrapper(*args, **kwargs):\r\n        rank = kwargs.get(\"rank\")\r\n        disable_tqdm = kwargs.get(\"disable_tqdm\", False)\r\n        disable_tqdm = True if rank is not None and rank > 0 else disable_tqdm\r\n        kwargs[\"disable_tqdm\"] = disable_tqdm\r\n        return func(*args, **kwargs)\r\n    return wrapper\r\n    \r\ndatasets.Dataset._map_single = progress_only_on_rank_0(datasets.Dataset._map_single)\r\n``` \r\n\r\nEDIT: Ups, closed by accident.\r\n\r\nThanks for the provided links. `Trainer` requires this for training in multi-node distributed setting. However, `Dataset.map` doesn't support that yet.\r\n\r\nDo you have an API for this in mind? `Dataset.map` is already bloated with the arguments, so IMO it's not a good idea to add a new arg there.\r\n\r\n","body":"It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets.\r\n\r\n```python\r\ndataset.map(lambda examples: tokenize(examples[\"text\"]), batched=True, num_proc=6)\r\n```\r\n\r\nThe above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down.\r\n\r\n```\r\n #0:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #1:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #2:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #3:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #4:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #5:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #0:   8%|          | 1\/13 [00:00<?, ?ba\/s]\r\n #1:   8%|          | 1\/13 [00:00<?, ?ba\/s]\r\n...\r\n```\r\n\r\nInstead, it would be welcome if we had the option to only show the progress of rank 0.","comment_length":129,"text":"More control over TQDM when using map\/filter with multiple processes \n It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets.\r\n\r\n```python\r\ndataset.map(lambda examples: tokenize(examples[\"text\"]), batched=True, num_proc=6)\r\n```\r\n\r\nThe above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down.\r\n\r\n```\r\n #0:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #1:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #2:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #3:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #4:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #5:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #0:   8%|          | 1\/13 [00:00<?, ?ba\/s]\r\n #1:   8%|          | 1\/13 [00:00<?, ?ba\/s]\r\n...\r\n```\r\n\r\nInstead, it would be welcome if we had the option to only show the progress of rank 0. \n Hi,\r\n\r\nIt's hard to provide an API that would cover all use-cases with tqdm in this project.\r\n\r\nHowever, you can make it work by defining a custom decorator (a bit hacky tho) as follows:\r\n```python\r\nimport datasets\r\n\r\ndef progress_only_on_rank_0(func):\r\n    def wrapper(*args, **kwargs):\r\n        rank = kwargs.get(\"rank\")\r\n        disable_tqdm = kwargs.get(\"disable_tqdm\", False)\r\n        disable_tqdm = True if rank is not None and rank > 0 else disable_tqdm\r\n        kwargs[\"disable_tqdm\"] = disable_tqdm\r\n        return func(*args, **kwargs)\r\n    return wrapper\r\n    \r\ndatasets.Dataset._map_single = progress_only_on_rank_0(datasets.Dataset._map_single)\r\n``` \r\n\r\nEDIT: Ups, closed by accident.\r\n\r\nThanks for the provided links. `Trainer` requires this for training in multi-node distributed setting. However, `Dataset.map` doesn't support that yet.\r\n\r\nDo you have an API for this in mind? `Dataset.map` is already bloated with the arguments, so IMO it's not a good idea to add a new arg there.\r\n\r\n","embeddings":[-0.4308310151,-0.1890671849,-0.1275618225,-0.2148895264,0.2067867815,-0.4247947037,0.2956126928,0.2832435668,-0.0885271206,0.1811403185,0.0224597845,0.5895185471,-0.3055739403,0.3410094976,-0.1393371969,-0.0427787937,-0.2126194537,-0.0323360451,-0.0557467081,0.303363204,-0.311552465,0.1690868735,-0.1908487529,-0.0169026088,0.083374016,-0.188131094,0.2211441696,-0.0185975768,0.0988403708,-0.5225311518,-0.2091057599,0.5296835303,-0.0627193972,0.4850282073,-0.0001148572,0.0013584985,0.1195267588,0.1042941585,-0.0476054773,-0.122552529,0.0642663389,-0.5208238959,0.1492702812,-0.2199396342,0.1000950336,0.225867793,-0.3060847223,-0.4031676054,0.4587512314,-0.0706014633,0.0965236723,0.4603024125,-0.6107484102,0.312443763,0.0127600897,-0.2070994824,-0.1807243824,-0.2112840712,0.6496046185,-0.0543531217,-0.2045805007,0.537106216,-0.3084450364,-0.0040309224,0.0602236576,0.0687544495,0.8249087334,-0.7553758025,0.1820384264,0.3523980975,-0.1085855141,-0.2747583091,-0.2967470586,-0.2797618806,-0.0472391434,-0.4471073151,-0.0331499577,0.0151954042,-0.1511999518,0.0797242671,-0.8251951933,0.1631236523,0.051900696,0.0735422447,-0.4492529929,0.247291714,0.0151401106,0.0163529981,0.2508830428,0.0327074453,0.0431395285,-0.057836242,0.1835708767,0.3129428029,-0.3747931123,-0.245941788,0.3771615922,-0.0692538694,-0.013039385,0.1626438349,-0.10774564,0.3663351238,0.0358357988,0.1256347746,0.0610055551,-0.042171441,0.36352548,-0.0087995306,0.4548891485,-0.3369383514,0.2452256978,0.1936721504,0.244555667,-0.2460112423,0.1326200664,0.2575453818,-0.1277342141,0.1611255705,-0.1085958406,0.1317795962,0.2498379499,0.0827220008,0.2349221557,0.0705911443,0.4069457054,0.4860851467,-0.0855152085,0.1643733829,-0.1270094663,-0.1030368656,0.0207836442,-0.3312841654,-0.1116858348,-0.0571596436,-0.0351049826,0.1304402947,-0.0859857351,-0.0285229981,0.2325696349,0.29457286,0.4892117083,-0.0318104997,0.3284104168,0.6149432659,-0.1570804417,0.0181030948,0.3410227001,0.0156682357,-0.3508782983,0.4715284705,-0.0816509277,-0.145363152,0.1368791908,0.098603785,0.0383206941,0.1980012655,-0.4375406504,0.5194078088,-0.2131972015,0.169773683,0.0347335115,-0.0469575115,-0.4323513508,-0.2168844342,0.2196736485,0.3428824842,-0.3551807106,-0.2892405093,-0.3879410923,-0.0375486091,0.1254660934,-0.0011685306,-0.1117359251,0.0879132599,0.1021294594,0.1766670793,0.640273571,-0.5258668661,-0.2805989981,0.515255332,-0.3983382881,-0.0656134933,-0.0379131176,0.149761796,0.3066088259,-0.1166178435,-0.0034483694,0.2129353881,-0.2427787483,0.3387228847,-0.0791481435,0.0030513839,0.2799412906,-0.0328505673,0.1697036922,-0.0766257718,-0.0455943048,-0.7425559163,0.2315538377,0.0691493154,0.0939860418,0.0542089529,0.1477711946,-0.1542446166,-0.2021473795,-0.3261949122,-0.0590643026,0.2479391992,0.072521165,0.0655876696,-0.2830637991,-0.2511856258,-0.0379135385,-0.0476734601,-0.0562175177,0.0395904295,0.1156698242,-0.3136801422,-0.3568829596,-0.1668849438,-0.2195767015,-0.1592024863,0.3167149425,-0.0602468662,0.5436050296,0.2269660085,0.299115032,-0.0678445771,-0.1496875882,0.1506832242,0.108919397,-0.0509539805,0.1356011778,0.624691844,0.2527222931,-0.1727449,0.2495176196,0.3853091002,0.2987546325,0.2346537411,-0.0092481095,0.2948203087,-0.2537184954,0.051678814,0.0321503654,0.6309404373,-0.014931635,0.3147058189,-0.1267742366,0.1278659254,0.0360466279,-0.0309857875,-0.2166053504,-0.3182306886,0.0765563846,0.5746425986,0.0324062444,-0.3334642649,-0.0195964817,0.4699484706,0.0585662797,0.0809671283,0.0481169634,-0.2125704288,-0.0960638896,0.3254280686,-0.0657024235,0.0461687408,0.1034181118,0.2440157533,0.0270085782,0.0495731831,-0.1090703607,-0.023925541,0.0646370947,0.373488009,-0.1742328852,0.0894106925,0.0581408963,0.108873345,-0.0745349228,-0.4096830785,-0.030872751,0.1270198524,-0.2195718884,-0.1059783101,-0.1564873755,0.0310423207,0.2368571162,-0.0612479933,-0.1958824396,-0.3607873321,0.2704037726,0.0002102557,-0.2369707525,0.3326048255,-0.0445939563,0.2200144231,0.0132758543,-0.0868176967,0.1145739183,-0.364333421,-0.0914066136,-0.0024245013,0.2999726832,-0.0423785895,0.6094264388,-0.0259925462,-0.0687723011,-0.1498768479,-0.4431316257,-0.0279218722,-0.1625172049,0.4117764831,-0.0233462285,0.2589902282,-0.3701545894,0.3188964128,0.1239787787,-0.1208255216,-0.2365747094,-0.0136089548,0.0735199153,0.0537707247,-0.3301969469,0.1379592717,-0.0903058201,-0.3353261352,-0.0142631512,-0.2883002162,0.3493576646,-0.1686572582,-0.2898531556,-0.0202122163,0.15974769,-0.2690550387,0.0729827136,-0.3323304355,0.0548886396,-0.1665711403,0.0502764471,0.2083737105,-0.0962056145,-0.0410933122,0.4076682925,-0.1494530588,-0.4140759408,0.1039565802,0.4255425632,-0.0408835784,0.0547968186,0.4998475909,0.2552913725,-0.0905966237,0.0226898473,-0.184668079,-0.2942548692,-0.0335817486,-0.1943857074,-0.1103770807,0.2154734582,0.1443075985,0.7530956864,0.0798752755,0.2324292809,-0.129108727,0.0441983864,0.0942310914,0.1052306965,-0.0685762614,-0.331332922,0.11009021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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3177","title":"More control over TQDM when using map\/filter with multiple processes","comments":"Inspiration may be found at `transformers`.\r\n\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/4a394cf53f05e73ab9bbb4b179a40236a5ffe45a\/src\/transformers\/trainer.py#L1231-L1233\r\n\r\nTo get unique IDs for each worker, see https:\/\/stackoverflow.com\/a\/10192611\/1150683","body":"It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets.\r\n\r\n```python\r\ndataset.map(lambda examples: tokenize(examples[\"text\"]), batched=True, num_proc=6)\r\n```\r\n\r\nThe above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down.\r\n\r\n```\r\n #0:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #1:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #2:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #3:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #4:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #5:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #0:   8%|          | 1\/13 [00:00<?, ?ba\/s]\r\n #1:   8%|          | 1\/13 [00:00<?, ?ba\/s]\r\n...\r\n```\r\n\r\nInstead, it would be welcome if we had the option to only show the progress of rank 0.","comment_length":16,"text":"More control over TQDM when using map\/filter with multiple processes \n It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets.\r\n\r\n```python\r\ndataset.map(lambda examples: tokenize(examples[\"text\"]), batched=True, num_proc=6)\r\n```\r\n\r\nThe above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down.\r\n\r\n```\r\n #0:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #1:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #2:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #3:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #4:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #5:   0%|          | 0\/13 [00:00<?, ?ba\/s]\r\n #0:   8%|          | 1\/13 [00:00<?, ?ba\/s]\r\n #1:   8%|          | 1\/13 [00:00<?, ?ba\/s]\r\n...\r\n```\r\n\r\nInstead, it would be welcome if we had the option to only show the progress of rank 0. \n Inspiration may be found at `transformers`.\r\n\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/4a394cf53f05e73ab9bbb4b179a40236a5ffe45a\/src\/transformers\/trainer.py#L1231-L1233\r\n\r\nTo get unique IDs for each worker, see https:\/\/stackoverflow.com\/a\/10192611\/1150683","embeddings":[-0.3891158402,-0.2537089586,-0.1010456905,-0.1923471093,0.2609182,-0.4491050541,0.561211884,0.3046912849,-0.1326828897,0.2505340576,0.0430049375,0.4588485956,-0.3234093785,0.3719579875,-0.0753827915,-0.0509361885,-0.1899892986,-0.0078425314,-0.090316996,0.2520215809,-0.257038027,0.2113479674,-0.1396318078,-0.0092122545,-0.1701571196,-0.1101574749,0.2882323861,0.0243733115,0.1481699049,-0.4589737654,-0.2807808518,0.5592170954,-0.0881311074,0.491517067,-0.0001185518,0.0586644188,0.0643324852,0.1580015719,-0.0678700581,-0.1461372972,0.0228629299,-0.5203016996,0.1783992797,-0.246951133,0.1389308423,0.302803278,-0.1758898199,-0.2331063002,0.4582578242,-0.0854634717,0.0617944859,0.5493264198,-0.6976995468,0.3524906337,0.0275906269,-0.1426097155,-0.1743516624,-0.1831694543,0.6869828105,-0.0292393602,-0.2020935416,0.5249745846,-0.202571556,-0.0487350337,-0.0005855765,0.1074931845,0.9600194097,-0.7401763201,0.2662090063,0.4347898364,-0.1880081892,-0.1738138348,-0.2002182752,-0.2800697982,-0.0116347997,-0.3009382188,-0.0066673402,0.1240594164,-0.0772038624,0.1499624997,-0.8625445962,0.211499691,0.0475510731,0.0566544831,-0.4653140306,0.3657103777,0.0730060637,-0.0049612569,0.3385875821,0.0831795484,0.1302760243,0.0254146457,0.1679090112,0.2480480373,-0.2850793898,-0.2479438484,0.2617900372,-0.1502373517,-0.0304843653,0.0922311246,-0.1614449769,0.3531843424,0.0599265918,0.0575616844,0.0926551372,-0.1398381144,0.309063375,0.0671843663,0.4235623181,-0.3416377902,0.1733921468,0.1627539992,0.1811364442,-0.2295928895,0.0731631517,0.2768250704,-0.1533476412,0.1973620504,-0.0382854752,0.0711896196,0.200217247,0.0105186077,0.2024003416,0.1017096117,0.3765785098,0.559330523,-0.106084533,0.1870782375,-0.1884030998,-0.1521342546,0.0080375429,-0.384008944,-0.0144263627,-0.154758811,-0.0832019523,0.065436095,0.0224203523,-0.0207372475,0.259090066,0.1906217188,0.4199035168,0.0008653307,0.2464148849,0.6163259745,-0.164891392,-0.0123427529,0.2770441175,-0.0219170619,-0.2753283978,0.4926710427,-0.1023091599,-0.1826321781,0.0100402739,0.0464343913,0.0561839752,0.2681395411,-0.3590455651,0.4042212963,-0.1780247837,0.2227259725,-0.052466087,-0.0466784537,-0.3326429129,-0.1200269833,0.0870886371,0.3319008648,-0.3201565742,-0.3935419023,-0.3440549672,0.0089246426,0.2454256266,0.0708913058,-0.1899321675,0.1174927503,0.1053163335,0.2027683705,0.564204812,-0.4466151297,-0.2076997459,0.5597566366,-0.48244977,-0.0861889273,0.1295083463,0.1725863367,0.3200224936,-0.0432289168,-0.1812171787,0.1060202941,-0.2086614072,0.4496418834,-0.1057193801,-0.0511744767,0.27572155,-0.0211138148,0.2137649208,0.0126352105,-0.0883580446,-0.4926640391,0.3617992699,0.0064284261,0.0275255572,-0.0478537157,0.09970624,-0.1992169768,-0.1829972565,-0.2052148879,-0.075568229,0.1542934775,0.0144554731,0.2087358683,-0.2537456155,-0.203070879,-0.0044671241,-0.1185344607,-0.0569650196,0.0839954168,0.0381485634,-0.3131380081,-0.5101329088,-0.2995295823,-0.1969642639,-0.0955092534,0.2695788741,-0.0294158738,0.5302406549,0.3033751547,0.172711134,0.0543763414,-0.1886641085,0.1555583775,0.1192021444,-0.1126515046,0.1578168869,0.590939045,0.2054243684,-0.176236704,0.2570674717,0.4738164246,0.4173777699,0.182725504,-0.0091146873,0.2500742078,-0.293729037,0.05432209,-0.0069073923,0.5941438079,0.0038699459,0.2458104193,-0.1593243778,0.1527003199,0.005752244,-0.1024633646,-0.1839679182,-0.3400194347,0.0833265185,0.6647580862,-0.0429836437,-0.1713749468,-0.1390772611,0.4642447233,0.107172139,0.092911005,0.0636564046,-0.2177752107,-0.0632280409,0.3123133779,-0.1501605958,0.0290051363,-0.0164658614,0.2546280921,-0.0806785971,-0.0108354855,-0.0648140758,-0.1081978083,0.0514824018,0.3043230772,-0.0854582563,0.1056129336,0.0773887336,0.0210360866,-0.1193187684,-0.4288011491,0.0038734081,0.1477967054,-0.3083959818,-0.125499472,-0.0869305506,0.1349983066,0.1600978971,-0.0002257395,-0.2952763736,-0.3686139584,0.2851698399,0.0981946886,-0.1707389355,0.3687419593,0.0090115098,0.2567463815,-0.025662981,0.1143569946,0.0892858729,-0.274428457,-0.1000490114,-0.0491773635,0.2954649329,-0.0690152198,0.4327454269,-0.0747947469,-0.1313676536,-0.211360693,-0.4130224288,-0.0668226257,-0.1371024102,0.4504956901,-0.0463076159,0.2709876299,-0.4300172031,0.214660123,0.0570697337,-0.1451872736,-0.2291475683,-0.023973437,0.0309011061,0.0508563183,-0.2485610098,0.0661574751,-0.101180777,-0.2414950877,0.0072093611,-0.2861698866,0.3770836294,-0.0816245228,-0.2183855176,-0.1367134601,0.1966686696,-0.2821018994,0.0489802398,-0.4344125688,0.0388189889,-0.1085398272,0.0501244292,0.1083351746,-0.0631529465,-0.0887395591,0.3546773791,-0.1685383618,-0.2654781044,0.0951209664,0.268797189,0.0296843369,0.0785839036,0.4544781446,0.3423356414,-0.0873148367,0.1304941475,-0.1603890359,-0.2848637402,0.1354690343,-0.0948128551,-0.1525224447,0.1465718746,0.117056109,0.7582410574,0.0245839171,0.2541175485,-0.2149057388,-0.0497986935,0.0475242808,0.2085903287,-0.14340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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3172","title":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`","comments":"NB: even if the error is raised, the dataset is successfully cached. So restarting the script after every `map()` allows to ultimately run the whole preprocessing. But this prevents to realistically run the code over multiple nodes.","body":"## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":37,"text":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1` \n ## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n NB: even if the error is raised, the dataset is successfully cached. So restarting the script after every `map()` allows to ultimately run the whole preprocessing. But this prevents to realistically run the code over multiple nodes.","embeddings":[-0.4948829114,0.0149978213,0.1314037293,0.2600638866,0.3205188513,0.003436357,0.4633166492,0.022410823,0.020504294,0.1974067539,0.1711900532,0.5095179677,-0.2995518744,-0.2648986578,-0.2079018801,0.2099909335,0.0466202609,-0.158107847,-0.2430629134,0.4450131953,-0.4338955283,0.1656216383,-0.2789233625,0.1814815551,-0.1627852917,0.0370968059,-0.0776934475,0.3484531641,0.0860636979,-0.4888789952,0.2598617673,-0.1325254142,0.2476991862,0.539838016,-0.0001272483,0.0460720882,0.2578922808,-0.1024023965,-0.3156309426,-0.0267749932,-0.3072267473,0.1170789972,0.0201563016,-0.0358048417,0.2532772422,0.1260475665,-0.0460102409,-0.3230037987,0.1596814692,0.4001812339,0.0492289513,0.2706763446,-0.1699380279,-0.0399686843,0.119054541,0.2666269541,-0.0602311455,0.3845191002,0.232191205,-0.2951954603,0.1350493431,-0.0893992633,-0.1739127636,-0.0045469832,0.351059258,-0.0409840569,0.0232145321,-0.4895769358,-0.1028225869,0.1353896558,-0.061366301,-0.3817997277,-0.2259420753,-0.0742599666,-0.1902289391,-0.2047738433,-0.0998715237,-0.0249890592,-0.1718678027,0.2097606361,0.0638549626,-0.0485782847,-0.2467308789,0.1295641959,0.0103536081,0.4980970919,0.0289838053,0.3650413752,-0.1222733334,-0.0264730696,-0.4142027199,0.1232839599,-0.0591069385,0.0954193398,-0.0946489051,-0.0601429828,0.029420495,-0.4986720383,0.3885614574,-0.3821167052,0.2268929183,0.1152443215,0.4275257885,0.3449108005,0.3994286954,-0.2721115053,0.2052562684,0.44940117,0.3322494626,0.1261995137,0.2210263908,-0.0018742961,0.1574818939,-0.1251471639,0.2052964419,0.4317996204,0.2413813472,-0.2272038311,-0.0174590666,0.3960921764,-0.1598087549,0.0779929087,0.108998999,0.1883090436,0.4819608629,-0.0319109522,0.0973626003,0.0852163434,-0.3469715118,0.0697055683,-0.0165418107,-0.1447067559,-0.2634496987,0.1307265162,0.1109895185,0.1422276944,0.0236042738,0.028884992,-0.2137302011,-0.1220506653,0.3333074152,-0.144238174,0.0426446162,0.435718447,-0.3199972212,0.2193744481,0.1305710226,0.1577418149,-0.109030433,0.4621482193,-0.0068926616,-0.4316526949,-0.1327646971,0.0437069871,-0.1331690997,0.3940798938,-0.1725145876,-0.037882179,0.5560599566,-0.2402358055,0.1401234567,-0.4384783506,-0.56090343,-0.185416609,-0.0538082905,0.3940500021,-0.090118207,-0.032434471,-0.0583023429,-0.20397228,-0.0181756783,-0.1308783293,-0.2065330744,0.235063225,-0.2520937026,0.0857783183,0.3741846085,-0.1164216399,-0.5244367719,0.2536506951,-0.4085045457,-0.1177071705,-0.4146097898,0.1758607328,0.063368775,-0.0385139361,0.1088760793,0.1612363458,-0.1654227674,0.0422913991,-0.2292452455,-0.020257052,-0.0226200856,0.122850053,0.3228340447,0.0421642102,0.1730150133,0.1101934984,0.2251438946,-0.0360025913,0.0431244783,0.3198361397,0.2104032189,-0.1014226973,-0.038890101,-0.5471792817,-0.0904445127,0.2984935045,-0.2046448886,-0.0949442983,-0.4452675283,0.0026962177,0.0058615077,0.340518266,-0.0987058356,-0.3571216762,-0.0807114094,0.099500522,-0.3186143339,-0.2070311457,0.0025403022,0.227699697,-0.1679095328,0.140409857,-0.5382887721,-0.1865255833,0.0094538452,-0.4891035557,-0.2562515438,-0.0527232736,-0.0526046976,-0.054093536,-0.2584116161,0.3309262693,0.3661310375,-0.5121654272,-0.2837775648,0.016083004,0.0031394619,0.0102091385,-0.1074366421,0.1130131036,0.1510249972,0.1967200488,-0.0199702438,0.2717971206,-0.3765301406,0.3035849929,-0.1066413224,0.1059920341,0.0149016948,0.2249100208,-0.1494161487,-0.3018305898,-0.071553126,0.2841205299,-0.0581321418,0.0478570387,0.2563273907,0.0223195534,0.1165330037,0.0069207284,0.1036128551,0.0942058787,0.0301585048,0.1494592428,0.1820777506,-0.0936225578,0.7560111284,-0.0835572407,-0.1237435713,0.1788657904,-0.0323520936,0.0961244777,0.016920533,0.3523868322,0.4663647115,0.2155160159,0.2119994611,0.2142360657,0.0836377442,-0.537694335,-0.175676927,0.2406241298,-0.4153206646,0.5134964585,-0.2037619501,0.0930119902,-0.198110655,-0.0625839084,-0.1029864103,-0.4223908782,-0.2592758238,0.3001408279,-0.3008062541,0.1081114337,0.0544911288,-0.1594304889,0.0861883834,0.101374723,0.3512019813,-0.2590734065,-0.0052097901,-0.2097328007,0.2074596733,0.0198079217,0.2121817768,0.1231948882,-0.3192383051,0.0487815924,-0.3365660906,0.1015573069,0.1328027099,0.6923864484,0.207278952,0.0852453932,-0.1494085938,0.0258820951,0.2258660942,-0.1865838319,0.0009122335,0.3407760859,0.1408562064,0.0455648378,-0.3188873231,-0.1834890246,-0.1682402045,-0.2703425288,-0.0995852202,-0.3767812848,0.1229388341,0.1574546099,0.1579884291,0.2216858417,-0.0129297227,0.0073684445,0.112623848,0.3374052346,-0.0788994059,-0.273580879,-0.1783542633,-0.0092285154,-0.0247026756,-0.1286484152,0.2723337114,-0.3488796949,-0.243644461,0.0243682265,0.1317642182,-0.0563964322,-0.2671625316,0.5877298713,0.0857561231,0.1951408833,-0.1148177236,-0.1879141927,0.1556041688,-0.2940722108,0.0032642973,0.1971099228,0.6781890988,0.0620537065,0.9768182039,0.3290988505,-0.1423387825,0.0260338914,-0.0972261727,0.0620759018,-0.1731206924,-0.2154784054,-0.0603202395,0.1031092107,-0.1072251648,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3172","title":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`","comments":"Hi,\r\n\r\nIt's not easy to debug the problem without the script. I may be wrong since I'm not very familiar with PyTorch Lightning, but shouldn't you preprocess the data in the `prepare_data` function of `LightningDataModule` and not in the `setup` function.\r\nAs you can't modify the module state in `prepare_data` (according to the docs), use the `cache_file_name` argument in `Dataset.map` there, and reload the processed data in `setup` with `Dataset.from_file(cache_file_name)`. If `num_proc>1`, check the docs on the `suffix_template` argument of `Dataset.map` to get an idea what the final `cache_file_names` are going to be.\r\n\r\nLet me know if this helps.","body":"## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":99,"text":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1` \n ## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n Hi,\r\n\r\nIt's not easy to debug the problem without the script. I may be wrong since I'm not very familiar with PyTorch Lightning, but shouldn't you preprocess the data in the `prepare_data` function of `LightningDataModule` and not in the `setup` function.\r\nAs you can't modify the module state in `prepare_data` (according to the docs), use the `cache_file_name` argument in `Dataset.map` there, and reload the processed data in `setup` with `Dataset.from_file(cache_file_name)`. If `num_proc>1`, check the docs on the `suffix_template` argument of `Dataset.map` to get an idea what the final `cache_file_names` are going to be.\r\n\r\nLet me know if this helps.","embeddings":[-0.4948829114,0.0149978213,0.1314037293,0.2600638866,0.3205188513,0.003436357,0.4633166492,0.022410823,0.020504294,0.1974067539,0.1711900532,0.5095179677,-0.2995518744,-0.2648986578,-0.2079018801,0.2099909335,0.0466202609,-0.158107847,-0.2430629134,0.4450131953,-0.4338955283,0.1656216383,-0.2789233625,0.1814815551,-0.1627852917,0.0370968059,-0.0776934475,0.3484531641,0.0860636979,-0.4888789952,0.2598617673,-0.1325254142,0.2476991862,0.539838016,-0.0001272483,0.0460720882,0.2578922808,-0.1024023965,-0.3156309426,-0.0267749932,-0.3072267473,0.1170789972,0.0201563016,-0.0358048417,0.2532772422,0.1260475665,-0.0460102409,-0.3230037987,0.1596814692,0.4001812339,0.0492289513,0.2706763446,-0.1699380279,-0.0399686843,0.119054541,0.2666269541,-0.0602311455,0.3845191002,0.232191205,-0.2951954603,0.1350493431,-0.0893992633,-0.1739127636,-0.0045469832,0.351059258,-0.0409840569,0.0232145321,-0.4895769358,-0.1028225869,0.1353896558,-0.061366301,-0.3817997277,-0.2259420753,-0.0742599666,-0.1902289391,-0.2047738433,-0.0998715237,-0.0249890592,-0.1718678027,0.2097606361,0.0638549626,-0.0485782847,-0.2467308789,0.1295641959,0.0103536081,0.4980970919,0.0289838053,0.3650413752,-0.1222733334,-0.0264730696,-0.4142027199,0.1232839599,-0.0591069385,0.0954193398,-0.0946489051,-0.0601429828,0.029420495,-0.4986720383,0.3885614574,-0.3821167052,0.2268929183,0.1152443215,0.4275257885,0.3449108005,0.3994286954,-0.2721115053,0.2052562684,0.44940117,0.3322494626,0.1261995137,0.2210263908,-0.0018742961,0.1574818939,-0.1251471639,0.2052964419,0.4317996204,0.2413813472,-0.2272038311,-0.0174590666,0.3960921764,-0.1598087549,0.0779929087,0.108998999,0.1883090436,0.4819608629,-0.0319109522,0.0973626003,0.0852163434,-0.3469715118,0.0697055683,-0.0165418107,-0.1447067559,-0.2634496987,0.1307265162,0.1109895185,0.1422276944,0.0236042738,0.028884992,-0.2137302011,-0.1220506653,0.3333074152,-0.144238174,0.0426446162,0.435718447,-0.3199972212,0.2193744481,0.1305710226,0.1577418149,-0.109030433,0.4621482193,-0.0068926616,-0.4316526949,-0.1327646971,0.0437069871,-0.1331690997,0.3940798938,-0.1725145876,-0.037882179,0.5560599566,-0.2402358055,0.1401234567,-0.4384783506,-0.56090343,-0.185416609,-0.0538082905,0.3940500021,-0.090118207,-0.032434471,-0.0583023429,-0.20397228,-0.0181756783,-0.1308783293,-0.2065330744,0.235063225,-0.2520937026,0.0857783183,0.3741846085,-0.1164216399,-0.5244367719,0.2536506951,-0.4085045457,-0.1177071705,-0.4146097898,0.1758607328,0.063368775,-0.0385139361,0.1088760793,0.1612363458,-0.1654227674,0.0422913991,-0.2292452455,-0.020257052,-0.0226200856,0.122850053,0.3228340447,0.0421642102,0.1730150133,0.1101934984,0.2251438946,-0.0360025913,0.0431244783,0.3198361397,0.2104032189,-0.1014226973,-0.038890101,-0.5471792817,-0.0904445127,0.2984935045,-0.2046448886,-0.0949442983,-0.4452675283,0.0026962177,0.0058615077,0.340518266,-0.0987058356,-0.3571216762,-0.0807114094,0.099500522,-0.3186143339,-0.2070311457,0.0025403022,0.227699697,-0.1679095328,0.140409857,-0.5382887721,-0.1865255833,0.0094538452,-0.4891035557,-0.2562515438,-0.0527232736,-0.0526046976,-0.054093536,-0.2584116161,0.3309262693,0.3661310375,-0.5121654272,-0.2837775648,0.016083004,0.0031394619,0.0102091385,-0.1074366421,0.1130131036,0.1510249972,0.1967200488,-0.0199702438,0.2717971206,-0.3765301406,0.3035849929,-0.1066413224,0.1059920341,0.0149016948,0.2249100208,-0.1494161487,-0.3018305898,-0.071553126,0.2841205299,-0.0581321418,0.0478570387,0.2563273907,0.0223195534,0.1165330037,0.0069207284,0.1036128551,0.0942058787,0.0301585048,0.1494592428,0.1820777506,-0.0936225578,0.7560111284,-0.0835572407,-0.1237435713,0.1788657904,-0.0323520936,0.0961244777,0.016920533,0.3523868322,0.4663647115,0.2155160159,0.2119994611,0.2142360657,0.0836377442,-0.537694335,-0.175676927,0.2406241298,-0.4153206646,0.5134964585,-0.2037619501,0.0930119902,-0.198110655,-0.0625839084,-0.1029864103,-0.4223908782,-0.2592758238,0.3001408279,-0.3008062541,0.1081114337,0.0544911288,-0.1594304889,0.0861883834,0.101374723,0.3512019813,-0.2590734065,-0.0052097901,-0.2097328007,0.2074596733,0.0198079217,0.2121817768,0.1231948882,-0.3192383051,0.0487815924,-0.3365660906,0.1015573069,0.1328027099,0.6923864484,0.207278952,0.0852453932,-0.1494085938,0.0258820951,0.2258660942,-0.1865838319,0.0009122335,0.3407760859,0.1408562064,0.0455648378,-0.3188873231,-0.1834890246,-0.1682402045,-0.2703425288,-0.0995852202,-0.3767812848,0.1229388341,0.1574546099,0.1579884291,0.2216858417,-0.0129297227,0.0073684445,0.112623848,0.3374052346,-0.0788994059,-0.273580879,-0.1783542633,-0.0092285154,-0.0247026756,-0.1286484152,0.2723337114,-0.3488796949,-0.243644461,0.0243682265,0.1317642182,-0.0563964322,-0.2671625316,0.5877298713,0.0857561231,0.1951408833,-0.1148177236,-0.1879141927,0.1556041688,-0.2940722108,0.0032642973,0.1971099228,0.6781890988,0.0620537065,0.9768182039,0.3290988505,-0.1423387825,0.0260338914,-0.0972261727,0.0620759018,-0.1731206924,-0.2154784054,-0.0603202395,0.1031092107,-0.1072251648,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3172","title":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`","comments":"Hi @mariosasko, thank you for the hint, that helped me to move forward with that issue. \r\n\r\nI did a major refactoring of my project to disentangle my `LightningDataModule` and `Dataset`.  Just FYI, it looks like:\r\n\r\n```python\r\nclass Builder():\r\n   def __call__() -> DatasetDict:\r\n      # load and preprocess the data\r\n      return dataset\r\n\r\nclass DataModule(LightningDataModule):\r\n    def prepare_data():\r\n       self.builder()\r\n    def setup():\r\n       self.dataset = self.builder()\r\n```\r\n\r\nUnfortunately, the entanglement between `LightningDataModule` and `Dataset` was not the issue.\r\n\r\nThe culprit was `hydra` and a slight adjustment of the structure of my project solved this issue. The problematic project  structure was:\r\n\r\n```\r\nsrc\/\r\n | - cli.py\r\n | - training\/\r\n      | -experiment.py\r\n\r\n# code in experiment.py\r\ndef run_experiment(config):\r\n     # preprocess data and run\r\n     \r\n# code in cli.py\r\n@hydra.main(...)\r\ndef run(config):\r\n    return run_experiment(config)\r\n```\r\n\r\nMoving `run()` from `clip.py` to `training.experiment.py` solved the issue with `SystemError 15`. No idea why. \r\n\r\nEven if the traceback was referring to `Dataset.__del__`, the problem does not seem to be primarily related to `datasets`, so I will close this issue. Thank you for your help!","body":"## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":170,"text":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1` \n ## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n Hi @mariosasko, thank you for the hint, that helped me to move forward with that issue. \r\n\r\nI did a major refactoring of my project to disentangle my `LightningDataModule` and `Dataset`.  Just FYI, it looks like:\r\n\r\n```python\r\nclass Builder():\r\n   def __call__() -> DatasetDict:\r\n      # load and preprocess the data\r\n      return dataset\r\n\r\nclass DataModule(LightningDataModule):\r\n    def prepare_data():\r\n       self.builder()\r\n    def setup():\r\n       self.dataset = self.builder()\r\n```\r\n\r\nUnfortunately, the entanglement between `LightningDataModule` and `Dataset` was not the issue.\r\n\r\nThe culprit was `hydra` and a slight adjustment of the structure of my project solved this issue. The problematic project  structure was:\r\n\r\n```\r\nsrc\/\r\n | - cli.py\r\n | - training\/\r\n      | -experiment.py\r\n\r\n# code in experiment.py\r\ndef run_experiment(config):\r\n     # preprocess data and run\r\n     \r\n# code in cli.py\r\n@hydra.main(...)\r\ndef run(config):\r\n    return run_experiment(config)\r\n```\r\n\r\nMoving `run()` from `clip.py` to `training.experiment.py` solved the issue with `SystemError 15`. No idea why. \r\n\r\nEven if the traceback was referring to `Dataset.__del__`, the problem does not seem to be primarily related to `datasets`, so I will close this issue. Thank you for your help!","embeddings":[-0.4948829114,0.0149978213,0.1314037293,0.2600638866,0.3205188513,0.003436357,0.4633166492,0.022410823,0.020504294,0.1974067539,0.1711900532,0.5095179677,-0.2995518744,-0.2648986578,-0.2079018801,0.2099909335,0.0466202609,-0.158107847,-0.2430629134,0.4450131953,-0.4338955283,0.1656216383,-0.2789233625,0.1814815551,-0.1627852917,0.0370968059,-0.0776934475,0.3484531641,0.0860636979,-0.4888789952,0.2598617673,-0.1325254142,0.2476991862,0.539838016,-0.0001272483,0.0460720882,0.2578922808,-0.1024023965,-0.3156309426,-0.0267749932,-0.3072267473,0.1170789972,0.0201563016,-0.0358048417,0.2532772422,0.1260475665,-0.0460102409,-0.3230037987,0.1596814692,0.4001812339,0.0492289513,0.2706763446,-0.1699380279,-0.0399686843,0.119054541,0.2666269541,-0.0602311455,0.3845191002,0.232191205,-0.2951954603,0.1350493431,-0.0893992633,-0.1739127636,-0.0045469832,0.351059258,-0.0409840569,0.0232145321,-0.4895769358,-0.1028225869,0.1353896558,-0.061366301,-0.3817997277,-0.2259420753,-0.0742599666,-0.1902289391,-0.2047738433,-0.0998715237,-0.0249890592,-0.1718678027,0.2097606361,0.0638549626,-0.0485782847,-0.2467308789,0.1295641959,0.0103536081,0.4980970919,0.0289838053,0.3650413752,-0.1222733334,-0.0264730696,-0.4142027199,0.1232839599,-0.0591069385,0.0954193398,-0.0946489051,-0.0601429828,0.029420495,-0.4986720383,0.3885614574,-0.3821167052,0.2268929183,0.1152443215,0.4275257885,0.3449108005,0.3994286954,-0.2721115053,0.2052562684,0.44940117,0.3322494626,0.1261995137,0.2210263908,-0.0018742961,0.1574818939,-0.1251471639,0.2052964419,0.4317996204,0.2413813472,-0.2272038311,-0.0174590666,0.3960921764,-0.1598087549,0.0779929087,0.108998999,0.1883090436,0.4819608629,-0.0319109522,0.0973626003,0.0852163434,-0.3469715118,0.0697055683,-0.0165418107,-0.1447067559,-0.2634496987,0.1307265162,0.1109895185,0.1422276944,0.0236042738,0.028884992,-0.2137302011,-0.1220506653,0.3333074152,-0.144238174,0.0426446162,0.435718447,-0.3199972212,0.2193744481,0.1305710226,0.1577418149,-0.109030433,0.4621482193,-0.0068926616,-0.4316526949,-0.1327646971,0.0437069871,-0.1331690997,0.3940798938,-0.1725145876,-0.037882179,0.5560599566,-0.2402358055,0.1401234567,-0.4384783506,-0.56090343,-0.185416609,-0.0538082905,0.3940500021,-0.090118207,-0.032434471,-0.0583023429,-0.20397228,-0.0181756783,-0.1308783293,-0.2065330744,0.235063225,-0.2520937026,0.0857783183,0.3741846085,-0.1164216399,-0.5244367719,0.2536506951,-0.4085045457,-0.1177071705,-0.4146097898,0.1758607328,0.063368775,-0.0385139361,0.1088760793,0.1612363458,-0.1654227674,0.0422913991,-0.2292452455,-0.020257052,-0.0226200856,0.122850053,0.3228340447,0.0421642102,0.1730150133,0.1101934984,0.2251438946,-0.0360025913,0.0431244783,0.3198361397,0.2104032189,-0.1014226973,-0.038890101,-0.5471792817,-0.0904445127,0.2984935045,-0.2046448886,-0.0949442983,-0.4452675283,0.0026962177,0.0058615077,0.340518266,-0.0987058356,-0.3571216762,-0.0807114094,0.099500522,-0.3186143339,-0.2070311457,0.0025403022,0.227699697,-0.1679095328,0.140409857,-0.5382887721,-0.1865255833,0.0094538452,-0.4891035557,-0.2562515438,-0.0527232736,-0.0526046976,-0.054093536,-0.2584116161,0.3309262693,0.3661310375,-0.5121654272,-0.2837775648,0.016083004,0.0031394619,0.0102091385,-0.1074366421,0.1130131036,0.1510249972,0.1967200488,-0.0199702438,0.2717971206,-0.3765301406,0.3035849929,-0.1066413224,0.1059920341,0.0149016948,0.2249100208,-0.1494161487,-0.3018305898,-0.071553126,0.2841205299,-0.0581321418,0.0478570387,0.2563273907,0.0223195534,0.1165330037,0.0069207284,0.1036128551,0.0942058787,0.0301585048,0.1494592428,0.1820777506,-0.0936225578,0.7560111284,-0.0835572407,-0.1237435713,0.1788657904,-0.0323520936,0.0961244777,0.016920533,0.3523868322,0.4663647115,0.2155160159,0.2119994611,0.2142360657,0.0836377442,-0.537694335,-0.175676927,0.2406241298,-0.4153206646,0.5134964585,-0.2037619501,0.0930119902,-0.198110655,-0.0625839084,-0.1029864103,-0.4223908782,-0.2592758238,0.3001408279,-0.3008062541,0.1081114337,0.0544911288,-0.1594304889,0.0861883834,0.101374723,0.3512019813,-0.2590734065,-0.0052097901,-0.2097328007,0.2074596733,0.0198079217,0.2121817768,0.1231948882,-0.3192383051,0.0487815924,-0.3365660906,0.1015573069,0.1328027099,0.6923864484,0.207278952,0.0852453932,-0.1494085938,0.0258820951,0.2258660942,-0.1865838319,0.0009122335,0.3407760859,0.1408562064,0.0455648378,-0.3188873231,-0.1834890246,-0.1682402045,-0.2703425288,-0.0995852202,-0.3767812848,0.1229388341,0.1574546099,0.1579884291,0.2216858417,-0.0129297227,0.0073684445,0.112623848,0.3374052346,-0.0788994059,-0.273580879,-0.1783542633,-0.0092285154,-0.0247026756,-0.1286484152,0.2723337114,-0.3488796949,-0.243644461,0.0243682265,0.1317642182,-0.0563964322,-0.2671625316,0.5877298713,0.0857561231,0.1951408833,-0.1148177236,-0.1879141927,0.1556041688,-0.2940722108,0.0032642973,0.1971099228,0.6781890988,0.0620537065,0.9768182039,0.3290988505,-0.1423387825,0.0260338914,-0.0972261727,0.0620759018,-0.1731206924,-0.2154784054,-0.0603202395,0.1031092107,-0.1072251648,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3172","title":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`","comments":"Please allow me to revive this discussion, as I have an extremely similar issue. Instead of an error, my datasets functions simply aren't caching properly. My setup is almost the same as yours, with hydra to configure my experiment parameters.\r\n\r\n@vlievin Could you confirm if your code correctly loads the cache? If so, do you have any public code that I can reference for comparison?\r\n\r\nI will post a full example with hydra that illustrates this problem in a little bit, probably on another thread.","body":"## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":85,"text":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1` \n ## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n Please allow me to revive this discussion, as I have an extremely similar issue. Instead of an error, my datasets functions simply aren't caching properly. My setup is almost the same as yours, with hydra to configure my experiment parameters.\r\n\r\n@vlievin Could you confirm if your code correctly loads the cache? If so, do you have any public code that I can reference for comparison?\r\n\r\nI will post a full example with hydra that illustrates this problem in a little bit, probably on another thread.","embeddings":[-0.4948829114,0.0149978213,0.1314037293,0.2600638866,0.3205188513,0.003436357,0.4633166492,0.022410823,0.020504294,0.1974067539,0.1711900532,0.5095179677,-0.2995518744,-0.2648986578,-0.2079018801,0.2099909335,0.0466202609,-0.158107847,-0.2430629134,0.4450131953,-0.4338955283,0.1656216383,-0.2789233625,0.1814815551,-0.1627852917,0.0370968059,-0.0776934475,0.3484531641,0.0860636979,-0.4888789952,0.2598617673,-0.1325254142,0.2476991862,0.539838016,-0.0001272483,0.0460720882,0.2578922808,-0.1024023965,-0.3156309426,-0.0267749932,-0.3072267473,0.1170789972,0.0201563016,-0.0358048417,0.2532772422,0.1260475665,-0.0460102409,-0.3230037987,0.1596814692,0.4001812339,0.0492289513,0.2706763446,-0.1699380279,-0.0399686843,0.119054541,0.2666269541,-0.0602311455,0.3845191002,0.232191205,-0.2951954603,0.1350493431,-0.0893992633,-0.1739127636,-0.0045469832,0.351059258,-0.0409840569,0.0232145321,-0.4895769358,-0.1028225869,0.1353896558,-0.061366301,-0.3817997277,-0.2259420753,-0.0742599666,-0.1902289391,-0.2047738433,-0.0998715237,-0.0249890592,-0.1718678027,0.2097606361,0.0638549626,-0.0485782847,-0.2467308789,0.1295641959,0.0103536081,0.4980970919,0.0289838053,0.3650413752,-0.1222733334,-0.0264730696,-0.4142027199,0.1232839599,-0.0591069385,0.0954193398,-0.0946489051,-0.0601429828,0.029420495,-0.4986720383,0.3885614574,-0.3821167052,0.2268929183,0.1152443215,0.4275257885,0.3449108005,0.3994286954,-0.2721115053,0.2052562684,0.44940117,0.3322494626,0.1261995137,0.2210263908,-0.0018742961,0.1574818939,-0.1251471639,0.2052964419,0.4317996204,0.2413813472,-0.2272038311,-0.0174590666,0.3960921764,-0.1598087549,0.0779929087,0.108998999,0.1883090436,0.4819608629,-0.0319109522,0.0973626003,0.0852163434,-0.3469715118,0.0697055683,-0.0165418107,-0.1447067559,-0.2634496987,0.1307265162,0.1109895185,0.1422276944,0.0236042738,0.028884992,-0.2137302011,-0.1220506653,0.3333074152,-0.144238174,0.0426446162,0.435718447,-0.3199972212,0.2193744481,0.1305710226,0.1577418149,-0.109030433,0.4621482193,-0.0068926616,-0.4316526949,-0.1327646971,0.0437069871,-0.1331690997,0.3940798938,-0.1725145876,-0.037882179,0.5560599566,-0.2402358055,0.1401234567,-0.4384783506,-0.56090343,-0.185416609,-0.0538082905,0.3940500021,-0.090118207,-0.032434471,-0.0583023429,-0.20397228,-0.0181756783,-0.1308783293,-0.2065330744,0.235063225,-0.2520937026,0.0857783183,0.3741846085,-0.1164216399,-0.5244367719,0.2536506951,-0.4085045457,-0.1177071705,-0.4146097898,0.1758607328,0.063368775,-0.0385139361,0.1088760793,0.1612363458,-0.1654227674,0.0422913991,-0.2292452455,-0.020257052,-0.0226200856,0.122850053,0.3228340447,0.0421642102,0.1730150133,0.1101934984,0.2251438946,-0.0360025913,0.0431244783,0.3198361397,0.2104032189,-0.1014226973,-0.038890101,-0.5471792817,-0.0904445127,0.2984935045,-0.2046448886,-0.0949442983,-0.4452675283,0.0026962177,0.0058615077,0.340518266,-0.0987058356,-0.3571216762,-0.0807114094,0.099500522,-0.3186143339,-0.2070311457,0.0025403022,0.227699697,-0.1679095328,0.140409857,-0.5382887721,-0.1865255833,0.0094538452,-0.4891035557,-0.2562515438,-0.0527232736,-0.0526046976,-0.054093536,-0.2584116161,0.3309262693,0.3661310375,-0.5121654272,-0.2837775648,0.016083004,0.0031394619,0.0102091385,-0.1074366421,0.1130131036,0.1510249972,0.1967200488,-0.0199702438,0.2717971206,-0.3765301406,0.3035849929,-0.1066413224,0.1059920341,0.0149016948,0.2249100208,-0.1494161487,-0.3018305898,-0.071553126,0.2841205299,-0.0581321418,0.0478570387,0.2563273907,0.0223195534,0.1165330037,0.0069207284,0.1036128551,0.0942058787,0.0301585048,0.1494592428,0.1820777506,-0.0936225578,0.7560111284,-0.0835572407,-0.1237435713,0.1788657904,-0.0323520936,0.0961244777,0.016920533,0.3523868322,0.4663647115,0.2155160159,0.2119994611,0.2142360657,0.0836377442,-0.537694335,-0.175676927,0.2406241298,-0.4153206646,0.5134964585,-0.2037619501,0.0930119902,-0.198110655,-0.0625839084,-0.1029864103,-0.4223908782,-0.2592758238,0.3001408279,-0.3008062541,0.1081114337,0.0544911288,-0.1594304889,0.0861883834,0.101374723,0.3512019813,-0.2590734065,-0.0052097901,-0.2097328007,0.2074596733,0.0198079217,0.2121817768,0.1231948882,-0.3192383051,0.0487815924,-0.3365660906,0.1015573069,0.1328027099,0.6923864484,0.207278952,0.0852453932,-0.1494085938,0.0258820951,0.2258660942,-0.1865838319,0.0009122335,0.3407760859,0.1408562064,0.0455648378,-0.3188873231,-0.1834890246,-0.1682402045,-0.2703425288,-0.0995852202,-0.3767812848,0.1229388341,0.1574546099,0.1579884291,0.2216858417,-0.0129297227,0.0073684445,0.112623848,0.3374052346,-0.0788994059,-0.273580879,-0.1783542633,-0.0092285154,-0.0247026756,-0.1286484152,0.2723337114,-0.3488796949,-0.243644461,0.0243682265,0.1317642182,-0.0563964322,-0.2671625316,0.5877298713,0.0857561231,0.1951408833,-0.1148177236,-0.1879141927,0.1556041688,-0.2940722108,0.0032642973,0.1971099228,0.6781890988,0.0620537065,0.9768182039,0.3290988505,-0.1423387825,0.0260338914,-0.0972261727,0.0620759018,-0.1731206924,-0.2154784054,-0.0603202395,0.1031092107,-0.1072251648,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3172","title":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`","comments":"Hello @mariomeissner, very sorry for the late reply, I hope you have found a solution to your problem!\r\n\r\nI don't have public code at the moment. I have not experienced any other issue with hydra, even if I don't understand why changing the location of the definition of `run()` fixed the problem. \r\n\r\nOverall, I don't have issue with caching anymore, even when \r\n1. using custom fingerprints using the argument `new_fingerprint \r\n2. when using `num_proc>1`","body":"## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":74,"text":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1` \n ## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n Hello @mariomeissner, very sorry for the late reply, I hope you have found a solution to your problem!\r\n\r\nI don't have public code at the moment. I have not experienced any other issue with hydra, even if I don't understand why changing the location of the definition of `run()` fixed the problem. \r\n\r\nOverall, I don't have issue with caching anymore, even when \r\n1. using custom fingerprints using the argument `new_fingerprint \r\n2. when using `num_proc>1`","embeddings":[-0.4948829114,0.0149978213,0.1314037293,0.2600638866,0.3205188513,0.003436357,0.4633166492,0.022410823,0.020504294,0.1974067539,0.1711900532,0.5095179677,-0.2995518744,-0.2648986578,-0.2079018801,0.2099909335,0.0466202609,-0.158107847,-0.2430629134,0.4450131953,-0.4338955283,0.1656216383,-0.2789233625,0.1814815551,-0.1627852917,0.0370968059,-0.0776934475,0.3484531641,0.0860636979,-0.4888789952,0.2598617673,-0.1325254142,0.2476991862,0.539838016,-0.0001272483,0.0460720882,0.2578922808,-0.1024023965,-0.3156309426,-0.0267749932,-0.3072267473,0.1170789972,0.0201563016,-0.0358048417,0.2532772422,0.1260475665,-0.0460102409,-0.3230037987,0.1596814692,0.4001812339,0.0492289513,0.2706763446,-0.1699380279,-0.0399686843,0.119054541,0.2666269541,-0.0602311455,0.3845191002,0.232191205,-0.2951954603,0.1350493431,-0.0893992633,-0.1739127636,-0.0045469832,0.351059258,-0.0409840569,0.0232145321,-0.4895769358,-0.1028225869,0.1353896558,-0.061366301,-0.3817997277,-0.2259420753,-0.0742599666,-0.1902289391,-0.2047738433,-0.0998715237,-0.0249890592,-0.1718678027,0.2097606361,0.0638549626,-0.0485782847,-0.2467308789,0.1295641959,0.0103536081,0.4980970919,0.0289838053,0.3650413752,-0.1222733334,-0.0264730696,-0.4142027199,0.1232839599,-0.0591069385,0.0954193398,-0.0946489051,-0.0601429828,0.029420495,-0.4986720383,0.3885614574,-0.3821167052,0.2268929183,0.1152443215,0.4275257885,0.3449108005,0.3994286954,-0.2721115053,0.2052562684,0.44940117,0.3322494626,0.1261995137,0.2210263908,-0.0018742961,0.1574818939,-0.1251471639,0.2052964419,0.4317996204,0.2413813472,-0.2272038311,-0.0174590666,0.3960921764,-0.1598087549,0.0779929087,0.108998999,0.1883090436,0.4819608629,-0.0319109522,0.0973626003,0.0852163434,-0.3469715118,0.0697055683,-0.0165418107,-0.1447067559,-0.2634496987,0.1307265162,0.1109895185,0.1422276944,0.0236042738,0.028884992,-0.2137302011,-0.1220506653,0.3333074152,-0.144238174,0.0426446162,0.435718447,-0.3199972212,0.2193744481,0.1305710226,0.1577418149,-0.109030433,0.4621482193,-0.0068926616,-0.4316526949,-0.1327646971,0.0437069871,-0.1331690997,0.3940798938,-0.1725145876,-0.037882179,0.5560599566,-0.2402358055,0.1401234567,-0.4384783506,-0.56090343,-0.185416609,-0.0538082905,0.3940500021,-0.090118207,-0.032434471,-0.0583023429,-0.20397228,-0.0181756783,-0.1308783293,-0.2065330744,0.235063225,-0.2520937026,0.0857783183,0.3741846085,-0.1164216399,-0.5244367719,0.2536506951,-0.4085045457,-0.1177071705,-0.4146097898,0.1758607328,0.063368775,-0.0385139361,0.1088760793,0.1612363458,-0.1654227674,0.0422913991,-0.2292452455,-0.020257052,-0.0226200856,0.122850053,0.3228340447,0.0421642102,0.1730150133,0.1101934984,0.2251438946,-0.0360025913,0.0431244783,0.3198361397,0.2104032189,-0.1014226973,-0.038890101,-0.5471792817,-0.0904445127,0.2984935045,-0.2046448886,-0.0949442983,-0.4452675283,0.0026962177,0.0058615077,0.340518266,-0.0987058356,-0.3571216762,-0.0807114094,0.099500522,-0.3186143339,-0.2070311457,0.0025403022,0.227699697,-0.1679095328,0.140409857,-0.5382887721,-0.1865255833,0.0094538452,-0.4891035557,-0.2562515438,-0.0527232736,-0.0526046976,-0.054093536,-0.2584116161,0.3309262693,0.3661310375,-0.5121654272,-0.2837775648,0.016083004,0.0031394619,0.0102091385,-0.1074366421,0.1130131036,0.1510249972,0.1967200488,-0.0199702438,0.2717971206,-0.3765301406,0.3035849929,-0.1066413224,0.1059920341,0.0149016948,0.2249100208,-0.1494161487,-0.3018305898,-0.071553126,0.2841205299,-0.0581321418,0.0478570387,0.2563273907,0.0223195534,0.1165330037,0.0069207284,0.1036128551,0.0942058787,0.0301585048,0.1494592428,0.1820777506,-0.0936225578,0.7560111284,-0.0835572407,-0.1237435713,0.1788657904,-0.0323520936,0.0961244777,0.016920533,0.3523868322,0.4663647115,0.2155160159,0.2119994611,0.2142360657,0.0836377442,-0.537694335,-0.175676927,0.2406241298,-0.4153206646,0.5134964585,-0.2037619501,0.0930119902,-0.198110655,-0.0625839084,-0.1029864103,-0.4223908782,-0.2592758238,0.3001408279,-0.3008062541,0.1081114337,0.0544911288,-0.1594304889,0.0861883834,0.101374723,0.3512019813,-0.2590734065,-0.0052097901,-0.2097328007,0.2074596733,0.0198079217,0.2121817768,0.1231948882,-0.3192383051,0.0487815924,-0.3365660906,0.1015573069,0.1328027099,0.6923864484,0.207278952,0.0852453932,-0.1494085938,0.0258820951,0.2258660942,-0.1865838319,0.0009122335,0.3407760859,0.1408562064,0.0455648378,-0.3188873231,-0.1834890246,-0.1682402045,-0.2703425288,-0.0995852202,-0.3767812848,0.1229388341,0.1574546099,0.1579884291,0.2216858417,-0.0129297227,0.0073684445,0.112623848,0.3374052346,-0.0788994059,-0.273580879,-0.1783542633,-0.0092285154,-0.0247026756,-0.1286484152,0.2723337114,-0.3488796949,-0.243644461,0.0243682265,0.1317642182,-0.0563964322,-0.2671625316,0.5877298713,0.0857561231,0.1951408833,-0.1148177236,-0.1879141927,0.1556041688,-0.2940722108,0.0032642973,0.1971099228,0.6781890988,0.0620537065,0.9768182039,0.3290988505,-0.1423387825,0.0260338914,-0.0972261727,0.0620759018,-0.1731206924,-0.2154784054,-0.0603202395,0.1031092107,-0.107225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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3172","title":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1`","comments":"I solved my issue by turning the map callable into a class static method, like they do in `lightning-transformers`. Very strange...","body":"## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n","comment_length":21,"text":"`SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1` \n ## Describe the bug\r\nI use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow. \r\n \r\nThe exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup. \r\n\r\nIn my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.\r\n\r\nI could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome. \r\n\r\n```python\r\nclass Dataset()\r\n    ...\r\n    def __del__(self):\r\n        if hasattr(self, \"_data\"):\r\n            _ = self._data # <- ugly trick that allows avoiding the issue.\r\n            del self._data\r\n        if hasattr(self, \"_indices\"):\r\n            del self._indices\r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Unfortunately I couldn't isolate the bug.\r\n```\r\n\r\n## Expected results\r\nCalling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception\/traceback.\r\n\r\n## Actual results\r\n```\r\nException ignored in: <function Dataset.__del__ at 0x7f7cec179160>\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:05<00:00,  1.17ba\/s]\r\nTraceback (most recent call last):\r\n  File \"...\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 906, in __del__\r\n    del self._data\r\n  File \"...\/python3.8\/site-packages\/ray\/worker.py\", line 1033, in sigterm_handler\r\n    sys.exit(signum)\r\nSystemExit: 15\r\n\r\n```\r\n\r\n## Environment info\r\n\r\nTested on 2 environments:\r\n\r\n**Environment 1.**\r\n- `datasets` version: 1.14.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n**Environment 2.**\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28\r\n- Python version: 3.9.7\r\n- PyArrow version: 6.0.0\r\n \n I solved my issue by turning the map callable into a class static method, like they do in `lightning-transformers`. Very strange...","embeddings":[-0.4948829114,0.0149978213,0.1314037293,0.2600638866,0.3205188513,0.003436357,0.4633166492,0.022410823,0.020504294,0.1974067539,0.1711900532,0.5095179677,-0.2995518744,-0.2648986578,-0.2079018801,0.2099909335,0.0466202609,-0.158107847,-0.2430629134,0.4450131953,-0.4338955283,0.1656216383,-0.2789233625,0.1814815551,-0.1627852917,0.0370968059,-0.0776934475,0.3484531641,0.0860636979,-0.4888789952,0.2598617673,-0.1325254142,0.2476991862,0.539838016,-0.0001272483,0.0460720882,0.2578922808,-0.1024023965,-0.3156309426,-0.0267749932,-0.3072267473,0.1170789972,0.0201563016,-0.0358048417,0.2532772422,0.1260475665,-0.0460102409,-0.3230037987,0.1596814692,0.4001812339,0.0492289513,0.2706763446,-0.1699380279,-0.0399686843,0.119054541,0.2666269541,-0.0602311455,0.3845191002,0.232191205,-0.2951954603,0.1350493431,-0.0893992633,-0.1739127636,-0.0045469832,0.351059258,-0.0409840569,0.0232145321,-0.4895769358,-0.1028225869,0.1353896558,-0.061366301,-0.3817997277,-0.2259420753,-0.0742599666,-0.1902289391,-0.2047738433,-0.0998715237,-0.0249890592,-0.1718678027,0.2097606361,0.0638549626,-0.0485782847,-0.2467308789,0.1295641959,0.0103536081,0.4980970919,0.0289838053,0.3650413752,-0.1222733334,-0.0264730696,-0.4142027199,0.1232839599,-0.0591069385,0.0954193398,-0.0946489051,-0.0601429828,0.029420495,-0.4986720383,0.3885614574,-0.3821167052,0.2268929183,0.1152443215,0.4275257885,0.3449108005,0.3994286954,-0.2721115053,0.2052562684,0.44940117,0.3322494626,0.1261995137,0.2210263908,-0.0018742961,0.1574818939,-0.1251471639,0.2052964419,0.4317996204,0.2413813472,-0.2272038311,-0.0174590666,0.3960921764,-0.1598087549,0.0779929087,0.108998999,0.1883090436,0.4819608629,-0.0319109522,0.0973626003,0.0852163434,-0.3469715118,0.0697055683,-0.0165418107,-0.1447067559,-0.2634496987,0.1307265162,0.1109895185,0.1422276944,0.0236042738,0.028884992,-0.2137302011,-0.1220506653,0.3333074152,-0.144238174,0.0426446162,0.435718447,-0.3199972212,0.2193744481,0.1305710226,0.1577418149,-0.109030433,0.4621482193,-0.0068926616,-0.4316526949,-0.1327646971,0.0437069871,-0.1331690997,0.3940798938,-0.1725145876,-0.037882179,0.5560599566,-0.2402358055,0.1401234567,-0.4384783506,-0.56090343,-0.185416609,-0.0538082905,0.3940500021,-0.090118207,-0.032434471,-0.0583023429,-0.20397228,-0.0181756783,-0.1308783293,-0.2065330744,0.235063225,-0.2520937026,0.0857783183,0.3741846085,-0.1164216399,-0.5244367719,0.2536506951,-0.4085045457,-0.1177071705,-0.4146097898,0.1758607328,0.063368775,-0.0385139361,0.1088760793,0.1612363458,-0.1654227674,0.0422913991,-0.2292452455,-0.020257052,-0.0226200856,0.122850053,0.3228340447,0.0421642102,0.1730150133,0.1101934984,0.2251438946,-0.0360025913,0.0431244783,0.3198361397,0.2104032189,-0.1014226973,-0.038890101,-0.5471792817,-0.0904445127,0.2984935045,-0.2046448886,-0.0949442983,-0.4452675283,0.0026962177,0.0058615077,0.340518266,-0.0987058356,-0.3571216762,-0.0807114094,0.099500522,-0.3186143339,-0.2070311457,0.0025403022,0.227699697,-0.1679095328,0.140409857,-0.5382887721,-0.1865255833,0.0094538452,-0.4891035557,-0.2562515438,-0.0527232736,-0.0526046976,-0.054093536,-0.2584116161,0.3309262693,0.3661310375,-0.5121654272,-0.2837775648,0.016083004,0.0031394619,0.0102091385,-0.1074366421,0.1130131036,0.1510249972,0.1967200488,-0.0199702438,0.2717971206,-0.3765301406,0.3035849929,-0.1066413224,0.1059920341,0.0149016948,0.2249100208,-0.1494161487,-0.3018305898,-0.071553126,0.2841205299,-0.0581321418,0.0478570387,0.2563273907,0.0223195534,0.1165330037,0.0069207284,0.1036128551,0.0942058787,0.0301585048,0.1494592428,0.1820777506,-0.0936225578,0.7560111284,-0.0835572407,-0.1237435713,0.1788657904,-0.0323520936,0.0961244777,0.016920533,0.3523868322,0.4663647115,0.2155160159,0.2119994611,0.2142360657,0.0836377442,-0.537694335,-0.175676927,0.2406241298,-0.4153206646,0.5134964585,-0.2037619501,0.0930119902,-0.198110655,-0.0625839084,-0.1029864103,-0.4223908782,-0.2592758238,0.3001408279,-0.3008062541,0.1081114337,0.0544911288,-0.1594304889,0.0861883834,0.101374723,0.3512019813,-0.2590734065,-0.0052097901,-0.2097328007,0.2074596733,0.0198079217,0.2121817768,0.1231948882,-0.3192383051,0.0487815924,-0.3365660906,0.1015573069,0.1328027099,0.6923864484,0.207278952,0.0852453932,-0.1494085938,0.0258820951,0.2258660942,-0.1865838319,0.0009122335,0.3407760859,0.1408562064,0.0455648378,-0.3188873231,-0.1834890246,-0.1682402045,-0.2703425288,-0.0995852202,-0.3767812848,0.1229388341,0.1574546099,0.1579884291,0.2216858417,-0.0129297227,0.0073684445,0.112623848,0.3374052346,-0.0788994059,-0.273580879,-0.1783542633,-0.0092285154,-0.0247026756,-0.1286484152,0.2723337114,-0.3488796949,-0.243644461,0.0243682265,0.1317642182,-0.0563964322,-0.2671625316,0.5877298713,0.0857561231,0.1951408833,-0.1148177236,-0.1879141927,0.1556041688,-0.2940722108,0.0032642973,0.1971099228,0.6781890988,0.0620537065,0.9768182039,0.3290988505,-0.1423387825,0.0260338914,-0.0972261727,0.0620759018,-0.1731206924,-0.2154784054,-0.0603202395,0.1031092107,-0.10722516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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3171","title":"Raise exceptions instead of using assertions for control flow","comments":"Adding the remaining tasks for this issue to help new code contributors.  \r\n$ cd src\/datasets && ack assert -lc \r\n- [x] commands\/convert.py:1\r\n- [x] arrow_reader.py:3\r\n- [x] load.py:7\r\n- [x] utils\/py_utils.py:2\r\n- [x] features\/features.py:9\r\n- [x] arrow_writer.py:7\r\n- [x] search.py:6\r\n- [x] table.py:1\r\n- [x] metric.py:3\r\n- [x] tasks\/image_classification.py:1\r\n- [x] arrow_dataset.py:17\r\n- [x] fingerprint.py:6\r\n- [x] io\/json.py:1\r\n- [x] io\/csv.py:1","body":"Motivated by https:\/\/github.com\/huggingface\/transformers\/issues\/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.\r\n\r\nCurrently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src\/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR.","comment_length":61,"text":"Raise exceptions instead of using assertions for control flow \n Motivated by https:\/\/github.com\/huggingface\/transformers\/issues\/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.\r\n\r\nCurrently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src\/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR. \n Adding the remaining tasks for this issue to help new code contributors.  \r\n$ cd src\/datasets && ack assert -lc \r\n- [x] commands\/convert.py:1\r\n- [x] arrow_reader.py:3\r\n- [x] load.py:7\r\n- [x] utils\/py_utils.py:2\r\n- [x] features\/features.py:9\r\n- [x] arrow_writer.py:7\r\n- [x] search.py:6\r\n- [x] table.py:1\r\n- [x] metric.py:3\r\n- [x] tasks\/image_classification.py:1\r\n- [x] arrow_dataset.py:17\r\n- [x] fingerprint.py:6\r\n- [x] io\/json.py:1\r\n- [x] 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3171","title":"Raise exceptions instead of using assertions for control flow","comments":"Hi all,\r\nI am interested in taking up `fingerprint.py`, `search.py`, `arrow_writer.py` and `metric.py`. Will raise a PR soon!","body":"Motivated by https:\/\/github.com\/huggingface\/transformers\/issues\/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.\r\n\r\nCurrently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src\/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR.","comment_length":18,"text":"Raise exceptions instead of using assertions for control flow \n Motivated by https:\/\/github.com\/huggingface\/transformers\/issues\/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.\r\n\r\nCurrently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src\/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR. \n Hi all,\r\nI am interested in taking up `fingerprint.py`, `search.py`, `arrow_writer.py` and `metric.py`. Will raise a PR soon!","embeddings":[-0.0443973131,-0.3855049908,-0.0965585262,0.0352227353,0.2973783612,-0.4148050845,0.16322653,0.305850774,-0.0634226203,0.2538896203,0.2832563519,-0.111668475,-0.0943636671,0.1367025971,-0.0752318352,-0.3936472833,-0.016545428,0.0376644321,0.1048266813,-0.0178149063,-0.117592819,0.022701608,-0.0550462045,0.0515687726,-0.4929392338,-0.1577432454,0.0396956354,0.0080961566,0.0034159643,-0.6304950714,0.0225893017,0.3503121436,0.0602492876,0.5564227104,-0.0001080529,0.0079952488,0.3682622313,0.0435871631,0.1379034966,0.4090200961,0.1559152305,-0.3093356192,-0.010130276,-0.0623225085,0.1100680158,-0.3748114407,-0.117802687,0.4675937295,0.4955402017,0.2173317075,0.2472783625,0.5136277676,0.153931573,0.0871138871,0.0833809152,0.357989043,0.0342415124,-0.0697482377,0.4139145315,-0.0550201461,-0.1766918898,0.106161423,0.1114478409,-0.1337872893,0.1015222967,-0.3289579749,0.1560181975,-0.2726629376,0.0224331543,0.2641471028,0.0942806751,-0.1697351784,-0.5638930798,-0.4778563082,-0.0611666329,-0.2056973428,0.1154563427,0.1467808038,-0.1408530772,0.1503971368,-0.2703333497,-0.1204671487,-0.2784129977,-0.297868073,-0.1625622064,0.2546880245,0.1082980931,0.014319594,0.2957330942,-0.0872823671,0.0597032495,0.0404071286,-0.1229627505,-0.2055559605,-0.1467737705,-0.2311653197,0.0545159653,-0.0060877856,0.2341570705,0.1485357583,-0.2558372319,0.1095786765,0.0426650271,0.0007373096,0.1723356396,0.2423827499,-0.3118127584,0.3290785551,0.4951616824,0.1766760796,0.1809313446,0.045465786,0.0117025813,-0.2209623158,-0.0396052115,0.1572436541,0.1173072681,-0.1060320064,0.3168834746,-0.1047117338,0.0179522112,-0.0272599831,0.1369427741,0.2252496183,0.1310708225,0.560064733,0.1301067919,0.0949326605,-0.0663482547,-0.0977972075,-0.2061595619,-0.0123217339,-0.1162941903,-0.0824685916,0.0946944058,0.1730432063,0.1480430663,-0.1594902873,0.207092315,-0.1003186107,0.3334524632,0.0513906851,0.2417224199,0.5240749121,-0.4533220828,-0.1798014045,-0.1476250291,-0.0629224405,-0.1961715817,0.097796008,0.0936141163,-0.4668838382,0.0961250067,0.2099661976,-0.2735571861,0.0175614599,0.0716029778,0.0320127346,0.0898628086,-0.0828214735,0.3354223669,-0.0669291988,0.2312240899,-0.04661154,0.1529256701,0.4079343379,0.0868165046,-0.1335089356,0.0848860368,-0.1842863262,-0.0591025651,0.5728435516,-0.2156318724,0.1726903617,-0.138073355,-0.2174676508,0.0260612722,-0.3545252979,-0.2374695241,0.0634922832,-0.0860260576,-0.0486663841,0.3459973633,-0.0581727438,-0.037964467,-0.3205471635,-0.2557015121,0.0453680269,-0.2663160861,0.4371153414,-0.3299966455,-0.3249102533,-0.234212622,0.0986731574,0.367292434,-0.028609883,-0.1139254794,0.0536748543,0.1827501059,-0.2990556657,-0.0615343414,0.0923045576,0.4559177756,-0.2674093246,0.0548928306,0.0565712713,-0.0579679795,0.0976718515,-0.2862743139,-0.0044918144,0.139566645,-0.1986404508,-0.0761091113,0.1087931842,0.1886405945,-0.3328647614,0.1860733628,0.0168999117,-0.0193088334,-0.341399163,-0.1419970393,0.0006965335,-0.0904091075,0.1742922962,-0.1268024743,-0.0385634713,-0.1558128893,-0.1023029462,-0.1272954047,0.0249140412,-0.1019632369,-0.1478797644,-0.112821281,0.2802673578,-0.0242674053,0.3148951232,0.3188633919,0.5227810144,0.2761103809,-0.1469001472,0.1767627448,0.0399196595,-0.1516554356,0.1605863273,-0.4779449105,0.2870228589,-0.0454999171,0.0885780752,0.1315409988,0.0312656462,0.1154811531,-0.0815072432,-0.3221706152,0.2324202955,-0.0571842678,0.2367855161,0.1814435571,0.3105854392,-0.2238204628,-0.1394132823,0.1789246649,-0.2086843401,0.0216782521,0.4463310242,-0.0805174559,-0.0663929209,0.0977407172,0.2899337709,0.0681947172,0.4081125855,-0.0460216179,-0.1513637751,0.1582057476,-0.3459095657,0.2634891272,0.4897969067,-0.4019296765,0.1929724514,0.3291119635,0.0588623956,-0.316657424,-0.2629854083,-0.2980671823,0.0220816303,-0.0784920529,0.0843388885,0.2250579596,-0.0297478177,-0.2395957261,0.0701971278,-0.3414030075,-0.0951558948,0.1910140216,-0.0047753397,-0.4281342924,0.2272886634,0.0768975317,0.5166133046,0.0291044712,-0.0355410911,-0.3046271205,-0.1172070652,-0.028194923,0.1015873924,0.1168140024,-0.1349297166,0.3720982671,0.0315622874,0.1201361865,-0.1583744287,-0.5135610104,0.048645284,-0.0515249372,-0.0456338003,0.2541992664,0.060059052,0.3515126109,-0.249120295,0.0693657324,-0.2643868625,-0.0345378853,0.101572983,-0.0001274921,0.0923434868,-0.1270198524,-0.1786590964,0.0679454133,-0.6265596151,0.6084980965,0.0825430527,-0.0148923751,0.5006606579,0.227458939,-0.0092804544,-0.0891698226,0.2207549512,0.2586966753,-0.1780376583,0.1302426308,0.1459489018,-0.2321412563,-0.326036185,-0.4993501604,-0.2117638886,0.0944416001,-0.2091025412,-0.4601055086,-0.358055234,0.1347699761,0.1308307797,0.2584318817,0.3315148354,0.1831547171,-0.270601511,0.0964184105,-0.1869493723,-0.0090520186,0.168751359,0.2279992849,-0.1637886912,-0.0492803939,0.1133867502,0.6102945805,0.2513975501,-0.0685018077,0.0528288558,0.1713784635,0.1963612139,-0.0080119641,-0.0962425843,0.2697042823,0.1324074715,-0.136336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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3168","title":"OpenSLR\/83 is empty","comments":"Hi @tyrius02, thanks for reporting. I see you self-assigned this issue: are you working on this?","body":"## Describe the bug\r\nAs the summary says, openslr \/ SLR83 \/ train is empty.\r\n\r\nThe dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**.\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\n\r\ndatasets.load_dataset('openslr', 'SLR83')\r\n```\r\n\r\n## Expected results\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['path', 'audio', 'sentence'],\r\n        num_rows: 17877\r\n    })\r\n})\r\n```\r\n## Actual results\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['path', 'audio', 'sentence'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.1.dev0 (master HEAD)\r\n- Platform: Ubuntu 20.04\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":16,"text":"OpenSLR\/83 is empty \n ## Describe the bug\r\nAs the summary says, openslr \/ SLR83 \/ train is empty.\r\n\r\nThe dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**.\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\n\r\ndatasets.load_dataset('openslr', 'SLR83')\r\n```\r\n\r\n## Expected results\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['path', 'audio', 'sentence'],\r\n        num_rows: 17877\r\n    })\r\n})\r\n```\r\n## Actual results\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['path', 'audio', 'sentence'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.1.dev0 (master HEAD)\r\n- Platform: Ubuntu 20.04\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n \n Hi @tyrius02, thanks for reporting. I see you self-assigned this issue: are you working on this?","embeddings":[-0.1339389235,0.2001887262,0.0174183901,0.258898735,0.1328079402,0.0173652191,0.5546807051,0.3825567961,0.0937650055,0.3072177768,-0.0423920006,0.4279452562,-0.2365681082,-0.0011825875,0.0797994062,-0.0660125241,0.0724032745,0.112232253,-0.0167126935,-0.2577761412,-0.1738891006,0.1973001361,-0.4404388964,0.0219875965,-0.476067245,0.2874427736,-0.1160585433,0.2264235765,0.0059463223,-0.3488579392,0.2378400415,-0.2668090761,0.3661424816,0.4156529903,-0.0001074947,0.0536475293,0.3103165329,-0.0600966029,-0.1963830888,-0.4586192667,0.0289960802,-0.306840688,0.3235343397,-0.2114791572,0.0451221205,-0.1022677496,-0.1241810843,-0.1516533196,0.0771075338,0.4594588578,0.2310301811,0.1654524654,0.0535357259,0.113345556,0.6033672094,0.0759337768,-0.0824452564,0.1355312616,0.0173617937,0.0375859253,0.019887045,0.4335467219,0.0137290852,-0.1673817337,0.0966822356,0.3411175311,0.1706942022,-0.1972605437,-0.0689508319,0.2826718986,0.4058531523,-0.1388438046,-0.1039383933,-0.1441076696,0.0596058145,-0.573918879,0.1065692306,0.4767598212,-0.0987913609,0.1742700487,-0.0853631347,0.120096378,-0.1859288663,0.160207361,-0.2754465342,0.4038644731,0.1457085162,-0.0957935676,0.0738280192,-0.0166640952,0.2240304947,-0.0880395249,-0.1395947039,0.2573345006,-0.5644059181,0.069734849,0.1490813941,-0.2585193217,-0.1216115132,0.2926425338,0.1842294037,0.1403398812,0.0259394534,0.0529414751,0.242637977,0.0207219329,-0.1661788523,0.3000276685,-0.0158982631,0.0749859884,-0.0560864881,-0.1259216815,-0.3072428107,-0.1631622165,0.1713112146,-0.0954250842,0.4492141008,-0.1971363723,-0.0599498525,0.2471516579,-0.6559746265,-0.1564712077,-0.0526031069,0.310803473,-0.1164228916,0.246660307,0.3426423967,0.1338528097,-0.124578543,-0.0300735831,-0.2738963068,0.1685159355,-0.2836501598,-0.2520354986,0.2220945209,-0.0805452615,0.3566674888,-0.0334838629,0.0295626875,-0.2554585338,0.2281299382,-0.061558798,0.0152515508,0.3154494464,0.0930812284,0.0417945758,0.0006741689,-0.059993282,0.0517238341,0.535584271,-0.3183783293,-0.4038652182,-0.4787496626,0.302541405,-0.1449673772,-0.0422838032,-0.0102687357,0.1338716149,-0.190815106,-0.0412601568,-0.0046463045,-0.1308538318,0.0377218165,-0.034035109,0.4729420841,0.1463146061,-0.3379404843,-0.0438943729,0.0537971184,-0.1157375872,0.1803102046,-0.1854588687,-0.1455039084,-0.0379164666,-0.0918879658,0.2157681733,0.6409285069,-0.0579958111,-0.2828888893,-0.043131724,0.0303223655,-0.2189032137,0.0447632335,0.0824392661,-0.1834608763,0.2904071212,0.1808480918,0.122006841,0.1158021539,-0.0327714086,-0.5234895349,0.0889064893,-0.1801308095,0.4005849957,0.2021250725,-0.1826084554,-0.1839065254,0.0930835009,0.451038003,-0.0740571022,0.0088868262,0.3758169711,0.5334427357,0.272079736,0.0922183841,-0.1599160135,-0.2967684269,0.0891456455,-0.0696032271,0.1586752385,0.1494923681,-0.0342311338,-0.2316956967,0.0656537563,-0.1902727634,-0.1132397801,0.1874236166,-0.0486588851,-0.1088248268,0.0890154019,-0.4337845147,-0.1367288083,-0.2332295626,0.1486929208,-0.2778755724,0.6138692498,-0.1640325636,-0.2482625842,-0.0181563944,0.2350123227,0.1213506907,0.0486227982,0.0487719476,0.2549422979,0.2513914108,-0.1970444322,-0.1492139846,0.1104448363,0.0230975263,-0.2163980454,-0.0881139114,0.2661984563,0.1640079618,0.2087931037,-0.1997843534,0.1223830506,-0.27826792,0.0775689259,-0.032945089,-0.0419265926,-0.0520545878,-0.2706236839,-0.154271245,-0.0607360154,0.1095288396,-0.1135191321,0.2369916886,0.2813269198,-0.3355119228,0.0058808536,0.2374134809,-0.0671864226,-0.0144379595,0.1366146356,-0.4012351036,-0.1870545745,-0.0853126496,-0.0608347505,0.0910429955,0.3117313385,0.1081931219,0.1866482943,0.0124504529,-0.0989648476,0.276288569,0.2483089864,0.2068839967,0.2672985196,0.2692834735,0.0784996748,-0.1788472831,-0.2592964768,0.0402203351,0.3426870108,-0.2969507873,-0.0476476476,-0.1959915906,-0.0042325542,-0.2478990108,-0.2674124539,-0.0806678236,-0.2428775132,0.0097342385,-0.1212822646,-0.3213605881,0.1964369863,-0.4679158628,-0.0790942013,0.1946040392,0.1737379879,0.164957583,-0.1275922954,-0.2993332446,0.0611210279,0.0027666788,0.0047943885,-0.0487088226,-0.064050898,-0.1444775164,-0.0969190449,-0.3034469485,0.1520527601,0.1732318252,-0.1043752953,0.1292590201,0.020527916,-0.3482244611,-0.1586630642,0.2710627317,0.1036013961,-0.2716562748,0.1809124202,0.1850718856,0.2777193189,-0.240468502,-0.5770915747,-0.2284031808,-0.3522010446,-0.1730075628,-0.051633805,0.12478403,0.0928276777,0.207186535,0.1526166052,-0.1643395126,-0.1665129513,-0.1565100402,-0.0894437209,0.1864400357,0.0154262008,-0.4444541037,0.0937252194,-0.1993379444,-0.1444304436,-0.0663008764,-0.5485389233,-0.2253895551,0.0493241735,0.319142133,-0.2963668406,0.0554981679,0.4496922493,0.2543046474,-0.1806269884,-0.1611900181,-0.0690425038,0.0841886699,-0.1453248262,0.183712393,-0.2252485007,0.4837565124,-0.0801649541,0.4129234552,0.1188939437,-0.065508619,0.3645163774,-0.1454742849,0.1753111035,-0.1935375333,-0.0396240093,0.2334945649,0.210654363,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3168","title":"OpenSLR\/83 is empty","comments":"@albertvillanova Yes. Figured I introduced the broken config, I should fix it too.\r\n\r\nI've got it working, but I'm struggling with one of the tests. I've started a PR so I\/we can work through it.","body":"## Describe the bug\r\nAs the summary says, openslr \/ SLR83 \/ train is empty.\r\n\r\nThe dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**.\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\n\r\ndatasets.load_dataset('openslr', 'SLR83')\r\n```\r\n\r\n## Expected results\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['path', 'audio', 'sentence'],\r\n        num_rows: 17877\r\n    })\r\n})\r\n```\r\n## Actual results\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['path', 'audio', 'sentence'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.1.dev0 (master HEAD)\r\n- Platform: Ubuntu 20.04\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":35,"text":"OpenSLR\/83 is empty \n ## Describe the bug\r\nAs the summary says, openslr \/ SLR83 \/ train is empty.\r\n\r\nThe dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**.\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\n\r\ndatasets.load_dataset('openslr', 'SLR83')\r\n```\r\n\r\n## Expected results\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['path', 'audio', 'sentence'],\r\n        num_rows: 17877\r\n    })\r\n})\r\n```\r\n## Actual results\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['path', 'audio', 'sentence'],\r\n        num_rows: 0\r\n    })\r\n})\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.1.dev0 (master HEAD)\r\n- Platform: Ubuntu 20.04\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n \n @albertvillanova Yes. Figured I introduced the broken config, I should fix it too.\r\n\r\nI've got it working, but I'm struggling with one of the tests. I've started a PR so I\/we can work through 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3167","title":"bookcorpusopen no longer works","comments":"I tried with the latest changes from #3280 on google colab and it worked fine :)\r\nWe'll do a new release soon, in the meantime you can use the updated version with:\r\n```python\r\nload_dataset(\"bookcorpusopen\", revision=\"master\")\r\n```","body":"## Describe the bug\r\n\r\nWhen using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples\/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...).\r\n\r\nThis did not happen with 1.4.1.\r\nI tried also `rm -rf ~\/.cache\/huggingface` but did not help.\r\nChanging python version between 3.7, 3.8 and 3.9 did not help too.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nd = datasets.load_dataset('bookcorpusopen')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 4.0.1\r\n","comment_length":36,"text":"bookcorpusopen no longer works \n ## Describe the bug\r\n\r\nWhen using the latest version of datasets (1.14.0), I cannot use the `bookcorpusopen` dataset. The process blocks always around `9924 examples [00:06, 1439.61 examples\/s]` when preparing the dataset. I also noticed that after half an hour the process is automatically killed because of the RAM usage (the machine has 1TB of RAM...).\r\n\r\nThis did not happen with 1.4.1.\r\nI tried also `rm -rf ~\/.cache\/huggingface` but did not help.\r\nChanging python version between 3.7, 3.8 and 3.9 did not help too.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nd = datasets.load_dataset('bookcorpusopen')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-5.4.0-1054-aws-x86_64-with-glibc2.27\r\n- Python version: 3.9.7\r\n- PyArrow version: 4.0.1\r\n \n I tried with the latest changes from #3280 on google colab and it worked fine :)\r\nWe'll do a new release soon, in the meantime you can use the updated version with:\r\n```python\r\nload_dataset(\"bookcorpusopen\", revision=\"master\")\r\n```","embeddings":[-0.3898613751,0.0123078059,0.0758038983,0.2028929889,-0.0502231047,-0.2223528326,0.3556592464,0.0869096443,-0.2748003602,0.1474600285,-0.1287825108,0.3929777443,0.164361313,0.3723462522,-0.0846743584,-0.0717373788,0.1279461831,-0.091514729,0.0168373864,0.2605871558,-0.4226183295,0.2889651954,-0.3729495406,-0.0984447226,0.0025999364,-0.0406945907,-0.1569525152,-0.027046632,-0.0629279912,-0.3901459277,0.4275419712,0.2666738927,0.1095135957,0.7125322223,-0.000123781,-0.029311832,0.5024093986,0.1239169613,-0.6058193445,-0.1059754193,0.0056565329,-0.1608937979,0.2945019603,-0.1363799274,-0.2217486948,0.0814674646,0.2588741481,-0.0959241092,0.1095857695,0.2762271166,0.1497513503,0.3037849963,0.3654173911,-0.0995595083,0.1748429835,0.0361379609,-0.4197972715,0.0598902106,0.522489965,-0.0301381554,0.0965158641,0.2241009325,-0.0849929377,0.0193074364,0.2831943929,-0.043869473,0.0229164194,-0.2325598747,0.3575329781,0.1055824459,0.4983660281,-0.4020620584,-0.33522439,-0.1355646551,0.009944262,-0.4120332301,0.1868991107,0.2354363799,-0.0684512332,0.0731014982,-0.3529761732,-0.2871640325,-0.081213966,0.2736923099,0.0547116771,0.2628900707,-0.1975141317,0.1978100091,0.4899702072,0.0308897756,0.0140868705,-0.0494472533,-0.1924459934,0.2122741491,-0.2705284953,0.0548210181,0.0120825069,0.704714179,0.0356016904,0.0831115022,-0.0876442045,0.119022049,0.0673817918,0.0860987008,0.3255117834,-0.0034627386,0.1700455546,0.036715284,0.4299221933,0.7873386145,0.097010985,-0.1208450645,0.1579384059,-0.1742531657,0.1368739009,-0.3688420057,0.2958277166,-0.3816065192,-0.1234823838,0.2389943004,-0.1276120991,0.0564410686,0.0590956621,0.3074043989,-0.1866201162,-0.0186880864,0.4500317872,-0.1632380933,-0.3314071894,-0.1563636661,-0.013792119,-0.0420244783,-0.3018776774,0.1211763397,0.3291480541,-0.3234835565,0.2096002102,0.1153364182,-0.1089195311,-0.155056864,0.2256602198,-0.3327973783,-0.1405166239,0.2691767812,-0.0593973361,-0.0297667254,-0.2764542699,-0.203455314,-0.0786505863,0.174363777,-0.0944891274,-0.0792355388,-0.2922044396,0.0038318227,-0.0831093937,0.0486025512,-0.5156857371,0.111579746,0.27778247,-0.182879135,0.0679193512,-0.2762611508,-0.3309889734,0.112011902,0.2938703299,0.4954016805,-0.224709034,-0.197833091,-0.566316247,0.1634205878,0.1191923246,0.1255797744,-0.135357514,0.0134814624,-0.052179534,-0.3361288309,-0.1484716833,-0.074300386,-0.4862655401,0.1870550364,0.2342873514,0.2743138969,-0.3590197563,-0.0846104771,-0.2000626773,-0.0004056702,0.244402498,-0.0915732905,0.0828425214,-0.1844215244,-0.3587605953,-0.4910703599,-0.050653141,0.34103477,0.2920826375,0.0273589529,-0.083276324,-0.0576467961,0.284183383,-0.0754768178,0.1077875569,0.4884551466,0.4227895737,-0.0821871907,0.2850288749,-0.2369254529,-0.2888115942,0.2824302018,0.0063420665,0.2739982307,-0.0862052217,-0.1260887384,-0.1685448438,-0.0836992189,0.0406248719,-0.1362911463,-0.1154961362,-0.0703689232,-0.0394091457,0.1284043491,0.0551098585,0.5862648487,-0.2416293174,0.198679179,-0.6034765244,0.0981101468,-0.0115173841,-0.2622712553,-0.0289311633,-0.0258152056,0.0606634654,-0.0271427035,-0.0369359888,0.453294307,0.026103925,0.13772434,-0.0144091602,-0.0312104691,0.1659799665,-0.1492672414,-0.0280274246,0.2467896938,0.2313128263,0.1455643028,-0.1155114025,0.2314558923,0.2422409058,0.2612873316,0.2787800729,-0.1035927236,0.0464529246,-0.1944346577,-0.0563363954,0.1089803725,0.3832002282,-0.2366936505,0.2097789645,-0.038429603,-0.4186981618,0.050104931,0.2248213291,0.0291959215,0.0442765467,0.2432195395,-0.2284255326,0.0180438794,0.3836129308,-0.0429178514,0.2823895216,0.105701372,-0.0873445198,0.1887234598,-0.1871912181,-0.0694509,0.2413115054,0.1532374322,-0.0796860754,-0.1558023989,0.2063116729,-0.0792706013,-0.3113120496,-0.2721199691,-0.1656867713,0.3360219002,-0.1040094048,0.2369026095,-0.2183511406,-0.2341489047,-0.0096262917,0.3806242645,-0.1688463837,-0.1452967674,-0.1966799945,0.7166187167,0.1593089849,0.0603419505,-0.0844969153,0.3879717588,-0.1754706055,-0.0535687245,-0.4342113137,-0.0492240451,-0.3178181946,-0.0304966141,0.1978004426,-0.0813581347,0.12722826,-0.4605333805,-0.3687522113,-0.1709653288,-0.3561968207,0.1349200308,0.037260104,0.6029319763,-0.0289426502,0.0136938309,-0.6014420986,0.0210788064,0.1511324197,-0.3201876879,0.0636936203,-0.1042696983,-0.1868638247,-0.1216010153,-0.2700521648,-0.0816641748,-0.1091076583,-0.2385918945,0.5066685677,0.3138301671,0.0161011927,0.2606229186,0.0559482053,0.2213387042,0.3386722207,-0.005243713,-0.2544813454,-0.1708033979,0.2020791024,-0.0742471516,-0.2520222366,0.0263226163,0.1815366447,-0.0254926514,0.1572324485,-0.6425096989,0.0160676949,-0.484778583,0.0580380075,0.0048751454,0.2352338284,0.2089194804,0.0245160498,0.0800348148,0.010784856,-0.4969175756,-0.1500092894,0.2188635021,0.3703863323,0.0713815093,0.0354843512,0.0575211793,0.5301241279,0.2229252756,-0.1739251912,0.3980806172,0.0242356434,0.4932112396,-0.3355484903,-0.325889945,-0.0183858685,-0.2324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3164","title":"Add raw data files to the Hub with GitHub LFS for canonical dataset","comments":"Hi @zlucia, I would actually suggest hosting the dataset as a huggingface.co-hosted dataset.\r\n\r\nThe only difference with a \"canonical\"\/legacy dataset is that it's nested under an organization (here `stanford` or `stanfordnlp` for instance \u2013 completely up to you) but then you can upload your data using git-lfs (unlike \"canonical\" datasets where we don't host the data)\r\n\r\nLet me know if this fits your use case!\r\n\r\ncc'ing @osanseviero @lhoestq and rest of the team \ud83e\udd17","body":"I'm interested in sharing the CaseHOLD dataset (https:\/\/arxiv.org\/abs\/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!","comment_length":74,"text":"Add raw data files to the Hub with GitHub LFS for canonical dataset \n I'm interested in sharing the CaseHOLD dataset (https:\/\/arxiv.org\/abs\/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks! \n Hi @zlucia, I would actually suggest hosting the dataset as a huggingface.co-hosted dataset.\r\n\r\nThe only difference with a \"canonical\"\/legacy dataset is that it's nested under an organization (here `stanford` or `stanfordnlp` for instance \u2013 completely up to you) but then you can upload your data using git-lfs (unlike \"canonical\" datasets where we don't host the data)\r\n\r\nLet me know if this fits your use case!\r\n\r\ncc'ing @osanseviero @lhoestq and rest of the team \ud83e\udd17","embeddings":[-0.2665247619,-0.1827454269,-0.0062526753,0.0318809561,-0.0109190214,0.1445141882,-0.0868280828,0.3903292716,0.2624930143,-0.0303210467,-0.2295877337,-0.0343147144,-0.2536514699,0.3241310418,0.1303450316,0.2218015045,0.0054547596,0.0797924846,0.1123959869,-0.0398901552,0.1150908321,0.0766838789,0.1367830336,-0.2238130271,-0.207427457,0.1061086282,-0.0725089535,0.2290413082,-0.1743649095,-0.1096047834,0.3940238357,0.3765412569,0.1405112147,0.3050995171,-0.0001218822,0.0072088777,0.2501145601,-0.1687199175,-0.3471151888,-0.3165862858,-0.1878392547,0.0829296634,-0.097591199,0.0858463272,-0.4969352186,0.0640934631,0.173453629,-0.1044894233,0.1173067763,-0.2587693334,0.0145133883,0.1892106533,0.0844805837,-0.1029904261,0.0773179308,0.4221263826,-0.2412165254,0.5497739911,0.4649930894,0.1216786653,-0.0640395507,-0.0981299803,0.2521438301,-0.0165032055,-0.0571033359,0.095956862,-0.3532751203,-0.1128141955,0.1489445716,0.2189080715,0.4228686094,-0.376070559,-0.4358508885,-0.2145006657,0.2201534808,-0.2514029741,0.0206678323,0.5102843642,-0.2002692819,0.3463026285,-0.0497153588,-0.577688098,0.0117326062,0.0008290606,0.2111346871,0.2713479102,-0.1319758147,0.0097797159,0.1333130449,-0.0561518595,-0.3542377949,-0.2427235842,0.0927683264,-0.0338045619,0.2184588611,-0.0912949741,-0.1975586414,0.2318164259,0.409027487,0.1166934595,-0.2240317613,-0.1028894931,-0.3284315467,-0.1152379289,0.3487409055,-0.2042653561,-0.0826646611,0.2181473523,0.2479392588,0.262852639,0.1039625332,0.0798643753,-0.2126901895,0.1609093994,-0.7712407112,-0.2206630409,0.2897870243,-0.198813349,0.0796233714,-0.2017210722,0.3035264611,0.2749489248,0.2383247316,-0.0523487553,-0.0154146617,-0.2587596476,-0.0441734381,0.1497699022,0.0375958309,-0.0829463974,-0.0780499652,-0.0470850058,0.2264404893,0.1108429357,0.0497290306,-0.6066895723,0.0873495638,0.1912712008,0.4863931835,0.0626433268,-0.1197493523,0.2309165895,-0.1405654401,0.2416115403,0.1819144934,0.1449535042,0.1819226295,-0.3962028325,-0.2727973163,0.0491474122,-0.1082646996,-0.389785856,-0.318287313,-0.094307892,-0.254378438,0.2402719408,-0.6651906967,0.0809432492,-0.2214653492,0.2130226195,-0.0201545674,0.2615922689,0.0114757949,-0.0277532712,0.3683043718,0.4206543267,0.0995107144,-0.2001755536,0.0907240659,0.2529321909,-0.0597527884,0.2877044082,-0.1582577527,-0.1315704435,-0.2787149251,0.1662851721,0.4121833444,-0.6380051374,-0.5084888935,-0.030273715,-0.1928982735,-0.1399565041,-0.0113607375,0.1017674282,0.2115483433,-0.155597508,-0.0758043826,0.458339572,0.0232086089,-0.1269019246,-0.176453501,-0.3753015101,-0.0077879764,0.1338988096,-0.1937423795,0.0876091942,0.171277687,0.152049318,0.2850721776,-0.0894175321,0.3909377456,0.118917346,0.5414788127,0.0715971664,-0.1675703675,0.0709394366,-0.1707430184,0.2139844596,0.0235006474,0.1752338707,-0.1261011362,-0.3766761124,-0.0095724333,-0.0971162766,0.0164345335,-0.0004602033,0.0011731475,0.0047996291,0.2792337835,0.2050064057,-0.0782152414,0.1759434789,-0.1685063988,0.1475927234,-0.2774280012,0.5396146774,0.0198051631,-0.1506018341,0.3039488494,0.2477888763,0.0449777506,-0.1457323581,0.0174376182,0.2061316073,-0.0575044453,0.4176465273,0.574041605,0.4769644439,0.4514027536,-0.0232711248,-0.1856721789,-0.1281621307,-0.2184778005,0.1755773574,-0.4029955268,0.4061101973,0.0050035808,-0.2898288965,-0.0500473678,0.3792815208,0.014146056,0.2231318802,-0.1843406856,-0.1845345795,-0.0673127621,-0.0675746575,0.4000763595,-0.2898490727,-0.1833952665,0.0229000356,0.0351698697,0.0013580917,0.1494608223,0.1572178602,-0.1590988636,-0.0319525674,0.4809268415,-0.1128565371,0.0460004695,0.0522192456,0.2284600288,0.1255472004,0.1804151088,-0.0777032226,-0.0049036774,-0.3158061802,-0.1887284368,-0.2076808363,0.2962703407,-0.0121352654,-0.0750022829,0.1295110285,-0.1333547533,-0.0479939282,-0.0127186067,-0.241105929,-0.1619769931,0.110429965,-0.0248833001,-0.0148099121,-0.2748353779,-0.1186574772,0.1592400372,0.6206403971,-0.0131520964,0.1258528382,0.2597209215,0.4867326915,-0.0785967633,0.0862981528,-0.1729710996,-0.1589923501,0.1131701097,0.0310209189,0.2098183483,-0.1889981627,0.3592494428,-0.1263188869,0.1208611578,-0.6279175282,-0.1716773212,0.1020906791,-0.1656258702,0.2280641198,0.157873109,0.0274634697,-0.1100896969,0.0675820932,-0.0278850179,-0.2415905744,0.0257203281,-0.2843967676,-0.187493667,-0.0275013316,-0.0526905581,0.5303860307,-0.4226076603,-0.3629897237,0.5493775606,0.3335313797,-0.2026878148,-0.0734988973,-0.1354938,0.0240468141,0.1688289046,-0.2675638497,-0.0934204236,-0.4611538053,0.1257448941,-0.4575265348,-0.3302558959,-0.0003680453,0.1490739435,0.1577557772,-0.0688265711,-0.4292908013,-0.4489057958,0.1090679094,0.4061559141,0.1496835053,0.0636186451,0.2679275572,-0.1184317321,0.1586984396,-0.0685587153,-0.2075570077,-0.0572718456,0.4874179065,-0.0395560861,0.1287686676,-0.30012694,0.3065260947,0.7234535813,0.4919941723,-0.0849989578,0.1363577992,-0.2671541572,0.4214400947,-0.2574196756,0.1121242642,0.0161890425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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3164","title":"Add raw data files to the Hub with GitHub LFS for canonical dataset","comments":"Hi @zlucia,\r\n\r\nAs @julien-c pointed out, the way to store\/host raw data files in our Hub is by using what we call \"community\" datasets:\r\n- either at your personal namespace: `load_dataset(\"zlucia\/casehold\")`\r\n- or at an organization namespace: for example, if you create the organization `reglab`, then `load_dataset(\"reglab\/casehold\")`\r\n\r\nPlease note that \"canonical\" datasets do not normally store\/host their raw data at our Hub, but in a third-party server. For \"canonical\" datasets, we just host the \"loading script\", that is, a Python script that downloads the raw data from a third-party server, creates the HuggingFace dataset from it and caches it locally.\r\n\r\nIn order to create an organization namespace in our Hub, please follow this link: https:\/\/huggingface.co\/organizations\/new\r\n\r\nThere are already many organizations at our Hub (complete list here: https:\/\/huggingface.co\/organizations), such as:\r\n- Stanford CRFM: https:\/\/huggingface.co\/stanford-crfm\r\n- Stanford NLP: https:\/\/huggingface.co\/stanfordnlp\r\n- Stanford CS329S: Machine Learning Systems Design: https:\/\/huggingface.co\/stanford-cs329s\r\n\r\nAlso note that you in your organization namespace:\r\n- you can add any number of members\r\n- you can store both raw datasets and models, and those can be immediately accessed using `datasets` and `transformers`\r\n\r\nOnce you have created an organization, these are the steps to upload\/host a raw dataset: \r\n- The no-code procedure: https:\/\/huggingface.co\/docs\/datasets\/upload_dataset.html\r\n- Using the command line (terminal): https:\/\/huggingface.co\/docs\/datasets\/share.html#add-a-community-dataset\r\n\r\nPlease, feel free to ping me if you have any further questions or need help.\r\n","body":"I'm interested in sharing the CaseHOLD dataset (https:\/\/arxiv.org\/abs\/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!","comment_length":222,"text":"Add raw data files to the Hub with GitHub LFS for canonical dataset \n I'm interested in sharing the CaseHOLD dataset (https:\/\/arxiv.org\/abs\/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks! \n Hi @zlucia,\r\n\r\nAs @julien-c pointed out, the way to store\/host raw data files in our Hub is by using what we call \"community\" datasets:\r\n- either at your personal namespace: `load_dataset(\"zlucia\/casehold\")`\r\n- or at an organization namespace: for example, if you create the organization `reglab`, then `load_dataset(\"reglab\/casehold\")`\r\n\r\nPlease note that \"canonical\" datasets do not normally store\/host their raw data at our Hub, but in a third-party server. For \"canonical\" datasets, we just host the \"loading script\", that is, a Python script that downloads the raw data from a third-party server, creates the HuggingFace dataset from it and caches it locally.\r\n\r\nIn order to create an organization namespace in our Hub, please follow this link: https:\/\/huggingface.co\/organizations\/new\r\n\r\nThere are already many organizations at our Hub (complete list here: https:\/\/huggingface.co\/organizations), such as:\r\n- Stanford CRFM: https:\/\/huggingface.co\/stanford-crfm\r\n- Stanford NLP: https:\/\/huggingface.co\/stanfordnlp\r\n- Stanford CS329S: Machine Learning Systems Design: https:\/\/huggingface.co\/stanford-cs329s\r\n\r\nAlso note that you in your organization namespace:\r\n- you can add any number of members\r\n- you can store both raw datasets and models, and those can be immediately accessed using `datasets` and `transformers`\r\n\r\nOnce you have created an organization, these are the steps to upload\/host a raw dataset: \r\n- The no-code procedure: https:\/\/huggingface.co\/docs\/datasets\/upload_dataset.html\r\n- Using the command line (terminal): https:\/\/huggingface.co\/docs\/datasets\/share.html#add-a-community-dataset\r\n\r\nPlease, feel free to ping me if you have any further questions or need help.\r\n","embeddings":[-0.2289416939,-0.0566738881,-0.0150375813,0.0101022394,0.0391705744,0.0713323951,-0.0123488773,0.3533782959,0.3484463394,0.0711126775,-0.3602599502,-0.0362023748,-0.2267822176,0.2558304667,0.082069844,0.2637310326,-0.0132171223,0.0790142789,0.2102047503,-0.0459959507,0.0696717724,0.0801071078,0.1755230576,-0.225580886,-0.2424690425,0.1790591627,-0.0154947294,0.2225513011,-0.1476863921,-0.0763740018,0.4192401767,0.4168629348,0.1235429645,0.3801312745,-0.0001217591,0.102267921,0.30244115,-0.1765778661,-0.3889103234,-0.3695986271,-0.1753301024,0.0104388371,-0.162067771,0.1045613959,-0.4605769813,-0.0172288194,0.2128417492,-0.1144231483,0.1005250216,-0.2434495986,-0.0057904753,0.128263697,-0.0025864125,-0.1003332138,0.1070976555,0.4919632971,-0.2844495475,0.5346364379,0.3674664199,0.050963752,-0.104855448,-0.0811720341,0.2657429874,-0.0084291613,-0.0048646764,0.1290423274,-0.4811240733,-0.1339906454,0.1393608749,0.236444205,0.3538213968,-0.4089080691,-0.472435236,-0.2859213948,0.1858097166,-0.2970905006,0.0359917991,0.4753831923,-0.264662236,0.3224124908,-0.1876666099,-0.6103287935,-0.02729211,0.0974333063,0.2486462593,0.2919163704,-0.1473026723,0.0313416161,0.0733094811,-0.0495871194,-0.2720704079,-0.2294728309,0.0971252918,-0.0311887637,0.1580685079,-0.0634277239,-0.2403774858,0.2212851942,0.4159781337,0.1984440833,-0.1606664509,-0.1246071383,-0.2967112362,-0.1187424213,0.2743867934,-0.1263878047,-0.0399361886,0.217242673,0.2872417569,0.2611219585,0.1160352603,0.0650826097,-0.2321233749,0.1446001083,-0.7998683453,-0.2408125848,0.2785772681,-0.0750854164,0.1232147962,-0.1731604934,0.3006311059,0.2339327186,0.313577652,0.0234345011,-0.0033772232,-0.2124573737,-0.0794733763,0.1745622456,0.1228402331,-0.1085846722,-0.0527003296,-0.0896479711,0.1655945778,0.2216679603,0.1309725195,-0.4986114204,0.1340181828,0.1912742257,0.581332624,-0.03043635,-0.1015290022,0.1716553569,-0.1288489997,0.2906460464,0.2397774607,0.1689422727,0.1652786136,-0.3512501419,-0.2649674416,0.0394093283,-0.1079717726,-0.4682827294,-0.3302841485,-0.0924922377,-0.2245678455,0.316193819,-0.6732621193,0.0386788063,-0.1848047674,0.1369432807,-0.0245977174,0.2543033957,-0.0185172129,-0.0318338685,0.4215041995,0.3626413047,0.1283767074,-0.1223132312,-0.0132790338,0.2733826637,-0.055937279,0.2761846185,-0.2108077854,-0.0339222848,-0.2740959823,0.1926782876,0.3350191116,-0.6549581289,-0.5144544244,0.0766465813,-0.1155442148,-0.1820701957,0.0649720877,0.1024818867,0.1899865866,-0.1671630442,-0.0271581728,0.4742614329,0.0283705611,-0.093274124,-0.1378289312,-0.3161193132,-0.0585996807,0.1304533333,-0.2158692777,0.0459204614,0.2064811438,0.2363731861,0.1897941083,-0.1216365546,0.3804343343,0.1068380699,0.4320045412,0.1613536626,-0.1318620741,0.1476364434,-0.2112721205,0.2452945411,0.0292870179,0.128834039,-0.0761343986,-0.3818157911,-0.0817468613,-0.0881604999,0.0207765531,0.0112177152,-0.0137714399,0.0778216645,0.2996225953,0.1033405736,-0.0801335722,0.2641204894,-0.2595405281,0.0972644165,-0.2883906364,0.5789307952,-0.0271306317,-0.1088960543,0.2874490619,0.2413368523,0.0780656338,-0.1417326331,0.0595442653,0.2694085538,0.0033764669,0.4471620917,0.5883654356,0.4300043285,0.4169282615,-0.0178621337,-0.0990522429,-0.079664357,-0.1917492002,0.1951129884,-0.3184892833,0.4425115883,-0.0686291382,-0.2483042926,-0.0750448927,0.3116086125,0.0824402049,0.1778650582,-0.203067854,-0.2037797272,-0.0430678427,0.0767545104,0.4374572933,-0.2889710069,-0.1317538172,-0.0234483071,0.0244190693,-0.0181572735,0.1730671972,0.136074245,-0.1732062995,-0.1116979867,0.4302823842,-0.0344253704,0.0894580781,0.0209814459,0.1668467969,0.1042368487,0.1518803835,-0.1527378559,-0.0143259354,-0.2298695743,-0.0617134385,-0.0567003526,0.3264980316,0.031113347,-0.1025086939,0.0467547774,-0.0421335362,-0.0918668434,-0.0715972558,-0.2171820998,-0.0990764573,0.1246889681,0.0273604132,-0.155213654,-0.2848011851,-0.1160440817,0.1702086031,0.5452514291,-0.0324460827,0.1210783795,0.3661157787,0.5227205157,-0.1356993169,0.0601785332,-0.2815464735,-0.1186736301,0.1161927879,-0.0358841643,0.2400545776,-0.2261710614,0.2538428009,-0.1452169269,0.1049689427,-0.5245934725,-0.1037112921,0.084408015,-0.1513347775,0.2855918109,0.127277419,0.0364757143,-0.0345725417,-0.0161850844,-0.0081887376,-0.2265819758,0.021389069,-0.2890096605,-0.1822023094,0.0120903254,-0.0820674226,0.5350761414,-0.4383313358,-0.3754745424,0.4410324395,0.3280362487,-0.1596620679,-0.0109546203,-0.1457764506,0.0081219589,0.1790076643,-0.3094938397,-0.1351656765,-0.5066210628,0.1772020012,-0.5204622149,-0.2116716206,0.0307908002,0.1091418415,0.197384879,-0.0545891188,-0.345048964,-0.4182310998,0.1344371289,0.3758225739,0.1441229731,0.0423594601,0.2510948777,-0.1470134407,0.1563168615,-0.0560333282,-0.1967369467,-0.1197849289,0.4281424284,-0.0546032824,0.1368464231,-0.4074230492,0.3371656239,0.8386976123,0.4731461406,-0.0152542638,0.1146985665,-0.2327662706,0.4259349108,-0.2218108773,0.134030968,0.0434224568,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3164","title":"Add raw data files to the Hub with GitHub LFS for canonical dataset","comments":"Ah I see, I think I was unclear whether there were benefits to uploading a canonical dataset vs. a community provided dataset. Thanks for clarifying. I'll see if we want to create an organization namespace and otherwise, will upload the dataset under my personal namespace.","body":"I'm interested in sharing the CaseHOLD dataset (https:\/\/arxiv.org\/abs\/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks!","comment_length":45,"text":"Add raw data files to the Hub with GitHub LFS for canonical dataset \n I'm interested in sharing the CaseHOLD dataset (https:\/\/arxiv.org\/abs\/2104.08671) as a canonical dataset on the HuggingFace Hub and would like to add the raw data files to the Hub with GitHub LFS, since it seems like a more sustainable long term storage solution, compared to other storage solutions available to my team. From what I can tell, this option is not immediately supported if one follows the sharing steps detailed here: [https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#sharing-a-canonical-dataset), since GitHub LFS is not supported for public forks. Is there a way to request this? Thanks! \n Ah I see, I think I was unclear whether there were benefits to uploading a canonical dataset vs. a community provided dataset. Thanks for clarifying. I'll see if we want to create an organization namespace and otherwise, will upload the dataset under my personal 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3162","title":"`datasets-cli test` should work with datasets without scripts","comments":"> It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).\r\n> \r\n> I wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n\r\nwhy don't you try to share that info with people, so you can also save some days.","body":"It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n","comment_length":75,"text":"`datasets-cli test` should work with datasets without scripts \n It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n \n > It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).\r\n> \r\n> I wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n\r\nwhy don't you try to share that info with people, so you can also save some days.","embeddings":[-0.5560206175,-0.1414554119,-0.155322656,-0.0454635099,-0.1118018925,0.0510701463,0.4913174808,0.3185858428,0.4026770592,0.1730814427,-0.0837670565,0.078228578,-0.1506558508,0.3814932108,0.0290263928,0.1978032291,-0.1345369965,0.1956578344,-0.0688029304,0.0441200212,-0.1408363432,-0.1069490612,0.0969227478,0.040238779,-0.2073049992,-0.1505503803,-0.2179204971,0.3114549518,-0.0366938747,-0.2238072604,0.3590606153,0.4466850162,0.2573334873,0.3568913639,-0.0000989236,0.1222640499,0.2891544998,-0.0616438463,-0.2667553425,0.0962774381,-0.3794642389,0.097289063,0.0364207029,-0.3920642734,-0.2846099734,0.1487987489,0.0574815236,-0.4912440777,0.1197385043,0.1843055487,0.3275878429,0.5559114814,-0.3701720536,-0.323125869,-0.061504472,0.1970516443,-0.0800338611,-0.036939498,0.2754113078,0.0829574317,-0.0381291695,0.1978677362,0.0158485901,0.066596441,0.129184857,-0.0718483925,-0.1140081286,-0.2348290086,0.0424599424,0.4118003845,0.5436511636,-0.5197178721,-0.3008647859,-0.0746664703,-0.1030099392,-0.1940701753,0.1533671468,0.2417721599,0.0771721229,0.2190376669,-0.4076789618,-0.085003376,-0.1312141567,-0.0931139663,0.0244527087,0.0361832455,0.0010570403,0.0602215864,-0.0285616107,-0.1386741996,-0.1320223957,-0.146500051,-0.2558895648,0.0341240652,-0.0063421722,-0.4983389378,0.0558891408,0.3896739185,0.2723721266,0.3891405463,0.0642129704,0.2567560077,-0.1292240471,0.0862235948,0.0355462283,0.090458937,0.3950497806,0.1496598274,0.6636338234,0.300680697,-0.0415794924,-0.0104960958,0.1138679609,-0.0651361421,-0.1452970058,0.0006895777,0.1474985778,-0.2273817509,-0.361002773,-0.0016840138,0.1800074279,0.0097118169,0.2611899674,0.6791660786,0.1878275573,-0.1814540476,-0.0188493636,0.1611685902,0.013686656,-0.242044881,-0.2139463574,0.2622241378,-0.007825355,0.1103936508,0.4803416133,0.016674187,0.1254927516,-0.1570509374,-0.0523336045,0.1415273547,0.1869857907,0.2295870185,0.2179132849,0.3386209905,0.2603665292,-0.1656473577,-0.0658719316,-0.2081975639,-0.1962547302,0.0986238346,0.0038633174,-0.2171615064,0.0362961181,0.2491504252,-0.4209603071,-0.0635475516,-0.0909086317,-0.0210495908,-0.21584405,-0.0624870248,0.3120268583,0.0629144832,-0.0952779129,-0.0338467769,0.1318486333,0.4137842655,-0.40114519,0.0573635958,-0.0279802848,-0.3918381929,-0.239628464,-0.0775596201,0.0086393058,0.1331721544,-0.2681317627,0.09621007,0.1120050848,-0.4669944644,-0.1013242677,0.1668251306,0.0333215594,-0.07665167,0.052888982,0.1399772316,-0.1104988977,-0.111177817,-0.4577392042,0.025656905,0.0615885705,0.1947799325,-0.0234357901,-0.3171971738,0.0971010104,0.2353202552,0.1998639554,-0.1090594828,0.1773309708,-0.0601381324,0.3271969855,-0.2842562497,0.0508645512,0.0507219061,0.2634806335,-0.1643544734,-0.078975074,-0.2637184858,-0.4044247866,0.4092674255,0.002947421,0.2443298697,0.050695207,-0.3969169557,-0.1700858623,-0.039829161,-0.1442283541,-0.1887393296,0.2112944871,0.2058694661,0.3379813433,-0.2706896961,-0.2878726125,0.2489084452,-0.1752168685,0.1640066057,-0.3009534776,0.1791168004,0.1145626903,-0.0617117472,0.1204020306,-0.0322532468,-0.1270552874,-0.2945046723,0.0855396688,0.5120895505,-0.0599603839,0.139053151,0.3478790522,0.2529201806,-0.0419125408,-0.0737212896,0.0478794649,0.1600911319,-0.0087542161,0.2519087493,-0.475120753,0.4520224035,0.1945286244,0.0063482425,0.097689122,-0.0126993004,0.0922011361,-0.2902010381,-0.3776557148,-0.197774291,0.1076750979,-0.0329754837,0.2483908236,-0.0900150016,-0.2259749919,0.0459101498,0.0529985949,-0.2937115729,0.4414080083,-0.1138806492,-0.0558391213,0.3156947196,0.2266923338,0.2140238881,0.2733525932,0.2037542015,0.036601834,0.0472546108,-0.0296586677,-0.0859853625,0.1789832562,-0.1098945439,-0.0059571206,-0.2478307784,0.2119050473,-0.3464793265,-0.2746471167,-0.1140613928,-0.0745495036,0.2145202607,-0.4993107319,-0.1490456015,-0.2037403733,-0.0653900728,0.2671711147,0.0479252078,-0.1034545898,-0.1103026345,0.2763113081,-0.0071624792,-0.1896475852,0.0315988921,-0.2146940678,0.4651938081,-0.0654138103,-0.2308571488,-0.2652416229,-0.1782327741,-0.0477480404,0.278221339,0.3075315356,0.2596981525,0.44906196,-0.2030298412,0.0297437869,-0.4068724811,-0.3205240369,0.199365586,-0.1127401963,0.156813249,0.2713254392,0.2458337098,0.1270510852,0.0714863613,0.1436058432,-0.3423073888,-0.1623191386,-0.1521087438,0.0727904141,-0.2473239154,-0.385003686,-0.3155545592,-0.1074414253,-0.2349250466,0.3046237528,0.1232592613,0.0503762923,0.1212302074,-0.0311304852,0.1270192116,-0.1406521052,0.0467275046,-0.0819104016,-0.4273731112,0.2143923193,-0.4195079803,-0.2963676155,0.0663670599,-0.058477208,0.1351304352,-0.102387391,-0.2900463939,-0.1623853296,-0.2472109199,0.3377851844,0.2033721358,-0.0923425183,0.3013337851,0.0069995956,-0.1180432513,-0.1271206141,0.1413706541,0.1416210383,-0.2573839426,-0.0434522592,-0.1607074738,0.1140668988,-0.0693456158,0.1026124805,0.0271491054,-0.1951802373,0.1565760374,-0.352978766,0.5672581792,-0.0550593212,-0.0134187052,0.084144935,0.1242650598,-0.2929179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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3162","title":"`datasets-cli test` should work with datasets without scripts","comments":"Hi ! You can run the command if you download the repository\r\n```\r\ngit clone https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\r\n```\r\nand run the command\r\n```\r\ndatasets-cli test DataMeasurementsTest\/DataMeasurementsTest.py\r\n```\r\n\r\n(though on my side it doesn't manage to download the data since the dataset is private ^^)","body":"It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n","comment_length":43,"text":"`datasets-cli test` should work with datasets without scripts \n It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n \n Hi ! You can run the command if you download the repository\r\n```\r\ngit clone https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\r\n```\r\nand run the command\r\n```\r\ndatasets-cli test DataMeasurementsTest\/DataMeasurementsTest.py\r\n```\r\n\r\n(though on my side it doesn't manage to download the data since the dataset is private ^^)","embeddings":[-0.5413658619,-0.1230082735,-0.0824002475,-0.0086423587,-0.1236357838,0.2065103203,0.3399676979,0.4094341397,0.4431852996,0.0522725992,-0.1899445504,0.0832231715,-0.2094292343,0.3715486526,0.1386321485,0.1913190335,-0.1385656297,0.1844176054,-0.0423576832,0.0380894169,-0.1440089345,-0.0470076352,0.0733576193,0.0771224201,-0.1492717415,-0.1789040118,-0.1724954098,0.3012987375,-0.0357919484,-0.3550520539,0.5933483243,0.4876578152,0.3083233833,0.3212812245,-0.0001084944,0.1166497096,0.306201905,-0.0898676291,-0.2739932239,-0.0753657147,-0.348796308,0.0299253855,0.1927207857,-0.3817478418,-0.1775608808,0.2047631443,0.0765340552,-0.3029182851,0.0904086232,0.2083657682,0.2202314287,0.6419224739,-0.3167460859,-0.2536001801,-0.1344452798,0.2462202162,-0.0396219604,0.0443268865,0.2999961972,0.1339148581,0.1145809963,0.0939213634,-0.026920883,0.101454936,0.1905096918,-0.06336914,-0.163635686,-0.2358217388,-0.0049079424,0.4782274067,0.5150632262,-0.6297235489,-0.2977357805,-0.1852237433,-0.111994952,-0.2063328177,0.0725241899,0.2824873328,-0.0313482545,0.1760843992,-0.4396591783,-0.1498287916,-0.0929354578,-0.0881749541,0.027889546,0.0462046079,-0.0142617011,0.1022706553,0.0400919318,-0.0913678184,-0.1529484391,-0.0705157146,-0.2819609642,0.2896372974,-0.1200855076,-0.5150676966,0.1366438568,0.4175721407,0.3653280139,0.3775800765,-0.0761629492,0.2952550352,-0.2891990244,0.1497060061,0.0455269217,0.075117141,0.3800842166,0.2709921598,0.6967614293,0.3882277012,0.0412700996,-0.0137017043,0.0418683179,-0.0405947268,-0.1412283331,-0.0779128894,0.2832305133,-0.3054704666,-0.2825123072,-0.0178040862,0.1899565011,0.0202892944,0.2363708764,0.6728634238,0.2603140473,-0.200703457,0.0551806465,0.2154921293,0.0487369969,-0.1673451662,-0.1744373888,0.1580706835,0.1401270926,0.1123772934,0.4933282733,-0.1058294028,0.1858788133,-0.2125648409,-0.0309229512,0.0882319584,0.206837818,0.2145855129,0.1571100205,0.374841392,0.2142892331,-0.1537417918,0.0044225785,-0.1510621756,-0.2671703398,0.1064048782,-0.0970497057,-0.3103985488,0.1482904702,0.1283786595,-0.4986415207,-0.1044910997,-0.1500388533,-0.0543141887,-0.3174698353,-0.0733138472,0.3530022502,0.0649333596,-0.0168012474,-0.0277595818,0.2254769653,0.6246485114,-0.2523320317,-0.0317417681,-0.0223019011,-0.463301152,-0.3560507298,-0.1487303525,-0.0113890646,0.1718815416,-0.3076068461,0.1108841822,0.1650387347,-0.5764830709,-0.1707879007,0.0971560925,0.004974097,0.0429601036,0.0127092078,0.1926679313,-0.1295133531,-0.021908775,-0.450589776,0.0671919286,0.1105329469,0.116911225,0.0614614412,-0.2584028244,0.0781961307,0.3027754128,0.2132481635,-0.0859518498,0.1767591834,-0.0235749241,0.3864534199,-0.2427488714,-0.0342732556,-0.0476114787,0.2266608626,-0.0831084698,-0.0255304165,-0.3104475141,-0.4002406001,0.5124018192,0.0053474251,0.1853506863,-0.0483599193,-0.3517081738,-0.1230364963,-0.0674450397,-0.2248211056,-0.188586399,0.1219930872,0.0495611653,0.4296850264,-0.0802948624,-0.3161459267,0.2858039439,-0.1793110669,0.317088306,-0.3419393301,0.2753562629,0.2688277662,0.0300963353,-0.0174322985,-0.0375326686,-0.1331026554,-0.3903397322,0.0036379432,0.558432579,0.011155271,0.2133595496,0.3596339226,0.3012940586,-0.0531010963,-0.0391197726,0.0453462861,0.1616414934,0.0285707209,0.2330410182,-0.3761376739,0.5234043002,0.2247912586,0.142480433,0.0155805992,0.0287635401,0.147999078,-0.2390398979,-0.3827520013,-0.1276706308,0.1310301125,-0.0364965051,0.2653720081,-0.2376777083,-0.1200699285,-0.0695999861,-0.0376464389,-0.3425667584,0.4516459405,0.0246881675,-0.1543013602,0.4261362851,0.2205684036,0.2442471683,0.3664429188,0.04883736,-0.067145437,0.2046158016,0.0309310071,-0.0291010607,0.1520280093,-0.1225931421,-0.0966600254,-0.3179332912,0.1317305714,-0.3076865971,-0.3241184056,-0.0997104347,-0.1021350473,0.3214961886,-0.4172161222,-0.1341458559,-0.1398774683,-0.0763945505,0.2612318695,-0.0256477222,-0.1336647123,-0.0700408369,0.12013188,-0.0189302731,-0.0149610732,-0.0443254411,-0.1983743161,0.4373173714,-0.0086917998,-0.3791740537,-0.319381088,-0.0940636396,0.0148491496,0.1604795754,0.2452368736,0.3199107051,0.3705597818,-0.2720610499,0.0221049823,-0.4438967109,-0.2719181478,0.1419530958,-0.1162409782,0.1400315911,0.4177233279,0.2430116087,0.1962140799,0.2148953229,0.1365872175,-0.3857600093,-0.1105941311,-0.0416478589,0.077440463,-0.2247451395,-0.2861839533,-0.1476282775,-0.2171434164,-0.2374385893,0.3948898017,0.2241599262,0.0563082881,0.0848930627,-0.075780265,0.1537754387,-0.2241571248,-0.030219499,0.0443239175,-0.4801723957,0.1724244058,-0.3469870389,-0.1961857826,0.1837985367,0.0146243898,0.1895901263,-0.0707219541,-0.2822997272,-0.2450829446,-0.2603213191,0.3904298246,0.1955579966,0.0729195029,0.3131706715,-0.0803538635,0.040646188,-0.0321367122,0.1546767503,0.0882173628,-0.1464568824,-0.0703835562,-0.0747549757,0.0570098646,-0.0250857733,0.1151336357,0.1541651934,-0.152020663,0.1367477924,-0.373218298,0.5896906853,-0.0117156776,-0.1013891175,0.0606655292,0.12385462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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3162","title":"`datasets-cli test` should work with datasets without scripts","comments":"> Hi ! You can run the command if you download the repository\r\n> \r\n> ```\r\n> git clone https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\r\n> ```\r\n> \r\n> and run the command\r\n> \r\n> ```\r\n> datasets-cli test DataMeasurementsTest\/DataMeasurementsTest.py\r\n> ```\r\n> \r\n> (though on my side it doesn't manage to download the data since the dataset is private ^^)\r\n\r\nHi! Thanks for the info. \r\ngit cannot find the repository. Do you know if they have depreciated these tests and created a new one?","body":"It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n","comment_length":80,"text":"`datasets-cli test` should work with datasets without scripts \n It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n \n > Hi ! You can run the command if you download the repository\r\n> \r\n> ```\r\n> git clone https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\r\n> ```\r\n> \r\n> and run the command\r\n> \r\n> ```\r\n> datasets-cli test DataMeasurementsTest\/DataMeasurementsTest.py\r\n> ```\r\n> \r\n> (though on my side it doesn't manage to download the data since the dataset is private ^^)\r\n\r\nHi! Thanks for the info. \r\ngit cannot find the repository. Do you know if they have depreciated these tests and created a new one?","embeddings":[-0.4793646038,-0.0350034907,-0.0811262131,0.0051560947,-0.1159431562,0.2101322412,0.3284417689,0.4366989732,0.3811268806,-0.0298917685,-0.1913800687,-0.0401112549,-0.1729567945,0.2411368191,0.1206410453,0.1749819517,-0.1350269169,0.1931909472,0.0534980521,-0.0214347932,-0.0516277552,-0.0518683642,0.0147820795,0.0822283626,-0.1341726929,-0.2102362812,-0.2200966179,0.2186568081,-0.0007003553,-0.2946762145,0.6724280715,0.4579106569,0.2746323049,0.5644158125,-0.0001169587,0.1217297539,0.328995496,-0.063724868,-0.3074445426,-0.0495321825,-0.3842837512,0.0348106585,0.1632794738,-0.335088551,-0.1931089014,0.3004218638,0.0717748702,-0.4423832297,0.0042152302,0.2623841166,0.1621757597,0.6054902673,-0.3275639713,-0.2601145804,-0.0860616863,0.3606820703,-0.0296012666,0.1400972158,0.4355516434,0.1198339462,0.2278399169,-0.079035297,-0.0649261028,0.0759593025,0.1600216627,-0.048090037,-0.1634925753,-0.2541019022,0.0484856069,0.4351792932,0.5665469766,-0.6391467452,-0.3396843374,-0.2421278805,-0.1441447735,-0.2059525251,-0.0014795752,0.2565573454,-0.065184541,0.2072599679,-0.5502041578,-0.3302358091,-0.171149075,-0.0751144364,0.0297766328,0.017239796,0.042988915,0.1183330268,-0.0039446368,-0.0914930552,-0.2143327296,-0.0401226208,-0.2938643992,0.3311138153,-0.0440836288,-0.5796714425,0.0525046512,0.3996300101,0.3417097032,0.425542295,-0.1804389954,0.212502569,-0.3701970279,0.0718619823,0.0355058275,0.1443957984,0.3573254347,0.3099835515,0.741383791,0.3407741189,0.0554997995,-0.0184589811,0.0020675356,-0.0340798832,-0.1378539503,-0.1220604554,0.2901415527,-0.4243540466,-0.2101333588,-0.0427252166,0.0878321454,0.0020767814,0.140102312,0.5331292748,0.3161356151,-0.1457105726,0.0696696341,0.1419331729,0.0241601914,-0.2461204678,-0.1626888812,0.1233859062,0.1534830928,0.1628362685,0.5111418366,-0.1238003373,0.1600495875,-0.2312073857,-0.0193462558,0.1517569721,0.030876575,0.2377658188,0.0803635791,0.4198556244,0.0691682622,-0.0538456514,0.0848032907,-0.2472534478,-0.2020612955,0.1089738235,-0.1685108095,-0.3066085279,0.1128854752,0.0202344302,-0.5946002007,-0.1545372009,-0.3065297306,-0.0989966542,-0.2779984772,-0.1169825494,0.3799086213,0.0023628389,0.0104780141,0.0062559745,0.1905445606,0.6673040986,-0.3182727695,-0.0799058452,-0.1225792989,-0.3955584764,-0.3251459599,-0.0237534381,-0.0192654934,0.2276733816,-0.2756209075,-0.0259552691,0.1421019733,-0.6164165139,-0.1666887105,0.1011471748,0.0451459736,0.1117658988,-0.0219174847,0.0548410229,-0.1657374352,-0.1598905176,-0.5183991194,-0.1050224602,0.0538248867,-0.0004197109,0.0058027371,-0.2623270154,0.0348798223,0.2813653648,0.248915568,-0.0782804415,0.2280702591,-0.0728461519,0.3314044178,-0.2273528129,-0.0244541895,0.0032446955,0.3236002922,-0.006252382,0.0009979531,-0.3365165889,-0.4372736812,0.5132391453,0.0550050586,0.2197541296,-0.1355510056,-0.2600660026,-0.0559498779,-0.1874911338,-0.1966729164,-0.1220000312,0.0040061222,-0.0116572995,0.4840429127,-0.0297554452,-0.2898127437,0.2965969741,-0.0931805372,0.2591381371,-0.3018274605,0.3876906633,0.2953745127,-0.0051677264,-0.060425736,0.0487632267,-0.1575047076,-0.4263753593,-0.0277419314,0.5033205152,-0.0870922655,0.2198927999,0.4431917071,0.4085289538,0.0069716536,-0.0821099132,0.0988277495,0.1389948279,0.0532171093,0.2253283858,-0.3482511342,0.3991467953,0.3206999302,0.2061223537,0.0426956266,0.0434999987,0.1085657403,-0.2083387524,-0.289840132,-0.0097503588,0.1813403815,-0.0249073263,0.322707504,-0.1948347986,-0.1143599376,-0.0735563412,0.0167112276,-0.4162097573,0.3858821988,0.0809625462,-0.1269963682,0.4530119002,0.2252419293,0.219838053,0.4197409749,-0.0323360525,-0.0960689634,0.1728778183,0.0694627091,-0.0756909549,0.13551642,-0.0800687969,0.013121333,-0.3186014295,0.1703654826,-0.2200761586,-0.19504188,-0.0955156684,-0.0539791845,0.3701503575,-0.454859525,-0.0483524986,-0.228325814,-0.042294506,0.2881699204,0.0113386875,-0.1962646991,-0.0566561259,0.0680729374,-0.0454422571,0.0841889977,-0.0862096697,-0.1605086625,0.6107150316,-0.0350476503,-0.4494149685,-0.3606436253,-0.0919733346,-0.0436117053,0.0939483196,0.2737179399,0.2388507575,0.2868131101,-0.2255786508,-0.0284568667,-0.5197084546,-0.2781671584,0.1731308252,-0.1884097159,0.1885800958,0.404823035,0.2131846547,0.1991953254,0.2877584696,0.1187307388,-0.3820068836,-0.0814849958,-0.0639127418,0.0036102321,-0.1854220033,-0.3005761802,-0.1368411183,-0.2462842166,-0.1156666726,0.4008357525,0.2220220864,0.0142196584,0.1480108052,-0.0507078134,0.1563611776,-0.1821073294,-0.1247434393,0.1665425301,-0.4966342747,0.2169125974,-0.301509738,-0.2235348076,0.1967263222,0.1123247892,0.2349604815,-0.1439007223,-0.3738929331,-0.0956714973,-0.2520744205,0.4707767665,0.2826868594,0.0293982215,0.3348072767,-0.17663607,0.1475589871,-0.0998964235,0.1311476678,0.1330378056,-0.184173584,-0.0058557224,0.0046643959,0.1116213426,-0.0014279952,0.2266184986,0.1940636039,-0.1771638393,0.0581194423,-0.332827121,0.5661172867,-0.0064239958,-0.1414975226,0.0626929551,0.159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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3162","title":"`datasets-cli test` should work with datasets without scripts","comments":"I think it's become private, but feel free to try with any other dataset like `lhoestq\/test` for example at `https:\/\/huggingface.co\/datasets\/lhoestq\/test`","body":"It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n","comment_length":20,"text":"`datasets-cli test` should work with datasets without scripts \n It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n \n I think it's become private, but feel free to try with any other dataset like `lhoestq\/test` for example at `https:\/\/huggingface.co\/datasets\/lhoestq\/test`","embeddings":[-0.6349617243,-0.0156224938,-0.1090960503,-0.0938601941,-0.1955118626,0.1658551246,0.4447168708,0.4181768,0.5121307373,0.0517273396,-0.1489698589,0.1513026059,-0.2129507512,0.3969886601,0.1485299021,0.2338596582,-0.1907201111,0.1554762572,-0.0101323202,0.0428648964,-0.1532213092,-0.1193444803,0.0233733207,0.0961474627,-0.2027205974,-0.2144661099,-0.1602944136,0.2837404013,0.0266019069,-0.3098110557,0.5493773818,0.5170654655,0.3770486414,0.2661829293,-0.0001052348,0.1520181745,0.3456892669,-0.0492840335,-0.3078452051,-0.0791100711,-0.4322427511,0.0540607348,0.1815715134,-0.4128523171,-0.1894226372,0.2416944951,0.1010977626,-0.420322746,0.0084342649,0.2326942384,0.2437996268,0.5983241796,-0.4993752837,-0.2506774068,-0.0888074711,0.3069192469,-0.0803096294,-0.0378425196,0.3784542978,0.1098653302,-0.0198184866,0.0767708793,-0.030264074,0.0053233951,0.1549842805,-0.1423492879,-0.2348434627,-0.1867733151,-0.0433787033,0.4399002492,0.5971586108,-0.6137309074,-0.2141859382,-0.0962434337,-0.1684596539,-0.1439333856,0.1592623144,0.1977103502,0.0797146037,0.2201581597,-0.4162266552,-0.1500200629,-0.0629228503,-0.1147263274,0.0867130235,0.027107697,0.0842197612,0.1496914923,-0.0206319019,-0.114797838,0.0095368093,-0.1054157019,-0.3608535528,0.170024842,-0.1481675655,-0.5458719134,0.1356848776,0.4645478725,0.3018706739,0.4313438237,-0.0286346767,0.341916889,-0.2586274743,0.180521667,0.0684600621,0.077253513,0.4929506481,0.2716988921,0.8175003529,0.272261709,0.0263106544,0.0109551772,0.0467688181,-0.111115925,-0.1623896956,-0.0198535938,0.2210606039,-0.2459720075,-0.3047313094,0.0067765173,0.1816067696,-0.0104510747,0.3082921803,0.5885189176,0.3705600202,-0.1555698067,0.0778789073,0.1878980547,0.1163264662,-0.2642421424,-0.1708342135,0.2051250488,0.0528772622,0.1440212131,0.5038614273,-0.0188218188,0.1394019872,-0.1922031343,-0.0801693425,0.1044010818,0.2321355492,0.2755089998,0.1098343953,0.3066087961,0.2562095225,-0.1984027624,-0.0736666322,-0.148527652,-0.2028270364,0.2178044468,-0.1322733164,-0.2935736179,0.1396026909,0.1420992762,-0.4787715375,-0.098988764,-0.2367629409,-0.0171512067,-0.3649938405,-0.0757328197,0.3593787849,0.0592293739,-0.0656950176,0.057108257,0.0725716129,0.5406169891,-0.3243830502,0.0640756637,-0.0349360108,-0.3583969474,-0.3518805206,-0.2167252898,-0.0265982319,0.2199654877,-0.3085413277,0.1331959218,0.2601792812,-0.581607461,-0.0978088602,0.1787860394,0.0196949244,-0.0775292367,-0.0413116477,0.1964696348,-0.1223289892,-0.0951330513,-0.5069365501,0.0292957835,0.1181308478,0.1422331184,0.0660161749,-0.2260217667,0.0428345501,0.3173208833,0.2591685057,-0.0856147259,0.1527502388,-0.0001810986,0.3328505456,-0.2455343008,0.0083032837,-0.0124219786,0.2023594379,-0.1461529881,-0.0107798912,-0.3476917744,-0.4025418758,0.4919832349,0.0445285402,0.1811458468,0.1293016374,-0.3338459134,-0.05822023,-0.0592688657,-0.1952442527,-0.1086966097,0.1327032745,0.0883663967,0.3998385668,-0.1451084763,-0.3349273205,0.2142728269,-0.1948884279,0.2074405402,-0.3240228593,0.2410450429,0.2089907527,-0.0078229103,-0.0580034442,-0.0895789638,-0.1550443172,-0.3532863259,0.016405819,0.5282897353,0.0249824096,0.1572011113,0.4234713614,0.3124665022,-0.061656788,-0.0136375166,0.0679692999,0.0882854611,-0.0300240181,0.2671087086,-0.42639184,0.4767917991,0.1756155044,0.09226951,-0.0618223697,0.0195896011,0.1083803847,-0.2485022098,-0.3831190169,-0.0856605172,0.1401787698,0.0518053435,0.3296487927,-0.2115803659,-0.101791054,-0.0760504082,-0.0477185138,-0.3409608901,0.4847204983,-0.0056227739,-0.0418774672,0.3982764781,0.2810359299,0.2187141478,0.3610836864,0.0640065894,-0.0825091153,0.0958383158,0.0297181346,-0.0672448799,0.142880246,-0.1435712129,-0.0556558073,-0.301338762,0.1822006255,-0.3005086482,-0.3518089354,-0.1882080436,-0.014627466,0.3050140142,-0.3962845802,-0.1027285829,-0.2098782361,-0.0112025989,0.3378657401,0.0514789,-0.0602035411,-0.0910093188,0.2210662961,-0.0213461686,-0.1056037396,0.0420258157,-0.1354057938,0.5118541718,-0.0629092455,-0.3071706891,-0.3978358805,-0.1404344141,-0.0299602207,0.1919422448,0.330962956,0.2827973664,0.295866102,-0.2353932709,0.0389535837,-0.4877454638,-0.3105126321,0.1652044803,-0.0434243716,0.1275438219,0.3720425069,0.1973076016,0.1784810126,0.210162282,0.0847171769,-0.330413729,-0.0946999118,-0.0911657885,0.1234377995,-0.2418704778,-0.3103995919,-0.1595096737,-0.0952498987,-0.2647661567,0.2971675694,0.0885084569,0.0346279293,0.0108629158,-0.0543142259,0.1271023452,-0.1411781013,-0.1075419337,0.0328506418,-0.4495217204,0.1861077994,-0.390297085,-0.1898955107,0.1061630249,-0.0028580786,0.1267159283,-0.0889587551,-0.2976309657,-0.1873643994,-0.2431078851,0.3567128479,0.2126811296,-0.0947333872,0.3040803671,-0.0302770771,0.0556255914,-0.0285351202,0.1254731566,0.1214331239,-0.1719695032,-0.1267793477,-0.0094390949,0.1257778108,-0.0663022697,0.0089807538,0.0165892933,-0.1502206475,0.1481196433,-0.3247397542,0.5599175096,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3162","title":"`datasets-cli test` should work with datasets without scripts","comments":"> I think it's become private, but feel free to try with any other dataset like `lhoestq\/test` for example at `https:\/\/huggingface.co\/datasets\/lhoestq\/test`\r\n\r\nyour example repo and this page `https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html` helped me to solve.. thanks a lot","body":"It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n","comment_length":35,"text":"`datasets-cli test` should work with datasets without scripts \n It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached  to them (whether the datasets are private or not).\r\n\r\nI wasn't able to run the script for a private test dataset that I had created on the hub (https:\/\/huggingface.co\/datasets\/huggingface\/DataMeasurementsTest\/tree\/main) -- although @lhoestq came to save the day!\r\n \n > I think it's become private, but feel free to try with any other dataset like `lhoestq\/test` for example at `https:\/\/huggingface.co\/datasets\/lhoestq\/test`\r\n\r\nyour example repo and this page `https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html` helped me to solve.. thanks a lot","embeddings":[-0.5352519155,-0.0844738632,-0.0369244441,-0.0430359393,-0.0821808875,0.1424027681,0.3522937298,0.3502660692,0.471252054,0.0475400165,-0.2139710784,0.1264724582,-0.1820225716,0.4219509661,0.1478931606,0.148932904,-0.1257574856,0.1198423952,-0.03550772,0.0078229997,-0.17062594,-0.039780695,-0.0034139866,-0.0034461722,-0.1993757337,-0.1791826487,-0.1679854691,0.301169306,-0.040284276,-0.2641881108,0.6000769138,0.456846118,0.3752204478,0.3984924257,-0.0001096238,0.1531711519,0.330619216,-0.1126229092,-0.3055999577,-0.2000812441,-0.4102603793,0.0505574271,0.1724772304,-0.3441166878,-0.2248616517,0.2500847578,0.0557850674,-0.3859581947,0.0953368396,0.2396574616,0.2185447961,0.6243333817,-0.3937688768,-0.2925622761,-0.044279743,0.342872113,-0.105231382,0.080254212,0.4130091369,0.1383507848,0.0265159775,0.0972133949,-0.0214721244,-0.0079796249,0.1640398353,-0.1041518599,-0.2099265903,-0.2286209017,-0.0043635871,0.4416225553,0.5569645762,-0.6281040907,-0.2534695268,-0.1140451506,-0.1352103651,-0.1855720878,0.1637008041,0.2285473347,0.1023692042,0.1975900084,-0.5202790499,-0.124502331,-0.0780534595,-0.0868508369,0.1477108598,-0.021698799,-0.0010333337,0.1484971344,0.018746905,-0.1658633053,-0.2735182643,-0.0772248656,-0.3841667771,0.2764624655,-0.1351077259,-0.4910592735,0.1121809557,0.4507446587,0.3981649578,0.4771333337,-0.1524893641,0.3339529932,-0.2721913457,0.1690147221,0.0371528938,0.1567709893,0.5104634166,0.3090956509,0.7483816147,0.3974915743,0.0657338053,-0.0442644171,0.049580086,-0.0815345868,-0.2495731115,-0.1151921302,0.2799656987,-0.2725791633,-0.2518103421,0.0089743352,0.0825765803,-0.0268880371,0.3041672111,0.6570934057,0.3366855681,-0.2402465045,0.1121594459,0.2946697176,0.0224895366,-0.2291639596,-0.2005655915,0.1843916327,0.0221113674,0.2006018609,0.538164258,-0.0779385194,0.232296288,-0.1376285553,0.0249045286,0.07928776,0.1404269487,0.2700879872,0.0160757545,0.320810318,0.2818194032,-0.078941755,0.0212115571,-0.2050206959,-0.1804387867,0.1489757001,-0.1530311555,-0.3082692325,0.0894552395,0.0978143588,-0.5159097314,-0.080804266,-0.3172417283,-0.0226193275,-0.3226175606,-0.0586473159,0.3460631967,0.0972125381,-0.0718269944,0.0584883019,0.1700266451,0.6006401777,-0.2232009768,-0.0424817391,0.0409840643,-0.3904187977,-0.2952346802,-0.1417484432,-0.0230253208,0.1581072509,-0.3337393105,0.0913021713,0.2215818167,-0.6308197379,-0.1567746103,0.1938838065,0.036409609,-0.0170592312,-0.0467478372,0.1000299156,-0.1344303787,-0.0278262701,-0.5280157924,-0.0062049492,0.1411053389,0.1340725422,0.031282369,-0.1841890216,-0.0012388836,0.2970654964,0.1502640396,-0.0422059484,0.1067042574,-0.0646804571,0.3414486647,-0.2153326124,0.0692404956,0.0446431264,0.3650635481,-0.0347277261,0.0209490247,-0.3609853983,-0.4852699339,0.4834528863,0.0413010083,0.2644351125,0.0075484668,-0.322119683,-0.1236599237,-0.058487542,-0.2611745298,-0.2313589603,0.0760403723,0.0412972085,0.4343155622,-0.0977699459,-0.3058801591,0.2908141613,-0.1276759058,0.2741721869,-0.3858571649,0.2956764996,0.1645809114,-0.034886755,-0.0357157327,-0.0325374454,-0.091674082,-0.3854194582,0.0171198528,0.5513330102,-0.0563165992,0.1506063938,0.4141300619,0.2657607198,-0.0017745781,-0.0486012995,0.0628898665,0.0316780172,0.0035994207,0.2473424226,-0.4466792345,0.4576910138,0.1665409356,0.1668155938,-0.0561288334,0.0596741326,0.1559274346,-0.2676960826,-0.3420620859,-0.1192651242,0.1617529541,0.0163351428,0.3559247851,-0.2275562286,-0.089308165,-0.0920502394,-0.0388766304,-0.3215028346,0.3636484444,0.0149718346,-0.1073401347,0.3896721303,0.280601114,0.2055968344,0.3644350767,0.0478178374,-0.0849502534,0.1253394037,0.0216463078,-0.0789140984,0.0850756019,-0.2042112797,-0.0479326695,-0.2641328871,0.1886255294,-0.3010458052,-0.3341765404,-0.1452605575,0.0171618778,0.3333785236,-0.4020013213,-0.0968645811,-0.2364292592,-0.07104031,0.2520609498,-0.0257671326,-0.1899444014,-0.0725030825,0.1589692831,-0.0260071587,-0.0373157188,0.0910713598,-0.0313776508,0.4296782017,-0.0548359416,-0.3660787046,-0.3848163784,-0.0465309285,-0.0668185875,0.1406887621,0.3174901903,0.2436400801,0.3353519738,-0.2912317812,-0.0266081132,-0.4996435046,-0.3579373956,0.1856772602,-0.1180992723,0.1858188063,0.3670450747,0.1555396616,0.1794197857,0.2078730315,0.2094285339,-0.3292770982,-0.1392238289,-0.0714935958,0.1166491285,-0.1633933038,-0.3490847349,-0.1557148844,-0.0619167127,-0.2283777744,0.352612555,0.1288238168,-0.0002534515,0.0566854849,-0.0109894089,0.1717432737,-0.1755271852,-0.111769408,0.0257778782,-0.441073209,0.2065200657,-0.3786635697,-0.2555679977,0.2139500231,0.0350180455,0.144881174,-0.1034578681,-0.305128634,-0.2580841184,-0.2489449978,0.3965071738,0.2493252307,-0.0004035606,0.3757328093,-0.0887727886,0.1111695915,-0.0718501434,-0.0236780103,0.1959438175,-0.2211524695,-0.0307810623,-0.0360920727,0.2569295168,-0.1146405712,0.1677817702,0.118466042,-0.1468899697,0.1615326405,-0.3532542586,0.5700926185,-0.0224052705,-0.066373311,0.0563576184,0.1657965481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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3156","title":"Rouge and Meteor for multiple references","comments":"Hi @avinashsai ,\r\n\r\ncurrently, multiple references are not supported. However, we could add a `multiref` config to fix that. When working with multiple references, we can accumulate them by either taking an average or the best score. Would you like to work on that?","body":"Hi,\r\n\r\nCurrently rogue and meteor supports only single references. Can we use these metrics to calculate for multiple references?","comment_length":44,"text":"Rouge and Meteor for multiple references \n Hi,\r\n\r\nCurrently rogue and meteor supports only single references. Can we use these metrics to calculate for multiple references? \n Hi @avinashsai ,\r\n\r\ncurrently, multiple references are not supported. However, we could add a `multiref` config to fix that. When working with multiple references, we can accumulate them by either taking an average or the best score. Would you like to work on that?","embeddings":[-0.2006473392,-0.3965419829,-0.0614075288,0.3612065911,0.1863977462,-0.3232399225,0.1122063473,-0.1550198495,0.0509822816,0.2434678227,-0.3342248499,-0.0029786755,-0.1092280671,-0.3390773833,-0.1908658445,-0.194698751,-0.0195315816,-0.1624713689,0.2199575454,0.0569956973,0.0293881893,0.1764993817,0.0839980766,-0.0139367888,-0.061448697,0.0897646248,-0.1522077322,-0.0796465501,-0.018869577,-0.0325120725,-0.223461017,0.3469818234,0.0289587658,0.280964613,-0.0001058969,-0.2209191918,0.1398139596,-0.0278730262,0.1128037348,-0.2206878066,-0.3002561331,0.1667781472,0.0005170061,-0.2214780152,-0.1831668764,-0.170452252,-0.2663925588,-0.2812636197,0.3677088916,-0.0228913669,0.2517790198,-0.2068436593,0.0709822699,-0.2426408231,0.5898231268,0.0200350787,-0.1285903305,0.3211148679,0.310487628,0.0907983556,-0.1113907993,0.2825616598,0.2285732031,0.019111637,0.3263856173,-0.1069565788,0.1167628542,0.0668038726,-0.1456190795,0.2103588283,0.4278614819,-0.0395683013,-0.454293102,0.2995744944,-0.0152025912,-0.0495105721,-0.4708033502,-0.1472477019,0.1182632074,-0.3225021362,-0.1976673156,-0.2332185209,-0.0285379142,-0.0224786475,0.0829701349,0.0214261264,0.0265461635,0.062117219,0.3106827438,0.097609289,0.0076313457,0.1078302339,-0.0150407357,-0.2742308676,-0.2083007991,-0.1169604287,0.0165994465,0.3246042132,0.3177952766,0.2997745275,0.5002148151,0.1982102543,-0.1346913278,0.3149813116,0.0461056866,-0.2614542842,0.1243773252,-0.3140147328,0.3520402014,0.2881336212,-0.1199389994,-0.1268554479,0.2264431119,-0.232081145,-0.2438084036,0.3602294624,-0.2705093622,-0.2860136628,0.125097096,-0.3177197874,-0.0039008507,-0.1495594084,0.2739169002,0.202103734,-0.2136802226,0.0572168455,-0.1148475558,0.0214601289,0.128633827,-0.2882445455,-0.058746554,0.2323526144,-0.2436712533,0.0334197469,0.1591088474,0.2321346402,0.1570466012,0.0265597682,0.3655699492,-0.3783807456,0.2802885771,-0.4674782455,-0.0510098673,-0.1811249554,-0.0154984109,-0.0607326292,0.0093765398,-0.277346164,-0.2515019178,-0.0997938141,-0.3049696684,-0.053527236,0.1925965697,0.135174185,0.0475660041,0.0172038171,-0.0582136586,0.4031031132,-0.0390218683,-0.2869913876,-0.1838857979,0.0331637971,-0.1967530102,-0.1603133976,0.1067419276,0.2929647863,-0.0782642588,-0.0709377825,0.1886646301,-0.0583652742,-0.2995364964,0.2986941934,-0.36369735,-0.2062778026,-0.0252227131,-0.6121824384,0.479170382,-0.5290030837,0.139862448,-0.4507588446,0.1469555795,-0.1859387308,0.2395943552,0.2988832891,0.0720744804,-0.1210816577,0.2483628541,-0.1413971037,0.0198783055,-0.0720405728,-0.122116819,0.049055744,-0.0569678061,0.1548675746,0.2110263109,-0.3637344241,0.1769493967,0.2235827893,0.0887798592,-0.3463477492,-0.0065092398,0.2382932305,0.2117232829,-0.1896890551,0.1507794559,-0.2817701995,0.0773990899,0.132438764,-0.1612773389,0.0108025139,0.7908697724,-0.2554762065,0.0640689284,-0.1990547031,0.1955898255,0.1128291562,0.1680552512,0.1374145746,-0.042798385,-0.1667709053,-0.1187781245,-0.1607209742,0.0057472927,-0.096107915,-0.1257892251,0.0520499758,-0.3301888704,0.2967614233,0.2476128191,0.2364581227,0.0571677946,0.0623119622,0.3698891401,-0.0163390655,0.4553200006,-0.0399641767,0.4811357558,0.3368361294,0.3671682775,-0.1116711572,0.1535943449,-0.0799557865,0.2614457309,-0.2282571644,-0.4210207462,0.2855210006,-0.1176332757,0.1725213975,0.05913366,-0.053930562,0.2245807946,-0.0374960713,-0.219892025,-0.1123331934,0.1423816532,0.1473018229,0.0652884394,-0.1046644002,-0.25832358,-0.1085733995,-0.172059238,0.2318546027,0.0923218131,0.1425011903,-0.0681707039,-0.1652110517,-0.0918607637,0.1571730077,0.1726608574,0.2089892179,0.2791935205,-0.4240639806,-0.0266976524,-0.2953686416,0.0411162302,-0.1316496432,0.0205132142,0.2330175042,0.3825842738,-0.1424123496,0.0165236369,-0.5652412176,0.1673866808,-0.2179877758,-0.1213600338,-0.1776112765,0.2057293206,0.2225185931,-0.28153947,-0.4604882598,-0.2517856658,-0.2660823464,0.2703669071,0.0268901233,0.1219318956,-0.0814697891,0.4797797501,0.3193744123,-0.3393964171,0.2886294723,-0.0645192191,-0.5124973655,0.0973399132,0.143998608,-0.0847791061,-0.2453273684,0.2170069069,0.0796486735,-0.0528740063,-0.1077848524,-0.1876734048,-0.1059301645,0.2799499333,0.1985013038,-0.0401226915,-0.4909838438,0.1888005883,0.3361598551,0.3671739101,-0.0386858247,0.2003214955,-0.081364058,-0.1642340571,-0.1156011447,-0.0449692383,-0.0637786612,0.0602957532,-0.1658675373,-0.0063087344,0.2462585717,-0.0820497051,-0.0839436948,-0.0838442817,-0.0863137841,-0.0333934799,0.2191066146,-0.0243677776,-0.0805547386,0.3149220049,-0.3286988139,-0.3267739415,0.0195063166,0.2045660019,0.1670956463,0.1194825917,0.0955412984,-0.7805389762,0.3536307216,-0.3824895024,0.0327375121,0.2036887258,0.0551359132,0.1605888158,-0.2601475418,-0.0681848004,-0.0805083066,0.1516291052,-0.2570616901,0.3633846045,-0.1554851979,-0.1140041798,0.1199980378,0.8116832972,0.3189427555,-0.0373840295,-0.0026702238,0.1662789732,-0.0057142237,0.0978959426,-0.1421974301,-0.1104570329,-0.17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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3155","title":"Illegal instruction (core dumped) at datasets import","comments":"It seems to be an issue with how conda-forge is building the binaries. It works on some machines, but not a machine with AMD Opteron 8384 processors.","body":"## Describe the bug\r\nI install datasets using conda and when I import datasets I get: \"Illegal instruction (core dumped)\"\r\n\r\n## Steps to reproduce the bug\r\n\r\n```\r\nconda create --prefix path\/to\/env\r\nconda activate path\/to\/env\r\nconda install -c huggingface -c conda-forge datasets\r\n# exits with output \"Illegal instruction (core dumped)\"\r\npython -m datasets\r\n```\r\n\r\n## Environment info\r\n\r\nWhen I run \"datasets-cli env\", I also get \"Illegal instruction (core dumped)\"\r\n\r\nIf I run the following commands:\r\n\r\n```\r\nconda create --prefix path\/to\/another\/new\/env\r\nconda activate path\/to\/another\/new\/env\r\nconda install -c huggingface transformers\r\ntransformers-cli env\r\n```\r\n\r\nThen I get:\r\n\r\n- `transformers` version: 4.11.3\r\n- Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyTorch version (GPU?): not installed (NA)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Flax version (CPU?\/GPU?\/TPU?): not installed (NA)\r\n- Jax version: not installed\r\n- JaxLib version: not installed\r\n- Using GPU in script?: No\r\n- Using distributed or parallel set-up in script?: No\r\n\r\nLet me know what additional information you need in order to debug this issue. Thanks in advance!","comment_length":27,"text":"Illegal instruction (core dumped) at datasets import \n ## Describe the bug\r\nI install datasets using conda and when I import datasets I get: \"Illegal instruction (core dumped)\"\r\n\r\n## Steps to reproduce the bug\r\n\r\n```\r\nconda create --prefix path\/to\/env\r\nconda activate path\/to\/env\r\nconda install -c huggingface -c conda-forge datasets\r\n# exits with output \"Illegal instruction (core dumped)\"\r\npython -m datasets\r\n```\r\n\r\n## Environment info\r\n\r\nWhen I run \"datasets-cli env\", I also get \"Illegal instruction (core dumped)\"\r\n\r\nIf I run the following commands:\r\n\r\n```\r\nconda create --prefix path\/to\/another\/new\/env\r\nconda activate path\/to\/another\/new\/env\r\nconda install -c huggingface transformers\r\ntransformers-cli env\r\n```\r\n\r\nThen I get:\r\n\r\n- `transformers` version: 4.11.3\r\n- Platform: Linux-5.4.0-67-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.12\r\n- PyTorch version (GPU?): not installed (NA)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Flax version (CPU?\/GPU?\/TPU?): not installed (NA)\r\n- Jax version: not installed\r\n- JaxLib version: not installed\r\n- Using GPU in script?: No\r\n- Using distributed or parallel set-up in script?: No\r\n\r\nLet me know what additional information you need in order to debug this issue. Thanks in advance! \n It seems to be an issue with how conda-forge is building the binaries. It works on some machines, but not a machine with AMD Opteron 8384 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3154","title":"Sacrebleu unexpected behaviour\/requirement for data format","comments":"Hi @BramVanroy!\r\n\r\nGood question. This project relies on PyArrow (tables) to store data too big to fit in RAM. In the case of metrics, this means that the number of predictions and references has to match to form a table.\r\n\r\nThat's why your example throws an error even though it matches the schema:\r\n```python\r\nrefs = [\r\n    ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],\r\n    ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],\r\n]  # len(refs) = 2\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nInstead, it should be:\r\n```python\r\nrefs = [\r\n    ['The dog bit the man.', 'The dog had bit the man.'],\r\n    ['It was not unexpected.', 'No one was surprised.'],\r\n    ['The man bit him first.', 'The man had bitten the dog.'], \r\n]  # len(refs) = 3\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nHowever, `sacreblue` works with the format that's described in your example, hence this part:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L94-L99\r\n\r\nHope you get an idea!","body":"## Describe the bug\r\nWhen comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https:\/\/github.com\/huggingface\/datasets\/pull\/3153).\r\n\r\nIn the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport sacrebleu\r\nimport datasets\r\n\r\nrefs = [\r\n    ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],\r\n    ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],\r\n]\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.']\r\n\r\nexpected_bleu = 48.530827\r\n\r\nds_bleu = datasets.load_metric(\"sacrebleu\")\r\n\r\nbleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score\r\nprint(bleu_score_sb, expected_bleu)\r\n# works: 48.5308...\r\nbleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)[\"score\"]\r\nprint(bleu_score_ds, expected_bleu)\r\n# ValueError: Predictions and\/or references don't match the expected format.\r\n```\r\nThis seems to be related to how datasets forces the features format here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L94-L99\r\n\r\nand then manipulates the references during the compute stage here\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L119-L122\r\n\r\nI do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https:\/\/github.com\/mjpost\/sacrebleu\/blob\/2787185dd0f8d224c72ee5a831d163c2ac711a47\/sacrebleu\/metrics\/base.py#L229). \r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\bramv\\AppData\\Roaming\\JetBrains\\PyCharm2020.3\\scratches\\scratch_23.py\", line 23, in <module>\r\n    bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)[\"score\"]\r\n  File \"C:\\dev\\python\\datasets\\src\\datasets\\metric.py\", line 392, in compute\r\n    self.add_batch(predictions=predictions, references=references)\r\n  File \"C:\\dev\\python\\datasets\\src\\datasets\\metric.py\", line 439, in add_batch\r\n    raise ValueError(\r\nValueError: Predictions and\/or references don't match the expected format.\r\nExpected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},\r\nInput predictions: ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'],\r\nInput references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.1.dev0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.9.2\r\n- PyArrow version: 4.0.1\r\n\r\n","comment_length":197,"text":"Sacrebleu unexpected behaviour\/requirement for data format \n ## Describe the bug\r\nWhen comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https:\/\/github.com\/huggingface\/datasets\/pull\/3153).\r\n\r\nIn the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport sacrebleu\r\nimport datasets\r\n\r\nrefs = [\r\n    ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],\r\n    ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],\r\n]\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.']\r\n\r\nexpected_bleu = 48.530827\r\n\r\nds_bleu = datasets.load_metric(\"sacrebleu\")\r\n\r\nbleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score\r\nprint(bleu_score_sb, expected_bleu)\r\n# works: 48.5308...\r\nbleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)[\"score\"]\r\nprint(bleu_score_ds, expected_bleu)\r\n# ValueError: Predictions and\/or references don't match the expected format.\r\n```\r\nThis seems to be related to how datasets forces the features format here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L94-L99\r\n\r\nand then manipulates the references during the compute stage here\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L119-L122\r\n\r\nI do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https:\/\/github.com\/mjpost\/sacrebleu\/blob\/2787185dd0f8d224c72ee5a831d163c2ac711a47\/sacrebleu\/metrics\/base.py#L229). \r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\bramv\\AppData\\Roaming\\JetBrains\\PyCharm2020.3\\scratches\\scratch_23.py\", line 23, in <module>\r\n    bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)[\"score\"]\r\n  File \"C:\\dev\\python\\datasets\\src\\datasets\\metric.py\", line 392, in compute\r\n    self.add_batch(predictions=predictions, references=references)\r\n  File \"C:\\dev\\python\\datasets\\src\\datasets\\metric.py\", line 439, in add_batch\r\n    raise ValueError(\r\nValueError: Predictions and\/or references don't match the expected format.\r\nExpected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},\r\nInput predictions: ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'],\r\nInput references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.1.dev0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.9.2\r\n- PyArrow version: 4.0.1\r\n\r\n \n Hi @BramVanroy!\r\n\r\nGood question. This project relies on PyArrow (tables) to store data too big to fit in RAM. In the case of metrics, this means that the number of predictions and references has to match to form a table.\r\n\r\nThat's why your example throws an error even though it matches the schema:\r\n```python\r\nrefs = [\r\n    ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],\r\n    ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],\r\n]  # len(refs) = 2\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nInstead, it should be:\r\n```python\r\nrefs = [\r\n    ['The dog bit the man.', 'The dog had bit the man.'],\r\n    ['It was not unexpected.', 'No one was surprised.'],\r\n    ['The man bit him first.', 'The man had bitten the dog.'], \r\n]  # len(refs) = 3\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'] # len(hyps) = 3\r\n```\r\n\r\nHowever, `sacreblue` works with the format that's described in your example, hence this part:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L94-L99\r\n\r\nHope you get an idea!","embeddings":[0.0317310318,-0.2080372721,0.0581462234,0.1247448251,0.3749686182,-0.0902196616,0.170554921,0.1604260653,-0.3364596367,0.0518430732,-0.0757988095,0.2882937491,-0.0812798291,0.2799241543,0.0603364222,-0.1692026258,0.2169517875,0.2796366811,0.229610607,-0.0013863477,-0.0580700636,0.0824512169,-0.321213156,-0.1622766107,-0.3530726731,0.2892410755,-0.0370381139,-0.0285126884,-0.391792953,-0.4078976214,0.3379500806,0.0358569808,-0.1415418535,0.6881942749,-0.000122776,-0.1983245313,0.0475013815,-0.1819527149,-0.4215900004,-0.2807797194,-0.2676606178,-0.3110386431,0.0075277449,-0.2268883735,-0.2807044983,-0.2899981141,-0.1817334741,-0.1974864453,0.0456378385,0.2402633727,0.1405072361,0.4610384703,-0.3269178569,-0.1773908138,0.3765436709,0.3578309715,-0.2097972929,0.0421400182,0.0439859815,0.2386363894,0.1036146283,0.1915555149,-0.1671423167,-0.0364760496,0.2031667084,0.0517835356,-0.5668801665,-0.086965926,0.084760882,0.1742599905,0.4631301463,-0.3084827363,-0.6328508854,-0.111727111,-0.3257716298,-0.1827742904,0.1079265326,0.0412677042,0.1532302648,0.1278625876,-0.4699285924,0.2582840919,-0.0158911087,0.1142017469,-0.2946029007,-0.1979317665,-0.4510428309,0.0183213763,-0.114013955,0.0518356264,-0.2205842137,-0.1044205427,-0.0592639036,0.0610239394,-0.2661688328,-0.1096411794,-0.1018491983,0.085114561,0.2666997015,0.3706920743,0.1574822068,0.0832778215,-0.3361518383,0.0476020537,0.0736066848,0.2986440063,0.0355686955,-0.100632295,0.0707379878,0.1470650285,0.1487566531,-0.0361495465,0.3862420022,-0.2469170839,0.1397444308,-0.0349188261,0.30399701,-0.3889895678,-0.3404229581,0.2912738323,0.0396895558,0.2124418318,-0.0406285599,0.045301836,-0.2439430952,0.077968277,-0.147694692,0.1812999249,-0.224208653,-0.1301690489,-0.1956047565,-0.2090708315,-0.4047017097,-0.202728197,0.1874872893,-0.3455869257,0.3793722689,0.3439644873,0.2739528716,-0.0790528357,-0.132792592,-0.2681227326,0.3768129945,-0.0934437886,-0.4716941118,0.1950533539,0.0511941947,-0.1956318617,-0.0016997496,-0.0869866461,-0.3642956614,0.018719146,0.1574371755,0.1374967247,-0.4574984908,-0.0927081779,-0.2508966923,0.2614222467,0.1145225614,-0.245760873,0.0634412542,-0.1808056235,-0.3381125331,-0.2041364312,0.4688743651,0.3654601276,-0.3295740187,-0.0172316711,0.4282448888,-0.0466207936,0.161125347,0.1564275473,0.0470471568,-0.0027401226,-0.2008215189,-0.0276420973,0.0598686449,-0.1669043899,-0.4678935409,0.1675121635,0.1311185956,0.2630412877,0.045251403,0.1285016239,0.2586842775,-0.0493509695,-0.0103606442,-0.1011584625,-0.0235989429,-0.2062923014,-0.1641660333,-0.2063200772,0.0194536336,-0.2110918015,0.094985202,0.3565945923,0.1454799771,0.3896767199,0.36655882,-0.1118037701,-0.020633854,0.1515947431,0.3117903769,-0.1273692101,0.2747133672,-0.5003556013,-0.1666878313,0.3074694276,0.3255886137,0.1186324432,0.1110498458,-0.0798222572,-0.4921579063,0.0737609267,0.0128343031,-0.1892291009,0.0057038153,0.119781293,-0.0513669401,0.095065549,-0.1068139002,0.0258229245,-0.0998941213,0.0519921109,-0.2619096637,0.0854428038,0.0226419792,-0.1490587294,-0.1726226062,0.3147755563,0.0658039376,-0.1032702774,-0.1215437651,0.352510184,0.4304808676,0.23517932,-0.2996089458,0.0821239203,0.2040390819,-0.0384490229,-0.1290630847,0.4433319271,0.2218438387,-0.0182539318,-0.1191772968,0.3095273674,-0.1752451956,0.0909822658,-0.087150611,-0.1526123285,0.0509057492,0.0666255876,-0.1823847294,-0.4403994977,0.1939836591,-0.1146944314,0.0000478808,0.1023100689,-0.3825535476,-0.0161636528,0.2689918578,0.0596403368,-0.1266870052,0.0132081555,-0.027244525,0.0393436849,0.0443553589,0.2605520487,0.5791496038,0.0041056145,0.1914570034,0.1731903702,0.0664428994,-0.3382095098,0.0391626582,0.1107903048,-0.4332155287,0.1631759703,0.3441346884,0.2131804973,-0.0497877449,0.2156024575,-0.0853808969,-0.1639244258,-0.4157847762,0.448867023,-0.4556628466,0.019329723,-0.5457910895,-0.0442539193,0.1051302478,-0.0995023772,-0.0517056845,0.0867241323,-0.017743418,0.1359563768,-0.0396637656,0.1590863764,-0.0386683941,-0.1103770211,0.1252460182,0.0769863054,-0.18443048,-0.0014535087,-0.2557236552,-0.1308986247,0.1903683245,-0.1765409559,-0.1722281128,-0.4279376864,-0.537086904,0.144268468,-0.1039336547,0.6145106554,0.3804366887,-0.3088343143,-0.0054257577,0.1331433356,0.1473065913,0.0517996997,-0.0858951434,0.2584977746,-0.0827185884,-0.1648394316,-0.3129447997,-0.3725875616,0.2889754772,-0.0598307364,0.3633621037,0.0184714533,-0.0196832847,0.1708986312,0.3312630653,0.1382445097,0.159263745,-0.0033276523,-0.1916251779,-0.1673801839,0.4011286199,-0.1759748459,-0.5639656186,-0.0581715256,0.1368482411,0.0535849892,0.5197384953,-0.3321480155,-0.3354057074,-0.0640598759,-0.2263320833,0.1279833019,0.0831694528,-0.1586357653,0.0327537991,-0.0622241795,-0.2718763947,-0.1644657403,0.1752987802,-0.0792187005,0.1274837703,0.0325107314,-0.0691035166,0.4407610297,0.3369850218,0.4358158112,-0.3011395633,0.1825987399,0.126127094,0.4447239637,-0.1453529298,-0.2570250928,0.1331665814,-0.3175551295,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3154","title":"Sacrebleu unexpected behaviour\/requirement for data format","comments":"Thanks, that makes sense. It is a bit unfortunate because it may be confusing to users since the input format is suddenly different than what they may expect from the underlying library\/metric. But it is understandable due to how `datasets` works!","body":"## Describe the bug\r\nWhen comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https:\/\/github.com\/huggingface\/datasets\/pull\/3153).\r\n\r\nIn the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport sacrebleu\r\nimport datasets\r\n\r\nrefs = [\r\n    ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],\r\n    ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],\r\n]\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.']\r\n\r\nexpected_bleu = 48.530827\r\n\r\nds_bleu = datasets.load_metric(\"sacrebleu\")\r\n\r\nbleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score\r\nprint(bleu_score_sb, expected_bleu)\r\n# works: 48.5308...\r\nbleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)[\"score\"]\r\nprint(bleu_score_ds, expected_bleu)\r\n# ValueError: Predictions and\/or references don't match the expected format.\r\n```\r\nThis seems to be related to how datasets forces the features format here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L94-L99\r\n\r\nand then manipulates the references during the compute stage here\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L119-L122\r\n\r\nI do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https:\/\/github.com\/mjpost\/sacrebleu\/blob\/2787185dd0f8d224c72ee5a831d163c2ac711a47\/sacrebleu\/metrics\/base.py#L229). \r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\bramv\\AppData\\Roaming\\JetBrains\\PyCharm2020.3\\scratches\\scratch_23.py\", line 23, in <module>\r\n    bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)[\"score\"]\r\n  File \"C:\\dev\\python\\datasets\\src\\datasets\\metric.py\", line 392, in compute\r\n    self.add_batch(predictions=predictions, references=references)\r\n  File \"C:\\dev\\python\\datasets\\src\\datasets\\metric.py\", line 439, in add_batch\r\n    raise ValueError(\r\nValueError: Predictions and\/or references don't match the expected format.\r\nExpected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},\r\nInput predictions: ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'],\r\nInput references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.1.dev0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.9.2\r\n- PyArrow version: 4.0.1\r\n\r\n","comment_length":41,"text":"Sacrebleu unexpected behaviour\/requirement for data format \n ## Describe the bug\r\nWhen comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https:\/\/github.com\/huggingface\/datasets\/pull\/3153).\r\n\r\nIn the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport sacrebleu\r\nimport datasets\r\n\r\nrefs = [\r\n    ['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],\r\n    ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],\r\n]\r\n\r\nhyps = ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.']\r\n\r\nexpected_bleu = 48.530827\r\n\r\nds_bleu = datasets.load_metric(\"sacrebleu\")\r\n\r\nbleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score\r\nprint(bleu_score_sb, expected_bleu)\r\n# works: 48.5308...\r\nbleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)[\"score\"]\r\nprint(bleu_score_ds, expected_bleu)\r\n# ValueError: Predictions and\/or references don't match the expected format.\r\n```\r\nThis seems to be related to how datasets forces the features format here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L94-L99\r\n\r\nand then manipulates the references during the compute stage here\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/87c71b9c29a40958973004910f97e4892559dfed\/metrics\/sacrebleu\/sacrebleu.py#L119-L122\r\n\r\nI do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https:\/\/github.com\/mjpost\/sacrebleu\/blob\/2787185dd0f8d224c72ee5a831d163c2ac711a47\/sacrebleu\/metrics\/base.py#L229). \r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\bramv\\AppData\\Roaming\\JetBrains\\PyCharm2020.3\\scratches\\scratch_23.py\", line 23, in <module>\r\n    bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)[\"score\"]\r\n  File \"C:\\dev\\python\\datasets\\src\\datasets\\metric.py\", line 392, in compute\r\n    self.add_batch(predictions=predictions, references=references)\r\n  File \"C:\\dev\\python\\datasets\\src\\datasets\\metric.py\", line 439, in add_batch\r\n    raise ValueError(\r\nValueError: Predictions and\/or references don't match the expected format.\r\nExpected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},\r\nInput predictions: ['The dog bit the man.', \"It wasn't surprising.\", 'The man had just bitten him.'],\r\nInput references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.1.dev0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.9.2\r\n- PyArrow version: 4.0.1\r\n\r\n \n Thanks, that makes sense. It is a bit unfortunate because it may be confusing to users since the input format is suddenly different than what they may expect from the underlying library\/metric. But it is understandable due to how `datasets` works!","embeddings":[0.0317310318,-0.2080372721,0.0581462234,0.1247448251,0.3749686182,-0.0902196616,0.170554921,0.1604260653,-0.3364596367,0.0518430732,-0.0757988095,0.2882937491,-0.0812798291,0.2799241543,0.0603364222,-0.1692026258,0.2169517875,0.2796366811,0.229610607,-0.0013863477,-0.0580700636,0.0824512169,-0.321213156,-0.1622766107,-0.3530726731,0.2892410755,-0.0370381139,-0.0285126884,-0.391792953,-0.4078976214,0.3379500806,0.0358569808,-0.1415418535,0.6881942749,-0.000122776,-0.1983245313,0.0475013815,-0.1819527149,-0.4215900004,-0.2807797194,-0.2676606178,-0.3110386431,0.0075277449,-0.2268883735,-0.2807044983,-0.2899981141,-0.1817334741,-0.1974864453,0.0456378385,0.2402633727,0.1405072361,0.4610384703,-0.3269178569,-0.1773908138,0.3765436709,0.3578309715,-0.2097972929,0.0421400182,0.0439859815,0.2386363894,0.1036146283,0.1915555149,-0.1671423167,-0.0364760496,0.2031667084,0.0517835356,-0.5668801665,-0.086965926,0.084760882,0.1742599905,0.4631301463,-0.3084827363,-0.6328508854,-0.111727111,-0.3257716298,-0.1827742904,0.1079265326,0.0412677042,0.1532302648,0.1278625876,-0.4699285924,0.2582840919,-0.0158911087,0.1142017469,-0.2946029007,-0.1979317665,-0.4510428309,0.0183213763,-0.114013955,0.0518356264,-0.2205842137,-0.1044205427,-0.0592639036,0.0610239394,-0.2661688328,-0.1096411794,-0.1018491983,0.085114561,0.2666997015,0.3706920743,0.1574822068,0.0832778215,-0.3361518383,0.0476020537,0.0736066848,0.2986440063,0.0355686955,-0.100632295,0.0707379878,0.1470650285,0.1487566531,-0.0361495465,0.3862420022,-0.2469170839,0.1397444308,-0.0349188261,0.30399701,-0.3889895678,-0.3404229581,0.2912738323,0.0396895558,0.2124418318,-0.0406285599,0.045301836,-0.2439430952,0.077968277,-0.147694692,0.1812999249,-0.224208653,-0.1301690489,-0.1956047565,-0.2090708315,-0.4047017097,-0.202728197,0.1874872893,-0.3455869257,0.3793722689,0.3439644873,0.2739528716,-0.0790528357,-0.132792592,-0.2681227326,0.3768129945,-0.0934437886,-0.4716941118,0.1950533539,0.0511941947,-0.1956318617,-0.0016997496,-0.0869866461,-0.3642956614,0.018719146,0.1574371755,0.1374967247,-0.4574984908,-0.0927081779,-0.2508966923,0.2614222467,0.1145225614,-0.245760873,0.0634412542,-0.1808056235,-0.3381125331,-0.2041364312,0.4688743651,0.3654601276,-0.3295740187,-0.0172316711,0.4282448888,-0.0466207936,0.161125347,0.1564275473,0.0470471568,-0.0027401226,-0.2008215189,-0.0276420973,0.0598686449,-0.1669043899,-0.4678935409,0.1675121635,0.1311185956,0.2630412877,0.045251403,0.1285016239,0.2586842775,-0.0493509695,-0.0103606442,-0.1011584625,-0.0235989429,-0.2062923014,-0.1641660333,-0.2063200772,0.0194536336,-0.2110918015,0.094985202,0.3565945923,0.1454799771,0.3896767199,0.36655882,-0.1118037701,-0.020633854,0.1515947431,0.3117903769,-0.1273692101,0.2747133672,-0.5003556013,-0.1666878313,0.3074694276,0.3255886137,0.1186324432,0.1110498458,-0.0798222572,-0.4921579063,0.0737609267,0.0128343031,-0.1892291009,0.0057038153,0.119781293,-0.0513669401,0.095065549,-0.1068139002,0.0258229245,-0.0998941213,0.0519921109,-0.2619096637,0.0854428038,0.0226419792,-0.1490587294,-0.1726226062,0.3147755563,0.0658039376,-0.1032702774,-0.1215437651,0.352510184,0.4304808676,0.23517932,-0.2996089458,0.0821239203,0.2040390819,-0.0384490229,-0.1290630847,0.4433319271,0.2218438387,-0.0182539318,-0.1191772968,0.3095273674,-0.1752451956,0.0909822658,-0.087150611,-0.1526123285,0.0509057492,0.0666255876,-0.1823847294,-0.4403994977,0.1939836591,-0.1146944314,0.0000478808,0.1023100689,-0.3825535476,-0.0161636528,0.2689918578,0.0596403368,-0.1266870052,0.0132081555,-0.027244525,0.0393436849,0.0443553589,0.2605520487,0.5791496038,0.0041056145,0.1914570034,0.1731903702,0.0664428994,-0.3382095098,0.0391626582,0.1107903048,-0.4332155287,0.1631759703,0.3441346884,0.2131804973,-0.0497877449,0.2156024575,-0.0853808969,-0.1639244258,-0.4157847762,0.448867023,-0.4556628466,0.019329723,-0.5457910895,-0.0442539193,0.1051302478,-0.0995023772,-0.0517056845,0.0867241323,-0.017743418,0.1359563768,-0.0396637656,0.1590863764,-0.0386683941,-0.1103770211,0.1252460182,0.0769863054,-0.18443048,-0.0014535087,-0.2557236552,-0.1308986247,0.1903683245,-0.1765409559,-0.1722281128,-0.4279376864,-0.537086904,0.144268468,-0.1039336547,0.6145106554,0.3804366887,-0.3088343143,-0.0054257577,0.1331433356,0.1473065913,0.0517996997,-0.0858951434,0.2584977746,-0.0827185884,-0.1648394316,-0.3129447997,-0.3725875616,0.2889754772,-0.0598307364,0.3633621037,0.0184714533,-0.0196832847,0.1708986312,0.3312630653,0.1382445097,0.159263745,-0.0033276523,-0.1916251779,-0.1673801839,0.4011286199,-0.1759748459,-0.5639656186,-0.0581715256,0.1368482411,0.0535849892,0.5197384953,-0.3321480155,-0.3354057074,-0.0640598759,-0.2263320833,0.1279833019,0.0831694528,-0.1586357653,0.0327537991,-0.0622241795,-0.2718763947,-0.1644657403,0.1752987802,-0.0792187005,0.1274837703,0.0325107314,-0.0691035166,0.4407610297,0.3369850218,0.4358158112,-0.3011395633,0.1825987399,0.126127094,0.4447239637,-0.1453529298,-0.2570250928,0.1331665814,-0.3175551295,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3148","title":"Streaming with num_workers != 0","comments":"I can confirm that I was able to reproduce the bug. This seems odd given that #3423 reports duplicate data retrieval when `num_workers` and `streaming` are used together, which is obviously different from what is reported here. ","body":"## Describe the bug\r\nWhen using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.\r\n\r\nThe code owner is likely @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n\r\nFor your convenience, we've prepped a colab notebook that reproduces the bug\r\nhttps:\/\/colab.research.google.com\/drive\/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing\r\n```python\r\n!pip install datasets==1.14.0\r\n\r\nshould_freeze_forever = True\r\n# ^-- set this to True in order to freeze forever, set to False in order to work normally\r\n\r\nimport torch\r\nfrom datasets import load_dataset\r\n\r\ndata = load_dataset(\"oscar\", \"unshuffled_deduplicated_bn\", split=\"train\", streaming=True)\r\ndata = data.map(lambda x: {\"text\": x[\"text\"], \"orig\": f\"oscar[{x['id']}]\"}, batched=True)\r\ndata = data.shuffle(100, seed=1337)\r\n\r\ndata = data.with_format(\"torch\")\r\nloader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)\r\n\r\n# v-- the code should freeze forever at this line\r\nfor i, row in enumerate(loader):\r\n  print(row)\r\n  if i > 10: break\r\nprint(\"DONE!\")\r\n```\r\n\r\n## Expected results\r\nThe code should not freeze forever with num_workers=2\r\n\r\n## Actual results\r\nThe code freezes forever with num_workers=2\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0 (also found in previous versions)\r\n- Platform: google colab (also locally)\r\n- Python version: 3.7, (also 3.8)\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":37,"text":"Streaming with num_workers != 0 \n ## Describe the bug\r\nWhen using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.\r\n\r\nThe code owner is likely @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n\r\nFor your convenience, we've prepped a colab notebook that reproduces the bug\r\nhttps:\/\/colab.research.google.com\/drive\/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing\r\n```python\r\n!pip install datasets==1.14.0\r\n\r\nshould_freeze_forever = True\r\n# ^-- set this to True in order to freeze forever, set to False in order to work normally\r\n\r\nimport torch\r\nfrom datasets import load_dataset\r\n\r\ndata = load_dataset(\"oscar\", \"unshuffled_deduplicated_bn\", split=\"train\", streaming=True)\r\ndata = data.map(lambda x: {\"text\": x[\"text\"], \"orig\": f\"oscar[{x['id']}]\"}, batched=True)\r\ndata = data.shuffle(100, seed=1337)\r\n\r\ndata = data.with_format(\"torch\")\r\nloader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)\r\n\r\n# v-- the code should freeze forever at this line\r\nfor i, row in enumerate(loader):\r\n  print(row)\r\n  if i > 10: break\r\nprint(\"DONE!\")\r\n```\r\n\r\n## Expected results\r\nThe code should not freeze forever with num_workers=2\r\n\r\n## Actual results\r\nThe code freezes forever with num_workers=2\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0 (also found in previous versions)\r\n- Platform: google colab (also locally)\r\n- Python version: 3.7, (also 3.8)\r\n- PyArrow version: 3.0.0\r\n\r\n \n I can confirm that I was able to reproduce the bug. This seems odd given that #3423 reports duplicate data retrieval when `num_workers` and `streaming` are used together, which is obviously different from what is reported here. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3148","title":"Streaming with num_workers != 0","comments":"Any update? A possible solution is to have multiple arrow files as shards, and handle them like what webdatasets does.\r\n![image](https:\/\/user-images.githubusercontent.com\/11533479\/148176637-72746b2c-c122-47aa-bbfe-224b13ee9a71.png)\r\n\r\nPytorch's new dataset RFC is supporting sharding now, which may helps avoid duplicate data under streaming mode. (https:\/\/github.com\/pytorch\/pytorch\/blob\/master\/torch\/utils\/data\/datapipes\/iter\/grouping.py#L13)\r\n","body":"## Describe the bug\r\nWhen using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.\r\n\r\nThe code owner is likely @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n\r\nFor your convenience, we've prepped a colab notebook that reproduces the bug\r\nhttps:\/\/colab.research.google.com\/drive\/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing\r\n```python\r\n!pip install datasets==1.14.0\r\n\r\nshould_freeze_forever = True\r\n# ^-- set this to True in order to freeze forever, set to False in order to work normally\r\n\r\nimport torch\r\nfrom datasets import load_dataset\r\n\r\ndata = load_dataset(\"oscar\", \"unshuffled_deduplicated_bn\", split=\"train\", streaming=True)\r\ndata = data.map(lambda x: {\"text\": x[\"text\"], \"orig\": f\"oscar[{x['id']}]\"}, batched=True)\r\ndata = data.shuffle(100, seed=1337)\r\n\r\ndata = data.with_format(\"torch\")\r\nloader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)\r\n\r\n# v-- the code should freeze forever at this line\r\nfor i, row in enumerate(loader):\r\n  print(row)\r\n  if i > 10: break\r\nprint(\"DONE!\")\r\n```\r\n\r\n## Expected results\r\nThe code should not freeze forever with num_workers=2\r\n\r\n## Actual results\r\nThe code freezes forever with num_workers=2\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0 (also found in previous versions)\r\n- Platform: google colab (also locally)\r\n- Python version: 3.7, (also 3.8)\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":39,"text":"Streaming with num_workers != 0 \n ## Describe the bug\r\nWhen using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.\r\n\r\nThe code owner is likely @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n\r\nFor your convenience, we've prepped a colab notebook that reproduces the bug\r\nhttps:\/\/colab.research.google.com\/drive\/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing\r\n```python\r\n!pip install datasets==1.14.0\r\n\r\nshould_freeze_forever = True\r\n# ^-- set this to True in order to freeze forever, set to False in order to work normally\r\n\r\nimport torch\r\nfrom datasets import load_dataset\r\n\r\ndata = load_dataset(\"oscar\", \"unshuffled_deduplicated_bn\", split=\"train\", streaming=True)\r\ndata = data.map(lambda x: {\"text\": x[\"text\"], \"orig\": f\"oscar[{x['id']}]\"}, batched=True)\r\ndata = data.shuffle(100, seed=1337)\r\n\r\ndata = data.with_format(\"torch\")\r\nloader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)\r\n\r\n# v-- the code should freeze forever at this line\r\nfor i, row in enumerate(loader):\r\n  print(row)\r\n  if i > 10: break\r\nprint(\"DONE!\")\r\n```\r\n\r\n## Expected results\r\nThe code should not freeze forever with num_workers=2\r\n\r\n## Actual results\r\nThe code freezes forever with num_workers=2\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0 (also found in previous versions)\r\n- Platform: google colab (also locally)\r\n- Python version: 3.7, (also 3.8)\r\n- PyArrow version: 3.0.0\r\n\r\n \n Any update? A possible solution is to have multiple arrow files as shards, and handle them like what webdatasets does.\r\n![image](https:\/\/user-images.githubusercontent.com\/11533479\/148176637-72746b2c-c122-47aa-bbfe-224b13ee9a71.png)\r\n\r\nPytorch's new dataset RFC is supporting sharding now, which may helps avoid duplicate data under streaming mode. (https:\/\/github.com\/pytorch\/pytorch\/blob\/master\/torch\/utils\/data\/datapipes\/iter\/grouping.py#L13)\r\n","embeddings":[-0.461596638,0.0840081647,-0.0917685628,0.1433269233,-0.1277629733,-0.2838304043,0.6224709153,0.2356565595,0.0436165594,0.3701072037,0.2003805637,0.3652403951,-0.5244144797,-0.1296162009,-0.1155709103,0.1345635653,-0.1440051496,0.213054359,-0.1751621217,0.1614886671,0.0630867928,0.0283181071,-0.1368732601,-0.3835963309,-0.7374165058,0.1155783832,0.1648601592,0.0814532414,0.1128169075,-0.2605651319,0.0886151567,0.0597968772,-0.1121509746,0.6455814838,-0.0001162078,-0.0387910046,0.4676326811,0.1345288903,-0.6384675503,-0.309319973,0.1686165333,-0.2287610769,0.2106488794,0.0480406322,0.1425430924,0.0849133953,0.1730154455,-0.3305674195,0.2686712146,0.2175594717,0.0856185257,0.4063210785,-0.2820675671,0.3103516698,0.1310315281,-0.1396586299,-0.2351375222,0.229577437,0.3924074173,-0.0670586526,-0.214244172,0.2687139511,-0.2799442708,0.1257233322,0.1269943565,-0.0989730507,-0.2335141301,-0.6294925213,0.1656221002,0.2479878813,0.007874866,-0.0693365782,-0.0756301805,-0.4273682535,0.0145042334,-0.2572437823,0.14625144,0.3188640773,-0.4664770365,-0.1490090936,-0.209010452,0.2165494859,-0.1546195149,0.3447423875,-0.1058521941,0.3180482686,-0.1220530197,0.0291444156,0.191093117,0.0453495272,0.6301649213,-0.1929227561,0.0229600221,-0.0133988708,-0.3280262947,0.1866904944,-0.0873127133,0.148588255,-0.0832736045,-0.2093458474,0.4092452526,0.2010458708,0.2821329534,0.2278542817,0.2349042594,-0.1946620941,0.0178909898,-0.120793812,0.5377947688,0.066168949,0.0065827463,0.1903312355,0.0043799412,-0.4356060922,0.1911130697,0.0592479147,0.3574859798,0.0085329134,-0.0903629288,0.0203512963,-0.1798358411,0.0442476124,0.2468069792,0.1529729366,-0.2967973053,0.3693980277,-0.0971112996,-0.1109659523,-0.4364367425,-0.1669523418,-0.1252868026,-0.194288373,-0.0447913781,-0.0070663644,0.488648802,-0.4631687701,0.2122552097,0.0747724473,0.5314906836,0.1154672429,0.3113245368,-0.2635485232,0.0788435563,0.3261722028,0.0190124549,0.2229300886,-0.0420923643,0.0808244124,0.0298324637,0.5602990389,-0.0744844973,-0.1325143874,-0.1314005703,0.1612412035,-0.2957392335,0.1007018983,-0.3321024477,-0.0664680749,0.1246926934,-0.0315264761,0.2606884539,-0.3304385245,0.0405385755,-0.1430798024,0.0388987623,0.634155035,-0.180888772,-0.0129789952,0.1518668383,0.0941597596,0.3056043684,0.098648943,-0.3293596804,-0.4173762202,-0.1197885945,0.1613762379,0.1841504425,0.2218615413,-0.3250557482,0.2197610289,-0.2447834462,0.101739414,0.2811567783,0.4975207746,0.4399672747,-0.2268902808,0.2996389568,0.3158227801,-0.1091269627,0.261271596,-0.4395676255,-0.068305701,-0.0197126586,0.297383219,-0.0538248047,-0.1804976016,-0.0758569464,-0.13898471,0.0784427822,-0.1193517223,0.1375365257,0.134177804,0.0437218174,0.0954043865,-0.0717149228,-0.1435351372,-0.6150406599,0.2157732397,0.1537981629,0.0008421724,-0.235185504,0.033835981,0.1785726845,-0.0013183397,-0.2086162269,-0.1329335421,0.0038541888,-0.0441620313,-0.1432221383,0.0896591917,-0.1638952792,0.5435912013,-0.159011811,0.0077639334,-0.2681691349,0.4484792054,-0.0182634518,-0.483766377,-0.2552691698,-0.1071327403,0.0338500477,-0.0465846546,-0.1040764526,0.3443760574,-0.0660257936,0.123327978,-0.3504337966,0.0265636351,0.3699008226,-0.0935290828,0.1608651727,0.3548155725,0.0572155975,0.02640431,0.0772498026,0.2529551983,-0.4101242721,0.1560890824,-0.0700975806,-0.130151853,-0.092705138,0.1763370335,-0.1572454274,0.2699421346,0.4539076388,-0.1660534143,0.2386310399,0.1235293224,-0.2645144165,0.3699781597,0.1024749875,0.0807826146,0.0099624926,-0.0414981134,-0.3126255572,-0.0438970514,0.2174340189,0.1223860979,0.2964060009,0.2253801823,0.033662077,-0.1922153085,-0.1501042694,-0.3918687403,0.2277211994,0.2594929039,0.0629669875,0.2116518468,0.0876168162,-0.1563093364,-0.2519596219,-0.3591313958,0.0264743883,0.2507943511,-0.2796293795,0.2187856287,-0.1389969736,-0.0761647522,-0.1790859252,-0.1305818409,-0.3786975741,-0.3084186614,-0.0532181673,0.759532094,-0.1909827888,0.1129107177,-0.3325247169,-0.116395317,0.0846185237,0.0394872837,-0.3609859943,-0.1666886806,-0.1446967721,-0.065626204,0.4298759699,-0.0517394878,0.1879480481,0.2138379067,-0.3249357045,-0.2858007848,0.0304160174,-0.0127660045,0.1018608063,0.2415071577,-0.0890487507,0.4059051573,-0.1448232383,-0.2074292898,-0.0027750875,-0.211782068,-0.0571698844,0.2074415237,0.0127560515,0.3589396775,-0.2913258076,-0.0607178621,-0.2924726009,-0.4315826595,-0.0740517452,-0.1791705191,0.2070242018,0.0766984373,0.4659692645,0.2550257742,0.1419045925,-0.2540527284,0.0923470035,-0.2363913953,0.6167689562,-0.0159812551,-0.2508721054,-0.0035845239,-0.0908344835,-0.3518592119,0.3114838302,-0.2124379426,-0.1030734628,-0.2987543344,0.2352301627,-0.1020669118,-0.0404773057,0.462849915,0.2564911842,-0.1282548904,-0.0861959159,0.1138767302,-0.0931885242,-0.0863535255,0.0435799919,0.0114048915,0.2072336227,0.0853210911,0.9141860604,-0.1430669278,-0.2195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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3148","title":"Streaming with num_workers != 0","comments":"Hi ! Thanks for the insights :) Note that in streaming mode there're usually no arrow files. The data are streamed from TAR, ZIP, text, etc. files directly from the web. Though for sharded datasets we can definitely adopt a similar strategy !","body":"## Describe the bug\r\nWhen using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.\r\n\r\nThe code owner is likely @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n\r\nFor your convenience, we've prepped a colab notebook that reproduces the bug\r\nhttps:\/\/colab.research.google.com\/drive\/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing\r\n```python\r\n!pip install datasets==1.14.0\r\n\r\nshould_freeze_forever = True\r\n# ^-- set this to True in order to freeze forever, set to False in order to work normally\r\n\r\nimport torch\r\nfrom datasets import load_dataset\r\n\r\ndata = load_dataset(\"oscar\", \"unshuffled_deduplicated_bn\", split=\"train\", streaming=True)\r\ndata = data.map(lambda x: {\"text\": x[\"text\"], \"orig\": f\"oscar[{x['id']}]\"}, batched=True)\r\ndata = data.shuffle(100, seed=1337)\r\n\r\ndata = data.with_format(\"torch\")\r\nloader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)\r\n\r\n# v-- the code should freeze forever at this line\r\nfor i, row in enumerate(loader):\r\n  print(row)\r\n  if i > 10: break\r\nprint(\"DONE!\")\r\n```\r\n\r\n## Expected results\r\nThe code should not freeze forever with num_workers=2\r\n\r\n## Actual results\r\nThe code freezes forever with num_workers=2\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0 (also found in previous versions)\r\n- Platform: google colab (also locally)\r\n- Python version: 3.7, (also 3.8)\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":43,"text":"Streaming with num_workers != 0 \n ## Describe the bug\r\nWhen using dataset streaming with pytorch DataLoader, the setting num_workers to anything other than 0 causes the code to freeze forever before yielding the first batch.\r\n\r\nThe code owner is likely @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n\r\nFor your convenience, we've prepped a colab notebook that reproduces the bug\r\nhttps:\/\/colab.research.google.com\/drive\/1Mgl0oTZSNIE3UeGl_oX9wPCOIxRg19h1?usp=sharing\r\n```python\r\n!pip install datasets==1.14.0\r\n\r\nshould_freeze_forever = True\r\n# ^-- set this to True in order to freeze forever, set to False in order to work normally\r\n\r\nimport torch\r\nfrom datasets import load_dataset\r\n\r\ndata = load_dataset(\"oscar\", \"unshuffled_deduplicated_bn\", split=\"train\", streaming=True)\r\ndata = data.map(lambda x: {\"text\": x[\"text\"], \"orig\": f\"oscar[{x['id']}]\"}, batched=True)\r\ndata = data.shuffle(100, seed=1337)\r\n\r\ndata = data.with_format(\"torch\")\r\nloader = torch.utils.data.DataLoader(data, batch_size=2, num_workers=2 if should_freeze_forever else 0)\r\n\r\n# v-- the code should freeze forever at this line\r\nfor i, row in enumerate(loader):\r\n  print(row)\r\n  if i > 10: break\r\nprint(\"DONE!\")\r\n```\r\n\r\n## Expected results\r\nThe code should not freeze forever with num_workers=2\r\n\r\n## Actual results\r\nThe code freezes forever with num_workers=2\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0 (also found in previous versions)\r\n- Platform: google colab (also locally)\r\n- Python version: 3.7, (also 3.8)\r\n- PyArrow version: 3.0.0\r\n\r\n \n Hi ! Thanks for the insights :) Note that in streaming mode there're usually no arrow files. The data are streamed from TAR, ZIP, text, etc. files directly from the web. Though for sharded datasets we can definitely adopt a similar strategy !","embeddings":[-0.4143067896,0.0090086041,-0.0575313233,0.0904793888,-0.088911362,-0.2557559609,0.5883558393,0.1792390347,-0.0135438479,0.4365208447,0.1794317961,0.3351478577,-0.4951831698,-0.0813723877,-0.0375348292,0.0914545804,-0.1148621291,0.1715027243,-0.1394739598,0.1825371087,0.0248708799,0.0596142523,-0.1937873363,-0.3801695704,-0.6788660288,0.1745756119,0.1381022781,0.0978576392,0.1170412824,-0.2365559787,0.1210270077,0.0576245748,-0.1082673073,0.6546927691,-0.0001147963,-0.092971392,0.4668411314,0.1498444527,-0.6293586493,-0.3716201186,0.2061192244,-0.1965381652,0.130331248,0.0720089525,0.080631651,0.1794765294,0.160539344,-0.3033618331,0.1996501237,0.2648699284,0.0913729519,0.4180984497,-0.3160583675,0.2894591987,0.1164905354,-0.1814072579,-0.2618105114,0.2382862121,0.4549101591,-0.0690267384,-0.1946635991,0.2166133523,-0.2756496966,0.0997344479,0.184602797,-0.1018593013,-0.3252633214,-0.6266245842,0.2068677396,0.3251905739,-0.0786996707,-0.0458017997,0.0033466893,-0.4331632555,0.0561903156,-0.3432873785,0.2050534785,0.2637581527,-0.4875221252,-0.1639727354,-0.2828736901,0.2825276554,-0.1118493304,0.305939734,-0.1513921171,0.3185962439,-0.1385757923,0.0005079497,0.2005148232,0.0113753518,0.6334400177,-0.2142485827,0.0530544743,0.0092937499,-0.3408907354,0.1763084382,-0.0089910356,0.1700001955,-0.0646438226,-0.2631527185,0.3998757303,0.1602894664,0.3048624992,0.2122563124,0.2396824956,-0.1929454654,-0.0461905897,-0.1478025466,0.5269643664,0.1415031701,0.0365634225,0.168983236,0.042615734,-0.3580476642,0.2679621875,0.0419395752,0.3315667808,-0.0586193614,-0.0768562779,0.0756916404,-0.227569297,0.0579706728,0.2504349649,0.1838665754,-0.3236344755,0.3339579105,-0.1074472815,-0.140632689,-0.474896878,-0.1831724495,-0.1523777992,-0.1975597888,-0.019332964,-0.0348582193,0.5200933218,-0.5533132553,0.2654011548,0.1034350544,0.5184746385,0.1459904164,0.3025617599,-0.2561679482,0.0899208263,0.3293526769,-0.0075100204,0.2073692977,-0.1046175435,0.1487017572,0.0545301028,0.6070765853,-0.008047088,-0.1558095217,-0.0818992183,0.1823070347,-0.3328891397,0.0685565397,-0.2411823571,-0.0321272947,0.1160638854,-0.0317029133,0.249590829,-0.3895556629,0.0306856446,-0.0771086589,0.0849686712,0.6202540398,-0.1003903449,-0.0281969979,0.1377502382,0.076352194,0.3365947604,0.0259391796,-0.2726678848,-0.3834512532,-0.0890697613,0.1444645673,0.1197381765,0.2711753547,-0.3187184632,0.2208511829,-0.2486623377,0.172434628,0.2601599097,0.4975770712,0.409173131,-0.2063548267,0.421438247,0.2876707613,-0.1153914854,0.2442931384,-0.4581514895,-0.0271951221,-0.0113342069,0.3150866032,-0.0444306768,-0.1054886431,-0.125816375,-0.0382357277,-0.0031273859,-0.0961135402,0.0445443317,0.1447230577,-0.0260809679,0.1086728647,-0.0691126436,-0.1770243049,-0.5775411129,0.2097817361,0.1999306828,0.0893740207,-0.1363966316,0.0704863444,0.192343384,-0.0211656615,-0.1745738983,-0.1653850675,-0.0044731689,-0.0495896488,-0.0737673193,0.0889715254,-0.1652091593,0.5554657578,-0.1474585235,-0.0204413459,-0.3419874609,0.3482140303,-0.0287830029,-0.4959204495,-0.1895937324,-0.0714169964,0.0221485868,0.0098657263,-0.0854224041,0.3253193796,-0.0975778326,0.1105832309,-0.4619435966,0.0523654073,0.3082005382,-0.0191839207,0.1700605899,0.3610631526,0.0606975593,0.0100711277,0.0454659723,0.238169834,-0.4133294821,0.1323227435,-0.0751530826,-0.2135985792,-0.0857460648,0.1516050547,-0.1772001684,0.2870028913,0.5555788279,-0.1620020717,0.2082915157,0.1518478245,-0.3982335627,0.3277072012,0.1472977847,0.0122053577,-0.0027638553,-0.0501570143,-0.3261628449,-0.1041030362,0.1548923999,0.1228175014,0.3184901476,0.2007983029,0.0579126142,-0.0880295858,-0.1592079103,-0.4354149699,0.2463444322,0.1956181377,-0.0711938366,0.2082786858,0.0620087832,-0.1298514158,-0.2751624882,-0.3336796761,-0.0275427345,0.3139257133,-0.3498507142,0.2225510329,-0.1220412776,-0.0283671077,-0.1635342836,-0.1026950777,-0.3984391391,-0.3401980102,-0.0692667067,0.8024847507,-0.1296646893,0.0333677083,-0.318153441,-0.1062951386,0.0851158798,0.1017463058,-0.3761912584,-0.0842927769,-0.2030498236,-0.0457857214,0.3303076327,-0.0897168666,0.1587561518,0.1305692196,-0.3298098743,-0.1763086915,0.0236710683,-0.0078850361,0.0500125028,0.3111427724,-0.0102406852,0.4574684203,-0.136262536,-0.1045806259,0.013481278,-0.2362448275,-0.0389205888,0.2542268038,0.0406496115,0.3545817733,-0.2941040993,-0.1162615642,-0.208410427,-0.3543187082,-0.0911517739,-0.2220822126,0.1822967678,0.0695010424,0.4158731699,0.2884176075,0.1328886598,-0.2431596965,0.0427383147,-0.4045704007,0.6566904187,0.0090980735,-0.2190770507,0.0402334109,-0.066742152,-0.4168599248,0.2120265812,-0.2094388753,-0.0328240208,-0.2816250026,0.2073866427,-0.0746820569,-0.06842269,0.4505012631,0.2071974725,-0.1341877878,-0.0577157363,0.0076994421,-0.1333267689,-0.0337151773,0.0919505581,0.0036397572,0.21492441,0.0697267354,0.817630589,-0.156683892,-0.1846788824,0.1381716579,0.0541654564,0.0395319425,0.1300270855,-0.2791482508,0.1246860027,-0.000877305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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3145","title":"[when Image type will exist] provide a way to get the data as binary + filename","comments":"@severo I'll keep that in mind.\r\n\r\nYou can track progress on the Image feature in #3163 (still in the early stage).  ","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nWhen a dataset cell contains a value of type Image (be it from a remote URL, an Array2D\/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.\r\n\r\nNote: this issue would apply exactly the same for the `Audio` type.\r\n\r\n**Describe the solution you'd like**\r\n\r\nIf a \"cell\" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:\r\n\r\n```python\r\n  filename: str\r\n  data: bytes\r\n```\r\n\r\n**Describe alternatives you've considered**\r\n\r\nA way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.\r\n","comment_length":21,"text":"[when Image type will exist] provide a way to get the data as binary + filename \n **Is your feature request related to a problem? Please describe.**\r\n\r\nWhen a dataset cell contains a value of type Image (be it from a remote URL, an Array2D\/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.\r\n\r\nNote: this issue would apply exactly the same for the `Audio` type.\r\n\r\n**Describe the solution you'd like**\r\n\r\nIf a \"cell\" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:\r\n\r\n```python\r\n  filename: str\r\n  data: bytes\r\n```\r\n\r\n**Describe alternatives you've considered**\r\n\r\nA way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.\r\n \n @severo I'll keep that in mind.\r\n\r\nYou can track progress on the Image feature in #3163 (still in the early stage).  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3145","title":"[when Image type will exist] provide a way to get the data as binary + filename","comments":"Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the dataset viewer doesn't need to disable decoding after all","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nWhen a dataset cell contains a value of type Image (be it from a remote URL, an Array2D\/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.\r\n\r\nNote: this issue would apply exactly the same for the `Audio` type.\r\n\r\n**Describe the solution you'd like**\r\n\r\nIf a \"cell\" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:\r\n\r\n```python\r\n  filename: str\r\n  data: bytes\r\n```\r\n\r\n**Describe alternatives you've considered**\r\n\r\nA way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.\r\n","comment_length":30,"text":"[when Image type will exist] provide a way to get the data as binary + filename \n **Is your feature request related to a problem? Please describe.**\r\n\r\nWhen a dataset cell contains a value of type Image (be it from a remote URL, an Array2D\/3D, or any other way to represent images), I want to be able to write the image to the disk, with the correct filename, and optionally to know its mimetype, in order to serve it on the web.\r\n\r\nNote: this issue would apply exactly the same for the `Audio` type.\r\n\r\n**Describe the solution you'd like**\r\n\r\nIf a \"cell\" has the type `Image`, provide a way to get the binary content of the file, and the filename, eg as:\r\n\r\n```python\r\n  filename: str\r\n  data: bytes\r\n```\r\n\r\n**Describe alternatives you've considered**\r\n\r\nA way to write the cell to the disk (passing a local directory), and then return the pathname, filename, and mimetype.\r\n \n Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the dataset viewer doesn't need to disable decoding after 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3142","title":"Provide a way to write a streamed dataset to the disk","comments":"Yes, I agree this feature is much needed. We could do something similar to what TF does (https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/data\/Dataset#cache). \r\n\r\nIdeally, if the entire streamed dataset is consumed\/cached, the generated cache should be reusable for the Arrow dataset.","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nThe streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.\r\n\r\n**Describe the solution you'd like**\r\n\r\nProvide a way to write the streamed rows of a dataset on the disk, and to load from it later.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nProvide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data. \r\n","comment_length":36,"text":"Provide a way to write a streamed dataset to the disk \n **Is your feature request related to a problem? Please describe.**\r\n\r\nThe streaming mode allows to get the 100 first rows of a dataset very quickly. But it does not cache the answer, so a posterior call to get the same 100 rows will send a request to the server again and again.\r\n\r\n**Describe the solution you'd like**\r\n\r\nProvide a way to write the streamed rows of a dataset on the disk, and to load from it later.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nProvide a third mode: `lazy`, which would use the local cache for the data that have already been fetched previously, and use streaming to get the rest of the requested data. \r\n \n Yes, I agree this feature is much needed. We could do something similar to what TF does (https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/data\/Dataset#cache). \r\n\r\nIdeally, if the entire streamed dataset is consumed\/cached, the generated cache should be reusable for the Arrow dataset.","embeddings":[-0.2683078051,-0.3177527189,-0.1327392608,-0.0794552714,-0.0398469642,-0.0026228279,0.1889888048,0.4413464367,0.2273311019,0.2164763659,0.0959097072,0.2253637463,-0.082694605,0.0931222141,0.3210359216,-0.1213707849,-0.1736004353,0.3577263057,-0.0479810946,-0.1806283891,0.0291440058,-0.3862545788,0.1405440569,-0.348553896,-0.1995935142,-0.2212200314,-0.0105794547,-0.1523149312,0.1954342425,-0.1705537587,0.2767568231,0.1031696945,0.1600535363,0.183074683,-0.0001072699,-0.0706434995,0.129365474,-0.1187411547,-0.434499234,-0.0045738579,-0.2073056847,-0.1697868705,0.2750028372,-0.5239251852,-0.1582238525,-0.0396628641,0.1302939206,-0.2445212007,0.5526272058,0.1529222727,0.1737425327,0.4968924224,-0.1920740306,0.1887398809,-0.0049222163,0.2274743617,-0.3571186364,0.1248469204,0.3581561744,0.1704996079,-0.1059932038,0.4844270349,-0.0071469275,0.2589959502,0.3585144877,0.185296014,0.0815429613,-0.19863455,0.0081557017,0.1857807189,0.6061736941,-0.1190698594,-0.4739542902,-0.4949228764,-0.0184712876,-0.4900220037,0.068787083,0.2698987424,-0.4480802417,0.196088776,0.0659932494,-0.3207785785,-0.2918585241,0.1155638173,-0.0041976268,0.0065355957,0.0378386267,-0.0921428874,0.1441111863,0.1816064715,0.5019687414,-0.0369845554,0.2983961403,0.2413528413,-0.517768383,-0.252982229,0.0622552522,-0.3458671272,0.0137280189,0.2609600723,0.6722894311,0.1488690376,-0.1981077939,0.119910486,0.0425218306,0.1644453853,-0.1212974712,-0.1763976812,0.0676590204,-0.0804085657,0.0476211756,-0.0673368797,-0.0224535707,0.2799984217,0.493603617,0.0034186493,0.0900226235,0.0618773438,0.1057713255,0.1763333082,0.0256082732,-0.0217121262,-0.0932750404,0.126025781,-0.0151038449,0.3739366233,0.0747174099,-0.2110420018,-0.1149776205,-0.048276294,-0.0875908509,-0.2752632499,0.1445355862,0.2371526808,0.2353582531,-0.3091238141,0.1815153062,-0.2493493259,0.1179906502,-0.0416967273,0.4315355122,-0.3178493679,0.2423471063,0.3134660721,-0.2034629136,0.0242162477,0.0240955818,0.2765097022,-0.2126499563,0.4340803325,-0.1035418808,-0.50426507,0.1037561223,0.1871642768,0.1037146524,-0.0193237029,-0.1993616968,0.2268260866,-0.0244353209,0.1046940982,0.0963672251,0.1462945938,-0.4315117598,-0.2111319602,0.1100612655,0.1109121144,-0.4432621002,0.0041889986,0.0017448344,0.2692562938,-0.0367849953,0.3546657264,-0.2817357183,0.1523495615,0.0109763704,0.0449820645,0.5064898133,-0.1593227834,-0.4188682437,0.1452448368,-0.1723188907,0.1169995442,0.1757153869,0.3481389582,0.3208596408,0.0505655333,0.2220486701,0.3800950348,-0.136855796,0.0586428978,-0.2918034494,-0.3282575607,-0.2300407737,0.4149032831,-0.1843444556,-0.0608908869,-0.0698981881,-0.2912910283,0.3415612876,-0.1833444089,0.1497200727,0.0460674018,0.2277943045,0.1293726563,-0.1079795957,-0.0818063319,-0.5282505751,0.1749492735,0.199638024,-0.4244826734,0.1597496569,-0.2424968034,-0.0144710606,0.1066239998,0.1538407207,0.1307675689,0.0648353025,-0.0484942235,0.103641808,0.1303360611,-0.4630797803,0.1945945472,-0.3924369514,-0.1429431289,-0.0664398521,0.1627307981,0.2655311525,-0.0953738764,-0.1092800722,-0.0985180065,-0.1110353395,0.0359240845,-0.061273776,0.045283217,-0.1469905227,0.3863787055,-0.2278850079,0.4562308788,0.1903046221,0.1191329211,0.0179455485,0.1497143656,0.0499428026,-0.1514408588,-0.4336445928,0.309402585,-0.1195782721,0.0985242203,-0.0872955024,-0.2024530768,0.1542353481,0.0180869605,-0.2225313336,-0.0637943,0.2895719111,-0.2090573609,0.1230714098,-0.1422940344,-0.3379748464,0.2734129429,0.4200360179,-0.0529952683,0.2930787802,0.136373803,0.1665653437,-0.0542297587,0.2733798623,0.1897316128,0.2981443703,0.2536485791,0.1835673153,-0.0466220081,0.2052661926,-0.0987638831,0.1878378838,-0.1489667296,0.362362355,0.1661680937,0.0737799928,-0.13697128,-0.2217129171,0.1104893237,0.1804289818,0.0806246772,0.1076273546,-0.1621623635,0.0852834433,-0.0914063379,-0.1550440043,-0.0840477422,0.2487413436,-0.0035627875,0.0370724946,0.560739398,-0.2518762052,0.0767617002,-0.2095046043,0.5090606809,0.191510275,-0.1071185693,-0.324341327,-0.1709412187,0.2365398556,0.0472975634,-0.1677060127,-0.1900826544,0.6288195252,0.1484630406,0.1635990143,-0.3681810796,0.0079361629,0.0484101698,0.2001388669,-0.1296309233,-0.2176757902,0.4395520687,-0.0959917381,-0.1948433965,0.0149049517,-0.3012704849,-0.0332006104,-0.25383389,0.2184496224,0.3224828243,-0.1601122022,-0.1852590293,-0.3129262328,-0.5129247308,0.3237226903,-0.0443505235,0.4395585358,0.1697692573,0.0076361052,0.0593201853,0.2342262566,-0.0846809298,-0.1504049301,-0.4503547251,0.291749686,-0.1694887727,-0.1870311648,-0.13103728,0.0108885877,-0.039290458,0.4408939183,-0.5197466016,-0.3249295056,-0.1160458252,0.2834420204,-0.456407696,-0.1526499838,0.3529306352,-0.135788992,-0.0644845366,-0.1135522723,0.0936567932,-0.0465529598,0.2807445526,-0.1383016706,0.2033104748,0.3375997841,0.3140206039,0.7874845862,-0.0598836206,0.0002463713,0.0475400276,0.2468037009,-0.0978813395,-0.2131865621,-0.2709493637,-0.0174795464,-0.1852563024,-0.24441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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3135","title":"Make inspect.get_dataset_config_names always return a non-empty list of configs","comments":"Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nCurrently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to\r\n\r\n**Describe the solution you'd like**\r\n\r\nIn that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check\/region_1`).\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af\/src\/datasets\/inspect.py#L161\r\n","comment_length":43,"text":"Make inspect.get_dataset_config_names always return a non-empty list of configs \n **Is your feature request related to a problem? Please describe.**\r\n\r\nCurrently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to\r\n\r\n**Describe the solution you'd like**\r\n\r\nIn that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check\/region_1`).\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af\/src\/datasets\/inspect.py#L161\r\n \n Hi @severo, I guess this issue requests not only to be able to access the configuration name (by using `inspect.get_dataset_config_names`), but the configuration itself as well (I mean you use the name to get the configuration afterwards, maybe using `builder_cls.builder_configs`), is this right?","embeddings":[-0.1959384233,-0.039393086,-0.086716406,0.2019755691,0.3481402695,0.1019181609,0.2270129472,0.461691916,0.0720913559,0.6261892915,-0.0640387833,0.3278577328,-0.0607229322,0.2241170555,-0.0723212883,0.2225233316,-0.2583372295,0.3215780556,-0.0100070387,0.0468213335,-0.1702592969,-0.2241367102,0.0781604871,-0.0461676344,-0.1003569216,0.1415476352,0.2304190546,-0.1391489357,0.0186756197,-0.6572409868,0.2234815359,0.1989043206,-0.4255686104,0.0693519637,-0.0001110376,0.2244479954,0.3528083861,0.0388477184,-0.5182683468,-0.1933932453,-0.2672390044,-0.4833592474,0.2906863689,-0.1147353724,-0.2835974693,-0.1487339139,0.0870580077,-0.1586876512,-0.173538819,0.0200252235,0.1960031986,0.2004272044,-0.2426570803,-0.0933089182,0.0630540028,0.277669698,-0.2956315875,-0.1127577052,0.0804485008,0.1904269159,0.154870674,0.3677093685,-0.1610150039,-0.0107884305,0.0748035982,-0.0822514966,0.0687064156,-0.3162957728,0.3752181828,0.565238297,0.5786011219,-0.21987997,-0.2005219907,-0.4037021399,0.2033671886,-0.0489761122,0.1670882255,0.1474890709,-0.3679387867,0.2678747475,-0.1129504889,-0.48393929,-0.1116797701,-0.1416140497,-0.0734359175,0.2190535665,-0.0441842973,0.0945545807,-0.0024599098,-0.1388072371,0.1994498223,-0.3203270733,-0.1752383411,0.0893554613,-0.4130401015,-0.0658878163,0.3217000961,0.0640344173,0.2501985729,0.5676413178,-0.1647889465,0.0838268846,-0.1348368526,0.0246589556,-0.0299826618,-0.1104093641,0.5471462011,0.45391348,0.1456599981,-0.1224309877,0.168057844,-0.0307406485,0.0272639859,0.0481356531,-0.058544565,0.0205425434,0.5758944154,-0.1331561655,0.0193219315,0.3181627393,0.0365128443,0.008971232,0.2629740834,0.478657335,-0.0632589385,0.3437306285,-0.0169642232,-0.1319732219,0.031527698,-0.1304221004,-0.2280666828,-0.3893209994,0.0188858435,0.2144661844,0.1009576693,-0.2881239057,0.3406375051,0.0537812598,0.0736508518,-0.0585598536,0.2391284257,-0.0543665513,0.2546288669,0.3422959149,-0.1886843592,-0.0615912527,0.2175421417,-0.2652130723,-0.3602988422,0.0847412273,-0.1007121205,-0.4504194856,-0.0330600739,0.2018903047,-0.247514531,0.3033879399,0.194542855,0.1195167825,-0.1528192163,0.2279151976,-0.0312416721,0.038487684,0.1562954634,-0.107570447,0.2217018455,0.4701797366,-0.1791074872,-0.0722725913,0.1227691397,-0.3365766704,-0.3208185732,-0.2323925197,-0.16432181,-0.0216580797,-0.2443053573,-0.2132276446,0.5509496331,-0.2235957533,-0.0414219052,0.2092016041,-0.2034505308,0.0148414103,0.2633711696,0.0366299078,-0.3152832687,0.2129547,-0.0748643801,-0.152221337,-0.0655175522,-0.010319775,-0.0408627763,-0.2560619414,0.1272127926,-0.1339309514,-0.1146780327,0.2541871667,-0.1153529361,-0.3087101281,0.3998308778,-0.2179086059,-0.1138052121,0.2828002572,0.6059144139,0.3508161604,0.0376675278,-0.1276495606,-0.4507604539,0.2771319747,0.0476992987,-0.0231503639,0.0218216665,-0.4317792058,-0.0325504169,0.1522016823,-0.1756982952,-0.0482311174,0.1161221787,0.3900378048,-0.2242332101,-0.2545667887,-0.1897058934,0.0864796937,-0.3799450397,0.1254774481,-0.3186782598,0.084125638,0.2545931935,0.3039532304,-0.2960426211,0.0332568809,0.11298953,0.0411812067,0.0975852758,0.2061693221,0.2992254794,-0.0798445195,0.174434498,0.2657062113,-0.1008226797,-0.1726621985,0.0452646501,0.0393186584,0.0339248925,-0.1151458323,-0.1918477267,0.3620772362,0.3134702146,0.5693539381,-0.1884537488,0.0209852308,0.1558391005,-0.1732034385,-0.5035433769,-0.2430479228,-0.085372813,0.1035384461,0.2578534186,0.1520322561,-0.5671442151,-0.1849046052,0.2030064762,0.1621283442,-0.1709274352,-0.0647311136,0.204336226,0.0766700581,0.0494499095,-0.0664176792,0.1643402427,0.321397692,-0.1490915269,0.1652550399,-0.067321077,0.073981449,0.2737995386,0.0591498576,0.1516791135,0.2020286918,0.0846377313,-0.1541451067,-0.1252696514,-0.3802018464,0.0894180536,-0.0079724444,-0.3926845193,-0.035403613,-0.2151961774,-0.132676214,-0.0969560668,0.0710726902,-0.3255751431,-0.1928885728,-0.0591352843,-0.006001519,-0.2963115573,0.4112299383,-0.4222435057,0.4480034411,0.1499307454,-0.3696016073,-0.1088932529,0.0597083084,-0.0689574033,-0.0161506385,-0.1823832095,-0.2994285226,0.4465332031,-0.0831150562,-0.1522802562,-0.4172019958,-0.2782405615,0.3350115716,0.0945878103,0.2299931496,0.4732413888,0.2531484365,0.4345406592,-0.1736396998,0.2177949101,0.4027148187,-0.068419382,0.0129048107,0.1353895217,0.0839028656,-0.1290623546,-0.1474435627,0.0411922447,-0.3457290232,0.2438776642,0.0456944741,0.3258518875,0.608130157,-0.1407056302,0.0638407469,-0.3629420698,0.2904008031,-0.1566164941,-0.2963267863,0.0692306012,-0.0037670513,0.0485049784,-0.0760870278,-0.2665855587,0.1629572064,-0.0278858654,-0.4113143384,-0.2560601234,-0.204739064,0.3191523552,0.1252966672,0.1132084578,0.0726670697,0.2248085588,-0.1096810997,-0.001605352,-0.5240080357,0.1263992488,-0.0700512156,0.3815968633,0.2243554741,-0.2458628714,0.0379536264,0.314381361,0.3325382471,0.1412073076,-0.1166588366,-0.3768304884,0.6222693324,-0.0226900671,-0.4071136117,-0.0999524668,0.3115707934,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3135","title":"Make inspect.get_dataset_config_names always return a non-empty list of configs","comments":"Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:\r\n- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).\r\n- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check\/region_1`)","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nCurrently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to\r\n\r\n**Describe the solution you'd like**\r\n\r\nIn that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check\/region_1`).\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af\/src\/datasets\/inspect.py#L161\r\n","comment_length":71,"text":"Make inspect.get_dataset_config_names always return a non-empty list of configs \n **Is your feature request related to a problem? Please describe.**\r\n\r\nCurrently, some datasets have a configuration, while others don't. It would be simpler for the user to always have configuration names to refer to\r\n\r\n**Describe the solution you'd like**\r\n\r\nIn that sense inspect.get_dataset_config_names should always return at least one configuration name, be it `default` or `Check___region_1` (for community datasets like `Check\/region_1`).\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c5747a5e1dde2670b7f2ca6e79e2ffd99dff85af\/src\/datasets\/inspect.py#L161\r\n \n Yes, maybe the issue could be reformulated. As a user, I want to avoid having to manage special cases:\r\n- I want to be able to get the names of a dataset's configs, and use them in the rest of the API (get the data, get the split names, etc).\r\n- I don't want to have to manage datasets with named configs (`glue`) differently from datasets without named configs (`acronym_identification`, `Check\/region_1`)","embeddings":[-0.1433714479,-0.0727250203,-0.0968914703,0.1169705018,0.2310527563,0.1749295294,0.1810451001,0.4647791088,0.2811766565,0.4464251101,-0.1508253813,0.3579241037,-0.0074268258,0.2346330583,-0.1006171554,0.1920577735,-0.2181449831,0.3768829703,0.0402733088,-0.0437977314,-0.1790204346,-0.1275996715,0.1375390887,-0.1373451948,0.0585947819,0.0190024897,0.1934047341,-0.1649044156,0.0602194443,-0.5103995204,0.275187254,0.1749853194,-0.4080824554,-0.098295182,-0.0001147764,0.1454396844,0.3931483626,0.0295219645,-0.2994023561,-0.2757667303,-0.2195849121,-0.4347120523,0.3364683986,-0.0902142674,-0.1970413029,-0.267999053,0.0424465761,-0.3975795805,-0.0429605916,-0.0228175726,0.1328009516,0.124713704,-0.2763917446,-0.0263262,-0.0710506365,0.2565395534,-0.2219058275,-0.1989444345,-0.0270209555,0.0230686348,0.2815820873,0.272718519,-0.249713555,0.0805142298,0.0367143005,-0.1255477667,-0.2886051536,-0.489778161,0.3804355264,0.696700573,0.477411598,-0.2139567137,-0.2099900395,-0.4865967631,0.2095875442,-0.0447107069,0.1629623473,0.24189879,-0.1787386686,0.2783285975,0.0136889517,-0.3687569797,-0.0539654233,-0.0973068327,-0.0343693122,0.149717465,0.011972717,0.0801065043,0.142380476,-0.1292158067,0.0451950766,-0.3058245182,-0.219697848,0.1384254992,-0.4764614105,-0.1456150115,0.2492707074,0.0765425339,0.3076213598,0.6439833641,-0.1088594124,0.1659890413,-0.1020130292,0.0376433767,-0.0155918933,-0.1399624348,0.6405239105,0.4763416648,0.1353594065,-0.2433071733,0.3037060499,-0.0080176098,-0.0228333343,0.190676108,-0.1720104516,-0.0456799604,0.4999154806,-0.1936784536,0.0495912954,0.2485846281,-0.0847657397,0.073144339,0.0992767513,0.3354879022,-0.0468668155,0.3651702702,-0.0309498124,-0.0105231628,0.0860726982,-0.1213569492,-0.1734200567,-0.2953152955,0.2703095973,0.3748980463,0.1186389774,-0.1560012251,0.338460952,0.0228795558,-0.0494391918,0.0805170909,0.4005951285,-0.1108885482,0.2407984883,0.3809286058,-0.0227739476,0.06550017,0.1970492154,-0.3520115316,-0.5092692375,0.1010978147,-0.1016530618,-0.3922618032,-0.0458398648,0.1580730826,-0.461869657,0.290248841,0.0820924118,0.1887720078,-0.2089480758,0.2367161065,-0.0019098431,0.0967034996,0.1646993905,-0.1576855779,0.1581623256,0.5411083698,-0.1641574353,-0.1867616177,0.0654291287,-0.2275778204,-0.3789492846,-0.1162819713,0.0093351956,-0.0637383536,-0.2095452845,-0.0143288784,0.5601008534,-0.3848939836,-0.0215389915,0.1212667823,-0.0539876446,0.0529679395,0.2674377561,0.0559360534,-0.082318455,0.282117188,-0.0735671446,-0.0496556573,-0.0967447013,-0.0756412074,-0.0400323346,-0.2979436517,0.0648301765,-0.0754033849,-0.2461987585,0.1373710185,-0.1365503669,-0.5016213655,0.3489428759,-0.1242315546,-0.1839516163,0.3089510798,0.616076827,0.3104616106,0.0805870295,-0.1474644095,-0.5678797364,0.235576421,0.1546317637,0.0770225748,0.0135863908,-0.4639113545,0.0194692295,0.0002360453,-0.1342291087,0.0375442952,0.0528414734,0.1781301647,-0.0916892588,-0.089394033,-0.1662817001,0.2143174857,-0.2517029345,0.2234838158,-0.4982383549,0.1455301493,0.4295929372,0.3374190629,-0.1703814268,0.0356957689,0.121431075,0.0012094672,0.1707779467,0.1362569928,0.4226947129,-0.0479407609,0.1528661102,0.4331666231,-0.0521639176,-0.1085044071,0.0241043903,0.0039144903,0.0095844055,-0.1314717233,-0.2157047838,0.4401795566,0.3573460281,0.6186777353,-0.2321943939,0.0959280506,0.1745720506,-0.1923827231,-0.5609423518,-0.2448148429,-0.082401447,0.026682945,0.3762546778,0.1373793334,-0.4646809101,-0.1517627686,0.1965176165,0.112081036,-0.1041244492,-0.0948176458,0.0521226414,0.0873310193,0.1184345931,0.0826083347,0.2212688178,0.3244652748,-0.1226682961,0.1137938723,0.0025885452,0.0728863105,0.283475697,0.0507215187,0.1389396042,0.060694702,0.0183994323,-0.1019578651,-0.1506782174,-0.4149255157,-0.0263536498,-0.0393612683,-0.3869342208,-0.1196660399,-0.278862536,-0.2356785685,-0.0043059881,0.002943058,-0.3053967953,-0.2297122031,-0.0842821598,-0.0404859558,-0.3021886051,0.452585876,-0.334528327,0.4767783582,0.0723965913,-0.433542788,-0.1751534939,0.0997809991,-0.0578094721,-0.0404256806,-0.1687264442,-0.1898216158,0.5054405928,-0.0038826864,-0.1361233443,-0.3728593886,-0.346255064,0.282535851,0.0931028873,0.25808236,0.516064465,0.2449319363,0.4300899208,-0.1194875836,0.1513722688,0.3626991808,-0.034310177,-0.022616921,0.143134281,0.109689422,-0.1567588896,-0.15690808,-0.0054104854,-0.2816168368,0.410582006,0.0528377593,0.3476487994,0.533885777,-0.2079223543,-0.0055794944,-0.3637748659,0.1124836206,-0.0825155601,-0.2567253411,0.2289350033,0.0574575812,0.0772296786,-0.0649041086,-0.3172123134,0.206775561,-0.0296609905,-0.3859019876,-0.4528616965,-0.1999424994,0.2624132931,0.0676219463,0.1735281795,0.0938236341,0.2409356236,-0.0256587695,0.0029372633,-0.605063796,0.2089140415,-0.0440758243,0.1887290776,0.2837440968,-0.2053195089,-0.1013536677,0.3613682985,0.307852447,0.1235211343,-0.1117342934,-0.3102417588,0.6340025067,0.041168876,-0.3187806308,-0.2290784866,0.2787203789,-0.117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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3134","title":"Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py","comments":"Hi,\r\n\r\nDid you try to run the code multiple times (GitHub URLs can be down sometimes for various reasons)? I can access `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py`, so this code is working without an error on my side. \r\n\r\nAdditionally, can you please run the `datasets-cli env` command because it seems to me that you are using the `datasets` version different from `1.12.1`?","body":"datasets version: 1.12.1\r\n\r\n`metric = datasets.load_metric('rouge')`\r\n\r\nThe error:\r\n\r\n> ConnectionError                           Traceback (most recent call last)\r\n> <ipython-input-3-dd10a0c5212f> in <module>\r\n> ----> 1 metric = datasets.load_metric('rouge')\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)\r\n>     613         download_config=download_config,\r\n>     614         download_mode=download_mode,\r\n> --> 615         dataset=False,\r\n>     616     )\r\n>     617     metric_cls = import_main_class(module_path, dataset=False)\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)\r\n>     328                 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n>     329                 try:\r\n> --> 330                     local_path = cached_path(file_path, download_config=download_config)\r\n>     331                 except FileNotFoundError:\r\n>     332                     if script_version is not None:\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n>     296             use_etag=download_config.use_etag,\r\n>     297             max_retries=download_config.max_retries,\r\n> --> 298             use_auth_token=download_config.use_auth_token,\r\n>     299         )\r\n>     300     elif os.path.exists(url_or_filename):\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n>     603             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n>     604         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n> --> 605         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n>     606\r\n>     607     # Try a second time\r\n> \r\n> ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py\r\n\r\n\r\nIs there any remedy to solve the connection issue ?","comment_length":58,"text":"Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py \n datasets version: 1.12.1\r\n\r\n`metric = datasets.load_metric('rouge')`\r\n\r\nThe error:\r\n\r\n> ConnectionError                           Traceback (most recent call last)\r\n> <ipython-input-3-dd10a0c5212f> in <module>\r\n> ----> 1 metric = datasets.load_metric('rouge')\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)\r\n>     613         download_config=download_config,\r\n>     614         download_mode=download_mode,\r\n> --> 615         dataset=False,\r\n>     616     )\r\n>     617     metric_cls = import_main_class(module_path, dataset=False)\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)\r\n>     328                 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n>     329                 try:\r\n> --> 330                     local_path = cached_path(file_path, download_config=download_config)\r\n>     331                 except FileNotFoundError:\r\n>     332                     if script_version is not None:\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n>     296             use_etag=download_config.use_etag,\r\n>     297             max_retries=download_config.max_retries,\r\n> --> 298             use_auth_token=download_config.use_auth_token,\r\n>     299         )\r\n>     300     elif os.path.exists(url_or_filename):\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n>     603             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n>     604         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n> --> 605         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n>     606\r\n>     607     # Try a second time\r\n> \r\n> ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py\r\n\r\n\r\nIs there any remedy to solve the connection issue ? \n Hi,\r\n\r\nDid you try to run the code multiple times (GitHub URLs can be down sometimes for various reasons)? I can access `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py`, so this code is working without an error on my side. \r\n\r\nAdditionally, can you please run the `datasets-cli env` command because it seems to me that you are using the `datasets` version different from `1.12.1`?","embeddings":[-0.4028955698,-0.091716215,-0.1140874401,0.2032690793,0.2335223407,-0.0791541114,0.0977335498,0.4012298286,0.1732944548,0.292118609,-0.3156027198,-0.0292550325,0.0664938241,0.0566061288,0.1343692392,-0.0982900336,-0.1961651742,-0.0955756232,-0.2617654502,0.1735692769,-0.1562049985,0.2479441613,-0.0610231385,0.1988897175,0.0647327602,-0.0930981264,0.1221594959,0.2962450087,-0.4615675211,-0.4462738335,0.3729447424,0.0537409447,0.1342707425,0.5007219911,-0.0001111456,0.2051056623,0.3346630037,-0.0609423667,-0.4902224541,-0.6396036148,-0.239111349,-0.1106907502,0.1620517671,-0.0708997622,-0.1534717381,0.2025640309,0.0105373971,-0.2176420838,0.4202255905,0.5358589292,0.2260995358,0.5080780387,0.3908087015,-0.2263844609,0.1627917141,-0.1687018275,-0.0075963419,0.8425220251,0.0675038621,-0.0139086135,0.0614181124,0.2156310529,0.1526988894,0.2065245062,0.3073361814,-0.1842178851,0.3627560437,-0.1989940852,-0.0022881888,0.0231645145,0.1620704234,-0.2893258333,-0.4648668766,0.1519877166,-0.0389807299,-0.6075189114,0.2533769906,-0.0769177079,-0.2029350102,0.0204769392,-0.3510054648,-0.318610847,-0.3203493059,0.1770494431,-0.0430527925,-0.0150429821,-0.2803620398,0.1023681387,0.1011376455,-0.1008139849,-0.2903202772,0.1227265075,-0.1664374471,0.211817041,-0.1926621795,0.0178747158,0.0295450762,-0.1235276684,0.2105481625,0.2714715898,-0.0309210028,-0.0044636666,0.1105521694,0.1100028902,0.0323088355,0.1711502373,0.0010292578,0.2829097509,0.2745903134,0.6173326969,0.0732795447,-0.0066845203,-0.1303241253,-0.3659859598,-0.0657290518,0.2089111507,0.4930972159,-0.2139486223,-0.2350700945,0.1885440648,-0.0584999844,-0.1023369804,0.0419672616,0.2563569546,-0.2652182579,-0.075281173,0.1136622503,0.1762934327,-0.2488433272,0.1500772238,-0.2428801209,0.1336621046,-0.1552988291,0.1361046731,0.0989214554,-0.1735591441,0.1436371803,-0.1222379282,0.1787065566,-0.2080038935,0.2229410261,0.0121264532,-0.2444592118,0.2979534566,0.0410452038,0.0915408134,0.2260122001,-0.2707825005,-0.110792093,-0.2070261985,-0.3982375562,-0.4079214334,-0.0309666283,0.2210294753,-0.0491231158,0.0621324144,-0.3226237595,-0.1749109924,-0.0211229771,-0.1313864738,-0.1213966012,-0.1584642231,-0.1029095799,-0.1630706936,0.4859734476,0.41819188,0.0717642531,-0.1625388265,0.0906270966,-0.1173116714,0.0385931805,0.1696136296,-0.0856976211,0.0188256893,-0.3086384535,-0.2789125741,0.6501632929,-0.6785372496,-0.4723157883,0.3038990796,-0.1134298891,-0.006939515,0.0896838978,-0.2527339458,0.2923912406,-0.0245885365,0.4318203926,0.0718152374,-0.0383056104,-0.2738701701,-0.1059285477,-0.0947448984,0.0300331339,0.061208576,0.2298381627,0.2225991338,0.1938076615,-0.1399755925,0.2568139136,0.2142125964,-0.0451662503,0.3252111971,0.0714541897,0.1115394086,-0.0805458054,-0.2215158939,-0.0335603505,0.1528939307,-0.226977095,-0.0033852644,-0.2462601066,-0.0065566851,-0.5282768607,0.055067692,0.013122634,-0.1911691576,0.1303531229,0.1321271658,0.1379092932,0.2595019341,-0.1853679121,0.3281309605,-0.2172051668,0.2060409337,-0.3939418495,0.2265941054,-0.2306167036,-0.0846330673,0.2492671758,0.1102450043,0.1850965172,-0.2445450574,-0.1428142637,0.2696176767,0.0781223401,0.119499065,0.3623811901,0.1378722787,0.1654004455,-0.0693063214,-0.0484114885,-0.0910792574,0.0615042783,0.2113356739,0.1499656141,0.1725246757,-0.0836475119,0.2312521935,0.1438015997,0.0934036896,0.2962749004,0.215372473,-0.1921023875,0.0857501179,0.4044250548,0.0632342696,0.251308918,-0.203662768,-0.0695393234,-0.0492584743,0.3880657256,-0.0716116875,-0.0119202081,0.1328728795,0.142764017,-0.1056780443,-0.0279339701,0.0461299941,0.2462921292,0.0275421273,0.1360163838,0.2337549627,-0.1139558926,0.0066711707,0.0820674822,-0.0510199815,0.0019911958,0.2081708312,-0.0959367529,-0.0093752593,-0.0120975729,-0.3281028867,-0.1260832846,0.3014913201,-0.2548196316,0.2159495056,-0.1751920879,0.0687984899,0.1250672489,-0.4654536843,-0.3020919859,-0.2618439496,-0.1894523501,0.3307758868,0.1972889155,0.2216158062,0.155264169,0.0893133432,0.2035491765,-0.1746786833,-0.1663403809,-0.2236740887,-0.0392353125,0.0689444318,0.0742272958,-0.3100693226,0.3720661998,-0.464076221,-0.1145649925,-0.1542761177,-0.1854745597,-0.11623393,-0.0989381149,0.2891538143,0.3395434916,0.4083218873,-0.0036551922,0.1563250273,0.428876549,0.0404865816,-0.1701290607,0.0572603308,-0.1989900917,-0.0159630179,0.0900020748,-0.2380993217,-0.363535434,-0.5015983582,0.2459298372,0.1915348917,0.0858115181,0.2453097999,0.1705062687,0.1764076203,0.1050436422,0.1702939421,-0.1885237545,-0.6819544435,0.2097891271,-0.3668945432,-0.3407714069,0.2215177417,0.2399463356,0.3687411547,0.221523881,-0.5437683463,-0.482924819,0.0638976917,0.3006433547,0.0436028689,-0.0622707009,0.1850144714,-0.1984946281,-0.0342264473,0.0328603275,-0.2459640503,-0.0066251918,-0.0505960248,0.2106594443,0.00885869,0.5591800213,-0.0989438817,0.6215458512,0.2605271935,0.1507215649,0.2617112696,-0.0829316229,0.5427127481,-0.2461127043,-0.3602122366,0.1052059159,-0.0460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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3134","title":"Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py","comments":"Same issue when running `metric = datasets.load_metric(\"accuracy\")`.\r\nError info is:\r\n```\r\nmetric = datasets.load_metric(\"accuracy\")\r\nTraceback (most recent call last):\r\n\r\n  File \"<ipython-input-2-d25db38b26c5>\", line 1, in <module>\r\n    metric = datasets.load_metric(\"accuracy\")\r\n\r\n  File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 610, in load_metric\r\n    module_path, _ = prepare_module(\r\n\r\n  File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 330, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n\r\n  File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 288, in cached_path\r\n    output_path = get_from_cache(\r\n\r\n  File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 605, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/accuracy\/accuracy.py\r\n```\r\n\r\n\r\n My `datasets-cli env` result is as follows:\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n@yananchen1989 did you find a way to solve this?","body":"datasets version: 1.12.1\r\n\r\n`metric = datasets.load_metric('rouge')`\r\n\r\nThe error:\r\n\r\n> ConnectionError                           Traceback (most recent call last)\r\n> <ipython-input-3-dd10a0c5212f> in <module>\r\n> ----> 1 metric = datasets.load_metric('rouge')\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)\r\n>     613         download_config=download_config,\r\n>     614         download_mode=download_mode,\r\n> --> 615         dataset=False,\r\n>     616     )\r\n>     617     metric_cls = import_main_class(module_path, dataset=False)\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)\r\n>     328                 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n>     329                 try:\r\n> --> 330                     local_path = cached_path(file_path, download_config=download_config)\r\n>     331                 except FileNotFoundError:\r\n>     332                     if script_version is not None:\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n>     296             use_etag=download_config.use_etag,\r\n>     297             max_retries=download_config.max_retries,\r\n> --> 298             use_auth_token=download_config.use_auth_token,\r\n>     299         )\r\n>     300     elif os.path.exists(url_or_filename):\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n>     603             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n>     604         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n> --> 605         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n>     606\r\n>     607     # Try a second time\r\n> \r\n> ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py\r\n\r\n\r\nIs there any remedy to solve the connection issue ?","comment_length":103,"text":"Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py \n datasets version: 1.12.1\r\n\r\n`metric = datasets.load_metric('rouge')`\r\n\r\nThe error:\r\n\r\n> ConnectionError                           Traceback (most recent call last)\r\n> <ipython-input-3-dd10a0c5212f> in <module>\r\n> ----> 1 metric = datasets.load_metric('rouge')\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)\r\n>     613         download_config=download_config,\r\n>     614         download_mode=download_mode,\r\n> --> 615         dataset=False,\r\n>     616     )\r\n>     617     metric_cls = import_main_class(module_path, dataset=False)\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)\r\n>     328                 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n>     329                 try:\r\n> --> 330                     local_path = cached_path(file_path, download_config=download_config)\r\n>     331                 except FileNotFoundError:\r\n>     332                     if script_version is not None:\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n>     296             use_etag=download_config.use_etag,\r\n>     297             max_retries=download_config.max_retries,\r\n> --> 298             use_auth_token=download_config.use_auth_token,\r\n>     299         )\r\n>     300     elif os.path.exists(url_or_filename):\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n>     603             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n>     604         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n> --> 605         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n>     606\r\n>     607     # Try a second time\r\n> \r\n> ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py\r\n\r\n\r\nIs there any remedy to solve the connection issue ? \n Same issue when running `metric = datasets.load_metric(\"accuracy\")`.\r\nError info is:\r\n```\r\nmetric = datasets.load_metric(\"accuracy\")\r\nTraceback (most recent call last):\r\n\r\n  File \"<ipython-input-2-d25db38b26c5>\", line 1, in <module>\r\n    metric = datasets.load_metric(\"accuracy\")\r\n\r\n  File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 610, in load_metric\r\n    module_path, _ = prepare_module(\r\n\r\n  File \"D:\\anaconda3\\lib\\site-packages\\datasets\\load.py\", line 330, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n\r\n  File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 288, in cached_path\r\n    output_path = get_from_cache(\r\n\r\n  File \"D:\\anaconda3\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 605, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/accuracy\/accuracy.py\r\n```\r\n\r\n\r\n My `datasets-cli env` result is as follows:\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.8\r\n- PyArrow version: 6.0.0\r\n\r\n@yananchen1989 did you find a way to solve this?","embeddings":[-0.4028955698,-0.091716215,-0.1140874401,0.2032690793,0.2335223407,-0.0791541114,0.0977335498,0.4012298286,0.1732944548,0.292118609,-0.3156027198,-0.0292550325,0.0664938241,0.0566061288,0.1343692392,-0.0982900336,-0.1961651742,-0.0955756232,-0.2617654502,0.1735692769,-0.1562049985,0.2479441613,-0.0610231385,0.1988897175,0.0647327602,-0.0930981264,0.1221594959,0.2962450087,-0.4615675211,-0.4462738335,0.3729447424,0.0537409447,0.1342707425,0.5007219911,-0.0001111456,0.2051056623,0.3346630037,-0.0609423667,-0.4902224541,-0.6396036148,-0.239111349,-0.1106907502,0.1620517671,-0.0708997622,-0.1534717381,0.2025640309,0.0105373971,-0.2176420838,0.4202255905,0.5358589292,0.2260995358,0.5080780387,0.3908087015,-0.2263844609,0.1627917141,-0.1687018275,-0.0075963419,0.8425220251,0.0675038621,-0.0139086135,0.0614181124,0.2156310529,0.1526988894,0.2065245062,0.3073361814,-0.1842178851,0.3627560437,-0.1989940852,-0.0022881888,0.0231645145,0.1620704234,-0.2893258333,-0.4648668766,0.1519877166,-0.0389807299,-0.6075189114,0.2533769906,-0.0769177079,-0.2029350102,0.0204769392,-0.3510054648,-0.318610847,-0.3203493059,0.1770494431,-0.0430527925,-0.0150429821,-0.2803620398,0.1023681387,0.1011376455,-0.1008139849,-0.2903202772,0.1227265075,-0.1664374471,0.211817041,-0.1926621795,0.0178747158,0.0295450762,-0.1235276684,0.2105481625,0.2714715898,-0.0309210028,-0.0044636666,0.1105521694,0.1100028902,0.0323088355,0.1711502373,0.0010292578,0.2829097509,0.2745903134,0.6173326969,0.0732795447,-0.0066845203,-0.1303241253,-0.3659859598,-0.0657290518,0.2089111507,0.4930972159,-0.2139486223,-0.2350700945,0.1885440648,-0.0584999844,-0.1023369804,0.0419672616,0.2563569546,-0.2652182579,-0.075281173,0.1136622503,0.1762934327,-0.2488433272,0.1500772238,-0.2428801209,0.1336621046,-0.1552988291,0.1361046731,0.0989214554,-0.1735591441,0.1436371803,-0.1222379282,0.1787065566,-0.2080038935,0.2229410261,0.0121264532,-0.2444592118,0.2979534566,0.0410452038,0.0915408134,0.2260122001,-0.2707825005,-0.110792093,-0.2070261985,-0.3982375562,-0.4079214334,-0.0309666283,0.2210294753,-0.0491231158,0.0621324144,-0.3226237595,-0.1749109924,-0.0211229771,-0.1313864738,-0.1213966012,-0.1584642231,-0.1029095799,-0.1630706936,0.4859734476,0.41819188,0.0717642531,-0.1625388265,0.0906270966,-0.1173116714,0.0385931805,0.1696136296,-0.0856976211,0.0188256893,-0.3086384535,-0.2789125741,0.6501632929,-0.6785372496,-0.4723157883,0.3038990796,-0.1134298891,-0.006939515,0.0896838978,-0.2527339458,0.2923912406,-0.0245885365,0.4318203926,0.0718152374,-0.0383056104,-0.2738701701,-0.1059285477,-0.0947448984,0.0300331339,0.061208576,0.2298381627,0.2225991338,0.1938076615,-0.1399755925,0.2568139136,0.2142125964,-0.0451662503,0.3252111971,0.0714541897,0.1115394086,-0.0805458054,-0.2215158939,-0.0335603505,0.1528939307,-0.226977095,-0.0033852644,-0.2462601066,-0.0065566851,-0.5282768607,0.055067692,0.013122634,-0.1911691576,0.1303531229,0.1321271658,0.1379092932,0.2595019341,-0.1853679121,0.3281309605,-0.2172051668,0.2060409337,-0.3939418495,0.2265941054,-0.2306167036,-0.0846330673,0.2492671758,0.1102450043,0.1850965172,-0.2445450574,-0.1428142637,0.2696176767,0.0781223401,0.119499065,0.3623811901,0.1378722787,0.1654004455,-0.0693063214,-0.0484114885,-0.0910792574,0.0615042783,0.2113356739,0.1499656141,0.1725246757,-0.0836475119,0.2312521935,0.1438015997,0.0934036896,0.2962749004,0.215372473,-0.1921023875,0.0857501179,0.4044250548,0.0632342696,0.251308918,-0.203662768,-0.0695393234,-0.0492584743,0.3880657256,-0.0716116875,-0.0119202081,0.1328728795,0.142764017,-0.1056780443,-0.0279339701,0.0461299941,0.2462921292,0.0275421273,0.1360163838,0.2337549627,-0.1139558926,0.0066711707,0.0820674822,-0.0510199815,0.0019911958,0.2081708312,-0.0959367529,-0.0093752593,-0.0120975729,-0.3281028867,-0.1260832846,0.3014913201,-0.2548196316,0.2159495056,-0.1751920879,0.0687984899,0.1250672489,-0.4654536843,-0.3020919859,-0.2618439496,-0.1894523501,0.3307758868,0.1972889155,0.2216158062,0.155264169,0.0893133432,0.2035491765,-0.1746786833,-0.1663403809,-0.2236740887,-0.0392353125,0.0689444318,0.0742272958,-0.3100693226,0.3720661998,-0.464076221,-0.1145649925,-0.1542761177,-0.1854745597,-0.11623393,-0.0989381149,0.2891538143,0.3395434916,0.4083218873,-0.0036551922,0.1563250273,0.428876549,0.0404865816,-0.1701290607,0.0572603308,-0.1989900917,-0.0159630179,0.0900020748,-0.2380993217,-0.363535434,-0.5015983582,0.2459298372,0.1915348917,0.0858115181,0.2453097999,0.1705062687,0.1764076203,0.1050436422,0.1702939421,-0.1885237545,-0.6819544435,0.2097891271,-0.3668945432,-0.3407714069,0.2215177417,0.2399463356,0.3687411547,0.221523881,-0.5437683463,-0.482924819,0.0638976917,0.3006433547,0.0436028689,-0.0622707009,0.1850144714,-0.1984946281,-0.0342264473,0.0328603275,-0.2459640503,-0.0066251918,-0.0505960248,0.2106594443,0.00885869,0.5591800213,-0.0989438817,0.6215458512,0.2605271935,0.1507215649,0.2617112696,-0.0829316229,0.5427127481,-0.2461127043,-0.3602122366,0.1052059159,-0.04606219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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3134","title":"Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py","comments":"It seems to be able to solve this issue by adding the equivalent `accuracy.py` locally. \r\nchange `metric = datasets.load_metric(\"accuracy\")` to  `metric = datasets.load_metric(path = \".\/accuracy.py\")`.\r\nCopy `accuracy.py` from browser at [accuracy.py](https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/accuracy\/accuracy.py)","body":"datasets version: 1.12.1\r\n\r\n`metric = datasets.load_metric('rouge')`\r\n\r\nThe error:\r\n\r\n> ConnectionError                           Traceback (most recent call last)\r\n> <ipython-input-3-dd10a0c5212f> in <module>\r\n> ----> 1 metric = datasets.load_metric('rouge')\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)\r\n>     613         download_config=download_config,\r\n>     614         download_mode=download_mode,\r\n> --> 615         dataset=False,\r\n>     616     )\r\n>     617     metric_cls = import_main_class(module_path, dataset=False)\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)\r\n>     328                 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n>     329                 try:\r\n> --> 330                     local_path = cached_path(file_path, download_config=download_config)\r\n>     331                 except FileNotFoundError:\r\n>     332                     if script_version is not None:\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n>     296             use_etag=download_config.use_etag,\r\n>     297             max_retries=download_config.max_retries,\r\n> --> 298             use_auth_token=download_config.use_auth_token,\r\n>     299         )\r\n>     300     elif os.path.exists(url_or_filename):\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n>     603             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n>     604         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n> --> 605         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n>     606\r\n>     607     # Try a second time\r\n> \r\n> ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py\r\n\r\n\r\nIs there any remedy to solve the connection issue ?","comment_length":31,"text":"Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py \n datasets version: 1.12.1\r\n\r\n`metric = datasets.load_metric('rouge')`\r\n\r\nThe error:\r\n\r\n> ConnectionError                           Traceback (most recent call last)\r\n> <ipython-input-3-dd10a0c5212f> in <module>\r\n> ----> 1 metric = datasets.load_metric('rouge')\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)\r\n>     613         download_config=download_config,\r\n>     614         download_mode=download_mode,\r\n> --> 615         dataset=False,\r\n>     616     )\r\n>     617     metric_cls = import_main_class(module_path, dataset=False)\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)\r\n>     328                 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n>     329                 try:\r\n> --> 330                     local_path = cached_path(file_path, download_config=download_config)\r\n>     331                 except FileNotFoundError:\r\n>     332                     if script_version is not None:\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n>     296             use_etag=download_config.use_etag,\r\n>     297             max_retries=download_config.max_retries,\r\n> --> 298             use_auth_token=download_config.use_auth_token,\r\n>     299         )\r\n>     300     elif os.path.exists(url_or_filename):\r\n> \r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n>     603             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n>     604         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n> --> 605         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n>     606\r\n>     607     # Try a second time\r\n> \r\n> ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/rouge\/rouge.py\r\n\r\n\r\nIs there any remedy to solve the connection issue ? \n It seems to be able to solve this issue by adding the equivalent `accuracy.py` locally. \r\nchange `metric = datasets.load_metric(\"accuracy\")` to  `metric = datasets.load_metric(path = \".\/accuracy.py\")`.\r\nCopy `accuracy.py` from browser at [accuracy.py](https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.11.0\/metrics\/accuracy\/accuracy.py)","embeddings":[-0.4028955698,-0.091716215,-0.1140874401,0.2032690793,0.2335223407,-0.0791541114,0.0977335498,0.4012298286,0.1732944548,0.292118609,-0.3156027198,-0.0292550325,0.0664938241,0.0566061288,0.1343692392,-0.0982900336,-0.1961651742,-0.0955756232,-0.2617654502,0.1735692769,-0.1562049985,0.2479441613,-0.0610231385,0.1988897175,0.0647327602,-0.0930981264,0.1221594959,0.2962450087,-0.4615675211,-0.4462738335,0.3729447424,0.0537409447,0.1342707425,0.5007219911,-0.0001111456,0.2051056623,0.3346630037,-0.0609423667,-0.4902224541,-0.6396036148,-0.239111349,-0.1106907502,0.1620517671,-0.0708997622,-0.1534717381,0.2025640309,0.0105373971,-0.2176420838,0.4202255905,0.5358589292,0.2260995358,0.5080780387,0.3908087015,-0.2263844609,0.1627917141,-0.1687018275,-0.0075963419,0.8425220251,0.0675038621,-0.0139086135,0.0614181124,0.2156310529,0.1526988894,0.2065245062,0.3073361814,-0.1842178851,0.3627560437,-0.1989940852,-0.0022881888,0.0231645145,0.1620704234,-0.2893258333,-0.4648668766,0.1519877166,-0.0389807299,-0.6075189114,0.2533769906,-0.0769177079,-0.2029350102,0.0204769392,-0.3510054648,-0.318610847,-0.3203493059,0.1770494431,-0.0430527925,-0.0150429821,-0.2803620398,0.1023681387,0.1011376455,-0.1008139849,-0.2903202772,0.1227265075,-0.1664374471,0.211817041,-0.1926621795,0.0178747158,0.0295450762,-0.1235276684,0.2105481625,0.2714715898,-0.0309210028,-0.0044636666,0.1105521694,0.1100028902,0.0323088355,0.1711502373,0.0010292578,0.2829097509,0.2745903134,0.6173326969,0.0732795447,-0.0066845203,-0.1303241253,-0.3659859598,-0.0657290518,0.2089111507,0.4930972159,-0.2139486223,-0.2350700945,0.1885440648,-0.0584999844,-0.1023369804,0.0419672616,0.2563569546,-0.2652182579,-0.075281173,0.1136622503,0.1762934327,-0.2488433272,0.1500772238,-0.2428801209,0.1336621046,-0.1552988291,0.1361046731,0.0989214554,-0.1735591441,0.1436371803,-0.1222379282,0.1787065566,-0.2080038935,0.2229410261,0.0121264532,-0.2444592118,0.2979534566,0.0410452038,0.0915408134,0.2260122001,-0.2707825005,-0.110792093,-0.2070261985,-0.3982375562,-0.4079214334,-0.0309666283,0.2210294753,-0.0491231158,0.0621324144,-0.3226237595,-0.1749109924,-0.0211229771,-0.1313864738,-0.1213966012,-0.1584642231,-0.1029095799,-0.1630706936,0.4859734476,0.41819188,0.0717642531,-0.1625388265,0.0906270966,-0.1173116714,0.0385931805,0.1696136296,-0.0856976211,0.0188256893,-0.3086384535,-0.2789125741,0.6501632929,-0.6785372496,-0.4723157883,0.3038990796,-0.1134298891,-0.006939515,0.0896838978,-0.2527339458,0.2923912406,-0.0245885365,0.4318203926,0.0718152374,-0.0383056104,-0.2738701701,-0.1059285477,-0.0947448984,0.0300331339,0.061208576,0.2298381627,0.2225991338,0.1938076615,-0.1399755925,0.2568139136,0.2142125964,-0.0451662503,0.3252111971,0.0714541897,0.1115394086,-0.0805458054,-0.2215158939,-0.0335603505,0.1528939307,-0.226977095,-0.0033852644,-0.2462601066,-0.0065566851,-0.5282768607,0.055067692,0.013122634,-0.1911691576,0.1303531229,0.1321271658,0.1379092932,0.2595019341,-0.1853679121,0.3281309605,-0.2172051668,0.2060409337,-0.3939418495,0.2265941054,-0.2306167036,-0.0846330673,0.2492671758,0.1102450043,0.1850965172,-0.2445450574,-0.1428142637,0.2696176767,0.0781223401,0.119499065,0.3623811901,0.1378722787,0.1654004455,-0.0693063214,-0.0484114885,-0.0910792574,0.0615042783,0.2113356739,0.1499656141,0.1725246757,-0.0836475119,0.2312521935,0.1438015997,0.0934036896,0.2962749004,0.215372473,-0.1921023875,0.0857501179,0.4044250548,0.0632342696,0.251308918,-0.203662768,-0.0695393234,-0.0492584743,0.3880657256,-0.0716116875,-0.0119202081,0.1328728795,0.142764017,-0.1056780443,-0.0279339701,0.0461299941,0.2462921292,0.0275421273,0.1360163838,0.2337549627,-0.1139558926,0.0066711707,0.0820674822,-0.0510199815,0.0019911958,0.2081708312,-0.0959367529,-0.0093752593,-0.0120975729,-0.3281028867,-0.1260832846,0.3014913201,-0.2548196316,0.2159495056,-0.1751920879,0.0687984899,0.1250672489,-0.4654536843,-0.3020919859,-0.2618439496,-0.1894523501,0.3307758868,0.1972889155,0.2216158062,0.155264169,0.0893133432,0.2035491765,-0.1746786833,-0.1663403809,-0.2236740887,-0.0392353125,0.0689444318,0.0742272958,-0.3100693226,0.3720661998,-0.464076221,-0.1145649925,-0.1542761177,-0.1854745597,-0.11623393,-0.0989381149,0.2891538143,0.3395434916,0.4083218873,-0.0036551922,0.1563250273,0.428876549,0.0404865816,-0.1701290607,0.0572603308,-0.1989900917,-0.0159630179,0.0900020748,-0.2380993217,-0.363535434,-0.5015983582,0.2459298372,0.1915348917,0.0858115181,0.2453097999,0.1705062687,0.1764076203,0.1050436422,0.1702939421,-0.1885237545,-0.6819544435,0.2097891271,-0.3668945432,-0.3407714069,0.2215177417,0.2399463356,0.3687411547,0.221523881,-0.5437683463,-0.482924819,0.0638976917,0.3006433547,0.0436028689,-0.0622707009,0.1850144714,-0.1984946281,-0.0342264473,0.0328603275,-0.2459640503,-0.0066251918,-0.0505960248,0.2106594443,0.00885869,0.5591800213,-0.0989438817,0.6215458512,0.26052719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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3127","title":"datasets-cli: convertion of a tfds dataset to a huggingface one.","comments":"Hi,\r\n\r\nthe MNIST dataset is already available on the Hub. You can use it as follows:\r\n```python\r\nimport datasets\r\ndataset_dict = datasets.load_dataset(\"mnist\")\r\n```\r\n\r\nAs for the conversion of TFDS datasets to HF datasets, we will be working on it in the coming months, so stay tuned.","body":"### Discussed in https:\/\/github.com\/huggingface\/datasets\/discussions\/3079\r\n\r\n<div type='discussions-op-text'>\r\n\r\n<sup>Originally posted by **vitalyshalumov** October 14, 2021<\/sup>\r\nI'm trying to convert a tfds dataset to a huggingface one.\r\n\r\nI've tried:\r\n\r\n1.       datasets-cli convert  --tfds_path ~\/tensorflow_datasets\/mnist\/3.0.1\/ --datasets_directory ~\/.cache\/huggingface\/datasets\/mnist\/3.0.1\/\r\n\r\n2.       datasets-cli convert  --tfds_path ~\/tensorflow_datasets\/mnist\/3.0.1\/ --datasets_directory ~\/.cache\/huggingface\/datasets\/\r\n\r\n\r\nand other permutations.\r\nThe script appears to be running and finishing without an error but when looking in the  huggingface\/datasets\/ folder nothing is created.\r\n\r\n\r\n<\/div>","comment_length":46,"text":"datasets-cli: convertion of a tfds dataset to a huggingface one. \n ### Discussed in https:\/\/github.com\/huggingface\/datasets\/discussions\/3079\r\n\r\n<div type='discussions-op-text'>\r\n\r\n<sup>Originally posted by **vitalyshalumov** October 14, 2021<\/sup>\r\nI'm trying to convert a tfds dataset to a huggingface one.\r\n\r\nI've tried:\r\n\r\n1.       datasets-cli convert  --tfds_path ~\/tensorflow_datasets\/mnist\/3.0.1\/ --datasets_directory ~\/.cache\/huggingface\/datasets\/mnist\/3.0.1\/\r\n\r\n2.       datasets-cli convert  --tfds_path ~\/tensorflow_datasets\/mnist\/3.0.1\/ --datasets_directory ~\/.cache\/huggingface\/datasets\/\r\n\r\n\r\nand other permutations.\r\nThe script appears to be running and finishing without an error but when looking in the  huggingface\/datasets\/ folder nothing is created.\r\n\r\n\r\n<\/div> \n Hi,\r\n\r\nthe MNIST dataset is already available on the Hub. You can use it as follows:\r\n```python\r\nimport datasets\r\ndataset_dict = datasets.load_dataset(\"mnist\")\r\n```\r\n\r\nAs for the conversion of TFDS datasets to HF datasets, we will be working on it in the coming months, so stay tuned.","embeddings":[-0.1648911536,-0.411280483,-0.0158712976,0.0792157128,0.2669316232,0.2255699188,-0.0300496463,0.3266834319,0.1026159376,0.128515169,-0.4481954873,-0.0443411097,-0.2346506864,0.2429739386,0.2353374362,-0.0526281409,0.2293660492,0.1147148088,-0.2987782359,-0.1758611649,-0.1143736318,0.4553712308,0.1947537661,-0.0943606794,-0.2140311897,0.0632476732,-0.2156209648,0.3068083823,-0.1974937469,0.0785198063,0.407473892,0.1288553774,0.3076045811,0.9031054974,-0.0001176619,0.1035624444,0.0770372078,0.0183310863,-0.1491824985,-0.2649513483,-0.0654089451,-0.0583234392,-0.001722962,0.1351431012,-0.1704145372,-0.0957549438,-0.042944856,-0.2761211097,0.2943459153,0.3742785752,0.1364397556,0.641417861,0.1126429364,-0.0918210819,-0.146025613,0.3847137988,-0.2380075306,0.2382901311,-0.0169247407,0.1123111174,0.3870151341,0.1114597097,-0.0422698408,-0.2082799822,0.6722182631,0.0787079781,-0.1185647249,-0.3814941049,0.1779486537,0.3222829103,0.3145783544,-0.54581815,-0.3819338679,-0.0322664641,-0.0107391067,-0.0347258821,0.1161965951,0.0666061863,-0.17836757,0.1975577176,-0.2564900517,-0.3320019543,-0.1914514899,0.0833563432,-0.1894272864,-0.0938348547,-0.0842759386,-0.0404548235,0.2743360102,-0.2028793097,-0.1018055975,-0.2093822509,0.1576105356,0.1957501322,-0.2910375297,-0.2681077719,-0.0156666487,0.2289363593,0.1728545278,0.0753593594,-0.2325066626,-0.0799445659,-0.2544602752,-0.0390396714,-0.1685219854,0.1191585958,0.0093294699,0.1019297317,0.204700157,0.1212196574,0.0405918919,0.105945006,-0.1957068741,-0.2644931674,-0.0974034667,-0.2959907651,0.2290275246,-0.0804002509,-0.0581064075,-0.0319240689,-0.0073814522,0.1177538931,0.0679652765,0.28333655,0.1381744444,-0.016510142,0.294225961,0.2597621977,-0.3386052549,0.1279685795,-0.2473294735,0.2322494984,-0.0785698071,0.095228985,0.1278781593,-0.3557142913,-0.1348437965,-0.081731379,0.2395924479,0.0665210336,-0.0794441029,0.2777225673,0.1969356835,0.4052303135,0.0879951417,0.1785387844,0.3538282216,-0.2660952508,-0.1469752938,0.0170703735,-0.1270171553,-0.0388547406,-0.3430979848,0.0828505829,-0.1334641576,-0.1012380421,-0.420306325,0.114112325,0.0006747517,0.040014755,0.1046376526,-0.126305148,-0.1290212125,-0.1774504036,0.4161355197,0.5823609829,-0.3970506787,-0.017536398,0.0848497301,-0.2861663103,-0.2057555318,0.2484476417,0.0731186047,0.0971920043,-0.3245048821,-0.092311658,-0.0034449731,-0.5120362639,-0.0317203738,0.2744568288,-0.1969935447,0.118397966,-0.1307956129,0.0581689253,-0.2474885881,-0.1540415883,-0.1155405492,-0.2703346014,-0.0714007169,-0.1841675341,-0.0988296047,-0.1992491633,0.0802315325,-0.2128945291,0.2160906643,0.2891929746,-0.0253714249,0.1706589311,0.1941291988,-0.1411958188,0.2589686811,0.3458690941,0.1944485307,0.2842564881,-0.040288683,-0.210718751,-0.6065875292,0.1452765614,0.0855025575,0.228628099,-0.1379564106,-0.1419072747,-0.2167517096,0.0025007753,-0.0583706982,0.0533461533,0.0275818668,-0.0392102897,0.4835892618,-0.0442922153,-0.3477332592,0.3699811399,-0.1279627979,0.3048413694,-0.4012543857,0.5816088319,0.1548337638,-0.0992616117,-0.0724769235,0.268566072,-0.0283487421,-0.2871650755,0.0063507333,0.3673804104,-0.1132019013,0.4100806713,0.1847901344,0.3856836557,0.1985163838,-0.1981128603,0.0679021776,-0.0253363568,-0.0597930662,-0.0255460013,-0.1785299331,0.1148391142,-0.0390425697,0.3238583803,0.1479392052,0.0077467072,-0.0478747673,0.0990773365,-0.2169593275,-0.1831818372,0.0223232005,-0.1015937775,0.2260504961,0.0044146772,-0.2912732959,0.228355661,0.2206148207,-0.2823570371,-0.1105513945,0.21198605,-0.1498780102,0.1052306816,0.1299797744,0.2943784595,0.3469110727,0.072729893,0.1985188425,0.2922204733,-0.0842516422,-0.0526979491,0.1732628644,0.0160458665,0.2079773843,-0.0086958082,-0.0168531686,-0.0105103785,-0.3031794429,0.1227688119,-0.0858282819,0.1738738716,-0.4836605787,-0.1099989638,-0.3512434661,-0.1739867777,-0.1407673061,-0.0475968011,-0.3504057229,-0.3079296649,-0.2626383901,0.3540430963,0.2149427086,-0.1545413882,-0.0274497624,-0.1332238317,-0.161482513,-0.1979889423,-0.4101575613,-0.018738091,-0.2513023615,-0.0172438752,0.3406272233,0.0418139249,0.2592490315,-0.3714254797,0.2028076947,-0.5838525295,-0.303293705,0.1039334461,-0.3183479905,0.0498384051,0.246547699,0.4412375391,-0.2077016532,-0.0099060694,0.12583749,-0.1980945915,-0.1830414683,0.0413797237,-0.1689615995,-0.1573883146,-0.0855485797,-0.0762825757,-0.3684204221,-0.1856380403,0.1232286394,-0.121367,-0.020308692,0.4174486697,0.0696876496,0.210537374,-0.2112541944,-0.2251598388,0.0087770699,-0.4232058227,0.3801833689,-0.4478027225,-0.4045419991,0.2524513304,0.1100743935,0.2839270532,-0.0728737563,-0.3338084221,-0.0901468247,-0.3284910619,0.2091330886,-0.0214111041,0.0599997379,0.36434111,-0.0861565843,0.0312415659,-0.2290471941,0.0844668299,0.1416712552,-0.0247929953,0.2591267824,0.0017641906,0.2045703977,-0.0286231302,0.3668397069,0.3739620149,-0.156957224,0.2157032043,-0.2398674488,0.618439734,-0.2504611015,-0.4026356041,0.1155281961,0.1510549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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3126","title":"\"arabic_billion_words\" dataset does not create the full dataset","comments":"Thanks for reporting, @vitalyshalumov.\r\n\r\nApparently the script to parse the data has a bug, and does not generate the entire dataset.\r\n\r\nI'm fixing it.","body":"## Describe the bug\r\nWhen running: \r\nraw_dataset = load_dataset('arabic_billion_words','Alittihad')\r\nthe correct dataset file is pulled from the url.\r\nBut, the generated dataset includes just a small portion of the data included in the file.\r\nThis is true for all other portions of the \"arabic_billion_words\" dataset ('Almasryalyoum',.....)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nraw_dataset = load_dataset('arabic_billion_words','Alittihad')\r\n\r\n#The screen message\r\nDownloading and preparing dataset arabic_billion_words\/Alittihad (download: 332.13 MiB, generated: 20.62 MiB, post-processed: Unknown size, total: 352.74 MiB) \r\n\r\n## Expected results\r\nover 100K sentences\r\n\r\n## Actual results\r\nonly 11K sentences\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n\r\n","comment_length":24,"text":"\"arabic_billion_words\" dataset does not create the full dataset \n ## Describe the bug\r\nWhen running: \r\nraw_dataset = load_dataset('arabic_billion_words','Alittihad')\r\nthe correct dataset file is pulled from the url.\r\nBut, the generated dataset includes just a small portion of the data included in the file.\r\nThis is true for all other portions of the \"arabic_billion_words\" dataset ('Almasryalyoum',.....)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nraw_dataset = load_dataset('arabic_billion_words','Alittihad')\r\n\r\n#The screen message\r\nDownloading and preparing dataset arabic_billion_words\/Alittihad (download: 332.13 MiB, generated: 20.62 MiB, post-processed: Unknown size, total: 352.74 MiB) \r\n\r\n## Expected results\r\nover 100K sentences\r\n\r\n## Actual results\r\nonly 11K sentences\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n\r\n \n Thanks for reporting, @vitalyshalumov.\r\n\r\nApparently the script to parse the data has a bug, and does not generate the entire dataset.\r\n\r\nI'm fixing it.","embeddings":[-0.0286145974,0.0881006718,-0.0398160852,0.4470803738,-0.0541642718,0.1820726842,0.0881034657,0.4025404453,-0.0948741212,0.0163805503,0.2197004259,-0.1283018887,0.2180958688,0.1541099548,0.0881127268,0.0227929894,0.145227313,-0.0231750961,-0.1374332607,-0.1756127775,-0.2381924093,0.2698273957,-0.199213475,-0.2039990574,-0.4456401467,0.1676325053,-0.1632733941,-0.0635794699,-0.1843623221,-0.257866323,0.0919585675,-0.2433368266,0.084918797,0.4776031971,-0.0001088292,-0.2206329107,0.5023479462,-0.1118985564,-0.4034661055,-0.3848635554,0.0355659164,-0.3234835267,0.0307379272,-0.2087685317,0.0238543376,-0.0831142515,0.002925908,-0.1123989597,0.2253303826,0.342549175,0.2435446531,-0.1121717766,0.2035398781,0.0319853835,0.1550090164,0.0470183417,-0.1615523994,0.0871606469,0.1023731232,0.0703571439,-0.13108854,0.2172883004,0.0001453455,0.0416009277,0.1334565133,0.12789765,-0.1598103791,-0.622928679,0.4248596132,0.274725467,0.3804432452,-0.221715495,-0.4288478494,-0.2201075256,-0.0231262799,-0.1542038023,0.1516387612,0.6916108727,-0.2772276998,0.2186613679,-0.1506364197,-0.0398950838,-0.219780907,0.135017693,-0.3431139588,0.2500344515,-0.1061585695,-0.0388703682,-0.0151683483,-0.002198382,0.1005699635,-0.4978498816,-0.0422838517,0.1698680371,-0.2431504279,0.1753534228,-0.1003269255,0.0660815537,0.4918048382,-0.1318801045,0.044523675,-0.0373536795,0.0255123954,0.0239351168,0.1401286721,0.0411044583,0.0701485798,0.0033497673,0.098625876,0.0592800602,-0.0515251793,0.0176411811,-0.0161438286,0.1974155605,-0.1515598744,-0.2380447686,0.1054733545,-0.1540675014,-0.2276155353,0.3563838601,-0.3293886483,-0.1530779749,-0.3679720461,0.151406154,-0.2379037738,0.1093183234,0.1320767999,0.3215911388,-0.2217429876,-0.4376674294,-0.1450057328,-0.1139065698,-0.1167903021,0.120769918,0.3398093283,-0.0325787961,0.3868946433,0.0024005375,0.0531718805,-0.2295836657,0.1267257482,-0.3613075018,0.1805514395,0.2296861112,0.3778501749,0.3998395801,-0.1190919802,-0.1038645357,0.0736527219,0.57554245,-0.0727925599,-0.081556119,-0.1748432517,0.2306032628,-0.2592100501,-0.0234385002,0.0235207547,0.5305867791,0.439578563,0.2396567762,-0.1242128015,-0.1348515749,-0.1769691259,-0.1975351423,0.3434276879,0.4946720004,-0.4158701003,-0.0487564243,-0.034116365,0.2067747414,0.3079302609,0.1762385666,0.0381441526,0.3497665823,-0.143360734,0.2342196256,-0.0185387172,0.1762320548,-0.3684453964,0.4762641191,0.1337172091,0.1651507765,0.1558812559,-0.0755714476,0.2125988007,0.1379378438,0.339473784,0.5168277621,0.0178827867,-0.0830451697,-0.3762100637,-0.1958620846,0.0544983372,0.0318193808,0.1419245601,0.1507170498,0.0055883764,-0.1335388571,0.5737940073,0.0584243499,0.3523125648,0.1516795307,-0.1481349915,0.057971105,0.1505539268,-0.0581482835,-0.0713479072,0.0759717301,0.0030591297,0.3061108887,-0.3237095177,-0.0573885329,-0.3818063736,-0.0373029821,-0.0686785206,-0.2595747113,0.0993638709,0.1658283323,0.0236060396,0.1869384646,0.0280446559,0.0725914389,-0.1516501606,0.1148675308,-0.4662271738,0.2030524164,-0.0833292156,-0.1655611247,0.1491265893,0.0129904384,0.0345683508,-0.03955267,-0.1300476938,0.1855137497,-0.0949675888,-0.0810459182,-0.1057298332,-0.0021824879,0.1308677346,-0.2438005358,0.2065781504,0.2460821122,0.1862364113,-0.0674534515,0.0126613192,0.1819051653,0.1060353667,0.2599342763,0.0500394925,-0.1356127709,0.3852490187,-0.1078271493,0.1342313737,-0.1632529646,0.3109799027,-0.0300231241,0.1672322452,0.0661249459,-0.0720107555,0.0530035645,0.3902860284,-0.3246495724,0.0707296506,0.3767690063,-0.3420104086,-0.2788617909,-0.1851684898,0.1887776107,0.2930834293,0.317263037,0.2019102722,0.0761118084,0.1780348569,-0.3230369389,0.2173184156,0.1017627493,-0.0395575911,0.260635525,0.1906799227,-0.021018168,-0.3817103803,0.1521875411,0.0480205044,0.2777329385,-0.1173457429,0.1095080823,0.045707088,-0.0662522838,-0.0528181531,-0.0900236294,-0.1821064353,-0.2297857106,-0.0663709417,0.0785831362,0.0323547386,-0.0557912104,-0.2609686852,-0.0103501678,0.0871909112,-0.1272549927,-0.0935620666,-0.0499482565,-0.5044417381,0.1436324269,0.1440043598,-0.0655109361,0.070501782,-0.2973750234,-0.1454303265,-0.1476977319,-0.0592223182,0.2702582479,-0.1891826093,0.3846007288,-0.2577128112,0.1871441901,-0.0600917898,0.058670748,0.1227956265,-0.0630927682,-0.2196058929,0.130449906,-0.075095512,-0.0418693423,0.0851370692,-0.5043628216,-0.1832008809,-0.1847125739,0.0074737552,0.1847725511,0.2795472145,-0.0099890269,0.1514579654,-0.1435367614,-0.1225183681,-0.0658691078,-0.2603884041,-0.3574770391,0.4084274769,-0.1438036412,-0.5462388396,0.0685859993,-0.1858547032,-0.0138565199,0.1752384007,-0.6390581131,0.3277481794,-0.5411503911,0.1657116264,-0.2699119151,0.1590081602,0.1299925447,-0.263771683,-0.0036698279,-0.2388724387,-0.1556261629,-0.0077239508,-0.0571244694,0.4045508504,-0.0376793481,0.2968532145,0.1221717224,0.3267689645,0.2896201611,0.1191212013,0.3440839648,-0.0147239966,0.1905517727,-0.3405968845,-0.1005732492,0.0690073222,-0.1051691994,-0.025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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3123","title":"Segmentation fault when loading datasets from file","comments":"Hi ! I created an issue on Arrow's JIRA after making a minimum reproducible example\r\n\r\nhttps:\/\/issues.apache.org\/jira\/browse\/ARROW-14439\r\n\r\n```python\r\nimport io\r\n\r\nimport pyarrow.json as paj\r\n\r\nbatch = b'{\"a\": [], \"b\": 1}\\n{\"b\": 1}'\r\nblock_size = 12\r\n\r\npaj.read_json(\r\n    io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)\r\n)\r\n```\r\n\r\nI don't see a way to workaround this properly now without hurting the performance of the JSON loader significantly though","body":"## Describe the bug\r\nCustom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features\/\r\n\r\n## Steps to reproduce the bug\r\n\r\nDownload an example file:\r\n```\r\nwget https:\/\/gist.githubusercontent.com\/TevenLeScao\/11e2184394b3fa47d693de2550942c6b\/raw\/4232704d08fbfcaf93e5b51def9e5051507651ad\/tiny_kelm.jsonl\r\n```\r\nThen in Python:\r\n```\r\nimport datasets\r\ntiny_kelm = datasets.load_dataset(\"json\", data_files=\"tiny_kelm.jsonl\", chunksize=100000)\r\n```\r\n\r\n## Expected results\r\na `tiny_kelm` functional dataset\r\n\r\n## Actual results\r\n\u2620\ufe0f `Segmentation fault (core dumped)` \u2620\ufe0f\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0","comment_length":58,"text":"Segmentation fault when loading datasets from file \n ## Describe the bug\r\nCustom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features\/\r\n\r\n## Steps to reproduce the bug\r\n\r\nDownload an example file:\r\n```\r\nwget https:\/\/gist.githubusercontent.com\/TevenLeScao\/11e2184394b3fa47d693de2550942c6b\/raw\/4232704d08fbfcaf93e5b51def9e5051507651ad\/tiny_kelm.jsonl\r\n```\r\nThen in Python:\r\n```\r\nimport datasets\r\ntiny_kelm = datasets.load_dataset(\"json\", data_files=\"tiny_kelm.jsonl\", chunksize=100000)\r\n```\r\n\r\n## Expected results\r\na `tiny_kelm` functional dataset\r\n\r\n## Actual results\r\n\u2620\ufe0f `Segmentation fault (core dumped)` \u2620\ufe0f\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0 \n Hi ! I created an issue on Arrow's JIRA after making a minimum reproducible example\r\n\r\nhttps:\/\/issues.apache.org\/jira\/browse\/ARROW-14439\r\n\r\n```python\r\nimport io\r\n\r\nimport pyarrow.json as paj\r\n\r\nbatch = b'{\"a\": [], \"b\": 1}\\n{\"b\": 1}'\r\nblock_size = 12\r\n\r\npaj.read_json(\r\n    io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)\r\n)\r\n```\r\n\r\nI don't see a way to workaround this properly now without hurting the performance of the JSON loader significantly though","embeddings":[-0.221605584,0.0462872945,-0.0461311825,0.3980242312,0.2773250341,0.1302966326,0.4007089138,0.4724463224,-0.262596786,-0.0488693342,0.0218500886,0.5417183638,-0.0916598961,-0.1903081685,-0.0640659928,-0.1771656424,0.0981778502,0.0973746628,-0.0141670192,-0.012553419,-0.2562197447,-0.0560517386,-0.1102395132,0.0849382058,-0.1556875557,0.0115971342,0.14521119,0.3879400492,-0.0625489578,-0.5099861622,0.2821138799,-0.2407681495,0.0446112566,0.2868835032,-0.0001123592,0.1390264332,0.4759358168,0.022709161,-0.24694103,0.2645267844,-0.235292092,-0.173948586,0.0418367013,-0.2738325298,0.1732014716,-0.2128401995,-0.206406638,-0.0277917068,0.4520408809,0.2071412355,0.1630880088,0.0785674453,-0.1414091438,0.1519347131,0.5740485191,0.2817715704,-0.0905650556,0.4997977018,0.0421377756,-0.075127013,-0.25558272,0.1747101992,-0.0596408695,-0.0509764962,0.0880904049,-0.101865381,-0.189122051,-0.250059545,0.0684959814,0.348677963,0.2594338953,-0.2350414395,-0.259872824,-0.2506487966,-0.0998470858,-0.413494885,0.1293164343,0.2884502113,-0.1790688187,0.0971407369,0.2144644856,0.0861793756,-0.2454946637,0.0177508015,0.0249226801,0.3554042876,0.1664780527,0.0705153495,0.0180654041,-0.260833323,-0.1484968215,0.2133002728,-0.5500562787,0.2827174962,-0.2968090177,0.0191073138,-0.0657928362,-0.1952113509,0.3904781342,0.2183542997,0.3374666274,0.099783808,0.2069177926,0.0194743685,0.4279438853,-0.0438201204,-0.1440996826,0.4070798159,0.1380331069,0.1287357956,-0.2121904492,0.0739370361,-0.0511373915,-0.188466236,0.2399909496,-0.0627126247,-0.0172544178,-0.1591181159,-0.2573587894,0.1316414326,-0.5078763962,0.0057503302,-0.0103549045,0.299248457,0.0306511167,0.1430562139,-0.2126277387,0.1253627688,-0.2801526785,-0.139183864,-0.124476172,-0.081183292,-0.2614840865,-0.0889936313,0.1668784171,-0.2727865279,0.3856733739,-0.0384647287,-0.1798031479,-0.2193139493,-0.3691850305,-0.2453140467,-0.2819586396,0.221899882,-0.2575138509,0.0152653893,-0.0341507122,0.1473247409,-0.0739361271,0.1324351281,-0.2381289303,-0.2341753542,0.1409041882,0.1954628974,-0.2478666455,0.0903600976,0.0337653458,-0.0754913464,0.1685124338,-0.3441918492,0.0697185174,-0.0952544883,0.1113582924,-0.3139527738,0.055439841,0.3389277458,-0.5728892088,-0.2156763226,-0.2238027453,-0.0550122783,0.2022037953,0.3335134983,-0.1161196232,0.4219344854,-0.197771579,0.4660539031,0.5630300641,-0.0010422167,-0.5784207582,0.355795294,-0.2032341063,0.0704929978,0.0196038373,0.2035707086,0.3436391056,-0.0336897336,0.0538897067,0.5422451496,0.0069927587,0.1079376042,-0.2757103741,-0.0330382548,-0.2924931347,0.3397628963,0.0080734687,-0.2369634807,0.3083426356,-0.1700802743,0.2838055491,-0.1287005097,0.1103432328,0.2882897556,0.1234363019,-0.3386018872,0.1912812442,-0.1444408298,-0.3866171837,0.2047242522,-0.2137306035,-0.0900970995,-0.1573768258,0.0637638047,-0.3672558069,0.1882307231,-0.1796551049,-0.0505042784,0.0616008081,-0.1237851009,-0.1436090618,0.0496679135,-0.1734348983,-0.0068401196,0.0666731969,0.2822467387,-0.1183877066,0.3455177248,0.1098546609,-0.3701758683,0.0344968289,0.2685461044,0.0246440358,-0.211390242,-0.195269987,0.2861360013,0.2696336806,0.3125638366,-0.2642026246,-0.1232759431,0.0994486436,-0.1385982633,0.0744259134,0.2112017274,0.1333164126,-0.0914144367,-0.042479869,0.5776666999,-0.188370809,0.1782927662,0.0071952199,-0.1034138724,0.2388171554,-0.1040792391,-0.0587426424,-0.2408674955,0.2845827639,0.0319572836,0.1298465133,0.3460408151,-0.4589185715,-0.2158197463,0.450863868,-0.0563560165,0.0936530232,0.1642145514,-0.3932670355,-0.0102755968,0.1970409155,0.1955841631,0.2559323907,0.0823422968,-0.1792046279,-0.0600138307,0.2606877983,-0.0606072694,0.1187147424,0.1648446023,0.711681664,0.4595626295,0.1783031225,-0.0091598155,-0.1337963194,-0.3248572648,-0.0982473865,0.1129503697,-0.3129493892,0.1563327014,-0.1185368821,-0.1944894344,-0.2035536319,-0.2847201228,-0.169016093,-0.2733292878,-0.0243717767,-0.0352148153,-0.3120147288,0.0961500406,-0.305174768,0.1072429791,0.2588108182,-0.3611755669,-0.1390076131,-0.2868054509,-0.3106594384,0.0600661635,0.344175905,0.2264542282,-0.0389865153,-0.0205796696,-0.0758690536,0.0657646134,0.0167170465,0.0827547908,0.0130464248,0.3093847632,0.1217027605,0.0808955505,-0.0338228196,-0.3260700405,0.1601104587,0.0035952439,-0.2894241512,0.4521140754,-0.069026649,0.3773836493,-0.1344536096,-0.2433420271,0.0515746661,-0.4230126739,0.3029527962,0.2432290167,0.1131563187,0.0832997039,0.2293303907,-0.0727687702,0.0639879555,-0.0832017586,0.2250120789,-0.020242162,0.3274050951,-0.2005006373,-0.157688722,-0.0501231551,-0.2484262288,0.3035387397,0.1735507399,-0.5167227983,0.4446373582,-0.0456955582,0.2214260846,-0.2998895943,-0.3068346679,-0.1103231758,0.081444554,0.0200825874,-0.0290862601,0.1139624193,-0.0569637865,-0.0202093255,0.1185986251,-0.0370282382,0.7219757438,0.0483334921,0.8045208454,0.0409953147,-0.0692867935,0.3652346432,0.0598133579,-0.0528410561,-0.2030092478,-0.1332765669,0.2159008533,-0.1262719184,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3123","title":"Segmentation fault when loading datasets from file","comments":"The issue has been fixed in pyarrow 6.0.0, please update pyarrow :)\r\n\r\nThe issue was due to missing fields in the JSON data of type list. Now it's working fine and missing list fields are replaced with empty lists","body":"## Describe the bug\r\nCustom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features\/\r\n\r\n## Steps to reproduce the bug\r\n\r\nDownload an example file:\r\n```\r\nwget https:\/\/gist.githubusercontent.com\/TevenLeScao\/11e2184394b3fa47d693de2550942c6b\/raw\/4232704d08fbfcaf93e5b51def9e5051507651ad\/tiny_kelm.jsonl\r\n```\r\nThen in Python:\r\n```\r\nimport datasets\r\ntiny_kelm = datasets.load_dataset(\"json\", data_files=\"tiny_kelm.jsonl\", chunksize=100000)\r\n```\r\n\r\n## Expected results\r\na `tiny_kelm` functional dataset\r\n\r\n## Actual results\r\n\u2620\ufe0f `Segmentation fault (core dumped)` \u2620\ufe0f\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0","comment_length":39,"text":"Segmentation fault when loading datasets from file \n ## Describe the bug\r\nCustom dataset loading sometimes segfaults and kills the process if chunks contain a variety of features\/\r\n\r\n## Steps to reproduce the bug\r\n\r\nDownload an example file:\r\n```\r\nwget https:\/\/gist.githubusercontent.com\/TevenLeScao\/11e2184394b3fa47d693de2550942c6b\/raw\/4232704d08fbfcaf93e5b51def9e5051507651ad\/tiny_kelm.jsonl\r\n```\r\nThen in Python:\r\n```\r\nimport datasets\r\ntiny_kelm = datasets.load_dataset(\"json\", data_files=\"tiny_kelm.jsonl\", chunksize=100000)\r\n```\r\n\r\n## Expected results\r\na `tiny_kelm` functional dataset\r\n\r\n## Actual results\r\n\u2620\ufe0f `Segmentation fault (core dumped)` \u2620\ufe0f\r\n\r\n## Environment info\r\n- `datasets` version: 1.14.0\r\n- Platform: Linux-5.11.0-38-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0 \n The issue has been fixed in pyarrow 6.0.0, please update pyarrow :)\r\n\r\nThe issue was due to missing fields in the JSON data of type list. Now it's working fine and missing list fields are replaced with empty lists","embeddings":[-0.2918099165,0.121430032,-0.0827054307,0.3285434842,0.2168405056,0.1759034246,0.4262993932,0.4501456618,-0.1168767214,-0.0356809944,-0.0088389916,0.4317321777,-0.0735193565,-0.1450855285,0.0433540866,-0.2367312312,0.0988489613,0.1945872158,0.0212002434,-0.0470168591,-0.3097120523,0.009922944,-0.2753229141,0.0240142792,-0.0618330427,0.0798942894,0.0055653467,0.4071408212,-0.0610916838,-0.4775451422,0.0961332023,-0.2853013277,0.0704784021,0.2516076267,-0.0001071634,0.1179605126,0.3458616138,0.0579590425,-0.2285083234,0.0595329367,-0.142582342,-0.2246127278,0.1000740677,-0.3406712413,0.1174899116,-0.2985037565,-0.0583706722,0.2825622559,0.3278872073,0.216968894,0.241840601,0.1461565048,0.0648333952,0.1432899386,0.66293329,0.1798786819,-0.0988814384,0.4694985151,0.0925357416,-0.1290549338,-0.0867849663,0.1264720559,-0.1382033527,-0.0642648786,0.1143599302,0.0624193512,-0.2006952763,-0.3929519355,-0.0047307229,0.2205432355,0.2763769031,-0.2993310988,-0.1974207461,-0.2203943878,-0.0762351975,-0.6523587704,0.1665418744,0.4311146736,-0.1317387968,0.1498046368,0.0405508578,0.0215342697,-0.1675586551,0.2037259191,-0.0932426229,0.2465570867,0.1563674361,0.0249535684,-0.111709483,-0.1566171646,-0.3185904622,-0.0222119521,-0.2943530977,0.2650361955,-0.2715198994,-0.0931318328,-0.0153355999,-0.0133472607,0.381599158,0.2088992298,0.1359739006,0.1028358862,0.3156547546,0.1051695421,0.2657701671,-0.0841623843,-0.1544484645,0.156277895,0.199801147,-0.0283483453,-0.1149821058,0.0828846097,0.0142490501,-0.0847683996,0.1554763466,-0.0665237084,0.1366336048,0.0116305919,-0.2576901317,0.1284848303,-0.5171625614,-0.0423919037,-0.0807352439,0.3980869055,-0.0022926214,-0.0626937822,-0.2234251052,0.1109166741,-0.2770853639,-0.1238286719,-0.1840717345,-0.0135288332,-0.1840054691,-0.1019439697,0.1591253728,-0.2818835676,0.332729131,0.1258439124,-0.208056137,-0.0870823115,-0.1511071026,-0.185886994,-0.322763592,0.2604021132,-0.164487794,0.0673506632,-0.057250984,-0.0110572306,-0.0591599457,0.2522027194,-0.1928112656,-0.1586403996,-0.140776813,0.2303975075,-0.2062385231,0.1054344922,0.0742589012,0.1178543344,0.1249415129,-0.3388925493,-0.0115909828,-0.0706394762,0.0729904771,-0.3966017962,0.0796643868,0.2618735433,-0.6067892909,-0.154826656,-0.1292985231,-0.1278661638,0.1880634576,0.3075351715,-0.1117323041,0.3459045589,-0.1622916162,0.7070807815,0.4934939444,0.2397246659,-0.4732916653,0.3044734001,-0.0659738705,0.0593904518,-0.1498849541,0.1641961187,0.3186173439,0.03778423,-0.1284880489,0.2756296992,0.0619142838,0.1308682114,-0.3311683536,-0.0936562121,0.0880353153,0.2849454284,0.0306397192,-0.0702107549,0.2555612922,-0.0627643093,0.2737771273,-0.2454644144,-0.0023805008,0.3222180009,0.2429715395,-0.1740373224,0.1736062914,-0.3627472818,-0.6328650117,0.0891137198,-0.0971371382,0.0748264715,-0.1568674445,0.0295939501,-0.3496383429,0.1422255486,-0.0629001483,-0.0007520755,0.1465374082,-0.0654866472,-0.064567171,0.0269743148,-0.2135264874,-0.1856558621,0.0097288406,0.322925359,-0.1873952448,0.4950961769,-0.0500722267,-0.4322348237,-0.03552581,0.1339970678,0.1449486166,-0.262191087,0.0396851934,0.342387706,0.3585014939,0.0682128742,-0.3767340183,-0.0028267694,0.1771764308,-0.0501334704,0.0266503338,0.1111535877,0.1508514583,-0.0225144178,0.058573395,0.3733451366,0.0212671831,-0.0140367225,-0.0211170167,-0.1850604862,0.1319806278,-0.1667291224,0.1363778412,-0.0831818581,0.2561385632,0.1283501089,0.032696303,0.3540171087,-0.5563915968,-0.1424492449,0.5803522468,-0.1736175865,0.2540675104,0.1871566027,-0.4211393595,-0.059587054,0.1676716059,-0.2155299783,0.3650292158,0.1340764314,-0.1880152673,0.0629993156,0.1503530443,-0.0604173169,0.1884623021,0.0627666861,0.4829987884,0.3494061232,0.238363564,0.0484827012,-0.2497020215,-0.2753812969,0.0868439749,0.1772166789,-0.313598007,0.0274571143,-0.1577531993,0.0770981237,-0.030388739,-0.2400131077,-0.2895748019,-0.1923346967,0.1079372242,0.136182934,-0.2626644075,0.1495297104,-0.0420786366,0.0163779352,0.2609810233,-0.1793540716,-0.2925779819,-0.2322919369,-0.3041021526,0.1057989299,0.3839275837,0.1333932877,-0.0890821815,0.0345550068,-0.3096343577,0.0257636309,-0.2375027835,0.1160189286,-0.1164889038,0.4348429441,0.1793019921,0.21213907,-0.0954027325,-0.2661333084,0.2407329082,0.1119572893,-0.3348252773,0.4194238484,-0.0814046934,0.2867171764,-0.2858223617,-0.3179768622,-0.1767689437,-0.2761531472,0.3072343469,0.2338083535,0.1426363438,0.0027276301,0.2530322373,-0.1746165156,-0.0738176107,-0.1283878386,0.0438373499,0.1562934667,0.3257735074,-0.2792034149,-0.331363678,-0.0435021818,-0.1857441813,0.1009748131,0.143144086,-0.5068827271,0.197363168,-0.0731494129,0.3436178565,-0.2792085409,-0.2486218661,-0.0080984449,0.072745733,-0.0014525562,-0.0021341203,0.0122707365,-0.247662276,0.028285021,0.158812359,-0.2153235674,0.494135648,-0.0059231264,0.7905435562,-0.0169307888,-0.1117257997,0.2927450836,-0.1193504557,-0.0058985041,-0.1725427061,-0.0875328407,0.2907531261,-0.1406132132,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3122","title":"OSError with a custom dataset loading script","comments":"Hi,\r\n\r\nthere is a difference in how the `data_dir` is zipped between the `classla\/janes_tag` and the `classla\/reldi_hr` dataset. After unzipping, for the former, the data files (`*.conllup`) are in the root directory (root -> data files), and for the latter, they are inside the `data` directory (root -> `data` -> data files).\r\n\r\nThis can be fixed by removing the `os.path.join` call in https:\/\/huggingface.co\/datasets\/classla\/janes_tag\/blob\/main\/janes_tag.py#L86\r\n\r\nLet me know if this works for you.","body":"## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":71,"text":"OSError with a custom dataset loading script \n ## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n \n Hi,\r\n\r\nthere is a difference in how the `data_dir` is zipped between the `classla\/janes_tag` and the `classla\/reldi_hr` dataset. After unzipping, for the former, the data files (`*.conllup`) are in the root directory (root -> data files), and for the latter, they are inside the `data` directory (root -> `data` -> data files).\r\n\r\nThis can be fixed by removing the `os.path.join` call in https:\/\/huggingface.co\/datasets\/classla\/janes_tag\/blob\/main\/janes_tag.py#L86\r\n\r\nLet me know if this works for you.","embeddings":[-0.1128959954,0.2815290093,-0.0133344904,0.3955747187,0.2419004142,0.1173842326,0.4183782339,0.3338423967,0.399305582,0.1943426877,-0.2293029577,0.3192375302,-0.2365897745,-0.0586510971,-0.0276406296,0.0171539187,-0.1140037775,0.1235067025,0.0152646853,0.0538216457,-0.2668657601,0.1890418231,0.0730201155,0.1165179238,-0.1524645835,0.2888302207,-0.305568099,0.6062158346,-0.1058221906,-0.4976432025,0.411575675,-0.0848031938,0.3153779507,0.9328231215,-0.0001182188,0.2439122349,0.1822360009,-0.1082940698,-0.3610416055,-0.3233253062,0.1418734044,-0.0559697747,0.0167580526,-0.2509720623,0.107310012,-0.1073527634,-0.063804917,-0.3272550404,0.1470365524,0.243046701,0.1508885771,-0.0782089829,-0.2656306922,-0.3175562322,0.2304670662,0.3083327115,0.1454181969,0.345936805,0.2215478271,-0.2759937346,-0.0904202983,-0.0664639845,-0.1675293595,0.1449217647,0.2412745804,0.227457568,-0.0587955005,-0.1350414455,0.0041873236,0.3970922828,0.6253734827,-0.1800610423,-0.3252730668,-0.1314567924,0.0302401986,-0.2941183448,0.3250662684,0.2082556188,-0.1266013235,0.0780134052,0.1090783998,0.1126024202,-0.1752075553,0.3246009648,-0.1998942941,0.0976590887,0.0400923453,0.0607715026,0.0194340236,-0.0370791554,-0.0513202921,-0.057304427,0.0523795113,0.2560680509,-0.214629218,0.0880448222,-0.1322696954,-0.160373956,-0.0199079383,0.0104806162,0.2611761391,-0.1694516987,-0.4164377749,0.1147027835,0.1924516112,0.2341755927,0.0028592155,0.0696605891,0.2455968261,0.3264804482,-0.3402524292,0.00250661,-0.2539592683,-0.1772051007,0.2750910223,-0.0635992736,0.55353266,-0.0913137943,-0.6092348099,0.0851995349,-0.0907426551,-0.1220401302,0.0167008061,0.350426048,0.1556562334,0.0076237121,0.324208796,0.3544691801,-0.2622276843,-0.09909565,-0.1103033796,0.2185394168,-0.0110623334,0.2203117162,0.3367286921,-0.1516910642,0.2326345146,0.1329758018,-0.0343423858,-0.1373286694,-0.0624273121,0.1760949641,-0.3333288133,0.1167247742,0.013814901,0.2963364422,0.3627976775,-0.1896024346,-0.1238491237,0.3359745145,-0.511523664,-0.5266267657,-0.1148847342,0.1564936489,-0.2748809159,-0.0949096307,-0.0207241066,-0.2097221762,0.2548092604,-0.3180552125,-0.0229650903,-0.1040266827,-0.5212677717,-0.2807867527,-0.0835549384,0.6397893429,-0.5403725505,-0.0046029598,-0.2959841192,0.0659917593,0.0528749079,0.2252856195,-0.1808517128,0.4837553203,-0.4920635521,0.0551535003,0.1831505001,-0.4322178066,-0.3191882968,0.0916377306,-0.3816460669,0.3135969341,0.1789076775,0.1348926276,0.0342426822,-0.0998480618,-0.0080061089,0.0334071256,0.0391690247,-0.1163586378,-0.1003423557,-0.1150628552,0.2950371802,0.3189660609,-0.0525983013,-0.0284808464,0.0728865042,-0.0832465887,0.2127765864,-0.163817361,-0.1320706457,0.1578202099,0.329498142,0.2000402659,0.1325257421,-0.0937297344,-0.3722987473,0.4385554194,0.203245163,-0.0487933122,0.053555131,-0.0575330257,-0.3631984293,-0.0736556053,-0.4429955482,-0.1980914623,-0.0112997387,0.2344987988,-0.0294579472,-0.0375628062,-0.1806129515,0.4302507043,-0.3583678305,0.073811397,-0.2808938622,0.1908035725,-0.072052367,-0.0913957655,-0.0957284942,0.111958757,0.1296710372,0.000445568,-0.0718535855,0.346457541,0.3362167776,-0.1385053545,-0.0276074223,0.0314185135,-0.000874043,-0.0213833749,0.1846695691,0.2478931397,0.1665903777,-0.1895471066,-0.0732969269,0.1872237921,-0.1838081926,0.2477006912,-0.0536598228,-0.1778230965,0.0044104611,-0.0287910867,0.1164985746,-0.043293152,0.4287044406,-0.141619131,0.3427115679,0.0684483498,0.0491025485,-0.0600036867,0.1305796802,0.0284004379,0.108604908,0.1169634163,-0.0362798646,0.1426839828,-0.3513677418,0.2691136897,0.8010326028,0.0404969342,-0.1698909551,0.179069519,0.0917893425,0.0040805689,-0.0075934026,-0.0389094874,0.151727438,0.3422045112,0.1113567874,0.2531370819,-0.0732817054,-0.3762145936,-0.0807913616,0.3256628811,-0.5139819384,0.0058893552,-0.2444292605,-0.0086611249,-0.3238428235,-0.1037614718,-0.2189128101,-0.1865138412,-0.039064344,0.1296098679,-0.0096950391,-0.0156510603,-0.14024508,0.197912693,0.1155453175,-0.2887761295,0.1485815048,-0.0220534243,-0.1977469027,-0.1064597666,0.259642154,-0.0955125391,0.1163765863,-0.2262869775,-0.011609816,-0.3291104734,-0.2606185675,-0.1494769603,0.1949064881,0.1227230355,0.2664646208,0.2509476542,0.3389138281,-0.354885906,0.3041362464,-0.2651309073,-0.2922275662,0.2627813816,0.0242236461,0.1615139544,-0.0260517858,-0.4274685383,-0.2367953062,-0.3845948875,-0.1018426195,0.0268561281,0.2221177071,0.2095914185,0.0703785866,0.0630866736,0.1186972111,0.0982181951,-0.1481311172,-0.3428363204,0.2690017819,-0.1362376064,-0.133478716,0.0459205545,-0.0404601805,0.1930996478,-0.0156030785,-0.5530503392,-0.2346846461,-0.0609784164,0.3486636281,-0.2883737683,-0.0819918737,0.1984854639,-0.1505389363,0.0831359476,-0.1674246639,-0.3545556366,0.0060318839,0.0953259692,0.2359377742,-0.1034476906,0.4342650473,-0.2289342731,0.6846206784,-0.0281379726,-0.2245501578,0.502378881,-0.1753670871,0.5141823292,-0.1999611706,-0.3403879404,-0.1677144766,-0.0122255329,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3122","title":"OSError with a custom dataset loading script","comments":"Hi Mario,\r\n\r\nI had already tried that before, but it didn't work. I have now recreated the `classla\/janes_tag`  zip file so that it also contains the `data` directory, but I am still getting the same error.","body":"## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":36,"text":"OSError with a custom dataset loading script \n ## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n \n Hi Mario,\r\n\r\nI had already tried that before, but it didn't work. I have now recreated the `classla\/janes_tag`  zip file so that it also contains the `data` directory, but I am still getting the same error.","embeddings":[-0.1128959954,0.2815290093,-0.0133344904,0.3955747187,0.2419004142,0.1173842326,0.4183782339,0.3338423967,0.399305582,0.1943426877,-0.2293029577,0.3192375302,-0.2365897745,-0.0586510971,-0.0276406296,0.0171539187,-0.1140037775,0.1235067025,0.0152646853,0.0538216457,-0.2668657601,0.1890418231,0.0730201155,0.1165179238,-0.1524645835,0.2888302207,-0.305568099,0.6062158346,-0.1058221906,-0.4976432025,0.411575675,-0.0848031938,0.3153779507,0.9328231215,-0.0001182188,0.2439122349,0.1822360009,-0.1082940698,-0.3610416055,-0.3233253062,0.1418734044,-0.0559697747,0.0167580526,-0.2509720623,0.107310012,-0.1073527634,-0.063804917,-0.3272550404,0.1470365524,0.243046701,0.1508885771,-0.0782089829,-0.2656306922,-0.3175562322,0.2304670662,0.3083327115,0.1454181969,0.345936805,0.2215478271,-0.2759937346,-0.0904202983,-0.0664639845,-0.1675293595,0.1449217647,0.2412745804,0.227457568,-0.0587955005,-0.1350414455,0.0041873236,0.3970922828,0.6253734827,-0.1800610423,-0.3252730668,-0.1314567924,0.0302401986,-0.2941183448,0.3250662684,0.2082556188,-0.1266013235,0.0780134052,0.1090783998,0.1126024202,-0.1752075553,0.3246009648,-0.1998942941,0.0976590887,0.0400923453,0.0607715026,0.0194340236,-0.0370791554,-0.0513202921,-0.057304427,0.0523795113,0.2560680509,-0.214629218,0.0880448222,-0.1322696954,-0.160373956,-0.0199079383,0.0104806162,0.2611761391,-0.1694516987,-0.4164377749,0.1147027835,0.1924516112,0.2341755927,0.0028592155,0.0696605891,0.2455968261,0.3264804482,-0.3402524292,0.00250661,-0.2539592683,-0.1772051007,0.2750910223,-0.0635992736,0.55353266,-0.0913137943,-0.6092348099,0.0851995349,-0.0907426551,-0.1220401302,0.0167008061,0.350426048,0.1556562334,0.0076237121,0.324208796,0.3544691801,-0.2622276843,-0.09909565,-0.1103033796,0.2185394168,-0.0110623334,0.2203117162,0.3367286921,-0.1516910642,0.2326345146,0.1329758018,-0.0343423858,-0.1373286694,-0.0624273121,0.1760949641,-0.3333288133,0.1167247742,0.013814901,0.2963364422,0.3627976775,-0.1896024346,-0.1238491237,0.3359745145,-0.511523664,-0.5266267657,-0.1148847342,0.1564936489,-0.2748809159,-0.0949096307,-0.0207241066,-0.2097221762,0.2548092604,-0.3180552125,-0.0229650903,-0.1040266827,-0.5212677717,-0.2807867527,-0.0835549384,0.6397893429,-0.5403725505,-0.0046029598,-0.2959841192,0.0659917593,0.0528749079,0.2252856195,-0.1808517128,0.4837553203,-0.4920635521,0.0551535003,0.1831505001,-0.4322178066,-0.3191882968,0.0916377306,-0.3816460669,0.3135969341,0.1789076775,0.1348926276,0.0342426822,-0.0998480618,-0.0080061089,0.0334071256,0.0391690247,-0.1163586378,-0.1003423557,-0.1150628552,0.2950371802,0.3189660609,-0.0525983013,-0.0284808464,0.0728865042,-0.0832465887,0.2127765864,-0.163817361,-0.1320706457,0.1578202099,0.329498142,0.2000402659,0.1325257421,-0.0937297344,-0.3722987473,0.4385554194,0.203245163,-0.0487933122,0.053555131,-0.0575330257,-0.3631984293,-0.0736556053,-0.4429955482,-0.1980914623,-0.0112997387,0.2344987988,-0.0294579472,-0.0375628062,-0.1806129515,0.4302507043,-0.3583678305,0.073811397,-0.2808938622,0.1908035725,-0.072052367,-0.0913957655,-0.0957284942,0.111958757,0.1296710372,0.000445568,-0.0718535855,0.346457541,0.3362167776,-0.1385053545,-0.0276074223,0.0314185135,-0.000874043,-0.0213833749,0.1846695691,0.2478931397,0.1665903777,-0.1895471066,-0.0732969269,0.1872237921,-0.1838081926,0.2477006912,-0.0536598228,-0.1778230965,0.0044104611,-0.0287910867,0.1164985746,-0.043293152,0.4287044406,-0.141619131,0.3427115679,0.0684483498,0.0491025485,-0.0600036867,0.1305796802,0.0284004379,0.108604908,0.1169634163,-0.0362798646,0.1426839828,-0.3513677418,0.2691136897,0.8010326028,0.0404969342,-0.1698909551,0.179069519,0.0917893425,0.0040805689,-0.0075934026,-0.0389094874,0.151727438,0.3422045112,0.1113567874,0.2531370819,-0.0732817054,-0.3762145936,-0.0807913616,0.3256628811,-0.5139819384,0.0058893552,-0.2444292605,-0.0086611249,-0.3238428235,-0.1037614718,-0.2189128101,-0.1865138412,-0.039064344,0.1296098679,-0.0096950391,-0.0156510603,-0.14024508,0.197912693,0.1155453175,-0.2887761295,0.1485815048,-0.0220534243,-0.1977469027,-0.1064597666,0.259642154,-0.0955125391,0.1163765863,-0.2262869775,-0.011609816,-0.3291104734,-0.2606185675,-0.1494769603,0.1949064881,0.1227230355,0.2664646208,0.2509476542,0.3389138281,-0.354885906,0.3041362464,-0.2651309073,-0.2922275662,0.2627813816,0.0242236461,0.1615139544,-0.0260517858,-0.4274685383,-0.2367953062,-0.3845948875,-0.1018426195,0.0268561281,0.2221177071,0.2095914185,0.0703785866,0.0630866736,0.1186972111,0.0982181951,-0.1481311172,-0.3428363204,0.2690017819,-0.1362376064,-0.133478716,0.0459205545,-0.0404601805,0.1930996478,-0.0156030785,-0.5530503392,-0.2346846461,-0.0609784164,0.3486636281,-0.2883737683,-0.0819918737,0.1984854639,-0.1505389363,0.0831359476,-0.1674246639,-0.3545556366,0.0060318839,0.0953259692,0.2359377742,-0.1034476906,0.4342650473,-0.2289342731,0.6846206784,-0.0281379726,-0.2245501578,0.502378881,-0.1753670871,0.5141823292,-0.1999611706,-0.3403879404,-0.1677144766,-0.0122255329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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3122","title":"OSError with a custom dataset loading script","comments":"Hi,\r\n\r\nI just tried to download the `classla\/janes_tag` dataset, and this time the zip file is extracted correctly. However, the script is now throwing the IndexError, probably due to a bug in the `_generate_examples`.\r\n\r\nLet me know if you are still getting the same error.","body":"## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":45,"text":"OSError with a custom dataset loading script \n ## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n \n Hi,\r\n\r\nI just tried to download the `classla\/janes_tag` dataset, and this time the zip file is extracted correctly. However, the script is now throwing the IndexError, probably due to a bug in the `_generate_examples`.\r\n\r\nLet me know if you are still getting the same error.","embeddings":[-0.1128959954,0.2815290093,-0.0133344904,0.3955747187,0.2419004142,0.1173842326,0.4183782339,0.3338423967,0.399305582,0.1943426877,-0.2293029577,0.3192375302,-0.2365897745,-0.0586510971,-0.0276406296,0.0171539187,-0.1140037775,0.1235067025,0.0152646853,0.0538216457,-0.2668657601,0.1890418231,0.0730201155,0.1165179238,-0.1524645835,0.2888302207,-0.305568099,0.6062158346,-0.1058221906,-0.4976432025,0.411575675,-0.0848031938,0.3153779507,0.9328231215,-0.0001182188,0.2439122349,0.1822360009,-0.1082940698,-0.3610416055,-0.3233253062,0.1418734044,-0.0559697747,0.0167580526,-0.2509720623,0.107310012,-0.1073527634,-0.063804917,-0.3272550404,0.1470365524,0.243046701,0.1508885771,-0.0782089829,-0.2656306922,-0.3175562322,0.2304670662,0.3083327115,0.1454181969,0.345936805,0.2215478271,-0.2759937346,-0.0904202983,-0.0664639845,-0.1675293595,0.1449217647,0.2412745804,0.227457568,-0.0587955005,-0.1350414455,0.0041873236,0.3970922828,0.6253734827,-0.1800610423,-0.3252730668,-0.1314567924,0.0302401986,-0.2941183448,0.3250662684,0.2082556188,-0.1266013235,0.0780134052,0.1090783998,0.1126024202,-0.1752075553,0.3246009648,-0.1998942941,0.0976590887,0.0400923453,0.0607715026,0.0194340236,-0.0370791554,-0.0513202921,-0.057304427,0.0523795113,0.2560680509,-0.214629218,0.0880448222,-0.1322696954,-0.160373956,-0.0199079383,0.0104806162,0.2611761391,-0.1694516987,-0.4164377749,0.1147027835,0.1924516112,0.2341755927,0.0028592155,0.0696605891,0.2455968261,0.3264804482,-0.3402524292,0.00250661,-0.2539592683,-0.1772051007,0.2750910223,-0.0635992736,0.55353266,-0.0913137943,-0.6092348099,0.0851995349,-0.0907426551,-0.1220401302,0.0167008061,0.350426048,0.1556562334,0.0076237121,0.324208796,0.3544691801,-0.2622276843,-0.09909565,-0.1103033796,0.2185394168,-0.0110623334,0.2203117162,0.3367286921,-0.1516910642,0.2326345146,0.1329758018,-0.0343423858,-0.1373286694,-0.0624273121,0.1760949641,-0.3333288133,0.1167247742,0.013814901,0.2963364422,0.3627976775,-0.1896024346,-0.1238491237,0.3359745145,-0.511523664,-0.5266267657,-0.1148847342,0.1564936489,-0.2748809159,-0.0949096307,-0.0207241066,-0.2097221762,0.2548092604,-0.3180552125,-0.0229650903,-0.1040266827,-0.5212677717,-0.2807867527,-0.0835549384,0.6397893429,-0.5403725505,-0.0046029598,-0.2959841192,0.0659917593,0.0528749079,0.2252856195,-0.1808517128,0.4837553203,-0.4920635521,0.0551535003,0.1831505001,-0.4322178066,-0.3191882968,0.0916377306,-0.3816460669,0.3135969341,0.1789076775,0.1348926276,0.0342426822,-0.0998480618,-0.0080061089,0.0334071256,0.0391690247,-0.1163586378,-0.1003423557,-0.1150628552,0.2950371802,0.3189660609,-0.0525983013,-0.0284808464,0.0728865042,-0.0832465887,0.2127765864,-0.163817361,-0.1320706457,0.1578202099,0.329498142,0.2000402659,0.1325257421,-0.0937297344,-0.3722987473,0.4385554194,0.203245163,-0.0487933122,0.053555131,-0.0575330257,-0.3631984293,-0.0736556053,-0.4429955482,-0.1980914623,-0.0112997387,0.2344987988,-0.0294579472,-0.0375628062,-0.1806129515,0.4302507043,-0.3583678305,0.073811397,-0.2808938622,0.1908035725,-0.072052367,-0.0913957655,-0.0957284942,0.111958757,0.1296710372,0.000445568,-0.0718535855,0.346457541,0.3362167776,-0.1385053545,-0.0276074223,0.0314185135,-0.000874043,-0.0213833749,0.1846695691,0.2478931397,0.1665903777,-0.1895471066,-0.0732969269,0.1872237921,-0.1838081926,0.2477006912,-0.0536598228,-0.1778230965,0.0044104611,-0.0287910867,0.1164985746,-0.043293152,0.4287044406,-0.141619131,0.3427115679,0.0684483498,0.0491025485,-0.0600036867,0.1305796802,0.0284004379,0.108604908,0.1169634163,-0.0362798646,0.1426839828,-0.3513677418,0.2691136897,0.8010326028,0.0404969342,-0.1698909551,0.179069519,0.0917893425,0.0040805689,-0.0075934026,-0.0389094874,0.151727438,0.3422045112,0.1113567874,0.2531370819,-0.0732817054,-0.3762145936,-0.0807913616,0.3256628811,-0.5139819384,0.0058893552,-0.2444292605,-0.0086611249,-0.3238428235,-0.1037614718,-0.2189128101,-0.1865138412,-0.039064344,0.1296098679,-0.0096950391,-0.0156510603,-0.14024508,0.197912693,0.1155453175,-0.2887761295,0.1485815048,-0.0220534243,-0.1977469027,-0.1064597666,0.259642154,-0.0955125391,0.1163765863,-0.2262869775,-0.011609816,-0.3291104734,-0.2606185675,-0.1494769603,0.1949064881,0.1227230355,0.2664646208,0.2509476542,0.3389138281,-0.354885906,0.3041362464,-0.2651309073,-0.2922275662,0.2627813816,0.0242236461,0.1615139544,-0.0260517858,-0.4274685383,-0.2367953062,-0.3845948875,-0.1018426195,0.0268561281,0.2221177071,0.2095914185,0.0703785866,0.0630866736,0.1186972111,0.0982181951,-0.1481311172,-0.3428363204,0.2690017819,-0.1362376064,-0.133478716,0.0459205545,-0.0404601805,0.1930996478,-0.0156030785,-0.5530503392,-0.2346846461,-0.0609784164,0.3486636281,-0.2883737683,-0.0819918737,0.1984854639,-0.1505389363,0.0831359476,-0.1674246639,-0.3545556366,0.0060318839,0.0953259692,0.2359377742,-0.1034476906,0.4342650473,-0.2289342731,0.6846206784,-0.0281379726,-0.2245501578,0.502378881,-0.1753670871,0.5141823292,-0.1999611706,-0.3403879404,-0.1677144766,-0.0122255329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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3122","title":"OSError with a custom dataset loading script","comments":"Hi, \r\n\r\ncould you try to download the dataset with a different `cache_dir` like so:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation', cache_dir=\"path\/to\/different\/cache\/dir\")\r\n```\r\nIf this works, then most likely the cached extracted data is causing issues. This data is stored at `~\/.cache\/huggingface\/datasets\/downloads\/extracted` and needs to be deleted, and then it should work (you can easily locate the directory with the path given in the `OSError` message). Additionally, I'd suggest you to update `datasets` to the newest version with:\r\n```\r\npip install -U datasets\r\n```","body":"## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":84,"text":"OSError with a custom dataset loading script \n ## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n \n Hi, \r\n\r\ncould you try to download the dataset with a different `cache_dir` like so:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation', cache_dir=\"path\/to\/different\/cache\/dir\")\r\n```\r\nIf this works, then most likely the cached extracted data is causing issues. This data is stored at `~\/.cache\/huggingface\/datasets\/downloads\/extracted` and needs to be deleted, and then it should work (you can easily locate the directory with the path given in the `OSError` message). Additionally, I'd suggest you to update `datasets` to the newest version with:\r\n```\r\npip install -U datasets\r\n```","embeddings":[-0.1128959954,0.2815290093,-0.0133344904,0.3955747187,0.2419004142,0.1173842326,0.4183782339,0.3338423967,0.399305582,0.1943426877,-0.2293029577,0.3192375302,-0.2365897745,-0.0586510971,-0.0276406296,0.0171539187,-0.1140037775,0.1235067025,0.0152646853,0.0538216457,-0.2668657601,0.1890418231,0.0730201155,0.1165179238,-0.1524645835,0.2888302207,-0.305568099,0.6062158346,-0.1058221906,-0.4976432025,0.411575675,-0.0848031938,0.3153779507,0.9328231215,-0.0001182188,0.2439122349,0.1822360009,-0.1082940698,-0.3610416055,-0.3233253062,0.1418734044,-0.0559697747,0.0167580526,-0.2509720623,0.107310012,-0.1073527634,-0.063804917,-0.3272550404,0.1470365524,0.243046701,0.1508885771,-0.0782089829,-0.2656306922,-0.3175562322,0.2304670662,0.3083327115,0.1454181969,0.345936805,0.2215478271,-0.2759937346,-0.0904202983,-0.0664639845,-0.1675293595,0.1449217647,0.2412745804,0.227457568,-0.0587955005,-0.1350414455,0.0041873236,0.3970922828,0.6253734827,-0.1800610423,-0.3252730668,-0.1314567924,0.0302401986,-0.2941183448,0.3250662684,0.2082556188,-0.1266013235,0.0780134052,0.1090783998,0.1126024202,-0.1752075553,0.3246009648,-0.1998942941,0.0976590887,0.0400923453,0.0607715026,0.0194340236,-0.0370791554,-0.0513202921,-0.057304427,0.0523795113,0.2560680509,-0.214629218,0.0880448222,-0.1322696954,-0.160373956,-0.0199079383,0.0104806162,0.2611761391,-0.1694516987,-0.4164377749,0.1147027835,0.1924516112,0.2341755927,0.0028592155,0.0696605891,0.2455968261,0.3264804482,-0.3402524292,0.00250661,-0.2539592683,-0.1772051007,0.2750910223,-0.0635992736,0.55353266,-0.0913137943,-0.6092348099,0.0851995349,-0.0907426551,-0.1220401302,0.0167008061,0.350426048,0.1556562334,0.0076237121,0.324208796,0.3544691801,-0.2622276843,-0.09909565,-0.1103033796,0.2185394168,-0.0110623334,0.2203117162,0.3367286921,-0.1516910642,0.2326345146,0.1329758018,-0.0343423858,-0.1373286694,-0.0624273121,0.1760949641,-0.3333288133,0.1167247742,0.013814901,0.2963364422,0.3627976775,-0.1896024346,-0.1238491237,0.3359745145,-0.511523664,-0.5266267657,-0.1148847342,0.1564936489,-0.2748809159,-0.0949096307,-0.0207241066,-0.2097221762,0.2548092604,-0.3180552125,-0.0229650903,-0.1040266827,-0.5212677717,-0.2807867527,-0.0835549384,0.6397893429,-0.5403725505,-0.0046029598,-0.2959841192,0.0659917593,0.0528749079,0.2252856195,-0.1808517128,0.4837553203,-0.4920635521,0.0551535003,0.1831505001,-0.4322178066,-0.3191882968,0.0916377306,-0.3816460669,0.3135969341,0.1789076775,0.1348926276,0.0342426822,-0.0998480618,-0.0080061089,0.0334071256,0.0391690247,-0.1163586378,-0.1003423557,-0.1150628552,0.2950371802,0.3189660609,-0.0525983013,-0.0284808464,0.0728865042,-0.0832465887,0.2127765864,-0.163817361,-0.1320706457,0.1578202099,0.329498142,0.2000402659,0.1325257421,-0.0937297344,-0.3722987473,0.4385554194,0.203245163,-0.0487933122,0.053555131,-0.0575330257,-0.3631984293,-0.0736556053,-0.4429955482,-0.1980914623,-0.0112997387,0.2344987988,-0.0294579472,-0.0375628062,-0.1806129515,0.4302507043,-0.3583678305,0.073811397,-0.2808938622,0.1908035725,-0.072052367,-0.0913957655,-0.0957284942,0.111958757,0.1296710372,0.000445568,-0.0718535855,0.346457541,0.3362167776,-0.1385053545,-0.0276074223,0.0314185135,-0.000874043,-0.0213833749,0.1846695691,0.2478931397,0.1665903777,-0.1895471066,-0.0732969269,0.1872237921,-0.1838081926,0.2477006912,-0.0536598228,-0.1778230965,0.0044104611,-0.0287910867,0.1164985746,-0.043293152,0.4287044406,-0.141619131,0.3427115679,0.0684483498,0.0491025485,-0.0600036867,0.1305796802,0.0284004379,0.108604908,0.1169634163,-0.0362798646,0.1426839828,-0.3513677418,0.2691136897,0.8010326028,0.0404969342,-0.1698909551,0.179069519,0.0917893425,0.0040805689,-0.0075934026,-0.0389094874,0.151727438,0.3422045112,0.1113567874,0.2531370819,-0.0732817054,-0.3762145936,-0.0807913616,0.3256628811,-0.5139819384,0.0058893552,-0.2444292605,-0.0086611249,-0.3238428235,-0.1037614718,-0.2189128101,-0.1865138412,-0.039064344,0.1296098679,-0.0096950391,-0.0156510603,-0.14024508,0.197912693,0.1155453175,-0.2887761295,0.1485815048,-0.0220534243,-0.1977469027,-0.1064597666,0.259642154,-0.0955125391,0.1163765863,-0.2262869775,-0.011609816,-0.3291104734,-0.2606185675,-0.1494769603,0.1949064881,0.1227230355,0.2664646208,0.2509476542,0.3389138281,-0.354885906,0.3041362464,-0.2651309073,-0.2922275662,0.2627813816,0.0242236461,0.1615139544,-0.0260517858,-0.4274685383,-0.2367953062,-0.3845948875,-0.1018426195,0.0268561281,0.2221177071,0.2095914185,0.0703785866,0.0630866736,0.1186972111,0.0982181951,-0.1481311172,-0.3428363204,0.2690017819,-0.1362376064,-0.133478716,0.0459205545,-0.0404601805,0.1930996478,-0.0156030785,-0.5530503392,-0.2346846461,-0.0609784164,0.3486636281,-0.2883737683,-0.0819918737,0.1984854639,-0.1505389363,0.0831359476,-0.1674246639,-0.3545556366,0.0060318839,0.0953259692,0.2359377742,-0.1034476906,0.4342650473,-0.2289342731,0.6846206784,-0.0281379726,-0.2245501578,0.502378881,-0.1753670871,0.5141823292,-0.1999611706,-0.3403879404,-0.1677144766,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3122","title":"OSError with a custom dataset loading script","comments":"Thank you, deleting the `~\/.cache\/huggingface\/datasets\/downloads\/extracted` directory helped. However, I am still having problems.\r\n\r\nThere was indeed a bug in the script that was throwing an `IndexError`, which I have now corrected (added the condition to skip the lines starting with '# text') and it is working locally, but still throws an error when I try to load the dataset from HuggingFace. I literally copied and pasted the `_generate_examples` function and ran it on the `dev_all.conllup` file, which I even re-downloaded from the repository to be certain that the files are exactly the same. I also deleted everything again just in case, but it didn't help. The code works locally, but throws an `IndexError` when loading from `datasets.`","body":"## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":117,"text":"OSError with a custom dataset loading script \n ## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n \n Thank you, deleting the `~\/.cache\/huggingface\/datasets\/downloads\/extracted` directory helped. However, I am still having problems.\r\n\r\nThere was indeed a bug in the script that was throwing an `IndexError`, which I have now corrected (added the condition to skip the lines starting with '# text') and it is working locally, but still throws an error when I try to load the dataset from HuggingFace. I literally copied and pasted the `_generate_examples` function and ran it on the `dev_all.conllup` file, which I even re-downloaded from the repository to be certain that the files are exactly the same. I also deleted everything again just in case, but it didn't help. The code works locally, but throws an `IndexError` when loading from `datasets.`","embeddings":[-0.1128959954,0.2815290093,-0.0133344904,0.3955747187,0.2419004142,0.1173842326,0.4183782339,0.3338423967,0.399305582,0.1943426877,-0.2293029577,0.3192375302,-0.2365897745,-0.0586510971,-0.0276406296,0.0171539187,-0.1140037775,0.1235067025,0.0152646853,0.0538216457,-0.2668657601,0.1890418231,0.0730201155,0.1165179238,-0.1524645835,0.2888302207,-0.305568099,0.6062158346,-0.1058221906,-0.4976432025,0.411575675,-0.0848031938,0.3153779507,0.9328231215,-0.0001182188,0.2439122349,0.1822360009,-0.1082940698,-0.3610416055,-0.3233253062,0.1418734044,-0.0559697747,0.0167580526,-0.2509720623,0.107310012,-0.1073527634,-0.063804917,-0.3272550404,0.1470365524,0.243046701,0.1508885771,-0.0782089829,-0.2656306922,-0.3175562322,0.2304670662,0.3083327115,0.1454181969,0.345936805,0.2215478271,-0.2759937346,-0.0904202983,-0.0664639845,-0.1675293595,0.1449217647,0.2412745804,0.227457568,-0.0587955005,-0.1350414455,0.0041873236,0.3970922828,0.6253734827,-0.1800610423,-0.3252730668,-0.1314567924,0.0302401986,-0.2941183448,0.3250662684,0.2082556188,-0.1266013235,0.0780134052,0.1090783998,0.1126024202,-0.1752075553,0.3246009648,-0.1998942941,0.0976590887,0.0400923453,0.0607715026,0.0194340236,-0.0370791554,-0.0513202921,-0.057304427,0.0523795113,0.2560680509,-0.214629218,0.0880448222,-0.1322696954,-0.160373956,-0.0199079383,0.0104806162,0.2611761391,-0.1694516987,-0.4164377749,0.1147027835,0.1924516112,0.2341755927,0.0028592155,0.0696605891,0.2455968261,0.3264804482,-0.3402524292,0.00250661,-0.2539592683,-0.1772051007,0.2750910223,-0.0635992736,0.55353266,-0.0913137943,-0.6092348099,0.0851995349,-0.0907426551,-0.1220401302,0.0167008061,0.350426048,0.1556562334,0.0076237121,0.324208796,0.3544691801,-0.2622276843,-0.09909565,-0.1103033796,0.2185394168,-0.0110623334,0.2203117162,0.3367286921,-0.1516910642,0.2326345146,0.1329758018,-0.0343423858,-0.1373286694,-0.0624273121,0.1760949641,-0.3333288133,0.1167247742,0.013814901,0.2963364422,0.3627976775,-0.1896024346,-0.1238491237,0.3359745145,-0.511523664,-0.5266267657,-0.1148847342,0.1564936489,-0.2748809159,-0.0949096307,-0.0207241066,-0.2097221762,0.2548092604,-0.3180552125,-0.0229650903,-0.1040266827,-0.5212677717,-0.2807867527,-0.0835549384,0.6397893429,-0.5403725505,-0.0046029598,-0.2959841192,0.0659917593,0.0528749079,0.2252856195,-0.1808517128,0.4837553203,-0.4920635521,0.0551535003,0.1831505001,-0.4322178066,-0.3191882968,0.0916377306,-0.3816460669,0.3135969341,0.1789076775,0.1348926276,0.0342426822,-0.0998480618,-0.0080061089,0.0334071256,0.0391690247,-0.1163586378,-0.1003423557,-0.1150628552,0.2950371802,0.3189660609,-0.0525983013,-0.0284808464,0.0728865042,-0.0832465887,0.2127765864,-0.163817361,-0.1320706457,0.1578202099,0.329498142,0.2000402659,0.1325257421,-0.0937297344,-0.3722987473,0.4385554194,0.203245163,-0.0487933122,0.053555131,-0.0575330257,-0.3631984293,-0.0736556053,-0.4429955482,-0.1980914623,-0.0112997387,0.2344987988,-0.0294579472,-0.0375628062,-0.1806129515,0.4302507043,-0.3583678305,0.073811397,-0.2808938622,0.1908035725,-0.072052367,-0.0913957655,-0.0957284942,0.111958757,0.1296710372,0.000445568,-0.0718535855,0.346457541,0.3362167776,-0.1385053545,-0.0276074223,0.0314185135,-0.000874043,-0.0213833749,0.1846695691,0.2478931397,0.1665903777,-0.1895471066,-0.0732969269,0.1872237921,-0.1838081926,0.2477006912,-0.0536598228,-0.1778230965,0.0044104611,-0.0287910867,0.1164985746,-0.043293152,0.4287044406,-0.141619131,0.3427115679,0.0684483498,0.0491025485,-0.0600036867,0.1305796802,0.0284004379,0.108604908,0.1169634163,-0.0362798646,0.1426839828,-0.3513677418,0.2691136897,0.8010326028,0.0404969342,-0.1698909551,0.179069519,0.0917893425,0.0040805689,-0.0075934026,-0.0389094874,0.151727438,0.3422045112,0.1113567874,0.2531370819,-0.0732817054,-0.3762145936,-0.0807913616,0.3256628811,-0.5139819384,0.0058893552,-0.2444292605,-0.0086611249,-0.3238428235,-0.1037614718,-0.2189128101,-0.1865138412,-0.039064344,0.1296098679,-0.0096950391,-0.0156510603,-0.14024508,0.197912693,0.1155453175,-0.2887761295,0.1485815048,-0.0220534243,-0.1977469027,-0.1064597666,0.259642154,-0.0955125391,0.1163765863,-0.2262869775,-0.011609816,-0.3291104734,-0.2606185675,-0.1494769603,0.1949064881,0.1227230355,0.2664646208,0.2509476542,0.3389138281,-0.354885906,0.3041362464,-0.2651309073,-0.2922275662,0.2627813816,0.0242236461,0.1615139544,-0.0260517858,-0.4274685383,-0.2367953062,-0.3845948875,-0.1018426195,0.0268561281,0.2221177071,0.2095914185,0.0703785866,0.0630866736,0.1186972111,0.0982181951,-0.1481311172,-0.3428363204,0.2690017819,-0.1362376064,-0.133478716,0.0459205545,-0.0404601805,0.1930996478,-0.0156030785,-0.5530503392,-0.2346846461,-0.0609784164,0.3486636281,-0.2883737683,-0.0819918737,0.1984854639,-0.1505389363,0.0831359476,-0.1674246639,-0.3545556366,0.0060318839,0.0953259692,0.2359377742,-0.1034476906,0.4342650473,-0.2289342731,0.6846206784,-0.0281379726,-0.2245501578,0.502378881,-0.1753670871,0.5141823292,-0.1999611706,-0.3403879404,-0.1677144766,-0.01222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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3122","title":"OSError with a custom dataset loading script","comments":"Hi,\r\n\r\nDid some investigation.\r\n\r\nTo fix the dataset script on the Hub, append the following labels to the `names` list of the `upos_tags` field:\r\n```'INTJ NOUN', 'AUX PRON', 'PART ADV', 'PRON ADP', 'INTJ INTJ', 'VERB NOUN', 'NOUN AUX'```.\r\n\r\nThis step is required to avoid an error due to missing labels in the following step which is:\r\n```python\r\nload_dataset(\"classla\/janes_tag\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\nThis will generate and cache the dataset, so specifying `download_mode` will not be required anymore unless you update the script\/data on the Hub.","body":"## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":84,"text":"OSError with a custom dataset loading script \n ## Describe the bug\r\nI am getting an OS error when trying to load the newly uploaded dataset classla\/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla\/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = datasets.load_dataset('classla\/janes_tag', split='validation')\r\n```\r\n\r\n## Expected results\r\nDataset correctly loaded.\r\n\r\n## Actual results\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/mypath\/test.py\", line 91, in <module>\r\n    load_and_print('janes_tag')\r\n  File \"C:\/mypath\/test.py\", line 32, in load_and_print\r\n    dataset = datasets.load_dataset('classla\/{}'.format(ds_name), split='validation')\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"C:\\mypath\\venv\\lib\\site-packages\\datasets\\builder.py\", line 704, in _download_and_prepare\r\n    ) from None\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] No such file or directory: 'C:\\\\mypath\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\\\data\\\\train_all.conllup'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.14.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.5\r\n- PyArrow version: 3.0.0\r\n \n Hi,\r\n\r\nDid some investigation.\r\n\r\nTo fix the dataset script on the Hub, append the following labels to the `names` list of the `upos_tags` field:\r\n```'INTJ NOUN', 'AUX PRON', 'PART ADV', 'PRON ADP', 'INTJ INTJ', 'VERB NOUN', 'NOUN AUX'```.\r\n\r\nThis step is required to avoid an error due to missing labels in the following step which is:\r\n```python\r\nload_dataset(\"classla\/janes_tag\", split=\"validation\", download_mode=\"force_redownload\")\r\n```\r\nThis will generate and cache the dataset, so specifying `download_mode` will not be required anymore unless you update the script\/data on the Hub.","embeddings":[-0.1128959954,0.2815290093,-0.0133344904,0.3955747187,0.2419004142,0.1173842326,0.4183782339,0.3338423967,0.399305582,0.1943426877,-0.2293029577,0.3192375302,-0.2365897745,-0.0586510971,-0.0276406296,0.0171539187,-0.1140037775,0.1235067025,0.0152646853,0.0538216457,-0.2668657601,0.1890418231,0.0730201155,0.1165179238,-0.1524645835,0.2888302207,-0.305568099,0.6062158346,-0.1058221906,-0.4976432025,0.411575675,-0.0848031938,0.3153779507,0.9328231215,-0.0001182188,0.2439122349,0.1822360009,-0.1082940698,-0.3610416055,-0.3233253062,0.1418734044,-0.0559697747,0.0167580526,-0.2509720623,0.107310012,-0.1073527634,-0.063804917,-0.3272550404,0.1470365524,0.243046701,0.1508885771,-0.0782089829,-0.2656306922,-0.3175562322,0.2304670662,0.3083327115,0.1454181969,0.345936805,0.2215478271,-0.2759937346,-0.0904202983,-0.0664639845,-0.1675293595,0.1449217647,0.2412745804,0.227457568,-0.0587955005,-0.1350414455,0.0041873236,0.3970922828,0.6253734827,-0.1800610423,-0.3252730668,-0.1314567924,0.0302401986,-0.2941183448,0.3250662684,0.2082556188,-0.1266013235,0.0780134052,0.1090783998,0.1126024202,-0.1752075553,0.3246009648,-0.1998942941,0.0976590887,0.0400923453,0.0607715026,0.0194340236,-0.0370791554,-0.0513202921,-0.057304427,0.0523795113,0.2560680509,-0.214629218,0.0880448222,-0.1322696954,-0.160373956,-0.0199079383,0.0104806162,0.2611761391,-0.1694516987,-0.4164377749,0.1147027835,0.1924516112,0.2341755927,0.0028592155,0.0696605891,0.2455968261,0.3264804482,-0.3402524292,0.00250661,-0.2539592683,-0.1772051007,0.2750910223,-0.0635992736,0.55353266,-0.0913137943,-0.6092348099,0.0851995349,-0.0907426551,-0.1220401302,0.0167008061,0.350426048,0.1556562334,0.0076237121,0.324208796,0.3544691801,-0.2622276843,-0.09909565,-0.1103033796,0.2185394168,-0.0110623334,0.2203117162,0.3367286921,-0.1516910642,0.2326345146,0.1329758018,-0.0343423858,-0.1373286694,-0.0624273121,0.1760949641,-0.3333288133,0.1167247742,0.013814901,0.2963364422,0.3627976775,-0.1896024346,-0.1238491237,0.3359745145,-0.511523664,-0.5266267657,-0.1148847342,0.1564936489,-0.2748809159,-0.0949096307,-0.0207241066,-0.2097221762,0.2548092604,-0.3180552125,-0.0229650903,-0.1040266827,-0.5212677717,-0.2807867527,-0.0835549384,0.6397893429,-0.5403725505,-0.0046029598,-0.2959841192,0.0659917593,0.0528749079,0.2252856195,-0.1808517128,0.4837553203,-0.4920635521,0.0551535003,0.1831505001,-0.4322178066,-0.3191882968,0.0916377306,-0.3816460669,0.3135969341,0.1789076775,0.1348926276,0.0342426822,-0.0998480618,-0.0080061089,0.0334071256,0.0391690247,-0.1163586378,-0.1003423557,-0.1150628552,0.2950371802,0.3189660609,-0.0525983013,-0.0284808464,0.0728865042,-0.0832465887,0.2127765864,-0.163817361,-0.1320706457,0.1578202099,0.329498142,0.2000402659,0.1325257421,-0.0937297344,-0.3722987473,0.4385554194,0.203245163,-0.0487933122,0.053555131,-0.0575330257,-0.3631984293,-0.0736556053,-0.4429955482,-0.1980914623,-0.0112997387,0.2344987988,-0.0294579472,-0.0375628062,-0.1806129515,0.4302507043,-0.3583678305,0.073811397,-0.2808938622,0.1908035725,-0.072052367,-0.0913957655,-0.0957284942,0.111958757,0.1296710372,0.000445568,-0.0718535855,0.346457541,0.3362167776,-0.1385053545,-0.0276074223,0.0314185135,-0.000874043,-0.0213833749,0.1846695691,0.2478931397,0.1665903777,-0.1895471066,-0.0732969269,0.1872237921,-0.1838081926,0.2477006912,-0.0536598228,-0.1778230965,0.0044104611,-0.0287910867,0.1164985746,-0.043293152,0.4287044406,-0.141619131,0.3427115679,0.0684483498,0.0491025485,-0.0600036867,0.1305796802,0.0284004379,0.108604908,0.1169634163,-0.0362798646,0.1426839828,-0.3513677418,0.2691136897,0.8010326028,0.0404969342,-0.1698909551,0.179069519,0.0917893425,0.0040805689,-0.0075934026,-0.0389094874,0.151727438,0.3422045112,0.1113567874,0.2531370819,-0.0732817054,-0.3762145936,-0.0807913616,0.3256628811,-0.5139819384,0.0058893552,-0.2444292605,-0.0086611249,-0.3238428235,-0.1037614718,-0.2189128101,-0.1865138412,-0.039064344,0.1296098679,-0.0096950391,-0.0156510603,-0.14024508,0.197912693,0.1155453175,-0.2887761295,0.1485815048,-0.0220534243,-0.1977469027,-0.1064597666,0.259642154,-0.0955125391,0.1163765863,-0.2262869775,-0.011609816,-0.3291104734,-0.2606185675,-0.1494769603,0.1949064881,0.1227230355,0.2664646208,0.2509476542,0.3389138281,-0.354885906,0.3041362464,-0.2651309073,-0.2922275662,0.2627813816,0.0242236461,0.1615139544,-0.0260517858,-0.4274685383,-0.2367953062,-0.3845948875,-0.1018426195,0.0268561281,0.2221177071,0.2095914185,0.0703785866,0.0630866736,0.1186972111,0.0982181951,-0.1481311172,-0.3428363204,0.2690017819,-0.1362376064,-0.133478716,0.0459205545,-0.0404601805,0.1930996478,-0.0156030785,-0.5530503392,-0.2346846461,-0.0609784164,0.3486636281,-0.2883737683,-0.0819918737,0.1984854639,-0.1505389363,0.0831359476,-0.1674246639,-0.3545556366,0.0060318839,0.0953259692,0.2359377742,-0.1034476906,0.4342650473,-0.2289342731,0.6846206784,-0.0281379726,-0.2245501578,0.502378881,-0.1753670871,0.5141823292,-0.1999611706,-0.3403879404,-0.1677144766,-0.0122255329,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3119","title":"Add OpenSLR 83 - Crowdsourced high-quality UK and Ireland English Dialect speech","comments":"Ugh. The index files for SLR83 are CSV, not TSV. I need to add logic to process these index files.","body":"## Adding a Dataset\r\n- **Name:** *openslr**\r\n- **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.*\r\n- **Paper:** *https:\/\/www.openslr.org\/resources\/83\/about.html*\r\n- **Data:** *Eleven separate data files can be found via https:\/\/www.openslr.org\/resources\/83\/*\r\n- **Motivation:** *Increase english ASR data with UK and Irish dialects*\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nThe *openslr* dataset already exists, this will add additional subset, *SLR83*.","comment_length":20,"text":"Add OpenSLR 83 - Crowdsourced high-quality UK and Ireland English Dialect speech \n ## Adding a Dataset\r\n- **Name:** *openslr**\r\n- **Description:** *Data set which contains male and female recordings of English from various dialects of the UK and Ireland.*\r\n- **Paper:** *https:\/\/www.openslr.org\/resources\/83\/about.html*\r\n- **Data:** *Eleven separate data files can be found via https:\/\/www.openslr.org\/resources\/83\/*\r\n- **Motivation:** *Increase english ASR data with UK and Irish dialects*\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nThe *openslr* dataset already exists, this will add additional subset, *SLR83*. \n Ugh. The index files for SLR83 are CSV, not TSV. I need to add logic to process these index files.","embeddings":[-0.0509534553,0.2338021398,-0.1422743648,0.104232274,-0.3136020005,0.2464337349,-0.0554704629,0.2928812802,0.5650348067,0.2243563682,-0.1979468614,0.151480034,-0.4145608544,0.2902002037,-0.04630059,0.2556519508,0.0738376603,0.1669848561,0.0678538159,-0.1343690306,-0.1931578368,0.0323270261,0.0292691998,-0.4757165909,0.1941168904,0.0646487027,-0.146458596,0.1191218793,-0.4239875376,-0.1926948577,-0.0828790367,0.1462191194,0.4242410362,0.2993045151,-0.000120317,-0.1240756363,0.0498129353,-0.1574859619,-0.1657472402,-0.0447544232,-0.1879732013,-0.1172536984,-0.1636561453,-0.2847345769,0.0109924106,-0.1401000321,-0.0040259631,-0.4738585949,0.3443059921,0.4983550012,0.0810898393,-0.2362529337,-0.0446554534,0.1376219839,0.5199350715,0.3255409002,0.0566006936,-0.1557752639,0.1246465817,0.2177200317,0.0643828064,0.2777470648,-0.2605280578,-0.4083625972,-0.0659870058,0.0378827266,-0.1326918155,-0.1573368609,-0.1533317417,0.2418283969,0.5251845717,0.0429909602,-0.2338139862,-0.0666675046,-0.2356085777,-0.5749571323,-0.0652378872,-0.065824233,0.0468185358,0.2967836261,0.2624107003,-0.2391830087,-0.274898082,0.2700686157,0.1597602963,0.5638093352,0.0469546579,-0.2372244,0.5630198717,-0.0697602928,0.2946172655,0.0387221575,0.0378459468,0.2328002006,-0.4092856646,-0.0824057683,0.1123387963,-0.3244323432,-0.004356279,0.027701728,0.2231144309,0.1077826098,0.0131821977,0.0114476932,0.0053057643,0.2598889172,-0.0336399153,0.0075371712,0.0787887275,-0.129198283,0.0562401377,-0.0779841319,-0.3173889518,-0.3271739483,-0.3687657118,0.0027745366,0.0145457722,-0.1879828274,-0.0469698645,0.2333133817,-0.0643969476,-0.4009294212,0.0387273319,0.0086923027,0.132949844,0.2325969934,0.1835964471,0.2476230562,-0.1305820346,0.0541622043,0.0180970561,0.2261682749,0.1842285991,0.0799576789,0.1816687286,-0.2603930533,-0.056761194,0.2779752612,0.0226736255,-0.0179040693,0.0760478675,-0.0239887442,0.2114244848,0.0286036208,0.0673325956,-0.083231248,-0.1425860077,-0.3877199292,-0.1439108253,0.2580006421,-0.172458753,-0.3384285867,-0.6734220386,0.1089843139,-0.2504033446,-0.3172882497,-0.0885847732,0.5379897356,-0.0850939676,-0.1611691862,0.0379926786,0.248031497,-0.087062411,0.0390384234,0.1053834334,0.1240279526,-0.6394197941,0.4462562203,-0.5473041534,0.0708155632,0.1171505824,0.0194134098,-0.0403636023,0.0638471767,-0.1777105629,0.2216798216,0.3539551795,-0.2851119041,-0.0975629613,0.172297433,-0.2426548451,-0.2729947269,0.8565294743,0.1974641979,0.4108784795,0.0416895784,0.0094674136,0.2636066079,-0.0107659334,-0.3581661582,-0.0554178543,-0.0009453372,0.1381151229,0.4360538721,0.0029819496,0.068006359,0.2427411228,-0.2749914527,0.3254119158,-0.2881462574,0.2662016451,0.0143961292,0.5430256128,0.3106611669,0.1235949993,-0.1681961417,-0.1778963059,-0.2098633945,0.0823280662,0.1115249395,-0.0235179812,-0.24944821,-0.3565727472,-0.2896191776,-0.0225429367,0.1970887035,0.0226405375,0.0554991663,-0.15925394,-0.241422534,-0.3710379004,-0.1013992429,0.0657217056,-0.1766549051,-0.2498583198,0.2081737518,-0.2103737593,-0.1036920771,0.1463564634,0.3502123952,-0.0665807053,-0.1764823496,0.1758454442,0.0455710329,0.0550361276,-0.027890563,0.7096669078,0.0472317636,-0.0349558927,-0.178776294,0.1177027002,-0.044811368,0.0767873451,0.0194275081,-0.2615093291,0.5356485248,-0.3654962778,0.2776793242,-0.2429318875,-0.1112512127,0.0210768376,-0.1462047845,-0.0727854595,-0.2937822342,0.2116627991,0.1317346245,-0.0930195898,0.200318113,-0.3462790847,0.3120553493,0.2731941044,-0.1874342412,0.0025405786,0.3556023836,0.1732566953,0.0534109212,-0.1632880867,-0.210160166,0.0275419522,0.2441107333,0.0965517163,-0.0394419208,0.3879839182,-0.1221379414,0.2174078226,-0.1312504858,-0.2307742089,0.4156410098,0.1559899598,-0.0856602788,-0.3132009506,-0.2532366216,0.059968479,-0.0368590876,-0.1244010106,-0.1515340954,-0.0676487833,0.068713285,-0.3935278058,-0.2099524736,0.0155025776,-0.1352455169,0.0311402138,-0.0589432418,-0.297984302,-0.1612521112,-0.2785433233,0.0052926615,-0.1062872708,-0.3683427572,0.280564487,-0.3393211365,0.0290886071,0.0584301837,0.298758328,0.2636342347,0.0171876214,-0.1923506856,0.3628999591,-0.1646125466,-0.4911407232,0.0534825101,-0.2734577954,-0.0255231652,0.0592791662,-0.1290898323,-0.261015445,-0.7199594975,-0.2041206956,0.4557150304,-0.3778246343,-0.0697562248,-0.0150965909,0.1534132212,-0.1108855233,-0.8450393677,-0.3586763144,-0.4437713027,0.2386009395,-0.122569859,0.2775231004,-0.1137254611,0.1369560808,0.0736127049,-0.1709694564,0.1169446334,-0.1538461596,0.1334352642,0.535217762,-0.1651065499,-0.0441974849,0.1229194179,-0.1087678969,-0.3636181653,-0.2102993131,-0.3078949153,-0.025166776,-0.0375449546,0.2223182321,-0.061148867,0.3615266085,0.5438821912,0.1638171375,-0.1597710848,-0.2693540752,-0.1892402917,0.3662846684,0.3068470657,-0.0560317449,0.0342046954,0.6313487291,-0.2182052583,0.7873808742,-0.0472775884,0.0668312162,0.4801153243,0.1973683834,0.2165360451,0.140827179,-0.2295309007,0.2855811417,0.1948391646,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3114","title":"load_from_disk in DatasetsDict\/Dataset not working with PyArrowHDFS wrapper implementing fsspec.spec.AbstractFileSystem","comments":"Hi ! Can you try again with pyarrow 6.0.0 ? I think it includes some changes regarding filesystems compatibility with fsspec.","body":"## Describe the bug\r\nPassing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter.\r\n\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe documentation for the `fs` parameter states:\r\n\r\n```\r\nfs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``):\r\n                Instance of the remote filesystem used to download the files from.\r\n```\r\n\r\n`PyArrowHDFS` from [fsspec](https:\/\/filesystem-spec.readthedocs.io\/en\/latest\/_modules\/fsspec\/implementations\/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error.\r\n\r\n```python\r\nfrom fsspec.implementations.hdfs import PyArrowHDFS\r\n...\r\ntransformed_corpus_path = \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nfs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)\r\ndss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)\r\n```\r\n\r\n## Expected results\r\n\r\nPrevious to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing:\r\n```python\r\ntransformed_corpus_path = \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nfs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)\r\nmy_datasets.save_to_disk(transformed_corpus_path, fs=fs)\r\n```\r\n\r\nAs I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS:\r\n\r\n```sh\r\n$ hadoop fs -ls \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nFound 4 items\r\n-rw-------   3 my_user users         43 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/dataset_dict.json\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/test\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/train\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/validation\r\n```\r\n\r\nI would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above. \r\n\r\n## Actual results\r\n\r\nHowever, when trying to recover the saved datasets, I get this error:\r\n\r\n```\r\n...\r\n  File \"\/home\/fperez\/dev\/neuromancer\/neuromancer\/corpus.py\", line 186, in load_transformed_corpus_from_disk\r\n    dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)\r\n  File \"\/home\/fperez\/anaconda3\/envs\/neuromancer\/lib\/python3.9\/site-packages\/datasets\/dataset_dict.py\", line 748, in load_from_disk\r\n    dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/home\/fperez\/anaconda3\/envs\/neuromancer\/lib\/python3.9\/site-packages\/datasets\/arrow_dataset.py\", line 1048, in load_from_disk\r\n    fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)\r\n  File \"pyarrow\/_hdfsio.pyx\", line 438, in pyarrow._hdfsio.HadoopFileSystem.download\r\nTypeError: download() got an unexpected keyword argument 'recursive'\r\n```\r\n\r\nExamining the [signature of the download method in pyarrow 5.0.0](https:\/\/github.com\/apache\/arrow\/blob\/54d2bd89c99df72fa091b025452f85dd5d88e3cf\/python\/pyarrow\/_hdfsio.pyx#L438) we can see that there's no download parameter:\r\n\r\n```python\r\n    def download(self, path, stream, buffer_size=None):\r\n        with self.open(path, 'rb') as f:\r\n            f.download(stream, buffer_size=buffer_size)\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.3\r\n- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":21,"text":"load_from_disk in DatasetsDict\/Dataset not working with PyArrowHDFS wrapper implementing fsspec.spec.AbstractFileSystem \n ## Describe the bug\r\nPassing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter.\r\n\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe documentation for the `fs` parameter states:\r\n\r\n```\r\nfs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``):\r\n                Instance of the remote filesystem used to download the files from.\r\n```\r\n\r\n`PyArrowHDFS` from [fsspec](https:\/\/filesystem-spec.readthedocs.io\/en\/latest\/_modules\/fsspec\/implementations\/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error.\r\n\r\n```python\r\nfrom fsspec.implementations.hdfs import PyArrowHDFS\r\n...\r\ntransformed_corpus_path = \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nfs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)\r\ndss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)\r\n```\r\n\r\n## Expected results\r\n\r\nPrevious to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing:\r\n```python\r\ntransformed_corpus_path = \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nfs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)\r\nmy_datasets.save_to_disk(transformed_corpus_path, fs=fs)\r\n```\r\n\r\nAs I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS:\r\n\r\n```sh\r\n$ hadoop fs -ls \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nFound 4 items\r\n-rw-------   3 my_user users         43 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/dataset_dict.json\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/test\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/train\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/validation\r\n```\r\n\r\nI would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above. \r\n\r\n## Actual results\r\n\r\nHowever, when trying to recover the saved datasets, I get this error:\r\n\r\n```\r\n...\r\n  File \"\/home\/fperez\/dev\/neuromancer\/neuromancer\/corpus.py\", line 186, in load_transformed_corpus_from_disk\r\n    dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)\r\n  File \"\/home\/fperez\/anaconda3\/envs\/neuromancer\/lib\/python3.9\/site-packages\/datasets\/dataset_dict.py\", line 748, in load_from_disk\r\n    dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/home\/fperez\/anaconda3\/envs\/neuromancer\/lib\/python3.9\/site-packages\/datasets\/arrow_dataset.py\", line 1048, in load_from_disk\r\n    fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)\r\n  File \"pyarrow\/_hdfsio.pyx\", line 438, in pyarrow._hdfsio.HadoopFileSystem.download\r\nTypeError: download() got an unexpected keyword argument 'recursive'\r\n```\r\n\r\nExamining the [signature of the download method in pyarrow 5.0.0](https:\/\/github.com\/apache\/arrow\/blob\/54d2bd89c99df72fa091b025452f85dd5d88e3cf\/python\/pyarrow\/_hdfsio.pyx#L438) we can see that there's no download parameter:\r\n\r\n```python\r\n    def download(self, path, stream, buffer_size=None):\r\n        with self.open(path, 'rb') as f:\r\n            f.download(stream, buffer_size=buffer_size)\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.3\r\n- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Hi ! Can you try again with pyarrow 6.0.0 ? I think it includes some changes regarding filesystems compatibility with 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3114","title":"load_from_disk in DatasetsDict\/Dataset not working with PyArrowHDFS wrapper implementing fsspec.spec.AbstractFileSystem","comments":"Hi @lhoestq! I ended up using `fsspec.implementations.arrow.HadoopFileSystem` which doesn't have the problem I described with pyarrow 5.0.0.\r\n\r\nI'll try again with `PyArrowHDFS` once I update arrow to 6.0.0.\r\n\r\nThanks!","body":"## Describe the bug\r\nPassing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter.\r\n\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe documentation for the `fs` parameter states:\r\n\r\n```\r\nfs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``):\r\n                Instance of the remote filesystem used to download the files from.\r\n```\r\n\r\n`PyArrowHDFS` from [fsspec](https:\/\/filesystem-spec.readthedocs.io\/en\/latest\/_modules\/fsspec\/implementations\/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error.\r\n\r\n```python\r\nfrom fsspec.implementations.hdfs import PyArrowHDFS\r\n...\r\ntransformed_corpus_path = \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nfs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)\r\ndss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)\r\n```\r\n\r\n## Expected results\r\n\r\nPrevious to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing:\r\n```python\r\ntransformed_corpus_path = \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nfs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)\r\nmy_datasets.save_to_disk(transformed_corpus_path, fs=fs)\r\n```\r\n\r\nAs I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS:\r\n\r\n```sh\r\n$ hadoop fs -ls \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nFound 4 items\r\n-rw-------   3 my_user users         43 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/dataset_dict.json\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/test\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/train\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/validation\r\n```\r\n\r\nI would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above. \r\n\r\n## Actual results\r\n\r\nHowever, when trying to recover the saved datasets, I get this error:\r\n\r\n```\r\n...\r\n  File \"\/home\/fperez\/dev\/neuromancer\/neuromancer\/corpus.py\", line 186, in load_transformed_corpus_from_disk\r\n    dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)\r\n  File \"\/home\/fperez\/anaconda3\/envs\/neuromancer\/lib\/python3.9\/site-packages\/datasets\/dataset_dict.py\", line 748, in load_from_disk\r\n    dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/home\/fperez\/anaconda3\/envs\/neuromancer\/lib\/python3.9\/site-packages\/datasets\/arrow_dataset.py\", line 1048, in load_from_disk\r\n    fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)\r\n  File \"pyarrow\/_hdfsio.pyx\", line 438, in pyarrow._hdfsio.HadoopFileSystem.download\r\nTypeError: download() got an unexpected keyword argument 'recursive'\r\n```\r\n\r\nExamining the [signature of the download method in pyarrow 5.0.0](https:\/\/github.com\/apache\/arrow\/blob\/54d2bd89c99df72fa091b025452f85dd5d88e3cf\/python\/pyarrow\/_hdfsio.pyx#L438) we can see that there's no download parameter:\r\n\r\n```python\r\n    def download(self, path, stream, buffer_size=None):\r\n        with self.open(path, 'rb') as f:\r\n            f.download(stream, buffer_size=buffer_size)\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.3\r\n- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":29,"text":"load_from_disk in DatasetsDict\/Dataset not working with PyArrowHDFS wrapper implementing fsspec.spec.AbstractFileSystem \n ## Describe the bug\r\nPassing a PyArrowHDFS implementation of fsspec.spec.AbstractFileSystem (in the `fs` param required by `load_from_disk` methods in `DatasetDict` (in datasets_dict.py) and `Dataset` (in arrow_dataset.py) results in an error when calling the download method in the `fs` parameter.\r\n\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe documentation for the `fs` parameter states:\r\n\r\n```\r\nfs (:class:`~filesystems.S3FileSystem` or ``fsspec.spec.AbstractFileSystem``, optional, default ``None``):\r\n                Instance of the remote filesystem used to download the files from.\r\n```\r\n\r\n`PyArrowHDFS` from [fsspec](https:\/\/filesystem-spec.readthedocs.io\/en\/latest\/_modules\/fsspec\/implementations\/hdfs.html) implements `fsspec.spec.AbstractFileSystem`. However, when using it as shown below, I get an error.\r\n\r\n```python\r\nfrom fsspec.implementations.hdfs import PyArrowHDFS\r\n...\r\ntransformed_corpus_path = \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nfs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)\r\ndss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)\r\n```\r\n\r\n## Expected results\r\n\r\nPrevious to load from disk, I have managed to successfully store in HDFS the data and meta-information of a DatasetDict by doing:\r\n```python\r\ntransformed_corpus_path = \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nfs = PyArrowHDFS(host, port, user, kerb_ticket=kerb_ticket)\r\nmy_datasets.save_to_disk(transformed_corpus_path, fs=fs)\r\n```\r\n\r\nAs I have 3 datasets in the DatasetDict named `my_datasets`, the previous Python code creates the following contents in HDFS:\r\n\r\n```sh\r\n$ hadoop fs -ls \"\/user\/my_user\/clickbait\/transformed_ds\/\"\r\nFound 4 items\r\n-rw-------   3 my_user users         43 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/dataset_dict.json\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/test\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/train\r\ndrwx------   - my_user users          0 2021-10-19 03:08 \/user\/my_user\/clickbait\/transformed_ds\/validation\r\n```\r\n\r\nI would expect to recover on `dss` the Arrow-backed datasets I previously saved in HDFS calling the `save_to_disk` method on the `DatasetDict` object when invoking `DatasetDict.load_from_disk(...)` as described above. \r\n\r\n## Actual results\r\n\r\nHowever, when trying to recover the saved datasets, I get this error:\r\n\r\n```\r\n...\r\n  File \"\/home\/fperez\/dev\/neuromancer\/neuromancer\/corpus.py\", line 186, in load_transformed_corpus_from_disk\r\n    dss = DatasetDict.load_from_disk(transformed_corpus_path, fs, True)\r\n  File \"\/home\/fperez\/anaconda3\/envs\/neuromancer\/lib\/python3.9\/site-packages\/datasets\/dataset_dict.py\", line 748, in load_from_disk\r\n    dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/home\/fperez\/anaconda3\/envs\/neuromancer\/lib\/python3.9\/site-packages\/datasets\/arrow_dataset.py\", line 1048, in load_from_disk\r\n    fs.download(src_dataset_path, dataset_path.as_posix(), recursive=True)\r\n  File \"pyarrow\/_hdfsio.pyx\", line 438, in pyarrow._hdfsio.HadoopFileSystem.download\r\nTypeError: download() got an unexpected keyword argument 'recursive'\r\n```\r\n\r\nExamining the [signature of the download method in pyarrow 5.0.0](https:\/\/github.com\/apache\/arrow\/blob\/54d2bd89c99df72fa091b025452f85dd5d88e3cf\/python\/pyarrow\/_hdfsio.pyx#L438) we can see that there's no download parameter:\r\n\r\n```python\r\n    def download(self, path, stream, buffer_size=None):\r\n        with self.open(path, 'rb') as f:\r\n            f.download(stream, buffer_size=buffer_size)\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.3\r\n- Platform: Linux-3.10.0-1160.15.2.el7.x86_64-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Hi @lhoestq! I ended up using `fsspec.implementations.arrow.HadoopFileSystem` which doesn't have the problem I described with pyarrow 5.0.0.\r\n\r\nI'll try again with `PyArrowHDFS` once I update arrow to 6.0.0.\r\n\r\nThanks!","embeddings":[-0.3349548876,0.2241349369,0.1027955115,0.1352018863,0.1960357428,-0.2252625078,0.3615587652,0.0040488029,-0.0512576252,-0.1026412696,-0.1220099926,0.4690686762,0.1646990478,0.032615196,0.115963392,0.037640471,0.2662324309,-0.0224410295,-0.0546424538,-0.0038120253,-0.0468189083,0.1966888756,-0.1476573348,-0.0208664406,-0.041236639,0.2302885354,-0.2657260597,0.3903673887,-0.0680893883,-0.3523623645,0.659252286,0.1840914339,0.4344583452,0.3419649005,-0.0001241506,0.1030732542,0.2805522382,-0.1367635876,-0.2313629538,-0.115709722,0.3009370267,-0.334964335,0.1414179206,-0.3048173487,-0.0972508714,-0.2475433052,-0.1224901751,0.0915353671,0.2256359905,0.2455889881,0.0811108351,0.1188376099,-0.0034247704,0.0279203635,0.4743972123,0.4964672327,-0.2376571447,0.3314310312,0.1718963981,-0.0127169276,-0.073388949,-0.1258909106,-0.3266922534,0.1095578372,0.2582924068,0.0762625039,-0.0644787624,0.0952942297,-0.1973095685,0.0023757152,0.5005263686,-0.6183691621,-0.4538958669,-0.1590423733,-0.2705807388,0.179809466,0.1894540042,0.164800331,-0.2678621411,0.1896121204,0.0118059246,-0.2344639748,-0.1498088688,0.3241241574,-0.213906467,-0.4162372947,-0.1617649198,0.1615715623,0.2402797639,-0.0451984294,0.2079426944,-0.3892836571,0.0767163783,0.1676045358,-0.0626498461,-0.0673043057,0.1609551162,-0.0554368086,0.1013188139,-0.1642184556,-0.2042354792,0.0012925676,0.0194097869,0.1953823715,0.2329110503,-0.0572115667,-0.088082619,0.1559267789,0.0426892266,0.1365589052,0.1190023273,-0.0552875698,-0.0832739249,-0.1961075515,0.1052543074,-0.1820534021,0.2968954146,-0.2645761967,-0.2291024029,-0.0231679101,0.1238994673,0.0546311252,0.0125761852,0.2835527658,0.0384483784,0.1877819747,0.1875625998,0.131967634,-0.1937467158,0.0559891425,-0.1020837128,0.1040537804,-0.2838178575,0.0866511017,0.3212416172,-0.2065195292,0.0385490917,0.1511926353,-0.2096682936,0.1097601578,-0.235933125,-0.2982353866,0.1868723929,0.2723735571,-0.106958352,0.1796817631,0.0695802569,0.1101202518,-0.1590230316,0.4295528829,-0.3818666339,-0.3355217278,-0.2488647848,0.053253796,-0.2094864994,0.1516818255,-0.9970975518,-0.0709662735,0.078282997,-0.1534861773,0.0121118892,-0.2660575807,-0.2727920115,-0.2400301397,0.2514426708,0.3093753159,-0.3793880641,0.0004594017,-0.144403398,-0.3248268366,0.2518029511,0.1285364032,-0.2419516742,0.4255224466,-0.3465428054,0.3773615956,0.26941064,-0.315856427,-0.620844841,0.6082898974,0.2249345332,0.0885229632,-0.0120230969,-0.0248352457,0.4436499476,-0.2672280669,-0.1859655231,0.3175537288,0.1195630133,0.1444232017,-0.2017782927,-0.2124797851,-0.1056744233,0.3348723948,-0.0771100745,0.1499777883,0.3789489269,0.1038330123,0.3402035236,-0.0186061878,0.1657093465,0.2660182416,0.2807666063,0.1099919304,-0.0116428221,-0.180390805,-0.600462079,0.2230315506,-0.0150352977,-0.1344621181,-0.2526041567,-0.1625391245,-0.0372832417,0.1343709081,-0.1196581945,0.1085762456,-0.0790817365,0.0407998227,0.147639066,-0.0628859773,0.0091525121,0.0283027776,-0.1528121531,0.0464811064,-0.0380090959,0.7559700608,-0.0185694862,-0.3635178208,-0.1487202942,0.0572084077,0.3924450874,-0.0994054973,-0.0039331024,0.5101383924,0.398229599,0.1366243213,-0.3077687621,0.1475266963,0.1672148108,-0.1928482354,0.1135950014,0.3128064871,0.25559026,0.117229484,0.0334880687,0.1333461404,0.0322043747,-0.0406403951,0.1586370766,-0.080774039,0.1852014661,0.0696371794,0.1108281016,0.0764955953,-0.039459873,0.3130888939,0.249199599,0.0481727347,-0.0323603638,0.1146053001,0.2795549333,-0.0532877594,-0.0101495907,0.1259939671,-0.0930722132,-0.1840510815,0.4165039062,0.2011199892,0.4155315161,0.1255696267,0.0014678703,0.0594747812,-0.1338419318,0.0660482794,0.1770570427,0.0206731185,-0.0137727382,0.1328145415,0.0291898362,0.0227723122,-0.211441502,-0.1640914232,0.1413927078,0.0482288338,-0.5286555886,0.0681854486,-0.1113610566,-0.0815002024,-0.1460059881,-0.275187999,0.0334523693,0.0642525554,-0.3306065202,0.5829567909,-0.1921337247,-0.0200600196,0.092704691,-0.1033721194,-0.0401010178,-0.2644488513,-0.1566375643,0.0839576349,-0.3414261937,-0.1576366723,0.2627204061,0.0248307362,0.0596252382,0.0208840799,-0.0658893958,-0.791772604,0.1146835834,0.2108013779,-0.0271244049,0.338873595,0.0921419486,0.1485252082,-0.1379126459,0.0144681931,0.2203326225,-0.1100958213,-0.2514595985,0.1915113479,0.0189551786,-0.1697292328,-0.1620199978,0.0378339365,-0.3503848016,-0.2025852799,0.0125960466,-0.335141331,0.1549157202,-0.0255503431,-0.029840678,0.038169086,0.0020049717,0.0068811132,-0.0876438469,-0.3234086633,0.3013534844,-0.0881954581,-0.5032919049,0.1077487916,0.1354489774,-0.3445076048,0.3160082996,-0.4374325871,-0.1431199163,0.0600982606,0.3037619591,-0.0495443866,0.170914337,0.4621812403,0.0393853076,0.1347218007,-0.2583552897,-0.243255049,0.1395734847,-0.0308966115,-0.0729526505,-0.1673983783,0.3971686363,0.0118228439,0.516313374,0.0089964028,-0.0707268789,0.7223641276,-0.0523866601,0.1376953572,0.0099097192,-0.2078965753,-0.0854830295,-0.1685241461,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3112","title":"OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB","comments":"I am very unsure on why you tagged me here. I am not a maintainer of the Datasets library and have no idea how to help you.","body":"## Describe the bug\r\nDespite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error :\r\n\r\n> OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\nNote that I always run `batch_size=writer_batch_size` :\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndatasets.map(lambda example : {\"column_name\" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc=\"blablabla\")\r\n```\r\n## Introspecting CUDA memory during bug\r\n\r\nPlaced within `function(arguments)` the following statement to introspect memory usage, merely a little over 1\/4 of 2Gb\r\n`print(torch.cuda.memory_summary(device=device, abbreviated=False))`\r\n\r\n> |===========================================================================|\r\n |                  PyTorch CUDA memory summary, device ID 0                 |\r\n |---------------------------------------------------------------------------|\r\n |            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\r\n |===========================================================================|\r\n |        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\r\n |---------------------------------------------------------------------------|\r\n | Allocated memory      |  541418 KB |  545725 KB |  555695 KB |   14276 KB |\r\n |       from large pool |  540672 KB |  544431 KB |  544431 KB |    3759 KB |\r\n |       from small pool |     746 KB |    1714 KB |   11264 KB |   10517 KB |\r\n |---------------------------------------------------------------------------|\r\n | Active memory         |  541418 KB |  545725 KB |  555695 KB |   14276 KB |\r\n |       from large pool |  540672 KB |  544431 KB |  544431 KB |    3759 KB |\r\n |       from small pool |     746 KB |    1714 KB |   11264 KB |   10517 KB |\r\n |---------------------------------------------------------------------------|\r\n | GPU reserved memory   |  598016 KB |  598016 KB |  598016 KB |       0 B  |\r\n |       from large pool |  595968 KB |  595968 KB |  595968 KB |       0 B  |\r\n |       from small pool |    2048 KB |    2048 KB |    2048 KB |       0 B  |\r\n |---------------------------------------------------------------------------|\r\n | Non-releasable memory |   36117 KB |   52292 KB |  274275 KB |  238158 KB |\r\n|       from large pool |   34816 KB |   51537 KB |  261713 KB |  226897 KB |\r\n |       from small pool |    1301 KB |    2045 KB |   12562 KB |   11261 KB |\r\n |---------------------------------------------------------------------------|\r\n | Allocations           |     198    |     224    |     478    |     280    |\r\n |       from large pool |      74    |      75    |      75    |       1    |\r\n |       from small pool |     124    |     150    |     403    |     279    |\r\n |---------------------------------------------------------------------------|\r\n | Active allocs         |     198    |     224    |     478    |     280    |\r\n |       from large pool |      74    |      75    |      75    |       1    |\r\n |       from small pool |     124    |     150    |     403    |     279    |\r\n |---------------------------------------------------------------------------|\r\n | GPU reserved segments |      21    |      21    |      21    |       0    |\r\n |       from large pool |      20    |      20    |      20    |       0    |\r\n |       from small pool |       1    |       1    |       1    |       0    |\r\n |---------------------------------------------------------------------------|\r\n | Non-releasable allocs |      18    |      23    |     166    |     148    |\r\n |       from large pool |      17    |      18    |      19    |       2    |\r\n |       from small pool |       1    |       6    |     147    |     146    |\r\n |===========================================================================|\r\n\r\n## Expected results\r\nEfficiently process the datasets and write it down to disk.\r\n\r\n## Actual results\r\n--------------------------------------------------------------------------\r\nOverflowError                             Traceback (most recent call last)\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2390                             else:\r\n-> 2391                                 writer.write(example)\r\n   2392                 else:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    367 \r\n--> 368             self.write_examples_on_file()\r\n    369 \r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n    316                 if not isinstance(pa_array[0], pa.lib.FloatScalar):\r\n--> 317                     raise OverflowError(\r\n    318                         \"There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB\".format(\r\n\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOverflowError                             Traceback (most recent call last)\r\n~\\AppData\\Local\\Temp\/ipykernel_16268\/2456940807.py in <module>\r\n      3 #tracker = OfflineEmissionsTracker(country_iso_code=\"FRA\", project_name='xxx'+time_stamp,output_dir='.\/codecarbon')\r\n      4 #tracker.start()\r\n----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True,\r\n      6                  clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False,\r\n      7                  max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) #\r\n\r\n~\\xxx\\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc)\r\n    481             for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False):\r\n    482                 if \"xxx_\" in column:\r\n--> 483                     dataset = dataset.map(lambda example :\r\n    484                                                 {\"embeddings_\"+str(column).replace(\"translated_\",\"\"):function(input_ids=example[column],\r\n    485                                                                                                                             token_type_ids=example[column.replace(\"input_ids\",\"token_type_ids\")],\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   2034 \r\n   2035         if num_proc is None or num_proc == 1:\r\n-> 2036             return self._map_single(\r\n   2037                 function=function,\r\n   2038                 with_indices=with_indices,\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in wrapper(*args, **kwargs)\r\n    501             self: \"Dataset\" = kwargs.pop(\"self\")\r\n    502         # apply actual function\r\n--> 503         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    504         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    505         for dataset in datasets:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in wrapper(*args, **kwargs)\r\n    468         }\r\n    469         # apply actual function\r\n--> 470         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    471         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    472         # re-apply format to the output\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\fingerprint.py in wrapper(*args, **kwargs)\r\n    404             # Call actual function\r\n    405 \r\n--> 406             out = func(self, *args, **kwargs)\r\n    407 \r\n    408             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2425                 if update_data:\r\n   2426                     if writer is not None:\r\n-> 2427                         writer.finalize()\r\n   2428                     if tmp_file is not None:\r\n   2429                         tmp_file.close()\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in finalize(self, close_stream)\r\n    440             # Re-intializing to empty list for next batch\r\n    441             self.hkey_record = []\r\n--> 442         self.write_examples_on_file()\r\n    443         if self.pa_writer is None:\r\n    444             if self._schema is not None:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n    315                 # This check fails with FloatArrays with nans, which is not what we want, so account for that:\r\n    316                 if not isinstance(pa_array[0], pa.lib.FloatScalar):\r\n--> 317                     raise OverflowError(\r\n    318                         \"There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB\".format(\r\n    319                             type(pa_array)\r\n\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.8.11\r\n- PyArrow version: 3.0.0\r\n\r\n##Next steps\r\nTesting on Linux.\r\n@albertvillanova \r\n","comment_length":27,"text":"OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB \n ## Describe the bug\r\nDespite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error :\r\n\r\n> OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\nNote that I always run `batch_size=writer_batch_size` :\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndatasets.map(lambda example : {\"column_name\" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc=\"blablabla\")\r\n```\r\n## Introspecting CUDA memory during bug\r\n\r\nPlaced within `function(arguments)` the following statement to introspect memory usage, merely a little over 1\/4 of 2Gb\r\n`print(torch.cuda.memory_summary(device=device, abbreviated=False))`\r\n\r\n> |===========================================================================|\r\n |                  PyTorch CUDA memory summary, device ID 0                 |\r\n |---------------------------------------------------------------------------|\r\n |            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\r\n |===========================================================================|\r\n |        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\r\n |---------------------------------------------------------------------------|\r\n | Allocated memory      |  541418 KB |  545725 KB |  555695 KB |   14276 KB |\r\n |       from large pool |  540672 KB |  544431 KB |  544431 KB |    3759 KB |\r\n |       from small pool |     746 KB |    1714 KB |   11264 KB |   10517 KB |\r\n |---------------------------------------------------------------------------|\r\n | Active memory         |  541418 KB |  545725 KB |  555695 KB |   14276 KB |\r\n |       from large pool |  540672 KB |  544431 KB |  544431 KB |    3759 KB |\r\n |       from small pool |     746 KB |    1714 KB |   11264 KB |   10517 KB |\r\n |---------------------------------------------------------------------------|\r\n | GPU reserved memory   |  598016 KB |  598016 KB |  598016 KB |       0 B  |\r\n |       from large pool |  595968 KB |  595968 KB |  595968 KB |       0 B  |\r\n |       from small pool |    2048 KB |    2048 KB |    2048 KB |       0 B  |\r\n |---------------------------------------------------------------------------|\r\n | Non-releasable memory |   36117 KB |   52292 KB |  274275 KB |  238158 KB |\r\n|       from large pool |   34816 KB |   51537 KB |  261713 KB |  226897 KB |\r\n |       from small pool |    1301 KB |    2045 KB |   12562 KB |   11261 KB |\r\n |---------------------------------------------------------------------------|\r\n | Allocations           |     198    |     224    |     478    |     280    |\r\n |       from large pool |      74    |      75    |      75    |       1    |\r\n |       from small pool |     124    |     150    |     403    |     279    |\r\n |---------------------------------------------------------------------------|\r\n | Active allocs         |     198    |     224    |     478    |     280    |\r\n |       from large pool |      74    |      75    |      75    |       1    |\r\n |       from small pool |     124    |     150    |     403    |     279    |\r\n |---------------------------------------------------------------------------|\r\n | GPU reserved segments |      21    |      21    |      21    |       0    |\r\n |       from large pool |      20    |      20    |      20    |       0    |\r\n |       from small pool |       1    |       1    |       1    |       0    |\r\n |---------------------------------------------------------------------------|\r\n | Non-releasable allocs |      18    |      23    |     166    |     148    |\r\n |       from large pool |      17    |      18    |      19    |       2    |\r\n |       from small pool |       1    |       6    |     147    |     146    |\r\n |===========================================================================|\r\n\r\n## Expected results\r\nEfficiently process the datasets and write it down to disk.\r\n\r\n## Actual results\r\n--------------------------------------------------------------------------\r\nOverflowError                             Traceback (most recent call last)\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2390                             else:\r\n-> 2391                                 writer.write(example)\r\n   2392                 else:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    367 \r\n--> 368             self.write_examples_on_file()\r\n    369 \r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n    316                 if not isinstance(pa_array[0], pa.lib.FloatScalar):\r\n--> 317                     raise OverflowError(\r\n    318                         \"There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB\".format(\r\n\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOverflowError                             Traceback (most recent call last)\r\n~\\AppData\\Local\\Temp\/ipykernel_16268\/2456940807.py in <module>\r\n      3 #tracker = OfflineEmissionsTracker(country_iso_code=\"FRA\", project_name='xxx'+time_stamp,output_dir='.\/codecarbon')\r\n      4 #tracker.start()\r\n----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True,\r\n      6                  clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False,\r\n      7                  max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) #\r\n\r\n~\\xxx\\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc)\r\n    481             for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False):\r\n    482                 if \"xxx_\" in column:\r\n--> 483                     dataset = dataset.map(lambda example :\r\n    484                                                 {\"embeddings_\"+str(column).replace(\"translated_\",\"\"):function(input_ids=example[column],\r\n    485                                                                                                                             token_type_ids=example[column.replace(\"input_ids\",\"token_type_ids\")],\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   2034 \r\n   2035         if num_proc is None or num_proc == 1:\r\n-> 2036             return self._map_single(\r\n   2037                 function=function,\r\n   2038                 with_indices=with_indices,\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in wrapper(*args, **kwargs)\r\n    501             self: \"Dataset\" = kwargs.pop(\"self\")\r\n    502         # apply actual function\r\n--> 503         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    504         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    505         for dataset in datasets:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in wrapper(*args, **kwargs)\r\n    468         }\r\n    469         # apply actual function\r\n--> 470         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    471         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    472         # re-apply format to the output\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\fingerprint.py in wrapper(*args, **kwargs)\r\n    404             # Call actual function\r\n    405 \r\n--> 406             out = func(self, *args, **kwargs)\r\n    407 \r\n    408             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2425                 if update_data:\r\n   2426                     if writer is not None:\r\n-> 2427                         writer.finalize()\r\n   2428                     if tmp_file is not None:\r\n   2429                         tmp_file.close()\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in finalize(self, close_stream)\r\n    440             # Re-intializing to empty list for next batch\r\n    441             self.hkey_record = []\r\n--> 442         self.write_examples_on_file()\r\n    443         if self.pa_writer is None:\r\n    444             if self._schema is not None:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n    315                 # This check fails with FloatArrays with nans, which is not what we want, so account for that:\r\n    316                 if not isinstance(pa_array[0], pa.lib.FloatScalar):\r\n--> 317                     raise OverflowError(\r\n    318                         \"There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB\".format(\r\n    319                             type(pa_array)\r\n\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.8.11\r\n- PyArrow version: 3.0.0\r\n\r\n##Next steps\r\nTesting on Linux.\r\n@albertvillanova \r\n \n I am very unsure on why you tagged me here. I am not a maintainer of the Datasets library and have no idea how to help you.","embeddings":[-0.4791873693,-0.101569891,-0.1007575244,0.3905477524,0.2512300611,-0.0684596002,0.1106997803,0.2645795643,-0.1296792924,0.3843229413,0.2181477696,0.3294644952,-0.1310049444,-0.1716288775,0.0497368723,-0.0603307411,0.0917913765,-0.2516672611,-0.0394297168,0.0323777758,-0.2504085004,-0.086918503,-0.1130417511,-0.0151135139,-0.0834402964,-0.3049120307,0.139835,0.1140331998,-0.2340847999,-0.2418782413,0.0897170603,-0.1915828288,0.221023649,0.2542475462,-0.0001156218,-0.0681016445,0.2426097393,0.0645803362,-0.1508473754,0.0526856966,0.0428462401,-0.4404893517,-0.0704084039,-0.3497677445,0.2266319245,-0.146756053,-0.010643567,-0.1709754169,0.2077379376,0.2966848314,0.1989971995,-0.0138504459,0.1279185861,0.0502240248,0.5973913074,-0.0425611809,-0.1547393948,0.1817258,0.4113594592,-0.3212555051,-0.3399894238,0.0642786995,-0.3123115003,0.0723138303,0.0735213086,0.0755391493,-0.0019404354,-0.243086189,0.0262548123,-0.1978913546,0.0675821826,-0.394800514,-0.0795195699,-0.1233015135,-0.0157664791,-0.4527913332,0.0995018631,0.2328349054,-0.4075728357,-0.0989535749,-0.4193741977,0.1155350581,-0.1239218414,0.2330795377,-0.0592705607,0.3074740767,0.0047142105,0.2095742822,0.196607545,-0.0053504114,0.5129474401,-0.0709110051,-0.0113269864,0.2013025433,-0.4676406384,0.1051284969,0.2939867079,-0.0502983257,0.5077893734,-0.0957446694,0.1430463493,-0.1436831504,0.3505896926,0.0293883644,0.2170954049,0.4565422237,-0.2786490619,0.4197795987,0.2109335214,-0.0157594718,0.1513209641,-0.119827114,-0.0296273939,-0.2943778336,0.2821809947,-0.0091492198,0.240355581,-0.156307295,-0.3086295724,0.1988816857,-0.3600850999,0.0612888001,-0.0218717158,0.3002025485,0.1207000017,-0.0293847173,0.0294683818,-0.1035251617,-0.0404463708,0.0078178877,-0.2214457244,-0.0186168626,-0.3967349231,0.0554239415,0.1488305926,0.0385815836,0.265104264,0.1318566352,0.3805131912,-0.1918962747,0.1950099617,-0.2590519786,0.2879867554,0.242953822,-0.0098109245,0.196389541,0.1639664769,0.3001648784,-0.0144200921,0.6081849337,-0.385751456,-0.4894140959,-0.0089114644,0.1488809288,0.14920789,0.3424102664,-0.3296617866,-0.1756064147,0.6020561457,-0.1773954481,0.1349130869,-0.2100277245,0.1021258831,-0.4289412796,0.1298334002,0.1081753597,-0.7026266456,0.2321748734,-0.0112394346,0.0854156166,0.4426007867,0.6264768839,-0.3793056607,0.1955496818,-0.4166079164,-0.0423507392,0.165656969,-0.2223813832,-0.6628284454,0.0662823096,-0.0400794968,0.1180478036,0.021409208,0.1136311516,0.4583936334,0.1032361388,0.3721669614,0.2542293668,-0.3773194253,0.2213815004,-0.3497407734,-0.2212026268,0.0168303568,-0.043936912,0.0643142536,-0.1385787129,0.0896298289,-0.1657464206,0.2688992023,-0.2118351758,0.036254514,0.1838579178,0.289696455,-0.1664539129,-0.11468371,-0.1004822999,-0.2999524772,0.0535286665,-0.2676375508,-0.1073116139,-0.2515851259,-0.2944732606,-0.0730568394,0.1995472908,0.0247375183,-0.0814571753,0.11329294,0.0186100379,0.1513357311,-0.1031116322,0.1201159358,0.1809509397,-0.4612051547,-0.0600751974,-0.2285695821,0.1009557098,-0.1340085715,-0.3965064883,-0.1587676108,0.010481637,0.0488835983,-0.0901868939,-0.1125799343,0.2798411548,0.023992762,-0.3586603105,-0.3417615592,0.0024195323,0.1557076871,-0.1216348559,0.2745077014,0.0010634054,0.0594687238,-0.0877711028,-0.0765739828,0.209531188,0.1757121831,0.1426491737,-0.1151323617,0.0512540527,0.0651137233,0.1298433691,0.175810799,0.0157208536,0.1712497473,0.2229860127,0.3608556986,-0.0580638759,-0.1580515355,0.0946137086,0.4701109827,-0.0197819546,0.194552049,0.138112992,-0.2135622203,-0.1268219054,0.222679913,-0.1140938699,0.3751450479,0.2416901439,-0.0602771342,-0.1704898179,-0.0751106516,-0.0352937244,0.0658045113,-0.004286489,0.2925325036,0.0399406962,0.4612780809,-0.0292339884,-0.2392118722,-0.0579590499,0.0077317851,0.3310962319,-0.1639625579,0.0594538338,0.1934713721,-0.2000398934,0.0213530734,-0.2389669269,-0.1020776853,-0.1513523906,-0.0046538529,0.2886989117,-0.0756482854,0.236316964,0.4554078281,0.3434215486,0.3280366063,-0.1805103272,0.1633731723,-0.1627669632,-0.150253132,0.1015176997,0.3950359523,-0.3732878268,0.2742282152,0.2455913424,-0.1506754309,-0.193423003,-0.125122875,-0.1164164841,0.0526928529,0.3520668745,0.0071147545,0.2117315233,-0.1039761677,-0.2358806729,0.0012239725,-0.0071636862,-0.2696326673,-0.0120425597,-0.0319462754,-0.0347352326,-0.1717883199,-0.1776195914,-0.0253133569,-0.5092660189,-0.0226758812,0.0186971389,0.1199764535,-0.2123201191,0.3679845333,0.1896440834,0.0993028134,-0.2219925821,-0.0520104133,-0.1427073032,0.1922285855,-0.1869854629,-0.3465240896,0.0265490077,0.0426833406,0.0125978608,0.3760277331,-0.4561062753,0.0023040739,-0.2894494832,0.391746819,-0.1043551862,0.0885514319,0.4165962636,0.2006848156,-0.0801264271,0.0194083378,-0.3021274805,-0.1463979632,-0.0703578442,0.3098169863,-0.2234727442,0.5141125917,0.155668512,0.8973591328,0.151639685,-0.0577581748,0.0690155774,0.0083153993,0.1206522807,-0.1576684564,-0.2264817655,0.1268743426,-0.3355574012,-0.33230438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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3112","title":"OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB","comments":"Ok got it, tensor full of NaNs, cf.\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:","body":"## Describe the bug\r\nDespite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error :\r\n\r\n> OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\nNote that I always run `batch_size=writer_batch_size` :\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndatasets.map(lambda example : {\"column_name\" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc=\"blablabla\")\r\n```\r\n## Introspecting CUDA memory during bug\r\n\r\nPlaced within `function(arguments)` the following statement to introspect memory usage, merely a little over 1\/4 of 2Gb\r\n`print(torch.cuda.memory_summary(device=device, abbreviated=False))`\r\n\r\n> |===========================================================================|\r\n |                  PyTorch CUDA memory summary, device ID 0                 |\r\n |---------------------------------------------------------------------------|\r\n |            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\r\n |===========================================================================|\r\n |        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\r\n |---------------------------------------------------------------------------|\r\n | Allocated memory      |  541418 KB |  545725 KB |  555695 KB |   14276 KB |\r\n |       from large pool |  540672 KB |  544431 KB |  544431 KB |    3759 KB |\r\n |       from small pool |     746 KB |    1714 KB |   11264 KB |   10517 KB |\r\n |---------------------------------------------------------------------------|\r\n | Active memory         |  541418 KB |  545725 KB |  555695 KB |   14276 KB |\r\n |       from large pool |  540672 KB |  544431 KB |  544431 KB |    3759 KB |\r\n |       from small pool |     746 KB |    1714 KB |   11264 KB |   10517 KB |\r\n |---------------------------------------------------------------------------|\r\n | GPU reserved memory   |  598016 KB |  598016 KB |  598016 KB |       0 B  |\r\n |       from large pool |  595968 KB |  595968 KB |  595968 KB |       0 B  |\r\n |       from small pool |    2048 KB |    2048 KB |    2048 KB |       0 B  |\r\n |---------------------------------------------------------------------------|\r\n | Non-releasable memory |   36117 KB |   52292 KB |  274275 KB |  238158 KB |\r\n|       from large pool |   34816 KB |   51537 KB |  261713 KB |  226897 KB |\r\n |       from small pool |    1301 KB |    2045 KB |   12562 KB |   11261 KB |\r\n |---------------------------------------------------------------------------|\r\n | Allocations           |     198    |     224    |     478    |     280    |\r\n |       from large pool |      74    |      75    |      75    |       1    |\r\n |       from small pool |     124    |     150    |     403    |     279    |\r\n |---------------------------------------------------------------------------|\r\n | Active allocs         |     198    |     224    |     478    |     280    |\r\n |       from large pool |      74    |      75    |      75    |       1    |\r\n |       from small pool |     124    |     150    |     403    |     279    |\r\n |---------------------------------------------------------------------------|\r\n | GPU reserved segments |      21    |      21    |      21    |       0    |\r\n |       from large pool |      20    |      20    |      20    |       0    |\r\n |       from small pool |       1    |       1    |       1    |       0    |\r\n |---------------------------------------------------------------------------|\r\n | Non-releasable allocs |      18    |      23    |     166    |     148    |\r\n |       from large pool |      17    |      18    |      19    |       2    |\r\n |       from small pool |       1    |       6    |     147    |     146    |\r\n |===========================================================================|\r\n\r\n## Expected results\r\nEfficiently process the datasets and write it down to disk.\r\n\r\n## Actual results\r\n--------------------------------------------------------------------------\r\nOverflowError                             Traceback (most recent call last)\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2390                             else:\r\n-> 2391                                 writer.write(example)\r\n   2392                 else:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    367 \r\n--> 368             self.write_examples_on_file()\r\n    369 \r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n    316                 if not isinstance(pa_array[0], pa.lib.FloatScalar):\r\n--> 317                     raise OverflowError(\r\n    318                         \"There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB\".format(\r\n\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOverflowError                             Traceback (most recent call last)\r\n~\\AppData\\Local\\Temp\/ipykernel_16268\/2456940807.py in <module>\r\n      3 #tracker = OfflineEmissionsTracker(country_iso_code=\"FRA\", project_name='xxx'+time_stamp,output_dir='.\/codecarbon')\r\n      4 #tracker.start()\r\n----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True,\r\n      6                  clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False,\r\n      7                  max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) #\r\n\r\n~\\xxx\\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc)\r\n    481             for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False):\r\n    482                 if \"xxx_\" in column:\r\n--> 483                     dataset = dataset.map(lambda example :\r\n    484                                                 {\"embeddings_\"+str(column).replace(\"translated_\",\"\"):function(input_ids=example[column],\r\n    485                                                                                                                             token_type_ids=example[column.replace(\"input_ids\",\"token_type_ids\")],\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   2034 \r\n   2035         if num_proc is None or num_proc == 1:\r\n-> 2036             return self._map_single(\r\n   2037                 function=function,\r\n   2038                 with_indices=with_indices,\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in wrapper(*args, **kwargs)\r\n    501             self: \"Dataset\" = kwargs.pop(\"self\")\r\n    502         # apply actual function\r\n--> 503         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    504         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    505         for dataset in datasets:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in wrapper(*args, **kwargs)\r\n    468         }\r\n    469         # apply actual function\r\n--> 470         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    471         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    472         # re-apply format to the output\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\fingerprint.py in wrapper(*args, **kwargs)\r\n    404             # Call actual function\r\n    405 \r\n--> 406             out = func(self, *args, **kwargs)\r\n    407 \r\n    408             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2425                 if update_data:\r\n   2426                     if writer is not None:\r\n-> 2427                         writer.finalize()\r\n   2428                     if tmp_file is not None:\r\n   2429                         tmp_file.close()\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in finalize(self, close_stream)\r\n    440             # Re-intializing to empty list for next batch\r\n    441             self.hkey_record = []\r\n--> 442         self.write_examples_on_file()\r\n    443         if self.pa_writer is None:\r\n    444             if self._schema is not None:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n    315                 # This check fails with FloatArrays with nans, which is not what we want, so account for that:\r\n    316                 if not isinstance(pa_array[0], pa.lib.FloatScalar):\r\n--> 317                     raise OverflowError(\r\n    318                         \"There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB\".format(\r\n    319                             type(pa_array)\r\n\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.8.11\r\n- PyArrow version: 3.0.0\r\n\r\n##Next steps\r\nTesting on Linux.\r\n@albertvillanova \r\n","comment_length":30,"text":"OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB \n ## Describe the bug\r\nDespite having batches way under 2Gb when running `datasets.map()`, after processing correctly the data of the first batch without fuss and irrespective of writer_batch_size (say 2,4,8,16,32,64 and 128 in my case), it returns the following error :\r\n\r\n> OverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\nNote that I always run `batch_size=writer_batch_size` :\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndatasets.map(lambda example : {\"column_name\" : function(arguments)}, batched=False, remove_columns = datasets.column_names, batch_size=batch_size, writer_batch_size=batch_size, disable_nullable=True, num_proc=None, desc=\"blablabla\")\r\n```\r\n## Introspecting CUDA memory during bug\r\n\r\nPlaced within `function(arguments)` the following statement to introspect memory usage, merely a little over 1\/4 of 2Gb\r\n`print(torch.cuda.memory_summary(device=device, abbreviated=False))`\r\n\r\n> |===========================================================================|\r\n |                  PyTorch CUDA memory summary, device ID 0                 |\r\n |---------------------------------------------------------------------------|\r\n |            CUDA OOMs: 0            |        cudaMalloc retries: 0         |\r\n |===========================================================================|\r\n |        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |\r\n |---------------------------------------------------------------------------|\r\n | Allocated memory      |  541418 KB |  545725 KB |  555695 KB |   14276 KB |\r\n |       from large pool |  540672 KB |  544431 KB |  544431 KB |    3759 KB |\r\n |       from small pool |     746 KB |    1714 KB |   11264 KB |   10517 KB |\r\n |---------------------------------------------------------------------------|\r\n | Active memory         |  541418 KB |  545725 KB |  555695 KB |   14276 KB |\r\n |       from large pool |  540672 KB |  544431 KB |  544431 KB |    3759 KB |\r\n |       from small pool |     746 KB |    1714 KB |   11264 KB |   10517 KB |\r\n |---------------------------------------------------------------------------|\r\n | GPU reserved memory   |  598016 KB |  598016 KB |  598016 KB |       0 B  |\r\n |       from large pool |  595968 KB |  595968 KB |  595968 KB |       0 B  |\r\n |       from small pool |    2048 KB |    2048 KB |    2048 KB |       0 B  |\r\n |---------------------------------------------------------------------------|\r\n | Non-releasable memory |   36117 KB |   52292 KB |  274275 KB |  238158 KB |\r\n|       from large pool |   34816 KB |   51537 KB |  261713 KB |  226897 KB |\r\n |       from small pool |    1301 KB |    2045 KB |   12562 KB |   11261 KB |\r\n |---------------------------------------------------------------------------|\r\n | Allocations           |     198    |     224    |     478    |     280    |\r\n |       from large pool |      74    |      75    |      75    |       1    |\r\n |       from small pool |     124    |     150    |     403    |     279    |\r\n |---------------------------------------------------------------------------|\r\n | Active allocs         |     198    |     224    |     478    |     280    |\r\n |       from large pool |      74    |      75    |      75    |       1    |\r\n |       from small pool |     124    |     150    |     403    |     279    |\r\n |---------------------------------------------------------------------------|\r\n | GPU reserved segments |      21    |      21    |      21    |       0    |\r\n |       from large pool |      20    |      20    |      20    |       0    |\r\n |       from small pool |       1    |       1    |       1    |       0    |\r\n |---------------------------------------------------------------------------|\r\n | Non-releasable allocs |      18    |      23    |     166    |     148    |\r\n |       from large pool |      17    |      18    |      19    |       2    |\r\n |       from small pool |       1    |       6    |     147    |     146    |\r\n |===========================================================================|\r\n\r\n## Expected results\r\nEfficiently process the datasets and write it down to disk.\r\n\r\n## Actual results\r\n--------------------------------------------------------------------------\r\nOverflowError                             Traceback (most recent call last)\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2390                             else:\r\n-> 2391                                 writer.write(example)\r\n   2392                 else:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    367 \r\n--> 368             self.write_examples_on_file()\r\n    369 \r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n    316                 if not isinstance(pa_array[0], pa.lib.FloatScalar):\r\n--> 317                     raise OverflowError(\r\n    318                         \"There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB\".format(\r\n\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOverflowError                             Traceback (most recent call last)\r\n~\\AppData\\Local\\Temp\/ipykernel_16268\/2456940807.py in <module>\r\n      3 #tracker = OfflineEmissionsTracker(country_iso_code=\"FRA\", project_name='xxx'+time_stamp,output_dir='.\/codecarbon')\r\n      4 #tracker.start()\r\n----> 5 process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection=['wikipedia'], from_scratch=True,\r\n      6                  clean_sentences=False, negative_sampling=False, translate=False, tokenize=False, generate_embeddings=True, concatenate_embeddings=False,\r\n      7                  max_sample=10000, padding='do_not_pad', truncation=True, cpu_batch_size=1000, gpu_batch_size=2, cpu_writer_batch_size=1000, gpu_writer_batch_size=2, disable_nullable=True, num_proc=None) #\r\n\r\n~\\xxx\\xxx.py in process_datasets(source_datasets_paths, dataset_dir, LM_tokenizer, LMhead_model, datasets_selection, from_scratch, clean_sentences, translate, negative_sampling, tokenize, generate_embeddings, concatenate_embeddings, max_sample, padding, truncation, cpu_batch_size, gpu_batch_size, cpu_writer_batch_size, gpu_writer_batch_size, disable_nullable, num_proc)\r\n    481             for column in tqdm(dataset.column_names, desc=f'Processing column', leave=False):\r\n    482                 if \"xxx_\" in column:\r\n--> 483                     dataset = dataset.map(lambda example :\r\n    484                                                 {\"embeddings_\"+str(column).replace(\"translated_\",\"\"):function(input_ids=example[column],\r\n    485                                                                                                                             token_type_ids=example[column.replace(\"input_ids\",\"token_type_ids\")],\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   2034 \r\n   2035         if num_proc is None or num_proc == 1:\r\n-> 2036             return self._map_single(\r\n   2037                 function=function,\r\n   2038                 with_indices=with_indices,\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in wrapper(*args, **kwargs)\r\n    501             self: \"Dataset\" = kwargs.pop(\"self\")\r\n    502         # apply actual function\r\n--> 503         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    504         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    505         for dataset in datasets:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in wrapper(*args, **kwargs)\r\n    468         }\r\n    469         # apply actual function\r\n--> 470         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    471         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    472         # re-apply format to the output\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\fingerprint.py in wrapper(*args, **kwargs)\r\n    404             # Call actual function\r\n    405 \r\n--> 406             out = func(self, *args, **kwargs)\r\n    407 \r\n    408             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2425                 if update_data:\r\n   2426                     if writer is not None:\r\n-> 2427                         writer.finalize()\r\n   2428                     if tmp_file is not None:\r\n   2429                         tmp_file.close()\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in finalize(self, close_stream)\r\n    440             # Re-intializing to empty list for next batch\r\n    441             self.hkey_record = []\r\n--> 442         self.write_examples_on_file()\r\n    443         if self.pa_writer is None:\r\n    444             if self._schema is not None:\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n    315                 # This check fails with FloatArrays with nans, which is not what we want, so account for that:\r\n    316                 if not isinstance(pa_array[0], pa.lib.FloatScalar):\r\n--> 317                     raise OverflowError(\r\n    318                         \"There was an overflow in the {}. Try to reduce writer_batch_size to have batches smaller than 2GB\".format(\r\n    319                             type(pa_array)\r\n\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.ListArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.8.11\r\n- PyArrow version: 3.0.0\r\n\r\n##Next steps\r\nTesting on Linux.\r\n@albertvillanova \r\n \n Ok got it, tensor full of NaNs, cf.\r\n\r\n~\\anaconda3\\envs\\xxx\\lib\\site-packages\\datasets\\arrow_writer.py in write_examples_on_file(self)\r\n315 # This check fails with FloatArrays with nans, which is not what we want, so account for that:","embeddings":[-0.4791873693,-0.101569891,-0.1007575244,0.3905477524,0.2512300611,-0.0684596002,0.1106997803,0.2645795643,-0.1296792924,0.3843229413,0.2181477696,0.3294644952,-0.1310049444,-0.1716288775,0.0497368723,-0.0603307411,0.0917913765,-0.2516672611,-0.0394297168,0.0323777758,-0.2504085004,-0.086918503,-0.1130417511,-0.0151135139,-0.0834402964,-0.3049120307,0.139835,0.1140331998,-0.2340847999,-0.2418782413,0.0897170603,-0.1915828288,0.221023649,0.2542475462,-0.0001156218,-0.0681016445,0.2426097393,0.0645803362,-0.1508473754,0.0526856966,0.0428462401,-0.4404893517,-0.0704084039,-0.3497677445,0.2266319245,-0.146756053,-0.010643567,-0.1709754169,0.2077379376,0.2966848314,0.1989971995,-0.0138504459,0.1279185861,0.0502240248,0.5973913074,-0.0425611809,-0.1547393948,0.1817258,0.4113594592,-0.3212555051,-0.3399894238,0.0642786995,-0.3123115003,0.0723138303,0.0735213086,0.0755391493,-0.0019404354,-0.243086189,0.0262548123,-0.1978913546,0.0675821826,-0.394800514,-0.0795195699,-0.1233015135,-0.0157664791,-0.4527913332,0.0995018631,0.2328349054,-0.4075728357,-0.0989535749,-0.4193741977,0.1155350581,-0.1239218414,0.2330795377,-0.0592705607,0.3074740767,0.0047142105,0.2095742822,0.196607545,-0.0053504114,0.5129474401,-0.0709110051,-0.0113269864,0.2013025433,-0.4676406384,0.1051284969,0.2939867079,-0.0502983257,0.5077893734,-0.0957446694,0.1430463493,-0.1436831504,0.3505896926,0.0293883644,0.2170954049,0.4565422237,-0.2786490619,0.4197795987,0.2109335214,-0.0157594718,0.1513209641,-0.119827114,-0.0296273939,-0.2943778336,0.2821809947,-0.0091492198,0.240355581,-0.156307295,-0.3086295724,0.1988816857,-0.3600850999,0.0612888001,-0.0218717158,0.3002025485,0.1207000017,-0.0293847173,0.0294683818,-0.1035251617,-0.0404463708,0.0078178877,-0.2214457244,-0.0186168626,-0.3967349231,0.0554239415,0.1488305926,0.0385815836,0.265104264,0.1318566352,0.3805131912,-0.1918962747,0.1950099617,-0.2590519786,0.2879867554,0.242953822,-0.0098109245,0.196389541,0.1639664769,0.3001648784,-0.0144200921,0.6081849337,-0.385751456,-0.4894140959,-0.0089114644,0.1488809288,0.14920789,0.3424102664,-0.3296617866,-0.1756064147,0.6020561457,-0.1773954481,0.1349130869,-0.2100277245,0.1021258831,-0.4289412796,0.1298334002,0.1081753597,-0.7026266456,0.2321748734,-0.0112394346,0.0854156166,0.4426007867,0.6264768839,-0.3793056607,0.1955496818,-0.4166079164,-0.0423507392,0.165656969,-0.2223813832,-0.6628284454,0.0662823096,-0.0400794968,0.1180478036,0.021409208,0.1136311516,0.4583936334,0.1032361388,0.3721669614,0.2542293668,-0.3773194253,0.2213815004,-0.3497407734,-0.2212026268,0.0168303568,-0.043936912,0.0643142536,-0.1385787129,0.0896298289,-0.1657464206,0.2688992023,-0.2118351758,0.036254514,0.1838579178,0.289696455,-0.1664539129,-0.11468371,-0.1004822999,-0.2999524772,0.0535286665,-0.2676375508,-0.1073116139,-0.2515851259,-0.2944732606,-0.0730568394,0.1995472908,0.0247375183,-0.0814571753,0.11329294,0.0186100379,0.1513357311,-0.1031116322,0.1201159358,0.1809509397,-0.4612051547,-0.0600751974,-0.2285695821,0.1009557098,-0.1340085715,-0.3965064883,-0.1587676108,0.010481637,0.0488835983,-0.0901868939,-0.1125799343,0.2798411548,0.023992762,-0.3586603105,-0.3417615592,0.0024195323,0.1557076871,-0.1216348559,0.2745077014,0.0010634054,0.0594687238,-0.0877711028,-0.0765739828,0.209531188,0.1757121831,0.1426491737,-0.1151323617,0.0512540527,0.0651137233,0.1298433691,0.175810799,0.0157208536,0.1712497473,0.2229860127,0.3608556986,-0.0580638759,-0.1580515355,0.0946137086,0.4701109827,-0.0197819546,0.194552049,0.138112992,-0.2135622203,-0.1268219054,0.222679913,-0.1140938699,0.3751450479,0.2416901439,-0.0602771342,-0.1704898179,-0.0751106516,-0.0352937244,0.0658045113,-0.004286489,0.2925325036,0.0399406962,0.4612780809,-0.0292339884,-0.2392118722,-0.0579590499,0.0077317851,0.3310962319,-0.1639625579,0.0594538338,0.1934713721,-0.2000398934,0.0213530734,-0.2389669269,-0.1020776853,-0.1513523906,-0.0046538529,0.2886989117,-0.0756482854,0.236316964,0.4554078281,0.3434215486,0.3280366063,-0.1805103272,0.1633731723,-0.1627669632,-0.150253132,0.1015176997,0.3950359523,-0.3732878268,0.2742282152,0.2455913424,-0.1506754309,-0.193423003,-0.125122875,-0.1164164841,0.0526928529,0.3520668745,0.0071147545,0.2117315233,-0.1039761677,-0.2358806729,0.0012239725,-0.0071636862,-0.2696326673,-0.0120425597,-0.0319462754,-0.0347352326,-0.1717883199,-0.1776195914,-0.0253133569,-0.5092660189,-0.0226758812,0.0186971389,0.1199764535,-0.2123201191,0.3679845333,0.1896440834,0.0993028134,-0.2219925821,-0.0520104133,-0.1427073032,0.1922285855,-0.1869854629,-0.3465240896,0.0265490077,0.0426833406,0.0125978608,0.3760277331,-0.4561062753,0.0023040739,-0.2894494832,0.391746819,-0.1043551862,0.0885514319,0.4165962636,0.2006848156,-0.0801264271,0.0194083378,-0.3021274805,-0.1463979632,-0.0703578442,0.3098169863,-0.2234727442,0.5141125917,0.155668512,0.8973591328,0.151639685,-0.0577581748,0.0690155774,0.0083153993,0.1206522807,-0.1576684564,-0.2264817655,0.1268743426,-0.3355574012,-0.3323043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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3099","title":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'","comments":"Hi @JTWang2000, thanks for reporting.\r\n\r\nHowever, I cannot reproduce your reported bug:\r\n```python\r\n>>> from datasets import load_dataset\r\n\r\n>>> dataset = load_dataset(\"sst\", \"default\")\r\n>>> dataset\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'label', 'tokens', 'tree'],\r\n        num_rows: 8544\r\n    })\r\n    validation: Dataset({\r\n        features: ['sentence', 'label', 'tokens', 'tree'],\r\n        num_rows: 1101\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'label', 'tokens', 'tree'],\r\n        num_rows: 2210\r\n    })\r\n})\r\n```\r\n\r\nMaybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n```\r\npip install -U huggingface_hub\r\n```","body":"## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":90,"text":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo' \n ## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n Hi @JTWang2000, thanks for reporting.\r\n\r\nHowever, I cannot reproduce your reported bug:\r\n```python\r\n>>> from datasets import load_dataset\r\n\r\n>>> dataset = load_dataset(\"sst\", \"default\")\r\n>>> dataset\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'label', 'tokens', 'tree'],\r\n        num_rows: 8544\r\n    })\r\n    validation: Dataset({\r\n        features: ['sentence', 'label', 'tokens', 'tree'],\r\n        num_rows: 1101\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'label', 'tokens', 'tree'],\r\n        num_rows: 2210\r\n    })\r\n})\r\n```\r\n\r\nMaybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n```\r\npip install -U huggingface_hub\r\n```","embeddings":[-0.4111835957,-0.2402751297,-0.068508558,0.4320226908,0.2099773288,0.0242408048,0.2803736329,0.4057574272,0.1470828801,0.0813486874,-0.2076940686,0.3919779956,-0.0483211279,0.0626082942,-0.0157078728,-0.1405222714,0.0408855118,0.2579434216,-0.3039067388,-0.1304223388,0.0125605129,0.2599345148,-0.0942888409,0.0197069794,-0.363888979,-0.1373621821,0.2644459009,0.0650361925,-0.2278311253,-0.4140820503,0.2953846157,-0.1304875761,0.0288115274,0.3824133277,-0.0001108762,0.2163034528,0.3906912506,-0.0061177472,-0.3889297843,-0.1615571529,-0.406158179,-0.0434615947,0.1681846678,-0.1199032664,-0.0249250028,-0.0406566784,-0.1425966024,-0.3305263519,0.3294387758,0.3395951986,0.2705219984,0.4959825575,0.3331592381,-0.2063226253,-0.0917058736,0.2692639232,-0.2287531197,0.1491523981,-0.0694679245,0.1074016094,0.0091462368,0.2462645769,-0.1224697158,0.1727231592,0.4893963337,-0.0743135437,-0.2408602238,-0.3432418108,0.0028929322,0.1411681175,0.3232000768,-0.4975568056,-0.5184058547,-0.1447170377,0.0205587782,-0.0667386502,0.2386431396,0.0537124611,-0.110344179,0.0854035765,-0.1581896693,-0.1327443272,-0.0845850706,-0.0041269297,-0.0971259028,0.2198684067,-0.1888699234,0.0896138176,-0.0369771309,-0.1413608789,0.1013766825,-0.1235723644,-0.2180247158,0.1011404023,-0.3869355023,-0.0875710696,0.020100737,0.0376694575,0.1170411333,0.3247123063,-0.1191577017,-0.1074449047,0.0774691254,0.1526909918,-0.0633710697,0.251049906,0.0717843994,0.4055881202,0.1644883603,0.2256294489,0.1903326511,-0.0372535028,-0.0465321206,-0.1879572421,-0.0354170091,-0.0767263472,0.3911944926,-0.1857683808,-0.4609494805,0.0816526636,-0.0192961972,-0.0126494439,0.1657830477,0.3901851475,-0.0508910641,0.195847854,0.0338386074,0.2480371743,-0.0987039804,-0.0549307913,-0.2178040147,0.1822751462,0.0442955643,-0.0613648966,0.2665494084,-0.2043992132,0.3732978702,-0.0423154943,0.3616090417,0.0847419202,0.0510842279,-0.1454524249,-0.0255684759,0.396805793,0.0152088162,0.2378199548,0.1801996827,-0.1035066023,-0.1647804826,-0.0958936587,-0.1918443292,-0.1836182177,-0.3192626536,0.2162049264,-0.0210420564,-0.0921766534,-0.3226115704,-0.3689464331,0.0944248512,-0.0182121526,0.1137155965,-0.0906746089,0.0215387493,-0.1323784292,0.4526686668,0.5081090331,-0.0831920207,-0.1450252086,0.0850341246,-0.2529964149,-0.0855873674,0.0920887291,-0.0484305695,0.2740024328,-0.2953411639,-0.067179434,0.4383234978,-0.7279800773,-0.5659128428,0.1156728417,0.1477627754,0.1233794764,0.1726186424,-0.0775599778,0.1376910657,0.1192028373,0.1501132101,0.1212865189,0.0992891043,-0.0077223522,-0.1008376405,-0.17664285,-0.1245832667,0.19220002,0.1282687932,0.092015028,0.060645178,-0.0919139013,0.1492720842,0.0344355851,0.1913653016,0.3376875818,0.1375068873,-0.0066799424,-0.0293334946,-0.3104025126,-0.3723601997,0.2383066714,0.0184980091,-0.0546190962,-0.4142485559,-0.1267382056,-0.3679560721,0.1336208433,-0.2611930668,-0.3326177895,0.1561790258,0.1425204128,0.0898754597,-0.0342777036,-0.2702408433,0.629942596,0.1620313823,0.2853499353,-0.5041022897,0.4159918129,-0.2283565551,-0.1928143352,0.0165431872,0.2877674401,0.0604734086,-0.1350450814,-0.1276095957,0.2860280275,-0.073757194,0.141970247,-0.1714648306,-0.0211530551,0.254278332,-0.368799001,0.1102794483,-0.1610150486,0.152554661,0.1148898602,0.155960232,-0.067275174,-0.0296016466,0.1813689768,0.056223046,0.1284665614,0.1555491239,0.0018368594,-0.0128844185,-0.3161047697,0.2342734486,-0.0157591552,0.3669007123,-0.1330784857,-0.0506014191,0.0338333063,0.3739252388,0.1390889883,0.0664237514,0.0390165858,-0.3434298337,0.397697866,0.332878828,0.0874230713,0.3656415939,0.1534752548,-0.3630251586,0.087031953,-0.0286811888,-0.0013226286,0.2386494875,0.1084731668,0.164944008,0.1434297115,-0.0002986772,0.1899613589,-0.1344562322,-0.5046703219,-0.2546652555,0.1954541355,-0.364171505,-0.0019555893,-0.2530147135,0.0832497552,-0.109307304,-0.4048540294,-0.1465938687,-0.2161157578,-0.2081963867,0.2030976415,0.0224118233,0.2812790573,-0.2393125445,-0.062586613,0.1110455394,-0.2723608911,-0.2848054171,-0.0283505246,-0.0647846535,0.0693342015,0.1385720074,0.1202724203,0.3721858859,-0.2688125074,0.1781259328,-0.2829397023,-0.2431177497,0.2052356601,-0.1936722249,0.2931850255,0.3369629979,0.0967691615,0.0580183789,-0.3787179291,0.308119148,-0.0661887303,-0.2518463433,0.0720032379,-0.1423130333,-0.3741618991,-0.0584933832,-0.2144790441,-0.2503893375,-0.5462630391,0.1873145252,0.2677696645,0.138693735,0.2515629828,0.1481534839,0.2667450905,-0.0929773971,0.349395752,0.0188705251,-0.1835743934,0.3339470923,-0.3508706391,-0.2841075957,-0.0293090325,-0.096019648,0.3760782182,-0.0521921292,-0.4940158427,-0.3822026551,-0.3773941398,0.4545093179,-0.2008544803,0.0889410228,0.3171696365,0.2307834327,0.0254349783,-0.19756715,-0.1063174829,0.0195998866,-0.2402971834,-0.0674572736,0.0507310294,0.2947299778,-0.1363618672,0.4734269679,0.4305485785,-0.13804169,0.3442363441,-0.1096343473,0.3224722445,-0.2545557618,-0.6419271231,0.11272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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3099","title":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'","comments":"Im facing the same issue. I did run the upgrade command but that doesnt seem to resolve the issue","body":"## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":19,"text":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo' \n ## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n Im facing the same issue. I did run the upgrade command but that doesnt seem to resolve the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3099","title":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'","comments":"Hi @aneeshjain, could you please specify which `huggingface_hub` version you are using?\r\n\r\nBesides that, please run `datasets-cli env` and copy-and-paste its output below.","body":"## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":23,"text":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo' \n ## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n Hi @aneeshjain, could you please specify which `huggingface_hub` version you are using?\r\n\r\nBesides that, please run `datasets-cli env` and copy-and-paste its output below.","embeddings":[-0.4111835957,-0.2402751297,-0.068508558,0.4320226908,0.2099773288,0.0242408048,0.2803736329,0.4057574272,0.1470828801,0.0813486874,-0.2076940686,0.3919779956,-0.0483211279,0.0626082942,-0.0157078728,-0.1405222714,0.0408855118,0.2579434216,-0.3039067388,-0.1304223388,0.0125605129,0.2599345148,-0.0942888409,0.0197069794,-0.363888979,-0.1373621821,0.2644459009,0.0650361925,-0.2278311253,-0.4140820503,0.2953846157,-0.1304875761,0.0288115274,0.3824133277,-0.0001108762,0.2163034528,0.3906912506,-0.0061177472,-0.3889297843,-0.1615571529,-0.406158179,-0.0434615947,0.1681846678,-0.1199032664,-0.0249250028,-0.0406566784,-0.1425966024,-0.3305263519,0.3294387758,0.3395951986,0.2705219984,0.4959825575,0.3331592381,-0.2063226253,-0.0917058736,0.2692639232,-0.2287531197,0.1491523981,-0.0694679245,0.1074016094,0.0091462368,0.2462645769,-0.1224697158,0.1727231592,0.4893963337,-0.0743135437,-0.2408602238,-0.3432418108,0.0028929322,0.1411681175,0.3232000768,-0.4975568056,-0.5184058547,-0.1447170377,0.0205587782,-0.0667386502,0.2386431396,0.0537124611,-0.110344179,0.0854035765,-0.1581896693,-0.1327443272,-0.0845850706,-0.0041269297,-0.0971259028,0.2198684067,-0.1888699234,0.0896138176,-0.0369771309,-0.1413608789,0.1013766825,-0.1235723644,-0.2180247158,0.1011404023,-0.3869355023,-0.0875710696,0.020100737,0.0376694575,0.1170411333,0.3247123063,-0.1191577017,-0.1074449047,0.0774691254,0.1526909918,-0.0633710697,0.251049906,0.0717843994,0.4055881202,0.1644883603,0.2256294489,0.1903326511,-0.0372535028,-0.0465321206,-0.1879572421,-0.0354170091,-0.0767263472,0.3911944926,-0.1857683808,-0.4609494805,0.0816526636,-0.0192961972,-0.0126494439,0.1657830477,0.3901851475,-0.0508910641,0.195847854,0.0338386074,0.2480371743,-0.0987039804,-0.0549307913,-0.2178040147,0.1822751462,0.0442955643,-0.0613648966,0.2665494084,-0.2043992132,0.3732978702,-0.0423154943,0.3616090417,0.0847419202,0.0510842279,-0.1454524249,-0.0255684759,0.396805793,0.0152088162,0.2378199548,0.1801996827,-0.1035066023,-0.1647804826,-0.0958936587,-0.1918443292,-0.1836182177,-0.3192626536,0.2162049264,-0.0210420564,-0.0921766534,-0.3226115704,-0.3689464331,0.0944248512,-0.0182121526,0.1137155965,-0.0906746089,0.0215387493,-0.1323784292,0.4526686668,0.5081090331,-0.0831920207,-0.1450252086,0.0850341246,-0.2529964149,-0.0855873674,0.0920887291,-0.0484305695,0.2740024328,-0.2953411639,-0.067179434,0.4383234978,-0.7279800773,-0.5659128428,0.1156728417,0.1477627754,0.1233794764,0.1726186424,-0.0775599778,0.1376910657,0.1192028373,0.1501132101,0.1212865189,0.0992891043,-0.0077223522,-0.1008376405,-0.17664285,-0.1245832667,0.19220002,0.1282687932,0.092015028,0.060645178,-0.0919139013,0.1492720842,0.0344355851,0.1913653016,0.3376875818,0.1375068873,-0.0066799424,-0.0293334946,-0.3104025126,-0.3723601997,0.2383066714,0.0184980091,-0.0546190962,-0.4142485559,-0.1267382056,-0.3679560721,0.1336208433,-0.2611930668,-0.3326177895,0.1561790258,0.1425204128,0.0898754597,-0.0342777036,-0.2702408433,0.629942596,0.1620313823,0.2853499353,-0.5041022897,0.4159918129,-0.2283565551,-0.1928143352,0.0165431872,0.2877674401,0.0604734086,-0.1350450814,-0.1276095957,0.2860280275,-0.073757194,0.141970247,-0.1714648306,-0.0211530551,0.254278332,-0.368799001,0.1102794483,-0.1610150486,0.152554661,0.1148898602,0.155960232,-0.067275174,-0.0296016466,0.1813689768,0.056223046,0.1284665614,0.1555491239,0.0018368594,-0.0128844185,-0.3161047697,0.2342734486,-0.0157591552,0.3669007123,-0.1330784857,-0.0506014191,0.0338333063,0.3739252388,0.1390889883,0.0664237514,0.0390165858,-0.3434298337,0.397697866,0.332878828,0.0874230713,0.3656415939,0.1534752548,-0.3630251586,0.087031953,-0.0286811888,-0.0013226286,0.2386494875,0.1084731668,0.164944008,0.1434297115,-0.0002986772,0.1899613589,-0.1344562322,-0.5046703219,-0.2546652555,0.1954541355,-0.364171505,-0.0019555893,-0.2530147135,0.0832497552,-0.109307304,-0.4048540294,-0.1465938687,-0.2161157578,-0.2081963867,0.2030976415,0.0224118233,0.2812790573,-0.2393125445,-0.062586613,0.1110455394,-0.2723608911,-0.2848054171,-0.0283505246,-0.0647846535,0.0693342015,0.1385720074,0.1202724203,0.3721858859,-0.2688125074,0.1781259328,-0.2829397023,-0.2431177497,0.2052356601,-0.1936722249,0.2931850255,0.3369629979,0.0967691615,0.0580183789,-0.3787179291,0.308119148,-0.0661887303,-0.2518463433,0.0720032379,-0.1423130333,-0.3741618991,-0.0584933832,-0.2144790441,-0.2503893375,-0.5462630391,0.1873145252,0.2677696645,0.138693735,0.2515629828,0.1481534839,0.2667450905,-0.0929773971,0.349395752,0.0188705251,-0.1835743934,0.3339470923,-0.3508706391,-0.2841075957,-0.0293090325,-0.096019648,0.3760782182,-0.0521921292,-0.4940158427,-0.3822026551,-0.3773941398,0.4545093179,-0.2008544803,0.0889410228,0.3171696365,0.2307834327,0.0254349783,-0.19756715,-0.1063174829,0.0195998866,-0.2402971834,-0.0674572736,0.0507310294,0.2947299778,-0.1363618672,0.4734269679,0.4305485785,-0.13804169,0.3442363441,-0.1096343473,0.3224722445,-0.2545557618,-0.6419271231,0.1127227545,0.09007753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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3099","title":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'","comments":"The problem seems to be with the latest version of `datasets`. After running `pip install -U datasets huggingface_hub`, I get the following: \r\n\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/__init__.py\", line 37, in <module>\r\n    from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 44, in <module>\r\n    from .data_files import DataFilesDict, _sanitize_patterns\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 122, in <module>\r\n    allowed_extensions: Optional[list] = None,\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n````\r\nNote that pip reports the latest `datasets` version as \r\n```bash\r\n pip show datasets\r\nName: datasets\r\nVersion: 1.14.0\r\n```\r\nHowever, if I downgrade datasets with `pip install datasets==1.11.0`, things now work\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\ndvers=1.11.0\r\n````","body":"## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":128,"text":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo' \n ## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n The problem seems to be with the latest version of `datasets`. After running `pip install -U datasets huggingface_hub`, I get the following: \r\n\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/__init__.py\", line 37, in <module>\r\n    from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 44, in <module>\r\n    from .data_files import DataFilesDict, _sanitize_patterns\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/data_files.py\", line 122, in <module>\r\n    allowed_extensions: Optional[list] = None,\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n````\r\nNote that pip reports the latest `datasets` version as \r\n```bash\r\n pip show datasets\r\nName: datasets\r\nVersion: 1.14.0\r\n```\r\nHowever, if I downgrade datasets with `pip install datasets==1.11.0`, things now work\r\n```bash\r\npython -c \"import huggingface_hub; print(f'hbvers={huggingface_hub.__version__}'); import datasets; print(f'dvers={datasets.__version__}')\"\r\nhbvers=0.0.8\r\ndvers=1.11.0\r\n````","embeddings":[-0.4111835957,-0.2402751297,-0.068508558,0.4320226908,0.2099773288,0.0242408048,0.2803736329,0.4057574272,0.1470828801,0.0813486874,-0.2076940686,0.3919779956,-0.0483211279,0.0626082942,-0.0157078728,-0.1405222714,0.0408855118,0.2579434216,-0.3039067388,-0.1304223388,0.0125605129,0.2599345148,-0.0942888409,0.0197069794,-0.363888979,-0.1373621821,0.2644459009,0.0650361925,-0.2278311253,-0.4140820503,0.2953846157,-0.1304875761,0.0288115274,0.3824133277,-0.0001108762,0.2163034528,0.3906912506,-0.0061177472,-0.3889297843,-0.1615571529,-0.406158179,-0.0434615947,0.1681846678,-0.1199032664,-0.0249250028,-0.0406566784,-0.1425966024,-0.3305263519,0.3294387758,0.3395951986,0.2705219984,0.4959825575,0.3331592381,-0.2063226253,-0.0917058736,0.2692639232,-0.2287531197,0.1491523981,-0.0694679245,0.1074016094,0.0091462368,0.2462645769,-0.1224697158,0.1727231592,0.4893963337,-0.0743135437,-0.2408602238,-0.3432418108,0.0028929322,0.1411681175,0.3232000768,-0.4975568056,-0.5184058547,-0.1447170377,0.0205587782,-0.0667386502,0.2386431396,0.0537124611,-0.110344179,0.0854035765,-0.1581896693,-0.1327443272,-0.0845850706,-0.0041269297,-0.0971259028,0.2198684067,-0.1888699234,0.0896138176,-0.0369771309,-0.1413608789,0.1013766825,-0.1235723644,-0.2180247158,0.1011404023,-0.3869355023,-0.0875710696,0.020100737,0.0376694575,0.1170411333,0.3247123063,-0.1191577017,-0.1074449047,0.0774691254,0.1526909918,-0.0633710697,0.251049906,0.0717843994,0.4055881202,0.1644883603,0.2256294489,0.1903326511,-0.0372535028,-0.0465321206,-0.1879572421,-0.0354170091,-0.0767263472,0.3911944926,-0.1857683808,-0.4609494805,0.0816526636,-0.0192961972,-0.0126494439,0.1657830477,0.3901851475,-0.0508910641,0.195847854,0.0338386074,0.2480371743,-0.0987039804,-0.0549307913,-0.2178040147,0.1822751462,0.0442955643,-0.0613648966,0.2665494084,-0.2043992132,0.3732978702,-0.0423154943,0.3616090417,0.0847419202,0.0510842279,-0.1454524249,-0.0255684759,0.396805793,0.0152088162,0.2378199548,0.1801996827,-0.1035066023,-0.1647804826,-0.0958936587,-0.1918443292,-0.1836182177,-0.3192626536,0.2162049264,-0.0210420564,-0.0921766534,-0.3226115704,-0.3689464331,0.0944248512,-0.0182121526,0.1137155965,-0.0906746089,0.0215387493,-0.1323784292,0.4526686668,0.5081090331,-0.0831920207,-0.1450252086,0.0850341246,-0.2529964149,-0.0855873674,0.0920887291,-0.0484305695,0.2740024328,-0.2953411639,-0.067179434,0.4383234978,-0.7279800773,-0.5659128428,0.1156728417,0.1477627754,0.1233794764,0.1726186424,-0.0775599778,0.1376910657,0.1192028373,0.1501132101,0.1212865189,0.0992891043,-0.0077223522,-0.1008376405,-0.17664285,-0.1245832667,0.19220002,0.1282687932,0.092015028,0.060645178,-0.0919139013,0.1492720842,0.0344355851,0.1913653016,0.3376875818,0.1375068873,-0.0066799424,-0.0293334946,-0.3104025126,-0.3723601997,0.2383066714,0.0184980091,-0.0546190962,-0.4142485559,-0.1267382056,-0.3679560721,0.1336208433,-0.2611930668,-0.3326177895,0.1561790258,0.1425204128,0.0898754597,-0.0342777036,-0.2702408433,0.629942596,0.1620313823,0.2853499353,-0.5041022897,0.4159918129,-0.2283565551,-0.1928143352,0.0165431872,0.2877674401,0.0604734086,-0.1350450814,-0.1276095957,0.2860280275,-0.073757194,0.141970247,-0.1714648306,-0.0211530551,0.254278332,-0.368799001,0.1102794483,-0.1610150486,0.152554661,0.1148898602,0.155960232,-0.067275174,-0.0296016466,0.1813689768,0.056223046,0.1284665614,0.1555491239,0.0018368594,-0.0128844185,-0.3161047697,0.2342734486,-0.0157591552,0.3669007123,-0.1330784857,-0.0506014191,0.0338333063,0.3739252388,0.1390889883,0.0664237514,0.0390165858,-0.3434298337,0.397697866,0.332878828,0.0874230713,0.3656415939,0.1534752548,-0.3630251586,0.087031953,-0.0286811888,-0.0013226286,0.2386494875,0.1084731668,0.164944008,0.1434297115,-0.0002986772,0.1899613589,-0.1344562322,-0.5046703219,-0.2546652555,0.1954541355,-0.364171505,-0.0019555893,-0.2530147135,0.0832497552,-0.109307304,-0.4048540294,-0.1465938687,-0.2161157578,-0.2081963867,0.2030976415,0.0224118233,0.2812790573,-0.2393125445,-0.062586613,0.1110455394,-0.2723608911,-0.2848054171,-0.0283505246,-0.0647846535,0.0693342015,0.1385720074,0.1202724203,0.3721858859,-0.2688125074,0.1781259328,-0.2829397023,-0.2431177497,0.2052356601,-0.1936722249,0.2931850255,0.3369629979,0.0967691615,0.0580183789,-0.3787179291,0.308119148,-0.0661887303,-0.2518463433,0.0720032379,-0.1423130333,-0.3741618991,-0.0584933832,-0.2144790441,-0.2503893375,-0.5462630391,0.1873145252,0.2677696645,0.138693735,0.2515629828,0.1481534839,0.2667450905,-0.0929773971,0.349395752,0.0188705251,-0.1835743934,0.3339470923,-0.3508706391,-0.2841075957,-0.0293090325,-0.096019648,0.3760782182,-0.0521921292,-0.4940158427,-0.3822026551,-0.3773941398,0.4545093179,-0.2008544803,0.0889410228,0.3171696365,0.2307834327,0.0254349783,-0.19756715,-0.1063174829,0.0195998866,-0.2402971834,-0.0674572736,0.0507310294,0.2947299778,-0.1363618672,0.4734269679,0.4305485785,-0.13804169,0.3442363441,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3099","title":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'","comments":"> Hi @JTWang2000, thanks for reporting.\r\n> \r\n> However, I cannot reproduce your reported bug:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> \r\n> >>> dataset = load_dataset(\"sst\", \"default\")\r\n> >>> dataset\r\n> DatasetDict({\r\n>     train: Dataset({\r\n>         features: ['sentence', 'label', 'tokens', 'tree'],\r\n>         num_rows: 8544\r\n>     })\r\n>     validation: Dataset({\r\n>         features: ['sentence', 'label', 'tokens', 'tree'],\r\n>         num_rows: 1101\r\n>     })\r\n>     test: Dataset({\r\n>         features: ['sentence', 'label', 'tokens', 'tree'],\r\n>         num_rows: 2210\r\n>     })\r\n> })\r\n> ```\r\n> \r\n> Maybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n> \r\n> ```\r\n> pip install -U huggingface_hub\r\n> ```\r\n\r\nMy problem solved after updating huggingface hub command. Thanks a lot and sorry for the late reply. ","body":"## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":137,"text":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo' \n ## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n > Hi @JTWang2000, thanks for reporting.\r\n> \r\n> However, I cannot reproduce your reported bug:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> \r\n> >>> dataset = load_dataset(\"sst\", \"default\")\r\n> >>> dataset\r\n> DatasetDict({\r\n>     train: Dataset({\r\n>         features: ['sentence', 'label', 'tokens', 'tree'],\r\n>         num_rows: 8544\r\n>     })\r\n>     validation: Dataset({\r\n>         features: ['sentence', 'label', 'tokens', 'tree'],\r\n>         num_rows: 1101\r\n>     })\r\n>     test: Dataset({\r\n>         features: ['sentence', 'label', 'tokens', 'tree'],\r\n>         num_rows: 2210\r\n>     })\r\n> })\r\n> ```\r\n> \r\n> Maybe, the cause is that you have a quite old version of `huggingface_hub`. Could you please try to update it and confirm if the problem persists?\r\n> \r\n> ```\r\n> pip install -U huggingface_hub\r\n> ```\r\n\r\nMy problem solved after updating huggingface hub command. Thanks a lot and sorry for the late reply. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3099","title":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'","comments":"@tjruwase, please note that versions of `datsets` and `huggingface_hub` must be compatible one with each other:\r\n- In `datasets` version `1.11.0`, the requirement on `huggingface_hub` was: `huggingface_hub<0.1.0`\r\n  https:\/\/github.com\/huggingface\/datasets\/blob\/2cc00f372a96133e701275eec4d6b26d15257289\/setup.py#L90\r\n  - Therefore, your installed `huggingface_hub` version `0.0.8` was compatible\r\n- In `datasets` version `1.12.0`, the requirement on `huggingface_hub` was: `huggingface_hub>=0.0.14,<0.1.0`\r\n  https:\/\/github.com\/huggingface\/datasets\/blob\/6c766f9115d686182d76b1b937cb27e099c45d68\/setup.py#L104\r\n  - Therefore, your installed `huggingface_hub` version `0.0.8` was no longer compatible \r\n- Currently, in `datasets` version `1.15.1`, the requirement on `huggingface_hub` is: `huggingface_hub>=0.1.0,<1.0.0`\r\n  https:\/\/github.com\/huggingface\/datasets\/blob\/018100679d21cf27136f0eccb1c50e3a9c968ce2\/setup.py#L102\r\n\r\n@JTWang2000, thanks for your answer. I close this issue then.","body":"## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":83,"text":"AttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo' \n ## Describe the bug\r\nWhen using `pip install datasets`\r\nor use `conda install -c huggingface -c conda-forge datasets`\r\ncannot install datasets\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"sst\", \"default\")\r\n```\r\n\r\n## Actual results\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-fbe7981e6e21> in <module>\r\n      1 import torch\r\n      2 import transformers\r\n----> 3 from datasets import load_dataset\r\n      4 \r\n      5 dataset = load_dataset(\"sst\", \"default\")\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/__init__.py in <module>\r\n     35 from .arrow_reader import ArrowReader, ReadInstruction\r\n     36 from .arrow_writer import ArrowWriter\r\n---> 37 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder\r\n     38 from .combine import interleave_datasets\r\n     39 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/builder.py in <module>\r\n     42 )\r\n     43 from .arrow_writer import ArrowWriter, BeamWriter\r\n---> 44 from .data_files import DataFilesDict, _sanitize_patterns\r\n     45 from .dataset_dict import DatasetDict, IterableDatasetDict\r\n     46 from .fingerprint import Hasher\r\n\r\n~\/miniforge3\/envs\/actor\/lib\/python3.8\/site-packages\/datasets\/data_files.py in <module>\r\n    118 \r\n    119 def _exec_patterns_in_dataset_repository(\r\n--> 120     dataset_info: huggingface_hub.hf_api.DatasetInfo,\r\n    121     patterns: List[str],\r\n    122     allowed_extensions: Optional[list] = None,\r\n\r\nAttributeError: module 'huggingface_hub.hf_api' has no attribute 'DatasetInfo'\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.13.3\r\n- Platform: macOS-11.3.1-arm64-arm-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n @tjruwase, please note that versions of `datsets` and `huggingface_hub` must be compatible one with each other:\r\n- In `datasets` version `1.11.0`, the requirement on `huggingface_hub` was: `huggingface_hub<0.1.0`\r\n  https:\/\/github.com\/huggingface\/datasets\/blob\/2cc00f372a96133e701275eec4d6b26d15257289\/setup.py#L90\r\n  - Therefore, your installed `huggingface_hub` version `0.0.8` was compatible\r\n- In `datasets` version `1.12.0`, the requirement on `huggingface_hub` was: `huggingface_hub>=0.0.14,<0.1.0`\r\n  https:\/\/github.com\/huggingface\/datasets\/blob\/6c766f9115d686182d76b1b937cb27e099c45d68\/setup.py#L104\r\n  - Therefore, your installed `huggingface_hub` version `0.0.8` was no longer compatible \r\n- Currently, in `datasets` version `1.15.1`, the requirement on `huggingface_hub` is: `huggingface_hub>=0.1.0,<1.0.0`\r\n  https:\/\/github.com\/huggingface\/datasets\/blob\/018100679d21cf27136f0eccb1c50e3a9c968ce2\/setup.py#L102\r\n\r\n@JTWang2000, thanks for your answer. I close this issue then.","embeddings":[-0.4111835957,-0.2402751297,-0.068508558,0.4320226908,0.2099773288,0.0242408048,0.2803736329,0.4057574272,0.1470828801,0.0813486874,-0.2076940686,0.3919779956,-0.0483211279,0.0626082942,-0.0157078728,-0.1405222714,0.0408855118,0.2579434216,-0.3039067388,-0.1304223388,0.0125605129,0.2599345148,-0.0942888409,0.0197069794,-0.363888979,-0.1373621821,0.2644459009,0.0650361925,-0.2278311253,-0.4140820503,0.2953846157,-0.1304875761,0.0288115274,0.3824133277,-0.0001108762,0.2163034528,0.3906912506,-0.0061177472,-0.3889297843,-0.1615571529,-0.406158179,-0.0434615947,0.1681846678,-0.1199032664,-0.0249250028,-0.0406566784,-0.1425966024,-0.3305263519,0.3294387758,0.3395951986,0.2705219984,0.4959825575,0.3331592381,-0.2063226253,-0.0917058736,0.2692639232,-0.2287531197,0.1491523981,-0.0694679245,0.1074016094,0.0091462368,0.2462645769,-0.1224697158,0.1727231592,0.4893963337,-0.0743135437,-0.2408602238,-0.3432418108,0.0028929322,0.1411681175,0.3232000768,-0.4975568056,-0.5184058547,-0.1447170377,0.0205587782,-0.0667386502,0.2386431396,0.0537124611,-0.110344179,0.0854035765,-0.1581896693,-0.1327443272,-0.0845850706,-0.0041269297,-0.0971259028,0.2198684067,-0.1888699234,0.0896138176,-0.0369771309,-0.1413608789,0.1013766825,-0.1235723644,-0.2180247158,0.1011404023,-0.3869355023,-0.0875710696,0.020100737,0.0376694575,0.1170411333,0.3247123063,-0.1191577017,-0.1074449047,0.0774691254,0.1526909918,-0.0633710697,0.251049906,0.0717843994,0.4055881202,0.1644883603,0.2256294489,0.1903326511,-0.0372535028,-0.0465321206,-0.1879572421,-0.0354170091,-0.0767263472,0.3911944926,-0.1857683808,-0.4609494805,0.0816526636,-0.0192961972,-0.0126494439,0.1657830477,0.3901851475,-0.0508910641,0.195847854,0.0338386074,0.2480371743,-0.0987039804,-0.0549307913,-0.2178040147,0.1822751462,0.0442955643,-0.0613648966,0.2665494084,-0.2043992132,0.3732978702,-0.0423154943,0.3616090417,0.0847419202,0.0510842279,-0.1454524249,-0.0255684759,0.396805793,0.0152088162,0.2378199548,0.1801996827,-0.1035066023,-0.1647804826,-0.0958936587,-0.1918443292,-0.1836182177,-0.3192626536,0.2162049264,-0.0210420564,-0.0921766534,-0.3226115704,-0.3689464331,0.0944248512,-0.0182121526,0.1137155965,-0.0906746089,0.0215387493,-0.1323784292,0.4526686668,0.5081090331,-0.0831920207,-0.1450252086,0.0850341246,-0.2529964149,-0.0855873674,0.0920887291,-0.0484305695,0.2740024328,-0.2953411639,-0.067179434,0.4383234978,-0.7279800773,-0.5659128428,0.1156728417,0.1477627754,0.1233794764,0.1726186424,-0.0775599778,0.1376910657,0.1192028373,0.1501132101,0.1212865189,0.0992891043,-0.0077223522,-0.1008376405,-0.17664285,-0.1245832667,0.19220002,0.1282687932,0.092015028,0.060645178,-0.0919139013,0.1492720842,0.0344355851,0.1913653016,0.3376875818,0.1375068873,-0.0066799424,-0.0293334946,-0.3104025126,-0.3723601997,0.2383066714,0.0184980091,-0.0546190962,-0.4142485559,-0.1267382056,-0.3679560721,0.1336208433,-0.2611930668,-0.3326177895,0.1561790258,0.1425204128,0.0898754597,-0.0342777036,-0.2702408433,0.629942596,0.1620313823,0.2853499353,-0.5041022897,0.4159918129,-0.2283565551,-0.1928143352,0.0165431872,0.2877674401,0.0604734086,-0.1350450814,-0.1276095957,0.2860280275,-0.073757194,0.141970247,-0.1714648306,-0.0211530551,0.254278332,-0.368799001,0.1102794483,-0.1610150486,0.152554661,0.1148898602,0.155960232,-0.067275174,-0.0296016466,0.1813689768,0.056223046,0.1284665614,0.1555491239,0.0018368594,-0.0128844185,-0.3161047697,0.2342734486,-0.0157591552,0.3669007123,-0.1330784857,-0.0506014191,0.0338333063,0.3739252388,0.1390889883,0.0664237514,0.0390165858,-0.3434298337,0.397697866,0.332878828,0.0874230713,0.3656415939,0.1534752548,-0.3630251586,0.087031953,-0.0286811888,-0.0013226286,0.2386494875,0.1084731668,0.164944008,0.1434297115,-0.0002986772,0.1899613589,-0.1344562322,-0.5046703219,-0.2546652555,0.1954541355,-0.364171505,-0.0019555893,-0.2530147135,0.0832497552,-0.109307304,-0.4048540294,-0.1465938687,-0.2161157578,-0.2081963867,0.2030976415,0.0224118233,0.2812790573,-0.2393125445,-0.062586613,0.1110455394,-0.2723608911,-0.2848054171,-0.0283505246,-0.0647846535,0.0693342015,0.1385720074,0.1202724203,0.3721858859,-0.2688125074,0.1781259328,-0.2829397023,-0.2431177497,0.2052356601,-0.1936722249,0.2931850255,0.3369629979,0.0967691615,0.0580183789,-0.3787179291,0.308119148,-0.0661887303,-0.2518463433,0.0720032379,-0.1423130333,-0.3741618991,-0.0584933832,-0.2144790441,-0.2503893375,-0.5462630391,0.1873145252,0.2677696645,0.138693735,0.2515629828,0.1481534839,0.2667450905,-0.0929773971,0.349395752,0.0188705251,-0.1835743934,0.3339470923,-0.3508706391,-0.2841075957,-0.0293090325,-0.096019648,0.3760782182,-0.0521921292,-0.4940158427,-0.3822026551,-0.3773941398,0.4545093179,-0.2008544803,0.0889410228,0.3171696365,0.2307834327,0.0254349783,-0.19756715,-0.1063174829,0.0195998866,-0.2402971834,-0.0674572736,0.0507310294,0.2947299778,-0.1363618672,0.4734269679,0.4305485785,-0.13804169,0.3442363441,-0.1096343473,0.3224722445,-0.2545557618,-0.6419271231,0.1127227545,0.090077534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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3095","title":"`cast_column` makes audio decoding fail","comments":"Thanks for reporting, @patrickvonplaten.\r\n\r\nI think the issue is related to mp3 resampling, not to `cast_column`.\r\n\r\nYou can check that `cast_column` works OK with non-mp3 audio files:\r\n```python\r\nfrom datasets import load_dataset\r\nimport datasets\r\nds = load_dataset(\"arabic_speech_corpus\", split=\"train\")\r\nds = ds.cast_column(\"audio\", datasets.features.Audio(sampling_rate=16_000))\r\nprint(ds[0][\"audio\"])\r\n```\r\n\r\nI'm fixing it.","body":"## Describe the bug\r\n\r\nAfter changing the sampling rate automatic decoding fails.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport datasets\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\nds = ds.cast_column(\"audio\", datasets.features.Audio(sampling_rate=16_000))\r\n\r\nprint(ds[0][\"audio\"])  # <- this fails currently\r\n```\r\n\r\nyields:\r\n\r\n```\r\nTypeError: forward() takes 2 positional arguments but 4 were given\r\n```\r\n\r\n## Expected results\r\nno failure\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 1.13.2 (master)\r\n- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":47,"text":"`cast_column` makes audio decoding fail \n ## Describe the bug\r\n\r\nAfter changing the sampling rate automatic decoding fails.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nimport datasets\r\n\r\nds = load_dataset(\"common_voice\", \"ab\", split=\"train\")\r\n\r\nds = ds.cast_column(\"audio\", datasets.features.Audio(sampling_rate=16_000))\r\n\r\nprint(ds[0][\"audio\"])  # <- this fails currently\r\n```\r\n\r\nyields:\r\n\r\n```\r\nTypeError: forward() takes 2 positional arguments but 4 were given\r\n```\r\n\r\n## Expected results\r\nno failure\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 1.13.2 (master)\r\n- Platform: Linux-5.11.0-1019-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n Thanks for reporting, @patrickvonplaten.\r\n\r\nI think the issue is related to mp3 resampling, not to `cast_column`.\r\n\r\nYou can check that `cast_column` works OK with non-mp3 audio files:\r\n```python\r\nfrom datasets import load_dataset\r\nimport datasets\r\nds = load_dataset(\"arabic_speech_corpus\", split=\"train\")\r\nds = ds.cast_column(\"audio\", datasets.features.Audio(sampling_rate=16_000))\r\nprint(ds[0][\"audio\"])\r\n```\r\n\r\nI'm fixing it.","embeddings":[-0.344961375,0.0809731483,0.0290253758,-0.1023004353,0.5135595798,-0.0765637979,0.3012085557,0.2489968091,-0.141864568,0.186738953,-0.2701360583,0.4867174327,0.0446885228,-0.1314158738,-0.3210033178,-0.292571038,0.1798268259,0.0890951902,-0.0834233314,-0.0481002182,-0.3176523149,0.306037575,-0.3996774852,-0.0373014323,0.2222272754,0.1928584427,0.0543187857,-0.3391494751,-0.1822615713,-0.18457672,-0.0594690368,-0.0365387723,0.236424312,0.4648657143,-0.0001053389,0.0007051463,0.3777334988,0.0462336093,-0.2197378874,-0.207267344,-0.207270965,0.0201439522,-0.197671473,-0.180917263,-0.252471745,-0.1413533986,-0.328742981,-0.1692548841,0.2775698304,0.3828302622,0.2956799567,0.1690138429,-0.1836949438,0.0336887687,0.2160948664,-0.1325875819,-0.2135980278,0.0396198332,0.141680941,0.3767261505,-0.1270405203,0.2859011292,-0.4151909649,0.1047778204,-0.0718418583,-0.0087776762,0.0776621401,-0.124574542,0.3770923018,0.060470026,0.7983108163,-0.3676582575,-0.3543493152,0.0489502177,0.2948670089,-0.2519976497,0.0515813082,0.1364448518,-0.0881375894,0.2334896922,-0.1687191278,0.2179821283,0.1454938799,0.0681414008,-0.1564674824,0.2573390007,-0.1629470438,0.0102946935,0.2681015134,-0.15769355,0.043258667,0.0677657574,-0.0937255472,0.1866409779,-0.5204972029,-0.2621381581,-0.0441939235,-0.0268956106,-0.1150507107,0.18864353,0.1597753763,0.3290556073,0.2473379821,0.1465437561,0.0074538924,-0.079515852,-0.1272968203,0.352889657,0.2860917151,0.2613309324,0.0291946586,-0.0776750892,0.1012396067,-0.2413478196,-0.0478986017,0.0262441095,-0.1553739309,-0.1953722686,-0.2192506045,0.038713485,-0.1295908391,-0.0303756911,0.0223304313,0.268717587,0.165938437,0.2164547741,0.0989412367,0.4155560434,-0.1189424992,-0.2073539793,-0.0624062456,0.0301009137,-0.0109610884,0.0484938286,0.0069297566,0.0690905601,0.1390097141,0.3160800934,0.0519832224,-0.1286739409,-0.0369593054,-0.048434563,-0.0944308788,0.3246289194,-0.0400445238,0.3260273933,-0.2159683555,0.1436411738,-0.0463801771,0.3565734923,-0.1808855981,-0.18500413,-0.0759029463,0.2142332494,0.067671746,-0.089740932,0.0325524956,0.2903163433,0.3790346086,-0.5632781386,0.0779878944,-0.2508467138,-0.313344121,-0.0325339474,0.2222625911,-0.0758934245,-0.1201800033,0.0182713401,-0.2794311345,0.276501596,0.4511859715,0.341848135,-0.1031687856,-0.2812899947,-0.0589829832,0.3118816614,0.4476523697,-0.1174024791,-0.3510469198,0.2269939184,0.2499116957,-0.0513914712,0.0173281822,-0.1631331295,0.2610942721,-0.2571058273,-0.1215981543,0.5106365085,0.1183719784,0.2105490565,-0.1357177198,-0.1571892649,0.0917717665,0.1592175812,-0.1492059976,0.1671786904,-0.0031393254,0.1503482759,0.140658617,-0.2397478521,-0.0223079957,0.1785107255,0.0318156853,-0.3498952985,0.2548889518,-0.3011934757,0.1805476397,0.0061924434,0.2778680623,0.1910151541,-0.1984942108,-0.2170055807,-0.2080298513,-0.2124697417,-0.0244746823,0.2528625131,0.1450855732,-0.0667070076,0.0050181667,0.0023657435,-0.1229430214,-0.2412697673,-0.1593769789,-0.0379574597,-0.1818118393,0.1207712814,0.0019269672,-0.2949457467,0.1429748982,0.1333989203,0.2182686329,0.1969207078,-0.3609715104,0.4032324255,0.0128290411,0.4149238765,-0.6723421216,0.0519546047,0.1092597619,-0.1187672168,0.1229803562,0.0888604596,0.258467555,-0.0345963947,0.199009046,0.2430265695,0.188905552,0.1710927933,0.0103078922,-0.1771746725,0.3711967766,0.0265534222,-0.2208574265,-0.0791862756,0.0186767448,-0.122043781,0.0332033746,-0.0126940673,-0.2701613605,0.0471895114,0.5708184242,-0.2198930681,0.1009101123,0.1899841726,-0.1792981923,-0.2092709392,0.1678676158,0.2484969348,0.4910354018,0.1546022296,0.3089252114,-0.0988950878,-0.1900452822,-0.2730255425,0.2405868918,0.1305104941,-0.1630518287,0.3238586485,0.1208306104,-0.1157568395,-0.4485521019,-0.1555052698,0.1886261404,0.2734315693,-0.3962467611,-0.0017575666,-0.3865879178,-0.0219330788,-0.0628553405,-0.2412037849,0.1635367721,-0.1281116754,0.0197747294,0.4063360095,-0.3343636096,0.297139734,-0.0485793389,-0.0473757163,-0.0128934914,0.0376649164,-0.2636221051,0.0251881666,-0.055789195,0.1422659457,0.1981192827,0.0936664417,-0.0569622479,0.0019342275,-0.1272775978,-0.0272117276,-0.0867732987,-0.0322964266,-0.1978644878,0.0632015467,-0.1392142922,0.0124869542,-0.1418849081,-0.2218722552,0.2023818195,-0.0425733067,0.0687748343,0.391308248,-0.0025125106,-0.0755852684,-0.3350450397,-0.2484964877,-0.0712958947,-0.4585553408,-0.2903002501,-0.1814407706,-0.0732128769,-0.224592641,0.277856648,-0.0836509466,0.0960080996,0.2552135289,-0.4077554345,-0.1043933406,0.4614216089,-0.1774923503,-0.2846854925,-0.2384538502,0.172807917,0.265258342,0.0886555687,-0.1405791044,0.2162232548,-0.3612727225,-0.0677037612,-0.0640318319,0.2609565556,0.0717424229,0.0576566644,-0.0637433156,-0.0079241106,-0.0222729854,-0.2573252916,0.3364306986,0.2316390574,0.0983347893,0.3216475546,-0.025711108,0.1738758236,0.144430697,-0.2387274057,0.3019502461,-0.1285698116,0.0388520434,-0.2165510207,0.0186564699,0.0900127813,-0.0366592966,-0.10199836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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3093","title":"Error loading json dataset with multiple splits if keys in nested dicts have a different order","comments":"Hi, \r\n\r\neven Pandas, which is less strict compared to PyArrow when it comes to reading JSON, doesn't support different orderings:\r\n```python\r\nimport io\r\nimport pandas as pd\r\n\r\ns = \"\"\"\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n\"\"\"\r\n\r\nbuffer = io.StringIO(s)\r\ndf = pd.read_json(buffer, lines=True)\r\n\r\nprint(df.shape[0]) # 0\r\n```\r\n\r\nSo we can't even fall back to Pandas in such cases.\r\n\r\nIt seems the only option is a script that recursively re-orders fields to enforce deterministic order:\r\n```python\r\nwith open(\"train.json\", \"r\") as fin:\r\n    with open(\"train_reordered.json\", \"w\") as fout:\r\n        for line in fin:\r\n            obj_jsonl = json.loads(line.strip())\r\n            fout.write(json.dumps(obj_jsonl, sort_keys=True) + \"\\n\")\r\n```","body":"## Describe the bug\r\nLoading a json dataset with multiple splits that have nested dicts with keys in different order results in the error below.\r\n\r\nIf the keys in the nested dicts always have the same order or even if you just load a single split in which the nested dicts don't have the same order, everything works fine.\r\n\r\n## Steps to reproduce the bug\r\nCreate two json files:\r\n\r\ntrain.json\r\n```\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n```\r\n\r\ntest.json\r\n```\r\n{\"a\": {\"b\": 1, \"c\": 2}}\r\n{\"a\": {\"b\": 3, \"c\": 4}}\r\n```\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n# Loading the files individually works (even though the keys in train.json don't have the same order)\r\nload_dataset('json', data_files={\"test\": \"test.json\"})\r\nload_dataset('json', data_files={\"train\": \"train.json\"})\r\n# Loading both splits fails\r\nload_dataset('json', data_files={\"train\": \"train.json\", \"test\": \"test.json\"})\r\n```\r\n\r\n## Expected results\r\nLoading both splits should not give an error whether the nested dicts are have the same order or not.\r\n\r\n## Actual results\r\n```\r\n>>> load_dataset('json', data_files={\"train\": \"train.json\", \"test\": \"test.json\"})\r\nUsing custom data configuration default-f1bc76fd07398c4c\r\nDownloading and preparing dataset json\/default to \/home\/dthulke\/.cache\/huggingface\/datasets\/json\/default-f1bc76fd07398c4c\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:00<00:00, 477.82it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1159, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1596, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/table.pxi\", line 592, in pyarrow.lib._sanitize_arrays\r\n  File \"pyarrow\/array.pxi\", line 329, in pyarrow.lib.asarray\r\n  File \"pyarrow\/table.pxi\", line 277, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/pyarrow\/compute.py\", line 297, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 527, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 337, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 120, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.2\r\n- Platform: Linux-4.15.0-147-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 5.0.0\r\n","comment_length":102,"text":"Error loading json dataset with multiple splits if keys in nested dicts have a different order \n ## Describe the bug\r\nLoading a json dataset with multiple splits that have nested dicts with keys in different order results in the error below.\r\n\r\nIf the keys in the nested dicts always have the same order or even if you just load a single split in which the nested dicts don't have the same order, everything works fine.\r\n\r\n## Steps to reproduce the bug\r\nCreate two json files:\r\n\r\ntrain.json\r\n```\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n```\r\n\r\ntest.json\r\n```\r\n{\"a\": {\"b\": 1, \"c\": 2}}\r\n{\"a\": {\"b\": 3, \"c\": 4}}\r\n```\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n# Loading the files individually works (even though the keys in train.json don't have the same order)\r\nload_dataset('json', data_files={\"test\": \"test.json\"})\r\nload_dataset('json', data_files={\"train\": \"train.json\"})\r\n# Loading both splits fails\r\nload_dataset('json', data_files={\"train\": \"train.json\", \"test\": \"test.json\"})\r\n```\r\n\r\n## Expected results\r\nLoading both splits should not give an error whether the nested dicts are have the same order or not.\r\n\r\n## Actual results\r\n```\r\n>>> load_dataset('json', data_files={\"train\": \"train.json\", \"test\": \"test.json\"})\r\nUsing custom data configuration default-f1bc76fd07398c4c\r\nDownloading and preparing dataset json\/default to \/home\/dthulke\/.cache\/huggingface\/datasets\/json\/default-f1bc76fd07398c4c\/0.0.0\/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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2\/2 [00:00<00:00, 477.82it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1632, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 697, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 1159, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1596, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/table.pxi\", line 592, in pyarrow.lib._sanitize_arrays\r\n  File \"pyarrow\/array.pxi\", line 329, in pyarrow.lib.asarray\r\n  File \"pyarrow\/table.pxi\", line 277, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/home\/dthulke\/venvs\/venv_torch_transformers\/lib\/python3.6\/site-packages\/pyarrow\/compute.py\", line 297, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 527, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 337, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 120, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.2\r\n- Platform: Linux-4.15.0-147-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 5.0.0\r\n \n Hi, \r\n\r\neven Pandas, which is less strict compared to PyArrow when it comes to reading JSON, doesn't support different orderings:\r\n```python\r\nimport io\r\nimport pandas as pd\r\n\r\ns = \"\"\"\r\n{\"a\": {\"c\": 8, \"b\": 5}}\r\n{\"a\": {\"b\": 7, \"c\": 6}}\r\n\"\"\"\r\n\r\nbuffer = io.StringIO(s)\r\ndf = pd.read_json(buffer, lines=True)\r\n\r\nprint(df.shape[0]) # 0\r\n```\r\n\r\nSo we can't even fall back to Pandas in such cases.\r\n\r\nIt seems the only option is a script that recursively re-orders fields to enforce deterministic order:\r\n```python\r\nwith open(\"train.json\", \"r\") as fin:\r\n    with open(\"train_reordered.json\", \"w\") as fout:\r\n        for line in fin:\r\n            obj_jsonl = json.loads(line.strip())\r\n            fout.write(json.dumps(obj_jsonl, sort_keys=True) + \"\\n\")\r\n```","embeddings":[0.0832339898,-0.2473291457,-0.0849279314,0.4981232285,-0.0341761746,0.092523396,0.473840028,0.2425033003,0.4540646374,-0.0762439147,0.0957989171,0.3503094912,-0.0273538064,0.1923105419,-0.4392796159,-0.3062129319,0.1353110671,0.0029309716,0.2856114209,0.2145694196,-0.2677586675,0.4219860137,-0.2506572604,-0.0870384201,-0.1292538047,-0.1254863739,-0.2167165726,0.3957204521,0.1152153984,-0.4368095398,0.3336633742,0.1480521858,-0.1053347141,0.3599999547,-0.0001107044,0.183123529,0.3657305539,-0.064558737,-0.096832782,-0.4886764586,-0.3687759638,-0.1478070468,0.032807488,-0.2557142973,-0.0578952394,-0.1994666755,-0.3122704029,-0.4717562199,0.8244403005,0.0067789736,0.1945836693,-0.0980121419,0.0316921398,-0.1429001987,0.1581694186,0.1960919797,0.1081515253,0.1555108875,0.0031480915,0.0758982375,0.0521504767,0.047054328,-0.0809774548,0.2331052125,-0.0050106593,-0.02581832,0.0009370064,-0.0631741211,-0.1879121363,0.3248148859,0.3290959299,-0.0596830919,-0.2868614197,-0.5015627742,0.0747120082,-0.1104731262,0.4031126499,0.1024425551,0.0293998122,0.078563787,0.0645569712,0.0587738641,-0.0242206957,0.0305642225,-0.0450764857,0.0913375691,-0.0512975678,0.0944296196,0.0445447005,-0.2445055395,0.0770113766,-0.239748016,-0.2185720056,-0.0449783318,-0.4027816355,-0.0959675834,-0.0221837927,-0.3604857922,0.0712332055,0.2007465363,0.0229707547,-0.1043820977,-0.0430626012,0.040381372,0.6374557018,0.0440495797,-0.0302748717,0.2815842927,-0.0140819531,0.4600857794,-0.1894746721,0.0386916809,0.1659073085,-0.2601965964,-0.2329406589,0.2749159038,0.2563062012,-0.0129238823,-0.3903878331,-0.0083090737,-0.3796016872,-0.1985426545,0.1165086553,0.2785375118,0.1416193098,0.3316738307,0.0210311599,0.364239037,-0.0714406222,0.1461274922,-0.1550713331,0.0217340793,0.0350730717,0.0234129168,0.2252332866,0.1573016793,0.1591329426,0.4804239273,-0.0634091571,-0.414511472,0.0372620448,0.0220868941,-0.1855535656,0.159374997,0.0625567138,0.2160947025,0.1256238669,-0.1515461951,-0.2374009043,0.0220389962,-0.4572146535,-0.3092395663,0.2442093641,0.2159642726,-0.3086921275,0.0167247988,-0.5496590137,0.207562238,0.2142513394,-0.2409013212,-0.1716308892,-0.141263783,-0.2096139938,-0.182050854,-0.0327319875,0.4727683663,-0.2295395136,-0.0511631109,-0.4683582187,-0.1293668896,0.1181911454,0.2323840111,-0.1847354919,0.1758099794,-0.3658026159,0.4606004655,-0.0540028028,-0.3485622704,-0.3616455793,0.3621631861,-0.065222092,0.382327348,0.1218415648,-0.1514881551,0.2859786451,-0.1497787237,0.1010317057,0.4230573475,0.0862270668,0.0726963505,-0.1191036999,-0.0560389534,0.1950559914,0.2336264402,-0.3723067641,-0.2367299199,-0.0159121193,0.343108356,0.1683058143,-0.0232571047,-0.2583092451,0.2677446008,-0.0854805782,0.0703554675,0.1019485667,-0.0994763002,-0.6694178581,0.3769905567,-0.1641759127,-0.2352900505,-0.348182112,0.0272322278,-0.2266201973,-0.0394872725,-0.5185477138,0.0311717819,0.1730409563,0.1743474007,-0.050169114,0.0340895839,-0.0813144073,0.0858603045,0.1826336086,0.0082552824,-0.1941962093,0.5570828915,-0.0527562834,0.1363331676,0.167903021,0.1956671774,0.2212681025,-0.2614814341,-0.0615088716,0.3136240244,0.4257158339,-0.0116565339,-0.27243945,-0.4078136683,0.2277289629,-0.1655471921,-0.215231061,0.3736804724,-0.0872404426,-0.2830619514,-0.2574501336,0.587653935,-0.2200129777,0.3982827663,-0.1646175981,-0.111778304,0.1969648898,-0.0924868062,0.0333528891,-0.4297274947,0.0819367245,0.0038248634,-0.0445481464,-0.1404555887,-0.0764199197,0.0134654865,0.4384989738,0.0773344636,0.0010496845,-0.1247930899,0.2947217524,-0.0813309029,-0.0340290219,0.4937986135,0.5339331031,0.1765296161,-0.2302022427,-0.0351961702,0.1376588792,-0.108190313,0.1377434283,-0.0493509136,0.2462355345,0.5046729445,0.171158269,-0.044352673,-0.2507885098,-0.3934268355,0.2088359445,0.0583242141,-0.3603430986,0.1051310003,-0.4051795602,0.0068077818,-0.1658841372,-0.1507852525,-0.2163145542,-0.3028441966,0.1574076265,0.0236644335,-0.3122570515,0.0966826528,0.2349251658,0.092878744,0.063064158,-0.1791818589,-0.2357362509,-0.0773358569,-0.0822453573,0.0423694961,0.4529551566,0.0320167094,0.1023063585,-0.0800494179,-0.4902331531,0.1398572028,-0.0170758907,-0.0797473118,-0.2178429514,0.1513770819,0.3310345113,0.1548567861,0.1933784336,-0.3647662997,0.2399509847,0.3468094766,-0.3739336133,0.2880286276,0.1934474856,0.177377224,-0.1737246662,-0.4609985352,-0.1343500763,-0.3501883447,0.3151539862,-0.0047269035,0.0720183775,-0.2132380903,-0.1429645717,-0.1715976894,0.1552699804,-0.0661982149,-0.2230028808,-0.04575409,-0.0175125897,-0.1394261867,-0.0874988213,0.025484411,0.0036787465,-0.0350358821,-0.3163465559,-0.5239716768,0.0931532606,-0.0066500963,0.1555081606,-0.1819789708,-0.2520923018,0.123397693,0.1503535509,0.0064758528,-0.229849875,-0.0444472544,0.1513226777,-0.09090624,0.1023089215,-0.0785665289,0.4628233016,0.0011935573,0.5358224511,0.4185201824,0.1086598784,0.2101682872,0.0492569171,0.0273923799,-0.0896059945,-0.2067691982,-0.2643717229,-0.038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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3091","title":"`blog_authorship_corpus` is broken","comments":"Hi @fdtomasi, thanks for reporting.\r\n\r\nYou are right: the original host data URL does no longer exist.\r\n\r\nI've contacted the authors of the dataset to ask them if they host this dataset in another URL.","body":"## Describe the bug\r\nThe dataset `blog_authorship_corpus` is broken.\r\nBy bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty.\r\nI suspect it is because the data download url is broken (http:\/\/www.cs.biu.ac.il\/~koppel\/blogs\/blogs.zip).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"blog_authorship_corpus\", split=\"train\", download_mode='force_redownload')\r\n```\r\n\r\n## Expected results\r\nNo error.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\/tmp\/ipykernel_5237\/1729238701.py in <module>\r\n      2 ds = load_dataset(\r\n      3     \"blog_authorship_corpus\", split=\"train\",\r\n----> 4     download_mode='force_redownload'\r\n      5 )\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n   1115         ignore_verifications=ignore_verifications,\r\n   1116         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1117         use_auth_token=use_auth_token,\r\n   1118     )\r\n   1119 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    635                     if not downloaded_from_gcs:\r\n    636                         self._download_and_prepare(\r\n--> 637                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    638                         )\r\n    639                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    707         if verify_infos:\r\n    708             verify_checksums(\r\n--> 709                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    710             )\r\n    711 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.biu.ac.il\/~koppel\/blogs\/blogs.zip']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.2\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11\r\n- Python version: 3.7.10\r\n- PyArrow version: 5.0.0","comment_length":35,"text":"`blog_authorship_corpus` is broken \n ## Describe the bug\r\nThe dataset `blog_authorship_corpus` is broken.\r\nBy bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty.\r\nI suspect it is because the data download url is broken (http:\/\/www.cs.biu.ac.il\/~koppel\/blogs\/blogs.zip).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"blog_authorship_corpus\", split=\"train\", download_mode='force_redownload')\r\n```\r\n\r\n## Expected results\r\nNo error.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\/tmp\/ipykernel_5237\/1729238701.py in <module>\r\n      2 ds = load_dataset(\r\n      3     \"blog_authorship_corpus\", split=\"train\",\r\n----> 4     download_mode='force_redownload'\r\n      5 )\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n   1115         ignore_verifications=ignore_verifications,\r\n   1116         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1117         use_auth_token=use_auth_token,\r\n   1118     )\r\n   1119 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    635                     if not downloaded_from_gcs:\r\n    636                         self._download_and_prepare(\r\n--> 637                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    638                         )\r\n    639                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    707         if verify_infos:\r\n    708             verify_checksums(\r\n--> 709                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    710             )\r\n    711 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.biu.ac.il\/~koppel\/blogs\/blogs.zip']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.2\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11\r\n- Python version: 3.7.10\r\n- PyArrow version: 5.0.0 \n Hi @fdtomasi, thanks for reporting.\r\n\r\nYou are right: the original host data URL does no longer exist.\r\n\r\nI've contacted the authors of the dataset to ask them if they host this dataset in another URL.","embeddings":[-0.0562497713,0.4549156427,-0.0059021055,0.2907783091,0.0022764034,0.203263849,0.3583987951,0.3165001273,0.0460983329,-0.0797858983,-0.1252787411,0.1028039083,0.1488698572,-0.2176860869,0.0624525174,0.1197560951,0.0223904662,0.0131194014,-0.0396165103,-0.1130264327,-0.258438617,0.173091799,-0.5407016277,-0.1771992743,0.1287778616,0.1898820102,0.0294032693,0.4739401639,-0.2036675215,-0.3458685875,0.2739189565,0.1237058714,-0.1284815669,0.5154027939,-0.0001175366,0.0210454464,0.5677784681,-0.0733784214,-0.1515372843,-0.4283043444,-0.3305858374,-0.0286644064,-0.3226741254,-0.2600146234,0.2285935879,0.2501410842,-0.1276016533,-0.0415078253,-0.073110871,0.5933796167,0.1946098059,0.1965470463,0.0797101036,0.0710579306,0.2051685899,0.0943982229,-0.0187649857,0.3861164451,0.2865898907,-0.2356249839,-0.1877569407,0.1918154061,-0.5152372718,-0.0284457523,0.2086835653,-0.0382144898,0.2911122143,-0.0454506502,0.2807458639,0.2146054208,0.6078283787,-0.0456122346,-0.0967268795,0.1149548441,0.0032838134,0.0163844619,0.4927814305,0.4497661889,-0.345646441,0.1712204665,-0.4183926284,0.1020211801,-0.1247238368,0.1432517916,0.0230687093,0.316270858,0.0796429515,0.0394369327,-0.2270576805,0.1477726698,0.5298452973,-0.1775356382,-0.171585694,-0.0319676474,-0.1666919291,0.042455826,0.2628034055,0.2457204908,0.3852871358,0.4644738138,0.2309204638,0.0300172511,0.1323720813,-0.0769817978,0.1633687913,-0.0325687788,-0.0266182348,0.2564399242,0.3981122673,0.2932431698,-0.0797115862,0.0269054342,-0.0563323013,-0.2070539147,0.335637629,0.0772547126,0.2912136614,-0.4616053104,-0.2208432406,0.2300801128,-0.0640776232,-0.0186850615,0.0230151843,0.4758158028,-0.3349999189,-0.0102567784,0.072884962,0.0538804494,-0.2344471216,-0.0806923211,-0.1371881068,-0.1417044848,-0.0878920555,-0.0856084451,0.1774396747,-0.3970324993,0.3790173829,-0.0875650868,0.1221125349,-0.3959283233,0.1682016551,-0.1995535046,0.2365662307,0.286290735,0.0501304455,0.1428810656,0.1178594753,-0.2939453423,-0.0031421101,0.2348094732,-0.3411171138,-0.3841234744,-0.0674369559,0.146407336,-0.4109938145,-0.1460254192,-0.3116435707,-0.1180968806,0.26207304,-0.2313367277,0.246118769,0.0836490989,-0.2958272994,-0.1942913979,0.1575956196,0.6883987188,0.3353039324,-0.0320032313,0.1838966906,0.0495909266,0.1735774875,-0.3189142048,0.0161538292,0.3211499155,-0.2240234762,0.0012115338,0.0918682516,-0.523306787,-0.522126317,0.0946466848,0.1921862364,0.1526589245,-0.1431559324,-0.1051851213,0.1169563532,-0.2258260846,0.2228447795,0.442851305,0.0694353655,0.166479826,-0.3666193187,-0.1975312084,-0.0557984151,0.0327224061,0.2255838513,0.3249875009,0.2355345488,-0.0384469219,0.3495755494,0.0162414201,0.0182247758,0.3924268186,0.0703326389,0.0658895522,0.1781128794,-0.1512549669,-0.1944592744,0.2105384171,-0.0043566385,0.1750834286,-0.1962530017,-0.0339200906,-0.4856854379,-0.0931698009,-0.1027180254,-0.1884219944,0.0546724014,0.2442555726,0.114501901,0.0681222081,-0.1919654161,0.1694284827,-0.351713419,0.1770364344,-0.4465617239,0.5283479691,0.0425435267,-0.1421764791,0.0976662859,0.159623459,0.2642476559,-0.041519586,-0.1875714213,0.4823961854,-0.2166809291,0.3621063828,-0.2178569287,-0.0485538282,0.0688805357,-0.5010010004,-0.0342844315,0.3659473956,0.2796648145,-0.1273828596,0.0827901512,0.109085843,-0.3991433382,0.2072757035,-0.0735473931,0.3174446225,0.1260162443,-0.3371314406,-0.240010798,-0.0939150974,0.5193185806,-0.2243626714,-0.1208702102,-0.1622358412,0.0063209338,-0.0731715783,0.1223805174,-0.0328616835,0.0570477843,0.0197380073,-0.1723070145,-0.002941143,0.0070525501,0.2199326754,0.4185923934,-0.0728402361,0.1047066972,0.095260784,-0.027752433,-0.1943772137,0.3323221803,-0.0731077045,-0.1470601857,0.387655288,0.2240037918,-0.0216279589,-0.3915558159,-0.1572110951,0.1719677299,0.2584986091,-0.4957592785,0.003248479,-0.0541189872,-0.0690850541,-0.027350219,-0.1780719161,-0.0977313221,-0.209331423,-0.2477351576,0.3272693455,-0.0813720822,0.071106337,-0.5586530566,0.1249354258,0.0156812277,-0.2328921258,0.0032782706,-0.0019289065,-0.0446637049,-0.0176293086,0.3655637503,0.2692746818,0.1923629344,-0.3903419077,-0.1112874672,-0.3884452581,-0.2082286626,0.0564380214,-0.0130518405,0.1784356534,0.1140801981,-0.0339967161,0.3579772115,0.0772601739,0.2472269386,-0.260327965,-0.2289704829,0.2943036854,0.2081555575,0.0636168495,-0.0599201992,-0.1167053133,0.0530765131,-0.3387315869,-0.3224053085,0.1251635253,0.1566274911,0.3387782574,-0.055351682,-0.0055501573,0.1081811339,0.4075170159,-0.0881281719,-0.467367053,0.383657217,-0.1355945915,-0.4556314349,-0.0193859674,-0.0848916024,0.2073908746,0.1848551333,-0.7427706122,-0.0485434681,-0.0603209287,0.0184499919,-0.21080257,-0.1129944399,0.2184087336,0.0236277264,-0.0093285842,-0.1288456619,-0.1308840662,-0.1989981234,-0.0542319119,0.3603271544,-0.0468465835,-0.0467921011,-0.0536165796,0.1725937128,0.5298574567,0.3258613348,0.1528925896,0.0302763321,0.2942607105,0.1146890372,-0.3815487325,0.2307293862,-0.103383407,-0.0116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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3091","title":"`blog_authorship_corpus` is broken","comments":"Hi, @fdtomasi, the URL is fixed.\r\n\r\nThe fix is already in our master branch and it will be accessible in our next release.\r\n\r\nIn the meantime, you can include the fix if you install the `datasets` library from the master branch:\r\n```\r\npip install -U git+ssh:\/\/git@github.com\/huggingface\/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```\r\npip install -U git+https:\/\/github.com\/huggingface\/datasets.git@master#egg=datasets\r\n```","body":"## Describe the bug\r\nThe dataset `blog_authorship_corpus` is broken.\r\nBy bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty.\r\nI suspect it is because the data download url is broken (http:\/\/www.cs.biu.ac.il\/~koppel\/blogs\/blogs.zip).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"blog_authorship_corpus\", split=\"train\", download_mode='force_redownload')\r\n```\r\n\r\n## Expected results\r\nNo error.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\/tmp\/ipykernel_5237\/1729238701.py in <module>\r\n      2 ds = load_dataset(\r\n      3     \"blog_authorship_corpus\", split=\"train\",\r\n----> 4     download_mode='force_redownload'\r\n      5 )\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n   1115         ignore_verifications=ignore_verifications,\r\n   1116         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1117         use_auth_token=use_auth_token,\r\n   1118     )\r\n   1119 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    635                     if not downloaded_from_gcs:\r\n    636                         self._download_and_prepare(\r\n--> 637                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    638                         )\r\n    639                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    707         if verify_infos:\r\n    708             verify_checksums(\r\n--> 709                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    710             )\r\n    711 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.biu.ac.il\/~koppel\/blogs\/blogs.zip']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.2\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11\r\n- Python version: 3.7.10\r\n- PyArrow version: 5.0.0","comment_length":54,"text":"`blog_authorship_corpus` is broken \n ## Describe the bug\r\nThe dataset `blog_authorship_corpus` is broken.\r\nBy bypassing the checksum checks, the loading does not return any error but the resulting dataset is empty.\r\nI suspect it is because the data download url is broken (http:\/\/www.cs.biu.ac.il\/~koppel\/blogs\/blogs.zip).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"blog_authorship_corpus\", split=\"train\", download_mode='force_redownload')\r\n```\r\n\r\n## Expected results\r\nNo error.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\/tmp\/ipykernel_5237\/1729238701.py in <module>\r\n      2 ds = load_dataset(\r\n      3     \"blog_authorship_corpus\", split=\"train\",\r\n----> 4     download_mode='force_redownload'\r\n      5 )\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n   1115         ignore_verifications=ignore_verifications,\r\n   1116         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1117         use_auth_token=use_auth_token,\r\n   1118     )\r\n   1119 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    635                     if not downloaded_from_gcs:\r\n    636                         self._download_and_prepare(\r\n--> 637                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    638                         )\r\n    639                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    707         if verify_infos:\r\n    708             verify_checksums(\r\n--> 709                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    710             )\r\n    711 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.biu.ac.il\/~koppel\/blogs\/blogs.zip']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.13.2\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11\r\n- Python version: 3.7.10\r\n- PyArrow version: 5.0.0 \n Hi, @fdtomasi, the URL is fixed.\r\n\r\nThe fix is already in our master branch and it will be accessible in our next release.\r\n\r\nIn the meantime, you can include the fix if you install the `datasets` library from the master branch:\r\n```\r\npip install -U git+ssh:\/\/git@github.com\/huggingface\/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```\r\npip install -U git+https:\/\/github.com\/huggingface\/datasets.git@master#egg=datasets\r\n```","embeddings":[-0.0562497713,0.4549156427,-0.0059021055,0.2907783091,0.0022764034,0.203263849,0.3583987951,0.3165001273,0.0460983329,-0.0797858983,-0.1252787411,0.1028039083,0.1488698572,-0.2176860869,0.0624525174,0.1197560951,0.0223904662,0.0131194014,-0.0396165103,-0.1130264327,-0.258438617,0.173091799,-0.5407016277,-0.1771992743,0.1287778616,0.1898820102,0.0294032693,0.4739401639,-0.2036675215,-0.3458685875,0.2739189565,0.1237058714,-0.1284815669,0.5154027939,-0.0001175366,0.0210454464,0.5677784681,-0.0733784214,-0.1515372843,-0.4283043444,-0.3305858374,-0.0286644064,-0.3226741254,-0.2600146234,0.2285935879,0.2501410842,-0.1276016533,-0.0415078253,-0.073110871,0.5933796167,0.1946098059,0.1965470463,0.0797101036,0.0710579306,0.2051685899,0.0943982229,-0.0187649857,0.3861164451,0.2865898907,-0.2356249839,-0.1877569407,0.1918154061,-0.5152372718,-0.0284457523,0.2086835653,-0.0382144898,0.2911122143,-0.0454506502,0.2807458639,0.2146054208,0.6078283787,-0.0456122346,-0.0967268795,0.1149548441,0.0032838134,0.0163844619,0.4927814305,0.4497661889,-0.345646441,0.1712204665,-0.4183926284,0.1020211801,-0.1247238368,0.1432517916,0.0230687093,0.316270858,0.0796429515,0.0394369327,-0.2270576805,0.1477726698,0.5298452973,-0.1775356382,-0.171585694,-0.0319676474,-0.1666919291,0.042455826,0.2628034055,0.2457204908,0.3852871358,0.4644738138,0.2309204638,0.0300172511,0.1323720813,-0.0769817978,0.1633687913,-0.0325687788,-0.0266182348,0.2564399242,0.3981122673,0.2932431698,-0.0797115862,0.0269054342,-0.0563323013,-0.2070539147,0.335637629,0.0772547126,0.2912136614,-0.4616053104,-0.2208432406,0.2300801128,-0.0640776232,-0.0186850615,0.0230151843,0.4758158028,-0.3349999189,-0.0102567784,0.072884962,0.0538804494,-0.2344471216,-0.0806923211,-0.1371881068,-0.1417044848,-0.0878920555,-0.0856084451,0.1774396747,-0.3970324993,0.3790173829,-0.0875650868,0.1221125349,-0.3959283233,0.1682016551,-0.1995535046,0.2365662307,0.286290735,0.0501304455,0.1428810656,0.1178594753,-0.2939453423,-0.0031421101,0.2348094732,-0.3411171138,-0.3841234744,-0.0674369559,0.146407336,-0.4109938145,-0.1460254192,-0.3116435707,-0.1180968806,0.26207304,-0.2313367277,0.246118769,0.0836490989,-0.2958272994,-0.1942913979,0.1575956196,0.6883987188,0.3353039324,-0.0320032313,0.1838966906,0.0495909266,0.1735774875,-0.3189142048,0.0161538292,0.3211499155,-0.2240234762,0.0012115338,0.0918682516,-0.523306787,-0.522126317,0.0946466848,0.1921862364,0.1526589245,-0.1431559324,-0.1051851213,0.1169563532,-0.2258260846,0.2228447795,0.442851305,0.0694353655,0.166479826,-0.3666193187,-0.1975312084,-0.0557984151,0.0327224061,0.2255838513,0.3249875009,0.2355345488,-0.0384469219,0.3495755494,0.0162414201,0.0182247758,0.3924268186,0.0703326389,0.0658895522,0.1781128794,-0.1512549669,-0.1944592744,0.2105384171,-0.0043566385,0.1750834286,-0.1962530017,-0.0339200906,-0.4856854379,-0.0931698009,-0.1027180254,-0.1884219944,0.0546724014,0.2442555726,0.114501901,0.0681222081,-0.1919654161,0.1694284827,-0.351713419,0.1770364344,-0.4465617239,0.5283479691,0.0425435267,-0.1421764791,0.0976662859,0.159623459,0.2642476559,-0.041519586,-0.1875714213,0.4823961854,-0.2166809291,0.3621063828,-0.2178569287,-0.0485538282,0.0688805357,-0.5010010004,-0.0342844315,0.3659473956,0.2796648145,-0.1273828596,0.0827901512,0.109085843,-0.3991433382,0.2072757035,-0.0735473931,0.3174446225,0.1260162443,-0.3371314406,-0.240010798,-0.0939150974,0.5193185806,-0.2243626714,-0.1208702102,-0.1622358412,0.0063209338,-0.0731715783,0.1223805174,-0.0328616835,0.0570477843,0.0197380073,-0.1723070145,-0.002941143,0.0070525501,0.2199326754,0.4185923934,-0.0728402361,0.1047066972,0.095260784,-0.027752433,-0.1943772137,0.3323221803,-0.0731077045,-0.1470601857,0.387655288,0.2240037918,-0.0216279589,-0.3915558159,-0.1572110951,0.1719677299,0.2584986091,-0.4957592785,0.003248479,-0.0541189872,-0.0690850541,-0.027350219,-0.1780719161,-0.0977313221,-0.209331423,-0.2477351576,0.3272693455,-0.0813720822,0.071106337,-0.5586530566,0.1249354258,0.0156812277,-0.2328921258,0.0032782706,-0.0019289065,-0.0446637049,-0.0176293086,0.3655637503,0.2692746818,0.1923629344,-0.3903419077,-0.1112874672,-0.3884452581,-0.2082286626,0.0564380214,-0.0130518405,0.1784356534,0.1140801981,-0.0339967161,0.3579772115,0.0772601739,0.2472269386,-0.260327965,-0.2289704829,0.2943036854,0.2081555575,0.0636168495,-0.0599201992,-0.1167053133,0.0530765131,-0.3387315869,-0.3224053085,0.1251635253,0.1566274911,0.3387782574,-0.055351682,-0.0055501573,0.1081811339,0.4075170159,-0.0881281719,-0.467367053,0.383657217,-0.1355945915,-0.4556314349,-0.0193859674,-0.0848916024,0.2073908746,0.1848551333,-0.7427706122,-0.0485434681,-0.0603209287,0.0184499919,-0.21080257,-0.1129944399,0.2184087336,0.0236277264,-0.0093285842,-0.1288456619,-0.1308840662,-0.1989981234,-0.0542319119,0.3603271544,-0.0468465835,-0.0467921011,-0.0536165796,0.1725937128,0.5298574567,0.3258613348,0.1528925896,0.0302763321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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3089","title":"JNLPBA Dataset","comments":"# Steps to reproduce\r\n\r\nTo reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('jnlpba')\r\n\r\ndataset['train'].features['ner_tags']\r\n```\r\nOutput:\r\n```python\r\nSequence(feature=ClassLabel(num_classes=3, names=['O', 'B', 'I'], names_file=None, id=None), length=-1, id=None)\r\n```\r\n\r\n","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nThe dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/jnlpba\/jnlpba.py#L81-L83) are: O, B, and I.  The correct entities from the original data file are: \r\n\r\n['O',\r\n 'B-DNA',\r\n 'I-DNA',\r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein']\r\n\r\n## Actual results\r\nThe dataset loader script needs to include the following NER names:\r\n\r\n['O',\r\n 'B-DNA',\r\n 'I-DNA',\r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein']\r\n\r\nAnd the [data](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/jnlpba\/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":27,"text":"JNLPBA Dataset \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nThe dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/jnlpba\/jnlpba.py#L81-L83) are: O, B, and I.  The correct entities from the original data file are: \r\n\r\n['O',\r\n 'B-DNA',\r\n 'I-DNA',\r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein']\r\n\r\n## Actual results\r\nThe dataset loader script needs to include the following NER names:\r\n\r\n['O',\r\n 'B-DNA',\r\n 'I-DNA',\r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein']\r\n\r\nAnd the [data](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/jnlpba\/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n # Steps to reproduce\r\n\r\nTo reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('jnlpba')\r\n\r\ndataset['train'].features['ner_tags']\r\n```\r\nOutput:\r\n```python\r\nSequence(feature=ClassLabel(num_classes=3, names=['O', 'B', 'I'], names_file=None, id=None), length=-1, id=None)\r\n```\r\n\r\n","embeddings":[0.1463599503,-0.019174017,0.0117601454,0.1949786991,0.2566967905,0.005295936,0.260189116,0.3991116881,0.1160132289,0.2989786267,-0.0805610418,0.4474710822,0.1182021126,0.1445477307,0.2085643411,-0.0914916471,0.0858131051,0.2013557106,0.0577948987,-0.0255821589,-0.3360538483,0.2513848543,-0.1098310426,0.2690391243,-0.2118879706,0.2803019881,-0.0952114537,0.3429387212,0.0787797719,-0.54062289,0.0587688014,-0.1115869507,-0.1144979075,0.0836253464,-0.0000995758,0.0145969493,0.2259087116,0.0167553443,-0.2911888957,0.1107614413,-0.0642669722,-0.1970922798,-0.035090629,-0.2666274011,-0.1085737646,-0.0839340165,0.0456143916,-0.2984905541,0.1269731373,0.2899158895,0.3103593588,0.1636269242,-0.1090562642,0.0113943471,0.3258366883,-0.0905954987,-0.0421469286,0.0557155907,0.0873346999,-0.2442819178,-0.186922431,0.1201017499,-0.090162769,-0.032418903,0.3049845695,0.1963013411,0.0829772279,-0.1598600745,0.0621584095,0.1293514669,0.0082759811,-0.2991339862,-0.1368646175,-0.2586815059,0.1599678099,-0.4779088199,0.2018825114,-0.0778620765,-0.1334771365,0.0169440005,0.1629645675,0.147173956,-0.0175578929,-0.0262146816,-0.2255602926,0.105803825,0.0856314152,-0.012482319,-0.1957400292,-0.1070160344,-0.2087607831,-0.1217098683,0.1379988045,0.1134552881,-0.3052332997,0.1128420383,0.1268153042,-0.0158511419,0.1522596478,0.3030883372,0.3197424412,-0.1836346239,-0.0971259102,0.134380877,-0.0547342114,0.1507384777,-0.1012674868,-0.0075488598,0.2436989844,0.1801058948,-0.0959189758,0.0936131552,0.1251742244,-0.0808034614,0.311971724,0.0444377922,0.262738049,-0.1939215064,-0.4318827093,0.234637931,-0.1696214825,0.0399943851,-0.125239715,0.2835684717,-0.2557702363,0.178002134,0.2292883992,0.0475603789,-0.1701579243,-0.3886728585,-0.3570978343,-0.0288499482,-0.3093796074,-0.0841219574,0.326787293,-0.0753740147,0.3364931643,-0.0566537268,-0.3255327046,-0.0573195219,-0.2185440958,-0.0182524603,-0.0344960392,0.2155147493,-0.0187463649,-0.0810464248,-0.043280486,-0.2400835901,0.0931017026,0.1197203249,-0.0939782858,0.0461263508,-0.0610425323,0.3555841446,-0.0290879644,-0.0418131165,-0.0161719676,-0.1399125755,0.1377732456,-0.2332128286,-0.0651673377,-0.102222167,-0.230196923,-0.2721506655,0.2064383328,0.3416980505,-0.2484472543,-0.2681770325,-0.1877987534,-0.0043540448,0.261919111,0.2430831194,0.0374767072,0.2239933908,-0.4189516306,0.4572798312,0.1251330525,-0.2661541998,-0.3847001791,-0.1103240699,-0.3015440702,0.1265032738,0.1089021713,0.1903227568,0.0282005146,0.0032976461,0.3662708104,0.4877804816,-0.0340398476,0.1878727674,-0.3471518457,-0.0038198028,0.1862048507,0.1984105408,0.0328478068,0.1977490038,-0.0282966942,-0.003502737,0.2963935435,-0.1360437721,-0.0016061509,0.1760522127,0.0704080909,0.1267873198,0.1215298325,-0.1289236099,-0.4459973872,0.1767302006,-0.5278157592,0.1567814052,-0.212806344,-0.0938561037,-0.4465481341,0.0017753867,-0.2179547697,-0.1521268189,0.3087831438,0.3826569915,-0.1562136412,0.0544431992,-0.0001431178,0.5229550004,-0.0848164707,0.2076076865,-0.8496698737,0.3504163623,-0.2548978031,0.0411184318,0.190580219,0.3541077673,0.1531741023,0.0956461579,-0.1535535008,0.2666716874,0.1260719001,-0.1173193306,-0.1097214594,0.0633564219,-0.096826151,-0.2956201732,0.1220706999,0.4672617614,0.2476159781,-0.1148581803,-0.1032522023,0.320220381,-0.016657386,0.1936981082,0.0672038123,-0.0159943476,0.1915370524,-0.2973662019,-0.1578668952,-0.2219605297,0.1719375253,0.1999064833,0.293815881,0.1884365231,-0.4167760313,-0.1194061041,0.5031082034,0.0422314331,0.0937423706,0.1307168603,-0.3267630339,-0.1163202524,-0.1790597439,0.044907432,0.2279786915,0.254702419,-0.1725228578,0.2014107555,-0.1616564691,-0.1836086959,0.1382440925,0.0555133298,0.1643090546,0.3248467743,0.4020846784,0.1810911894,-0.3269583583,-0.3477650881,-0.2029145062,0.1982141286,-0.5685585737,0.1790288985,-0.1477200091,-0.4355048239,-0.403418988,0.056761466,-0.1608054638,-0.4438098371,0.1425551623,0.2031991184,-0.1206562817,0.1330447942,-0.0643003508,0.257327348,-0.0386120528,0.1302260011,-0.2416203022,0.0358174406,-0.3062455356,0.18812114,-0.002393004,0.1904333383,0.3202706873,-0.3084126711,0.073678568,-0.0333177932,-0.4163534939,0.0679719672,-0.1694073975,0.1468749195,0.2279840261,-0.0180283505,0.2907314897,-0.0949796811,0.2520330548,-0.0497788154,-0.337390244,0.3117396235,0.0783136711,0.1036425531,-0.0584134869,-0.4725947976,-0.0067321057,-0.3326849043,0.0523947664,0.0701839924,0.1783392876,0.3609488904,-0.2437036783,0.0992261842,0.2264300585,0.2708568275,-0.0952143818,0.1996335834,0.1272150874,-0.1807060242,-0.2138819396,-0.2146209627,-0.2079818249,0.2615096569,-0.1770761907,-0.3571328819,0.0071646902,-0.0365778878,0.18720676,0.0964012891,-0.0986048952,-0.0089867096,0.1561374515,-0.2820245028,-0.2509303093,-0.1882360876,-0.0787646472,-0.1478708088,0.1132579446,0.0114853894,0.2378219068,-0.0925859362,-0.1042098552,0.1227938756,-0.2132066488,0.4248072505,-0.1675913781,0.0151974801,-0.2937702239,-0.1926690489,-0.12176884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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3089","title":"JNLPBA Dataset","comments":"Since I cannot create a branch here is the updated code:\r\n\r\n```python\r\n\r\n# coding=utf-8\r\n# Copyright 2020 HuggingFace Datasets Authors.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n#     http:\/\/www.apache.org\/licenses\/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\n# Lint as: python3\r\n\"\"\"Introduction to the Bio-Entity Recognition Task at JNLPBA\"\"\"\r\n\r\nimport os\r\n\r\nimport datasets\r\n\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\n_CITATION = \"\"\"\\\r\n@inproceedings{kim2004introduction,\r\n               title={Introduction to the bio-entity recognition task at JNLPBA},\r\n               author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},\r\n               booktitle={Proceedings of the international joint workshop on natural language processing in biomedicine and its applications},\r\n               pages={70--75},\r\n               year={2004},\r\n               organization={Citeseer}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThe data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search\r\non MEDLINE using the MeSH terms \u0018human\u0019, \u0018blood cells\u0019 and \u0018transcription factors\u0019. From this search 2,000 abstracts\r\nwere selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification.\r\nAmong the classes, 36 terminal classes were used to annotate the GENIA corpus.\r\n\"\"\"\r\n\r\n_HOMEPAGE = \"http:\/\/www.geniaproject.org\/shared-tasks\/bionlp-jnlpba-shared-task-2004\"\r\n_TRAIN_URL = \"http:\/\/www.nactem.ac.uk\/GENIA\/current\/Shared-tasks\/JNLPBA\/Train\/Genia4ERtraining.tar.gz\"\r\n_VAL_URL = 'http:\/\/www.nactem.ac.uk\/GENIA\/current\/Shared-tasks\/JNLPBA\/Evaluation\/Genia4ERtest.tar.gz'\r\n\r\n\r\n_URLS = {\r\n    \"train\": _TRAIN_URL,\r\n    \"val\": _VAL_URL,\r\n}\r\n\r\n_TRAIN_DIRECTORY = \"Genia4ERtraining\"\r\n_VAL_DIRECTORY = \"Genia4ERtest\"\r\n\r\n_TRAIN_FILE = \"Genia4ERtask1.iob2\"\r\n_VAL_FILE = \"Genia4EReval1.iob2\"\r\n\r\n\r\nclass JNLPBAConfig(datasets.BuilderConfig):\r\n    \"\"\"BuilderConfig for JNLPBA\"\"\"\r\n\r\n    def __init__(self, **kwargs):\r\n        \"\"\"BuilderConfig for JNLPBA.\r\n        Args:\r\n          **kwargs: keyword arguments forwarded to super.\r\n        \"\"\"\r\n        super(JNLPBAConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass JNLPBA(datasets.GeneratorBasedBuilder):\r\n    \"\"\"JNLPBA dataset.\"\"\"\r\n\r\n    BUILDER_CONFIGS = [\r\n        JNLPBAConfig(name=\"jnlpba\", version=datasets.Version(\"1.0.0\"), description=\"JNLPBA dataset\"),\r\n    ]\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"ner_tags\": datasets.Sequence(\r\n                        datasets.features.ClassLabel(\r\n                            names=[\r\n                                'O',\r\n                                'B-DNA',\r\n                                'I-DNA',                             \r\n                                'B-RNA',\r\n                                'I-RNA',\r\n                                'B-cell_line',\r\n                                'I-cell_line',\r\n                                'B-cell_type',\r\n                                'I-cell_type',\r\n                                'B-protein',\r\n                                'I-protein',\r\n                            ]\r\n                        )\r\n                    ),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        downloaded_files = dl_manager.download_and_extract(_URLS)\r\n                    \r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, \r\n                                    gen_kwargs={\"filepath\": os.path.join(downloaded_files['train'], _TRAIN_FILE)}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, \r\n                                    gen_kwargs={\"filepath\": os.path.join(downloaded_files['val'], _VAL_FILE)})\r\n        ]\r\n    \r\n\r\n    def _generate_examples(self, filepath):\r\n        logger.info(\"\u23f3 Generating examples from = %s\", filepath)\r\n        with open(filepath, encoding=\"utf-8\") as f:\r\n            guid = 0\r\n            tokens = []\r\n            ner_tags = []\r\n            for line in f:\r\n                if line.startswith('###'):\r\n                    continue\r\n                if line == '' or line == '\\n':\r\n                    if tokens:\r\n                        yield guid, {\r\n                            \"id\": str(guid),\r\n                            \"tokens\": tokens,\r\n                            \"ner_tags\": ner_tags,\r\n                        }\r\n                        guid += 1\r\n                        tokens = []\r\n                        ner_tags = []\r\n                else:\r\n                    # tokens are tab separated\r\n                    splits = line.split(\"\\t\")\r\n                    #print(splits)\r\n                    #print(len(splits))\r\n                    if len(splits) < 2:\r\n                        splits = splits[0].split()\r\n                    tokens.append(splits[0])\r\n                    ner_tags.append(splits[1].rstrip())\r\n            # last example\r\n            yield guid, {\r\n                \"id\": str(guid),\r\n                \"tokens\": tokens,\r\n                \"ner_tags\": ner_tags,\r\n            }\r\n```","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nThe dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/jnlpba\/jnlpba.py#L81-L83) are: O, B, and I.  The correct entities from the original data file are: \r\n\r\n['O',\r\n 'B-DNA',\r\n 'I-DNA',\r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein']\r\n\r\n## Actual results\r\nThe dataset loader script needs to include the following NER names:\r\n\r\n['O',\r\n 'B-DNA',\r\n 'I-DNA',\r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein']\r\n\r\nAnd the [data](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/jnlpba\/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":455,"text":"JNLPBA Dataset \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nThe dataset loading script for this dataset is incorrect. This is a biomedical dataset used for named entity recognition. The entities in the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/jnlpba\/jnlpba.py#L81-L83) are: O, B, and I.  The correct entities from the original data file are: \r\n\r\n['O',\r\n 'B-DNA',\r\n 'I-DNA',\r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein']\r\n\r\n## Actual results\r\nThe dataset loader script needs to include the following NER names:\r\n\r\n['O',\r\n 'B-DNA',\r\n 'I-DNA',\r\n 'B-RNA',\r\n 'I-RNA',\r\n 'B-cell_line',\r\n 'I-cell_line',\r\n 'B-cell_type',\r\n 'I-cell_type',\r\n 'B-protein',\r\n 'I-protein']\r\n\r\nAnd the [data](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/jnlpba\/jnlpba.py#L46) that is being pulled has been modified from the original dataset and does not include the original NER tags.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform:\r\n- Python version:\r\n- PyArrow version:\r\n \n Since I cannot create a branch here is the updated code:\r\n\r\n```python\r\n\r\n# coding=utf-8\r\n# Copyright 2020 HuggingFace Datasets Authors.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n#     http:\/\/www.apache.org\/licenses\/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\r\n# Lint as: python3\r\n\"\"\"Introduction to the Bio-Entity Recognition Task at JNLPBA\"\"\"\r\n\r\nimport os\r\n\r\nimport datasets\r\n\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\n_CITATION = \"\"\"\\\r\n@inproceedings{kim2004introduction,\r\n               title={Introduction to the bio-entity recognition task at JNLPBA},\r\n               author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},\r\n               booktitle={Proceedings of the international joint workshop on natural language processing in biomedicine and its applications},\r\n               pages={70--75},\r\n               year={2004},\r\n               organization={Citeseer}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThe data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search\r\non MEDLINE using the MeSH terms \u0018human\u0019, \u0018blood cells\u0019 and \u0018transcription factors\u0019. From this search 2,000 abstracts\r\nwere selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification.\r\nAmong the classes, 36 terminal classes were used to annotate the GENIA corpus.\r\n\"\"\"\r\n\r\n_HOMEPAGE = \"http:\/\/www.geniaproject.org\/shared-tasks\/bionlp-jnlpba-shared-task-2004\"\r\n_TRAIN_URL = \"http:\/\/www.nactem.ac.uk\/GENIA\/current\/Shared-tasks\/JNLPBA\/Train\/Genia4ERtraining.tar.gz\"\r\n_VAL_URL = 'http:\/\/www.nactem.ac.uk\/GENIA\/current\/Shared-tasks\/JNLPBA\/Evaluation\/Genia4ERtest.tar.gz'\r\n\r\n\r\n_URLS = {\r\n    \"train\": _TRAIN_URL,\r\n    \"val\": _VAL_URL,\r\n}\r\n\r\n_TRAIN_DIRECTORY = \"Genia4ERtraining\"\r\n_VAL_DIRECTORY = \"Genia4ERtest\"\r\n\r\n_TRAIN_FILE = \"Genia4ERtask1.iob2\"\r\n_VAL_FILE = \"Genia4EReval1.iob2\"\r\n\r\n\r\nclass JNLPBAConfig(datasets.BuilderConfig):\r\n    \"\"\"BuilderConfig for JNLPBA\"\"\"\r\n\r\n    def __init__(self, **kwargs):\r\n        \"\"\"BuilderConfig for JNLPBA.\r\n        Args:\r\n          **kwargs: keyword arguments forwarded to super.\r\n        \"\"\"\r\n        super(JNLPBAConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass JNLPBA(datasets.GeneratorBasedBuilder):\r\n    \"\"\"JNLPBA dataset.\"\"\"\r\n\r\n    BUILDER_CONFIGS = [\r\n        JNLPBAConfig(name=\"jnlpba\", version=datasets.Version(\"1.0.0\"), description=\"JNLPBA dataset\"),\r\n    ]\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"ner_tags\": datasets.Sequence(\r\n                        datasets.features.ClassLabel(\r\n                            names=[\r\n                                'O',\r\n                                'B-DNA',\r\n                                'I-DNA',                             \r\n                                'B-RNA',\r\n                                'I-RNA',\r\n                                'B-cell_line',\r\n                                'I-cell_line',\r\n                                'B-cell_type',\r\n                                'I-cell_type',\r\n                                'B-protein',\r\n                                'I-protein',\r\n                            ]\r\n                        )\r\n                    ),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        downloaded_files = dl_manager.download_and_extract(_URLS)\r\n                    \r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, \r\n                                    gen_kwargs={\"filepath\": os.path.join(downloaded_files['train'], _TRAIN_FILE)}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, \r\n                                    gen_kwargs={\"filepath\": os.path.join(downloaded_files['val'], _VAL_FILE)})\r\n        ]\r\n    \r\n\r\n    def _generate_examples(self, filepath):\r\n        logger.info(\"\u23f3 Generating examples from = %s\", filepath)\r\n        with open(filepath, encoding=\"utf-8\") as f:\r\n            guid = 0\r\n            tokens = []\r\n            ner_tags = []\r\n            for line in f:\r\n                if line.startswith('###'):\r\n                    continue\r\n                if line == '' or line == '\\n':\r\n                    if tokens:\r\n                        yield guid, {\r\n                            \"id\": str(guid),\r\n                            \"tokens\": tokens,\r\n                            \"ner_tags\": ner_tags,\r\n                        }\r\n                        guid += 1\r\n                        tokens = []\r\n                        ner_tags = []\r\n                else:\r\n                    # tokens are tab separated\r\n                    splits = line.split(\"\\t\")\r\n                    #print(splits)\r\n                    #print(len(splits))\r\n                    if len(splits) < 2:\r\n                        splits = splits[0].split()\r\n                    tokens.append(splits[0])\r\n                    ner_tags.append(splits[1].rstrip())\r\n            # last example\r\n            yield guid, {\r\n                \"id\": str(guid),\r\n                \"tokens\": tokens,\r\n                \"ner_tags\": ner_tags,\r\n            }\r\n```","embeddings":[0.1486540884,-0.1739539802,0.0470736288,0.2513508201,0.2042786926,-0.0211735647,0.1656199992,0.4094050825,0.1123946384,0.2550163567,-0.185700655,0.375578016,0.151699394,0.1216228828,0.25610888,-0.1432026029,0.0996648371,0.1449369341,-0.111402899,-0.0391409583,-0.3574819267,0.3411877453,-0.0987748429,0.2195612043,-0.1219135523,0.2415012717,-0.0927377567,0.4354267716,0.0538867079,-0.4518612921,0.0315414667,-0.1311707795,-0.158991456,0.074800849,-0.0000998077,0.017651448,0.2747310102,-0.0076889074,-0.337741524,0.1166043282,-0.0471348241,-0.1622763276,-0.0783113986,-0.1985010207,-0.0640286282,-0.1041449085,0.0424184352,-0.3402503729,0.2538080215,0.2019366622,0.3115350306,0.3132965267,-0.0104707042,-0.0118709113,0.2484316379,-0.0746831894,-0.0220026243,0.0596909821,0.0891813487,-0.2076164484,-0.1792510152,0.0853665248,-0.0747522637,-0.0255771875,0.3158915639,0.1592070311,0.0407510959,-0.0886038691,0.1557084173,0.1198274046,-0.0091902334,-0.3641085625,-0.1562201828,-0.2859893739,0.1652780622,-0.3108338416,0.2314648479,-0.0242923032,-0.1560340226,0.0408795811,0.1020192876,0.1644132137,-0.0294987932,-0.020210214,-0.19260557,-0.0071329847,-0.0003409741,0.0252779089,-0.1362309605,-0.1014293507,-0.2966951132,-0.1499617994,0.0975762904,0.0955893695,-0.3157710135,0.0780467093,0.1170095354,0.1942019314,0.2848684192,0.2893327773,0.3281622827,-0.1506716013,-0.1320067793,0.181833148,-0.0125356661,0.1307728738,-0.1099271923,-0.0973194614,0.2371180356,0.1785688251,-0.0377605185,0.0847995356,0.1637207717,-0.0625106841,0.1610235423,-0.0619294383,0.263756603,-0.1708233804,-0.4105324149,0.2043031603,-0.1301448941,0.0287312865,-0.0922354609,0.40574494,-0.2607686222,0.0956299752,0.2312331796,0.0488529615,-0.159777537,-0.4378924668,-0.3238989413,-0.0521840341,-0.2988617122,-0.0354350209,0.3166689575,-0.1213096082,0.3524872959,0.0058465428,-0.2567110658,0.0006499414,-0.2640992105,0.0600089133,-0.0302440766,0.211954698,0.0139650423,-0.1476164162,-0.0209179055,-0.2071858346,0.100954026,0.0573403612,-0.0117389802,0.0636264831,-0.1165741682,0.3356428742,-0.0460309647,0.0018759224,-0.0469794348,-0.1121605709,0.076058425,-0.2198970169,-0.0983341113,-0.0247347038,-0.0932751521,-0.2576322258,0.2355141193,0.4389727116,-0.1779844761,-0.2355246395,-0.2344189584,-0.0277835242,0.2220378518,0.2717775702,0.0717509985,0.1474504471,-0.4442260563,0.4108539224,-0.035127975,-0.1894631535,-0.3233064115,-0.0740291849,-0.3137940168,0.174614653,0.1123689637,0.1378769577,-0.0584506579,-0.0120946979,0.2980913818,0.4342546463,-0.0221327301,0.218913421,-0.370631367,-0.0379235335,0.0156316701,0.154279843,-0.0380543806,0.2045679241,0.0491086729,0.0366613492,0.3298110664,-0.1429270059,0.019629335,0.1501951963,0.0348413959,0.1237310246,0.1393734068,-0.1063572839,-0.4866110384,0.1464646906,-0.5134999752,0.1034794748,-0.218875438,-0.1047423705,-0.471784085,0.0555043034,-0.145548448,-0.1484253109,0.2992916703,0.3687244654,-0.0919169188,0.0509220213,-0.0215500761,0.5519652963,0.0327054486,0.23799254,-0.925172925,0.3091940582,-0.2369477898,0.0847021863,0.2500907481,0.3630146086,0.1217428595,0.0178271737,-0.0618554763,0.2816226184,0.0228089932,0.0103069684,-0.0169690922,0.0206092,-0.0776505768,-0.2262040526,0.1686142981,0.371665448,0.2862478495,-0.0976485536,-0.1370399147,0.3213839531,-0.0802313462,0.147745505,0.0939130336,-0.0149694756,0.1766810119,-0.3173463941,-0.184962824,-0.270310849,0.2467224747,0.23207362,0.375370115,0.1426813006,-0.4702478051,-0.1110664308,0.4145902693,-0.0201826692,0.1152621061,0.1399078518,-0.3419932723,-0.1128963977,-0.1474757195,0.0894416273,0.2654878199,0.2984972,-0.1855389178,0.2214673907,-0.1002203226,-0.2083885521,0.1768093556,0.0278409272,0.0836080015,0.2856020331,0.3537326753,0.158897981,-0.4217227101,-0.2582751811,-0.2430136204,0.1711105704,-0.5954359174,0.1429012269,-0.1243700758,-0.4651801884,-0.4144418836,0.155681923,-0.2186879516,-0.4113916159,0.1314998418,0.2580562532,-0.1569703519,0.1083914936,0.0159621984,0.3620449305,-0.0896230415,0.1762821376,-0.3268767297,0.1042961925,-0.2564565241,0.1810634434,0.0630739182,0.1480295509,0.4264529943,-0.2966628671,0.0785707012,-0.1034216732,-0.4119957089,0.0893337429,-0.2357762307,0.1846895069,0.2707220316,0.0499492176,0.2237727642,-0.1040504873,0.267035991,-0.085361965,-0.3717879057,0.2826578021,0.1080286875,0.0379803143,-0.0763392076,-0.3449460566,0.1134342104,-0.2979823649,0.2148338258,0.0244685728,0.1538879126,0.3093248904,-0.265229851,0.0868471637,0.0888370797,0.2499281317,-0.1681040674,0.0860412046,0.0531006083,-0.2217128277,-0.2689089179,-0.315486908,-0.1304766983,0.2968777418,-0.2253634483,-0.373254627,-0.0474584103,-0.0715329498,0.1360913217,0.0954225361,-0.0199732389,-0.0502945185,0.1049475819,-0.2946063876,-0.3287935257,-0.2536298335,-0.0935362056,-0.1894426793,0.1623021364,0.0766092762,0.2075240761,-0.1003227308,-0.105167523,0.2292075902,-0.1119061708,0.438390255,-0.1526432782,0.103279233,-0.2589288354,-0.2632179558,-0.0684086531,0.0473156087,-0.0328588672,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3084","title":"VisibleDeprecationWarning when using `set_format(\"numpy\")`","comments":"I just opened a PR and I verified that the code you provided doesn't show any deprecation warning :)","body":"Code to reproduce:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"glue\", \"mnli\")\r\n\r\nfrom transformers import AutoTokenizer\r\ntokenizer = AutoTokenizer.from_pretrained('distilbert-base-cased')\r\n\r\ndef tokenize_function(dataset):\r\n    return tokenizer(dataset['premise'])\r\n\r\ntokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset['train'].features)\r\n\r\ntokenized_datasets.set_format(\"numpy\")\r\n\r\ntokenized_datasets['train'][5:8]\r\n```\r\nOutputs:\r\n\r\n```\r\npython3.9\/site-packages\/datasets\/formatting\/formatting.py:167: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\r\n  return np.array(array, copy=False, **self.np_array_kwargs)\r\n```","comment_length":19,"text":"VisibleDeprecationWarning when using `set_format(\"numpy\")` \n Code to reproduce:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"glue\", \"mnli\")\r\n\r\nfrom transformers import AutoTokenizer\r\ntokenizer = AutoTokenizer.from_pretrained('distilbert-base-cased')\r\n\r\ndef tokenize_function(dataset):\r\n    return tokenizer(dataset['premise'])\r\n\r\ntokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=dataset['train'].features)\r\n\r\ntokenized_datasets.set_format(\"numpy\")\r\n\r\ntokenized_datasets['train'][5:8]\r\n```\r\nOutputs:\r\n\r\n```\r\npython3.9\/site-packages\/datasets\/formatting\/formatting.py:167: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\r\n  return np.array(array, copy=False, **self.np_array_kwargs)\r\n``` \n I just opened a PR and I verified that the code you provided doesn't show any deprecation warning :)","embeddings":[-0.2761725187,-0.1842716783,-0.0695532858,-0.1787901819,0.4122675955,-0.0492862798,0.558613658,0.3872012198,-0.2987531424,-0.0875778869,0.0455404744,0.4911158979,-0.1978973895,-0.1039189547,-0.0582352169,-0.1153113991,0.2907197773,0.2943542004,0.2120756507,0.1158553138,-0.2081122994,0.1070954576,-0.4302379191,0.2284090072,-0.4189924598,-0.1525948048,0.0408214889,-0.2931671143,-0.3349087536,-0.7788208723,0.0660459101,0.079761751,0.0744713023,0.2448985875,-0.0001098288,-0.0975438878,0.3597273529,-0.0770139247,-0.1247081757,0.074202776,0.3560260236,-0.4634509087,0.0592154451,-0.2812361419,-0.1970688403,-0.409788698,-0.1290643513,-0.3477350771,0.6323642135,0.2891099453,0.2063379139,0.4852136374,0.2613784969,0.0175006464,-0.3670915961,0.1488031745,-0.1340202987,0.1205146089,0.1063919738,-0.035455551,0.0198830515,0.2753426135,-0.1398331821,-0.0459282026,0.1863449216,0.1255587488,0.075742349,-0.206248343,-0.1665849537,0.1073327214,0.0700932145,-0.321030736,-0.278200537,-0.2677000165,-0.069075793,-0.5681188107,0.0612050444,-0.0869588107,-0.2751303613,0.0758052319,-0.3220216334,0.0978007615,-0.2289743423,0.1881926358,0.0347904041,0.6022424698,-0.1596241146,0.0375894308,-0.2223696411,-0.1647968292,0.1067260206,-0.002741439,0.2140187323,0.1306845546,-0.0757352859,-0.4122517109,-0.126764372,-0.6793171763,0.2326527983,0.1209922284,-0.143196702,0.3046106398,-0.006097673,-0.0194614958,-0.0591362864,0.30570364,0.0737466365,0.1135525629,-0.0347447544,0.1902703494,0.4996115863,-0.0076952716,0.0276339613,-0.1617165655,0.1515793204,0.2835575044,0.2841199636,0.0173878651,-0.2379263639,-0.0870275199,-0.2753245234,-0.098315306,-0.1502567083,0.0370622166,0.1859235615,-0.2279872745,-0.2188481688,0.3251948059,-0.0943176746,-0.0486953445,-0.1035270765,-0.1674337238,-0.3179579079,0.1329163313,-0.0886545032,0.2242263556,0.1560443491,0.1465636939,-0.0324214771,0.0602972656,0.0799856037,-0.0460453033,0.5269075036,-0.0566714704,-0.3072919548,0.2131788135,0.2221236676,-0.0400520936,-0.2100965679,-0.1289281845,-0.0139501849,-0.1564739496,-0.5129193068,0.1726043969,-0.0554577485,0.2254410088,0.3058399856,0.1406797469,0.5754808187,-0.1065602303,0.2052521706,-0.3490458727,-0.2888505757,-0.310546428,0.2269766033,0.2763702273,-0.1726520211,-0.0707580969,0.1366371363,-0.0064445813,0.3465929925,0.2848232388,-0.1192948893,0.0688241497,-0.287109673,0.4187419116,0.358689338,0.0476352982,-0.0891477913,0.0125328451,-0.258991003,-0.0262385458,-0.0572705902,-0.16227974,0.0134197092,-0.2037441283,0.2283157855,0.3728840351,0.2259037495,-0.1221142709,-0.2184075564,-0.2819759548,0.3238856792,-0.2155996859,-0.1016643867,-0.121549502,-0.2503406107,0.1340948641,0.2821192741,-0.1773385257,-0.0170629807,-0.0387141556,0.1763840318,0.0143638244,-0.1300521493,-0.0771002322,-0.0962042734,-0.0671343654,0.0520721562,0.1208134964,-0.3858441412,-0.3550818563,-0.3253433704,0.125751093,-0.3299167156,-0.174274832,0.2626980245,0.1670860797,0.1895420402,0.1613108963,-0.0519949533,-0.0429784693,-0.036872007,0.190599218,-0.2597706616,0.1361260861,-0.1346711218,-0.2055520266,-0.0391565822,0.3673565984,0.2699649036,-0.0914394036,-0.1568429321,0.3795862198,0.0887942314,-0.4257693887,-0.7925295234,0.0752188563,0.2111434489,-0.2160459757,0.1591641456,0.4370615482,-0.2216649055,-0.0073352223,-0.1069032997,0.4940537214,0.0331711657,-0.0660154074,-0.2693096697,0.2354245633,0.0660544187,0.2270790339,-0.0189904664,-0.7010733485,-0.2925937772,-0.0455618203,0.0268346053,-0.1644273698,0.0306235943,0.1477360874,0.3273435533,-0.088928543,0.2476334274,-0.0263726916,-0.0485697985,-0.299977392,0.1472763717,-0.019436894,0.3213820159,0.0447840095,-0.0328442678,0.1148985624,-0.2355351746,-0.2843548954,0.1139830723,0.2980045676,0.1014595106,0.0132735418,0.1308699846,0.0618221276,-0.156989187,-0.2117524147,-0.0598078556,-0.0292049237,-0.161760062,0.0283771213,-0.1302662492,-0.1216727197,-0.246578455,-0.1667916328,0.0293444674,-0.2007003278,-0.1053787768,-0.1808106899,0.0346927084,0.3763521612,0.2113144249,0.1528421342,0.3686487675,0.1845226735,0.0241100974,0.030454183,-0.1500765532,0.0849774331,-0.0184949115,0.0062558232,0.3324939311,0.0571211427,0.1146108434,-0.2055234313,-0.4484799504,0.24633044,-0.1142501086,-0.000320256,0.4293905795,0.0370680615,-0.1065906286,-0.1725502759,0.0679828897,-0.0005197408,-0.145363614,-0.0901245549,0.0515017174,0.1096314192,-0.2149844021,-0.0992181897,-0.4176307619,-0.0787719563,-0.1055382863,-0.0199240409,0.1435371041,0.3431904912,-0.0873066261,0.1519757807,-0.0564907417,-0.0899892226,-0.1629415751,0.3400265872,0.2989009619,-0.3487618864,-0.1448611468,-0.3322185874,-0.1197795421,0.1044682562,0.0479923934,-0.090023078,-0.3131167889,-0.1404427737,0.0738078728,-0.0438244827,-0.302870661,0.3333740532,0.3414775729,-0.0922711492,0.0533920191,0.0581372902,-0.0094670644,-0.4543273151,0.1178998798,0.0117356218,0.3174470365,0.2539557517,0.305644393,0.3840444386,-0.4130399823,0.1981722862,0.0329028107,0.4008171558,-0.2731593251,0.0298931655,-0.0963919982,0.1519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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3073","title":"Import error installing with ppc64le","comments":"This seems to be an issue with importing PyArrow so I posted the problem [here](https:\/\/issues.apache.org\/jira\/browse\/ARROW-14323), and I'm closing this issue.\r\n","body":"## Describe the bug\r\nInstalling the datasets library with a computer running with ppc64le seems to cause an issue when importing the datasets library.\r\n\r\n\r\n```\r\npython\r\nPython 3.6.13 | packaged by conda-forge | (default, Sep 23 2021, 07:37:44) \r\n[GCC 9.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import datasets\r\nIllegal instruction (core dumped)\r\n```\r\n\r\nError when importing\r\n`Illegal instruction (core dumped)`\r\n\r\n## Steps to reproduce the bug\r\nI get this error when installing the library by using conda.  I can't install with pip I believe because pyarrow only has the ppc64le library on conda forge\r\n```\r\nconda create --name transformers_py36_v2 python=3.6 \r\nconda activate transformers_py36_v2 \r\nconda install datasets \r\n```\r\n\r\n## Tracebacks\r\nconda create --name transformers_py36_v2 python=3.6\r\n\r\n\r\n```\r\nCollecting package metadata (current_repodata.json): done\r\nSolving environment: done\r\n\r\n\r\n==> WARNING: A newer version of conda exists. <==\r\n  current version: 4.9.2\r\n  latest version: 4.10.3\r\n\r\nPlease update conda by running\r\n\r\n    $ conda update -n base -c defaults conda\r\n\r\n\r\n\r\n## Package Plan ##\r\n\r\n  environment location: \/p\/home\/gerryc\/.conda\/envs\/transformers_py36_v2\r\n\r\n  added \/ updated specs:\r\n    - python=3.6\r\n\r\n\r\nThe following NEW packages will be INSTALLED:\r\n\r\n  _libgcc_mutex      conda-forge\/linux-ppc64le::_libgcc_mutex-0.1-conda_forge\r\n  _openmp_mutex      conda-forge\/linux-ppc64le::_openmp_mutex-4.5-1_gnu\r\n  ca-certificates    conda-forge\/linux-ppc64le::ca-certificates-2021.10.8-h1084571_0\r\n  certifi            pkgs\/main\/linux-ppc64le::certifi-2020.12.5-py36h6ffa863_0\r\n  ld_impl_linux-ppc~ conda-forge\/linux-ppc64le::ld_impl_linux-ppc64le-2.36.1-ha35d02b_2\r\n  libffi             conda-forge\/linux-ppc64le::libffi-3.4.2-h3b9df90_4\r\n  libgcc-ng          conda-forge\/linux-ppc64le::libgcc-ng-11.2.0-h7698a5e_11\r\n  libgomp            conda-forge\/linux-ppc64le::libgomp-11.2.0-h7698a5e_11\r\n  libstdcxx-ng       conda-forge\/linux-ppc64le::libstdcxx-ng-11.2.0-habdf983_11\r\n  libzlib            conda-forge\/linux-ppc64le::libzlib-1.2.11-h339bb43_1013\r\n  ncurses            conda-forge\/linux-ppc64le::ncurses-6.2-hea85c5d_4\r\n  openssl            conda-forge\/linux-ppc64le::openssl-1.1.1l-h4e0d66e_0\r\n  pip                conda-forge\/noarch::pip-21.3-pyhd8ed1ab_0\r\n  python             conda-forge\/linux-ppc64le::python-3.6.13-h57873ef_2_cpython\r\n  readline           conda-forge\/linux-ppc64le::readline-8.1-h5c45dff_0\r\n  setuptools         pkgs\/main\/linux-ppc64le::setuptools-58.0.4-py36h6ffa863_0\r\n  sqlite             conda-forge\/linux-ppc64le::sqlite-3.36.0-h4e2196e_2\r\n  tk                 conda-forge\/linux-ppc64le::tk-8.6.11-h41c6715_1\r\n  wheel              conda-forge\/noarch::wheel-0.37.0-pyhd8ed1ab_1\r\n  xz                 conda-forge\/linux-ppc64le::xz-5.2.5-h6eb9509_1\r\n  zlib               conda-forge\/linux-ppc64le::zlib-1.2.11-h339bb43_1013\r\n\r\n\r\nProceed ([y]\/n)? y\r\n\r\nPreparing transaction: done\r\nVerifying transaction: done\r\nExecuting transaction: done\r\n#\r\n# To activate this environment, use\r\n#\r\n#     $ conda activate transformers_py36_v2\r\n#\r\n# To deactivate an active environment, use\r\n#\r\n#     $ conda deactivate\r\n```\r\n\r\n\r\nconda activate transformers_py36_v2\r\nconda install datasets\r\n```\r\nCollecting package metadata (current_repodata.json): done\r\nSolving environment: failed with initial frozen solve. Retrying with flexible solve.\r\nSolving environment: failed with repodata from current_repodata.json, will retry with next repodata source.\r\nCollecting package metadata (repodata.json): done\r\nSolving environment: done\r\n\r\n\r\n==> WARNING: A newer version of conda exists. <==\r\n  current version: 4.9.2\r\n  latest version: 4.10.3\r\n\r\nPlease update conda by running\r\n\r\n    $ conda update -n base -c defaults conda\r\n\r\n\r\n\r\n## Package Plan ##\r\n\r\n  environment location: \/p\/home\/gerryc\/.conda\/envs\/transformers_py36_v2\r\n\r\n  added \/ updated specs:\r\n    - datasets\r\n\r\n\r\nThe following NEW packages will be INSTALLED:\r\n\r\n  abseil-cpp         conda-forge\/linux-ppc64le::abseil-cpp-20210324.2-h3b9df90_0\r\n  aiohttp            conda-forge\/linux-ppc64le::aiohttp-3.7.4.post0-py36hc33305d_0\r\n  arrow-cpp          conda-forge\/linux-ppc64le::arrow-cpp-5.0.0-py36hf9cf308_8_cpu\r\n  async-timeout      conda-forge\/noarch::async-timeout-3.0.1-py_1000\r\n  attrs              conda-forge\/noarch::attrs-21.2.0-pyhd8ed1ab_0\r\n  aws-c-cal          conda-forge\/linux-ppc64le::aws-c-cal-0.5.11-hb3fac3d_0\r\n  aws-c-common       conda-forge\/linux-ppc64le::aws-c-common-0.6.2-h4e0d66e_0\r\n  aws-c-event-stream conda-forge\/linux-ppc64le::aws-c-event-stream-0.2.7-h76da5f2_13\r\n  aws-c-io           conda-forge\/linux-ppc64le::aws-c-io-0.10.5-hf6a6c7c_0\r\n  aws-checksums      conda-forge\/linux-ppc64le::aws-checksums-0.1.11-hfe76d68_7\r\n  aws-sdk-cpp        conda-forge\/linux-ppc64le::aws-sdk-cpp-1.8.186-h90855e8_3\r\n  brotlipy           conda-forge\/linux-ppc64le::brotlipy-0.7.0-py36hc33305d_1001\r\n  bzip2              conda-forge\/linux-ppc64le::bzip2-1.0.8-h4e0d66e_4\r\n  c-ares             conda-forge\/linux-ppc64le::c-ares-1.17.2-h4e0d66e_0\r\n  cffi               conda-forge\/linux-ppc64le::cffi-1.14.6-py36h021ab3c_1\r\n  chardet            conda-forge\/linux-ppc64le::chardet-4.0.0-py36h270354c_1\r\n  colorama           conda-forge\/noarch::colorama-0.4.4-pyh9f0ad1d_0\r\n  cryptography       conda-forge\/linux-ppc64le::cryptography-3.4.7-py36hc71b123_0\r\n  dataclasses        conda-forge\/noarch::dataclasses-0.8-pyh787bdff_2\r\n  datasets           conda-forge\/noarch::datasets-1.12.1-pyhd8ed1ab_1\r\n  dill               conda-forge\/noarch::dill-0.3.4-pyhd8ed1ab_0\r\n  filelock           conda-forge\/noarch::filelock-3.3.0-pyhd8ed1ab_0\r\n  fsspec             conda-forge\/noarch::fsspec-2021.10.0-pyhd8ed1ab_0\r\n  gflags             conda-forge\/linux-ppc64le::gflags-2.2.2-hb209c28_1004\r\n  glog               conda-forge\/linux-ppc64le::glog-0.5.0-h4040248_0\r\n  grpc-cpp           conda-forge\/linux-ppc64le::grpc-cpp-1.40.0-h2bf711c_2\r\n  huggingface_hub    conda-forge\/noarch::huggingface_hub-0.0.19-pyhd8ed1ab_0\r\n  idna               conda-forge\/noarch::idna-2.10-pyh9f0ad1d_0\r\n  idna_ssl           conda-forge\/noarch::idna_ssl-1.0.0-0\r\n  importlib-metadata conda-forge\/linux-ppc64le::importlib-metadata-4.8.1-py36h270354c_0\r\n  importlib_metadata conda-forge\/noarch::importlib_metadata-4.8.1-hd8ed1ab_0\r\n  krb5               conda-forge\/linux-ppc64le::krb5-1.19.2-haf43566_2\r\n  libblas            conda-forge\/linux-ppc64le::libblas-3.9.0-11_linuxppc64le_openblas\r\n  libbrotlicommon    conda-forge\/linux-ppc64le::libbrotlicommon-1.0.9-h4e0d66e_5\r\n  libbrotlidec       conda-forge\/linux-ppc64le::libbrotlidec-1.0.9-h4e0d66e_5\r\n  libbrotlienc       conda-forge\/linux-ppc64le::libbrotlienc-1.0.9-h4e0d66e_5\r\n  libcblas           conda-forge\/linux-ppc64le::libcblas-3.9.0-11_linuxppc64le_openblas\r\n  libcurl            conda-forge\/linux-ppc64le::libcurl-7.79.1-he415e40_1\r\n  libedit            conda-forge\/linux-ppc64le::libedit-3.1.20191231-h41a240f_2\r\n  libev              conda-forge\/linux-ppc64le::libev-4.33-h6eb9509_1\r\n  libevent           conda-forge\/linux-ppc64le::libevent-2.1.10-h97db324_4\r\n  libgfortran-ng     conda-forge\/linux-ppc64le::libgfortran-ng-11.2.0-hfdc3801_11\r\n  libgfortran5       conda-forge\/linux-ppc64le::libgfortran5-11.2.0-he58fbb4_11\r\n  liblapack          conda-forge\/linux-ppc64le::liblapack-3.9.0-11_linuxppc64le_openblas\r\n  libnghttp2         conda-forge\/linux-ppc64le::libnghttp2-1.43.0-h42039ad_1\r\n  libopenblas        conda-forge\/linux-ppc64le::libopenblas-0.3.17-pthreads_h486567c_1\r\n  libprotobuf        conda-forge\/linux-ppc64le::libprotobuf-3.18.1-h690f14c_0\r\n  libssh2            conda-forge\/linux-ppc64le::libssh2-1.10.0-ha5a9321_2\r\n  libthrift          conda-forge\/linux-ppc64le::libthrift-0.15.0-h54f692e_1\r\n  libutf8proc        conda-forge\/linux-ppc64le::libutf8proc-2.6.1-h4e0d66e_0\r\n  lz4-c              conda-forge\/linux-ppc64le::lz4-c-1.9.3-h3b9df90_1\r\n  multidict          conda-forge\/linux-ppc64le::multidict-5.2.0-py36hc33305d_0\r\n  multiprocess       conda-forge\/linux-ppc64le::multiprocess-0.70.12.2-py36hc33305d_0\r\n  numpy              conda-forge\/linux-ppc64le::numpy-1.19.5-py36h86665d4_1\r\n  orc                conda-forge\/linux-ppc64le::orc-1.7.0-hae6b4bd_0\r\n  packaging          conda-forge\/noarch::packaging-21.0-pyhd8ed1ab_0\r\n  pandas             conda-forge\/linux-ppc64le::pandas-1.1.5-py36hab1a6e6_0\r\n  parquet-cpp        conda-forge\/noarch::parquet-cpp-1.5.1-2\r\n  pyarrow            conda-forge\/linux-ppc64le::pyarrow-5.0.0-py36h7a46c7e_8_cpu\r\n  pycparser          conda-forge\/noarch::pycparser-2.20-pyh9f0ad1d_2\r\n  pyopenssl          conda-forge\/noarch::pyopenssl-21.0.0-pyhd8ed1ab_0\r\n  pyparsing          conda-forge\/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\r\n  pysocks            conda-forge\/linux-ppc64le::pysocks-1.7.1-py36h270354c_3\r\n  python-dateutil    conda-forge\/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0\r\n  python-xxhash      conda-forge\/linux-ppc64le::python-xxhash-2.0.2-py36hc33305d_0\r\n  python_abi         conda-forge\/linux-ppc64le::python_abi-3.6-2_cp36m\r\n  pytz               conda-forge\/noarch::pytz-2021.3-pyhd8ed1ab_0\r\n  pyyaml             conda-forge\/linux-ppc64le::pyyaml-5.4.1-py36hc33305d_1\r\n  re2                conda-forge\/linux-ppc64le::re2-2021.09.01-h3b9df90_0\r\n  requests           conda-forge\/noarch::requests-2.25.1-pyhd3deb0d_0\r\n  s2n                conda-forge\/linux-ppc64le::s2n-1.0.10-h97db324_0\r\n  six                conda-forge\/noarch::six-1.16.0-pyh6c4a22f_0\r\n  snappy             conda-forge\/linux-ppc64le::snappy-1.1.8-hb209c28_3\r\n  tqdm               conda-forge\/noarch::tqdm-4.62.3-pyhd8ed1ab_0\r\n  typing-extensions  conda-forge\/noarch::typing-extensions-3.10.0.2-hd8ed1ab_0\r\n  typing_extensions  conda-forge\/noarch::typing_extensions-3.10.0.2-pyha770c72_0\r\n  urllib3            conda-forge\/noarch::urllib3-1.26.7-pyhd8ed1ab_0\r\n  xxhash             conda-forge\/linux-ppc64le::xxhash-0.8.0-h4e0d66e_3\r\n  yaml               conda-forge\/linux-ppc64le::yaml-0.2.5-h6eb9509_0\r\n  yarl               conda-forge\/linux-ppc64le::yarl-1.6.3-py36hc33305d_2\r\n  zipp               conda-forge\/noarch::zipp-3.6.0-pyhd8ed1ab_0\r\n  zstd               conda-forge\/linux-ppc64le::zstd-1.5.0-h65c4b1a_0\r\n\r\nThe following packages will be UPDATED:\r\n\r\n  certifi            pkgs\/main::certifi-2020.12.5-py36h6ff~ --> conda-forge::certifi-2021.5.30-py36h270354c_0\r\n\r\n\r\nProceed ([y]\/n)? y\r\n\r\nPreparing transaction: done\r\nVerifying transaction: done\r\nExecuting transaction: done\r\n```\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Red Hat Enterprise Linux 8.2 (Ootpa)\r\n- Python version: 3.6\r\n- PyArrow version: pyarrow - 5.0.0 - py36h7a46c7e_8_cpu - conda-forge\r\n\r\n\r\nAny help would be appreciated! I've been struggling on installing datasets on this machine.\r\n","comment_length":20,"text":"Import error installing with ppc64le \n ## Describe the bug\r\nInstalling the datasets library with a computer running with ppc64le seems to cause an issue when importing the datasets library.\r\n\r\n\r\n```\r\npython\r\nPython 3.6.13 | packaged by conda-forge | (default, Sep 23 2021, 07:37:44) \r\n[GCC 9.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import datasets\r\nIllegal instruction (core dumped)\r\n```\r\n\r\nError when importing\r\n`Illegal instruction (core dumped)`\r\n\r\n## Steps to reproduce the bug\r\nI get this error when installing the library by using conda.  I can't install with pip I believe because pyarrow only has the ppc64le library on conda forge\r\n```\r\nconda create --name transformers_py36_v2 python=3.6 \r\nconda activate transformers_py36_v2 \r\nconda install datasets \r\n```\r\n\r\n## Tracebacks\r\nconda create --name transformers_py36_v2 python=3.6\r\n\r\n\r\n```\r\nCollecting package metadata (current_repodata.json): done\r\nSolving environment: done\r\n\r\n\r\n==> WARNING: A newer version of conda exists. <==\r\n  current version: 4.9.2\r\n  latest version: 4.10.3\r\n\r\nPlease update conda by running\r\n\r\n    $ conda update -n base -c defaults conda\r\n\r\n\r\n\r\n## Package Plan ##\r\n\r\n  environment location: \/p\/home\/gerryc\/.conda\/envs\/transformers_py36_v2\r\n\r\n  added \/ updated specs:\r\n    - python=3.6\r\n\r\n\r\nThe following NEW packages will be INSTALLED:\r\n\r\n  _libgcc_mutex      conda-forge\/linux-ppc64le::_libgcc_mutex-0.1-conda_forge\r\n  _openmp_mutex      conda-forge\/linux-ppc64le::_openmp_mutex-4.5-1_gnu\r\n  ca-certificates    conda-forge\/linux-ppc64le::ca-certificates-2021.10.8-h1084571_0\r\n  certifi            pkgs\/main\/linux-ppc64le::certifi-2020.12.5-py36h6ffa863_0\r\n  ld_impl_linux-ppc~ conda-forge\/linux-ppc64le::ld_impl_linux-ppc64le-2.36.1-ha35d02b_2\r\n  libffi             conda-forge\/linux-ppc64le::libffi-3.4.2-h3b9df90_4\r\n  libgcc-ng          conda-forge\/linux-ppc64le::libgcc-ng-11.2.0-h7698a5e_11\r\n  libgomp            conda-forge\/linux-ppc64le::libgomp-11.2.0-h7698a5e_11\r\n  libstdcxx-ng       conda-forge\/linux-ppc64le::libstdcxx-ng-11.2.0-habdf983_11\r\n  libzlib            conda-forge\/linux-ppc64le::libzlib-1.2.11-h339bb43_1013\r\n  ncurses            conda-forge\/linux-ppc64le::ncurses-6.2-hea85c5d_4\r\n  openssl            conda-forge\/linux-ppc64le::openssl-1.1.1l-h4e0d66e_0\r\n  pip                conda-forge\/noarch::pip-21.3-pyhd8ed1ab_0\r\n  python             conda-forge\/linux-ppc64le::python-3.6.13-h57873ef_2_cpython\r\n  readline           conda-forge\/linux-ppc64le::readline-8.1-h5c45dff_0\r\n  setuptools         pkgs\/main\/linux-ppc64le::setuptools-58.0.4-py36h6ffa863_0\r\n  sqlite             conda-forge\/linux-ppc64le::sqlite-3.36.0-h4e2196e_2\r\n  tk                 conda-forge\/linux-ppc64le::tk-8.6.11-h41c6715_1\r\n  wheel              conda-forge\/noarch::wheel-0.37.0-pyhd8ed1ab_1\r\n  xz                 conda-forge\/linux-ppc64le::xz-5.2.5-h6eb9509_1\r\n  zlib               conda-forge\/linux-ppc64le::zlib-1.2.11-h339bb43_1013\r\n\r\n\r\nProceed ([y]\/n)? y\r\n\r\nPreparing transaction: done\r\nVerifying transaction: done\r\nExecuting transaction: done\r\n#\r\n# To activate this environment, use\r\n#\r\n#     $ conda activate transformers_py36_v2\r\n#\r\n# To deactivate an active environment, use\r\n#\r\n#     $ conda deactivate\r\n```\r\n\r\n\r\nconda activate transformers_py36_v2\r\nconda install datasets\r\n```\r\nCollecting package metadata (current_repodata.json): done\r\nSolving environment: failed with initial frozen solve. Retrying with flexible solve.\r\nSolving environment: failed with repodata from current_repodata.json, will retry with next repodata source.\r\nCollecting package metadata (repodata.json): done\r\nSolving environment: done\r\n\r\n\r\n==> WARNING: A newer version of conda exists. <==\r\n  current version: 4.9.2\r\n  latest version: 4.10.3\r\n\r\nPlease update conda by running\r\n\r\n    $ conda update -n base -c defaults conda\r\n\r\n\r\n\r\n## Package Plan ##\r\n\r\n  environment location: \/p\/home\/gerryc\/.conda\/envs\/transformers_py36_v2\r\n\r\n  added \/ updated specs:\r\n    - datasets\r\n\r\n\r\nThe following NEW packages will be INSTALLED:\r\n\r\n  abseil-cpp         conda-forge\/linux-ppc64le::abseil-cpp-20210324.2-h3b9df90_0\r\n  aiohttp            conda-forge\/linux-ppc64le::aiohttp-3.7.4.post0-py36hc33305d_0\r\n  arrow-cpp          conda-forge\/linux-ppc64le::arrow-cpp-5.0.0-py36hf9cf308_8_cpu\r\n  async-timeout      conda-forge\/noarch::async-timeout-3.0.1-py_1000\r\n  attrs              conda-forge\/noarch::attrs-21.2.0-pyhd8ed1ab_0\r\n  aws-c-cal          conda-forge\/linux-ppc64le::aws-c-cal-0.5.11-hb3fac3d_0\r\n  aws-c-common       conda-forge\/linux-ppc64le::aws-c-common-0.6.2-h4e0d66e_0\r\n  aws-c-event-stream conda-forge\/linux-ppc64le::aws-c-event-stream-0.2.7-h76da5f2_13\r\n  aws-c-io           conda-forge\/linux-ppc64le::aws-c-io-0.10.5-hf6a6c7c_0\r\n  aws-checksums      conda-forge\/linux-ppc64le::aws-checksums-0.1.11-hfe76d68_7\r\n  aws-sdk-cpp        conda-forge\/linux-ppc64le::aws-sdk-cpp-1.8.186-h90855e8_3\r\n  brotlipy           conda-forge\/linux-ppc64le::brotlipy-0.7.0-py36hc33305d_1001\r\n  bzip2              conda-forge\/linux-ppc64le::bzip2-1.0.8-h4e0d66e_4\r\n  c-ares             conda-forge\/linux-ppc64le::c-ares-1.17.2-h4e0d66e_0\r\n  cffi               conda-forge\/linux-ppc64le::cffi-1.14.6-py36h021ab3c_1\r\n  chardet            conda-forge\/linux-ppc64le::chardet-4.0.0-py36h270354c_1\r\n  colorama           conda-forge\/noarch::colorama-0.4.4-pyh9f0ad1d_0\r\n  cryptography       conda-forge\/linux-ppc64le::cryptography-3.4.7-py36hc71b123_0\r\n  dataclasses        conda-forge\/noarch::dataclasses-0.8-pyh787bdff_2\r\n  datasets           conda-forge\/noarch::datasets-1.12.1-pyhd8ed1ab_1\r\n  dill               conda-forge\/noarch::dill-0.3.4-pyhd8ed1ab_0\r\n  filelock           conda-forge\/noarch::filelock-3.3.0-pyhd8ed1ab_0\r\n  fsspec             conda-forge\/noarch::fsspec-2021.10.0-pyhd8ed1ab_0\r\n  gflags             conda-forge\/linux-ppc64le::gflags-2.2.2-hb209c28_1004\r\n  glog               conda-forge\/linux-ppc64le::glog-0.5.0-h4040248_0\r\n  grpc-cpp           conda-forge\/linux-ppc64le::grpc-cpp-1.40.0-h2bf711c_2\r\n  huggingface_hub    conda-forge\/noarch::huggingface_hub-0.0.19-pyhd8ed1ab_0\r\n  idna               conda-forge\/noarch::idna-2.10-pyh9f0ad1d_0\r\n  idna_ssl           conda-forge\/noarch::idna_ssl-1.0.0-0\r\n  importlib-metadata conda-forge\/linux-ppc64le::importlib-metadata-4.8.1-py36h270354c_0\r\n  importlib_metadata conda-forge\/noarch::importlib_metadata-4.8.1-hd8ed1ab_0\r\n  krb5               conda-forge\/linux-ppc64le::krb5-1.19.2-haf43566_2\r\n  libblas            conda-forge\/linux-ppc64le::libblas-3.9.0-11_linuxppc64le_openblas\r\n  libbrotlicommon    conda-forge\/linux-ppc64le::libbrotlicommon-1.0.9-h4e0d66e_5\r\n  libbrotlidec       conda-forge\/linux-ppc64le::libbrotlidec-1.0.9-h4e0d66e_5\r\n  libbrotlienc       conda-forge\/linux-ppc64le::libbrotlienc-1.0.9-h4e0d66e_5\r\n  libcblas           conda-forge\/linux-ppc64le::libcblas-3.9.0-11_linuxppc64le_openblas\r\n  libcurl            conda-forge\/linux-ppc64le::libcurl-7.79.1-he415e40_1\r\n  libedit            conda-forge\/linux-ppc64le::libedit-3.1.20191231-h41a240f_2\r\n  libev              conda-forge\/linux-ppc64le::libev-4.33-h6eb9509_1\r\n  libevent           conda-forge\/linux-ppc64le::libevent-2.1.10-h97db324_4\r\n  libgfortran-ng     conda-forge\/linux-ppc64le::libgfortran-ng-11.2.0-hfdc3801_11\r\n  libgfortran5       conda-forge\/linux-ppc64le::libgfortran5-11.2.0-he58fbb4_11\r\n  liblapack          conda-forge\/linux-ppc64le::liblapack-3.9.0-11_linuxppc64le_openblas\r\n  libnghttp2         conda-forge\/linux-ppc64le::libnghttp2-1.43.0-h42039ad_1\r\n  libopenblas        conda-forge\/linux-ppc64le::libopenblas-0.3.17-pthreads_h486567c_1\r\n  libprotobuf        conda-forge\/linux-ppc64le::libprotobuf-3.18.1-h690f14c_0\r\n  libssh2            conda-forge\/linux-ppc64le::libssh2-1.10.0-ha5a9321_2\r\n  libthrift          conda-forge\/linux-ppc64le::libthrift-0.15.0-h54f692e_1\r\n  libutf8proc        conda-forge\/linux-ppc64le::libutf8proc-2.6.1-h4e0d66e_0\r\n  lz4-c              conda-forge\/linux-ppc64le::lz4-c-1.9.3-h3b9df90_1\r\n  multidict          conda-forge\/linux-ppc64le::multidict-5.2.0-py36hc33305d_0\r\n  multiprocess       conda-forge\/linux-ppc64le::multiprocess-0.70.12.2-py36hc33305d_0\r\n  numpy              conda-forge\/linux-ppc64le::numpy-1.19.5-py36h86665d4_1\r\n  orc                conda-forge\/linux-ppc64le::orc-1.7.0-hae6b4bd_0\r\n  packaging          conda-forge\/noarch::packaging-21.0-pyhd8ed1ab_0\r\n  pandas             conda-forge\/linux-ppc64le::pandas-1.1.5-py36hab1a6e6_0\r\n  parquet-cpp        conda-forge\/noarch::parquet-cpp-1.5.1-2\r\n  pyarrow            conda-forge\/linux-ppc64le::pyarrow-5.0.0-py36h7a46c7e_8_cpu\r\n  pycparser          conda-forge\/noarch::pycparser-2.20-pyh9f0ad1d_2\r\n  pyopenssl          conda-forge\/noarch::pyopenssl-21.0.0-pyhd8ed1ab_0\r\n  pyparsing          conda-forge\/noarch::pyparsing-2.4.7-pyh9f0ad1d_0\r\n  pysocks            conda-forge\/linux-ppc64le::pysocks-1.7.1-py36h270354c_3\r\n  python-dateutil    conda-forge\/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0\r\n  python-xxhash      conda-forge\/linux-ppc64le::python-xxhash-2.0.2-py36hc33305d_0\r\n  python_abi         conda-forge\/linux-ppc64le::python_abi-3.6-2_cp36m\r\n  pytz               conda-forge\/noarch::pytz-2021.3-pyhd8ed1ab_0\r\n  pyyaml             conda-forge\/linux-ppc64le::pyyaml-5.4.1-py36hc33305d_1\r\n  re2                conda-forge\/linux-ppc64le::re2-2021.09.01-h3b9df90_0\r\n  requests           conda-forge\/noarch::requests-2.25.1-pyhd3deb0d_0\r\n  s2n                conda-forge\/linux-ppc64le::s2n-1.0.10-h97db324_0\r\n  six                conda-forge\/noarch::six-1.16.0-pyh6c4a22f_0\r\n  snappy             conda-forge\/linux-ppc64le::snappy-1.1.8-hb209c28_3\r\n  tqdm               conda-forge\/noarch::tqdm-4.62.3-pyhd8ed1ab_0\r\n  typing-extensions  conda-forge\/noarch::typing-extensions-3.10.0.2-hd8ed1ab_0\r\n  typing_extensions  conda-forge\/noarch::typing_extensions-3.10.0.2-pyha770c72_0\r\n  urllib3            conda-forge\/noarch::urllib3-1.26.7-pyhd8ed1ab_0\r\n  xxhash             conda-forge\/linux-ppc64le::xxhash-0.8.0-h4e0d66e_3\r\n  yaml               conda-forge\/linux-ppc64le::yaml-0.2.5-h6eb9509_0\r\n  yarl               conda-forge\/linux-ppc64le::yarl-1.6.3-py36hc33305d_2\r\n  zipp               conda-forge\/noarch::zipp-3.6.0-pyhd8ed1ab_0\r\n  zstd               conda-forge\/linux-ppc64le::zstd-1.5.0-h65c4b1a_0\r\n\r\nThe following packages will be UPDATED:\r\n\r\n  certifi            pkgs\/main::certifi-2020.12.5-py36h6ff~ --> conda-forge::certifi-2021.5.30-py36h270354c_0\r\n\r\n\r\nProceed ([y]\/n)? y\r\n\r\nPreparing transaction: done\r\nVerifying transaction: done\r\nExecuting transaction: done\r\n```\r\n\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Red Hat Enterprise Linux 8.2 (Ootpa)\r\n- Python version: 3.6\r\n- PyArrow version: pyarrow - 5.0.0 - py36h7a46c7e_8_cpu - conda-forge\r\n\r\n\r\nAny help would be appreciated! I've been struggling on installing datasets on this machine.\r\n \n This seems to be an issue with importing PyArrow so I posted the problem [here](https:\/\/issues.apache.org\/jira\/browse\/ARROW-14323), and I'm closing this issue.\r\n","embeddings":[-0.1681167483,0.1741703153,0.0191157497,0.2278843522,0.2522399426,0.2248133123,0.3580006063,0.230405882,-0.3304049671,-0.151977241,-0.1553059816,0.3825336993,-0.0719908923,-0.1810767949,0.1235700995,-0.1017434224,0.2075530142,0.1592649072,-0.355894804,0.0104453051,0.0393025242,0.0991444066,-0.2588663995,-0.0131988069,-0.2772497535,-0.0752695277,0.1208346561,0.2642084956,-0.3207326531,-0.4877249002,0.4658457041,0.0226073861,0.2885294855,0.5494134426,-0.0001185895,0.0614437833,0.1450084597,0.0612168536,-0.3407801986,0.1261223257,-0.2262351066,-0.1255428642,0.0684670284,-0.3169618249,0.2733924091,-0.1515469998,-0.2632461488,-0.0266987123,0.3241505027,0.3755487502,0.1832677573,0.4011484981,0.4120345414,-0.1107694432,-0.1212255135,-0.0766370222,-0.1382636279,0.4454354644,-0.090663597,0.0604198053,0.2833495736,-0.1074118763,-0.4097567499,-0.1004437059,0.0622541308,-0.2555211484,0.0053105778,-0.3622724116,-0.0770322159,0.0977199599,0.4057969451,-0.5244999528,-0.3740674555,-0.1321486384,0.0982755423,-0.1443511546,0.0384261198,0.3288904428,-0.026884377,0.4496766031,-0.136501193,-0.1078773215,-0.1149715483,-0.1255889237,-0.1303062737,0.2772199214,0.0795140341,0.0621868819,0.3043601215,0.0229977202,0.2511222064,-0.0277939998,-0.1933030635,0.2085258514,-0.2977025211,0.0448928997,-0.0169851929,0.3024373055,0.057473436,-0.0868089199,-0.4460232258,-0.2258909196,-0.1284603179,0.4202296138,0.0785937682,0.1287722588,-0.2310458124,0.6227793694,0.172263369,0.007031525,0.1491643637,0.1725920439,-0.026954582,-0.2498265058,-0.1854353249,0.0644905344,0.2040732354,-0.3807359338,-0.6193609238,0.103422679,-0.0806259215,0.0323945954,0.0463369451,0.3786564469,-0.0428492539,0.267095089,-0.0900381356,0.1296769679,-0.0506841317,-0.1650948077,-0.0085962797,0.1578431427,-0.1412445456,-0.1000752673,0.1378428191,-0.2330300957,0.1386909783,0.3562193811,0.190565303,-0.0444914363,-0.0948983356,-0.1628222018,-0.0110508194,0.5177474022,-0.1413873881,0.1911185533,0.0758848712,-0.1573005766,-0.1927098334,0.3283714652,-0.275898844,-0.2721927464,-0.3044765294,0.0428348146,-0.2530064285,-0.0404013395,-0.1022374779,-0.3184184432,0.1914589703,-0.353746146,0.0429671817,-0.2561556995,0.2510426641,-0.3389224112,0.160877496,0.3802629709,-0.3298732936,-0.0096446471,-0.1356581151,-0.1638524234,0.0697237253,0.3967148066,-0.0640763268,0.1426299959,-0.1877413392,0.059267804,0.1169532165,-0.6120995879,-0.4776151478,0.1241763085,-0.0072813733,0.1154374331,-0.1382435858,-0.0577720888,0.1037281677,-0.2769392133,-0.0251639504,0.1974217445,-0.1917708665,-0.0228508245,-0.2093313038,-0.3600585759,0.1762980968,0.0794564262,-0.0742749423,0.122618027,0.0729383677,-0.126017794,0.1474113911,-0.0943460464,-0.1373960078,0.3990389109,0.4590025246,-0.0205616467,-0.0649981275,-0.0202442817,-0.4134480059,0.1031624302,0.1382993013,0.11922279,-0.5537195206,-0.2069346309,-0.1882449985,0.1518903077,-0.0850869194,-0.1587788016,-0.0216947924,-0.1237902567,0.0485547967,0.071910575,0.0880477875,0.4626564384,0.236557737,0.3100979626,-0.0276080407,0.0221242495,-0.3907720745,-0.0591377541,-0.0258063339,0.2370866686,0.3949136138,-0.2027698457,-0.0885349885,0.3065521121,0.0062376084,-0.0289252773,-0.2997791171,0.320317179,0.3272383511,-0.3317565918,0.014508252,-0.162565276,0.0816528574,0.1551696211,0.3347972035,0.1416384429,0.0082721459,-0.0588766001,0.0277062487,0.2139033079,0.472817868,0.1253752112,0.1064170375,-0.3569251597,0.2567155957,0.2894699872,0.3512606621,0.1421397626,-0.0006213235,-0.1016296968,0.3808919489,-0.0531530716,-0.0684341267,0.0168288406,-0.1259626746,0.2775593698,0.4140891433,0.2114192545,0.4604665339,0.1008697078,-0.2999583185,0.0760149956,-0.0933910608,-0.0398392342,0.1069488004,0.0131417317,0.0878641084,-0.1818794012,0.0280347764,0.165189594,-0.0290527344,-0.3745678365,-0.0146601796,0.3565303683,-0.2384230047,0.3197468221,-0.3326584697,0.0320671313,-0.2852928042,-0.4586717784,-0.1252925098,0.0092238439,-0.2183821648,0.0172385629,-0.0140595278,0.2438283712,-0.1330532581,0.0772265419,-0.1235713959,-0.3615942597,0.0415624194,-0.0689863041,-0.2089127749,-0.0383707434,0.3161186576,-0.0648595914,0.245308727,-0.0631549433,-0.0479900613,0.0697784424,-0.5097413063,0.1870933622,-0.0384004489,0.2822638154,-0.0371473655,0.0251931213,0.0235878173,-0.3928706944,0.044580508,-0.0598565191,0.0154011697,-0.0135760307,-0.1440773159,-0.3158510029,-0.3546175361,-0.1802105159,-0.3794827759,-0.2938169837,0.1727311462,0.212023288,0.066311501,0.0445277765,0.1872196198,-0.017345937,0.0800274462,0.0523343757,0.0117527414,-0.1039147601,0.3370037377,-0.2633851171,-0.286316514,-0.0022649732,-0.0298108011,0.4419113398,0.115319632,-0.2360550612,0.2469995469,-0.3731722534,0.6139401793,0.0336668566,-0.0038018874,0.1522491723,0.1148400381,0.218961969,-0.1268131584,-0.2673543096,-0.0462666303,-0.0977062732,-0.0782295242,-0.1110155284,0.1414683312,0.0398476534,0.4579199255,-0.0062669292,-0.2128994018,0.1838432103,0.0348559842,0.3761181831,-0.0162685849,-0.2655890584,0.0858873278,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3071","title":"Custom plain text dataset, plain json dataset and plain csv dataset are remove from datasets template folder","comments":"Hi @zixiliuUSC, \r\n\r\nAs explained in the documentation (https:\/\/huggingface.co\/docs\/datasets\/loading.html#json), we support loading any dataset in JSON (as well as CSV, text, Parquet) format:\r\n```python\r\nds = load_dataset('json', data_files='my_file.json')\r\n```","body":"## Adding a Dataset\r\n- **Name:** text, json, csv\r\n- **Description:** I am developing a customized dataset loading script. The problem is  mainly about my custom dataset is seperate into many files and I only find a dataset loading template in [https:\/\/github.com\/huggingface\/datasets\/blob\/1.2.1\/datasets\/json\/json.py](https:\/\/github.com\/huggingface\/datasets\/blob\/1.2.1\/datasets\/json\/json.py) that can handle my circumstance. I'm afraid these templates are too old to use. Could you re-add these three templates to current master branch?\r\n\r\n","comment_length":28,"text":"Custom plain text dataset, plain json dataset and plain csv dataset are remove from datasets template folder \n ## Adding a Dataset\r\n- **Name:** text, json, csv\r\n- **Description:** I am developing a customized dataset loading script. The problem is  mainly about my custom dataset is seperate into many files and I only find a dataset loading template in [https:\/\/github.com\/huggingface\/datasets\/blob\/1.2.1\/datasets\/json\/json.py](https:\/\/github.com\/huggingface\/datasets\/blob\/1.2.1\/datasets\/json\/json.py) that can handle my circumstance. I'm afraid these templates are too old to use. Could you re-add these three templates to current master branch?\r\n\r\n \n Hi @zixiliuUSC, \r\n\r\nAs explained in the documentation (https:\/\/huggingface.co\/docs\/datasets\/loading.html#json), we support loading any dataset in JSON (as well as CSV, text, Parquet) format:\r\n```python\r\nds = load_dataset('json', data_files='my_file.json')\r\n```","embeddings":[0.2397149652,-0.332098335,-0.0408486649,0.3953284323,0.1127283052,0.2876622975,0.192507267,0.1587701589,0.2423331589,-0.1266014874,-0.3314574659,0.1281609535,-0.1774952263,0.2487665564,-0.1037117466,-0.1301275194,0.0871858746,0.2242773026,0.0038963947,-0.0486043803,-0.2215840518,0.385548681,0.1164100021,-0.2304294854,-0.0053815199,0.0183712803,-0.2670787871,0.1515718102,-0.2334827185,-0.4484162927,0.170611605,0.260355413,0.2933289111,0.3570078015,-0.0001270182,0.085569568,0.189528212,-0.1176054552,-0.4165238142,-0.1284475178,-0.3328685164,-0.3567738235,0.3995290697,0.0487887636,-0.1988208741,-0.4922281206,-0.1915897429,-0.1317308843,0.5035949945,0.2088761926,0.0827731192,0.0383541137,-0.1548607796,-0.1078837663,0.0494983383,0.6002784371,-0.0121491766,0.2295688093,0.4843372703,0.0863152519,0.1955571026,0.0946905091,0.015024567,0.0831780583,0.4629555047,0.0208377931,0.0096444078,-0.1580663025,-0.1981608868,0.1376045942,0.4504593015,-0.3374323547,-0.4038012922,-0.5566542149,0.0242961366,-0.3005296588,-0.0943672508,-0.0194394104,0.1944104582,0.1993808299,-0.2086917609,-0.4415774047,-0.077005282,0.239884302,0.2977451682,-0.0771941915,-0.2071599364,-0.0595258102,0.029685013,0.0251892675,0.0668663532,-0.3175983727,-0.0541374758,0.0322853066,0.2081820518,0.0221924931,0.0432682559,-0.1497154832,0.0414740331,0.4109562337,0.3230081499,0.0359937735,-0.1076194942,-0.0438911133,0.3086813986,-0.1575551331,0.0908620134,-0.240191415,0.1258212179,0.3245790005,0.1483537257,-0.2518955469,0.0023087154,0.1757754236,-0.2206161916,-0.0294433758,0.3159923851,-0.1350587904,0.1094480008,-0.2391660511,0.1018563882,-0.1552807242,0.0484643839,0.1559966803,0.0737821907,0.1199022382,-0.1991564035,0.1684161574,-0.0012954691,-0.0881190374,-0.0571255907,0.107263349,-0.1512747258,0.0625320077,0.3269740641,-0.4262293279,0.1753560603,0.3152215779,0.1370832175,-0.1612693667,-0.103187345,0.0785498619,0.0275691524,-0.0194569957,-0.1305346042,0.3504505157,0.1866627783,-0.0250368714,-0.2299426645,-0.3176362813,-0.479657948,-0.4421070218,0.0077491789,0.0281270295,-0.1840628386,-0.0144506833,-0.6208382845,0.0688326135,-0.2443321645,0.0150119076,-0.0344267264,-0.223718822,0.0454168916,-0.1178498492,-0.100964725,0.6085813642,-0.4475254416,-0.2051450759,-0.1917374432,-0.1002579927,-0.1242297292,0.4132974446,-0.2120757997,0.3355857432,-0.4235167205,-0.2918106914,0.1010816023,-0.3979909718,-0.0674588829,0.27717188,0.1737129688,0.3372457922,0.1782293022,-0.254313916,0.2949810922,-0.0832665265,0.107468769,-0.0382446162,0.2500596941,-0.0525424294,0.0474158078,-0.2523852885,-0.0321014374,0.1409665793,-0.0292491093,0.1200666577,0.0816777721,-0.134635374,0.1620994657,-0.1568421274,0.0757809281,0.5549691319,0.1322447807,0.1574085057,0.1006558165,-0.0453234054,-0.7832226753,0.145457074,0.1175631732,0.0269903075,-0.1429139376,0.0296681635,-0.1743668914,-0.2783115804,-0.5661079884,0.0545508377,-0.0431761928,0.2712217569,0.2243048102,-0.2082612216,-0.183610782,0.1898403466,-0.1496697217,0.3285489976,-0.460842818,0.1921831816,0.2395593971,0.0531999096,0.2668137848,0.3015272319,0.1350753307,-0.3465775549,0.1267358214,0.046185568,0.091738157,0.2308921516,0.0867484659,0.2341104001,0.1296196133,-0.1140034795,0.2505323589,0.1158945411,-0.1319603473,-0.2133677006,-0.2606644332,0.5038955808,-0.0521393418,0.3009726405,-0.0150966542,-0.210855931,0.317379117,0.1088863388,-0.096501708,-0.4358348846,-0.0398298614,0.2355497628,0.2854887545,0.0052205729,-0.2784567773,0.0539461635,0.454062283,-0.1289983243,0.0450710841,0.1819260269,-0.2471076995,-0.1772562414,0.0697941631,0.047881797,0.4118821621,0.1054504812,-0.12262135,0.0757028535,0.3979727626,-0.3457880914,0.1606607884,-0.0312539041,-0.0161785427,0.35899055,0.3641776443,-0.1705216914,-0.5319751501,0.0122345043,0.0764483288,0.2014374584,-0.2536534071,-0.064399682,-0.5378640294,-0.1128427908,-0.1268002391,-0.1751130074,-0.3331820965,-0.2573069036,0.0661084056,0.0955177397,0.035788741,-0.0074391621,0.2278428525,0.262253195,-0.3420829177,-0.2399759889,-0.4923952818,0.0763999224,0.0508606471,-0.063507162,0.1058089882,-0.0384407751,0.3041072488,-0.1968717426,0.0930492654,-0.5297384858,-0.0595384724,0.1809660643,-0.1568977833,0.1744407415,0.4398272634,0.2253004164,0.1713788211,0.0832549185,0.1815666705,0.0382151455,-0.1479779035,-0.0162074696,-0.2678100169,0.3210030496,0.0637022927,-0.2019527555,-0.3397630751,-0.2309111059,0.3177537322,0.1296043843,-0.0362552591,0.2082753628,0.0296567343,-0.1614693999,-0.3054532409,0.1565807462,-0.224349454,-0.5733680129,0.264767766,-0.1850538999,-0.0171695091,0.1135958731,0.1869444698,0.2208567262,-0.1500973254,-0.3529197276,-0.0823227093,0.0041405899,0.0001955193,0.1444591135,0.0141253602,0.1902522296,0.1661314219,0.0782992318,-0.0757938921,-0.1163697466,-0.2609504461,0.0338216014,0.1316007525,0.0152777275,0.4550558329,-0.184087649,0.6422142982,0.2391354591,-0.0652935356,0.3699788749,0.0501432046,0.5908274055,-0.2770814896,-0.5382220745,-0.1669814885,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3064","title":"Make `interleave_datasets` more robust","comments":"Hi ! Sorry for the late response\r\n\r\nI agree `interleave_datasets` would benefit a lot from having more flexibility. If I understand correctly it would be nice to be able to define stopping strategies like `stop=\"first_exhausted\"` (default) or `stop=\"all_exhausted\"`. If you'd like to contribute this feature I'd be happy to give you some pointers :)\r\n\r\nAlso one can already set the max number of iterations per dataset by doing `dataset.take(n)` on the dataset that should only have `n` samples.\r\n\r\nRegarding the `iter_cnt` counter, I think this requires a bit more thoughts, since we might have to be able to backpropagate the the counter if `map` or other transforms have been applied after `interleave_datasets`. ","body":"**Is your feature request related to a problem? Please describe.**\r\nRight now there are few hiccups using `interleave_datasets`. Interleaved dataset iterates until the smallest dataset completes it's iterator. In this way larger datasets may not complete full epoch of iteration. \r\nIt creates new problems in calculation of epoch since there are no way to track which dataset from `interleave_datasets` completes how many epoch.\r\n\r\n**Describe the solution you'd like**\r\nFor `interleave_datasets` module, \r\n- [ ] Add a boolean argument `--stop-iter` in `interleave_datasets` that enables dataset to either iterate infinite amount of time or not. That means it should not return `StopIterator` exception in case `--stop-iter=False`.\r\n- [ ] Internal list variable `iter_cnt` that explains how many times (in steps\/epochs) each dataset iterates at a given point.\r\n- [ ] Add an argument `--max-iter` (list type) that explain maximum amount of time each of the dataset can iterate. After complete  `--max-iter` of one dataset, other dataset should continue sampling and when all the dataset finish their respective `--max-iter`, only then return `StopIterator`\r\n\r\nNote: I'm new to `datasets` api. May be these features are already there in the datasets. \r\n\r\nSince multitask training is the latest trends, I believe this feature would make the `datasets` api more popular.\r\n\r\n@lhoestq ","comment_length":112,"text":"Make `interleave_datasets` more robust \n **Is your feature request related to a problem? Please describe.**\r\nRight now there are few hiccups using `interleave_datasets`. Interleaved dataset iterates until the smallest dataset completes it's iterator. In this way larger datasets may not complete full epoch of iteration. \r\nIt creates new problems in calculation of epoch since there are no way to track which dataset from `interleave_datasets` completes how many epoch.\r\n\r\n**Describe the solution you'd like**\r\nFor `interleave_datasets` module, \r\n- [ ] Add a boolean argument `--stop-iter` in `interleave_datasets` that enables dataset to either iterate infinite amount of time or not. That means it should not return `StopIterator` exception in case `--stop-iter=False`.\r\n- [ ] Internal list variable `iter_cnt` that explains how many times (in steps\/epochs) each dataset iterates at a given point.\r\n- [ ] Add an argument `--max-iter` (list type) that explain maximum amount of time each of the dataset can iterate. After complete  `--max-iter` of one dataset, other dataset should continue sampling and when all the dataset finish their respective `--max-iter`, only then return `StopIterator`\r\n\r\nNote: I'm new to `datasets` api. May be these features are already there in the datasets. \r\n\r\nSince multitask training is the latest trends, I believe this feature would make the `datasets` api more popular.\r\n\r\n@lhoestq  \n Hi ! Sorry for the late response\r\n\r\nI agree `interleave_datasets` would benefit a lot from having more flexibility. If I understand correctly it would be nice to be able to define stopping strategies like `stop=\"first_exhausted\"` (default) or `stop=\"all_exhausted\"`. If you'd like to contribute this feature I'd be happy to give you some pointers :)\r\n\r\nAlso one can already set the max number of iterations per dataset by doing `dataset.take(n)` on the dataset that should only have `n` samples.\r\n\r\nRegarding the `iter_cnt` counter, I think this requires a bit more thoughts, since we might have to be able to backpropagate the the counter if `map` or other transforms have been applied after `interleave_datasets`. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3063","title":"Windows CI is unable to test streaming properly because of SSL issues","comments":"I think this problem is already fixed:\r\n```python\r\nIn [4]: import fsspec\r\n   ...:\r\n   ...: url = \"https:\/\/moon-staging.huggingface.co\/datasets\/__DUMMY_TRANSFORMERS_USER__\/my-dataset-16242824690709\/resolve\/main\/.gitattributes\"\r\n   ...:\r\n   ...: fsspec.open(url).open()\r\nOut[4]: <File-like object HTTPFileSystem, https:\/\/moon-staging.huggingface.co\/datasets\/__DUMMY_TRANSFORMERS_USER__\/my-dataset-16242824690709\/resolve\/main\/.gitattribu\r\n```","body":"In https:\/\/github.com\/huggingface\/datasets\/pull\/3041 the windows tests were skipped because of SSL issues with moon-staging.huggingface.co:443\r\n\r\nThe issue appears only on windows with asyncio. On Linux it works. With requests it works as well. And with the production environment huggingface.co it also works.\r\n\r\nto reproduce on windows:\r\n```python\r\nimport fsspec\r\n\r\n# use any URL to a file in a dataset repo\r\nurl = \"https:\/\/moon-staging.huggingface.co\/datasets\/__DUMMY_TRANSFORMERS_USER__\/my-dataset-16242824690709\/resolve\/main\/.gitattributes\"\r\n\r\nfsspec.open(url).open()\r\n```\r\nraises\r\n```python\r\nFileNotFoundError: https:\/\/moon-staging.huggingface.co\/datasets\/__DUMMY_TRANSFORMERS_USER__\/my-dataset-16242824690709\/resolve\/main\/.gitattributes\r\n```\r\nbecause of\r\n```python\r\naiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')]\r\n```","comment_length":26,"text":"Windows CI is unable to test streaming properly because of SSL issues \n In https:\/\/github.com\/huggingface\/datasets\/pull\/3041 the windows tests were skipped because of SSL issues with moon-staging.huggingface.co:443\r\n\r\nThe issue appears only on windows with asyncio. On Linux it works. With requests it works as well. And with the production environment huggingface.co it also works.\r\n\r\nto reproduce on windows:\r\n```python\r\nimport fsspec\r\n\r\n# use any URL to a file in a dataset repo\r\nurl = \"https:\/\/moon-staging.huggingface.co\/datasets\/__DUMMY_TRANSFORMERS_USER__\/my-dataset-16242824690709\/resolve\/main\/.gitattributes\"\r\n\r\nfsspec.open(url).open()\r\n```\r\nraises\r\n```python\r\nFileNotFoundError: https:\/\/moon-staging.huggingface.co\/datasets\/__DUMMY_TRANSFORMERS_USER__\/my-dataset-16242824690709\/resolve\/main\/.gitattributes\r\n```\r\nbecause of\r\n```python\r\naiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host moon-staging.huggingface.co:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)')]\r\n``` \n I think this problem is already fixed:\r\n```python\r\nIn [4]: import fsspec\r\n   ...:\r\n   ...: url = \"https:\/\/moon-staging.huggingface.co\/datasets\/__DUMMY_TRANSFORMERS_USER__\/my-dataset-16242824690709\/resolve\/main\/.gitattributes\"\r\n   ...:\r\n   ...: fsspec.open(url).open()\r\nOut[4]: <File-like object HTTPFileSystem, https:\/\/moon-staging.huggingface.co\/datasets\/__DUMMY_TRANSFORMERS_USER__\/my-dataset-16242824690709\/resolve\/main\/.gitattribu\r\n```","embeddings":[-0.3543521762,0.0034220477,0.0198071115,0.0342934057,0.0507179797,-0.1108401865,0.211902529,0.131444782,0.0287686903,-0.0833963081,0.0598859489,-0.2223210037,0.1826362759,0.2124500275,-0.0842108428,-0.1026160121,0.0015288843,-0.2221381664,0.0797981843,0.1013270393,-0.1483759731,0.2218105793,-0.1631116867,-0.2483710647,0.0282209441,-0.0970839486,-0.1524079293,0.226184085,-0.0843155086,-0.1519061476,0.3168168068,-0.0242451709,0.1006002426,0.3102076948,-0.0001256052,0.1159605756,0.4257006049,-0.0735931173,-0.1649602205,-0.089656882,-0.0487797335,0.1262587309,-0.0519584008,0.1272165179,-0.1898976117,0.2872740626,-0.0654392615,-0.2645670772,0.3044877648,0.3697357476,0.0502638258,0.648626864,-0.1948173493,0.133277759,-0.0445335247,0.2740882337,-0.1210538223,0.1870938689,0.2344524115,-0.1632012874,0.011435004,0.0519906469,0.0575878136,-0.0248539075,-0.2013207972,-0.0252438504,-0.061537005,-0.4755307734,0.1112513021,-0.0192216374,0.0919255018,-0.1820927262,-0.3352394998,0.0066398242,-0.0352364965,-0.0330462158,0.3889964521,0.4129362404,-0.2233092487,-0.1475996971,-0.2206351906,0.1108857989,-0.2988286316,0.24561207,0.1633144319,0.056529928,-0.1586245447,0.1210289672,0.0075566866,-0.1215826347,-0.2040593326,-0.3106710017,-0.3100169599,0.0135264341,-0.3462364078,-0.1128351614,-0.1071452647,0.3016144335,-0.0281999484,0.4423442483,-0.0670456737,0.0521029048,0.3095791042,0.0470107384,0.0270674936,0.3488001227,0.2669855952,-0.0430113599,0.5553990602,0.5416621566,0.1450486779,0.0070086652,-0.079612948,-0.4109809995,-0.5638800859,-0.006512288,0.681571424,-0.4159496725,-0.3877807856,0.0852680728,-0.3326855004,-0.0487378277,0.2230423093,0.3753294945,0.1078907549,0.155603677,0.0414419733,0.3619268537,-0.1772617251,0.1067658141,0.0516681187,-0.1916960776,-0.0455030911,0.2198717296,0.5214277506,-0.0763024613,0.2492323369,0.0417199768,0.2694272697,-0.0677996725,-0.0662206039,0.0689453855,0.2365228832,0.3781507015,0.1813642234,0.0680557638,0.0001430752,-0.0634002015,-0.069523111,-0.0530408472,0.0870255828,-0.0660537034,0.2422847599,0.0555333495,-0.4479528964,-0.0627523363,-0.1213541701,-0.0325014517,-0.1565562487,-0.013666681,0.1629217416,-0.0629070252,-0.1408006102,0.1388531029,0.3069664538,0.452858597,0.3826233447,-0.2743981779,0.1454558372,-0.0962856561,0.1617183238,0.3852910697,0.0428228825,-0.3558398485,-0.3128454387,-0.0219431128,0.015155823,-0.2400061637,-0.5203033686,0.5559719205,0.0358434245,0.2131424546,0.1763976216,-0.0707731918,0.3806973398,0.0235636756,-0.0875860453,0.1388106197,-0.007286781,0.0403363183,-0.2413896769,-0.2984218895,-0.1722083241,0.1608168185,-0.2495590448,0.1097410619,0.1748133749,-0.0763373598,0.2224630117,-0.017278038,-0.0035834806,0.1541457325,0.5449124575,-0.1085759029,0.0107049765,0.2868559957,-0.5678369999,0.1539328843,0.0707012787,0.1635650247,-0.262601018,-0.2345349938,0.1239504814,0.0243811216,-0.1081525981,-0.543730557,-0.0796095058,0.2259243429,0.3211647272,-0.0757878572,0.0946624428,0.0847374201,-0.0091291536,0.0257860217,-0.1007332578,0.3716412783,-0.1310213655,-0.1361418217,0.2129083127,0.0635740161,0.2995783091,-0.3150562942,-0.0858871043,0.1598911136,-0.1246628165,0.3734053373,0.20780617,0.4243704975,0.4627768397,-0.4986632168,-0.2132524848,0.6479541063,-0.0407587327,0.1814229041,-0.2019170076,0.4523843825,-0.2229152918,0.3383367062,0.1330448985,0.2408159077,0.2969433069,-0.0994414911,-0.4279637933,0.1542135179,0.6061840653,-0.2365878373,-0.1563205123,-0.122171253,-0.1459845603,0.0173814315,0.1187772825,-0.1694775969,-0.1393552721,0.1243504807,-0.1064150184,0.1199971735,0.2527738512,-0.0673193112,0.4469708204,-0.0795335248,-0.1146678478,0.326713711,-0.0286481157,-0.4041205645,0.3785718977,0.1742649972,-0.5299690366,0.1651252955,0.0334792398,0.0171059035,-0.4112291038,-0.0617914796,0.2394534796,0.0368293189,-0.4693355858,0.1741779,-0.0134169133,-0.3946727514,-0.2672727704,-0.0977180153,-0.2567535639,-0.1608512998,-0.0846929923,0.454342097,-0.3679554164,0.2305577248,-0.1533100307,0.3281268179,0.2198004574,-0.3388391435,-0.268055588,0.0348573364,-0.0899653509,0.0275853574,0.4766932726,-0.3319572806,0.2846599519,-0.2303344905,-0.0388999283,-0.4945641756,-0.4030043185,-0.0059383423,0.1876982599,0.5563310981,-0.2929016352,0.1156144217,0.090092741,-0.2526043952,0.2472028881,-0.4906109869,-0.0893013105,0.0471942313,0.0073552746,-0.2612396181,-0.3027014136,-0.1954978257,0.1596343368,-0.244118169,0.2415452451,-0.2124281228,-0.0576146729,-0.0586842261,-0.0694766864,-0.0450455286,0.0413018838,-0.2106054723,-0.0918873101,-0.4115782976,0.2073516846,-0.0037106494,-0.5209705234,0.0635151118,0.1812869608,0.0283918772,-0.0270630512,-0.4344809055,-0.2439691871,-0.1412710994,0.3977643847,0.1795970052,-0.1648497134,0.0508692525,-0.2716762722,0.0724270865,-0.1170073673,-0.1901363283,0.07045497,0.1410788298,0.2725571096,0.0390480421,0.3403044939,-0.03619157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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3061","title":"Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?) ","comments":"@lhoestq, @albertvillanova can we have `**tqdm_kwargs` in `map`? If there are any fields that are important to our tqdm (like iterable or unit), we can pop them before initialising the tqdm object so as to avoid duplicity.","body":"**A clear and concise description of what you want to happen.**\r\n\r\nIt would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nBy the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish?\r\n\r\n**Additional context**\r\n\r\nFurthering tqdm integration #2374 and huggingface\/transformers#11797 solutioned by huggingface\/transformers#12226 provided with tqdm description as `desc=`\r\n\r\n@sgugger @bhavitvyamalik","comment_length":37,"text":"Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?)  \n **A clear and concise description of what you want to happen.**\r\n\r\nIt would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nBy the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish?\r\n\r\n**Additional context**\r\n\r\nFurthering tqdm integration #2374 and huggingface\/transformers#11797 solutioned by huggingface\/transformers#12226 provided with tqdm description as `desc=`\r\n\r\n@sgugger @bhavitvyamalik \n @lhoestq, @albertvillanova can we have `**tqdm_kwargs` in `map`? If there are any fields that are important to our tqdm (like iterable or unit), we can pop them before initialising the tqdm object so as to avoid duplicity.","embeddings":[-0.307960242,-0.4473892152,-0.0674709454,-0.1451708972,0.1558560282,-0.0401671715,0.3228400648,0.1836802363,-0.2261488587,0.1609634161,-0.2775018811,0.5143535137,-0.0580763184,0.4776197672,-0.0116167944,-0.0091587165,-0.0279597752,-0.0164656416,-0.57832551,0.2839638293,-0.2386759669,0.1177632436,-0.2052085251,0.014030843,-0.1283077747,-0.2181258202,-0.0727002025,0.0891768262,0.2378109843,-0.4380876422,0.1363578737,0.4961166084,-0.0845471174,0.3012764454,-0.0001123342,-0.1186002865,0.2314579785,0.0678118989,0.0905242637,0.2309414148,0.0704173371,-0.3954606652,0.2171638757,-0.2037576139,0.0039231419,0.0778369606,-0.2011007667,-0.2251551002,0.6380643845,-0.3206146955,0.2035883218,0.5751177073,-0.3614924252,-0.0999107808,-0.0673890635,-0.1439171582,-0.347907424,0.1304291189,0.6831461191,-0.0604309812,-0.4105485976,0.3436622322,0.1126456931,-0.1097342521,0.4672141671,0.1040427387,0.3632031083,-0.3507681787,-0.0940831378,0.3984415829,0.2598362267,-0.4390260577,-0.3076882958,-0.5278615952,-0.114856489,0.0443192795,0.1853931993,-0.4412871003,-0.2632372975,0.2405505031,-0.752294898,-0.2109112293,-0.0951795429,-0.1344124824,-0.0059548309,-0.1206197441,-0.0429761298,-0.0671918839,0.2441700548,0.057843674,-0.1306304485,0.16591838,0.0655495226,0.0699952766,-0.0650798604,-0.3877479136,0.3482091129,0.1820209622,0.3610499203,0.4487198889,-0.2333018184,0.2086693347,-0.2075906992,0.1894376278,0.0458500087,0.0350824855,0.2099637389,-0.6823462248,0.3317911625,-0.0910224169,-0.0064956513,0.066315636,0.4740935862,-0.3375525475,0.0122793894,0.1485329717,0.0290585533,0.2503143847,0.0655544177,0.0614340864,0.440054059,0.0181998294,0.2161862254,0.2555684447,0.2323504835,-0.157708019,-0.1825529933,0.2060040087,0.0525524504,-0.2602236569,-0.104851149,-0.1484538317,-0.0115255034,0.1889915615,-0.1758653075,-0.057874918,0.1143177748,0.1035193801,0.2416128665,0.3793934882,0.0099139716,0.2401102036,0.4376016557,0.1714744866,-0.1935151815,-0.0332564153,0.0231527872,-0.0273961499,-0.2909912765,0.1589658856,0.1512226611,-0.3906770051,-0.0128474217,0.0756797791,-0.2792850733,-0.0237765033,-0.1771696359,0.4996314049,-0.1845145822,0.2341129035,0.2909584045,0.2248272747,-0.3258741796,-0.1309424192,0.2822689116,0.5106950402,-0.0458280295,-0.3792687058,-0.1266288757,-0.2145805657,-0.1766173542,-0.0663959086,-0.0261237249,0.2818356752,-0.2176332176,0.3067010343,0.1366119683,-0.3151678145,-0.3274073303,0.3661196828,-0.2542598248,0.0249007791,-0.0088647744,-0.0543352216,0.2999833822,-0.0839863867,0.0227355082,0.1365933567,-0.2591518164,0.0553288795,-0.1001938432,-0.1960333139,0.259019196,-0.0960544571,-0.0300321113,0.0128161488,0.2516794503,-0.522557497,0.3692955375,0.1973553598,0.1636330783,0.0467098206,0.1135135517,-0.3524396122,-0.3397786617,-0.2655186653,-0.5118914247,0.2580673993,0.1038686261,0.0176615566,-0.0429076962,-0.2247077227,-0.2403955013,0.0763083473,-0.156558007,-0.1037588939,0.0943128169,-0.3427300155,-0.1491201073,-0.0871926323,-0.4087624252,-0.0702994615,0.2064335197,0.1776584834,0.1126251593,0.2186546326,0.2959976196,-0.0322700515,0.0210868474,0.3266243339,0.0145896459,0.001206702,0.112738736,0.3950480223,-0.1125115231,0.310988158,0.2542632222,0.1912207305,0.2630429566,-0.0523688309,-0.2642048597,-0.0862910002,-0.3357798159,0.0653109476,-0.0492222868,0.358941257,-0.0728801787,0.1659179926,-0.1286818385,0.359597683,-0.1110253781,-0.1444559842,-0.4122587442,-0.5489957929,-0.1054244861,0.1291071326,0.1533344984,-0.3521595895,0.0710343644,0.5648074746,0.3756909966,0.2095337361,-0.0012230065,-0.1501525789,-0.0600566193,0.1590242088,0.2295419872,0.2353700846,0.2767284214,0.2107118368,0.2865697145,0.43455863,0.0733938292,-0.1249688268,-0.0961179659,0.1896224022,-0.0576759726,-0.0183899794,-0.1182684526,0.1323442608,-0.2599257827,-0.1802595556,-0.1607861817,-0.117503427,-0.115086332,-0.2908856869,-0.1277654618,-0.0496662818,0.1294685751,-0.1993996203,-0.3898548782,-0.3082318902,0.2844219208,-0.0311284605,-0.4053133428,0.58853966,0.2347526401,0.3873145878,-0.080263488,0.2060899734,-0.3127573431,-0.2263648063,0.3307778835,0.0512521006,-0.0053935586,-0.2187380493,0.436296463,-0.0970736593,0.2674237192,-0.3897657692,-0.3612946868,0.1512942761,-0.060056217,0.2392493337,0.1514693648,0.1445385814,-0.0809321404,0.3444690704,0.2372220904,-0.154677242,-0.4039933383,-0.3191626668,0.1283710748,0.0445722118,-0.3029712439,0.3092248142,-0.111561954,-0.5240487456,0.4612877965,-0.3300383985,-0.0152331339,0.0264014378,-0.2810370326,0.0605544262,-0.2266512811,-0.0913030282,-0.0311454777,-0.4570119679,0.2387091666,-0.441364795,-0.0169710498,0.1272779852,-0.1762617081,0.2201118469,-0.0842842609,-0.2619678974,-0.4975880086,-0.058040306,0.6153677106,-0.0036953003,0.1284215003,0.2658995986,0.2732687891,-0.030714266,-0.0217982493,-0.1772908717,0.160282895,-0.0554740466,-0.1159456596,-0.2003400326,0.1310293376,0.1367267966,0.3860693574,0.2768524289,0.2165236473,-0.0172464848,-0.1478403062,0.2377219349,-0.0361539945,-0.2676184773,-0.24632243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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3061","title":"Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?) ","comments":"Hi ! Sounds like a good idea :)\r\n\r\nAlso I think it would be better to have this as an actual parameters instead of kwargs to make it clearer","body":"**A clear and concise description of what you want to happen.**\r\n\r\nIt would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nBy the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish?\r\n\r\n**Additional context**\r\n\r\nFurthering tqdm integration #2374 and huggingface\/transformers#11797 solutioned by huggingface\/transformers#12226 provided with tqdm description as `desc=`\r\n\r\n@sgugger @bhavitvyamalik","comment_length":29,"text":"Feature request : add leave=True to dataset.map to enable tqdm nested bars (and whilst we're at it couldn't we get a way to access directly tqdm underneath?)  \n **A clear and concise description of what you want to happen.**\r\n\r\nIt would be so nice to be able to nest HuggingFace `Datasets.map() ` progress bars in the grander scheme of things and whilst we're at it why not other functions.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nBy the way is there not a way to directly interact with underlying tqdm module ? **kwargs-ish?\r\n\r\n**Additional context**\r\n\r\nFurthering tqdm integration #2374 and huggingface\/transformers#11797 solutioned by huggingface\/transformers#12226 provided with tqdm description as `desc=`\r\n\r\n@sgugger @bhavitvyamalik \n Hi ! Sounds like a good idea :)\r\n\r\nAlso I think it would be better to have this as an actual parameters instead of kwargs to make it clearer","embeddings":[-0.3040524423,-0.4401254952,-0.0733553693,-0.1609275043,0.1207132116,-0.0838483945,0.361603111,0.1839645356,-0.2422844619,0.1404825598,-0.2260065675,0.5033694506,-0.020245567,0.5093126893,-0.0045802835,-0.0898853168,-0.0212959666,0.0006455621,-0.5398647785,0.2524979711,-0.2420831472,0.1321516037,-0.1777400672,0.0366458818,-0.1847932935,-0.2185875326,-0.0785340294,0.1085541695,0.2125882506,-0.4118732214,0.1750570536,0.5118029714,-0.0924812406,0.2173535973,-0.0001114059,-0.1403513402,0.2472056895,0.0254464522,0.0924290195,0.2744339406,0.0252313484,-0.3768673837,0.2074661702,-0.2283930629,-0.0416655242,0.0749301091,-0.2352409512,-0.1270353347,0.6391503811,-0.3022738993,0.2194512039,0.576659739,-0.3688623607,-0.1105951145,-0.0363935977,-0.0876147076,-0.3601963818,0.0621498264,0.6224269867,-0.0555487052,-0.4059790373,0.4138614833,0.0508022979,-0.1347206086,0.4756100178,0.097201243,0.3391195238,-0.3385726511,-0.0631171986,0.3900970221,0.2385011911,-0.4620216191,-0.3017434776,-0.5928298235,-0.1061994508,0.0222147685,0.14176929,-0.4166000485,-0.3606654406,0.2118471265,-0.7446248531,-0.1906603277,-0.1046320349,-0.1145515665,-0.013261144,-0.0598558187,-0.038301345,-0.0679127127,0.2490650415,0.0716847628,-0.1656601429,0.1634162664,0.0414614007,0.0729134008,-0.0615867376,-0.3877871335,0.357175529,0.2380764633,0.3436084688,0.4493226409,-0.2292794883,0.1998808235,-0.1638224125,0.1955307573,0.0026398588,0.0687609389,0.2191921622,-0.7096219063,0.3801071048,-0.0600185916,0.0188221373,0.0724772513,0.5096796751,-0.3414834738,-0.0232225601,0.1497010589,0.0764117762,0.1847016215,0.0494535677,0.0659919083,0.4892995358,0.0403440557,0.1812064052,0.2243424952,0.1911826432,-0.1756914258,-0.1308942884,0.2162923664,0.0709819496,-0.1664342284,-0.1119686738,-0.1433534026,-0.0387450382,0.2185349464,-0.1589704007,-0.0334539972,0.1354027838,0.1309197843,0.2110914141,0.3881798983,-0.0323359035,0.3138100505,0.3814648688,0.1778326035,-0.224428311,-0.0523890592,-0.0012408064,-0.0032916814,-0.2528928518,0.1842806339,0.1515641063,-0.3615579605,-0.0192728583,0.0795272514,-0.3050708473,-0.0699194446,-0.1832270622,0.4967521131,-0.1570297182,0.2529943883,0.3311838508,0.2205490321,-0.3214269876,-0.1257992685,0.2778689861,0.5231160522,-0.0092250099,-0.4374570549,-0.1174479425,-0.2013557255,-0.1516336501,-0.0473439805,-0.0687665641,0.1946769208,-0.2248228341,0.3129492998,0.1484861523,-0.2415237576,-0.3402500749,0.342725724,-0.2442864776,0.0049838228,0.0701161399,-0.0205515642,0.3069837689,-0.0192295555,-0.0141084967,0.1990292668,-0.2121517509,0.0793273374,-0.1258684844,-0.2303996384,0.2429479063,-0.0506800301,0.0117277391,0.0063216193,0.2339881957,-0.5024089217,0.4072954953,0.1495748609,0.1827936172,0.0449886732,0.1284586936,-0.3524285555,-0.3019922078,-0.2372846454,-0.4979465008,0.1862058043,0.0497492328,0.0373184197,-0.0976102576,-0.272834897,-0.2762614191,0.0262670368,-0.1709609479,-0.1258241534,0.0885970145,-0.3336369693,-0.1573251635,-0.0924692452,-0.370447427,-0.0190581903,0.208893925,0.1469105482,0.1239176095,0.2826065421,0.3062502742,-0.0597985573,0.003973776,0.321285516,0.0433724262,0.0574327223,0.133415997,0.336808145,-0.1559200138,0.3341625929,0.2001488507,0.1550644487,0.2686703205,-0.0097825481,-0.1922695488,-0.0485801809,-0.3267363906,0.0513550453,-0.1022885069,0.4136492312,-0.062152613,0.1770100743,-0.1507863849,0.3069581985,-0.1460065693,-0.1824867278,-0.405061543,-0.5421484113,-0.0835584104,0.1085510105,0.1905282587,-0.3169668615,0.0083622262,0.5795347095,0.3920890391,0.2365939915,0.0256101284,-0.0879432037,-0.0739521682,0.1709176898,0.2626287937,0.2539455891,0.2087326646,0.265432179,0.2266521007,0.4055883586,0.0679124296,-0.1610924304,-0.0923363119,0.1664828062,-0.0830577463,0.0289005693,-0.1275924295,0.0821364373,-0.3430142701,-0.1516446173,-0.1561581492,-0.1161333546,-0.1314951628,-0.2932076752,-0.100169681,-0.143450141,0.0620872676,-0.2277710736,-0.4332298338,-0.3049092591,0.3392318189,0.0068841241,-0.4466090202,0.5754475594,0.1684544832,0.4101639986,-0.1021777391,0.16996333,-0.3442566991,-0.2505159974,0.3481220603,0.0732137412,-0.0645268187,-0.1695849597,0.4388557076,-0.0586820245,0.2961875796,-0.3456121087,-0.3851083517,0.0845564604,-0.0978166983,0.2158661783,0.1557662189,0.205077365,-0.0546315797,0.2470743805,0.2673864067,-0.1088445112,-0.3712069392,-0.3445515633,0.1556957215,0.0345049873,-0.3399263024,0.3345479071,-0.1021224037,-0.5162625909,0.4678388238,-0.3595427871,-0.0251820032,0.0904206485,-0.2080748528,0.0643903688,-0.1926524788,-0.0894384831,-0.0026041516,-0.4241378307,0.2659586072,-0.4216688871,0.0232190248,0.0545669384,-0.146790877,0.1811044365,-0.0995190442,-0.2360970825,-0.5348483324,-0.1099297404,0.6421715021,0.0428269245,0.1460507065,0.2659364641,0.2887934446,-0.0550249591,-0.0144863147,-0.2059701532,0.1521559656,-0.0602107085,-0.0986836106,-0.195897162,0.1586375087,0.0881339163,0.4038794637,0.2201348394,0.18105115,0.0500223488,-0.1471749395,0.2583084404,-0.0364407152,-0.304258287,-0.243243351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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3060","title":"load_dataset('openwebtext') yields \"Compressed file ended before the end-of-stream marker was reached\"","comments":"Hi @RylanSchaeffer, thanks for reporting.\r\n\r\nI'm sorry, but I was not able to reproduce your problem.\r\n\r\nNormally, the reason for this type of error is that, during your download of the data files, this was not fully complete.\r\n\r\nCould you please try to load the dataset again but forcing its redownload? Please use:\r\n```python\r\ndataset = load_dataset(\"openwebtext\", download_mode=\"FORCE_REDOWNLOAD\")\r\n```\r\n\r\nLet me know if the problem persists.","body":"## Describe the bug\r\nWhen I try `load_dataset('openwebtext')`, I receive a \"EOFError: Compressed file ended before the end-of-stream marker was reached\" error.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('openwebtext')\r\n```\r\n\r\n## Expected results\r\nI expect the `dataset` variable to be properly constructed.\r\n\r\n## Actual results\r\n\r\n```\r\nFile \"\/home\/rschaef\/CoCoSci-Language-Distillation\/distillation_v2\/ratchet_learning\/tasks\/base.py\", line 37, in create_dataset\r\n    dataset_str,\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1117, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 637, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 704, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/rschaef\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/openwebtext\/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1\/openwebtext.py\", line 61, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(_URL)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 261, in extract\r\n    partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 197, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 316, in cached_path\r\n    output_path, force_extract=download_config.force_extract\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 40, in extract\r\n    self.extractor.extract(input_path, output_path, extractor=extractor)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 179, in extract\r\n    return extractor.extract(input_path, output_path)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 53, in extract\r\n    tar_file.extractall(output_path)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2010, in extractall\r\n    numeric_owner=numeric_owner)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2052, in extract\r\n    numeric_owner=numeric_owner)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2122, in _extract_member\r\n    self.makefile(tarinfo, targetpath)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2171, in makefile\r\n    copyfileobj(source, target, tarinfo.size, ReadError, bufsize)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 249, in copyfileobj\r\n    buf = src.read(bufsize)\r\n  File \"\/usr\/lib\/python3.6\/lzma.py\", line 200, in read\r\n    return self._buffer.read(size)\r\n  File \"\/usr\/lib\/python3.6\/_compression.py\", line 68, in readinto\r\n    data = self.read(len(byte_view))\r\n  File \"\/usr\/lib\/python3.6\/_compression.py\", line 99, in read\r\n    raise EOFError(\"Compressed file ended before the \"\r\npython-BaseException\r\nEOFError: Compressed file ended before the end-of-stream marker was reached\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial\r\n- Python version: 3.6.10\r\n- PyArrow version: 5.0.0","comment_length":66,"text":"load_dataset('openwebtext') yields \"Compressed file ended before the end-of-stream marker was reached\" \n ## Describe the bug\r\nWhen I try `load_dataset('openwebtext')`, I receive a \"EOFError: Compressed file ended before the end-of-stream marker was reached\" error.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('openwebtext')\r\n```\r\n\r\n## Expected results\r\nI expect the `dataset` variable to be properly constructed.\r\n\r\n## Actual results\r\n\r\n```\r\nFile \"\/home\/rschaef\/CoCoSci-Language-Distillation\/distillation_v2\/ratchet_learning\/tasks\/base.py\", line 37, in create_dataset\r\n    dataset_str,\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1117, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 637, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 704, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/rschaef\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/openwebtext\/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1\/openwebtext.py\", line 61, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(_URL)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 261, in extract\r\n    partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 197, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 316, in cached_path\r\n    output_path, force_extract=download_config.force_extract\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 40, in extract\r\n    self.extractor.extract(input_path, output_path, extractor=extractor)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 179, in extract\r\n    return extractor.extract(input_path, output_path)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 53, in extract\r\n    tar_file.extractall(output_path)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2010, in extractall\r\n    numeric_owner=numeric_owner)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2052, in extract\r\n    numeric_owner=numeric_owner)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2122, in _extract_member\r\n    self.makefile(tarinfo, targetpath)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2171, in makefile\r\n    copyfileobj(source, target, tarinfo.size, ReadError, bufsize)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 249, in copyfileobj\r\n    buf = src.read(bufsize)\r\n  File \"\/usr\/lib\/python3.6\/lzma.py\", line 200, in read\r\n    return self._buffer.read(size)\r\n  File \"\/usr\/lib\/python3.6\/_compression.py\", line 68, in readinto\r\n    data = self.read(len(byte_view))\r\n  File \"\/usr\/lib\/python3.6\/_compression.py\", line 99, in read\r\n    raise EOFError(\"Compressed file ended before the \"\r\npython-BaseException\r\nEOFError: Compressed file ended before the end-of-stream marker was reached\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial\r\n- Python version: 3.6.10\r\n- PyArrow version: 5.0.0 \n Hi @RylanSchaeffer, thanks for reporting.\r\n\r\nI'm sorry, but I was not able to reproduce your problem.\r\n\r\nNormally, the reason for this type of error is that, during your download of the data files, this was not fully complete.\r\n\r\nCould you please try to load the dataset again but forcing its redownload? Please use:\r\n```python\r\ndataset = load_dataset(\"openwebtext\", download_mode=\"FORCE_REDOWNLOAD\")\r\n```\r\n\r\nLet me know if the problem persists.","embeddings":[-0.3312982917,-0.0927271843,0.0039606234,0.4996192157,0.217184186,0.0211864468,0.1519742608,0.3008345962,-0.0964677632,0.2880558074,0.0456490815,0.4010349512,0.1005000621,0.1572422385,-0.1340148598,0.1229326576,-0.0532352775,0.2270653993,-0.3442712426,-0.1649358571,-0.3830607235,0.1808816791,-0.2773353755,0.062877655,-0.111182943,-0.0469831042,-0.0431883782,0.4383739829,-0.1630149931,-0.2796119452,0.2136618793,-0.2695507407,0.05395687,0.6261737943,-0.0001161531,-0.09260986,0.3415843844,-0.1594275385,-0.5765936971,-0.3982520103,-0.3595910966,-0.1711031497,-0.0351360217,-0.2234364599,0.1131682247,-0.3637066483,-0.0003497917,-0.903801918,0.4014372528,0.3819840252,0.1780741513,0.2864637971,0.1049718857,0.0262464751,0.2313112319,-0.13254489,-0.1141464934,0.0244511627,0.1257959157,0.0218918305,0.0266759731,0.1473567933,0.0009369374,0.2005739808,0.1641595513,0.1365946978,0.0048945518,-0.2551273108,0.0068793972,0.2073964924,0.4921969771,-0.174404487,-0.4262576699,-0.1401749849,-0.1636835039,-0.2943094671,0.3465163112,0.4004844129,-0.0935192704,0.2845868468,-0.1182845831,0.0399829075,-0.2083890438,0.244759813,-0.1515064389,0.510620892,-0.1901015639,0.1200768948,-0.0266891364,0.0143490331,0.2320238948,-0.2181996107,-0.232309863,0.1040356681,-0.287116766,0.0572169088,0.0521775559,0.0754541531,0.3376552463,0.3926326632,0.42264691,0.0361555219,-0.0578872785,0.0168296676,0.4293915629,0.4896800816,0.1440073252,0.3416921198,0.1281625181,0.0548673198,-0.1055298969,-0.0860902295,-0.1812628955,-0.3085367084,0.199510172,0.0358867422,0.4818568826,-0.2016379535,-0.4085258842,0.0601204708,0.001118586,-0.0490917824,-0.1270450503,0.3839947879,-0.2501535714,0.1746220738,0.2353313565,0.1076785028,-0.1670911461,-0.3009111881,-0.1993411779,-0.0774161816,-0.1510998011,-0.1715002358,0.0437888093,-0.4479243755,0.3691596091,-0.0233906657,0.0399989821,-0.2815095782,0.1289481968,-0.111001052,0.2524846196,-0.0073828977,0.0215788651,0.1751914024,0.2928537726,-0.0335109532,-0.0665789843,0.2539945245,-0.3219537139,-0.3707032204,-0.2056256533,0.2098768055,-0.2052979469,-0.0307979714,-0.1397868544,-0.1158810705,0.3711585104,0.0239743162,-0.2101038098,-0.0913011581,-0.3554463387,-0.1127246544,0.2910822928,0.4761274755,-0.2243707478,-0.1116755977,-0.0586354733,0.0355621167,0.3517952263,0.0072030169,-0.1956916153,0.1678815186,-0.3378806412,-0.0389308259,0.4461311102,-0.3789560497,-0.502972126,0.2232002765,-0.1793850213,0.2654213011,0.0531434715,-0.4097696245,0.2695766985,-0.0952484384,0.1045243964,0.3907127082,-0.1543832123,0.2037483305,-0.2764239907,-0.2854374349,0.422817409,0.0796487033,-0.1850759387,0.1122501865,0.1673440933,0.118088983,0.2080085874,-0.0696033612,0.127702415,-0.1067377031,0.0522449575,-0.1227007061,0.1166329309,-0.0956515819,-0.1017507464,0.1730471253,0.0438241214,0.097248897,-0.2194466293,0.0940841362,-0.4863502383,0.0839416459,-0.4505892396,-0.2358104736,0.0910426974,0.277038157,0.0987461805,0.0791374147,-0.1225559264,0.3559840322,-0.0997196808,0.0703955963,-0.2106678337,0.0668482855,0.0505845919,0.0439951085,0.1814975441,0.1181750223,0.0629802719,-0.336386919,-0.4088573754,0.4617052972,-0.045771759,0.2274643779,-0.135981068,-0.1161875725,0.135300383,-0.28203547,0.1351788193,0.1544815451,0.1860885769,0.0585611165,0.0533635803,0.1390992701,0.3611154556,0.1057877243,0.0357286558,-0.0800552592,0.0920302048,-0.134666577,0.061086487,-0.2376108766,0.1896296144,-0.0795633271,0.3487709463,-0.083051756,0.3082584143,0.1251770258,0.5375611186,0.0301415678,0.0406418145,0.413620472,-0.1843680739,-0.1502149403,-0.1502020508,0.2045324147,0.4924976826,0.2075417191,0.1358688623,0.0057758032,0.1321398765,-0.0433419086,0.462179482,0.1222941577,0.2442747653,0.3935107589,0.0905452669,0.011096037,-0.1202894375,-0.179365322,-0.0723673999,0.1638967693,-0.3875527084,0.1813223362,-0.3384018838,0.1017300412,-0.0136564886,0.0421165489,0.0607790574,-0.2998276949,-0.1119073108,-0.0727648512,-0.1296863109,0.2440655529,-0.0383238532,0.2057429403,0.2878133655,0.0611641333,0.1041040495,-0.1987074763,-0.3906424344,0.0209228918,0.2030639797,-0.0895029828,0.1980610043,-0.2523890436,-0.0874002054,-0.0960293934,-0.0607471168,0.2478203177,0.0834883675,0.3964467347,0.1029937044,0.1237240136,0.1531072855,-0.3236405551,0.2378218472,-0.0654020831,-0.2483451366,0.1270753592,0.030228721,0.045463603,-0.0411411338,-0.5968298912,-0.2533139884,-0.5044028759,-0.0213336963,-0.0189509522,0.1721522361,0.2751091719,0.1224149242,0.0566172116,0.1341888607,0.1148343608,-0.2150580287,-0.5697579384,0.3551082313,-0.2896880507,-0.31971699,-0.0250026546,0.0350137278,0.0273049828,0.1775408089,-0.4853973985,0.2039097399,-0.199174881,-0.1216235757,-0.3832300603,0.2537437975,0.190308392,0.0784887969,-0.0645286292,-0.3062009215,-0.0401628725,0.1397818774,-0.0769658908,0.3363414407,-0.0793640614,0.3724684417,0.0283797476,0.0935261473,0.3273434639,0.005919972,0.3541775942,0.0358775258,0.2581323385,-0.109708786,-0.1072162092,0.1250615865,-0.1380739361,-0.0532418378,0.421604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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3060","title":"load_dataset('openwebtext') yields \"Compressed file ended before the end-of-stream marker was reached\"","comments":"I close this issue for the moment. Feel free to re-open it again if the problem persists.","body":"## Describe the bug\r\nWhen I try `load_dataset('openwebtext')`, I receive a \"EOFError: Compressed file ended before the end-of-stream marker was reached\" error.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('openwebtext')\r\n```\r\n\r\n## Expected results\r\nI expect the `dataset` variable to be properly constructed.\r\n\r\n## Actual results\r\n\r\n```\r\nFile \"\/home\/rschaef\/CoCoSci-Language-Distillation\/distillation_v2\/ratchet_learning\/tasks\/base.py\", line 37, in create_dataset\r\n    dataset_str,\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1117, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 637, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 704, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/rschaef\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/openwebtext\/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1\/openwebtext.py\", line 61, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(_URL)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 261, in extract\r\n    partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 197, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 316, in cached_path\r\n    output_path, force_extract=download_config.force_extract\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 40, in extract\r\n    self.extractor.extract(input_path, output_path, extractor=extractor)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 179, in extract\r\n    return extractor.extract(input_path, output_path)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 53, in extract\r\n    tar_file.extractall(output_path)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2010, in extractall\r\n    numeric_owner=numeric_owner)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2052, in extract\r\n    numeric_owner=numeric_owner)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2122, in _extract_member\r\n    self.makefile(tarinfo, targetpath)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2171, in makefile\r\n    copyfileobj(source, target, tarinfo.size, ReadError, bufsize)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 249, in copyfileobj\r\n    buf = src.read(bufsize)\r\n  File \"\/usr\/lib\/python3.6\/lzma.py\", line 200, in read\r\n    return self._buffer.read(size)\r\n  File \"\/usr\/lib\/python3.6\/_compression.py\", line 68, in readinto\r\n    data = self.read(len(byte_view))\r\n  File \"\/usr\/lib\/python3.6\/_compression.py\", line 99, in read\r\n    raise EOFError(\"Compressed file ended before the \"\r\npython-BaseException\r\nEOFError: Compressed file ended before the end-of-stream marker was reached\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial\r\n- Python version: 3.6.10\r\n- PyArrow version: 5.0.0","comment_length":17,"text":"load_dataset('openwebtext') yields \"Compressed file ended before the end-of-stream marker was reached\" \n ## Describe the bug\r\nWhen I try `load_dataset('openwebtext')`, I receive a \"EOFError: Compressed file ended before the end-of-stream marker was reached\" error.\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('openwebtext')\r\n```\r\n\r\n## Expected results\r\nI expect the `dataset` variable to be properly constructed.\r\n\r\n## Actual results\r\n\r\n```\r\nFile \"\/home\/rschaef\/CoCoSci-Language-Distillation\/distillation_v2\/ratchet_learning\/tasks\/base.py\", line 37, in create_dataset\r\n    dataset_str,\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 1117, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 637, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 704, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/rschaef\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/openwebtext\/85b3ae7051d2d72e7c5fdf6dfb462603aaa26e9ed506202bf3a24d261c6c40a1\/openwebtext.py\", line 61, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(_URL)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 284, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 261, in extract\r\n    partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 197, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 316, in cached_path\r\n    output_path, force_extract=download_config.force_extract\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 40, in extract\r\n    self.extractor.extract(input_path, output_path, extractor=extractor)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 179, in extract\r\n    return extractor.extract(input_path, output_path)\r\n  File \"\/home\/rschaef\/CoCoSci-Language-Distillation\/cocosci\/lib\/python3.6\/site-packages\/datasets\/utils\/extract.py\", line 53, in extract\r\n    tar_file.extractall(output_path)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2010, in extractall\r\n    numeric_owner=numeric_owner)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2052, in extract\r\n    numeric_owner=numeric_owner)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2122, in _extract_member\r\n    self.makefile(tarinfo, targetpath)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 2171, in makefile\r\n    copyfileobj(source, target, tarinfo.size, ReadError, bufsize)\r\n  File \"\/usr\/lib\/python3.6\/tarfile.py\", line 249, in copyfileobj\r\n    buf = src.read(bufsize)\r\n  File \"\/usr\/lib\/python3.6\/lzma.py\", line 200, in read\r\n    return self._buffer.read(size)\r\n  File \"\/usr\/lib\/python3.6\/_compression.py\", line 68, in readinto\r\n    data = self.read(len(byte_view))\r\n  File \"\/usr\/lib\/python3.6\/_compression.py\", line 99, in read\r\n    raise EOFError(\"Compressed file ended before the \"\r\npython-BaseException\r\nEOFError: Compressed file ended before the end-of-stream marker was reached\r\n```\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial\r\n- Python version: 3.6.10\r\n- PyArrow version: 5.0.0 \n I close this issue for the moment. Feel free to re-open it again if the problem persists.","embeddings":[-0.3312982917,-0.0927271843,0.0039606234,0.4996192157,0.217184186,0.0211864468,0.1519742608,0.3008345962,-0.0964677632,0.2880558074,0.0456490815,0.4010349512,0.1005000621,0.1572422385,-0.1340148598,0.1229326576,-0.0532352775,0.2270653993,-0.3442712426,-0.1649358571,-0.3830607235,0.1808816791,-0.2773353755,0.062877655,-0.111182943,-0.0469831042,-0.0431883782,0.4383739829,-0.1630149931,-0.2796119452,0.2136618793,-0.2695507407,0.05395687,0.6261737943,-0.0001161531,-0.09260986,0.3415843844,-0.1594275385,-0.5765936971,-0.3982520103,-0.3595910966,-0.1711031497,-0.0351360217,-0.2234364599,0.1131682247,-0.3637066483,-0.0003497917,-0.903801918,0.4014372528,0.3819840252,0.1780741513,0.2864637971,0.1049718857,0.0262464751,0.2313112319,-0.13254489,-0.1141464934,0.0244511627,0.1257959157,0.0218918305,0.0266759731,0.1473567933,0.0009369374,0.2005739808,0.1641595513,0.1365946978,0.0048945518,-0.2551273108,0.0068793972,0.2073964924,0.4921969771,-0.174404487,-0.4262576699,-0.1401749849,-0.1636835039,-0.2943094671,0.3465163112,0.4004844129,-0.0935192704,0.2845868468,-0.1182845831,0.0399829075,-0.2083890438,0.244759813,-0.1515064389,0.510620892,-0.1901015639,0.1200768948,-0.0266891364,0.0143490331,0.2320238948,-0.2181996107,-0.232309863,0.1040356681,-0.287116766,0.0572169088,0.0521775559,0.0754541531,0.3376552463,0.3926326632,0.42264691,0.0361555219,-0.0578872785,0.0168296676,0.4293915629,0.4896800816,0.1440073252,0.3416921198,0.1281625181,0.0548673198,-0.1055298969,-0.0860902295,-0.1812628955,-0.3085367084,0.199510172,0.0358867422,0.4818568826,-0.2016379535,-0.4085258842,0.0601204708,0.001118586,-0.0490917824,-0.1270450503,0.3839947879,-0.2501535714,0.1746220738,0.2353313565,0.1076785028,-0.1670911461,-0.3009111881,-0.1993411779,-0.0774161816,-0.1510998011,-0.1715002358,0.0437888093,-0.4479243755,0.3691596091,-0.0233906657,0.0399989821,-0.2815095782,0.1289481968,-0.111001052,0.2524846196,-0.0073828977,0.0215788651,0.1751914024,0.2928537726,-0.0335109532,-0.0665789843,0.2539945245,-0.3219537139,-0.3707032204,-0.2056256533,0.2098768055,-0.2052979469,-0.0307979714,-0.1397868544,-0.1158810705,0.3711585104,0.0239743162,-0.2101038098,-0.0913011581,-0.3554463387,-0.1127246544,0.2910822928,0.4761274755,-0.2243707478,-0.1116755977,-0.0586354733,0.0355621167,0.3517952263,0.0072030169,-0.1956916153,0.1678815186,-0.3378806412,-0.0389308259,0.4461311102,-0.3789560497,-0.502972126,0.2232002765,-0.1793850213,0.2654213011,0.0531434715,-0.4097696245,0.2695766985,-0.0952484384,0.1045243964,0.3907127082,-0.1543832123,0.2037483305,-0.2764239907,-0.2854374349,0.422817409,0.0796487033,-0.1850759387,0.1122501865,0.1673440933,0.118088983,0.2080085874,-0.0696033612,0.127702415,-0.1067377031,0.0522449575,-0.1227007061,0.1166329309,-0.0956515819,-0.1017507464,0.1730471253,0.0438241214,0.097248897,-0.2194466293,0.0940841362,-0.4863502383,0.0839416459,-0.4505892396,-0.2358104736,0.0910426974,0.277038157,0.0987461805,0.0791374147,-0.1225559264,0.3559840322,-0.0997196808,0.0703955963,-0.2106678337,0.0668482855,0.0505845919,0.0439951085,0.1814975441,0.1181750223,0.0629802719,-0.336386919,-0.4088573754,0.4617052972,-0.045771759,0.2274643779,-0.135981068,-0.1161875725,0.135300383,-0.28203547,0.1351788193,0.1544815451,0.1860885769,0.0585611165,0.0533635803,0.1390992701,0.3611154556,0.1057877243,0.0357286558,-0.0800552592,0.0920302048,-0.134666577,0.061086487,-0.2376108766,0.1896296144,-0.0795633271,0.3487709463,-0.083051756,0.3082584143,0.1251770258,0.5375611186,0.0301415678,0.0406418145,0.413620472,-0.1843680739,-0.1502149403,-0.1502020508,0.2045324147,0.4924976826,0.2075417191,0.1358688623,0.0057758032,0.1321398765,-0.0433419086,0.462179482,0.1222941577,0.2442747653,0.3935107589,0.0905452669,0.011096037,-0.1202894375,-0.179365322,-0.0723673999,0.1638967693,-0.3875527084,0.1813223362,-0.3384018838,0.1017300412,-0.0136564886,0.0421165489,0.0607790574,-0.2998276949,-0.1119073108,-0.0727648512,-0.1296863109,0.2440655529,-0.0383238532,0.2057429403,0.2878133655,0.0611641333,0.1041040495,-0.1987074763,-0.3906424344,0.0209228918,0.2030639797,-0.0895029828,0.1980610043,-0.2523890436,-0.0874002054,-0.0960293934,-0.0607471168,0.2478203177,0.0834883675,0.3964467347,0.1029937044,0.1237240136,0.1531072855,-0.3236405551,0.2378218472,-0.0654020831,-0.2483451366,0.1270753592,0.030228721,0.045463603,-0.0411411338,-0.5968298912,-0.2533139884,-0.5044028759,-0.0213336963,-0.0189509522,0.1721522361,0.2751091719,0.1224149242,0.0566172116,0.1341888607,0.1148343608,-0.2150580287,-0.5697579384,0.3551082313,-0.2896880507,-0.31971699,-0.0250026546,0.0350137278,0.0273049828,0.1775408089,-0.4853973985,0.2039097399,-0.199174881,-0.1216235757,-0.3832300603,0.2537437975,0.190308392,0.0784887969,-0.0645286292,-0.3062009215,-0.0401628725,0.1397818774,-0.0769658908,0.3363414407,-0.0793640614,0.3724684417,0.0283797476,0.0935261473,0.3273434639,0.005919972,0.3541775942,0.0358775258,0.2581323385,-0.109708786,-0.1072162092,0.1250615865,-0.1380739361,-0.0532418378,0.421604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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3058","title":"Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader.","comments":"Hi ! I think this issue is more related to the `transformers` project. Could you open an issue on https:\/\/github.com\/huggingface\/transformers ?\r\n\r\nAnyway I think the issue could be that both wikipedia and bookcorpusopen have an additional \"title\" column, contrary to wikitext which only has a \"text\" column. After calling `load_dataset`, can you try doing `dataset = dataset.remove_columns(\"title\")` ?","body":"## Describe the bug\r\nI have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`.\r\n\r\n## Steps to reproduce the bug\r\nRun the `run_mlm_no_trainer.py` and the given script on this [link](https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/pytorch\/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3.\r\n\r\n## Expected results\r\nThe data batchs are fetched from the data loader and train.\r\n\r\n## Actual results\r\nThe first time to fetch data batch occurs error.\r\n`Traceback (most recent call last):\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 705, in convert_to_tensors\r\n    tensor = as_tensor(value)\r\nValueError: too many dimensions 'str'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"src\/original_run_mlm_no_trainer.py\", line 528, in <module>\r\n    main()\r\n  File \"src\/original_run_mlm_no_trainer.py\", line 488, in main\r\n    for step, batch in enumerate(train_dataloader):\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/accelerate\/data_loader.py\", line 303, in __iter__\r\n    for batch in super().__iter__():\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 517, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 557, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 47, in fetch\r\n    return self.collate_fn(data)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/data\/data_collator.py\", line 41, in __call__\r\n    return self.torch_call(features)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/data\/data_collator.py\", line 671, in torch_call\r\n    batch = self.tokenizer.pad(examples, return_tensors=\"pt\", pad_to_multiple_of=self.pad_to_multiple_of)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2774, in pad\r\n    return BatchEncoding(batch_outputs, tensor_type=return_tensors)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 210, in __init__\r\n    self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 722, in convert_to_tensors\r\n    \"Unable to create tensor, you should probably activate truncation and\/or padding \"\r\nValueError: Unable to create tensor, you should probably activate truncation and\/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.\r\n`\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.6\r\n- PyArrow version: 5.0.0\r\n\r\n","comment_length":58,"text":"Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader. \n ## Describe the bug\r\nI have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`.\r\n\r\n## Steps to reproduce the bug\r\nRun the `run_mlm_no_trainer.py` and the given script on this [link](https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/pytorch\/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3.\r\n\r\n## Expected results\r\nThe data batchs are fetched from the data loader and train.\r\n\r\n## Actual results\r\nThe first time to fetch data batch occurs error.\r\n`Traceback (most recent call last):\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 705, in convert_to_tensors\r\n    tensor = as_tensor(value)\r\nValueError: too many dimensions 'str'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"src\/original_run_mlm_no_trainer.py\", line 528, in <module>\r\n    main()\r\n  File \"src\/original_run_mlm_no_trainer.py\", line 488, in main\r\n    for step, batch in enumerate(train_dataloader):\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/accelerate\/data_loader.py\", line 303, in __iter__\r\n    for batch in super().__iter__():\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 517, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 557, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 47, in fetch\r\n    return self.collate_fn(data)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/data\/data_collator.py\", line 41, in __call__\r\n    return self.torch_call(features)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/data\/data_collator.py\", line 671, in torch_call\r\n    batch = self.tokenizer.pad(examples, return_tensors=\"pt\", pad_to_multiple_of=self.pad_to_multiple_of)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2774, in pad\r\n    return BatchEncoding(batch_outputs, tensor_type=return_tensors)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 210, in __init__\r\n    self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 722, in convert_to_tensors\r\n    \"Unable to create tensor, you should probably activate truncation and\/or padding \"\r\nValueError: Unable to create tensor, you should probably activate truncation and\/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.\r\n`\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.6\r\n- PyArrow version: 5.0.0\r\n\r\n \n Hi ! I think this issue is more related to the `transformers` project. Could you open an issue on https:\/\/github.com\/huggingface\/transformers ?\r\n\r\nAnyway I think the issue could be that both wikipedia and bookcorpusopen have an additional \"title\" column, contrary to wikitext which only has a \"text\" column. After calling `load_dataset`, can you try doing `dataset = dataset.remove_columns(\"title\")` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3058","title":"Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader.","comments":"Removing the \"title\" column works! Thanks for your advice.\r\n\r\nMaybe I should still create an issue to `transformers' to mark this solution?","body":"## Describe the bug\r\nI have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`.\r\n\r\n## Steps to reproduce the bug\r\nRun the `run_mlm_no_trainer.py` and the given script on this [link](https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/pytorch\/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3.\r\n\r\n## Expected results\r\nThe data batchs are fetched from the data loader and train.\r\n\r\n## Actual results\r\nThe first time to fetch data batch occurs error.\r\n`Traceback (most recent call last):\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 705, in convert_to_tensors\r\n    tensor = as_tensor(value)\r\nValueError: too many dimensions 'str'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"src\/original_run_mlm_no_trainer.py\", line 528, in <module>\r\n    main()\r\n  File \"src\/original_run_mlm_no_trainer.py\", line 488, in main\r\n    for step, batch in enumerate(train_dataloader):\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/accelerate\/data_loader.py\", line 303, in __iter__\r\n    for batch in super().__iter__():\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 517, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 557, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 47, in fetch\r\n    return self.collate_fn(data)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/data\/data_collator.py\", line 41, in __call__\r\n    return self.torch_call(features)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/data\/data_collator.py\", line 671, in torch_call\r\n    batch = self.tokenizer.pad(examples, return_tensors=\"pt\", pad_to_multiple_of=self.pad_to_multiple_of)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2774, in pad\r\n    return BatchEncoding(batch_outputs, tensor_type=return_tensors)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 210, in __init__\r\n    self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 722, in convert_to_tensors\r\n    \"Unable to create tensor, you should probably activate truncation and\/or padding \"\r\nValueError: Unable to create tensor, you should probably activate truncation and\/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.\r\n`\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.6\r\n- PyArrow version: 5.0.0\r\n\r\n","comment_length":22,"text":"Dataset wikipedia and Bookcorpusopen cannot be fetched from dataloader. \n ## Describe the bug\r\nI have used the previous version of `transformers` and `datasets`. The dataset `wikipedia` can be successfully used. Recently, I upgrade them to the newest version and find it raises errors. I also tried other datasets. The `wikitext` works and the `bookcorpusopen` raises the same errors as `wikipedia`.\r\n\r\n## Steps to reproduce the bug\r\nRun the `run_mlm_no_trainer.py` and the given script on this [link](https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/pytorch\/language-modeling). Change the dataset from wikitext to wikipedia or bookcorpusopen. BTW, the library transformers is of version 4.11.3.\r\n\r\n## Expected results\r\nThe data batchs are fetched from the data loader and train.\r\n\r\n## Actual results\r\nThe first time to fetch data batch occurs error.\r\n`Traceback (most recent call last):\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 705, in convert_to_tensors\r\n    tensor = as_tensor(value)\r\nValueError: too many dimensions 'str'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"src\/original_run_mlm_no_trainer.py\", line 528, in <module>\r\n    main()\r\n  File \"src\/original_run_mlm_no_trainer.py\", line 488, in main\r\n    for step, batch in enumerate(train_dataloader):\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/accelerate\/data_loader.py\", line 303, in __iter__\r\n    for batch in super().__iter__():\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 517, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 557, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 47, in fetch\r\n    return self.collate_fn(data)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/data\/data_collator.py\", line 41, in __call__\r\n    return self.torch_call(features)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/data\/data_collator.py\", line 671, in torch_call\r\n    batch = self.tokenizer.pad(examples, return_tensors=\"pt\", pad_to_multiple_of=self.pad_to_multiple_of)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2774, in pad\r\n    return BatchEncoding(batch_outputs, tensor_type=return_tensors)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 210, in __init__\r\n    self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)\r\n  File \"\/home\/zyli\/anaconda3\/envs\/LatestStacking\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 722, in convert_to_tensors\r\n    \"Unable to create tensor, you should probably activate truncation and\/or padding \"\r\nValueError: Unable to create tensor, you should probably activate truncation and\/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.\r\n`\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.8.0-59-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.6\r\n- PyArrow version: 5.0.0\r\n\r\n \n Removing the \"title\" column works! Thanks for your advice.\r\n\r\nMaybe I should still create an issue to `transformers' to mark this solution?","embeddings":[-0.1171705127,-0.0888662115,0.028719984,0.5902852416,0.1981569827,0.0083725248,0.4193758965,0.2448292971,0.0080311252,0.0493947454,-0.261428237,0.0099563533,0.096120134,-0.1403682679,-0.1862075329,-0.5285600424,0.0875841826,0.1113324687,-0.4384570718,-0.059108153,-0.2347154766,0.2510098219,-0.2473161966,0.1419709027,-0.4807752073,-0.1802387238,-0.038659215,-0.0276199505,-0.0100243632,-0.2176781148,0.4571982324,-0.0493428819,0.4165698588,0.6459467411,-0.0001281459,0.1455685943,0.3086574078,-0.0806097537,-0.4064445198,-0.3418373168,0.0844811499,-0.3148394525,0.3177813292,-0.291639924,-0.1006432921,-0.1809128672,-0.1234996542,-0.1434838176,0.4752549827,0.4050860107,0.0876131952,0.489905566,0.2896855772,-0.0910833478,0.4336786568,0.2441261411,-0.1686101854,0.1206395403,0.2548327744,-0.0317675322,-0.0894666463,0.2407542318,-0.0562882833,0.2046242803,0.2193748504,0.1903476417,0.1454553604,-0.49637115,0.0623132102,0.282484442,0.6721055508,-0.2857173085,-0.5109415054,-0.2408045083,-0.1129163653,0.0782910287,0.3663063943,0.1060311869,-0.0971273035,-0.0947541445,-0.1954414099,-0.2011725754,-0.2782560587,0.3333951533,-0.2293273062,0.6383188963,0.0342503227,0.2232230902,0.1940723509,-0.0854955986,0.27222839,-0.141229555,0.2133427113,0.3040318191,-0.2496562153,-0.0634408891,-0.1190442964,-0.4535715282,-0.224459514,-0.1983699948,-0.0865397751,-0.2103539407,-0.0466721505,0.077692911,0.3683885634,0.3098663688,-0.0909319893,0.5451512933,0.378529042,0.3910867274,-0.0818721652,-0.015421805,0.0179256443,-0.0151242241,-0.1180352867,0.1055031121,0.3192512989,-0.1981033534,0.0307710897,-0.031447731,-0.1382980645,-0.2237645388,0.1434346884,0.2013051212,-0.2833177447,0.4591163099,0.2934342325,0.2424464524,-0.1315660924,-0.1539353728,-0.0161549281,0.2229108214,-0.22530891,0.1299220622,0.4849185646,-0.0583618917,0.2079950124,0.1109641194,0.0612233914,-0.2180410624,-0.0050380449,-0.4270085692,-0.092646338,0.209138304,-0.0764837861,0.4124706089,0.0667618439,0.0055022221,-0.132686466,0.1776529551,-0.5252428651,-0.0866228491,-0.1289568543,0.0689049438,-0.1025051326,0.1387518197,-0.2886662185,0.1624601334,0.43787691,-0.2315801829,0.0823424086,-0.3417636752,-0.0098681934,-0.0583569668,0.1542676687,0.2835454941,-0.2258250862,-0.2675381303,-0.0562045984,-0.0233822558,0.2037673295,0.3895207644,-0.4370259941,0.2606168985,-0.1095651388,0.0259380601,0.3239626288,-0.3485063612,-0.5067795515,0.0822412819,0.1392828375,0.1930871308,-0.0024984479,0.0781059414,0.2480258644,-0.0567223802,-0.0810232311,0.4235652089,0.1310390234,0.1502450556,-0.2206773311,-0.3404788375,0.4318116605,0.3021400869,0.5274093151,0.050625816,0.0029891061,0.5645059347,0.1752803177,-0.2198449671,0.1318730712,0.3785246909,-0.0768740773,-0.0214927606,0.213898167,-0.1895807087,-0.5815826654,0.2631791234,-0.4444324076,0.327044636,-0.0658803806,0.0346552879,-0.0846822113,0.0045284196,-0.1166417077,-0.1295528859,0.0068399264,0.0296967756,0.0795701295,0.127587527,0.1058196574,-0.0091674756,-0.0868385583,0.1810750067,-0.4302278757,0.6699711084,-0.0863272101,-0.1038033515,-0.2037702501,0.0931572616,0.2730454504,-0.1878029853,-0.1515579969,0.0965949073,0.0587591305,0.0082263919,-0.2379564345,0.1389171481,0.4958928227,-0.3601185381,0.2688722312,0.3903026581,0.1611915976,-0.0840756893,-0.0987916142,0.0845496207,0.0638569817,0.3242057264,0.1774785221,0.0445013829,0.1756588668,-0.0550794601,-0.0217931755,-0.1192778423,0.0884167925,-0.106205076,0.0800939053,-0.0524799414,-0.0873327777,-0.1037753448,0.4015245736,0.0314117856,0.2027187496,0.2310858667,-0.4405787885,-0.0669260621,-0.0037616908,0.0802354515,0.1953769773,-0.0142135732,0.0023399587,-0.1138558835,-0.0544700921,-0.0266034268,0.2037663013,0.4041627049,0.2687699795,0.1880783886,0.2951119244,0.0657678992,-0.1009708643,-0.2959223092,0.0578403175,0.3295824826,-0.2637157142,0.1906746924,-0.1156069785,-0.17973952,-0.3497118056,-0.2760043442,-0.4515840709,-0.2150101364,-0.2188574374,0.4294582903,-0.0150341187,0.1557552367,0.0451589711,-0.0121267168,0.049292177,0.0540843122,-0.1029282659,0.000299115,-0.2656458914,-0.0639111996,0.1351941824,-0.2079045624,-0.092497237,-0.0064727101,-0.0969610214,-0.4026873112,-0.4037209153,0.2633169591,0.0498793982,0.3535006344,-0.0341055617,0.2956372797,-0.0522303283,-0.4987383485,0.2341709435,-0.2217988074,0.0341940559,0.2018872648,-0.0928441808,0.1023057103,-0.0496037938,-0.5121685266,-0.4480066895,-0.3077338934,-0.0585260987,0.1117494777,0.1526694,0.4017450809,0.0558004603,0.0782742724,0.2752707601,0.1532314271,-0.0719691142,0.0218491182,0.4701942503,-0.0880750939,-0.364893347,-0.1620924026,-0.1677841246,0.0799676403,0.2666200101,-0.5871055722,-0.0607878417,-0.24962008,0.09433873,-0.0796474218,0.1099923998,0.1963579357,0.0037184553,0.1361057162,-0.0127291027,-0.027274346,0.0776349232,0.1489040405,0.3079800904,0.0135953194,0.3438874185,-0.1361531913,0.9339314103,0.1448736936,-0.1148532405,0.2808001339,0.2016262263,0.1213692054,0.0132454066,-0.5149044394,-0.1874068975,-0.2477413267,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3057","title":"Error in per class precision computation ","comments":"Hi @tidhamecha2, thanks for reporting.\r\n\r\nIndeed, we fixed this issue just one week ago: #3008\r\n\r\nThe fix will be included in our next version release.\r\n\r\nIn the meantime, you can incorporate the fix by installing `datasets` from the master branch:\r\n```\r\npip install -U git+ssh:\/\/git@github.com\/huggingface\/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```\r\npip install -U git+https:\/\/github.com\/huggingface\/datasets.git@master#egg=datasets\r\n```","body":"## Describe the bug\r\nWhen trying to get the per class precision values by providing `average=None`, following error is thrown `ValueError: can only convert an array of size 1 to a Python scalar`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\nprecision_metric = load_metric(\"precision\")\r\npredictions = [0, 2, 1, 0, 0, 1]\r\nreferences = [0, 1, 2, 0, 1, 2]\r\nresults = precision_metric.compute(predictions=predictions, references=references, average=None)\r\n```\r\n\r\n## Expected results\r\n`  {'precision': array([0.66666667, 0.        , 0.        ])}`\r\nas per https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/precision\/precision.py\r\n\r\n## Actual results\r\n```\r\n   output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/precision\/94709a71c6fe37171ef49d3466fec24dee9a79846c9f176dff66a649e9811690\/precision.py\", line 110, in _compute\r\n    sample_weight=sample_weight,\r\nValueError: can only convert an array of size 1 to a Python scalar\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: linux\r\n- Python version: 3.6.9\r\n- PyArrow version: 5.0.0\r\n","comment_length":53,"text":"Error in per class precision computation  \n ## Describe the bug\r\nWhen trying to get the per class precision values by providing `average=None`, following error is thrown `ValueError: can only convert an array of size 1 to a Python scalar`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\nprecision_metric = load_metric(\"precision\")\r\npredictions = [0, 2, 1, 0, 0, 1]\r\nreferences = [0, 1, 2, 0, 1, 2]\r\nresults = precision_metric.compute(predictions=predictions, references=references, average=None)\r\n```\r\n\r\n## Expected results\r\n`  {'precision': array([0.66666667, 0.        , 0.        ])}`\r\nas per https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/precision\/precision.py\r\n\r\n## Actual results\r\n```\r\n   output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/precision\/94709a71c6fe37171ef49d3466fec24dee9a79846c9f176dff66a649e9811690\/precision.py\", line 110, in _compute\r\n    sample_weight=sample_weight,\r\nValueError: can only convert an array of size 1 to a Python scalar\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: linux\r\n- Python version: 3.6.9\r\n- PyArrow version: 5.0.0\r\n \n Hi @tidhamecha2, thanks for reporting.\r\n\r\nIndeed, we fixed this issue just one week ago: #3008\r\n\r\nThe fix will be included in our next version release.\r\n\r\nIn the meantime, you can incorporate the fix by installing `datasets` from the master branch:\r\n```\r\npip install -U git+ssh:\/\/git@github.com\/huggingface\/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```\r\npip install -U git+https:\/\/github.com\/huggingface\/datasets.git@master#egg=datasets\r\n```","embeddings":[-0.1642783582,-0.4141964614,-0.0621301793,0.3582137525,0.541162312,0.1221356243,0.0809526071,0.1405230016,-0.0493662544,0.6348304749,-0.0748262405,0.1664378494,-0.0171342753,0.1542898417,-0.1672086418,-0.1889112741,-0.076621756,0.2445581853,-0.2314058244,-0.0811217427,-0.2428577542,0.1677612811,-0.2075735778,-0.1134949327,-0.034236744,-0.0433458649,0.0320153013,-0.0701471567,-0.1811205596,-0.2452348918,0.585454762,-0.1199147031,-0.0246815179,0.5421234369,-0.000109252,0.0344296247,0.1995838135,-0.0679611266,0.0439431109,-0.012563251,0.1732243299,-0.3171208799,0.0496170148,-0.3605561554,-0.0957354382,0.1044576913,-0.1465981156,-0.4334391057,0.1332168132,0.1467652768,0.2312608957,0.3048004806,0.1135815457,-0.1844075769,-0.1475373954,-0.0382655822,-0.058189746,0.2983249426,-0.1915526539,0.2531233728,-0.1129280776,0.2372668087,-0.0117657026,0.0728569478,0.3472217023,0.0826094598,0.1160903871,-0.2710084021,-0.0767336935,-0.0471142866,0.0637195855,-0.2755132616,-0.4951063693,-0.2424662709,0.1073005572,-0.478644371,-0.1084214747,0.2575415075,-0.206534341,-0.0887252986,-0.4264825583,0.1642452925,-0.4307794273,-0.0183306895,0.0105908485,0.0735078901,-0.1967831552,0.1328269988,0.1659915745,-0.0923231766,-0.366584897,0.1136856303,0.3257921934,0.2553650141,-0.4922661781,-0.1202569678,-0.0069112093,-0.0941244289,0.1079658419,-0.1188328713,0.2723199725,0.2519577742,0.1970919967,0.111839734,-0.0486192927,0.5292060971,0.1057306603,0.1572881937,0.2328597307,0.363365978,0.0790527314,0.1107819155,0.3502543867,-0.147895515,0.0180973839,0.050395634,0.0839345306,-0.1485374272,-0.2498825788,0.1619853228,-0.114643164,0.1252571791,0.0074048932,0.1748040617,0.140748322,0.3416101336,0.2224615067,0.1269019693,-0.134176746,-0.3123119771,-0.2685231268,0.0823626444,-0.1030989513,0.1653857231,0.1571233869,-0.0920844823,0.1091013774,0.2144410163,0.2966879606,-0.3067883551,-0.110645093,-0.0527689606,-0.159620896,0.2275317907,-0.1653754711,0.0245515071,0.2818194032,-0.1193355173,0.0505630076,0.1132711545,-0.2201224416,-0.1010886729,0.2045732588,0.2235243022,-0.1137974635,0.0192237794,-0.3351350427,0.3108475506,0.2783310413,0.0677505583,-0.1220610812,-0.206777513,-0.4401348233,-0.3280390501,0.3332184553,-0.0279469509,-0.1649877578,-0.1342420876,0.2150936276,-0.1916710436,0.1266342849,0.2586930692,0.078428708,0.1456570178,-0.1856607646,0.218973726,0.0652346984,-0.323242873,-0.3575804532,-0.0842360258,0.0535265915,-0.1628313363,0.1398750842,0.0354869552,0.2864261568,0.10632658,-0.0179317463,-0.0202652104,0.0516321883,0.0028288995,-0.460021615,-0.1886983663,0.1565812677,0.1908868104,0.1541031152,0.1737861335,-0.0167413373,-0.1339861304,0.1712926328,-0.4382627308,0.0141523425,0.3480501473,0.175099805,-0.3897571862,0.1666066796,-0.4405604601,-0.2117417157,0.3000274301,-0.1076468304,0.181844607,0.1039116308,-0.0475897938,-0.3701078296,-0.1257906854,-0.0862575173,0.0743678287,0.2409291267,-0.2931118011,0.3394930959,-0.0490062274,-0.0258724596,0.0741943717,-0.3482827544,0.1723859757,-0.330614388,0.0988354161,0.000280097,-0.3719058931,0.0021474776,0.1552318633,0.2212848514,-0.0484935381,-0.2107051164,0.36287117,0.0959679037,0.0079476349,0.0661718994,0.2228797376,0.0445763245,-0.1284398139,0.0397050045,0.1112956032,0.2196475267,-0.0409307778,-0.1681111008,0.809614718,0.1643650681,0.0814210847,-0.0043152394,0.1086638123,0.1563540697,0.0717949793,0.0073268483,-0.1458165646,0.2394854426,-0.3244332373,0.3670498729,-0.1767276376,-0.2085167021,-0.1059382111,0.1006933451,-0.1409001648,0.0489815511,0.0957311913,0.1083060354,0.118331857,-0.1365237236,0.0232546572,0.4859464467,0.097036548,-0.1279264838,0.2964468002,-0.0409602597,0.0279606339,0.0629497841,0.0925423577,0.1806247532,0.1303048581,0.0704176575,-0.0265598446,-0.4172140658,0.1046436504,-0.2550738752,0.1777276695,-0.3576974273,0.0332620107,-0.0505348034,0.3547062576,-0.2645374537,-0.0980491117,-0.3411876559,-0.3228208423,-0.1120152399,0.0200069752,-0.0697506964,-0.0079260832,0.0052729119,0.331538409,0.1463102698,0.2327750027,-0.2720112801,0.1737282127,-0.1649525464,-0.0070555359,0.0366067924,-0.1410153061,0.2186914682,0.0287690442,0.0895845965,-0.0630352274,-0.3871897161,-0.29359743,-0.2791937292,0.330284059,0.2702201903,-0.1042711511,-0.2102464288,0.0762788951,0.413654536,-0.3010396957,-0.157901153,0.3228687048,-0.0900866985,0.042228803,-0.3177701235,-0.1696226597,-0.2323242277,-0.0392226838,0.0643530861,0.1191188097,0.1743204594,-0.229236275,0.0867128596,0.2089665234,-0.0061571607,0.0451914072,-0.3585213721,-0.1896906942,0.1969542801,-0.0836359113,-0.4709044099,-0.0525663011,-0.0546275079,0.4495645463,-0.1232962534,-0.2685032189,-0.2677353024,-0.1713537872,0.1907431781,-0.4779602289,0.2603838742,0.1594991535,-0.1738595068,-0.0435348824,-0.2163251042,-0.2287625968,0.0640429184,-0.4279389977,-0.1116902307,-0.2180623412,0.3225432932,0.2558867037,0.5785421133,0.4294579029,-0.1259106994,0.0114290947,0.0859187022,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3052","title":"load_dataset cannot download the data and hangs on forever if cache dir specified","comments":"Issue was environment inconsistency, updating packages did the trick\r\n\r\n`conda install -c huggingface -c conda-forge datasets`\r\n\r\n> Collecting package metadata (current_repodata.json): done\r\n> Solving environment: |\r\n> The environment is inconsistent, please check the package plan carefully\r\n> The following packages are causing the inconsistency:\r\n> \r\n>   - conda-forge\/noarch::datasets==1.12.1=pyhd8ed1ab_1\r\n>   - conda-forge\/win-64::multiprocess==0.70.12.2=py38h294d835_0\r\n> done\r\n> \r\n> Package Plan\r\n> \r\n>   environment location: C:\\xxx\\anaconda3\\envs\\UnBias-94-1\r\n> \r\n>   added \/ updated specs:\r\n>     - datasets\r\n> \r\n> \r\n> The following NEW packages will be INSTALLED:\r\n> \r\n>   dill               conda-forge\/noarch::dill-0.3.4-pyhd8ed1ab_0\r\n> \r\n> The following packages will be UPDATED:\r\n> \r\n>   ca-certificates    pkgs\/main::ca-certificates-2021.9.30-~ --> conda-forge::ca-certificates-2021.10.8-h5b45459_0\r\n>   certifi            pkgs\/main::certifi-2021.5.30-py38haa9~ --> conda-forge::certifi-2021.10.8-py38haa244fe_0\r\n> \r\n> The following packages will be SUPERSEDED by a higher-priority channel:\r\n> ","body":"## Describe the bug\r\nAfter updating datasets, a code that ran just fine for ages began to fail. Specifying _datasets.load_dataset_'s _cache_dir_ optional argument on Windows 10 machine results in data download to hang on forever. Same call without cache_dir works just fine. Surprisingly exact same code just runs perfectly fine on Linux docker instance running in cloud.\r\n\r\nUnfortunately I updated Windows also at the same time and I can't remember which version of datasets was running in my conda environment prior to the update otherwise I would have tried both to check this out.  :(\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\ncache_dir = 'c:\/data\/datasets'\r\ndataset = load_dataset('wikipedia', '20200501.en', split='train',cache_dir=cache_dir) \r\n```\r\nNote that exact same code without specifying _cache_dir_ argument works perfectly fine.\r\n```\r\ncache_dir = 'c:\/data\/datasets'\r\ndataset = load_dataset('wikipedia', '20200501.en', split='train') \r\n```\r\n\r\n## Expected results\r\nDownloads the dataset and cache is handled in the _cache_dir_ directory\r\n## Actual results\r\nData download keeps hanging on forever, **NO TRACEBACK**!\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.8.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":118,"text":"load_dataset cannot download the data and hangs on forever if cache dir specified \n ## Describe the bug\r\nAfter updating datasets, a code that ran just fine for ages began to fail. Specifying _datasets.load_dataset_'s _cache_dir_ optional argument on Windows 10 machine results in data download to hang on forever. Same call without cache_dir works just fine. Surprisingly exact same code just runs perfectly fine on Linux docker instance running in cloud.\r\n\r\nUnfortunately I updated Windows also at the same time and I can't remember which version of datasets was running in my conda environment prior to the update otherwise I would have tried both to check this out.  :(\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\n```\r\ncache_dir = 'c:\/data\/datasets'\r\ndataset = load_dataset('wikipedia', '20200501.en', split='train',cache_dir=cache_dir) \r\n```\r\nNote that exact same code without specifying _cache_dir_ argument works perfectly fine.\r\n```\r\ncache_dir = 'c:\/data\/datasets'\r\ndataset = load_dataset('wikipedia', '20200501.en', split='train') \r\n```\r\n\r\n## Expected results\r\nDownloads the dataset and cache is handled in the _cache_dir_ directory\r\n## Actual results\r\nData download keeps hanging on forever, **NO TRACEBACK**!\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.8.11\r\n- PyArrow version: 3.0.0\r\n \n Issue was environment inconsistency, updating packages did the trick\r\n\r\n`conda install -c huggingface -c conda-forge datasets`\r\n\r\n> Collecting package metadata (current_repodata.json): done\r\n> Solving environment: |\r\n> The environment is inconsistent, please check the package plan carefully\r\n> The following packages are causing the inconsistency:\r\n> \r\n>   - conda-forge\/noarch::datasets==1.12.1=pyhd8ed1ab_1\r\n>   - conda-forge\/win-64::multiprocess==0.70.12.2=py38h294d835_0\r\n> done\r\n> \r\n> Package Plan\r\n> \r\n>   environment location: C:\\xxx\\anaconda3\\envs\\UnBias-94-1\r\n> \r\n>   added \/ updated specs:\r\n>     - datasets\r\n> \r\n> \r\n> The following NEW packages will be INSTALLED:\r\n> \r\n>   dill               conda-forge\/noarch::dill-0.3.4-pyhd8ed1ab_0\r\n> \r\n> The following packages will be UPDATED:\r\n> \r\n>   ca-certificates    pkgs\/main::ca-certificates-2021.9.30-~ --> conda-forge::ca-certificates-2021.10.8-h5b45459_0\r\n>   certifi            pkgs\/main::certifi-2021.5.30-py38haa9~ --> conda-forge::certifi-2021.10.8-py38haa244fe_0\r\n> \r\n> The following packages will be SUPERSEDED by a higher-priority channel:\r\n> 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3051","title":"Non-Matching Checksum Error with crd3 dataset","comments":"I got the same error for another dataset (`multi_woz_v22`):\r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json']\r\n```","body":"## Describe the bug\r\nWhen I try loading the crd3 dataset (https:\/\/huggingface.co\/datasets\/crd3), an error is thrown.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('crd3', split='train')\r\n```\r\n\r\n## Expected results\r\nI expect no error to be thrown.\r\n\r\n\r\n## Actual results\r\nA non-matching checksum error is thrown.\r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/RevanthRameshkumar\/CRD3\/archive\/master.zip']\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial\r\n- Python version: 3.6.10\r\n- PyArrow version: 5.0.0\r\n\r\n","comment_length":21,"text":"Non-Matching Checksum Error with crd3 dataset \n ## Describe the bug\r\nWhen I try loading the crd3 dataset (https:\/\/huggingface.co\/datasets\/crd3), an error is thrown.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('crd3', split='train')\r\n```\r\n\r\n## Expected results\r\nI expect no error to be thrown.\r\n\r\n\r\n## Actual results\r\nA non-matching checksum error is thrown.\r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/RevanthRameshkumar\/CRD3\/archive\/master.zip']\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial\r\n- Python version: 3.6.10\r\n- PyArrow version: 5.0.0\r\n\r\n \n I got the same error for another dataset (`multi_woz_v22`):\r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json']\r\n```","embeddings":[0.0334027447,-0.0302849021,0.0008103768,0.2788744867,0.0175490454,-0.0104441941,0.2656279206,0.3295178413,-0.0376745462,-0.0168872196,0.0127356406,0.1477052569,0.1147076041,0.0427600704,-0.1140765473,-0.0273570064,0.3024690449,0.0541090593,0.1257389337,-0.0328547098,-0.1098990142,0.1389318109,-0.2276907265,0.0113812573,-0.0697474927,0.2781861424,0.0035582439,0.3464343846,0.0303115211,-0.0911191329,0.5705821514,0.1612658948,0.1096183658,0.6796619296,-0.0001145116,-0.0514412224,0.3022954166,-0.1902944148,-0.1386958957,-0.3195831478,-0.4316028953,-0.2920105755,-0.0718898028,-0.224814117,0.0259777922,0.2991978824,-0.2701681256,-0.015815204,-0.0014768416,-0.1409104615,0.2136478722,0.6153893471,0.1170377582,0.1221040487,0.0792808086,0.1203393713,0.0231934693,0.6040298343,0.3690384328,-0.0561720803,-0.0219493173,0.1442451924,-0.3300770521,0.1867080629,0.1652891785,-0.0224611834,-0.2784799933,0.0593476519,-0.0232624486,0.6013781428,0.0544583909,-0.2938160896,-0.3598979712,-0.117025584,-0.0946374163,-0.4865979254,0.3369043767,0.1865321398,-0.1341735572,-0.1014839336,-0.520621419,0.1967919171,0.0010658881,0.1569826603,0.0405208282,0.0763623863,0.0658672303,-0.0411715098,0.1133173406,-0.2226431817,0.1944141537,-0.2851200998,-0.2304153442,0.0046868189,-0.6093125343,-0.2500115633,-0.2335205674,0.5036349893,0.4081351459,0.3797470629,-0.0246288683,0.1881062686,0.0500273257,0.0253397319,0.1391056925,0.1356039345,-0.0616216995,0.3133065701,0.18283768,0.3394802809,-0.0898902863,0.1667277813,-0.0264582839,-0.1692324132,0.6086750031,0.0562597997,0.1235864908,-0.3384875655,-0.0623678155,0.2706259191,0.0637870282,-0.0351836346,0.0791989937,0.2678135931,-0.2224630564,0.1412708312,-0.1128853485,0.323813051,-0.257812947,0.0398011878,-0.1379939467,-0.1428777426,-0.110193029,-0.043379005,0.1768587232,-0.1917888224,0.3768378496,-0.1522655338,0.407471776,-0.010665576,0.0010699329,0.0462024398,-0.0042666183,0.2696405351,0.0373193026,0.1827977598,0.1469234973,0.1544154733,0.0166845638,0.0283675082,-0.2679578364,-0.2680885792,-0.3479526043,0.1990946382,-0.7517385483,-0.105678007,-0.0398925096,-0.4248099327,0.2718976736,-0.2394351512,0.016400164,-0.31643942,-0.2812264264,-0.2570580244,0.146551773,0.1059917063,0.0172466505,0.258803457,-0.2220121622,-0.1420883834,0.2174441516,0.2261406481,-0.0803290531,-0.2999190092,-0.2428376228,0.0969996676,0.0712970495,-0.5103929043,-0.5260890126,0.0877703205,0.0451156199,0.346947521,-0.0794484988,0.1229037642,-0.0831523538,-0.2634933889,0.2411931604,0.0855117813,0.0120581025,0.2704756558,-0.0931820571,-0.1950541288,0.2055423409,0.1910841465,-0.0510346629,-0.1572391689,0.0612891167,-0.1809057891,0.4714629352,-0.1275043041,-0.1305404902,0.0314627849,0.5298314095,-0.0231175516,0.120679222,-0.1474152803,-0.4394855201,0.4606895447,-0.1143050492,-0.1152104065,-0.047123149,0.0850278512,-0.230835408,-0.14280954,-0.4497535229,0.2097351104,0.1144584119,0.0779342279,0.0232081506,-0.2181335092,-0.2973220646,-0.0552935675,-0.3006828725,0.1130245924,-0.2625952065,0.2755222619,-0.1537062377,-0.1574437767,0.0881763324,0.2783002555,0.0660187528,-0.0398223735,-0.1973527521,0.440356344,0.1893935651,0.1783676594,-0.2212109417,0.174372986,-0.0205574185,-0.5043703914,-0.1972319335,0.2222740203,0.1445057988,-0.0836563185,0.1542642713,0.3256594539,0.0469937846,0.2012369931,-0.1187429428,-0.0244210549,0.0809818804,-0.3183220625,-0.1043487862,-0.314383477,0.3306580186,0.0669425353,-0.0498435833,0.3644227982,-0.1433967054,-0.2364378273,0.2562755942,-0.1630935073,0.0166427307,-0.026204586,0.0526052117,0.0161143877,-0.136538744,0.5215741396,0.3676496148,0.0759982094,-0.0556291379,0.2547418475,-0.1684829146,0.0051104827,-0.1327620596,0.030811673,0.4413366616,0.574624896,0.0918349028,-0.0614508204,-0.1359842718,-0.1500114799,0.0318570659,0.0972656906,-0.4689367712,-0.0825086012,-0.3472769856,-0.0520653799,-0.2333784997,-0.4814291,-0.4252476096,-0.1412815899,-0.2088073045,0.2533640862,-0.1031531543,0.1241185293,-0.5149744153,-0.2665119469,-0.0047690147,-0.2358535826,0.041226089,-0.1164910197,-0.0986882672,0.0302726589,0.358402133,-0.123590976,0.3141858578,-0.3931678236,-0.127362594,-0.2688835263,-0.2275175452,0.0751745552,0.0303657614,0.2022959292,0.3887101412,0.0176298656,0.177481845,-0.3341087997,0.2367105782,-0.0342042744,-0.4332152009,0.4542856216,-0.0338085964,-0.0439042598,0.1590162814,0.1034131423,0.0255463775,-0.1891103387,-0.2271229327,-0.1617025137,0.1750960797,0.3050272167,-0.0588234849,0.1737568974,-0.2385280579,0.2457228303,-0.0914774537,-0.3930357695,0.4473374784,0.1348597407,-0.389441371,-0.0014067368,-0.138111189,0.2023784369,0.0179263689,-0.2059975863,-0.2789627612,-0.3191369176,-0.2040819824,0.2696875334,-0.1908815503,0.1036993563,-0.1405059993,0.0116645629,-0.3016442657,-0.2897188663,0.0866188258,0.1963247955,0.5354513526,-0.0306565408,0.1845716834,-0.000315347,0.4918311834,0.5818005204,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3051","title":"Non-Matching Checksum Error with crd3 dataset","comments":"I'm seeing the same issue as @RylanSchaeffer:\r\nPython 3.7.11, macOs 11.4\r\ndatasets==1.14.0\r\n\r\nfails on:\r\n```python\r\ndataset = datasets.load_dataset(\"multi_woz_v22\")\r\n```","body":"## Describe the bug\r\nWhen I try loading the crd3 dataset (https:\/\/huggingface.co\/datasets\/crd3), an error is thrown.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('crd3', split='train')\r\n```\r\n\r\n## Expected results\r\nI expect no error to be thrown.\r\n\r\n\r\n## Actual results\r\nA non-matching checksum error is thrown.\r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/RevanthRameshkumar\/CRD3\/archive\/master.zip']\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial\r\n- Python version: 3.6.10\r\n- PyArrow version: 5.0.0\r\n\r\n","comment_length":19,"text":"Non-Matching Checksum Error with crd3 dataset \n ## Describe the bug\r\nWhen I try loading the crd3 dataset (https:\/\/huggingface.co\/datasets\/crd3), an error is thrown.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('crd3', split='train')\r\n```\r\n\r\n## Expected results\r\nI expect no error to be thrown.\r\n\r\n\r\n## Actual results\r\nA non-matching checksum error is thrown.\r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/RevanthRameshkumar\/CRD3\/archive\/master.zip']\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.4.0-173-generic-x86_64-with-Ubuntu-16.04-xenial\r\n- Python version: 3.6.10\r\n- PyArrow version: 5.0.0\r\n\r\n \n I'm seeing the same issue as @RylanSchaeffer:\r\nPython 3.7.11, macOs 11.4\r\ndatasets==1.14.0\r\n\r\nfails on:\r\n```python\r\ndataset = datasets.load_dataset(\"multi_woz_v22\")\r\n```","embeddings":[-0.0050891992,-0.0740974769,0.0115769189,0.2142193019,0.0446548425,-0.0815157816,0.219915539,0.2908578515,-0.020732671,-0.0038819646,0.0393178239,0.2184230834,-0.0073496089,0.1165498421,-0.1907244325,-0.0479839928,0.3433541059,0.0568102747,0.0270324126,-0.0146719711,-0.1217484176,0.1897556335,-0.2262431532,-0.0176009052,-0.0114533184,0.3141429126,-0.126532793,0.3303384185,-0.0143034989,-0.0601225719,0.6190877557,0.1798831522,0.1300380975,0.6831793189,-0.0001163469,-0.0475460216,0.2488115579,-0.1716599017,-0.1160657629,-0.378292948,-0.3892392218,-0.3070961237,-0.0062210625,-0.1876083612,-0.0163909253,0.2598288655,-0.2455654293,0.0698740631,0.1047334671,-0.0001954177,0.2071569413,0.6591682434,0.1159940362,0.0595753267,0.0807697698,0.1534064561,0.0017084951,0.5446962714,0.3403493762,-0.2183008492,0.0440145396,0.1729720682,-0.2633571327,0.1657975763,0.1468816698,-0.054348018,-0.2399257421,0.0805292726,-0.0212839469,0.5182216167,0.059143614,-0.234651953,-0.4316556752,-0.102145575,-0.0664191768,-0.5562385321,0.3821810782,0.2015203387,-0.1566859931,-0.0776732937,-0.4879797995,0.2152165323,0.000652196,0.2196807414,-0.1058544144,0.0862760097,0.0302637592,-0.0457014106,0.2073961794,-0.1850510985,0.2528309226,-0.2778702676,-0.1077203229,0.0007032199,-0.631821394,-0.2522801757,-0.2542658448,0.5570594668,0.3400395811,0.3121531606,-0.1901735514,0.1766638905,-0.0098920548,0.009496172,0.0896567181,0.0675861016,-0.2068274766,0.2910379767,0.2482634038,0.3254955411,-0.1401990354,0.1437121928,-0.0361439139,-0.1321337968,0.5210404396,0.002048048,0.1366412938,-0.3246911466,-0.1277599484,0.2106404006,0.1474869847,-0.0480394363,-0.0522452369,0.3029046953,-0.2518993318,0.1874470413,-0.090214856,0.3765105307,-0.3248196542,0.1031466573,-0.135008201,-0.1989864707,-0.1647198647,-0.0518906862,0.1542470753,-0.189479515,0.3332675099,-0.1281971782,0.2518437803,0.0167214256,-0.0220428295,0.087046504,0.0131781269,0.3861887753,-0.0182063654,0.1935081184,0.1541045606,0.2382917553,-0.0286406241,0.1050754711,-0.3319675326,-0.2624969184,-0.380623877,0.1772113442,-0.7136672139,-0.1537100077,-0.1200048029,-0.3985891044,0.2354296148,-0.0649562776,-0.0567608103,-0.4134115875,-0.3182626069,-0.2599987686,0.1167358682,0.0206809398,0.0420251042,0.2987655997,-0.3632175922,-0.1163749322,0.2428524494,0.215058133,-0.0810534209,-0.3425643742,-0.2393646538,-0.0893207267,0.2158229351,-0.5109860897,-0.4660956264,0.0118561983,0.0660677254,0.3486479521,-0.0430671871,0.0808348581,-0.2013578415,-0.14148283,0.1494971067,0.0776483417,-0.0261582676,0.2240615487,-0.0761471391,-0.2241599858,0.2241112143,0.3258959949,0.0461160615,-0.1371053904,0.0084664039,-0.2884565294,0.4022703171,-0.1850484461,-0.2157305479,-0.0031192056,0.6294670105,-0.025983924,0.1472249627,-0.1330263168,-0.3578472137,0.4545338452,0.1552175432,-0.0783921257,-0.0471028909,0.1216536015,-0.1741367579,-0.0999754295,-0.4120608568,0.1904596984,0.0489371978,0.0591338649,0.0370274931,-0.1525634676,-0.3469006121,0.0350571573,-0.3284326494,0.1572928131,-0.2522117794,0.2887921631,-0.1682868302,-0.1193462759,0.0962610468,0.2829837501,-0.0479974523,-0.0246672854,-0.1631454825,0.3613803685,0.2994857132,0.0781223774,-0.2115116566,0.273858875,-0.0612724423,-0.5040163398,-0.1860647947,0.1962278783,0.0509089716,-0.0120964395,0.1028113067,0.358720839,0.1528955102,0.1631416976,-0.0800277591,-0.0455882624,0.0936294869,-0.3036854863,-0.0761333033,-0.2706749141,0.3285456896,-0.1124864221,-0.0460960902,0.2538232803,-0.1733798236,-0.1929416955,0.2653232515,-0.1394605786,-0.0033856491,0.0253237691,0.0985907987,0.0749491379,-0.1329105496,0.5222578049,0.4665668309,0.0308967847,-0.047724802,0.3658854067,-0.2190231234,-0.0082348231,-0.1465048939,0.0391668454,0.3142392635,0.4739094079,0.169755742,-0.1059725434,-0.1650492549,-0.1430268139,0.0060736425,0.1290060282,-0.4080835879,0.0221757349,-0.3125736415,-0.0695161745,-0.3103097081,-0.3648861945,-0.4391922355,-0.2065061033,-0.1677440554,0.3744711876,-0.0996104926,0.0897784531,-0.4655693769,-0.2300638705,0.018182274,-0.1904291213,0.0938805863,-0.1477371007,-0.0599843934,0.0177278072,0.3180305064,-0.2250942439,0.3155865371,-0.4139696062,-0.1411272734,-0.2258401662,-0.2934674621,0.0246613231,-0.0640822947,0.1182565168,0.4367730916,-0.0116511416,0.1331203282,-0.2618660331,0.2756678462,-0.0951335356,-0.4010117948,0.3309478164,-0.0003832174,-0.0188512672,0.1426872909,0.0919117257,-0.0133981509,-0.1658942997,-0.2025047541,-0.1406132132,0.1450517178,0.2710607648,0.0166773181,0.0633990094,-0.1788412929,0.1415931284,-0.145910725,-0.3692005873,0.438944757,0.1344796419,-0.4036253393,0.0226769354,-0.108101584,0.2670501769,0.1023031324,-0.3367684782,-0.3280656636,-0.3281985521,-0.1448690295,0.2304747552,-0.0184544548,0.0375305712,-0.1474923939,0.0226096883,-0.3131790161,-0.2207556963,0.0349096991,0.2063782066,0.5681167245,0.015653966,0.1972703487,-0.0391963795,0.6299889684,0.4885998368,-0.3487401009,0.2094762474,0.1457841247,0.509437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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3048","title":"Identify which shard data belongs to","comments":"Independently of this I think it raises the need to allow multiprocessing during streaming so that we get samples from multiple shards in one batch.","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nI'm training on a large dataset made of multiple sub-datasets.\r\nDuring training I can observe some jumps in loss which may correspond to different shards.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/715491\/136668758-521263aa-a9b2-4ad2-8d22-060b6bf86a1c.png)\r\n\r\nMy suspicion is that either:\r\n* some of the sub-datasets are harder for the model than others\r\n* some of the sub-datasets are not formatted properly\r\n\r\nI'd like to identify which shards correspond to those jumps.\r\n\r\n**Describe the solution you'd like**\r\n\r\nIt would be nice to have a key associated to each data sample or data batch containing details on where the data comes from (shard idx + item idx within the shard).\r\nThis should be supported both in local and streaming mode.\r\n\r\n**Describe alternatives you've considered**\r\nA\u00a0fix would be for me to add myself details (shard id, sample id) as part of each data sample.\r\nThe inconvenient is that it requires users to process\/reupload every dataset when they need this feature.","comment_length":25,"text":"Identify which shard data belongs to \n **Is your feature request related to a problem? Please describe.**\r\n\r\nI'm training on a large dataset made of multiple sub-datasets.\r\nDuring training I can observe some jumps in loss which may correspond to different shards.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/715491\/136668758-521263aa-a9b2-4ad2-8d22-060b6bf86a1c.png)\r\n\r\nMy suspicion is that either:\r\n* some of the sub-datasets are harder for the model than others\r\n* some of the sub-datasets are not formatted properly\r\n\r\nI'd like to identify which shards correspond to those jumps.\r\n\r\n**Describe the solution you'd like**\r\n\r\nIt would be nice to have a key associated to each data sample or data batch containing details on where the data comes from (shard idx + item idx within the shard).\r\nThis should be supported both in local and streaming mode.\r\n\r\n**Describe alternatives you've considered**\r\nA\u00a0fix would be for me to add myself details (shard id, sample id) as part of each data sample.\r\nThe inconvenient is that it requires users to process\/reupload every dataset when they need this feature. \n Independently of this I think it raises the need to allow multiprocessing during streaming so that we get samples from multiple shards in one batch.","embeddings":[-0.5709904432,-0.2696205378,-0.0461119227,0.3155333102,-0.2868875265,-0.2091676295,0.3178082705,0.1625359654,-0.1746657342,0.1725880951,-0.0845629722,-0.0563050844,-0.1931996346,0.3816592395,0.2079123706,-0.1998601705,0.0493255779,-0.0795502737,0.1737651676,-0.2677969038,0.100716278,-0.0156259108,0.1929812282,-0.1184963956,-0.2689400911,0.1326909363,-0.1220511124,-0.0179285668,0.0469087325,-0.2242325693,0.2826404572,0.1475595832,0.2918368578,-0.0138965463,-0.0001092341,-0.0732626691,0.22638008,-0.0900219753,-0.3096092641,0.0119993137,-0.869181335,0.0032535705,-0.0575844608,-0.3002436757,-0.1204273254,0.2608662546,0.1185263023,0.0329473354,0.0694157779,0.0252298452,0.1884849668,0.1801660806,-0.0429716669,0.0438886434,0.489653945,0.4038176835,-0.0066060843,-0.0340817869,0.258482635,0.1488655359,-0.1401785612,0.4460330606,0.0370764583,-0.0463608615,0.1250325441,0.032745447,-0.1728486717,-0.1596881449,-0.0829678923,0.498172909,0.0808277801,-0.190813601,-0.2543672323,-0.2508694232,0.1018680111,-0.2345200628,-0.0288367532,0.1048876494,-0.1443794072,0.1257146448,-0.254045248,-0.0069289282,-0.1915407926,-0.0093550887,-0.0878727064,0.0050905221,0.0395428091,0.0493331738,-0.0164439604,-0.0469880551,-0.2272800356,0.023964107,0.0019556363,-0.0861117169,-0.5485095978,-0.3809557557,-0.003099465,0.1173760071,-0.0590870157,0.1542110145,0.2136579007,0.1981616169,0.0814375579,0.2998208404,0.060788665,0.0385002457,0.3691852093,-0.127941072,-0.1163832396,-0.1969382316,-0.2451207191,-0.0549961515,0.049750749,0.1683234721,-0.0405017175,0.1245944127,0.0775967762,0.0302684121,-0.1466989815,0.1345967948,-0.0929486826,-0.3084986508,0.4297229648,0.1652702838,0.4270984828,-0.2202009112,-0.4966801703,0.01934544,-0.0134651409,-0.4856950641,-0.1782249361,0.0875018388,-0.1560877711,0.1891102046,-0.0451836102,-0.1182164475,-0.2167025208,-0.0512671657,0.1708140075,0.0237275679,-0.2127480209,-0.0437665991,0.3513394892,0.1852004528,-0.2973463535,-0.0719529614,0.0395710431,-0.1169115305,-0.3072593212,-0.0212838762,-0.1907701045,-0.3584463894,0.115486376,0.1365481019,-0.2593171597,0.0086187283,-0.3699962199,0.4144397974,-0.1433676332,-0.0585846677,0.0655909926,0.0048977253,-0.218744114,-0.2537170053,0.0688017681,0.0364816263,-0.3367956877,-0.2591070235,-0.3486136198,-0.3160948157,0.1899280399,0.0004183175,-0.2406380922,-0.1886382103,0.0932203755,0.770663023,0.3409430385,0.0027935901,-0.4715899527,0.2323596328,-0.2772668302,-0.142316699,0.4000468552,0.3716962337,0.5799997449,-0.1628333479,0.0218060389,0.3921266496,-0.1520334631,-0.0574037582,-0.28107512,-0.254021585,-0.5342463851,0.2569099963,0.0350381061,-0.1558594704,0.5246927738,-0.1178574339,0.1159577966,0.0381564051,0.0497313142,-0.0474537909,0.753731668,0.0548378862,-0.1998084337,-0.4527462423,0.0020754817,0.221543476,0.1477131099,-0.08911746,0.0106477961,-0.1777780652,-0.0012475641,0.1311786026,0.0224370454,0.131006673,0.1884071529,0.2130464464,-0.1453604549,-0.3276982009,-0.2419621795,-0.0508454517,-0.40286538,-0.0672674477,-0.309409976,0.3224691749,0.2072730958,0.0151806511,-0.090137586,0.1501726061,0.2638182938,-0.0662732795,0.0661468804,0.1760363728,0.0774164647,0.3352478445,0.2951071262,0.4660004079,0.087736018,-0.2359364182,0.061238315,-0.1254999936,-0.1598585099,-0.0155548584,-0.1181284338,0.4157899916,0.0041962364,0.1115229651,-0.0253948551,0.1543070525,0.0687085986,-0.1903565228,0.0677894428,-0.4307661951,-0.0305564478,-0.2374735773,-0.3773954511,0.1976535767,-0.2500165105,0.4033872485,0.2813197374,-0.1678200811,0.1705036759,0.2647622824,-0.2081879377,-0.0473208986,0.2916650176,1.0890598297,0.36237064,0.2931347489,0.2414596081,-0.2788496912,-0.0343659036,-0.0997084901,0.1302981675,-0.0440129563,-0.3383914828,-0.0427164882,0.0310580637,0.0291775633,-0.1062831655,0.0969518423,-0.0133818127,-0.083464317,-0.2195937485,0.017699711,-0.2781891823,-0.0682408735,-0.3582413793,-0.1415423751,-0.0356760286,-0.3407170773,0.2814799845,0.0546865463,-0.1540181041,0.0119063677,0.244937703,0.3509860635,-0.3554840982,0.1644364893,0.0175709017,-0.4776811004,0.1714634895,0.1502482593,0.3307827413,0.4914042354,0.4550990164,-0.0402204394,0.2229998112,-0.2494082302,-0.2849799395,-0.1306205094,-0.1294596046,0.3039523959,-0.0480902568,0.2825911641,-0.3412027657,-0.4481682777,0.0902033448,-0.0687749535,-0.404014796,0.1928904057,-0.0292488094,0.0132226227,0.2052280009,0.0548711643,-0.3481010497,-0.0955850333,0.1017728746,-0.1834001988,0.4312797189,-0.1468974352,-0.2735154927,-0.19614245,-0.1716307104,-0.0070545562,-0.1397174299,-0.3102643788,-0.0768587217,-0.2146062404,0.0327053256,0.0669093058,0.0724084452,-0.0446243882,0.6150416136,0.0086349687,0.0784670636,-0.0671600103,0.0811796114,0.2925291359,-0.2237593979,0.1966492534,0.1818594337,-0.0821954161,0.0150330048,0.2159875184,-0.0804598555,0.2648128569,0.2655796707,-0.1124488711,0.1327181011,0.3001618683,1.1926677227,-0.0522533581,-0.5359844565,-0.0129083162,0.2003907561,-0.302718997,0.0968871787,-0.2958998084,0.36221838,-0.1440248787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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3044","title":"Inconsistent caching behaviour when using `Dataset.map()` with a `new_fingerprint` and `num_proc>1`","comments":"Following the discussion in #3045 if would be nice to have a way to let users have a nice experience with caching even if the function is not hashable.\r\n\r\nCurrently a workaround is to make the function picklable. This can be done by implementing a callable class instead, that can be pickled using by implementing a custom `__getstate__` method for example.\r\n\r\nHowever it sounds pretty complicated for a simple thing. Maybe one idea would be to have something similar to streamlit: they allow users to register the hashing of their own objects.\r\n\r\nSee the documentation about their `hash_funcs` here: https:\/\/docs.streamlit.io\/library\/advanced-features\/caching#the-hash_funcs-parameter\r\n\r\nHere is the example they give:\r\n\r\n```python\r\nclass FileReference:\r\n    def __init__(self, filename):\r\n        self.filename = filename\r\n\r\ndef hash_file_reference(file_reference):\r\n    filename = file_reference.filename\r\n    return (filename, os.path.getmtime(filename))\r\n\r\n@st.cache(hash_funcs={FileReference: hash_file_reference})\r\ndef func(file_reference):\r\n    ...\r\n```","body":"## Describe the bug\r\nCaching does not work when using `Dataset.map()` with:\r\n1. a function that cannot be deterministically fingerprinted \r\n2. `num_proc>1`\r\n3.  using a custom fingerprint set with the argument `new_fingerprint`. \r\n\r\nThis means that the dataset will be mapped with the function for each and every call, which does not happen if `num_proc==1`. In that case (`num_proc==1`) subsequent calls will load the transformed dataset from the cache, which is the  expected behaviour. The example can easily be translated into a unit test.\r\n\r\nI have a fix and will submit a pull request asap. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport hashlib\r\nimport json\r\nimport os\r\nfrom typing import Dict, Any\r\n\r\nimport numpy as np\r\nfrom datasets import load_dataset, Dataset\r\n\r\nBatch = Dict[str, Any]\r\nfilename = 'example.json'\r\n\r\n\r\nclass Transformation():\r\n    \"\"\"A transformation with a random state that cannot be fingerprinted\"\"\"\r\n\r\n    def __init__(self):\r\n        self.state = np.random.random()\r\n\r\n    def __call__(self, batch: Batch) -> Batch:\r\n        batch['x'] = [np.random.random() for _ in batch['x']]\r\n        return batch\r\n\r\n\r\ndef generate_dataset():\r\n    \"\"\"generate a simple dataset\"\"\"\r\n    rgn = np.random.RandomState(24)\r\n    data = {\r\n        'data': [{'x': float(y), 'y': -float(y)} for y in\r\n                 rgn.random(size=(1000,))]}\r\n    if not os.path.exists(filename):\r\n        with open(filename, 'w') as f:\r\n            f.write(json.dumps(data))\r\n\r\n    return filename\r\n\r\n\r\ndef process_dataset_with_cache(num_proc=1, remove_cache=False,\r\n                               cache_expected_to_exist=False):\r\n\r\n    # load the generated dataset\r\n    dset: Dataset = next(\r\n        iter(load_dataset('json', data_files=filename, field='data').values()))\r\n    new_fingerprint = hashlib.md5(\"static-id\".encode(\"utf8\")).hexdigest()\r\n\r\n    # get the expected cached path\r\n    cache_path = dset._get_cache_file_path(new_fingerprint)\r\n    if remove_cache and os.path.exists(cache_path):\r\n        os.remove(cache_path)\r\n\r\n     # check that the cache exists, and print a statement\r\n    # if was actually expected to exist\r\n    cache_exist = os.path.exists(cache_path)\r\n    print(f\"> cache file exists={cache_exist}\")\r\n    if cache_expected_to_exist and not cache_exist:\r\n        print(\"=== Cache does not exist! ====\")\r\n\r\n    # apply the transformation with the new fingerprint\r\n    dset = dset.map(\r\n        Transformation(),\r\n        batched=True,\r\n        num_proc=num_proc,\r\n        new_fingerprint=new_fingerprint,\r\n        desc=\"mapping dataset with transformation\")\r\n\r\n\r\ngenerate_dataset()\r\n\r\nfor num_proc in [1, 2]:\r\n    print(f\"# num_proc={num_proc}, first pass\")\r\n    # first pass to generate the cache (always create a new cache here)\r\n    process_dataset_with_cache(remove_cache=True,\r\n                               num_proc=num_proc,\r\n                               cache_expected_to_exist=False)\r\n    print(f\"# num_proc={num_proc}, second pass\")\r\n    # second pass, expects the cache to exist\r\n    process_dataset_with_cache(remove_cache=False,\r\n                               num_proc=num_proc,\r\n                               cache_expected_to_exist=True)\r\n\r\nos.remove(filename)\r\n\r\n```\r\n\r\n## Expected results\r\nIn the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache`  (\"=== Cache does not exist! ====\" should not be printed). \r\nWhen the cache is successfully created, `map()` is called only one time.\r\n\r\n## Actual results\r\nIn the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing \"=== Cache does not exist! ====\"). \r\nBecause the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 5.0.0\r\n","comment_length":129,"text":"Inconsistent caching behaviour when using `Dataset.map()` with a `new_fingerprint` and `num_proc>1` \n ## Describe the bug\r\nCaching does not work when using `Dataset.map()` with:\r\n1. a function that cannot be deterministically fingerprinted \r\n2. `num_proc>1`\r\n3.  using a custom fingerprint set with the argument `new_fingerprint`. \r\n\r\nThis means that the dataset will be mapped with the function for each and every call, which does not happen if `num_proc==1`. In that case (`num_proc==1`) subsequent calls will load the transformed dataset from the cache, which is the  expected behaviour. The example can easily be translated into a unit test.\r\n\r\nI have a fix and will submit a pull request asap. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport hashlib\r\nimport json\r\nimport os\r\nfrom typing import Dict, Any\r\n\r\nimport numpy as np\r\nfrom datasets import load_dataset, Dataset\r\n\r\nBatch = Dict[str, Any]\r\nfilename = 'example.json'\r\n\r\n\r\nclass Transformation():\r\n    \"\"\"A transformation with a random state that cannot be fingerprinted\"\"\"\r\n\r\n    def __init__(self):\r\n        self.state = np.random.random()\r\n\r\n    def __call__(self, batch: Batch) -> Batch:\r\n        batch['x'] = [np.random.random() for _ in batch['x']]\r\n        return batch\r\n\r\n\r\ndef generate_dataset():\r\n    \"\"\"generate a simple dataset\"\"\"\r\n    rgn = np.random.RandomState(24)\r\n    data = {\r\n        'data': [{'x': float(y), 'y': -float(y)} for y in\r\n                 rgn.random(size=(1000,))]}\r\n    if not os.path.exists(filename):\r\n        with open(filename, 'w') as f:\r\n            f.write(json.dumps(data))\r\n\r\n    return filename\r\n\r\n\r\ndef process_dataset_with_cache(num_proc=1, remove_cache=False,\r\n                               cache_expected_to_exist=False):\r\n\r\n    # load the generated dataset\r\n    dset: Dataset = next(\r\n        iter(load_dataset('json', data_files=filename, field='data').values()))\r\n    new_fingerprint = hashlib.md5(\"static-id\".encode(\"utf8\")).hexdigest()\r\n\r\n    # get the expected cached path\r\n    cache_path = dset._get_cache_file_path(new_fingerprint)\r\n    if remove_cache and os.path.exists(cache_path):\r\n        os.remove(cache_path)\r\n\r\n     # check that the cache exists, and print a statement\r\n    # if was actually expected to exist\r\n    cache_exist = os.path.exists(cache_path)\r\n    print(f\"> cache file exists={cache_exist}\")\r\n    if cache_expected_to_exist and not cache_exist:\r\n        print(\"=== Cache does not exist! ====\")\r\n\r\n    # apply the transformation with the new fingerprint\r\n    dset = dset.map(\r\n        Transformation(),\r\n        batched=True,\r\n        num_proc=num_proc,\r\n        new_fingerprint=new_fingerprint,\r\n        desc=\"mapping dataset with transformation\")\r\n\r\n\r\ngenerate_dataset()\r\n\r\nfor num_proc in [1, 2]:\r\n    print(f\"# num_proc={num_proc}, first pass\")\r\n    # first pass to generate the cache (always create a new cache here)\r\n    process_dataset_with_cache(remove_cache=True,\r\n                               num_proc=num_proc,\r\n                               cache_expected_to_exist=False)\r\n    print(f\"# num_proc={num_proc}, second pass\")\r\n    # second pass, expects the cache to exist\r\n    process_dataset_with_cache(remove_cache=False,\r\n                               num_proc=num_proc,\r\n                               cache_expected_to_exist=True)\r\n\r\nos.remove(filename)\r\n\r\n```\r\n\r\n## Expected results\r\nIn the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache`  (\"=== Cache does not exist! ====\" should not be printed). \r\nWhen the cache is successfully created, `map()` is called only one time.\r\n\r\n## Actual results\r\nIn the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing \"=== Cache does not exist! ====\"). \r\nBecause the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 5.0.0\r\n \n Following the discussion in #3045 if would be nice to have a way to let users have a nice experience with caching even if the function is not hashable.\r\n\r\nCurrently a workaround is to make the function picklable. This can be done by implementing a callable class instead, that can be pickled using by implementing a custom `__getstate__` method for example.\r\n\r\nHowever it sounds pretty complicated for a simple thing. Maybe one idea would be to have something similar to streamlit: they allow users to register the hashing of their own objects.\r\n\r\nSee the documentation about their `hash_funcs` here: https:\/\/docs.streamlit.io\/library\/advanced-features\/caching#the-hash_funcs-parameter\r\n\r\nHere is the example they give:\r\n\r\n```python\r\nclass FileReference:\r\n    def __init__(self, filename):\r\n        self.filename = filename\r\n\r\ndef hash_file_reference(file_reference):\r\n    filename = file_reference.filename\r\n    return (filename, os.path.getmtime(filename))\r\n\r\n@st.cache(hash_funcs={FileReference: hash_file_reference})\r\ndef func(file_reference):\r\n    ...\r\n```","embeddings":[-0.112587072,0.1165609583,-0.0157670714,0.0611557886,-0.1184845567,-0.1240612119,0.3861943483,0.3515930772,0.3012607694,-0.0997982845,0.2086912394,0.3295017183,0.0391607657,-0.1431074888,-0.0197526887,0.3910209537,0.3168308139,-0.0682445914,-0.0389391929,-0.0995171517,-0.4793722928,0.0277485196,-0.3548876941,-0.2784349918,-0.3435046077,0.0711033866,-0.1592223793,0.1362336874,0.121988602,-0.3866126239,0.2304465026,0.0593740195,-0.0752180591,0.4310933352,-0.000114172,-0.0567237288,0.2517128587,0.0079106614,0.0590457655,-0.0195848327,-0.3739239573,-0.2685649395,-0.2234235257,-0.1998500377,-0.1342521459,-0.0989063755,0.0821544379,-0.4035897851,0.1880825609,0.2669548988,0.2029106766,0.0146495132,-0.1780854166,0.1081886292,0.0760926902,0.1785471141,-0.1503020376,-0.0250822697,0.2232385874,-0.1936356425,-0.3218946755,0.1730556637,-0.000653106,0.07362587,0.4684592485,0.2219025046,0.2286393046,-0.2857306004,0.1566766798,-0.003106605,0.0585948899,-0.2211934477,-0.4096222222,-0.3232640624,-0.4060796499,-0.0082029533,0.3329617381,-0.0361971855,-0.0168460421,0.0615802817,-0.5032262802,0.3311186135,0.292362839,0.1471600533,0.0637912601,0.0509290285,0.08861956,0.1207855642,-0.0620211773,-0.2344514579,0.0843856409,-0.2585548162,-0.1525861472,0.269222647,-0.0252908561,0.0286145825,0.2956112623,0.1723501235,0.1807041317,-0.0028712137,0.1553745121,0.1463024318,-0.0380611382,0.2179177552,0.1920387447,0.1609915197,0.0323731378,0.3685817122,0.0826500207,-0.2335250378,-0.4429801106,0.1069171056,0.3875388503,-0.1421610266,0.6534640193,0.0437276624,-0.0715634078,-0.1008541808,0.0942281634,0.0042007482,-0.1544425786,-0.1449454278,0.2402852774,0.2114507556,-0.0122845126,0.0895248353,-0.1645038873,0.1004158556,-0.2435953766,0.2616867721,-0.2362700999,-0.062756598,-0.3793402612,0.2962090373,0.2557350099,0.0279155876,0.2042231262,0.2528306544,0.0236647204,-0.1804893911,0.2260394096,-0.269852519,0.5383321047,-0.0242645349,-0.3987633288,0.1220848188,-0.0585364141,0.129760161,-0.2044865787,0.2739317417,-0.1989354789,0.0436989553,0.3474624157,0.1077479497,-0.3829645216,0.1285947561,-0.0849244446,0.1277649105,0.325925678,-0.3512022495,0.0299431887,-0.2341234535,-0.5851396322,-0.377001971,-0.1053824872,0.471426785,0.1104359776,-0.1469236463,0.1996140778,0.3393431902,0.1269707829,0.1585380286,-0.1798787415,-0.0122608887,-0.3548344076,0.3470664322,0.0527057573,-0.4631510973,-0.8054310679,0.1940662712,0.0198369585,0.2849624753,-0.2524037659,0.2596901953,0.0837854519,-0.3804090023,-0.1488813311,0.2633269727,0.0669291988,0.2377872616,-0.3906365037,-0.1816049218,0.2042836696,-0.1243238077,0.0286707934,0.3220615983,0.1909097731,-0.1716929227,0.1565608084,-0.0259961002,0.1394070536,0.2325990945,0.1335546225,-0.3540163636,0.0975812599,0.1032650024,-0.3816807568,0.4530992508,-0.1013765186,-0.2259747386,-0.0974101871,0.1298207343,0.1011133716,-0.1546224207,-0.1785900295,-0.2162836194,0.0639761612,0.2628129423,0.3670861423,-0.2023704797,0.0809102729,0.3404198587,0.219929412,-0.1081745401,-0.3324360847,-0.0261589885,0.2252345383,-0.1553074121,-0.2981742024,-0.0245638769,0.4082883596,-0.1921357214,-0.2038392872,0.2058954984,0.1627078205,0.1013164967,-0.2682017982,0.2445409149,0.0725817531,-0.0229116585,0.2189580202,0.154879868,0.1265120655,-0.2247427255,-0.1205230951,0.6013142467,0.0204221103,0.1298080534,-0.1672376245,-0.2604888976,0.0164148621,-0.0084770732,0.1057498977,-0.1996718496,-0.1569326073,0.131908223,0.5416613817,0.1203986779,0.017565975,0.335160017,0.3188402057,0.0951519459,0.022005517,0.0444787368,-0.0942569375,-0.3260909617,0.0667862073,0.2325774133,0.5157186389,0.0600492321,0.0977213681,-0.0077187861,0.002170135,-0.091049701,-0.0255523715,-0.196142897,-0.0521435216,-0.036757838,0.413872838,-0.1103311256,-0.0029256411,-0.0147618055,0.2290459573,0.0421559401,-0.1423854679,0.1586479694,-0.1542544067,0.4265763164,0.0226105284,-0.1968333721,0.0263903793,-0.3279195726,-0.1847746223,0.3959241509,0.017462723,0.0593912527,-0.2548629642,-0.2206837535,-0.0935521722,-0.3701072335,-0.2463575751,-0.146275714,-0.1450060159,-0.057753019,0.1283845156,0.0363081135,0.011430745,-0.1244665384,-0.1121077538,-0.2261992842,-0.1451638639,-0.009519103,-0.095323734,0.1750873476,-0.1545361876,-0.1427361965,-0.2333955467,0.0803028271,0.2684287429,-0.4073776603,-0.176920563,0.0594680011,0.0661968142,-0.084753409,-0.01485522,-0.0339586511,-0.1269848943,-0.0625391901,0.1935477108,0.1140768901,0.1619168818,0.2994751334,0.1833603531,-0.1050815806,0.0553901829,-0.0250174664,-0.2818129361,-0.5573965907,0.4793079197,-0.1213013008,-0.1359288096,-0.137742579,-0.1052189693,-0.1139261425,0.4554575086,-0.4010355473,-0.3724507093,-0.1838816702,0.749366641,0.1569766104,-0.2589917183,0.3056098819,-0.0359726064,-0.0486654229,-0.383977592,-0.4920690656,0.0787425488,0.0907914639,0.1645452827,0.3155908883,0.1497921199,0.0306629464,0.6485084295,0.3672061563,-0.5198047161,0.1849380881,0.1144148037,0.0605884306,-0.2385769337,-0.2028326094,0.0684988871,-0.0570874959,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3044","title":"Inconsistent caching behaviour when using `Dataset.map()` with a `new_fingerprint` and `num_proc>1`","comments":"My solution was to generate a custom hash, and use the hash as a `new_fingerprint` argument to the `map()` method to enable caching. This works, but is quite hacky.\r\n\r\n@lhoestq, this approach is very neat, this would make the whole caching mechanic more explicit. I don't have so much time to look into this right now, but I might give it a try in the future. ","body":"## Describe the bug\r\nCaching does not work when using `Dataset.map()` with:\r\n1. a function that cannot be deterministically fingerprinted \r\n2. `num_proc>1`\r\n3.  using a custom fingerprint set with the argument `new_fingerprint`. \r\n\r\nThis means that the dataset will be mapped with the function for each and every call, which does not happen if `num_proc==1`. In that case (`num_proc==1`) subsequent calls will load the transformed dataset from the cache, which is the  expected behaviour. The example can easily be translated into a unit test.\r\n\r\nI have a fix and will submit a pull request asap. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport hashlib\r\nimport json\r\nimport os\r\nfrom typing import Dict, Any\r\n\r\nimport numpy as np\r\nfrom datasets import load_dataset, Dataset\r\n\r\nBatch = Dict[str, Any]\r\nfilename = 'example.json'\r\n\r\n\r\nclass Transformation():\r\n    \"\"\"A transformation with a random state that cannot be fingerprinted\"\"\"\r\n\r\n    def __init__(self):\r\n        self.state = np.random.random()\r\n\r\n    def __call__(self, batch: Batch) -> Batch:\r\n        batch['x'] = [np.random.random() for _ in batch['x']]\r\n        return batch\r\n\r\n\r\ndef generate_dataset():\r\n    \"\"\"generate a simple dataset\"\"\"\r\n    rgn = np.random.RandomState(24)\r\n    data = {\r\n        'data': [{'x': float(y), 'y': -float(y)} for y in\r\n                 rgn.random(size=(1000,))]}\r\n    if not os.path.exists(filename):\r\n        with open(filename, 'w') as f:\r\n            f.write(json.dumps(data))\r\n\r\n    return filename\r\n\r\n\r\ndef process_dataset_with_cache(num_proc=1, remove_cache=False,\r\n                               cache_expected_to_exist=False):\r\n\r\n    # load the generated dataset\r\n    dset: Dataset = next(\r\n        iter(load_dataset('json', data_files=filename, field='data').values()))\r\n    new_fingerprint = hashlib.md5(\"static-id\".encode(\"utf8\")).hexdigest()\r\n\r\n    # get the expected cached path\r\n    cache_path = dset._get_cache_file_path(new_fingerprint)\r\n    if remove_cache and os.path.exists(cache_path):\r\n        os.remove(cache_path)\r\n\r\n     # check that the cache exists, and print a statement\r\n    # if was actually expected to exist\r\n    cache_exist = os.path.exists(cache_path)\r\n    print(f\"> cache file exists={cache_exist}\")\r\n    if cache_expected_to_exist and not cache_exist:\r\n        print(\"=== Cache does not exist! ====\")\r\n\r\n    # apply the transformation with the new fingerprint\r\n    dset = dset.map(\r\n        Transformation(),\r\n        batched=True,\r\n        num_proc=num_proc,\r\n        new_fingerprint=new_fingerprint,\r\n        desc=\"mapping dataset with transformation\")\r\n\r\n\r\ngenerate_dataset()\r\n\r\nfor num_proc in [1, 2]:\r\n    print(f\"# num_proc={num_proc}, first pass\")\r\n    # first pass to generate the cache (always create a new cache here)\r\n    process_dataset_with_cache(remove_cache=True,\r\n                               num_proc=num_proc,\r\n                               cache_expected_to_exist=False)\r\n    print(f\"# num_proc={num_proc}, second pass\")\r\n    # second pass, expects the cache to exist\r\n    process_dataset_with_cache(remove_cache=False,\r\n                               num_proc=num_proc,\r\n                               cache_expected_to_exist=True)\r\n\r\nos.remove(filename)\r\n\r\n```\r\n\r\n## Expected results\r\nIn the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache`  (\"=== Cache does not exist! ====\" should not be printed). \r\nWhen the cache is successfully created, `map()` is called only one time.\r\n\r\n## Actual results\r\nIn the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing \"=== Cache does not exist! ====\"). \r\nBecause the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 5.0.0\r\n","comment_length":66,"text":"Inconsistent caching behaviour when using `Dataset.map()` with a `new_fingerprint` and `num_proc>1` \n ## Describe the bug\r\nCaching does not work when using `Dataset.map()` with:\r\n1. a function that cannot be deterministically fingerprinted \r\n2. `num_proc>1`\r\n3.  using a custom fingerprint set with the argument `new_fingerprint`. \r\n\r\nThis means that the dataset will be mapped with the function for each and every call, which does not happen if `num_proc==1`. In that case (`num_proc==1`) subsequent calls will load the transformed dataset from the cache, which is the  expected behaviour. The example can easily be translated into a unit test.\r\n\r\nI have a fix and will submit a pull request asap. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport hashlib\r\nimport json\r\nimport os\r\nfrom typing import Dict, Any\r\n\r\nimport numpy as np\r\nfrom datasets import load_dataset, Dataset\r\n\r\nBatch = Dict[str, Any]\r\nfilename = 'example.json'\r\n\r\n\r\nclass Transformation():\r\n    \"\"\"A transformation with a random state that cannot be fingerprinted\"\"\"\r\n\r\n    def __init__(self):\r\n        self.state = np.random.random()\r\n\r\n    def __call__(self, batch: Batch) -> Batch:\r\n        batch['x'] = [np.random.random() for _ in batch['x']]\r\n        return batch\r\n\r\n\r\ndef generate_dataset():\r\n    \"\"\"generate a simple dataset\"\"\"\r\n    rgn = np.random.RandomState(24)\r\n    data = {\r\n        'data': [{'x': float(y), 'y': -float(y)} for y in\r\n                 rgn.random(size=(1000,))]}\r\n    if not os.path.exists(filename):\r\n        with open(filename, 'w') as f:\r\n            f.write(json.dumps(data))\r\n\r\n    return filename\r\n\r\n\r\ndef process_dataset_with_cache(num_proc=1, remove_cache=False,\r\n                               cache_expected_to_exist=False):\r\n\r\n    # load the generated dataset\r\n    dset: Dataset = next(\r\n        iter(load_dataset('json', data_files=filename, field='data').values()))\r\n    new_fingerprint = hashlib.md5(\"static-id\".encode(\"utf8\")).hexdigest()\r\n\r\n    # get the expected cached path\r\n    cache_path = dset._get_cache_file_path(new_fingerprint)\r\n    if remove_cache and os.path.exists(cache_path):\r\n        os.remove(cache_path)\r\n\r\n     # check that the cache exists, and print a statement\r\n    # if was actually expected to exist\r\n    cache_exist = os.path.exists(cache_path)\r\n    print(f\"> cache file exists={cache_exist}\")\r\n    if cache_expected_to_exist and not cache_exist:\r\n        print(\"=== Cache does not exist! ====\")\r\n\r\n    # apply the transformation with the new fingerprint\r\n    dset = dset.map(\r\n        Transformation(),\r\n        batched=True,\r\n        num_proc=num_proc,\r\n        new_fingerprint=new_fingerprint,\r\n        desc=\"mapping dataset with transformation\")\r\n\r\n\r\ngenerate_dataset()\r\n\r\nfor num_proc in [1, 2]:\r\n    print(f\"# num_proc={num_proc}, first pass\")\r\n    # first pass to generate the cache (always create a new cache here)\r\n    process_dataset_with_cache(remove_cache=True,\r\n                               num_proc=num_proc,\r\n                               cache_expected_to_exist=False)\r\n    print(f\"# num_proc={num_proc}, second pass\")\r\n    # second pass, expects the cache to exist\r\n    process_dataset_with_cache(remove_cache=False,\r\n                               num_proc=num_proc,\r\n                               cache_expected_to_exist=True)\r\n\r\nos.remove(filename)\r\n\r\n```\r\n\r\n## Expected results\r\nIn the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache`  (\"=== Cache does not exist! ====\" should not be printed). \r\nWhen the cache is successfully created, `map()` is called only one time.\r\n\r\n## Actual results\r\nIn the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing \"=== Cache does not exist! ====\"). \r\nBecause the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 5.0.0\r\n \n My solution was to generate a custom hash, and use the hash as a `new_fingerprint` argument to the `map()` method to enable caching. This works, but is quite hacky.\r\n\r\n@lhoestq, this approach is very neat, this would make the whole caching mechanic more explicit. I don't have so much time to look into this right now, but I might give it a try in the future. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3040","title":"[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset","comments":"Hi,\r\n\r\nthe `save_to_disk` docstring explains that `flatten_indices` has to be called on a dataset before saving it to save only the shard\/slice of the dataset.","body":"## Describe the bug\r\n\r\nWhen only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo\/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset, save_to_disk\r\n\r\nnlp = load_dataset(\"glue\", \"mnli\", split=\"train\")\r\nnlp.save_to_disk(\"full\")\r\n\r\nnlp = nlp.select(range(100))\r\nnlp.save_to_disk(\"dummy\")\r\n```\r\n\r\nNow one can see that both `\"dummy\"` and `\"full\"` have the same size. This shouldn't be the case IMO.\r\n\r\n## Expected results\r\n\r\nIMO `\"dummy\"` should be much smaller so that one can easily play around with the dataset on the hub.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\n","comment_length":25,"text":"[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset \n ## Describe the bug\r\n\r\nWhen only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo\/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset, save_to_disk\r\n\r\nnlp = load_dataset(\"glue\", \"mnli\", split=\"train\")\r\nnlp.save_to_disk(\"full\")\r\n\r\nnlp = nlp.select(range(100))\r\nnlp.save_to_disk(\"dummy\")\r\n```\r\n\r\nNow one can see that both `\"dummy\"` and `\"full\"` have the same size. This shouldn't be the case IMO.\r\n\r\n## Expected results\r\n\r\nIMO `\"dummy\"` should be much smaller so that one can easily play around with the dataset on the hub.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\n \n Hi,\r\n\r\nthe `save_to_disk` docstring explains that `flatten_indices` has to be called on a dataset before saving it to save only the shard\/slice of the dataset.","embeddings":[-0.1809476018,-0.1576080471,0.0806469321,0.2456983179,0.1063632667,0.1908277869,0.2535607815,0.4150105715,0.0799375176,0.4557596147,0.1509387344,0.3872533143,-0.1541714817,0.0044505592,0.0994568989,0.0693319812,0.1867135316,0.1767673194,0.0718551576,-0.2415307015,-0.3384591639,-0.0049714069,-0.2006051987,-0.2561906278,-0.5940170884,-0.1241070703,-0.2723130584,0.3448466361,-0.2400342226,-0.0883391723,0.2154426575,-0.2062250823,-0.0196470171,0.3707505763,-0.0001124717,-0.0168191269,0.3227279782,-0.1523152441,-0.534442544,-0.0364098102,-0.1071832776,-0.2971006334,-0.0003248023,-0.3562377691,0.1194449365,0.2600404024,0.0269447379,-0.0901064277,0.0172032099,0.0128549859,0.219539687,0.3787954748,-0.2591149807,-0.2375458777,0.1458400786,0.2124068886,-0.2900447845,0.3401015997,-0.0082976846,0.0553625524,0.073697567,0.0374357067,-0.103789255,-0.204268232,-0.022133274,0.2229923606,0.0837853849,-0.4117188454,0.1977331042,0.1641881317,0.2704138756,-0.3860102892,-0.2915239632,-0.3839529157,0.0568632074,-0.1729192734,0.0673257783,0.3428153396,0.056405928,0.041440323,-0.0169608593,0.0706529468,-0.0531341806,0.0719644204,0.1494865716,0.0740206987,0.0289723556,0.1422629654,0.2648313046,0.0623221286,0.0208589267,-0.4156859219,-0.0609482229,-0.0203198455,-0.336183548,-0.2736121118,-0.0287884586,-0.1164757535,0.2221485376,0.1423020512,0.0341674834,-0.0448523909,0.1295352429,0.0894861743,0.1833876967,0.3511478603,-0.0091150347,0.2439825535,0.2207394987,-0.0590819456,0.2513430119,0.1189330891,-0.0364380218,-0.0208001621,0.2362941504,-0.1164130867,0.1002893224,-0.0884994939,-0.4727648199,0.15679425,-0.0143859629,0.180195272,-0.110802047,0.2001823336,-0.0485560223,-0.1313421875,-0.1518021375,0.364173919,-0.3945655227,0.1070972756,-0.2818075716,0.0034412779,0.0790928304,-0.0707915425,0.1393574923,-0.1525524557,0.145628497,0.1426642537,0.0012432778,0.2042501569,0.2205497772,-0.3194167018,0.1457872093,0.3391509056,0.2245105803,0.1646439284,-0.024024656,-0.5527133346,-0.192545101,0.3616388142,-0.0139216073,-0.2650310099,-0.2030185461,0.1908952594,-0.0774476454,0.093444936,-0.2279696614,0.2242336869,0.1070291027,0.0205888767,0.0771217793,-0.0499763563,-0.1576577723,-0.4104116261,0.2029597163,0.2047045231,-0.3116405606,0.1689976007,-0.0091345282,0.0901456475,0.2009032965,0.2160713524,-0.0251559578,0.1247703061,-0.1635745019,0.4801803231,0.2066607922,-0.0120371869,-0.6134503484,-0.0278496724,-0.1356237382,0.0737617016,-0.0404504053,0.2520146072,0.1007740572,0.0652423352,0.1290011853,0.3021902144,0.0169159267,-0.0766480342,-0.5265501142,-0.1743296981,0.0576389916,0.1135145426,-0.0811974555,0.0534860976,0.0205377582,0.168155387,0.3211294115,0.0329175033,0.1902280003,0.2687304914,0.2511722147,-0.5698814988,-0.0044411151,-0.1871612966,-0.3816381395,0.212325111,-0.0997601599,0.0296603963,0.18443802,-0.3699108958,-0.4125839472,-0.048557844,-0.0925682634,0.0275843479,0.0951025337,-0.0448911227,0.2003566027,0.0263494272,-0.0641916543,0.0739021376,-0.2241148204,-0.1007087752,-0.5723357797,0.3845404685,-0.1050581113,-0.3537456989,-0.005892403,0.1754187495,0.1860033423,-0.1179626733,-0.1333574206,0.3074066639,-0.0640339702,0.0244817473,-0.0390252732,-0.0326384455,0.1187225729,0.0773053095,0.1364892423,0.2566510439,0.2901672721,-0.0358008407,-0.4762172401,0.2256448567,-0.2718867064,-0.0681953058,-0.1276810616,-0.0561304502,0.0809247196,-0.0691309571,-0.0378520563,-0.2077889293,0.0562364571,0.2686213851,0.0495259166,-0.0954025984,-0.2391554117,0.1801368296,0.3591174185,-0.207241267,0.3417232037,0.2792402804,-0.1928321719,-0.145322755,0.0338279791,-0.0260973927,0.566364944,0.1432142556,0.1267023832,-0.1102956906,-0.2060811669,-0.1857474893,0.2950297594,0.1248138174,-0.1738295406,0.3017813861,0.0941795185,-0.0765593871,-0.3963547349,0.412289381,0.0633921698,0.1480904967,-0.0435104184,-0.0700092763,-0.2579234242,0.0369152091,0.0873150751,-0.0427786782,-0.1553040296,-0.2956053615,0.0584962144,0.524361372,-0.1894658506,-0.0053955545,0.0184224918,0.4486205876,0.1498339772,0.0209981501,-0.0243952833,0.1089819074,-0.3649989069,0.0884363353,0.0903603584,0.322453022,0.33138147,-0.0400640815,-0.0172033012,-0.1452057213,-0.1694861054,-0.0037442634,-0.1928982884,0.5611333251,0.0071932334,0.1484984159,-0.2511810362,0.1157363281,0.0695089623,0.120740816,-0.1740410179,0.2399406135,0.0645513535,-0.1891043931,-0.2574205995,-0.3324154019,-0.3096147478,-0.2162970155,0.137068212,-0.0024601561,0.1374368966,0.1419246644,0.2257581949,0.0039065671,0.1730032116,-0.3041781187,-0.4262359142,-0.2583120465,0.3981920779,-0.1674565822,-0.4312326014,-0.1307578832,-0.412043184,-0.2913600504,-0.2491648197,-0.5085943937,-0.2731799781,-0.2647990286,0.0767692178,-0.1012892947,0.1876304448,0.4329630136,-0.1366920173,0.0072904443,-0.1077184752,-0.3787584603,0.1094163656,0.6233230829,0.3391466737,-0.1750592738,0.0740101561,0.1227031723,0.2648212016,0.5041719675,0.1349874586,0.3246047497,-0.0258418191,0.338665247,-0.4063775241,-0.3055651784,0.0249322169,-0.0418925248,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3040","title":"[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset","comments":"That works! Thansk!\r\n\r\nMight be worth doing that automatically actually in case the `save_to_disk` is called on a dataset that has an indices mapping :-)","body":"## Describe the bug\r\n\r\nWhen only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo\/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset, save_to_disk\r\n\r\nnlp = load_dataset(\"glue\", \"mnli\", split=\"train\")\r\nnlp.save_to_disk(\"full\")\r\n\r\nnlp = nlp.select(range(100))\r\nnlp.save_to_disk(\"dummy\")\r\n```\r\n\r\nNow one can see that both `\"dummy\"` and `\"full\"` have the same size. This shouldn't be the case IMO.\r\n\r\n## Expected results\r\n\r\nIMO `\"dummy\"` should be much smaller so that one can easily play around with the dataset on the hub.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\n","comment_length":25,"text":"[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset \n ## Describe the bug\r\n\r\nWhen only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo\/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset, save_to_disk\r\n\r\nnlp = load_dataset(\"glue\", \"mnli\", split=\"train\")\r\nnlp.save_to_disk(\"full\")\r\n\r\nnlp = nlp.select(range(100))\r\nnlp.save_to_disk(\"dummy\")\r\n```\r\n\r\nNow one can see that both `\"dummy\"` and `\"full\"` have the same size. This shouldn't be the case IMO.\r\n\r\n## Expected results\r\n\r\nIMO `\"dummy\"` should be much smaller so that one can easily play around with the dataset on the hub.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\n \n That works! Thansk!\r\n\r\nMight be worth doing that automatically actually in case the `save_to_disk` is called on a dataset that has an indices mapping :-)","embeddings":[-0.1977871209,-0.1849523336,0.072868377,0.2110224813,0.1640478075,0.1720052361,0.2364401966,0.4040086269,0.1711797118,0.4311816096,0.1333657056,0.4044636786,-0.1757008433,-0.0216433331,0.1236589998,0.0688508376,0.1950649321,0.1609200388,0.0407716259,-0.2092083395,-0.366563648,-0.0393781625,-0.2007808834,-0.2394990921,-0.56791085,-0.1390289366,-0.2599999011,0.3170101643,-0.223950997,-0.1409922391,0.2029298395,-0.1835681498,-0.0130428905,0.3703455925,-0.0001100582,-0.037762776,0.3083373606,-0.1472821683,-0.5062392354,-0.0701982155,-0.0457464419,-0.3110975623,-0.0088483626,-0.3288088739,0.0949149877,0.2151666135,0.0791756213,-0.0913621113,0.0012331076,0.0505701676,0.2513641715,0.4108485579,-0.2465483099,-0.2575872242,0.1223161966,0.1864281893,-0.3110144734,0.396617502,0.0498438999,0.0242470372,0.0749574527,0.0207484793,-0.0738475323,-0.2160372585,0.0505224839,0.1869021952,0.0945415944,-0.3978109062,0.1829328835,0.1901502013,0.2695260942,-0.3909193575,-0.2869184911,-0.3718158901,0.0727202669,-0.1521388143,0.099284783,0.347353667,0.042013064,0.0252482314,0.0034331924,0.0928483978,-0.0706309304,0.0694204867,0.1227566674,0.1449013203,0.0458698571,0.1241287813,0.2717329264,0.0736979097,-0.0033491838,-0.3851346076,-0.0898636729,-0.0056832726,-0.3101033568,-0.2732902765,-0.0305429772,-0.1067174599,0.1926317811,0.1444732398,0.0142678283,-0.0591375828,0.1108084545,0.0905303508,0.171371609,0.3588756323,-0.0566962957,0.2353720069,0.2473843843,-0.0384708457,0.2235182971,0.1161126569,-0.0470465533,-0.0591058731,0.2131260782,-0.1278297752,0.0539076291,-0.0707068592,-0.4778515697,0.1104311123,-0.0428477377,0.2320133001,-0.0496196412,0.2169025391,-0.1059914827,-0.1307794601,-0.1272582561,0.3900390267,-0.3908438087,0.1520224661,-0.2745407224,0.0693812668,-0.0031495066,-0.1155261621,0.1257149875,-0.1084947512,0.1539542824,0.1367454529,-0.0360459089,0.1717463285,0.2328800708,-0.3360840976,0.1397536993,0.3224724829,0.230978936,0.1605302393,-0.0720767304,-0.5434202552,-0.197037667,0.3449588716,0.0451547019,-0.2426753491,-0.1659772396,0.2179300785,-0.0989995897,0.057384979,-0.1879909635,0.2307098806,0.0449056476,0.0413295329,0.1090964302,-0.0087232869,-0.1554277092,-0.3899649978,0.1429950297,0.2100995034,-0.2669969499,0.1345271468,0.0578441322,0.0673973709,0.1484009773,0.1618484855,-0.0336384922,0.17856659,-0.169621557,0.4384046495,0.2284028828,-0.0879608318,-0.6092956066,-0.0444820449,-0.139168933,0.0385290012,-0.0218065921,0.3064077199,0.0780755579,0.0500046946,0.1382081658,0.3394072354,0.0300909821,-0.0311813839,-0.4885333776,-0.1677111685,0.0653208643,0.1110184044,-0.0843687057,0.02162035,0.0252542086,0.1852714419,0.323373735,-0.0269022714,0.1599854678,0.277000457,0.2414495945,-0.5311183929,-0.0465841778,-0.1805998832,-0.297118485,0.2276287824,-0.043540746,0.0396551564,0.2141442299,-0.3296993971,-0.4157267213,-0.0731681287,-0.0965764597,0.0023366187,0.1209072694,-0.0278384704,0.1901722252,0.0195098128,-0.0420802832,0.1634548157,-0.1658813208,-0.0654124618,-0.5854560137,0.3622556925,-0.098635599,-0.3251305819,-0.0199055411,0.2262206823,0.185797438,-0.0878899097,-0.1168790013,0.3178603351,-0.0640415326,0.049384445,-0.0407994725,-0.0390559919,0.1458150446,0.0960776061,0.135174349,0.2477272302,0.2851672471,-0.0157451797,-0.459449321,0.2119687647,-0.2450076491,-0.0865968689,-0.1098814309,-0.0511474535,0.0795151517,-0.0634498298,-0.0242459476,-0.1720589697,0.0626823008,0.3055984676,0.1486764848,-0.1058013812,-0.2393774986,0.1272456497,0.3296100199,-0.1535168141,0.3674607873,0.215952605,-0.2434613705,-0.1331490576,0.0090645505,0.0136435833,0.5998482704,0.155180186,0.0968212932,-0.0777540356,-0.2424423248,-0.1839168668,0.3035931587,0.1049737632,-0.1753926575,0.2799507976,0.1332300305,-0.0944037586,-0.433057338,0.4002631605,0.0444567353,0.1431965828,-0.0320624225,-0.0706118569,-0.2251722515,-0.0026557366,0.0920098722,-0.0347136371,-0.1509559751,-0.2977165282,0.0549133569,0.5223662853,-0.1731408089,-0.0183339324,0.0556407273,0.424443543,0.1024656817,0.0142149152,0.0103437472,0.1148999482,-0.376762718,0.1110295281,0.0764084235,0.2917808592,0.4058229625,-0.0029618584,-0.0199720729,-0.1476207227,-0.1871918887,-0.0304832738,-0.1638308614,0.5393264294,0.0278336871,0.1262161732,-0.1849325895,0.1418166012,0.0625836253,0.0791317225,-0.1860046238,0.2147678733,0.0651813671,-0.1725206524,-0.2546781301,-0.3686538041,-0.3149786294,-0.2519778907,0.1370450854,0.0119559988,0.1155910641,0.1756904423,0.2255754471,-0.0037887332,0.2176188827,-0.3331574798,-0.4415977001,-0.190395385,0.4190735817,-0.2170313001,-0.4003567994,-0.1111468598,-0.4238256812,-0.2446251959,-0.2530270517,-0.4782190323,-0.3155081272,-0.2766214311,0.0851034448,-0.1338302344,0.1919114143,0.438248843,-0.1354264617,-0.0175971724,-0.1220151111,-0.3827521801,0.0951114073,0.6282557845,0.3503098786,-0.1803290844,0.091885075,0.1060120016,0.1967720985,0.4577824771,0.1304442286,0.3684527278,-0.0855639204,0.3643560112,-0.3971609473,-0.2799599469,0.0358195342,-0.0419341922,-0.188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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3040","title":"[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset","comments":"I agree with @patrickvonplaten: this issue is reported recurrently, so better if we implement the `.flatten_indices()` automatically?","body":"## Describe the bug\r\n\r\nWhen only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo\/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset, save_to_disk\r\n\r\nnlp = load_dataset(\"glue\", \"mnli\", split=\"train\")\r\nnlp.save_to_disk(\"full\")\r\n\r\nnlp = nlp.select(range(100))\r\nnlp.save_to_disk(\"dummy\")\r\n```\r\n\r\nNow one can see that both `\"dummy\"` and `\"full\"` have the same size. This shouldn't be the case IMO.\r\n\r\n## Expected results\r\n\r\nIMO `\"dummy\"` should be much smaller so that one can easily play around with the dataset on the hub.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\n","comment_length":17,"text":"[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset \n ## Describe the bug\r\n\r\nWhen only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo\/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset, save_to_disk\r\n\r\nnlp = load_dataset(\"glue\", \"mnli\", split=\"train\")\r\nnlp.save_to_disk(\"full\")\r\n\r\nnlp = nlp.select(range(100))\r\nnlp.save_to_disk(\"dummy\")\r\n```\r\n\r\nNow one can see that both `\"dummy\"` and `\"full\"` have the same size. This shouldn't be the case IMO.\r\n\r\n## Expected results\r\n\r\nIMO `\"dummy\"` should be much smaller so that one can easily play around with the dataset on the hub.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\n \n I agree with @patrickvonplaten: this issue is reported recurrently, so better if we implement the `.flatten_indices()` automatically?","embeddings":[-0.1824214309,-0.113809213,0.0977014527,0.3485224545,0.1396677941,0.2466376573,0.2202678174,0.4321449995,0.0931753591,0.4231401384,0.2067136467,0.4109375477,-0.1631097049,0.0241226405,0.0257547349,0.0898384005,0.1856226325,0.1863040924,0.1133494005,-0.186726436,-0.3679889441,-0.034697175,-0.2251633704,-0.2867272794,-0.5878717899,-0.1352666765,-0.2150022984,0.2400487959,-0.2191441208,-0.1255783141,0.2466321141,-0.1921943724,0.010856268,0.3942228556,-0.0001180514,-0.0306326449,0.2950567007,-0.1383220404,-0.5959519148,-0.0872453153,-0.1021330282,-0.3036228716,0.0431589186,-0.3366670012,0.1549148411,0.1752594113,-0.0215229169,-0.1752218753,-0.0500183478,-0.109815672,0.1773789376,0.3541215062,-0.2485176623,-0.1996886134,0.0963103771,0.260673672,-0.291703254,0.4178822935,-0.0252499096,0.012755868,0.0795758292,0.0772192329,-0.1269273311,-0.2274494767,0.0135579389,0.1542257071,0.2082137018,-0.4271066785,0.0888291746,0.1758713871,0.3514673114,-0.4096342027,-0.3287012279,-0.4679723382,0.1070840508,-0.1454008818,0.0790995359,0.3163139224,0.1117239445,0.0284491554,-0.0080435462,0.0165638328,-0.0406812169,0.0566666014,0.1716334075,0.0803278834,0.0659949183,0.1112479493,0.3244348764,0.0607176311,0.1576551646,-0.4257392883,-0.0498691574,-0.051387243,-0.345939517,-0.3144321144,-0.0646128431,-0.2331473976,0.2141068131,0.1466633379,0.0828898847,-0.0512655936,0.1039324775,0.0972091183,0.2802479863,0.4042062461,-0.0045046434,0.2779263854,0.2386250943,0.0031678628,0.3111637533,0.1591968536,-0.0543330982,-0.0277814027,0.2901394069,-0.1067797765,0.107901521,-0.0390054807,-0.5261319876,0.2403636575,-0.0785611644,0.1253744364,-0.0990725458,0.045020137,0.0012911992,0.0460561179,-0.1295695007,0.3285558224,-0.360068351,0.1768575162,-0.2048030943,-0.0818338841,0.1339152455,-0.0262590125,0.1351950616,-0.1808651388,0.0544019863,0.1963542402,0.0110338898,0.1525797546,0.2140342444,-0.3012073338,0.154763937,0.3042809963,0.16463314,0.1646241099,-0.0107291946,-0.5549112558,-0.2624775171,0.4457949996,-0.0471825041,-0.2813354731,-0.1600618064,0.1427329928,-0.0473381206,0.1045618355,-0.2495882809,0.2826406956,0.1285541058,0.0049477494,0.0937051848,-0.0598873943,-0.2070244104,-0.3390337229,0.1187296882,0.2781873047,-0.3131510019,0.1823755652,0.0934561938,0.1286162585,0.1621197313,0.2420837432,-0.0059808274,0.1465028524,-0.1775266379,0.3466580212,0.1117451862,0.0016743994,-0.624917686,0.0209838822,-0.1478678286,0.1297532767,-0.0276126433,0.238225013,0.1030509174,0.0496229157,0.2067223191,0.287035048,-0.0057135648,-0.0930916592,-0.5067902207,-0.2917131484,0.1325445175,0.0996118486,-0.1132607311,0.1168393567,-0.0541846156,0.152791962,0.2984363139,0.0115783615,0.1294958293,0.2538181245,0.2744988203,-0.5812739134,0.0073869359,-0.194184944,-0.4004679024,0.2022342235,-0.0575538129,-0.0334873609,0.207796216,-0.4359383881,-0.354832083,-0.0341037884,-0.1060037538,0.1953344792,0.0236695409,-0.1109825447,0.1759208143,0.0710311607,-0.0550084636,0.0924753621,-0.2118326724,-0.0880593285,-0.5480938554,0.3700671196,-0.0334891975,-0.269952476,-0.0477563925,0.2148385197,0.2061794996,-0.1658431441,-0.1213964149,0.2399321049,-0.0687814653,-0.0206424519,-0.0287741702,0.0087209363,0.2264522612,0.136241734,0.089087896,0.3028978109,0.2653177381,-0.0386852995,-0.3866869807,0.2356378436,-0.3323497474,-0.0228695385,-0.1523447484,-0.0745188743,0.0411554351,-0.0315316767,-0.046096582,-0.2976568341,-0.0055451184,0.2622754872,0.0386884063,-0.0888222158,-0.241841495,0.1259613037,0.2611568272,-0.1725546122,0.3259118497,0.3228480816,-0.1374326646,-0.0832929611,0.0070280307,-0.063134551,0.5287135243,0.0956825167,0.0931685567,-0.1179796383,-0.1051544324,-0.147058323,0.3237075508,0.1603641659,-0.1255931258,0.4035588205,0.089024514,-0.0175057612,-0.398368448,0.4373594522,0.0874038488,0.157419771,-0.0741253197,-0.0384848192,-0.25409621,0.0013750383,0.0856668949,-0.0780276954,-0.1355518848,-0.2828027606,0.0547019653,0.5230623484,-0.2340425849,0.0045056823,-0.0343557149,0.5282377005,0.1925749183,-0.0800615177,0.014167225,0.1471464336,-0.3374717236,0.0165416878,0.1492857188,0.1304275692,0.3169644773,-0.0181378368,-0.0124610458,-0.178982541,-0.2209400684,0.0149250375,-0.1337041259,0.4910607636,-0.0371617749,0.0739381239,-0.2581019998,0.0902984142,0.0065871407,0.1845211238,-0.1270188689,0.1990924478,0.0488377698,-0.1689403206,-0.2667791247,-0.3044629693,-0.2898525,-0.1931368709,0.1496868432,-0.079442665,0.1808066517,0.1349316835,0.2245853543,-0.1167479903,0.1796952188,-0.3212722242,-0.4062312245,-0.1991112828,0.4217100739,-0.1044516861,-0.326051712,-0.2064410597,-0.4594880044,-0.3912559152,-0.2283607125,-0.5592181087,-0.2990948558,-0.2514797151,0.0949836597,-0.1802689284,0.2258631289,0.3877160847,-0.1121095493,0.0381691419,-0.0341174528,-0.3819514215,0.1171431243,0.6907037497,0.3149606586,-0.1198196337,0.0892537907,0.1076900661,0.1742885262,0.5434101224,0.1835006624,0.3248952627,0.0176830254,0.3773004413,-0.3554061353,-0.2926700711,-0.0809607431,-0.0957102999,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3040","title":"[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset","comments":"That would be great indeed - I don't really see a use case where one would not like to call `.flatten_indices()` before calling `save_to_disk`","body":"## Describe the bug\r\n\r\nWhen only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo\/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset, save_to_disk\r\n\r\nnlp = load_dataset(\"glue\", \"mnli\", split=\"train\")\r\nnlp.save_to_disk(\"full\")\r\n\r\nnlp = nlp.select(range(100))\r\nnlp.save_to_disk(\"dummy\")\r\n```\r\n\r\nNow one can see that both `\"dummy\"` and `\"full\"` have the same size. This shouldn't be the case IMO.\r\n\r\n## Expected results\r\n\r\nIMO `\"dummy\"` should be much smaller so that one can easily play around with the dataset on the hub.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\n","comment_length":24,"text":"[save_to_disk] Using `select()` followed by `save_to_disk` saves complete dataset making it hard to create dummy dataset \n ## Describe the bug\r\n\r\nWhen only keeping a dummy size of a dataset (say the first 100 samples), and then saving it  to disk to upload it in the following to the hub for easy demo\/use - not just the small dataset is saved but the whole dataset with an indices file. The problem with this is that the dataset is still very big.\r\n\r\n## Steps to reproduce the bug\r\n\r\nE.g. run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset, save_to_disk\r\n\r\nnlp = load_dataset(\"glue\", \"mnli\", split=\"train\")\r\nnlp.save_to_disk(\"full\")\r\n\r\nnlp = nlp.select(range(100))\r\nnlp.save_to_disk(\"dummy\")\r\n```\r\n\r\nNow one can see that both `\"dummy\"` and `\"full\"` have the same size. This shouldn't be the case IMO.\r\n\r\n## Expected results\r\n\r\nIMO `\"dummy\"` should be much smaller so that one can easily play around with the dataset on the hub.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\n \n That would be great indeed - I don't really see a use case where one would not like to call `.flatten_indices()` before calling `save_to_disk`","embeddings":[-0.1977923065,-0.1417636424,0.0666226447,0.2484790981,0.1402246356,0.2132203281,0.2300786972,0.4092865586,0.1048447192,0.4188419878,0.179849416,0.4132264853,-0.183023423,-0.0113671264,0.0974143744,0.081590198,0.2100723535,0.1471538693,0.0857832357,-0.1938105971,-0.3538570404,-0.0429385714,-0.2031716257,-0.2402988821,-0.5825731754,-0.1538551897,-0.2552961707,0.3503507376,-0.1936282665,-0.1488086432,0.1829911619,-0.152149111,-0.0034606026,0.3732103407,-0.0001118755,-0.0325940438,0.2789495587,-0.1452435106,-0.5515750647,-0.0471205972,-0.0590481497,-0.3082062006,-0.0078996588,-0.3504585624,0.1207971051,0.1892545074,0.0210367404,-0.1303116679,0.0142751839,-0.0035851586,0.238408193,0.3948935866,-0.2610619068,-0.2463668138,0.116863057,0.2067168504,-0.2616846859,0.3971014917,0.0437283628,0.023273889,0.0879835039,0.0416664705,-0.0989569575,-0.2398887575,0.0274792183,0.1991413981,0.1252735555,-0.3865589499,0.151944682,0.1520225406,0.3265634477,-0.4074038267,-0.3049967885,-0.4118420184,0.0830994099,-0.1462811083,0.0741890594,0.3014725447,0.059208408,0.0424719304,-0.0021616309,0.0335388817,-0.0522372909,0.0580203533,0.1535101533,0.1139572188,0.0477708578,0.1177479252,0.2698823512,0.0782432035,0.057556022,-0.4144047499,-0.0511409976,-0.0261351671,-0.280151844,-0.3041948378,-0.0125459218,-0.1516771019,0.1897178739,0.1437531114,0.0515998453,-0.0759510994,0.1328125447,0.0882505402,0.2017663866,0.3657119572,-0.0611900352,0.2186083645,0.2735137045,-0.0123815089,0.2792712152,0.1109457612,-0.0763516948,-0.042150557,0.2625474632,-0.1083037257,0.100431703,-0.0761471987,-0.5143136382,0.1527712196,-0.049001202,0.1797046065,-0.0629053414,0.1861971319,-0.0773151591,-0.068577081,-0.1280660331,0.3488468528,-0.3783810139,0.1637241989,-0.2652187645,0.0272064973,0.0539234877,-0.0693392381,0.1273181736,-0.1293837428,0.108636111,0.1448056698,-0.0045946557,0.2118902802,0.20695436,-0.3078584373,0.1424489021,0.3278591931,0.1940366179,0.1614962816,-0.066417709,-0.5430278182,-0.2594834864,0.3924203813,0.0038941912,-0.2803139985,-0.1710090935,0.2075323313,-0.114318423,0.0635976195,-0.1667931825,0.2285083085,0.0753565654,0.0062483414,0.1177989393,-0.0211899038,-0.1414744258,-0.4120807946,0.1449577361,0.2291033864,-0.3432753384,0.1477131397,0.0558114387,0.0793075264,0.1419508904,0.1744058877,-0.0441937223,0.1885417849,-0.1577699333,0.411806792,0.2100580931,-0.0976462215,-0.6079847217,-0.0466692187,-0.1598028839,0.1248292252,-0.0274634883,0.2837903202,0.0746336877,0.0379477441,0.1873244494,0.3415531814,0.0179857574,-0.0658537298,-0.5190656185,-0.2099470943,0.065526925,0.136282444,-0.1064373776,0.0395039953,0.0087476429,0.1809938401,0.3446835577,-0.020211529,0.1579243094,0.256741643,0.2704083622,-0.5342032909,-0.0411682576,-0.1754533499,-0.351436168,0.2400709838,-0.0526099168,-0.0125049558,0.1998316199,-0.394423902,-0.4044263065,-0.0553547479,-0.0877609029,0.0728787929,0.0979471877,-0.0615156442,0.1825806946,0.0425919853,-0.0759793594,0.1585500389,-0.1935742497,-0.0618260168,-0.5536085367,0.3859690428,-0.0915120468,-0.3280573487,-0.0350283757,0.2126302272,0.1772057712,-0.1135065407,-0.0940909982,0.3162437081,-0.0951056406,0.0406973884,-0.0201186892,0.0025903997,0.160834983,0.1244406998,0.1571010202,0.2498223037,0.2735660672,-0.0235077329,-0.4771657884,0.2311612517,-0.2927592695,-0.0810446665,-0.1208495125,-0.0608975179,0.0319917984,-0.0428190269,-0.0425485037,-0.1763475686,0.0703341886,0.2909359038,0.0909604877,-0.1151610762,-0.2419188619,0.1735586226,0.3396463096,-0.1919737607,0.3474773169,0.2817615569,-0.2003467381,-0.1335473508,0.0373678952,-0.0377689227,0.6007300615,0.1558988243,0.0883580744,-0.1008334532,-0.2186989486,-0.1595448256,0.2964480221,0.1223166138,-0.1436362267,0.2958210111,0.1177835166,-0.0496107936,-0.3815622628,0.3660567701,0.0397349335,0.1403037161,-0.0746119916,-0.0468751565,-0.2220275253,0.0091476431,0.097569041,-0.0845478773,-0.1518618166,-0.2975795567,0.0770290196,0.5313760638,-0.1911174804,-0.0311000254,0.023234304,0.5041885972,0.1211440414,-0.0425538979,-0.0122351851,0.1154704243,-0.343416065,0.1002829224,0.0953971744,0.2601560056,0.400315702,0.002141617,-0.0197245106,-0.1349077672,-0.1973682642,-0.0259969048,-0.1635120958,0.541750133,0.0183579959,0.0991628021,-0.1966389716,0.0967615396,0.0561495684,0.1406345665,-0.1366091967,0.1727806181,0.0687410757,-0.1653536111,-0.2481952161,-0.325432986,-0.3048186302,-0.2542737126,0.153766498,-0.0122883124,0.1313152462,0.1448341608,0.2448848486,0.0041391086,0.2224031538,-0.310112834,-0.4191607237,-0.2238923311,0.3832211494,-0.1818078011,-0.3602207303,-0.1531229913,-0.4102198482,-0.3148635626,-0.2500453591,-0.5173293352,-0.3352140188,-0.2655044794,0.1003340483,-0.1530312896,0.1927640289,0.4105791152,-0.1144198775,-0.0117200501,-0.0861051977,-0.3866618276,0.0798540711,0.6662328243,0.3316209614,-0.1858379543,0.045934543,0.1075594053,0.2206328064,0.4430142343,0.1405547708,0.3218773007,-0.0192041006,0.3616746366,-0.3665599227,-0.2806676328,-0.012996261,-0.067006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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3036","title":"Protect master branch to force contributions via Pull Requests","comments":"It would be nice to protect the master from direct commits, but still having a way to merge our own PRs when no review is required (for example when updating a dataset_infos.json file, or minor bug fixes - things that happen quite often actually).\r\nDo you know if there's a way ?","body":"In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed.\r\n- The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution\r\n- The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context\/discussions)\r\n\r\nNote that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests.\r\n\r\nPlease note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release.\r\n\r\nThe only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.\r\n","comment_length":52,"text":"Protect master branch to force contributions via Pull Requests \n In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed.\r\n- The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution\r\n- The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context\/discussions)\r\n\r\nNote that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests.\r\n\r\nPlease note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release.\r\n\r\nThe only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.\r\n \n It would be nice to protect the master from direct commits, but still having a way to merge our own PRs when no review is required (for example when updating a dataset_infos.json file, or minor bug fixes - things that happen quite often actually).\r\nDo you know if there's a way 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3036","title":"Protect master branch to force contributions via Pull Requests","comments":"This is done. Now the master branch is protected:\r\n- [x] Require a pull request before merging: all commits must be made to a non-protected branch and submitted via a pull request\r\n  - Required number of approvals before merging: 1 \r\n- [x] Require linear history: prevent merge commits from being pushed\r\n- [x] These requirements are not enforced for administrators\r\n- [x] Additionally, the master branch is also protected against deletion and force pushes\r\n\r\nCC: @lhoestq @julien-c @thomwolf ","body":"In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed.\r\n- The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution\r\n- The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context\/discussions)\r\n\r\nNote that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests.\r\n\r\nPlease note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release.\r\n\r\nThe only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.\r\n","comment_length":78,"text":"Protect master branch to force contributions via Pull Requests \n In order to have a clearer Git history in the master branch, I propose to protect it so that all contributions must be done through a Pull Request and no direct commits to master are allowed.\r\n- The Pull Request allows to give context, discuss any potential issues and improve the quality of the contribution\r\n- The Pull Request will eventually be squashed and merged into master with a single commit that links to the Pull Request page (with all the context\/discussions)\r\n\r\nNote that we already implemented a protection in the master branch to avoid *merge* commits and ensure a linear history. This proposal goes one step further by avoiding all kind of direct commits and forcing contributions **only** through Pull Requests.\r\n\r\nPlease note that we can temporarily deactivate this protection if we need to make a direct commit, e.g. at each new version release.\r\n\r\nThe only way GitHub allows this kind or protection is by requiring a minimal number (at least one) of approvals of the Pull Request. The inconvenient is that the PR creator cannot approve their own PR: another person must approve it before it can be merged into master. To circumvent this, we could eventually disable this protection in the master branch when an urgent commit is needed (e.g. for a hotfix) and there is no other person available at that time to approve the PR.\r\n \n This is done. Now the master branch is protected:\r\n- [x] Require a pull request before merging: all commits must be made to a non-protected branch and submitted via a pull request\r\n  - Required number of approvals before merging: 1 \r\n- [x] Require linear history: prevent merge commits from being pushed\r\n- [x] These requirements are not enforced for administrators\r\n- [x] Additionally, the master branch is also protected against deletion and force pushes\r\n\r\nCC: @lhoestq @julien-c @thomwolf 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3035","title":"`load_dataset` does not work with uploaded arrow file","comments":"Hi ! This is not a bug, this is simply not implemented.\r\n`save_to_disk` is for on-disk serialization and was not made compatible for the Hub.\r\nThat being said, I agree we actually should make it work with the Hub x)","body":"## Describe the bug\r\n\r\nI've preprocessed and uploaded a dataset here: https:\/\/huggingface.co\/datasets\/ami-wav2vec2\/ami_headset_single_preprocessed . The dataset is in `.arrow` format.\r\n\r\nThe dataset can correctly be loaded when doing:\r\n\r\n```bash\r\ngit lfs install\r\ngit clone https:\/\/huggingface.co\/datasets\/ami-wav2vec2\/ami_headset_single_preprocessed\r\n```\r\n\r\nfollowed by \r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\nds = load_from_disk(\".\/ami_headset_single_preprocessed\")\r\n```\r\n\r\nHowever when I try to directly download the dataset as follows:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"ami-wav2vec2\/ami_headset_single_preprocessed\")\r\n```\r\n\r\nthe following error occurs:\r\n\r\n```bash\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n   1115         ignore_verifications=ignore_verifications,\r\n   1116         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1117         use_auth_token=use_auth_token,\r\n   1118     )\r\n   1119 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    635                     if not downloaded_from_gcs:\r\n    636                         self._download_and_prepare(\r\n--> 637                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    638                         )\r\n    639                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    724             try:\r\n    725                 # Prepare split will record examples associated to the split\r\n--> 726                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    727             except OSError as e:\r\n    728                 raise OSError(\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1186                 generator, unit=\" tables\", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)\r\n   1187             ):\r\n-> 1188                 writer.write_table(table)\r\n   1189             num_examples, num_bytes = writer.finalize()\r\n   1190 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in write_table(self, pa_table, writer_batch_size)\r\n    424         # reorder the arrays if necessary + cast to self._schema\r\n    425         # we can't simply use .cast here because we may need to change the order of the columns\r\n--> 426         pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n    427         batches: List[pa.RecordBatch] = pa_table.to_batches(max_chunksize=writer_batch_size)\r\n    428         self._num_bytes += sum(batch.nbytes for batch in batches)\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_arrays()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib._sanitize_arrays()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.ChunkedArray.cast()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/compute.py in cast(arr, target_type, safe)\r\n    279     else:\r\n    280         options = CastOptions.unsafe(target_type)\r\n--> 281     return call_function(\"cast\", [arr], options)\r\n    282 \r\n    283 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/_compute.pyx in pyarrow._compute.call_function()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/_compute.pyx in pyarrow._compute.Function.call()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: Unsupported cast from struct<train: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, validation: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, test: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>> to list using function cast_list\r\n```\r\n\r\n## Expected results\r\n\r\nThe dataset should be correctly loaded with `load_dataset` IMO.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n","comment_length":40,"text":"`load_dataset` does not work with uploaded arrow file \n ## Describe the bug\r\n\r\nI've preprocessed and uploaded a dataset here: https:\/\/huggingface.co\/datasets\/ami-wav2vec2\/ami_headset_single_preprocessed . The dataset is in `.arrow` format.\r\n\r\nThe dataset can correctly be loaded when doing:\r\n\r\n```bash\r\ngit lfs install\r\ngit clone https:\/\/huggingface.co\/datasets\/ami-wav2vec2\/ami_headset_single_preprocessed\r\n```\r\n\r\nfollowed by \r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\nds = load_from_disk(\".\/ami_headset_single_preprocessed\")\r\n```\r\n\r\nHowever when I try to directly download the dataset as follows:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"ami-wav2vec2\/ami_headset_single_preprocessed\")\r\n```\r\n\r\nthe following error occurs:\r\n\r\n```bash\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n   1115         ignore_verifications=ignore_verifications,\r\n   1116         try_from_hf_gcs=try_from_hf_gcs,\r\n-> 1117         use_auth_token=use_auth_token,\r\n   1118     )\r\n   1119 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    635                     if not downloaded_from_gcs:\r\n    636                         self._download_and_prepare(\r\n--> 637                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    638                         )\r\n    639                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    724             try:\r\n    725                 # Prepare split will record examples associated to the split\r\n--> 726                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    727             except OSError as e:\r\n    728                 raise OSError(\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1186                 generator, unit=\" tables\", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)\r\n   1187             ):\r\n-> 1188                 writer.write_table(table)\r\n   1189             num_examples, num_bytes = writer.finalize()\r\n   1190 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in write_table(self, pa_table, writer_batch_size)\r\n    424         # reorder the arrays if necessary + cast to self._schema\r\n    425         # we can't simply use .cast here because we may need to change the order of the columns\r\n--> 426         pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n    427         batches: List[pa.RecordBatch] = pa_table.to_batches(max_chunksize=writer_batch_size)\r\n    428         self._num_bytes += sum(batch.nbytes for batch in batches)\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_arrays()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib._sanitize_arrays()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.ChunkedArray.cast()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/compute.py in cast(arr, target_type, safe)\r\n    279     else:\r\n    280         options = CastOptions.unsafe(target_type)\r\n--> 281     return call_function(\"cast\", [arr], options)\r\n    282 \r\n    283 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/_compute.pyx in pyarrow._compute.call_function()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/_compute.pyx in pyarrow._compute.Function.call()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: Unsupported cast from struct<train: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, validation: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>, test: struct<name: string, num_bytes: int64, num_examples: int64, dataset_name: string>> to list using function cast_list\r\n```\r\n\r\n## Expected results\r\n\r\nThe dataset should be correctly loaded with `load_dataset` IMO.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n \n Hi ! This is not a bug, this is simply not implemented.\r\n`save_to_disk` is for on-disk serialization and was not made compatible for the Hub.\r\nThat being said, I agree we actually should make it work with the Hub x)","embeddings":[-0.3928041458,-0.0385760143,0.0167482831,0.2226261795,0.1754212677,-0.0094853742,0.5628116727,0.085137479,0.2055559009,-0.017303789,0.0880218446,0.4524751604,-0.0171561372,0.0238934606,-0.0179098044,0.0333888605,0.0853121728,0.1029189825,-0.3803859651,0.0196575839,-0.1442720145,0.2501572669,-0.2272602171,0.1319896877,-0.2122670114,0.1587938219,0.2464158684,0.4314405024,-0.1021873131,-0.4040693939,0.4824698269,0.0766632557,0.1761621982,0.5237109661,-0.0001266862,0.2380869985,0.4700069427,-0.1962800622,-0.4834672213,-0.3951976895,-0.3488003016,-0.1319031268,0.3145769238,-0.0640478805,0.1231687665,-0.4004472196,-0.2247779965,-0.420604378,0.4897970855,0.1957876235,0.1233585775,0.1910223812,0.2313356698,-0.0189750828,0.0745915622,0.3110907674,-0.1752477735,0.3732423484,-0.0138064735,0.132981509,0.2628335059,0.153995052,0.0515643843,0.1624945104,0.3405152559,-0.0448476858,-0.1577795446,-0.0908572152,0.0367789567,0.2183103859,0.6042827368,-0.3275972903,-0.5339693427,-0.2552392781,0.0452337377,-0.208891809,0.2747498155,0.1828291714,-0.0922193974,0.1072444618,0.0007658764,-0.1723815352,-0.2028947771,0.1726585031,0.1561828703,-0.0302459393,-0.1611978114,0.2643852532,0.0948311388,-0.0824208409,0.0147343539,-0.155741632,-0.2428599298,0.3321098685,-0.2457002848,0.314684689,-0.2571251392,-0.3491085172,0.1799942255,0.4133507013,0.3458416462,0.0946355537,-0.1347861886,0.1890298128,0.3255594373,0.1741341203,0.0474796072,0.1320378482,0.2025796324,0.2669740021,-0.0124070244,-0.0857027248,-0.3571215272,-0.3845462203,0.3005620539,-0.1831320226,0.255812943,-0.0825493261,-0.218900159,-0.0353709571,-0.0499065183,-0.0716862679,-0.0803835168,0.1496354789,0.256111294,0.2459229678,0.2073506266,0.5286036134,-0.0260195844,-0.0935111269,0.0167674702,-0.2516648173,-0.0489038229,-0.0488448553,0.2139725089,-0.2573982179,0.4595925808,-0.0499899425,0.0545420237,-0.3271282315,0.0426556878,-0.0441374704,0.1464913785,0.3049205542,0.1913828105,0.1889034063,0.0683100671,0.0127459131,-0.0452578664,0.3910391033,-0.405847609,-0.226178214,-0.1780997515,0.0657504499,-0.1617332101,-0.01068989,-0.6739574075,-0.0922302306,-0.0615394525,-0.185182631,-0.0032767472,-0.2532427311,-0.2216698825,-0.196109578,0.380597502,0.3868361712,-0.4505640566,-0.1303249002,-0.3670470417,-0.0786690488,0.0814643279,0.2125306576,-0.2620464265,0.2521254718,-0.4439152181,0.2112655491,0.6082788706,-0.4810082018,-0.3692701161,0.3420464993,0.0852795318,0.3052792251,-0.0568237044,-0.1006570384,-0.2048591226,-0.0342247114,0.0161902308,0.3535475433,0.2185233533,-0.0379906036,-0.1238767505,-0.3244946897,-0.1962777823,0.021968985,-0.2239887416,0.0114567541,0.2258779109,-0.2454333603,0.2816851735,0.0187822692,0.0667978302,0.1274472475,0.1235368177,-0.1271107197,0.037144281,-0.0091041178,-0.8017456532,0.2910636365,-0.0601295233,-0.3009422719,-0.4226384163,0.0678754598,-0.3960783482,0.2581460178,-0.472035259,0.1035216972,-0.1119291931,0.0592124052,0.0588045456,-0.0274452884,-0.2426957637,0.25913167,-0.0610545762,0.1389798671,-0.1125165299,0.1307205856,0.2221420854,-0.1746871024,0.0325513892,-0.0048124031,0.0524020828,-0.0885367468,-0.2877314091,0.4403909743,-0.0073632197,0.2840344608,-0.0813916922,-0.1785120964,0.0485589057,-0.3220831156,-0.1697811186,0.1743814498,0.3360582292,-0.0005537861,-0.1871083826,0.2288465798,-0.1562402844,0.3641760647,0.1003365517,-0.0662215427,0.2525092065,0.2296377718,-0.1318341494,-0.1903583854,0.0053258119,0.0672820807,0.4375481904,-0.050209932,0.0184765607,-0.1339345425,0.2760748863,0.0936544314,0.0431072712,0.1131166667,-0.3350430429,-0.0052084662,-0.0236543603,0.2396187484,0.4954312146,0.0771514997,-0.0373495705,-0.0615654625,0.0860914215,-0.1167547479,0.297329843,0.0092099924,0.5016457438,0.5203508139,-0.0380838737,-0.0429958105,-0.1453178525,0.0857745484,0.0123480223,0.1249866709,-0.4560661018,0.1069254875,-0.2204195708,0.1423386484,-0.1825630516,-0.2159544379,-0.2390973717,-0.0587080568,-0.2701593041,0.1804379076,-0.0484640673,0.0130079035,-0.3017247319,0.0297416188,0.1759513915,-0.6335482001,-0.2036150396,0.1180636585,-0.1505991668,-0.1450366825,0.2618308663,0.0294442829,0.108705841,0.1060070321,0.1152716503,-0.2883921564,0.1273940653,0.0943462327,0.0325360671,0.3252062798,0.0032012002,0.0128995469,0.195506379,0.0235370621,0.2107594162,-0.0609451644,-0.1564697176,0.136918202,0.0409022644,-0.0140008079,-0.2085007876,-0.087110512,-0.0875754654,-0.4136025608,0.148088336,0.210526213,0.0891035125,0.2594256699,0.3279232681,0.0459988602,0.2531627715,-0.068163462,-0.0998170972,-0.2595494986,0.4124750495,-0.1651926786,-0.295486629,0.120020844,0.0689311028,0.2598119378,0.3976527452,-0.2447840124,0.3146594763,-0.0863738209,0.1802365929,-0.1535918415,0.1298538297,0.0940975547,0.0569917187,0.0842632204,-0.2452422976,-0.1371392459,0.4301894605,-0.0857712701,-0.0394985005,0.1920611113,0.2589606345,-0.1920582801,0.9589234591,0.1657007337,-0.1899140179,0.4515737593,-0.0626030341,0.2651635408,-0.2975690067,-0.1488751322,-0.3305054307,-0.0949041769,0.383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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3032","title":"Error when loading private dataset with \"data_files\" arg","comments":"We'll do a release tomorrow or on wednesday to make the fix available :)\r\n\r\nThanks for reproting !","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nPrivate datasets with no loading script can't be loaded using `data_files` parameter.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndata_files = {\"train\": \"**\/train\/*\/*.jsonl\", \"valid\": \"**\/valid\/*\/*.jsonl\"}\r\ndataset = load_dataset('dalle-mini\/encoded', data_files=data_files, use_auth_token=True, streaming=True)\r\n```\r\n\r\nSame error happens in non-streaming mode.\r\n\r\n## Expected results\r\nFiles should be loaded (whether in streaming or not).\r\n\r\n## Actual results\r\nError:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)\r\n    539                 try:\r\n--> 540                     local_path = cached_path(file_path, download_config=download_config)\r\n    541                 except FileNotFoundError:\r\n\r\n8 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/huggingface.co\/datasets\/dalle-mini\/encoded\/resolve\/main\/encoded.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nHTTPError                                 Traceback (most recent call last)\r\nHTTPError: 404 Client Error: Not Found for url: https:\/\/huggingface.co\/api\/datasets\/dalle-mini\/encoded?full=true\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)\r\n    547                     except Exception:\r\n    548                         raise FileNotFoundError(\r\n--> 549                             f\"Couldn't find a directory or a {resource_type} named '{path}'. \"\r\n    550                             f\"It doesn't exist locally at {expected_dir_for_combined_path_abs} or remotely on {hf_api.endpoint}\/datasets\"\r\n    551                         )\r\n\r\nFileNotFoundError: Couldn't find a directory or a dataset named 'dalle-mini\/encoded'. It doesn't exist locally at \/content\/dalle-mini\/encoded or remotely on https:\/\/huggingface.co\/datasets\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n\r\n@lhoestq ","comment_length":18,"text":"Error when loading private dataset with \"data_files\" arg \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nPrivate datasets with no loading script can't be loaded using `data_files` parameter.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndata_files = {\"train\": \"**\/train\/*\/*.jsonl\", \"valid\": \"**\/valid\/*\/*.jsonl\"}\r\ndataset = load_dataset('dalle-mini\/encoded', data_files=data_files, use_auth_token=True, streaming=True)\r\n```\r\n\r\nSame error happens in non-streaming mode.\r\n\r\n## Expected results\r\nFiles should be loaded (whether in streaming or not).\r\n\r\n## Actual results\r\nError:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)\r\n    539                 try:\r\n--> 540                     local_path = cached_path(file_path, download_config=download_config)\r\n    541                 except FileNotFoundError:\r\n\r\n8 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/huggingface.co\/datasets\/dalle-mini\/encoded\/resolve\/main\/encoded.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nHTTPError                                 Traceback (most recent call last)\r\nHTTPError: 404 Client Error: Not Found for url: https:\/\/huggingface.co\/api\/datasets\/dalle-mini\/encoded?full=true\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, return_associated_base_path, data_files, **download_kwargs)\r\n    547                     except Exception:\r\n    548                         raise FileNotFoundError(\r\n--> 549                             f\"Couldn't find a directory or a {resource_type} named '{path}'. \"\r\n    550                             f\"It doesn't exist locally at {expected_dir_for_combined_path_abs} or remotely on {hf_api.endpoint}\/datasets\"\r\n    551                         )\r\n\r\nFileNotFoundError: Couldn't find a directory or a dataset named 'dalle-mini\/encoded'. It doesn't exist locally at \/content\/dalle-mini\/encoded or remotely on https:\/\/huggingface.co\/datasets\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n\r\n@lhoestq  \n We'll do a release tomorrow or on wednesday to make the fix available :)\r\n\r\nThanks for reproting !","embeddings":[-0.2903700173,0.1561493129,0.0307244956,0.430328697,0.1911779493,0.025779346,0.4299965799,0.4009223282,0.1551969796,0.0551270284,-0.1564763486,0.230697006,-0.1625583321,-0.0824603885,0.0650250465,0.0666342825,-0.084966667,0.1085280254,0.0225836858,0.0579892285,-0.2959386408,0.1074577942,-0.2376065403,-0.1826736182,0.1591904312,-0.0057861619,0.1428616196,0.4598133266,-0.1129729971,-0.3364360034,0.1604583412,-0.2739299536,0.3451860845,0.59301126,-0.0001165896,0.3240575194,0.5787169933,-0.1371546239,-0.5110728145,-0.3130856156,-0.4098590016,-0.0029053765,0.1808122396,-0.1826783121,-0.1313366741,-0.3966876268,-0.0281594731,-0.421859622,0.5495311022,0.3786801696,0.1711920202,0.3091495633,-0.1985797584,-0.1525232345,0.1244914383,0.1165711656,-0.0178763177,0.2135336697,-0.013781325,0.1712692827,-0.1668857485,0.28704983,-0.3148833513,-0.0619247779,0.3347285688,0.1140482649,-0.09244968,-0.209716484,0.1381493509,0.1114524826,0.7266331911,-0.2186493725,-0.3816860616,-0.2797209322,0.1342336237,-0.2664972842,0.1291685104,0.2379081845,-0.1455373168,0.0822928548,-0.1256530732,-0.0256853849,-0.2556142807,0.2286863774,0.3236038089,-0.1296074241,-0.0598883927,0.0780799463,-0.1961849332,-0.1295455843,0.0115271769,-0.1408375055,-0.091888234,0.2059097886,-0.0049391412,0.1666925251,0.1341944486,0.0815421194,0.1596106589,0.3349063396,0.2448388338,-0.0739026964,-0.1038275287,0.0755733177,0.0272546504,0.158567518,0.0338095762,0.1374547184,0.4046430588,0.4681552052,-0.0009268171,0.010207057,-0.1450332999,-0.0577476956,-0.1240944862,-0.1071584672,0.5488464236,-0.2591855228,-0.4094162285,-0.00856164,-0.0853659362,0.0556016788,0.0278590657,0.4063490331,-0.0116301551,-0.0949265435,0.2150746584,0.1685712934,-0.0836135745,-0.235735774,-0.1969790012,-0.1580425501,0.0442066416,0.1360569596,0.3235349059,-0.1387713552,0.3186034262,0.1817256361,0.0174636655,0.0129203191,0.1237941608,0.0407030992,-0.2395694405,0.3833339512,0.1197658628,0.0658134446,0.2378252,-0.3579256833,0.0498055331,0.1413372457,-0.3321944475,-0.4053561389,0.0861589983,0.1552071422,-0.3704569638,0.117034398,-0.3429154754,-0.0725015774,0.0652615353,-0.3319216371,0.0167287216,-0.1941316277,-0.3359287083,-0.2444748729,0.3545154929,0.8923536539,-0.2905390263,-0.103223443,-0.2116887867,-0.3194523752,0.2664178312,0.144648537,-0.1367924809,-0.0281723533,-0.4125277698,0.0816559345,0.5212769508,-0.2712144256,-0.4752932191,0.6177859902,-0.1550975144,0.3110102713,0.2835176885,0.0969902948,0.1750849187,-0.028084876,0.3497490883,0.2068122029,0.0469445325,-0.0708378479,-0.3066215217,-0.1830465943,0.2643436491,0.2997501194,0.0115307756,0.150228247,0.1405100673,-0.0236654673,0.2652821243,0.0223092623,-0.0503898859,0.1050876677,0.2761021554,-0.0372775644,0.1814168245,0.0396462567,-0.5989335179,0.2171809822,-0.0399853215,-0.1016433388,-0.2662972808,-0.0913016871,-0.1901819855,-0.1478462815,-0.2657321393,-0.1175490767,0.0181949884,0.1151557341,-0.1710781008,0.1136909798,-0.2751096189,0.4989174902,-0.3676986992,0.1053173617,-0.4798952341,0.1261621416,0.1682884991,-0.2656128407,0.1940930933,-0.017059423,0.2895063162,-0.0321599506,-0.1867906302,0.3034256995,0.3349787593,0.1036499664,0.1065268889,-0.0694364235,0.092259936,-0.1398902684,0.0556779131,0.4280016124,0.229541555,0.1058368757,0.075229153,0.2571936548,-0.0469540022,0.2572446167,0.0489852615,-0.1629816145,0.0540583283,-0.0314040333,-0.3879783154,-0.2681676447,0.3927337229,-0.2225757539,0.3901296258,0.0790241361,-0.2781417072,-0.0226776525,0.0105618592,-0.0504221208,0.0651867762,0.271617204,-0.2775190771,0.084648028,-0.045197174,0.4522235394,0.6720915437,0.1380175948,-0.1731875241,0.1056028381,0.0350390486,-0.2162675858,0.1507758945,-0.0568150431,0.1227646694,0.3448737562,-0.0328348577,0.0852068961,-0.1000743806,-0.2531380653,-0.0609647408,0.1922787726,-0.5153962374,-0.1262349635,-0.4613442719,-0.1893223077,0.0668304563,-0.0625128597,-0.1729181558,0.0013633077,-0.0467901751,-0.0019259414,-0.1101061553,0.0273680072,-0.5067340732,0.2731795907,0.2176575363,-0.4486321807,-0.3038973808,0.0813087299,-0.0804772824,-0.029368775,0.3875233233,-0.0793236047,0.2327370197,-0.1770058125,-0.3405685425,-0.214531824,-0.1017042026,-0.0872658864,0.0106230024,0.559032023,0.0862335712,0.2060578167,0.3480833471,0.0271177609,0.2462596148,-0.0568853058,-0.1871891469,0.1424201131,-0.0919245929,0.0820614249,-0.4319984615,-0.3623188138,-0.2072741538,-0.4887992442,0.1106831804,0.1485090703,0.1030100957,0.1551558673,0.3054748178,0.255785495,0.0543630607,0.1256958395,-0.128133595,-0.227732107,0.6152669191,-0.2603027821,-0.323928237,0.2170604765,0.0550892986,0.09618444,0.0684607476,-0.5978400111,-0.2890000343,0.0116827935,-0.0207069479,-0.1982985735,-0.2223130465,0.0216023549,-0.1161593571,0.0259666294,-0.2537699342,-0.2282101959,0.0158920102,-0.1488070786,0.1596875787,0.083707571,0.5658364892,-0.0231266469,0.6997222304,0.2779694498,0.0421082191,0.1989482194,-0.0802294165,0.4185763001,-0.1145561263,-0.1348790526,-0.0882574171,0.0967891142,-0.2061605603,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3027","title":"Resolve data_files by split name","comments":"Awesome @lhoestq I like the proposal and it works great on my JSON community dataset. Here is the [log](https:\/\/gist.github.com\/vblagoje\/714babc325bcbdd5de579fd8e1648892). ","body":"This issue is about discussing the default behavior when someone loads a dataset that consists in data files. For example:\r\n```python\r\nload_dataset(\"lhoestq\/demo1\")\r\n```\r\nshould return two splits \"train\" and \"test\" since the dataset repostiory is like\r\n```\r\ndata\/\r\n\u251c\u2500\u2500 train.csv\r\n\u2514\u2500\u2500 test.csv\r\n```\r\nCurrently it returns only one split \"train\" which contains the data of both files\r\n\r\n\r\nI started playing with this idea on this branch btw: `resolve-data_files-by-split-name`\r\nBasically the idea is that if you named you data files after split names then the default pattern is\r\n```python\r\n{\r\n    \"train\": [\"*train*\"],\r\n    \"test\": [\"*test*\"],\r\n    \"validation\": [\"*dev*\", \"valid\"],\r\n}\r\n```\r\notherwise it's\r\n```python\r\n{\r\n    \"train\": [\"*\"]\r\n}\r\n```\r\n\r\nLet me know what you think !\r\ncc @albertvillanova @LysandreJik @vblagoje ","comment_length":19,"text":"Resolve data_files by split name \n This issue is about discussing the default behavior when someone loads a dataset that consists in data files. For example:\r\n```python\r\nload_dataset(\"lhoestq\/demo1\")\r\n```\r\nshould return two splits \"train\" and \"test\" since the dataset repostiory is like\r\n```\r\ndata\/\r\n\u251c\u2500\u2500 train.csv\r\n\u2514\u2500\u2500 test.csv\r\n```\r\nCurrently it returns only one split \"train\" which contains the data of both files\r\n\r\n\r\nI started playing with this idea on this branch btw: `resolve-data_files-by-split-name`\r\nBasically the idea is that if you named you data files after split names then the default pattern is\r\n```python\r\n{\r\n    \"train\": [\"*train*\"],\r\n    \"test\": [\"*test*\"],\r\n    \"validation\": [\"*dev*\", \"valid\"],\r\n}\r\n```\r\notherwise it's\r\n```python\r\n{\r\n    \"train\": [\"*\"]\r\n}\r\n```\r\n\r\nLet me know what you think !\r\ncc @albertvillanova @LysandreJik @vblagoje  \n Awesome @lhoestq I like the proposal and it works great on my JSON community dataset. Here is the [log](https:\/\/gist.github.com\/vblagoje\/714babc325bcbdd5de579fd8e1648892). 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3027","title":"Resolve data_files by split name","comments":"From my discussion with @borisdayma it would be more general the files match if their paths contains the split name - not only if the filename contains the split name. For example for a dataset like this:\r\n```\r\ntrain\/\r\n\u2514\u2500\u2500 data.csv\r\ntest\/\r\n\u2514\u2500\u2500 data.csv\r\n```\r\n\r\nBut IMO the default should be \r\n```\r\ndata\/\r\n\u251c\u2500\u2500 train.csv\r\n\u2514\u2500\u2500 test.csv\r\n```\r\nbecause it allows people to have other directories if they have different subsets of their data (different configurations, not splits)","body":"This issue is about discussing the default behavior when someone loads a dataset that consists in data files. For example:\r\n```python\r\nload_dataset(\"lhoestq\/demo1\")\r\n```\r\nshould return two splits \"train\" and \"test\" since the dataset repostiory is like\r\n```\r\ndata\/\r\n\u251c\u2500\u2500 train.csv\r\n\u2514\u2500\u2500 test.csv\r\n```\r\nCurrently it returns only one split \"train\" which contains the data of both files\r\n\r\n\r\nI started playing with this idea on this branch btw: `resolve-data_files-by-split-name`\r\nBasically the idea is that if you named you data files after split names then the default pattern is\r\n```python\r\n{\r\n    \"train\": [\"*train*\"],\r\n    \"test\": [\"*test*\"],\r\n    \"validation\": [\"*dev*\", \"valid\"],\r\n}\r\n```\r\notherwise it's\r\n```python\r\n{\r\n    \"train\": [\"*\"]\r\n}\r\n```\r\n\r\nLet me know what you think !\r\ncc @albertvillanova @LysandreJik @vblagoje ","comment_length":78,"text":"Resolve data_files by split name \n This issue is about discussing the default behavior when someone loads a dataset that consists in data files. For example:\r\n```python\r\nload_dataset(\"lhoestq\/demo1\")\r\n```\r\nshould return two splits \"train\" and \"test\" since the dataset repostiory is like\r\n```\r\ndata\/\r\n\u251c\u2500\u2500 train.csv\r\n\u2514\u2500\u2500 test.csv\r\n```\r\nCurrently it returns only one split \"train\" which contains the data of both files\r\n\r\n\r\nI started playing with this idea on this branch btw: `resolve-data_files-by-split-name`\r\nBasically the idea is that if you named you data files after split names then the default pattern is\r\n```python\r\n{\r\n    \"train\": [\"*train*\"],\r\n    \"test\": [\"*test*\"],\r\n    \"validation\": [\"*dev*\", \"valid\"],\r\n}\r\n```\r\notherwise it's\r\n```python\r\n{\r\n    \"train\": [\"*\"]\r\n}\r\n```\r\n\r\nLet me know what you think !\r\ncc @albertvillanova @LysandreJik @vblagoje  \n From my discussion with @borisdayma it would be more general the files match if their paths contains the split name - not only if the filename contains the split name. For example for a dataset like this:\r\n```\r\ntrain\/\r\n\u2514\u2500\u2500 data.csv\r\ntest\/\r\n\u2514\u2500\u2500 data.csv\r\n```\r\n\r\nBut IMO the default should be \r\n```\r\ndata\/\r\n\u251c\u2500\u2500 train.csv\r\n\u2514\u2500\u2500 test.csv\r\n```\r\nbecause it allows people to have other directories if they have different subsets of their data (different configurations, not splits)","embeddings":[0.0270638969,-0.0892496482,-0.1097631752,0.1351384968,0.0538495407,-0.1521057934,0.3358280659,0.5063108206,0.1621048748,-0.0166822691,0.2228466868,0.1351216584,-0.1136881188,0.2894292474,-0.1998567879,-0.2272005975,-0.0563693009,0.0884636939,0.0838887095,0.0457754955,-0.3167465627,0.0998689607,-0.1170416325,0.1179772168,-0.3923658729,0.2584532499,0.0115597369,0.3857000172,-0.037764024,-0.3534212112,0.2145462483,0.181407392,0.1407522261,0.163941443,-0.0001185402,-0.072109513,0.4363927841,-0.1522338092,0.220181182,-0.367803961,-0.4510225058,-0.2540358305,0.3084780276,-0.4188295603,-0.0676248148,-0.139395982,0.0806283057,-0.2843283117,0.3402704298,-0.0552421547,0.0952068791,-0.2253267169,-0.0863787159,0.1467776746,0.1762958318,0.3417215049,0.1126108319,0.2838568389,0.0524288453,-0.2838109136,0.1041060835,-0.102822043,-0.0584659912,0.176157549,0.1245812252,0.1175099015,-0.1545900702,-0.3509627283,-0.1656112373,0.3802145422,0.266092509,-0.0478129312,-0.194650352,-0.51572299,-0.1008810103,-0.0895038769,0.31681934,0.2614824176,-0.0516103618,0.1412387341,0.1236385331,-0.0023075859,0.0237717573,0.0797262862,-0.4112017751,0.2440209389,-0.1261061281,0.2495102435,0.1241004765,0.0006029287,0.0852397829,-0.3216855228,0.1844170094,0.1678652614,-0.3733210862,-0.1084709018,-0.0317973942,0.1747062355,0.279307574,0.0383365229,-0.0225678105,-0.0568160564,-0.1061782017,0.0022388955,0.3379701078,0.2131718695,0.2825787663,-0.0370281674,0.2807859778,0.0367979147,-0.263692528,0.1309579015,-0.0679046065,-0.3493283391,-0.3827217519,0.1193391681,0.4428101778,0.0174136069,-0.1657602042,-0.0423221514,-0.1241664663,-0.4989936948,0.1497683823,0.2121201456,0.0484752208,0.5931286812,-0.3226957619,0.0946145281,0.075377278,-0.1129216552,-0.0209343452,-0.3209089935,-0.4212556183,-0.0491283163,0.1755793095,-0.0049979761,0.1248717606,0.1342567503,0.0588836707,-0.1949623972,0.3118009567,-0.1139665917,0.0684163943,0.1643889844,-0.1177615821,0.1898178905,0.0866966546,-0.2004945278,-0.3348344862,0.3298317194,-0.7846764326,-0.4369293451,0.1633819491,0.1231913194,-0.1893168539,0.4690492451,-0.005164172,0.034833923,0.0976273715,-0.1030716375,-0.0631070733,-0.3695322871,0.1701549441,-0.3755721152,0.2521471679,0.1412463039,-0.3451225162,0.1652319431,-0.323967725,-0.2678652108,-0.0005796389,0.4028593302,-0.3052545488,0.1310660094,-0.4376269281,0.4604645371,0.3293556273,-0.1856958717,-0.3755874932,0.3393337131,0.0338668041,0.1399630904,0.2919980288,0.0367675386,0.1881822497,-0.072258167,0.1695459783,0.2610369623,-0.0801317692,0.045574192,-0.0977777466,-0.2554684281,-0.0259623565,-0.0219876356,-0.2103601545,-0.0115204966,0.0805358216,-0.1600058675,0.5113764405,0.0065154196,-0.1614560783,-0.0932050869,0.1350596398,0.1334781498,0.2491547763,0.180725202,-0.4748724103,0.2074028552,-0.1224530339,-0.5045092106,-0.2311446071,-0.3620306849,-0.152839303,-0.1678920984,-0.3231675923,0.4038153887,0.0623151399,0.1333552152,-0.2513743043,-0.1129919142,-0.3945623934,0.2968928814,-0.306931138,0.0122589981,-0.4265828133,0.4831362069,0.1446909308,0.2009452432,-0.346020788,0.0433638431,0.041418191,-0.1183137819,0.2100969106,0.444914639,0.4787169397,0.1327355802,0.1388509572,0.2663660049,0.0823091492,-0.2328074723,0.0662160367,0.0787866116,-0.0238834228,-0.0550849028,-0.2458170652,0.321051836,-0.5423387289,0.3390042782,0.0063306685,-0.3598770499,0.2438615561,-0.2079190016,-0.3672236204,-0.2697355747,0.0550815985,0.2147869021,0.4444977939,0.3003084362,-0.2010456473,-0.1982327551,0.4653484523,-0.2203814536,0.0030789089,0.1786044091,0.0384304002,-0.0726509094,-0.0788449943,0.4924039543,0.6452199817,0.1851120293,0.0622964539,-0.0040566451,0.0917088017,-0.2873215973,0.1623195261,-0.0126949092,-0.2111666501,0.4826069772,-0.1999593824,-0.0064448179,-0.2408777326,-0.1191613227,0.0133677842,-0.1565806121,-0.3247489929,-0.1735367477,-0.1142806634,0.0186563022,-0.2934766114,-0.0460514762,-0.3018512726,-0.09840893,0.0589908734,-0.2609007657,-0.2200454473,0.0481567569,0.0537725016,0.4499253035,-0.1041512489,-0.4823902547,0.010930378,-0.1258500963,0.0902595147,0.0256826971,0.0116398307,0.0233025998,0.1768388003,-0.449742198,-0.1201990917,-0.0309916213,-0.5094630718,0.0606880449,0.0879433155,0.1646253616,0.3704209328,0.2070943862,0.509442687,-0.4992419481,0.0513431281,0.3034721315,-0.0733082741,-0.1588930339,0.3971946836,0.1793325394,-0.1391334534,-0.3126041591,-0.2604975998,-0.4406672418,0.4037440717,-0.0275973696,0.0368138403,-0.2688921094,-0.241542846,-0.1569707394,-0.4251074493,-0.0523571782,-0.1164751947,-0.0321598239,0.1537635773,0.1665874422,0.0599495023,-0.0274719559,-0.2557914257,0.0776544958,0.1224521697,-0.1252755374,-0.202287063,-0.1485557407,0.226999566,0.0817574114,0.0948812887,0.0927125737,0.206980288,0.0487037823,-0.0922679231,0.1175594777,0.0906672031,0.0926110148,0.1174343452,0.5096938014,0.0975977927,-0.093182154,0.7195664644,0.2749606669,0.0252628271,0.0763146952,0.134448126,0.1696209759,-0.0121271536,-0.133404687,0.0079035917,-0.009010219,-0.216818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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3027","title":"Resolve data_files by split name","comments":"I just created a PR for this at https:\/\/github.com\/huggingface\/datasets\/pull\/3221, let me know what you think :)","body":"This issue is about discussing the default behavior when someone loads a dataset that consists in data files. For example:\r\n```python\r\nload_dataset(\"lhoestq\/demo1\")\r\n```\r\nshould return two splits \"train\" and \"test\" since the dataset repostiory is like\r\n```\r\ndata\/\r\n\u251c\u2500\u2500 train.csv\r\n\u2514\u2500\u2500 test.csv\r\n```\r\nCurrently it returns only one split \"train\" which contains the data of both files\r\n\r\n\r\nI started playing with this idea on this branch btw: `resolve-data_files-by-split-name`\r\nBasically the idea is that if you named you data files after split names then the default pattern is\r\n```python\r\n{\r\n    \"train\": [\"*train*\"],\r\n    \"test\": [\"*test*\"],\r\n    \"validation\": [\"*dev*\", \"valid\"],\r\n}\r\n```\r\notherwise it's\r\n```python\r\n{\r\n    \"train\": [\"*\"]\r\n}\r\n```\r\n\r\nLet me know what you think !\r\ncc @albertvillanova @LysandreJik @vblagoje ","comment_length":16,"text":"Resolve data_files by split name \n This issue is about discussing the default behavior when someone loads a dataset that consists in data files. For example:\r\n```python\r\nload_dataset(\"lhoestq\/demo1\")\r\n```\r\nshould return two splits \"train\" and \"test\" since the dataset repostiory is like\r\n```\r\ndata\/\r\n\u251c\u2500\u2500 train.csv\r\n\u2514\u2500\u2500 test.csv\r\n```\r\nCurrently it returns only one split \"train\" which contains the data of both files\r\n\r\n\r\nI started playing with this idea on this branch btw: `resolve-data_files-by-split-name`\r\nBasically the idea is that if you named you data files after split names then the default pattern is\r\n```python\r\n{\r\n    \"train\": [\"*train*\"],\r\n    \"test\": [\"*test*\"],\r\n    \"validation\": [\"*dev*\", \"valid\"],\r\n}\r\n```\r\notherwise it's\r\n```python\r\n{\r\n    \"train\": [\"*\"]\r\n}\r\n```\r\n\r\nLet me know what you think !\r\ncc @albertvillanova @LysandreJik @vblagoje  \n I just created a PR for this at https:\/\/github.com\/huggingface\/datasets\/pull\/3221, let me know what you think 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3018","title":"Support multiple zipped CSV data files","comments":"@lhoestq I would like to draw your attention to the proposed API by @lewtun, using `data_dir` to pass the ZIP URL.\r\n\r\nI'm not totally convinced with this... What do you think?\r\n\r\nMaybe we could discuss other approaches...\r\n\r\nOne brainstorming idea: what about using URL chaining with the hop operator in `data_files`?","body":"As requested by @lewtun, support loading multiple zipped CSV data files.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https:\/\/domain.org\/filename.zip\"\r\ndata_files = {\"train\": \"train_filename.csv\", \"test\": \"test_filename.csv\"}\r\ndataset = load_dataset(\"csv\", data_dir=url, data_files=data_files)\r\n```\r\n","comment_length":51,"text":"Support multiple zipped CSV data files \n As requested by @lewtun, support loading multiple zipped CSV data files.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https:\/\/domain.org\/filename.zip\"\r\ndata_files = {\"train\": \"train_filename.csv\", \"test\": \"test_filename.csv\"}\r\ndataset = load_dataset(\"csv\", data_dir=url, data_files=data_files)\r\n```\r\n \n @lhoestq I would like to draw your attention to the proposed API by @lewtun, using `data_dir` to pass the ZIP URL.\r\n\r\nI'm not totally convinced with this... What do you think?\r\n\r\nMaybe we could discuss other approaches...\r\n\r\nOne brainstorming idea: what about using URL chaining with the hop operator in `data_files`?","embeddings":[-0.0436359681,0.0263970699,-0.2615996301,-0.0185476616,0.0255620033,-0.0422075875,0.3967733681,0.251575619,0.3005161285,0.1386564225,0.0246005636,0.3511762917,-0.073824279,0.4689003527,-0.0553035028,-0.0315262266,0.1049564332,0.0371837877,-0.2543559074,0.1798334271,-0.4606361687,0.1624909043,-0.2674738467,-0.2959582508,0.1731171459,0.1512974799,-0.4463757873,0.1381659806,-0.1852311492,-0.2603342831,0.3103385866,0.4319655299,0.0832271054,0.3215489388,-0.000100246,-0.0111010885,0.1156264916,-0.038266249,-0.2498729527,-0.4966268241,-0.0618846193,-0.5920066237,0.1507882923,-0.3615575433,-0.0270741619,-0.2116896361,0.0227410998,-0.3560551405,0.5224738121,0.0643954128,0.247826308,-0.1120522171,-0.2025864571,-0.0920370519,0.0234452002,0.142921418,-0.0554822683,0.2300870568,0.3940664232,-0.3152674437,-0.1596295834,0.0186503809,-0.0154167255,0.1507207155,0.2266869247,0.029269468,-0.2085193396,-0.2015845925,-0.2037310451,-0.0787250996,0.5302681327,-0.1150343791,-0.4657896161,-0.2253689617,-0.1263517886,-0.2694242895,0.0023906189,0.2622334957,-0.090764828,0.201509133,-0.1180955991,-0.1633691639,-0.2983178794,0.1397203356,0.016654335,0.4006111324,-0.0975455716,0.1752037257,0.1303572506,0.0758952871,0.3332109749,-0.2281384319,0.4936175048,0.1271244287,-0.2799767852,-0.1529814303,-0.0303083006,-0.1952121854,0.2260189205,0.4028481543,0.2682906687,-0.0182480849,-0.1999376267,0.1068641618,0.3378008306,0.0317783765,-0.1353937238,-0.0460312665,0.2748930454,0.1731686294,0.1857204735,-0.1363654435,-0.0758869052,-0.1692975461,-0.2145167291,0.2636391222,0.1367031336,0.2679504454,-0.1150045469,-0.1711585671,-0.1239368543,-0.33249107,0.0483511537,0.1693087816,-0.2614633143,0.4086423218,-0.091106385,0.2081848979,0.1356903315,-0.0016185699,-0.0810256153,0.0636236593,-0.2558551133,0.1231587008,0.2967294753,0.0194938127,0.0508868173,0.2665131092,0.2375354767,-0.0483861119,0.2765945792,0.0274856593,0.1173676997,-0.0025271254,0.134636268,0.112548165,0.2061368823,-0.1383029372,-0.1542518735,0.2669121921,-0.6838110089,-0.2387516499,0.0750438645,0.2425269186,-0.111831367,0.01224038,-0.2211935222,0.2078766078,-0.2809883654,-0.0636904463,-0.0652964264,0.1397292614,-0.212067157,-0.427516818,0.0195958242,0.421941638,-0.164459303,0.0397631489,-0.1026984304,0.06388814,-0.0415248983,0.1556163281,-0.4455311,-0.0630902126,-0.2526977956,0.1106395498,0.4125576615,-0.5059450865,-0.1921289861,0.5535314083,0.0032554511,0.153346613,0.3123893738,-0.0982754454,0.1020748392,-0.1097075716,0.2193707973,0.4260475039,-0.0111637805,0.0763111115,-0.1428826749,-0.4190199971,0.2785040736,0.2693854868,-0.1089916825,-0.127796486,-0.0768968537,-0.7075768709,0.5397415161,-0.1505293101,0.0171480551,-0.203527078,0.0456460938,0.1132194623,-0.0430056453,0.1150097549,-0.4236178696,0.1653789878,-0.0717538819,-0.1156512871,-0.1464955509,-0.2864287198,-0.1096562296,-0.0159441791,-0.183744356,-0.0471788459,0.2330162674,0.0539154634,-0.2111237496,-0.0962468684,-0.2758667469,0.2824772298,0.2066917419,0.1845916361,-0.1800290644,0.3112853169,0.0513940938,0.312639147,0.0851909071,0.0641660318,0.1782820374,-0.3219421506,0.148112148,0.2800832391,0.1103477925,0.0278668385,0.2154511213,-0.0246928427,0.2957789302,0.019136453,0.1111767963,0.2755138874,0.0702282712,-0.1409219503,-0.3595096469,0.5948723555,-0.3154379129,0.135562405,0.0929266587,0.0885934681,0.2146630436,-0.1898740381,-0.274146229,-0.0101427166,-0.0003114044,0.2729881704,0.2020614594,-0.1027798057,-0.0831912979,0.0039276755,-0.0492047109,-0.170523569,-0.2223509997,0.1758680195,0.0612683408,-0.1614272892,0.1547977477,0.5783026814,0.5669012666,0.3175036311,-0.0222533774,-0.1476722658,0.1143330857,-0.2679820657,0.2796592414,-0.0565998852,-0.0011971236,0.1733516455,0.023410121,0.0007522935,-0.1147570163,-0.3495039046,0.2905475795,-0.0434748419,-0.0525842719,0.0775065869,-0.1852651387,-0.4047700167,-0.169755891,-0.2295651883,0.1156113148,-0.2231193185,0.0008892582,0.0627503395,-0.1485074461,0.0615699701,-0.0149181373,0.4625588655,-0.1160438508,-0.2457060516,0.074019976,0.120310992,0.1111497134,0.2595192194,0.3215943575,-0.1730076522,0.3103784025,0.3767892718,0.1228503361,-0.2942708433,-0.0264552217,-0.0198007803,0.0405317955,0.2414725721,0.1998558342,0.0220445469,0.2829360962,-0.19152233,0.0830708519,0.2160791904,0.1934794188,-0.2949893773,0.222293064,0.010738614,-0.1970660836,-0.2688370645,-0.4581337571,-0.230117783,0.413351655,-0.0224769544,0.1804953367,0.1178757176,0.0956539884,-0.1148528159,0.3196907043,-0.3219422102,-0.1252059042,-0.5076751709,0.2632461786,-0.2517259121,-0.1867696941,-0.1030997187,-0.018434979,0.2382460386,0.0730056465,-0.1397068352,-0.2729614377,-0.0326203965,0.2985886037,-0.1966245919,0.001050706,0.2337563038,0.1340591609,-0.1706003398,0.0104451245,-0.1902242005,-0.0386980958,0.0194456466,-0.0252129547,0.2876065075,0.0379486308,0.176584363,0.5748739243,0.2305502445,0.3427622318,0.4909664094,0.0696866736,0.1371338218,0.2529777586,-0.4236736,-0.1547653675,-0.2582744062,-0.09930184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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3018","title":"Support multiple zipped CSV data files","comments":"`data_dir` is currently exclusively used for manually downloaded data.\r\n\r\nMaybe we can have an API that only uses data_files as you are suggesting, using URL chaining ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https:\/\/domain.org\/filename.zip\"\r\ndata_files = {\"train\": \"zip:\/\/train_filename.csv::\" + url, \"test\": \"zip:\/\/test_filename.csv::\" + url}\r\ndataset = load_dataset(\"csv\", data_files=data_files)\r\n```\r\n\r\nURL chaining is used by `fsspec` to get access to files in nested filesystems of any kind. Since `fsspec` is being used by `pandas`, `dask` and also extensively by `datasets` I think it would be nice to use it here too","body":"As requested by @lewtun, support loading multiple zipped CSV data files.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https:\/\/domain.org\/filename.zip\"\r\ndata_files = {\"train\": \"train_filename.csv\", \"test\": \"test_filename.csv\"}\r\ndataset = load_dataset(\"csv\", data_dir=url, data_files=data_files)\r\n```\r\n","comment_length":91,"text":"Support multiple zipped CSV data files \n As requested by @lewtun, support loading multiple zipped CSV data files.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https:\/\/domain.org\/filename.zip\"\r\ndata_files = {\"train\": \"train_filename.csv\", \"test\": \"test_filename.csv\"}\r\ndataset = load_dataset(\"csv\", data_dir=url, data_files=data_files)\r\n```\r\n \n `data_dir` is currently exclusively used for manually downloaded data.\r\n\r\nMaybe we can have an API that only uses data_files as you are suggesting, using URL chaining ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https:\/\/domain.org\/filename.zip\"\r\ndata_files = {\"train\": \"zip:\/\/train_filename.csv::\" + url, \"test\": \"zip:\/\/test_filename.csv::\" + url}\r\ndataset = load_dataset(\"csv\", data_files=data_files)\r\n```\r\n\r\nURL chaining is used by `fsspec` to get access to files in nested filesystems of any kind. Since `fsspec` is being used by `pandas`, `dask` and also extensively by `datasets` I think it would be nice to use it here too","embeddings":[-0.1517476439,0.0207619295,-0.2865626514,0.0424872823,0.0839063898,0.014205439,0.3182390928,0.3192950785,0.3058342934,0.1624240726,-0.1752528995,0.20039621,0.0179093126,0.5145025849,-0.0635827482,-0.0473536588,0.0735590309,0.0708632395,-0.3205333054,0.262632966,-0.3963668048,0.1230586395,-0.1051666662,-0.2234488726,0.1980773211,0.0668318346,-0.4705534875,0.1946741343,-0.0826175362,-0.3332024217,0.4631043077,0.3740043342,0.1444803625,0.3535309732,-0.000095427,0.0268231276,0.057708472,-0.0029178008,-0.2493804544,-0.501334846,-0.08666832,-0.5928838253,0.1744819134,-0.3621188402,-0.0669679567,-0.4214963913,0.2277116776,-0.3604801595,0.63446033,0.0914133862,0.2918238044,-0.1238210052,-0.1096094921,-0.110483788,-0.0202037003,-0.0644003302,0.0519749336,0.0950659066,0.2567530572,-0.1588013321,-0.0923803151,-0.0536552183,-0.0746654123,0.1222234294,0.1295357347,0.0855314285,-0.1597155631,-0.2494312078,-0.1584970504,0.0611480735,0.3725822866,-0.2339421213,-0.4823827744,-0.3244562149,-0.1475008279,-0.3678247929,0.0439283773,0.2593337893,0.0593575574,0.2394163907,0.0746818557,-0.0245949328,-0.2102389783,0.2114364058,0.0374441892,0.2570928037,-0.0528409742,0.0802577958,0.2477474511,-0.003437879,0.1880755723,-0.2032278776,0.3410642445,0.1467856914,-0.2746537924,-0.110355854,0.0277993232,-0.4258928597,0.1661972702,0.3727761507,0.1601403058,0.0797076672,-0.2558286786,0.1621586978,0.3093790412,-0.0050069876,-0.1044976413,0.0268960074,0.4262836874,0.1114850417,0.0930270031,-0.1796223521,-0.07527899,-0.1046856791,-0.3051145375,0.2063324302,0.0616394877,0.2672472298,-0.1085622236,-0.2054920048,0.0716335401,-0.1665533185,0.0057243807,0.2181023806,-0.2045259476,0.4197296798,-0.0973519906,0.1610346287,0.1887912303,0.1751844585,-0.1137487963,0.0797238573,-0.2004719526,0.1409806758,0.2628248334,-0.0074886763,0.0917161405,0.1222578585,0.0567254834,0.0792336985,0.268506676,-0.0856208801,0.0937947035,0.1572926342,0.1905155033,0.0627135485,0.1468758881,-0.2112132013,-0.2346839607,0.0290272348,-0.659656167,-0.1931822747,-0.0467702635,0.2708576918,-0.0789047182,0.0069903,-0.3474181294,0.2615852058,-0.2269384116,-0.2031121403,-0.0578410998,0.1977536529,-0.2535289824,-0.3325170279,-0.0680390298,0.244126901,-0.13402237,0.0134058027,-0.1122744083,-0.0367560647,-0.058759734,0.1993150264,-0.285110414,-0.142625615,-0.2443638444,0.1777216047,0.3718696833,-0.5111823082,-0.2273078412,0.4465683103,0.0671629161,0.0914146751,0.3676990569,-0.0317131095,0.1330670863,-0.0759340301,0.1098299623,0.453759253,0.153314665,0.051591035,0.0385947749,-0.4713403285,0.1335056573,0.1816042662,-0.151159063,-0.0284719672,0.1218284965,-0.5766505599,0.4545602202,-0.2646490037,0.0182905179,-0.0200406145,0.0656290725,0.1353217959,-0.085995473,-0.1473808587,-0.409394294,0.240105167,-0.2323813289,-0.1999493241,-0.2438223511,-0.4181776941,0.0164647028,-0.0941949487,-0.13364847,-0.0503293835,0.2771934271,0.0942307785,-0.1174575239,-0.065660581,-0.278460592,0.2017042041,0.2097977549,0.100248307,-0.1811527014,0.3082320392,0.1087107435,0.2168254703,0.231453374,0.0560380258,0.1086083725,-0.2269898355,0.2170995027,0.3387130201,0.1782852411,0.0415182672,0.2386908531,0.1749959737,0.2587143779,0.1388196796,-0.0780994147,0.2099059671,0.0672314391,-0.0417028703,-0.372871995,0.548894763,-0.1888819337,0.1369534135,0.2453732193,0.0445912518,0.4368447959,-0.2325271219,-0.1930615008,-0.1327948123,-0.1502898335,0.1999642998,0.2144888639,-0.0484176129,-0.1878434271,0.1058548018,0.0368792303,-0.0984973237,-0.0634765401,0.1418624967,0.0621674731,-0.1127964854,0.1598243415,0.4857783318,0.4743587375,0.4172950685,0.1153218821,-0.0490274914,0.0835932046,-0.2294195294,0.313809067,-0.0881874338,-0.0647734106,0.1869556457,0.0817846134,-0.1013003513,-0.1387664378,-0.3249024749,0.1932832003,-0.0525833406,-0.0428627543,0.0586358942,-0.457682997,-0.4572099447,-0.08494813,-0.0761711076,0.1027502045,-0.1674534678,-0.0111895632,0.241240561,-0.2840839326,0.0993320718,-0.0794990435,0.3238812387,0.0096628321,-0.1817501634,0.030561693,0.0447897799,0.1886534393,0.288407892,0.3991660774,-0.0614717416,0.3297426701,0.3392837644,0.0257114377,-0.3063805997,0.010246194,0.0096594449,0.1448065192,0.0870326683,0.1366738975,0.0875370279,0.2304453254,-0.2303735316,0.074572973,0.2003789395,0.0297633093,-0.1962109357,0.1881733537,0.0258774944,-0.1892684996,-0.2019287944,-0.4472970366,-0.3572184145,0.4445303977,-0.0131666223,0.2705977261,0.1263919324,0.1365581304,-0.0533348918,0.2467122674,-0.2157731205,-0.0748075992,-0.4944863319,0.3398918509,-0.3314995468,-0.268818289,-0.118735455,-0.0709726289,0.1381789893,0.0270801112,-0.1865261644,-0.3547495604,-0.114297092,0.3215599358,0.0044124667,0.0172241312,0.2845311463,0.1248978004,-0.2337913066,0.0395010225,0.0011549433,0.0825960487,-0.0608014688,-0.0762293786,0.2126134336,-0.0082777925,0.1692153811,0.5917462111,0.2397565097,0.2618874907,0.4686522782,-0.1027538106,0.2164989114,0.1689040661,-0.3466769457,-0.3183970749,-0.3281102479,0.0541434214,0.3167024553,0.0592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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3018","title":"Support multiple zipped CSV data files","comments":"URL chaining sounds super nice to me! And it's also a nice way to leverage the same concepts we currently have in the docs around `fsspec` :)","body":"As requested by @lewtun, support loading multiple zipped CSV data files.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https:\/\/domain.org\/filename.zip\"\r\ndata_files = {\"train\": \"train_filename.csv\", \"test\": \"test_filename.csv\"}\r\ndataset = load_dataset(\"csv\", data_dir=url, data_files=data_files)\r\n```\r\n","comment_length":27,"text":"Support multiple zipped CSV data files \n As requested by @lewtun, support loading multiple zipped CSV data files.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nurl = \"https:\/\/domain.org\/filename.zip\"\r\ndata_files = {\"train\": \"train_filename.csv\", \"test\": \"test_filename.csv\"}\r\ndataset = load_dataset(\"csv\", data_dir=url, data_files=data_files)\r\n```\r\n \n URL chaining sounds super nice to me! And it's also a nice way to leverage the same concepts we currently have in the docs around `fsspec` :)","embeddings":[-0.215741694,-0.1642609835,-0.2551280558,-0.1260849684,0.1416186094,-0.0345901325,0.2652949393,0.2513987124,0.2857457399,0.2783493102,-0.0589769147,0.0554859862,0.0669120029,0.7944067717,-0.0695711225,0.0996731445,0.1315492392,0.0356056616,-0.3957926631,0.2155075669,-0.278981179,0.1462833285,-0.1596725285,-0.2834044397,0.0354687162,0.2469332069,-0.3867260516,0.236173749,-0.1187658831,-0.4162017703,0.3688868284,0.358510673,0.0933774784,0.234732151,-0.0000980808,-0.0945333391,0.0160437953,-0.0352337323,-0.1828584075,-0.217905134,-0.108141616,-0.4635151923,0.0317368805,-0.2648179829,-0.0504494607,-0.2034476399,0.228882283,-0.306523025,0.515529871,0.2722182572,0.3127776086,-0.0248599648,-0.2082156539,-0.1984893233,0.1797328889,-0.048844438,-0.0489977859,-0.132962808,0.0696741715,-0.340575844,-0.0303088501,-0.0205441993,-0.0121269822,0.1321771592,0.2149746418,0.0162105728,-0.0079651549,-0.2031974941,-0.2286120355,-0.0217994787,0.3442907631,-0.0939159766,-0.4007879198,-0.1533208191,-0.1404773742,-0.2054237425,-0.0394445136,0.1286773682,0.027152596,0.1428263336,0.0536003485,0.0084748222,-0.1783855557,0.0893784761,-0.0928044841,0.1740282327,-0.1740203202,0.0921061188,0.3015056849,-0.0435776599,-0.0307641774,-0.0921929777,0.2762883008,0.1254871488,-0.3249480426,-0.1768933982,0.1537485272,-0.2983544171,0.1702657193,0.3295579851,0.1499403566,-0.0213927832,-0.1038951501,0.1752705276,0.2417341918,-0.035350699,-0.0202113539,-0.0926374123,0.2776362598,-0.0061339829,-0.0037257273,-0.1233321503,-0.0167558026,-0.2298651636,-0.3768136799,0.1609529406,0.0012654887,0.0850995183,-0.171522215,-0.0003453998,0.2652061582,-0.2182274908,-0.1445250362,0.3320532441,-0.1835781187,0.5021541119,-0.0777961463,0.0837063044,-0.0339622386,-0.0077921744,-0.1327905208,0.107872501,-0.3132188916,0.0964963585,0.2124572247,-0.0198067706,0.0747843608,0.0558276512,0.1187866181,-0.0474798232,0.1043487713,-0.0153018376,0.1125190929,0.2089585215,0.1510067731,-0.0021080393,0.0973031074,-0.0674061775,-0.2266640067,0.0384083092,-0.4948365688,-0.1444410384,0.0091523938,0.2752681673,-0.0567333885,-0.1325119585,-0.2498712838,0.2060031593,-0.2639375925,-0.1724477112,-0.0998122916,0.0526816882,-0.364033252,-0.2438754439,-0.0080637811,0.4704191983,-0.1158204451,-0.0491679721,0.0112157706,-0.1040657014,-0.1362750083,0.1489475965,-0.177974537,-0.0763803646,-0.2472878844,0.2269233316,0.3529200852,-0.5110210776,-0.089820914,0.5618731976,0.0946200117,0.0262687784,0.3620938957,-0.1419815719,0.1646303684,-0.048069723,0.0558189154,0.3949379325,0.0091230115,0.1270930171,-0.0500399061,-0.4293938875,0.1944680065,0.2060690969,-0.0432046317,0.0155206164,0.0942449048,-0.5977076292,0.4833750427,-0.2354205549,-0.0538848974,-0.053725224,0.1125920266,0.1886627674,-0.0871134326,-0.1970135719,-0.3970642686,0.1463342607,-0.1394941062,-0.1496264488,0.0256632492,-0.3065262437,-0.1116735563,-0.0315369144,-0.1081228256,-0.1135563254,0.2205096334,0.2133321464,-0.0901463181,-0.1916046888,-0.2823676467,0.2204559743,0.1619003862,0.0983805954,-0.1196255237,0.332717061,0.0822899267,0.1791755259,0.1649319082,0.1662979126,0.2124338597,-0.2559519112,0.1811172515,0.4326789975,0.3693719804,-0.0247308929,0.321670413,0.2530790865,0.1243453994,-0.0970492139,-0.1293404549,0.2210512906,-0.0004753611,0.0159898847,-0.345348388,0.5397232175,-0.1364029497,0.1619253904,0.2254572362,0.0652265325,0.3308142126,-0.26096192,-0.2954784036,0.0022280405,0.0395020209,0.3374920189,0.1094359457,0.0640005916,-0.1317662299,0.1006029397,0.0627012178,-0.1299193054,-0.2401369363,0.1926115453,0.0346701331,-0.084926255,0.1949141324,0.3218875527,0.4343255162,0.398983568,-0.0712769777,-0.0341414995,-0.027783813,-0.1839130521,0.2907153666,-0.1937363893,-0.0375946127,0.159848541,0.0725800619,-0.0745242462,-0.3108382225,-0.2874616385,0.1163385138,-0.1457634866,-0.1253509969,0.1449902654,-0.2655478418,-0.3774935305,-0.2270383388,0.1996171176,0.1414245069,-0.1938112825,-0.0056359163,0.2908972502,-0.3346062899,0.0573233254,-0.1238074973,0.4895161986,0.0134137254,-0.1013060883,0.0795344636,0.0931583196,0.1122078001,0.2371439487,0.3086263835,-0.0633461103,0.2404560149,0.285018295,0.0387707464,-0.3997160494,-0.0444292426,0.0623621866,0.105078049,0.1742317528,0.1177354082,0.0550629422,0.3674738109,-0.1767395586,0.0725335553,-0.0070011471,0.036393255,-0.1584515125,0.1898028255,-0.1301562935,-0.2549802363,-0.3965546787,-0.3332663178,-0.2602420747,0.3856277168,-0.1999123991,0.2120864093,0.0862786472,0.0863485336,-0.1735616922,0.0657743216,-0.2049981058,-0.1973900497,-0.4386597574,0.3372315466,-0.3546915352,-0.3027249277,-0.0598984659,-0.0117832059,-0.0791255757,-0.0388853773,-0.2088930458,-0.1570748091,-0.2669195533,0.2284502685,-0.121536158,0.1355875134,0.3548524678,0.0863458142,-0.3350389898,-0.0596757531,-0.0322274603,0.086353071,0.053283006,0.0153099364,-0.0147202266,-0.0597400777,0.1214100793,0.5251368284,0.3025655746,0.2898776829,0.4303888679,-0.1726694852,0.1158742234,0.149608776,-0.3078602254,0.0161989871,-0.38231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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3013","title":"Improve `get_dataset_infos`?","comments":"To keeps things simple maybe we should use `load_dataset_builder` in `get_dataset_infos`.\r\n`load_dataset_builder` instantiates a builder and runs the _infos() method in order to give you the most up-to-date infos, even if the dataset_infos.json is outdated or missing.","body":"Using the dedicated function `get_dataset_infos` on a dataset that has no dataset-info.json file returns an empty info:\r\n\r\n```\r\n>>> from datasets import get_dataset_infos\r\n>>> get_dataset_infos('wit')\r\n{}\r\n```\r\n\r\nWhile it's totally possible to get it (regenerate it) with:\r\n\r\n```\r\n>>> from datasets import load_dataset_builder\r\n>>> builder = load_dataset_builder('wit')\r\n>>> builder.info\r\nDatasetInfo(description='Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set\\n of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its\\n size enables WIT to be used as a pretraining dataset for multimodal machine learning models.\\n', citation='@article{srinivasan2021wit,\\n  title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},\\n  author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},\\n  journal={arXiv preprint arXiv:2103.01913},\\n  year={2021}\\n}\\n', homepage='https:\/\/github.com\/google-research-datasets\/wit', license='', features={'b64_bytes': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'image_url': Value(dtype='string', id=None), 'metadata_url': Value(dtype='string', id=None), 'original_height': Value(dtype='int32', id=None), 'original_width': Value(dtype='int32', id=None), 'mime_type': Value(dtype='string', id=None), 'caption_attribution_description': Value(dtype='string', id=None), 'wit_features': Sequence(feature={'language': Value(dtype='string', id=None), 'page_url': Value(dtype='string', id=None), 'attribution_passes_lang_id': Value(dtype='string', id=None), 'caption_alt_text_description': Value(dtype='string', id=None), 'caption_reference_description': Value(dtype='string', id=None), 'caption_title_and_reference_description': Value(dtype='string', id=None), 'context_page_description': Value(dtype='string', id=None), 'context_section_description': Value(dtype='string', id=None), 'hierarchical_section_title': Value(dtype='string', id=None), 'is_main_image': Value(dtype='string', id=None), 'page_changed_recently': Value(dtype='string', id=None), 'page_title': Value(dtype='string', id=None), 'section_title': Value(dtype='string', id=None)}, length=-1, id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='wit', config_name='default', version=0.0.0, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None)\r\n```\r\n\r\nShould we test if info is empty, and in that case regenerate it? Or always generate it?","comment_length":37,"text":"Improve `get_dataset_infos`? \n Using the dedicated function `get_dataset_infos` on a dataset that has no dataset-info.json file returns an empty info:\r\n\r\n```\r\n>>> from datasets import get_dataset_infos\r\n>>> get_dataset_infos('wit')\r\n{}\r\n```\r\n\r\nWhile it's totally possible to get it (regenerate it) with:\r\n\r\n```\r\n>>> from datasets import load_dataset_builder\r\n>>> builder = load_dataset_builder('wit')\r\n>>> builder.info\r\nDatasetInfo(description='Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set\\n of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its\\n size enables WIT to be used as a pretraining dataset for multimodal machine learning models.\\n', citation='@article{srinivasan2021wit,\\n  title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},\\n  author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},\\n  journal={arXiv preprint arXiv:2103.01913},\\n  year={2021}\\n}\\n', homepage='https:\/\/github.com\/google-research-datasets\/wit', license='', features={'b64_bytes': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'image_url': Value(dtype='string', id=None), 'metadata_url': Value(dtype='string', id=None), 'original_height': Value(dtype='int32', id=None), 'original_width': Value(dtype='int32', id=None), 'mime_type': Value(dtype='string', id=None), 'caption_attribution_description': Value(dtype='string', id=None), 'wit_features': Sequence(feature={'language': Value(dtype='string', id=None), 'page_url': Value(dtype='string', id=None), 'attribution_passes_lang_id': Value(dtype='string', id=None), 'caption_alt_text_description': Value(dtype='string', id=None), 'caption_reference_description': Value(dtype='string', id=None), 'caption_title_and_reference_description': Value(dtype='string', id=None), 'context_page_description': Value(dtype='string', id=None), 'context_section_description': Value(dtype='string', id=None), 'hierarchical_section_title': Value(dtype='string', id=None), 'is_main_image': Value(dtype='string', id=None), 'page_changed_recently': Value(dtype='string', id=None), 'page_title': Value(dtype='string', id=None), 'section_title': Value(dtype='string', id=None)}, length=-1, id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name='wit', config_name='default', version=0.0.0, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None)\r\n```\r\n\r\nShould we test if info is empty, and in that case regenerate it? Or always generate it? \n To keeps things simple maybe we should use `load_dataset_builder` in `get_dataset_infos`.\r\n`load_dataset_builder` instantiates a builder and runs the _infos() method in order to give you the most up-to-date infos, even if the dataset_infos.json is outdated or missing.","embeddings":[-0.0334542207,0.2061552703,-0.1745597869,0.3140958548,0.275837779,-0.0529510602,0.1713561863,0.5251324773,-0.069187358,0.1756513566,0.0600979589,0.2291978598,0.0621007159,0.1081236303,-0.065336898,0.1299309283,-0.217101723,0.2951028347,0.1627132297,-0.3937523067,-0.1867577285,-0.0563144647,-0.2302012295,-0.3213418722,-0.3358125389,0.153682068,-0.1194726974,0.1497913003,-0.1494498551,-0.1949355453,0.0807013959,-0.0780498683,-0.1489067376,0.1531967223,-0.0001038918,0.0524569601,0.5534229279,-0.1777953357,-0.3418049514,-0.156708613,-0.324539721,-0.5854389071,-0.1491033286,-0.4891826808,-0.1760082841,-0.2954667509,0.0380491465,-0.1407111436,0.1658508927,0.3137062192,0.2565643191,-0.0870700032,0.0932613835,0.0769788697,0.1724296808,0.406337291,-0.1518058628,-0.2664296329,-0.0768409297,-0.0628424659,0.1223376542,0.6122500896,-0.224766016,-0.0494002812,0.1435616314,0.0622973032,-0.1373683065,-0.2564626932,0.3453699648,0.271297574,0.7522829175,-0.0253585763,-0.2978535295,-0.3384350538,0.2018364519,0.0596269742,0.0889257863,0.1734776646,-0.0637805983,0.110283263,-0.2851331532,-0.2304969877,-0.1781084985,0.0896933898,-0.1758005619,0.3796133101,0.0320524052,0.0400053225,-0.1805095226,-0.2603105903,0.0641288087,-0.3207340837,-0.308748126,0.1897556037,-0.0681720152,-0.0819919109,0.4134169519,-0.1274333298,0.1784699708,0.1100468785,-0.1594198346,0.1657499969,-0.3443576992,-0.1088484302,0.2411605567,-0.1968438625,0.1918195188,0.0167743806,0.2059240937,-0.0136213684,-0.0483301617,-0.1238968968,0.115211226,0.1859358251,0.0162745472,-0.0506279245,0.356515944,-0.0002164763,0.0118709914,0.2050759643,-0.3223838806,-0.0233698152,-0.0770181492,0.2244777977,-0.2602082491,0.3806174099,0.1886270195,-0.2091175616,-0.0791548789,-0.2555490434,-0.2285604328,0.0406154059,0.0085600298,-0.0528355762,0.2444270402,0.3101569414,0.3834151328,-0.0601820424,-0.1776162237,0.0479826778,0.1572519839,-0.0362689346,0.2999795377,0.3553059697,0.1466278285,0.2769989371,-0.0198824126,-0.2852192223,0.0563426167,0.1521654427,-0.340488553,-0.2834471464,-0.4425541759,0.2581709027,-0.3184605837,0.2689374089,-0.0084117027,0.5213388205,-0.2392914891,-0.104998596,0.0303914286,0.157341823,-0.1347372532,-0.1958785355,0.4010678232,0.495536536,-0.2726320028,0.0863534361,-0.1794573218,-0.0366972573,-0.0535454452,-0.118438594,-0.1413103789,0.4051979184,-0.0547600575,-0.0060904264,0.4116458893,-0.2144985199,-0.3026271164,0.2057461888,0.1920412034,0.2037703544,0.0045246743,0.1405543685,-0.0406869538,0.3160429597,-0.0006625348,0.3190124035,0.003484172,0.084879823,-0.1785145998,-0.1158487648,0.0172239523,0.1232072562,0.1329958886,-0.0204973035,0.1022523567,0.2846868038,0.4099693894,-0.2249460667,0.0721268207,0.3886172771,0.2679021358,-0.0117173148,0.0785825998,-0.1293994635,-0.6393068433,0.2591580153,0.2079567909,0.2568542957,-0.4003850818,-0.2732289433,0.1295411289,-0.0133795124,0.056356702,-0.3491611779,0.2960683405,0.1283245981,0.2354371548,0.1595014632,0.0541410111,0.1102450863,-0.2360835969,-0.0676045418,-0.407179296,0.3269284368,0.1947237402,0.0377086923,0.0928125754,0.0599980019,0.0896959007,0.1459403783,-0.0249641985,0.1651206464,0.1401599795,0.1033202261,0.1114771441,0.167466417,0.1247643381,-0.0977261737,-0.0412240587,0.0512822904,0.2474001795,-0.1452587098,-0.384737581,0.2439996153,0.2047152668,0.2836335897,-0.0171040837,0.0775974244,0.2408284247,0.0049435035,-0.0706445873,-0.1657139659,-0.2895042896,0.2500273883,-0.142453298,0.0883784518,-0.2719704509,0.2651036084,0.638428092,0.0802806243,-0.0317703374,-0.101863876,-0.1843247414,-0.263867408,0.3413107693,-0.1086781174,0.1312226355,0.2773258388,-0.1568631679,0.056414254,0.2343965173,0.0310186092,0.2734320462,0.1170413569,0.1636386067,0.122844629,0.1248969287,0.0004230138,-0.1073728427,-0.25319314,-0.0225666892,0.4294435978,-0.12447422,-0.2474554926,-0.3552822471,-0.2883386016,0.1937556416,0.1298277229,-0.1636924148,-0.348731339,-0.125952661,-0.0782927573,-0.2252166271,0.1918267161,-0.0344631858,-0.0089529445,0.2262825966,-0.3319551647,-0.2581144571,-0.2339914739,-0.0110380463,0.1595903188,0.0216111317,0.3327693641,0.0718957186,-0.180365786,0.0014981448,-0.5359110236,-0.347638011,0.2214894891,-0.2525462806,0.1643024087,0.1965313554,0.3480834365,-0.0522335544,0.1019697711,0.1464661509,0.0549008176,-0.0940030366,-0.0414607599,-0.0177693069,0.0083958702,-0.2611916065,-0.4069519043,-0.0017969044,-0.2890123129,0.1443513036,0.1285297722,0.1703273356,0.2320342213,0.3752901256,0.1171036139,0.0706071034,0.0562450103,-0.2634882927,-0.2355572134,0.4781817198,-0.0444191881,-0.2352214903,0.0902412459,-0.0903828144,0.1149698123,0.0934406444,-0.6104696393,-0.1491386592,-0.0897699818,0.3234093785,-0.1087138131,0.2258106023,0.3890783787,-0.0003674568,-0.1241855994,-0.1488192081,-0.2123655826,-0.150899291,-0.2982991636,0.1165625155,0.1073614657,0.2004256099,0.0181675423,0.3399748504,-0.1375774145,0.0091610067,-0.0707905814,-0.1692601144,0.6448239088,-0.0441294685,-0.2197175175,0.1882587373,0.3172581494,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3011","title":"load_dataset_builder should error if \"name\" does not exist?","comments":"Yes I think it should raise an error. Currently it looks like it instantiates a custom configuration with the name given by the user:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/ba27ce33bf568374cf23a07669fdd875b5718bc2\/src\/datasets\/builder.py#L391-L397","body":"```\r\nimport datasets as ds\r\nbuilder = ds.load_dataset_builder('sent_comp', name=\"doesnotexist\")\r\nbuilder.info.config_name\r\n```\r\n\r\nreturns\r\n\r\n```\r\n'doesnotexist'\r\n```\r\n\r\nShouldn't it raise an error instead?\r\n\r\nFor this dataset, the only valid values for `name` should be: `\"default\"` or `None` (ie. argument not passed)","comment_length":25,"text":"load_dataset_builder should error if \"name\" does not exist? \n ```\r\nimport datasets as ds\r\nbuilder = ds.load_dataset_builder('sent_comp', name=\"doesnotexist\")\r\nbuilder.info.config_name\r\n```\r\n\r\nreturns\r\n\r\n```\r\n'doesnotexist'\r\n```\r\n\r\nShouldn't it raise an error instead?\r\n\r\nFor this dataset, the only valid values for `name` should be: `\"default\"` or `None` (ie. argument not passed) \n Yes I think it should raise an error. Currently it looks like it instantiates a custom configuration with the name given by the user:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/ba27ce33bf568374cf23a07669fdd875b5718bc2\/src\/datasets\/builder.py#L391-L397","embeddings":[-0.1365154982,-0.0678217039,0.0515268557,0.4797345698,0.1939743757,0.1313637644,0.4507783651,0.0584304631,0.1885161698,0.4071680307,0.2744373381,0.0654158667,-0.1559426785,0.0452126265,0.0748665184,0.1092219353,0.0515938401,0.4053525329,0.0932740942,-0.0820035115,-0.3264883161,0.0690019056,-0.2051035464,0.205421716,-0.3182511926,0.19244802,0.0245388523,0.3618241251,0.0310830809,-0.5019716024,0.3296846151,0.1269435287,-0.0798212886,0.4256618619,-0.0001227875,0.2216548771,0.3875079155,-0.1257542521,-0.3384044468,-0.2878855169,-0.4218553007,-0.3141176999,0.1963293105,-0.3155790865,-0.1880995333,-0.0356336087,-0.02947817,-0.0846264586,0.2010957748,0.3315222859,0.1019258276,0.1995676309,-0.0928660855,-0.1097772494,-0.0304008592,0.1422150731,-0.0506707691,-0.0311583243,-0.2171076089,0.3104867935,0.3165410459,-0.0031834748,-0.2034775168,0.0226768702,0.211984992,0.0614573099,0.29549402,-0.2096066624,0.2671177983,0.4647545218,0.6100264788,-0.2184839845,-0.1589092016,-0.3488064706,0.1456149817,-0.1771451384,0.4762196541,-0.1190970913,-0.4372709095,-0.0038972565,0.0531537272,-0.3702909946,-0.0274389666,0.004406482,-0.1794804484,0.3111835718,-0.2952216566,0.2918322384,-0.2532501221,-0.0634101406,0.0950682536,-0.5572370887,-0.0322666988,0.0354070999,-0.3109021187,-0.1681941301,0.1004493535,-0.0235654321,0.046179533,-0.0766152442,0.019302994,-0.100376904,0.2705607712,0.2008221447,0.3124349117,-0.1943702549,0.4022127688,0.0244297981,0.1532547325,-0.1888620108,0.0153587004,-0.1326110363,-0.220169574,-0.2243310213,0.2224155962,-0.1130873337,0.5471590757,-0.0859905779,-0.2607185841,0.3700968921,-0.0309429299,-0.0235011503,0.3581155241,0.4340705276,-0.3459785879,-0.0602266528,-0.0109691629,0.0287872106,0.1162926704,-0.2185178399,-0.2448519468,-0.286475271,-0.1581101716,0.014114934,0.1101059914,-0.1437315196,0.3157342076,0.0209471062,0.0158054531,-0.1146633327,0.0012024743,0.0427888855,0.1737973988,0.4335072041,-0.077383101,-0.0208789408,0.1779347509,-0.5118983984,-0.1616971195,0.3676540554,-0.3195556402,-0.5034795403,-0.1444896162,0.1987370104,-0.3553117216,0.0682408139,-0.0166228376,0.1797619164,0.3039962947,-0.1931894124,-0.004100793,-0.0153900459,-0.0566249788,-0.3033479452,0.1927752644,0.6054324508,-0.3153461218,-0.1551710218,-0.1266624779,-0.2349188179,-0.1522910148,-0.1622673571,-0.2928962708,0.1857941598,-0.3503902853,-0.1219199002,0.6622512937,0.052531559,0.0514902398,0.1271199882,-0.0560246445,-0.0180821382,0.2535822988,0.0531497672,-0.4748903513,-0.2082740068,0.0320996195,-0.0735040084,-0.2670388222,-0.1277974695,0.0581023768,0.0805699006,0.1849423349,0.0290551726,0.1483401954,0.2096758783,-0.1969891936,0.1204601377,0.1779195219,0.0442268327,-0.2986889184,0.0708641037,0.0739729926,0.1280709505,-0.0184650868,0.0518806055,-0.5132173896,0.395257175,0.2898358405,0.0761165768,0.1880132258,-0.2100041658,-0.0236213338,0.2549449205,-0.231332913,0.2855654359,-0.0021310789,0.2137175947,-0.0200031549,-0.1633899808,-0.5911722183,0.0730035901,-0.5128945708,0.1134406328,-0.2511410117,0.0570106171,0.0657259598,0.0840133056,-0.3317811191,0.1456491351,0.0831190497,0.1114531234,0.0315280743,0.3394143581,-0.0292688552,-0.3182825446,-0.3687495887,-0.1365355998,-0.1261918843,-0.1312016398,0.0479407534,0.2263949513,0.2594276667,-0.2273846269,0.0545666628,0.1653786749,0.0768361166,0.3123579919,0.0045266883,-0.1620179117,0.0270768646,0.0312676094,-0.199678421,-0.2576739788,-0.0366315208,-0.0837541074,0.2844649255,0.1125899255,-0.3142172396,-0.4374168515,0.1415325105,-0.0316721499,-0.038884744,-0.1186527833,0.0424314104,0.1533552557,0.0811427906,0.1794043779,0.7374492884,0.1646928042,-0.2438902706,0.2097111791,-0.1587426066,-0.0906147435,0.1747174263,0.2098544538,0.3317415714,0.3183526993,-0.2306793332,-0.000087323,-0.4921308458,-0.1336327493,0.2420618832,-0.0612105429,-0.5944673419,-0.0289827045,0.2129337639,-0.2268887609,-0.1055230051,-0.1379869878,-0.5043967962,-0.1896234453,-0.0380214639,-0.0401812755,-0.0914028063,0.1641109884,-0.2888195217,0.034227442,-0.1356271356,-0.6813523769,-0.2077850103,0.0796029344,-0.2166487426,-0.0068199388,-0.1274287552,-0.1644721478,0.2829401493,-0.3081445396,-0.1579722911,-0.0115649523,-0.1641715914,0.3320311904,-0.1385515034,0.267516017,0.5650125742,-0.0227343142,0.4870811105,0.0076122717,0.472279191,0.1429279745,-0.0553504452,0.0823882893,0.2437620163,-0.2491675168,-0.1030339748,-0.0316978395,-0.0804234743,-0.1919903159,-0.3154265881,0.2807677686,0.2267272472,0.6292543411,0.3469144702,0.1828418374,0.1006417722,0.2297695428,-0.3656279147,-0.5135587454,0.057532791,-0.028656546,-0.0654869825,0.1561422646,-0.2154023349,0.3854542375,-0.15879412,-0.2987373769,0.027892774,0.0142793711,0.3960189223,-0.0403920673,0.3405085206,0.3123459816,0.2468714118,-0.0190675166,-0.1924038529,-0.5061350465,0.2484142631,0.0114157731,0.326438278,0.303284198,0.2244390994,-0.42601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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3010","title":"Chain filtering is leaking","comments":"### Update:\r\nI wrote a bit cleaner code snippet (without transforming to json) that can expose leaking.\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = ['ab', 'c', 'df']\r\n\r\nds = datasets.Dataset.from_dict({'col': items})\r\nprint(list(ds))\r\n# > Prints: [{'col': 'ab'}, {'col': 'c'}, {'col': 'df'}]\r\n\r\nfiltered = ds\r\n\r\n# get all items that are starting with a character with ascii code bigger than 'a'\r\nfiltered = filtered.filter(lambda x: x['col'][0] > 'a', load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'c'}, {'col': 'df'}] as expected\r\n\r\n# get all items that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(x['col']) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'ab'}] -> this is a leaked item from the first filter\r\n# > Should be: [{'col': 'c'}]\r\n```","body":"## Describe the bug\r\nAs there's no support for lists within dataset fields, I convert my lists to json-string format. However, the bug described is occurring even when the data format is 'string'.\r\nThese samples show that filtering behavior diverges from what's expected when chaining filterings.\r\nOn sample 2 the second filtering leads to \"leaking\" of data that should've been filtered on the first filtering into the results.\r\n\r\n## Steps to reproduce the bug\r\nSample 1:\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = [[1, 2], [3], [4]]\r\njsoned_items = map(json.dumps, [[1, 2], [3], [4]])\r\nds = datasets.Dataset.from_dict({'a': jsoned_items})\r\nprint(list(ds))\r\n# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] as expected\r\n\r\nfiltered = ds\r\n\r\n# get all lists that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[3]'}, {'a': '[4]'}] as expected\r\n\r\n# get all lists, which have a value bigger than 3 on its zero index\r\nfiltered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Should be: [{'a': [4]}]\r\n# > Prints: [{'a': [3]}]\r\n```\r\nSample 2:\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = [[1, 2], [3], [4]]\r\njsoned_items = map(json.dumps, [[1, 2], [3], [4]])\r\nds = datasets.Dataset.from_dict({'a': jsoned_items})\r\nprint(list(ds))\r\n# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}]\r\n\r\nfiltered = ds\r\n\r\n# get all lists, which have a value bigger than 3 on its zero index\r\nfiltered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[4]'}] as expected\r\n\r\n# get all lists that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[1, 2]'}]\r\n# > Should be: [{'a': '[4]'}] (remain intact)\r\n```\r\n\r\n## Expected results\r\nExpected and actual results are attached to the code snippets.\r\n\r\n## Actual results\r\nExpected and actual results are attached to the code snippets.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":118,"text":"Chain filtering is leaking \n ## Describe the bug\r\nAs there's no support for lists within dataset fields, I convert my lists to json-string format. However, the bug described is occurring even when the data format is 'string'.\r\nThese samples show that filtering behavior diverges from what's expected when chaining filterings.\r\nOn sample 2 the second filtering leads to \"leaking\" of data that should've been filtered on the first filtering into the results.\r\n\r\n## Steps to reproduce the bug\r\nSample 1:\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = [[1, 2], [3], [4]]\r\njsoned_items = map(json.dumps, [[1, 2], [3], [4]])\r\nds = datasets.Dataset.from_dict({'a': jsoned_items})\r\nprint(list(ds))\r\n# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] as expected\r\n\r\nfiltered = ds\r\n\r\n# get all lists that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[3]'}, {'a': '[4]'}] as expected\r\n\r\n# get all lists, which have a value bigger than 3 on its zero index\r\nfiltered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Should be: [{'a': [4]}]\r\n# > Prints: [{'a': [3]}]\r\n```\r\nSample 2:\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = [[1, 2], [3], [4]]\r\njsoned_items = map(json.dumps, [[1, 2], [3], [4]])\r\nds = datasets.Dataset.from_dict({'a': jsoned_items})\r\nprint(list(ds))\r\n# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}]\r\n\r\nfiltered = ds\r\n\r\n# get all lists, which have a value bigger than 3 on its zero index\r\nfiltered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[4]'}] as expected\r\n\r\n# get all lists that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[1, 2]'}]\r\n# > Should be: [{'a': '[4]'}] (remain intact)\r\n```\r\n\r\n## Expected results\r\nExpected and actual results are attached to the code snippets.\r\n\r\n## Actual results\r\nExpected and actual results are attached to the code snippets.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n ### Update:\r\nI wrote a bit cleaner code snippet (without transforming to json) that can expose leaking.\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = ['ab', 'c', 'df']\r\n\r\nds = datasets.Dataset.from_dict({'col': items})\r\nprint(list(ds))\r\n# > Prints: [{'col': 'ab'}, {'col': 'c'}, {'col': 'df'}]\r\n\r\nfiltered = ds\r\n\r\n# get all items that are starting with a character with ascii code bigger than 'a'\r\nfiltered = filtered.filter(lambda x: x['col'][0] > 'a', load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'c'}, {'col': 'df'}] as expected\r\n\r\n# get all items that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(x['col']) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'ab'}] -> this is a leaked item from the first filter\r\n# > Should be: [{'col': 'c'}]\r\n```","embeddings":[-0.1847150773,0.161246106,-0.1516270339,0.2235311568,-0.1269432008,-0.1254191846,0.2978745103,0.2360771894,0.3307059407,-0.2883337736,-0.0847590938,0.5628129244,0.1776726097,0.0540364422,-0.1557195634,-0.0142781883,0.0818480402,-0.2436116785,-0.07721176,0.0644921213,-0.0099008018,0.1094733477,-0.0502915904,-0.1966574043,0.1898589581,-0.1831228733,0.1703651696,-0.0448103361,0.0313826203,-0.3397786915,0.1861234158,0.1549551487,-0.1752405316,0.2725081146,-0.0001136461,0.0179228839,0.4603436589,-0.0023184209,-0.2560626566,-0.2721797228,-0.6779726148,-0.3843247592,0.4751814604,0.1045294181,0.1581596434,-0.1409381479,-0.3943981826,-0.5809842944,0.5498983264,0.0836501271,0.1865929067,0.1239459366,0.2013133317,0.2279952466,0.3071090579,0.0612769611,-0.1329182088,-0.0985753238,0.5059636235,0.1816353202,-0.0606279448,0.2830307782,-0.3123322725,-0.179931283,-0.0456996635,0.051492516,-0.0805766955,-0.400259167,0.0559847243,0.1327341348,0.1209445745,-0.1222665161,-0.4533758461,-0.4508509338,-0.3495272994,-0.243267715,0.0314363353,0.1414371878,-0.3538603485,0.2027987391,-0.2764708698,-0.1556459516,0.0727501288,0.0935490802,-0.0499437712,0.3183329701,-0.0550209209,-0.0942812189,-0.1449712515,-0.0841661245,0.2327211201,-0.4712362289,-0.04653446,-0.0989451036,-0.154906258,-0.091554001,0.1965167969,-0.1078545526,0.1243452653,0.137086004,-0.3421191871,0.0678285211,0.0103614721,0.1023122519,0.6082578897,-0.0646017864,0.083492294,0.3204421401,-0.1884859949,0.0361530595,-0.0940531641,-0.0970924273,0.5751081109,0.2424972951,0.0703016669,-0.0573820993,0.2419258952,-0.2012381703,-0.2759465575,0.1202593818,-0.4374152422,0.0493062213,0.2863562405,-0.0316956416,0.0712674856,0.4016806185,0.089793019,0.2228570729,0.0414939448,0.0240086485,-0.1476510614,-0.3927489221,-0.0927363783,0.0332224034,-0.0720575824,-0.2217340469,0.1971174479,0.3013776839,0.0302122198,-0.0620290376,-0.0699578226,-0.0182696972,0.1309518069,0.3366765678,-0.031535916,0.1930772364,0.3408396542,-0.4169307053,0.0629105791,0.2294757962,-0.3842694461,0.2078428566,-0.0051671634,0.1213130578,-0.1119322181,0.0607558042,-0.1206251606,0.3163619637,0.295581013,0.1773178279,-0.071336396,-0.1740193069,-0.2285037041,-0.1540698558,-0.1343206465,0.4101733267,-0.3921201825,-0.062313389,0.0087976335,0.17045331,0.2360628992,0.2257158458,-0.3418206871,-0.093267113,0.0923364386,-0.0911326259,0.1958352476,-0.3295056224,-0.4398537576,0.4421161115,0.0211870167,0.4617260993,0.0218369104,-0.1658025235,0.2958743274,-0.2383985668,0.2526781261,0.1585456133,-0.2539972365,-0.0153491218,-0.2694908381,0.0518369935,0.1175766662,-0.3335338533,-0.1858093143,0.3327952325,0.0100903902,0.1793176681,0.1677996665,0.0830730051,0.1988558471,0.2763452828,0.0519531108,0.0703643188,-0.1275638938,-0.0912705958,-0.259722054,-0.1354537606,0.0208348576,-0.3145239055,-0.5268619657,-0.1500761807,-0.0556619801,0.3636551797,0.0319159105,0.0590852015,0.1626487225,0.0887826234,-0.3272232115,0.0365304202,-0.153263092,0.20625256,0.0190534052,0.0694461614,-0.2959235013,0.1946905702,0.293622762,0.0214410126,-0.0025371017,-0.1091895923,0.3430593014,0.2286581993,-0.0589699969,0.2945375144,0.5386202335,-0.1742345691,-0.1106797978,0.2507929206,0.2530082464,0.0762549788,-0.0700857192,0.4580593705,0.1266881526,-0.0846773088,-0.1948206276,0.4551439881,-0.4292136431,0.1899206489,0.0670578107,0.2497699708,0.3304589391,-0.098609671,-0.3311886787,-0.3288036585,-0.2801124156,-0.2165813297,0.2056818157,-0.1593928784,-0.4222958982,0.4927533269,0.2779901922,0.2250119597,0.0210310835,0.1032613665,0.1426526904,-0.0311795492,-0.0244386941,0.3411134183,0.3493514359,0.2354830205,0.2533789575,-0.0617805347,0.0149179148,-0.2069470584,0.2583582997,0.2557946146,0.1444420069,0.1069826633,0.3125623763,-0.0940604806,-0.0703385696,-0.1985916346,-0.0905192271,-0.0239294637,-0.246761173,0.5204171538,-0.531216085,-0.1981952786,-0.182739526,-0.511631608,0.1445445269,-0.4857126176,0.0923855826,0.4313834608,-0.2941233218,-0.0876571164,-0.1119941473,-0.0655377805,0.2373668551,-0.0889304802,-0.1484933347,-0.0783227831,-0.1540423632,0.0402275771,0.4182909131,-0.1659476459,-0.0945213512,0.2740504146,-0.2734484375,-0.1864078343,-0.4067754447,0.2100645453,0.0406006388,0.3307211697,0.1401688457,-0.1133892015,-0.2044884115,-0.2377706468,-0.0199922808,0.0368938632,-0.2550298274,0.0597670414,0.0730562061,0.1169711798,-0.0949098691,-0.1607797891,-0.1777294725,-0.1410635561,0.0827369168,0.1372183114,0.0686011985,0.4495679736,-0.1690391898,-0.3041932583,0.0321646556,0.0085217049,-0.0780886933,-0.4054821134,0.2219271809,-0.2311036587,-0.2220573127,-0.1003448814,0.094280012,-0.1370528191,0.2631193101,-0.3425102532,0.0981490463,0.0878791884,0.0352271162,-0.0163980462,-0.0600471497,0.0181788914,0.1340132505,-0.0330037549,-0.3138585687,0.0062159505,-0.0829842612,-0.2392150164,0.4358153641,-0.0488199741,0.462400347,0.1643844098,0.3649653494,0.3674896955,0.0879176036,0.1632308662,0.1379345804,0.0847538635,-0.1678284556,-0.328999728,-0.6673765779,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3010","title":"Chain filtering is leaking","comments":"I just pushed a fix ! We'll do a new release soon.\r\nIn the meantime feel free to install `datasets` from source to play with it","body":"## Describe the bug\r\nAs there's no support for lists within dataset fields, I convert my lists to json-string format. However, the bug described is occurring even when the data format is 'string'.\r\nThese samples show that filtering behavior diverges from what's expected when chaining filterings.\r\nOn sample 2 the second filtering leads to \"leaking\" of data that should've been filtered on the first filtering into the results.\r\n\r\n## Steps to reproduce the bug\r\nSample 1:\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = [[1, 2], [3], [4]]\r\njsoned_items = map(json.dumps, [[1, 2], [3], [4]])\r\nds = datasets.Dataset.from_dict({'a': jsoned_items})\r\nprint(list(ds))\r\n# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] as expected\r\n\r\nfiltered = ds\r\n\r\n# get all lists that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[3]'}, {'a': '[4]'}] as expected\r\n\r\n# get all lists, which have a value bigger than 3 on its zero index\r\nfiltered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Should be: [{'a': [4]}]\r\n# > Prints: [{'a': [3]}]\r\n```\r\nSample 2:\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = [[1, 2], [3], [4]]\r\njsoned_items = map(json.dumps, [[1, 2], [3], [4]])\r\nds = datasets.Dataset.from_dict({'a': jsoned_items})\r\nprint(list(ds))\r\n# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}]\r\n\r\nfiltered = ds\r\n\r\n# get all lists, which have a value bigger than 3 on its zero index\r\nfiltered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[4]'}] as expected\r\n\r\n# get all lists that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[1, 2]'}]\r\n# > Should be: [{'a': '[4]'}] (remain intact)\r\n```\r\n\r\n## Expected results\r\nExpected and actual results are attached to the code snippets.\r\n\r\n## Actual results\r\nExpected and actual results are attached to the code snippets.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":26,"text":"Chain filtering is leaking \n ## Describe the bug\r\nAs there's no support for lists within dataset fields, I convert my lists to json-string format. However, the bug described is occurring even when the data format is 'string'.\r\nThese samples show that filtering behavior diverges from what's expected when chaining filterings.\r\nOn sample 2 the second filtering leads to \"leaking\" of data that should've been filtered on the first filtering into the results.\r\n\r\n## Steps to reproduce the bug\r\nSample 1:\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = [[1, 2], [3], [4]]\r\njsoned_items = map(json.dumps, [[1, 2], [3], [4]])\r\nds = datasets.Dataset.from_dict({'a': jsoned_items})\r\nprint(list(ds))\r\n# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}] as expected\r\n\r\nfiltered = ds\r\n\r\n# get all lists that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[3]'}, {'a': '[4]'}] as expected\r\n\r\n# get all lists, which have a value bigger than 3 on its zero index\r\nfiltered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Should be: [{'a': [4]}]\r\n# > Prints: [{'a': [3]}]\r\n```\r\nSample 2:\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = [[1, 2], [3], [4]]\r\njsoned_items = map(json.dumps, [[1, 2], [3], [4]])\r\nds = datasets.Dataset.from_dict({'a': jsoned_items})\r\nprint(list(ds))\r\n# > Prints: [{'a': '[1, 2]'}, {'a': '[3]'}, {'a': '[4]'}]\r\n\r\nfiltered = ds\r\n\r\n# get all lists, which have a value bigger than 3 on its zero index\r\nfiltered = filtered.filter(lambda x: json.loads(x['a'])[0] > 3, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[4]'}] as expected\r\n\r\n# get all lists that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(json.loads(x['a'])) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'a': '[1, 2]'}]\r\n# > Should be: [{'a': '[4]'}] (remain intact)\r\n```\r\n\r\n## Expected results\r\nExpected and actual results are attached to the code snippets.\r\n\r\n## Actual results\r\nExpected and actual results are attached to the code snippets.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n I just pushed a fix ! We'll do a new release soon.\r\nIn the meantime feel free to install `datasets` from source to play with it","embeddings":[-0.1847150773,0.161246106,-0.1516270339,0.2235311568,-0.1269432008,-0.1254191846,0.2978745103,0.2360771894,0.3307059407,-0.2883337736,-0.0847590938,0.5628129244,0.1776726097,0.0540364422,-0.1557195634,-0.0142781883,0.0818480402,-0.2436116785,-0.07721176,0.0644921213,-0.0099008018,0.1094733477,-0.0502915904,-0.1966574043,0.1898589581,-0.1831228733,0.1703651696,-0.0448103361,0.0313826203,-0.3397786915,0.1861234158,0.1549551487,-0.1752405316,0.2725081146,-0.0001136461,0.0179228839,0.4603436589,-0.0023184209,-0.2560626566,-0.2721797228,-0.6779726148,-0.3843247592,0.4751814604,0.1045294181,0.1581596434,-0.1409381479,-0.3943981826,-0.5809842944,0.5498983264,0.0836501271,0.1865929067,0.1239459366,0.2013133317,0.2279952466,0.3071090579,0.0612769611,-0.1329182088,-0.0985753238,0.5059636235,0.1816353202,-0.0606279448,0.2830307782,-0.3123322725,-0.179931283,-0.0456996635,0.051492516,-0.0805766955,-0.400259167,0.0559847243,0.1327341348,0.1209445745,-0.1222665161,-0.4533758461,-0.4508509338,-0.3495272994,-0.243267715,0.0314363353,0.1414371878,-0.3538603485,0.2027987391,-0.2764708698,-0.1556459516,0.0727501288,0.0935490802,-0.0499437712,0.3183329701,-0.0550209209,-0.0942812189,-0.1449712515,-0.0841661245,0.2327211201,-0.4712362289,-0.04653446,-0.0989451036,-0.154906258,-0.091554001,0.1965167969,-0.1078545526,0.1243452653,0.137086004,-0.3421191871,0.0678285211,0.0103614721,0.1023122519,0.6082578897,-0.0646017864,0.083492294,0.3204421401,-0.1884859949,0.0361530595,-0.0940531641,-0.0970924273,0.5751081109,0.2424972951,0.0703016669,-0.0573820993,0.2419258952,-0.2012381703,-0.2759465575,0.1202593818,-0.4374152422,0.0493062213,0.2863562405,-0.0316956416,0.0712674856,0.4016806185,0.089793019,0.2228570729,0.0414939448,0.0240086485,-0.1476510614,-0.3927489221,-0.0927363783,0.0332224034,-0.0720575824,-0.2217340469,0.1971174479,0.3013776839,0.0302122198,-0.0620290376,-0.0699578226,-0.0182696972,0.1309518069,0.3366765678,-0.031535916,0.1930772364,0.3408396542,-0.4169307053,0.0629105791,0.2294757962,-0.3842694461,0.2078428566,-0.0051671634,0.1213130578,-0.1119322181,0.0607558042,-0.1206251606,0.3163619637,0.295581013,0.1773178279,-0.071336396,-0.1740193069,-0.2285037041,-0.1540698558,-0.1343206465,0.4101733267,-0.3921201825,-0.062313389,0.0087976335,0.17045331,0.2360628992,0.2257158458,-0.3418206871,-0.093267113,0.0923364386,-0.0911326259,0.1958352476,-0.3295056224,-0.4398537576,0.4421161115,0.0211870167,0.4617260993,0.0218369104,-0.1658025235,0.2958743274,-0.2383985668,0.2526781261,0.1585456133,-0.2539972365,-0.0153491218,-0.2694908381,0.0518369935,0.1175766662,-0.3335338533,-0.1858093143,0.3327952325,0.0100903902,0.1793176681,0.1677996665,0.0830730051,0.1988558471,0.2763452828,0.0519531108,0.0703643188,-0.1275638938,-0.0912705958,-0.259722054,-0.1354537606,0.0208348576,-0.3145239055,-0.5268619657,-0.1500761807,-0.0556619801,0.3636551797,0.0319159105,0.0590852015,0.1626487225,0.0887826234,-0.3272232115,0.0365304202,-0.153263092,0.20625256,0.0190534052,0.0694461614,-0.2959235013,0.1946905702,0.293622762,0.0214410126,-0.0025371017,-0.1091895923,0.3430593014,0.2286581993,-0.0589699969,0.2945375144,0.5386202335,-0.1742345691,-0.1106797978,0.2507929206,0.2530082464,0.0762549788,-0.0700857192,0.4580593705,0.1266881526,-0.0846773088,-0.1948206276,0.4551439881,-0.4292136431,0.1899206489,0.0670578107,0.2497699708,0.3304589391,-0.098609671,-0.3311886787,-0.3288036585,-0.2801124156,-0.2165813297,0.2056818157,-0.1593928784,-0.4222958982,0.4927533269,0.2779901922,0.2250119597,0.0210310835,0.1032613665,0.1426526904,-0.0311795492,-0.0244386941,0.3411134183,0.3493514359,0.2354830205,0.2533789575,-0.0617805347,0.0149179148,-0.2069470584,0.2583582997,0.2557946146,0.1444420069,0.1069826633,0.3125623763,-0.0940604806,-0.0703385696,-0.1985916346,-0.0905192271,-0.0239294637,-0.246761173,0.5204171538,-0.531216085,-0.1981952786,-0.182739526,-0.511631608,0.1445445269,-0.4857126176,0.0923855826,0.4313834608,-0.2941233218,-0.0876571164,-0.1119941473,-0.0655377805,0.2373668551,-0.0889304802,-0.1484933347,-0.0783227831,-0.1540423632,0.0402275771,0.4182909131,-0.1659476459,-0.0945213512,0.2740504146,-0.2734484375,-0.1864078343,-0.4067754447,0.2100645453,0.0406006388,0.3307211697,0.1401688457,-0.1133892015,-0.2044884115,-0.2377706468,-0.0199922808,0.0368938632,-0.2550298274,0.0597670414,0.0730562061,0.1169711798,-0.0949098691,-0.1607797891,-0.1777294725,-0.1410635561,0.0827369168,0.1372183114,0.0686011985,0.4495679736,-0.1690391898,-0.3041932583,0.0321646556,0.0085217049,-0.0780886933,-0.4054821134,0.2219271809,-0.2311036587,-0.2220573127,-0.1003448814,0.094280012,-0.1370528191,0.2631193101,-0.3425102532,0.0981490463,0.0878791884,0.0352271162,-0.0163980462,-0.0600471497,0.0181788914,0.1340132505,-0.0330037549,-0.3138585687,0.0062159505,-0.0829842612,-0.2392150164,0.4358153641,-0.0488199741,0.462400347,0.1643844098,0.3649653494,0.3674896955,0.0879176036,0.1632308662,0.1379345804,0.0847538635,-0.1678284556,-0.328999728,-0.6673765779,-0.1120911688,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3005","title":"DatasetDict.filter and Dataset.filter crashes with any \"fn_kwargs\" argument","comments":"Hi @DrMatters, thanks for reporting.\r\n\r\nThis issue was fixed 14 days ago: #2950.\r\n\r\nCurrently, the fix is only in the master branch and will be made available in our next library release.\r\n\r\nIn the meantime, you can incorporate the fix by installing datasets from the master branch:\r\n```shell\r\npip install -U git+ssh:\/\/git@github.com\/huggingface\/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```shell\r\npip install -U git+https:\/\/github.com\/huggingface\/datasets.git@master#egg=datasets\r\n```","body":"## Describe the bug\r\nThe \".filter\" method of DatasetDict or Dataset objects fails when passing any \"fn_kwargs\" argument\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\nexample_dataset = datasets.Dataset.from_dict({\"a\": {1, 2, 3, 4}})\r\n\r\ndef filter_value(example, value):\r\n    return example['a'] == value\r\n\r\nfiltered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})\r\n```\r\n\r\n## Expected results\r\n`filtered` is a dataset containing {\"a\": {3}}\r\n\r\n## Actual results\r\n\r\n> Traceback (most recent call last):\r\n>   File \"C:\\Users\\qsemi\\Documents\\git\\nlp_experiments\\gpt_celebrity\\src\\test_faulty_filter.py\", line 8, in <module>\r\n>     filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 185, in wrapper\r\n>     out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\fingerprint.py\", line 398, in wrapper\r\n>     out = func(self, *args, **kwargs)\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 2169, in filter\r\n>     indices = self.map(\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1686, in map\r\n>     return self._map_single(\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 185, in wrapper\r\n>     out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\fingerprint.py\", line 398, in wrapper\r\n>     out = func(self, *args, **kwargs)\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 2048, in _map_single\r\n>     batch = apply_function_on_filtered_inputs(\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1939, in apply_function_on_filtered_inputs\r\n>     function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n> TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'value'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":60,"text":"DatasetDict.filter and Dataset.filter crashes with any \"fn_kwargs\" argument \n ## Describe the bug\r\nThe \".filter\" method of DatasetDict or Dataset objects fails when passing any \"fn_kwargs\" argument\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\nexample_dataset = datasets.Dataset.from_dict({\"a\": {1, 2, 3, 4}})\r\n\r\ndef filter_value(example, value):\r\n    return example['a'] == value\r\n\r\nfiltered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})\r\n```\r\n\r\n## Expected results\r\n`filtered` is a dataset containing {\"a\": {3}}\r\n\r\n## Actual results\r\n\r\n> Traceback (most recent call last):\r\n>   File \"C:\\Users\\qsemi\\Documents\\git\\nlp_experiments\\gpt_celebrity\\src\\test_faulty_filter.py\", line 8, in <module>\r\n>     filtered = example_dataset.filter(filter_value, fn_kwargs={'value': 3})\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 185, in wrapper\r\n>     out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\fingerprint.py\", line 398, in wrapper\r\n>     out = func(self, *args, **kwargs)\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 2169, in filter\r\n>     indices = self.map(\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1686, in map\r\n>     return self._map_single(\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 185, in wrapper\r\n>     out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\fingerprint.py\", line 398, in wrapper\r\n>     out = func(self, *args, **kwargs)\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 2048, in _map_single\r\n>     batch = apply_function_on_filtered_inputs(\r\n>   File \"C:\\Users\\qsemi\\miniconda3\\envs\\main\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1939, in apply_function_on_filtered_inputs\r\n>     function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n> TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'value'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.19042-SP0\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Hi @DrMatters, thanks for reporting.\r\n\r\nThis issue was fixed 14 days ago: #2950.\r\n\r\nCurrently, the fix is only in the master branch and will be made available in our next library release.\r\n\r\nIn the meantime, you can incorporate the fix by installing datasets from the master branch:\r\n```shell\r\npip install -U git+ssh:\/\/git@github.com\/huggingface\/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```shell\r\npip install -U git+https:\/\/github.com\/huggingface\/datasets.git@master#egg=datasets\r\n```","embeddings":[-0.1690023839,0.1079778224,-0.0990461707,0.2168680727,0.0916615054,-0.1244720593,0.061660368,0.3426544368,0.1288871616,-0.0045325565,-0.0775926486,0.5116116405,-0.2248641849,0.122256346,-0.1808165312,-0.1617235243,0.0669156983,-0.0306671262,-0.0628625676,-0.0279793255,-0.2775100172,0.1459847242,-0.3633542657,0.2622735202,0.0042149145,-0.0581638701,0.0071831997,0.1170642003,-0.0976910517,-0.3816309869,0.3197533488,0.1038153768,-0.1884299368,0.5421863198,-0.0001097719,0.226201728,0.3147906661,-0.070683375,-0.4898494184,0.0444603898,-0.1691540927,0.0168048292,0.087924242,-0.204738602,0.0350152887,-0.3247827888,-0.3048883975,-0.3992136419,0.3202290833,0.5379633307,0.2239295244,0.1217353866,0.0986725837,0.0586761013,-0.0500810966,0.2121106684,-0.1163619459,0.0562368147,0.2613699734,-0.1781239957,0.0318880565,0.1617822945,-0.3746824265,-0.229896158,0.0832073539,-0.2383361012,0.0139814653,-0.1912639588,0.2338468432,-0.0241400655,0.0294487532,-0.3859303296,-0.3008982241,-0.2289018333,-0.1748608351,-0.0726025328,-0.1212465316,-0.0688958168,-0.5144794583,0.1725679487,-0.1525181234,0.1442772746,-0.0306611639,0.2332361639,-0.0133534083,0.502459228,-0.0743390247,0.2503433228,0.0968454778,-0.1396994591,0.0332270376,-0.3823406696,-0.1965303123,-0.0147394929,-0.2340064347,-0.0410276242,0.2007407099,-0.0717609152,0.2608825564,-0.1736513823,-0.0509063341,0.1881482303,0.2851507664,0.1732221991,0.171738252,0.0074455808,0.4710155725,0.3466989398,0.1967473626,-0.2313842773,-0.2463459074,-0.0041107959,0.2134088427,-0.1784032136,0.0254817456,-0.0321092196,0.3665739298,-0.2347309142,-0.4233624041,-0.1240891963,-0.2274082899,0.2877785563,0.3114433885,0.3256751299,0.180887714,0.1783580035,0.076754652,0.0257870667,-0.0109376526,0.047032252,-0.2045731097,-0.1862517148,0.0743368343,-0.0065202871,0.1274014711,-0.261480689,0.29820472,-0.168930918,-0.0635608956,-0.0420556515,0.1778305918,-0.1095379889,0.1115081683,0.3363687396,-0.2705056369,0.0683040842,0.2735778987,-0.2712777853,0.0021330174,-0.0582729541,-0.3282701075,-0.1916853637,-0.0025131672,0.2654926181,0.0905712023,0.0367515385,-0.3647672236,0.3528071344,0.1909295768,-0.1470300257,-0.032391876,-0.4032224119,-0.4427422881,-0.4158336222,0.3200588226,0.3416737318,-0.3922725916,-0.0818345249,-0.2981072366,-0.1453659683,0.1026888564,0.1037627533,-0.2703128755,0.0615401752,-0.2082357109,-0.018870391,0.5001471639,-0.2920774519,-0.3207316399,0.1842789948,0.1780927628,0.3510619402,-0.1247512251,-0.0254241098,0.1028444096,-0.1985166073,0.2865837514,0.2779538929,-0.1625914723,0.0370189287,-0.1797908694,-0.1232539564,0.1489046663,0.0747718513,0.0576800741,-0.0216233619,0.0575903766,-0.3828399479,0.1891635507,-0.0804140717,-0.09083312,0.1207932234,0.508813858,0.0317654982,0.2157405615,-0.2936540246,-0.3297331035,0.1875579357,0.2089963108,-0.2932587564,-0.2724069357,-0.3248451948,-0.1238651648,0.2228353024,-0.2720043063,-0.2518545091,0.2614545226,0.0444962792,0.1267432272,0.0372401215,-0.0124831861,0.2853068113,-0.2086112797,-0.0152132539,-0.0001506377,0.2370602041,-0.0306496657,-0.1998751312,-0.1662997156,0.0556900539,0.351331085,0.0384941772,-0.0839657187,0.3290603459,0.3637548387,-0.3067316413,-0.0325929821,-0.0140985884,-0.0653480068,-0.2510518432,-0.0255352575,0.464628756,0.2510257661,-0.2040247619,-0.025207052,0.2618360221,-0.0279459469,0.1923236251,0.0660528764,0.174213469,0.1815208495,0.1254569143,-0.1330012232,-0.0482447706,0.024385415,-0.2300138026,0.2080644369,-0.0876666084,-0.308668077,0.1638466418,0.239335373,0.2033559978,0.2983902693,0.1153902784,-0.1193821579,0.1276553422,0.0142925503,0.2795273066,0.6075778604,0.20278956,-0.1540360302,-0.0446508713,-0.1209474802,-0.0704544857,0.2789675593,0.2302939147,0.0704754069,0.1182083413,0.22699821,0.0280451756,-0.3119044602,-0.2885091901,-0.1402565241,0.1148215905,-0.2915370762,0.3386082649,-0.150477007,-0.3110824227,-0.0568547808,0.0691218153,0.1010144427,-0.4233440757,-0.0085941553,0.2850896716,-0.2425683439,0.1305285096,-0.2098611444,0.1827579588,0.2200361788,-0.1005342975,-0.0754382983,-0.2359612882,-0.2100058049,0.0882686377,0.2592342794,0.2625150979,0.4690301716,0.3622205555,-0.102318652,-0.26371786,-0.5074987411,-0.0968931466,-0.0947879925,0.688157618,0.3183228374,0.2780736685,-0.0341825336,-0.3162075579,0.0745366141,0.0525511764,-0.1096285582,0.2327319235,0.1051626503,-0.0674749985,-0.2983950675,-0.0807787105,-0.5075434446,-0.4076522887,-0.1995864958,-0.1943225265,0.0909395143,0.4722770452,0.2439128309,0.309543103,-0.116949372,0.093938902,0.0560869873,0.1693790406,0.1855395436,-0.1524574012,-0.2668396831,-0.2921789289,-0.0485368297,-0.0816716105,0.1622701734,-0.3921108544,-0.3828908801,-0.1746285558,0.1137081161,0.2465953082,-0.1569054723,0.0052775042,0.18441315,-0.0608546659,-0.2471649945,-0.1549735069,-0.0268793851,-0.2190561593,0.0287502483,0.1731504947,0.5743712783,0.1642218083,0.5488057137,0.3525410593,-0.2236235738,0.2097297162,0.0601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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2997","title":"Dataset has incorrect labels","comments":"Hi @marshmellow77, thanks for reporting.\r\n\r\nThat issue is fixed since `datasets` version 1.9.0 (see 16bc665f2753677c765011ef79c84e55486d4347).\r\n\r\nPlease, update `datasets` with: `pip install -U datasets`","body":"The dataset https:\/\/huggingface.co\/datasets\/turkish_product_reviews has incorrect labels - all reviews are labelled with \"1\" (positive sentiment). None of the reviews is labelled with \"0\". See screenshot attached:\r\n\r\n![Capture](https:\/\/user-images.githubusercontent.com\/63367770\/135617428-14ce0b27-5208-4e66-a3ee-71542e3257b4.PNG)\r\n","comment_length":23,"text":"Dataset has incorrect labels \n The dataset https:\/\/huggingface.co\/datasets\/turkish_product_reviews has incorrect labels - all reviews are labelled with \"1\" (positive sentiment). None of the reviews is labelled with \"0\". See screenshot attached:\r\n\r\n![Capture](https:\/\/user-images.githubusercontent.com\/63367770\/135617428-14ce0b27-5208-4e66-a3ee-71542e3257b4.PNG)\r\n \n Hi @marshmellow77, thanks for reporting.\r\n\r\nThat issue is fixed since `datasets` version 1.9.0 (see 16bc665f2753677c765011ef79c84e55486d4347).\r\n\r\nPlease, update `datasets` with: `pip install -U datasets`","embeddings":[0.0827171877,-0.3300799429,-0.1685770303,0.1547696739,0.0381951816,0.2151863575,0.3849951029,0.0040706899,-0.1630113274,-0.1092670634,-0.3049570918,0.1451764256,0.1469206661,0.5568861365,0.0549145043,0.1013108343,0.2955243289,-0.1342056543,0.0368612409,-0.2108732611,-0.2134607136,0.194399029,-0.1512174606,0.1098829284,-0.3690980971,0.0368765444,0.1958554238,-0.4579506218,-0.0822031125,-0.5934593678,0.2520820796,0.1521275192,-0.304956913,0.4699079394,-0.000106652,-0.0895583332,0.3018386364,0.0657603815,-0.0152209504,-0.3397645354,0.0891106427,-0.500615418,-0.138909474,-0.0888425633,-0.0891889855,-0.3566967547,-0.0852885544,0.1974131614,0.0824165121,0.13689138,0.2477026731,-0.0203011353,-0.0823324025,0.0664727464,0.1534873843,0.3733443022,-0.2127846032,0.0524579063,0.0907359421,-0.0468921103,0.1876504123,0.5746165514,-0.1498907655,-0.0271101072,0.1352486908,-0.0230336506,0.0235660765,-0.4660262764,0.353866905,0.3876511157,0.7163805962,-0.4202762842,-0.5077399015,-0.243821606,-0.0662693009,-0.3324439526,0.0335708112,0.2680910528,0.0553379394,0.0598464608,-0.2984196246,0.0403498933,0.0872156471,0.1596512794,0.0931742862,0.345520556,-0.1406511068,0.0286883768,0.1736680567,-0.1051784009,0.2998772264,0.1837810278,-0.1075255275,0.0825617537,-0.3549919128,-0.0566540249,-0.0309354644,0.2112621963,0.2016006708,-0.0769798458,0.1708560288,0.0461283475,-0.0315932073,-0.0361344777,0.1173599884,0.1751489043,0.5971417427,0.163715601,-0.1375490874,0.1272883117,0.2048583329,0.0595585331,0.1390394568,-0.1338866949,0.1390378475,0.0254069474,-0.2410084605,-0.2312716395,-0.3642416894,0.3782798946,-0.2107875794,-0.0069284793,0.0735932663,0.2173342407,-0.0965236872,0.0732789561,-0.2972214222,0.2261812538,0.0239167344,-0.073055163,-0.1905498803,0.1514112949,-0.1710591018,-0.099608548,0.3232781291,-0.1479810774,0.5818253756,0.0012215993,-0.0948678479,-0.2763219178,-0.1950514466,-0.3302877843,0.2633200884,0.2982042432,-0.2006141692,0.2761179209,0.1448080987,0.060983438,-0.0716900453,0.2649276257,-0.2769354582,0.1679261923,0.1086128056,0.2046224177,-0.1708952785,-0.49598822,-0.2338262498,0.2875287533,0.1208472773,0.1583428234,0.0514334403,-0.0128536606,-0.0952210352,-0.0563930981,0.0308222249,0.1114382446,-0.4609392285,-0.17288737,-0.3021656275,-0.0675708279,-0.0119010005,0.3279077411,0.1735320389,0.1309546977,0.1932584494,0.1423788667,-0.026404446,-0.2673941851,-0.3747159839,-0.0825351477,0.0482754298,-0.1660183072,-0.1321782917,0.2046722025,0.1108912081,0.1752911359,-0.143445015,-0.1185267195,0.3017588854,0.1596747786,-0.2708196938,-0.2343392521,0.2006430179,0.186800614,0.2092593163,0.016237827,-0.3023100793,-0.3459333479,0.1930293292,0.1690391898,-0.1395741701,0.4623102248,0.4301410317,0.3649873137,0.2181995958,0.0083189197,-0.3710203767,-0.0064482302,-0.2046899199,0.1879344285,0.1789573729,-0.3164820075,-0.513330102,0.0074115335,-0.1040575802,-0.1723653376,0.2363257408,0.0233682133,-0.1459814906,0.2099978477,-0.218049258,0.2396514118,0.1250265092,0.2449726015,0.1181604639,0.1170343384,0.0344781131,0.0624390319,0.1335916519,0.2213991731,0.238728866,0.1818671823,-0.0255704727,0.1588410139,-0.1704668552,0.2229384929,-0.051041577,0.0280747507,0.13966465,-0.3854644001,-0.3065173328,0.2486745119,0.0177028663,0.0173459966,-0.1784909368,0.2858126163,-0.0830879286,0.0402111299,-0.3636784554,0.0962586701,0.0925723016,-0.1040457487,-0.0137479911,-0.4995589554,0.4471006095,-0.2386276871,0.3529191315,0.1672911197,-0.0173809119,0.2589150965,0.3035748601,-0.0090610152,0.0336592309,0.0664557964,-0.0538186952,-0.0121130385,0.2746826112,0.1138037071,0.033278849,0.1877104044,-0.1904313713,0.1206140518,0.0055718403,-0.0881646797,0.0033493468,-0.0641940087,-0.06505806,0.1952707469,0.1882728487,-0.2315630168,-0.3235929608,0.1872285455,0.1560435444,0.3456493616,-0.4204522371,0.0696233064,-0.2037783116,-0.1737859994,-0.24009642,-0.3208136261,-0.5267137885,-0.4627149701,0.2274193466,-0.0428111814,-0.0801798329,0.245827049,0.1455912292,-0.0985158607,-0.2612766623,0.1230359226,-0.2608943582,-0.3647432923,-0.1340235174,-0.0096429028,-0.0974872038,0.1770117432,0.3007126153,-0.4100846648,-0.2838755846,-0.281675458,-0.3878369331,0.1596682966,-0.2572085261,0.0755259544,-0.0235007517,-0.071987696,-0.2795679569,0.1634620279,0.3201038241,0.0036613515,0.0297686867,-0.0542249531,-0.1600922793,-0.1995168924,-0.0513563603,-0.3800828755,-0.1324866712,-0.0038539241,0.1867566258,-0.1426092982,0.1730260402,0.0082483813,-0.1696839482,-0.1181550995,-0.279181987,-0.0226116702,-0.3751546144,-0.2114236951,0.0083400682,-0.0920758694,-0.2112526,0.0576865375,0.2249599546,-0.0284003019,-0.0916418508,-0.3723655641,0.0010773009,0.014457792,0.0563241281,0.137837708,-0.0476227812,-0.1190446168,0.1150853857,-0.0473135822,-0.2623840868,-0.2892486155,0.1363867223,-0.197619468,0.210954994,-0.1558859497,0.1090041101,0.0137911644,0.1759948283,0.3166746199,-0.086108692,0.1254953146,-0.1626474112,0.2985578179,0.1513293684,-0.4280117452,0.171750173,0.0811673775,0.2911856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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2997","title":"Dataset has incorrect labels","comments":"Thanks. Please note that the dataset explorer (https:\/\/huggingface.co\/datasets\/viewer\/?dataset=turkish_product_reviews) still shows the incorrect state. The sentiment for the first few customer reviews is actually negative and should be labelled with \"0\", see screenshot:\r\n\r\n![Capture1](https:\/\/user-images.githubusercontent.com\/63367770\/135637150-93d9b09b-f1dd-4701-97a5-5cb2672ec0c7.PNG)\r\n\r\n\r\n","body":"The dataset https:\/\/huggingface.co\/datasets\/turkish_product_reviews has incorrect labels - all reviews are labelled with \"1\" (positive sentiment). None of the reviews is labelled with \"0\". See screenshot attached:\r\n\r\n![Capture](https:\/\/user-images.githubusercontent.com\/63367770\/135617428-14ce0b27-5208-4e66-a3ee-71542e3257b4.PNG)\r\n","comment_length":33,"text":"Dataset has incorrect labels \n The dataset https:\/\/huggingface.co\/datasets\/turkish_product_reviews has incorrect labels - all reviews are labelled with \"1\" (positive sentiment). None of the reviews is labelled with \"0\". See screenshot attached:\r\n\r\n![Capture](https:\/\/user-images.githubusercontent.com\/63367770\/135617428-14ce0b27-5208-4e66-a3ee-71542e3257b4.PNG)\r\n \n Thanks. Please note that the dataset explorer (https:\/\/huggingface.co\/datasets\/viewer\/?dataset=turkish_product_reviews) still shows the incorrect state. The sentiment for the first few customer reviews is actually negative and should be labelled with \"0\", see screenshot:\r\n\r\n![Capture1](https:\/\/user-images.githubusercontent.com\/63367770\/135637150-93d9b09b-f1dd-4701-97a5-5cb2672ec0c7.PNG)\r\n\r\n\r\n","embeddings":[0.0925552994,-0.342415601,-0.1470151693,0.2214364111,-0.1244753525,0.1750766635,0.2092049122,-0.0090172756,-0.1904669702,-0.2527097464,-0.3256235719,-0.0248506907,0.1052714437,0.4831328988,0.0100236041,0.0766140297,0.3029942811,-0.1475577503,0.1747662872,-0.2336751074,-0.1630173028,0.244733274,-0.184375912,0.1097194329,-0.4816226959,-0.0086973803,0.1464185119,-0.4138464034,-0.0807928592,-0.5444647074,0.2882569134,0.2182916254,-0.2296825498,0.4476241469,-0.0001088794,-0.097786136,0.2252599597,0.0276983697,0.1330188364,-0.2660121024,0.0255316105,-0.51647228,-0.085971877,-0.0465791076,-0.165632233,-0.4607760608,-0.1222815737,0.152180478,0.1188045964,0.1382310987,0.2050516754,-0.0610743053,-0.1624113321,0.0877262279,0.0246965569,0.5398183465,-0.1285413951,0.0463006385,0.0237158872,0.0301638525,0.015869353,0.4995584488,-0.0722464696,-0.1840715706,0.1814396977,-0.0906175599,-0.1119099408,-0.3975764513,0.3953448534,0.3383181989,0.7514029145,-0.3189276159,-0.5381092429,-0.2498255372,-0.0654208139,-0.292666018,0.0706187859,0.1658866853,0.0437180921,0.0782257542,-0.3169127405,0.0486426093,0.1096847057,0.1309595853,-0.0818448737,0.2622310817,-0.1950609833,-0.0505639128,0.1685505062,-0.1881686598,0.240237385,0.2002963126,-0.1427433491,0.0550988615,-0.4516893923,-0.1323699206,0.0277020112,0.2582725883,0.1765512079,-0.0797229186,0.1655906141,0.0487647094,-0.0254061017,-0.1323493868,0.1936000884,0.1914471686,0.655913651,-0.0652809218,-0.1739184707,0.1009602845,0.1301327944,0.0597785749,0.1106910631,-0.146958515,0.0868450552,-0.0563054942,-0.350145191,-0.2870482504,-0.3128223419,0.3707848489,-0.2109360844,-0.0604800284,0.1282981336,0.1985507011,-0.0388556533,0.1409612596,-0.3294707239,0.1969042867,0.0257305019,-0.1328757703,-0.1810901165,0.1208986416,-0.2200973183,-0.0021920071,0.2898962498,-0.179118976,0.5723329186,-0.0001026082,-0.1410488933,-0.3301346898,-0.2811888754,-0.4343366921,0.3018733263,0.2096795291,-0.2175999284,0.3176564872,0.1271997392,0.0361565612,-0.0298634171,0.2122237831,-0.1901507229,0.2018220872,0.0402877703,0.1588368118,-0.2872497141,-0.4701351821,-0.0771028474,0.3606425226,0.0451969095,0.159164384,0.022608405,0.0299825538,-0.0489245877,0.0285789315,0.1254715025,0.1486033946,-0.5311074257,-0.1466320604,-0.2928225398,-0.090993017,-0.0071209436,0.2930852473,0.1774894893,0.1673538983,0.1578117311,0.1614206731,-0.047522001,-0.1990639418,-0.4214951396,-0.0159146637,0.0571546778,-0.1256390065,-0.1184962392,0.2174258828,0.2066157311,0.2937484384,-0.1759041846,-0.2056261152,0.3906102777,0.1544347554,-0.2913843691,-0.1915049553,0.2208830118,0.1764069796,0.18626827,0.048102729,-0.264269799,-0.2381033748,0.2731867731,0.2121208161,-0.1287885755,0.4339455962,0.2817262113,0.3914189041,0.1509340703,0.1307968348,-0.4305352867,-0.0130271027,-0.2612330616,0.1762263477,0.2740207613,-0.2632717788,-0.5795595646,-0.0911950693,-0.1274681985,-0.1828567982,0.2207563818,0.0863781497,-0.2229521126,0.2503077984,-0.2417708784,0.2290884107,0.1896991581,0.1300667226,0.1291898936,0.1048270836,0.0725776777,0.1052134931,0.1782899946,0.1867225915,0.2586902678,0.1860181093,-0.0064022699,0.1396360546,-0.2065950632,0.2743647695,-0.0908350199,0.0400513187,0.1414872855,-0.4296844602,-0.3346498311,0.2124006748,-0.040398065,0.0142854247,-0.2832313776,0.3753648996,0.0314655341,0.0283531509,-0.3977449238,0.0931193456,0.1092704833,-0.0987701938,-0.0257523805,-0.5242962837,0.3948393166,-0.2650360167,0.4269766212,0.2789642513,-0.1135712638,0.2573894262,0.2287765294,0.0457124598,0.0348060057,0.0860750973,-0.103399016,-0.1026964784,0.3983831108,0.0348389447,-0.0806954131,0.1857797951,-0.2074537277,0.018011203,0.0003833283,-0.0542805418,-0.0025297825,-0.0928601325,0.0596835613,0.2430813909,0.0986878276,-0.2536664307,-0.3579102755,0.1261610538,0.1257320195,0.3963796496,-0.4340934753,-0.0324924812,-0.2033091635,-0.3048999608,-0.2153482735,-0.3079586923,-0.5704945326,-0.5030295849,0.1617797017,-0.1631568819,-0.0779118389,0.1797312647,0.035612084,-0.0228621662,-0.3221244216,0.1976554841,-0.274332732,-0.3320001364,-0.155189693,-0.0377388224,-0.0806242302,0.2178602517,0.2423862368,-0.3808186948,-0.2089700848,-0.1904132813,-0.2644409835,0.1463931948,-0.2205648869,0.0350181088,-0.0906732678,-0.019084299,-0.3397002816,0.1482518911,0.2232294977,0.0881788284,0.0379660204,-0.0547000393,-0.1406261623,-0.1526518464,-0.0025037243,-0.3232073188,-0.0744337663,0.0207083486,0.0946891978,-0.1430284232,0.1534765512,0.0472829938,-0.1591156572,-0.015072532,-0.339607954,-0.0251221713,-0.2941179276,-0.2728949189,0.0080346745,-0.0517541245,-0.2171221823,0.0061312239,0.2858325839,-0.028120609,-0.1632909477,-0.2719991803,0.3200101852,-0.011716106,0.0714912564,0.1710425019,-0.2226872146,-0.1518601179,0.094519183,-0.0586658381,-0.1701666862,-0.2833763957,0.1189975962,-0.2403701246,0.2660479844,-0.2029168457,-0.05887512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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2993","title":"Can't download `trivia_qa\/unfiltered`","comments":"wooo that was fast! thank you @lhoestq !\r\nit is able to process now, though it's ignoring all files and ending up with 0 examples now haha :\/\r\n\r\nFor subset \"unfiltered\":\r\n```python\r\n>>> load_dataset(\"trivia_qa\", \"unfiltered\")\r\nDownloading and preparing dataset trivia_qa\/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to \/gpfsscratch\/rech\/six\/commun\/datasets\/trivia_qa\/unfiltered\/1.1.0\/910043a609bb2bdf62b4874f68e0c24fb648cf81e40a358f4bd54c919d72c9ab...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1354.53it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 40.60it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1198, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 647, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 748, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=2906575347, num_examples=10832, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='validation', num_bytes=3038966234, num_examples=11313, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}]\r\n```\r\nFor subset \"rc\":\r\n```python\r\n>>> load_dataset(\"trivia_qa\", \"rc\")\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/gpfsscratch\/rech\/six\/commun\/datasets\/trivia_qa\/rc\/1.1.0\/910043a609bb2bdf62b4874f68e0c24fb648cf81e40a358f4bd54c919d72c9ab...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 3806.08it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 51.57it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1198, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 647, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 748, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=1577814583, num_examples=17210, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='train', num_bytes=12750976012, num_examples=138384, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='validation', num_bytes=1688535379, num_examples=18669, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}]\r\n```\r\n\r\nCould you look into that when you get a chance?\r\nI wonder if it's not something they changed on the file to download? i couldn't find any information","body":"## Describe the bug\r\n\r\nFor some reason, I can't download `trivia_qa\/unfilted`. A file seems to be missing... I am able to see it fine though the viewer tough...\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> load_dataset(\"trivia_qa\", \"unfiltered\")\r\nDownloading and preparing dataset trivia_qa\/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to \/gpfsscratch\/rech\/six\/commun\/datasets\/trivia_qa\/unfiltered\/1.1.0\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6...\r\nTraceback (most recent call last):\r\n  File \"\/gpfswork\/rech\/six\/commun\/modules\/datasets_modules\/datasets\/trivia_qa\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6\/trivia_qa.py\", line 251, in _add_context\r\n    with open(os.path.join(file_dir, fname), encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/gpfsscratch\/rech\/six\/commun\/datasets\/downloads\/extracted\/9fcb7eddc6afd46fd074af3c5128931dfe4b548f933c925a23847faf4c1995ad\/evidence\/wikipedia\/Peanuts.txt'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 852, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 616, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/gpfswork\/rech\/six\/commun\/modules\/datasets_modules\/datasets\/trivia_qa\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6\/trivia_qa.py\", line 303, in _generate_examples\r\n    example = parse_example(article)\r\n  File \"\/gpfswork\/rech\/six\/commun\/modules\/datasets_modules\/datasets\/trivia_qa\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6\/trivia_qa.py\", line 274, in parse_example\r\n    _add_context(article.get(\"EntityPages\", []), \"WikiContext\", wiki_dir),\r\n  File \"\/gpfswork\/rech\/six\/commun\/modules\/datasets_modules\/datasets\/trivia_qa\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6\/trivia_qa.py\", line 253, in _add_context\r\n    except (IOError, datasets.Value(\"errors\").NotFoundError):\r\n  File \"<string>\", line 5, in __init__\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 265, in __post_init__\r\n    self.pa_type = string_to_arrow(self.dtype)\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 134, in string_to_arrow\r\n    f\"Neither {datasets_dtype} nor {datasets_dtype + '_'} seems to be a pyarrow data type. \"\r\nValueError: Neither errors nor errors_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\n## Expected results\r\nI am able to load another subset (`rc`), but unable to load.\r\nI am not sure why the try\/except doesn't catch it...\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/9675a5a1e7b99a86f9c250f6ea5fa5d1e6d5cc7d\/datasets\/trivia_qa\/trivia_qa.py#L253\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-4.18.0-147.51.2.el8_1.x86_64-x86_64-with-redhat-8.1-Ootpa\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":264,"text":"Can't download `trivia_qa\/unfiltered` \n ## Describe the bug\r\n\r\nFor some reason, I can't download `trivia_qa\/unfilted`. A file seems to be missing... I am able to see it fine though the viewer tough...\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> load_dataset(\"trivia_qa\", \"unfiltered\")\r\nDownloading and preparing dataset trivia_qa\/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to \/gpfsscratch\/rech\/six\/commun\/datasets\/trivia_qa\/unfiltered\/1.1.0\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6...\r\nTraceback (most recent call last):\r\n  File \"\/gpfswork\/rech\/six\/commun\/modules\/datasets_modules\/datasets\/trivia_qa\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6\/trivia_qa.py\", line 251, in _add_context\r\n    with open(os.path.join(file_dir, fname), encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/gpfsscratch\/rech\/six\/commun\/datasets\/downloads\/extracted\/9fcb7eddc6afd46fd074af3c5128931dfe4b548f933c925a23847faf4c1995ad\/evidence\/wikipedia\/Peanuts.txt'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 852, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 616, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/gpfswork\/rech\/six\/commun\/modules\/datasets_modules\/datasets\/trivia_qa\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6\/trivia_qa.py\", line 303, in _generate_examples\r\n    example = parse_example(article)\r\n  File \"\/gpfswork\/rech\/six\/commun\/modules\/datasets_modules\/datasets\/trivia_qa\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6\/trivia_qa.py\", line 274, in parse_example\r\n    _add_context(article.get(\"EntityPages\", []), \"WikiContext\", wiki_dir),\r\n  File \"\/gpfswork\/rech\/six\/commun\/modules\/datasets_modules\/datasets\/trivia_qa\/9977a5d6f72acfd92f587de052403e8138b43bb0d1ce595016c3baf7e14deba6\/trivia_qa.py\", line 253, in _add_context\r\n    except (IOError, datasets.Value(\"errors\").NotFoundError):\r\n  File \"<string>\", line 5, in __init__\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 265, in __post_init__\r\n    self.pa_type = string_to_arrow(self.dtype)\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 134, in string_to_arrow\r\n    f\"Neither {datasets_dtype} nor {datasets_dtype + '_'} seems to be a pyarrow data type. \"\r\nValueError: Neither errors nor errors_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\n## Expected results\r\nI am able to load another subset (`rc`), but unable to load.\r\nI am not sure why the try\/except doesn't catch it...\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/9675a5a1e7b99a86f9c250f6ea5fa5d1e6d5cc7d\/datasets\/trivia_qa\/trivia_qa.py#L253\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-4.18.0-147.51.2.el8_1.x86_64-x86_64-with-redhat-8.1-Ootpa\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n \n wooo that was fast! thank you @lhoestq !\r\nit is able to process now, though it's ignoring all files and ending up with 0 examples now haha :\/\r\n\r\nFor subset \"unfiltered\":\r\n```python\r\n>>> load_dataset(\"trivia_qa\", \"unfiltered\")\r\nDownloading and preparing dataset trivia_qa\/unfiltered (download: 3.07 GiB, generated: 27.23 GiB, post-processed: Unknown size, total: 30.30 GiB) to \/gpfsscratch\/rech\/six\/commun\/datasets\/trivia_qa\/unfiltered\/1.1.0\/910043a609bb2bdf62b4874f68e0c24fb648cf81e40a358f4bd54c919d72c9ab...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 1354.53it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 40.60it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1198, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 647, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 748, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=2906575347, num_examples=10832, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='validation', num_bytes=3038966234, num_examples=11313, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}]\r\n```\r\nFor subset \"rc\":\r\n```python\r\n>>> load_dataset(\"trivia_qa\", \"rc\")\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/gpfsscratch\/rech\/six\/commun\/datasets\/trivia_qa\/rc\/1.1.0\/910043a609bb2bdf62b4874f68e0c24fb648cf81e40a358f4bd54c919d72c9ab...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 3806.08it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 51.57it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 1198, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 647, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 748, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/gpfswork\/rech\/six\/commun\/conda\/victor\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=1577814583, num_examples=17210, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='train', num_bytes=12750976012, num_examples=138384, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}, {'expected': SplitInfo(name='validation', num_bytes=1688535379, num_examples=18669, dataset_name='trivia_qa'), 'recorded': SplitInfo(name='validation', num_bytes=0, num_examples=0, dataset_name='trivia_qa')}]\r\n```\r\n\r\nCould you look into that when you get a chance?\r\nI wonder if it's not something they changed on the file to download? i couldn't find any information","embeddings":[-0.1447088867,-0.1315827966,-0.0809885338,0.2758610845,0.4623157978,0.3147660196,0.0820351392,0.2365654409,0.18648763,0.1213065237,-0.1047465727,0.0168522466,0.0071841441,-0.1792368889,-0.026129527,-0.138726294,-0.0643537641,-0.0873087719,0.2322930843,0.1013253704,-0.2905995548,0.2706827819,-0.2917580605,-0.0539276972,-0.1867733002,0.0020684204,0.0271448921,0.3517014086,-0.1980820298,-0.1902807802,0.0393858179,0.0315682031,-0.0264077913,0.5587806106,-0.000116145,-0.0502303541,0.2307258993,-0.2165746391,-0.2700590193,-0.2972961068,-0.3187274635,0.0021523214,-0.1289208084,-0.0246363077,-0.009166332,0.1179234758,0.2188946307,-0.3271940351,0.0243420321,0.2475953251,0.215443179,-0.1399555951,0.2990696728,-0.2421391159,0.2115184367,-0.2175885886,-0.0901852176,0.3148315549,0.0893244669,0.1804854721,0.3296200931,0.19915995,-0.0921910554,-0.0493850708,0.0908847377,-0.1865285635,-0.3333748877,-0.4321648479,0.3027580082,0.4389563501,0.3177227974,-0.0553484261,-0.6114198565,-0.0403180122,0.2227832675,-0.1834741533,0.1175453886,0.2676682174,-0.2445856929,0.1215388328,0.0523084924,-0.4803204834,-0.0321349949,0.0082692839,-0.262054503,0.1919169873,-0.0798988342,0.0957580507,-0.2800920606,0.0229388457,-0.0834696889,-0.2190042287,0.0380742289,0.1810049117,-0.1479461938,0.2362419367,0.1901223361,-0.0407778844,0.0179842785,0.0846960321,-0.1568715423,-0.0612834133,-0.3658030629,0.1785548031,0.1689216942,0.0156500097,-0.1296779066,0.3591116965,0.2757448256,0.3697735071,-0.1477806568,-0.2764611542,-0.1887272298,0.0508408993,-0.0619390979,0.1390022486,0.3612701297,-0.2981185019,-0.544950366,-0.0386098288,-0.1987038851,0.257060349,-0.2661457956,0.169818759,-0.1961322129,0.2867230475,0.0430716835,0.2466429025,-0.1259749383,-0.191923663,-0.2074618042,0.0615258031,0.1302971393,0.0187396109,0.0131411431,-0.3410980105,0.4129911661,-0.282458365,-0.1302912682,-0.0641593859,-0.0678202361,-0.2422218323,-0.3612655401,0.5236302018,0.1769208014,0.1769420505,0.1364932507,-0.1574611664,0.0909204632,0.2715309858,-0.2481537014,-0.1442258209,-0.1438059062,0.2079810798,-0.2853924632,-0.1031061262,0.1829573214,0.0338009074,-0.030808907,-0.4103814065,0.0878869519,-0.1517980397,-0.2252562046,-0.4477551281,0.2062779218,0.5398128033,-0.4783023,0.0291701313,-0.2839588523,-0.1050835326,0.0734501332,0.050775744,0.0715679452,0.1863813251,-0.2867978811,0.301361233,0.4690093398,-0.622554183,-0.4140735567,0.1154510006,-0.176652655,0.4788354039,0.1688828021,0.0216218866,0.2153728902,-0.2463669032,0.2774094045,0.2589016855,-0.3009458482,-0.3227989674,-0.2626438141,-0.2620822489,0.1029927284,0.0544753112,0.3256145418,0.0818218812,0.1170076579,0.0771208033,0.4368186593,0.2516320646,0.0720793381,0.3099306524,0.6050543189,0.2903930843,0.0146320714,-0.5332842469,-0.3027372658,0.2410594523,0.0941625088,-0.2037673146,-0.2042154968,-0.3050153255,-0.4655900002,-0.0192314964,-0.0295519251,-0.3123147488,0.1047571898,0.1841634363,-0.0032199018,0.3160443008,-0.0337121524,0.1506026387,0.0383378118,0.1362492442,-0.0513874404,0.0444195308,-0.0133984145,-0.0646501929,0.1156273484,-0.0054199207,0.3512855172,-0.0185674876,-0.0031675778,0.4685291648,0.263120681,0.0308185946,0.2065355629,0.1996358037,0.1877035201,-0.0820684135,-0.1333812624,0.3762407303,0.2601445317,-0.0505385213,-0.2880823314,0.0112224594,-0.0401988812,0.0113410503,0.1489484608,0.2050505877,0.3028258383,0.0143018104,0.0433061756,0.0447259061,0.2907612622,-0.0340044983,-0.34141922,-0.1923977435,-0.2089301944,0.4489889145,0.3202460408,0.002198651,-0.0990961269,0.0785985887,-0.15581581,0.0818559751,-0.0537346266,0.1818118691,0.592979908,0.1440632343,0.0991725624,-0.0960208252,0.1360939592,-0.3111836314,0.233067289,0.185114935,-0.0470308512,0.3925483823,0.0441476107,0.2095210701,0.1311470568,-0.1531832665,-0.056977395,0.2275854796,-0.3553038239,-0.0056569828,-0.2527720332,-0.1379151046,0.0456786901,-0.1074184701,-0.0023536296,-0.4432319701,0.1308874041,0.4339793622,0.0446267314,-0.0972888321,-0.3843178451,0.0537541434,0.0904447213,-0.3527255952,-0.1774734408,-0.010787827,-0.0839815289,0.098902382,0.0984127596,-0.0513513312,0.2351607382,0.0566682406,-0.0139915766,-0.3682755828,-0.3342293203,-0.1983941793,-0.0815904811,0.6031446457,0.2807516158,0.4727473259,-0.050437659,-0.0891787261,0.1254507452,-0.1059367433,-0.1098454148,-0.0953692496,0.0004086908,-0.0682881474,-0.1030942202,-0.4439965785,-0.2509035468,-0.4766795337,0.0762200803,0.1652491093,-0.0378195196,0.0379894711,-0.0217819847,0.0736739114,0.1130339056,-0.1586565375,-0.1104300842,-0.0097244186,0.5126011968,-0.2777706087,-0.3480978906,0.4913222194,0.0755775422,0.1444995403,0.1707935929,-0.6691317558,-0.0374883637,-0.1030585542,0.2736333609,-0.1462507099,-0.1364404857,0.246869728,-0.2043958902,-0.0122730061,-0.1415847987,-0.0162227489,-0.1445461512,0.0117694121,0.311570704,-0.2259241343,0.1614000797,0.0643461123,0.6258360147,0.2716143727,0.6232298613,0.0524457805,-0.1454946846,0.4174816012,-0.1778517962,-0.2010696083,0.2108574659,-0.00560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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"Hi ! Could you check the length of the `self.dataset` object (i.e. the Dataset object passed to the data loader) ? It looks like the dataset is empty.\r\nNot sure why the SWA optimizer would cause this though.","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":38,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n Hi ! Could you check the length of the `self.dataset` object (i.e. the Dataset object passed to the data loader) ? It looks like the dataset is empty.\r\nNot sure why the SWA optimizer would cause this though.","embeddings":[-0.389613986,-0.2896887064,-0.0146302115,0.3087924719,0.3473482132,-0.2418410331,0.3561576009,0.2430759966,0.1943363994,0.3443817794,0.0316291973,0.1856550276,-0.1241295412,0.068187736,-0.363416642,-0.3182570636,-0.1385273039,0.1319549829,-0.2823426723,0.1612484306,-0.2188625783,0.2060973197,-0.2639538944,0.1758927256,-0.0497712679,-0.0371602289,0.0992189422,-0.0780251622,0.0320226103,-0.2494231611,0.3354479074,-0.346852392,0.2457087934,0.7273562551,-0.0001192912,-0.025481293,0.4413975179,-0.2230760157,-0.0005031597,-0.0746245086,0.6110761166,-0.3019441366,-0.0140578914,-0.2531581521,0.2060740441,-0.3909291029,-0.1341194361,-0.0279495362,0.1947908998,0.2587778866,0.14318344,0.3059898019,0.0780828446,-0.2722992003,-0.1123694852,-0.2118056417,0.0402175114,0.2579285502,0.0773182064,-0.174048245,-0.2438988239,0.0654503778,0.1149050593,-0.0566485077,0.5178043246,-0.2840248942,0.1143898815,-0.1758093238,-0.0145956818,-0.00342199,0.2611017227,-0.0868977532,-0.1931508631,-0.3589656949,0.0625937134,-0.0254444499,0.0988237485,0.0703022853,0.0224990975,0.05238875,0.1724583507,0.3939803541,-0.1061743945,0.225917697,-0.0800592527,0.3932600021,-0.0756288841,0.2439516634,0.5022857785,-0.1024582237,0.1065810695,0.2865984738,0.0634872764,0.2548939884,-0.3530240059,-0.2010882944,0.2330770195,-0.6732481718,-0.1928143203,0.2007482648,0.1331625879,-0.0908272117,0.6408691406,-0.1230339259,0.2253909856,0.3644768894,-0.2757491171,0.1300418228,0.3418738544,0.2227095217,0.1012675986,-0.2118380368,0.2360481769,-0.3004505634,0.0831962079,-0.0416878946,-0.0521921478,0.1723070592,-0.1586503237,0.2281520069,-0.0353684463,-0.1432163119,0.0790323019,0.3053472042,0.148478061,0.2386116683,0.3460195959,0.1962521076,-0.2135006189,0.2145754546,-0.1948221773,0.1809065789,0.0893448591,-0.2781764269,-0.0089508276,-0.0071537942,0.4308076501,-0.0701530874,0.3631477654,-0.5140989423,0.1039239168,-0.1084562987,-0.2815326452,0.4845505655,-0.1215516105,-0.0517095365,0.1621133089,-0.0360111222,-0.1242828965,0.2764679492,-0.4824527204,-0.6505846381,0.0183498394,0.0918173417,-0.2360075265,0.2977598906,-0.0981007516,-0.1434908658,0.2951731384,0.0648202747,0.1849305481,-0.39213866,-0.0430691019,-0.0496006273,0.0602658428,0.0568472743,-0.0471673533,-0.2099940032,-0.0448071845,-0.0833948404,0.125947997,0.0874721706,-0.3543171883,0.3284276426,-0.3312932849,0.1692542285,0.3340460956,-0.2291650474,-0.709107995,0.0061513516,-0.4203280509,-0.150533855,0.0903914645,0.1434873194,0.1855770797,-0.004922823,-0.0824156925,0.1605361253,0.0381931551,0.0124474373,-0.1024393141,-0.0544400476,0.2747745812,0.0930822268,0.2478365302,0.0688723475,-0.0113420561,0.4366556108,-0.0544372499,-0.0169295259,0.0306486972,0.1363259256,0.1023456007,-0.2260520756,0.3137185574,-0.2096313834,0.0550971664,0.1035370231,-0.3195706606,0.1881973296,-0.0363559276,-0.0363876037,-0.3540016711,0.0793980435,0.1037681028,-0.0991274491,0.0352030657,-0.1450605839,-0.0832667202,-0.388479054,0.0341508873,-0.0037553702,-0.4048275054,-0.0301961396,-0.0421601012,0.1399486512,-0.2281762362,-0.1746620536,-0.2353018969,0.1891423464,-0.0752717257,-0.183382377,-0.3118394315,0.2674703002,-0.1548387557,-0.0372150429,-0.3062781692,0.1764156967,0.1957689971,-0.050919205,0.2716096044,0.4083098769,0.1179642826,-0.2562609315,0.4541432261,0.6124860048,-0.1288583577,0.0579432212,-0.1382121295,-0.0600695387,0.3139534295,-0.0176601112,-0.0880969986,-0.0222126264,0.0208588988,-0.3356958628,0.0802437738,-0.2782365084,-0.1084268689,-0.2599354684,0.0221382268,0.1173917577,-0.0186677184,0.2555037141,-0.0347185954,0.2116461694,-0.2827578783,0.2482461631,0.5192136765,0.0696473047,-0.2654940188,0.1591648161,0.3544076979,-0.0453876071,0.2292569876,0.3359416723,0.2728696764,0.466919899,0.1930990964,0.1399871856,0.0085987858,-0.3180223107,-0.0641661659,0.4424804151,-0.2965271175,0.2216306329,0.134372741,0.0398937613,-0.1693696529,-0.1187544242,-0.1792631298,-0.2337691486,-0.1237298101,0.3886534274,-0.4316176772,0.0530367754,-0.2674812078,0.288205266,0.2576822937,0.3259709179,0.1478629261,0.2398411036,0.0139712077,-0.1078580841,0.0940019935,-0.0251650214,-0.0192727335,0.1582969874,0.056291651,0.4700152874,-0.4344233274,-0.0599876791,0.0343006402,0.1294091493,0.2592258751,0.1120613292,-0.1111286804,-0.2542455196,0.2870855331,-0.1661201715,-0.1753746569,0.2520132959,-0.0450242199,0.2733143866,-0.3296175599,-0.2529786229,-0.3249234557,-0.4631488323,-0.1274587065,-0.0206832197,0.2834064364,-0.0659301355,0.3948688507,0.1004976705,0.3003748357,-0.0638103858,-0.0256142989,0.1553850174,0.387717545,-0.0366312899,-0.3885851502,-0.0218417924,-0.1496914625,-0.0235516038,-0.4220234752,-0.5347440243,-0.0104106301,-0.1061309054,-0.155281052,-0.3330346644,0.0399059616,0.6237717867,-0.1732101589,-0.0035062337,-0.1227628216,-0.0843582973,0.3050920069,-0.0285022948,0.1049818248,0.1410135627,0.5239762068,0.0909968838,0.6615387201,0.0805728212,-0.2219898999,0.0731115863,0.0339721218,-0.1155951768,-0.1329817623,-0.2787259221,-0.1261444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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"Any updates on this? \r\nThe same error occurred to me too when running `cardiffnlp\/twitter-roberta-base-sentiment` on a custom dataset. This happened when I tried to do `model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3])` without using sagemaker distribution. \r\nPython: 3.6.13\r\ndatasets: 1.6.2","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":40,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n Any updates on this? \r\nThe same error occurred to me too when running `cardiffnlp\/twitter-roberta-base-sentiment` on a custom dataset. This happened when I tried to do `model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3])` without using sagemaker distribution. \r\nPython: 3.6.13\r\ndatasets: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"Hi @ruisi-su, do you have this issue while using SWA as well, or just data parallel ?\r\n\r\nIf you have a code example to reproduce this issue it would also be helpful","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":32,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n Hi @ruisi-su, do you have this issue while using SWA as well, or just data parallel ?\r\n\r\nIf you have a code example to reproduce this issue it would also be helpful","embeddings":[-0.389613986,-0.2896887064,-0.0146302115,0.3087924719,0.3473482132,-0.2418410331,0.3561576009,0.2430759966,0.1943363994,0.3443817794,0.0316291973,0.1856550276,-0.1241295412,0.068187736,-0.363416642,-0.3182570636,-0.1385273039,0.1319549829,-0.2823426723,0.1612484306,-0.2188625783,0.2060973197,-0.2639538944,0.1758927256,-0.0497712679,-0.0371602289,0.0992189422,-0.0780251622,0.0320226103,-0.2494231611,0.3354479074,-0.346852392,0.2457087934,0.7273562551,-0.0001192912,-0.025481293,0.4413975179,-0.2230760157,-0.0005031597,-0.0746245086,0.6110761166,-0.3019441366,-0.0140578914,-0.2531581521,0.2060740441,-0.3909291029,-0.1341194361,-0.0279495362,0.1947908998,0.2587778866,0.14318344,0.3059898019,0.0780828446,-0.2722992003,-0.1123694852,-0.2118056417,0.0402175114,0.2579285502,0.0773182064,-0.174048245,-0.2438988239,0.0654503778,0.1149050593,-0.0566485077,0.5178043246,-0.2840248942,0.1143898815,-0.1758093238,-0.0145956818,-0.00342199,0.2611017227,-0.0868977532,-0.1931508631,-0.3589656949,0.0625937134,-0.0254444499,0.0988237485,0.0703022853,0.0224990975,0.05238875,0.1724583507,0.3939803541,-0.1061743945,0.225917697,-0.0800592527,0.3932600021,-0.0756288841,0.2439516634,0.5022857785,-0.1024582237,0.1065810695,0.2865984738,0.0634872764,0.2548939884,-0.3530240059,-0.2010882944,0.2330770195,-0.6732481718,-0.1928143203,0.2007482648,0.1331625879,-0.0908272117,0.6408691406,-0.1230339259,0.2253909856,0.3644768894,-0.2757491171,0.1300418228,0.3418738544,0.2227095217,0.1012675986,-0.2118380368,0.2360481769,-0.3004505634,0.0831962079,-0.0416878946,-0.0521921478,0.1723070592,-0.1586503237,0.2281520069,-0.0353684463,-0.1432163119,0.0790323019,0.3053472042,0.148478061,0.2386116683,0.3460195959,0.1962521076,-0.2135006189,0.2145754546,-0.1948221773,0.1809065789,0.0893448591,-0.2781764269,-0.0089508276,-0.0071537942,0.4308076501,-0.0701530874,0.3631477654,-0.5140989423,0.1039239168,-0.1084562987,-0.2815326452,0.4845505655,-0.1215516105,-0.0517095365,0.1621133089,-0.0360111222,-0.1242828965,0.2764679492,-0.4824527204,-0.6505846381,0.0183498394,0.0918173417,-0.2360075265,0.2977598906,-0.0981007516,-0.1434908658,0.2951731384,0.0648202747,0.1849305481,-0.39213866,-0.0430691019,-0.0496006273,0.0602658428,0.0568472743,-0.0471673533,-0.2099940032,-0.0448071845,-0.0833948404,0.125947997,0.0874721706,-0.3543171883,0.3284276426,-0.3312932849,0.1692542285,0.3340460956,-0.2291650474,-0.709107995,0.0061513516,-0.4203280509,-0.150533855,0.0903914645,0.1434873194,0.1855770797,-0.004922823,-0.0824156925,0.1605361253,0.0381931551,0.0124474373,-0.1024393141,-0.0544400476,0.2747745812,0.0930822268,0.2478365302,0.0688723475,-0.0113420561,0.4366556108,-0.0544372499,-0.0169295259,0.0306486972,0.1363259256,0.1023456007,-0.2260520756,0.3137185574,-0.2096313834,0.0550971664,0.1035370231,-0.3195706606,0.1881973296,-0.0363559276,-0.0363876037,-0.3540016711,0.0793980435,0.1037681028,-0.0991274491,0.0352030657,-0.1450605839,-0.0832667202,-0.388479054,0.0341508873,-0.0037553702,-0.4048275054,-0.0301961396,-0.0421601012,0.1399486512,-0.2281762362,-0.1746620536,-0.2353018969,0.1891423464,-0.0752717257,-0.183382377,-0.3118394315,0.2674703002,-0.1548387557,-0.0372150429,-0.3062781692,0.1764156967,0.1957689971,-0.050919205,0.2716096044,0.4083098769,0.1179642826,-0.2562609315,0.4541432261,0.6124860048,-0.1288583577,0.0579432212,-0.1382121295,-0.0600695387,0.3139534295,-0.0176601112,-0.0880969986,-0.0222126264,0.0208588988,-0.3356958628,0.0802437738,-0.2782365084,-0.1084268689,-0.2599354684,0.0221382268,0.1173917577,-0.0186677184,0.2555037141,-0.0347185954,0.2116461694,-0.2827578783,0.2482461631,0.5192136765,0.0696473047,-0.2654940188,0.1591648161,0.3544076979,-0.0453876071,0.2292569876,0.3359416723,0.2728696764,0.466919899,0.1930990964,0.1399871856,0.0085987858,-0.3180223107,-0.0641661659,0.4424804151,-0.2965271175,0.2216306329,0.134372741,0.0398937613,-0.1693696529,-0.1187544242,-0.1792631298,-0.2337691486,-0.1237298101,0.3886534274,-0.4316176772,0.0530367754,-0.2674812078,0.288205266,0.2576822937,0.3259709179,0.1478629261,0.2398411036,0.0139712077,-0.1078580841,0.0940019935,-0.0251650214,-0.0192727335,0.1582969874,0.056291651,0.4700152874,-0.4344233274,-0.0599876791,0.0343006402,0.1294091493,0.2592258751,0.1120613292,-0.1111286804,-0.2542455196,0.2870855331,-0.1661201715,-0.1753746569,0.2520132959,-0.0450242199,0.2733143866,-0.3296175599,-0.2529786229,-0.3249234557,-0.4631488323,-0.1274587065,-0.0206832197,0.2834064364,-0.0659301355,0.3948688507,0.1004976705,0.3003748357,-0.0638103858,-0.0256142989,0.1553850174,0.387717545,-0.0366312899,-0.3885851502,-0.0218417924,-0.1496914625,-0.0235516038,-0.4220234752,-0.5347440243,-0.0104106301,-0.1061309054,-0.155281052,-0.3330346644,0.0399059616,0.6237717867,-0.1732101589,-0.0035062337,-0.1227628216,-0.0843582973,0.3050920069,-0.0285022948,0.1049818248,0.1410135627,0.5239762068,0.0909968838,0.6615387201,0.0805728212,-0.2219898999,0.0731115863,0.0339721218,-0.1155951768,-0.1329817623,-0.2787259221,-0.1261444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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"@lhoestq I had this issue without SWA. I followed [this](https:\/\/github.com\/huggingface\/notebooks\/blob\/master\/sagemaker\/03_distributed_training_data_parallelism\/sagemaker-notebook.ipynb) notebook to utilize multiple gpus on the [roberta-base](https:\/\/huggingface.co\/cardiffnlp\/twitter-roberta-base-sentiment) model. This tutorial could only work if I am on `ml.p3.16xlarge`, which I don't have access to. So I tried using just `model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3]` before calling `trainer.fit()`. But maybe this is not the right way to do distributed training. I can provide a code example if that will be more helpful.","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":74,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n @lhoestq I had this issue without SWA. I followed [this](https:\/\/github.com\/huggingface\/notebooks\/blob\/master\/sagemaker\/03_distributed_training_data_parallelism\/sagemaker-notebook.ipynb) notebook to utilize multiple gpus on the [roberta-base](https:\/\/huggingface.co\/cardiffnlp\/twitter-roberta-base-sentiment) model. This tutorial could only work if I am on `ml.p3.16xlarge`, which I don't have access to. So I tried using just `model = torch.nn.DataParallel(model, device_ids=[0, 1, 2, 3]` before calling `trainer.fit()`. But maybe this is not the right way to do distributed training. I can provide a code example if that will be more helpful.","embeddings":[-0.389613986,-0.2896887064,-0.0146302115,0.3087924719,0.3473482132,-0.2418410331,0.3561576009,0.2430759966,0.1943363994,0.3443817794,0.0316291973,0.1856550276,-0.1241295412,0.068187736,-0.363416642,-0.3182570636,-0.1385273039,0.1319549829,-0.2823426723,0.1612484306,-0.2188625783,0.2060973197,-0.2639538944,0.1758927256,-0.0497712679,-0.0371602289,0.0992189422,-0.0780251622,0.0320226103,-0.2494231611,0.3354479074,-0.346852392,0.2457087934,0.7273562551,-0.0001192912,-0.025481293,0.4413975179,-0.2230760157,-0.0005031597,-0.0746245086,0.6110761166,-0.3019441366,-0.0140578914,-0.2531581521,0.2060740441,-0.3909291029,-0.1341194361,-0.0279495362,0.1947908998,0.2587778866,0.14318344,0.3059898019,0.0780828446,-0.2722992003,-0.1123694852,-0.2118056417,0.0402175114,0.2579285502,0.0773182064,-0.174048245,-0.2438988239,0.0654503778,0.1149050593,-0.0566485077,0.5178043246,-0.2840248942,0.1143898815,-0.1758093238,-0.0145956818,-0.00342199,0.2611017227,-0.0868977532,-0.1931508631,-0.3589656949,0.0625937134,-0.0254444499,0.0988237485,0.0703022853,0.0224990975,0.05238875,0.1724583507,0.3939803541,-0.1061743945,0.225917697,-0.0800592527,0.3932600021,-0.0756288841,0.2439516634,0.5022857785,-0.1024582237,0.1065810695,0.2865984738,0.0634872764,0.2548939884,-0.3530240059,-0.2010882944,0.2330770195,-0.6732481718,-0.1928143203,0.2007482648,0.1331625879,-0.0908272117,0.6408691406,-0.1230339259,0.2253909856,0.3644768894,-0.2757491171,0.1300418228,0.3418738544,0.2227095217,0.1012675986,-0.2118380368,0.2360481769,-0.3004505634,0.0831962079,-0.0416878946,-0.0521921478,0.1723070592,-0.1586503237,0.2281520069,-0.0353684463,-0.1432163119,0.0790323019,0.3053472042,0.148478061,0.2386116683,0.3460195959,0.1962521076,-0.2135006189,0.2145754546,-0.1948221773,0.1809065789,0.0893448591,-0.2781764269,-0.0089508276,-0.0071537942,0.4308076501,-0.0701530874,0.3631477654,-0.5140989423,0.1039239168,-0.1084562987,-0.2815326452,0.4845505655,-0.1215516105,-0.0517095365,0.1621133089,-0.0360111222,-0.1242828965,0.2764679492,-0.4824527204,-0.6505846381,0.0183498394,0.0918173417,-0.2360075265,0.2977598906,-0.0981007516,-0.1434908658,0.2951731384,0.0648202747,0.1849305481,-0.39213866,-0.0430691019,-0.0496006273,0.0602658428,0.0568472743,-0.0471673533,-0.2099940032,-0.0448071845,-0.0833948404,0.125947997,0.0874721706,-0.3543171883,0.3284276426,-0.3312932849,0.1692542285,0.3340460956,-0.2291650474,-0.709107995,0.0061513516,-0.4203280509,-0.150533855,0.0903914645,0.1434873194,0.1855770797,-0.004922823,-0.0824156925,0.1605361253,0.0381931551,0.0124474373,-0.1024393141,-0.0544400476,0.2747745812,0.0930822268,0.2478365302,0.0688723475,-0.0113420561,0.4366556108,-0.0544372499,-0.0169295259,0.0306486972,0.1363259256,0.1023456007,-0.2260520756,0.3137185574,-0.2096313834,0.0550971664,0.1035370231,-0.3195706606,0.1881973296,-0.0363559276,-0.0363876037,-0.3540016711,0.0793980435,0.1037681028,-0.0991274491,0.0352030657,-0.1450605839,-0.0832667202,-0.388479054,0.0341508873,-0.0037553702,-0.4048275054,-0.0301961396,-0.0421601012,0.1399486512,-0.2281762362,-0.1746620536,-0.2353018969,0.1891423464,-0.0752717257,-0.183382377,-0.3118394315,0.2674703002,-0.1548387557,-0.0372150429,-0.3062781692,0.1764156967,0.1957689971,-0.050919205,0.2716096044,0.4083098769,0.1179642826,-0.2562609315,0.4541432261,0.6124860048,-0.1288583577,0.0579432212,-0.1382121295,-0.0600695387,0.3139534295,-0.0176601112,-0.0880969986,-0.0222126264,0.0208588988,-0.3356958628,0.0802437738,-0.2782365084,-0.1084268689,-0.2599354684,0.0221382268,0.1173917577,-0.0186677184,0.2555037141,-0.0347185954,0.2116461694,-0.2827578783,0.2482461631,0.5192136765,0.0696473047,-0.2654940188,0.1591648161,0.3544076979,-0.0453876071,0.2292569876,0.3359416723,0.2728696764,0.466919899,0.1930990964,0.1399871856,0.0085987858,-0.3180223107,-0.0641661659,0.4424804151,-0.2965271175,0.2216306329,0.134372741,0.0398937613,-0.1693696529,-0.1187544242,-0.1792631298,-0.2337691486,-0.1237298101,0.3886534274,-0.4316176772,0.0530367754,-0.2674812078,0.288205266,0.2576822937,0.3259709179,0.1478629261,0.2398411036,0.0139712077,-0.1078580841,0.0940019935,-0.0251650214,-0.0192727335,0.1582969874,0.056291651,0.4700152874,-0.4344233274,-0.0599876791,0.0343006402,0.1294091493,0.2592258751,0.1120613292,-0.1111286804,-0.2542455196,0.2870855331,-0.1661201715,-0.1753746569,0.2520132959,-0.0450242199,0.2733143866,-0.3296175599,-0.2529786229,-0.3249234557,-0.4631488323,-0.1274587065,-0.0206832197,0.2834064364,-0.0659301355,0.3948688507,0.1004976705,0.3003748357,-0.0638103858,-0.0256142989,0.1553850174,0.387717545,-0.0366312899,-0.3885851502,-0.0218417924,-0.1496914625,-0.0235516038,-0.4220234752,-0.5347440243,-0.0104106301,-0.1061309054,-0.155281052,-0.3330346644,0.0399059616,0.6237717867,-0.1732101589,-0.0035062337,-0.1227628216,-0.0843582973,0.3050920069,-0.0285022948,0.1049818248,0.1410135627,0.5239762068,0.0909968838,0.6615387201,0.0805728212,-0.2219898999,0.0731115863,0.0339721218,-0.1155951768,-0.1329817623,-0.2787259221,-0.126144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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"It might be an issue with old versions of `datasets`, can you try updating `datasets` ?","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":16,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n It might be an issue with old versions of `datasets`, can you try updating `datasets` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"FYI I encountered the exact same error using the latest versions of `datasets`, `transformers` and `pyarrow`, without using any kind of SWA or dataparallel: \r\n\r\n```\r\n# packages in environment at C:\\Users\\zhang\\mambaforge:\r\n#\r\n# Name                    Version                   Build  Channel\r\ncudatoolkit               11.0.3               h3f58a73_9    https:\/\/mirrors.ustc.edu.cn\/anaconda\/cloud\/conda-forge\r\ndatasets                  1.17.0                   pypi_0    pypi\r\npyarrow                   6.0.1                    pypi_0    pypi\r\npytorch                   1.7.1           py3.9_cuda110_cudnn8_0    pytorch\r\ntornado                   6.1              py39hb82d6ee_2    https:\/\/mirrors.ustc.edu.cn\/anaconda\/cloud\/conda-forge\r\n```\r\n\r\n```\r\n> python --version\r\n> 3.9.7\r\n```","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":65,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n FYI I encountered the exact same error using the latest versions of `datasets`, `transformers` and `pyarrow`, without using any kind of SWA or dataparallel: \r\n\r\n```\r\n# packages in environment at C:\\Users\\zhang\\mambaforge:\r\n#\r\n# Name                    Version                   Build  Channel\r\ncudatoolkit               11.0.3               h3f58a73_9    https:\/\/mirrors.ustc.edu.cn\/anaconda\/cloud\/conda-forge\r\ndatasets                  1.17.0                   pypi_0    pypi\r\npyarrow                   6.0.1                    pypi_0    pypi\r\npytorch                   1.7.1           py3.9_cuda110_cudnn8_0    pytorch\r\ntornado                   6.1              py39hb82d6ee_2    https:\/\/mirrors.ustc.edu.cn\/anaconda\/cloud\/conda-forge\r\n```\r\n\r\n```\r\n> python --version\r\n> 3.9.7\r\n```","embeddings":[-0.389613986,-0.2896887064,-0.0146302115,0.3087924719,0.3473482132,-0.2418410331,0.3561576009,0.2430759966,0.1943363994,0.3443817794,0.0316291973,0.1856550276,-0.1241295412,0.068187736,-0.363416642,-0.3182570636,-0.1385273039,0.1319549829,-0.2823426723,0.1612484306,-0.2188625783,0.2060973197,-0.2639538944,0.1758927256,-0.0497712679,-0.0371602289,0.0992189422,-0.0780251622,0.0320226103,-0.2494231611,0.3354479074,-0.346852392,0.2457087934,0.7273562551,-0.0001192912,-0.025481293,0.4413975179,-0.2230760157,-0.0005031597,-0.0746245086,0.6110761166,-0.3019441366,-0.0140578914,-0.2531581521,0.2060740441,-0.3909291029,-0.1341194361,-0.0279495362,0.1947908998,0.2587778866,0.14318344,0.3059898019,0.0780828446,-0.2722992003,-0.1123694852,-0.2118056417,0.0402175114,0.2579285502,0.0773182064,-0.174048245,-0.2438988239,0.0654503778,0.1149050593,-0.0566485077,0.5178043246,-0.2840248942,0.1143898815,-0.1758093238,-0.0145956818,-0.00342199,0.2611017227,-0.0868977532,-0.1931508631,-0.3589656949,0.0625937134,-0.0254444499,0.0988237485,0.0703022853,0.0224990975,0.05238875,0.1724583507,0.3939803541,-0.1061743945,0.225917697,-0.0800592527,0.3932600021,-0.0756288841,0.2439516634,0.5022857785,-0.1024582237,0.1065810695,0.2865984738,0.0634872764,0.2548939884,-0.3530240059,-0.2010882944,0.2330770195,-0.6732481718,-0.1928143203,0.2007482648,0.1331625879,-0.0908272117,0.6408691406,-0.1230339259,0.2253909856,0.3644768894,-0.2757491171,0.1300418228,0.3418738544,0.2227095217,0.1012675986,-0.2118380368,0.2360481769,-0.3004505634,0.0831962079,-0.0416878946,-0.0521921478,0.1723070592,-0.1586503237,0.2281520069,-0.0353684463,-0.1432163119,0.0790323019,0.3053472042,0.148478061,0.2386116683,0.3460195959,0.1962521076,-0.2135006189,0.2145754546,-0.1948221773,0.1809065789,0.0893448591,-0.2781764269,-0.0089508276,-0.0071537942,0.4308076501,-0.0701530874,0.3631477654,-0.5140989423,0.1039239168,-0.1084562987,-0.2815326452,0.4845505655,-0.1215516105,-0.0517095365,0.1621133089,-0.0360111222,-0.1242828965,0.2764679492,-0.4824527204,-0.6505846381,0.0183498394,0.0918173417,-0.2360075265,0.2977598906,-0.0981007516,-0.1434908658,0.2951731384,0.0648202747,0.1849305481,-0.39213866,-0.0430691019,-0.0496006273,0.0602658428,0.0568472743,-0.0471673533,-0.2099940032,-0.0448071845,-0.0833948404,0.125947997,0.0874721706,-0.3543171883,0.3284276426,-0.3312932849,0.1692542285,0.3340460956,-0.2291650474,-0.709107995,0.0061513516,-0.4203280509,-0.150533855,0.0903914645,0.1434873194,0.1855770797,-0.004922823,-0.0824156925,0.1605361253,0.0381931551,0.0124474373,-0.1024393141,-0.0544400476,0.2747745812,0.0930822268,0.2478365302,0.0688723475,-0.0113420561,0.4366556108,-0.0544372499,-0.0169295259,0.0306486972,0.1363259256,0.1023456007,-0.2260520756,0.3137185574,-0.2096313834,0.0550971664,0.1035370231,-0.3195706606,0.1881973296,-0.0363559276,-0.0363876037,-0.3540016711,0.0793980435,0.1037681028,-0.0991274491,0.0352030657,-0.1450605839,-0.0832667202,-0.388479054,0.0341508873,-0.0037553702,-0.4048275054,-0.0301961396,-0.0421601012,0.1399486512,-0.2281762362,-0.1746620536,-0.2353018969,0.1891423464,-0.0752717257,-0.183382377,-0.3118394315,0.2674703002,-0.1548387557,-0.0372150429,-0.3062781692,0.1764156967,0.1957689971,-0.050919205,0.2716096044,0.4083098769,0.1179642826,-0.2562609315,0.4541432261,0.6124860048,-0.1288583577,0.0579432212,-0.1382121295,-0.0600695387,0.3139534295,-0.0176601112,-0.0880969986,-0.0222126264,0.0208588988,-0.3356958628,0.0802437738,-0.2782365084,-0.1084268689,-0.2599354684,0.0221382268,0.1173917577,-0.0186677184,0.2555037141,-0.0347185954,0.2116461694,-0.2827578783,0.2482461631,0.5192136765,0.0696473047,-0.2654940188,0.1591648161,0.3544076979,-0.0453876071,0.2292569876,0.3359416723,0.2728696764,0.466919899,0.1930990964,0.1399871856,0.0085987858,-0.3180223107,-0.0641661659,0.4424804151,-0.2965271175,0.2216306329,0.134372741,0.0398937613,-0.1693696529,-0.1187544242,-0.1792631298,-0.2337691486,-0.1237298101,0.3886534274,-0.4316176772,0.0530367754,-0.2674812078,0.288205266,0.2576822937,0.3259709179,0.1478629261,0.2398411036,0.0139712077,-0.1078580841,0.0940019935,-0.0251650214,-0.0192727335,0.1582969874,0.056291651,0.4700152874,-0.4344233274,-0.0599876791,0.0343006402,0.1294091493,0.2592258751,0.1120613292,-0.1111286804,-0.2542455196,0.2870855331,-0.1661201715,-0.1753746569,0.2520132959,-0.0450242199,0.2733143866,-0.3296175599,-0.2529786229,-0.3249234557,-0.4631488323,-0.1274587065,-0.0206832197,0.2834064364,-0.0659301355,0.3948688507,0.1004976705,0.3003748357,-0.0638103858,-0.0256142989,0.1553850174,0.387717545,-0.0366312899,-0.3885851502,-0.0218417924,-0.1496914625,-0.0235516038,-0.4220234752,-0.5347440243,-0.0104106301,-0.1061309054,-0.155281052,-0.3330346644,0.0399059616,0.6237717867,-0.1732101589,-0.0035062337,-0.1227628216,-0.0843582973,0.3050920069,-0.0285022948,0.1049818248,0.1410135627,0.5239762068,0.0909968838,0.6615387201,0.0805728212,-0.2219898999,0.0731115863,0.0339721218,-0.1155951768,-0.1329817623,-0.2787259221,-0.12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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"Same error here! Datasets version `1.18.3` freshly updated.\r\n\r\n`IndexError: Invalid key: 90 is out of bounds for size 0`\r\n\r\nMy task is finetuning the model for token classification.\r\n\r\n**Solved**: I make a mistake while updating the dataset during the map, you should check that you return the correct values.\r\n","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":49,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n Same error here! Datasets version `1.18.3` freshly updated.\r\n\r\n`IndexError: Invalid key: 90 is out of bounds for size 0`\r\n\r\nMy task is finetuning the model for token classification.\r\n\r\n**Solved**: I make a mistake while updating the dataset during the map, you should check that you return the correct values.\r\n","embeddings":[-0.389613986,-0.2896887064,-0.0146302115,0.3087924719,0.3473482132,-0.2418410331,0.3561576009,0.2430759966,0.1943363994,0.3443817794,0.0316291973,0.1856550276,-0.1241295412,0.068187736,-0.363416642,-0.3182570636,-0.1385273039,0.1319549829,-0.2823426723,0.1612484306,-0.2188625783,0.2060973197,-0.2639538944,0.1758927256,-0.0497712679,-0.0371602289,0.0992189422,-0.0780251622,0.0320226103,-0.2494231611,0.3354479074,-0.346852392,0.2457087934,0.7273562551,-0.0001192912,-0.025481293,0.4413975179,-0.2230760157,-0.0005031597,-0.0746245086,0.6110761166,-0.3019441366,-0.0140578914,-0.2531581521,0.2060740441,-0.3909291029,-0.1341194361,-0.0279495362,0.1947908998,0.2587778866,0.14318344,0.3059898019,0.0780828446,-0.2722992003,-0.1123694852,-0.2118056417,0.0402175114,0.2579285502,0.0773182064,-0.174048245,-0.2438988239,0.0654503778,0.1149050593,-0.0566485077,0.5178043246,-0.2840248942,0.1143898815,-0.1758093238,-0.0145956818,-0.00342199,0.2611017227,-0.0868977532,-0.1931508631,-0.3589656949,0.0625937134,-0.0254444499,0.0988237485,0.0703022853,0.0224990975,0.05238875,0.1724583507,0.3939803541,-0.1061743945,0.225917697,-0.0800592527,0.3932600021,-0.0756288841,0.2439516634,0.5022857785,-0.1024582237,0.1065810695,0.2865984738,0.0634872764,0.2548939884,-0.3530240059,-0.2010882944,0.2330770195,-0.6732481718,-0.1928143203,0.2007482648,0.1331625879,-0.0908272117,0.6408691406,-0.1230339259,0.2253909856,0.3644768894,-0.2757491171,0.1300418228,0.3418738544,0.2227095217,0.1012675986,-0.2118380368,0.2360481769,-0.3004505634,0.0831962079,-0.0416878946,-0.0521921478,0.1723070592,-0.1586503237,0.2281520069,-0.0353684463,-0.1432163119,0.0790323019,0.3053472042,0.148478061,0.2386116683,0.3460195959,0.1962521076,-0.2135006189,0.2145754546,-0.1948221773,0.1809065789,0.0893448591,-0.2781764269,-0.0089508276,-0.0071537942,0.4308076501,-0.0701530874,0.3631477654,-0.5140989423,0.1039239168,-0.1084562987,-0.2815326452,0.4845505655,-0.1215516105,-0.0517095365,0.1621133089,-0.0360111222,-0.1242828965,0.2764679492,-0.4824527204,-0.6505846381,0.0183498394,0.0918173417,-0.2360075265,0.2977598906,-0.0981007516,-0.1434908658,0.2951731384,0.0648202747,0.1849305481,-0.39213866,-0.0430691019,-0.0496006273,0.0602658428,0.0568472743,-0.0471673533,-0.2099940032,-0.0448071845,-0.0833948404,0.125947997,0.0874721706,-0.3543171883,0.3284276426,-0.3312932849,0.1692542285,0.3340460956,-0.2291650474,-0.709107995,0.0061513516,-0.4203280509,-0.150533855,0.0903914645,0.1434873194,0.1855770797,-0.004922823,-0.0824156925,0.1605361253,0.0381931551,0.0124474373,-0.1024393141,-0.0544400476,0.2747745812,0.0930822268,0.2478365302,0.0688723475,-0.0113420561,0.4366556108,-0.0544372499,-0.0169295259,0.0306486972,0.1363259256,0.1023456007,-0.2260520756,0.3137185574,-0.2096313834,0.0550971664,0.1035370231,-0.3195706606,0.1881973296,-0.0363559276,-0.0363876037,-0.3540016711,0.0793980435,0.1037681028,-0.0991274491,0.0352030657,-0.1450605839,-0.0832667202,-0.388479054,0.0341508873,-0.0037553702,-0.4048275054,-0.0301961396,-0.0421601012,0.1399486512,-0.2281762362,-0.1746620536,-0.2353018969,0.1891423464,-0.0752717257,-0.183382377,-0.3118394315,0.2674703002,-0.1548387557,-0.0372150429,-0.3062781692,0.1764156967,0.1957689971,-0.050919205,0.2716096044,0.4083098769,0.1179642826,-0.2562609315,0.4541432261,0.6124860048,-0.1288583577,0.0579432212,-0.1382121295,-0.0600695387,0.3139534295,-0.0176601112,-0.0880969986,-0.0222126264,0.0208588988,-0.3356958628,0.0802437738,-0.2782365084,-0.1084268689,-0.2599354684,0.0221382268,0.1173917577,-0.0186677184,0.2555037141,-0.0347185954,0.2116461694,-0.2827578783,0.2482461631,0.5192136765,0.0696473047,-0.2654940188,0.1591648161,0.3544076979,-0.0453876071,0.2292569876,0.3359416723,0.2728696764,0.466919899,0.1930990964,0.1399871856,0.0085987858,-0.3180223107,-0.0641661659,0.4424804151,-0.2965271175,0.2216306329,0.134372741,0.0398937613,-0.1693696529,-0.1187544242,-0.1792631298,-0.2337691486,-0.1237298101,0.3886534274,-0.4316176772,0.0530367754,-0.2674812078,0.288205266,0.2576822937,0.3259709179,0.1478629261,0.2398411036,0.0139712077,-0.1078580841,0.0940019935,-0.0251650214,-0.0192727335,0.1582969874,0.056291651,0.4700152874,-0.4344233274,-0.0599876791,0.0343006402,0.1294091493,0.2592258751,0.1120613292,-0.1111286804,-0.2542455196,0.2870855331,-0.1661201715,-0.1753746569,0.2520132959,-0.0450242199,0.2733143866,-0.3296175599,-0.2529786229,-0.3249234557,-0.4631488323,-0.1274587065,-0.0206832197,0.2834064364,-0.0659301355,0.3948688507,0.1004976705,0.3003748357,-0.0638103858,-0.0256142989,0.1553850174,0.387717545,-0.0366312899,-0.3885851502,-0.0218417924,-0.1496914625,-0.0235516038,-0.4220234752,-0.5347440243,-0.0104106301,-0.1061309054,-0.155281052,-0.3330346644,0.0399059616,0.6237717867,-0.1732101589,-0.0035062337,-0.1227628216,-0.0843582973,0.3050920069,-0.0285022948,0.1049818248,0.1410135627,0.5239762068,0.0909968838,0.6615387201,0.0805728212,-0.2219898999,0.0731115863,0.0339721218,-0.1155951768,-0.1329817623,-0.2787259221,-0.126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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"cc @sgugger This probably comes from the `Trainer` removing all the columns of a dataset, do you think we can improve the error message in this case ?","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":28,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n cc @sgugger This probably comes from the `Trainer` removing all the columns of a dataset, do you think we can improve the error message in this case ?","embeddings":[-0.389613986,-0.2896887064,-0.0146302115,0.3087924719,0.3473482132,-0.2418410331,0.3561576009,0.2430759966,0.1943363994,0.3443817794,0.0316291973,0.1856550276,-0.1241295412,0.068187736,-0.363416642,-0.3182570636,-0.1385273039,0.1319549829,-0.2823426723,0.1612484306,-0.2188625783,0.2060973197,-0.2639538944,0.1758927256,-0.0497712679,-0.0371602289,0.0992189422,-0.0780251622,0.0320226103,-0.2494231611,0.3354479074,-0.346852392,0.2457087934,0.7273562551,-0.0001192912,-0.025481293,0.4413975179,-0.2230760157,-0.0005031597,-0.0746245086,0.6110761166,-0.3019441366,-0.0140578914,-0.2531581521,0.2060740441,-0.3909291029,-0.1341194361,-0.0279495362,0.1947908998,0.2587778866,0.14318344,0.3059898019,0.0780828446,-0.2722992003,-0.1123694852,-0.2118056417,0.0402175114,0.2579285502,0.0773182064,-0.174048245,-0.2438988239,0.0654503778,0.1149050593,-0.0566485077,0.5178043246,-0.2840248942,0.1143898815,-0.1758093238,-0.0145956818,-0.00342199,0.2611017227,-0.0868977532,-0.1931508631,-0.3589656949,0.0625937134,-0.0254444499,0.0988237485,0.0703022853,0.0224990975,0.05238875,0.1724583507,0.3939803541,-0.1061743945,0.225917697,-0.0800592527,0.3932600021,-0.0756288841,0.2439516634,0.5022857785,-0.1024582237,0.1065810695,0.2865984738,0.0634872764,0.2548939884,-0.3530240059,-0.2010882944,0.2330770195,-0.6732481718,-0.1928143203,0.2007482648,0.1331625879,-0.0908272117,0.6408691406,-0.1230339259,0.2253909856,0.3644768894,-0.2757491171,0.1300418228,0.3418738544,0.2227095217,0.1012675986,-0.2118380368,0.2360481769,-0.3004505634,0.0831962079,-0.0416878946,-0.0521921478,0.1723070592,-0.1586503237,0.2281520069,-0.0353684463,-0.1432163119,0.0790323019,0.3053472042,0.148478061,0.2386116683,0.3460195959,0.1962521076,-0.2135006189,0.2145754546,-0.1948221773,0.1809065789,0.0893448591,-0.2781764269,-0.0089508276,-0.0071537942,0.4308076501,-0.0701530874,0.3631477654,-0.5140989423,0.1039239168,-0.1084562987,-0.2815326452,0.4845505655,-0.1215516105,-0.0517095365,0.1621133089,-0.0360111222,-0.1242828965,0.2764679492,-0.4824527204,-0.6505846381,0.0183498394,0.0918173417,-0.2360075265,0.2977598906,-0.0981007516,-0.1434908658,0.2951731384,0.0648202747,0.1849305481,-0.39213866,-0.0430691019,-0.0496006273,0.0602658428,0.0568472743,-0.0471673533,-0.2099940032,-0.0448071845,-0.0833948404,0.125947997,0.0874721706,-0.3543171883,0.3284276426,-0.3312932849,0.1692542285,0.3340460956,-0.2291650474,-0.709107995,0.0061513516,-0.4203280509,-0.150533855,0.0903914645,0.1434873194,0.1855770797,-0.004922823,-0.0824156925,0.1605361253,0.0381931551,0.0124474373,-0.1024393141,-0.0544400476,0.2747745812,0.0930822268,0.2478365302,0.0688723475,-0.0113420561,0.4366556108,-0.0544372499,-0.0169295259,0.0306486972,0.1363259256,0.1023456007,-0.2260520756,0.3137185574,-0.2096313834,0.0550971664,0.1035370231,-0.3195706606,0.1881973296,-0.0363559276,-0.0363876037,-0.3540016711,0.0793980435,0.1037681028,-0.0991274491,0.0352030657,-0.1450605839,-0.0832667202,-0.388479054,0.0341508873,-0.0037553702,-0.4048275054,-0.0301961396,-0.0421601012,0.1399486512,-0.2281762362,-0.1746620536,-0.2353018969,0.1891423464,-0.0752717257,-0.183382377,-0.3118394315,0.2674703002,-0.1548387557,-0.0372150429,-0.3062781692,0.1764156967,0.1957689971,-0.050919205,0.2716096044,0.4083098769,0.1179642826,-0.2562609315,0.4541432261,0.6124860048,-0.1288583577,0.0579432212,-0.1382121295,-0.0600695387,0.3139534295,-0.0176601112,-0.0880969986,-0.0222126264,0.0208588988,-0.3356958628,0.0802437738,-0.2782365084,-0.1084268689,-0.2599354684,0.0221382268,0.1173917577,-0.0186677184,0.2555037141,-0.0347185954,0.2116461694,-0.2827578783,0.2482461631,0.5192136765,0.0696473047,-0.2654940188,0.1591648161,0.3544076979,-0.0453876071,0.2292569876,0.3359416723,0.2728696764,0.466919899,0.1930990964,0.1399871856,0.0085987858,-0.3180223107,-0.0641661659,0.4424804151,-0.2965271175,0.2216306329,0.134372741,0.0398937613,-0.1693696529,-0.1187544242,-0.1792631298,-0.2337691486,-0.1237298101,0.3886534274,-0.4316176772,0.0530367754,-0.2674812078,0.288205266,0.2576822937,0.3259709179,0.1478629261,0.2398411036,0.0139712077,-0.1078580841,0.0940019935,-0.0251650214,-0.0192727335,0.1582969874,0.056291651,0.4700152874,-0.4344233274,-0.0599876791,0.0343006402,0.1294091493,0.2592258751,0.1120613292,-0.1111286804,-0.2542455196,0.2870855331,-0.1661201715,-0.1753746569,0.2520132959,-0.0450242199,0.2733143866,-0.3296175599,-0.2529786229,-0.3249234557,-0.4631488323,-0.1274587065,-0.0206832197,0.2834064364,-0.0659301355,0.3948688507,0.1004976705,0.3003748357,-0.0638103858,-0.0256142989,0.1553850174,0.387717545,-0.0366312899,-0.3885851502,-0.0218417924,-0.1496914625,-0.0235516038,-0.4220234752,-0.5347440243,-0.0104106301,-0.1061309054,-0.155281052,-0.3330346644,0.0399059616,0.6237717867,-0.1732101589,-0.0035062337,-0.1227628216,-0.0843582973,0.3050920069,-0.0285022948,0.1049818248,0.1410135627,0.5239762068,0.0909968838,0.6615387201,0.0805728212,-0.2219898999,0.0731115863,0.0339721218,-0.1155951768,-0.1329817623,-0.2787259221,-0.1261444092,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"The `Trainer` clearly logs when it removes columns in the dataset. I'm not too sure of where the bug appears as I haven't seen a clear reproducer. Happy to display a more helpful error message, but I'd need a reproducer to see what the exact problem is to design the right test and warning :-) ","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":55,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n The `Trainer` clearly logs when it removes columns in the dataset. I'm not too sure of where the bug appears as I haven't seen a clear reproducer. Happy to display a more helpful error message, but I'd need a reproducer to see what the exact problem is to design the right test and warning :-) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"Well, if I can try to suggest how to reproduce, please try by do not returning any updated content in the map function used to tokenize input (e.g., in TokenClassification). I can leave here my wrong version for reference:\r\n\r\n```python\r\ndef preprocess_function(examples):\r\n\r\n    text = examples[\"text\"]\r\n    \r\n    inputs = tokenizer(\r\n        text,\r\n        max_length=512,\r\n        truncation=\"only_second\",\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n    offset_mapping = inputs.pop(\"offset_mapping\")\r\n    # ... processing code\r\n\r\n    inputs[\"labels\"] = label_ids\r\n    #return inputs\r\n    \r\ntrain_ds = train_ds.map(preprocess_function, batched=False)\r\ntest_ds = test_ds.map(preprocess_function, batched=False)\r\neval_ds = eval_ds.map(preprocess_function, batched=False)\r\n```\r\n\r\nOf course, returning inputs solved the problem. As suggestion, a possible error message could display \"IndexError: the `key` required by trainer are not found in the dataset\" (just an hypothesis, I think there could be something better). \r\n\r\nPlease tell me if you need more details to reproduce, glad to help!","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":129,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n Well, if I can try to suggest how to reproduce, please try by do not returning any updated content in the map function used to tokenize input (e.g., in TokenClassification). I can leave here my wrong version for reference:\r\n\r\n```python\r\ndef preprocess_function(examples):\r\n\r\n    text = examples[\"text\"]\r\n    \r\n    inputs = tokenizer(\r\n        text,\r\n        max_length=512,\r\n        truncation=\"only_second\",\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n    offset_mapping = inputs.pop(\"offset_mapping\")\r\n    # ... processing code\r\n\r\n    inputs[\"labels\"] = label_ids\r\n    #return inputs\r\n    \r\ntrain_ds = train_ds.map(preprocess_function, batched=False)\r\ntest_ds = test_ds.map(preprocess_function, batched=False)\r\neval_ds = eval_ds.map(preprocess_function, batched=False)\r\n```\r\n\r\nOf course, returning inputs solved the problem. As suggestion, a possible error message could display \"IndexError: the `key` required by trainer are not found in the dataset\" (just an hypothesis, I think there could be something better). \r\n\r\nPlease tell me if you need more details to reproduce, glad to help!","embeddings":[-0.389613986,-0.2896887064,-0.0146302115,0.3087924719,0.3473482132,-0.2418410331,0.3561576009,0.2430759966,0.1943363994,0.3443817794,0.0316291973,0.1856550276,-0.1241295412,0.068187736,-0.363416642,-0.3182570636,-0.1385273039,0.1319549829,-0.2823426723,0.1612484306,-0.2188625783,0.2060973197,-0.2639538944,0.1758927256,-0.0497712679,-0.0371602289,0.0992189422,-0.0780251622,0.0320226103,-0.2494231611,0.3354479074,-0.346852392,0.2457087934,0.7273562551,-0.0001192912,-0.025481293,0.4413975179,-0.2230760157,-0.0005031597,-0.0746245086,0.6110761166,-0.3019441366,-0.0140578914,-0.2531581521,0.2060740441,-0.3909291029,-0.1341194361,-0.0279495362,0.1947908998,0.2587778866,0.14318344,0.3059898019,0.0780828446,-0.2722992003,-0.1123694852,-0.2118056417,0.0402175114,0.2579285502,0.0773182064,-0.174048245,-0.2438988239,0.0654503778,0.1149050593,-0.0566485077,0.5178043246,-0.2840248942,0.1143898815,-0.1758093238,-0.0145956818,-0.00342199,0.2611017227,-0.0868977532,-0.1931508631,-0.3589656949,0.0625937134,-0.0254444499,0.0988237485,0.0703022853,0.0224990975,0.05238875,0.1724583507,0.3939803541,-0.1061743945,0.225917697,-0.0800592527,0.3932600021,-0.0756288841,0.2439516634,0.5022857785,-0.1024582237,0.1065810695,0.2865984738,0.0634872764,0.2548939884,-0.3530240059,-0.2010882944,0.2330770195,-0.6732481718,-0.1928143203,0.2007482648,0.1331625879,-0.0908272117,0.6408691406,-0.1230339259,0.2253909856,0.3644768894,-0.2757491171,0.1300418228,0.3418738544,0.2227095217,0.1012675986,-0.2118380368,0.2360481769,-0.3004505634,0.0831962079,-0.0416878946,-0.0521921478,0.1723070592,-0.1586503237,0.2281520069,-0.0353684463,-0.1432163119,0.0790323019,0.3053472042,0.148478061,0.2386116683,0.3460195959,0.1962521076,-0.2135006189,0.2145754546,-0.1948221773,0.1809065789,0.0893448591,-0.2781764269,-0.0089508276,-0.0071537942,0.4308076501,-0.0701530874,0.3631477654,-0.5140989423,0.1039239168,-0.1084562987,-0.2815326452,0.4845505655,-0.1215516105,-0.0517095365,0.1621133089,-0.0360111222,-0.1242828965,0.2764679492,-0.4824527204,-0.6505846381,0.0183498394,0.0918173417,-0.2360075265,0.2977598906,-0.0981007516,-0.1434908658,0.2951731384,0.0648202747,0.1849305481,-0.39213866,-0.0430691019,-0.0496006273,0.0602658428,0.0568472743,-0.0471673533,-0.2099940032,-0.0448071845,-0.0833948404,0.125947997,0.0874721706,-0.3543171883,0.3284276426,-0.3312932849,0.1692542285,0.3340460956,-0.2291650474,-0.709107995,0.0061513516,-0.4203280509,-0.150533855,0.0903914645,0.1434873194,0.1855770797,-0.004922823,-0.0824156925,0.1605361253,0.0381931551,0.0124474373,-0.1024393141,-0.0544400476,0.2747745812,0.0930822268,0.2478365302,0.0688723475,-0.0113420561,0.4366556108,-0.0544372499,-0.0169295259,0.0306486972,0.1363259256,0.1023456007,-0.2260520756,0.3137185574,-0.2096313834,0.0550971664,0.1035370231,-0.3195706606,0.1881973296,-0.0363559276,-0.0363876037,-0.3540016711,0.0793980435,0.1037681028,-0.0991274491,0.0352030657,-0.1450605839,-0.0832667202,-0.388479054,0.0341508873,-0.0037553702,-0.4048275054,-0.0301961396,-0.0421601012,0.1399486512,-0.2281762362,-0.1746620536,-0.2353018969,0.1891423464,-0.0752717257,-0.183382377,-0.3118394315,0.2674703002,-0.1548387557,-0.0372150429,-0.3062781692,0.1764156967,0.1957689971,-0.050919205,0.2716096044,0.4083098769,0.1179642826,-0.2562609315,0.4541432261,0.6124860048,-0.1288583577,0.0579432212,-0.1382121295,-0.0600695387,0.3139534295,-0.0176601112,-0.0880969986,-0.0222126264,0.0208588988,-0.3356958628,0.0802437738,-0.2782365084,-0.1084268689,-0.2599354684,0.0221382268,0.1173917577,-0.0186677184,0.2555037141,-0.0347185954,0.2116461694,-0.2827578783,0.2482461631,0.5192136765,0.0696473047,-0.2654940188,0.1591648161,0.3544076979,-0.0453876071,0.2292569876,0.3359416723,0.2728696764,0.466919899,0.1930990964,0.1399871856,0.0085987858,-0.3180223107,-0.0641661659,0.4424804151,-0.2965271175,0.2216306329,0.134372741,0.0398937613,-0.1693696529,-0.1187544242,-0.1792631298,-0.2337691486,-0.1237298101,0.3886534274,-0.4316176772,0.0530367754,-0.2674812078,0.288205266,0.2576822937,0.3259709179,0.1478629261,0.2398411036,0.0139712077,-0.1078580841,0.0940019935,-0.0251650214,-0.0192727335,0.1582969874,0.056291651,0.4700152874,-0.4344233274,-0.0599876791,0.0343006402,0.1294091493,0.2592258751,0.1120613292,-0.1111286804,-0.2542455196,0.2870855331,-0.1661201715,-0.1753746569,0.2520132959,-0.0450242199,0.2733143866,-0.3296175599,-0.2529786229,-0.3249234557,-0.4631488323,-0.1274587065,-0.0206832197,0.2834064364,-0.0659301355,0.3948688507,0.1004976705,0.3003748357,-0.0638103858,-0.0256142989,0.1553850174,0.387717545,-0.0366312899,-0.3885851502,-0.0218417924,-0.1496914625,-0.0235516038,-0.4220234752,-0.5347440243,-0.0104106301,-0.1061309054,-0.155281052,-0.3330346644,0.0399059616,0.6237717867,-0.1732101589,-0.0035062337,-0.1227628216,-0.0843582973,0.3050920069,-0.0285022948,0.1049818248,0.1410135627,0.5239762068,0.0909968838,0.6615387201,0.0805728212,-0.2219898999,0.0731115863,0.0339721218,-0.1155951768,-0.1329817623,-0.2787259221,-0.126144409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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2988","title":"IndexError: Invalid key: 14 is out of bounds for size 0","comments":"That's the thing though. The `Trainer` has no idea which inputs are required or not since all models can have different kinds of inputs, and it can work for models outside of the Transformers library. I can add a clear error message if I get an empty batch, as this is easy to detect, but that's pretty much it.","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n","comment_length":59,"text":"IndexError: Invalid key: 14 is out of bounds for size 0 \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\nHi. I am trying to implement stochastic weighted averaging optimizer with transformer library as described here https:\/\/pytorch.org\/blog\/pytorch-1.6-now-includes-stochastic-weight-averaging\/ , for this I am using a run_clm.py codes which is working fine before adding SWA optimizer, the moment I modify the model with `swa_model = AveragedModel(model)` in this script, I am getting the below error,  since I am NOT touching the dataloader part, I am confused why this is occurring, I very much appreciate your opinion on this @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_clm.py\", line 723, in <module>\r\n    main()\r\n  File \"run_clm.py\", line 669, in main\r\n    train_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/transformers\/trainer.py\", line 1258, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 475, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1530, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1517, in _getitem\r\n    pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 368, in query_table\r\n    _check_valid_index_key(key, size)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets\/formatting\/formatting.py\", line 311, in _check_valid_index_key\r\n    raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\r\nIndexError: Invalid key: 14 is out of bounds for size 0\r\n```\r\n\r\n\r\n\r\n## Expected results\r\nnot getting the index error\r\n\r\n## Actual results\r\nPlease see the above\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets                  1.12.1 \r\n- Platform: linux\r\n- Python version: 3.7.11 \r\n- PyArrow version:  5.0.0 \r\n\r\n\r\n\r\n \n That's the thing though. The `Trainer` has no idea which inputs are required or not since all models can have different kinds of inputs, and it can work for models outside of the Transformers library. I can add a clear error message if I get an empty batch, as this is easy to detect, but that's pretty much it.","embeddings":[-0.389613986,-0.2896887064,-0.0146302115,0.3087924719,0.3473482132,-0.2418410331,0.3561576009,0.2430759966,0.1943363994,0.3443817794,0.0316291973,0.1856550276,-0.1241295412,0.068187736,-0.363416642,-0.3182570636,-0.1385273039,0.1319549829,-0.2823426723,0.1612484306,-0.2188625783,0.2060973197,-0.2639538944,0.1758927256,-0.0497712679,-0.0371602289,0.0992189422,-0.0780251622,0.0320226103,-0.2494231611,0.3354479074,-0.346852392,0.2457087934,0.7273562551,-0.0001192912,-0.025481293,0.4413975179,-0.2230760157,-0.0005031597,-0.0746245086,0.6110761166,-0.3019441366,-0.0140578914,-0.2531581521,0.2060740441,-0.3909291029,-0.1341194361,-0.0279495362,0.1947908998,0.2587778866,0.14318344,0.3059898019,0.0780828446,-0.2722992003,-0.1123694852,-0.2118056417,0.0402175114,0.2579285502,0.0773182064,-0.174048245,-0.2438988239,0.0654503778,0.1149050593,-0.0566485077,0.5178043246,-0.2840248942,0.1143898815,-0.1758093238,-0.0145956818,-0.00342199,0.2611017227,-0.0868977532,-0.1931508631,-0.3589656949,0.0625937134,-0.0254444499,0.0988237485,0.0703022853,0.0224990975,0.05238875,0.1724583507,0.3939803541,-0.1061743945,0.225917697,-0.0800592527,0.3932600021,-0.0756288841,0.2439516634,0.5022857785,-0.1024582237,0.1065810695,0.2865984738,0.0634872764,0.2548939884,-0.3530240059,-0.2010882944,0.2330770195,-0.6732481718,-0.1928143203,0.2007482648,0.1331625879,-0.0908272117,0.6408691406,-0.1230339259,0.2253909856,0.3644768894,-0.2757491171,0.1300418228,0.3418738544,0.2227095217,0.1012675986,-0.2118380368,0.2360481769,-0.3004505634,0.0831962079,-0.0416878946,-0.0521921478,0.1723070592,-0.1586503237,0.2281520069,-0.0353684463,-0.1432163119,0.0790323019,0.3053472042,0.148478061,0.2386116683,0.3460195959,0.1962521076,-0.2135006189,0.2145754546,-0.1948221773,0.1809065789,0.0893448591,-0.2781764269,-0.0089508276,-0.0071537942,0.4308076501,-0.0701530874,0.3631477654,-0.5140989423,0.1039239168,-0.1084562987,-0.2815326452,0.4845505655,-0.1215516105,-0.0517095365,0.1621133089,-0.0360111222,-0.1242828965,0.2764679492,-0.4824527204,-0.6505846381,0.0183498394,0.0918173417,-0.2360075265,0.2977598906,-0.0981007516,-0.1434908658,0.2951731384,0.0648202747,0.1849305481,-0.39213866,-0.0430691019,-0.0496006273,0.0602658428,0.0568472743,-0.0471673533,-0.2099940032,-0.0448071845,-0.0833948404,0.125947997,0.0874721706,-0.3543171883,0.3284276426,-0.3312932849,0.1692542285,0.3340460956,-0.2291650474,-0.709107995,0.0061513516,-0.4203280509,-0.150533855,0.0903914645,0.1434873194,0.1855770797,-0.004922823,-0.0824156925,0.1605361253,0.0381931551,0.0124474373,-0.1024393141,-0.0544400476,0.2747745812,0.0930822268,0.2478365302,0.0688723475,-0.0113420561,0.4366556108,-0.0544372499,-0.0169295259,0.0306486972,0.1363259256,0.1023456007,-0.2260520756,0.3137185574,-0.2096313834,0.0550971664,0.1035370231,-0.3195706606,0.1881973296,-0.0363559276,-0.0363876037,-0.3540016711,0.0793980435,0.1037681028,-0.0991274491,0.0352030657,-0.1450605839,-0.0832667202,-0.388479054,0.0341508873,-0.0037553702,-0.4048275054,-0.0301961396,-0.0421601012,0.1399486512,-0.2281762362,-0.1746620536,-0.2353018969,0.1891423464,-0.0752717257,-0.183382377,-0.3118394315,0.2674703002,-0.1548387557,-0.0372150429,-0.3062781692,0.1764156967,0.1957689971,-0.050919205,0.2716096044,0.4083098769,0.1179642826,-0.2562609315,0.4541432261,0.6124860048,-0.1288583577,0.0579432212,-0.1382121295,-0.0600695387,0.3139534295,-0.0176601112,-0.0880969986,-0.0222126264,0.0208588988,-0.3356958628,0.0802437738,-0.2782365084,-0.1084268689,-0.2599354684,0.0221382268,0.1173917577,-0.0186677184,0.2555037141,-0.0347185954,0.2116461694,-0.2827578783,0.2482461631,0.5192136765,0.0696473047,-0.2654940188,0.1591648161,0.3544076979,-0.0453876071,0.2292569876,0.3359416723,0.2728696764,0.466919899,0.1930990964,0.1399871856,0.0085987858,-0.3180223107,-0.0641661659,0.4424804151,-0.2965271175,0.2216306329,0.134372741,0.0398937613,-0.1693696529,-0.1187544242,-0.1792631298,-0.2337691486,-0.1237298101,0.3886534274,-0.4316176772,0.0530367754,-0.2674812078,0.288205266,0.2576822937,0.3259709179,0.1478629261,0.2398411036,0.0139712077,-0.1078580841,0.0940019935,-0.0251650214,-0.0192727335,0.1582969874,0.056291651,0.4700152874,-0.4344233274,-0.0599876791,0.0343006402,0.1294091493,0.2592258751,0.1120613292,-0.1111286804,-0.2542455196,0.2870855331,-0.1661201715,-0.1753746569,0.2520132959,-0.0450242199,0.2733143866,-0.3296175599,-0.2529786229,-0.3249234557,-0.4631488323,-0.1274587065,-0.0206832197,0.2834064364,-0.0659301355,0.3948688507,0.1004976705,0.3003748357,-0.0638103858,-0.0256142989,0.1553850174,0.387717545,-0.0366312899,-0.3885851502,-0.0218417924,-0.1496914625,-0.0235516038,-0.4220234752,-0.5347440243,-0.0104106301,-0.1061309054,-0.155281052,-0.3330346644,0.0399059616,0.6237717867,-0.1732101589,-0.0035062337,-0.1227628216,-0.0843582973,0.3050920069,-0.0285022948,0.1049818248,0.1410135627,0.5239762068,0.0909968838,0.6615387201,0.0805728212,-0.2219898999,0.0731115863,0.0339721218,-0.1155951768,-0.1329817623,-0.2787259221,-0.1261444092,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2987","title":"ArrowInvalid: Can only convert 1-dimensional array values","comments":"Hi @NielsRogge, thanks for reporting!\r\n\r\nIn `datasets`, we were handling N-dimensional arrays only when passed as an instance of `np.array`, not when passed as a list of `np.array`s.\r\n\r\nI'm fixing it.","body":"## Describe the bug\r\n\r\nFor the ViT and LayoutLMv2 demo notebooks in my [Transformers-Tutorials repo](https:\/\/github.com\/NielsRogge\/Transformers-Tutorials), people reported an ArrowInvalid issue after applying the following function to a Dataset:\r\n\r\n```\r\ndef preprocess_data(examples):\r\n  images = [Image.open(path).convert(\"RGB\") for path in examples['image_path']]\r\n  words = examples['words']\r\n  boxes = examples['bboxes']\r\n  word_labels = examples['ner_tags']\r\n  \r\n  encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,\r\n                             padding=\"max_length\", truncation=True)\r\n  \r\n  return encoded_inputs \r\n```\r\n\r\n```\r\nFull trace:\r\n\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-8-0fc3efc6f0c2> in <module>()\r\n     27 \r\n     28 train_dataset = datasets['train'].map(preprocess_data, batched=True, remove_columns=datasets['train'].column_names,\r\n---> 29                                       features=features)\r\n     30 test_dataset = datasets['test'].map(preprocess_data, batched=True, remove_columns=datasets['test'].column_names,\r\n     31                                       features=features)\r\n\r\n13 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1701                 new_fingerprint=new_fingerprint,\r\n   1702                 disable_tqdm=disable_tqdm,\r\n-> 1703                 desc=desc,\r\n   1704             )\r\n   1705         else:\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    183         }\r\n    184         # apply actual function\r\n--> 185         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    186         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    187         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    396             # Call actual function\r\n    397 \r\n--> 398             out = func(self, *args, **kwargs)\r\n    399 \r\n    400             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2063                                 writer.write_table(batch)\r\n   2064                             else:\r\n-> 2065                                 writer.write_batch(batch)\r\n   2066                 if update_data and writer is not None:\r\n   2067                     writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    409             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n    410             typed_sequence_examples[col] = typed_sequence\r\n--> 411         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    412         self.write_table(pa_table, writer_batch_size)\r\n    413 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    106                     storage = numpy_to_pyarrow_listarray(self.data, type=type.value_type)\r\n    107                 else:\r\n--> 108                     storage = pa.array(self.data, type.storage_dtype)\r\n    109                 out = pa.ExtensionArray.from_storage(type, storage)\r\n    110             elif isinstance(self.data, np.ndarray):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Can only convert 1-dimensional array values\r\n```\r\nIt can be fixed by adding the following line:\r\n\r\n```diff\r\ndef preprocess_data(examples):\r\n  images = [Image.open(path).convert(\"RGB\") for path in examples['image_path']]\r\n  words = examples['words']\r\n  boxes = examples['bboxes']\r\n  word_labels = examples['ner_tags']\r\n  \r\n  encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,\r\n                             padding=\"max_length\", truncation=True)\r\n+ encoded_inputs[\"image\"] = np.array(encoded_inputs[\"image\"])\r\n  \r\n  return encoded_inputs \r\n```\r\n\r\nHowever, would be great if this can be fixed within Datasets itself.","comment_length":31,"text":"ArrowInvalid: Can only convert 1-dimensional array values \n ## Describe the bug\r\n\r\nFor the ViT and LayoutLMv2 demo notebooks in my [Transformers-Tutorials repo](https:\/\/github.com\/NielsRogge\/Transformers-Tutorials), people reported an ArrowInvalid issue after applying the following function to a Dataset:\r\n\r\n```\r\ndef preprocess_data(examples):\r\n  images = [Image.open(path).convert(\"RGB\") for path in examples['image_path']]\r\n  words = examples['words']\r\n  boxes = examples['bboxes']\r\n  word_labels = examples['ner_tags']\r\n  \r\n  encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,\r\n                             padding=\"max_length\", truncation=True)\r\n  \r\n  return encoded_inputs \r\n```\r\n\r\n```\r\nFull trace:\r\n\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-8-0fc3efc6f0c2> in <module>()\r\n     27 \r\n     28 train_dataset = datasets['train'].map(preprocess_data, batched=True, remove_columns=datasets['train'].column_names,\r\n---> 29                                       features=features)\r\n     30 test_dataset = datasets['test'].map(preprocess_data, batched=True, remove_columns=datasets['test'].column_names,\r\n     31                                       features=features)\r\n\r\n13 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1701                 new_fingerprint=new_fingerprint,\r\n   1702                 disable_tqdm=disable_tqdm,\r\n-> 1703                 desc=desc,\r\n   1704             )\r\n   1705         else:\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    183         }\r\n    184         # apply actual function\r\n--> 185         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    186         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    187         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    396             # Call actual function\r\n    397 \r\n--> 398             out = func(self, *args, **kwargs)\r\n    399 \r\n    400             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\r\n   2063                                 writer.write_table(batch)\r\n   2064                             else:\r\n-> 2065                                 writer.write_batch(batch)\r\n   2066                 if update_data and writer is not None:\r\n   2067                     writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    409             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)\r\n    410             typed_sequence_examples[col] = typed_sequence\r\n--> 411         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    412         self.write_table(pa_table, writer_batch_size)\r\n    413 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n    106                     storage = numpy_to_pyarrow_listarray(self.data, type=type.value_type)\r\n    107                 else:\r\n--> 108                     storage = pa.array(self.data, type.storage_dtype)\r\n    109                 out = pa.ExtensionArray.from_storage(type, storage)\r\n    110             elif isinstance(self.data, np.ndarray):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Can only convert 1-dimensional array values\r\n```\r\nIt can be fixed by adding the following line:\r\n\r\n```diff\r\ndef preprocess_data(examples):\r\n  images = [Image.open(path).convert(\"RGB\") for path in examples['image_path']]\r\n  words = examples['words']\r\n  boxes = examples['bboxes']\r\n  word_labels = examples['ner_tags']\r\n  \r\n  encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,\r\n                             padding=\"max_length\", truncation=True)\r\n+ encoded_inputs[\"image\"] = np.array(encoded_inputs[\"image\"])\r\n  \r\n  return encoded_inputs \r\n```\r\n\r\nHowever, would be great if this can be fixed within Datasets itself. \n Hi @NielsRogge, thanks for reporting!\r\n\r\nIn `datasets`, we were handling N-dimensional arrays only when passed as an instance of `np.array`, not when passed as a list of `np.array`s.\r\n\r\nI'm fixing 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2984","title":"Exceeded maximum rows when reading large files","comments":"Hi @zijwang, thanks for reporting this issue.\r\n\r\nYou did not mention which `datasets` version you are using, but looking at the code in the stack trace, it seems you are using an old version.\r\n\r\nCould you please update `datasets` (`pip install -U datasets`) and check if the problem persists?","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\nWhen using `load_dataset` with json files, if the files are too large, there will be \"Exceeded maximum rows\" error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('json', data_files=data_files) # data files have 3M rows in a single file\r\n```\r\n\r\n## Expected results\r\nNo error\r\n\r\n## Actual results\r\n```\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files)\r\n    134                                 with open(file, encoding=\"utf-8\") as f:\r\n--> 135                                     dataset = json.load(f)\r\n    136                             except json.JSONDecodeError:\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/json\/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    292     \"\"\"\r\n--> 293     return loads(fp.read(),\r\n    294         cls=cls, object_hook=object_hook,\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/json\/__init__.py in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    345             parse_constant is None and object_pairs_hook is None and not kw):\r\n--> 346         return _default_decoder.decode(s)\r\n    347     if cls is None:\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/json\/decoder.py in decode(self, s, _w)\r\n    339         if end != len(s):\r\n--> 340             raise JSONDecodeError(\"Extra data\", s, end)\r\n    341         return obj\r\n\r\nJSONDecodeError: Extra data: line 2 column 1 (char 20321)\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-20-ab3718a6482f> in <module>\r\n----> 1 dataset = load_dataset('json', data_files=data_files)\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    841 \r\n    842     # Download and prepare data\r\n--> 843     builder_instance.download_and_prepare(\r\n    844         download_config=download_config,\r\n    845         download_mode=download_mode,\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    606                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    607                     if not downloaded_from_gcs:\r\n--> 608                         self._download_and_prepare(\r\n    609                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    610                         )\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    684             try:\r\n    685                 # Prepare split will record examples associated to the split\r\n--> 686                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    687             except OSError as e:\r\n    688                 raise OSError(\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1153         generator = self._generate_tables(**split_generator.gen_kwargs)\r\n   1154         with ArrowWriter(features=self.info.features, path=fpath) as writer:\r\n-> 1155             for key, table in utils.tqdm(\r\n   1156                 generator, unit=\" tables\", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)\r\n   1157             ):\r\n\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files)\r\n    135                                     dataset = json.load(f)\r\n    136                             except json.JSONDecodeError:\r\n--> 137                                 raise e\r\n    138                             raise ValueError(\r\n    139                                 f\"Not able to read records in the JSON file at {file}. \"\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files)\r\n    114                             while True:\r\n    115                                 try:\r\n--> 116                                     pa_table = paj.read_json(\r\n    117                                         BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)\r\n    118                                     )\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/pyarrow\/_json.pyx in pyarrow._json.read_json()\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Exceeded maximum rows\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Linux\r\n- Python version: 3.9\r\n- PyArrow version: 4.0.1\r\n","comment_length":49,"text":"Exceeded maximum rows when reading large files \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\nWhen using `load_dataset` with json files, if the files are too large, there will be \"Exceeded maximum rows\" error.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('json', data_files=data_files) # data files have 3M rows in a single file\r\n```\r\n\r\n## Expected results\r\nNo error\r\n\r\n## Actual results\r\n```\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files)\r\n    134                                 with open(file, encoding=\"utf-8\") as f:\r\n--> 135                                     dataset = json.load(f)\r\n    136                             except json.JSONDecodeError:\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/json\/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    292     \"\"\"\r\n--> 293     return loads(fp.read(),\r\n    294         cls=cls, object_hook=object_hook,\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/json\/__init__.py in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    345             parse_constant is None and object_pairs_hook is None and not kw):\r\n--> 346         return _default_decoder.decode(s)\r\n    347     if cls is None:\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/json\/decoder.py in decode(self, s, _w)\r\n    339         if end != len(s):\r\n--> 340             raise JSONDecodeError(\"Extra data\", s, end)\r\n    341         return obj\r\n\r\nJSONDecodeError: Extra data: line 2 column 1 (char 20321)\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-20-ab3718a6482f> in <module>\r\n----> 1 dataset = load_dataset('json', data_files=data_files)\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    841 \r\n    842     # Download and prepare data\r\n--> 843     builder_instance.download_and_prepare(\r\n    844         download_config=download_config,\r\n    845         download_mode=download_mode,\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    606                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    607                     if not downloaded_from_gcs:\r\n--> 608                         self._download_and_prepare(\r\n    609                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    610                         )\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    684             try:\r\n    685                 # Prepare split will record examples associated to the split\r\n--> 686                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    687             except OSError as e:\r\n    688                 raise OSError(\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1153         generator = self._generate_tables(**split_generator.gen_kwargs)\r\n   1154         with ArrowWriter(features=self.info.features, path=fpath) as writer:\r\n-> 1155             for key, table in utils.tqdm(\r\n   1156                 generator, unit=\" tables\", leave=False, disable=bool(logging.get_verbosity() == logging.NOTSET)\r\n   1157             ):\r\n\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files)\r\n    135                                     dataset = json.load(f)\r\n    136                             except json.JSONDecodeError:\r\n--> 137                                 raise e\r\n    138                             raise ValueError(\r\n    139                                 f\"Not able to read records in the JSON file at {file}. \"\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files)\r\n    114                             while True:\r\n    115                                 try:\r\n--> 116                                     pa_table = paj.read_json(\r\n    117                                         BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)\r\n    118                                     )\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/pyarrow\/_json.pyx in pyarrow._json.read_json()\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda3\/envs\/python\/lib\/python3.9\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Exceeded maximum rows\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Linux\r\n- Python version: 3.9\r\n- PyArrow version: 4.0.1\r\n \n Hi @zijwang, thanks for reporting this issue.\r\n\r\nYou did not mention which `datasets` version you are using, but looking at the code in the stack trace, it seems you are using an old version.\r\n\r\nCould you please update `datasets` (`pip install -U datasets`) and check if the problem persists?","embeddings":[-0.0769460425,-0.1556029618,-0.1352694035,0.5384629965,0.1540833414,0.2282284349,0.0905873403,0.3988286853,0.2924410403,0.0929042175,0.0769099295,0.2426915467,-0.0044684885,0.1653082222,-0.0385244414,0.1086360589,0.0569194146,-0.0218207724,-0.0694187284,0.1177438721,-0.5645869374,-0.011686285,-0.1165467724,-0.2815795243,-0.1374031752,0.0457933806,0.07671386,0.1193501428,-0.085102126,-0.2741531432,0.0589793734,-0.1251615137,0.0506067313,0.5795961022,-0.0001026162,-0.0741348863,0.2823173106,-0.1580396742,-0.1914461255,0.0524788871,-0.158820644,-0.4901194572,-0.044607047,-0.4243276417,0.0520130321,-0.4441848099,-0.4582794607,-0.3565166295,0.2467675209,0.3993396163,0.3029395938,-0.1107094884,0.354678303,0.050669767,0.4560398161,0.0097794626,-0.0159025323,0.5112149715,0.3727463484,0.0250599571,-0.1267336309,0.0451631248,0.1047978774,0.0746333227,0.2478966713,-0.0161891114,0.0251847673,-0.0967551842,0.1495010108,0.095724009,0.3640725017,-0.162356481,-0.2619970739,-0.4543037117,0.0967043564,-0.1490243077,0.2010118365,0.4033388793,-0.2749597728,0.0939767063,-0.0490384512,-0.160940066,-0.2947116494,0.1080343574,-0.182806626,-0.2629229426,-0.0703701526,-0.0984110162,0.3635616601,-0.3042796552,0.1641813219,0.0202350952,-0.1379190832,-0.0182395671,-0.5389512181,-0.1775956899,0.2404949665,-0.1867658198,0.2379968166,-0.0483622327,0.3267845809,-0.0661213845,0.076591216,0.1607316583,0.3481045663,0.1145051196,-0.3595595956,0.1402717084,0.0746400505,0.2876774371,-0.0522531122,0.0039939373,0.0930582657,-0.141484037,-0.0326825604,-0.0824714452,0.1688966453,-0.2456578165,-0.0044773179,0.5148532987,-0.3092027307,0.0840378925,0.0231890716,0.1685452908,0.1330818385,0.1576659828,0.0416583717,0.1885663569,-0.070212692,-0.1640753895,-0.1597316563,0.001066783,0.0312006138,-0.0391439423,0.3320554197,0.0046412405,0.0608899929,0.0960599333,0.06003768,-0.4427105188,0.0654765889,-0.3470648825,0.1403877586,0.2625469863,0.0437018909,-0.2483319491,0.0361790434,0.0029275522,-0.0336715691,0.3558743298,-0.3037769794,-0.1970202625,0.0143788345,0.3456524312,-0.1912354827,0.149573043,-0.5972539783,0.1198014095,0.022067152,-0.0501473211,0.084824264,-0.2966217995,0.1100101173,-0.2016491592,-0.0097574452,0.1833482981,-0.4004988074,0.2971410155,0.2097386718,0.074264586,-0.1032633185,0.2526578903,-0.2942622006,0.1684643924,-0.1902893782,-0.1819896996,0.0819212273,-0.1772242785,-0.5631121397,0.2971054912,0.0119614638,0.3826853037,0.0697120577,-0.2484331131,0.3310584128,-0.1010151803,0.3935411274,0.2224805653,0.1044247299,0.1172244176,-0.2926820517,-0.0988510698,0.018676009,0.0683439299,-0.1223839074,-0.0449453667,0.1086771712,-0.1179352254,0.3372189105,-0.0796818733,-0.084260799,0.4627375305,-0.1942699552,0.1334639937,0.0591717996,-0.038828183,-0.2622411549,0.058191929,-0.2745420039,-0.0599440373,-0.1047359928,-0.1330450028,-0.0580645651,0.0340415724,0.0111573255,0.1280299127,0.2872228324,-0.047243502,0.0931340456,0.0641457513,0.1053901836,-0.0541077554,-0.265065223,0.0566911362,-0.5503027439,0.2793127,0.0221922714,-0.2252403349,0.2196929306,0.0734969825,0.0890995935,-0.1802639216,-0.1316659451,-0.0517141372,0.2320383787,-0.1232432723,-0.2157121748,0.116701968,0.0795538649,0.0344190225,-0.0082532866,0.3060638905,0.1110781655,0.0954571813,-0.1548586637,0.2150399983,-0.198570922,0.0695444271,-0.0264873672,-0.2882179916,0.2465692759,-0.0104236538,0.1066720635,-0.1563573927,0.2474524677,0.4364832342,-0.1290821433,-0.027694311,-0.0338883363,0.0037997682,0.5654012561,-0.1987548321,-0.0526676476,0.3433435261,-0.0317901894,-0.1613455266,-0.0379093848,0.3309772015,0.3182141483,0.3212843537,-0.0907967836,-0.0485608354,0.2348047197,-0.2390993685,0.118269302,0.2215557098,0.2458982319,0.2771693468,0.4468896985,-0.1705667078,-0.1867524832,-0.275077641,0.1440755278,0.4473401606,-0.2858642936,0.0932745859,-0.1721931845,-0.1151289418,-0.091481097,-0.007512223,-0.0168443024,-0.3007216752,-0.0141265644,0.1908277571,-0.246838212,-0.2141839564,-0.1206997633,0.1980581731,0.2621046603,-0.0484112874,-0.1677565724,-0.0144752888,-0.1371250153,0.1408514529,0.2910279036,-0.1248307824,0.0997065678,0.0531263277,0.0047614025,0.1143891066,-0.1024243757,-0.0409114249,-0.0404357202,0.313834399,-0.095005624,0.1014426649,0.1307107955,-0.0399070568,-0.0089611802,0.0772754997,-0.1263584197,0.1655675024,0.2210281044,0.3231648803,-0.0048628552,-0.3695445955,-0.0782347247,-0.6428579688,0.4804486334,0.1689866334,0.1132769436,-0.0732696652,0.2564467788,0.1899723858,0.1395002604,0.128543511,-0.2430884093,-0.2960593998,0.3488708436,-0.0963601395,-0.325068146,-0.0451484844,0.0047923885,0.0153847747,-0.0012870019,-0.6893302202,-0.0174114965,-0.0912322477,0.169613272,-0.4645300508,-0.2990463972,-0.0401448086,-0.1662213355,-0.0940188542,0.0402935967,0.0415423252,-0.1925214231,0.0580609627,-0.0108711431,-0.0757356957,0.4708294272,0.0531801581,0.009357729,0.2749343514,0.088010475,0.2473265976,0.0826684982,0.3654446602,-0.1884826273,-0.2260721773,-0.1005348414,-0.1110956371,-0.000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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2980","title":"OpenSLR 25: ASR data for Amharic, Swahili and Wolof","comments":"Whoever handles this just needs to: \r\n\r\n- [ ] fork the HuggingFace Datasets repo\r\n- [ ] update the [existing dataset script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/openslr\/openslr.py) to add SLR25. Lots of copypasting from other sections of the script should make that easy. \r\nAmharic URL: https:\/\/www.openslr.org\/resources\/25\/data_readspeech_am.tar.bz2. \r\nSwahili URL: https:\/\/www.openslr.org\/resources\/25\/data_broadcastnews_sw.tar.bz2, \r\nWolof URL: https:\/\/www.openslr.org\/resources\/25\/data_readspeech_wo.tar.bz2\r\n- [ ] update the [data card](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/openslr\/README.md) to include information about SLR25. There's lots of other examples to draw from. \r\n- [ ] add the appropriate language tags to the data card as well. https:\/\/www.w3.org\/International\/questions\/qa-choosing-language-tags, or just use `sw`, `am`, and `wo` for consistency. \r\n- [ ] make a pull request to merge your changes back into HuggingFace's repo","body":"## Adding a Dataset\r\n- **Name:** *SLR25*\r\n- **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https:\/\/huggingface.co\/datasets\/openslr, 25 covers Amharic, Swahili and Wolof data*\r\n- **Paper:** *https:\/\/www.openslr.org\/25\/ has citations for each of the three subsubsets. *\r\n- **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps:\/\/www.openslr.org\/25\/*\r\n- **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.*\r\n\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/openslr\/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do. \r\n\r\n","comment_length":106,"text":"OpenSLR 25: ASR data for Amharic, Swahili and Wolof \n ## Adding a Dataset\r\n- **Name:** *SLR25*\r\n- **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https:\/\/huggingface.co\/datasets\/openslr, 25 covers Amharic, Swahili and Wolof data*\r\n- **Paper:** *https:\/\/www.openslr.org\/25\/ has citations for each of the three subsubsets. *\r\n- **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps:\/\/www.openslr.org\/25\/*\r\n- **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.*\r\n\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/openslr\/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do. \r\n\r\n \n Whoever handles this just needs to: \r\n\r\n- [ ] fork the HuggingFace Datasets repo\r\n- [ ] update the [existing dataset script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/openslr\/openslr.py) to add SLR25. Lots of copypasting from other sections of the script should make that easy. \r\nAmharic URL: https:\/\/www.openslr.org\/resources\/25\/data_readspeech_am.tar.bz2. \r\nSwahili URL: https:\/\/www.openslr.org\/resources\/25\/data_broadcastnews_sw.tar.bz2, \r\nWolof URL: https:\/\/www.openslr.org\/resources\/25\/data_readspeech_wo.tar.bz2\r\n- [ ] update the [data card](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/openslr\/README.md) to include information about SLR25. There's lots of other examples to draw from. \r\n- [ ] add the appropriate language tags to the data card as well. https:\/\/www.w3.org\/International\/questions\/qa-choosing-language-tags, or just use `sw`, `am`, and `wo` for consistency. \r\n- [ ] make a pull request to merge your changes back into HuggingFace's 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2980","title":"OpenSLR 25: ASR data for Amharic, Swahili and Wolof","comments":"... also the example in \"use in datasets library\" should be updated. It currently says \r\n![image](https:\/\/user-images.githubusercontent.com\/4109253\/135115980-8583a44a-cae6-4121-b699-00667020849f.png)\r\nBut you actually have to specify a subset, e.g. \r\n```python\r\ndataset = load_dataset(\"openslr\", \"SLR32\")\r\n```","body":"## Adding a Dataset\r\n- **Name:** *SLR25*\r\n- **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https:\/\/huggingface.co\/datasets\/openslr, 25 covers Amharic, Swahili and Wolof data*\r\n- **Paper:** *https:\/\/www.openslr.org\/25\/ has citations for each of the three subsubsets. *\r\n- **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps:\/\/www.openslr.org\/25\/*\r\n- **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.*\r\n\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/openslr\/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do. \r\n\r\n","comment_length":31,"text":"OpenSLR 25: ASR data for Amharic, Swahili and Wolof \n ## Adding a Dataset\r\n- **Name:** *SLR25*\r\n- **Description:** *Subset 25 from OpenSLR. Other subsets have been added to https:\/\/huggingface.co\/datasets\/openslr, 25 covers Amharic, Swahili and Wolof data*\r\n- **Paper:** *https:\/\/www.openslr.org\/25\/ has citations for each of the three subsubsets. *\r\n- **Data:** *Currently the three links to the .tar.bz2 files can be found a thttps:\/\/www.openslr.org\/25\/*\r\n- **Motivation:** *Increase ASR data for underrepresented African languages. Also, other subsets of OpenSLR speech recognition have been uploaded, so this would be easy.*\r\n\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/openslr\/openslr.py already has been created for various other OpenSLR subsets, this should be relatively straightforward to do. \r\n\r\n \n ... also the example in \"use in datasets library\" should be updated. It currently says \r\n![image](https:\/\/user-images.githubusercontent.com\/4109253\/135115980-8583a44a-cae6-4121-b699-00667020849f.png)\r\nBut you actually have to specify a subset, e.g. \r\n```python\r\ndataset = load_dataset(\"openslr\", \"SLR32\")\r\n```","embeddings":[-0.2541204393,0.0149931684,-0.1568882316,-0.0666442811,0.1727309823,0.0196193997,0.1956541985,0.2351339757,-0.0779473409,0.2244037539,-0.3227958381,0.1105092987,-0.049799066,0.1022861972,-0.0044498206,-0.1112288684,0.0189730115,0.0934513137,-0.3194477558,-0.1371395886,-0.2414939553,0.2651551068,-0.1138584465,-0.1909170747,-0.1503929198,0.1428505182,-0.1503926963,-0.1785715967,-0.3060544729,-0.274415642,-0.1375572979,0.1245220229,0.4141058922,-0.0399010889,-0.0001008441,-0.274947077,0.2809059918,-0.2132053971,-0.3460813761,-0.2972496748,-0.0373456478,-0.3054199219,-0.2194031924,-0.0194295216,0.0122143691,-0.3532194197,0.2722992599,-0.2762002051,0.0923043191,0.5146610737,0.2764804065,0.0332708955,0.1432783157,0.0251406599,0.3011720181,0.0438667722,-0.02497245,0.1159955859,0.3335841,0.0393728577,-0.3175997734,0.4325484037,0.0790365487,-0.2621071935,0.093207702,-0.0896535069,0.1102014706,-0.5220060945,0.2202041,0.1582264006,0.3744142056,-0.1099912599,-0.3572245538,-0.0998233855,-0.1053372025,-0.0111113936,0.0054637198,0.6642064452,-0.1384600997,0.3150061667,0.0565097667,-0.0438702852,-0.171704933,0.4587357342,0.028763542,0.7883151174,-0.2050682455,-0.0999381691,0.1189725399,-0.2580257952,0.2014998049,-0.0021524492,0.0366708636,0.163373664,-0.3590475917,0.0530339181,0.2335587442,-0.036310181,0.3703724444,0.2321751118,0.4167259634,0.2082057148,-0.2686636448,0.0990938991,-0.224038288,0.1749963164,0.163171187,0.0148614394,0.1078866124,0.012318817,0.1973840594,-0.0987885669,-0.0841266885,-0.0747229084,-0.4523786008,0.0001058206,0.1480620056,-0.262562871,-0.0882684365,-0.0765629038,-0.4151188135,-0.3293423057,-0.1381580979,0.1747362912,0.1431473196,0.1287214011,-0.0293960311,0.3676012754,-0.055669643,-0.1400238276,-0.0847909823,0.1708003134,-0.0793044046,0.1554360241,0.1880742908,-0.1196832955,0.3211278319,-0.0857966393,0.0188267808,-0.0667793155,0.1255374849,0.0698902905,0.079088822,0.0632786229,-0.0021239007,-0.0051187826,-0.1046999395,-0.0233135056,-0.2753021419,0.0357771292,-0.0166923534,-0.0674436316,-0.2392840236,0.2772396207,-0.1464591026,-0.1688998193,0.2688502371,0.6259340644,-0.0056782062,-0.1500781924,0.0447545461,0.154251188,-0.4611468315,-0.1305501014,0.0223310664,0.1516464353,-0.116531454,-0.1572005451,0.2840601504,0.0148355495,0.2018292993,-0.1614563763,-0.0298157856,-0.2606808245,-0.1937262714,0.2632634044,0.2235270441,-0.2847888768,-0.3911616206,0.1267349422,0.1357152015,-0.2811870873,0.5081934333,0.038524501,0.047437381,-0.0889036134,0.1394535899,0.5031155348,0.0883360431,0.0303485598,-0.2834263146,-0.1665796638,-0.0167930126,0.5109137297,-0.0634110793,0.0375587977,0.2223459333,-0.147313863,0.4631279111,-0.4892173707,0.2842029631,-0.0165501181,0.0889720023,-0.1726130396,-0.0230672713,-0.1378189027,0.1041877344,-0.0520765446,-0.3508652449,0.4322320819,-0.1415486336,-0.0480840355,-0.0818893313,-0.3467095196,-0.115593113,-0.0708303675,0.2422638237,0.3329282701,0.1923549771,0.1777946204,-0.0571699291,0.2157732993,-0.2157714069,-0.0980018675,-0.3658516109,0.4185876548,-0.0182682853,-0.1468577832,0.4904655218,0.2812775373,0.0224047732,0.0430950969,-0.0700185522,0.1535644829,-0.223676309,0.1935110539,0.0014751299,0.2192423195,0.0252719857,-0.3586250842,0.3649781346,0.1815707535,0.0375134535,0.1346200556,-0.1087152809,0.3779659271,0.0846328735,0.1312419176,0.2544039786,0.2674127221,0.2050270587,0.0104312366,-0.4168974161,-0.0846613422,0.0587140173,-0.1700367182,0.3173223138,0.0535835437,-0.0895218998,0.3256073296,0.3877068758,-0.2326274365,0.0994570404,0.1777904332,-0.3705998361,-0.1157528087,0.0431218222,0.236683771,0.1814141572,0.3706633449,-0.0251789112,-0.1855591685,0.2290616184,-0.0991503745,0.2222584635,0.3611578047,-0.2086779773,0.215872094,0.3066404164,-0.1062060222,-0.3816695511,-0.2779486477,-0.15186508,0.0971296355,-0.0136740264,-0.0291563813,-0.0979848653,-0.1947394907,-0.1633709222,0.0489861108,-0.2585560679,-0.1879456043,0.0249118414,-0.1066499054,-0.1447274536,0.15784958,-0.1753017902,0.1839389801,-0.1423019469,0.2133677304,0.0930672735,0.0068374854,-0.1011719927,0.1796436459,0.3995366096,0.0026324941,0.4266234636,-0.1119567826,0.1583374143,-0.1168125495,-0.4218910038,0.0153533909,-0.0273147877,-0.0396156237,0.0433007069,-0.3961341381,-0.3890517354,-0.3723088503,0.048213575,-0.0381921865,-0.3012907207,-0.0722718909,-0.0838293955,0.278832078,-0.1262899786,-0.1988776177,-0.4832143784,-0.3863205016,0.1878187805,0.1298211217,0.1384068131,0.2832690775,0.1244394332,0.0005444381,0.0419948101,0.0194720384,-0.2920700014,-0.1330598742,0.4992426932,-0.3392697573,-0.4198156595,-0.0687823966,-0.1743166298,0.0692266375,-0.0860466957,-0.2532230318,-0.2108917236,-0.2155050635,0.0839107484,-0.0512041152,0.2224143445,0.4714519978,0.2697158754,-0.1482656598,-0.1524413526,-0.2305158973,0.0819239914,0.0948793963,0.1056794375,-0.0212242454,0.1554244459,-0.0417786762,0.3390539289,0.0693387985,0.1193260327,0.5018689632,0.1942719072,-0.1161997095,0.0111237112,-0.2000567019,0.457397878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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2978","title":"Run CI tests against non-production server","comments":"Hey @albertvillanova could you provide more context, including extracts from the discussion we had ?\r\n\r\nLet's ping @Pierrci @julien-c and @n1t0 for their opinion about that","body":"Currently, the CI test suite performs requests to the HF production server.\r\n\r\nAs discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`.","comment_length":26,"text":"Run CI tests against non-production server \n Currently, the CI test suite performs requests to the HF production server.\r\n\r\nAs discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`. \n Hey @albertvillanova could you provide more context, including extracts from the discussion we had ?\r\n\r\nLet's ping @Pierrci @julien-c and @n1t0 for their opinion about that","embeddings":[-0.2581966221,-0.2636909187,-0.1136403084,-0.2357592285,-0.3444662988,-0.2215364426,0.4800029397,0.2981351912,0.1589351147,0.1931143701,-0.0116512487,-0.1929859519,0.055465389,0.5981814265,-0.0119556785,-0.0694414899,-0.2003654987,0.2295342386,-0.453727901,0.0589953549,-0.0489894859,0.0819214657,-0.0140340691,0.2060643435,-0.224554196,-0.280407846,-0.078469485,0.2029875219,0.2365461588,-0.0769797862,0.1586601436,0.3057914972,-0.1212864667,0.2439483851,-0.0000982162,0.2524148822,0.2183801532,-0.16406174,-0.1261836737,0.1516645402,0.2107732147,0.3568190038,-0.2911554575,-0.0046596685,-0.4559673667,0.1889890581,-0.1793922335,0.0320580006,0.0936319977,0.3777403235,0.3349733055,0.6081647873,-0.6110967994,-0.2137931734,-0.229847312,0.3583364785,-0.1512903422,-0.2111722231,0.3925922513,-0.1274534315,-0.229959622,0.0460558534,0.1208739281,0.075642392,-0.2281793207,-0.0495905578,0.0586778186,-0.097301282,0.2002833635,0.0888528228,0.1330516785,0.0797856674,-0.0752842724,0.3061264753,-0.0347949862,-0.1004173607,0.4344212711,-0.0960532054,-0.0858062431,0.1986433864,-0.559602499,-0.2080490738,-0.2361356467,-0.2760004699,0.0451590642,-0.1231975704,-0.037678238,0.0630118325,-0.0329802148,0.1285116971,-0.0008785185,0.009778657,-0.1128719673,-0.2493593991,-0.019624263,-0.3633943796,-0.0791018605,0.2912258804,0.1155337468,0.3436814547,-0.3110245764,0.077164866,0.1100231335,0.0698873475,-0.2554711103,0.1506787688,-0.0724785924,0.0045216042,0.2942040265,0.4493350983,-0.173477307,0.0503992438,0.2301414162,-0.3306617141,-0.424671948,-0.1809380502,-0.1057191268,-0.3326224983,-0.0849522948,0.0033340829,0.1601043642,0.1471257508,0.1802926213,0.4874814451,0.1678507924,0.1627970487,0.0714607388,0.1183635369,-0.1185085922,-0.1066217571,-0.1939216107,0.1641836911,-0.0533226281,0.2761271894,0.3227761388,-0.197835505,0.204302758,0.0892878324,0.5762524605,0.0146700488,0.2346501499,0.2547370791,0.1740652472,0.400241971,0.0842058361,-0.2558113933,0.0694847107,0.0695627406,0.0338338986,-0.049914781,-0.2086454183,-0.2899094522,0.1604922861,0.2470573336,-0.2252909094,-0.176444456,-0.0457118861,-0.3540529311,-0.45603019,-0.0720743909,0.1713737845,0.0508965403,0.0008694308,0.2565489113,-0.0667316765,0.2634350657,0.1388750672,0.1118542254,0.2989209592,-0.0252789799,0.0389819294,0.2006594688,-0.1384980828,-0.0716528744,-0.1476915479,-0.4803523421,0.0507494174,-0.6395078301,0.099300608,0.3226574957,-0.2871626318,-0.146357581,0.0510061421,-0.2201724797,0.1391236782,0.093371667,-0.3286855817,0.0946367308,-0.1640975028,0.2651784718,-0.4013496339,-0.3191486597,-0.2550348043,0.087898545,0.3312149644,0.0554539263,-0.082907103,-0.1112430617,0.1865355819,-0.214501366,-0.1126509756,-0.1332752705,0.1059435531,-0.0800948441,-0.1363365352,0.3422494233,0.0576851405,0.2449731231,-0.3302891552,0.391502291,0.5867274404,-0.0525695309,-0.3458616734,-0.125093922,0.1672413051,-0.2430202812,0.1900217533,0.2343509644,0.0490480587,-0.1685324162,-0.1034120917,0.1922826171,-0.0335430056,0.0215357561,0.124927707,0.2115403712,-0.1932802945,0.0791982934,0.032993719,-0.1397313625,-0.0265122578,-0.1478770822,-0.1470818073,0.1770461947,-0.117443651,0.3919013739,0.4954921305,0.2195422798,0.144489333,-0.2348134667,-0.0192898586,0.0238450747,-0.1445747316,0.1852428615,0.0612739287,0.4294350445,-0.1603239179,0.1028200835,0.0418799147,0.0928947106,0.1820460707,-0.2280775011,-0.145365417,0.0529976748,0.1474513859,-0.158129409,-0.084284313,0.003867276,0.0264218729,-0.1095034108,0.1353758574,-0.0849447772,0.0710451156,-0.1413820684,0.2226624638,-0.0694037676,0.2553245723,-0.1463145018,0.1799787581,0.1963089556,-0.071626462,0.1647903472,-0.0758744702,-0.1261290014,0.1898153722,0.0424706377,0.0885777697,-0.2360117882,0.1619625539,-0.1015686765,-0.1850802749,-0.1761343777,0.0408759266,-0.0339598097,-0.4417907298,0.2206096798,0.0107665993,0.367977351,-0.0345867202,0.3067292571,-0.1608922482,-0.1483866125,0.3072610199,0.2463775277,-0.3023309112,0.1178856045,-0.1992936879,0.5673119426,-0.2471861243,-0.3327311277,-0.2653438747,0.0860616639,-0.278427422,0.2573415339,-0.0287117306,0.0148465764,0.2936495543,-0.2988934517,0.0970843509,-0.0609058663,-0.2338626236,0.234575361,-0.0250406023,0.1252272278,0.0847299546,0.0345536843,-0.018983094,-0.1262509823,0.0240016915,-0.5183690786,-0.004764033,0.0237041451,-0.1660868973,0.1026714817,-0.1956017315,-0.4401955307,0.1564083844,-0.3722034991,-0.1086867079,-0.1182132736,-0.0106577063,0.1772188693,-0.1514628381,-0.0575483255,0.0305766184,0.0911666453,-0.1915976852,-0.5478982925,-0.0075743934,-0.3402535319,-0.0971214399,0.0783299655,-0.1222266331,0.2481231689,-0.1708081812,-0.4140253961,-0.2771827579,0.1934852749,0.1647317857,0.3267793655,-0.3197095096,0.2082230002,-0.111139439,-0.2431177944,-0.0162039548,0.1495752186,0.0964283943,0.2615318596,0.0019405694,0.0342323929,0.0508093648,-0.0063387151,0.5217655897,0.0905855969,0.0044298768,0.2895391285,-0.0607832521,0.2372371554,-0.0332485475,-0.1196545586,0.0041959882,0.159187451,-0.257807135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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2978","title":"Run CI tests against non-production server","comments":"@julien-c increased the huggingface.co production workers in order to see if it solve [the 502 you had this morning](https:\/\/app.circleci.com\/pipelines\/github\/huggingface\/datasets\/7843\/workflows\/fc83fa32-18f5-4dc3-9e2f-ba277ae1af74)\r\n\r\nFor the decision process: be aware that moon-staging does not have persistent repos (they are deleted regularly). as a consequence, **if the moon-staging solution is validated**, you should consider a way to keep the repository that are loaded in tests. These are the ones I found : https:\/\/github.com\/huggingface\/datasets\/blob\/d488db2f64f312f88f72bbc57a09b7eddb329182\/tests\/test_load.py and https:\/\/github.com\/huggingface\/datasets\/blob\/40773111c3e7db8a992fa1c48af32d900a1018d6\/tests\/test_streaming_download_manager.","body":"Currently, the CI test suite performs requests to the HF production server.\r\n\r\nAs discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`.","comment_length":69,"text":"Run CI tests against non-production server \n Currently, the CI test suite performs requests to the HF production server.\r\n\r\nAs discussed with @elishowk, we should refactor our tests to use the HF staging server instead, like `huggingface_hub` and `transformers`. \n @julien-c increased the huggingface.co production workers in order to see if it solve [the 502 you had this morning](https:\/\/app.circleci.com\/pipelines\/github\/huggingface\/datasets\/7843\/workflows\/fc83fa32-18f5-4dc3-9e2f-ba277ae1af74)\r\n\r\nFor the decision process: be aware that moon-staging does not have persistent repos (they are deleted regularly). as a consequence, **if the moon-staging solution is validated**, you should consider a way to keep the repository that are loaded in tests. These are the ones I found : https:\/\/github.com\/huggingface\/datasets\/blob\/d488db2f64f312f88f72bbc57a09b7eddb329182\/tests\/test_load.py and https:\/\/github.com\/huggingface\/datasets\/blob\/40773111c3e7db8a992fa1c48af32d900a1018d6\/tests\/test_streaming_download_manager.","embeddings":[-0.1305260807,-0.428791821,-0.0245480351,-0.050143823,-0.0372761972,-0.3314857781,0.3419258893,0.2687295973,0.0688499436,0.1076420993,-0.1735488325,-0.0307782032,0.0130698746,0.5772927403,0.0340342261,0.0195309613,-0.1025431007,-0.0250553098,-0.3881973326,0.1347023398,-0.2016983777,0.2019162774,0.1960047483,-0.0386469103,-0.234919697,-0.0435035229,-0.0427291207,0.4001532197,-0.0812707245,-0.2070282549,0.2559806108,0.4386577606,-0.0837760568,0.3032745123,-0.0001019077,0.2145999968,0.3183282912,-0.0816071853,-0.2325219959,-0.005546913,0.3747113943,0.0506133251,-0.1416216046,-0.0578030609,-0.3539259434,0.4318255782,-0.2415277511,0.0497061312,0.4061840177,0.2655678093,0.2836879194,0.7704788446,-0.2371009141,-0.2227017879,0.0298394263,0.1852059513,0.0178450178,0.2147376835,0.4013447464,0.0758646801,-0.1427794844,0.1316497922,0.106075123,0.0911727473,-0.2191720903,-0.0748041347,0.1811399311,-0.331484437,0.202807337,0.0730936974,-0.0733722523,0.0227783006,-0.2250918746,-0.0378156565,-0.063523449,0.0001727998,0.3170170486,0.0299292691,-0.1095095128,0.0124672875,-0.556878984,-0.0795901418,-0.0316532068,-0.2211887389,-0.193407163,0.0283719618,-0.1798201501,-0.011013533,0.1277316958,-0.1033686623,-0.3625870943,0.0944387317,-0.1530098617,-0.0434590504,-0.1634571403,-0.3332136869,0.1339760721,0.1313949972,0.3033831716,0.4456387162,-0.420232296,0.0752668902,0.0043677781,0.1956190318,-0.0664114952,0.0903562233,-0.1379484832,-0.1031073034,0.4602323174,0.5199607611,-0.0329788588,0.1140600219,0.1476299316,-0.2922465503,-0.4059171677,-0.3547381759,0.1801455766,-0.3801060021,-0.229711473,0.0762291402,-0.0119287204,0.1839698851,0.2951189578,0.5688844323,0.1172172725,0.2492411733,0.0761988387,0.2057667971,-0.1622645408,-0.0106244059,-0.2375398129,-0.0012927753,-0.0639139041,0.1776282042,0.3426247239,-0.1980267167,0.4622862339,0.0304967463,0.3654654026,-0.084650293,0.0439804792,0.2002164572,0.0126490071,0.4738184512,0.0730411783,-0.0687334985,0.008919646,0.0433885567,-0.0888427198,-0.2946822941,-0.1090787351,-0.2951481938,0.2062453777,0.2537802458,-0.3415855169,-0.0016138195,-0.0846980214,-0.209957093,-0.0935171396,0.0528581999,0.0973718762,0.128681466,-0.1524395496,0.0822777376,0.300472945,0.5897501111,0.2419102341,-0.1463538408,0.1591778249,-0.0322307236,0.084283486,0.24403584,-0.081517823,-0.2264968753,-0.3224546015,-0.2526633441,-0.178951472,-0.4867736101,-0.1077312753,0.3008035123,-0.4121317267,-0.0498253964,0.1161187515,-0.1185392737,-0.0025664351,-0.1603716016,0.027264718,-0.067588985,-0.1215333864,0.2308692932,-0.4781524241,-0.3641106486,-0.4165862203,0.2496831119,0.2646138668,0.0107757244,-0.0459679216,-0.1707911938,0.1450500339,-0.0893955752,-0.0307694953,-0.0142055526,0.3855175078,0.003998769,-0.0409512892,0.1950000525,0.0614410639,0.2799167931,-0.4930280745,0.1695724726,0.2498505414,-0.2613472342,-0.4218215048,0.0352229252,0.0819679499,-0.410043329,0.1281490177,0.180193454,0.1426625401,-0.0784475133,-0.1742781252,0.2218937725,-0.2013100684,0.1731221229,-0.2767748535,0.3417881429,-0.063049458,-0.0526741296,0.255936116,0.0133430716,-0.0532899834,-0.1558797508,-0.0087803323,0.2850829959,-0.3434054255,0.3426477015,0.3365835547,0.2865997553,0.1321083456,0.0430410914,-0.0857936665,0.0332708359,-0.1033129618,0.1402410567,-0.0539640151,0.6008309722,-0.179118529,0.0073939795,0.1079471186,0.1032739282,0.41193977,-0.153486833,-0.152873382,-0.0583414659,0.1286037415,-0.1215991974,0.2406557947,-0.011597611,-0.0666927695,0.1434533,0.2606137991,-0.0441656001,-0.1087513268,0.0190664604,-0.0465875156,-0.0960484818,0.1431568712,0.2174973041,0.3879112899,0.196086809,-0.1493971348,0.149561137,0.0118369088,-0.2195417732,0.2303372175,0.0464181714,-0.1608099192,-0.1187171489,0.183478713,-0.1135804132,-0.2781247795,0.2012189329,0.0151856076,0.0731933713,-0.5638827682,0.0144620137,0.0327580124,-0.246879369,-0.171857059,0.3994782865,-0.1676181555,-0.3322205245,0.2349867076,0.4071694016,-0.3905113339,0.1045719981,0.0341117941,0.4749476016,-0.1525156647,-0.1558888853,-0.21988298,0.0050392384,-0.0228422675,0.1788763255,0.1698391438,-0.272433579,0.4506469369,-0.2980763912,-0.0735514164,-0.2653982341,-0.5127334595,0.198834002,0.0249762647,0.2091193944,0.0517902821,0.1490320712,0.0035864972,-0.0516887009,0.1779605001,-0.488973856,-0.1670517027,0.0861840323,-0.0083166379,-0.1112084165,-0.2962723076,-0.3347021043,0.2434136271,-0.4091585279,0.1474370956,-0.0706823021,-0.0814463943,0.0447581671,-0.1744526327,0.1564539075,0.042986773,-0.0669217557,-0.3200812638,-0.6977434158,0.0929190964,-0.3355582654,-0.1884703934,0.1322403848,-0.0321530737,0.2468560934,-0.2034340054,-0.2921003401,-0.4990878105,-0.0531158783,0.2948757708,0.3427002132,-0.1022512913,0.2474038303,-0.1210139468,-0.2232376784,-0.0338859707,0.0734118521,0.2557754815,0.2977091372,0.1111202165,0.0211386047,0.2101588547,-0.1241494715,0.718044281,0.26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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2977","title":"Impossible to load compressed csv","comments":"Hi @Valahaar, thanks for reporting and for your investigation about the source cause.\r\n\r\nYou are right and that commit prevents `pandas` from inferring the compression. On the other hand, @lhoestq did that change to support loading that dataset in streaming mode. \r\n\r\nI'm fixing it.","body":"## Describe the bug\r\nIt is not possible to load from a compressed csv anymore.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('csv', data_files=['\/path\/to\/csv.bz2'])\r\n```\r\n\r\n## Problem and possible solution\r\nThis used to work, but the commit that broke it is [this one](https:\/\/github.com\/huggingface\/datasets\/commit\/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782).\r\n\r\n`pandas` usually gets the compression information from the filename itself (which was previously directly passed). Now, since it gets a file descriptor, it might be good to auto-infer the compression or let the user pass the `compression` kwarg to `load_dataset` (or maybe warn the user if the file ends with a commonly known compression scheme?).\r\n\r\n## Environment info\r\n- `datasets` version: 1.10.0 (and over)\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":44,"text":"Impossible to load compressed csv \n ## Describe the bug\r\nIt is not possible to load from a compressed csv anymore.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset('csv', data_files=['\/path\/to\/csv.bz2'])\r\n```\r\n\r\n## Problem and possible solution\r\nThis used to work, but the commit that broke it is [this one](https:\/\/github.com\/huggingface\/datasets\/commit\/ad489d4597381fc2d12c77841642cbeaecf7a2e0#diff-6f60f8d0552b75be8b3bfd09994480fd60dcd4e7eb08d02f721218c3acdd2782).\r\n\r\n`pandas` usually gets the compression information from the filename itself (which was previously directly passed). Now, since it gets a file descriptor, it might be good to auto-infer the compression or let the user pass the `compression` kwarg to `load_dataset` (or maybe warn the user if the file ends with a commonly known compression scheme?).\r\n\r\n## Environment info\r\n- `datasets` version: 1.10.0 (and over)\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 3.0.0\r\n \n Hi @Valahaar, thanks for reporting and for your investigation about the source cause.\r\n\r\nYou are right and that commit prevents `pandas` from inferring the compression. On the other hand, @lhoestq did that change to support loading that dataset in streaming mode. \r\n\r\nI'm fixing it.","embeddings":[-0.0614784919,-0.3051363528,-0.0514281765,0.1528820097,0.2586481571,0.1544819027,0.2230625004,0.4252105951,-0.0204916149,0.1378156692,0.0169656146,0.2389486581,0.0854324177,0.0874538943,0.1481859535,-0.0222840346,0.0329402722,0.3155577481,0.025474498,0.1752863228,-0.2651688755,0.2333434075,-0.2270932049,-0.0026514835,-0.0454326123,0.0845377073,-0.0833105445,0.2256980091,-0.2592105865,-0.335591346,0.4326028824,-0.0934356079,0.300979197,0.3950255513,-0.0001020591,-0.1226730049,0.3961129189,-0.1634769291,-0.2991411984,-0.4494857788,-0.0744108707,-0.5960688591,0.0620010793,-0.1266969889,-0.1324295253,-0.2974301577,-0.0414285101,-0.2888426483,0.426289022,0.1822604537,0.3117643893,0.1046867371,0.1300227046,-0.3514536321,-0.0887358412,-0.1995870024,-0.1068224758,0.3647935987,0.1556926668,-0.103243731,0.0251124073,-0.0338424407,-0.2219094783,0.0337756053,0.2967721224,0.0648002177,-0.1226707995,-0.1931183338,0.080347009,0.1834528446,0.3346794248,-0.0148657691,-0.3791021407,-0.2410831004,0.1307502538,-0.4078870416,0.2141067386,0.4929457903,-0.1863753796,0.1931043565,-0.0965447724,0.0579843782,-0.1435084939,-0.0664467961,-0.0769095197,-0.1868752241,-0.3574590981,-0.0625623465,0.3124009073,0.1585372984,0.0063965241,-0.1115629822,-0.1625657231,0.2177528888,-0.2956256568,0.1825872958,0.1144231334,-0.0692853332,0.1220659688,-0.0564758703,0.3107755482,-0.1003030613,0.0719245374,0.1369924098,0.171230197,0.2081516981,-0.0948732346,-0.1305073649,0.2413035333,0.2853882611,0.1821537018,-0.1272707283,-0.1778649092,-0.4902085662,0.0759299397,0.092035681,0.234822914,-0.1621237248,-0.1904001683,-0.0231375415,-0.122148335,0.1345783174,-0.2048909366,0.2325454205,-0.1804688573,0.1637525558,0.086311087,0.1327105314,-0.0438669622,0.1497035921,-0.1511116326,-0.0941085443,-0.0169293582,-0.2026002556,0.297003001,-0.8424346447,0.1161726713,0.3937287629,0.1805177927,-0.1558415145,-0.2820665836,-0.0572569817,0.0675132424,0.3330464959,0.1154348403,0.0970241949,0.3162524402,-0.1966735721,-0.1140297353,0.0809397548,-0.4638030529,-0.2509192526,-0.221967712,0.2281769663,-0.1592300385,0.1608195752,-0.3123032451,0.1663164645,-0.1435447484,-0.1247068495,-0.2849939167,-0.0366115756,0.1117133573,-0.234318465,0.3406657279,0.081134811,-0.6112296581,0.1169065759,-0.0734358355,0.0971404389,0.2391807884,0.2400678545,-0.3667945266,-0.3219702244,-0.1816941053,0.0668908954,0.0588902868,0.0942305177,-0.4198100567,0.2584793866,-0.0787819102,0.4545955956,0.0716635957,-0.1879388988,0.2347466499,-0.1293647587,0.2298521996,0.2900304794,0.077056706,-0.1587311774,-0.3293112218,-0.3160006404,0.3811201453,-0.1898719817,0.0272722226,0.2593712211,0.0711647645,-0.1506721526,0.282541573,-0.1110936552,-0.074505724,0.1238492951,0.1348832697,-0.1400374025,0.1719699204,-0.1556208581,-0.1367852092,0.2315117717,-0.0186726507,-0.0747142658,-0.2643135786,-0.0891062096,-0.2631087303,-0.0998089984,0.0742412135,-0.1738074869,0.1776614636,-0.1006594896,0.0009260487,-0.0045170174,-0.3178519309,0.3350274563,-0.1553823352,0.0147161195,-0.2690112293,0.0986140147,0.0442542247,0.0223450866,0.1994422674,-0.1628925651,0.1641370356,-0.1156037673,-0.3098381162,0.1505504549,-0.1560315937,0.3875695169,-0.1568922698,-0.3463493586,0.093769379,0.0192601848,0.1126813814,0.1716166437,0.1878187656,0.1233414412,-0.2314393967,0.3860321939,-0.079521969,-0.0045005758,0.000496055,-0.0539542921,0.5088993311,-0.1236602366,-0.1814280301,0.0247874428,0.2003861517,-0.0397362746,-0.1284886152,0.2388229519,-0.2220730484,-0.1313382983,0.3371980488,-0.0209167451,-0.2600600719,0.3741085529,-0.1720174104,-0.0311834607,0.1263818294,0.1629732698,0.2791542411,0.124352783,-0.1436999589,-0.1017004326,-0.0282963365,-0.1736962795,0.3861041069,-0.0922942683,0.215626657,0.1153679937,0.0355318226,0.292755276,-0.3319061697,0.2514705956,-0.0146857696,-0.0053637284,-0.3241723776,0.2128042877,-0.4665183127,0.0131100146,0.0960567519,-0.1042594463,-0.07520625,-0.1262213141,-0.1226208434,0.3095210493,0.0883765817,-0.0422576554,-0.1398976296,0.4042471051,0.1357427984,-0.0780687332,-0.2033827603,-0.0445186682,-0.0247036312,0.1894179583,0.0382957123,-0.1239802763,0.0594423711,0.0243147593,-0.117554009,-0.019072406,-0.2250999808,-0.0292579029,-0.2230969667,0.3362352848,-0.0795056298,0.3071867228,-0.1086852998,-0.226210326,0.0917054862,-0.1023674682,-0.0364182703,0.2584679425,0.0443127081,-0.1336543113,-0.1774822325,-0.2208343297,-0.3752494156,-0.390093267,0.331550926,0.075713098,-0.0391846597,0.2658914626,0.1550409347,-0.035000667,0.2767851949,0.0553357564,-0.2475008965,-0.4915826619,0.538897872,-0.1564398408,-0.615229249,0.0602858961,0.1061399505,0.035742268,0.1724653691,-0.2620434761,0.1376273036,-0.147770077,0.0643920973,0.1130523533,-0.0800906122,0.0127502335,-0.0834319368,-0.1570314616,-0.1017764509,-0.1610552967,-0.278686583,0.3573161364,0.381806016,0.0494190417,0.2696268559,0.1211111173,0.353438884,0.165976271,0.0152643844,0.3193958104,0.0790443718,0.5739271045,-0.1405611187,-0.4306120872,-0.10565231,-0.3462277949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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2976","title":"Can't load dataset","comments":"Hi @mskovalova, \r\n\r\nSome datasets have multiple configurations. Therefore, in order to load them, you have to specify both the *dataset name* and the *configuration name*.\r\n\r\nIn the error message you got, you have a usage example:\r\n- To load the 'wikitext-103-raw-v1' configuration of the 'wikitext' dataset, you should use: \r\n  ```python\r\n  load_dataset('wikitext', 'wikitext-103-raw-v1')\r\n  ```\r\n\r\nIn your case, if you would like to load the 'wikitext-2-v1' configuration of the 'wikitext' dataset, please use:\r\n```python\r\nraw_datasets = load_dataset(\"wikitext\", \"wikitext-2-v1\")\r\n```","body":"I'm trying to load a wikitext dataset\r\n\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"wikitext\")\r\n```\r\n\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['wikitext-103-raw-v1', 'wikitext-2-raw-v1', 'wikitext-103-v1', 'wikitext-2-v1']\r\nExample of usage:\r\n\t`load_dataset('wikitext', 'wikitext-103-raw-v1')`.\r\n\r\nIf I try\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"wikitext-2-v1\")\r\n```\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.12.1\/datasets\/wikitext-2-v1\/wikitext-2-v1.py\r\n\r\n#### Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic (colab)\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n","comment_length":77,"text":"Can't load dataset \n I'm trying to load a wikitext dataset\r\n\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"wikitext\")\r\n```\r\n\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['wikitext-103-raw-v1', 'wikitext-2-raw-v1', 'wikitext-103-v1', 'wikitext-2-v1']\r\nExample of usage:\r\n\t`load_dataset('wikitext', 'wikitext-103-raw-v1')`.\r\n\r\nIf I try\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"wikitext-2-v1\")\r\n```\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.12.1\/datasets\/wikitext-2-v1\/wikitext-2-v1.py\r\n\r\n#### Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic (colab)\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n \n Hi @mskovalova, \r\n\r\nSome datasets have multiple configurations. Therefore, in order to load them, you have to specify both the *dataset name* and the *configuration name*.\r\n\r\nIn the error message you got, you have a usage example:\r\n- To load the 'wikitext-103-raw-v1' configuration of the 'wikitext' dataset, you should use: \r\n  ```python\r\n  load_dataset('wikitext', 'wikitext-103-raw-v1')\r\n  ```\r\n\r\nIn your case, if you would like to load the 'wikitext-2-v1' configuration of the 'wikitext' dataset, please use:\r\n```python\r\nraw_datasets = load_dataset(\"wikitext\", \"wikitext-2-v1\")\r\n```","embeddings":[-0.0967338681,-0.2605879903,-0.0797852352,0.0848610178,0.248342663,0.3990706205,0.3288686574,0.1829105765,0.2315174788,0.2177287787,0.1168219671,0.2477994412,0.0077033122,0.1976594329,0.0933573917,-0.1330858022,0.0495663621,0.2156874686,0.2971088886,0.0440123118,-0.2111310512,0.2297869176,-0.2723999918,0.2842084169,-0.288128674,0.1674170494,-0.0682811439,0.1798944324,-0.1121602207,-0.4030483663,0.46938923,-0.0903508961,0.1952043623,0.3504015803,-0.0001096411,0.1461240947,0.3931308091,-0.1093517691,-0.3158326745,-0.3690313101,-0.4439369738,-0.376432687,0.219206661,-0.2139492929,-0.1764792651,-0.3019708991,0.2062354088,-0.2297460586,0.1217174307,0.1413179487,0.1870117188,0.1579394639,0.3519153893,-0.1046540216,-0.020349402,-0.1574950814,-0.0406100713,0.1109330729,0.1776449531,-0.1708909869,0.5759911537,0.0713046789,-0.0390349589,-0.0635157675,0.4338323474,0.12295378,0.0190565027,-0.418022424,0.2929027677,0.3656431437,0.6226111054,-0.2067046165,-0.3309411705,-0.2132035792,0.1552220881,-0.2214615792,0.3412881196,0.0913870782,-0.1872748584,0.1216237098,-0.2422992885,-0.2899409533,-0.0343743488,0.2559742928,-0.1588421464,0.5330593586,-0.0333521776,0.2572159171,-0.0448901206,-0.1157067195,0.1821154356,-0.2638184428,0.0199274104,0.2313269377,-0.4350584745,0.2946489751,-0.0778230652,-0.0644006953,-0.0448440537,-0.2138106972,-0.0089808563,-0.1653630883,0.0790497512,0.37736848,0.2167265862,0.0134134702,0.3524945974,0.1803928614,-0.1300219595,0.0226341933,0.0569218807,-0.2290414125,-0.3014462888,-0.3933193684,-0.1312492043,-0.0778773054,0.1285660118,-0.0695294738,-0.1767128259,-0.1538100243,-0.0302220099,0.0649976879,0.2150942087,0.2775916457,-0.1054002419,0.2821864784,0.0311108027,0.173089534,-0.0395783521,0.0599998049,-0.2499151826,0.0582094826,-0.1042519733,-0.0144238127,0.0354135856,-0.2822659016,0.4318057895,-0.016221324,0.1326906532,-0.0678477585,-0.0869918689,0.1205855533,-0.1974942386,0.372931093,0.1184865832,0.3420417905,0.2322843671,-0.4099903405,-0.2282729149,0.1049007475,-0.6848441362,-0.3727274537,-0.1056438237,0.2250058204,-0.1399939507,-0.1954797804,-0.3823266625,-0.022532884,0.110819988,-0.3745190799,-0.2001091391,-0.2051955014,-0.0919691101,-0.3380680084,0.4893407226,0.2005493194,-0.4506958425,-0.1715909988,-0.1755935401,-0.207870394,-0.1180201247,-0.0793776736,-0.4771016538,0.2959241271,-0.1663758904,0.0240165889,0.5427584052,-0.4997714162,-0.3670861125,-0.0388153605,0.0164756402,0.006510579,-0.0226851702,-0.0601873472,-0.198573187,-0.1382555664,0.1578788608,0.2091637254,0.0152790006,-0.0450913645,0.1309405267,-0.3848307729,0.1281730384,0.1044121534,-0.0062654051,-0.1929264367,-0.0387516841,-0.0808495656,0.3238803446,-0.1858879477,0.0030231301,0.2656099498,0.1519828439,0.1895815283,-0.0782619789,0.0168156754,-0.5132263303,0.5098343492,0.049432192,-0.2252335697,-0.0218973625,0.0614066385,-0.2356507033,0.0711350739,-0.3809774518,-0.1296530068,0.115592733,0.2986520827,0.021011062,-0.0146629848,-0.2925006151,0.1121136621,-0.0568627492,0.0773401037,-0.3017234504,0.2296636701,0.1323438138,0.0655929819,0.0426369607,0.0339101292,0.1366013885,0.0110238539,-0.1282252073,0.3610387444,0.0401118957,0.2575386763,0.041255258,-0.107247971,-0.0600844957,-0.1924877167,-0.0224139765,-0.0820748582,0.2843626142,-0.1188217998,-0.0187057462,0.0494139083,0.0446844213,0.4075766504,-0.0990610197,0.0411033519,0.1067151576,-0.0311274994,0.0445069075,-0.1102051586,0.0010922673,0.0453730337,0.0790856034,-0.0876222104,0.0758719519,-0.0595282391,0.3527704477,0.1528524011,-0.1356673539,-0.0422571301,-0.2403200567,0.063287206,-0.0674187392,0.1263209879,0.4199226797,0.1626887619,-0.2575629652,-0.2052465528,-0.0263274014,0.0217933785,0.3873999119,0.2675890625,0.409160018,0.0910493061,-0.0285145827,-0.0623926967,0.0397466011,-0.2164422721,0.2676001489,0.0978023335,-0.1957415342,0.1033532023,-0.3668818474,0.1999860406,-0.028099902,-0.4390399754,-0.2368553877,-0.4396343529,-0.1165380478,-0.1062572449,0.1159805357,0.1606515348,-0.2165023088,-0.0821672156,-0.0414665639,-0.3020502329,-0.0633363053,-0.3563521802,-0.2902115583,0.0356805809,0.1454374194,0.0693259612,0.2568148077,-0.3082197905,-0.0597087555,-0.0593358576,-0.1410859823,0.1215532422,-0.2163666487,0.3394034803,0.0783828348,0.3125860989,-0.063487187,-0.4796459377,0.519258678,-0.0121383313,-0.0762073025,-0.0260333773,0.125619173,-0.2077433914,0.1737444848,-0.2605305314,-0.4377222955,-0.2272718847,-0.158695519,-0.0587786138,0.0097740302,0.2992061377,0.2388386428,0.1309825331,-0.1197606698,0.0935206413,-0.0719077736,-0.4158431292,0.29374668,-0.2859342098,-0.2169199139,0.1834628731,0.2357595861,0.2410969287,0.0645949766,-0.3696052134,-0.1180433109,0.002099622,0.0961794555,0.0398277454,0.2944115996,0.1469348073,0.0885295346,0.0421755277,-0.2046012729,-0.2511930764,-0.0728481859,-0.0803249329,0.5748785734,0.1494718343,0.2559167445,-0.3147684932,0.6954649687,0.3260699809,0.0000622796,0.1809890568,-0.2448104024,0.2308385968,-0.2309320867,-0.509894371,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2972","title":"OSError: Not enough disk space.","comments":"Maybe we can change the disk space calculating API from `shutil.disk_usage` to  `os.statvfs` in UNIX-like system, which can provide correct results.\r\n```\r\nstatvfs = os.statvfs('path')\r\navail_space_bytes = statvfs.f_frsize * statvfs.f_bavail\r\n```","body":"## Describe the bug\r\nI'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `\/` disk having no enough space. \r\n\r\nThe file system structure is like below. The root `\/` has `115G` disk space available, and the `sda1` is mounted to `\/mnt`, which has `1.2T` disk space available:\r\n```\r\n\/\r\n\/mnt\/sda1\/path\/to\/args.dataset_cache_dir \r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset_config = DownloadConfig(\r\n    cache_dir=os.path.abspath(args.dataset_cache_dir),\r\n    resume_download=True,\r\n)\r\ndataset = load_dataset(\"natural_questions\", download_config=dataset_config)\r\n```\r\n\r\n## Expected results\r\n\r\nCan download the dataset without an error.\r\n\r\n## Actual results\r\n\r\nThe following error raised:\r\n```\r\nOSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Ubuntu 18.04\r\n- Python version: 3.8.10\r\n- PyArrow version:\r\n","comment_length":31,"text":"OSError: Not enough disk space. \n ## Describe the bug\r\nI'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `\/` disk having no enough space. \r\n\r\nThe file system structure is like below. The root `\/` has `115G` disk space available, and the `sda1` is mounted to `\/mnt`, which has `1.2T` disk space available:\r\n```\r\n\/\r\n\/mnt\/sda1\/path\/to\/args.dataset_cache_dir \r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset_config = DownloadConfig(\r\n    cache_dir=os.path.abspath(args.dataset_cache_dir),\r\n    resume_download=True,\r\n)\r\ndataset = load_dataset(\"natural_questions\", download_config=dataset_config)\r\n```\r\n\r\n## Expected results\r\n\r\nCan download the dataset without an error.\r\n\r\n## Actual results\r\n\r\nThe following error raised:\r\n```\r\nOSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Ubuntu 18.04\r\n- Python version: 3.8.10\r\n- PyArrow version:\r\n \n Maybe we can change the disk space calculating API from `shutil.disk_usage` to  `os.statvfs` in UNIX-like system, which can provide correct results.\r\n```\r\nstatvfs = os.statvfs('path')\r\navail_space_bytes = statvfs.f_frsize * statvfs.f_bavail\r\n```","embeddings":[-0.0483117625,-0.2650125027,-0.1014163494,0.4828662276,0.0775186792,0.125428319,-0.2298763394,0.451880306,0.306951344,0.2453740388,0.0781501904,-0.3200185001,-0.0492318608,-0.3311833143,0.0627448186,0.1433423012,0.044361122,-0.0126373898,0.2707397342,0.0378632024,-0.2826903462,0.2443154156,-0.2258920819,-0.3227071762,-0.2242964655,0.1010245904,-0.2902759612,0.2154649645,-0.0411231071,-0.3155759275,0.4027944207,-0.0841757283,0.0840346888,0.693170011,-0.0001227214,-0.1555825472,0.0862489939,-0.1632558852,-0.5597650409,-0.2008342594,0.1888247281,-0.6115403175,-0.075049907,-0.3282054067,0.0743473247,-0.0160431396,0.0657260194,-0.4729304314,0.0375218466,0.2735341787,0.1334287375,-0.0294978619,0.1958460808,0.0285463873,0.1346616298,0.2381906658,0.0663541034,0.6081162095,0.2268736809,0.1576585323,0.1857670695,0.1266636997,0.0412560403,0.2906077206,0.3219506741,0.1340879947,0.0225306209,-0.252055198,0.1095144525,0.0155193359,0.5439798832,-0.3082551658,-0.3940526545,-0.4138250053,0.0333954394,-0.2799567878,0.3140417933,0.2301519513,-0.5507558584,0.0223602708,-0.1646193862,-0.3364186883,-0.0527600832,0.2172588855,-0.0387702025,0.1343382895,-0.1687610745,0.115997389,0.2244087756,0.0765300021,0.1890666783,-0.296551019,0.1879142672,0.1297443211,-0.4401780367,-0.1109682024,-0.0168489274,0.140935272,0.1592553109,0.0533146448,-0.2479382902,-0.2559824884,-0.2616776228,-0.0811362192,0.1745799929,0.4847319424,-0.306012392,0.1869135946,0.0049681813,0.1419613212,0.0260269307,-0.2461785823,0.1455345154,-0.308396548,0.2213537693,-0.2205417603,0.2423892319,-0.1568432897,-0.1978182346,0.0816144645,-0.056985002,0.0895368233,0.1581038833,0.2711423337,-0.1124085635,0.2333161831,0.3339409828,0.3943675756,-0.0816277564,0.1422207803,-0.1094974354,0.0753834322,0.0055848993,-0.0501602255,0.1430165917,0.0099912314,0.1418014616,0.0294670444,0.1587927639,-0.246119678,-0.0672238171,-0.4152612388,0.0279095434,0.3896477818,0.1615676135,0.1737033576,0.186954543,0.1475630403,-0.1396175325,0.4222061932,-0.3708596528,-0.2254736125,-0.144778356,0.0746017173,-0.3282575607,-0.0970010385,-0.3211320043,-0.2398508042,0.3386922479,-0.2368621826,0.1199311316,-0.0682271421,-0.224448204,-0.1532811373,0.19493635,0.4025303125,-0.0504233427,0.224533841,-0.4339043796,0.2981555462,0.1873593926,0.3178592622,0.0989431813,0.3977374434,-0.6145943403,0.2145382613,0.2425823212,-0.3106400073,-0.6953368783,0.2175887972,-0.0547902621,0.0636579022,0.0832141042,0.2463435978,-0.1071875617,-0.0109767215,-0.0690358058,0.1144295186,-0.0609783828,-0.0638776273,-0.4297259748,-0.1411557049,0.1125511527,0.1543643624,0.2233882248,-0.0437205434,0.0127734747,0.0299706552,0.3276563287,0.204854086,0.2048880607,0.3353894949,0.4992460608,-0.0654498339,-0.0429810397,-0.2636959851,-0.3488045931,0.3205153644,0.1313492656,-0.0754328966,-0.2659978271,0.0885727257,-0.3640156686,0.0743718445,0.0784490108,-0.0363996029,-0.024008492,0.1717920899,0.1351293027,-0.1531063318,-0.0828776136,0.1165560409,-0.1822514534,0.0991578549,-0.0847870708,0.3593726456,-0.1360959411,-0.0268859509,-0.2466160357,-0.0420186073,0.1175989807,0.0447950885,-0.2857835293,0.4597989917,0.0980816633,-0.2837499082,0.1816985905,-0.0013553012,0.2244534492,-0.0308725517,0.1531259418,0.2797998786,0.1640133113,0.1522729993,-0.2192896754,-0.2408362031,0.0114156501,0.0912391096,-0.2266647816,0.0725638792,0.3409914672,-0.069984369,0.3898643255,0.011901333,0.2974434793,-0.1528408527,0.3281003237,-0.1778043658,0.4156008661,-0.002571763,0.1722847968,0.0111423247,0.2959091067,0.4236969948,0.0563095771,-0.2051933557,-0.1029914469,0.3139263988,0.6357459426,0.1418750584,0.1012347341,-0.1571074277,0.3621191084,-0.1422180831,0.2960793376,-0.028650796,-0.1725246757,0.2115655094,-0.1189480498,-0.0454106443,0.0218234602,-0.4014765918,0.1147883609,0.3954373896,-0.2941667736,-0.2452555299,-0.0902295113,0.0754166692,0.0976644158,-0.0877977908,-0.1713177413,-0.2449693233,-0.0628587082,0.3013705909,-0.0706708282,-0.1989257634,-0.2204564214,0.0996796712,0.251950264,-0.4279265702,-0.0057244808,0.202740103,-0.0869457126,-0.0579262525,0.1862246692,-0.2343982607,0.206414327,-0.2179809064,0.0828104913,-0.3807497323,-0.0036385444,-0.0544189177,0.1421558261,0.2551337183,0.0285257716,0.4676262438,-0.0978369638,0.1490528882,0.0095403427,-0.0499292314,-0.1611362696,0.0478008986,0.2276856899,-0.0234736912,-0.3540166616,-0.164182514,-0.3538464606,-0.4644952416,-0.0699181408,-0.0544025972,0.2067489922,0.1965935081,0.2587899566,-0.0422573015,0.1219059229,0.0217380561,-0.2371945679,-0.4583791792,0.1267108917,0.1998536289,-0.0379579589,0.1002714485,-0.0330554396,-0.0869313404,0.2507907152,-0.7376928329,-0.2579144537,-0.2682241499,0.5005323887,-0.1573341787,0.1762371808,0.408649981,-0.4275276363,0.0464052111,-0.0902253538,-0.237482667,-0.2078114599,0.2359808981,0.4737018049,0.0763955116,0.1733407974,0.0804072097,0.4434355497,-0.0436965711,0.3275301456,0.5047436953,0.0264634658,0.6254559159,-0.0680015907,-0.0800560117,0.1004764065,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2972","title":"OSError: Not enough disk space.","comments":"`DownloadConfig` only sets the location to download the files. On the other hand, `cache_dir` sets the location for both downloading and caching the data. You can find more information here: https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#cache-directory","body":"## Describe the bug\r\nI'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `\/` disk having no enough space. \r\n\r\nThe file system structure is like below. The root `\/` has `115G` disk space available, and the `sda1` is mounted to `\/mnt`, which has `1.2T` disk space available:\r\n```\r\n\/\r\n\/mnt\/sda1\/path\/to\/args.dataset_cache_dir \r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset_config = DownloadConfig(\r\n    cache_dir=os.path.abspath(args.dataset_cache_dir),\r\n    resume_download=True,\r\n)\r\ndataset = load_dataset(\"natural_questions\", download_config=dataset_config)\r\n```\r\n\r\n## Expected results\r\n\r\nCan download the dataset without an error.\r\n\r\n## Actual results\r\n\r\nThe following error raised:\r\n```\r\nOSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Ubuntu 18.04\r\n- Python version: 3.8.10\r\n- PyArrow version:\r\n","comment_length":31,"text":"OSError: Not enough disk space. \n ## Describe the bug\r\nI'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `\/` disk having no enough space. \r\n\r\nThe file system structure is like below. The root `\/` has `115G` disk space available, and the `sda1` is mounted to `\/mnt`, which has `1.2T` disk space available:\r\n```\r\n\/\r\n\/mnt\/sda1\/path\/to\/args.dataset_cache_dir \r\n```\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset_config = DownloadConfig(\r\n    cache_dir=os.path.abspath(args.dataset_cache_dir),\r\n    resume_download=True,\r\n)\r\ndataset = load_dataset(\"natural_questions\", download_config=dataset_config)\r\n```\r\n\r\n## Expected results\r\n\r\nCan download the dataset without an error.\r\n\r\n## Actual results\r\n\r\nThe following error raised:\r\n```\r\nOSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Ubuntu 18.04\r\n- Python version: 3.8.10\r\n- PyArrow version:\r\n \n `DownloadConfig` only sets the location to download the files. On the other hand, `cache_dir` sets the location for both downloading and caching the data. You can find more information here: https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#cache-directory","embeddings":[-0.0635233447,-0.2830458581,-0.0885159895,0.4970606267,0.0879720077,0.1864337772,-0.1798836142,0.4213719666,0.2735773325,0.2332109213,0.0468635932,-0.3344708383,-0.0403603427,-0.3025632203,0.0909605622,0.0889558047,0.0419809744,0.0356531776,0.2770933509,0.0795286521,-0.2876849771,0.2624314427,-0.2058112919,-0.2824216187,-0.2445370108,0.087432757,-0.2864069939,0.2500259578,-0.047800798,-0.3173517883,0.4837854803,-0.1142797619,0.1130313054,0.6810320616,-0.0001228752,-0.1106924564,0.1300180852,-0.1663176119,-0.5987583995,-0.1993252337,0.0672086105,-0.5795090199,-0.0562238246,-0.2680239677,0.0665681586,0.0624798015,0.0890348256,-0.4190043509,0.0600294471,0.2604207098,0.1240676492,0.070566237,0.1978161335,0.0219345577,0.1323047429,0.2642109692,0.0178821292,0.5894095302,0.2349644005,0.1923785061,0.1750001609,0.1309752017,0.0138330944,0.3019683957,0.3325861096,0.1170020923,0.0367041342,-0.2519949377,0.1177152172,0.0224530455,0.5822769403,-0.3558640778,-0.4414693713,-0.4437248409,0.0059300801,-0.2740187347,0.3592785299,0.2121209353,-0.4913748205,0.0718674585,-0.2001358569,-0.3971711993,-0.0525568649,0.2504213452,-0.0873224437,0.1087133437,-0.1914394498,0.0910865813,0.2366619855,0.1039999425,0.2103602439,-0.2841476202,0.1797291785,0.128219679,-0.4327701032,-0.0506381877,-0.0222896803,0.2459597886,0.1734153777,0.0829613507,-0.1922983229,-0.2317530662,-0.2255528271,-0.0701190159,0.1975115538,0.5353708267,-0.2956285477,0.1536401957,-0.0018689308,0.1543458402,0.0369914025,-0.2411973327,0.1252182722,-0.2464449406,0.2199292481,-0.2739791572,0.2896855474,-0.125759542,-0.2084155232,0.0423556231,-0.0318695232,0.0508132279,0.1519080848,0.3132840693,-0.0876725912,0.2727206051,0.2921749353,0.454521805,-0.0948524028,0.1252304763,-0.1412675828,0.0484295003,-0.0014129804,0.0523086525,0.1622110754,-0.0374780782,0.1862364411,0.0396869369,0.2253507823,-0.1993430406,-0.0906973928,-0.3640328348,-0.0105018429,0.4011726379,0.1225024238,0.2600064576,0.1426282972,0.0873305798,-0.1323470473,0.3872962296,-0.369161129,-0.2355871201,-0.1481734663,0.0575596429,-0.3133731782,-0.0706200525,-0.3859627545,-0.2532059848,0.4336040914,-0.2351290137,0.1642589271,-0.0450017825,-0.2203207463,-0.1682055742,0.1917436421,0.4826413691,-0.0405564085,0.1455830783,-0.3912421763,0.3041979671,0.1395224035,0.3218920529,0.0274894685,0.4158928096,-0.6230331659,0.1619236767,0.2286601514,-0.3694984317,-0.729906857,0.2008679211,-0.0774621218,0.1158250347,0.1044472009,0.188143149,-0.1545188874,0.0188357886,-0.0776496157,0.1156541705,-0.0520383231,-0.044588957,-0.4045834839,-0.1671212912,0.0754799172,0.1557203233,0.1597132385,-0.0104549937,-0.082747139,0.0370147824,0.3118736446,0.1792691052,0.2168410569,0.3594471812,0.3891606629,-0.0374321379,-0.0842315704,-0.1996255368,-0.4655118585,0.3553546369,0.1088182479,-0.0678451806,-0.2929954231,0.1053464115,-0.3854989409,0.0936517566,0.0117403241,-0.0657352209,-0.0498357974,0.2212378085,0.2067638934,-0.1031758487,-0.115308255,0.2549064755,-0.1413604021,0.1209999844,-0.1966580302,0.3492388129,-0.1703708321,-0.0319430195,-0.2959093451,-0.0974916816,0.0660771877,-0.009697035,-0.2376769334,0.44501701,0.0570794158,-0.2732319534,0.0839802548,0.0521385968,0.245448336,-0.0485376604,0.1506576836,0.2443732917,0.1474779248,0.1198615655,-0.18351762,-0.2291077524,0.0583010204,0.1187079847,-0.2436077297,0.0265198164,0.2993576825,-0.0655696243,0.3339160383,0.0173476785,0.2688305974,-0.1857594103,0.3863316476,-0.1270760745,0.3761842549,0.0269678682,0.1899333596,0.0365135707,0.2688363791,0.3934065402,0.0576955304,-0.2031284273,-0.1123779044,0.354052037,0.6171280742,0.1232227683,0.1110493541,-0.1440573484,0.3499374092,-0.1438457668,0.2074887007,-0.012414082,-0.1729935408,0.164728567,-0.0900972784,-0.038208738,0.0509872325,-0.3579010069,0.1176405698,0.3616828918,-0.3154420257,-0.1312954426,-0.0672084391,0.0020317216,0.0773874074,-0.067679435,-0.2015193403,-0.2130254209,-0.082363449,0.334278971,-0.0011189883,-0.181077674,-0.2875178158,0.1274569929,0.2103916854,-0.4946582317,-0.0752126873,0.2515457869,-0.125142917,-0.0790992603,0.2197958976,-0.2577578127,0.148080498,-0.2777003646,0.1049135178,-0.3337102234,0.0704019591,-0.0702826604,0.1774657965,0.231453523,0.0021069434,0.5112779737,-0.1617596149,0.0734581128,0.0698858202,-0.0246067457,-0.1811518669,0.0166409798,0.2682532966,0.0327466652,-0.2949332893,-0.1357983053,-0.3509215117,-0.4543667436,-0.0091634952,-0.1419839263,0.183249101,0.2186906636,0.2438648939,-0.0441508144,0.0373043381,0.0277107451,-0.231631279,-0.5477306843,0.1127147824,0.1870409995,-0.03105467,0.0768924728,-0.0367420204,0.010541168,0.2669086158,-0.7017439604,-0.2344659865,-0.311104089,0.5713159442,-0.176007092,0.1478950977,0.4506879747,-0.376293391,0.0594140999,-0.0761811659,-0.2797216475,-0.2243181169,0.2443921864,0.4906610548,0.0558765866,0.1475636363,-0.0039278627,0.495521456,-0.0667166859,0.2920241356,0.548112452,0.0429993235,0.6437495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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2969","title":"medical-dialog error","comments":"Hi @smeyerhot, thanks for reporting.\r\n\r\nYou are right: there is an issue with the dataset metadata. I'm fixing it.\r\n\r\nIn the meantime, you can circumvent the issue by passing `ignore_verifications=True`:\r\n```python\r\nraw_datasets = load_dataset(\"medical_dialog\", \"en\", split=\"train\", download_mode=\"force_redownload\", data_dir=\".\/Medical-Dialogue-Dataset-English\", ignore_verifications=True)\r\n```","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\nWhen I attempt to download the huggingface datatset medical_dialog it errors out midway through\r\n## Steps to reproduce the bug\r\n```python\r\nraw_datasets = load_dataset(\"medical_dialog\", \"en\", split=\"train\", download_mode=\"force_redownload\", data_dir=\".\/Medical-Dialogue-Dataset-English\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\nNo error\r\n## Actual results\r\n\r\n```\r\n3 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/info_utils.py in verify_splits(expected_splits, recorded_splits)\r\n     72     ]\r\n     73     if len(bad_splits) > 0:\r\n---> 74         raise NonMatchingSplitsSizesError(str(bad_splits))\r\n     75     logger.info(\"All the splits matched successfully.\")\r\n     76 \r\n\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=295097913, num_examples=229674, dataset_name='medical_dialog')}]\r\n```\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.21.1\r\n- Platform: colab\r\n- Python version: colab 3.7\r\n- PyArrow version: N\/A\r\n","comment_length":40,"text":"medical-dialog error \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\nWhen I attempt to download the huggingface datatset medical_dialog it errors out midway through\r\n## Steps to reproduce the bug\r\n```python\r\nraw_datasets = load_dataset(\"medical_dialog\", \"en\", split=\"train\", download_mode=\"force_redownload\", data_dir=\".\/Medical-Dialogue-Dataset-English\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\nNo error\r\n## Actual results\r\n\r\n```\r\n3 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/info_utils.py in verify_splits(expected_splits, recorded_splits)\r\n     72     ]\r\n     73     if len(bad_splits) > 0:\r\n---> 74         raise NonMatchingSplitsSizesError(str(bad_splits))\r\n     75     logger.info(\"All the splits matched successfully.\")\r\n     76 \r\n\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='medical_dialog'), 'recorded': SplitInfo(name='train', num_bytes=295097913, num_examples=229674, dataset_name='medical_dialog')}]\r\n```\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.21.1\r\n- Platform: colab\r\n- Python version: colab 3.7\r\n- PyArrow version: N\/A\r\n \n Hi @smeyerhot, thanks for reporting.\r\n\r\nYou are right: there is an issue with the dataset metadata. I'm fixing it.\r\n\r\nIn the meantime, you can circumvent the issue by passing `ignore_verifications=True`:\r\n```python\r\nraw_datasets = load_dataset(\"medical_dialog\", \"en\", split=\"train\", download_mode=\"force_redownload\", data_dir=\".\/Medical-Dialogue-Dataset-English\", ignore_verifications=True)\r\n```","embeddings":[-0.1769695729,-0.1679425687,0.1324310005,0.3051021993,0.0987705961,0.1788934916,0.2574727237,0.3572397232,-0.1842424721,0.1025890782,-0.2651023865,0.1361616552,-0.0293867178,0.4398861229,-0.1116999313,-0.1225640699,-0.1173762754,0.128063187,-0.0444529653,0.0845177174,-0.2706306875,0.17604357,-0.4015025496,0.1251937002,-0.260456562,-0.1920609325,-0.0500055514,0.1964506954,-0.187369734,-0.3622837365,0.0246013328,-0.1420357376,-0.1834929734,0.5016120672,-0.0001205272,-0.1494741142,0.4438034892,-0.0312318467,-0.7869769335,-0.29640764,-0.2228539586,0.0233006682,0.165361464,-0.0670552924,0.0577693991,0.1188884154,-0.0292042531,-0.1568406075,0.3194740713,0.4523730576,0.1425131112,0.36996153,-0.0349191576,-0.0790767744,0.0967894047,0.1101072878,-0.1509435624,0.3499753177,0.2005819976,0.0454699993,0.3381055295,0.3952894509,-0.0747318938,0.0068175867,-0.064802058,0.1322317123,0.0341860056,-0.3173151612,0.1479791403,0.5003923178,0.4976690412,-0.3424368799,-0.3184413314,-0.4580037296,0.0061690123,-0.3762101829,0.3186460137,0.2919501364,-0.2969644368,0.1543126255,-0.1780059487,-0.180392921,0.0048961868,0.0972719118,-0.1185565442,0.062697351,-0.073078014,0.1329293102,0.2000085711,0.0486144088,0.1498819739,0.0358141214,-0.0136758303,0.1708940268,-0.2461961508,-0.0449028313,-0.1836041808,0.5213916302,0.3509040773,-0.0532025807,-0.1872241795,-0.1476790607,-0.2851104438,0.0450585335,0.4613186419,-0.1036597937,0.100552395,0.2130735964,0.1542563438,0.3851316571,0.1553562135,0.0154383434,0.1469233185,-0.1992664039,0.0606076717,-0.0282273553,0.28639099,-0.2664484084,-0.3326632977,0.1081310436,-0.3204188943,0.2324723452,0.0700191334,0.107919015,-0.3211482465,-0.0591365583,0.1940511167,-0.1348733604,-0.2768239677,-0.2596821487,-0.1074372008,0.2144895047,-0.2531350255,0.179311201,0.0558745265,-0.2652102113,0.1660884023,0.005608324,0.3041837215,-0.0926884413,-0.2417583615,-0.2114887238,-0.1477476656,0.3744513988,0.0002233915,0.3184859753,-0.0221835226,-0.3037138879,-0.0763860345,0.0821065307,-0.2018779665,-0.2816458941,-0.2584341168,0.1209683046,-0.3434649706,0.1432389319,-0.3991204202,-0.0316096731,0.1611027271,-0.2906996608,0.0893816873,-0.2029214501,-0.2834694386,-0.112754643,0.1212018505,0.7058104277,-0.276930958,-0.0607606173,-0.2715162635,-0.1081986278,0.114710696,0.3042283356,-0.1753933132,0.1213620752,-0.4867250025,0.368455708,-0.0184237752,-0.4604404569,-0.5586315989,0.0751289502,-0.1979199201,0.2133274078,-0.0389808826,-0.126893729,0.1592346281,-0.2046138644,0.1519342959,0.3224082887,-0.0467534475,0.0877224207,-0.3800099492,-0.3064818978,0.0242313128,0.1133447886,0.1342862695,-0.0102615738,-0.0383153148,0.2593236864,0.5065190196,0.1001932323,-0.0156097887,0.0680442154,0.2086708546,0.114063032,0.0377330221,-0.4227130413,-0.7257651091,0.1071037278,0.2285232693,0.1055045277,-0.4268517196,-0.2155922651,-0.3992097974,0.0458045565,-0.1664608568,-0.0064150449,0.0437591411,0.2123741955,-0.0414033607,0.256991744,-0.0902733132,0.7574388385,0.0882175192,0.4163082242,-0.3237505555,0.341327697,-0.0027731375,-0.1668810844,0.0973345265,-0.0836585015,0.2162520289,-0.0705511495,-0.1997118294,0.5262144208,0.2810688317,0.2378268838,-0.3514769375,-0.0367622115,0.4658617377,-0.2215694189,-0.1697006971,-0.1935651153,0.2047336996,0.0692469105,-0.060829509,-0.1499872208,-0.2671663165,0.1102423519,0.1957953572,0.1823008806,-0.0363292769,-0.2097198516,-0.2261216938,-0.2612205446,0.405755192,-0.0124695031,0.096864216,0.0318366475,-0.3492918313,0.2115348428,0.5548493266,-0.1745636314,0.0934518799,0.049600672,-0.135145098,0.0473821834,-0.0690171868,0.0292057637,0.3376858234,0.1313350797,0.016015159,0.3664110005,-0.0385731719,-0.1196624488,0.1683080494,0.1869595945,0.1598453671,0.2509024143,-0.0641339645,0.1346060038,-0.2410595715,-0.048917722,0.0530161336,0.2455534637,-0.5014851689,0.0130829029,-0.4143124223,-0.1430115104,-0.305663228,-0.5674601197,-0.1427650899,-0.2658532262,0.0028019985,0.2338791639,-0.0110072,0.1109669954,0.0586551018,0.1400514394,-0.0108723436,-0.0558104813,-0.0067467056,-0.0825364143,-0.1926916689,-0.0512556173,0.2244787812,0.1345146149,0.3748652935,-0.3790003955,-0.262911737,0.0062633338,-0.1930830479,0.3398481607,-0.0909417495,0.3690991104,0.2176611274,0.3530425727,-0.0265271217,-0.0465140678,0.0713956505,0.0743730962,-0.134606272,-0.1390089244,0.2276300043,0.1288413554,-0.1968261302,-0.2306727171,-0.2243405879,-0.3627335429,0.1929699183,-0.3550292253,0.1105440482,0.6773463488,-0.0102068586,0.2450000942,-0.1639090478,0.1868559867,-0.2450705469,0.0795373768,0.1418534517,-0.0248325448,-0.1745527089,-0.1229750365,-0.0805803463,-0.0522321984,0.1095211282,-0.5826975107,-0.3080058098,-0.2354388088,0.0074047274,-0.1070072651,-0.0515541472,0.4485073686,-0.0904415473,0.0394975618,-0.1898174137,-0.2948574424,-0.0597758666,-0.0146797011,0.3497594595,-0.0930357948,0.3620761335,0.1532794237,0.4296871722,0.1892595589,0.0353851989,0.0528169535,-0.1689925492,0.3412167132,-0.0295794383,-0.4730042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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2968","title":"`DatasetDict` cannot be exported to parquet if the splits have different features","comments":"This is because you have to specify which split corresponds to what file:\r\n```python\r\ndata_files = {\"train\": \"train\/split.parquet\", \"validation\": \"validation\/split.parquet\"}\r\nbrand_new_dataset_2 = load_dataset(\"ds\", data_files=data_files)\r\n```\r\n\r\nOtherwise it tries to concatenate the two splits, and it fails because they don't have the same features.\r\n\r\nIt works with save_to_disk\/load_from_disk because it also stores json files that contain the information about which files goes into which split","body":"## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n","comment_length":64,"text":"`DatasetDict` cannot be exported to parquet if the splits have different features \n ## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n \n This is because you have to specify which split corresponds to what file:\r\n```python\r\ndata_files = {\"train\": \"train\/split.parquet\", \"validation\": \"validation\/split.parquet\"}\r\nbrand_new_dataset_2 = load_dataset(\"ds\", data_files=data_files)\r\n```\r\n\r\nOtherwise it tries to concatenate the two splits, and it fails because they don't have the same features.\r\n\r\nIt works with save_to_disk\/load_from_disk because it also stores json files that contain the information about which files goes into which 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2968","title":"`DatasetDict` cannot be exported to parquet if the splits have different features","comments":"I may be mistaken but I think the following doesn't work either:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\ndata_files = {\"train\": \"train\/split.parquet\", \"validation\": \"validation\/split.parquet\"}\r\nbrand_new_dataset_2 = load_dataset(\"ds\", data_files=data_files)\r\n```","body":"## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n","comment_length":45,"text":"`DatasetDict` cannot be exported to parquet if the splits have different features \n ## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n \n I may be mistaken but I think the following doesn't work either:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\ndata_files = {\"train\": \"train\/split.parquet\", \"validation\": \"validation\/split.parquet\"}\r\nbrand_new_dataset_2 = load_dataset(\"ds\", data_files=data_files)\r\n```","embeddings":[-0.1533447504,-0.0085505815,0.0541425422,0.4590764344,0.0908742622,0.0531120785,0.2564251125,0.204687953,0.1695544571,0.0804405808,0.2533523738,0.3629656136,-0.1307234466,0.4581688046,-0.1649915427,-0.2605801821,0.1808207929,-0.0397759825,0.1971472502,0.0392243527,-0.1355071962,0.2122626156,0.094932884,-0.0579855405,0.0764198601,0.0857248604,-0.1721858829,0.0313951448,-0.0407176763,-0.4404964447,0.1308065057,0.1139079481,0.0330194607,0.0638304576,-0.0001209551,0.126368016,0.1089218706,-0.1468749344,-0.1553466767,-0.2253277302,0.0722789466,-0.3276149631,0.1342098266,-0.2398258895,-0.072140716,-0.0230652466,-0.1887051612,-0.3661788404,0.0575958677,0.0872431472,0.0884058252,0.2435974628,0.2534649372,-0.1587028056,0.3093519807,0.2460288703,-0.3525647819,-0.0957963541,0.2942000926,-0.0267965253,0.2372006029,-0.0125332521,-0.0131008094,-0.1866216809,-0.0100193704,0.0357764922,0.2016760111,-0.0472216308,-0.0048582056,0.1708362401,0.3730287552,-0.3078299463,-0.2139721662,-0.4796130359,-0.0043476122,-0.4512827396,0.1248775795,0.2219220996,0.3061760366,0.0717384964,0.2438835949,-0.3051103055,-0.019504955,-0.1060554013,-0.2055142373,-0.036159467,0.0323047489,0.2756302953,-0.2607961595,-0.1307297647,-0.1952963322,-0.0395402387,-0.1048606709,-0.0872063041,-0.288130343,-0.1636976451,0.021831993,-0.5100026727,0.0840099603,0.0521086231,0.1298688799,-0.1045378894,0.210742712,0.0668166354,0.6997101307,-0.2117575556,0.0629034638,0.3308934867,-0.2021674514,0.3535165191,-0.2645005882,-0.0922604725,0.21472691,0.0130990278,0.0660821944,-0.061799854,0.7261018753,-0.2778936028,-0.2827643752,0.314060539,-0.0690315589,-0.0184658691,-0.0227580052,0.1725393236,0.274394244,0.1146718115,-0.178026706,0.5176942348,-0.1419944912,-0.3822630942,-0.1794261932,0.0076865726,-0.0897204652,0.2087680399,-0.0494074076,0.1495054066,-0.2162174582,0.4734737575,-0.3237044215,-0.1224931329,-0.0673533753,-0.3026716113,-0.1163356751,-0.1683811843,0.0091825156,0.1893606186,0.2657749057,-0.2059383243,-0.3632978499,-0.1637562364,-0.4159124196,-0.1169884726,-0.066379413,0.1262874901,-0.1668713093,0.0606247336,-0.3843916655,0.4787818193,0.1406238526,-0.358817637,-0.0864635631,-0.1940063089,-0.2968921065,-0.2229759693,0.0135747455,0.4260392785,-0.3764185905,0.0151709579,0.2066549659,-0.2078363448,0.2506180704,0.3543793559,-0.2833498418,0.1510397494,-0.2686647475,0.0858026147,0.361854583,-0.0420141146,-0.1179696023,0.1215711087,-0.3412570059,0.3513429165,0.2501019537,-0.2739672363,0.7012817264,-0.0806037635,-0.102758199,0.2733205855,-0.1434348226,-0.158481732,-0.3888079822,-0.1002107933,0.4952836931,-0.0738501921,0.0348834246,-0.1343848705,0.0709742755,0.394562453,0.1850542575,-0.1021511033,0.0324044004,0.0347256735,0.1851088405,0.4772476852,-0.0399264991,-0.3850698173,-0.4081051052,0.3002918363,-0.1372296214,-0.1115630642,-0.0120542087,-0.010708537,-0.0465532206,0.0372191891,-0.1143149287,-0.0311569143,0.0697760805,0.1805256605,-0.040180698,0.1399053782,-0.1801367998,0.1831670552,-0.0679245442,0.0580777973,-0.365580529,0.5434340835,0.059666682,-0.4079326093,0.0663541257,0.1222490817,0.3252889514,-0.231349647,0.0326350704,0.2040225863,0.3464330435,0.1921724081,-0.2224576324,-0.1261529326,0.0411716998,-0.1582911462,-0.0217442624,0.2696456015,0.1211625561,-0.0049493723,-0.3899843991,0.3378594816,0.0666849688,0.302473098,0.2049649358,-0.0344837904,0.1528332978,-0.0858370215,-0.1044744328,-0.2849783003,-0.040402405,-0.1805185527,-0.2627166808,-0.0407801196,-0.4981930554,0.1941738874,0.4554045498,0.1275276542,0.1528485715,-0.2623436153,0.2779436111,-0.109375447,-0.0063420981,0.1921729892,0.4044851363,0.0994521305,0.1481258571,0.0026019081,-0.1942891777,0.06037407,0.1571056098,0.0571372956,0.0014193667,0.4877780974,0.2879687548,0.1425783038,-0.2236469388,-0.0820934251,-0.0090236347,-0.1301547587,-0.3222863078,0.1376560181,-0.3830343187,0.3732868135,-0.6004145145,0.1781218499,-0.2033672482,-0.2806482911,0.0596009046,0.3162017465,-0.375900358,-0.0766379461,-0.2617491186,-0.0673811585,-0.2911250591,-0.3365999162,0.2566405237,-0.1513856351,-0.0714571998,-0.0811375529,0.0521242768,0.303072989,0.0942187607,0.027974939,-0.1763356179,-0.5506234169,-0.1967489123,0.2618514001,-0.1779257804,-0.0185542908,0.1727975011,-0.1009669006,0.2337462008,-0.4144860804,0.4925787151,-0.0358078331,-0.0066017727,0.2643794417,0.0900097191,-0.0831367522,0.1307204366,-0.31131199,-0.2345553637,-0.2256342471,0.1592255831,-0.0407043025,0.1187285334,-0.3200721443,-0.0507357568,-0.142388925,0.0648478419,0.023819102,-0.100065358,0.0829668939,0.0487475954,-0.0658936501,-0.2630805671,0.04452594,0.218389973,-0.0337153822,0.2957593203,-0.2080755085,0.3681337237,-0.081969209,0.0343025886,-0.1908912957,-0.1358336657,0.319472909,0.2093870938,0.0743385106,-0.1095198095,-0.2680376768,0.0924855918,0.2580272853,0.1739187986,-0.3795464635,0.471278578,-0.0465764143,0.6406767964,0.0505417064,0.22840859,0.6799256206,0.0201883391,0.2697415948,-0.1240961999,-0.310335964,0.137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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2968","title":"`DatasetDict` cannot be exported to parquet if the splits have different features","comments":"It works on my side as soon as the directories named `ds\/train` and `ds\/validation` exist (otherwise it returns a FileNotFoundError). What error are you getting ?","body":"## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n","comment_length":26,"text":"`DatasetDict` cannot be exported to parquet if the splits have different features \n ## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n \n It works on my side as soon as the directories named `ds\/train` and `ds\/validation` exist (otherwise it returns a FileNotFoundError). What error are you getting 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2968","title":"`DatasetDict` cannot be exported to parquet if the splits have different features","comments":"Also we may introduce a default mapping for the data files:\r\n```python\r\n{\r\n    \"train\": [\"*train*\"],\r\n    \"test\": [\"*test*\"],\r\n    \"validation\": [\"*dev*\", \"valid\"],\r\n}\r\n```\r\nthis way if you name your files according to the splits you won't have to specify the data_files parameter. What do you think ?\r\n\r\nI moved this discussion to #3027 ","body":"## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n","comment_length":52,"text":"`DatasetDict` cannot be exported to parquet if the splits have different features \n ## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n \n Also we may introduce a default mapping for the data files:\r\n```python\r\n{\r\n    \"train\": [\"*train*\"],\r\n    \"test\": [\"*test*\"],\r\n    \"validation\": [\"*dev*\", \"valid\"],\r\n}\r\n```\r\nthis way if you name your files according to the splits you won't have to specify the data_files parameter. What do you think ?\r\n\r\nI moved this discussion to #3027 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2968","title":"`DatasetDict` cannot be exported to parquet if the splits have different features","comments":"I'm getting the following error:\r\n\r\n```\r\nDownloading and preparing dataset custom_squad\/plain_text to \/home\/lysandre\/.cache\/huggingface\/datasets\/lhoestq___custom_squad)\/plain_text\/1.0.0\/397916d1ae99584877e0fb4f5b8b6f01e66fcbbeff4d178afb30c933a8d0d93a...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 7760.04it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 2020.38it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"<input>\", line 1, in <module>\r\n  File \"\/opt\/pycharm-professional\/plugins\/python\/helpers\/pydev\/_pydev_bundle\/pydev_umd.py\", line 198, in runfile\r\n    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script\r\n  File \"\/opt\/pycharm-professional\/plugins\/python\/helpers\/pydev\/_pydev_imps\/_pydev_execfile.py\", line 18, in execfile\r\n    exec(compile(contents+\"\\n\", file, 'exec'), glob, loc)\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 12, in <module>\r\n    ds = load_dataset(\"lhoestq\/custom_squad\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1207, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 823, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 854, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 924, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n\r\nTried on current master, after updating latest dependencies and obtained the same result","body":"## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n","comment_length":237,"text":"`DatasetDict` cannot be exported to parquet if the splits have different features \n ## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n \n I'm getting the following error:\r\n\r\n```\r\nDownloading and preparing dataset custom_squad\/plain_text to \/home\/lysandre\/.cache\/huggingface\/datasets\/lhoestq___custom_squad)\/plain_text\/1.0.0\/397916d1ae99584877e0fb4f5b8b6f01e66fcbbeff4d178afb30c933a8d0d93a...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 7760.04it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 2020.38it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]Traceback (most recent call last):\r\n  File \"<input>\", line 1, in <module>\r\n  File \"\/opt\/pycharm-professional\/plugins\/python\/helpers\/pydev\/_pydev_bundle\/pydev_umd.py\", line 198, in runfile\r\n    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script\r\n  File \"\/opt\/pycharm-professional\/plugins\/python\/helpers\/pydev\/_pydev_imps\/_pydev_execfile.py\", line 18, in execfile\r\n    exec(compile(contents+\"\\n\", file, 'exec'), glob, loc)\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 12, in <module>\r\n    ds = load_dataset(\"lhoestq\/custom_squad\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1207, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 823, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 854, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 924, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n\r\nTried on current master, after updating latest dependencies and obtained the same result","embeddings":[-0.1533447504,-0.0085505815,0.0541425422,0.4590764344,0.0908742622,0.0531120785,0.2564251125,0.204687953,0.1695544571,0.0804405808,0.2533523738,0.3629656136,-0.1307234466,0.4581688046,-0.1649915427,-0.2605801821,0.1808207929,-0.0397759825,0.1971472502,0.0392243527,-0.1355071962,0.2122626156,0.094932884,-0.0579855405,0.0764198601,0.0857248604,-0.1721858829,0.0313951448,-0.0407176763,-0.4404964447,0.1308065057,0.1139079481,0.0330194607,0.0638304576,-0.0001209551,0.126368016,0.1089218706,-0.1468749344,-0.1553466767,-0.2253277302,0.0722789466,-0.3276149631,0.1342098266,-0.2398258895,-0.072140716,-0.0230652466,-0.1887051612,-0.3661788404,0.0575958677,0.0872431472,0.0884058252,0.2435974628,0.2534649372,-0.1587028056,0.3093519807,0.2460288703,-0.3525647819,-0.0957963541,0.2942000926,-0.0267965253,0.2372006029,-0.0125332521,-0.0131008094,-0.1866216809,-0.0100193704,0.0357764922,0.2016760111,-0.0472216308,-0.0048582056,0.1708362401,0.3730287552,-0.3078299463,-0.2139721662,-0.4796130359,-0.0043476122,-0.4512827396,0.1248775795,0.2219220996,0.3061760366,0.0717384964,0.2438835949,-0.3051103055,-0.019504955,-0.1060554013,-0.2055142373,-0.036159467,0.0323047489,0.2756302953,-0.2607961595,-0.1307297647,-0.1952963322,-0.0395402387,-0.1048606709,-0.0872063041,-0.288130343,-0.1636976451,0.021831993,-0.5100026727,0.0840099603,0.0521086231,0.1298688799,-0.1045378894,0.210742712,0.0668166354,0.6997101307,-0.2117575556,0.0629034638,0.3308934867,-0.2021674514,0.3535165191,-0.2645005882,-0.0922604725,0.21472691,0.0130990278,0.0660821944,-0.061799854,0.7261018753,-0.2778936028,-0.2827643752,0.314060539,-0.0690315589,-0.0184658691,-0.0227580052,0.1725393236,0.274394244,0.1146718115,-0.178026706,0.5176942348,-0.1419944912,-0.3822630942,-0.1794261932,0.0076865726,-0.0897204652,0.2087680399,-0.0494074076,0.1495054066,-0.2162174582,0.4734737575,-0.3237044215,-0.1224931329,-0.0673533753,-0.3026716113,-0.1163356751,-0.1683811843,0.0091825156,0.1893606186,0.2657749057,-0.2059383243,-0.3632978499,-0.1637562364,-0.4159124196,-0.1169884726,-0.066379413,0.1262874901,-0.1668713093,0.0606247336,-0.3843916655,0.4787818193,0.1406238526,-0.358817637,-0.0864635631,-0.1940063089,-0.2968921065,-0.2229759693,0.0135747455,0.4260392785,-0.3764185905,0.0151709579,0.2066549659,-0.2078363448,0.2506180704,0.3543793559,-0.2833498418,0.1510397494,-0.2686647475,0.0858026147,0.361854583,-0.0420141146,-0.1179696023,0.1215711087,-0.3412570059,0.3513429165,0.2501019537,-0.2739672363,0.7012817264,-0.0806037635,-0.102758199,0.2733205855,-0.1434348226,-0.158481732,-0.3888079822,-0.1002107933,0.4952836931,-0.0738501921,0.0348834246,-0.1343848705,0.0709742755,0.394562453,0.1850542575,-0.1021511033,0.0324044004,0.0347256735,0.1851088405,0.4772476852,-0.0399264991,-0.3850698173,-0.4081051052,0.3002918363,-0.1372296214,-0.1115630642,-0.0120542087,-0.010708537,-0.0465532206,0.0372191891,-0.1143149287,-0.0311569143,0.0697760805,0.1805256605,-0.040180698,0.1399053782,-0.1801367998,0.1831670552,-0.0679245442,0.0580777973,-0.365580529,0.5434340835,0.059666682,-0.4079326093,0.0663541257,0.1222490817,0.3252889514,-0.231349647,0.0326350704,0.2040225863,0.3464330435,0.1921724081,-0.2224576324,-0.1261529326,0.0411716998,-0.1582911462,-0.0217442624,0.2696456015,0.1211625561,-0.0049493723,-0.3899843991,0.3378594816,0.0666849688,0.302473098,0.2049649358,-0.0344837904,0.1528332978,-0.0858370215,-0.1044744328,-0.2849783003,-0.040402405,-0.1805185527,-0.2627166808,-0.0407801196,-0.4981930554,0.1941738874,0.4554045498,0.1275276542,0.1528485715,-0.2623436153,0.2779436111,-0.109375447,-0.0063420981,0.1921729892,0.4044851363,0.0994521305,0.1481258571,0.0026019081,-0.1942891777,0.06037407,0.1571056098,0.0571372956,0.0014193667,0.4877780974,0.2879687548,0.1425783038,-0.2236469388,-0.0820934251,-0.0090236347,-0.1301547587,-0.3222863078,0.1376560181,-0.3830343187,0.3732868135,-0.6004145145,0.1781218499,-0.2033672482,-0.2806482911,0.0596009046,0.3162017465,-0.375900358,-0.0766379461,-0.2617491186,-0.0673811585,-0.2911250591,-0.3365999162,0.2566405237,-0.1513856351,-0.0714571998,-0.0811375529,0.0521242768,0.303072989,0.0942187607,0.027974939,-0.1763356179,-0.5506234169,-0.1967489123,0.2618514001,-0.1779257804,-0.0185542908,0.1727975011,-0.1009669006,0.2337462008,-0.4144860804,0.4925787151,-0.0358078331,-0.0066017727,0.2643794417,0.0900097191,-0.0831367522,0.1307204366,-0.31131199,-0.2345553637,-0.2256342471,0.1592255831,-0.0407043025,0.1187285334,-0.3200721443,-0.0507357568,-0.142388925,0.0648478419,0.023819102,-0.100065358,0.0829668939,0.0487475954,-0.0658936501,-0.2630805671,0.04452594,0.218389973,-0.0337153822,0.2957593203,-0.2080755085,0.3681337237,-0.081969209,0.0343025886,-0.1908912957,-0.1358336657,0.319472909,0.2093870938,0.0743385106,-0.1095198095,-0.2680376768,0.0924855918,0.2580272853,0.1739187986,-0.3795464635,0.471278578,-0.0465764143,0.6406767964,0.0505417064,0.22840859,0.6799256206,0.0201883391,0.2697415948,-0.1240961999,-0.310335964,0.1371597946,-0.1938099712,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2968","title":"`DatasetDict` cannot be exported to parquet if the splits have different features","comments":"I just tried again on colab by installing `datasets` from source with pyarrow 3.0.0 and didn't get any error.\r\n\r\nYou error seems to happen when doing\r\n```python\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n```\r\n\r\nMore specifically it fails when trying to read the arrow file that just got generated. I haven't issues like this before. Can you make sure you have a recent version of `pyarrow` ? Maybe it was an old version that wrote the arrow file and some header was missing.","body":"## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n","comment_length":80,"text":"`DatasetDict` cannot be exported to parquet if the splits have different features \n ## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n \n I just tried again on colab by installing `datasets` from source with pyarrow 3.0.0 and didn't get any error.\r\n\r\nYou error seems to happen when doing\r\n```python\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n```\r\n\r\nMore specifically it fails when trying to read the arrow file that just got generated. I haven't issues like this before. Can you make sure you have a recent version of `pyarrow` ? Maybe it was an old version that wrote the arrow file and some header was 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2968","title":"`DatasetDict` cannot be exported to parquet if the splits have different features","comments":"Thank you for your pointer! This seems to have been linked to Python 3.9.7: it works flawlessly with Python 3.8.6. This can be closed, thanks a lot for your help.","body":"## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n","comment_length":30,"text":"`DatasetDict` cannot be exported to parquet if the splits have different features \n ## Describe the bug\r\n\r\nI'm trying to use parquet as a means of serialization for both `Dataset` and `DatasetDict` objects. Using `to_parquet` alongside `from_parquet` or `load_dataset` for a `Dataset` works perfectly. \r\n\r\nFor `DatasetDict`, I use `to_parquet` on each split to save the parquet files in individual folders representing individual splits. This works too, as long as the splits have identical features. If a split has different features to neighboring splits, then loading the dataset will fail: a single schema is used to load both splits, resulting in a failure to load the second parquet file.\r\n\r\n## Steps to reproduce the bug\r\n\r\nThe following works as expected:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n\r\nModifying a single split to add a new feature ends up in a crash:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\nds['train'].to_parquet(\".\/ds\/train\/split.parquet\")\r\nds['validation'].to_parquet(\".\/ds\/validation\/split.parquet\")\r\n\r\nbrand_new_dataset = load_dataset(\"ds\")\r\n```\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 26, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1151, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 642, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 732, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 1194, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in write_table\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_writer.py\", line 428, in <listcomp>\r\n    pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1257, in pyarrow.lib.Table.__getitem__\r\n  File \"pyarrow\/table.pxi\", line 1833, in pyarrow.lib.Table.column\r\n  File \"pyarrow\/table.pxi\", line 1808, in pyarrow.lib.Table._ensure_integer_index\r\nKeyError: 'Field \"identical_answers\" does not exist in table schema'\r\n```\r\n\r\nIt does work, however, to use the `save_to_disk` and `load_from_disk` methods:\r\n\r\n```py\r\nfrom datasets import load_from_disk\r\n\r\nds = load_dataset(\"lhoestq\/custom_squad\")\r\n\r\n\r\ndef identical_answers(e):\r\n    e['identical_answers'] = len(set(e['answers']['text'])) == 1\r\n    return e\r\n\r\n\r\nds['validation'] = ds['validation'].map(identical_answers)\r\n\r\nds.save_to_disk(\"local_path\")\r\nbrand_new_dataset = load_from_disk(\"local_path\")\r\n```\r\n\r\n## Expected results\r\n\r\nThe saving works correctly - but the loading fails. I would expect either an error when saving or an error-less instantiation of the dataset through the parquet files.\r\n\r\nIf it's helpful, I've traced a possible patch to the `write_table` method here: \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L424-L425\r\n\r\nThe writer is built only if the parquet writer is `None`, but I expect we would want to build a new writer as the table schema has changed. Furthermore, it relies on having the property `update_features` set to `True` in order to update the features:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/arrow_writer.py#L254-L255\r\n\r\nbut the `ArrowWriter` is instantiated without that option in the `_prepare_split` method of the `ArrowBasedBuilder`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/26ff41aa3a642e46489db9e95be1e9a8c4e64bea\/src\/datasets\/builder.py#L1190\r\n\r\nUpdating these two parts to recreate a schema on each split results in an error that is, unfortunately, out of my expertise:\r\n\r\n```\r\n  File \"\/home\/lysandre\/.config\/JetBrains\/PyCharm2021.2\/scratches\/datasets\/upload_dataset.py\", line 27, in <module>\r\n    brand_new_dataset = load_dataset(\"ds\")\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/load.py\", line 1163, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 819, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 207, in map_nested\r\n    mapped = [\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 208, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True))\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/utils\/py_utils.py\", line 143, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 850, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/builder.py\", line 920, in _as_dataset\r\n    dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 217, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 238, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 173, in _read_files\r\n    pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 308, in _get_table_from_filename\r\n    table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/arrow_reader.py\", line 327, in read_table\r\n    return table_cls.from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 458, in from_file\r\n    table = _memory_mapped_arrow_table_from_file(filename)\r\n  File \"\/home\/lysandre\/Workspaces\/Python\/datasets\/src\/datasets\/table.py\", line 45, in _memory_mapped_arrow_table_from_file\r\n    pa_table = opened_stream.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 563, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 114, in pyarrow.lib.check_status\r\nOSError: Header-type of flatbuffer-encoded Message is not RecordBatch.\r\n```\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.2.dev0\r\n- Platform: Linux-5.14.7-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\r\n\r\n \n Thank you for your pointer! This seems to have been linked to Python 3.9.7: it works flawlessly with Python 3.8.6. This can be closed, thanks a lot for your 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2964","title":"Error when calculating Matthews Correlation Coefficient loaded with `load_metric`","comments":"After some more tests I've realized that this \"issue\" is due to the `numpy.float64` to `float` conversion, but when defining a function named `compute_metrics` as it follows:\r\n\r\n```python\r\ndef compute_metrics(eval_preds):\r\n    metric = load_metric(\"matthews_correlation\")\r\n    logits, labels = eval_preds\r\n    predictions = np.argmax(logits, axis=1)\r\n    return metric.compute(predictions=predictions, references=labels)\r\n```\r\n\r\nIt fails when the evaluation metrics are computed in the `Trainer` with the same error code `AttributeError: 'float' object has no attribute 'item'` as the output is not a `numpy.float64`... Maybe I'm doing something wrong, not sure!","body":"## Describe the bug\r\n\r\nAfter loading the metric named \"[Matthews Correlation Coefficient](https:\/\/huggingface.co\/metrics\/matthews_correlation)\" from `\ud83e\udd17datasets`, the `.compute` method fails with the following exception `AttributeError: 'float' object has no attribute 'item'` (complete stack trace can be provided if required).\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nimport torch\r\npredictions = torch.ones((10,))\r\nreferences = torch.zeros((10,))\r\n\r\nfrom datasets import load_metric\r\n\r\nMETRIC = load_metric(\"matthews_correlation\")\r\n\r\nresult = METRIC.compute(predictions=predictions, references=references)\r\n```\r\n\r\n## Expected results\r\n\r\nWe should expect a Python `dict` as it follows:\r\n\r\n```\r\n{\r\n    \"matthews_correlation\": float()\r\n}\r\n```\r\n\r\nas defined in https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/matthews_correlation\/matthews_correlation.py, so the fix will imply removing `.item()`, since the value returned by the `scikit-learn` function is not a `torch.Tensor` but a `float`, which means that the `.item()` will fail.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/cli.py\", line 59, in main\r\n    app()\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/typer\/main.py\", line 214, in __call__\r\n    return get_command(self)(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1137, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1062, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1668, in invoke\r\n    return _process_result(sub_ctx.command.invoke(sub_ctx))\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1404, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 763, in invoke\r\n    return __callback(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/typer\/main.py\", line 500, in wrapper\r\n    return callback(**use_params)  # type: ignore\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/cli.py\", line 43, in train\r\n    metrics = trainer.evaluate()\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/transformers\/trainer.py\", line 2051, in evaluate\r\n    output = eval_loop(\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/transformers\/trainer.py\", line 2292, in evaluation_loop\r\n    metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/metrics.py\", line 20, in compute_metrics\r\n    res = METRIC.compute(predictions=predictions, references=eval_preds.label_ids)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/lang\/lib\/python3.9\/site-packages\/datasets\/metric.py\", line 402, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/matthews_correlation\/0275f1e9a4d318e3ea8cdd87547ee0d58d894966616052e3d18444ac8ddd2357\/matthews_correlation.py\", line 88, in _compute\r\n    \"matthews_correlation\": matthews_corrcoef(references, predictions, sample_weight=sample_weight).item(),\r\nAttributeError: 'float' object has no attribute 'item'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.15.0-1113-azure-x86_64-with-glibc2.23\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":82,"text":"Error when calculating Matthews Correlation Coefficient loaded with `load_metric` \n ## Describe the bug\r\n\r\nAfter loading the metric named \"[Matthews Correlation Coefficient](https:\/\/huggingface.co\/metrics\/matthews_correlation)\" from `\ud83e\udd17datasets`, the `.compute` method fails with the following exception `AttributeError: 'float' object has no attribute 'item'` (complete stack trace can be provided if required).\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nimport torch\r\npredictions = torch.ones((10,))\r\nreferences = torch.zeros((10,))\r\n\r\nfrom datasets import load_metric\r\n\r\nMETRIC = load_metric(\"matthews_correlation\")\r\n\r\nresult = METRIC.compute(predictions=predictions, references=references)\r\n```\r\n\r\n## Expected results\r\n\r\nWe should expect a Python `dict` as it follows:\r\n\r\n```\r\n{\r\n    \"matthews_correlation\": float()\r\n}\r\n```\r\n\r\nas defined in https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/matthews_correlation\/matthews_correlation.py, so the fix will imply removing `.item()`, since the value returned by the `scikit-learn` function is not a `torch.Tensor` but a `float`, which means that the `.item()` will fail.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/cli.py\", line 59, in main\r\n    app()\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/typer\/main.py\", line 214, in __call__\r\n    return get_command(self)(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1137, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1062, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1668, in invoke\r\n    return _process_result(sub_ctx.command.invoke(sub_ctx))\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1404, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 763, in invoke\r\n    return __callback(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/typer\/main.py\", line 500, in wrapper\r\n    return callback(**use_params)  # type: ignore\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/cli.py\", line 43, in train\r\n    metrics = trainer.evaluate()\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/transformers\/trainer.py\", line 2051, in evaluate\r\n    output = eval_loop(\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/transformers\/trainer.py\", line 2292, in evaluation_loop\r\n    metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/metrics.py\", line 20, in compute_metrics\r\n    res = METRIC.compute(predictions=predictions, references=eval_preds.label_ids)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/lang\/lib\/python3.9\/site-packages\/datasets\/metric.py\", line 402, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/matthews_correlation\/0275f1e9a4d318e3ea8cdd87547ee0d58d894966616052e3d18444ac8ddd2357\/matthews_correlation.py\", line 88, in _compute\r\n    \"matthews_correlation\": matthews_corrcoef(references, predictions, sample_weight=sample_weight).item(),\r\nAttributeError: 'float' object has no attribute 'item'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.15.0-1113-azure-x86_64-with-glibc2.23\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n After some more tests I've realized that this \"issue\" is due to the `numpy.float64` to `float` conversion, but when defining a function named `compute_metrics` as it follows:\r\n\r\n```python\r\ndef compute_metrics(eval_preds):\r\n    metric = load_metric(\"matthews_correlation\")\r\n    logits, labels = eval_preds\r\n    predictions = np.argmax(logits, axis=1)\r\n    return metric.compute(predictions=predictions, references=labels)\r\n```\r\n\r\nIt fails when the evaluation metrics are computed in the `Trainer` with the same error code `AttributeError: 'float' object has no attribute 'item'` as the output is not a `numpy.float64`... Maybe I'm doing something wrong, not sure!","embeddings":[-0.1346632242,-0.3452954292,0.0786115974,0.3539230824,0.2162498087,0.0481337979,0.2270004898,0.2281398326,0.1603158861,0.3655945063,-0.0764110386,0.5783470869,0.0116952946,0.054774411,-0.2028557807,-0.2456581891,-0.0192866959,0.1806842685,-0.0553318076,0.1620573252,-0.0679709092,0.3707422614,-0.2087529898,-0.0300136041,-0.043491561,-0.1876723319,0.1610840112,0.1529950947,-0.1207342297,-0.1370849162,0.3690682054,-0.0479772761,-0.0842530876,0.4826037586,-0.0001183492,-0.0784961879,0.5682637691,-0.0873692185,-0.1573249251,-0.6145122051,0.3821884692,-0.0605810992,0.5335688591,-0.2195534855,-0.0819899738,0.2434536666,-0.3503492177,-0.215829581,0.4596735537,0.1992693543,0.149792254,0.6678832769,0.1522447169,-0.2836018801,0.4087767303,0.0498383567,-0.0386289395,0.602099061,0.0675273389,-0.2266095728,-0.0079390174,0.1865924895,-0.0014301348,0.1472048163,0.3905893266,0.0608718172,0.0238725878,-0.1174537763,0.1825753301,0.1177005619,0.3312846124,-0.1309376955,-0.0656309128,0.1499143988,0.1480368227,-0.201024577,0.2491047978,0.0418888591,-0.2340114564,-0.4360597134,0.0711818635,0.1361631751,-0.2239286751,0.1957768798,-0.1927128583,0.5140115023,-0.3415762186,0.1640295833,0.3555520177,0.1447671354,0.2262019813,0.1812290102,-0.158239305,0.192152828,-0.412594527,0.0229211506,-0.0268711951,-0.0203171168,0.1825253665,-0.0297108945,0.4302613735,0.0836861581,0.3610846698,0.3273840845,0.0492598563,0.4633021057,-0.0939973816,0.2572134435,0.1083849221,0.5407493711,-0.0412839837,-0.2337539643,0.0500202142,-0.2245226502,-0.0711309016,0.0103233233,0.031536255,-0.2133668065,-0.5427336693,-0.0136268018,-0.0703158602,0.1904647499,0.2921550572,0.3912591338,-0.0739528388,0.381675303,0.208919242,0.1880535483,-0.0716072544,0.1786257327,-0.2126839906,0.32400769,-0.0426125228,0.0283595175,0.1451171488,-0.212011531,0.4561870992,0.0674151108,0.735268712,-0.3100109398,0.0481332913,-0.0233878177,0.1161648259,0.319141537,-0.2411396205,-0.0280447043,0.2253657281,-0.1722656339,-0.0964902788,-0.0180939846,-0.6707656384,-0.2343034446,-0.0400030352,0.1149021462,-0.2478627563,-0.0554928482,-0.1843859702,0.1920413375,0.0349757634,-0.0885058939,-0.1514078677,-0.0415073261,-0.5505473018,-0.1501431465,0.4664611816,0.2627126575,-0.2241636068,-0.2059201598,0.0356471054,0.2769384086,0.0194115341,-0.1975536495,-0.3452398181,0.2707131207,-0.135389924,-0.2437268347,0.2000696659,-0.5237429142,-0.1384710968,-0.1423726082,-0.1229476258,-0.0338327587,0.0069783595,-0.1309604496,0.1898611039,-0.0208825413,0.2575111091,-0.0268692188,-0.016863957,-0.0471271053,-0.2678294182,-0.1509103477,0.4108137786,0.1170555055,0.0910868347,-0.0006382915,0.0221006311,0.0015586525,0.0946039706,-0.0538667887,-0.1377039552,0.1160319597,-0.0724801272,0.0080363285,0.2567482591,-0.0669364408,-0.2991153896,0.2253068089,-0.4390331805,0.2879671752,-0.3368987441,-0.0491041541,-0.4912465811,0.1344736516,-0.1397566199,-0.1776331812,0.0919182301,-0.2390707284,0.147562772,0.0178248212,-0.085425429,0.0544818863,-0.1399712265,0.0795449987,-0.2808509767,0.2387433648,-0.0735696331,-0.1042926535,0.2588180006,0.2821485996,0.3205311894,-0.1555354893,-0.3949601054,0.4376566112,-0.2053664476,0.130528897,0.1792734563,0.0096063986,0.157531485,-0.1323380768,0.0002761623,-0.001434169,0.4274198115,-0.1360719651,0.1647747755,0.5870984197,0.0388808288,0.2791692019,-0.323521018,0.0526048243,-0.1318980753,0.2652962804,0.013210563,0.0291834883,0.14466618,0.087300688,-0.0369309746,-0.4472593963,0.1518435031,-0.1620588601,-0.1269605607,0.0269343909,-0.1393050551,0.1139153093,-0.1971994191,-0.061094258,-0.0573173687,-0.261954397,0.6510858536,0.0255859364,-0.2132512033,-0.0062850192,-0.0777452961,-0.1695220172,0.1457534879,0.4468921423,-0.011175626,0.0480809547,0.5717403293,0.0411028378,-0.0684938878,0.2097741365,-0.0657143593,0.0974915475,-0.29070732,0.4062485695,0.2107157558,0.4068445563,-0.0313422456,0.1208730787,-0.3450481594,-0.4318835139,0.0391076654,0.3303699791,-0.0258272532,0.0254777335,0.2210880816,0.3651057184,0.2474809289,-0.1855097264,-0.2161348611,-0.3324939311,-0.1272173673,-0.0206375942,-0.0570767708,0.05103378,0.2589173317,-0.0825503692,0.0954612046,-0.0633746311,-0.533891201,-0.0226389728,-0.0388583653,0.3302341402,0.2104026675,-0.1415504068,-0.214765504,-0.1327038258,0.4183850586,-0.4494492114,0.0817596987,0.2543350756,-0.0786546469,0.0405789092,-0.0404849648,-0.1780363023,-0.0586071648,-0.2344229817,-0.0575566813,0.2160505354,0.0464005992,-0.3639867008,-0.0104480954,0.0993913114,-0.1161062568,0.2406509817,-0.3235772848,-0.5167632699,0.2562184036,-0.2362062335,-0.4920795262,0.075631991,0.0048547774,0.0955302864,-0.0576559193,-0.2322703153,-0.7143223882,-0.0504001118,0.0930658653,-0.4623407125,0.2928229272,0.1933390349,0.0223863684,0.0279302914,-0.2276706696,-0.1376697421,0.2510820925,-0.0521573462,-0.015929874,-0.0673382953,-0.0615743734,-0.1437338591,0.4234569967,0.2290211469,-0.3653404713,0.1840812862,-0.0590453856,0.3185498714,-0.2130847722,-0.1574260145,-0.0907831416,-0.152223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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2964","title":"Error when calculating Matthews Correlation Coefficient loaded with `load_metric`","comments":"Ok after some more experiments I've realized that it's an issue from my side, at first I thought it was due to `fp16=True` in `TrainingArguments`, but in the end that may not be the issue, so I'll close this for now and check later, since the mistake is on my side :weary: Sorry for the inconvenience!","body":"## Describe the bug\r\n\r\nAfter loading the metric named \"[Matthews Correlation Coefficient](https:\/\/huggingface.co\/metrics\/matthews_correlation)\" from `\ud83e\udd17datasets`, the `.compute` method fails with the following exception `AttributeError: 'float' object has no attribute 'item'` (complete stack trace can be provided if required).\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nimport torch\r\npredictions = torch.ones((10,))\r\nreferences = torch.zeros((10,))\r\n\r\nfrom datasets import load_metric\r\n\r\nMETRIC = load_metric(\"matthews_correlation\")\r\n\r\nresult = METRIC.compute(predictions=predictions, references=references)\r\n```\r\n\r\n## Expected results\r\n\r\nWe should expect a Python `dict` as it follows:\r\n\r\n```\r\n{\r\n    \"matthews_correlation\": float()\r\n}\r\n```\r\n\r\nas defined in https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/matthews_correlation\/matthews_correlation.py, so the fix will imply removing `.item()`, since the value returned by the `scikit-learn` function is not a `torch.Tensor` but a `float`, which means that the `.item()` will fail.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/cli.py\", line 59, in main\r\n    app()\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/typer\/main.py\", line 214, in __call__\r\n    return get_command(self)(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1137, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1062, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1668, in invoke\r\n    return _process_result(sub_ctx.command.invoke(sub_ctx))\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1404, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 763, in invoke\r\n    return __callback(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/typer\/main.py\", line 500, in wrapper\r\n    return callback(**use_params)  # type: ignore\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/cli.py\", line 43, in train\r\n    metrics = trainer.evaluate()\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/transformers\/trainer.py\", line 2051, in evaluate\r\n    output = eval_loop(\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/transformers\/trainer.py\", line 2292, in evaluation_loop\r\n    metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/metrics.py\", line 20, in compute_metrics\r\n    res = METRIC.compute(predictions=predictions, references=eval_preds.label_ids)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/lang\/lib\/python3.9\/site-packages\/datasets\/metric.py\", line 402, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/matthews_correlation\/0275f1e9a4d318e3ea8cdd87547ee0d58d894966616052e3d18444ac8ddd2357\/matthews_correlation.py\", line 88, in _compute\r\n    \"matthews_correlation\": matthews_corrcoef(references, predictions, sample_weight=sample_weight).item(),\r\nAttributeError: 'float' object has no attribute 'item'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.15.0-1113-azure-x86_64-with-glibc2.23\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":56,"text":"Error when calculating Matthews Correlation Coefficient loaded with `load_metric` \n ## Describe the bug\r\n\r\nAfter loading the metric named \"[Matthews Correlation Coefficient](https:\/\/huggingface.co\/metrics\/matthews_correlation)\" from `\ud83e\udd17datasets`, the `.compute` method fails with the following exception `AttributeError: 'float' object has no attribute 'item'` (complete stack trace can be provided if required).\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nimport torch\r\npredictions = torch.ones((10,))\r\nreferences = torch.zeros((10,))\r\n\r\nfrom datasets import load_metric\r\n\r\nMETRIC = load_metric(\"matthews_correlation\")\r\n\r\nresult = METRIC.compute(predictions=predictions, references=references)\r\n```\r\n\r\n## Expected results\r\n\r\nWe should expect a Python `dict` as it follows:\r\n\r\n```\r\n{\r\n    \"matthews_correlation\": float()\r\n}\r\n```\r\n\r\nas defined in https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/matthews_correlation\/matthews_correlation.py, so the fix will imply removing `.item()`, since the value returned by the `scikit-learn` function is not a `torch.Tensor` but a `float`, which means that the `.item()` will fail.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/cli.py\", line 59, in main\r\n    app()\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/typer\/main.py\", line 214, in __call__\r\n    return get_command(self)(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1137, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1062, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1668, in invoke\r\n    return _process_result(sub_ctx.command.invoke(sub_ctx))\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 1404, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/click\/core.py\", line 763, in invoke\r\n    return __callback(*args, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/typer\/main.py\", line 500, in wrapper\r\n    return callback(**use_params)  # type: ignore\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/cli.py\", line 43, in train\r\n    metrics = trainer.evaluate()\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/transformers\/trainer.py\", line 2051, in evaluate\r\n    output = eval_loop(\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/xxx\/lib\/python3.9\/site-packages\/transformers\/trainer.py\", line 2292, in evaluation_loop\r\n    metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))\r\n  File \"\/home\/alvaro.bartolome\/XXX\/xxx\/metrics.py\", line 20, in compute_metrics\r\n    res = METRIC.compute(predictions=predictions, references=eval_preds.label_ids)\r\n  File \"\/home\/alvaro.bartolome\/miniconda3\/envs\/lang\/lib\/python3.9\/site-packages\/datasets\/metric.py\", line 402, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/alvaro.bartolome\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/matthews_correlation\/0275f1e9a4d318e3ea8cdd87547ee0d58d894966616052e3d18444ac8ddd2357\/matthews_correlation.py\", line 88, in _compute\r\n    \"matthews_correlation\": matthews_corrcoef(references, predictions, sample_weight=sample_weight).item(),\r\nAttributeError: 'float' object has no attribute 'item'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-4.15.0-1113-azure-x86_64-with-glibc2.23\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Ok after some more experiments I've realized that it's an issue from my side, at first I thought it was due to `fp16=True` in `TrainingArguments`, but in the end that may not be the issue, so I'll close this for now and check later, since the mistake is on my side :weary: Sorry for the inconvenience!","embeddings":[-0.1346632242,-0.3452954292,0.0786115974,0.3539230824,0.2162498087,0.0481337979,0.2270004898,0.2281398326,0.1603158861,0.3655945063,-0.0764110386,0.5783470869,0.0116952946,0.054774411,-0.2028557807,-0.2456581891,-0.0192866959,0.1806842685,-0.0553318076,0.1620573252,-0.0679709092,0.3707422614,-0.2087529898,-0.0300136041,-0.043491561,-0.1876723319,0.1610840112,0.1529950947,-0.1207342297,-0.1370849162,0.3690682054,-0.0479772761,-0.0842530876,0.4826037586,-0.0001183492,-0.0784961879,0.5682637691,-0.0873692185,-0.1573249251,-0.6145122051,0.3821884692,-0.0605810992,0.5335688591,-0.2195534855,-0.0819899738,0.2434536666,-0.3503492177,-0.215829581,0.4596735537,0.1992693543,0.149792254,0.6678832769,0.1522447169,-0.2836018801,0.4087767303,0.0498383567,-0.0386289395,0.602099061,0.0675273389,-0.2266095728,-0.0079390174,0.1865924895,-0.0014301348,0.1472048163,0.3905893266,0.0608718172,0.0238725878,-0.1174537763,0.1825753301,0.1177005619,0.3312846124,-0.1309376955,-0.0656309128,0.1499143988,0.1480368227,-0.201024577,0.2491047978,0.0418888591,-0.2340114564,-0.4360597134,0.0711818635,0.1361631751,-0.2239286751,0.1957768798,-0.1927128583,0.5140115023,-0.3415762186,0.1640295833,0.3555520177,0.1447671354,0.2262019813,0.1812290102,-0.158239305,0.192152828,-0.412594527,0.0229211506,-0.0268711951,-0.0203171168,0.1825253665,-0.0297108945,0.4302613735,0.0836861581,0.3610846698,0.3273840845,0.0492598563,0.4633021057,-0.0939973816,0.2572134435,0.1083849221,0.5407493711,-0.0412839837,-0.2337539643,0.0500202142,-0.2245226502,-0.0711309016,0.0103233233,0.031536255,-0.2133668065,-0.5427336693,-0.0136268018,-0.0703158602,0.1904647499,0.2921550572,0.3912591338,-0.0739528388,0.381675303,0.208919242,0.1880535483,-0.0716072544,0.1786257327,-0.2126839906,0.32400769,-0.0426125228,0.0283595175,0.1451171488,-0.212011531,0.4561870992,0.0674151108,0.735268712,-0.3100109398,0.0481332913,-0.0233878177,0.1161648259,0.319141537,-0.2411396205,-0.0280447043,0.2253657281,-0.1722656339,-0.0964902788,-0.0180939846,-0.6707656384,-0.2343034446,-0.0400030352,0.1149021462,-0.2478627563,-0.0554928482,-0.1843859702,0.1920413375,0.0349757634,-0.0885058939,-0.1514078677,-0.0415073261,-0.5505473018,-0.1501431465,0.4664611816,0.2627126575,-0.2241636068,-0.2059201598,0.0356471054,0.2769384086,0.0194115341,-0.1975536495,-0.3452398181,0.2707131207,-0.135389924,-0.2437268347,0.2000696659,-0.5237429142,-0.1384710968,-0.1423726082,-0.1229476258,-0.0338327587,0.0069783595,-0.1309604496,0.1898611039,-0.0208825413,0.2575111091,-0.0268692188,-0.016863957,-0.0471271053,-0.2678294182,-0.1509103477,0.4108137786,0.1170555055,0.0910868347,-0.0006382915,0.0221006311,0.0015586525,0.0946039706,-0.0538667887,-0.1377039552,0.1160319597,-0.0724801272,0.0080363285,0.2567482591,-0.0669364408,-0.2991153896,0.2253068089,-0.4390331805,0.2879671752,-0.3368987441,-0.0491041541,-0.4912465811,0.1344736516,-0.1397566199,-0.1776331812,0.0919182301,-0.2390707284,0.147562772,0.0178248212,-0.085425429,0.0544818863,-0.1399712265,0.0795449987,-0.2808509767,0.2387433648,-0.0735696331,-0.1042926535,0.2588180006,0.2821485996,0.3205311894,-0.1555354893,-0.3949601054,0.4376566112,-0.2053664476,0.130528897,0.1792734563,0.0096063986,0.157531485,-0.1323380768,0.0002761623,-0.001434169,0.4274198115,-0.1360719651,0.1647747755,0.5870984197,0.0388808288,0.2791692019,-0.323521018,0.0526048243,-0.1318980753,0.2652962804,0.013210563,0.0291834883,0.14466618,0.087300688,-0.0369309746,-0.4472593963,0.1518435031,-0.1620588601,-0.1269605607,0.0269343909,-0.1393050551,0.1139153093,-0.1971994191,-0.061094258,-0.0573173687,-0.261954397,0.6510858536,0.0255859364,-0.2132512033,-0.0062850192,-0.0777452961,-0.1695220172,0.1457534879,0.4468921423,-0.011175626,0.0480809547,0.5717403293,0.0411028378,-0.0684938878,0.2097741365,-0.0657143593,0.0974915475,-0.29070732,0.4062485695,0.2107157558,0.4068445563,-0.0313422456,0.1208730787,-0.3450481594,-0.4318835139,0.0391076654,0.3303699791,-0.0258272532,0.0254777335,0.2210880816,0.3651057184,0.2474809289,-0.1855097264,-0.2161348611,-0.3324939311,-0.1272173673,-0.0206375942,-0.0570767708,0.05103378,0.2589173317,-0.0825503692,0.0954612046,-0.0633746311,-0.533891201,-0.0226389728,-0.0388583653,0.3302341402,0.2104026675,-0.1415504068,-0.214765504,-0.1327038258,0.4183850586,-0.4494492114,0.0817596987,0.2543350756,-0.0786546469,0.0405789092,-0.0404849648,-0.1780363023,-0.0586071648,-0.2344229817,-0.0575566813,0.2160505354,0.0464005992,-0.3639867008,-0.0104480954,0.0993913114,-0.1161062568,0.2406509817,-0.3235772848,-0.5167632699,0.2562184036,-0.2362062335,-0.4920795262,0.075631991,0.0048547774,0.0955302864,-0.0576559193,-0.2322703153,-0.7143223882,-0.0504001118,0.0930658653,-0.4623407125,0.2928229272,0.1933390349,0.0223863684,0.0279302914,-0.2276706696,-0.1376697421,0.2510820925,-0.0521573462,-0.015929874,-0.0673382953,-0.0615743734,-0.1437338591,0.4234569967,0.2290211469,-0.3653404713,0.1840812862,-0.0590453856,0.3185498714,-0.2130847722,-0.1574260145,-0.0907831416,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2957","title":"MultiWOZ Dataset NonMatchingChecksumError","comments":"Hi Brady! I met the similar issue, it stuck in the downloading stage instead of download anything, maybe it is broken. After I change the downloading from URLs to one url of the [Multiwoz project](https:\/\/github.com\/budzianowski\/multiwoz\/archive\/44f0f8479f11721831c5591b839ad78827da197b.zip) and use dirs to get separate files, the problems gone.","body":"## Describe the bug\r\nThe checksums for the downloaded MultiWOZ dataset and source MultiWOZ dataset aren't matching.\r\n\r\n## Steps to reproduce the bug\r\nBoth of the below dataset versions yield the checksum error:\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('multi_woz_v22', 'v2.2')\r\ndataset = load_dataset('multi_woz_v22', 'v2.2_active_only')\r\n```\r\n\r\n## Expected results\r\nFor the above calls to `load_dataset` to work.\r\n\r\n## Actual results\r\nNonMatchingChecksumError. Traceback:\r\n> Traceback (most recent call last):\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/IPython\/core\/interactiveshell.py\", line 3441, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n  File \"<ipython-input-15-4e91280e112e>\", line 1, in <module>\r\n    dataset = load_dataset('multi_woz_v22', 'v2.2')\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 847, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 615, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json']\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":45,"text":"MultiWOZ Dataset NonMatchingChecksumError \n ## Describe the bug\r\nThe checksums for the downloaded MultiWOZ dataset and source MultiWOZ dataset aren't matching.\r\n\r\n## Steps to reproduce the bug\r\nBoth of the below dataset versions yield the checksum error:\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('multi_woz_v22', 'v2.2')\r\ndataset = load_dataset('multi_woz_v22', 'v2.2_active_only')\r\n```\r\n\r\n## Expected results\r\nFor the above calls to `load_dataset` to work.\r\n\r\n## Actual results\r\nNonMatchingChecksumError. Traceback:\r\n> Traceback (most recent call last):\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/IPython\/core\/interactiveshell.py\", line 3441, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n  File \"<ipython-input-15-4e91280e112e>\", line 1, in <module>\r\n    dataset = load_dataset('multi_woz_v22', 'v2.2')\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 847, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 615, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 675, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/brady\/anaconda3\/envs\/elysium\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 40, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json']\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n Hi Brady! I met the similar issue, it stuck in the downloading stage instead of download anything, maybe it is broken. After I change the downloading from URLs to one url of the [Multiwoz project](https:\/\/github.com\/budzianowski\/multiwoz\/archive\/44f0f8479f11721831c5591b839ad78827da197b.zip) and use dirs to get separate files, the problems gone.","embeddings":[-0.1857132912,0.2824348807,-0.0103633087,0.2273115218,-0.1089396626,0.0266023558,0.4214707613,0.4297234118,0.1776718646,0.0903899819,-0.1264118403,0.1356127411,0.0912543535,-0.0172352474,-0.0992788821,0.2393863499,0.1802412122,-0.0172633901,-0.3148810267,-0.0254053734,-0.160951063,0.2217589468,-0.2102820426,-0.1824971288,-0.0456188209,0.2666471601,0.1223759055,0.1869054586,-0.0484859832,-0.1660533249,0.4412839711,0.1968481839,-0.013311469,0.4252355993,-0.0001183016,0.1124094799,0.296803087,-0.1296401024,-0.2232105583,-0.1682910472,-0.4560235143,-0.43868348,-0.0885319933,-0.1898672134,0.170491308,0.4208543897,-0.1168579236,-0.0827398226,0.0629306734,-0.0126963155,0.1581587642,0.5822010636,0.4030981064,0.1252417713,0.1970892102,0.0393281728,-0.117853418,0.1882817894,0.2040813118,-0.0312332418,-0.1780881435,0.2824749947,-0.3017633855,0.3099508584,-0.0178374946,-0.0548682176,0.0882390589,-0.098189801,0.0019518912,0.4938593805,0.1008166075,-0.3008319438,-0.3892050683,-0.2051616758,-0.3296724856,0.1635203958,0.3679273427,0.1957933754,-0.274276644,-0.1802024543,-0.3902156651,0.3427878022,0.0677079782,0.1836716235,0.014304081,0.2648561895,0.2137290239,0.003746127,-0.0517350622,-0.1973327249,0.1935978532,-0.4476892352,0.0077399551,0.1180825159,-0.5939763188,-0.1695201546,-0.0754331574,0.3176149428,0.4821313918,0.4230730534,0.2929350734,0.1173708588,-0.2319894582,0.1290969849,0.2468466908,0.0862759575,0.0014587779,-0.0412894785,0.0506132096,0.3339034617,-0.0159097649,0.2367849499,0.1620214731,-0.3424532712,0.5505930781,0.1863106936,0.3187479675,-0.4685881734,-0.2995807528,0.2195347548,-0.2689187527,-0.2491639256,0.3218149543,0.0196468309,-0.2147006392,0.3384554982,-0.1812158376,0.3032727838,-0.2416976988,-0.1844143867,-0.1945900321,-0.0540542305,0.0140766362,0.0679238662,0.3222481012,-0.0300805047,0.4282167256,0.1399196982,0.3867962658,-0.126673162,0.3571106195,-0.0007348769,-0.0462410711,0.3179152906,-0.0102224108,0.1153942347,0.1931466907,0.0721090138,-0.0510717407,0.1817475259,-0.2787762582,-0.2925911844,-0.0718404502,0.1701146662,-0.4714779258,-0.1411759257,0.1903770268,-0.5398696065,0.492957145,-0.4410258234,0.0588067845,-0.3429527581,-0.0873087049,-0.1363504678,0.2187083215,0.2090427428,0.1447187811,0.1217784286,-0.1248662323,-0.1126581505,0.3954070807,0.1465790421,-0.0756230727,-0.1730584353,-0.2184591293,0.0177632049,0.2371676117,-0.3490881026,-0.3837649226,-0.0385042801,0.0520001762,0.7727557421,0.1339309961,0.0986835584,-0.0696789697,-0.2922151685,0.191440329,-0.0046279449,-0.0267085601,0.1605678201,-0.2763486505,-0.2653129697,0.3215686977,-0.0103946058,-0.0300548468,-0.1173767149,0.1304577291,-0.1090817302,0.4816509187,0.0167928934,-0.122387968,-0.0125266453,0.2112238854,-0.084235616,0.041095458,-0.1484988779,-0.5830713511,0.3500460982,-0.2894625068,0.0793371499,0.0441911332,-0.013754623,-0.1174861938,-0.1051901206,-0.3605993986,-0.0613709204,0.0931162909,0.2512524128,0.1876758784,-0.2360634804,0.0983460248,0.2980125546,-0.1005239934,0.0937533453,-0.3599634767,0.2759971023,-0.1360352486,-0.0026744837,-0.0441815667,0.2741098106,0.1801469028,0.0314876847,-0.2281171829,0.4233845472,0.2513296604,0.0915886611,-0.1960860193,0.2484433353,-0.0060740341,-0.0442373343,0.1253615767,0.4152407646,0.078162238,-0.1159746647,-0.0873372108,0.4383071661,-0.1502492726,0.2534452975,-0.0705960914,0.1820661873,0.1730350405,-0.0170390289,-0.2775233984,-0.263692379,0.3745577335,-0.0109729785,-0.1087726504,0.1891141087,-0.0705662891,0.0369836837,0.3300088346,-0.0409071743,0.0281632096,-0.0614857115,0.1271936446,-0.1778979897,0.224224329,0.5954227448,0.4448838532,0.1070267707,0.1795392036,0.0346116312,-0.32358253,-0.0532696322,0.0621295087,0.0045760139,0.1233897582,0.3781661987,0.0509032272,-0.0632907152,-0.1719755381,0.1237589344,-0.0444963202,0.0501845963,-0.5337992311,-0.2700666785,-0.3799063861,0.2092472166,-0.5469834208,-0.3301280737,-0.4705201089,-0.3462615907,0.0097750798,0.5019389391,-0.0336737707,0.334241271,-0.4741787612,-0.1968929768,-0.1534341425,-0.2213002592,0.0375547037,0.0770918503,-0.099674046,-0.0007810764,0.4961389303,0.0071368823,0.2611541152,-0.294490248,-0.1273926944,-0.3649143577,-0.3177824318,-0.0738705322,0.0519118495,0.0148224924,0.2240076661,0.0710083172,0.2262447178,-0.4085937142,0.194751054,0.0442344956,-0.503709197,0.2894505262,-0.0566867739,-0.0870469734,0.1449697912,-0.1841807514,-0.0518224537,-0.1697621644,-0.1883084029,0.1560915709,0.2967365086,0.1249212772,-0.0799535662,-0.069409959,-0.0038179082,0.388718456,-0.2261020541,-0.6709474921,0.4219002128,-0.0117652798,-0.20349738,-0.171230197,-0.0835984871,0.1688533127,0.2346090376,-0.2976332009,-0.2025556862,-0.3522112668,0.0260210447,0.2862877846,0.0065287603,0.3455328345,0.2454901934,0.003499611,-0.2818132043,-0.2827116251,0.2768625617,-0.0182156824,0.4016954303,-0.1409533918,0.1183669269,-0.0307261702,0.560443759,0.5893762112,0.1702058464,0.1602053046,0.1725814641,0.2031650841,-0.1808362156,-0.3893718123,0.0644636452,-0.06914059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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2953","title":"Trying to get in touch regarding a security issue","comments":"Hi @JamieSlome,\r\n\r\nThanks for reaching out. Yes, you are right: I'm opening a PR to add the `SECURITY.md` file and a contact method.\r\n\r\nIn the meantime, please feel free to report the security issue to: feedback@huggingface.co","body":"Hey there!\n\nI'd like to report a security issue but cannot find contact instructions on your repository.\n\nIf not a hassle, might you kindly add a `SECURITY.md` file with an email, or another contact method? GitHub [recommends](https:\/\/docs.github.com\/en\/code-security\/getting-started\/adding-a-security-policy-to-your-repository) this best practice to ensure security issues are responsibly disclosed, and it would serve as a simple instruction for security researchers in the future.\n\nThank you for your consideration, and I look forward to hearing from you!\n\n(cc @huntr-helper)","comment_length":36,"text":"Trying to get in touch regarding a security issue \n Hey there!\n\nI'd like to report a security issue but cannot find contact instructions on your repository.\n\nIf not a hassle, might you kindly add a `SECURITY.md` file with an email, or another contact method? GitHub [recommends](https:\/\/docs.github.com\/en\/code-security\/getting-started\/adding-a-security-policy-to-your-repository) this best practice to ensure security issues are responsibly disclosed, and it would serve as a simple instruction for security researchers in the future.\n\nThank you for your consideration, and I look forward to hearing from you!\n\n(cc @huntr-helper) \n Hi @JamieSlome,\r\n\r\nThanks for reaching out. Yes, you are right: I'm opening a PR to add the `SECURITY.md` file and a contact method.\r\n\r\nIn the meantime, please feel free to report the security issue to: feedback@huggingface.co","embeddings":[0.2186535299,-0.1629607975,-0.142506972,-0.1004328355,0.185160622,-0.1031139567,0.3181802928,-0.1012666747,-0.1313705444,0.4227073491,0.2270617932,0.0571598783,-0.0121725751,0.2170338035,0.1085698083,0.2790280879,-0.1240275204,-0.2373872399,0.3156681657,0.0582364909,0.1769996136,0.0810235217,0.0039010879,0.0446605459,0.0098963538,0.0930073783,-0.1246273294,0.4496959448,0.1082037985,-0.270145148,0.3850389123,0.0491372384,-0.1182155013,0.1254822016,-0.0000965996,0.0396931656,0.3298994303,0.0268374644,0.0001174756,0.0823182389,0.1790399402,-0.6113688946,-0.0278172009,0.0783896372,-0.1868285835,0.4804748595,0.2070450038,0.0234168991,0.5059351325,0.157572791,0.3344627321,0.0587490201,0.5902059078,-0.0439039618,0.1273012161,-0.0482194647,-0.0500769541,0.2850489616,0.2533285022,0.274408102,0.2047336549,0.0853587762,0.1878501028,-0.1136896908,0.0600220375,0.0611489266,0.2654817998,-0.254378438,0.027584333,-0.0843033716,0.1713146418,-0.0212308895,-0.2210464627,-0.1931808293,0.1317444742,-0.2573877871,0.4460543394,0.2202801853,-0.2861532271,0.1926689595,-0.2557120323,-0.2708847821,-0.0342833661,0.0059901644,0.0811089426,-0.1336501986,-0.1981843859,-0.155624792,-0.0442018732,-0.364349544,-0.2063210607,0.3382320702,-0.0837426037,0.1543415487,0.070487693,-0.2926999629,0.364187181,0.0682249069,0.2650503218,0.3983977735,-0.002788357,0.1773603857,-0.3701789975,-0.0372372605,0.1142850146,-0.0041438239,-0.0933001861,0.1987629086,0.509789288,0.2794699371,0.4129339159,0.1565452367,-0.0970191583,0.0075627426,-0.0258872136,0.0297542419,0.3324610591,-0.4054474533,-0.0662545934,0.2657257617,-0.2179051638,-0.092885673,-0.1060036346,0.2074237168,0.2551059723,-0.2366822809,0.1271453947,-0.4456458092,0.0714959353,0.061276447,-0.1995547265,-0.070740737,-0.3747984469,0.2753647864,-0.022518713,-0.0781670809,-0.0199483279,0.1700403839,0.2198875844,0.0030297711,-0.1372493505,0.4214530289,-0.0805090964,0.4741144776,-0.0731608197,-0.3155764639,-0.0810498595,-0.2570367455,-0.0302660018,-0.0682931915,-0.3203935921,-0.0540543869,-0.2375963628,0.283923775,-0.0266592335,0.2131969929,0.1176687852,-0.080514051,-0.2998147607,0.0816607699,0.3131218553,0.3426598012,0.2536273897,-0.1591525078,0.1389365196,0.3485186696,0.2420519888,-0.1765068173,-0.0470203497,-0.0838602409,0.4095532894,0.2057964951,-0.1720517278,-0.2624775767,-0.3719707727,0.051056888,0.00675245,-0.3616034985,-0.2091307193,0.0243775379,-0.7009596825,-0.1723297685,0.0465557612,-0.0651908591,-0.1146286801,-0.12784338,0.0126011986,-0.146745339,-0.2860130966,0.2074053735,-0.2274108678,-0.1561577171,-0.1869653165,-0.0415098555,-0.1570719481,0.0859098732,0.2366808951,-0.1140159518,0.2389257252,-0.0850061625,-0.2952872217,0.1620395482,0.3350214064,0.3262414336,-0.3032127023,0.178787753,0.1010295376,0.0042966106,0.1408965886,0.3541580737,-0.0627841279,-0.2590534389,-0.0284352731,-0.1143797338,0.0304272007,-0.3394260406,0.3055904508,0.2394157499,-0.0121590812,-0.151816994,0.0180012025,0.5105559826,-0.3734747469,0.2341839075,-0.1048416346,0.1772060394,-0.1914599687,-0.2515123785,0.1202365682,0.2091029286,0.2587940097,0.0913000181,0.2138013691,0.212645337,-0.1964057833,-0.1337648034,0.2960145473,0.4127744138,0.2689934671,-0.0198462959,0.0615033917,-0.0176897328,-0.1563456804,0.3779199421,-0.0581042916,0.2245612293,0.1328174174,-0.3083682358,-0.0605074465,0.2274338156,0.1826453805,-0.1204258427,-0.2156412303,-0.0192205403,0.0075909561,0.0134622725,0.0440552011,-0.3674945235,-0.4067901969,0.1730782986,0.1649362892,-0.1202769205,0.0043011736,0.2058712095,0.2499071658,0.0619490966,0.2164054811,0.0284186173,-0.0215610527,0.18863599,-0.1294030249,0.1568417698,-0.0459261164,-0.1609865576,-0.0449463762,-0.1972198188,-0.1158359349,-0.2535040975,0.4214959741,0.0381972976,0.0087435488,-0.1069221646,-0.2781147361,-0.0478855595,-0.3684829175,-0.1846963614,-0.026906047,-0.0684127212,-0.1163773686,0.1281846017,-0.1842909008,0.0011452953,-0.0419020019,-0.2139053643,-0.1651486903,-0.0937363058,-0.1577631533,0.3605837822,-0.1110761166,0.5761532784,0.0520852879,0.1629662514,-0.1208504066,0.2495586425,-0.0210658778,-0.2983735502,0.5696049929,-0.2909913957,0.0740288645,-0.471295774,-0.4845912457,0.2485635728,-0.0262477994,-0.1499004662,0.1036492586,0.0376190729,0.1663997471,-0.1724300981,0.0673286989,0.180249989,-0.2771223187,-0.0980912298,0.0967492685,0.1341659129,-0.3827569187,-0.1321300268,-0.1525329947,-0.1259342134,0.3682902753,-0.0027658537,0.1859066188,0.2089606673,-0.220552206,0.1354504377,-0.2270277292,0.0350076258,-0.1908678114,-0.6111551523,0.2154478282,-0.2631402314,-0.2290403247,0.2007332891,0.0967875868,-0.1973006725,-0.2840696871,-0.4267006516,-0.322820276,-0.0494272262,0.1994755715,0.1742792875,0.0790401101,0.4597175419,0.034147948,-0.3432197273,-0.0173600353,-0.4325991571,-0.0652106479,-0.1599386632,0.1804701984,-0.0005131537,0.1099920943,-0.1268743128,0.4469510317,-0.0819336921,-0.0416341089,-0.0273823217,0.0961322039,0.2669239044,0.4730257988,0.0395700671,0.105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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2945","title":"Protect master branch","comments":"@lhoestq now the 2 are implemented.\r\n\r\nPlease note that for the the second protection, finally I have chosen to protect the master branch only from **merge commits** (see update comment above), so no need to disable\/re-enable the protection on each release (direct commits, different from merge commits, can be pushed to the remote master branch; and eventually reverted without messing up the repo history).","body":"After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n  - Currently, simple merge commits are already disabled\r\n  - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n  - ~~This protection would reject direct pushes to master branch~~\r\n  - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n  - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n  - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution.","comment_length":64,"text":"Protect master branch \n After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n  - Currently, simple merge commits are already disabled\r\n  - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n  - ~~This protection would reject direct pushes to master branch~~\r\n  - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n  - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n  - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution. \n @lhoestq now the 2 are implemented.\r\n\r\nPlease note that for the the second protection, finally I have chosen to protect the master branch only from **merge commits** (see update comment above), so no need to disable\/re-enable the protection on each release (direct commits, different from merge commits, can be pushed to the remote master branch; and eventually reverted without messing up the repo history).","embeddings":[-0.1553194672,-0.1002306268,-0.070321016,-0.0798171908,-0.1042534709,-0.1879874468,0.0104035577,0.2728635967,-0.0098489737,-0.0841399208,0.2926148772,-0.0778751224,-0.1324572563,0.2158903182,-0.0562292449,0.1705456823,0.2003232092,-0.0407981202,-0.0923330337,0.0279233586,0.0226084664,-0.0687029436,0.0832000151,0.0436301306,-0.1837819666,0.0054013738,-0.0148117775,0.1564962417,-0.4397666156,-0.4695702493,0.1452465504,0.2322402596,0.0251065325,0.570535183,-0.0001043637,-0.0003454557,0.1694866419,-0.0406010039,-0.1401022524,-0.2154636681,-0.4901072085,-0.4447958767,-0.07006336,-0.0083542913,0.1288067251,0.0528678969,-0.132499963,0.0238457564,0.3848451674,0.1055523977,0.2386109382,-0.1028517336,0.1812405735,0.0137694413,0.392008841,0.5474528074,-0.066634573,0.3031997979,0.0540179275,0.0006757497,0.0611058585,0.1741086096,-0.0823729113,-0.2866700888,0.2270194292,-0.1779912561,0.5181742311,-0.4243531227,-0.2415838391,-0.0831176639,0.0523793846,-0.1781465262,-0.4089041948,-0.2419367433,-0.1499476582,0.0076339091,0.0677802935,0.2468644679,0.039739579,0.0036496122,-0.3773649931,-0.2718090415,-0.004407926,0.0492040403,0.141940549,-0.1626293063,0.0664263666,-0.1270385236,0.2942230105,-0.0473279878,-0.1469833106,-0.3676346838,-0.2413333207,-0.0125874653,-0.1026133746,-0.1957468837,-0.2462065965,-0.3248277903,0.4251304567,0.3889683783,-0.3832727969,0.0137396995,-0.0304892361,-0.0810169131,0.2071851492,0.1178578511,-0.0104900496,0.0584886484,0.530713141,0.3144408166,0.0051584225,0.3255857527,0.3015701473,0.0030471815,0.0344459116,0.574118197,0.4289444685,-0.26292032,0.1276476383,0.1959735751,-0.3412007391,-0.3845081031,0.0711311549,-0.0861726329,0.1103260964,0.0271337908,-0.1147114784,0.0668015406,0.0451908335,-0.0380863659,-0.3079027534,-0.141485557,-0.3744001687,-0.0685305148,0.1860401928,-0.4571051598,0.1491677761,0.4325242639,-0.0680372268,-0.1992845535,0.0445522442,0.1292257011,0.0555874184,0.2961196899,-0.1537979692,-0.4526284337,0.2235967368,0.1313333064,-0.0668994114,0.1940043122,-0.1598993689,-0.2616221011,-0.1049757376,0.2977656722,-0.1482721567,0.3071561158,-0.5237665772,0.0363732092,-0.0044837268,0.3506707847,0.4910375476,0.3025480509,0.3280785084,-0.1685939282,0.0617566295,0.1903455257,0.3235116601,-0.0516864397,0.0623045377,-0.2877363563,0.1095039099,0.1450733244,-0.3121602237,0.0175557248,-0.0052360594,-0.1312822402,0.0107761566,-0.0714070052,-0.1119878516,-0.0989294425,-0.3666853309,0.0694030076,-0.0407375731,-0.1007770002,0.2079568803,-0.3201756477,-0.0947941095,0.2050814927,-0.1481231451,0.0208985135,-0.2919872105,-0.6061385274,-0.1636558473,-0.06343887,0.1249144152,0.2275642306,0.3607693315,-0.0050310493,-0.0283617247,-0.1714023799,-0.0093972106,0.1125069708,0.3421364129,-0.1496780068,-0.2415423691,0.1322465539,-0.3545884192,0.0911409035,-0.0785121024,-0.0097747799,0.0344485678,-0.3157884181,0.3412798941,-0.0440529883,-0.1208016798,-0.1141509414,0.2415172756,0.095951654,-0.1747335494,-0.2039210498,-0.2074069828,0.2601909339,-0.2408306301,0.3018657863,0.1300136894,0.1026374176,-0.1411807239,-0.0364533328,-0.0250960924,0.36193645,0.2363044769,-0.0899174288,-0.0591584407,0.1687609404,-0.0490626656,0.2035702467,0.2246947736,0.4252423346,0.3736152053,-0.1026815102,0.0626009405,-0.0917925984,-0.0787246898,0.0245947056,-0.3291273713,0.3280371726,0.073694557,-0.1394644678,-0.1740321219,-0.1033326834,-0.1740740985,-0.2202692628,-0.1707722992,-0.012951361,-0.1690982729,0.1943652332,-0.3365918398,0.2223692834,-0.2772080004,0.148579374,0.0150526064,-0.2516064942,-0.1024010181,0.0682980418,-0.0059674503,-0.1327149123,0.1241709664,0.3773829341,0.1278743744,0.1701000035,0.0042741634,0.0201513059,0.2572831213,-0.1164359227,0.353325963,0.1443069726,-0.1054452285,0.054064367,0.3364559114,0.1812186539,0.0643318817,0.346919328,-0.0033243245,0.0352581739,-0.2084068358,-0.0459848903,-0.2602106631,-0.5283415914,0.2192376256,-0.110637784,-0.3552163243,-0.0085041393,-0.0453661792,0.047418233,-0.5248722434,0.136029914,0.1835808307,0.2905260623,-0.3178029358,-0.1199780703,0.1263155937,-0.0225951802,0.0123441136,0.1285599768,0.3631602824,-0.2266809344,0.45945099,0.2389809936,-0.1695563644,-0.4726397991,-0.3434132934,0.0191301201,-0.2039363831,-0.1179748252,0.0098189935,-0.0989309773,0.0921538174,-0.3585401177,-0.3257124722,-0.018773742,-0.3730046451,-0.1600959152,0.0695955083,0.0628860295,-0.2346176803,-0.114040032,-0.386590004,-0.2821527123,0.4793556035,-0.0164330266,0.1156589612,-0.0431727543,-0.2923772335,-0.0459178463,0.0874827728,0.1366390288,-0.0000448525,-0.3609322906,0.0325667374,-0.0406603813,-0.0293471292,0.352609843,0.1854910851,-0.4302407205,0.0712517202,-0.3217604458,-0.1558190286,-0.0520843901,0.4864443243,0.3141199946,-0.0973737389,0.0710799471,0.1035510823,-0.1919001341,0.0550895296,-0.1816118211,0.1472238153,0.3454884887,0.2857186198,0.0378993787,0.1458674669,0.0254973993,0.7140879035,0.3778497577,-0.059657298,0.210337624,0.3106974065,-0.0277180839,0.0039902786,-0.2601569593,0.065046832,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2944","title":"Add  `remove_columns` to `IterableDataset ` ","comments":"Hi ! Good idea :)\r\nIf you are interested in contributing, feel free to give it a try and open a Pull Request. Also let me know if I can help you with this or if you have questions","body":"**Is your feature request related to a problem? Please describe.**\r\nA clear and concise description of what the problem is.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"c4\", 'realnewslike', streaming =True, split='train')\r\ndataset = dataset.remove_columns('url')\r\n```\r\n```\r\nAttributeError: 'IterableDataset' object has no attribute 'remove_columns'\r\n```\r\n\r\n**Describe the solution you'd like**\r\n\r\nIt would be nice to have `.remove_columns()` to match the `Datasets` api. \r\n\r\n\r\n**Describe alternatives you've considered**\r\n\r\nThis can be done with a single call to `.map()`, \r\n\r\nI can try to help add this. \ud83e\udd17","comment_length":39,"text":"Add  `remove_columns` to `IterableDataset `  \n **Is your feature request related to a problem? Please describe.**\r\nA clear and concise description of what the problem is.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"c4\", 'realnewslike', streaming =True, split='train')\r\ndataset = dataset.remove_columns('url')\r\n```\r\n```\r\nAttributeError: 'IterableDataset' object has no attribute 'remove_columns'\r\n```\r\n\r\n**Describe the solution you'd like**\r\n\r\nIt would be nice to have `.remove_columns()` to match the `Datasets` api. \r\n\r\n\r\n**Describe alternatives you've considered**\r\n\r\nThis can be done with a single call to `.map()`, \r\n\r\nI can try to help add this. \ud83e\udd17 \n Hi ! Good idea :)\r\nIf you are interested in contributing, feel free to give it a try and open a Pull Request. Also let me know if I can help you with this or if you have 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Hi ! I guess the caching mechanism should have considered the new `filter` to be different from the old one, and don't use cached results from the old `filter`.\r\nTo avoid other users from having this issue we could make the caching differentiate the two, what do you think ?","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question:  This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n  File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in <module>\r\n    ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n    indices = self.map(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n    return self._map_single(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n    return cls(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":50,"text":"Backwards compatibility broken for cached datasets that use `.filter()` \n ## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question:  This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n  File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in <module>\r\n    ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n    indices = self.map(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n    return self._map_single(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n    return cls(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n Hi ! I guess the caching mechanism should have considered the new `filter` to be different from the old one, and don't use cached results from the old `filter`.\r\nTo avoid other users from having this issue we could make the caching differentiate the two, what do you think ?","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.1531914324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"If it's easy enough to implement, then yes please \ud83d\ude04  But this issue can be low-priority, since I've only encountered it in a couple of `transformers` CI tests.","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question:  This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n  File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in <module>\r\n    ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n    indices = self.map(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n    return self._map_single(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n    return cls(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":28,"text":"Backwards compatibility broken for cached datasets that use `.filter()` \n ## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question:  This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n  File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in <module>\r\n    ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n    indices = self.map(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n    return self._map_single(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n    return cls(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n If it's easy enough to implement, then yes please \ud83d\ude04  But this issue can be low-priority, since I've only encountered it in a couple of `transformers` CI 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Well it can cause issue with anyone that updates `datasets` and re-run some code that uses filter, so I'm creating a PR","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question:  This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n  File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in <module>\r\n    ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n    indices = self.map(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n    return self._map_single(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n    return cls(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":22,"text":"Backwards compatibility broken for cached datasets that use `.filter()` \n ## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question:  This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n  File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in <module>\r\n    ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n    indices = self.map(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n    return self._map_single(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n    return cls(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n Well it can cause issue with anyone that updates `datasets` and re-run some code that uses filter, so I'm creating a PR","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.153191432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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"I just merged a fix, let me know if you're still having this kind of issues :)\r\n\r\nWe'll do a release soon to make this fix available","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question:  This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n  File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in <module>\r\n    ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n    indices = self.map(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n    return self._map_single(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n    return cls(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":27,"text":"Backwards compatibility broken for cached datasets that use `.filter()` \n ## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question:  This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n  File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in <module>\r\n    ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n    indices = self.map(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n    return self._map_single(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n    return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n    return cls(\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n    self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n    return Features(recursive_reorder(self, other))\r\n  File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n    raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n \n I just merged a fix, let me know if you're still having this kind of issues :)\r\n\r\nWe'll do a release soon to make this fix 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2937","title":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied","comments":"Hi @daqieq, thanks for reporting.\r\n\r\nUnfortunately, I was not able to reproduce this bug:\r\n```ipython\r\nIn [1]: from datasets import load_dataset\r\n   ...: ds = load_dataset('wiki_bio')\r\nDownloading: 7.58kB [00:00, 26.3kB\/s]\r\nDownloading: 2.71kB [00:00, ?B\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\\r\n1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nDownloading: 334MB [01:17, 4.32MB\/s]\r\nDataset wiki_bio downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9. Subsequent calls will reuse thi\r\ns data.\r\n```\r\n\r\nThis kind of error messages usually happen because:\r\n- Your running Python script hasn't write access to that directory\r\n- You have another program (the File Explorer?) already browsing inside that directory","body":"## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n    next(self.gen)\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n    os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n","comment_length":109,"text":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied \n ## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n    next(self.gen)\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n    os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n \n Hi @daqieq, thanks for reporting.\r\n\r\nUnfortunately, I was not able to reproduce this bug:\r\n```ipython\r\nIn [1]: from datasets import load_dataset\r\n   ...: ds = load_dataset('wiki_bio')\r\nDownloading: 7.58kB [00:00, 26.3kB\/s]\r\nDownloading: 2.71kB [00:00, ?B\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\\r\n1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nDownloading: 334MB [01:17, 4.32MB\/s]\r\nDataset wiki_bio downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9. Subsequent calls will reuse thi\r\ns data.\r\n```\r\n\r\nThis kind of error messages usually happen because:\r\n- Your running Python script hasn't write access to that directory\r\n- You have another program (the File Explorer?) already browsing inside that directory","embeddings":[-0.2289305478,0.3834535182,0.040345341,0.2550398409,-0.0121113108,0.2622572184,0.5036683083,0.1329532564,0.3442817032,0.1357175708,-0.1089743897,0.0772839189,0.073150374,-0.0056987684,-0.1256225109,0.0789283216,0.0364452153,-0.0053882273,0.214949578,0.1039548293,-0.4238833189,0.1072954684,-0.172855407,0.1861151755,0.0260949377,-0.1363423616,0.1212603897,0.3607298434,-0.0307886619,-0.3922062814,0.3530231118,0.0634532124,0.4639182985,0.2582460642,-0.000119246,-0.0137069086,0.2306841165,0.1244994402,-0.0737274736,0.0870998204,-0.1839853674,-0.1409676671,0.1652300805,-0.0976862535,0.1767440438,0.3634920418,-0.1021372527,-0.2009297311,-0.0130413789,0.379034102,0.1573001444,-0.0381855965,-0.1186785623,-0.0752038136,0.4933359921,-0.1592982262,-0.133179754,0.2798380852,0.0790250301,-0.11457026,-0.0989914164,0.1995727867,-0.1621495485,0.1471577436,0.0186581202,0.0311292373,0.2162310332,-0.4581103027,0.3521417677,0.1512579024,0.9656507373,-0.1955735534,-0.1914045215,-0.0887490958,0.0133037586,-0.0330028497,0.4326035678,0.4286232889,-0.3245685697,0.2523709536,0.0659693852,0.0835653022,-0.0825510696,-0.0019090191,0.0712853372,0.1486521512,-0.0504703857,0.1195566207,-0.1147312596,-0.0834005401,0.3863353431,-0.2138270736,-0.254601866,0.191666767,-0.3394370973,0.2898705602,0.0534532256,0.7778053284,0.157877773,0.3155301809,0.1250241548,-0.0540518314,0.2136244029,0.0651903003,0.0085929586,0.3214896023,-0.1122384295,-0.0169230793,0.4271060824,0.1174419895,-0.0816169605,-0.0089906445,-0.0200457647,-0.4294741154,0.3538792133,0.1054070443,0.4081188738,-0.2856419981,-0.1217012107,-0.0002951851,0.1581317037,-0.0239709932,0.0588830411,0.5011533499,-0.2717713416,0.0426839963,0.0682517141,0.1794451177,0.0186107904,0.0977605507,-0.0877998695,-0.0890028104,-0.2667180896,-0.0514384918,0.408646673,-0.3225747645,0.0955644995,0.2566769719,0.0270482041,-0.1792946011,0.1918320805,-0.0414977111,-0.0328572541,0.3576870263,0.397295326,0.3376339078,0.174932614,-0.2122963965,-0.0349953882,0.1863114685,-0.4872902334,-0.4025040567,-0.0336632058,0.1213277355,-0.0833240524,0.0632052422,-0.1980368942,-0.0482573472,0.3823129535,-0.3008547723,-0.0618359111,-0.0137255481,-0.2206203043,-0.5042767525,0.040707428,0.4012890756,-0.3596991003,0.1303672194,-0.0915537477,-0.2640866041,0.1504325122,0.0056375656,0.0056341127,0.3616814017,-0.2812198102,0.0156073133,0.2774584889,-0.3938943148,-1.0015972853,-0.0210030079,0.0730995089,0.1660424769,0.2444063723,0.1191371009,0.3659378588,-0.0303458292,0.1704769284,0.3187128901,0.1300752312,0.1119742319,-0.0629962236,-0.2038549483,0.2796608508,0.1339266002,0.0883391201,0.3009034991,0.312600106,-0.0569339506,0.251095593,-0.1872612089,0.116804406,0.1925157905,0.0114956526,-0.0351362228,0.1375146806,-0.1277379841,-0.420409292,0.3514482081,-0.00811728,0.0851668194,0.0759790465,-0.2027431279,-0.3184642494,0.0913407728,-0.5447552204,-0.2889970243,0.0851063654,0.2938987911,0.2124249339,0.0236818604,0.1891641766,0.3374149799,-0.2082268894,0.1271959245,-0.1811406165,0.0386061855,-0.1883375496,-0.1364492327,-0.2122476399,0.0469153933,0.2001280636,0.2527483404,-0.2134325951,0.1961012632,0.151213333,0.0374986753,-0.3785572946,-0.0595560744,-0.0223776083,0.1217297763,0.1568485051,0.1381781846,0.1483232975,-0.0443943143,-0.1922187656,0.1377540231,-0.1596345007,0.14373146,-0.3266511559,0.2430362701,0.2447061241,0.0900503173,0.0856966749,0.0344529897,0.3054705858,0.0241174567,0.0745961443,-0.2858042717,0.1012615561,-0.0778078139,0.0593021847,0.0225473158,0.0812815726,0.2497533113,-0.1746012717,0.0168862902,0.2869935334,0.349679023,0.5377136469,-0.0409865156,0.0515500903,0.0667970031,0.0479120463,0.0896691009,0.1192515045,0.0857714713,-0.0702273324,0.2634492815,0.1284338385,0.2241659462,-0.0647975802,-0.1138611957,-0.107541278,0.4194297791,-0.4875138104,-0.084387593,0.1000552922,0.0748399198,0.1812013388,-0.2428474277,-0.0887820199,-0.1460988075,-0.2430461347,0.3116364777,0.0940705463,0.0835373625,-0.511957705,0.164441213,0.0075377976,-0.2018063366,-0.1137571484,0.1238797531,-0.1658339351,-0.0141071854,0.1862449348,-0.1799398512,0.1873875707,-0.2951588631,0.2437517196,-0.3901760876,-0.1954325885,0.0151099013,0.0578858256,0.4098414481,0.1254311204,0.1551068723,0.1864105314,-0.3417646587,0.0566025972,-0.1075194404,-0.1510609835,0.2333712131,-0.0550265461,0.0938554928,0.0341966748,-0.2493757308,-0.5408244133,-0.2501789927,-0.0095754005,0.3858899772,0.0983009189,-0.0028510524,-0.1126530915,0.0033437847,0.267029494,0.3937678933,-0.2108280957,-0.2492020875,0.2102492452,-0.2092680335,-0.3346993625,0.0819493756,-0.1042304859,0.0445711389,0.2523835003,-0.4788489342,-0.0906342939,-0.3600659072,0.4096082449,-0.232615903,0.0938282236,0.3463047147,0.0892930999,0.1463989317,-0.0417488404,-0.2757158875,-0.0651353374,-0.2946413159,-0.068232581,0.3381980062,0.4274490774,-0.0575702377,0.3770318925,0.3002279699,0.3604496717,0.3344713151,0.04584153,0.3711133003,0.0542609356,-0.2594737113,0.1087757051,-0.1943459958,-0.140160799,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2937","title":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied","comments":"Thanks @albertvillanova for looking at it! I tried on my personal Windows machine and it downloaded just fine.\r\n\r\nRunning on my work machine and on a colleague's machine it is consistently hitting this error. It's not a write access issue because the `.incomplete` directory is written just fine. It just won't rename and then it deletes the directory in the `finally` step. Also the zip file is written and extracted fine in the downloads directory.\r\n\r\nThat leaves another program that might be interfering, and there are plenty of those in my work machine ... (full antivirus, data loss prevention, etc.). So the question remains, why not extend the `try` block to allow catching the error and circle back to the rename after the unknown program is finished doing its 'stuff'. This is the approach that I read about in the linked repo (see my comments above).\r\n\r\nIf it's not high priority, that's fine. However, if someone were to write an PR that solved this issue in our environment in an `except` clause, would it be reviewed for inclusion in a future release? Just wondering whether I should spend any more time on this issue.","body":"## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n    next(self.gen)\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n    os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n","comment_length":194,"text":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied \n ## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n    next(self.gen)\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n    os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n \n Thanks @albertvillanova for looking at it! I tried on my personal Windows machine and it downloaded just fine.\r\n\r\nRunning on my work machine and on a colleague's machine it is consistently hitting this error. It's not a write access issue because the `.incomplete` directory is written just fine. It just won't rename and then it deletes the directory in the `finally` step. Also the zip file is written and extracted fine in the downloads directory.\r\n\r\nThat leaves another program that might be interfering, and there are plenty of those in my work machine ... (full antivirus, data loss prevention, etc.). So the question remains, why not extend the `try` block to allow catching the error and circle back to the rename after the unknown program is finished doing its 'stuff'. This is the approach that I read about in the linked repo (see my comments above).\r\n\r\nIf it's not high priority, that's fine. However, if someone were to write an PR that solved this issue in our environment in an `except` clause, would it be reviewed for inclusion in a future release? Just wondering whether I should spend any more time on this issue.","embeddings":[-0.2289305478,0.3834535182,0.040345341,0.2550398409,-0.0121113108,0.2622572184,0.5036683083,0.1329532564,0.3442817032,0.1357175708,-0.1089743897,0.0772839189,0.073150374,-0.0056987684,-0.1256225109,0.0789283216,0.0364452153,-0.0053882273,0.214949578,0.1039548293,-0.4238833189,0.1072954684,-0.172855407,0.1861151755,0.0260949377,-0.1363423616,0.1212603897,0.3607298434,-0.0307886619,-0.3922062814,0.3530231118,0.0634532124,0.4639182985,0.2582460642,-0.000119246,-0.0137069086,0.2306841165,0.1244994402,-0.0737274736,0.0870998204,-0.1839853674,-0.1409676671,0.1652300805,-0.0976862535,0.1767440438,0.3634920418,-0.1021372527,-0.2009297311,-0.0130413789,0.379034102,0.1573001444,-0.0381855965,-0.1186785623,-0.0752038136,0.4933359921,-0.1592982262,-0.133179754,0.2798380852,0.0790250301,-0.11457026,-0.0989914164,0.1995727867,-0.1621495485,0.1471577436,0.0186581202,0.0311292373,0.2162310332,-0.4581103027,0.3521417677,0.1512579024,0.9656507373,-0.1955735534,-0.1914045215,-0.0887490958,0.0133037586,-0.0330028497,0.4326035678,0.4286232889,-0.3245685697,0.2523709536,0.0659693852,0.0835653022,-0.0825510696,-0.0019090191,0.0712853372,0.1486521512,-0.0504703857,0.1195566207,-0.1147312596,-0.0834005401,0.3863353431,-0.2138270736,-0.254601866,0.191666767,-0.3394370973,0.2898705602,0.0534532256,0.7778053284,0.157877773,0.3155301809,0.1250241548,-0.0540518314,0.2136244029,0.0651903003,0.0085929586,0.3214896023,-0.1122384295,-0.0169230793,0.4271060824,0.1174419895,-0.0816169605,-0.0089906445,-0.0200457647,-0.4294741154,0.3538792133,0.1054070443,0.4081188738,-0.2856419981,-0.1217012107,-0.0002951851,0.1581317037,-0.0239709932,0.0588830411,0.5011533499,-0.2717713416,0.0426839963,0.0682517141,0.1794451177,0.0186107904,0.0977605507,-0.0877998695,-0.0890028104,-0.2667180896,-0.0514384918,0.408646673,-0.3225747645,0.0955644995,0.2566769719,0.0270482041,-0.1792946011,0.1918320805,-0.0414977111,-0.0328572541,0.3576870263,0.397295326,0.3376339078,0.174932614,-0.2122963965,-0.0349953882,0.1863114685,-0.4872902334,-0.4025040567,-0.0336632058,0.1213277355,-0.0833240524,0.0632052422,-0.1980368942,-0.0482573472,0.3823129535,-0.3008547723,-0.0618359111,-0.0137255481,-0.2206203043,-0.5042767525,0.040707428,0.4012890756,-0.3596991003,0.1303672194,-0.0915537477,-0.2640866041,0.1504325122,0.0056375656,0.0056341127,0.3616814017,-0.2812198102,0.0156073133,0.2774584889,-0.3938943148,-1.0015972853,-0.0210030079,0.0730995089,0.1660424769,0.2444063723,0.1191371009,0.3659378588,-0.0303458292,0.1704769284,0.3187128901,0.1300752312,0.1119742319,-0.0629962236,-0.2038549483,0.2796608508,0.1339266002,0.0883391201,0.3009034991,0.312600106,-0.0569339506,0.251095593,-0.1872612089,0.116804406,0.1925157905,0.0114956526,-0.0351362228,0.1375146806,-0.1277379841,-0.420409292,0.3514482081,-0.00811728,0.0851668194,0.0759790465,-0.2027431279,-0.3184642494,0.0913407728,-0.5447552204,-0.2889970243,0.0851063654,0.2938987911,0.2124249339,0.0236818604,0.1891641766,0.3374149799,-0.2082268894,0.1271959245,-0.1811406165,0.0386061855,-0.1883375496,-0.1364492327,-0.2122476399,0.0469153933,0.2001280636,0.2527483404,-0.2134325951,0.1961012632,0.151213333,0.0374986753,-0.3785572946,-0.0595560744,-0.0223776083,0.1217297763,0.1568485051,0.1381781846,0.1483232975,-0.0443943143,-0.1922187656,0.1377540231,-0.1596345007,0.14373146,-0.3266511559,0.2430362701,0.2447061241,0.0900503173,0.0856966749,0.0344529897,0.3054705858,0.0241174567,0.0745961443,-0.2858042717,0.1012615561,-0.0778078139,0.0593021847,0.0225473158,0.0812815726,0.2497533113,-0.1746012717,0.0168862902,0.2869935334,0.349679023,0.5377136469,-0.0409865156,0.0515500903,0.0667970031,0.0479120463,0.0896691009,0.1192515045,0.0857714713,-0.0702273324,0.2634492815,0.1284338385,0.2241659462,-0.0647975802,-0.1138611957,-0.107541278,0.4194297791,-0.4875138104,-0.084387593,0.1000552922,0.0748399198,0.1812013388,-0.2428474277,-0.0887820199,-0.1460988075,-0.2430461347,0.3116364777,0.0940705463,0.0835373625,-0.511957705,0.164441213,0.0075377976,-0.2018063366,-0.1137571484,0.1238797531,-0.1658339351,-0.0141071854,0.1862449348,-0.1799398512,0.1873875707,-0.2951588631,0.2437517196,-0.3901760876,-0.1954325885,0.0151099013,0.0578858256,0.4098414481,0.1254311204,0.1551068723,0.1864105314,-0.3417646587,0.0566025972,-0.1075194404,-0.1510609835,0.2333712131,-0.0550265461,0.0938554928,0.0341966748,-0.2493757308,-0.5408244133,-0.2501789927,-0.0095754005,0.3858899772,0.0983009189,-0.0028510524,-0.1126530915,0.0033437847,0.267029494,0.3937678933,-0.2108280957,-0.2492020875,0.2102492452,-0.2092680335,-0.3346993625,0.0819493756,-0.1042304859,0.0445711389,0.2523835003,-0.4788489342,-0.0906342939,-0.3600659072,0.4096082449,-0.232615903,0.0938282236,0.3463047147,0.0892930999,0.1463989317,-0.0417488404,-0.2757158875,-0.0651353374,-0.2946413159,-0.068232581,0.3381980062,0.4274490774,-0.0575702377,0.3770318925,0.3002279699,0.3604496717,0.3344713151,0.04584153,0.3711133003,0.0542609356,-0.2594737113,0.1087757051,-0.1943459958,-0.140160799,0.3007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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2937","title":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied","comments":"Hi @albertvillanova, even I am facing the same issue on my work machine:\r\n\r\n`Downloading and preparing dataset json\/c4-en-html-with-metadata to C:\\Users\\......\\.cache\\huggingface\\datasets\\json\\c4-en-html-with-metadata-4635c2fd9249f62d\\0.0.0\\c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 983.42it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 209.01it\/s]\r\nTraceback (most recent call last):\r\n  File \"bsmetadata\/preprocessing_utils.py\", line 710, in <module>\r\n    ds = load_dataset(\r\n  File \"C:\\Users\\.......\\AppData\\Roaming\\Python\\Python38\\site-packages\\datasets\\load.py\", line 1694, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\........\\AppData\\Roaming\\Python\\Python38\\site-packages\\datasets\\builder.py\", line 603, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\..........\\AppData\\Local\\Programs\\Python\\Python38\\lib\\contextlib.py\", line 120, in __exit__\r\n    next(self.gen)\r\n  File \"C:\\Users\\.....\\AppData\\Roaming\\Python\\Python38\\site-packages\\datasets\\builder.py\", line 557, in incomplete_dir\r\n    os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\.........\\\\.cache\\\\huggingface\\\\datasets\\\\json\\\\c4-en-html-with-metadata-4635c2fd9249f62d\\\\0.0.0\\\\c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde.incomplete' -> 'C:\\\\Users\\\\I355109\\\\.cache\\\\huggingface\\\\datasets\\\\json\\\\c4-en-html-with-metadata-4635c2fd9249f62d\\\\0.0.0\\\\c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde'`","body":"## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n    next(self.gen)\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n    os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n","comment_length":80,"text":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied \n ## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n    next(self.gen)\r\n  File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n    os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n \n Hi @albertvillanova, even I am facing the same issue on my work machine:\r\n\r\n`Downloading and preparing dataset json\/c4-en-html-with-metadata to C:\\Users\\......\\.cache\\huggingface\\datasets\\json\\c4-en-html-with-metadata-4635c2fd9249f62d\\0.0.0\\c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 983.42it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 209.01it\/s]\r\nTraceback (most recent call last):\r\n  File \"bsmetadata\/preprocessing_utils.py\", line 710, in <module>\r\n    ds = load_dataset(\r\n  File \"C:\\Users\\.......\\AppData\\Roaming\\Python\\Python38\\site-packages\\datasets\\load.py\", line 1694, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\........\\AppData\\Roaming\\Python\\Python38\\site-packages\\datasets\\builder.py\", line 603, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\..........\\AppData\\Local\\Programs\\Python\\Python38\\lib\\contextlib.py\", line 120, in __exit__\r\n    next(self.gen)\r\n  File \"C:\\Users\\.....\\AppData\\Roaming\\Python\\Python38\\site-packages\\datasets\\builder.py\", line 557, in incomplete_dir\r\n    os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\.........\\\\.cache\\\\huggingface\\\\datasets\\\\json\\\\c4-en-html-with-metadata-4635c2fd9249f62d\\\\0.0.0\\\\c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde.incomplete' -> 'C:\\\\Users\\\\I355109\\\\.cache\\\\huggingface\\\\datasets\\\\json\\\\c4-en-html-with-metadata-4635c2fd9249f62d\\\\0.0.0\\\\c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde'`","embeddings":[-0.2289305478,0.3834535182,0.040345341,0.2550398409,-0.0121113108,0.2622572184,0.5036683083,0.1329532564,0.3442817032,0.1357175708,-0.1089743897,0.0772839189,0.073150374,-0.0056987684,-0.1256225109,0.0789283216,0.0364452153,-0.0053882273,0.214949578,0.1039548293,-0.4238833189,0.1072954684,-0.172855407,0.1861151755,0.0260949377,-0.1363423616,0.1212603897,0.3607298434,-0.0307886619,-0.3922062814,0.3530231118,0.0634532124,0.4639182985,0.2582460642,-0.000119246,-0.0137069086,0.2306841165,0.1244994402,-0.0737274736,0.0870998204,-0.1839853674,-0.1409676671,0.1652300805,-0.0976862535,0.1767440438,0.3634920418,-0.1021372527,-0.2009297311,-0.0130413789,0.379034102,0.1573001444,-0.0381855965,-0.1186785623,-0.0752038136,0.4933359921,-0.1592982262,-0.133179754,0.2798380852,0.0790250301,-0.11457026,-0.0989914164,0.1995727867,-0.1621495485,0.1471577436,0.0186581202,0.0311292373,0.2162310332,-0.4581103027,0.3521417677,0.1512579024,0.9656507373,-0.1955735534,-0.1914045215,-0.0887490958,0.0133037586,-0.0330028497,0.4326035678,0.4286232889,-0.3245685697,0.2523709536,0.0659693852,0.0835653022,-0.0825510696,-0.0019090191,0.0712853372,0.1486521512,-0.0504703857,0.1195566207,-0.1147312596,-0.0834005401,0.3863353431,-0.2138270736,-0.254601866,0.191666767,-0.3394370973,0.2898705602,0.0534532256,0.7778053284,0.157877773,0.3155301809,0.1250241548,-0.0540518314,0.2136244029,0.0651903003,0.0085929586,0.3214896023,-0.1122384295,-0.0169230793,0.4271060824,0.1174419895,-0.0816169605,-0.0089906445,-0.0200457647,-0.4294741154,0.3538792133,0.1054070443,0.4081188738,-0.2856419981,-0.1217012107,-0.0002951851,0.1581317037,-0.0239709932,0.0588830411,0.5011533499,-0.2717713416,0.0426839963,0.0682517141,0.1794451177,0.0186107904,0.0977605507,-0.0877998695,-0.0890028104,-0.2667180896,-0.0514384918,0.408646673,-0.3225747645,0.0955644995,0.2566769719,0.0270482041,-0.1792946011,0.1918320805,-0.0414977111,-0.0328572541,0.3576870263,0.397295326,0.3376339078,0.174932614,-0.2122963965,-0.0349953882,0.1863114685,-0.4872902334,-0.4025040567,-0.0336632058,0.1213277355,-0.0833240524,0.0632052422,-0.1980368942,-0.0482573472,0.3823129535,-0.3008547723,-0.0618359111,-0.0137255481,-0.2206203043,-0.5042767525,0.040707428,0.4012890756,-0.3596991003,0.1303672194,-0.0915537477,-0.2640866041,0.1504325122,0.0056375656,0.0056341127,0.3616814017,-0.2812198102,0.0156073133,0.2774584889,-0.3938943148,-1.0015972853,-0.0210030079,0.0730995089,0.1660424769,0.2444063723,0.1191371009,0.3659378588,-0.0303458292,0.1704769284,0.3187128901,0.1300752312,0.1119742319,-0.0629962236,-0.2038549483,0.2796608508,0.1339266002,0.0883391201,0.3009034991,0.312600106,-0.0569339506,0.251095593,-0.1872612089,0.116804406,0.1925157905,0.0114956526,-0.0351362228,0.1375146806,-0.1277379841,-0.420409292,0.3514482081,-0.00811728,0.0851668194,0.0759790465,-0.2027431279,-0.3184642494,0.0913407728,-0.5447552204,-0.2889970243,0.0851063654,0.2938987911,0.2124249339,0.0236818604,0.1891641766,0.3374149799,-0.2082268894,0.1271959245,-0.1811406165,0.0386061855,-0.1883375496,-0.1364492327,-0.2122476399,0.0469153933,0.2001280636,0.2527483404,-0.2134325951,0.1961012632,0.151213333,0.0374986753,-0.3785572946,-0.0595560744,-0.0223776083,0.1217297763,0.1568485051,0.1381781846,0.1483232975,-0.0443943143,-0.1922187656,0.1377540231,-0.1596345007,0.14373146,-0.3266511559,0.2430362701,0.2447061241,0.0900503173,0.0856966749,0.0344529897,0.3054705858,0.0241174567,0.0745961443,-0.2858042717,0.1012615561,-0.0778078139,0.0593021847,0.0225473158,0.0812815726,0.2497533113,-0.1746012717,0.0168862902,0.2869935334,0.349679023,0.5377136469,-0.0409865156,0.0515500903,0.0667970031,0.0479120463,0.0896691009,0.1192515045,0.0857714713,-0.0702273324,0.2634492815,0.1284338385,0.2241659462,-0.0647975802,-0.1138611957,-0.107541278,0.4194297791,-0.4875138104,-0.084387593,0.1000552922,0.0748399198,0.1812013388,-0.2428474277,-0.0887820199,-0.1460988075,-0.2430461347,0.3116364777,0.0940705463,0.0835373625,-0.511957705,0.164441213,0.0075377976,-0.2018063366,-0.1137571484,0.1238797531,-0.1658339351,-0.0141071854,0.1862449348,-0.1799398512,0.1873875707,-0.2951588631,0.2437517196,-0.3901760876,-0.1954325885,0.0151099013,0.0578858256,0.4098414481,0.1254311204,0.1551068723,0.1864105314,-0.3417646587,0.0566025972,-0.1075194404,-0.1510609835,0.2333712131,-0.0550265461,0.0938554928,0.0341966748,-0.2493757308,-0.5408244133,-0.2501789927,-0.0095754005,0.3858899772,0.0983009189,-0.0028510524,-0.1126530915,0.0033437847,0.267029494,0.3937678933,-0.2108280957,-0.2492020875,0.2102492452,-0.2092680335,-0.3346993625,0.0819493756,-0.1042304859,0.0445711389,0.2523835003,-0.4788489342,-0.0906342939,-0.3600659072,0.4096082449,-0.232615903,0.0938282236,0.3463047147,0.0892930999,0.1463989317,-0.0417488404,-0.2757158875,-0.0651353374,-0.2946413159,-0.068232581,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2934","title":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows","comments":"I did some investigation and, as it seems, the bug stems from [this line](https:\/\/github.com\/huggingface\/datasets\/blob\/8004d7c3e1d74b29c3e5b0d1660331cd26758363\/src\/datasets\/arrow_dataset.py#L325). The lifecycle of the dataset from the linked line is bound to one of the returned `tf.data.Dataset`. So my (hacky) solution involves wrapping the linked dataset with `weakref.proxy` and adding a custom `__del__` to `tf.python.data.ops.dataset_ops.TensorSliceDataset` (this is the type of a dataset that is returned by `tf.data.Dataset.from_tensor_slices`; this works for TF 2.x, but I'm not sure `tf.python.data.ops.dataset_ops` is a valid path for TF 1.x) that deletes the linked dataset, which is assigned to the dataset object as a property. Will open a draft PR soon!","body":"To reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1 ","comment_length":99,"text":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows \n To reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1  \n I did some investigation and, as it seems, the bug stems from [this line](https:\/\/github.com\/huggingface\/datasets\/blob\/8004d7c3e1d74b29c3e5b0d1660331cd26758363\/src\/datasets\/arrow_dataset.py#L325). The lifecycle of the dataset from the linked line is bound to one of the returned `tf.data.Dataset`. So my (hacky) solution involves wrapping the linked dataset with `weakref.proxy` and adding a custom `__del__` to `tf.python.data.ops.dataset_ops.TensorSliceDataset` (this is the type of a dataset that is returned by `tf.data.Dataset.from_tensor_slices`; this works for TF 2.x, but I'm not sure `tf.python.data.ops.dataset_ops` is a valid path for TF 1.x) that deletes the linked dataset, which is assigned to the dataset object as a property. Will open a draft PR soon!","embeddings":[0.0456905663,0.334508419,0.1148890257,0.080451034,0.2304305583,0.118887417,0.3988847136,0.2548755109,-0.1099170819,0.2674386799,-0.3353850842,0.4040720165,-0.1644811481,-0.1074629501,-0.0342661999,-0.1066431478,0.0511111543,0.1451645941,-0.1666638404,-0.1226122081,-0.2103365809,0.0351907536,-0.1274755448,0.0721159056,-0.1387630254,-0.2811448276,0.0105889933,0.1006439403,0.2441080213,-0.0183550231,0.0682790056,0.0211760364,-0.177796185,0.4797623456,-0.0001166652,0.4142073989,-0.0061280271,0.1301537901,-0.235580802,0.0565112084,-0.0690313205,0.1760492623,0.1245584637,-0.0599729307,0.1349684894,-0.2177767009,-0.0255724229,0.0832738504,0.2824953496,0.4290578365,0.1738771498,0.6678111553,0.049049627,-0.0070738695,0.3827846944,0.0638971701,-0.2742590308,0.1161701158,-0.2370098084,-0.1935149431,0.0655605793,0.5781883597,0.0173822027,-0.0747985765,-0.1346866488,0.0793781951,-0.2474848479,-0.2132097334,0.099155657,0.3864619732,0.2942140996,-0.3177287579,-0.0704426542,-0.1286842972,-0.0647842139,-0.1454346925,0.1913480461,-0.046207156,0.017386917,0.1348499954,0.0582152456,-0.1622668952,-0.2615066469,-0.0061726281,-0.2810887992,-0.1072200239,0.0857438222,0.0801384374,0.414106369,-0.0271513741,-0.068637602,0.0416288003,0.0201638471,0.1307915747,-0.2023018003,-0.1820205152,-0.039258711,-0.1770301908,-0.0281791855,-0.0708624199,0.2392520756,-0.1570445448,-0.2443445474,0.0677398071,0.1644194871,-0.0705889463,-0.6655332446,0.1451997906,0.2680508792,-0.1933338344,-0.1119194329,-0.0286712628,0.0027096812,-0.4981434345,0.2861165106,-0.2107922286,0.4638061523,0.0174133722,-0.5834477544,0.1703355759,-0.1943601817,0.4077934325,-0.0392662771,0.3421227634,-0.0097245919,0.2793687582,0.3236465752,-0.0036117642,-0.4970700741,-0.0162012,-0.0546676517,-0.0396631137,-0.2995604873,-0.3319226205,0.1675927192,0.0439531542,-0.0478496067,0.1728075743,0.0610827021,0.1025774032,0.2539456487,-0.2808155119,0.5246316791,0.4812721908,-0.0750983432,0.2450949699,0.094958134,-0.2507022023,-0.1292222887,0.3534173071,-0.271070987,-0.1585650742,-0.2603646219,0.1245645955,-0.1202328429,-0.1552468389,-0.2238021493,0.0450044461,0.0343124531,-0.0595466755,0.0862919539,-0.1370920688,-0.5523484945,-0.2646558583,0.0109041464,0.3650912344,-0.1991525888,0.1795514375,-0.0254384503,-0.3931910396,-0.1594114155,0.2656008899,0.0646049082,0.315382421,-0.4097916186,-0.1304864734,0.4838194549,-0.2320678681,-0.4512842894,0.2350057364,-0.2089946121,0.2142525762,-0.3518173099,0.1303172708,0.2125170231,-0.2066658884,0.0123629132,-0.0070450343,-0.0623103678,-0.009127249,-0.3236092329,-0.0108151864,-0.0680190474,-0.1952706128,-0.0157284178,-0.0124156829,0.1318649352,-0.0493954197,0.3902634382,0.2196914554,0.1933261156,0.1417482793,0.3756607473,-0.1729133576,-0.0790883675,0.0432887785,-0.3775175512,0.1395243853,0.4056796134,0.0777031407,-0.1249045953,-0.1479362994,-0.0149693638,0.1985371262,-0.1919496357,0.0200802218,0.0784440786,-0.1194245666,-0.3427836597,0.2097568363,-0.0087737013,-0.1780350357,-0.2768910527,0.0820086896,0.1028026193,0.4687242806,0.2687642872,-0.0055732708,-0.2448134571,0.1943388134,-0.1494650543,-0.0300622191,-0.2719019651,0.4269395471,0.1747722477,0.3178130984,-0.1541973948,0.5814441442,0.1066848487,-0.5981359482,-0.2948667407,0.345680505,0.1319477111,-0.028623648,-0.0179552659,0.1456983984,-0.2007223219,0.171325475,0.0537690409,0.2392641008,0.0408273004,0.0214642044,-0.0363970362,-0.3410162628,-0.048381418,0.3614164293,-0.3146298528,0.2986148596,-0.6084029675,0.1965304911,0.3866648376,0.0311069563,-0.1689249873,0.2358304709,-0.1694948971,0.0617295206,0.0715380758,0.3181353509,0.476054281,0.0223621521,0.1727905273,0.1367983967,-0.193739742,-0.0902521908,0.2373642176,0.0887134001,0.3124321103,0.2039208263,0.25704512,0.193103686,-0.3024033606,0.0330183432,0.130203411,0.1728958189,-0.4525787234,0.1747216135,-0.1807385981,-0.0388464183,-0.307489574,0.0117461048,0.0377215482,-0.134800002,0.3154597282,0.3967652917,-0.4561233222,0.153922379,-0.3289419115,0.1290333718,0.0227688793,-0.2959315479,0.1803024411,-0.169179216,-0.2714836895,0.0344429053,0.3643865883,-0.24427782,0.4180335999,-0.0013580687,-0.0747494251,-0.1528537571,-0.2631629407,0.0897795483,-0.0949533954,0.1932279766,0.2937165499,0.3368107378,-0.1232672036,-0.1301765293,0.1462340504,-0.4851278663,-0.1786133051,0.1372669786,-0.1227175817,0.209010601,0.0203978065,-0.1520884037,0.0741765723,-0.3432299793,0.0670822263,-0.191379711,0.127728045,0.6203324199,0.1520412415,-0.0800016448,0.3789710402,0.10767968,-0.1736004055,-0.1127346307,0.200914368,-0.2037230134,-0.4296337366,0.2909044921,0.0338889062,0.1325789392,0.295558393,-0.6434158087,-0.1689423323,-0.0437356085,0.1776636243,-0.1088872477,-0.0470069051,0.1665738225,0.1164368093,-0.1197147071,-0.3432469964,0.0827863663,0.3821257651,-0.227080822,-0.0485802852,0.0422705784,0.0882449597,0.313570559,0.6243577003,0.0508327,-0.3429434299,0.0208036136,-0.2982521057,0.5073607564,-0.159090668,-0.2795748413,0.0158007275,0.0420313291,-0.13273637,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Hi, the filename here is less than 255\r\n```python\r\n>>> len(\"_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock\")\r\n154\r\n```\r\nso not sure why it's considered too long for your filesystem.\r\n(also note that the lock files we use always have smaller filenames than 255)\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152\/src\/datasets\/utils\/filelock.py#L135-L135","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":39,"text":"\"File name too long\" error for file locks \n ## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Hi, the filename here is less than 255\r\n```python\r\n>>> len(\"_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock\")\r\n154\r\n```\r\nso not sure why it's considered too long for your filesystem.\r\n(also note that the lock files we use always have smaller filenames than 255)\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152\/src\/datasets\/utils\/filelock.py#L135-L135","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.11607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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Yes, you're right! I need to get you more info here. Either there's something going with the name itself that the file system doesn't like (an encoding that blows up the name length??) or perhaps there's something with the path that's causing the entire string to  be used as a name. I haven't seen this on any system before and the Internet's not forthcoming with any info.","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":67,"text":"\"File name too long\" error for file locks \n ## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Yes, you're right! I need to get you more info here. Either there's something going with the name itself that the file system doesn't like (an encoding that blows up the name length??) or perhaps there's something with the path that's causing the entire string to  be used as a name. I haven't seen this on any system before and the Internet's not forthcoming with any info.","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.1160778701,0.4532136321,0.3616654873,0.0753480569,0.1953798383,-0.1376693696,0.1843574643,-0.2590326071,-0.1269457787,0.0239379536,0.1744713485,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Snap, encountered when trying to run [this example from PyTorch Lightning Flash](https:\/\/lightning-flash.readthedocs.io\/en\/latest\/reference\/speech_recognition.html):\r\n\r\n```py\r\nimport torch\r\n\r\nimport flash\r\nfrom flash.audio import SpeechRecognition, SpeechRecognitionData\r\nfrom flash.core.data.utils import download_data\r\n\r\n# 1. Create the DataModule\r\ndownload_data(\"https:\/\/pl-flash-data.s3.amazonaws.com\/timit_data.zip\", \".\/data\")\r\n\r\ndatamodule = SpeechRecognitionData.from_json(\r\n    input_fields=\"file\",\r\n    target_fields=\"text\",\r\n    train_file=\"data\/timit\/train.json\",\r\n    test_file=\"data\/timit\/test.json\",\r\n)\r\n```\r\n\r\nGave this traceback:\r\n\r\n```py\r\nTraceback (most recent call last):\r\n  File \"lf_ft.py\", line 10, in <module>\r\n    datamodule = SpeechRecognitionData.from_json(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/core\/data\/data_module.py\", line 1005, in from_json\r\n    return cls.from_data_source(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/core\/data\/data_module.py\", line 571, in from_data_source\r\n    train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/core\/data\/data_source.py\", line 307, in to_datasets\r\n    train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING)\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/core\/data\/data_source.py\", line 344, in generate_dataset\r\n    data = load_data(data, mock_dataset)\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/audio\/speech_recognition\/data.py\", line 103, in load_data\r\n    dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)})\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1599, in load_dataset\r\n    builder_instance = load_dataset_builder(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1457, in load_dataset_builder\r\n    builder_instance: DatasetBuilder = builder_cls(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 285, in __init__\r\n    with FileLock(lock_path):\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/home\/louis\/.cache\/huggingface\/datasets\/_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock'\r\n```\r\n\r\nMy home directory is encrypted, therefore the maximum length is 143 ([source 1](https:\/\/github.com\/ray-project\/ray\/issues\/1463#issuecomment-425674521), [source 2](https:\/\/stackoverflow.com\/a\/6571568\/2668831))\r\n\r\nFrom what I've read I think the error is in reference to the file name (just the final part of the path) which is 145 chars long:\r\n\r\n```py\r\n>>> len(\"_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock\")\r\n145\r\n```\r\n\r\nI also have a file in this directory (i.e. whose length is not a problem):\r\n\r\n```py\r\n>>> len(\"_home_louis_.cache_huggingface_datasets_librispeech_asr_clean_2.1.0_468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1.lock\")\r\n137\r\n```","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":238,"text":"\"File name too long\" error for file locks \n ## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Snap, encountered when trying to run [this example from PyTorch Lightning Flash](https:\/\/lightning-flash.readthedocs.io\/en\/latest\/reference\/speech_recognition.html):\r\n\r\n```py\r\nimport torch\r\n\r\nimport flash\r\nfrom flash.audio import SpeechRecognition, SpeechRecognitionData\r\nfrom flash.core.data.utils import download_data\r\n\r\n# 1. Create the DataModule\r\ndownload_data(\"https:\/\/pl-flash-data.s3.amazonaws.com\/timit_data.zip\", \".\/data\")\r\n\r\ndatamodule = SpeechRecognitionData.from_json(\r\n    input_fields=\"file\",\r\n    target_fields=\"text\",\r\n    train_file=\"data\/timit\/train.json\",\r\n    test_file=\"data\/timit\/test.json\",\r\n)\r\n```\r\n\r\nGave this traceback:\r\n\r\n```py\r\nTraceback (most recent call last):\r\n  File \"lf_ft.py\", line 10, in <module>\r\n    datamodule = SpeechRecognitionData.from_json(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/core\/data\/data_module.py\", line 1005, in from_json\r\n    return cls.from_data_source(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/core\/data\/data_module.py\", line 571, in from_data_source\r\n    train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/core\/data\/data_source.py\", line 307, in to_datasets\r\n    train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING)\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/core\/data\/data_source.py\", line 344, in generate_dataset\r\n    data = load_data(data, mock_dataset)\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/flash\/audio\/speech_recognition\/data.py\", line 103, in load_data\r\n    dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)})\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1599, in load_dataset\r\n    builder_instance = load_dataset_builder(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1457, in load_dataset_builder\r\n    builder_instance: DatasetBuilder = builder_cls(\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 285, in __init__\r\n    with FileLock(lock_path):\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"\/home\/louis\/miniconda3\/envs\/w2vlf\/lib\/python3.8\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/home\/louis\/.cache\/huggingface\/datasets\/_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock'\r\n```\r\n\r\nMy home directory is encrypted, therefore the maximum length is 143 ([source 1](https:\/\/github.com\/ray-project\/ray\/issues\/1463#issuecomment-425674521), [source 2](https:\/\/stackoverflow.com\/a\/6571568\/2668831))\r\n\r\nFrom what I've read I think the error is in reference to the file name (just the final part of the path) which is 145 chars long:\r\n\r\n```py\r\n>>> len(\"_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock\")\r\n145\r\n```\r\n\r\nI also have a file in this directory (i.e. whose length is not a problem):\r\n\r\n```py\r\n>>> len(\"_home_louis_.cache_huggingface_datasets_librispeech_asr_clean_2.1.0_468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1.lock\")\r\n137\r\n```","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Perhaps this could be exposed as a config setting so you could change it manually?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152\/src\/datasets\/utils\/filelock.py#L135-L135\r\n\r\nRather than hard-code 255, default it to 255, and allow it to be changed, the same way is done for `datasets.config.IN_MEMORY_MAX_SIZE`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/12b7e13bc568b9f92705f64b249e148f3bc9a9ea\/src\/datasets\/config.py#L171-L173\r\n\r\nIn fact there already appears to be an existing variable to do so:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/12b7e13bc568b9f92705f64b249e148f3bc9a9ea\/src\/datasets\/config.py#L187\r\n\r\nIt's used here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/efe89edd36e4ffa562fc3eebaf07a5fec26e6dac\/src\/datasets\/builder.py#L163-L165\r\n\r\nPerhaps it could be set based on a test (trying to create a 255 char length named lock file and seeing if it fails)","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":80,"text":"\"File name too long\" error for file locks \n ## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Perhaps this could be exposed as a config setting so you could change it manually?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152\/src\/datasets\/utils\/filelock.py#L135-L135\r\n\r\nRather than hard-code 255, default it to 255, and allow it to be changed, the same way is done for `datasets.config.IN_MEMORY_MAX_SIZE`:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/12b7e13bc568b9f92705f64b249e148f3bc9a9ea\/src\/datasets\/config.py#L171-L173\r\n\r\nIn fact there already appears to be an existing variable to do so:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/12b7e13bc568b9f92705f64b249e148f3bc9a9ea\/src\/datasets\/config.py#L187\r\n\r\nIt's used here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/efe89edd36e4ffa562fc3eebaf07a5fec26e6dac\/src\/datasets\/builder.py#L163-L165\r\n\r\nPerhaps it could be set based on a test (trying to create a 255 char length named lock file and seeing if it fails)","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.1160778701,0.4532136321,0.3616654873,0.0753480569,0.1953798383,-0.1376693696,0.1843574643,-0.2590326071,-0.1269457787,0.0239379536,0.1744713485,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Hi @lmmx @gar1t ,\r\n\r\nit would be helpful if you could run the following code and copy-paste the output here:\r\n```python\r\nimport datasets\r\nimport os\r\nos.statvfs(datasets.config.HF_DATASETS_CACHE)\r\n```","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":27,"text":"\"File name too long\" error for file locks \n ## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Hi @lmmx @gar1t ,\r\n\r\nit would be helpful if you could run the following code and copy-paste the output here:\r\n```python\r\nimport datasets\r\nimport os\r\nos.statvfs(datasets.config.HF_DATASETS_CACHE)\r\n```","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.1160778701,0.4532136321,0.3616654873,0.0753480569,0.1953798383,-0.1376693696,0.1843574643,-0.25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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Hi @lmmx,\r\n\r\nThanks for providing the result of the command. I've opened a PR, and it would be great if you could verify that the fix works on your system. To install the version of the datasets with the fix, please run the following command:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git@fix-2924\r\n```\r\n\r\nBtw, I saw your PR, and I appreciate your effort. However, my approach is a bit simpler for the end-user, so that's why I decided to fix the issue myself.","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":80,"text":"\"File name too long\" error for file locks \n ## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n Hi @lmmx,\r\n\r\nThanks for providing the result of the command. I've opened a PR, and it would be great if you could verify that the fix works on your system. To install the version of the datasets with the fix, please run the following command:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git@fix-2924\r\n```\r\n\r\nBtw, I saw your PR, and I appreciate your effort. However, my approach is a bit simpler for the end-user, so that's why I decided to fix the issue myself.","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.1160778701,0.4532136321,0.3616654873,0.0753480569,0.1953798383,-0.1376693696,0.1843574643,-0.2590326071,-0.1269457787,0.0239379536,0.1744713485,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"No problem Mario I didn't know that was where that value was recorded so I learnt something :smiley: I just wanted to get a local version working, of course you should implement whatever fix is best for HF. Yes can confirm this fixes it too. Thanks!","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":46,"text":"\"File name too long\" error for file locks \n ## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n    self._save_info()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n    with FileLock(lock_path):\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"<python_venv>\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n    fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '<user>\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n \n No problem Mario I didn't know that was where that value was recorded so I learnt something :smiley: I just wanted to get a local version working, of course you should implement whatever fix is best for HF. Yes can confirm this fixes it too. Thanks!","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.1160778701,0.4532136321,0.3616654873,0.0753480569,0.1953798383,-0.1376693696,0.1843574643,-0.2590326071,-0.1269457787,0.0239379536,0.1744713485,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2918","title":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming","comments":"Hi @SBrandeis, thanks for reporting! ^^\r\n\r\nI think this is an issue with `fsspec`: https:\/\/github.com\/intake\/filesystem_spec\/issues\/389\r\n\r\nI will ask them if they are planning to fix it...","body":"## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n    load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n    339\r\n    340     def __iter__(self):\r\n--> 341         for key, example in self._iter():\r\n    342             if self.features:\r\n    343                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n    336         else:\r\n    337             ex_iterable = self._ex_iterable\r\n--> 338         yield from ex_iterable\r\n    339\r\n    340     def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n     76\r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n    162\r\n    163         with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164             for id_, row in enumerate(f):\r\n    165                 data = json.loads(row)\r\n    166                 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n    496         else:\r\n    497             length = min(self.size - self.loc, length)\r\n--> 498         return super().read(length)\r\n    499\r\n    500     async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n   1481             # don't even bother calling fetch\r\n   1482             return b\"\"\r\n-> 1483         out = self.cache._fetch(self.loc, self.loc + length)\r\n   1484         self.loc += len(out)\r\n   1485         return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n    378         elif start < self.start:\r\n    379             if self.end - end > self.blocksize:\r\n--> 380                 self.cache = self.fetcher(start, bend)\r\n    381                 self.start = start\r\n    382             else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n     86     def wrapper(*args, **kwargs):\r\n     87         self = obj or args[0]\r\n---> 88         return sync(self.loop, func, *args, **kwargs)\r\n     89\r\n     90     return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n     67         raise FSTimeoutError\r\n     68     if isinstance(result[0], BaseException):\r\n---> 69         raise result[0]\r\n     70     return result[0]\r\n     71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n     23         coro = asyncio.wait_for(coro, timeout=timeout)\r\n     24     try:\r\n---> 25         result[0] = await coro\r\n     26     except Exception as ex:\r\n     27         result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n    538             if r.status == 206:\r\n    539                 # partial content, as expected\r\n--> 540                 out = await r.read()\r\n    541             elif \"Content-Length\" in r.headers:\r\n    542                 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n   1030         if self._body is None:\r\n   1031             try:\r\n-> 1032                 self._body = await self.content.read()\r\n   1033                 for trace in self._traces:\r\n   1034                     await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n    342     async def read(self, n: int = -1) -> bytes:\r\n    343         if self._exception is not None:\r\n--> 344             raise self._exception\r\n    345\r\n    346         # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n","comment_length":26,"text":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming \n ## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n    load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n    339\r\n    340     def __iter__(self):\r\n--> 341         for key, example in self._iter():\r\n    342             if self.features:\r\n    343                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n    336         else:\r\n    337             ex_iterable = self._ex_iterable\r\n--> 338         yield from ex_iterable\r\n    339\r\n    340     def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n     76\r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n    162\r\n    163         with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164             for id_, row in enumerate(f):\r\n    165                 data = json.loads(row)\r\n    166                 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n    496         else:\r\n    497             length = min(self.size - self.loc, length)\r\n--> 498         return super().read(length)\r\n    499\r\n    500     async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n   1481             # don't even bother calling fetch\r\n   1482             return b\"\"\r\n-> 1483         out = self.cache._fetch(self.loc, self.loc + length)\r\n   1484         self.loc += len(out)\r\n   1485         return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n    378         elif start < self.start:\r\n    379             if self.end - end > self.blocksize:\r\n--> 380                 self.cache = self.fetcher(start, bend)\r\n    381                 self.start = start\r\n    382             else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n     86     def wrapper(*args, **kwargs):\r\n     87         self = obj or args[0]\r\n---> 88         return sync(self.loop, func, *args, **kwargs)\r\n     89\r\n     90     return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n     67         raise FSTimeoutError\r\n     68     if isinstance(result[0], BaseException):\r\n---> 69         raise result[0]\r\n     70     return result[0]\r\n     71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n     23         coro = asyncio.wait_for(coro, timeout=timeout)\r\n     24     try:\r\n---> 25         result[0] = await coro\r\n     26     except Exception as ex:\r\n     27         result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n    538             if r.status == 206:\r\n    539                 # partial content, as expected\r\n--> 540                 out = await r.read()\r\n    541             elif \"Content-Length\" in r.headers:\r\n    542                 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n   1030         if self._body is None:\r\n   1031             try:\r\n-> 1032                 self._body = await self.content.read()\r\n   1033                 for trace in self._traces:\r\n   1034                     await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n    342     async def read(self, n: int = -1) -> bytes:\r\n    343         if self._exception is not None:\r\n--> 344             raise self._exception\r\n    345\r\n    346         # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n \n Hi @SBrandeis, thanks for reporting! ^^\r\n\r\nI think this is an issue with `fsspec`: https:\/\/github.com\/intake\/filesystem_spec\/issues\/389\r\n\r\nI will ask them if they are planning to fix it...","embeddings":[-0.3864652812,-0.2204957455,0.0894813165,0.4387415349,0.2157086134,0.1229229122,-0.0369101539,0.3093882799,0.2422775328,0.0955026373,-0.2264311761,0.4261536002,-0.0641756877,0.3035327792,-0.0094654057,-0.2584786713,-0.0045060921,0.2337655127,-0.1049250886,0.1268760264,-0.0005556306,0.1941259056,-0.2469931841,-0.0304233823,0.0522285365,-0.1081046611,0.0124065261,0.2805253863,-0.059583243,-0.4462363422,0.1259049624,-0.0615482517,0.4121141732,0.3375741839,-0.0001145032,0.2734483778,0.4721427262,-0.1227841005,-0.4853440225,-0.2121149004,-0.218665719,0.0961271524,0.0428345352,-0.1566341817,-0.0091345869,0.0711133331,-0.0400046185,-0.7192984819,0.3178500533,0.3456359804,0.1778500229,0.0426006429,-0.0489487238,0.0268223863,0.0688386783,-0.2986646891,-0.0920066237,0.1218173951,0.2367273569,0.2287404835,-0.359573245,0.3845903277,-0.0658884048,0.0601840951,0.0944148228,0.0967238322,-0.1749501675,-0.4655051231,-0.0073944977,0.1884141415,0.6776787043,-0.2555166781,-0.4161978662,-0.2319881767,-0.0021713183,-0.6144573689,0.1465310603,0.1489273459,-0.4673651159,0.1280597448,0.2459630817,-0.0087530883,-0.327994585,-0.0458728224,-0.246799171,0.3882223368,-0.0767857283,-0.0237270929,0.0272943433,-0.0866289064,0.4627741277,-0.2110346556,-0.28459391,-0.0639790371,-0.4397552311,-0.0420655161,-0.0765123144,-0.094109349,0.1681466401,0.215389967,0.2953203917,0.0882523879,0.1543645263,0.0926442817,0.3555265665,0.3608669043,0.0598316565,0.1853774786,0.1311810017,0.0994313955,0.1366237104,-0.1818924248,-0.146488592,-0.0372809209,0.0281632524,-0.0131140957,0.262858659,-0.1912374645,-0.4138476253,0.0297920927,-0.3057210743,-0.0794672072,0.0055905147,0.2817168832,0.0958337784,0.4187803268,0.0194585584,0.1178673506,-0.2396837324,-0.4573938251,-0.0878112167,-0.1247182265,-0.0010082311,0.1233467832,0.0153353186,-0.1886832416,0.1515737325,-0.0465371571,0.1621010602,-0.0012185768,-0.0208561029,-0.1100598425,0.0216292907,0.2049841285,0.2180556655,0.1640353203,0.1795151085,-0.1997850239,-0.130682379,0.0886226073,-0.1416330338,-0.1671275645,-0.1788374335,0.2166469097,-0.0094024939,-0.3506264389,-0.0937892571,0.2814688385,0.1409590542,-0.2037903368,-0.1410701424,-0.0391465798,-0.1241319329,-0.0865156949,0.1792775393,0.5796180964,-0.1855915189,-0.1380216628,-0.1521381587,0.0008939481,0.5833057165,0.3344025612,-0.0607914478,0.0897968188,-0.170722425,0.1476544142,0.5017140508,-0.1436584145,-0.5796036124,0.5436661243,-0.0396082066,0.5637281537,0.2707032561,0.0225366186,0.1742344499,0.0431196988,0.313529104,0.3299024701,-0.1484592706,0.0074822325,-0.3334669471,-0.0399836674,0.4416950643,0.1912285686,0.0674290732,0.2502861321,-0.0288742278,-0.0626677349,0.3689779639,-0.0752563924,0.0763451308,0.0410080664,0.1468882412,0.0120370993,0.1379382461,-0.2090770155,-0.310682714,0.2972311378,0.0749307722,0.0127100674,-0.3174338341,0.2031723857,-0.1906455308,0.0017469627,-0.2628925443,-0.2590405643,0.1243173257,0.3170801699,0.0756207108,0.0505895875,-0.3084766865,0.7747215033,-0.078255251,-0.0131349247,-0.4549141228,0.2024929523,-0.0707768798,-0.2014573216,0.0164402872,-0.1265656799,0.0672556311,-0.0161919687,-0.2579914331,0.3313200772,-0.2366138101,0.2640713155,-0.1954375803,-0.1731976271,0.2897448838,-0.3644788265,-0.0866843313,0.2706028223,0.1075036898,0.125209704,-0.0176983867,0.1480839401,0.2070915401,0.2361335903,0.0434485786,0.1143487841,0.1961675137,-0.0816748515,-0.0686837137,-0.0850090533,0.3240485787,-0.3548789024,0.0467500761,-0.1989230514,-0.2722995877,0.0823631957,0.3586791456,-0.1403829306,-0.0492737889,0.3303245604,-0.2885322273,-0.1639078707,0.3145605624,0.1940099597,0.4723360538,0.0815219656,0.2573353648,-0.0455882996,-0.1886483878,-0.0735060051,0.3051697612,0.1374839693,0.0314638466,0.2464114279,0.0300063528,0.1635747999,-0.3229160309,-0.3497166634,-0.0566318817,0.1123416647,-0.3201874793,0.306302011,-0.38826859,-0.4651744664,0.0463509336,0.1560085416,0.0689049438,-0.3235587776,-0.1817550063,0.2325055152,-0.0038823313,0.1636838913,-0.2682063282,0.0895485729,0.2671770155,-0.0134687945,-0.3418698907,-0.1808474958,-0.2729629874,0.0591977872,0.1749951094,-0.4228037596,0.1769195795,0.0074260007,-0.1160532683,-0.1401735991,-0.076423429,0.3197818398,-0.1554995328,0.1568958163,-0.2774865627,0.1493911892,0.2213633955,-0.2503491044,0.1219482496,0.0908335969,-0.1187844425,0.2801248431,0.0940897167,-0.0241610892,-0.0305832736,-0.3010807931,-0.3024666011,-0.6177494526,0.0549683981,0.0454257689,0.2135515809,0.1643050015,0.0170347784,0.0968246013,0.3292069137,0.0991601422,-0.114212282,-0.4199434817,0.3747448325,-0.1670146883,-0.2868409157,-0.3248367012,-0.0681597292,0.1972901672,0.2589270473,-0.5295243263,0.2950162292,-0.4081035852,0.0848925635,-0.3834516108,-0.0974560827,-0.0261515174,0.0762885734,-0.0349893123,-0.1878576428,0.0707459524,-0.1169513538,0.0990076959,0.2066107839,0.1086307243,0.3698712885,0.1008493528,0.3327504098,0.4812112153,-0.0195496976,0.3407551944,0.082891129,0.1538007557,-0.1004334986,-0.0627885908,-0.0955621898,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2918","title":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming","comments":"Code to reproduce the bug: `ClientPayloadError: 400, message='Can not decode content-encoding: gzip'`\r\n```python\r\nIn [1]: import fsspec\r\n\r\nIn [2]: import json\r\n\r\nIn [3]: with fsspec.open('https:\/\/raw.githubusercontent.com\/allenai\/scitldr\/master\/SciTLDR-Data\/SciTLDR-FullText\/test.jsonl', encoding=\"utf-8\") as f:\r\n   ...:     for row in f:\r\n   ...:         data = json.loads(row)\r\n   ...:\r\n---------------------------------------------------------------------------\r\nClientPayloadError                        Traceback (most recent call last)\r\n```","body":"## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n    load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n    339\r\n    340     def __iter__(self):\r\n--> 341         for key, example in self._iter():\r\n    342             if self.features:\r\n    343                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n    336         else:\r\n    337             ex_iterable = self._ex_iterable\r\n--> 338         yield from ex_iterable\r\n    339\r\n    340     def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n     76\r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n    162\r\n    163         with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164             for id_, row in enumerate(f):\r\n    165                 data = json.loads(row)\r\n    166                 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n    496         else:\r\n    497             length = min(self.size - self.loc, length)\r\n--> 498         return super().read(length)\r\n    499\r\n    500     async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n   1481             # don't even bother calling fetch\r\n   1482             return b\"\"\r\n-> 1483         out = self.cache._fetch(self.loc, self.loc + length)\r\n   1484         self.loc += len(out)\r\n   1485         return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n    378         elif start < self.start:\r\n    379             if self.end - end > self.blocksize:\r\n--> 380                 self.cache = self.fetcher(start, bend)\r\n    381                 self.start = start\r\n    382             else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n     86     def wrapper(*args, **kwargs):\r\n     87         self = obj or args[0]\r\n---> 88         return sync(self.loop, func, *args, **kwargs)\r\n     89\r\n     90     return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n     67         raise FSTimeoutError\r\n     68     if isinstance(result[0], BaseException):\r\n---> 69         raise result[0]\r\n     70     return result[0]\r\n     71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n     23         coro = asyncio.wait_for(coro, timeout=timeout)\r\n     24     try:\r\n---> 25         result[0] = await coro\r\n     26     except Exception as ex:\r\n     27         result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n    538             if r.status == 206:\r\n    539                 # partial content, as expected\r\n--> 540                 out = await r.read()\r\n    541             elif \"Content-Length\" in r.headers:\r\n    542                 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n   1030         if self._body is None:\r\n   1031             try:\r\n-> 1032                 self._body = await self.content.read()\r\n   1033                 for trace in self._traces:\r\n   1034                     await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n    342     async def read(self, n: int = -1) -> bytes:\r\n    343         if self._exception is not None:\r\n--> 344             raise self._exception\r\n    345\r\n    346         # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n","comment_length":46,"text":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming \n ## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n    load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n    339\r\n    340     def __iter__(self):\r\n--> 341         for key, example in self._iter():\r\n    342             if self.features:\r\n    343                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n    336         else:\r\n    337             ex_iterable = self._ex_iterable\r\n--> 338         yield from ex_iterable\r\n    339\r\n    340     def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n     76\r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n    162\r\n    163         with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164             for id_, row in enumerate(f):\r\n    165                 data = json.loads(row)\r\n    166                 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n    496         else:\r\n    497             length = min(self.size - self.loc, length)\r\n--> 498         return super().read(length)\r\n    499\r\n    500     async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n   1481             # don't even bother calling fetch\r\n   1482             return b\"\"\r\n-> 1483         out = self.cache._fetch(self.loc, self.loc + length)\r\n   1484         self.loc += len(out)\r\n   1485         return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n    378         elif start < self.start:\r\n    379             if self.end - end > self.blocksize:\r\n--> 380                 self.cache = self.fetcher(start, bend)\r\n    381                 self.start = start\r\n    382             else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n     86     def wrapper(*args, **kwargs):\r\n     87         self = obj or args[0]\r\n---> 88         return sync(self.loop, func, *args, **kwargs)\r\n     89\r\n     90     return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n     67         raise FSTimeoutError\r\n     68     if isinstance(result[0], BaseException):\r\n---> 69         raise result[0]\r\n     70     return result[0]\r\n     71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n     23         coro = asyncio.wait_for(coro, timeout=timeout)\r\n     24     try:\r\n---> 25         result[0] = await coro\r\n     26     except Exception as ex:\r\n     27         result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n    538             if r.status == 206:\r\n    539                 # partial content, as expected\r\n--> 540                 out = await r.read()\r\n    541             elif \"Content-Length\" in r.headers:\r\n    542                 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n   1030         if self._body is None:\r\n   1031             try:\r\n-> 1032                 self._body = await self.content.read()\r\n   1033                 for trace in self._traces:\r\n   1034                     await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n    342     async def read(self, n: int = -1) -> bytes:\r\n    343         if self._exception is not None:\r\n--> 344             raise self._exception\r\n    345\r\n    346         # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n \n Code to reproduce the bug: `ClientPayloadError: 400, message='Can not decode content-encoding: gzip'`\r\n```python\r\nIn [1]: import fsspec\r\n\r\nIn [2]: import json\r\n\r\nIn [3]: with fsspec.open('https:\/\/raw.githubusercontent.com\/allenai\/scitldr\/master\/SciTLDR-Data\/SciTLDR-FullText\/test.jsonl', encoding=\"utf-8\") as f:\r\n   ...:     for row in f:\r\n   ...:         data = json.loads(row)\r\n   ...:\r\n---------------------------------------------------------------------------\r\nClientPayloadError                        Traceback (most recent call last)\r\n```","embeddings":[-0.3864652812,-0.2204957455,0.0894813165,0.4387415349,0.2157086134,0.1229229122,-0.0369101539,0.3093882799,0.2422775328,0.0955026373,-0.2264311761,0.4261536002,-0.0641756877,0.3035327792,-0.0094654057,-0.2584786713,-0.0045060921,0.2337655127,-0.1049250886,0.1268760264,-0.0005556306,0.1941259056,-0.2469931841,-0.0304233823,0.0522285365,-0.1081046611,0.0124065261,0.2805253863,-0.059583243,-0.4462363422,0.1259049624,-0.0615482517,0.4121141732,0.3375741839,-0.0001145032,0.2734483778,0.4721427262,-0.1227841005,-0.4853440225,-0.2121149004,-0.218665719,0.0961271524,0.0428345352,-0.1566341817,-0.0091345869,0.0711133331,-0.0400046185,-0.7192984819,0.3178500533,0.3456359804,0.1778500229,0.0426006429,-0.0489487238,0.0268223863,0.0688386783,-0.2986646891,-0.0920066237,0.1218173951,0.2367273569,0.2287404835,-0.359573245,0.3845903277,-0.0658884048,0.0601840951,0.0944148228,0.0967238322,-0.1749501675,-0.4655051231,-0.0073944977,0.1884141415,0.6776787043,-0.2555166781,-0.4161978662,-0.2319881767,-0.0021713183,-0.6144573689,0.1465310603,0.1489273459,-0.4673651159,0.1280597448,0.2459630817,-0.0087530883,-0.327994585,-0.0458728224,-0.246799171,0.3882223368,-0.0767857283,-0.0237270929,0.0272943433,-0.0866289064,0.4627741277,-0.2110346556,-0.28459391,-0.0639790371,-0.4397552311,-0.0420655161,-0.0765123144,-0.094109349,0.1681466401,0.215389967,0.2953203917,0.0882523879,0.1543645263,0.0926442817,0.3555265665,0.3608669043,0.0598316565,0.1853774786,0.1311810017,0.0994313955,0.1366237104,-0.1818924248,-0.146488592,-0.0372809209,0.0281632524,-0.0131140957,0.262858659,-0.1912374645,-0.4138476253,0.0297920927,-0.3057210743,-0.0794672072,0.0055905147,0.2817168832,0.0958337784,0.4187803268,0.0194585584,0.1178673506,-0.2396837324,-0.4573938251,-0.0878112167,-0.1247182265,-0.0010082311,0.1233467832,0.0153353186,-0.1886832416,0.1515737325,-0.0465371571,0.1621010602,-0.0012185768,-0.0208561029,-0.1100598425,0.0216292907,0.2049841285,0.2180556655,0.1640353203,0.1795151085,-0.1997850239,-0.130682379,0.0886226073,-0.1416330338,-0.1671275645,-0.1788374335,0.2166469097,-0.0094024939,-0.3506264389,-0.0937892571,0.2814688385,0.1409590542,-0.2037903368,-0.1410701424,-0.0391465798,-0.1241319329,-0.0865156949,0.1792775393,0.5796180964,-0.1855915189,-0.1380216628,-0.1521381587,0.0008939481,0.5833057165,0.3344025612,-0.0607914478,0.0897968188,-0.170722425,0.1476544142,0.5017140508,-0.1436584145,-0.5796036124,0.5436661243,-0.0396082066,0.5637281537,0.2707032561,0.0225366186,0.1742344499,0.0431196988,0.313529104,0.3299024701,-0.1484592706,0.0074822325,-0.3334669471,-0.0399836674,0.4416950643,0.1912285686,0.0674290732,0.2502861321,-0.0288742278,-0.0626677349,0.3689779639,-0.0752563924,0.0763451308,0.0410080664,0.1468882412,0.0120370993,0.1379382461,-0.2090770155,-0.310682714,0.2972311378,0.0749307722,0.0127100674,-0.3174338341,0.2031723857,-0.1906455308,0.0017469627,-0.2628925443,-0.2590405643,0.1243173257,0.3170801699,0.0756207108,0.0505895875,-0.3084766865,0.7747215033,-0.078255251,-0.0131349247,-0.4549141228,0.2024929523,-0.0707768798,-0.2014573216,0.0164402872,-0.1265656799,0.0672556311,-0.0161919687,-0.2579914331,0.3313200772,-0.2366138101,0.2640713155,-0.1954375803,-0.1731976271,0.2897448838,-0.3644788265,-0.0866843313,0.2706028223,0.1075036898,0.125209704,-0.0176983867,0.1480839401,0.2070915401,0.2361335903,0.0434485786,0.1143487841,0.1961675137,-0.0816748515,-0.0686837137,-0.0850090533,0.3240485787,-0.3548789024,0.0467500761,-0.1989230514,-0.2722995877,0.0823631957,0.3586791456,-0.1403829306,-0.0492737889,0.3303245604,-0.2885322273,-0.1639078707,0.3145605624,0.1940099597,0.4723360538,0.0815219656,0.2573353648,-0.0455882996,-0.1886483878,-0.0735060051,0.3051697612,0.1374839693,0.0314638466,0.2464114279,0.0300063528,0.1635747999,-0.3229160309,-0.3497166634,-0.0566318817,0.1123416647,-0.3201874793,0.306302011,-0.38826859,-0.4651744664,0.0463509336,0.1560085416,0.0689049438,-0.3235587776,-0.1817550063,0.2325055152,-0.0038823313,0.1636838913,-0.2682063282,0.0895485729,0.2671770155,-0.0134687945,-0.3418698907,-0.1808474958,-0.2729629874,0.0591977872,0.1749951094,-0.4228037596,0.1769195795,0.0074260007,-0.1160532683,-0.1401735991,-0.076423429,0.3197818398,-0.1554995328,0.1568958163,-0.2774865627,0.1493911892,0.2213633955,-0.2503491044,0.1219482496,0.0908335969,-0.1187844425,0.2801248431,0.0940897167,-0.0241610892,-0.0305832736,-0.3010807931,-0.3024666011,-0.6177494526,0.0549683981,0.0454257689,0.2135515809,0.1643050015,0.0170347784,0.0968246013,0.3292069137,0.0991601422,-0.114212282,-0.4199434817,0.3747448325,-0.1670146883,-0.2868409157,-0.3248367012,-0.0681597292,0.1972901672,0.2589270473,-0.5295243263,0.2950162292,-0.4081035852,0.0848925635,-0.3834516108,-0.0974560827,-0.0261515174,0.0762885734,-0.0349893123,-0.1878576428,0.0707459524,-0.1169513538,0.0990076959,0.2066107839,0.1086307243,0.3698712885,0.1008493528,0.3327504098,0.4812112153,-0.0195496976,0.3407551944,0.082891129,0.1538007557,-0.1004334986,-0.0627885908,-0.095562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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2917","title":"windows download abnormal","comments":"Hi ! Is there some kind of proxy that is configured in your browser that gives you access to internet ? If it's the case it could explain why it doesn't work in the code, since the proxy wouldn't be used","body":"## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n","comment_length":41,"text":"windows download abnormal \n ## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n \n Hi ! Is there some kind of proxy that is configured in your browser that gives you access to internet ? If it's the case it could explain why it doesn't work in the code, since the proxy wouldn't be used","embeddings":[-0.1240484565,-0.2018248588,-0.0703396499,0.1894593984,0.2147738934,-0.1120505109,0.0276671592,0.0718739256,0.2805814743,0.0850560516,0.1648736149,0.1174071059,0.1554815173,0.1022035182,0.1588474363,-0.2615045309,0.0301855393,0.0393100753,-0.1691515595,0.0088643078,-0.3215381503,0.0859283432,-0.3066691756,-0.1127134934,0.1635590941,0.0324402116,-0.2886959612,0.3004715145,-0.2545996904,-0.1694043726,0.099369511,-0.0709087476,0.1850661635,0.3519495726,-0.0001087431,0.0813257769,0.3922842741,0.0235310104,0.0174283162,-0.0581749007,-0.2938911617,-0.1413686872,-0.1911946684,-0.3151716888,0.0997065529,0.2347301394,0.0454377979,-0.1112284884,0.244362548,0.4491443336,0.3060822189,0.4809369445,-0.1150025278,0.0517268442,0.4554628432,0.0505648442,-0.1816764176,0.2491989583,0.3063491285,-0.1419377476,0.2570019662,-0.059411183,-0.1210118234,0.0115583753,0.0310037415,0.1206353381,-0.0494647175,-0.5093158484,0.1234319583,0.1743744612,0.31266132,-0.1531091034,-0.1585402191,0.1158476546,-0.1493998766,-0.185696885,0.2733875215,0.2762599587,-0.1393169761,0.1871818155,-0.2805486917,0.1743876189,-0.2056800723,0.2596932352,-0.1191073507,0.3299077153,-0.0759498328,0.1894465089,-0.1710143685,0.3167176545,0.03771016,-0.3272551894,-0.0156770907,-0.0039893929,0.0856893435,0.105707854,0.0493557453,0.5118294954,0.05730417,0.2643461227,0.0334886052,-0.0560189262,0.2410898954,0.0700188726,0.090272449,0.0949797034,-0.2958666682,0.066043593,0.3817819655,0.313916266,-0.0065793474,-0.026200084,-0.0330495574,-0.3155067265,0.2965119183,-0.0053916001,0.486035049,-0.04742698,-0.556910634,0.0026351295,0.0146284271,0.0908918232,-0.0023530021,0.2526860833,-0.32183218,-0.022580795,0.1233704016,0.3000146747,-0.1240452528,0.1943567246,0.004063094,0.1806814522,-0.2520693839,-0.2260728776,0.3475835323,-0.0671294406,0.12012472,0.1935619116,-0.0914587379,-0.042873878,0.0132011967,-0.0805152804,0.0271826498,0.4316020012,0.1952559203,0.3702535331,-0.0094595077,0.0780785382,-0.0707269013,0.2062932998,-0.1463157386,-0.0249700788,0.021134628,0.2608017921,-0.3265893757,-0.1885598153,0.0397941098,-0.2664661705,-0.0409113131,-0.0293878373,0.2495985031,-0.1964796036,-0.2891480029,-0.3841315806,0.0929920301,0.5247913599,-0.3482265472,0.2068513781,0.0614641719,-0.271443069,0.4014218748,0.0586097874,0.1309562773,0.1605100483,-0.4302997291,0.0415051356,0.117131643,-0.3501324654,-0.4970429242,0.3584975302,-0.1899531484,0.3206911981,0.1783815473,0.0919841528,0.293910265,-0.0833817273,-0.012038324,0.3098945022,-0.022483252,0.0582434274,-0.1245782077,-0.1297618896,0.1300003082,0.1085215658,0.1208592206,0.1432953179,0.293230623,-0.1360394806,0.3164023459,0.1788195372,0.0964889973,0.2375457287,0.2550174594,0.2680895925,0.1236548349,-0.2269855589,0.0015702655,0.2861069441,-0.0804182291,-0.1590327024,-0.2410328686,-0.2218061388,-0.5582574606,0.0224495344,-0.1886620373,-0.2219815105,0.1653012484,0.1372602582,0.249552846,0.0426937975,0.0754473731,0.179336831,-0.0071864342,-0.068459399,0.0751905069,0.3602052331,-0.3439503014,-0.050523717,-0.096596159,-0.0721758753,0.2299172729,-0.0566853061,-0.0152284885,0.197268486,0.0202615354,-0.0426289514,0.0362087451,0.0486600362,0.0167178959,-0.1805609912,0.2716660798,0.5724846125,0.2685571015,-0.0967838913,-0.0322295241,0.0053620976,-0.075025402,0.0177561473,-0.0222576819,0.1486974061,0.1936407834,-0.188959837,-0.1011113226,0.0021014542,0.3638005555,0.0613047183,-0.0991422012,-0.1685334742,-0.1679939777,0.167080909,0.5450258255,-0.2752330005,0.3014795184,0.0706219375,-0.0966639817,0.2094732076,-0.1800906807,0.1059181914,0.6080446839,0.033296559,0.112960726,0.3711541593,0.0088629806,-0.1436058879,0.2149284184,0.1461233199,-0.2989706993,0.1730046868,-0.0520784967,0.1769192368,-0.2095150501,-0.2376021594,-0.0571574084,0.0526774004,-0.3452227414,0.2429896444,-0.1614445746,-0.1740004569,-0.3291527927,-0.086957626,-0.0766705945,-0.0632937625,0.0246917997,0.2807940841,-0.1450743377,-0.1818347871,-0.4370901287,0.092590116,0.2119114399,-0.3590200245,-0.0015145239,0.1992023885,-0.2863779962,0.1333586127,0.1517552882,-0.0984132662,0.5485373139,-0.5067276955,0.0579211973,-0.3667593896,-0.1009359881,-0.0654943958,0.0553083122,0.3579990566,0.182639122,0.1857500374,0.0548134558,0.1031560078,0.2081247717,-0.2389806062,-0.179515481,0.1597154588,0.0564894304,-0.3985366821,-0.2559876144,-0.2491457164,-0.2066613436,-0.3509724736,0.0161345936,0.1100101173,0.1269879043,-0.1030690521,0.0080765542,0.0372453369,0.0731868744,0.2246696055,-0.083026208,0.0263501108,0.3867479861,-0.0423500165,-0.6439450383,0.2405601293,-0.0189747661,-0.3578401804,0.3052423,-0.5030599833,-0.132392019,-0.2873800397,0.2506045699,0.0892270058,0.2778801024,0.3353272378,-0.2777280211,-0.0245965142,-0.3039755225,-0.0659073442,-0.2901861966,-0.1096735597,0.2428965271,0.1826853007,0.3031284213,-0.0289380215,0.2855456471,0.2206116915,0.1294395328,0.4378712475,-0.3864388764,0.4741620421,0.001300477,-0.1765860617,0.035116639,-0.2722176611,0.0180080682,0.2376574576,0.02238789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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2913","title":"timit_asr dataset only includes one text phrase","comments":"Hi @margotwagner, \r\nThis bug was fixed in #1995. Upgrading the datasets should work (min v1.8.0 ideally)","body":"## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n    picks = []\r\n    for _ in range(num_examples):\r\n        pick = random.randint(0, len(dataset)-1)\r\n        while pick in picks:\r\n            pick = random.randint(0, len(dataset)-1)\r\n        picks.append(pick)\r\n    \r\n    df = pd.DataFrame(dataset[picks])\r\n    display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n","comment_length":16,"text":"timit_asr dataset only includes one text phrase \n ## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n    picks = []\r\n    for _ in range(num_examples):\r\n        pick = random.randint(0, len(dataset)-1)\r\n        while pick in picks:\r\n            pick = random.randint(0, len(dataset)-1)\r\n        picks.append(pick)\r\n    \r\n    df = pd.DataFrame(dataset[picks])\r\n    display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n \n Hi @margotwagner, \r\nThis bug was fixed in #1995. Upgrading the datasets should work (min v1.8.0 ideally)","embeddings":[0.161290288,-0.138053894,-0.0205535926,0.2273859978,0.1176241562,-0.1575979143,0.2670057416,0.2965668142,-0.5154975653,0.2804078162,0.1454417259,0.4508609474,-0.0935282409,-0.1810210198,0.0642997622,-0.0575755686,0.1221427992,0.2484459728,-0.0715344921,-0.2342915833,0.1333340406,0.3007956445,-0.257455945,-0.064661555,-0.2737528682,0.0847610086,-0.0766698644,-0.2693378031,-0.0532864109,-0.4953400791,0.1539394706,0.011122671,0.1049450785,0.3338848054,-0.0001155144,-0.0851756856,0.0369373672,0.0938122272,-0.2382418513,-0.157761991,-0.0969283953,0.0886226818,-0.0698391646,-0.0270568896,-0.0834104121,0.1937476546,0.0529345647,-0.0981618613,0.5147064328,0.2915839553,0.1400677115,-0.0102435909,-0.252120316,-0.0659528822,0.3198694587,0.0313076191,0.0850752518,-0.1780210286,0.2137241364,0.0314790495,-0.0516679287,0.6180685163,-0.2813329399,0.3980180621,-0.2359145284,0.1242306605,-0.3711774647,-0.5308332443,0.266836673,0.2161651254,0.6465566754,-0.2296243757,-0.2044666409,-0.2378615439,0.3199083507,-0.1560648084,-0.0421698391,-0.0161785055,-0.2728021145,0.2350625694,-0.0094950655,0.047647696,-0.2204018831,0.1288805455,0.055811543,-0.1074196771,-0.0731363669,0.1385569125,-0.177100122,0.0994098634,-0.085863933,0.2703583241,-0.1442609429,0.0341251902,-0.149762243,-0.0674901083,0.1925161034,-0.3049418628,0.2917183042,-0.0602199472,0.3855580986,0.0374377258,-0.1856715977,-0.0881002173,0.3239292204,0.019808244,-0.0233677495,0.1496766508,0.2298973352,-0.2340689301,-0.2531856596,0.0737158284,-0.0144180572,0.2162737995,0.331879437,-0.265029937,0.2829249203,-0.367644608,-0.5658828616,0.0642497912,-0.4400376976,-0.1030385345,-0.2528013289,0.1816738844,0.1693309546,0.3009272516,0.1449253261,0.2226763219,0.0693393499,-0.5628325343,-0.1818621904,0.0267980024,0.2098604441,0.0308408234,0.211329326,-0.3237065375,0.3398516774,0.3417337835,0.195452705,-0.4315991104,-0.2874112725,-0.1301766485,0.0255610328,0.0091858534,-0.0264903046,0.3916681111,0.0738202855,-0.0182900447,0.0072356486,0.0610072762,-0.106556423,0.0068646008,0.2134016603,0.1364102364,-0.0805880576,-0.167114675,0.1232486665,0.3605877757,0.0779450312,-0.3008698225,0.0030109661,-0.1827422976,-0.3919641674,-0.0386591218,0.0773314983,0.3024609089,-0.3355484903,0.0954273939,0.178534463,0.4213981032,0.3944750428,0.2484540939,-0.0465098992,0.3734385073,-0.1168166548,0.3001444042,0.2041984349,-0.4621309042,-0.3920545578,0.2112352401,-0.049603641,0.2464142591,0.1548911184,-0.1627833694,0.3027338982,0.1427075267,0.3759355247,0.153478384,0.1457359195,0.0334793814,-0.2446887195,-0.057892438,0.0868306831,-0.0131406998,0.0021537091,0.0955836996,0.0370380655,0.232970655,0.5271517038,0.0745838359,0.0924516246,0.1029673368,0.1659648567,-0.1012314931,0.2239779383,-0.2631005943,0.2564861178,-0.0488976687,0.2068272382,0.2837985456,0.1540315896,0.0793671757,-0.3015450537,-0.2188883722,-0.2991884053,-0.3250726461,0.1145813912,0.3257373571,-0.0144317094,0.0420466512,-0.1898579001,0.3409897685,-0.2203264982,-0.0741857439,0.0233059432,0.0991652608,0.0638567135,-0.2447074801,0.5248855948,0.2528992593,0.18514961,0.2016844004,-0.1669406295,0.22355178,-0.0147676235,0.0219675135,-0.2637168467,-0.3288924992,0.1099477187,-0.5017575622,-0.1113948897,0.2344709635,0.3181717396,-0.0044999369,-0.3036757708,-0.1834709942,0.1390077174,0.2569387853,-0.0554442666,-0.0651190057,0.3011586368,0.0662527978,-0.3537996113,-0.3246975541,0.2526897192,-0.234980613,0.2662216723,0.0202848148,-0.3417770565,0.1585986316,0.3928949833,0.2245113999,0.085208714,0.2943886817,-0.2468326539,-0.027017273,-0.1227289811,0.0949118286,0.2907651365,0.2881050408,0.0543022901,-0.3764547706,-0.1287488788,-0.1132321432,0.1393357515,-0.1307561845,-0.3556297719,0.4351170063,0.3585450351,0.0863876268,-0.4493839145,-0.1341284662,-0.1424040794,0.4703677297,-0.3349600732,-0.2316320539,-0.4356057346,-0.1205524206,-0.109005712,0.0662912875,0.0797044486,0.225550577,0.2190566808,-0.0127121434,0.0054567931,0.1367991865,0.1719908118,0.2340619862,0.0712051392,0.1117983311,0.086305052,0.3191404045,-0.306956917,-0.0242702272,0.0259727612,0.271612525,0.1074295118,-0.1422471404,-0.1639021039,-0.4067965448,0.0590180866,0.2675643563,-0.3301686049,0.5113489628,0.1133633777,0.1602485478,-0.2251659632,-0.3130813539,0.0026724054,-0.0268870983,-0.1686861217,0.3555683196,0.0829433203,-0.0056142341,-0.0482558794,-0.698974669,0.099392809,-0.2500089407,0.2359433323,-0.0328674652,0.2103785425,0.3641358316,0.0606071204,0.0812700838,-0.0587117635,0.2053699046,-0.410209775,-0.2545935214,0.3561107218,0.2606044412,-0.3348694146,-0.3405695558,0.0509579033,0.2652952075,-0.0322290994,-0.4001087248,0.1003541723,-0.2674658895,0.1453480273,-0.2972603142,0.2826198339,0.3854675591,0.0129939336,-0.1495047212,-0.1692215204,-0.1078334898,0.2658356428,0.3553734422,0.0058843573,0.1011472642,0.0699137822,-0.0593266487,0.3506978154,0.6459594965,0.2025162727,0.2600066662,-0.1765539348,0.0942354053,-0.0324934721,-0.3995798528,0.0270797946,-0.1173247173,-0.062567167,0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2913","title":"timit_asr dataset only includes one text phrase","comments":"Hi @margotwagner,\r\n\r\nYes, as @bhavitvyamalik has commented, this bug was fixed in `datasets` version 1.5.0. You need to update it, as your current version is 1.4.1:\r\n> Environment info\r\n> - `datasets` version: 1.4.1","body":"## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n    picks = []\r\n    for _ in range(num_examples):\r\n        pick = random.randint(0, len(dataset)-1)\r\n        while pick in picks:\r\n            pick = random.randint(0, len(dataset)-1)\r\n        picks.append(pick)\r\n    \r\n    df = pd.DataFrame(dataset[picks])\r\n    display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n","comment_length":34,"text":"timit_asr dataset only includes one text phrase \n ## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n    picks = []\r\n    for _ in range(num_examples):\r\n        pick = random.randint(0, len(dataset)-1)\r\n        while pick in picks:\r\n            pick = random.randint(0, len(dataset)-1)\r\n        picks.append(pick)\r\n    \r\n    df = pd.DataFrame(dataset[picks])\r\n    display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n \n Hi @margotwagner,\r\n\r\nYes, as @bhavitvyamalik has commented, this bug was fixed in `datasets` version 1.5.0. You need to update it, as your current version is 1.4.1:\r\n> Environment info\r\n> - `datasets` version: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2904","title":"FORCE_REDOWNLOAD does not work","comments":"Hi ! Thanks for reporting. The error seems to happen only if you use compressed files.\r\n\r\nThe second dataset is prepared in another dataset cache directory than the first - which is normal, since the source file is different. However, it doesn't uncompress the new data file because it finds the old uncompressed data in the extraction cache directory.\r\n\r\nIf we fix the extraction cache mechanism to uncompress a local file if it changed then it should fix the issue.\r\nCurrently the extraction cache mechanism only takes into account the path of the compressed file, which is an issue.","body":"## Describe the bug\r\nWith GenerateMode.FORCE_REDOWNLOAD, the documentation says \r\n    +------------------------------------+-----------+---------+\r\n    |                                    | Downloads | Dataset |\r\n    +====================================+===========+=========+\r\n    | `REUSE_DATASET_IF_EXISTS` (default)| Reuse     | Reuse   |\r\n    +------------------------------------+-----------+---------+\r\n    | `REUSE_CACHE_IF_EXISTS`            | Reuse     | Fresh   |\r\n    +------------------------------------+-----------+---------+\r\n    | `FORCE_REDOWNLOAD`                 | Fresh     | Fresh   |\r\n    +------------------------------------+-----------+---------+\r\n\r\nHowever, the old dataset is loaded even when FORCE_REDOWNLOAD is chosen.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport pandas as pd\r\nfrom datasets import load_dataset, GenerateMode\r\npd.DataFrame(range(5), columns=['numbers']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\npd.DataFrame(range(10), columns=['numerals']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\n\r\n```\r\n\r\n## Expected results\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\nDataset({\r\n    features: ['numerals'],\r\n    num_rows: 10\r\n})\r\n\r\n## Actual results\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":99,"text":"FORCE_REDOWNLOAD does not work \n ## Describe the bug\r\nWith GenerateMode.FORCE_REDOWNLOAD, the documentation says \r\n    +------------------------------------+-----------+---------+\r\n    |                                    | Downloads | Dataset |\r\n    +====================================+===========+=========+\r\n    | `REUSE_DATASET_IF_EXISTS` (default)| Reuse     | Reuse   |\r\n    +------------------------------------+-----------+---------+\r\n    | `REUSE_CACHE_IF_EXISTS`            | Reuse     | Fresh   |\r\n    +------------------------------------+-----------+---------+\r\n    | `FORCE_REDOWNLOAD`                 | Fresh     | Fresh   |\r\n    +------------------------------------+-----------+---------+\r\n\r\nHowever, the old dataset is loaded even when FORCE_REDOWNLOAD is chosen.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport pandas as pd\r\nfrom datasets import load_dataset, GenerateMode\r\npd.DataFrame(range(5), columns=['numbers']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\npd.DataFrame(range(10), columns=['numerals']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\n\r\n```\r\n\r\n## Expected results\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\nDataset({\r\n    features: ['numerals'],\r\n    num_rows: 10\r\n})\r\n\r\n## Actual results\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n \n Hi ! Thanks for reporting. The error seems to happen only if you use compressed files.\r\n\r\nThe second dataset is prepared in another dataset cache directory than the first - which is normal, since the source file is different. However, it doesn't uncompress the new data file because it finds the old uncompressed data in the extraction cache directory.\r\n\r\nIf we fix the extraction cache mechanism to uncompress a local file if it changed then it should fix the issue.\r\nCurrently the extraction cache mechanism only takes into account the path of the compressed file, which is an issue.","embeddings":[-0.1253399998,0.0207806267,0.0185128842,0.0257142521,0.1821040809,0.0251934566,0.4788660109,0.2438331693,0.145796001,-0.2338699996,-0.1162827983,0.2687395215,0.0850747079,0.1121127084,-0.0132031068,0.3137487769,0.1101020277,0.2144826502,-0.0722493976,-0.0051923986,-0.2910993099,0.0634416714,-0.1833185554,-0.2195639163,-0.2065767497,0.2937990129,-0.0426936708,0.3026081324,0.0084062209,-0.3731202781,0.1836812794,0.2550069988,0.1820975393,0.6611076593,-0.000109716,0.0617883578,0.1586239785,-0.1793150753,-0.1704429984,-0.1858083606,0.0419964381,-0.0455286205,-0.3461463749,-0.0842089579,-0.0005326021,-0.2207529545,-0.1748757958,-0.346149832,0.5304936767,0.4028010368,0.2320071906,-0.1104161516,0.1202409416,-0.0721178129,0.1787120998,0.1414143443,-0.0699524358,0.3557813466,0.1045252755,-0.2072234303,-0.090631254,0.071447365,-0.1301329136,0.0515931137,-0.100776881,-0.0266386643,0.3280929923,-0.1360899806,0.0856568739,0.1706219614,0.60730654,-0.3377125561,-0.4172843397,0.0072846375,0.0183145087,-0.2442766875,0.1650239527,0.1792488247,-0.0074186656,0.368666321,-0.0126197841,-0.0254521314,0.0721380934,-0.1948064566,-0.0308580399,-0.117819868,-0.1004166082,-0.0362135768,0.0516206622,0.0602480508,0.4213585556,-0.021616118,-0.0566806123,0.0342854224,-0.029152602,-0.1342204958,0.2153061479,-0.3018356264,0.0747370049,-0.0281923842,-0.0764476284,-0.1077359319,-0.0592024475,0.04897983,0.0738615543,0.2261448056,-0.1387252212,-0.090120174,0.3910107613,0.1767412573,-0.251378119,-0.0726185068,0.0294129029,-0.2027270049,0.5261009336,-0.0647600591,0.2614033818,-0.1472218335,-0.4044618905,-0.0181814078,-0.1070784777,0.0032655229,-0.2742642164,0.1767624319,0.0207748469,0.3530659974,0.1341454834,0.1856252104,0.0581398085,-0.206356883,-0.2226337641,-0.1226871684,-0.3091842234,0.0251519568,0.2947835922,-0.1708823442,0.3440055251,0.5470414758,-0.3297725916,-0.5027301908,-0.0621353462,-0.0314323269,0.145252198,0.2964736819,-0.0633033141,-0.0863981768,0.2666994929,-0.0901367441,-0.0168399569,0.3552974463,-0.0914471522,-0.3069287539,0.1436540186,0.1975634098,0.0511063114,0.1958488077,-0.127950877,0.0155278603,0.3042720556,-0.0705190748,-0.0457962081,-0.0500780307,-0.0136534544,-0.2484514862,0.0476663969,0.7337824702,-0.4681162834,0.0081418594,-0.2922013104,-0.0890840143,0.2404547036,0.0785243809,0.0047035967,0.0423925631,-0.4686902165,-0.3603310585,0.1667583287,-0.1669529229,-0.4369600415,0.0058205375,-0.2142421305,0.2575328946,0.1665146351,-0.0449789762,-0.1501356214,-0.0172337554,0.0970478505,0.2490073442,0.0923939124,0.044823505,-0.3246170282,-0.288821131,0.0455681123,-0.1329662651,0.2855156362,0.405680865,0.3521436155,0.166620627,0.3699389398,0.0625928491,0.0155102359,0.0143866315,0.3367364407,-0.0180097409,0.0330760218,0.1765212268,-0.5221686959,0.3075172901,-0.0336038433,-0.2515624166,0.0728959292,-0.0701956674,-0.5719491243,-0.228338778,-0.0720977858,-0.3877546787,0.1725475937,0.4713421166,-0.033435449,0.0184990596,-0.1434893459,0.4725981653,-0.3845011294,-0.0480710976,-0.0413006209,0.2855195999,-0.1932275444,-0.1814612597,-0.2195678353,-0.1187965199,0.2447945774,-0.1625743508,-0.1215589717,0.4299698472,0.186048165,0.2720868587,-0.3619140387,-0.1950368881,0.1652197391,-0.016063001,-0.1465488821,0.1185459942,0.2459682077,-0.0776265264,-0.0173951481,0.2789047956,-0.4031001925,-0.0885871574,0.071374476,-0.0462191142,0.2666130662,-0.2367841005,0.2828178704,-0.340397954,-0.3403313756,0.2947020233,0.0018241567,0.1279272139,0.0989251807,0.1121156812,0.2867794335,0.0066712452,0.047518149,-0.0890123621,-0.1765656024,-0.2945634723,0.0131209595,0.3500014544,0.5929611921,0.1657259762,-0.0166612044,0.1451728493,-0.0583074652,-0.253809005,0.2043861002,-0.2613338828,-0.0064706737,0.3311824501,0.1878525168,0.0763362646,-0.6370830536,0.186015591,0.5140926838,0.2118025422,-0.2195149958,-0.1909853667,-0.1673647612,0.4125154912,0.0630170777,0.1435509324,-0.0300561506,-0.1065669209,-0.138272956,0.5955287814,-0.0589990281,0.073292084,-0.1850811243,-0.0749260634,-0.0461661331,-0.1426966637,-0.1472802907,-0.1764150858,-0.1414824873,0.0910957381,0.2313582748,-0.3419226408,0.1477311403,-0.1828764379,-0.1267566234,-0.3267619908,-0.0833430737,-0.1293941885,0.0340040401,0.2224018276,-0.2250489891,0.1471374333,0.169757694,0.0325683653,-0.004715248,-0.3554546833,-0.3341683745,-0.1029282659,0.1511999369,0.2633759975,-0.2600107789,-0.4554549158,0.1090232432,-0.3711392879,-0.0406573713,0.1015611589,-0.0919760466,0.3833361566,0.2238112539,-0.0310825258,0.4020083547,0.2698833942,-0.4376188517,-0.2790603936,0.0805035159,-0.1014774665,-0.2633699775,0.0147913452,0.0609156676,-0.0121200867,0.2737061381,-0.4307680726,-0.0845259503,-0.2824058533,0.0837785453,-0.0585749894,0.2336959094,0.2081224918,0.1207609028,-0.2136411071,-0.1040799022,-0.0956789032,-0.041495204,0.233491689,0.126982823,0.0212046467,0.2292778045,0.0935394913,0.6404239535,0.0503102541,-0.1254811585,0.5086991787,0.125423491,0.7294853926,-0.074036397,-0.339060694,-0.0056349542,-0.1845843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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2904","title":"FORCE_REDOWNLOAD does not work","comments":"Facing the same issue, is there any way to overtake this issue until it will be fixed? ","body":"## Describe the bug\r\nWith GenerateMode.FORCE_REDOWNLOAD, the documentation says \r\n    +------------------------------------+-----------+---------+\r\n    |                                    | Downloads | Dataset |\r\n    +====================================+===========+=========+\r\n    | `REUSE_DATASET_IF_EXISTS` (default)| Reuse     | Reuse   |\r\n    +------------------------------------+-----------+---------+\r\n    | `REUSE_CACHE_IF_EXISTS`            | Reuse     | Fresh   |\r\n    +------------------------------------+-----------+---------+\r\n    | `FORCE_REDOWNLOAD`                 | Fresh     | Fresh   |\r\n    +------------------------------------+-----------+---------+\r\n\r\nHowever, the old dataset is loaded even when FORCE_REDOWNLOAD is chosen.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport pandas as pd\r\nfrom datasets import load_dataset, GenerateMode\r\npd.DataFrame(range(5), columns=['numbers']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\npd.DataFrame(range(10), columns=['numerals']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\n\r\n```\r\n\r\n## Expected results\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\nDataset({\r\n    features: ['numerals'],\r\n    num_rows: 10\r\n})\r\n\r\n## Actual results\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":17,"text":"FORCE_REDOWNLOAD does not work \n ## Describe the bug\r\nWith GenerateMode.FORCE_REDOWNLOAD, the documentation says \r\n    +------------------------------------+-----------+---------+\r\n    |                                    | Downloads | Dataset |\r\n    +====================================+===========+=========+\r\n    | `REUSE_DATASET_IF_EXISTS` (default)| Reuse     | Reuse   |\r\n    +------------------------------------+-----------+---------+\r\n    | `REUSE_CACHE_IF_EXISTS`            | Reuse     | Fresh   |\r\n    +------------------------------------+-----------+---------+\r\n    | `FORCE_REDOWNLOAD`                 | Fresh     | Fresh   |\r\n    +------------------------------------+-----------+---------+\r\n\r\nHowever, the old dataset is loaded even when FORCE_REDOWNLOAD is chosen.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport pandas as pd\r\nfrom datasets import load_dataset, GenerateMode\r\npd.DataFrame(range(5), columns=['numbers']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\npd.DataFrame(range(10), columns=['numerals']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\n\r\n```\r\n\r\n## Expected results\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\nDataset({\r\n    features: ['numerals'],\r\n    num_rows: 10\r\n})\r\n\r\n## Actual results\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\nDataset({\r\n    features: ['numbers'],\r\n    num_rows: 5\r\n})\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n \n Facing the same issue, is there any way to overtake this issue until it will be fixed? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2902","title":"Add WIT Dataset","comments":"WikiMedia is now hosting the pixel values directly which should make it a lot easier!\r\nThe files can be found here:\r\nhttps:\/\/techblog.wikimedia.org\/2021\/09\/09\/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research\/\r\nhttps:\/\/analytics.wikimedia.org\/published\/datasets\/one-off\/caption_competition\/training\/image_pixels\/","body":"## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:**  (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":23,"text":"Add WIT Dataset \n ## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:**  (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n WikiMedia is now hosting the pixel values directly which should make it a lot easier!\r\nThe files can be found here:\r\nhttps:\/\/techblog.wikimedia.org\/2021\/09\/09\/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research\/\r\nhttps:\/\/analytics.wikimedia.org\/published\/datasets\/one-off\/caption_competition\/training\/image_pixels\/","embeddings":[-0.0497860387,-0.0547444522,-0.1280666143,0.0003004618,-0.039354112,-0.0071313963,0.3546046019,0.1659145951,0.1936855763,0.2055810541,0.0639799759,0.1460067928,-0.0242924541,0.1997424215,-0.01487609,-0.2061530054,-0.0474179126,0.007556899,-0.095439963,-0.0953547359,-0.1237470731,0.1472279876,-0.1375917941,-0.2112537324,-0.4580724537,-0.0875768587,-0.1730553955,-0.0253455658,-0.1721293628,-0.0776184052,-0.1209003329,0.1993556619,-0.1435845345,0.3342963159,-0.0000973471,-0.1190001816,0.2309069782,-0.1802841574,-0.0871033221,0.0576613285,0.1233595088,-0.0183559302,-0.3197696805,-0.4497412145,-0.1917415559,-0.0919262245,0.250259757,0.0872264057,0.220723629,-0.0171945672,0.3280394673,-0.1210444793,0.10394793,0.0242980495,0.0408663712,0.1121114418,-0.1330121011,-0.0007004917,0.0150091052,-0.3246178627,-0.1604316086,0.6847470999,0.0171727017,0.0272535756,0.0614412166,0.0563659444,-0.1721417308,-0.3297246695,0.1790350825,0.4349500835,0.5310005546,0.0022286586,-0.1660662293,-0.0531980433,-0.0875593573,-0.0650093853,0.1432961375,0.2999428809,-0.1368227452,0.0239426252,-0.2666973472,0.0028924709,-0.2545390427,0.1593592912,-0.1959471852,0.4131114483,0.021566527,0.0326524712,-0.000182682,-0.2101522833,-0.4369372725,-0.0127320085,0.2125476599,0.0099950507,0.0381090157,-0.2509273589,-0.0503862202,-0.1546090245,0.3692227602,-0.197378248,-0.0403584838,0.0733242184,-0.1825137436,0.3001660109,0.1151571646,-0.273580879,-0.1728782058,0.0215191487,0.1827842295,0.0874059945,-0.2466638684,0.1344642043,0.2253025919,-0.1029728055,-0.1772990078,-0.087800771,0.1070196927,-0.0466616042,-0.1075610965,-0.008583298,-0.146704793,-0.0710129738,-0.4360583127,0.2165165991,-0.0335759073,0.1098506823,0.1278970838,0.1473859996,-0.2188078016,-0.1533710063,-0.1057867333,0.1009146869,0.0557398014,0.2790454626,0.1211024597,0.3311381638,0.256734401,-0.0824029595,0.1746457219,0.0947210044,0.0416478626,-0.0953107029,0.2555246055,-0.008912893,0.0164618678,-0.1227277219,-0.051241383,-0.2793621719,-0.19902426,0.0270652734,-0.1084727868,0.2036321461,-0.3212899566,0.2668367922,0.1446974277,-0.0583522059,-0.0045630517,0.6982100606,0.0959759504,0.0308421906,0.1365310401,0.1445246041,-0.3958842158,-0.0636466146,0.2453516275,0.4490688741,-0.0886282399,0.0255282167,0.3061005771,0.3564617038,0.0193696469,0.1932205856,0.0245506912,-0.0181516893,0.0488758124,0.2630175054,-0.1056210548,-0.2358684838,-0.1195387468,-0.1759907305,-0.0965119898,0.1020767167,0.0326437689,0.2860510647,0.270050317,-0.102775909,0.093534559,0.6013006568,-0.1892949641,0.2137656063,-0.2130067647,-0.3863318861,-0.0069002835,0.3273807466,0.1285638511,-0.4685770869,0.2250231653,0.1909397244,0.0319985636,-0.3845872879,0.0987228006,0.0075334026,-0.0310356412,0.0995845795,0.091828078,-0.2398949265,-0.0079521695,0.0546789393,0.2049163729,0.4832212925,-0.1773444712,-0.2481343299,-0.038995903,-0.1513748169,-0.1065134704,-0.3698706031,0.3537430465,0.1205859855,-0.0045493012,0.2210574299,0.0784497336,-0.0594155379,-0.3163442016,-0.0456017256,0.1199062467,0.1867598891,-0.2623721361,-0.0185750853,0.1218658164,0.0860536918,-0.0835613608,0.1807654053,0.0911751837,0.1913248599,0.0461769477,0.3455418348,0.2677955925,0.0530583225,0.3559015393,-0.6900479198,-0.0690085068,0.19123438,0.0030610359,0.0122600449,-0.2659728527,0.1819274873,0.2717046738,0.0452171303,-0.0804450437,-0.0083080204,0.1473466903,-0.0244393963,0.0583699085,-0.3646619022,-0.0810375884,0.2129996717,-0.2515609264,-0.2326057106,-0.1890244186,0.1808756441,0.5132712126,0.2433172613,0.3687907755,0.1684178561,-0.1199678779,-0.1401048452,-0.0068703438,-0.0960154831,-0.1688125134,0.3742109239,-0.053772375,-0.1260635257,-0.2724704742,-0.0922951698,0.1780956537,-0.0158920027,0.2231312841,0.0486644618,0.2492239773,-0.0249909423,-0.4577436149,-0.1271143258,-0.1574495137,-0.1973033249,0.056282267,0.0784645602,-0.071331881,-0.1991609186,-0.066964902,0.2833610475,-0.3087125421,-0.1143085882,0.3522223532,0.0152183259,-0.1103117839,0.183313325,0.0873240456,0.1733572483,0.1862344891,0.0245582648,-0.1337936968,-0.5455780029,-0.0611162968,0.2686133087,0.1743589044,-0.0161512867,0.5377413034,-0.1781012118,0.0367738679,-0.2884244919,-0.705337286,0.1020467132,-0.1840780228,0.1969555616,0.0155186988,0.2760653794,-0.2234164178,-0.1541128606,0.1546294391,-0.1017461941,-0.1935486495,-0.2209588885,0.0480773337,0.0420998633,-0.2291868478,-0.2148961276,-0.1442073584,-0.2447485179,0.1066020951,0.3489588201,0.1721870601,0.0647365227,0.3838174343,0.0280661602,-0.0238699075,0.2764138281,-0.3571163714,-0.032202661,0.3682462573,-0.3039095998,-0.403814584,-0.1645279825,-0.182767272,0.1699132323,0.0591959544,-0.3852038085,-0.3932056427,0.0160926636,0.1964574009,0.2606021762,0.0776553899,0.1688962281,0.1696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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2902","title":"Add WIT Dataset","comments":"> @hassiahk is working on it #2810\r\n\r\nThank you @bhavitvyamalik! Added this issue so we could track progress \ud83d\ude04 . Just linked the PR as well for visibility. ","body":"## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:**  (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":28,"text":"Add WIT Dataset \n ## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:**  (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n > @hassiahk is working on it #2810\r\n\r\nThank you @bhavitvyamalik! Added this issue so we could track progress \ud83d\ude04 . Just linked the PR as well for visibility. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2902","title":"Add WIT Dataset","comments":"Hey folks, we are now hosting the merged pixel values + embeddings + metadata ourselves. I gave it a try - [nateraw\/wit](https:\/\/huggingface.co\/datasets\/nateraw\/wit)\r\n\r\n**\u26a0\ufe0f - Make sure you add `streaming=True` unless you're prepared to download 400GB of data!**\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('nateraw\/wit', streaming=True)\r\nexample = next(iter(ds))\r\n```\r\n\r\n```python\r\n>>> example = next(iter(ds['train']))\r\n>>> example.keys()\r\ndict_keys(['b64_bytes', 'original_width', 'image_url', 'wit_features', 'original_height', 'metadata_url', 'mime_type', 'caption_attribution_description', 'embedding'])\r\n>>> example['wit_features'].keys()\r\ndict_keys(['hierarchical_section_title', 'language', 'attribution_passes_lang_id', 'context_section_description', 'is_main_image', 'page_title', 'caption_title_and_reference_description', 'caption_alt_text_description', 'caption_reference_description', 'page_url', 'context_page_description', 'section_title', 'page_changed_recently'])\r\n```","body":"## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:**  (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":82,"text":"Add WIT Dataset \n ## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:**  (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hey folks, we are now hosting the merged pixel values + embeddings + metadata ourselves. I gave it a try - [nateraw\/wit](https:\/\/huggingface.co\/datasets\/nateraw\/wit)\r\n\r\n**\u26a0\ufe0f - Make sure you add `streaming=True` unless you're prepared to download 400GB of data!**\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('nateraw\/wit', streaming=True)\r\nexample = next(iter(ds))\r\n```\r\n\r\n```python\r\n>>> example = next(iter(ds['train']))\r\n>>> example.keys()\r\ndict_keys(['b64_bytes', 'original_width', 'image_url', 'wit_features', 'original_height', 'metadata_url', 'mime_type', 'caption_attribution_description', 'embedding'])\r\n>>> example['wit_features'].keys()\r\ndict_keys(['hierarchical_section_title', 'language', 'attribution_passes_lang_id', 'context_section_description', 'is_main_image', 'page_title', 'caption_title_and_reference_description', 'caption_alt_text_description', 'caption_reference_description', 'page_url', 'context_page_description', 'section_title', 'page_changed_recently'])\r\n```","embeddings":[-0.1127936468,-0.3660557866,-0.0743472204,0.0989870876,0.1571707577,0.0002976077,0.3201468885,0.1798595488,0.1064553112,0.1802819967,-0.1281458288,0.0916985646,-0.0961453915,0.2781312466,0.0500217602,-0.2135280818,-0.0313868634,0.0803588629,-0.1130700707,-0.0754943341,-0.0617344491,0.195978567,-0.089915283,-0.2751672566,-0.4703181982,0.1444536895,-0.3146583438,0.1389909834,-0.2078276426,0.1107922047,-0.1769427061,0.1067766175,-0.2275704443,0.5229244232,-0.00009933,-0.0197977126,0.2415332943,-0.1750340909,-0.0644826367,-0.0778388232,0.234139204,-0.0634752065,-0.280452013,-0.3421323299,-0.2317476273,-0.1847543418,0.1015041322,-0.011839183,0.4073025882,0.056796968,0.3128572106,0.0304383356,0.0779094175,0.0463778339,0.1003950238,0.1235639974,-0.1647861749,-0.0414207689,0.0091499314,-0.2282686234,-0.0891304985,0.6468824148,0.0142807029,-0.0998940617,0.1765292287,0.0900045186,-0.1455237418,-0.3033283055,0.0756126866,0.3445054591,0.4071141481,-0.0724962652,-0.2233341932,0.036188975,-0.1426528394,-0.305775553,0.1205694824,0.2737214863,-0.1172480509,0.0602812171,-0.1651700586,0.1006650627,-0.2075355053,0.2664605677,-0.265029639,0.3764568865,-0.016189944,-0.0339372382,0.0149117764,-0.1650952548,-0.2769257128,-0.0310796853,0.2455943972,-0.0275462642,0.0170438755,-0.1150247455,0.1005613059,-0.2237215638,0.2705687284,-0.0771672279,-0.0536473803,0.0963175744,-0.1730285883,0.2887856364,-0.0153745124,-0.1895415187,-0.1080746949,-0.0999811366,0.2123668492,0.0843739361,-0.118824169,0.0783975422,0.2026643008,-0.0383944623,-0.2172788531,-0.2400268465,0.1216245294,0.0683212131,-0.0867482722,-0.0308441855,-0.2619360387,-0.0105714314,-0.3383368254,0.2436919361,0.0414685644,0.0920979306,0.2444183528,0.2414946705,-0.1277333498,0.0254563,-0.1962920129,0.1458347142,-0.1061614752,0.1172731817,0.1671883911,0.2310720682,0.2650675476,-0.0414919779,0.2681098282,0.0103542786,-0.088271901,-0.0234218799,0.2350068539,0.1316095442,-0.0299728308,-0.0822103843,0.0115291718,-0.2654998004,-0.1302790195,0.0447920635,-0.071818985,-0.0214206949,-0.3733005226,0.2578840256,0.1412623078,0.0757177919,0.0819769502,0.6322957873,0.0833620876,0.083024919,0.0574023947,0.1813165694,-0.4125094116,-0.0992664695,0.3997363746,0.433960557,-0.2317686379,-0.165040791,0.2389319837,0.3340359032,0.0815450177,0.3074741662,-0.0339201875,-0.1124214083,-0.0426018462,0.3753934205,0.0426759236,-0.2846666276,-0.1661852002,-0.0628164187,-0.0271609109,0.121067971,0.1125156432,0.2895497978,0.148621887,-0.0841926336,0.1877522022,0.6975086331,-0.0504955575,0.1915243566,-0.2837094665,-0.3887051642,0.0250388514,0.2955477536,0.0446941145,-0.4839538932,0.1468640119,0.0844427124,0.1555161178,-0.3033557236,0.1012263596,0.0437979661,0.0007848545,0.199647665,0.0841274932,-0.1682545394,-0.0323356539,0.0856209174,0.2162829041,0.5262115598,-0.2870110869,-0.2510940135,-0.0655869916,-0.1933980882,-0.1747309715,-0.4910915196,0.3567461073,0.2140885741,-0.1384143829,0.1379236132,-0.0776988119,-0.0019407844,-0.1633296758,-0.0107133891,-0.0556919128,0.3489171863,-0.2462145686,-0.0811416954,-0.0008055486,0.2053411156,-0.1188986227,0.09803617,0.0262854341,0.3709500134,-0.0042960886,0.3041270375,0.0776459351,0.1161036044,0.3713479042,-0.5518855453,0.098378107,0.0743917897,-0.0125628831,-0.152046755,-0.3225552142,0.0440614857,0.3144774735,0.1018015146,0.0284636617,0.0239155665,0.1517513394,0.0100216959,-0.1084221229,-0.3836122155,0.0093868058,0.0397226326,-0.0725952536,-0.1801057756,-0.1373412013,0.1949926168,0.5173015594,0.1614923775,0.2218614668,0.0869821161,-0.2707193494,-0.1511384249,-0.0393371694,-0.2554870248,-0.0111633968,0.373790741,0.0467149355,0.0264951792,-0.1724006087,-0.0379969925,0.1969967037,0.0204086043,0.1709062457,0.148644045,0.2892823219,0.0007013938,-0.5360543132,0.0332579613,-0.1098512486,-0.2198702246,-0.0840391219,0.1056190059,-0.1298488975,-0.2686516643,-0.0975158587,0.0630487576,-0.360316366,-0.2706814706,0.2066226304,0.0115099037,-0.1689703614,0.2770496607,0.3699896634,0.167567417,0.2477453053,-0.0077357548,-0.1534488499,-0.2951208949,-0.0308689009,0.1947922409,0.2185022384,0.0902849287,0.5967864394,-0.17600061,-0.040235959,-0.1415278167,-0.7260532975,0.2035658509,-0.3150738478,0.1984147578,0.0552007072,0.1283572614,-0.0661231503,-0.1377191991,0.2716807723,-0.0415155031,-0.1723564565,-0.0956234932,0.2038718909,0.0772237703,-0.3101857603,-0.1672160327,-0.20552665,-0.4030854404,0.1192120761,0.2301404625,0.1426580697,0.0832017213,0.3863643408,0.0729206875,0.0972099975,0.1468118131,-0.382947892,0.0334181562,0.3265535831,-0.3666166663,-0.3955132961,-0.1492932141,0.0139312716,0.1929573417,0.0814097971,-0.408488214,-0.3970986009,-0.1105476692,0.1815385669,0.1899611652,0.0998048708,0.2236189842,0.1507980973,-0.2739018798,-0.1178891361,-0.1631104499,0.0498990342,-0.0084563438,0.0889707953,0.1153720692,0.271898061,0.2369403839,0.4327150583,-0.0874639228,-0.0254739504,0.3919620216,-0.1100328937,0.2966561317,-0.1588880867,-0.2245686948,0.4265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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2901","title":"Incompatibility with pytest","comments":"Sorry, my bad... When implementing `xpathopen`, I just considered the use case in the COUNTER dataset... I'm fixing it!","body":"## Describe the bug\r\n\r\npytest complains about xpathopen \/ path.open(\"w\")\r\n\r\n## Steps to reproduce the bug\r\n\r\nCreate a test file, `test.py`:\r\n\r\n```python\r\nimport datasets as ds\r\ndef load_dataset():\r\n    ds.load_dataset(\"counter\", split=\"train\", streaming=True)\r\n```\r\n\r\nAnd launch it with pytest:\r\n\r\n```bash\r\npython -m pytest test.py\r\n```\r\n\r\n## Expected results\r\n\r\nIt should give something like:\r\n\r\n```\r\ncollected 1 item\r\n\r\ntest.py .                                                                                                                                                                                                                                             [100%]\r\n\r\n======= 1 passed in 3.15s =======\r\n```\r\n\r\n## Actual results\r\n\r\n```\r\n============================================================================================================================= test session starts ==============================================================================================================================\r\nplatform linux -- Python 3.8.11, pytest-6.2.5, py-1.10.0, pluggy-1.0.0\r\nrootdir: \/home\/slesage\/hf\/datasets-preview-backend, configfile: pyproject.toml\r\nplugins: anyio-3.3.1\r\ncollected 1 item\r\n\r\ntests\/queries\/test_rows.py .                                                                                                                                                                                                                                             [100%]Traceback (most recent call last):\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 194, in _run_module_as_main\r\n    return _run_code(code, main_globals, None,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 87, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pytest\/__main__.py\", line 5, in <module>\r\n    raise SystemExit(pytest.console_main())\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 185, in console_main\r\n    code = main()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 162, in main\r\n    ret: Union[ExitCode, int] = config.hook.pytest_cmdline_main(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n    return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n    return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 60, in _multicall\r\n    return outcome.get_result()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n    raise ex[1].with_traceback(ex[2])\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n    res = hook_impl.function(*args)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 316, in pytest_cmdline_main\r\n    return wrap_session(config, _main)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 304, in wrap_session\r\n    config.hook.pytest_sessionfinish(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n    return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n    return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 55, in _multicall\r\n    gen.send(outcome)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/terminal.py\", line 803, in pytest_sessionfinish\r\n    outcome.get_result()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n    raise ex[1].with_traceback(ex[2])\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n    res = hook_impl.function(*args)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 428, in pytest_sessionfinish\r\n    config.cache.set(\"cache\/nodeids\", sorted(self.cached_nodeids))\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 188, in set\r\n    f = path.open(\"w\")\r\nTypeError: xpathopen() takes 1 positional argument but 2 were given\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Linux-5.11.0-1017-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":19,"text":"Incompatibility with pytest \n ## Describe the bug\r\n\r\npytest complains about xpathopen \/ path.open(\"w\")\r\n\r\n## Steps to reproduce the bug\r\n\r\nCreate a test file, `test.py`:\r\n\r\n```python\r\nimport datasets as ds\r\ndef load_dataset():\r\n    ds.load_dataset(\"counter\", split=\"train\", streaming=True)\r\n```\r\n\r\nAnd launch it with pytest:\r\n\r\n```bash\r\npython -m pytest test.py\r\n```\r\n\r\n## Expected results\r\n\r\nIt should give something like:\r\n\r\n```\r\ncollected 1 item\r\n\r\ntest.py .                                                                                                                                                                                                                                             [100%]\r\n\r\n======= 1 passed in 3.15s =======\r\n```\r\n\r\n## Actual results\r\n\r\n```\r\n============================================================================================================================= test session starts ==============================================================================================================================\r\nplatform linux -- Python 3.8.11, pytest-6.2.5, py-1.10.0, pluggy-1.0.0\r\nrootdir: \/home\/slesage\/hf\/datasets-preview-backend, configfile: pyproject.toml\r\nplugins: anyio-3.3.1\r\ncollected 1 item\r\n\r\ntests\/queries\/test_rows.py .                                                                                                                                                                                                                                             [100%]Traceback (most recent call last):\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 194, in _run_module_as_main\r\n    return _run_code(code, main_globals, None,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 87, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pytest\/__main__.py\", line 5, in <module>\r\n    raise SystemExit(pytest.console_main())\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 185, in console_main\r\n    code = main()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 162, in main\r\n    ret: Union[ExitCode, int] = config.hook.pytest_cmdline_main(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n    return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n    return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 60, in _multicall\r\n    return outcome.get_result()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n    raise ex[1].with_traceback(ex[2])\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n    res = hook_impl.function(*args)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 316, in pytest_cmdline_main\r\n    return wrap_session(config, _main)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 304, in wrap_session\r\n    config.hook.pytest_sessionfinish(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n    return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n    return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 55, in _multicall\r\n    gen.send(outcome)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/terminal.py\", line 803, in pytest_sessionfinish\r\n    outcome.get_result()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n    raise ex[1].with_traceback(ex[2])\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n    res = hook_impl.function(*args)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 428, in pytest_sessionfinish\r\n    config.cache.set(\"cache\/nodeids\", sorted(self.cached_nodeids))\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 188, in set\r\n    f = path.open(\"w\")\r\nTypeError: xpathopen() takes 1 positional argument but 2 were given\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Linux-5.11.0-1017-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Sorry, my bad... When implementing `xpathopen`, I just considered the use case in the COUNTER dataset... I'm fixing it!","embeddings":[-0.3016421199,-0.1903872788,-0.0241492949,0.0990293771,0.3130396903,-0.1301406622,0.3114988804,0.2646481693,-0.1026777923,0.2515563369,-0.0329412185,0.3579744101,-0.0499672778,0.0878717452,-0.1619878709,0.1268500686,-0.0505254902,0.0685187206,0.0408935249,0.1136002094,-0.5712682605,-0.1570851803,-0.2168759555,0.0572490133,0.0126794586,0.0686679184,0.0916296914,0.2422822416,-0.0774931237,-0.469889611,0.1960368752,-0.0263328627,-0.0422579646,0.4722329378,-0.0001045304,-0.0628838241,0.4584931433,0.2026191652,-0.4859764278,-0.2095862329,-0.1490363926,0.2063343376,0.2531500459,-0.254386425,-0.1224498227,-0.2215161026,0.0800599158,-0.5224384069,0.0328101404,0.2298583686,0.2646005154,0.5858446956,0.1764907539,-0.1220323741,0.3920012712,-0.1865620166,-0.0988033265,0.1512855589,0.2791418135,0.0342658088,-0.1552184969,0.2738155425,-0.251668334,0.2581939995,0.0830629617,0.0403149687,0.1071799397,0.1236744151,0.0054618004,0.1477268934,-0.0244684517,-0.2447356284,-0.187163204,0.0132809207,-0.1560323685,-0.4709234536,0.0660294145,0.2990931869,-0.1927743256,0.0707391798,-0.1533978879,0.285312593,-0.2178352475,0.2086598277,-0.0414643139,0.474942416,0.0106467595,0.0582296215,0.0282520335,0.0535728522,0.3718392253,-0.029089909,0.0258877985,-0.2166382521,-0.0353300683,0.0447735675,0.3098430037,0.1299089044,0.1545516998,0.1684173644,-0.0050124396,-0.0647071525,0.1099802256,0.1257055998,-0.0156705379,-0.0932240486,-0.0586434491,0.1788802147,0.1195266545,0.1648669541,-0.1360280216,0.0084639816,-0.0668128058,-0.3070941567,0.0900237709,0.1294367164,0.5126132965,-0.0637784451,-0.3585409522,-0.1637833863,-0.3168658912,0.1315581501,-0.0887979344,0.1646843106,-0.3344549835,0.0525158457,0.2567434609,0.1665857583,-0.3985979259,0.1734841466,-0.1729390174,-0.0025560693,-0.2071772516,-0.1981868595,0.0244949311,-0.0061054123,0.0369944014,0.0258494634,-0.0169168096,-0.0553664789,0.1729714125,-0.0304495171,0.0978282169,0.0834876001,0.1038077101,0.0230855085,0.0646957457,0.2365556359,0.0004667089,0.4194874167,-0.1334887296,-0.2440327555,0.1164916679,0.35601753,0.0041353889,-0.1565836817,0.1820517033,-0.3767896891,-0.1220365986,-0.0800681487,0.0026528577,-0.2985853255,-0.1478585154,-0.2639125884,0.2254217416,0.2425370067,0.2605780959,0.0505344905,-0.0282764584,0.1627342254,0.3797311783,0.1669939458,-0.1939092427,-0.0896540061,-0.1379573345,0.1596079618,-0.0486291014,-0.4239594936,-0.3278156817,-0.1607752889,-0.1731502265,-0.07503663,0.049631156,0.0323371701,0.3179516196,0.0479337424,-0.0318585075,0.412953496,-0.0740917623,0.0748518556,-0.4666415751,-0.3798174262,0.5641200542,0.151705578,0.0570301823,-0.3497416377,0.2054489851,0.0608398058,0.5148956776,-0.1897210926,-0.1518411934,-0.2993411422,0.752466023,-0.0474503674,0.0110379141,-0.0960698351,0.0665274039,0.0204419531,0.2316295356,-0.2802456915,-0.1499119997,-0.0891542956,-0.1524517834,0.1319255084,-0.3466832638,-0.382157594,0.2474724054,0.2782579362,-0.1456098258,-0.1309039593,-0.0118953492,-0.0933069214,-0.3045151532,0.100494273,0.0386145003,0.1053284183,-0.0396609642,-0.094124943,-0.1193556041,0.298748225,0.162939176,-0.0600543693,-0.1179709211,0.3312662542,0.2449479997,0.1955460757,-0.0468190089,0.0072500408,0.0820179358,-0.3156889975,-0.2024822384,0.3221007586,0.0975408554,-0.002581625,0.0227513239,0.1893991232,-0.2354014963,0.1002926081,0.2986403406,0.0012304113,-0.0390900932,-0.1708397567,0.0159268808,0.3352193236,0.253539145,0.4091952145,0.4384313226,0.0471034944,-0.1185913086,-0.0291307271,0.1720729768,0.0163347404,0.2327632755,0.1700844765,-0.400013119,-0.0777249038,-0.0843988508,0.3033127487,0.3952056468,0.275578618,0.0969723538,0.0887242258,-0.1324126124,-0.2870429754,0.2992976308,0.1152055711,0.0208725408,0.2415834963,0.2297437042,0.1021427438,-0.2554797828,-0.0732267201,-0.1389924884,0.0566167571,-0.1460091919,-0.0478304811,-0.1640127003,0.2011270672,-0.1140680909,-0.5005605817,0.1816411167,-0.2008605003,0.0143492073,0.3810865879,-0.2022752911,0.1200969592,-0.2770578265,0.0942122191,0.0885685533,-0.0884958655,0.1415922791,-0.2295171022,-0.1760160774,0.1794952601,0.0489556827,-0.1769523323,0.3923769593,-0.0935229883,-0.0265750755,-0.1780978739,-0.1293669194,0.0228035133,0.2426382601,0.0753103569,0.111499086,0.05481565,0.108430557,-0.2810976505,0.3347675204,-0.2257572263,-0.0527905673,0.1222594678,0.0974359363,-0.0859619454,-0.1513888091,-0.6303583384,-0.1750367284,-0.3355152905,0.0044383388,-0.107812658,0.1289740354,0.4354717731,0.0387976281,0.1618680805,-0.1536655724,0.121643737,-0.0872593373,-0.0211115107,0.2169368863,-0.1349027008,-0.4010963142,-0.198347047,-0.1213339865,-0.1045445204,0.433926791,-0.2609218061,-0.2592196167,-0.2313274145,0.0872525126,-0.1294534057,-0.0232153423,0.3986389339,0.2484317124,-0.2569616735,-0.2864581048,0.0643375963,-0.0433718897,-0.0686287582,0.0381864421,-0.087544404,0.1517151892,0.1127801165,0.2772788107,-0.0392005183,-0.0387176163,0.1129376143,-0.2495963275,0.1978779882,-0.0433227047,-0.256916225,0.1621052921,-0.2219971567,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2892","title":"Error when encoding a dataset with None objects with a Sequence feature","comments":"This has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2900\r\nWe're doing a new release 1.12 today to make the fix available :)","body":"There is an error when encoding a dataset with None objects with a Sequence feature\r\n\r\nTo reproduce:\r\n```python\r\nfrom datasets import Dataset, Features, Value, Sequence\r\ndata = {\"a\": [[0], None]}\r\nfeatures = Features({\"a\": Sequence(Value(\"int32\"))})\r\ndataset = Dataset.from_dict(data, features=features)\r\n```\r\nraises\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-24-40add67f8751> in <module>\r\n      2 data = {\"a\": [[0], None]}\r\n      3 features = Features({\"a\": Sequence(Value(\"int32\"))})\r\n----> 4 dataset = Dataset.from_dict(data, features=features)\r\n[...]\r\n~\/datasets\/features.py in encode_nested_example(schema, obj)\r\n    888         if isinstance(obj, str):  # don't interpret a string as a list\r\n    889             raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n--> 890         return [encode_nested_example(schema.feature, o) for o in obj]\r\n    891     # Object with special encoding:\r\n    892     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n\r\nTypeError: 'NoneType' object is not iterable\r\n```\r\n\r\nInstead, if should run without error, as if the `features` were not passed","comment_length":19,"text":"Error when encoding a dataset with None objects with a Sequence feature \n There is an error when encoding a dataset with None objects with a Sequence feature\r\n\r\nTo reproduce:\r\n```python\r\nfrom datasets import Dataset, Features, Value, Sequence\r\ndata = {\"a\": [[0], None]}\r\nfeatures = Features({\"a\": Sequence(Value(\"int32\"))})\r\ndataset = Dataset.from_dict(data, features=features)\r\n```\r\nraises\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-24-40add67f8751> in <module>\r\n      2 data = {\"a\": [[0], None]}\r\n      3 features = Features({\"a\": Sequence(Value(\"int32\"))})\r\n----> 4 dataset = Dataset.from_dict(data, features=features)\r\n[...]\r\n~\/datasets\/features.py in encode_nested_example(schema, obj)\r\n    888         if isinstance(obj, str):  # don't interpret a string as a list\r\n    889             raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n--> 890         return [encode_nested_example(schema.feature, o) for o in obj]\r\n    891     # Object with special encoding:\r\n    892     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n\r\nTypeError: 'NoneType' object is not iterable\r\n```\r\n\r\nInstead, if should run without error, as if the `features` were not passed \n This has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2900\r\nWe're doing a new release 1.12 today to make the fix available 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2888","title":"v1.11.1 release date","comments":"@albertvillanova i think this issue is still valid and should not be closed till `>1.11.0` is published :)","body":"Hello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release?","comment_length":18,"text":"v1.11.1 release date \n Hello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release? \n @albertvillanova i think this issue is still valid and should not be closed till `>1.11.0` is published :)","embeddings":[-0.2534325421,-0.1435918212,-0.1170087606,0.1250979602,-0.0803622156,-0.2069433331,0.3182261586,0.2595030665,-0.2662116587,0.2432924211,0.4498234391,-0.0775993839,-0.0092685595,0.3775973022,-0.3972175717,-0.0890159234,0.0787209198,0.1089016348,-0.0526827313,0.0449188799,-0.2720680833,0.068657726,-0.3964475691,0.1608792841,0.0663995072,-0.0716037601,-0.1215934381,-0.254347086,-0.6671610475,-0.5313281417,0.3133550286,0.2034503967,0.0760271847,0.271275878,-0.0001110468,-0.423866421,0.5205672979,0.1486699134,-0.2614662647,-0.1513033807,-0.416443944,-0.5353802443,-0.1008592322,-0.0137669845,-0.3072520196,-0.1077144369,0.0117483158,-0.1943197548,-0.0628163069,-0.0156020885,0.2480115443,0.1673386395,0.4019794762,-0.3542853892,0.1863051802,-0.1159243658,-0.3703640699,-0.2499655932,0.5717850924,0.2568038106,0.2268878967,0.384983182,0.1912631989,-0.1922257096,0.1507626772,-0.0097723147,0.2396764457,-0.266032964,0.0415400378,0.0777787417,0.9955607653,0.0203635413,-0.3793464899,0.2034740597,-0.0927332118,-0.2712348402,0.2412474453,0.027283011,-0.0292447396,0.149242878,0.0873354375,-0.4584193528,-0.1516828388,0.1602145284,-0.0272049885,0.459104538,-0.0887170136,0.1067651212,-0.0215255283,-0.3015371263,0.2346984148,0.0200975835,0.0850308686,0.156445384,0.0514521152,-0.4402125776,0.1060789451,-0.0172465947,0.1498136967,0.1924144477,-0.2606184483,-0.249166131,-0.0464439057,0.0133378366,0.6594331861,0.0745627508,0.3405662179,0.188009277,0.4329450428,0.2551412284,0.0644607022,0.0997430384,0.0618424639,-0.0808662996,-0.1455136389,0.2052437365,0.4584421515,-0.5718432069,0.3661362231,0.1454990655,0.0115315011,-0.073997356,-0.3209313154,-0.1508467942,-0.0373871252,0.3257052004,0.0893606171,-0.1983124912,0.1348611414,-0.1805576384,-0.1951919943,-0.1088341549,-0.200981006,-0.0183628146,-0.031561859,-0.3116750121,-0.2775349915,0.1134495363,-0.1254057586,-0.0434127748,0.0564601496,0.1302085966,-0.2246665657,0.4118489623,-0.2466320097,0.082844384,-0.3096274436,0.2402789593,-0.0819456652,0.3500746489,-0.0424564332,-0.1780534983,-0.3391320705,0.2919041216,-0.1666521877,-0.0776967406,0.2701286674,0.1953231692,-0.2030190676,-0.2382987589,-0.0494303294,0.1722414643,-0.2394903451,-0.256931901,-0.0695379525,0.0432331599,-0.1193417981,0.1540283114,-0.2632963657,-0.2000599653,0.1197276041,0.0051401095,-0.1842305511,-0.3186813593,0.1262588203,-0.2455536276,0.3016958833,-0.3433002532,-0.639559865,0.3815340102,-0.284142822,-0.3894364536,0.4114672244,0.1741337925,0.2954373658,-0.3212958872,-0.1409738809,-0.0816188827,-0.1616334766,-0.3559683263,-0.4418994188,-0.4019602537,0.0429010168,0.0181122757,0.2204887122,0.1503366083,0.0952749476,0.4235877395,0.0749824941,0.2797828615,0.2078803927,0.0765660107,0.482803762,-0.1286068708,0.0529898442,-0.1006552279,-0.2767008245,0.0938937142,-0.1435723305,0.1591186672,0.4384330511,-0.3189628124,-0.2085864544,-0.0561050922,0.3260715008,-0.0709523261,0.0817816257,0.0915738642,-0.2478206754,0.1673493236,-0.4060981572,0.1368636489,-0.0098665319,-0.07066378,-0.088441737,0.0452465005,-0.2908729911,0.0994255021,0.1788440347,0.0838302895,0.1694515795,-0.2390569299,0.1745637655,-0.0815123394,-0.0723995641,0.3089856207,0.3561557233,0.1862898916,0.2350969911,0.2040317804,0.0746104494,0.1710554659,-0.0586112514,0.2303554267,-0.2253153324,0.3460877836,0.3549923301,-0.2632007301,-0.0564746819,0.144726038,0.1307961047,-0.2599101663,-0.0981654152,-0.2556478679,0.0817662179,-0.221117571,-0.2914198935,0.2252827138,-0.2763938606,0.2965317667,0.5968988538,0.0184195228,-0.047829736,0.0809677541,0.2573701739,-0.0809906572,0.1679659784,-0.0462063886,0.0884929076,0.2539899945,-0.021018533,0.0049677342,0.1221921593,0.0425285064,0.1027600989,0.0455321558,-0.4042115211,-0.0299377795,0.0530678742,0.0633863062,0.115278326,0.1767398119,0.1918154806,0.2266006619,0.0650810748,-0.3978857994,-0.2835028172,-0.0675668642,0.0291238204,-0.0194902401,0.0619625486,0.0534454733,0.1382157803,0.1468727291,0.0316265896,0.1826277375,-0.1548491269,0.7969358563,-0.3889087141,0.4114349484,-0.156639576,-0.1199053377,-0.3110970855,0.2345053405,-0.0124809062,-0.2776047289,0.2567169964,0.0537328571,0.2090214789,-0.6258595586,-0.4577414989,0.0528508835,-0.144126296,0.0452978648,0.0343037061,-0.2054453045,-0.107023187,0.2207032591,0.0384894237,-0.0587673448,-0.2087223232,-0.1666585952,-0.0068196445,0.0721502379,-0.2570260763,-0.6848427057,-0.1318627,-0.3363646269,-0.0185509361,-0.1078772321,0.1569403857,-0.1232450008,-0.051425416,-0.1049095914,0.0217202213,-0.3342834115,-0.0948758051,-0.048731003,0.0945054814,-0.0082828654,-0.4569739103,0.1993883997,0.1774878055,-0.2025116682,0.1531127393,-0.5314667225,-0.2725237608,0.206520468,-0.043862544,0.1573558748,-0.1776329577,0.5536837578,-0.1086382791,-0.2538419664,-0.1345501691,-0.2866043448,0.0720614642,0.1712462157,0.3246344626,0.409180969,0.2624562085,0.0242744964,0.3892090321,0.1903090924,0.0267370865,0.2341599315,0.1795129925,0.1924058646,-0.0937660858,-0.2098539323,0.0676550344,0.1260128468,0.161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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2885","title":"Adding an Elastic Search index to a Dataset","comments":"Hi, is this bug deterministic in your poetry env ? I mean, does it always stop at 90% or is it random ?\r\n\r\nAlso, can you try using another version of Elasticsearch ? Maybe there's an issue with the one of you poetry env","body":"## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589     | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n    \"settings\": {\r\n        \"number_of_shards\": 1,\r\n        \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n    },\r\n    \"mappings\": {\r\n        \"properties\": {\r\n            \"idx\" : {\"type\" : \"keyword\"},\r\n            \"title\" : {\"type\" : \"keyword\"},\r\n            \"text\": {\r\n                \"type\": \"text\",\r\n                \"analyzer\": \"standard\",\r\n                \"similarity\": \"BM25\"\r\n            },\r\n        }\r\n    },\r\n}\r\nclass IndexBuilder:\r\n    \"\"\"\r\n    Elastic search indexing of a corpus\r\n    \"\"\"\r\n    def __init__(\r\n        self,\r\n        *args,\r\n        #corpus : None,\r\n        dataset : squad,\r\n        index_name = str,\r\n        query = str,\r\n        config = dict,\r\n        **kwargs,\r\n    ):\r\n        #instantiate HuggingFace dataset\r\n        self.dataset = dataset\r\n        #instantiate ElasticSearch config\r\n        self.config = config\r\n        self.es = Elasticsearch()\r\n        self.index_name = index_name\r\n        self.query = query\r\n    def elastic_index(self):\r\n        print(self.es.info)\r\n        self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n        search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n        return search_index\r\n    def exact_match_method(self, index):\r\n        scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n        return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n    print(type(squad))\r\n    Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n    search_index = Index.elastic_index()\r\n    scores, examples = Index.exact_match_method(search_index)\r\n    print(scores, examples)\r\n    for name in squad.column_names:\r\n        print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n","comment_length":44,"text":"Adding an Elastic Search index to a Dataset \n ## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589     | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n    \"settings\": {\r\n        \"number_of_shards\": 1,\r\n        \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n    },\r\n    \"mappings\": {\r\n        \"properties\": {\r\n            \"idx\" : {\"type\" : \"keyword\"},\r\n            \"title\" : {\"type\" : \"keyword\"},\r\n            \"text\": {\r\n                \"type\": \"text\",\r\n                \"analyzer\": \"standard\",\r\n                \"similarity\": \"BM25\"\r\n            },\r\n        }\r\n    },\r\n}\r\nclass IndexBuilder:\r\n    \"\"\"\r\n    Elastic search indexing of a corpus\r\n    \"\"\"\r\n    def __init__(\r\n        self,\r\n        *args,\r\n        #corpus : None,\r\n        dataset : squad,\r\n        index_name = str,\r\n        query = str,\r\n        config = dict,\r\n        **kwargs,\r\n    ):\r\n        #instantiate HuggingFace dataset\r\n        self.dataset = dataset\r\n        #instantiate ElasticSearch config\r\n        self.config = config\r\n        self.es = Elasticsearch()\r\n        self.index_name = index_name\r\n        self.query = query\r\n    def elastic_index(self):\r\n        print(self.es.info)\r\n        self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n        search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n        return search_index\r\n    def exact_match_method(self, index):\r\n        scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n        return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n    print(type(squad))\r\n    Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n    search_index = Index.elastic_index()\r\n    scores, examples = Index.exact_match_method(search_index)\r\n    print(scores, examples)\r\n    for name in squad.column_names:\r\n        print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n \n Hi, is this bug deterministic in your poetry env ? I mean, does it always stop at 90% or is it random ?\r\n\r\nAlso, can you try using another version of Elasticsearch ? Maybe there's an issue with the one of you poetry 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2885","title":"Adding an Elastic Search index to a Dataset","comments":"I face similar issue with oscar dataset on remote ealsticsearch instance. It was mainly due to timeout of batch indexing requests and I solve these by adding large request_timeout param in `search.py`\r\n\r\n```\r\n       for ok, action in es.helpers.streaming_bulk(\r\n            client=self.es_client,\r\n            index=index_name,\r\n            actions=passage_generator(),\r\n            request_timeout=3600,\r\n        )\r\n        ```","body":"## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589     | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n    \"settings\": {\r\n        \"number_of_shards\": 1,\r\n        \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n    },\r\n    \"mappings\": {\r\n        \"properties\": {\r\n            \"idx\" : {\"type\" : \"keyword\"},\r\n            \"title\" : {\"type\" : \"keyword\"},\r\n            \"text\": {\r\n                \"type\": \"text\",\r\n                \"analyzer\": \"standard\",\r\n                \"similarity\": \"BM25\"\r\n            },\r\n        }\r\n    },\r\n}\r\nclass IndexBuilder:\r\n    \"\"\"\r\n    Elastic search indexing of a corpus\r\n    \"\"\"\r\n    def __init__(\r\n        self,\r\n        *args,\r\n        #corpus : None,\r\n        dataset : squad,\r\n        index_name = str,\r\n        query = str,\r\n        config = dict,\r\n        **kwargs,\r\n    ):\r\n        #instantiate HuggingFace dataset\r\n        self.dataset = dataset\r\n        #instantiate ElasticSearch config\r\n        self.config = config\r\n        self.es = Elasticsearch()\r\n        self.index_name = index_name\r\n        self.query = query\r\n    def elastic_index(self):\r\n        print(self.es.info)\r\n        self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n        search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n        return search_index\r\n    def exact_match_method(self, index):\r\n        scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n        return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n    print(type(squad))\r\n    Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n    search_index = Index.elastic_index()\r\n    scores, examples = Index.exact_match_method(search_index)\r\n    print(scores, examples)\r\n    for name in squad.column_names:\r\n        print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n","comment_length":44,"text":"Adding an Elastic Search index to a Dataset \n ## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589     | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n    \"settings\": {\r\n        \"number_of_shards\": 1,\r\n        \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n    },\r\n    \"mappings\": {\r\n        \"properties\": {\r\n            \"idx\" : {\"type\" : \"keyword\"},\r\n            \"title\" : {\"type\" : \"keyword\"},\r\n            \"text\": {\r\n                \"type\": \"text\",\r\n                \"analyzer\": \"standard\",\r\n                \"similarity\": \"BM25\"\r\n            },\r\n        }\r\n    },\r\n}\r\nclass IndexBuilder:\r\n    \"\"\"\r\n    Elastic search indexing of a corpus\r\n    \"\"\"\r\n    def __init__(\r\n        self,\r\n        *args,\r\n        #corpus : None,\r\n        dataset : squad,\r\n        index_name = str,\r\n        query = str,\r\n        config = dict,\r\n        **kwargs,\r\n    ):\r\n        #instantiate HuggingFace dataset\r\n        self.dataset = dataset\r\n        #instantiate ElasticSearch config\r\n        self.config = config\r\n        self.es = Elasticsearch()\r\n        self.index_name = index_name\r\n        self.query = query\r\n    def elastic_index(self):\r\n        print(self.es.info)\r\n        self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n        search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n        return search_index\r\n    def exact_match_method(self, index):\r\n        scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n        return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n    print(type(squad))\r\n    Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n    search_index = Index.elastic_index()\r\n    scores, examples = Index.exact_match_method(search_index)\r\n    print(scores, examples)\r\n    for name in squad.column_names:\r\n        print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n \n I face similar issue with oscar dataset on remote ealsticsearch instance. It was mainly due to timeout of batch indexing requests and I solve these by adding large request_timeout param in `search.py`\r\n\r\n```\r\n       for ok, action in es.helpers.streaming_bulk(\r\n            client=self.es_client,\r\n            index=index_name,\r\n            actions=passage_generator(),\r\n            request_timeout=3600,\r\n        )\r\n        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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2885","title":"Adding an Elastic Search index to a Dataset","comments":"Hi @MotzWanted - are there any errors in the Elasticsearch cluster logs? Since it works in your local environment and the cluster versions are different between your poetry env and your local env, it is possible that it is some difference in the cluster - either settings or the cluster being under a different load etc that has this effect, so it would be useful to see if any errors are thrown in the cluster's logs when you try to ingest. \r\nWhich elasticsearch client method is the function `add_elasticsearch_index` from your code using under the hood? Is it `helpers.bulk` or is the indexing performed using something else? You can try adding a timeout to the indexing method to see if this helps. Also, you mention that it stops at around 90%  - do you know if the timeout\/hanging happens always when a particular document is being indexed or does it happen randomly at around 90% completeness but on different documents?","body":"## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589     | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n    \"settings\": {\r\n        \"number_of_shards\": 1,\r\n        \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n    },\r\n    \"mappings\": {\r\n        \"properties\": {\r\n            \"idx\" : {\"type\" : \"keyword\"},\r\n            \"title\" : {\"type\" : \"keyword\"},\r\n            \"text\": {\r\n                \"type\": \"text\",\r\n                \"analyzer\": \"standard\",\r\n                \"similarity\": \"BM25\"\r\n            },\r\n        }\r\n    },\r\n}\r\nclass IndexBuilder:\r\n    \"\"\"\r\n    Elastic search indexing of a corpus\r\n    \"\"\"\r\n    def __init__(\r\n        self,\r\n        *args,\r\n        #corpus : None,\r\n        dataset : squad,\r\n        index_name = str,\r\n        query = str,\r\n        config = dict,\r\n        **kwargs,\r\n    ):\r\n        #instantiate HuggingFace dataset\r\n        self.dataset = dataset\r\n        #instantiate ElasticSearch config\r\n        self.config = config\r\n        self.es = Elasticsearch()\r\n        self.index_name = index_name\r\n        self.query = query\r\n    def elastic_index(self):\r\n        print(self.es.info)\r\n        self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n        search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n        return search_index\r\n    def exact_match_method(self, index):\r\n        scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n        return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n    print(type(squad))\r\n    Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n    search_index = Index.elastic_index()\r\n    scores, examples = Index.exact_match_method(search_index)\r\n    print(scores, examples)\r\n    for name in squad.column_names:\r\n        print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n","comment_length":160,"text":"Adding an Elastic Search index to a Dataset \n ## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589     | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n    \"settings\": {\r\n        \"number_of_shards\": 1,\r\n        \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n    },\r\n    \"mappings\": {\r\n        \"properties\": {\r\n            \"idx\" : {\"type\" : \"keyword\"},\r\n            \"title\" : {\"type\" : \"keyword\"},\r\n            \"text\": {\r\n                \"type\": \"text\",\r\n                \"analyzer\": \"standard\",\r\n                \"similarity\": \"BM25\"\r\n            },\r\n        }\r\n    },\r\n}\r\nclass IndexBuilder:\r\n    \"\"\"\r\n    Elastic search indexing of a corpus\r\n    \"\"\"\r\n    def __init__(\r\n        self,\r\n        *args,\r\n        #corpus : None,\r\n        dataset : squad,\r\n        index_name = str,\r\n        query = str,\r\n        config = dict,\r\n        **kwargs,\r\n    ):\r\n        #instantiate HuggingFace dataset\r\n        self.dataset = dataset\r\n        #instantiate ElasticSearch config\r\n        self.config = config\r\n        self.es = Elasticsearch()\r\n        self.index_name = index_name\r\n        self.query = query\r\n    def elastic_index(self):\r\n        print(self.es.info)\r\n        self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n        search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n        return search_index\r\n    def exact_match_method(self, index):\r\n        scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n        return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n    print(type(squad))\r\n    Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n    search_index = Index.elastic_index()\r\n    scores, examples = Index.exact_match_method(search_index)\r\n    print(scores, examples)\r\n    for name in squad.column_names:\r\n        print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n \n Hi @MotzWanted - are there any errors in the Elasticsearch cluster logs? Since it works in your local environment and the cluster versions are different between your poetry env and your local env, it is possible that it is some difference in the cluster - either settings or the cluster being under a different load etc that has this effect, so it would be useful to see if any errors are thrown in the cluster's logs when you try to ingest. \r\nWhich elasticsearch client method is the function `add_elasticsearch_index` from your code using under the hood? Is it `helpers.bulk` or is the indexing performed using something else? You can try adding a timeout to the indexing method to see if this helps. Also, you mention that it stops at around 90%  - do you know if the timeout\/hanging happens always when a particular document is being indexed or does it happen randomly at around 90% completeness but on different documents?","embeddings":[-0.1423357278,0.172727868,-0.0044709793,0.0400959924,0.0997370332,0.0273672696,-0.013204325,0.0684226677,0.0138257872,0.0531148016,-0.0395590216,0.3000072539,0.3499440253,-0.3781624734,-0.1049256846,0.1306153685,0.0909727961,0.0941495076,0.2756751478,0.1979281008,-0.0359147601,0.095908314,-0.1247543246,-0.0892915428,-0.3346315026,0.0212135501,-0.0316708237,-0.1235372126,-0.0287176576,-0.4167428911,0.3019476235,-0.1501852423,-0.1225858629,0.3576337695,-0.0001185079,0.0780737773,0.4348543286,-0.0329714008,-0.0183227472,-0.2907201052,-0.0723804981,-0.1354442686,0.279198885,-0.1591316015,-0.0953472778,0.1617633402,0.0987489,-0.6114605665,0.4415228069,0.3961337507,0.221011281,0.3180061877,0.3437509239,-0.1243836433,0.4198286533,-0.1281127334,-0.0489515252,-0.2564239502,0.2530343533,-0.0389002413,-0.0290674474,0.1452289224,-0.0944889709,-0.2707443833,0.108504355,0.1481345296,-0.4625434577,-0.2957313359,0.3149895072,0.1134225652,0.1910260916,-0.0712343082,-0.2519367039,0.0451840535,-0.0008995398,0.1622108668,0.1994201243,0.1894568503,0.0606099963,0.1190423146,0.0653904825,-0.1248976737,-0.2391608059,0.4135331213,-0.0537097417,0.1734440774,-0.097541891,0.1064755395,0.1365941018,-0.146080032,0.0782291442,0.2522954047,-0.2729330957,-0.0555161387,-0.3764431775,0.1024475098,-0.1168943718,-0.2002719194,0.4185051024,-0.0634525344,-0.0926844999,0.1597323418,0.6514115334,0.082370162,0.2860307097,-0.0631037876,0.0613816716,0.3085791171,0.0735873207,-0.3172648847,-0.3174724579,-0.2593558729,0.285479039,-0.2149694413,0.4180778563,-0.4818145633,0.2134571224,-0.0300275907,-0.159065932,0.2193003744,-0.5879529715,-0.0061579379,-0.0753503293,0.1353493631,0.1364613473,-0.4953055084,0.0383693911,0.2725166976,-0.2289299369,0.1452205926,-0.2356664091,-0.2659835815,0.0408693254,0.0179617349,0.1160360128,-0.6583971977,0.2295712978,0.0021879284,0.0842600539,-0.3945707381,0.2630119622,-0.0829003006,0.1773642153,0.218855992,0.0615348555,0.023488922,0.0232272167,-0.4385558963,-0.0351555534,-0.1221938878,-0.3814532757,-0.2521406412,-0.2764842808,0.2102274299,-0.1385770142,0.0171780493,0.1357520074,0.2181541324,0.1103124693,-0.0082744509,0.0775755048,-0.0403560661,-0.2897684872,0.0814163461,0.2997894585,0.2074805498,0.1160847694,-0.1290065497,0.034548115,0.4276873767,-0.2609300017,0.2141182125,-0.2313275784,0.0364292637,-0.1731410325,0.4179225564,0.3350299299,-0.4630022347,-0.613461256,0.1387205869,-0.2306159735,-0.0950086787,-0.272356391,0.0607838072,0.6483929753,0.0107715735,0.1798678637,0.2476815581,-0.1762580425,-0.1678980738,-0.1523010135,-0.0007278286,-0.1430873871,-0.177202031,0.0628991276,0.0903355628,0.2576126158,0.2004109323,-0.0055740727,0.1895459443,0.0115436343,0.3066953421,0.3564867079,0.1077488884,0.2526801229,-0.1288016289,-0.2565320432,0.2232705653,0.0895305648,0.0836865902,-0.1827768236,-0.1198632121,-0.1379051954,0.0071618739,-0.1569918245,0.0309961122,0.107497111,-0.2377432734,-0.2198884338,0.1922539026,0.0002298418,-0.1667473465,0.0728107169,0.2382162064,-0.2376390845,0.3120537996,-0.0830118656,-0.0867530704,-0.1883609593,-0.0127894552,0.2368167192,-0.2060383856,-0.1446202993,0.332541883,-0.0994061977,0.3171947896,0.1542841047,0.025194684,0.1261184365,-0.6889179945,-0.1038779616,0.1420570463,0.0571958348,-0.2761912048,-0.0003128937,0.0152491853,0.0072259768,0.3724019229,-0.2171028852,0.0868740231,-0.0073972992,-0.1734815091,-0.4597905576,-0.0337329842,-0.0198460873,-0.3641361892,-0.0424907617,-0.1129411384,-0.118290931,0.0319062024,0.0161155853,0.1542524546,0.104662396,0.3709858358,0.0202301163,-0.10019207,-0.3800418377,-0.0208591763,0.2398631126,0.1784040332,0.1413077563,-0.0003945187,0.016000025,-0.0571260825,0.3174203336,0.3449330926,0.0851912573,0.3284900784,0.4267836213,0.080735378,-0.2349987477,-0.0556238145,-0.2187708616,-0.0349604785,-0.2483057678,0.4837775528,-0.0381795317,0.1216896772,-0.142181322,0.036698062,-0.2608760893,-0.440820545,-0.1000710577,0.1918876469,-0.0298058689,0.010196479,-0.389636904,-0.0280155968,0.2536537051,-0.2719688416,0.1400227845,-0.13825804,-0.1423628628,-0.0342508368,0.1852953732,0.0559359342,0.239090845,0.2277609557,-0.1308041066,-0.3857408166,-0.4401707351,0.1353846639,0.0270662643,0.3381630778,0.1061841398,-0.2070374787,-0.4189092517,-0.139235571,0.2391747087,-0.1569141895,0.0960531235,0.0989907682,0.0812273249,0.165456146,0.252183646,-0.2988062501,-0.1856297553,-0.1773062944,0.0319295116,-0.1300621033,-0.0701901093,0.3265482187,-0.0100668529,0.1334317029,-0.117993705,-0.0943661481,-0.1205612123,-0.1552785039,0.0512902848,-0.1617365777,-0.3146482408,0.1664025784,-0.0358661972,-0.2476519793,0.2399175614,-0.3951127529,0.2129151374,0.1262060553,-0.2579472363,-0.3092864454,0.0559353307,0.0856449679,-0.2632970214,-0.0152955577,-0.0331743136,0.0292060673,0.1754637212,0.2231558114,0.0925877765,-0.0978393182,0.2361078113,-0.0709363371,0.6817715764,0.1603981704,-0.0134615982,0.1587851346,-0.1708619744,0.3219510317,-0.2369931191,-0.5023804903,-0.1412394941,-0.10594407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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2882","title":"`load_dataset('docred')` results in a `NonMatchingChecksumError` ","comments":"Hi @tmpr, thanks for reporting.\r\n\r\nTwo weeks ago (23th Aug), the host of the source `docred` dataset updated one of the files (`dev.json`): you can see it [here](https:\/\/drive.google.com\/drive\/folders\/1c5-0YwnoJx8NS6CV2f-NoTHR__BdkNqw).\r\n\r\nTherefore, the checksum needs to be updated.\r\n\r\nNormally, in the meantime, you could avoid the error by passing `ignore_verifications=True` to `load_dataset`. However, as the old link points to a non-existing file, the link must be updated too.\r\n\r\nI'm fixing all this.\r\n\r\n","body":"## Describe the bug\r\nI get consistent `NonMatchingChecksumError: Checksums didn't match for dataset source files` errors when trying to execute `datasets.load_dataset('docred')`.\r\n\r\n## Steps to reproduce the bug\r\nIt is quasi only this code:\r\n```python\r\nimport datasets\r\ndata = datasets.load_dataset('docred')\r\n```\r\n\r\n## Expected results\r\nThe DocRED dataset should be loaded without any problems.\r\n\r\n## Actual results\r\n```\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-4-b1b83f25a16c> in <module>\r\n----> 1 d = datasets.load_dataset('docred')\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    845 \r\n    846     # Download and prepare data\r\n--> 847     builder_instance.download_and_prepare(\r\n    848         download_config=download_config,\r\n    849         download_mode=download_mode,\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    613                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    614                     if not downloaded_from_gcs:\r\n--> 615                         self._download_and_prepare(\r\n    616                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    617                         )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    673         # Checksums verification\r\n    674         if verify_infos:\r\n--> 675             verify_checksums(\r\n    676                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    677             )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.11.0-7633-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\nThis error also happened on my Windows-partition, after freshly installing python 3.9 and `datasets`.\r\n\r\n## Remarks\r\n\r\n- I have already called `rm -rf \/home\/<user>\/.cache\/huggingface`, i.e., I have tried clearing the cache.\r\n- The problem does not exist for other datasets, i.e., it seems to be DocRED-specific.","comment_length":69,"text":"`load_dataset('docred')` results in a `NonMatchingChecksumError`  \n ## Describe the bug\r\nI get consistent `NonMatchingChecksumError: Checksums didn't match for dataset source files` errors when trying to execute `datasets.load_dataset('docred')`.\r\n\r\n## Steps to reproduce the bug\r\nIt is quasi only this code:\r\n```python\r\nimport datasets\r\ndata = datasets.load_dataset('docred')\r\n```\r\n\r\n## Expected results\r\nThe DocRED dataset should be loaded without any problems.\r\n\r\n## Actual results\r\n```\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-4-b1b83f25a16c> in <module>\r\n----> 1 d = datasets.load_dataset('docred')\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    845 \r\n    846     # Download and prepare data\r\n--> 847     builder_instance.download_and_prepare(\r\n    848         download_config=download_config,\r\n    849         download_mode=download_mode,\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    613                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    614                     if not downloaded_from_gcs:\r\n--> 615                         self._download_and_prepare(\r\n    616                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    617                         )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    673         # Checksums verification\r\n    674         if verify_infos:\r\n--> 675             verify_checksums(\r\n    676                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    677             )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.11.0-7633-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\nThis error also happened on my Windows-partition, after freshly installing python 3.9 and `datasets`.\r\n\r\n## Remarks\r\n\r\n- I have already called `rm -rf \/home\/<user>\/.cache\/huggingface`, i.e., I have tried clearing the cache.\r\n- The problem does not exist for other datasets, i.e., it seems to be DocRED-specific. \n Hi @tmpr, thanks for reporting.\r\n\r\nTwo weeks ago (23th Aug), the host of the source `docred` dataset updated one of the files (`dev.json`): you can see it [here](https:\/\/drive.google.com\/drive\/folders\/1c5-0YwnoJx8NS6CV2f-NoTHR__BdkNqw).\r\n\r\nTherefore, the checksum needs to be updated.\r\n\r\nNormally, in the meantime, you could avoid the error by passing `ignore_verifications=True` to `load_dataset`. However, as the old link points to a non-existing file, the link must be updated too.\r\n\r\nI'm fixing all this.\r\n\r\n","embeddings":[-0.2659113109,0.3422692716,0.0355651341,0.3098572195,0.214197889,0.0404327884,0.3646671176,0.4201550186,0.3040324748,0.0715031847,-0.2768665254,0.190118432,0.1306955665,-0.1951108724,-0.1612918228,0.3339123726,0.1246936545,0.1053829789,-0.2242237628,-0.1252798289,-0.3106808662,0.1985796541,-0.2184446454,-0.2843059599,-0.0529530197,0.1835617423,0.1532600224,0.2324340791,-0.120700039,-0.3587095439,0.2788212895,0.2785292566,0.173633635,0.4296127558,-0.0001249282,0.1207987443,0.2817707658,-0.0242785923,-0.501280129,-0.2013867348,-0.563354671,-0.3742280006,-0.0070420792,-0.1583375037,-0.0344875716,0.2363334298,-0.0177811794,-0.2750721872,0.0001792605,0.3736942112,0.1168587953,0.3918669224,0.1610017419,0.0871690363,0.3812574446,0.0504982956,-0.0901036188,0.4119758606,0.219390288,-0.0385965593,-0.2582117915,0.1683268249,-0.4234545529,0.3069981039,0.232055366,0.0618822426,0.0648707673,-0.1373666972,0.253241241,0.2994743288,0.4981358647,-0.4146616459,-0.3278160989,-0.2285789698,-0.298303932,-0.3188637793,0.3791346848,0.1398549974,-0.1967773736,0.0336276442,-0.3680784702,0.1499671191,0.0037718869,0.1940130293,0.2422606051,0.0545322224,0.0316536799,-0.0187701844,0.0221102182,0.0298728757,0.3937070668,-0.5399644375,-0.2046732903,0.2048074454,-0.5590269566,0.0730827823,0.0080572087,0.2777058184,0.352638036,0.4925115705,0.08901117,0.2042775005,-0.0375440791,0.188513726,0.1643131375,0.2119750381,-0.0647413805,0.4100411534,0.2708570957,0.3656848669,-0.1347787827,0.0432133712,-0.0054317582,-0.1764253378,0.6712611914,-0.1259947717,0.3084286749,-0.4890398979,-0.4388926029,0.271324724,0.0082045635,-0.0947970897,0.0869470164,0.322021246,-0.226584658,0.1516590118,0.1470181793,0.1476904005,-0.2125054002,-0.0541327372,-0.187337622,-0.0764364675,-0.0596771426,0.1976072788,0.2942281067,-0.1821978688,0.2553656697,0.1083121598,0.2190237492,-0.1960808337,0.2999760807,-0.1955657154,-0.0533633046,0.3180696368,-0.0322253928,0.0961275995,0.2476602346,-0.1812210828,-0.1439391971,0.1828757226,-0.2557730675,-0.2137421519,-0.1269117147,0.1511545181,-0.3514274061,0.0082077142,-0.2378200293,-0.400021553,0.4063311517,-0.3223438859,-0.0154792899,-0.3542110622,-0.2637994885,-0.353944093,0.2389694899,0.4025940299,0.0419260897,0.0426536016,-0.0905655473,-0.0114815729,0.294190079,0.183512345,0.0093251551,0.1056279317,-0.4124949574,-0.0635623261,0.2606512606,-0.3542023897,-0.6170941591,0.1036195084,0.1277518421,0.6295824647,0.0476492308,0.0750021562,0.1831660569,-0.0502420217,0.2216965407,0.1532353014,-0.038785696,0.1244795322,-0.252807796,-0.0325587615,-0.0434744433,0.1044865996,0.0201719031,0.2498004735,0.2889224589,-0.1661153138,0.2637803555,-0.0953656435,-0.1257363111,0.2321082801,0.2991938293,0.046871759,0.0639734343,-0.0167484935,-0.7693794966,0.3423186839,-0.1505941451,-0.0280080661,-0.0733518675,-0.0444171354,-0.1803081781,-0.1141534746,-0.3073681295,-0.0912047699,-0.0125983637,0.3906361461,0.2074201256,0.0201071296,0.0379508324,0.4137744009,-0.3047952354,0.1248284727,-0.3346244991,0.4118489027,0.0743400902,-0.1029386371,0.1318240315,-0.0030252386,0.1328218132,-0.0562920161,-0.2745426893,0.4387036562,0.3811519444,0.1363846362,-0.1962373555,0.3414201438,-0.0699199662,-0.1395473629,-0.012621915,0.4393106699,0.1203998923,-0.1690554321,-0.1533752978,0.3473964334,-0.0006330401,0.1946744621,0.0062628747,0.0575816073,0.2450569719,-0.1167023629,-0.1461344063,-0.261870563,0.3650653958,0.1296010762,0.2266256809,0.196940884,-0.1572066098,-0.0258856118,0.4515280128,-0.0735068321,-0.0205244031,0.1288514733,0.0474654101,-0.0436953641,0.0753767639,0.2370779216,0.5027197599,0.0668875426,-0.04627854,0.1425257325,-0.1718104333,-0.0678285286,0.0968001559,-0.0407392457,0.1989537925,0.6168555617,0.1380470097,-0.1526708454,-0.2818833888,-0.0774433017,-0.1217622161,0.3029148579,-0.4903008938,0.0769678801,-0.1072904691,0.0858299136,-0.3731725514,-0.1268986762,-0.1855977178,-0.2465110421,-0.2533238232,0.6255096793,0.0291794557,0.1770886779,-0.543838203,0.0261698551,-0.0482735336,-0.3658800423,0.0178647786,0.2034313083,-0.2111107856,-0.154736951,0.5788052082,-0.0910895392,0.3742569685,-0.4110652506,-0.0268509947,-0.5500833392,-0.2233636677,0.0945177004,0.0162294693,0.2236746848,0.171183303,0.0406548828,-0.0192004945,-0.2501042485,0.2409217954,-0.0442294776,-0.2682688534,0.3236261308,-0.1510273069,-0.0955459252,0.1709357947,-0.1985139698,-0.0594486669,-0.3050559163,-0.1297381818,0.2319205552,0.2133797705,0.1936335266,-0.0064717545,0.0152918147,0.1389674693,0.337371707,-0.1682054251,-0.6134564281,0.4448585808,-0.0378274098,-0.220205918,-0.1443342119,-0.1069903895,0.1174569875,0.3974053264,-0.5095450878,0.0167492125,-0.288091749,0.0945630446,0.0950631127,-0.0240606703,0.2160272449,0.145353362,0.0829630345,-0.2499599308,-0.2719066143,0.1908720434,-0.1372274011,0.1996578574,0.0226447172,0.3025070429,-0.2673497498,0.5462415814,0.4274444282,0.0061363615,0.2280853242,0.0467436984,0.526879847,-0.1603343338,-0.3179928064,-0.0785081387,-0.2098642141,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2879","title":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"","comments":"Hi @rcgale, thanks for reporting.\r\n\r\nPlease note that this bug was fixed on `datasets` version 1.5.0: https:\/\/github.com\/huggingface\/datasets\/commit\/a23c73e526e1c30263834164f16f1fdf76722c8c#diff-f12a7a42d4673bb6c2ca5a40c92c29eb4fe3475908c84fd4ce4fad5dc2514878\r\n\r\nIf you update `datasets` version, that should work.\r\n\r\nOn the other hand, would it be possible for @patrickvonplaten to update the [blog post](https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english) with the correct version of `datasets`?","body":"## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n","comment_length":46,"text":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\" \n ## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n \n Hi @rcgale, thanks for reporting.\r\n\r\nPlease note that this bug was fixed on `datasets` version 1.5.0: https:\/\/github.com\/huggingface\/datasets\/commit\/a23c73e526e1c30263834164f16f1fdf76722c8c#diff-f12a7a42d4673bb6c2ca5a40c92c29eb4fe3475908c84fd4ce4fad5dc2514878\r\n\r\nIf you update `datasets` version, that should work.\r\n\r\nOn the other hand, would it be possible for @patrickvonplaten to update the [blog post](https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english) with the correct version of `datasets`?","embeddings":[0.124852851,-0.0535938032,0.0293307602,0.0739796385,0.2047281116,-0.0245766733,0.2489377707,0.4213005006,-0.4746908545,0.1751622707,0.1361838877,0.5513864756,-0.3347690701,-0.0850955397,0.0344839208,0.1865644157,-0.0921123177,0.1384560466,-0.2678906322,-0.2130741179,0.0398925841,0.3963992298,-0.1676281989,-0.0685962737,-0.3741768599,0.244877398,0.2273771465,-0.2314912826,0.1054832488,-0.4600336254,0.2924744487,0.1486980319,0.0194595307,0.3702304661,-0.0001191299,0.0063226023,0.2757813334,0.0352541432,-0.2775231004,-0.4479430318,-0.0650265291,0.1085864976,0.0530338921,0.1475536525,-0.4656309485,-0.1793126911,0.2337317616,-0.3409471512,0.0292004794,0.4485591054,0.0685843602,-0.0634173304,-0.2496642619,-0.0824669749,0.3689354062,0.0125433672,-0.0625187159,0.0592703931,-0.0683355555,0.2411289513,0.2196747363,0.3755480051,-0.0313242152,0.0129956314,-0.1954175979,-0.0935640186,-0.2540271878,-0.3487864733,0.0747668445,0.2519178092,0.7106900215,-0.2958623171,-0.3215118349,-0.2683330774,0.3369950652,-0.0542203225,0.2911206484,0.0254176874,-0.2944933772,0.1627251506,-0.5629917383,0.0742394626,-0.3041745424,0.1429014206,-0.0974289998,-0.359634608,-0.143771261,0.1845409572,-0.1975932419,0.1163858026,0.114155367,0.1884384304,-0.0396124236,-0.0923857763,-0.3206233084,-0.0985560194,0.0559260733,-0.0815377459,0.0717144236,0.3172878623,0.2842969596,0.0675945058,-0.4174665213,-0.135505408,0.0322321728,0.1481688768,0.1823079735,-0.1778396666,0.101101391,-0.0720301494,-0.1533745825,0.0139923282,0.0189836212,-0.0720990822,-0.1062536985,-0.0153032569,0.3571920395,-0.1175772175,-0.5631766319,0.1525668353,-0.5025030375,0.2135901749,-0.0520560816,0.1947724819,-0.2154174149,0.1350183487,0.2971945703,0.0870628357,0.02487945,-0.3115249574,-0.0998023972,-0.1971572787,0.1828780323,0.0968628898,0.2440094203,-0.2103739977,0.2123047858,0.257135421,0.2615037858,-0.1538840383,-0.0926653668,-0.0326226279,0.2529811561,0.2668441832,-0.1248196512,0.3939130902,-0.0565123409,0.1157927513,0.106670782,0.2571235001,-0.0651218817,-0.0061710193,0.5623431206,0.060588941,-0.1836533099,-0.3006524444,-0.1071216986,0.3333047032,0.0442795344,-0.0668319687,0.2504209578,-0.2751781344,-0.2271512002,0.0158824958,0.1358926445,0.1566538513,-0.5853290558,0.0243286006,0.1945606619,0.0684714913,0.3222911954,0.3032742143,0.1651955247,0.1811709255,-0.0816664621,0.1170219034,-0.219933033,-0.3883649707,-0.2060429454,0.2092838287,-0.2708128095,0.264259249,-0.0832697079,-0.2192167342,0.0802669078,0.0991331413,0.3640693426,-0.1891600937,0.24668172,-0.1056700721,-0.3831144273,-0.1445571929,0.1893382818,0.1755265892,-0.0177115817,0.0532647818,-0.2982362509,-0.0164561383,0.4027053714,0.0394103304,-0.0291296821,-0.025731707,0.3644500375,-0.1775300503,0.263861388,-0.1554233581,0.2872300744,0.0272912737,0.1604733467,0.2198717296,0.5499910116,-0.1693307608,-0.1779452264,-0.0762331262,-0.0728189349,-0.0683157369,0.0582602769,0.1486589164,-0.1489184052,0.3247836828,-0.0285170637,0.4712553024,-0.4881338477,-0.0153098824,-0.0879812539,-0.1088415384,0.3033908308,-0.0987990201,0.1165293604,0.2411418706,0.0172993075,0.0872968137,-0.110142529,0.3636822999,0.2269500643,0.1219172627,-0.3682011068,-0.4027226269,0.2316687405,-0.5078880787,-0.4552857876,0.5677568913,-0.0276648402,0.2008169293,-0.1534452736,0.060441643,0.0807491466,0.0479999073,0.0348503962,0.0360783227,-0.019409189,-0.1079213247,-0.470849663,-0.1607666463,0.2607947588,-0.2098269463,0.1590214819,-0.1161290258,-0.320378989,0.1368902475,0.315872401,-0.0502400734,0.0395470038,0.2744301558,-0.1326723844,0.209983319,0.0375697017,0.1719760001,0.2373924702,0.2009201497,0.0807271153,-0.0535479188,-0.0781926885,-0.2605938315,0.2923918068,0.1945406348,-0.3651409447,0.3840377629,0.2203695476,0.0489813201,-0.4372256994,-0.0049824319,-0.1904428899,0.2824560404,-0.449403286,-0.1749291867,-0.3977611363,0.1078346595,-0.1361607611,-0.0218015406,0.0710958019,0.0436779261,0.3717918992,-0.0027077466,-0.0859973133,0.2743452489,0.1501078755,0.1915652007,-0.110422954,0.1713414043,-0.0096689994,0.1939062178,-0.4710183442,0.0452458076,-0.1606466472,-0.2644656301,0.1504383832,-0.1925743371,-0.1099141166,-0.4143466055,-0.071546413,0.1478731036,-0.1916618943,0.4232137799,0.2326952517,-0.0426136926,-0.1860425919,-0.194430843,-0.1887598336,-0.0310719088,-0.0671303943,0.0714476034,-0.1346394867,0.0415853858,-0.256131798,-0.8888216615,0.1003023162,-0.2204675525,0.7185165882,0.0599351153,-0.0460496917,0.5412366986,0.2822042704,0.2012919486,0.0792539567,0.1863065064,-0.3713881075,-0.1230798736,0.0792584717,0.0293458365,-0.4573093057,-0.298314631,0.1936385185,0.1548020095,0.2074109167,-0.4953384995,0.3497347236,-0.1089945436,-0.1306295842,0.0102862855,-0.1989106387,0.2906790078,-0.2038048357,-0.1159159765,-0.2554523647,0.1175914109,0.3896949589,0.1017955169,0.112711817,0.3398635685,0.2160488367,0.2003065348,0.3506759107,0.7158643007,0.0828187764,0.039017871,-0.1280344874,0.4244537055,-0.0728549138,-0.052832868,0.1600767821,0.0590854175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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2879","title":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"","comments":"I just proposed a change in the blog post.\r\n\r\nI had assumed there was a data format change that broke a previous version of the code, since presumably @patrickvonplaten tested the tutorial with the version they explicitly referenced. But that fix you linked suggests a problem in the code, which surprised me.\r\n\r\nI still wonder, though, is there a way for downloads to be invalidated server-side? If the client can announce its version during a download request, perhaps the server could reject known incompatibilities? It would save much valuable time if `datasets` raised an informative error on a known problem (\"Error: the requested data set requires `datasets>=1.5.0`.\"). This kind of API versioning is a prudent move anyhow, as there will surely come a time when you'll need to make a breaking change to data.","body":"## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n","comment_length":134,"text":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\" \n ## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n \n I just proposed a change in the blog post.\r\n\r\nI had assumed there was a data format change that broke a previous version of the code, since presumably @patrickvonplaten tested the tutorial with the version they explicitly referenced. But that fix you linked suggests a problem in the code, which surprised me.\r\n\r\nI still wonder, though, is there a way for downloads to be invalidated server-side? If the client can announce its version during a download request, perhaps the server could reject known incompatibilities? It would save much valuable time if `datasets` raised an informative error on a known problem (\"Error: the requested data set requires `datasets>=1.5.0`.\"). This kind of API versioning is a prudent move anyhow, as there will surely come a time when you'll need to make a breaking change to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2877","title":"Don't keep the dummy data folder or dataset_infos.json when resolving data files","comments":"Hi @lhoestq I am new to huggingface datasets, I would like to work on this issue!\r\n","body":"When there's no dataset script, all the data files of a folder or a repository on the Hub are loaded as data files.\r\n\r\nThere are already a few exceptions:\r\n- files starting with \".\" are ignored\r\n- the dataset card \"README.md\" is ignored\r\n- any file named \"config.json\" is ignored (currently it isn't used anywhere, but it could be used in the future to define splits or configs for example, but not 100% sure)\r\n\r\nHowever any data files in a folder named \"dummy\" should be ignored as well as they should only be used to test the dataset.\r\nSame for \"dataset_infos.json\" which should only be used to get the `dataset.info`","comment_length":16,"text":"Don't keep the dummy data folder or dataset_infos.json when resolving data files \n When there's no dataset script, all the data files of a folder or a repository on the Hub are loaded as data files.\r\n\r\nThere are already a few exceptions:\r\n- files starting with \".\" are ignored\r\n- the dataset card \"README.md\" is ignored\r\n- any file named \"config.json\" is ignored (currently it isn't used anywhere, but it could be used in the future to define splits or configs for example, but not 100% sure)\r\n\r\nHowever any data files in a folder named \"dummy\" should be ignored as well as they should only be used to test the dataset.\r\nSame for \"dataset_infos.json\" which should only be used to get the `dataset.info` \n Hi @lhoestq I am new to huggingface datasets, I would like to work on this issue!\r\n","embeddings":[-0.1615232974,-0.0727220699,0.0000138009,0.2843921781,0.1758460253,0.0413103998,0.2075548768,0.3739945889,0.2408152968,0.2205160707,-0.1030789837,0.2478830069,0.0299951751,0.399069488,-0.1797669828,0.1182415485,-0.1221318021,0.0999347791,-0.1338039488,-0.1163479313,-0.3770118058,0.1916589886,0.0433103964,-0.0381870531,-0.5834296346,-0.101494655,0.0468457676,0.4716686904,-0.2405844629,-0.3236856461,0.2634019554,0.4223190844,0.0704587102,0.3016800284,-0.0001107126,0.0050795982,0.4527167082,-0.0880949572,-0.196537748,0.0499534495,-0.0938266069,-0.1166392043,0.2795168459,-0.1301650256,-0.3243519962,-0.0931576714,0.0000017519,-0.008876509,0.3529389203,0.1096101105,0.1999024153,0.3339097202,-0.009643646,-0.167508468,0.0687669739,0.4267164767,-0.2080415934,0.2854217291,0.0414379202,0.112251915,-0.1726083755,0.2326886207,0.004255285,0.2497414351,0.5036461949,0.0573791116,-0.1103253514,-0.4123876691,0.0717473999,0.2694647908,0.3935631812,-0.1716999859,-0.258156091,-0.3632938862,-0.0567461327,-0.0329703204,0.4961109161,0.0966847911,-0.0149801681,0.2028457969,-0.2355035841,-0.3898991942,-0.1176654696,-0.0147239678,0.0472258069,-0.0216477234,-0.1069339365,0.0018954237,0.225696072,-0.1198107153,-0.0044523813,-0.3516441286,-0.2117131352,-0.0181489177,-0.1719541401,-0.2847643495,0.0058673169,0.5682479739,0.5639811754,0.1021969691,0.1325013041,0.1527167112,-0.1631947756,0.011532926,0.2939195037,0.2053645104,0.1767932773,-0.0027622916,0.5741882324,0.123442404,0.186656788,-0.0393960252,0.0057562217,-0.0090916008,-0.3533385694,-0.0632805601,0.4478315413,-0.3045849502,0.1084480882,-0.0474523157,0.357242018,0.0729707256,0.2083865702,0.3238162696,-0.055841852,0.2860808074,-0.2386604249,0.2186374068,-0.1960887462,-0.0528185777,-0.1811157018,-0.0749750063,-0.0727381185,0.1280302107,0.3819963634,-0.2583300769,0.3725114465,0.0729547217,-0.0221839249,0.1266508698,0.1644002944,-0.1025907174,0.1860305816,0.3421511948,0.0962513387,0.1020353287,-0.216803357,-0.1738631874,-0.165402025,0.0577777177,-0.3886924684,-0.2653143406,-0.0602510646,0.1679856479,-0.4854981899,0.2392626107,-0.3236392438,0.1635597497,-0.2916353643,0.0651620477,0.0514752604,0.036580544,-0.0802114382,-0.1342493296,0.2304943502,0.5579197407,-0.0552425198,0.0633949041,-0.0337160863,-0.4196458757,-0.2812909484,0.1999997646,-0.0703347474,0.1934586316,-0.409032017,0.1171927676,-0.0075731566,-0.6183504462,-0.2939766943,0.0314322039,-0.0515222289,0.1528531164,-0.0340668783,0.0124775451,0.1916750222,-0.21057944,-0.0235275943,0.1818129271,-0.0616264679,-0.002965187,-0.3211553991,-0.3672722876,-0.1839456558,0.1671412587,-0.0651525036,0.2309067994,-0.1049308553,-0.0032304954,-0.034166757,-0.10685087,0.0506608449,0.178031534,0.1308198571,-0.0580769926,0.0686535314,0.0406652763,-0.8880108595,0.3015938699,0.0746994019,-0.1015320867,-0.064832218,-0.3816497326,0.0030324827,-0.2458530813,-0.2665680349,-0.0127946101,0.0995143279,0.0750600025,0.1926473528,0.0558963083,-0.3450708687,0.3587606847,-0.0104612326,0.3684038222,-0.5800520778,0.2228638381,0.156446293,-0.1596039683,0.2487043589,0.0692418888,0.043793872,-0.2785088718,0.1370572895,0.3687668443,0.1007846817,0.3230682611,0.4689800739,0.3601872623,0.2676127255,-0.0621861741,0.0493939929,-0.1251096576,-0.0088892672,0.0588150285,-0.3381732404,0.2184064835,-0.3091188967,0.0080783628,-0.0506492108,-0.0165555459,0.3123957217,-0.1788854897,-0.2673778534,-0.2660437524,0.0826557577,0.2761753798,0.1772094816,0.2516744137,-0.3228478134,0.1642692238,0.5452516079,-0.1820382327,0.0241765399,0.1554514617,-0.1291537732,0.0100030629,0.2294883579,0.093718268,0.2721302807,0.2145863473,0.0207565911,0.204296425,-0.0272397958,-0.2929233015,0.10421177,0.1748328209,-0.2157121301,0.1954936236,-0.138726145,-0.1916698664,-0.4630394876,0.1589559764,-0.0439645462,0.1378001571,-0.3489494622,-0.2716997266,-0.394059509,-0.0696454719,0.1327864826,-0.0952010378,-0.3179853857,-0.1796410084,0.2468465865,0.4097900689,-0.3833031952,0.1052015722,0.1590542346,0.5716611147,-0.2255376875,0.0515021197,-0.4354623258,-0.1249688193,-0.1082022339,0.0758214965,0.2743503451,0.2648706436,0.3428465128,-0.5347328782,0.1965736598,-0.3266199827,-0.0976982489,0.2047813088,-0.1360629201,0.3442364037,0.1152855754,0.3487888277,0.1497742534,-0.0299345572,0.1534506828,-0.1364970207,-0.0470054224,0.0543665774,0.2001294643,-0.0861148387,-0.3340204358,-0.3727873266,-0.0675748214,-0.4655825794,0.2968955636,-0.1273219138,0.0790255442,0.0400375761,0.0064036422,0.113703981,-0.4716822207,-0.2102858424,-0.2793780565,-0.656442523,0.4258553982,-0.1808549613,-0.3651298285,0.2850432694,-0.1435688734,0.0122700315,-0.1654518098,-0.3582283854,-0.6084073782,-0.0630329326,0.1842316538,0.135371834,-0.0234863143,0.3452751338,0.0221191589,-0.0950559825,-0.1026623994,-0.1565981954,0.2955849469,0.0474835858,0.0743655041,0.1053192914,-0.0106370719,-0.2988037765,0.2903437316,0.2562226057,0.1732869148,0.2589078844,0.0715381205,0.5482520461,-0.0861470699,-0.0750412568,0.0703618228,0.1526068896,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2877","title":"Don't keep the dummy data folder or dataset_infos.json when resolving data files","comments":"Thanks for the help :) \r\n\r\nAs mentioned in the PR, excluding files named \"dummy_data.zip\" is actually more general than excluding the files inside a \"dummy\" folder. I just did the change in the PR, I think we can merge it now","body":"When there's no dataset script, all the data files of a folder or a repository on the Hub are loaded as data files.\r\n\r\nThere are already a few exceptions:\r\n- files starting with \".\" are ignored\r\n- the dataset card \"README.md\" is ignored\r\n- any file named \"config.json\" is ignored (currently it isn't used anywhere, but it could be used in the future to define splits or configs for example, but not 100% sure)\r\n\r\nHowever any data files in a folder named \"dummy\" should be ignored as well as they should only be used to test the dataset.\r\nSame for \"dataset_infos.json\" which should only be used to get the `dataset.info`","comment_length":41,"text":"Don't keep the dummy data folder or dataset_infos.json when resolving data files \n When there's no dataset script, all the data files of a folder or a repository on the Hub are loaded as data files.\r\n\r\nThere are already a few exceptions:\r\n- files starting with \".\" are ignored\r\n- the dataset card \"README.md\" is ignored\r\n- any file named \"config.json\" is ignored (currently it isn't used anywhere, but it could be used in the future to define splits or configs for example, but not 100% sure)\r\n\r\nHowever any data files in a folder named \"dummy\" should be ignored as well as they should only be used to test the dataset.\r\nSame for \"dataset_infos.json\" which should only be used to get the `dataset.info` \n Thanks for the help :) \r\n\r\nAs mentioned in the PR, excluding files named \"dummy_data.zip\" is actually more general than excluding the files inside a \"dummy\" folder. I just did the change in the PR, I think we can merge it now","embeddings":[-0.2584907413,0.1885458529,-0.091537565,0.0349398218,0.1493660063,-0.0548265763,0.1679391414,0.5366549492,0.1263960153,0.2296374887,0.0419927314,0.4573587179,0.0253700297,0.219680354,-0.2307560891,0.1939048916,-0.0863876194,0.1343439072,-0.0014823446,-0.0621362515,-0.3409141004,0.0437207632,0.013741876,0.0527167805,-0.5012488961,-0.071370557,0.1572821289,0.3634360135,-0.319209367,-0.3698277473,0.1853763312,0.3923356235,0.1234950647,0.2126041651,-0.0001033153,0.0452668481,0.479061842,-0.1432207972,-0.1641294062,0.0662710592,-0.2789699733,-0.0978354812,0.1289302409,-0.1087823585,-0.1370750964,-0.1474805921,-0.0049808556,0.0181441121,0.1052267775,0.2115420103,0.2602944076,0.2100945115,-0.0161006618,-0.1639690548,0.0839416832,0.29742378,-0.1661355793,0.2901608348,0.173230201,0.0665511563,-0.1383562833,0.2036249787,0.0078015067,0.2006860673,0.3671165109,-0.0361864679,0.0071710306,-0.4366571307,0.0761354417,0.0692212507,0.4618232548,-0.251370132,-0.137899965,-0.1813551635,-0.0552107394,-0.0162242521,0.4868282676,0.1057866663,-0.1308716983,0.2175550461,-0.171978265,-0.3544054925,-0.1828456521,-0.0247504767,0.0486877412,0.2201775014,0.1126083434,-0.03551884,0.1032359153,0.0287048873,0.3421466351,-0.3884492218,-0.2100473195,-0.1494087279,-0.0861975476,-0.4166833758,-0.0597917736,0.3653749526,0.4870933592,0.1717763245,0.1307550669,0.1747074276,-0.0869545788,-0.0056641349,0.2978257537,0.2281374037,0.2067386508,0.2572255731,0.4828513265,0.1193500683,0.1154651642,0.1081528291,0.0054023461,0.0171766914,-0.3893154562,0.1643334776,0.4104681611,-0.3133634627,0.0185487382,-0.1285407394,0.3099914491,-0.0500102527,0.1027603075,0.2147358656,0.0806987956,0.3675919771,-0.3688464463,0.221489355,-0.1637146771,-0.0420804694,-0.1777688265,0.0507949144,-0.2282931954,-0.0394407697,0.4680235684,-0.0555599853,0.2985544801,0.0265330039,-0.0117351869,0.1090770289,0.4164348543,-0.0561771505,0.2786061168,0.3324085176,0.1442686766,-0.0880200788,-0.1806058586,-0.0944631472,-0.1536616385,0.2040932775,-0.3791877031,-0.3445593417,-0.0288875159,0.2771149874,-0.3445024788,0.1524355561,-0.0546493679,0.0587798767,-0.2990573347,0.0084109865,0.0889014751,0.1063332334,0.011773793,-0.16026555,0.0515748486,0.3365086615,-0.2313521504,0.2782838047,-0.0680489764,-0.4057779014,-0.2888948023,-0.0855201185,-0.2161610723,0.2594636977,-0.3874742687,0.116407223,0.0832145885,-0.6279163957,-0.3516995013,0.1600248516,-0.0638394207,0.0043003559,0.0013079424,0.0581687391,0.1906201541,-0.3163522184,-0.0640323758,0.1472735852,-0.1022391841,-0.0083086574,-0.3177677393,-0.3574671745,-0.1299354434,0.1251968443,0.0481172763,0.2333724946,-0.0177575108,0.0660047755,0.0228110105,-0.0318253078,0.0231999438,0.1106577665,0.0562924817,-0.1561985761,0.0670390576,0.0804513544,-0.7157747746,0.2028120607,-0.0950807035,-0.1198869646,-0.0153277135,-0.4100680053,-0.0071173352,-0.2987995744,-0.2089089453,-0.0488592535,0.2389232963,0.1185706407,0.0688450634,-0.2154602557,-0.3188628256,0.2758254409,0.0661817789,0.234124437,-0.4188548326,0.1638617367,0.0274378266,-0.1194342151,0.2590581477,-0.0692614615,0.033455871,-0.2376096696,0.0798937455,0.306755513,0.1931920499,0.2653445303,0.5392461419,0.2998707294,0.2326886803,-0.096791856,0.1545903236,-0.0309665985,-0.0237437319,0.1341338456,-0.3767640591,0.272426188,-0.2549965382,-0.0254833102,-0.0937079564,-0.1204330847,0.2983860672,-0.2568772733,-0.2168403268,-0.3233260214,0.0015929871,0.4489439428,0.107380949,0.1949823201,-0.1372593939,0.1789402664,0.4063425958,-0.0876029506,0.065411754,0.108755365,-0.0151245613,-0.0523157418,0.1664425731,0.2340708226,0.3490403295,0.2934250832,0.0562090836,0.1091100797,-0.0509645864,-0.3482300341,0.206209138,0.209228605,-0.2106100172,0.3508513868,-0.1737700999,-0.1698773205,-0.4362843931,-0.0124081476,0.1137078106,0.0828043595,-0.1850234866,-0.3028166592,-0.3026649356,-0.0358501934,0.286858201,0.0044815401,-0.0778741688,-0.2661060393,0.3397377431,0.3447647989,-0.4579791725,0.2018215805,0.0801410303,0.3877770305,-0.3059741259,0.0390353091,-0.3688223362,-0.1527916044,-0.0929959938,0.19826217,0.3045088351,0.2871028185,0.3225335479,-0.4126716554,0.1767686754,-0.3296550512,-0.1167954057,0.18467471,-0.1269944906,0.1531803459,0.1017692089,0.2018316537,0.24842228,0.0044393521,-0.0549465939,-0.1519651264,0.0019746111,0.0332691409,0.1111631542,-0.0649110824,-0.3804906309,-0.5711312294,-0.1806981415,-0.4743154347,-0.0186327118,-0.0269580875,0.1275357157,-0.0826035663,0.0994188562,-0.0892376155,-0.2584009767,-0.3807595372,-0.1817255467,-0.5130351186,0.401483953,-0.2126709372,-0.1911867112,0.3533429205,-0.1736063063,0.1025503725,-0.1299996376,-0.258564204,-0.6175332069,0.0674608424,0.1639103293,0.0100645842,-0.197764799,0.3711945713,0.1263228208,-0.2374122143,-0.1193460524,0.0140743246,0.2056955546,0.0791667029,0.128445819,0.0137274703,-0.1636790931,-0.1799904257,0.2561790943,0.2817142606,0.1936584711,0.1713872552,0.1823856384,0.5017807484,0.0018629656,0.1169970781,-0.0834934562,0.1291079372,-0.0738923177,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Hi @bwang482,\r\n\r\nI'm sorry but I'm not able to reproduce your bug.\r\n\r\nPlease note that in our current master branch, we made a commit (d03223d4d64b89e76b48b00602aba5aa2f817f1e) that simultaneously modified:\r\n- test_dataset_common.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-a1bc225bd9a5bade373d1f140e24d09cbbdc97971c2f73bb627daaa803ada002L289 that introduces the usage of `datasets.config.PYARROW_VERSION.major`\r\n- but also changed config.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-e021fcfc41811fb970fab889b8d245e68382bca8208e63eaafc9a396a336f8f2L40, so that `datasets.config.PYARROW_VERSION.major` exists\r\n","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n   packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in <module>\r\n      1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":47,"text":"datasets.config.PYARROW_VERSION has no attribute 'major' \n In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n   packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in <module>\r\n      1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n \n Hi @bwang482,\r\n\r\nI'm sorry but I'm not able to reproduce your bug.\r\n\r\nPlease note that in our current master branch, we made a commit (d03223d4d64b89e76b48b00602aba5aa2f817f1e) that simultaneously modified:\r\n- test_dataset_common.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-a1bc225bd9a5bade373d1f140e24d09cbbdc97971c2f73bb627daaa803ada002L289 that introduces the usage of `datasets.config.PYARROW_VERSION.major`\r\n- but also changed config.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-e021fcfc41811fb970fab889b8d245e68382bca8208e63eaafc9a396a336f8f2L40, so that `datasets.config.PYARROW_VERSION.major` exists\r\n","embeddings":[-0.3742081225,0.1478579193,0.0884070471,0.0458860211,0.2804539502,0.1281492561,0.1531104892,0.3776514232,-0.2427559048,0.2011216879,0.3052013814,0.4177031815,-0.1865011901,0.1149607599,-0.1479020417,0.0099561084,0.0530921482,0.2520797253,0.1291816086,0.0060467068,-0.1062645391,0.1722441018,-0.1942494661,0.2815794349,-0.2942166626,0.1177279726,-0.0419812873,-0.0479392149,-0.1290827245,-0.5930629969,0.3376209438,-0.1589786112,0.2437154949,0.3399909437,-0.0001267467,0.1114126891,0.4063085616,-0.0081883511,-0.2549678385,-0.5021974444,-0.1217937693,-0.0536484718,0.3664952815,-0.0817655995,0.102711834,-0.7000570297,0.013414585,0.049935773,-0.1659477353,0.329718858,0.1360016465,0.0637335405,0.2616915703,-0.097520113,0.2251864374,0.0621270798,-0.0079679107,0.4649414122,0.0723097548,-0.1101893187,0.2356003225,-0.0848251805,0.0687128976,-0.0513685308,0.0992346108,0.1825542748,0.2629990876,-0.1627095938,0.1989027411,0.0318586826,0.5882601738,-0.626342833,-0.4298146665,-0.0176614802,0.220206961,-0.2607834935,0.2262852788,0.2429365963,-0.10094136,0.1628725231,-0.0570024997,-0.1206212416,-0.063535206,-0.0164811891,-0.4222541451,0.3365793526,0.1881286055,0.140308246,-0.1132394746,-0.009329834,0.0025072652,-0.1097331718,0.10199292,-0.078015849,-0.2746736109,-0.0144315083,-0.0775343105,0.1633089334,0.1757595986,0.208408609,0.0030351472,-0.2792110145,0.2704052031,0.0860595629,0.1035962552,0.1269080788,-0.0204388909,0.1562927812,0.1005842909,-0.1070765704,0.0690039098,-0.0031423518,-0.0581314154,-0.0568243526,0.3815536797,0.0283413101,0.742669642,-0.2355119437,-0.2066710442,0.1739302725,-0.3724194169,-0.179325074,-0.2660050988,0.2187235057,0.0305577051,0.3607175052,-0.1849215776,0.3320734203,-0.0956049785,-0.2356070876,-0.113595061,0.1457725167,0.0022741908,-0.1549615115,0.0268715788,-0.3101926744,-0.0878965631,0.3905238211,0.0014736976,0.1260318905,-0.1415989846,-0.0541735664,-0.1536239386,0.4754760563,-0.2805734873,0.2540762722,0.1305656582,-0.4681425095,-0.3429700136,0.2116095126,-0.2033770382,-0.4039486051,-0.4072811902,0.1281087101,-0.1272283196,-0.1809758395,-0.1497177035,-0.0052619977,0.2306341529,-0.1509694159,-0.0146371135,-0.6668522954,0.1032236964,-0.319904536,0.2467015982,0.1543766707,-0.6335021853,0.0087504741,0.1704790741,-0.1900213808,0.165815562,-0.1197979152,0.005515072,-0.1398759037,0.0290386174,0.0476634167,0.3468930423,-0.1955857724,-0.3685946465,0.0728684291,0.058083415,-0.0002380217,0.2594134212,-0.3139027059,0.0474226773,0.1808189899,-0.1679071486,0.0811104253,0.001909597,0.0160693731,-0.1740115881,-0.0421568789,0.3828534484,0.2783960104,0.1499212235,-0.1601757854,0.0293607879,-0.2005846798,0.1286726147,-0.0868610367,0.0851052552,0.0264915284,0.4046365619,-0.1430395395,0.033946842,-0.2175856382,-0.3197008371,0.1867123246,0.0698746145,0.0265328661,-0.3560784161,-0.1848592609,-0.1795900613,0.1088649258,-0.1084898785,-0.0283278916,0.0058021918,0.3832255006,0.0711459517,0.1115474254,-0.2974824309,0.2475375384,0.0242408346,0.1781353056,-0.0212112162,0.4727578163,-0.4082576334,-0.3153122663,0.0902254656,0.0457065813,0.102861464,0.0391666442,-0.0002053427,0.2879419625,-0.0028909601,0.0584653802,-0.0276767816,0.2250142992,0.0138650322,-0.1725012511,-0.192661047,0.2565892935,0.0011452289,0.4009828568,0.1184246242,0.3715967536,0.5518648624,0.3002988398,-0.0234602094,0.09365049,-0.1508531719,-0.0168528631,0.1085570678,-0.0839205682,0.0393525958,-0.1069295108,0.2100666016,-0.0077000619,-0.5035149455,0.0840201378,0.3833167255,-0.0595607199,0.1452025324,0.1436303854,-0.2609054446,0.0484301373,0.0339332297,0.1345786303,0.2741984427,0.1518546194,-0.1246563643,0.1309271455,-0.3188162744,0.164799884,0.1828395575,0.1089477018,0.1759979427,0.1831200421,0.2829288542,0.086618349,-0.0567072183,-0.3240150213,0.1387683749,0.3523797393,-0.4344475269,0.2530074716,-0.3444136083,0.250282228,-0.1669087112,-0.1862104386,-0.4147616029,-0.2429396659,-0.0304380916,0.0963686109,0.1005030423,0.2397005558,-0.3122081459,-0.1296312362,0.0462817326,-0.3988128304,-0.2186687887,-0.1758570671,-0.4486293495,-0.019090917,0.2013365775,-0.3951259255,0.039651487,-0.2199624628,-0.2137857676,-0.3060598373,-0.1851968169,0.0833885372,-0.2393283546,0.2992102802,0.2597303987,-0.038242083,-0.0473477803,-0.0988906175,0.3235451281,-0.3321583569,-0.2847580612,0.1006459966,-0.0942858085,0.0356610715,-0.0603935421,-0.4970048368,-0.3575113714,-0.1462482959,0.0124236261,0.041099906,0.1555687338,-0.0624720603,0.2309518009,-0.1249248758,-0.1489116102,-0.0105736004,-0.0276867412,0.0039672754,0.2373251915,0.0062804506,-0.2969486415,-0.0511556789,0.0064248098,0.0896267369,0.0367034785,-0.3144406974,-0.1439506412,0.0547291487,0.5389251113,-0.3207369447,0.0192000363,0.2970771492,0.2694778144,-0.0367961302,0.0630886257,-0.1787241399,-0.1892798841,0.1446789801,0.1415685862,0.1744132191,0.2410916984,-0.0849480182,0.5790010095,-0.0703439489,-0.298630327,0.2510004938,-0.29209131,0.3411943913,0.0630541816,-0.1798293442,-0.0886980519,-0.0483298227,0.01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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Reopening this. Although the `test_dataset_common.py` script works fine now.\r\n\r\nHas this got something to do with my pull request not passing `ci\/circleci: run_dataset_script_tests_pyarrow` tests?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/2873","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n   packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in <module>\r\n      1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":25,"text":"datasets.config.PYARROW_VERSION has no attribute 'major' \n In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n   packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in <module>\r\n      1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n \n Reopening this. Although the `test_dataset_common.py` script works fine now.\r\n\r\nHas this got something to do with my pull request not passing `ci\/circleci: run_dataset_script_tests_pyarrow` tests?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/2873","embeddings":[-0.3413378894,0.2006237954,0.049372945,0.0571891814,0.1107941419,0.0854446366,0.2353403866,0.2816726863,-0.1760852039,0.1386214346,0.438138634,0.3304321766,-0.1294845939,0.297735095,-0.1947026998,0.0704638883,0.0133854281,0.1427540034,0.3433145285,0.0562438518,-0.1254506111,0.239258036,-0.1946723908,0.1283952147,-0.1329981238,0.0372122005,-0.1708889008,0.0408576243,-0.1387818605,-0.4680930376,0.4385558963,0.0902260914,0.1819554418,0.6217026114,-0.0001299516,0.1380508989,0.4102708101,-0.0885429606,-0.2731285393,-0.5470474362,0.0774918571,-0.0126978597,0.3988084793,-0.1040790603,0.0965566337,-0.3847824633,-0.0448813178,0.1092322022,-0.067902863,0.3774314225,0.0892633647,0.2561304271,0.0892846957,0.0130179506,0.2664367259,0.3202229738,-0.1790551394,0.4346646965,0.163472265,-0.1368527114,0.18223162,-0.0818933249,-0.0010008782,0.0381129086,0.058766406,0.0650137067,-0.0054221773,-0.2756882608,0.2095608413,-0.0046765376,0.4738269448,-0.5655617118,-0.4999672771,0.0325702988,0.1310693324,-0.2759967446,0.1989559233,0.1092876121,-0.2475142032,0.1120673195,-0.1768936068,-0.1210495085,-0.0797204599,-0.0597053356,-0.452073127,0.1955433339,0.1415925175,0.1933697611,0.0005672796,-0.0080453893,0.0825758576,-0.1077340245,0.1832358539,-0.0202006698,-0.2383793592,-0.0761251375,0.0723483413,0.3277744949,0.2713033557,0.4195830226,0.0403551459,-0.1306673586,0.174266085,0.0946188346,0.0676531792,0.2015321553,-0.0325329341,0.1100755706,0.26883623,0.0022651241,0.0251369309,-0.0340531245,-0.0820883214,-0.118339479,0.2201674134,0.0307946801,0.8329048157,-0.3100616932,-0.264413327,0.0647196248,-0.4131411612,-0.1456345022,-0.1949632913,0.2120336592,0.1063020974,0.3991495371,-0.054112941,0.3935264349,-0.0928063095,-0.0335153863,-0.1254985034,0.0960216671,0.0741094276,-0.0745831132,0.2243506163,-0.3418166041,-0.0214521512,0.2785852551,0.2317495495,0.1688358784,0.0847869664,0.1707445085,-0.0158238746,0.5882755518,-0.1170846,0.3030574322,0.1808865517,-0.4662673175,-0.3240087628,0.2942477167,-0.3417190313,-0.3119837642,-0.1544470787,0.0395626612,-0.3309824169,-0.2279408276,-0.2230107188,-0.199565202,0.230595544,-0.09554369,0.0377825759,-0.6431552768,0.1101010367,-0.2451968342,0.2660878897,0.2522025704,-0.4532644153,0.0551288687,-0.0058161258,0.0077627655,0.1686531156,0.108184278,-0.0699362084,-0.1368115842,-0.0259974599,0.0800719559,0.4520925283,-0.4171760976,-0.3087326586,0.2504969537,-0.0502103381,0.0525274239,0.2544584572,-0.4099239409,0.0670625865,0.1091564149,-0.1023410782,-0.0559723116,-0.013213655,0.1008469164,-0.1170869917,-0.0518476814,0.4205104113,0.2592461705,0.0945970491,-0.1817267984,-0.0615676045,-0.3520296216,0.2080374062,-0.058173541,-0.00003425,-0.0472278409,0.4154104888,-0.3017407358,0.0376632102,-0.0054553566,-0.2810015082,0.2353983223,0.1397245526,0.1832517534,-0.4007819295,-0.2460963428,-0.2957423627,0.1120926812,-0.1815855503,-0.0250835977,-0.0734229907,0.3676602542,0.0943571031,0.0908512399,-0.2459866107,0.1231615022,-0.0549701601,0.121638678,-0.0157055464,0.36100775,-0.39767465,-0.3114204407,0.1486184299,0.0152983777,0.1552105695,-0.0953354239,0.0492075197,0.1977923065,0.018292509,-0.0615071394,0.0249138251,0.2371177673,0.0974489674,-0.1439389586,-0.1320542544,0.2738496065,-0.0527514368,0.2288922668,0.0089537343,0.4483506978,0.433740288,0.4135373831,-0.0052913735,0.133137241,-0.0789800286,0.0464649163,-0.1026584506,0.0235418081,0.0623203032,-0.1579686403,0.2385576516,-0.0083107622,-0.4083735943,0.1789080948,0.4755972028,0.0045280191,0.0512079373,0.1263181567,-0.0464047939,0.0135155227,0.1427060068,0.1196604073,0.3690716922,0.1315706223,-0.1269986928,0.1280188709,-0.3089667261,-0.000240603,0.0881366134,0.0855403543,-0.0177239105,0.1066989228,0.2934764922,0.0094751427,-0.1714618802,-0.5069288611,0.1386284977,0.3523923159,-0.5544535518,0.2957420945,-0.2599960566,0.2412185669,-0.0563951358,-0.099517554,-0.3512249887,-0.3400330842,-0.0426338091,0.1322477013,0.1855418086,0.1987326741,-0.3156895339,0.085814856,0.0278856121,-0.5474075079,-0.3988513947,-0.2250475734,-0.3432812393,-0.0082413657,0.335031271,-0.3440192044,0.1043744832,-0.3536006212,-0.0615599714,-0.3151578307,-0.3276818395,0.0971032903,-0.3790067136,0.3400279582,0.264110893,0.1390078962,-0.0936909392,-0.0775850788,0.2825623751,-0.3451506793,-0.3498209715,0.0558034815,-0.0393706858,-0.081658788,-0.2297518551,-0.4395770431,-0.1756117046,-0.1256152838,0.1797543317,0.1345760971,0.0894499645,0.1602129638,0.2617307603,-0.1119602174,0.0015942445,-0.080045335,0.0416262671,-0.1853195578,0.311385572,-0.1229889318,-0.2905154526,0.0768143982,0.0901876315,0.2567841113,0.0905639157,-0.3195661008,-0.2088564485,0.0071465177,0.5721033216,-0.1528172642,0.0589576438,0.3950414658,0.1801723242,0.033580374,0.0303174295,-0.2007779479,-0.0994261503,0.1576837599,0.0561942831,0.1956194937,0.2270442694,-0.0645815507,0.7777855992,-0.0912658498,-0.247752741,0.3013986051,-0.3482937813,0.4125493765,0.092459954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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Hi @bwang482,\r\n\r\nIf you click on `Details` (on the right of your non passing CI test names: `ci\/circleci: run_dataset_script_tests_pyarrow`), you can have more information about the non-passing tests.\r\n\r\nFor example, for [\"ci\/circleci: run_dataset_script_tests_pyarrow_1\" details](https:\/\/circleci.com\/gh\/huggingface\/datasets\/46324?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link), you can see that the only non-passing test has to do with the dataset card (missing information in the `README.md` file): `test_changed_dataset_card`\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests\/test_dataset_cards.py::test_changed_dataset_card[swedish_medical_ner]\r\n= 1 failed, 3214 passed, 2874 skipped, 2 xfailed, 1 xpassed, 15 warnings in 175.59s (0:02:55) =\r\n```\r\n\r\nTherefore, your PR non-passing test has nothing to do with this issue.","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n   packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in <module>\r\n      1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":95,"text":"datasets.config.PYARROW_VERSION has no attribute 'major' \n In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n   packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in <module>\r\n      1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n \n Hi @bwang482,\r\n\r\nIf you click on `Details` (on the right of your non passing CI test names: `ci\/circleci: run_dataset_script_tests_pyarrow`), you can have more information about the non-passing tests.\r\n\r\nFor example, for [\"ci\/circleci: run_dataset_script_tests_pyarrow_1\" details](https:\/\/circleci.com\/gh\/huggingface\/datasets\/46324?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link), you can see that the only non-passing test has to do with the dataset card (missing information in the `README.md` file): `test_changed_dataset_card`\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests\/test_dataset_cards.py::test_changed_dataset_card[swedish_medical_ner]\r\n= 1 failed, 3214 passed, 2874 skipped, 2 xfailed, 1 xpassed, 15 warnings in 175.59s (0:02:55) =\r\n```\r\n\r\nTherefore, your PR non-passing test has nothing to do with this issue.","embeddings":[-0.4502635896,0.1362894326,0.0727608278,-0.0077378419,0.210440293,0.1455685943,0.1347785145,0.3891866505,-0.2612731457,0.1966487318,0.2423591018,0.4409922659,-0.174534291,0.1955467612,-0.2465572804,0.0201375429,-0.0359608233,0.1991022974,0.1349838227,0.0418360345,-0.1486246139,0.1625906974,-0.2162213922,0.2647956908,-0.1537034959,-0.0562001877,-0.0889388993,-0.0443796068,-0.1304095089,-0.543780148,0.374103874,-0.0596004017,0.1746742874,0.5324847698,-0.0001234938,0.1340290904,0.3579029739,-0.099743627,-0.3201577663,-0.4985058904,0.0303555876,0.0250716098,0.3309290409,-0.1497163624,0.0457028411,-0.5905317068,-0.0560617782,0.011204035,-0.0264583249,0.4921305478,0.1557275653,0.1648605168,0.0755554661,-0.137879923,0.210254997,0.089855887,-0.0760812536,0.4706763327,0.1216390133,-0.0717137381,0.1600072682,-0.1023208573,-0.0217084177,-0.0166188478,-0.0197366606,0.0890951231,0.2701399326,-0.2540642619,0.2758241594,0.0213556439,0.6139847636,-0.6099302769,-0.402813077,0.0584969297,0.1510484666,-0.1977946013,0.2043727338,0.1740199775,-0.2345986068,0.1370747983,-0.1940619051,-0.0381158032,-0.0790201724,-0.1012421995,-0.3796566129,0.2510077655,0.1984764487,0.1712024063,-0.0156713389,-0.1160794795,0.0763115138,-0.1649231166,0.170402199,0.0014240057,-0.2107874453,-0.129683286,-0.0683172196,0.3605410159,0.2446825057,0.2437425703,0.0395206213,-0.2193709165,0.2899466455,0.1493432522,-0.0002065366,0.060830459,0.0282580554,0.1525740027,0.1156841591,-0.0743039697,0.0116414698,0.0125757102,-0.0356712267,-0.1347546428,0.2806890607,-0.0175586436,0.747718215,-0.386092782,-0.3294264078,0.1127082333,-0.3191033304,-0.1121121868,-0.1496730894,0.3698425591,0.0801952556,0.3064732254,-0.2025549859,0.3027328849,-0.148769632,-0.1783850491,-0.0953058898,0.0878898203,-0.0219249018,-0.1331822574,0.1687428951,-0.2979690433,-0.0465056896,0.3469100296,0.1025219187,0.1879935563,0.0652616769,-0.0026953628,-0.0544139817,0.4611659646,-0.2218509614,0.2516638935,0.1780836433,-0.5087434649,-0.3634715378,0.2743989229,-0.2164251655,-0.3534741104,-0.2432028204,0.151281327,-0.0920187533,-0.2586004138,-0.0489280932,-0.0613507144,0.1802086085,-0.1575635076,-0.0042943428,-0.6692387462,0.1291497648,-0.3172443509,0.2979747355,0.2355631441,-0.5403274298,0.0511988066,0.1171005592,-0.1538690478,0.2208563685,-0.0480547622,0.0285186488,-0.1684476435,0.008382176,0.0004034062,0.3619309366,-0.3157839477,-0.2808323205,0.2037823498,0.1373576522,-0.0054161474,0.258110404,-0.3559542,0.0676776916,0.1332530528,-0.2449017912,0.0707520768,-0.0634788871,0.0394995026,-0.1724347621,0.0111094378,0.4310903251,0.2942523658,0.0776945651,-0.1869189143,0.0541623756,-0.2536583543,0.1724235415,-0.153491497,0.0145558426,-0.095210731,0.4228506982,-0.3071483076,0.0091015995,-0.0820851251,-0.3720258772,0.214207381,0.1426869631,0.0427836291,-0.3331008852,-0.2545279264,-0.3418053687,0.0929807276,-0.1178567037,-0.0309027005,0.0290229879,0.3834606111,0.0836455449,0.0666082948,-0.1785346568,0.1259467155,0.0109810112,0.1843898743,-0.021914199,0.4052859545,-0.3848957419,-0.3287788928,0.1164525375,0.0147691462,0.2110700905,-0.0747004673,-0.0244301055,0.3569452167,0.0668140128,0.0324969031,0.0375453681,0.2812748551,0.0095077008,-0.2744845748,-0.2255578786,0.2845185697,-0.0563144833,0.335890919,-0.0477804728,0.4245945215,0.4690216482,0.2440936267,0.0434890911,0.1207157895,-0.0876624584,0.0307110269,0.0175956078,-0.1027696207,0.1239546984,-0.0975145176,0.2429556996,0.0073814266,-0.4534864426,0.0181120038,0.3326230049,-0.1036967561,0.079722181,0.1088910624,-0.1348168701,0.0713322908,0.1310128421,0.1286019683,0.2671003044,0.1545218527,-0.0664283633,0.1459353566,-0.3957768083,0.0493688993,0.1474568546,0.046951104,0.0843830109,0.1178493723,0.2030754983,0.0692258701,-0.1964526772,-0.4386540353,0.1690077484,0.3975126445,-0.4556099176,0.2525030673,-0.2593023777,0.2879627645,-0.1762989759,-0.1013129577,-0.2812756002,-0.2871729434,0.0298087746,0.1613865793,0.1280528903,0.2844814956,-0.3949874341,0.1308537275,0.0604371689,-0.5389617682,-0.3587738574,-0.1788505465,-0.4358465075,0.0564847142,0.2930062115,-0.2936736047,0.1870015562,-0.3385720551,-0.1041571423,-0.2780030966,-0.2546269596,0.1000234187,-0.2732539177,0.4224722683,0.2331346571,0.0653007999,-0.0509411618,-0.1394465566,0.2985119224,-0.5006918907,-0.2382101864,0.1075783893,-0.0609142967,-0.0281601492,-0.0992578343,-0.6026321054,-0.2700941563,-0.1767314076,-0.0738294199,0.1012948826,0.1171792299,0.0153186703,0.1794993281,-0.0414384827,-0.1064003855,0.040731471,0.0227799695,-0.0935673937,0.2638745904,-0.1412262917,-0.247062102,0.0030329677,0.0593990013,0.3075082004,0.0263136849,-0.3157670796,-0.1785085499,0.0870808735,0.5713326931,-0.213754639,-0.0088340752,0.2017015219,0.3253875971,-0.0461313352,0.0438692309,-0.1561382264,-0.2257906049,0.0831051841,0.1174168065,0.1173686087,0.2478049397,-0.0001471604,0.7000100017,0.0901702568,-0.2647122443,0.2456341684,-0.290442735,0.3646113276,0.0215920843,-0.2104302496,-0.0293033496,-0.0032851116,-0.08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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"Hi, @Chenfei-Kang.\r\n\r\nI'm sorry, but I'm not able to reproduce your bug:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"glue\", 'cola')\r\nds\r\n```\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 8551\r\n    })\r\n    validation: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 1043\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 1063\r\n    })\r\n})\r\n```\r\n\r\nCould you please give more details and environment info (platform, PyArrow version)?","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":66,"text":"TypeError: 'NoneType' object is not callable \n ## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n \n Hi, @Chenfei-Kang.\r\n\r\nI'm sorry, but I'm not able to reproduce your bug:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"glue\", 'cola')\r\nds\r\n```\r\n```\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 8551\r\n    })\r\n    validation: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 1043\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'label', 'idx'],\r\n        num_rows: 1063\r\n    })\r\n})\r\n```\r\n\r\nCould you please give more details and environment info (platform, PyArrow version)?","embeddings":[-0.0594903305,-0.1199711189,0.0148641206,0.2438831329,0.4732634127,-0.0013416682,0.4414617419,0.1253976971,-0.0142953452,0.2675631642,-0.1799774915,0.4658890367,-0.1191558838,0.1054266095,0.0933514312,-0.1634460539,-0.1493485421,0.1789052486,-0.0950517431,0.0041558379,-0.2951413095,-0.1227453202,-0.2647020519,0.1601062417,-0.4545831382,-0.2473363578,-0.1237274557,-0.0289071202,-0.2947974801,-0.3072052002,0.3905930817,0.045531936,0.0802016407,0.573389709,-0.0001046333,0.085742943,0.5220624804,0.0795245767,-0.1019385606,-0.4897305667,-0.1364545226,-0.1668332815,0.2139172852,-0.393879205,-0.174881503,-0.0434178077,0.0156303495,0.1047198623,0.3135638833,0.4849516451,0.3089385629,0.4509330392,0.0464522541,-0.2673178315,0.2966395915,0.1515992731,-0.0532518923,0.3601016104,-0.0297630094,-0.1026376411,0.1846834719,0.1567065567,-0.2669480145,-0.0967591628,0.1903866827,0.0687865466,-0.0415391438,-0.290898174,-0.0751495287,0.4143870771,0.3387809098,-0.4593112171,-0.2788108885,0.1180645153,0.2122831494,-0.2495187223,0.2602464557,0.0856177285,-0.0128446911,0.1975728273,-0.0867947638,0.2796069086,-0.2793028951,0.3049078584,-0.164794296,0.1444952488,-0.0630867183,0.0345241651,0.2060508877,-0.1919636279,0.059791442,-0.1310273856,0.0356741138,0.1007023752,-0.243607074,-0.1369945854,0.1091515124,-0.0550249554,0.1305607557,-0.2993285656,0.1565279961,-0.0825746059,-0.0047007087,0.4082542658,0.183615461,0.3742778003,0.2353915423,0.146733135,0.2031041682,0.1250560284,-0.2914271057,0.0851671919,-0.1916304976,-0.0795564353,0.3350311518,0.141778037,0.4543803036,0.004213843,-0.4538616538,-0.0484698229,-0.4231696129,0.0795800835,0.2269208282,0.4578568935,-0.1490905881,0.111972034,0.0448685922,0.1539013535,-0.1707451195,-0.2831352949,-0.2869074643,0.4119275212,-0.3327414989,-0.2005894333,0.1725695282,0.0631759688,-0.0440938734,-0.0142761068,-0.124532178,0.0147927273,0.0312171597,-0.4864667356,-0.1069177911,0.1802367717,0.0366909429,-0.1434192806,0.2177432328,-0.4385626912,0.0038601621,0.2104463279,-0.1390264481,-0.2205248028,-0.0644018948,0.2921482325,-0.3297555149,-0.1617497504,-0.1220805645,0.1581521481,0.158894524,0.0152816763,0.0386043973,-0.0845265612,-0.0218608137,-0.3067058027,0.0268780459,0.3902682364,-0.3743338883,-0.2277735025,-0.1163482741,-0.0573342443,0.3309760094,-0.0552941114,-0.0139303515,0.2842520177,-0.0162361618,0.1270191222,0.6166725755,-0.3811469376,-0.3189837933,-0.0871518478,-0.0525565632,-0.0323508754,-0.0000088416,-0.1415092349,0.0952094793,0.1851929128,0.4305967093,0.1825711876,0.0108494731,-0.0068763741,-0.2708131671,-0.2111279964,0.1911941022,0.0895309374,0.1389753819,-0.0566433519,-0.0780088603,-0.24225308,0.0179314129,-0.1191949025,-0.1323612928,-0.0616726913,0.5806082487,-0.0895553157,-0.1220896021,-0.5331310034,-0.2384607494,0.1451272964,-0.0384060368,0.4400658906,-0.1647625715,-0.2132972479,-0.4087546468,0.0467493199,-0.0534322485,-0.0422442332,0.2887170017,0.0327202827,-0.162372008,0.1476718634,-0.093931675,0.0690900609,-0.1891066879,0.1402491182,0.1150690317,0.3248268962,-0.052656617,-0.4338489175,0.0160555448,0.2450485379,0.4278887212,0.0615985915,-0.0518304221,0.3103211224,0.0966809914,-0.2166442722,-0.332929194,-0.0375642255,0.1061588004,-0.3284879327,-0.1916384846,-0.02861006,0.1814015508,-0.0039089932,0.0155871715,0.5066564679,0.073046267,0.0751549006,-0.0834585428,0.1962543428,-0.0521300845,-0.1202241629,-0.1189233959,-0.0935665593,0.0853727981,-0.0516477935,0.1604620069,0.0839664191,-0.1486921012,-0.0939611942,0.6276284456,0.066133365,0.4066179395,-0.1278576404,-0.253084898,0.1469340771,0.0753541663,0.0935161412,0.3560036421,0.309184581,-0.1741830856,-0.012002632,-0.1699138284,0.0200339202,0.0936975554,-0.0300577246,0.3375006616,-0.0191667695,0.326565057,0.0025228276,-0.3129945993,0.1154984683,0.0383162536,0.1894647479,-0.3931485116,0.1806956828,-0.3155397177,-0.1539050788,0.0592735447,-0.1398235261,-0.0421994813,-0.2707495987,-0.085526906,0.1815255284,0.0589705072,0.264362663,0.1597475857,-0.0327072591,0.0847381577,-0.0655731633,-0.102235347,-0.2201068252,-0.1743910462,0.1428499222,0.1274092495,0.2078600228,0.2995347381,-0.1425538957,0.0480940603,0.0138055841,-0.3602250516,0.0857338384,-0.1215342283,0.4401930273,0.3425597847,0.0728836656,-0.2368693203,0.0173933227,0.3619660437,-0.3473314047,-0.108051084,0.3983776271,-0.1979978234,0.0502504855,-0.2161714584,-0.3248876333,-0.510535121,-0.318955183,0.1311067194,-0.0294710808,0.0872791484,0.3062438071,0.2281634808,0.450894624,-0.052571062,0.0395837203,-0.1739951223,-0.0077805598,0.3531797528,-0.0148417056,-0.3720351756,0.0079913763,-0.1651002616,0.1313911527,-0.0117581366,-0.1790904701,-0.5396494269,-0.1400610059,0.288399756,0.0229437444,-0.0957489237,0.3742546439,0.0539732315,-0.1510512084,-0.1974764913,-0.082263872,0.0317742862,0.3172571063,0.0219645612,-0.069488965,0.2908799648,0.0552209876,0.2811395824,-0.113323316,-0.2513748407,0.4198065102,-0.2915056348,0.2461425811,-0.0716580451,-0.4766027927,-0.0776230395,-0.05550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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"> Hi, @Chenfei-Kang.\r\n> \r\n> I'm sorry, but I'm not able to reproduce your bug:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\"glue\", 'cola')\r\n> ds\r\n> ```\r\n> \r\n> ```\r\n> DatasetDict({\r\n>     train: Dataset({\r\n>         features: ['sentence', 'label', 'idx'],\r\n>         num_rows: 8551\r\n>     })\r\n>     validation: Dataset({\r\n>         features: ['sentence', 'label', 'idx'],\r\n>         num_rows: 1043\r\n>     })\r\n>     test: Dataset({\r\n>         features: ['sentence', 'label', 'idx'],\r\n>         num_rows: 1063\r\n>     })\r\n> })\r\n> ```\r\n> \r\n> Could you please give more details and environment info (platform, PyArrow version)?\r\n\r\nSorry to reply you so late.\r\nplatform: pycharm 2021 + anaconda with python 3.7\r\nPyArrow version: 5.0.0\r\nhuggingface-hub: 0.0.16\r\ndatasets: 1.9.0\r\n","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":116,"text":"TypeError: 'NoneType' object is not callable \n ## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n \n > Hi, @Chenfei-Kang.\r\n> \r\n> I'm sorry, but I'm not able to reproduce your bug:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\"glue\", 'cola')\r\n> ds\r\n> ```\r\n> \r\n> ```\r\n> DatasetDict({\r\n>     train: Dataset({\r\n>         features: ['sentence', 'label', 'idx'],\r\n>         num_rows: 8551\r\n>     })\r\n>     validation: Dataset({\r\n>         features: ['sentence', 'label', 'idx'],\r\n>         num_rows: 1043\r\n>     })\r\n>     test: Dataset({\r\n>         features: ['sentence', 'label', 'idx'],\r\n>         num_rows: 1063\r\n>     })\r\n> })\r\n> ```\r\n> \r\n> Could you please give more details and environment info (platform, PyArrow version)?\r\n\r\nSorry to reply you so late.\r\nplatform: pycharm 2021 + anaconda with python 3.7\r\nPyArrow version: 5.0.0\r\nhuggingface-hub: 0.0.16\r\ndatasets: 1.9.0\r\n","embeddings":[-0.0170860495,-0.3240526319,0.062121436,0.2103672326,0.3840116858,-0.0155181745,0.415794313,0.1785911471,0.1879396886,0.3116176724,-0.2562711835,0.2617366314,-0.0900785625,0.1555979401,0.134646982,-0.1783233583,-0.1780708879,0.2362426817,-0.1429384202,-0.0610416606,-0.307266444,0.016021125,-0.2765933871,0.1563270539,-0.3710355163,-0.2232295871,-0.0727495775,-0.0337860063,-0.3516702652,-0.3736093938,0.3502697647,-0.0149404071,0.1077515855,0.3936695755,-0.000109273,0.039748542,0.5531897545,0.0916725695,-0.1215192825,-0.486445725,0.0033005883,-0.153592959,0.2640893161,-0.2971876264,-0.2491431981,-0.1678692102,-0.0046053282,0.1546724737,0.3977606893,0.5068286061,0.2735918462,0.5179857016,0.1557451636,-0.3057215512,0.2476199716,0.1295629144,-0.0830335468,0.3645498157,0.0868025422,0.0324823782,0.2476591766,0.22567074,-0.2615945935,-0.1020329148,0.2481998503,0.0714492425,0.0565817356,-0.3359338641,-0.0874417275,0.3458363712,0.2176503092,-0.4307984114,-0.2740110457,-0.0156725049,0.1589335203,-0.2778740823,0.2152642608,0.1117749512,-0.0614302717,0.2610106766,-0.0477596298,0.2916436493,-0.2724869251,0.2264740765,0.0332289934,0.0998286605,-0.0974352807,0.0864828005,0.3184731901,-0.1978602111,-0.0712477863,-0.0744847059,0.0765407234,0.1997876465,-0.2625720799,-0.1569988281,0.0412150547,-0.0527565517,0.2478982806,-0.0623121895,0.010698121,-0.0784372389,0.0866332129,0.3945228755,0.1457946301,0.3695580363,0.2746926844,0.0596430153,0.147126019,0.2683903873,-0.1179920584,0.0550732911,-0.1812906712,-0.0505757257,0.2737024724,0.1010781154,0.4157111049,-0.0123857539,-0.3835292757,-0.0340619497,-0.249388665,0.2141660452,0.2170223594,0.4983271658,-0.1098527387,-0.006581971,0.1920767128,0.1782709658,-0.1211596727,-0.1304862946,-0.2357181609,0.34223032,-0.3215116858,-0.0617322512,0.2265961319,0.0308971405,0.0039272024,-0.0047287471,0.0682608932,-0.0418036729,-0.0186945554,-0.3479027152,-0.0821475908,0.2535481453,0.032342352,-0.0685248151,0.2264001071,-0.410043478,-0.0981175676,0.1309780478,-0.0746355355,-0.1795981675,-0.2304216176,0.2343917638,-0.2560875714,-0.0213031564,-0.0934348702,0.0212540068,0.1447509378,0.1013658345,0.1272418797,-0.0190788247,0.0178890768,-0.2180711776,0.1380720735,0.3654916883,-0.100190863,-0.3205661774,0.0321522094,-0.0025296691,0.1171867996,-0.0173707716,0.0019335701,0.2421703041,-0.0305478908,0.1668857634,0.4985628724,-0.62414819,-0.3442799449,-0.2747382522,-0.0806370825,-0.0161460973,0.0393288732,-0.2286882102,0.1914647818,0.2106938213,0.4453007579,0.1527808011,0.1522087157,0.0167638846,-0.2598265111,-0.2841210067,-0.1492304802,0.1216640398,0.0572228469,-0.0611823201,-0.1246782616,-0.3315826058,0.0439961776,-0.1464243233,-0.0947787762,-0.0285352468,0.4802513719,-0.0267939679,-0.0334615521,-0.5103312731,-0.3110117316,0.1444107145,-0.0827431381,0.4963111877,-0.2935872972,-0.2401018441,-0.3797984123,0.0216788147,-0.0260307416,-0.1073646471,0.2608529925,-0.0292999409,-0.0793902054,0.2039854974,-0.148156628,0.3061811626,-0.0629678369,0.3361934423,-0.0899120644,0.2520775199,-0.0519085974,-0.4535894096,0.0178125389,0.3152451813,0.407485038,0.0184305087,0.0400744975,0.2059644312,0.120073542,-0.2516606152,-0.3879702389,-0.2059795707,0.0908953026,-0.2052687109,-0.1253317297,-0.1193971634,0.1875582188,0.043237146,0.0816024616,0.4845341146,0.0002889726,0.1447360814,-0.0010887757,0.2192268968,0.0464254729,-0.2223508209,-0.1943704933,-0.1116545573,0.1098374575,0.0302241202,0.2480703294,0.0179758705,-0.1454162598,0.0168016292,0.6618235111,0.0420250148,0.258257091,-0.1182069257,-0.3660911322,0.2513479888,0.1739427298,0.087606214,0.3652841747,0.2524144948,-0.2956422269,0.0845419914,-0.1635932028,0.0505567454,0.0976488069,-0.0679601654,0.3684989512,-0.0912532359,0.3075386584,0.0185329355,-0.3090053499,0.0744575188,-0.0793073252,0.1062876061,-0.3757323921,0.1150494143,-0.2569516301,-0.1478515416,-0.0088819563,-0.133373037,0.0266572647,-0.1974048913,-0.0598685481,0.3584897518,0.0893847048,0.282640487,0.2069196403,0.0192367453,-0.0590613931,-0.1030487418,-0.1551313996,-0.108617492,-0.11700131,0.0823388919,0.0588655174,0.1915499568,0.3331587911,-0.3143273294,0.0909607261,0.1370236278,-0.3026359379,0.0858223662,-0.1740436107,0.2888210118,0.2684489191,0.1589440256,-0.1759042591,-0.0624604672,0.4340486825,-0.3824962378,-0.192428574,0.2827731073,-0.1414361447,-0.0550831221,-0.2623379529,-0.2640984952,-0.5197219253,-0.2946316004,0.2550328076,0.0227000788,0.0717564151,0.3472696841,0.234048292,0.452318579,-0.3834219277,-0.0229388513,-0.1441199481,0.0412894003,0.2728778422,-0.0820257813,-0.4259680212,-0.0077680945,-0.0855097622,0.2539603114,-0.1286979169,-0.0568948649,-0.4479478002,-0.1266304255,0.3238252997,0.0086144321,-0.1531503648,0.6029988527,0.0156400762,-0.1078105569,-0.1583341658,-0.1767390072,0.1045932546,0.1382757425,0.0056557627,-0.0700706616,0.2453286648,-0.0265153348,0.351564467,-0.0049929833,-0.2051898241,0.3471206427,-0.304443121,0.3146957457,-0.1218410507,-0.5708717108,-0.0727783665,-0.02652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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"- For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n- In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":69,"text":"TypeError: 'NoneType' object is not callable \n ## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n \n - For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n- In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?","embeddings":[-0.2277715802,-0.3193501234,0.0008596373,0.3918522596,0.3677237034,0.0557456017,0.3176339269,0.1545283645,0.1733444631,0.2960280478,-0.1426855326,0.4552958608,-0.0776570067,0.2298437357,0.0493332893,-0.1651720107,-0.2105034888,0.2611335516,-0.1191705689,0.0262278002,-0.5092200041,-0.0741478875,-0.1640852094,0.1349925548,-0.237754494,-0.2719812989,-0.1240942478,0.0408258066,-0.1921427697,-0.2838498354,0.3645710647,-0.0801881999,0.2122620046,0.5988925099,-0.0001008489,-0.1514540613,0.435205698,0.0475999489,-0.1254921108,-0.3523042202,-0.2946639061,-0.3847207725,0.18985717,-0.3827019334,-0.0743031427,-0.0082017854,0.0286093634,-0.2426373065,0.3481251895,0.3484524488,0.3506994545,0.415902108,0.0833576843,-0.2823812366,0.1884763539,0.0775724575,-0.1048533171,0.2966803014,0.0623726882,-0.0164799206,0.3145338893,0.1602044255,-0.2830132842,-0.0542992242,0.2551215291,0.0645872205,-0.0053246845,-0.3559243679,0.0310364459,0.3066703379,0.4195721149,-0.3946249187,-0.1872214228,0.0607764684,0.1299087554,-0.2234973758,0.1539727449,-0.0115599288,-0.0064773369,0.0658862591,-0.0040367073,0.1557925344,-0.236804232,0.2183835655,-0.1664240062,0.0901961327,-0.2121003419,0.107063584,0.0670970306,-0.1992394775,0.0452290624,-0.1839859337,0.0351355337,0.1096044704,-0.221126616,-0.1452792883,0.1299011409,0.1111679673,0.1270107031,-0.2455778271,0.1168725938,-0.0483121127,-0.0236970503,0.4071713388,0.239894405,0.2160654962,0.197072491,0.1496332735,0.2160288244,0.0441346094,-0.1853791922,-0.0519746169,-0.1255763024,-0.1014563665,0.2814617455,0.1962061971,0.5696896911,-0.0633770376,-0.4451116025,-0.0369891338,-0.207089901,0.0923674777,0.2541407347,0.3783914447,-0.1840863526,0.1531827301,0.2149179727,0.0695832223,-0.1426405609,-0.2327828705,-0.2402686328,0.3336758912,-0.2726967335,-0.232448265,0.0732233152,0.0800087824,-0.1016519293,0.0515346751,-0.0466053039,0.0736266375,0.1052564383,-0.382973969,-0.1715993732,0.1613502353,0.1342369616,-0.0109220734,0.3575491905,-0.413230598,-0.1455421895,0.1080859974,-0.3179209232,-0.0927535221,-0.0903914943,0.3212557733,-0.2362898439,-0.079209581,-0.1951911002,-0.0047342735,0.0772167519,-0.2021209151,0.0243879221,-0.1612926275,0.0099704852,-0.4124752581,0.0396714061,0.5434519649,-0.3702253997,-0.131931603,-0.3560011387,-0.0526704639,0.3650825024,-0.1617242545,0.0869184732,0.2049263865,-0.1619556099,-0.0783798695,0.5953560472,-0.3626013696,-0.1942619234,0.0556299053,-0.079251498,-0.0580402203,-0.067448847,-0.0140706375,0.0950176865,0.1139309034,0.4095413983,0.2211544514,-0.0633310452,-0.027870506,-0.1950395554,-0.1733900309,0.1649025828,0.0918372497,0.1436722875,0.1626258194,0.0630830303,-0.2037843466,0.0184938218,-0.0397915058,-0.09291742,-0.0652414113,0.6565243602,-0.0872236714,-0.0788739324,-0.5945641994,-0.3253455162,0.1684785038,-0.0677354112,0.2807234824,-0.1447329372,-0.1707682014,-0.3664011657,0.0677765012,-0.0498286709,0.0480884425,0.34265396,0.1271567047,-0.1998722106,0.0451182313,-0.1443472803,0.1779200137,-0.1498600394,0.1019958928,-0.0057357312,0.3032817245,-0.0105569726,-0.3550938368,0.0437213145,0.2375442684,0.5392077565,0.0301111341,-0.0714498162,0.2961714268,0.0839738399,-0.1378158778,-0.1490661353,-0.0828541517,0.1191909611,-0.2615092099,0.0761332735,0.113510862,0.341347903,-0.0806762725,0.1379154474,0.550291419,0.070542492,0.218250677,-0.0425794087,0.2359925807,-0.0458750986,-0.0404749922,-0.09132009,-0.1391232908,0.1310136467,0.0969333351,0.1783950478,-0.0640903935,-0.1166954562,0.0628789961,0.5675632358,0.0985855833,0.3846487105,-0.0710379705,-0.3002262712,0.1548450738,0.0890177488,0.2822793424,0.4458229542,0.3082370758,-0.1348168105,0.0028449628,-0.105473116,0.0909257606,0.069577001,-0.008273948,0.2112074643,-0.0183985941,0.1782516539,0.0799446777,-0.1778929234,-0.0452206172,-0.2235442847,0.2644327581,-0.3588179946,0.1916420162,-0.2675382495,-0.1693612486,0.0416815057,-0.0662412271,-0.1583892852,-0.343996793,-0.2322412729,0.0256404057,0.1133051515,0.1315740049,0.114424713,0.1559722424,0.0080657853,0.0855939835,-0.1656484157,-0.1026751921,-0.1289932728,0.1639403254,0.1211261749,0.2124070823,0.4851669073,-0.2281131297,0.2450820208,-0.0264441986,-0.227994293,0.0638851374,-0.0960038304,0.4924280643,0.4533854723,0.170710057,-0.0828904957,-0.0167101845,0.3197818995,-0.3656996191,0.0297314506,0.2110722065,-0.2212308943,0.1077203006,-0.1586056501,-0.1741414219,-0.5684420466,-0.4111053944,0.1035611331,0.1680119336,0.1657065153,0.1624201983,0.1247417033,0.5334134102,0.1375749111,0.1933974028,-0.195846498,-0.0487356074,0.2836108506,-0.122860238,-0.2586844563,0.1470841169,-0.1050434485,0.2065625638,0.0155344047,-0.1752419621,-0.5393431783,-0.1731456071,0.1546034515,-0.0829334036,0.0919005796,0.2602021098,0.1155401021,-0.1390545517,-0.2339776456,-0.1428654939,-0.1661490351,0.2557976246,0.0672933385,0.0401755236,0.3397756815,-0.178367734,0.1294477135,0.0065674935,-0.2346490473,0.3460306227,-0.2629454136,0.3489436209,-0.1248321906,-0.5024338961,-0.1444402039,0.0058216783,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"> * For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n> * In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?\r\n\r\n1. For the platform, here are the output:\r\n        - datasets` version: 1.11.0\r\n        - Platform: Windows-10-10.0.19041-SP0\r\n        - Python version: 3.7.10\r\n        - PyArrow version: 5.0.0\r\n2. For the code and error\uff1a\r\n     ```python\r\n     from datasets import load_dataset, load_metric\r\n     dataset = load_dataset(\"glue\", \"cola\")\r\n    ```\r\n    ```python\r\n    Traceback (most recent call last):\r\n    ....\r\n    ....\r\n    File \"my_file.py\", line 2, in <module>\r\n    dataset = load_dataset(\"glue\", \"cola\")\r\n    File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 830, in load_dataset\r\n    **config_kwargs,\r\n     File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 710, in load_dataset_builder\r\n    **config_kwargs,\r\n    TypeError: 'NoneType' object is not callable\r\n    ```\r\n   Thank you!","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":154,"text":"TypeError: 'NoneType' object is not callable \n ## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n \n > * For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n> * In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?\r\n\r\n1. For the platform, here are the output:\r\n        - datasets` version: 1.11.0\r\n        - Platform: Windows-10-10.0.19041-SP0\r\n        - Python version: 3.7.10\r\n        - PyArrow version: 5.0.0\r\n2. For the code and error\uff1a\r\n     ```python\r\n     from datasets import load_dataset, load_metric\r\n     dataset = load_dataset(\"glue\", \"cola\")\r\n    ```\r\n    ```python\r\n    Traceback (most recent call last):\r\n    ....\r\n    ....\r\n    File \"my_file.py\", line 2, in <module>\r\n    dataset = load_dataset(\"glue\", \"cola\")\r\n    File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 830, in load_dataset\r\n    **config_kwargs,\r\n     File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 710, in load_dataset_builder\r\n    **config_kwargs,\r\n    TypeError: 'NoneType' object is not callable\r\n    ```\r\n   Thank you!","embeddings":[-0.2333926857,-0.2121910155,0.0151841119,0.3540363908,0.431312561,0.0565467253,0.4275258183,0.1668837517,0.1619586647,0.2543876767,-0.1491201818,0.4838864207,-0.032627441,0.2008449435,0.1351143867,-0.1875769347,-0.1780486703,0.1836340725,-0.2075739056,0.0554907173,-0.530339241,-0.0694576576,-0.131100297,0.1142207906,-0.165304631,-0.2441832423,-0.1580201536,0.0987920165,-0.1795856655,-0.3501777351,0.4064702392,-0.1164285839,0.2196373641,0.562065959,-0.0001015193,-0.0459203348,0.4622377753,0.0741885006,-0.1826767772,-0.3177576065,-0.2952965498,-0.3818572462,0.1700716317,-0.3912402391,-0.0481698103,-0.0371986367,-0.0039750608,-0.2519207001,0.2980602384,0.3795799315,0.328802675,0.4538740516,0.0911629125,-0.2488764822,0.1904570758,0.0910028517,-0.0621268973,0.3023785055,0.0538949445,-0.0567670763,0.2113882601,0.1328090131,-0.3628790081,-0.0556369275,0.2719312906,0.0631388128,0.0336346067,-0.3873294294,0.0748671964,0.3000453711,0.4482415617,-0.3896304667,-0.2469754517,0.0235931426,0.1223488674,-0.1994509548,0.2123644501,0.0509505086,0.0008063291,0.0616286434,0.0196442436,0.1935778111,-0.1930177808,0.2497536093,-0.1554912627,0.1598448157,-0.1672741175,0.1182282194,0.0708542541,-0.2033922374,0.049795296,-0.2369977683,0.007765641,0.1136214361,-0.2622630298,-0.0578037463,0.1595869213,0.1328142434,0.0924718305,-0.2031244338,0.0840387568,-0.0385783762,0.0005321783,0.3734622598,0.2657110691,0.2719011307,0.2630656064,0.1053493395,0.3113079071,0.0968508199,-0.1035405621,0.0171599817,-0.1741403788,-0.1962881684,0.2919623256,0.27147156,0.5614536405,-0.0071394164,-0.4003361762,-0.112911351,-0.1762718856,0.0414780118,0.1914937347,0.3488394022,-0.1659963131,0.1930406094,0.2003636211,0.1248331815,-0.2139800191,-0.2602629662,-0.2342291027,0.277436316,-0.2562621832,-0.1992109269,0.1157746315,-0.0126312999,-0.0741725713,0.0112992479,-0.1771455258,0.1546337306,0.1403729916,-0.3297455609,-0.1945308298,0.1805401146,0.0513517223,0.0468542874,0.3396641314,-0.3767827153,-0.1449093074,0.0857277736,-0.2972788513,-0.1406729817,-0.1555437893,0.3137725294,-0.203810975,-0.0596022606,-0.2211033255,-0.0271769408,0.1290350109,-0.1592079401,0.0224947892,-0.1568120122,-0.0343936272,-0.4068643153,0.0919899419,0.6147862673,-0.4071787894,-0.1555482447,-0.3654867709,-0.1499285698,0.3206854463,-0.2192046493,0.0420476161,0.205342114,-0.1884394288,-0.0670418441,0.5587269068,-0.4109594524,-0.2658071816,0.071330905,-0.1287924349,-0.0868593082,0.0406506956,-0.0299981683,0.0722305849,0.1137080416,0.4100970328,0.275131911,0.0011142003,0.0079012448,-0.1864448488,-0.2357116938,0.1991765499,0.0649753883,0.1384467632,0.1023558527,0.1406214237,-0.1971790642,0.0246631335,-0.0487639643,-0.0327245519,-0.057789661,0.5558087826,-0.0468545221,-0.044843737,-0.5518350005,-0.4227520227,0.1897544414,-0.0799524039,0.3541664779,-0.1335036159,-0.1872806996,-0.3264653981,0.0583028831,-0.1389628798,-0.0200660415,0.3176531494,0.1545748264,-0.1688733399,0.0288684927,-0.0564694032,0.2119377255,-0.0756313056,0.0736123845,-0.14293015,0.3080175817,-0.057636708,-0.3339879513,0.0377683677,0.2538016438,0.5117610097,-0.0004488377,-0.0633197874,0.3244804144,0.0866748691,-0.0496543907,-0.1650345176,-0.0999800116,0.1509415507,-0.2289367616,0.0971660241,0.0872650221,0.3168590069,-0.0662048236,0.0954951942,0.4775231183,0.0889250115,0.2869647741,-0.0390479192,0.2417568564,-0.0411586203,-0.0287830383,-0.0849919841,-0.1743532121,0.0761678517,0.1894836426,0.2709217072,-0.0320920832,-0.0377041772,0.0921959504,0.5580118299,0.0443215631,0.3538882732,-0.0804426596,-0.3818507195,0.0884140804,0.1868627965,0.2703929543,0.4774660766,0.3163056076,-0.1177275926,-0.0135746626,-0.0687375143,0.068971917,0.0899053961,0.0000686368,0.2182746232,0.0326346494,0.2071536779,0.0608280376,-0.2113714963,-0.0529055037,-0.1733764559,0.2231471837,-0.4077748656,0.1184415296,-0.3216187358,-0.1545066088,-0.0203860439,0.0009128465,-0.1665667146,-0.3700627089,-0.2923040688,0.0090758018,0.0586140528,0.1355309337,0.0729432553,0.0848084092,0.0303801391,0.0038109233,-0.1432915479,-0.1506637037,-0.197236672,0.1309271753,0.1724003851,0.2035219669,0.4579099715,-0.1723938286,0.1496831626,-0.0668234155,-0.2592738867,0.0691069737,-0.0358921215,0.5192118287,0.4751577079,0.1759489179,0.0373363346,-0.0723712072,0.3917131424,-0.337565273,-0.0363603607,0.2813171744,-0.2262491435,0.1912381798,-0.1627744734,-0.2709549367,-0.5850613117,-0.4314647317,0.0900498033,0.1581933945,0.1574169397,0.2290733755,0.1376304924,0.4989827573,0.0554460995,0.173627466,-0.1574210674,-0.0517017953,0.2869262695,-0.128268525,-0.3188622594,0.0627037063,-0.1610585004,0.2123719454,0.0095064696,-0.2091803849,-0.5421624184,-0.1937738359,0.248898387,-0.0088543072,0.1406644732,0.2820861042,0.1652371287,-0.171896413,-0.2008978277,-0.1451961696,-0.0958748311,0.2647386193,0.003278581,0.1268489063,0.3415829241,-0.1613709927,0.1591750383,0.0212520342,-0.1993087977,0.414999783,-0.2640288472,0.3553538322,-0.1553532481,-0.501110673,-0.1315999925,-0.034155868,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":20,"text":"TypeError: 'NoneType' object is not callable \n ## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n \n For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.","embeddings":[-0.2608940005,-0.2007066011,0.0660000592,0.400195688,0.4645994604,0.0115246382,0.3240968585,0.0941318348,0.1896000355,0.3001818657,-0.2849094868,0.4667589068,-0.0366437919,0.1668581367,0.0777649358,-0.1253727525,-0.2606472373,0.2636680901,-0.1777470112,0.0613667145,-0.5233634114,0.0231986102,-0.2373635471,0.0858903304,-0.2768001556,-0.1533993483,-0.0281501114,0.079713583,-0.2462176234,-0.2805505991,0.4611611068,-0.0159628261,0.1640476584,0.5104134083,-0.0001023479,-0.0184572823,0.5836995244,0.0530032516,-0.0761517882,-0.4211900532,-0.2255626768,-0.3977999389,0.2870038748,-0.3548148572,-0.1275422126,0.0055780676,0.0405133776,-0.1819047779,0.262139827,0.3204336464,0.3242153823,0.5296897888,0.0114422301,-0.3599714339,0.1965120882,0.0808303058,-0.0636900738,0.3710546196,0.0265845973,-0.0474929884,0.2439645529,0.0870305225,-0.3353538513,0.0527608916,0.2847363651,0.0382548533,0.0095302081,-0.270170182,0.0964757726,0.3694062233,0.3855828047,-0.382620275,-0.1514433473,0.1750092059,0.2239649594,-0.2032708824,0.211649254,0.0468775555,0.0163049828,0.0907445624,-0.0130718844,0.1712200791,-0.2445577234,0.2749375999,-0.2020063251,0.1065498963,-0.1935495585,0.1447966695,0.0849972516,-0.1188601851,0.0763338953,-0.1345976889,-0.0619969517,0.0890901908,-0.2083802223,0.0349296518,0.1152055487,0.1535798907,0.1636114269,-0.2843680978,0.1018852517,-0.0238335412,0.0397984199,0.4501174688,0.2414609641,0.1897560805,0.2724782526,0.088692598,0.2660125494,0.066787757,-0.2417135537,-0.0120890485,-0.1543584764,-0.0610723533,0.2427943051,0.1411847323,0.5124645233,0.0189152192,-0.4501633942,-0.1742377281,-0.172133401,0.0913005546,0.2716411948,0.4277633727,-0.1526402384,0.0820783153,0.1332417876,0.1743281931,-0.1884514242,-0.2918041646,-0.3183253706,0.3539678454,-0.2528480887,-0.2349549532,0.1911386847,0.0579657778,-0.0449788868,0.0134671535,-0.0958473086,0.1027007028,0.0886686146,-0.2908604145,-0.2310647517,0.3132710755,0.2443055063,-0.0628327206,0.2800607383,-0.4668283463,-0.091596745,0.2292028666,-0.2898909748,-0.1671735048,-0.0732437372,0.2667024732,-0.3390546739,-0.0697349459,-0.2441662848,0.0466724299,0.0489577651,-0.1566343755,-0.1024087444,-0.0962615982,-0.112656869,-0.4234251976,0.0446183719,0.5165780783,-0.3751866817,-0.1992407143,-0.3078093231,-0.1091819853,0.352284044,-0.147301212,0.0145670017,0.0629001632,-0.1706566364,-0.0006857364,0.6008648276,-0.3847208321,-0.2815137208,0.0563009419,-0.0371869579,-0.1165539101,-0.0517222472,-0.0470438264,0.0718874335,0.135642916,0.4128215313,0.2446535677,0.0493588299,-0.0324675739,-0.2109587342,-0.2381093949,0.1280453652,0.151658386,0.1272704005,0.1630408317,0.0712505579,-0.2506299913,-0.0557164401,-0.0061886362,-0.1022448465,-0.0301638599,0.5365030169,-0.1599618644,-0.0399641804,-0.532881856,-0.3950176537,0.2330813259,-0.0779283196,0.2541694939,-0.105489023,-0.1700357646,-0.352982074,0.1019510254,-0.0170240067,0.0607618615,0.2853280008,0.0909614265,-0.2011494935,0.1454881579,-0.0809026584,0.1637727916,-0.0873494446,0.0512318462,0.0335052349,0.291667223,0.0747782141,-0.3356175423,0.0352951288,0.1897852123,0.4574481249,-0.0104598496,-0.0672703311,0.2640901804,0.070892185,-0.0788056329,-0.2070860565,-0.05381817,0.0879925042,-0.313064307,-0.0303661674,0.0379851758,0.3187099099,-0.0943738967,0.0625385866,0.6038931608,0.0668431222,0.1744620055,-0.0730603263,0.2714183331,0.0653238744,-0.0385558642,-0.149077177,-0.1286280155,0.0459849499,0.1176183671,0.2486371994,0.0061038891,-0.0880215764,-0.0231885035,0.5769147873,0.0913832113,0.3607913256,-0.1375178844,-0.4204133153,0.1137951836,0.0370061696,0.2525543571,0.5032669902,0.332315892,-0.1068636402,0.023629915,-0.0571720079,0.0404726267,0.1032921076,0.0494471341,0.2328974009,-0.0000240203,0.3057212234,-0.0209420472,-0.3462260365,0.1613915414,-0.053847719,0.1842062026,-0.3760870993,0.1845387518,-0.2747030258,-0.076757893,0.1425513774,-0.1027247906,-0.125005424,-0.3286290169,-0.2664072216,0.1081375405,0.1210905612,0.1938206255,0.1422476321,0.0763199255,0.0087453714,-0.0255289134,-0.1826611906,-0.1326549202,-0.1483921856,0.0880739987,0.1653355807,0.181941092,0.4119828641,-0.1496375948,0.2284882814,-0.0511268564,-0.245598793,0.100177817,-0.0573291741,0.5299291015,0.4086084068,0.0602920428,-0.1410644501,0.1004512459,0.4188910425,-0.4658019245,0.0264790654,0.363583684,-0.3070645034,0.0837264657,-0.2518776655,-0.1060993373,-0.5478299856,-0.4280380607,0.0683310926,0.0958201066,0.0449768677,0.172417596,0.1191176772,0.444096446,0.112392433,0.0157600436,-0.2330423892,-0.0854077637,0.3311132789,-0.1693256646,-0.3608907759,0.2042488754,-0.1323478222,0.2548744977,0.0285864808,-0.1609783471,-0.5243912339,-0.1556174606,0.2053533643,-0.005188724,-0.0110916914,0.2912253737,0.0402853899,-0.1367866844,-0.2726752162,-0.1413018703,-0.0488153882,0.2352696806,-0.0683882236,0.0471838973,0.2825388908,-0.1223018318,0.1436379254,-0.1094394699,-0.1853011698,0.4059675932,-0.3138792515,0.3742715716,-0.1514657587,-0.4665456712,-0.2108906806,0.0278709959,0.04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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"One naive question: do you have internet access from the machine where you execute the code?","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":16,"text":"TypeError: 'NoneType' object is not callable \n ## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n \n One naive question: do you have internet access from the machine where you execute the code?","embeddings":[-0.2466751933,-0.2166070193,-0.0038837458,0.3586860299,0.4009475708,0.035536956,0.302520901,0.1521895081,0.1650357544,0.3566101193,-0.1779196858,0.4452134073,-0.0382566825,0.1990447938,0.0816193596,-0.0899617374,-0.2033715993,0.2447062582,-0.1842225194,0.010521763,-0.472876668,-0.0185202286,-0.1821441352,0.0199327096,-0.2480738759,-0.2002862543,-0.1163984835,0.0645010024,-0.2148991227,-0.2530295849,0.3632104993,0.0450640544,0.112367928,0.5467213392,-0.0001019842,-0.0862105861,0.4975403249,0.0762401372,-0.1133384928,-0.2227149755,-0.2704135478,-0.3431518078,0.1823247373,-0.4260804653,-0.0916544795,0.028890904,0.0123812426,-0.2481914461,0.3053150177,0.3072139323,0.339708358,0.4426216781,-0.0190175083,-0.3322893977,0.2162288725,0.0421040244,-0.0323108882,0.362215519,0.0633602291,-0.0537314415,0.1767412126,0.1824111044,-0.2867747545,-0.0029550304,0.2504888773,0.0876018703,-0.0245356578,-0.2595610619,0.1022056341,0.2668823004,0.2856886089,-0.4520843923,-0.1245521009,0.1114801839,0.1043578833,-0.2367105186,0.1430763751,-0.0036112065,-0.0197007451,0.1060361415,-0.0351593755,0.133691445,-0.231664747,0.2396647781,-0.209117204,0.1492936164,-0.1894275993,0.0967105776,0.0752299801,-0.0852814317,0.0532211438,-0.1713415682,0.021075597,0.0105691114,-0.1507948637,-0.0472187474,0.1830180287,0.1402942985,0.1534052491,-0.254579097,0.1078871489,-0.0058753905,0.1186242029,0.4159373343,0.154383108,0.2415641695,0.2150634825,0.0880800635,0.2187133878,0.1302518398,-0.2568061352,-0.0115337186,-0.0923354477,-0.0363530554,0.3084871173,0.1605716944,0.4978731573,0.0148101076,-0.43729496,-0.0482422002,-0.2314789593,0.1063998938,0.2851623595,0.3828612864,-0.2197893709,0.0280763004,0.1954128742,0.1463474184,-0.167907998,-0.2896850407,-0.3228367269,0.3451814651,-0.3253240883,-0.1996293813,0.1255358905,0.0956025869,-0.078586556,0.0480372384,-0.035712216,0.0553010367,0.0763480887,-0.2473653257,-0.1706801951,0.1856051236,0.2096802443,-0.1042110175,0.3234429955,-0.4836687446,-0.1027234495,0.1058211848,-0.2070778757,-0.0896052122,-0.0359199308,0.3180175722,-0.3513986468,-0.1008676514,-0.151536569,0.1008075848,0.0612458289,-0.1549359858,0.0268286895,-0.0838796198,-0.0460206904,-0.443877846,0.0987593383,0.5773766041,-0.3923660815,-0.1583535075,-0.3149743676,-0.0410091579,0.3569568694,-0.2316169292,0.1328972578,0.1612990648,-0.1505951583,0.0307376627,0.5494579673,-0.3594247699,-0.298289597,-0.0194503367,-0.1195071116,-0.0845604688,-0.0080690281,-0.1045471951,0.0758646503,0.1502961367,0.4117963314,0.2450776398,-0.0244919565,-0.0403468348,-0.2356397361,-0.2162138522,0.0301526804,0.107242316,0.1855029613,0.160957098,0.1086669937,-0.2254877985,0.0150828492,-0.0165604558,-0.0678272471,-0.0664632171,0.5562682748,-0.1198019162,-0.1030108109,-0.548601687,-0.3364979625,0.2137777209,-0.0644096881,0.2583818138,-0.1622745097,-0.1771243662,-0.4169964492,0.1173746884,-0.0591691211,0.0415956117,0.3479426801,0.1584892571,-0.1316381693,0.1201645657,-0.038923569,0.1554773301,-0.0975586995,0.0615419187,0.002867274,0.3624258637,0.0288160499,-0.3402500749,0.0125437025,0.2390864491,0.492028594,0.0251288041,-0.0366603509,0.3425714076,0.0153975571,-0.0589240976,-0.0713611618,0.0129818767,0.0355378203,-0.3102554679,0.0903075784,0.0740944669,0.274332881,-0.0501489863,0.0455586836,0.5526733398,0.0639084801,0.1631180197,-0.0774783418,0.2747522891,-0.1043333039,-0.0652063265,-0.1591193974,-0.1482455432,0.1165027544,0.0529977977,0.1941261292,-0.0445721783,-0.102411747,0.0711867437,0.6148303747,0.07447882,0.410061866,-0.1350679696,-0.3626484573,0.1363148987,0.0632819161,0.2627360523,0.4373947978,0.3606951535,-0.0552522913,0.0725060552,-0.0956002772,0.0463584624,0.0818468109,0.0780013651,0.1710066646,-0.0235593766,0.2648818195,0.0000485086,-0.302197367,0.0393880606,-0.2115725577,0.1999685317,-0.3442276716,0.2703234255,-0.2761572301,-0.1083705425,0.0665704906,-0.1341651529,-0.1119984984,-0.3972260058,-0.189691484,0.0495734848,0.0846979097,0.1596637964,0.1331482828,0.1780963689,0.0435850658,-0.0506396033,-0.2169364691,-0.0929441899,-0.1491832733,0.124519974,0.1127889976,0.2195498496,0.4563970566,-0.2264528573,0.2653894424,0.0405263342,-0.2521411479,0.1091040745,-0.1113009229,0.5382031798,0.4217015207,0.0849712491,-0.1375489533,0.0479233935,0.4025655687,-0.3987693489,0.0345607921,0.2760668993,-0.1733168364,0.0993671566,-0.2214981169,-0.1091302112,-0.4937289059,-0.430516243,0.0535355024,0.0940166637,0.0907848328,0.1661356688,0.1647697985,0.5161489248,0.0903599858,0.0853421465,-0.2309448719,-0.0928328782,0.2652721107,-0.1356292665,-0.3170882165,0.1164372712,-0.1327296942,0.1882433891,0.0346632898,-0.2173748314,-0.5696399808,-0.1761069298,0.2179042101,-0.0343912281,0.0933210403,0.26999861,0.0949118435,-0.1569222659,-0.2845886648,-0.1625180691,-0.09149611,0.1541405916,-0.0318299644,-0.0249708015,0.2786458135,-0.1161085069,0.1320557296,-0.0665605962,-0.2075443119,0.3825691044,-0.3480911255,0.3984667063,-0.0996405408,-0.5200996399,-0.1736067683,0.0353287831,-0.0609325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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"> For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.\r\n\r\nBut I can download other task dataset such as `dataset = load_dataset('squad')`. I don't know what went wrong.  Thank you so much!","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":43,"text":"TypeError: 'NoneType' object is not callable \n ## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n \n > For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.\r\n\r\nBut I can download other task dataset such as `dataset = load_dataset('squad')`. I don't know what went wrong.  Thank you so much!","embeddings":[-0.2479815185,-0.3217719793,0.0763500705,0.4701036215,0.4953816831,0.0185086466,0.3540442288,0.0614947863,0.2402586043,0.3128617108,-0.276409179,0.4540319443,-0.0626192093,0.1033334509,0.1556583196,-0.1557852626,-0.2255954146,0.1679677218,-0.0779853985,0.1020552889,-0.4996086359,-0.0293463878,-0.2632772624,0.1951634139,-0.1559653133,-0.1863708049,-0.0737683624,0.0807150304,-0.1478742063,-0.2537325919,0.5681656003,-0.1083382666,0.2122284323,0.5568339229,-0.0001079977,0.0223296881,0.5475671887,0.0222912077,-0.181262657,-0.5533769727,-0.1755931079,-0.4152663052,0.3201848865,-0.3743572831,-0.1145967916,0.07909403,0.1459887475,-0.1117727757,0.3590354919,0.4540637732,0.2855804861,0.7018626928,-0.0197485741,-0.342117399,0.1333463937,0.0541447364,-0.1285513192,0.2871590555,0.1371662915,-0.0082335789,0.4334596992,-0.0373312868,-0.3705081344,0.090445593,0.3338054717,0.0173116531,0.0456819162,-0.4084408581,0.0989959762,0.3526202738,0.429132849,-0.2789865732,-0.1445523649,0.1062167063,0.2393290848,-0.061995618,0.1765795946,0.0757426322,-0.0298103616,0.0413528346,0.0463871136,0.1718294621,-0.2779862285,0.2125990689,-0.1284300834,-0.0353670754,-0.1932722479,0.2176218927,0.038036257,-0.0971409827,0.0310842935,-0.1765830368,0.0018496352,0.1451958716,-0.3074558675,-0.0370389931,0.1003599092,0.0206406731,0.2188021392,-0.2397266477,0.2141465396,-0.0814069584,0.0014896236,0.3541437387,0.346270442,0.2386882752,0.2158100009,0.1408458501,0.1517539918,0.022729395,-0.1789033264,0.0621397234,-0.2835336924,-0.0460709594,0.2485666424,0.2519290149,0.62191993,0.0226177946,-0.5257906914,-0.1946161389,-0.0697027519,0.0744651854,0.1489258707,0.4044239819,-0.2522181571,0.0734108984,0.0253312159,0.2898434997,-0.1949325502,-0.3826381862,-0.250428021,0.1526836008,-0.268599242,-0.270571053,0.24026227,0.0925443023,0.0312479846,0.0430606492,-0.2270833105,0.0859438777,0.1672883034,-0.3710010648,-0.3183163404,0.203129068,0.2981905341,0.013275166,0.2797108889,-0.3238164485,-0.0456261188,0.2975198925,-0.3877721429,-0.1205919012,-0.1263929754,0.1935602129,-0.3629306555,-0.1565003395,-0.5364289284,0.0805210099,0.0401180312,-0.0892851651,-0.0573687814,-0.1425406635,-0.0697560161,-0.3583665192,-0.0551820286,0.4761920869,-0.2147590965,-0.2288669497,-0.3011833727,-0.2443191111,0.3596111238,-0.1455561668,-0.0476030186,0.2456554323,-0.1385620087,0.0195395816,0.642037034,-0.5051142573,-0.2810824811,0.1532612592,-0.1181739345,-0.076440759,-0.0949228108,-0.0446148701,0.2863956094,0.0758701265,0.3716195226,0.3349784911,0.0008509188,-0.0305648036,-0.131107524,-0.2105619609,0.2909449637,0.1949024647,0.2361101955,0.2211598307,0.0545335151,-0.1998473704,-0.0358049236,0.0527693219,-0.0813176557,-0.0201197267,0.4355514646,-0.0436968356,-0.0257663354,-0.5290393233,-0.5686805844,0.1899500042,-0.0841612145,0.1826199889,-0.115729481,-0.0673960596,-0.3040428758,0.042624291,-0.078021042,-0.0217457879,0.182378903,0.0794131309,-0.2668655217,0.0558810793,-0.0770036429,0.1909017116,-0.1526100785,0.095781371,0.1212609336,0.2611952722,0.1016982198,-0.3987404406,-0.0017724648,0.1362133324,0.4781362414,0.0183626059,-0.0826574564,0.1729604006,0.0458101444,-0.1020453051,-0.2171081603,-0.1762046367,0.0575558543,-0.3366027176,-0.0447619446,0.1347156912,0.3460848331,-0.160481751,0.0836437568,0.4571962357,-0.0167928878,0.3421861231,-0.1437793821,0.2406296581,0.0525164865,-0.0606726483,-0.1331075728,0.1804715544,0.1290700138,0.0913747177,0.2827326059,-0.0355854295,-0.2706822753,0.0046682684,0.6162089109,0.0299597997,0.3194147646,-0.0831516907,-0.33788234,0.1462022662,0.0000325413,0.0418264307,0.5353927612,0.2632282078,-0.1103599295,-0.0164734125,0.0007065669,0.0515690558,0.0333776362,0.069090277,0.2856582701,0.0461304933,0.2759691477,0.0386635847,-0.2005325556,0.1444482505,-0.0084361024,0.1951348335,-0.3969622254,0.1878786534,-0.1423569769,-0.1165724248,-0.0304603409,0.1370230019,-0.1042248011,-0.1990451217,-0.3134959638,0.1589778066,0.2137130797,0.1265260726,0.1616361737,0.0088405497,-0.0113126803,-0.0408857763,-0.1634947658,-0.1458583921,-0.1861838251,0.0462163053,0.1946974099,0.0735260546,0.3495370448,-0.1900552809,0.1245678738,-0.1000485346,-0.1240719408,0.0189164113,-0.0774424672,0.5409654975,0.2683835626,0.2594893575,-0.162481755,0.1194115579,0.3791869879,-0.4782688916,-0.0512078144,0.3330359757,-0.334943682,0.0967640132,-0.2121215761,-0.3186377287,-0.5942174196,-0.3938197494,0.2054920793,-0.0129524982,0.069686912,0.3293658495,0.1026407331,0.4716491699,0.2177550197,0.0916105509,-0.1844632626,-0.0342024155,0.2794252634,-0.1346675754,-0.3884647787,0.1575419456,-0.0971030593,0.2852425575,0.0799094364,-0.1245648041,-0.2630615234,-0.0669115633,0.197574988,-0.1246708706,-0.1207072735,0.3231537342,-0.0845761523,-0.0469946526,-0.2115352452,-0.1213957146,-0.0482246727,0.3319038451,-0.0071725766,0.0945167914,0.3415936828,-0.1624031961,0.3309932351,-0.0868292972,-0.0393967107,0.4093084633,-0.3032317758,0.2776997089,-0.0965931192,-0.4811325371,0.0073162122,0.0563486181,0.06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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Hi @severo, thanks for reporting.\r\n\r\nJust note that currently not all canonical datasets support streaming mode: this is one case!\r\n\r\nAll datasets that use `pathlib` joins (using `\/`) instead of `os.path.join` (as in this dataset) do not support streaming mode yet.","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":41,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode \n ## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Hi @severo, thanks for reporting.\r\n\r\nJust note that currently not all canonical datasets support streaming mode: this is one case!\r\n\r\nAll datasets that use `pathlib` joins (using `\/`) instead of `os.path.join` (as in this dataset) do not support streaming mode 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"OK. Do you think it's possible to detect this, and raise an exception (maybe `NotImplementedError`, or a specific `StreamingError`)?","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":19,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode \n ## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n OK. Do you think it's possible to detect this, and raise an exception (maybe `NotImplementedError`, or a specific `StreamingError`)?","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.13208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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"We should definitely support datasets using `pathlib` in streaming mode...\r\n\r\nFor non-supported datasets in streaming mode, we have already a request of raising an error\/warning: see #2654.","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":27,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode \n ## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n We should definitely support datasets using `pathlib` in streaming mode...\r\n\r\nFor non-supported datasets in streaming mode, we have already a request of raising an error\/warning: see #2654.","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332,0.2081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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Hi @severo, please note that \"counter\" dataset will be streamable (at least until it arrives at the missing file, error already in normal mode) once these PRs are merged:\r\n- #2874\r\n- #2876\r\n- #2880\r\n\r\nI have tested it. \ud83d\ude09 ","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":40,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode \n ## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Hi @severo, please note that \"counter\" dataset will be streamable (at least until it arrives at the missing file, error already in normal mode) once these PRs are merged:\r\n- #2874\r\n- #2876\r\n- #2880\r\n\r\nI have tested it. \ud83d\ude09 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Now (on master), we get:\r\n\r\n```\r\nimport datasets as ds\r\nds.load_dataset('counter', split=\"train\", streaming=False)\r\n```\r\n\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\nThe error is now the same with or without streaming. I close the issue, thanks @albertvillanova and @lhoestq!\r\n","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":191,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode \n ## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Now (on master), we get:\r\n\r\n```\r\nimport datasets as ds\r\nds.load_dataset('counter', split=\"train\", streaming=False)\r\n```\r\n\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\nThe error is now the same with or without streaming. I close the issue, thanks @albertvillanova and @lhoestq!\r\n","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Note that we might want to open an issue to fix the \"counter\" dataset by itself, but I let it up to you.","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":23,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode \n ## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Note that we might want to open an issue to fix the \"counter\" dataset by itself, but I let it up to you.","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332,0.208188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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"On master, I get:\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> iterable_dataset = ds.load_dataset('counter', split=\"train\", streaming=True)\r\n>>> rows = list(iterable_dataset.take(100))\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/iterable_dataset.py\", line 341, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/iterable_dataset.py\", line 338, in _iter\r\n    yield from ex_iterable\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/iterable_dataset.py\", line 273, in __iter__\r\n    yield from islice(self.ex_iterable, self.n)\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/iterable_dataset.py\", line 78, in __iter__\r\n    for key, example in self.generate_examples_fn(**self.kwargs):\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/b9e4378dbd3f5ce235d2302e48168c00196e67bbcd13cc7e1f6e69ef82c0cf2a\/counter.py\", line 153, in _generate_examples\r\n    files = sorted(base_path.glob(r\"[0-9][0-9][0-9][0-9].xml\"))\r\nTypeError: xpathglob() missing 1 required positional argument: 'pattern'\r\n```","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":90,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode \n ## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n On master, I get:\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> iterable_dataset = ds.load_dataset('counter', split=\"train\", streaming=True)\r\n>>> rows = list(iterable_dataset.take(100))\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/iterable_dataset.py\", line 341, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/iterable_dataset.py\", line 338, in _iter\r\n    yield from ex_iterable\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/iterable_dataset.py\", line 273, in __iter__\r\n    yield from islice(self.ex_iterable, self.n)\r\n  File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/iterable_dataset.py\", line 78, in __iter__\r\n    for key, example in self.generate_examples_fn(**self.kwargs):\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/b9e4378dbd3f5ce235d2302e48168c00196e67bbcd13cc7e1f6e69ef82c0cf2a\/counter.py\", line 153, in _generate_examples\r\n    files = sorted(base_path.glob(r\"[0-9][0-9][0-9][0-9].xml\"))\r\nTypeError: xpathglob() missing 1 required positional argument: 'pattern'\r\n```","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Associated to the above exception, if I create a test and run it with pytest, I get an awful traceback.\r\n\r\n- create a file `test_counter.py`\r\n\r\n```python\r\nimport pytest\r\nfrom datasets import load_dataset, IterableDataset\r\nfrom typing import Any, cast\r\n\r\n\r\ndef test_counter() -> Any:\r\n    iterable_dataset = cast(IterableDataset, load_dataset(\"counter\", split=\"train\", streaming=True))\r\n    with pytest.raises(TypeError):\r\n        list(iterable_dataset.take(100))\r\n```\r\n\r\n- run the test with pytest\r\n\r\n```bash\r\n$ python -m pytest -x test_counter.py\r\n============================================================================================================================= test session starts ==============================================================================================================================\r\nplatform linux -- Python 3.9.6, pytest-6.2.5, py-1.10.0, pluggy-1.0.0\r\nrootdir: \/home\/slesage\/hf\/datasets-preview-backend, configfile: pyproject.toml\r\nplugins: anyio-3.3.2, cov-2.12.1\r\ncollected 1 item\r\n\r\ntests\/test_counter.py .                                                                                                                                                                                                                                                  [100%]Traceback (most recent call last):\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/runpy.py\", line 197, in _run_module_as_main\r\n    return _run_code(code, main_globals, None,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/runpy.py\", line 87, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pytest\/__main__.py\", line 5, in <module>\r\n    raise SystemExit(pytest.console_main())\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/config\/__init__.py\", line 185, in console_main\r\n    code = main()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/config\/__init__.py\", line 162, in main\r\n    ret: Union[ExitCode, int] = config.hook.pytest_cmdline_main(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n    return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n    return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_callers.py\", line 60, in _multicall\r\n    return outcome.get_result()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n    raise ex[1].with_traceback(ex[2])\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n    res = hook_impl.function(*args)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/main.py\", line 316, in pytest_cmdline_main\r\n    return wrap_session(config, _main)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/main.py\", line 304, in wrap_session\r\n    config.hook.pytest_sessionfinish(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n    return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n    return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_callers.py\", line 55, in _multicall\r\n    gen.send(outcome)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/terminal.py\", line 803, in pytest_sessionfinish\r\n    outcome.get_result()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n    raise ex[1].with_traceback(ex[2])\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n    res = hook_impl.function(*args)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/cacheprovider.py\", line 428, in pytest_sessionfinish\r\n    config.cache.set(\"cache\/nodeids\", sorted(self.cached_nodeids))\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/cacheprovider.py\", line 188, in set\r\n    f = path.open(\"w\")\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/streaming_download_manager.py\", line 199, in xpathopen\r\n    return xopen(_as_posix(path), *args, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/streaming_download_manager.py\", line 117, in _as_posix\r\n    path_as_posix = path.as_posix()\r\nAttributeError: 'str' object has no attribute 'as_posix'\r\n```\r\n","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":301,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode \n ## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Associated to the above exception, if I create a test and run it with pytest, I get an awful traceback.\r\n\r\n- create a file `test_counter.py`\r\n\r\n```python\r\nimport pytest\r\nfrom datasets import load_dataset, IterableDataset\r\nfrom typing import Any, cast\r\n\r\n\r\ndef test_counter() -> Any:\r\n    iterable_dataset = cast(IterableDataset, load_dataset(\"counter\", split=\"train\", streaming=True))\r\n    with pytest.raises(TypeError):\r\n        list(iterable_dataset.take(100))\r\n```\r\n\r\n- run the test with pytest\r\n\r\n```bash\r\n$ python -m pytest -x test_counter.py\r\n============================================================================================================================= test session starts ==============================================================================================================================\r\nplatform linux -- Python 3.9.6, pytest-6.2.5, py-1.10.0, pluggy-1.0.0\r\nrootdir: \/home\/slesage\/hf\/datasets-preview-backend, configfile: pyproject.toml\r\nplugins: anyio-3.3.2, cov-2.12.1\r\ncollected 1 item\r\n\r\ntests\/test_counter.py .                                                                                                                                                                                                                                                  [100%]Traceback (most recent call last):\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/runpy.py\", line 197, in _run_module_as_main\r\n    return _run_code(code, main_globals, None,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.9.6\/lib\/python3.9\/runpy.py\", line 87, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pytest\/__main__.py\", line 5, in <module>\r\n    raise SystemExit(pytest.console_main())\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/config\/__init__.py\", line 185, in console_main\r\n    code = main()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/config\/__init__.py\", line 162, in main\r\n    ret: Union[ExitCode, int] = config.hook.pytest_cmdline_main(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n    return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n    return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_callers.py\", line 60, in _multicall\r\n    return outcome.get_result()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n    raise ex[1].with_traceback(ex[2])\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n    res = hook_impl.function(*args)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/main.py\", line 316, in pytest_cmdline_main\r\n    return wrap_session(config, _main)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/main.py\", line 304, in wrap_session\r\n    config.hook.pytest_sessionfinish(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n    return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n    return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_callers.py\", line 55, in _multicall\r\n    gen.send(outcome)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/terminal.py\", line 803, in pytest_sessionfinish\r\n    outcome.get_result()\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n    raise ex[1].with_traceback(ex[2])\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n    res = hook_impl.function(*args)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/cacheprovider.py\", line 428, in pytest_sessionfinish\r\n    config.cache.set(\"cache\/nodeids\", sorted(self.cached_nodeids))\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/_pytest\/cacheprovider.py\", line 188, in set\r\n    f = path.open(\"w\")\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/streaming_download_manager.py\", line 199, in xpathopen\r\n    return xopen(_as_posix(path), *args, **kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.9\/site-packages\/datasets\/utils\/streaming_download_manager.py\", line 117, in _as_posix\r\n    path_as_posix = path.as_posix()\r\nAttributeError: 'str' object has no attribute 'as_posix'\r\n```\r\n","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.13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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"I opened a PR to fix these issues.\r\nAlso in your test you expect a TypeError but I don't know why. On my side it works fine without raising a TypeError","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":31,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode \n ## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n    with derived_file.open(encoding=\"utf-8\") as f:\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n    return io.open(self, mode, buffering, encoding, errors, newline,\r\n  File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n    return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n I opened a PR to fix these issues.\r\nAlso in your test you expect a TypeError but I don't know why. On my side it works fine without raising a TypeError","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2860","title":"Cannot download TOTTO dataset","comments":"Hola @mrm8488, thanks for reporting.\r\n\r\nApparently, the data source host changed their URL one week ago: https:\/\/github.com\/google-research-datasets\/ToTTo\/commit\/cebeb430ec2a97747e704d16a9354f7d9073ff8f\r\n\r\nI'm fixing it.","body":"Error: Couldn't find file at https:\/\/storage.googleapis.com\/totto\/totto_data.zip\r\n\r\n`datasets version: 1.11.0`\r\n# How to reproduce:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('totto')\r\n```\r\n\r\n\r\n","comment_length":20,"text":"Cannot download TOTTO dataset \n Error: Couldn't find file at https:\/\/storage.googleapis.com\/totto\/totto_data.zip\r\n\r\n`datasets version: 1.11.0`\r\n# How to reproduce:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('totto')\r\n```\r\n\r\n\r\n \n Hola @mrm8488, thanks for reporting.\r\n\r\nApparently, the data source host changed their URL one week ago: https:\/\/github.com\/google-research-datasets\/ToTTo\/commit\/cebeb430ec2a97747e704d16a9354f7d9073ff8f\r\n\r\nI'm fixing it.","embeddings":[-0.294410497,0.3900933862,-0.1317222118,0.0641968921,0.4353683293,0.1393284649,0.1679639369,0.5879850388,-0.0261453092,0.234341681,-0.1947766393,-0.0473091528,0.0594171956,0.318708539,0.0643599778,-0.3084107637,0.0652796775,-0.0130061815,-0.1395519972,0.0308540575,-0.0759798363,0.2782622278,0.0100256903,0.0654641986,0.0181091316,0.0936704874,-0.0290926713,-0.0979447067,-0.2686378956,-0.4347373247,0.415338546,0.0613755472,0.1697377264,0.3808855712,-0.0001073767,0.1472330391,0.3291011751,0.0086168032,-0.2500196695,-0.3531953394,-0.3288857937,-0.1141764298,-0.1965035796,-0.1480810791,-0.0830533355,0.2127362341,0.2038826346,-0.2607125342,0.0417160429,0.5573630333,0.2718278766,0.0907747969,0.3079364896,-0.3647713363,0.2657265365,0.1475394666,-0.0365158468,0.0705632791,0.0785895288,0.0108668972,0.3562817872,0.0460000634,-0.133997038,0.0743308142,-0.1553672999,-0.0840224102,-0.0924824327,-0.3424230218,0.2141403407,0.3073774278,0.5096561313,-0.2044652998,-0.3611649573,0.2267780751,-0.0598624535,-0.279548645,0.2698038518,0.1802222282,0.0900912583,0.1698183566,0.1732570976,-0.4855846167,-0.1890135705,0.0528651476,-0.4141792953,0.107604593,-0.1177637801,0.0048586847,-0.105921559,-0.1203932837,0.3016455472,-0.0547915176,-0.0375608727,0.2590126991,-0.2745456994,-0.1841468364,0.1035571247,0.0311484244,0.1920053959,0.0490685366,0.1362317801,-0.1017651632,-0.5062013865,-0.0666135922,0.2418693751,-0.1095379815,-0.000267334,0.3804118037,0.2949267328,0.2416359335,0.1484333426,0.0473140553,-0.1586337984,-0.358691752,0.0269331522,-0.113104403,0.4928361177,-0.2085420638,-0.3899927437,0.1616978347,-0.090081729,-0.1214746758,-0.0176121797,0.1517945826,-0.0244206693,0.0753581598,0.0918698311,0.1404550672,-0.008870828,-0.1377051771,-0.2661000192,0.2041446567,-0.0125199547,0.0713024735,0.2309996784,-0.3448672891,0.0952998549,-0.0258917771,-0.4624833763,0.0828349143,-0.1361531913,-0.0750162899,-0.2458771169,0.3632289171,0.2265905589,0.1045650393,0.0013822372,0.1134399772,-0.2949780524,0.2792558968,-0.3678761125,-0.0678893328,-0.1227772906,0.2941953242,-0.3227276504,-0.2466399968,-0.5071802735,-0.0339648984,-0.0525038354,-0.3056934178,-0.1234595478,-0.1745043397,-0.1805351675,-0.2515176237,0.1124921739,0.6013523936,-0.2063911855,0.020885611,-0.4196625054,-0.4201267958,0.1899637729,0.269320339,-0.0341406576,0.4477233887,-0.3025847673,0.0885180831,0.4392078221,-0.1254837364,-0.7618820667,-0.0033206183,-0.2596687675,-0.1898869723,0.1790016443,0.0790314004,0.2497510761,-0.1000057384,0.2460572869,0.1370749325,0.012917717,-0.0459471196,-0.2387001067,-0.0105643263,-0.0045891618,0.1839413047,0.1048776805,0.2050982267,0.1501675397,-0.0725682601,0.2357553095,0.1607331038,0.0994152948,0.3408914506,0.3313904405,-0.0265133232,-0.0199446026,-0.1835746318,-0.1992596537,0.1232289374,-0.2162778378,0.1084155291,-0.2086808383,-0.1304647177,-0.4467419088,0.0594626628,-0.0653845966,-0.093042925,0.1499165893,-0.0002756922,0.310736388,0.3321071863,-0.02035648,0.320682168,0.2677029073,0.1030120254,-0.1375443637,0.5665908456,-0.1339783072,0.0568048991,0.4536594152,-0.2412828058,0.236403808,-0.0816816241,-0.0623007454,0.1545941979,0.0604691543,0.3422275782,0.4771338403,0.2396415472,0.1721659005,-0.4354346693,0.2116640955,0.343268007,0.0783024356,0.1411449164,-0.1909071058,0.0213618409,0.1539851874,0.0350163281,-0.0176072661,0.2682003379,0.544712007,0.0763235763,0.0633015558,-0.0642063022,0.2679164112,0.5545850396,0.2224782109,-0.0677626878,-0.2419174314,0.1457102746,0.538705945,-0.0372180901,0.0527861379,0.2355953008,-0.1476681978,-0.0631909966,-0.1953097135,0.2097867578,0.1718096882,0.0596293472,0.1565221548,0.0106348377,-0.0777703449,-0.2667880654,0.0551331863,0.0725212246,0.1452946216,0.1418423057,0.1219759658,0.2061336339,-0.08200939,-0.3087290227,0.0309936032,0.3056665659,-0.1311020702,-0.0795388594,-0.2592677772,0.0049411356,-0.2732787728,-0.3159630299,-0.2113306969,-0.3952906728,0.0605420209,0.2657611072,0.0656001344,-0.0102982745,-0.0560371764,0.0340000875,0.2436152548,-0.3098428249,-0.1341092587,-0.150049597,-0.1041894853,0.170614779,0.2377597988,-0.3077156246,0.3982344866,-0.6368309259,0.1199382693,-0.5480054617,-0.234621942,-0.043117255,-0.1899068207,0.0942285731,0.175614506,0.4022015929,0.3427299261,0.2505168021,0.1937542409,-0.0247938484,-0.0510064363,0.0891098529,-0.1387198567,-0.0441237539,0.1827340573,-0.4613678753,-0.3021485507,-0.2280495763,0.0444356278,0.0629076734,0.2366307676,-0.2858436108,0.1709893048,0.0659947768,0.1328113973,-0.3080635071,-0.1222961098,-0.1796017885,0.4985628128,-0.2371805012,-0.2333060801,0.1059011444,0.130261451,0.4757525921,0.1674677283,-0.4497697651,0.179243952,-0.3459710777,0.1534799039,-0.0468465574,0.0758880824,0.3201242983,0.1453730166,-0.1137198061,-0.0569933727,-0.1610036939,-0.050038822,-0.3855092824,0.2330114543,-0.0367576629,0.2503889203,-0.1210072637,0.398199141,0.2551098466,0.07454741,0.3040669858,-0.1943264455,0.1306042373,-0.1279257536,-0.1889470071,-0.0984253138,0.1577033997,-0.116324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2842","title":"always requiring the username in the dataset name when there is one","comments":"From what I can understand, you want the saved arrow file directory to have username as well instead of just dataset name if it was downloaded with the user prefix?","body":"Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you!","comment_length":30,"text":"always requiring the username in the dataset name when there is one \n Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you! \n From what I can understand, you want the saved arrow file directory to have username as well instead of just dataset name if it was downloaded with the user prefix?","embeddings":[-0.1190379634,0.1679278612,-0.0471789353,0.083183147,0.0581347011,-0.1882434636,0.2695405185,0.0901198387,0.0316710472,0.300450474,0.1571788788,-0.0406884216,-0.0339300595,0.0137734013,-0.2211274058,0.0345355049,-0.037572436,0.4607703388,-0.1611818522,-0.0188344046,-0.2140433043,0.0586200505,0.0266007949,0.1466935724,0.092917338,0.058842849,0.3130096197,0.2868224382,-0.346788317,-0.4643648267,0.3015707433,0.2356016636,0.0029155812,0.2852761447,-0.0001282645,-0.0333672389,0.2038314193,-0.0626532808,-0.4011749923,-0.4367166162,-0.5333263278,-0.2659046352,0.3054177761,-0.3457644284,0.0780374855,-0.2222444117,0.0719516128,-0.7639085054,0.5935953856,0.173073858,0.0422012918,0.2400033772,-0.058429502,0.0676861182,0.0910241902,0.1828541756,-0.1983405799,0.0576104783,0.2272553295,0.1533893198,0.2952234745,0.2544378638,-0.1423670352,0.1849683672,0.4132423401,-0.2334423661,0.1443344504,-0.1818229556,-0.0621791855,0.4150875509,1.119764924,0.1163945422,-0.2934359312,-0.1209268048,-0.1289778799,0.0856915265,0.4294357896,-0.028681282,-0.2189789265,0.0680714622,0.049464494,-0.2302126884,-0.2342119068,0.0908740088,-0.2012763619,0.4058734477,-0.029753875,0.2471694201,0.0076992027,-0.1546998769,0.1335837245,0.0433808118,-0.0445635058,0.0337282456,-0.3212507665,-0.1692341864,0.090835236,-0.4048357606,-0.0476178452,0.5174331069,0.4020608962,0.0327300504,-0.3441611528,-0.0132745011,0.1745274514,0.190897122,0.6876799464,-0.0796756595,0.4477432966,-0.2132449448,0.1390418559,-0.1412651986,-0.2152579576,-0.2961265147,0.1313990057,0.0904852152,0.6935943961,-0.2948879004,-0.1523353606,0.0342134871,-0.1434054077,-0.2593048513,0.0486348569,0.0749922767,-0.0735887289,0.1086224318,0.1906049252,-0.0469755083,0.2274105549,0.0317912996,0.0382744707,-0.4006378651,-0.1337124705,-0.0184564274,0.2508587837,-0.3766240478,0.4235996902,-0.069896698,-0.0486346111,-0.0030262368,0.2811372578,0.0505682491,0.1693538427,0.1912305951,-0.0317290314,0.1745623946,0.0232970379,-0.2015249431,-0.2798311412,0.4101695418,-0.3928644657,-0.2087896764,-0.458591193,-0.0268326569,-0.2381677628,0.0136597864,-0.1689317673,-0.3450929523,-0.061842192,-0.0526142754,0.2095700502,0.2401569933,-0.061588455,-0.1526175439,-0.0715633631,0.3001872301,-0.2423455864,-0.1067422256,-0.2043185681,-0.0189731102,-0.158886537,0.1915175617,-0.4447416663,0.0448998325,-0.2232249677,0.1948380321,0.5952612162,-0.495640099,-0.0760816261,0.1809930801,-0.1531474739,0.0020545234,0.5405682325,-0.1667470187,-0.1783379763,-0.273727715,-0.5374604464,0.2701226771,0.2313288748,-0.0610755235,0.0770300031,-0.1622279584,0.0180178434,-0.2001359463,-0.237470299,0.0979825258,0.1585738361,-0.1249815747,0.3110917807,-0.2299071401,-0.0763425902,0.0260678306,0.280485779,0.1328603923,-0.0561477169,0.1408209652,-0.3887729943,0.209692806,0.0229794681,-0.0062835971,-0.1900233626,-0.3237221539,-0.2919830382,-0.0033373197,-0.1393821537,0.4665467143,-0.1532784253,0.2935825884,-0.2240790874,-0.1394454837,-0.1043931916,0.3928515017,-0.061280597,0.1351870298,-0.2509830892,-0.0169803556,0.136933744,0.2311661541,-0.049291268,-0.168064788,0.3726317585,-0.0220886804,-0.1133118421,0.5330518484,-0.0282319766,0.0319849886,-0.1048781797,0.1954684258,0.1114103943,-0.2514074445,0.1943351328,0.1735413671,0.1979680359,-0.110180378,0.0263108853,0.1806216091,-0.0178217087,0.0831885189,0.0754430369,-0.1566095501,-0.0618395284,-0.2291377485,-0.3702287674,-0.1156575456,0.0781684741,0.028383391,0.1068357155,-0.0578241237,-0.2347726524,-0.2743072808,0.3148424625,0.0879814997,0.1735295206,-0.0414846316,0.0935693234,-0.0817902759,0.1874943078,0.0105516203,0.414855361,0.1526489854,0.1386745274,-0.2018697709,0.3261217773,-0.1958365887,0.4617404044,0.0994198173,-0.1915585101,0.0323105305,-0.2889240384,-0.000456579,-0.2048773319,-0.1207685098,0.1294811517,-0.1390759945,-0.5578092933,-0.1775350273,-0.1947994381,-0.177322939,-0.0711520389,-0.389251858,-0.1408149898,-0.0222053286,-0.0295884032,0.3878869712,-0.0663037002,0.3042517006,-0.2504909933,0.2322497219,-0.2513068914,-0.4720901251,-0.2935732305,0.1879668981,0.1781593114,-0.0206833128,0.1983250529,-0.0375114866,0.3403944969,0.0168807711,0.3036705256,-0.4464102685,-0.2624958158,-0.1112394556,-0.0941314921,0.0098525006,0.1129646525,0.2278973162,0.1429516822,-0.3494535685,-0.0963938758,-0.130720526,-0.140706256,0.0009915548,0.1941831559,-0.0720808953,0.0421999432,-0.0923184529,-0.0686048046,-0.1738803238,0.1846844405,0.0589118898,0.3326393366,0.4182104766,0.024567036,0.0229594819,0.0603554808,0.0939202309,-0.1221084967,-0.4152963758,0.2814902365,-0.221095562,-0.0365174599,0.1737717241,0.232513532,0.0525959171,0.0665095448,-0.2961342633,-0.0420534499,-0.2180652767,0.4774214327,-0.2487527877,0.2532512844,0.567563653,0.3703916371,-0.1215839982,-0.1645440161,-0.0928736031,0.1102988049,0.0533315688,0.1783058792,0.1550331414,-0.0761385411,-0.3044638634,0.702835381,-0.0307355858,0.3346610367,0.5488693714,0.1978102028,0.3732857704,0.0515313633,-0.3388848603,-0.3989714384,0.15294368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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2842","title":"always requiring the username in the dataset name when there is one","comments":"I don't think the user cares of how this is done, but the 2nd command should fail, IMHO, as its dataset name is invalid:\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nMoreover, if someone were to create `openwebtext-10k` w\/o the prefix, they will now get the wrong dataset, if they previously downloaded `stas\/openwebtext-10k`.\r\n\r\nAnd if there are 2 users with the same dataset name `foo\/ds` and `bar\/ds` - currently this won't work to get the correct dataset.\r\n\r\nSo really there 3 unrelated issues hiding in the current behavior.","body":"Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you!","comment_length":115,"text":"always requiring the username in the dataset name when there is one \n Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you! \n I don't think the user cares of how this is done, but the 2nd command should fail, IMHO, as its dataset name is invalid:\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nMoreover, if someone were to create `openwebtext-10k` w\/o the prefix, they will now get the wrong dataset, if they previously downloaded `stas\/openwebtext-10k`.\r\n\r\nAnd if there are 2 users with the same dataset name `foo\/ds` and `bar\/ds` - currently this won't work to get the correct dataset.\r\n\r\nSo really there 3 unrelated issues hiding in the current behavior.","embeddings":[-0.0112160547,0.1447476298,-0.039360974,0.0958193168,0.0011399824,-0.21814017,0.4295825958,0.1111663133,0.1069055125,0.3369613886,0.1158719733,-0.0281225555,0.012058734,0.0387605466,-0.1626236439,0.15405114,-0.0621639118,0.3452987075,-0.1390585154,0.0082569737,-0.1663803458,-0.0051505985,0.0509563424,0.0512073487,0.1581596732,0.0974032432,0.3119137287,0.1957424432,-0.2975040972,-0.4824235439,0.3003055453,0.3615881801,-0.0639261305,0.4107335508,-0.0001179238,-0.0590646155,0.2076957226,0.0588892549,-0.5383250713,-0.4455489516,-0.4917058647,-0.2482866049,0.2102007866,-0.3056302369,0.0239957049,-0.0770888329,0.0180540141,-0.7141695619,0.4935949147,0.1425914317,0.1235402897,0.4247736037,-0.0561008267,-0.0546838194,0.0977879092,0.0445507616,-0.1430555433,0.0673177168,0.2824338078,0.1683505923,0.3565243781,0.2082258761,-0.2156513482,0.1110855341,0.3078671992,-0.297311604,0.1378888935,-0.1850421876,-0.0196566731,0.4707064927,1.0010164976,0.1690532565,-0.4670833349,-0.1757358611,-0.1048424318,0.0698701069,0.4081428647,0.1350550354,-0.1117157638,0.1679628044,-0.0835038573,-0.0350223184,-0.0327410065,-0.0183726028,-0.2701371312,0.463075012,-0.0382801704,0.2346923351,-0.126982227,-0.1310454905,0.1787928343,-0.0570791326,0.0163661037,-0.0085559338,-0.4005134404,-0.1691485345,0.1516158283,-0.4226424098,0.0428753421,0.3494897187,0.2651739419,0.0839743465,-0.3256967366,-0.0293966886,0.2081443816,0.2185648829,0.5727408528,-0.1588097215,0.4727654457,-0.2071726769,0.2513425052,-0.0986485332,-0.0985091105,-0.1068097875,0.0423010625,0.0020768261,0.5621961951,-0.2326089889,-0.2164977491,0.0521594696,-0.0958086252,-0.2232666016,0.0548773855,0.0210059099,-0.1488406956,0.1968661994,0.084558256,-0.0511213467,0.1819668114,-0.1001203507,-0.0620180219,-0.3828084171,-0.1790466756,0.0472363196,0.2418147027,-0.4406692982,0.3566652834,0.0988026187,-0.114215441,0.0436313599,0.1698496491,0.0908757001,-0.0172396172,0.190460071,-0.0580173172,0.1688790023,-0.0506810285,-0.2279134095,-0.2390823513,0.3085813522,-0.390411824,-0.2045666426,-0.4336534142,0.1097150519,-0.2935425639,0.066132538,-0.0812161043,-0.2224237472,-0.0917920545,-0.0883756503,0.1027795821,0.1798978746,-0.1870242059,-0.1584708095,-0.0523947552,0.3964365125,-0.1756321788,-0.0502207018,-0.1365360767,0.029464392,-0.1163142025,0.1394523829,-0.4678532481,0.1224646345,-0.1508981884,-0.0489705689,0.3005219698,-0.5388397574,-0.0999098346,0.2233630568,-0.1950829178,0.2370555699,0.515113771,-0.1638976336,-0.1690343618,-0.3403670788,-0.38592875,0.2341131121,0.1532597393,0.0108526954,-0.0428429842,-0.2005463839,0.1890724003,-0.0827106163,-0.1046393141,0.1461027265,0.1451874524,0.0288746953,0.2422480285,-0.2445032746,-0.1366341412,-0.1447138786,0.2217039317,0.0762651041,-0.1103753522,0.0332951918,-0.4837906957,0.3472396135,0.1079088002,0.2498538941,-0.0062445723,-0.3379675746,-0.2230136245,-0.0397204049,-0.0805989653,0.3676057756,-0.0520953275,0.2502617836,-0.1500829607,-0.1581352502,-0.0297868438,0.5793233514,-0.1334762275,0.1298864037,-0.3625038862,-0.1167224422,0.0630655214,0.3549443483,-0.0472435132,-0.0926375985,0.3039370775,-0.0752965957,-0.032413844,0.4737949967,0.1176315397,-0.163392067,-0.2493177205,0.2496862262,0.2226758897,-0.0240225773,0.1172254756,0.0922882482,0.3392164409,-0.1424901634,-0.0014673754,0.2404722124,-0.0266190972,0.0524699837,0.0816142038,-0.2937871218,0.0200445335,-0.2223831415,-0.3907833099,-0.1125158742,0.0381230712,0.0196339674,0.0680680349,0.0361104049,-0.1875891834,-0.2777249813,0.2804121077,0.0181260668,0.0682562515,-0.0759634972,0.2254895121,-0.1054001525,0.2398348451,-0.136148423,0.3855210245,0.2088996172,0.059797354,-0.2161650807,0.2650007308,-0.2814117074,0.3381402791,0.0773393586,-0.3269681633,-0.0710419416,-0.1665609181,0.1119682863,-0.1857122034,-0.1120791584,0.2062527388,-0.144352138,-0.5838578939,-0.1948499233,-0.1586288065,-0.0823749378,-0.0362053961,-0.3682667911,-0.2206246853,-0.1217518374,0.0648033395,0.3796051443,-0.1369002163,0.1926307976,-0.2595559359,0.2505998909,-0.387212038,-0.3063415289,-0.2005381584,0.1017693058,0.0251546223,0.0505025312,0.2794833779,-0.0904417783,0.4055159688,-0.1425036788,0.0767796338,-0.284624368,-0.4424414039,-0.0563289598,-0.1785662323,0.1260644644,0.0438687876,-0.0142092248,0.2531159818,-0.3800748587,-0.1869846284,-0.1745903641,-0.0908754691,0.0464824699,0.1490806341,-0.2406558543,-0.0902432725,-0.1752282232,-0.0152530055,-0.2159507722,0.0440757349,-0.0034682797,0.2913244665,0.5777443051,-0.1085720286,-0.092646189,0.0440641046,0.2822085023,-0.241314888,-0.4220570326,0.1361677647,-0.1282571852,-0.1011924967,0.1170816571,0.2195591033,0.1298719347,0.1245224699,-0.3237330914,-0.0901662037,-0.2222660482,0.4953493774,-0.1317195296,0.2276004553,0.4823291898,0.3495170772,-0.1312914342,-0.1352271438,-0.110282965,0.0061191386,-0.0955961198,0.1160098016,0.1497413665,-0.1784745306,-0.2178835422,0.5322826505,-0.0081038838,0.1795237362,0.4474458992,0.2515381873,0.4841234088,-0.0553931706,-0.3686906993,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2842","title":"always requiring the username in the dataset name when there is one","comments":"This has been fixed now, and we'll do a new release of the library today.\r\n\r\nNow the stas\/openwebtext-10k dataset is cached at `.cache\/huggingface\/datasets\/stas___openwebtext10k` and openwebtext-10k would be at `.cache\/huggingface\/datasets\/openwebtext10k`. Since they are different, the cache won't fall back on loading the wrong one anymore.\r\n\r\nSame for the python script used to generate the dataset: stas\/openwebtext-10k is cached at `.cache\/huggingface\/modules\/datasets_modules\/datasets\/stas___openwebtext10k` and openwebtext-10k would be at `.cache\/huggingface\/modules\/datasets_modules\/datasets\/openwebtext10k`","body":"Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you!","comment_length":65,"text":"always requiring the username in the dataset name when there is one \n Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you! \n This has been fixed now, and we'll do a new release of the library today.\r\n\r\nNow the stas\/openwebtext-10k dataset is cached at `.cache\/huggingface\/datasets\/stas___openwebtext10k` and openwebtext-10k would be at `.cache\/huggingface\/datasets\/openwebtext10k`. Since they are different, the cache won't fall back on loading the wrong one anymore.\r\n\r\nSame for the python script used to generate the dataset: stas\/openwebtext-10k is cached at `.cache\/huggingface\/modules\/datasets_modules\/datasets\/stas___openwebtext10k` and openwebtext-10k would be at `.cache\/huggingface\/modules\/datasets_modules\/datasets\/openwebtext10k`","embeddings":[0.0375944376,0.0824939683,-0.062389642,0.189189285,0.0797621161,-0.1698654443,0.3240476549,0.1753910333,0.0988098755,0.2571041286,-0.0334544182,-0.005713027,0.086840637,0.0152159454,-0.0175968595,0.1378327161,-0.0254061203,0.329660058,-0.1355326027,0.0548913628,-0.1549164057,0.17095384,0.1209011897,-0.0124111632,0.077868402,0.1203935072,0.3031094372,0.2197110057,-0.1996740997,-0.4276252985,0.4127321243,0.3344061673,-0.1120956168,0.4064258933,-0.0001154478,-0.011840065,0.2260462493,-0.0157717355,-0.4490821958,-0.3961157501,-0.3092710674,-0.2415910661,0.1481006145,-0.2513083518,-0.0750782788,0.0520001352,0.1243218482,-0.8216949105,0.4474804699,0.0437123962,0.1597146094,0.3984534144,-0.0662301183,-0.0339964777,0.195396632,-0.0242579058,-0.2378288656,-0.0330576561,0.2683281004,0.2136081457,0.2348719835,0.3823943436,-0.1320448071,0.1214565411,0.5294762254,-0.1905008703,-0.0765320137,-0.0523217767,0.0627270713,0.4552723169,0.9934232831,0.0988379344,-0.4571218491,-0.2672223449,-0.1201256067,0.161817044,0.516744554,0.0830884501,-0.1558988243,0.1172698587,-0.2003969699,-0.1372404546,0.0205607265,-0.1234013364,-0.1353400499,0.2168562412,-0.107538946,0.1391433924,-0.0142687736,-0.2034431696,-0.0362406,0.0745288953,-0.1067777723,0.0448119901,-0.3880922794,-0.117680788,0.1456662863,-0.2159736156,0.0906699747,0.4436990023,0.316975534,0.0655232891,-0.357278049,-0.0557985306,0.1364842206,0.3374383748,0.5676668882,-0.2773848772,0.4061650038,-0.1175324693,0.1740215868,-0.1573353261,0.0191520788,-0.1939193904,-0.0007182655,-0.1182994395,0.4845440388,-0.3503368199,-0.1724876016,0.0147446999,0.0866394117,-0.1949363351,0.0858671591,0.1353599429,-0.2128481269,0.1548174024,0.1387090534,-0.1193475872,0.0730489492,0.0121808676,-0.2253031284,-0.2874251902,-0.1149150431,0.1724596769,0.2542384565,-0.5877205729,0.4259341955,-0.0012006037,-0.0699641928,0.026610652,0.1783616096,0.1688966155,0.1100478843,0.2089374363,-0.0693488196,0.1823322624,-0.010585146,-0.1790511161,-0.342687428,0.1561022103,-0.3777935505,-0.2844825089,-0.2765375078,0.1333822608,-0.309530288,0.016709365,0.0032431264,-0.2123597264,0.1110164896,-0.0681817979,0.1985924244,0.2615762353,-0.2429310828,-0.1313876212,-0.0422853716,0.4807311893,-0.0208388139,-0.2878079116,-0.0464031622,0.0286547765,-0.1911574602,0.140012309,-0.3698641658,-0.1404031813,-0.2386260331,-0.005867769,0.2615377903,-0.5422500372,-0.1753261387,0.2094224393,-0.1829869002,0.228848502,0.5161494613,-0.1081246957,-0.1796392202,-0.3369978964,-0.3160737753,0.1745828688,0.1238078102,0.0089166965,-0.0150062107,-0.2921795547,0.1959774643,-0.1530795246,-0.1326317638,0.119127728,0.0649605468,0.1155806929,0.2017225772,-0.1567181051,-0.0798916072,0.0918692499,0.1158241555,0.1057760566,-0.0553290546,-0.0761156008,-0.3990009725,0.3699044585,0.0047424366,0.1510597616,-0.0381602459,-0.3035133183,-0.2081749588,-0.1519041955,-0.103091225,0.1407390833,-0.0399605036,0.2882585526,-0.0206609704,-0.0848372132,-0.1380134672,0.6630226374,-0.0821959302,0.1411767304,-0.605294764,-0.1928301603,0.1094563454,0.2213483155,-0.0821277946,-0.0498441234,0.2705253661,-0.0836341158,-0.0362478942,0.4826480448,-0.0253505092,-0.0777289569,-0.1643601358,0.3060417771,0.1677652746,-0.0649727136,0.1342426538,-0.0101499846,0.1675351411,-0.1151787415,0.1162599623,0.2596082091,-0.1182160974,0.0340584926,0.1283718944,-0.2436627746,-0.0020315542,-0.1679558754,-0.3611088097,-0.1741486788,0.1734526455,-0.0966658145,0.2100705653,0.0032940314,-0.187973842,-0.1772351414,0.3280988038,0.0652870461,0.0247033462,-0.0490054339,0.1099447533,-0.1526254565,0.1590571404,-0.0789734572,0.3677211404,0.2072459757,0.1408207119,-0.2069216967,0.2591053843,-0.2363043875,0.3576541841,0.0444861203,-0.3380260468,-0.1850038916,-0.1805044264,0.0743287206,-0.222916469,0.0631989241,0.1214975864,-0.180281654,-0.515289247,-0.1083763614,-0.2212189883,-0.18575643,-0.0817603916,-0.3339968622,-0.4126357138,0.023136612,0.019387478,0.3944680989,0.0120265996,0.2852747738,-0.249261111,0.3125061393,-0.3813315332,-0.2637667954,-0.2887052894,0.1078852117,0.0179940872,0.0776809156,0.3039681315,-0.1619673371,0.3960868716,-0.1949276179,0.0832813382,-0.4336537123,-0.3930888772,-0.0385031737,-0.1903375536,0.1014984995,0.1863474548,0.0943361595,0.2056379914,-0.3380207419,-0.0639304519,-0.3201238513,-0.2125253975,0.0137129948,0.0797390342,-0.0470290743,-0.1253706515,-0.1166010797,0.1017374471,-0.1972443163,0.1969526708,-0.0309529714,0.2142860144,0.6474480033,-0.1165280193,-0.0800771192,-0.0099893799,0.3416545987,-0.3617219329,-0.6655094624,0.1575684845,-0.2076197267,-0.1257559359,0.122562848,0.2855867147,0.1571497023,0.0301424842,-0.3921298683,-0.1861689389,-0.2656190097,0.3884158731,-0.1062542498,0.2340859026,0.4980401993,0.2453362495,-0.1455229521,-0.140803352,-0.2526674271,0.0589347109,-0.1568568647,0.2337859869,0.1340134144,-0.21853441,-0.1535795033,0.5687243938,0.0035127429,0.1735146195,0.506649971,0.3821831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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2841","title":"Adding GLUECoS Hinglish and Spanglish code-switching bemchmark","comments":"Hi @yjernite I am interested in adding this dataset. \r\nIn the repo they have also added a code mixed MT task from English to Hinglish [here](https:\/\/github.com\/microsoft\/GLUECoS#code-mixed-machine-translation-task). I think this could be a good dataset addition in itself and then I can add the rest of the GLUECoS tasks as one dataset. What do you think?","body":"## Adding a Dataset\r\n- **Name:** GLUECoS\r\n- **Description:** a Microsoft Benchmark to evaluate code-switching for only two language pairs but a variety of tasks\r\n- **Paper:** https:\/\/aclanthology.org\/2020.acl-main.329\/\r\n- **Data:** https:\/\/github.com\/microsoft\/GLUECoS\r\n- **Motivation:** We currently only have [one other](https:\/\/huggingface.co\/datasets\/lince) dataset for code-switching\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":55,"text":"Adding GLUECoS Hinglish and Spanglish code-switching bemchmark \n ## Adding a Dataset\r\n- **Name:** GLUECoS\r\n- **Description:** a Microsoft Benchmark to evaluate code-switching for only two language pairs but a variety of tasks\r\n- **Paper:** https:\/\/aclanthology.org\/2020.acl-main.329\/\r\n- **Data:** https:\/\/github.com\/microsoft\/GLUECoS\r\n- **Motivation:** We currently only have [one other](https:\/\/huggingface.co\/datasets\/lince) dataset for code-switching\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hi @yjernite I am interested in adding this dataset. \r\nIn the repo they have also added a code mixed MT task from English to Hinglish [here](https:\/\/github.com\/microsoft\/GLUECoS#code-mixed-machine-translation-task). I think this could be a good dataset addition in itself and then I can add the rest of the GLUECoS tasks as one dataset. What do you think?","embeddings":[-0.4562041163,-0.0375740156,-0.1403836459,0.0984709784,-0.0866128579,0.1747595966,0.1034360081,0.1597854942,0.1243993938,-0.127530843,-0.3663089871,0.0582287237,-0.166472286,0.4522484541,0.4579002261,-0.0750973895,0.1682832241,-0.1863522232,-0.2975812256,-0.0285653546,-0.2414671779,0.2591425776,-0.0748849809,-0.2490670085,0.1195182577,-0.0600319542,-0.2158005089,0.0281797834,-0.2166912258,-0.3669120669,0.2070242614,0.2018546015,-0.0001944442,0.3099788129,-0.0001212761,-0.0454128906,0.2508905232,-0.0865600482,-0.1637153924,0.0253809541,-0.2250319272,-0.4286168814,-0.3869867623,0.022446448,-0.1007063836,0.365392983,-0.2201342583,-0.9435053468,0.2026268542,0.2867703438,0.0809130818,0.2432606071,-0.1424294561,-0.0372652858,0.7352899909,-0.1337578446,-0.2202915549,0.2272410095,0.6726600528,-0.2170975059,0.0930986032,0.3815473914,-0.1620406806,-0.3303690851,-0.2662052512,-0.0435483046,-0.040546529,-0.1095841527,0.0432634056,0.3855702579,0.1063566133,-0.2388777286,-0.2566063702,-0.4398989677,0.0243869387,-0.0287990645,-0.245624572,0.1916798502,0.0747111738,0.3524116874,0.1448379457,-0.3725741804,0.2412807643,0.0874847323,0.2643488646,-0.055611793,-0.2258850038,0.0737464428,0.1882336885,0.1209186539,0.2201785296,-0.2032581568,-0.1668458283,0.2319615483,-0.376214385,-0.1901734173,0.2024731934,0.1758103371,0.1956194043,-0.1517497599,0.0767290145,0.0106660491,-0.06453605,-0.2784256637,0.0454485118,-0.0186138637,0.3070380688,0.0766615272,0.1068392023,-0.2859810591,0.4655205309,0.1304586977,0.0806110799,-0.0863334462,-0.3141005635,-0.0095944125,0.1537840366,-0.1434822083,-0.362654686,-0.0277232844,-0.2088481933,-0.3821384013,0.1138347834,0.2250814438,0.3041009903,-0.2810276151,-0.1514193714,0.6095604897,0.0756695867,-0.1866672635,-0.0463029407,0.3217450678,-0.201774478,0.2090623379,0.1378411651,0.0049377554,-0.1987736374,0.1945095211,-0.1816968471,0.1418224424,0.1004326865,-0.0555508435,-0.0043899007,-0.1610300988,-0.092538476,0.0321346633,-0.0459238738,-0.1850142479,-0.345557332,-0.2124434859,0.1162323505,-0.0959634483,-0.0822607502,0.0319889151,-0.2499201149,-0.1897381991,-0.2471116036,0.6594047546,0.1014635786,0.1009019241,0.2697681487,0.1177111343,-0.1489271075,-0.1702114791,-0.2519376874,0.6982992291,-0.1051398218,-0.1983122975,0.2227164358,-0.0263970271,0.0097541083,-0.0311246496,-0.1665099561,0.0859855339,-0.1244314536,0.0410051979,0.2208697498,-0.6630020738,-0.0372317284,0.5112774372,-0.0672470257,0.0222848933,0.3917962909,0.2232131213,0.1814109236,-0.1477971971,0.1134216189,0.217004016,-0.069765918,-0.142048806,-0.1766978204,-0.2959159613,0.2276780754,0.3646274209,0.1544316262,-0.1501962095,0.246703729,0.1386543512,0.0468995497,-0.1326189935,0.3884868026,-0.0526061691,0.0649195015,-0.1179859564,-0.1198043302,-0.1624260247,-0.3035241365,-0.0673430935,0.1742854565,0.3465016782,0.171958372,-0.2279568464,-0.0264772438,-0.1765334308,-0.2174317539,-0.2586343586,0.0319890156,-0.1133362651,0.1491020471,-0.1919874698,-0.2116900682,0.7850537896,-0.0857680738,-0.061007522,0.0560463257,0.4692820907,0.1327565759,0.0785388425,0.1129377857,0.5679251552,0.0094590345,-0.3007210195,0.1097042859,0.142452091,-0.0804616213,0.0245189648,0.6064789891,0.1087974906,0.308499366,-0.2306319922,0.442273587,-0.2406692803,0.0576789491,-0.3362897933,0.014712926,0.2891784906,-0.1666901708,0.2974423766,-0.0736287832,-0.0333827734,0.0399036258,-0.1557314694,-0.5101320744,-0.1167823523,0.4150747657,-0.2120473385,0.5949844718,-0.1531043202,-0.2547149956,0.1771592051,0.4270577133,-0.138554424,0.0630073175,-0.1863427907,-0.0634287894,0.0967811495,-0.1388385445,0.3731571138,0.5468965769,0.2336337566,0.1551204175,0.1052155197,0.144607082,-0.1012262702,0.1400053948,-0.1023547947,0.0240267459,-0.0150082707,0.0018948076,-0.0823107362,-0.2932803929,-0.0677899718,0.0624599047,0.0017761353,-0.1852775365,-0.0936834887,0.0828696638,-0.2058594823,-0.3967961371,-0.0119660515,-0.1377779841,-0.1254186481,0.1600348204,-0.2357913107,0.1440874487,0.0406980664,0.171438098,0.3780547082,-0.5666148067,-0.2546240091,0.1896491498,-0.2795822024,-0.0693916902,0.0511924289,0.417733103,-0.1247263253,0.2618254423,-0.4135529399,0.0773484856,-0.2194329351,-0.3052051663,0.2013590932,-0.2877219319,0.2322921157,0.1570303142,-0.2804115415,0.1059050709,-0.0931596756,-0.0331333466,0.0732637495,-0.2135927379,-0.3110412061,-0.1847257018,-0.0901200622,-0.0302940961,-0.1354500204,-0.4298542738,-0.3466406167,0.0700607076,-0.2995126247,-0.3958471119,-0.5084580779,-0.2140938491,0.1673551053,-0.1113384217,-0.1393941641,-0.3711221814,-0.5005770922,0.0580936931,-0.0644598156,-0.1387184709,-0.2087160349,-0.0592253953,0.4133212864,-0.0504316092,-0.2962018549,0.1412037015,-0.1058873758,0.3201633394,0.1048124507,0.2532625794,0.2049774975,0.1003272608,0.106704399,-0.185974434,-0.3719950616,0.0748692751,0.1357494444,0.047928758,-0.0209082384,0.0205908939,-0.0066340356,0.5636225343,0.4314819574,0.206521973,0.4313124418,-0.0233349148,0.3596463501,0.0776876956,-0.4756762385,0.13892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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2839","title":"OpenWebText: NonMatchingSplitsSizesError","comments":"I just regenerated the verifications metadata and noticed that nothing changed: the data file is fine (the checksum didn't change), and the number of examples is still 8013769. Not sure how you managed to get 7982430 examples.\r\n\r\nCan you try to delete your cache ( by default at `~\/.cache\/huggingface\/datasets`) and try again please ?\r\nAlso, on which platform are you (linux\/macos\/windows) ?","body":"## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading:   0%|          | 0.00\/12.9G [00:00<?, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n","comment_length":62,"text":"OpenWebText: NonMatchingSplitsSizesError \n ## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading:   0%|          | 0.00\/12.9G [00:00<?, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n \n I just regenerated the verifications metadata and noticed that nothing changed: the data file is fine (the checksum didn't change), and the number of examples is still 8013769. Not sure how you managed to get 7982430 examples.\r\n\r\nCan you try to delete your cache ( by default at `~\/.cache\/huggingface\/datasets`) and try again please ?\r\nAlso, on which platform are you (linux\/macos\/windows) ?","embeddings":[-0.2325653881,-0.1025962085,0.0594487116,0.4710067809,0.099145472,0.0983288512,0.001799254,0.5494816303,-0.0201674923,0.1316695958,-0.1176461726,0.0032898269,-0.1183154136,0.091308251,-0.0851507634,0.1036110669,-0.1839791536,0.1432445645,-0.2236059755,0.0837038904,-0.1660715044,0.2510773838,-0.2940531671,-0.136175856,-0.2839494944,-0.0273162425,-0.2651298642,0.2377939373,-0.2557264864,-0.4160161018,0.1758898795,-0.3987876177,-0.0372202508,0.5550357699,-0.0001198019,0.053337954,0.2946435809,-0.2280974835,-0.130750522,-0.1020404696,-0.4944339693,-0.3940509856,-0.1452365369,-0.1462632567,0.1926995069,-0.128324911,0.0508886799,-0.2793247402,0.45934093,0.4411795735,0.1552283317,0.397815913,0.2411608696,0.0371606573,0.3650759459,-0.2219923884,-0.131810233,-0.0362073407,0.0326931886,0.0405232348,0.0806393847,0.1172676533,0.010848294,0.208711639,-0.0005465942,-0.04086693,0.2286907285,-0.2362536341,0.1534994245,0.4934612215,0.6223924756,-0.136980623,-0.3544664681,-0.3586990833,-0.4166816473,0.0730490908,0.2820883095,0.4564271271,0.0207831394,0.2088509351,-0.3920858502,0.0627977401,0.0887393206,0.1010190621,-0.219721511,0.1737966686,-0.0613732822,0.0936699957,0.0337460227,0.1205220819,0.2228565961,-0.1298710406,-0.0552684665,0.130376339,-0.2470706999,-0.0399980396,0.0465838946,-0.1719465107,0.3725028634,0.3157573938,0.3307099342,-0.2253412157,-0.2792927325,-0.0260545649,0.1021305472,0.486409694,-0.0030222856,0.181135416,-0.0994259194,0.153441906,0.0841908902,-0.038324248,-0.0627070367,-0.3573563993,0.0782192796,0.0519293919,0.5506390929,-0.313762635,-0.5638862252,0.1355626136,-0.271795541,-0.2499685287,-0.2926042974,-0.0598699637,-0.3987960219,0.21013017,0.1625957936,-0.1578373164,-0.3149763942,-0.3888362348,-0.274458766,-0.0227039084,-0.1876686811,0.1219247207,0.0013910971,-0.1314144135,0.4226850271,-0.0792298242,-0.0405353419,-0.2938423455,0.16571486,0.011818096,0.0518370979,0.1675295681,-0.2069491893,0.1224142388,0.0686967969,0.0914774612,-0.2862041891,0.3105949759,-0.2906442583,-0.4093714058,-0.1381831467,0.1050514579,-0.3705904186,-0.160875082,0.3073712587,-0.0599066541,0.3331327736,-0.1210963205,-0.0874515325,-0.1715030372,-0.1489326507,-0.1385395974,0.136148721,0.4009007215,-0.2815036774,-0.1870612502,0.2009663135,-0.1067375243,0.2657059729,0.4419420362,0.0383116044,0.1077955142,-0.2697066367,0.1994151175,0.1035528705,-0.3222500086,-0.6370256543,0.3166928291,-0.0614389703,0.1890935451,0.1131784543,-0.2998300493,0.0997247845,-0.1133560538,-0.1074843481,0.1425459236,-0.1855406165,0.0653884783,-0.6042557955,-0.7404735684,0.4050538242,-0.0152202202,0.2199621052,-0.2009830177,0.0258024931,0.2023736984,0.4867910445,-0.0068813604,0.0082399799,0.1832945794,0.1684482396,0.0200096425,-0.0262848958,-0.3034965396,-0.1270769089,0.2578174174,-0.1189626977,-0.0252107475,0.2395293266,-0.1284899116,-0.5201492906,-0.2797634602,-0.027436683,-0.2890127301,-0.0011977525,0.0537340343,0.3314978778,0.070443891,0.1006710827,0.3630115986,0.0587042272,0.1622793376,-0.3886450529,0.2116378248,-0.0419298112,-0.1590762138,0.147472024,-0.0411890857,0.157839641,-0.1788159907,-0.3645519018,0.6570310593,0.1383420527,0.02780932,-0.0206335951,0.1761494279,0.2389790714,-0.232609421,0.1361853629,0.3155079186,-0.0367029049,-0.0358329862,0.0357469767,-0.0957837403,0.1101791039,-0.027026739,-0.0629577935,0.0031620259,-0.0677025169,-0.2016023397,-0.0996729061,-0.1194978952,0.0788498223,-0.0529226586,-0.1825649738,0.0738681331,0.1595273167,-0.1473862082,0.7876149416,-0.0265092328,-0.0810908601,0.2676542401,-0.2830883563,-0.1285564452,-0.183779493,0.1333068758,0.5281811357,0.2116181999,0.1979825199,0.2271813452,0.0595819056,-0.0967165157,0.1486262977,0.2788825929,0.2119769752,0.301492095,0.0253650583,0.0782746673,-0.1882289052,0.1017744318,0.116242066,0.3736377954,-0.4455088973,-0.1506477296,-0.499330163,0.0167021099,-0.2890394628,0.1519827545,-0.1953706294,-0.2340987176,-0.0569427386,0.0578246228,-0.0190754719,0.2200787216,-0.1844465733,0.1744143516,0.177943483,0.2221967876,0.0834514573,-0.0005307973,-0.5165869594,0.0194541551,0.3351318538,-0.249814257,0.4144765437,-0.5614904165,-0.0921603888,-0.201660797,-0.2970202863,-0.0893634483,0.0089835012,0.1129095405,0.292773813,0.2110989392,0.0249922592,-0.0226391368,0.1041431502,-0.320573926,-0.2713530958,-0.0989822373,0.0973132402,0.2661294639,-0.0641359314,-0.5627799034,-0.216370061,-0.2757206857,0.326700151,0.1139719784,0.202980265,0.0925925523,0.0694469884,0.1050399691,-0.0523555838,0.1160848886,-0.2542546391,-0.5217090845,0.3188512623,-0.0026786192,-0.3333176374,-0.0070148506,0.1511377543,-0.0392282307,0.0255860798,-0.6288368106,-0.2420555353,-0.4020483494,-0.0416228026,-0.1522629708,0.2767257392,0.4846800268,-0.030819742,-0.0632560626,-0.1522991359,-0.3440429568,-0.0237116944,0.1624833941,0.6210068464,-0.1053256467,0.1852699518,0.4056099951,0.4629566371,0.5385028124,0.0481480174,-0.0136958975,-0.0321367905,0.1083640978,-0.1104782,-0.0401165858,0.2920781076,-0.1999634057,0.0356370732,0.29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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2839","title":"OpenWebText: NonMatchingSplitsSizesError","comments":"I'll try without deleting the whole cache (we have large datasets already stored). I was under the impression that `download_mode=\"force_redownload\"` would bypass cache.\r\nSorry plateform should be linux (Redhat version 8.1)","body":"## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading:   0%|          | 0.00\/12.9G [00:00<?, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n","comment_length":31,"text":"OpenWebText: NonMatchingSplitsSizesError \n ## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading:   0%|          | 0.00\/12.9G [00:00<?, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n \n I'll try without deleting the whole cache (we have large datasets already stored). I was under the impression that `download_mode=\"force_redownload\"` would bypass cache.\r\nSorry plateform should be linux (Redhat version 8.1)","embeddings":[-0.3607826829,-0.239854753,0.0516973063,0.4607081115,-0.0456267372,0.1327427775,0.066052109,0.6086549163,0.1439708173,0.1374783516,-0.2283655256,0.1216922924,-0.0511874668,0.24087888,0.0173501056,0.0554530993,-0.1288683265,0.209086746,0.0327513143,0.0964000374,-0.2407321781,0.165215224,-0.3307679594,-0.0877942294,-0.0919432417,0.0468051881,-0.0785730928,0.2525186837,-0.1423963159,-0.3720365465,0.3101969957,-0.3209818304,0.0526632667,0.4554589093,-0.0001236256,0.058411181,0.3002471924,-0.2492422163,-0.16436176,-0.0202188827,-0.4952323437,-0.4582964778,-0.1188494414,-0.1141500697,0.184963122,-0.0521582067,0.0923798084,-0.2381899059,0.3523260951,0.1760241985,0.1076085791,0.2688066065,0.1909793615,-0.0141789233,0.3994997442,-0.288500309,-0.2018007636,-0.026038751,0.2025960684,0.2345232815,-0.0004501377,0.2051186413,0.0155433789,0.3176065981,0.1230182052,-0.0852457508,0.1734517515,-0.223595798,0.1765745282,0.4632994831,0.7116635442,-0.2908660173,-0.3333330154,-0.4549492896,-0.4818343818,-0.0767983198,0.3402119577,0.3721658885,0.0110199759,0.2835704088,-0.2383954376,-0.0144351302,0.0867683887,0.0248056687,-0.2652613223,0.1660380363,-0.0655873343,0.0525521077,-0.0053646825,0.1384350061,0.2647070587,-0.0735872909,0.0548304617,0.1759223789,-0.2408612818,-0.0740365535,0.0165432449,-0.2352584451,0.3727194965,0.3354320526,0.3635959923,-0.1954646856,-0.3061286211,-0.0127849197,0.202025786,0.4631952047,-0.1391366273,0.0402779281,-0.1740580797,0.0575832874,0.111650236,-0.1003556475,-0.0169872101,-0.3424649537,0.1643748879,0.0634849742,0.6322448254,-0.3608763218,-0.5638146996,0.0620397069,-0.2243329287,-0.2449309826,-0.2871406674,-0.0788733289,-0.3087580204,0.3446002007,0.1083343402,-0.1410493702,-0.250838846,-0.4097034633,-0.2513109446,-0.1282017827,-0.1443449259,0.0415491126,0.0319383219,-0.138546139,0.2920025587,0.0044018342,-0.0483897887,-0.1885268092,0.1772954911,-0.0495248884,0.0395917334,0.2417313159,-0.0637856051,0.2164584994,0.0311726723,0.1261219084,-0.3001105189,0.3434175551,-0.3207673132,-0.3199581802,-0.0984371156,0.0781468749,-0.2246572673,-0.1677706391,0.1053349003,-0.047128059,0.3671295941,-0.2251332849,-0.0991620123,-0.1212022156,-0.1245307252,-0.1096512154,0.0449683294,0.5349205136,-0.4528928995,-0.1801821291,0.185575366,-0.1425025314,0.3732614815,0.3822270036,0.0877135545,0.0240434296,-0.2359959036,0.0825527161,0.0203862526,-0.4213356078,-0.8024846911,0.4292296767,-0.063970007,0.1961514801,0.1568332464,-0.1446538568,0.1170874834,-0.1508550644,0.057605356,0.1717762351,-0.2016098052,0.0442328788,-0.629065454,-0.8196583986,0.3851351142,-0.0254817307,0.2239643931,-0.1056236476,0.0249994807,0.2732915282,0.368870616,0.1013237163,0.0660170689,0.1658230275,0.0039065285,0.0954902247,-0.0445364453,-0.2933856547,-0.3191492856,0.2778995037,-0.2501968443,-0.1287551522,0.3011008203,-0.1411423236,-0.5185483098,-0.2888207138,-0.0862790644,-0.0926862955,-0.0315534584,0.0389909446,0.2252077758,0.0351627357,0.0298353322,0.3739862442,0.116735056,0.1384649873,-0.2917839289,0.1245396957,-0.0396486931,-0.0166225843,0.0037614766,-0.1315949559,0.189512223,-0.1118277237,-0.3834511042,0.6283049583,-0.0011398828,0.1150420979,-0.090775691,-0.0132446112,0.2655419707,-0.1831507087,0.1516956985,0.3305782974,-0.0388676003,-0.1138824895,0.0377740338,-0.1808829308,0.1214482114,0.0156273618,-0.1123446822,0.0120707881,-0.0592523217,-0.1683395058,0.0520322248,-0.1470990628,0.041033186,-0.132320255,0.1531793624,-0.058693409,0.089498423,-0.0775124133,0.6427761316,-0.0461898148,-0.0024128065,0.2773858607,-0.31353724,-0.1165984571,-0.149243772,0.3093588948,0.4315313101,0.2351769954,0.1816487759,0.214854151,-0.0480404198,-0.0663257837,0.0623969994,0.2788154185,0.1103492528,0.278703928,0.0314646326,0.0604010746,-0.2338596284,0.1105607077,0.1959756166,0.3361184299,-0.4241134524,-0.1764782369,-0.4222451448,-0.0984081179,-0.2253969163,0.1779851317,-0.1684412062,-0.228486225,-0.0869306996,0.0673669577,-0.0112149464,0.18163158,-0.2745671272,0.1598093063,0.139281556,0.0402397811,0.0125676868,0.0235119388,-0.4680196047,-0.0232420117,0.3328770697,-0.314013958,0.3491358757,-0.535438776,-0.0107724341,-0.0256563537,-0.1533284187,0.0009073421,0.082536146,-0.0696029216,0.2342223376,0.2336276174,0.0563837104,0.1101676375,0.1208643317,-0.270549655,-0.2377162576,-0.1075411141,0.1214817017,0.3437823653,-0.0233938284,-0.5561491847,-0.2882370651,-0.3905978799,0.2452228218,0.1488028616,0.2224647105,0.3056887388,0.081562236,0.1264165938,0.1255260706,0.086612016,-0.2552498579,-0.3846787512,0.27545017,0.0955306813,-0.2659647465,-0.0228641741,0.2200033963,-0.0600050166,0.1335960776,-0.5093490481,-0.1830071062,-0.4022891521,-0.2084587067,-0.1236945465,0.2260227352,0.4829974174,0.0146279922,-0.0300645493,-0.1376815438,-0.330088079,0.0368260704,0.1950068027,0.4865730703,-0.1092981622,0.238016054,0.3572044671,0.4601337314,0.468192935,0.1195461452,0.0832175612,0.0417757891,0.2068868279,-0.1063739881,0.0597097725,0.1245609075,-0.2355274707,-0.0282634329,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2839","title":"OpenWebText: NonMatchingSplitsSizesError","comments":"Sorry I haven't had time to work on this. I'll close and re-open if I can't figure out why I'm having this issue. Thanks for taking a look !","body":"## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading:   0%|          | 0.00\/12.9G [00:00<?, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n","comment_length":29,"text":"OpenWebText: NonMatchingSplitsSizesError \n ## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading:   0%|          | 0.00\/12.9G [00:00<?, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n \n Sorry I haven't had time to work on this. I'll close and re-open if I can't figure out why I'm having this issue. Thanks for taking a look !","embeddings":[-0.3353740871,-0.18841286,0.0487625636,0.4328111112,0.0591351986,0.1115414873,-0.0852611586,0.5720363855,-0.0169446841,0.1263842881,-0.1240672097,0.0030228929,-0.0780212209,0.0841974169,-0.1284149289,0.028532682,-0.1261273474,0.1875724196,-0.2567581236,0.1037800536,-0.289475292,0.2635649145,-0.3147125542,0.0043351529,-0.1761120707,0.0403093137,-0.1997045577,0.1530286819,-0.2597185671,-0.3653438091,0.2717806399,-0.3823173642,0.0590334535,0.4936454296,-0.0001182356,0.0295847729,0.3093135059,-0.2089018524,-0.0846336931,-0.1598861217,-0.4955505431,-0.4291180968,-0.0661034137,-0.1210819632,0.1902426779,-0.1171414033,0.126828596,-0.3275127709,0.3349781334,0.3231547475,0.178308785,0.4126829505,0.2072996944,0.0013006629,0.3759169579,-0.2919977009,-0.2213473916,-0.0704078153,0.0860572755,0.0268128999,0.0031038474,0.0268938132,0.0232798997,0.2867479622,0.1630622447,-0.0368349515,0.3667397797,-0.2062833607,0.1011864692,0.421010673,0.7274805903,-0.215399608,-0.2575476766,-0.3073272705,-0.3769135177,0.0297992546,0.2773856521,0.4866474867,0.0031598224,0.1984095871,-0.3036155999,0.057540372,0.038236998,0.1616750956,-0.2182648033,0.2967064977,-0.0009074002,0.1527033448,-0.0929155275,0.0851415694,0.2732588649,-0.1273201704,0.0084036961,0.1611264944,-0.2615137696,0.0117760235,-0.028219644,-0.2475543171,0.2653850913,0.3092905581,0.3690367043,-0.3131037354,-0.1379642636,-0.0037725242,0.239037782,0.379905194,-0.0437345132,0.1846986711,-0.1888067573,0.0326588899,0.1132931337,-0.0755623877,-0.0282561257,-0.3621478677,0.0659071729,-0.0113287931,0.6134747267,-0.2487007678,-0.6051997542,0.0658672377,-0.2793935835,-0.2565443218,-0.3295621574,-0.1635749787,-0.3112907708,0.2582394481,0.1950361133,-0.0819413513,-0.3276638687,-0.3721604943,-0.2869725823,-0.1174980029,-0.1620406508,0.0461693704,-0.104130201,-0.0131914904,0.3832283616,-0.0587805249,-0.0423808992,-0.2699390352,0.1448607892,0.0324001946,-0.0699524432,0.150408566,-0.163368687,0.1828367412,0.0472206064,0.084708415,-0.356433779,0.3897977173,-0.2615618706,-0.3636491001,-0.2217823118,0.1456974,-0.3320489228,-0.2151723057,0.3394041359,-0.0417384505,0.2415718585,-0.2057961076,-0.1408115029,-0.2203736901,-0.2054502219,-0.1400124133,0.0715175718,0.3740119636,-0.2969316244,-0.218428269,0.2159541398,-0.0582996793,0.4005611539,0.4265969098,0.0306359641,0.0068852236,-0.1978981346,0.251529634,0.1011661589,-0.2532138526,-0.6780224442,0.4099708498,-0.1634244025,0.1814756691,0.1188286543,-0.0990723297,0.0737057328,-0.131755352,-0.0850457847,0.2120765597,-0.2106408179,0.0707255006,-0.5871102214,-0.7869003415,0.3780463934,-0.0584511533,0.1627453417,-0.1797488481,-0.0278539229,0.2599582672,0.4960872531,0.1067426801,0.0147278421,0.1068839282,0.0614952855,0.0681387484,-0.0840681568,-0.2501348555,-0.1588561684,0.2199242413,-0.1815293133,-0.0562988445,0.3016198874,-0.1083739847,-0.5671508908,-0.205630973,-0.0594865642,-0.0970369652,0.0005349878,0.0013908687,0.1822182983,0.0878181085,0.1549400836,0.3509557545,0.0925608948,0.1153081357,-0.3258306086,0.2187996805,-0.042067606,-0.1144090295,0.1862325072,-0.0807211772,0.1894461066,-0.0506584011,-0.4008100629,0.6621257663,-0.0508649424,0.1304217279,-0.0485616773,0.0759109631,0.208716765,-0.1647550017,0.1396922916,0.4040056467,-0.0078118043,-0.0893657655,0.1285756379,-0.1466649324,0.1681570411,0.0385813452,-0.1399838179,0.0219172258,-0.1082341596,-0.1906882823,-0.0218825191,-0.1363818944,0.1214792728,-0.0143770063,-0.1486689448,0.0490543544,0.1307161152,-0.1663719416,0.735114038,-0.0395391472,-0.0493242703,0.3084146678,-0.3009499013,-0.0696082339,-0.1869142652,0.2009025812,0.5170977712,0.2373419106,0.1705388427,0.2232394367,0.0059838467,-0.1573754251,0.1209169701,0.298640728,0.2252731025,0.2332677841,-0.0040379744,0.0442569368,-0.1431460232,0.1358934343,0.054894004,0.3265087605,-0.4214719534,-0.2221026719,-0.4799452126,-0.0070513934,-0.332985729,0.185629949,-0.1814439297,-0.3045488298,-0.0691658854,0.0729711354,-0.0868554711,0.1772733927,-0.2617514133,0.1166194379,0.1726839989,0.1684265584,0.1131397113,-0.0296697319,-0.5200146437,0.0163424835,0.2846112549,-0.2646366358,0.3774171472,-0.553154707,0.0195056908,-0.0720698833,-0.1443551183,-0.1200665608,0.0596865043,0.0407049321,0.1904513389,0.1615996957,0.0788518563,-0.0324806385,0.1823148876,-0.2791775167,-0.1931467801,-0.0665636063,0.1417395473,0.2153919935,-0.0159327202,-0.5946922898,-0.2640362382,-0.3566413522,0.2469536364,0.0686296374,0.1870593131,0.0002924684,0.082385689,0.0191314239,0.042840708,0.0874992684,-0.2150725871,-0.4413594306,0.3366400301,0.0674884766,-0.3040826321,-0.0139753176,0.2830919027,-0.0750178322,0.0852657259,-0.6037398577,-0.1121485978,-0.3842428923,-0.1020442545,-0.1089007631,0.1909778714,0.4259518683,0.0320949741,-0.0522993766,-0.1426744163,-0.3267737031,-0.0091122296,0.2860001922,0.5351224542,-0.0486740395,0.2700761557,0.3071492612,0.437469542,0.536436379,0.0791866481,0.0161778517,-0.038535431,0.0828423277,-0.1028325781,0.053590294,0.191081807,-0.2176369429,0.0492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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2832","title":"Logging levels not taken into account","comments":"I just take a look at all the outputs produced by `datasets` using the different log-levels.\r\nAs far as i can tell using `datasets==1.17.0` they overall issue seems to be fixed.\r\n\r\nHowever, I noticed that there is one tqdm based progress indicator appearing on STDERR that I can simply not suppress.\r\n```\r\nResolving data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 652\/652 [00:00<00:00, 1604.52it\/s]\r\n```\r\n\r\nAccording to _get_origin_metadata_locally_or_by_urls it shold be supressable by using the `NOTSET` log-level\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/1406a04c3e911cec2680d8bc513653e0cafcaaa4\/src\/datasets\/data_files.py#L491-L501\r\nSadly when specifiing the log-level `NOTSET` it seems to has no effect.\r\n\r\nBut appart from it not having any effect I must admit that it seems unintuitive to me.\r\nI would suggest changing this such that it is only shown when the log-level is greater or equal to INFO.\r\n\r\nThis would conform better to INFO according to the [documentation](https:\/\/huggingface.co\/docs\/datasets\/v1.0.0\/package_reference\/logging_methods.html#datasets.logging.set_verbosity_info).\r\n> This will display most of the logging information and tqdm bars.\r\n\r\nAny inputs on this?\r\nI will be happy to supply a PR if desired \ud83d\udc4d ","body":"## Describe the bug\r\n\r\nThe `logging` module isn't working as intended relative to the levels to set.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import logging\r\n\r\nlogging.set_verbosity_debug()\r\nlogger = logging.get_logger()\r\n\r\nlogger.error(\"ERROR\")\r\nlogger.warning(\"WARNING\")\r\nlogger.info(\"INFO\")\r\nlogger.debug(\"DEBUG\"\r\n```\r\n\r\n## Expected results\r\n\r\nI expect all logs to be output since I'm putting a `debug` level.\r\n\r\n## Actual results\r\n\r\nOnly the two first logs are output.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.13.9-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.6\r\n- PyArrow version: 5.0.0\r\n\r\n## To go further\r\n\r\nThis logging issue appears in `datasets` but not in `transformers`. It happens because there is no handler defined for the logger. When no handler is defined, the `logging` library will output a one-off error to stderr, using a `StderrHandler` with level `WARNING`.\r\n\r\n`transformers` sets a default `StreamHandler` [here](https:\/\/github.com\/huggingface\/transformers\/blob\/5c6eca71a983bae2589eed01e5c04fcf88ba5690\/src\/transformers\/utils\/logging.py#L86)","comment_length":159,"text":"Logging levels not taken into account \n ## Describe the bug\r\n\r\nThe `logging` module isn't working as intended relative to the levels to set.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import logging\r\n\r\nlogging.set_verbosity_debug()\r\nlogger = logging.get_logger()\r\n\r\nlogger.error(\"ERROR\")\r\nlogger.warning(\"WARNING\")\r\nlogger.info(\"INFO\")\r\nlogger.debug(\"DEBUG\"\r\n```\r\n\r\n## Expected results\r\n\r\nI expect all logs to be output since I'm putting a `debug` level.\r\n\r\n## Actual results\r\n\r\nOnly the two first logs are output.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.13.9-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.6\r\n- PyArrow version: 5.0.0\r\n\r\n## To go further\r\n\r\nThis logging issue appears in `datasets` but not in `transformers`. It happens because there is no handler defined for the logger. When no handler is defined, the `logging` library will output a one-off error to stderr, using a `StderrHandler` with level `WARNING`.\r\n\r\n`transformers` sets a default `StreamHandler` [here](https:\/\/github.com\/huggingface\/transformers\/blob\/5c6eca71a983bae2589eed01e5c04fcf88ba5690\/src\/transformers\/utils\/logging.py#L86) \n I just take a look at all the outputs produced by `datasets` using the different log-levels.\r\nAs far as i can tell using `datasets==1.17.0` they overall issue seems to be fixed.\r\n\r\nHowever, I noticed that there is one tqdm based progress indicator appearing on STDERR that I can simply not suppress.\r\n```\r\nResolving data files: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 652\/652 [00:00<00:00, 1604.52it\/s]\r\n```\r\n\r\nAccording to _get_origin_metadata_locally_or_by_urls it shold be supressable by using the `NOTSET` log-level\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/1406a04c3e911cec2680d8bc513653e0cafcaaa4\/src\/datasets\/data_files.py#L491-L501\r\nSadly when specifiing the log-level `NOTSET` it seems to has no effect.\r\n\r\nBut appart from it not having any effect I must admit that it seems unintuitive to me.\r\nI would suggest changing this such that it is only shown when the log-level is greater or equal to INFO.\r\n\r\nThis would conform better to INFO according to the [documentation](https:\/\/huggingface.co\/docs\/datasets\/v1.0.0\/package_reference\/logging_methods.html#datasets.logging.set_verbosity_info).\r\n> This will display most of the logging information and tqdm bars.\r\n\r\nAny inputs on this?\r\nI will be happy to supply a PR if desired \ud83d\udc4d 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2832","title":"Logging levels not taken into account","comments":"Hi! This should disable the tqdm output:\r\n```python\r\nimport datasets\r\ndatasets.set_progress_bar_enabled(False)\r\n```\r\n\r\nOn a side note: I believe the issue with logging (not tqdm) is still relevant on master.","body":"## Describe the bug\r\n\r\nThe `logging` module isn't working as intended relative to the levels to set.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import logging\r\n\r\nlogging.set_verbosity_debug()\r\nlogger = logging.get_logger()\r\n\r\nlogger.error(\"ERROR\")\r\nlogger.warning(\"WARNING\")\r\nlogger.info(\"INFO\")\r\nlogger.debug(\"DEBUG\"\r\n```\r\n\r\n## Expected results\r\n\r\nI expect all logs to be output since I'm putting a `debug` level.\r\n\r\n## Actual results\r\n\r\nOnly the two first logs are output.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.13.9-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.6\r\n- PyArrow version: 5.0.0\r\n\r\n## To go further\r\n\r\nThis logging issue appears in `datasets` but not in `transformers`. It happens because there is no handler defined for the logger. When no handler is defined, the `logging` library will output a one-off error to stderr, using a `StderrHandler` with level `WARNING`.\r\n\r\n`transformers` sets a default `StreamHandler` [here](https:\/\/github.com\/huggingface\/transformers\/blob\/5c6eca71a983bae2589eed01e5c04fcf88ba5690\/src\/transformers\/utils\/logging.py#L86)","comment_length":29,"text":"Logging levels not taken into account \n ## Describe the bug\r\n\r\nThe `logging` module isn't working as intended relative to the levels to set.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import logging\r\n\r\nlogging.set_verbosity_debug()\r\nlogger = logging.get_logger()\r\n\r\nlogger.error(\"ERROR\")\r\nlogger.warning(\"WARNING\")\r\nlogger.info(\"INFO\")\r\nlogger.debug(\"DEBUG\"\r\n```\r\n\r\n## Expected results\r\n\r\nI expect all logs to be output since I'm putting a `debug` level.\r\n\r\n## Actual results\r\n\r\nOnly the two first logs are output.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.13.9-arch1-1-x86_64-with-glibc2.33\r\n- Python version: 3.9.6\r\n- PyArrow version: 5.0.0\r\n\r\n## To go further\r\n\r\nThis logging issue appears in `datasets` but not in `transformers`. It happens because there is no handler defined for the logger. When no handler is defined, the `logging` library will output a one-off error to stderr, using a `StderrHandler` with level `WARNING`.\r\n\r\n`transformers` sets a default `StreamHandler` [here](https:\/\/github.com\/huggingface\/transformers\/blob\/5c6eca71a983bae2589eed01e5c04fcf88ba5690\/src\/transformers\/utils\/logging.py#L86) \n Hi! This should disable the tqdm output:\r\n```python\r\nimport datasets\r\ndatasets.set_progress_bar_enabled(False)\r\n```\r\n\r\nOn a side note: I believe the issue with logging (not tqdm) is still relevant on master.","embeddings":[-0.1961631626,-0.1857534796,0.1107907444,0.14803949,0.3297182322,-0.0822736099,0.5039983392,0.1595657766,-0.3525216281,0.0196762551,-0.0581960939,0.3992099464,-0.3322146535,0.0895235166,-0.2066877782,-0.0721533,-0.0582956858,-0.1462147087,-0.7243373394,0.0119105158,0.0195580833,0.1667648703,-0.2251543254,-0.031044025,-0.8175336719,0.0477162264,0.2403507978,0.1338019669,0.1247154623,-0.4091963768,-0.0588396601,-0.1771491021,0.4029044509,0.6211340427,-0.0001126207,0.164409712,0.4035289586,0.0473797396,-0.3274336159,-0.1172748804,-0.1890802085,-0.5097724795,0.3058553934,0.0477110706,0.1475175023,-0.2378335446,0.0696510151,-0.1291801929,0.4339434803,0.2530440092,0.2087228447,0.2816491127,-0.1198415533,0.2419210821,0.0305378158,0.0303584728,-0.2681500912,-0.0914779603,0.2344406396,0.0488845594,-0.4311731458,0.194169119,0.0194285288,0.065440625,0.1002029106,0.1689889431,0.566624701,-0.2679894865,0.2360870987,0.1478902549,0.5088433623,-0.3595345914,-0.3113983274,-0.2981720865,-0.1628542244,-0.2935987711,0.3128688633,-0.1524338871,-0.3761093318,0.1747971028,-0.2810962796,0.2337384522,-0.1563786864,-0.0268146265,-0.285045594,0.2692164183,-0.1607903093,-0.0963201225,-0.2304420173,0.1415987611,0.359727174,0.0579432398,0.0447671749,0.0487129763,-0.6872738004,0.0309156422,0.564855516,-0.008383533,-0.2129118294,0.2596915066,-0.3558063507,0.1998559088,0.146699816,0.0373592228,0.3965774179,0.1945800036,0.2000785619,0.4664000869,0.2577106953,-0.1843634397,0.096906729,0.067274712,0.2396020591,-0.2853828371,0.7518724799,0.326287061,0.337495178,-0.1994034797,0.1333765686,0.0334258191,0.142145887,0.0238745306,-0.1016982347,0.2498173416,0.1464494318,0.0842108727,-0.2175168842,0.2263491899,-0.1252815723,-0.0578348823,-0.1602466851,-0.3468601704,-0.1907569915,-0.0038836733,0.2922649682,0.0947797522,0.1197690368,0.2418019176,0.1227588579,-0.0453739204,0.253985852,-0.0451220833,0.180202201,0.3891866803,-0.1877993494,0.3774586618,0.1221587434,-0.2311720848,-0.1906292886,0.2874311805,0.0527112968,-0.2976286113,-0.0702843219,0.1449474692,-0.2183810323,0.0385466851,-0.3331749737,0.1909691542,0.4282231331,0.2084953189,0.1515865624,-0.2178490311,0.077216953,-0.0886494517,0.1347335279,0.289529711,-0.2243276536,-0.3214562833,-0.4558845758,-0.0703269467,0.4135137498,-0.1000462621,0.0729908273,0.3193694353,-0.1175439358,-0.1573952585,0.3047197461,-0.2394626886,-0.1108431593,0.2009868473,-0.2378268689,0.2311310619,0.2109929919,-0.0107220523,0.2284746021,-0.1858541369,0.0317303017,-0.0965961292,-0.0761840418,-0.011527543,-0.2013270855,0.1903138906,-0.02809605,0.0513514169,0.2954565585,-0.1307561696,-0.0628356263,-0.2544024587,0.2757626772,0.0596944839,0.2939062119,0.3580637872,0.0496150963,-0.2431005538,0.0570562668,-0.2956659794,-0.2710455358,0.1286030263,-0.068712391,0.3166913688,-0.1662701666,-0.1360496134,-0.1555981487,-0.0677530468,-0.2247299105,-0.3235754967,0.1252478957,0.1171186939,-0.4840775132,-0.1226032972,-0.4014335573,0.0886065289,0.0570016466,0.1415705532,-0.075730592,0.1488713324,0.112022087,-0.2919694185,0.1143543422,0.1264327914,0.1433572918,0.2091687322,-0.0441686586,0.1328215152,-0.0128590353,0.3123262227,-0.035526447,0.2624204457,0.0120606404,-0.2896254957,-0.1676879525,0.4245975316,-0.264757365,0.2080600262,0.0324222259,0.0356401093,0.1069643945,0.2309785932,-0.2238096297,0.1077130958,-0.0675715432,-0.1784480512,-0.1800573915,-0.2999486923,0.0748855621,0.0032704019,0.108223699,-0.1484938562,0.0070483671,0.3163109422,0.4622617364,-0.031582579,0.1585717648,-0.0436266623,-0.2921994925,0.118711032,0.188999936,0.1749476641,0.3119925857,0.1866185516,0.451299727,0.1608059406,-0.0904001519,-0.0431504883,0.0107072117,0.2198346853,-0.3489575386,0.3686018884,0.0724782422,0.0651265606,-0.1239082143,0.0399236381,-0.3184741139,0.1378659904,-0.4924426079,0.1809544563,-0.3752939105,-0.100427106,-0.3149755001,-0.1193744391,-0.3806522191,-0.2864054441,0.1889881641,-0.0876627043,-0.2258024961,0.3630396426,0.0304593667,-0.137546733,0.1519270837,0.0302967131,-0.3355326951,0.0377983823,-0.2157171518,0.0924028978,0.0885362327,-0.0445694812,0.1381099522,-0.0509386137,-0.0554657914,0.1526848823,-0.2914133668,0.2315916866,-0.1484282166,0.3853763938,-0.2647819221,-0.2195249945,0.0947788209,0.1816896647,-0.1378818154,-0.1246391982,0.0160609856,0.1018250436,-0.0249030162,0.2027850449,-0.1329838336,-0.0404179469,0.0034179986,-0.4241661429,-0.1651818901,-0.1784505248,0.1941047311,0.2435721904,-0.3272636235,0.1154976115,-0.1252847761,0.3236821592,0.2372186333,-0.2294468135,0.31422472,-0.2238786668,-0.2484498322,-0.0604179166,-0.0575501136,-0.1547350287,-0.2085780352,-0.3361050189,-0.0273777265,-0.1448063403,-0.2392134666,0.0314082317,0.328843236,0.2584891617,0.2707686126,-0.0694302097,-0.0711980239,-0.0978967026,-0.2681845427,0.1198685765,0.2962121964,-0.3872793019,0.0889027193,0.1711236984,0.3883065283,0.4493600428,0.2492602617,-0.1704489291,-0.109523423,0.3249649107,-0.0348286927,-0.2589968741,-0.1947960705,-0.1468948126,-0.347249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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2831","title":"ArrowInvalid when mapping dataset with missing values","comments":"Hi ! It fails because of the feature type inference.\r\n\r\nBecause the first 1000 examples all have null values in the \"match\" field, then it infers that the type for this field is `null` type before writing the data on disk. But as soon as it tries to map an example with a non-null \"match\" field, then it fails.\r\n\r\nTo fix that you can either:\r\n- increase the writer_batch_size to >2000 (default is 1000) so that some non-null values will be in the first batch written to disk\r\n```python\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], writer_batch_size=2000)\r\n```\r\n- OR force the feature type with:\r\n```python\r\nfrom datasets import Features, Value\r\n\r\nfeatures = Features({\r\n    'conflict': Value('int64'),\r\n    'date': Value('string'),\r\n    'headline': Value('string'),\r\n    'match': Value('float64'),\r\n    'label': Value('float64')\r\n})\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], features=features)\r\n```","body":"## Describe the bug\r\nI encountered an `ArrowInvalid` when mapping dataset with missing values. \r\nHere are the files for a minimal example. The exception is only thrown when the first line in the csv has a missing value (if you move the last line to the top it isn't thrown).\r\n[data_small.csv](https:\/\/github.com\/huggingface\/datasets\/files\/7037838\/data_small.csv)\r\n[data.csv](https:\/\/github.com\/huggingface\/datasets\/files\/7037842\/data.csv)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndatasets = load_dataset(\"csv\", data_files=['data_small.csv'])\r\n\r\ndatasets = datasets.map(lambda e: {'labels': e['match']},\r\n                        remove_columns=['id'])\r\n```\r\n\r\n## Expected results\r\nNo error\r\n\r\n## Actual results\r\n```\r\nFile \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Invalid null value\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.5.0\r\n- Platform: Linux-5.11.0-25-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyTorch version (GPU?): 1.7.1+cpu (False)\r\n- Tensorflow version (GPU?): 2.4.1 (False)\r\n- Using GPU in script?: no\r\n- Using distributed or parallel set-up in script?: no\r\n","comment_length":134,"text":"ArrowInvalid when mapping dataset with missing values \n ## Describe the bug\r\nI encountered an `ArrowInvalid` when mapping dataset with missing values. \r\nHere are the files for a minimal example. The exception is only thrown when the first line in the csv has a missing value (if you move the last line to the top it isn't thrown).\r\n[data_small.csv](https:\/\/github.com\/huggingface\/datasets\/files\/7037838\/data_small.csv)\r\n[data.csv](https:\/\/github.com\/huggingface\/datasets\/files\/7037842\/data.csv)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndatasets = load_dataset(\"csv\", data_files=['data_small.csv'])\r\n\r\ndatasets = datasets.map(lambda e: {'labels': e['match']},\r\n                        remove_columns=['id'])\r\n```\r\n\r\n## Expected results\r\nNo error\r\n\r\n## Actual results\r\n```\r\nFile \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Invalid null value\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.5.0\r\n- Platform: Linux-5.11.0-25-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyTorch version (GPU?): 1.7.1+cpu (False)\r\n- Tensorflow version (GPU?): 2.4.1 (False)\r\n- Using GPU in script?: no\r\n- Using distributed or parallel set-up in script?: no\r\n \n Hi ! It fails because of the feature type inference.\r\n\r\nBecause the first 1000 examples all have null values in the \"match\" field, then it infers that the type for this field is `null` type before writing the data on disk. But as soon as it tries to map an example with a non-null \"match\" field, then it fails.\r\n\r\nTo fix that you can either:\r\n- increase the writer_batch_size to >2000 (default is 1000) so that some non-null values will be in the first batch written to disk\r\n```python\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], writer_batch_size=2000)\r\n```\r\n- OR force the feature type with:\r\n```python\r\nfrom datasets import Features, Value\r\n\r\nfeatures = Features({\r\n    'conflict': Value('int64'),\r\n    'date': Value('string'),\r\n    'headline': Value('string'),\r\n    'match': Value('float64'),\r\n    'label': Value('float64')\r\n})\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], features=features)\r\n```","embeddings":[-0.0327268355,-0.1531262994,0.1040895432,0.2286237329,0.0726004243,0.1366074681,0.3329794109,0.4905757904,-0.0207695644,0.2300736755,0.2338506132,0.4390177727,0.0271520261,-0.1494164914,-0.1128671244,-0.0434916839,0.036237888,0.1175362915,-0.0013278106,-0.0447311327,-0.378742218,0.0844402388,-0.3389022946,0.0575111024,-0.3012346625,-0.1077298597,-0.0090291733,0.0034838135,0.1298887879,-0.4910511672,0.2923193276,-0.5293874741,-0.1505012959,0.1626194119,-0.0001158711,-0.0339868702,0.2518496215,0.0519128256,-0.0784546211,-0.2758250237,-0.1568179429,-0.2185934782,-0.0141735272,-0.1653571576,0.1762025803,-0.0375164859,0.0760230497,-0.1961084604,-0.0201382134,0.2709065378,0.1460493654,0.4126615822,0.217869252,0.0892388672,0.4545611143,0.0507913604,-0.2142468095,0.3872686923,0.150176838,-0.2261426896,0.1055756882,0.3145453632,-0.0312069766,0.0160816535,0.3101096451,0.176736027,-0.0633132234,-0.2293644398,-0.0956780314,0.1412264705,0.121313028,-0.4014458358,-0.430261761,-0.2851817012,0.0836299956,-0.3390862942,0.3403961062,0.168567881,-0.1564158648,0.1623672247,0.1155143604,0.2014273405,-0.192848891,0.1499879211,-0.1042830124,0.0833818689,0.0503504686,0.2609374523,-0.0560738258,-0.2023003697,-0.2976311743,-0.1810262501,0.0063066739,0.2630151212,-0.3672583699,-0.2198308855,-0.1504853517,-0.1560584158,0.1592742801,-0.2276551127,0.2990320325,-0.1067543626,0.0777331293,0.4128786623,0.0608040318,-0.1506104469,0.1527083069,0.108326219,0.1780028194,-0.4061113,0.2636467516,-0.0342918187,0.1340156496,-0.5859510303,0.5141164064,0.2606024444,0.6728469133,-0.1126297936,-0.4689024091,0.1276107281,-0.7892329097,0.3337589502,-0.135780707,0.1627140641,0.0130745769,0.0863919556,0.0484194085,0.2774145603,-0.1620678008,-0.2743251026,0.0147257512,-0.0746785253,-0.0540683046,-0.0446719974,0.3431840837,-0.189890027,0.2458141446,0.1759653986,-0.1122789308,-0.0045988979,0.2626313269,-0.3046376705,0.1480023861,0.1737484187,-0.4271009266,0.2794370651,0.0387199447,-0.146589011,-0.1892317235,0.2775795162,-0.1205456331,-0.1588583738,-0.2165751904,0.1482956111,0.0022299229,-0.2128342688,-0.0540005676,-0.0019999465,0.242374599,-0.173490569,0.1846102625,-0.219979018,0.0187497139,-0.3760899603,-0.0113685085,0.3736341894,-0.6385915279,0.070836775,0.1267937571,-0.0185744036,-0.0337321162,0.3634842634,-0.1870690137,-0.013671454,-0.0591617823,0.5252671838,-0.0086465375,-0.1326466799,-0.4373976886,0.131386131,-0.074395977,-0.2444548905,0.0401039533,0.2435707748,0.1417698264,-0.1496624351,-0.01733483,-0.1213846132,-0.1300859749,0.2243183255,-0.3260702193,-0.0411789082,0.1720778644,0.0506165735,-0.1469437182,-0.2109326124,0.1945364773,-0.6835855842,0.0329079628,-0.2068118751,0.2666673362,0.3962259293,0.4044350684,-0.0465761051,0.1552023888,-0.1333547831,-0.8110236526,0.1985868216,0.100847736,-0.1821604669,-0.4811460078,-0.4098103642,-0.1061270237,0.3230985403,0.169046849,0.1539756507,0.0751769021,-0.0067508742,-0.122685194,-0.0598925687,-0.2849860191,-0.298654139,-0.2172655761,0.1865486056,-0.2249355167,0.2575946748,-0.013920987,-0.2167794704,-0.0960751399,0.0344255976,0.1761991829,-0.1273743063,0.013591324,0.4820096791,0.1067622378,0.1463912725,-0.1182710975,0.0272354595,0.3414061964,-0.4875248373,-0.2777242064,0.3717956841,0.3092466295,0.0654949769,-0.1880118698,0.367030859,-0.0085762525,0.142661944,-0.243231982,-0.0240107384,0.0476458743,-0.0050884029,-0.1706681848,-0.0740773231,-0.0837703869,-0.0132249454,0.2308350503,0.1707009226,-0.648462832,0.0064496514,0.1777651906,-0.1262826473,0.1471400559,0.0463455319,-0.2821579576,0.3659059703,0.3918358088,0.0322905146,0.3437526524,0.197664246,-0.0775276646,0.0050951457,-0.1521711648,-0.0824590325,0.3486306369,-0.0050706733,0.0830795988,0.1302965432,0.1771517992,0.2778594494,-0.1754120141,0.0187847074,0.2271973938,0.1906450242,-0.5900598764,-0.0668672845,-0.1082044542,0.0791586116,-0.3532581627,-0.1602440476,-0.0837709084,-0.2679263949,-0.0238328073,0.1787119508,-0.4540548921,0.2962757349,-0.0503031909,0.0645017549,0.2097569555,-0.2873474061,-0.1986872554,-0.1805445403,-0.206972599,0.0005408119,0.1689912528,-0.0692994669,0.2951590121,0.2671799064,-0.105649069,-0.5031932592,-0.4341720939,0.0272096228,-0.228622973,0.1287030727,0.1987026632,0.2116244137,-0.0771777779,-0.3438918293,0.1970792711,0.0353334136,-0.2919718623,0.1465553045,-0.0171925724,-0.0445718765,-0.1751176119,-0.4850569963,-0.038820982,-0.228159681,0.2234225869,-0.3103996813,0.0502251722,0.163063243,0.3292430937,0.1380271018,-0.1170724183,-0.1576803476,0.1402166784,0.2776943147,0.5713930726,-0.0197698306,-0.3324659467,0.2355109453,-0.1852093339,-0.0800568014,0.4506347477,-0.2126406878,-0.0239198282,-0.1131706908,0.6397444606,-0.0702653602,-0.0265891757,0.3226291835,0.2941908538,0.0133324713,-0.1408387423,-0.3169084787,0.062488731,-0.0236808807,0.2940809429,0.0561678745,0.3970873654,0.0143699041,0.7554779053,0.1163189709,-0.2453815788,0.1562790424,-0.2184980959,-0.0008914333,0.1268464029,-0.1204357743,-0.117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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2826","title":"Add a Text Classification dataset: KanHope","comments":"Hi ! In your script it looks like you're trying to load the dataset `bn_hate_speech,`, not KanHope.\r\n\r\nMoreover the error `KeyError: ' '` means that you have a feature of type ClassLabel, but for a certain example of the dataset, it looks like the label is empty (it's just a string with a space). Can you make sure that the data don't have missing labels, and that your dataset script parses the labels correctly ?","body":"## Adding a Dataset\r\n- **Name:** *KanHope*\r\n- **Description:** *A code-mixed English-Kannada dataset for Hope speech detection*\r\n- **Paper:** *https:\/\/arxiv.org\/abs\/2108.04616* (I am the author of the paper}\r\n- **Author:** *[AdeepH](https:\/\/github.com\/adeepH)*\r\n- **Data:** *https:\/\/github.com\/adeepH\/KanHope\/tree\/main\/dataset*\r\n- **Motivation:** *The dataset is amongst the very few resources available for code-mixed Dravidian languages*\r\n\r\n- I tried following the steps as per the instructions. However, could not resolve an error. Any help would be appreciated.\r\n\r\n- The dataset card and the scripts for the dataset *https:\/\/github.com\/adeepH\/datasets\/tree\/multilingual-hope-speech\/datasets\/mhs_eval*\r\n\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset bn_hate_speech\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/bn_hate_speech\/default\/0.0.0\/5f417ddc89777278abd29988f909f39495f0ec802090f7d8fa63b5bffb121762...\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-114-4a9cdb519e4c> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 data = load_dataset('\/content\/bn')\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    850         ignore_verifications=ignore_verifications,\r\n    851         try_from_hf_gcs=try_from_hf_gcs,\r\n--> 852         use_auth_token=use_auth_token,\r\n    853     )\r\n    854 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    614                     if not downloaded_from_gcs:\r\n    615                         self._download_and_prepare(\r\n--> 616                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    617                         )\r\n    618                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    691             try:\r\n    692                 # Prepare split will record examples associated to the split\r\n--> 693                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    694             except OSError as e:\r\n    695                 raise OSError(\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1107                     disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n   1108                 ):\r\n-> 1109                     example = self.info.features.encode_example(record)\r\n   1110                     writer.write(example, key)\r\n   1111             finally:\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_example(self, example)\r\n   1015         \"\"\"\r\n   1016         example = cast_to_python_objects(example)\r\n-> 1017         return encode_nested_example(self, example)\r\n   1018 \r\n   1019     def encode_batch(self, batch):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_nested_example(schema, obj)\r\n    863     if isinstance(schema, dict):\r\n    864         return {\r\n--> 865             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    866         }\r\n    867     elif isinstance(schema, (list, tuple)):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in <dictcomp>(.0)\r\n    863     if isinstance(schema, dict):\r\n    864         return {\r\n--> 865             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    866         }\r\n    867     elif isinstance(schema, (list, tuple)):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_nested_example(schema, obj)\r\n    890     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    891     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 892         return schema.encode_example(obj)\r\n    893     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    894     return obj\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_example(self, example_data)\r\n    665         # If a string is given, convert to associated integer\r\n    666         if isinstance(example_data, str):\r\n--> 667             example_data = self.str2int(example_data)\r\n    668 \r\n    669         # Allowing -1 to mean no label.\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in str2int(self, values)\r\n    623                 if value not in self._str2int:\r\n    624                     value = str(value).strip()\r\n--> 625                 output.append(self._str2int[str(value)])\r\n    626             else:\r\n    627                 # No names provided, try to integerize\r\n\r\nKeyError: ' '\r\n```","comment_length":75,"text":"Add a Text Classification dataset: KanHope \n ## Adding a Dataset\r\n- **Name:** *KanHope*\r\n- **Description:** *A code-mixed English-Kannada dataset for Hope speech detection*\r\n- **Paper:** *https:\/\/arxiv.org\/abs\/2108.04616* (I am the author of the paper}\r\n- **Author:** *[AdeepH](https:\/\/github.com\/adeepH)*\r\n- **Data:** *https:\/\/github.com\/adeepH\/KanHope\/tree\/main\/dataset*\r\n- **Motivation:** *The dataset is amongst the very few resources available for code-mixed Dravidian languages*\r\n\r\n- I tried following the steps as per the instructions. However, could not resolve an error. Any help would be appreciated.\r\n\r\n- The dataset card and the scripts for the dataset *https:\/\/github.com\/adeepH\/datasets\/tree\/multilingual-hope-speech\/datasets\/mhs_eval*\r\n\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset bn_hate_speech\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/bn_hate_speech\/default\/0.0.0\/5f417ddc89777278abd29988f909f39495f0ec802090f7d8fa63b5bffb121762...\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-114-4a9cdb519e4c> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 data = load_dataset('\/content\/bn')\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    850         ignore_verifications=ignore_verifications,\r\n    851         try_from_hf_gcs=try_from_hf_gcs,\r\n--> 852         use_auth_token=use_auth_token,\r\n    853     )\r\n    854 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    614                     if not downloaded_from_gcs:\r\n    615                         self._download_and_prepare(\r\n--> 616                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    617                         )\r\n    618                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    691             try:\r\n    692                 # Prepare split will record examples associated to the split\r\n--> 693                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    694             except OSError as e:\r\n    695                 raise OSError(\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1107                     disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n   1108                 ):\r\n-> 1109                     example = self.info.features.encode_example(record)\r\n   1110                     writer.write(example, key)\r\n   1111             finally:\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_example(self, example)\r\n   1015         \"\"\"\r\n   1016         example = cast_to_python_objects(example)\r\n-> 1017         return encode_nested_example(self, example)\r\n   1018 \r\n   1019     def encode_batch(self, batch):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_nested_example(schema, obj)\r\n    863     if isinstance(schema, dict):\r\n    864         return {\r\n--> 865             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    866         }\r\n    867     elif isinstance(schema, (list, tuple)):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in <dictcomp>(.0)\r\n    863     if isinstance(schema, dict):\r\n    864         return {\r\n--> 865             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    866         }\r\n    867     elif isinstance(schema, (list, tuple)):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_nested_example(schema, obj)\r\n    890     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    891     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 892         return schema.encode_example(obj)\r\n    893     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    894     return obj\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_example(self, example_data)\r\n    665         # If a string is given, convert to associated integer\r\n    666         if isinstance(example_data, str):\r\n--> 667             example_data = self.str2int(example_data)\r\n    668 \r\n    669         # Allowing -1 to mean no label.\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in str2int(self, values)\r\n    623                 if value not in self._str2int:\r\n    624                     value = str(value).strip()\r\n--> 625                 output.append(self._str2int[str(value)])\r\n    626             else:\r\n    627                 # No names provided, try to integerize\r\n\r\nKeyError: ' '\r\n``` \n Hi ! In your script it looks like you're trying to load the dataset `bn_hate_speech,`, not KanHope.\r\n\r\nMoreover the error `KeyError: ' '` means that you have a feature of type ClassLabel, but for a certain example of the dataset, it looks like the label is empty (it's just a string with a space). Can you make sure that the data don't have missing labels, and that your dataset script parses the labels correctly ?","embeddings":[-0.2438095212,-0.0837450847,-0.1076410264,0.1681506783,0.2711259723,0.0154425018,0.1767380387,0.4127865434,0.1465682834,0.086858876,-0.1648917794,-0.0779896751,-0.2590732574,0.2801799476,-0.2317627817,-0.2915211916,-0.0884709209,0.1522613019,-0.1177391112,-0.0643954799,-0.0891423598,0.3205003142,-0.1597214788,0.1476871818,-0.2826568484,-0.3294512033,-0.1136720181,0.0935218558,-0.3491749763,-0.4404600263,0.265022397,-0.052807495,0.1387756914,0.3857922852,-0.0001095746,-0.0700783134,0.31012097,-0.1751648784,-0.3034772277,-0.2981868386,-0.0789678693,-0.0469495505,-0.023372205,-0.3721512854,-0.2534294724,0.1050642058,-0.0610323809,-0.4590233564,0.5346906781,0.650282979,0.229791224,0.0913035348,0.3871519566,-0.2322711349,0.0315533839,0.2178065032,-0.2045287341,0.3952790499,0.2633341849,-0.1257786155,0.0324165039,0.3559170365,0.0472595692,0.1438392997,0.2195971012,-0.015439433,0.1111363098,-0.2860589921,0.2297817022,0.1622222811,0.320484072,-0.4211920202,-0.1435187608,-0.1698827595,0.0571510829,-0.3703622818,0.3384325504,0.0452004038,-0.3210283518,0.219812423,0.2442415953,-0.0520663597,-0.1897224188,0.1275731176,0.2060876638,0.3765225112,-0.102869004,0.0157828555,-0.0386532173,-0.1966774017,0.0999093354,-0.2033937275,0.0466274358,0.2891245186,-0.3035188317,-0.0678539053,0.0391921364,-0.1798283607,0.1154339984,-0.2513130009,-0.0251798052,0.0710902959,0.002385335,0.009314036,0.0932091624,0.1263341308,0.2573832273,0.2938033938,0.3097373545,0.2206825316,-0.0685122982,0.0444711931,0.0038334976,-0.0998946726,0.0476477519,0.1706878841,0.2578557134,-0.1094403118,-0.3618175387,0.0034739668,-0.0324972756,0.0244196821,-0.00028303,0.3031305075,0.1252112687,-0.1238038316,0.1699262112,0.2675572634,-0.1435555369,-0.324788481,-0.1181280389,0.3308382332,-0.1890414208,-0.1475578994,0.2492350042,-0.1676839888,-0.0145412395,-0.0785941333,0.1480413526,-0.0763944089,-0.1346729398,-0.1982849091,-0.1027288511,0.1339935511,-0.0330300145,0.0655056462,0.3730125427,-0.528439343,-0.1443895847,0.054698877,-0.0276944656,-0.2500066161,-0.0474639162,0.2256289274,0.0221192595,0.0573181361,-0.3491019309,0.4549744427,0.2764905989,-0.0304751769,-0.0142187122,-0.2228385508,-0.0456286781,-0.1861785799,0.1986172944,0.5018196702,-0.3949016333,-0.0255267676,-0.295882225,-0.0158854276,0.1709475815,0.3286664486,-0.1296211034,0.3348804116,-0.3377097845,0.2680733502,0.3865487278,-0.3286767006,-0.265244931,0.0421010889,-0.0843798369,-0.016684752,0.1469514221,-0.0085748956,0.1543220282,0.0568281338,0.212637499,0.199009344,0.0098339142,0.0903028995,-0.2157630771,0.0875527486,0.2370745391,0.1217467189,0.2510007024,-0.0429093987,0.1854168773,0.1080474779,0.2132850289,-0.3157053888,0.3144257963,0.0235330593,0.2331555039,-0.064878948,-0.0976154953,-0.3182422221,-0.034065105,0.1612092853,-0.0904948041,0.3825849593,-0.5090478659,-0.1393896788,-0.4510042071,0.0097899446,-0.4143909812,-0.2540181875,0.2000551075,0.0524621159,0.1434838027,0.1402629018,-0.1512691677,0.5673833489,-0.3430083096,0.0166805387,-0.0704813972,0.3489124179,-0.3280907273,-0.2981008291,0.2780768275,0.2281981111,0.0782784671,-0.1092452332,-0.138141498,0.286190182,-0.253243506,0.002743836,0.1318633556,-0.2189347744,-0.0235489141,-0.4651159942,0.1460891962,0.1815542579,0.2387862951,-0.1993078142,0.0126365162,0.4493567646,0.323233515,0.1039566621,-0.0041023707,0.0095627103,0.4009858966,0.0525740013,-0.0183920767,-0.198618561,0.2568436861,-0.0751996413,0.2822484672,-0.2066947073,-0.2829215825,0.0135723483,0.3775028884,0.0491406284,0.4471918046,0.1533788741,-0.1032971516,-0.0195986778,-0.1601633728,0.1207781211,0.3303133547,0.0281339604,-0.0462334566,0.2144807875,-0.1015012562,-0.184566617,0.2178310454,-0.0088697011,0.0528022572,0.2028618306,0.1282225102,-0.0992851406,-0.3807548583,-0.2188048363,-0.0102537293,0.3387620449,-0.2657980621,0.0051065776,-0.127150625,-0.3741125762,-0.1996125132,0.2105915397,-0.1274860203,-0.2177482992,0.0389078073,-0.0407144018,0.0290992465,0.1327520162,0.0913390741,0.0010963383,0.1120714322,-0.1972098649,-0.0206984226,-0.2795613706,-0.1944819093,0.1158320531,0.3588798046,0.1520616114,0.4850715101,-0.3050047457,0.0137912193,-0.1548298895,-0.4496572316,-0.0058443835,-0.3447466791,0.0721900761,0.0923592448,0.2036583275,-0.0428540371,-0.459580332,0.3144107461,-0.0724989846,-0.3400882185,0.1471819133,-0.0350828357,-0.0772237629,-0.0065320386,-0.7115244865,-0.302619487,-0.375590235,-0.0545475073,-0.1178930029,0.1017842889,0.0756666735,0.2219262719,0.233664915,0.0522240996,0.1142273992,-0.2015258968,-0.2000739127,0.3361239731,-0.3108819127,-0.3760727942,0.0944502652,-0.1340978891,0.3507673144,-0.2822908759,-0.3692141473,0.20770441,-0.2674362361,0.4491765797,0.0926628783,-0.1399540901,0.3198357522,0.0110754361,-0.0922598988,0.0202658288,-0.221143201,0.1299603432,-0.0406076089,0.3319708109,0.0096705416,0.47392717,0.0118622035,0.623893857,0.2154695541,0.0228207409,0.4156720936,0.1736146361,0.26692608,-0.3062385917,-0.4208293557,0.2276843935,-0.0628337711,0.119622983,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2825","title":"The datasets.map function does not load cached dataset after moving python script","comments":"This also happened to me on COLAB.\r\nDetails:\r\nI ran the `run_mlm.py` in two different notebooks. \r\nIn the first notebook, I do tokenization since I can get 4 CPU cores without any GPUs, and save the cache into a folder which I copy to drive.\r\nIn the second notebook, I copy the cache folder from drive and re-run the run_mlm.py script (this time I uncomment the trainer code which happens after the tokenization)\r\n\r\nNote: I didn't change anything in the arguments, not even the preprocessing_num_workers\r\n ","body":"## Describe the bug\r\nThe datasets.map function caches the processed data to a certain directory. When the map function is called another time with totally the same parameters, the cached data are supposed to be reloaded instead of re-processing. However, it doesn't reuse cached data sometimes. I use the common data processing in different tasks, the datasets are processed again, the only difference is that I run them in different files.\r\n\r\n## Steps to reproduce the bug\r\nJust run the following codes in different .py files.\r\n```python\r\nif __name__ == '__main__':\r\n    from datasets import load_dataset\r\n    from transformers import AutoTokenizer\r\n    raw_datasets = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\r\n\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\n\r\n    def tokenize_function(examples):\r\n        return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\r\n\r\n\r\n    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n```\r\n\r\n## Expected results\r\nThe map function should reload data in the second or any later runs.\r\n\r\n## Actual results\r\nThe processing happens in each run.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: linux\r\n- Python version: 3.7.6\r\n- PyArrow version: 3.0.0\r\n\r\nThis is the first time I report a bug. If there is any problem or confusing description, please let me know \ud83d\ude04.\r\n","comment_length":85,"text":"The datasets.map function does not load cached dataset after moving python script \n ## Describe the bug\r\nThe datasets.map function caches the processed data to a certain directory. When the map function is called another time with totally the same parameters, the cached data are supposed to be reloaded instead of re-processing. However, it doesn't reuse cached data sometimes. I use the common data processing in different tasks, the datasets are processed again, the only difference is that I run them in different files.\r\n\r\n## Steps to reproduce the bug\r\nJust run the following codes in different .py files.\r\n```python\r\nif __name__ == '__main__':\r\n    from datasets import load_dataset\r\n    from transformers import AutoTokenizer\r\n    raw_datasets = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\r\n\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\n\r\n    def tokenize_function(examples):\r\n        return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\r\n\r\n\r\n    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n```\r\n\r\n## Expected results\r\nThe map function should reload data in the second or any later runs.\r\n\r\n## Actual results\r\nThe processing happens in each run.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: linux\r\n- Python version: 3.7.6\r\n- PyArrow version: 3.0.0\r\n\r\nThis is the first time I report a bug. If there is any problem or confusing description, please let me know \ud83d\ude04.\r\n \n This also happened to me on COLAB.\r\nDetails:\r\nI ran the `run_mlm.py` in two different notebooks. \r\nIn the first notebook, I do tokenization since I can get 4 CPU cores without any GPUs, and save the cache into a folder which I copy to drive.\r\nIn the second notebook, I copy the cache folder from drive and re-run the run_mlm.py script (this time I uncomment the trainer code which happens after the tokenization)\r\n\r\nNote: I didn't change anything in the arguments, not even the preprocessing_num_workers\r\n 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2825","title":"The datasets.map function does not load cached dataset after moving python script","comments":"#2854 fixed the issue :)\r\n\r\nWe'll do a new release of `datasets` soon to make the fix available.\r\nIn the meantime, feel free to try it out by installing `datasets` from source\r\n\r\nIf you have other issues or any question, feel free to re-open the issue :)","body":"## Describe the bug\r\nThe datasets.map function caches the processed data to a certain directory. When the map function is called another time with totally the same parameters, the cached data are supposed to be reloaded instead of re-processing. However, it doesn't reuse cached data sometimes. I use the common data processing in different tasks, the datasets are processed again, the only difference is that I run them in different files.\r\n\r\n## Steps to reproduce the bug\r\nJust run the following codes in different .py files.\r\n```python\r\nif __name__ == '__main__':\r\n    from datasets import load_dataset\r\n    from transformers import AutoTokenizer\r\n    raw_datasets = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\r\n\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\n\r\n    def tokenize_function(examples):\r\n        return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\r\n\r\n\r\n    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n```\r\n\r\n## Expected results\r\nThe map function should reload data in the second or any later runs.\r\n\r\n## Actual results\r\nThe processing happens in each run.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: linux\r\n- Python version: 3.7.6\r\n- PyArrow version: 3.0.0\r\n\r\nThis is the first time I report a bug. If there is any problem or confusing description, please let me know \ud83d\ude04.\r\n","comment_length":47,"text":"The datasets.map function does not load cached dataset after moving python script \n ## Describe the bug\r\nThe datasets.map function caches the processed data to a certain directory. When the map function is called another time with totally the same parameters, the cached data are supposed to be reloaded instead of re-processing. However, it doesn't reuse cached data sometimes. I use the common data processing in different tasks, the datasets are processed again, the only difference is that I run them in different files.\r\n\r\n## Steps to reproduce the bug\r\nJust run the following codes in different .py files.\r\n```python\r\nif __name__ == '__main__':\r\n    from datasets import load_dataset\r\n    from transformers import AutoTokenizer\r\n    raw_datasets = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\r\n\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\n\r\n    def tokenize_function(examples):\r\n        return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\r\n\r\n\r\n    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n```\r\n\r\n## Expected results\r\nThe map function should reload data in the second or any later runs.\r\n\r\n## Actual results\r\nThe processing happens in each run.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: linux\r\n- Python version: 3.7.6\r\n- PyArrow version: 3.0.0\r\n\r\nThis is the first time I report a bug. If there is any problem or confusing description, please let me know \ud83d\ude04.\r\n \n #2854 fixed the issue :)\r\n\r\nWe'll do a new release of `datasets` soon to make the fix available.\r\nIn the meantime, feel free to try it out by installing `datasets` from source\r\n\r\nIf you have other issues or any question, feel free to re-open the issue :)","embeddings":[-0.1273975223,0.0724143684,0.0914586037,0.0095804129,0.1599638164,-0.0660084039,0.4047463834,0.246048376,0.2259855419,-0.0873799026,0.1144409701,0.5168568492,-0.0583694465,-0.1686522663,0.1454256177,0.1743037403,0.2147076875,0.1613187045,0.0151957124,-0.1748613268,-0.2260828018,-0.0004946861,-0.3888833523,-0.0530203693,-0.4167590141,0.1189529076,-0.1496831626,-0.0641775802,0.2103359103,-0.4711386859,0.3424901962,0.1252883971,0.2924120724,0.3714997768,-0.0001175742,-0.0325822458,0.2381018996,-0.0887213871,-0.1687702239,-0.1984855384,-0.0911653638,-0.0784300789,0.1140753031,-0.1740205139,0.0148621257,-0.0670339316,-0.0580180101,-0.5517778993,0.3449013233,0.2660760283,0.1569478512,0.091664575,-0.1324367374,0.1409147382,-0.0919476375,-0.1474609375,-0.0402100831,-0.1767199039,0.288394928,-0.4073186815,-0.1861928105,0.2407978177,-0.1470916569,0.0880867541,0.3459538817,0.0264889579,-0.1978945434,-0.3031310439,0.3096983433,-0.0609247796,0.3651449382,-0.4083526433,-0.2633101344,-0.2716084719,-0.1292909235,-0.0306372046,0.1167385951,-0.2174400091,0.2154372633,0.1586684734,-0.4689263701,-0.1160352752,0.1113872156,0.1911925524,-0.126292944,0.4278686345,-0.0862307549,0.1805843711,-0.1728989333,-0.0524413548,0.1630068719,-0.1279247999,0.0592711233,0.5038397312,-0.1977031082,-0.0271315686,0.1539091021,-0.0486140214,0.0574085824,0.1103376374,-0.009577659,0.2165228575,-0.1987567395,-0.0295949746,0.1525702477,0.5008949041,0.1595460474,0.4105488658,0.1037071869,-0.0912700221,-0.6493957043,-0.1686208397,-0.1285147518,-0.0576798096,0.6006772518,0.0352326371,0.203829214,-0.0009124889,0.1098072156,0.0979165137,-0.071602203,-0.1900148988,0.0337545089,0.449154079,-0.0397840627,0.3059886694,-0.0941899493,0.2121893764,-0.1355014145,0.0562306903,-0.1956659108,-0.2108473182,-0.1978409588,0.2894202173,0.2324400395,0.1111553609,0.3557316959,0.0897305757,-0.0325618498,-0.120944269,0.277336508,0.0440268628,0.6040584445,0.1215034574,-0.0231417157,0.5818362236,0.2637910247,0.0014681084,-0.151149407,0.1716978252,-0.6476752162,-0.1697181016,0.3885863125,0.0899603292,-0.2056822926,-0.1571826786,-0.1573634148,-0.0107510462,0.5134230852,-0.2149523944,-0.0122531233,-0.3363991678,-0.3412780762,-0.3847541511,0.1996460408,0.4401876926,-0.051230289,-0.1549954563,-0.1365014464,0.3355631232,0.1325760931,0.1070944518,-0.3555155993,0.3302602172,-0.2539390326,0.3772550225,0.2882091701,-0.448125571,-0.7045115232,0.0289333984,0.014852711,0.2682565451,-0.2539271712,-0.1451850682,0.2909582853,-0.1724201739,-0.0794617459,0.2683820128,0.2199956477,0.3350924253,-0.2560369372,-0.1363706589,0.3257187307,-0.1690098792,0.2152385265,0.0236729011,-0.0220000949,0.0955117047,0.1455851644,-0.0097120069,0.2444512248,0.1607978344,0.1761437058,0.0880905166,0.3228500485,-0.0743065029,-0.2499892563,0.2898178399,-0.1547434926,-0.4490094483,-0.097189784,-0.106667392,-0.1024616584,-0.1602702439,-0.5025526285,-0.2756501436,0.1304307282,0.2702072859,0.2430558652,0.1169801876,-0.2288282216,0.1204118058,0.3192523122,-0.1263379157,-0.0870229453,0.1495259702,0.1631319225,-0.0988768414,-0.2654795647,0.0836536586,0.4113591611,-0.091093339,-0.2791558206,0.2201692164,0.2130369991,0.2288496047,-0.06588386,0.0021975208,0.023006035,-0.0422747508,0.1455105543,0.2342197299,0.0880005285,-0.1172928885,0.1115299612,0.2227391154,0.2169865966,0.2037428319,-0.0055456567,-0.0320434906,0.1114534512,-0.1695540398,0.0991046652,-0.2482886761,-0.2217267752,-0.1447315514,0.3873695731,0.1267309636,0.3542512357,0.334225744,0.6654902101,0.1740915775,0.1755018085,0.0477037206,-0.6600905657,-0.2747760713,-0.0900236368,0.1625278145,0.2718919814,-0.003267417,-0.0523946099,-0.0592408516,0.1783802509,0.0688536093,0.0634294674,-0.0239987858,0.1010288224,0.1833835542,0.1937384009,0.1529010087,-0.1032355726,0.174034372,0.1970168203,0.2634407282,-0.1638142318,0.0178135596,-0.4033915699,0.4058876038,-0.011080293,-0.2489236444,-0.0473926626,-0.3511256576,-0.0560280718,0.2632542551,0.0609169528,0.2647619247,0.0576650612,-0.0182358995,0.006251486,-0.3572273254,-0.2716269493,-0.4146781266,-0.2249661982,-0.0644470006,0.159751907,-0.1184373647,0.1242043227,-0.0222660135,0.0828909203,-0.3509310484,-0.2879324257,-0.0605935492,0.0784289464,-0.0372277647,-0.2462186068,0.0106649986,-0.2674310803,-0.1137792692,0.1633513719,-0.5391669869,-0.0414720327,0.1899370402,0.0173565503,-0.0609043166,0.1396540403,-0.334335655,-0.1855303198,0.0146545516,0.0790277645,-0.0025065762,-0.0581584163,-0.0783223137,-0.0806789175,-0.1290310621,-0.2679095566,-0.1495465487,-0.3555653691,-0.3247364461,0.3774479628,-0.2381370068,-0.2658275664,-0.0918252617,-0.0233523957,0.2156021148,0.218480289,-0.3069497049,-0.0684558377,-0.183655411,0.1931986213,0.0127494996,0.1517698914,0.4884290695,0.1360828131,0.0904133543,-0.215189144,-0.0438366532,0.0594989993,0.1414827704,0.0310218632,0.0702757984,0.0630455017,-0.0158293452,0.8712025881,0.3394305706,-0.2434746325,0.3906833827,-0.1166394204,0.1712316871,-0.2813171446,-0.368688643,-0.0921083391,-0.19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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2823","title":"HF_DATASETS_CACHE variable in Windows","comments":"Agh - I'm a muppet. No quote marks are needed.\r\nset HF_DATASETS_CACHE = C:\\Datasets\r\nworks as intended.","body":"I can't seem to use a custom Cache directory in Windows. I have tried:\r\n\r\nset HF_DATASETS_CACHE = \"C:\\Datasets\"\r\nset HF_DATASETS_CACHE = \"C:\/Datasets\"\r\nset HF_DATASETS_CACHE = \"C:\\\\Datasets\"\r\nset HF_DATASETS_CACHE = \"r'C:\\Datasets'\"\r\nset HF_DATASETS_CACHE = \"\\Datasets\"\r\nset HF_DATASETS_CACHE = \"\/Datasets\"\r\n\r\nIn each instance I get the \"[WinError 123] The filename, directory name, or volume label syntax is incorrect\" error when attempting to load a dataset","comment_length":17,"text":"HF_DATASETS_CACHE variable in Windows \n I can't seem to use a custom Cache directory in Windows. I have tried:\r\n\r\nset HF_DATASETS_CACHE = \"C:\\Datasets\"\r\nset HF_DATASETS_CACHE = \"C:\/Datasets\"\r\nset HF_DATASETS_CACHE = \"C:\\\\Datasets\"\r\nset HF_DATASETS_CACHE = \"r'C:\\Datasets'\"\r\nset HF_DATASETS_CACHE = \"\\Datasets\"\r\nset HF_DATASETS_CACHE = \"\/Datasets\"\r\n\r\nIn each instance I get the \"[WinError 123] The filename, directory name, or volume label syntax is incorrect\" error when attempting to load a dataset \n Agh - I'm a muppet. No quote marks are needed.\r\nset HF_DATASETS_CACHE = C:\\Datasets\r\nworks as intended.","embeddings":[-0.2557515204,0.4681499004,-0.0086679254,0.0938130617,-0.0932577625,0.2809450626,0.3591798544,0.0130967433,0.5492925644,0.1723245382,-0.1086298451,-0.3207066953,0.0269634761,-0.1362952143,-0.0884396136,0.1600556374,-0.0124639487,0.2051330358,0.3308285177,0.1052643135,-0.3471739888,0.194610998,-0.1163545176,0.1732594371,-0.2201039195,-0.0273021515,-0.2286344022,0.3452448845,0.1202253625,-0.0732737556,0.2985122204,0.1823600531,0.4611557126,0.4373255372,-0.0001192506,-0.0092273718,0.1367600113,-0.1833856255,-0.1629035026,0.1200159863,-0.2397752106,0.2823110223,-0.2644101381,0.0286023542,-0.002813136,0.2012717724,0.008684691,-0.633574605,-0.2559023499,0.3518752158,0.144456163,-0.2560602129,-0.4754669964,-0.1310121119,0.3233233988,0.4254634976,-0.1895282269,0.1743329912,0.0022010743,-0.2547373772,-0.1301951408,0.1984852701,0.0741537735,0.3853290975,0.3594753742,0.2023977488,-0.0893624648,0.1987679154,0.2981185019,-0.028904222,0.911711812,-0.3926505446,-0.067581974,0.1137470528,-0.1882463098,-0.3810411096,0.3569802046,0.1096177548,-0.2152440995,0.0923623815,0.0322469994,-0.209403649,-0.2581728101,0.1735568196,0.1536321193,-0.0576701201,0.0166171566,-0.0853283554,0.209992066,-0.1379805952,0.304087162,-0.242267251,-0.4997873604,0.1586566865,-0.2232942432,0.1928556412,-0.2934031487,0.5975059867,-0.0478701368,0.034979865,0.0655475706,0.1633851677,-0.1220712364,-0.0252837315,-0.0709528327,0.4531860054,0.0865988582,0.0687403828,0.0591183044,-0.1701724678,-0.1758125871,-0.088145569,-0.2163306922,-0.449667871,0.218222633,0.060151238,-0.1500684768,-0.5533458591,0.0617661439,-0.2378155887,0.1814159453,-0.1227409691,0.282826215,0.3287512064,0.181951344,-0.155424729,-0.2453553677,0.1222565249,-0.1248539314,-0.0093623875,-0.044213336,0.115095675,-0.3545196056,0.4502945244,0.4435072243,0.1504650712,-0.0859052166,-0.1223051623,0.2869002819,0.1731900722,-0.0152334422,-0.2569104731,-0.0175518487,0.1623936146,-0.0230819732,0.3286900818,0.0982614011,-0.1849636436,-0.2361326665,0.2819387317,-0.2798592448,-0.4200795293,-0.1352794021,-0.004328853,-0.073356919,-0.3321877122,-0.2920281291,0.0190325361,0.0367763527,-0.0160885863,0.0514025465,0.0777065381,-0.292544961,-0.1867620945,-0.1598431617,0.4258094728,-0.6456723809,0.0549008623,-0.1299296767,0.0349943377,0.1590850204,0.3617969155,-0.1314047426,0.144155547,-0.3559282422,0.0094692772,0.291824162,-0.520745635,-0.457478404,0.0855572671,0.3869653642,-0.2977321446,0.284693718,0.0809179693,0.1606728584,-0.1620028168,0.0116999075,0.216101557,0.0686244294,-0.2469922751,-0.0120328413,-0.1828505844,0.0438937731,0.2415065765,-0.0034101778,0.4013135135,0.1921704412,0.0047577857,0.0524615049,-0.0943140462,0.1270525903,0.30668208,0.3205009103,0.4424470663,0.1727309972,0.1762866676,-0.2260034233,0.291208148,-0.1170075089,-0.2172814012,0.2878508568,0.0115911169,0.0161375888,-0.2953485548,-0.220785588,-0.1811322868,-0.0076177837,0.2608892024,0.2349206507,-0.2933544219,-0.02679554,0.6589478254,0.2265408784,-0.1941644102,0.2957310677,-0.0269145686,-0.1871454716,-0.0424646065,-0.1201222688,-0.0311330575,0.2132748365,0.1775698513,-0.2325893193,0.3005902171,0.0022563951,-0.0776350871,0.3121986389,0.3358964622,-0.0462791063,-0.0129095605,0.5341506004,0.0875013471,-0.0112856096,-0.0671750233,-0.2536233366,-0.028579792,-0.1555690765,0.3571642339,-0.2178194523,-0.0085013164,0.1321295351,-0.0853915066,0.3848129809,-0.2251411974,0.3857040703,-0.2674026787,0.1608100682,-0.204515174,-0.0223741475,0.0500294492,0.2000289559,0.0464820378,0.0900041834,0.3841385543,-0.2304574251,-0.061300002,0.1181385964,0.1175849214,0.6406487823,0.1460811198,-0.0062330784,0.0381106734,-0.1333918869,-0.0382689051,0.1126071587,-0.1810551733,-0.0053234501,-0.040674448,-0.0634999946,-0.1481620371,0.1249394044,-0.353921324,-0.0089608701,0.0603265539,-0.402530998,0.125486508,-0.2663137317,-0.2450944632,-0.0418880805,-0.3795005977,-0.1935695559,-0.1571077108,-0.0439107344,0.0742222071,0.3458555937,-0.0554470196,-0.6163679361,0.2907798588,-0.1938139796,-0.5244791508,-0.2568773627,-0.0308192391,-0.3730294704,-0.0545444898,0.0315489955,0.0334092863,0.0565818474,0.0013542117,0.402936697,-0.433188349,0.2553689778,0.172015205,0.3753192127,0.2596966922,0.0151372654,-0.0903709978,0.0453084819,-0.1888089627,0.2970713973,-0.0839478523,0.1273833066,-0.0312315002,-0.1942108572,-0.0280021261,0.0193983261,0.2274570316,-0.3716921508,-0.2120231241,-0.2424019873,0.143338412,0.0797449723,0.3831820488,-0.2004886121,0.0953654572,0.2932655513,-0.3508400619,-0.3323981464,-0.3654141128,0.5393200517,-0.4161731005,0.0118386988,0.1936699301,0.2055070102,-0.2015186995,0.2151324451,-0.4242047369,0.0964951068,-0.1795964092,0.0807430893,0.0342938155,-0.088356927,0.1362947226,-0.2433892041,-0.0392160565,-0.2442471087,-0.5972727537,0.2365330309,-0.0724125803,0.4560070038,0.3650609851,0.1059790179,-0.0916182622,0.4457309246,0.0964218825,0.1595916003,0.4605713785,-0.0735017136,0.3184109926,-0.0136250146,-0.0809273124,0.1316006333,-0.0595001951,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2821","title":"Cannot load linnaeus dataset","comments":"Thanks for reporting ! #2852 fixed this error\r\n\r\nWe'll do a new release of `datasets` soon :)","body":"## Describe the bug\r\nThe [linnaeus](https:\/\/huggingface.co\/datasets\/linnaeus) dataset cannot be loaded. To reproduce:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndatasets = load_dataset(\"linnaeus\")\r\n```\r\nThis results in:\r\n```\r\nDownloading and preparing dataset linnaeus\/linnaeus (download: 17.36 MiB, generated: 8.74 MiB, post-processed: Unknown size, total: 26.10 MiB) to \/root\/.cache\/huggingface\/datasets\/linnaeus\/linnaeus\/1.0.0\/2ff05dbc256108233262f596e09e322dbc3db067202de14286913607cd9cb704...\r\n---------------------------------------------------------------------------\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-4-7ef3a88f6276> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 datasets = load_dataset(\"linnaeus\")\r\n\r\n11 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    603             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    604         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 605         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    606 \r\n    607     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/u\/0\/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\/\r\n```","comment_length":17,"text":"Cannot load linnaeus dataset \n ## Describe the bug\r\nThe [linnaeus](https:\/\/huggingface.co\/datasets\/linnaeus) dataset cannot be loaded. To reproduce:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndatasets = load_dataset(\"linnaeus\")\r\n```\r\nThis results in:\r\n```\r\nDownloading and preparing dataset linnaeus\/linnaeus (download: 17.36 MiB, generated: 8.74 MiB, post-processed: Unknown size, total: 26.10 MiB) to \/root\/.cache\/huggingface\/datasets\/linnaeus\/linnaeus\/1.0.0\/2ff05dbc256108233262f596e09e322dbc3db067202de14286913607cd9cb704...\r\n---------------------------------------------------------------------------\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-4-7ef3a88f6276> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 datasets = load_dataset(\"linnaeus\")\r\n\r\n11 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    603             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    604         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 605         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    606 \r\n    607     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/u\/0\/uc?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh&export=download\/\r\n``` \n Thanks for reporting ! #2852 fixed this error\r\n\r\nWe'll do a new release of `datasets` soon :)","embeddings":[-0.1098577157,-0.143165946,-0.0036667234,0.5683761835,0.2294618189,-0.0716874748,0.0764331371,0.3127034903,0.1373578608,0.0487763658,-0.3296411037,0.0350638591,-0.0180285536,-0.1834695786,0.1603863835,-0.2352236807,-0.1053614989,-0.0622279905,-0.4106493592,0.1044703573,-0.1308865696,0.3101219535,-0.0382798947,-0.0052360063,-0.0595476739,-0.0635992363,0.0269933958,0.4038961232,-0.0817026794,-0.5142930746,0.3658116162,-0.025758775,0.2221749574,0.6335695386,-0.0001129523,0.1002365649,0.3394467235,-0.0037233825,-0.417912811,-0.3696373105,-0.1447288692,-0.1747751683,0.147974357,-0.1763244122,-0.1769499779,0.1925733835,-0.0365836434,-0.4481865764,0.2486859858,0.2116878182,0.2418952137,0.2672031522,0.3284202516,-0.1788068116,0.0594662465,-0.0254503135,-0.104390353,0.7655639052,0.1173543409,0.2063794136,0.0510814972,0.3760934174,0.0908738375,0.16046004,0.4749838114,0.0394841954,0.2304551601,-0.2688692212,0.1130298451,0.1952230185,0.4000908136,-0.1607928276,-0.3665402234,-0.1934892684,0.0951567963,-0.3805069327,0.3234522939,0.1300037503,-0.2127992958,0.1132354736,-0.123492457,-0.1773454696,-0.2792692482,0.2881274521,-0.0447788946,-0.1690954268,-0.1575406343,0.1463273019,0.1806666553,-0.1248240247,-0.0539279319,0.0094063003,-0.0262687579,0.2579780817,-0.2460853308,0.1773656458,-0.0310983844,0.1817291528,0.2558598816,0.0904838815,-0.1404848844,0.0120945303,-0.0953951403,0.1886289269,0.298963815,0.0496456474,-0.0871780366,0.0046279761,0.2070226222,0.5039040446,-0.2293083221,-0.1274598688,0.0119244009,-0.2724320292,0.1770056784,-0.1214359626,0.4966045618,-0.3770104945,-0.1082005426,0.0917365104,-0.0458589755,0.0481627844,0.151230976,0.4551626444,-0.25800246,0.1544597,0.095444411,0.0614505634,-0.1204147637,-0.1457547247,-0.2277960777,0.1879870743,-0.177434206,0.0885476619,0.3645627201,-0.5190119743,0.1639645547,-0.014977023,0.2522332966,-0.1167270392,-0.0186835006,-0.2165719122,-0.107789509,0.311350435,0.0228776522,0.0862024426,0.1767017841,-0.1567439437,-0.1832847595,-0.0628963485,-0.3582116961,-0.2106400877,-0.0830561221,0.1629568785,-0.2142596841,0.0662850142,-0.3244718015,-0.0464086831,-0.1090323478,-0.1943716407,-0.1086106449,-0.1355385631,-0.1367982626,-0.209141925,0.3754203022,0.6375408173,-0.0808179602,0.0584750362,-0.1234021932,-0.0431577228,0.1322187781,0.3780945539,-0.1198968068,-0.0201250315,-0.2446826398,-0.1078016236,0.2805714011,-0.3060962558,-0.3442858756,0.11498072,-0.0337683484,0.3318838775,-0.0532563291,-0.0374132134,-0.0318786912,0.1323215216,0.1931566149,0.2832884789,-0.0563489199,-0.1293168962,-0.2122825384,-0.259457767,0.1632746458,0.2174010128,0.1880996227,0.1501255631,0.0770919248,0.0468290523,0.130879879,-0.0778299496,0.0669316649,0.1227842644,0.1351918876,0.4310694635,-0.0718185753,-0.3716206551,-0.4436382651,0.3363806009,-0.03146106,0.1913928241,-0.146351248,0.0657709762,-0.4101037383,0.0308009479,-0.0787293762,-0.1457380056,0.1097632498,0.0998461172,0.0353457816,0.2634499669,-0.2242711782,0.4821530581,0.0530593172,0.1775317788,-0.4551772773,0.257725209,-0.034705095,-0.0915881023,0.03112779,0.1576486081,0.1089980304,-0.0902797282,-0.103441529,0.3063961267,0.0274115913,-0.0405845679,0.1088259593,-0.1070616171,0.0980541557,-0.1691553593,0.0670722127,0.0534188636,0.1921955347,-0.0061213728,-0.3353888392,0.1313283145,-0.1543354392,0.2098008394,0.0107588526,0.0329193026,0.3360375762,-0.0081185251,-0.0462266766,-0.0963451713,0.3444593251,-0.108463861,0.4994331598,-0.0924285054,-0.3686579168,-0.1824690849,-0.1115126386,0.0226241816,0.0863872617,0.2621725202,-0.1716367751,-0.0400449112,0.0067801168,0.1041773707,0.3363501132,0.0850468576,0.0939544365,0.0940393284,0.0719725564,-0.0245300326,0.1491383314,-0.0482987016,0.2200093418,0.0769660249,0.1324644238,0.064665772,-0.4159032702,-0.3394742906,-0.0652086064,0.3232426643,-0.3425343037,0.1666306853,-0.1244778037,-0.3035856783,-0.2150221616,-0.2546468079,-0.4462381005,-0.2368631661,-0.3029857278,0.5309480429,0.1654843688,0.0352378562,-0.3215266764,0.0583712086,-0.040452648,-0.1734482944,-0.1826367676,-0.0247984342,-0.1803610772,0.0409550183,0.2866387963,0.1651409119,0.4407707751,-0.0842784494,-0.0442237966,-0.2323412746,-0.3730412424,-0.0294400081,0.0091494331,0.4914520383,0.1717602611,0.4271701574,-0.1075664908,-0.2444170862,0.4784312844,-0.0908168703,0.0685489252,0.0768222138,-0.203672111,0.0741951391,0.0008809214,-0.5234006047,-0.3201754391,-0.296497196,0.3565408289,0.0888761356,0.0241043288,0.4940642715,-0.0086904792,0.1140922159,-0.0923546031,0.1628053635,-0.3461090624,-0.618103981,0.3059367537,-0.2781543732,-0.4102417231,0.0600088499,0.1253888458,0.3633446097,-0.0327594392,-0.6117784381,-0.0045396043,-0.1223770529,0.1743086725,-0.0899428129,-0.1310316175,0.0207807552,-0.1267365217,-0.0387925059,0.0129932296,-0.1978799403,-0.1798412353,-0.0088726301,0.1235184073,0.0039644828,0.3006074727,-0.0057001635,0.5055919886,0.1251589656,0.0853411406,0.3692272902,0.05112122,0.4881687164,-0.3019167483,-0.5070818067,-0.1335797608,-0.1893318295,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2820","title":"Downloading \u201creddit\u201d dataset keeps timing out.","comments":"```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset reddit\/default (download: 2.93 GiB, generated: 17.64 GiB, post-processed: Unknown size, total: 20.57 GiB) to \/Volumes\/My Passport for Mac\/og-chat-data\/reddit\/default\/1.0.0\/98ba5abea674d3178f7588aa6518a5510dc0c6fa8176d9653a3546d5afcb3969...\r\nDownloading: 13%\r\n403M\/3.14G [44:39<2:27:09, 310kB\/s]\r\n---------------------------------------------------------------------------\r\ntimeout                                   Traceback (most recent call last)\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in _error_catcher(self)\r\n    437             try:\r\n--> 438                 yield\r\n    439 \r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in read(self, amt, decode_content, cache_content)\r\n    518                 cache_content = False\r\n--> 519                 data = self._fp.read(amt) if not fp_closed else b\"\"\r\n    520                 if (\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/http\/client.py in read(self, amt)\r\n    458             b = bytearray(amt)\r\n--> 459             n = self.readinto(b)\r\n    460             return memoryview(b)[:n].tobytes()\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/http\/client.py in readinto(self, b)\r\n    502         # (for example, reading in 1k chunks)\r\n--> 503         n = self.fp.readinto(b)\r\n    504         if not n and b:\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/socket.py in readinto(self, b)\r\n    703             try:\r\n--> 704                 return self._sock.recv_into(b)\r\n    705             except timeout:\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/ssl.py in recv_into(self, buffer, nbytes, flags)\r\n   1240                   self.__class__)\r\n-> 1241             return self.read(nbytes, buffer)\r\n   1242         else:\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/ssl.py in read(self, len, buffer)\r\n   1098             if buffer is not None:\r\n-> 1099                 return self._sslobj.read(len, buffer)\r\n   1100             else:\r\n\r\ntimeout: The read operation timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nReadTimeoutError                          Traceback (most recent call last)\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/requests\/models.py in generate()\r\n    757                 try:\r\n--> 758                     for chunk in self.raw.stream(chunk_size, decode_content=True):\r\n    759                         yield chunk\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in stream(self, amt, decode_content)\r\n    575             while not is_fp_closed(self._fp):\r\n--> 576                 data = self.read(amt=amt, decode_content=decode_content)\r\n    577 \r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in read(self, amt, decode_content, cache_content)\r\n    540                         # Content-Length are caught.\r\n--> 541                         raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\r\n    542 \r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/contextlib.py in __exit__(self, type, value, traceback)\r\n    134             try:\r\n--> 135                 self.gen.throw(type, value, traceback)\r\n    136             except StopIteration as exc:\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in _error_catcher(self)\r\n    442                 # there is yet no clean way to get at it from this context.\r\n--> 443                 raise ReadTimeoutError(self._pool, None, \"Read timed out.\")\r\n    444 \r\n\r\nReadTimeoutError: HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out.\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\/var\/folders\/3f\/md0t9sgj6rz8xy01fskttqdc0000gn\/T\/ipykernel_89016\/1133441872.py in <module>\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 dataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    845 \r\n    846     # Download and prepare data\r\n--> 847     builder_instance.download_and_prepare(\r\n    848         download_config=download_config,\r\n    849         download_mode=download_mode,\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    613                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    614                     if not downloaded_from_gcs:\r\n--> 615                         self._download_and_prepare(\r\n    616                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    617                         )\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    669         split_dict = SplitDict(dataset_name=self.name)\r\n    670         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 671         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    672 \r\n    673         # Checksums verification\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/reddit\/98ba5abea674d3178f7588aa6518a5510dc0c6fa8176d9653a3546d5afcb3969\/reddit.py in _split_generators(self, dl_manager)\r\n     73     def _split_generators(self, dl_manager):\r\n     74         \"\"\"Returns SplitGenerators.\"\"\"\r\n---> 75         dl_path = dl_manager.download_and_extract(_URL)\r\n     76         return [\r\n     77             datasets.SplitGenerator(\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py in download_and_extract(self, url_or_urls)\r\n    287             extracted_path(s): `str`, extracted paths of given URL(s).\r\n    288         \"\"\"\r\n--> 289         return self.extract(self.download(url_or_urls))\r\n    290 \r\n    291     def get_recorded_sizes_checksums(self):\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py in download(self, url_or_urls)\r\n    195 \r\n    196         start_time = datetime.now()\r\n--> 197         downloaded_path_or_paths = map_nested(\r\n    198             download_func,\r\n    199             url_or_urls,\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n    194     # Singleton\r\n    195     if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 196         return function(data_struct)\r\n    197 \r\n    198     disable_tqdm = bool(logger.getEffectiveLevel() > logging.INFO) or not utils.is_progress_bar_enabled()\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py in _download(self, url_or_filename, download_config)\r\n    218             # append the relative path to the base_path\r\n    219             url_or_filename = url_or_path_join(self._base_path, url_or_filename)\r\n--> 220         return cached_path(url_or_filename, download_config=download_config)\r\n    221 \r\n    222     def iter_archive(self, path):\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    286     if is_remote_url(url_or_filename):\r\n    287         # URL, so get it from the cache (downloading if necessary)\r\n--> 288         output_path = get_from_cache(\r\n    289             url_or_filename,\r\n    290             cache_dir=cache_dir,\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    643                 ftp_get(url, temp_file)\r\n    644             else:\r\n--> 645                 http_get(\r\n    646                     url,\r\n    647                     temp_file,\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries)\r\n    451         disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n    452     )\r\n--> 453     for chunk in response.iter_content(chunk_size=1024):\r\n    454         if chunk:  # filter out keep-alive new chunks\r\n    455             progress.update(len(chunk))\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/requests\/models.py in generate()\r\n    763                     raise ContentDecodingError(e)\r\n    764                 except ReadTimeoutError as e:\r\n--> 765                     raise ConnectionError(e)\r\n    766             else:\r\n    767                 # Standard file-like object.\r\n\r\nConnectionError: HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out.\r\n```","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n","comment_length":646,"text":"Downloading \u201creddit\u201d dataset keeps timing out. \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n \n ```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset reddit\/default (download: 2.93 GiB, generated: 17.64 GiB, post-processed: Unknown size, total: 20.57 GiB) to \/Volumes\/My Passport for Mac\/og-chat-data\/reddit\/default\/1.0.0\/98ba5abea674d3178f7588aa6518a5510dc0c6fa8176d9653a3546d5afcb3969...\r\nDownloading: 13%\r\n403M\/3.14G [44:39<2:27:09, 310kB\/s]\r\n---------------------------------------------------------------------------\r\ntimeout                                   Traceback (most recent call last)\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in _error_catcher(self)\r\n    437             try:\r\n--> 438                 yield\r\n    439 \r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in read(self, amt, decode_content, cache_content)\r\n    518                 cache_content = False\r\n--> 519                 data = self._fp.read(amt) if not fp_closed else b\"\"\r\n    520                 if (\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/http\/client.py in read(self, amt)\r\n    458             b = bytearray(amt)\r\n--> 459             n = self.readinto(b)\r\n    460             return memoryview(b)[:n].tobytes()\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/http\/client.py in readinto(self, b)\r\n    502         # (for example, reading in 1k chunks)\r\n--> 503         n = self.fp.readinto(b)\r\n    504         if not n and b:\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/socket.py in readinto(self, b)\r\n    703             try:\r\n--> 704                 return self._sock.recv_into(b)\r\n    705             except timeout:\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/ssl.py in recv_into(self, buffer, nbytes, flags)\r\n   1240                   self.__class__)\r\n-> 1241             return self.read(nbytes, buffer)\r\n   1242         else:\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/ssl.py in read(self, len, buffer)\r\n   1098             if buffer is not None:\r\n-> 1099                 return self._sslobj.read(len, buffer)\r\n   1100             else:\r\n\r\ntimeout: The read operation timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nReadTimeoutError                          Traceback (most recent call last)\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/requests\/models.py in generate()\r\n    757                 try:\r\n--> 758                     for chunk in self.raw.stream(chunk_size, decode_content=True):\r\n    759                         yield chunk\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in stream(self, amt, decode_content)\r\n    575             while not is_fp_closed(self._fp):\r\n--> 576                 data = self.read(amt=amt, decode_content=decode_content)\r\n    577 \r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in read(self, amt, decode_content, cache_content)\r\n    540                         # Content-Length are caught.\r\n--> 541                         raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\r\n    542 \r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/contextlib.py in __exit__(self, type, value, traceback)\r\n    134             try:\r\n--> 135                 self.gen.throw(type, value, traceback)\r\n    136             except StopIteration as exc:\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/urllib3\/response.py in _error_catcher(self)\r\n    442                 # there is yet no clean way to get at it from this context.\r\n--> 443                 raise ReadTimeoutError(self._pool, None, \"Read timed out.\")\r\n    444 \r\n\r\nReadTimeoutError: HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out.\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\/var\/folders\/3f\/md0t9sgj6rz8xy01fskttqdc0000gn\/T\/ipykernel_89016\/1133441872.py in <module>\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 dataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    845 \r\n    846     # Download and prepare data\r\n--> 847     builder_instance.download_and_prepare(\r\n    848         download_config=download_config,\r\n    849         download_mode=download_mode,\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    613                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    614                     if not downloaded_from_gcs:\r\n--> 615                         self._download_and_prepare(\r\n    616                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    617                         )\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    669         split_dict = SplitDict(dataset_name=self.name)\r\n    670         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 671         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    672 \r\n    673         # Checksums verification\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/reddit\/98ba5abea674d3178f7588aa6518a5510dc0c6fa8176d9653a3546d5afcb3969\/reddit.py in _split_generators(self, dl_manager)\r\n     73     def _split_generators(self, dl_manager):\r\n     74         \"\"\"Returns SplitGenerators.\"\"\"\r\n---> 75         dl_path = dl_manager.download_and_extract(_URL)\r\n     76         return [\r\n     77             datasets.SplitGenerator(\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py in download_and_extract(self, url_or_urls)\r\n    287             extracted_path(s): `str`, extracted paths of given URL(s).\r\n    288         \"\"\"\r\n--> 289         return self.extract(self.download(url_or_urls))\r\n    290 \r\n    291     def get_recorded_sizes_checksums(self):\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py in download(self, url_or_urls)\r\n    195 \r\n    196         start_time = datetime.now()\r\n--> 197         downloaded_path_or_paths = map_nested(\r\n    198             download_func,\r\n    199             url_or_urls,\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n    194     # Singleton\r\n    195     if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 196         return function(data_struct)\r\n    197 \r\n    198     disable_tqdm = bool(logger.getEffectiveLevel() > logging.INFO) or not utils.is_progress_bar_enabled()\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py in _download(self, url_or_filename, download_config)\r\n    218             # append the relative path to the base_path\r\n    219             url_or_filename = url_or_path_join(self._base_path, url_or_filename)\r\n--> 220         return cached_path(url_or_filename, download_config=download_config)\r\n    221 \r\n    222     def iter_archive(self, path):\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    286     if is_remote_url(url_or_filename):\r\n    287         # URL, so get it from the cache (downloading if necessary)\r\n--> 288         output_path = get_from_cache(\r\n    289             url_or_filename,\r\n    290             cache_dir=cache_dir,\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    643                 ftp_get(url, temp_file)\r\n    644             else:\r\n--> 645                 http_get(\r\n    646                     url,\r\n    647                     temp_file,\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries)\r\n    451         disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n    452     )\r\n--> 453     for chunk in response.iter_content(chunk_size=1024):\r\n    454         if chunk:  # filter out keep-alive new chunks\r\n    455             progress.update(len(chunk))\r\n\r\n\/usr\/local\/anaconda3\/envs\/og-data-env\/lib\/python3.9\/site-packages\/requests\/models.py in generate()\r\n    763                     raise ContentDecodingError(e)\r\n    764                 except ReadTimeoutError as e:\r\n--> 765                     raise ConnectionError(e)\r\n    766             else:\r\n    767                 # Standard file-like object.\r\n\r\nConnectionError: HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out.\r\n```","embeddings":[-0.3638951778,-0.1421309114,-0.0797189623,0.1200471893,0.166841507,0.1872061193,0.0386046171,0.2438804507,-0.0571571589,-0.1331617236,-0.0627646521,0.1441400796,0.3488289118,-0.0019752642,-0.0935343206,0.2313060313,0.0492559485,-0.1813283712,-0.0657732934,0.0541189387,-0.0644872263,0.1003924385,-0.1485519111,-0.1538381428,-0.1942003816,-0.0592060834,-0.1093631983,-0.1202174351,-0.2129873037,-0.2641814351,0.31763798,0.1678793132,-0.0866455138,0.5624829531,-0.0001095642,-0.2061458379,0.3377981782,0.0788483992,-0.4498393536,-0.4648267329,-0.261334002,-0.1185770333,0.0888731331,0.049372673,0.0792772025,0.1067883223,0.0705106482,-0.4283182025,0.317329675,0.4327323437,0.2017596811,0.178360939,0.2336223423,-0.1382245421,0.1232367456,0.0802627355,0.0134522729,0.4224596918,0.1959228814,-0.1228379458,0.2677885592,0.0269400552,-0.0859171823,0.2206049412,-0.1385429054,-0.0203245785,0.1799729913,-0.4049907625,-0.0455894917,0.1644668132,0.8760608435,-0.0498092026,-0.412339747,-0.2699545324,0.1099989936,-0.0107127959,0.3056143522,0.3553996682,-0.4313032925,0.2249759734,-0.2838400006,0.0700306892,0.0303666573,0.3651785851,0.1223645285,-0.0159724392,0.0214315131,0.2920616865,0.0948145241,-0.0455701128,0.2709402442,-0.3481400013,0.0262930784,-0.0731001273,-0.4070403874,-0.1091760322,0.0345305689,0.1573893279,0.1790525764,0.1141359657,-0.0329113863,-0.1888143271,-0.0093015349,0.1332781017,0.3627251089,-0.0514031611,0.0227710307,0.1718333662,0.2054774463,-0.0747328475,-0.0656795129,-0.0005851863,0.0896900445,-0.0134078031,0.1178109944,-0.1248832345,0.333463639,-0.2927748561,-0.264020294,-0.0683694184,0.1210783646,-0.0408359207,-0.1997187883,0.1755993515,-0.4186320901,0.1737193018,-0.0235131476,0.12073607,-0.2860050499,-0.3834685087,-0.0888734832,-0.0542666838,-0.2270246595,-0.0607817583,-0.0255941823,-0.0167295821,0.0956359059,0.1742198765,0.0805225372,-0.3122357428,-0.0038488104,-0.2295095474,-0.1563984901,0.1975986063,-0.1175193116,0.4563015997,0.0885375962,0.1118027046,-0.0920328721,-0.0471643582,-0.1970358193,-0.2452467978,-0.1614522636,0.2359225899,-0.209383145,-0.2534177899,-0.1873574108,-0.10031268,0.2682973742,0.0549779795,-0.0631795004,-0.0574919246,0.0485177338,-0.10668277,-0.0858378708,0.3686818182,-0.4268350601,0.32156232,-0.2427403778,0.1214148998,0.2209982276,0.2091517597,-0.2188085467,0.0070210267,-0.1598643512,-0.1748522222,0.2636782527,-0.2803451121,-0.6896670461,0.0776350945,-0.3067837954,0.0360194631,-0.1616358459,0.2824025154,0.3321720064,-0.1226263493,0.0851028785,0.4505003691,-0.2175913453,0.0324916169,-0.407615304,-0.1520121694,-0.0775400996,0.1463225335,-0.0387063511,0.016049223,-0.1382055879,-0.0291029382,0.2990521491,0.4490671158,0.1406909823,0.2543386519,0.09554144,0.2424472421,0.0095893964,-0.1858817786,-0.3774220943,0.1745990217,0.5166456699,0.0398234054,-0.234704867,-0.0346935764,-0.4541685879,0.0166554451,-0.0438087694,0.0101372059,0.0289865471,0.1677503139,0.288700372,0.1997802109,0.0198443532,0.6505975723,-0.0681825802,0.3121290505,-0.0864717141,0.2073747516,-0.1682162732,-0.1355968118,0.1590008885,-0.3182855546,0.1997077614,-0.1743712276,-0.1728224605,0.3266712427,0.0589874312,0.101190798,-0.0256031081,-0.2437624186,0.2322071791,-0.140318647,-0.1642286181,0.3527719676,0.10086371,0.0236945394,-0.2600862682,-0.0127559723,0.2208568305,0.1852484792,-0.2386240661,0.1970193684,0.1934301555,-0.0050591128,0.1545086652,-0.0676915795,0.2783944309,-0.2629438937,0.1345344037,-0.2053741664,0.0025805165,0.3789856434,0.3293512464,0.1056887582,-0.2198491246,0.1804495305,0.1296850443,-0.1198631078,-0.0056320135,0.5065407753,0.3067332804,0.1704119295,0.2266139984,-0.0701207072,-0.2436689585,-0.1433323473,0.1890535206,0.0757508501,0.0555560812,-0.0969582424,0.1349174827,-0.0243189335,-0.0290592741,-0.3912774026,0.1723955423,0.3068226576,-0.0378288329,0.0120927421,-0.2496584654,-0.1520566344,0.1568963826,-0.1114778221,-0.2432442009,-0.5550534725,-0.1170367375,0.2648683786,-0.0485743247,0.0361475125,-0.1683561802,-0.1456118822,0.3770270646,-0.2226378322,-0.0041139335,-0.15879637,-0.0620049685,0.0850587562,0.2173152864,-0.1189311743,0.3502157032,-0.2401047051,-0.2311211377,-0.3148239851,-0.0313548632,0.0154778501,-0.1471475661,0.3772631288,-0.228700012,0.4140965343,0.0851948634,0.1193854585,0.1587168425,-0.1645901799,-0.0706984699,-0.1050101146,0.0962466225,0.1030078828,-0.0575042479,-0.2533045411,-0.2038371563,-0.3415128887,0.1356620789,-0.1374951899,-0.0539904311,0.1529115736,0.2279939353,0.0301787183,-0.1151634753,0.0764709264,-0.1927661747,-0.3900161684,0.4926212132,-0.0878467113,-0.1976853907,0.0133778267,0.212452352,0.0070858877,0.3136509359,-0.6629764438,0.0568315722,-0.3185949028,0.2716561258,-0.0546660349,-0.2824904025,0.2686043084,-0.4917546213,-0.1053478792,0.0398719497,0.0277596097,-0.0297617838,0.1054959446,0.4257929027,-0.1738022715,0.2901479602,-0.0058969217,0.1678647697,0.2327540368,-0.0230306946,0.0852697045,0.1211807057,0.2642677128,-0.0063393824,-0.3034160435,-0.0176089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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2820","title":"Downloading \u201creddit\u201d dataset keeps timing out.","comments":"It also doesn't seem to be \"smart caching\" and I received an error about a file not being found...","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n","comment_length":19,"text":"Downloading \u201creddit\u201d dataset keeps timing out. \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n \n It also doesn't seem to be \"smart caching\" and I received an error about a file not being found...","embeddings":[-0.3514554501,-0.1771149635,-0.0796713531,0.1682872921,0.1475948989,0.223283723,0.0226032361,0.2211398929,0.0123542016,-0.1897378862,-0.0006052735,0.0798814148,0.2927660644,-0.0234991312,0.004596815,0.2605564594,0.1746075749,-0.1837051511,0.0295606758,0.0212488603,0.0039186464,0.1042710021,-0.2045951933,-0.189121902,-0.2868826687,-0.0632349402,-0.1026237905,-0.0651948974,-0.1613499969,-0.2562071085,0.2678031325,0.1747539937,-0.1125701368,0.5722392797,-0.0001108744,-0.200259611,0.3267357647,0.0606783293,-0.3902634084,-0.4879054725,-0.2514712811,-0.1367273331,0.0648576394,0.0613183603,0.1100816876,0.0650017411,0.0765786842,-0.3915479481,0.2778304815,0.4178677201,0.1937408149,0.3087404072,0.1997336745,-0.1075381562,0.2146615088,-0.0059832106,0.0433712266,0.3852337301,0.1850037426,-0.2150467038,0.2256681919,0.0387121551,-0.0873250589,0.2124781311,-0.0270646233,0.0023178114,0.0624300987,-0.4295829535,-0.0266268235,0.1627349108,0.9098489881,0.0155012272,-0.4210056663,-0.2765198648,0.0643732026,0.0191605408,0.4107702971,0.3963220119,-0.3528384864,0.2195534706,-0.2865401804,0.0169617962,0.0585765578,0.3455071747,0.1300351769,-0.0365858451,0.0514852069,0.2121029496,0.077693738,-0.0127777942,0.318225354,-0.3606897593,0.0638849661,-0.0454120524,-0.4223773479,-0.059610758,0.041024752,0.2965488732,0.2180076838,0.0376025513,0.0090417573,-0.2331798077,-0.1563536525,0.1810906082,0.414400667,0.0328045376,-0.0863438174,0.1566242725,0.2490015477,-0.1563360095,-0.0637261122,0.0281155314,0.0535564683,-0.0046373708,0.2060956359,-0.160881415,0.2642780542,-0.2941417098,-0.2341362834,-0.0317058675,0.1319109648,-0.1088596955,-0.2119629532,0.2084040791,-0.5190799236,0.2473203689,-0.1339633018,0.1147477552,-0.3465745449,-0.3769268692,-0.0995051712,0.047918506,-0.2433881313,-0.0284864716,-0.0462480299,-0.0000035711,0.0765833184,0.2044056356,0.0176847167,-0.3638130426,-0.0099054035,-0.2387177795,-0.1129431501,0.1991788596,-0.151095584,0.4225296676,0.0376464613,0.1037934422,-0.0805965811,-0.0162504371,-0.3319402337,-0.2365739495,-0.1203527078,0.2128539681,-0.3113930821,-0.3375158906,-0.1700793654,-0.0349231586,0.2402767092,-0.0020042018,-0.0957966819,-0.0359608307,0.0914469063,-0.1080953255,-0.0803297758,0.417519629,-0.4671492875,0.2881300449,-0.1859707385,0.1363562047,0.1721723527,0.2570499778,-0.1467755288,0.0454530977,-0.2080725729,-0.1775383949,0.2739038169,-0.1860808581,-0.7467337847,0.057802964,-0.2843503654,0.0424199812,-0.1178643554,0.369484961,0.2407179475,-0.1426741034,0.0391038395,0.4280474484,-0.2491465658,0.0049372939,-0.393066287,-0.2006654292,0.023305757,0.1867526621,0.0173271932,0.0746781379,-0.1123708561,-0.0463864729,0.2413773984,0.42074278,0.1031232849,0.2581168711,0.027649628,0.3117121458,0.034169782,-0.2208822668,-0.4634932578,0.1929716021,0.4974279106,-0.0402292684,-0.2256632,-0.0038947146,-0.3863794506,-0.0058577508,-0.0664516315,0.0201453753,0.0067058112,0.1483602524,0.2730265558,0.2620780468,-0.0428324305,0.6063830853,0.0634972528,0.2556881607,-0.1081701815,0.2015016973,-0.1358214915,-0.1573535502,0.1642462015,-0.331902653,0.2064648718,-0.1426869333,-0.1712403446,0.3065044582,0.0427156873,0.1618183255,0.0114986664,-0.1000133455,0.2866928577,-0.143895641,-0.1582967639,0.3258193433,0.1136549339,0.0031757897,-0.2952041924,0.017541334,0.160854429,0.1463085264,-0.2422055751,0.128293097,0.118898876,0.0218801927,0.2346511185,-0.0179281887,0.3345374763,-0.281360507,0.1407999247,-0.1899896413,-0.0198666658,0.3656054139,0.3421819508,0.1151358187,-0.1574933529,0.1737980098,0.1267798096,-0.1751209199,-0.0009873186,0.4331418276,0.2764455974,0.1672484577,0.2333202958,-0.0859269425,-0.1909573674,-0.163375169,0.1053178534,0.0348649882,-0.0363411382,-0.0639247745,0.1348118186,-0.0042759427,-0.0754822642,-0.3622912169,0.1658114791,0.2567993999,-0.0312261395,0.0473792218,-0.2350285798,-0.0895154327,0.1797646433,-0.0626701862,-0.3132677972,-0.534617722,-0.0570352897,0.2572369874,-0.0239659008,0.0314369202,-0.2107655704,-0.0662339777,0.4184938669,-0.2246605158,-0.0019356724,-0.1966605633,-0.0031246259,0.0666251704,0.2288979292,-0.1572305411,0.2978439629,-0.2575805485,-0.2320070267,-0.4382397234,0.010009652,0.0240634885,-0.1659139246,0.3262791634,-0.2745301723,0.4014875293,0.0467613973,0.184700042,0.1293170452,-0.215952307,-0.0568516403,-0.0901853219,0.1098197922,0.0916875973,0.0130148195,-0.2832642794,-0.2611865997,-0.3255770802,0.1303075105,-0.1285695136,-0.05958331,0.0994416028,0.2131880373,-0.0286169928,-0.1400176436,0.1327975541,-0.2126912624,-0.4661632776,0.574690938,-0.1025391966,-0.2443284094,0.0728578046,0.2274207771,-0.0849176496,0.3090695441,-0.730183363,0.0651278421,-0.3316624165,0.2532573938,-0.0661315992,-0.1861431897,0.2206130922,-0.4908768535,-0.0971385464,0.0012669493,0.036649555,-0.0588362068,0.0865296572,0.430654943,-0.0872969702,0.2449732572,-0.0117747048,0.2368469834,0.1969712526,0.0393538512,0.1359301209,0.1315881014,0.2887610793,0.038164176,-0.1975024194,0.005636089,-0.0781105012,-0.17137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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2820","title":"Downloading \u201creddit\u201d dataset keeps timing out.","comments":"To be clear, the error I get when I try to \"re-instantiate\" the download after failure is: \r\n```\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 20] Not a directory: <HOME>\/.cache\/huggingface\/datasets\/downloads\/1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c\/corpus-webis-tldr-17.json'\r\n```","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n","comment_length":32,"text":"Downloading \u201creddit\u201d dataset keeps timing out. \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n \n To be clear, the error I get when I try to \"re-instantiate\" the download after failure is: \r\n```\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 20] Not a directory: <HOME>\/.cache\/huggingface\/datasets\/downloads\/1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c\/corpus-webis-tldr-17.json'\r\n```","embeddings":[-0.3457060754,-0.1134913117,-0.0889911205,0.1294443458,0.1474923193,0.2143732458,0.0480176024,0.1998876333,0.0338550657,-0.0955315158,-0.030202182,0.1168750599,0.2437810749,-0.0771484822,-0.0491200723,0.231423229,0.0577855818,-0.1622307599,-0.0559198335,0.0735022649,0.011059083,0.1274359077,-0.0714068413,-0.1832770556,-0.2978641093,-0.0655245036,-0.1329081357,0.0713861063,-0.2183753848,-0.3113506734,0.2569487691,0.1230735406,0.0103951171,0.6663289666,-0.0001116103,-0.1592568904,0.2437488288,0.0426406115,-0.4877470732,-0.5513868928,-0.1342119128,-0.1945220381,0.0699981824,-0.0238278359,0.1194951162,0.0872305483,0.1862618923,-0.4260832667,0.2967655361,0.4254150689,0.1753057986,0.2387185842,0.1722792983,-0.0251178443,0.1737603545,0.1074786335,0.0559896566,0.4737626314,0.1752824336,-0.1304802895,0.2195627391,-0.0209688228,-0.1029600576,0.2084075212,-0.0964718014,0.1018205136,0.0989019349,-0.4662339687,-0.0699396804,0.1454073787,0.931850791,-0.0477511846,-0.4556200504,-0.2536899745,0.0674694479,-0.0081540942,0.3597680926,0.3981687725,-0.3702106476,0.2384305149,-0.2310329676,-0.0379162356,0.0456236973,0.3663991988,0.1222335845,0.0011730166,0.0717665702,0.1935005784,0.0912001356,-0.0108822351,0.1897068471,-0.2875150144,0.0827833563,-0.0230276976,-0.3163494468,-0.0040634186,0.0655484796,0.1514580846,0.1957388222,0.0782986283,0.0556203872,-0.2048885673,-0.1919862777,0.1541327089,0.3801209629,0.0180899296,-0.031185098,0.0855460837,0.3153648973,-0.0658992603,-0.0671005026,0.0107101351,0.0720502138,0.0049376562,0.2472783923,-0.1595617533,0.3136942387,-0.1568962187,-0.2649344802,-0.0032858315,-0.0083135916,-0.1022393033,-0.1726380885,0.1798466295,-0.4610124528,0.2657701671,0.0795594454,0.0874037296,-0.373341918,-0.3142513633,-0.0771974847,0.0610377826,-0.2870959342,-0.0180023052,0.0141784763,-0.1022643745,0.1352740228,0.1678624302,0.0020395012,-0.3097982407,-0.017695101,-0.1359445453,-0.1976197511,0.2188748419,-0.1506328285,0.4216026962,0.0583742112,0.1022207737,-0.0403342545,-0.0383255184,-0.2669510841,-0.2788278162,-0.1351445317,0.2164530307,-0.2373245806,-0.2125957608,-0.1456176937,-0.1163150445,0.2005085945,-0.012750349,-0.0898415819,-0.0423631743,0.1261980236,-0.1159448773,-0.0254689995,0.4587135613,-0.5205498934,0.2254990339,-0.210218206,0.105047226,0.1674224436,0.2220856547,-0.1680978537,0.125601545,-0.3032050431,-0.1276328117,0.326361984,-0.2088168263,-0.6565337181,-0.0064632804,-0.4225059748,0.0436369553,-0.0398843214,0.3199849427,0.2116360217,-0.0621474087,0.0237390995,0.3340667784,-0.192074582,0.0146882283,-0.3758515716,-0.1495868415,-0.0532919168,0.1565191895,0.0131836329,0.0386526622,-0.0940399021,0.0106654167,0.291351825,0.3134852052,0.1198113337,0.280466944,0.0698081106,0.3056702912,0.0819984227,-0.2470279485,-0.4421099126,0.2378842086,0.505831778,0.0975984633,-0.2918375731,-0.0104449233,-0.4422037899,0.0312912799,-0.0256959517,0.0083430177,0.0235647857,0.1981825382,0.2984959185,0.2305762917,-0.0688344389,0.5827645063,-0.0630007908,0.2926049829,-0.2162380219,0.3001052439,-0.185246408,-0.1520486623,0.073565647,-0.3078400791,0.0781649947,-0.2067961991,-0.1422501504,0.342199862,-0.0093691116,0.1589464247,0.0476749353,-0.065210022,0.2794476449,-0.1224916577,-0.1327505857,0.3763543665,0.0903322697,0.0723876134,-0.2474758774,-0.0002933883,0.1602976024,0.1797787845,-0.1811097562,0.1444840431,0.1153942198,-0.0085444888,0.1329468489,-0.044731155,0.4311594963,-0.3700483143,0.1274306178,-0.2081591934,-0.0152934119,0.3702557981,0.2613644898,0.0569560155,-0.1585609466,0.170138821,0.124994725,-0.1409881264,-0.0852991566,0.3960923254,0.3165245056,0.1875201315,0.2048451602,-0.0945082903,-0.1836341172,-0.1254289597,0.1274700314,0.0232612975,-0.0187184308,0.0660415143,0.1705086082,0.0169539042,-0.1588126868,-0.4864618182,0.1523246616,0.34003371,-0.1289244741,0.0425567515,-0.258289516,-0.0811866969,0.1528228819,-0.0556136668,-0.3009979725,-0.5474972725,-0.0488037132,0.3517224491,-0.0317387059,0.0333206505,-0.168702364,-0.1326798201,0.3515864015,-0.1978571713,-0.0380522572,-0.1242396235,-0.0250989161,0.0519403741,0.2081225663,-0.1554661691,0.3499416113,-0.3303993344,-0.215082854,-0.5077780485,-0.0573956966,0.0420378633,-0.071146816,0.345436275,-0.1364248991,0.4982603192,0.051556401,0.0235980954,0.1339967996,-0.2258448005,-0.109969832,-0.0887669101,0.0489946045,0.1446504891,-0.0279510859,-0.4025306702,-0.198005408,-0.3813938498,0.1214804798,-0.1050283685,-0.0400379263,0.1701100171,0.1836489588,0.0213121362,0.0021810639,0.1341613382,-0.1613623798,-0.4538007975,0.4539409876,-0.1210563257,-0.1833670288,0.0870497376,0.1361739635,-0.0487819202,0.3200146556,-0.7256786227,0.0329235345,-0.2690056562,0.2915369272,-0.1593283266,-0.1423478276,0.2895046473,-0.403314203,-0.1086893454,0.0458066911,0.0284443609,-0.0452256687,0.1700766385,0.4130652845,-0.1201204509,0.2722564936,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2820","title":"Downloading \u201creddit\u201d dataset keeps timing out.","comments":"Hi ! Since https:\/\/github.com\/huggingface\/datasets\/pull\/2803 we've changed the time out from 10sec to 100sec.\r\nThis should prevent the `ReadTimeoutError`. Feel free to try it out by installing `datasets` from source\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```\r\n\r\nWhen re-running your code you said you get a `OSError`, could you try deleting the file at the path returned by the error ? (the one after `[Errno 20] Not a directory:`). Ideally when a download fails you should be able to re-run it without error; there might be an issue here.\r\n\r\nFinally not sure what we can do about `ConnectionError`, this must be an issue from zenodo. If it happens you simply need to try again\r\n","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n","comment_length":111,"text":"Downloading \u201creddit\u201d dataset keeps timing out. \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n \n Hi ! Since https:\/\/github.com\/huggingface\/datasets\/pull\/2803 we've changed the time out from 10sec to 100sec.\r\nThis should prevent the `ReadTimeoutError`. Feel free to try it out by installing `datasets` from source\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```\r\n\r\nWhen re-running your code you said you get a `OSError`, could you try deleting the file at the path returned by the error ? (the one after `[Errno 20] Not a directory:`). Ideally when a download fails you should be able to re-run it without error; there might be an issue here.\r\n\r\nFinally not sure what we can do about `ConnectionError`, this must be an issue from zenodo. If it happens you simply need to try again\r\n","embeddings":[-0.4538246691,-0.0359525084,-0.1072578579,0.2148068547,0.225401029,0.0983382016,0.1591716409,0.2268794477,-0.0526379123,0.0000226978,-0.162777245,0.1547378004,0.1955923587,-0.1334099025,-0.097253561,0.0610446818,-0.0565706789,-0.0677602589,-0.1615533531,0.0819472596,0.0188205875,0.1436862499,-0.1121324673,-0.0026506882,-0.0766938329,-0.1644875854,-0.1467983723,0.050724186,-0.2956551611,-0.3328927457,0.1694848686,0.2418556511,-0.0690903589,0.6850526929,-0.0001101031,-0.1397206187,0.2245097011,0.0490238145,-0.3112405539,-0.5094980597,-0.1957669705,-0.1742007434,-0.0006458978,-0.0291792974,0.185485512,0.0554979406,0.1089929864,-0.2749736309,0.3389205039,0.5729048848,0.1995919943,0.3697789013,0.3693856597,-0.0535559133,0.2664019465,-0.012151652,0.031192204,0.4169182479,0.138078779,-0.0596361831,0.1828976423,0.0227427837,-0.0756145343,0.1545686871,-0.1193917766,0.0333615504,0.2555910051,-0.3887176514,-0.1383722574,0.2219341695,0.7953239083,-0.0027446952,-0.4068219364,-0.2871252596,0.1696534157,-0.0129158003,0.3349570334,0.2615113556,-0.4169999659,0.2174756229,-0.2613837123,-0.0808658972,-0.1266333461,0.4406132996,0.1170140058,0.0012068402,-0.0256973505,0.3007272482,0.2633582652,-0.1087947264,0.2221509814,-0.3168979883,-0.0292462241,0.0137489038,-0.3383377492,-0.1888522357,0.0329177901,0.1638691276,0.3037260771,0.0318557173,0.0471077301,-0.1961562186,-0.0839972943,0.1398137212,0.3832516074,0.0297968816,0.0367336459,0.0558998697,0.2139374614,0.0805045292,-0.0963447616,0.0382728539,-0.0520347729,0.0375237539,0.10670311,-0.1745736599,0.4148418903,-0.2983430624,-0.1657698452,0.0255850274,0.0757377893,-0.1702812314,-0.1688547879,0.2133611888,-0.4682379067,0.2446539104,-0.0922744349,0.0652114749,-0.3638776839,-0.3287384808,-0.0513934009,0.0137415705,-0.1487400681,-0.0431218706,-0.0900757164,-0.2335726917,0.1790361106,0.1448258609,0.0673104748,-0.2530994117,0.0061690058,-0.2436126471,-0.1551749706,0.1279544085,-0.0695514008,0.2915445566,0.1237974241,0.1339074969,-0.0637521893,0.0186464153,-0.2753818631,-0.3336540759,-0.200213477,0.2280961424,-0.335934788,-0.2744778097,0.025252834,-0.133858785,0.3139727712,-0.1475003511,-0.1339813918,-0.1570851356,0.0600346029,-0.0604759827,-0.0414292365,0.3157823086,-0.3593474329,0.1914790273,-0.2504243255,-0.0099842027,0.1681512147,0.2309772223,-0.2553583384,0.1200761124,-0.2136332244,-0.2048165798,0.3608438373,-0.3011219203,-0.600212872,0.003167416,-0.3776432574,0.1416860372,-0.1392541677,0.237651974,0.3605267107,-0.0069612009,-0.0734062865,0.3193845451,-0.2023209482,-0.0070757899,-0.3626311421,-0.1656437218,-0.1414755285,0.2422949374,0.0788861513,-0.0042997641,-0.0377065167,0.0240184031,0.2975076735,0.3169288635,0.0668257326,0.2782160342,0.1488048136,0.3036114872,-0.0502669141,-0.2682843506,-0.2213944495,0.1641264856,0.5927707553,0.1514855921,-0.2055570185,0.055662524,-0.3700690269,0.0064080376,-0.0968770906,-0.0029626812,0.0381577797,0.2506614029,0.2869864702,0.2637932003,-0.0415531583,0.4684087932,-0.0279490668,0.3550293744,-0.1794379801,0.2723223269,-0.2326211184,-0.1905997396,0.0346551053,-0.2605832219,0.1190185398,-0.1672925353,-0.1346308589,0.3733447194,0.0169509817,0.0580253042,0.1264173687,-0.1198541522,0.2207273692,-0.1083708256,-0.1503271461,0.248680532,0.1149940789,0.1063638106,-0.1766043752,0.1053012237,0.143037051,0.2057059705,-0.1959834993,0.1009418294,0.1564978808,0.0406317189,0.0823936462,0.0261111017,0.4374154508,-0.3641561568,0.0406906791,-0.1805986017,-0.0071351598,0.3008781075,0.410961926,0.0310156327,-0.1827630848,0.1770051122,0.1970965564,-0.089612104,-0.0317267142,0.4135085344,0.4253745079,0.2163395286,0.0385433398,0.0027145692,-0.17714113,-0.0891942158,0.166731149,0.0989230424,0.1816506088,0.0949445963,-0.0189945735,-0.0067830626,-0.0840521604,-0.5713555217,0.0794092417,0.4571004212,-0.0892287493,-0.137603879,-0.1908154786,-0.0958798304,0.1683706343,0.016017437,-0.475083977,-0.5786948204,-0.0676092505,0.452991873,-0.0524929017,0.0596475191,-0.2232982218,-0.1037176475,0.3926707804,-0.0736510977,0.0445313528,-0.2548263669,0.0125565547,0.0856887326,0.2230580002,-0.1910485476,0.2809529901,-0.3305025995,-0.2099800855,-0.4389460683,-0.043910604,0.1156763509,-0.2164092809,0.3292762339,-0.1231699139,0.5877863169,0.0931101665,0.0436491631,0.2046536505,-0.3436298072,-0.0776778907,-0.056204807,0.0713258833,0.1451002359,0.0598632395,-0.2994942069,-0.2301851958,-0.4278173745,0.1740367413,-0.0479382537,0.0908307359,0.2794734538,0.2059393525,0.0834495574,-0.2537406683,0.1692144722,-0.1258783191,-0.5617018342,0.4327825308,-0.1177528277,-0.263663888,0.0996503979,0.305850327,-0.020622395,0.1772894859,-0.6788051724,-0.0035098523,-0.2368997186,0.3396564424,-0.2022636831,-0.1831388772,0.3164763153,-0.4094335437,-0.1001709104,0.0228217896,0.109788537,-0.0111628193,0.1557815075,0.4228840172,-0.067544274,0.3687635362,-0.0110914931,0.3627903163,0.0647312254,0.0253924541,0.0362944342,0.0257080924,0.3557808995,0.0485108197,-0.3596302271,-0.0280463509,-0.01104220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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2820","title":"Downloading \u201creddit\u201d dataset keeps timing out.","comments":"@lhoestq thanks for the update. The directory specified by the OSError ie. \r\n```\r\n1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c\/corpus-webis-tldr-17.json \r\n```\r\n was not actually in that directory so I can't delete it. ","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n","comment_length":26,"text":"Downloading \u201creddit\u201d dataset keeps timing out. \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n \n @lhoestq thanks for the update. The directory specified by the OSError ie. \r\n```\r\n1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c\/corpus-webis-tldr-17.json \r\n```\r\n was not actually in that directory so I can't delete it. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2820","title":"Downloading \u201creddit\u201d dataset keeps timing out.","comments":"Oh, then could you try deleting the parent directory `1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c` instead ?\r\nThis way the download manager will know that it has to uncompress the data again","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n","comment_length":27,"text":"Downloading \u201creddit\u201d dataset keeps timing out. \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n \n Oh, then could you try deleting the parent directory `1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c` instead ?\r\nThis way the download manager will know that it has to uncompress the data again","embeddings":[-0.3229547739,-0.1548745632,-0.0958515629,0.2122767121,0.1727839857,0.2089084834,0.0311661009,0.2200947851,-0.0430057198,-0.0564695038,-0.0722842738,0.1025691852,0.2630372643,-0.0284359343,-0.1015666798,0.2591447234,0.0751857385,-0.1532897949,-0.134002611,0.093762137,-0.0453595072,0.1273859441,-0.1522027403,-0.1518076509,-0.2515835166,-0.0813376531,-0.1123948395,-0.0133465808,-0.2417642772,-0.3136616349,0.2217783332,0.1977145821,-0.0670335516,0.6311795712,-0.0001082193,-0.1661073864,0.2970055044,0.0607004948,-0.4434760809,-0.5188804865,-0.215862155,-0.1876016706,0.0243747514,0.0184114669,0.1335865259,0.0764069483,0.0671559721,-0.3609519005,0.3123517632,0.3766123056,0.2216305137,0.2938492298,0.1855626553,-0.1055657491,0.1551821679,0.0236803293,0.0808866918,0.4364547729,0.1442379951,-0.1347500831,0.1696618199,0.0660864338,-0.0995426252,0.2206776589,-0.1114257351,-0.0089217285,0.1844033003,-0.4553858042,-0.0359131321,0.1294727474,0.8906873465,-0.0397688448,-0.3760148585,-0.2968247235,0.1337402761,0.0270687733,0.4262497425,0.4283311367,-0.3859825432,0.2389271259,-0.266323179,-0.0228596497,0.0520490929,0.336849153,0.0947214887,-0.0229382161,-0.0076306579,0.1902257353,0.192469582,-0.0476929285,0.3261296749,-0.3512678742,0.0251167547,-0.0928472653,-0.3812167645,-0.0857717767,0.0138084851,0.17820324,0.1665260047,0.0568927824,-0.0361496769,-0.2665970623,-0.1561307907,0.1570051759,0.4282910824,0.0166722089,-0.0358770266,0.147023052,0.2235934436,-0.0597804934,-0.0454057567,0.0735234693,0.0501849167,-0.0342417248,0.1098434329,-0.0982877016,0.2976776063,-0.1693594307,-0.2574085295,-0.0296510197,0.1553851068,-0.1137762293,-0.253431648,0.1721235663,-0.456833899,0.3014909625,-0.0733489394,0.103349559,-0.3224068582,-0.3715544641,-0.0770322829,0.0178418625,-0.2535534203,-0.114999868,-0.0478267111,-0.0865907222,0.0556586869,0.2549179494,-0.0034713037,-0.3169311881,-0.0021104482,-0.2342968434,-0.0961762667,0.2281832099,-0.0899430886,0.4282738268,0.0563638732,0.1315538138,-0.0567201562,-0.0037883378,-0.3410360515,-0.2845475376,-0.1375982761,0.248136878,-0.2255693376,-0.2428179085,-0.152455315,-0.0870867968,0.1295226216,0.0438936315,-0.095159106,-0.0379463248,0.1215736866,-0.1129857972,-0.0805240273,0.3380515575,-0.4335297644,0.2870418429,-0.2611810863,0.0653512627,0.2386940569,0.2952560186,-0.2074873894,0.0207775664,-0.2533313632,-0.1445062757,0.2831653357,-0.166037336,-0.7092910409,0.0670329779,-0.3865574896,0.0544933341,-0.0951566547,0.2693455815,0.2380082756,-0.1328064352,0.0359675288,0.4281702936,-0.240654856,0.0688112453,-0.3896734715,-0.2089087516,0.0580077805,0.1221133545,-0.0680812821,0.0384233408,-0.1475014538,-0.0306050535,0.3082167804,0.3910114169,0.0956725478,0.3066321015,-0.0085600819,0.267819345,0.0175748114,-0.1669080406,-0.3697960079,0.181688875,0.4722077549,0.0637292862,-0.2675577104,-0.0389684476,-0.4558921158,0.0080367196,-0.0186161622,0.0451203398,0.0606043823,0.2065854967,0.2361144871,0.2035789937,-0.0189470612,0.54888767,0.0479960591,0.2936903834,-0.1253661215,0.1977761686,-0.1798651814,-0.1205080673,0.1851098835,-0.363132596,0.1816332191,-0.2229929417,-0.1726033986,0.3127637804,0.0540517494,0.1943844557,0.0477315299,-0.2152786702,0.2617830634,-0.1447607279,-0.1480248272,0.4077906013,0.1012227014,0.059215676,-0.2254851013,0.005725048,0.2113429308,0.1735051274,-0.2585220337,0.1331920773,0.1047107801,-0.0047231638,0.2456095964,-0.0934913978,0.3387973905,-0.248947531,0.1024932265,-0.1653571427,0.0717944503,0.3274765313,0.3101591766,0.054316476,-0.2026435286,0.2211813182,0.0865456536,-0.1284258813,-0.0187284015,0.4358161092,0.2632165253,0.1839127392,0.2763888538,-0.0652680472,-0.1404545903,-0.1486685276,0.1811475903,0.0201219767,-0.0072506336,-0.0460743718,0.1301448345,-0.0028621759,-0.1005294994,-0.3409176171,0.1631841809,0.3060806096,-0.0270071886,-0.0144398874,-0.3174676895,-0.1755051315,0.2243029475,-0.0507291108,-0.2910987437,-0.5441323519,-0.0666458905,0.2732475698,-0.0621724129,0.0719594136,-0.1098337919,-0.1164225638,0.3750080466,-0.1574857235,-0.0236032493,-0.1277323514,0.0175013412,0.10863024,0.2345093787,-0.2008160204,0.3059839606,-0.2675752044,-0.2694882452,-0.4199394584,-0.0039956979,0.0205786712,-0.1766990721,0.2704044878,-0.1738457233,0.469563365,0.1040984094,0.1371683776,0.1168253645,-0.1985445321,-0.0714371502,-0.0763074234,0.088183172,0.1546370238,-0.0620138794,-0.3198411167,-0.2525842488,-0.3489462435,0.1429210901,-0.1798455417,-0.010337851,0.0810252801,0.195770517,-0.0042211888,-0.0697445124,0.1001497284,-0.2080837488,-0.4533269703,0.4969059229,-0.0752501264,-0.2108899504,0.0671744496,0.2153131217,-0.0347476453,0.2523428798,-0.6985370517,0.1177348047,-0.3417280614,0.2311582863,-0.0959614962,-0.0915403962,0.1849167198,-0.4445741773,-0.1377573609,0.0578001961,0.0158680771,-0.119964458,0.1828929335,0.4253915846,-0.1589241475,0.2361423671,0.0096748201,0.1478535831,0.1611280739,0.0944745913,0.1329361498,0.0986260995,0.3116336167,0.0382882319,-0.236718595,-0.0082221748,-0.052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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2820","title":"Downloading \u201creddit\u201d dataset keeps timing out.","comments":"It seems to have worked. It only took like 20min! I think the extra timeout length did the trick! One thing is that it downloaded a total of 41gb instead of 20gb but at least it finished. ","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n","comment_length":37,"text":"Downloading \u201creddit\u201d dataset keeps timing out. \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\nEverytime I try and download the reddit dataset it times out before finishing and I have to try again.\r\n\r\nThere is some timeout error that I will post once it happens again.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"\/Volumes\/My Passport for Mac\/og-chat-data\")\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\nI would expect the download to finish, or at least provide a parameter to extend the read timeout window.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nShown below in error message.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: macOS \r\n- Python version: 3.9.6 (conda env)\r\n- PyArrow version: N\/A\r\n \n It seems to have worked. It only took like 20min! I think the extra timeout length did the trick! One thing is that it downloaded a total of 41gb instead of 20gb but at least it finished. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2818","title":"cannot load data from my loacal path","comments":"Hi ! The `data_files` parameter must be a string, a list\/tuple or a python dict.\r\n\r\nCan you check the type of your `config.train_path` please ? Or use `data_files=str(config.train_path)` ?","body":"## Describe the bug\r\nI just want to directly load data from my local path,but find a bug.And I compare it with pandas to provide my local path is real.\r\n\r\nhere is my code\r\n```python3\r\n# print my local path\r\nprint(config.train_path)\r\n# read data and print data length\r\ntarin=pd.read_csv(config.train_path)\r\nprint(len(tarin))\r\n\r\n# loading data by load_dataset \r\ndata = load_dataset('csv',data_files=config.train_path)\r\n\r\nprint(len(data))\r\n```\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nC:\\Users\\wie\\Documents\\\u9879\u76ee\\\u6587\u672c\u5206\u7c7b\\data\\train.csv\r\n7613\r\nTraceback (most recent call last):\r\n  File \"c:\/Users\/wie\/Documents\/\u9879\u76ee\/\u6587\u672c\u5206\u7c7b\/lib\/DataPrecess.py\", line 17, in <module>\r\n    data = load_dataset('csv',data_files=config.train_path)\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\load.py\", line 830, in load_dataset\r\n    **config_kwargs,\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\load.py\", line 710, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\builder.py\", line 271, in __init__\r\n    **config_kwargs,\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\builder.py\", line 386, in _create_builder_config\r\n    config_kwargs, custom_features=custom_features, use_auth_token=self.use_auth_token\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\builder.py\", line 156, in create_config_id\r\n    raise ValueError(\"Please provide a valid `data_files` in `DatasetBuilder`\")\r\nValueError: Please provide a valid `data_files` in `DatasetBuilder`\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: win10\r\n- Python version: 3.7.9\r\n- PyArrow version: 5.0.0\r\n","comment_length":29,"text":"cannot load data from my loacal path \n ## Describe the bug\r\nI just want to directly load data from my local path,but find a bug.And I compare it with pandas to provide my local path is real.\r\n\r\nhere is my code\r\n```python3\r\n# print my local path\r\nprint(config.train_path)\r\n# read data and print data length\r\ntarin=pd.read_csv(config.train_path)\r\nprint(len(tarin))\r\n\r\n# loading data by load_dataset \r\ndata = load_dataset('csv',data_files=config.train_path)\r\n\r\nprint(len(data))\r\n```\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nC:\\Users\\wie\\Documents\\\u9879\u76ee\\\u6587\u672c\u5206\u7c7b\\data\\train.csv\r\n7613\r\nTraceback (most recent call last):\r\n  File \"c:\/Users\/wie\/Documents\/\u9879\u76ee\/\u6587\u672c\u5206\u7c7b\/lib\/DataPrecess.py\", line 17, in <module>\r\n    data = load_dataset('csv',data_files=config.train_path)\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\load.py\", line 830, in load_dataset\r\n    **config_kwargs,\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\load.py\", line 710, in load_dataset_builder\r\n    **config_kwargs,\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\builder.py\", line 271, in __init__\r\n    **config_kwargs,\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\builder.py\", line 386, in _create_builder_config\r\n    config_kwargs, custom_features=custom_features, use_auth_token=self.use_auth_token\r\n  File \"C:\\Users\\wie\\Miniconda3\\lib\\site-packages\\datasets\\builder.py\", line 156, in create_config_id\r\n    raise ValueError(\"Please provide a valid `data_files` in `DatasetBuilder`\")\r\nValueError: Please provide a valid `data_files` in `DatasetBuilder`\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:  1.11.0\r\n- Platform: win10\r\n- Python version: 3.7.9\r\n- PyArrow version: 5.0.0\r\n \n Hi ! The `data_files` parameter must be a string, a list\/tuple or a python dict.\r\n\r\nCan you check the type of your `config.train_path` please ? Or use `data_files=str(config.train_path)` ?","embeddings":[-0.0574387945,-0.0158846732,0.0742202699,0.5506054163,0.3027433455,-0.1935664266,0.4147061408,0.0769136027,0.0825464055,0.0638927221,-0.0089269225,0.586360693,0.0122840842,-0.0284527745,0.1138430834,-0.0146789085,0.1873939633,0.085528031,-0.12411616,-0.2560271919,-0.3155732453,0.1688574553,-0.1460751295,0.1901938915,-0.0699701011,0.2109431922,-0.2822049856,0.4675122499,0.0227660947,-0.2501043975,0.4729166329,-0.280921042,0.4408823252,0.4645849764,-0.0001151561,-0.0097315684,0.475538075,0.0375795662,-0.2390377969,-0.607242763,-0.1662139148,-0.2815756202,0.4258596599,-0.1936817765,-0.066541642,-0.1174363792,-0.223506853,-0.1684545875,0.4106475413,0.4155715406,0.1782934666,0.1498636007,-0.2141826451,-0.1690315753,0.3525300026,0.1742020547,0.0461488776,0.4088488817,0.0037086802,-0.251622349,0.1068805382,-0.0742010474,-0.1378999054,0.0324060842,0.2563998401,0.2106460184,-0.0583171584,0.002279914,0.1797527224,0.3070548475,0.5116797686,-0.0626182184,-0.3027721345,-0.0975569487,-0.0389354527,-0.1113292128,0.2599177659,0.0741580278,0.0159020461,0.1967173517,0.1175490245,0.2056593299,-0.0650330633,0.4197382033,-0.1461278051,0.1639842838,-0.1312114894,0.0889243931,0.0116657214,0.1690495759,0.6599962711,-0.0752312168,0.0472111329,0.3930088878,-0.1994341314,0.127945751,0.3355509639,0.1851821095,-0.0519484505,-0.0531266108,0.3470786512,-0.1044920757,-0.1862101108,0.0079265162,0.3611758351,0.1985619366,-0.3046008646,0.2134357691,0.189971745,0.2259806991,-0.0859122425,-0.1602514088,-0.2420356125,-0.2013465911,-0.1269379407,0.1843207031,0.3140102029,-0.3728873134,-0.2214430571,-0.0581827797,0.0346098766,0.1836173087,0.2119463384,0.5136289597,-0.2088429928,0.5151565671,0.0225076247,0.2508969009,-0.0922638774,0.1784021258,-0.2614133954,0.0694378614,-0.1460306197,-0.1126512215,-0.0430093072,0.021556098,0.2521870434,-0.0096220551,-0.1722253859,-0.3173284531,-0.3492340446,-0.4206415713,0.0861275718,0.0746736377,-0.0392393172,0.0950153396,0.2757533491,-0.0933145955,-0.2179812938,0.1728485972,-0.2247143984,-0.4391539097,-0.3228242695,0.1312871128,-0.2310711443,0.0401465781,0.088622123,-0.2725886703,0.0588152781,0.3045935929,-0.1377795637,0.1218146011,-0.3652853966,-0.2556673288,0.4419429898,0.6512079239,-0.4164176881,0.2217454165,-0.2636246383,0.0769634545,0.410280019,0.1699589044,-0.3566013277,0.381391257,-0.1161803231,-0.151099667,0.3857566714,-0.5012984872,-0.3714636862,0.599517107,-0.0560738929,-0.2375229001,-0.0514174961,-0.0200658031,-0.1240407601,0.0844895691,0.3112476468,0.4983115494,0.1864834577,0.1523343176,-0.4068825841,-0.4395462871,-0.126450792,0.2480695993,-0.118607074,0.05363556,0.241335094,-0.3943933249,0.3047114909,-0.0594210476,-0.2168198824,0.4881590903,0.3690141439,0.0848213509,-0.0759779662,0.1972519308,-0.2346253097,0.2298525572,-0.0617966019,-0.1506754607,-0.1290706098,-0.003085624,-0.4466368556,0.0966235995,-0.2612614632,-0.1387730539,0.0527373739,0.1784128845,-0.0079229819,0.0783594474,-0.1965729594,0.1575737298,-0.1202824935,-0.1758037955,-0.1213267222,0.2304284424,-0.0853113458,0.0754689351,0.0699482262,0.4098211527,0.216741994,-0.0333156399,-0.0657609478,0.3856574595,0.1693173498,0.2863016129,-0.1166194975,-0.3804411292,0.2069358528,-0.0697807446,0.0948281735,0.0371947475,0.2641209066,0.1456101686,-0.2078539133,0.1532488018,-0.1123906225,0.0651696399,0.045180466,0.0005378057,0.5214226842,-0.2430495471,0.0346963443,-0.3120870292,0.2649819553,0.4043836594,0.5120335221,0.1754516065,-0.3331454694,-0.0802893192,0.1967051625,0.1612119824,0.0592381768,0.0705425516,-0.0783512518,-0.0168797802,-0.0027853975,0.4694555998,0.4198943377,0.1177514121,0.146663323,0.1681153625,0.0641562715,-0.1689205617,0.206251353,-0.0474292114,0.2692136168,0.0539128967,-0.0148290703,0.3034391105,-0.1236819029,0.0268181115,0.0028711224,0.299459219,-0.3273457587,0.0614307411,-0.3752581179,-0.1776741594,-0.145605877,-0.331272155,0.2078628689,-0.1352641135,-0.3229803741,0.032826785,-0.1912081838,-0.0376699418,-0.1218343377,0.0566690713,-0.0594361313,-0.2732097805,-0.2022083998,-0.1779984683,-0.525094986,-0.0782819316,0.3557379246,0.157008186,-0.087319918,-0.177217871,-0.0133176837,-0.099757798,0.1238210499,0.02284877,0.178985104,0.6002935767,0.1714735478,0.0395605043,0.1790844202,-0.1626320332,0.2645680308,-0.2557523549,-0.1662978083,0.006235044,0.0991331264,-0.1168443635,-0.1966537833,-0.1383365691,-0.3782124221,-0.2874901891,-0.0389488526,0.0985015407,0.1825945973,0.1845143437,0.216906026,0.2336980253,0.1198514402,-0.077794157,-0.1604256183,-0.4417062402,0.2158462107,-0.028256245,-0.5365150571,-0.3180679977,-0.1649757177,0.3878697157,-0.152408421,-0.5176630616,-0.0888612121,-0.2314541489,0.1201902553,-0.0813609213,-0.1304138452,0.346337527,-0.1021287441,-0.0298768412,-0.0978019238,-0.0786362141,0.0038152558,0.183000952,0.0856306627,0.1478830129,0.3921531439,-0.1356151104,0.6314616799,0.0274883565,-0.0541715883,0.5767683983,-0.1661159545,0.4468920827,-0.1700372249,-0.3950130641,-0.2945566177,0.0130058555,-0.2421169132,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2813","title":"Remove compression from xopen","comments":"After discussing with @lhoestq, a reasonable alternative:\r\n- `download_manager.extract(urlpath)` adds prefixes to `urlpath` in the same way as `fsspec` does for protocols, but we implement custom prefixes for all compression formats: \r\n  `bz2::http:\/\/domain.org\/filename.bz2`\r\n- `xopen` parses the `urlpath` and extracts the `compression` parameter and passes it to `fsspec.open`:\r\n  `fsspec.open(\"http:\/\/domain.org\/filename.bz2\", compression=\"bz2\")`\r\n\r\nPros:\r\n- clean solution that continues giving support to all compression formats\r\n- no breaking change when opening non-decompressed files: if no compression-protocol-like is passed, fsspec.open does not uncompress (passes compression=None)\r\n\r\nCons:\r\n- we create a \"private\" convention for the format of `urlpath`: although similar to `fsspec` protocols, we add custom prefixes for the `compression` argument","body":"We implemented support for streaming with 2 requirements:\r\n- transparent use for the end user: just needs to pass the parameter `streaming=True`\r\n- no additional work for the contributors: previous loading scripts should also work in streaming mode with no (or minor) changes; and new loading scripts should not involve additional code to support streaming\r\n\r\nIn order to fulfill these requirements, streaming implementation patched some Python functions:\r\n- the `open(urlpath)` function was patched with `fsspec.open(urlpath)`\r\n- the `os.path.join(urlpath, *others)` function was patched in order to add to `urlpath` hops (`::`) and extractor protocols (`zip:\/\/`), which are required by `fsspec.open`\r\n\r\nRecently, we implemented support for streaming all archive+compression formats: zip, tar, gz, bz2, lz4, xz, zst; tar.gz, tar.bz2,...\r\nUnder the hood, the implementation:\r\n- passes an additional parameter `compression` to `fsspec.open`, so that it performs the decompression on the fly: `fsspec.open(urlpath, compression=...)`\r\n\r\nSome concerns have been raised about passing the parameter `compression` to `fsspec.open`:\r\n- https:\/\/github.com\/huggingface\/datasets\/pull\/2786#discussion_r689550254\r\n- #2811 \r\n\r\nThe main argument is that if `open` decompresses the file and afterwards we call `gzip.open` on it, that will raise an error in `oscar` dataset:\r\n```python\r\ngzip.open(open(urlpath\r\n```\r\nWhile this is true:\r\n- it is not natural\/usual to call `open` inside `gzip.open` (never seen this before)\r\n- indeed, this was recently (2 months ago) coded that way in `datasets` in order to allow streaming support (with previous implementation of streaming)\r\n\r\nIn this particular case, there is a natural fix solution: #2811:\r\n- Revert the `open` inside the `gzip.open` (change done 2 months ago): `gzip.open(open(urlpath` => `gzip.open(urlpath`\r\n- Patch `gzip.open(urlpath` with `fsspec.open(urlpath, compression=\"gzip\"` \r\n\r\nAre there other issues apart from this?\r\n\r\nNote that there is an issue just because the open inside of the gzip.open. There is no issue in the other cases where datasets loading scripts use just\r\n- `gzip.open` \r\n- `open` (after having called dl_manager.download_and_extract)\r\n\r\nTODO:\r\n- [ ] Is this really an issue? Please enumerate the `datasets` loading scripts where this is problematic.\r\n  - For the moment, there are only 3 datasets where we have an `open` inside a `gzip.open`:\r\n    - oscar (since 23 June), mc4 (since 2 July) and c4 (since 2 July)\r\n  - In the 3 datasets, the only reason to put an open inside a gzip.open was indeed to force supporting streaming\r\n- [ ] If this is indeed an issue, which are the possible alternatives? Pros\/cons?","comment_length":105,"text":"Remove compression from xopen \n We implemented support for streaming with 2 requirements:\r\n- transparent use for the end user: just needs to pass the parameter `streaming=True`\r\n- no additional work for the contributors: previous loading scripts should also work in streaming mode with no (or minor) changes; and new loading scripts should not involve additional code to support streaming\r\n\r\nIn order to fulfill these requirements, streaming implementation patched some Python functions:\r\n- the `open(urlpath)` function was patched with `fsspec.open(urlpath)`\r\n- the `os.path.join(urlpath, *others)` function was patched in order to add to `urlpath` hops (`::`) and extractor protocols (`zip:\/\/`), which are required by `fsspec.open`\r\n\r\nRecently, we implemented support for streaming all archive+compression formats: zip, tar, gz, bz2, lz4, xz, zst; tar.gz, tar.bz2,...\r\nUnder the hood, the implementation:\r\n- passes an additional parameter `compression` to `fsspec.open`, so that it performs the decompression on the fly: `fsspec.open(urlpath, compression=...)`\r\n\r\nSome concerns have been raised about passing the parameter `compression` to `fsspec.open`:\r\n- https:\/\/github.com\/huggingface\/datasets\/pull\/2786#discussion_r689550254\r\n- #2811 \r\n\r\nThe main argument is that if `open` decompresses the file and afterwards we call `gzip.open` on it, that will raise an error in `oscar` dataset:\r\n```python\r\ngzip.open(open(urlpath\r\n```\r\nWhile this is true:\r\n- it is not natural\/usual to call `open` inside `gzip.open` (never seen this before)\r\n- indeed, this was recently (2 months ago) coded that way in `datasets` in order to allow streaming support (with previous implementation of streaming)\r\n\r\nIn this particular case, there is a natural fix solution: #2811:\r\n- Revert the `open` inside the `gzip.open` (change done 2 months ago): `gzip.open(open(urlpath` => `gzip.open(urlpath`\r\n- Patch `gzip.open(urlpath` with `fsspec.open(urlpath, compression=\"gzip\"` \r\n\r\nAre there other issues apart from this?\r\n\r\nNote that there is an issue just because the open inside of the gzip.open. There is no issue in the other cases where datasets loading scripts use just\r\n- `gzip.open` \r\n- `open` (after having called dl_manager.download_and_extract)\r\n\r\nTODO:\r\n- [ ] Is this really an issue? Please enumerate the `datasets` loading scripts where this is problematic.\r\n  - For the moment, there are only 3 datasets where we have an `open` inside a `gzip.open`:\r\n    - oscar (since 23 June), mc4 (since 2 July) and c4 (since 2 July)\r\n  - In the 3 datasets, the only reason to put an open inside a gzip.open was indeed to force supporting streaming\r\n- [ ] If this is indeed an issue, which are the possible alternatives? Pros\/cons? \n After discussing with @lhoestq, a reasonable alternative:\r\n- `download_manager.extract(urlpath)` adds prefixes to `urlpath` in the same way as `fsspec` does for protocols, but we implement custom prefixes for all compression formats: \r\n  `bz2::http:\/\/domain.org\/filename.bz2`\r\n- `xopen` parses the `urlpath` and extracts the `compression` parameter and passes it to `fsspec.open`:\r\n  `fsspec.open(\"http:\/\/domain.org\/filename.bz2\", compression=\"bz2\")`\r\n\r\nPros:\r\n- clean solution that continues giving support to all compression formats\r\n- no breaking change when opening non-decompressed files: if no compression-protocol-like is passed, fsspec.open does not uncompress (passes compression=None)\r\n\r\nCons:\r\n- we create a \"private\" convention for the format of `urlpath`: although similar to `fsspec` protocols, we add custom prefixes for the `compression` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"Hi @lewtun, thanks for reporting.\r\n\r\nApparently, `pyarrow.json` tries to cast timestamp-like fields in your JSON file to pyarrow timestamp type, and it fails with `ArrowNotImplementedError`.\r\n\r\nI will investigate if there is a way to tell pyarrow not to try that timestamp casting.","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":42,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n Hi @lewtun, thanks for reporting.\r\n\r\nApparently, `pyarrow.json` tries to cast timestamp-like fields in your JSON file to pyarrow timestamp type, and it fails with `ArrowNotImplementedError`.\r\n\r\nI will investigate if there is a way to tell pyarrow not to try that timestamp casting.","embeddings":[-0.0681871027,0.1841620058,0.0364155807,0.3780699968,0.2355371863,-0.0062702368,0.4846684635,0.3670629561,0.4180777371,-0.0028386964,0.0605546869,0.5898874402,0.0276210085,-0.148857981,-0.2172110528,-0.1838730276,0.0313557796,0.1905159801,0.1274458319,0.0285853427,-0.3122327328,0.2942055166,-0.214816317,0.1017739549,-0.0391265377,-0.0952268541,0.1041623503,0.3933393061,-0.126418829,-0.3826465905,0.4134063721,-0.0130348513,0.2161162943,0.5308527946,-0.0001203599,0.1964653581,0.4152528644,-0.092444241,-0.2868113518,-0.4780595005,-0.2790301442,-0.3253917992,0.537475884,-0.1200379506,0.1052053347,-0.2437469363,-0.0497698858,-0.2207819521,0.6015406847,0.2307226658,0.1048027351,0.429939568,0.0260289963,0.0192190502,0.0637280419,0.2108367532,-0.1217756793,0.4542036653,0.008248603,0.1270525903,-0.1715968549,-0.0036361166,-0.102641046,0.1538670212,0.2191231996,0.0476008654,-0.1704021692,0.0818345174,0.0492499694,0.2946319878,0.2845771015,-0.0508809648,-0.4122377634,-0.3489953876,-0.1207850203,0.1302726865,0.2388218939,0.2473598123,-0.1509169936,0.1259337515,0.0328827612,-0.0184712112,-0.274197191,0.2383805364,0.0036435723,0.0111869918,-0.1582194567,0.10729184,-0.1295856088,-0.020824587,-0.1499335319,-0.2244829088,-0.3057584167,0.1283780038,0.0374987274,0.11600402,-0.0355978459,-0.4100777805,0.2355834544,0.3558476567,0.3206709325,0.2080192715,-0.2673651874,0.2078464776,0.5903261304,0.2016133815,-0.0549013875,0.0114986571,0.2564998567,0.330514431,0.0607299209,-0.0435533673,-0.2637991011,-0.1108330786,0.0103772897,-0.197450906,0.3086808324,-0.1726259738,-0.2238530517,0.0755632967,-0.6134242415,-0.0573209412,-0.0002183132,0.3672920167,-0.1971875429,0.4987778664,0.2744282484,0.4868384302,0.0442172214,0.0317274667,-0.1022381261,-0.0353484266,-0.1634835154,-0.2962282002,0.2371660173,0.0525447652,0.0861053467,0.1265179962,-0.0630014017,-0.4126393497,-0.0742599964,0.0477150232,-0.0974051356,0.16696693,0.1920519024,0.132871151,0.1559260786,-0.4045022726,-0.1376460344,0.0323025733,-0.376919657,0.0055578146,-0.3176852167,0.0546303503,-0.2702908218,-0.0120817386,-0.7623512149,-0.0521201082,-0.1122782081,0.0660267845,0.0638756305,-0.0244168527,0.2227595448,-0.2470848262,0.0749816447,0.28331393,-0.3005584776,-0.2378236353,-0.0629288927,-0.037491519,0.1896335036,0.2650878727,-0.2005772442,0.2845451832,-0.1637352109,0.2106698602,0.3121518195,-0.3327314556,-0.2219264358,0.605520308,0.0193049405,0.5248504877,0.0198842697,-0.2308956832,-0.0092007685,0.1884041429,0.3035949767,0.4013707638,0.1422874182,0.0679152384,-0.2457050234,-0.2022635043,-0.1346425563,0.3127692342,0.0487670116,0.1889399141,0.0995356888,0.0244781338,0.2506872714,-0.1951391548,-0.0266671889,0.1624232382,0.0911331177,0.0282151625,-0.0057259151,-0.0044113393,-0.6142941117,0.1499558836,-0.0511226989,0.109201923,-0.408085674,0.0093207462,-0.2547832429,0.1260678917,-0.1280752718,0.1847681105,0.0374528877,0.1086534187,-0.1821872592,0.2108015567,-0.1838937998,0.3152327836,0.1626450419,0.2816524506,-0.4834640026,0.4377061725,0.210255906,-0.1212381497,0.0271184649,0.1853716373,-0.097817637,-0.1626708806,-0.2391357571,0.1727546602,0.1394181401,0.1797138602,-0.0562768616,-0.2375711501,-0.0822836161,0.0838592127,-0.1951901019,0.3068773448,0.2217241377,-0.0245843939,-0.0280968156,0.8052088618,-0.0741070136,0.4431970716,-0.015723357,-0.0205509756,0.4561716616,0.0615659803,-0.364153266,-0.3053402305,0.2388348728,0.3186245859,0.0603672415,0.0040045432,-0.3794290721,-0.1885213256,0.3982873261,0.005179801,0.2424625754,0.1532346159,-0.0953567475,-0.0996729881,0.0158600174,0.1401785463,0.2382987291,0.0758770034,-0.2070478052,-0.0434467606,-0.0409476832,-0.0247451495,0.1555987,0.306535244,0.2641779482,0.0933471769,0.1558886766,-0.020962242,-0.2815460265,-0.238003552,-0.2863601744,0.1189386174,-0.6182570457,0.2991375625,-0.4743469954,0.1188910827,0.035333585,-0.3410826325,-0.126299873,-0.322971493,-0.3007420599,0.2734274864,-0.1147493422,-0.1341666132,-0.2667239308,0.3151707649,-0.0232129041,-0.3463473022,-0.2113419473,-0.0117184091,-0.184648335,-0.0234055538,0.3875870109,0.1092963666,0.1544730067,-0.1289402992,0.047313679,0.1418192536,-0.0863964856,0.1482000202,-0.1994989067,0.2889936268,0.2154709399,0.1969099492,-0.0978527516,-0.1516460776,0.2886652946,0.0957521275,-0.3427708745,0.3241927326,-0.1354984939,0.0278997961,-0.1492165327,-0.0291694198,0.1219549254,-0.4392543733,0.5247157216,0.2458687872,0.0581384897,-0.0170072187,0.0242614429,0.1799215674,0.1687064171,0.0159113072,-0.1107467487,-0.1546859592,0.2326568216,-0.160832569,-0.2501766384,0.0764547884,-0.1965518147,0.1685908139,-0.0865133032,-0.390976578,0.0259424336,0.0707196668,0.1136565655,-0.0444040187,-0.295239538,0.0588247478,-0.0127509227,0.0851902887,-0.1855715215,-0.2022252828,0.1206316799,-0.0438129231,0.0216035321,0.0565150753,0.7252899408,-0.0942959189,0.1807754487,0.3117997646,-0.0382717438,0.394974649,-0.0264437217,0.1030089855,-0.4057941437,-0.1191259399,-0.3808179796,-0.0971131027,0.0786372274,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"I think the issue is more complex than that...\r\n\r\nI just took one of your JSON lines and pyarrow.json read it without problem.","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":23,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n I think the issue is more complex than that...\r\n\r\nI just took one of your JSON lines and pyarrow.json read it without problem.","embeddings":[-0.0681871027,0.1841620058,0.0364155807,0.3780699968,0.2355371863,-0.0062702368,0.4846684635,0.3670629561,0.4180777371,-0.0028386964,0.0605546869,0.5898874402,0.0276210085,-0.148857981,-0.2172110528,-0.1838730276,0.0313557796,0.1905159801,0.1274458319,0.0285853427,-0.3122327328,0.2942055166,-0.214816317,0.1017739549,-0.0391265377,-0.0952268541,0.1041623503,0.3933393061,-0.126418829,-0.3826465905,0.4134063721,-0.0130348513,0.2161162943,0.5308527946,-0.0001203599,0.1964653581,0.4152528644,-0.092444241,-0.2868113518,-0.4780595005,-0.2790301442,-0.3253917992,0.537475884,-0.1200379506,0.1052053347,-0.2437469363,-0.0497698858,-0.2207819521,0.6015406847,0.2307226658,0.1048027351,0.429939568,0.0260289963,0.0192190502,0.0637280419,0.2108367532,-0.1217756793,0.4542036653,0.008248603,0.1270525903,-0.1715968549,-0.0036361166,-0.102641046,0.1538670212,0.2191231996,0.0476008654,-0.1704021692,0.0818345174,0.0492499694,0.2946319878,0.2845771015,-0.0508809648,-0.4122377634,-0.3489953876,-0.1207850203,0.1302726865,0.2388218939,0.2473598123,-0.1509169936,0.1259337515,0.0328827612,-0.0184712112,-0.274197191,0.2383805364,0.0036435723,0.0111869918,-0.1582194567,0.10729184,-0.1295856088,-0.020824587,-0.1499335319,-0.2244829088,-0.3057584167,0.1283780038,0.0374987274,0.11600402,-0.0355978459,-0.4100777805,0.2355834544,0.3558476567,0.3206709325,0.2080192715,-0.2673651874,0.2078464776,0.5903261304,0.2016133815,-0.0549013875,0.0114986571,0.2564998567,0.330514431,0.0607299209,-0.0435533673,-0.2637991011,-0.1108330786,0.0103772897,-0.197450906,0.3086808324,-0.1726259738,-0.2238530517,0.0755632967,-0.6134242415,-0.0573209412,-0.0002183132,0.3672920167,-0.1971875429,0.4987778664,0.2744282484,0.4868384302,0.0442172214,0.0317274667,-0.1022381261,-0.0353484266,-0.1634835154,-0.2962282002,0.2371660173,0.0525447652,0.0861053467,0.1265179962,-0.0630014017,-0.4126393497,-0.0742599964,0.0477150232,-0.0974051356,0.16696693,0.1920519024,0.132871151,0.1559260786,-0.4045022726,-0.1376460344,0.0323025733,-0.376919657,0.0055578146,-0.3176852167,0.0546303503,-0.2702908218,-0.0120817386,-0.7623512149,-0.0521201082,-0.1122782081,0.0660267845,0.0638756305,-0.0244168527,0.2227595448,-0.2470848262,0.0749816447,0.28331393,-0.3005584776,-0.2378236353,-0.0629288927,-0.037491519,0.1896335036,0.2650878727,-0.2005772442,0.2845451832,-0.1637352109,0.2106698602,0.3121518195,-0.3327314556,-0.2219264358,0.605520308,0.0193049405,0.5248504877,0.0198842697,-0.2308956832,-0.0092007685,0.1884041429,0.3035949767,0.4013707638,0.1422874182,0.0679152384,-0.2457050234,-0.2022635043,-0.1346425563,0.3127692342,0.0487670116,0.1889399141,0.0995356888,0.0244781338,0.2506872714,-0.1951391548,-0.0266671889,0.1624232382,0.0911331177,0.0282151625,-0.0057259151,-0.0044113393,-0.6142941117,0.1499558836,-0.0511226989,0.109201923,-0.408085674,0.0093207462,-0.2547832429,0.1260678917,-0.1280752718,0.1847681105,0.0374528877,0.1086534187,-0.1821872592,0.2108015567,-0.1838937998,0.3152327836,0.1626450419,0.2816524506,-0.4834640026,0.4377061725,0.210255906,-0.1212381497,0.0271184649,0.1853716373,-0.097817637,-0.1626708806,-0.2391357571,0.1727546602,0.1394181401,0.1797138602,-0.0562768616,-0.2375711501,-0.0822836161,0.0838592127,-0.1951901019,0.3068773448,0.2217241377,-0.0245843939,-0.0280968156,0.8052088618,-0.0741070136,0.4431970716,-0.015723357,-0.0205509756,0.4561716616,0.0615659803,-0.364153266,-0.3053402305,0.2388348728,0.3186245859,0.0603672415,0.0040045432,-0.3794290721,-0.1885213256,0.3982873261,0.005179801,0.2424625754,0.1532346159,-0.0953567475,-0.0996729881,0.0158600174,0.1401785463,0.2382987291,0.0758770034,-0.2070478052,-0.0434467606,-0.0409476832,-0.0247451495,0.1555987,0.306535244,0.2641779482,0.0933471769,0.1558886766,-0.020962242,-0.2815460265,-0.238003552,-0.2863601744,0.1189386174,-0.6182570457,0.2991375625,-0.4743469954,0.1188910827,0.035333585,-0.3410826325,-0.126299873,-0.322971493,-0.3007420599,0.2734274864,-0.1147493422,-0.1341666132,-0.2667239308,0.3151707649,-0.0232129041,-0.3463473022,-0.2113419473,-0.0117184091,-0.184648335,-0.0234055538,0.3875870109,0.1092963666,0.1544730067,-0.1289402992,0.047313679,0.1418192536,-0.0863964856,0.1482000202,-0.1994989067,0.2889936268,0.2154709399,0.1969099492,-0.0978527516,-0.1516460776,0.2886652946,0.0957521275,-0.3427708745,0.3241927326,-0.1354984939,0.0278997961,-0.1492165327,-0.0291694198,0.1219549254,-0.4392543733,0.5247157216,0.2458687872,0.0581384897,-0.0170072187,0.0242614429,0.1799215674,0.1687064171,0.0159113072,-0.1107467487,-0.1546859592,0.2326568216,-0.160832569,-0.2501766384,0.0764547884,-0.1965518147,0.1685908139,-0.0865133032,-0.390976578,0.0259424336,0.0707196668,0.1136565655,-0.0444040187,-0.295239538,0.0588247478,-0.0127509227,0.0851902887,-0.1855715215,-0.2022252828,0.1206316799,-0.0438129231,0.0216035321,0.0565150753,0.7252899408,-0.0942959189,0.1807754487,0.3117997646,-0.0382717438,0.394974649,-0.0264437217,0.1030089855,-0.4057941437,-0.1191259399,-0.3808179796,-0.0971131027,0.0786372274,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"> I just took one of your JSON lines an pyarrow.json read it without problem.\r\n\r\nyes, and for some peculiar reason the error is non-deterministic (i was eventually able to load the whole dataset by just re-running the `load_dataset` cell multiple times \ud83e\udd14)\r\n\r\nthanks for looking into this \ud83d\ude4f !","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":50,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n > I just took one of your JSON lines an pyarrow.json read it without problem.\r\n\r\nyes, and for some peculiar reason the error is non-deterministic (i was eventually able to load the whole dataset by just re-running the `load_dataset` cell multiple times \ud83e\udd14)\r\n\r\nthanks for looking into this \ud83d\ude4f !","embeddings":[-0.0681871027,0.1841620058,0.0364155807,0.3780699968,0.2355371863,-0.0062702368,0.4846684635,0.3670629561,0.4180777371,-0.0028386964,0.0605546869,0.5898874402,0.0276210085,-0.148857981,-0.2172110528,-0.1838730276,0.0313557796,0.1905159801,0.1274458319,0.0285853427,-0.3122327328,0.2942055166,-0.214816317,0.1017739549,-0.0391265377,-0.0952268541,0.1041623503,0.3933393061,-0.126418829,-0.3826465905,0.4134063721,-0.0130348513,0.2161162943,0.5308527946,-0.0001203599,0.1964653581,0.4152528644,-0.092444241,-0.2868113518,-0.4780595005,-0.2790301442,-0.3253917992,0.537475884,-0.1200379506,0.1052053347,-0.2437469363,-0.0497698858,-0.2207819521,0.6015406847,0.2307226658,0.1048027351,0.429939568,0.0260289963,0.0192190502,0.0637280419,0.2108367532,-0.1217756793,0.4542036653,0.008248603,0.1270525903,-0.1715968549,-0.0036361166,-0.102641046,0.1538670212,0.2191231996,0.0476008654,-0.1704021692,0.0818345174,0.0492499694,0.2946319878,0.2845771015,-0.0508809648,-0.4122377634,-0.3489953876,-0.1207850203,0.1302726865,0.2388218939,0.2473598123,-0.1509169936,0.1259337515,0.0328827612,-0.0184712112,-0.274197191,0.2383805364,0.0036435723,0.0111869918,-0.1582194567,0.10729184,-0.1295856088,-0.020824587,-0.1499335319,-0.2244829088,-0.3057584167,0.1283780038,0.0374987274,0.11600402,-0.0355978459,-0.4100777805,0.2355834544,0.3558476567,0.3206709325,0.2080192715,-0.2673651874,0.2078464776,0.5903261304,0.2016133815,-0.0549013875,0.0114986571,0.2564998567,0.330514431,0.0607299209,-0.0435533673,-0.2637991011,-0.1108330786,0.0103772897,-0.197450906,0.3086808324,-0.1726259738,-0.2238530517,0.0755632967,-0.6134242415,-0.0573209412,-0.0002183132,0.3672920167,-0.1971875429,0.4987778664,0.2744282484,0.4868384302,0.0442172214,0.0317274667,-0.1022381261,-0.0353484266,-0.1634835154,-0.2962282002,0.2371660173,0.0525447652,0.0861053467,0.1265179962,-0.0630014017,-0.4126393497,-0.0742599964,0.0477150232,-0.0974051356,0.16696693,0.1920519024,0.132871151,0.1559260786,-0.4045022726,-0.1376460344,0.0323025733,-0.376919657,0.0055578146,-0.3176852167,0.0546303503,-0.2702908218,-0.0120817386,-0.7623512149,-0.0521201082,-0.1122782081,0.0660267845,0.0638756305,-0.0244168527,0.2227595448,-0.2470848262,0.0749816447,0.28331393,-0.3005584776,-0.2378236353,-0.0629288927,-0.037491519,0.1896335036,0.2650878727,-0.2005772442,0.2845451832,-0.1637352109,0.2106698602,0.3121518195,-0.3327314556,-0.2219264358,0.605520308,0.0193049405,0.5248504877,0.0198842697,-0.2308956832,-0.0092007685,0.1884041429,0.3035949767,0.4013707638,0.1422874182,0.0679152384,-0.2457050234,-0.2022635043,-0.1346425563,0.3127692342,0.0487670116,0.1889399141,0.0995356888,0.0244781338,0.2506872714,-0.1951391548,-0.0266671889,0.1624232382,0.0911331177,0.0282151625,-0.0057259151,-0.0044113393,-0.6142941117,0.1499558836,-0.0511226989,0.109201923,-0.408085674,0.0093207462,-0.2547832429,0.1260678917,-0.1280752718,0.1847681105,0.0374528877,0.1086534187,-0.1821872592,0.2108015567,-0.1838937998,0.3152327836,0.1626450419,0.2816524506,-0.4834640026,0.4377061725,0.210255906,-0.1212381497,0.0271184649,0.1853716373,-0.097817637,-0.1626708806,-0.2391357571,0.1727546602,0.1394181401,0.1797138602,-0.0562768616,-0.2375711501,-0.0822836161,0.0838592127,-0.1951901019,0.3068773448,0.2217241377,-0.0245843939,-0.0280968156,0.8052088618,-0.0741070136,0.4431970716,-0.015723357,-0.0205509756,0.4561716616,0.0615659803,-0.364153266,-0.3053402305,0.2388348728,0.3186245859,0.0603672415,0.0040045432,-0.3794290721,-0.1885213256,0.3982873261,0.005179801,0.2424625754,0.1532346159,-0.0953567475,-0.0996729881,0.0158600174,0.1401785463,0.2382987291,0.0758770034,-0.2070478052,-0.0434467606,-0.0409476832,-0.0247451495,0.1555987,0.306535244,0.2641779482,0.0933471769,0.1558886766,-0.020962242,-0.2815460265,-0.238003552,-0.2863601744,0.1189386174,-0.6182570457,0.2991375625,-0.4743469954,0.1188910827,0.035333585,-0.3410826325,-0.126299873,-0.322971493,-0.3007420599,0.2734274864,-0.1147493422,-0.1341666132,-0.2667239308,0.3151707649,-0.0232129041,-0.3463473022,-0.2113419473,-0.0117184091,-0.184648335,-0.0234055538,0.3875870109,0.1092963666,0.1544730067,-0.1289402992,0.047313679,0.1418192536,-0.0863964856,0.1482000202,-0.1994989067,0.2889936268,0.2154709399,0.1969099492,-0.0978527516,-0.1516460776,0.2886652946,0.0957521275,-0.3427708745,0.3241927326,-0.1354984939,0.0278997961,-0.1492165327,-0.0291694198,0.1219549254,-0.4392543733,0.5247157216,0.2458687872,0.0581384897,-0.0170072187,0.0242614429,0.1799215674,0.1687064171,0.0159113072,-0.1107467487,-0.1546859592,0.2326568216,-0.160832569,-0.2501766384,0.0764547884,-0.1965518147,0.1685908139,-0.0865133032,-0.390976578,0.0259424336,0.0707196668,0.1136565655,-0.0444040187,-0.295239538,0.0588247478,-0.0127509227,0.0851902887,-0.1855715215,-0.2022252828,0.1206316799,-0.0438129231,0.0216035321,0.0565150753,0.7252899408,-0.0942959189,0.1807754487,0.3117997646,-0.0382717438,0.394974649,-0.0264437217,0.1030089855,-0.4057941437,-0.1191259399,-0.3808179796,-0.0971131027,0.0786372274,0.219302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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"The code works fine on my side.\r\nNot sure what's going on here :\/\r\n\r\nI remember we did a few changes in the JSON loader in #2638 , did you do an update `datasets` when debugging this ?\r\n","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":38,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n The code works fine on my side.\r\nNot sure what's going on here :\/\r\n\r\nI remember we did a few changes in the JSON loader in #2638 , did you do an update `datasets` when debugging this ?\r\n","embeddings":[-0.0681871027,0.1841620058,0.0364155807,0.3780699968,0.2355371863,-0.0062702368,0.4846684635,0.3670629561,0.4180777371,-0.0028386964,0.0605546869,0.5898874402,0.0276210085,-0.148857981,-0.2172110528,-0.1838730276,0.0313557796,0.1905159801,0.1274458319,0.0285853427,-0.3122327328,0.2942055166,-0.214816317,0.1017739549,-0.0391265377,-0.0952268541,0.1041623503,0.3933393061,-0.126418829,-0.3826465905,0.4134063721,-0.0130348513,0.2161162943,0.5308527946,-0.0001203599,0.1964653581,0.4152528644,-0.092444241,-0.2868113518,-0.4780595005,-0.2790301442,-0.3253917992,0.537475884,-0.1200379506,0.1052053347,-0.2437469363,-0.0497698858,-0.2207819521,0.6015406847,0.2307226658,0.1048027351,0.429939568,0.0260289963,0.0192190502,0.0637280419,0.2108367532,-0.1217756793,0.4542036653,0.008248603,0.1270525903,-0.1715968549,-0.0036361166,-0.102641046,0.1538670212,0.2191231996,0.0476008654,-0.1704021692,0.0818345174,0.0492499694,0.2946319878,0.2845771015,-0.0508809648,-0.4122377634,-0.3489953876,-0.1207850203,0.1302726865,0.2388218939,0.2473598123,-0.1509169936,0.1259337515,0.0328827612,-0.0184712112,-0.274197191,0.2383805364,0.0036435723,0.0111869918,-0.1582194567,0.10729184,-0.1295856088,-0.020824587,-0.1499335319,-0.2244829088,-0.3057584167,0.1283780038,0.0374987274,0.11600402,-0.0355978459,-0.4100777805,0.2355834544,0.3558476567,0.3206709325,0.2080192715,-0.2673651874,0.2078464776,0.5903261304,0.2016133815,-0.0549013875,0.0114986571,0.2564998567,0.330514431,0.0607299209,-0.0435533673,-0.2637991011,-0.1108330786,0.0103772897,-0.197450906,0.3086808324,-0.1726259738,-0.2238530517,0.0755632967,-0.6134242415,-0.0573209412,-0.0002183132,0.3672920167,-0.1971875429,0.4987778664,0.2744282484,0.4868384302,0.0442172214,0.0317274667,-0.1022381261,-0.0353484266,-0.1634835154,-0.2962282002,0.2371660173,0.0525447652,0.0861053467,0.1265179962,-0.0630014017,-0.4126393497,-0.0742599964,0.0477150232,-0.0974051356,0.16696693,0.1920519024,0.132871151,0.1559260786,-0.4045022726,-0.1376460344,0.0323025733,-0.376919657,0.0055578146,-0.3176852167,0.0546303503,-0.2702908218,-0.0120817386,-0.7623512149,-0.0521201082,-0.1122782081,0.0660267845,0.0638756305,-0.0244168527,0.2227595448,-0.2470848262,0.0749816447,0.28331393,-0.3005584776,-0.2378236353,-0.0629288927,-0.037491519,0.1896335036,0.2650878727,-0.2005772442,0.2845451832,-0.1637352109,0.2106698602,0.3121518195,-0.3327314556,-0.2219264358,0.605520308,0.0193049405,0.5248504877,0.0198842697,-0.2308956832,-0.0092007685,0.1884041429,0.3035949767,0.4013707638,0.1422874182,0.0679152384,-0.2457050234,-0.2022635043,-0.1346425563,0.3127692342,0.0487670116,0.1889399141,0.0995356888,0.0244781338,0.2506872714,-0.1951391548,-0.0266671889,0.1624232382,0.0911331177,0.0282151625,-0.0057259151,-0.0044113393,-0.6142941117,0.1499558836,-0.0511226989,0.109201923,-0.408085674,0.0093207462,-0.2547832429,0.1260678917,-0.1280752718,0.1847681105,0.0374528877,0.1086534187,-0.1821872592,0.2108015567,-0.1838937998,0.3152327836,0.1626450419,0.2816524506,-0.4834640026,0.4377061725,0.210255906,-0.1212381497,0.0271184649,0.1853716373,-0.097817637,-0.1626708806,-0.2391357571,0.1727546602,0.1394181401,0.1797138602,-0.0562768616,-0.2375711501,-0.0822836161,0.0838592127,-0.1951901019,0.3068773448,0.2217241377,-0.0245843939,-0.0280968156,0.8052088618,-0.0741070136,0.4431970716,-0.015723357,-0.0205509756,0.4561716616,0.0615659803,-0.364153266,-0.3053402305,0.2388348728,0.3186245859,0.0603672415,0.0040045432,-0.3794290721,-0.1885213256,0.3982873261,0.005179801,0.2424625754,0.1532346159,-0.0953567475,-0.0996729881,0.0158600174,0.1401785463,0.2382987291,0.0758770034,-0.2070478052,-0.0434467606,-0.0409476832,-0.0247451495,0.1555987,0.306535244,0.2641779482,0.0933471769,0.1558886766,-0.020962242,-0.2815460265,-0.238003552,-0.2863601744,0.1189386174,-0.6182570457,0.2991375625,-0.4743469954,0.1188910827,0.035333585,-0.3410826325,-0.126299873,-0.322971493,-0.3007420599,0.2734274864,-0.1147493422,-0.1341666132,-0.2667239308,0.3151707649,-0.0232129041,-0.3463473022,-0.2113419473,-0.0117184091,-0.184648335,-0.0234055538,0.3875870109,0.1092963666,0.1544730067,-0.1289402992,0.047313679,0.1418192536,-0.0863964856,0.1482000202,-0.1994989067,0.2889936268,0.2154709399,0.1969099492,-0.0978527516,-0.1516460776,0.2886652946,0.0957521275,-0.3427708745,0.3241927326,-0.1354984939,0.0278997961,-0.1492165327,-0.0291694198,0.1219549254,-0.4392543733,0.5247157216,0.2458687872,0.0581384897,-0.0170072187,0.0242614429,0.1799215674,0.1687064171,0.0159113072,-0.1107467487,-0.1546859592,0.2326568216,-0.160832569,-0.2501766384,0.0764547884,-0.1965518147,0.1685908139,-0.0865133032,-0.390976578,0.0259424336,0.0707196668,0.1136565655,-0.0444040187,-0.295239538,0.0588247478,-0.0127509227,0.0851902887,-0.1855715215,-0.2022252828,0.1206316799,-0.0438129231,0.0216035321,0.0565150753,0.7252899408,-0.0942959189,0.1807754487,0.3117997646,-0.0382717438,0.394974649,-0.0264437217,0.1030089855,-0.4057941437,-0.1191259399,-0.3808179796,-0.0971131027,0.0786372274,0.21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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"OK after upgrading `datasets` to v1.12.1 the issue seems to have gone away. Closing this now :)","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":17,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n OK after upgrading `datasets` to v1.12.1 the issue seems to have gone away. Closing this now :)","embeddings":[-0.0681871027,0.1841620058,0.0364155807,0.3780699968,0.2355371863,-0.0062702368,0.4846684635,0.3670629561,0.4180777371,-0.0028386964,0.0605546869,0.5898874402,0.0276210085,-0.148857981,-0.2172110528,-0.1838730276,0.0313557796,0.1905159801,0.1274458319,0.0285853427,-0.3122327328,0.2942055166,-0.214816317,0.1017739549,-0.0391265377,-0.0952268541,0.1041623503,0.3933393061,-0.126418829,-0.3826465905,0.4134063721,-0.0130348513,0.2161162943,0.5308527946,-0.0001203599,0.1964653581,0.4152528644,-0.092444241,-0.2868113518,-0.4780595005,-0.2790301442,-0.3253917992,0.537475884,-0.1200379506,0.1052053347,-0.2437469363,-0.0497698858,-0.2207819521,0.6015406847,0.2307226658,0.1048027351,0.429939568,0.0260289963,0.0192190502,0.0637280419,0.2108367532,-0.1217756793,0.4542036653,0.008248603,0.1270525903,-0.1715968549,-0.0036361166,-0.102641046,0.1538670212,0.2191231996,0.0476008654,-0.1704021692,0.0818345174,0.0492499694,0.2946319878,0.2845771015,-0.0508809648,-0.4122377634,-0.3489953876,-0.1207850203,0.1302726865,0.2388218939,0.2473598123,-0.1509169936,0.1259337515,0.0328827612,-0.0184712112,-0.274197191,0.2383805364,0.0036435723,0.0111869918,-0.1582194567,0.10729184,-0.1295856088,-0.020824587,-0.1499335319,-0.2244829088,-0.3057584167,0.1283780038,0.0374987274,0.11600402,-0.0355978459,-0.4100777805,0.2355834544,0.3558476567,0.3206709325,0.2080192715,-0.2673651874,0.2078464776,0.5903261304,0.2016133815,-0.0549013875,0.0114986571,0.2564998567,0.330514431,0.0607299209,-0.0435533673,-0.2637991011,-0.1108330786,0.0103772897,-0.197450906,0.3086808324,-0.1726259738,-0.2238530517,0.0755632967,-0.6134242415,-0.0573209412,-0.0002183132,0.3672920167,-0.1971875429,0.4987778664,0.2744282484,0.4868384302,0.0442172214,0.0317274667,-0.1022381261,-0.0353484266,-0.1634835154,-0.2962282002,0.2371660173,0.0525447652,0.0861053467,0.1265179962,-0.0630014017,-0.4126393497,-0.0742599964,0.0477150232,-0.0974051356,0.16696693,0.1920519024,0.132871151,0.1559260786,-0.4045022726,-0.1376460344,0.0323025733,-0.376919657,0.0055578146,-0.3176852167,0.0546303503,-0.2702908218,-0.0120817386,-0.7623512149,-0.0521201082,-0.1122782081,0.0660267845,0.0638756305,-0.0244168527,0.2227595448,-0.2470848262,0.0749816447,0.28331393,-0.3005584776,-0.2378236353,-0.0629288927,-0.037491519,0.1896335036,0.2650878727,-0.2005772442,0.2845451832,-0.1637352109,0.2106698602,0.3121518195,-0.3327314556,-0.2219264358,0.605520308,0.0193049405,0.5248504877,0.0198842697,-0.2308956832,-0.0092007685,0.1884041429,0.3035949767,0.4013707638,0.1422874182,0.0679152384,-0.2457050234,-0.2022635043,-0.1346425563,0.3127692342,0.0487670116,0.1889399141,0.0995356888,0.0244781338,0.2506872714,-0.1951391548,-0.0266671889,0.1624232382,0.0911331177,0.0282151625,-0.0057259151,-0.0044113393,-0.6142941117,0.1499558836,-0.0511226989,0.109201923,-0.408085674,0.0093207462,-0.2547832429,0.1260678917,-0.1280752718,0.1847681105,0.0374528877,0.1086534187,-0.1821872592,0.2108015567,-0.1838937998,0.3152327836,0.1626450419,0.2816524506,-0.4834640026,0.4377061725,0.210255906,-0.1212381497,0.0271184649,0.1853716373,-0.097817637,-0.1626708806,-0.2391357571,0.1727546602,0.1394181401,0.1797138602,-0.0562768616,-0.2375711501,-0.0822836161,0.0838592127,-0.1951901019,0.3068773448,0.2217241377,-0.0245843939,-0.0280968156,0.8052088618,-0.0741070136,0.4431970716,-0.015723357,-0.0205509756,0.4561716616,0.0615659803,-0.364153266,-0.3053402305,0.2388348728,0.3186245859,0.0603672415,0.0040045432,-0.3794290721,-0.1885213256,0.3982873261,0.005179801,0.2424625754,0.1532346159,-0.0953567475,-0.0996729881,0.0158600174,0.1401785463,0.2382987291,0.0758770034,-0.2070478052,-0.0434467606,-0.0409476832,-0.0247451495,0.1555987,0.306535244,0.2641779482,0.0933471769,0.1558886766,-0.020962242,-0.2815460265,-0.238003552,-0.2863601744,0.1189386174,-0.6182570457,0.2991375625,-0.4743469954,0.1188910827,0.035333585,-0.3410826325,-0.126299873,-0.322971493,-0.3007420599,0.2734274864,-0.1147493422,-0.1341666132,-0.2667239308,0.3151707649,-0.0232129041,-0.3463473022,-0.2113419473,-0.0117184091,-0.184648335,-0.0234055538,0.3875870109,0.1092963666,0.1544730067,-0.1289402992,0.047313679,0.1418192536,-0.0863964856,0.1482000202,-0.1994989067,0.2889936268,0.2154709399,0.1969099492,-0.0978527516,-0.1516460776,0.2886652946,0.0957521275,-0.3427708745,0.3241927326,-0.1354984939,0.0278997961,-0.1492165327,-0.0291694198,0.1219549254,-0.4392543733,0.5247157216,0.2458687872,0.0581384897,-0.0170072187,0.0242614429,0.1799215674,0.1687064171,0.0159113072,-0.1107467487,-0.1546859592,0.2326568216,-0.160832569,-0.2501766384,0.0764547884,-0.1965518147,0.1685908139,-0.0865133032,-0.390976578,0.0259424336,0.0707196668,0.1136565655,-0.0444040187,-0.295239538,0.0588247478,-0.0127509227,0.0851902887,-0.1855715215,-0.2022252828,0.1206316799,-0.0438129231,0.0216035321,0.0565150753,0.7252899408,-0.0942959189,0.1807754487,0.3117997646,-0.0382717438,0.394974649,-0.0264437217,0.1030089855,-0.4057941437,-0.1191259399,-0.3808179796,-0.0971131027,0.0786372274,0.21930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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"Oops, I spoke too soon \ud83d\ude13 \r\n\r\nAfter deleting the cache and trying the above code snippet again I am hitting the same error. You can also reproduce it in the Colab notebook I linked to in the issue description. ","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":39,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n Oops, I spoke too soon \ud83d\ude13 \r\n\r\nAfter deleting the cache and trying the above code snippet again I am hitting the same error. You can also reproduce it in the Colab notebook I linked to in the issue description. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"@albertvillanova @lhoestq I noticed the same issue using datasets v1.12.1. Is there an update on when this could be fixed?","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":20,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n @albertvillanova @lhoestq I noticed the same issue using datasets v1.12.1. Is there an update on when this could be fixed?","embeddings":[-0.0681871027,0.1841620058,0.0364155807,0.3780699968,0.2355371863,-0.0062702368,0.4846684635,0.3670629561,0.4180777371,-0.0028386964,0.0605546869,0.5898874402,0.0276210085,-0.148857981,-0.2172110528,-0.1838730276,0.0313557796,0.1905159801,0.1274458319,0.0285853427,-0.3122327328,0.2942055166,-0.214816317,0.1017739549,-0.0391265377,-0.0952268541,0.1041623503,0.3933393061,-0.126418829,-0.3826465905,0.4134063721,-0.0130348513,0.2161162943,0.5308527946,-0.0001203599,0.1964653581,0.4152528644,-0.092444241,-0.2868113518,-0.4780595005,-0.2790301442,-0.3253917992,0.537475884,-0.1200379506,0.1052053347,-0.2437469363,-0.0497698858,-0.2207819521,0.6015406847,0.2307226658,0.1048027351,0.429939568,0.0260289963,0.0192190502,0.0637280419,0.2108367532,-0.1217756793,0.4542036653,0.008248603,0.1270525903,-0.1715968549,-0.0036361166,-0.102641046,0.1538670212,0.2191231996,0.0476008654,-0.1704021692,0.0818345174,0.0492499694,0.2946319878,0.2845771015,-0.0508809648,-0.4122377634,-0.3489953876,-0.1207850203,0.1302726865,0.2388218939,0.2473598123,-0.1509169936,0.1259337515,0.0328827612,-0.0184712112,-0.274197191,0.2383805364,0.0036435723,0.0111869918,-0.1582194567,0.10729184,-0.1295856088,-0.020824587,-0.1499335319,-0.2244829088,-0.3057584167,0.1283780038,0.0374987274,0.11600402,-0.0355978459,-0.4100777805,0.2355834544,0.3558476567,0.3206709325,0.2080192715,-0.2673651874,0.2078464776,0.5903261304,0.2016133815,-0.0549013875,0.0114986571,0.2564998567,0.330514431,0.0607299209,-0.0435533673,-0.2637991011,-0.1108330786,0.0103772897,-0.197450906,0.3086808324,-0.1726259738,-0.2238530517,0.0755632967,-0.6134242415,-0.0573209412,-0.0002183132,0.3672920167,-0.1971875429,0.4987778664,0.2744282484,0.4868384302,0.0442172214,0.0317274667,-0.1022381261,-0.0353484266,-0.1634835154,-0.2962282002,0.2371660173,0.0525447652,0.0861053467,0.1265179962,-0.0630014017,-0.4126393497,-0.0742599964,0.0477150232,-0.0974051356,0.16696693,0.1920519024,0.132871151,0.1559260786,-0.4045022726,-0.1376460344,0.0323025733,-0.376919657,0.0055578146,-0.3176852167,0.0546303503,-0.2702908218,-0.0120817386,-0.7623512149,-0.0521201082,-0.1122782081,0.0660267845,0.0638756305,-0.0244168527,0.2227595448,-0.2470848262,0.0749816447,0.28331393,-0.3005584776,-0.2378236353,-0.0629288927,-0.037491519,0.1896335036,0.2650878727,-0.2005772442,0.2845451832,-0.1637352109,0.2106698602,0.3121518195,-0.3327314556,-0.2219264358,0.605520308,0.0193049405,0.5248504877,0.0198842697,-0.2308956832,-0.0092007685,0.1884041429,0.3035949767,0.4013707638,0.1422874182,0.0679152384,-0.2457050234,-0.2022635043,-0.1346425563,0.3127692342,0.0487670116,0.1889399141,0.0995356888,0.0244781338,0.2506872714,-0.1951391548,-0.0266671889,0.1624232382,0.0911331177,0.0282151625,-0.0057259151,-0.0044113393,-0.6142941117,0.1499558836,-0.0511226989,0.109201923,-0.408085674,0.0093207462,-0.2547832429,0.1260678917,-0.1280752718,0.1847681105,0.0374528877,0.1086534187,-0.1821872592,0.2108015567,-0.1838937998,0.3152327836,0.1626450419,0.2816524506,-0.4834640026,0.4377061725,0.210255906,-0.1212381497,0.0271184649,0.1853716373,-0.097817637,-0.1626708806,-0.2391357571,0.1727546602,0.1394181401,0.1797138602,-0.0562768616,-0.2375711501,-0.0822836161,0.0838592127,-0.1951901019,0.3068773448,0.2217241377,-0.0245843939,-0.0280968156,0.8052088618,-0.0741070136,0.4431970716,-0.015723357,-0.0205509756,0.4561716616,0.0615659803,-0.364153266,-0.3053402305,0.2388348728,0.3186245859,0.0603672415,0.0040045432,-0.3794290721,-0.1885213256,0.3982873261,0.005179801,0.2424625754,0.1532346159,-0.0953567475,-0.0996729881,0.0158600174,0.1401785463,0.2382987291,0.0758770034,-0.2070478052,-0.0434467606,-0.0409476832,-0.0247451495,0.1555987,0.306535244,0.2641779482,0.0933471769,0.1558886766,-0.020962242,-0.2815460265,-0.238003552,-0.2863601744,0.1189386174,-0.6182570457,0.2991375625,-0.4743469954,0.1188910827,0.035333585,-0.3410826325,-0.126299873,-0.322971493,-0.3007420599,0.2734274864,-0.1147493422,-0.1341666132,-0.2667239308,0.3151707649,-0.0232129041,-0.3463473022,-0.2113419473,-0.0117184091,-0.184648335,-0.0234055538,0.3875870109,0.1092963666,0.1544730067,-0.1289402992,0.047313679,0.1418192536,-0.0863964856,0.1482000202,-0.1994989067,0.2889936268,0.2154709399,0.1969099492,-0.0978527516,-0.1516460776,0.2886652946,0.0957521275,-0.3427708745,0.3241927326,-0.1354984939,0.0278997961,-0.1492165327,-0.0291694198,0.1219549254,-0.4392543733,0.5247157216,0.2458687872,0.0581384897,-0.0170072187,0.0242614429,0.1799215674,0.1687064171,0.0159113072,-0.1107467487,-0.1546859592,0.2326568216,-0.160832569,-0.2501766384,0.0764547884,-0.1965518147,0.1685908139,-0.0865133032,-0.390976578,0.0259424336,0.0707196668,0.1136565655,-0.0444040187,-0.295239538,0.0588247478,-0.0127509227,0.0851902887,-0.1855715215,-0.2022252828,0.1206316799,-0.0438129231,0.0216035321,0.0565150753,0.7252899408,-0.0942959189,0.1807754487,0.3117997646,-0.0382717438,0.394974649,-0.0264437217,0.1030089855,-0.4057941437,-0.1191259399,-0.3808179796,-0.0971131027,0.0786372274,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"Apparently it's possible to make it work by increasing the `block_size`, let me open a PR","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":16,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n Apparently it's possible to make it work by increasing the `block_size`, let me open a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"I just opened a PR with a fix, feel free to install `datasets` from source from source and let me know if it helps","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":24,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n I just opened a PR with a fix, feel free to install `datasets` from source from source and let me know if it 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2799","title":"Loading JSON throws ArrowNotImplementedError","comments":"@zijwang did PR #3000 solve the problem for you? It did for me, so it all is good on your end we can close this issue. Thanks again to @lhoestq for the pyarrow magic \ud83e\udd2f ","body":"## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":35,"text":"Loading JSON throws ArrowNotImplementedError \n ## Describe the bug\r\nI have created a [dataset](https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).\r\n\r\nCuriously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.\r\n\r\nYou can find a Colab notebook which reproduces the error [here](https:\/\/colab.research.google.com\/drive\/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).\r\n\r\n**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom huggingface_hub import hf_hub_url\r\nimport pandas as pd\r\n\r\n# returns https:\/\/huggingface.co\/datasets\/lewtun\/github-issues-test\/resolve\/main\/issues-datasets.jsonl\r\ndata_files = hf_hub_url(repo_id=\"lewtun\/github-issues-test\", filename=\"issues-datasets.jsonl\", repo_type=\"dataset\")\r\n# throws ArrowNotImplementedError\r\ndset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n# no problem with pandas ...\r\ndf = pd.read_json(data_files, orient=\"records\", lines=True)\r\ndf.head()\r\n```\r\n\r\n## Expected results\r\nI can load any line-separated JSON file, similar to `pandas`.\r\n\r\n## Actual results\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-7-5b8e82b6c3a2> in <module>()\r\n----> 1 dset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n @zijwang did PR #3000 solve the problem for you? It did for me, so it all is good on your end we can close this issue. Thanks again to @lhoestq for the pyarrow magic \ud83e\udd2f 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2788","title":"How to sample every file in a list of files making up a split in a dataset when loading?","comments":"Hi ! This is not possible just with `load_dataset`.\r\n\r\nYou can do something like this instead:\r\n```python\r\nseed=42\r\ndata_files_dict = {\r\n    \"train\": [train_file1, train_file2],\r\n    \"test\": [test_file1, test_file2],\r\n    \"val\": [val_file1, val_file2]\r\n}\r\ndataset = datasets.load_dataset(\r\n    \"csv\",\r\n    data_files=data_files_dict,\r\n).shuffle(seed=seed)\r\n\r\nsample_dataset = {splitname: split.select(range(8)) for splitname, split in dataset.items()}\r\n```\r\n\r\nAnother alternative is loading each file separately with `split=\"train[:8]\"` and then use `concatenate_datasets` to merge the sample of each file.","body":"I am loading a dataset with multiple train, test, and validation files like this:\r\n\r\n```\r\ndata_files_dict = {\r\n    \"train\": [train_file1, train_file2],\r\n    \"test\": [test_file1, test_file2],\r\n    \"val\": [val_file1, val_file2]\r\n}\r\ndataset = datasets.load_dataset(\r\n    \"csv\",\r\n    data_files=data_files_dict,\r\n    split=['train[:8]', 'test[:8]', 'val[:8]']\r\n)\r\n\r\n```\r\n\r\nHowever, this only selects the first 8 rows from train_file1, test_file1, val_file1, since they are the first files in the lists.\r\n\r\nI'm trying to formulate a split argument that can sample from each file specified in my list of files that make up each split.\r\n\r\nIs this type of splitting supported? If so, how can I do it?","comment_length":67,"text":"How to sample every file in a list of files making up a split in a dataset when loading? \n I am loading a dataset with multiple train, test, and validation files like this:\r\n\r\n```\r\ndata_files_dict = {\r\n    \"train\": [train_file1, train_file2],\r\n    \"test\": [test_file1, test_file2],\r\n    \"val\": [val_file1, val_file2]\r\n}\r\ndataset = datasets.load_dataset(\r\n    \"csv\",\r\n    data_files=data_files_dict,\r\n    split=['train[:8]', 'test[:8]', 'val[:8]']\r\n)\r\n\r\n```\r\n\r\nHowever, this only selects the first 8 rows from train_file1, test_file1, val_file1, since they are the first files in the lists.\r\n\r\nI'm trying to formulate a split argument that can sample from each file specified in my list of files that make up each split.\r\n\r\nIs this type of splitting supported? If so, how can I do it? \n Hi ! This is not possible just with `load_dataset`.\r\n\r\nYou can do something like this instead:\r\n```python\r\nseed=42\r\ndata_files_dict = {\r\n    \"train\": [train_file1, train_file2],\r\n    \"test\": [test_file1, test_file2],\r\n    \"val\": [val_file1, val_file2]\r\n}\r\ndataset = datasets.load_dataset(\r\n    \"csv\",\r\n    data_files=data_files_dict,\r\n).shuffle(seed=seed)\r\n\r\nsample_dataset = {splitname: split.select(range(8)) for splitname, split in dataset.items()}\r\n```\r\n\r\nAnother alternative is loading each file separately with `split=\"train[:8]\"` and then use `concatenate_datasets` to merge the sample of each file.","embeddings":[-0.3138794303,-0.1796855778,-0.1142874882,0.145461306,-0.0157872085,0.3574570119,0.444955796,0.4948764741,0.556786716,0.0195083302,-0.0745141283,0.198577702,0.0281437356,0.1434578001,0.1276390553,-0.3237335086,-0.0534550883,0.1187504977,0.2719002068,0.0101069016,-0.1883502007,0.091860868,-0.4363408387,-0.3013267517,-0.1955500692,0.0335028172,-0.3832268119,0.0586887076,0.0298341662,-0.2322053164,0.0476163216,0.2474765778,0.0852342919,0.011206409,-0.0001181749,0.0020298953,0.1929554939,-0.3049511909,-0.2270892859,-0.3292902708,-0.0461659841,0.0632799491,-0.0771353766,-0.3171573579,-0.1542430818,-0.1601599008,-0.0316538103,-0.138571009,0.4643646777,0.1826563776,0.0203301627,0.1105126143,-0.3269145489,-0.1088883802,0.3746608496,0.3522197604,0.2187461704,0.0444287285,0.4358339012,-0.0470805466,-0.1141447574,-0.193022579,-0.0340736024,0.2639250457,0.1277291179,0.1246679351,-0.3276381791,-0.3322709799,-0.1635397971,0.4533859789,0.2487736046,0.0091191055,-0.4796769917,-0.6428300142,0.0095056836,-0.3940193355,-0.1848472953,0.4530909657,-0.3995965123,0.0952166766,0.1818254739,-0.0572032072,0.0219684429,0.2663127482,0.0569325797,0.1254368424,-0.067655161,0.0810493603,0.7294518948,0.1416414231,-0.0421174206,-0.2076791376,-0.079792425,0.1188273728,-0.4636723995,-0.1048142835,-0.2771571279,-0.0089037791,0.1892953068,0.3294673264,0.0680096671,-0.2414481789,0.1494103819,0.170034349,0.6460336447,-0.2381347865,-0.0347361565,0.1712915152,-0.1695314646,0.0176247302,0.0331937373,-0.0454387814,-0.0381043367,-0.1814244986,-0.3282948732,0.1058130413,-0.1274303645,0.0860395506,-0.1948265135,-0.4890435636,-0.2440893203,-0.5034528971,0.0762869567,0.3599081933,0.2342460155,-0.0838767216,-0.1735524237,0.4428135753,-0.0889260322,-0.274707526,-0.1148847938,0.2223401219,-0.1089815348,0.0057191653,0.1465914994,-0.188432321,0.2893144488,-0.0865546241,0.2269657701,-0.2306074351,0.3035942018,-0.138725698,0.1588564962,0.134075433,0.2950143814,-0.0119690951,0.2660682797,-0.0398498178,-0.1329244077,0.2388462722,-0.2571510375,-0.191285789,0.2006834,-0.0225589946,0.0105375061,0.1674260944,-0.362051636,0.2915106118,0.1189149395,0.1359364688,-0.1135267764,-0.1590327024,0.0445085838,-0.0197431128,0.1557989419,0.3986886144,-0.2346587479,-0.123491399,-0.2077334225,-0.1717362553,0.3626834452,0.0350856408,-0.3634830415,0.241454497,-0.3660298884,0.7993331552,0.3345078826,0.1338744164,-0.0137383081,0.4271205068,0.0220962726,0.0380513445,0.2731009722,0.0632520467,0.5865426064,-0.0518562123,0.0598010197,0.505310595,-0.2618050277,0.0240476914,0.1377211064,-0.0513437763,0.1542183459,0.3551742136,-0.0846145749,0.1678067297,-0.0063042361,0.2048270851,0.5283377767,-0.2432685345,-0.036706645,0.0304862726,-0.0026645798,0.2762807608,0.0030848575,-0.209514752,-0.1535419524,0.1135422587,0.278737247,-0.5721077919,0.3249298036,-0.3869333565,-0.122447297,-0.0005192388,-0.1241058335,0.1367531568,0.0101979896,0.1256721169,-0.1288661659,-0.5308008194,-0.3429537714,-0.0541972294,-0.2381863445,0.1940638423,-0.2353833616,0.3334453106,0.2488534898,0.0130883576,0.1835725605,-0.0925863832,-0.0169581082,-0.3653472364,0.0474871881,0.2036670148,0.8958287835,-0.0555032603,0.1376837194,-0.2449238896,0.0282209273,0.0709381327,-0.1368812919,-0.1341382563,-0.0789420307,-0.1361997575,-0.2074255496,0.5919697881,-0.5605755448,0.4632727206,-0.1452792734,0.2544754744,0.1349827349,-0.0727637112,-0.2149717957,-0.0677865893,0.0911614895,-0.1049719304,0.3877750039,0.062067505,-0.2700069547,-0.1236140803,0.226440832,-0.2351535559,-0.021593364,0.1420031786,0.0250722636,0.0769358501,0.1343010515,0.2210197002,0.2190335542,0.1367043406,0.2197372913,-0.2373443842,0.1761601716,-0.1678719819,0.090482682,0.2804896235,-0.1848636568,0.2034763992,-0.5162175894,-0.2349596769,-0.1231027246,0.0242495108,0.2009834945,-0.0500389449,-0.1504891515,0.0563311279,-0.1548143774,0.0014892309,-0.1733236015,-0.2208731472,0.1084049791,-0.0190758575,-0.2101282328,0.1752094328,-0.014967124,-0.046751678,-0.0501355603,0.3053672314,-0.0454382449,-0.172108233,0.0894083381,-0.1400024593,0.1235911474,0.0462605245,0.5049254298,0.5502218604,0.1374114454,0.0413787812,-0.216308251,0.2009606212,0.3659947217,0.0553424805,-0.1022311673,0.1487470418,0.0951430202,0.393389374,-0.0603435375,-0.3966614306,-0.2915034294,0.4472600818,-0.05621567,-0.1207681373,0.1564860195,0.0250614211,-0.1724932492,-0.2683087289,-0.3343343437,-0.1703346223,0.1064798757,0.1056603864,0.0908488706,-0.0613080114,0.0446206331,-0.1298354268,-0.2531605363,-0.3052235842,-0.0670007989,-0.1719997525,0.028853571,-0.0586904548,-0.1425167173,0.0911052972,-0.1706534475,-0.1698983461,0.3151335418,0.0565422364,-0.119005613,0.0536041893,-0.0231910441,-0.1919582039,-0.0447354652,0.0237636454,-0.2283338606,0.1782976985,-0.1228153259,0.076862894,0.3081637621,-0.1329180747,0.2364634871,0.2309126407,0.0722262487,0.0287414677,0.9346376061,0.4259983301,0.133753702,0.1054857075,-0.0050867302,-0.1113102809,0.0277696364,-0.2091869265,0.0696029216,-0.0040837573,-0.222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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2787","title":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com","comments":"the bug code locate in \uff1a\r\n    if data_args.task_name is not None:\r\n        # Downloading and loading a dataset from the hub.\r\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)","body":"Hello,\r\nI am trying to run run_glue.py and it gives me this error -\r\n\r\nTraceback (most recent call last):\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 546, in <module>\r\n    main()\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 250, in main\r\n    datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 718, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py\r\n\r\nTrying to do python run_glue.py  --model_name_or_path\r\nbert-base-cased\r\n--task_name\r\nmrpc\r\n--do_train\r\n--do_eval\r\n--max_seq_length\r\n128\r\n--per_device_train_batch_size\r\n32\r\n--learning_rate\r\n2e-5\r\n--num_train_epochs\r\n3\r\n--output_dir\r\n.\/tmp\/mrpc\/\r\n\r\nIs this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.\r\nThank you!\r\n","comment_length":25,"text":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com \n Hello,\r\nI am trying to run run_glue.py and it gives me this error -\r\n\r\nTraceback (most recent call last):\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 546, in <module>\r\n    main()\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 250, in main\r\n    datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 718, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py\r\n\r\nTrying to do python run_glue.py  --model_name_or_path\r\nbert-base-cased\r\n--task_name\r\nmrpc\r\n--do_train\r\n--do_eval\r\n--max_seq_length\r\n128\r\n--per_device_train_batch_size\r\n32\r\n--learning_rate\r\n2e-5\r\n--num_train_epochs\r\n3\r\n--output_dir\r\n.\/tmp\/mrpc\/\r\n\r\nIs this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.\r\nThank you!\r\n \n the bug code locate in \uff1a\r\n    if data_args.task_name is not None:\r\n        # Downloading and loading a dataset from the hub.\r\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)","embeddings":[-0.2038715184,-0.1601331234,-0.0819259807,0.046902068,0.2665438056,-0.0723191276,0.1546953321,0.3012448549,0.1090682521,-0.0955984294,-0.1769392192,-0.1569310576,0.0424281619,0.0098089986,0.0353741758,-0.1757960469,-0.0955913514,-0.0462971069,-0.2514628172,0.187785551,-0.1379356235,0.1785493493,-0.0379736349,0.2863889635,-0.1777602732,-0.2044938058,-0.0835378692,0.2092444748,-0.1527038217,-0.2665781081,0.4261870086,-0.1104120836,0.1023879945,0.4276525974,-0.0001111302,0.2171017975,0.4489411414,0.0764487162,-0.1543845832,-0.3074429333,-0.0379915237,0.0689299181,0.2134657204,-0.2233344316,-0.1960697025,0.3461775184,0.0491150022,-0.1808769554,0.4765046239,0.2705875933,0.2488420606,0.3377011716,0.4446749985,-0.2152077854,0.0332203731,-0.2912712395,0.0024016653,0.6216793656,0.1213354543,-0.2661054432,0.2426152825,0.2062644213,-0.1433541477,-0.0120363627,0.0456216745,-0.1558198184,0.0829060301,-0.2861295044,0.1528282017,0.0686278045,-0.141816169,-0.2192167342,-0.2591913342,-0.0737397969,-0.0262958892,-0.1296845824,0.331905216,0.1610924304,-0.2213149071,0.2277653813,-0.0923094675,-0.1391131133,-0.2099060416,0.2466940284,0.0454544127,0.2128365934,-0.285688132,0.2644905746,0.4337483943,-0.0378364623,-0.2852620184,0.0696709752,0.1732451916,0.1920874119,-0.2251850963,0.0389768854,-0.3047998548,0.0818184689,0.0858520195,0.0211909339,-0.2492952347,-0.1187235191,0.2668730021,0.1873599738,-0.0882738084,0.3309412599,0.0141430199,0.1095975041,0.3459482193,0.3594096899,-0.1148068979,0.0365825891,-0.2656762898,-0.46562168,-0.3594941199,0.0650140643,0.2611832321,0.0185204595,-0.1696608961,0.0698689297,-0.1784015596,0.0330274105,0.1020842791,0.5010195374,-0.264885366,-0.3661019206,0.0398671776,0.2935795486,0.0180174913,0.0218921565,-0.1782663316,0.157259956,-0.2640621364,0.0905434713,0.0618727505,-0.254320085,0.2353595346,-0.2026710063,0.0230699349,-0.1711889654,0.1205860972,-0.2005208731,-0.158144623,0.3388409019,0.0797365904,0.180475682,0.4291989803,-0.3894393742,-0.0596620627,-0.0774279088,-0.2149029821,-0.4968355596,0.0238060597,0.1501094401,-0.0620120503,0.1604385972,-0.0989380255,-0.248925671,0.2021318972,0.1912706196,-0.0140952608,-0.0100923842,0.1434962451,-0.2804526091,0.5142765641,0.4544944763,0.1708977968,-0.1090477556,-0.0004218963,0.0640206262,-0.0517981537,0.2329597026,-0.0699741095,0.1279715449,-0.3756048977,0.1551721245,0.3715333343,-0.653388679,-0.2841059566,0.3186047971,-0.274140209,-0.0540166125,0.1668800265,-0.2321895212,-0.0450625047,0.0239412375,0.3900935948,0.0740385205,0.0700897276,-0.2052421421,-0.0241824016,-0.3783530891,0.1280156225,0.0518320054,0.1179565564,0.0941041037,0.0289575048,-0.2993083894,0.2291134,0.1804360002,0.0232359786,0.22256051,0.0875867456,0.1844524741,-0.0589135289,-0.1022363752,-0.0982042775,0.1569275558,-0.13933146,0.3022058308,-0.4215773046,0.0652287006,-0.3841099143,0.0516632423,-0.2309429497,-0.3040854037,0.1636356562,-0.0034321281,0.4423214495,0.1496356577,-0.0936227217,0.4874028862,-0.1470745057,0.2835756838,-0.3597308099,0.2300837487,-0.3057549596,-0.1062674224,-0.156371057,0.166188091,0.351362288,-0.2343126982,-0.0265929922,0.4231664836,-0.2641439438,0.0973605886,0.2657681406,0.0963225588,0.031523183,-0.2190815657,0.0253635254,-0.1022461355,-0.0166736674,0.0638581291,0.6379241347,0.0479976051,0.1513416469,0.7245124578,0.1906802058,0.2567310035,0.1417647898,-0.0172172338,-0.2446911037,0.0072813323,0.2052772939,0.0509162173,0.4524137974,-0.3265386522,0.1677902639,-0.2114031017,0.1441133022,-0.035479892,0.2344904095,0.1491603702,-0.3617250323,0.2157289535,-0.1119084656,0.3587546647,0.3501404822,0.0897394866,-0.0167159215,0.2718507648,-0.1022959575,-0.0745456368,0.3431275487,0.0640020669,0.0710157901,-0.1801293045,-0.0368262008,-0.1345124245,-0.1243019998,-0.2394788861,-0.1977582723,0.1469757408,-0.2880876958,0.1693797261,-0.1144179553,-0.3013064861,-0.1543430984,-0.3460415006,-0.4186742306,-0.2409940213,-0.3461467028,0.0472732708,0.0670877323,0.2756136656,0.1089546755,0.0105085308,-0.0052667689,-0.1842108667,-0.034712214,-0.1745305657,-0.1391935796,0.0206357259,-0.0188447759,-0.2574251294,0.2771224976,-0.1947549433,-0.1127352193,0.0472366884,-0.2349187285,0.0479723103,-0.0904428661,0.1711955369,0.328553766,0.3805794716,0.1841480136,-0.3997780979,0.5727685094,0.0603625476,0.1144243404,-0.1596369445,0.1083576158,0.0168828517,0.1767283529,0.0041359356,-0.6324492693,-0.3507916033,0.3485687375,-0.0266018584,0.1511572897,0.1762987822,0.0596724488,0.2824338078,-0.3841683567,0.0531042032,-0.2631133497,-0.4323770106,0.1425144523,-0.2498616874,-0.220136717,-0.0182346441,0.1905058622,0.2702789605,-0.2122463733,-0.3237496614,-0.3732507825,-0.2246032059,-0.0373766795,0.1037429646,0.1593064964,0.2925694287,-0.1132495999,-0.0387336724,0.1269527972,-0.2187675238,0.2200421691,-0.197969988,0.1281968504,0.2286254913,0.3870925903,0.0413929857,0.5444860458,0.2436498553,-0.0796830058,0.3192813396,-0.1745523363,0.2922652662,-0.037890166,-0.5508058667,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2787","title":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com","comments":"Hi @jinec,\r\n\r\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https:\/\/raw.githubusercontent.com\r\n\r\nNormally, it should work if you wait a little and then retry.\r\n\r\nCould you please confirm if the problem persists?","body":"Hello,\r\nI am trying to run run_glue.py and it gives me this error -\r\n\r\nTraceback (most recent call last):\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 546, in <module>\r\n    main()\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 250, in main\r\n    datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 718, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py\r\n\r\nTrying to do python run_glue.py  --model_name_or_path\r\nbert-base-cased\r\n--task_name\r\nmrpc\r\n--do_train\r\n--do_eval\r\n--max_seq_length\r\n128\r\n--per_device_train_batch_size\r\n32\r\n--learning_rate\r\n2e-5\r\n--num_train_epochs\r\n3\r\n--output_dir\r\n.\/tmp\/mrpc\/\r\n\r\nIs this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.\r\nThank you!\r\n","comment_length":38,"text":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com \n Hello,\r\nI am trying to run run_glue.py and it gives me this error -\r\n\r\nTraceback (most recent call last):\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 546, in <module>\r\n    main()\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 250, in main\r\n    datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 718, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py\r\n\r\nTrying to do python run_glue.py  --model_name_or_path\r\nbert-base-cased\r\n--task_name\r\nmrpc\r\n--do_train\r\n--do_eval\r\n--max_seq_length\r\n128\r\n--per_device_train_batch_size\r\n32\r\n--learning_rate\r\n2e-5\r\n--num_train_epochs\r\n3\r\n--output_dir\r\n.\/tmp\/mrpc\/\r\n\r\nIs this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.\r\nThank you!\r\n \n Hi @jinec,\r\n\r\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https:\/\/raw.githubusercontent.com\r\n\r\nNormally, it should work if you wait a little and then retry.\r\n\r\nCould you please confirm if the problem persists?","embeddings":[-0.2038715184,-0.1601331234,-0.0819259807,0.046902068,0.2665438056,-0.0723191276,0.1546953321,0.3012448549,0.1090682521,-0.0955984294,-0.1769392192,-0.1569310576,0.0424281619,0.0098089986,0.0353741758,-0.1757960469,-0.0955913514,-0.0462971069,-0.2514628172,0.187785551,-0.1379356235,0.1785493493,-0.0379736349,0.2863889635,-0.1777602732,-0.2044938058,-0.0835378692,0.2092444748,-0.1527038217,-0.2665781081,0.4261870086,-0.1104120836,0.1023879945,0.4276525974,-0.0001111302,0.2171017975,0.4489411414,0.0764487162,-0.1543845832,-0.3074429333,-0.0379915237,0.0689299181,0.2134657204,-0.2233344316,-0.1960697025,0.3461775184,0.0491150022,-0.1808769554,0.4765046239,0.2705875933,0.2488420606,0.3377011716,0.4446749985,-0.2152077854,0.0332203731,-0.2912712395,0.0024016653,0.6216793656,0.1213354543,-0.2661054432,0.2426152825,0.2062644213,-0.1433541477,-0.0120363627,0.0456216745,-0.1558198184,0.0829060301,-0.2861295044,0.1528282017,0.0686278045,-0.141816169,-0.2192167342,-0.2591913342,-0.0737397969,-0.0262958892,-0.1296845824,0.331905216,0.1610924304,-0.2213149071,0.2277653813,-0.0923094675,-0.1391131133,-0.2099060416,0.2466940284,0.0454544127,0.2128365934,-0.285688132,0.2644905746,0.4337483943,-0.0378364623,-0.2852620184,0.0696709752,0.1732451916,0.1920874119,-0.2251850963,0.0389768854,-0.3047998548,0.0818184689,0.0858520195,0.0211909339,-0.2492952347,-0.1187235191,0.2668730021,0.1873599738,-0.0882738084,0.3309412599,0.0141430199,0.1095975041,0.3459482193,0.3594096899,-0.1148068979,0.0365825891,-0.2656762898,-0.46562168,-0.3594941199,0.0650140643,0.2611832321,0.0185204595,-0.1696608961,0.0698689297,-0.1784015596,0.0330274105,0.1020842791,0.5010195374,-0.264885366,-0.3661019206,0.0398671776,0.2935795486,0.0180174913,0.0218921565,-0.1782663316,0.157259956,-0.2640621364,0.0905434713,0.0618727505,-0.254320085,0.2353595346,-0.2026710063,0.0230699349,-0.1711889654,0.1205860972,-0.2005208731,-0.158144623,0.3388409019,0.0797365904,0.180475682,0.4291989803,-0.3894393742,-0.0596620627,-0.0774279088,-0.2149029821,-0.4968355596,0.0238060597,0.1501094401,-0.0620120503,0.1604385972,-0.0989380255,-0.248925671,0.2021318972,0.1912706196,-0.0140952608,-0.0100923842,0.1434962451,-0.2804526091,0.5142765641,0.4544944763,0.1708977968,-0.1090477556,-0.0004218963,0.0640206262,-0.0517981537,0.2329597026,-0.0699741095,0.1279715449,-0.3756048977,0.1551721245,0.3715333343,-0.653388679,-0.2841059566,0.3186047971,-0.274140209,-0.0540166125,0.1668800265,-0.2321895212,-0.0450625047,0.0239412375,0.3900935948,0.0740385205,0.0700897276,-0.2052421421,-0.0241824016,-0.3783530891,0.1280156225,0.0518320054,0.1179565564,0.0941041037,0.0289575048,-0.2993083894,0.2291134,0.1804360002,0.0232359786,0.22256051,0.0875867456,0.1844524741,-0.0589135289,-0.1022363752,-0.0982042775,0.1569275558,-0.13933146,0.3022058308,-0.4215773046,0.0652287006,-0.3841099143,0.0516632423,-0.2309429497,-0.3040854037,0.1636356562,-0.0034321281,0.4423214495,0.1496356577,-0.0936227217,0.4874028862,-0.1470745057,0.2835756838,-0.3597308099,0.2300837487,-0.3057549596,-0.1062674224,-0.156371057,0.166188091,0.351362288,-0.2343126982,-0.0265929922,0.4231664836,-0.2641439438,0.0973605886,0.2657681406,0.0963225588,0.031523183,-0.2190815657,0.0253635254,-0.1022461355,-0.0166736674,0.0638581291,0.6379241347,0.0479976051,0.1513416469,0.7245124578,0.1906802058,0.2567310035,0.1417647898,-0.0172172338,-0.2446911037,0.0072813323,0.2052772939,0.0509162173,0.4524137974,-0.3265386522,0.1677902639,-0.2114031017,0.1441133022,-0.035479892,0.2344904095,0.1491603702,-0.3617250323,0.2157289535,-0.1119084656,0.3587546647,0.3501404822,0.0897394866,-0.0167159215,0.2718507648,-0.1022959575,-0.0745456368,0.3431275487,0.0640020669,0.0710157901,-0.1801293045,-0.0368262008,-0.1345124245,-0.1243019998,-0.2394788861,-0.1977582723,0.1469757408,-0.2880876958,0.1693797261,-0.1144179553,-0.3013064861,-0.1543430984,-0.3460415006,-0.4186742306,-0.2409940213,-0.3461467028,0.0472732708,0.0670877323,0.2756136656,0.1089546755,0.0105085308,-0.0052667689,-0.1842108667,-0.034712214,-0.1745305657,-0.1391935796,0.0206357259,-0.0188447759,-0.2574251294,0.2771224976,-0.1947549433,-0.1127352193,0.0472366884,-0.2349187285,0.0479723103,-0.0904428661,0.1711955369,0.328553766,0.3805794716,0.1841480136,-0.3997780979,0.5727685094,0.0603625476,0.1144243404,-0.1596369445,0.1083576158,0.0168828517,0.1767283529,0.0041359356,-0.6324492693,-0.3507916033,0.3485687375,-0.0266018584,0.1511572897,0.1762987822,0.0596724488,0.2824338078,-0.3841683567,0.0531042032,-0.2631133497,-0.4323770106,0.1425144523,-0.2498616874,-0.220136717,-0.0182346441,0.1905058622,0.2702789605,-0.2122463733,-0.3237496614,-0.3732507825,-0.2246032059,-0.0373766795,0.1037429646,0.1593064964,0.2925694287,-0.1132495999,-0.0387336724,0.1269527972,-0.2187675238,0.2200421691,-0.197969988,0.1281968504,0.2286254913,0.3870925903,0.0413929857,0.5444860458,0.2436498553,-0.0796830058,0.3192813396,-0.1745523363,0.2922652662,-0.037890166,-0.5508058667,-0.1344224811,-0.15217116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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2787","title":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com","comments":"> I can access https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py without problem...\r\n\r\nI can not access https:\/\/raw.githubusercontent.com\/huggingface\/datasets either,   I am in China","body":"Hello,\r\nI am trying to run run_glue.py and it gives me this error -\r\n\r\nTraceback (most recent call last):\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 546, in <module>\r\n    main()\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 250, in main\r\n    datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 718, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py\r\n\r\nTrying to do python run_glue.py  --model_name_or_path\r\nbert-base-cased\r\n--task_name\r\nmrpc\r\n--do_train\r\n--do_eval\r\n--max_seq_length\r\n128\r\n--per_device_train_batch_size\r\n32\r\n--learning_rate\r\n2e-5\r\n--num_train_epochs\r\n3\r\n--output_dir\r\n.\/tmp\/mrpc\/\r\n\r\nIs this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.\r\nThank you!\r\n","comment_length":17,"text":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com \n Hello,\r\nI am trying to run run_glue.py and it gives me this error -\r\n\r\nTraceback (most recent call last):\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 546, in <module>\r\n    main()\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 250, in main\r\n    datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 718, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py\r\n\r\nTrying to do python run_glue.py  --model_name_or_path\r\nbert-base-cased\r\n--task_name\r\nmrpc\r\n--do_train\r\n--do_eval\r\n--max_seq_length\r\n128\r\n--per_device_train_batch_size\r\n32\r\n--learning_rate\r\n2e-5\r\n--num_train_epochs\r\n3\r\n--output_dir\r\n.\/tmp\/mrpc\/\r\n\r\nIs this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.\r\nThank you!\r\n \n > I can access https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py without problem...\r\n\r\nI can not access https:\/\/raw.githubusercontent.com\/huggingface\/datasets either,   I am in China","embeddings":[-0.2038715184,-0.1601331234,-0.0819259807,0.046902068,0.2665438056,-0.0723191276,0.1546953321,0.3012448549,0.1090682521,-0.0955984294,-0.1769392192,-0.1569310576,0.0424281619,0.0098089986,0.0353741758,-0.1757960469,-0.0955913514,-0.0462971069,-0.2514628172,0.187785551,-0.1379356235,0.1785493493,-0.0379736349,0.2863889635,-0.1777602732,-0.2044938058,-0.0835378692,0.2092444748,-0.1527038217,-0.2665781081,0.4261870086,-0.1104120836,0.1023879945,0.4276525974,-0.0001111302,0.2171017975,0.4489411414,0.0764487162,-0.1543845832,-0.3074429333,-0.0379915237,0.0689299181,0.2134657204,-0.2233344316,-0.1960697025,0.3461775184,0.0491150022,-0.1808769554,0.4765046239,0.2705875933,0.2488420606,0.3377011716,0.4446749985,-0.2152077854,0.0332203731,-0.2912712395,0.0024016653,0.6216793656,0.1213354543,-0.2661054432,0.2426152825,0.2062644213,-0.1433541477,-0.0120363627,0.0456216745,-0.1558198184,0.0829060301,-0.2861295044,0.1528282017,0.0686278045,-0.141816169,-0.2192167342,-0.2591913342,-0.0737397969,-0.0262958892,-0.1296845824,0.331905216,0.1610924304,-0.2213149071,0.2277653813,-0.0923094675,-0.1391131133,-0.2099060416,0.2466940284,0.0454544127,0.2128365934,-0.285688132,0.2644905746,0.4337483943,-0.0378364623,-0.2852620184,0.0696709752,0.1732451916,0.1920874119,-0.2251850963,0.0389768854,-0.3047998548,0.0818184689,0.0858520195,0.0211909339,-0.2492952347,-0.1187235191,0.2668730021,0.1873599738,-0.0882738084,0.3309412599,0.0141430199,0.1095975041,0.3459482193,0.3594096899,-0.1148068979,0.0365825891,-0.2656762898,-0.46562168,-0.3594941199,0.0650140643,0.2611832321,0.0185204595,-0.1696608961,0.0698689297,-0.1784015596,0.0330274105,0.1020842791,0.5010195374,-0.264885366,-0.3661019206,0.0398671776,0.2935795486,0.0180174913,0.0218921565,-0.1782663316,0.157259956,-0.2640621364,0.0905434713,0.0618727505,-0.254320085,0.2353595346,-0.2026710063,0.0230699349,-0.1711889654,0.1205860972,-0.2005208731,-0.158144623,0.3388409019,0.0797365904,0.180475682,0.4291989803,-0.3894393742,-0.0596620627,-0.0774279088,-0.2149029821,-0.4968355596,0.0238060597,0.1501094401,-0.0620120503,0.1604385972,-0.0989380255,-0.248925671,0.2021318972,0.1912706196,-0.0140952608,-0.0100923842,0.1434962451,-0.2804526091,0.5142765641,0.4544944763,0.1708977968,-0.1090477556,-0.0004218963,0.0640206262,-0.0517981537,0.2329597026,-0.0699741095,0.1279715449,-0.3756048977,0.1551721245,0.3715333343,-0.653388679,-0.2841059566,0.3186047971,-0.274140209,-0.0540166125,0.1668800265,-0.2321895212,-0.0450625047,0.0239412375,0.3900935948,0.0740385205,0.0700897276,-0.2052421421,-0.0241824016,-0.3783530891,0.1280156225,0.0518320054,0.1179565564,0.0941041037,0.0289575048,-0.2993083894,0.2291134,0.1804360002,0.0232359786,0.22256051,0.0875867456,0.1844524741,-0.0589135289,-0.1022363752,-0.0982042775,0.1569275558,-0.13933146,0.3022058308,-0.4215773046,0.0652287006,-0.3841099143,0.0516632423,-0.2309429497,-0.3040854037,0.1636356562,-0.0034321281,0.4423214495,0.1496356577,-0.0936227217,0.4874028862,-0.1470745057,0.2835756838,-0.3597308099,0.2300837487,-0.3057549596,-0.1062674224,-0.156371057,0.166188091,0.351362288,-0.2343126982,-0.0265929922,0.4231664836,-0.2641439438,0.0973605886,0.2657681406,0.0963225588,0.031523183,-0.2190815657,0.0253635254,-0.1022461355,-0.0166736674,0.0638581291,0.6379241347,0.0479976051,0.1513416469,0.7245124578,0.1906802058,0.2567310035,0.1417647898,-0.0172172338,-0.2446911037,0.0072813323,0.2052772939,0.0509162173,0.4524137974,-0.3265386522,0.1677902639,-0.2114031017,0.1441133022,-0.035479892,0.2344904095,0.1491603702,-0.3617250323,0.2157289535,-0.1119084656,0.3587546647,0.3501404822,0.0897394866,-0.0167159215,0.2718507648,-0.1022959575,-0.0745456368,0.3431275487,0.0640020669,0.0710157901,-0.1801293045,-0.0368262008,-0.1345124245,-0.1243019998,-0.2394788861,-0.1977582723,0.1469757408,-0.2880876958,0.1693797261,-0.1144179553,-0.3013064861,-0.1543430984,-0.3460415006,-0.4186742306,-0.2409940213,-0.3461467028,0.0472732708,0.0670877323,0.2756136656,0.1089546755,0.0105085308,-0.0052667689,-0.1842108667,-0.034712214,-0.1745305657,-0.1391935796,0.0206357259,-0.0188447759,-0.2574251294,0.2771224976,-0.1947549433,-0.1127352193,0.0472366884,-0.2349187285,0.0479723103,-0.0904428661,0.1711955369,0.328553766,0.3805794716,0.1841480136,-0.3997780979,0.5727685094,0.0603625476,0.1144243404,-0.1596369445,0.1083576158,0.0168828517,0.1767283529,0.0041359356,-0.6324492693,-0.3507916033,0.3485687375,-0.0266018584,0.1511572897,0.1762987822,0.0596724488,0.2824338078,-0.3841683567,0.0531042032,-0.2631133497,-0.4323770106,0.1425144523,-0.2498616874,-0.220136717,-0.0182346441,0.1905058622,0.2702789605,-0.2122463733,-0.3237496614,-0.3732507825,-0.2246032059,-0.0373766795,0.1037429646,0.1593064964,0.2925694287,-0.1132495999,-0.0387336724,0.1269527972,-0.2187675238,0.2200421691,-0.197969988,0.1281968504,0.2286254913,0.3870925903,0.0413929857,0.5444860458,0.2436498553,-0.0796830058,0.3192813396,-0.1745523363,0.2922652662,-0.037890166,-0.5508058667,-0.1344224811,-0.1521711648,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2787","title":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com","comments":"> Finally i can access it, by the superfast software. Thanks\r\n\r\nExcuse me, I have the same problem as you, could you please tell me how to solve it\uff1f","body":"Hello,\r\nI am trying to run run_glue.py and it gives me this error -\r\n\r\nTraceback (most recent call last):\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 546, in <module>\r\n    main()\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 250, in main\r\n    datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 718, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py\r\n\r\nTrying to do python run_glue.py  --model_name_or_path\r\nbert-base-cased\r\n--task_name\r\nmrpc\r\n--do_train\r\n--do_eval\r\n--max_seq_length\r\n128\r\n--per_device_train_batch_size\r\n32\r\n--learning_rate\r\n2e-5\r\n--num_train_epochs\r\n3\r\n--output_dir\r\n.\/tmp\/mrpc\/\r\n\r\nIs this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.\r\nThank you!\r\n","comment_length":29,"text":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com \n Hello,\r\nI am trying to run run_glue.py and it gives me this error -\r\n\r\nTraceback (most recent call last):\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 546, in <module>\r\n    main()\r\n  File \"E:\/BERT\/pytorch_hugging\/transformers\/examples\/pytorch\/text-classification\/run_glue.py\", line 250, in main\r\n    datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 718, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"C:\\install\\Anaconda3\\envs\\huggingface\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/datasets\/glue\/glue.py\r\n\r\nTrying to do python run_glue.py  --model_name_or_path\r\nbert-base-cased\r\n--task_name\r\nmrpc\r\n--do_train\r\n--do_eval\r\n--max_seq_length\r\n128\r\n--per_device_train_batch_size\r\n32\r\n--learning_rate\r\n2e-5\r\n--num_train_epochs\r\n3\r\n--output_dir\r\n.\/tmp\/mrpc\/\r\n\r\nIs this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.\r\nThank you!\r\n \n > Finally i can access it, by the superfast software. Thanks\r\n\r\nExcuse me, I have the same problem as you, could you please tell me how to solve it\uff1f","embeddings":[-0.2038715184,-0.1601331234,-0.0819259807,0.046902068,0.2665438056,-0.0723191276,0.1546953321,0.3012448549,0.1090682521,-0.0955984294,-0.1769392192,-0.1569310576,0.0424281619,0.0098089986,0.0353741758,-0.1757960469,-0.0955913514,-0.0462971069,-0.2514628172,0.187785551,-0.1379356235,0.1785493493,-0.0379736349,0.2863889635,-0.1777602732,-0.2044938058,-0.0835378692,0.2092444748,-0.1527038217,-0.2665781081,0.4261870086,-0.1104120836,0.1023879945,0.4276525974,-0.0001111302,0.2171017975,0.4489411414,0.0764487162,-0.1543845832,-0.3074429333,-0.0379915237,0.0689299181,0.2134657204,-0.2233344316,-0.1960697025,0.3461775184,0.0491150022,-0.1808769554,0.4765046239,0.2705875933,0.2488420606,0.3377011716,0.4446749985,-0.2152077854,0.0332203731,-0.2912712395,0.0024016653,0.6216793656,0.1213354543,-0.2661054432,0.2426152825,0.2062644213,-0.1433541477,-0.0120363627,0.0456216745,-0.1558198184,0.0829060301,-0.2861295044,0.1528282017,0.0686278045,-0.141816169,-0.2192167342,-0.2591913342,-0.0737397969,-0.0262958892,-0.1296845824,0.331905216,0.1610924304,-0.2213149071,0.2277653813,-0.0923094675,-0.1391131133,-0.2099060416,0.2466940284,0.0454544127,0.2128365934,-0.285688132,0.2644905746,0.4337483943,-0.0378364623,-0.2852620184,0.0696709752,0.1732451916,0.1920874119,-0.2251850963,0.0389768854,-0.3047998548,0.0818184689,0.0858520195,0.0211909339,-0.2492952347,-0.1187235191,0.2668730021,0.1873599738,-0.0882738084,0.3309412599,0.0141430199,0.1095975041,0.3459482193,0.3594096899,-0.1148068979,0.0365825891,-0.2656762898,-0.46562168,-0.3594941199,0.0650140643,0.2611832321,0.0185204595,-0.1696608961,0.0698689297,-0.1784015596,0.0330274105,0.1020842791,0.5010195374,-0.264885366,-0.3661019206,0.0398671776,0.2935795486,0.0180174913,0.0218921565,-0.1782663316,0.157259956,-0.2640621364,0.0905434713,0.0618727505,-0.254320085,0.2353595346,-0.2026710063,0.0230699349,-0.1711889654,0.1205860972,-0.2005208731,-0.158144623,0.3388409019,0.0797365904,0.180475682,0.4291989803,-0.3894393742,-0.0596620627,-0.0774279088,-0.2149029821,-0.4968355596,0.0238060597,0.1501094401,-0.0620120503,0.1604385972,-0.0989380255,-0.248925671,0.2021318972,0.1912706196,-0.0140952608,-0.0100923842,0.1434962451,-0.2804526091,0.5142765641,0.4544944763,0.1708977968,-0.1090477556,-0.0004218963,0.0640206262,-0.0517981537,0.2329597026,-0.0699741095,0.1279715449,-0.3756048977,0.1551721245,0.3715333343,-0.653388679,-0.2841059566,0.3186047971,-0.274140209,-0.0540166125,0.1668800265,-0.2321895212,-0.0450625047,0.0239412375,0.3900935948,0.0740385205,0.0700897276,-0.2052421421,-0.0241824016,-0.3783530891,0.1280156225,0.0518320054,0.1179565564,0.0941041037,0.0289575048,-0.2993083894,0.2291134,0.1804360002,0.0232359786,0.22256051,0.0875867456,0.1844524741,-0.0589135289,-0.1022363752,-0.0982042775,0.1569275558,-0.13933146,0.3022058308,-0.4215773046,0.0652287006,-0.3841099143,0.0516632423,-0.2309429497,-0.3040854037,0.1636356562,-0.0034321281,0.4423214495,0.1496356577,-0.0936227217,0.4874028862,-0.1470745057,0.2835756838,-0.3597308099,0.2300837487,-0.3057549596,-0.1062674224,-0.156371057,0.166188091,0.351362288,-0.2343126982,-0.0265929922,0.4231664836,-0.2641439438,0.0973605886,0.2657681406,0.0963225588,0.031523183,-0.2190815657,0.0253635254,-0.1022461355,-0.0166736674,0.0638581291,0.6379241347,0.0479976051,0.1513416469,0.7245124578,0.1906802058,0.2567310035,0.1417647898,-0.0172172338,-0.2446911037,0.0072813323,0.2052772939,0.0509162173,0.4524137974,-0.3265386522,0.1677902639,-0.2114031017,0.1441133022,-0.035479892,0.2344904095,0.1491603702,-0.3617250323,0.2157289535,-0.1119084656,0.3587546647,0.3501404822,0.0897394866,-0.0167159215,0.2718507648,-0.1022959575,-0.0745456368,0.3431275487,0.0640020669,0.0710157901,-0.1801293045,-0.0368262008,-0.1345124245,-0.1243019998,-0.2394788861,-0.1977582723,0.1469757408,-0.2880876958,0.1693797261,-0.1144179553,-0.3013064861,-0.1543430984,-0.3460415006,-0.4186742306,-0.2409940213,-0.3461467028,0.0472732708,0.0670877323,0.2756136656,0.1089546755,0.0105085308,-0.0052667689,-0.1842108667,-0.034712214,-0.1745305657,-0.1391935796,0.0206357259,-0.0188447759,-0.2574251294,0.2771224976,-0.1947549433,-0.1127352193,0.0472366884,-0.2349187285,0.0479723103,-0.0904428661,0.1711955369,0.328553766,0.3805794716,0.1841480136,-0.3997780979,0.5727685094,0.0603625476,0.1144243404,-0.1596369445,0.1083576158,0.0168828517,0.1767283529,0.0041359356,-0.6324492693,-0.3507916033,0.3485687375,-0.0266018584,0.1511572897,0.1762987822,0.0596724488,0.2824338078,-0.3841683567,0.0531042032,-0.2631133497,-0.4323770106,0.1425144523,-0.2498616874,-0.220136717,-0.0182346441,0.1905058622,0.2702789605,-0.2122463733,-0.3237496614,-0.3732507825,-0.2246032059,-0.0373766795,0.1037429646,0.1593064964,0.2925694287,-0.1132495999,-0.0387336724,0.1269527972,-0.2187675238,0.2200421691,-0.197969988,0.1281968504,0.2286254913,0.3870925903,0.0413929857,0.5444860458,0.2436498553,-0.0796830058,0.3192813396,-0.1745523363,0.2922652662,-0.037890166,-0.5508058667,-0.1344224811,-0.152171164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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2775","title":"`generate_random_fingerprint()` deterministic with \ud83e\udd17Transformers' `set_seed()`","comments":"I dug into what I believe is the root of this issue and added a repro in my comment. If this is better addressed as a cross-team issue, let me know and I can open an issue in the Transformers repo","body":"## Describe the bug\r\n\r\n**Update:** I dug into this to try to reproduce the underlying issue, and I believe it's that `set_seed()` from the `transformers` library makes the \"random\" fingerprint identical each time. I believe this is still a bug, because `datasets` is used exactly this way in `transformers` after `set_seed()` has been called, and I think that using `set_seed()` is a standard procedure to aid reproducibility. I've added more details to reproduce this below.\r\n\r\nHi there! I'm using my own local dataset and custom preprocessing function. My preprocessing function seems to be unpickle-able, perhaps because it is from a closure (will debug this separately). I get this warning, which is expected:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/450b9174765374111e5c6daab0ed294bc3d9b639\/src\/datasets\/fingerprint.py#L260-L265\r\n\r\nHowever, what's not expected is that the `datasets` actually _does_ seem to cache and reuse this dataset between runs! After that line, the next thing that's logged looks like:\r\n\r\n```text\r\n Loading cached processed dataset at \/home\/xxx\/.cache\/huggingface\/datasets\/csv\/default-xxx\/0.0.0\/xxx\/cache-xxx.arrow\r\n```\r\n\r\nThe path is exactly the same each run (e.g., last 26 runs).\r\n\r\nThis becomes a problem because I'll pass in the `--max_eval_samples` flag to the HuggingFace example script I'm running off of ([run_swag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/multiple-choice\/run_swag.py)).  The fact that the cached dataset is reused means this flag gets ignored. I'll try to load 100 examples, and it will load the full cached 1,000,000.\r\n\r\nI think that\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/450b9174765374111e5c6daab0ed294bc3d9b639\/src\/datasets\/fingerprint.py#L248\r\n\r\n... is actually consistent because randomness is being controlled in HuggingFace\/Transformers for reproducibility. I've added a demo of this below.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n# Contents of print_fingerprint.py\r\nfrom transformers import set_seed\r\nfrom datasets.fingerprint import generate_random_fingerprint\r\nset_seed(42)\r\nprint(generate_random_fingerprint())\r\n```\r\n\r\n```bash\r\nfor i in {0..10}; do\r\n    python print_fingerprint.py\r\ndone\r\n\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n```\r\n\r\n## Expected results\r\nAfter the \"random hash\" warning is emitted, a random hash is generated, and no outdated cached datasets are reused.\r\n\r\n## Actual results\r\nAfter the \"random hash\" warning is emitted, an identical hash is generated each time, and an outdated cached dataset is reused each run.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 4.0.1","comment_length":41,"text":"`generate_random_fingerprint()` deterministic with \ud83e\udd17Transformers' `set_seed()` \n ## Describe the bug\r\n\r\n**Update:** I dug into this to try to reproduce the underlying issue, and I believe it's that `set_seed()` from the `transformers` library makes the \"random\" fingerprint identical each time. I believe this is still a bug, because `datasets` is used exactly this way in `transformers` after `set_seed()` has been called, and I think that using `set_seed()` is a standard procedure to aid reproducibility. I've added more details to reproduce this below.\r\n\r\nHi there! I'm using my own local dataset and custom preprocessing function. My preprocessing function seems to be unpickle-able, perhaps because it is from a closure (will debug this separately). I get this warning, which is expected:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/450b9174765374111e5c6daab0ed294bc3d9b639\/src\/datasets\/fingerprint.py#L260-L265\r\n\r\nHowever, what's not expected is that the `datasets` actually _does_ seem to cache and reuse this dataset between runs! After that line, the next thing that's logged looks like:\r\n\r\n```text\r\n Loading cached processed dataset at \/home\/xxx\/.cache\/huggingface\/datasets\/csv\/default-xxx\/0.0.0\/xxx\/cache-xxx.arrow\r\n```\r\n\r\nThe path is exactly the same each run (e.g., last 26 runs).\r\n\r\nThis becomes a problem because I'll pass in the `--max_eval_samples` flag to the HuggingFace example script I'm running off of ([run_swag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/multiple-choice\/run_swag.py)).  The fact that the cached dataset is reused means this flag gets ignored. I'll try to load 100 examples, and it will load the full cached 1,000,000.\r\n\r\nI think that\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/450b9174765374111e5c6daab0ed294bc3d9b639\/src\/datasets\/fingerprint.py#L248\r\n\r\n... is actually consistent because randomness is being controlled in HuggingFace\/Transformers for reproducibility. I've added a demo of this below.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n# Contents of print_fingerprint.py\r\nfrom transformers import set_seed\r\nfrom datasets.fingerprint import generate_random_fingerprint\r\nset_seed(42)\r\nprint(generate_random_fingerprint())\r\n```\r\n\r\n```bash\r\nfor i in {0..10}; do\r\n    python print_fingerprint.py\r\ndone\r\n\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n```\r\n\r\n## Expected results\r\nAfter the \"random hash\" warning is emitted, a random hash is generated, and no outdated cached datasets are reused.\r\n\r\n## Actual results\r\nAfter the \"random hash\" warning is emitted, an identical hash is generated each time, and an outdated cached dataset is reused each run.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 4.0.1 \n I dug into what I believe is the root of this issue and added a repro in my comment. If this is better addressed as a cross-team issue, let me know and I can open an issue in the Transformers 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2775","title":"`generate_random_fingerprint()` deterministic with \ud83e\udd17Transformers' `set_seed()`","comments":"Hi !\r\n\r\nIMO we shouldn't try to modify `set_seed` from transformers but maybe make `datasets` have its own RNG just to generate random fingerprints.\r\n\r\nAny opinion on this @LysandreJik ?","body":"## Describe the bug\r\n\r\n**Update:** I dug into this to try to reproduce the underlying issue, and I believe it's that `set_seed()` from the `transformers` library makes the \"random\" fingerprint identical each time. I believe this is still a bug, because `datasets` is used exactly this way in `transformers` after `set_seed()` has been called, and I think that using `set_seed()` is a standard procedure to aid reproducibility. I've added more details to reproduce this below.\r\n\r\nHi there! I'm using my own local dataset and custom preprocessing function. My preprocessing function seems to be unpickle-able, perhaps because it is from a closure (will debug this separately). I get this warning, which is expected:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/450b9174765374111e5c6daab0ed294bc3d9b639\/src\/datasets\/fingerprint.py#L260-L265\r\n\r\nHowever, what's not expected is that the `datasets` actually _does_ seem to cache and reuse this dataset between runs! After that line, the next thing that's logged looks like:\r\n\r\n```text\r\n Loading cached processed dataset at \/home\/xxx\/.cache\/huggingface\/datasets\/csv\/default-xxx\/0.0.0\/xxx\/cache-xxx.arrow\r\n```\r\n\r\nThe path is exactly the same each run (e.g., last 26 runs).\r\n\r\nThis becomes a problem because I'll pass in the `--max_eval_samples` flag to the HuggingFace example script I'm running off of ([run_swag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/multiple-choice\/run_swag.py)).  The fact that the cached dataset is reused means this flag gets ignored. I'll try to load 100 examples, and it will load the full cached 1,000,000.\r\n\r\nI think that\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/450b9174765374111e5c6daab0ed294bc3d9b639\/src\/datasets\/fingerprint.py#L248\r\n\r\n... is actually consistent because randomness is being controlled in HuggingFace\/Transformers for reproducibility. I've added a demo of this below.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n# Contents of print_fingerprint.py\r\nfrom transformers import set_seed\r\nfrom datasets.fingerprint import generate_random_fingerprint\r\nset_seed(42)\r\nprint(generate_random_fingerprint())\r\n```\r\n\r\n```bash\r\nfor i in {0..10}; do\r\n    python print_fingerprint.py\r\ndone\r\n\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n```\r\n\r\n## Expected results\r\nAfter the \"random hash\" warning is emitted, a random hash is generated, and no outdated cached datasets are reused.\r\n\r\n## Actual results\r\nAfter the \"random hash\" warning is emitted, an identical hash is generated each time, and an outdated cached dataset is reused each run.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 4.0.1","comment_length":30,"text":"`generate_random_fingerprint()` deterministic with \ud83e\udd17Transformers' `set_seed()` \n ## Describe the bug\r\n\r\n**Update:** I dug into this to try to reproduce the underlying issue, and I believe it's that `set_seed()` from the `transformers` library makes the \"random\" fingerprint identical each time. I believe this is still a bug, because `datasets` is used exactly this way in `transformers` after `set_seed()` has been called, and I think that using `set_seed()` is a standard procedure to aid reproducibility. I've added more details to reproduce this below.\r\n\r\nHi there! I'm using my own local dataset and custom preprocessing function. My preprocessing function seems to be unpickle-able, perhaps because it is from a closure (will debug this separately). I get this warning, which is expected:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/450b9174765374111e5c6daab0ed294bc3d9b639\/src\/datasets\/fingerprint.py#L260-L265\r\n\r\nHowever, what's not expected is that the `datasets` actually _does_ seem to cache and reuse this dataset between runs! After that line, the next thing that's logged looks like:\r\n\r\n```text\r\n Loading cached processed dataset at \/home\/xxx\/.cache\/huggingface\/datasets\/csv\/default-xxx\/0.0.0\/xxx\/cache-xxx.arrow\r\n```\r\n\r\nThe path is exactly the same each run (e.g., last 26 runs).\r\n\r\nThis becomes a problem because I'll pass in the `--max_eval_samples` flag to the HuggingFace example script I'm running off of ([run_swag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/pytorch\/multiple-choice\/run_swag.py)).  The fact that the cached dataset is reused means this flag gets ignored. I'll try to load 100 examples, and it will load the full cached 1,000,000.\r\n\r\nI think that\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/450b9174765374111e5c6daab0ed294bc3d9b639\/src\/datasets\/fingerprint.py#L248\r\n\r\n... is actually consistent because randomness is being controlled in HuggingFace\/Transformers for reproducibility. I've added a demo of this below.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n# Contents of print_fingerprint.py\r\nfrom transformers import set_seed\r\nfrom datasets.fingerprint import generate_random_fingerprint\r\nset_seed(42)\r\nprint(generate_random_fingerprint())\r\n```\r\n\r\n```bash\r\nfor i in {0..10}; do\r\n    python print_fingerprint.py\r\ndone\r\n\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n1c80317fa3b1799d\r\n```\r\n\r\n## Expected results\r\nAfter the \"random hash\" warning is emitted, a random hash is generated, and no outdated cached datasets are reused.\r\n\r\n## Actual results\r\nAfter the \"random hash\" warning is emitted, an identical hash is generated each time, and an outdated cached dataset is reused each run.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31\r\n- Python version: 3.9.6\r\n- PyArrow version: 4.0.1 \n Hi !\r\n\r\nIMO we shouldn't try to modify `set_seed` from transformers but maybe make `datasets` have its own RNG just to generate random fingerprints.\r\n\r\nAny opinion on this @LysandreJik ?","embeddings":[-0.1622430533,-0.0606823638,0.0938825384,0.0836025402,0.3144044876,-0.1429734379,0.6139574647,0.0132132526,-0.1196164414,0.0058159092,0.165034622,0.2017952353,-0.0787890479,0.0945226923,-0.0190071128,0.2130856961,0.0278785881,-0.1402870119,-0.2763957083,-0.1410640776,-0.2284770757,0.152345553,-0.0874382332,-0.1617389768,-0.6726455092,-0.102102913,-0.1329014301,0.1628411114,0.1795904189,-0.4169393778,0.1931263804,0.23521474,-0.1014345139,0.3875524104,-0.0001199422,-0.0937381238,0.3522936404,-0.0650267377,-0.2904088199,-0.1039032489,0.0416674875,0.1874276549,-0.0534762479,-0.1191118434,-0.0600779131,0.0070903306,0.1324010938,-0.4158923626,0.3937298059,-0.1208229214,0.1669470221,0.2793320715,-0.117078729,0.0917676166,0.1264911741,0.700026989,-0.2477301359,-0.0409514718,0.0038184212,0.0352796055,-0.2997260094,0.2550401092,0.1456892937,0.1156733781,0.5079758167,0.1923030913,-0.1786665469,-0.1532367319,0.1148629934,0.2767552435,0.2311255336,-0.3748752773,-0.3369910121,-0.6084536314,-0.2362681925,-0.2321816534,0.1527197361,0.0096535189,-0.1577541232,0.0793170258,-0.3549202085,0.1265327483,0.0707441419,-0.1952131391,0.2468887717,0.221877411,0.0072371704,0.1679401398,0.1587349176,0.0604980215,0.169935748,-0.1181586757,-0.0938741639,0.039496325,0.0180432964,0.1057436615,0.1586054713,0.1534633934,0.3189108968,0.5014269352,0.0610095821,0.1023851931,0.1677440405,-0.073842831,0.2467009127,0.2197239995,-0.058061745,0.3566723764,0.0670858175,0.0370436385,-0.0029727847,0.0142731853,0.2652676702,-0.0426026322,0.501455307,-0.0270120259,0.2397684753,-0.0510366186,0.0650820434,0.1498213857,0.0121367406,-0.0750536099,-0.0779314637,0.2501386106,-0.1982081831,-0.0242463928,-0.0956238508,-0.0449064262,-0.1348835528,-0.0826703608,-0.2837889791,-0.1389882714,-0.2515237331,0.2813723683,-0.0713063851,-0.080778636,0.3410976827,0.2519500256,0.1854823083,-0.1556297839,0.1785332561,-0.0622604117,0.4497914612,0.1312009692,-0.1929613948,0.1577107608,0.1532040387,0.0786590576,-0.1610734612,0.0881237611,0.086571835,-0.2364135236,0.1173688397,0.0525634661,-0.5501559377,0.1222918853,-0.0983982906,-0.0366495587,0.4920062125,0.1749353409,0.2651320398,-0.1899252236,-0.4315011203,-0.291819036,0.072933957,0.7805565596,0.0292768963,-0.2914335132,0.0271981601,0.1518498659,0.178817749,0.1570243537,-0.1241752878,0.2212250829,-0.460788548,0.2205772847,-0.2322400659,-0.1423754692,-0.2322938591,0.3027459085,-0.0936336219,0.383153975,0.038360171,0.0561094806,-0.111499615,-0.1254639775,-0.238865599,0.2245150059,-0.0068208943,0.1098389551,-0.4005638957,-0.3897634745,0.0655296817,-0.0483645014,0.0519645959,0.0094370665,0.1318699718,-0.1288129389,0.2877875865,-0.106851168,0.1581378281,0.1528533399,0.1587304026,0.2246325463,0.0351854488,0.168915838,-0.541993916,0.3820074201,-0.241048187,-0.1893585026,-0.1494206786,-0.3210414052,-0.0753021613,0.0960759893,-0.4060974121,-0.3152598143,-0.0154513624,0.5506615639,0.2708693445,-0.0051996605,0.0968249068,0.2403063625,0.1423283666,-0.0027180747,-0.9128639102,0.0567852408,0.0906823352,-0.1725624204,-0.2306703925,0.0778862163,0.0968683288,-0.2287880331,0.0384364836,0.3598063886,0.1580559313,0.262817502,-0.0984206945,0.1928106546,0.0074188476,-0.1086183786,-0.0394932814,0.1521769762,0.0554043837,-0.1021172255,-0.0983805135,0.4918055832,-0.2128903121,0.1159908548,-0.1526506692,-0.0862316415,-0.0628082529,-0.0525967814,-0.2161844373,-0.1971542835,0.0899825916,-0.0390489846,0.5391991735,0.2282948643,-0.3941819072,0.2312160581,-0.0016536118,-0.0491704121,0.1437010616,0.0852040425,0.0712452978,-0.3131351769,0.0560087487,0.3389393389,0.4275982082,0.0159966983,0.0583637543,0.0512276031,-0.1064069122,-0.166511476,-0.0071328133,0.000592747,-0.0298429485,0.2195874751,0.1412554532,0.0112569081,-0.4387521446,-0.0286888238,0.1356961429,-0.0273100324,-0.3191818893,0.2899666727,-0.1755524725,0.445558995,-0.3127735555,-0.1545071006,-0.2895255387,-0.2514543235,-0.1213659272,0.4109185636,-0.1166299582,0.1950098127,-0.1968613714,0.2971345484,-0.0853328109,-0.2759700119,-0.2541822493,-0.1093628407,-0.2183861136,-0.0602291338,0.120375514,-0.0034396118,0.2321113497,-0.1100810915,-0.0712264851,-0.3098180592,-0.1055286676,0.222975418,-0.1086345837,0.5486388803,0.2477721125,-0.2188455909,-0.1793799549,-0.2130807936,0.2121192366,-0.1938274652,-0.0862900466,-0.1702485383,-0.0922699794,-0.0537805334,0.0295689162,0.0690156519,-0.0948401466,0.0121203233,0.2557660639,-0.0645927638,0.0405180492,0.5897526145,0.0284133237,-0.0574366823,-0.1054536477,-0.0440537892,-0.5146172047,-0.4949686527,0.2611507177,-0.1634007245,-0.2163863778,-0.3258367181,-0.2972288728,0.0188390184,0.1888897568,-0.6068610549,-0.0516650379,-0.2685230672,0.4238205254,-0.2032543421,0.0177746955,0.3344092071,0.0985963792,0.1076801494,-0.0211104527,-0.0474801324,0.2567791939,-0.3898849487,0.3817112148,0.1048754081,0.060253825,0.0892471969,1.0046882629,0.2359312177,-0.3699997962,0.1978331804,-0.0644092187,0.4749623835,-0.297985971,-0.2079370916,0.1310502589,-0.0590401702,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2768","title":"`ArrowInvalid: Added column's length must match table's length.` after using `select`","comments":"Hi,\r\n\r\nthe `select` method creates an indices mapping and doesn't modify the underlying PyArrow table by default for better performance. To modify the underlying table after the `select` call, call `flatten_indices` on the dataset object as follows:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"tweets_hate_speech_detection\")['train'].select(range(128))\r\nds = ds.flatten_indices()\r\nds = ds.add_column('ones', [1]*128)\r\n```","body":"## Describe the bug\r\nI would like to add a column to a downsampled dataset. However I get an error message saying the length don't match with the length of the unsampled dataset indicated. I suspect that the dataset size is not updated when calling `select`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"tweets_hate_speech_detection\")['train'].select(range(128))\r\nds = ds.add_column('ones', [1]*128)\r\n```\r\n\r\n## Expected results\r\nI would expect a new column named `ones` filled with `1`. When I check the length of `ds` it says `128`. Interestingly, it works when calling `ds = ds.map(lambda x: x)` before adding the column.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```python\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n\/var\/folders\/l4\/2905jygx4tx5jv8_kn03vxsw0000gn\/T\/ipykernel_6301\/868709636.py in <module>\r\n      1 from datasets import load_dataset\r\n      2 ds = load_dataset(\"tweets_hate_speech_detection\")['train'].select(range(128))\r\n----> 3 ds = ds.add_column('ones', [0]*128)\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    183         }\r\n    184         # apply actual function\r\n--> 185         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    186         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    187         # re-apply format to the output\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_column(self, name, column, new_fingerprint)\r\n   2965         column_table = InMemoryTable.from_pydict({name: column})\r\n   2966         # Concatenate tables horizontally\r\n-> 2967         table = ConcatenationTable.from_tables([self._data, column_table], axis=1)\r\n   2968         # Update features\r\n   2969         info = self.info.copy()\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/table.py in from_tables(cls, tables, axis)\r\n    715             table_blocks = to_blocks(table)\r\n    716             blocks = _extend_blocks(blocks, table_blocks, axis=axis)\r\n--> 717         return cls.from_blocks(blocks)\r\n    718 \r\n    719     @property\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/table.py in from_blocks(cls, blocks)\r\n    663             return cls(table, blocks)\r\n    664         else:\r\n--> 665             table = cls._concat_blocks_horizontally_and_vertically(blocks)\r\n    666             return cls(table, blocks)\r\n    667 \r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/table.py in _concat_blocks_horizontally_and_vertically(cls, blocks)\r\n    623             if not tables:\r\n    624                 continue\r\n--> 625             pa_table_horizontally_concatenated = cls._concat_blocks(tables, axis=1)\r\n    626             pa_tables_to_concat_vertically.append(pa_table_horizontally_concatenated)\r\n    627         return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0)\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/table.py in _concat_blocks(blocks, axis)\r\n    612                 else:\r\n    613                     for name, col in zip(table.column_names, table.columns):\r\n--> 614                         pa_table = pa_table.append_column(name, col)\r\n    615             return pa_table\r\n    616         else:\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.append_column()\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.add_column()\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Added column's length must match table's length. Expected length 31962 but got length 128\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n","comment_length":53,"text":"`ArrowInvalid: Added column's length must match table's length.` after using `select` \n ## Describe the bug\r\nI would like to add a column to a downsampled dataset. However I get an error message saying the length don't match with the length of the unsampled dataset indicated. I suspect that the dataset size is not updated when calling `select`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"tweets_hate_speech_detection\")['train'].select(range(128))\r\nds = ds.add_column('ones', [1]*128)\r\n```\r\n\r\n## Expected results\r\nI would expect a new column named `ones` filled with `1`. When I check the length of `ds` it says `128`. Interestingly, it works when calling `ds = ds.map(lambda x: x)` before adding the column.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```python\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n\/var\/folders\/l4\/2905jygx4tx5jv8_kn03vxsw0000gn\/T\/ipykernel_6301\/868709636.py in <module>\r\n      1 from datasets import load_dataset\r\n      2 ds = load_dataset(\"tweets_hate_speech_detection\")['train'].select(range(128))\r\n----> 3 ds = ds.add_column('ones', [0]*128)\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    183         }\r\n    184         # apply actual function\r\n--> 185         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    186         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    187         # re-apply format to the output\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py in add_column(self, name, column, new_fingerprint)\r\n   2965         column_table = InMemoryTable.from_pydict({name: column})\r\n   2966         # Concatenate tables horizontally\r\n-> 2967         table = ConcatenationTable.from_tables([self._data, column_table], axis=1)\r\n   2968         # Update features\r\n   2969         info = self.info.copy()\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/table.py in from_tables(cls, tables, axis)\r\n    715             table_blocks = to_blocks(table)\r\n    716             blocks = _extend_blocks(blocks, table_blocks, axis=axis)\r\n--> 717         return cls.from_blocks(blocks)\r\n    718 \r\n    719     @property\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/table.py in from_blocks(cls, blocks)\r\n    663             return cls(table, blocks)\r\n    664         else:\r\n--> 665             table = cls._concat_blocks_horizontally_and_vertically(blocks)\r\n    666             return cls(table, blocks)\r\n    667 \r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/table.py in _concat_blocks_horizontally_and_vertically(cls, blocks)\r\n    623             if not tables:\r\n    624                 continue\r\n--> 625             pa_table_horizontally_concatenated = cls._concat_blocks(tables, axis=1)\r\n    626             pa_tables_to_concat_vertically.append(pa_table_horizontally_concatenated)\r\n    627         return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0)\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/datasets\/table.py in _concat_blocks(blocks, axis)\r\n    612                 else:\r\n    613                     for name, col in zip(table.column_names, table.columns):\r\n--> 614                         pa_table = pa_table.append_column(name, col)\r\n    615             return pa_table\r\n    616         else:\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.append_column()\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.add_column()\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/git\/semantic-clustering\/env\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Added column's length must match table's length. Expected length 31962 but got length 128\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n \n Hi,\r\n\r\nthe `select` method creates an indices mapping and doesn't modify the underlying PyArrow table by default for better performance. To modify the underlying table after the `select` call, call `flatten_indices` on the dataset object as follows:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"tweets_hate_speech_detection\")['train'].select(range(128))\r\nds = ds.flatten_indices()\r\nds = ds.add_column('ones', [1]*128)\r\n```","embeddings":[-0.2274188101,-0.1898306608,-0.0089051146,0.0325093158,0.0982829332,0.0415741466,0.0728505924,0.1837980449,0.0604875386,0.164753437,0.2048401088,0.6761353016,0.0139961764,-0.3138830364,0.0691086799,-0.2314466685,0.0780239403,0.1225910485,-0.3308289647,-0.0652318522,-0.3497133255,0.0132752853,-0.3354176581,-0.3151179254,-0.35253039,-0.1137229875,-0.1477196962,-0.076647304,-0.3253406882,-0.3277154565,0.081593357,-0.3840053678,0.055132024,0.2379106879,-0.0001264378,-0.1121125668,0.2349809557,-0.1064973325,-0.2713165581,0.0904975608,-0.1300846487,-0.3753260374,0.1473688334,-0.1593483537,0.1864854842,-0.1655192822,-0.6239047647,-0.3172651231,-0.0759102702,0.1652894318,0.161454007,0.2517942488,0.0120389545,-0.2805069983,-0.0970271379,0.1960537583,-0.2620949745,0.3448806703,0.060268797,-0.0941637009,0.3812075257,0.3098258078,0.2030231357,-0.0177224353,0.1855849028,-0.0706433281,-0.0906358734,-0.0210477207,0.4785783589,-0.0847314149,0.7143198848,-0.2248954773,-0.2491259128,-0.1734179258,0.4096648395,-0.3938191831,-0.0271732341,-0.1862057149,0.2897660434,0.0206637345,-0.1405392736,0.0848093703,0.1604089737,0.1899269074,-0.0069546569,0.5650565028,0.2024693191,0.3704702258,-0.192640841,-0.1704318523,0.0081759496,0.0942436606,-0.2345474809,0.3636321723,-0.3872019649,-0.2049816102,-0.2336306423,-0.2155933231,0.1200502291,0.3310541511,-0.1377871931,-0.2782112956,0.3323386014,-0.2003704607,0.2972340584,0.3154058158,0.0448100008,0.606928587,-0.3473197818,-0.2117927074,0.3857125938,0.0074288007,0.1638017446,-0.2288597971,0.3544318676,-0.2207029164,0.1014282778,0.0487019904,-0.218203485,0.2133063972,-0.1640473157,-0.0807819366,-0.1688757539,0.1329697818,0.1167246401,0.090414986,0.1235700473,0.3109091222,-0.1185306236,-0.0169276446,-0.2082299143,-0.0679130778,-0.0348821245,-0.0294247828,-0.0645576492,0.3572279811,0.2836541533,-0.0721221864,-0.2275483906,-0.2286396921,-0.1471447945,-0.0540051572,0.1518873572,0.2009018809,-0.2485596836,0.0838380605,-0.0692494884,-0.3781675398,-0.1850917041,0.3647522926,-0.2289008796,-0.2965632975,-0.6092182994,0.0443059616,0.0293099396,-0.1413225532,0.1089118347,0.268869698,0.4819933474,-0.1184430197,0.4236471653,-0.3621317446,-0.3157013059,-0.1269381344,0.151450038,0.2686063945,-0.8413779736,-0.065250054,0.0082486253,0.2485842705,0.1492479891,0.2491782606,-0.0317912586,-0.1156833395,0.1023319066,0.2750084698,0.444047004,0.2298650444,-0.8025736809,-0.1254057884,-0.4141768813,0.1022443846,-0.119038485,0.1956773549,0.0839703605,0.2012964487,0.3524823189,0.095638141,-0.0832370371,-0.1414693147,-0.3857779205,-0.1018519029,0.2032636553,-0.1336515397,0.0435380042,-0.0106330048,0.3127953708,-0.0638056919,0.3429020643,0.3277736902,0.2038774341,0.3033575714,0.2194056958,-0.0338491276,0.2668210864,-0.2382794619,-0.2710856199,0.0977192894,0.1223185733,-0.1519711763,-0.1563115418,-0.240345329,-0.7025293708,0.2183455229,0.1364157349,0.005338741,-0.0692306608,-0.0590435602,0.218089819,-0.1341995001,-0.0326836184,-0.0548646972,-0.3558381498,0.1471114308,-0.1049330831,0.2896157801,0.0534480587,-0.3693044186,-0.2833218575,0.1731369942,0.1423828155,0.1439751387,-0.3318285346,0.5284386277,-0.02505772,-0.1870957315,-0.0710229203,-0.3655413687,0.1858620644,-0.0491627306,0.2788014412,-0.0748981237,0.1783838868,0.0045647947,0.011803302,-0.0678275377,-0.2379916459,0.2784359455,-0.3470553756,-0.1087519154,0.0590355732,0.1315242648,-0.1129913703,-0.3320047855,-0.2876385152,0.0122851627,-0.0364267863,-0.0151267387,-0.229577601,0.060114406,0.2146398723,-0.0264009442,0.1114841998,0.1653437465,-0.2672156692,0.1005395725,0.3662687838,0.2415162772,0.7434340119,0.1991255879,0.093924351,-0.0243315827,-0.1202091128,-0.0079243649,0.046591796,-0.0483894534,0.0148665728,0.2591744661,0.2337099463,-0.1083140671,-0.0253790636,0.3794060349,0.1265302747,0.1463618428,-0.3006027639,0.0304230396,-0.3964523673,0.1090054885,-0.1138181686,-0.1068870127,-0.04723268,-0.167769134,0.0350237526,0.0338079929,-0.2729739249,0.275567174,-0.4887412786,0.1287105381,0.2696689665,-0.11721275,0.1353976578,0.1072930694,-0.1601349413,-0.0256597102,-0.3478003442,0.0767987147,0.1265491843,0.3528582156,-0.0809799954,-0.1846956015,-0.6639268398,-0.0905888379,-0.1658451408,0.1347124577,0.1048660129,0.1017715037,-0.1720081121,-0.1064303219,0.1442970484,0.2994417846,0.0071018846,0.0552978106,0.0515188649,-0.1211503819,-0.1023785919,-0.4178765714,-0.2243611962,-0.2537752986,0.254355371,0.1677352786,-0.0982267484,-0.1927160919,0.4928369224,-0.3301513791,0.0152297141,-0.0172036998,-0.3184478879,0.36839661,0.4220629632,-0.0695865527,-0.1449093372,0.0337172411,-0.2432203293,-0.0239139777,0.0918513238,-0.1472073197,0.093353346,-0.1383074075,0.1208327562,-0.032450594,0.2197681218,0.5915466547,-0.0773879215,0.1023480073,-0.1245045438,-0.4013107717,0.2273926437,0.2349444479,0.5312868953,-0.1336196959,0.4529953897,-0.0000410322,0.2885741591,0.0917197391,-0.2183245718,-0.0316433199,-0.2098070681,0.1782779098,-0.3310292363,0.0301831886,-0.2201043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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2767","title":"equal operation to perform unbatch for huggingface datasets ","comments":"Hi @lhoestq \r\nMaybe this is clearer to explain like this, currently map function, map one example to \"one\" modified one, lets assume we want to map one example to \"multiple\" examples, in which we do not know in advance how many examples they would be per each entry. I greatly appreciate telling me how I can handle this operation, thanks a lot","body":"Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n","comment_length":62,"text":"equal operation to perform unbatch for huggingface datasets  \n Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n \n Hi @lhoestq \r\nMaybe this is clearer to explain like this, currently map function, map one example to \"one\" modified one, lets assume we want to map one example to \"multiple\" examples, in which we do not know in advance how many examples they would be per each entry. I greatly appreciate telling me how I can handle this operation, thanks a lot","embeddings":[-0.0286772158,-0.7930662632,0.0335285924,-0.0818505213,-0.0049160868,-0.1033486947,0.2304782718,-0.0015914276,0.4284729958,0.2395398617,-0.324056536,-0.045957718,0.0236226693,0.4699031711,0.1768155247,-0.234804824,0.0796521008,0.1120863855,-0.2576453686,-0.0738277659,-0.1641778648,-0.0214592591,-0.5562424064,-0.1872487962,0.3530071676,-0.3256388605,-0.1314136684,-0.2317972332,-0.1461296082,0.315525502,0.3914791048,0.3159993589,0.1450021267,0.4359943569,-0.000124996,0.2683015168,-0.2683853507,-0.160119608,0.124135524,-0.1457855105,-0.1318628341,-0.0308620147,-0.086130254,-0.2909473479,-0.1553762704,0.0247164331,0.0838053226,-0.3370779455,0.6689295173,0.1163539812,0.0033910642,0.2023021877,-0.0828164369,0.0687010959,-0.0968771428,0.0643510893,0.0033202854,0.0944168419,0.0819834173,-0.0196641665,0.0691599995,0.3062365949,-0.0207522139,-0.0487893857,0.0508214012,0.07096719,0.0294626951,-0.4244825244,0.0995178819,0.3601127863,0.1911817938,-0.3335200548,-0.5461017489,-0.0424865298,0.0593188368,-0.0293313079,-0.2361653447,0.157397002,-0.1050995514,0.2068406343,-0.4860775769,-0.1199070066,-0.0401413664,0.0794255883,0.1040407047,-0.0883648247,-0.3136317432,0.304833591,0.0857107341,0.0686647147,-0.1580198109,-0.1046706513,0.1547605395,0.2256595939,-0.6143533587,-0.2198613882,-0.2562040687,-0.018030664,0.3166288137,0.3536277115,0.5462899804,-0.0548854396,-0.0812086761,0.1295626163,0.0867581517,0.5366975069,-0.0814011171,-0.2418981045,-0.0120940544,0.1348869503,0.0322757177,-0.0191909596,0.3165329695,-0.0755865127,-0.364189893,0.1535307169,0.3737836182,-0.0768215284,-0.3276385069,-0.2054927647,-0.355368495,-0.1100299507,0.013368411,0.149778232,-0.1751033515,0.0324243531,0.1829235554,0.0582386367,-0.2670663595,-0.0648584366,-0.15654625,0.2346510738,-0.1623347849,0.0303167943,-0.0835497528,-0.1439877152,0.0143885342,0.4878042042,-0.0132796234,-0.0494600348,0.2268330753,-0.1175174713,0.3311557174,-0.012914151,0.1702439636,-0.1079322323,0.1320791095,0.048105862,-0.047323171,-0.020109592,-0.1124603525,-0.251969099,0.0791813955,-0.0342014916,-0.280818522,0.0415980518,-0.2271566242,0.3425572515,-0.3150840104,0.3320280612,0.4480588734,0.3958907723,-0.0926601663,-0.08121676,0.3662144542,0.0135225635,-0.0299308486,-0.2975441515,0.3306922317,-0.0206188466,0.2846172154,0.2693359554,-0.0772321746,0.2560566664,-0.2479436398,-0.1416988522,0.2170896083,-0.1550867856,-0.422665,0.2669496834,-0.1116820797,0.3041907847,-0.0293439664,0.0453690514,-0.0606513955,0.1691665053,-0.0211248212,0.2345538884,-0.0976088271,-0.0916917697,-0.0077355248,-0.1554212123,0.3678473532,-0.1237492189,-0.1123933941,0.3373726308,0.1141549349,-0.0311683882,0.2400601208,-0.4657922089,0.2133105695,0.2559942603,0.2300509512,0.1441592425,0.2475076914,-0.162230745,-0.3966977596,0.1127589867,-0.3874371946,0.1097859219,0.1357524693,-0.3187190294,-0.1147378087,-0.2083285451,-0.1158750355,-0.0275504105,-0.0913064778,-0.07691627,0.4252487719,0.0141551876,-0.4040580094,0.2884075344,-0.1211187318,0.1116678566,-0.4417361319,0.0506424047,0.2840945125,0.0014731303,-0.0312433951,0.169615671,-0.0143173551,-0.3851283193,0.0938997865,0.3167589009,-0.2716920376,-0.0050706319,-0.0730890334,0.0714602321,0.0649735183,-0.1072305143,-0.1467594951,0.1161518022,0.0738630369,-0.0635086298,-0.1146612242,0.755053401,-0.0631501153,0.1573880762,0.1018702015,-0.0422718599,-0.1553642303,-0.2558642626,-0.1365821511,0.2526302338,0.1801749468,-0.2043041736,0.2563439608,-0.1358107626,-0.227570802,-0.2907540798,0.2738241255,0.0077716927,0.3027178347,0.1412793249,0.0636070445,0.1619970351,0.0168047398,-0.1653858274,0.3542358279,-0.0300219264,0.2275405973,0.3308396637,0.2882860005,0.1496148556,0.0311066695,-0.2790497243,-0.040879406,-0.0289257523,0.0171885472,0.0865658298,-0.3360079825,-0.0112243909,0.0899158865,0.1401436925,-0.0238192286,-0.0793158188,-0.1206796318,-0.1589755714,-0.198860988,0.0308305621,-0.2242706716,-0.028090097,-0.0675436109,0.0957195312,0.0862422884,0.4852786958,0.3077232838,0.4453638792,-0.0495040603,0.3921653926,-0.0835309774,0.0408271775,0.0993405282,-0.0459782109,-0.1255243868,-0.2114326358,0.204008922,-0.0066473591,-0.0031043291,-0.514117837,-0.2244881839,0.2302787453,0.0412578695,-0.2499296367,0.3766584098,0.0238632113,-0.5575558543,-0.1051275283,-0.029765375,-0.0915054232,-0.5620676875,0.2115024328,-0.1757505834,0.1443349719,-0.2802774608,-0.3391255736,-0.0725459903,-0.2252959311,0.9792701006,-0.3612010479,0.0411205776,0.1631681174,-0.2286432534,0.0631909594,-0.0209391639,0.1144876555,-0.0962952077,-0.543505311,-0.171720624,0.0018520691,-0.1162723675,-0.1359869838,-0.0556084886,0.0778616071,0.5772361159,0.0053357044,-0.190570876,-0.3950435221,-0.2697645128,-0.0862358287,0.4126008451,0.2028821409,-0.3294523656,0.1059352309,-0.0606171004,-0.0498360544,0.233362332,0.0149459997,-0.0965534598,0.216025725,-0.0281821564,0.3622387648,0.3341759145,0.3676950336,-0.1023174226,0.0332149342,-0.2708882987,-0.0947812423,0.1031512022,-0.3197565675,0.0953792408,0.12446883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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2767","title":"equal operation to perform unbatch for huggingface datasets ","comments":"Hi,\r\nthis is also my question on how to perform similar operation as \"unbatch\" in tensorflow in great huggingface dataset library. \r\nthanks.","body":"Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n","comment_length":22,"text":"equal operation to perform unbatch for huggingface datasets  \n Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n \n Hi,\r\nthis is also my question on how to perform similar operation as \"unbatch\" in tensorflow in great huggingface dataset library. \r\nthanks.","embeddings":[-0.0424975231,-0.7478794456,0.0441974327,-0.0638020411,0.0602153614,-0.0741190612,0.2164928764,0.0234034453,0.4041276276,0.2328329533,-0.375790894,-0.0309473332,0.008743871,0.4603340924,0.2217881382,-0.2381002754,0.0493126996,0.0999169722,-0.2166887224,-0.0692890733,-0.07732407,-0.0117729409,-0.5677039623,-0.1649728417,0.3511196375,-0.2511515319,-0.1151801869,-0.2309311926,-0.1251270175,0.2847944796,0.4793148041,0.2426975667,0.1669390798,0.4491536617,-0.0001256292,0.2773347199,-0.2205358744,-0.1515981555,0.1575898677,-0.151048407,-0.1850503683,-0.0384183079,-0.0539824218,-0.3068354428,-0.1764235646,-0.0184586626,0.0631059781,-0.3345097601,0.6362299919,0.1719640344,0.0013318665,0.2099072486,-0.0798648074,0.0820844993,-0.1022054255,0.0367429368,-0.0256353393,0.1146042943,0.0846524984,0.0455240831,0.0681281611,0.2344957441,-0.0564624779,-0.0299290791,0.0353240147,0.1095685884,-0.032894548,-0.3611135781,0.1364402771,0.3937362134,0.2183602303,-0.373719424,-0.5333204269,-0.0268120263,0.0756629482,-0.0685920864,-0.205910027,0.2482982725,-0.0961421728,0.2391859442,-0.4491079748,-0.1502820253,-0.0860688835,0.0137132183,0.0574082434,-0.1004855558,-0.3021975756,0.2693094313,0.0576845296,0.0820068941,-0.0920690149,-0.0770096332,0.1063661948,0.158407554,-0.672842741,-0.2440767139,-0.2414087653,0.0239156168,0.2683830857,0.3667656779,0.5453345776,-0.0524723865,-0.0262592211,0.0717241764,0.050923761,0.5504601598,-0.0927488953,-0.2812029421,0.037321765,0.1110856161,-0.0138820438,-0.0510671809,0.3221217394,-0.0530241765,-0.344066143,0.14720577,0.3968828321,-0.1028705165,-0.355604291,-0.1796554625,-0.3000112772,-0.1298760325,0.0673620328,0.1227707714,-0.1877583712,0.016536355,0.1142916307,0.0416766889,-0.2851560414,-0.0781527162,-0.1623554677,0.2975337207,-0.1561620086,0.0250501819,-0.0284833647,-0.2422840297,0.0106540537,0.487816751,-0.0249357056,-0.0006227787,0.2213852406,-0.1916823983,0.2998886406,-0.0221225787,0.1274335682,-0.1672376692,0.1175889149,0.0563347861,-0.0360738076,0.0110055776,-0.0750050694,-0.2657646239,0.0381360538,-0.0416547023,-0.3246122003,0.0032485053,-0.1793220937,0.2697196901,-0.3806002438,0.4313870072,0.4621528983,0.3615436256,-0.0420184471,-0.0687471926,0.4144029617,-0.0749240369,-0.0679476485,-0.3054028749,0.3379549384,-0.0421895646,0.3259534538,0.2724405229,-0.0680568144,0.3023763001,-0.2311029583,-0.1621000916,0.235185951,-0.1755775064,-0.4458527267,0.1876093298,-0.0978450701,0.2507426143,-0.0481086299,0.0729169548,-0.0773575604,0.2221725732,-0.0441651233,0.2555157542,-0.0487267859,-0.1383284032,-0.015506614,-0.1830752939,0.3481126726,-0.0824953467,-0.0941848382,0.3812470138,0.1271146983,-0.0217743367,0.2218708396,-0.4553268254,0.1601547897,0.2863127887,0.2274770886,0.1333514005,0.2319566011,-0.1823991835,-0.3266887963,0.1416940689,-0.3598377407,0.0889272392,0.115725413,-0.3049005866,-0.1305134594,-0.2349703014,-0.1013237908,0.0059276456,-0.1083109602,-0.0970226675,0.4688789845,0.0513625555,-0.4691463411,0.353730619,-0.1237802282,0.1276918054,-0.4118320942,0.0413681194,0.2954889536,-0.0169274807,-0.0135378065,0.2284951657,-0.0155147873,-0.3583829999,0.0819730461,0.3086032569,-0.3341932893,0.0336165279,-0.0568824895,0.0462707952,0.0354421847,-0.1527609974,-0.1786395758,0.0535832122,0.0892216489,-0.011261384,-0.0550484695,0.7477804422,-0.0993619338,0.1371473223,0.1400215626,-0.0569110103,-0.1730741113,-0.3248525262,-0.1202724278,0.2766099274,0.2228631377,-0.2027987391,0.2167761475,-0.1463133544,-0.2560457289,-0.33674106,0.3105781972,0.0451067872,0.3367241919,0.1336293221,0.0417049378,0.2017525434,0.0126549862,-0.1468297839,0.4227997363,-0.0309492908,0.2567645311,0.379553616,0.3169096708,0.1312439889,0.0841790512,-0.2490986586,-0.0797817633,-0.0145397317,0.0557714365,0.0699486136,-0.3710449636,-0.0041458462,0.0700621605,0.1621102691,0.0241403021,-0.1014374867,-0.1798603833,-0.2002354413,-0.2028765678,0.0098963231,-0.1672875881,-0.0132869389,-0.0906698033,0.0028247344,0.1491720974,0.4050225914,0.2213249952,0.4259082973,-0.09626095,0.3810700178,-0.1173515394,0.0341566578,0.091918461,-0.0392951705,-0.1389828175,-0.1874711066,0.2197643965,0.0147329243,0.0398427658,-0.5104528069,-0.163260147,0.2542126179,0.0426331311,-0.2061795145,0.3554162085,0.056791164,-0.535736084,-0.0846047625,-0.028251566,-0.0885254443,-0.5490326285,0.1930105537,-0.1596266627,0.1691580564,-0.3059814274,-0.3406267464,-0.0838258937,-0.216371581,0.9462254643,-0.3164279759,0.0208242256,0.1576191187,-0.3140150905,0.1281804889,-0.0236970019,0.1545455158,-0.0536154322,-0.5723289847,-0.1633879691,0.0267136283,-0.1361408532,-0.1201191023,-0.0198404156,0.1375264078,0.5128785968,-0.0431780182,-0.1274823099,-0.4869631529,-0.2536957264,-0.0911310539,0.4112828374,0.1251698136,-0.3662730157,0.1088478789,-0.0184606872,-0.0162020139,0.213346824,0.004052876,-0.0966634229,0.2594733834,-0.0480677076,0.3650169373,0.3248707056,0.3047578335,-0.1051920429,0.0402547941,-0.297282666,-0.0865996405,0.0679808035,-0.3497266769,0.123785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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2767","title":"equal operation to perform unbatch for huggingface datasets ","comments":"Hi,\r\n\r\n`Dataset.map` in the batched mode allows you to map a single row to multiple rows. So to perform \"unbatch\", you can do the following:\r\n```python\r\nimport collections\r\n\r\ndef unbatch(batch):\r\n    new_batch = collections.defaultdict(list)\r\n    keys = batch.keys()\r\n    for values in zip(*batch.values()):\r\n        ex = {k: v for k, v in zip(keys, values)}\r\n        inputs = f\"record query: {ex['query']} entities: {', '.join(ex['entities'])} passage: {ex['passage']}\"\r\n        new_batch[\"inputs\"].extend([inputs] * len(ex[\"answers\"]))\r\n        new_batch[\"targets\"].extend(ex[\"answers\"])\r\n    return new_batch\r\n\r\ndset = dset.map(unbatch, batched=True, remove_columns=dset.column_names)\r\n```","body":"Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n","comment_length":72,"text":"equal operation to perform unbatch for huggingface datasets  \n Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n \n Hi,\r\n\r\n`Dataset.map` in the batched mode allows you to map a single row to multiple rows. So to perform \"unbatch\", you can do the following:\r\n```python\r\nimport collections\r\n\r\ndef unbatch(batch):\r\n    new_batch = collections.defaultdict(list)\r\n    keys = batch.keys()\r\n    for values in zip(*batch.values()):\r\n        ex = {k: v for k, v in zip(keys, values)}\r\n        inputs = f\"record query: {ex['query']} entities: {', '.join(ex['entities'])} passage: {ex['passage']}\"\r\n        new_batch[\"inputs\"].extend([inputs] * len(ex[\"answers\"]))\r\n        new_batch[\"targets\"].extend(ex[\"answers\"])\r\n    return new_batch\r\n\r\ndset = dset.map(unbatch, batched=True, remove_columns=dset.column_names)\r\n```","embeddings":[-0.0753810331,-0.7735245228,0.0261516012,-0.0693187863,0.0228945483,-0.0482676737,0.3034118414,0.040893551,0.397575289,0.2398023307,-0.3650856912,-0.0645896122,0.0264364704,0.411909461,0.179759115,-0.2043053061,0.0633047,0.0990086794,-0.2375567555,-0.0585383028,-0.1362456232,0.0407442078,-0.5339214802,-0.176741302,0.3353881836,-0.2973919511,-0.1161579415,-0.2635371983,-0.1034479588,0.3052071929,0.4921708405,0.309329927,0.2077566832,0.4856838882,-0.0001246521,0.3010813892,-0.2492474616,-0.1526912153,0.1145772412,-0.0873676911,-0.1308563501,-0.0882824585,-0.0693368986,-0.3121809363,-0.1677762419,-0.0135150421,0.086169593,-0.3338784277,0.6199630499,0.107661739,0.0035845411,0.3279936612,-0.0736484453,0.0471059866,-0.161874786,0.0315583572,-0.0162977967,0.1471144408,0.1239845455,0.0130380867,0.0811594427,0.3311883807,-0.1018417254,-0.034806475,0.0685208738,0.1417723894,0.0737149864,-0.4227611721,0.1653945744,0.3434442282,0.1286982298,-0.3457928002,-0.5522564054,-0.1110518277,0.0664463043,-0.0784133896,-0.2424910516,0.1549121588,-0.1231821254,0.1915919334,-0.4587093592,-0.085062556,-0.064205125,0.0513771512,0.0663282946,-0.0215883255,-0.2763303518,0.270816803,0.0563461557,0.0941273719,-0.1524153501,-0.1206801981,0.1649204046,0.2293738127,-0.6707261205,-0.2004776746,-0.2176058739,-0.0827744827,0.2486542463,0.3064738214,0.5127056837,-0.0695319474,-0.0014590117,0.0912642032,0.069061473,0.5457304716,-0.1010435522,-0.2234711945,0.010707383,0.1190956309,-0.0006037139,-0.0132912463,0.3079895079,-0.0960773826,-0.2861174643,0.186826244,0.3991137147,-0.0048522572,-0.3670807183,-0.1827206463,-0.3305942416,-0.1504132301,0.0168250147,0.1619478613,-0.195544973,0.038826786,0.1834592521,0.096247904,-0.2616742551,-0.0341715328,-0.1754765362,0.2005480081,-0.1355688274,-0.0149284443,0.0025351986,-0.1964241862,0.0182543136,0.405243963,0.045959089,-0.0078745354,0.1850574166,-0.1379251629,0.3300073445,0.0267928485,0.2054771781,-0.1043841615,0.1196802184,0.1109361276,-0.0676129758,0.0062329699,-0.0762393475,-0.2370306104,0.1029392928,-0.0261911377,-0.290127635,-0.0064336713,-0.2655008733,0.2805640399,-0.3139200509,0.4107523561,0.4642283022,0.3083906174,-0.0579389855,-0.0884879678,0.3909344673,-0.033580035,-0.1268098652,-0.270321548,0.3334641159,-0.0070434152,0.3165129125,0.3111931682,-0.0583803356,0.3475653827,-0.2451771051,-0.0472126268,0.2052667141,-0.1820423901,-0.4236242473,0.2309683859,-0.0761009604,0.2990359664,-0.035675738,0.0611703806,-0.0361164622,0.1729475856,0.0400792658,0.2885244787,-0.0879403278,-0.0819173232,0.0025714759,-0.1750308424,0.2911466062,-0.0937554762,-0.0195052046,0.2881815135,0.0503391959,-0.1503593475,0.2743276358,-0.5115571618,0.2057096809,0.2351751029,0.2331880778,0.1862762272,0.2376929522,-0.1802090406,-0.4620914459,0.1702177674,-0.375782311,0.1592279524,0.0510786287,-0.3364458084,-0.1325394809,-0.1899868399,-0.1449082196,0.0438793339,-0.0932907164,-0.133311078,0.462906003,0.0250245333,-0.4002581835,0.2373046726,-0.0836174414,0.1097686961,-0.4155571163,0.0613827072,0.2461878359,-0.0186510831,-0.0090569798,0.2117616385,0.0163055379,-0.3814625442,0.1080989093,0.2936584353,-0.3484901488,0.019219039,-0.0798182786,0.0817219391,0.0673079863,-0.0596716553,-0.129542917,0.0506830029,0.0736499205,-0.0641615167,-0.0339300036,0.8116818666,-0.0951392353,0.1268706173,0.067018576,-0.0672764555,-0.1310650855,-0.2365904897,-0.1849679053,0.2044692636,0.1865125,-0.23161982,0.2167523503,-0.1897063702,-0.2400353551,-0.2519877851,0.291700542,0.0077059921,0.3112775683,0.1127476171,0.042579215,0.2121856213,-0.0388295315,-0.1469406784,0.3771910965,0.0011109207,0.2564551532,0.3336131275,0.2784111798,0.1565333158,0.0501440316,-0.260941714,-0.0400376283,0.010296341,0.0104519352,0.1022086889,-0.3529451191,-0.0023584473,0.0579043962,0.1635893732,0.0091688875,-0.0685236752,-0.093329832,-0.1590349078,-0.2032993138,-0.0484064296,-0.199980557,-0.0636533871,-0.0596891753,0.0650865659,0.0827660188,0.4527523816,0.2323810607,0.4247006178,-0.0046510827,0.3034425378,-0.1403293908,0.0124633079,0.0758061558,-0.0436794795,-0.1188975722,-0.236073032,0.2823859453,-0.0057214219,0.0372352451,-0.5395433903,-0.1603367329,0.2113296688,-0.065205209,-0.2380534261,0.3718391657,0.1210858002,-0.5385232568,-0.1301630884,0.0426791012,-0.1647605151,-0.54490906,0.1823602468,-0.151724875,0.1190217435,-0.2554599643,-0.3128781915,-0.1122351065,-0.193388164,0.9071428776,-0.360363394,0.0387682952,0.1697901636,-0.2453997433,0.0909720957,0.0120815188,0.0438339747,-0.1046573743,-0.5249735713,-0.1223860905,0.0322275795,-0.137395978,-0.1270463914,-0.0300666932,0.1196217984,0.5564443469,-0.0042878212,-0.146527648,-0.4110369682,-0.1484779865,-0.0592323169,0.4029557705,0.1972219199,-0.3497493267,0.1031162441,-0.0321001783,-0.0572186373,0.1777200252,-0.0003169307,-0.1117025539,0.2010336071,0.014789477,0.3613646626,0.423307687,0.2689908445,-0.1124324426,0.0211761966,-0.2406370044,-0.0781438947,0.0687934458,-0.4165217578,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2767","title":"equal operation to perform unbatch for huggingface datasets ","comments":"Dear @mariosasko \r\nFirst, thank you very much for coming back to me on this, I appreciate it a lot. I tried this solution,  I am getting errors, do you mind\r\ngiving me one test example to be able to run your code, to understand better the format of the inputs to your function?\r\nin this function https:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L952 they copy each example to the number of \"answers\", do you mean one should not do the copying part and use directly your function? \r\n\r\n\r\nthank you very much for your help and time.","body":"Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n","comment_length":90,"text":"equal operation to perform unbatch for huggingface datasets  \n Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n \n Dear @mariosasko \r\nFirst, thank you very much for coming back to me on this, I appreciate it a lot. I tried this solution,  I am getting errors, do you mind\r\ngiving me one test example to be able to run your code, to understand better the format of the inputs to your function?\r\nin this function https:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L952 they copy each example to the number of \"answers\", do you mean one should not do the copying part and use directly your function? \r\n\r\n\r\nthank you very much for your help and time.","embeddings":[-0.0108777815,-0.7745167613,0.0500379056,-0.0438205153,0.0585958362,-0.1315132678,0.2449042946,0.0355795249,0.403403908,0.207198292,-0.3647301793,-0.0576848835,0.0258308109,0.4528597593,0.1368795037,-0.2415413558,0.0354214869,0.130074814,-0.3146570027,-0.0971326157,-0.1279555559,0.0090236766,-0.5449327826,-0.1741686314,0.3397312164,-0.285574764,-0.1111921817,-0.2267976999,-0.2018394172,0.3307215273,0.421207577,0.276545018,0.1566703469,0.5160192847,-0.0001262681,0.2817387581,-0.2239812016,-0.1529459655,0.0926806554,-0.1573763192,-0.141733855,-0.0440882891,-0.0630615577,-0.2752128839,-0.1816470772,-0.0017648741,0.0454082973,-0.3386259675,0.6838319302,0.1817790866,-0.0048463992,0.206730932,-0.0597356409,0.092067562,-0.1350352913,0.0671231896,-0.0204418618,0.0735917613,0.0742384195,0.0391847678,0.0825988725,0.2869920731,-0.0342607386,-0.0616690628,0.0821961388,0.1065226719,0.0024306127,-0.3898434937,0.1111200899,0.3619070351,0.2063346505,-0.3542043865,-0.5544352531,-0.0521407835,0.0253042858,-0.047070466,-0.2078715265,0.1657986045,-0.1159716845,0.2144903541,-0.4811854959,-0.0987051576,-0.0723049492,0.0731838271,0.0504783355,-0.1077801734,-0.2960430086,0.2540514767,0.0699314401,0.0905945897,-0.1174799427,-0.1175418869,0.123034589,0.2084047049,-0.6481525302,-0.2360532731,-0.2577502429,-0.0207786467,0.263964802,0.3931050301,0.5452445745,-0.105997473,-0.0683631003,0.0775518268,0.0718001723,0.5941019654,-0.093124032,-0.2449739575,-0.0216667969,0.1149332821,0.033849474,-0.045613125,0.3210249543,-0.0735206679,-0.3377156556,0.1442278922,0.3928581178,-0.098125428,-0.3077066541,-0.1714103371,-0.3482732773,-0.1062351093,0.0052448702,0.1235989332,-0.1738000065,0.0956065878,0.2250313759,0.0844191462,-0.2863166034,-0.0946582407,-0.1676394641,0.2888308764,-0.1838473678,0.0078385184,-0.0555583052,-0.1803662032,-0.0213175565,0.5038354993,0.0103862332,-0.0349442065,0.2061427534,-0.1644645035,0.3446747363,-0.0276211277,0.1581510454,-0.1279955357,0.1393094361,-0.0182058513,-0.0253074467,0.0004724024,-0.0577381849,-0.2445571721,0.0647586286,-0.0385767519,-0.3195185959,0.0288104545,-0.193225801,0.3196071088,-0.3369585574,0.3980179131,0.454992801,0.3295466602,-0.0548327714,-0.0711227283,0.4004134536,0.0320708752,-0.0892611146,-0.2960408628,0.3439548612,0.0093569336,0.3034724593,0.2937402427,-0.0132582132,0.2759523392,-0.2429623306,-0.0962143987,0.2000613511,-0.1653339267,-0.3978582919,0.2438246906,-0.0936977118,0.2858174145,-0.0008192139,0.0327162445,-0.0864739269,0.1881908178,-0.0270804409,0.2525263131,-0.0661386475,-0.1053133681,-0.0651599318,-0.1617499441,0.3505885303,-0.0655955747,-0.0829995051,0.3464676738,0.0652354285,-0.0619511642,0.2206347138,-0.5067725778,0.1742602289,0.2860680223,0.2737710476,0.1422733814,0.2397804707,-0.1471372098,-0.3221267164,0.1042289734,-0.3572678864,0.1865509748,0.1231021062,-0.2822453678,-0.174836114,-0.2101640403,-0.1613721251,-0.0220703632,-0.092828609,-0.0894160867,0.4296045601,0.062740095,-0.3935889304,0.3531248271,-0.1103326455,0.11737369,-0.4565909505,0.0766896084,0.2712408602,-0.0212028157,-0.0294926483,0.202091679,0.0250198003,-0.3182093203,0.0931531414,0.2768290639,-0.3111835718,-0.0000497935,-0.0807028562,0.1129693165,0.0448838323,-0.0933870897,-0.1356566399,0.1158975288,0.0989421085,-0.0575681403,-0.0803855881,0.7735747695,-0.0630798414,0.1183306128,0.1151418835,-0.0541957915,-0.1563129872,-0.2935832739,-0.1384200007,0.2834094763,0.2511129677,-0.2120889574,0.2328744829,-0.1283389032,-0.2621282637,-0.2862565815,0.2700942159,0.0284279771,0.3281134963,0.1195155457,0.0314621255,0.1946714073,-0.0153375231,-0.1554953903,0.3815603852,-0.0071744379,0.2247863263,0.3685889542,0.3105094731,0.1431070119,0.0553678721,-0.2981645763,-0.0729001313,0.0393902995,0.0086060138,0.1030274034,-0.3797222078,0.0236477815,0.0302486364,0.1702512354,-0.081622079,-0.0730784461,-0.1584304422,-0.1832448989,-0.2011881322,0.0363762155,-0.1941183209,-0.0438365713,-0.0806341022,0.0661064312,0.1269943118,0.4456271529,0.2406216264,0.4628773928,-0.0280903801,0.3665049374,-0.1363861561,0.0575946607,0.0705956444,-0.0461736582,-0.1194234192,-0.2033166885,0.2038011402,-0.0031708898,0.0439757034,-0.4660874009,-0.2374643683,0.2677345872,0.0232883915,-0.2113489062,0.3508021235,0.0557054542,-0.5462496281,-0.0783203766,-0.0216935202,-0.0884826779,-0.5615617633,0.252897054,-0.1770302355,0.1233159378,-0.3099218905,-0.3661047518,-0.0872962326,-0.2064203322,0.9370263219,-0.3762081861,0.0057926476,0.1996505111,-0.2222321033,0.0677220523,0.0126034934,0.1556550413,-0.0976721346,-0.5041771531,-0.073125653,0.0281708445,-0.1376679242,-0.148702845,-0.0546869412,0.0802529082,0.5497124195,-0.0282657202,-0.1994812638,-0.4520470798,-0.2961638272,-0.0728063732,0.4209002852,0.1771828979,-0.3457151055,0.1205206513,-0.0375313312,-0.0695850253,0.2295768261,0.0187303331,-0.1270513982,0.2347080708,0.0014079961,0.3598975837,0.3538529873,0.3470194936,-0.0990255922,0.0370055921,-0.2861382365,-0.0852763057,0.0900597051,-0.3696490228,0.10219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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2767","title":"equal operation to perform unbatch for huggingface datasets ","comments":"Hi @mariosasko \r\nI think finally I got this, I think you mean to do things in one step, here is the full example for completeness:\r\n\r\n```\r\ndef unbatch(batch):\r\n    new_batch = collections.defaultdict(list)\r\n    keys = batch.keys()\r\n    for values in zip(*batch.values()):\r\n        ex = {k: v for k, v in zip(keys, values)}\r\n        # updates the passage.\r\n        passage = ex['passage']\r\n        passage = re.sub(r'(\\.|\\?|\\!|\\\"|\\')\\n@highlight\\n', r'\\1 ', passage)\r\n        passage = re.sub(r'\\n@highlight\\n', '. ', passage)\r\n        inputs = f\"record query: {ex['query']} entities: {', '.join(ex['entities'])} passage: {passage}\"\r\n        # duplicates the samples based on  number of answers.\r\n        num_answers = len(ex[\"answers\"])\r\n        num_duplicates = np.maximum(1, num_answers)\r\n        new_batch[\"inputs\"].extend([inputs] * num_duplicates) #len(ex[\"answers\"]))\r\n        new_batch[\"targets\"].extend(ex[\"answers\"] if num_answers > 0 else [\"<unk>\"])\r\n    return new_batch\r\n\r\ndata = datasets.load_dataset('super_glue', 'record', split=\"train\", script_version=\"master\")\r\ndata = data.map(unbatch, batched=True, remove_columns=data.column_names)\r\n```\r\n\r\nThanks a lot again, this was a super great way to do it.","body":"Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n","comment_length":131,"text":"equal operation to perform unbatch for huggingface datasets  \n Hi\r\nI need to use \"unbatch\" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:\r\n\r\nI am considering \"record\" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:\r\n\r\nhttps:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/3c58859b8fe72c2dbca6a43bc775aa510ba7e706\/t5\/data\/preprocessors.py#L925\r\n\r\nHere please find an example:\r\n\r\n  For example, a typical example from ReCoRD might look like\r\n  {\r\n      'passsage': 'This is the passage.',\r\n      'query': 'A @placeholder is a bird.',\r\n      'entities': ['penguin', 'potato', 'pigeon'],\r\n      'answers': ['penguin', 'pigeon'],\r\n  }\r\n  and I need a prosessor which would turn this example into the following two examples:\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'penguin',\r\n  }\r\n  and\r\n  {\r\n      'inputs': 'record query: A @placeholder is a bird. entities: penguin, '\r\n                'potato, pigeon passage: This is the passage.',\r\n      'targets': 'pigeon',\r\n  }\r\n\r\n\r\nFor doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with  huggingface datasets library and greatly appreciate your help\r\n\r\n@lhoestq \r\n\r\nThank you very much.\r\n \n Hi @mariosasko \r\nI think finally I got this, I think you mean to do things in one step, here is the full example for completeness:\r\n\r\n```\r\ndef unbatch(batch):\r\n    new_batch = collections.defaultdict(list)\r\n    keys = batch.keys()\r\n    for values in zip(*batch.values()):\r\n        ex = {k: v for k, v in zip(keys, values)}\r\n        # updates the passage.\r\n        passage = ex['passage']\r\n        passage = re.sub(r'(\\.|\\?|\\!|\\\"|\\')\\n@highlight\\n', r'\\1 ', passage)\r\n        passage = re.sub(r'\\n@highlight\\n', '. ', passage)\r\n        inputs = f\"record query: {ex['query']} entities: {', '.join(ex['entities'])} passage: {passage}\"\r\n        # duplicates the samples based on  number of answers.\r\n        num_answers = len(ex[\"answers\"])\r\n        num_duplicates = np.maximum(1, num_answers)\r\n        new_batch[\"inputs\"].extend([inputs] * num_duplicates) #len(ex[\"answers\"]))\r\n        new_batch[\"targets\"].extend(ex[\"answers\"] if num_answers > 0 else [\"<unk>\"])\r\n    return new_batch\r\n\r\ndata = datasets.load_dataset('super_glue', 'record', split=\"train\", script_version=\"master\")\r\ndata = data.map(unbatch, batched=True, remove_columns=data.column_names)\r\n```\r\n\r\nThanks a lot again, this was a super great way to do it.","embeddings":[-0.0244649611,-0.8023660183,0.0363254026,-0.0556131192,0.0216097701,-0.0733311102,0.2536010146,0.0494495481,0.3815232515,0.241488114,-0.3534253836,-0.0654722452,0.0393712558,0.4445899725,0.1729435027,-0.2635638416,0.0600867756,0.1323877722,-0.2501762509,-0.0962025076,-0.1124165952,0.0328882895,-0.5142920017,-0.179090023,0.3367595375,-0.2938053906,-0.1501288116,-0.25004673,-0.1282937527,0.3018818796,0.4609180391,0.2909281254,0.2123069316,0.4509742856,-0.0001249902,0.292660445,-0.2429701388,-0.1337586045,0.1145015657,-0.0880547985,-0.0968523026,-0.094203338,-0.0344842076,-0.3119998872,-0.1772888601,-0.0073471898,0.0576628409,-0.3176602423,0.6801812053,0.1236015633,0.0111872517,0.2365360111,-0.0750250593,0.0690964758,-0.1374193281,0.0161666367,-0.0209393706,0.0664196014,0.0917303488,0.0396539941,0.1078623459,0.3016746044,-0.0618220083,-0.0389599092,0.0509128645,0.130355835,0.0570813902,-0.4106472731,0.1430849731,0.371230334,0.1794088334,-0.3381175995,-0.5565244555,-0.0625604987,0.0454883538,-0.1229719147,-0.2359695733,0.1706840247,-0.1305094957,0.2065273523,-0.410728395,-0.0659740493,-0.0432806909,0.0421258323,0.076096341,-0.0634093508,-0.2866249084,0.2641929984,0.0687270314,0.118497096,-0.1335151792,-0.1068736017,0.1343878061,0.2130965739,-0.6556074023,-0.2227413803,-0.243380785,-0.0494689941,0.278621912,0.3848898113,0.5002590418,-0.0848697722,-0.0207165629,0.0651059002,0.0739711523,0.5639448166,-0.0917669907,-0.2520310283,-0.0166104343,0.1402390152,0.0175611489,-0.0419357605,0.3055624664,-0.0990368202,-0.2601804733,0.2037353814,0.4017173946,-0.071072191,-0.3239939213,-0.1834992617,-0.3271969855,-0.1466225237,-0.0070036929,0.1262951344,-0.1984352022,0.0695280209,0.1843550801,0.0698361024,-0.2867665887,-0.0619153231,-0.1795290262,0.2524328232,-0.1391302496,-0.0016128101,-0.0439372435,-0.2109626979,0.0124201532,0.4680207968,0.0447812714,-0.0492153391,0.222948581,-0.1477324069,0.2782618105,-0.0027510433,0.1948138475,-0.1123818979,0.1089916527,0.0534727201,-0.0731872618,-0.0069980486,-0.0442639664,-0.2340401858,0.1050291061,-0.0208555199,-0.2917470336,0.0252854489,-0.2057438791,0.2881164253,-0.3421461284,0.4115400612,0.4393236637,0.3590464294,-0.0536828786,-0.0564322136,0.4131744206,-0.019270733,-0.0965282768,-0.26799196,0.3482652009,0.0231547076,0.3075779974,0.2882292271,-0.0513803102,0.3208956718,-0.2531568706,-0.0951119587,0.2231654227,-0.1747700125,-0.4179792106,0.2593773901,-0.0811822712,0.2943813801,0.0126556782,0.0581352301,-0.1151295975,0.1797288209,-0.0140454564,0.2801768184,-0.0816658288,-0.0973386616,-0.0200460423,-0.1788855791,0.3267515302,-0.0937372372,-0.0511453375,0.3327536583,0.041573327,-0.1167278588,0.2596202195,-0.4877478182,0.181680128,0.2622479498,0.2451246232,0.1432557255,0.2428885102,-0.152628094,-0.3867460489,0.1505290866,-0.3342051208,0.1509622037,0.0888974518,-0.3247162998,-0.1425441802,-0.2196986079,-0.1516406536,-0.0205253456,-0.0847825333,-0.113110289,0.4627571106,0.0237465501,-0.3968051374,0.276607275,-0.082532309,0.0979911983,-0.4222601652,0.0488031805,0.2384614348,0.0088137798,-0.0059175733,0.1739051789,0.0389285833,-0.3525582552,0.0861845389,0.2756893635,-0.3201452792,0.007060708,-0.0751751736,0.0669735745,0.0377308093,-0.0897159949,-0.1346691698,0.0368004777,0.0751844123,-0.0604840592,-0.0372871533,0.7736294866,-0.0813849419,0.1259882301,0.1066019908,-0.0489501767,-0.1498593986,-0.2755477428,-0.1673119962,0.2761720717,0.2356814444,-0.2201273739,0.2520665228,-0.1806640178,-0.2266646326,-0.2730321288,0.2895759344,0.0222219992,0.3233869076,0.1219824627,0.0554224737,0.2160241306,-0.0402212627,-0.1559975892,0.3685371578,-0.003990531,0.254512459,0.3580181301,0.2879855633,0.152347818,0.0509685427,-0.2573584616,-0.074441731,0.0054119737,-0.010024149,0.1193401515,-0.3581905961,0.0063078338,0.0412622169,0.1751889735,0.0069136517,-0.0505397953,-0.1088419259,-0.1534914821,-0.1993155479,-0.0013042735,-0.2027613223,-0.047777351,-0.0915739834,0.0144527415,0.0921797231,0.4318853021,0.2483216375,0.4400375187,-0.0472345874,0.3629509509,-0.1473098248,0.0066064852,0.0675346255,-0.0385228246,-0.1326177418,-0.2288424373,0.2457871139,-0.0202647522,0.0167320855,-0.5255942941,-0.1940316558,0.2381523252,-0.0047633923,-0.23789832,0.3722145557,0.0893638283,-0.5399628878,-0.111228779,0.018306395,-0.1314908564,-0.5532984734,0.2090689838,-0.1560288221,0.131594196,-0.2751115561,-0.3274399936,-0.1012824178,-0.1940861493,0.9283853173,-0.3411178291,0.0465672724,0.1961683631,-0.2678838968,0.073897168,0.0258137938,0.1224486679,-0.0997033864,-0.5181102157,-0.1364708543,0.0263615157,-0.1522428393,-0.1412647218,-0.063107878,0.1339441687,0.5387528539,-0.0023167669,-0.2072089016,-0.4411291778,-0.227887556,-0.1010209024,0.4474504292,0.1770002246,-0.3411288857,0.1188594401,-0.0365313701,-0.053765934,0.2113044113,-0.0068767234,-0.1427294016,0.1921971738,0.025802467,0.3620700836,0.3707354367,0.3190152943,-0.0835306942,0.0542934053,-0.2608816326,-0.1027698666,0.0708574727,-0.3895360231,0.0945675373,0.136618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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2765","title":"BERTScore Error","comments":"Hi,\r\n\r\nThe `use_fast_tokenizer` argument has been recently added to the bert-score lib. I've opened a PR with the fix. In the meantime, you can try to downgrade the version of bert-score with the following command to make the code work:\r\n```\r\npip uninstall bert-score\r\npip install \"bert-score<0.3.10\"\r\n```","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\npredictions = [\"hello there\", \"general kenobi\"]\r\nreferences = [\"hello there\", \"general kenobi\"]\r\nbert = load_metric('bertscore')\r\nbert.compute(predictions=predictions, references=references,lang='en')\r\n```\r\n\r\n# Bug\r\n`TypeError: get_hash() missing 1 required positional argument: 'use_fast_tokenizer'`\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Colab \r\n- Python version:\r\n- PyArrow version:\r\n","comment_length":48,"text":"BERTScore Error \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\npredictions = [\"hello there\", \"general kenobi\"]\r\nreferences = [\"hello there\", \"general kenobi\"]\r\nbert = load_metric('bertscore')\r\nbert.compute(predictions=predictions, references=references,lang='en')\r\n```\r\n\r\n# Bug\r\n`TypeError: get_hash() missing 1 required positional argument: 'use_fast_tokenizer'`\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:\r\n- Platform: Colab \r\n- Python version:\r\n- PyArrow version:\r\n \n Hi,\r\n\r\nThe `use_fast_tokenizer` argument has been recently added to the bert-score lib. I've opened a PR with the fix. In the meantime, you can try to downgrade the version of bert-score with the following command to make the code work:\r\n```\r\npip uninstall bert-score\r\npip install \"bert-score<0.3.10\"\r\n```","embeddings":[-0.1357685477,0.1683022529,0.0354213715,0.2092981488,0.3473314941,0.0116616022,0.228991881,0.3530413806,-0.1199427098,0.3440085948,0.0769439936,0.4752254784,-0.1141471267,-0.116274558,-0.0818043575,-0.3451915979,0.0918747634,0.3889160156,0.0978735834,-0.1766297966,-0.2902543843,-0.0321730375,-0.1890511215,0.14117378,-0.2226971686,0.0321636721,-0.2295636237,-0.1066831648,-0.2944519818,-0.4702112675,0.244017005,-0.0262592826,-0.0497719832,0.4349934757,-0.00011068,-0.0363304466,0.2661401331,-0.042828802,0.0534035116,-0.3680902421,-0.5844059587,-0.2591186166,0.0363761783,-0.3373253345,0.0206000004,0.0272910316,-0.0091573633,-0.205401808,0.2047609687,0.3369059265,0.2373391241,0.1403486729,-0.1228036061,-0.3247485459,0.3542505503,-0.2880485058,0.0359722786,0.239534542,-0.2517361939,-0.1955946982,-0.1582666934,0.3248482943,-0.0513245836,0.0017433926,0.2958139181,0.244579941,-0.0856363773,0.0226623286,0.0879988819,0.1774827838,0.0153080029,-0.2262357622,-0.3905266821,0.2721064687,-0.0508809835,-0.2326862812,0.008509024,-0.2376612872,0.0329267941,-0.2067704797,-0.3441335857,-0.1278514266,-0.1945914477,0.0551801622,-0.0131272878,0.3923610151,0.0053676334,0.0391234159,0.3017946482,-0.163325876,-0.0880642459,-0.0763620809,0.104990907,0.2026166916,-0.0702825934,0.0108945398,0.2954882681,0.090971075,0.1854871511,-0.5502127409,0.0220071506,0.0181753691,0.0533534288,0.20646061,0.0191061515,0.3505941331,-0.1541594267,0.216454044,0.2325025052,-0.0649732351,-0.171160236,-0.0547930077,-0.031594649,-0.0788692012,0.489043355,0.1721761376,-0.2210557312,-0.2010472119,-0.2376409769,0.1853826642,-0.4741438329,-0.0452962145,0.210642457,0.4373003542,-0.1917456836,0.1406194717,-0.1900238246,0.0471833199,-0.1788125485,0.1952169985,-0.2828834355,0.2730635405,-0.3546588421,-0.0977147445,0.1049495712,-0.1991661042,0.37559551,0.0675270855,0.4369859695,0.1716331095,-0.1321011335,-0.2742588222,0.1550827473,-0.0190750062,0.0458559394,-0.0588260926,0.1923587322,-0.1388753951,-0.2338359207,0.1322214901,-0.2897829115,-0.3272272348,0.0407599024,0.2200025469,-0.1817957163,-0.0427128002,-0.2142801881,0.0517423637,0.1565991342,-0.2517822087,-0.196469456,-0.2444114089,-0.3549409509,-0.2286157608,0.3791269064,0.1625283957,-0.2698113024,-0.0479812771,-0.1046242043,0.2611281872,0.2512706518,0.193728596,-0.2317173481,0.3143136501,-0.1103289425,0.0838198364,0.3269455433,-0.5421458483,-0.4934960902,-0.2773634493,0.1178701892,-0.1706846952,0.0330090113,0.0672046989,0.0950100273,0.039079804,0.2723297775,-0.1352109462,0.0036767162,0.1628976017,-0.2928624153,-0.1766243279,0.1568981707,-0.0429335237,0.4267222881,-0.2014783621,-0.1615751088,0.2878130078,-0.0385640301,-0.1464606225,-0.0206569377,0.080988571,0.5700151324,-0.286860615,0.2906683087,-0.2317186296,0.0181312673,0.1943648458,-0.0077656615,0.2645762265,-0.2120677233,-0.1155014187,-0.4618649483,0.1162807196,-0.0927695483,0.1053282917,0.1548778564,-0.0557345711,0.2700780928,0.1727413982,-0.0629527047,-0.2985925674,0.0740361884,-0.1582509279,0.0757353231,0.2299352288,-0.2951509356,-0.2042697817,0.3250257969,0.2769492567,0.3096359074,0.0713861287,-0.1819415092,0.1881348491,-0.0460106172,-0.0693427995,0.0917252153,0.2145000547,0.0582688972,-0.4049297869,0.082875289,0.1304061264,0.3137616217,0.0475625284,-0.1744232923,0.4764544368,0.0648464933,0.1258464605,-0.0076349145,-0.0176294688,0.267881155,0.1416642368,0.2068474591,-0.3284746706,0.1557706892,-0.0036571291,0.1681103259,-0.021475194,0.1855958551,-0.1167835146,0.5808283687,0.3723367751,0.0887974873,0.2555126846,-0.227700904,-0.1590948552,-0.1334581673,-0.2853350341,0.3417616487,0.2336738855,-0.0786180198,-0.0336476155,-0.1537461579,-0.0775062963,0.1249396354,0.1540715545,-0.0738965794,-0.0380365551,0.3315090835,-0.0597750694,-0.190403372,-0.1201440543,-0.0203250069,0.4591867328,-0.3168023527,0.2947408557,-0.1316542774,0.1890826821,-0.0493894964,-0.6532367468,-0.059199784,-0.3793861568,0.2373259962,-0.0883880109,-0.0403929539,0.3508841395,0.057935819,0.1776039749,0.1711506397,-0.1074415892,-0.2738973796,-0.5320510864,-0.2634779811,0.0622547679,-0.1156594306,0.0657804832,0.2517986298,-0.2744704187,0.1120415255,-0.1333208084,-0.5113040209,0.0996796861,-0.3408310115,0.4703126848,0.3321333826,-0.1779076606,-0.5641243458,-0.2329175919,0.4654655755,-0.189506948,-0.0704404712,0.2736673951,-0.0602836832,-0.0971532837,0.0020504901,-0.3575296402,0.0341204032,0.0028462575,0.0291580129,0.0796931237,0.1652203798,-0.1709430367,0.2613537312,0.2987735868,-0.0680780932,-0.0711867362,-0.2196063846,-0.4031369686,0.5500207543,-0.1909077018,-0.2825049758,-0.1657274663,-0.169257924,0.198781997,0.0487793349,-0.1322614551,-0.59427917,-0.0986286029,0.1793272197,-0.0764526576,0.2097877115,0.1528716385,0.3122169971,-0.1486094594,-0.2454244941,-0.1619692296,0.1745098978,-0.1710664481,0.1097458526,-0.2303149998,0.3325130045,0.1021188423,0.92150563,0.1928438544,-0.2365649939,0.260007441,-0.1881907582,0.1139804795,-0.1771749705,-0.424674809,0.095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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2763","title":"English wikipedia datasets is not clean","comments":"Hi ! Certain users might need these data (for training or simply to explore\/index the dataset).\r\n\r\nFeel free to implement a map function that gets rid of these paragraphs and process the wikipedia dataset with it before training","body":"## Describe the bug\r\nWikipedia english dumps contain many wikipedia paragraphs like \"References\", \"Category:\" and \"See Also\" that should not be used for training.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nw = load_dataset('wikipedia', '20200501.en')\r\nprint(w['train'][0]['text'])\r\n```\r\n\r\n> 'Yangliuqing () is a market town in Xiqing District, in the western suburbs of Tianjin, People\\'s Republic of China. Despite its relatively small size, it has been named since 2006 in the \"famous historical and cultural market towns in China\".\\n\\nIt is best known in China for creating nianhua or Yangliuqing nianhua. For more than 400 years, Yangliuqing has in effect specialised in the creation of these woodcuts for the New Year.  wood block prints using vivid colourschemes to portray traditional scenes of children\\'s games often interwoven with auspiciouse objects.\\n\\n, it had 27 residential communities () and 25 villages under its administration.\\n\\nShi Family Grand Courtyard\\n\\nShi Family Grand Courtyard (Ti\u0101nj\u012bn Sh\u00ed Ji\u0101 D\u00e0 Yu\u00e0n, \u5929\u6d25\u77f3\u5bb6\u5927\u9662) is situated in Yangliuqing Town of Xiqing District, which is the former residence of wealthy merchant Shi Yuanshi - the 4th son of Shi Wancheng, one of the eight great masters in Tianjin. First built in 1875, it covers over 6,000 square meters, including large and small yards and over 200 folk houses, a theater and over 275 rooms that served as apartments and places of business and worship for this powerful family. Shifu Garden, which finished its expansion in October 2003, covers 1,200 square meters, incorporates the elegance of imperial garden and delicacy of south garden. Now the courtyard of Shi family covers about 10,000 square meters, which is called the first mansion in North China. Now it serves as the folk custom museum in Yangliuqing, which has a large collection of folk custom museum in Yanliuqing, which has a large collection of folk art pieces like Yanliuqing New Year pictures, brick sculpture.\\n\\nShi\\'s ancestor came from Dong\\'e County in Shandong Province, engaged in water transport of grain. As the wealth gradually accumulated, the Shi Family moved to Yangliuqing and bought large tracts of land and set up their residence. Shi Yuanshi came from the fourth generation of the family, who was a successful businessman and a good household manager, and the residence was thus enlarged for several times until it acquired the present scale. It is believed to be the first mansion in the west of Tianjin.\\n\\nThe residence is symmetric based on the axis formed by a passageway in the middle, on which there are four archways. On the east side of the courtyard, there are traditional single-story houses with rows of rooms around the four sides, which was once the living area for the Shi Family. The rooms on north side were the accountants\\' office. On the west are the major constructions including the family hall for worshipping Buddha, theater and the south reception room. On both sides of the residence are side yard rooms for maids and servants.\\n\\nToday, the Shi mansion, located in the township of Yangliuqing to the west of central Tianjin, stands as a surprisingly well-preserved monument to China\\'s pre-revolution mercantile spirit. It also serves as an on-location shoot for many of China\\'s popular historical dramas. Many of the rooms feature period furniture, paintings and calligraphy, and the extensive Shifu Garden.\\n\\nPart of the complex has been turned into the Yangliuqing Museum, which includes displays focused on symbolic aspects of the courtyards\\'  construction, local folk art and customs, and traditional period furnishings and crafts.\\n\\n**See also \\n\\nList of township-level divisions of Tianjin\\n\\nReferences \\n\\n http:\/\/arts.cultural-china.com\/en\/65Arts4795.html\\n\\nCategory:Towns in Tianjin'**\r\n\r\n## Expected results\r\nI expect no junk in the data.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":38,"text":"English wikipedia datasets is not clean \n ## Describe the bug\r\nWikipedia english dumps contain many wikipedia paragraphs like \"References\", \"Category:\" and \"See Also\" that should not be used for training.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nw = load_dataset('wikipedia', '20200501.en')\r\nprint(w['train'][0]['text'])\r\n```\r\n\r\n> 'Yangliuqing () is a market town in Xiqing District, in the western suburbs of Tianjin, People\\'s Republic of China. Despite its relatively small size, it has been named since 2006 in the \"famous historical and cultural market towns in China\".\\n\\nIt is best known in China for creating nianhua or Yangliuqing nianhua. For more than 400 years, Yangliuqing has in effect specialised in the creation of these woodcuts for the New Year.  wood block prints using vivid colourschemes to portray traditional scenes of children\\'s games often interwoven with auspiciouse objects.\\n\\n, it had 27 residential communities () and 25 villages under its administration.\\n\\nShi Family Grand Courtyard\\n\\nShi Family Grand Courtyard (Ti\u0101nj\u012bn Sh\u00ed Ji\u0101 D\u00e0 Yu\u00e0n, \u5929\u6d25\u77f3\u5bb6\u5927\u9662) is situated in Yangliuqing Town of Xiqing District, which is the former residence of wealthy merchant Shi Yuanshi - the 4th son of Shi Wancheng, one of the eight great masters in Tianjin. First built in 1875, it covers over 6,000 square meters, including large and small yards and over 200 folk houses, a theater and over 275 rooms that served as apartments and places of business and worship for this powerful family. Shifu Garden, which finished its expansion in October 2003, covers 1,200 square meters, incorporates the elegance of imperial garden and delicacy of south garden. Now the courtyard of Shi family covers about 10,000 square meters, which is called the first mansion in North China. Now it serves as the folk custom museum in Yangliuqing, which has a large collection of folk custom museum in Yanliuqing, which has a large collection of folk art pieces like Yanliuqing New Year pictures, brick sculpture.\\n\\nShi\\'s ancestor came from Dong\\'e County in Shandong Province, engaged in water transport of grain. As the wealth gradually accumulated, the Shi Family moved to Yangliuqing and bought large tracts of land and set up their residence. Shi Yuanshi came from the fourth generation of the family, who was a successful businessman and a good household manager, and the residence was thus enlarged for several times until it acquired the present scale. It is believed to be the first mansion in the west of Tianjin.\\n\\nThe residence is symmetric based on the axis formed by a passageway in the middle, on which there are four archways. On the east side of the courtyard, there are traditional single-story houses with rows of rooms around the four sides, which was once the living area for the Shi Family. The rooms on north side were the accountants\\' office. On the west are the major constructions including the family hall for worshipping Buddha, theater and the south reception room. On both sides of the residence are side yard rooms for maids and servants.\\n\\nToday, the Shi mansion, located in the township of Yangliuqing to the west of central Tianjin, stands as a surprisingly well-preserved monument to China\\'s pre-revolution mercantile spirit. It also serves as an on-location shoot for many of China\\'s popular historical dramas. Many of the rooms feature period furniture, paintings and calligraphy, and the extensive Shifu Garden.\\n\\nPart of the complex has been turned into the Yangliuqing Museum, which includes displays focused on symbolic aspects of the courtyards\\'  construction, local folk art and customs, and traditional period furnishings and crafts.\\n\\n**See also \\n\\nList of township-level divisions of Tianjin\\n\\nReferences \\n\\n http:\/\/arts.cultural-china.com\/en\/65Arts4795.html\\n\\nCategory:Towns in Tianjin'**\r\n\r\n## Expected results\r\nI expect no junk in the data.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n \n Hi ! Certain users might need these data (for training or simply to explore\/index the dataset).\r\n\r\nFeel free to implement a map function that gets rid of these paragraphs and process the wikipedia dataset with it before 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2761","title":"Error loading C4 realnewslike dataset","comments":"Hi @danshirron, \r\n`c4` was updated few days back by @lhoestq. The new configs are `['en', 'en.noclean', 'en.realnewslike', 'en.webtextlike'].` You'll need to remove any older version of this dataset you previously downloaded and then run `load_dataset` again with new configuration.","body":"## Describe the bug\r\nError loading C4 realnewslike dataset. Validation part mismatch\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)\r\n## Expected results\r\nsuccess on data loading\r\n## Actual results\r\nDownloading: 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15.3M\/15.3M [00:00<00:00, 28.1MB\/s]Traceback (most recent call last):                                                                                                                                                                                                                                             \r\n  File \"run_mlm_tf.py\", line 794, in <module>                                                                                                                                                                                                                                  \r\n    main()                                                                                                                                                                                                                                                                     \r\n  File \"run_mlm_tf.py\", line 425, in main                                                                                                                                                                                                                                      \r\n    raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)                                                                                                                                                           File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 843, in load_dataset                                                                                                                                                                         \r\n    builder_instance.download_and_prepare(                                                                                                                                                                                                                                     \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare                                                                                                                                                              \r\n    self._download_and_prepare(                                                                                                                                                                                                                                                \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 698, in _download_and_prepare                                                                                                                                                                 verify_splits(self.info.splits, split_dict)                                                                                                                                                                                                                                  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits                                                                                                                                                             \r\n    raise NonMatchingSplitsSizesError(str(bad_splits))                                                                                                                                                                                                                         \r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}] \r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1","comment_length":39,"text":"Error loading C4 realnewslike dataset \n ## Describe the bug\r\nError loading C4 realnewslike dataset. Validation part mismatch\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)\r\n## Expected results\r\nsuccess on data loading\r\n## Actual results\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 15.3M\/15.3M [00:00<00:00, 28.1MB\/s]Traceback (most recent call last):                                                                                                                                                                                                                                             \r\n  File \"run_mlm_tf.py\", line 794, in <module>                                                                                                                                                                                                                                  \r\n    main()                                                                                                                                                                                                                                                                     \r\n  File \"run_mlm_tf.py\", line 425, in main                                                                                                                                                                                                                                      \r\n    raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)                                                                                                                                                           File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 843, in load_dataset                                                                                                                                                                         \r\n    builder_instance.download_and_prepare(                                                                                                                                                                                                                                     \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare                                                                                                                                                              \r\n    self._download_and_prepare(                                                                                                                                                                                                                                                \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 698, in _download_and_prepare                                                                                                                                                                 verify_splits(self.info.splits, split_dict)                                                                                                                                                                                                                                  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits                                                                                                                                                             \r\n    raise NonMatchingSplitsSizesError(str(bad_splits))                                                                                                                                                                                                                         \r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}] \r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1 \n Hi @danshirron, \r\n`c4` was updated few days back by @lhoestq. The new configs are `['en', 'en.noclean', 'en.realnewslike', 'en.webtextlike'].` You'll need to remove any older version of this dataset you previously downloaded and then run `load_dataset` again with new configuration.","embeddings":[-0.2315260619,0.0246403441,-0.0223660674,0.4574569166,0.1744244695,0.2295811772,-0.0073711821,0.5081834197,-0.0316528082,0.0191251654,-0.1842969209,-0.0640127659,-0.0091638044,0.3338081241,-0.043365553,-0.2194700241,-0.1107666269,0.1606068611,0.1407687962,0.0597507246,-0.1448137611,0.2009049803,-0.2154413909,-0.0725571662,-0.2707114816,-0.0130072096,0.1758372933,0.3205577433,0.0850609317,-0.5288669467,0.2025581449,-0.1739532501,0.0258219484,0.5030124187,-0.0001112284,0.0486640595,0.4246914983,-0.0443259999,-0.3031130135,0.0802712366,-0.309867084,-0.242859751,0.144365117,-0.0320097134,-0.3313594162,-0.1901502609,-0.2336005718,-0.337558955,0.2493240535,0.6167075038,0.238580361,0.3121445775,0.0608730987,0.0165075231,-0.1109841093,-0.1475731134,-0.022677483,0.3512011766,-0.0215199199,0.031706702,0.1784890443,0.2144178748,0.028754618,0.1094985083,0.1285811663,-0.1285620183,0.2033363879,-0.1063502431,0.1449157298,0.3846841753,0.3563730419,-0.2420736998,-0.1519511491,-0.3080828488,-0.1986611784,-0.2773949504,0.256582588,0.1999745071,-0.162488386,0.1661835164,-0.3945821524,0.2987675369,0.0589543805,0.0859668255,-0.1932324171,0.2412162274,0.151707381,-0.0265462417,0.0266118441,0.0962596685,0.1632707119,-0.1978420466,-0.0648603812,0.2136579305,-0.6099298596,-0.2218074054,-0.3099172413,0.4977063537,-0.0144231617,-0.051483281,-0.1140118986,-0.0171039887,0.1712173671,-0.186304301,0.1688324213,0.2142098099,-0.198009029,0.1207100525,0.1434043944,-0.1067880541,0.0161335897,-0.0791779235,0.162792176,-0.1318754554,-0.0637650043,0.2420955002,0.0574146733,-0.317915082,-0.6927220821,0.2458004653,-0.3038004041,-0.036881227,0.0007450588,0.3466166258,-0.3842547238,0.2641151547,0.1001107544,-0.1724211723,-0.2799533308,-0.2688709497,-0.2056975961,0.0522305109,-0.3685680926,0.0012723056,0.2524729669,-0.2094730288,0.4003704488,-0.0511978604,0.1751805246,-0.0478972159,0.209127903,-0.3762721717,-0.1081526875,0.2255997509,-0.3169129789,0.037633013,0.2689840496,-0.0927065387,0.0103949495,-0.0342599787,0.1593452543,-0.3435829878,-0.1637990624,0.287226826,-0.0145503711,0.0288387816,0.0684598163,-0.0540249497,0.3395628631,-0.1157185137,-0.1957776248,-0.2394090295,0.0330544971,-0.058229614,0.1889537871,0.4896891117,-0.4754011929,0.0806836635,-0.3405704498,0.1221416891,0.4467144907,0.2935122252,-0.0330286399,0.276563555,-0.2077404857,0.0237064268,-0.0174367968,-0.3686727285,-0.3444623053,0.5175626874,-0.0822370797,0.345344156,-0.0547711328,0.043166969,0.1374017298,0.0436606482,0.1547780186,0.2047380656,-0.1418510228,0.1543143243,-0.562957108,-0.1220002472,0.3635740578,0.0133565171,0.1890151352,-0.2069269419,0.1235904247,-0.0846622884,0.4912214279,-0.0084104314,-0.1206199005,-0.0321739204,0.2111113369,-0.0240932405,0.1006423309,-0.2743466496,-0.5518809557,0.2668113708,-0.0237271488,0.152853027,0.1286136508,-0.046923276,-0.589758873,-0.1467690319,-0.1562295854,-0.2404751182,0.1061195433,0.1226577386,0.2428137511,0.0796359479,-0.0546249487,0.5485526323,-0.0577965789,0.1413292736,-0.0799551159,0.341332972,-0.1525628865,-0.0305092093,-0.0302448831,0.1346586496,0.2463358045,-0.09839461,-0.3567022681,0.6420468092,0.1248638704,0.2066819072,-0.4426276088,0.0053086914,0.3827438354,-0.3125794828,0.0957020447,0.5969313979,0.2378956974,0.0181498863,0.0953133032,0.424510628,-0.0290682651,-0.0525034592,0.1060230434,-0.1878120452,0.1633400321,-0.1469224691,0.200978145,-0.3520150185,0.4401290417,-0.086315088,0.3760469556,-0.0243641734,-0.1455283016,0.0485069752,0.4009242058,-0.0737881437,-0.0185004827,-0.1634851992,-0.1819177121,-0.1184910983,0.3020989597,0.3241461515,0.6374415755,0.1745533198,-0.2192538083,0.2381288111,-0.000430839,-0.2599272132,0.4831356406,-0.1653624773,0.0127727846,0.4583308399,0.008977511,0.1091809645,-0.4487721622,0.1450789571,0.123402968,0.1818011999,-0.360792309,0.0788164586,-0.215221718,0.0968676656,-0.3518635631,-0.2619572878,0.029402364,-0.313379705,-0.0178715326,0.1368231773,-0.0503815673,0.2308113575,-0.4672618806,0.0846562535,0.1124338806,-0.123098582,-0.2004061192,0.0737325698,-0.3612599671,0.0988924429,0.253513217,0.0678496435,0.2908097208,-0.1956795603,-0.1511840522,0.0241016988,-0.4036478996,0.1278229356,0.1180695146,0.2275419384,0.0969556868,0.0744806901,0.3463863432,-0.4121953249,-0.071068041,-0.1308414191,-0.2183184475,0.17054151,0.0332118087,0.0247350875,-0.1156041622,-0.5987548232,-0.4374191463,-0.3892827332,-0.0498279557,-0.1645166576,-0.0728507191,0.1988957822,-0.1343782842,0.0514261425,0.1836190671,-0.0720977336,-0.4009529352,0.2122821659,0.1445584893,-0.0392751917,-0.2241797745,-0.1384652853,-0.0772775933,-0.0527735427,-0.0458171144,-0.3023410439,0.254326731,-0.4559999704,0.2435563952,-0.1985964179,-0.213123858,0.2872216702,0.0318476632,-0.0354612023,-0.1347290725,-0.0344382711,-0.0722915977,-0.1071643755,0.1636640579,-0.1976882517,0.394243598,0.1363981962,0.4813589752,0.2416753024,-0.5439124703,0.0680660531,0.1705723852,0.2048651129,-0.0990280136,-0.3353740275,0.3906064034,-0.0682675168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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2761","title":"Error loading C4 realnewslike dataset","comments":"@bhavitvyamalik @lhoestq , just tried the above and got:\r\n>>> a=datasets.load_dataset('c4','en.realnewslike')\r\nDownloading: 3.29kB [00:00, 1.66MB\/s]                                                                                                                                                                                                                                          \r\nDownloading: 2.40MB [00:00, 12.6MB\/s]                                                                                                                                                                                                                                          \r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 819, in load_dataset\r\n    builder_instance = load_dataset_builder(\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 701, in load_dataset_builder\r\n    builder_instance: DatasetBuilder = builder_cls(\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1049, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 268, in __init__\r\n    self.config, self.config_id = self._create_builder_config(\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 360, in _create_builder_config\r\n    raise ValueError(\r\nValueError: BuilderConfig en.realnewslike not found. Available: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']\r\n>>> \r\n\r\ndatasets version is 1.11.0\r\n","body":"## Describe the bug\r\nError loading C4 realnewslike dataset. Validation part mismatch\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)\r\n## Expected results\r\nsuccess on data loading\r\n## Actual results\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 15.3M\/15.3M [00:00<00:00, 28.1MB\/s]Traceback (most recent call last):                                                                                                                                                                                                                                             \r\n  File \"run_mlm_tf.py\", line 794, in <module>                                                                                                                                                                                                                                  \r\n    main()                                                                                                                                                                                                                                                                     \r\n  File \"run_mlm_tf.py\", line 425, in main                                                                                                                                                                                                                                      \r\n    raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)                                                                                                                                                           File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 843, in load_dataset                                                                                                                                                                         \r\n    builder_instance.download_and_prepare(                                                                                                                                                                                                                                     \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare                                                                                                                                                              \r\n    self._download_and_prepare(                                                                                                                                                                                                                                                \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 698, in _download_and_prepare                                                                                                                                                                 verify_splits(self.info.splits, split_dict)                                                                                                                                                                                                                                  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits                                                                                                                                                             \r\n    raise NonMatchingSplitsSizesError(str(bad_splits))                                                                                                                                                                                                                         \r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}] \r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1","comment_length":91,"text":"Error loading C4 realnewslike dataset \n ## Describe the bug\r\nError loading C4 realnewslike dataset. Validation part mismatch\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)\r\n## Expected results\r\nsuccess on data loading\r\n## Actual results\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 15.3M\/15.3M [00:00<00:00, 28.1MB\/s]Traceback (most recent call last):                                                                                                                                                                                                                                             \r\n  File \"run_mlm_tf.py\", line 794, in <module>                                                                                                                                                                                                                                  \r\n    main()                                                                                                                                                                                                                                                                     \r\n  File \"run_mlm_tf.py\", line 425, in main                                                                                                                                                                                                                                      \r\n    raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)                                                                                                                                                           File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 843, in load_dataset                                                                                                                                                                         \r\n    builder_instance.download_and_prepare(                                                                                                                                                                                                                                     \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare                                                                                                                                                              \r\n    self._download_and_prepare(                                                                                                                                                                                                                                                \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 698, in _download_and_prepare                                                                                                                                                                 verify_splits(self.info.splits, split_dict)                                                                                                                                                                                                                                  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits                                                                                                                                                             \r\n    raise NonMatchingSplitsSizesError(str(bad_splits))                                                                                                                                                                                                                         \r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}] \r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1 \n @bhavitvyamalik @lhoestq , just tried the above and got:\r\n>>> a=datasets.load_dataset('c4','en.realnewslike')\r\nDownloading: 3.29kB [00:00, 1.66MB\/s]                                                                                                                                                                                                                                          \r\nDownloading: 2.40MB [00:00, 12.6MB\/s]                                                                                                                                                                                                                                          \r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 819, in load_dataset\r\n    builder_instance = load_dataset_builder(\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 701, in load_dataset_builder\r\n    builder_instance: DatasetBuilder = builder_cls(\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1049, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 268, in __init__\r\n    self.config, self.config_id = self._create_builder_config(\r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 360, in _create_builder_config\r\n    raise ValueError(\r\nValueError: BuilderConfig en.realnewslike not found. Available: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']\r\n>>> \r\n\r\ndatasets version is 1.11.0\r\n","embeddings":[-0.2315260619,0.0246403441,-0.0223660674,0.4574569166,0.1744244695,0.2295811772,-0.0073711821,0.5081834197,-0.0316528082,0.0191251654,-0.1842969209,-0.0640127659,-0.0091638044,0.3338081241,-0.043365553,-0.2194700241,-0.1107666269,0.1606068611,0.1407687962,0.0597507246,-0.1448137611,0.2009049803,-0.2154413909,-0.0725571662,-0.2707114816,-0.0130072096,0.1758372933,0.3205577433,0.0850609317,-0.5288669467,0.2025581449,-0.1739532501,0.0258219484,0.5030124187,-0.0001112284,0.0486640595,0.4246914983,-0.0443259999,-0.3031130135,0.0802712366,-0.309867084,-0.242859751,0.144365117,-0.0320097134,-0.3313594162,-0.1901502609,-0.2336005718,-0.337558955,0.2493240535,0.6167075038,0.238580361,0.3121445775,0.0608730987,0.0165075231,-0.1109841093,-0.1475731134,-0.022677483,0.3512011766,-0.0215199199,0.031706702,0.1784890443,0.2144178748,0.028754618,0.1094985083,0.1285811663,-0.1285620183,0.2033363879,-0.1063502431,0.1449157298,0.3846841753,0.3563730419,-0.2420736998,-0.1519511491,-0.3080828488,-0.1986611784,-0.2773949504,0.256582588,0.1999745071,-0.162488386,0.1661835164,-0.3945821524,0.2987675369,0.0589543805,0.0859668255,-0.1932324171,0.2412162274,0.151707381,-0.0265462417,0.0266118441,0.0962596685,0.1632707119,-0.1978420466,-0.0648603812,0.2136579305,-0.6099298596,-0.2218074054,-0.3099172413,0.4977063537,-0.0144231617,-0.051483281,-0.1140118986,-0.0171039887,0.1712173671,-0.186304301,0.1688324213,0.2142098099,-0.198009029,0.1207100525,0.1434043944,-0.1067880541,0.0161335897,-0.0791779235,0.162792176,-0.1318754554,-0.0637650043,0.2420955002,0.0574146733,-0.317915082,-0.6927220821,0.2458004653,-0.3038004041,-0.036881227,0.0007450588,0.3466166258,-0.3842547238,0.2641151547,0.1001107544,-0.1724211723,-0.2799533308,-0.2688709497,-0.2056975961,0.0522305109,-0.3685680926,0.0012723056,0.2524729669,-0.2094730288,0.4003704488,-0.0511978604,0.1751805246,-0.0478972159,0.209127903,-0.3762721717,-0.1081526875,0.2255997509,-0.3169129789,0.037633013,0.2689840496,-0.0927065387,0.0103949495,-0.0342599787,0.1593452543,-0.3435829878,-0.1637990624,0.287226826,-0.0145503711,0.0288387816,0.0684598163,-0.0540249497,0.3395628631,-0.1157185137,-0.1957776248,-0.2394090295,0.0330544971,-0.058229614,0.1889537871,0.4896891117,-0.4754011929,0.0806836635,-0.3405704498,0.1221416891,0.4467144907,0.2935122252,-0.0330286399,0.276563555,-0.2077404857,0.0237064268,-0.0174367968,-0.3686727285,-0.3444623053,0.5175626874,-0.0822370797,0.345344156,-0.0547711328,0.043166969,0.1374017298,0.0436606482,0.1547780186,0.2047380656,-0.1418510228,0.1543143243,-0.562957108,-0.1220002472,0.3635740578,0.0133565171,0.1890151352,-0.2069269419,0.1235904247,-0.0846622884,0.4912214279,-0.0084104314,-0.1206199005,-0.0321739204,0.2111113369,-0.0240932405,0.1006423309,-0.2743466496,-0.5518809557,0.2668113708,-0.0237271488,0.152853027,0.1286136508,-0.046923276,-0.589758873,-0.1467690319,-0.1562295854,-0.2404751182,0.1061195433,0.1226577386,0.2428137511,0.0796359479,-0.0546249487,0.5485526323,-0.0577965789,0.1413292736,-0.0799551159,0.341332972,-0.1525628865,-0.0305092093,-0.0302448831,0.1346586496,0.2463358045,-0.09839461,-0.3567022681,0.6420468092,0.1248638704,0.2066819072,-0.4426276088,0.0053086914,0.3827438354,-0.3125794828,0.0957020447,0.5969313979,0.2378956974,0.0181498863,0.0953133032,0.424510628,-0.0290682651,-0.0525034592,0.1060230434,-0.1878120452,0.1633400321,-0.1469224691,0.200978145,-0.3520150185,0.4401290417,-0.086315088,0.3760469556,-0.0243641734,-0.1455283016,0.0485069752,0.4009242058,-0.0737881437,-0.0185004827,-0.1634851992,-0.1819177121,-0.1184910983,0.3020989597,0.3241461515,0.6374415755,0.1745533198,-0.2192538083,0.2381288111,-0.000430839,-0.2599272132,0.4831356406,-0.1653624773,0.0127727846,0.4583308399,0.008977511,0.1091809645,-0.4487721622,0.1450789571,0.123402968,0.1818011999,-0.360792309,0.0788164586,-0.215221718,0.0968676656,-0.3518635631,-0.2619572878,0.029402364,-0.313379705,-0.0178715326,0.1368231773,-0.0503815673,0.2308113575,-0.4672618806,0.0846562535,0.1124338806,-0.123098582,-0.2004061192,0.0737325698,-0.3612599671,0.0988924429,0.253513217,0.0678496435,0.2908097208,-0.1956795603,-0.1511840522,0.0241016988,-0.4036478996,0.1278229356,0.1180695146,0.2275419384,0.0969556868,0.0744806901,0.3463863432,-0.4121953249,-0.071068041,-0.1308414191,-0.2183184475,0.17054151,0.0332118087,0.0247350875,-0.1156041622,-0.5987548232,-0.4374191463,-0.3892827332,-0.0498279557,-0.1645166576,-0.0728507191,0.1988957822,-0.1343782842,0.0514261425,0.1836190671,-0.0720977336,-0.4009529352,0.2122821659,0.1445584893,-0.0392751917,-0.2241797745,-0.1384652853,-0.0772775933,-0.0527735427,-0.0458171144,-0.3023410439,0.254326731,-0.4559999704,0.2435563952,-0.1985964179,-0.213123858,0.2872216702,0.0318476632,-0.0354612023,-0.1347290725,-0.0344382711,-0.0722915977,-0.1071643755,0.1636640579,-0.1976882517,0.394243598,0.1363981962,0.4813589752,0.2416753024,-0.5439124703,0.0680660531,0.1705723852,0.2048651129,-0.0990280136,-0.3353740275,0.3906064034,-0.0682675168,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2761","title":"Error loading C4 realnewslike dataset","comments":"I think I had an older version of datasets installed and that's why I commented the old configurations in my last comment, my bad! I re-checked and updated it to latest version (`datasets==1.11.0`) and it's showing `available configs: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']`. \r\n\r\nI tried `raw_datasets = load_dataset('c4', 'realnewslike')` and the download started. Make sure you don't have any old copy of this dataset and you download it fresh using the latest version of datasets. Sorry for the mix up!","body":"## Describe the bug\r\nError loading C4 realnewslike dataset. Validation part mismatch\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)\r\n## Expected results\r\nsuccess on data loading\r\n## Actual results\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 15.3M\/15.3M [00:00<00:00, 28.1MB\/s]Traceback (most recent call last):                                                                                                                                                                                                                                             \r\n  File \"run_mlm_tf.py\", line 794, in <module>                                                                                                                                                                                                                                  \r\n    main()                                                                                                                                                                                                                                                                     \r\n  File \"run_mlm_tf.py\", line 425, in main                                                                                                                                                                                                                                      \r\n    raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)                                                                                                                                                           File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 843, in load_dataset                                                                                                                                                                         \r\n    builder_instance.download_and_prepare(                                                                                                                                                                                                                                     \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare                                                                                                                                                              \r\n    self._download_and_prepare(                                                                                                                                                                                                                                                \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 698, in _download_and_prepare                                                                                                                                                                 verify_splits(self.info.splits, split_dict)                                                                                                                                                                                                                                  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits                                                                                                                                                             \r\n    raise NonMatchingSplitsSizesError(str(bad_splits))                                                                                                                                                                                                                         \r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}] \r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1","comment_length":79,"text":"Error loading C4 realnewslike dataset \n ## Describe the bug\r\nError loading C4 realnewslike dataset. Validation part mismatch\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)\r\n## Expected results\r\nsuccess on data loading\r\n## Actual results\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 15.3M\/15.3M [00:00<00:00, 28.1MB\/s]Traceback (most recent call last):                                                                                                                                                                                                                                             \r\n  File \"run_mlm_tf.py\", line 794, in <module>                                                                                                                                                                                                                                  \r\n    main()                                                                                                                                                                                                                                                                     \r\n  File \"run_mlm_tf.py\", line 425, in main                                                                                                                                                                                                                                      \r\n    raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)                                                                                                                                                           File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 843, in load_dataset                                                                                                                                                                         \r\n    builder_instance.download_and_prepare(                                                                                                                                                                                                                                     \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare                                                                                                                                                              \r\n    self._download_and_prepare(                                                                                                                                                                                                                                                \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 698, in _download_and_prepare                                                                                                                                                                 verify_splits(self.info.splits, split_dict)                                                                                                                                                                                                                                  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits                                                                                                                                                             \r\n    raise NonMatchingSplitsSizesError(str(bad_splits))                                                                                                                                                                                                                         \r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}] \r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1 \n I think I had an older version of datasets installed and that's why I commented the old configurations in my last comment, my bad! I re-checked and updated it to latest version (`datasets==1.11.0`) and it's showing `available configs: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']`. \r\n\r\nI tried `raw_datasets = load_dataset('c4', 'realnewslike')` and the download started. Make sure you don't have any old copy of this dataset and you download it fresh using the latest version of datasets. Sorry for the mix up!","embeddings":[-0.2315260619,0.0246403441,-0.0223660674,0.4574569166,0.1744244695,0.2295811772,-0.0073711821,0.5081834197,-0.0316528082,0.0191251654,-0.1842969209,-0.0640127659,-0.0091638044,0.3338081241,-0.043365553,-0.2194700241,-0.1107666269,0.1606068611,0.1407687962,0.0597507246,-0.1448137611,0.2009049803,-0.2154413909,-0.0725571662,-0.2707114816,-0.0130072096,0.1758372933,0.3205577433,0.0850609317,-0.5288669467,0.2025581449,-0.1739532501,0.0258219484,0.5030124187,-0.0001112284,0.0486640595,0.4246914983,-0.0443259999,-0.3031130135,0.0802712366,-0.309867084,-0.242859751,0.144365117,-0.0320097134,-0.3313594162,-0.1901502609,-0.2336005718,-0.337558955,0.2493240535,0.6167075038,0.238580361,0.3121445775,0.0608730987,0.0165075231,-0.1109841093,-0.1475731134,-0.022677483,0.3512011766,-0.0215199199,0.031706702,0.1784890443,0.2144178748,0.028754618,0.1094985083,0.1285811663,-0.1285620183,0.2033363879,-0.1063502431,0.1449157298,0.3846841753,0.3563730419,-0.2420736998,-0.1519511491,-0.3080828488,-0.1986611784,-0.2773949504,0.256582588,0.1999745071,-0.162488386,0.1661835164,-0.3945821524,0.2987675369,0.0589543805,0.0859668255,-0.1932324171,0.2412162274,0.151707381,-0.0265462417,0.0266118441,0.0962596685,0.1632707119,-0.1978420466,-0.0648603812,0.2136579305,-0.6099298596,-0.2218074054,-0.3099172413,0.4977063537,-0.0144231617,-0.051483281,-0.1140118986,-0.0171039887,0.1712173671,-0.186304301,0.1688324213,0.2142098099,-0.198009029,0.1207100525,0.1434043944,-0.1067880541,0.0161335897,-0.0791779235,0.162792176,-0.1318754554,-0.0637650043,0.2420955002,0.0574146733,-0.317915082,-0.6927220821,0.2458004653,-0.3038004041,-0.036881227,0.0007450588,0.3466166258,-0.3842547238,0.2641151547,0.1001107544,-0.1724211723,-0.2799533308,-0.2688709497,-0.2056975961,0.0522305109,-0.3685680926,0.0012723056,0.2524729669,-0.2094730288,0.4003704488,-0.0511978604,0.1751805246,-0.0478972159,0.209127903,-0.3762721717,-0.1081526875,0.2255997509,-0.3169129789,0.037633013,0.2689840496,-0.0927065387,0.0103949495,-0.0342599787,0.1593452543,-0.3435829878,-0.1637990624,0.287226826,-0.0145503711,0.0288387816,0.0684598163,-0.0540249497,0.3395628631,-0.1157185137,-0.1957776248,-0.2394090295,0.0330544971,-0.058229614,0.1889537871,0.4896891117,-0.4754011929,0.0806836635,-0.3405704498,0.1221416891,0.4467144907,0.2935122252,-0.0330286399,0.276563555,-0.2077404857,0.0237064268,-0.0174367968,-0.3686727285,-0.3444623053,0.5175626874,-0.0822370797,0.345344156,-0.0547711328,0.043166969,0.1374017298,0.0436606482,0.1547780186,0.2047380656,-0.1418510228,0.1543143243,-0.562957108,-0.1220002472,0.3635740578,0.0133565171,0.1890151352,-0.2069269419,0.1235904247,-0.0846622884,0.4912214279,-0.0084104314,-0.1206199005,-0.0321739204,0.2111113369,-0.0240932405,0.1006423309,-0.2743466496,-0.5518809557,0.2668113708,-0.0237271488,0.152853027,0.1286136508,-0.046923276,-0.589758873,-0.1467690319,-0.1562295854,-0.2404751182,0.1061195433,0.1226577386,0.2428137511,0.0796359479,-0.0546249487,0.5485526323,-0.0577965789,0.1413292736,-0.0799551159,0.341332972,-0.1525628865,-0.0305092093,-0.0302448831,0.1346586496,0.2463358045,-0.09839461,-0.3567022681,0.6420468092,0.1248638704,0.2066819072,-0.4426276088,0.0053086914,0.3827438354,-0.3125794828,0.0957020447,0.5969313979,0.2378956974,0.0181498863,0.0953133032,0.424510628,-0.0290682651,-0.0525034592,0.1060230434,-0.1878120452,0.1633400321,-0.1469224691,0.200978145,-0.3520150185,0.4401290417,-0.086315088,0.3760469556,-0.0243641734,-0.1455283016,0.0485069752,0.4009242058,-0.0737881437,-0.0185004827,-0.1634851992,-0.1819177121,-0.1184910983,0.3020989597,0.3241461515,0.6374415755,0.1745533198,-0.2192538083,0.2381288111,-0.000430839,-0.2599272132,0.4831356406,-0.1653624773,0.0127727846,0.4583308399,0.008977511,0.1091809645,-0.4487721622,0.1450789571,0.123402968,0.1818011999,-0.360792309,0.0788164586,-0.215221718,0.0968676656,-0.3518635631,-0.2619572878,0.029402364,-0.313379705,-0.0178715326,0.1368231773,-0.0503815673,0.2308113575,-0.4672618806,0.0846562535,0.1124338806,-0.123098582,-0.2004061192,0.0737325698,-0.3612599671,0.0988924429,0.253513217,0.0678496435,0.2908097208,-0.1956795603,-0.1511840522,0.0241016988,-0.4036478996,0.1278229356,0.1180695146,0.2275419384,0.0969556868,0.0744806901,0.3463863432,-0.4121953249,-0.071068041,-0.1308414191,-0.2183184475,0.17054151,0.0332118087,0.0247350875,-0.1156041622,-0.5987548232,-0.4374191463,-0.3892827332,-0.0498279557,-0.1645166576,-0.0728507191,0.1988957822,-0.1343782842,0.0514261425,0.1836190671,-0.0720977336,-0.4009529352,0.2122821659,0.1445584893,-0.0392751917,-0.2241797745,-0.1384652853,-0.0772775933,-0.0527735427,-0.0458171144,-0.3023410439,0.254326731,-0.4559999704,0.2435563952,-0.1985964179,-0.213123858,0.2872216702,0.0318476632,-0.0354612023,-0.1347290725,-0.0344382711,-0.0722915977,-0.1071643755,0.1636640579,-0.1976882517,0.394243598,0.1363981962,0.4813589752,0.2416753024,-0.5439124703,0.0680660531,0.1705723852,0.2048651129,-0.0990280136,-0.3353740275,0.3906064034,-0.0682675168,0.02395800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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2761","title":"Error loading C4 realnewslike dataset","comments":"It works. I probably had some issue with the cache. after cleaning it im able to download the dataset. Thanks","body":"## Describe the bug\r\nError loading C4 realnewslike dataset. Validation part mismatch\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)\r\n## Expected results\r\nsuccess on data loading\r\n## Actual results\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 15.3M\/15.3M [00:00<00:00, 28.1MB\/s]Traceback (most recent call last):                                                                                                                                                                                                                                             \r\n  File \"run_mlm_tf.py\", line 794, in <module>                                                                                                                                                                                                                                  \r\n    main()                                                                                                                                                                                                                                                                     \r\n  File \"run_mlm_tf.py\", line 425, in main                                                                                                                                                                                                                                      \r\n    raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)                                                                                                                                                           File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 843, in load_dataset                                                                                                                                                                         \r\n    builder_instance.download_and_prepare(                                                                                                                                                                                                                                     \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare                                                                                                                                                              \r\n    self._download_and_prepare(                                                                                                                                                                                                                                                \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 698, in _download_and_prepare                                                                                                                                                                 verify_splits(self.info.splits, split_dict)                                                                                                                                                                                                                                  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits                                                                                                                                                             \r\n    raise NonMatchingSplitsSizesError(str(bad_splits))                                                                                                                                                                                                                         \r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}] \r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1","comment_length":20,"text":"Error loading C4 realnewslike dataset \n ## Describe the bug\r\nError loading C4 realnewslike dataset. Validation part mismatch\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)\r\n## Expected results\r\nsuccess on data loading\r\n## Actual results\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 15.3M\/15.3M [00:00<00:00, 28.1MB\/s]Traceback (most recent call last):                                                                                                                                                                                                                                             \r\n  File \"run_mlm_tf.py\", line 794, in <module>                                                                                                                                                                                                                                  \r\n    main()                                                                                                                                                                                                                                                                     \r\n  File \"run_mlm_tf.py\", line 425, in main                                                                                                                                                                                                                                      \r\n    raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)                                                                                                                                                           File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 843, in load_dataset                                                                                                                                                                         \r\n    builder_instance.download_and_prepare(                                                                                                                                                                                                                                     \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 608, in download_and_prepare                                                                                                                                                              \r\n    self._download_and_prepare(                                                                                                                                                                                                                                                \r\n  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 698, in _download_and_prepare                                                                                                                                                                 verify_splits(self.info.splits, split_dict)                                                                                                                                                                                                                                  File \"\/home\/dshirron\/.local\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits                                                                                                                                                             \r\n    raise NonMatchingSplitsSizesError(str(bad_splits))                                                                                                                                                                                                                         \r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}] \r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1 \n It works. I probably had some issue with the cache. after cleaning it im able to download the dataset. Thanks","embeddings":[-0.2315260619,0.0246403441,-0.0223660674,0.4574569166,0.1744244695,0.2295811772,-0.0073711821,0.5081834197,-0.0316528082,0.0191251654,-0.1842969209,-0.0640127659,-0.0091638044,0.3338081241,-0.043365553,-0.2194700241,-0.1107666269,0.1606068611,0.1407687962,0.0597507246,-0.1448137611,0.2009049803,-0.2154413909,-0.0725571662,-0.2707114816,-0.0130072096,0.1758372933,0.3205577433,0.0850609317,-0.5288669467,0.2025581449,-0.1739532501,0.0258219484,0.5030124187,-0.0001112284,0.0486640595,0.4246914983,-0.0443259999,-0.3031130135,0.0802712366,-0.309867084,-0.242859751,0.144365117,-0.0320097134,-0.3313594162,-0.1901502609,-0.2336005718,-0.337558955,0.2493240535,0.6167075038,0.238580361,0.3121445775,0.0608730987,0.0165075231,-0.1109841093,-0.1475731134,-0.022677483,0.3512011766,-0.0215199199,0.031706702,0.1784890443,0.2144178748,0.028754618,0.1094985083,0.1285811663,-0.1285620183,0.2033363879,-0.1063502431,0.1449157298,0.3846841753,0.3563730419,-0.2420736998,-0.1519511491,-0.3080828488,-0.1986611784,-0.2773949504,0.256582588,0.1999745071,-0.162488386,0.1661835164,-0.3945821524,0.2987675369,0.0589543805,0.0859668255,-0.1932324171,0.2412162274,0.151707381,-0.0265462417,0.0266118441,0.0962596685,0.1632707119,-0.1978420466,-0.0648603812,0.2136579305,-0.6099298596,-0.2218074054,-0.3099172413,0.4977063537,-0.0144231617,-0.051483281,-0.1140118986,-0.0171039887,0.1712173671,-0.186304301,0.1688324213,0.2142098099,-0.198009029,0.1207100525,0.1434043944,-0.1067880541,0.0161335897,-0.0791779235,0.162792176,-0.1318754554,-0.0637650043,0.2420955002,0.0574146733,-0.317915082,-0.6927220821,0.2458004653,-0.3038004041,-0.036881227,0.0007450588,0.3466166258,-0.3842547238,0.2641151547,0.1001107544,-0.1724211723,-0.2799533308,-0.2688709497,-0.2056975961,0.0522305109,-0.3685680926,0.0012723056,0.2524729669,-0.2094730288,0.4003704488,-0.0511978604,0.1751805246,-0.0478972159,0.209127903,-0.3762721717,-0.1081526875,0.2255997509,-0.3169129789,0.037633013,0.2689840496,-0.0927065387,0.0103949495,-0.0342599787,0.1593452543,-0.3435829878,-0.1637990624,0.287226826,-0.0145503711,0.0288387816,0.0684598163,-0.0540249497,0.3395628631,-0.1157185137,-0.1957776248,-0.2394090295,0.0330544971,-0.058229614,0.1889537871,0.4896891117,-0.4754011929,0.0806836635,-0.3405704498,0.1221416891,0.4467144907,0.2935122252,-0.0330286399,0.276563555,-0.2077404857,0.0237064268,-0.0174367968,-0.3686727285,-0.3444623053,0.5175626874,-0.0822370797,0.345344156,-0.0547711328,0.043166969,0.1374017298,0.0436606482,0.1547780186,0.2047380656,-0.1418510228,0.1543143243,-0.562957108,-0.1220002472,0.3635740578,0.0133565171,0.1890151352,-0.2069269419,0.1235904247,-0.0846622884,0.4912214279,-0.0084104314,-0.1206199005,-0.0321739204,0.2111113369,-0.0240932405,0.1006423309,-0.2743466496,-0.5518809557,0.2668113708,-0.0237271488,0.152853027,0.1286136508,-0.046923276,-0.589758873,-0.1467690319,-0.1562295854,-0.2404751182,0.1061195433,0.1226577386,0.2428137511,0.0796359479,-0.0546249487,0.5485526323,-0.0577965789,0.1413292736,-0.0799551159,0.341332972,-0.1525628865,-0.0305092093,-0.0302448831,0.1346586496,0.2463358045,-0.09839461,-0.3567022681,0.6420468092,0.1248638704,0.2066819072,-0.4426276088,0.0053086914,0.3827438354,-0.3125794828,0.0957020447,0.5969313979,0.2378956974,0.0181498863,0.0953133032,0.424510628,-0.0290682651,-0.0525034592,0.1060230434,-0.1878120452,0.1633400321,-0.1469224691,0.200978145,-0.3520150185,0.4401290417,-0.086315088,0.3760469556,-0.0243641734,-0.1455283016,0.0485069752,0.4009242058,-0.0737881437,-0.0185004827,-0.1634851992,-0.1819177121,-0.1184910983,0.3020989597,0.3241461515,0.6374415755,0.1745533198,-0.2192538083,0.2381288111,-0.000430839,-0.2599272132,0.4831356406,-0.1653624773,0.0127727846,0.4583308399,0.008977511,0.1091809645,-0.4487721622,0.1450789571,0.123402968,0.1818011999,-0.360792309,0.0788164586,-0.215221718,0.0968676656,-0.3518635631,-0.2619572878,0.029402364,-0.313379705,-0.0178715326,0.1368231773,-0.0503815673,0.2308113575,-0.4672618806,0.0846562535,0.1124338806,-0.123098582,-0.2004061192,0.0737325698,-0.3612599671,0.0988924429,0.253513217,0.0678496435,0.2908097208,-0.1956795603,-0.1511840522,0.0241016988,-0.4036478996,0.1278229356,0.1180695146,0.2275419384,0.0969556868,0.0744806901,0.3463863432,-0.4121953249,-0.071068041,-0.1308414191,-0.2183184475,0.17054151,0.0332118087,0.0247350875,-0.1156041622,-0.5987548232,-0.4374191463,-0.3892827332,-0.0498279557,-0.1645166576,-0.0728507191,0.1988957822,-0.1343782842,0.0514261425,0.1836190671,-0.0720977336,-0.4009529352,0.2122821659,0.1445584893,-0.0392751917,-0.2241797745,-0.1384652853,-0.0772775933,-0.0527735427,-0.0458171144,-0.3023410439,0.254326731,-0.4559999704,0.2435563952,-0.1985964179,-0.213123858,0.2872216702,0.0318476632,-0.0354612023,-0.1347290725,-0.0344382711,-0.0722915977,-0.1071643755,0.1636640579,-0.1976882517,0.394243598,0.1363981962,0.4813589752,0.2416753024,-0.5439124703,0.0680660531,0.1705723852,0.2048651129,-0.0990280136,-0.3353740275,0.3906064034,-0.0682675168,0.02395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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2759","title":"the meteor metric seems not consist with the official version","comments":"the issue is caused by the differences between varied meteor versions:\r\nmeteor1.0 is for https:\/\/aclanthology.org\/W07-0734.pdf\r\nmeteor1.5 is for https:\/\/aclanthology.org\/W14-3348.pdf\r\n\r\nhere is a very similar issue in NLTK\r\nhttps:\/\/github.com\/nltk\/nltk\/issues\/2655","body":"## Describe the bug\r\nThe computed meteor score seems strange because the value is very different from the scores computed by other tools. For example, I use the meteor score computed by [NLGeval](https:\/\/github.com\/Maluuba\/nlg-eval) as the reference (which reuses the official jar file for the computation)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_metric\r\nfrom nlgeval import NLGEval, compute_individual_metrics\r\n\r\nmeteor = load_metric('meteor')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = meteor.compute(predictions=predictions, references=references)\r\n# print the actual result\r\nprint(round(results[\"meteor\"], 4))\r\nmetrics_dict = compute_individual_metrics(references, predictions[0])\r\n# print the expected result\r\nprint(round(metrics_dict[\"METEOR\"], 4))\r\n```\r\nBy the way, you need to install the `nlg-eval` library first. Please check the installation guide [here](https:\/\/github.com\/Maluuba\/nlg-eval#setup), thanks!\r\n\r\n## Expected results\r\n`0.4474`\r\n\r\n## Actual results\r\n`0.7398`\r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 4.0.1\r\n","comment_length":28,"text":"the meteor metric seems not consist with the official version \n ## Describe the bug\r\nThe computed meteor score seems strange because the value is very different from the scores computed by other tools. For example, I use the meteor score computed by [NLGeval](https:\/\/github.com\/Maluuba\/nlg-eval) as the reference (which reuses the official jar file for the computation)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_metric\r\nfrom nlgeval import NLGEval, compute_individual_metrics\r\n\r\nmeteor = load_metric('meteor')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = meteor.compute(predictions=predictions, references=references)\r\n# print the actual result\r\nprint(round(results[\"meteor\"], 4))\r\nmetrics_dict = compute_individual_metrics(references, predictions[0])\r\n# print the expected result\r\nprint(round(metrics_dict[\"METEOR\"], 4))\r\n```\r\nBy the way, you need to install the `nlg-eval` library first. Please check the installation guide [here](https:\/\/github.com\/Maluuba\/nlg-eval#setup), thanks!\r\n\r\n## Expected results\r\n`0.4474`\r\n\r\n## Actual results\r\n`0.7398`\r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 4.0.1\r\n \n the issue is caused by the differences between varied meteor versions:\r\nmeteor1.0 is for https:\/\/aclanthology.org\/W07-0734.pdf\r\nmeteor1.5 is for https:\/\/aclanthology.org\/W14-3348.pdf\r\n\r\nhere is a very similar issue in NLTK\r\nhttps:\/\/github.com\/nltk\/nltk\/issues\/2655","embeddings":[-0.1358162314,-0.2349779308,-0.0059329974,0.4061324,0.1851489544,-0.2260374576,-0.1716093123,0.2357305288,-0.0438345596,0.0740853623,0.0668207631,0.3859927952,-0.0958541557,-0.4473044574,-0.0392022952,-0.0029859832,-0.0017699267,-0.2732459307,-0.1561685205,-0.1142284125,-0.0879566446,0.2557639182,-0.0586425364,0.0983389989,0.1512336284,0.2146469653,0.2339310795,-0.0853132606,-0.0735771358,-0.2372512668,0.4464174807,-0.2547119856,0.1061593369,0.5718839765,-0.0001092228,-0.1097544208,0.5471622348,-0.0057448437,-0.1640698314,-0.3211094439,-0.2009696811,-0.3649702668,0.3099010885,-0.2298337817,-0.1332615465,0.0013612013,-0.2734860778,-0.0977881551,0.5416399837,0.2425263971,0.2468080968,-0.0707297027,-0.032946784,-0.0735689253,0.0056162365,0.0035616672,-0.1855246723,0.4780148864,-0.2764458358,0.0538799241,-0.0193888284,0.1980319619,0.192500025,0.288261801,0.3047470748,-0.0374400355,0.205665499,0.1865397841,0.1254914254,0.2122563273,0.2753864527,-0.3759817779,-0.3653778434,0.1167431921,-0.0383625068,-0.4420903921,0.1010578945,0.0597524419,0.0617230311,-0.2433200181,-0.417296648,0.1959596425,-0.1231979579,0.1582983881,-0.0885476321,0.4110731184,-0.1813751906,0.1683813632,-0.1701655388,0.0795141235,0.1006190255,-0.1356270909,-0.0568454787,-0.0334441997,-0.1606684327,0.0442760922,-0.1364964545,-0.1545810848,0.0622734055,0.0040408052,0.2865042686,0.0556127653,0.4499130845,-0.0662461743,-0.083218731,0.1952988952,0.4034390152,-0.0914597362,0.2229097635,0.2841408253,0.071210146,0.0319353864,0.1968729347,-0.2307173014,0.5221300125,0.1731864214,-0.0789206848,-0.2163648307,-0.3225902021,0.0125966817,0.0460242592,0.0061025503,0.0650951937,-0.2966371477,-0.1263106912,-0.1950011104,0.0078040059,0.1437561363,-0.2404645085,-0.0113249114,-0.2011920661,0.1084214449,-0.1019063964,0.3199006915,-0.0643684641,0.1869410425,0.377910465,-0.0284285676,0.2165042758,-0.2593049705,-0.1498461068,-0.0044140611,-0.0484350361,0.0778284371,-0.1474400461,0.2168372571,0.2294588089,-0.2374290079,-0.1664121747,-0.0783710554,-0.0966554061,-0.2084736526,0.1554417312,0.2021888196,-0.3552140296,0.0330935195,-0.0279508755,0.1025886759,0.0691780522,-0.0243306719,0.0344141945,-0.2691453397,-0.2135478854,-0.0733912811,0.3713421822,-0.00008299,-0.2837857902,-0.0432695337,-0.3907486498,0.3113590181,0.109224923,-0.1440795809,-0.0074118418,0.0101120882,-0.1593013555,-0.4902365506,0.383169353,-0.2884459496,-0.3212443292,-0.1635175347,0.273311615,0.2212444246,-0.0917895138,0.0542549081,-0.2431739569,-0.0811820924,0.3172591031,-0.0166001562,0.3480672836,-0.1355618834,-0.5385566354,-0.0393849127,0.1130519435,0.0065353308,0.3192085624,-0.4252249002,0.1794968545,0.2294232994,0.0155098522,0.1722091585,0.0421790294,0.0602997728,0.0968743935,-0.4579890966,0.0290459003,-0.2238137126,-0.0304726809,0.2628813088,-0.204327181,-0.0593313463,0.1946983635,-0.0019937321,-0.5488238931,-0.058180809,-0.0294286348,-0.1989923418,0.1879660785,0.1149051413,-0.0626429021,-0.0436301716,0.0775676966,-0.1338417977,-0.3600866795,-0.0965371579,-0.0805342868,-0.0284555368,-0.1556739807,-0.1163060144,0.3951698244,0.2015009224,0.2209815085,-0.0824580118,-0.254796654,0.3142406344,0.2135628462,0.075458698,0.215813756,0.085991405,-0.1157252565,-0.2263933122,-0.1310671717,0.3775625229,0.1458840221,0.1624935418,-0.1309821159,0.3952777386,0.3242181242,0.093959786,-0.0954202637,-0.1156595871,-0.0092390869,-0.0147527177,-0.1969424337,-0.2568131983,0.1512787044,-0.0860581547,-0.021663608,-0.1682965606,0.2429612726,-0.1447513402,0.3565318286,0.0545579791,0.229637295,0.1990041733,-0.2496376783,-0.2357937098,-0.212897718,-0.162734434,0.3426095545,0.0317688026,0.0964511633,0.0694159269,-0.2506141961,-0.3128019273,0.1340085268,0.290913552,-0.0806699321,0.3633867204,0.3177281618,0.0116890101,-0.1174107641,0.2528041005,-0.0544438027,0.0673096851,-0.0671748221,0.2033380419,-0.2143352032,0.2841469049,-0.4203428626,-0.507707417,-0.6033649445,-0.4780286551,0.0246390644,-0.0339770317,0.2161393613,0.1809400916,0.3163756132,0.1196284816,0.0307187699,0.1664606631,-0.0013496839,-0.3262858391,-0.4050019383,0.1059098169,-0.0300928317,-0.0299215578,0.342389971,-0.1375697702,0.1093680933,-0.1515025944,-0.4955455661,0.02520556,-0.0457470231,0.7252534032,0.1718586832,-0.4527870119,0.0372483842,0.3458481431,0.1602809876,-0.3668874204,0.1735024303,0.2218369693,-0.2250281572,-0.2084215581,-0.0190014392,-0.22030209,0.1442066282,-0.1351414174,-0.2315960824,0.348626852,0.07076215,0.2468064278,-0.1577878743,0.1344600469,0.1748230159,0.3467097878,-0.2913876176,-0.1006377265,0.2914351225,-0.1825645417,-0.329429239,-0.0032992463,0.1110964864,0.4584417343,0.0616888627,-0.2635655701,-0.6254201531,-0.028324496,-0.0230475776,-0.0609051622,-0.1638613194,0.0816947371,-0.2560128272,-0.0483233407,-0.2598661482,-0.2592063546,-0.0988845974,0.1421607584,0.363828063,-0.1304811835,0.0940264612,0.1602395475,0.63237679,0.2696518302,-0.1276919395,-0.1066972688,0.1169157997,0.104596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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2759","title":"the meteor metric seems not consist with the official version","comments":"Hi @jianguda, thanks for reporting.\r\n\r\nCurrently, at \ud83e\udd17 `datasets` we are using METEOR 1.0 (indeed using NLTK: `from nltk.translate import meteor_score`): See the [citation here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/meteor\/meteor.py#L23-L35).\r\n\r\nIf there is some open source implementation of METEOR 1.5, that could be an interesting contribution! \ud83d\ude09 ","body":"## Describe the bug\r\nThe computed meteor score seems strange because the value is very different from the scores computed by other tools. For example, I use the meteor score computed by [NLGeval](https:\/\/github.com\/Maluuba\/nlg-eval) as the reference (which reuses the official jar file for the computation)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_metric\r\nfrom nlgeval import NLGEval, compute_individual_metrics\r\n\r\nmeteor = load_metric('meteor')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = meteor.compute(predictions=predictions, references=references)\r\n# print the actual result\r\nprint(round(results[\"meteor\"], 4))\r\nmetrics_dict = compute_individual_metrics(references, predictions[0])\r\n# print the expected result\r\nprint(round(metrics_dict[\"METEOR\"], 4))\r\n```\r\nBy the way, you need to install the `nlg-eval` library first. Please check the installation guide [here](https:\/\/github.com\/Maluuba\/nlg-eval#setup), thanks!\r\n\r\n## Expected results\r\n`0.4474`\r\n\r\n## Actual results\r\n`0.7398`\r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 4.0.1\r\n","comment_length":42,"text":"the meteor metric seems not consist with the official version \n ## Describe the bug\r\nThe computed meteor score seems strange because the value is very different from the scores computed by other tools. For example, I use the meteor score computed by [NLGeval](https:\/\/github.com\/Maluuba\/nlg-eval) as the reference (which reuses the official jar file for the computation)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_metric\r\nfrom nlgeval import NLGEval, compute_individual_metrics\r\n\r\nmeteor = load_metric('meteor')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = meteor.compute(predictions=predictions, references=references)\r\n# print the actual result\r\nprint(round(results[\"meteor\"], 4))\r\nmetrics_dict = compute_individual_metrics(references, predictions[0])\r\n# print the expected result\r\nprint(round(metrics_dict[\"METEOR\"], 4))\r\n```\r\nBy the way, you need to install the `nlg-eval` library first. Please check the installation guide [here](https:\/\/github.com\/Maluuba\/nlg-eval#setup), thanks!\r\n\r\n## Expected results\r\n`0.4474`\r\n\r\n## Actual results\r\n`0.7398`\r\n\r\n## Environment info\r\n- `datasets` version: 1.10.2\r\n- Platform: macOS-10.16-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 4.0.1\r\n \n Hi @jianguda, thanks for reporting.\r\n\r\nCurrently, at \ud83e\udd17 `datasets` we are using METEOR 1.0 (indeed using NLTK: `from nltk.translate import meteor_score`): See the [citation here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/meteor\/meteor.py#L23-L35).\r\n\r\nIf there is some open source implementation of METEOR 1.5, that could be an interesting contribution! \ud83d\ude09 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2757","title":"Unexpected type after `concatenate_datasets`","comments":"Hi @JulesBelveze, thanks for your question.\r\n\r\nNote that \ud83e\udd17  `datasets` internally store their data in Apache Arrow format.\r\n\r\nHowever, when accessing dataset columns, by default they are returned as native Python objects (lists in this case).\r\n\r\nIf you would like their columns to be returned in a more suitable format for your use case (torch arrays), you can use the method `set_format()`:\r\n```python\r\nconcat_dataset.set_format(type=\"torch\")\r\n```\r\n\r\nYou have detailed information in our docs:\r\n- [Using a Dataset with PyTorch\/Tensorflow](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html)\r\n- [Dataset.set_format()](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html#datasets.Dataset.set_format)","body":"## Describe the bug\r\nI am trying to concatenate two `Dataset` using `concatenate_datasets` but it turns out that after concatenation the features are casted from `torch.Tensor` to `list`. \r\nIt then leads to a weird tensors when trying to convert it to a `DataLoader`. However, if I use each `Dataset` separately everything behave as expected.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> featurized_teacher\r\nDataset({\r\n    features: ['t_labels', 't_input_ids', 't_token_type_ids', 't_attention_mask'],\r\n    num_rows: 502\r\n})\r\n>>> for f in featurized_teacher.features:\r\n     print(featurized_teacher[f].shape)\r\ntorch.Size([502])\r\ntorch.Size([502, 300])\r\ntorch.Size([502, 300])\r\ntorch.Size([502, 300])\r\n\r\n>>> featurized_student\r\nDataset({\r\n    features: ['s_features', 's_labels'],\r\n    num_rows: 502\r\n})\r\n>>> for f in featurized_student.features:\r\n     print(featurized_student[f].shape)\r\ntorch.Size([502, 64])\r\ntorch.Size([502])\r\n```\r\nThe shapes seem alright to me. Then the results after concatenation are as follow:\r\n```python\r\n>>> concat_dataset = datasets.concatenate_datasets([featurized_student, featurized_teacher], axis=1)\r\n>>> type(concat_dataset[\"t_labels\"])\r\n<class 'list'>\r\n```\r\nOne would expect to obtain the same type as the one before concatenation.\r\n\r\nAm I doing something wrong here? Any idea on how to fix this unexpected behavior?\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-10.14.6-x86_64-i386-64bit\r\n- Python version: 3.9.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":80,"text":"Unexpected type after `concatenate_datasets` \n ## Describe the bug\r\nI am trying to concatenate two `Dataset` using `concatenate_datasets` but it turns out that after concatenation the features are casted from `torch.Tensor` to `list`. \r\nIt then leads to a weird tensors when trying to convert it to a `DataLoader`. However, if I use each `Dataset` separately everything behave as expected.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> featurized_teacher\r\nDataset({\r\n    features: ['t_labels', 't_input_ids', 't_token_type_ids', 't_attention_mask'],\r\n    num_rows: 502\r\n})\r\n>>> for f in featurized_teacher.features:\r\n     print(featurized_teacher[f].shape)\r\ntorch.Size([502])\r\ntorch.Size([502, 300])\r\ntorch.Size([502, 300])\r\ntorch.Size([502, 300])\r\n\r\n>>> featurized_student\r\nDataset({\r\n    features: ['s_features', 's_labels'],\r\n    num_rows: 502\r\n})\r\n>>> for f in featurized_student.features:\r\n     print(featurized_student[f].shape)\r\ntorch.Size([502, 64])\r\ntorch.Size([502])\r\n```\r\nThe shapes seem alright to me. Then the results after concatenation are as follow:\r\n```python\r\n>>> concat_dataset = datasets.concatenate_datasets([featurized_student, featurized_teacher], axis=1)\r\n>>> type(concat_dataset[\"t_labels\"])\r\n<class 'list'>\r\n```\r\nOne would expect to obtain the same type as the one before concatenation.\r\n\r\nAm I doing something wrong here? Any idea on how to fix this unexpected behavior?\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-10.14.6-x86_64-i386-64bit\r\n- Python version: 3.9.5\r\n- PyArrow version: 3.0.0\r\n \n Hi @JulesBelveze, thanks for your question.\r\n\r\nNote that \ud83e\udd17  `datasets` internally store their data in Apache Arrow format.\r\n\r\nHowever, when accessing dataset columns, by default they are returned as native Python objects (lists in this case).\r\n\r\nIf you would like their columns to be returned in a more suitable format for your use case (torch arrays), you can use the method `set_format()`:\r\n```python\r\nconcat_dataset.set_format(type=\"torch\")\r\n```\r\n\r\nYou have detailed information in our docs:\r\n- [Using a Dataset with PyTorch\/Tensorflow](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html)\r\n- [Dataset.set_format()](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html#datasets.Dataset.set_format)","embeddings":[0.0659025609,-0.2509687543,0.0296784658,0.507555902,0.4053797126,0.1719074398,0.4817784131,0.1346458942,-0.3654304147,-0.1315238774,-0.1543308496,0.395609051,-0.0383024774,-0.0193488616,-0.089732863,-0.2919967473,0.2896149158,-0.0016607346,-0.2081793547,-0.2273828089,-0.0381968655,0.1582772583,-0.2841176987,0.035756506,-0.2161132842,0.03157226,-0.0633054227,-0.1039584205,0.0063897204,0.0390482917,0.4256393015,0.0004014391,0.2480084449,0.5711277723,-0.0001217747,0.0700064898,0.1109226272,0.0230530966,-0.0054579936,-0.0247396491,-0.2727138102,-0.3866924644,0.1001271084,-0.2212410122,0.0955420285,-0.276202023,-0.2802899182,-0.456353426,-0.0949754938,0.1694531739,0.151410982,0.2291548699,0.0298358481,0.1134988666,0.1770875156,0.1690293849,-0.1362749636,-0.1136704534,-0.0572797284,0.2449533194,0.447096467,-0.0400952436,-0.1846583337,-0.1947271824,0.0227859411,0.2623610497,-0.314884007,-0.3963642418,-0.1733084768,0.1674891263,0.526042819,-0.1522032768,-0.1825706363,-0.1398912817,0.0140495589,0.1007185876,0.1467597932,0.1790630221,0.1674614847,-0.0629968941,0.001977049,0.2578364313,-0.0612737238,0.2807202637,-0.2607089579,-0.0113037117,-0.1205294207,0.2740452588,-0.0921653509,-0.1820497215,0.186672464,-0.2658762336,-0.0710545853,-0.0162656344,-0.5050219297,-0.0840443373,0.0590057038,-0.509517312,-0.1505635381,-0.142667532,0.1211892888,0.0527787209,-0.2076213062,0.1359153986,0.2071558088,0.3062790036,-0.0643244088,0.3882654905,-0.0904146731,-0.0109213414,-0.0627332702,0.1054688171,-0.030391179,-0.3052833378,0.0818851665,0.025435878,0.3865280449,-0.0147889825,-0.3727706671,0.0108852461,-0.3847967088,0.2573918104,0.0848566517,-0.1533523351,-0.3881433904,0.5925773978,0.2986513972,0.2811685205,0.0565008447,-0.0198840369,-0.0571242161,-0.2771323323,-0.0768730938,-0.2397636622,0.0317757875,-0.1434997618,-0.2257492691,0.4488455653,0.1257311851,0.0125266463,-0.0595633425,-0.0809916928,0.2891119421,-0.0434466861,-0.0761660263,0.2290890217,0.1811477989,-0.0985061228,-0.0362648219,0.2386940569,-0.4926404357,-0.0590336435,-0.381783247,0.1135702878,-0.016453132,0.0104641356,-0.073190771,0.183647275,0.5666251183,0.2480074018,0.0704077184,-0.2878100574,0.0038009512,-0.2995419502,-0.0008175711,0.0209132563,-0.2185069621,-0.0127195939,-0.1650024205,0.0993296802,0.4306170046,0.3159378171,-0.1027809456,0.3083853126,-0.3631752133,0.0680128336,0.5011175275,-0.0826230198,-0.0558355302,-0.0620212071,-0.007606979,0.4844593406,-0.0192016512,0.0540582873,-0.0028558637,0.0904539824,0.4210165143,0.2623525858,0.1196406558,-0.0385204628,-0.172674045,0.099473469,0.6978393793,-0.09616445,-0.1197120473,0.3986996412,-0.028801024,0.0267649945,0.1838664412,-0.20131962,0.0032166641,0.0487347618,-0.1022671163,0.1278575659,0.1434339583,0.0885536224,-0.477280885,0.0218084715,-0.1082340553,0.1001599357,-0.201389879,-0.1823511124,0.172307089,-0.0312149823,-0.0837009475,0.2704252601,0.0632626712,0.0763835013,-0.4588813782,-0.0922061875,0.1424329728,0.1000564471,0.1427499056,0.0255112257,-0.4670328796,0.2207724154,0.0516190268,-0.0008800761,-0.3370990753,0.1691160947,0.1627229452,-0.0121848844,-0.1386409104,0.2362546623,-0.240252912,0.0432870612,-0.1662945598,0.0633411631,0.0786366761,0.0362607315,-0.1191926226,0.1988856643,0.1499744952,-0.2119936496,0.296115756,0.469109118,0.2151079774,0.1262290925,-0.3541630805,0.1088888571,0.0366480425,0.0466697365,-0.1513889283,0.0986699015,0.0332320556,0.1303197294,-0.1636215597,0.1962623745,-0.5942215919,0.205019325,0.4457781315,-0.029396804,0.1831894219,-0.0318305567,-0.1152455062,0.1445436776,0.0400426909,-0.4003143907,0.598656714,-0.0762853101,0.160049051,-0.062399704,0.0319034867,-0.0884366781,0.1578813493,0.1634771675,-0.1648807526,0.2422760278,0.5488060713,-0.0258924793,0.0026322759,0.2411987334,-0.0258664507,-0.0695296004,-0.4299575984,0.40895316,-0.4493633211,-0.0130516933,-0.4337830544,-0.4268899262,-0.2325565964,-0.1914565712,-0.3151691854,0.0831534639,-0.0308005959,0.1409175694,0.2315021604,0.208858788,0.4176336229,-0.3352909088,-0.0116184205,0.2565828562,-0.2936905921,-0.0194220729,0.1011185199,-0.474614054,-0.0927021652,-0.0140165407,0.1141560152,-0.2371975183,-0.3689379394,-0.0497705899,-0.2383907735,0.0373716392,0.2184629291,-0.0252511688,-0.0695573762,-0.3585180342,0.0914961845,0.3910309672,0.0402582474,0.5428994298,-0.120610252,-0.1459313184,-0.2737415433,-0.414254427,-0.1589875221,-0.1897293776,0.3760147989,0.0556129292,0.0255450513,0.2554220557,0.1048957482,-0.1140453443,0.3653511405,0.1799469888,-0.2550261319,-0.3012900949,0.5462107658,-0.0352578685,-0.2991886735,-0.1919014752,-0.181500569,0.086480394,0.3125531077,-0.3383074999,0.2758895457,-0.2269130498,0.5231872797,-0.264123112,0.037150573,0.033702679,0.2198939174,0.0525674894,-0.1942944378,0.2089818865,0.2619978786,0.1683866382,0.4185818434,-0.0266035572,-0.0517275445,0.0500230528,0.2800492644,0.0553948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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2750","title":"Second concatenation of datasets produces errors","comments":"Hi @Aktsvigun! We are planning to address this issue before our next release, in a couple of weeks at most. \ud83d\ude05 \r\n\r\nIn the meantime, if you would like to contribute, feel free to open a Pull Request. You are welcome. Here you can find more information: [How to contribute to Datasets?](CONTRIBUTING.md)","body":"Hi,\r\n\r\nI am need to concatenate my dataset with others several times, and after I concatenate it for the second time, the features of features (e.g. tags names) are collapsed. This hinders, for instance, the usage of tokenize function with `data.map`.\r\n\r\n```\r\nfrom datasets import load_dataset, concatenate_datasets\r\n\r\ndata = load_dataset('trec')['train']\r\nconcatenated = concatenate_datasets([data, data])\r\nconcatenated_2 = concatenate_datasets([concatenated, concatenated])\r\nprint('True features of features:', concatenated.features)\r\nprint('\\nProduced features of features:', concatenated_2.features)\r\n```\r\noutputs \r\n\r\n```\r\nTrue features of features: {'label-coarse': ClassLabel(num_classes=6, names=['DESC', 'ENTY', 'ABBR', 'HUM', 'NUM', 'LOC'], names_file=None, id=None), 'label-fine': ClassLabel(num_classes=47, names=['manner', 'cremat', 'animal', 'exp', 'ind', 'gr', 'title', 'def', 'date', 'reason', 'event', 'state', 'desc', 'count', 'other', 'letter', 'religion', 'food', 'country', 'color', 'termeq', 'city', 'body', 'dismed', 'mount', 'money', 'product', 'period', 'substance', 'sport', 'plant', 'techmeth', 'volsize', 'instru', 'abb', 'speed', 'word', 'lang', 'perc', 'code', 'dist', 'temp', 'symbol', 'ord', 'veh', 'weight', 'currency'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}\r\n\r\nProduced features of features: {'label-coarse': Value(dtype='int64', id=None), 'label-fine': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}\r\n```\r\n\r\nI am using `datasets` v.1.11.0","comment_length":51,"text":"Second concatenation of datasets produces errors \n Hi,\r\n\r\nI am need to concatenate my dataset with others several times, and after I concatenate it for the second time, the features of features (e.g. tags names) are collapsed. This hinders, for instance, the usage of tokenize function with `data.map`.\r\n\r\n```\r\nfrom datasets import load_dataset, concatenate_datasets\r\n\r\ndata = load_dataset('trec')['train']\r\nconcatenated = concatenate_datasets([data, data])\r\nconcatenated_2 = concatenate_datasets([concatenated, concatenated])\r\nprint('True features of features:', concatenated.features)\r\nprint('\\nProduced features of features:', concatenated_2.features)\r\n```\r\noutputs \r\n\r\n```\r\nTrue features of features: {'label-coarse': ClassLabel(num_classes=6, names=['DESC', 'ENTY', 'ABBR', 'HUM', 'NUM', 'LOC'], names_file=None, id=None), 'label-fine': ClassLabel(num_classes=47, names=['manner', 'cremat', 'animal', 'exp', 'ind', 'gr', 'title', 'def', 'date', 'reason', 'event', 'state', 'desc', 'count', 'other', 'letter', 'religion', 'food', 'country', 'color', 'termeq', 'city', 'body', 'dismed', 'mount', 'money', 'product', 'period', 'substance', 'sport', 'plant', 'techmeth', 'volsize', 'instru', 'abb', 'speed', 'word', 'lang', 'perc', 'code', 'dist', 'temp', 'symbol', 'ord', 'veh', 'weight', 'currency'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}\r\n\r\nProduced features of features: {'label-coarse': Value(dtype='int64', id=None), 'label-fine': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}\r\n```\r\n\r\nI am using `datasets` v.1.11.0 \n Hi @Aktsvigun! We are planning to address this issue before our next release, in a couple of weeks at most. \ud83d\ude05 \r\n\r\nIn the meantime, if you would like to contribute, feel free to open a Pull Request. You are welcome. Here you can find more information: [How to contribute to Datasets?](CONTRIBUTING.md)","embeddings":[-0.1504657269,-0.0591697134,-0.0577093922,0.2559515834,0.193053782,0.0901743174,0.0821338445,0.2622757852,-0.2609196305,-0.0551395863,0.0358312577,0.1265247911,0.2174618989,0.090199098,-0.2850414217,-0.1801928431,0.1788396835,0.0559669882,0.0485580564,-0.118900381,-0.1244952604,0.1265652031,-0.3151787817,0.0769735575,-0.4671195447,0.1625408679,-0.3227909505,0.007621164,-0.0032958225,-0.3098783493,-0.0556449778,0.3040577769,-0.0451283157,0.32534796,-0.0001139846,-0.1404050738,0.020385908,-0.0589029714,-0.1106781885,-0.1804829538,-0.1020738631,-0.5905038118,-0.0913802311,-0.2002821118,0.1462708861,-0.6070088148,-0.2705745995,-0.2687054873,0.2620135248,-0.1385006309,0.159783259,-0.0676152855,0.1747838706,-0.1806190014,-0.1066621318,0.0965181813,0.0347622149,-0.0004105456,-0.1437833905,-0.0325336047,0.4414526224,0.3706357181,-0.1782261729,-0.3017144203,-0.1555945128,0.1617710441,0.0063124946,-0.3817135096,-0.0536329299,0.1203863546,0.3290899098,-0.3443950415,-0.3714585006,-0.4346679449,0.1693651974,-0.2393826842,0.03127525,-0.0691222697,0.2867306471,0.2179713845,-0.0308873206,-0.189049691,0.0395146236,0.1386504024,-0.0608814657,0.2488592714,0.0057220296,0.1144914106,0.1244022697,-0.5368865728,0.0223361254,-0.4283556044,-0.0197522454,0.0175422635,-0.5440704823,-0.0473727919,0.0594298653,-0.6148066521,0.2270136178,0.1771862656,-0.0383978337,0.0237068944,-0.0668149516,-0.0024822024,0.2470452338,0.4188669622,0.2312257588,0.2866076529,-0.0470639057,-0.225259766,-0.2453390211,-0.0734846815,-0.1264434159,-0.2911265194,0.1000443324,0.0113737918,0.396143049,-0.0294776019,-0.0793904811,0.11111404,-0.3544274569,-0.1133779958,0.1028654203,0.0086840028,-0.0921811163,0.6188397408,-0.1172470599,0.2566657662,0.1120014265,-0.1708726287,-0.1573576778,-0.1931791455,0.0105560739,0.1908484846,-0.009235274,-0.2236082852,0.0366123281,0.2651329041,-0.0237969682,-0.1290379465,0.0272331573,-0.3101315796,0.0045604357,0.0218815319,0.1250576526,0.0520775132,0.0854914114,-0.0011943799,0.024049921,0.0454844385,-0.5595525503,-0.1502062678,-0.3701137304,0.2136218846,0.0852637514,0.2996404767,0.0801235214,0.302719593,0.2509583235,0.0506003797,-0.1885678768,-0.0870354995,0.0076499549,-0.1807177514,0.082131654,0.0898533612,0.0677425563,0.0127707971,0.1103849784,0.1843047738,0.2871360183,0.4206843376,-0.1703023314,0.3344914913,-0.2040976286,0.4971638322,0.5261321664,-0.0743064508,-0.1810768843,0.2042457908,-0.0661210865,0.3550088406,0.1406594813,-0.1781974137,0.0503419302,0.0754448324,0.4194709361,0.123826772,0.0369044282,-0.3195022941,-0.0728152394,0.0247223936,0.7286319733,-0.3416662514,-0.2831137478,0.2036883235,-0.0616782382,-0.0981448218,0.1693049967,-0.3445923924,-0.05869589,0.1913804859,-0.125977084,0.1830993742,0.0787284821,0.0552581102,-0.3375196755,0.0893760026,-0.0399330445,-0.2428482473,-0.167930007,-0.2065499425,0.0221018586,-0.0164528918,-0.1647251695,0.2443644553,0.1765640825,0.3324424326,-0.1891664118,-0.0546176396,-0.0573655665,0.4379684627,0.0619030967,0.0928421468,-0.1567480415,0.3573679924,0.0526526645,0.0333255939,-0.0547466129,0.1343888491,0.5898091793,-0.1381362081,-0.0987757519,0.4499067068,-0.1939736903,-0.1137822419,-0.0809097514,-0.0667266548,0.10392946,0.0040466455,0.0680115074,-0.1234541014,-0.0436430983,-0.1680187434,0.0705930367,0.2275555879,0.2849643826,0.1425679475,0.0359224901,-0.0949881747,0.1146106496,-0.1052120999,-0.1035783067,-0.4422333241,0.2089213729,0.0312789194,-0.2695364952,0.0007309509,-0.4664670825,0.2603234649,0.3221491277,0.1330774277,0.0780856013,0.0291613564,0.0404994898,0.0632191226,0.0192058738,-0.2066784352,0.4321274161,0.1638639122,-0.003215817,0.1260049194,-0.094315432,0.0408444293,0.182420522,-0.0683083534,0.0810810626,0.4733741879,0.2709379196,0.0947590172,-0.0473091118,-0.1575174481,0.0607751161,-0.1024566963,-0.1658180505,0.128069371,-0.6087813377,-0.1715297848,-0.2396755964,-0.5274429917,-0.2216403484,-0.4587311447,-0.1826675832,0.0086168805,-0.1529063731,0.172349304,0.1213834211,-0.1309144199,0.1670149118,0.0995271653,0.1163859367,0.1581566781,-0.0811959282,-0.0527282991,-0.1512535363,-0.0578469522,0.0354970507,-0.0889788717,-0.0157376137,-0.3222573996,-0.2392690182,0.1334006041,-0.2884916961,-0.1630610228,-0.0221155547,-0.0977419615,0.04354183,-0.4574145079,0.3913764656,0.5567986965,0.0928269625,0.2175679207,0.3165223002,-0.3759053051,-0.2535746992,-0.3681758046,-0.2974682748,-0.0178930797,0.1713598073,-0.256580472,0.1625857204,0.5247507095,0.0695422068,-0.2070610821,0.2132777125,0.2021140903,-0.1964456588,-0.1422774345,0.351760447,-0.2002482265,0.0002508448,-0.2620691359,-0.0276876092,-0.1174924597,0.2562609017,-0.2546110153,-0.0034958834,0.029826317,0.0769765601,-0.3703232408,0.0944289044,0.0625614673,0.3755552471,-0.1497161239,0.0357851461,-0.1428721547,0.0463107303,0.3056418896,0.4419403374,-0.182499513,-0.0159339756,-0.1022930294,-0.0357602537,0.2336682528,-0.0100663975,0.2705745697,-0.0379700698,0.2105386555,-0.0199496839,-0.6065669656,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2749","title":"Raise a proper exception when trying to stream a dataset that requires to manually download files","comments":"Hi @severo, thanks for reporting.\r\n\r\nAs discussed, datasets requiring manual download should be:\r\n- programmatically identifiable\r\n- properly handled with more clear error message when trying to load them with streaming\r\n\r\nIn relation with programmatically identifiability, note that for datasets requiring manual download, their builder have a property `manual_download_instructions` which is not None:\r\n```python\r\n# Dataset requiring manual download:\r\nbuilder.manual_download_instructions is not None\r\n```","body":"## Describe the bug\r\n\r\nAt least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"reclor\", streaming=True)\r\n```\r\n\r\n## Expected results\r\n\r\nIdeally: raise a specific exception, something like `ManualDownloadError`.\r\n\r\nOr at least give the reason in the message, as when we load in normal mode:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"reclor\")\r\n```\r\n\r\n```\r\nAssertionError: The dataset reclor with config default requires manual data.\r\n Please follow the manual download instructions:   to use ReClor you need to download it manually. Please go to its homepage (http:\/\/whyu.me\/reclor\/) fill the google\r\n  form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path\/to\/folder\/folder_name')\r\n  .\r\n Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\n## Actual results\r\n\r\n```\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: macOS-11.5-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":64,"text":"Raise a proper exception when trying to stream a dataset that requires to manually download files \n ## Describe the bug\r\n\r\nAt least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"reclor\", streaming=True)\r\n```\r\n\r\n## Expected results\r\n\r\nIdeally: raise a specific exception, something like `ManualDownloadError`.\r\n\r\nOr at least give the reason in the message, as when we load in normal mode:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"reclor\")\r\n```\r\n\r\n```\r\nAssertionError: The dataset reclor with config default requires manual data.\r\n Please follow the manual download instructions:   to use ReClor you need to download it manually. Please go to its homepage (http:\/\/whyu.me\/reclor\/) fill the google\r\n  form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path\/to\/folder\/folder_name')\r\n  .\r\n Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\n## Actual results\r\n\r\n```\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: macOS-11.5-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Hi @severo, thanks for reporting.\r\n\r\nAs discussed, datasets requiring manual download should be:\r\n- programmatically identifiable\r\n- properly handled with more clear error message when trying to load them with streaming\r\n\r\nIn relation with programmatically identifiability, note that for datasets requiring manual download, their builder have a property `manual_download_instructions` which is not None:\r\n```python\r\n# Dataset requiring manual download:\r\nbuilder.manual_download_instructions is not None\r\n```","embeddings":[-0.2240490466,0.0735050663,0.068855688,0.1454883069,0.3467077017,0.0917349607,0.1795138866,0.0385224037,0.1048629507,0.0732556209,0.0714531541,0.2278849632,-0.306107223,0.168981418,-0.0743809417,-0.0853384212,-0.0969869718,0.1296140105,0.2995474637,0.1218056008,-0.1967432946,0.037302237,-0.2146420479,0.0178642627,-0.0018434762,-0.1568493396,0.2580464482,0.2388842106,-0.12881051,-0.3902961612,0.3372131884,0.0121841822,0.4855319858,0.5165213346,-0.0001199634,0.0070316265,0.3886614144,-0.1586243212,-0.5747519135,-0.1283361316,-0.4686380327,-0.0777676925,0.0901651233,-0.0698137358,0.1049859673,-0.1720953882,0.162341401,-0.3645288646,0.2047697306,0.4541603923,0.1066825688,0.2829991579,0.2462426722,0.163706407,0.1327375621,0.351267308,-0.0302184448,0.3713254631,0.0608687028,0.1510274708,0.1606027037,0.1628775001,-0.1228826195,0.0393839516,0.1880534738,-0.1661041975,-0.2295856774,-0.4277891219,-0.1103247255,0.4421196878,0.5148940086,-0.3407017291,-0.4609524012,-0.3798696399,0.0458856784,-0.1476799548,0.1311267167,0.4878360331,-0.4216932058,0.3501260877,-0.0883329585,-0.3158322573,-0.3065256178,0.2305598706,0.0059048333,-0.1519111991,0.055758439,0.0611203685,-0.0835495442,0.1057094559,0.2180747986,-0.309345305,-0.0396562181,0.0888883397,-0.204697147,-0.0041701873,-0.0657843724,-0.0414948389,0.1965882033,0.0238626655,0.3733995557,0.0567336902,-0.1529811174,0.2431636602,0.1901954114,0.0264550317,0.0991011336,0.1258841455,0.4811724126,0.3951644301,0.1332431287,-0.1177569702,-0.0253069643,0.0886065438,0.3152233064,0.2048721611,0.4681082666,-0.1049640924,-0.4039188921,0.0096668303,-0.6102744937,0.1056832075,-0.02290681,0.1641360372,-0.1616206318,0.2007362247,0.2162867635,0.3693430424,-0.0011653588,-0.2878369689,-0.0102987355,-0.0643678904,0.1261957884,0.0260482784,0.1795365959,-0.5311354995,0.0900712088,0.093942605,-0.3286801875,-0.0527605303,0.3502986729,-0.0545802228,-0.159295544,0.3757533729,0.1402717531,0.0273190979,0.1152361333,-0.1576324105,-0.016434364,0.2604483366,-0.0598954596,-0.531606853,0.1063527539,0.1647614092,-0.0167459399,-0.0777090043,-0.5121468306,0.0411837399,0.0013843826,-0.3260016441,-0.0188462473,-0.1534196436,0.3346794546,-0.2917099595,0.0453913435,0.5106771588,-0.3724917471,0.0134214601,-0.4481051266,-0.2220885307,0.7759043574,0.1512121409,-0.2369718254,0.2332099974,-0.3799983263,0.0408191308,0.4848953784,-0.0353461541,-0.3184098303,0.4427749515,0.0254455581,0.4210849106,0.1377783716,0.2182543427,-0.0400385782,-0.1267595738,-0.0261647534,0.5302296281,-0.2185987681,0.0431420021,-0.2089989781,-0.3620250821,0.2253880501,0.2703531682,0.0270372368,0.2280398458,0.3364054561,0.0141229415,0.2515027523,-0.1984639168,-0.1834649891,-0.0087441904,0.4122576416,-0.0866532698,-0.2348504364,-0.1183793917,-0.3734786808,0.2647877336,0.0439619459,0.0354769155,-0.2135419101,-0.3262125254,-0.1027309895,0.0139823742,-0.2305373102,0.0171953775,0.0360648334,0.3708552122,-0.3016510606,0.1690964848,-0.1053365096,0.4814932644,-0.5749636889,0.2593649924,-0.1490311772,0.1025912687,0.0344819985,-0.1005370393,-0.1384553462,-0.3296217024,0.0505902618,-0.0311502721,-0.2466446459,0.4579785466,0.2499203086,0.341388911,-0.2688518167,-0.0825616494,0.0779676735,-0.197544679,-0.227867946,0.5077203512,0.1895116568,-0.0363306254,-0.0994233862,0.2132187188,0.2448489964,0.1440677643,0.0862258375,0.213060692,0.1893789172,0.2060934901,-0.1933785826,0.0910151675,0.1567736268,-0.1239111423,-0.0441933163,-0.2678855062,-0.7136908174,-0.2057197094,0.0648716241,0.0464329794,0.4829679728,0.3096090853,-0.1382143945,0.0907322541,0.25253883,0.3169167042,0.4295144379,0.0733744875,0.11609786,0.0864411369,0.1807482988,-0.2610627711,0.254388839,0.2185361236,-0.2846085429,0.1634678841,0.1206299067,0.1041521356,-0.2988964915,-0.163327828,-0.0516937599,0.1894502938,-0.5312809348,0.0377002135,0.0376430117,-0.1813216358,-0.3296357691,-0.3708743155,-0.1727309972,-0.1337380558,-0.1167202145,0.3897580206,-0.1965971291,0.207007736,-0.0349070318,0.2335080653,-0.0607416183,-0.4499779046,-0.0715346262,0.21721977,-0.2908616662,-0.0184098668,0.3773886561,-0.1365685612,0.4272453785,-0.2955581248,0.0621297061,-0.3178853393,-0.1042977273,-0.0536005981,0.2045689821,0.2286831588,0.3041532934,0.4670447111,0.3198855221,-0.3050571978,0.0509364195,-0.0395637155,-0.0034178786,0.1977665573,-0.0418911129,0.3320870996,-0.1186343729,-0.2866623104,-0.4388065636,-0.4254140556,-0.0446366221,-0.0688903108,0.1023061424,0.3437128961,0.1085468233,0.1080609486,0.2089250386,0.1163057312,0.0747015178,-0.172325626,0.2136213034,0.0754034743,-0.1863212883,0.095716998,-0.0790749639,-0.1870462745,0.6288800836,-0.4854286611,-0.0773327276,-0.2160004526,0.0934364274,-0.1314488202,-0.163055405,0.1723690778,0.0485098325,0.0638696179,0.0521184541,-0.0092215231,-0.1064659432,0.1419488341,0.3870232403,0.1204741821,0.4440130293,-0.0056693312,0.4280276,0.1485658288,0.0279103853,0.5554195046,0.037950702,0.2381644249,0.1094087139,-0.1581960022,-0.0390722267,-0.0468433239,-0.2562383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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2746","title":"Cannot load `few-nerd` dataset","comments":"Hi @Mehrad0711,\r\n\r\nI'm afraid there is no \"canonical\" Hugging Face dataset named \"few-nerd\".\r\n\r\nThere are 2 kinds of datasets hosted at the Hugging Face Hub:\r\n- canonical datasets (their identifier contains no slash \"\/\"): we, the Hugging Face team, supervise their implementation and we make sure they work correctly by means of our test suite\r\n- community datasets (their identifier contains a slash \"\/\", where before the slash it is the username or the organization name): those datasets are uploaded to the Hub by the community, and we, the Hugging Face team, do not supervise them; it is the responsibility of the user\/organization implementing them properly if they want them to be used by other users.\r\n\r\nIn this specific case, there is no \"canonical\" dataset named \"few-nerd\". On the other hand, there are two \"community\" datasets named \"few-nerd\":\r\n- [\"nbroad\/few-nerd\"](https:\/\/huggingface.co\/datasets\/nbroad\/few-nerd)\r\n- [\"dfki-nlp\/few-nerd\"](https:\/\/huggingface.co\/datasets\/dfki-nlp\/few-nerd)\r\n\r\nIf they were properly implemented, you should be able to load them this way:\r\n```python\r\n# \"nbroad\/few-nerd\" community dataset\r\nds = load_dataset(\"nbroad\/few-nerd\", \"supervised\")\r\n\r\n# \"dfki-nlp\/few-nerd\" community dataset\r\nds = load_dataset(\"dfki-nlp\/few-nerd\", \"supervised\")\r\n```\r\n\r\nHowever, they are not correctly implemented and both of them give errors:\r\n- \"nbroad\/few-nerd\":\r\n  ```\r\n  TypeError: expected str, bytes or os.PathLike object, not dict\r\n  ```\r\n- \"dfki-nlp\/few-nerd\":\r\n  ```\r\n  ConnectionError: Couldn't reach https:\/\/cloud.tsinghua.edu.cn\/f\/09265750ae6340429827\/?dl=1\r\n  ```\r\n\r\nYou could try to contact their users\/organizations to inform them about their bugs and ask them if they are planning to fix them. Alternatively you could try to implement your own script for this dataset.","body":"## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n","comment_length":242,"text":"Cannot load `few-nerd` dataset \n ## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n \n Hi @Mehrad0711,\r\n\r\nI'm afraid there is no \"canonical\" Hugging Face dataset named \"few-nerd\".\r\n\r\nThere are 2 kinds of datasets hosted at the Hugging Face Hub:\r\n- canonical datasets (their identifier contains no slash \"\/\"): we, the Hugging Face team, supervise their implementation and we make sure they work correctly by means of our test suite\r\n- community datasets (their identifier contains a slash \"\/\", where before the slash it is the username or the organization name): those datasets are uploaded to the Hub by the community, and we, the Hugging Face team, do not supervise them; it is the responsibility of the user\/organization implementing them properly if they want them to be used by other users.\r\n\r\nIn this specific case, there is no \"canonical\" dataset named \"few-nerd\". On the other hand, there are two \"community\" datasets named \"few-nerd\":\r\n- [\"nbroad\/few-nerd\"](https:\/\/huggingface.co\/datasets\/nbroad\/few-nerd)\r\n- [\"dfki-nlp\/few-nerd\"](https:\/\/huggingface.co\/datasets\/dfki-nlp\/few-nerd)\r\n\r\nIf they were properly implemented, you should be able to load them this way:\r\n```python\r\n# \"nbroad\/few-nerd\" community dataset\r\nds = load_dataset(\"nbroad\/few-nerd\", \"supervised\")\r\n\r\n# \"dfki-nlp\/few-nerd\" community dataset\r\nds = load_dataset(\"dfki-nlp\/few-nerd\", \"supervised\")\r\n```\r\n\r\nHowever, they are not correctly implemented and both of them give errors:\r\n- \"nbroad\/few-nerd\":\r\n  ```\r\n  TypeError: expected str, bytes or os.PathLike object, not dict\r\n  ```\r\n- \"dfki-nlp\/few-nerd\":\r\n  ```\r\n  ConnectionError: Couldn't reach https:\/\/cloud.tsinghua.edu.cn\/f\/09265750ae6340429827\/?dl=1\r\n  ```\r\n\r\nYou could try to contact their users\/organizations to inform them about their bugs and ask them if they are planning to fix them. Alternatively you could try to implement your own script for this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2746","title":"Cannot load `few-nerd` dataset","comments":"Hello, @Mehrad0711; Hi, @albertvillanova  !\r\nI am the maintainer of the `dfki\/few-nerd\" dataset script, sorry for the very late reply and hope this message finds you well!\r\nWe should use\r\n```\r\ndataset = load_dataset(\"dfki-nlp\/few-nerd\", name=\"supervised\")\r\n```\r\ninstead of not specifying the \"name\" argument, where name is from `[\"supervised\", \"inter\", \"intra\"]`. Otherwise the method just treats \"supervised\" as `split`, which we reserve after specifying the name, since for each name, there are three splits: train, dev and test.\r\n\r\nAlso we use Tsinghua server source to download data files since it is the official source referred in the paper where the dataset is released (even though it is cc-by-sa-4.0 licensed, means we can copy the data anywhere after mentioning the license\r\n). Sometimes the server just runs down due to high pressure, kinda weird (we encountered the same server problem serveral times a month when we conducted experiments on Few-NERD XD). I tried the script just now and it works perfectly!\r\n```\r\n>> dataset\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 131767\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 18824\r\n    })\r\n    test: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 37648\r\n    })\r\n})\r\n>>> dataset[\"train\"]\r\nDataset({\r\n    features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n    num_rows: 131767\r\n})\r\n>>> dataset[\"train\"][0]\r\n{'id': '0', 'tokens': ['Paul', 'International', 'airport', '.'], 'ner_tags': [0, 0, 0, 0], 'fine_ner_tags': [0, 0, 0, 0]}\r\n```\r\nAnyways if you cannot stand the pain with the server and its slow download speed, you can also download the `dfki\/few-nerd.py` script from HF and change the `_URLs` to your personal drive (after you once successfully download the data and upload to your cloud drive), and then load the .py script locally.\r\n\r\nHope this reply can still be any help. If you still have problems with it, feel free to ask here and I am glad to help!\r\nBest wishes.","body":"## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n","comment_length":305,"text":"Cannot load `few-nerd` dataset \n ## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n \n Hello, @Mehrad0711; Hi, @albertvillanova  !\r\nI am the maintainer of the `dfki\/few-nerd\" dataset script, sorry for the very late reply and hope this message finds you well!\r\nWe should use\r\n```\r\ndataset = load_dataset(\"dfki-nlp\/few-nerd\", name=\"supervised\")\r\n```\r\ninstead of not specifying the \"name\" argument, where name is from `[\"supervised\", \"inter\", \"intra\"]`. Otherwise the method just treats \"supervised\" as `split`, which we reserve after specifying the name, since for each name, there are three splits: train, dev and test.\r\n\r\nAlso we use Tsinghua server source to download data files since it is the official source referred in the paper where the dataset is released (even though it is cc-by-sa-4.0 licensed, means we can copy the data anywhere after mentioning the license\r\n). Sometimes the server just runs down due to high pressure, kinda weird (we encountered the same server problem serveral times a month when we conducted experiments on Few-NERD XD). I tried the script just now and it works perfectly!\r\n```\r\n>> dataset\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 131767\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 18824\r\n    })\r\n    test: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 37648\r\n    })\r\n})\r\n>>> dataset[\"train\"]\r\nDataset({\r\n    features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n    num_rows: 131767\r\n})\r\n>>> dataset[\"train\"][0]\r\n{'id': '0', 'tokens': ['Paul', 'International', 'airport', '.'], 'ner_tags': [0, 0, 0, 0], 'fine_ner_tags': [0, 0, 0, 0]}\r\n```\r\nAnyways if you cannot stand the pain with the server and its slow download speed, you can also download the `dfki\/few-nerd.py` script from HF and change the `_URLs` to your personal drive (after you once successfully download the data and upload to your cloud drive), and then load the .py script locally.\r\n\r\nHope this reply can still be any help. If you still have problems with it, feel free to ask here and I am glad to help!\r\nBest wishes.","embeddings":[-0.3042272031,-0.0985626653,0.007841493,0.5131354332,0.328545779,-0.0576422513,0.5201516747,0.2553012073,0.1991576403,0.1681091636,-0.4604775012,-0.0910487175,-0.2877757847,-0.2970745862,0.4347799718,0.0320020169,-0.02518804,-0.0349554755,0.1119609401,0.0084387101,-0.2257288843,0.2362412065,-0.2453141659,0.0727761388,-0.419000715,-0.0549565963,0.0889210105,0.4892740846,-0.0719758198,-0.6296027303,0.460472703,0.014389392,0.2181515396,0.4235012233,-0.0001091994,0.144184947,0.4189922512,-0.019266123,-0.2884244621,-0.3601104915,0.0940838531,-0.5144337416,0.1233332306,-0.2608023882,-0.406347245,0.0236194301,0.1217370182,-0.2709337175,0.490170449,0.488686204,0.288490653,0.1309136897,-0.0160082523,-0.3164563179,0.4296740592,0.0675480366,-0.1612952203,0.2777442336,0.3432971835,-0.0043072193,-0.0363358036,0.0306214392,-0.0457585603,-0.1231167316,0.39013201,0.0024617843,-0.1256728023,-0.2126000524,0.1296324283,0.4381466508,0.2064512968,-0.2134825289,-0.2328190356,-0.1929797977,0.0738780946,-0.1819786429,0.1654340327,0.0319197588,-0.1912625879,0.0741785392,-0.0262068659,-0.0630680993,-0.1722616404,0.1423974484,-0.110440284,0.1547157764,-0.150649339,0.0314562917,-0.1483332068,-0.0989765152,-0.2203025371,0.1539326459,-0.0331301875,0.2842571437,-0.3529803753,0.1692639887,0.0968754515,0.3016742766,0.2464153916,0.34680143,-0.0859935731,-0.011230737,-0.031366203,0.1054866239,0.0539561436,0.1550135016,0.3106099367,0.1709685922,0.2803744376,0.4544360042,-0.0940064788,0.0249504186,-0.2067869008,-0.1653557122,0.0443974584,0.0359360576,0.3279303312,-0.233940199,-0.4098480344,0.1534145772,0.2209211886,0.088786453,0.2583982944,0.4830324352,-0.2361518294,-0.0013515315,-0.0064407205,0.0255305003,-0.1511354744,-0.1756568849,-0.3392073214,0.1940045655,-0.080814667,0.0127045149,0.3254746199,-0.1232190207,0.3092910647,-0.1528524309,-0.0015991911,-0.1232205778,-0.1105265766,-0.1869745404,-0.0999141708,0.2051265985,-0.0700348094,0.0878425166,0.1172278821,-0.3199294508,-0.2411322892,-0.1124539599,-0.1270280331,-0.3474444747,-0.0937899575,0.2281974107,-0.2979833782,0.1321427822,-0.0671058372,0.0436785258,0.0764682218,-0.2537538111,-0.1240771115,-0.1402088106,-0.4273744822,-0.3767170906,0.6006539464,0.6969179511,-0.2007522732,-0.1508898139,-0.5498247147,-0.3447906077,0.105987072,0.3750484884,-0.2740003765,0.1505955309,-0.2850112915,0.3159655631,0.3138710558,-0.4321829379,-0.4340073168,0.2631569505,-0.2292088717,0.4060312808,-0.0842331722,0.085828267,0.1212194487,-0.1092525572,0.3998244703,0.4777024686,0.0203810725,-0.1821041256,-0.2581339777,-0.1423353404,0.1062361747,0.1785901934,0.2633876503,0.078766875,0.1548193544,0.0659918264,0.2326668501,0.208025232,0.0624008998,0.1595210284,0.2724159062,0.2057394385,0.0333990641,-0.4820550382,-0.6707289219,0.4395003617,-0.1612329334,-0.0476558656,-0.1690658629,-0.1581422389,-0.419980526,0.0298832543,-0.0987409204,-0.2591835856,0.179764092,0.2151272893,0.2117394209,0.0778774843,-0.2501817942,0.5149942636,0.0565136932,0.0629970059,-0.3822929561,0.1305712163,-0.1241281852,-0.1103749722,-0.1185646728,0.3505547345,0.2312965542,-0.185125351,0.0676198527,0.5000377893,0.0044357101,-0.1740285009,-0.0906504989,-0.4309688807,0.0396165289,-0.1043563485,0.1348259002,0.2153398097,0.1075613499,-0.0511244535,0.1082193404,0.3727986515,0.0354981348,0.2296619713,0.0288257133,-0.001395671,0.1831604987,0.0764853358,0.1574743092,-0.2301817834,0.4892827272,0.2627514303,0.1726315022,0.207790941,-0.2014015764,-0.0117084198,0.085846737,-0.0487292819,0.0544365048,0.2945663631,-0.1510484666,-0.0472731255,0.0568785742,0.3697474301,0.3691950142,0.1709932387,-0.026174238,0.0965143219,-0.0314382352,-0.0691685751,0.1280409545,0.0516601652,0.0810704231,0.2116550356,0.069557637,0.1970311254,-0.2670096457,-0.2561357021,-0.0517689362,0.3250854015,-0.3932227194,0.1741223335,-0.2071428001,-0.1251394451,-0.0495271608,0.0053799627,-0.4605352283,-0.0769572705,0.0503376685,0.1106792986,0.226770401,0.1833500862,-0.1267197728,0.1852060109,-0.3189344406,0.0570205934,-0.2792580724,0.0003344137,-0.3292350471,0.0763938129,0.3980509639,-0.0389914773,0.3228530884,-0.2194560468,-0.2569383085,-0.1921415925,-0.0463258214,-0.1839065105,0.0817929432,0.6579142213,0.1881101578,0.248079896,-0.0574077852,-0.1280138791,0.3673250675,-0.302280426,-0.1845697612,0.0297332909,-0.1068385243,0.0871499255,-0.1282043159,-0.3432548344,-0.6057456136,-0.4530194402,0.309641391,0.1487502158,0.1859292686,0.3676010668,-0.1163644344,0.092038922,-0.048556868,0.2006089389,-0.1484298408,-0.2746390998,0.2829445004,-0.4200580418,-0.3737150729,-0.1102969795,0.0587631874,0.0354793705,0.0825509056,-0.45354864,0.3604623675,-0.1324108988,0.0797707886,-0.1327626556,-0.0603401028,0.0686467588,-0.1221249402,-0.0532959439,0.0417382829,-0.1169573069,0.0133847743,-0.2853544354,0.0561641678,-0.0636483952,0.3072242141,-0.0019497601,0.4964491725,0.0580832027,-0.1058206409,0.2891736031,-0.08743117,0.4604646862,-0.1876951754,-0.437345475,0.2259440571,0.0792094916,0.102100268,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2746","title":"Cannot load `few-nerd` dataset","comments":"Hi @chen-yuxuan, thanks for your answer.\r\n\r\nJust a few comments:\r\n\r\n- Please, note that as we use `datasets.load_dataset` implementation, we can pass the configuration name as the second positional argument (no need to pass explicitly `name=`) and it downloads the 3 splits:\r\n```python\r\n  In [4]: ds = load_dataset(\"dfki-nlp\/few-nerd\", \"supervised\")\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 11.5k\/11.5k [00:00<00:00, 2.85MB\/s]\r\nDownloading and preparing dataset few_nerd\/supervised to .cache\\huggingface\\datasets\\dfki-nlp___few_nerd\\supervised\\0.0.0\\e40882b71f037a4a1f232025899170fbe8113cd2f4a26dddd2add7222a077255...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6M\/14.6M [01:16<00:00, 190kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 11.9M\/11.9M [01:14<00:00, 160kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12.0M\/12.0M [01:04<00:00, 186kB\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [03:58<00:00, 79.45s\/it]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00,  3.11it\/s]\r\n```\r\n\r\n- On the other hand, please note that your script does not work on Windows machines, because you call `open()` without passing the encoding parameter:\r\n```\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\dfki-nlp___few-nerd\\e40882b71f037a4a1f232025899170fbe8113cd2f4a26dddd2add7222a077255\\few-nerd.py in <genexpr>(.0)\r\n    276         assert filepath[-4:] == \".txt\"\r\n    277\r\n--> 278         num_lines = sum(1 for _ in open(filepath))\r\n    279         id = 0\r\n    280\r\n\r\n.venv\\lib\\encodings\\cp1252.py in decode(self, input, final)\r\n     21 class IncrementalDecoder(codecs.IncrementalDecoder):\r\n     22     def decode(self, input, final=False):\r\n---> 23         return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\n     24\r\n     25 class StreamWriter(Codec,codecs.StreamWriter):\r\n\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x8d in position 5238: character maps to <undefined>\r\n```\r\n\r\nIf you would like your script to be usable on Windows machines, you should pass `encoding=\"utf-8\"` to every `open()` function:\r\n- line 278: `num_lines = sum(1 for _ in open(filepath, encoding=\"utf-8\"))`\r\n- line 281: `with open(filepath, \"r\", encoding=\"utf-8\")`","body":"## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n","comment_length":208,"text":"Cannot load `few-nerd` dataset \n ## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n \n Hi @chen-yuxuan, thanks for your answer.\r\n\r\nJust a few comments:\r\n\r\n- Please, note that as we use `datasets.load_dataset` implementation, we can pass the configuration name as the second positional argument (no need to pass explicitly `name=`) and it downloads the 3 splits:\r\n```python\r\n  In [4]: ds = load_dataset(\"dfki-nlp\/few-nerd\", \"supervised\")\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 11.5k\/11.5k [00:00<00:00, 2.85MB\/s]\r\nDownloading and preparing dataset few_nerd\/supervised to .cache\\huggingface\\datasets\\dfki-nlp___few_nerd\\supervised\\0.0.0\\e40882b71f037a4a1f232025899170fbe8113cd2f4a26dddd2add7222a077255...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6M\/14.6M [01:16<00:00, 190kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 11.9M\/11.9M [01:14<00:00, 160kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 12.0M\/12.0M [01:04<00:00, 186kB\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [03:58<00:00, 79.45s\/it]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00,  3.11it\/s]\r\n```\r\n\r\n- On the other hand, please note that your script does not work on Windows machines, because you call `open()` without passing the encoding parameter:\r\n```\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\dfki-nlp___few-nerd\\e40882b71f037a4a1f232025899170fbe8113cd2f4a26dddd2add7222a077255\\few-nerd.py in <genexpr>(.0)\r\n    276         assert filepath[-4:] == \".txt\"\r\n    277\r\n--> 278         num_lines = sum(1 for _ in open(filepath))\r\n    279         id = 0\r\n    280\r\n\r\n.venv\\lib\\encodings\\cp1252.py in decode(self, input, final)\r\n     21 class IncrementalDecoder(codecs.IncrementalDecoder):\r\n     22     def decode(self, input, final=False):\r\n---> 23         return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\n     24\r\n     25 class StreamWriter(Codec,codecs.StreamWriter):\r\n\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x8d in position 5238: character maps to <undefined>\r\n```\r\n\r\nIf you would like your script to be usable on Windows machines, you should pass `encoding=\"utf-8\"` to every `open()` function:\r\n- line 278: `num_lines = sum(1 for _ in open(filepath, encoding=\"utf-8\"))`\r\n- line 281: `with open(filepath, \"r\", encoding=\"utf-8\")`","embeddings":[-0.3042272031,-0.0985626653,0.007841493,0.5131354332,0.328545779,-0.0576422513,0.5201516747,0.2553012073,0.1991576403,0.1681091636,-0.4604775012,-0.0910487175,-0.2877757847,-0.2970745862,0.4347799718,0.0320020169,-0.02518804,-0.0349554755,0.1119609401,0.0084387101,-0.2257288843,0.2362412065,-0.2453141659,0.0727761388,-0.419000715,-0.0549565963,0.0889210105,0.4892740846,-0.0719758198,-0.6296027303,0.460472703,0.014389392,0.2181515396,0.4235012233,-0.0001091994,0.144184947,0.4189922512,-0.019266123,-0.2884244621,-0.3601104915,0.0940838531,-0.5144337416,0.1233332306,-0.2608023882,-0.406347245,0.0236194301,0.1217370182,-0.2709337175,0.490170449,0.488686204,0.288490653,0.1309136897,-0.0160082523,-0.3164563179,0.4296740592,0.0675480366,-0.1612952203,0.2777442336,0.3432971835,-0.0043072193,-0.0363358036,0.0306214392,-0.0457585603,-0.1231167316,0.39013201,0.0024617843,-0.1256728023,-0.2126000524,0.1296324283,0.4381466508,0.2064512968,-0.2134825289,-0.2328190356,-0.1929797977,0.0738780946,-0.1819786429,0.1654340327,0.0319197588,-0.1912625879,0.0741785392,-0.0262068659,-0.0630680993,-0.1722616404,0.1423974484,-0.110440284,0.1547157764,-0.150649339,0.0314562917,-0.1483332068,-0.0989765152,-0.2203025371,0.1539326459,-0.0331301875,0.2842571437,-0.3529803753,0.1692639887,0.0968754515,0.3016742766,0.2464153916,0.34680143,-0.0859935731,-0.011230737,-0.031366203,0.1054866239,0.0539561436,0.1550135016,0.3106099367,0.1709685922,0.2803744376,0.4544360042,-0.0940064788,0.0249504186,-0.2067869008,-0.1653557122,0.0443974584,0.0359360576,0.3279303312,-0.233940199,-0.4098480344,0.1534145772,0.2209211886,0.088786453,0.2583982944,0.4830324352,-0.2361518294,-0.0013515315,-0.0064407205,0.0255305003,-0.1511354744,-0.1756568849,-0.3392073214,0.1940045655,-0.080814667,0.0127045149,0.3254746199,-0.1232190207,0.3092910647,-0.1528524309,-0.0015991911,-0.1232205778,-0.1105265766,-0.1869745404,-0.0999141708,0.2051265985,-0.0700348094,0.0878425166,0.1172278821,-0.3199294508,-0.2411322892,-0.1124539599,-0.1270280331,-0.3474444747,-0.0937899575,0.2281974107,-0.2979833782,0.1321427822,-0.0671058372,0.0436785258,0.0764682218,-0.2537538111,-0.1240771115,-0.1402088106,-0.4273744822,-0.3767170906,0.6006539464,0.6969179511,-0.2007522732,-0.1508898139,-0.5498247147,-0.3447906077,0.105987072,0.3750484884,-0.2740003765,0.1505955309,-0.2850112915,0.3159655631,0.3138710558,-0.4321829379,-0.4340073168,0.2631569505,-0.2292088717,0.4060312808,-0.0842331722,0.085828267,0.1212194487,-0.1092525572,0.3998244703,0.4777024686,0.0203810725,-0.1821041256,-0.2581339777,-0.1423353404,0.1062361747,0.1785901934,0.2633876503,0.078766875,0.1548193544,0.0659918264,0.2326668501,0.208025232,0.0624008998,0.1595210284,0.2724159062,0.2057394385,0.0333990641,-0.4820550382,-0.6707289219,0.4395003617,-0.1612329334,-0.0476558656,-0.1690658629,-0.1581422389,-0.419980526,0.0298832543,-0.0987409204,-0.2591835856,0.179764092,0.2151272893,0.2117394209,0.0778774843,-0.2501817942,0.5149942636,0.0565136932,0.0629970059,-0.3822929561,0.1305712163,-0.1241281852,-0.1103749722,-0.1185646728,0.3505547345,0.2312965542,-0.185125351,0.0676198527,0.5000377893,0.0044357101,-0.1740285009,-0.0906504989,-0.4309688807,0.0396165289,-0.1043563485,0.1348259002,0.2153398097,0.1075613499,-0.0511244535,0.1082193404,0.3727986515,0.0354981348,0.2296619713,0.0288257133,-0.001395671,0.1831604987,0.0764853358,0.1574743092,-0.2301817834,0.4892827272,0.2627514303,0.1726315022,0.207790941,-0.2014015764,-0.0117084198,0.085846737,-0.0487292819,0.0544365048,0.2945663631,-0.1510484666,-0.0472731255,0.0568785742,0.3697474301,0.3691950142,0.1709932387,-0.026174238,0.0965143219,-0.0314382352,-0.0691685751,0.1280409545,0.0516601652,0.0810704231,0.2116550356,0.069557637,0.1970311254,-0.2670096457,-0.2561357021,-0.0517689362,0.3250854015,-0.3932227194,0.1741223335,-0.2071428001,-0.1251394451,-0.0495271608,0.0053799627,-0.4605352283,-0.0769572705,0.0503376685,0.1106792986,0.226770401,0.1833500862,-0.1267197728,0.1852060109,-0.3189344406,0.0570205934,-0.2792580724,0.0003344137,-0.3292350471,0.0763938129,0.3980509639,-0.0389914773,0.3228530884,-0.2194560468,-0.2569383085,-0.1921415925,-0.0463258214,-0.1839065105,0.0817929432,0.6579142213,0.1881101578,0.248079896,-0.0574077852,-0.1280138791,0.3673250675,-0.302280426,-0.1845697612,0.0297332909,-0.1068385243,0.0871499255,-0.1282043159,-0.3432548344,-0.6057456136,-0.4530194402,0.309641391,0.1487502158,0.1859292686,0.3676010668,-0.1163644344,0.092038922,-0.048556868,0.2006089389,-0.1484298408,-0.2746390998,0.2829445004,-0.4200580418,-0.3737150729,-0.1102969795,0.0587631874,0.0354793705,0.0825509056,-0.45354864,0.3604623675,-0.1324108988,0.0797707886,-0.1327626556,-0.0603401028,0.0686467588,-0.1221249402,-0.0532959439,0.0417382829,-0.1169573069,0.0133847743,-0.2853544354,0.0561641678,-0.0636483952,0.3072242141,-0.0019497601,0.4964491725,0.0580832027,-0.1058206409,0.2891736031,-0.08743117,0.4604646862,-0.1876951754,-0.437345475,0.2259440571,0.0792094916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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2746","title":"Cannot load `few-nerd` dataset","comments":"Thank you @albertvillanova for your detailed feedback!\r\n\r\n> no need to pass explicitly `name=`\r\n\r\nGood catch! I thought `split` stands before `name` in the argument list... but now it is all clear to me, sounds cool! Thanks for the explanation.\r\n\r\nAnyways in our old code it still looks bit confusing if we only want one split but the function downloads all, so to allow efficient downloading, I optimized the code a bit so that only the specified split data is downloaded. now we get\r\n```\r\n>>> x = load_dataset(\"dfki-nlp\/few-nerd\", \"supervised\")\r\nDownloading and preparing dataset few_nerd\/supervised to \/home\/user\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/8e7ab598946cd5b395dcec6ea239123c8dff5b58b8e1c03b0c595b540248a885...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6M\/14.6M [01:01<00:00, 238kB\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3359329\/3359329 [00:12<00:00, 275462.84it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 482037\/482037 [00:01<00:00, 278633.64it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 958765\/958765 [00:03<00:00, 267472.83it\/s]\r\nDataset few_nerd downloaded and prepared to \/home\/user\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/8e7ab598946cd5b395dcec6ea239123c8dff5b58b8e1c03b0c595b540248a885. Subsequent calls will reuse this data.\r\n```\r\nwhere only one progress bar indicates downloading, and the three others just indicate pre-processing for the train, dev, test set.\r\n\r\nFor the encoding issue, I have made corresponding changes for the two lines you pointed out. However, I have no windows machine at hand, I would really appreciate it if you could help test on your end.\r\n\r\nAll the updates are uploaded to HF under `dfki-nlp` account where I am working for. \r\nThank you again for your kind help!\r\n","body":"## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n","comment_length":207,"text":"Cannot load `few-nerd` dataset \n ## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n \n Thank you @albertvillanova for your detailed feedback!\r\n\r\n> no need to pass explicitly `name=`\r\n\r\nGood catch! I thought `split` stands before `name` in the argument list... but now it is all clear to me, sounds cool! Thanks for the explanation.\r\n\r\nAnyways in our old code it still looks bit confusing if we only want one split but the function downloads all, so to allow efficient downloading, I optimized the code a bit so that only the specified split data is downloaded. now we get\r\n```\r\n>>> x = load_dataset(\"dfki-nlp\/few-nerd\", \"supervised\")\r\nDownloading and preparing dataset few_nerd\/supervised to \/home\/user\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/8e7ab598946cd5b395dcec6ea239123c8dff5b58b8e1c03b0c595b540248a885...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6M\/14.6M [01:01<00:00, 238kB\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3359329\/3359329 [00:12<00:00, 275462.84it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 482037\/482037 [00:01<00:00, 278633.64it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 958765\/958765 [00:03<00:00, 267472.83it\/s]\r\nDataset few_nerd downloaded and prepared to \/home\/user\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/8e7ab598946cd5b395dcec6ea239123c8dff5b58b8e1c03b0c595b540248a885. Subsequent calls will reuse this data.\r\n```\r\nwhere only one progress bar indicates downloading, and the three others just indicate pre-processing for the train, dev, test set.\r\n\r\nFor the encoding issue, I have made corresponding changes for the two lines you pointed out. However, I have no windows machine at hand, I would really appreciate it if you could help test on your end.\r\n\r\nAll the updates are uploaded to HF under `dfki-nlp` account where I am working for. \r\nThank you again for your kind help!\r\n","embeddings":[-0.3042272031,-0.0985626653,0.007841493,0.5131354332,0.328545779,-0.0576422513,0.5201516747,0.2553012073,0.1991576403,0.1681091636,-0.4604775012,-0.0910487175,-0.2877757847,-0.2970745862,0.4347799718,0.0320020169,-0.02518804,-0.0349554755,0.1119609401,0.0084387101,-0.2257288843,0.2362412065,-0.2453141659,0.0727761388,-0.419000715,-0.0549565963,0.0889210105,0.4892740846,-0.0719758198,-0.6296027303,0.460472703,0.014389392,0.2181515396,0.4235012233,-0.0001091994,0.144184947,0.4189922512,-0.019266123,-0.2884244621,-0.3601104915,0.0940838531,-0.5144337416,0.1233332306,-0.2608023882,-0.406347245,0.0236194301,0.1217370182,-0.2709337175,0.490170449,0.488686204,0.288490653,0.1309136897,-0.0160082523,-0.3164563179,0.4296740592,0.0675480366,-0.1612952203,0.2777442336,0.3432971835,-0.0043072193,-0.0363358036,0.0306214392,-0.0457585603,-0.1231167316,0.39013201,0.0024617843,-0.1256728023,-0.2126000524,0.1296324283,0.4381466508,0.2064512968,-0.2134825289,-0.2328190356,-0.1929797977,0.0738780946,-0.1819786429,0.1654340327,0.0319197588,-0.1912625879,0.0741785392,-0.0262068659,-0.0630680993,-0.1722616404,0.1423974484,-0.110440284,0.1547157764,-0.150649339,0.0314562917,-0.1483332068,-0.0989765152,-0.2203025371,0.1539326459,-0.0331301875,0.2842571437,-0.3529803753,0.1692639887,0.0968754515,0.3016742766,0.2464153916,0.34680143,-0.0859935731,-0.011230737,-0.031366203,0.1054866239,0.0539561436,0.1550135016,0.3106099367,0.1709685922,0.2803744376,0.4544360042,-0.0940064788,0.0249504186,-0.2067869008,-0.1653557122,0.0443974584,0.0359360576,0.3279303312,-0.233940199,-0.4098480344,0.1534145772,0.2209211886,0.088786453,0.2583982944,0.4830324352,-0.2361518294,-0.0013515315,-0.0064407205,0.0255305003,-0.1511354744,-0.1756568849,-0.3392073214,0.1940045655,-0.080814667,0.0127045149,0.3254746199,-0.1232190207,0.3092910647,-0.1528524309,-0.0015991911,-0.1232205778,-0.1105265766,-0.1869745404,-0.0999141708,0.2051265985,-0.0700348094,0.0878425166,0.1172278821,-0.3199294508,-0.2411322892,-0.1124539599,-0.1270280331,-0.3474444747,-0.0937899575,0.2281974107,-0.2979833782,0.1321427822,-0.0671058372,0.0436785258,0.0764682218,-0.2537538111,-0.1240771115,-0.1402088106,-0.4273744822,-0.3767170906,0.6006539464,0.6969179511,-0.2007522732,-0.1508898139,-0.5498247147,-0.3447906077,0.105987072,0.3750484884,-0.2740003765,0.1505955309,-0.2850112915,0.3159655631,0.3138710558,-0.4321829379,-0.4340073168,0.2631569505,-0.2292088717,0.4060312808,-0.0842331722,0.085828267,0.1212194487,-0.1092525572,0.3998244703,0.4777024686,0.0203810725,-0.1821041256,-0.2581339777,-0.1423353404,0.1062361747,0.1785901934,0.2633876503,0.078766875,0.1548193544,0.0659918264,0.2326668501,0.208025232,0.0624008998,0.1595210284,0.2724159062,0.2057394385,0.0333990641,-0.4820550382,-0.6707289219,0.4395003617,-0.1612329334,-0.0476558656,-0.1690658629,-0.1581422389,-0.419980526,0.0298832543,-0.0987409204,-0.2591835856,0.179764092,0.2151272893,0.2117394209,0.0778774843,-0.2501817942,0.5149942636,0.0565136932,0.0629970059,-0.3822929561,0.1305712163,-0.1241281852,-0.1103749722,-0.1185646728,0.3505547345,0.2312965542,-0.185125351,0.0676198527,0.5000377893,0.0044357101,-0.1740285009,-0.0906504989,-0.4309688807,0.0396165289,-0.1043563485,0.1348259002,0.2153398097,0.1075613499,-0.0511244535,0.1082193404,0.3727986515,0.0354981348,0.2296619713,0.0288257133,-0.001395671,0.1831604987,0.0764853358,0.1574743092,-0.2301817834,0.4892827272,0.2627514303,0.1726315022,0.207790941,-0.2014015764,-0.0117084198,0.085846737,-0.0487292819,0.0544365048,0.2945663631,-0.1510484666,-0.0472731255,0.0568785742,0.3697474301,0.3691950142,0.1709932387,-0.026174238,0.0965143219,-0.0314382352,-0.0691685751,0.1280409545,0.0516601652,0.0810704231,0.2116550356,0.069557637,0.1970311254,-0.2670096457,-0.2561357021,-0.0517689362,0.3250854015,-0.3932227194,0.1741223335,-0.2071428001,-0.1251394451,-0.0495271608,0.0053799627,-0.4605352283,-0.0769572705,0.0503376685,0.1106792986,0.226770401,0.1833500862,-0.1267197728,0.1852060109,-0.3189344406,0.0570205934,-0.2792580724,0.0003344137,-0.3292350471,0.0763938129,0.3980509639,-0.0389914773,0.3228530884,-0.2194560468,-0.2569383085,-0.1921415925,-0.0463258214,-0.1839065105,0.0817929432,0.6579142213,0.1881101578,0.248079896,-0.0574077852,-0.1280138791,0.3673250675,-0.302280426,-0.1845697612,0.0297332909,-0.1068385243,0.0871499255,-0.1282043159,-0.3432548344,-0.6057456136,-0.4530194402,0.309641391,0.1487502158,0.1859292686,0.3676010668,-0.1163644344,0.092038922,-0.048556868,0.2006089389,-0.1484298408,-0.2746390998,0.2829445004,-0.4200580418,-0.3737150729,-0.1102969795,0.0587631874,0.0354793705,0.0825509056,-0.45354864,0.3604623675,-0.1324108988,0.0797707886,-0.1327626556,-0.0603401028,0.0686467588,-0.1221249402,-0.0532959439,0.0417382829,-0.1169573069,0.0133847743,-0.2853544354,0.0561641678,-0.0636483952,0.3072242141,-0.0019497601,0.4964491725,0.0580832027,-0.1058206409,0.2891736031,-0.08743117,0.4604646862,-0.1876951754,-0.437345475,0.2259440571,0.0792094916,0.10210026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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2746","title":"Cannot load `few-nerd` dataset","comments":"Hi @chen-yuxuan,\r\n\r\nI have tested on Windows and now it works perfectly, after the fixing of the encoding issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"dfki-nlp\/few-nerd\", \"supervised\")\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 11.5k\/11.5k [00:00<?, ?B\/s]\r\nDownloading and preparing dataset few_nerd\/supervised to C:\\Users\\username\\.cache\\huggingface\\datasets\\dfki-nlp___few_nerd\\supervised\\0.0.0\\e1ceeaee82073fea12206e4461c7cfcd67e68c8f3ebeca179bddcacee00c4511...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 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958765\/958765 [00:06<00:00, 143152.35it\/s]\r\nDataset few_nerd downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\dfki-nlp___few_nerd\\supervised\\0.0.0\\e1ceeaee82073fea12206e4461c7cfcd67e68c8f3ebeca179bddcacee00c4511. Subsequent calls will reuse this data.765 [00:06<00:00, 139045.03it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 174.71it\/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 131767\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 18824\r\n    })\r\n    test: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 37648\r\n    })\r\n})\r\n```","body":"## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n","comment_length":112,"text":"Cannot load `few-nerd` dataset \n ## Describe the bug\r\n\r\nCannot load `few-nerd` dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset('few-nerd', 'supervised')\r\n```\r\n\r\n## Actual results\r\n\r\nExecuting above code will give the following error:\r\n\r\n```\r\nUsing the latest cached version of the module from \/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun  2 11:34:25 2021) since it couldn't be found locally at \/Users\/Mehrad\/Documents\/GitHub\/genienlp\/few-nerd\/few-nerd.py, or remotely (FileNotFoundError).\r\nDownloading and preparing dataset few_nerd\/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/Mehrad\/.cache\/huggingface\/datasets\/few_nerd\/supervised\/0.0.0\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1107, in _prepare_split\r\n    disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n  File \"\/Users\/Mehrad\/opt\/anaconda3\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/Users\/Mehrad\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/few-nerd\/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53\/few-nerd.py\", line 196, in _generate_examples\r\n    with open(filepath, encoding=\"utf-8\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/Users\/Mehrad\/.cache\/huggingface\/datasets\/downloads\/supervised\/train.json'\r\n```\r\nThe bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https:\/\/github.com\/nbroad1881\/few-nerd\/tree\/main\/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Python version: 3.8\r\n- PyArrow version: 1.0.1\r\n \n Hi @chen-yuxuan,\r\n\r\nI have tested on Windows and now it works perfectly, after the fixing of the encoding issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"dfki-nlp\/few-nerd\", \"supervised\")\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 11.5k\/11.5k [00:00<?, ?B\/s]\r\nDownloading and preparing dataset few_nerd\/supervised to C:\\Users\\username\\.cache\\huggingface\\datasets\\dfki-nlp___few_nerd\\supervised\\0.0.0\\e1ceeaee82073fea12206e4461c7cfcd67e68c8f3ebeca179bddcacee00c4511...\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3359329\/3359329 [00:25<00:00, 129427.23it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 482037\/482037 [00:03<00:00, 134513.66it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 958765\/958765 [00:06<00:00, 143152.35it\/s]\r\nDataset few_nerd downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\dfki-nlp___few_nerd\\supervised\\0.0.0\\e1ceeaee82073fea12206e4461c7cfcd67e68c8f3ebeca179bddcacee00c4511. Subsequent calls will reuse this data.765 [00:06<00:00, 139045.03it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 3\/3 [00:00<00:00, 174.71it\/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 131767\r\n    })\r\n    validation: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 18824\r\n    })\r\n    test: Dataset({\r\n        features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n        num_rows: 37648\r\n    })\r\n})\r\n```","embeddings":[-0.3042272031,-0.0985626653,0.007841493,0.5131354332,0.328545779,-0.0576422513,0.5201516747,0.2553012073,0.1991576403,0.1681091636,-0.4604775012,-0.0910487175,-0.2877757847,-0.2970745862,0.4347799718,0.0320020169,-0.02518804,-0.0349554755,0.1119609401,0.0084387101,-0.2257288843,0.2362412065,-0.2453141659,0.0727761388,-0.419000715,-0.0549565963,0.0889210105,0.4892740846,-0.0719758198,-0.6296027303,0.460472703,0.014389392,0.2181515396,0.4235012233,-0.0001091994,0.144184947,0.4189922512,-0.019266123,-0.2884244621,-0.3601104915,0.0940838531,-0.5144337416,0.1233332306,-0.2608023882,-0.406347245,0.0236194301,0.1217370182,-0.2709337175,0.490170449,0.488686204,0.288490653,0.1309136897,-0.0160082523,-0.3164563179,0.4296740592,0.0675480366,-0.1612952203,0.2777442336,0.3432971835,-0.0043072193,-0.0363358036,0.0306214392,-0.0457585603,-0.1231167316,0.39013201,0.0024617843,-0.1256728023,-0.2126000524,0.1296324283,0.4381466508,0.2064512968,-0.2134825289,-0.2328190356,-0.1929797977,0.0738780946,-0.1819786429,0.1654340327,0.0319197588,-0.1912625879,0.0741785392,-0.0262068659,-0.0630680993,-0.1722616404,0.1423974484,-0.110440284,0.1547157764,-0.150649339,0.0314562917,-0.1483332068,-0.0989765152,-0.2203025371,0.1539326459,-0.0331301875,0.2842571437,-0.3529803753,0.1692639887,0.0968754515,0.3016742766,0.2464153916,0.34680143,-0.0859935731,-0.011230737,-0.031366203,0.1054866239,0.0539561436,0.1550135016,0.3106099367,0.1709685922,0.2803744376,0.4544360042,-0.0940064788,0.0249504186,-0.2067869008,-0.1653557122,0.0443974584,0.0359360576,0.3279303312,-0.233940199,-0.4098480344,0.1534145772,0.2209211886,0.088786453,0.2583982944,0.4830324352,-0.2361518294,-0.0013515315,-0.0064407205,0.0255305003,-0.1511354744,-0.1756568849,-0.3392073214,0.1940045655,-0.080814667,0.0127045149,0.3254746199,-0.1232190207,0.3092910647,-0.1528524309,-0.0015991911,-0.1232205778,-0.1105265766,-0.1869745404,-0.0999141708,0.2051265985,-0.0700348094,0.0878425166,0.1172278821,-0.3199294508,-0.2411322892,-0.1124539599,-0.1270280331,-0.3474444747,-0.0937899575,0.2281974107,-0.2979833782,0.1321427822,-0.0671058372,0.0436785258,0.0764682218,-0.2537538111,-0.1240771115,-0.1402088106,-0.4273744822,-0.3767170906,0.6006539464,0.6969179511,-0.2007522732,-0.1508898139,-0.5498247147,-0.3447906077,0.105987072,0.3750484884,-0.2740003765,0.1505955309,-0.2850112915,0.3159655631,0.3138710558,-0.4321829379,-0.4340073168,0.2631569505,-0.2292088717,0.4060312808,-0.0842331722,0.085828267,0.1212194487,-0.1092525572,0.3998244703,0.4777024686,0.0203810725,-0.1821041256,-0.2581339777,-0.1423353404,0.1062361747,0.1785901934,0.2633876503,0.078766875,0.1548193544,0.0659918264,0.2326668501,0.208025232,0.0624008998,0.1595210284,0.2724159062,0.2057394385,0.0333990641,-0.4820550382,-0.6707289219,0.4395003617,-0.1612329334,-0.0476558656,-0.1690658629,-0.1581422389,-0.419980526,0.0298832543,-0.0987409204,-0.2591835856,0.179764092,0.2151272893,0.2117394209,0.0778774843,-0.2501817942,0.5149942636,0.0565136932,0.0629970059,-0.3822929561,0.1305712163,-0.1241281852,-0.1103749722,-0.1185646728,0.3505547345,0.2312965542,-0.185125351,0.0676198527,0.5000377893,0.0044357101,-0.1740285009,-0.0906504989,-0.4309688807,0.0396165289,-0.1043563485,0.1348259002,0.2153398097,0.1075613499,-0.0511244535,0.1082193404,0.3727986515,0.0354981348,0.2296619713,0.0288257133,-0.001395671,0.1831604987,0.0764853358,0.1574743092,-0.2301817834,0.4892827272,0.2627514303,0.1726315022,0.207790941,-0.2014015764,-0.0117084198,0.085846737,-0.0487292819,0.0544365048,0.2945663631,-0.1510484666,-0.0472731255,0.0568785742,0.3697474301,0.3691950142,0.1709932387,-0.026174238,0.0965143219,-0.0314382352,-0.0691685751,0.1280409545,0.0516601652,0.0810704231,0.2116550356,0.069557637,0.1970311254,-0.2670096457,-0.2561357021,-0.0517689362,0.3250854015,-0.3932227194,0.1741223335,-0.2071428001,-0.1251394451,-0.0495271608,0.0053799627,-0.4605352283,-0.0769572705,0.0503376685,0.1106792986,0.226770401,0.1833500862,-0.1267197728,0.1852060109,-0.3189344406,0.0570205934,-0.2792580724,0.0003344137,-0.3292350471,0.0763938129,0.3980509639,-0.0389914773,0.3228530884,-0.2194560468,-0.2569383085,-0.1921415925,-0.0463258214,-0.1839065105,0.0817929432,0.6579142213,0.1881101578,0.248079896,-0.0574077852,-0.1280138791,0.3673250675,-0.302280426,-0.1845697612,0.0297332909,-0.1068385243,0.0871499255,-0.1282043159,-0.3432548344,-0.6057456136,-0.4530194402,0.309641391,0.1487502158,0.1859292686,0.3676010668,-0.1163644344,0.092038922,-0.048556868,0.2006089389,-0.1484298408,-0.2746390998,0.2829445004,-0.4200580418,-0.3737150729,-0.1102969795,0.0587631874,0.0354793705,0.0825509056,-0.45354864,0.3604623675,-0.1324108988,0.0797707886,-0.1327626556,-0.0603401028,0.0686467588,-0.1221249402,-0.0532959439,0.0417382829,-0.1169573069,0.0133847743,-0.2853544354,0.0561641678,-0.0636483952,0.3072242141,-0.0019497601,0.4964491725,0.0580832027,-0.1058206409,0.2891736031,-0.08743117,0.4604646862,-0.1876951754,-0.437345475,0.2259440571,0.0792094916,0.10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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2743","title":"Dataset JSON is incorrect","comments":"As discussed, the metadata JSON files must be regenerated because the keys were nor properly generated and they will not be read by the builder:\r\n> Indeed there is some problem\/bug while reading the datasets_info.json file: there is a mismatch with the config.name keys in the file...\r\nIn the meanwhile, in order to be able to use the datasets_info.json file content, you can create the builder without passing the name :\r\n```\r\nIn [25]: builder = datasets.load_dataset_builder(\"journalists_questions\")\r\nIn [26]: builder.info.splits\r\nOut[26]: {'train': SplitInfo(name='train', num_bytes=342296, num_examples=10077, dataset_name='journalists_questions')}\r\n```\r\n\r\nAfter regenerating the metadata JSON file for this dataset, I get the right key:\r\n```\r\n{\"plain_text\": {\"description\": \"The journalists_questions corpus (\r\n```","body":"## Describe the bug\r\n\r\nThe JSON file generated for https:\/\/github.com\/huggingface\/datasets\/blob\/573f3d35081cee239d1b962878206e9abe6cde91\/datasets\/journalists_questions\/journalists_questions.py is https:\/\/github.com\/huggingface\/datasets\/blob\/573f3d35081cee239d1b962878206e9abe6cde91\/datasets\/journalists_questions\/dataset_infos.json.\r\n\r\nThe only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead.\r\n\r\n```json\r\n{\r\n  \"journalists_questions\": {\r\n    \"description\": \"The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\\n\",\r\n    ...\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nLook at the files.\r\n\r\n## Expected results\r\n\r\nThe first key should be `plain_text`:\r\n\r\n```json\r\n{\r\n  \"plain_text\": {\r\n    \"description\": \"The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\\n\",\r\n    ...\r\n```\r\n\r\n## Actual results\r\n\r\n```json\r\n{\r\n  \"journalists_questions\": {\r\n    \"description\": \"The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\\n\",\r\n    ...\r\n```\r\n\r\n","comment_length":109,"text":"Dataset JSON is incorrect \n ## Describe the bug\r\n\r\nThe JSON file generated for https:\/\/github.com\/huggingface\/datasets\/blob\/573f3d35081cee239d1b962878206e9abe6cde91\/datasets\/journalists_questions\/journalists_questions.py is https:\/\/github.com\/huggingface\/datasets\/blob\/573f3d35081cee239d1b962878206e9abe6cde91\/datasets\/journalists_questions\/dataset_infos.json.\r\n\r\nThe only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead.\r\n\r\n```json\r\n{\r\n  \"journalists_questions\": {\r\n    \"description\": \"The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\\n\",\r\n    ...\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nLook at the files.\r\n\r\n## Expected results\r\n\r\nThe first key should be `plain_text`:\r\n\r\n```json\r\n{\r\n  \"plain_text\": {\r\n    \"description\": \"The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\\n\",\r\n    ...\r\n```\r\n\r\n## Actual results\r\n\r\n```json\r\n{\r\n  \"journalists_questions\": {\r\n    \"description\": \"The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\\n\",\r\n    ...\r\n```\r\n\r\n \n As discussed, the metadata JSON files must be regenerated because the keys were nor properly generated and they will not be read by the builder:\r\n> Indeed there is some problem\/bug while reading the datasets_info.json file: there is a mismatch with the config.name keys in the file...\r\nIn the meanwhile, in order to be able to use the datasets_info.json file content, you can create the builder without passing the name :\r\n```\r\nIn [25]: builder = datasets.load_dataset_builder(\"journalists_questions\")\r\nIn [26]: builder.info.splits\r\nOut[26]: {'train': SplitInfo(name='train', num_bytes=342296, num_examples=10077, dataset_name='journalists_questions')}\r\n```\r\n\r\nAfter regenerating the metadata JSON file for this dataset, I get the right key:\r\n```\r\n{\"plain_text\": {\"description\": \"The journalists_questions corpus (\r\n```","embeddings":[0.1457878947,0.0401426293,-0.052989196,0.4768224955,0.106341809,0.2229720503,0.1550247669,0.3400089443,-0.239094466,0.0082270056,0.0357939377,0.4321314096,0.0995691568,0.138274163,-0.0019154998,-0.190864414,0.0582643151,-0.0400956571,0.0514679737,0.0135782566,-0.2260534912,0.4534671605,-0.0086119361,-0.0035447541,-0.2412652671,-0.0820137113,-0.0630130991,0.1975128502,-0.1947797537,-0.2986234725,0.2238184363,-0.0007780976,-0.2992435992,0.4079233706,-0.000105777,0.1115242764,0.3177174926,-0.0546918698,-0.2763531208,-0.3126670718,-0.1179979593,-0.1995790899,-0.1801231951,-0.3108087778,-0.224489361,-0.4072842896,-0.0359193645,-0.1976814717,0.4339139163,0.2254712582,0.288928777,0.0939777195,0.3655744493,0.0138632851,0.1234832406,0.3481938839,-0.0990860835,0.1498708278,-0.0261066817,0.2428706437,0.0618628412,0.5340320468,0.0121384664,-0.1359423101,0.3161493242,-0.0881342813,-0.1343109161,-0.2345810682,0.3665965199,0.2039393783,0.5051333308,-0.3382620215,-0.2950498462,-0.3582285941,-0.0540362,0.0441976264,0.3174577951,0.1573789716,-0.0850032195,0.2246190459,-0.1565230787,-0.128318727,-0.0585856065,0.1186569482,0.0848706737,0.1772662103,-0.1670170426,0.116541326,-0.1810568422,-0.2725031674,-0.0729672238,-0.3031312525,-0.2434514761,0.1057290584,-0.1498844028,-0.1740510315,0.1840366125,-0.1226231456,0.3561204076,0.0199349467,-0.0834137425,0.030259829,-0.1282664686,0.0784088969,0.2941545248,0.0664669797,0.2865293026,0.1005908623,0.0432852842,0.3054365218,-0.0957473516,-0.0153024169,0.2361126393,-0.2029713392,-0.0158883203,-0.0455252007,0.3907977045,-0.1099102944,-0.1303402781,0.316121757,-0.3122202456,-0.137791276,-0.0887379572,0.3365528882,-0.1041637957,-0.1737522781,0.0579555221,0.1500877142,-0.0759791508,-0.1040373445,-0.2383497059,-0.0655816421,-0.0106331585,0.0740007833,0.048797749,-0.3156566024,0.5672348738,0.1093741059,-0.0547511838,-0.407150954,0.089046672,0.0703376606,0.0181154348,0.2034135014,-0.1953361928,0.2121665329,0.0029763628,-0.2547075152,-0.1380570531,0.0224881601,-0.3546994925,-0.2375632823,-0.1867226958,0.2677464783,-0.0865126401,0.1017250717,-0.1889074743,0.2865265608,0.2674294412,-0.0266436636,0.0112008266,0.1325137913,0.0306418482,-0.2472921759,0.1174986064,0.3622021377,-0.2075220048,-0.1408250928,0.1055187657,0.1386903077,-0.0218916573,0.0166031532,-0.1378027946,0.2620957494,-0.206310302,0.1565947533,-0.0173679851,-0.350587666,-0.2432554811,0.2392064482,0.0047564222,0.2748712003,0.1912210286,-0.0517991073,-0.0962357223,0.1135012731,0.3098188341,0.0791999847,0.1609462202,0.2221574336,-0.2047823519,-0.4019450247,-0.0886192471,0.0951185599,-0.1413315386,0.0513563305,0.1474854946,-0.0433722213,0.3624107838,-0.0345611423,0.0621011369,0.3707377613,-0.0492520295,-0.089791216,0.2745544314,-0.0244210009,-0.5704759955,0.1188770086,0.0124510657,0.2495318949,-0.2598682046,-0.2070997953,-0.3331198394,-0.1166165248,-0.3170585036,-0.2710929513,0.2438492924,0.1982493848,0.0582692213,-0.0029124236,-0.1119038165,-0.068286553,0.1345844269,0.1485811025,-0.8418689966,0.0498394817,-0.2129893899,0.0804177523,0.2350145429,0.1320937872,0.1785342991,-0.1546062678,-0.0937623978,0.3380415142,0.0430230461,0.2134327143,-0.0276991129,0.2834200263,0.1190686226,-0.1272166818,0.0026943104,0.328243047,0.0061988663,-0.07700409,-0.1966425329,0.5660506487,0.1556259543,0.1487797946,-0.1113553196,-0.0827234909,0.3291027844,0.0196899921,-0.1956842542,-0.3322224915,0.1007923558,-0.0287543554,0.0456186794,0.0467657298,-0.3251135349,0.1408963799,0.6347138882,0.0261397306,0.0073852828,0.0732497573,-0.2073472589,-0.0869325474,0.0815157443,0.2034507245,0.3029956818,0.2047734112,-0.2401095629,0.1650703996,0.1505337656,-0.2791338861,0.2635053992,-0.0394194722,0.1640027463,0.2611382306,0.2849473953,-0.063887924,-0.3041082621,0.1152425036,-0.037274763,0.1826793104,-0.3774652779,0.2238548547,-0.1475861669,-0.1229848489,-0.2147876024,-0.2431918532,-0.3269706964,-0.2343281209,0.1149376258,-0.0334599651,-0.2041691989,0.1390652508,0.2810516655,0.0298622083,-0.0279995073,-0.0193298943,-0.417232126,-0.2008635998,-0.1176668927,0.0509741902,0.1550177485,-0.1096065491,0.2130268812,-0.3792505562,-0.1458819658,-0.3154368401,-0.4999270737,0.252818346,-0.1550461501,0.5203313231,0.2081254721,0.2512639761,-0.0175760482,-0.2799645364,0.3669069409,0.2514350712,-0.3487674892,0.1275802404,-0.1383172423,-0.0640730932,-0.0629763976,-0.4735097885,-0.1007245705,-0.2338860184,0.3656719029,0.1424440444,0.2632218003,0.6181697249,0.0286191851,-0.1326425225,-0.3063797653,0.4378258586,-0.3224540055,-0.6928203702,0.1458532512,-0.2717385292,-0.3099220693,-0.0869625881,0.1160565987,0.2011223435,-0.3413428068,-0.5350948572,-0.2200455219,-0.0060969437,0.1768632382,0.0011520705,0.219134897,0.1107885689,0.1014790908,-0.0868371278,-0.2585103214,-0.3038441241,0.0307266228,-0.046148967,0.4130176008,-0.0031142351,0.2360234261,-0.0675421655,0.2807981074,0.4191533625,-0.1038315371,0.1003282592,-0.1449592561,0.1923125088,-0.2088390738,-0.3092525303,0.1237522066,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2742","title":"Improve detection of streamable file types","comments":"maybe we should rather attempt to download a `Range` from the server and see if it works?","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\n```python\r\nfrom datasets import load_dataset_builder\r\nfrom datasets.utils.streaming_download_manager import StreamingDownloadManager\r\nbuilder = load_dataset_builder(\"journalists_questions\", name=\"plain_text\")\r\nbuilder._split_generators(StreamingDownloadManager(base_path=builder.base_path))\r\n```\r\n\r\nraises\r\n\r\n```\r\nNotImplementedError: Extraction protocol for file at https:\/\/drive.google.com\/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet\r\n```\r\n\r\nBut the file at https:\/\/drive.google.com\/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed:\r\n\r\n```bash\r\ncurl --header \"Range: bytes=0-100\" -L https:\/\/drive.google.com\/uc\\?export\\=download\\&id\\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U\r\n506938088174940160      yes     1\r\n302221719412830209      yes     1\r\n289761704907268096      yes     1\r\n513820885032378369      yes     %\r\n```\r\n\r\nYet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats.\r\n\r\n**Describe the solution you'd like**\r\n\r\nIn the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text\/html; charset=UTF-8`.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nAdd a variable in the dataset script to set the data format by hand.\r\n","comment_length":17,"text":"Improve detection of streamable file types \n **Is your feature request related to a problem? Please describe.**\r\n\r\n```python\r\nfrom datasets import load_dataset_builder\r\nfrom datasets.utils.streaming_download_manager import StreamingDownloadManager\r\nbuilder = load_dataset_builder(\"journalists_questions\", name=\"plain_text\")\r\nbuilder._split_generators(StreamingDownloadManager(base_path=builder.base_path))\r\n```\r\n\r\nraises\r\n\r\n```\r\nNotImplementedError: Extraction protocol for file at https:\/\/drive.google.com\/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet\r\n```\r\n\r\nBut the file at https:\/\/drive.google.com\/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed:\r\n\r\n```bash\r\ncurl --header \"Range: bytes=0-100\" -L https:\/\/drive.google.com\/uc\\?export\\=download\\&id\\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U\r\n506938088174940160      yes     1\r\n302221719412830209      yes     1\r\n289761704907268096      yes     1\r\n513820885032378369      yes     %\r\n```\r\n\r\nYet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats.\r\n\r\n**Describe the solution you'd like**\r\n\r\nIn the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text\/html; charset=UTF-8`.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nAdd a variable in the dataset script to set the data format by hand.\r\n \n maybe we should rather attempt to download a `Range` from the server and see if it works?","embeddings":[-0.4683198929,-0.0467546023,-0.0979575142,0.1375530511,0.1663962454,-0.2239752114,0.1926611513,0.4674963355,0.0628534257,0.2362468243,-0.0067694117,-0.0470789447,-0.4149926007,0.2554931045,0.0666618496,-0.1503152698,0.0009771778,0.0538053922,0.2009106427,0.0540023446,-0.204148069,-0.060409423,-0.0322798118,-0.3867236376,0.1372658163,0.1212853044,0.1237365007,-0.1005211771,-0.5439122319,-0.474157244,-0.0997944027,0.3655941784,0.3324325681,0.445081681,-0.0001090667,-0.1867188662,0.3261322081,-0.2225363702,-0.3541294932,-0.3582729399,-0.3125940263,-0.0820247084,0.1180793792,-0.0586481243,-0.0832975954,-0.0529165454,0.0051997509,-0.4320715666,0.2591350675,0.4835053086,0.1480431259,0.2258724123,-0.0629004538,0.3467933834,0.3124316335,0.3924538493,-0.1639557183,0.0028929068,0.2594112754,0.1911760718,-0.0581254363,0.1383457035,-0.1698191315,0.103310965,-0.0566330105,0.0279623643,-0.2206067294,-0.5073229074,-0.0161742251,0.5063699484,0.4199713469,0.0651927963,-0.4760579169,-0.2841737866,0.1523337513,0.0003063231,0.2511096299,0.1182074249,-0.3653725684,0.1618278921,-0.3606187999,-0.0820078999,-0.2226942927,0.1248639673,-0.1203310639,0.2459635735,-0.043402236,-0.0680061206,0.0655301437,0.1497651935,0.4056751728,-0.2479868233,0.3087380528,0.0348310992,-0.0393290222,-0.2037237287,-0.1606526524,0.0232138038,0.139927879,0.2566772699,0.3086072505,0.2102730125,-0.1491983533,0.2135013491,0.0305546075,-0.0383743644,-0.0746152326,-0.0697007105,0.2174675316,0.5407654643,-0.0032599913,-0.0687255338,-0.0720643029,-0.0631122068,0.1853893548,0.1223772541,0.4066690803,-0.0915880501,-0.3426751196,-0.150570482,-0.3363800049,-0.253600508,0.1648764163,0.256169498,-0.1719850004,0.426687181,-0.0319470987,0.3137049079,0.0381046534,-0.2230694443,0.1180704385,0.065703012,0.2028967142,-0.0114821773,0.3926180303,-0.2817879915,-0.2386939377,-0.0895161629,-0.0203915052,0.070417285,0.1470766217,-0.0729336962,0.4420581162,-0.0253235754,-0.1359640807,-0.0063474299,0.0284750815,-0.1936660707,-0.263530761,0.0223022848,-0.2017050236,-0.0364400446,0.0798283145,0.2122864425,-0.196270898,-0.1137211472,-0.2630351782,0.336170435,-0.3221484721,0.0124914376,0.033613544,-0.0683287829,-0.041918356,-0.0847013518,0.2602454722,0.5462822914,-0.0980606303,-0.043349836,-0.2405214012,-0.1707349867,0.5798384547,-0.0437973626,-0.0083254362,-0.0659563169,-0.1838844419,0.1395738423,0.4809137881,-0.1480768174,-0.1279195249,0.4295482635,-0.0217103604,0.4528400004,0.608165741,-0.1676059514,0.2023663819,-0.1279568076,-0.0170407351,0.5179781318,-0.161183551,-0.0107255802,-0.3075853288,-0.3225665689,0.2332601845,0.1089288965,0.2063652277,0.1164506003,0.3067207932,-0.1911922246,0.1745196581,-0.180751577,0.094783254,-0.2880555093,0.5135552287,-0.1454490572,-0.1516349018,-0.2969726622,-0.2235253751,-0.0345313996,-0.0053609889,0.1067875773,-0.5588930845,-0.3448258936,-0.1206655875,-0.1046227217,-0.1249413565,0.0593878478,0.194429189,0.2214226276,-0.2366072237,0.0891409293,-0.2099660188,-0.0000200507,-0.1156358197,0.1792646646,0.109158054,0.2531509995,0.158861354,0.0651161149,0.4046080112,-0.0879118964,0.2208455652,-0.0490316004,-0.2288409323,0.1611866504,0.1499025971,0.4502606988,0.1342955381,0.1967618763,0.3344070911,-0.2108052522,-0.1991127133,0.5702888966,0.1838453412,0.1385805905,-0.1338566691,0.2535959482,0.072673738,0.0099196509,0.2294880599,-0.0721119195,0.1688815355,0.0099147763,-0.1288588196,-0.2819772363,0.1058612242,-0.012520642,-0.1538639814,0.0313940495,-0.1393785477,0.0554552376,0.344843179,-0.1956211776,0.0726768151,0.3713411391,-0.0236742068,-0.2375599891,0.0598795488,0.481107235,0.2880631983,0.0595036335,0.3818356693,-0.1275047213,0.1442793757,-0.2373071015,0.2923407555,0.0002398107,-0.2734445333,0.1132262871,0.0885429978,-0.0658793598,-0.3768078983,0.0178051908,-0.003829428,-0.0365524292,-0.4186419845,0.0290679652,-0.5251237154,-0.0360287502,-0.24801974,-0.1389541775,-0.0589400642,-0.1279436797,0.2919638753,0.2840204835,-0.1698154211,-0.0176665634,-0.2772929072,0.4388016462,0.0414904952,-0.206550926,-0.0967263281,0.1288214326,0.0043228273,0.1860977262,0.6305446029,0.1073832139,0.4484096169,-0.3081641793,0.0962823257,-0.624953866,-0.2172114253,0.0335564986,0.0587058291,0.4059967697,0.0810813755,0.2308991998,0.1287959367,-0.3905832469,-0.0668752044,-0.1540997922,-0.000573795,0.2530429363,-0.0182126667,0.0828403682,-0.1011619419,-0.6140940785,-0.1934521198,-0.3065446913,0.1677130163,0.0852085128,0.1408643723,0.0646377355,-0.0652581304,-0.1110421121,0.0727033243,0.2945084274,-0.0126775391,-0.3376013935,0.4186655879,-0.1411740929,-0.2259037048,0.0813955963,-0.0794175118,-0.155160293,0.7107824087,-0.0593888238,-0.1276237816,-0.0179897584,-0.0429530255,0.1628359109,-0.2291655391,0.1578842551,0.0400509462,-0.0961626768,-0.1579543501,0.4025491774,-0.1899679899,0.1545591652,0.3249195218,0.1847119629,0.0663637668,0.1059691384,0.2219640762,-0.0338329673,-0.0687349513,0.6299155354,0.1036639512,-0.0545411929,0.1048766822,0.0151892668,0.3104054332,-0.2058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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2737","title":"SacreBLEU update","comments":"Hi @devrimcavusoglu, \r\nI tried your code with latest version of `datasets`and `sacrebleu==1.5.1` and it's running fine after changing one small thing:\r\n```\r\nsacrebleu = datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [[\"It is a guide to action that ensures that the military will forever heed Party commands\"]]  # double brackets here should do the work\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\noutput: {'score': 41.180376356915765, 'counts': [11, 8, 6, 4], 'totals': [18, 17, 16, 15], 'precisions': [61.111111111111114, 47.05882352941177, 37.5, 26.666666666666668], 'bp': 1.0, 'sys_len': 18, 'ref_len': 16}\r\n```","body":"With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n","comment_length":101,"text":"SacreBLEU update \n With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n \n Hi @devrimcavusoglu, \r\nI tried your code with latest version of `datasets`and `sacrebleu==1.5.1` and it's running fine after changing one small thing:\r\n```\r\nsacrebleu = datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [[\"It is a guide to action that ensures that the military will forever heed Party commands\"]]  # double brackets here should do the work\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\noutput: {'score': 41.180376356915765, 'counts': [11, 8, 6, 4], 'totals': [18, 17, 16, 15], 'precisions': [61.111111111111114, 47.05882352941177, 37.5, 26.666666666666668], 'bp': 1.0, 'sys_len': 18, 'ref_len': 16}\r\n```","embeddings":[-0.3851117492,0.180214569,-0.0012566224,-0.0679098815,0.5497623682,-0.2189842165,0.0627019629,0.3480107784,-0.2101948857,0.1243230999,0.0086564887,0.3338840604,-0.0440222882,-0.0246399418,-0.1152012944,-0.0089820018,0.1046468839,0.0598538108,0.3436266184,0.0075876298,-0.3184528649,0.1174141839,-0.1806857735,-0.0027261802,-0.2734516859,0.0927983373,-0.0575441308,-0.0664327666,-0.4653164446,-0.7149304748,0.173110649,0.0709628239,-0.0604935363,0.4433312714,-0.000109066,-0.1288360953,0.2636282146,-0.2208125442,-0.4332722723,-0.4529219866,-0.1555378437,-0.2812544405,0.0101911267,-0.1026846096,-0.0918698385,-0.3785425127,-0.0429842286,-0.0021675148,0.2421511263,0.3916675746,0.256303072,0.2788763046,0.0878582969,-0.3278107643,-0.0474800244,0.0717536137,-0.0416628681,0.2062654793,-0.1564477384,0.0919184536,0.069754228,0.1396521628,-0.2196805179,0.1190987453,0.025156932,-0.0775799155,-0.1114928201,-0.0601761714,0.1122960523,-0.047787834,0.3497331738,-0.4587009549,-0.382728368,0.1970104724,0.0173574872,-0.4542078376,-0.0254984181,-0.1006393731,0.0167840701,-0.1670011282,0.0344170108,0.0062938989,-0.0445957854,0.1155721471,-0.159620747,0.3486795425,-0.2516730428,0.0796109736,0.0394845605,-0.0542566106,-0.4969714582,0.107368283,-0.238305971,0.0230437443,-0.3772975802,-0.0650168061,-0.0828539804,-0.3516820669,0.0969957709,0.3739204109,0.0210683458,0.2644359767,0.1377246678,0.1189339012,-0.0622321777,0.5510037541,0.3230520189,0.1967891306,0.0707692876,0.2654051781,-0.0412965603,-0.0453923941,0.0042824158,-0.2698685825,0.2463271171,0.106711179,0.2557301223,-0.1353619397,-0.3971830904,0.1474429816,0.237819761,-0.0447644703,-0.1319333911,0.4307968915,-0.2377204746,0.1725683957,-0.0958559588,0.2664754987,-0.2389557958,-0.3424350023,-0.2415787727,0.0390883572,-0.3046321571,-0.1079160646,0.1816323996,0.2411848903,0.1305253953,-0.0414056592,0.4660554528,-0.2226983905,0.2804109454,-0.230205372,0.0603220426,-0.018826507,-0.119918853,0.0238396004,0.516158402,-0.610388577,0.0151248639,-0.1123187244,-0.2561796904,-0.2588275671,0.343396157,0.2879838645,-0.2528564334,-0.3133336008,-0.4201458991,0.2051164061,0.0603080615,0.0521517247,-0.001207719,-0.0462268293,-0.1970331371,-0.2614487112,0.3741241395,0.320263952,-0.1077721417,-0.1051594615,0.135828808,-0.1713021845,0.0858757123,-0.2513420284,0.0804902092,0.2804731429,0.1020598337,-0.0613257922,0.2927131057,-0.357960999,-0.5469436646,-0.0862499177,0.0579455532,-0.2599137723,0.0062024249,-0.110605292,0.5220208168,0.015823571,-0.0154365925,-0.0312897712,0.1183197945,-0.137460351,-0.2333331704,-0.4542905092,-0.0332773179,0.0685944259,0.5740548372,-0.0187632386,0.2364591807,0.3137117922,0.2896054089,0.0075053233,0.0306334458,0.1310765445,0.595379889,-0.1788109094,0.2888144553,-0.4382771552,0.0940980241,0.1595152766,0.0832317844,0.0736056566,0.0023776656,-0.2065862715,-0.5189365745,0.01281083,-0.2269904166,0.024094034,0.2367413193,0.1432618499,-0.0748593882,0.1702120453,-0.2773281932,-0.0676012337,-0.1973088235,0.2919341028,0.117482841,0.2413860857,-0.0157091059,-0.3451884985,0.2164435387,0.3111023903,0.294957608,-0.0879629478,-0.1517492831,0.4523438215,0.3043161929,0.1105450764,-0.0066715823,0.2907354534,0.0779274553,-0.2848576605,-0.0822741091,0.4827958941,0.0123695042,-0.0732993707,0.0758537129,0.3865855336,0.0324071869,0.1609558612,0.0566230379,0.2090730816,0.0145331286,-0.044533886,-0.1115885377,-0.2812256515,-0.1029724181,-0.2905320227,-0.1413835585,-0.244855538,0.0898525119,0.0704853386,0.2296757549,0.1971512437,0.1057656109,0.0084909536,-0.1468926072,0.0489098392,-0.1874101758,0.2557059824,0.2897592187,0.1384315789,0.0444402024,0.0740775838,0.0620137081,0.0123118209,0.1836156994,0.146964848,-0.3406762183,0.2635623217,0.0756593868,0.0884651467,-0.1997726709,0.0275216233,-0.0363636836,0.3163969815,-0.0755243748,0.1237703562,-0.0936109424,0.194595322,-0.0755663365,-0.2289679497,-0.0397202075,-0.201972723,-0.0094109001,-0.1007340997,0.1501653939,0.4106091559,0.0232313927,0.2186760455,0.0232600998,-0.151840359,0.1562186629,-0.1369847953,0.0295171738,0.0613607392,-0.0998253226,-0.2353059351,0.3304955363,-0.1720774174,-0.0926687866,-0.1907322705,-0.3452540934,0.0945268869,0.0551952235,0.3043855429,0.172596246,-0.1930793077,-0.0501054414,-0.0340395384,0.3287703693,-0.2375615537,0.0633648634,0.3274709284,-0.1111545712,-0.1580805331,-0.0383770168,-0.5256832242,-0.0342421457,-0.3555924296,-0.0401233099,0.3284459412,-0.0220830236,0.1165082529,0.3360468149,0.1921483576,0.1437692046,0.1019056812,-0.215679273,-0.0004950132,0.4178360105,-0.2397291362,-0.407648921,0.2128310055,-0.0815080181,0.2679240704,-0.0091952663,-0.3185970783,-0.4406927526,0.0532261543,-0.0969674885,-0.0705453008,0.1420745254,0.0384497531,0.0709822178,-0.2444822937,-0.1577114761,-0.1952126473,0.0239673052,-0.1525555998,0.288175106,-0.2231129408,-0.0932206884,0.5048215985,0.8064700961,0.3794753551,-0.1979299188,-0.052221451,0.0602564961,0.359092325,0.148836568,-0.3842603564,0.159408778,-0.3968274891,0.11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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2737","title":"SacreBLEU update","comments":"@bhavitvyamalik hmm. I forgot double brackets, but still didn't work when used it with double brackets. It may be an isseu with platform (using win-10 currently), or versions. What is your platform and your version info for datasets, python, and sacrebleu ?","body":"With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n","comment_length":42,"text":"SacreBLEU update \n With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n \n @bhavitvyamalik hmm. I forgot double brackets, but still didn't work when used it with double brackets. It may be an isseu with platform (using win-10 currently), or versions. What is your platform and your version info for datasets, python, and sacrebleu ?","embeddings":[-0.3610989153,0.1312095225,-0.0056600953,-0.1026852429,0.4761410058,-0.1557441801,0.1335223466,0.3350856304,-0.0614675321,0.1180700734,0.0635044575,0.414668411,-0.0860366523,-0.1110804603,-0.0810954571,-0.0622073822,0.132537201,0.0672766715,0.3208294213,-0.0251739398,-0.2997123003,0.1096768677,-0.2105325013,0.0695405751,-0.2469487488,0.0275084693,-0.0339476764,-0.0466949157,-0.4527229071,-0.6465780139,0.2405426353,0.1384067237,-0.0376656353,0.4237561822,-0.0001106591,-0.1028935835,0.303573072,-0.1960429102,-0.4353863597,-0.4215492606,-0.151994884,-0.3628089428,0.0174232349,-0.1062814593,-0.0368342102,-0.3589268029,-0.0217846669,-0.0480082631,0.2329918742,0.4084700346,0.2475418895,0.3381185234,0.0511740781,-0.3478782177,-0.1333429813,0.0854820535,-0.045865424,0.20325239,-0.0625104606,0.0833064616,0.0917622522,0.0576002821,-0.2753891647,0.118760936,0.1333260685,-0.1312291473,-0.1327502429,-0.0118353097,0.083412528,0.0151524441,0.3650402725,-0.3823320866,-0.3735580742,0.2127169222,0.0366065875,-0.3824598491,0.009144906,-0.1123226285,-0.0093321521,-0.1700323224,0.0664357245,-0.1201473475,-0.0205986146,0.0703741759,-0.2472744286,0.4081158936,-0.2506107986,0.0469713584,0.0798312947,-0.0848306268,-0.4981856346,0.0868942365,-0.1984250247,0.0335591696,-0.340089798,-0.0250237826,-0.0763024688,-0.3464218378,0.0813369378,0.3945920169,0.048743818,0.2591862977,0.1699689478,0.20894216,-0.1301805824,0.4959830642,0.2420791984,0.220037058,0.1657265276,0.201977089,-0.066154331,-0.0687891021,-0.1287662685,-0.2297536731,0.2602816522,0.0804346278,0.3282777965,-0.0755720735,-0.513369441,0.1752422154,0.2400903702,-0.0607881695,-0.0440927558,0.3927013874,-0.2399066389,0.1500324458,-0.153525725,0.2635989785,-0.2359527349,-0.366746217,-0.2468986809,0.0178700257,-0.2878113389,-0.1278100163,0.1567808092,0.1984203309,0.1318725646,-0.0593957193,0.3452292979,-0.1717815995,0.2690585852,-0.2084930241,-0.0340363085,-0.0009564048,-0.1924007088,0.0749266669,0.4489347041,-0.731613934,-0.1049167067,-0.1289740205,-0.3792109191,-0.2902465165,0.2612980604,0.2740133703,-0.2312810719,-0.3622233272,-0.4179069698,0.2116141766,-0.0123012662,0.0543336086,-0.0246524755,-0.037327271,-0.2178055197,-0.265399307,0.3607482016,0.3120439947,-0.1571212262,-0.1470865756,0.0469976664,-0.1452475488,0.046555765,-0.1418345124,0.0897663683,0.3025019765,0.0039869021,-0.0378518663,0.3772502244,-0.3398386836,-0.4797562063,-0.0732089207,0.0153638031,-0.2394689918,0.0264777839,-0.0947665423,0.5503355265,-0.011440808,0.014831217,0.0521674044,0.118539989,-0.2458545566,-0.1919911206,-0.3879248798,0.000823137,0.0664248466,0.5991947055,0.0690891594,0.2336752415,0.3648743033,0.2155066431,0.0335599668,-0.0352310091,0.2000242025,0.625720799,-0.1667003334,0.2624933422,-0.5427994728,0.0894249454,0.139965117,0.1393514425,0.0019271293,0.0035258583,-0.146954596,-0.5078863502,0.0668669492,-0.1458019167,0.0626409575,0.1951815188,0.1813527048,-0.1051507071,0.2351346016,-0.2970178723,-0.0262000915,-0.113038525,0.2730753422,0.1114348769,0.2101031691,-0.0158116538,-0.2850116491,0.2238170207,0.2779830098,0.2404205501,-0.0966570005,-0.1334513724,0.4079409242,0.2851322889,0.1962303668,0.0060256477,0.3215174675,0.0572762825,-0.3230302334,-0.1335025281,0.4925211966,0.0297270156,-0.0744329318,0.0689793304,0.3435507715,0.1100498289,0.1087410003,0.0434566252,0.2713367939,0.0451424047,-0.0510619655,-0.0742415488,-0.2531876862,-0.0879332051,-0.2256300002,-0.0866144001,-0.1809440106,0.0871166214,0.0022355102,0.3021745682,0.1648402512,0.1735638082,0.0365758501,-0.2439983338,0.0406366624,-0.2193615437,0.2514435053,0.3448076546,0.1203919202,0.0628764853,0.0848325193,-0.0402030684,-0.0243767574,0.2261278778,0.1047915518,-0.4793460071,0.2292708904,0.035115622,0.1252464056,-0.1896268278,0.021619333,0.0023161245,0.3032466173,-0.1119338125,0.1487003863,-0.1841072887,0.2805725038,-0.0352871083,-0.1682987064,-0.0367314555,-0.1768313199,0.0316732451,0.0094892653,0.1756763905,0.4028092325,-0.015124619,0.3465368748,0.0519646257,-0.2131378949,0.1350440234,-0.0875090808,-0.0391141139,0.0214336626,0.0093573881,-0.2057677805,0.2946458757,-0.1813661158,0.0153842447,-0.3245871663,-0.3740167916,0.072844103,0.0797597468,0.4360913038,0.1809325963,-0.1662887633,-0.0249089375,-0.0552834235,0.3301906884,-0.2599101961,0.0595741384,0.3426205814,-0.0848998129,-0.2088809013,0.0111935334,-0.4363763332,-0.0135472305,-0.394340992,-0.0577675626,0.2302500308,-0.1176094115,0.0639883652,0.2232749313,0.1488091648,0.1653535217,0.1498259902,-0.1831990778,-0.1005549282,0.5080993176,-0.192951858,-0.4019785821,0.1647427082,-0.1132767797,0.2334119231,0.0597606115,-0.286100775,-0.3820084035,0.0010044457,-0.005481902,-0.0573163442,0.1456347257,0.075875476,0.0291795786,-0.2026308328,-0.1702332199,-0.2736971974,-0.0046370388,-0.1380315125,0.253814131,-0.1768521816,-0.1441241354,0.4649412036,0.8005051613,0.3342849612,-0.2882514,0.0038186598,0.0407599658,0.3907130659,0.2285870016,-0.4275695682,0.0983100608,-0.3324217796,0.1017847508,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2737","title":"SacreBLEU update","comments":"You can check that here, I've reproduced your code in [Google colab](https:\/\/colab.research.google.com\/drive\/1X90fHRgMLKczOVgVk7NDEw_ciZFDjaCM?usp=sharing). Looks like there was some issue in `sacrebleu` which was fixed later from what I've found [here](https:\/\/github.com\/pytorch\/fairseq\/issues\/2049#issuecomment-622367967). Upgrading `sacrebleu` to latest version should work.","body":"With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n","comment_length":36,"text":"SacreBLEU update \n With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n \n You can check that here, I've reproduced your code in [Google colab](https:\/\/colab.research.google.com\/drive\/1X90fHRgMLKczOVgVk7NDEw_ciZFDjaCM?usp=sharing). Looks like there was some issue in `sacrebleu` which was fixed later from what I've found [here](https:\/\/github.com\/pytorch\/fairseq\/issues\/2049#issuecomment-622367967). Upgrading `sacrebleu` to latest version should work.","embeddings":[-0.3578206599,0.1911823153,-0.0130563695,-0.1219669282,0.4591220915,-0.2587324679,0.1023559123,0.3462676704,-0.1047268733,0.1702917069,0.0292684827,0.387314558,-0.0286068097,-0.1296031922,-0.0896717906,-0.0818408206,0.0223368928,0.1103716716,0.2108440399,-0.0650736988,-0.2887302041,0.0873918533,-0.1631985605,0.0852666646,-0.2492381483,-0.0023167096,-0.0760261863,-0.0779753551,-0.4405121207,-0.606107235,0.1186018363,0.1210269481,-0.0479873531,0.3743449152,-0.0001058455,-0.1107484773,0.305143863,-0.1988532692,-0.4026468098,-0.3009606898,-0.1276697367,-0.2364968956,0.1063758135,-0.1455808282,-0.1818398535,-0.3046250343,0.0252749342,0.0940319672,0.2512167692,0.4529435635,0.3059908152,0.4062624276,0.0230126735,-0.3524657786,-0.0199980736,-0.0242332816,-0.0131464573,0.2505607605,-0.0646727458,-0.0202181023,-0.0520274863,0.0627775341,-0.1706924438,0.1623967141,-0.0237090085,-0.0873843431,-0.1177439317,-0.0695197657,0.0494956113,-0.0812642649,0.2804450393,-0.38291201,-0.4305168688,0.2086233795,0.0718514994,-0.2917512655,-0.0081197824,-0.118237175,-0.0117069725,-0.1671764553,0.0797321945,-0.0309032053,-0.0469655395,0.0218466222,-0.2776467204,0.3991086185,-0.2167878002,0.0295434184,0.1019448638,-0.0798586756,-0.5395296812,0.1725980639,-0.1630486846,-0.0472776443,-0.3372485936,-0.0520965829,-0.035220474,-0.3039965332,0.079724595,0.2673193514,0.0789336413,0.3234218955,0.172597751,0.2088111937,-0.1074544266,0.5174102187,0.1569871008,0.2109557837,0.1481880993,0.2084748149,-0.1582446992,0.0132106123,-0.0868140534,-0.2773756385,0.1811378449,0.0504109226,0.2989059985,-0.1296224296,-0.5161451101,0.1212263703,0.3249453306,0.0074683409,-0.0784414336,0.460008651,-0.275907129,0.0837625787,-0.1150488257,0.2334918678,-0.3387951851,-0.3230011165,-0.2766523659,0.0408223793,-0.37425071,-0.0881016254,0.2087676078,0.2642614841,0.1801964492,-0.0865474418,0.3493546546,-0.1316841245,0.258661449,-0.2561262548,-0.0517255925,-0.0069767064,-0.1430691183,-0.0315161273,0.4688054621,-0.6108638644,-0.0566984527,-0.1254149824,-0.3261383176,-0.2362999618,0.2884625196,0.3258454204,-0.2371550053,-0.2956566513,-0.3317900896,0.2374335825,0.0726340786,0.027764719,-0.0176040698,-0.122636795,-0.2025411278,-0.2438831478,0.3008957803,0.3525292873,-0.1587591618,-0.1621088684,0.1584931165,-0.1695159525,0.0624839589,-0.0763147175,0.0115527418,0.3146710992,0.0382410213,-0.0237806197,0.2955042422,-0.2857785523,-0.5236398578,-0.0819974169,-0.0154975886,-0.2500258684,-0.0530064143,-0.0627646521,0.6092697382,0.0600496829,-0.0038255965,-0.0115368627,0.1557662636,-0.0427967533,-0.2675361335,-0.3750508428,-0.0079508908,0.0790766925,0.5792466998,0.0346505754,0.2244628966,0.1579549909,0.1856236309,0.0233877096,0.0189662781,0.2308950126,0.6588175297,-0.1663859934,0.247043848,-0.4124954045,0.0987278447,0.1414683759,-0.0113154342,0.0937688276,0.0519420467,-0.1947705895,-0.4691921771,-0.0183418598,-0.2268976122,0.0119521422,0.2715171576,0.1378232688,-0.0797377303,0.2032039464,-0.2275076956,0.0311874021,-0.1075829268,0.2439023256,0.1114137322,0.2018918395,-0.1064896807,-0.3009128273,0.3025524914,0.2251977921,0.2552266419,-0.026972048,-0.1187357083,0.4040307999,0.2718547285,0.1211067364,0.1103964373,0.2345688492,0.0470449403,-0.3110350668,-0.1486893743,0.4316517413,0.0484483428,-0.0514895581,0.0397672914,0.4062992334,0.1410156786,0.0460959598,0.067911312,0.1683560163,0.0360379927,-0.1195729524,-0.0889129713,-0.1963648051,-0.0799371526,-0.1591659784,-0.0623397231,-0.1417454928,0.0780003294,0.0879584253,0.3270541131,0.248662591,0.2066837102,0.076920718,-0.1798181534,-0.0212408919,-0.17702052,0.2161441892,0.3647597432,0.1700563878,0.0438454151,0.0763294026,-0.0463225432,-0.0378088616,0.179175958,0.0653135777,-0.4200987816,0.1537058055,0.1766033918,0.0890044197,-0.1963602304,0.0585504919,0.0622049607,0.2806884348,-0.0912156478,0.1762315631,-0.1289596409,0.3334774971,0.0158991348,-0.2181564569,-0.0460447557,-0.170931235,0.1734342575,0.0025810634,0.0736619085,0.3721660078,0.0109238736,0.3784334064,0.2001804113,-0.2112026513,0.1066684648,-0.2119735032,-0.0180871375,0.0760566518,-0.1197405308,-0.0421262793,0.3133828342,-0.1565406173,-0.0128217386,-0.3236809075,-0.3269429803,0.1113748699,0.0142587032,0.455611378,0.1397294104,-0.1708728969,-0.1437091231,-0.0577829257,0.3358262777,-0.2341038287,-0.0047566285,0.3123632967,-0.1245187744,-0.1732459068,-0.1278391331,-0.4561173618,0.0026664534,-0.3819520175,-0.0850622058,0.1646662205,-0.0369072109,0.0480373465,0.2475905418,0.1148171499,0.0956451893,0.0587986745,-0.2714680135,-0.132200256,0.5068076253,-0.3097522855,-0.4754901528,0.1518469155,-0.1226331145,0.2845422626,0.0372845605,-0.2821929157,-0.5299497247,0.0195122976,0.0280244816,-0.0609569177,0.0801196769,0.1169326678,0.0231861994,-0.2826243043,-0.1794141382,-0.2012349367,0.1383204758,-0.0638355538,0.2962293327,-0.2882066965,-0.1492156088,0.3420609236,0.8126741648,0.2221950144,-0.2842615247,0.028703196,-0.0046370728,0.3649331629,0.1545112431,-0.3445688188,0.180458858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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2737","title":"SacreBLEU update","comments":"It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https:\/\/colab.research.google.com\/drive\/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing\r\n\r\nI'm reopening this Issue and making a Pull Request to fix it.","body":"With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n","comment_length":33,"text":"SacreBLEU update \n With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n \n It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https:\/\/colab.research.google.com\/drive\/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing\r\n\r\nI'm reopening this Issue and making a Pull Request to fix it.","embeddings":[-0.3739028871,0.2329158187,0.0166788381,-0.1529369652,0.3844794631,-0.228001982,0.1411173046,0.3971856236,-0.1434217542,0.1172362044,0.0634808242,0.367531687,-0.0747411028,-0.0431298316,-0.0400885046,0.0182079747,0.1286734492,0.0568156801,0.2899592519,0.0479621105,-0.300147146,0.1294311881,-0.1781707853,-0.0137694543,-0.229881078,0.0428284891,-0.0431472957,-0.0255484059,-0.4351977408,-0.6946918368,0.2592553496,0.1504914165,-0.1331426054,0.4043775201,-0.0001106694,-0.0649239719,0.2988054156,-0.1715772152,-0.4254031479,-0.4097581804,-0.1907384396,-0.3609961271,0.0138522377,-0.0621109158,0.0258586202,-0.3896156251,-0.0413792729,0.0150133381,0.2358204424,0.3832374215,0.2394982576,0.430285275,-0.0015964248,-0.3362753689,-0.1122719795,0.0617029145,-0.047362607,0.1549793929,-0.0962243304,0.0600672923,-0.0194021594,0.0392365605,-0.186813578,0.1169505566,0.0850505829,-0.1944031566,-0.2114242166,-0.0239331648,0.0887817144,0.0142879719,0.3667894006,-0.3311085999,-0.4713633358,0.2233430147,0.0124881687,-0.3764422238,0.0764675438,-0.1040389091,-0.0425245799,-0.1320866495,0.0299374238,-0.0979778469,-0.0454691686,0.0143784024,-0.2644269466,0.3045631051,-0.2460582405,0.031894356,0.0465924069,-0.0460272171,-0.4297217131,0.1479371786,-0.2655233145,-0.0081698149,-0.4187926054,-0.0802067667,-0.0276516937,-0.2785298526,0.1515285224,0.4640417397,0.0794282258,0.2926971018,0.1231563091,0.160395056,-0.0903690904,0.5370455384,0.1759420782,0.1939258277,0.206447497,0.2096591294,-0.0102406796,-0.0219156723,0.0056200782,-0.2501822114,0.1966059357,0.0381130204,0.2866578996,-0.102828823,-0.467641592,0.1577839106,0.2295634151,-0.0384413898,-0.0725245103,0.3364729285,-0.2498460859,0.2119585127,-0.19204171,0.2145524919,-0.2049995661,-0.3765915334,-0.2550670505,-0.0666445047,-0.3085423708,-0.0407444835,0.1703774184,0.1910991967,0.172594294,-0.0540474169,0.3929164112,-0.1610344052,0.2505149841,-0.1740705371,0.0372872762,0.0719731227,-0.1483312696,-0.0247008689,0.3736082315,-0.6256074905,-0.0404642522,-0.2014552057,-0.329965055,-0.2798798084,0.2722164094,0.2741959095,-0.2737762332,-0.2794387639,-0.4384195507,0.2355144471,-0.0362764634,0.0633847713,-0.0532350242,-0.0661690459,-0.2227806598,-0.2717621624,0.3008926809,0.3167229295,-0.1638348848,-0.1671287566,0.1063645706,-0.1123757958,0.0829697698,-0.1165450588,0.0046931482,0.2391459644,0.0060909223,-0.020558333,0.2737216949,-0.2885116637,-0.5547575951,-0.0408842228,0.0688699484,-0.2400221527,-0.0268476401,-0.118897602,0.620539546,-0.0705594793,-0.0200987514,0.0195240062,0.06320595,-0.246159181,-0.1765811592,-0.4268577993,0.0149049275,0.0077161361,0.6241717339,0.0516901277,0.2833348811,0.3863051236,0.1588709205,0.0476233736,-0.0055336566,0.2308053672,0.5782594681,-0.1743898541,0.3671568036,-0.5228919983,0.0775714293,0.2116926163,0.122955896,0.005603936,0.031119138,-0.1564725488,-0.4288707972,0.0594722442,-0.1472803056,0.042211622,0.2035235167,0.1826676577,-0.1190581098,0.2278323919,-0.2830649018,-0.0059437174,-0.1857161671,0.2656484246,0.1051391289,0.2024466693,0.0008289005,-0.2361797243,0.221500203,0.2695159912,0.2052172273,-0.1491564661,-0.1476213038,0.428235203,0.3534136117,0.1693412662,0.014657395,0.3167547286,0.0962429345,-0.3463172615,-0.1519589424,0.4838857353,-0.0357929021,-0.0805382878,0.0886524469,0.3073081374,0.1158766598,0.0653569996,0.091283612,0.2688135207,0.0663800836,-0.0455183536,-0.1914458573,-0.2590549886,-0.1344322264,-0.2435743809,-0.0288173221,-0.2215945274,0.0934262052,0.0021051702,0.269428581,0.1448980123,0.1267837137,0.0566761009,-0.177900672,-0.0677237585,-0.2078700215,0.3414859176,0.2781054378,0.1037743017,0.1054759771,0.02401153,-0.0426237918,-0.0134245958,0.1805579215,0.1803596765,-0.3909809887,0.2518912256,0.1336865127,0.1309971809,-0.2239989787,0.0206262972,0.0429931916,0.2813043892,-0.0507084541,0.1824110001,-0.2047246248,0.299683094,-0.0462534949,-0.1584311575,-0.0436794795,-0.2114619762,0.009243397,-0.0766247064,0.1495618373,0.4088260233,-0.0732618421,0.3769261837,0.0553985387,-0.0642862841,0.1847970337,-0.067712456,0.0117821423,0.026519049,-0.0211126916,-0.2400423437,0.2869223654,-0.1938946247,-0.098600179,-0.3416166902,-0.4383988976,0.1606461853,0.0614481717,0.4100453854,0.2410633117,-0.2603682876,-0.0223946311,0.0411864892,0.1884096414,-0.2412289381,-0.0302342027,0.3084082603,-0.1350417882,-0.1841018647,-0.0438066795,-0.454743892,0.0212295149,-0.3150006831,-0.032749787,0.2424473166,-0.0739828199,0.0971793681,0.190607518,0.1345986426,0.1341573,0.0485786386,-0.2081841081,-0.1073848754,0.4198906422,-0.1874568015,-0.414606601,0.1693682373,-0.0941706821,0.3023670614,0.1207817942,-0.2617169619,-0.3486177921,0.0223566964,0.0841832682,-0.1161616594,0.0005431383,0.0513884872,0.0615979768,-0.1983550191,-0.220217824,-0.1936366409,-0.0119821178,-0.121142149,0.1930122972,-0.1696726233,-0.1834976971,0.5552967191,0.8563187718,0.3107652962,-0.2941723168,-0.0198015105,0.0878220648,0.3871375024,0.2270503342,-0.3990766704,0.1633845121,-0.4135036469,0.1365126967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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2737","title":"SacreBLEU update","comments":"> It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https:\/\/colab.research.google.com\/drive\/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing\r\n> \r\n> I'm reopening this Issue and making a Pull Request to fix it.\r\n\r\nHow did you solve him","body":"With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n","comment_length":41,"text":"SacreBLEU update \n With the latest release of [sacrebleu](https:\/\/github.com\/mjpost\/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.\r\n\r\n    AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'\r\n\r\nthis happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nsacrebleu= datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.11.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: Python 3.8.0\r\n- PyArrow version: 5.0.0\r\n \n > It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https:\/\/colab.research.google.com\/drive\/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing\r\n> \r\n> I'm reopening this Issue and making a Pull Request to fix it.\r\n\r\nHow did you solve him","embeddings":[-0.362845242,0.2175995559,0.004086663,-0.1242439002,0.4111887217,-0.220629707,0.1483426839,0.3998838663,-0.1654299498,0.1352215111,0.0552655384,0.3734941781,-0.0774145201,-0.0580643378,-0.0607180409,0.0035374502,0.1176181808,0.077919215,0.2915459275,0.0191621576,-0.2759603262,0.1011037678,-0.1640696228,0.0015134461,-0.2383580059,0.0175304934,-0.0485035963,-0.0062925327,-0.4295663238,-0.6999056935,0.2532283962,0.1361992359,-0.1402985156,0.3979230821,-0.0001091248,-0.0477900319,0.3237999678,-0.1652971208,-0.4128567278,-0.4182387292,-0.1641360074,-0.3391427994,-0.0007420061,-0.0666378662,-0.0077729942,-0.3945741057,-0.031256821,0.0479365401,0.2492387891,0.3934241235,0.2612756491,0.4141533971,0.0598831736,-0.3366800249,-0.084694311,0.0487544239,-0.0530635342,0.1865386367,-0.0910621732,0.0802499279,-0.0152818505,0.0361198597,-0.1820260733,0.1328264773,0.0885252133,-0.1782829314,-0.1637523621,-0.0435524248,0.0892583504,-0.0144922342,0.3788432479,-0.3090939522,-0.4297057688,0.2353573292,0.0354057141,-0.3800626397,0.0903579295,-0.0930901542,-0.0574086308,-0.1484018415,0.0412221923,-0.0888383165,-0.0620558001,0.0255064685,-0.2523965538,0.3010900319,-0.2612532675,0.0295433328,0.0508799478,-0.0524701476,-0.4290782809,0.1503403485,-0.2684570253,0.0096407775,-0.4180711508,-0.1008463055,-0.0331796184,-0.2912682593,0.1340479702,0.4417672157,0.0648405999,0.295745194,0.1138017327,0.1407485902,-0.1012537628,0.5619254112,0.1575083286,0.201601401,0.1946683824,0.2054576278,-0.0348405242,-0.028091928,-0.0163337272,-0.2552348673,0.2034204006,0.0415412486,0.303562969,-0.1217258424,-0.4554688334,0.1578488797,0.2282483727,-0.0357066058,-0.1020590514,0.3510771692,-0.2918943763,0.177814275,-0.1800833344,0.207565248,-0.2244511098,-0.3472722471,-0.2624337375,-0.0457039662,-0.320168376,-0.053205993,0.1509980857,0.185586974,0.173975721,-0.0481089354,0.3808067143,-0.1590554118,0.2545006871,-0.1856216639,0.0052504949,0.0599296354,-0.1274773777,-0.0022429698,0.3736338615,-0.6265415549,-0.0417177752,-0.1719615012,-0.3389930129,-0.2898697257,0.26037696,0.2917865813,-0.2713740468,-0.2895100713,-0.4276383221,0.2135307789,-0.0468292497,0.0605981871,-0.0583837517,-0.0446359143,-0.2061901093,-0.2938715219,0.2755730748,0.2930354178,-0.1708279252,-0.1627940983,0.0977060124,-0.1224904284,0.0954295248,-0.1266034245,0.0110307047,0.2641767263,0.0080838604,-0.0431722254,0.2989524901,-0.2925361991,-0.5710143447,-0.0437304266,0.0513182357,-0.225692153,0.0024336502,-0.1334952116,0.636012435,-0.0520209298,-0.0179191176,0.0119223008,0.0628868416,-0.240095675,-0.1924994886,-0.4101452231,0.0121167349,0.0220781956,0.6297089458,0.0523260757,0.2871435583,0.3598300219,0.1547829658,0.029587971,-0.0077790949,0.2395474613,0.5837540627,-0.1494227797,0.3492383063,-0.4936032891,0.0886991844,0.2013271898,0.0828694031,0.0365235582,0.0206890944,-0.1645201743,-0.4781960845,0.0126041938,-0.1703341156,0.0282473266,0.2213649899,0.1812157333,-0.0934645683,0.2186927646,-0.2754317522,-0.011173429,-0.1760546267,0.2706193328,0.0918599665,0.1965899765,-0.0252818931,-0.2301000804,0.2074280381,0.2511385679,0.2311755419,-0.1749943048,-0.1680489779,0.4167262614,0.3145976365,0.1679479629,0.0192301013,0.2878352106,0.1102960855,-0.3607653677,-0.1324299872,0.4603368938,-0.021306498,-0.1118078902,0.1202070713,0.285066843,0.1171938032,0.0649008825,0.0793462321,0.2440064251,0.0813206509,-0.0572495647,-0.1561620384,-0.219244346,-0.1082308739,-0.2363705039,-0.0297118742,-0.1994159818,0.0933506191,0.0130750816,0.2695565224,0.1522714198,0.1060369611,0.052756615,-0.1749832481,-0.0493182242,-0.2231974304,0.3209460974,0.2988562286,0.117444545,0.0725452974,0.0561309829,-0.0225261878,-0.0044958876,0.2074726522,0.1797425002,-0.3590680361,0.268536061,0.1167499572,0.1422433108,-0.1909759045,0.0221756063,0.0750568062,0.3056630492,-0.04740569,0.1486675441,-0.2036342919,0.2859741449,-0.0500305295,-0.1926962137,-0.0586603098,-0.1730152965,0.0255516693,-0.0870093554,0.1808108389,0.4239195287,-0.045412112,0.3644551635,0.0582846738,-0.0375052653,0.1838572472,-0.0868803412,-0.0099477293,0.0442618541,0.0141760502,-0.2238788009,0.3022320867,-0.2006902993,-0.114104718,-0.3337305486,-0.4218053222,0.151176095,0.0432135575,0.3995781541,0.2472610176,-0.2453445792,-0.0234301668,0.0311603528,0.2166615278,-0.2334047705,-0.0346827395,0.3050890267,-0.1537602842,-0.1630684882,-0.0333057754,-0.4809807241,-0.0054834681,-0.3314464688,-0.0303046443,0.2654610276,-0.0699679852,0.1131580621,0.199969545,0.1414227784,0.1660169065,0.0845947489,-0.2070979625,-0.1117456257,0.4133189917,-0.2170682997,-0.4127288759,0.1629554629,-0.0948741585,0.3039220572,0.0820327625,-0.2719516754,-0.3753357232,-0.0013626755,0.0699584559,-0.1577484757,0.0153763425,0.0468733013,0.0491943136,-0.2190265357,-0.2190441191,-0.1967024654,-0.0044867746,-0.1227650344,0.2221055627,-0.1427264661,-0.1629656255,0.520396173,0.8731858134,0.3052797019,-0.2945716083,-0.0228119195,0.0916860625,0.3633904159,0.2280341685,-0.4240376353,0.1679856181,-0.39236196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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2736","title":"Add Microsoft Building Footprints dataset","comments":"Motivation: this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. I'll see if I can figure out how to add it!","body":"## Adding a Dataset\r\n- **Name:** Microsoft Building Footprints\r\n- **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge.\r\n- **Paper:** *link to the dataset paper if available*\r\n- **Data:** https:\/\/www.microsoft.com\/en-us\/maps\/building-footprints\r\n- **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nReported by: @sashavor","comment_length":29,"text":"Add Microsoft Building Footprints dataset \n ## Adding a Dataset\r\n- **Name:** Microsoft Building Footprints\r\n- **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge.\r\n- **Paper:** *link to the dataset paper if available*\r\n- **Data:** https:\/\/www.microsoft.com\/en-us\/maps\/building-footprints\r\n- **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nReported by: @sashavor \n Motivation: this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. I'll see if I can figure out how to add it!","embeddings":[-0.6230252385,0.0684200227,-0.2241073251,-0.0011123391,-0.0653921142,-0.0625759736,0.1025177017,0.2215958387,0.1189827546,0.3236289024,0.2269601375,0.217902258,-0.2382512838,0.4009601474,0.0867363885,-0.0855379552,-0.0039106542,0.0257602166,-0.3132502437,-0.0593195222,-0.2509870827,-0.0065763914,0.0509937145,-0.1066526622,-0.1733564287,-0.2124451697,-0.2157186568,0.0495205931,-0.1937619001,-0.1133068278,-0.1484749913,0.0985472873,-0.0310337208,0.1762388051,-0.0000950589,0.0303419419,0.0873692408,-0.0173286181,-0.1495585293,0.0322935879,-0.1998689026,-0.2357267588,-0.1913833767,-0.0995227471,-0.2482879758,-0.30550313,0.1753943413,-0.1604298651,0.1601273865,0.1716109365,0.4217909276,0.0971787944,-0.0057386984,-0.0885416195,0.1435337365,0.3612351418,-0.1792609245,-0.1117324308,0.2214247882,0.056765236,-0.1190083548,0.3845185637,-0.1306173801,-0.0669638515,0.1537836045,-0.0036901901,0.4531482756,-0.0692371801,0.0502346717,0.2914082706,0.369674325,-0.0297207776,0.0431030579,-0.2419794798,-0.0569375604,-0.1633583754,0.0782722682,0.3049122095,0.0192148574,0.0670702979,-0.3540785015,-0.2523000538,-0.1428038329,0.2731838524,0.0099574272,0.1090829,-0.2636860907,-0.1367986649,-0.0572412089,-0.0115478802,-0.0016431562,-0.0419681743,-0.0403620191,-0.1071382686,0.158935219,-0.2846086919,0.2675792575,0.3740527034,0.2439793497,0.0790721178,0.217136234,-0.01563178,0.1460665911,0.0936232284,0.0302221999,-0.0052096932,-0.2541327178,-0.0691237301,0.0850651637,0.0107921567,-0.036097236,-0.0156645365,0.0807580575,-0.0613360144,-0.3894498348,0.0395667851,0.1997938752,-0.1449997425,-0.2350879014,-0.1658115387,-0.0954438522,0.0673334375,0.2127809972,0.1905244887,0.1502644271,-0.2285726964,-0.0628765076,0.1328581125,0.087707974,0.0078050257,-0.1772198081,-0.022458028,-0.1922894567,0.2782240808,0.1723856628,0.1065273508,0.3462498486,-0.2274960428,0.122201778,0.1039486155,0.2224634141,0.2691270709,0.3936697245,0.0028162545,-0.1251039803,-0.3085380495,-0.0050263433,0.0725640431,-0.3081750572,-0.1603627801,-0.1034094244,-0.1174105555,-0.6414904594,0.325807035,0.0167046953,-0.1306327581,0.345561415,0.4194295108,-0.0807239786,0.1015363485,0.1099148616,0.5468338132,-0.2805459797,-0.1869441122,0.0710544214,0.3174801469,0.2171293497,-0.1784773916,-0.0948300138,0.0587568693,-0.1399818212,-0.2157729864,-0.0245057698,0.3536578715,-0.214051947,-0.1769830585,0.0251720808,-0.0991853923,-0.1977200061,0.0249370094,-0.4823987782,-0.3185199797,0.1451163441,0.0448162667,0.2196041197,-0.0234549902,-0.1469695866,0.2988389432,-0.1710606962,0.0904276967,-0.2635376453,-0.3852531016,-0.074767828,0.2344444841,-0.0112594897,0.0674141943,0.2207692564,0.1787233651,0.0239800196,-0.1878061444,0.225879699,0.2710601985,0.1962817013,-0.068755284,-0.3588473201,-0.1836929917,-0.2154411227,-0.2696718276,-0.3207305074,-0.1458588094,0.0978271216,-0.1733013391,0.0119499546,-0.1436832547,-0.1488265246,-0.0343428142,0.4483795464,0.1232353747,0.1259393245,0.0121793225,0.1993558109,-0.1470786184,-0.0002672509,-0.0755678192,0.1166488379,0.2808676064,-0.1394074708,-0.0229548384,0.1239664704,0.3052729368,0.0543753803,-0.0755761638,0.2510988712,0.247122407,-0.4497462809,0.1395659,0.2692772746,0.6203433275,0.0687890872,-0.2716166675,0.2816954255,0.0852933526,-0.1750495434,0.0361091383,-0.3376236856,0.1835892648,0.2295089662,0.0209701434,-0.1751335412,0.3101272583,-0.0707540885,-0.2436029762,-0.1267685443,-0.3893863261,0.085204199,0.116021201,0.0240438227,-0.0940998048,0.0616934486,0.2042934746,0.1607656926,0.041861482,0.3469276428,0.0788425729,-0.3081942499,0.0382645391,0.1487524062,-0.0959710702,0.0808029398,0.3439022303,0.227925539,0.009722596,0.0924154297,-0.0774166137,0.1614506692,0.1702503711,-0.0818793252,0.1255161911,0.0986687839,0.0706480071,-0.6637936234,0.0366603695,0.0985401422,-0.0511817485,0.0899757817,-0.1714110225,-0.0347530991,-0.4059104919,-0.0967215896,-0.0804718807,-0.0178093724,-0.2373978645,0.2716466486,0.021604253,-0.2388008833,0.2244889885,0.1554945856,0.3148770034,-0.1643851101,0.0231538918,-0.0639288798,-0.3015567064,0.0390949585,0.3722415566,0.1592794657,0.0796657652,0.5405745506,-0.2245623916,0.5198444128,-0.5102851391,-0.6112650633,0.0957111418,0.1514920592,-0.2901204526,0.2286679149,0.1449795961,0.1110247672,-0.193341732,0.0316030979,-0.0301307458,-0.0487857945,-0.2493214011,-0.2277149707,-0.1805243194,-0.2456382513,0.1113199666,-0.0120784584,-0.5066966414,0.2648170292,0.2403164953,0.0354922302,0.1074682325,0.1158182621,0.2038116604,-0.6048650742,0.2234700918,-0.1103955284,-0.3730153143,0.0482158922,-0.3968025446,-0.0938651934,-0.150873974,-0.1597165614,-0.0215467401,-0.064376466,-0.3661301136,-0.2579316795,-0.1849817038,0.2595286071,0.3191812336,0.0671480596,0.4511183798,0.3124196231,-0.2581288218,-0.0313524641,-0.0152908619,-0.0152023891,0.3127680123,0.3218161762,-0.110319756,-0.1075894684,-0.0312795974,0.2447682768,0.27085343,-0.2615627646,0.3958711624,-0.0930594057,0.5226427317,-0.0089004543,-0.1838506162,0.3940806687,0.0300675631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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2730","title":"Update CommonVoice with new release","comments":"Does anybody know if there is a bundled link, which would allow direct data download instead of manual? \r\nSomething similar to: `https:\/\/voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com\/cv-corpus-6.1-2020-12-11\/ab.tar.gz` ? cc @patil-suraj \r\n","body":"## Adding a Dataset\r\n- **Name:** CommonVoice mid-2021 release\r\n- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).\r\n- **Paper:** https:\/\/discourse.mozilla.org\/t\/common-voice-2021-mid-year-dataset-release\/83812\r\n- **Data:** https:\/\/commonvoice.mozilla.org\/en\/datasets\r\n- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":25,"text":"Update CommonVoice with new release \n ## Adding a Dataset\r\n- **Name:** CommonVoice mid-2021 release\r\n- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).\r\n- **Paper:** https:\/\/discourse.mozilla.org\/t\/common-voice-2021-mid-year-dataset-release\/83812\r\n- **Data:** https:\/\/commonvoice.mozilla.org\/en\/datasets\r\n- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Does anybody know if there is a bundled link, which would allow direct data download instead of manual? \r\nSomething similar to: `https:\/\/voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com\/cv-corpus-6.1-2020-12-11\/ab.tar.gz` ? cc @patil-suraj \r\n","embeddings":[-0.3897120953,0.0689004436,-0.0436255857,-0.1407739818,-0.0075453636,0.2361044884,-0.0866278261,0.4099915326,-0.0332524851,0.0552654713,-0.2268214375,0.1267442852,-0.1374958158,0.3757566512,0.3153657019,-0.102551654,-0.0221795943,0.0116232457,0.3541306555,-0.2009497434,-0.1525128484,0.15630804,0.0348923542,-0.0708511695,-0.067562893,0.0845847875,-0.1082298458,0.0526001304,-0.1277033687,-0.3679584563,0.1854728758,0.3187081516,0.2517135143,0.2469440848,-0.0001240131,-0.4164184928,0.1370145082,-0.1946643889,-0.5836836696,-0.1107830554,-0.2506595254,0.0372974947,-0.1177459136,0.1902164668,0.1305427402,-0.0199203938,0.0473647751,-0.3805799186,0.0396899171,0.1962828189,0.0604044572,-0.1646220237,0.1000424176,-0.2372972071,0.0352670588,0.1634634286,-0.0934525654,0.3102356791,0.6635541916,0.2257751524,0.2486783713,0.0445476882,0.1510121226,-0.3072907627,-0.0004820801,0.1340319216,-0.2501448095,-0.4530813098,0.1524716914,0.5523585677,0.6914018989,-0.1817145646,-0.3851211667,-0.1164635122,0.1334374994,-0.5129933953,-0.1119492501,0.0647773072,-0.0460774861,0.1696078032,0.2209832817,-0.4564382136,-0.008320705,0.453556031,0.2018192112,0.18715702,0.0545919687,-0.1376045197,0.3618970513,-0.0942300037,-0.0713778585,0.0558883622,0.0256280061,0.434869498,0.0762254372,-0.3975177109,-0.0114526153,-0.5160592198,0.4938572943,-0.2566072047,0.0837338194,0.1518265754,-0.6276643276,-0.1240408123,0.3796098232,-0.260255456,-0.1462113559,0.0701046214,0.3797554076,0.1778353751,0.816459775,0.1496021003,-0.2681249678,0.066015549,-0.2071320713,0.1514522433,-0.0152980229,-0.3966103792,-0.1177079305,-0.0770759806,0.0033965383,0.0795907155,-0.3144282997,0.0444237068,0.0949636102,0.121604152,-0.0240253303,0.1572534144,0.1144951209,-0.2641264498,0.1021916121,0.1461638063,0.0444499068,0.1630934477,0.0774115473,-0.6380671263,0.0543287769,0.0733752102,0.3085193634,-0.1125856936,-0.3097593188,0.2996527851,-0.3038833141,-0.14780128,0.0384144932,0.1520600915,-0.2544485331,0.3241442442,-0.3895079494,0.12453641,-0.0625213608,-0.4871847928,-0.1174947172,0.053168539,0.1520292163,-0.3868251145,0.317874521,0.2663354576,-0.216191873,-0.3090520203,-0.0580613464,0.2228986472,-0.3956089616,0.029081177,0.0688039139,0.5167530179,-0.6113048792,-0.0970230773,-0.3922154605,0.1342055053,-0.2091183215,0.1252496839,-0.028499756,0.0580733046,-0.4365787208,0.0588036999,0.5816566944,-0.517485559,-0.2799951732,0.2649064064,-0.2213199884,-0.2088127732,0.1940362602,0.127349034,0.4670020342,-0.0127242673,0.1766999662,0.1752267182,-0.255661577,0.0175010562,-0.0913153142,-0.4835682213,-0.2657375336,0.16387932,0.1479585022,-0.1275347322,0.1275569052,-0.0209599696,0.4738978148,-0.0539265797,0.2950321734,0.2058830559,0.324365139,0.0320440307,-0.0217146464,0.104077816,-0.3587940633,0.0817736164,0.0326973535,0.1676013321,0.0703762844,-0.3706407249,-0.2648741603,-0.3648478389,-0.0455134474,-0.1106761172,-0.0740583986,0.2317182273,0.2323179394,-0.199136734,-0.2143363655,0.2044940144,0.2810825706,0.1102151275,0.0540390611,0.013871097,-0.0384281725,0.1122697815,0.2321036905,0.1626625806,0.2238751352,-0.2100117803,-0.107539326,0.2778187394,-0.0040991791,0.6636211872,0.2237515748,0.210928455,0.2058418989,0.0763214827,0.2137951404,0.2449189126,-0.1573190689,0.0034839413,-0.2436186671,0.2828460932,0.3007521629,0.0037648624,0.1546221972,0.230799377,0.1304882616,0.0746317357,0.0362140536,-0.1223573312,-0.1598810703,0.0585057773,0.3595196605,-0.1486743391,-0.5801500082,0.3750169873,0.4067924619,-0.1300884634,0.1598211974,0.2438954562,-0.3069536686,-0.1593468338,-0.0127577512,0.1984671205,0.1514736265,0.2299783975,0.0910491422,0.1847023964,0.2917860746,-0.0427496321,0.2333383262,-0.0779767632,-0.0976061672,0.423174262,-0.1869979948,0.1300887764,-0.3433693349,-0.1746968776,-0.0153600946,-0.0549228825,0.0351476632,-0.460986346,0.0200646538,0.1403170824,-0.4364106357,-0.040547926,-0.0631513,-0.3243238032,0.2186037749,0.3028701246,-0.0349642709,0.0587985627,-0.3607234359,0.2980546951,-0.3364134729,-0.4129265845,-0.0940513387,-0.0122429384,-0.248903513,0.0277524069,0.0449794382,-0.2859221399,0.2350471169,-0.2272629589,0.1947665662,-0.4353841245,-0.4220513403,0.1608829796,0.0167447273,-0.4277884662,0.1313138604,-0.2983762026,0.2825034261,0.0157571062,-0.3078204691,-0.1229287907,-0.1884716004,-0.4340620935,-0.137435779,0.2221311927,0.1926539242,-0.7328444123,-0.3953492641,-0.4587977529,0.1181819886,-0.2502526641,0.0705011189,0.0066022901,-0.0758031905,-0.1232876405,-0.2171438932,0.2414301634,-0.2790500224,0.2103625238,0.2944696844,-0.1946478039,-0.3588214517,0.0477154218,-0.2169554383,0.2603701055,0.0520536229,-0.3211286366,0.429033339,0.1537174284,0.231214121,-0.0417889804,-0.0362404361,0.5302618742,0.2607545555,-0.1030115783,0.0379973166,-0.0504230298,-0.2390133739,0.2745098472,0.2551525235,0.046681162,0.2509114444,0.1000865921,0.7929123044,0.0699423254,0.0917326212,0.0113678053,-0.1323479712,0.3699173331,0.0880693719,-0.16677095,-0.0713275969,-0.1005616486,0.1786989868,0.335919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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2728","title":"Concurrent use of same dataset (already downloaded)","comments":"Launching simultaneous job relying on the same datasets try some writing issue. I guess it is unexpected since I only need to load some already downloaded file.","body":"## Describe the bug\r\nWhen launching several jobs at the same time loading the same dataset trigger some errors see (last comments).\r\n\r\n## Steps to reproduce the bug\r\nexport HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets\r\nfor MODEL in \"bert-base-uncased\" \"roberta-base\" \"distilbert-base-cased\"; do #  \"bert-base-uncased\" \"bert-large-cased\" \"roberta-large\" \"albert-base-v1\" \"albert-large-v1\"; do\r\n  for TASK_NAME in \"mrpc\" \"rte\" 'imdb' \"paws\" \"mnli\"; do\r\n    export OUTPUT_DIR=${MODEL}_${TASK_NAME}\r\n    sbatch --job-name=${OUTPUT_DIR} \\\r\n      --gres=gpu:1 \\\r\n      --no-requeue \\\r\n      --cpus-per-task=10 \\\r\n      --hint=nomultithread \\\r\n      --time=1:00:00 \\\r\n      --output=jobinfo\/${OUTPUT_DIR}_%j.out \\\r\n      --error=jobinfo\/${OUTPUT_DIR}_%j.err \\\r\n      --qos=qos_gpu-t4 \\\r\n      --wrap=\"module purge; module load pytorch-gpu\/py3\/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets;  python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=\/gpfswork\/rech\/toto\/transformers_models\/$MODEL\"\r\n\r\n  done\r\ndone\r\n\r\n\r\n\r\n```python\r\n# Sample code to reproduce the bug\r\n        dataset_train = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                          batched=True).select(list(range(args.filter)))\r\n\r\n        dataset_val = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                      batched=True).select(list(range(args.filter, args.filter + 5000)))\r\n\r\n        dataset_test = load_dataset('imdb', split='test', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                        batched=True)\r\n```\r\n\r\n## Expected results\r\nI believe I am doing something wrong with the objects. \r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 983, in _prepare_split\r\n    check_duplicates=True,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/gpfswork\/rech\/tts\/unm25jp\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets==1.8.0\r\n- Platform: linux (jeanzay)\r\n- Python version: pyarrow==2.0.0\r\n- PyArrow version: 3.7.8\r\n","comment_length":27,"text":"Concurrent use of same dataset (already downloaded) \n ## Describe the bug\r\nWhen launching several jobs at the same time loading the same dataset trigger some errors see (last comments).\r\n\r\n## Steps to reproduce the bug\r\nexport HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets\r\nfor MODEL in \"bert-base-uncased\" \"roberta-base\" \"distilbert-base-cased\"; do #  \"bert-base-uncased\" \"bert-large-cased\" \"roberta-large\" \"albert-base-v1\" \"albert-large-v1\"; do\r\n  for TASK_NAME in \"mrpc\" \"rte\" 'imdb' \"paws\" \"mnli\"; do\r\n    export OUTPUT_DIR=${MODEL}_${TASK_NAME}\r\n    sbatch --job-name=${OUTPUT_DIR} \\\r\n      --gres=gpu:1 \\\r\n      --no-requeue \\\r\n      --cpus-per-task=10 \\\r\n      --hint=nomultithread \\\r\n      --time=1:00:00 \\\r\n      --output=jobinfo\/${OUTPUT_DIR}_%j.out \\\r\n      --error=jobinfo\/${OUTPUT_DIR}_%j.err \\\r\n      --qos=qos_gpu-t4 \\\r\n      --wrap=\"module purge; module load pytorch-gpu\/py3\/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets;  python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=\/gpfswork\/rech\/toto\/transformers_models\/$MODEL\"\r\n\r\n  done\r\ndone\r\n\r\n\r\n\r\n```python\r\n# Sample code to reproduce the bug\r\n        dataset_train = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                          batched=True).select(list(range(args.filter)))\r\n\r\n        dataset_val = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                      batched=True).select(list(range(args.filter, args.filter + 5000)))\r\n\r\n        dataset_test = load_dataset('imdb', split='test', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                        batched=True)\r\n```\r\n\r\n## Expected results\r\nI believe I am doing something wrong with the objects. \r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 983, in _prepare_split\r\n    check_duplicates=True,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/gpfswork\/rech\/tts\/unm25jp\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets==1.8.0\r\n- Platform: linux (jeanzay)\r\n- Python version: pyarrow==2.0.0\r\n- PyArrow version: 3.7.8\r\n \n Launching simultaneous job relying on the same datasets try some writing issue. I guess it is unexpected since I only need to load some already downloaded 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2728","title":"Concurrent use of same dataset (already downloaded)","comments":"If i have two jobs that use the same dataset. I got :\r\n\r\n\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 582, in download_and_prepare\r\n    self._save_info()\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 690, in _save_info\r\n    self.info.write_to_directory(self._cache_dir)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/info.py\", line 195, in write_to_directory\r\n    with open(os.path.join(dataset_info_dir, config.LICENSE_FILENAME), \"wb\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/LICENSE'","body":"## Describe the bug\r\nWhen launching several jobs at the same time loading the same dataset trigger some errors see (last comments).\r\n\r\n## Steps to reproduce the bug\r\nexport HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets\r\nfor MODEL in \"bert-base-uncased\" \"roberta-base\" \"distilbert-base-cased\"; do #  \"bert-base-uncased\" \"bert-large-cased\" \"roberta-large\" \"albert-base-v1\" \"albert-large-v1\"; do\r\n  for TASK_NAME in \"mrpc\" \"rte\" 'imdb' \"paws\" \"mnli\"; do\r\n    export OUTPUT_DIR=${MODEL}_${TASK_NAME}\r\n    sbatch --job-name=${OUTPUT_DIR} \\\r\n      --gres=gpu:1 \\\r\n      --no-requeue \\\r\n      --cpus-per-task=10 \\\r\n      --hint=nomultithread \\\r\n      --time=1:00:00 \\\r\n      --output=jobinfo\/${OUTPUT_DIR}_%j.out \\\r\n      --error=jobinfo\/${OUTPUT_DIR}_%j.err \\\r\n      --qos=qos_gpu-t4 \\\r\n      --wrap=\"module purge; module load pytorch-gpu\/py3\/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets;  python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=\/gpfswork\/rech\/toto\/transformers_models\/$MODEL\"\r\n\r\n  done\r\ndone\r\n\r\n\r\n\r\n```python\r\n# Sample code to reproduce the bug\r\n        dataset_train = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                          batched=True).select(list(range(args.filter)))\r\n\r\n        dataset_val = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                      batched=True).select(list(range(args.filter, args.filter + 5000)))\r\n\r\n        dataset_test = load_dataset('imdb', split='test', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                        batched=True)\r\n```\r\n\r\n## Expected results\r\nI believe I am doing something wrong with the objects. \r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 983, in _prepare_split\r\n    check_duplicates=True,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/gpfswork\/rech\/tts\/unm25jp\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets==1.8.0\r\n- Platform: linux (jeanzay)\r\n- Python version: pyarrow==2.0.0\r\n- PyArrow version: 3.7.8\r\n","comment_length":78,"text":"Concurrent use of same dataset (already downloaded) \n ## Describe the bug\r\nWhen launching several jobs at the same time loading the same dataset trigger some errors see (last comments).\r\n\r\n## Steps to reproduce the bug\r\nexport HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets\r\nfor MODEL in \"bert-base-uncased\" \"roberta-base\" \"distilbert-base-cased\"; do #  \"bert-base-uncased\" \"bert-large-cased\" \"roberta-large\" \"albert-base-v1\" \"albert-large-v1\"; do\r\n  for TASK_NAME in \"mrpc\" \"rte\" 'imdb' \"paws\" \"mnli\"; do\r\n    export OUTPUT_DIR=${MODEL}_${TASK_NAME}\r\n    sbatch --job-name=${OUTPUT_DIR} \\\r\n      --gres=gpu:1 \\\r\n      --no-requeue \\\r\n      --cpus-per-task=10 \\\r\n      --hint=nomultithread \\\r\n      --time=1:00:00 \\\r\n      --output=jobinfo\/${OUTPUT_DIR}_%j.out \\\r\n      --error=jobinfo\/${OUTPUT_DIR}_%j.err \\\r\n      --qos=qos_gpu-t4 \\\r\n      --wrap=\"module purge; module load pytorch-gpu\/py3\/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets;  python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=\/gpfswork\/rech\/toto\/transformers_models\/$MODEL\"\r\n\r\n  done\r\ndone\r\n\r\n\r\n\r\n```python\r\n# Sample code to reproduce the bug\r\n        dataset_train = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                          batched=True).select(list(range(args.filter)))\r\n\r\n        dataset_val = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                      batched=True).select(list(range(args.filter, args.filter + 5000)))\r\n\r\n        dataset_test = load_dataset('imdb', split='test', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                        batched=True)\r\n```\r\n\r\n## Expected results\r\nI believe I am doing something wrong with the objects. \r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 983, in _prepare_split\r\n    check_duplicates=True,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/gpfswork\/rech\/tts\/unm25jp\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets==1.8.0\r\n- Platform: linux (jeanzay)\r\n- Python version: pyarrow==2.0.0\r\n- PyArrow version: 3.7.8\r\n \n If i have two jobs that use the same dataset. I got :\r\n\r\n\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 582, in download_and_prepare\r\n    self._save_info()\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 690, in _save_info\r\n    self.info.write_to_directory(self._cache_dir)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/info.py\", line 195, in write_to_directory\r\n    with open(os.path.join(dataset_info_dir, config.LICENSE_FILENAME), \"wb\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/LICENSE'","embeddings":[-0.5779120326,-0.0810000375,-0.0453643724,0.4501565397,0.2459626794,0.0139751369,0.5565331578,0.2723817527,0.1843708605,0.1907434464,-0.0311780032,0.1464349478,0.0212427545,0.1441881359,-0.0885511786,0.0235604327,0.1309166104,-0.2257413864,-0.3968794644,-0.0203631092,-0.2406820655,0.1446244419,0.0855503231,0.1932705194,-0.5113247037,-0.3433853984,-0.0541765466,0.0557928272,0.2590035498,-0.0671327934,0.1664633751,0.2859389484,-0.0116243837,0.8678808808,-0.0001090042,0.1391353309,0.1289675683,0.1109726653,-0.0634465516,-0.2272718996,0.0330815762,-0.107974872,0.1186949238,-0.313777715,0.1189511046,0.0584998429,-0.030055631,-0.2093538344,0.5382676721,0.0736098737,0.2384028584,0.3819690347,-0.3065969348,-0.2297079861,0.1842454374,0.0569794737,0.0860361606,0.0703067929,0.1006150246,-0.160428226,-0.0243540276,0.4298269153,-0.128874436,0.205970034,-0.0231342856,0.0869602188,0.1051392555,-0.5513936877,0.0767241269,0.0688892454,-0.0115327006,-0.3123033047,-0.3542192578,-0.3473508954,-0.0639272109,-0.2551651597,0.0928215683,0.345266372,-0.2033960521,-0.1923074573,-0.0920517072,0.1867493987,-0.0755044073,-0.1349960566,0.2415366471,0.330652684,0.0830517709,0.2387527227,0.0692323297,0.0927333981,0.3280492723,-0.1612951756,-0.1014361978,-0.0775532722,-0.6200729012,0.0457460396,-0.0160569604,-0.465223223,0.1843742728,-0.1858731359,-0.1885086894,0.0499862283,0.1717092693,0.1031510457,0.2989788651,0.0960013494,-0.3177445531,0.2705483735,0.3438619673,0.1331980228,-0.5029964447,0.0652742609,-0.0156139219,-0.2567523718,0.2176098078,0.1873345226,0.2414573133,-0.0829889849,-0.3572193086,-0.0523138382,-0.0511178933,-0.0558524579,-0.1014149934,0.4524842501,0.1777906567,0.1567742676,0.0913131386,0.1969440281,-0.1822924018,0.0624874271,-0.3012304008,-0.0290754717,-0.0146923279,0.2143333256,0.115738757,-0.0643138587,0.2248848528,0.337944448,0.0498892926,-0.4685381353,0.262468487,-0.3657260239,-0.2471660674,0.1961344182,0.1851789206,0.1372065544,-0.1051604077,0.3842988014,0.0134874908,0.2587327957,-0.3288044035,-0.1393946558,0.080968149,0.2727389634,0.0137452884,0.1152774841,-0.3554980457,0.11369133,0.31862849,-0.0416694805,0.1086383611,-0.3720645308,-0.3164130151,-0.1393729448,-0.0205443148,0.5279052258,0.0768126845,0.0268207006,0.1770918816,-0.1090108529,0.0915662721,0.3510977328,-0.3423242867,0.4966166019,-0.3493314087,0.1288179904,0.2440502197,-0.3719643652,-0.1516722888,0.3383845389,-0.1646658182,0.1252206415,0.2112877369,-0.0568555631,0.2122339308,-0.135921821,-0.0084830578,0.1491883695,-0.1079862937,0.4015436471,-0.1918611526,-0.2839153409,0.0489497297,0.1688934714,0.3223809302,-0.093997553,0.0269821547,-0.2874779701,0.3875817657,-0.3304336965,-0.073193185,0.2158527076,-0.0103004053,0.0795356706,0.0810648277,-0.1096467897,-0.5169209242,0.1978815943,-0.3624865711,0.0432497934,0.0342297591,-0.1991609484,0.1067375392,-0.041770719,-0.2488535792,-0.188830331,0.1706272364,0.23171404,-0.1502679437,-0.1181374565,-0.1473599523,0.3599050343,-0.1582805365,0.0234616175,-0.0023934881,0.1705364734,-0.1951894164,-0.2185277343,0.1162100509,-0.0603484437,0.4591343403,-0.1816609353,-0.1646382064,0.234481588,0.1979359686,0.3151443601,-0.060830038,0.0168911535,0.1874209791,0.155690819,-0.0017908275,0.1425172091,0.264064461,-0.1452902257,0.0974556208,0.3178672493,0.0033611003,0.1383701563,0.0590600222,-0.2233062983,0.193423599,-0.1233381256,0.1891794056,0.1224577054,0.2253106385,0.1255556643,0.0167566203,-0.0816582367,-0.5741452575,0.0836443007,0.3775579929,0.1669413447,0.0734937191,0.0733089,-0.0474899113,0.0227888245,-0.1682934314,0.299851954,0.5353221893,0.1349111348,0.0457241945,0.0882089511,-0.1451271623,-0.0892191976,0.0666768849,0.1546055675,0.1948834658,0.5548338294,0.1994925439,-0.0699805468,-0.2803615928,-0.1119266897,0.2671932578,0.0941753089,-0.3748239875,0.1850156933,0.1274146289,-0.1747792363,-0.4213629663,0.0054045664,-0.0335235819,-0.1470950544,-0.0398527905,0.4568405449,-0.1997360289,0.3671449721,-0.0299287681,0.1571482718,-0.1867981851,0.2361011505,-0.1471163779,-0.0711986795,-0.1141485125,-0.0113953063,0.2849563956,0.0669397041,0.3187500536,0.0719447806,-0.3169528842,-0.3599549234,-0.0033945311,0.0783701614,0.0017772753,0.4231902361,-0.0844049677,-0.1965941489,0.0808480829,-0.4469995499,0.2024542987,-0.3591762781,-0.0702030137,-0.1327766627,-0.2264376134,-0.0671751946,-0.1766142398,-0.3789281249,-0.2513652146,-0.2676903903,-0.0906180367,-0.1727518439,0.2116888314,0.0763652846,-0.1411370635,-0.0400552638,0.1537126452,-0.0046703178,-0.3079814911,-0.3230585456,0.069326885,-0.1893555969,-0.3206602931,-0.0872680172,-0.14780882,0.0682690814,0.226744622,-0.4916568995,-0.3557165861,-0.1071279123,0.0577950142,-0.1461234093,-0.0371046998,0.0506975763,0.0610218383,-0.1380059868,-0.1171887517,-0.1728388518,0.2016058713,-0.0312829688,0.0052557569,-0.165980652,0.2671523094,-0.1580260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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2728","title":"Concurrent use of same dataset (already downloaded)","comments":"You can probably have a solution much faster than me (first time I use the library). But I suspect some write function are used when loading the dataset from cache.","body":"## Describe the bug\r\nWhen launching several jobs at the same time loading the same dataset trigger some errors see (last comments).\r\n\r\n## Steps to reproduce the bug\r\nexport HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets\r\nfor MODEL in \"bert-base-uncased\" \"roberta-base\" \"distilbert-base-cased\"; do #  \"bert-base-uncased\" \"bert-large-cased\" \"roberta-large\" \"albert-base-v1\" \"albert-large-v1\"; do\r\n  for TASK_NAME in \"mrpc\" \"rte\" 'imdb' \"paws\" \"mnli\"; do\r\n    export OUTPUT_DIR=${MODEL}_${TASK_NAME}\r\n    sbatch --job-name=${OUTPUT_DIR} \\\r\n      --gres=gpu:1 \\\r\n      --no-requeue \\\r\n      --cpus-per-task=10 \\\r\n      --hint=nomultithread \\\r\n      --time=1:00:00 \\\r\n      --output=jobinfo\/${OUTPUT_DIR}_%j.out \\\r\n      --error=jobinfo\/${OUTPUT_DIR}_%j.err \\\r\n      --qos=qos_gpu-t4 \\\r\n      --wrap=\"module purge; module load pytorch-gpu\/py3\/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets;  python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=\/gpfswork\/rech\/toto\/transformers_models\/$MODEL\"\r\n\r\n  done\r\ndone\r\n\r\n\r\n\r\n```python\r\n# Sample code to reproduce the bug\r\n        dataset_train = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                          batched=True).select(list(range(args.filter)))\r\n\r\n        dataset_val = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                      batched=True).select(list(range(args.filter, args.filter + 5000)))\r\n\r\n        dataset_test = load_dataset('imdb', split='test', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                        batched=True)\r\n```\r\n\r\n## Expected results\r\nI believe I am doing something wrong with the objects. \r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 983, in _prepare_split\r\n    check_duplicates=True,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/gpfswork\/rech\/tts\/unm25jp\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets==1.8.0\r\n- Platform: linux (jeanzay)\r\n- Python version: pyarrow==2.0.0\r\n- PyArrow version: 3.7.8\r\n","comment_length":30,"text":"Concurrent use of same dataset (already downloaded) \n ## Describe the bug\r\nWhen launching several jobs at the same time loading the same dataset trigger some errors see (last comments).\r\n\r\n## Steps to reproduce the bug\r\nexport HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets\r\nfor MODEL in \"bert-base-uncased\" \"roberta-base\" \"distilbert-base-cased\"; do #  \"bert-base-uncased\" \"bert-large-cased\" \"roberta-large\" \"albert-base-v1\" \"albert-large-v1\"; do\r\n  for TASK_NAME in \"mrpc\" \"rte\" 'imdb' \"paws\" \"mnli\"; do\r\n    export OUTPUT_DIR=${MODEL}_${TASK_NAME}\r\n    sbatch --job-name=${OUTPUT_DIR} \\\r\n      --gres=gpu:1 \\\r\n      --no-requeue \\\r\n      --cpus-per-task=10 \\\r\n      --hint=nomultithread \\\r\n      --time=1:00:00 \\\r\n      --output=jobinfo\/${OUTPUT_DIR}_%j.out \\\r\n      --error=jobinfo\/${OUTPUT_DIR}_%j.err \\\r\n      --qos=qos_gpu-t4 \\\r\n      --wrap=\"module purge; module load pytorch-gpu\/py3\/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets;  python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=\/gpfswork\/rech\/toto\/transformers_models\/$MODEL\"\r\n\r\n  done\r\ndone\r\n\r\n\r\n\r\n```python\r\n# Sample code to reproduce the bug\r\n        dataset_train = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                          batched=True).select(list(range(args.filter)))\r\n\r\n        dataset_val = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                      batched=True).select(list(range(args.filter, args.filter + 5000)))\r\n\r\n        dataset_test = load_dataset('imdb', split='test', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                        batched=True)\r\n```\r\n\r\n## Expected results\r\nI believe I am doing something wrong with the objects. \r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 983, in _prepare_split\r\n    check_duplicates=True,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/gpfswork\/rech\/tts\/unm25jp\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets==1.8.0\r\n- Platform: linux (jeanzay)\r\n- Python version: pyarrow==2.0.0\r\n- PyArrow version: 3.7.8\r\n \n You can probably have a solution much faster than me (first time I use the library). But I suspect some write function are used when loading the dataset from cache.","embeddings":[-0.5779120326,-0.0810000375,-0.0453643724,0.4501565397,0.2459626794,0.0139751369,0.5565331578,0.2723817527,0.1843708605,0.1907434464,-0.0311780032,0.1464349478,0.0212427545,0.1441881359,-0.0885511786,0.0235604327,0.1309166104,-0.2257413864,-0.3968794644,-0.0203631092,-0.2406820655,0.1446244419,0.0855503231,0.1932705194,-0.5113247037,-0.3433853984,-0.0541765466,0.0557928272,0.2590035498,-0.0671327934,0.1664633751,0.2859389484,-0.0116243837,0.8678808808,-0.0001090042,0.1391353309,0.1289675683,0.1109726653,-0.0634465516,-0.2272718996,0.0330815762,-0.107974872,0.1186949238,-0.313777715,0.1189511046,0.0584998429,-0.030055631,-0.2093538344,0.5382676721,0.0736098737,0.2384028584,0.3819690347,-0.3065969348,-0.2297079861,0.1842454374,0.0569794737,0.0860361606,0.0703067929,0.1006150246,-0.160428226,-0.0243540276,0.4298269153,-0.128874436,0.205970034,-0.0231342856,0.0869602188,0.1051392555,-0.5513936877,0.0767241269,0.0688892454,-0.0115327006,-0.3123033047,-0.3542192578,-0.3473508954,-0.0639272109,-0.2551651597,0.0928215683,0.345266372,-0.2033960521,-0.1923074573,-0.0920517072,0.1867493987,-0.0755044073,-0.1349960566,0.2415366471,0.330652684,0.0830517709,0.2387527227,0.0692323297,0.0927333981,0.3280492723,-0.1612951756,-0.1014361978,-0.0775532722,-0.6200729012,0.0457460396,-0.0160569604,-0.465223223,0.1843742728,-0.1858731359,-0.1885086894,0.0499862283,0.1717092693,0.1031510457,0.2989788651,0.0960013494,-0.3177445531,0.2705483735,0.3438619673,0.1331980228,-0.5029964447,0.0652742609,-0.0156139219,-0.2567523718,0.2176098078,0.1873345226,0.2414573133,-0.0829889849,-0.3572193086,-0.0523138382,-0.0511178933,-0.0558524579,-0.1014149934,0.4524842501,0.1777906567,0.1567742676,0.0913131386,0.1969440281,-0.1822924018,0.0624874271,-0.3012304008,-0.0290754717,-0.0146923279,0.2143333256,0.115738757,-0.0643138587,0.2248848528,0.337944448,0.0498892926,-0.4685381353,0.262468487,-0.3657260239,-0.2471660674,0.1961344182,0.1851789206,0.1372065544,-0.1051604077,0.3842988014,0.0134874908,0.2587327957,-0.3288044035,-0.1393946558,0.080968149,0.2727389634,0.0137452884,0.1152774841,-0.3554980457,0.11369133,0.31862849,-0.0416694805,0.1086383611,-0.3720645308,-0.3164130151,-0.1393729448,-0.0205443148,0.5279052258,0.0768126845,0.0268207006,0.1770918816,-0.1090108529,0.0915662721,0.3510977328,-0.3423242867,0.4966166019,-0.3493314087,0.1288179904,0.2440502197,-0.3719643652,-0.1516722888,0.3383845389,-0.1646658182,0.1252206415,0.2112877369,-0.0568555631,0.2122339308,-0.135921821,-0.0084830578,0.1491883695,-0.1079862937,0.4015436471,-0.1918611526,-0.2839153409,0.0489497297,0.1688934714,0.3223809302,-0.093997553,0.0269821547,-0.2874779701,0.3875817657,-0.3304336965,-0.073193185,0.2158527076,-0.0103004053,0.0795356706,0.0810648277,-0.1096467897,-0.5169209242,0.1978815943,-0.3624865711,0.0432497934,0.0342297591,-0.1991609484,0.1067375392,-0.041770719,-0.2488535792,-0.188830331,0.1706272364,0.23171404,-0.1502679437,-0.1181374565,-0.1473599523,0.3599050343,-0.1582805365,0.0234616175,-0.0023934881,0.1705364734,-0.1951894164,-0.2185277343,0.1162100509,-0.0603484437,0.4591343403,-0.1816609353,-0.1646382064,0.234481588,0.1979359686,0.3151443601,-0.060830038,0.0168911535,0.1874209791,0.155690819,-0.0017908275,0.1425172091,0.264064461,-0.1452902257,0.0974556208,0.3178672493,0.0033611003,0.1383701563,0.0590600222,-0.2233062983,0.193423599,-0.1233381256,0.1891794056,0.1224577054,0.2253106385,0.1255556643,0.0167566203,-0.0816582367,-0.5741452575,0.0836443007,0.3775579929,0.1669413447,0.0734937191,0.0733089,-0.0474899113,0.0227888245,-0.1682934314,0.299851954,0.5353221893,0.1349111348,0.0457241945,0.0882089511,-0.1451271623,-0.0892191976,0.0666768849,0.1546055675,0.1948834658,0.5548338294,0.1994925439,-0.0699805468,-0.2803615928,-0.1119266897,0.2671932578,0.0941753089,-0.3748239875,0.1850156933,0.1274146289,-0.1747792363,-0.4213629663,0.0054045664,-0.0335235819,-0.1470950544,-0.0398527905,0.4568405449,-0.1997360289,0.3671449721,-0.0299287681,0.1571482718,-0.1867981851,0.2361011505,-0.1471163779,-0.0711986795,-0.1141485125,-0.0113953063,0.2849563956,0.0669397041,0.3187500536,0.0719447806,-0.3169528842,-0.3599549234,-0.0033945311,0.0783701614,0.0017772753,0.4231902361,-0.0844049677,-0.1965941489,0.0808480829,-0.4469995499,0.2024542987,-0.3591762781,-0.0702030137,-0.1327766627,-0.2264376134,-0.0671751946,-0.1766142398,-0.3789281249,-0.2513652146,-0.2676903903,-0.0906180367,-0.1727518439,0.2116888314,0.0763652846,-0.1411370635,-0.0400552638,0.1537126452,-0.0046703178,-0.3079814911,-0.3230585456,0.069326885,-0.1893555969,-0.3206602931,-0.0872680172,-0.14780882,0.0682690814,0.226744622,-0.4916568995,-0.3557165861,-0.1071279123,0.0577950142,-0.1461234093,-0.0371046998,0.0506975763,0.0610218383,-0.1380059868,-0.1171887517,-0.1728388518,0.2016058713,-0.0312829688,0.0052557569,-0.165980652,0.2671523094,-0.1580260098,0.6628261209,0.2449266016,-0.0611025915,0.3297878206,-0.1419707984,0.0786561295,-0.0182802659,-0.4407642186,-0.0686313659,-0.4300889671,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2728","title":"Concurrent use of same dataset (already downloaded)","comments":"I have the same issue:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1040, in _prepare_split\r\n    with ArrowWriter(features=self.info.features, path=fpath) as writer:\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/dccstor\/tslm-gen\/.cache\/csv\/default-387f1f95c084d4df\/0.0.0\/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete\/csv-validation.arrow'. Detail: [errno 2] No such file or directory\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n  File \"\/dccstor\/tslm\/elron\/tslm-gen\/train.py\", line 510, in <module>\r\n    main()\r\n  File \"\/dccstor\/tslm\/elron\/tslm-gen\/train.py\", line 246, in main\r\n    datasets = prepare_dataset(dataset_args, logger)\r\n  File \"\/dccstor\/tslm\/elron\/tslm-gen\/data.py\", line 157, in prepare_dataset\r\n    datasets = load_dataset(extension, data_files=data_files, split=dataset_split, cache_dir=dataset_args.dataset_cache_dir, na_filter=False, download_mode=dataset_args.dataset_generate_mode)\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 742, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 654, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/dccstor\/tslm-gen\/.cache\/csv\/default-387f1f95c084d4df\/0.0.0\/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete\/csv-validation.arrow'. Detail: [errno 2] No such file or directory\r\n```","body":"## Describe the bug\r\nWhen launching several jobs at the same time loading the same dataset trigger some errors see (last comments).\r\n\r\n## Steps to reproduce the bug\r\nexport HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets\r\nfor MODEL in \"bert-base-uncased\" \"roberta-base\" \"distilbert-base-cased\"; do #  \"bert-base-uncased\" \"bert-large-cased\" \"roberta-large\" \"albert-base-v1\" \"albert-large-v1\"; do\r\n  for TASK_NAME in \"mrpc\" \"rte\" 'imdb' \"paws\" \"mnli\"; do\r\n    export OUTPUT_DIR=${MODEL}_${TASK_NAME}\r\n    sbatch --job-name=${OUTPUT_DIR} \\\r\n      --gres=gpu:1 \\\r\n      --no-requeue \\\r\n      --cpus-per-task=10 \\\r\n      --hint=nomultithread \\\r\n      --time=1:00:00 \\\r\n      --output=jobinfo\/${OUTPUT_DIR}_%j.out \\\r\n      --error=jobinfo\/${OUTPUT_DIR}_%j.err \\\r\n      --qos=qos_gpu-t4 \\\r\n      --wrap=\"module purge; module load pytorch-gpu\/py3\/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets;  python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=\/gpfswork\/rech\/toto\/transformers_models\/$MODEL\"\r\n\r\n  done\r\ndone\r\n\r\n\r\n\r\n```python\r\n# Sample code to reproduce the bug\r\n        dataset_train = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                          batched=True).select(list(range(args.filter)))\r\n\r\n        dataset_val = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                      batched=True).select(list(range(args.filter, args.filter + 5000)))\r\n\r\n        dataset_test = load_dataset('imdb', split='test', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                        batched=True)\r\n```\r\n\r\n## Expected results\r\nI believe I am doing something wrong with the objects. \r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 983, in _prepare_split\r\n    check_duplicates=True,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/gpfswork\/rech\/tts\/unm25jp\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets==1.8.0\r\n- Platform: linux (jeanzay)\r\n- Python version: pyarrow==2.0.0\r\n- PyArrow version: 3.7.8\r\n","comment_length":172,"text":"Concurrent use of same dataset (already downloaded) \n ## Describe the bug\r\nWhen launching several jobs at the same time loading the same dataset trigger some errors see (last comments).\r\n\r\n## Steps to reproduce the bug\r\nexport HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets\r\nfor MODEL in \"bert-base-uncased\" \"roberta-base\" \"distilbert-base-cased\"; do #  \"bert-base-uncased\" \"bert-large-cased\" \"roberta-large\" \"albert-base-v1\" \"albert-large-v1\"; do\r\n  for TASK_NAME in \"mrpc\" \"rte\" 'imdb' \"paws\" \"mnli\"; do\r\n    export OUTPUT_DIR=${MODEL}_${TASK_NAME}\r\n    sbatch --job-name=${OUTPUT_DIR} \\\r\n      --gres=gpu:1 \\\r\n      --no-requeue \\\r\n      --cpus-per-task=10 \\\r\n      --hint=nomultithread \\\r\n      --time=1:00:00 \\\r\n      --output=jobinfo\/${OUTPUT_DIR}_%j.out \\\r\n      --error=jobinfo\/${OUTPUT_DIR}_%j.err \\\r\n      --qos=qos_gpu-t4 \\\r\n      --wrap=\"module purge; module load pytorch-gpu\/py3\/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=\/gpfswork\/rech\/toto\/datasets;  python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=\/gpfswork\/rech\/toto\/transformers_models\/$MODEL\"\r\n\r\n  done\r\ndone\r\n\r\n\r\n\r\n```python\r\n# Sample code to reproduce the bug\r\n        dataset_train = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                          batched=True).select(list(range(args.filter)))\r\n\r\n        dataset_val = load_dataset('imdb', split='train', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                      batched=True).select(list(range(args.filter, args.filter + 5000)))\r\n\r\n        dataset_test = load_dataset('imdb', split='test', download_mode=\"reuse_cache_if_exists\")\r\n        dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),\r\n                                        batched=True)\r\n```\r\n\r\n## Expected results\r\nI believe I am doing something wrong with the objects. \r\n\r\n## Actual results\r\nTraceback (most recent call last):\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 983, in _prepare_split\r\n    check_duplicates=True,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/gpfswork\/rech\/tts\/unm25jp\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"compute_measures.py\", line 181, in <module>\r\n    train_loader, val_loader, test_loader = get_dataloader(args)\r\n  File \"\/gpfsdswork\/projects\/rech\/toto\/intRAOcular\/dataset_utils.py\", line 69, in get_dataloader\r\n    dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/gpfslocalsup\/pub\/anaconda-py3\/2020.02\/envs\/pytorch-gpu-1.7.0\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/gpfswork\/rech\/toto\/datasets\/paws\/labeled_final\/1.1.0\/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete\/paws-test.arrow'. Detail: [errno 2] No such file or directory\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets==1.8.0\r\n- Platform: linux (jeanzay)\r\n- Python version: pyarrow==2.0.0\r\n- PyArrow version: 3.7.8\r\n \n I have the same issue:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1040, in _prepare_split\r\n    with ArrowWriter(features=self.info.features, path=fpath) as writer:\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/arrow_writer.py\", line 192, in __init__\r\n    self.stream = pa.OSFile(self._path, \"wb\")\r\n  File \"pyarrow\/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n  File \"pyarrow\/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/dccstor\/tslm-gen\/.cache\/csv\/default-387f1f95c084d4df\/0.0.0\/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete\/csv-validation.arrow'. Detail: [errno 2] No such file or directory\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n  File \"\/dccstor\/tslm\/elron\/tslm-gen\/train.py\", line 510, in <module>\r\n    main()\r\n  File \"\/dccstor\/tslm\/elron\/tslm-gen\/train.py\", line 246, in main\r\n    datasets = prepare_dataset(dataset_args, logger)\r\n  File \"\/dccstor\/tslm\/elron\/tslm-gen\/data.py\", line 157, in prepare_dataset\r\n    datasets = load_dataset(extension, data_files=data_files, split=dataset_split, cache_dir=dataset_args.dataset_cache_dir, na_filter=False, download_mode=dataset_args.dataset_generate_mode)\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 742, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/tslm\/envs\/anaconda3\/envs\/trf-a100\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 654, in _download_and_prepare\r\n    raise OSError(\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] Failed to open local file '\/dccstor\/tslm-gen\/.cache\/csv\/default-387f1f95c084d4df\/0.0.0\/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete\/csv-validation.arrow'. Detail: [errno 2] No such file or directory\r\n```","embeddings":[-0.5779120326,-0.0810000375,-0.0453643724,0.4501565397,0.2459626794,0.0139751369,0.5565331578,0.2723817527,0.1843708605,0.1907434464,-0.0311780032,0.1464349478,0.0212427545,0.1441881359,-0.0885511786,0.0235604327,0.1309166104,-0.2257413864,-0.3968794644,-0.0203631092,-0.2406820655,0.1446244419,0.0855503231,0.1932705194,-0.5113247037,-0.3433853984,-0.0541765466,0.0557928272,0.2590035498,-0.0671327934,0.1664633751,0.2859389484,-0.0116243837,0.8678808808,-0.0001090042,0.1391353309,0.1289675683,0.1109726653,-0.0634465516,-0.2272718996,0.0330815762,-0.107974872,0.1186949238,-0.313777715,0.1189511046,0.0584998429,-0.030055631,-0.2093538344,0.5382676721,0.0736098737,0.2384028584,0.3819690347,-0.3065969348,-0.2297079861,0.1842454374,0.0569794737,0.0860361606,0.0703067929,0.1006150246,-0.160428226,-0.0243540276,0.4298269153,-0.128874436,0.205970034,-0.0231342856,0.0869602188,0.1051392555,-0.5513936877,0.0767241269,0.0688892454,-0.0115327006,-0.3123033047,-0.3542192578,-0.3473508954,-0.0639272109,-0.2551651597,0.0928215683,0.345266372,-0.2033960521,-0.1923074573,-0.0920517072,0.1867493987,-0.0755044073,-0.1349960566,0.2415366471,0.330652684,0.0830517709,0.2387527227,0.0692323297,0.0927333981,0.3280492723,-0.1612951756,-0.1014361978,-0.0775532722,-0.6200729012,0.0457460396,-0.0160569604,-0.465223223,0.1843742728,-0.1858731359,-0.1885086894,0.0499862283,0.1717092693,0.1031510457,0.2989788651,0.0960013494,-0.3177445531,0.2705483735,0.3438619673,0.1331980228,-0.5029964447,0.0652742609,-0.0156139219,-0.2567523718,0.2176098078,0.1873345226,0.2414573133,-0.0829889849,-0.3572193086,-0.0523138382,-0.0511178933,-0.0558524579,-0.1014149934,0.4524842501,0.1777906567,0.1567742676,0.0913131386,0.1969440281,-0.1822924018,0.0624874271,-0.3012304008,-0.0290754717,-0.0146923279,0.2143333256,0.115738757,-0.0643138587,0.2248848528,0.337944448,0.0498892926,-0.4685381353,0.262468487,-0.3657260239,-0.2471660674,0.1961344182,0.1851789206,0.1372065544,-0.1051604077,0.3842988014,0.0134874908,0.2587327957,-0.3288044035,-0.1393946558,0.080968149,0.2727389634,0.0137452884,0.1152774841,-0.3554980457,0.11369133,0.31862849,-0.0416694805,0.1086383611,-0.3720645308,-0.3164130151,-0.1393729448,-0.0205443148,0.5279052258,0.0768126845,0.0268207006,0.1770918816,-0.1090108529,0.0915662721,0.3510977328,-0.3423242867,0.4966166019,-0.3493314087,0.1288179904,0.2440502197,-0.3719643652,-0.1516722888,0.3383845389,-0.1646658182,0.1252206415,0.2112877369,-0.0568555631,0.2122339308,-0.135921821,-0.0084830578,0.1491883695,-0.1079862937,0.4015436471,-0.1918611526,-0.2839153409,0.0489497297,0.1688934714,0.3223809302,-0.093997553,0.0269821547,-0.2874779701,0.3875817657,-0.3304336965,-0.073193185,0.2158527076,-0.0103004053,0.0795356706,0.0810648277,-0.1096467897,-0.5169209242,0.1978815943,-0.3624865711,0.0432497934,0.0342297591,-0.1991609484,0.1067375392,-0.041770719,-0.2488535792,-0.188830331,0.1706272364,0.23171404,-0.1502679437,-0.1181374565,-0.1473599523,0.3599050343,-0.1582805365,0.0234616175,-0.0023934881,0.1705364734,-0.1951894164,-0.2185277343,0.1162100509,-0.0603484437,0.4591343403,-0.1816609353,-0.1646382064,0.234481588,0.1979359686,0.3151443601,-0.060830038,0.0168911535,0.1874209791,0.155690819,-0.0017908275,0.1425172091,0.264064461,-0.1452902257,0.0974556208,0.3178672493,0.0033611003,0.1383701563,0.0590600222,-0.2233062983,0.193423599,-0.1233381256,0.1891794056,0.1224577054,0.2253106385,0.1255556643,0.0167566203,-0.0816582367,-0.5741452575,0.0836443007,0.3775579929,0.1669413447,0.0734937191,0.0733089,-0.0474899113,0.0227888245,-0.1682934314,0.299851954,0.5353221893,0.1349111348,0.0457241945,0.0882089511,-0.1451271623,-0.0892191976,0.0666768849,0.1546055675,0.1948834658,0.5548338294,0.1994925439,-0.0699805468,-0.2803615928,-0.1119266897,0.2671932578,0.0941753089,-0.3748239875,0.1850156933,0.1274146289,-0.1747792363,-0.4213629663,0.0054045664,-0.0335235819,-0.1470950544,-0.0398527905,0.4568405449,-0.1997360289,0.3671449721,-0.0299287681,0.1571482718,-0.1867981851,0.2361011505,-0.1471163779,-0.0711986795,-0.1141485125,-0.0113953063,0.2849563956,0.0669397041,0.3187500536,0.0719447806,-0.3169528842,-0.3599549234,-0.0033945311,0.0783701614,0.0017772753,0.4231902361,-0.0844049677,-0.1965941489,0.0808480829,-0.4469995499,0.2024542987,-0.3591762781,-0.0702030137,-0.1327766627,-0.2264376134,-0.0671751946,-0.1766142398,-0.3789281249,-0.2513652146,-0.2676903903,-0.0906180367,-0.1727518439,0.2116888314,0.0763652846,-0.1411370635,-0.0400552638,0.1537126452,-0.0046703178,-0.3079814911,-0.3230585456,0.069326885,-0.1893555969,-0.3206602931,-0.0872680172,-0.14780882,0.0682690814,0.226744622,-0.4916568995,-0.3557165861,-0.1071279123,0.0577950142,-0.1461234093,-0.0371046998,0.0506975763,0.0610218383,-0.1380059868,-0.1171887517,-0.1728388518,0.2016058713,-0.0312829688,0.0052557569,-0.165980652,0.2671523094,-0.1580260098,0.6628261209,0.2449266016,-0.0611025915,0.3297878206,-0.1419707984,0.0786561295,-0.0182802659,-0.4407642186,-0.0686313659,-0.430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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2727","title":"Error in loading the Arabic Billion Words Corpus","comments":"I modified the dataset loading script to catch the `IndexError` and inspect the records at which the error is happening, and I found this:\r\nFor the `Techreen` config, the error happens in 36 records when trying to find the `Text` or `Dateline` tags. All these 36 records look something like:\r\n```\r\n<Techreen>\r\n <ID>TRN_ARB_0248167<\/ID>\r\n <URL>http:\/\/tishreen.news.sy\/tishreen\/public\/read\/248240<\/URL>\r\n <Headline>Removed, because the original articles was in English<\/Headline>\r\n<\/Techreen>\r\n```\r\n\r\nand all the 288 faulty records in the `Almustaqbal` config look like:\r\n```\r\n<Almustaqbal>\r\n <ID>MTL_ARB_0028398<\/ID>\r\n \r\n <URL>http:\/\/www.almustaqbal.com\/v4\/article.aspx?type=NP&ArticleID=179015<\/URL>\r\n <Headline> Removed because it is not available in the original site<\/Headline>\r\n<\/Almustaqbal>\r\n```\r\n\r\nso the error is happening because the articles were removed and so the associated records lack the `Text` tag.\r\n\r\nIn this case, I think we just need to catch the `IndexError` and ignore (pass) it.\r\n","body":"## Describe the bug\r\nI get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"arabic_billion_words\", \"Techreen\")\r\nload_dataset(\"arabic_billion_words\", \"Almustaqbal\")\r\n```\r\n\r\n## Expected results\r\nThe datasets load succefully.\r\n\r\n## Actual results\r\n```python\r\n_extract_tags(self, sample, tag)\r\n    139             if len(out) > 0:\r\n    140                 break\r\n--> 141         return out[0]\r\n    142 \r\n    143     def _clean_text(self, text):\r\n\r\nIndexError: list index out of range\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: Ubuntu 18.04.5 LTS\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":128,"text":"Error in loading the Arabic Billion Words Corpus \n ## Describe the bug\r\nI get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"arabic_billion_words\", \"Techreen\")\r\nload_dataset(\"arabic_billion_words\", \"Almustaqbal\")\r\n```\r\n\r\n## Expected results\r\nThe datasets load succefully.\r\n\r\n## Actual results\r\n```python\r\n_extract_tags(self, sample, tag)\r\n    139             if len(out) > 0:\r\n    140                 break\r\n--> 141         return out[0]\r\n    142 \r\n    143     def _clean_text(self, text):\r\n\r\nIndexError: list index out of range\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: Ubuntu 18.04.5 LTS\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n I modified the dataset loading script to catch the `IndexError` and inspect the records at which the error is happening, and I found this:\r\nFor the `Techreen` config, the error happens in 36 records when trying to find the `Text` or `Dateline` tags. All these 36 records look something like:\r\n```\r\n<Techreen>\r\n <ID>TRN_ARB_0248167<\/ID>\r\n <URL>http:\/\/tishreen.news.sy\/tishreen\/public\/read\/248240<\/URL>\r\n <Headline>Removed, because the original articles was in English<\/Headline>\r\n<\/Techreen>\r\n```\r\n\r\nand all the 288 faulty records in the `Almustaqbal` config look like:\r\n```\r\n<Almustaqbal>\r\n <ID>MTL_ARB_0028398<\/ID>\r\n \r\n <URL>http:\/\/www.almustaqbal.com\/v4\/article.aspx?type=NP&ArticleID=179015<\/URL>\r\n <Headline> Removed because it is not available in the original site<\/Headline>\r\n<\/Almustaqbal>\r\n```\r\n\r\nso the error is happening because the articles were removed and so the associated records lack the `Text` tag.\r\n\r\nIn this case, I think we just need to catch the `IndexError` and ignore (pass) it.\r\n","embeddings":[-0.1755402386,0.106749475,-0.1183999553,0.4231290519,-0.1488431394,0.2618189752,0.2323353887,0.3904677033,0.3619009554,-0.0010758634,-0.2331460863,-0.0116897542,0.058974117,0.0111060264,-0.0046201525,-0.2025989443,-0.030889973,-0.1021867767,0.2275917828,0.14214845,-0.1725380868,0.088298142,-0.2301309407,-0.0897333324,0.1276371777,-0.0596874207,0.0723104551,0.0443104617,0.2043147385,-0.5446294546,-0.0048863036,-0.4125088155,0.4355430603,0.4883056283,-0.0001138216,0.0546824187,0.3437693715,-0.0272877552,-0.3930965066,-0.4340239763,-0.3352651596,-0.2002716511,0.0597978495,-0.2232355028,-0.0848990083,-0.2553299963,-0.1447215676,-0.3347806036,0.1664268374,0.5260614753,0.1590858251,0.1715174317,0.1333598644,0.106060259,0.3838208318,-0.2292442918,-0.0983324423,0.2020831853,0.5291868448,0.1876037419,-0.0844436958,0.2945673764,-0.1038550511,-0.0169019997,0.1182348877,-0.0495264716,0.1696146429,-0.4821276665,0.0659714937,0.2115795463,0.3448188007,-0.3105709553,-0.4523892105,-0.2377697527,-0.1179611385,-0.3698241711,0.2565723658,0.2197514176,-0.195923686,0.129543364,0.1145651564,-0.0898964331,-0.0348887108,0.3676132858,-0.3026773036,0.3926457763,-0.077249445,-0.0298966225,0.1111189947,-0.1466866583,0.0816025436,0.002760611,0.0597463623,0.2146911174,-0.3444157541,0.2084279954,0.0736802295,-0.1433285624,0.0144621581,0.0827068537,0.2570385337,-0.034107022,-0.0585579835,0.0653540716,0.3505274355,0.1814717501,-0.1095268726,0.0925202146,-0.0106690628,0.303013593,0.0932591036,0.0778528303,0.1683073938,-0.2432312667,0.0530864,-0.0370576158,0.2929569185,-0.3523527086,-0.265804857,0.2261015177,-0.3138167858,-0.1832838506,0.0655586943,0.1817698777,-0.2392186075,-0.1005868986,0.2253467143,0.0679831579,-0.27737239,-0.0633981153,-0.1548104882,0.0935432166,-0.0421134941,0.1769756973,0.4418578446,-0.5744714141,0.1978181005,0.043781396,0.2652725875,-0.0362150185,0.0188351832,-0.4248972237,0.2651003003,0.4377249777,0.0096786022,0.0718528181,0.0458108075,0.0313299224,-0.1299722046,0.309678942,-0.2886221409,-0.1968882084,-0.0432505459,0.12648274,0.0378431939,0.1340029985,-0.1635893285,0.3765878677,0.5087185502,-0.1100702062,0.0502905846,-0.1411720365,-0.2798919678,-0.0035947154,0.1736454964,0.4014997184,-0.3967858851,-0.105560407,0.0184918921,0.565326333,-0.0560430773,0.1985335201,-0.2315893471,0.4627299011,-0.1715318412,0.2583889663,0.2030450553,-0.1243126094,-0.1949735135,0.1152663976,0.2389275134,-0.0143756084,-0.0578818396,-0.1760071516,0.4688828886,-0.1368215531,0.6135954261,0.344940871,-0.0424385816,-0.0966618583,-0.347731024,-0.1141509861,0.1973970234,0.1627766341,0.0313840397,-0.048485674,-0.0097026713,-0.2665247619,0.0788684636,0.1392221302,0.0231262427,0.3674274087,-0.6418525577,0.0916571841,0.2857526541,-0.2323861867,-0.1339323819,0.1271806806,-0.1287386864,0.2774348557,-0.3100445569,0.1869035959,-0.4867818058,0.015705755,-0.1359554082,0.1484376341,0.0637337714,0.0105744414,-0.1396457851,0.2280972898,-0.2554454803,-0.1438363194,0.068384625,-0.0334545262,-0.6968354583,0.2373879552,-0.2281327993,-0.13617751,0.1577715576,0.1020079553,0.140430674,-0.1101043522,-0.1807846725,-0.020392159,-0.1228406578,-0.1708034873,-0.3455619216,0.3240127861,0.3131725788,-0.3127225637,0.2518367469,0.405628264,0.2708936036,-0.0836238265,0.3261787295,0.2131666988,-0.1609645039,0.3445234895,-0.0217991453,-0.2369894981,0.2463639528,0.0210876931,-0.0098291654,-0.2796858251,0.2190893143,-0.1045974866,0.3646546006,0.0616709776,-0.0477095805,-0.058134757,0.1234448552,-0.0161477383,0.1691765636,0.2042669356,-0.0738272592,-0.1680463105,-0.1717485487,-0.3292060196,0.1732795835,0.107717663,-0.0089457426,-0.083401978,0.1157973781,-0.1058290303,0.3423880935,0.255276531,0.2736132741,0.2762809098,0.0783206522,0.1315883994,-0.3082402945,-0.4225620031,0.0142450947,0.2152878642,-0.4495734572,0.4153724015,0.1764909774,-0.3796938658,0.0393821411,-0.6723875403,-0.2069754153,-0.2337341309,0.0206673089,-0.1468473375,-0.2473726422,0.0721461475,-0.1190406457,0.09507218,0.2156872749,0.0018519799,-0.0147829652,0.0973429382,-0.3829988539,-0.0383320674,0.2576623559,-0.0327397026,0.095439449,-0.0960054174,-0.1535268277,-0.1855542362,-0.003608952,0.1915376484,0.0029507354,0.4744865894,0.0214510057,0.3548190296,-0.0678444281,-0.3108948767,0.1428106278,0.284109205,-0.0027109555,0.0835413486,0.0051922076,0.3517223001,0.0820932537,-0.6314830184,-0.2765565813,-0.3663812876,-0.2867331505,-0.0545874983,0.2838801742,0.3187383115,-0.0734038353,-0.1046390012,0.2247227281,0.3155885935,-0.2177784741,-0.0706695691,0.2444880903,-0.0777041316,-0.2715021968,-0.1034858078,-0.0088611031,0.0229867306,0.0225059278,-0.4185804427,0.3741729259,-0.2116332948,-0.1099837944,-0.2799408734,0.1427977234,0.1195821241,-0.1057285815,-0.0864008814,0.2175137401,-0.1457337588,-0.0083484845,-0.0701671541,0.1588692367,0.2455358207,0.3019486666,-0.150668174,0.2919903696,-0.0586923361,-0.040083617,0.6439816952,0.2175884843,0.1514039934,-0.2557828128,-0.3873148263,-0.1864755899,0.0446603149,-0.32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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2727","title":"Error in loading the Arabic Billion Words Corpus","comments":"Thanks @M-Salti for reporting this issue and for your investigation.\r\n\r\nIndeed, those `IndexError` should be catched and the corresponding record should be ignored.\r\n\r\nI'm opening a Pull Request to fix it.","body":"## Describe the bug\r\nI get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"arabic_billion_words\", \"Techreen\")\r\nload_dataset(\"arabic_billion_words\", \"Almustaqbal\")\r\n```\r\n\r\n## Expected results\r\nThe datasets load succefully.\r\n\r\n## Actual results\r\n```python\r\n_extract_tags(self, sample, tag)\r\n    139             if len(out) > 0:\r\n    140                 break\r\n--> 141         return out[0]\r\n    142 \r\n    143     def _clean_text(self, text):\r\n\r\nIndexError: list index out of range\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: Ubuntu 18.04.5 LTS\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":31,"text":"Error in loading the Arabic Billion Words Corpus \n ## Describe the bug\r\nI get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nload_dataset(\"arabic_billion_words\", \"Techreen\")\r\nload_dataset(\"arabic_billion_words\", \"Almustaqbal\")\r\n```\r\n\r\n## Expected results\r\nThe datasets load succefully.\r\n\r\n## Actual results\r\n```python\r\n_extract_tags(self, sample, tag)\r\n    139             if len(out) > 0:\r\n    140                 break\r\n--> 141         return out[0]\r\n    142 \r\n    143     def _clean_text(self, text):\r\n\r\nIndexError: list index out of range\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.2\r\n- Platform: Ubuntu 18.04.5 LTS\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n Thanks @M-Salti for reporting this issue and for your investigation.\r\n\r\nIndeed, those `IndexError` should be catched and the corresponding record should be ignored.\r\n\r\nI'm opening a Pull Request to fix it.","embeddings":[-0.1877755523,-0.046449706,-0.0886767581,0.4570911825,-0.1361012012,0.2721578479,0.2241880596,0.3228879273,0.4278388917,-0.0132559789,-0.2501672208,-0.0138163101,0.1214889213,0.0700821951,-0.0400824957,-0.2980046272,0.0483283624,-0.098388046,0.2569685876,0.0944317132,-0.2235391736,0.0493709408,-0.3123707175,-0.0771447942,0.1526142508,-0.0772051439,0.0157806892,-0.0589512959,0.209041208,-0.4896107316,0.086100854,-0.3483731449,0.5297790766,0.5618437529,-0.0001174112,-0.0130771194,0.4175146818,0.0011641483,-0.2492554486,-0.5517652631,-0.191778481,-0.1388527453,0.1426743865,-0.1907674372,-0.1093662828,-0.268625766,-0.1455368847,-0.2430966496,0.1646284759,0.3619323671,0.1498288214,0.1506914049,0.1792136282,0.0761944503,0.2918259203,-0.2640067935,-0.0309772193,0.1672476977,0.5540938973,0.0845655128,0.0206550229,0.1773422956,-0.1420636475,0.019520076,0.1509270966,-0.0254509784,0.1860082895,-0.6007181406,0.0481732525,0.2375650257,0.4379049242,-0.2943656743,-0.4859935641,-0.203239426,-0.0306405481,-0.3684281707,0.2340958267,0.3169795871,-0.1875098348,0.1218396127,0.1365371197,-0.163762331,-0.1190502718,0.3956000805,-0.2622692585,0.3255661428,-0.0335666724,0.0091807358,0.1443209648,-0.1356599182,0.0591473952,-0.0627769157,0.0089818519,0.2237094939,-0.3714162111,0.24262923,0.0462795645,-0.1512625068,0.0464232042,0.1270489544,0.1780865192,-0.0431698486,-0.0866993442,0.1870376319,0.3773936927,0.1415175349,-0.0474852845,0.1577919126,0.0590441376,0.2584305108,-0.036474321,0.0688213184,0.1227201745,-0.1632344127,0.0382446647,-0.1630247384,0.2272497565,-0.3855598569,-0.2166520804,0.2503850758,-0.400190413,-0.216493547,0.0175691918,0.1911917776,-0.2175972015,-0.1130019203,0.1756854653,0.2376640737,-0.1939764917,-0.0253841691,-0.1380524337,0.0888229981,-0.0909494609,0.0913553238,0.4032443464,-0.60462147,0.2357470393,-0.0026580137,0.3509509861,-0.0137081342,0.114214994,-0.3825574219,0.1943871826,0.4071758091,0.1387536079,0.079913117,0.1162825003,0.0233889893,-0.1573423296,0.4012973011,-0.391155839,-0.1710174978,-0.0674691871,0.102638647,0.000966474,0.0878515691,-0.2276010215,0.3403206468,0.5037471652,-0.0629233569,0.00321158,-0.1481618732,-0.3219294548,-0.0733049363,0.1235521734,0.3431198597,-0.3512627482,-0.0584017523,0.0450982563,0.4893796444,-0.0376650393,0.2192805856,-0.2559595108,0.4304127395,-0.1774687618,0.3240619898,0.2467717826,-0.1277134866,-0.2421393543,0.1979265958,0.2657414377,-0.0758553892,-0.1030729339,-0.2082653493,0.4517375231,-0.0546392873,0.6274052262,0.4939140677,-0.0511970967,-0.0900520161,-0.3175869882,-0.1195221618,0.3496678174,0.125807032,0.1293507218,-0.0166825335,0.0039982693,-0.2633425295,0.1419402808,0.1173850745,-0.0173712447,0.3858934045,-0.4759050906,0.0965217575,0.3008753657,-0.2243798375,0.0428482927,0.1723197103,-0.1413490325,0.2480078042,-0.2273525894,0.2863011956,-0.4168075323,0.0402079336,-0.2054211795,0.0908534005,0.0146368677,-0.0775188431,-0.0872636363,0.2759567499,-0.2865067124,-0.1691031456,0.0641296208,0.0150775602,-0.6049718261,0.3342185915,-0.176308617,-0.2357383072,0.0998833701,-0.0123946629,0.1410332173,-0.1680068374,-0.213278085,0.0060990844,-0.1226605475,-0.2166849971,-0.3014376163,0.2852753103,0.295688808,-0.373118192,0.3329123259,0.4022060037,0.1639844328,-0.0809130594,0.3661567569,0.2040123641,-0.1086356044,0.3784812987,-0.1051313728,-0.2154235393,0.2266115993,0.0307549872,0.0170902889,-0.1032828689,0.2849919796,-0.1050957665,0.3402002156,-0.0231685769,-0.131332323,-0.1196971312,0.0099507049,0.0059377626,0.2142357081,0.2765231133,-0.0754362345,-0.1280435026,-0.1962985843,-0.4573308527,0.2315344214,0.1442405879,-0.0854848549,-0.089523755,0.124490425,-0.0630655214,0.3120190203,0.1077940091,0.294393599,0.2502011359,0.1347865462,0.1014078036,-0.3171717227,-0.3249179423,0.0405472703,0.1729808897,-0.3517558277,0.4300263524,0.2592787147,-0.3555389047,0.0915019512,-0.6388462186,-0.2775249779,-0.1306153536,-0.0360215791,0.0063877609,-0.1150352284,0.1680921614,-0.1682744324,-0.0864626616,0.2360263318,-0.1264009923,0.0223936588,0.0920129195,-0.3701221347,-0.0770025775,0.2550441027,-0.074680008,0.1576037258,0.0727805719,-0.1214017197,-0.1043154299,-0.0292145535,0.1853601933,-0.0183185749,0.4640192986,-0.1149681211,0.3587628901,-0.1403543502,-0.2421628535,0.2271582037,0.18570669,-0.0218745098,0.1637769938,0.0100992899,0.2391341776,0.0944108143,-0.4680998027,-0.314943105,-0.3170129359,-0.2879398763,-0.0266062431,0.1678608358,0.3233623207,-0.0039898078,-0.2117659599,0.2140717953,0.2239971906,-0.174762398,-0.1660606414,0.2782445848,-0.0394990407,-0.3239015937,-0.0820351094,0.0130388215,-0.0526661798,0.0465412848,-0.4170078933,0.3313923776,-0.2046108246,-0.0389936902,-0.2311557084,0.1742732525,0.0914127827,-0.1448886395,-0.0160810445,0.1682032496,-0.0429368019,-0.0291244797,0.0024891554,0.1942388415,0.3245144188,0.2126039714,-0.1889989525,0.2093219459,-0.0382447913,0.0387954004,0.6535492539,0.2737876773,0.07669615,-0.2376984358,-0.4639846981,-0.1501168311,0.1125407815,-0.41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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2724","title":"404 Error when loading remote data files from private repo","comments":"I guess the issue is when computing the ETags of the remote files. Indeed `use_auth_token` must be passed to `request_etags` here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/35b5e4bc0cb2ed896e40f3eb2a4aa3de1cb1a6c5\/src\/datasets\/builder.py#L160-L160","body":"## Describe the bug\r\nWhen loading remote data files from a private repo, a 404 error is raised.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nurl = hf_hub_url(\"lewtun\/asr-preds-test\", \"preds.jsonl\", repo_type=\"dataset\")\r\ndset = load_dataset(\"json\", data_files=url, use_auth_token=True)\r\n# HTTPError: 404 Client Error: Not Found for url: https:\/\/huggingface.co\/datasets\/lewtun\/asr-preds-test\/resolve\/main\/preds.jsonl\r\n```\r\n\r\n## Expected results\r\nLoad dataset.\r\n\r\n## Actual results\r\n404 Error.\r\n\r\n","comment_length":22,"text":"404 Error when loading remote data files from private repo \n ## Describe the bug\r\nWhen loading remote data files from a private repo, a 404 error is raised.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nurl = hf_hub_url(\"lewtun\/asr-preds-test\", \"preds.jsonl\", repo_type=\"dataset\")\r\ndset = load_dataset(\"json\", data_files=url, use_auth_token=True)\r\n# HTTPError: 404 Client Error: Not Found for url: https:\/\/huggingface.co\/datasets\/lewtun\/asr-preds-test\/resolve\/main\/preds.jsonl\r\n```\r\n\r\n## Expected results\r\nLoad dataset.\r\n\r\n## Actual results\r\n404 Error.\r\n\r\n \n I guess the issue is when computing the ETags of the remote files. Indeed `use_auth_token` must be passed to `request_etags` here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/35b5e4bc0cb2ed896e40f3eb2a4aa3de1cb1a6c5\/src\/datasets\/builder.py#L160-L160","embeddings":[0.0871632025,0.025846079,0.0274070129,0.6045913696,-0.1784682125,-0.0898554325,0.2882218957,0.3268503845,0.0184905343,0.1675235331,-0.3693999052,-0.0839862376,0.2241544724,-0.04085657,0.0888220221,-0.0422795936,0.0376422778,0.1200491264,0.3278564811,-0.2406732142,-0.1716177166,0.1605942547,-0.0282529909,0.2701848447,0.069742389,0.1259597391,0.0421590768,0.5915513635,-0.1242948249,-0.1665705591,0.2873155773,-0.0173066445,0.5707443357,0.8942393064,-0.0001184026,0.168162629,0.2595363259,0.010358192,-0.2348511517,-0.1142685115,-0.4038441181,0.1302046031,0.0836360753,-0.1607075483,-0.1807733178,-0.0938127637,-0.3060295582,0.0864418074,0.4408935308,0.3926028609,0.1661800742,0.4484367371,0.0907083973,-0.1656462997,0.4235145748,0.0348576717,0.0053401585,0.2900849581,-0.0708383024,0.0867628902,0.0132969748,-0.0068785753,0.0141346008,0.0842933804,0.2092770934,0.0134577155,-0.0423866138,-0.0992748365,-0.1002172008,0.0658624619,0.4129537046,0.0002570056,-0.2669299543,0.026682891,-0.0367544442,-0.1364852935,0.158232078,0.2569408715,-0.1341071278,0.1208953932,-0.1735553145,-0.3134871423,-0.4048245251,0.3338383734,0.1114476398,-0.0551392734,0.0097003402,0.0252807047,-0.2526357472,-0.2069942504,-0.0964903906,-0.1750052273,0.021321848,0.1697475761,-0.0287924297,-0.0907949507,0.085866265,-0.031663157,0.2942894399,0.3939569592,-0.2702388763,-0.0663872436,-0.4752777517,0.1954914033,-0.017346004,0.3439953625,0.0193969086,0.2792779803,0.4515994191,0.4925678074,0.0249252655,0.0916304067,-0.3378535211,-0.0051901876,-0.1679282337,-0.1927329153,0.4646197557,-0.4979287684,-0.0039766082,0.1847059727,-0.2357386053,0.0277586654,-0.0260837879,0.3406423032,-0.0719407499,-0.1338147968,0.0686857253,0.0375015922,-0.2768373191,-0.0923992842,-0.1704371572,-0.1632038951,0.0213641208,0.259172529,0.1316786557,-0.0267508589,0.2024156749,-0.0369497463,0.3569190204,-0.2349615395,0.0001223852,0.1925103962,-0.1851204485,0.2597736418,0.1659449488,-0.0253740828,0.1190524772,-0.392562747,-0.0547073521,-0.1396570802,-0.6864687204,-0.4307950139,-0.0843315795,0.1072939336,-0.1942071468,0.0400028341,-0.5282006264,-0.3732130527,-0.1112889051,-0.0995284989,0.0376247726,0.0282477625,-0.0483968221,-0.1158322841,0.1585278064,0.585699439,0.3585839868,-0.0071596056,0.3294963539,-0.1886004359,0.2019345164,0.3431803584,-0.1851004809,0.0839203298,-0.2878257632,0.31100142,0.1597580165,-0.4815152586,-0.4112557471,0.645683229,-0.26988554,0.0339968279,0.098229751,-0.1084417254,0.1172511205,-0.2085673362,0.5326524377,0.4657271802,0.2107895911,-0.1300878823,-0.288569659,-0.2136393338,-0.3629421592,0.3998591304,-0.0017962889,0.2109307945,0.3625538647,-0.2569054961,0.2932198346,-0.0458183661,-0.1872207522,0.3072160184,0.3584540188,0.0192747992,0.2242029458,0.0763057843,-0.2873437405,0.1927688122,-0.3585609794,0.1491599977,-0.0419801287,-0.0627901629,-0.3100636303,-0.0910803974,-0.4545488954,0.186371997,0.0425487794,-0.0372703671,-0.1701895148,0.1651738584,0.0201012958,0.2720374167,-0.1222627088,0.2718659937,-0.2868469059,0.6367555857,0.0267659351,0.0750541836,0.1860989779,0.2177869529,0.3806755841,-0.0630792081,-0.1942438781,0.3124832213,0.1950450838,0.1324029118,0.7725487947,-0.0686830506,0.2205391824,-0.0882269591,0.0287845023,0.4030066431,0.0595477112,0.2151002139,0.1824583113,0.1951798499,0.0441593677,0.3703363836,0.0991877913,-0.108142063,0.1792208999,-0.1274946332,-0.1071734875,-0.0383460037,0.3741497993,0.0318130068,-0.2007786632,-0.0603227839,-0.1172189116,0.0278729647,0.2025966495,-0.3027195632,-0.0415041894,0.3303074241,-0.1391055584,-0.0167186912,0.2410385013,0.4205012023,0.2979258597,-0.0170525722,-0.1817577481,0.0656797588,-0.0896785483,-0.0831211284,-0.0638893247,0.0220544692,0.0340768434,0.1687859297,0.1015623882,-0.0376967192,-0.0569456555,-0.4256904721,-0.082486622,0.2719039619,-0.4516474009,-0.0140944477,-0.3728094697,-0.13232328,-0.076727353,-0.2615007162,-0.5702645779,-0.0763109252,0.028902594,-0.0809274614,-0.10595496,-0.1590922326,0.0347101949,0.2810231447,0.1168027893,-0.3547641933,-0.1858559549,-0.0121848453,-0.294015795,-0.0945232734,0.2922323942,0.020486027,0.3175248802,-0.4767365754,-0.0902839676,-0.4070107639,-0.1112848222,0.0356454179,0.1565070599,0.3482768238,-0.0398228914,0.072316125,0.2941636741,0.091648683,0.2570353448,-0.1088714674,-0.2068011612,0.2457992285,-0.1831875741,-0.0909194797,-0.1320876181,-0.0294611044,-0.2550732195,-0.1853720546,0.2371392697,0.3771695495,0.0953637883,-0.2680513561,0.07060536,0.2262733728,-0.1531769484,-0.1468227059,-0.171980992,-0.5023723245,0.2743113935,-0.4032094777,-0.2306600511,0.2652857006,0.2256273478,0.0434053689,-0.1237591431,-0.6602146029,-0.5772987008,-0.0836366713,0.2650004029,-0.2728845179,0.0472681038,0.0438520834,-0.1540024579,0.0778176486,0.04580402,-0.2724538147,0.1395338178,0.047789719,0.1411540806,0.0129360519,0.1984617561,-0.0642819479,0.599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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2724","title":"404 Error when loading remote data files from private repo","comments":"Yes, I remember having properly implemented that: \r\n- https:\/\/github.com\/huggingface\/datasets\/commit\/7a9c62f7cef9ecc293f629f859d4375a6bd26dc8#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R160\r\n- https:\/\/github.com\/huggingface\/datasets\/pull\/2628\/commits\/6350a03b4b830339a745f7b1da46ece784ca734c\r\n\r\nBut a subsequent refactoring accidentally removed it...","body":"## Describe the bug\r\nWhen loading remote data files from a private repo, a 404 error is raised.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nurl = hf_hub_url(\"lewtun\/asr-preds-test\", \"preds.jsonl\", repo_type=\"dataset\")\r\ndset = load_dataset(\"json\", data_files=url, use_auth_token=True)\r\n# HTTPError: 404 Client Error: Not Found for url: https:\/\/huggingface.co\/datasets\/lewtun\/asr-preds-test\/resolve\/main\/preds.jsonl\r\n```\r\n\r\n## Expected results\r\nLoad dataset.\r\n\r\n## Actual results\r\n404 Error.\r\n\r\n","comment_length":18,"text":"404 Error when loading remote data files from private repo \n ## Describe the bug\r\nWhen loading remote data files from a private repo, a 404 error is raised.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nurl = hf_hub_url(\"lewtun\/asr-preds-test\", \"preds.jsonl\", repo_type=\"dataset\")\r\ndset = load_dataset(\"json\", data_files=url, use_auth_token=True)\r\n# HTTPError: 404 Client Error: Not Found for url: https:\/\/huggingface.co\/datasets\/lewtun\/asr-preds-test\/resolve\/main\/preds.jsonl\r\n```\r\n\r\n## Expected results\r\nLoad dataset.\r\n\r\n## Actual results\r\n404 Error.\r\n\r\n \n Yes, I remember having properly implemented that: \r\n- https:\/\/github.com\/huggingface\/datasets\/commit\/7a9c62f7cef9ecc293f629f859d4375a6bd26dc8#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R160\r\n- https:\/\/github.com\/huggingface\/datasets\/pull\/2628\/commits\/6350a03b4b830339a745f7b1da46ece784ca734c\r\n\r\nBut a subsequent refactoring accidentally removed 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2722","title":"Missing cache file","comments":"This could be solved by going to the glue\/ directory and delete sst2 directory, then load the dataset again will help you redownload the dataset.","body":"Strangely missing cache file after I  restart my program again.\r\n\r\n`glue_dataset = datasets.load_dataset('glue', 'sst2')`\r\n\r\n`FileNotFoundError: [Errno 2] No such file or directory: \/Users\/chris\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad\/dataset_info.json'`\r\n","comment_length":25,"text":"Missing cache file \n Strangely missing cache file after I  restart my program again.\r\n\r\n`glue_dataset = datasets.load_dataset('glue', 'sst2')`\r\n\r\n`FileNotFoundError: [Errno 2] No such file or directory: \/Users\/chris\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad\/dataset_info.json'`\r\n \n This could be solved by going to the glue\/ directory and delete sst2 directory, then load the dataset again will help you redownload the dataset.","embeddings":[-0.1489497572,-0.2841119766,-0.090395011,0.146070689,0.2751778662,0.0493270718,0.0770320743,0.2098800093,0.2618972063,0.0594649278,-0.085697338,0.0969992578,0.0986218303,0.2517307997,0.1167899966,-0.14294447,-0.1188406721,0.2136434615,-0.3757638633,0.1028596535,-0.0300949439,0.4992606342,0.0187852196,-0.0301720798,-0.3194895089,0.015822893,-0.0725437552,0.3714369833,-0.0625675693,-0.2505569458,0.2042683363,-0.0081873154,0.1753682047,0.2365752012,-0.0001000679,-0.0085890358,0.1501080245,-0.0443094373,-0.131183669,0.1888656318,-0.123258099,-0.1802763939,-0.0042734607,-0.0790977329,-0.4431518614,0.1293307543,0.149391681,-0.2958288491,0.5744659305,-0.026167728,0.3707997501,0.28149876,0.1990785748,-0.2768567502,0.1207759976,-0.1539129466,-0.0656856894,0.1609669179,0.0918901637,-0.1530089974,0.134279713,0.4699548781,0.0848045349,0.1188219041,0.2851537168,-0.0147726713,-0.2164845616,-0.159825772,0.3803760409,-0.002382593,0.3897214234,-0.2139130086,-0.1837674677,-0.1025153399,-0.0050807288,-0.0097253323,0.1055578887,-0.0117374668,0.1646526754,0.3102853298,0.0797114074,-0.3766746223,0.134093985,-0.1535574496,0.1237724796,0.0248707775,-0.2039152235,0.0251798332,0.2108683288,-0.0817251205,-0.3896350265,0.2301623225,-0.4329809844,0.1080717221,-0.2919595838,0.0540039763,0.2727687061,0.0597484149,-0.0113160992,0.1861447245,0.0120569412,0.0881809369,0.2470487207,0.0937485844,0.1267062575,0.2551039755,0.2777359486,-0.0926357508,0.2941237688,-0.0005995972,-0.1350926906,-0.1442201883,0.0105527854,-0.0579530597,0.1486091614,-0.0236186329,0.0687815174,-0.1440523863,-0.0859181285,-0.0094244853,0.0210778452,0.0438704528,-0.0676199943,0.298885107,-0.1251085848,-0.0440157801,0.1222792491,-0.0032418079,-0.1049566492,-0.1209477261,-0.4230038822,0.0173482299,-0.1510064155,0.1446428597,0.2698577642,-0.2826415896,0.4717916846,-0.0908560678,-0.1431017667,-0.0054614809,0.1562077701,0.1193652377,-0.1149761304,0.3126595616,0.0720781311,0.2043959498,0.1971702725,-0.2255741358,0.0935301557,-0.0622500405,-0.3156168163,-0.3906930387,-0.2478711456,0.2746995389,-0.2598507702,0.0476593114,-0.1760026366,-0.0673485398,0.0220543332,-0.131791383,-0.0509734936,-0.0088433828,-0.105749093,-0.3027727306,0.3504111469,0.4563004076,-0.384116441,-0.3124049604,0.0743108615,-0.3012655675,-0.2867965102,0.2582087517,-0.069087632,0.0903619528,-0.3982503116,0.3396105766,0.086900495,-0.2457402796,-0.441323936,0.1408777982,-0.1561100483,-0.0168532152,0.0004647762,0.0846227854,0.0410560891,0.1609211564,-0.0110930866,0.0727054477,0.129570052,0.1198460981,-0.2933918834,-0.2234240025,-0.1068688482,0.1781915426,-0.0184352919,0.048017703,0.0894301012,-0.053266339,-0.0724112988,-0.0346956961,0.1421084404,0.5428279042,0.3240688145,0.1759873629,0.0710827932,-0.1112197042,-0.4236648977,0.2405983955,0.0110830301,0.19942756,-0.072741203,-0.1040809453,-0.1806593537,-0.1200453416,-0.1043450683,-0.3273666799,0.2714445591,0.2450683862,0.2367115617,0.1192792505,-0.1000405326,0.5312882066,0.2279617637,0.1042723954,-0.5642830729,0.2393198311,-0.1372600794,-0.3061442673,0.0747153759,0.0397150628,0.0365927853,-0.2077722847,-0.0809380114,0.50306952,-0.2509528399,0.1525256038,0.2653539777,0.1040038317,-0.0787997693,-0.2012809813,0.0443733335,-0.1353620589,0.0737980455,0.0207623709,0.1207825392,0.0742132217,0.0406173207,-0.0525603667,0.0079512419,0.0271904189,0.1815289557,-0.1884570271,-0.1001354456,-0.1488170475,0.1305884868,-0.220273152,0.2337646782,-0.0128627382,-0.1599577516,0.4027630091,0.5056480765,0.1514839828,0.1703585982,-0.0077866623,-0.3467351794,-0.0944548994,0.0424062833,0.3587125242,0.5050246119,0.2802165449,-0.0021780601,-0.0187231414,0.0887213126,-0.0561165623,0.1006643325,-0.2698319256,-0.059102416,0.0573711172,0.1500862986,-0.1143287048,-0.3618562222,0.1692366749,-0.1863103956,0.2012964934,-0.1493307799,-0.1143300906,-0.2247170806,-0.2197960317,0.1550284773,0.0793028772,-0.2900716364,-0.1471260786,0.0979439169,0.0780382156,0.0648432449,0.1727213264,0.140134871,0.1194718778,0.0232448783,0.0420770533,-0.2549952567,-0.1949689835,-0.1610857546,0.1396798491,-0.1114572063,0.0598114878,0.3552449942,-0.2490369231,0.2142373472,-0.4008228183,-0.3154816926,0.0011929446,-0.1015660465,0.2772091925,0.3434364498,0.480493933,-0.1362862736,-0.2463300675,0.4448930323,-0.3510623276,-0.1793381274,-0.0874464512,0.0341214389,-0.1851829588,-0.3269742727,-0.1720096916,-0.162594676,-0.3666308224,0.4123755693,-0.0911129639,-0.0702770799,-0.0009885175,-0.0258267056,0.1959026456,-0.2662552595,0.0188400969,-0.4876939952,-0.5863960981,0.2309202701,-0.3134086132,-0.3265423477,0.1614696234,0.1782240272,0.2323613465,-0.3869133294,-0.6166652441,-0.5167448521,-0.261787802,0.2029007673,-0.2004734427,0.1902625561,0.3913955986,-0.0382905975,-0.1795982569,-0.1816891283,-0.4716680944,0.2314304858,0.0114416629,0.2168355286,-0.2339811325,0.3932483494,0.04659044,0.4432985485,0.0131256552,-0.0416375548,0.321998477,-0.082459271,0.456438005,-0.2917788923,-0.3831224144,0.1857813448,-0.006060940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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2722","title":"Missing cache file","comments":"Hi ! Not sure why this file was missing, but yes the way to fix this is to delete the sst2 directory and to reload the dataset","body":"Strangely missing cache file after I  restart my program again.\r\n\r\n`glue_dataset = datasets.load_dataset('glue', 'sst2')`\r\n\r\n`FileNotFoundError: [Errno 2] No such file or directory: \/Users\/chris\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad\/dataset_info.json'`\r\n","comment_length":27,"text":"Missing cache file \n Strangely missing cache file after I  restart my program again.\r\n\r\n`glue_dataset = datasets.load_dataset('glue', 'sst2')`\r\n\r\n`FileNotFoundError: [Errno 2] No such file or directory: \/Users\/chris\/.cache\/huggingface\/datasets\/glue\/sst2\/1.0.0\/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad\/dataset_info.json'`\r\n \n Hi ! Not sure why this file was missing, but yes the way to fix this is to delete the sst2 directory and to reload the dataset","embeddings":[-0.0967947617,-0.2115666568,-0.0807496384,0.1962106824,0.3262526095,0.2369837016,0.0145864468,0.2186023891,0.304212749,0.0247304682,0.0359603092,0.0324181393,0.0695079118,0.0254838355,0.185232088,-0.1839707792,-0.0519994311,0.2447137982,-0.2526428699,0.1134489998,0.0262401756,0.5897667408,-0.016213784,-0.0534840636,-0.4070296288,0.0637794733,-0.1067256927,0.386438787,0.0837384537,-0.3092555702,0.3094595671,-0.1223242581,0.1665251106,0.2378737181,-0.0001069555,-0.0172668993,0.1097328812,-0.0738142133,-0.2003081441,0.193641603,-0.1755623519,-0.2121768892,-0.0431495495,-0.1683145612,-0.3127231598,0.2077138275,0.2080174387,-0.360209614,0.4669828415,-0.0779851526,0.3034725189,0.0734519064,0.1870388538,-0.2253172845,0.0996841341,-0.064716883,0.0180001147,0.1627905965,0.0280185528,-0.0881333426,0.2278116643,0.5137569308,0.0191269275,0.066894874,0.3252755105,0.0058021583,-0.2148669511,-0.1175146177,0.4294656217,0.0252462607,0.5012630224,-0.284591794,-0.2556747496,-0.1477597654,0.0607869476,-0.0376200266,0.1306268275,0.0382047892,0.240204677,0.3680465817,0.1573650092,-0.4963627458,0.1782764643,-0.1671724617,0.2400638312,-0.0252192542,-0.2089725286,0.0257383753,-0.0523489974,0.0602182299,-0.4202076197,0.09361808,-0.4452365041,0.2293759733,-0.3384398818,0.0177128166,0.1564031392,0.0586689785,-0.0672714189,0.1756927818,-0.0096155424,-0.01781529,0.1846621186,0.0050517819,0.1165803894,0.3475584388,0.3002185225,-0.0608562641,0.2772581577,-0.0344318226,-0.112605691,-0.2116935551,-0.0267669167,-0.0445203669,0.3394462168,0.0709249079,0.0471464656,-0.1273613423,-0.0748827234,0.0052467301,0.0094822235,0.0462573543,-0.0173616577,0.2279712409,-0.1619673818,0.0669734329,0.0752746016,-0.1120772958,-0.1022966504,-0.2396534979,-0.4385360777,0.0857379884,-0.1416300684,0.2605865002,0.2251195014,-0.3529346883,0.5194398165,-0.15446347,-0.1339461207,-0.0671917573,0.1733881682,0.0164418183,-0.111294508,0.3600179553,-0.0070324023,0.2911078334,0.2413915545,-0.2093438208,0.0329281725,0.0017530004,-0.4485351145,-0.4871952534,-0.2713923156,0.205846861,-0.3041187823,0.1193074808,-0.2237885147,-0.1324516982,0.1100723818,-0.3623006642,0.0665284321,-0.0316081457,-0.1814624518,-0.3980076015,0.318770647,0.5888073444,-0.4055528939,-0.3779479563,-0.0020709571,-0.2126722187,-0.3125277162,0.2282436341,-0.083651334,0.1937862188,-0.5655229092,0.328269273,0.2421826869,-0.4067055583,-0.5046402216,0.1763454527,-0.0761452243,0.0242318809,0.0388752893,0.1862749904,-0.048539076,0.0908513293,-0.0068641757,-0.0596245863,0.0911441445,0.0738905072,-0.2160988748,-0.185294494,-0.0194679964,0.1719608158,0.1014139429,0.0600856431,0.1387099177,-0.0865883604,-0.1059987321,0.0313375369,0.1604241729,0.6651590466,0.2728141248,0.1942803115,0.1240620986,-0.1508184969,-0.4816050828,0.3217637241,0.0186104774,-0.0582429692,-0.1431977302,-0.1247108653,-0.0210408289,-0.2005503923,-0.1294278651,-0.4022967219,0.1712466925,0.145832777,0.2671712935,0.1689704508,-0.0652441531,0.6422885656,0.1865052134,0.0755148605,-0.5425470471,0.2478586435,-0.0030789503,-0.2368048728,-0.0396937653,0.1197547689,0.091819033,-0.1683971435,-0.1228633597,0.4949476421,-0.2293965816,0.0498527549,0.3663037717,0.1016435996,-0.104260169,-0.137436837,0.036056675,-0.1573877782,0.1217031702,-0.0146611696,0.1188355982,-0.0206106734,0.0581252538,-0.1098696142,-0.0893905312,0.0144160241,0.2158410251,-0.1967654079,-0.1474705786,-0.132848531,0.2166163027,-0.2527221739,0.2928239405,0.0272486284,-0.0646641478,0.3660409749,0.5254284739,0.1012593806,0.1509121954,-0.0414987616,-0.3780709803,-0.0576856919,0.0197057799,0.371759057,0.6975572705,0.2514449656,-0.0356979221,-0.1242030114,0.1776922792,0.0119530903,0.0613102689,-0.2530559301,-0.0369476229,0.1276064068,0.1222911775,0.0318813436,-0.2021453679,0.0858177394,-0.1157987565,0.1562950611,-0.0868853554,-0.0478942692,-0.2492735535,-0.2735444903,0.1308374554,0.1976600587,-0.2253232002,-0.1098191813,0.0344392359,-0.050612133,0.235156551,0.1227781698,0.0337840356,0.1383581311,0.0554196648,-0.0962215737,-0.2523566484,-0.1669972092,-0.1882661432,0.035055209,-0.1320614517,-0.0566251986,0.3350159228,-0.3225826919,0.2025213242,-0.5201035738,-0.2556633353,0.0153616453,-0.0054264311,0.1994890124,0.3850772381,0.3450616002,-0.1408618689,-0.1468842179,0.4020046592,-0.3022862971,-0.1455642134,-0.0385316126,0.0002109645,-0.2106548846,-0.2796366811,-0.1135790125,-0.2354622334,-0.2539646029,0.2946382165,-0.177436769,-0.1345800757,-0.0957031623,-0.1538654715,0.1646320075,-0.2699987888,-0.0264159776,-0.471652329,-0.6318153143,0.2939579785,-0.2295247018,-0.2231517732,0.2445824891,0.0589947253,0.314601928,-0.3103459477,-0.6782829165,-0.4436450303,-0.2228266448,0.285623461,-0.4130557477,0.1635397077,0.4808389246,-0.1488039345,-0.1070157886,-0.1414729506,-0.4869025052,0.0894894451,0.0037858344,0.3139515817,-0.2787528932,0.4786124527,0.0733766481,0.5729888678,0.0530609712,0.0298595708,0.2796929777,0.0171320941,0.5509269834,-0.2649046481,-0.3026599884,0.2523865104,-0.062442757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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2716","title":"Calling shuffle on IterableDataset will disable batching in case any functions were mapped","comments":"Hi :) Good catch ! Feel free to open a PR if you want to contribute, this would be very welcome ;)","body":"When using dataset in streaming mode, if one applies `shuffle` method on the dataset and `map` method for which `batched=True` than the batching operation will not happen, instead `batched` will be set to `False`\r\n\r\nI did RCA on the dataset codebase, the problem is emerging from [this line of code](https:\/\/github.com\/huggingface\/datasets\/blob\/d25a0bf94d9f9a9aa6cabdf5b450b9c327d19729\/src\/datasets\/iterable_dataset.py#L197) here as it is\r\n`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batch_size=self.batch_size`, as one can see it is missing batched argument, which means that the iterator fallsback to default constructor value, which in this case is `False`.\r\nTo remedy the problem we can change this line to\r\n`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batched=self.batched, batch_size=self.batch_size`\r\n","comment_length":22,"text":"Calling shuffle on IterableDataset will disable batching in case any functions were mapped \n When using dataset in streaming mode, if one applies `shuffle` method on the dataset and `map` method for which `batched=True` than the batching operation will not happen, instead `batched` will be set to `False`\r\n\r\nI did RCA on the dataset codebase, the problem is emerging from [this line of code](https:\/\/github.com\/huggingface\/datasets\/blob\/d25a0bf94d9f9a9aa6cabdf5b450b9c327d19729\/src\/datasets\/iterable_dataset.py#L197) here as it is\r\n`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batch_size=self.batch_size`, as one can see it is missing batched argument, which means that the iterator fallsback to default constructor value, which in this case is `False`.\r\nTo remedy the problem we can change this line to\r\n`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batched=self.batched, batch_size=self.batch_size`\r\n \n Hi :) Good catch ! Feel free to open a PR if you want to contribute, this would be very welcome ;)","embeddings":[-0.4098215103,-0.2538074851,0.0164554343,-0.0311614387,0.3398851156,-0.0579713434,0.3023079932,0.0674438328,-0.2175251842,0.3773573041,-0.2310502231,0.2912554741,-0.1922606975,0.2657689154,-0.0942452475,0.1088107154,0.0774022713,0.0824446902,-0.4071667492,-0.0259761699,-0.2257343829,-0.0934465826,-0.5538959503,-0.2494303882,-0.0387422554,-0.1291296035,0.0840008408,0.069073014,-0.2499088496,-0.1048584506,0.1157220602,0.5466902852,-0.0666164756,0.4911211431,-0.0001046605,-0.0466864854,0.220941633,-0.1478953958,-0.1971089542,-0.0162350703,-0.2287014872,0.3616317511,-0.1729200929,-0.0470902249,-0.1105783731,-0.3326943219,0.0179095548,-0.3529171646,0.1090987101,-0.0607379526,0.250481993,0.1520516723,-0.1833851635,-0.0110956132,0.3300379515,0.1602014601,-0.1887402982,0.2795745432,0.3021202087,0.1214002371,-0.0215312038,0.3396961689,-0.0457591675,0.0973074287,0.2876346111,-0.1221546829,-0.0756907761,-0.4055928886,0.1629078388,0.1327944696,0.4041192234,-0.3270388246,-0.2338447571,-0.3728273809,0.0683641359,-0.2494541854,0.1298157573,-0.0826154873,-0.1251394153,-0.1127450392,-0.4295096397,0.1275163591,-0.0135839991,-0.033932738,0.5006251931,0.4225344658,-0.1381253302,0.1263806075,0.2588524818,-0.206630975,0.4659806788,-0.0802471787,-0.2135605961,0.3368564248,-0.4075320661,-0.130137682,0.1735796779,0.2387518883,0.346085012,0.0446178056,0.2171569765,0.2294618189,0.1571498662,0.0546244234,0.4331026077,0.0627880916,-0.0688361675,0.0454046875,0.1574959159,0.3058146536,0.023727756,-0.02492515,0.2888559699,-0.0860852078,0.002355594,-0.0567303747,0.1077041775,-0.1669803709,-0.0952625722,-0.3858787715,-0.1767874956,-0.1537875533,0.2711011767,0.3885038793,0.0594843924,0.0154892504,-0.3158887625,0.2343372405,0.0019726157,0.1345427632,-0.2632302344,-0.2480551451,-0.2327021211,0.0905819163,0.2369067967,-0.4453908801,0.3494920433,0.3258509934,0.0771219358,0.0918583646,0.1089512184,-0.1570501477,0.2872882783,0.0994779319,0.0974388123,0.056982927,0.1213003322,0.1756840199,0.0762383863,0.088006489,-0.0508972406,-0.0789761767,0.4025254846,0.2578459382,-0.1870023608,-0.0908630863,-0.5048840046,0.1181122959,0.0382432751,-0.3109931052,0.1444637179,-0.2712101638,0.0033719011,-0.254860729,0.1507634073,0.1824268699,0.0524112433,-0.0907988623,0.0347349122,-0.2587924302,0.6150118709,0.4008066952,-0.2116879225,0.0417136326,-0.2732911408,0.077940397,0.1313033402,0.1164889857,-0.3975092173,0.1741449237,-0.1221157238,0.4353640974,0.059893094,0.0428745709,0.3480425477,-0.1915022582,0.2754980326,0.2648024857,-0.2541203499,0.2964873016,-0.1950291097,-0.0763328299,-0.0320449248,0.0939136818,-0.2064952999,0.3401971459,0.1230375394,-0.1803273708,0.3168271184,-0.2674479485,0.1347766817,-0.0228714179,0.2825080156,0.0961350203,-0.0171809867,-0.1778329909,-0.243828997,0.0989337862,0.163568303,-0.0619942807,-0.0031122109,-0.3148007393,0.1615255326,-0.1159022003,-0.4890494049,-0.2133393586,0.21111314,0.2268390507,0.0447983257,-0.1561160833,-0.1767099053,0.0718606934,-0.2924746573,-0.1943624467,-0.3770171106,-0.0943284109,0.1136750355,-0.2265723348,0.1598410457,0.1383999735,0.1202635765,-0.1816586554,-0.08545243,0.4552502334,-0.0204102639,0.0224799942,-0.0471611284,0.2453854084,-0.1095877588,0.0012365161,-0.1277897954,-0.0703821704,0.2200995535,-0.1837346107,-0.058323849,0.4448705614,-0.4544275701,0.2487808019,0.2326847017,-0.2751173377,0.4183245897,0.2991345525,-0.2019591331,-0.1013117731,0.0643185824,-0.1856444031,0.1916487515,0.0225364417,-0.3827545345,0.0753574073,0.4850607216,0.1322100163,-0.1092718691,-0.0830761939,-0.1139723659,-0.2782618403,-0.0191041678,0.3351629972,0.406793952,0.238962993,0.2084313184,0.1073293909,0.1976170242,-0.1879748553,0.1229329035,0.0111799175,0.1723272353,0.2312593609,0.1828304231,-0.1218916997,-0.4867269099,-0.3557829261,0.0885691568,0.3144856095,-0.2118907422,-0.2529515922,-0.1668187231,-0.2122499049,-0.1276170015,-0.2103358656,-0.0831772387,-0.220365569,0.13692303,0.7543545365,-0.1674434692,0.2527231276,0.3151095808,0.1411622614,-0.0560657084,-0.09979821,-0.1466866136,-0.0236520562,-0.0085362075,0.1970877498,0.1575423181,-0.1427107006,0.3563771844,-0.0000349335,-0.3080062866,-0.3034623563,0.130791381,0.0878003165,-0.1911070347,0.0158843715,0.0466463268,0.1843953878,-0.0100606075,-0.2569463253,0.2034876943,-0.4171120524,-0.0513277687,0.0685134158,-0.1702582985,-0.0884037316,-0.2802341878,-0.09832526,-0.1416513622,-0.3688466549,0.2363346666,-0.1134562641,0.0388994925,0.2580617368,-0.0042469669,-0.1164416224,-0.1947188824,-0.2532929778,-0.3594523072,-0.6516541839,0.0104260119,-0.2006954849,-0.2527257204,0.0317443088,0.1986943781,0.178017959,0.6395937204,-0.032256078,0.1754576862,-0.1056497917,0.0781790614,-0.0048312638,0.1112322807,0.4022190869,0.0161642283,-0.1008445621,-0.2005631179,-0.0300819241,0.1122007668,0.0311471485,0.2318634093,0.1879943609,0.4561055303,-0.055017069,0.9363186359,0.1427913159,0.1208270714,0.3089776635,-0.0406129807,0.294936806,-0.3398709893,-0.1727134287,0.4189908206,0.1589230299,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2714","title":"add more precise information for size","comments":"We already have this information in the dataset_infos.json files of each dataset.\r\nMaybe we can parse these files in the backend to return their content with the endpoint at huggingface.co\/api\/datasets\r\n\r\nFor now if you want to access this info you have to load the json for each dataset. For example:\r\n- for a dataset on github like `squad` \r\n-   https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/squad\/dataset_infos.json\r\n- for a community dataset on the hub like `lhoestq\/squad`:\r\n  https:\/\/huggingface.co\/datasets\/lhoestq\/squad\/resolve\/main\/dataset_infos.json","body":"For the import into ELG, we would like a more precise description of the size of the dataset, instead of the current size categories. The size can be expressed in bytes, or any other preferred size unit. As suggested in the slack channel, perhaps this could be computed with a regex for existing datasets.","comment_length":71,"text":"add more precise information for size \n For the import into ELG, we would like a more precise description of the size of the dataset, instead of the current size categories. The size can be expressed in bytes, or any other preferred size unit. As suggested in the slack channel, perhaps this could be computed with a regex for existing datasets. \n We already have this information in the dataset_infos.json files of each dataset.\r\nMaybe we can parse these files in the backend to return their content with the endpoint at huggingface.co\/api\/datasets\r\n\r\nFor now if you want to access this info you have to load the json for each dataset. For example:\r\n- for a dataset on github like `squad` \r\n-   https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/squad\/dataset_infos.json\r\n- for a community dataset on the hub like `lhoestq\/squad`:\r\n  https:\/\/huggingface.co\/datasets\/lhoestq\/squad\/resolve\/main\/dataset_infos.json","embeddings":[-0.041354809,-0.5577720404,-0.1589672863,0.3919742107,0.2053750604,-0.0639249161,-0.0737590641,0.0503646508,0.1064483523,0.0798681453,-0.4394702017,0.0881778374,-0.0656972155,0.4548651874,0.0609381124,0.0388585962,-0.2295204401,-0.112665996,-0.1492429972,-0.2198024839,0.0234504249,0.2798558772,0.2534013987,-0.0295910668,-0.0154504152,0.1214822605,-0.254406631,0.2946007848,-0.5448058248,-0.2168553919,0.162506178,-0.0575589314,0.1592405736,-0.0210057553,-0.0001038512,-0.0647772849,0.1080954596,-0.2295954525,-0.3205727339,-0.0383348875,0.0025480704,-0.3959295452,0.1042662635,-0.0850442499,-0.1412745118,-0.1166362092,0.0335714296,-0.1261952221,0.4541440308,0.0918545425,0.2733739913,0.4500881135,0.1584033072,-0.1389549822,0.0338327177,0.4866320789,0.0143970624,0.2679385543,0.3840728402,0.3003026545,-0.0922307894,0.405079335,0.2523309886,-0.0259290654,0.2601458132,0.0314368606,-0.3214984238,-0.1370878816,0.1025048047,0.4285010099,0.1674049348,-0.3730247617,-0.5177248716,-0.4534083009,0.1588495076,-0.254727155,0.0493003577,0.2319036126,0.030197721,0.1767473519,-0.2547946274,-0.1507339627,-0.2382446975,-0.024006838,-0.1776671559,0.0789698511,-0.2915142477,-0.1185309142,0.0026724185,-0.4049320817,-0.6295150518,0.0317048617,-0.1120769009,0.204180643,-0.1458263695,-0.382292062,0.3146906793,0.2405808419,0.5023838878,0.1578407586,0.0075038429,0.1524950713,0.0011088211,0.1947938204,0.1977786869,0.3022005856,0.3661184311,-0.0779799819,0.1031197831,0.0316854157,0.1499069482,-0.1356841922,-0.213738367,0.0816751868,-0.3636180162,-0.2070543468,0.0226015691,-0.1112502217,-0.0727842227,-0.0099661201,0.1764284372,0.0235274211,0.0794332996,0.439917773,0.0285252072,0.0114363786,-0.094827041,0.2278685868,0.1659278572,-0.2580323517,-0.1681976616,0.0100567983,-0.0577120967,0.1395848393,0.0128062749,-0.3011850715,0.3934868276,-0.1103335544,0.1721936017,-0.0499964878,-0.0783385783,0.0496042967,0.1866596937,0.0405115597,0.0921569839,-0.2169891447,0.0278592855,-0.1454958767,-0.3767353296,-0.2053822875,-0.1997884065,-0.2601394951,-0.4404108524,0.2663024664,-0.234716177,-0.0646486208,-0.4436706305,0.3297384381,-0.1481907368,0.138103798,0.0043476312,0.089858152,-0.0293878783,-0.1064031199,0.0048883492,0.2305946648,-0.0331626125,-0.275741905,-0.0361056812,-0.2553452849,-0.2473154515,0.4085282981,-0.0546536222,-0.278449744,-0.1099671051,0.2847639918,0.3449617624,-0.4075211585,-0.0638781562,0.2323251367,0.0420696475,0.0920103788,0.0441864021,0.0051910719,0.2295906395,0.1645947546,0.0175024141,0.3218150139,-0.1843339354,0.1228394881,-0.1066689193,-0.4086617827,-0.175804019,0.1705343723,0.0425586514,-0.3353559673,0.2083470821,0.0844615996,0.2212419808,-0.1525700092,0.0652610213,0.2249132395,-0.0162420589,-0.0485722311,0.0501228198,-0.1123514995,-0.6196359992,0.1310897619,-0.0196532421,0.237208575,-0.0399618447,-0.4177995026,-0.3142914176,0.1552389115,0.0596921146,-0.0916106254,0.2139571309,-0.0335062295,0.2626999021,-0.0538792685,-0.1323897243,-0.0153961927,-0.1248895526,0.299482137,-0.6840795875,0.0677038357,-0.0187636875,0.0317224115,0.1639617532,0.1434708983,-0.2023173273,-0.0474745519,0.0281766895,0.0756473839,-0.1749101728,0.4565301239,0.260379523,0.4778719544,0.2401754558,-0.0033623995,0.2902507484,-0.1425657272,0.0222379602,0.1272934526,-0.5046000481,0.5015555024,0.3156271279,-0.0389141738,0.0748621151,-0.058989089,0.0190481506,-0.0133134108,-0.2305651307,-0.3546416163,0.0251254924,-0.0404179804,0.2994695902,0.0617636181,-0.3438564837,0.0625477806,0.5101698637,0.0132961441,-0.0046376036,0.345225811,-0.31604442,-0.2252226025,0.2095775306,0.055681482,0.1830606908,0.373601824,0.1089994982,0.1292484403,0.211334765,-0.0286117196,0.1002286598,0.1177811474,-0.0578761175,0.0437692255,0.3493605852,-0.0645666197,-0.4000751078,-0.1172626019,-0.2899731696,0.0852834731,-0.2282990217,-0.0175707508,-0.1347502768,-0.364479512,-0.1586054862,-0.099081628,-0.5645573139,-0.2226260751,0.185556218,-0.247871384,-0.3137249947,-0.2181916833,0.1011981815,0.2453226298,-0.0683198795,0.0089634182,-0.0908100903,-0.085657604,-0.0101825539,0.1782536358,0.1776801795,0.0589755811,0.3929869831,-0.2609719634,0.1044503301,0.006272601,-0.5086253285,0.2717722952,-0.1840400398,0.1842017323,0.0771672577,0.1117586568,0.0895381868,0.0349557251,0.1722739339,-0.0007507968,-0.3836705983,-0.093941763,-0.0080974372,0.1394441426,-0.161365211,-0.1860440522,-0.3122625053,-0.3296838999,0.3015921712,0.0786694139,0.2122530043,0.211279273,-0.0106809111,0.1425525248,-0.1424604058,0.0097210035,-0.3036101758,-0.4350679219,0.2735942304,-0.4120613337,-0.1951003671,-0.057308048,0.0915092304,0.2012043446,-0.2210039496,-0.1597380191,-0.5394004583,-0.122895591,-0.027982872,0.1173581108,0.0357880518,0.255788058,-0.1290501505,-0.1456703097,0.0228043478,-0.2735737562,-0.0556732379,0.2504128814,0.0304258671,-0.2115626633,0.2764234245,0.1665291935,0.2871811688,0.2273597419,-0.2123124897,0.1203336269,-0.1358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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2709","title":"Missing documentation for wnut_17 (ner_tags)","comments":"Hi @maxpel, thanks for reporting this issue.\r\n\r\nIndeed, the documentation in the dataset card is not complete. I\u2019m opening a Pull Request to fix it.\r\n\r\nAs the paper explains, there are 6 entity types and we have ordered them alphabetically: `corporation`, `creative-work`, `group`, `location`, `person` and `product`. \r\n\r\nEach of these entity types has 2 possible IOB2 format tags: \r\n- `B-`: to indicate that the token is the beginning of an entity name, and the \r\n- `I-`: to indicate that the token is inside an entity name. \r\n\r\nAdditionally, there is the standalone IOB2 tag \r\n- `O`: that indicates that the token belongs to no named entity. \r\n\r\nIn total there are 13 possible tags, which correspond to the following integer numbers:\r\n\r\n0. `O`\r\n1. `B-corporation`\r\n2. `I-corporation`\r\n3. `B-creative-work`\r\n4. `I-creative-work`\r\n5. `B-group`\r\n6. `I-group`\r\n7. `B-location`\r\n8. `I-location`\r\n9. `B-person`\r\n10. `I-person`\r\n11. `B-product`\r\n12. `I-product`","body":"On the info page of the wnut_17 data set (https:\/\/huggingface.co\/datasets\/wnut_17), the model output of ner-tags is only documented for these 5 cases:\r\n\r\n`ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).`\r\n\r\nI trained a model with the data and it gives me 13 classes:\r\n\r\n```\r\n\"id2label\": {\r\n    \"0\": 0,\r\n    \"1\": 1,\r\n    \"2\": 2,\r\n    \"3\": 3,\r\n    \"4\": 4,\r\n    \"5\": 5,\r\n    \"6\": 6,\r\n    \"7\": 7,\r\n    \"8\": 8,\r\n    \"9\": 9,\r\n    \"10\": 10,\r\n    \"11\": 11,\r\n    \"12\": 12\r\n  }\r\n\r\n  \"label2id\": {\r\n    \"0\": 0,\r\n    \"1\": 1,\r\n    \"10\": 10,\r\n    \"11\": 11,\r\n    \"12\": 12,\r\n    \"2\": 2,\r\n    \"3\": 3,\r\n    \"4\": 4,\r\n    \"5\": 5,\r\n    \"6\": 6,\r\n    \"7\": 7,\r\n    \"8\": 8,\r\n    \"9\": 9\r\n  }\r\n```\r\nThe paper (https:\/\/www.aclweb.org\/anthology\/W17-4418.pdf) explains those 6 categories, but the ordering does not match:\r\n\r\n```\r\n1. person\r\n2. location (including GPE, facility)\r\n3. corporation\r\n4. product (tangible goods, or well-defined\r\nservices)\r\n5. creative-work (song, movie, book and\r\nso on)\r\n6. group (subsuming music band, sports team,\r\nand non-corporate organisations)\r\n```\r\nI would be very helpful for me, if somebody could clarify the model ouputs and explain the \"B-\" and \"I-\" prefixes to me.\r\n\r\nReally great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner!","comment_length":145,"text":"Missing documentation for wnut_17 (ner_tags) \n On the info page of the wnut_17 data set (https:\/\/huggingface.co\/datasets\/wnut_17), the model output of ner-tags is only documented for these 5 cases:\r\n\r\n`ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).`\r\n\r\nI trained a model with the data and it gives me 13 classes:\r\n\r\n```\r\n\"id2label\": {\r\n    \"0\": 0,\r\n    \"1\": 1,\r\n    \"2\": 2,\r\n    \"3\": 3,\r\n    \"4\": 4,\r\n    \"5\": 5,\r\n    \"6\": 6,\r\n    \"7\": 7,\r\n    \"8\": 8,\r\n    \"9\": 9,\r\n    \"10\": 10,\r\n    \"11\": 11,\r\n    \"12\": 12\r\n  }\r\n\r\n  \"label2id\": {\r\n    \"0\": 0,\r\n    \"1\": 1,\r\n    \"10\": 10,\r\n    \"11\": 11,\r\n    \"12\": 12,\r\n    \"2\": 2,\r\n    \"3\": 3,\r\n    \"4\": 4,\r\n    \"5\": 5,\r\n    \"6\": 6,\r\n    \"7\": 7,\r\n    \"8\": 8,\r\n    \"9\": 9\r\n  }\r\n```\r\nThe paper (https:\/\/www.aclweb.org\/anthology\/W17-4418.pdf) explains those 6 categories, but the ordering does not match:\r\n\r\n```\r\n1. person\r\n2. location (including GPE, facility)\r\n3. corporation\r\n4. product (tangible goods, or well-defined\r\nservices)\r\n5. creative-work (song, movie, book and\r\nso on)\r\n6. group (subsuming music band, sports team,\r\nand non-corporate organisations)\r\n```\r\nI would be very helpful for me, if somebody could clarify the model ouputs and explain the \"B-\" and \"I-\" prefixes to me.\r\n\r\nReally great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner! \n Hi @maxpel, thanks for reporting this issue.\r\n\r\nIndeed, the documentation in the dataset card is not complete. I\u2019m opening a Pull Request to fix it.\r\n\r\nAs the paper explains, there are 6 entity types and we have ordered them alphabetically: `corporation`, `creative-work`, `group`, `location`, `person` and `product`. \r\n\r\nEach of these entity types has 2 possible IOB2 format tags: \r\n- `B-`: to indicate that the token is the beginning of an entity name, and the \r\n- `I-`: to indicate that the token is inside an entity name. \r\n\r\nAdditionally, there is the standalone IOB2 tag \r\n- `O`: that indicates that the token belongs to no named entity. \r\n\r\nIn total there are 13 possible tags, which correspond to the following integer numbers:\r\n\r\n0. `O`\r\n1. `B-corporation`\r\n2. `I-corporation`\r\n3. `B-creative-work`\r\n4. `I-creative-work`\r\n5. `B-group`\r\n6. `I-group`\r\n7. `B-location`\r\n8. `I-location`\r\n9. `B-person`\r\n10. `I-person`\r\n11. `B-product`\r\n12. `I-product`","embeddings":[0.2751238048,-0.2970445752,0.0208108258,0.4673909247,-0.05594486,0.0520152785,-0.0117354188,-0.2991321087,-0.2569106817,-0.218673259,0.0983796492,0.3813357353,-0.1071820334,0.2050385922,0.196168229,-0.0721679255,0.1684128791,-0.1887154281,0.3061253726,-0.1100955382,-0.3069913387,0.3437999487,-0.2482835054,0.4954278171,-0.3762372434,-0.0859183073,-0.1869406849,-0.1539729685,-0.404643625,-0.595091939,0.2896220684,0.1556397229,0.0723897889,-0.0579099618,-0.0001202806,-0.2460776716,0.0285192654,-0.1889371872,-0.1224052832,-0.1234155074,-0.0926301107,-0.2060739398,0.2835662961,-0.0094313715,-0.1429478377,-0.1868063211,0.2751163244,0.5176041126,-0.2508018613,0.1197170019,0.0497632809,-0.1621630192,0.2689089179,0.0288132094,0.3519521058,0.0908359066,-0.1232602298,0.3104263544,0.4276799262,-0.0534058437,-0.2571891546,0.4627604485,0.0963761806,-0.2820251882,0.2589055598,0.2285993099,0.3358659446,-0.4192578197,-0.1301675141,0.3817865849,0.2516928613,0.1147544533,-0.2154103816,-0.0157400407,-0.0526016802,-0.6351476908,0.0023943272,0.4393245876,0.0330131762,0.0414246507,-0.0036089518,0.1708147973,-0.2163026631,0.0360443704,-0.0949905366,0.6974461675,-0.1429927051,-0.1101660803,-0.0203673504,-0.0645245537,-0.3136532903,0.0532633699,0.0026731857,0.0485409833,-0.3527156413,0.0821140707,0.1113006175,0.6753001809,0.1034720987,0.1295490712,-0.1083086431,-0.1619324684,-0.1246846244,0.0191322826,0.0126678748,0.0671091676,0.1944057345,0.2011132985,-0.0446371771,0.3469562232,0.0323721841,-0.074082382,-0.1044222638,0.0455814935,-0.2820935845,0.0927808434,0.06808424,-0.0183761325,-0.296031177,0.2254844457,-0.3230934441,-0.0889051035,0.14402318,-0.0752068758,-0.0731510445,0.0357110389,0.1876580566,-0.0504871607,-0.0379349701,-0.482126683,-0.1482041627,0.0386484899,-0.4639268816,0.2523362935,0.1713847667,0.0126344152,0.2062462568,-0.1111709774,-0.4054633081,0.0354569443,-0.0658331662,-0.0313021578,0.0889694542,0.1795018017,-0.4525698423,0.3838886023,0.0929943621,-0.1642804146,-0.2671718597,0.0119260168,-0.4921444952,-0.3080555201,0.0690308735,0.1287891418,-0.1365886331,0.0609575473,0.1897367388,0.3318854868,-0.1153175086,0.1990909874,-0.1007408574,-0.0983121544,-0.4269200861,-0.2644012868,0.0072038556,0.0409698226,-0.3341094851,-0.1623819619,0.137377739,0.1871725172,0.311260432,-0.022741653,-0.0241282787,0.4247220755,-0.1090937629,0.1467428654,0.2499331087,-0.346585989,-0.0087535521,-0.1740071028,-0.0258988328,0.1329857558,-0.0895642564,0.166426003,-0.1744971722,-0.092697978,0.0160262547,-0.0840970501,-0.0957265422,-0.1990121007,0.0502361655,-0.1406760514,0.6332871318,0.1118355095,-0.1572697759,-0.1110158861,-0.373549819,0.1674022377,0.3539595902,-0.0071341656,0.0449136905,0.3623041809,0.087197952,0.3109552562,0.3268584311,0.05157727,-0.1253266633,-0.0166781731,0.1232129708,0.7216901183,0.1610009074,-0.1609766632,-0.059894897,-0.0297032725,-0.0421017967,-0.5228356719,0.1638392508,-0.0354805775,-0.0927694142,-0.0156906638,-0.2345708907,0.3003310561,-0.7553529739,0.1253665686,-0.5494101048,-0.2719165981,-0.3093493283,0.1723164767,0.2604398429,0.16060175,0.3212370872,0.2714565992,0.0924715698,-0.0461571552,0.1488591433,-0.6642785668,0.0911436304,0.6776841879,0.0883733034,-0.5249889493,-0.0968010277,0.0926435068,-0.1376922876,-0.0823116377,-0.1829960197,0.0447826535,-0.0842518657,0.2933146358,0.1141288579,0.0089593967,0.0623177178,0.0631054342,-0.3090403378,0.0669122711,0.1034506932,-0.1360894889,0.0211302061,0.1199387908,-0.0387462042,0.441496253,0.5638064742,-0.1863643229,-0.0692087114,0.2521673441,-0.1829850376,-0.0577115454,-0.2707814872,0.1821918637,0.2422930598,0.0731470287,0.2016145736,-0.1423315406,-0.2664191127,-0.210128665,0.1479953974,0.0713569671,-0.367538482,0.1322910786,-0.0141255315,0.0283213109,-0.100118272,-0.2369363159,-0.0526684187,0.1579366326,-0.2895200849,-0.0486256331,-0.0847701505,-0.0414972827,0.0493923016,-0.1332643181,-0.3768906295,-0.4618844688,0.0604123473,-0.2065777481,0.104614079,0.0916574374,0.0267902054,0.2577775121,0.0078206351,0.4968671501,-0.0199225992,-0.3135142326,-0.418705374,0.0002778663,-0.1932315826,-0.0689466745,-0.2793949246,-0.4715572894,-0.1962037534,0.1891828179,-0.6522645354,0.1992064714,-0.0307924859,0.0423557907,0.3619500101,0.0384898447,-0.2372522801,0.1165786311,0.012894474,-0.0482755043,-0.2377350032,-0.0630051047,-0.1078974307,0.0475881621,-0.2777667046,-0.3997900188,-0.4137221575,-0.0360726044,0.1471912116,-0.0564060211,0.0793977603,0.5000622272,-0.3187740743,0.0542291403,-0.0402783342,0.4291716218,-0.0579381771,-0.0362953581,0.3741432428,-0.1225995943,-0.2875945568,0.2860401571,-0.2776125073,-0.0642166361,-0.2167772502,-0.1659706831,0.0128629273,0.124749057,-0.2548129261,0.1925065517,0.1471295506,0.1604904383,-0.0510471091,0.1826126873,-0.1810187548,-0.135969758,0.1485753506,-0.1738654226,0.1625648886,-0.1120582893,-0.2352698743,0.1533918083,-0.0981056169,0.0958108157,-0.1561302841,-0.1958439052,0.1817343682,0.1204219311,0.1285723746,0.091467604,-0.120799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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2708","title":"QASC: incomplete training set ","comments":"Hi @danyaljj, thanks for reporting.\r\n\r\nUnfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:\r\n```ipython\r\nIn [10]: ds[\"train\"]\r\nOut[10]:\r\nDataset({\r\n    features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_question'],\r\n    num_rows: 8134\r\n})\r\n\r\nIn [11]: ds[\"train\"].shape\r\nOut[11]: (8134, 8)\r\n```\r\nand the content of the last 5 examples is:\r\n```ipython\r\nIn [12]: for i in range(8129, 8134):\r\n    ...:     print(json.dumps(ds[\"train\"][i]))\r\n    ...:\r\n{\"id\": \"3KAKFY4PGU1LGXM77JAK2700NGCI3X\", \"question\": \"Chitin can be used for protection by whom?\", \"choices\": {\"text\": [\"Fungi\", \"People\", \"Man\", \"Fish\", \"trees\", \"Dogs\", \"animal\", \"Birds\"], \"label\": [\"A\", \"B\",\r\n \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"D\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish scales are also composed of chitin.\", \"combinedfact\": \"Chitin can be used for prote\r\nction by fish.\", \"formatted_question\": \"Chitin can be used for protection by whom? (A) Fungi (B) People (C) Man (D) Fish (E) trees (F) Dogs (G) animal (H) Birds\"}\r\n{\"id\": \"336YQZE83VDAQVZ26HW59X51JZ9M5M\", \"question\": \"Which type of animal uses plates for protection?\", \"choices\": {\"text\": [\"squids\", \"reptiles\", \"sea urchins\", \"fish\", \"amphibians\", \"Frogs\", \"mammals\", \"salm\r\non\"], \"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"B\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Reptiles have scales or plates.\", \"combinedfact\": \"Reptiles use\r\n their plates for protection.\", \"formatted_question\": \"Which type of animal uses plates for protection? (A) squids (B) reptiles (C) sea urchins (D) fish (E) amphibians (F) Frogs (G) mammals (H) salmon\"}\r\n{\"id\": \"3WZ36BJEV3FGS66VGOOUYX0LN8GTBU\", \"question\": \"What are used for protection by fish?\", \"choices\": {\"text\": [\"scales\", \"fins\", \"streams.\", \"coral\", \"gills\", \"Collagen\", \"mussels\", \"whiskers\"], \"label\": [\"\r\nA\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"A\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish are backboned aquatic animals.\", \"combinedfact\": \"scales are used for prote\r\nction by fish \", \"formatted_question\": \"What are used for protection by fish? (A) scales (B) fins (C) streams. (D) coral (E) gills (F) Collagen (G) mussels (H) whiskers\"}\r\n{\"id\": \"3Z2R0DQ0JHDKFAO2706OYIXGNA4E28\", \"question\": \"What are pangolins covered in?\", \"choices\": {\"text\": [\"tunicates\", \"Echinoids\", \"shells\", \"exoskeleton\", \"blastoids\", \"barrel-shaped\", \"protection\", \"white\"\r\n], \"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"G\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Pangolins have an elongate and tapering body covered above with ov\r\nerlapping scales.\", \"combinedfact\": \"Pangolins are covered in overlapping protection.\", \"formatted_question\": \"What are pangolins covered in? (A) tunicates (B) Echinoids (C) shells (D) exoskeleton (E) blastoids\r\n (F) barrel-shaped (G) protection (H) white\"}\r\n{\"id\": \"3PMBY0YE272GIWPNWIF8IH5RBHVC9S\", \"question\": \"What are covered with protection?\", \"choices\": {\"text\": [\"apples\", \"trees\", \"coral\", \"clams\", \"roses\", \"wings\", \"hats\", \"fish\"], \"label\": [\"A\", \"B\", \"C\", \"D\r\n\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"H\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish are covered with scales.\", \"combinedfact\": \"Fish are covered with protection\", \"formatted_q\r\nuestion\": \"What are covered with protection? (A) apples (B) trees (C) coral (D) clams (E) roses (F) wings (G) hats (H) fish\"}\r\n```\r\n\r\nCould you please load again your dataset and print its shape, like this:\r\n```python\r\nds = load_dataset(\"qasc\", split=\"train)\r\nprint(ds.shape)\r\n```\r\nand confirm which is your output?","body":"## Describe the bug\r\nThe training instances are not loaded properly. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"qasc\", script_version='1.10.2')\r\n \r\ndef load_instances(split): \r\n    instances = dataset[split]\r\n    print(f\"split: {split} - size: {len(instances)}\")\r\n    for x in instances:\r\n        print(json.dumps(x))\r\n\r\n\r\nload_instances('test')\r\nload_instances('validation')\r\nload_instances('train')\r\n```\r\n\r\n##  results\r\nFor test and validation, we can see the examples in the output (which is good!): \r\n```\r\nsplit: test - size: 920\r\n{\"answerKey\": \"\", \"choices\": {\"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], \"text\": [\"Anthax\", \"under water\", \"uterus\", \"wombs\", \"two\", \"moles\", \"live\", \"embryo\"]}, \"combinedfact\": \"\", \"fact1\": \"\", \"fact2\": \"\", \"formatted_question\": \"What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo\", \"id\": \"3C44YUNSI1OBFBB8D36GODNOZN9DPA\", \"question\": \"What type of birth do therian mammals have?\"}\r\n{\"answerKey\": \"\", \"choices\": {\"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], \"text\": [\"Corvidae\", \"arthropods\", \"birds\", \"backbones\", \"keratin\", \"Jurassic\", \"front paws\", \"Parakeets.\"]}, \"combinedfact\": \"\", \"fact1\": \"\", \"fact2\": \"\", \"formatted_question\": \"By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.\", \"id\": \"3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ\", \"question\": \"By what time had mouse-sized viviparous mammals evolved?\"}\r\n{\"answerKey\": \"\", \"choices\": {\"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], \"text\": [\"Reduced friction\", \"causes infection\", \"vital to a good life\", \"prevents water loss\", \"camouflage from consumers\", \"Protection against predators\", \"spur the growth of the plant\", \"a smooth surface\"]}, \"combinedfact\": \"\", \"fact1\": \"\", \"fact2\": \"\", \"formatted_question\": \"What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface\", \"id\": \"3QRYMNZ7FYGITFVSJET3PS0F4S0NT9\", \"question\": \"What does a plant's skin do?\"}\r\n...\r\n```\r\nHowever, only a few instances are loaded for the training split, which is not correct. \r\n\r\n## Environment info\r\n- `datasets` version: '1.10.2' \r\n- Platform: MaxOS \r\n- Python version:3.7\r\n- PyArrow version: 3.0.0\r\n","comment_length":496,"text":"QASC: incomplete training set  \n ## Describe the bug\r\nThe training instances are not loaded properly. \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"qasc\", script_version='1.10.2')\r\n \r\ndef load_instances(split): \r\n    instances = dataset[split]\r\n    print(f\"split: {split} - size: {len(instances)}\")\r\n    for x in instances:\r\n        print(json.dumps(x))\r\n\r\n\r\nload_instances('test')\r\nload_instances('validation')\r\nload_instances('train')\r\n```\r\n\r\n##  results\r\nFor test and validation, we can see the examples in the output (which is good!): \r\n```\r\nsplit: test - size: 920\r\n{\"answerKey\": \"\", \"choices\": {\"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], \"text\": [\"Anthax\", \"under water\", \"uterus\", \"wombs\", \"two\", \"moles\", \"live\", \"embryo\"]}, \"combinedfact\": \"\", \"fact1\": \"\", \"fact2\": \"\", \"formatted_question\": \"What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo\", \"id\": \"3C44YUNSI1OBFBB8D36GODNOZN9DPA\", \"question\": \"What type of birth do therian mammals have?\"}\r\n{\"answerKey\": \"\", \"choices\": {\"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], \"text\": [\"Corvidae\", \"arthropods\", \"birds\", \"backbones\", \"keratin\", \"Jurassic\", \"front paws\", \"Parakeets.\"]}, \"combinedfact\": \"\", \"fact1\": \"\", \"fact2\": \"\", \"formatted_question\": \"By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.\", \"id\": \"3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ\", \"question\": \"By what time had mouse-sized viviparous mammals evolved?\"}\r\n{\"answerKey\": \"\", \"choices\": {\"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], \"text\": [\"Reduced friction\", \"causes infection\", \"vital to a good life\", \"prevents water loss\", \"camouflage from consumers\", \"Protection against predators\", \"spur the growth of the plant\", \"a smooth surface\"]}, \"combinedfact\": \"\", \"fact1\": \"\", \"fact2\": \"\", \"formatted_question\": \"What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface\", \"id\": \"3QRYMNZ7FYGITFVSJET3PS0F4S0NT9\", \"question\": \"What does a plant's skin do?\"}\r\n...\r\n```\r\nHowever, only a few instances are loaded for the training split, which is not correct. \r\n\r\n## Environment info\r\n- `datasets` version: '1.10.2' \r\n- Platform: MaxOS \r\n- Python version:3.7\r\n- PyArrow version: 3.0.0\r\n \n Hi @danyaljj, thanks for reporting.\r\n\r\nUnfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:\r\n```ipython\r\nIn [10]: ds[\"train\"]\r\nOut[10]:\r\nDataset({\r\n    features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_question'],\r\n    num_rows: 8134\r\n})\r\n\r\nIn [11]: ds[\"train\"].shape\r\nOut[11]: (8134, 8)\r\n```\r\nand the content of the last 5 examples is:\r\n```ipython\r\nIn [12]: for i in range(8129, 8134):\r\n    ...:     print(json.dumps(ds[\"train\"][i]))\r\n    ...:\r\n{\"id\": \"3KAKFY4PGU1LGXM77JAK2700NGCI3X\", \"question\": \"Chitin can be used for protection by whom?\", \"choices\": {\"text\": [\"Fungi\", \"People\", \"Man\", \"Fish\", \"trees\", \"Dogs\", \"animal\", \"Birds\"], \"label\": [\"A\", \"B\",\r\n \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"D\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish scales are also composed of chitin.\", \"combinedfact\": \"Chitin can be used for prote\r\nction by fish.\", \"formatted_question\": \"Chitin can be used for protection by whom? (A) Fungi (B) People (C) Man (D) Fish (E) trees (F) Dogs (G) animal (H) Birds\"}\r\n{\"id\": \"336YQZE83VDAQVZ26HW59X51JZ9M5M\", \"question\": \"Which type of animal uses plates for protection?\", \"choices\": {\"text\": [\"squids\", \"reptiles\", \"sea urchins\", \"fish\", \"amphibians\", \"Frogs\", \"mammals\", \"salm\r\non\"], \"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"B\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Reptiles have scales or plates.\", \"combinedfact\": \"Reptiles use\r\n their plates for protection.\", \"formatted_question\": \"Which type of animal uses plates for protection? (A) squids (B) reptiles (C) sea urchins (D) fish (E) amphibians (F) Frogs (G) mammals (H) salmon\"}\r\n{\"id\": \"3WZ36BJEV3FGS66VGOOUYX0LN8GTBU\", \"question\": \"What are used for protection by fish?\", \"choices\": {\"text\": [\"scales\", \"fins\", \"streams.\", \"coral\", \"gills\", \"Collagen\", \"mussels\", \"whiskers\"], \"label\": [\"\r\nA\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"A\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish are backboned aquatic animals.\", \"combinedfact\": \"scales are used for prote\r\nction by fish \", \"formatted_question\": \"What are used for protection by fish? (A) scales (B) fins (C) streams. (D) coral (E) gills (F) Collagen (G) mussels (H) whiskers\"}\r\n{\"id\": \"3Z2R0DQ0JHDKFAO2706OYIXGNA4E28\", \"question\": \"What are pangolins covered in?\", \"choices\": {\"text\": [\"tunicates\", \"Echinoids\", \"shells\", \"exoskeleton\", \"blastoids\", \"barrel-shaped\", \"protection\", \"white\"\r\n], \"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"G\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Pangolins have an elongate and tapering body covered above with ov\r\nerlapping scales.\", \"combinedfact\": \"Pangolins are covered in overlapping protection.\", \"formatted_question\": \"What are pangolins covered in? (A) tunicates (B) Echinoids (C) shells (D) exoskeleton (E) blastoids\r\n (F) barrel-shaped (G) protection (H) white\"}\r\n{\"id\": \"3PMBY0YE272GIWPNWIF8IH5RBHVC9S\", \"question\": \"What are covered with protection?\", \"choices\": {\"text\": [\"apples\", \"trees\", \"coral\", \"clams\", \"roses\", \"wings\", \"hats\", \"fish\"], \"label\": [\"A\", \"B\", \"C\", \"D\r\n\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"H\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish are covered with scales.\", \"combinedfact\": \"Fish are covered with protection\", \"formatted_q\r\nuestion\": \"What are covered with protection? (A) apples (B) trees (C) coral (D) clams (E) roses (F) wings (G) hats (H) fish\"}\r\n```\r\n\r\nCould you please load again your dataset and print its shape, like this:\r\n```python\r\nds = load_dataset(\"qasc\", split=\"train)\r\nprint(ds.shape)\r\n```\r\nand confirm which is your output?","embeddings":[-0.2450181693,-0.3615007997,-0.1671689451,0.3723612428,0.1236955449,0.1451464146,0.0164389201,0.4380207956,-0.1213213205,0.0934934169,0.1632602215,0.124971658,0.0430466495,0.2471365929,-0.0943753049,-0.1647269875,-0.0069864769,0.2966333032,-0.2305759043,-0.0744921416,-0.2418487519,0.3023805916,-0.2500701845,-0.0736244619,-0.1649780124,-0.2872940302,-0.0491756722,0.0133106634,0.0458690599,-0.320064187,-0.0480261333,-0.3247571588,-0.1014455259,0.4697218835,-0.0001062213,0.0782134309,0.1270972788,-0.1408859044,-0.3300843835,0.1074304581,-0.2933759093,-0.1728284657,0.0326805972,-0.0973909572,-0.2557199597,-0.2729076743,-0.0496537611,-0.0854506865,0.5871784687,0.289850086,0.2529067695,0.3633326888,0.033648029,-0.0832497925,-0.221334666,-0.504969418,-0.0405405499,0.0328143686,0.2264511585,0.0457118303,0.2468177974,0.2307891995,0.2760746777,-0.100784339,0.1479250938,-0.2171174735,0.1146207899,-0.1962149888,0.026363261,0.1972755641,0.3719688356,0.1936610043,-0.3621879816,-0.1538570821,-0.0319558084,-0.4144759178,-0.0372161455,0.2690791488,-0.0616779961,0.20732674,-0.1861333698,0.2345629483,-0.1997883022,0.1442641318,-0.1462110281,0.2244074494,-0.0654697195,-0.0954147652,-0.2495789528,0.1812457442,-0.0705581754,0.1260930896,-0.0084403893,0.1862768978,-0.5876232982,-0.1518306881,0.1735303998,-0.2187470049,0.05366778,-0.0276974067,0.4717944562,-0.1412032098,0.0702639893,0.0977402925,0.2745475769,0.510950923,0.201284498,0.1008572876,-0.1483419985,0.2257253379,-0.1809088588,0.1172315031,-0.1104004085,-0.1220034808,0.1084840372,0.2392062545,-0.1832301319,-0.1661884338,-0.4356065989,0.4669902325,-0.4517908394,0.1110789999,-0.2033956498,0.3239640892,-0.2341742665,0.2661927342,0.0968197584,0.2421134561,-0.4497469068,-0.1726887971,-0.3069637716,-0.0053717731,-0.1520239562,-0.1448992342,0.0667963028,-0.0198877677,0.6864049435,-0.1905841082,0.2406973541,-0.1494981796,0.1166925207,-0.1677237153,-0.1573780477,0.2168618292,0.0933346674,-0.1515265107,0.0986844525,0.1273582578,0.073553957,-0.0123291211,-0.1933779418,-0.0778044537,0.0731493235,0.3307126164,0.0673327073,0.1481941789,-0.0800899193,0.3283025026,0.3131912947,0.2075050324,-0.1654668152,-0.1291126758,-0.0672498494,-0.0664217025,0.2284736335,0.1068682075,-0.3879636526,-0.2831277251,0.2111899704,0.1523139328,-0.1277307868,0.1663789004,-0.0637842715,0.4463846385,-0.0574760288,0.1228245422,-0.1606709957,-0.0278364383,-0.0758509561,-0.0808564126,-0.0359562635,0.0637387708,0.0771537423,-0.3781818449,0.3808516264,0.1721238941,0.2533078194,0.2878333032,-0.0454363003,-0.0531011745,-0.3692813516,-0.333049655,-0.1322299391,0.0303953551,0.1376107484,-0.0659362376,-0.0196515061,-0.2123082876,0.4386965334,0.0753439441,-0.1268944889,0.2475073934,0.2016558796,-0.175553605,0.134940505,-0.0882637501,-0.4648680985,0.3856156766,-0.2136990875,0.1805250943,0.1455844641,0.0752157122,-0.5313087106,-0.0354604274,-0.0576039143,-0.3980095685,0.2089185566,-0.04827407,0.1521581262,-0.0416214764,-0.1192983985,0.0717149377,-0.2015973032,-0.0051512355,-0.2455405742,0.2948980629,-0.1022188514,-0.0338154435,0.2529664934,0.1713653058,0.0838688239,-0.2252237201,-0.0535434745,0.3246357739,0.1384629458,-0.0993399248,-0.1715636551,-0.1226139367,0.1180634499,-0.131923154,-0.2020104975,0.2057533711,0.1158741117,-0.0233052354,-0.1940400749,0.3919537663,0.1703977436,0.2448907644,0.2319546491,0.0529230796,0.1448597759,-0.0916807204,0.0975734591,-0.0638853163,0.189599961,-0.0497281142,-0.0844068453,-0.0868861899,-0.2769928873,0.1296674013,0.1336871833,0.0348882228,-0.0792626292,-0.162670821,-0.1537512541,0.1644689143,0.1590701044,0.1174459532,0.1510914415,0.30799824,-0.2536084652,-0.199834615,0.0356864035,0.0004941155,0.1626493484,0.0426238924,0.2464949638,0.2847855985,0.1936950982,-0.0943688676,-0.1065967083,0.1231058016,0.1845837831,0.2153210491,-0.3730052114,0.1432093829,-0.1024540812,0.2950365245,-0.1279529482,0.235516116,0.159334287,-0.2550415099,0.2276655138,0.0367487222,-0.053009022,0.264975369,-0.254773885,0.2745153606,0.1846229136,-0.1531972736,0.1978167146,0.1969082803,-0.3121934831,0.2985317111,-0.0876592323,0.1244520769,0.2559636235,-0.0741104484,-0.298353672,0.1935950369,-0.2393571287,0.1780943573,-0.1836472899,0.483614713,-0.141664952,0.1909188926,-0.3466756642,-0.3810741007,0.1948749274,-0.0581267588,-0.2577676773,0.1504852474,0.0410655998,-0.2013731003,-0.1593966931,-0.7575306296,0.0354381427,-0.1635408401,0.1972049624,0.1563578099,0.2769849002,0.0308583379,0.2668566406,-0.0354165621,-0.1260354668,-0.1651287824,-0.1709091961,-0.194642216,0.3205131292,-0.1530383378,-0.3015633821,-0.1450711936,-0.1393867731,0.5834782124,-0.0346679166,-0.3780317307,-0.2207747847,-0.0099999337,0.1090353206,-0.041204527,-0.1890207827,0.3572488725,-0.0990521386,-0.202939555,-0.0918208286,-0.0775542781,0.308878094,0.1910594404,0.258572638,-0.4090474844,0.2854526043,-0.0080062728,1.0584617853,0.3723424077,0.0747849196,0.0401264355,-0.0044042636,-0.1289398372,-0.1965896934,-0.1487231553,0.1607521623,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2707","title":"404 Not Found Error when loading LAMA dataset","comments":"Hi @dwil2444! I was able to reproduce your error when I downgraded to v1.1.2. Updating to the latest version of Datasets fixed the error for me :)","body":"The [LAMA](https:\/\/huggingface.co\/datasets\/viewer\/?dataset=lama) probing dataset is not available for download:  \r\n\r\nSteps to Reproduce: \r\n\r\n1. `from datasets import load_dataset`\r\n2. `dataset = load_dataset('lama', 'trex')`. \r\n\r\n\r\nResults:  \r\n`FileNotFoundError: Couldn't find file locally at lama\/lama.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/lama\/lama.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/lama\/lama.py`","comment_length":27,"text":"404 Not Found Error when loading LAMA dataset \n The [LAMA](https:\/\/huggingface.co\/datasets\/viewer\/?dataset=lama) probing dataset is not available for download:  \r\n\r\nSteps to Reproduce: \r\n\r\n1. `from datasets import load_dataset`\r\n2. `dataset = load_dataset('lama', 'trex')`. \r\n\r\n\r\nResults:  \r\n`FileNotFoundError: Couldn't find file locally at lama\/lama.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/lama\/lama.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/lama\/lama.py` \n Hi @dwil2444! I was able to reproduce your error when I downgraded to v1.1.2. Updating to the latest version of Datasets fixed the error for me :)","embeddings":[0.0275762063,-0.1798952222,-0.0416179113,0.5950444341,0.4188714027,0.1276884079,-0.1021334752,0.4045758247,-0.4222145975,0.2916164994,-0.3122213781,0.1641525775,-0.159445703,-0.2467197478,0.0084447721,-0.3867286146,-0.1002725586,-0.0234841295,-0.2141502798,0.1703372449,-0.0552273877,0.3564765453,-0.1458574384,0.1938980818,0.0413480662,0.1218298301,-0.1561390162,0.3231827319,-0.2132577449,-0.3137521744,0.376162082,-0.1966380328,0.2636945844,0.7459003329,-0.000113161,0.170933485,0.2560487688,-0.1524574012,-0.2275671512,-0.3300167024,-0.2031929493,0.020837808,0.0260597821,0.0620778389,-0.327978462,-0.0541198514,0.0296569485,-0.106870763,0.0726983771,0.2427031249,0.2222920507,0.5647499561,0.1862714142,-0.3274774551,0.155467093,-0.1818133444,-0.1400780976,0.6799879074,0.1498614252,0.4447278678,0.1170044169,0.4727893472,0.0081939111,-0.0207349416,0.2193838507,-0.1606039107,0.3265073597,-0.3726662099,0.2364120185,0.3105343878,0.398439914,-0.1125394702,-0.3003064394,-0.188016057,0.0631653368,-0.1206552461,0.2855997384,0.1726784557,-0.2515365183,0.2372489721,0.064684689,-0.3774136603,-0.030924784,0.2397215515,-0.0525427237,-0.3283061683,-0.1941554397,-0.0440329574,-0.0428908393,-0.1177555099,-0.1223740503,-0.1022243947,-0.0604988597,0.4073463678,-0.1801186353,-0.0222832523,0.1310425252,0.1632917374,0.0916107669,0.2455806285,-0.0600872375,0.107350491,-0.4293561876,0.1083620638,0.2306107134,0.0193927865,0.0387212373,0.0064264713,0.1022538543,0.559654057,0.1537634581,-0.0669870302,-0.3494531512,-0.3994399905,-0.4301458895,0.0453638174,0.3619140089,-0.2745935321,-0.1926773489,-0.2255994081,-0.1761281192,-0.1179646626,0.328471154,0.4818301797,-0.0135623077,0.1518219262,-0.2231666446,0.1077072024,-0.0843086615,-0.1456580907,-0.2482452691,0.0794835985,-0.2387344837,0.1790161431,0.1133854911,-0.6890286803,0.3001265824,-0.305708766,-0.1137952954,0.008293299,0.0363545194,-0.0737774596,-0.1109906659,0.2830492258,0.0126989959,-0.0248338543,-0.0502680764,-0.123763524,-0.135119915,0.0123523436,-0.2292298079,-0.2775953412,-0.0139294909,0.1285256743,-0.0168099627,0.203304857,0.1159618348,0.1686997861,-0.2712258697,-0.128826499,-0.0279897451,0.139417395,-0.0879680589,-0.1676285118,0.4258745015,0.5617586374,-0.1878831685,-0.0116636874,-0.0652727038,-0.4448719323,0.021887565,0.2667708099,-0.1007739604,0.2799500227,-0.5007410049,0.0761442259,0.3697856963,-0.3822958767,-0.2424640507,-0.017970413,-0.1458064318,-0.0362399928,0.045597408,-0.1990565956,-0.0711607784,-0.1117392629,0.4304517806,-0.0396544561,0.108241044,-0.2827382386,-0.1561469436,-0.1705923676,-0.0402733684,0.0282070879,0.0869352147,0.0649742782,0.1890324503,-0.1641197652,0.1505366564,0.017725084,0.0044502984,0.33847785,0.333594054,0.2531719208,0.0434678271,-0.1701409817,-0.0906351954,0.1604188234,-0.1763248742,0.2604502439,-0.3216400743,-0.0586831719,-0.3983004391,-0.0621902123,-0.2430595011,-0.1129879728,0.1386483759,0.0277117025,0.1554589719,0.3996631205,-0.1036331877,0.0866570175,0.0413638577,0.3868951499,-0.5217029452,0.2882936299,-0.1621196568,-0.0569980741,0.0447353981,0.1650550961,0.0889760703,-0.2264202088,0.0879154354,0.3215096891,0.1480682343,0.0254212078,0.6113657951,-0.2260311991,0.1211305782,-0.272159636,0.0900689587,-0.0116431955,0.1313446164,0.1288069934,0.063158825,0.1652727276,0.1123038009,0.0762929022,0.1483362764,0.0277672596,0.3416911662,-0.0324320681,-0.072627984,-0.2988233566,0.2953135371,0.1560999155,0.2211470455,-0.0873381495,-0.2797177434,-0.2418055683,-0.2270907015,-0.105482161,-0.0097520975,0.1181006506,-0.3057870865,0.2041134685,0.2184486538,0.3560207188,0.199327752,0.1325501353,0.1636909544,0.1632709503,0.1215503141,-0.1284631938,0.0921372399,0.1699588597,0.0739580318,0.1871473789,-0.1497145146,0.0457578301,-0.3276008964,-0.0989335477,-0.159858793,0.2108633518,-0.1368249655,-0.1308034062,-0.3650540113,-0.5920037031,-0.2478825003,-0.0549499802,-0.4640192091,-0.2258246094,-0.129240036,-0.0146912904,-0.0487080924,0.0384634621,-0.3173106909,0.1435844749,-0.08266332,-0.0794701278,-0.2091592699,0.0474949628,-0.0480669215,0.0619082414,0.3312280476,-0.0067720227,0.3388998806,-0.3461804688,0.110254623,-0.2857080996,-0.3271134496,0.1754175723,0.1256680638,0.2020720541,0.1722787917,0.1957675517,0.0749968067,-0.1231173426,0.3749608397,0.0885668844,-0.1042628959,0.0144845201,-0.022987701,0.2079499364,0.0007505329,-0.2067741305,-0.1117951125,-0.2796300352,-0.0600172356,0.075303629,-0.0136360526,0.0379966013,0.2016094923,0.3852606118,-0.5088940263,-0.0519054495,-0.0848602504,-0.3770296276,0.3114745021,-0.2667360902,-0.2900529802,0.3200337589,-0.2356281877,0.5331386328,-0.1657560617,-0.476556927,0.0859394297,0.0523845255,0.1605126709,0.2272054851,-0.1370075196,0.0498042554,0.0314962864,-0.113392286,0.0020012052,-0.0747741535,-0.3300971389,-0.0530968681,0.4950979054,-0.1138645932,0.0503144599,0.0733376518,0.4199269414,0.3726042509,0.1534758508,0.4652198553,0.1167078093,0.5983056426,-0.0907256156,-0.1926039904,-0.1169227883,0.0117179872,0.127961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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2707","title":"404 Not Found Error when loading LAMA dataset","comments":"Hi @dwil2444, thanks for reporting.\r\n\r\nCould you please confirm which `datasets` version you were using and if the problem persists after you update it to the latest version: `pip install -U datasets`?\r\n\r\nThanks @stevhliu for the hint to fix this! ;)","body":"The [LAMA](https:\/\/huggingface.co\/datasets\/viewer\/?dataset=lama) probing dataset is not available for download:  \r\n\r\nSteps to Reproduce: \r\n\r\n1. `from datasets import load_dataset`\r\n2. `dataset = load_dataset('lama', 'trex')`. \r\n\r\n\r\nResults:  \r\n`FileNotFoundError: Couldn't find file locally at lama\/lama.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/lama\/lama.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/lama\/lama.py`","comment_length":41,"text":"404 Not Found Error when loading LAMA dataset \n The [LAMA](https:\/\/huggingface.co\/datasets\/viewer\/?dataset=lama) probing dataset is not available for download:  \r\n\r\nSteps to Reproduce: \r\n\r\n1. `from datasets import load_dataset`\r\n2. `dataset = load_dataset('lama', 'trex')`. \r\n\r\n\r\nResults:  \r\n`FileNotFoundError: Couldn't find file locally at lama\/lama.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/lama\/lama.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/lama\/lama.py` \n Hi @dwil2444, thanks for reporting.\r\n\r\nCould you please confirm which `datasets` version you were using and if the problem persists after you update it to the latest version: `pip install -U datasets`?\r\n\r\nThanks @stevhliu for the hint to fix this! ;)","embeddings":[0.0376231372,-0.2442803234,-0.0386776887,0.6305751204,0.4072245955,0.0883304179,-0.0716991201,0.3489049077,-0.3947159946,0.264603734,-0.2870087922,0.2205041796,-0.1573990136,-0.2962248325,0.0108721973,-0.3568078876,-0.1149339452,-0.0169953573,-0.1815581769,0.1489533335,-0.0625042245,0.3521650434,-0.1265231669,0.1486698538,0.0735035911,0.1503960192,-0.1259550154,0.2814263105,-0.190541774,-0.324713707,0.4116790593,-0.1972879469,0.2471829355,0.7875105143,-0.0001171694,0.1473741084,0.2983234227,-0.1649733931,-0.2821417749,-0.3706027567,-0.2304017842,-0.0749065652,0.0254260506,0.0481946096,-0.3216995895,-0.0121680219,0.0023315025,-0.0769948438,0.1006459892,0.2473402768,0.1817989498,0.5502545834,0.179993704,-0.2549853325,0.1239052936,-0.1564266682,-0.1432594657,0.6937092543,0.212116614,0.4913322031,0.1655281186,0.466221422,-0.0164648257,-0.000398995,0.1752236336,-0.1469997317,0.3066895306,-0.3588722348,0.2023094743,0.3324887753,0.4127957225,-0.1082109511,-0.3176200092,-0.2034640759,0.0543417782,-0.1205691546,0.2801489532,0.2186776847,-0.2847771049,0.2256470174,0.0706216395,-0.4013845623,-0.0462253094,0.2663989961,-0.0475644954,-0.3123933375,-0.2254801095,-0.0457049645,-0.0561008006,-0.122848995,-0.1137527674,-0.1313110739,-0.050231088,0.4134670496,-0.1436476111,-0.0026620096,0.0764176324,0.196240738,0.0534739718,0.2188102007,-0.0718790889,0.1110744551,-0.3836347163,0.0944969058,0.2684501112,-0.0089992173,0.0116549749,0.0559998192,0.0936570838,0.5667535067,0.180558607,-0.0413758755,-0.3334462047,-0.3940544128,-0.4419755638,0.0216966718,0.3429944813,-0.2445634156,-0.181790486,-0.2328260243,-0.1784704328,-0.1435522437,0.331209898,0.4548911154,-0.0192042291,0.1615790725,-0.1978382319,0.0973857343,-0.07085976,-0.1500709653,-0.2035821974,0.0518461131,-0.227414161,0.1836643666,0.151868999,-0.7167775035,0.3160148561,-0.2461057305,-0.142082423,0.01632227,0.0305065606,-0.0926795825,-0.0823924765,0.2839703858,0.0460856035,0.0165917315,-0.0983635634,-0.1527360976,-0.1185643375,0.0476335138,-0.2459807396,-0.2467961907,-0.0715638027,0.0831063986,-0.0353121869,0.2100958228,0.0569599718,0.1596894711,-0.235934034,-0.1610614061,0.0122283325,0.1079684719,-0.0236636344,-0.1690436602,0.4432069957,0.5722745657,-0.1904916465,-0.0203828923,-0.1001596898,-0.4123748839,0.0347054265,0.2353795916,-0.105083622,0.3270788491,-0.5126182437,0.0094884476,0.4096075594,-0.4043959975,-0.2640866339,-0.0146399327,-0.103657946,0.0169052184,0.061881315,-0.2221135199,-0.0735695362,-0.0733288229,0.4005431831,-0.0504778139,0.1311737448,-0.2857051492,-0.180993855,-0.1583044231,-0.0461685397,0.027168408,0.0974410176,0.0271266028,0.1707299203,-0.1774171591,0.1637755483,0.0285246186,0.015392079,0.360457927,0.3656584024,0.3046260178,0.0483875982,-0.1808129847,-0.0931404829,0.1336420774,-0.1816187501,0.2133657932,-0.3849082887,-0.0144105749,-0.3852227628,-0.0800000578,-0.2602271736,-0.1229649261,0.093259193,0.0460892059,0.1697572619,0.4013627768,-0.1205973551,0.1332776248,0.0645349845,0.3940297067,-0.5741817951,0.2775955498,-0.1454002261,-0.0851296261,-0.0152847981,0.1977677494,0.0863129199,-0.232964769,0.116725333,0.2829639316,0.1624978781,-0.0122364433,0.5958023071,-0.2751281261,0.1441280395,-0.2933014035,0.0742353871,-0.0074059241,0.1238600835,0.1390321702,0.0064978856,0.1745623946,0.0916065499,0.1102492586,0.1289982051,0.0054872818,0.3452596664,-0.0348348208,-0.1165444255,-0.2764839828,0.3460576832,0.186855942,0.2750222981,-0.0800100863,-0.233538121,-0.247148782,-0.224796474,-0.0969630182,0.0205947887,0.1212189049,-0.3004150987,0.2324629277,0.154048413,0.3634408712,0.2016908675,0.1185744628,0.1071733236,0.1104388684,0.1724074483,-0.1105893403,0.0749386176,0.1939235926,0.0081138499,0.223238185,-0.1277025193,0.0503921695,-0.2690105438,-0.0030083468,-0.1563002616,0.1812726855,-0.146503523,-0.0971217304,-0.3710281849,-0.5473519564,-0.2929015756,-0.0843420625,-0.4625463486,-0.198014304,-0.1817056239,-0.0270366874,0.0327184871,0.0591555312,-0.2924275696,0.1223865151,-0.1217258722,-0.1199230775,-0.2157153934,0.0569689013,-0.0498639978,0.0278448034,0.3217616677,0.0231651142,0.331033498,-0.3448909223,0.1146355495,-0.2824027538,-0.3251257539,0.1471241266,0.1241642013,0.2616078854,0.1532170922,0.2105141431,0.072250925,-0.1196710095,0.3903245628,0.1341011971,-0.0772342011,0.007932202,-0.0552426577,0.2397370785,0.0300046932,-0.1942540258,-0.1402244717,-0.2553319037,-0.0677700192,0.0564313084,-0.0548759773,-0.0302682444,0.2058975846,0.353776902,-0.5599407554,-0.043356888,-0.0665352792,-0.3719189167,0.3360426724,-0.2776235938,-0.2710165679,0.3012785912,-0.2774116993,0.5949883461,-0.1596422195,-0.4551080465,0.1483637989,0.0645555556,0.1678711921,0.2163109481,-0.1184497252,0.0491827913,0.0213148855,-0.0523296706,0.0135797393,-0.081472598,-0.3569486141,-0.0511713736,0.5589962006,-0.0684969798,0.0786288455,0.0684092417,0.4786718488,0.402877599,0.1334718466,0.4516437948,0.0872956291,0.6159577966,-0.0439010262,-0.2505469918,-0.1680506021,0.0329114161,0.18023017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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2707","title":"404 Not Found Error when loading LAMA dataset","comments":"@stevhliu @albertvillanova  updating to the latest version of datasets did in fact fix this issue. Thanks a lot for your help!","body":"The [LAMA](https:\/\/huggingface.co\/datasets\/viewer\/?dataset=lama) probing dataset is not available for download:  \r\n\r\nSteps to Reproduce: \r\n\r\n1. `from datasets import load_dataset`\r\n2. `dataset = load_dataset('lama', 'trex')`. \r\n\r\n\r\nResults:  \r\n`FileNotFoundError: Couldn't find file locally at lama\/lama.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/lama\/lama.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/lama\/lama.py`","comment_length":21,"text":"404 Not Found Error when loading LAMA dataset \n The [LAMA](https:\/\/huggingface.co\/datasets\/viewer\/?dataset=lama) probing dataset is not available for download:  \r\n\r\nSteps to Reproduce: \r\n\r\n1. `from datasets import load_dataset`\r\n2. `dataset = load_dataset('lama', 'trex')`. \r\n\r\n\r\nResults:  \r\n`FileNotFoundError: Couldn't find file locally at lama\/lama.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/lama\/lama.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/lama\/lama.py` \n @stevhliu @albertvillanova  updating to the latest version of datasets did in fact fix this issue. Thanks a lot for your help!","embeddings":[0.0579447038,-0.2256722301,-0.0337308869,0.6018184423,0.396944344,0.1228495389,-0.0771401227,0.392708838,-0.4282576144,0.2528084219,-0.3340739012,0.1994093359,-0.1437425762,-0.263453573,0.0329603218,-0.3631291389,-0.1055584773,-0.035641022,-0.1782314926,0.1402931064,-0.0773811564,0.3491840661,-0.1713868082,0.1461031735,0.0305642188,0.164069742,-0.1523557901,0.2818200886,-0.1676369458,-0.3405278027,0.4034930468,-0.1659760773,0.2667887807,0.7545297146,-0.0001155138,0.1582778245,0.2871517241,-0.180117473,-0.2258957177,-0.3208471537,-0.2181340903,-0.0076235915,0.0299344957,0.0575377792,-0.3109140694,-0.0250622947,0.0147859557,-0.1243565828,0.0784541816,0.2548890114,0.1996642798,0.5669783354,0.15794155,-0.2838997245,0.121087864,-0.1600352079,-0.1550655216,0.6935021877,0.1979849786,0.5019051433,0.1076982692,0.4367845953,0.0512147099,-0.0000367471,0.17038849,-0.1627652496,0.3027929962,-0.362082839,0.2260492891,0.3412948549,0.411247015,-0.0895616338,-0.300208658,-0.2041232288,0.0630146936,-0.1047641039,0.2831819952,0.2124739885,-0.2709573507,0.2240071893,0.0596977212,-0.3989564478,-0.0375748686,0.2220338732,-0.0665908009,-0.3245233297,-0.2249275297,-0.0477187447,-0.0698751584,-0.1235073805,-0.1092792824,-0.0962813273,-0.0449433923,0.4012899101,-0.2000897378,0.0081394352,0.0980038121,0.1805590987,0.071898371,0.2427892238,-0.092317313,0.1385717392,-0.4070573747,0.0528271496,0.2572005093,-0.0139705762,0.0244916715,0.0129902465,0.1060971692,0.5729398131,0.1416838169,-0.0277773663,-0.3655084372,-0.4114148319,-0.4233098328,0.0277434904,0.3314841092,-0.2584204674,-0.1657229066,-0.2332686335,-0.1796723604,-0.1320354342,0.3118084073,0.4670644701,0.0070983572,0.1362685859,-0.194996044,0.1143863797,-0.0724026635,-0.1776781678,-0.2421129495,0.0321830474,-0.2401953042,0.1697179228,0.1667345017,-0.7531397343,0.3183695376,-0.2839638591,-0.1498496383,-0.0083345817,0.0144481035,-0.058931306,-0.0803298876,0.2743904293,0.0371800512,0.0159661993,-0.1051173583,-0.1115520671,-0.1097052768,0.0255913399,-0.2360412329,-0.2386386991,-0.04296856,0.0918830559,-0.0386213176,0.2000920475,0.0625077784,0.1967736185,-0.2370226979,-0.1061923727,-0.0301952213,0.1145451367,-0.0816156417,-0.1677071601,0.4616360664,0.5593075156,-0.2058154792,-0.0246673226,-0.0757972151,-0.4332314432,0.0128629459,0.2552057207,-0.0984490141,0.3181884885,-0.5351845026,0.0659497753,0.3240578473,-0.3493839502,-0.2325602919,-0.0395773239,-0.0958142653,0.0048005632,0.0316052288,-0.2354949266,-0.0602965765,-0.0936399773,0.4298363924,-0.0275842343,0.1246361881,-0.2872694731,-0.1498779356,-0.1584853381,-0.0458613411,0.0432468355,0.1069767177,0.0484397411,0.1735407561,-0.1535310298,0.1848183572,0.0487170815,0.0185187478,0.3353233337,0.3199564219,0.268153131,0.0523090921,-0.1446799934,-0.1134740412,0.1642012894,-0.160792008,0.2338319868,-0.3601338267,-0.0121705504,-0.3953308165,-0.0618144572,-0.2387167364,-0.1459473372,0.108727321,0.0498258807,0.1492777914,0.4205012619,-0.1292730868,0.1097558364,0.0478614606,0.3824838102,-0.5560265779,0.2685636878,-0.1329844892,-0.0726381317,0.0431461222,0.1757280827,0.0637694597,-0.2454117388,0.0792406499,0.2824501395,0.1595969051,0.0057037603,0.5873308182,-0.2710196972,0.1232921034,-0.3047973216,0.0646835789,0.0044439742,0.103925623,0.1204294115,0.0022746688,0.217210412,0.117086716,0.0793338716,0.1173363328,0.0420594774,0.3593285978,-0.0393532701,-0.0884055495,-0.2660077214,0.3219717145,0.1995388567,0.2781038284,-0.0838949308,-0.2605073154,-0.2462642938,-0.2269675434,-0.0955885351,0.0221516397,0.1218470037,-0.3248013258,0.2021924555,0.1915891021,0.3560812175,0.1836343557,0.1089359671,0.1458857656,0.1345479041,0.1329140365,-0.1289663017,0.0620400161,0.1720373183,0.0370383263,0.1893455386,-0.140074119,0.040612068,-0.3171598911,-0.0203471258,-0.1183285713,0.1915402561,-0.1601587385,-0.1411346197,-0.3798510134,-0.5577113032,-0.2689440846,-0.0469791926,-0.4519857466,-0.2289761752,-0.1617726833,-0.0296160374,-0.0148885734,0.0779268891,-0.3301406205,0.1626651287,-0.0877002031,-0.0723210499,-0.2258994728,0.0785861835,-0.0488439091,0.0400497913,0.3276121914,-0.0029401446,0.3127982616,-0.3487844169,0.1278117001,-0.2811443806,-0.3291572034,0.1592871994,0.1283323914,0.2206982523,0.1335715801,0.2108578086,0.0832080916,-0.1141858324,0.3580516875,0.0815688744,-0.0978443846,0.0026842158,-0.0238126554,0.2314572483,0.0320728421,-0.2025589645,-0.1074043065,-0.2520040274,-0.0685241893,0.0493981875,-0.0499446094,0.0068831188,0.1808726192,0.3758721948,-0.5315436721,-0.06250678,-0.0959486216,-0.4061956108,0.3328712285,-0.2945596576,-0.3111495674,0.3056975305,-0.2641014159,0.5637584329,-0.1587353647,-0.4655626714,0.199511081,0.0502379946,0.1819996834,0.2339470088,-0.1430178136,0.0354512893,-0.000908708,-0.0746007115,-0.0022464588,-0.0792549998,-0.3318125606,-0.0562983379,0.5509485006,-0.0979870036,0.04112719,0.1134254709,0.403195709,0.3871581554,0.1285032183,0.4658761621,0.1068727374,0.6118932366,-0.0472184755,-0.205602318,-0.1198549196,0.019816549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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2705","title":"404 not found error on loading WIKIANN dataset","comments":"Hi @ronbutan, thanks for reporting.\r\n\r\nYou are right: we have recently found that the link to the original PAN-X dataset (also called WikiANN), hosted at Dropbox, is no longer working.\r\n\r\nWe have opened an issue in the GitHub repository of the original dataset (afshinrahimi\/mmner#4) and we have also contacted the author by email to ask if they are planning to fix this issue. See the details here: https:\/\/github.com\/huggingface\/datasets\/issues\/2691#issuecomment-885463027\r\n\r\nI close this issue because it is the same as in #2691. Feel free to subscribe to that other issue to be informed about any updates.","body":"## Describe the bug\r\nUnable to retreive wikiann English dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import list_datasets, load_dataset, list_metrics, load_metric\r\nWIKIANN = load_dataset(\"wikiann\",\"en\")\r\n```\r\n\r\n## Expected results\r\nColab notebook should display successful download status\r\n\r\n## Actual results\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":94,"text":"404 not found error on loading WIKIANN dataset \n ## Describe the bug\r\nUnable to retreive wikiann English dataset\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import list_datasets, load_dataset, list_metrics, load_metric\r\nWIKIANN = load_dataset(\"wikiann\",\"en\")\r\n```\r\n\r\n## Expected results\r\nColab notebook should display successful download status\r\n\r\n## Actual results\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.1\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n Hi @ronbutan, thanks for reporting.\r\n\r\nYou are right: we have recently found that the link to the original PAN-X dataset (also called WikiANN), hosted at Dropbox, is no longer working.\r\n\r\nWe have opened an issue in the GitHub repository of the original dataset (afshinrahimi\/mmner#4) and we have also contacted the author by email to ask if they are planning to fix this issue. See the details here: https:\/\/github.com\/huggingface\/datasets\/issues\/2691#issuecomment-885463027\r\n\r\nI close this issue because it is the same as in #2691. Feel free to subscribe to that other issue to be informed about any updates.","embeddings":[-0.1983268559,0.0143738203,0.0021061967,0.236056,0.0338601805,0.0631663427,0.054253187,0.1971953064,-0.0232596956,0.1844016463,-0.1990168989,0.3482315838,0.1889100224,-0.0352049246,0.1387394071,-0.2517963946,0.029693706,0.263538152,0.0581075698,-0.097589165,-0.2116294056,0.1820748001,-0.4031837285,0.2455023229,-0.143373549,-0.0048098648,0.027901195,0.109960407,-0.3365817666,-0.2690229416,0.3799149394,-0.2225337178,0.3279035985,0.6063837409,-0.0001109609,-0.0759147629,0.4458803535,0.1000097468,-0.391836077,-0.4055833817,-0.4237225652,-0.0974915251,-0.0088995369,-0.2199463993,-0.033632338,-0.3518974781,-0.0263598748,0.0464493744,0.1087762266,0.408013314,0.2161208391,0.3451708853,0.1994796693,-0.0758984461,0.1112233698,-0.2041801065,-0.2938869298,0.2485886812,0.3716220856,0.0193181764,0.2147423625,0.4076726735,-0.012935644,-0.1191930324,0.0002604528,0.0962099433,-0.2352766991,-0.3271325827,0.1940369904,0.4193383157,0.7381356955,-0.0773248747,-0.3165581822,-0.1083422005,0.1998565197,-0.1682266593,0.2259471714,-0.0250789039,-0.1283596456,0.2104655802,0.1944339275,-0.3695300221,-0.1503367275,0.2880272865,-0.0677539706,0.6986863613,-0.0293509737,0.0186183192,-0.10053204,-0.2014934719,0.6673569679,-0.079500705,0.107167244,0.0952379629,-0.1010599658,-0.007474252,0.0782534406,0.270442307,0.0974567384,-0.5342894197,0.0890707225,0.071390301,-0.1726489216,0.1293272525,0.0966725275,0.4672059119,0.113636516,0.059606567,0.3231092989,0.5634970069,-0.0561737493,-0.0862273499,-0.2202486843,0.036675714,0.0765397027,-0.1212231293,0.2347895652,-0.2797554433,-0.1778755784,0.1110151708,0.0104963854,-0.1117839292,0.1340745986,0.3343401253,-0.0520977639,0.4685741365,-0.1090251505,-0.0353921875,-0.2961293459,-0.0141351288,-0.1227805093,0.2299466133,-0.2976323366,-0.0304367337,0.0177038424,-0.0002731988,0.3745611906,-0.1093472764,0.2685654461,-0.0799273401,0.0472409688,-0.0589807145,-0.1680402756,0.2072970122,0.1856461763,0.0938892514,-0.0393015221,-0.5908218622,-0.0037347227,0.2500285208,-0.5024211407,-0.111079976,-0.3087926805,0.207456544,-0.0394152291,-0.0953390598,-0.2219092399,0.0893992707,-0.1252564192,-0.2733161449,-0.0483465157,-0.1564322859,-0.0715266392,-0.0714866295,0.0706887245,0.1540295482,-0.3954985142,-0.0663112924,-0.0150679545,0.0924511626,0.0102316067,0.2509191632,-0.289742589,0.1805291474,-0.4087093472,0.2994852662,0.4748595357,-0.6277826428,-0.4702953398,-0.1551852077,-0.2183941603,0.1764796674,-0.1732209921,-0.1397213638,0.0623195209,-0.0246238504,0.3741478324,0.2529842854,0.181484431,-0.1765975654,-0.3372808397,-0.262729913,-0.1873220056,0.0544974245,0.2701176405,-0.0965783149,0.2220113426,0.09778779,0.2185599208,-0.3049603403,-0.0352812856,0.3708949983,0.2777453661,0.0029751016,0.0427017324,-0.1631835401,-0.2401775122,0.2356497496,-0.0651689544,0.2263505906,-0.1303499937,-0.1732662022,-0.5030014515,0.0282157417,-0.4124868512,0.0118033681,0.202769503,0.0639729351,-0.0951892212,0.3821229041,-0.1254786551,0.0553834289,-0.0648604855,0.0590664372,-0.1425856501,0.3004005551,-0.2772384584,0.0521437638,0.0099659944,0.3502000272,0.3246391714,0.0650507808,-0.058719255,0.141134426,-0.2738318741,-0.0084212022,0.6433116794,-0.0843106881,0.2559211552,-0.4660770297,0.2186956257,0.0654229969,0.184139654,0.0361105651,-0.0412480719,0.2195616364,0.2949728966,0.1033336893,0.164579019,0.0750200525,0.3924272358,-0.2022890002,0.0477028862,-0.3806104958,0.1593785882,-0.2764974833,0.1574205905,-0.1935892254,-0.206379056,0.2418937832,0.4138443172,-0.2443424612,0.2266424745,0.1589666158,-0.2687552273,-0.057376679,0.2165173739,0.3593724072,0.3308875859,0.157457009,0.3751510978,0.0756046474,-0.0619225949,-0.0286794025,0.2185885161,-0.0385723971,0.1342400312,0.2594102025,0.2180501372,0.0059732315,-0.3377846181,-0.2373338938,-0.1496368498,0.2096709609,-0.0474483185,-0.0373308845,-0.2840379775,-0.2394461334,-0.0997332111,-0.1130313873,-0.1936632395,-0.4324688613,0.0376594253,0.0136799458,0.1823806614,0.1621531695,-0.1680104733,-0.0975627005,-0.0643109456,-0.1069126651,-0.3256938457,-0.0213216618,-0.4002248347,0.0550165251,0.1399589479,0.0118893851,0.0437359549,-0.4289552569,0.0444942527,-0.3928014338,-0.4904257655,0.3152220845,-0.006297125,0.3915443718,-0.0743702799,0.2267785817,-0.0817342699,-0.1939721107,0.2416514754,-0.3259669244,0.0569712035,-0.0268688221,-0.081177406,-0.0201227218,0.1438263357,-0.2476803511,-0.3624427021,-0.098898232,-0.3223589957,-0.1130257398,-0.0283177085,0.166384384,0.0870398358,0.2570314407,-0.0655414239,-0.1978173852,-0.2999257743,-0.2391173542,0.4530379772,-0.2117198259,-0.2243974507,0.2539779544,-0.1193230674,0.2886121273,-0.2418434769,-0.5117617249,-0.4658155143,-0.0748426914,0.4123660028,0.0936156511,0.0990675166,0.2016833872,0.0559695065,-0.0554729663,-0.0769423917,-0.1939579695,-0.3744467795,0.0871133506,0.4336403012,-0.0294841379,0.0110902572,0.0580509566,0.4314289093,0.1037614644,0.3736900389,0.4079248607,0.0739920959,0.4846547842,-0.1832888722,-0.1795473844,-0.004560899,-0.1673390418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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2700","title":"from datasets import Dataset is failing ","comments":"Hi @kswamy15, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, you can circumvent this issue by updating the `tqdm` library: `!pip install -U tqdm`","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import Dataset\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n\r\n---------------------------------------------------------------------------\r\nNOTE: If your import is failing due to a missing package, you can\r\nmanually install dependencies using either !pip or !apt.\r\n\r\nTo view examples of installing some common dependencies, click the\r\n\"Open Examples\" button below.\r\n---------------------------------------------------------------------------\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: latest version as of 07\/21\/2021\r\n- Platform: Google Colab\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":39,"text":"from datasets import Dataset is failing  \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import Dataset\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n\r\n---------------------------------------------------------------------------\r\nNOTE: If your import is failing due to a missing package, you can\r\nmanually install dependencies using either !pip or !apt.\r\n\r\nTo view examples of installing some common dependencies, click the\r\n\"Open Examples\" button below.\r\n---------------------------------------------------------------------------\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: latest version as of 07\/21\/2021\r\n- Platform: Google Colab\r\n- Python version: 3.7\r\n- PyArrow version:\r\n \n Hi @kswamy15, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, you can circumvent this issue by updating the `tqdm` library: `!pip install -U tqdm`","embeddings":[-0.4085453153,-0.1528589129,-0.1239814758,0.0641506314,0.1121254042,0.1035053059,0.3430987298,0.223898381,-0.1365936399,0.0548991077,-0.0883905292,0.2876406014,0.0480185561,0.0611997545,-0.1352098435,0.0198291205,-0.0553882942,-0.0109331338,-0.3337739706,0.1266524345,-0.217322588,0.3459280729,-0.3196708858,-0.0665076375,-0.26490587,-0.2171354145,-0.0884475261,0.2700223327,-0.0890713185,-0.3350606263,0.0057384246,0.140682146,-0.0379140452,0.5401774645,-0.0001011049,-0.0078354618,0.3128858507,0.0935639068,-0.084417522,-0.0710472539,-0.3134652674,-0.4694289863,0.0806844607,-0.1916164458,-0.0022029711,0.1091363952,-0.0123654772,-0.3157416284,0.3191045225,0.3372055292,0.3364604712,0.5836625099,0.001960984,-0.306732744,0.0630692542,-0.3369416893,-0.1633457243,0.2865929902,0.4009721577,-0.1956418753,-0.0135743497,0.1969815046,-0.213966459,0.212846294,0.0626571998,0.0328398421,-0.0416467525,-0.4304128885,0.2624842227,0.1870162934,0.5149672627,-0.3271745145,-0.0373378322,-0.0568678826,0.0038167925,-0.2325145602,0.2190859914,-0.036922235,-0.1316181868,0.2236550599,-0.036217466,0.071047999,0.0526873171,0.0384646766,-0.2217678726,-0.0460676551,-0.0829893947,-0.0408100374,-0.0412158743,-0.136370182,0.3713529706,-0.0078106704,0.0284169465,-0.0238212813,-0.5177696943,-0.0575567298,0.3645920157,0.03556858,0.0987452865,0.0120878918,-0.0397548415,0.1094594598,-0.0714503527,0.3100284934,0.1683249623,-0.1073323935,-0.0055003739,0.0794885755,0.282115221,0.188070327,-0.2165435702,0.0819282457,0.0630704537,-0.4723846912,0.2384412438,0.1042847112,0.2806369662,0.1072816551,-0.3729566634,0.045274362,-0.0440311693,0.1376920044,-0.0906591117,0.4109575748,-0.109149456,0.0150475614,-0.0427163467,0.0600475036,-0.2108016759,-0.3220811188,-0.2802446783,0.0243945587,0.0463382415,-0.1378921568,-0.0416072346,-0.1625710577,0.2116598338,0.0482912175,0.157229349,0.0954252183,0.1668868065,-0.27029562,-0.0576179698,0.40771088,0.192671597,0.0409890004,0.2395561785,-0.0955689326,-0.0758202076,0.0929865688,-0.2596942186,-0.1863327324,-0.0069954796,0.3259136379,0.103960976,-0.2556672096,-0.0916619077,0.1133576035,0.0773157701,-0.1863555759,-0.0654879883,-0.2524957061,-0.0674690306,-0.3504024148,0.0776730925,0.5380800366,-0.4327085614,-0.0149561055,-0.159262374,-0.1078128815,0.0124575635,0.2770961225,-0.0810049549,0.1695788801,-0.1159966663,0.0491245463,0.2401681542,-0.319914937,-0.3626366258,0.0908700749,-0.301079005,-0.0427362472,-0.1367135346,0.093721427,0.295752883,-0.0094474945,0.4482402205,0.2462100089,-0.2692749798,0.0903337151,-0.1365799308,-0.2909478545,-0.0536916591,0.0469712801,0.2362893969,-0.0899253488,0.2524038553,-0.2295201272,0.1484709233,0.133948043,-0.0187071133,0.3950461149,0.3054838181,0.0365398787,-0.0323319994,-0.1834006906,-0.5057476163,0.249874711,0.3089755476,0.0818637311,-0.0234779958,-0.0089072566,-0.4486204088,0.2037529796,-0.1624403596,0.0956389755,0.2882280052,0.0894581974,-0.2714303136,0.0509770662,-0.247302711,0.4360179007,0.0321619958,0.08695364,-0.1087867022,0.288949877,-0.2754172385,-0.2581116557,0.2147305906,-0.0580091849,0.247653529,0.0031081075,-0.0581927523,0.329310447,0.0771936849,0.2450991124,0.1307125241,-0.0577195324,0.1969685405,-0.2697572708,-0.095454812,0.0938056782,0.1041573212,-0.0734270588,0.0805765018,0.1706543565,-0.2142150551,0.0104886293,0.0348369218,0.2288723588,0.3592408895,-0.0549508706,-0.0541916639,-0.1366294175,0.2221429497,0.118401818,0.012584161,-0.1474806666,-0.2470891625,0.2493074983,0.490115732,0.1015379876,0.1599902362,-0.1755920053,-0.2158996016,0.0977206901,0.133570075,0.3544579446,0.4767529964,0.2372669578,0.1458981931,0.181716755,-0.0352559723,-0.2257147729,0.0278197397,0.2088332325,0.0691447631,0.1396366805,0.2791813016,0.0575675294,-0.2701753378,-0.3984820545,0.0493996665,0.23472175,-0.3226417601,0.0203030147,-0.3022142053,-0.0669246912,-0.0339748487,-0.1841361821,-0.1792141944,-0.4988186955,0.0177649166,0.1733527333,-0.0362730883,0.2254777551,-0.0886924714,0.0382607728,0.1581800878,-0.234258011,-0.0838016868,-0.2235110551,-0.085879609,0.2016251981,0.1225927621,0.1275233775,0.4814586937,-0.3473612666,-0.0764092579,-0.3007541299,-0.2729535699,0.1080874056,-0.1079408526,0.5393425822,0.1690939814,0.1429461837,-0.189146474,0.0837831348,0.2729692161,-0.2841287255,-0.0361449495,0.1139917225,-0.0069278525,-0.1247009039,-0.0059449803,-0.2854000628,-0.259416014,-0.5138936043,-0.1164918765,0.0366632454,0.0855601579,0.2803019285,0.1171813011,0.25910905,0.0950798318,0.1597633809,-0.157387957,-0.27479285,0.2889837027,-0.2669566274,-0.4839946032,0.2523922622,-0.0436241478,0.248827219,0.0183746628,-0.291695416,-0.347537905,-0.0735730305,0.4598746002,-0.0146951051,0.139476046,0.2242235839,0.1021410152,-0.2486859262,-0.1503612399,-0.193666473,-0.2438552231,0.2690796256,0.0429665484,-0.0695938766,0.4731346071,0.1293032914,0.5410220623,0.2028915733,0.1337266862,0.3551773429,-0.3133036792,0.4701182246,-0.0137464684,-0.4632149339,-0.2093192041,-0.156620115,-0.2196600139,0.1839844286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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2699","title":"cannot combine splits merging and streaming?","comments":"Hi ! That's missing indeed. We'll try to implement this for the next version :)\r\n\r\nI guess we just need to implement #2564 first, and then we should be able to add support for splits combinations","body":"this does not work:\r\n`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`\r\nwith error:\r\n`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`\r\n\r\nthese work:\r\n`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`\r\n`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`\r\n`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`\r\n\r\ni could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits","comment_length":36,"text":"cannot combine splits merging and streaming? \n this does not work:\r\n`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`\r\nwith error:\r\n`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`\r\n\r\nthese work:\r\n`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`\r\n`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`\r\n`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`\r\n\r\ni could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits \n Hi ! That's missing indeed. We'll try to implement this for the next version :)\r\n\r\nI guess we just need to implement #2564 first, and then we should be able to add support for splits 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2695","title":"Cannot import load_dataset on Colab","comments":"I'm facing the same issue on Colab today too.\r\n\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-4-5833ac0f5437> in <module>()\r\n      3 \r\n      4 from ray import tune\r\n----> 5 from datasets import DatasetDict, Dataset\r\n      6 from datasets import load_dataset, load_metric\r\n      7 from dataclasses import dataclass\r\n\r\n7 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n\r\n---------------------------------------------------------------------------\r\nNOTE: If your import is failing due to a missing package, you can\r\nmanually install dependencies using either !pip or !apt.\r\n\r\nTo view examples of installing some common dependencies, click the\r\n\"Open Examples\" button below.\r\n---------------------------------------------------------------------------\r\n```","body":"## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":111,"text":"Cannot import load_dataset on Colab \n ## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n I'm facing the same issue on Colab today too.\r\n\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-4-5833ac0f5437> in <module>()\r\n      3 \r\n      4 from ray import tune\r\n----> 5 from datasets import DatasetDict, Dataset\r\n      6 from datasets import load_dataset, load_metric\r\n      7 from dataclasses import dataclass\r\n\r\n7 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n\r\n---------------------------------------------------------------------------\r\nNOTE: If your import is failing due to a missing package, you can\r\nmanually install dependencies using either !pip or !apt.\r\n\r\nTo view examples of installing some common dependencies, click the\r\n\"Open Examples\" button below.\r\n---------------------------------------------------------------------------\r\n```","embeddings":[-0.4603253901,-0.2148275971,-0.024375163,0.2682137489,0.1354128718,0.051994808,0.4973769188,-0.1074850261,0.1983702779,0.0849533305,-0.3266307712,0.4753232598,-0.1742698699,0.1487485617,-0.2584459782,0.0984701291,-0.0661861375,0.058203578,-0.2907032669,0.0956151709,0.0202920046,0.3308885992,-0.6335685253,-0.3468577266,-0.5786883831,-0.2100416869,-0.0277418792,0.168791458,-0.2702196836,-0.292011261,0.3478223681,0.1770586967,0.0921896547,0.5697174668,-0.0001172403,0.0715135038,0.3933092654,-0.0787535235,-0.2456898838,-0.3918715715,-0.3461531103,-0.3291964531,0.3686217368,-0.1073928103,0.1058271378,0.404165864,-0.1357311457,-0.133275196,0.2626952529,0.1352794766,0.1936342865,0.549197793,0.1630603671,-0.192492187,-0.1125865728,-0.193378076,-0.2020825595,0.7461523414,0.4174177051,0.0168563686,-0.0510248244,0.2604947388,-0.0992677361,0.2890874445,0.1862412095,-0.0334219635,0.1051012799,-0.4805828929,0.1006372571,0.1743771285,0.4806717336,-0.233426556,0.0443901122,-0.1220246106,0.2094738036,-0.2924674749,0.2619004846,-0.0985698551,-0.2118597776,0.0490080118,-0.0226965509,-0.0896168202,-0.0123074474,0.0164376963,-0.0933856592,0.3655072451,-0.1430076212,-0.1118264869,0.0698661357,-0.1096417829,0.5018094778,-0.0161823332,-0.0268674623,0.2063278407,-0.468000561,-0.0093295779,-0.098870948,-0.1408492327,-0.0516990833,-0.1014948189,-0.0361867324,0.0174666177,-0.1110087708,0.303483665,0.1848023385,0.1422498971,0.0347587094,0.0182988103,0.1226537004,0.2864284515,-0.2007629573,0.227394864,-0.259282738,-0.3118798137,0.1845084578,-0.029214235,0.2715788782,-0.0037106252,-0.319062084,-0.1818456352,0.0095681679,0.105042167,0.0446227603,0.3990536928,0.055795569,0.1057089195,-0.0346219428,0.0016938471,-0.3566534519,-0.2394378334,-0.0538450666,0.1936965287,-0.0642801523,-0.1152990013,0.0678034425,-0.1549987495,0.2601871789,-0.0124206394,0.3092051148,-0.0599603131,0.1036741883,-0.3025387824,-0.0606454387,0.331908226,0.1287349612,0.0872022063,-0.0623135045,-0.2055783719,-0.0778879523,0.2355169952,-0.4770620167,-0.1442362964,-0.0243145749,0.1429465264,-0.1339146942,-0.2244311124,-0.5850232244,0.1648343056,0.1447636336,-0.0812816098,-0.0849623531,-0.3062829673,-0.2330714166,-0.1535831094,0.1236880049,0.4482834041,-0.2309288979,-0.142314136,-0.2176906466,0.1205273345,0.1272705644,0.234823361,-0.2021541893,0.1736088246,-0.0962642506,-0.1171419844,0.490647763,-0.4326361418,-0.4671251178,-0.0203478895,-0.2660758793,-0.0052545536,-0.0897063687,0.0485790744,0.3577234745,-0.0669410899,0.5458390117,0.2629367113,-0.1390330195,-0.036604546,-0.1640905291,-0.4099052548,0.1294252127,0.0418195315,0.1834629029,-0.2177709639,0.203032732,-0.0533746518,0.1012382284,0.1859944463,-0.1211299896,0.3843335509,0.2429013252,-0.2223784029,0.0372738875,-0.1859572828,-0.4775209725,0.2240482867,0.409294188,0.0799716562,-0.0674993992,0.05132493,-0.4381961823,0.0791065395,-0.2594439685,0.0676428005,0.0994127542,-0.0666698813,-0.1751303822,0.0889260471,-0.2604830265,0.7403804064,0.0377074406,0.2175934911,-0.3183411956,0.2157882154,-0.1859039217,-0.3081798851,0.1762920618,0.1361715198,0.0753254518,-0.092272602,-0.2389075756,0.1421211809,0.2554138899,0.1713412106,0.061892774,-0.2776550353,0.4123665094,-0.274011761,0.0123248948,-0.1066840887,0.208570227,0.0058228662,0.2551185191,0.1565714628,-0.3707030118,-0.0410736091,-0.0305118244,0.0905382559,0.3726439178,0.1239964738,-0.0050928039,-0.1856487691,0.325183779,0.2222952098,0.192474246,0.0937420502,-0.1297319233,0.2881348133,0.3061757982,0.2452441901,0.038779363,-0.2685610354,-0.2554307878,0.0633648261,0.1922871321,0.2518091798,0.4446454048,0.1803891063,0.1049195379,0.0316309184,0.0318480134,-0.1561172903,0.1151199043,0.2919997871,0.2558412552,0.1311028153,0.4120824635,0.0967440531,-0.078998588,-0.3894120455,0.1753250211,0.1503109038,-0.3317721486,0.0766265243,-0.2146017402,-0.113947168,-0.1419459134,-0.4253391027,-0.1464539021,-0.1978348494,-0.1058005691,0.2810257375,0.2650622427,0.1936620623,-0.0239940546,0.0500169992,0.0625045747,-0.142483592,-0.0558168627,-0.2506769001,-0.0925878361,0.0015159415,0.327739507,0.246811226,0.3685063124,-0.3038308024,-0.0695626289,0.1437006891,-0.332098037,0.1763451695,-0.005682677,0.2919798791,-0.0134428795,-0.0779709816,-0.3206300437,-0.1939175874,0.0215363186,-0.1346231997,-0.1827463955,0.0472795218,-0.1800287366,-0.2297740728,-0.0019908124,-0.2926879525,-0.4344463348,-0.3998571932,-0.3682101369,0.0472466946,-0.0089030508,0.4603004754,-0.0427159443,0.2933925688,0.2250700146,0.0508381985,0.0179356467,-0.3330911994,0.4733818173,-0.2865113616,-0.2936578393,0.2801209092,-0.2680461109,0.3836089373,0.0181830842,-0.1104436517,-0.2135308832,-0.0214798469,0.3837370574,-0.1009398997,0.0416830219,0.0846360251,0.1136619002,0.0782582983,-0.092406489,-0.1815526336,-0.2040507346,0.2196319252,0.1930779815,-0.1657154113,0.2265984565,-0.0363357775,0.8421141505,0.0771993697,-0.0400505289,0.30156785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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2695","title":"Cannot import load_dataset on Colab","comments":"@phosseini \r\nI think it is related to [1.10.0](https:\/\/github.com\/huggingface\/datasets\/actions\/runs\/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\nFor now I just downgraded to 1.9.0 and it is working fine.","body":"## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":28,"text":"Cannot import load_dataset on Colab \n ## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n @phosseini \r\nI think it is related to [1.10.0](https:\/\/github.com\/huggingface\/datasets\/actions\/runs\/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\nFor now I just downgraded to 1.9.0 and it is working fine.","embeddings":[-0.4603253901,-0.2148275971,-0.024375163,0.2682137489,0.1354128718,0.051994808,0.4973769188,-0.1074850261,0.1983702779,0.0849533305,-0.3266307712,0.4753232598,-0.1742698699,0.1487485617,-0.2584459782,0.0984701291,-0.0661861375,0.058203578,-0.2907032669,0.0956151709,0.0202920046,0.3308885992,-0.6335685253,-0.3468577266,-0.5786883831,-0.2100416869,-0.0277418792,0.168791458,-0.2702196836,-0.292011261,0.3478223681,0.1770586967,0.0921896547,0.5697174668,-0.0001172403,0.0715135038,0.3933092654,-0.0787535235,-0.2456898838,-0.3918715715,-0.3461531103,-0.3291964531,0.3686217368,-0.1073928103,0.1058271378,0.404165864,-0.1357311457,-0.133275196,0.2626952529,0.1352794766,0.1936342865,0.549197793,0.1630603671,-0.192492187,-0.1125865728,-0.193378076,-0.2020825595,0.7461523414,0.4174177051,0.0168563686,-0.0510248244,0.2604947388,-0.0992677361,0.2890874445,0.1862412095,-0.0334219635,0.1051012799,-0.4805828929,0.1006372571,0.1743771285,0.4806717336,-0.233426556,0.0443901122,-0.1220246106,0.2094738036,-0.2924674749,0.2619004846,-0.0985698551,-0.2118597776,0.0490080118,-0.0226965509,-0.0896168202,-0.0123074474,0.0164376963,-0.0933856592,0.3655072451,-0.1430076212,-0.1118264869,0.0698661357,-0.1096417829,0.5018094778,-0.0161823332,-0.0268674623,0.2063278407,-0.468000561,-0.0093295779,-0.098870948,-0.1408492327,-0.0516990833,-0.1014948189,-0.0361867324,0.0174666177,-0.1110087708,0.303483665,0.1848023385,0.1422498971,0.0347587094,0.0182988103,0.1226537004,0.2864284515,-0.2007629573,0.227394864,-0.259282738,-0.3118798137,0.1845084578,-0.029214235,0.2715788782,-0.0037106252,-0.319062084,-0.1818456352,0.0095681679,0.105042167,0.0446227603,0.3990536928,0.055795569,0.1057089195,-0.0346219428,0.0016938471,-0.3566534519,-0.2394378334,-0.0538450666,0.1936965287,-0.0642801523,-0.1152990013,0.0678034425,-0.1549987495,0.2601871789,-0.0124206394,0.3092051148,-0.0599603131,0.1036741883,-0.3025387824,-0.0606454387,0.331908226,0.1287349612,0.0872022063,-0.0623135045,-0.2055783719,-0.0778879523,0.2355169952,-0.4770620167,-0.1442362964,-0.0243145749,0.1429465264,-0.1339146942,-0.2244311124,-0.5850232244,0.1648343056,0.1447636336,-0.0812816098,-0.0849623531,-0.3062829673,-0.2330714166,-0.1535831094,0.1236880049,0.4482834041,-0.2309288979,-0.142314136,-0.2176906466,0.1205273345,0.1272705644,0.234823361,-0.2021541893,0.1736088246,-0.0962642506,-0.1171419844,0.490647763,-0.4326361418,-0.4671251178,-0.0203478895,-0.2660758793,-0.0052545536,-0.0897063687,0.0485790744,0.3577234745,-0.0669410899,0.5458390117,0.2629367113,-0.1390330195,-0.036604546,-0.1640905291,-0.4099052548,0.1294252127,0.0418195315,0.1834629029,-0.2177709639,0.203032732,-0.0533746518,0.1012382284,0.1859944463,-0.1211299896,0.3843335509,0.2429013252,-0.2223784029,0.0372738875,-0.1859572828,-0.4775209725,0.2240482867,0.409294188,0.0799716562,-0.0674993992,0.05132493,-0.4381961823,0.0791065395,-0.2594439685,0.0676428005,0.0994127542,-0.0666698813,-0.1751303822,0.0889260471,-0.2604830265,0.7403804064,0.0377074406,0.2175934911,-0.3183411956,0.2157882154,-0.1859039217,-0.3081798851,0.1762920618,0.1361715198,0.0753254518,-0.092272602,-0.2389075756,0.1421211809,0.2554138899,0.1713412106,0.061892774,-0.2776550353,0.4123665094,-0.274011761,0.0123248948,-0.1066840887,0.208570227,0.0058228662,0.2551185191,0.1565714628,-0.3707030118,-0.0410736091,-0.0305118244,0.0905382559,0.3726439178,0.1239964738,-0.0050928039,-0.1856487691,0.325183779,0.2222952098,0.192474246,0.0937420502,-0.1297319233,0.2881348133,0.3061757982,0.2452441901,0.038779363,-0.2685610354,-0.2554307878,0.0633648261,0.1922871321,0.2518091798,0.4446454048,0.1803891063,0.1049195379,0.0316309184,0.0318480134,-0.1561172903,0.1151199043,0.2919997871,0.2558412552,0.1311028153,0.4120824635,0.0967440531,-0.078998588,-0.3894120455,0.1753250211,0.1503109038,-0.3317721486,0.0766265243,-0.2146017402,-0.113947168,-0.1419459134,-0.4253391027,-0.1464539021,-0.1978348494,-0.1058005691,0.2810257375,0.2650622427,0.1936620623,-0.0239940546,0.0500169992,0.0625045747,-0.142483592,-0.0558168627,-0.2506769001,-0.0925878361,0.0015159415,0.327739507,0.246811226,0.3685063124,-0.3038308024,-0.0695626289,0.1437006891,-0.332098037,0.1763451695,-0.005682677,0.2919798791,-0.0134428795,-0.0779709816,-0.3206300437,-0.1939175874,0.0215363186,-0.1346231997,-0.1827463955,0.0472795218,-0.1800287366,-0.2297740728,-0.0019908124,-0.2926879525,-0.4344463348,-0.3998571932,-0.3682101369,0.0472466946,-0.0089030508,0.4603004754,-0.0427159443,0.2933925688,0.2250700146,0.0508381985,0.0179356467,-0.3330911994,0.4733818173,-0.2865113616,-0.2936578393,0.2801209092,-0.2680461109,0.3836089373,0.0181830842,-0.1104436517,-0.2135308832,-0.0214798469,0.3837370574,-0.1009398997,0.0416830219,0.0846360251,0.1136619002,0.0782582983,-0.092406489,-0.1815526336,-0.2040507346,0.2196319252,0.1930779815,-0.1657154113,0.2265984565,-0.0363357775,0.8421141505,0.0771993697,-0.0400505289,0.3015678525,-0.061655391,0.3064921498,-0.1064245924,-0.5121492743,-0.0696846843,-0.1073111445,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2695","title":"Cannot import load_dataset on Colab","comments":"> @phosseini\r\n> I think it is related to [1.10.0](https:\/\/github.com\/huggingface\/datasets\/actions\/runs\/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\n> For now I just downgraded to 1.9.0 and it is working fine.\r\n\r\nSame here, downgraded to 1.9.0 for now and works fine.","body":"## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":41,"text":"Cannot import load_dataset on Colab \n ## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n > @phosseini\r\n> I think it is related to [1.10.0](https:\/\/github.com\/huggingface\/datasets\/actions\/runs\/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\n> For now I just downgraded to 1.9.0 and it is working fine.\r\n\r\nSame here, downgraded to 1.9.0 for now and works 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2695","title":"Cannot import load_dataset on Colab","comments":"Hi, \r\n\r\nupdating tqdm to the newest version resolves the issue for me. You can do this as follows in Colab:\r\n```\r\n!pip install tqdm --upgrade\r\n```","body":"## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":26,"text":"Cannot import load_dataset on Colab \n ## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n Hi, \r\n\r\nupdating tqdm to the newest version resolves the issue for me. You can do this as follows in Colab:\r\n```\r\n!pip install tqdm --upgrade\r\n```","embeddings":[-0.4603253901,-0.2148275971,-0.024375163,0.2682137489,0.1354128718,0.051994808,0.4973769188,-0.1074850261,0.1983702779,0.0849533305,-0.3266307712,0.4753232598,-0.1742698699,0.1487485617,-0.2584459782,0.0984701291,-0.0661861375,0.058203578,-0.2907032669,0.0956151709,0.0202920046,0.3308885992,-0.6335685253,-0.3468577266,-0.5786883831,-0.2100416869,-0.0277418792,0.168791458,-0.2702196836,-0.292011261,0.3478223681,0.1770586967,0.0921896547,0.5697174668,-0.0001172403,0.0715135038,0.3933092654,-0.0787535235,-0.2456898838,-0.3918715715,-0.3461531103,-0.3291964531,0.3686217368,-0.1073928103,0.1058271378,0.404165864,-0.1357311457,-0.133275196,0.2626952529,0.1352794766,0.1936342865,0.549197793,0.1630603671,-0.192492187,-0.1125865728,-0.193378076,-0.2020825595,0.7461523414,0.4174177051,0.0168563686,-0.0510248244,0.2604947388,-0.0992677361,0.2890874445,0.1862412095,-0.0334219635,0.1051012799,-0.4805828929,0.1006372571,0.1743771285,0.4806717336,-0.233426556,0.0443901122,-0.1220246106,0.2094738036,-0.2924674749,0.2619004846,-0.0985698551,-0.2118597776,0.0490080118,-0.0226965509,-0.0896168202,-0.0123074474,0.0164376963,-0.0933856592,0.3655072451,-0.1430076212,-0.1118264869,0.0698661357,-0.1096417829,0.5018094778,-0.0161823332,-0.0268674623,0.2063278407,-0.468000561,-0.0093295779,-0.098870948,-0.1408492327,-0.0516990833,-0.1014948189,-0.0361867324,0.0174666177,-0.1110087708,0.303483665,0.1848023385,0.1422498971,0.0347587094,0.0182988103,0.1226537004,0.2864284515,-0.2007629573,0.227394864,-0.259282738,-0.3118798137,0.1845084578,-0.029214235,0.2715788782,-0.0037106252,-0.319062084,-0.1818456352,0.0095681679,0.105042167,0.0446227603,0.3990536928,0.055795569,0.1057089195,-0.0346219428,0.0016938471,-0.3566534519,-0.2394378334,-0.0538450666,0.1936965287,-0.0642801523,-0.1152990013,0.0678034425,-0.1549987495,0.2601871789,-0.0124206394,0.3092051148,-0.0599603131,0.1036741883,-0.3025387824,-0.0606454387,0.331908226,0.1287349612,0.0872022063,-0.0623135045,-0.2055783719,-0.0778879523,0.2355169952,-0.4770620167,-0.1442362964,-0.0243145749,0.1429465264,-0.1339146942,-0.2244311124,-0.5850232244,0.1648343056,0.1447636336,-0.0812816098,-0.0849623531,-0.3062829673,-0.2330714166,-0.1535831094,0.1236880049,0.4482834041,-0.2309288979,-0.142314136,-0.2176906466,0.1205273345,0.1272705644,0.234823361,-0.2021541893,0.1736088246,-0.0962642506,-0.1171419844,0.490647763,-0.4326361418,-0.4671251178,-0.0203478895,-0.2660758793,-0.0052545536,-0.0897063687,0.0485790744,0.3577234745,-0.0669410899,0.5458390117,0.2629367113,-0.1390330195,-0.036604546,-0.1640905291,-0.4099052548,0.1294252127,0.0418195315,0.1834629029,-0.2177709639,0.203032732,-0.0533746518,0.1012382284,0.1859944463,-0.1211299896,0.3843335509,0.2429013252,-0.2223784029,0.0372738875,-0.1859572828,-0.4775209725,0.2240482867,0.409294188,0.0799716562,-0.0674993992,0.05132493,-0.4381961823,0.0791065395,-0.2594439685,0.0676428005,0.0994127542,-0.0666698813,-0.1751303822,0.0889260471,-0.2604830265,0.7403804064,0.0377074406,0.2175934911,-0.3183411956,0.2157882154,-0.1859039217,-0.3081798851,0.1762920618,0.1361715198,0.0753254518,-0.092272602,-0.2389075756,0.1421211809,0.2554138899,0.1713412106,0.061892774,-0.2776550353,0.4123665094,-0.274011761,0.0123248948,-0.1066840887,0.208570227,0.0058228662,0.2551185191,0.1565714628,-0.3707030118,-0.0410736091,-0.0305118244,0.0905382559,0.3726439178,0.1239964738,-0.0050928039,-0.1856487691,0.325183779,0.2222952098,0.192474246,0.0937420502,-0.1297319233,0.2881348133,0.3061757982,0.2452441901,0.038779363,-0.2685610354,-0.2554307878,0.0633648261,0.1922871321,0.2518091798,0.4446454048,0.1803891063,0.1049195379,0.0316309184,0.0318480134,-0.1561172903,0.1151199043,0.2919997871,0.2558412552,0.1311028153,0.4120824635,0.0967440531,-0.078998588,-0.3894120455,0.1753250211,0.1503109038,-0.3317721486,0.0766265243,-0.2146017402,-0.113947168,-0.1419459134,-0.4253391027,-0.1464539021,-0.1978348494,-0.1058005691,0.2810257375,0.2650622427,0.1936620623,-0.0239940546,0.0500169992,0.0625045747,-0.142483592,-0.0558168627,-0.2506769001,-0.0925878361,0.0015159415,0.327739507,0.246811226,0.3685063124,-0.3038308024,-0.0695626289,0.1437006891,-0.332098037,0.1763451695,-0.005682677,0.2919798791,-0.0134428795,-0.0779709816,-0.3206300437,-0.1939175874,0.0215363186,-0.1346231997,-0.1827463955,0.0472795218,-0.1800287366,-0.2297740728,-0.0019908124,-0.2926879525,-0.4344463348,-0.3998571932,-0.3682101369,0.0472466946,-0.0089030508,0.4603004754,-0.0427159443,0.2933925688,0.2250700146,0.0508381985,0.0179356467,-0.3330911994,0.4733818173,-0.2865113616,-0.2936578393,0.2801209092,-0.2680461109,0.3836089373,0.0181830842,-0.1104436517,-0.2135308832,-0.0214798469,0.3837370574,-0.1009398997,0.0416830219,0.0846360251,0.1136619002,0.0782582983,-0.092406489,-0.1815526336,-0.2040507346,0.2196319252,0.1930779815,-0.1657154113,0.2265984565,-0.0363357775,0.8421141505,0.0771993697,-0.0400505289,0.3015678525,-0.061655391,0.3064921498,-0.1064245924,-0.5121492743,-0.0696846843,-0.10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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2695","title":"Cannot import load_dataset on Colab","comments":"Hi @bayartsogt-ya and @phosseini, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, as pointed out by @mariosasko, you can circumvent this issue by updating the `tqdm` library: \r\n```\r\n!pip install -U tqdm\r\n```","body":"## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n","comment_length":48,"text":"Cannot import load_dataset on Colab \n ## Describe the bug\r\nGot tqdm concurrent module not found error during importing load_dataset from datasets.\r\n\r\n## Steps to reproduce the bug\r\nHere [colab notebook](https:\/\/colab.research.google.com\/drive\/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error\r\n\r\nOn colab:\r\n```python\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n```\r\n\r\n## Expected results\r\nWorks without error\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n```\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-2-8cc7de4c69eb> in <module>()\r\n----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value\r\n      2 from sklearn.metrics import mean_squared_error\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/__init__.py in <module>()\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in <module>()\r\n     40 from tqdm.auto import tqdm\r\n     41 \r\n---> 42 from datasets.tasks.text_classification import TextClassification\r\n     43 \r\n     44 from . import config, utils\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/tasks\/__init__.py in <module>()\r\n      1 from typing import Optional\r\n      2 \r\n----> 3 from ..utils.logging import get_logger\r\n      4 from .automatic_speech_recognition import AutomaticSpeechRecognition\r\n      5 from .base import TaskTemplate\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/__init__.py in <module>()\r\n     19 \r\n     20 from . import logging\r\n---> 21 from .download_manager import DownloadManager, GenerateMode\r\n     22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed\r\n     23 from .mock_download_manager import MockDownloadManager\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/download_manager.py in <module>()\r\n     24 \r\n     25 from .. import config\r\n---> 26 from .file_utils import (\r\n     27     DownloadConfig,\r\n     28     cached_path,\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in <module>()\r\n     25 import posixpath\r\n     26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n     28 \r\n     29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.10.0\r\n- Platform: Colab\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n \n Hi @bayartsogt-ya and @phosseini, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, as pointed out by @mariosasko, you can circumvent this issue by updating the `tqdm` library: \r\n```\r\n!pip install -U tqdm\r\n```","embeddings":[-0.4603253901,-0.2148275971,-0.024375163,0.2682137489,0.1354128718,0.051994808,0.4973769188,-0.1074850261,0.1983702779,0.0849533305,-0.3266307712,0.4753232598,-0.1742698699,0.1487485617,-0.2584459782,0.0984701291,-0.0661861375,0.058203578,-0.2907032669,0.0956151709,0.0202920046,0.3308885992,-0.6335685253,-0.3468577266,-0.5786883831,-0.2100416869,-0.0277418792,0.168791458,-0.2702196836,-0.292011261,0.3478223681,0.1770586967,0.0921896547,0.5697174668,-0.0001172403,0.0715135038,0.3933092654,-0.0787535235,-0.2456898838,-0.3918715715,-0.3461531103,-0.3291964531,0.3686217368,-0.1073928103,0.1058271378,0.404165864,-0.1357311457,-0.133275196,0.2626952529,0.1352794766,0.1936342865,0.549197793,0.1630603671,-0.192492187,-0.1125865728,-0.193378076,-0.2020825595,0.7461523414,0.4174177051,0.0168563686,-0.0510248244,0.2604947388,-0.0992677361,0.2890874445,0.1862412095,-0.0334219635,0.1051012799,-0.4805828929,0.1006372571,0.1743771285,0.4806717336,-0.233426556,0.0443901122,-0.1220246106,0.2094738036,-0.2924674749,0.2619004846,-0.0985698551,-0.2118597776,0.0490080118,-0.0226965509,-0.0896168202,-0.0123074474,0.0164376963,-0.0933856592,0.3655072451,-0.1430076212,-0.1118264869,0.0698661357,-0.1096417829,0.5018094778,-0.0161823332,-0.0268674623,0.2063278407,-0.468000561,-0.0093295779,-0.098870948,-0.1408492327,-0.0516990833,-0.1014948189,-0.0361867324,0.0174666177,-0.1110087708,0.303483665,0.1848023385,0.1422498971,0.0347587094,0.0182988103,0.1226537004,0.2864284515,-0.2007629573,0.227394864,-0.259282738,-0.3118798137,0.1845084578,-0.029214235,0.2715788782,-0.0037106252,-0.319062084,-0.1818456352,0.0095681679,0.105042167,0.0446227603,0.3990536928,0.055795569,0.1057089195,-0.0346219428,0.0016938471,-0.3566534519,-0.2394378334,-0.0538450666,0.1936965287,-0.0642801523,-0.1152990013,0.0678034425,-0.1549987495,0.2601871789,-0.0124206394,0.3092051148,-0.0599603131,0.1036741883,-0.3025387824,-0.0606454387,0.331908226,0.1287349612,0.0872022063,-0.0623135045,-0.2055783719,-0.0778879523,0.2355169952,-0.4770620167,-0.1442362964,-0.0243145749,0.1429465264,-0.1339146942,-0.2244311124,-0.5850232244,0.1648343056,0.1447636336,-0.0812816098,-0.0849623531,-0.3062829673,-0.2330714166,-0.1535831094,0.1236880049,0.4482834041,-0.2309288979,-0.142314136,-0.2176906466,0.1205273345,0.1272705644,0.234823361,-0.2021541893,0.1736088246,-0.0962642506,-0.1171419844,0.490647763,-0.4326361418,-0.4671251178,-0.0203478895,-0.2660758793,-0.0052545536,-0.0897063687,0.0485790744,0.3577234745,-0.0669410899,0.5458390117,0.2629367113,-0.1390330195,-0.036604546,-0.1640905291,-0.4099052548,0.1294252127,0.0418195315,0.1834629029,-0.2177709639,0.203032732,-0.0533746518,0.1012382284,0.1859944463,-0.1211299896,0.3843335509,0.2429013252,-0.2223784029,0.0372738875,-0.1859572828,-0.4775209725,0.2240482867,0.409294188,0.0799716562,-0.0674993992,0.05132493,-0.4381961823,0.0791065395,-0.2594439685,0.0676428005,0.0994127542,-0.0666698813,-0.1751303822,0.0889260471,-0.2604830265,0.7403804064,0.0377074406,0.2175934911,-0.3183411956,0.2157882154,-0.1859039217,-0.3081798851,0.1762920618,0.1361715198,0.0753254518,-0.092272602,-0.2389075756,0.1421211809,0.2554138899,0.1713412106,0.061892774,-0.2776550353,0.4123665094,-0.274011761,0.0123248948,-0.1066840887,0.208570227,0.0058228662,0.2551185191,0.1565714628,-0.3707030118,-0.0410736091,-0.0305118244,0.0905382559,0.3726439178,0.1239964738,-0.0050928039,-0.1856487691,0.325183779,0.2222952098,0.192474246,0.0937420502,-0.1297319233,0.2881348133,0.3061757982,0.2452441901,0.038779363,-0.2685610354,-0.2554307878,0.0633648261,0.1922871321,0.2518091798,0.4446454048,0.1803891063,0.1049195379,0.0316309184,0.0318480134,-0.1561172903,0.1151199043,0.2919997871,0.2558412552,0.1311028153,0.4120824635,0.0967440531,-0.078998588,-0.3894120455,0.1753250211,0.1503109038,-0.3317721486,0.0766265243,-0.2146017402,-0.113947168,-0.1419459134,-0.4253391027,-0.1464539021,-0.1978348494,-0.1058005691,0.2810257375,0.2650622427,0.1936620623,-0.0239940546,0.0500169992,0.0625045747,-0.142483592,-0.0558168627,-0.2506769001,-0.0925878361,0.0015159415,0.327739507,0.246811226,0.3685063124,-0.3038308024,-0.0695626289,0.1437006891,-0.332098037,0.1763451695,-0.005682677,0.2919798791,-0.0134428795,-0.0779709816,-0.3206300437,-0.1939175874,0.0215363186,-0.1346231997,-0.1827463955,0.0472795218,-0.1800287366,-0.2297740728,-0.0019908124,-0.2926879525,-0.4344463348,-0.3998571932,-0.3682101369,0.0472466946,-0.0089030508,0.4603004754,-0.0427159443,0.2933925688,0.2250700146,0.0508381985,0.0179356467,-0.3330911994,0.4733818173,-0.2865113616,-0.2936578393,0.2801209092,-0.2680461109,0.3836089373,0.0181830842,-0.1104436517,-0.2135308832,-0.0214798469,0.3837370574,-0.1009398997,0.0416830219,0.0846360251,0.1136619002,0.0782582983,-0.092406489,-0.1815526336,-0.2040507346,0.2196319252,0.1930779815,-0.1657154113,0.2265984565,-0.0363357775,0.8421141505,0.0771993697,-0.0400505289,0.3015678525,-0.061655391,0.3064921498,-0.1064245924,-0.5121492743,-0.0696846843,-0.1073111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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2691","title":"xtreme \/ pan-x cannot be downloaded","comments":"Hi @severo, thanks for reporting.\r\n\r\nHowever I have not been able to reproduce this issue. Could you please confirm if the problem persists for you?\r\n\r\nMaybe Dropbox (where the data source is hosted) was temporarily unavailable when you tried.","body":"## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":39,"text":"xtreme \/ pan-x cannot be downloaded \n ## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Hi @severo, thanks for reporting.\r\n\r\nHowever I have not been able to reproduce this issue. Could you please confirm if the problem persists for you?\r\n\r\nMaybe Dropbox (where the data source is hosted) was temporarily unavailable when you tried.","embeddings":[-0.352686584,-0.386775732,-0.0510648638,0.2910829186,0.2072106749,0.0486671701,-0.116992861,0.2295708954,0.1450724155,0.0749082938,-0.2071777731,0.2856611907,0.0034074627,0.0860847235,0.196489796,-0.2445528358,0.070219554,0.1196101531,0.0714935958,-0.0395350382,-0.0941303298,0.1315491349,-0.2260093987,0.0777683258,-0.2466472387,0.126108855,0.0916940719,0.2066926062,-0.3236722052,-0.4029152989,0.6594854593,-0.0882715508,0.2548996508,0.6138737202,-0.0001095451,0.0444418713,0.2389015704,-0.0772670582,0.008579297,-0.5894992352,-0.3442205787,-0.4234595001,-0.0681286678,-0.182247594,-0.0317808576,-0.1360592842,-0.1195598543,-0.126975894,0.304120928,0.2758345604,0.2564079463,0.4237165451,0.2048839927,-0.1892297268,0.424253881,0.0090922257,-0.3090731204,0.2538466454,0.4219304025,0.1650082916,0.4848668873,0.2082423866,-0.0695545673,0.0420786776,0.129794836,0.0679554939,-0.1117196828,-0.4720572829,0.0596620217,0.3128014803,0.4577904046,-0.2595132291,-0.3999505937,-0.1368682683,0.1110483035,-0.2919364572,0.3227855265,0.2403816432,-0.0112175364,0.0619956963,0.0464067198,-0.0516867079,-0.0386229977,0.1513980031,-0.042721048,0.2568822503,-0.2622476518,-0.0077699278,0.1031031981,-0.0465255752,0.3317069113,-0.0003910958,-0.0721538886,0.1808130741,-0.2971533835,0.033614397,-0.081234023,-0.1032688469,0.325927943,-0.0659449324,-0.1524967253,-0.0552354939,-0.1848699003,0.0646039173,0.2071894258,0.2051487714,0.0498812981,0.0647757202,0.2026147544,0.1957772225,0.0656497926,-0.0646910891,-0.1810179353,-0.0279590804,0.0963962451,-0.0940713957,0.4540398717,-0.3807743788,-0.3451695442,-0.0102128312,-0.0039379629,0.0608344525,0.1219215095,0.1474575251,-0.0968996659,0.3871399462,-0.104413569,0.3990936875,-0.2333694994,-0.042109821,-0.1832315773,0.353790462,-0.2850381434,-0.201285556,0.2524963915,-0.2233882695,0.3617868721,0.1000485346,0.3133207262,-0.20624955,0.042804569,-0.0248363614,-0.352999717,0.4104738533,0.1801795512,0.2340242118,-0.1464570761,0.0782419667,-0.1296510547,0.1654407978,-0.6251087785,-0.0875181332,-0.098489657,0.1942233145,-0.2081999928,-0.1046866104,-0.6656982303,-0.1676555723,-0.0900125429,0.1431583315,0.0834209844,-0.2053960711,0.1542958617,-0.1790491939,0.2005880773,0.2411441654,-0.2552346289,0.0609929524,-0.1533209234,-0.2647586763,-0.0765785873,0.126275599,-0.2085569054,0.010368458,-0.3422111869,0.323212862,0.4552656114,-0.6140804887,-0.5974395871,-0.1052325368,-0.2622517943,0.0898787528,-0.0617282391,0.1077232808,-0.0357164182,-0.0788005739,0.1014333293,0.2451176792,0.0692812279,-0.0631380528,-0.1651225388,-0.2046420574,-0.3625925779,0.3529902399,0.2083215714,-0.0578963794,0.078610681,0.0761333331,0.0877819508,-0.0318486802,-0.0306234825,0.4155043662,0.3037109375,-0.0001337734,-0.1492662728,-0.36823228,-0.3647809327,0.3155393898,-0.0051267017,-0.2266247869,-0.016196372,-0.0862171724,-0.4500852823,0.1982221305,-0.2209279835,-0.0606530905,0.0769481286,0.0073385714,0.2201862037,0.0836802647,-0.2290256172,0.1458614469,0.0163225271,0.1431554854,-0.4204463959,0.3151150942,-0.048910588,-0.1553777009,0.0702609345,0.0526242554,0.2588835657,-0.2040776908,-0.1541559994,0.2960607111,-0.1171703413,0.0546539836,0.2451883256,0.1121123135,0.1407469213,-0.5683162808,0.2428395599,0.0568337068,0.0048016249,0.1328017712,-0.080193989,0.3285500109,0.0742878169,-0.1284792274,-0.144333154,0.1246474534,0.3701762557,-0.1742715091,0.0623171479,-0.0386976339,0.3798928857,-0.1570974737,0.2001883686,-0.1817019731,-0.3316303194,0.0980749577,0.2502304316,-0.1069736704,0.1860957593,0.1444752514,-0.1026409641,0.1593287289,-0.0966135561,0.1676412821,0.5179623365,0.1285328418,-0.006795112,0.0641476959,0.008289258,-0.1184643656,0.0590544976,0.1046777964,-0.323988378,0.0827619657,0.1612743586,0.1036033481,-0.225023374,-0.1348782033,-0.1345205456,0.0970061123,-0.1478446126,-0.0257360358,-0.1935744584,-0.1062394008,-0.0235615224,-0.062759541,-0.26309219,-0.1395761967,-0.0624651127,0.321655184,0.3296003342,0.1525023878,-0.3904215693,-0.0954002142,0.0352769271,-0.3149357736,-0.2127218843,0.3404073715,-0.0474719182,0.0662847012,0.0962966308,0.0808142498,0.2356974632,-0.370800525,0.2722423375,-0.4419664443,-0.2029943317,0.0966422856,-0.2410521209,0.4696161151,0.1120234504,0.126544103,-0.0322053209,0.2229399383,0.3095308542,-0.4043672681,-0.0039248331,0.0307903998,0.0260589682,-0.1758607477,0.0369341634,-0.1270143539,-0.3618342578,-0.3396022618,0.0283496343,0.080137223,-0.0221556723,0.0101633742,0.0757040083,-0.0299586933,-0.1044041738,-0.1533589661,-0.2170976549,-0.7377814054,0.5196609497,-0.2019710839,-0.4470081627,0.3016469777,0.0556515008,0.2421492785,0.0431420989,-0.5795800686,-0.3328723907,-0.20937334,0.3857634068,0.0262414403,0.172588855,0.179884553,-0.3582758904,0.0249791555,-0.2073284984,-0.240735516,-0.3274493814,0.1376802623,0.3069241941,0.0616082996,0.1685754955,-0.1148670241,0.4480379224,0.1403064281,0.1453653872,0.5500609875,-0.0225298703,0.325284332,-0.1545571536,-0.3528732061,-0.1701274067,0.0941827074,0.119336538,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2691","title":"xtreme \/ pan-x cannot be downloaded","comments":"Hmmm, the file (https:\/\/www.dropbox.com\/s\/dl\/12h3qqog6q4bjve\/panx_dataset.tar) really seems to be unavailable... I tried from various connexions and machines and got the same 404 error. Maybe the dataset has been loaded from the cache in your case?","body":"## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":34,"text":"xtreme \/ pan-x cannot be downloaded \n ## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Hmmm, the file (https:\/\/www.dropbox.com\/s\/dl\/12h3qqog6q4bjve\/panx_dataset.tar) really seems to be unavailable... I tried from various connexions and machines and got the same 404 error. Maybe the dataset has been loaded from the cache in your case?","embeddings":[-0.2679742277,-0.4418168366,-0.1176263168,0.2636517584,0.2272869796,0.1094410568,-0.1848932803,0.2379712462,0.1063496396,0.1472171843,-0.1563756764,0.2612072229,0.0340300761,0.0054809009,0.1887651682,-0.29292804,-0.0231135618,0.157192722,0.085480608,-0.0127716027,-0.04482596,0.1623114944,-0.1777581871,0.1660579741,-0.2347056568,0.0610586517,0.0814340189,0.1718645096,-0.3454205096,-0.4189862609,0.5661595464,-0.0949465558,0.2182051539,0.7195156813,-0.0001037239,0.0292130355,0.2176596671,-0.0261945613,0.0566013306,-0.4336361289,-0.3120516241,-0.2680827677,-0.0547174066,-0.2174528837,-0.1400291026,-0.1007528901,-0.0880559012,-0.1750144213,0.2505108416,0.3105109632,0.3232139647,0.3803195655,0.1769934148,-0.20664078,0.3996114135,-0.094190076,-0.3172445893,0.0890595615,0.2912508249,0.1761108935,0.5273684859,0.3237523437,-0.0359906144,0.0154759418,0.1438701749,0.0410690084,-0.062304955,-0.4709228575,0.0612652376,0.3000105917,0.4271570444,-0.2391207963,-0.3885632753,-0.1054575145,0.1049027145,-0.4078127444,0.2444296181,0.1799363494,-0.016278049,0.1052497625,0.0096645588,-0.0978146642,-0.0419347472,0.0619605519,-0.0776690245,0.2221194804,-0.2184873968,-0.0683026388,0.0588096492,-0.0759769455,0.2078842819,0.0511512533,-0.0275817458,0.1276533753,-0.2452935725,0.0600650944,0.0632193387,-0.1291561872,0.2096273601,-0.0791392401,-0.0892370194,0.0114943245,-0.1473243833,0.0619761087,0.0659476146,0.2439481467,0.0383303538,0.0237593241,0.2091334462,0.1862054467,0.0814153999,-0.1025757566,-0.1737058163,0.0113051254,0.1954597384,-0.0294615906,0.3959265947,-0.3529497981,-0.2808386385,0.0221817661,0.0397591889,0.0408062376,0.0866130665,0.2145688236,-0.0824295655,0.4011310637,-0.1501498818,0.3440098464,-0.2506442964,0.0770461783,-0.2169536054,0.2924671173,-0.2931419313,-0.1526376605,0.1920557916,-0.0812762156,0.3481796086,-0.0350379422,0.3502663672,-0.1696178615,0.1593687534,0.0612697862,-0.2728770971,0.4204449952,0.1726548076,0.1674812585,-0.0856520236,0.0686986148,-0.1584209353,0.0594495535,-0.5414469242,-0.2124148011,-0.0291898306,0.298630625,-0.1243534461,-0.0292220488,-0.5017438531,-0.2424408942,-0.0926839411,0.2001235187,0.0688052848,-0.2365992665,0.1697010547,-0.2065946758,0.1218228713,0.1404036134,-0.1871487647,0.0682941899,-0.0762738213,-0.2601469457,-0.1281082034,0.0846763924,-0.1892635971,0.0538473502,-0.3212268054,0.2798063755,0.5203265548,-0.6607077718,-0.5881974101,-0.1088319495,-0.3328568339,0.0381410457,-0.032900013,0.1168181747,-0.098201707,0.0026601506,0.2206364572,0.2092215717,0.0729324967,-0.0459184907,-0.1469891369,-0.1996123493,-0.3954456747,0.2889850438,0.2185926884,-0.115557,0.0630726442,0.0086837495,0.1266668141,-0.0867342278,-0.0569839217,0.4788995385,0.3095561564,0.0865387619,-0.0792881995,-0.3216750026,-0.2247870266,0.2472707182,-0.0705728233,-0.0963476226,-0.0327415653,-0.1887047589,-0.4533225,0.143108353,-0.3158530295,-0.1230281666,0.1752057672,0.1260690987,0.2419695854,0.0138474563,-0.2411731631,0.0591528639,0.1169226617,0.1251550317,-0.4320315719,0.2213039547,-0.1491878033,-0.1053038836,0.0922812745,0.1673108488,0.2437001467,-0.2250935286,-0.0908466578,0.324670881,-0.096702151,0.0355161875,0.3533547521,0.0978430361,0.0845166221,-0.5908098221,0.2334867269,0.1035210714,-0.0187885165,0.1300654113,-0.0431514904,0.3024262488,0.1514284164,-0.1150512546,-0.0487053618,0.0742619485,0.4298829436,-0.1736487895,0.0714132115,-0.1306804419,0.2847753465,-0.1419808865,0.1040666774,-0.229980275,-0.2976059914,0.2326201349,0.3081139028,-0.1277648509,0.1826978624,0.0562879071,-0.0640333146,0.1722058952,-0.0551215112,0.1123171449,0.3923814595,0.1955979764,0.0783934593,0.0904865712,0.0251341909,-0.0745071098,0.0514602475,0.0309503525,-0.2735628784,0.007225696,0.1577754319,0.0551329926,-0.2244767994,-0.219876349,-0.1738971472,0.126578778,-0.0564300008,-0.0923083276,-0.2825479507,-0.1587558985,-0.0015430114,-0.0670296699,-0.2526369691,-0.2226977199,0.0491071939,0.2205242068,0.2909301519,0.1391824782,-0.4456433356,-0.1586828381,0.1205253229,-0.3013077974,-0.1710583866,0.2445183694,-0.0701966882,0.1309379637,-0.0495745577,-0.0147706587,0.2298841774,-0.403102845,0.2660707533,-0.4210811555,-0.2356017977,0.1419922858,-0.2103907466,0.2974480689,0.1424486339,0.0910667479,-0.0189679265,0.1650261581,0.3228319585,-0.4411507547,-0.0466851778,0.0402265303,0.0551933236,-0.2243185937,0.0146075087,-0.1893948317,-0.3284139037,-0.3596836627,-0.0406748131,0.1506467164,0.0344251506,-0.018506743,0.0803386122,0.0885249823,-0.0685613677,-0.0511880368,-0.2514695823,-0.7017880082,0.4636758566,-0.2644920349,-0.4040011168,0.403811574,0.0343751051,0.2874763906,0.0281600729,-0.6414683461,-0.426507473,-0.2081430703,0.4057197571,0.0910308436,0.1623046994,0.248888731,-0.1939970553,-0.0804983974,-0.1296487451,-0.2731976211,-0.3393236399,0.1231079474,0.351418674,0.0049005449,0.1693899184,-0.0566528216,0.4797626734,0.1411705464,0.2660491765,0.4828196764,0.0074648089,0.4104550183,-0.1721997857,-0.2594890296,-0.1180761755,0.0553667545,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2691","title":"xtreme \/ pan-x cannot be downloaded","comments":"Yes @severo, weird... I could access the file when I answered to you, but now I cannot longer access it either... Maybe it was from the cache as you point out.\r\n\r\nAnyway, I have opened an issue in the GitHub repository responsible for the original dataset: https:\/\/github.com\/afshinrahimi\/mmner\/issues\/4\r\nI have also contacted the maintainer by email.\r\n\r\nI'll keep you informed with their answer.","body":"## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":62,"text":"xtreme \/ pan-x cannot be downloaded \n ## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Yes @severo, weird... I could access the file when I answered to you, but now I cannot longer access it either... Maybe it was from the cache as you point out.\r\n\r\nAnyway, I have opened an issue in the GitHub repository responsible for the original dataset: https:\/\/github.com\/afshinrahimi\/mmner\/issues\/4\r\nI have also contacted the maintainer by email.\r\n\r\nI'll keep you informed with their answer.","embeddings":[-0.3182061911,-0.3272892833,-0.1103755236,0.2835794687,0.2328954488,0.0658764243,-0.0919090956,0.1860670298,0.0463901907,0.2400702983,-0.2260717303,0.2097327858,-0.0343383439,-0.148910597,0.1411536783,-0.233079657,-0.0239754748,0.0649312139,0.0869294629,-0.0030195133,-0.0435504913,0.0999886692,-0.1346054673,0.1627422869,-0.2816883326,0.1641907394,0.1339256316,0.1528057605,-0.361030668,-0.5697411299,0.5499168038,-0.0718998834,0.2162335813,0.6330673099,-0.0001045263,0.0274301097,0.2513790429,-0.0701240972,0.0219657589,-0.3303210735,-0.1703127176,-0.3326670825,-0.0559186675,-0.2326419502,-0.0492845885,-0.1610853821,-0.0430011265,-0.2736641169,0.1877941489,0.3142366409,0.3028452396,0.3407095969,0.197421059,-0.1840539575,0.4177657962,-0.1173002496,-0.2915065885,0.0986323431,0.2888224125,0.2544907928,0.3358385861,0.3467045724,-0.0624667481,0.136175558,0.1152224466,0.1114344373,0.0359211527,-0.4036871791,0.1034546196,0.3053645194,0.3668734133,-0.2257569581,-0.3703781068,-0.128019318,-0.0011336268,-0.413595587,0.2864317298,0.1885604411,-0.0614449792,0.0487341769,0.0590127409,-0.0640952811,-0.0094630001,0.0822293609,-0.236755088,0.3045526147,-0.2398663014,-0.0810085982,0.0611778311,-0.0976329967,0.4266878366,0.1055221632,-0.049861975,0.1894536316,-0.1910706609,0.0861553177,0.0916260183,-0.031315688,0.187951237,-0.0939677805,-0.0870378762,0.0212328956,-0.1213041991,0.0533422381,-0.0236649718,0.2236055732,0.0136994431,0.0123606063,0.3364660442,0.1675424278,-0.0252039582,-0.1039092392,-0.0610516407,0.0210201573,0.3471226394,0.0151840132,0.2679439485,-0.4040792882,-0.1462789476,0.108835727,0.0811567977,0.0040619867,0.0753683075,0.2696229219,-0.1310441941,0.5004304647,-0.143953681,0.277445972,-0.1786134094,-0.0545943379,-0.2305138558,0.287528187,-0.3185658455,-0.1640536487,0.268139869,-0.0885947198,0.342446655,0.0193034634,0.4485731423,-0.2276663333,0.1455594152,-0.039132528,-0.1567942351,0.3950023055,0.2186512351,0.2417788953,-0.0570860915,0.1070095226,-0.1398312002,0.054744076,-0.5332157612,-0.1445197165,0.0484364741,0.2813412249,-0.1109327674,0.0443463624,-0.4615261555,-0.2585163414,0.0184416547,0.0368481353,0.0654701218,-0.2370756119,0.1036681756,-0.2727521062,0.1194799766,0.1861193627,-0.1496269554,0.0811404139,-0.1508214325,-0.1840720326,-0.1036881953,0.0949762687,-0.2324253768,-0.0280532092,-0.343136102,0.1788948774,0.4966636598,-0.5603690147,-0.6766209602,-0.1945018023,-0.216724813,0.1057187915,0.0522407591,0.1284447461,-0.1195854843,-0.075569354,0.116512984,0.2314845324,0.0689336732,-0.0431960672,-0.2029492408,-0.1580140293,-0.3941799104,0.2986288667,0.3304900229,-0.0866440758,0.1213661656,-0.0463840812,0.0500180684,-0.1777494848,0.0538886599,0.4001027644,0.0876315162,0.0499624014,-0.1079476029,-0.3960916698,-0.26929003,0.3049806654,-0.231490761,-0.0901017115,0.042286627,-0.1657970548,-0.3381282389,0.1252348125,-0.2549578846,-0.1380519867,0.19801943,0.217994526,0.210733667,0.0432911031,-0.1856295615,0.2093699574,0.1347272545,0.0454653166,-0.393473357,0.0988777801,-0.1647421569,-0.0848025233,-0.0186090562,0.0318331234,0.2233188301,-0.1131895483,-0.1313253194,0.2966018319,-0.1410743296,0.1947293133,0.1766807586,0.1290343851,0.0698762462,-0.5461265445,0.3711177111,0.0836980268,0.0137755852,0.1292071939,-0.1238272563,0.3975926638,0.2258637846,-0.1046828404,-0.0073532304,0.0574757345,0.400721401,-0.1839652807,0.137681365,-0.2009602338,0.3223162889,-0.0778860152,0.0890906081,-0.1408875883,-0.3618681431,0.2239440829,0.2993720174,-0.0730524808,0.27456972,0.0513551347,-0.2327725291,0.1006511152,-0.1656292975,0.1345457137,0.4002487957,0.1655014306,0.1585409492,0.1127974987,0.0303282104,-0.1219317317,0.0640179291,0.0798129663,-0.3082915545,0.138644591,0.2200647295,0.0478178822,-0.2584237158,-0.0435232669,-0.1759923846,0.0869173855,-0.1290674359,-0.054927662,-0.2891584337,-0.0187092144,-0.0212734994,-0.1296854913,-0.326795578,-0.2894042134,0.0833147317,0.301923573,0.3182380199,0.0738872439,-0.4791664779,-0.1091631353,-0.0258458965,-0.1513308287,-0.2301011682,0.1849558353,-0.0872135237,0.1294902563,-0.0128820883,-0.0319500677,0.2024096251,-0.3810040355,0.2993235588,-0.3908240497,-0.2395477742,0.1227841303,-0.096419245,0.3663290143,0.1242185086,0.0071552503,0.0495898463,0.2909899652,0.3144429326,-0.5699630976,-0.0599179268,-0.0251489207,-0.0103814062,-0.113144584,-0.002694956,-0.213219583,-0.4026871622,-0.3585482538,-0.1287237555,0.2220827937,0.0845836923,-0.1363319308,-0.0353298225,-0.0234601684,-0.0528173819,0.0132873515,-0.3657125235,-0.7164649367,0.5234928131,-0.3666719198,-0.3437446356,0.3153213263,0.0493955836,0.3250024021,0.0266776681,-0.5940799117,-0.4159550369,-0.192096591,0.2497019023,0.07498952,0.1958060116,0.2518181801,-0.2047294676,-0.1169368848,-0.1770107299,-0.1927513331,-0.3299808204,-0.0104139606,0.274867475,-0.0486648791,0.2104867995,-0.0205360781,0.5043942928,-0.0040310412,0.2921929657,0.5138947964,-0.0157214403,0.387863189,-0.1739789844,-0.2270195484,-0.100549981,0.0359631404,0.11801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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2691","title":"xtreme \/ pan-x cannot be downloaded","comments":"Reply from the author\/maintainer: \r\n> Will fix the issue and let you know during the weekend.","body":"## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":16,"text":"xtreme \/ pan-x cannot be downloaded \n ## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n Reply from the author\/maintainer: \r\n> Will fix the issue and let you know during the weekend.","embeddings":[-0.3508202434,-0.4881745875,-0.1012572348,0.2679373324,0.242426917,0.0743881837,-0.1332196444,0.2452753931,0.1442698389,0.1187140942,-0.2436651886,0.2646325231,0.0461617969,0.0465012193,0.1917267889,-0.326759398,0.0447652899,0.1279419363,0.0111566251,-0.0376448408,-0.1420017928,0.1677828431,-0.2066324651,0.1202998906,-0.2243205905,0.1020483896,0.0773360208,0.1569079757,-0.310025692,-0.4581514299,0.6455733776,-0.0633814484,0.2681769133,0.604803741,-0.0001053799,0.0210377499,0.2643092871,-0.0464517288,0.164749071,-0.4361985624,-0.285949707,-0.3566252887,-0.0301674232,-0.2249145359,-0.0407119244,-0.2227444649,-0.1089594513,-0.114901796,0.2539050877,0.224211067,0.294454962,0.3886256516,0.1530675143,-0.2150014043,0.4348991215,-0.0721100867,-0.3141875565,0.1084877253,0.3806249797,0.1838413328,0.464098841,0.2748147547,-0.0413430333,0.0587843582,0.158915177,0.0977327004,-0.0495259948,-0.4637801945,0.038880758,0.3281199038,0.387986362,-0.2812048197,-0.3449712694,-0.0700660646,0.0634025484,-0.3715293407,0.2863736153,0.1802633405,-0.0139233274,0.0406311527,0.0181270111,-0.048152443,-0.0553825833,0.110701099,-0.0341184102,0.2764595449,-0.2252991349,-0.0331717096,0.1091583148,-0.0374769866,0.2385877073,0.086752139,-0.0555851869,0.1689654291,-0.2702018917,0.0684053004,0.007478517,-0.0838781968,0.3481557667,-0.077706039,-0.1982533634,0.0007951256,-0.1292144805,0.0622540116,0.0946067944,0.1408195496,0.0807005838,0.0324028134,0.2074715644,0.1673329324,0.084922336,-0.0790967569,-0.1301611364,-0.0352247357,0.1570245177,-0.041583214,0.3730689585,-0.393217653,-0.2654044032,0.0548107438,0.0834704265,0.0691127703,0.0720816329,0.2562322915,-0.1046686918,0.337223798,-0.1489224434,0.4322533607,-0.220555678,0.0384466723,-0.2084830403,0.3019188046,-0.2957861722,-0.2560020089,0.2096585184,-0.1504617035,0.3913116455,0.0729194656,0.3850796223,-0.199715212,0.0614920445,0.0084678633,-0.309230268,0.3815722466,0.191742301,0.1878200024,-0.1222999021,0.1563587487,-0.1546243429,0.0993809178,-0.5259683728,-0.0668005794,-0.0467678905,0.260764122,-0.1234370545,-0.0680576935,-0.5894210339,-0.1413500905,-0.0553176366,0.1839159578,0.1050052494,-0.2398908734,0.1146361604,-0.2036355883,0.2371749133,0.187357828,-0.2137732953,-0.0268489625,-0.0182714853,-0.3518374264,-0.0915610716,0.1520219445,-0.192415148,-0.019013904,-0.2748942077,0.3359982967,0.475649029,-0.5591967106,-0.5651315451,-0.0963319466,-0.2180439532,0.0007037257,-0.0200067069,0.0962054431,-0.1117716432,-0.0004895465,0.0994872823,0.2488730401,0.0631219074,-0.0226262789,-0.1768683344,-0.2266003489,-0.3862449527,0.3425415158,0.2518791258,-0.0944090635,0.0663152635,-0.0002348693,0.1595528126,-0.083132036,-0.0486579649,0.3730819225,0.2286723554,0.0447254702,-0.1036694273,-0.3814466298,-0.3556773961,0.2895233035,-0.092832014,-0.1557418108,0.0468271151,-0.154229328,-0.4698528647,0.2062272131,-0.2509517968,-0.0803833529,0.132697165,0.0898680985,0.2055639625,0.0326894782,-0.2376073897,0.07682693,0.0526436307,0.1150268391,-0.3940125406,0.2354759574,-0.0872816145,-0.1635206789,0.0684082732,0.0649870113,0.212233156,-0.2378917485,-0.1321241409,0.3139254153,-0.0963537097,0.1405933201,0.241208896,0.0367050841,0.06820032,-0.6196578145,0.2208238542,0.0719355792,-0.0106893228,0.1309360862,-0.0878985301,0.3659490645,0.0959123895,-0.112328954,-0.0786752477,0.0964418203,0.3741547763,-0.1869223863,0.1258788109,-0.0770237446,0.352786392,-0.087723054,0.130599454,-0.2188171744,-0.3352217674,0.0951015875,0.2439258397,-0.0809091628,0.2046398371,0.0871258527,-0.1089674085,0.1696591526,-0.0836644247,0.0586608984,0.4200647771,0.1910061389,0.0222819075,0.109670192,-0.0126072252,-0.1149557158,0.0522856936,0.0696804002,-0.2818588614,0.0545434989,0.110459581,0.0407713465,-0.3076771498,-0.1345253289,-0.1521248221,0.1130894795,-0.1194994673,-0.1115960851,-0.1741676033,-0.0930008516,-0.0067214267,-0.0441843234,-0.2230140716,-0.1807046831,-0.0032339063,0.2508816123,0.3006366789,0.1857052892,-0.4258006215,-0.1767810285,0.0492325723,-0.3311206698,-0.241443634,0.2739618719,-0.0648465529,0.1281394213,0.0012627601,0.1026067287,0.2570173144,-0.3908737004,0.2834922373,-0.3780142367,-0.2129531354,0.0874035731,-0.2608731687,0.3951687813,0.1670734584,0.0618837886,-0.1002265811,0.2385386676,0.3719349504,-0.4895252287,-0.0249968544,0.0311351214,0.0116852922,-0.2028901577,0.0012992581,-0.2291317284,-0.3406861126,-0.3332809806,0.0251754094,0.1601017863,0.0542623773,-0.0320996121,0.0846425891,0.0127804996,-0.1503263116,-0.1215310022,-0.24921,-0.6595410109,0.4708841145,-0.2177618295,-0.4426346123,0.285890162,0.0693380013,0.274273634,0.0797217265,-0.5939115286,-0.3438007236,-0.2070501447,0.4223075509,0.0485908538,0.2106080204,0.1916267872,-0.2807761729,-0.0558228046,-0.1982801408,-0.2254240066,-0.3088796735,0.0396661535,0.2459839135,-0.0177332219,0.2006799877,-0.1411966532,0.4559248388,0.1566533744,0.2250502259,0.5607686043,-0.0784542561,0.2969378531,-0.1781841666,-0.3331464827,-0.0779972598,0.1333859414,0.1393454522,0.00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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2691","title":"xtreme \/ pan-x cannot be downloaded","comments":"The author told that apparently Dropbox has changed their policy and no longer allow downloading the file without having signed in first. The author asked Hugging Face to host their dataset.","body":"## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":31,"text":"xtreme \/ pan-x cannot be downloaded \n ## Describe the bug\r\n\r\nDataset xtreme \/ pan-x cannot be loaded\r\n\r\nSeems related to https:\/\/github.com\/huggingface\/datasets\/pull\/2326\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoad the dataset\r\n\r\n## Actual results\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/www.dropbox.com\/s\/12h3qqog6q4bjve\/panx_dataset.tar?dl=1\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: macOS-11.4-x86_64-i386-64bit\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n \n The author told that apparently Dropbox has changed their policy and no longer allow downloading the file without having signed in first. The author asked Hugging Face to host their dataset.","embeddings":[-0.2759372294,-0.3480373025,-0.059366364,0.3470798731,0.1086417437,0.0554847009,0.0214382652,0.2132673413,0.3107877672,0.068113707,-0.2372275144,0.2172878534,-0.0742678568,0.1829396188,0.2306470424,-0.1670987904,0.018207306,0.0383961536,0.1040748507,-0.1059666947,-0.0068049063,0.0688059554,-0.2352527827,0.0341514982,-0.2067681998,-0.0287322812,0.0926772133,0.3318829536,-0.3699327707,-0.375195086,0.6139099002,-0.0842426792,0.1851760298,0.5841585398,-0.0001120175,0.0652964935,0.1374395192,-0.1021743938,-0.077500619,-0.5789530277,-0.3472652435,-0.4007537663,-0.0861783698,-0.1636051387,-0.0511669517,-0.1117286012,-0.0150200641,-0.1729518473,0.5082504153,0.3695927262,0.2240834236,0.5469501019,0.1720808595,-0.1378317922,0.3611240685,0.1562046111,-0.2377878875,0.4367509186,0.3216775954,0.1157636195,0.411288619,0.17754592,-0.0996762365,-0.1689160168,0.1308137774,0.0133560635,-0.2266700417,-0.4470198154,0.0784026906,0.2077765316,0.3536137342,-0.1338850856,-0.4127598405,-0.3614983559,0.1108664125,-0.1548257917,0.2727961242,0.1956167519,-0.0359452404,0.1448778957,0.0599417686,-0.1014324799,-0.0129188905,0.0593793765,0.0173093267,0.3373428583,-0.2577695251,0.0144006712,0.1092236415,-0.0751527324,0.3978737295,0.0532085076,-0.0479291938,0.1232226193,-0.1411840767,0.0053743836,-0.0540445745,0.0137527417,0.2482397109,0.0194021352,-0.0785555169,-0.080885604,-0.2006908357,0.0241392329,0.1848617345,0.304869771,0.1491407752,-0.1033317223,0.1745783836,0.3312750459,0.196003139,-0.0434585921,-0.2963788509,0.0445669107,-0.0722321942,-0.1512659788,0.5243734717,-0.2715309858,-0.3970927298,-0.019526897,0.0200423934,-0.0885450915,0.1340081245,0.2182936966,-0.1068647355,0.2549786568,-0.07606861,0.2554853559,-0.163050577,-0.1515774131,-0.1914504617,0.405141592,-0.2343253791,-0.1603657901,0.1848150939,-0.2435047626,0.4186853468,0.1288653165,0.3946613073,-0.150030002,0.1027104482,0.0922761708,-0.1681749523,0.3451004326,0.1561530232,0.1269650757,-0.0141464854,-0.234855175,-0.1157528386,0.0571507141,-0.5933461785,-0.1403056085,-0.1993746758,0.1679313332,-0.2249724567,-0.130701378,-0.4889059663,-0.1503004879,-0.1152251363,0.0413695909,0.1745775342,-0.093983531,0.2278492898,-0.1294245869,0.1880383492,0.4361646771,-0.2327737212,0.0334096067,-0.1792382747,-0.099394165,-0.0730862916,0.0946990103,-0.2904944718,-0.0520451032,-0.4483394921,0.1997848898,0.2893691659,-0.7500990033,-0.5598580837,-0.089861609,-0.4706421494,0.2330068946,0.0047258893,0.0929392725,-0.0257564187,-0.1167187244,0.1843946427,0.0955513045,0.1699332446,-0.1224311441,-0.2045544237,-0.2444781512,-0.2624104321,0.2842634618,0.0622716285,0.0681209564,0.0910937339,0.2827214301,0.2098217756,-0.0228558481,-0.0933938175,0.3326901197,0.2923719287,0.0233746152,-0.28684026,-0.2477786839,-0.423281312,0.3309375346,0.0280762967,-0.105102919,-0.0007369105,-0.2265768051,-0.5678087473,0.1613327563,-0.188889429,-0.1785442084,0.067773439,0.1239690706,0.1513993591,0.0496586636,-0.2474329919,0.2233787626,0.0188436583,0.1543605179,-0.5641939044,0.2311494946,-0.1155117154,-0.0604094341,0.035268791,0.0466389135,0.2501816452,-0.2004779428,-0.0556469597,0.3803123236,-0.1920132637,0.0159702394,0.2529326081,0.0522421375,0.1939553469,-0.4988432229,0.243588537,0.0546597093,0.0235157963,0.1672837436,-0.2205287069,0.4448288381,0.0305797495,-0.2373160869,-0.1401200145,0.1262514889,0.2230827957,-0.1306058466,-0.0272244941,-0.1473646462,0.2565690875,-0.2958555818,0.4428711832,-0.1918583959,-0.3940776289,0.0924434811,0.2551046014,-0.0954708979,0.1634525359,0.1062978208,0.0065807784,0.0754789263,0.0918328539,0.1748152524,0.4484563172,0.1287842691,0.0455228947,0.1220075339,0.1126532406,-0.1875597686,0.1822135746,-0.0599472448,-0.4153219759,0.0525691845,0.2299790233,0.0714469552,-0.3545280993,-0.2406418622,-0.1877214909,0.0300265606,-0.2494356334,-0.0179142952,-0.1286338568,-0.2612890899,-0.0283743665,-0.0056921877,-0.3994728625,-0.141236186,-0.1487310529,0.2708365619,0.249328509,0.0064039682,-0.3584647775,0.1185194328,-0.0075160903,-0.1367571652,-0.3121984899,0.3624312282,-0.0285946559,0.048477158,0.1105538458,-0.0293610375,0.2358980477,-0.415718168,0.1052324548,-0.3573950231,-0.3269350231,0.0451696552,-0.2162965387,0.428937763,0.1453777999,0.2217922211,-0.0008089605,0.0529852659,0.2060077935,-0.21674788,-0.0299977288,-0.210040167,0.0807595924,-0.1460517198,0.0060590436,-0.026528053,-0.4058999717,-0.3775566816,0.2012751102,0.1482295543,-0.0348030217,0.2005346715,0.0097332513,0.1122852936,-0.1734445691,-0.1491319835,-0.1994180381,-0.8692612052,0.4091035426,-0.2042848319,-0.3979110122,0.2263697088,0.1963291615,0.1721195132,-0.1776609868,-0.5201548934,-0.4196139276,-0.2218331993,0.4353312254,-0.0021964402,0.137933746,0.2515825629,-0.2824039757,0.0077370708,-0.1384647042,-0.2788476348,-0.2825559378,0.226845935,0.1722354889,-0.0015180076,0.0224355981,-0.0498864055,0.3794842064,0.1308884919,0.161136061,0.5610560179,-0.0744288862,0.4397812784,-0.1766300052,-0.4628420472,-0.1886907965,0.0963781402,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2689","title":"cannot save the dataset to disk after rename_column","comments":"Hi ! That's because you are trying to overwrite a file that is already open and being used.\r\nIndeed `foo\/dataset.arrow` is open and used by your `dataset` object.\r\n\r\nWhen you do `rename_column`, the resulting dataset reads the data from the same arrow file.\r\nIn other cases like when using `map` on the other hand, the resulting dataset reads the data from another arrow file that is the result of the map transform.\r\n\r\nTherefore overwriting a dataset after `rename_column` is not possible, but it is possible after `map`, since `rename_column` doesn't switch to using another arrow file (the actual data stay the same).","body":"## Describe the bug\r\nIf you use `rename_column` and do no other modification, you will be unable to save the dataset using `save_to_disk`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nIn [1]: from datasets import Dataset, load_from_disk\r\nIn [5]: dataset=Dataset.from_dict({'foo': [0]})\r\nIn [7]: dataset.save_to_disk('foo')\r\nIn [8]: dataset=load_from_disk('foo')\r\nIn [10]: dataset=dataset.rename_column('foo', 'bar')\r\nIn [11]: dataset.save_to_disk('foo')\r\n---------------------------------------------------------------------------\r\nPermissionError                           Traceback (most recent call last)\r\n<ipython-input-11-a3bc0d4fc339> in <module>\r\n----> 1 dataset.save_to_disk('foo')\r\n\r\n\/mnt\/beegfs\/projects\/meerqat\/anaconda3\/envs\/meerqat\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in save_to_disk(self, dataset_path\r\n, fs)\r\n    597             if Path(dataset_path, config.DATASET_ARROW_FILENAME) in cache_files_paths:\r\n    598                 raise PermissionError(\r\n--> 599                     f\"Tried to overwrite {Path(dataset_path, config.DATASET_ARROW_FILENAME)} but a dataset can't overwrite itself.\"\r\n    600                 )\r\n    601             if Path(dataset_path, config.DATASET_INDICES_FILENAME) in cache_files_paths:\r\n\r\nPermissionError: Tried to overwrite foo\/dataset.arrow but a dataset can't overwrite itself.\r\n```\r\n\r\nN. B. I created the dataset from dict to enable easy reproduction but the same happens if you load an existing dataset (e.g. starting from `In [8]`)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-3.10.0-1160.11.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":102,"text":"cannot save the dataset to disk after rename_column \n ## Describe the bug\r\nIf you use `rename_column` and do no other modification, you will be unable to save the dataset using `save_to_disk`\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nIn [1]: from datasets import Dataset, load_from_disk\r\nIn [5]: dataset=Dataset.from_dict({'foo': [0]})\r\nIn [7]: dataset.save_to_disk('foo')\r\nIn [8]: dataset=load_from_disk('foo')\r\nIn [10]: dataset=dataset.rename_column('foo', 'bar')\r\nIn [11]: dataset.save_to_disk('foo')\r\n---------------------------------------------------------------------------\r\nPermissionError                           Traceback (most recent call last)\r\n<ipython-input-11-a3bc0d4fc339> in <module>\r\n----> 1 dataset.save_to_disk('foo')\r\n\r\n\/mnt\/beegfs\/projects\/meerqat\/anaconda3\/envs\/meerqat\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in save_to_disk(self, dataset_path\r\n, fs)\r\n    597             if Path(dataset_path, config.DATASET_ARROW_FILENAME) in cache_files_paths:\r\n    598                 raise PermissionError(\r\n--> 599                     f\"Tried to overwrite {Path(dataset_path, config.DATASET_ARROW_FILENAME)} but a dataset can't overwrite itself.\"\r\n    600                 )\r\n    601             if Path(dataset_path, config.DATASET_INDICES_FILENAME) in cache_files_paths:\r\n\r\nPermissionError: Tried to overwrite foo\/dataset.arrow but a dataset can't overwrite itself.\r\n```\r\n\r\nN. B. I created the dataset from dict to enable easy reproduction but the same happens if you load an existing dataset (e.g. starting from `In [8]`)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-3.10.0-1160.11.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n\r\n \n Hi ! That's because you are trying to overwrite a file that is already open and being used.\r\nIndeed `foo\/dataset.arrow` is open and used by your `dataset` object.\r\n\r\nWhen you do `rename_column`, the resulting dataset reads the data from the same arrow file.\r\nIn other cases like when using `map` on the other hand, the resulting dataset reads the data from another arrow file that is the result of the map transform.\r\n\r\nTherefore overwriting a dataset after `rename_column` is not possible, but it is possible after `map`, since `rename_column` doesn't switch to using another arrow file (the actual data stay the same).","embeddings":[-0.0523223989,0.2342098802,-0.0231681652,-0.0133467484,0.3893551528,0.2707957029,0.4929154813,0.246301353,0.0219596829,0.1201097146,-0.0814653933,0.4607105553,-0.1719950885,-0.1512238532,-0.0136408731,-0.0714220777,0.2988217771,-0.1098150685,-0.0071087461,0.1580941379,-0.4297677875,-0.0167975575,-0.0632793829,0.0525315329,-0.1448873281,-0.1060153246,-0.0191482995,0.0699789524,-0.0107616372,-0.2462689877,0.0901805609,-0.0555133633,0.1750023365,0.3935277462,-0.0001104696,-0.1174478754,0.0169522669,-0.0254376344,-0.3840672076,-0.1677256376,-0.2794014215,-0.22320804,-0.1534653902,-0.5357638001,0.2765216529,-0.0022124713,-0.1228611171,-0.3125068247,0.1796944588,0.2632053494,0.294092983,0.1083573475,0.273332864,-0.1635734141,0.0736200586,0.0986325443,-0.3788083792,0.2685394287,-0.2693726122,-0.0911083221,0.1226996332,0.2915157378,-0.1082791165,-0.0690102279,0.1018080711,0.0468242764,0.1152402088,-0.2415997535,0.2724038661,-0.1014997885,0.4159988761,-0.545006156,-0.4493023753,-0.0069589685,0.2386427075,-0.3515321016,0.2436310351,0.1155513525,0.0246032923,0.1335383654,0.0393228084,-0.2681631148,-0.1927657425,0.0060169129,0.0300406311,-0.2012541443,-0.1730899066,0.1861053854,0.0252395608,-0.1277827471,0.3110419214,-0.0618427843,-0.0558179356,0.0676211342,-0.4282257557,-0.0441422723,-0.0765138939,-0.1082729995,-0.1432772726,0.234134987,-0.0629033148,-0.1409233361,0.2153322846,0.2729771733,-0.0386802107,0.2238942534,-0.0026314,0.4684874117,0.3518581092,0.119733721,0.0032541524,-0.1048212945,0.078337796,0.0482479073,0.6009219289,-0.0478974693,0.4625107348,-0.0603512265,-0.3091149926,0.3789649606,0.1044593304,-0.0342989787,0.0012844725,0.2447030544,0.1551560313,0.0356650017,0.0988404751,0.3300382793,0.1512357295,0.167356506,-0.1845288128,-0.0235995818,-0.0770769566,0.1205223575,0.1922730654,0.2134322971,0.0603944696,0.1418900788,-0.1296938509,-0.0818710178,0.063822411,-0.1976366341,0.0316390581,0.3230943084,0.0812872648,0.2417824864,0.1332234889,-0.2870061994,0.0473018102,0.219573319,-0.125742957,-0.1329015791,-0.4467063248,0.2365868688,0.1273117065,0.1414909065,-0.4421401322,-0.2893771231,0.434397608,-0.0966240466,0.2320122719,-0.0182891302,0.0811488405,-0.409035027,0.0521090887,0.221598804,-0.1067450941,0.2233543247,-0.3136855066,0.1113037318,0.1221690103,0.158576414,0.0759357885,-0.0761187598,-0.1343706846,0.0381270684,0.4585378468,-0.3772488832,-0.491268158,-0.1910640746,-0.1587199569,-0.0277118497,0.0377318859,0.0575775579,0.2001122385,-0.1936412901,-0.0350448787,0.1537034363,0.1532455683,0.0386600159,-0.2126651406,-0.0145917758,-0.0151801519,-0.1022223905,-0.0853901356,0.1332870275,0.1747178137,-0.0071969456,0.1526002586,-0.0898960382,0.2490469515,0.2958875,0.4298882782,0.0385020413,-0.0166205857,-0.276522994,-0.6019979119,0.1072554365,0.2519675195,-0.2931177616,-0.3241817653,-0.0773591474,-0.2935066819,0.0149025014,-0.2205447853,0.0091186985,0.1636463404,0.2512086332,0.0141901895,-0.0200967919,-0.245231986,0.2676571906,-0.3195354939,0.1948679537,-0.2083370388,0.201210171,-0.2217801511,-0.2969256938,-0.1804705113,0.1420997977,0.2723119855,0.0925014988,-0.2997994125,0.4652596712,0.0059302798,0.0698818564,-0.129803732,-0.0326074213,-0.1389693618,-0.1473561078,0.0775982514,-0.0047066477,0.2419632226,0.0690333918,-0.362295717,0.0900742859,-0.0637699068,0.2205827683,0.0301657431,0.1150783226,0.1708155274,-0.1170947775,-0.0064452961,-0.2951321304,0.0090424838,0.066455327,0.2198874354,-0.2932216227,-0.1216397136,0.0560927466,0.4650214016,0.064876847,0.1760996878,0.1861424446,-0.0901378095,0.0198986512,-0.0121839065,0.614377141,0.3814362288,0.0090051927,-0.0581259727,-0.0293169972,-0.1447795033,-0.0531236529,0.1978241801,0.010228049,0.1976764053,0.1335366517,0.336348325,0.0209108517,-0.1498912573,-0.1165845022,0.1740596294,0.3704490364,-0.3543183208,0.0263497047,-0.1302931756,0.1459719688,0.0057367785,-0.2700253725,-0.139059633,-0.3707582057,-0.3373607099,0.6752127409,0.0063468511,0.0844874233,-0.1134621948,-0.1232812926,0.0266843792,0.1229012609,-0.3791603148,-0.043895971,0.0552867576,0.0447739065,0.0763220713,-0.2286300957,0.5205156803,-0.0397290662,0.0512861572,-0.5295776129,-0.1521557868,-0.2153201699,-0.127969563,0.3062783182,0.1174204946,0.3138974607,-0.1450230181,-0.2756850421,0.2403118312,0.1121567413,-0.1098957136,0.1784870476,0.1534111351,-0.0302763302,-0.0489839315,-0.1432929337,-0.2808938026,-0.3785196543,0.3248309791,-0.0642383248,0.1248255149,0.0986435562,-0.1798856705,0.1114000976,0.0895826668,0.0878933594,-0.2645496428,-0.1945210695,0.4365966916,-0.238999784,-0.2203456312,0.033036951,0.1541690975,0.1376350522,-0.0165131632,-0.248448804,-0.0653736219,-0.248131305,0.4719092846,-0.3034968078,0.1303994358,0.2934890389,0.3040732443,-0.0327981412,-0.0750491172,-0.2942451537,-0.0698595047,0.2907999754,0.2731865048,-0.1038846597,0.4481271505,-0.109040536,0.2552041411,0.1094086394,0.170207873,0.3902416527,0.0170501973,0.1714869291,-0.2735756636,-0.5204055309,-0.0096631926,-0.2813251019,-0.0576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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2688","title":"hebrew language codes he and iw should be treated as aliases","comments":"Hi @eyaler, thanks for reporting.\r\n\r\nWhile you are true with respect the Hebrew language tag (\"iw\" is deprecated and \"he\" is the preferred value), in the \"mc4\" dataset (which is a derived dataset) we have kept the language tags present in the original dataset: [Google C4](https:\/\/www.tensorflow.org\/datasets\/catalog\/c4).","body":"https:\/\/huggingface.co\/datasets\/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability. ","comment_length":46,"text":"hebrew language codes he and iw should be treated as aliases \n https:\/\/huggingface.co\/datasets\/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability.  \n Hi @eyaler, thanks for reporting.\r\n\r\nWhile you are true with respect the Hebrew language tag (\"iw\" is deprecated and \"he\" is the preferred value), in the \"mc4\" dataset (which is a derived dataset) we have kept the language tags present in the original dataset: [Google C4](https:\/\/www.tensorflow.org\/datasets\/catalog\/c4).","embeddings":[-0.1847832352,-0.0222230945,-0.1543211937,0.0329199657,0.0282858852,0.0720350742,0.5509453416,0.3117233217,0.0522171184,0.1043045521,-0.3407955766,-0.2625412643,0.0178138241,0.0098183285,0.1778062731,0.103592433,0.0763270855,0.0028082454,0.0307024941,-0.259934932,-0.2483024001,0.1667094529,0.0356423743,0.230902493,-0.3309554458,0.07476753,0.235079512,-0.0006581638,0.0945436433,-0.2600386441,-0.050563775,-0.0218149852,-0.1151202247,0.4908214509,-0.0001046046,0.200144574,0.3720237911,-0.1461262107,-0.2577262819,-0.402392596,0.0496585295,-0.0652501434,-0.3410946429,-0.1979911625,-0.3883949816,-0.2093005627,-0.0824905932,-0.425860852,-0.0239149425,0.2144082189,0.2111899704,0.4698423445,-0.1391437799,0.106838204,-0.0199652482,-0.2126427442,-0.0785034969,0.2142321914,0.0535802431,0.5240815878,0.2998705506,0.5605412722,0.2111984044,-0.0797846168,-0.122580193,0.2412062436,-0.1161051169,-0.1545200348,0.2309415787,0.2940369546,0.3640371263,-0.3231701851,-0.1078307852,0.0924717933,-0.0515936539,-0.2096336335,0.4878683686,0.1322401017,-0.0833423212,0.2722433805,-0.0162745751,-0.2900920808,0.02577658,0.2554773986,-0.239437148,0.5161865354,0.100475274,0.0479175523,0.102319859,-0.2073668838,0.2490313947,-0.2282048613,0.1534506381,0.094461903,0.0936851203,-0.2620147467,-0.2162770778,0.0145027619,-0.3406135738,-0.3154471219,0.0759732872,0.2595084012,-0.1825710088,0.0420564152,-0.3105461001,0.0559434555,0.430518508,-0.2673490942,0.452997297,-0.2308996022,0.0026237562,-0.1428270787,-0.0379159562,-0.0601268299,-0.3148117661,-0.1479606628,-0.0055956719,0.298242718,0.022936007,-0.0491462201,-0.4983114302,-0.5706431866,-0.0304256361,0.3789435327,0.1616676897,-0.3083958924,-0.1461149007,-0.0396290906,-0.3200576603,-0.541968286,-0.2043112516,0.1096073166,-0.1466782838,-0.0825254545,0.0807791725,-0.1820848584,0.3079834878,-0.0946761668,0.0314200222,0.1600391567,-0.2420831472,0.0018396663,0.3078255057,0.0659999773,-0.1263970733,0.0821514726,-0.0706280172,-0.2200494111,-0.1961161941,0.1325772256,-0.3130845428,0.1174960509,0.2477213144,0.2029940188,-0.1501318961,-0.004773824,0.062870726,0.2855404615,-0.0142905442,0.0629693493,0.3462482393,-0.0515146144,0.0772701204,-0.1047443822,-0.104922384,0.2785845995,-0.4833551645,-0.4456771016,0.1126218736,-0.0339967497,-0.0810493007,0.5016024113,-0.2918809354,0.172396794,-0.174690783,0.3707315326,0.2921551168,-0.305390507,-0.3107973635,-0.0459155217,0.0442110077,-0.0580154881,-0.1685320586,-0.1529696882,0.0498177521,-0.1401434541,0.0799029768,-0.06562832,0.2023756951,-0.1784927249,-0.0507951081,0.0377850682,0.3984745741,-0.012750457,0.1060536429,-0.0219229851,0.1121249497,-0.0090246676,0.2545722127,-0.1508146524,0.2193585634,0.0614949539,0.0400639139,0.1059669182,0.1841665953,-0.0109765185,-0.1952796727,0.0795870051,-0.1208200753,0.261523813,-0.0632150024,0.0669061467,0.0704118013,-0.1375233829,-0.1428229362,-0.1268767118,0.2377082407,0.035947483,-0.1261019558,0.2591712177,-0.2723131776,0.0940321535,-0.3269435465,0.0285445116,-0.1553587168,0.1628706157,-0.1221369654,0.0393177085,-0.2129364759,0.3802617788,0.1243670732,-0.4671669304,-0.0933214128,0.0844818577,-0.2620695233,0.1656294763,0.1111799106,0.5108319521,0.5489289761,-0.3658974767,0.3155207038,0.1858113557,-0.0510408618,-0.1089917719,0.2349341065,0.2308352292,0.3495796919,0.1166996881,0.1217742115,-0.2267134488,0.4270852506,0.0358429216,-0.1411843598,-0.3088137209,-0.0927452818,0.1146655604,0.5891355872,0.2524322271,-0.1919254959,0.0864684582,1.0686205626,-0.2139691412,-0.0719673559,0.0956165865,-0.2867286205,0.1048127189,0.034709055,-0.1156415418,0.0591344461,0.1322740465,0.2981178164,0.1810235977,0.1942354739,-0.2748283744,0.3244132698,0.2057235241,0.0029113751,-0.003260385,0.0801432282,-0.1853951067,-0.4277556837,0.1569376886,0.0582927838,-0.190404743,-0.227196306,0.2576307952,-0.4687194228,-0.1034525409,-0.363117069,-0.4276822507,-0.5842372179,-0.0740773231,0.0689160675,-0.2383491844,-0.0100949146,0.2999588251,0.3809861243,0.1638946831,-0.3429346383,0.0557869337,-0.2732775211,-0.1871110052,-0.2862946093,0.1406190097,0.116354309,-0.3061376214,0.2784307897,-0.3796431124,-0.2748767734,-0.1865570247,-0.4455599785,0.4134855866,-0.168808952,-0.1180184036,-0.1187332571,-0.0272593647,-0.0475958586,-0.0813059881,-0.0034812698,-0.2797719538,-0.2955234349,-0.2735096216,-0.0544816703,0.0387494825,-0.0833983943,-0.5276796222,-0.6048954129,-0.0471697003,0.0081236148,-0.1959532201,-0.0276988689,0.1241637021,-0.2377651036,-0.1558343023,0.2911198437,-0.2068577558,-0.3472431004,-0.092574589,0.203680858,-0.4344355166,-0.0920788348,0.1560345143,0.0218184832,0.4703041017,0.0739345849,-0.0249648634,0.3892793953,0.0256208591,0.2439542264,0.2953723967,-0.0541684367,0.2268235832,0.005541089,-0.1026123986,-0.3346844316,0.0345104076,0.2723380923,0.0560688637,0.3472976387,0.4592175782,0.1106088683,0.1023152396,0.4332106709,0.1335740983,-0.3373230398,0.317191869,0.194379285,0.3283284009,-0.224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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2688","title":"hebrew language codes he and iw should be treated as aliases","comments":"For discoverability on the website I updated the YAML tags at the top of the mC4 dataset card https:\/\/github.com\/huggingface\/datasets\/commit\/38288087b1b02f97586e0346e8f28f4960f1fd37\r\n\r\nOnce the website is updated, mC4 will be listed in https:\/\/huggingface.co\/datasets?filter=languages:he\r\n\r\n","body":"https:\/\/huggingface.co\/datasets\/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability. ","comment_length":30,"text":"hebrew language codes he and iw should be treated as aliases \n https:\/\/huggingface.co\/datasets\/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability.  \n For discoverability on the website I updated the YAML tags at the top of the mC4 dataset card https:\/\/github.com\/huggingface\/datasets\/commit\/38288087b1b02f97586e0346e8f28f4960f1fd37\r\n\r\nOnce the website is updated, mC4 will be listed in https:\/\/huggingface.co\/datasets?filter=languages:he\r\n\r\n","embeddings":[-0.1667793095,-0.0835691169,-0.0892025903,0.0611215979,0.2324795276,-0.0180359893,0.2219808996,0.4122384489,-0.0158847161,-0.0184125621,-0.3140928745,-0.1698120981,0.0033469584,0.2545406222,0.1932530552,0.1975350529,0.0163776241,-0.0476836413,-0.0980868042,-0.1150792018,-0.1693115979,0.2598110735,0.0212985426,0.1030976996,-0.3453690708,0.181439966,0.093154043,0.2252604961,-0.1960123926,-0.3190945983,-0.0182252023,0.0939656943,-0.0006310958,0.4357659221,-0.0001059205,0.0330444202,0.3800244629,-0.1992282271,-0.3762791753,-0.2892414033,0.0209638663,-0.2393657118,-0.2559764087,-0.0811617821,-0.4343551099,-0.1628268808,-0.1122972742,-0.4339210689,0.1044049785,0.1989211887,0.2612394691,0.3563450873,-0.032994628,-0.0351646729,-0.117643632,0.0228875559,-0.1134202108,0.1792144179,0.1580500305,0.3489226699,0.1483013779,0.54327178,0.2903345823,-0.0764120519,-0.1122616827,0.1147642285,0.0032077453,0.0572590679,0.3240350485,0.14874883,0.3166532815,-0.2415895164,-0.1554276496,-0.0568174794,-0.1319238693,0.0243273973,0.5333500504,0.174634397,-0.1649092883,0.2587651312,-0.2961873412,-0.3385000825,0.1553282291,0.2498288751,-0.0771950483,0.4242895544,-0.0888629854,-0.0967942774,0.2540954351,-0.3760229051,0.2349133193,-0.0890634507,0.066388838,0.119261086,0.1198623553,-0.1865350008,-0.0233282428,0.4402767122,-0.0654596612,-0.1952186078,-0.0206408892,0.4489215314,0.0282775033,-0.1221648157,-0.2154051512,0.0195822045,0.406290561,-0.2921652198,0.4357168972,0.1794189215,0.0299781561,-0.1222584695,-0.0275853407,0.0099782944,-0.3433042467,-0.1535710096,0.0033574468,-0.0158009231,0.05647083,-0.2361889184,-0.3366084099,-0.5890074372,0.0629421026,0.5665414333,0.131193161,-0.3434547186,-0.1309618205,0.026705008,-0.3744470179,-0.547901094,-0.1846733838,0.0057189474,-0.279368192,0.0802297145,0.2298758477,-0.0261130817,0.2447016388,-0.0560064688,0.0425881706,0.1433309019,-0.2035028934,0.2507776916,0.19294779,-0.0188651159,-0.0510998629,0.0914350227,0.0767901465,-0.0955582783,-0.1163833886,-0.2118172497,-0.0828713104,-0.0378749818,0.3168015182,0.213849172,-0.0961016864,0.0332690775,0.1287437528,0.2874478996,-0.2442082614,-0.0501865856,0.0877918005,0.1112369597,0.1360358894,0.0165860951,0.1065112203,0.5581029058,-0.1508264244,-0.4534163177,0.0059125475,0.0684777871,-0.0526169203,0.2169591486,-0.265027225,-0.0396188609,-0.2780256569,0.199904263,0.2216721624,-0.2611607313,-0.3147504032,0.104289934,-0.0508712083,0.078357622,-0.0582682565,-0.2508698106,-0.0913714021,-0.0312877409,0.14336285,-0.0250134896,0.2120948881,-0.1466607153,-0.1613957137,0.0091552874,0.1078615263,0.0310231112,0.0563399568,0.0332748666,0.2991711795,0.032914944,0.304586798,-0.1429280937,0.3440973759,0.1078212783,0.1012447178,0.1082189232,0.1702220887,0.0276004244,-0.1284164637,-0.0133595234,-0.1266474724,0.4452771246,-0.3261275291,0.0027909689,-0.0843923315,-0.164378792,-0.2925625145,-0.2476252466,0.2804225385,0.2389830053,0.1320311278,0.2433049232,-0.0412337109,0.2225045115,-0.047925584,0.0427353047,-0.1773307621,0.1300029159,-0.1718190014,0.088953115,-0.050500121,0.3366715908,0.2604981959,-0.4208653569,-0.0333489552,0.1978801042,-0.2265546322,0.0181902926,0.4222949445,0.3469171226,0.4615173936,-0.4829180837,0.2479090393,0.2108457834,-0.0170133114,-0.0548951961,0.1468935758,0.2738035917,0.1913130879,0.1775021404,0.2303002328,0.0313565098,0.5397229195,0.0832959339,-0.11530637,-0.5012766123,0.0700699836,-0.1024854556,0.5470167398,0.049325645,-0.2217351049,0.2288285494,0.5540849566,-0.071282573,-0.0024071324,0.0742132291,-0.3126943409,0.0342741758,0.1063324362,-0.0146783413,-0.0114304386,0.2696539462,0.1796233952,0.1794149131,0.1878886521,-0.2812915444,0.3473748565,0.1071499512,-0.0631066337,-0.0326031186,0.1392660737,-0.2676081359,-0.5354726911,0.0588487796,-0.1164289638,-0.240103811,-0.2407671958,0.1507294774,-0.4924585223,-0.2797491848,-0.326993376,-0.4875526428,-0.5873590112,-0.1275563836,0.1273369193,-0.2287453264,-0.0304019973,0.2408620417,0.1011902913,0.0555539355,-0.3660441637,0.0910643041,-0.252566278,-0.11768803,-0.2850552797,0.1581056714,0.2618581355,-0.1413695961,0.3274270296,-0.5285533667,-0.1004667431,-0.403367579,-0.5248311758,0.4519945383,-0.0507598296,0.0864281729,0.1022053137,0.0659651458,-0.1033915803,-0.0635006353,0.3658282459,-0.2740926445,-0.2576243877,-0.0471568182,-0.0331300646,-0.0117174881,-0.1012047157,-0.4515068829,-0.4519301355,-0.1368679404,-0.0734952018,0.0303943362,-0.0678286999,0.0535799935,-0.1967331767,-0.119337514,-0.0210138187,-0.0974937975,-0.3391346037,-0.0635035932,0.3806593716,-0.4907185137,-0.2501942813,0.093572624,0.084723033,0.3325749636,-0.0734401569,-0.0107869888,0.2763570845,-0.0586664565,0.1256780922,0.2447043657,0.1293507218,0.2293200344,0.196904704,-0.1986111999,-0.2833264768,-0.0769396573,0.1819898039,-0.221714735,0.5080351233,0.2685968578,0.0753014386,0.0591795444,0.2819244266,0.2316615582,-0.0314475037,0.5248018503,0.0978564918,0.3908880949,-0.10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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2681","title":"5 duplicate datasets","comments":"Yes this was documented in the PR that added this hf->paperswithcode mapping (https:\/\/github.com\/huggingface\/datasets\/pull\/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix\r\n\r\nFor context on the paperswithcode mapping you can also refer to https:\/\/github.com\/huggingface\/huggingface_hub\/pull\/43 which contains a lot of background discussion ","body":"## Describe the bug\r\n\r\nIn 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are:\r\n\r\n- https:\/\/paperswithcode.com\/dataset\/multinli -> https:\/\/huggingface.co\/datasets\/multi_nli and https:\/\/huggingface.co\/datasets\/multi_nli_mismatch\r\n  \r\n  <img width=\"838\" alt=\"Capture d\u2019e\u0301cran 2021-07-20 a\u0300 16 33 58\" src=\"https:\/\/user-images.githubusercontent.com\/1676121\/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png\">\r\n\r\n- https:\/\/paperswithcode.com\/dataset\/squad -> https:\/\/huggingface.co\/datasets\/squad and https:\/\/huggingface.co\/datasets\/squad_v2\r\n- https:\/\/paperswithcode.com\/dataset\/narrativeqa -> https:\/\/huggingface.co\/datasets\/narrativeqa and https:\/\/huggingface.co\/datasets\/narrativeqa_manual\r\n- https:\/\/paperswithcode.com\/dataset\/hate-speech-and-offensive-language -> https:\/\/huggingface.co\/datasets\/hate_offensive and https:\/\/huggingface.co\/datasets\/hate_speech_offensive\r\n- https:\/\/paperswithcode.com\/dataset\/newsph-nli -> https:\/\/huggingface.co\/datasets\/newsph and https:\/\/huggingface.co\/datasets\/newsph_nli\r\n\r\nPossible solutions:\r\n- don't fix (it works)\r\n- for each pair of duplicate datasets, remove one, and create an alias to the other.\r\n\r\n## Steps to reproduce the bug\r\n\r\nVisit the Paperswithcode links, and look at the \"Dataset Loaders\" section\r\n\r\n## Expected results\r\n\r\nThere should only be one reference to a Hugging Face dataset loader\r\n\r\n## Actual results\r\n\r\nTwo Hugging Face dataset loaders\r\n","comment_length":45,"text":"5 duplicate datasets \n ## Describe the bug\r\n\r\nIn 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are:\r\n\r\n- https:\/\/paperswithcode.com\/dataset\/multinli -> https:\/\/huggingface.co\/datasets\/multi_nli and https:\/\/huggingface.co\/datasets\/multi_nli_mismatch\r\n  \r\n  <img width=\"838\" alt=\"Capture d\u2019e\u0301cran 2021-07-20 a\u0300 16 33 58\" src=\"https:\/\/user-images.githubusercontent.com\/1676121\/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png\">\r\n\r\n- https:\/\/paperswithcode.com\/dataset\/squad -> https:\/\/huggingface.co\/datasets\/squad and https:\/\/huggingface.co\/datasets\/squad_v2\r\n- https:\/\/paperswithcode.com\/dataset\/narrativeqa -> https:\/\/huggingface.co\/datasets\/narrativeqa and https:\/\/huggingface.co\/datasets\/narrativeqa_manual\r\n- https:\/\/paperswithcode.com\/dataset\/hate-speech-and-offensive-language -> https:\/\/huggingface.co\/datasets\/hate_offensive and https:\/\/huggingface.co\/datasets\/hate_speech_offensive\r\n- https:\/\/paperswithcode.com\/dataset\/newsph-nli -> https:\/\/huggingface.co\/datasets\/newsph and https:\/\/huggingface.co\/datasets\/newsph_nli\r\n\r\nPossible solutions:\r\n- don't fix (it works)\r\n- for each pair of duplicate datasets, remove one, and create an alias to the other.\r\n\r\n## Steps to reproduce the bug\r\n\r\nVisit the Paperswithcode links, and look at the \"Dataset Loaders\" section\r\n\r\n## Expected results\r\n\r\nThere should only be one reference to a Hugging Face dataset loader\r\n\r\n## Actual results\r\n\r\nTwo Hugging Face dataset loaders\r\n \n Yes this was documented in the PR that added this hf->paperswithcode mapping (https:\/\/github.com\/huggingface\/datasets\/pull\/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix\r\n\r\nFor context on the paperswithcode mapping you can also refer to https:\/\/github.com\/huggingface\/huggingface_hub\/pull\/43 which contains a lot of background discussion 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2679","title":"Cannot load the blog_authorship_corpus due to codec errors","comments":"Hi @izaskr, thanks for reporting.\r\n\r\nHowever the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...\r\n\r\nI'm going to have a look at the dataset anyway...","body":"## Describe the bug\r\nA codec error is raised while loading the blog_authorship_corpus. \r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"blog_authorship_corpus\")\r\n```\r\n\r\n\r\n## Expected results\r\nLoading the dataset without errors.\r\n\r\n## Actual results\r\nAn error similar to the one below was raised for (what seems like) every XML file.\r\n\/home\/izaskr\/.cache\/huggingface\/datasets\/downloads\/extracted\/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a\/blogs\/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte\r\n\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 856, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 583, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 671, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyArrow version: 4.0.1\r\n\r\n","comment_length":43,"text":"Cannot load the blog_authorship_corpus due to codec errors \n ## Describe the bug\r\nA codec error is raised while loading the blog_authorship_corpus. \r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"blog_authorship_corpus\")\r\n```\r\n\r\n\r\n## Expected results\r\nLoading the dataset without errors.\r\n\r\n## Actual results\r\nAn error similar to the one below was raised for (what seems like) every XML file.\r\n\/home\/izaskr\/.cache\/huggingface\/datasets\/downloads\/extracted\/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a\/blogs\/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte\r\n\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 856, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 583, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 671, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyArrow version: 4.0.1\r\n\r\n \n Hi @izaskr, thanks for reporting.\r\n\r\nHowever the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...\r\n\r\nI'm going to have a look at the dataset anyway...","embeddings":[-0.2084893584,0.4780819416,-0.0540075116,0.3998489976,0.3617135882,0.2268402874,0.0785168484,0.4288438559,-0.1586735994,0.2435750514,0.0468282923,0.2679276764,-0.0666647777,-0.210872665,0.0490831248,0.0175640285,-0.0882997364,0.1426831633,0.2282305956,-0.2342811376,-0.2054748684,0.1818809807,-0.1930260658,0.0730281472,-0.2108267397,-0.0789327174,0.1461101174,0.6233423352,-0.1766088754,-0.6049428582,0.1305889636,0.0211400893,0.1271802336,0.3438818455,-0.0001129081,-0.0840206072,0.5609151125,-0.0908816382,-0.2077678293,-0.2428600639,-0.2933349609,0.0893766582,-0.1876998544,-0.2272577435,0.0519394279,0.1707786471,0.043988727,-0.5675269365,0.3152764142,0.5750642419,0.2217924744,-0.0919267312,0.0331094116,-0.0800493881,0.3778606355,0.045461297,0.1114239097,0.4461227059,0.4049824476,-0.106069237,-0.0645619035,0.3080867231,-0.3914480805,-0.0931516141,0.0810792297,-0.1104288027,0.2468281239,-0.3147915006,0.6316772699,0.1682745665,0.7650758624,-0.2163730562,-0.081857644,0.0109544322,0.1951058507,-0.273940444,0.1889482737,0.3846224546,-0.3076028824,0.0981161445,-0.0437706299,-0.0178626496,-0.2440257967,-0.0302386694,-0.1594134271,0.0686632022,-0.0978547037,0.0309896469,-0.2485807091,0.0831889808,0.3431307375,-0.087953411,-0.2779461741,0.3806451559,0.0356344506,0.1754511893,0.1777861267,0.0299991332,-0.0623696409,0.1734724194,-0.0929429084,0.1898393929,0.1581863463,0.2450632006,0.0953593701,-0.052051533,0.1189913675,0.3499027193,0.2957804501,-0.0092535857,-0.0625836328,-0.0884831399,-0.3842275739,-0.385307163,-0.113360554,0.2477751374,0.2131842673,-0.3699801266,-0.4927609563,0.3212743998,-0.0740120709,0.0194718782,-0.0607549623,0.485186547,0.1276381463,0.0470056459,0.2408136576,0.2430116534,-0.1895996183,-0.0786771104,-0.1897735894,0.1530003697,-0.0485872775,-0.0438676737,0.1317065358,-0.5761977434,0.1176543161,-0.0184416268,0.3553464711,-0.320558995,-0.3360753059,-0.0648012385,-0.1094540656,0.0805431306,-0.0559896976,0.0071818228,0.1801805049,-0.3210016191,-0.0618890561,-0.0260087419,-0.1589974463,-0.3241128623,-0.0479485355,0.2177026421,0.0325050503,0.1240164414,-0.2615481317,0.098749727,0.4569669068,-0.1838753968,0.0121197803,-0.0366272479,-0.1992596835,-0.1116067916,0.0685284734,0.7190669775,0.0927176028,0.0617560372,0.4470205903,0.220451206,0.2396672964,-0.0346576385,-0.0303007979,0.194758296,-0.2161441147,0.1940477937,0.173850432,-0.6035790443,-0.0017290334,0.1910260767,0.0441783108,0.1295305341,0.0547363125,-0.3995582759,0.4223296642,-0.3531521559,0.1809348166,0.4003559351,-0.0078087784,0.0422336236,-0.2096998394,-0.0094913309,-0.0592940226,0.1407908052,0.0956548601,0.0371472165,0.4447281957,0.0195302796,0.5081382394,-0.2271026224,-0.0435088165,0.2044648826,-0.0637107193,0.0057082479,-0.0157999657,-0.2292633206,-0.1008971632,0.2268027067,-0.0385448001,-0.0186795779,-0.3047531843,-0.1203672215,-0.3363896608,0.0941970125,-0.1724899709,-0.0096021341,0.1468279064,0.0825056061,0.1227488443,0.211104691,-0.2485478967,0.2506260276,-0.3510062695,0.1294386089,-0.5143997073,0.2901625633,0.0366383158,-0.1802812517,0.3910512626,0.5273911357,0.2063739449,-0.1315859556,-0.2450412661,0.3295736909,-0.2906770408,0.6357571483,-0.3684304059,-0.2102894187,0.1556368917,-0.3921348453,0.1912713796,0.1315571219,0.2919704914,-0.1065121442,0.2268299609,0.1296588629,0.0019443497,0.0616183653,0.1570881456,-0.0124361692,0.3217045665,-0.0014806424,-0.1980579346,-0.306335181,0.6450024843,0.2185741812,0.1565416306,-0.105205372,-0.2416180521,0.0593610816,0.4993925989,-0.0996020436,-0.0267609991,0.035702005,-0.0107226865,0.0234232377,0.0127148628,-0.1461623758,0.1471957862,0.0217034426,-0.1039544493,0.0323545709,0.0716076419,-0.3352051079,0.3919107318,-0.1687837392,0.0142022185,0.4510241747,0.0282918811,0.0123838438,-0.3308029473,-0.1881506145,0.0471752398,0.0889911354,-0.5186877847,0.0840452239,-0.2582147419,-0.2728693783,-0.2343662679,-0.3481079042,-0.0588170327,-0.1013338491,-0.061281193,0.1418228,-0.0765725076,0.0614699721,-0.3226076365,0.1384917498,-0.1622715741,0.1579311192,-0.1613741368,-0.1625829339,-0.1952576339,-0.0374632478,0.4106931388,0.2090358436,0.145612061,-0.3906173706,0.0756685361,-0.1550291479,-0.1967244893,0.1922615021,-0.0072839498,0.0445013195,-0.096276179,-0.1030256748,0.6314195991,-0.1873525977,0.0774794072,0.0403758325,-0.00959244,0.4120475352,0.1020493954,-0.4308469594,-0.0713257641,-0.239278838,-0.3840438426,-0.5113678575,-0.2183594406,0.0891492143,0.2631490529,0.4002134502,-0.2125230432,-0.0471867919,-0.0296742711,0.3316637278,-0.1299691945,-0.2295024246,0.203759864,-0.2909738719,-0.3221518993,0.1063784137,0.0682759359,0.3978834748,-0.0266790073,-0.2990857959,0.200989604,0.0736920312,0.0807962045,-0.1829186678,-0.0326646566,0.1681961864,0.0659452304,0.0074228281,-0.076543875,0.0337187611,-0.3264494538,-0.0253692903,-0.1287384629,-0.0559628531,0.2026890516,-0.1725133657,0.061919529,0.4852911234,0.0982005894,0.2867949903,0.0515121557,0.21362333,0.0147207957,-0.2460832298,0.4707962275,-0.19306546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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2679","title":"Cannot load the blog_authorship_corpus due to codec errors","comments":"Hi @izaskr, thanks again for having reported this issue.\r\n\r\nAfter investigation, I have created a Pull Request (#2685) to fix several issues with this dataset:\r\n- the `NonMatchingSplitsSizesError`\r\n- the `UnicodeDecodeError`\r\n\r\nOnce the Pull Request merged into master, you will be able to load this dataset if you install `datasets` from our GitHub repository master branch. Otherwise, you will be able to use it after our next release, by updating `datasets`: `pip install -U datasets`.","body":"## Describe the bug\r\nA codec error is raised while loading the blog_authorship_corpus. \r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"blog_authorship_corpus\")\r\n```\r\n\r\n\r\n## Expected results\r\nLoading the dataset without errors.\r\n\r\n## Actual results\r\nAn error similar to the one below was raised for (what seems like) every XML file.\r\n\/home\/izaskr\/.cache\/huggingface\/datasets\/downloads\/extracted\/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a\/blogs\/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte\r\n\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 856, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 583, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 671, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyArrow version: 4.0.1\r\n\r\n","comment_length":75,"text":"Cannot load the blog_authorship_corpus due to codec errors \n ## Describe the bug\r\nA codec error is raised while loading the blog_authorship_corpus. \r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"blog_authorship_corpus\")\r\n```\r\n\r\n\r\n## Expected results\r\nLoading the dataset without errors.\r\n\r\n## Actual results\r\nAn error similar to the one below was raised for (what seems like) every XML file.\r\n\/home\/izaskr\/.cache\/huggingface\/datasets\/downloads\/extracted\/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a\/blogs\/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte\r\n\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 856, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 583, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 671, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyArrow version: 4.0.1\r\n\r\n \n Hi @izaskr, thanks again for having reported this issue.\r\n\r\nAfter investigation, I have created a Pull Request (#2685) to fix several issues with this dataset:\r\n- the `NonMatchingSplitsSizesError`\r\n- the `UnicodeDecodeError`\r\n\r\nOnce the Pull Request merged into master, you will be able to load this dataset if you install `datasets` from our GitHub repository master branch. Otherwise, you will be able to use it after our next release, by updating `datasets`: `pip install -U datasets`.","embeddings":[-0.2084893584,0.4780819416,-0.0540075116,0.3998489976,0.3617135882,0.2268402874,0.0785168484,0.4288438559,-0.1586735994,0.2435750514,0.0468282923,0.2679276764,-0.0666647777,-0.210872665,0.0490831248,0.0175640285,-0.0882997364,0.1426831633,0.2282305956,-0.2342811376,-0.2054748684,0.1818809807,-0.1930260658,0.0730281472,-0.2108267397,-0.0789327174,0.1461101174,0.6233423352,-0.1766088754,-0.6049428582,0.1305889636,0.0211400893,0.1271802336,0.3438818455,-0.0001129081,-0.0840206072,0.5609151125,-0.0908816382,-0.2077678293,-0.2428600639,-0.2933349609,0.0893766582,-0.1876998544,-0.2272577435,0.0519394279,0.1707786471,0.043988727,-0.5675269365,0.3152764142,0.5750642419,0.2217924744,-0.0919267312,0.0331094116,-0.0800493881,0.3778606355,0.045461297,0.1114239097,0.4461227059,0.4049824476,-0.106069237,-0.0645619035,0.3080867231,-0.3914480805,-0.0931516141,0.0810792297,-0.1104288027,0.2468281239,-0.3147915006,0.6316772699,0.1682745665,0.7650758624,-0.2163730562,-0.081857644,0.0109544322,0.1951058507,-0.273940444,0.1889482737,0.3846224546,-0.3076028824,0.0981161445,-0.0437706299,-0.0178626496,-0.2440257967,-0.0302386694,-0.1594134271,0.0686632022,-0.0978547037,0.0309896469,-0.2485807091,0.0831889808,0.3431307375,-0.087953411,-0.2779461741,0.3806451559,0.0356344506,0.1754511893,0.1777861267,0.0299991332,-0.0623696409,0.1734724194,-0.0929429084,0.1898393929,0.1581863463,0.2450632006,0.0953593701,-0.052051533,0.1189913675,0.3499027193,0.2957804501,-0.0092535857,-0.0625836328,-0.0884831399,-0.3842275739,-0.385307163,-0.113360554,0.2477751374,0.2131842673,-0.3699801266,-0.4927609563,0.3212743998,-0.0740120709,0.0194718782,-0.0607549623,0.485186547,0.1276381463,0.0470056459,0.2408136576,0.2430116534,-0.1895996183,-0.0786771104,-0.1897735894,0.1530003697,-0.0485872775,-0.0438676737,0.1317065358,-0.5761977434,0.1176543161,-0.0184416268,0.3553464711,-0.320558995,-0.3360753059,-0.0648012385,-0.1094540656,0.0805431306,-0.0559896976,0.0071818228,0.1801805049,-0.3210016191,-0.0618890561,-0.0260087419,-0.1589974463,-0.3241128623,-0.0479485355,0.2177026421,0.0325050503,0.1240164414,-0.2615481317,0.098749727,0.4569669068,-0.1838753968,0.0121197803,-0.0366272479,-0.1992596835,-0.1116067916,0.0685284734,0.7190669775,0.0927176028,0.0617560372,0.4470205903,0.220451206,0.2396672964,-0.0346576385,-0.0303007979,0.194758296,-0.2161441147,0.1940477937,0.173850432,-0.6035790443,-0.0017290334,0.1910260767,0.0441783108,0.1295305341,0.0547363125,-0.3995582759,0.4223296642,-0.3531521559,0.1809348166,0.4003559351,-0.0078087784,0.0422336236,-0.2096998394,-0.0094913309,-0.0592940226,0.1407908052,0.0956548601,0.0371472165,0.4447281957,0.0195302796,0.5081382394,-0.2271026224,-0.0435088165,0.2044648826,-0.0637107193,0.0057082479,-0.0157999657,-0.2292633206,-0.1008971632,0.2268027067,-0.0385448001,-0.0186795779,-0.3047531843,-0.1203672215,-0.3363896608,0.0941970125,-0.1724899709,-0.0096021341,0.1468279064,0.0825056061,0.1227488443,0.211104691,-0.2485478967,0.2506260276,-0.3510062695,0.1294386089,-0.5143997073,0.2901625633,0.0366383158,-0.1802812517,0.3910512626,0.5273911357,0.2063739449,-0.1315859556,-0.2450412661,0.3295736909,-0.2906770408,0.6357571483,-0.3684304059,-0.2102894187,0.1556368917,-0.3921348453,0.1912713796,0.1315571219,0.2919704914,-0.1065121442,0.2268299609,0.1296588629,0.0019443497,0.0616183653,0.1570881456,-0.0124361692,0.3217045665,-0.0014806424,-0.1980579346,-0.306335181,0.6450024843,0.2185741812,0.1565416306,-0.105205372,-0.2416180521,0.0593610816,0.4993925989,-0.0996020436,-0.0267609991,0.035702005,-0.0107226865,0.0234232377,0.0127148628,-0.1461623758,0.1471957862,0.0217034426,-0.1039544493,0.0323545709,0.0716076419,-0.3352051079,0.3919107318,-0.1687837392,0.0142022185,0.4510241747,0.0282918811,0.0123838438,-0.3308029473,-0.1881506145,0.0471752398,0.0889911354,-0.5186877847,0.0840452239,-0.2582147419,-0.2728693783,-0.2343662679,-0.3481079042,-0.0588170327,-0.1013338491,-0.061281193,0.1418228,-0.0765725076,0.0614699721,-0.3226076365,0.1384917498,-0.1622715741,0.1579311192,-0.1613741368,-0.1625829339,-0.1952576339,-0.0374632478,0.4106931388,0.2090358436,0.145612061,-0.3906173706,0.0756685361,-0.1550291479,-0.1967244893,0.1922615021,-0.0072839498,0.0445013195,-0.096276179,-0.1030256748,0.6314195991,-0.1873525977,0.0774794072,0.0403758325,-0.00959244,0.4120475352,0.1020493954,-0.4308469594,-0.0713257641,-0.239278838,-0.3840438426,-0.5113678575,-0.2183594406,0.0891492143,0.2631490529,0.4002134502,-0.2125230432,-0.0471867919,-0.0296742711,0.3316637278,-0.1299691945,-0.2295024246,0.203759864,-0.2909738719,-0.3221518993,0.1063784137,0.0682759359,0.3978834748,-0.0266790073,-0.2990857959,0.200989604,0.0736920312,0.0807962045,-0.1829186678,-0.0326646566,0.1681961864,0.0659452304,0.0074228281,-0.076543875,0.0337187611,-0.3264494538,-0.0253692903,-0.1287384629,-0.0559628531,0.2026890516,-0.1725133657,0.061919529,0.4852911234,0.0982005894,0.2867949903,0.0515121557,0.21362333,0.0147207957,-0.2460832298,0.4707962275,-0.1930654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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2679","title":"Cannot load the blog_authorship_corpus due to codec errors","comments":"@albertvillanova \r\nCan you shed light on how this fix works?\r\n\r\nWe're experiencing a similar issue. \r\n\r\nIf we run several runs (eg in a Wandb sweep) the first run \"works\" but then we get `NonMatchingSplitsSizesError`\r\n\r\n| run num | actual train examples # | expected example # | recorded example # |\r\n| ------- | -------------- | ----------------- | -------- |\r\n| 1       | 100            | 100               | 100      |\r\n| 2       | 102            | 100               | 102      |\r\n| 3       | 100            | 100               | 202      | \r\n| 4       | 40             | 100               | 40       |\r\n| 5       | 40             | 100               | 40       |\r\n| 6       | 40             | 100               | 40       | \r\n\r\n\r\nThe second through the nth all crash with \r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=19980970, num_examples=100, dataset_name='cies'), 'recorded': SplitInfo(name='train', num_bytes=40163811, num_examples=202, dataset_name='cies')}]\r\n\r\n```","body":"## Describe the bug\r\nA codec error is raised while loading the blog_authorship_corpus. \r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"blog_authorship_corpus\")\r\n```\r\n\r\n\r\n## Expected results\r\nLoading the dataset without errors.\r\n\r\n## Actual results\r\nAn error similar to the one below was raised for (what seems like) every XML file.\r\n\/home\/izaskr\/.cache\/huggingface\/datasets\/downloads\/extracted\/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a\/blogs\/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte\r\n\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 856, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 583, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 671, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyArrow version: 4.0.1\r\n\r\n","comment_length":135,"text":"Cannot load the blog_authorship_corpus due to codec errors \n ## Describe the bug\r\nA codec error is raised while loading the blog_authorship_corpus. \r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom datasets import load_dataset\r\nraw_datasets = load_dataset(\"blog_authorship_corpus\")\r\n```\r\n\r\n\r\n## Expected results\r\nLoading the dataset without errors.\r\n\r\n## Actual results\r\nAn error similar to the one below was raised for (what seems like) every XML file.\r\n\/home\/izaskr\/.cache\/huggingface\/datasets\/downloads\/extracted\/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a\/blogs\/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte\r\n\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 856, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 583, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 671, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/home\/izaskr\/anaconda3\/envs\/local_vae_older\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 74, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyArrow version: 4.0.1\r\n\r\n \n @albertvillanova \r\nCan you shed light on how this fix works?\r\n\r\nWe're experiencing a similar issue. \r\n\r\nIf we run several runs (eg in a Wandb sweep) the first run \"works\" but then we get `NonMatchingSplitsSizesError`\r\n\r\n| run num | actual train examples # | expected example # | recorded example # |\r\n| ------- | -------------- | ----------------- | -------- |\r\n| 1       | 100            | 100               | 100      |\r\n| 2       | 102            | 100               | 102      |\r\n| 3       | 100            | 100               | 202      | \r\n| 4       | 40             | 100               | 40       |\r\n| 5       | 40             | 100               | 40       |\r\n| 6       | 40             | 100               | 40       | \r\n\r\n\r\nThe second through the nth all crash with \r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=19980970, num_examples=100, dataset_name='cies'), 'recorded': SplitInfo(name='train', num_bytes=40163811, num_examples=202, dataset_name='cies')}]\r\n\r\n```","embeddings":[-0.2084893584,0.4780819416,-0.0540075116,0.3998489976,0.3617135882,0.2268402874,0.0785168484,0.4288438559,-0.1586735994,0.2435750514,0.0468282923,0.2679276764,-0.0666647777,-0.210872665,0.0490831248,0.0175640285,-0.0882997364,0.1426831633,0.2282305956,-0.2342811376,-0.2054748684,0.1818809807,-0.1930260658,0.0730281472,-0.2108267397,-0.0789327174,0.1461101174,0.6233423352,-0.1766088754,-0.6049428582,0.1305889636,0.0211400893,0.1271802336,0.3438818455,-0.0001129081,-0.0840206072,0.5609151125,-0.0908816382,-0.2077678293,-0.2428600639,-0.2933349609,0.0893766582,-0.1876998544,-0.2272577435,0.0519394279,0.1707786471,0.043988727,-0.5675269365,0.3152764142,0.5750642419,0.2217924744,-0.0919267312,0.0331094116,-0.0800493881,0.3778606355,0.045461297,0.1114239097,0.4461227059,0.4049824476,-0.106069237,-0.0645619035,0.3080867231,-0.3914480805,-0.0931516141,0.0810792297,-0.1104288027,0.2468281239,-0.3147915006,0.6316772699,0.1682745665,0.7650758624,-0.2163730562,-0.081857644,0.0109544322,0.1951058507,-0.273940444,0.1889482737,0.3846224546,-0.3076028824,0.0981161445,-0.0437706299,-0.0178626496,-0.2440257967,-0.0302386694,-0.1594134271,0.0686632022,-0.0978547037,0.0309896469,-0.2485807091,0.0831889808,0.3431307375,-0.087953411,-0.2779461741,0.3806451559,0.0356344506,0.1754511893,0.1777861267,0.0299991332,-0.0623696409,0.1734724194,-0.0929429084,0.1898393929,0.1581863463,0.2450632006,0.0953593701,-0.052051533,0.1189913675,0.3499027193,0.2957804501,-0.0092535857,-0.0625836328,-0.0884831399,-0.3842275739,-0.385307163,-0.113360554,0.2477751374,0.2131842673,-0.3699801266,-0.4927609563,0.3212743998,-0.0740120709,0.0194718782,-0.0607549623,0.485186547,0.1276381463,0.0470056459,0.2408136576,0.2430116534,-0.1895996183,-0.0786771104,-0.1897735894,0.1530003697,-0.0485872775,-0.0438676737,0.1317065358,-0.5761977434,0.1176543161,-0.0184416268,0.3553464711,-0.320558995,-0.3360753059,-0.0648012385,-0.1094540656,0.0805431306,-0.0559896976,0.0071818228,0.1801805049,-0.3210016191,-0.0618890561,-0.0260087419,-0.1589974463,-0.3241128623,-0.0479485355,0.2177026421,0.0325050503,0.1240164414,-0.2615481317,0.098749727,0.4569669068,-0.1838753968,0.0121197803,-0.0366272479,-0.1992596835,-0.1116067916,0.0685284734,0.7190669775,0.0927176028,0.0617560372,0.4470205903,0.220451206,0.2396672964,-0.0346576385,-0.0303007979,0.194758296,-0.2161441147,0.1940477937,0.173850432,-0.6035790443,-0.0017290334,0.1910260767,0.0441783108,0.1295305341,0.0547363125,-0.3995582759,0.4223296642,-0.3531521559,0.1809348166,0.4003559351,-0.0078087784,0.0422336236,-0.2096998394,-0.0094913309,-0.0592940226,0.1407908052,0.0956548601,0.0371472165,0.4447281957,0.0195302796,0.5081382394,-0.2271026224,-0.0435088165,0.2044648826,-0.0637107193,0.0057082479,-0.0157999657,-0.2292633206,-0.1008971632,0.2268027067,-0.0385448001,-0.0186795779,-0.3047531843,-0.1203672215,-0.3363896608,0.0941970125,-0.1724899709,-0.0096021341,0.1468279064,0.0825056061,0.1227488443,0.211104691,-0.2485478967,0.2506260276,-0.3510062695,0.1294386089,-0.5143997073,0.2901625633,0.0366383158,-0.1802812517,0.3910512626,0.5273911357,0.2063739449,-0.1315859556,-0.2450412661,0.3295736909,-0.2906770408,0.6357571483,-0.3684304059,-0.2102894187,0.1556368917,-0.3921348453,0.1912713796,0.1315571219,0.2919704914,-0.1065121442,0.2268299609,0.1296588629,0.0019443497,0.0616183653,0.1570881456,-0.0124361692,0.3217045665,-0.0014806424,-0.1980579346,-0.306335181,0.6450024843,0.2185741812,0.1565416306,-0.105205372,-0.2416180521,0.0593610816,0.4993925989,-0.0996020436,-0.0267609991,0.035702005,-0.0107226865,0.0234232377,0.0127148628,-0.1461623758,0.1471957862,0.0217034426,-0.1039544493,0.0323545709,0.0716076419,-0.3352051079,0.3919107318,-0.1687837392,0.0142022185,0.4510241747,0.0282918811,0.0123838438,-0.3308029473,-0.1881506145,0.0471752398,0.0889911354,-0.5186877847,0.0840452239,-0.2582147419,-0.2728693783,-0.2343662679,-0.3481079042,-0.0588170327,-0.1013338491,-0.061281193,0.1418228,-0.0765725076,0.0614699721,-0.3226076365,0.1384917498,-0.1622715741,0.1579311192,-0.1613741368,-0.1625829339,-0.1952576339,-0.0374632478,0.4106931388,0.2090358436,0.145612061,-0.3906173706,0.0756685361,-0.1550291479,-0.1967244893,0.1922615021,-0.0072839498,0.0445013195,-0.096276179,-0.1030256748,0.6314195991,-0.1873525977,0.0774794072,0.0403758325,-0.00959244,0.4120475352,0.1020493954,-0.4308469594,-0.0713257641,-0.239278838,-0.3840438426,-0.5113678575,-0.2183594406,0.0891492143,0.2631490529,0.4002134502,-0.2125230432,-0.0471867919,-0.0296742711,0.3316637278,-0.1299691945,-0.2295024246,0.203759864,-0.2909738719,-0.3221518993,0.1063784137,0.0682759359,0.3978834748,-0.0266790073,-0.2990857959,0.200989604,0.0736920312,0.0807962045,-0.1829186678,-0.0326646566,0.1681961864,0.0659452304,0.0074228281,-0.076543875,0.0337187611,-0.3264494538,-0.0253692903,-0.1287384629,-0.0559628531,0.2026890516,-0.1725133657,0.061919529,0.4852911234,0.0982005894,0.2867949903,0.0515121557,0.21362333,0.0147207957,-0.2460832298,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2678","title":"Import Error in Kaggle notebook","comments":"@lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.\r\n\r\nEdit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstalling will change anything.\r\n```\r\nInstall Trace of datasets:\r\n\r\nCollecting datasets\r\n  Downloading datasets-1.9.0-py3-none-any.whl (262 kB)\r\n     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 262 kB 834 kB\/s eta 0:00:01\r\nRequirement already satisfied: dill in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (0.3.4)\r\nCollecting pyarrow!=4.0.0,>=1.0.0\r\n  Downloading pyarrow-4.0.1-cp37-cp37m-manylinux2014_x86_64.whl (21.8 MB)\r\n     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 21.8 MB 6.2 MB\/s eta 0:00:01\r\nRequirement already satisfied: importlib-metadata in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (3.4.0)\r\nRequirement already satisfied: huggingface-hub<0.1.0 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (0.0.8)\r\nRequirement already satisfied: pandas in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (1.2.4)\r\nRequirement already satisfied: requests>=2.19.0 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: fsspec>=2021.05.0 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (2021.6.1)\r\nRequirement already satisfied: multiprocess in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: packaging in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (20.9)\r\nCollecting xxhash\r\n  Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)\r\n     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 243 kB 23.7 MB\/s eta 0:00:01\r\nRequirement already satisfied: numpy>=1.17 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: tqdm>=4.27 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (4.61.1)\r\nRequirement already satisfied: filelock in \/opt\/conda\/lib\/python3.7\/site-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in \/opt\/conda\/lib\/python3.7\/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in \/opt\/conda\/lib\/python3.7\/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: certifi>=2017.4.17 in \/opt\/conda\/lib\/python3.7\/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in \/opt\/conda\/lib\/python3.7\/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: typing-extensions>=3.6.4 in \/opt\/conda\/lib\/python3.7\/site-packages (from importlib-metadata->datasets) (3.7.4.3)\r\nRequirement already satisfied: zipp>=0.5 in \/opt\/conda\/lib\/python3.7\/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: pyparsing>=2.0.2 in \/opt\/conda\/lib\/python3.7\/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in \/opt\/conda\/lib\/python3.7\/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.3 in \/opt\/conda\/lib\/python3.7\/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: six>=1.5 in \/opt\/conda\/lib\/python3.7\/site-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\r\nInstalling collected packages: xxhash, pyarrow, datasets\r\n  Attempting uninstall: pyarrow\r\n    Found existing installation: pyarrow 4.0.0\r\n    Uninstalling pyarrow-4.0.0:\r\n      Successfully uninstalled pyarrow-4.0.0\r\nSuccessfully installed datasets-1.9.0 pyarrow-4.0.1 xxhash-2.0.2\r\nWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https:\/\/pip.pypa.io\/warnings\/venv\r\n```","body":"## Describe the bug\r\nNot able to import datasets library in kaggle notebooks\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n!pip install datasets\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nNo such error\r\n\r\n## Actual results\r\n```\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-9-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     36 import pandas as pd\r\n     37 import pyarrow as pa\r\n---> 38 import pyarrow.compute as pc\r\n     39 from multiprocess import Pool, RLock\r\n     40 from tqdm.auto import tqdm\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/compute.py in <module>\r\n     16 # under the License.\r\n     17 \r\n---> 18 from pyarrow._compute import (  # noqa\r\n     19     Function,\r\n     20     FunctionOptions,\r\n\r\nImportError: \/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Kaggle\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":322,"text":"Import Error in Kaggle notebook \n ## Describe the bug\r\nNot able to import datasets library in kaggle notebooks\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n!pip install datasets\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nNo such error\r\n\r\n## Actual results\r\n```\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-9-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     36 import pandas as pd\r\n     37 import pyarrow as pa\r\n---> 38 import pyarrow.compute as pc\r\n     39 from multiprocess import Pool, RLock\r\n     40 from tqdm.auto import tqdm\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/compute.py in <module>\r\n     16 # under the License.\r\n     17 \r\n---> 18 from pyarrow._compute import (  # noqa\r\n     19     Function,\r\n     20     FunctionOptions,\r\n\r\nImportError: \/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Kaggle\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n \n @lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.\r\n\r\nEdit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstalling will change anything.\r\n```\r\nInstall Trace of datasets:\r\n\r\nCollecting datasets\r\n  Downloading datasets-1.9.0-py3-none-any.whl (262 kB)\r\n     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 262 kB 834 kB\/s eta 0:00:01\r\nRequirement already satisfied: dill in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (0.3.4)\r\nCollecting pyarrow!=4.0.0,>=1.0.0\r\n  Downloading pyarrow-4.0.1-cp37-cp37m-manylinux2014_x86_64.whl (21.8 MB)\r\n     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 21.8 MB 6.2 MB\/s eta 0:00:01\r\nRequirement already satisfied: importlib-metadata in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (3.4.0)\r\nRequirement already satisfied: huggingface-hub<0.1.0 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (0.0.8)\r\nRequirement already satisfied: pandas in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (1.2.4)\r\nRequirement already satisfied: requests>=2.19.0 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: fsspec>=2021.05.0 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (2021.6.1)\r\nRequirement already satisfied: multiprocess in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: packaging in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (20.9)\r\nCollecting xxhash\r\n  Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)\r\n     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 243 kB 23.7 MB\/s eta 0:00:01\r\nRequirement already satisfied: numpy>=1.17 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: tqdm>=4.27 in \/opt\/conda\/lib\/python3.7\/site-packages (from datasets) (4.61.1)\r\nRequirement already satisfied: filelock in \/opt\/conda\/lib\/python3.7\/site-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in \/opt\/conda\/lib\/python3.7\/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in \/opt\/conda\/lib\/python3.7\/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: certifi>=2017.4.17 in \/opt\/conda\/lib\/python3.7\/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in \/opt\/conda\/lib\/python3.7\/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: typing-extensions>=3.6.4 in \/opt\/conda\/lib\/python3.7\/site-packages (from importlib-metadata->datasets) (3.7.4.3)\r\nRequirement already satisfied: zipp>=0.5 in \/opt\/conda\/lib\/python3.7\/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: pyparsing>=2.0.2 in \/opt\/conda\/lib\/python3.7\/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in \/opt\/conda\/lib\/python3.7\/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.3 in \/opt\/conda\/lib\/python3.7\/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: six>=1.5 in \/opt\/conda\/lib\/python3.7\/site-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\r\nInstalling collected packages: xxhash, pyarrow, datasets\r\n  Attempting uninstall: pyarrow\r\n    Found existing installation: pyarrow 4.0.0\r\n    Uninstalling pyarrow-4.0.0:\r\n      Successfully uninstalled pyarrow-4.0.0\r\nSuccessfully installed datasets-1.9.0 pyarrow-4.0.1 xxhash-2.0.2\r\nWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https:\/\/pip.pypa.io\/warnings\/venv\r\n```","embeddings":[-0.3157348335,-0.0831313878,-0.1109722927,0.1658830196,0.1794455051,-0.0220792536,0.3208149672,0.2708318233,-0.0284393821,-0.0525305681,-0.1436260194,0.85928303,0.0922810957,0.2378659844,0.0209103543,-0.0479097851,0.161305666,0.2256399542,-0.0434857272,0.0514687337,-0.0643853098,0.0771296248,-0.2960688174,0.2006766051,-0.059248656,-0.3657685816,0.1798947603,-0.014297124,-0.2524811327,-0.3473741114,0.4036728442,-0.1720401645,0.1640073508,0.3787457347,-0.0001087982,0.0669417232,0.3618468642,-0.0291197337,-0.3702855408,-0.1077230796,-0.2176077515,-0.6008007526,0.0832865685,-0.2847102284,0.4702086747,-0.2857757211,-0.2630595267,-0.1921920329,0.2056123465,0.1816900522,0.220649913,0.4774709642,0.4807039201,0.0092315031,0.2772545218,-0.1289513111,-0.2072615474,0.302177608,0.2401313484,-0.2112718225,0.1166253239,0.1265563369,-0.0676916912,0.0300699398,0.1025223359,0.0168172158,-0.1558161676,-0.3838818073,-0.0678934753,0.0581074692,0.4930505753,-0.5021786094,-0.2771857083,-0.1238675043,0.1140542701,0.0022397963,0.1400085837,0.1730056405,-0.0677028969,-0.0962271243,0.2432511002,-0.2378110439,-0.2651634812,0.1864016056,-0.1860895604,0.4596527219,-0.0485247113,0.0963278115,0.2849809527,-0.1344873905,0.6502164602,-0.0968685225,-0.1329707056,0.1146485433,-0.4117366672,0.0971322283,0.010409127,-0.0425877497,0.1165604293,0.1059470251,0.1066146642,0.1188913435,0.0963285193,0.3061642647,0.0781596452,0.3834520876,0.0288374443,0.3633151054,0.2639465928,0.2905315459,0.245966047,-0.0625741929,-0.0748769566,-0.3948988616,0.0809105411,-0.0593818463,0.2644909322,0.1566989869,-0.4245138466,-0.0065868539,-0.2908203006,-0.0526703186,0.0005867296,0.1260784417,-0.2189185619,0.1570490748,0.153926149,0.1813415289,-0.1178863421,-0.0593994446,-0.1201268286,0.3064025939,-0.0218912102,-0.2258309722,0.0449589416,-0.2936345041,0.2442829013,0.0245810207,0.2295258194,0.1750361025,0.1705325842,-0.1257321984,-0.1919405162,0.3506008089,0.0321080126,0.115392603,0.1277721226,-0.3931867778,-0.1246997267,0.0788087621,-0.1662564874,-0.0804766044,-0.2768648565,0.1809964627,-0.290689826,0.051424928,-0.5761383176,-0.2516323924,0.0038182673,-0.0501182638,-0.0043983608,-0.1965858638,0.234431088,-0.2544774711,0.2506346703,0.0534614958,-0.3594920933,0.0474446714,-0.2210427821,-0.2070468217,-0.0644442812,0.1289324909,-0.2450414598,0.1776161641,-0.2288636714,0.134849295,0.6730281711,-0.6061068773,-0.5256843567,0.0257641803,-0.0934768692,0.0148919905,-0.0130191706,0.0944576636,-0.1029906943,0.2157653421,0.0677377284,0.1622854322,-0.0314902738,-0.1910270452,-0.2592289746,-0.2871662676,0.0578858815,0.1806648374,0.0925267786,-0.2924076617,0.1847915649,-0.1539341211,0.0381951183,0.0600379854,0.1202748492,0.0565657131,0.3603731394,0.0527839586,-0.0124018872,-0.1248254329,-0.2917067111,0.268514514,-0.1398256272,0.0348941907,-0.406406641,-0.0832441077,-0.3611127138,0.3012945354,-0.217399776,0.0525478832,0.1697994173,-0.123736605,0.0105001181,0.3521030843,-0.0077052573,0.3821054101,0.1918429434,0.1550069898,-0.3192882836,0.3061139882,-0.3814661801,-0.1457504779,0.0615457892,0.1301296651,-0.1030459404,0.0251288638,-0.0443161391,0.1139586046,0.077213712,0.0239458177,0.010299528,-0.2309055328,0.1762903035,-0.3153473437,0.1356556416,0.1335422546,0.3049322069,0.0928746164,0.0961892903,0.095304653,-0.0592509471,-0.095798105,0.014867017,0.257581383,0.2067648321,0.2018836439,0.061224442,-0.1222234517,0.1843215227,-0.0238401052,0.1030386165,-0.0872515887,-0.1367312521,-0.023938071,0.360041827,0.0096609853,0.1436843425,0.0097316708,-0.3445076942,0.2186189443,0.2709667087,0.0152541101,0.3870508969,0.2118288875,-0.2134576887,-0.0735057443,-0.0510983989,-0.1095043346,0.1225212216,0.3149383664,0.391687423,0.1771174818,0.245856002,-0.0654154643,0.0114169847,-0.2596939802,-0.04270285,0.2147675902,-0.1877250969,0.2559342682,-0.2284218222,-0.2371326089,-0.162731111,-0.1992757618,0.0117198545,-0.4043591022,-0.2368007302,0.2449380606,0.1685368866,0.1849227846,0.1470604688,-0.1181396544,0.0749962181,-0.2854743898,-0.2432315946,-0.3484172225,-0.3759360015,0.1030787006,0.3510778248,0.1604800373,0.2290232033,-0.1745557785,0.2487835139,-0.4051178396,-0.4474343657,-0.210863471,-0.0932771042,0.4236423969,0.266956836,0.1613865495,-0.2377539724,-0.2812199891,0.3301022947,0.1585963517,-0.2695578933,0.1333747208,-0.1104923189,-0.2200868726,0.0303354114,-0.3240668774,-0.3991329074,-0.4153462648,0.3101412356,0.1853808314,-0.1316485703,-0.1519116759,0.3288983107,0.2101772875,0.1746779233,0.0226537231,0.0737200826,0.2688193619,0.5995613337,-0.1175564229,-0.4101842046,0.2229814231,-0.2326618135,0.3032893836,-0.0754950792,-0.4256069362,0.0147363963,-0.173968941,0.650064826,-0.2176861614,0.2231082469,0.1810691208,0.2036795765,0.0705053508,-0.1243862733,-0.2514392436,-0.1216403469,-0.1191993132,0.0573351607,0.0553836375,0.1176579595,0.0425810367,0.5509263277,0.0537305214,-0.0652405843,0.2583059371,-0.0818953067,0.2138422579,0.0764959231,-0.4019142687,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2678","title":"Import Error in Kaggle notebook","comments":"You may need to restart your kaggle notebook after installing a newer version of `pyarrow`.\r\n\r\nIf it doesn't work we'll probably have to create an issue on [arrow's JIRA](https:\/\/issues.apache.org\/jira\/projects\/ARROW\/issues\/), and maybe ask kaggle why it could fail","body":"## Describe the bug\r\nNot able to import datasets library in kaggle notebooks\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n!pip install datasets\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nNo such error\r\n\r\n## Actual results\r\n```\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-9-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     36 import pandas as pd\r\n     37 import pyarrow as pa\r\n---> 38 import pyarrow.compute as pc\r\n     39 from multiprocess import Pool, RLock\r\n     40 from tqdm.auto import tqdm\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/compute.py in <module>\r\n     16 # under the License.\r\n     17 \r\n---> 18 from pyarrow._compute import (  # noqa\r\n     19     Function,\r\n     20     FunctionOptions,\r\n\r\nImportError: \/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Kaggle\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":37,"text":"Import Error in Kaggle notebook \n ## Describe the bug\r\nNot able to import datasets library in kaggle notebooks\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n!pip install datasets\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nNo such error\r\n\r\n## Actual results\r\n```\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-9-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     36 import pandas as pd\r\n     37 import pyarrow as pa\r\n---> 38 import pyarrow.compute as pc\r\n     39 from multiprocess import Pool, RLock\r\n     40 from tqdm.auto import tqdm\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/compute.py in <module>\r\n     16 # under the License.\r\n     17 \r\n---> 18 from pyarrow._compute import (  # noqa\r\n     19     Function,\r\n     20     FunctionOptions,\r\n\r\nImportError: \/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Kaggle\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n \n You may need to restart your kaggle notebook after installing a newer version of `pyarrow`.\r\n\r\nIf it doesn't work we'll probably have to create an issue on [arrow's JIRA](https:\/\/issues.apache.org\/jira\/projects\/ARROW\/issues\/), and maybe ask kaggle why it could 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2678","title":"Import Error in Kaggle notebook","comments":"> You may need to restart your kaggle notebook before after installing a newer version of `pyarrow`.\r\n> \r\n> If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https:\/\/issues.apache.org\/jira\/projects\/ARROW\/issues\/), and maybe ask kaggle why it could fail\r\n\r\nIt works after restarting.\r\nMy bad, I forgot to restart the notebook. Sorry for the trouble!","body":"## Describe the bug\r\nNot able to import datasets library in kaggle notebooks\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n!pip install datasets\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nNo such error\r\n\r\n## Actual results\r\n```\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-9-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     36 import pandas as pd\r\n     37 import pyarrow as pa\r\n---> 38 import pyarrow.compute as pc\r\n     39 from multiprocess import Pool, RLock\r\n     40 from tqdm.auto import tqdm\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/compute.py in <module>\r\n     16 # under the License.\r\n     17 \r\n---> 18 from pyarrow._compute import (  # noqa\r\n     19     Function,\r\n     20     FunctionOptions,\r\n\r\nImportError: \/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Kaggle\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":57,"text":"Import Error in Kaggle notebook \n ## Describe the bug\r\nNot able to import datasets library in kaggle notebooks\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n!pip install datasets\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nNo such error\r\n\r\n## Actual results\r\n```\r\nImportError                               Traceback (most recent call last)\r\n<ipython-input-9-652e886d387f> in <module>\r\n----> 1 import datasets\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/__init__.py in <module>\r\n     31     )\r\n     32 \r\n---> 33 from .arrow_dataset import Dataset, concatenate_datasets\r\n     34 from .arrow_reader import ArrowReader, ReadInstruction\r\n     35 from .arrow_writer import ArrowWriter\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in <module>\r\n     36 import pandas as pd\r\n     37 import pyarrow as pa\r\n---> 38 import pyarrow.compute as pc\r\n     39 from multiprocess import Pool, RLock\r\n     40 from tqdm.auto import tqdm\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/compute.py in <module>\r\n     16 # under the License.\r\n     17 \r\n---> 18 from pyarrow._compute import (  # noqa\r\n     19     Function,\r\n     20     FunctionOptions,\r\n\r\nImportError: \/opt\/conda\/lib\/python3.7\/site-packages\/pyarrow\/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Kaggle\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n \n > You may need to restart your kaggle notebook before after installing a newer version of `pyarrow`.\r\n> \r\n> If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https:\/\/issues.apache.org\/jira\/projects\/ARROW\/issues\/), and maybe ask kaggle why it could fail\r\n\r\nIt works after restarting.\r\nMy bad, I forgot to restart the notebook. Sorry for the trouble!","embeddings":[-0.3438675702,-0.0770802647,-0.1142193824,0.1320918351,0.1916217655,0.0109465495,0.340642184,0.3310463727,-0.0405511446,-0.0315704569,-0.1770029217,0.8259581327,0.0784066692,0.2167555988,0.042296242,-0.0751289576,0.1151352748,0.2059997469,-0.0540337525,0.0684145167,-0.1204729155,0.0821902528,-0.2884452641,0.2308666408,-0.0536760129,-0.335398227,0.1634015739,-0.0156411771,-0.2512480915,-0.389315486,0.3791489899,-0.2030484974,0.1786520332,0.3298630118,-0.0001054691,0.0557772815,0.3331196308,-0.001695561,-0.3189184666,-0.0971903503,-0.1449750513,-0.5241268873,0.1139851436,-0.3169448674,0.4252889752,-0.3302152753,-0.2376512587,-0.1207024008,0.2349157035,0.2009016424,0.264329344,0.4617816806,0.4861875176,-0.004376133,0.3844998479,-0.1263221502,-0.167839393,0.3752985597,0.2364216447,-0.2263902128,0.0413371772,0.1175254211,-0.0614103116,0.0632822588,0.1276656091,0.0492599458,-0.1038790867,-0.3674464226,-0.0683321431,0.0503972881,0.5075068474,-0.5111188889,-0.2169642001,-0.0854614452,0.1243208125,-0.0331041217,0.1380392164,0.2016976625,-0.053589806,-0.1101543531,0.2808272839,-0.2064090818,-0.2585773468,0.1937158555,-0.1518905312,0.4403881431,-0.0387481116,0.0953781009,0.2886867523,-0.1460981816,0.6073461771,-0.0855476558,-0.1203497052,0.1216696128,-0.3787596524,0.1202813387,0.068331331,-0.0643597394,0.0970348492,0.0882156864,0.0919665843,0.1456046999,0.1305030137,0.3127405643,0.1170410216,0.3272093236,0.0092065716,0.291288048,0.2798186541,0.2912294865,0.1691599041,-0.0766246244,-0.0699370801,-0.3918393254,0.1344002932,-0.0694812238,0.3126778305,0.1718910336,-0.3556835651,-0.0494576171,-0.3413330615,-0.0610720553,-0.018745685,0.1171982065,-0.2134622037,0.1104203761,0.1844510287,0.1440460533,-0.1670670509,-0.0719212443,-0.1517760754,0.3045976162,-0.0294002872,-0.2297071368,0.0208934434,-0.3086881638,0.1870203167,0.0515802316,0.2191489041,0.1520774961,0.190795511,-0.1156799346,-0.1989659369,0.3034534454,0.0597290881,0.075162217,0.1255255342,-0.4011129141,-0.129481256,0.1090210378,-0.2078445405,-0.1130267158,-0.2632022798,0.2298652679,-0.2152012885,0.0539015308,-0.5561850667,-0.204222247,-0.0161544625,-0.0957035124,0.0088539263,-0.2448432595,0.2065392137,-0.2754352987,0.2436387539,0.0379972048,-0.3599003255,0.019681843,-0.2543708384,-0.2262233198,-0.0632984191,0.159200877,-0.2863209844,0.19538486,-0.2295816392,0.1917762458,0.638063252,-0.5143753886,-0.4943232834,-0.0183402691,-0.1120874807,-0.0296253059,-0.056907054,0.1142123267,-0.1233816445,0.2207448334,0.0638165101,0.1699175239,-0.03144373,-0.1549661905,-0.2469737381,-0.2476423383,0.1074169576,0.1619285345,0.0861109868,-0.3101319671,0.1939121932,-0.1340430975,0.0820696577,0.0488968976,0.0861349106,0.0410352498,0.3639855683,0.0073456755,0.0412799157,-0.1453770697,-0.2789386213,0.2334987968,-0.2037810087,0.043385569,-0.3181882203,-0.0976251066,-0.3575016558,0.2770489454,-0.2133723199,0.0811754838,0.2015091926,-0.1052717045,-0.0017692286,0.3523833752,-0.0231055394,0.2652153671,0.1516367644,0.1324870586,-0.33897686,0.3570480943,-0.3791424334,-0.1911290884,0.1065430865,0.1010324731,-0.0990490168,0.0375528336,-0.0400208198,0.1391092241,0.0869648904,0.0505159572,-0.0299680922,-0.197322458,0.1561044604,-0.3874383867,0.0762953386,0.1411544085,0.3138978481,0.0901013389,0.0919348896,0.1251188368,-0.0571356192,-0.1056846157,0.0100074066,0.2146949768,0.1489283592,0.1847998351,0.1219904274,-0.0952658504,0.1637495458,-0.0575594567,0.0876649097,-0.0831241906,-0.1441524774,-0.0253729504,0.4067189991,0.0275045056,0.1840658188,0.0293449238,-0.3974842131,0.1613838822,0.2377908081,-0.0388252065,0.3521713614,0.2281310111,-0.2038653791,-0.0367030092,-0.0257220864,-0.1459667534,0.1384558678,0.2991797626,0.3653391302,0.1721170843,0.2751637101,-0.0793751255,-0.0749894753,-0.2025559545,-0.0662610829,0.2388169616,-0.1793177426,0.2328462303,-0.2155467868,-0.2813491225,-0.1677555144,-0.168367967,-0.0178475194,-0.4374024868,-0.2076959312,0.2531118691,0.1370605528,0.1795032322,0.1891433746,-0.1191048995,0.0914462358,-0.2401306182,-0.2524135709,-0.3897508681,-0.3771707416,0.1407493949,0.3231416643,0.1610304266,0.2322367877,-0.1523366272,0.2447451055,-0.4053628743,-0.4641484022,-0.1996776909,-0.0798342302,0.3753569424,0.2712040544,0.1453299969,-0.2507660091,-0.3088523746,0.3701548576,0.0678835213,-0.2780373991,0.1633874774,-0.110238649,-0.1721238196,0.0301491357,-0.3643354177,-0.3662023544,-0.4178311229,0.3031972051,0.1823398471,-0.1202225611,-0.1583278924,0.2869707346,0.2404126525,0.1297005862,-0.0267205127,0.0517497584,0.2594495714,0.5824719071,-0.1134833768,-0.4176762998,0.1986724287,-0.2575908601,0.2834931612,-0.120212473,-0.4212935269,0.0007623154,-0.1604905128,0.6609592438,-0.1991951168,0.21468997,0.1496120393,0.1904706359,0.0129404534,-0.0890318006,-0.2557511628,-0.1402997077,-0.0363350734,0.0108378865,0.0229870286,0.1207668483,0.015704697,0.5826402903,-0.0225849897,-0.0533317551,0.27514112,-0.1212432683,0.2058857828,0.0454710908,-0.4002604187,-0.0308185071,-0.14823524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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2677","title":"Error when downloading C4","comments":"Hi Thanks for reporting !\r\nIt looks like these files are not correctly reported in the list of expected files to download, let me fix that ;)","body":"Hi,\r\nI am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:\r\n`datasets.load_dataset('c4', 'en')`\r\n\r\nIs this a bug or do I have some configurations missing on my server? \r\nThanks!\r\n\r\n\r\n<img width=\"1014\" alt=\"\u0421\u043d\u0438\u043c\u043e\u043a \u044d\u043a\u0440\u0430\u043d\u0430 2021-07-20 \u0432 11 37 17\" src=\"https:\/\/user-images.githubusercontent.com\/36672861\/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png\">","comment_length":27,"text":"Error when downloading C4 \n Hi,\r\nI am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:\r\n`datasets.load_dataset('c4', 'en')`\r\n\r\nIs this a bug or do I have some configurations missing on my server? \r\nThanks!\r\n\r\n\r\n<img width=\"1014\" alt=\"\u0421\u043d\u0438\u043c\u043e\u043a \u044d\u043a\u0440\u0430\u043d\u0430 2021-07-20 \u0432 11 37 17\" src=\"https:\/\/user-images.githubusercontent.com\/36672861\/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png\"> \n Hi Thanks for reporting !\r\nIt looks like these files are not correctly reported in the list of expected files to download, let me fix that ;)","embeddings":[0.096845977,-0.0686112046,-0.0539066978,0.4750131071,0.2311235666,0.2395801693,0.0058158985,0.1676318944,-0.0170363579,-0.1058175042,-0.0259849485,-0.2992729247,0.1618457735,-0.0318008177,0.0029821228,-0.1193322763,0.135855034,0.084860906,-0.1391199231,-0.3514162302,-0.1153822467,0.0911915377,-0.1484110653,0.1158548519,-0.026407348,-0.0274939761,0.0056613502,0.0816003084,-0.3496825397,-0.3620357513,0.555793941,0.1042395383,0.1785736382,0.0744807795,-0.0001167735,-0.1865388751,0.3024600446,-0.3341219127,-0.1340886652,-0.121632129,-0.3307465017,-0.2062899321,-0.2419678271,-0.3494567275,-0.0442584194,-0.140995115,0.1553079635,-0.1196931154,0.0351108015,0.4630728066,0.1809932441,-0.1043740585,0.0083733583,-0.1809168756,-0.0925607607,0.0102551347,0.0669598803,0.1523742378,0.1125920042,0.295470953,0.3457050622,0.1438502967,0.0234140679,-0.0461742245,0.2648062408,-0.1932270974,0.1687993854,-0.3840813935,0.3017909229,0.2715108693,0.9384877682,-0.1433071792,-0.1691133976,0.2467643768,0.1892973334,-0.1855994314,0.294626236,0.6772632599,-0.0679940879,0.1251312643,-0.3260817826,-0.1865656674,-0.1197463498,0.2777039707,-0.1292812526,-0.2179443389,0.0771927834,-0.051841978,0.1002988219,0.2046857923,0.0032096968,-0.4079837203,-0.177982986,0.2119580954,-0.1153169423,0.0007458262,-0.4127675593,0.3904469013,-0.0880833864,-0.0488232821,-0.2443313748,-0.0720183104,-0.1606342196,0.0810397491,0.1806429774,-0.0447047949,0.1368902624,0.1567291468,0.0802775994,-0.1490299851,0.1545657068,-0.0944330469,-0.1808453649,-0.3186020255,-0.0616494119,0.2068343312,-0.1288014203,-0.4318065941,-0.2298416942,0.2312493473,-0.3119583428,0.0769578367,-0.1125138998,0.4242171347,-0.4325264394,-0.0528689064,-0.0815890431,0.1484345198,-0.0591337755,-0.5108196139,-0.1177364141,0.0567053556,-0.2883011997,-0.2188446075,0.2805007994,-0.5110835433,0.2950258553,0.0521375276,0.1721790731,-0.1180123091,-0.0835570171,-0.3251758814,0.0874162763,0.2726626396,-0.1362612098,0.1245287657,0.2622080147,-0.2980207503,0.0288882442,-0.0410171784,-0.3503580689,-0.120409824,-0.1634520143,0.1190706417,-0.2125154436,-0.0093940236,-0.349647373,-0.2559763491,0.2443347275,-0.0921485797,0.1594803333,-0.3166207373,0.031773597,-0.1681563109,-0.0448281765,0.3502218127,-0.3620434105,0.0180544611,-0.0329119675,-0.1109365821,0.361064136,0.2010711879,-0.1729782671,0.3088462949,-0.2813074589,0.2549065053,0.0488998368,-0.5429531932,-0.4550856054,0.2392617166,0.1014861241,-0.0787607357,-0.226033479,-0.0706493407,0.388184011,-0.1845718324,-0.0514269136,0.0685296431,0.0910026878,0.0454453379,-0.1369255334,0.0582367703,0.1933749467,-0.0361915529,0.2007416189,-0.0915700868,0.2835775614,-0.1294594705,0.2464183122,-0.190301314,0.1727260649,0.2742322385,0.1823205501,-0.1228114814,0.1321302801,0.3525640666,-0.4031892121,0.1628526002,-0.043868646,-0.000149133,-0.0202082377,0.0030592661,-0.2633752823,-0.1960603148,-0.2828921676,-0.0816282704,0.029565528,-0.1728123724,0.0364455581,0.1513742357,0.1686381549,0.5813813806,-0.2374359816,0.0732231513,0.1647721529,0.3052451909,-0.1146178693,0.3243879974,0.0605081208,-0.197623983,0.301115036,-0.1183037311,-0.1741787195,0.1802297086,0.2748375535,0.2119431049,-0.3257678449,0.0051281089,0.4052165747,-0.3786321282,0.1828547269,0.4098116457,0.1813715845,-0.0424861833,0.0105122207,-0.0025696948,-0.0898008645,-0.2186424285,0.0273648687,0.1081600562,0.3547260463,-0.0161224678,-0.2691572905,-0.1640057266,0.3425188959,-0.2733933926,0.0401486084,-0.0079950253,-0.1774633229,-0.3209866583,0.4895766377,0.0607112646,-0.0399639159,0.1609406471,-0.0280320551,0.0574853681,0.3541265428,0.2005570829,0.5117332935,-0.0272196513,-0.2033050656,0.2647412419,0.1374890059,-0.1859330535,0.3109610081,-0.2197519541,0.1099894643,0.1862859279,-0.0879818574,-0.1205739602,-0.3998787403,0.150488466,0.3180182278,0.0884106085,-0.3093161583,0.1285469532,-0.4081644118,-0.2306700349,-0.2915096581,-0.1382839978,-0.0635014698,0.0557817519,0.0039454689,-0.1081699133,-0.1405950338,-0.0048358324,-0.6207512021,-0.0432001874,-0.0407774523,-0.1709545106,0.0394609161,0.2573703825,-0.235153839,0.0094771134,0.1098938584,0.0883889273,0.3553434014,-0.3984425068,0.262776792,-0.3834376931,-0.2011980861,0.2526563704,0.0835286155,-0.1793435067,-0.0235761423,0.2764116228,-0.3303965926,-0.1471963376,-0.0817749277,0.05934266,-0.159355104,0.198516652,0.0402036086,-0.1698155254,-0.1646369845,-0.5502979755,-0.4189038277,-0.0854859203,-0.1607704014,-0.1752345115,0.0396212041,0.4361684918,0.0182257835,-0.0780427381,0.1090334654,-0.1596165448,-0.2542777956,-0.1209918112,0.4298145175,-0.2539363503,-0.5836586952,0.1029922739,0.0107242055,0.1744459718,0.3262527287,-0.3350965381,0.3499177694,0.1039251313,0.0213261619,0.0253066868,-0.2157391608,0.3457696736,-0.2229926735,0.1244463027,-0.1363958716,0.2712985277,0.1059970111,0.2097367644,0.5329785943,-0.4421275258,-0.0302732196,0.1538648307,0.2319278717,0.1277960241,-0.2935450375,0.5387865901,0.2865954041,0.3581418395,0.2793970704,-0.3494909108,0.3341584504,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2677","title":"Error when downloading C4","comments":"Alright this is fixed now. We'll do a new release soon to make the fix available.\r\n\r\nIn the meantime feel free to simply pass `ignore_verifications=True` to `load_dataset` to skip this error","body":"Hi,\r\nI am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:\r\n`datasets.load_dataset('c4', 'en')`\r\n\r\nIs this a bug or do I have some configurations missing on my server? \r\nThanks!\r\n\r\n\r\n<img width=\"1014\" alt=\"\u0421\u043d\u0438\u043c\u043e\u043a \u044d\u043a\u0440\u0430\u043d\u0430 2021-07-20 \u0432 11 37 17\" src=\"https:\/\/user-images.githubusercontent.com\/36672861\/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png\">","comment_length":31,"text":"Error when downloading C4 \n Hi,\r\nI am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:\r\n`datasets.load_dataset('c4', 'en')`\r\n\r\nIs this a bug or do I have some configurations missing on my server? \r\nThanks!\r\n\r\n\r\n<img width=\"1014\" alt=\"\u0421\u043d\u0438\u043c\u043e\u043a \u044d\u043a\u0440\u0430\u043d\u0430 2021-07-20 \u0432 11 37 17\" src=\"https:\/\/user-images.githubusercontent.com\/36672861\/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png\"> \n Alright this is fixed now. We'll do a new release soon to make the fix available.\r\n\r\nIn the meantime feel free to simply pass `ignore_verifications=True` to `load_dataset` to skip this error","embeddings":[0.0360019878,-0.0390101783,-0.0446487889,0.4204434156,0.2243454158,0.2426010668,-0.0268920865,0.1530843377,-0.0672710538,-0.0291624796,0.0164733157,-0.2508128285,0.1566776633,0.0499308072,0.0004604018,-0.0572996549,0.1233928427,0.0768573433,-0.1300052702,-0.2965222001,-0.1279532611,0.0637142062,-0.1454876363,0.0978287309,-0.0157246217,0.0041988972,-0.0361221768,0.112853542,-0.3306386173,-0.4125275612,0.5867489576,0.0864503309,0.2514703274,0.0607930385,-0.0001131244,-0.1866180599,0.3207838237,-0.2820208967,-0.1749012768,-0.1334163994,-0.325238049,-0.1429717839,-0.2270385921,-0.3486924767,-0.0586523786,-0.0870727822,0.1525995135,-0.1404471695,0.0293257777,0.3692456782,0.2102141678,-0.1186661646,0.0342105515,-0.2088637203,-0.146671623,-0.0970341563,0.0677079633,0.1624719352,0.136817649,0.2822684348,0.3550719321,0.2087816447,-0.041719079,-0.0173644219,0.3091590405,-0.2235092521,0.2251718789,-0.3702123761,0.3360903561,0.279658556,0.965049088,-0.1046360657,-0.1588131785,0.2854292095,0.2251126915,-0.1731430739,0.3463103771,0.6254925132,-0.0934754759,0.1446568221,-0.3239200711,-0.2792814374,-0.1134175807,0.2988192141,-0.1196650937,-0.1817182899,0.1240483895,-0.0344568565,0.1894722432,0.2115551978,0.051272653,-0.3572794497,-0.229422465,0.2440073341,-0.1396593153,-0.0109471083,-0.3934450746,0.4090083539,-0.1072911099,-0.0308373738,-0.3079855144,-0.0492989607,-0.1120638251,0.0944594964,0.2030055076,-0.0697778836,0.1198696271,0.1149196029,0.1016233638,-0.1529776305,0.2040787488,-0.0545782261,-0.1837911159,-0.3530216813,-0.0942231119,0.155848965,-0.1233038604,-0.3783267438,-0.1654168069,0.1764023751,-0.3575060666,0.1171477735,-0.137021333,0.4213334322,-0.3903787732,-0.050491225,-0.0359500907,0.1107082516,-0.1120523885,-0.5132819414,-0.1210590228,0.0789467245,-0.2682026923,-0.2268249393,0.2764569819,-0.51893121,0.2074765712,0.0441430844,0.1534954309,-0.0671436042,-0.0873310044,-0.282404989,0.0264247432,0.2698502243,-0.120303981,0.0706778541,0.2335549742,-0.3244762123,0.0129573429,0.0130953621,-0.3358463943,-0.1569307148,-0.2096270174,0.1417196691,-0.1472776085,-0.0375684462,-0.3525378704,-0.2765506208,0.2389992476,-0.0917323455,0.1340071261,-0.2895190418,-0.012052021,-0.1646085829,-0.0726770982,0.3515331745,-0.3475580812,0.0224019084,-0.063313365,-0.0984154344,0.3028386533,0.1756015271,-0.1984318197,0.2672305703,-0.2671558261,0.2501603365,-0.0137765417,-0.5401049852,-0.509370625,0.2695917487,0.055113297,-0.0686934739,-0.2188578397,-0.035527721,0.3348928094,-0.192320019,0.0134166107,0.1395029426,0.0648256168,0.0564677566,-0.1089969426,-0.0176786371,0.2234360576,-0.0455377512,0.2158854306,-0.0558993369,0.2599472702,-0.0973496363,0.2286307067,-0.2337479144,0.1718613952,0.3047979772,0.2269141823,-0.1765137315,0.0588294864,0.3398589194,-0.4050041735,0.2001463026,0.0163853783,0.0992064029,-0.0325783491,-0.0069131684,-0.194969207,-0.1770458966,-0.2826213539,-0.099384889,0.0356473997,-0.2061429918,0.0358737074,0.1314911544,0.190925315,0.5959507227,-0.2169481367,0.0794818178,0.136102736,0.2638708055,-0.1812299639,0.3510243893,0.0228076521,-0.1756054759,0.2562204301,-0.1429301947,-0.1528047174,0.2349878699,0.2168685496,0.2181942463,-0.3224677145,-0.0151658105,0.3912174702,-0.3939552009,0.142312929,0.3844948113,0.2187794298,-0.016003333,0.0914730579,-0.046353627,-0.1119231656,-0.2208696008,0.0427727215,0.0983948335,0.3558832705,0.002010664,-0.2500748932,-0.1520540118,0.3440111876,-0.3176958561,-0.0068033431,-0.0388007686,-0.1095327437,-0.3359957337,0.4060756862,0.0946510211,-0.0373951457,0.1653100997,-0.0237267278,0.0274253991,0.3157754838,0.248559311,0.447175622,-0.0136000365,-0.2143898904,0.2998844683,0.1573820561,-0.1602239013,0.3488217294,-0.2297730446,0.1348233819,0.0508116186,-0.1089659184,-0.0918366611,-0.3125115931,0.116694212,0.3133603334,0.1552191228,-0.2792727649,0.1318763644,-0.4109159708,-0.2489753813,-0.1848032624,-0.1850156784,-0.0852527842,0.0458248407,-0.0152919302,-0.0633327588,-0.1338995546,-0.0200535469,-0.5972525477,-0.0605826043,-0.0365189649,-0.2365607917,0.0650054887,0.1695266664,-0.2182215899,0.0348358937,0.1568024307,0.0094862776,0.3665184677,-0.3867044449,0.2242770791,-0.3541082144,-0.234388411,0.2437724024,0.0588889793,-0.1631189287,-0.0350466035,0.2716336548,-0.3771987855,-0.1262652874,-0.1053924859,0.0165898465,-0.164206937,0.2119745761,0.1110279858,-0.1994582415,-0.1752018034,-0.5475433469,-0.4019851089,-0.0843536481,-0.1555342674,-0.1761154383,0.0200413894,0.4436435997,0.0465703383,-0.0550639629,0.1353532821,-0.2508653402,-0.3042049706,-0.106329754,0.319399029,-0.2624073327,-0.5575564504,0.1595307887,0.0274067931,0.1751662493,0.3157703578,-0.3637105525,0.3887701333,0.0978396684,0.1054476723,-0.0301313512,-0.2219379991,0.4351092875,-0.2020962983,0.0946493298,-0.0956711918,0.276397109,0.0846992806,0.1521805078,0.5275927186,-0.428185761,-0.03821465,0.1636305153,0.176832363,0.0551307313,-0.2453256696,0.5363041162,0.2724509239,0.396664083,0.2414665669,-0.3665806055,0.337654233,-0.2462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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2669","title":"Metric kwargs are not passed to underlying external metric f1_score","comments":"Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst, note that `\"min\"` is not an allowed value for `average`. According to scikit-learn [documentation](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html), `average` can only take the values: `{\"micro\", \"macro\", \"samples\", \"weighted\", \"binary\"} or None, default=\"binary\"`.\r\n\r\nSecond, you should take into account that all additional metric-specific argument should be passed in the method `compute` (and not in the method `load_metric`). You can find more information in our documentation: https:\/\/huggingface.co\/docs\/datasets\/using_metrics.html#computing-the-metric-scores\r\n\r\nSo for example, if you would like to calculate the macro-averaged F1 score, you should use:\r\n```python\r\nimport datasets\r\n\r\nf1 = datasets.load_metric(\"f1\", keep_in_memory=True)\r\nf1.add_batch(predictions=[0,2,3], references=[1, 2, 3])\r\nf1.compute(average=\"macro\")\r\n```","body":"## Describe the bug\r\nWhen I want to use F1 score with average=\"min\", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html) throws an error telling me so.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nf1 = datasets.load_metric(\"f1\", keep_in_memory=True, average=\"min\")\r\nf1.add_batch(predictions=[0,2,3], references=[1, 2, 3])\r\nf1.compute()\r\n```\r\n\r\n## Expected results\r\nNo error, because `average=\"min\"` should be passed correctly to f1_score in sklearn.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\datasets\\metric.py\", line 402, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\metrics\\f1\\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\\f1.py\", line 97, in _compute\r\n    \"f1\": f1_score(\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1071, in f1_score\r\n    return fbeta_score(y_true, y_pred, beta=1, labels=labels,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1195, in fbeta_score\r\n    _, _, f, _ = precision_recall_fscore_support(y_true, y_pred,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1464, in precision_recall_fscore_support\r\n    labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1294, in _check_set_wise_labels\r\n    raise ValueError(\"Target is %s but average='binary'. Please \"\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.9.2\r\n- PyArrow version: 4.0.1","comment_length":96,"text":"Metric kwargs are not passed to underlying external metric f1_score \n ## Describe the bug\r\nWhen I want to use F1 score with average=\"min\", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html) throws an error telling me so.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nf1 = datasets.load_metric(\"f1\", keep_in_memory=True, average=\"min\")\r\nf1.add_batch(predictions=[0,2,3], references=[1, 2, 3])\r\nf1.compute()\r\n```\r\n\r\n## Expected results\r\nNo error, because `average=\"min\"` should be passed correctly to f1_score in sklearn.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\datasets\\metric.py\", line 402, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\metrics\\f1\\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\\f1.py\", line 97, in _compute\r\n    \"f1\": f1_score(\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1071, in f1_score\r\n    return fbeta_score(y_true, y_pred, beta=1, labels=labels,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1195, in fbeta_score\r\n    _, _, f, _ = precision_recall_fscore_support(y_true, y_pred,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1464, in precision_recall_fscore_support\r\n    labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1294, in _check_set_wise_labels\r\n    raise ValueError(\"Target is %s but average='binary'. Please \"\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.9.2\r\n- PyArrow version: 4.0.1 \n Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst, note that `\"min\"` is not an allowed value for `average`. According to scikit-learn [documentation](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html), `average` can only take the values: `{\"micro\", \"macro\", \"samples\", \"weighted\", \"binary\"} or None, default=\"binary\"`.\r\n\r\nSecond, you should take into account that all additional metric-specific argument should be passed in the method `compute` (and not in the method `load_metric`). You can find more information in our documentation: https:\/\/huggingface.co\/docs\/datasets\/using_metrics.html#computing-the-metric-scores\r\n\r\nSo for example, if you would like to calculate the macro-averaged F1 score, you should use:\r\n```python\r\nimport datasets\r\n\r\nf1 = datasets.load_metric(\"f1\", keep_in_memory=True)\r\nf1.add_batch(predictions=[0,2,3], references=[1, 2, 3])\r\nf1.compute(average=\"macro\")\r\n```","embeddings":[-0.0516288541,-0.5501691103,0.0873140618,0.2038566321,0.4681911767,-0.0643797889,0.1748601645,-0.1953885257,0.3451755941,0.2828977704,0.0679134801,0.4462169111,0.1783719808,0.0242547859,-0.0182039514,0.0616982467,0.1058040708,-0.3587132096,-0.0846228451,-0.2987218797,-0.4068837166,0.0102044912,-0.4048623741,0.0202550888,-0.2006222904,0.0613628998,-0.0347853079,-0.1700376719,0.0016798849,-0.003900961,0.3077568412,0.1265656352,-0.0970672965,0.3663867116,-0.0001235781,-0.0986129567,0.4526257515,-0.1368988007,-0.0371827036,-0.0111093875,-0.1764513552,0.014776093,-0.1244774237,-0.3509167433,0.1918561906,0.1058521941,-0.1739191711,-0.3824925721,0.2338166088,0.3685019314,0.0636828318,0.1573851407,-0.2092087269,-0.1720214784,-0.2959969938,-0.1044454128,-0.1850389093,0.4786297977,-0.6841754317,-0.0733038336,-0.1676173359,0.0917701647,0.1501622796,-0.0126522053,0.7468219995,0.0399315543,0.3019666672,0.1136633381,0.1792099476,-0.1155982167,0.0679363832,-0.3869571686,-0.403575331,-0.1462424695,0.0940086693,-0.7693849206,0.1077826694,0.0144160725,-0.2267361879,-0.092972286,-0.1046825647,-0.1168835163,-0.3561751246,0.0764986202,-0.050303366,0.6123213172,0.0015167097,0.0829183236,0.192719996,0.1838299781,0.0406339429,-0.2930784225,0.0284065641,0.071691215,-0.2631740272,0.2108209878,-0.0395305119,-0.0636173189,0.27936849,0.0515006147,-0.0760850012,0.0437829681,0.430503428,0.0532589592,0.197408691,0.5313013792,0.4781782627,0.3300953805,-0.0164008401,0.0775587261,-0.1973581165,-0.2480203807,0.271512419,-0.0058645201,0.2495119125,0.1664949208,0.0198457837,-0.07760185,-0.2983479202,-0.0815996081,0.0488367118,-0.013127977,0.0346073136,0.159644261,-0.0479189232,0.3419955671,0.0582272336,0.3695012033,-0.112433292,-0.0151641183,-0.2224202901,0.1949929297,-0.4361763597,0.1441707015,-0.2022401094,-0.1239357069,0.5087715983,-0.0023832025,0.606444478,-0.4638635218,-0.0930645168,0.0385823548,-0.0192406494,-0.012212811,-0.4288246334,0.2412556112,0.033799082,0.0614981018,0.0438620262,-0.0164111089,-0.3245539367,-0.0843018368,0.4818834066,0.041315563,-0.2956653833,-0.132889986,-0.1859076768,0.2178625315,0.0208467692,0.114158757,0.0367673673,-0.1453685611,-0.5320333838,-0.2027044743,0.7653902769,0.0672897398,0.1244359612,-0.2514540553,-0.1332684457,0.0805685669,-0.0005603431,0.0552252233,0.0327283815,0.2792529762,-0.0457489416,0.0623041987,0.2406337559,-0.6767405272,-0.4408147037,0.0251210853,0.046009697,-0.0332321338,0.2000237256,-0.1751340479,0.3521326184,-0.0047277925,0.1999831498,0.430185914,-0.1823700368,-0.0675364807,-0.421084106,-0.0479820445,0.048952315,-0.1104324088,0.4257763326,0.0662507042,0.0844967216,0.2618041635,0.0360832065,0.1915479749,-0.1473145038,0.0495125614,0.0701558739,-0.3530410528,0.0856516659,0.055507049,-0.1457139701,0.1035256162,-0.1434553862,-0.168143332,0.1927152425,-0.3029265404,-0.7353894711,0.2379848659,-0.0126784286,-0.1675952822,0.027924424,-0.0007104453,-0.1402493715,0.2118350416,0.1246928424,-0.2877166867,-0.2155011594,0.0689764768,0.2514443398,0.0879586414,-0.0275266338,-0.1178222597,-0.0540140606,0.26175946,0.4194566607,0.0281710755,-0.0429728068,0.5366603136,0.2259910554,0.1409419775,0.1608157605,0.0340641066,0.24411273,-0.0328771435,0.0478120819,0.3331083655,0.1005686298,-0.1979617476,-0.0977448374,0.4207776785,0.1251930445,0.3327291906,-0.0869361833,0.2726321518,-0.2567584515,0.0971228853,-0.1023771539,-0.2068930119,-0.0779832676,-0.4906929433,-0.0203643702,-0.2348927855,0.0249764565,0.1215319857,0.4332318902,0.1995496154,0.1523185372,-0.076057896,-0.2277942002,-0.0624574721,0.0835695267,-0.1111955717,0.4364539385,0.1562025398,0.0343498029,0.2055181116,-0.1461104602,-0.095109567,0.1814320683,0.4817385674,-0.0805645362,0.2435981035,-0.0937529802,-0.1131483465,-0.0635224879,0.2218562812,0.0521538183,-0.0792746693,-0.5283737779,0.2364412844,-0.0392129347,0.2011196017,-0.2159098983,0.0986009464,-0.1711424738,-0.7540907264,-0.1033217013,0.0821495727,-0.1489276886,0.3635009825,-0.2049851418,0.2673458159,-0.0496508144,0.0223412737,0.036626853,-0.3193083107,-0.1708577275,-0.1480315328,0.2124335468,-0.0287868083,0.3443077803,0.3873269558,0.2164435089,0.0731538311,-0.4658792913,-0.1431510746,0.081477575,0.918420434,-0.0383187644,-0.1673643887,-0.3328225613,-0.0775397047,0.354608506,-0.2792412341,0.0753313825,0.1667623371,0.0544317216,0.1211809143,-0.3318283856,-0.1408891678,0.0599886477,-0.1981328428,-0.1365239322,-0.1284700185,-0.2889370024,0.3429214656,0.1867969185,0.0009099875,-0.0350905024,-0.0836620182,-0.097604759,0.0677944645,0.3448297381,-0.1971108466,-0.2251143754,0.0398046635,-0.0092014624,0.2385275364,-0.0336470567,-0.3195250928,-0.4439285696,0.2071544677,0.1057022214,0.0439924188,0.1653718948,-0.0892879516,-0.4593803585,0.1171314344,-0.2870640457,-0.219100967,0.3560729325,-0.2641616166,0.1047707945,-0.1369574219,0.3790746927,0.2572436631,0.6966174245,0.0503653102,0.1582950801,-0.0299590658,0.0022024186,0.2247810364,0.0723269656,-0.31291243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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2669","title":"Metric kwargs are not passed to underlying external metric f1_score","comments":"Thanks, that was it. A bit strange though, since `load_metric` had an argument `metric_init_kwargs`. I assume that that's for specific initialisation arguments whereas `average` is for the function itself.","body":"## Describe the bug\r\nWhen I want to use F1 score with average=\"min\", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html) throws an error telling me so.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nf1 = datasets.load_metric(\"f1\", keep_in_memory=True, average=\"min\")\r\nf1.add_batch(predictions=[0,2,3], references=[1, 2, 3])\r\nf1.compute()\r\n```\r\n\r\n## Expected results\r\nNo error, because `average=\"min\"` should be passed correctly to f1_score in sklearn.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\datasets\\metric.py\", line 402, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\metrics\\f1\\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\\f1.py\", line 97, in _compute\r\n    \"f1\": f1_score(\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1071, in f1_score\r\n    return fbeta_score(y_true, y_pred, beta=1, labels=labels,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1195, in fbeta_score\r\n    _, _, f, _ = precision_recall_fscore_support(y_true, y_pred,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1464, in precision_recall_fscore_support\r\n    labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1294, in _check_set_wise_labels\r\n    raise ValueError(\"Target is %s but average='binary'. Please \"\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.9.2\r\n- PyArrow version: 4.0.1","comment_length":29,"text":"Metric kwargs are not passed to underlying external metric f1_score \n ## Describe the bug\r\nWhen I want to use F1 score with average=\"min\", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html) throws an error telling me so.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\nf1 = datasets.load_metric(\"f1\", keep_in_memory=True, average=\"min\")\r\nf1.add_batch(predictions=[0,2,3], references=[1, 2, 3])\r\nf1.compute()\r\n```\r\n\r\n## Expected results\r\nNo error, because `average=\"min\"` should be passed correctly to f1_score in sklearn.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\datasets\\metric.py\", line 402, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\metrics\\f1\\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\\f1.py\", line 97, in _compute\r\n    \"f1\": f1_score(\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1071, in f1_score\r\n    return fbeta_score(y_true, y_pred, beta=1, labels=labels,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1195, in fbeta_score\r\n    _, _, f, _ = precision_recall_fscore_support(y_true, y_pred,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\r\n    return f(*args, **kwargs)\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1464, in precision_recall_fscore_support\r\n    labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n  File \"C:\\Users\\bramv\\.virtualenvs\\pipeline-TpEsXVex\\lib\\site-packages\\sklearn\\metrics\\_classification.py\", line 1294, in _check_set_wise_labels\r\n    raise ValueError(\"Target is %s but average='binary'. Please \"\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.9.2\r\n- PyArrow version: 4.0.1 \n Thanks, that was it. A bit strange though, since `load_metric` had an argument `metric_init_kwargs`. I assume that that's for specific initialisation arguments whereas `average` is for the function itself.","embeddings":[-0.0516288541,-0.5501691103,0.0873140618,0.2038566321,0.4681911767,-0.0643797889,0.1748601645,-0.1953885257,0.3451755941,0.2828977704,0.0679134801,0.4462169111,0.1783719808,0.0242547859,-0.0182039514,0.0616982467,0.1058040708,-0.3587132096,-0.0846228451,-0.2987218797,-0.4068837166,0.0102044912,-0.4048623741,0.0202550888,-0.2006222904,0.0613628998,-0.0347853079,-0.1700376719,0.0016798849,-0.003900961,0.3077568412,0.1265656352,-0.0970672965,0.3663867116,-0.0001235781,-0.0986129567,0.4526257515,-0.1368988007,-0.0371827036,-0.0111093875,-0.1764513552,0.014776093,-0.1244774237,-0.3509167433,0.1918561906,0.1058521941,-0.1739191711,-0.3824925721,0.2338166088,0.3685019314,0.0636828318,0.1573851407,-0.2092087269,-0.1720214784,-0.2959969938,-0.1044454128,-0.1850389093,0.4786297977,-0.6841754317,-0.0733038336,-0.1676173359,0.0917701647,0.1501622796,-0.0126522053,0.7468219995,0.0399315543,0.3019666672,0.1136633381,0.1792099476,-0.1155982167,0.0679363832,-0.3869571686,-0.403575331,-0.1462424695,0.0940086693,-0.7693849206,0.1077826694,0.0144160725,-0.2267361879,-0.092972286,-0.1046825647,-0.1168835163,-0.3561751246,0.0764986202,-0.050303366,0.6123213172,0.0015167097,0.0829183236,0.192719996,0.1838299781,0.0406339429,-0.2930784225,0.0284065641,0.071691215,-0.2631740272,0.2108209878,-0.0395305119,-0.0636173189,0.27936849,0.0515006147,-0.0760850012,0.0437829681,0.430503428,0.0532589592,0.197408691,0.5313013792,0.4781782627,0.3300953805,-0.0164008401,0.0775587261,-0.1973581165,-0.2480203807,0.271512419,-0.0058645201,0.2495119125,0.1664949208,0.0198457837,-0.07760185,-0.2983479202,-0.0815996081,0.0488367118,-0.013127977,0.0346073136,0.159644261,-0.0479189232,0.3419955671,0.0582272336,0.3695012033,-0.112433292,-0.0151641183,-0.2224202901,0.1949929297,-0.4361763597,0.1441707015,-0.2022401094,-0.1239357069,0.5087715983,-0.0023832025,0.606444478,-0.4638635218,-0.0930645168,0.0385823548,-0.0192406494,-0.012212811,-0.4288246334,0.2412556112,0.033799082,0.0614981018,0.0438620262,-0.0164111089,-0.3245539367,-0.0843018368,0.4818834066,0.041315563,-0.2956653833,-0.132889986,-0.1859076768,0.2178625315,0.0208467692,0.114158757,0.0367673673,-0.1453685611,-0.5320333838,-0.2027044743,0.7653902769,0.0672897398,0.1244359612,-0.2514540553,-0.1332684457,0.0805685669,-0.0005603431,0.0552252233,0.0327283815,0.2792529762,-0.0457489416,0.0623041987,0.2406337559,-0.6767405272,-0.4408147037,0.0251210853,0.046009697,-0.0332321338,0.2000237256,-0.1751340479,0.3521326184,-0.0047277925,0.1999831498,0.430185914,-0.1823700368,-0.0675364807,-0.421084106,-0.0479820445,0.048952315,-0.1104324088,0.4257763326,0.0662507042,0.0844967216,0.2618041635,0.0360832065,0.1915479749,-0.1473145038,0.0495125614,0.0701558739,-0.3530410528,0.0856516659,0.055507049,-0.1457139701,0.1035256162,-0.1434553862,-0.168143332,0.1927152425,-0.3029265404,-0.7353894711,0.2379848659,-0.0126784286,-0.1675952822,0.027924424,-0.0007104453,-0.1402493715,0.2118350416,0.1246928424,-0.2877166867,-0.2155011594,0.0689764768,0.2514443398,0.0879586414,-0.0275266338,-0.1178222597,-0.0540140606,0.26175946,0.4194566607,0.0281710755,-0.0429728068,0.5366603136,0.2259910554,0.1409419775,0.1608157605,0.0340641066,0.24411273,-0.0328771435,0.0478120819,0.3331083655,0.1005686298,-0.1979617476,-0.0977448374,0.4207776785,0.1251930445,0.3327291906,-0.0869361833,0.2726321518,-0.2567584515,0.0971228853,-0.1023771539,-0.2068930119,-0.0779832676,-0.4906929433,-0.0203643702,-0.2348927855,0.0249764565,0.1215319857,0.4332318902,0.1995496154,0.1523185372,-0.076057896,-0.2277942002,-0.0624574721,0.0835695267,-0.1111955717,0.4364539385,0.1562025398,0.0343498029,0.2055181116,-0.1461104602,-0.095109567,0.1814320683,0.4817385674,-0.0805645362,0.2435981035,-0.0937529802,-0.1131483465,-0.0635224879,0.2218562812,0.0521538183,-0.0792746693,-0.5283737779,0.2364412844,-0.0392129347,0.2011196017,-0.2159098983,0.0986009464,-0.1711424738,-0.7540907264,-0.1033217013,0.0821495727,-0.1489276886,0.3635009825,-0.2049851418,0.2673458159,-0.0496508144,0.0223412737,0.036626853,-0.3193083107,-0.1708577275,-0.1480315328,0.2124335468,-0.0287868083,0.3443077803,0.3873269558,0.2164435089,0.0731538311,-0.4658792913,-0.1431510746,0.081477575,0.918420434,-0.0383187644,-0.1673643887,-0.3328225613,-0.0775397047,0.354608506,-0.2792412341,0.0753313825,0.1667623371,0.0544317216,0.1211809143,-0.3318283856,-0.1408891678,0.0599886477,-0.1981328428,-0.1365239322,-0.1284700185,-0.2889370024,0.3429214656,0.1867969185,0.0009099875,-0.0350905024,-0.0836620182,-0.097604759,0.0677944645,0.3448297381,-0.1971108466,-0.2251143754,0.0398046635,-0.0092014624,0.2385275364,-0.0336470567,-0.3195250928,-0.4439285696,0.2071544677,0.1057022214,0.0439924188,0.1653718948,-0.0892879516,-0.4593803585,0.1171314344,-0.2870640457,-0.219100967,0.3560729325,-0.2641616166,0.1047707945,-0.1369574219,0.3790746927,0.2572436631,0.6966174245,0.0503653102,0.1582950801,-0.0299590658,0.0022024186,0.2247810364,0.0723269656,-0.3129124343,0.1803696454,0.0302197263,-0.1768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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2663","title":"[`to_json`] add multi-proc sharding support","comments":"Hi @stas00, \r\nI want to work on this issue and I was thinking why don't we use `imap` [in this loop](https:\/\/github.com\/huggingface\/datasets\/blob\/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84\/src\/datasets\/io\/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert  pyarrow table to `json` using multiprocessing. I've a small code snippet for some clarity:\r\n```\r\nresult = list(\r\n            pool.imap(self._apply_df, [(offset, batch_size) for offset in range(0, len(self.dataset), batch_size)])\r\n        )\r\n```\r\n`_apply_df` is a function which will return `batch.to_pandas().to_json(path_or_buf=None, orient=\"records\", lines=True)` which is basically json version of the batched pyarrow table. Later on we can concatenate it to form json file? \r\n\r\nI think the only downside here is to write file from `imap` output (output would be a list and we'll need to iterate over it and write in a file) which might add a little overhead cost. What do you think about this?","body":"As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR.\r\n\r\nI implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards?\r\n\r\nI think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process.\r\n\r\nI'm not sure whether the user should be responsible to concatenate the shards at the end  or `datasets`, either way works for my needs.\r\n\r\nThe code I was using:\r\n\r\n```\r\nfrom multiprocessing import cpu_count, Process, Queue\r\n\r\n[...]\r\n\r\nfiltered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count())\r\n\r\nDATASET_NAME = \"oscar\"\r\nSHARDS = 10\r\ndef process_shard(idx):\r\n    print(f\"Sharding {idx}\")\r\n    ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True)\r\n    # ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling\r\n    print(f\"Saving {DATASET_NAME}-{idx}.jsonl\")\r\n    ds_shard.to_json(f\"{DATASET_NAME}-{idx}.jsonl\", orient=\"records\", lines=True, force_ascii=False)\r\n\r\nqueue = Queue()\r\nprocesses = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)]\r\nfor p in processes:\r\n    p.start()\r\n\r\nfor p in processes:\r\n    p.join()\r\n```\r\n\r\nThank you!\r\n\r\n@lhoestq ","comment_length":139,"text":"[`to_json`] add multi-proc sharding support \n As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR.\r\n\r\nI implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards?\r\n\r\nI think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process.\r\n\r\nI'm not sure whether the user should be responsible to concatenate the shards at the end  or `datasets`, either way works for my needs.\r\n\r\nThe code I was using:\r\n\r\n```\r\nfrom multiprocessing import cpu_count, Process, Queue\r\n\r\n[...]\r\n\r\nfiltered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count())\r\n\r\nDATASET_NAME = \"oscar\"\r\nSHARDS = 10\r\ndef process_shard(idx):\r\n    print(f\"Sharding {idx}\")\r\n    ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True)\r\n    # ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling\r\n    print(f\"Saving {DATASET_NAME}-{idx}.jsonl\")\r\n    ds_shard.to_json(f\"{DATASET_NAME}-{idx}.jsonl\", orient=\"records\", lines=True, force_ascii=False)\r\n\r\nqueue = Queue()\r\nprocesses = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)]\r\nfor p in processes:\r\n    p.start()\r\n\r\nfor p in processes:\r\n    p.join()\r\n```\r\n\r\nThank you!\r\n\r\n@lhoestq  \n Hi @stas00, \r\nI want to work on this issue and I was thinking why don't we use `imap` [in this loop](https:\/\/github.com\/huggingface\/datasets\/blob\/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84\/src\/datasets\/io\/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert  pyarrow table to `json` using multiprocessing. I've a small code snippet for some clarity:\r\n```\r\nresult = list(\r\n            pool.imap(self._apply_df, [(offset, batch_size) for offset in range(0, len(self.dataset), batch_size)])\r\n        )\r\n```\r\n`_apply_df` is a function which will return `batch.to_pandas().to_json(path_or_buf=None, orient=\"records\", lines=True)` which is basically json version of the batched pyarrow table. Later on we can concatenate it to form json file? \r\n\r\nI think the only downside here is to write file from `imap` output (output would be a list and we'll need to iterate over it and write in a file) which might add a little overhead cost. What do you think about this?","embeddings":[-0.242046684,-0.3245055377,-0.0334172063,-0.0440206081,-0.0977220312,-0.0860110968,0.4022955596,0.110401459,-0.0120916022,0.3323682249,-0.0399441943,0.2764456272,-0.1313705295,0.220484823,-0.2396433651,-0.0810559019,0.1407508403,-0.1243918613,0.4335382879,0.1816553771,-0.2526787221,0.3090566695,-0.1334240586,-0.2920532227,-0.1443659961,-0.1116204932,0.1702491939,0.1693739146,0.1813992709,-0.2050732076,-0.2966971099,0.1466355026,-0.1574244648,-0.0663380995,-0.0001212066,-0.0165756941,0.3360563517,-0.0251887646,0.1674968004,0.0298161469,-0.2610717416,-0.2657977343,-0.1118150875,-0.3679598272,0.1589178443,0.2573316395,0.079610385,-0.1090120748,0.2735993862,0.1824122667,0.0266379192,-0.0381312259,0.2731877565,0.1871727109,-0.1231689528,-0.0584274754,-0.134199053,-0.1373107284,0.1717839539,0.2883924544,0.1722884029,0.2656449974,-0.071409367,-0.0292127803,-0.0493074544,0.0790251568,0.0676737875,-0.2999537587,-0.0728117675,0.1627262384,-0.1009485945,-0.0941404179,-0.3561589122,-0.2951528728,-0.1113355607,-0.5862678289,-0.0910202265,-0.0569902956,-0.0046954835,0.0016200872,-0.2583362758,-0.1352896541,-0.1115969121,-0.0975015834,0.055972673,-0.0492389314,0.1559401751,0.2313601971,0.4239072502,-0.116016157,-0.2813234031,-0.036755722,0.1901650429,-0.0995850414,-0.318210125,-0.2331991494,0.067072466,-0.2741035819,-0.1916322261,0.2405323833,-0.0652907044,0.3501672149,0.1451126337,0.2800191641,-0.0082594501,-0.3676018119,0.1055693775,-0.2028030753,0.0866219476,0.0854692459,-0.0438373163,0.0142234955,0.1041972712,-0.1864198446,0.2379478663,0.0610435084,-0.224745959,0.0134621486,-0.1326915771,0.1245782599,0.0409150533,-0.1848429441,-0.1038549617,0.1181193963,0.1211151928,0.45546031,-0.2897269428,0.1267385185,-0.1991168708,0.0845798627,-0.0060030092,0.1752078086,-0.1212147102,0.3038285971,-0.0945648476,0.3363530636,-0.1660330445,-0.1813056916,0.4267489612,0.0143280402,0.0071663768,-0.16150783,-0.0277643669,0.3133506477,-0.052582223,-0.0263768546,-0.0027026199,-0.6090400219,-0.5215034485,0.0872401595,-0.424731642,-0.5617137551,0.0674493909,-0.0105560878,0.0413319431,0.053050071,-0.7100686431,0.1912888587,0.1468509734,0.0172505639,-0.2996538877,-0.0313656293,-0.3974209726,-0.3379457891,0.0040908754,0.1846761703,-0.0345499441,0.1666767001,-0.1057315916,0.1573319584,0.4911988974,0.3862863481,0.0417262204,0.1512308419,0.1384150386,0.4020533562,0.1216073856,-0.2359776348,-0.3532421887,0.343623966,-0.1950227618,0.1283876598,0.58977139,0.1619928926,0.1941811889,-0.1963891238,0.5543405414,0.084137179,-0.0459819511,0.2191111892,-0.288382709,-0.0190754812,-0.6522377133,0.2896907926,0.0680862591,0.0845365077,0.1911191493,-0.2741482556,0.2790185213,-0.1253771037,0.0269500688,0.1180229038,0.2163124979,-0.1152399108,-0.263433367,0.1062837616,-0.0953335464,0.3388625681,0.2482239306,0.1104214638,-0.0917066708,-0.1606258154,0.2462689579,0.1796581149,0.0966826454,0.1453395188,-0.0540784188,-0.2201896161,-0.148558557,-0.0962636843,-0.0684250891,0.5936162472,0.1674351841,-0.0775221288,-0.4122172594,0.1772023886,0.0901107192,-0.1776477545,0.0167182907,0.1800415516,0.0830760226,-0.1811238974,0.2029942572,0.0858528763,0.2311604619,0.1759352535,-0.020178318,0.3995901942,0.058495447,0.1984225512,0.0361033008,-0.2241056561,0.0027489795,-0.2517606318,-0.1687451601,0.7329337001,0.2339997739,0.5969922543,0.1784469783,-0.1098150834,0.2974082232,0.0058557852,0.0032608882,-0.0948940367,0.1027392,0.433375895,-0.4786273241,-0.2609365582,-0.3565048575,0.4948657155,0.325558573,0.161405161,-0.2580355406,0.29643327,0.3624889851,0.0195348449,0.1627064049,0.3779317737,0.6566362381,0.2248315811,-0.1344006956,-0.0622735098,0.0452874824,-0.3232685626,0.050376039,0.09435381,0.369332552,0.0394429304,0.207812205,0.015269028,0.0442837626,-0.5829306841,-0.0670747459,0.0321427658,-0.147529617,0.1656359881,-0.1907572448,0.5931070447,0.0694528222,0.0002796644,0.1994153708,-0.4266071022,-0.018969696,0.5152558684,-0.15105775,-0.1607409716,0.1470748484,0.0544022396,-0.3848678172,-0.3304622769,-0.2615265548,-0.0200198349,0.1954409033,-0.0881892592,0.3558399677,-0.0667843595,0.2389365733,-0.0046784831,0.0458213799,0.0259417854,-0.189389497,-0.0488842279,-0.1514607072,0.1716158539,-0.0206062961,-0.1644494385,-0.0581386164,-0.2877838314,0.2799780369,0.1137813032,-0.0842128918,0.2283849418,-0.2359640598,-0.1618583351,0.1569346488,0.0827050433,-0.2531999052,-0.2911528945,0.5311349034,0.1381748021,0.3013705313,-0.4351008534,0.1738480628,-0.1655333787,0.0203211103,0.0540141091,0.0509408452,-0.4110952616,0.3229976892,-0.0502166562,-0.0940238982,-0.1275964826,-0.0198118761,0.1817742884,0.1251268536,-0.2338487804,-0.386282891,-0.2070031315,0.1083227545,-0.0194043163,-0.2067757547,0.4428941905,0.1868667454,-0.1519838125,0.1370428652,-0.1170813367,0.1773805022,-0.1496586055,-0.1970183253,-0.0474350415,0.5124171376,0.1980293244,0.6017048359,0.256328851,-0.0557649992,0.2804168165,0.1276686937,-0.1844818592,-0.1950068325,-0.1390612125,-0.0172410626,-0.1265537441,0.03351020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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2655","title":"Allow the selection of multiple columns at once","comments":"Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense. \r\nIs there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like one? Please let me know if I'm missing something.","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nSimilar to pandas, it would be great if we could select multiple columns at once.\r\n\r\n\r\n**Describe the solution you'd like**\r\n```python\r\nmy_dataset = ...  # Has columns ['idx', 'sentence', 'label']\r\nidx, label = my_dataset[['idx', 'label']]\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nwe can do `[dataset[col] for col in ('idx', 'label')]`\r\n\r\n**Additional context**\r\nThis is of course very minor.\r\n","comment_length":64,"text":"Allow the selection of multiple columns at once \n **Is your feature request related to a problem? Please describe.**\r\n\r\nSimilar to pandas, it would be great if we could select multiple columns at once.\r\n\r\n\r\n**Describe the solution you'd like**\r\n```python\r\nmy_dataset = ...  # Has columns ['idx', 'sentence', 'label']\r\nidx, label = my_dataset[['idx', 'label']]\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nwe can do `[dataset[col] for col in ('idx', 'label')]`\r\n\r\n**Additional context**\r\nThis is of course very minor.\r\n \n Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense. \r\nIs there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like one? Please let me know if I'm missing something.","embeddings":[-0.0573705472,-0.2296809554,-0.196985513,0.0512581915,0.2014224976,0.2186503857,0.5181803107,0.1173711866,0.3343811333,0.4082842171,-0.1473215073,0.3830779195,0.0007235444,0.2195994854,-0.2610401511,-0.3490301371,-0.1463272572,0.1411355287,0.0857272297,0.0135873593,-0.1727700979,-0.3008389771,-0.34792009,0.0155922901,-0.0639111847,-0.2046385854,-0.2487484068,-0.2731154859,-0.374528259,-0.2284196466,-0.0509770252,0.4818705618,-0.0998930559,0.0420988575,-0.0001022023,-0.2425405979,0.0494161844,0.0450042561,-0.372803688,-0.3197800517,-0.1985395104,-0.2248339653,0.3259703517,-0.3805671632,-0.1459850371,-0.176906988,-0.2034198344,-0.2526134551,-0.0332642533,0.087867476,0.3098186553,-0.014179701,-0.212574631,0.0007960759,-0.0395887345,0.1632219702,-0.1948770136,-0.0862278566,0.4251423776,-0.022268353,0.2404662669,0.1023098603,-0.2389610261,0.0799369439,0.0789678991,0.1655120254,0.2781155109,-0.2613496184,0.0809774324,0.2927647829,0.7717722058,-0.1894667894,-0.4718903303,-0.2102407068,0.4684536457,-0.1977852434,-0.3191841245,0.2664964199,-0.2287949473,0.2275188863,0.1129403636,-0.2688922286,-0.1090144664,0.1486848146,-0.155721277,0.3326571584,-0.0430136062,0.0551568158,0.131431073,-0.0222349931,0.1233634427,-0.1049084663,0.1292737126,0.1556303203,-0.6503581405,-0.2172845006,-0.2239073962,-0.2029287517,0.1681587398,0.2159994692,-0.0236092377,-0.0231380388,-0.1188300475,0.316463232,0.4838551879,-0.0458058529,-0.0439748168,0.389067024,-0.0343437567,-0.1517443806,0.0094906045,-0.0180744138,-0.091057241,0.0409744494,0.0095805982,0.2227684557,0.0332288779,0.0254826564,-0.2143536806,-0.2053003162,-0.1048714966,-0.2566322088,-0.06128674,0.4625502229,0.1120596603,0.3907713294,-0.1936305612,0.2314957231,0.2148041874,-0.2290373892,-0.2068743706,0.2864536047,0.1582296789,-0.1184982359,0.1220543459,0.0670838952,-0.1788309067,0.3173830509,0.0300921556,-0.3956840336,-0.1217478663,-0.3682628572,0.0412878841,0.1645923257,-0.0171067212,0.1042750329,0.2690171897,-0.2168727666,-0.3145562708,0.3942932487,0.0467404574,-0.1573522538,-0.2171412557,0.1881261617,0.1842950583,-0.3066887558,-0.0916226506,0.5061647296,-0.0624875426,0.3623635471,0.1700692475,0.0290999245,0.0495873243,-0.3428075016,-0.0607744679,0.3431543112,-0.5089313388,0.0842315033,0.1649499536,-0.0967291221,-0.0080227749,0.1633627415,-0.2780387402,0.086305052,0.2251906097,0.0358405933,0.3047424257,-0.3884338439,-0.1144216284,0.1912813038,0.0785425976,0.1773522496,0.458563298,0.1077365652,0.1881625503,0.2333642244,0.057728298,0.1933408678,0.0248733461,0.0773601532,0.2218871117,-0.4066192508,0.2764487863,0.1477501839,-0.1539059281,0.1683893204,-0.1599247307,-0.1816745698,0.3445219994,-0.2589389384,-0.1385620385,0.1810883731,0.3131214976,0.1123533547,-0.0972969234,-0.4043030441,-0.2775419056,0.1191289872,0.4883978069,0.1380021274,0.0122433724,-0.4381889105,-0.1534580588,0.0582767762,-0.1595164239,0.1654387265,0.2506510615,-0.2094160467,-0.1075731367,-0.1019803286,-0.5070766211,0.3666983247,-0.1956880093,0.1371779293,0.1988463253,0.1711434573,0.0042811586,0.1743839979,0.0299108475,0.0195631087,0.200883016,0.1793606728,0.1491049379,0.0841175914,0.1976446509,-0.0701240003,-0.3016873598,-0.0303100739,0.074177511,0.1923077404,-0.0160453785,-0.2838518322,0.1254215091,0.0264123604,-0.1852659434,0.0936241671,-0.1243551448,0.231679529,0.0130066602,0.0152296619,0.1907401383,-0.1493622959,-0.1304418594,-0.3635964096,0.0054596518,-0.0161964651,-0.0854954123,-0.0368625186,-0.4798964262,-0.023961775,0.0446463041,0.1415292472,0.1112430543,0.0083408942,0.206122309,0.1885337234,-0.0144463023,0.2635808587,0.3555055261,0.2335148752,-0.2185611576,-0.1486164629,-0.0873393267,0.0437040031,0.177231133,0.1707769781,0.0495433249,0.198735714,0.1211190522,0.0136511913,-0.156597212,-0.1618948281,0.2901345193,0.1482460499,-0.0390661284,-0.0574769676,-0.4436930418,-0.3970943987,0.2507375777,-0.1709859669,0.2013518363,-0.2210760862,-0.0407893099,-0.0284860525,0.0827126801,-0.1632941365,-0.1330616027,0.3956558108,-0.0260782279,-0.0435184315,0.1253520697,-0.0867817253,0.2436129451,0.1832947284,0.0363313928,0.0684396252,0.1590244025,0.509021461,-0.1408690959,-0.282323271,0.0954570919,0.0301749706,-0.1105845273,0.157165274,0.2084631026,0.2270528078,-0.0286244433,-0.0005949585,0.1542893797,0.5449559689,-0.0339258127,-0.1696295887,0.293104887,-0.0085181464,-0.0150808459,-0.1630134434,-0.4817564189,-0.3427665234,0.2621435821,-0.0571562424,0.1788183749,-0.0686620474,-0.0465505086,-0.3099000156,0.1274475306,0.3333838582,-0.3092621863,0.1138573214,0.1015176699,-0.1127338856,-0.1350675225,-0.2750650048,0.0670812726,0.0355206728,-0.1064128503,-0.0677800998,-0.2876259089,-0.2491898388,-0.1500188261,-0.223416701,0.145767495,0.0861834884,0.1993306428,-0.0843114704,-0.0341170803,-0.1865898669,0.0430478305,0.3065355122,-0.1867840141,0.1513662636,0.2028809041,0.0744919926,0.0181314535,0.4157295823,0.2260370255,0.2796542943,-0.0739429668,-0.037148688,0.0357310697,-0.3922090232,-0.3193703592,-0.204772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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2655","title":"Allow the selection of multiple columns at once","comments":"Hi! Sorry for the delay.\r\n\r\nIn this case, the dataset would be a `datasets.Dataset` and we want to select multiple columns, the `idx` and `label` columns for example.\r\n\r\nMy issue is that my dataset is too big for memory if I load everything into pandas.","body":"**Is your feature request related to a problem? Please describe.**\r\n\r\nSimilar to pandas, it would be great if we could select multiple columns at once.\r\n\r\n\r\n**Describe the solution you'd like**\r\n```python\r\nmy_dataset = ...  # Has columns ['idx', 'sentence', 'label']\r\nidx, label = my_dataset[['idx', 'label']]\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nwe can do `[dataset[col] for col in ('idx', 'label')]`\r\n\r\n**Additional context**\r\nThis is of course very minor.\r\n","comment_length":45,"text":"Allow the selection of multiple columns at once \n **Is your feature request related to a problem? Please describe.**\r\n\r\nSimilar to pandas, it would be great if we could select multiple columns at once.\r\n\r\n\r\n**Describe the solution you'd like**\r\n```python\r\nmy_dataset = ...  # Has columns ['idx', 'sentence', 'label']\r\nidx, label = my_dataset[['idx', 'label']]\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nwe can do `[dataset[col] for col in ('idx', 'label')]`\r\n\r\n**Additional context**\r\nThis is of course very minor.\r\n \n Hi! Sorry for the delay.\r\n\r\nIn this case, the dataset would be a `datasets.Dataset` and we want to select multiple columns, the `idx` and `label` columns for example.\r\n\r\nMy issue is that my dataset is too big for memory if I load everything into pandas.","embeddings":[-0.112305522,-0.3079831898,-0.1617852896,0.17450881,0.276807785,0.2954530716,0.4830079079,0.1078617051,0.2709566057,0.4354278445,-0.1071548834,0.1811603904,-0.0057086572,0.1110229865,-0.1205765307,-0.4149233699,-0.1460112482,0.1239385754,0.1093645617,0.1609404683,-0.1801461279,-0.4086984694,-0.2840020657,-0.0011803384,-0.092296347,-0.1219144464,-0.3127509058,-0.3032180369,-0.3770621419,-0.2049789727,-0.0196613744,0.3044013083,-0.0134685794,0.1382125765,-0.0001026555,-0.2590750456,0.1286849827,0.1175028831,-0.244921267,-0.1337271929,-0.2202558815,-0.339571625,0.2827405035,-0.3361616433,-0.0875567272,-0.1732636839,-0.2556638122,-0.1358377486,-0.260029763,0.0658948272,0.3234616816,0.152897656,-0.1889439076,0.0023041901,0.0063465508,0.1277681589,-0.2579077184,-0.1608469784,0.4758292139,-0.1474863589,0.3507170975,-0.0039973366,-0.1867642105,0.0326898396,0.0836206153,0.1034264043,0.2566024959,-0.2503734529,0.1066819206,0.2868083417,0.7255767584,-0.1725481153,-0.4039957821,-0.1537713856,0.5212610364,-0.3376422524,-0.2131011933,0.5081314445,-0.1903934181,0.1000797153,-0.0466958694,-0.3265556097,-0.0729803517,0.2228528261,-0.1081898361,0.2732259333,0.0282639675,0.03926358,0.3872614205,-0.0016128597,0.2152474821,-0.1840659678,0.1768385917,0.2321210653,-0.7773068547,-0.2501906157,-0.2356759906,-0.1116905734,0.2165343165,0.1640052348,0.027720917,-0.1002437547,-0.182840392,0.2871480584,0.4455347359,-0.0151288416,-0.3066451252,0.3155571222,0.1022447273,-0.0562237911,0.1859585643,-0.0062980005,0.0271456502,0.0207334477,0.2195993811,0.1581209749,-0.1780441105,0.0976189449,-0.0328994766,-0.26516518,-0.0894478783,-0.2567326725,-0.0282300059,0.367639184,0.0666408986,0.3588038385,-0.1916099191,0.1649855226,0.1718434393,-0.2895754874,-0.2468194515,0.3928829134,0.0549767315,-0.0291879643,0.2498265207,-0.0250980668,-0.2088542879,0.373406291,-0.0406710505,-0.3881934583,-0.049061574,-0.413400948,-0.014645732,0.2703150511,0.0037309716,0.0997229442,0.1696856171,-0.0508779138,-0.3953902125,0.4790834486,-0.002467064,-0.3585241437,-0.0582644343,0.1234737337,0.2299954742,-0.125632748,-0.2516845167,0.3921030462,0.0448530354,0.4098561406,0.2616648972,0.0018020573,0.0157517269,-0.2986845672,-0.0468573235,0.3674305379,-0.3242014647,0.1272618324,0.0768462941,-0.1038113534,-0.0715092868,0.2260803282,-0.2931676209,-0.0978576913,0.1805267334,-0.013063659,0.2161767185,-0.2442466021,-0.090066433,0.0857360959,0.0422934406,0.0927949771,0.4033911824,0.2204860449,-0.0306513738,0.2627088726,0.057953164,0.3283009529,-0.052146107,0.2525752187,0.1994085312,-0.4831075668,0.2263852805,0.1378232837,0.0445587449,0.0151862036,-0.1832596809,-0.0865488648,0.2737626135,-0.1824587137,-0.1385209858,0.2540957034,0.1792686433,0.0199871175,-0.1123359725,-0.394777298,-0.301106602,0.1568817049,0.4884350598,0.0642902479,0.1696430743,-0.2737737894,0.039078299,0.0045270924,-0.0981860906,0.2652836144,0.1543953717,-0.2062114626,-0.0029782839,-0.1011045724,-0.3993161023,0.4738367796,-0.229688406,0.1321178377,0.0275053978,0.2511871159,0.0649993196,0.124628894,0.164604485,-0.1319043934,0.2843926549,0.1517538428,0.0908201858,0.1125609502,0.1458586454,0.0646826997,-0.3552847505,0.1177156195,0.1671918184,0.1794776767,0.0260349084,-0.3332150877,0.0846312642,0.0246968698,-0.123212263,0.0588422939,-0.3414193094,0.2632939517,0.1129575223,-0.0123408604,0.1553319991,-0.1083925366,-0.1047378629,-0.1918958426,0.1666090488,0.0842899159,-0.2391910702,-0.0248147231,-0.6293264627,-0.1653970778,-0.0721647069,0.1629372388,0.0634677485,-0.0024512354,0.1556061655,0.20158723,0.0425239652,0.1142363697,0.47955966,0.1736518294,-0.2230082452,-0.1962168813,-0.0580710843,0.0025859727,0.183200106,0.2066932321,0.0457415655,0.1509783417,0.2637327015,-0.0220886581,-0.1352917701,-0.0007264406,0.4631795585,0.1383932978,0.0121772531,-0.0780441687,-0.4353890717,-0.2944473624,0.1450490654,-0.2059286833,0.1311485469,-0.0954132006,-0.0634456277,0.1592476219,0.1958408058,-0.2035662979,-0.292973578,0.5270133018,-0.0790494829,0.0136274798,0.1328639686,0.1082940251,0.2760705352,0.1199759021,0.1261203885,0.0356749892,0.1787355095,0.6488932371,-0.1277385652,-0.3254690766,0.1147138327,-0.0649893209,-0.0020178144,0.0803012699,0.0581910424,0.1841234267,0.0059469636,-0.0061487369,0.1868944615,0.4603095055,0.0214347299,-0.1087111086,0.2561409175,0.0313772298,-0.003277506,-0.0456619225,-0.4483949244,-0.3211632371,0.232732892,-0.1309588999,0.1882290244,-0.1114174053,-0.0094253719,-0.4182296693,0.0695729703,0.100777097,-0.2108718604,0.0373317115,0.1368307322,-0.1218452677,-0.1473861635,-0.2751776278,0.1980687529,0.0036962135,-0.0153313,-0.1016477272,-0.18618159,-0.365926832,-0.2651929557,-0.2874382138,0.1633550972,0.1649812758,0.1100185066,-0.0244297124,0.0806228667,-0.2668939233,-0.0728356987,0.345305264,-0.0653501973,0.203998521,0.2225221545,0.0862639323,0.0524850711,0.364397645,0.2674494982,0.3109446466,-0.0122172162,-0.1367586702,0.0528562814,-0.5359568596,-0.3218106329,-0.203645587,0.1261132509,0.063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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2654","title":"Give a user feedback if the dataset he loads is streamable or not","comments":"I understand it already raises a `NotImplementedError` exception, eg:\r\n\r\n```\r\n>>> dataset = load_dataset(\"journalists_questions\", name=\"plain_text\", split=\"train\", streaming=True)\r\n\r\n[...]\r\nNotImplementedError: Extraction protocol for file at https:\/\/drive.google.com\/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet\r\n```\r\n","body":"**Is your feature request related to a problem? Please describe.**\r\nI would love to know if a `dataset` is with the current implementation streamable or not. \r\n\r\n**Describe the solution you'd like**\r\nWe could show a warning when a dataset is loaded with `load_dataset('...',streaming=True)` when its lot streamable, e.g. if it is an archive. \r\n\r\n**Describe alternatives you've considered**\r\nAdd a new metadata tag for \"streaming\"\r\n","comment_length":30,"text":"Give a user feedback if the dataset he loads is streamable or not \n **Is your feature request related to a problem? Please describe.**\r\nI would love to know if a `dataset` is with the current implementation streamable or not. \r\n\r\n**Describe the solution you'd like**\r\nWe could show a warning when a dataset is loaded with `load_dataset('...',streaming=True)` when its lot streamable, e.g. if it is an archive. \r\n\r\n**Describe alternatives you've considered**\r\nAdd a new metadata tag for \"streaming\"\r\n \n I understand it already raises a `NotImplementedError` exception, eg:\r\n\r\n```\r\n>>> dataset = load_dataset(\"journalists_questions\", name=\"plain_text\", split=\"train\", streaming=True)\r\n\r\n[...]\r\nNotImplementedError: Extraction protocol for file at https:\/\/drive.google.com\/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet\r\n```\r\n","embeddings":[-0.3121467233,0.1084250659,-0.0998005643,0.0617054515,0.1377473921,-0.1033977941,0.1825834215,0.2757679522,-0.0348217525,0.2482772321,0.23722516,0.2212012112,-0.41875422,0.2228206396,-0.2498128265,-0.1057739332,-0.1940762699,0.2266800851,0.1973604113,0.0241289698,0.050080765,-0.1404721588,0.125907585,-0.2275450975,0.0955903977,0.040822193,0.2770479918,0.0032736778,-0.4309591651,-0.4094036222,-0.1264721453,0.5943691134,0.1119763926,0.3047804236,-0.0001064388,-0.1363653243,0.4294652045,-0.0646332726,-0.4166698158,-0.2811459601,-0.2709993124,-0.04569529,0.3828910887,-0.0648992881,-0.2366087437,0.0000304834,0.1893444657,-0.2507543266,0.398091197,0.3040390313,0.1841346622,0.1431601793,-0.1689657271,0.1103191748,0.2620275021,0.2505020797,-0.2353683561,0.1992798001,0.134420082,0.3304212689,-0.3621218204,0.3760129213,-0.1080571488,0.0278845839,0.2396207899,-0.1162741035,-0.2675046921,-0.3587150276,-0.1097213551,0.3916170299,0.8121817112,-0.0677935779,-0.266923964,-0.2799865305,0.2192740738,-0.1652314067,0.2707595229,0.0779115707,-0.3087148368,0.2289714813,-0.3739491403,-0.3058995605,-0.4223290384,0.0608525462,0.0020896047,0.2876546085,0.0454271957,-0.0564265884,0.2633794248,0.184820801,0.22921893,0.0431781076,0.023470154,-0.0227358416,-0.3078721762,-0.3380286992,0.0473389663,-0.1607312709,0.1749161631,0.3495371044,0.3600506186,0.1747979075,-0.0686454102,0.2610440254,0.3030857444,-0.0043789367,0.1163038835,-0.2984133065,0.3219976127,0.1630396098,0.139344126,-0.1453951448,0.1518913358,0.1023415998,0.1967503428,0.1389230788,0.4190607369,-0.17866081,-0.1202374622,0.044085402,-0.2055466771,-0.3528243303,0.0303816646,0.2117040008,-0.2265862525,0.1878919005,-0.1177252755,0.1621883065,0.1279754937,-0.3675708175,0.0093998937,0.0382103808,-0.2182808965,0.203861773,0.1490737647,-0.5337638259,-0.0831713602,0.0130760521,-0.1549495608,-0.0313117877,0.3512848914,-0.1534520835,-0.0033806057,0.1138476729,-0.016180858,-0.0984127149,-0.0082277646,0.1018308923,-0.260963589,0.2310131639,-0.0887562186,-0.2245736569,0.0018799404,0.2684470117,-0.234155044,-0.153254807,-0.1981976628,0.5610991716,-0.4761672318,-0.2225428671,0.1492543668,0.3053433895,-0.0972417444,-0.1726650596,-0.0284204539,0.4456055164,-0.2409359366,-0.1790953726,-0.3171626031,-0.4264673293,0.3974467516,0.0774606764,-0.1762910336,0.0792955533,0.0524514392,0.0932530761,0.5550786257,-0.0697788671,-0.1110180095,0.5365390182,-0.3236947656,0.3182698786,0.3217315972,0.13688308,0.3014834225,0.0313737877,0.0506621562,0.3943997324,-0.097603865,-0.0137693277,-0.313816458,-0.2959581614,0.1970930994,0.2616508305,0.1503327042,0.1946424991,0.1730065495,-0.1789409816,0.1596208215,0.0251363069,-0.0353978425,-0.0746823326,0.4696786702,-0.0059243557,-0.3908084631,0.0882130414,-0.3534206748,0.0092368284,0.1217710376,0.0998350084,0.0756967887,-0.3154946864,-0.1760160923,-0.1409995705,-0.1186433136,0.0458630547,0.1992529184,0.2442771494,-0.2380592078,0.0990362465,-0.2280892432,0.2491263598,-0.2165423781,0.2083421946,-0.2282067388,0.1588588208,0.1062748432,-0.0724145398,0.1579423845,-0.302803129,0.1809504479,0.0156517364,-0.0901722163,0.3711771369,0.0464416705,0.5940019488,0.071555391,0.2712281346,0.2272628695,-0.2836053371,0.1909567863,0.3033688664,0.0110063823,0.0073280735,-0.3751382232,0.2143037617,0.1132769063,-0.1076527461,0.1807173342,-0.1787669212,0.1305251271,-0.0777626261,-0.3311727643,-0.0216072369,0.1609404981,0.0429362245,-0.1634137034,0.0293836836,-0.3633941114,-0.1784788668,0.2748422027,0.0663698167,-0.0496440642,0.5686532855,-0.0119932629,-0.3284745216,0.2952456772,0.215292111,0.3742587268,0.2192719579,0.2608889043,0.1018619016,0.3289047182,-0.1284110546,0.1090840995,0.0433938652,-0.1185555309,0.1851857901,0.0774018094,0.0425153822,-0.4282302558,-0.1988510191,0.0296159647,0.1814826429,-0.0836001039,-0.0599258654,-0.0986200348,-0.1597470939,0.0016256224,-0.1829215139,-0.1229931936,-0.122131899,0.2737377882,0.6344076395,-0.2617421448,-0.0066787251,-0.2472235411,0.4284985065,-0.1542272568,-0.0365397893,-0.3052411079,0.0085203778,0.1265051067,0.1804899424,0.1358112246,-0.0603530928,0.7215875387,0.2235303223,0.2180972099,-0.4833516181,-0.2957260311,0.1400237232,0.0619995482,0.0927764699,-0.0645019412,0.2419243008,0.1780843586,-0.4123814404,0.0639034808,-0.0645541772,-0.014477253,0.2115996182,0.0463888086,0.2912009358,-0.0600490235,-0.4394127429,-0.378875792,-0.528772831,0.2629510462,-0.0132583296,0.0757973269,0.2690798938,0.0719243214,-0.1398300081,0.1498427093,0.0919446871,-0.1639776975,-0.3578228951,0.0425293148,-0.1043366268,-0.0163401142,0.0637978762,0.1183894575,-0.1711734384,0.5343636274,-0.385127157,-0.0583212338,-0.1136077791,-0.2023053318,-0.1155278906,-0.5180271864,0.2743529081,0.157575056,-0.2281766981,0.1127378047,-0.0284653194,-0.2289383858,0.1795026213,0.1157948822,0.0684842169,0.418427527,0.0242061075,0.3933297098,0.0401668958,-0.0478451028,0.4635876119,0.2107439041,0.2624152601,0.0386320874,-0.0146240201,0.1145379841,0.0727314949,-0.344008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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2653","title":"Add SD task for SUPERB","comments":"Note that this subset requires us to:\r\n\r\n* generate the LibriMix corpus from LibriSpeech\r\n* prepare the corpus for diarization\r\n\r\nAs suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https:\/\/huggingface.co\/datasets\/superb\/superb-data\r\n\r\nThen we can use the URLs for the files to load the data in `superb`'s dataset loading script.\r\n\r\nFor consistency, I suggest we name the folders in `superb-data` in the same way as the configs in the dataset loading script - e.g. use `sd` for speech diarization in both places :)","body":"Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https:\/\/arxiv.org\/abs\/2105.01051) and `s3prl` [instructions](https:\/\/github.com\/s3prl\/s3prl\/tree\/master\/s3prl\/downstream#sd-speaker-diarization).\r\n\r\nSteps:\r\n- [x] Generate the LibriMix corpus\r\n- [x] Prepare the corpus for diarization\r\n- [x] Upload these files to the superb-data repo\r\n- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script\r\n- [ ] README: tags + description sections\r\n\r\nRelated to #2619.\r\n\r\ncc: @lewtun \r\n","comment_length":94,"text":"Add SD task for SUPERB \n Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https:\/\/arxiv.org\/abs\/2105.01051) and `s3prl` [instructions](https:\/\/github.com\/s3prl\/s3prl\/tree\/master\/s3prl\/downstream#sd-speaker-diarization).\r\n\r\nSteps:\r\n- [x] Generate the LibriMix corpus\r\n- [x] Prepare the corpus for diarization\r\n- [x] Upload these files to the superb-data repo\r\n- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script\r\n- [ ] README: tags + description sections\r\n\r\nRelated to #2619.\r\n\r\ncc: @lewtun \r\n \n Note that this subset requires us to:\r\n\r\n* generate the LibriMix corpus from LibriSpeech\r\n* prepare the corpus for diarization\r\n\r\nAs suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https:\/\/huggingface.co\/datasets\/superb\/superb-data\r\n\r\nThen we can use the URLs for the files to load the data in `superb`'s dataset loading script.\r\n\r\nFor consistency, I suggest we name the folders in `superb-data` in the same way as the configs in the dataset loading script - e.g. use `sd` for speech diarization in both places :)","embeddings":[-0.2387332767,-0.1557696015,0.0066516008,0.1112360433,0.3777109385,-0.1991261393,0.008201831,-0.1370199472,0.1117523089,0.3464581966,-0.3380066156,0.4965123236,-0.1501338184,0.4443328083,0.2138432264,0.2012203336,0.0950579941,0.2073054612,-0.2991645336,0.0513462573,-0.1497841179,-0.11977157,-0.1633818448,-0.1436896175,-0.2040832639,0.2193612754,0.0727682114,0.0811632127,-0.2905584872,-0.2938543558,0.162700817,0.5291999578,0.4015758932,0.3271007538,-0.0001083251,-0.1900360137,0.3640153706,-0.4076463878,-0.5083063245,-0.170351848,-0.2225591242,0.2536762953,-0.1591915041,-0.0934045389,-0.6251901388,-0.1363317817,0.0133751472,-0.1100857332,0.2596839964,0.1732192189,0.2259884179,-0.0555906668,-0.0955966935,-0.1510584205,0.1919825822,0.2502139807,-0.1117708832,-0.0226814114,0.1821225435,0.0146341296,0.1411370486,0.2883913517,-0.1353493929,-0.1251489073,0.6657478213,-0.1592933089,-0.0340276808,-0.0476164259,-0.0653878674,0.2897382677,0.6062102914,-0.2307612896,-0.3986759782,0.1317663938,0.0489644669,-0.1367883086,-0.0511328392,-0.1670542508,-0.1804630458,-0.0097852154,-0.159321174,-0.2900246978,0.0168993883,-0.0991930068,0.1257368028,0.4029803574,-0.2149695754,-0.2486239821,0.1087753102,-0.1918498725,-0.2503000498,0.2007246464,0.2491452545,0.1213822961,-0.2323536426,-0.1240352243,0.2190023512,-0.2282164246,0.0134818926,0.0134721603,0.2555201948,0.0663685948,0.0736561865,0.0467751846,0.2122075111,0.3263137639,0.3418327272,-0.0758142918,0.1266981065,0.3235274851,0.1213152185,-0.0469559021,-0.4792706966,0.0506935194,-0.3832933605,0.12917687,0.0326539949,-0.2910527587,-0.175021708,-0.1362396181,0.2221731246,0.0894525126,0.1904240847,0.6082089543,-0.1698205918,0.01628289,-0.1385700107,0.1991364509,-0.2245851606,0.3818306923,-0.1250522435,0.1218158081,-0.2075890452,0.2060779929,0.155845657,-0.3326058686,0.135431245,-0.036553368,0.0925743058,-0.0350978263,-0.3106779158,0.2560380697,0.1394774467,0.011242196,0.2154384404,-0.024268534,-0.4070394933,-0.0185336247,-0.2668024898,0.0970803052,0.2298041582,-0.3090216815,-0.2402658314,0.2687863111,-0.2112889886,-0.2399874479,-0.2093415409,0.2070339173,-0.025995804,-0.3565532267,-0.0472566225,-0.1550932378,-0.1873577982,0.0806875154,0.3618192077,0.0493871719,0.1410747916,-0.2385227829,-0.0613222308,-0.0213319585,0.1526767313,-0.0223494153,0.1086406261,0.1549884528,0.0454300493,0.3059645891,0.0213385373,-0.7309918404,-0.295655489,-0.0253892522,-0.0704014972,0.0473956242,0.0758811012,0.0266980901,0.5626787543,-0.3666591644,0.0267688185,0.4169299006,0.1045972407,0.0271830522,-0.1137812883,-0.1186165512,0.1551050991,0.333193481,-0.1412375122,-0.0132466843,-0.0201382544,0.5371373892,0.1115940213,-0.2153895199,0.1351972073,-0.0304381102,0.1972648948,0.2505992651,0.0189217851,-0.1343054771,-0.1566025317,-0.0714810267,0.3544766307,0.364461422,-0.2444358766,-0.4812817872,-0.0186536182,-0.2085857242,-0.2348953933,-0.0008711324,0.1966120899,0.1794876307,0.4501173198,-0.2948080301,-0.103123419,-0.4538341463,-0.1539292485,-0.0776344165,-0.0457611158,0.0276464708,-0.0710129365,-0.0754553452,0.2740697563,0.4282013476,-0.0134114316,0.0006371762,0.0198208001,0.360601902,-0.1204632446,0.2716153562,-0.0330983698,0.4144019186,0.0200404413,-0.5199398994,0.1516585201,0.060767103,0.0611061342,-0.0336799286,-0.085207656,0.0801730677,-0.215658471,0.1141562238,0.2457428873,-0.2012441456,0.2822172642,0.0160779636,-0.1429870427,-0.1003037095,-0.0837649554,-0.0753232315,0.1822261214,0.0374899656,-0.295640856,0.0134174684,0.1434490085,-0.1508319676,0.2064888775,-0.00133868,-0.0963153392,-0.2068926096,0.1192235276,-0.1725337356,0.0588459037,0.2269429713,0.2886661291,-0.0056762486,-0.08356978,-0.1300175935,0.1359495372,-0.0962685645,-0.0186919682,0.3149695694,0.0194331501,-0.0111387698,-0.4310698211,0.0500695966,-0.0565520786,-0.0238469187,-0.0844350681,-0.4412799776,-0.0452111848,-0.6270555258,0.0019319929,-0.4387516379,0.1230187714,0.1873015165,0.254145354,0.0001312008,-0.3270376027,0.2696534395,0.3459562659,0.2631192207,-0.2504715025,-0.0477190204,-0.3070183098,-0.2446109354,-0.0061026989,0.1591077894,0.1529820859,0.0119618867,0.0585236326,0.0105655249,0.2202136815,-0.2467608601,-0.1310711056,0.2598434389,-0.0594292767,0.1896293908,0.0486859381,-0.0084419986,0.2503096461,-0.0831067935,0.0541906469,-0.3319964409,-0.1546895653,-0.0413330644,0.0950936154,-0.005971218,-0.3511022329,-0.3864653111,-0.1713501513,-0.4853638709,-0.2212005705,-0.1197456941,0.097088635,-0.0555566549,-0.3157558143,0.0252460707,0.1532766223,0.4391430616,-0.2017246932,-0.0928222016,0.2107275128,-0.4319680035,-0.1795963496,-0.162950471,-0.0613188371,0.2938212454,0.0343351439,-0.1930086911,-0.0772728845,0.1486816108,-0.1429511905,0.0608095899,-0.0430621617,0.3532962203,0.1241763309,-0.0949400961,-0.1835152358,-0.2840723395,0.0802196786,0.3533506095,0.1515348107,-0.0702311844,0.024417419,-0.0395405479,0.3105427623,0.0657296479,-0.030512413,0.2941798568,0.0216222163,0.1571723819,-0.0036712699,0.1609631628,0.411649555,0.091672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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2653","title":"Add SD task for SUPERB","comments":"@lewtun @lhoestq: \r\n\r\nI have already generated the LibriMix corpus and prepared the corpus for diarization. The output is 3 dirs (train, dev, test), each one containing 6 files: reco2dur  rttm  segments  spk2utt  utt2spk  wav.scp\r\n\r\nNext steps:\r\n- Upload these files to the superb-data repo\r\n- Transcribe the corresponding s3prl processing of these files into our superb loading script\r\n\r\nNote that processing of these files is a bit more intricate than usual datasets: https:\/\/github.com\/s3prl\/s3prl\/blob\/master\/s3prl\/downstream\/diarization\/dataset.py#L233\r\n\r\n","body":"Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https:\/\/arxiv.org\/abs\/2105.01051) and `s3prl` [instructions](https:\/\/github.com\/s3prl\/s3prl\/tree\/master\/s3prl\/downstream#sd-speaker-diarization).\r\n\r\nSteps:\r\n- [x] Generate the LibriMix corpus\r\n- [x] Prepare the corpus for diarization\r\n- [x] Upload these files to the superb-data repo\r\n- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script\r\n- [ ] README: tags + description sections\r\n\r\nRelated to #2619.\r\n\r\ncc: @lewtun \r\n","comment_length":73,"text":"Add SD task for SUPERB \n Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https:\/\/arxiv.org\/abs\/2105.01051) and `s3prl` [instructions](https:\/\/github.com\/s3prl\/s3prl\/tree\/master\/s3prl\/downstream#sd-speaker-diarization).\r\n\r\nSteps:\r\n- [x] Generate the LibriMix corpus\r\n- [x] Prepare the corpus for diarization\r\n- [x] Upload these files to the superb-data repo\r\n- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script\r\n- [ ] README: tags + description sections\r\n\r\nRelated to #2619.\r\n\r\ncc: @lewtun \r\n \n @lewtun @lhoestq: \r\n\r\nI have already generated the LibriMix corpus and prepared the corpus for diarization. The output is 3 dirs (train, dev, test), each one containing 6 files: reco2dur  rttm  segments  spk2utt  utt2spk  wav.scp\r\n\r\nNext steps:\r\n- Upload these files to the superb-data repo\r\n- Transcribe the corresponding s3prl processing of these files into our superb loading script\r\n\r\nNote that processing of these files is a bit more intricate than usual datasets: https:\/\/github.com\/s3prl\/s3prl\/blob\/master\/s3prl\/downstream\/diarization\/dataset.py#L233\r\n\r\n","embeddings":[-0.2306326032,-0.3324863613,-0.0547317527,0.0661656931,0.3352736831,-0.2812387645,0.0837412626,-0.061148867,-0.11445418,0.4088612795,-0.4140782356,0.4271566272,-0.1571262032,0.3467774987,0.157153517,0.0596581884,0.0408407263,0.2455734313,-0.3144540787,-0.0464579388,-0.0659857914,0.0155268218,-0.1118666157,-0.287522912,-0.1676342189,0.3125640154,0.0159357525,-0.0790378004,-0.3433384299,-0.2468428016,0.1256893575,0.4726737738,0.281218946,0.3263559341,-0.0001044987,-0.2434346527,0.1248142496,-0.4071696699,-0.3041563332,0.0205105282,-0.1400114894,0.2529032528,-0.2431382239,-0.1004232243,-0.621483326,-0.1066604331,-0.0715739205,0.0587374866,0.3838650882,0.2615119219,0.252238214,-0.1253829151,0.0187662579,-0.0416322239,0.3106273413,0.237228334,-0.1541283429,-0.0043616295,0.2208904028,0.0400725566,0.0306639131,0.2973972261,-0.2016076446,-0.0407217927,0.4242485166,-0.3073334396,0.0961292982,-0.1444908977,0.0631378889,0.4065577984,0.4522716999,-0.2049413025,-0.2887031436,0.2152059525,0.0077375132,-0.250554055,-0.2491929084,-0.0404339358,-0.2725820839,-0.0228523165,-0.2060943842,-0.1376397312,0.0324130356,-0.1914403737,0.019091947,0.3704536855,-0.2671468258,-0.3009182811,0.2260408401,-0.1345985085,-0.3198322654,0.3320156038,0.2018844038,0.1263996065,-0.2140351385,-0.184205547,0.2491409332,-0.3830596209,-0.0471485704,-0.0462135226,0.2658673823,0.1086573899,0.2545675039,0.0696091652,0.0248165876,0.1501286775,0.2364243269,-0.035419587,-0.0448732153,0.1341962665,-0.0506398194,-0.0012747318,-0.4389600158,0.0330511592,-0.3648111522,0.1746805459,-0.0583863072,-0.310341537,-0.2033723295,-0.1287636161,-0.0196185149,0.2213390023,0.0794401541,0.4776504636,-0.0948933885,0.0649780482,-0.0509123318,0.2270583659,-0.2490281165,0.3620907068,-0.1059040204,0.1659913957,-0.1457807273,0.0199768879,0.2315411419,-0.3207785785,0.1398188919,-0.049939312,0.1733028889,-0.1151324287,-0.2575062513,0.1903565079,0.2721084058,-0.0316013694,0.1445753127,-0.1708843857,-0.3705866635,0.0417678021,-0.2342026979,-0.0167721901,0.2933934927,-0.228061229,-0.2369891256,0.3036904633,-0.1506552696,-0.1359421462,0.0305428952,0.2015447617,0.0977666527,-0.3038559556,-0.0548577122,-0.2512845695,-0.2239756733,0.1178684458,0.3892634213,-0.070179753,0.1160391346,-0.2245630324,-0.0051192115,-0.0084878551,0.2964225709,-0.0331700109,0.2258887142,0.0678634122,0.032679975,0.1900208592,-0.0479926355,-0.5481700301,-0.2245559543,-0.0074401433,0.0226993319,0.0313451886,0.1355641186,0.0045564678,0.6181932688,-0.3744726479,-0.0381599963,0.3380509019,0.0725140944,0.0997640416,-0.1310929358,-0.0191139672,0.0118534891,0.2393464893,-0.0813841149,-0.0808948353,-0.0676738396,0.497025758,0.2334402651,-0.1725580692,0.1217373312,-0.0119452635,0.2894847691,0.0910597369,0.0217040274,-0.1855188459,0.0678440779,-0.1747249216,0.2588680089,0.383089751,-0.0389183201,-0.4150964022,-0.0010084159,-0.1662146896,-0.2090087384,-0.0032301026,0.2927136123,0.2428493798,0.4071397781,-0.3336694539,-0.0167746805,-0.6764424443,-0.0711311549,-0.0923162699,-0.0045005032,-0.0199995451,-0.0798712224,-0.1189093962,0.3851715624,0.5336657166,-0.0507785231,0.0314621627,0.0887996107,0.2988008857,-0.072608754,0.2658647895,-0.2084178776,0.4722294211,-0.0895455033,-0.5653336644,0.0470015146,0.1999008805,0.0958383605,-0.0654768944,0.0055325581,0.1372514814,-0.0953737646,0.0778418556,0.2655323148,-0.2062670887,0.2208839804,-0.0152462181,-0.1619107425,0.0088333944,0.0560737848,-0.2033192366,0.0120154358,0.0018841662,-0.2424480915,0.041762691,0.1776093543,-0.1071049571,0.1440399289,-0.0182286855,-0.0981846228,-0.2613983452,-0.0173643734,-0.0852323174,-0.102264747,0.2748094499,0.3855220675,-0.1190942526,-0.1484159082,-0.0794157386,0.193910867,-0.0809500739,-0.1137179211,0.4792473912,0.0618225522,-0.0789260715,-0.4903302193,0.0071991622,-0.0477589369,-0.0150727332,-0.0146992607,-0.3657634556,0.0245484039,-0.5591490269,-0.0659198239,-0.5107609034,0.1796441078,0.1567976028,0.3538485467,0.1014042571,-0.2838631868,0.3210274577,0.3669550717,0.2201403975,-0.0898769721,0.0792877004,-0.1918426752,-0.194029212,0.0848434642,0.2379512787,0.1225406379,0.0649443045,0.0667414218,-0.0530556776,0.0843598172,-0.1486164927,-0.1039231494,0.2008406073,-0.0775581524,0.087992534,0.0492757745,0.0500362925,0.1740358621,-0.1463154107,-0.0014404202,-0.2926126719,-0.1680888534,0.1636350304,0.0077726361,-0.0098427515,-0.3968880773,-0.3802548349,-0.0190264583,-0.4988487661,-0.1282486618,-0.0362591445,0.2246869355,0.0443339944,-0.1743770689,0.0240723472,0.1243400872,0.4566859305,-0.1572700292,-0.1098150015,0.1976679713,-0.3751398027,-0.2254721224,-0.2104405314,-0.0910398439,0.3619688451,0.0524688028,-0.0941483155,-0.0015293293,0.1633959115,-0.2450573891,-0.0099363634,0.0543501079,0.265822053,0.114933081,-0.116809085,-0.2195414603,0.0235201474,0.0505326279,0.2868507504,0.230368495,-0.1580718756,-0.0439049937,0.0079755094,0.3405783176,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2651","title":"Setting log level higher than warning does not suppress progress bar","comments":"Hi,\r\n\r\nyou can suppress progress bars by patching logging as follows:\r\n```python\r\nimport datasets\r\nimport logging\r\ndatasets.logging.get_verbosity = lambda: logging.NOTSET\r\n# map call ...\r\n```","body":"## Describe the bug\r\nI would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).\r\nAccording to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.\r\n\r\nI also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\nfrom datasets.utils.logging import set_verbosity_error\r\n\r\nset_verbosity_error()\r\n\r\ndef dummy_map(batch):\r\n    return batch\r\n\r\ncommon_voice_train = datasets.load_dataset(\"common_voice\", \"de\", split=\"train\")\r\ncommon_voice_test = datasets.load_dataset(\"common_voice\", \"de\", split=\"test\")\r\n\r\ncommon_voice_train.map(dummy_map)\r\n```\r\n\r\n## Expected results\r\n- The progress bar for `.map` call won't be shown\r\n\r\n## Actual results\r\n- The progress bar for `.map` is still shown \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.5\r\n- PyArrow version: 4.0.1\r\n","comment_length":25,"text":"Setting log level higher than warning does not suppress progress bar \n ## Describe the bug\r\nI would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).\r\nAccording to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.\r\n\r\nI also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\nfrom datasets.utils.logging import set_verbosity_error\r\n\r\nset_verbosity_error()\r\n\r\ndef dummy_map(batch):\r\n    return batch\r\n\r\ncommon_voice_train = datasets.load_dataset(\"common_voice\", \"de\", split=\"train\")\r\ncommon_voice_test = datasets.load_dataset(\"common_voice\", \"de\", split=\"test\")\r\n\r\ncommon_voice_train.map(dummy_map)\r\n```\r\n\r\n## Expected results\r\n- The progress bar for `.map` call won't be shown\r\n\r\n## Actual results\r\n- The progress bar for `.map` is still shown \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.5\r\n- PyArrow version: 4.0.1\r\n \n Hi,\r\n\r\nyou can suppress progress bars by patching logging as follows:\r\n```python\r\nimport datasets\r\nimport logging\r\ndatasets.logging.get_verbosity = lambda: logging.NOTSET\r\n# map call ...\r\n```","embeddings":[-0.4419386983,-0.1637800336,0.0876798704,-0.1625066102,0.1394929886,-0.0227062833,0.4293670058,0.2288739532,0.0989456922,0.1445457786,0.1754810065,0.6159744263,-0.1407675892,0.1008445546,-0.1938808262,0.1940040886,0.0229355711,0.0618275292,0.097369045,-0.0051870239,-0.2738218009,-0.2124589086,-0.4537506104,0.1177136377,-0.1908455044,-0.0994138941,0.2819594145,-0.0676383972,-0.1347370744,-0.6845767498,0.3213669062,0.065580003,0.1590007544,0.6035616398,-0.0001319013,-0.062754631,0.5607278943,0.1790860295,-0.2247193009,-0.1718731225,-0.5930761695,-0.3090308607,0.3925269246,-0.0706966892,0.029787939,-0.3474459648,-0.1272029579,-0.2612386048,0.2345679253,0.1812126786,-0.0071892273,0.2482845783,-0.0372400172,0.5450522304,0.2635576129,0.1993847489,-0.2768193781,0.1551691741,0.4623755217,-0.2347632349,-0.3073786795,0.3043422997,-0.3391894996,0.0891906247,0.364815563,-0.1111718714,0.6513959169,-0.5470018983,0.3078687191,0.1294251531,0.3183060586,-0.2856887281,-0.388346523,-0.1922209859,-0.0462128073,-0.0714263916,0.1643112451,-0.0450842045,-0.3889573812,0.1488934159,-0.7792407274,-0.1206325367,-0.2446362525,0.2550941408,0.1909737736,0.2744412422,-0.0276818555,0.1187562421,0.1178881973,-0.1365887076,0.0607780181,-0.2545288503,0.0116920443,0.2483185679,-0.4572415054,-0.0615720265,0.3918232322,0.1325479448,0.1498936415,-0.0930807739,-0.1562084556,0.1936067343,0.1616427749,0.1653095484,0.2875440717,0.149799183,0.5941331983,0.2832121849,0.2572874129,0.1503210217,0.1399834305,0.2325322032,0.3986858726,-0.4250461757,0.4426813722,0.3641909361,0.3208539784,-0.2956034541,0.0545899868,0.0405408517,-0.4391988218,-0.0771040246,0.2471935302,-0.1380392611,0.4477908313,-0.3311774433,0.1545620263,0.2359691113,-0.1745847017,0.0984209254,0.0096889799,-0.2765092254,-0.2854017317,0.184328407,0.2677907348,-0.2136614025,0.2089000493,0.1451055557,-0.2515017092,0.0489353426,0.305675149,0.0159485061,0.5277969837,0.4136826694,-0.2733989954,0.2552737594,-0.011760084,0.0148656834,-0.1286605746,0.4761300683,-0.0766501874,-0.4540790319,-0.181950748,-0.0578345284,-0.4170138538,0.1642577648,-0.5429635048,0.3450361192,0.2866149247,-0.0346266739,0.3771919906,-0.1488109231,-0.5182569027,-0.098286815,0.1885202378,0.4915161431,-0.6797257662,-0.1832246631,-0.3486949801,-0.255328685,0.2047462612,0.0311415233,-0.235932216,0.0198393911,-0.0590228215,0.3680252433,-0.0158654414,-0.1317313015,-0.3091971874,0.3006400168,-0.051639691,0.1174315512,0.0648248047,-0.2404747754,-0.1172710061,-0.2352151722,0.0669461638,-0.0542757884,0.2040422261,-0.193476662,-0.2301561236,-0.2625632882,0.0543837547,0.0057217423,-0.1277088225,0.0963604599,0.1668698341,-0.4069348872,0.4588558376,0.0186784789,0.175115183,0.2314312011,0.3669462502,-0.4304539859,-0.0262334719,-0.0806005299,-0.1245752275,0.1955326349,-0.1073027998,-0.0875074416,-0.3066435456,-0.3172891438,0.0935739428,0.0031500207,-0.2190496922,-0.2050161064,-0.1374476701,-0.2400769889,-0.1346881092,-0.1813044697,-0.2845744193,-0.2089660466,-0.3723792732,0.0575660132,-0.1472546011,0.0575117543,0.2907574475,-0.3923129439,0.0755687431,0.1688372046,0.2368117571,0.1207592487,-0.125924021,0.3243703246,-0.1417277902,0.5567009449,-0.100467436,0.2094581425,0.121137239,-0.1209335476,0.026592046,0.1609217376,-0.2660254836,0.2535660267,0.1031406373,0.0171375033,0.4108292758,0.2966626287,-0.0813289285,-0.0227405485,0.0175928492,-0.1252802014,-0.2696110904,-0.407341212,0.2404932231,0.274861604,0.4156718552,-0.0455322601,0.1888778955,0.0303593129,0.5161691904,0.0442289338,0.1040256396,-0.0162763689,-0.206837967,-0.1898768693,0.3138594031,-0.0990591645,0.3751361072,0.1118451878,0.2199739665,0.149659425,-0.0212915689,-0.0491001941,0.1307388395,0.2823371887,0.0909593552,0.2894970477,0.0859388933,-0.1233443096,-0.1833081543,-0.1638492942,-0.1781530082,0.0441813581,-0.500053525,0.0947351381,-0.2914811969,-0.1945669055,-0.0300083589,0.0856101364,-0.2602653801,0.0320401378,0.1747450978,0.0906224549,-0.2227553278,0.4843930006,0.2578085363,-0.1074472219,0.1933289915,-0.1457282752,-0.4724210799,-0.0866331384,-0.1253925562,-0.1823430657,-0.0189420171,-0.2148829699,-0.1284613907,0.2228270024,-0.1062044948,-0.1972326785,-0.0630908608,-0.0496863946,-0.1086649671,0.2292987555,0.0186894909,0.068712309,-0.0976074189,0.1998907328,-0.0956420973,-0.3264266253,-0.0166796353,-0.1359514594,-0.2407793403,0.2194422483,-0.1885677576,0.066867575,-0.2478818446,-0.1415907145,-0.0791148022,-0.358297646,0.1285268664,0.2675815225,0.1696363389,0.3616370559,-0.1621623784,-0.2807433903,0.0995376557,-0.5332383513,0.2486508191,-0.0036411504,0.0805034637,-0.0706206337,0.0875727832,-0.0307491459,0.3047397435,-0.3325500786,-0.0666837692,-0.2412404567,0.0422770791,0.0657984614,0.0382698439,0.2339995056,0.0500805117,0.0235204082,-0.0735215843,-0.3944574893,0.0005847008,0.2307642847,0.3519811332,0.1808226705,0.2470562011,0.0638627782,0.4577203989,0.5331856012,-0.0457769856,0.0876507014,0.2052827477,0.2106728107,0.0915259421,-0.1375364959,-0.2229065597,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2651","title":"Setting log level higher than warning does not suppress progress bar","comments":"Note also that you can disable the progress bar with\r\n\r\n```python\r\nfrom datasets.utils import disable_progress_bar\r\ndisable_progress_bar()\r\n```\r\n\r\nSee https:\/\/github.com\/huggingface\/datasets\/blob\/8814b393984c1c2e1800ba370de2a9f7c8644908\/src\/datasets\/utils\/tqdm_utils.py#L84","body":"## Describe the bug\r\nI would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).\r\nAccording to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.\r\n\r\nI also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\nfrom datasets.utils.logging import set_verbosity_error\r\n\r\nset_verbosity_error()\r\n\r\ndef dummy_map(batch):\r\n    return batch\r\n\r\ncommon_voice_train = datasets.load_dataset(\"common_voice\", \"de\", split=\"train\")\r\ncommon_voice_test = datasets.load_dataset(\"common_voice\", \"de\", split=\"test\")\r\n\r\ncommon_voice_train.map(dummy_map)\r\n```\r\n\r\n## Expected results\r\n- The progress bar for `.map` call won't be shown\r\n\r\n## Actual results\r\n- The progress bar for `.map` is still shown \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.5\r\n- PyArrow version: 4.0.1\r\n","comment_length":19,"text":"Setting log level higher than warning does not suppress progress bar \n ## Describe the bug\r\nI would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).\r\nAccording to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.\r\n\r\nI also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\nfrom datasets.utils.logging import set_verbosity_error\r\n\r\nset_verbosity_error()\r\n\r\ndef dummy_map(batch):\r\n    return batch\r\n\r\ncommon_voice_train = datasets.load_dataset(\"common_voice\", \"de\", split=\"train\")\r\ncommon_voice_test = datasets.load_dataset(\"common_voice\", \"de\", split=\"test\")\r\n\r\ncommon_voice_train.map(dummy_map)\r\n```\r\n\r\n## Expected results\r\n- The progress bar for `.map` call won't be shown\r\n\r\n## Actual results\r\n- The progress bar for `.map` is still shown \r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.5\r\n- PyArrow version: 4.0.1\r\n \n Note also that you can disable the progress bar with\r\n\r\n```python\r\nfrom datasets.utils import disable_progress_bar\r\ndisable_progress_bar()\r\n```\r\n\r\nSee https:\/\/github.com\/huggingface\/datasets\/blob\/8814b393984c1c2e1800ba370de2a9f7c8644908\/src\/datasets\/utils\/tqdm_utils.py#L84","embeddings":[-0.4388272762,-0.2037361413,0.0796426833,-0.152261883,0.1692188829,-0.0159658007,0.4777553976,0.2021972537,0.0318350866,0.1475353092,0.1682003886,0.5794948339,-0.152897656,0.10003113,-0.1835140139,0.1944968998,0.0263119973,0.0424377173,0.0457620323,0.0004414686,-0.2606731355,-0.1466900259,-0.4761810601,0.0941639617,-0.2263159901,-0.0694701001,0.2926193774,0.0035402456,-0.1176254302,-0.6455771327,0.3015622199,0.0900377035,0.1431134343,0.6660018563,-0.000133249,-0.0878026485,0.5361055136,0.1657677442,-0.1852545738,-0.1453343928,-0.5602222681,-0.3629795909,0.390527755,-0.0617991835,0.0420391709,-0.3005341291,-0.1574251056,-0.2664179504,0.2540456951,0.1718673557,-0.0169129483,0.3063340485,-0.1073400155,0.5482039452,0.2376393527,0.1451119483,-0.3086778224,0.1676180363,0.4750162661,-0.2120764256,-0.2969590425,0.3610367775,-0.3199104667,0.1028575674,0.3066386878,-0.0769179389,0.6109765172,-0.562685132,0.3343413472,0.1769725382,0.3472414911,-0.3141160011,-0.3868072629,-0.1731614619,-0.0694993213,-0.092866905,0.205878377,-0.0921727419,-0.3756278753,0.1914187521,-0.798966229,-0.107239224,-0.2327204645,0.2482954711,0.1075972766,0.2289103717,-0.0718901008,0.0963486731,0.0785074681,-0.1218960881,0.1031514108,-0.2171088457,0.004403878,0.2491951883,-0.5141398311,-0.1063526124,0.3615424633,0.0719984099,0.1568298489,-0.0595684089,-0.1863827854,0.2191510051,0.1158890873,0.1340003759,0.2554060221,0.1776839197,0.5470439196,0.2377239168,0.2636432648,0.1237090528,0.1506489515,0.2026610225,0.3975674808,-0.435788244,0.4497306943,0.3733786643,0.3258314729,-0.278327316,0.007087599,0.0931878909,-0.3374129832,-0.0380666889,0.2372532934,-0.0866661444,0.4455689192,-0.2540481091,0.1459576041,0.267873168,-0.1896029413,0.0222190097,0.0186259449,-0.2887332141,-0.2559722364,0.1881934255,0.2404662967,-0.1552522779,0.2149009109,0.1360751688,-0.1472955197,0.0388564803,0.3051345646,-0.0191650707,0.5638685226,0.4335379303,-0.2885813117,0.2676416039,0.0248658936,0.0252257604,-0.0963666365,0.4703077972,-0.0585900992,-0.4574696422,-0.1870668828,-0.0671591312,-0.4046756625,0.1836805195,-0.5151902437,0.3845124543,0.2827964723,-0.0090500852,0.402459681,-0.1470163465,-0.5378506184,-0.088026017,0.2301702052,0.5276051164,-0.6987097859,-0.1768608838,-0.3421043754,-0.2491247952,0.1895651221,0.0326977149,-0.209911719,0.0540279038,-0.0955106393,0.3148594201,0.072308585,-0.1670954973,-0.3650942743,0.3568647206,-0.0652225092,0.1153442338,0.0775962546,-0.2185321301,-0.1156786755,-0.2229297906,0.0675712153,-0.0615469627,0.1715471298,-0.2080003768,-0.2356216311,-0.2607070506,0.0544957444,-0.0067107948,-0.0851498842,0.1419218928,0.1761008352,-0.435642153,0.4553629458,0.073969692,0.1693066806,0.2582277656,0.333901763,-0.4463591576,-0.0426594056,-0.1058060229,-0.1639454812,0.2082848698,-0.1032622904,-0.0945881158,-0.2895023525,-0.3256093562,0.070658274,0.0064348616,-0.2393781543,-0.2069596052,-0.1521600932,-0.2338041216,-0.1510626227,-0.2036368847,-0.3345766664,-0.1761711389,-0.3320506811,0.0417802036,-0.1015787125,0.0562134869,0.3076057732,-0.3617397547,0.0521020964,0.1781619638,0.2282764465,0.1035378501,-0.1218878329,0.3652700186,-0.1437883079,0.5218519568,-0.0476370454,0.1986086071,0.1101089269,-0.1610442549,0.0058754878,0.1750309169,-0.2611900866,0.2790229619,0.1503447443,0.0661970675,0.3930872083,0.282735765,-0.0947121605,-0.0167267304,0.0013401011,-0.1263046265,-0.2820746303,-0.4369442165,0.2476718724,0.2622611523,0.3680646718,-0.1068669483,0.1074983478,0.0735359117,0.4914260507,0.0519005768,0.1097518578,-0.0605801828,-0.2102842778,-0.1543627232,0.2606515288,-0.0956025198,0.4311898649,0.093353264,0.2250624746,0.1879181713,0.0072549391,-0.0469730012,0.1144525483,0.284550786,0.0379744954,0.3296860456,0.0763445422,-0.1105656847,-0.156406194,-0.1601752043,-0.1460017413,0.0240580291,-0.5067870021,0.1301323175,-0.3039036393,-0.1741355062,-0.029542502,0.0431750864,-0.2786378264,-0.0110479686,0.1811807007,0.0700712129,-0.2074576765,0.4660838246,0.2094022036,-0.087380223,0.1957890391,-0.1263174862,-0.4703644216,-0.0578536466,-0.1195598319,-0.191275835,-0.0444651283,-0.1912553757,-0.1227570251,0.1609010994,-0.1007062122,-0.2174821645,-0.0513029061,-0.042095691,-0.0791766122,0.3041088283,0.0238878708,0.065711543,-0.1246171296,0.2760334909,-0.0570036396,-0.3113236725,-0.0033401474,-0.1241060272,-0.2416000217,0.1921311766,-0.1650097072,0.0801849514,-0.2275784761,-0.1445658803,-0.1452714205,-0.4155721962,0.1291881055,0.27291888,0.1285600066,0.3616063893,-0.17458345,-0.2520793378,0.0768212676,-0.5869483948,0.2583813071,-0.0286887027,0.0308609623,-0.0457013771,0.0982180908,-0.0320495032,0.2712278366,-0.3657181561,-0.1137825698,-0.2194886208,0.0420212746,0.0528603457,0.0495054238,0.2127379775,0.017098818,0.0331126489,-0.0813796222,-0.3952858746,-0.054773204,0.2042000294,0.3584367335,0.1497317106,0.2788911164,0.0666010082,0.523348212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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2646","title":"downloading of yahoo_answers_topics dataset failed","comments":"Hi ! I just tested and it worked fine today for me.\r\n\r\nI think this is because the dataset is stored on Google Drive which has a quota limit for the number of downloads per day, see this similar issue https:\/\/github.com\/huggingface\/datasets\/issues\/996 \r\n\r\nFeel free to try again today, now that the quota was reset","body":"## Describe the bug\r\nI get an error datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files when I try to download the yahoo_answers_topics dataset\r\n\r\n## Steps to reproduce the bug\r\n   self.dataset = load_dataset(\r\n                'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')\r\n# Sample code to reproduce the bug\r\n   self.dataset = load_dataset(\r\n                'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files\r\n\r\n","comment_length":53,"text":"downloading of yahoo_answers_topics dataset failed \n ## Describe the bug\r\nI get an error datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files when I try to download the yahoo_answers_topics dataset\r\n\r\n## Steps to reproduce the bug\r\n   self.dataset = load_dataset(\r\n                'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')\r\n# Sample code to reproduce the bug\r\n   self.dataset = load_dataset(\r\n                'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files\r\n\r\n \n Hi ! I just tested and it worked fine today for me.\r\n\r\nI think this is because the dataset is stored on Google Drive which has a quota limit for the number of downloads per day, see this similar issue https:\/\/github.com\/huggingface\/datasets\/issues\/996 \r\n\r\nFeel free to try again today, now that the quota was reset","embeddings":[-0.4173349738,0.143848151,-0.0522662066,0.2103923559,0.2381456643,-0.0566483326,0.2377109826,0.2905799747,0.1442447603,0.0573149025,-0.0823222697,-0.0347232893,0.0095932391,0.2967808545,-0.1141854674,0.1532201618,0.1207118854,-0.2423342615,-0.3290193081,0.187282294,0.0082971463,0.3475176692,-0.1365118027,-0.186028257,-0.2225944102,0.0802132934,0.0324274227,0.1877681762,-0.3060182929,-0.1939620823,0.499863416,0.1681091785,0.048498448,0.2420327514,-0.0001159498,-0.0456452072,0.3564919531,-0.023051843,-0.2754577696,-0.171936661,-0.6136645675,-0.3641073406,-0.162503168,-0.0510163009,0.2578029037,0.4485144913,-0.0789878741,-0.0727403313,0.2911607027,0.3613387048,0.2246932238,0.5078260899,0.5233133435,0.0212832503,0.6007751226,0.0284277834,-0.1523155123,0.3020832241,0.0485347733,0.0955684856,0.0357895978,0.2261752188,-0.0547714457,0.2072292268,0.0509779118,-0.131506905,0.0101163024,-0.2643214762,0.18099913,0.3761467338,0.7534170747,-0.0676243082,-0.3866149187,-0.2335674614,-0.1377534717,-0.0953435823,0.3624245822,0.1967544854,-0.1790942848,0.1182354912,-0.3431764841,-0.0559018403,-0.1109952107,0.229899779,0.1896436065,-0.0589184761,-0.2049210817,-0.0243713334,-0.0747148991,-0.1185131967,0.3572820723,-0.277400285,-0.0273528006,0.1389751434,-0.5924146175,-0.1630174518,-0.147222802,0.3785592318,0.5116468072,0.5190519094,0.0123613179,0.1149433553,-0.27763924,-0.1024332941,0.3908933997,0.4025636017,-0.0215438753,0.1509063542,0.4378218949,0.3599639237,0.0442593172,0.0989443734,0.0606571287,-0.1273132861,0.3956009448,-0.0489524379,0.2884015441,-0.4871147275,-0.3083412647,0.2666872144,-0.2881489694,-0.2556790113,-0.034938097,0.1287734658,-0.2728326321,0.3193659782,-0.1581632942,0.231635198,0.0761210695,-0.1068308949,-0.2661254108,-0.0457298346,-0.1917149127,0.1251797229,0.409027487,-0.3089469969,0.3847400844,0.0583134443,-0.0366222151,-0.1010764167,0.0702504516,-0.0097570438,-0.3626892865,0.4796497226,-0.0283761043,0.3199997842,0.0633891225,0.205698505,-0.0027630047,0.188662529,-0.3618530929,-0.3521518409,0.0451591946,0.1900095344,-0.3650646806,-0.1490500867,-0.2103958875,-0.0985899344,0.0886896625,-0.0989632681,-0.0011395429,-0.0808662251,-0.1798315793,-0.1877604127,0.1709344387,0.2947294712,-0.1214032397,0.0492805317,-0.2126547396,-0.2295565754,0.0075604594,0.0988980085,-0.102120772,0.0516035929,-0.3121572137,0.1910166144,-0.0304746944,-0.044129435,-0.8213795424,0.112007983,0.2127331048,0.1995853484,0.0226960126,0.1158608794,-0.0115395924,-0.0558772534,0.0683128536,0.2879424691,-0.111356616,0.0531982332,-0.2135489434,-0.2146818191,-0.1412281245,0.1924072504,0.2178845257,0.179527387,0.1498346031,-0.0014782615,0.2000293434,0.0601047017,0.02646528,0.4225367606,0.2325090021,0.1264427751,-0.179184556,-0.4266201854,-0.6495069861,0.2739134729,-0.1510443538,-0.1101723239,-0.0169516113,-0.1082540005,-0.4812152982,-0.1396791339,-0.0671041086,-0.120860599,0.0543915816,0.1184010804,0.1921287328,0.0473960415,-0.0808084756,0.6759991646,-0.2277866602,0.1251005828,-0.2809175849,0.3650693893,-0.1465261579,-0.1149823815,0.1866227388,-0.0570824929,0.1539196968,0.0262796674,-0.1638719887,0.3405667543,0.0817586109,-0.0481769145,0.1589739025,0.3192985952,0.1357056648,-0.2003445774,-0.1071018875,0.2686326802,0.0888122767,-0.0193650126,-0.3108480275,0.1076376289,-0.3952946365,0.0862755626,-0.0847249702,0.2748892605,0.2487687469,-0.1529650986,-0.2354714423,0.0756012946,0.4902819693,0.1661131233,-0.015917832,0.0276128631,-0.2370831817,0.0720305815,0.3816649318,-0.1854065806,-0.0236775093,0.231529519,-0.0424432643,-0.084859401,0.0524934307,0.3492453992,0.4121186733,0.0774499029,0.1270791888,0.0192581601,-0.0698235929,-0.2119279206,-0.0097266156,0.1050680801,0.0516628176,0.3873591721,0.0891756043,0.0201651081,-0.3156518042,0.017886471,0.0662738904,0.2733680606,-0.4959610701,-0.0993697047,-0.2257489115,-0.0083330069,-0.1796372533,-0.2272870094,-0.4338906407,-0.4144547582,-0.0414733104,0.4321182966,-0.0073697041,0.1584973484,-0.1760055721,0.0766011328,0.1298984587,-0.1298920363,-0.0110590551,0.1239375621,-0.030557489,0.0674769878,0.4870757163,-0.1200205684,0.2683666348,-0.3797082305,0.0057567521,-0.5876286626,-0.0252418835,0.031945128,-0.0429755375,0.329652667,0.0681997612,0.3912466168,-0.3351227045,0.089258641,0.0412826017,0.0300798323,-0.2807072699,0.172067076,0.0588248335,0.0429034531,0.0793969035,-0.1639138311,-0.1592887789,-0.2847833335,0.0246164221,-0.1046027541,0.0429372713,0.3333446681,-0.0596335828,-0.0055995085,0.0306974165,0.0233642235,-0.3865577877,-0.6483180523,0.5215483904,0.0017794424,-0.4173825085,0.0635498688,0.1082302183,-0.1324106157,0.3234136701,-0.5212422609,-0.0796859786,-0.3460487425,0.2091495544,0.1091664284,0.1893132478,0.4114702046,-0.2102251202,-0.0587379932,-0.0052901902,-0.1677402854,0.0951371267,-0.0565814525,0.2765411735,0.0760917589,0.0933395401,-0.0337052345,0.6843774319,0.2569456398,0.3306367099,0.4992987216,0.1553970277,0.3406203985,0.0140705667,-0.1394452304,0.0300782286,-0.2988120019,0.11100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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2645","title":"load_dataset processing failed with OS error after downloading a dataset","comments":"Hi ! It looks like an issue with pytorch.\r\n\r\nCould you try to run `import torch` and see if it raises an error ?","body":"## Describe the bug\r\nAfter downloading a dataset like opus100, there is a bug that \r\nOSError: Cannot find data file.\r\nOriginal error:\r\ndlopen: cannot load any more object with static TLS\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nthis_dataset = load_dataset('opus100', 'af-en')\r\n```\r\n\r\n## Expected results\r\nthere is no error when running load_dataset.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prep\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 989, in _prepare_split\r\n    example = self.info.features.encode_example(record)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 952, in encode_example\r\n    example = cast_to_python_objects(example)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 219, in cast_to_python_ob\r\n    return _cast_to_python_objects(obj)[0]\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 165, in _cast_to_python_o\r\n    import torch\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/torch\/__init__.py\", line 188, in <module>\r\n    _load_global_deps()\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/torch\/__init__.py\", line 141, in _load_global_deps\r\n    ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/ctypes\/__init__.py\", line 348, in __init__\r\n    self._handle = _dlopen(self._name, mode)\r\nOSError: dlopen: cannot load any more object with static TLS\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"download_hub_opus100.py\", line 9, in <module>\r\n    this_dataset = load_dataset('opus100', language_pair)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepa\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prep\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\ndlopen: cannot load any more object with static TLS\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid\r\n- Python version: 3.6.6\r\n- PyArrow version: 3.0.0\r\n\r\n\r\n","comment_length":24,"text":"load_dataset processing failed with OS error after downloading a dataset \n ## Describe the bug\r\nAfter downloading a dataset like opus100, there is a bug that \r\nOSError: Cannot find data file.\r\nOriginal error:\r\ndlopen: cannot load any more object with static TLS\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nthis_dataset = load_dataset('opus100', 'af-en')\r\n```\r\n\r\n## Expected results\r\nthere is no error when running load_dataset.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prep\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 989, in _prepare_split\r\n    example = self.info.features.encode_example(record)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 952, in encode_example\r\n    example = cast_to_python_objects(example)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 219, in cast_to_python_ob\r\n    return _cast_to_python_objects(obj)[0]\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 165, in _cast_to_python_o\r\n    import torch\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/torch\/__init__.py\", line 188, in <module>\r\n    _load_global_deps()\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/torch\/__init__.py\", line 141, in _load_global_deps\r\n    ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/ctypes\/__init__.py\", line 348, in __init__\r\n    self._handle = _dlopen(self._name, mode)\r\nOSError: dlopen: cannot load any more object with static TLS\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"download_hub_opus100.py\", line 9, in <module>\r\n    this_dataset = load_dataset('opus100', language_pair)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepa\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prep\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\ndlopen: cannot load any more object with static TLS\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid\r\n- Python version: 3.6.6\r\n- PyArrow version: 3.0.0\r\n\r\n\r\n \n Hi ! It looks like an issue with pytorch.\r\n\r\nCould you try to run `import torch` and see if it raises an error ?","embeddings":[-0.4430502951,0.2031198442,-0.0454366133,0.484775424,0.30398193,-0.0233756881,0.2349691838,0.3660457134,-0.1082289293,0.0927524492,-0.1625688374,0.4678144753,-0.0074352133,-0.0836081207,-0.0174168609,-0.1323251277,0.0387222506,0.2778642178,-0.6045764089,-0.0482679792,-0.4312471151,0.3115380704,-0.1030293554,-0.0990164578,0.0378610119,0.0749130622,-0.1906295121,0.3197152019,-0.0947884694,-0.260250777,-0.020933656,-0.1710368693,0.2840125263,0.5108674169,-0.0001183986,0.2902742326,0.0925462469,-0.0907713547,-0.402387321,-0.1111452058,-0.0832587332,-0.1031597853,0.148237586,-0.3790362477,-0.0424588993,-0.2175002843,-0.0282115731,-0.5162989497,0.2104209661,0.3379337788,0.1746215373,0.374222666,0.2902139723,-0.148411572,0.254062593,-0.0283484124,-0.0414075442,0.048455026,0.1556954682,-0.0565809533,0.0651961565,0.292029947,-0.1614065915,0.1469640583,0.2089904547,0.0581361279,0.2701870203,-0.33701846,0.3568376005,0.1577442288,0.5403915048,-0.0280469693,-0.3331862986,-0.187848255,0.0130488845,-0.4298108518,0.2435779721,0.2214604914,-0.0285913814,0.0868347734,0.1412747353,-0.1238947809,-0.0940341353,0.3054019213,-0.042060364,0.2001214623,-0.0576187335,-0.0591642372,0.0631149262,0.2239584625,0.2793515921,0.0497795753,-0.0100777987,0.3172575235,-0.4745251834,0.1773097366,0.1719004661,0.0040303003,0.0933709666,-0.0650276318,0.1036134586,-0.2318625599,-0.0230106711,-0.0736536384,0.2052522302,0.0471473411,-0.2928019464,-0.1984692216,0.1404688209,0.0625927523,-0.1818164736,-0.0956845358,-0.1932927221,-0.3410633504,0.0759849101,0.1347163916,0.5756460428,-0.1788666844,-0.3567049801,-0.0537942536,-0.1881234646,-0.0532148778,0.106923528,0.2891930044,-0.0607561283,0.4205152392,0.2440235764,0.1303139478,-0.3833307922,0.0157029722,-0.1193970069,-0.2396061271,-0.1751903147,-0.018225871,0.064439863,-0.4865529239,0.3154323995,0.1732182652,-0.0735439509,-0.3623705506,0.3777589798,-0.079421401,-0.0954614356,0.2311086208,-0.0021412387,-0.1261712462,0.1479644179,-0.0137931965,0.0977248177,0.4053345621,-0.4959002435,-0.5249584317,-0.4787271023,0.171328932,-0.3053381145,0.1398524195,0.3070881665,-0.1751901656,0.1507404149,-0.1544900537,-0.098591283,-0.2091853321,-0.3328261971,-0.1935880631,0.4081570506,0.4952768385,-0.3194479048,0.0553343706,-0.252432704,-0.1881270856,-0.1042026207,-0.0230471604,-0.0677800998,0.3877436221,-0.5029060841,-0.2613579333,0.2853173614,-0.442951858,-0.6753488779,0.0294269733,-0.2476943135,0.0424231142,0.3182734847,0.0589966103,0.3740208149,-0.0094537772,-0.1303220242,0.2131943554,0.1354910731,0.0185981672,-0.1482967734,-0.1480104178,0.0453190804,0.2436876893,0.0738346502,0.0752403513,0.1276132911,0.1137061119,0.2993844151,-0.1438609511,-0.1832547486,0.1096934155,0.2658593357,0.1396879703,0.2437562943,-0.0872695222,-0.4674577117,0.0827427655,-0.0533501357,0.0326676778,-0.2187159657,-0.0448126234,-0.1293059886,0.0956431851,-0.2936310172,-0.3219188452,0.0730179325,0.1981949508,0.0066885557,0.0158333015,-0.086826086,0.3457705677,0.1164511517,0.1674299091,-0.2508371174,0.330353111,0.1207666919,-0.0379181989,-0.0812918246,0.3204132915,0.0453062765,-0.2102501094,-0.2788046896,0.4593667984,0.1484796256,-0.0225258488,-0.0986613557,0.1753183156,0.2358965874,-0.2645653784,-0.098218061,0.0409373902,0.2143257558,0.0708438754,0.1004788578,0.0042706537,-0.1008393019,0.1319384128,0.1286811531,-0.004212575,0.0599547587,-0.2399789393,0.035048604,-0.0927790403,0.364774555,-0.0100674117,0.3398686349,0.1148416102,-0.2486354411,-0.1168410107,0.4487637579,-0.0409491025,0.2245076895,0.1049687639,-0.2105497122,0.2330356687,-0.0582606643,0.1338374168,0.763418138,0.1158927903,-0.3087420464,0.0303173065,0.1568048,-0.0560591407,0.3047151864,0.2135780603,0.5176039934,0.4227158427,0.0548551455,0.2111869901,-0.1041079387,-0.4372707903,0.1301827282,0.2429524511,-0.3268970549,-0.0282367282,-0.2275322825,-0.0035175544,-0.17453444,-0.0063033402,-0.1977397352,-0.1105283424,-0.003894232,0.11990612,-0.0541347824,0.0521627367,-0.2768771946,0.0833762586,0.2396606207,-0.2275040299,0.1765099317,-0.0787875429,-0.1228342876,-0.0798948407,0.3425710499,-0.1298445314,0.2565789223,-0.3259460628,0.1347655356,-0.1751976609,-0.212033093,0.1560975164,0.2870919406,0.1585514694,0.1268410534,0.212009266,0.1711961478,-0.2368269414,0.2334577441,-0.1223337799,-0.0551896133,0.111345306,-0.0120252324,0.2442098558,-0.047289893,-0.4643704593,-0.3488773406,-0.4888262451,-0.0874160677,-0.0671280771,0.2688062191,0.1994024813,0.1261017323,0.2629390359,0.2930875719,0.0473046899,-0.0196140371,-0.1887223721,0.2164980024,-0.303324461,-0.2178200781,-0.2564584911,0.0712681711,0.2940013111,0.0316479616,-0.6996409893,-0.3688041568,-0.0958594903,0.1920544207,-0.4736225605,-0.0218408015,0.2243337035,0.0982552692,0.023132002,-0.3120465875,0.0890534148,0.0811186284,-0.1509288847,0.2234877795,-0.1041746438,0.4505559206,-0.2182901651,0.5835216045,-0.0896609947,-0.2326279879,0.494356215,-0.1522163749,0.3561162949,-0.1552245617,-0.2526461184,-0.204453364,-0.0917278007,0.011840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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2645","title":"load_dataset processing failed with OS error after downloading a dataset","comments":"> Hi ! It looks like an issue with pytorch.\r\n> \r\n> Could you try to run `import torch` and see if it raises an error ?\r\n\r\nIt works. Thank you!","body":"## Describe the bug\r\nAfter downloading a dataset like opus100, there is a bug that \r\nOSError: Cannot find data file.\r\nOriginal error:\r\ndlopen: cannot load any more object with static TLS\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nthis_dataset = load_dataset('opus100', 'af-en')\r\n```\r\n\r\n## Expected results\r\nthere is no error when running load_dataset.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prep\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 989, in _prepare_split\r\n    example = self.info.features.encode_example(record)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 952, in encode_example\r\n    example = cast_to_python_objects(example)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 219, in cast_to_python_ob\r\n    return _cast_to_python_objects(obj)[0]\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 165, in _cast_to_python_o\r\n    import torch\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/torch\/__init__.py\", line 188, in <module>\r\n    _load_global_deps()\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/torch\/__init__.py\", line 141, in _load_global_deps\r\n    ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/ctypes\/__init__.py\", line 348, in __init__\r\n    self._handle = _dlopen(self._name, mode)\r\nOSError: dlopen: cannot load any more object with static TLS\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"download_hub_opus100.py\", line 9, in <module>\r\n    this_dataset = load_dataset('opus100', language_pair)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepa\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prep\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\ndlopen: cannot load any more object with static TLS\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid\r\n- Python version: 3.6.6\r\n- PyArrow version: 3.0.0\r\n\r\n\r\n","comment_length":31,"text":"load_dataset processing failed with OS error after downloading a dataset \n ## Describe the bug\r\nAfter downloading a dataset like opus100, there is a bug that \r\nOSError: Cannot find data file.\r\nOriginal error:\r\ndlopen: cannot load any more object with static TLS\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nthis_dataset = load_dataset('opus100', 'af-en')\r\n```\r\n\r\n## Expected results\r\nthere is no error when running load_dataset.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prep\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 989, in _prepare_split\r\n    example = self.info.features.encode_example(record)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 952, in encode_example\r\n    example = cast_to_python_objects(example)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 219, in cast_to_python_ob\r\n    return _cast_to_python_objects(obj)[0]\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/features.py\", line 165, in _cast_to_python_o\r\n    import torch\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/torch\/__init__.py\", line 188, in <module>\r\n    _load_global_deps()\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/torch\/__init__.py\", line 141, in _load_global_deps\r\n    ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/ctypes\/__init__.py\", line 348, in __init__\r\n    self._handle = _dlopen(self._name, mode)\r\nOSError: dlopen: cannot load any more object with static TLS\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"download_hub_opus100.py\", line 9, in <module>\r\n    this_dataset = load_dataset('opus100', language_pair)\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepa\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/anaconda3\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 658, in _download_and_prep\r\n    + str(e)\r\nOSError: Cannot find data file.\r\nOriginal error:\r\ndlopen: cannot load any more object with static TLS\r\n\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid\r\n- Python version: 3.6.6\r\n- PyArrow version: 3.0.0\r\n\r\n\r\n \n > Hi ! It looks like an issue with pytorch.\r\n> \r\n> Could you try to run `import torch` and see if it raises an error ?\r\n\r\nIt works. Thank you!","embeddings":[-0.4430502951,0.2031198442,-0.0454366133,0.484775424,0.30398193,-0.0233756881,0.2349691838,0.3660457134,-0.1082289293,0.0927524492,-0.1625688374,0.4678144753,-0.0074352133,-0.0836081207,-0.0174168609,-0.1323251277,0.0387222506,0.2778642178,-0.6045764089,-0.0482679792,-0.4312471151,0.3115380704,-0.1030293554,-0.0990164578,0.0378610119,0.0749130622,-0.1906295121,0.3197152019,-0.0947884694,-0.260250777,-0.020933656,-0.1710368693,0.2840125263,0.5108674169,-0.0001183986,0.2902742326,0.0925462469,-0.0907713547,-0.402387321,-0.1111452058,-0.0832587332,-0.1031597853,0.148237586,-0.3790362477,-0.0424588993,-0.2175002843,-0.0282115731,-0.5162989497,0.2104209661,0.3379337788,0.1746215373,0.374222666,0.2902139723,-0.148411572,0.254062593,-0.0283484124,-0.0414075442,0.048455026,0.1556954682,-0.0565809533,0.0651961565,0.292029947,-0.1614065915,0.1469640583,0.2089904547,0.0581361279,0.2701870203,-0.33701846,0.3568376005,0.1577442288,0.5403915048,-0.0280469693,-0.3331862986,-0.187848255,0.0130488845,-0.4298108518,0.2435779721,0.2214604914,-0.0285913814,0.0868347734,0.1412747353,-0.1238947809,-0.0940341353,0.3054019213,-0.042060364,0.2001214623,-0.0576187335,-0.0591642372,0.0631149262,0.2239584625,0.2793515921,0.0497795753,-0.0100777987,0.3172575235,-0.4745251834,0.1773097366,0.1719004661,0.0040303003,0.0933709666,-0.0650276318,0.1036134586,-0.2318625599,-0.0230106711,-0.0736536384,0.2052522302,0.0471473411,-0.2928019464,-0.1984692216,0.1404688209,0.0625927523,-0.1818164736,-0.0956845358,-0.1932927221,-0.3410633504,0.0759849101,0.1347163916,0.5756460428,-0.1788666844,-0.3567049801,-0.0537942536,-0.1881234646,-0.0532148778,0.106923528,0.2891930044,-0.0607561283,0.4205152392,0.2440235764,0.1303139478,-0.3833307922,0.0157029722,-0.1193970069,-0.2396061271,-0.1751903147,-0.018225871,0.064439863,-0.4865529239,0.3154323995,0.1732182652,-0.0735439509,-0.3623705506,0.3777589798,-0.079421401,-0.0954614356,0.2311086208,-0.0021412387,-0.1261712462,0.1479644179,-0.0137931965,0.0977248177,0.4053345621,-0.4959002435,-0.5249584317,-0.4787271023,0.171328932,-0.3053381145,0.1398524195,0.3070881665,-0.1751901656,0.1507404149,-0.1544900537,-0.098591283,-0.2091853321,-0.3328261971,-0.1935880631,0.4081570506,0.4952768385,-0.3194479048,0.0553343706,-0.252432704,-0.1881270856,-0.1042026207,-0.0230471604,-0.0677800998,0.3877436221,-0.5029060841,-0.2613579333,0.2853173614,-0.442951858,-0.6753488779,0.0294269733,-0.2476943135,0.0424231142,0.3182734847,0.0589966103,0.3740208149,-0.0094537772,-0.1303220242,0.2131943554,0.1354910731,0.0185981672,-0.1482967734,-0.1480104178,0.0453190804,0.2436876893,0.0738346502,0.0752403513,0.1276132911,0.1137061119,0.2993844151,-0.1438609511,-0.1832547486,0.1096934155,0.2658593357,0.1396879703,0.2437562943,-0.0872695222,-0.4674577117,0.0827427655,-0.0533501357,0.0326676778,-0.2187159657,-0.0448126234,-0.1293059886,0.0956431851,-0.2936310172,-0.3219188452,0.0730179325,0.1981949508,0.0066885557,0.0158333015,-0.086826086,0.3457705677,0.1164511517,0.1674299091,-0.2508371174,0.330353111,0.1207666919,-0.0379181989,-0.0812918246,0.3204132915,0.0453062765,-0.2102501094,-0.2788046896,0.4593667984,0.1484796256,-0.0225258488,-0.0986613557,0.1753183156,0.2358965874,-0.2645653784,-0.098218061,0.0409373902,0.2143257558,0.0708438754,0.1004788578,0.0042706537,-0.1008393019,0.1319384128,0.1286811531,-0.004212575,0.0599547587,-0.2399789393,0.035048604,-0.0927790403,0.364774555,-0.0100674117,0.3398686349,0.1148416102,-0.2486354411,-0.1168410107,0.4487637579,-0.0409491025,0.2245076895,0.1049687639,-0.2105497122,0.2330356687,-0.0582606643,0.1338374168,0.763418138,0.1158927903,-0.3087420464,0.0303173065,0.1568048,-0.0560591407,0.3047151864,0.2135780603,0.5176039934,0.4227158427,0.0548551455,0.2111869901,-0.1041079387,-0.4372707903,0.1301827282,0.2429524511,-0.3268970549,-0.0282367282,-0.2275322825,-0.0035175544,-0.17453444,-0.0063033402,-0.1977397352,-0.1105283424,-0.003894232,0.11990612,-0.0541347824,0.0521627367,-0.2768771946,0.0833762586,0.2396606207,-0.2275040299,0.1765099317,-0.0787875429,-0.1228342876,-0.0798948407,0.3425710499,-0.1298445314,0.2565789223,-0.3259460628,0.1347655356,-0.1751976609,-0.212033093,0.1560975164,0.2870919406,0.1585514694,0.1268410534,0.212009266,0.1711961478,-0.2368269414,0.2334577441,-0.1223337799,-0.0551896133,0.111345306,-0.0120252324,0.2442098558,-0.047289893,-0.4643704593,-0.3488773406,-0.4888262451,-0.0874160677,-0.0671280771,0.2688062191,0.1994024813,0.1261017323,0.2629390359,0.2930875719,0.0473046899,-0.0196140371,-0.1887223721,0.2164980024,-0.303324461,-0.2178200781,-0.2564584911,0.0712681711,0.2940013111,0.0316479616,-0.6996409893,-0.3688041568,-0.0958594903,0.1920544207,-0.4736225605,-0.0218408015,0.2243337035,0.0982552692,0.023132002,-0.3120465875,0.0890534148,0.0811186284,-0.1509288847,0.2234877795,-0.1041746438,0.4505559206,-0.2182901651,0.5835216045,-0.0896609947,-0.2326279879,0.494356215,-0.1522163749,0.3561162949,-0.1552245617,-0.2526461184,-0.204453364,-0.0917278007,0.011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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2644","title":"Batched `map` not allowed to return 0 items","comments":"Hi ! Thanks for reporting. Indeed it looks like type inference makes it fail. We should probably just ignore this step until a non-empty batch is passed.","body":"## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":27,"text":"Batched `map` not allowed to return 0 items \n ## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Hi ! Thanks for reporting. Indeed it looks like type inference makes it fail. We should probably just ignore this step until a non-empty batch is passed.","embeddings":[-0.2328359336,-0.3903888166,-0.0444566682,0.1644899547,-0.1085444242,-0.0687690005,0.1163394973,0.4056320786,0.6400417089,0.1178812236,-0.0565718673,0.2460853755,-0.3954412043,0.1043543741,-0.1629210711,0.1608026177,-0.0449331626,0.1405789405,-0.1691175699,-0.1914925724,-0.3362929821,0.163199544,-0.3979255855,-0.0501046143,-0.1487712115,-0.2919759154,0.286480099,-0.0362105146,-0.162814334,-0.2454052418,0.206091553,-0.2328298539,-0.2091356516,0.5783210993,-0.0001299276,-0.0231365338,0.1505937427,-0.1851885766,-0.0897614583,-0.3029606342,-0.1908181012,-0.1715300977,-0.1157183424,-0.2824648917,0.1819460839,-0.2716506422,-0.1956346184,-0.4482398331,0.1071964726,0.2587007582,0.043321602,0.4188180268,0.003066103,0.1764480174,0.3312488496,0.2664009035,-0.0753516629,0.1288110614,0.7205767035,-0.2881007195,0.0226270352,0.3385902047,-0.3921834528,0.0709065422,0.1367558688,-0.0309035499,0.230168879,-0.5515586734,0.2697506845,0.3675674498,-0.0899998844,0.0607856587,-0.433337301,-0.6687820554,-0.0403036699,-0.0954985172,-0.0388978831,0.2810007632,-0.4210395217,-0.0564594232,-0.4846186638,0.1217950135,0.0078949472,0.1618547142,0.0306755304,0.4589543343,-0.0930409953,0.2003241777,0.3451099396,-0.1618324965,-0.0994021893,-0.1672711968,-0.2236276567,0.5847230554,-0.0366987288,-0.1459876001,0.3893203735,0.2197681516,0.2230358422,-0.3132426441,-0.050306201,-0.0742755681,0.2646799386,-0.0234315637,0.4159452617,0.0793761313,0.2686936855,0.3493200243,-0.005052017,-0.0590938851,0.0676903278,0.1131693944,0.113442108,-0.0890844166,-0.0096516423,-0.0764087662,0.2567855418,-0.0787523314,-0.1218986362,0.2341007143,-0.4100169539,0.1369067729,0.0620478503,0.1250830591,0.1700579822,-0.1776706576,0.1202128828,0.1126287505,-0.1570285559,0.1897115409,-0.0772467405,-0.1316168755,-0.192502588,-0.3208249807,0.1964786202,-0.3342862427,0.2418166101,-0.1585832834,0.0198275335,-0.0615327619,0.0957399309,-0.2645617723,0.7512272596,0.4937881231,-0.119511351,0.2913580537,0.3966238201,-0.2185574174,-0.1752834171,0.4062903821,-0.3826135099,-0.2777348757,0.2100417018,-0.0401864871,-0.2380758375,0.346570462,-0.5073054433,0.2185845971,0.1782458276,0.0822512209,0.2914729118,-0.2697472274,-0.105394572,-0.2360114455,0.0425621271,0.6938397884,-0.63877213,-0.049924884,0.1101892963,0.1013790518,-0.0613662116,0.3478800058,-0.0454604253,0.3481105566,-0.4107360542,0.3059869707,0.0873124897,-0.1264710426,-0.3275630772,0.3134204149,-0.0358151942,0.2356395572,-0.0210545417,-0.0642482191,0.5519376397,-0.1873890162,0.3676523268,-0.1700588763,-0.279956907,0.1041627601,-0.0503817089,-0.039843861,0.0603797175,-0.2420822233,0.19492428,0.0165877249,0.0390782356,-0.5488622189,0.1286391169,-0.1508790404,0.3383628428,0.2401701212,0.262108326,0.1233314499,0.0182514824,-0.2563515604,-0.4713618159,0.3001765013,-0.4880333245,0.021248579,-0.4338338673,-0.1033039168,0.0309646595,0.1834742576,-0.0350200944,0.0330956019,-0.0749208331,-0.4975802898,0.1765710711,-0.0213433541,-0.2262366265,-0.2370698303,-0.1358649582,-0.0084387707,-0.3567225337,0.071359843,0.2501084507,-0.2921575904,-0.2542045712,0.2466300279,0.1149316132,-0.1176109165,0.117354244,0.1758526713,-0.0348161124,-0.1673572958,-0.1400127411,0.131253615,0.0117278975,-0.0458009467,0.101837188,0.1450134069,0.3444767892,-0.2378433347,-0.0694068223,0.5054207444,-0.1876149476,0.5359300375,-0.1796862483,0.0512384214,-0.1996322572,0.042606812,-0.2059215009,-0.3850508034,0.0762836039,-0.1577371508,0.0513374284,0.0319146588,0.0012563785,-0.1989199072,0.0856170282,-0.046962332,0.16477938,0.0144881429,-0.0630519465,0.2266895324,0.0446924791,-0.0142778121,0.3432699442,0.2191425711,-0.0887260064,-0.1521824449,0.250440836,0.0001223587,0.4139182568,0.18166776,0.0260531791,0.1550095528,-0.003640976,-0.0657203272,-0.2528341115,-0.1814302802,0.005715027,0.149533838,-0.3952980638,-0.0354201086,0.1101150513,-0.158504501,0.0689762086,-0.0765032619,-0.290856123,-0.3032285273,-0.0453336127,0.2601582408,-0.2722713947,-0.02400795,-0.4013136625,0.0116532287,0.1975094527,-0.2747197151,-0.2812999189,-0.1685465872,-0.0756110176,-0.0741591007,0.0731139854,-0.1339878589,0.3615631759,0.2521368861,-0.2612816691,-0.243771404,-0.0951137543,0.0065136496,-0.5370389223,0.2054035813,0.4100922644,0.3981708586,-0.2923846245,0.1470615864,0.2793858051,-0.1071430445,-0.2480682433,-0.0079056751,0.0223337524,0.153262645,-0.179215163,0.1474315226,0.0644616261,-0.3209299445,0.1781849116,-0.1891590655,0.2615512013,0.2194533795,0.2780077457,0.1538574994,-0.3693777621,-0.4285494387,0.1223512962,-0.1416769028,0.4460211396,0.0390229337,-0.1469308287,0.1844055504,0.0262182392,-0.1853193641,0.2405568659,-0.0966859683,0.0939638689,-0.0562738925,0.1234829947,0.190006569,0.3668004274,0.4985276759,0.0864884332,0.0460447706,-0.2846722603,-0.1089555398,0.0812640563,0.0222764555,-0.064514026,0.422529459,0.3217896223,0.0287001207,0.7504062057,0.3654765189,0.1272135675,0.0946453437,0.0725369975,0.3822363317,-0.1140064001,-0.379692018,-0.2082244754,0.1538844705,-0.084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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2644","title":"Batched `map` not allowed to return 0 items","comments":"Sounds good! Do you want me to propose a PR? I'm quite busy right now, but if it's not too urgent I could take a look next week.","body":"## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":28,"text":"Batched `map` not allowed to return 0 items \n ## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Sounds good! Do you want me to propose a PR? I'm quite busy right now, but if it's not too urgent I could take a look next week.","embeddings":[-0.2328359336,-0.3903888166,-0.0444566682,0.1644899547,-0.1085444242,-0.0687690005,0.1163394973,0.4056320786,0.6400417089,0.1178812236,-0.0565718673,0.2460853755,-0.3954412043,0.1043543741,-0.1629210711,0.1608026177,-0.0449331626,0.1405789405,-0.1691175699,-0.1914925724,-0.3362929821,0.163199544,-0.3979255855,-0.0501046143,-0.1487712115,-0.2919759154,0.286480099,-0.0362105146,-0.162814334,-0.2454052418,0.206091553,-0.2328298539,-0.2091356516,0.5783210993,-0.0001299276,-0.0231365338,0.1505937427,-0.1851885766,-0.0897614583,-0.3029606342,-0.1908181012,-0.1715300977,-0.1157183424,-0.2824648917,0.1819460839,-0.2716506422,-0.1956346184,-0.4482398331,0.1071964726,0.2587007582,0.043321602,0.4188180268,0.003066103,0.1764480174,0.3312488496,0.2664009035,-0.0753516629,0.1288110614,0.7205767035,-0.2881007195,0.0226270352,0.3385902047,-0.3921834528,0.0709065422,0.1367558688,-0.0309035499,0.230168879,-0.5515586734,0.2697506845,0.3675674498,-0.0899998844,0.0607856587,-0.433337301,-0.6687820554,-0.0403036699,-0.0954985172,-0.0388978831,0.2810007632,-0.4210395217,-0.0564594232,-0.4846186638,0.1217950135,0.0078949472,0.1618547142,0.0306755304,0.4589543343,-0.0930409953,0.2003241777,0.3451099396,-0.1618324965,-0.0994021893,-0.1672711968,-0.2236276567,0.5847230554,-0.0366987288,-0.1459876001,0.3893203735,0.2197681516,0.2230358422,-0.3132426441,-0.050306201,-0.0742755681,0.2646799386,-0.0234315637,0.4159452617,0.0793761313,0.2686936855,0.3493200243,-0.005052017,-0.0590938851,0.0676903278,0.1131693944,0.113442108,-0.0890844166,-0.0096516423,-0.0764087662,0.2567855418,-0.0787523314,-0.1218986362,0.2341007143,-0.4100169539,0.1369067729,0.0620478503,0.1250830591,0.1700579822,-0.1776706576,0.1202128828,0.1126287505,-0.1570285559,0.1897115409,-0.0772467405,-0.1316168755,-0.192502588,-0.3208249807,0.1964786202,-0.3342862427,0.2418166101,-0.1585832834,0.0198275335,-0.0615327619,0.0957399309,-0.2645617723,0.7512272596,0.4937881231,-0.119511351,0.2913580537,0.3966238201,-0.2185574174,-0.1752834171,0.4062903821,-0.3826135099,-0.2777348757,0.2100417018,-0.0401864871,-0.2380758375,0.346570462,-0.5073054433,0.2185845971,0.1782458276,0.0822512209,0.2914729118,-0.2697472274,-0.105394572,-0.2360114455,0.0425621271,0.6938397884,-0.63877213,-0.049924884,0.1101892963,0.1013790518,-0.0613662116,0.3478800058,-0.0454604253,0.3481105566,-0.4107360542,0.3059869707,0.0873124897,-0.1264710426,-0.3275630772,0.3134204149,-0.0358151942,0.2356395572,-0.0210545417,-0.0642482191,0.5519376397,-0.1873890162,0.3676523268,-0.1700588763,-0.279956907,0.1041627601,-0.0503817089,-0.039843861,0.0603797175,-0.2420822233,0.19492428,0.0165877249,0.0390782356,-0.5488622189,0.1286391169,-0.1508790404,0.3383628428,0.2401701212,0.262108326,0.1233314499,0.0182514824,-0.2563515604,-0.4713618159,0.3001765013,-0.4880333245,0.021248579,-0.4338338673,-0.1033039168,0.0309646595,0.1834742576,-0.0350200944,0.0330956019,-0.0749208331,-0.4975802898,0.1765710711,-0.0213433541,-0.2262366265,-0.2370698303,-0.1358649582,-0.0084387707,-0.3567225337,0.071359843,0.2501084507,-0.2921575904,-0.2542045712,0.2466300279,0.1149316132,-0.1176109165,0.117354244,0.1758526713,-0.0348161124,-0.1673572958,-0.1400127411,0.131253615,0.0117278975,-0.0458009467,0.101837188,0.1450134069,0.3444767892,-0.2378433347,-0.0694068223,0.5054207444,-0.1876149476,0.5359300375,-0.1796862483,0.0512384214,-0.1996322572,0.042606812,-0.2059215009,-0.3850508034,0.0762836039,-0.1577371508,0.0513374284,0.0319146588,0.0012563785,-0.1989199072,0.0856170282,-0.046962332,0.16477938,0.0144881429,-0.0630519465,0.2266895324,0.0446924791,-0.0142778121,0.3432699442,0.2191425711,-0.0887260064,-0.1521824449,0.250440836,0.0001223587,0.4139182568,0.18166776,0.0260531791,0.1550095528,-0.003640976,-0.0657203272,-0.2528341115,-0.1814302802,0.005715027,0.149533838,-0.3952980638,-0.0354201086,0.1101150513,-0.158504501,0.0689762086,-0.0765032619,-0.290856123,-0.3032285273,-0.0453336127,0.2601582408,-0.2722713947,-0.02400795,-0.4013136625,0.0116532287,0.1975094527,-0.2747197151,-0.2812999189,-0.1685465872,-0.0756110176,-0.0741591007,0.0731139854,-0.1339878589,0.3615631759,0.2521368861,-0.2612816691,-0.243771404,-0.0951137543,0.0065136496,-0.5370389223,0.2054035813,0.4100922644,0.3981708586,-0.2923846245,0.1470615864,0.2793858051,-0.1071430445,-0.2480682433,-0.0079056751,0.0223337524,0.153262645,-0.179215163,0.1474315226,0.0644616261,-0.3209299445,0.1781849116,-0.1891590655,0.2615512013,0.2194533795,0.2780077457,0.1538574994,-0.3693777621,-0.4285494387,0.1223512962,-0.1416769028,0.4460211396,0.0390229337,-0.1469308287,0.1844055504,0.0262182392,-0.1853193641,0.2405568659,-0.0966859683,0.0939638689,-0.0562738925,0.1234829947,0.190006569,0.3668004274,0.4985276759,0.0864884332,0.0460447706,-0.2846722603,-0.1089555398,0.0812640563,0.0222764555,-0.064514026,0.422529459,0.3217896223,0.0287001207,0.7504062057,0.3654765189,0.1272135675,0.0946453437,0.0725369975,0.3822363317,-0.1140064001,-0.379692018,-0.2082244754,0.1538844705,-0.08407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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2644","title":"Batched `map` not allowed to return 0 items","comments":"Sure if you're interested feel free to open a PR :)\r\n\r\nYou can also ping me anytime if you have questions or if I can help !","body":"## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":27,"text":"Batched `map` not allowed to return 0 items \n ## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Sure if you're interested feel free to open a PR :)\r\n\r\nYou can also ping me anytime if you have questions or if I can help !","embeddings":[-0.2328359336,-0.3903888166,-0.0444566682,0.1644899547,-0.1085444242,-0.0687690005,0.1163394973,0.4056320786,0.6400417089,0.1178812236,-0.0565718673,0.2460853755,-0.3954412043,0.1043543741,-0.1629210711,0.1608026177,-0.0449331626,0.1405789405,-0.1691175699,-0.1914925724,-0.3362929821,0.163199544,-0.3979255855,-0.0501046143,-0.1487712115,-0.2919759154,0.286480099,-0.0362105146,-0.162814334,-0.2454052418,0.206091553,-0.2328298539,-0.2091356516,0.5783210993,-0.0001299276,-0.0231365338,0.1505937427,-0.1851885766,-0.0897614583,-0.3029606342,-0.1908181012,-0.1715300977,-0.1157183424,-0.2824648917,0.1819460839,-0.2716506422,-0.1956346184,-0.4482398331,0.1071964726,0.2587007582,0.043321602,0.4188180268,0.003066103,0.1764480174,0.3312488496,0.2664009035,-0.0753516629,0.1288110614,0.7205767035,-0.2881007195,0.0226270352,0.3385902047,-0.3921834528,0.0709065422,0.1367558688,-0.0309035499,0.230168879,-0.5515586734,0.2697506845,0.3675674498,-0.0899998844,0.0607856587,-0.433337301,-0.6687820554,-0.0403036699,-0.0954985172,-0.0388978831,0.2810007632,-0.4210395217,-0.0564594232,-0.4846186638,0.1217950135,0.0078949472,0.1618547142,0.0306755304,0.4589543343,-0.0930409953,0.2003241777,0.3451099396,-0.1618324965,-0.0994021893,-0.1672711968,-0.2236276567,0.5847230554,-0.0366987288,-0.1459876001,0.3893203735,0.2197681516,0.2230358422,-0.3132426441,-0.050306201,-0.0742755681,0.2646799386,-0.0234315637,0.4159452617,0.0793761313,0.2686936855,0.3493200243,-0.005052017,-0.0590938851,0.0676903278,0.1131693944,0.113442108,-0.0890844166,-0.0096516423,-0.0764087662,0.2567855418,-0.0787523314,-0.1218986362,0.2341007143,-0.4100169539,0.1369067729,0.0620478503,0.1250830591,0.1700579822,-0.1776706576,0.1202128828,0.1126287505,-0.1570285559,0.1897115409,-0.0772467405,-0.1316168755,-0.192502588,-0.3208249807,0.1964786202,-0.3342862427,0.2418166101,-0.1585832834,0.0198275335,-0.0615327619,0.0957399309,-0.2645617723,0.7512272596,0.4937881231,-0.119511351,0.2913580537,0.3966238201,-0.2185574174,-0.1752834171,0.4062903821,-0.3826135099,-0.2777348757,0.2100417018,-0.0401864871,-0.2380758375,0.346570462,-0.5073054433,0.2185845971,0.1782458276,0.0822512209,0.2914729118,-0.2697472274,-0.105394572,-0.2360114455,0.0425621271,0.6938397884,-0.63877213,-0.049924884,0.1101892963,0.1013790518,-0.0613662116,0.3478800058,-0.0454604253,0.3481105566,-0.4107360542,0.3059869707,0.0873124897,-0.1264710426,-0.3275630772,0.3134204149,-0.0358151942,0.2356395572,-0.0210545417,-0.0642482191,0.5519376397,-0.1873890162,0.3676523268,-0.1700588763,-0.279956907,0.1041627601,-0.0503817089,-0.039843861,0.0603797175,-0.2420822233,0.19492428,0.0165877249,0.0390782356,-0.5488622189,0.1286391169,-0.1508790404,0.3383628428,0.2401701212,0.262108326,0.1233314499,0.0182514824,-0.2563515604,-0.4713618159,0.3001765013,-0.4880333245,0.021248579,-0.4338338673,-0.1033039168,0.0309646595,0.1834742576,-0.0350200944,0.0330956019,-0.0749208331,-0.4975802898,0.1765710711,-0.0213433541,-0.2262366265,-0.2370698303,-0.1358649582,-0.0084387707,-0.3567225337,0.071359843,0.2501084507,-0.2921575904,-0.2542045712,0.2466300279,0.1149316132,-0.1176109165,0.117354244,0.1758526713,-0.0348161124,-0.1673572958,-0.1400127411,0.131253615,0.0117278975,-0.0458009467,0.101837188,0.1450134069,0.3444767892,-0.2378433347,-0.0694068223,0.5054207444,-0.1876149476,0.5359300375,-0.1796862483,0.0512384214,-0.1996322572,0.042606812,-0.2059215009,-0.3850508034,0.0762836039,-0.1577371508,0.0513374284,0.0319146588,0.0012563785,-0.1989199072,0.0856170282,-0.046962332,0.16477938,0.0144881429,-0.0630519465,0.2266895324,0.0446924791,-0.0142778121,0.3432699442,0.2191425711,-0.0887260064,-0.1521824449,0.250440836,0.0001223587,0.4139182568,0.18166776,0.0260531791,0.1550095528,-0.003640976,-0.0657203272,-0.2528341115,-0.1814302802,0.005715027,0.149533838,-0.3952980638,-0.0354201086,0.1101150513,-0.158504501,0.0689762086,-0.0765032619,-0.290856123,-0.3032285273,-0.0453336127,0.2601582408,-0.2722713947,-0.02400795,-0.4013136625,0.0116532287,0.1975094527,-0.2747197151,-0.2812999189,-0.1685465872,-0.0756110176,-0.0741591007,0.0731139854,-0.1339878589,0.3615631759,0.2521368861,-0.2612816691,-0.243771404,-0.0951137543,0.0065136496,-0.5370389223,0.2054035813,0.4100922644,0.3981708586,-0.2923846245,0.1470615864,0.2793858051,-0.1071430445,-0.2480682433,-0.0079056751,0.0223337524,0.153262645,-0.179215163,0.1474315226,0.0644616261,-0.3209299445,0.1781849116,-0.1891590655,0.2615512013,0.2194533795,0.2780077457,0.1538574994,-0.3693777621,-0.4285494387,0.1223512962,-0.1416769028,0.4460211396,0.0390229337,-0.1469308287,0.1844055504,0.0262182392,-0.1853193641,0.2405568659,-0.0966859683,0.0939638689,-0.0562738925,0.1234829947,0.190006569,0.3668004274,0.4985276759,0.0864884332,0.0460447706,-0.2846722603,-0.1089555398,0.0812640563,0.0222764555,-0.064514026,0.422529459,0.3217896223,0.0287001207,0.7504062057,0.3654765189,0.1272135675,0.0946453437,0.0725369975,0.3822363317,-0.1140064001,-0.379692018,-0.2082244754,0.1538844705,-0.084078185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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2644","title":"Batched `map` not allowed to return 0 items","comments":"Sorry to ping you, @lhoestq, did you have a chance to take a look at the proposed PR? Thank you!","body":"## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":20,"text":"Batched `map` not allowed to return 0 items \n ## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Sorry to ping you, @lhoestq, did you have a chance to take a look at the proposed PR? Thank you!","embeddings":[-0.2328359336,-0.3903888166,-0.0444566682,0.1644899547,-0.1085444242,-0.0687690005,0.1163394973,0.4056320786,0.6400417089,0.1178812236,-0.0565718673,0.2460853755,-0.3954412043,0.1043543741,-0.1629210711,0.1608026177,-0.0449331626,0.1405789405,-0.1691175699,-0.1914925724,-0.3362929821,0.163199544,-0.3979255855,-0.0501046143,-0.1487712115,-0.2919759154,0.286480099,-0.0362105146,-0.162814334,-0.2454052418,0.206091553,-0.2328298539,-0.2091356516,0.5783210993,-0.0001299276,-0.0231365338,0.1505937427,-0.1851885766,-0.0897614583,-0.3029606342,-0.1908181012,-0.1715300977,-0.1157183424,-0.2824648917,0.1819460839,-0.2716506422,-0.1956346184,-0.4482398331,0.1071964726,0.2587007582,0.043321602,0.4188180268,0.003066103,0.1764480174,0.3312488496,0.2664009035,-0.0753516629,0.1288110614,0.7205767035,-0.2881007195,0.0226270352,0.3385902047,-0.3921834528,0.0709065422,0.1367558688,-0.0309035499,0.230168879,-0.5515586734,0.2697506845,0.3675674498,-0.0899998844,0.0607856587,-0.433337301,-0.6687820554,-0.0403036699,-0.0954985172,-0.0388978831,0.2810007632,-0.4210395217,-0.0564594232,-0.4846186638,0.1217950135,0.0078949472,0.1618547142,0.0306755304,0.4589543343,-0.0930409953,0.2003241777,0.3451099396,-0.1618324965,-0.0994021893,-0.1672711968,-0.2236276567,0.5847230554,-0.0366987288,-0.1459876001,0.3893203735,0.2197681516,0.2230358422,-0.3132426441,-0.050306201,-0.0742755681,0.2646799386,-0.0234315637,0.4159452617,0.0793761313,0.2686936855,0.3493200243,-0.005052017,-0.0590938851,0.0676903278,0.1131693944,0.113442108,-0.0890844166,-0.0096516423,-0.0764087662,0.2567855418,-0.0787523314,-0.1218986362,0.2341007143,-0.4100169539,0.1369067729,0.0620478503,0.1250830591,0.1700579822,-0.1776706576,0.1202128828,0.1126287505,-0.1570285559,0.1897115409,-0.0772467405,-0.1316168755,-0.192502588,-0.3208249807,0.1964786202,-0.3342862427,0.2418166101,-0.1585832834,0.0198275335,-0.0615327619,0.0957399309,-0.2645617723,0.7512272596,0.4937881231,-0.119511351,0.2913580537,0.3966238201,-0.2185574174,-0.1752834171,0.4062903821,-0.3826135099,-0.2777348757,0.2100417018,-0.0401864871,-0.2380758375,0.346570462,-0.5073054433,0.2185845971,0.1782458276,0.0822512209,0.2914729118,-0.2697472274,-0.105394572,-0.2360114455,0.0425621271,0.6938397884,-0.63877213,-0.049924884,0.1101892963,0.1013790518,-0.0613662116,0.3478800058,-0.0454604253,0.3481105566,-0.4107360542,0.3059869707,0.0873124897,-0.1264710426,-0.3275630772,0.3134204149,-0.0358151942,0.2356395572,-0.0210545417,-0.0642482191,0.5519376397,-0.1873890162,0.3676523268,-0.1700588763,-0.279956907,0.1041627601,-0.0503817089,-0.039843861,0.0603797175,-0.2420822233,0.19492428,0.0165877249,0.0390782356,-0.5488622189,0.1286391169,-0.1508790404,0.3383628428,0.2401701212,0.262108326,0.1233314499,0.0182514824,-0.2563515604,-0.4713618159,0.3001765013,-0.4880333245,0.021248579,-0.4338338673,-0.1033039168,0.0309646595,0.1834742576,-0.0350200944,0.0330956019,-0.0749208331,-0.4975802898,0.1765710711,-0.0213433541,-0.2262366265,-0.2370698303,-0.1358649582,-0.0084387707,-0.3567225337,0.071359843,0.2501084507,-0.2921575904,-0.2542045712,0.2466300279,0.1149316132,-0.1176109165,0.117354244,0.1758526713,-0.0348161124,-0.1673572958,-0.1400127411,0.131253615,0.0117278975,-0.0458009467,0.101837188,0.1450134069,0.3444767892,-0.2378433347,-0.0694068223,0.5054207444,-0.1876149476,0.5359300375,-0.1796862483,0.0512384214,-0.1996322572,0.042606812,-0.2059215009,-0.3850508034,0.0762836039,-0.1577371508,0.0513374284,0.0319146588,0.0012563785,-0.1989199072,0.0856170282,-0.046962332,0.16477938,0.0144881429,-0.0630519465,0.2266895324,0.0446924791,-0.0142778121,0.3432699442,0.2191425711,-0.0887260064,-0.1521824449,0.250440836,0.0001223587,0.4139182568,0.18166776,0.0260531791,0.1550095528,-0.003640976,-0.0657203272,-0.2528341115,-0.1814302802,0.005715027,0.149533838,-0.3952980638,-0.0354201086,0.1101150513,-0.158504501,0.0689762086,-0.0765032619,-0.290856123,-0.3032285273,-0.0453336127,0.2601582408,-0.2722713947,-0.02400795,-0.4013136625,0.0116532287,0.1975094527,-0.2747197151,-0.2812999189,-0.1685465872,-0.0756110176,-0.0741591007,0.0731139854,-0.1339878589,0.3615631759,0.2521368861,-0.2612816691,-0.243771404,-0.0951137543,0.0065136496,-0.5370389223,0.2054035813,0.4100922644,0.3981708586,-0.2923846245,0.1470615864,0.2793858051,-0.1071430445,-0.2480682433,-0.0079056751,0.0223337524,0.153262645,-0.179215163,0.1474315226,0.0644616261,-0.3209299445,0.1781849116,-0.1891590655,0.2615512013,0.2194533795,0.2780077457,0.1538574994,-0.3693777621,-0.4285494387,0.1223512962,-0.1416769028,0.4460211396,0.0390229337,-0.1469308287,0.1844055504,0.0262182392,-0.1853193641,0.2405568659,-0.0966859683,0.0939638689,-0.0562738925,0.1234829947,0.190006569,0.3668004274,0.4985276759,0.0864884332,0.0460447706,-0.2846722603,-0.1089555398,0.0812640563,0.0222764555,-0.064514026,0.422529459,0.3217896223,0.0287001207,0.7504062057,0.3654765189,0.1272135675,0.0946453437,0.0725369975,0.3822363317,-0.1140064001,-0.379692018,-0.2082244754,0.1538844705,-0.084078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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2644","title":"Batched `map` not allowed to return 0 items","comments":"Yes and it's all good, thank you :)\r\n\r\nFeel free to close this issue if it's good for you","body":"## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":19,"text":"Batched `map` not allowed to return 0 items \n ## Describe the bug\r\nI'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.\r\n\r\nHowever, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L100), but no elements were returned in this case.\r\n\r\nFor this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    # The following line simulates no matches found, so we return an empty batch\r\n    result = {'key': []}\r\n    return result\r\n\r\nfiltered_dataset = dataset.map(\r\n    select_rows,\r\n    remove_columns = dataset.column_names,\r\n    batched = True,\r\n    num_proc = 1,\r\n    desc = \"Selecting rows with images that exist\"\r\n)\r\n```\r\n\r\nThe code above immediately triggers the exception. If we use the following instead:\r\n\r\n```python\r\ndef select_rows(examples):\r\n    # `key` is a column name that exists in the original dataset\r\n    result = {'key': []}   # or defaultdict or whatever\r\n    \r\n    # code to check for condition and append elements to result\r\n    # some_items_found will be set to True if there were any matching elements in the batch\r\n    \r\n    return result if some_items_found else {}\r\n```\r\n\r\nThen it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.\r\n\r\nIn my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.\r\n\r\n## Expected results\r\nThe dataset would be filtered and only the matching fields would be returned.\r\n\r\n## Actual results\r\nAn exception is encountered, as described. Using a workaround makes it fail further along the line.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 4.0.1\r\n \n Yes and it's all good, thank you :)\r\n\r\nFeel free to close this issue if it's good for you","embeddings":[-0.2328359336,-0.3903888166,-0.0444566682,0.1644899547,-0.1085444242,-0.0687690005,0.1163394973,0.4056320786,0.6400417089,0.1178812236,-0.0565718673,0.2460853755,-0.3954412043,0.1043543741,-0.1629210711,0.1608026177,-0.0449331626,0.1405789405,-0.1691175699,-0.1914925724,-0.3362929821,0.163199544,-0.3979255855,-0.0501046143,-0.1487712115,-0.2919759154,0.286480099,-0.0362105146,-0.162814334,-0.2454052418,0.206091553,-0.2328298539,-0.2091356516,0.5783210993,-0.0001299276,-0.0231365338,0.1505937427,-0.1851885766,-0.0897614583,-0.3029606342,-0.1908181012,-0.1715300977,-0.1157183424,-0.2824648917,0.1819460839,-0.2716506422,-0.1956346184,-0.4482398331,0.1071964726,0.2587007582,0.043321602,0.4188180268,0.003066103,0.1764480174,0.3312488496,0.2664009035,-0.0753516629,0.1288110614,0.7205767035,-0.2881007195,0.0226270352,0.3385902047,-0.3921834528,0.0709065422,0.1367558688,-0.0309035499,0.230168879,-0.5515586734,0.2697506845,0.3675674498,-0.0899998844,0.0607856587,-0.433337301,-0.6687820554,-0.0403036699,-0.0954985172,-0.0388978831,0.2810007632,-0.4210395217,-0.0564594232,-0.4846186638,0.1217950135,0.0078949472,0.1618547142,0.0306755304,0.4589543343,-0.0930409953,0.2003241777,0.3451099396,-0.1618324965,-0.0994021893,-0.1672711968,-0.2236276567,0.5847230554,-0.0366987288,-0.1459876001,0.3893203735,0.2197681516,0.2230358422,-0.3132426441,-0.050306201,-0.0742755681,0.2646799386,-0.0234315637,0.4159452617,0.0793761313,0.2686936855,0.3493200243,-0.005052017,-0.0590938851,0.0676903278,0.1131693944,0.113442108,-0.0890844166,-0.0096516423,-0.0764087662,0.2567855418,-0.0787523314,-0.1218986362,0.2341007143,-0.4100169539,0.1369067729,0.0620478503,0.1250830591,0.1700579822,-0.1776706576,0.1202128828,0.1126287505,-0.1570285559,0.1897115409,-0.0772467405,-0.1316168755,-0.192502588,-0.3208249807,0.1964786202,-0.3342862427,0.2418166101,-0.1585832834,0.0198275335,-0.0615327619,0.0957399309,-0.2645617723,0.7512272596,0.4937881231,-0.119511351,0.2913580537,0.3966238201,-0.2185574174,-0.1752834171,0.4062903821,-0.3826135099,-0.2777348757,0.2100417018,-0.0401864871,-0.2380758375,0.346570462,-0.5073054433,0.2185845971,0.1782458276,0.0822512209,0.2914729118,-0.2697472274,-0.105394572,-0.2360114455,0.0425621271,0.6938397884,-0.63877213,-0.049924884,0.1101892963,0.1013790518,-0.0613662116,0.3478800058,-0.0454604253,0.3481105566,-0.4107360542,0.3059869707,0.0873124897,-0.1264710426,-0.3275630772,0.3134204149,-0.0358151942,0.2356395572,-0.0210545417,-0.0642482191,0.5519376397,-0.1873890162,0.3676523268,-0.1700588763,-0.279956907,0.1041627601,-0.0503817089,-0.039843861,0.0603797175,-0.2420822233,0.19492428,0.0165877249,0.0390782356,-0.5488622189,0.1286391169,-0.1508790404,0.3383628428,0.2401701212,0.262108326,0.1233314499,0.0182514824,-0.2563515604,-0.4713618159,0.3001765013,-0.4880333245,0.021248579,-0.4338338673,-0.1033039168,0.0309646595,0.1834742576,-0.0350200944,0.0330956019,-0.0749208331,-0.4975802898,0.1765710711,-0.0213433541,-0.2262366265,-0.2370698303,-0.1358649582,-0.0084387707,-0.3567225337,0.071359843,0.2501084507,-0.2921575904,-0.2542045712,0.2466300279,0.1149316132,-0.1176109165,0.117354244,0.1758526713,-0.0348161124,-0.1673572958,-0.1400127411,0.131253615,0.0117278975,-0.0458009467,0.101837188,0.1450134069,0.3444767892,-0.2378433347,-0.0694068223,0.5054207444,-0.1876149476,0.5359300375,-0.1796862483,0.0512384214,-0.1996322572,0.042606812,-0.2059215009,-0.3850508034,0.0762836039,-0.1577371508,0.0513374284,0.0319146588,0.0012563785,-0.1989199072,0.0856170282,-0.046962332,0.16477938,0.0144881429,-0.0630519465,0.2266895324,0.0446924791,-0.0142778121,0.3432699442,0.2191425711,-0.0887260064,-0.1521824449,0.250440836,0.0001223587,0.4139182568,0.18166776,0.0260531791,0.1550095528,-0.003640976,-0.0657203272,-0.2528341115,-0.1814302802,0.005715027,0.149533838,-0.3952980638,-0.0354201086,0.1101150513,-0.158504501,0.0689762086,-0.0765032619,-0.290856123,-0.3032285273,-0.0453336127,0.2601582408,-0.2722713947,-0.02400795,-0.4013136625,0.0116532287,0.1975094527,-0.2747197151,-0.2812999189,-0.1685465872,-0.0756110176,-0.0741591007,0.0731139854,-0.1339878589,0.3615631759,0.2521368861,-0.2612816691,-0.243771404,-0.0951137543,0.0065136496,-0.5370389223,0.2054035813,0.4100922644,0.3981708586,-0.2923846245,0.1470615864,0.2793858051,-0.1071430445,-0.2480682433,-0.0079056751,0.0223337524,0.153262645,-0.179215163,0.1474315226,0.0644616261,-0.3209299445,0.1781849116,-0.1891590655,0.2615512013,0.2194533795,0.2780077457,0.1538574994,-0.3693777621,-0.4285494387,0.1223512962,-0.1416769028,0.4460211396,0.0390229337,-0.1469308287,0.1844055504,0.0262182392,-0.1853193641,0.2405568659,-0.0966859683,0.0939638689,-0.0562738925,0.1234829947,0.190006569,0.3668004274,0.4985276759,0.0864884332,0.0460447706,-0.2846722603,-0.1089555398,0.0812640563,0.0222764555,-0.064514026,0.422529459,0.3217896223,0.0287001207,0.7504062057,0.3654765189,0.1272135675,0.0946453437,0.0725369975,0.3822363317,-0.1140064001,-0.379692018,-0.2082244754,0.1538844705,-0.0840781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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2643","title":"Enum used in map functions will raise a RecursionError with dill.","comments":"I'm running into this as well. (Thank you so much for reporting @jorgeecardona \u2014 was staring at this massive stack trace and unsure what exactly was wrong!)","body":"## Describe the bug\r\n\r\nEnums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nIn my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom enum import Enum\r\n\r\nclass A(Enum):\r\n    a = 'a'\r\n\r\ndef main():\r\n    a = A.a\r\n    \r\n    def f(x):\r\n        return {} if a == a.a else x\r\n    \r\n    ds = load_dataset('cnn_dailymail', '3.0.0')['test']\r\n    ds = ds.map(f, num_proc=15)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Expected results\r\nThe known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.\r\n\r\n## Actual results\r\n\r\n```python\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1373, in save_type\r\n    pickler.save_reduce(_create_type, (type(obj), obj.__name__,\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 534, in save\r\n    self.framer.commit_frame()\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 220, in commit_frame\r\n    if f.tell() >= self._FRAME_SIZE_TARGET or force:\r\nRecursionError: maximum recursion depth exceeded while calling a Python object\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":27,"text":"Enum used in map functions will raise a RecursionError with dill. \n ## Describe the bug\r\n\r\nEnums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nIn my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom enum import Enum\r\n\r\nclass A(Enum):\r\n    a = 'a'\r\n\r\ndef main():\r\n    a = A.a\r\n    \r\n    def f(x):\r\n        return {} if a == a.a else x\r\n    \r\n    ds = load_dataset('cnn_dailymail', '3.0.0')['test']\r\n    ds = ds.map(f, num_proc=15)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Expected results\r\nThe known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.\r\n\r\n## Actual results\r\n\r\n```python\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1373, in save_type\r\n    pickler.save_reduce(_create_type, (type(obj), obj.__name__,\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 534, in save\r\n    self.framer.commit_frame()\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 220, in commit_frame\r\n    if f.tell() >= self._FRAME_SIZE_TARGET or force:\r\nRecursionError: maximum recursion depth exceeded while calling a Python object\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n \n I'm running into this as well. (Thank you so much for reporting @jorgeecardona \u2014 was staring at this massive stack trace and unsure what exactly was wrong!)","embeddings":[0.1053604856,0.1992541552,0.0405323058,0.1406992823,-0.0743715987,-0.0783433765,0.1477604657,0.2337792367,0.1263172179,0.1690644473,0.1453331262,0.9332155585,-0.2908696234,-0.3072986007,-0.0783376396,0.0934459493,0.0223675463,0.0455224887,-0.6181129813,-0.245678708,-0.3375143707,-0.0052422048,-0.1024470925,-0.0100890975,-0.2116018087,0.2239997536,0.0477665961,0.4010353982,0.0841139033,-0.2200670987,0.3333719671,-0.0734497905,0.1974620223,0.3142178059,-0.0001113917,0.0850248039,0.118756108,-0.2274410874,-0.365465194,-0.2395340651,0.1474908143,0.1226186752,0.0726437643,-0.4154757559,0.2226357162,0.3753210306,-0.0025735646,-0.2636497021,-0.0621624291,0.0543684065,0.1718367338,-0.1396808624,0.2170403898,0.0761598274,0.4435100555,-0.0182866361,0.0448160022,0.5206541419,0.1653602272,-0.3055960536,-0.1643484682,0.1722366214,-0.0976958573,-0.0254113507,0.3883353472,-0.1136089861,0.6294152737,-0.0288456883,-0.1640148461,0.2222030163,-0.1290313452,-0.2840961218,-0.2295470983,0.040171016,-0.0794383436,-0.1017234176,0.3087863922,-0.0180283096,-0.0133565376,-0.0775611177,0.124069795,-0.2159891874,0.0056776186,0.4278802872,-0.1641043723,0.5943023562,0.1378127486,0.1661039293,0.2780688107,-0.1684818417,-0.1965375096,0.1178516373,0.1020064875,0.0950327516,-0.2208731472,-0.0939793363,0.2846585512,-0.3601528108,0.3353612125,-0.1542606801,0.0143164145,0.1036642119,-0.0685879216,0.3336909115,0.3899123371,-0.141833812,-0.0516568385,0.5599200726,0.2155424654,-0.112243861,0.0307026245,0.3607044518,0.0600120537,0.091906555,-0.0083617987,-0.1110875979,0.0112875355,-0.0954111367,-0.1062042788,0.4172789752,-0.474712491,-0.1136129498,0.1349558085,0.1240020096,-0.1058503166,0.0211870689,0.1341360658,0.0897011906,-0.0525646433,0.4216713011,-0.2110072672,-0.0060294033,0.0234192684,-0.2131149173,-0.1768059134,0.0548265763,0.2632889152,0.0713272691,0.1323001087,-0.0223733783,0.0825912654,-0.2246848345,0.1783074588,0.102531299,-0.5080521703,-0.083212778,0.2530028224,-0.2604123056,-0.2579993904,0.2515308559,-0.453178972,-0.0923266262,-0.3719526231,0.2141515613,0.1512587517,0.142075941,0.1465840489,-0.1181596518,0.3029899895,-0.1372455209,0.1552291662,-0.4185498357,-0.3111720085,-0.2451112121,0.0752648413,-0.0309842099,-0.072397247,-0.2012919784,-0.2666209936,0.1593035311,0.175672859,-0.049040623,-0.1848199815,-0.1414388418,-0.2404543161,0.2867933214,-0.1029907167,-0.1263521612,-0.3646345437,-0.0630571842,-0.0521467067,0.1691658795,-0.254185915,-0.1637441516,0.2021380663,-0.2736370265,0.5204458833,0.175168708,0.1138640717,-0.0142076705,-0.3202032149,-0.113395296,0.3278877735,0.1209530756,-0.1715014875,-0.0926166549,-0.2653790116,-0.1963817775,0.3977228403,-0.2698430121,0.0405843817,0.131158188,0.108336933,0.0882655382,0.0628296435,-0.1535954922,-0.3434669971,0.1982899308,-0.2558756173,-0.0215071645,0.0164881572,-0.1004741266,0.0963971615,0.2580535114,-0.0351012833,-0.0973073915,0.1891377568,0.0994406566,-0.1080731302,-0.021638101,-0.0003865003,-0.329185307,-0.4009248614,-0.0294388644,0.0524632335,0.302303493,-0.2305079699,-0.2322060466,-0.2058906853,0.1653586477,-0.0919003114,0.0035678879,-0.2516871393,0.0290701613,0.387491405,-0.5933543444,-0.2805407345,-0.0545177348,-0.1167532355,-0.274884969,0.0368423834,0.4684727192,0.1454917938,-0.2771622539,0.2459003329,0.1754849255,0.1758498102,0.1228594929,0.0697168633,0.2272963524,-0.199826479,-0.1135167107,-0.0290244762,-0.3736829162,-0.1019667089,0.0934689865,0.0345105156,0.1148613542,-0.0388505384,-0.2529444695,0.1679152846,0.2472826391,0.2872360945,0.2680383921,-0.1414113045,0.0800695792,0.1683732569,0.056109257,0.1142272428,-0.0879762098,0.097669974,0.1423885375,-0.1171692982,0.1136519611,-0.0736678466,0.0754612312,0.3981694579,0.1457406729,-0.0600115098,0.0496477075,-0.0412557833,-0.2883348167,-0.0536869168,0.102130346,-0.0684398487,0.0745906532,-0.4567013681,0.4747098982,0.1333308071,-0.5235562921,-0.2458880842,-0.2118292898,-0.170154497,0.1294812113,-0.523945272,0.2111630291,-0.1791205406,-0.4466718137,0.1996660978,-0.344062984,0.2858987749,-0.3210588396,-0.2967031598,-0.0235466622,0.1581546515,-0.1790600866,0.0374892913,0.262714833,-0.3319718242,0.0355784371,-0.2012701929,0.146975711,0.0324610099,0.1711266041,0.2742405534,0.0931569561,-0.4386810958,-0.4228983819,0.1325159371,0.1339356452,-0.0586651936,0.060103815,0.2963472307,0.2722679377,-0.2222012132,-0.3065881729,-0.1973068565,-0.3845987916,-0.2084994912,0.0703798085,0.4546933472,0.2742165625,-0.0160644073,0.4758411646,0.3324288726,0.3127490878,-0.3234798014,0.3757663071,0.2017489374,-0.1626095921,-0.2093153149,-0.1637184769,-0.5420069695,0.0844266042,0.1182149425,-0.5454444885,-0.2052110136,0.1891743392,0.0723087415,-0.2491007149,-0.1957806796,0.1190048307,0.2233190536,0.0628091916,-0.0219544377,-0.2726022601,0.2237222791,0.2733663917,0.151839152,0.178386867,-0.0109509388,-0.0432436094,0.3684876859,0.3489730954,-0.0253312588,0.4116002917,0.3529876173,-0.0865499526,0.0425222665,0.0363262221,0.1121196747,0.0395058356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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2643","title":"Enum used in map functions will raise a RecursionError with dill.","comments":"Hi ! Thanks for reporting :)\r\n\r\nUntil this is fixed on `dill`'s side, we could implement a custom saving in our Pickler indefined in utils.py_utils.py\r\nThere is already a suggestion in this message about how to do it:\r\nhttps:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nLet me know if such a workaround could help, and feel free to open a PR if you want to contribute !","body":"## Describe the bug\r\n\r\nEnums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nIn my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom enum import Enum\r\n\r\nclass A(Enum):\r\n    a = 'a'\r\n\r\ndef main():\r\n    a = A.a\r\n    \r\n    def f(x):\r\n        return {} if a == a.a else x\r\n    \r\n    ds = load_dataset('cnn_dailymail', '3.0.0')['test']\r\n    ds = ds.map(f, num_proc=15)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Expected results\r\nThe known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.\r\n\r\n## Actual results\r\n\r\n```python\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1373, in save_type\r\n    pickler.save_reduce(_create_type, (type(obj), obj.__name__,\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 534, in save\r\n    self.framer.commit_frame()\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 220, in commit_frame\r\n    if f.tell() >= self._FRAME_SIZE_TARGET or force:\r\nRecursionError: maximum recursion depth exceeded while calling a Python object\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":61,"text":"Enum used in map functions will raise a RecursionError with dill. \n ## Describe the bug\r\n\r\nEnums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nIn my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom enum import Enum\r\n\r\nclass A(Enum):\r\n    a = 'a'\r\n\r\ndef main():\r\n    a = A.a\r\n    \r\n    def f(x):\r\n        return {} if a == a.a else x\r\n    \r\n    ds = load_dataset('cnn_dailymail', '3.0.0')['test']\r\n    ds = ds.map(f, num_proc=15)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Expected results\r\nThe known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.\r\n\r\n## Actual results\r\n\r\n```python\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1373, in save_type\r\n    pickler.save_reduce(_create_type, (type(obj), obj.__name__,\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 534, in save\r\n    self.framer.commit_frame()\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 220, in commit_frame\r\n    if f.tell() >= self._FRAME_SIZE_TARGET or force:\r\nRecursionError: maximum recursion depth exceeded while calling a Python object\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n \n Hi ! Thanks for reporting :)\r\n\r\nUntil this is fixed on `dill`'s side, we could implement a custom saving in our Pickler indefined in utils.py_utils.py\r\nThere is already a suggestion in this message about how to do it:\r\nhttps:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nLet me know if such a workaround could help, and feel free to open a PR if you want to contribute 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2643","title":"Enum used in map functions will raise a RecursionError with dill.","comments":"I have the same bug.\r\nthe code is as follows:\r\n![image](https:\/\/user-images.githubusercontent.com\/84262181\/139785849-620dd4ac-86ce-4212-8163-942bbca305aa.png)\r\nthe error is: \r\n![image](https:\/\/user-images.githubusercontent.com\/84262181\/139785899-88a9bd75-c60b-45a5-b819-830c7c096f3d.png)\r\n\r\nLook for the solution for this bug.","body":"## Describe the bug\r\n\r\nEnums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nIn my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom enum import Enum\r\n\r\nclass A(Enum):\r\n    a = 'a'\r\n\r\ndef main():\r\n    a = A.a\r\n    \r\n    def f(x):\r\n        return {} if a == a.a else x\r\n    \r\n    ds = load_dataset('cnn_dailymail', '3.0.0')['test']\r\n    ds = ds.map(f, num_proc=15)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Expected results\r\nThe known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.\r\n\r\n## Actual results\r\n\r\n```python\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1373, in save_type\r\n    pickler.save_reduce(_create_type, (type(obj), obj.__name__,\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 534, in save\r\n    self.framer.commit_frame()\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 220, in commit_frame\r\n    if f.tell() >= self._FRAME_SIZE_TARGET or force:\r\nRecursionError: maximum recursion depth exceeded while calling a Python object\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":22,"text":"Enum used in map functions will raise a RecursionError with dill. \n ## Describe the bug\r\n\r\nEnums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nIn my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom enum import Enum\r\n\r\nclass A(Enum):\r\n    a = 'a'\r\n\r\ndef main():\r\n    a = A.a\r\n    \r\n    def f(x):\r\n        return {} if a == a.a else x\r\n    \r\n    ds = load_dataset('cnn_dailymail', '3.0.0')['test']\r\n    ds = ds.map(f, num_proc=15)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Expected results\r\nThe known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.\r\n\r\n## Actual results\r\n\r\n```python\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1373, in save_type\r\n    pickler.save_reduce(_create_type, (type(obj), obj.__name__,\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 534, in save\r\n    self.framer.commit_frame()\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 220, in commit_frame\r\n    if f.tell() >= self._FRAME_SIZE_TARGET or force:\r\nRecursionError: maximum recursion depth exceeded while calling a Python object\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n \n I have the same bug.\r\nthe code is as follows:\r\n![image](https:\/\/user-images.githubusercontent.com\/84262181\/139785849-620dd4ac-86ce-4212-8163-942bbca305aa.png)\r\nthe error is: \r\n![image](https:\/\/user-images.githubusercontent.com\/84262181\/139785899-88a9bd75-c60b-45a5-b819-830c7c096f3d.png)\r\n\r\nLook for the solution for this bug.","embeddings":[0.1053604856,0.1992541552,0.0405323058,0.1406992823,-0.0743715987,-0.0783433765,0.1477604657,0.2337792367,0.1263172179,0.1690644473,0.1453331262,0.9332155585,-0.2908696234,-0.3072986007,-0.0783376396,0.0934459493,0.0223675463,0.0455224887,-0.6181129813,-0.245678708,-0.3375143707,-0.0052422048,-0.1024470925,-0.0100890975,-0.2116018087,0.2239997536,0.0477665961,0.4010353982,0.0841139033,-0.2200670987,0.3333719671,-0.0734497905,0.1974620223,0.3142178059,-0.0001113917,0.0850248039,0.118756108,-0.2274410874,-0.365465194,-0.2395340651,0.1474908143,0.1226186752,0.0726437643,-0.4154757559,0.2226357162,0.3753210306,-0.0025735646,-0.2636497021,-0.0621624291,0.0543684065,0.1718367338,-0.1396808624,0.2170403898,0.0761598274,0.4435100555,-0.0182866361,0.0448160022,0.5206541419,0.1653602272,-0.3055960536,-0.1643484682,0.1722366214,-0.0976958573,-0.0254113507,0.3883353472,-0.1136089861,0.6294152737,-0.0288456883,-0.1640148461,0.2222030163,-0.1290313452,-0.2840961218,-0.2295470983,0.040171016,-0.0794383436,-0.1017234176,0.3087863922,-0.0180283096,-0.0133565376,-0.0775611177,0.124069795,-0.2159891874,0.0056776186,0.4278802872,-0.1641043723,0.5943023562,0.1378127486,0.1661039293,0.2780688107,-0.1684818417,-0.1965375096,0.1178516373,0.1020064875,0.0950327516,-0.2208731472,-0.0939793363,0.2846585512,-0.3601528108,0.3353612125,-0.1542606801,0.0143164145,0.1036642119,-0.0685879216,0.3336909115,0.3899123371,-0.141833812,-0.0516568385,0.5599200726,0.2155424654,-0.112243861,0.0307026245,0.3607044518,0.0600120537,0.091906555,-0.0083617987,-0.1110875979,0.0112875355,-0.0954111367,-0.1062042788,0.4172789752,-0.474712491,-0.1136129498,0.1349558085,0.1240020096,-0.1058503166,0.0211870689,0.1341360658,0.0897011906,-0.0525646433,0.4216713011,-0.2110072672,-0.0060294033,0.0234192684,-0.2131149173,-0.1768059134,0.0548265763,0.2632889152,0.0713272691,0.1323001087,-0.0223733783,0.0825912654,-0.2246848345,0.1783074588,0.102531299,-0.5080521703,-0.083212778,0.2530028224,-0.2604123056,-0.2579993904,0.2515308559,-0.453178972,-0.0923266262,-0.3719526231,0.2141515613,0.1512587517,0.142075941,0.1465840489,-0.1181596518,0.3029899895,-0.1372455209,0.1552291662,-0.4185498357,-0.3111720085,-0.2451112121,0.0752648413,-0.0309842099,-0.072397247,-0.2012919784,-0.2666209936,0.1593035311,0.175672859,-0.049040623,-0.1848199815,-0.1414388418,-0.2404543161,0.2867933214,-0.1029907167,-0.1263521612,-0.3646345437,-0.0630571842,-0.0521467067,0.1691658795,-0.254185915,-0.1637441516,0.2021380663,-0.2736370265,0.5204458833,0.175168708,0.1138640717,-0.0142076705,-0.3202032149,-0.113395296,0.3278877735,0.1209530756,-0.1715014875,-0.0926166549,-0.2653790116,-0.1963817775,0.3977228403,-0.2698430121,0.0405843817,0.131158188,0.108336933,0.0882655382,0.0628296435,-0.1535954922,-0.3434669971,0.1982899308,-0.2558756173,-0.0215071645,0.0164881572,-0.1004741266,0.0963971615,0.2580535114,-0.0351012833,-0.0973073915,0.1891377568,0.0994406566,-0.1080731302,-0.021638101,-0.0003865003,-0.329185307,-0.4009248614,-0.0294388644,0.0524632335,0.302303493,-0.2305079699,-0.2322060466,-0.2058906853,0.1653586477,-0.0919003114,0.0035678879,-0.2516871393,0.0290701613,0.387491405,-0.5933543444,-0.2805407345,-0.0545177348,-0.1167532355,-0.274884969,0.0368423834,0.4684727192,0.1454917938,-0.2771622539,0.2459003329,0.1754849255,0.1758498102,0.1228594929,0.0697168633,0.2272963524,-0.199826479,-0.1135167107,-0.0290244762,-0.3736829162,-0.1019667089,0.0934689865,0.0345105156,0.1148613542,-0.0388505384,-0.2529444695,0.1679152846,0.2472826391,0.2872360945,0.2680383921,-0.1414113045,0.0800695792,0.1683732569,0.056109257,0.1142272428,-0.0879762098,0.097669974,0.1423885375,-0.1171692982,0.1136519611,-0.0736678466,0.0754612312,0.3981694579,0.1457406729,-0.0600115098,0.0496477075,-0.0412557833,-0.2883348167,-0.0536869168,0.102130346,-0.0684398487,0.0745906532,-0.4567013681,0.4747098982,0.1333308071,-0.5235562921,-0.2458880842,-0.2118292898,-0.170154497,0.1294812113,-0.523945272,0.2111630291,-0.1791205406,-0.4466718137,0.1996660978,-0.344062984,0.2858987749,-0.3210588396,-0.2967031598,-0.0235466622,0.1581546515,-0.1790600866,0.0374892913,0.262714833,-0.3319718242,0.0355784371,-0.2012701929,0.146975711,0.0324610099,0.1711266041,0.2742405534,0.0931569561,-0.4386810958,-0.4228983819,0.1325159371,0.1339356452,-0.0586651936,0.060103815,0.2963472307,0.2722679377,-0.2222012132,-0.3065881729,-0.1973068565,-0.3845987916,-0.2084994912,0.0703798085,0.4546933472,0.2742165625,-0.0160644073,0.4758411646,0.3324288726,0.3127490878,-0.3234798014,0.3757663071,0.2017489374,-0.1626095921,-0.2093153149,-0.1637184769,-0.5420069695,0.0844266042,0.1182149425,-0.5454444885,-0.2052110136,0.1891743392,0.0723087415,-0.2491007149,-0.1957806796,0.1190048307,0.2233190536,0.0628091916,-0.0219544377,-0.2726022601,0.2237222791,0.2733663917,0.151839152,0.178386867,-0.0109509388,-0.0432436094,0.3684876859,0.3489730954,-0.0253312588,0.4116002917,0.3529876173,-0.0865499526,0.0425222665,0.0363262221,0.1121196747,0.0395058356,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2643","title":"Enum used in map functions will raise a RecursionError with dill.","comments":"Hi ! I think your RecursionError comes from a different issue @BitcoinNLPer , could you open a separate issue please ?\r\n\r\nAlso which dataset are you using ? I tried loading `CodedotAI\/code_clippy` but I get a different error\r\n```python\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/quentinlhoest\/Desktop\/hf\/datasets\/src\/datasets\/load.py\", line 1615, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/Users\/quentinlhoest\/Desktop\/hf\/datasets\/src\/datasets\/load.py\", line 1446, in load_dataset_builder\r\n    builder_cls = import_main_class(dataset_module.module_path)\r\n  File \"\/Users\/quentinlhoest\/Desktop\/hf\/datasets\/src\/datasets\/load.py\", line 101, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n  File \"\/Users\/quentinlhoest\/.virtualenvs\/hf-datasets\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/Users\/quentinlhoest\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/CodedotAI___code_clippy\/d332f69d036e8c80f47bc9a96d676c3fa30cb50af7bb81e2d4d12e80b83efc4d\/code_clippy.py\", line 66, in <module>\r\n    url_elements = results.find_all(\"a\")\r\nAttributeError: 'NoneType' object has no attribute 'find_all'\r\n```","body":"## Describe the bug\r\n\r\nEnums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nIn my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom enum import Enum\r\n\r\nclass A(Enum):\r\n    a = 'a'\r\n\r\ndef main():\r\n    a = A.a\r\n    \r\n    def f(x):\r\n        return {} if a == a.a else x\r\n    \r\n    ds = load_dataset('cnn_dailymail', '3.0.0')['test']\r\n    ds = ds.map(f, num_proc=15)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Expected results\r\nThe known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.\r\n\r\n## Actual results\r\n\r\n```python\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1373, in save_type\r\n    pickler.save_reduce(_create_type, (type(obj), obj.__name__,\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 534, in save\r\n    self.framer.commit_frame()\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 220, in commit_frame\r\n    if f.tell() >= self._FRAME_SIZE_TARGET or force:\r\nRecursionError: maximum recursion depth exceeded while calling a Python object\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":144,"text":"Enum used in map functions will raise a RecursionError with dill. \n ## Describe the bug\r\n\r\nEnums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https:\/\/github.com\/uqfoundation\/dill\/issues\/250#issuecomment-852566284\r\n\r\nIn my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom enum import Enum\r\n\r\nclass A(Enum):\r\n    a = 'a'\r\n\r\ndef main():\r\n    a = A.a\r\n    \r\n    def f(x):\r\n        return {} if a == a.a else x\r\n    \r\n    ds = load_dataset('cnn_dailymail', '3.0.0')['test']\r\n    ds = ds.map(f, num_proc=15)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n## Expected results\r\nThe known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.\r\n\r\n## Actual results\r\n\r\n```python\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1373, in save_type\r\n    pickler.save_reduce(_create_type, (type(obj), obj.__name__,\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 534, in save\r\n    self.framer.commit_frame()\r\n  File \"\/home\/xxxx\/miniconda3\/lib\/python3.8\/pickle.py\", line 220, in commit_frame\r\n    if f.tell() >= self._FRAME_SIZE_TARGET or force:\r\nRecursionError: maximum recursion depth exceeded while calling a Python object\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n \n Hi ! I think your RecursionError comes from a different issue @BitcoinNLPer , could you open a separate issue please ?\r\n\r\nAlso which dataset are you using ? I tried loading `CodedotAI\/code_clippy` but I get a different error\r\n```python\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/quentinlhoest\/Desktop\/hf\/datasets\/src\/datasets\/load.py\", line 1615, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/Users\/quentinlhoest\/Desktop\/hf\/datasets\/src\/datasets\/load.py\", line 1446, in load_dataset_builder\r\n    builder_cls = import_main_class(dataset_module.module_path)\r\n  File \"\/Users\/quentinlhoest\/Desktop\/hf\/datasets\/src\/datasets\/load.py\", line 101, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n  File \"\/Users\/quentinlhoest\/.virtualenvs\/hf-datasets\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/Users\/quentinlhoest\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/CodedotAI___code_clippy\/d332f69d036e8c80f47bc9a96d676c3fa30cb50af7bb81e2d4d12e80b83efc4d\/code_clippy.py\", line 66, in <module>\r\n    url_elements = results.find_all(\"a\")\r\nAttributeError: 'NoneType' object has no attribute 'find_all'\r\n```","embeddings":[0.1053604856,0.1992541552,0.0405323058,0.1406992823,-0.0743715987,-0.0783433765,0.1477604657,0.2337792367,0.1263172179,0.1690644473,0.1453331262,0.9332155585,-0.2908696234,-0.3072986007,-0.0783376396,0.0934459493,0.0223675463,0.0455224887,-0.6181129813,-0.245678708,-0.3375143707,-0.0052422048,-0.1024470925,-0.0100890975,-0.2116018087,0.2239997536,0.0477665961,0.4010353982,0.0841139033,-0.2200670987,0.3333719671,-0.0734497905,0.1974620223,0.3142178059,-0.0001113917,0.0850248039,0.118756108,-0.2274410874,-0.365465194,-0.2395340651,0.1474908143,0.1226186752,0.0726437643,-0.4154757559,0.2226357162,0.3753210306,-0.0025735646,-0.2636497021,-0.0621624291,0.0543684065,0.1718367338,-0.1396808624,0.2170403898,0.0761598274,0.4435100555,-0.0182866361,0.0448160022,0.5206541419,0.1653602272,-0.3055960536,-0.1643484682,0.1722366214,-0.0976958573,-0.0254113507,0.3883353472,-0.1136089861,0.6294152737,-0.0288456883,-0.1640148461,0.2222030163,-0.1290313452,-0.2840961218,-0.2295470983,0.040171016,-0.0794383436,-0.1017234176,0.3087863922,-0.0180283096,-0.0133565376,-0.0775611177,0.124069795,-0.2159891874,0.0056776186,0.4278802872,-0.1641043723,0.5943023562,0.1378127486,0.1661039293,0.2780688107,-0.1684818417,-0.1965375096,0.1178516373,0.1020064875,0.0950327516,-0.2208731472,-0.0939793363,0.2846585512,-0.3601528108,0.3353612125,-0.1542606801,0.0143164145,0.1036642119,-0.0685879216,0.3336909115,0.3899123371,-0.141833812,-0.0516568385,0.5599200726,0.2155424654,-0.112243861,0.0307026245,0.3607044518,0.0600120537,0.091906555,-0.0083617987,-0.1110875979,0.0112875355,-0.0954111367,-0.1062042788,0.4172789752,-0.474712491,-0.1136129498,0.1349558085,0.1240020096,-0.1058503166,0.0211870689,0.1341360658,0.0897011906,-0.0525646433,0.4216713011,-0.2110072672,-0.0060294033,0.0234192684,-0.2131149173,-0.1768059134,0.0548265763,0.2632889152,0.0713272691,0.1323001087,-0.0223733783,0.0825912654,-0.2246848345,0.1783074588,0.102531299,-0.5080521703,-0.083212778,0.2530028224,-0.2604123056,-0.2579993904,0.2515308559,-0.453178972,-0.0923266262,-0.3719526231,0.2141515613,0.1512587517,0.142075941,0.1465840489,-0.1181596518,0.3029899895,-0.1372455209,0.1552291662,-0.4185498357,-0.3111720085,-0.2451112121,0.0752648413,-0.0309842099,-0.072397247,-0.2012919784,-0.2666209936,0.1593035311,0.175672859,-0.049040623,-0.1848199815,-0.1414388418,-0.2404543161,0.2867933214,-0.1029907167,-0.1263521612,-0.3646345437,-0.0630571842,-0.0521467067,0.1691658795,-0.254185915,-0.1637441516,0.2021380663,-0.2736370265,0.5204458833,0.175168708,0.1138640717,-0.0142076705,-0.3202032149,-0.113395296,0.3278877735,0.1209530756,-0.1715014875,-0.0926166549,-0.2653790116,-0.1963817775,0.3977228403,-0.2698430121,0.0405843817,0.131158188,0.108336933,0.0882655382,0.0628296435,-0.1535954922,-0.3434669971,0.1982899308,-0.2558756173,-0.0215071645,0.0164881572,-0.1004741266,0.0963971615,0.2580535114,-0.0351012833,-0.0973073915,0.1891377568,0.0994406566,-0.1080731302,-0.021638101,-0.0003865003,-0.329185307,-0.4009248614,-0.0294388644,0.0524632335,0.302303493,-0.2305079699,-0.2322060466,-0.2058906853,0.1653586477,-0.0919003114,0.0035678879,-0.2516871393,0.0290701613,0.387491405,-0.5933543444,-0.2805407345,-0.0545177348,-0.1167532355,-0.274884969,0.0368423834,0.4684727192,0.1454917938,-0.2771622539,0.2459003329,0.1754849255,0.1758498102,0.1228594929,0.0697168633,0.2272963524,-0.199826479,-0.1135167107,-0.0290244762,-0.3736829162,-0.1019667089,0.0934689865,0.0345105156,0.1148613542,-0.0388505384,-0.2529444695,0.1679152846,0.2472826391,0.2872360945,0.2680383921,-0.1414113045,0.0800695792,0.1683732569,0.056109257,0.1142272428,-0.0879762098,0.097669974,0.1423885375,-0.1171692982,0.1136519611,-0.0736678466,0.0754612312,0.3981694579,0.1457406729,-0.0600115098,0.0496477075,-0.0412557833,-0.2883348167,-0.0536869168,0.102130346,-0.0684398487,0.0745906532,-0.4567013681,0.4747098982,0.1333308071,-0.5235562921,-0.2458880842,-0.2118292898,-0.170154497,0.1294812113,-0.523945272,0.2111630291,-0.1791205406,-0.4466718137,0.1996660978,-0.344062984,0.2858987749,-0.3210588396,-0.2967031598,-0.0235466622,0.1581546515,-0.1790600866,0.0374892913,0.262714833,-0.3319718242,0.0355784371,-0.2012701929,0.146975711,0.0324610099,0.1711266041,0.2742405534,0.0931569561,-0.4386810958,-0.4228983819,0.1325159371,0.1339356452,-0.0586651936,0.060103815,0.2963472307,0.2722679377,-0.2222012132,-0.3065881729,-0.1973068565,-0.3845987916,-0.2084994912,0.0703798085,0.4546933472,0.2742165625,-0.0160644073,0.4758411646,0.3324288726,0.3127490878,-0.3234798014,0.3757663071,0.2017489374,-0.1626095921,-0.2093153149,-0.1637184769,-0.5420069695,0.0844266042,0.1182149425,-0.5454444885,-0.2052110136,0.1891743392,0.0723087415,-0.2491007149,-0.1957806796,0.1190048307,0.2233190536,0.0628091916,-0.0219544377,-0.2726022601,0.2237222791,0.2733663917,0.151839152,0.178386867,-0.0109509388,-0.0432436094,0.3684876859,0.3489730954,-0.0253312588,0.4116002917,0.3529876173,-0.0865499526,0.0425222665,0.0363262221,0.1121196747,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2642","title":"Support multi-worker with streaming dataset (IterableDataset).","comments":"Hi ! This is a great idea :)\r\nI think we could have something similar to what we have in `datasets.Dataset.map`, i.e. a `num_proc` parameter that tells how many processes to spawn to parallelize the data processing. \r\n\r\nRegarding AUTOTUNE, this could be a nice feature as well, we could see how to add it in a second step","body":"**Is your feature request related to a problem? Please describe.**\r\nThe current `.map` does not support multi-process, CPU can become bottleneck if the pre-processing is complex (e.g. t5 span masking).\r\n\r\n**Describe the solution you'd like**\r\nIdeally `.map` should support multi-worker like tfds, with `AUTOTUNE`.\r\n\r\n**Describe alternatives you've considered**\r\nA simpler solution is to shard the dataset and process it in parallel with pytorch dataloader. The shard does not need to be of equal size.\r\n* https:\/\/pytorch.org\/docs\/stable\/data.html#torch.utils.data.IterableDataset\r\n\r\n**Additional context**\r\n","comment_length":58,"text":"Support multi-worker with streaming dataset (IterableDataset). \n **Is your feature request related to a problem? Please describe.**\r\nThe current `.map` does not support multi-process, CPU can become bottleneck if the pre-processing is complex (e.g. t5 span masking).\r\n\r\n**Describe the solution you'd like**\r\nIdeally `.map` should support multi-worker like tfds, with `AUTOTUNE`.\r\n\r\n**Describe alternatives you've considered**\r\nA simpler solution is to shard the dataset and process it in parallel with pytorch dataloader. The shard does not need to be of equal size.\r\n* https:\/\/pytorch.org\/docs\/stable\/data.html#torch.utils.data.IterableDataset\r\n\r\n**Additional context**\r\n \n Hi ! This is a great idea :)\r\nI think we could have something similar to what we have in `datasets.Dataset.map`, i.e. a `num_proc` parameter that tells how many processes to spawn to parallelize the data processing. \r\n\r\nRegarding AUTOTUNE, this could be a nice feature as well, we could see how to add it in a second step","embeddings":[-0.6293717623,-0.5262192488,-0.1424407065,-0.0420489386,-0.1927177757,-0.0104304384,0.5165578127,0.1782796085,0.0827181712,0.1480467021,-0.0847553164,0.3020122349,-0.2687706351,0.2669042647,-0.0826474428,-0.2372147441,-0.1250282079,0.064319551,-0.0082344348,0.2308466136,-0.1249568984,0.0046403394,-0.1339984387,-0.2044828087,-0.3420331776,-0.0932869539,-0.0188155528,0.0379000381,0.317248106,-0.2443901002,0.0603744462,0.4343952835,0.1305341572,0.5852185488,-0.0001076541,-0.0930030048,0.1798295975,-0.000388135,-0.0898807794,0.0590006188,-0.0778129026,-0.1035521254,0.0630580485,-0.3442632556,-0.0596426465,-0.2667041421,0.175497666,-0.4042609334,0.2037626058,-0.1677056253,0.1624763161,0.3757341802,-0.1845998466,0.1089050248,-0.3134669662,-0.0016090807,-0.1212616712,0.069438614,0.6291935444,0.0375892594,-0.3766747415,0.3427779973,-0.2514382899,0.3703422844,0.2419779748,-0.1848531663,-0.3477918208,-0.4835861623,0.0880194157,0.3975020647,0.264756918,-0.2432525605,-0.1754490584,-0.4112393558,-0.1313700378,-0.0672614872,-0.0569014587,0.159991309,-0.2204431891,-0.0761081874,-0.3439476192,-0.0879495889,-0.2108642757,0.1095366105,0.1564038545,0.3157585263,0.0834367275,0.2758638561,0.260576278,0.1159337834,0.3285752237,-0.1067808419,0.332206279,0.1372993886,-0.4830509722,-0.2322729528,-0.0932076126,-0.4594946504,0.1724085361,-0.0372719429,0.2354434133,0.3031925261,-0.1467373371,0.3102487326,0.313496381,-0.273981899,-0.2789573073,-0.1171555594,0.4016439021,-0.1800670773,-0.1007044688,0.0791770443,0.1089924052,-0.3574824333,0.3322720528,0.211125344,0.1256034225,0.3532764018,0.1471811533,-0.3941155076,-0.1462440491,-0.2977169752,-0.063694261,0.1053121909,-0.064752005,0.3614375591,-0.1102483645,-0.1895647496,-0.0609045178,-0.26942119,-0.0108173992,-0.2652403414,-0.0306396093,0.1368186623,0.207841903,-0.5171914101,0.2112829685,-0.0188694466,0.3191460967,0.230833739,0.3875932395,-0.2626329362,0.2241920084,0.0336896703,0.026763346,-0.0016433458,-0.184890911,0.51435256,-0.1868384182,0.3826094866,-0.2418973148,-0.53414464,-0.0030775217,0.1260261983,0.0426396318,-0.0261148065,-0.1706091017,0.3209350407,0.2911277115,-0.2322874814,-0.0450492837,-0.0829442665,-0.3704201281,-0.2616883218,0.1099369377,0.1438874751,0.0900046006,-0.0141940154,-0.0176147521,0.052084934,0.0604343526,0.2784424126,-0.2516744733,-0.1148802638,-0.1209258139,0.2565873563,0.2521813214,-0.06233247,-0.1281469017,0.5759640932,-0.101285927,0.0670711622,0.2291383892,0.4602633417,0.4873190224,-0.1004529223,0.0499885306,0.4320580065,-0.3681764603,0.3279699683,-0.1328706741,-0.2349713296,0.2222042829,0.4411790669,0.2141229957,-0.1205196679,-0.0339910425,-0.3242702186,0.3923345506,-0.1728551537,0.2822696865,0.0077500418,-0.018366443,0.0428027883,0.103909038,-0.4952968061,-0.4326003492,0.1770922691,0.1475961655,-0.131574735,0.3082438409,-0.1840863228,0.2749662995,-0.1251863539,-0.1211157218,-0.1684861779,0.0596544668,0.1639610231,-0.2323682159,-0.4080905914,-0.5029201508,0.2063696533,-0.0860888213,-0.2651174664,-0.1824333817,0.1871777475,0.1442110389,-0.0866211578,-0.1398963183,-0.0140008954,-0.0666918233,-0.161142081,-0.0031100495,0.2786311805,-0.1334877312,0.2993046343,0.0858057737,0.5260654688,0.4518821836,-0.1562732011,0.0458431244,0.2426924109,-0.1348728091,-0.2062017322,-0.0090116095,0.1845544726,-0.33687976,0.2194125354,0.041712638,-0.0113582388,0.2030809373,0.0669188723,-0.1730810255,0.1992408782,-0.0525555797,-0.3027154803,0.0736511126,-0.0028769041,-0.5791730285,0.2516581714,0.3946070373,0.1065459698,-0.0127725545,0.0887364596,-0.1118107513,0.0106015094,0.1834913939,0.1043288931,0.3538615108,0.2624371052,0.1418777704,-0.1068482324,0.1929641515,-0.0949223638,-0.0051331879,0.0241868906,0.3479547799,0.2728956044,0.0987079814,-0.0123774242,-0.0414282717,-0.4256393611,0.2487670779,-0.0164841898,0.0392941087,-0.015385841,0.086906217,-0.0173120126,-0.0803795904,-0.1648901552,-0.0341393463,-0.0209993199,0.1612665355,0.7649838328,-0.1086928695,0.336827904,0.1869968772,0.1899265647,0.0012889812,-0.2053337544,-0.1098533422,-0.2062001526,0.2713899314,0.1082150042,0.1666261554,-0.2358656675,0.4359210432,0.3054795861,-0.0445546247,-0.4260461032,-0.0482704118,-0.0027315952,0.0770408586,-0.2029365301,0.0277969167,0.1210204139,0.2024903148,-0.2030792087,0.0591698736,-0.3240244985,0.0467326082,-0.1095950007,0.0144648943,0.010242519,-0.0533558987,-0.2154246122,-0.2746195495,-0.4614111483,0.3068508208,-0.19972682,0.3495417237,-0.1097627804,0.0805519074,-0.1313741058,0.3815685511,-0.1566507071,-0.0187258665,-0.4405144155,0.2183481753,-0.3194648027,-0.1749503016,-0.0687201545,0.0693106279,0.1051425934,0.4950210154,-0.1572958827,-0.1464728862,-0.1428736746,0.2285068035,-0.1550750434,-0.0148039218,0.4700108469,0.2209294885,-0.0559558645,0.0330369845,0.0362328701,-0.0336562544,0.2212250829,-0.1767287254,0.3686834872,0.1956779212,0.2241793126,0.9834297299,0.2730429769,-0.1933690012,0.1726339757,-0.097055681,-0.2342126518,-0.0213304907,-0.4307989478,0.1177888811,-0.316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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2641","title":"load_dataset(\"financial_phrasebank\") NonMatchingChecksumError","comments":"Hi! It's probably because this dataset is stored on google drive and it has a per day quota limit. It should work if you retry, I was able to initiate the download.\r\n\r\nSimilar issue [here](https:\/\/github.com\/huggingface\/datasets\/issues\/2646)","body":"## Describe the bug\r\nAttempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\r\n```\r\n\r\n## Expected results\r\nI expect to see the financial_phrasebank dataset downloaded successfully\r\n\r\n## Actual results\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.researchgate.net\/profile\/Pekka_Malo\/publication\/251231364_FinancialPhraseBank-v10\/data\/0c96051eee4fb1d56e000000\/FinancialPhraseBank-v10.zip']\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":35,"text":"load_dataset(\"financial_phrasebank\") NonMatchingChecksumError \n ## Describe the bug\r\nAttempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\r\n```\r\n\r\n## Expected results\r\nI expect to see the financial_phrasebank dataset downloaded successfully\r\n\r\n## Actual results\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.researchgate.net\/profile\/Pekka_Malo\/publication\/251231364_FinancialPhraseBank-v10\/data\/0c96051eee4fb1d56e000000\/FinancialPhraseBank-v10.zip']\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n \n Hi! It's probably because this dataset is stored on google drive and it has a per day quota limit. It should work if you retry, I was able to initiate the download.\r\n\r\nSimilar issue [here](https:\/\/github.com\/huggingface\/datasets\/issues\/2646)","embeddings":[-0.1221614629,0.2191510051,-0.0784073547,0.2884528637,0.2203612775,0.1347171366,0.0658889487,0.3310612142,0.2095745206,0.1821722388,-0.1414383203,0.0533875376,0.1674158573,0.0750155449,-0.141532883,0.0439132415,0.0427031741,-0.0287592523,0.0304532889,-0.1089905724,-0.1035575122,0.3996220529,-0.2143702209,-0.2048737407,-0.0571743771,0.0271739345,0.1256504357,0.1493478715,-0.355509609,-0.1062427387,0.3173011243,0.2356399447,-0.0364060998,0.4088571072,-0.0001165979,-0.1570466757,0.2954361141,-0.00232066,-0.3087729216,-0.4117237628,-0.3101617694,-0.5327080488,-0.0006328061,-0.1562370658,0.1472093612,0.2293883711,0.1187414825,-0.0404394418,0.1839256287,0.2563109994,0.1976445317,0.1766314059,0.1544101089,-0.0672572702,0.2356061935,-0.3061351478,-0.0442656465,0.2061338276,0.1339274347,0.1396629959,-0.1116091758,0.0986639559,-0.2409786433,0.3398787975,-0.0248231217,-0.0569328777,-0.1054870635,-0.4350318611,0.3071873486,0.4293161333,0.5019190311,-0.138317883,-0.333804518,-0.2137775719,-0.1314466596,-0.0050602425,0.4133678973,0.2254237831,-0.2224520743,-0.0487492941,-0.4026898146,0.2127029002,0.0522077084,0.1742328107,0.0008497239,-0.0786489025,-0.1465951353,-0.0958845466,0.2525239885,-0.0932485312,0.0778142214,-0.3102552295,-0.0711328387,0.2796263099,-0.3990389109,-0.0756752491,0.0446167625,0.5124953985,0.5575388074,0.372407496,0.1555419564,0.2275237739,-0.2916593254,0.0197752379,0.1210928038,0.2486981153,0.0146071771,-0.1616899967,0.2106146663,0.4556140304,-0.0590558872,0.1310696006,-0.0883604214,-0.074275732,0.5709369779,-0.0558999628,0.0653932542,-0.5341384411,-0.3482496142,0.444601208,-0.1251795739,-0.1410444379,-0.043768432,0.2691244781,-0.251355052,0.1109621525,-0.0669108108,0.1472851187,-0.0376438834,-0.1506907195,-0.2671844661,-0.0550954677,-0.1249775961,-0.0389060453,0.2755370736,-0.4455491304,0.3306753039,-0.0811800137,0.2223319262,-0.2022905946,0.0439216383,-0.1592709571,-0.2756517529,0.2825688422,0.136262551,0.2232762128,0.0382252634,0.0837551206,0.0077711293,0.01986387,-0.2361139506,-0.1198631674,0.0956027061,0.2120611072,-0.5457581282,-0.426435709,-0.0918331668,-0.2004864216,0.1569826752,-0.0646281019,-0.0429721363,-0.0988637283,-0.2170904577,-0.2330788523,-0.0128975147,0.2208419889,-0.1360917836,-0.0196837783,-0.1142555252,-0.0846794471,0.4203273058,0.3317056596,-0.0746124163,0.0467755422,-0.199337706,0.3415529132,0.2876548469,-0.0402506292,-0.8489012718,0.2736243308,-0.0461516269,0.3179981411,0.1135814711,0.0936846882,-0.0120978067,0.0986145288,0.0596022792,0.2110910118,0.0724116638,0.1698853076,-0.1996620446,-0.3329528868,-0.0283887461,0.1590505093,0.0541048683,0.1359466016,0.1838505566,-0.0445343107,0.289162606,-0.0262767635,0.0180040952,0.2336857468,0.2727536857,0.20902583,0.1021457314,-0.2314194143,-0.3615790606,0.3223058879,-0.2195711583,0.1002212018,-0.0158476625,-0.1486970633,-0.3613629639,-0.2824739516,-0.149021998,0.1300820112,0.0898795724,0.3411881328,0.3992163241,0.0405050106,-0.0459838361,0.3040063679,-0.1290018857,0.0751188993,-0.1407593936,0.2749923766,-0.0342067815,-0.0159849785,0.0301264729,-0.0177684445,0.1630057842,0.0362789519,-0.1874522567,0.2895474732,0.2095121741,0.1428240538,-0.0875202417,0.2737728953,-0.159500286,-0.0725642443,0.0289917383,0.4911872149,0.1428546757,-0.0163486991,-0.1632606685,0.3131428361,0.0755077377,0.0060959174,0.0377293304,0.159274742,0.3733704686,-0.1345328391,-0.0825447515,-0.1518207937,0.5095816851,-0.3409509659,0.2042862773,0.0117272995,-0.2768357694,-0.0628604516,0.2434820831,-0.1203336194,0.1307428926,0.185330078,0.0470325053,-0.075967975,-0.1524516791,0.5569287539,0.2706282437,0.2248675376,0.1861108243,0.0303609613,-0.0696507618,-0.2190412581,-0.0496182106,0.2269037813,0.1424941719,0.502320528,0.2711748779,0.0449598208,-0.4356445968,-0.0609106049,0.0420997292,0.327498734,-0.3210887015,-0.1184834614,-0.0740199834,-0.3158183694,-0.2219233215,-0.1268128157,-0.1441177279,-0.4463294744,-0.1008804739,0.3465779722,0.0165852327,0.0658375844,-0.2468461096,0.056430541,-0.0425222591,-0.2394363582,-0.1359230429,0.2502993047,-0.1967727393,0.0108748442,0.2604633868,-0.0329329446,0.3196413219,-0.391744405,-0.0326173566,-0.7137257457,-0.2451633215,0.0635802969,-0.2230637223,0.2376654297,0.2862507403,0.2511952221,-0.2726148367,0.0127000222,0.1277004629,-0.0658945367,-0.3835560977,0.2892594039,-0.1202679649,0.0041038278,0.0760868415,0.0091314269,-0.2212635875,-0.3392722309,0.105578728,0.1535753757,0.174302265,0.3875114918,-0.1369953603,0.0982941538,-0.0144528039,0.4250248969,-0.289943248,-0.5708986521,0.5505203605,0.1047234163,-0.4363671839,0.065043658,-0.0068894234,0.0403068103,0.2376913726,-0.5485623479,-0.1757599711,-0.4750021994,-0.0197928585,0.2069163471,0.0863772184,0.2451558262,-0.1192784235,-0.0731347576,-0.1427673697,-0.2040537,0.1644188315,0.045720946,0.2432695478,-0.2186840475,0.3314571381,0.0300983507,0.2455593199,0.4993488789,0.2258814722,0.3620388806,-0.0157043617,0.1015442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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2641","title":"load_dataset(\"financial_phrasebank\") NonMatchingChecksumError","comments":"Hi ! Loading the dataset works on my side as well.\r\nFeel free to try again and let us know if it works for you know","body":"## Describe the bug\r\nAttempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\r\n```\r\n\r\n## Expected results\r\nI expect to see the financial_phrasebank dataset downloaded successfully\r\n\r\n## Actual results\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.researchgate.net\/profile\/Pekka_Malo\/publication\/251231364_FinancialPhraseBank-v10\/data\/0c96051eee4fb1d56e000000\/FinancialPhraseBank-v10.zip']\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":26,"text":"load_dataset(\"financial_phrasebank\") NonMatchingChecksumError \n ## Describe the bug\r\nAttempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\r\n```\r\n\r\n## Expected results\r\nI expect to see the financial_phrasebank dataset downloaded successfully\r\n\r\n## Actual results\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.researchgate.net\/profile\/Pekka_Malo\/publication\/251231364_FinancialPhraseBank-v10\/data\/0c96051eee4fb1d56e000000\/FinancialPhraseBank-v10.zip']\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n \n Hi ! Loading the dataset works on my side as well.\r\nFeel free to try again and let us know if it works for you know","embeddings":[-0.1198334545,0.179409802,-0.0883815736,0.3396996856,0.187968567,0.1173867732,0.0060668611,0.3996661901,0.1419468075,0.1976888925,-0.161383763,0.2437268794,0.230403617,-0.1089785248,-0.092774637,0.140614748,0.1338221133,0.029889619,0.0345540233,-0.1668419242,-0.1839316636,0.3492645025,-0.1968892962,-0.1531044543,-0.0255643241,0.1277683973,0.1105171144,0.1647164822,-0.2496967465,-0.1128350124,0.3732021451,0.1607759744,-0.0779852793,0.4326273203,-0.0001198013,-0.1041963696,0.234977439,-0.0170198269,-0.2702702284,-0.3771175444,-0.4048727453,-0.5063537359,0.0147821624,-0.1779724061,0.1007493883,0.267157495,0.1191743761,-0.0723108277,0.0938146859,0.1856914759,0.1936353743,0.1554289162,0.0486355983,-0.0412597843,0.3124481142,-0.2527000308,-0.0651909336,0.1622196436,0.1873314977,0.1267400682,-0.2227674723,0.1186722293,-0.3079533577,0.3004409671,-0.0344962887,0.0108133554,0.0467392728,-0.3264696896,0.2752406299,0.4079024792,0.4325535297,-0.1871513426,-0.294485718,-0.1738485247,-0.1533012241,-0.0023674676,0.4001485109,0.1629086882,-0.2278741598,-0.0463881977,-0.3922587633,0.2648798525,0.0528610013,0.2153360695,0.0590297543,0.0483919531,-0.0609724782,-0.1191069931,0.1370472461,-0.0754750073,0.2253561467,-0.3740883768,-0.1097705364,0.2610442936,-0.4206713438,-0.0614455864,-0.0303055104,0.5376824737,0.5291839838,0.3535023034,0.1522078067,0.2514333427,-0.2445680499,0.0461458862,0.020478759,0.2093227208,0.0655857772,-0.0943782404,0.1245097518,0.3872912824,-0.1391385645,0.1805371195,-0.0368315205,-0.0456456318,0.6784492731,0.0181602072,0.1439567208,-0.5884661674,-0.3368144333,0.4549659789,-0.151741609,-0.1199077368,-0.0062457686,0.3078668714,-0.2831119597,0.1685542166,-0.1050597578,0.1378824711,-0.0804604143,-0.1539838761,-0.2957545519,-0.1397241205,-0.1504713148,-0.0632841587,0.2246820927,-0.3565534651,0.3352398276,-0.0515730828,0.2992823422,-0.1967782378,0.0159074217,-0.1158531681,-0.1921083927,0.2023259848,0.0298571456,0.2005928308,0.1549967825,0.0597271658,0.0133519163,0.0879230276,-0.2705900073,-0.1324612498,0.1108844131,0.2053999752,-0.5836594105,-0.393779397,0.0451772697,-0.2672811747,0.2497830391,-0.1411281079,0.0544548631,-0.109312281,-0.2154649645,-0.2687777579,-0.0199841242,0.1369310617,-0.1675184518,-0.0077661779,-0.0942448378,-0.1026165262,0.5374264717,0.2014993429,-0.0416983888,0.0285321809,-0.2054149061,0.2833956778,0.3906702995,-0.1673758924,-0.8798965812,0.259763062,-0.0273200367,0.4781271517,0.1399355829,0.1600446552,-0.0521174632,0.0658210292,0.1036422625,0.1195324734,0.0777687803,0.1902897507,-0.2474446148,-0.3442918062,0.0394470915,0.1539608985,0.0443249755,0.1407001317,0.1851332635,-0.0210841764,0.3947173357,-0.0458302759,-0.0327074528,0.0945947394,0.3341778517,0.2300528884,0.1512163281,-0.3137423694,-0.3739702404,0.3189919591,-0.2645703256,0.0681858212,0.0149742896,-0.1703307927,-0.3815482259,-0.2504169047,-0.2797917128,0.1239095032,0.0768753961,0.3769244254,0.360422194,-0.0117445569,-0.0419714786,0.3240392804,-0.0933088884,0.1179432124,-0.1803281754,0.2804037631,0.0320097543,-0.0506400988,-0.1284962445,0.0905664787,0.1922347993,0.110051766,-0.247399196,0.3598941863,0.2414757758,0.0673888624,-0.1680870503,0.2145634741,-0.1735956222,-0.1560672373,-0.030370187,0.523876071,0.1082741842,-0.002380654,-0.0148321139,0.2883145213,0.0723006129,0.0380463526,-0.0439890847,0.0857009962,0.3262713253,-0.1612974405,-0.0934867039,-0.2017478943,0.4962559342,-0.4073311985,0.1981087327,0.0379774496,-0.2952640951,-0.0991547853,0.1828975976,-0.1132807732,0.1603105068,0.1191875264,0.0249365289,-0.0544468425,-0.1000170708,0.5423449874,0.3044378459,0.2119982094,0.1004110053,0.1059395745,-0.1225009412,-0.1714084148,-0.0072758668,0.2283169925,0.2661945522,0.54904145,0.2414600253,0.0022512665,-0.409511894,-0.0301399939,-0.0552837364,0.2440394461,-0.4301237464,-0.0997081324,-0.1695824116,-0.3729839027,-0.2735750973,-0.2497572154,-0.1284107119,-0.4585259855,-0.0836521238,0.2773180604,0.0546669215,0.1180557311,-0.377423197,-0.0120092863,-0.0948292315,-0.2251132578,-0.1511166394,0.210032016,-0.1957802922,-0.0154231535,0.1645876467,-0.0427266024,0.2526283562,-0.4117342532,0.0165241696,-0.6264835596,-0.2493328154,0.0706534237,-0.1580813527,0.207870841,0.3840282857,0.1392316073,-0.2632396519,-0.0744720697,0.2209399939,-0.039336957,-0.3720573783,0.3436579108,-0.0934946314,0.014834187,0.1287033558,-0.0040662088,-0.2322083265,-0.2908137143,-0.0185987856,0.1010554507,0.2270297706,0.3653247654,-0.1820604354,0.0977607444,-0.116912134,0.4977903664,-0.2711399198,-0.5762326717,0.5287802219,0.1319577843,-0.3189797997,0.0123399375,-0.0178966019,0.0606252067,0.2794049978,-0.5194585323,-0.1023500934,-0.3720165491,-0.0292396992,0.1933085024,0.0693060234,0.1819202751,0.0034621039,-0.0545215607,-0.2112660259,-0.1410179138,0.1383913159,-0.0830824897,0.3895276487,-0.2822101712,0.295948118,0.0198221058,0.2290988415,0.5105573535,0.2102246732,0.2790658772,-0.022137912,0.0786330849,-0.1357040107,-0.2530153692,-0.3591159284,-0.09603871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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2641","title":"load_dataset(\"financial_phrasebank\") NonMatchingChecksumError","comments":"Thank you! I've been trying periodically for the past month, and no luck yet with this particular dataset. Just tried again and still hitting the checksum error.\r\n\r\nCode:\r\n\r\n`dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\") `\r\n\r\nTraceback:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-2-55cc2144f31e> in <module>\r\n----> 1 dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    859         ignore_verifications=ignore_verifications,\r\n    860         try_from_hf_gcs=try_from_hf_gcs,\r\n--> 861         use_auth_token=use_auth_token,\r\n    862     )\r\n    863 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    582                     if not downloaded_from_gcs:\r\n    583                         self._download_and_prepare(\r\n--> 584                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    585                         )\r\n    586                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    642         if verify_infos:\r\n    643             verify_checksums(\r\n--> 644                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    645             )\r\n    646 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.researchgate.net\/profile\/Pekka_Malo\/publication\/251231364_FinancialPhraseBank-v10\/data\/0c96051eee4fb1d56e000000\/FinancialPhraseBank-v10.zip']\r\n```","body":"## Describe the bug\r\nAttempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\r\n```\r\n\r\n## Expected results\r\nI expect to see the financial_phrasebank dataset downloaded successfully\r\n\r\n## Actual results\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.researchgate.net\/profile\/Pekka_Malo\/publication\/251231364_FinancialPhraseBank-v10\/data\/0c96051eee4fb1d56e000000\/FinancialPhraseBank-v10.zip']\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n","comment_length":174,"text":"load_dataset(\"financial_phrasebank\") NonMatchingChecksumError \n ## Describe the bug\r\nAttempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\r\n```\r\n\r\n## Expected results\r\nI expect to see the financial_phrasebank dataset downloaded successfully\r\n\r\n## Actual results\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.researchgate.net\/profile\/Pekka_Malo\/publication\/251231364_FinancialPhraseBank-v10\/data\/0c96051eee4fb1d56e000000\/FinancialPhraseBank-v10.zip']\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6\r\n- Python version: 3.7.10\r\n- PyArrow version: 4.0.1\r\n \n Thank you! I've been trying periodically for the past month, and no luck yet with this particular dataset. Just tried again and still hitting the checksum error.\r\n\r\nCode:\r\n\r\n`dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\") `\r\n\r\nTraceback:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-2-55cc2144f31e> in <module>\r\n----> 1 dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n    859         ignore_verifications=ignore_verifications,\r\n    860         try_from_hf_gcs=try_from_hf_gcs,\r\n--> 861         use_auth_token=use_auth_token,\r\n    862     )\r\n    863 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    582                     if not downloaded_from_gcs:\r\n    583                         self._download_and_prepare(\r\n--> 584                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    585                         )\r\n    586                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    642         if verify_infos:\r\n    643             verify_checksums(\r\n--> 644                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    645             )\r\n    646 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     38     if len(bad_urls) > 0:\r\n     39         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     41     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.researchgate.net\/profile\/Pekka_Malo\/publication\/251231364_FinancialPhraseBank-v10\/data\/0c96051eee4fb1d56e000000\/FinancialPhraseBank-v10.zip']\r\n```","embeddings":[-0.1219035983,0.2504358292,-0.0632824302,0.3291988373,0.1633206755,0.1306015104,0.0294304509,0.3981843889,0.1042740047,0.1001081914,-0.2370298058,0.251907289,0.2408214658,-0.1808868349,-0.1394717395,0.1404775232,0.1074734256,0.043175105,-0.0138186608,-0.0908906534,-0.1074014753,0.2812159956,-0.2145471871,-0.2022111565,-0.0256867241,0.0863929465,0.1104901284,0.0720111281,-0.2658267319,-0.1348887682,0.4920259714,0.1678609848,-0.0726743191,0.4778312445,-0.0001244328,-0.0519944988,0.271574378,-0.0486339889,-0.3567807078,-0.3707872033,-0.4534249604,-0.4662193358,-0.043372035,-0.2014847696,0.1378681362,0.3529549837,0.144514963,-0.1261881292,0.0545095019,0.1745295227,0.1397491992,0.2478656322,0.1467762291,0.0349865034,0.2998352647,-0.190959245,-0.1134894267,0.270352155,0.1460585147,0.0511562563,-0.2077224404,0.108807154,-0.3613056242,0.3707385063,-0.0008351873,0.0689157397,0.0588344298,-0.2889934778,0.2137651891,0.3574343026,0.4351553023,-0.218733266,-0.3650907278,-0.1239956617,-0.2084687203,-0.046321772,0.4173539877,0.1647838354,-0.283115685,-0.0629981011,-0.468390882,0.277602464,-0.0092773754,0.2345029265,0.0326497257,0.0620341748,-0.0185132474,-0.078727223,0.1915030032,-0.0618736297,0.265865773,-0.3590570688,-0.0391858891,0.2633775771,-0.4160850644,-0.1000158042,-0.0744050965,0.4421432912,0.4887327552,0.4190541208,0.1808799803,0.1608913392,-0.1823398769,0.0954611823,0.0509010777,0.1860753894,0.0430604406,0.0192637518,0.1798640043,0.4134500027,-0.1069433987,0.1626017839,-0.0692646354,-0.0316085517,0.7316473722,0.0094839688,0.2204508185,-0.6202270985,-0.3789457381,0.4553828239,-0.1631374806,-0.1740154773,0.0485575385,0.2002506554,-0.3129514456,0.1997944713,-0.0601880364,0.0835547298,-0.0794082284,-0.1734060645,-0.2265704125,-0.0589110851,-0.1227228791,0.0320249833,0.3139738142,-0.3250138164,0.3017095327,-0.0585967302,0.3189172447,-0.1349220276,0.1294149905,-0.14722161,-0.177792415,0.3243793249,-0.0608408451,0.1900531948,0.1779836416,0.0699722171,-0.1106022149,0.1508008689,-0.2157249302,-0.1449101269,0.0084858332,0.1288296133,-0.6136748791,-0.3836396337,0.074727647,-0.417157799,0.3301468492,-0.2364791781,0.0597680807,-0.1355568022,-0.2099694163,-0.2326627523,0.0224018674,0.1867602468,-0.1150826812,0.0642107129,-0.1237894073,-0.0815362334,0.537407577,0.2021664828,-0.0115692494,0.0198319256,-0.1981375515,0.1501951367,0.2272737771,-0.1579652578,-0.8371682167,0.2042014748,0.0438029431,0.5060791373,0.1131729558,0.1906835437,-0.0519395955,0.0921697095,0.1658838689,0.0820414498,-0.0265884344,0.084858343,-0.2368453592,-0.3326882124,0.1446264833,0.2096131593,0.0511388145,0.1455373615,0.1411495954,0.0321554951,0.2999107838,-0.0340311565,-0.0117764939,0.0532305762,0.4530851841,0.172077924,0.0931847841,-0.3631533682,-0.4774758518,0.2887387872,-0.1649101824,0.0705624968,-0.0916769207,-0.0877335221,-0.3856807351,-0.2757385969,-0.2165607363,0.1742729545,0.0195611566,0.3824872673,0.3753988445,-0.0675044581,-0.0457649902,0.422455281,-0.1418426484,0.1107591093,-0.2753636837,0.3411239386,-0.0082150716,-0.0534482971,-0.0830925554,0.0514111891,0.1445264965,0.0604137108,-0.2113299519,0.3504087925,0.2670076489,-0.0531184264,-0.1756032705,0.2846336067,-0.1455773115,-0.16696769,-0.0429787487,0.526586473,0.091803357,-0.0179369021,-0.0586445779,0.3515950143,-0.0240479726,0.0481224805,-0.0688220188,0.1417023689,0.3558284342,-0.1717816144,-0.1814339757,-0.1569634527,0.5086337924,-0.2532770038,0.1340863109,0.0424634553,-0.2152335197,-0.1337844431,0.1058997959,-0.1258063614,0.1081808358,0.1274549067,0.1138325185,-0.0263647847,-0.0446382761,0.497566402,0.3507782221,0.1526300609,0.1489660293,0.1117477864,-0.2153270841,-0.0640217364,-0.0453858338,0.1830278337,0.2811230719,0.5960560441,0.228765592,0.0227123145,-0.389703989,-0.163664937,-0.0485494509,0.3402167559,-0.4080109298,-0.0583371408,-0.2477993071,-0.207867682,-0.297673434,-0.3445234001,-0.2360521555,-0.4660623074,-0.1171728522,0.4518059492,0.0896143392,0.1947255731,-0.5383248329,0.01948178,-0.0496499166,-0.3143502176,-0.1638917327,0.1301636994,-0.1365332901,-0.0599620976,0.328393966,-0.0334941633,0.2559363544,-0.43783167,0.001131805,-0.5693760514,-0.3058415949,-0.0112471888,-0.095018439,0.2326525897,0.3062504232,0.1937238127,-0.2337453365,-0.2111416906,0.1895799041,-0.0036033427,-0.3972535431,0.3469931185,-0.1643625349,0.0363665074,0.1348143369,-0.0390872881,-0.1930673271,-0.2375830412,-0.020973986,0.1128812134,0.2617201805,0.3831792176,-0.1477780193,0.1182787195,-0.1250498742,0.5495764613,-0.222515583,-0.6153051853,0.6148754954,0.1510251164,-0.2769511044,0.060051091,-0.0439410582,0.1141605675,0.3257282972,-0.5195677876,-0.1178133339,-0.3152972758,-0.0033047076,0.1880886704,0.0336292945,0.1843613833,-0.0221023895,0.0029330023,-0.163847506,-0.2238503993,0.1285649091,-0.0007640535,0.4240906835,-0.270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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2630","title":"Progress bars are not properly rendered in Jupyter notebook","comments":"To add my experience when trying to debug this issue:\r\n\r\nSeems like previously the workaround given [here](https:\/\/github.com\/tqdm\/tqdm\/issues\/485#issuecomment-473338308) worked around this issue. But with the latest version of jupyter\/tqdm I still get terminal warnings that IPython tried to send a message from a forked process.","body":"## Describe the bug\r\nThe progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds.map(tokenize, num_proc=10)\r\n```\r\n\r\n## Expected results\r\nJupyter widgets displaying the progress bars.\r\n\r\n## Actual results\r\nSimple plane progress bars.\r\n\r\ncc: Reported by @thomwolf ","comment_length":44,"text":"Progress bars are not properly rendered in Jupyter notebook \n ## Describe the bug\r\nThe progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds.map(tokenize, num_proc=10)\r\n```\r\n\r\n## Expected results\r\nJupyter widgets displaying the progress bars.\r\n\r\n## Actual results\r\nSimple plane progress bars.\r\n\r\ncc: Reported by @thomwolf  \n To add my experience when trying to debug this issue:\r\n\r\nSeems like previously the workaround given [here](https:\/\/github.com\/tqdm\/tqdm\/issues\/485#issuecomment-473338308) worked around this issue. But with the latest version of jupyter\/tqdm I still get terminal warnings that IPython tried to send a message from a forked process.","embeddings":[0.1817371696,0.0266521983,-0.0048842984,0.0565507188,0.1184258536,-0.3483625948,0.556789875,0.4173842669,-0.2593963444,0.0024573819,-0.2125666291,0.6025976539,0.2658938468,0.1672159582,0.0038400963,-0.2379226387,-0.1584529132,0.1035003811,-0.3469713032,0.0024837342,-0.2896267474,0.1918030232,-0.2716622353,0.3635055125,-0.4041291773,0.0030341123,0.0743729547,0.0767616257,0.1247892603,-0.4877848625,0.1010513604,-0.0711862147,0.0366622657,0.3372041583,-0.0001156703,-0.0285918657,0.5814056396,0.0483196415,-0.0534029566,0.3121868372,0.4811497331,-0.4962196648,0.2921688855,-0.2768397629,0.0297931377,-0.1636991352,-0.3128537536,0.1014387682,0.5365033746,-0.1144312248,0.1887732148,0.5778613091,0.0724003091,0.201487571,0.0623273849,-0.0967912152,-0.232330367,0.0826817229,0.4364378154,-0.1705191731,-0.4072221816,0.086250186,-0.0827880129,0.3567173481,-0.0489209443,0.1682757884,0.8467930555,-0.095043242,-0.3010845482,0.1756129712,0.1570870727,-0.2217915058,-0.2138753235,-0.1908809394,-0.2720872462,-0.1241789237,-0.0247892942,-0.2117275,-0.1801390946,0.0630745143,-0.3082980812,0.1876827031,-0.0752101466,0.0611595735,-0.2718208432,0.0522353537,0.0162742119,0.0222461689,-0.4251683652,0.044707261,0.2143156826,-0.0899430662,0.0952503532,0.0319860168,0.0103438683,-0.1096736342,0.1929796934,-0.2212168574,-0.1708805859,0.062981531,-0.1323730499,0.0801317021,-0.1984624714,0.1380109638,0.227278918,-0.0242731273,0.2722266018,0.031876523,0.4079745412,0.1121730804,0.0771005675,0.020398302,0.1639780104,-0.197235167,0.1264158487,0.2029946595,-0.0121485041,-0.0115512963,-0.164659366,0.3339937925,0.3537817895,0.0827571824,0.1300661415,-0.0598980151,-0.112852715,-0.0985690653,0.1420909762,0.2114596516,-0.1650141031,-0.3398205638,-0.0455710255,-0.3156957328,-0.1131617427,-0.0326119512,-0.0819021687,0.5226358771,0.2015721053,0.1167481616,-0.013367529,-0.0608451813,-0.0039571417,-0.0454569608,0.1604146063,0.2736178041,-0.1801854223,0.0645642579,0.3331306279,-0.2147260755,0.1004189998,0.4017115831,0.0019665295,0.014730881,-0.3248888552,0.0271350294,-0.3449071944,0.2925201058,-0.3482396007,-0.2347100675,0.3120609522,0.0191090684,0.0911080837,-0.4993657768,0.0704650953,-0.0417196676,0.2428245693,0.2687725723,-0.5468888879,-0.3661729395,-0.2729978859,-0.1132058352,0.5822642446,0.4652715027,0.0913209617,0.3527902663,-0.0516454615,0.0799935684,-0.0715077668,0.0040799421,-0.3240094185,-0.0506807715,0.0342350155,0.3598086834,-0.2217023969,-0.1391577423,0.5451210141,0.0268424228,0.2225217074,0.1077181548,0.2201520503,0.0050674751,-0.5182207823,0.1198147163,0.1424679011,0.053952083,0.2264510393,-0.0413967334,-0.0760639831,-0.3514195979,0.3787595332,0.4695475399,0.0613986328,-0.1032748073,-0.1235893145,0.0379323103,0.2085112035,0.2291628867,0.1162103117,0.2249444723,-0.2089492977,-0.0343402848,0.0795870051,0.1317459792,-0.4423643649,0.0988320336,-0.3059394658,-0.141505003,0.1749251485,-0.1771274954,-0.0869025737,0.2503200471,0.1665762812,-0.1593328267,-0.0878702104,0.0841876566,0.2446449697,0.6247319579,0.2298951894,-0.6264795661,0.0758993849,0.1546844691,-0.0475362353,0.1062089875,-0.0996648744,0.0255801398,0.4109326303,-0.0742413998,-0.2628404498,-0.2066325843,0.24791646,0.0921126977,-0.0527587496,0.61665833,-0.1669974476,-0.0735857636,0.2651449144,0.108077243,-0.1507932097,0.216676712,-0.1668829918,0.3541098833,-0.0477515906,0.004200194,-0.1646630615,-0.0965929255,0.0865282565,0.051981438,0.1038385108,-0.2703277767,0.2899268568,0.2455769479,0.2856424451,0.0597281158,0.1261171252,-0.108426854,-0.442605257,0.39192909,0.35090819,-0.1244852766,-0.0641749203,-0.005992013,-0.0925087631,0.1071569398,-0.1056128591,-0.1911780536,-0.2419401109,0.2931978405,0.0717580467,0.2813779414,-0.0015513499,-0.0976843238,-0.1878172606,0.1744771004,-0.2419845313,0.0416071415,-0.2596999705,-0.1773075908,-0.2635091245,-0.0708165392,0.0574664697,-0.3950704634,-0.1241569594,-0.2460366786,0.0640075579,0.2776843011,-0.0457182713,0.4009768069,0.4703310728,-0.1269136071,0.2192731798,0.3190117776,-0.2036365867,-0.2075867504,-0.1263897866,0.0597334765,-0.0034549914,-0.0828465149,-0.0726938695,-0.4586654902,0.2137558609,-0.2816906273,-0.0525495075,0.0743593872,-0.0355221555,0.5063041449,-0.2617628574,0.2529340386,-0.1107394397,0.4106066525,0.4045543969,-0.2005270422,-0.1289593428,0.0199467838,0.0778892413,-0.2607973516,-0.0393591039,0.3344100118,-0.2608054578,-0.1424450278,0.2783357501,0.0502967797,0.169492811,-0.5509198308,-0.3159068227,0.0516400151,0.2530952692,-0.1564558446,-0.0282126497,-0.222311452,0.2857842743,-0.2854257226,-0.2039786428,-0.1333153844,-0.0329735503,0.021691991,-0.6995676756,-0.3913334608,-0.016667543,-0.151509434,0.5323093534,0.0880490392,0.0253739655,0.027831845,0.0030348997,-0.0044203573,-0.0663749129,-0.2988654971,0.0168136694,0.0403070152,0.3351403773,-0.0734657943,0.0413795263,-0.1678373963,0.2677576542,0.1905387193,0.0943192318,0.0324877612,-0.0937042907,0.0733191222,0.0126712257,-0.0974273905,-0.1152827963,-0.1858903766,-0.073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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2630","title":"Progress bars are not properly rendered in Jupyter notebook","comments":"Hi @mludv, thanks for the hint!!! :) \r\n\r\nWe will definitely take it into account to try to fix this issue... It seems somehow related to `multiprocessing` and `tqdm`...","body":"## Describe the bug\r\nThe progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds.map(tokenize, num_proc=10)\r\n```\r\n\r\n## Expected results\r\nJupyter widgets displaying the progress bars.\r\n\r\n## Actual results\r\nSimple plane progress bars.\r\n\r\ncc: Reported by @thomwolf ","comment_length":28,"text":"Progress bars are not properly rendered in Jupyter notebook \n ## Describe the bug\r\nThe progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal).\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nds.map(tokenize, num_proc=10)\r\n```\r\n\r\n## Expected results\r\nJupyter widgets displaying the progress bars.\r\n\r\n## Actual results\r\nSimple plane progress bars.\r\n\r\ncc: Reported by @thomwolf  \n Hi @mludv, thanks for the hint!!! :) \r\n\r\nWe will definitely take it into account to try to fix this issue... It seems somehow related to `multiprocessing` and `tqdm`...","embeddings":[0.0840747207,-0.0174471959,-0.0553433672,0.146748662,0.1418051273,-0.2826747596,0.4365513623,0.3081710339,-0.2813870907,0.1335172951,-0.1734683812,0.4958986938,0.302113533,0.360643357,-0.0934584588,-0.2819553018,-0.0838802904,0.1640948504,-0.3097079098,-0.0013430968,-0.3553539813,0.2289115191,-0.3600171804,0.274898231,-0.3260476887,-0.0157691464,0.0067369333,-0.0339760669,0.2025540769,-0.3707706332,0.0603209287,-0.0869974941,0.1291181743,0.4368245304,-0.0001134229,-0.1087477282,0.4875665009,0.0413400717,0.0329009667,0.2878982425,0.4559450746,-0.4877927303,0.2829349339,-0.2850020528,0.0710600391,-0.1794690639,-0.3954918087,-0.0420616306,0.406507194,-0.1567408293,0.2473642379,0.4405182004,-0.0182326809,0.2076785713,0.0391099975,-0.1461219341,-0.2083894163,-0.0282222517,0.4653917551,-0.3063619435,-0.3879884779,0.1063967794,-0.0486431532,0.4187018573,-0.0393807665,0.2133056968,0.9913772941,-0.0878651738,-0.2944223583,0.2115101814,0.2394176275,-0.105977878,-0.1512372196,-0.1202547997,-0.2735105455,-0.1160391644,-0.1966740787,-0.0861075521,-0.2104892582,0.0214731023,-0.3254862428,0.1474572867,0.0230799317,0.0039320379,-0.2227223516,0.0638749674,-0.0164266899,-0.0254160725,-0.4152092636,0.1107601821,0.1686975807,-0.1647108644,0.1662720144,-0.017423898,-0.0668758824,0.0514467619,0.1705864519,-0.0394774675,-0.1951253414,-0.0893868878,-0.0934441686,0.1065666303,-0.1109584719,0.1176110208,0.1380227953,0.0363824181,0.2670516074,-0.0737986863,0.4157478809,0.0265655257,0.0262287073,0.0510558039,0.2824546099,-0.2843369544,0.1190832481,0.2696622014,-0.1699028164,-0.0240198709,-0.1325654685,0.2839515805,0.3845815063,-0.1172237247,0.050101649,-0.0212007668,-0.1604377031,0.0458948351,0.0982232392,0.2016148567,-0.093886435,-0.2694262862,-0.1279253215,-0.2795524895,-0.2595213652,0.0063250475,-0.1258066744,0.3609222472,0.2234245986,0.1400429904,-0.0556914099,-0.1958279908,-0.0410629772,-0.0504104123,0.1032537669,0.2466241866,-0.0829610452,0.1367767453,0.2960921824,-0.2910871506,0.1265167892,0.425904572,-0.0393438265,0.0393931307,-0.3534792364,0.0832789913,-0.3191151619,0.3744330108,-0.4555279315,-0.2263981551,0.3937739134,0.0776729807,0.0967461243,-0.4163134694,-0.0178411622,-0.1785806417,0.17323713,0.1455981731,-0.5406771898,-0.3143543601,-0.0999097899,-0.1438417584,0.6745646596,0.3311755657,0.0616390258,0.3111010194,-0.0355523303,0.1803805232,-0.0299432483,-0.0998076946,-0.2703015506,-0.0446333215,0.0686812028,0.3434061408,-0.1168109104,-0.1047256365,0.387932837,0.0571679473,0.1605418772,0.1995611638,0.2249500453,-0.0040507358,-0.5000802875,0.1472550035,0.2613554001,0.1368755549,0.2515562475,-0.10519059,-0.1100691408,-0.4471721053,0.4548561275,0.3896095753,0.0735659003,-0.1086159647,-0.2065193206,0.0486687124,0.20959723,0.2003743201,0.0501533598,0.2725409865,-0.2228266299,-0.1505789608,0.1081631556,0.0841673166,-0.2802225947,0.0574537516,-0.3637309074,-0.2123386711,0.2303520441,-0.0214926694,-0.0324373543,0.1609050781,0.1928894222,-0.0381628126,0.0449481718,-0.0241240729,0.2177133262,0.6586594582,0.220101133,-0.5620321035,0.1005694792,0.1098099351,-0.0100126024,0.0599663928,-0.1167561784,0.105545871,0.3706769645,-0.0126156434,-0.2862404287,-0.2531236112,0.1941245794,0.1476222426,-0.1142541096,0.5261477828,-0.058354076,-0.1218652278,0.3449297249,0.087261498,-0.1932433844,0.3505972326,-0.133608982,0.2573168278,-0.0148074897,0.016546404,-0.0542389229,-0.1458518058,0.137533918,0.0343477614,0.0556813329,-0.2651257813,0.3374623358,0.2101247609,0.2718619108,0.0949251875,0.1694935709,-0.1708599627,-0.4523812532,0.3870063424,0.3288426995,-0.1260956228,0.1076468676,0.0887194499,-0.0556902587,0.0388398618,0.0346243121,-0.2729830742,-0.2650905252,0.2690735161,0.2852129042,0.3415223658,-0.0553954542,-0.1524236351,-0.0843226239,0.1791528463,-0.2676634192,0.0621223412,-0.1503647715,-0.216901809,-0.2802102268,-0.0814332962,0.1066746041,-0.2798359394,-0.0973197892,-0.2014974654,-0.0294565782,0.1356530339,0.0548096485,0.3602169752,0.5633695126,-0.1225382686,0.140152514,0.3674173951,-0.184812814,-0.2312560081,-0.2094030827,0.0796075463,0.0218693707,-0.0270249415,-0.1452461034,-0.5066241622,0.2695992291,-0.2906699181,0.0424460471,0.0552971922,0.0010296454,0.4239055812,-0.311807543,0.1914335191,-0.0486043878,0.4684601724,0.3779620826,-0.1273926646,-0.1681383252,0.0822190493,0.0849710479,-0.2132692188,-0.0504145734,0.3898370266,-0.331717968,-0.1092651263,0.2391228974,-0.0295659266,0.1968768239,-0.7200556397,-0.2799727619,-0.0238183253,0.2932691574,-0.0693960413,-0.1043622568,-0.1313590556,0.2333750129,-0.2860134244,-0.2543158233,-0.2362781763,-0.0590562932,0.0311986003,-0.6471823454,-0.4120875895,-0.0859325826,-0.2004361153,0.3781549633,0.0382053107,0.1749142706,0.1584655195,0.0033087095,-0.0538710952,-0.0849443451,-0.4036501348,0.0524333604,-0.016965311,0.3014837205,-0.0230090097,-0.0270725153,-0.1710710675,0.1382593215,0.2992339134,0.0753851905,0.1574761719,-0.0522209629,-0.0141519243,0.1012698188,-0.0767275617,-0.1693764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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2629","title":"Load datasets from the Hub without requiring a dataset script","comments":"This is so cool, let us know if we can help with anything on the hub side (@Pierrci @elishowk) \ud83c\udf89 ","body":"As a user I would like to be able to upload my csv\/json\/text\/parquet\/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script.\r\n\r\nMoreover I would like to be able to specify which file goes into which split using the `data_files` argument.\r\n\r\nThis feature should be compatible with private repositories and dataset streaming.\r\n\r\nThis can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv\/json\/text\/parquet\/etc.)","comment_length":20,"text":"Load datasets from the Hub without requiring a dataset script \n As a user I would like to be able to upload my csv\/json\/text\/parquet\/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script.\r\n\r\nMoreover I would like to be able to specify which file goes into which split using the `data_files` argument.\r\n\r\nThis feature should be compatible with private repositories and dataset streaming.\r\n\r\nThis can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv\/json\/text\/parquet\/etc.) \n This is so cool, let us know if we can help with anything on the hub side (@Pierrci @elishowk) \ud83c\udf89 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2622","title":"Integration with AugLy","comments":"Hi,\r\n\r\nyou can define your own custom formatting with `Dataset.set_transform()` and then run the tokenizer with the batches of augmented data as follows:\r\n```python\r\ndset = load_dataset(\"imdb\", split=\"train\")  # Let's say we are working with the IMDB dataset\r\ndset.set_transform(lambda ex: {\"text\": augly_text_augmentation(ex[\"text\"])}, columns=\"text\", output_all_columns=True)\r\ndataloader = torch.utils.data.DataLoader(dset, batch_size=32)\r\nfor epoch in range(5):\r\n    for batch in dataloader:\r\n       tokenizer_output = tokenizer(batch.pop(\"text\"), padding=True, truncation=True, return_tensors=\"pt\")\r\n       batch.update(tokenizer_output)\r\n       output = model(**batch)\r\n       ...\r\n```","body":"**Is your feature request related to a problem? Please describe.**\r\nFacebook recently launched a library, [AugLy](https:\/\/github.com\/facebookresearch\/AugLy) , that has a unified API for augmentations for image, video and text.\r\n\r\nIt would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial.\r\n\r\n**Describe the solution you'd like**\r\nThe biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nOne possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.\r\n","comment_length":68,"text":"Integration with AugLy \n **Is your feature request related to a problem? Please describe.**\r\nFacebook recently launched a library, [AugLy](https:\/\/github.com\/facebookresearch\/AugLy) , that has a unified API for augmentations for image, video and text.\r\n\r\nIt would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial.\r\n\r\n**Describe the solution you'd like**\r\nThe biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader.\r\n\r\n**Describe alternatives you've considered**\r\n\r\nOne possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.\r\n \n Hi,\r\n\r\nyou can define your own custom formatting with `Dataset.set_transform()` and then run the tokenizer with the batches of augmented data as follows:\r\n```python\r\ndset = load_dataset(\"imdb\", split=\"train\")  # Let's say we are working with the IMDB dataset\r\ndset.set_transform(lambda ex: {\"text\": augly_text_augmentation(ex[\"text\"])}, columns=\"text\", output_all_columns=True)\r\ndataloader = torch.utils.data.DataLoader(dset, batch_size=32)\r\nfor epoch in range(5):\r\n    for batch in dataloader:\r\n       tokenizer_output = tokenizer(batch.pop(\"text\"), padding=True, truncation=True, return_tensors=\"pt\")\r\n       batch.update(tokenizer_output)\r\n       output = model(**batch)\r\n       ...\r\n```","embeddings":[-0.1749232709,-0.1920810193,-0.1434521526,-0.239678964,0.1912634373,0.0263557434,0.167391479,0.3273121417,-0.3234899342,0.0160070173,0.0447254553,-0.0170348417,-0.2608955204,0.1105004624,-0.0723288879,-0.1290824711,0.1623859406,0.1287831068,0.0835960805,-0.0614518151,0.2458241582,-0.0801938996,-0.1216678768,0.0804531202,-0.4331211746,-0.2005291581,-0.0184633899,-0.1191418543,0.0323870108,-0.0549192354,-0.2768345773,0.2928821146,0.139453426,0.2861319482,-0.0001094861,-0.2278579026,-0.0457839854,-0.1373027265,-0.3240187466,-0.0370470993,0.8906273842,-0.4260549843,0.018437136,-0.1956669092,-0.2086928785,-0.2443619668,0.225955084,-0.1025847942,0.7065521479,-0.1400630176,0.1746794581,0.1642660201,-0.1695449203,0.1748896688,0.0812375322,0.6041795611,-0.0998975858,-0.1248127446,0.3372099102,-0.287638396,-0.1602464765,0.5248727798,0.0838095024,-0.0850363374,0.3582636416,0.0786501989,0.0502489135,-0.5240317583,-0.2224519402,0.1617945582,0.064426139,-0.1844712794,-0.5932481885,-0.3726418614,0.2279145569,-0.0164295323,-0.2347869426,0.1063614562,-0.1818405986,0.0403820574,-0.1904088408,-0.1769422144,-0.0590320975,0.2114746869,0.2574062645,0.2118017375,0.2327120304,-0.1030833274,-0.3152122796,-0.1901496947,-0.017550623,0.1092635542,0.216767475,0.3279650807,-0.0164926499,-0.1686119884,-0.2443469465,-0.3988581598,-0.0008842092,0.1280761212,0.0307479911,0.420787394,-0.4762347043,0.0391779132,0.0057944143,0.0282802861,0.0076813176,0.1638359725,0.3196819425,0.054072842,0.0039827083,-0.0232678968,0.0108465804,0.1630419195,0.0301643293,-0.1091242656,-0.3127089739,0.2283012867,-0.2628072798,0.0098471437,-0.1675759256,0.1402470917,0.0705191717,0.2345561385,-0.2274330705,0.2814390957,0.2564775944,0.1967922896,0.0014192906,0.035521768,-0.0433994904,0.1743155271,-0.3539296091,0.1625114679,0.280423969,0.5648206472,0.1444967091,0.0708900243,0.3205162585,0.0095686428,0.2328449339,-0.1425879747,0.4217463732,-0.0478327163,-0.1146397516,0.2201912552,-0.0657536536,0.0721538365,-0.119618915,-0.1751389802,-0.1528177708,0.1419904232,-0.2186926752,0.2464309633,0.3551001549,-0.0151164867,-0.2094570398,0.3241281509,0.0210421551,-0.3204990327,0.1727177501,-0.1375955194,0.0115348604,-0.0599739328,0.2275060564,0.0374388285,-0.0412992723,-0.2540357709,0.4374252558,0.2148566544,0.1135617793,0.3173480928,-0.2680597603,0.0712589696,-0.0477385707,0.0781117305,0.5492462516,-0.4070941806,0.051066298,0.2031701058,0.0156891905,0.063910991,-0.1526074409,0.3913735449,0.2438090593,0.0073967106,0.1471612751,0.5788085461,-0.0462913401,0.072911121,-0.0735741481,-0.3104627728,0.2083489448,0.0925204605,0.0948226675,-0.3573521674,-0.2048754394,0.0999733284,0.3083713055,-0.2700299323,0.3583818972,-0.0095584178,-0.2125834227,0.3153033555,-0.0342440456,-0.1558937132,0.0081812898,0.0151067451,-0.0870277882,0.3045280874,-0.1963891983,-0.3819638193,0.229483664,-0.0637034997,0.0682513863,-0.3417001963,0.1884030551,0.0611839779,-0.0851469785,-0.0551100858,-0.3363082409,0.1103338376,-0.1135299876,0.1098784581,0.0517388619,0.040688049,-0.03455396,-0.1430622339,-0.3016890287,0.3711787164,-0.0835312009,-0.0199187733,-0.0105550857,0.2912918925,-0.0540971868,0.1201765835,-0.0536797419,0.2401742935,0.3565207422,-0.0962436572,0.1682845652,0.1372682005,-0.0107516479,-0.289251864,-0.0039154035,0.5061603785,0.2224728614,0.2047206461,0.3028242588,-0.0844083056,0.0872405171,0.0437296331,-0.2721039951,-0.3616888821,-0.1609094441,-0.0939855874,0.1342760772,-0.3366361856,-0.5899156928,-0.0114842718,0.2806825638,0.2557839751,-0.0145958262,0.1399237812,-0.183690384,-0.1397485733,0.0756567791,-0.5855828524,-0.0750117525,0.2024073005,-0.0168656539,0.1435841024,-0.0854747966,-0.0052283532,0.0352681763,0.2353792638,-0.0255144238,0.0951068327,0.4083151817,0.3604509532,-0.1396538168,-0.3078117371,-0.1590868384,-0.4041635394,-0.0580309778,0.2476757914,-0.1718835831,-0.4956324697,-0.1368497014,-0.4858056307,-0.2315850407,0.0946224555,0.3938374221,-0.0339780822,-0.1213260442,0.3089965284,-0.0552519336,0.3120442033,-0.0148540866,-0.1032003462,-0.029511502,0.195880264,-0.2042996138,0.0272398032,0.003990253,0.047898218,0.4143897593,0.2367702872,-0.0613217652,-0.368853271,-0.5738261342,0.1989150047,-0.0435740761,-0.0754411444,0.1535942703,-0.2465477586,0.0378198773,-0.5249401331,0.0829260051,-0.2365261763,-0.0877230018,-0.3229259849,0.0898313299,-0.1227382049,-0.2532222569,0.0823828354,-0.3209186792,-0.350895375,-0.0673328415,0.213936165,0.0358337909,-0.1540177017,-0.260230571,-0.1037170589,0.1582040936,0.2489973009,0.0276557952,0.0360089839,0.2866538167,-0.3043049574,-0.1436741948,-0.1987398416,-0.3153315187,0.1066342443,0.2237939388,-0.1450603306,-0.2972123623,0.0744433552,0.0842172951,-0.248432532,-0.1239834875,0.0842127874,-0.0569638982,-0.1526043266,0.0490060262,-0.0183922965,0.1789525002,-0.1769568324,0.1623331755,-0.3505836427,0.2998358309,0.2065783292,0.4156169891,-0.1009587571,-0.1860577464,0.4628739059,-0.1015675366,-0.0547133237,-0.0337048657,-0.069356434,-0.01455988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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2618","title":"`filelock.py` Error","comments":"Hi @liyucheng09, thanks for reporting.\r\n\r\nApparently this issue has to do with your environment setup. One question: is your data in an NFS share? Some people have reported this error when using `fcntl` to write to an NFS share... If this is the case, then it might be that your NFS just may not be set up to provide file locks. You should ask your system administrator, or try these commands in the terminal:\r\n```shell\r\nsudo systemctl enable rpc-statd\r\nsudo systemctl start rpc-statd\r\n```","body":"## Describe the bug\r\n\r\nIt seems that the `filelock.py` went error. \r\n\r\n```\r\n>>> ds=load_dataset('xsum')\r\n\r\n^CTraceback (most recent call last):\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 402, in _acquire\r\n    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\nOSError: [Errno 37] No locks available\r\n```\r\n\r\nAccording to error log, it is OSError, but there is an `except` in the `_acquire` function.\r\n\r\n```\r\n    def _acquire(self):\r\n        open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC\r\n        try:\r\n            fd = os.open(self._lock_file, open_mode)\r\n        except (IOError, OSError):\r\n            pass\r\n        else:\r\n            self._lock_file_fd = fd\r\n        return None\r\n```\r\n\r\nI don't know why it stucked rather than `pass` directly.\r\n\r\nI am not quite familiar with filelock operation, so any help is highly appriciated.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nds = load_dataset('xsum')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\n```\r\n>>> ds=load_dataset('xsum')\r\n\r\n^CTraceback (most recent call last):\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 402, in _acquire\r\n    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\nOSError: [Errno 37] No locks available\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 818, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 470, in prepare_module\r\n    with FileLock(lock_path):\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 402, in _acquire\r\n    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\nKeyboardInterrupt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.13\r\n- PyArrow version: 4.0.1\r\n","comment_length":84,"text":"`filelock.py` Error \n ## Describe the bug\r\n\r\nIt seems that the `filelock.py` went error. \r\n\r\n```\r\n>>> ds=load_dataset('xsum')\r\n\r\n^CTraceback (most recent call last):\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 402, in _acquire\r\n    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\nOSError: [Errno 37] No locks available\r\n```\r\n\r\nAccording to error log, it is OSError, but there is an `except` in the `_acquire` function.\r\n\r\n```\r\n    def _acquire(self):\r\n        open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC\r\n        try:\r\n            fd = os.open(self._lock_file, open_mode)\r\n        except (IOError, OSError):\r\n            pass\r\n        else:\r\n            self._lock_file_fd = fd\r\n        return None\r\n```\r\n\r\nI don't know why it stucked rather than `pass` directly.\r\n\r\nI am not quite familiar with filelock operation, so any help is highly appriciated.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nds = load_dataset('xsum')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\n```\r\n>>> ds=load_dataset('xsum')\r\n\r\n^CTraceback (most recent call last):\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 402, in _acquire\r\n    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\nOSError: [Errno 37] No locks available\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 818, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 470, in prepare_module\r\n    with FileLock(lock_path):\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n    self.acquire()\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n    self._acquire()\r\n  File \"\/user\/HS502\/yl02706\/.conda\/envs\/lyc\/lib\/python3.6\/site-packages\/datasets\/utils\/filelock.py\", line 402, in _acquire\r\n    fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\nKeyboardInterrupt\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.0\r\n- Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid\r\n- Python version: 3.6.13\r\n- PyArrow version: 4.0.1\r\n \n Hi @liyucheng09, thanks for reporting.\r\n\r\nApparently this issue has to do with your environment setup. One question: is your data in an NFS share? Some people have reported this error when using `fcntl` to write to an NFS share... If this is the case, then it might be that your NFS just may not be set up to provide file locks. You should ask your system administrator, or try these commands in the terminal:\r\n```shell\r\nsudo systemctl enable rpc-statd\r\nsudo systemctl start rpc-statd\r\n```","embeddings":[0.1390438825,-0.3473012149,0.0151467938,0.0418614037,0.0302764755,0.0907045901,0.1623594612,0.1356668025,0.0592375062,0.0936220959,-0.1010852456,0.340319097,-0.0329612717,-0.4240655601,-0.459251672,0.1141825914,0.0069274814,-0.0698580444,-0.1149571389,-0.0138159692,-0.3595577478,0.3968342245,-0.4249511659,0.0698371232,-0.2793181241,-0.0480348431,-0.1366254836,0.3886517584,-0.1688994318,-0.1395633221,-0.0517245941,0.0867065936,0.0480411835,0.5414582491,-0.0001142323,-0.1104709134,0.0751497373,-0.0566150211,-0.3720932305,-0.0422811881,0.1407606304,-0.0910087451,-0.0822487995,-0.3874730766,0.2373984158,-0.3464207649,0.1434787065,-0.2871147692,0.4022390246,0.2639315426,0.1441263407,0.1482024789,0.4175293744,-0.178009063,0.1095130891,-0.4201950729,-0.0597184002,0.6798739433,0.7193904519,-0.1613604128,0.0171960033,0.0855058357,-0.148249507,-0.2049404085,0.2320636958,0.0193500929,0.3815196753,-0.4401879013,-0.2167775631,-0.0676430166,0.0476562157,-0.0331165791,-0.4759555459,-0.1618117094,0.4275055826,-0.07761994,0.0674172193,0.1474146098,-0.3159746826,0.3756872118,0.3556930125,-0.1153605878,-0.0145976534,0.0721763074,-0.0632616431,0.3086351454,0.0281341132,0.1370660216,0.262724787,-0.030876169,-0.0805696622,-0.0226215124,-0.0786239654,0.1791381687,-0.375010848,0.0532035343,0.1346542835,0.0576871149,0.0491891205,-0.2361772358,-0.3443281651,-0.246875599,-0.2653763294,0.2343066335,-0.1037879363,0.0391341224,0.0564300455,0.1757361293,0.3515813649,0.1015717313,-0.1955176741,0.013869076,-0.0682477951,-0.2627129257,0.1323340237,0.2916414738,0.407789737,-0.1522002071,-0.0398686491,0.233780697,-0.3236961961,0.018107092,0.1310848594,0.2916454077,0.1106775701,-0.0809489489,0.4470187724,0.1683355421,-0.1455666572,0.0815058947,-0.089699015,0.2096744478,-0.0342741646,-0.0005644005,-0.170863539,-0.3630220592,0.0755960718,0.3683515191,0.238421917,-0.3314391673,0.2022261024,-0.1128131896,-0.1492998302,0.1193967164,-0.1394654959,-0.0028866963,0.3368239105,-0.2456145138,0.128629908,0.171438545,-0.3326620162,-0.1702174097,-0.2444072664,0.144668743,-0.0669414401,0.0573142767,0.1982481927,-0.3010265827,0.0458981581,-0.315223217,0.1590230465,-0.3397700787,0.1084697098,-0.1530378461,0.0482599549,0.4075002074,-0.12052425,0.2917505801,0.1815027595,-0.1920057386,0.1625549197,0.0901744589,-0.02133926,0.1109134704,-0.4444103837,0.019757757,0.1067390218,-0.5011029243,-0.5333332419,0.0561696328,-0.4790895581,0.0537881702,0.1695948094,0.4211776853,-0.0173681006,-0.1641702652,0.2718978524,0.0371419527,-0.0135011952,-0.1450970024,-0.1528099775,0.0375398956,-0.0440814942,0.1406921446,-0.0029505112,0.1586375833,0.1399629563,0.0071283313,0.5296826959,-0.146213159,-0.2674141228,0.1628330052,0.4794795811,0.1399137527,0.0850243941,-0.1591180116,-0.189272061,0.2522282302,-0.1989827305,-0.0743363202,-0.0725066662,-0.0764314309,-0.0437692963,-0.0435342342,-0.0420152061,-0.0813650563,0.1175978482,-0.0148879644,-0.1799628735,0.0497146435,-0.1294880211,0.4790835083,-0.4433471859,0.2013792098,-0.1435981095,0.0725171342,-0.1064023823,-0.2321328968,-0.2575782537,-0.0071405102,0.2843230367,-0.1900358796,-0.2792275548,0.4126048088,0.2959444821,-0.1124275327,-0.2172019482,-0.2112116665,0.1744597554,0.3583770394,-0.2304764688,0.0148896417,0.0837068111,-0.0612622015,0.1202413812,0.1285025179,0.0746100321,-0.0397937819,-0.0832265541,0.2109800279,-0.0251819398,0.0652944073,0.0204290822,0.0194804948,0.4611050189,0.0080103828,0.0624385551,-0.1079219654,-0.0518783815,-0.0763699785,0.1907402128,0.0570197999,0.2793467343,0.1884344518,0.3128790557,0.3672698438,-0.193111077,0.5885143876,0.7208219767,0.101684466,-0.2875608802,0.0199206434,0.3027022481,-0.2104957104,-0.0469008498,-0.0330835022,-0.0418750085,0.2120727748,0.0586945824,0.2311490327,0.2032687366,-0.8740941286,-0.0125969276,0.2340481877,-0.004902795,-0.0489193536,-0.1694609076,0.1353217512,0.0601872131,0.3312356472,-0.0317488611,-0.3138143122,0.1344853938,0.4649529457,-0.074478209,0.0039711501,-0.3631457984,0.0643610656,0.1980468035,0.1316606551,0.1112059504,0.0367514491,-0.0958562642,-0.0664244443,0.1315078735,-0.0980742946,0.2080141157,-0.0003201759,-0.081226252,-0.4246044159,-0.1829681247,-0.0314671658,-0.1735013127,0.4597562253,0.056715481,0.3035485148,-0.0233455431,0.0249478146,0.1383039355,-0.0979297757,-0.0782772005,0.0798414126,0.0149578704,-0.0715152249,-0.3844693005,-0.034707766,-0.3898947835,-0.5175123811,0.1433671862,-0.1855088621,0.0664420128,0.3325220942,-0.1523124278,0.4005431831,0.1016704738,0.1809343398,0.1439602524,-0.292670399,0.2969040275,-0.0858423859,-0.4228855968,-0.3153539896,0.0855032727,-0.473782897,-0.1134320945,-0.2983644307,-0.5243878961,-0.3494296968,0.0939467922,-0.5529792309,0.0196183491,0.161808297,-0.2451497763,-0.0653789341,-0.2333385199,0.1794316769,0.1334120482,0.0864112228,0.1263547987,0.0655043945,0.0586766675,-0.0067032562,0.3639445603,0.108551845,0.2269399017,0.2979877591,-0.0920615122,0.268489629,0.0239143018,-0.2958976328,-0.203977406,0.26492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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2615","title":"Jsonlines export error","comments":"For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.","body":"## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1","comment_length":19,"text":"Jsonlines export error \n ## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1 \n For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.","embeddings":[-0.3504133224,0.1333585531,-0.0069016316,0.3097095788,0.0389037989,0.0805483982,0.2188645303,0.3883448839,0.0006037177,-0.0402306095,0.3231003284,0.0345943347,0.0673164427,0.0446980782,-0.168024078,-0.1674068421,0.2194828242,-0.0308502372,0.0336684771,0.2225417495,-0.2770715058,0.3025195897,-0.1921600103,-0.2260363847,-0.3988591135,-0.2168714553,0.1474183947,0.0140270805,-0.0263657849,-0.5282495618,-0.0391146727,-0.2388433814,0.4440392554,0.5343334675,-0.0001208482,-0.1544707865,0.333599031,0.0061301352,-0.2074822038,0.0264416467,-0.4390989244,-0.32505247,0.0626767799,-0.3321589828,0.4254887998,-0.043325413,-0.356161654,-0.4652174115,0.6484056711,0.4455591738,0.1272598058,0.0488448367,0.0954690576,-0.0134071819,-0.0303338505,0.1398149431,-0.0270308219,0.1463216394,-0.1673423201,0.2741565406,0.2642328143,-0.0571525879,-0.1586708724,-0.2541062534,-0.119923979,-0.085410364,-0.0593052246,-0.2858139575,0.1833256185,0.4000854194,0.1188576818,-0.1941767186,-0.2122763246,-0.3544609249,0.1109925061,-0.5715143085,0.0593010001,0.5090208054,-0.0011916923,0.1080368087,-0.2637678385,-0.114410229,-0.2154253274,-0.0444664508,0.0033301797,-0.1853815615,-0.1245927736,0.0236596912,-0.1236833036,-0.0916753635,-0.0017678378,-0.2792039514,-0.5480955839,-0.0483755842,-0.1531420797,0.0719255358,0.1683174223,-0.41027686,0.0765405893,-0.1706936359,0.2756137252,-0.3768747449,0.2522144914,0.0399434194,0.5629569292,0.1241348237,0.3155882359,-0.0116557395,0.2348292768,0.0134433247,0.0630376115,-0.0512274355,0.0182550773,-0.1685528904,0.1308631897,0.0527191199,0.0156177739,-0.0881923288,-0.0524228923,0.1799780875,-0.3575305343,-0.1791661978,-0.2505482733,-0.1906233877,-0.0992806479,0.2618553042,0.1635038704,0.2956346869,-0.1717634797,0.0063081384,-0.0204488989,-0.1102865338,-0.0897183716,-0.0236233976,-0.073195003,-0.2337141484,-0.0126970643,0.2607291639,-0.0713082403,-0.2439553589,-0.0935669243,-0.0429422632,-0.1192921028,0.2015205622,0.3051171303,0.1432213038,-0.1321808249,-0.1420604289,-0.0551187806,0.1836929768,-0.44878003,-0.0509726219,-0.3994896412,0.1303583235,-0.1831211299,0.1322419196,-0.4531036913,-0.0757280588,0.1063631997,0.1056371853,0.3775993288,-0.3547270596,0.5717495084,-0.2804208994,0.0224844217,0.2825922072,-0.7132418156,0.0905744135,0.0787857249,-0.018038623,0.4428821206,0.3599768877,-0.0533611178,0.3268298507,-0.1179183424,0.2507768273,0.1492944807,0.1822726578,0.0733188763,0.4902765751,-0.075096488,0.0082652271,-0.0178810563,-0.3451776803,0.6365368962,0.1081678718,0.5238189101,0.378954649,0.0567399897,0.247143209,-0.1749355942,-0.1934174299,-0.058229696,-0.1998623759,-0.3382233083,-0.0477836467,0.1436721087,-0.2043278515,0.3136581779,-0.2468699366,0.1187639832,0.4237191677,0.1386943907,0.1213777065,0.0007520858,0.2297507823,-0.4470793903,-0.1484595537,-0.1031326875,-0.1582561135,-0.1408949494,-0.0262280516,0.2096289694,0.2228936702,-0.0618911646,0.1483442932,0.0206855722,-0.1179929227,-0.0828323588,0.2088543773,-0.0439206995,0.1072661281,0.1892146617,0.0468167737,-0.2518898249,0.3014494181,0.2420794815,-0.2325034887,0.2049761862,0.1209193617,0.1598200649,-0.3047866523,-0.1392458826,0.2068885416,0.0553343631,-0.0437620915,-0.2117630988,0.0500013866,0.1228865013,0.1427299976,-0.1562665254,0.2958237231,-0.0600599162,0.0940000713,-0.0663738996,0.6036195755,-0.1680673957,0.3515254557,-0.0444824845,-0.0093329754,0.204870522,-0.1004328355,-0.0489793755,-0.2392224967,0.2890396416,0.2199492753,-0.1189383641,0.035022106,-0.2944984734,0.0433705114,0.6203617454,0.0286877751,0.0749300122,0.2449778318,-0.0279280301,-0.0106857792,0.0029470548,0.1193481237,0.34180516,0.1002836004,0.0062348549,0.1227353662,0.2248274833,-0.0821276978,0.2882922888,0.1530649364,0.3440402746,0.297332257,0.1898810863,0.1800428331,-0.1405833066,-0.3945007026,-0.2151729912,0.0726259276,-0.3205601275,0.3570815623,-0.181052506,-0.0075610415,-0.4912694693,-0.4257619977,-0.2852296531,-0.3221094012,-0.1869159192,-0.117719993,-0.4756629765,-0.1666974425,-0.1625766903,0.0679481179,0.1090966687,-0.062014658,0.0807449296,0.0071076346,-0.1285309643,-0.1226083934,-0.0314302854,-0.2198490202,0.0281818788,0.0962631404,0.09539105,-0.0799174607,0.1263902634,0.1485719532,0.0225120932,-0.0986567363,0.1772813201,0.1919338256,0.0379259177,-0.5070132017,0.044114504,0.1346117854,0.0048047537,0.1715757996,-0.0695156157,-0.1623263955,-0.0968115181,-0.0530371778,-0.0232678726,-0.3681439459,0.4280723035,-0.105002597,0.0783650875,-0.0927589759,0.1251449585,-0.0520538427,-0.0536761992,-0.2879790962,0.2134601921,-0.440947175,0.0076127052,-0.2837551236,-0.2592487037,0.1172973439,0.1513832062,-0.0292772949,0.0501040556,-0.3940513432,0.507517457,-0.1001565978,0.3795506954,-0.123145014,0.1970722824,0.1210306212,-0.0695032999,-0.0169989299,-0.0407808423,0.110844411,0.1125232428,0.0556513295,0.4821352065,-0.0872536749,0.7164244056,-0.2381183803,-0.1243974194,0.8051139712,0.162354365,0.2087133527,0.0996946692,0.1702729613,-0.1429881901,-0.0274126045,0.0307171643,-0.123238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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2615","title":"Jsonlines export error","comments":"@TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it must be due to pandas. Could you please check the pandas version causing the issue?","body":"## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1","comment_length":29,"text":"Jsonlines export error \n ## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1 \n @TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it must be due to pandas. Could you please check the pandas version causing the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2615","title":"Jsonlines export error","comments":"@TevenLeScao I have just checked it: this was a bug in `pandas` and it was fixed in version 1.2: https:\/\/github.com\/pandas-dev\/pandas\/pull\/36898","body":"## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1","comment_length":20,"text":"Jsonlines export error \n ## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1 \n @TevenLeScao I have just checked it: this was a bug in `pandas` and it was fixed in version 1.2: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2615","title":"Jsonlines export error","comments":"Sorry, I was also talking to teven offline so I already had the PR ready before noticing x)","body":"## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1","comment_length":18,"text":"Jsonlines export error \n ## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1 \n Sorry, I was also talking to teven offline so I already had the PR ready before noticing x)","embeddings":[-0.3764406741,0.0621025078,-0.0249254145,0.286175251,0.0446193144,0.0809495226,0.155846566,0.3971746266,0.0510593839,-0.0476375185,0.3336996734,0.004115683,0.0834176391,0.1582755297,-0.1321929693,-0.1995778978,0.1929605454,-0.0722633898,0.0871973783,0.2302482277,-0.2315653414,0.2663528323,-0.1839067787,-0.2031627148,-0.4015369117,-0.2735066712,0.1577025503,-0.0050987559,0.0436923541,-0.4615628123,-0.0991505682,-0.2882117629,0.4996251166,0.4922945499,-0.0001196299,-0.1714506298,0.2876414359,-0.0389574282,-0.2002032399,0.0875424296,-0.4354243875,-0.3970061839,-0.0468420871,-0.3596850038,0.3823304474,-0.1017141715,-0.348195076,-0.4584693611,0.6558184028,0.4028030336,0.1424899995,0.0162800271,0.1383096427,0.0170791205,-0.0757894963,0.0695119649,-0.0042966921,0.1023576409,-0.1442514062,0.3273944259,0.2477681339,-0.0633077919,-0.1642436981,-0.2750755847,-0.1225755438,-0.0865195766,-0.0925146863,-0.251667887,0.198066026,0.363743782,0.1841873974,-0.2028266639,-0.1457754076,-0.3202906549,0.0713843182,-0.6242572665,0.0502856746,0.5177451968,-0.0037660636,0.1118399054,-0.2235129178,-0.0908083618,-0.2312039435,-0.0297229458,0.0023631344,-0.2277643681,-0.1178186238,0.0040284269,-0.1674533933,-0.0984246954,-0.1111671031,-0.2227237374,-0.5720722079,-0.0140362605,-0.178409636,0.0443736389,0.136865288,-0.4408860207,0.1079220921,-0.0460403375,0.3337441683,-0.3853139877,0.2083592564,0.0500750467,0.5481048822,0.1906710416,0.2801466584,-0.1100368425,0.2598357201,-0.0213279296,0.0885011181,-0.0792431608,0.0471384935,-0.2001504153,0.0954323933,0.0385467969,-0.0271395184,-0.0987015888,-0.059927199,0.2017267793,-0.4078757763,-0.1518074423,-0.2807910442,-0.1851532608,-0.098366864,0.2293490767,0.1623693109,0.2720747292,-0.1685682833,-0.1085861698,-0.0424001999,-0.0828755498,-0.0747891665,-0.0433050208,-0.0337312743,-0.1575301588,-0.0203637742,0.2245582789,-0.0742422193,-0.2805363834,-0.1095870435,-0.0356901884,-0.1673441231,0.1796679348,0.285264045,0.1275248826,-0.1639618129,-0.2139405161,-0.0154010765,0.1968718767,-0.4836391211,-0.0749545172,-0.3604170382,0.1418749243,-0.1785311848,0.1125149131,-0.4827480912,-0.0880973339,0.1005527154,0.1740028113,0.3533980846,-0.2788318396,0.6303645968,-0.2761406004,0.0279032234,0.2305346727,-0.7488188744,0.0958139673,0.1825981885,0.0159449726,0.4496082664,0.3953610361,-0.0237344708,0.3770441413,-0.0881658271,0.2835766077,0.1006708369,0.1974977553,0.1171777025,0.4938797653,-0.072217837,-0.0063227187,0.018416658,-0.2992613018,0.6212943792,0.1084748283,0.5063618422,0.4043264091,0.0377526022,0.2353627086,-0.2143671811,-0.168838948,-0.0559027568,-0.1844295561,-0.3852595389,-0.055643566,0.1588097513,-0.174625054,0.296225518,-0.2451870888,0.118582204,0.4660310149,0.082875967,0.1488805562,0.0076211854,0.3006697297,-0.4398680031,-0.1985074878,-0.0882368535,-0.1634428203,-0.126803726,-0.0337225273,0.19733347,0.2187891901,-0.0381889343,0.1392454058,0.0512021445,-0.1272217929,-0.1436673552,0.17980434,-0.0061849961,0.2388722897,0.1822498143,0.0251253378,-0.3140966594,0.2853173912,0.2368263304,-0.2068870068,0.1841392964,0.1220637634,0.1675075591,-0.3401522636,-0.1555438042,0.2237614989,0.0257933401,-0.0869451091,-0.1305888891,0.124615401,0.128108263,0.0885499716,-0.1277747601,0.2919149697,-0.0615549199,0.0573416911,-0.1319109052,0.5513093472,-0.2694433033,0.3201516867,-0.0676286891,-0.0254177395,0.2279528677,-0.1107776985,-0.0442409292,-0.1289412677,0.2877562642,0.1512959301,-0.1880396307,-0.0283117518,-0.3569781184,0.0031246247,0.5258997679,0.0223967172,0.0300821047,0.1846691966,-0.0212222394,0.009899091,0.0738691986,0.0619092546,0.2687598765,0.1387556195,-0.0613799691,0.0710852742,0.2284822911,-0.067713663,0.2718981206,0.1209923401,0.2285152525,0.3146282434,0.1939519346,0.1624784768,-0.118125841,-0.4355782866,-0.2385646552,0.111990042,-0.3184938133,0.2901818156,-0.1427608579,-0.0482058153,-0.4705456197,-0.3678222001,-0.2575357556,-0.3024018109,-0.1673855931,-0.0986443087,-0.4747144878,-0.1406026036,-0.113705337,0.0823613703,0.0937031657,-0.1105913371,0.0388361849,-0.0006975631,-0.1376157254,-0.0897183269,0.0183231886,-0.1932256073,0.1084228531,0.0978774279,0.1040239632,-0.115459986,0.1239950135,0.1251721829,-0.0150250634,-0.0435006693,0.120246008,0.2838810086,-0.0367315859,-0.5128861666,0.033721026,0.1928918213,0.0333153158,0.1826905161,-0.0985661671,-0.174150452,-0.0930802599,-0.1191139147,-0.0344737433,-0.3550938666,0.4420220852,-0.0710536093,0.0302069243,-0.0339094698,0.1259305328,-0.1103534698,0.067188397,-0.284722507,0.2316463888,-0.4047389627,-0.0109364316,-0.2776227295,-0.2221847177,0.1345082223,0.1779789925,-0.0056694248,0.0136070233,-0.3946296573,0.6360439062,-0.0991959944,0.442253232,-0.1970859468,0.1547565311,0.1013635471,-0.0505038314,-0.0257120151,-0.0238358397,0.1536919326,0.1530253142,0.026020091,0.4894853532,-0.071928218,0.6651268601,-0.2164898962,-0.1908443719,0.7653546929,0.1234655678,0.2171909362,0.1095760241,0.0839432627,-0.1581361145,0.0156226475,0.0867911875,-0.12681382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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2615","title":"Jsonlines export error","comments":"I was also already working in my PR... Nevermind. Next time we should pay attention if there is somebody (self-)assigned to an issue and if he\/she is still working on it before overtaking it... \ud83d\ude04 ","body":"## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1","comment_length":35,"text":"Jsonlines export error \n ## Describe the bug\r\nWhen exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default\r\n\r\n## Steps to reproduce the bug\r\nThis what I'm running:\r\n\r\nin python:\r\n\r\n```\r\nfrom datasets import load_dataset\r\nptb = load_dataset(\"ptb_text_only\")\r\nptb[\"train\"].to_json(\"ptb.jsonl\")\r\n```\r\n\r\nthen out of python:\r\n\r\n```\r\nhead -10000 ptb.jsonl\r\n```\r\n\r\n## Expected results\r\nProperly separated lines\r\n\r\n## Actual results\r\nThe last line is a concatenation of two lines\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyArrow version: 4.0.1 \n I was also already working in my PR... Nevermind. Next time we should pay attention if there is somebody (self-)assigned to an issue and if he\/she is still working on it before overtaking it... \ud83d\ude04 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2607","title":"Streaming local gzip compressed JSON line files is not working","comments":"Hi @thomwolf, thanks for reporting.\r\n\r\nIt seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj.read_json`) to read the data without using the Python standard `open(file,...` (which is the one patched with `xopen` to work in streaming mode).\r\n\r\nThis has to be fixed.","body":"## Describe the bug\r\nUsing streaming to iterate on local gzip compressed JSON files raise a file not exist error\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nstreamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n\r\nnext(iter(streamed_dataset))\r\n```\r\n\r\n## Actual results\r\n```\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-6-27a664e29784> in <module>\r\n----> 1 next(iter(streamed_dataset))\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n    336 \r\n    337     def __iter__(self):\r\n--> 338         for key, example in self._iter():\r\n    339             if self.features:\r\n    340                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in _iter(self)\r\n    333         else:\r\n    334             ex_iterable = self._ex_iterable\r\n--> 335         yield from ex_iterable\r\n    336 \r\n    337     def __iter__(self):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n     76 \r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80 \r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in wrapper(**kwargs)\r\n    282     def wrapper(**kwargs):\r\n    283         python_formatter = PythonFormatter()\r\n--> 284         for key, table in generate_tables_fn(**kwargs):\r\n    285             batch = python_formatter.format_batch(table)\r\n    286             for i, example in enumerate(_batch_to_examples(batch)):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files, original_files)\r\n     85                         file,\r\n     86                         read_options=self.config.pa_read_options,\r\n---> 87                         parse_options=self.config.pa_parse_options,\r\n     88                     )\r\n     89                 except pa.ArrowInvalid as err:\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json.read_json()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json._get_reader()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_input_stream()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_native_file()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile.__cinit__()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile._open_readable()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nFileNotFoundError: [Errno 2] Failed to open local file 'gzip:\/\/file-000000000000.json::\/Users\/thomwolf\/github-dataset\/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Darwin-19.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.7\r\n- PyArrow version: 1.0.0","comment_length":49,"text":"Streaming local gzip compressed JSON line files is not working \n ## Describe the bug\r\nUsing streaming to iterate on local gzip compressed JSON files raise a file not exist error\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nstreamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n\r\nnext(iter(streamed_dataset))\r\n```\r\n\r\n## Actual results\r\n```\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-6-27a664e29784> in <module>\r\n----> 1 next(iter(streamed_dataset))\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n    336 \r\n    337     def __iter__(self):\r\n--> 338         for key, example in self._iter():\r\n    339             if self.features:\r\n    340                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in _iter(self)\r\n    333         else:\r\n    334             ex_iterable = self._ex_iterable\r\n--> 335         yield from ex_iterable\r\n    336 \r\n    337     def __iter__(self):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n     76 \r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80 \r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in wrapper(**kwargs)\r\n    282     def wrapper(**kwargs):\r\n    283         python_formatter = PythonFormatter()\r\n--> 284         for key, table in generate_tables_fn(**kwargs):\r\n    285             batch = python_formatter.format_batch(table)\r\n    286             for i, example in enumerate(_batch_to_examples(batch)):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files, original_files)\r\n     85                         file,\r\n     86                         read_options=self.config.pa_read_options,\r\n---> 87                         parse_options=self.config.pa_parse_options,\r\n     88                     )\r\n     89                 except pa.ArrowInvalid as err:\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json.read_json()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json._get_reader()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_input_stream()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_native_file()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile.__cinit__()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile._open_readable()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nFileNotFoundError: [Errno 2] Failed to open local file 'gzip:\/\/file-000000000000.json::\/Users\/thomwolf\/github-dataset\/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Darwin-19.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.7\r\n- PyArrow version: 1.0.0 \n Hi @thomwolf, thanks for reporting.\r\n\r\nIt seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj.read_json`) to read the data without using the Python standard `open(file,...` (which is the one patched with `xopen` to work in streaming mode).\r\n\r\nThis has to be fixed.","embeddings":[-0.1992494613,-0.1339781284,-0.0091445101,0.2391350567,0.0777640566,0.0168800913,0.3976738453,0.5178918839,0.1970213056,0.0789984018,0.1030013189,0.3306827545,-0.0883667096,0.0208737329,0.2288863659,-0.1809338927,-0.0436617956,0.3560613692,-0.0251667686,0.0015848613,-0.1305664033,0.2937984169,-0.1404591352,-0.0185168069,-0.0187981017,-0.0395553522,-0.0471503064,0.1500941366,-0.0939593539,-0.3326378763,0.0627696067,-0.1684151441,0.188022688,0.4454552829,-0.000114536,0.288641125,0.2961105406,-0.1629139781,-0.3886071146,-0.4712482095,-0.229901135,-0.1492993236,-0.1307716817,-0.2858453691,-0.1378189325,-0.3474362791,-0.0337565579,-0.5071999431,0.5416871309,0.2922603488,0.1556637138,0.0419651642,-0.061772272,0.0905790925,0.0390155055,0.1573239863,-0.0886082724,0.3150972426,0.0327884369,0.1149678901,0.0552849993,0.4286454916,-0.2254960537,-0.1747770905,0.1473699361,-0.0008157574,-0.0843830332,-0.1257867962,-0.1683152765,0.085735105,0.1845451146,-0.4122557342,-0.4398344159,-0.3761076331,0.0466741547,-0.44880265,0.2118544728,0.4092858434,-0.250308305,0.1969692856,-0.100665614,-0.130531922,-0.3545617759,0.0854526162,0.36184901,0.3480824232,-0.0946776345,0.0091285398,-0.0919867903,0.1193795577,-0.0839845538,-0.3859618604,-0.1857065707,0.227756381,-0.2421413958,-0.1349226832,0.1443624049,-0.6062148213,0.1511498541,0.1012284085,0.4615779221,-0.1257445961,0.3588074744,0.0546084084,0.2728678286,0.1870484799,0.1769655049,0.2638421357,0.0566591471,0.3898805082,-0.0865667164,-0.1980740726,-0.2363374233,-0.0394049771,-0.0915300399,0.1368107945,0.4833262861,-0.1576653421,-0.089093484,0.081910789,-0.5941808224,-0.1327005029,0.1576721966,0.0986096784,0.0661023408,0.2201749086,0.026484726,0.1610841751,-0.002104495,-0.0618949644,-0.0907956734,0.0235721208,0.0740586743,0.1347989291,0.251897037,-0.5213885903,0.2793646455,0.0087546054,-0.046029456,-0.1562338322,0.0058455556,-0.12555632,0.1504514217,0.146838814,-0.0259293783,0.0193639155,0.2058843374,-0.1102267727,-0.1738193929,0.1183539331,-0.2751955688,-0.2672133446,0.0372359268,0.2010167539,0.0381590351,0.009389149,-0.0081491908,0.0059310379,0.0556991696,-0.3565125763,0.023636166,-0.0094647072,-0.0558016375,-0.0841292068,0.2759922445,0.4360964596,-0.3608719409,-0.1035921797,0.0715648532,0.2175855786,0.7216550708,0.3425383568,-0.2379719913,0.1273078024,-0.3681203723,0.3619052172,0.6931351423,0.0160383359,-0.3624646366,0.8522378206,-0.1086349562,0.3817502558,0.4800842702,-0.20442909,0.1941484958,-0.0105180815,0.3352073431,0.2812868953,-0.0445720889,0.0385273471,-0.464271307,0.1312143207,0.0419497304,0.0378056802,-0.2513179779,0.2954408824,-0.0433923602,0.015032988,0.3520895541,-0.1800565869,0.1081850231,0.2904555202,0.2378064245,0.0199463479,0.2021734118,0.0092641879,-0.2178035229,0.0324927419,-0.3366108537,-0.2629368603,-0.2826167047,-0.0624102913,-0.1401127279,0.1487002075,-0.1843477637,-0.3533777595,0.1464820653,0.4111509025,0.3808980882,0.0224370789,-0.4623301327,0.3006553948,-0.2987254858,-0.1048559621,-0.2611705363,0.429363668,0.0721354857,-0.2631469369,0.1758847386,-0.0589892119,0.1717893928,-0.2416345477,-0.2221167684,0.2450815886,-0.1208557263,0.1709071249,0.1169753596,-0.2226256579,0.2957748175,-0.0062111109,-0.0720564499,0.3445874751,0.2193278074,0.2041208595,-0.2239909172,0.4739874303,-0.0305651519,0.3029977679,0.0209108982,-0.205836162,0.1924333572,-0.0084315231,-0.3421524465,0.0653704852,0.3345847428,-0.1258885711,0.0946051851,-0.0612957813,-0.3791344762,-0.0635569915,0.5271067023,-0.0830585435,-0.1615021825,0.1926877648,-0.1803122163,-0.0555438772,0.2965638041,-0.0067166113,0.3335767984,0.1575452387,0.104602471,0.0166130755,-0.1111166552,-0.2565548122,0.2091605067,-0.0900344998,0.1146262661,0.5449168086,0.0356366746,0.0449167937,-0.3995397389,-0.2428149432,-0.128882125,0.1455808133,-0.0990883484,0.384134531,-0.3911352754,-0.3713185489,-0.1201700941,-0.1128693894,-0.0630642995,-0.0857734159,-0.0269654337,0.2985586822,-0.2004606724,0.2329237461,0.0005526908,0.0394016802,0.251948297,-0.4227379262,-0.4818938971,-0.1630378664,-0.191156432,-0.0104031721,0.2656034231,-0.2878884673,0.3017160296,-0.0459037386,-0.3699751496,-0.2707997561,0.1194068417,0.1435605735,0.0721819699,-0.0227004942,-0.0912801325,0.2280841321,-0.0796872899,-0.3324358761,0.287009567,-0.2174522281,-0.1486556232,0.194851473,-0.0311844051,-0.0018445911,-0.2352986038,-0.2655209303,-0.1472442448,-0.4852220118,0.289681673,-0.0403695032,0.3086782992,0.2429099977,0.1440986991,0.1779596359,0.1556582451,-0.0546927191,-0.1509623826,-0.6362751126,0.3917452991,-0.1101357043,-0.2303782552,-0.0295004416,-0.1555970311,-0.0550973527,0.3612907529,-0.4562184811,0.237912789,0.0702732503,0.0834183842,-0.0950674713,-0.134057045,0.1914462298,-0.1075467318,-0.0603607446,-0.2590092719,0.0759437755,-0.0122350994,0.2527312934,0.1833108217,0.4013436437,0.7469967604,-0.039161507,0.2865437567,0.353391856,0.1458686143,0.5503771305,-0.1300928146,0.16025047,-0.2425827533,-0.2188410312,0.0596462861,0.1050836518,-0.31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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2607","title":"Streaming local gzip compressed JSON line files is not working","comments":"Sorry for reopening this, but I'm having the same issue as @thomwolf when streaming a gzipped JSON Lines file from the hub. Or is that just not possible by definition?\r\nI installed `datasets`in editable mode from source (so probably includes the fix from #2608 ?): \r\n```\r\n>>> datasets.__version__\r\n'1.9.1.dev0'\r\n```\r\n\r\n```\r\n>>> msmarco = datasets.load_dataset(\"webis\/msmarco\", \"corpus\", streaming=True)\r\nUsing custom data configuration corpus-174d3b7155eb68db\r\n>>> msmarco_iter = iter(msmarco['train'])\r\n>>> print(next(msmarco_iter))\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/media\/ssd\/TREC\/msmarco\/datasets\/src\/datasets\/iterable_dataset.py\", line 338, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/media\/ssd\/TREC\/msmarco\/datasets\/src\/datasets\/iterable_dataset.py\", line 335, in _iter\r\n    yield from ex_iterable\r\n  File \"\/media\/ssd\/TREC\/msmarco\/datasets\/src\/datasets\/iterable_dataset.py\", line 78, in __iter__\r\n    for key, example in self.generate_examples_fn(**self.kwargs):\r\n  File \"\/home\/christopher\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/msmarco\/eb63dff8d83107168e973c7a655a6082d37e08d71b4ac39a0afada479c138745\/msmarco.py\", line 96, in _generate_examples\r\n    with gzip.open(file, \"rt\", encoding=\"utf-8\") as f:\r\n  File \"\/usr\/lib\/python3.6\/gzip.py\", line 53, in open\r\n    binary_file = GzipFile(filename, gz_mode, compresslevel)\r\n  File \"\/usr\/lib\/python3.6\/gzip.py\", line 163, in __init__\r\n    fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')\r\nFileNotFoundError: [Errno 2] No such file or directory: 'https:\/\/huggingface.co\/datasets\/webis\/msmarco\/resolve\/main\/msmarco_doc_00.gz'\r\n```\r\n\r\nLoading the dataset without streaming set to True, works fine.","body":"## Describe the bug\r\nUsing streaming to iterate on local gzip compressed JSON files raise a file not exist error\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nstreamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n\r\nnext(iter(streamed_dataset))\r\n```\r\n\r\n## Actual results\r\n```\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-6-27a664e29784> in <module>\r\n----> 1 next(iter(streamed_dataset))\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n    336 \r\n    337     def __iter__(self):\r\n--> 338         for key, example in self._iter():\r\n    339             if self.features:\r\n    340                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in _iter(self)\r\n    333         else:\r\n    334             ex_iterable = self._ex_iterable\r\n--> 335         yield from ex_iterable\r\n    336 \r\n    337     def __iter__(self):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n     76 \r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80 \r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in wrapper(**kwargs)\r\n    282     def wrapper(**kwargs):\r\n    283         python_formatter = PythonFormatter()\r\n--> 284         for key, table in generate_tables_fn(**kwargs):\r\n    285             batch = python_formatter.format_batch(table)\r\n    286             for i, example in enumerate(_batch_to_examples(batch)):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files, original_files)\r\n     85                         file,\r\n     86                         read_options=self.config.pa_read_options,\r\n---> 87                         parse_options=self.config.pa_parse_options,\r\n     88                     )\r\n     89                 except pa.ArrowInvalid as err:\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json.read_json()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json._get_reader()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_input_stream()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_native_file()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile.__cinit__()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile._open_readable()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nFileNotFoundError: [Errno 2] Failed to open local file 'gzip:\/\/file-000000000000.json::\/Users\/thomwolf\/github-dataset\/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Darwin-19.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.7\r\n- PyArrow version: 1.0.0","comment_length":167,"text":"Streaming local gzip compressed JSON line files is not working \n ## Describe the bug\r\nUsing streaming to iterate on local gzip compressed JSON files raise a file not exist error\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nstreamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n\r\nnext(iter(streamed_dataset))\r\n```\r\n\r\n## Actual results\r\n```\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-6-27a664e29784> in <module>\r\n----> 1 next(iter(streamed_dataset))\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n    336 \r\n    337     def __iter__(self):\r\n--> 338         for key, example in self._iter():\r\n    339             if self.features:\r\n    340                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in _iter(self)\r\n    333         else:\r\n    334             ex_iterable = self._ex_iterable\r\n--> 335         yield from ex_iterable\r\n    336 \r\n    337     def __iter__(self):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n     76 \r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80 \r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in wrapper(**kwargs)\r\n    282     def wrapper(**kwargs):\r\n    283         python_formatter = PythonFormatter()\r\n--> 284         for key, table in generate_tables_fn(**kwargs):\r\n    285             batch = python_formatter.format_batch(table)\r\n    286             for i, example in enumerate(_batch_to_examples(batch)):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files, original_files)\r\n     85                         file,\r\n     86                         read_options=self.config.pa_read_options,\r\n---> 87                         parse_options=self.config.pa_parse_options,\r\n     88                     )\r\n     89                 except pa.ArrowInvalid as err:\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json.read_json()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json._get_reader()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_input_stream()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_native_file()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile.__cinit__()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile._open_readable()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nFileNotFoundError: [Errno 2] Failed to open local file 'gzip:\/\/file-000000000000.json::\/Users\/thomwolf\/github-dataset\/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Darwin-19.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.7\r\n- PyArrow version: 1.0.0 \n Sorry for reopening this, but I'm having the same issue as @thomwolf when streaming a gzipped JSON Lines file from the hub. Or is that just not possible by definition?\r\nI installed `datasets`in editable mode from source (so probably includes the fix from #2608 ?): \r\n```\r\n>>> datasets.__version__\r\n'1.9.1.dev0'\r\n```\r\n\r\n```\r\n>>> msmarco = datasets.load_dataset(\"webis\/msmarco\", \"corpus\", streaming=True)\r\nUsing custom data configuration corpus-174d3b7155eb68db\r\n>>> msmarco_iter = iter(msmarco['train'])\r\n>>> print(next(msmarco_iter))\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/media\/ssd\/TREC\/msmarco\/datasets\/src\/datasets\/iterable_dataset.py\", line 338, in __iter__\r\n    for key, example in self._iter():\r\n  File \"\/media\/ssd\/TREC\/msmarco\/datasets\/src\/datasets\/iterable_dataset.py\", line 335, in _iter\r\n    yield from ex_iterable\r\n  File \"\/media\/ssd\/TREC\/msmarco\/datasets\/src\/datasets\/iterable_dataset.py\", line 78, in __iter__\r\n    for key, example in self.generate_examples_fn(**self.kwargs):\r\n  File \"\/home\/christopher\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/msmarco\/eb63dff8d83107168e973c7a655a6082d37e08d71b4ac39a0afada479c138745\/msmarco.py\", line 96, in _generate_examples\r\n    with gzip.open(file, \"rt\", encoding=\"utf-8\") as f:\r\n  File \"\/usr\/lib\/python3.6\/gzip.py\", line 53, in open\r\n    binary_file = GzipFile(filename, gz_mode, compresslevel)\r\n  File \"\/usr\/lib\/python3.6\/gzip.py\", line 163, in __init__\r\n    fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')\r\nFileNotFoundError: [Errno 2] No such file or directory: 'https:\/\/huggingface.co\/datasets\/webis\/msmarco\/resolve\/main\/msmarco_doc_00.gz'\r\n```\r\n\r\nLoading the dataset without streaming set to True, works fine.","embeddings":[-0.1992494613,-0.1339781284,-0.0091445101,0.2391350567,0.0777640566,0.0168800913,0.3976738453,0.5178918839,0.1970213056,0.0789984018,0.1030013189,0.3306827545,-0.0883667096,0.0208737329,0.2288863659,-0.1809338927,-0.0436617956,0.3560613692,-0.0251667686,0.0015848613,-0.1305664033,0.2937984169,-0.1404591352,-0.0185168069,-0.0187981017,-0.0395553522,-0.0471503064,0.1500941366,-0.0939593539,-0.3326378763,0.0627696067,-0.1684151441,0.188022688,0.4454552829,-0.000114536,0.288641125,0.2961105406,-0.1629139781,-0.3886071146,-0.4712482095,-0.229901135,-0.1492993236,-0.1307716817,-0.2858453691,-0.1378189325,-0.3474362791,-0.0337565579,-0.5071999431,0.5416871309,0.2922603488,0.1556637138,0.0419651642,-0.061772272,0.0905790925,0.0390155055,0.1573239863,-0.0886082724,0.3150972426,0.0327884369,0.1149678901,0.0552849993,0.4286454916,-0.2254960537,-0.1747770905,0.1473699361,-0.0008157574,-0.0843830332,-0.1257867962,-0.1683152765,0.085735105,0.1845451146,-0.4122557342,-0.4398344159,-0.3761076331,0.0466741547,-0.44880265,0.2118544728,0.4092858434,-0.250308305,0.1969692856,-0.100665614,-0.130531922,-0.3545617759,0.0854526162,0.36184901,0.3480824232,-0.0946776345,0.0091285398,-0.0919867903,0.1193795577,-0.0839845538,-0.3859618604,-0.1857065707,0.227756381,-0.2421413958,-0.1349226832,0.1443624049,-0.6062148213,0.1511498541,0.1012284085,0.4615779221,-0.1257445961,0.3588074744,0.0546084084,0.2728678286,0.1870484799,0.1769655049,0.2638421357,0.0566591471,0.3898805082,-0.0865667164,-0.1980740726,-0.2363374233,-0.0394049771,-0.0915300399,0.1368107945,0.4833262861,-0.1576653421,-0.089093484,0.081910789,-0.5941808224,-0.1327005029,0.1576721966,0.0986096784,0.0661023408,0.2201749086,0.026484726,0.1610841751,-0.002104495,-0.0618949644,-0.0907956734,0.0235721208,0.0740586743,0.1347989291,0.251897037,-0.5213885903,0.2793646455,0.0087546054,-0.046029456,-0.1562338322,0.0058455556,-0.12555632,0.1504514217,0.146838814,-0.0259293783,0.0193639155,0.2058843374,-0.1102267727,-0.1738193929,0.1183539331,-0.2751955688,-0.2672133446,0.0372359268,0.2010167539,0.0381590351,0.009389149,-0.0081491908,0.0059310379,0.0556991696,-0.3565125763,0.023636166,-0.0094647072,-0.0558016375,-0.0841292068,0.2759922445,0.4360964596,-0.3608719409,-0.1035921797,0.0715648532,0.2175855786,0.7216550708,0.3425383568,-0.2379719913,0.1273078024,-0.3681203723,0.3619052172,0.6931351423,0.0160383359,-0.3624646366,0.8522378206,-0.1086349562,0.3817502558,0.4800842702,-0.20442909,0.1941484958,-0.0105180815,0.3352073431,0.2812868953,-0.0445720889,0.0385273471,-0.464271307,0.1312143207,0.0419497304,0.0378056802,-0.2513179779,0.2954408824,-0.0433923602,0.015032988,0.3520895541,-0.1800565869,0.1081850231,0.2904555202,0.2378064245,0.0199463479,0.2021734118,0.0092641879,-0.2178035229,0.0324927419,-0.3366108537,-0.2629368603,-0.2826167047,-0.0624102913,-0.1401127279,0.1487002075,-0.1843477637,-0.3533777595,0.1464820653,0.4111509025,0.3808980882,0.0224370789,-0.4623301327,0.3006553948,-0.2987254858,-0.1048559621,-0.2611705363,0.429363668,0.0721354857,-0.2631469369,0.1758847386,-0.0589892119,0.1717893928,-0.2416345477,-0.2221167684,0.2450815886,-0.1208557263,0.1709071249,0.1169753596,-0.2226256579,0.2957748175,-0.0062111109,-0.0720564499,0.3445874751,0.2193278074,0.2041208595,-0.2239909172,0.4739874303,-0.0305651519,0.3029977679,0.0209108982,-0.205836162,0.1924333572,-0.0084315231,-0.3421524465,0.0653704852,0.3345847428,-0.1258885711,0.0946051851,-0.0612957813,-0.3791344762,-0.0635569915,0.5271067023,-0.0830585435,-0.1615021825,0.1926877648,-0.1803122163,-0.0555438772,0.2965638041,-0.0067166113,0.3335767984,0.1575452387,0.104602471,0.0166130755,-0.1111166552,-0.2565548122,0.2091605067,-0.0900344998,0.1146262661,0.5449168086,0.0356366746,0.0449167937,-0.3995397389,-0.2428149432,-0.128882125,0.1455808133,-0.0990883484,0.384134531,-0.3911352754,-0.3713185489,-0.1201700941,-0.1128693894,-0.0630642995,-0.0857734159,-0.0269654337,0.2985586822,-0.2004606724,0.2329237461,0.0005526908,0.0394016802,0.251948297,-0.4227379262,-0.4818938971,-0.1630378664,-0.191156432,-0.0104031721,0.2656034231,-0.2878884673,0.3017160296,-0.0459037386,-0.3699751496,-0.2707997561,0.1194068417,0.1435605735,0.0721819699,-0.0227004942,-0.0912801325,0.2280841321,-0.0796872899,-0.3324358761,0.287009567,-0.2174522281,-0.1486556232,0.194851473,-0.0311844051,-0.0018445911,-0.2352986038,-0.2655209303,-0.1472442448,-0.4852220118,0.289681673,-0.0403695032,0.3086782992,0.2429099977,0.1440986991,0.1779596359,0.1556582451,-0.0546927191,-0.1509623826,-0.6362751126,0.3917452991,-0.1101357043,-0.2303782552,-0.0295004416,-0.1555970311,-0.0550973527,0.3612907529,-0.4562184811,0.237912789,0.0702732503,0.0834183842,-0.0950674713,-0.134057045,0.1914462298,-0.1075467318,-0.0603607446,-0.2590092719,0.0759437755,-0.0122350994,0.2527312934,0.1833108217,0.4013436437,0.7469967604,-0.039161507,0.2865437567,0.353391856,0.1458686143,0.5503771305,-0.1300928146,0.16025047,-0.2425827533,-0.2188410312,0.0596462861,0.1050836518,-0.319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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2607","title":"Streaming local gzip compressed JSON line files is not working","comments":"Hi ! To make the streaming work, we extend `open` in the dataset builder to work with urls.\r\n\r\nTherefore you just need to use `open` before using `gzip.open`:\r\n```diff\r\n- with gzip.open(file, \"rt\", encoding=\"utf-8\") as f:\r\n+ with gzip.open(open(file, \"rb\"), \"rt\", encoding=\"utf-8\") as f:\r\n```\r\n\r\nYou can see that it is the case for oscar.py and c4.py for example:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8814b393984c1c2e1800ba370de2a9f7c8644908\/datasets\/oscar\/oscar.py#L358-L358\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8814b393984c1c2e1800ba370de2a9f7c8644908\/datasets\/c4\/c4.py#L88-L88\r\n\r\n","body":"## Describe the bug\r\nUsing streaming to iterate on local gzip compressed JSON files raise a file not exist error\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nstreamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n\r\nnext(iter(streamed_dataset))\r\n```\r\n\r\n## Actual results\r\n```\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-6-27a664e29784> in <module>\r\n----> 1 next(iter(streamed_dataset))\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n    336 \r\n    337     def __iter__(self):\r\n--> 338         for key, example in self._iter():\r\n    339             if self.features:\r\n    340                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in _iter(self)\r\n    333         else:\r\n    334             ex_iterable = self._ex_iterable\r\n--> 335         yield from ex_iterable\r\n    336 \r\n    337     def __iter__(self):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n     76 \r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80 \r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in wrapper(**kwargs)\r\n    282     def wrapper(**kwargs):\r\n    283         python_formatter = PythonFormatter()\r\n--> 284         for key, table in generate_tables_fn(**kwargs):\r\n    285             batch = python_formatter.format_batch(table)\r\n    286             for i, example in enumerate(_batch_to_examples(batch)):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files, original_files)\r\n     85                         file,\r\n     86                         read_options=self.config.pa_read_options,\r\n---> 87                         parse_options=self.config.pa_parse_options,\r\n     88                     )\r\n     89                 except pa.ArrowInvalid as err:\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json.read_json()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json._get_reader()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_input_stream()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_native_file()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile.__cinit__()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile._open_readable()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nFileNotFoundError: [Errno 2] Failed to open local file 'gzip:\/\/file-000000000000.json::\/Users\/thomwolf\/github-dataset\/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Darwin-19.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.7\r\n- PyArrow version: 1.0.0","comment_length":61,"text":"Streaming local gzip compressed JSON line files is not working \n ## Describe the bug\r\nUsing streaming to iterate on local gzip compressed JSON files raise a file not exist error\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nstreamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n\r\nnext(iter(streamed_dataset))\r\n```\r\n\r\n## Actual results\r\n```\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-6-27a664e29784> in <module>\r\n----> 1 next(iter(streamed_dataset))\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n    336 \r\n    337     def __iter__(self):\r\n--> 338         for key, example in self._iter():\r\n    339             if self.features:\r\n    340                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in _iter(self)\r\n    333         else:\r\n    334             ex_iterable = self._ex_iterable\r\n--> 335         yield from ex_iterable\r\n    336 \r\n    337     def __iter__(self):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in __iter__(self)\r\n     76 \r\n     77     def __iter__(self):\r\n---> 78         for key, example in self.generate_examples_fn(**self.kwargs):\r\n     79             yield key, example\r\n     80 \r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/iterable_dataset.py in wrapper(**kwargs)\r\n    282     def wrapper(**kwargs):\r\n    283         python_formatter = PythonFormatter()\r\n--> 284         for key, table in generate_tables_fn(**kwargs):\r\n    285             batch = python_formatter.format_batch(table)\r\n    286             for i, example in enumerate(_batch_to_examples(batch)):\r\n\r\n~\/Documents\/GitHub\/datasets\/src\/datasets\/packaged_modules\/json\/json.py in _generate_tables(self, files, original_files)\r\n     85                         file,\r\n     86                         read_options=self.config.pa_read_options,\r\n---> 87                         parse_options=self.config.pa_parse_options,\r\n     88                     )\r\n     89                 except pa.ArrowInvalid as err:\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json.read_json()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/_json.pyx in pyarrow._json._get_reader()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_input_stream()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.get_native_file()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile.__cinit__()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/io.pxi in pyarrow.lib.OSFile._open_readable()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nFileNotFoundError: [Errno 2] Failed to open local file 'gzip:\/\/file-000000000000.json::\/Users\/thomwolf\/github-dataset\/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.9.1.dev0\r\n- Platform: Darwin-19.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.7\r\n- PyArrow version: 1.0.0 \n Hi ! To make the streaming work, we extend `open` in the dataset builder to work with urls.\r\n\r\nTherefore you just need to use `open` before using `gzip.open`:\r\n```diff\r\n- with gzip.open(file, \"rt\", encoding=\"utf-8\") as f:\r\n+ with gzip.open(open(file, \"rb\"), \"rt\", encoding=\"utf-8\") as f:\r\n```\r\n\r\nYou can see that it is the case for oscar.py and c4.py for example:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8814b393984c1c2e1800ba370de2a9f7c8644908\/datasets\/oscar\/oscar.py#L358-L358\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8814b393984c1c2e1800ba370de2a9f7c8644908\/datasets\/c4\/c4.py#L88-L88\r\n\r\n","embeddings":[-0.1992494613,-0.1339781284,-0.0091445101,0.2391350567,0.0777640566,0.0168800913,0.3976738453,0.5178918839,0.1970213056,0.0789984018,0.1030013189,0.3306827545,-0.0883667096,0.0208737329,0.2288863659,-0.1809338927,-0.0436617956,0.3560613692,-0.0251667686,0.0015848613,-0.1305664033,0.2937984169,-0.1404591352,-0.0185168069,-0.0187981017,-0.0395553522,-0.0471503064,0.1500941366,-0.0939593539,-0.3326378763,0.0627696067,-0.1684151441,0.188022688,0.4454552829,-0.000114536,0.288641125,0.2961105406,-0.1629139781,-0.3886071146,-0.4712482095,-0.229901135,-0.1492993236,-0.1307716817,-0.2858453691,-0.1378189325,-0.3474362791,-0.0337565579,-0.5071999431,0.5416871309,0.2922603488,0.1556637138,0.0419651642,-0.061772272,0.0905790925,0.0390155055,0.1573239863,-0.0886082724,0.3150972426,0.0327884369,0.1149678901,0.0552849993,0.4286454916,-0.2254960537,-0.1747770905,0.1473699361,-0.0008157574,-0.0843830332,-0.1257867962,-0.1683152765,0.085735105,0.1845451146,-0.4122557342,-0.4398344159,-0.3761076331,0.0466741547,-0.44880265,0.2118544728,0.4092858434,-0.250308305,0.1969692856,-0.100665614,-0.130531922,-0.3545617759,0.0854526162,0.36184901,0.3480824232,-0.0946776345,0.0091285398,-0.0919867903,0.1193795577,-0.0839845538,-0.3859618604,-0.1857065707,0.227756381,-0.2421413958,-0.1349226832,0.1443624049,-0.6062148213,0.1511498541,0.1012284085,0.4615779221,-0.1257445961,0.3588074744,0.0546084084,0.2728678286,0.1870484799,0.1769655049,0.2638421357,0.0566591471,0.3898805082,-0.0865667164,-0.1980740726,-0.2363374233,-0.0394049771,-0.0915300399,0.1368107945,0.4833262861,-0.1576653421,-0.089093484,0.081910789,-0.5941808224,-0.1327005029,0.1576721966,0.0986096784,0.0661023408,0.2201749086,0.026484726,0.1610841751,-0.002104495,-0.0618949644,-0.0907956734,0.0235721208,0.0740586743,0.1347989291,0.251897037,-0.5213885903,0.2793646455,0.0087546054,-0.046029456,-0.1562338322,0.0058455556,-0.12555632,0.1504514217,0.146838814,-0.0259293783,0.0193639155,0.2058843374,-0.1102267727,-0.1738193929,0.1183539331,-0.2751955688,-0.2672133446,0.0372359268,0.2010167539,0.0381590351,0.009389149,-0.0081491908,0.0059310379,0.0556991696,-0.3565125763,0.023636166,-0.0094647072,-0.0558016375,-0.0841292068,0.2759922445,0.4360964596,-0.3608719409,-0.1035921797,0.0715648532,0.2175855786,0.7216550708,0.3425383568,-0.2379719913,0.1273078024,-0.3681203723,0.3619052172,0.6931351423,0.0160383359,-0.3624646366,0.8522378206,-0.1086349562,0.3817502558,0.4800842702,-0.20442909,0.1941484958,-0.0105180815,0.3352073431,0.2812868953,-0.0445720889,0.0385273471,-0.464271307,0.1312143207,0.0419497304,0.0378056802,-0.2513179779,0.2954408824,-0.0433923602,0.015032988,0.3520895541,-0.1800565869,0.1081850231,0.2904555202,0.2378064245,0.0199463479,0.2021734118,0.0092641879,-0.2178035229,0.0324927419,-0.3366108537,-0.2629368603,-0.2826167047,-0.0624102913,-0.1401127279,0.1487002075,-0.1843477637,-0.3533777595,0.1464820653,0.4111509025,0.3808980882,0.0224370789,-0.4623301327,0.3006553948,-0.2987254858,-0.1048559621,-0.2611705363,0.429363668,0.0721354857,-0.2631469369,0.1758847386,-0.0589892119,0.1717893928,-0.2416345477,-0.2221167684,0.2450815886,-0.1208557263,0.1709071249,0.1169753596,-0.2226256579,0.2957748175,-0.0062111109,-0.0720564499,0.3445874751,0.2193278074,0.2041208595,-0.2239909172,0.4739874303,-0.0305651519,0.3029977679,0.0209108982,-0.205836162,0.1924333572,-0.0084315231,-0.3421524465,0.0653704852,0.3345847428,-0.1258885711,0.0946051851,-0.0612957813,-0.3791344762,-0.0635569915,0.5271067023,-0.0830585435,-0.1615021825,0.1926877648,-0.1803122163,-0.0555438772,0.2965638041,-0.0067166113,0.3335767984,0.1575452387,0.104602471,0.0166130755,-0.1111166552,-0.2565548122,0.2091605067,-0.0900344998,0.1146262661,0.5449168086,0.0356366746,0.0449167937,-0.3995397389,-0.2428149432,-0.128882125,0.1455808133,-0.0990883484,0.384134531,-0.3911352754,-0.3713185489,-0.1201700941,-0.1128693894,-0.0630642995,-0.0857734159,-0.0269654337,0.2985586822,-0.2004606724,0.2329237461,0.0005526908,0.0394016802,0.251948297,-0.4227379262,-0.4818938971,-0.1630378664,-0.191156432,-0.0104031721,0.2656034231,-0.2878884673,0.3017160296,-0.0459037386,-0.3699751496,-0.2707997561,0.1194068417,0.1435605735,0.0721819699,-0.0227004942,-0.0912801325,0.2280841321,-0.0796872899,-0.3324358761,0.287009567,-0.2174522281,-0.1486556232,0.194851473,-0.0311844051,-0.0018445911,-0.2352986038,-0.2655209303,-0.1472442448,-0.4852220118,0.289681673,-0.0403695032,0.3086782992,0.2429099977,0.1440986991,0.1779596359,0.1556582451,-0.0546927191,-0.1509623826,-0.6362751126,0.3917452991,-0.1101357043,-0.2303782552,-0.0295004416,-0.1555970311,-0.0550973527,0.3612907529,-0.4562184811,0.237912789,0.0702732503,0.0834183842,-0.0950674713,-0.134057045,0.191446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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"Hi !\r\nIf we want something more general, we could either\r\n1. delete the extracted files after the arrow data generation automatically, or \r\n2. delete each extracted file during the arrow generation right after it has been closed.\r\n\r\nSolution 2 is better to save disk space during the arrow generation. Is it what you had in mind ?\r\n\r\nThe API could look like\r\n```python\r\nload_dataset(..., delete_extracted_files_after_usage=True)\r\n```\r\n\r\nIn terms of implementation, here are some directions we could take for each solution:\r\n1. get the list of the extracted files from the DownloadManager and then delete them after the dataset is processed. This can be implemented in `download_and_prepare` I guess\r\n2. maybe wrap and mock `open` in the builder to make it delete the file when the file is closed.","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":129,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n Hi !\r\nIf we want something more general, we could either\r\n1. delete the extracted files after the arrow data generation automatically, or \r\n2. delete each extracted file during the arrow generation right after it has been closed.\r\n\r\nSolution 2 is better to save disk space during the arrow generation. Is it what you had in mind ?\r\n\r\nThe API could look like\r\n```python\r\nload_dataset(..., delete_extracted_files_after_usage=True)\r\n```\r\n\r\nIn terms of implementation, here are some directions we could take for each solution:\r\n1. get the list of the extracted files from the DownloadManager and then delete them after the dataset is processed. This can be implemented in `download_and_prepare` I guess\r\n2. maybe wrap and mock `open` in the builder to make it delete the file when the file is closed.","embeddings":[-0.0422243811,-0.052077923,-0.1375647485,0.249136284,-0.0890204087,0.1040985212,-0.1198280752,0.1847193092,0.234230876,0.2407588214,0.1567286253,0.5178807378,-0.3404889405,-0.0508526862,-0.1349290013,-0.020200653,-0.1992256045,0.3400244117,-0.0047850101,0.2303301692,-0.2209787965,0.0262257326,0.0232040863,-0.1026177779,0.0811791494,-0.3859203756,0.1359873116,-0.1867462993,-0.1622753888,-0.5164051652,0.05417528,0.5212088823,0.2322490513,0.0971321985,-0.0001223348,0.0322482027,0.2591515183,0.079954356,-0.4075611234,0.1244008169,-0.1461262256,-0.2453016639,0.2597362101,-0.1671171933,0.2101428211,-0.564896524,-0.2238956094,-0.6139017344,0.5910028219,-0.0977437869,0.0498433188,0.1384608746,-0.3260321617,0.2452622503,0.2004276514,0.2770322561,-0.0424004868,0.0223673433,0.4151899219,0.0967546105,0.1619669199,0.3807038665,-0.0550241023,-0.1603195667,0.4445157647,0.0631318614,-0.0319245309,-0.4852730334,0.1398253888,-0.0066928174,0.7061523795,-0.4330633283,-0.2672884464,-0.457439065,0.0280466024,-0.592244029,0.0385922231,0.2951566577,-0.1211811528,0.2089288384,-0.110395126,-0.4544366598,-0.3733914196,-0.0871406049,0.6077163815,-0.3243749738,-0.0568619892,-0.0656688809,0.3697440028,0.0736787319,0.2949277461,-0.2698952258,-0.1399102509,0.5059065223,-0.0271433536,-0.1845743209,-0.2607621849,-0.2897108495,0.11943537,0.3029824495,0.3516383767,-0.007010607,0.193062216,0.1110153273,0.1876697838,0.3751322925,0.0078877974,-0.0530541278,0.1721748412,0.2076802552,0.0642983392,-0.0915418714,0.0383336209,-0.1278788298,0.3636780679,0.0154576702,0.0716330782,0.0261886418,-0.1650816947,0.07824976,0.1291304678,-0.135167703,0.1513490379,0.2670413256,0.0587308556,0.1620476097,0.215889141,0.2350441068,-0.0299415961,-0.1465885341,0.1376228184,0.0731804147,-0.2401808649,-0.1884089112,0.3641346991,-0.0083342325,0.0416415893,0.0005885031,-0.3138786256,-0.0641817972,0.6676418781,-0.0993862525,0.2807014883,0.3421969414,-0.1093466878,-0.2436191589,-0.3248208165,-0.1198692247,-0.19655855,0.5875695944,-0.1568873972,-0.3170170784,-0.1368437409,0.1290899366,-0.0270507522,0.2437296063,-0.5556287766,0.1172113717,0.16315265,0.0627298281,-0.0224351045,0.2961559594,-0.0586108044,-0.1787772775,-0.189347744,0.3691442907,-0.5007237196,-0.1223104745,-0.3205631673,-0.1797679216,0.1424594522,-0.0010882767,-0.1239746287,0.37910676,-0.2620406151,0.091597259,0.5124638677,-0.181572035,-0.4231873453,0.1482755989,0.1100984067,0.2711139619,0.3149678707,0.2507965267,0.2787887752,0.0021172836,-0.038488593,0.4016497135,0.0736518428,0.0040990273,-0.2239464074,-0.4116459191,0.0513558574,0.2471985817,-0.0207932256,0.039883431,0.0623965673,-0.1024292186,0.3069228828,0.097825557,0.151983127,0.1327217519,0.1448920667,-0.2485721409,-0.2080590725,0.0697800443,-0.5534030199,0.173768878,-0.5192010403,-0.291768074,-0.4091202915,-0.3444458842,0.0820593238,0.1959255189,-0.2989016473,0.1966816336,0.0327640213,-0.1894039512,0.2831685543,-0.0064171986,-0.4284304976,0.0199055001,-0.1409000307,0.1741943359,-0.4501312375,0.1040370539,0.2124263197,-0.1986983567,-0.1110477,-0.0094022434,-0.2910374701,-0.3422919512,-0.093639046,0.2829712629,0.3651207089,0.268342793,0.1073635519,0.2281978279,0.2203880847,0.2218503952,0.0824293271,0.2590484917,0.0871407762,0.1824860573,-0.2291260958,0.5258798003,-0.0765097216,0.0241309945,-0.1283982992,-0.3022350371,0.2240570784,-0.0017422049,0.0128592802,-0.4730243087,-0.0542870536,0.3683068752,0.1288885623,0.0917666256,0.1476072818,-0.0150869377,0.0925110132,-0.103151001,-0.1026698127,0.3242192566,-0.0696177632,-0.1613177806,0.0573300086,0.4151658118,0.4481205642,0.0921721533,-0.1154637635,-0.078765668,0.4702810943,0.0514211953,0.2684764862,0.1953540295,0.0813863352,0.3618116379,0.1682407856,-0.062261533,-0.2748675942,-0.0914838165,0.0715449303,0.2726363242,0.2208339423,-0.0611376241,-0.1839365214,-0.2929829359,0.2759950757,0.1252204776,0.2172905952,-0.0161953643,0.4093545973,0.0779331475,-0.3893392384,0.0748196319,0.0082118474,0.5284100771,-0.0873366371,-0.2672452927,-0.3404185772,-0.1312678456,0.2366932034,-0.0275300201,0.3818286657,-0.198455438,0.5107133985,0.0785908923,0.0959751084,-0.0302718319,-0.0918449312,0.0615337417,0.4084796011,0.0522785895,0.1331733316,0.1684497297,0.1248740777,0.0609198548,-0.1308309436,-0.2205235362,-0.0963044167,0.1561925262,0.0524337031,0.0963251069,-0.2165250629,-0.0163153131,-0.3071950078,-0.3777163327,0.2557627857,0.1072915643,0.1372369677,0.0845113918,0.2943944335,-0.0587567501,0.1412729025,0.0703832358,0.0453881212,-0.5879281759,0.1189820543,-0.2073395699,0.0725701302,0.3074827492,-0.122104302,0.060423281,0.3388400972,-0.3763493299,-0.0693922415,-0.041573368,0.2056569159,-0.2972852886,-0.0365348272,0.1967595965,0.332067728,-0.0217487123,0.0054507819,0.2608991861,0.1648945659,0.2400366217,0.0658634081,0.2286016941,0.2869085073,0.0574271269,0.4774214923,-0.1240692511,0.0212154537,0.3330622911,0.3224788606,0.3370009363,-0.2226606905,0.2584810257,-0.5167121887,-0.0704164281,-0.1229764894,0.088660202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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"Also, if I delete the extracted files they need to be re-extracted again instead of loading from the Arrow cache files","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":21,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n Also, if I delete the extracted files they need to be re-extracted again instead of loading from the Arrow cache files","embeddings":[-0.0280625913,-0.1268454641,-0.1462638825,0.1517361701,-0.1755876094,0.1943831593,-0.2197522521,0.2515498996,0.1799505502,0.1638493091,0.1056418791,0.4813863039,-0.2407226562,-0.1040748432,-0.1887400746,-0.0030702341,-0.2355995178,0.2889693975,-0.007609963,0.1621083319,-0.1351312846,0.0150677748,0.0465388186,-0.1541214883,0.0537764616,-0.4090359211,0.2061123103,-0.2602271438,-0.1102860421,-0.3523688614,-0.0030772036,0.5342466831,0.2766074836,0.141957894,-0.0001208869,-0.0142732738,0.2875986993,0.0723435506,-0.4089965522,0.2255584151,-0.243676886,-0.1031287462,0.2598846257,-0.1265426427,0.2946948111,-0.4084398448,-0.2041454315,-0.6497346759,0.5037788749,-0.2386162877,0.0973825306,0.009610286,-0.5454075933,0.1060440689,0.3338082433,0.3475065231,-0.1310423166,-0.0081458325,0.4774487019,0.100996539,0.2767952979,0.3542622626,-0.0738362148,-0.2929219007,0.338668555,-0.0257929079,-0.0745974183,-0.2595772743,0.2915354371,0.0246846136,0.6543719769,-0.3459971547,-0.0916249156,-0.3327183425,0.1247224882,-0.586995244,0.0834856406,0.1749043018,0.0470301099,0.2470870614,-0.1042246521,-0.4791462421,-0.3593364954,-0.1478547901,0.6762899756,-0.4306482971,-0.0751291513,-0.1268769652,0.5420335531,0.1520411521,0.2174185514,-0.1760035306,-0.3039318025,0.4096335173,-0.0656304583,-0.1792774647,-0.4102030396,-0.2998050451,0.1566981822,0.1752879173,0.4254452884,-0.0052143037,0.1747297347,0.0212203227,0.2458343506,0.3867214024,0.0701313987,-0.0618336499,0.1884942502,0.1172566861,0.1380652636,-0.0492788553,0.0289419107,-0.0381849222,0.2580288351,0.0199359134,-0.038412381,-0.1320509911,-0.0718108863,0.0647786483,0.2061200589,-0.1974595934,0.086588949,0.1973827928,0.2360136807,0.0340708494,0.0615368113,0.2982646227,0.0124634709,-0.3605464697,0.1215328351,0.1221913174,-0.3169591129,-0.2377638072,0.4114531279,-0.0058699609,-0.0418000557,-0.1277221888,-0.3367883563,0.0653983206,0.5076660514,-0.1196554154,0.2788880467,0.277174741,-0.1964025646,-0.2270881534,-0.2803883255,-0.0114324261,-0.1321524382,0.5104100704,-0.1253011823,-0.2173408419,-0.066548638,0.1125036553,0.1528291404,0.2723378241,-0.5884199142,0.1173697188,0.1026504189,0.0425826795,0.0408399254,0.3486083448,0.0186311677,-0.0803235322,-0.3321526349,0.2598174214,-0.6571017504,-0.0888553485,-0.3545111716,-0.2567665279,0.2115150094,0.112507388,-0.1175073907,0.2376223356,-0.1577705294,0.1594096273,0.3421454728,-0.0935675725,-0.3924858272,0.1546457261,0.0958269089,0.1922517717,0.3176071346,0.3045813739,0.384672761,0.0386508517,-0.0686706528,0.2615580857,0.0880157351,-0.10837017,-0.3247458935,-0.4044742584,0.0343365036,0.2238271981,-0.0033786683,0.0553450696,0.2231910229,-0.0305488575,0.1710044295,0.149852097,0.1663506776,0.1606840342,0.1390224993,-0.2714569867,-0.2364161164,0.1407706439,-0.6055968404,-0.0088958433,-0.5798181891,-0.3777634799,-0.1828524619,-0.3519939184,0.1315580755,0.0131068761,-0.1980336308,0.1989232302,0.0346161276,-0.0616230592,0.2489040345,-0.0277927574,-0.4232954681,-0.0171468463,-0.0940821245,-0.0000205979,-0.4419443011,0.1254571229,0.1512196362,-0.259088397,-0.019973442,0.0140808979,-0.2875122428,-0.4913887978,-0.1318501681,0.2647311985,0.3056980669,0.2912358046,0.2880557775,0.2514718771,0.1814325601,0.1652215123,0.1045260057,0.1599608064,-0.0176257435,0.0884636194,-0.3933174014,0.3887653053,-0.2242788523,-0.0352116078,-0.1823385656,-0.3922522664,0.2207522392,-0.1020581275,0.1129981726,-0.5385864377,0.0472975224,0.3436099291,-0.1163012832,0.0287124459,0.1037619263,-0.1153912768,-0.0062079015,-0.044994086,-0.1471545547,0.3006251156,-0.0148150921,-0.1625528634,0.0789365247,0.3862743676,0.3511201739,0.1766192466,-0.1846850961,-0.1630650908,0.4579220116,-0.009584832,0.1368839145,0.0368465967,0.018498214,0.3677564859,0.0602166951,-0.0062987711,-0.2424093634,-0.0591289215,0.1128261387,0.2101317495,0.2819328308,-0.2630928755,-0.276941061,-0.4187943935,0.4745363891,0.3225945234,0.258238852,-0.0183715001,0.5235859752,0.0613533333,-0.4129450619,0.06954474,-0.0198271107,0.668682754,-0.1250792444,-0.2156380862,-0.4043356776,-0.1886476129,0.1578585804,0.0167335793,0.3731075823,-0.101245068,0.5151019096,0.1303833127,0.1142640114,-0.0901175216,-0.0323509462,0.0783112571,0.3688560426,-0.0753833354,0.0812374055,0.1757234782,-0.0526804589,0.1216848269,-0.1933170557,-0.2980662882,-0.124831304,0.1577711999,0.0885916725,-0.0213484634,-0.2257019877,-0.0321052261,-0.1681333482,-0.3950457871,0.2272086293,-0.0255394801,0.0000476393,0.1141305417,0.2313457876,0.0122267203,0.0164264757,0.0707674026,-0.0095825437,-0.588660121,0.132973209,-0.208014816,0.0741187483,0.2229170948,0.0186059121,-0.0014545916,0.3487145007,-0.3909091055,0.0475146547,0.0179734323,0.1181501076,-0.3372391164,-0.0764741078,0.214870289,0.2313204259,-0.0852172971,0.0311033241,0.1433147341,0.2431013286,0.3958516717,0.1196727753,0.0834100172,0.2145751119,0.1187930927,0.3881933689,-0.2566867471,0.0381416716,0.3195043206,0.3901723027,0.4156238735,-0.1908151805,0.2777892649,-0.4188162088,0.0556625202,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"I think we already opened an issue about this topic (suggested by @stas00): duplicated of #2481?\r\n\r\nThis is in our TODO list... \ud83d\ude05 ","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":23,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n I think we already opened an issue about this topic (suggested by @stas00): duplicated of #2481?\r\n\r\nThis is in our TODO list... \ud83d\ude05 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"I think the deletion of each extracted file could be implemented in our CacheManager and ExtractManager (once merged to master: #2295, #2277). \ud83d\ude09 ","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":23,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n I think the deletion of each extracted file could be implemented in our CacheManager and ExtractManager (once merged to master: #2295, #2277). \ud83d\ude09 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"Nevermind @thomwolf, I just mentioned the other issue so that both appear linked in GitHub and we do not forget to close both once we make the corresponding Pull Request... That was the main reason! \ud83d\ude04 ","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":36,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n Nevermind @thomwolf, I just mentioned the other issue so that both appear linked in GitHub and we do not forget to close both once we make the corresponding Pull Request... That was the main reason! \ud83d\ude04 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"Ok yes. I think this is an important feature to be able to use large datasets which are pretty much always compressed files.\r\n\r\nIn particular now this requires to keep the extracted file on the drive if you want to avoid reprocessing the dataset so in my case, this require using always ~400GB of drive instead of just 200GB (which is already significant). \r\n\r\nTwo nice features would be to:\r\n- allow to delete the extracted files without loosing the ability to load the dataset from the cached arrow-file\r\n- streamlined decompression when only the currently read file is extracted - this might require to read the list of files from the extracted archives before processing them?","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":116,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n Ok yes. I think this is an important feature to be able to use large datasets which are pretty much always compressed files.\r\n\r\nIn particular now this requires to keep the extracted file on the drive if you want to avoid reprocessing the dataset so in my case, this require using always ~400GB of drive instead of just 200GB (which is already significant). \r\n\r\nTwo nice features would be to:\r\n- allow to delete the extracted files without loosing the ability to load the dataset from the cached arrow-file\r\n- streamlined decompression when only the currently read file is extracted - this might require to read the list of files from the extracted archives before processing 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"Note that I'm confirming that with the current master branch of dataset, deleting extracted files (without deleting the arrow cache file) lead to **re-extracting** these files when reloading the dataset instead of directly loading the arrow cache file.","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":38,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n Note that I'm confirming that with the current master branch of dataset, deleting extracted files (without deleting the arrow cache file) lead to **re-extracting** these files when reloading the dataset instead of directly loading the arrow cache file.","embeddings":[-0.1638352126,0.0380310751,-0.1447685808,0.1780929118,-0.1592689753,0.2357144654,-0.1746569574,0.287337333,0.1265909523,0.1585224569,0.0674439222,0.5160763264,-0.2275414467,-0.0690875575,-0.2018715143,0.0688017979,-0.2498386949,0.2992703915,-0.1215841621,0.1475564241,-0.1509249061,0.0209516753,0.0688547119,-0.1775142699,-0.0016516814,-0.396625787,0.2204260677,-0.2167356908,-0.1347008646,-0.4136333764,0.0634001121,0.5557292104,0.2655682862,0.1864333749,-0.0001169127,-0.0113526583,0.2978251576,0.1052251905,-0.4620437622,0.1928018481,-0.2715247571,-0.1185481772,0.2721543312,-0.117644541,0.2377959192,-0.4201447964,-0.2090130597,-0.6489426494,0.479297787,-0.1898259521,0.1478467733,0.0329266563,-0.5153906345,0.0615889952,0.2572768927,0.3319445252,-0.0981928185,0.0039390293,0.3674480319,0.0887911618,0.1793729812,0.3715210557,-0.0340011679,-0.2261724919,0.3655919135,-0.0161389373,-0.1732269377,-0.2317009717,0.3097927868,0.0443826169,0.6939400434,-0.4388231933,-0.1589534432,-0.3909534216,0.142165795,-0.5054193735,0.1175634265,0.1817198545,0.0114592006,0.2422340661,-0.1476078779,-0.4430011213,-0.3262475133,-0.1966211945,0.5953676701,-0.4200973511,-0.0878205076,-0.1266939789,0.4859718382,0.0839420781,0.2252024263,-0.1792857945,-0.3020443618,0.3221302927,-0.1004943848,-0.1947907656,-0.3318036199,-0.2837395668,0.1633085012,0.1971438229,0.3921901286,0.0155776553,0.1589901894,-0.00245813,0.3650124967,0.325317502,0.0567488,-0.015445116,0.1955094486,0.1219282299,0.0944667011,-0.0626407117,0.0564416498,-0.0361695439,0.3055577278,-0.0224623028,0.03207618,-0.1643724442,-0.0773370042,0.0661545843,0.1919808686,-0.1818098426,0.0514081493,0.1919044852,0.1945622265,0.108414501,0.0120298024,0.2396143079,0.0325863101,-0.3729590178,0.0624157004,0.0842375457,-0.3152473867,-0.2167695761,0.4251202047,-0.0692520663,-0.0677439123,-0.0776425228,-0.332596451,0.1238681599,0.5096788406,-0.0929375216,0.2749297917,0.3011269271,-0.194448486,-0.2114191949,-0.2887327373,0.0115748607,-0.146205619,0.4718725681,-0.0836265981,-0.2405286133,-0.079288736,0.1582892239,0.09983401,0.2539297342,-0.6136035919,0.1032643318,0.1020422503,0.0629070476,0.0064155911,0.2727427483,-0.0109288385,-0.0711655617,-0.250028491,0.2974756062,-0.7026976347,-0.0151146194,-0.3810774088,-0.2615415752,0.1441979557,0.1360021681,-0.1141004711,0.2153520286,-0.1931096464,0.1585097462,0.2810723484,-0.127763778,-0.4494964182,0.1385644823,0.0316712596,0.221644789,0.2527033687,0.2599445879,0.4064200521,-0.0053484794,-0.0747046247,0.2618270814,0.0072225165,-0.0818888769,-0.31014961,-0.3716854751,0.0091329832,0.2902944982,-0.0464238934,0.0333958231,0.2457732558,-0.0116136139,0.1760638505,0.1377877891,0.1765563488,0.1564887613,0.1129363626,-0.2869826853,-0.2430571169,0.117363438,-0.6472294331,0.0515569635,-0.4302771389,-0.3367313743,-0.2597639859,-0.4017222226,0.1069798246,0.0003136142,-0.2254595608,0.1922654808,0.090428181,-0.0458627604,0.1582460403,-0.0665628016,-0.3913021982,0.0469880179,-0.065038465,0.0207478963,-0.4797946215,0.069530502,0.1714746952,-0.2255218178,0.0300064292,0.0377172492,-0.3066351116,-0.4373158813,-0.1367695034,0.2760284245,0.2677150667,0.2772708535,0.2583813965,0.2232232094,0.1723570675,0.1517799199,0.0993092805,0.1439179927,-0.0193581209,0.1048069149,-0.4482202828,0.3862940371,-0.1830082238,-0.0284164567,-0.1669945568,-0.3487372994,0.2705671489,-0.0806844011,0.0948654562,-0.6014310718,-0.0367365107,0.3301691711,-0.0589249842,0.0794566199,0.0906827226,0.0266449917,0.061290618,-0.0423662998,-0.1162151322,0.3406762481,-0.0697613433,-0.2100413293,0.1170764118,0.4911105633,0.3566942513,0.2214138061,-0.0846716613,-0.1309578121,0.4406019151,0.004432214,0.1452678591,0.0406752266,0.0857581198,0.3085535467,0.0913145542,-0.0110078687,-0.2508101761,-0.0662560016,0.104020901,0.2441475242,0.2336023152,-0.2658806741,-0.3304500878,-0.3818595707,0.4872902632,0.3326804638,0.2534179986,-0.0412212983,0.4535224438,0.1211714,-0.4766841531,0.1249921694,-0.0667654425,0.6247702837,-0.1010287553,-0.2049594671,-0.3821294308,-0.2168403566,0.1266383678,0.0564317144,0.390730232,-0.0646003336,0.4973318279,0.1180510968,0.1314409673,-0.1648124009,-0.0041278186,0.0818762034,0.3110399544,-0.0571077578,0.1339185089,0.1453293711,-0.0676509216,0.1235644221,-0.2136645615,-0.3039454818,-0.1567982435,0.1779378057,0.0637528077,-0.0044931006,-0.2445878983,-0.111591801,-0.1638434529,-0.434940815,0.2674321234,-0.0510960147,0.0239288732,0.1086262837,0.1742657274,-0.0175042432,-0.0008385375,0.0091251042,-0.0748917088,-0.5851693153,0.1650405675,-0.2132187635,0.0230189376,0.2357920557,0.0374633074,0.0251581594,0.3056431711,-0.4097911716,0.0278872866,-0.0611578524,0.1791488081,-0.2906417251,-0.1186671332,0.2030057013,0.2551263869,-0.1212828606,0.0341082998,0.1248948574,0.2755829096,0.3200286627,0.0323269442,0.0342403799,0.2859163582,0.1640781462,0.3512731493,-0.1879602075,0.0512155481,0.2993659973,0.3690423965,0.4838581383,-0.2564122379,0.2488680631,-0.3840307891,0.0199971236,-0.0404896438,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"Hi ! That's weird, it doesn't do that on my side (tested on master on my laptop by deleting the `extracted` folder in the download cache directory). You tested with one of the files at https:\/\/huggingface.co\/datasets\/thomwolf\/github-python that you have locally ?","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":41,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n Hi ! That's weird, it doesn't do that on my side (tested on master on my laptop by deleting the `extracted` folder in the download cache directory). You tested with one of the files at https:\/\/huggingface.co\/datasets\/thomwolf\/github-python that you have locally ?","embeddings":[-0.0332566053,-0.0617311411,-0.1518054754,0.2321407646,-0.0794417933,0.189954102,-0.1519192904,0.3226890564,0.2679444253,0.1859114766,-0.008661042,0.47859025,-0.2064822614,0.0198211502,-0.1249555647,0.0193799771,-0.2612201273,0.2673110068,-0.0352828801,0.1023952886,-0.1649813056,0.161548242,0.0307119377,-0.2143165022,-0.0014290169,-0.2806631327,0.177387327,-0.0900829583,-0.2368220091,-0.4120254219,0.0911949351,0.4586049616,0.2278508395,0.2842088342,-0.0001189412,0.0202764049,0.3218185008,0.066426903,-0.3416108489,0.0965557545,-0.1090628654,-0.2528342605,0.3365181684,-0.2395073324,0.1118235067,-0.2765804529,-0.2922463417,-0.5912124515,0.5315221548,-0.0952571779,0.1267584264,0.1466713101,-0.355958432,0.2192575932,0.2747636437,0.2166805863,-0.09886498,0.053727679,0.315277338,0.0675988942,0.2849488556,0.3909698725,-0.1213172525,-0.1054870561,0.3051481247,0.0537144914,-0.1651786566,-0.4498274624,0.263142556,-0.0311787631,0.5653121471,-0.4969917536,-0.2195424885,-0.3977091908,0.0205808245,-0.602556169,0.0792899057,0.3545735478,-0.0875537023,0.2304884493,-0.2468704283,-0.3859897554,-0.3075895905,-0.0835531577,0.4763791561,-0.2620851099,-0.1824117601,-0.1066635922,0.4677399695,0.1795823574,0.0424528904,-0.2289459109,-0.2521284521,0.3555209041,-0.1274119616,-0.2224962562,-0.2088644505,-0.3136998713,0.2307114452,0.2699070573,0.2709549963,0.0157219525,0.1045328081,-0.0305523351,0.3759339154,0.4152934551,-0.0611145161,0.1297464371,0.3189991117,0.1017591357,0.0494839922,-0.0757151172,0.109716095,-0.0244888943,0.3162862659,-0.1740889698,0.1749759167,-0.1531502008,-0.0912235156,0.1615382433,0.1361991167,-0.1264770925,0.0433214866,0.2908792198,0.030465981,0.087059319,0.0671079159,0.3209926486,-0.056101162,-0.1605153531,0.0770599991,0.1735166311,-0.2492263913,-0.2693278491,0.421579957,-0.1249038652,0.0887147859,-0.0435720794,-0.216262728,-0.0413144492,0.517576158,-0.1195725799,0.3411772251,0.4210411906,-0.0745880976,-0.114984937,-0.1722786129,-0.1657734364,-0.1743794382,0.460924834,-0.1498116553,-0.2387833297,-0.1282224804,0.1567413956,-0.00324932,0.2452864647,-0.6246505976,0.0005452317,0.1519329697,0.2135063261,0.0848618522,0.2263615876,0.0496609882,-0.1697266549,-0.1571180671,0.3347275853,-0.4668126404,-0.0333851315,-0.2098701894,-0.1474102885,0.1847275645,0.2946440578,-0.091226317,0.2407300323,-0.2020543963,0.1187637821,0.3351454735,-0.2505328953,-0.5275449753,0.2201987356,0.064316839,0.3103695214,0.263060987,0.238543421,0.3052237332,0.0946832523,0.0400316529,0.3342135251,0.0402658992,0.0579853579,-0.403421253,-0.2949002385,0.0445607603,0.2469552606,-0.0413194336,0.1011844873,0.1281719208,-0.1058721989,0.3076839447,0.0411467254,0.1362333149,0.2670237124,0.1770786941,-0.2603784204,-0.1623841375,0.0183591694,-0.4770290256,0.0945220068,-0.4080125093,-0.2634161413,-0.3683611453,-0.3869258463,0.0043123853,0.0810938403,-0.3423129916,0.0619365312,0.0587098934,-0.1631045491,0.3893430233,0.002705645,-0.3460029364,0.151015684,0.020282967,0.102244325,-0.4906042516,0.1922679096,0.1886428148,-0.251817435,-0.0409127697,-0.0152284065,-0.2488425225,-0.4605016112,-0.154968068,0.2493535727,0.2999372184,0.1253156364,0.206798479,0.2235670835,0.1670227051,0.2181142867,0.1772170961,0.222254023,0.0617597066,0.1005986854,-0.2781881988,0.4478914142,-0.1885373592,-0.0291268285,-0.1488328725,-0.3766204417,0.3021794856,-0.0755825639,-0.0541075468,-0.4702609181,-0.0302460603,0.3676839769,-0.0016348548,-0.0202190597,-0.0253918841,-0.047281839,0.1545387059,-0.0499397144,-0.125313133,0.405554086,-0.149950996,-0.1962159425,-0.0140799722,0.415500164,0.4606252313,0.1591747254,-0.0678065792,-0.0173171833,0.373567313,0.0155714555,0.1984344423,0.0710080042,-0.0462578759,0.3129602671,0.2086460143,-0.0325560942,-0.2569279671,0.0208207872,-0.0300837718,0.2917767763,0.1879808456,-0.0984210372,-0.319522351,-0.3765306175,0.4093852937,0.1857545227,0.1845454872,-0.1193320379,0.3986589015,0.1807380468,-0.3674204946,0.0814922825,0.0344223753,0.3880402744,-0.0016941144,-0.3106749356,-0.253988117,-0.1466290206,0.1887052804,-0.0019916501,0.2663029432,-0.1614186019,0.5131768584,-0.0222540926,-0.0057921638,-0.1705052555,0.0209889617,0.0856403336,0.2487094551,0.0692659765,0.1128563136,0.2418886721,0.0192850307,0.16073744,-0.0956477001,-0.2883743644,-0.1349900961,0.1901531368,0.139432922,0.0259200912,-0.2716072798,-0.104124479,-0.2724406123,-0.4422227442,0.3500591815,0.1526203156,0.0999123305,0.1743971407,0.2171635479,-0.0255618915,0.1482309699,0.0802659839,-0.1224801093,-0.6555259824,0.2202030569,-0.2332468033,-0.046239946,0.3175825477,0.0387545489,0.0010782455,0.2596187294,-0.4103809893,-0.1555810571,-0.193201825,0.2453217357,-0.1802003831,0.1367727518,0.1767840534,0.1448154598,-0.0755918175,0.0392446816,0.0460777506,0.1047629714,0.3434068263,-0.004457972,0.1057259217,0.320535779,0.1107369885,0.4776507914,-0.0486312658,0.0883385465,0.4071294069,0.2520943284,0.4920267165,-0.3374051452,0.1258828938,-0.4846446812,-0.093485795,-0.0854883119,0.1300485432,0.1559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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"@thomwolf I'm sorry but I can't reproduce this problem. I'm also using: \r\n```python\r\nds = load_dataset(\"json\", split=\"train\", data_files=data_files, cache_dir=cache_dir)\r\n```\r\nafter having removed the extracted files:\r\n```python\r\nassert sorted((cache_dir \/ \"downloads\" \/ \"extracted\").iterdir()) == []\r\n```\r\n\r\nI get the logging message:\r\n```shell\r\nWARNING  datasets.builder:builder.py:531 Reusing dataset json ...\r\n```","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":49,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n @thomwolf I'm sorry but I can't reproduce this problem. I'm also using: \r\n```python\r\nds = load_dataset(\"json\", split=\"train\", data_files=data_files, cache_dir=cache_dir)\r\n```\r\nafter having removed the extracted files:\r\n```python\r\nassert sorted((cache_dir \/ \"downloads\" \/ \"extracted\").iterdir()) == []\r\n```\r\n\r\nI get the logging message:\r\n```shell\r\nWARNING  datasets.builder:builder.py:531 Reusing dataset json ...\r\n```","embeddings":[0.0387398824,0.0011513438,-0.0731728151,0.2888256013,-0.0225971937,0.2442346811,0.0043715644,0.2252011448,0.211116001,0.1112262979,0.1300096661,0.4447046518,-0.3149459064,-0.152233839,-0.1879176497,0.0685452223,-0.1822635829,0.2303551137,0.1179905161,0.1538845748,-0.1485168636,0.0167127326,0.0281603187,-0.1277256161,-0.081199348,-0.3685379624,0.1793391258,-0.2001989782,-0.0709400177,-0.475566864,0.1935171932,0.4872877598,0.2634621859,0.2937750518,-0.0001270321,0.1402311623,0.2824359834,0.1706399322,-0.4946140647,0.073470667,-0.2185482085,-0.131723702,0.278342694,-0.1279088408,0.238957271,-0.5058012605,-0.2874324322,-0.7039566636,0.6001020074,-0.1444820762,0.0226126779,0.0881787464,-0.475264281,0.235341385,0.1795049906,0.3432503641,0.003216923,-0.014561764,0.397413075,0.0410049222,0.2530764639,0.3323472738,-0.1500108838,-0.2315425277,0.2820379734,-0.0412385538,-0.0308066588,-0.303809613,0.1847779602,-0.0036727618,0.6249611378,-0.3946924806,-0.255618304,-0.5423304439,0.0626548827,-0.6009432673,0.0232606772,0.279749155,-0.0825029537,0.2129976153,-0.1887128651,-0.3832755983,-0.3273220956,-0.1123421714,0.5984613895,-0.3998067677,-0.0372391976,-0.0458068214,0.4938412309,0.1461505145,0.2353725284,-0.3142920434,-0.2171897143,0.3846135437,-0.1873886138,-0.0936432704,-0.2239203453,-0.2821353078,0.1414932907,0.1589775831,0.2970098555,-0.1674041748,0.1185295656,0.0908483192,0.3935776651,0.4597049654,-0.0481675826,0.1128554419,0.100851737,0.1619086713,0.0482873619,-0.0979919359,0.1011848226,-0.0617838986,0.4250560403,-0.0321993716,0.0416536331,-0.107187964,-0.2610659897,0.0968772247,-0.0078233099,-0.2051997781,0.1666130871,0.1866741627,0.1723242849,0.0193716679,0.0672903135,0.4301665723,-0.0577309355,-0.2265523523,0.1032965183,0.1011031419,-0.3205889761,-0.3679001331,0.4237632453,-0.1298708916,-0.0259721037,-0.0048201988,-0.25508219,-0.0653327405,0.4840128422,-0.092918694,0.2538093925,0.3099845052,-0.2183120549,-0.0880336314,-0.2122648358,-0.1653785855,-0.1422930807,0.5295100808,-0.1849961281,-0.3411290646,-0.0856946632,0.0599792488,-0.0660016015,0.2800516188,-0.5685242414,0.0611999296,0.1911428124,0.0687458068,-0.041827403,0.2140063345,-0.0375086814,-0.0887151733,-0.2247154415,0.4024045765,-0.6254299879,-0.1333288848,-0.3174979091,-0.1452012062,0.2010465711,0.2420930415,-0.1749440432,0.3494221568,-0.315636009,0.0096579343,0.4006729126,-0.2328708619,-0.4543673396,0.1765417904,0.1190119013,0.4207156897,0.352494508,0.2543875277,0.2594757974,-0.0322538726,-0.0024930341,0.3350212872,0.0773673728,-0.0738491192,-0.2808282971,-0.3792129457,0.2256362885,0.1916751266,-0.0921854079,0.1105522737,0.1678093672,-0.0024405546,0.2609681487,0.1150512472,0.1132845357,0.1940720677,0.1154089645,-0.2010038793,-0.1774228364,0.1313792914,-0.5052556396,0.0986857489,-0.4657924771,-0.3321765363,-0.3756705225,-0.2849501669,0.038604483,0.1174927056,-0.389093101,0.1290554851,-0.0220173094,-0.1279522032,0.3280850351,0.0559472367,-0.3593132496,0.1520723104,-0.0983794555,0.0780223235,-0.5450092554,0.2050356865,0.1843023151,-0.2473080903,-0.1290709525,0.1400124282,-0.2306076288,-0.3627342284,-0.1654000431,0.2036164701,0.3391128778,0.2150984406,0.1560032964,0.2421350926,0.2304073125,0.2009599805,0.0469735563,0.1948219389,0.0115868561,0.0463874564,-0.2821243703,0.3812071681,-0.2219506055,0.0534807071,-0.1775822192,-0.4575609267,0.2535552382,-0.0570940077,0.079079181,-0.4799568653,0.1083344445,0.355214268,-0.0536249131,0.0245037731,0.1122616008,-0.1633778661,0.0036084938,-0.0947122648,-0.1975779533,0.2892430425,-0.1210098863,-0.1792408079,0.0539704822,0.4722078145,0.3872458041,0.0728120878,-0.0692079291,-0.1037085876,0.4217990637,0.0783267841,0.170580253,0.0563217103,-0.0379138552,0.3702068031,0.2599157393,-0.0925854817,-0.2354387939,0.0143223666,0.1043259799,0.2683180273,0.1289220899,-0.0334743373,-0.3978503048,-0.3873176575,0.3365691602,0.1967991292,0.1924797744,-0.146978572,0.3643906415,0.179900229,-0.2070634961,0.0305839833,-0.0449990556,0.4603663683,0.0073027909,-0.3709696531,-0.3444038033,-0.1632081717,0.1228643432,-0.0831061155,0.40229249,-0.2809521854,0.4440789819,0.1275637448,-0.0038780824,-0.0542435609,-0.0177100953,0.0376475342,0.3879231811,0.1876690239,0.1001782194,0.1531307995,0.0379875526,0.096379213,-0.0991242006,-0.144934833,-0.1022407338,0.3233754039,0.0661228448,0.1851321161,-0.1712577045,0.000553999,-0.2757932246,-0.2898995876,0.2013386488,0.0319607258,-0.0162346866,0.2689197361,0.2810165882,-0.0965232104,0.0841756389,0.1488934904,-0.0251834001,-0.7104842663,0.1607454866,-0.0769011527,0.1364143789,0.1890652776,-0.0697535276,-0.0293769576,0.3408835828,-0.4336383343,-0.0120831616,0.0660452917,0.1627597362,-0.2804273069,0.019199362,0.0958866924,0.2754341364,0.0378886424,0.0201795232,0.0985087976,0.1447329521,0.3198245764,0.1731146127,0.1262675673,0.2603633106,0.2043638676,0.4321883619,-0.0622241534,0.0196017493,0.3881896436,0.3163253069,0.4387296736,-0.2470133305,0.0989190862,-0.4738682806,0.0050000832,-0.21820475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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2604","title":"Add option to delete temporary files (e.g. extracted files) when loading dataset","comments":"> \r\n> \r\n> Do you confirm the extracted folder stays empty after reloading?\r\n\r\nYes, I have the above mentioned assertion on the emptiness of the extracted folder:\r\n```python\r\nassert sorted((cache_dir \/ \"downloads\" \/ \"extracted\").iterdir()) == []\r\n```\r\n","body":"I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution.","comment_length":37,"text":"Add option to delete temporary files (e.g. extracted files) when loading dataset \n I'm loading a dataset constituted of 44 GB of compressed JSON files.\r\n\r\nWhen loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables\r\n\r\nHaving a simple way to delete the extracted files after usage (or even better, to stream extraction\/delete) would be nice to avoid disk cluter.\r\n\r\nI can maybe tackle this one in the JSON script unless you want a more general solution. \n > \r\n> \r\n> Do you confirm the extracted folder stays empty after reloading?\r\n\r\nYes, I have the above mentioned assertion on the emptiness of the extracted folder:\r\n```python\r\nassert sorted((cache_dir \/ \"downloads\" \/ \"extracted\").iterdir()) == []\r\n```\r\n","embeddings":[0.0413070992,-0.0485373661,-0.144972533,0.224829793,-0.0466053225,0.2017893344,-0.1690857112,0.3027119935,0.2245082706,0.1342939734,0.0109500363,0.480185926,-0.268691659,-0.1230428964,-0.1824239641,-0.0093835704,-0.2349088937,0.2767875493,-0.003963063,0.1206632704,-0.1744759232,0.0921203047,0.0378465205,-0.1965695918,0.0525064655,-0.4080628157,0.1274044961,-0.0839395151,-0.1356525421,-0.4438681006,0.0438200198,0.4613906145,0.265135318,0.2756152451,-0.0001182811,-0.0052507161,0.321416229,0.0448974855,-0.4485212564,0.0787613317,-0.1391087621,-0.168904081,0.2623524964,-0.1987499744,0.223350808,-0.3563092947,-0.293863833,-0.6617410779,0.5167124271,-0.1608560979,0.1268815249,0.0683706999,-0.4738253355,0.1933883727,0.2738461792,0.2409303933,-0.0916814432,0.0385858454,0.3927947581,0.0045666494,0.2632922828,0.3668141961,-0.1002369002,-0.2114969343,0.3557851017,0.0218768865,-0.1461001784,-0.32141608,0.2498360574,-0.0287617948,0.6228607297,-0.3944323063,-0.2275240719,-0.3693217933,0.0613993891,-0.6855837107,0.0714127496,0.2646585107,-0.0256918222,0.238250345,-0.0805310756,-0.374007225,-0.3292851746,-0.1420151889,0.5611350536,-0.3349307179,-0.0679379329,-0.1523856223,0.4347500205,0.1455315799,0.2015200257,-0.2469354272,-0.2134846151,0.4398026466,-0.1156603768,-0.1684402525,-0.2826519608,-0.255082339,0.1541438252,0.2384481281,0.3873394728,-0.0318554565,0.1681101024,0.0122853788,0.302649945,0.4701262712,0.0035454736,0.0091398135,0.1823690534,0.0923326388,0.0784320012,-0.0593405664,-0.0071670548,0.0037678275,0.3564743698,-0.0054526506,-0.0003948941,-0.1036438271,-0.0663218647,0.1109705269,0.1465508342,-0.1764888465,0.0581149533,0.2550421357,0.1115225703,0.0797707289,0.1175451726,0.3180947602,-0.006252687,-0.1697294563,0.0901406556,0.1622937173,-0.2889664173,-0.3153970838,0.4541114271,-0.0812455714,0.0166768096,-0.1212240458,-0.2307779342,0.0247761011,0.5310213566,-0.1226880178,0.2855815589,0.3619463146,-0.1461788714,-0.1604757607,-0.1923618317,-0.0846228227,-0.1570649445,0.4940737188,-0.115770638,-0.2477866113,-0.0082167536,0.1721287072,0.0101683578,0.2284962088,-0.5198004842,0.0128179165,0.1061698347,0.0977833718,0.0089794928,0.3370685279,0.0592684001,-0.1662468165,-0.2143195868,0.360255301,-0.5754243135,-0.0783924982,-0.3162104785,-0.1381838024,0.1419519037,0.1934991479,-0.0748170614,0.2696565092,-0.2039485574,0.1503590345,0.3257901073,-0.2086213082,-0.4496496916,0.1334092915,0.0934550762,0.3512125611,0.2929980159,0.2657374442,0.3637495339,0.0370408632,0.0280236807,0.3027046919,0.0446932241,-0.0319463052,-0.3799369037,-0.3065942824,0.1273216903,0.2023749799,0.0401778482,0.0616224483,0.1113649085,-0.0308885518,0.241564095,0.1242302954,0.1132445931,0.2096996307,0.2101258337,-0.2184161395,-0.1411334872,0.0758418366,-0.533705771,0.1161193699,-0.4802550673,-0.2700078785,-0.3258729875,-0.3545676172,-0.0062890369,0.06039932,-0.2571125031,0.0709158108,0.100192897,-0.0983826444,0.3363024294,0.0053298594,-0.4017877579,0.0917589441,-0.0026925183,0.0702562034,-0.442569524,0.1793133169,0.1284513772,-0.2546826303,-0.0913694054,0.0198447704,-0.2252409309,-0.5050381422,-0.1748074442,0.2506622672,0.3291651309,0.233689636,0.2079574913,0.2225165367,0.1781661958,0.2550555766,0.0977902487,0.2198260725,0.0716378614,0.0666633397,-0.3593182564,0.4581740201,-0.226888895,-0.0226653814,-0.1261266172,-0.3892331421,0.2734319866,-0.1001379341,0.0814294145,-0.4512613416,0.0787403956,0.3533690274,-0.0491033792,0.0033766234,0.1296355873,-0.0848263949,-0.0107796937,-0.0596405938,-0.1538949609,0.3206886351,-0.1086348519,-0.1442068368,0.0110897776,0.4614611566,0.4385591745,0.1701882631,-0.1012916416,-0.11482694,0.4571801722,0.0365350693,0.181687206,0.0530165993,-0.048828043,0.3767079115,0.1704260856,0.0017932453,-0.3105232716,-0.0254260805,0.086679019,0.2245713323,0.2444339395,-0.1599863619,-0.2307387292,-0.3647020459,0.4693772793,0.1976751983,0.2197479308,-0.0726308525,0.4689161777,0.1539280713,-0.326340884,0.0959923044,-0.0333921723,0.5423595309,-0.01706874,-0.3114193976,-0.377166003,-0.1681100577,0.2098544985,0.0186219271,0.3155292273,-0.1995816827,0.4949300885,0.08884909,0.0585311837,-0.0464112572,-0.0641171038,0.0497560054,0.2917626202,0.0272737369,0.1036752015,0.2092254162,0.022574693,0.1079359278,-0.094643496,-0.3111015558,-0.1514032483,0.2132693827,0.1755683869,0.0509327352,-0.2745606899,-0.1111386269,-0.2817261815,-0.4231975377,0.2254269272,0.0398009978,0.0331125483,0.1260128021,0.1688347161,-0.061066892,0.1147645488,0.081115447,-0.0450237058,-0.6299257278,0.1831360906,-0.1912486255,-0.0007606788,0.2425493896,-0.0675699636,-0.0057533085,0.3238607943,-0.3674845099,-0.0564071722,-0.0332863927,0.1807260811,-0.3109463751,-0.02231168,0.2386684567,0.2549817562,-0.1006572545,-0.0001963221,0.0948112383,0.1612852961,0.3627622128,0.0858090073,0.0152382841,0.2255530208,0.1552601904,0.4575290084,-0.2319746614,0.0894569457,0.3778058887,0.3623319566,0.483091712,-0.2674523592,0.1350191981,-0.476162523,-0.059716668,-0.1635607779,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2598","title":"Unable to download omp dataset","comments":"Hi @erikadistefano , thanks for reporting the issue.\r\n\r\nI have created a Pull Request that should fix it. \r\n\r\nOnce merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp dataset.","body":"## Describe the bug\r\nThe omp dataset cannot be downloaded because of a DuplicatedKeysError\r\n\r\n## Steps to reproduce the bug\r\nfrom datasets import load_dataset\r\nomp = load_dataset('omp', 'posts_labeled')\r\nprint(omp)\r\n\r\n## Expected results\r\nThis code should download the omp dataset and print the dictionary\r\n\r\n## Actual results\r\nDownloading and preparing dataset omp\/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to \/home\/erika_distefano\/.cache\/huggingface\/datasets\/omp\/posts_labeled\/1.1.0\/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b...\r\n0 examples [00:00, ? examples\/s]2021-07-06 09:43:55.868815: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0\r\nTraceback (most recent call last):      \r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 990, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 338, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 349, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 3326\r\nKeys should be unique and deterministic in nature\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"hf_datasets.py\", line 32, in <module>\r\n    omp = load_dataset('omp', 'posts_labeled')\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 992, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 409, in finalize\r\n    self.check_duplicate_keys()\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 349, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 3326\r\nKeys should be unique and deterministic in nature\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Ubuntu 18.04.4 LTS\r\n- Python version: 3.6.9\r\n- PyArrow version: 3.0.0\r\n","comment_length":52,"text":"Unable to download omp dataset \n ## Describe the bug\r\nThe omp dataset cannot be downloaded because of a DuplicatedKeysError\r\n\r\n## Steps to reproduce the bug\r\nfrom datasets import load_dataset\r\nomp = load_dataset('omp', 'posts_labeled')\r\nprint(omp)\r\n\r\n## Expected results\r\nThis code should download the omp dataset and print the dictionary\r\n\r\n## Actual results\r\nDownloading and preparing dataset omp\/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to \/home\/erika_distefano\/.cache\/huggingface\/datasets\/omp\/posts_labeled\/1.1.0\/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b...\r\n0 examples [00:00, ? examples\/s]2021-07-06 09:43:55.868815: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0\r\nTraceback (most recent call last):      \r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 990, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 338, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 349, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 3326\r\nKeys should be unique and deterministic in nature\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"hf_datasets.py\", line 32, in <module>\r\n    omp = load_dataset('omp', 'posts_labeled')\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 992, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 409, in finalize\r\n    self.check_duplicate_keys()\r\n  File \"\/home\/erika_distefano\/.local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py\", line 349, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 3326\r\nKeys should be unique and deterministic in nature\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Ubuntu 18.04.4 LTS\r\n- Python version: 3.6.9\r\n- PyArrow version: 3.0.0\r\n \n Hi @erikadistefano , thanks for reporting the issue.\r\n\r\nI have created a Pull Request that should fix it. \r\n\r\nOnce merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2596","title":"Transformer Class on dataset","comments":"Hi ! Do you have an example in mind that shows how this could be useful ?","body":"Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":17,"text":"Transformer Class on dataset \n Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n \n Hi ! Do you have an example in mind that shows how this could be useful ?","embeddings":[-0.4188199937,-0.1118550226,-0.0211097654,0.164978072,0.4431482255,-0.0737165511,0.5200480819,0.025510639,-0.0000917429,-0.0612897165,0.1383574754,0.3554057777,-0.3664284945,0.1816854477,0.1437233835,-0.126732409,-0.0193027947,0.3366385698,-0.381377995,-0.1455226839,-0.181020543,-0.3653594255,-0.1667024642,0.020922713,-0.18708767,-0.0778727084,-0.0324452557,-0.4812172055,-0.0334892087,-0.0890976936,0.7275010943,0.049632404,-0.0156269856,0.6457778215,-0.0001180408,-0.1917837858,0.0963871926,-0.0768376365,0.0740181282,-0.3926313519,-0.470248878,-0.0931003764,0.0384803675,-0.2704013586,-0.4740886092,-0.0527881496,-0.0390679054,-0.0544612743,0.4018721581,0.1389983594,0.1720724106,-0.4391120672,-0.1384260803,-0.1219020709,-0.0793528929,0.4444640279,-0.2869962156,-0.1327419877,0.1904753894,0.3042643964,0.048442889,-0.0256284904,-0.0494295768,-0.1503827423,0.7289847732,-0.137820065,0.1042367965,-0.4418712556,-0.1786393821,0.4444399178,0.7537171841,-0.3168216944,-0.1675662994,-0.1634083986,0.2200718522,0.2761265039,-0.0231509525,0.2009141445,-0.0280606877,0.2865293026,-0.2226814628,-0.2574950457,-0.422495991,0.0322743244,-0.5201644301,0.1166955978,-0.068190895,0.0087468084,0.0091331927,-0.0526339896,0.2658056319,0.0693863928,0.1179423183,0.1734823734,-0.139836356,-0.5531538129,-0.3461993039,0.2137343585,-0.1130962968,-0.0050097806,-0.1444609761,-0.1590377986,0.1009187475,-0.0037088529,-0.045353055,0.2560715079,0.0444240943,0.2834724188,0.1548118144,-0.3329988122,-0.1865014434,0.1489244401,0.0044968962,-0.1453438699,0.1278155446,0.0848207772,0.0767351985,-0.1519827396,0.059555009,-0.2803952992,-0.0193673726,0.0458842702,-0.1318854988,0.0430733673,0.1812555194,0.0792005211,0.176816076,0.2458331138,0.2344777137,-0.2285812348,0.0617158823,0.2511284649,-0.297173351,-0.1399851143,0.0375425294,0.1639120281,-0.267642051,0.0062306882,-0.1234986782,0.3804902434,-0.0001207096,-0.26225245,0.1476206332,0.2959809303,-0.5519163609,-0.2185193449,0.1582685709,-0.1785986871,-0.2252624333,0.4631130099,-0.1985176504,0.0231161043,0.0901266113,0.1778133661,-0.0336978287,0.0682865679,-0.1685471088,0.3371283114,0.0445051827,-0.1100770682,0.1710585803,-0.3961261213,-0.1047371104,-0.1422396898,-0.0999873653,0.0916873813,-0.6140998602,-0.3623417616,-0.1760552973,-0.0748703852,0.1173579171,0.0689149648,-0.2285491526,0.3040558696,-0.1412665993,-0.1797803938,0.663918972,0.0727627352,-0.2718644738,0.2040672451,-0.5375030637,-0.1342455596,0.1489097625,0.3913097084,0.2420570403,-0.2189547867,-0.5890008211,0.2351159304,-0.0481770113,-0.1363944411,0.0984599069,-0.1721860915,0.5869368315,0.0207448918,0.2949224412,0.4651507437,0.182667166,0.5072157383,-0.1435580254,-0.3711574376,0.2393033206,-0.0810984671,0.4471267462,-0.045060996,0.0037965905,-0.3930869401,-0.0073088813,-0.1223513484,-0.0850009844,0.1331424862,0.3702483177,-0.090662472,0.0221196674,-0.3194077909,-0.1461212337,-0.1193116456,0.1447146386,-0.3999894261,0.280167073,-0.2651736438,-0.1051316038,0.4493666887,0.0060810461,0.0600682721,-0.4010302722,0.2931924462,0.0819307566,0.2227484286,-0.3591738641,-0.0449864492,-0.144190222,-0.1969311386,-0.1015450135,-0.1836638302,-0.0584041364,-0.0512780547,0.0085194856,0.439289093,0.2763770223,-0.4933925867,0.4896831512,0.0793199912,0.0957188308,-0.1939777583,-0.3488175869,0.4720558524,-0.150127992,0.2503143549,-0.3207320869,0.0362431817,0.186323598,-0.433570087,-0.1252280474,-0.0907754898,-0.0459950119,0.0280870683,0.1391773075,0.2028847486,0.0194785055,-0.1024754122,-0.0012694855,-0.0949835256,0.2904396653,0.4638142586,-0.235706374,0.2653409839,-0.2432362437,0.5296252966,0.6370874643,0.1556210518,-0.2533013523,0.2274798006,0.0958712026,-0.1503843367,0.2806729972,-0.0943110287,0.2716952562,0.0350976288,-0.1571458578,0.1369215101,0.070758529,-0.3582987487,-0.0043882094,0.1940810084,-0.1077887714,0.0459180549,-0.1841415167,-0.0973766968,-0.3303296268,0.0659516603,0.072096929,0.1469988227,-0.0945241451,-0.0728322789,0.281208396,0.0661741793,-0.0833279192,0.2607306242,0.236743018,-0.057936959,-0.1471119672,-0.070167996,0.0777691901,0.1580581963,-0.1741780341,0.0169900544,0.2542794049,-0.3422031999,0.1313803941,-0.0990186483,-0.4404471219,-0.0119380821,0.0566025935,0.16734083,0.3422213495,0.0247850493,-0.0896843076,0.0493691154,-0.0207102075,0.0460866168,-0.1860033423,-0.0157513116,-0.2754972279,0.2332953811,0.0840452462,-0.2450775504,-0.2703368366,-0.1052667275,0.1708030254,0.0575782806,-0.0584610067,0.0116345808,0.0825020745,-0.019699581,0.0160593316,0.1571203321,-0.0940387622,0.0360755548,0.2773386836,0.0892527401,-0.1180108115,-0.1655022353,-0.2728357911,-0.0858478546,0.2958499789,-0.3110006452,0.0010259798,-0.1123813093,0.0221872199,-0.0838600472,-0.145645529,0.5014488101,0.050975889,0.0237529818,-0.0218031537,0.0657578409,-0.0743142292,0.1865724623,0.3480320275,0.1766137034,0.4494115412,-0.0166078489,0.2254580557,-0.3424141705,-0.4381712079,-0.26404351,0.0538090803,-0.0042795348,0.1079061702,-0.4174182713,0.2436749786,0.2311609834,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2596","title":"Transformer Class on dataset","comments":"Example:\n\nMerge 2 datasets into one datasets\n\nLabel extraction from dataset\n\ndataset(text, label)\n   \u2014> dataset(text, newlabel)\n\nTextCleaning.\n\n\nFor image dataset, \nTransformation are easier (ie linear algebra).\n\n\n\n\n\n\n> On Jul 6, 2021, at 17:39, Quentin Lhoest ***@***.***> wrote:\n> \n> \ufeff\n> Hi ! Do you have an example in mind that shows how this could be useful ?\n> \n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n","body":"Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":83,"text":"Transformer Class on dataset \n Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n \n Example:\n\nMerge 2 datasets into one datasets\n\nLabel extraction from dataset\n\ndataset(text, label)\n   \u2014> dataset(text, newlabel)\n\nTextCleaning.\n\n\nFor image dataset, \nTransformation are easier (ie linear algebra).\n\n\n\n\n\n\n> On Jul 6, 2021, at 17:39, Quentin Lhoest ***@***.***> wrote:\n> \n> \ufeff\n> Hi ! Do you have an example in mind that shows how this could be useful ?\n> \n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n","embeddings":[-0.4854312241,0.1529312581,-0.0886402205,0.1896278262,0.4006462991,0.1068503782,0.507528007,0.1244744882,-0.0116836671,-0.0620446242,0.0533830151,0.3345322311,-0.3507431746,0.1875819862,0.0721608475,-0.1314892918,-0.0283112172,0.3180100322,-0.4435121417,-0.1215898991,-0.2172255516,-0.2452004105,-0.204332307,-0.0350172669,-0.2736256123,-0.1387131512,-0.1118711159,-0.2744330466,-0.1285729706,0.0032409544,0.5669198632,0.2155507505,-0.0712961778,0.5656960607,-0.0001113632,-0.1279024333,0.1100607142,-0.0080487533,-0.1362921596,-0.3996142447,-0.416369915,-0.0603963397,-0.0396468043,-0.2917911112,-0.2664454281,-0.1854079366,0.0237760376,-0.0645149946,0.4134106338,0.0751382485,0.2136539668,-0.4207115173,-0.081319578,-0.1326424181,-0.0725329444,0.4323451221,-0.1573563963,-0.0739583224,0.2559334636,0.2774946094,-0.005224322,0.2231015116,-0.1098077819,-0.2118475437,0.659743607,-0.086872831,-0.0518905669,-0.3680224121,-0.1720120013,0.3020718396,0.6202872396,-0.4165475667,-0.2718209624,-0.2353081554,0.3179525137,0.1380975544,-0.0936644822,0.1536032856,-0.0291529447,0.2862283587,-0.3013944328,-0.3070755005,-0.312664777,0.0769137442,-0.3927911818,0.1838325262,-0.0187025126,0.0787064508,0.0261065941,-0.0120617105,0.2786219716,0.0286741741,0.1734904945,0.1302508563,-0.1575931311,-0.4430968165,-0.347088933,-0.01647182,-0.1724005491,0.082052581,-0.0669375435,-0.1407574117,0.0001506267,0.0678739622,-0.0976173431,0.2801892459,0.0756248534,0.2835099399,0.0927253142,-0.2981130481,-0.1342790425,0.2335567474,-0.1500683874,-0.1828356385,0.0823884085,0.0751636773,0.0881752074,0.0607573465,0.0079319952,-0.3705034256,0.0102080423,0.0104207722,-0.1592562497,0.0114267236,0.2135364115,0.0537415035,0.2268779129,0.1977373362,0.2781538367,-0.2910178304,0.0033119151,0.3380631804,-0.3129458427,-0.1553813368,0.0573053025,0.0989251956,-0.3175148368,-0.0068458575,-0.2444904894,0.386097461,-0.0104768593,-0.2480302602,0.2561534345,0.1999243349,-0.4474495053,-0.1308980584,0.1515335888,-0.2037787586,-0.208618328,0.3421449959,-0.1957412213,0.1629634798,0.0655770227,0.2365572304,0.1148019582,0.0316513292,-0.298053503,0.3904443979,0.0113533875,-0.0630758777,0.102777414,-0.315286696,-0.063845478,-0.0778256282,-0.0714211613,0.1344807297,-0.6597012281,-0.3752602935,-0.0892390683,-0.0400439277,0.0257388875,0.1785104573,-0.1679376811,0.3726211786,-0.1166770533,-0.204284206,0.7212469578,-0.0659181923,-0.1949130446,0.1581442952,-0.5203281045,-0.1914043725,0.1640527993,0.3704347014,0.2596188784,-0.1747305542,-0.4552618265,0.3597725034,-0.1351937354,-0.1334810257,0.1524548978,-0.2831425667,0.4489609897,0.1342541575,0.2727267444,0.2775531411,0.0847700387,0.4158124328,-0.1401902288,-0.4404054284,0.284372896,0.0401456915,0.3044769764,-0.1269488037,-0.0328502059,-0.3859139979,-0.1105206832,-0.126223743,0.0478566401,0.1563088447,0.2692359388,-0.0514915511,-0.0509790517,-0.2854634821,-0.1628869027,-0.2248650044,0.2087797225,-0.3400037587,0.2504788339,-0.3147006035,-0.074569881,0.5503131747,0.0370456353,0.2137278318,-0.5016191602,0.3224230111,0.0449388511,0.2022389323,-0.3285857737,0.0471547246,-0.2616764307,-0.2003247589,0.0024983638,-0.1345227659,-0.0960846022,-0.0462613627,0.0916247442,0.3488053083,0.3109102845,-0.4374358356,0.3571309745,-0.083256118,0.196588859,-0.1748176813,-0.4034092724,0.3144291341,-0.2289667577,0.2899323106,-0.1774757355,0.0508714616,0.1357771158,-0.3550786376,-0.1169614047,-0.1495451182,-0.0605595149,-0.0075774002,0.2507326901,0.2566907704,0.0911047757,-0.0859348252,0.1485472023,-0.0672260299,0.2210759372,0.3868333101,-0.2876425087,0.2263994813,-0.2082078308,0.5587705374,0.4472160637,0.2356890738,-0.2560152709,0.2304827571,0.1021553427,-0.073639825,0.2621409893,0.0267599151,0.4427095056,-0.0132771386,-0.0441405512,0.2116562277,0.0442383848,-0.4906044304,0.0805869475,0.1066009551,-0.0207087882,0.0048884447,-0.1806955338,-0.1815158874,-0.4042205215,0.1540659219,0.0376808681,-0.0611128286,-0.0880035609,-0.0729024857,0.1077074334,0.1231755763,0.0667177662,0.2231042236,0.2378362566,-0.1540003717,-0.0582209639,-0.1108326763,0.057614509,0.1907510757,-0.0666936114,0.0429162681,0.3864461482,-0.3671724498,0.2392293364,-0.1411261559,-0.5004366636,0.1360855401,-0.0246935803,0.0368475728,0.3780713975,-0.0869703665,-0.101020515,-0.0403236784,0.0226899162,0.0577006899,-0.2035497576,0.0045408439,-0.2300645858,0.0799108595,0.0435018539,-0.2263130248,-0.3054023981,-0.2169413716,0.2214498371,-0.0128984582,-0.0439193919,0.0234342162,0.1507755816,0.0317601971,-0.0810158178,0.1676726788,-0.0809066594,0.0390486792,0.1736202836,0.12728104,-0.0919616893,-0.2783718407,-0.3630923927,0.0234737117,0.31731686,-0.2352731079,0.01467063,-0.1455161721,0.0455431454,-0.00765173,-0.0787579641,0.5167153478,0.135990575,-0.0250538439,-0.0448868461,0.0040792534,-0.0832194835,0.0459284186,0.1290344298,0.0709372386,0.3557409942,0.0434626602,0.2524471581,-0.1993198246,-0.3729278743,-0.2999459207,0.1988696754,0.099833928,0.0715788305,-0.4846233428,0.26655760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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2596","title":"Transformer Class on dataset","comments":"There are already a few transformations that you can apply on a dataset using methods like `dataset.map()`.\r\nYou can find examples in the documentation here:\r\nhttps:\/\/huggingface.co\/docs\/datasets\/processing.html\r\n\r\nYou can merge two datasets with `concatenate_datasets()` or do label extraction with `dataset.map()` for example","body":"Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":41,"text":"Transformer Class on dataset \n Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n \n There are already a few transformations that you can apply on a dataset using methods like `dataset.map()`.\r\nYou can find examples in the documentation here:\r\nhttps:\/\/huggingface.co\/docs\/datasets\/processing.html\r\n\r\nYou can merge two datasets with `concatenate_datasets()` or do label extraction with `dataset.map()` for example","embeddings":[-0.4685243368,-0.2306800336,-0.0531573892,0.141038999,0.3790983558,-0.1987152547,0.336720854,0.1695584059,0.1045032665,0.193190679,-0.1746759713,0.2893973589,-0.2316427976,0.5593631268,0.0868366435,-0.2147053182,-0.1576156914,0.3244946003,-0.4510017633,-0.0933915228,-0.2045138329,-0.1517907381,0.0859746113,0.0435048975,-0.2052290738,-0.2564643025,-0.0503218733,-0.1808667779,-0.0929280743,-0.0206905995,0.3956409097,0.2275130004,-0.1231825128,0.461050868,-0.0000994468,-0.107468307,-0.1551022679,-0.0925966576,0.1021683365,-0.168191731,-0.125418961,-0.084633626,0.0220993273,-0.2678519785,-0.5002477169,-0.1091537103,-0.0318488441,0.0631250218,0.5358985066,-0.0103142783,0.2958766818,0.0097887106,-0.0158978384,-0.1169515923,-0.0069825319,0.2734376788,-0.1275932193,-0.018073108,0.1740113497,0.2556479275,-0.1945536286,0.3934059739,-0.0161714107,-0.032958664,0.6259969473,0.0143669872,0.0547637083,-0.4071558416,-0.2678075433,0.4698746502,0.3069075644,-0.2341915965,-0.4158831835,-0.4240956008,0.1417989135,-0.0436883718,0.0182049982,0.0818796903,-0.0191098955,0.2180835307,-0.3345590532,-0.2262868732,-0.2550739348,-0.084840931,-0.4569258094,0.1557009965,-0.1581203043,-0.0320846364,0.208306849,-0.1093471721,-0.057075128,0.2332167178,0.0770113915,0.1242182925,-0.1319326907,-0.4667616785,-0.240433991,0.2420442402,0.0641125143,0.0925941318,-0.101852648,0.0364717245,-0.107458137,0.1789062619,-0.0019420339,0.3197201788,-0.0181512497,0.0871051997,0.2844573259,-0.0984446034,-0.0780216083,0.1426822394,0.1567533314,-0.1871579438,-0.1085611209,-0.047767289,0.0716382936,-0.1214730814,0.0511640087,-0.3087018728,0.267242372,0.1720949113,-0.1121375188,0.2878980935,0.0153888362,0.2037271857,0.3953179419,0.179369539,0.1763352156,-0.1484648734,-0.0825694278,0.1802886128,-0.0494106524,-0.0196711849,-0.0038546352,-0.0378014594,-0.0293676201,0.0261388272,-0.0889280736,0.3310028315,-0.0821674317,-0.1042408645,0.2652435601,0.1671462208,-0.2829819322,-0.2588634789,0.0745518431,-0.1645919681,-0.2382676601,-0.0137226693,-0.1708738953,0.0797658116,0.0186045971,0.2774107158,-0.0035122889,-0.0864806622,-0.21165663,0.4036306143,-0.0128508657,-0.0990921631,0.0406693034,-0.173594445,-0.1943620592,-0.025116479,0.0886336863,-0.0599144511,-0.3292738497,-0.4942750335,0.1610184759,-0.1373925507,-0.0452827141,0.2936730683,-0.0389954075,0.1977722794,-0.062210463,-0.0802091509,0.4939524531,-0.1065551266,-0.1814965606,0.068139568,-0.3399479985,-0.0184119456,0.0083446512,0.3178723752,0.2948327959,-0.0164953284,-0.3483887315,0.3703303933,-0.0752102658,0.1497977972,0.1050369292,-0.2864674032,0.2458959967,0.062972419,0.2355774343,0.2537992299,-0.069267422,0.1117845848,-0.0753493756,-0.4083535373,0.2005532086,0.0748429671,0.2569252849,-0.0320868492,-0.1039824635,-0.4349060059,-0.1174092293,-0.1057571024,-0.1769857854,0.2305080295,0.2835945785,-0.2835356891,-0.0504569672,-0.2405830026,-0.1328191012,-0.1575145721,0.2584118545,-0.2040453404,0.2221158147,-0.3294777274,-0.1069264635,0.4259645343,0.1077912152,0.2475834191,-0.4918275774,0.0894110501,0.0432433337,0.0613956377,0.0378549881,0.0642796755,-0.15450266,-0.2567332387,0.0715664104,0.0709938034,-0.0862856209,0.0828549117,-0.0011877238,0.3227497935,0.2609703243,-0.0784021616,0.143908903,-0.0525297672,0.1788266301,0.0433894731,-0.4305520058,0.6129770875,0.0180025399,0.1722464263,-0.0484185964,0.0495318137,0.300876379,-0.3305992186,-0.014370014,-0.1949433982,-0.1480185986,-0.1301355362,0.2936966717,0.1075424477,-0.2553980947,-0.0235894732,0.2760587335,0.0384417437,0.1950599551,0.3294142187,-0.258631587,0.2395893186,-0.0654359311,0.3712917864,0.2040140629,0.3569667935,-0.1667204201,0.239026159,0.0320154838,-0.0046142754,0.1888573617,-0.0338227116,0.2357216775,0.0783822462,0.0489155911,0.1945629418,-0.2278055698,-0.2059036046,-0.2645703852,0.0031590862,-0.2033651769,0.0034469608,-0.1967256665,-0.1450364143,-0.2393192053,0.0100171873,-0.3171900213,-0.078466624,0.1861803234,0.1125213653,-0.2316172123,0.1188410148,0.2403347492,0.2831951678,0.198699519,-0.0203866176,-0.127515614,-0.1675501466,0.1066242605,0.2194830179,-0.1370417625,0.0977809951,0.4182054698,-0.411868751,0.1238672361,-0.2679352164,-0.4999006391,0.2685489058,-0.1570931524,0.2256887406,0.3385981023,0.1490938365,0.0294232275,-0.0188311692,0.0859243199,-0.1540537477,-0.2891170382,-0.0687830076,-0.2152634859,0.0695159882,-0.1339472681,-0.1737843752,-0.2338173985,-0.35813272,0.3657938838,0.1533703953,0.0451712608,0.0105709638,0.0082780421,-0.0310073849,-0.215448305,0.3004665077,-0.2189868242,-0.178074494,0.2259613872,-0.0719293579,-0.1818494499,-0.2360566854,-0.1790878922,0.0673804134,0.2620018423,-0.1796708554,-0.2952071726,-0.2022519112,0.2044983953,0.0763566718,0.1147262529,0.5687099099,0.1093285531,-0.1792212576,-0.0555118062,-0.0229510516,0.1011578962,0.1597188413,0.0593476593,-0.0260262657,0.368496269,-0.038997788,0.3227413297,-0.3008691072,-0.4758778512,-0.0619685836,-0.0589893572,0.2156079859,0.0416006148,-0.2979401648,0.2278830409,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2596","title":"Transformer Class on dataset","comments":"Ok, sure.\n\nThanks for pointing on functional part.\nMy question is more\n\u201cPhilosophical\u201d\/Design perspective.\n\nThere are 2 perspetive:\n  Add transformation methods to \n     Dataset Class\n\n\n OR Create a Transformer Class\n   which operates on Dataset Class.\n\nT(Dataset) \u2014> Dataset\n\ndatasetnew = MyTransform.transform(dataset)\ndatasetNew.save(path)\n\n\nWhat would be the difficulty\nof implementing a Transformer Class\noperating at dataset level ?\n\n\nthanks\n\n\n\n\n\n\n\n\n\n> On Jul 6, 2021, at 22:00, Quentin Lhoest ***@***.***> wrote:\n> \n> \ufeff\n> There are already a few transformations that you can apply on a dataset using methods like dataset.map().\n> You can find examples in the documentation here:\n> https:\/\/huggingface.co\/docs\/datasets\/processing.html\n> \n> You can merge two datasets with concatenate_datasets() or do label extraction with dataset.map() for example\n> \n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n","body":"Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":142,"text":"Transformer Class on dataset \n Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n \n Ok, sure.\n\nThanks for pointing on functional part.\nMy question is more\n\u201cPhilosophical\u201d\/Design perspective.\n\nThere are 2 perspetive:\n  Add transformation methods to \n     Dataset Class\n\n\n OR Create a Transformer Class\n   which operates on Dataset Class.\n\nT(Dataset) \u2014> Dataset\n\ndatasetnew = MyTransform.transform(dataset)\ndatasetNew.save(path)\n\n\nWhat would be the difficulty\nof implementing a Transformer Class\noperating at dataset level ?\n\n\nthanks\n\n\n\n\n\n\n\n\n\n> On Jul 6, 2021, at 22:00, Quentin Lhoest ***@***.***> wrote:\n> \n> \ufeff\n> There are already a few transformations that you can apply on a dataset using methods like dataset.map().\n> You can find examples in the documentation here:\n> https:\/\/huggingface.co\/docs\/datasets\/processing.html\n> \n> You can merge two datasets with concatenate_datasets() or do label extraction with dataset.map() for example\n> \n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n","embeddings":[-0.1065422595,-0.0538378395,0.0260536186,0.1430307478,0.3365959227,-0.1399590075,0.5725405812,-0.0339156091,-0.0521859936,0.1631585509,0.1168567315,0.2448585033,-0.4620172977,0.4327042997,0.2910209894,-0.3197534084,0.0604292937,0.211038664,-0.5410810113,-0.053649202,-0.3712553084,-0.3355924487,-0.0531556383,0.0580872223,-0.2199225277,-0.1509220451,-0.1271557361,-0.1756850481,-0.1485715061,0.0726781711,0.7325074077,0.0547797345,0.1794544756,0.6730666757,-0.0001188061,-0.0387484692,-0.0175584517,-0.0500610694,-0.0148862395,-0.398799181,-0.2108461708,-0.124951303,-0.0250348169,-0.3303232193,-0.1954077333,-0.0154983262,0.0561694205,-0.0434550978,0.3625746667,-0.0595809221,0.1046545133,-0.2699905038,-0.0658378005,0.0467020571,-0.1154842526,0.3715500236,-0.2796650827,0.0010369624,0.1731648743,0.3063555658,-0.0288388617,0.0741662532,0.0859446824,-0.3187921643,0.912211597,-0.0709178597,-0.0384920985,-0.3968650103,-0.2046209127,0.2444629073,0.4599398673,-0.2721917629,-0.634357214,-0.2657699585,0.0709742159,0.158929944,0.2296385765,-0.0058804196,0.0921110287,0.2785584629,-0.3245720565,-0.2360975146,-0.4755647779,0.093837291,-0.5276674628,0.0506132618,-0.1485310942,0.0155018717,0.0621879064,-0.0744666085,0.1951581836,0.0976155698,0.2587227821,0.2356418073,-0.2632692754,-0.3962749243,-0.1543958336,0.2665106952,-0.0398138203,-0.0534418225,-0.1178392321,-0.1095124632,-0.1519872099,0.1321215183,0.2445401698,0.3347284794,-0.067885384,0.1746526212,0.2386979759,-0.3149813116,0.0606371351,0.0193097442,0.0075841905,-0.1074852049,0.0635666028,0.0290941913,0.2385463417,-0.0591094121,0.2498930842,-0.2912393808,-0.1080684885,-0.0159943588,-0.1475112438,0.0228482094,0.2536226213,0.3971214592,0.3115744293,0.0693930238,0.3559366167,-0.137148425,0.1234385967,0.0896292403,-0.2077120394,-0.0085141491,0.0254325047,-0.0882945061,-0.2700825334,-0.0413119048,-0.0517634265,0.3604640663,0.0132195698,-0.435118556,0.1218436435,0.3219591081,-0.4701114297,-0.2149737626,-0.1325143576,-0.0192559194,-0.1424986422,0.4075756073,-0.1337510794,-0.1032077968,0.2736286521,0.0855005085,0.0136810727,0.0905926451,-0.6206724048,0.3858294487,0.0393181667,-0.1848632097,0.0401023738,-0.3431012332,-0.1524264812,-0.1820155233,-0.1730383933,-0.0275773164,-0.4880181253,-0.3247206807,-0.0589682125,-0.0078160465,0.0338281095,0.2875152826,-0.3181762993,0.5770632625,-0.0649325103,-0.1183059886,0.7609329224,-0.1623450071,-0.3263252676,0.1355405003,-0.3932285011,0.1056018919,-0.0285580903,0.3570363522,0.3934704661,-0.3740145266,-0.5503361821,0.2517984509,-0.050300166,-0.0115070008,0.1889352947,-0.1970737278,0.497328788,0.0133823594,0.2665420771,0.3399263024,0.0465910397,0.3440959454,-0.2913905978,-0.4086551368,0.2489724904,0.0150376074,0.2712851465,-0.007466495,-0.0214922521,-0.4259054065,-0.2000454813,-0.031809289,-0.0618993305,0.326859504,0.3026998341,0.0083405804,0.1577600837,-0.1190226078,-0.2191333771,-0.27046296,-0.0120443031,-0.3185946643,0.3225788772,-0.5568806529,-0.3735568225,0.4142183661,-0.0540543348,0.2334824353,-0.5580812693,0.1156936437,0.0233746469,0.2491554767,-0.3481840193,-0.0079235677,-0.1335957795,-0.2917013466,-0.0953387693,-0.0589290373,-0.0261338111,-0.1575662792,-0.072056435,0.4762631655,0.2876897454,-0.2148327529,0.3182387352,0.0622443222,0.2261413932,-0.1458975971,-0.3460884988,0.2852140963,-0.0535704494,0.3659822345,-0.0880966336,-0.0648378655,0.1404396594,-0.2240234315,-0.2181909084,0.1399024129,-0.1129794866,-0.072604008,0.3441033959,0.1270374954,-0.0325209014,-0.181943059,0.2679167986,-0.1034208536,0.1382667422,0.4796472192,-0.1408270299,0.2142405212,-0.3483157754,0.4952425957,0.621920526,0.1169676855,-0.2442904115,0.3060922623,0.2635382712,-0.0524754114,0.1504600942,0.1145002246,0.236633569,-0.1106892005,0.0115367128,0.355188936,0.1476724297,-0.4427650273,-0.1622827202,-0.0296411291,-0.1099239439,0.0588960797,-0.0287238415,-0.2380894721,-0.3307723105,-0.0129873464,-0.1074386165,0.0961227864,-0.0999431238,-0.1937006116,0.0738501027,0.0243850909,0.1707930714,0.3343965113,0.1304818839,-0.1242378727,0.0231084675,0.0867313594,0.1082578972,0.0292638596,-0.0491125286,0.0383777544,0.2854854465,-0.3548637927,0.101978682,-0.049428083,-0.6258038282,0.2046298385,0.0114048105,0.1709541082,0.3630703688,0.0625498071,0.0274036489,0.0422574393,0.1247981116,-0.0741003156,-0.233915478,0.0146628302,-0.068667911,0.1758811772,-0.0053665605,-0.102647908,-0.1687109917,-0.2311032563,0.4608861506,0.1201040819,0.0761675462,-0.0585082509,-0.2050844431,0.0445598252,0.0041256682,0.2625152469,-0.0093501452,-0.1128629595,0.2273569554,0.0768129528,-0.2224090695,-0.2462584972,-0.2503373921,0.034182623,0.5150563121,-0.2194655985,-0.1157506183,-0.0979348272,0.1770817786,0.0443448499,0.1360614747,0.6754920483,0.1972072721,0.1080714241,-0.0683113262,-0.2257611006,-0.0008916733,0.2898741663,0.124386169,0.4621849954,0.5110164881,-0.0024083038,0.3339212537,-0.1510581374,-0.2582774758,-0.2768117189,0.1763470769,0.0443195552,0.0027491101,-0.4018685818,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2596","title":"Transformer Class on dataset","comments":"I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\r\n\r\nI guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)","body":"Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":64,"text":"Transformer Class on dataset \n Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n \n I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\r\n\r\nI guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)","embeddings":[-0.490424931,0.0872302279,-0.1906813085,-0.0839954689,0.3473585546,-0.1753206402,0.3381084502,0.2574364245,0.0903777182,0.019790493,0.2580311894,0.3618627489,-0.3850794137,0.4767219126,0.1930702329,-0.2324400842,-0.151134789,0.265183866,-0.4250871539,-0.0168538652,-0.3289776146,-0.1533589661,-0.0330886394,0.1328707039,0.002900715,-0.1466348916,-0.0935134068,-0.3280186951,-0.0702162236,-0.1045169681,0.3167803884,0.1368869245,-0.0556037799,0.4302741885,-0.0001056646,-0.1033560857,0.1528594941,0.0172446407,0.0270981509,-0.3660884202,-0.2878669798,-0.2310528606,-0.0796364918,-0.3515015543,-0.2724731266,-0.1547861993,0.1759620458,-0.3427095115,0.5134334564,0.1626292765,0.2492758334,-0.2664002478,-0.1245223358,-0.0106896963,-0.0541695543,0.4239853621,-0.3033707142,-0.5247583389,0.4609455764,0.23065494,-0.1012291089,0.1187341437,-0.0994046628,-0.1907213777,0.630456388,-0.1156991124,-0.0931705385,-0.5817266107,-0.078451395,0.295892179,0.410266459,-0.2705188394,-0.4758317471,-0.2998233736,-0.0145636881,0.1219541728,-0.2259934396,0.2029168755,-0.0218977723,0.327022016,-0.2893729508,-0.2254891992,-0.3472728729,0.0120361308,-0.2229361981,0.1600910425,-0.097874403,-0.025985254,0.0453669541,0.0403649323,0.2370888144,0.0952521786,0.1654207408,0.1429382712,-0.0912426338,-0.428411901,-0.122181274,0.0104457103,-0.2088413686,0.2001110315,0.0329007506,0.1262459904,-0.0182830803,0.049241744,0.0054219016,0.1322811097,0.0045804046,0.0382177345,0.2880474329,-0.3633047342,-0.0313483216,0.1370051354,0.0354362167,0.0212808382,-0.1402965784,0.1661054641,0.0147123355,0.0149487611,-0.0016047077,-0.2726562321,0.1066770554,0.1336745769,-0.1337660551,0.0749790147,0.1600473225,0.3427148461,0.4094092548,0.0567138046,0.2407179177,-0.1985290796,0.0100685144,0.171739921,-0.2010185272,-0.0314506255,0.0899902508,0.0984608829,-0.1902890354,-0.085748896,-0.1360107511,0.403221637,0.2586643994,-0.0103076641,0.3266676664,0.287661612,-0.3840346634,-0.2247590423,0.0478359126,-0.0214083195,-0.2645701468,0.1571627855,-0.2843391001,-0.0495866835,0.1654486954,0.2562448978,0.0745678619,-0.0825993717,-0.2585091889,0.4359450638,0.0350625217,-0.128864184,0.1879973412,-0.2153193802,-0.2061604261,-0.0929540545,-0.1613592058,-0.085530296,-0.5210264921,-0.4330343306,-0.2928813994,0.0670860708,0.1855181307,0.1258127093,-0.0995190516,0.2549536228,-0.0540064499,0.0176642574,0.6603587866,-0.1747204363,-0.0979027748,0.2688248754,-0.357213378,-0.1598703563,0.1279026419,0.4035031796,0.3632651269,-0.2125305235,-0.4146336615,0.353454262,-0.0909692273,0.0054099564,0.1527169347,-0.2682519853,0.2469784915,0.2128084153,0.3504933119,0.2810118496,0.1294330359,0.1401317567,-0.1105129644,-0.4927369952,0.3531637788,-0.0376637019,0.1912439615,0.0206499044,0.042872902,-0.6312668324,0.188138321,-0.1162087768,-0.270983994,0.1082687154,0.2710978687,-0.1145076528,0.1172646508,-0.155006215,0.0733629018,-0.1630351245,0.2363794148,-0.3905517161,0.1708755344,-0.2011028528,-0.2399889529,0.3937875926,0.0097546885,0.053466253,-0.4360540509,0.1432032436,0.2297930866,0.1855835766,-0.1641368717,0.1989326328,-0.3491078913,-0.2043966353,0.0468196012,-0.228336513,0.0734595135,0.0671370849,0.0497843623,0.5814134479,0.2575738728,-0.0738892183,0.271571666,0.0297425967,0.1512193084,-0.0686642528,-0.1690057069,0.6479952335,-0.0027132689,0.2556301355,-0.1393957436,0.1753908992,0.2669109702,-0.3181121349,-0.265509367,-0.0300369971,-0.03970946,-0.0646846816,0.2421483845,0.0481635667,0.0189805608,0.046282962,0.251547575,0.015413492,0.2425545454,0.3825644851,-0.1510885954,0.2011816353,-0.1937962472,0.578283608,0.4914995432,0.2047153115,-0.2291341573,0.1965982318,0.1805032939,-0.0880012214,0.2533913553,0.159966737,0.305857718,0.0324335583,-0.0279732086,0.1396907419,-0.0631873831,-0.4729793966,-0.1748874038,-0.0551563501,-0.0589844957,0.2467169911,-0.2987349033,-0.3389695585,-0.1829380691,-0.0542922653,0.2892733514,-0.0513308868,0.2056132406,-0.2524846494,-0.1110305563,0.1603519917,-0.0294568706,0.5149572492,0.1022286192,-0.0487784296,-0.2807581127,-0.2141819298,0.0795971602,0.1924239844,0.0171542037,0.0326939672,0.4736204445,-0.2052599788,0.355670929,-0.2228472233,-0.5828427076,0.0999873355,-0.0393820554,0.1567539126,0.3471787572,-0.0640847683,0.1175071076,-0.0812777951,-0.0054268581,-0.1949022859,-0.288954258,-0.0580740124,-0.1537415534,-0.0682870895,-0.1009423882,-0.1854184419,-0.3057166338,-0.3653067052,0.3621490896,0.0168432351,0.1068324968,-0.146415174,-0.0528573878,0.0466547571,0.1201931238,0.1184352413,-0.0254238285,-0.0544987619,0.1331170946,-0.1406106502,-0.1921079904,-0.2421644777,-0.3054256737,0.1779670268,0.3593081534,-0.1374822557,-0.0864887014,-0.179784596,0.0269064307,0.1405414641,-0.0950946137,0.5606482029,0.0580535121,-0.1028714776,-0.1430615485,0.0960488915,-0.1594544053,0.1404044777,0.062264584,0.2014611214,0.4887650311,0.0573834777,0.3966763914,-0.2763507962,-0.5605832934,-0.1552893072,0.0559178889,-0.1592636555,0.0357731134,-0.4223344922,0.1116604879,-0.0961359441,-0.184373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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2596","title":"Transformer Class on dataset","comments":"Thanks for reply.\n\nWhat would be the constraints\nto have\nDataset \u2014> Dataset consistency ?\n\nMain issue would be\nlarger than memory dataset and\nserialization on disk.\n\nTechnically,\none still process at atomic level\nand try to wrap the full results\ninto Dataset\u2026. (!)\n\nWhat would you think ?\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 16:51, Quentin Lhoest ***@***.***> wrote:\n> \n> \ufeff\n> I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\n> \n> I guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)\n> \n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n","body":"Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":155,"text":"Transformer Class on dataset \n Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n \n Thanks for reply.\n\nWhat would be the constraints\nto have\nDataset \u2014> Dataset consistency ?\n\nMain issue would be\nlarger than memory dataset and\nserialization on disk.\n\nTechnically,\none still process at atomic level\nand try to wrap the full results\ninto Dataset\u2026. (!)\n\nWhat would you think ?\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 16:51, Quentin Lhoest ***@***.***> wrote:\n> \n> \ufeff\n> I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\n> \n> I guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)\n> \n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n","embeddings":[-0.4273887277,0.2038237303,-0.0892710388,0.0677168295,0.522382021,-0.1340120435,0.3225390017,0.1911114901,0.013091499,0.0615377389,0.2266187072,0.3506041467,-0.4103445709,0.2984676659,0.0133415768,-0.0542735718,-0.0388857797,0.2965457439,-0.5386686921,-0.0739863887,-0.167423442,-0.2261765152,-0.0746393502,-0.1188812032,-0.0701206326,-0.0560871586,-0.1632028073,-0.4052655995,-0.3135545552,-0.0657363608,0.5253243446,0.2762766778,0.0223159511,0.4755660892,-0.0001143179,-0.1631057411,0.2398658395,0.0084741171,-0.1145883426,-0.443159759,-0.338857919,-0.2092615813,-0.0825239941,-0.3510289192,-0.2636530101,0.0173992235,0.0538823754,-0.3100696504,0.4917915761,-0.0407068171,0.1410671771,-0.3664943576,0.0061739157,0.0395747162,-0.2592406273,0.4160124958,-0.1895077974,-0.265927881,0.2792956829,0.2837553918,-0.107362546,-0.0279848874,-0.0470017865,-0.2350812554,0.6600618958,-0.1943365782,0.0043311757,-0.5277867317,-0.0465322025,0.3039246798,0.6490062475,-0.3981794417,-0.4264175594,-0.1945628077,-0.0383273773,0.1389341503,-0.0498606153,0.244358778,0.017654974,0.3849679828,-0.2907490432,-0.2940432727,-0.4222829044,0.0256696455,-0.3817669153,0.1451612711,-0.0047396049,-0.0499595143,-0.0384320952,0.1308086514,0.3123420775,-0.1127013713,0.2272325754,0.0686238334,-0.1626562476,-0.5328313112,-0.3075406849,0.09869425,0.0344533175,0.1020556912,-0.0561499521,0.0376796611,-0.0647402331,0.0121175535,0.137759611,0.2655591369,-0.0177641474,0.1145247743,0.1995014101,-0.3872465491,0.0304755718,0.2286774367,0.0088771312,-0.0593662374,0.1155702844,0.1483322382,0.0488787293,-0.0583638959,0.0696761757,-0.1357873678,0.0441751406,-0.045509316,-0.2064178139,-0.0037700229,0.210297972,0.4288657904,0.2672246695,0.152155742,0.20641689,-0.2488153875,0.077928558,0.0225015283,-0.3289786279,-0.0778612942,0.0461385325,0.0587496422,-0.310872376,0.1111935154,-0.16365771,0.4183041751,0.1364359558,-0.2913190126,0.1636843681,0.1784058511,-0.3646046817,-0.0293231923,-0.0457364544,0.0250666421,-0.2207814157,0.4000896811,0.0081141181,0.0546802916,0.1735207289,0.1761144698,-0.0178801324,0.136665225,-0.2848555148,0.2927871048,0.162032038,-0.0634124801,0.1134386882,-0.4430219233,-0.1859663129,-0.1998777539,-0.1033181474,0.0172009487,-0.627992034,-0.2453715205,-0.1362820268,0.1093932837,0.036538288,0.2848489285,-0.1213752925,0.225227952,-0.1534097493,-0.2330722362,0.5798242092,-0.0970395505,-0.2454657406,0.2289628536,-0.3728942275,-0.1394857615,0.1957407594,0.2967514098,0.4605460763,-0.2393575311,-0.4908559024,0.2818575203,-0.0648387074,-0.1180446744,0.0126638273,-0.2541800737,0.3696837127,-0.0692067295,0.2602415681,0.199729383,0.0043156072,0.3201088309,-0.0260312054,-0.4473751783,0.3603182435,-0.006686795,0.4549993277,0.0567456298,0.0585832372,-0.3478059173,-0.0341736339,0.0530903563,-0.0473659523,0.246687606,0.3147389293,-0.0091185896,0.1841320395,-0.1566165984,0.033421725,-0.1146244556,0.1116199046,-0.3407637179,0.2966465354,-0.1415416747,-0.2600739002,0.5447855592,0.0473795496,0.1205488965,-0.5010402799,0.3326478302,0.0500790179,0.1671906859,-0.1501583308,0.0597823299,-0.3445490003,-0.192339316,0.0210279152,-0.1629345566,0.131426543,0.0772699341,-0.0494827703,0.6066023707,0.4095857143,-0.1700623482,0.4739151001,0.0510801636,0.1334010363,-0.1194854155,-0.1270043999,0.492796272,-0.0984402373,0.2128577828,-0.0814322904,-0.0044127326,0.2524282634,-0.2985364497,-0.3720683455,-0.0959758386,0.1043319032,-0.0137542076,0.2253137827,0.2128076702,0.015363995,0.0124664828,0.1573317945,-0.0755052119,0.142143786,0.3831191361,-0.1746159941,0.2205764353,-0.2004304379,0.4899602234,0.5510008931,0.1705225408,-0.2451501489,0.1599907428,0.1105057374,-0.065086484,0.2228177488,0.0525744408,0.3805498779,0.2248176634,-0.0298500881,0.1878754646,0.0349261984,-0.3846750557,-0.0897410288,-0.0698584244,-0.0528352521,0.0159650911,-0.2960271239,-0.2227744311,-0.4385722578,-0.0818984509,0.0492287092,-0.0255590435,-0.0404092558,-0.0424437262,0.2382252216,0.1159824878,-0.1045534015,0.3931617141,0.114002265,-0.1347837299,-0.1402972639,-0.0884842128,0.0053355726,0.0395600609,0.0950410217,0.0166571345,0.4365256429,-0.1513846815,0.1293430775,-0.0760705844,-0.5865780711,0.071924217,0.0726535991,0.130654633,0.2849867046,0.0718221292,0.1427114457,0.0545829237,0.034046974,-0.1997928768,-0.3182959855,-0.0681257844,-0.0269995295,-0.0561735779,-0.0062386389,-0.1270177513,-0.3657001257,-0.1590499133,0.2604333758,-0.0370676331,0.0225017499,-0.1918992847,0.0366403386,-0.1675834954,0.0392668284,0.1488780379,-0.1130215228,-0.0118303867,0.2623637021,0.0692795813,-0.2676432431,-0.2812874317,-0.3509054184,0.1491180807,0.2953979671,-0.2414922565,0.0110005792,-0.1852772087,0.0474693291,0.0062265396,-0.0530401058,0.6440244913,0.1352549493,-0.0090843812,-0.1666705012,-0.0123998169,-0.0724096671,0.0961196497,0.1759649664,0.3626632094,0.4776412547,0.1353176981,0.4528478682,-0.0309897773,-0.5186715126,-0.2558555901,0.2427676022,-0.0109792529,0.0309007037,-0.4694468975,0.2096977979,-0.0902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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2596","title":"Transformer Class on dataset","comments":"We can be pretty flexible and not impose any constraints for transforms.\r\n\r\nMoreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like `map` work in a batched fashion to not fill up your RAM. So this shouldn't be an issue","body":"Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":59,"text":"Transformer Class on dataset \n Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n \n We can be pretty flexible and not impose any constraints for transforms.\r\n\r\nMoreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like `map` work in a batched fashion to not fill up your RAM. So this shouldn't be an issue","embeddings":[-0.540179193,-0.0189927835,-0.1440081596,0.1232815832,0.4372493029,-0.2170054168,0.245312959,0.1936611384,0.2192274332,0.130491212,0.0615159795,0.187767759,-0.2603258789,0.07238736,0.0481922179,-0.0115978345,-0.0782924369,0.2525763512,-0.5557228327,-0.0573627502,-0.3713938892,-0.1573706418,0.0135659892,-0.1440851539,-0.0328940004,-0.2428776622,0.0715212226,-0.01376657,-0.1378084719,0.0672599301,0.3069247305,0.1395094246,-0.1308079958,0.3437947035,-0.0000978967,-0.0323359221,0.0386952013,-0.1293641925,0.0463968217,0.1244557947,-0.1310040504,-0.1104852259,-0.1095086113,-0.3612022996,-0.2468073219,0.0423793793,0.0283964686,-0.3146081269,0.2295787483,0.1305739284,0.354373306,-0.1699274927,-0.1962252706,-0.053974133,0.2161227465,0.1079380736,-0.0890014172,-0.1663483828,0.2665728331,0.2456260324,-0.312132746,0.2356686741,-0.0461950712,0.0065835495,0.5308229327,-0.1477865726,0.0746076852,-0.1883723289,-0.0439591147,0.2111916542,0.3024063706,-0.3585546911,-0.1495787352,-0.1898489743,-0.0642794892,0.1807121933,0.1255578697,0.0910863951,-0.1162973195,0.1236252263,-0.2932367325,-0.2158868462,-0.185869202,0.0246883072,-0.3864739239,0.0843880698,0.0810551643,-0.0198896565,0.2320849746,-0.1219940484,0.0640956834,0.233398959,0.0315565765,0.0362053812,-0.107584089,-0.428006649,-0.3164810836,-0.0104847215,0.140488416,-0.0769903585,0.0305277966,0.2146950513,0.0684990287,0.0404782556,0.0683705509,0.2132992595,-0.3577131033,0.0629388914,0.3471935987,-0.2959295809,-0.0947281048,0.1421936303,0.1777654141,-0.1919908226,-0.0374680385,-0.187868312,-0.1458259523,-0.1118825898,0.0392557308,-0.1497556716,0.2410176247,0.2816529274,-0.0548955686,0.3797278702,0.0164872911,0.122593388,0.4158708155,-0.0704298615,-0.1016627997,-0.0267893784,-0.1234514937,0.1682244688,-0.2392304242,-0.1178169474,0.1755443364,0.2547565997,-0.0090133147,-0.1393259615,-0.0014683313,0.4502189457,0.1454497874,-0.1632993966,0.3711447716,0.3215205669,-0.3073906898,-0.2312166244,-0.1824297607,0.1355635822,-0.224871546,0.2574270666,-0.0100257434,-0.0601822175,0.2163721919,0.3365978003,-0.0965414643,0.0889814273,-0.0164763294,0.1412249804,0.0578206107,-0.2100098431,0.0223715231,-0.1844257265,-0.1561108828,-0.1307398826,0.0042210277,0.0234843884,-0.3599258363,-0.196546793,-0.0161801074,0.0299439281,0.0393635854,0.2355713397,-0.2584232688,-0.0666704699,-0.1518525928,-0.0959847718,0.5436256528,-0.0873637274,-0.5102764964,0.1716724485,-0.307172209,-0.2066991627,0.0392220244,0.5570349097,0.3030121326,-0.0606620498,-0.2779816985,0.4389851987,-0.0120839952,0.2393550426,-0.1522180885,-0.3807231188,0.2241461128,0.0999747664,0.4358043671,0.069594577,-0.004208317,0.4479361176,0.0191990659,-0.3247076273,0.2287278026,0.0903669521,0.2029692382,-0.1758003682,0.0514797978,-0.3881592751,-0.029957436,-0.0194903668,-0.1231638268,0.1746489406,0.3121495247,-0.0633318052,0.093969591,-0.2406131178,0.0662423149,0.052394297,0.3378172517,-0.0506487973,0.2560933828,-0.3789431751,0.0967584029,0.3695116937,-0.0359232277,0.0900141522,-0.4097872674,0.0930517539,0.0395775288,0.0411274321,0.0174408313,-0.0858047009,-0.159468174,-0.1214016229,0.0888800398,0.0884446204,0.1423340589,-0.0831505358,-0.225153029,0.3935472965,0.2967004776,-0.0864641517,0.4523482323,0.2250223905,0.1164681241,-0.054058414,-0.2326427102,0.3532539606,0.0675226301,0.1176048443,0.0894607902,-0.0354507528,0.3178417683,-0.2105885148,-0.0916877463,-0.2391735613,-0.0803821012,0.1462734789,0.2722612321,0.241806075,-0.1434707642,-0.1761649847,0.2574052215,0.095572263,0.2356152534,0.2567685843,-0.1985844076,0.168779552,-0.0230590459,0.3929041922,0.4536697268,0.3772838116,-0.0321456678,0.0570631735,-0.0334798992,-0.128506422,0.1743486375,-0.0611710101,0.2145086229,-0.0075750812,0.0224812124,-0.0848525614,-0.3140361011,-0.2202801704,0.0788237676,-0.0040893168,-0.1285701245,-0.0825884491,-0.2104412764,-0.2082371861,-0.1002784669,0.1774028689,-0.1065199599,0.1337847114,0.1469562948,0.0870128423,-0.1986066252,0.1812264919,0.0617287904,0.3059969544,0.1493957341,-0.2343177944,-0.010339994,0.0442344993,-0.0209342781,0.2839320004,0.0812600628,0.0301540904,0.3573117256,-0.0325139053,0.0159158316,-0.1075667888,-0.4170719087,0.021991238,0.0508672036,0.1327099055,0.2242882103,0.1550285071,0.1264584213,0.0545450002,0.0223775897,-0.2790865898,-0.3149189949,-0.0249228776,-0.0592014007,0.1274024546,-0.2830105424,-0.2389216274,-0.0929963067,-0.5055266619,0.2309706211,0.1451750249,0.0573420674,0.0028792156,0.2555831075,-0.175446555,0.1253508627,0.1729648709,-0.2668440342,0.023113925,0.4328384399,-0.0556156076,-0.2987732887,-0.1864276677,-0.2419093251,0.216374889,0.5209243298,-0.4316136837,-0.0116054965,-0.2875873744,-0.0104507301,0.0108654983,-0.0455288813,0.5706766844,0.0471077152,-0.2182017267,-0.0778356642,-0.1106931269,0.0597552098,0.150663957,0.016526347,-0.0133856088,0.2726939917,0.0209518801,0.2372093052,-0.4598437548,-0.3791241348,-0.0370535441,0.1754178852,0.132338658,-0.0176740009,-0.1120066792,0.1540552825,0.1146941185,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2596","title":"Transformer Class on dataset","comments":"Ok thanks.\n\nBut, Dataset has various flavors.\nIn current design of Dataset,\n   how the serialization on disk is done (?)\n\n\nThe main issue is serialization \nof   newdataset= Transform(Dataset)\n (ie thats why am referring to Out Of memory dataset\u2026):\n\n   Should be part of Transform or part of dataset ?\n\n\n\n\nMaybe, not, since the output is aimed to feed model in memory (?)\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 18:04, Quentin Lhoest ***@***.***> wrote:\n> \n> \ufeff\n> We can be pretty flexible and not impose any constraints for transforms.\n> \n> Moreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like map work in a batched fashion to not fill up your RAM. So this shouldn't be an issue\n> \n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n","body":"Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":162,"text":"Transformer Class on dataset \n Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n \n Ok thanks.\n\nBut, Dataset has various flavors.\nIn current design of Dataset,\n   how the serialization on disk is done (?)\n\n\nThe main issue is serialization \nof   newdataset= Transform(Dataset)\n (ie thats why am referring to Out Of memory dataset\u2026):\n\n   Should be part of Transform or part of dataset ?\n\n\n\n\nMaybe, not, since the output is aimed to feed model in memory (?)\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 18:04, Quentin Lhoest ***@***.***> wrote:\n> \n> \ufeff\n> We can be pretty flexible and not impose any constraints for transforms.\n> \n> Moreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like map work in a batched fashion to not fill up your RAM. So this shouldn't be an issue\n> \n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n","embeddings":[-0.3028283417,-0.0752469152,-0.05205011,0.2568613887,0.4963202775,0.0333011746,0.3464813828,0.1359570324,0.1084691659,-0.0489086248,0.1433253735,0.1807944179,-0.2497085929,0.0995197967,0.1433494985,0.0294342879,-0.0367588326,0.2082752585,-0.6057528853,-0.110377945,-0.2944284678,-0.1542579234,-0.105689235,-0.1136090979,-0.0622637235,-0.1996254027,-0.1581871808,-0.1262419373,-0.2569142282,-0.075012356,0.420137316,0.0989703834,0.1109477431,0.5628956556,-0.0001049531,-0.1980094761,0.0629667714,-0.1417288631,-0.0416416563,-0.1271471083,-0.110590592,-0.1241359413,-0.0914635956,-0.3788447976,-0.2515878081,-0.012632167,0.042666167,-0.1632636189,0.2673305273,0.1891238987,0.2877258658,-0.2571978569,0.0613154992,-0.1159944683,-0.0906342044,0.4980603456,-0.2132179588,-0.1317691058,0.1639421731,0.2951180041,-0.1768623143,0.1194915324,0.0316155441,-0.0623987168,0.6949888468,-0.1144897491,0.1647530645,-0.3416478038,-0.047490526,0.1770274639,0.6858400702,-0.4208053648,-0.2926061153,-0.1978127658,0.0413535796,0.1125580594,0.0736283138,0.2918520272,-0.1023894027,0.4228276312,-0.290635854,-0.1578572839,-0.212265715,-0.0273612179,-0.3538392782,-0.0272985063,-0.0485696979,0.0704357848,-0.0280704889,-0.0896413997,0.1152463704,0.0687420294,0.085941568,0.0053374721,-0.0471011549,-0.4889363945,-0.2492946684,-0.1057617217,0.1770293117,0.0206169728,-0.1055535376,-0.0825049132,0.0663288385,-0.0880444124,0.17521891,0.3654631376,-0.1550439894,0.1602373272,0.0713833496,-0.3452311158,-0.0499749109,0.0044703535,-0.0236585438,0.0380209535,0.2002761662,-0.0101096481,-0.0094334157,-0.0741236731,0.1005777717,-0.2199292779,0.1885883063,0.1436040848,-0.1644640416,0.256283164,0.1343160272,0.2364140302,0.2878935933,-0.0608223677,0.0728546306,-0.1835631281,-0.0920746624,0.2097067535,-0.3946242034,-0.0316818357,0.0614863746,0.262503773,-0.098427169,-0.1631225944,-0.1027049199,0.293238163,0.0450083837,-0.2885059714,0.37007761,0.2936200202,-0.5175172687,0.0034238908,-0.0082310745,0.090826489,-0.2388359904,0.3887875378,-0.0808293521,-0.0568780564,0.1232487559,0.2654306293,-0.0568023287,0.1304450184,-0.2425501645,0.2334434688,0.2355148196,-0.1140492633,0.0523458868,-0.2796918154,-0.0145570571,-0.2248253822,0.0019177148,0.1308251768,-0.550594151,-0.1371417493,-0.0929638222,0.0054792524,0.2257186919,0.2538051009,-0.2389266789,0.2112432718,-0.2781811357,-0.1598565429,0.5308234692,-0.0717295632,-0.3687420785,0.2272962779,-0.3469464183,-0.2316666543,0.1396826655,0.3911378384,0.3910191357,-0.0739194453,-0.3169713914,0.3255683184,-0.1547599733,0.0172961466,-0.1826123446,-0.2983471453,0.4146893024,0.0043986351,0.3740959764,0.3172221482,-0.0899922773,0.5544752479,0.0361762792,-0.1642745286,0.2736387849,0.0395088308,0.3699331284,-0.2187348157,0.0669548362,-0.3890495896,-0.1323824525,-0.0164715406,0.0716964751,0.2384104282,0.1521542966,0.0425674058,-0.0351381898,-0.1884233207,0.1005936936,-0.1441040337,0.2575828731,-0.0682228804,0.2908599973,-0.3608987927,-0.1201941073,0.3291369975,0.0169595778,0.0997303575,-0.4722500443,0.135290727,-0.1037477329,0.0637206659,-0.1334819943,-0.1195375398,-0.1471011341,-0.2059051692,0.027681306,0.045600161,0.1006959081,-0.072777003,-0.1369668692,0.4407846332,0.3833371401,-0.2213459909,0.4104960263,0.1753512174,0.197182104,-0.1581744701,-0.1563640684,0.3226000369,0.0591880493,0.0539624803,-0.0045891628,-0.0444103889,0.2749353051,-0.1846638918,-0.1495006084,-0.2213106602,-0.093039088,0.1528907418,0.2064580917,0.2672087252,-0.0180213116,-0.0872312263,0.2759074867,-0.0993304923,0.1754228622,0.2399032116,-0.2363844812,0.0967985913,-0.1155927703,0.3368810117,0.4515927434,0.2688371539,-0.0172095709,0.0994065106,0.2248305678,-0.1374188513,0.2106560916,-0.010123007,0.3061108887,0.0928651989,-0.0435660295,0.0148412464,-0.1426321864,-0.2731765807,0.2011459768,0.1052608714,-0.0868094042,-0.0827878341,-0.0791172609,-0.0586189143,-0.3210164011,0.1139508635,0.0012527817,0.1354949921,-0.0451803915,0.0133053074,0.0678712949,0.1548039466,-0.0258904975,0.2285887897,0.1403194815,-0.022201566,-0.0016004929,0.0719159022,-0.012956596,0.1860071272,0.0082577905,-0.0523206517,0.3632893264,0.0237425417,0.0723289773,-0.0993584543,-0.3952199817,0.100798808,-0.0392054431,0.1362577081,0.2487015575,0.1583067477,0.0638481453,0.073958613,0.1003383845,-0.1901111752,-0.2742712498,0.0383124985,-0.0836613625,0.1695398688,-0.1737304032,-0.3793549538,-0.2794141173,-0.3382386863,0.1888036579,0.0238642674,0.0039382828,0.1018079519,0.0851486549,-0.1637770087,0.1167969406,0.135999456,-0.2013310045,0.0089232298,0.3245040178,0.0960316285,-0.3421610594,-0.1381985247,-0.154382363,0.01020806,0.3889779449,-0.3169546425,-0.0313842595,-0.2151265293,-0.028546948,-0.0322704613,-0.1737960577,0.625001967,-0.0079119746,-0.1444297731,-0.0402277,-0.1125065014,-0.0756177604,0.1865358055,0.127004683,0.05392823,0.4150843918,0.1548915505,0.1784219891,-0.2313495427,-0.3481871486,-0.1597625017,0.2163899988,0.0208490193,-0.0015360153,-0.2815902829,0.2986260056,0.08677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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2596","title":"Transformer Class on dataset","comments":"I'm not sure I understand, could you elaborate a bit more please ?\r\n\r\nEach dataset is a wrapper of a PyArrow Table that contains all the data. The table is loaded from an arrow file on the disk.\r\nWe have an ArrowWriter and ArrowReader class to write\/read arrow tables on disk or in in-memory buffers.","body":"Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":55,"text":"Transformer Class on dataset \n Just wondering if you have intenttion to create\r\n\r\nTransformerClass :\r\n    dataset --> dataset\r\n\r\nand make determnistic transformation (ie not fit).\r\n\r\n\r\n\r\n\r\n\r\n \n I'm not sure I understand, could you elaborate a bit more please ?\r\n\r\nEach dataset is a wrapper of a PyArrow Table that contains all the data. The table is loaded from an arrow file on the disk.\r\nWe have an ArrowWriter and ArrowReader class to write\/read arrow tables on disk or in in-memory buffers.","embeddings":[-0.3604480028,-0.056124229,0.0116531681,0.2704983056,0.3946083188,-0.0971958861,0.3508632779,0.0482230633,-0.100028649,-0.1611097008,0.1317931861,0.3815632463,-0.3154996634,-0.0833913833,0.2542794347,-0.2325899005,0.0182949621,0.4430117309,-0.3059166968,-0.1537890881,-0.2024315447,-0.2975797653,-0.1411009431,0.1563541144,-0.108661823,-0.1525422484,-0.1539614797,-0.3630880117,-0.3081549406,-0.1566582322,0.6911965609,0.0511289686,0.0235325266,0.5484084487,-0.0001298558,-0.0235750005,0.0727151707,-0.1433799416,0.1298894733,-0.1923117191,-0.2210621834,-0.033829201,0.1811142266,-0.3892242312,-0.3804796934,-0.2979854345,-0.0040941914,0.051528912,0.5385863185,0.1942201555,0.0866092443,-0.3026257753,0.0551640689,0.1068512052,0.2160902172,0.3861628771,-0.2014035583,0.0175133739,0.1515752673,0.2822321057,-0.15152888,-0.1318753958,-0.1761910468,-0.0272739865,0.8690388799,0.0244412255,0.1509451866,-0.3632725477,-0.2043594569,0.2908260524,0.7759717107,-0.5236781836,-0.1550633758,-0.0679519549,0.1838050336,0.1550900936,0.0083903447,0.2853395939,-0.1065251231,0.2195320576,-0.1110390648,-0.2602996826,-0.5028033257,0.1028025374,-0.521471858,0.1457862109,-0.0008760057,0.1302547306,-0.0523233823,0.0741430968,0.420173645,0.0259791929,0.0769913048,0.2010128349,-0.1059709489,-0.4257588089,-0.4286718965,0.1216747537,-0.0518270582,0.0706445873,-0.0520828366,-0.2472423762,0.1668996215,0.0298987795,-0.0434882902,0.2679726779,-0.0275975652,0.3352452517,0.0658622608,-0.3162097633,-0.1533956379,0.1382795125,-0.0609802641,-0.2558608055,0.2248512506,0.0547069982,0.145677194,-0.0902904868,0.1242999882,-0.2215955853,-0.0831000656,-0.1453952938,-0.2076639533,0.1542184204,0.2172251642,0.0267329048,0.2401559651,0.2879996896,0.1310458928,-0.2053761184,0.080245629,0.2888303101,-0.4228098392,-0.1289700866,0.0684534237,0.169024229,-0.3287172318,0.0584953316,-0.2053339928,0.2638606131,0.0007679323,-0.321113646,0.251339674,0.3396914303,-0.4704187214,-0.1540033817,0.1538810432,-0.1303744316,-0.2196123451,0.6994740963,-0.1940143704,-0.0101558361,0.0722472891,0.0758103952,-0.0002412482,0.0676918253,-0.2430974692,0.166962117,0.2110141367,-0.1898816079,0.3288912773,-0.3618783653,0.1388187855,-0.2993890345,-0.088786222,0.1197607517,-0.7907062769,-0.2019080669,-0.014581684,-0.0888852552,0.184751302,0.2106100917,-0.3140335381,0.3180654943,-0.119111225,-0.0558806583,0.7501407862,0.0790741518,-0.3657938242,0.1410096586,-0.4655134976,-0.3085185885,0.2650138736,0.483253926,0.2425493002,-0.0904032215,-0.5927631259,0.1744460166,0.0494939573,-0.1906802803,0.0165515766,-0.1602030545,0.4672031403,0.0275996495,0.2890078723,0.2277741879,0.1661888808,0.1460767388,-0.0301545355,-0.3529479206,0.2881473005,0.0217043664,0.3800839484,-0.0008921814,0.0429793745,-0.2651746571,-0.0465587564,-0.116854921,-0.214377284,0.1626737565,0.1254605502,-0.085994035,0.0401732624,-0.218502894,-0.2053408772,-0.1495812833,0.0275096651,-0.376270473,0.3453819156,-0.3231988549,-0.204575941,0.3623326123,0.032749325,0.2075287998,-0.4299808443,0.4497601688,-0.0851841941,0.0589445829,-0.2049827874,-0.1223996505,-0.0487961695,-0.1631944478,-0.0185094532,-0.0753683671,0.0226385035,-0.2253183275,-0.0744982287,0.3795029819,0.2443033308,-0.6910700798,0.6359952688,-0.0714266971,0.1448139697,-0.2594786584,-0.1614321768,0.4715094864,-0.0678142235,0.2263817489,-0.2780797184,-0.0126201846,0.1119962484,-0.3335904181,-0.1063085422,0.1066768095,-0.0678891838,0.1117890179,0.2380772233,0.2574709952,0.0649888888,-0.186487183,0.1255374253,-0.1597726792,0.2351240218,0.4662559032,-0.2535711229,0.2055148482,-0.1791690141,0.3958261311,0.6401337385,0.1718729734,-0.3124704659,0.2381339371,0.0387583189,-0.0405567028,0.2972995937,-0.0722031221,0.3564677536,0.0087075802,-0.1942992806,0.1325372458,-0.0467172563,-0.2978574038,0.0913365409,0.1649499834,-0.2284638584,0.0794563442,-0.0708314478,-0.1709196419,-0.4144055843,-0.0693010539,0.0078859618,0.2818384171,-0.0862996578,-0.1729284525,0.3115230799,0.0573587082,-0.1573171616,0.1988903731,0.2325231135,-0.1714354753,-0.1934318691,-0.1323370934,-0.0262800548,0.0499790609,-0.0661576539,0.0992844701,0.2294830531,-0.2976982892,0.2404411435,-0.0680991858,-0.294751972,0.0200556256,-0.0614587069,0.022762306,0.2964047194,0.258754164,-0.1494279504,-0.1004934162,0.1512501091,-0.1010407507,-0.3608795702,0.1753015518,-0.2611690462,0.0857438892,0.179305926,-0.1949099451,-0.2642946243,-0.0162403081,0.1638755649,0.1531158835,0.0431888364,-0.1628793478,0.1258719712,-0.0752598196,-0.0013590266,0.100743182,0.0334941223,0.1717408448,0.3631780148,0.1620592624,-0.1893088967,-0.0503245331,-0.2994683385,0.0299040433,0.3481313288,-0.4328601956,0.0511916466,-0.1269654632,0.126892671,-0.147893399,-0.013433788,0.6234856248,0.1828055978,0.0386355966,-0.0361397006,-0.0435907207,-0.0636737719,0.016723467,0.1787310392,0.132709071,0.4253961444,-0.12833713,0.458335191,-0.3325755298,-0.35143435,-0.1117647588,0.1025599465,-0.0301402267,0.131637916,-0.3031069338,0.0468979031,0.1495187879,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2595","title":"ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets","comments":"Hi @profsatwinder.\r\n\r\nIt looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.","body":"Error traceback:\r\n---------------------------------------------------------------------------\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-8-a7b592d3bca0> in <module>()\r\n      1 from datasets import load_dataset, load_metric\r\n      2 \r\n----> 3 common_voice_train = load_dataset(\"common_voice\", \"pa-IN\", split=\"train+validation\")\r\n      4 common_voice_test = load_dataset(\"common_voice\", \"pa-IN\", split=\"test\")\r\n\r\n9 frames\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/common_voice\/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b\/common_voice.py in <module>()\r\n     19 \r\n     20 import datasets\r\n---> 21 from datasets.tasks import AutomaticSpeechRecognition\r\n     22 \r\n     23 \r\n\r\nModuleNotFoundError: No module named 'datasets.tasks'","comment_length":27,"text":"ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets \n Error traceback:\r\n---------------------------------------------------------------------------\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-8-a7b592d3bca0> in <module>()\r\n      1 from datasets import load_dataset, load_metric\r\n      2 \r\n----> 3 common_voice_train = load_dataset(\"common_voice\", \"pa-IN\", split=\"train+validation\")\r\n      4 common_voice_test = load_dataset(\"common_voice\", \"pa-IN\", split=\"test\")\r\n\r\n9 frames\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/common_voice\/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b\/common_voice.py in <module>()\r\n     19 \r\n     20 import datasets\r\n---> 21 from datasets.tasks import AutomaticSpeechRecognition\r\n     22 \r\n     23 \r\n\r\nModuleNotFoundError: No module named 'datasets.tasks' \n Hi @profsatwinder.\r\n\r\nIt looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.","embeddings":[-0.4680482745,-0.2621825933,-0.0422590971,-0.1249682978,0.2041023821,0.1450624764,0.4402432442,0.2543092072,0.2220357209,0.1348177344,-0.2110196054,0.2088879645,-0.279766947,-0.0780611783,0.0812453702,-0.0557086468,0.0423195399,0.2207903266,0.0042473162,-0.1911050677,-0.0319739655,0.1782887131,-0.3306013346,0.2055014223,-0.3548139334,-0.2180955708,0.0779372454,0.0423675068,-0.0422454886,-0.2438998669,0.1500824243,-0.0146506298,0.070500724,0.4818688333,-0.0001044994,0.0464495383,0.3543689251,-0.0503025986,-0.3212524354,-0.4222036004,-0.3318475187,0.0348274596,-0.0461382121,-0.1579176635,0.1033446863,0.1319067329,0.1263466626,-0.1368252337,0.5385984182,0.3482384384,0.2840605378,0.4131548703,0.1567139328,-0.1267151833,0.0197893772,0.2498226613,-0.0403087847,0.2972659767,0.0895575508,-0.1931072623,0.3447549641,0.2893808484,-0.2390359789,0.063667126,0.0798237845,-0.0465243123,-0.0414960198,-0.4952995181,0.0744473934,0.132193625,0.3183714747,-0.4103887081,-0.1930001378,-0.2050786465,0.1101318151,-0.2723012567,0.0004303494,-0.0182032287,-0.132379517,0.1062286645,0.1162843257,-0.1884781122,0.0229160469,0.0268927347,-0.0017303053,-0.0327819213,-0.0579357445,-0.1311632842,0.2440393865,-0.1913584322,-0.1221389621,0.236902833,0.1770546585,0.5055808425,-0.4833761454,-0.1723853052,0.2230124623,-0.2537678778,0.0828389078,0.0121455379,0.0260496307,0.1074604318,-0.1929781884,0.2938083112,0.1571602672,0.2055592388,0.2407598346,0.3080771863,0.2705402672,0.1983297765,-0.0529231802,0.1062482819,-0.2731648088,-0.16814138,0.2600198984,-0.0516615026,0.4945361614,-0.1721232682,-0.5520052314,0.0645157322,-0.1806008816,-0.0973164216,0.0437590554,0.2725822926,-0.0597683676,0.2682129443,0.0992677733,0.0980217084,-0.3125715852,-0.083981581,-0.2083452344,0.1204670966,-0.1339889616,0.0375394039,0.1968412548,-0.1620706022,0.1611405611,0.0262966827,0.1254214644,-0.0994550213,0.0607768632,-0.0804267302,-0.3314159214,0.1653537005,-0.0582629256,-0.0140873948,0.044594083,0.0253963694,-0.1504641473,0.0868899375,-0.3289185762,-0.1916423887,0.0417402312,0.2643729448,-0.0014749963,-0.259262681,-0.1852260083,0.0195644684,-0.0112829041,-0.3709941208,-0.1309114099,-0.3607839644,0.066148676,0.1108472869,0.3037349284,0.2592234015,-0.0803736299,-0.254234314,-0.1639973223,-0.1825810671,0.0133437915,0.3237006068,-0.0084946277,0.1405990422,-0.0637404993,0.4380916059,0.2472978979,-0.452693969,-0.2491369992,0.1340448409,0.0961463302,0.0678056851,-0.1399039924,-0.214760378,0.1372883916,-0.0111433854,0.3468581736,0.1733139008,-0.0133900391,-0.1076784283,-0.0589140691,-0.1439610422,0.1319515407,0.2343855649,0.0049605146,-0.04832251,-0.0427682996,0.1929655075,0.1269651949,-0.1789568216,0.0387045667,0.2070473135,0.4356282353,0.0695726126,-0.089049004,-0.4199829996,-0.1745185405,0.0376326963,0.021216793,0.2585080266,-0.2317058444,-0.0989650488,-0.405416131,-0.1051226109,-0.3773017526,0.1202742383,0.1669739634,0.099082917,-0.0610881634,0.0066819703,-0.2997961044,0.3757269382,-0.1313783824,0.0944234282,-0.3323701024,0.3736220896,-0.237708658,-0.2670727968,0.2409504801,0.411372602,-0.0041607246,0.0625160709,-0.1170939207,0.2125421613,-0.0076911575,0.172198087,-0.2736349404,-0.075165987,0.0657028928,-0.3961906731,0.0338857807,0.1110476479,0.0426315181,-0.0886901394,0.028556956,0.1296461821,0.1537565589,0.1063358858,0.1165112779,0.0868430957,0.1682522446,-0.0683105662,-0.2281047553,-0.0361748189,0.2848306596,-0.1852824241,0.4031330645,-0.0381330252,-0.6622078419,-0.3254399598,0.4939145148,-0.023165077,0.2633921504,0.0882816017,-0.2232540697,0.2004333287,0.1287862211,-0.1824391335,0.4371800423,0.2529809177,-0.1184101105,0.2785458565,-0.1213825792,-0.0148254698,-0.0021355702,0.0376375988,0.0944892615,0.1906543225,0.0539587885,0.1309286952,-0.4432955384,-0.2492175102,-0.2248527259,0.1723359972,-0.3543302417,-0.034156058,-0.2780617774,-0.048387751,-0.1897361577,-0.0976622403,-0.2422444671,-0.0885605216,-0.0185894705,0.1371502578,0.1353910863,0.4319052696,-0.0441789739,-0.0619513579,0.046910055,-0.4555506408,-0.1673716903,-0.1692593843,-0.0982952863,0.1030317545,0.3574747145,0.0153019419,0.2674027383,-0.2192237973,-0.0046595428,0.0495451801,-0.3638139665,-0.0044207326,-0.2471049726,0.1006778479,0.028096972,0.1944648176,-0.087781027,-0.2716493011,0.2568017244,-0.2902956009,-0.3031878173,0.0084943045,-0.0715098754,-0.2179573327,-0.1056061089,-0.6792387962,-0.4916081429,-0.3824001849,0.1891574264,-0.004323863,-0.0504479334,0.5760887861,-0.0038484496,0.1662248075,-0.0396011472,0.2001395673,-0.1855196953,-0.00141783,0.3684045672,-0.1762257516,-0.198447302,0.0650722608,-0.2073300034,0.6107491851,-0.1771792918,-0.1790088862,-0.0992143452,-0.1153991073,0.2072138935,-0.1426563114,0.1987741143,0.2562193871,0.2243711203,-0.0665225461,-0.0385431163,-0.2290917933,-0.0892246291,0.5440238714,0.1236746758,0.0299236327,0.3696120977,-0.2337699234,0.7312172651,0.2542453706,0.0529726259,0.225074172,-0.1787748784,0.2573381066,-0.2329882383,-0.4067516625,0.2250757366,0.05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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2595","title":"ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets","comments":"@albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again. ","body":"Error traceback:\r\n---------------------------------------------------------------------------\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-8-a7b592d3bca0> in <module>()\r\n      1 from datasets import load_dataset, load_metric\r\n      2 \r\n----> 3 common_voice_train = load_dataset(\"common_voice\", \"pa-IN\", split=\"train+validation\")\r\n      4 common_voice_test = load_dataset(\"common_voice\", \"pa-IN\", split=\"test\")\r\n\r\n9 frames\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/common_voice\/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b\/common_voice.py in <module>()\r\n     19 \r\n     20 import datasets\r\n---> 21 from datasets.tasks import AutomaticSpeechRecognition\r\n     22 \r\n     23 \r\n\r\nModuleNotFoundError: No module named 'datasets.tasks'","comment_length":17,"text":"ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets \n Error traceback:\r\n---------------------------------------------------------------------------\r\nModuleNotFoundError                       Traceback (most recent call last)\r\n<ipython-input-8-a7b592d3bca0> in <module>()\r\n      1 from datasets import load_dataset, load_metric\r\n      2 \r\n----> 3 common_voice_train = load_dataset(\"common_voice\", \"pa-IN\", split=\"train+validation\")\r\n      4 common_voice_test = load_dataset(\"common_voice\", \"pa-IN\", split=\"test\")\r\n\r\n9 frames\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/common_voice\/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b\/common_voice.py in <module>()\r\n     19 \r\n     20 import datasets\r\n---> 21 from datasets.tasks import AutomaticSpeechRecognition\r\n     22 \r\n     23 \r\n\r\nModuleNotFoundError: No module named 'datasets.tasks' \n @albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2591","title":"Cached dataset overflowing disk space","comments":"I'm using the datasets concatenate dataset to combine the datasets and then train.\r\ntrain_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])\r\n\r\n","body":"I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).\r\n\r\nThe cache folder is 500gb (and now my disk space is full).\r\n\r\nIs there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).\r\n\r\nThis might not technically be a bug, but I was unsure and I felt that the bug was the closest one.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 121, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 186, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 397, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1983, in _map_single\r\n    writer.finalize()\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 418, in finalize\r\n    self.pa_writer.close()\r\n  File \"pyarrow\/ipc.pxi\", line 402, in pyarrow.lib._CRecordBatchWriter.close\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nOSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n","comment_length":18,"text":"Cached dataset overflowing disk space \n I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).\r\n\r\nThe cache folder is 500gb (and now my disk space is full).\r\n\r\nIs there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).\r\n\r\nThis might not technically be a bug, but I was unsure and I felt that the bug was the closest one.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 121, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 186, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 397, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1983, in _map_single\r\n    writer.finalize()\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 418, in finalize\r\n    self.pa_writer.close()\r\n  File \"pyarrow\/ipc.pxi\", line 402, in pyarrow.lib._CRecordBatchWriter.close\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nOSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n \n I'm using the datasets concatenate dataset to combine the datasets and then train.\r\ntrain_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])\r\n\r\n","embeddings":[0.0375595093,-0.4222126603,0.0985903814,0.4514758587,0.1011737883,0.2050561309,0.0744853392,0.141904518,0.1445289999,0.0556368642,0.4010794461,-0.2280424833,-0.1242998913,0.1127196699,0.1466887593,0.0686893761,0.2591468096,-0.2458593994,-0.0610483401,0.1060899645,-0.1954928488,0.2656945884,0.0236382782,-0.1252904385,-0.2641810179,-0.1312523186,0.1114902273,0.0915549919,0.1169939488,-0.2215207964,0.6455171704,-0.037543118,0.2969840467,0.3342037201,-0.0001271115,-0.2188411504,0.0915412679,-0.181593284,-0.0922594294,0.5504892468,0.1914410889,-0.3445687294,-0.0522951484,0.0128892465,-0.1940163374,0.1353006959,-0.0969077572,-0.4302129745,0.2448510528,0.0724848434,0.0480299257,-0.0077622137,-0.1181686893,0.1708012968,-0.1092367396,-0.0044049905,-0.2483725548,0.1998682469,0.2498452663,-0.1698941886,-0.1137219444,0.5557486415,-0.0590143166,-0.0844287351,0.5253401995,0.0833818987,-0.3562970459,-0.1106624231,0.3813790679,-0.0801356807,0.314463526,-0.4211860299,-0.0359757133,-0.5416691303,0.1242926195,-0.4084812701,0.4917798042,0.1439301074,-0.3081754148,0.2055083513,-0.2643952072,-0.4621683359,-0.2639658749,-0.1211516634,-0.0510151535,-0.1692601889,-0.1779297143,0.0038869162,0.4981890023,-0.1243522316,-0.0180550069,-0.1148280278,-0.1485933661,0.1664890051,-0.4704052806,-0.1601628959,-0.1597560495,0.4838789701,0.1778159291,0.0343691669,0.0580152795,-0.2219776958,0.0135496575,-0.0545078851,0.0384909436,0.6338197589,-0.4302008152,0.1479855031,0.0260615703,-0.1004753411,-0.0664962679,-0.0840651393,-0.0061190045,-0.388866663,0.2431183755,-0.3094467223,0.0366061553,-0.266004473,-0.1329783052,0.1096001938,0.1877570152,0.1407824308,0.2514514029,0.3864438534,0.1821056455,0.3051152825,-0.322846204,0.2218101174,-0.2443430275,-0.1192895547,-0.0440737009,-0.0846645087,0.0494980812,0.3295774758,0.3280156553,-0.1158240363,-0.0438293442,-0.1668728292,0.2368198782,-0.0264211819,0.0309991352,-0.2585927844,0.2868451476,0.1123239547,-0.3598536849,0.225169003,0.2425775528,0.3989651501,-0.2411044687,0.1247824728,-0.3550453782,-0.2518386543,0.1480785757,-0.01426965,-0.1831807494,-0.0969632864,-0.429746151,0.2190823555,0.4013512135,-0.2894096375,0.0460615568,0.0167300683,-0.4555115998,-0.1271671653,0.2183404118,0.390550375,-0.1844455749,-0.0037565609,0.0871429145,0.4355638623,0.0485231653,0.6233537793,-0.1413376033,0.0224382952,-0.3221297562,-0.2130482346,-0.034653984,-0.5695789456,-0.7795040607,0.015956128,0.0507356077,0.2829304338,0.3464263976,0.1854577065,0.0370459333,-0.1173866913,-0.1299520284,0.2160572261,-0.0045058765,-0.0558645725,-0.3231567442,-0.5304920673,0.28534621,-0.1147800237,0.0466500521,0.1006035507,-0.2822220922,0.0011679371,0.1973249465,-0.0316379927,0.2541683018,0.3844375014,0.293066293,-0.3115614951,-0.0663591474,0.0042587491,-0.4575053751,0.1950948834,0.1783739477,-0.3753206432,0.1245837435,-0.1003920138,0.0144068571,-0.2264458388,-0.1339516789,-0.2168265283,-0.1374632716,-0.0339444168,0.4003777504,-0.176329121,-0.2991106212,0.3400560319,0.0025759132,-0.0406318903,-0.3259862363,-0.0154328085,0.1447563022,-0.0170684289,-0.2598402798,0.1888251454,0.0648194179,-0.0355281867,0.0368644558,0.2335698307,0.0226677191,0.3187166452,0.349332124,0.3796356022,0.2128698975,-0.0915223658,0.2820823789,0.2902056575,-0.0620261133,0.0524619706,-0.3751501739,0.2003801614,0.0710650906,0.1950615644,-0.113981016,-0.2368826121,0.251411289,0.1360599846,0.0944542736,-0.1216271147,0.2244897187,0.0017589895,0.392508775,0.0476589166,-0.0416111052,0.2306888103,0.3817815483,0.2312813848,0.0795109794,0.3927400112,-0.1257767677,-0.2583568692,0.1078153104,0.3400515914,0.5201392174,0.1659973711,0.1927121878,-0.08257889,0.0334070846,-0.0248670559,0.1405932903,-0.2922542989,-0.1662439853,0.0999909863,0.1785677075,-0.1703336537,-0.1151010394,0.2065084279,-0.0072104023,0.0374703854,-0.1230290011,0.2915499508,-0.0051883152,0.0669604018,-0.300422281,0.2018046081,-0.4009771645,-0.0063932566,0.1357572228,0.5235956907,0.0959122628,0.0231753308,-0.1968869716,0.7337744236,-0.0781162232,-0.2568981051,0.0806490034,-0.0120087182,-0.1814791262,-0.1487001628,0.3093964458,-0.5017920732,0.097177811,0.1724740267,-0.0851729885,-0.2979379892,0.1057201475,-0.0711285472,0.1724738181,0.0262167249,-0.3023276925,0.3171690702,0.0086356783,-0.0908934399,-0.086091809,-0.1090197414,-0.0805503279,-0.1161442846,0.0500084944,0.4038137794,0.1403172016,0.0388037674,-0.260648638,-0.230347693,0.2758387029,-0.1536940336,-0.0432945602,0.081139639,0.0112992609,-0.1315152347,-0.123419635,-0.2881757617,-0.2525757253,-0.7670419812,0.3175759912,0.0659810528,-0.0736874044,0.148288399,0.2214990407,0.3519730866,0.1975888908,-0.5262952447,-0.1080436409,-0.1927305013,0.2778547108,-0.2042674869,-0.2437518984,0.3360224962,-0.1253866404,0.1910074055,0.0439109616,-0.1972723454,0.2947356105,0.2608200908,0.3492497802,-0.075725235,-0.0599262416,0.0863768682,0.9180575013,0.0366472527,0.1179880798,0.234326914,0.2871668935,0.448636353,-0.1861969531,-0.2464789301,0.051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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2591","title":"Cached dataset overflowing disk space","comments":"Hi @BirgerMoell.\r\n\r\nYou have several options:\r\n- to set caching to be stored on a different path location, other than the default one (`~\/.cache\/huggingface\/datasets`):\r\n  - either setting the environment variable `HF_DATASETS_CACHE` with the path to the new cache location\r\n  - or by passing it with the parameter `cache_dir` when loading each of the datasets: `dataset = load_dataset(..., cache_dir=your_new_location)`\r\n\r\n  You can get all the information in the docs: https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#cache-directory\r\n- I wouldn't recommend disabling caching, because current implementation generates cache files anyway, although in a temporary directory and they are deleted when the session closes. See details here: https:\/\/huggingface.co\/docs\/datasets\/processing.html#enable-or-disable-caching\r\n- You could alternatively load the datasets in streaming mode. This is a new feature which allows loading the datasets without downloading the entire files. More information here: https:\/\/huggingface.co\/docs\/datasets\/dataset_streaming.html","body":"I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).\r\n\r\nThe cache folder is 500gb (and now my disk space is full).\r\n\r\nIs there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).\r\n\r\nThis might not technically be a bug, but I was unsure and I felt that the bug was the closest one.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 121, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 186, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 397, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1983, in _map_single\r\n    writer.finalize()\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 418, in finalize\r\n    self.pa_writer.close()\r\n  File \"pyarrow\/ipc.pxi\", line 402, in pyarrow.lib._CRecordBatchWriter.close\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nOSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n","comment_length":127,"text":"Cached dataset overflowing disk space \n I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).\r\n\r\nThe cache folder is 500gb (and now my disk space is full).\r\n\r\nIs there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).\r\n\r\nThis might not technically be a bug, but I was unsure and I felt that the bug was the closest one.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 121, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 186, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 397, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1983, in _map_single\r\n    writer.finalize()\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 418, in finalize\r\n    self.pa_writer.close()\r\n  File \"pyarrow\/ipc.pxi\", line 402, in pyarrow.lib._CRecordBatchWriter.close\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nOSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n \n Hi @BirgerMoell.\r\n\r\nYou have several options:\r\n- to set caching to be stored on a different path location, other than the default one (`~\/.cache\/huggingface\/datasets`):\r\n  - either setting the environment variable `HF_DATASETS_CACHE` with the path to the new cache location\r\n  - or by passing it with the parameter `cache_dir` when loading each of the datasets: `dataset = load_dataset(..., cache_dir=your_new_location)`\r\n\r\n  You can get all the information in the docs: https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#cache-directory\r\n- I wouldn't recommend disabling caching, because current implementation generates cache files anyway, although in a temporary directory and they are deleted when the session closes. See details here: https:\/\/huggingface.co\/docs\/datasets\/processing.html#enable-or-disable-caching\r\n- You could alternatively load the datasets in streaming mode. This is a new feature which allows loading the datasets without downloading the entire files. More information here: https:\/\/huggingface.co\/docs\/datasets\/dataset_streaming.html","embeddings":[0.0375595093,-0.4222126603,0.0985903814,0.4514758587,0.1011737883,0.2050561309,0.0744853392,0.141904518,0.1445289999,0.0556368642,0.4010794461,-0.2280424833,-0.1242998913,0.1127196699,0.1466887593,0.0686893761,0.2591468096,-0.2458593994,-0.0610483401,0.1060899645,-0.1954928488,0.2656945884,0.0236382782,-0.1252904385,-0.2641810179,-0.1312523186,0.1114902273,0.0915549919,0.1169939488,-0.2215207964,0.6455171704,-0.037543118,0.2969840467,0.3342037201,-0.0001271115,-0.2188411504,0.0915412679,-0.181593284,-0.0922594294,0.5504892468,0.1914410889,-0.3445687294,-0.0522951484,0.0128892465,-0.1940163374,0.1353006959,-0.0969077572,-0.4302129745,0.2448510528,0.0724848434,0.0480299257,-0.0077622137,-0.1181686893,0.1708012968,-0.1092367396,-0.0044049905,-0.2483725548,0.1998682469,0.2498452663,-0.1698941886,-0.1137219444,0.5557486415,-0.0590143166,-0.0844287351,0.5253401995,0.0833818987,-0.3562970459,-0.1106624231,0.3813790679,-0.0801356807,0.314463526,-0.4211860299,-0.0359757133,-0.5416691303,0.1242926195,-0.4084812701,0.4917798042,0.1439301074,-0.3081754148,0.2055083513,-0.2643952072,-0.4621683359,-0.2639658749,-0.1211516634,-0.0510151535,-0.1692601889,-0.1779297143,0.0038869162,0.4981890023,-0.1243522316,-0.0180550069,-0.1148280278,-0.1485933661,0.1664890051,-0.4704052806,-0.1601628959,-0.1597560495,0.4838789701,0.1778159291,0.0343691669,0.0580152795,-0.2219776958,0.0135496575,-0.0545078851,0.0384909436,0.6338197589,-0.4302008152,0.1479855031,0.0260615703,-0.1004753411,-0.0664962679,-0.0840651393,-0.0061190045,-0.388866663,0.2431183755,-0.3094467223,0.0366061553,-0.266004473,-0.1329783052,0.1096001938,0.1877570152,0.1407824308,0.2514514029,0.3864438534,0.1821056455,0.3051152825,-0.322846204,0.2218101174,-0.2443430275,-0.1192895547,-0.0440737009,-0.0846645087,0.0494980812,0.3295774758,0.3280156553,-0.1158240363,-0.0438293442,-0.1668728292,0.2368198782,-0.0264211819,0.0309991352,-0.2585927844,0.2868451476,0.1123239547,-0.3598536849,0.225169003,0.2425775528,0.3989651501,-0.2411044687,0.1247824728,-0.3550453782,-0.2518386543,0.1480785757,-0.01426965,-0.1831807494,-0.0969632864,-0.429746151,0.2190823555,0.4013512135,-0.2894096375,0.0460615568,0.0167300683,-0.4555115998,-0.1271671653,0.2183404118,0.390550375,-0.1844455749,-0.0037565609,0.0871429145,0.4355638623,0.0485231653,0.6233537793,-0.1413376033,0.0224382952,-0.3221297562,-0.2130482346,-0.034653984,-0.5695789456,-0.7795040607,0.015956128,0.0507356077,0.2829304338,0.3464263976,0.1854577065,0.0370459333,-0.1173866913,-0.1299520284,0.2160572261,-0.0045058765,-0.0558645725,-0.3231567442,-0.5304920673,0.28534621,-0.1147800237,0.0466500521,0.1006035507,-0.2822220922,0.0011679371,0.1973249465,-0.0316379927,0.2541683018,0.3844375014,0.293066293,-0.3115614951,-0.0663591474,0.0042587491,-0.4575053751,0.1950948834,0.1783739477,-0.3753206432,0.1245837435,-0.1003920138,0.0144068571,-0.2264458388,-0.1339516789,-0.2168265283,-0.1374632716,-0.0339444168,0.4003777504,-0.176329121,-0.2991106212,0.3400560319,0.0025759132,-0.0406318903,-0.3259862363,-0.0154328085,0.1447563022,-0.0170684289,-0.2598402798,0.1888251454,0.0648194179,-0.0355281867,0.0368644558,0.2335698307,0.0226677191,0.3187166452,0.349332124,0.3796356022,0.2128698975,-0.0915223658,0.2820823789,0.2902056575,-0.0620261133,0.0524619706,-0.3751501739,0.2003801614,0.0710650906,0.1950615644,-0.113981016,-0.2368826121,0.251411289,0.1360599846,0.0944542736,-0.1216271147,0.2244897187,0.0017589895,0.392508775,0.0476589166,-0.0416111052,0.2306888103,0.3817815483,0.2312813848,0.0795109794,0.3927400112,-0.1257767677,-0.2583568692,0.1078153104,0.3400515914,0.5201392174,0.1659973711,0.1927121878,-0.08257889,0.0334070846,-0.0248670559,0.1405932903,-0.2922542989,-0.1662439853,0.0999909863,0.1785677075,-0.1703336537,-0.1151010394,0.2065084279,-0.0072104023,0.0374703854,-0.1230290011,0.2915499508,-0.0051883152,0.0669604018,-0.300422281,0.2018046081,-0.4009771645,-0.0063932566,0.1357572228,0.5235956907,0.0959122628,0.0231753308,-0.1968869716,0.7337744236,-0.0781162232,-0.2568981051,0.0806490034,-0.0120087182,-0.1814791262,-0.1487001628,0.3093964458,-0.5017920732,0.097177811,0.1724740267,-0.0851729885,-0.2979379892,0.1057201475,-0.0711285472,0.1724738181,0.0262167249,-0.3023276925,0.3171690702,0.0086356783,-0.0908934399,-0.086091809,-0.1090197414,-0.0805503279,-0.1161442846,0.0500084944,0.4038137794,0.1403172016,0.0388037674,-0.260648638,-0.230347693,0.2758387029,-0.1536940336,-0.0432945602,0.081139639,0.0112992609,-0.1315152347,-0.123419635,-0.2881757617,-0.2525757253,-0.7670419812,0.3175759912,0.0659810528,-0.0736874044,0.148288399,0.2214990407,0.3519730866,0.1975888908,-0.5262952447,-0.1080436409,-0.1927305013,0.2778547108,-0.2042674869,-0.2437518984,0.3360224962,-0.1253866404,0.1910074055,0.0439109616,-0.1972723454,0.2947356105,0.2608200908,0.3492497802,-0.075725235,-0.0599262416,0.0863768682,0.9180575013,0.0366472527,0.1179880798,0.234326914,0.2871668935,0.4486363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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2591","title":"Cached dataset overflowing disk space","comments":"Hi @BirgerMoell,\r\n\r\nWe are planning to add a new feature to datasets, which could be interesting in your case: Add the option to delete temporary files (decompressed files) from the cache directory (see: #2481, #2604).\r\n\r\nWe will ping you once this feature is implemented, so that the size of your cache directory will be considerably reduced.","body":"I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).\r\n\r\nThe cache folder is 500gb (and now my disk space is full).\r\n\r\nIs there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).\r\n\r\nThis might not technically be a bug, but I was unsure and I felt that the bug was the closest one.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 121, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 186, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 397, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1983, in _map_single\r\n    writer.finalize()\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 418, in finalize\r\n    self.pa_writer.close()\r\n  File \"pyarrow\/ipc.pxi\", line 402, in pyarrow.lib._CRecordBatchWriter.close\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nOSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n","comment_length":56,"text":"Cached dataset overflowing disk space \n I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).\r\n\r\nThe cache folder is 500gb (and now my disk space is full).\r\n\r\nIs there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).\r\n\r\nThis might not technically be a bug, but I was unsure and I felt that the bug was the closest one.\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 121, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 186, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 397, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1983, in _map_single\r\n    writer.finalize()\r\n  File \"\/home\/birger\/miniconda3\/envs\/wav2vec2\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 418, in finalize\r\n    self.pa_writer.close()\r\n  File \"pyarrow\/ipc.pxi\", line 402, in pyarrow.lib._CRecordBatchWriter.close\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nOSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n \n Hi @BirgerMoell,\r\n\r\nWe are planning to add a new feature to datasets, which could be interesting in your case: Add the option to delete temporary files (decompressed files) from the cache directory (see: #2481, #2604).\r\n\r\nWe will ping you once this feature is implemented, so that the size of your cache directory will be considerably reduced.","embeddings":[0.0375595093,-0.4222126603,0.0985903814,0.4514758587,0.1011737883,0.2050561309,0.0744853392,0.141904518,0.1445289999,0.0556368642,0.4010794461,-0.2280424833,-0.1242998913,0.1127196699,0.1466887593,0.0686893761,0.2591468096,-0.2458593994,-0.0610483401,0.1060899645,-0.1954928488,0.2656945884,0.0236382782,-0.1252904385,-0.2641810179,-0.1312523186,0.1114902273,0.0915549919,0.1169939488,-0.2215207964,0.6455171704,-0.037543118,0.2969840467,0.3342037201,-0.0001271115,-0.2188411504,0.0915412679,-0.181593284,-0.0922594294,0.5504892468,0.1914410889,-0.3445687294,-0.0522951484,0.0128892465,-0.1940163374,0.1353006959,-0.0969077572,-0.4302129745,0.2448510528,0.0724848434,0.0480299257,-0.0077622137,-0.1181686893,0.1708012968,-0.1092367396,-0.0044049905,-0.2483725548,0.1998682469,0.2498452663,-0.1698941886,-0.1137219444,0.5557486415,-0.0590143166,-0.0844287351,0.5253401995,0.0833818987,-0.3562970459,-0.1106624231,0.3813790679,-0.0801356807,0.314463526,-0.4211860299,-0.0359757133,-0.5416691303,0.1242926195,-0.4084812701,0.4917798042,0.1439301074,-0.3081754148,0.2055083513,-0.2643952072,-0.4621683359,-0.2639658749,-0.1211516634,-0.0510151535,-0.1692601889,-0.1779297143,0.0038869162,0.4981890023,-0.1243522316,-0.0180550069,-0.1148280278,-0.1485933661,0.1664890051,-0.4704052806,-0.1601628959,-0.1597560495,0.4838789701,0.1778159291,0.0343691669,0.0580152795,-0.2219776958,0.0135496575,-0.0545078851,0.0384909436,0.6338197589,-0.4302008152,0.1479855031,0.0260615703,-0.1004753411,-0.0664962679,-0.0840651393,-0.0061190045,-0.388866663,0.2431183755,-0.3094467223,0.0366061553,-0.266004473,-0.1329783052,0.1096001938,0.1877570152,0.1407824308,0.2514514029,0.3864438534,0.1821056455,0.3051152825,-0.322846204,0.2218101174,-0.2443430275,-0.1192895547,-0.0440737009,-0.0846645087,0.0494980812,0.3295774758,0.3280156553,-0.1158240363,-0.0438293442,-0.1668728292,0.2368198782,-0.0264211819,0.0309991352,-0.2585927844,0.2868451476,0.1123239547,-0.3598536849,0.225169003,0.2425775528,0.3989651501,-0.2411044687,0.1247824728,-0.3550453782,-0.2518386543,0.1480785757,-0.01426965,-0.1831807494,-0.0969632864,-0.429746151,0.2190823555,0.4013512135,-0.2894096375,0.0460615568,0.0167300683,-0.4555115998,-0.1271671653,0.2183404118,0.390550375,-0.1844455749,-0.0037565609,0.0871429145,0.4355638623,0.0485231653,0.6233537793,-0.1413376033,0.0224382952,-0.3221297562,-0.2130482346,-0.034653984,-0.5695789456,-0.7795040607,0.015956128,0.0507356077,0.2829304338,0.3464263976,0.1854577065,0.0370459333,-0.1173866913,-0.1299520284,0.2160572261,-0.0045058765,-0.0558645725,-0.3231567442,-0.5304920673,0.28534621,-0.1147800237,0.0466500521,0.1006035507,-0.2822220922,0.0011679371,0.1973249465,-0.0316379927,0.2541683018,0.3844375014,0.293066293,-0.3115614951,-0.0663591474,0.0042587491,-0.4575053751,0.1950948834,0.1783739477,-0.3753206432,0.1245837435,-0.1003920138,0.0144068571,-0.2264458388,-0.1339516789,-0.2168265283,-0.1374632716,-0.0339444168,0.4003777504,-0.176329121,-0.2991106212,0.3400560319,0.0025759132,-0.0406318903,-0.3259862363,-0.0154328085,0.1447563022,-0.0170684289,-0.2598402798,0.1888251454,0.0648194179,-0.0355281867,0.0368644558,0.2335698307,0.0226677191,0.3187166452,0.349332124,0.3796356022,0.2128698975,-0.0915223658,0.2820823789,0.2902056575,-0.0620261133,0.0524619706,-0.3751501739,0.2003801614,0.0710650906,0.1950615644,-0.113981016,-0.2368826121,0.251411289,0.1360599846,0.0944542736,-0.1216271147,0.2244897187,0.0017589895,0.392508775,0.0476589166,-0.0416111052,0.2306888103,0.3817815483,0.2312813848,0.0795109794,0.3927400112,-0.1257767677,-0.2583568692,0.1078153104,0.3400515914,0.5201392174,0.1659973711,0.1927121878,-0.08257889,0.0334070846,-0.0248670559,0.1405932903,-0.2922542989,-0.1662439853,0.0999909863,0.1785677075,-0.1703336537,-0.1151010394,0.2065084279,-0.0072104023,0.0374703854,-0.1230290011,0.2915499508,-0.0051883152,0.0669604018,-0.300422281,0.2018046081,-0.4009771645,-0.0063932566,0.1357572228,0.5235956907,0.0959122628,0.0231753308,-0.1968869716,0.7337744236,-0.0781162232,-0.2568981051,0.0806490034,-0.0120087182,-0.1814791262,-0.1487001628,0.3093964458,-0.5017920732,0.097177811,0.1724740267,-0.0851729885,-0.2979379892,0.1057201475,-0.0711285472,0.1724738181,0.0262167249,-0.3023276925,0.3171690702,0.0086356783,-0.0908934399,-0.086091809,-0.1090197414,-0.0805503279,-0.1161442846,0.0500084944,0.4038137794,0.1403172016,0.0388037674,-0.260648638,-0.230347693,0.2758387029,-0.1536940336,-0.0432945602,0.081139639,0.0112992609,-0.1315152347,-0.123419635,-0.2881757617,-0.2525757253,-0.7670419812,0.3175759912,0.0659810528,-0.0736874044,0.148288399,0.2214990407,0.3519730866,0.1975888908,-0.5262952447,-0.1080436409,-0.1927305013,0.2778547108,-0.2042674869,-0.2437518984,0.3360224962,-0.1253866404,0.1910074055,0.0439109616,-0.1972723454,0.2947356105,0.2608200908,0.3492497802,-0.075725235,-0.0599262416,0.0863768682,0.9180575013,0.0366472527,0.1179880798,0.234326914,0.2871668935,0.448636353,-0.1861969531,-0.2464789301,0.0511529483,-0.134572371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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2585","title":"sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index","comments":"Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so that all leading blank spaces in the source dataset are kept, and there is no misalignment between the answer text and the answer_start within the context.","body":"## Describe the bug\r\nThe built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].\r\n\r\nFor example:\r\nid = '56d1f453e7d4791d009025bd'\r\nanswers = {'text': ['Pure Land'], 'answer_start': [146]}\r\nHowever the actual text in context at location 146 is 'ure Land,'\r\nWhich is an off-by-one error from the correct answer.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\ndef check_context_answer_alignment(example):\r\n    for a_idx in range(len(example['answers']['text'])):\r\n        # check raw dataset for answer consistency between context and answer\r\n        answer_text = example['answers']['text'][a_idx]\r\n        a_st_idx = example['answers']['answer_start'][a_idx]\r\n        a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])\r\n        answer_text_from_context = example['context'][a_st_idx:a_end_idx]\r\n        if answer_text != answer_text_from_context:\r\n            #print(example['id'])\r\n            return False\r\n    return True\r\n\r\ndataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)\r\n\r\nstart_len = len(dataset)\r\ndataset = dataset.filter(check_context_answer_alignment,\r\n                                num_proc=1,\r\n                                keep_in_memory=True)\r\nend_len = len(dataset)\r\nprint('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))\r\n```\r\n\r\n## Expected results\r\nThis code should result in 0 rows being filtered out from the dataset.\r\n\r\n## Actual results\r\nThis filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.\r\n\r\nThis code will reproduce the problem and produce the following count:\r\n\"258 instances contain mis-alignment between the answer text and answer index.\"\r\n\r\n## Environment info\r\nSteps to rebuilt the Conda environment:\r\n```\r\n# create a virtual environment to stuff all these packages into\r\nconda create -n round8 python=3.8 -y\r\n\r\n# activate the virtual environment\r\nconda activate round8\r\n\r\n# install pytorch (best done through conda to handle cuda dependencies)\r\nconda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia\r\n\r\npip install jsonpickle transformers datasets matplotlib\r\n```\r\n\r\nOS: Ubuntu 20.04\r\nPython 3.8\r\n\r\nResult of `conda env export`:\r\n```\r\nname: round8\r\nchannels:\r\n  - pytorch-lts\r\n  - nvidia\r\n  - defaults\r\ndependencies:\r\n  - _libgcc_mutex=0.1=main\r\n  - _openmp_mutex=4.5=1_gnu\r\n  - blas=1.0=mkl\r\n  - brotlipy=0.7.0=py38h27cfd23_1003\r\n  - bzip2=1.0.8=h7b6447c_0\r\n  - ca-certificates=2021.5.25=h06a4308_1\r\n  - certifi=2021.5.30=py38h06a4308_0\r\n  - cffi=1.14.5=py38h261ae71_0\r\n  - chardet=4.0.0=py38h06a4308_1003\r\n  - cryptography=3.4.7=py38hd23ed53_0\r\n  - cudatoolkit=11.1.74=h6bb024c_0\r\n  - ffmpeg=4.2.2=h20bf706_0\r\n  - freetype=2.10.4=h5ab3b9f_0\r\n  - gmp=6.2.1=h2531618_2\r\n  - gnutls=3.6.15=he1e5248_0\r\n  - idna=2.10=pyhd3eb1b0_0\r\n  - intel-openmp=2021.2.0=h06a4308_610\r\n  - jpeg=9b=h024ee3a_2\r\n  - lame=3.100=h7b6447c_0\r\n  - lcms2=2.12=h3be6417_0\r\n  - ld_impl_linux-64=2.35.1=h7274673_9\r\n  - libffi=3.3=he6710b0_2\r\n  - libgcc-ng=9.3.0=h5101ec6_17\r\n  - libgomp=9.3.0=h5101ec6_17\r\n  - libidn2=2.3.1=h27cfd23_0\r\n  - libopus=1.3.1=h7b6447c_0\r\n  - libpng=1.6.37=hbc83047_0\r\n  - libstdcxx-ng=9.3.0=hd4cf53a_17\r\n  - libtasn1=4.16.0=h27cfd23_0\r\n  - libtiff=4.2.0=h85742a9_0\r\n  - libunistring=0.9.10=h27cfd23_0\r\n  - libuv=1.40.0=h7b6447c_0\r\n  - libvpx=1.7.0=h439df22_0\r\n  - libwebp-base=1.2.0=h27cfd23_0\r\n  - lz4-c=1.9.3=h2531618_0\r\n  - mkl=2021.2.0=h06a4308_296\r\n  - mkl-service=2.3.0=py38h27cfd23_1\r\n  - mkl_fft=1.3.0=py38h42c9631_2\r\n  - mkl_random=1.2.1=py38ha9443f7_2\r\n  - ncurses=6.2=he6710b0_1\r\n  - nettle=3.7.3=hbbd107a_1\r\n  - ninja=1.10.2=hff7bd54_1\r\n  - numpy=1.20.2=py38h2d18471_0\r\n  - numpy-base=1.20.2=py38hfae3a4d_0\r\n  - olefile=0.46=py_0\r\n  - openh264=2.1.0=hd408876_0\r\n  - openssl=1.1.1k=h27cfd23_0\r\n  - pillow=8.2.0=py38he98fc37_0\r\n  - pip=21.1.2=py38h06a4308_0\r\n  - pycparser=2.20=py_2\r\n  - pyopenssl=20.0.1=pyhd3eb1b0_1\r\n  - pysocks=1.7.1=py38h06a4308_0\r\n  - python=3.8.10=h12debd9_8\r\n  - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0\r\n  - readline=8.1=h27cfd23_0\r\n  - requests=2.25.1=pyhd3eb1b0_0\r\n  - setuptools=52.0.0=py38h06a4308_0\r\n  - six=1.16.0=pyhd3eb1b0_0\r\n  - sqlite=3.35.4=hdfb4753_0\r\n  - tk=8.6.10=hbc83047_0\r\n  - torchtext=0.9.1=py38\r\n  - torchvision=0.9.1=py38_cu111\r\n  - typing_extensions=3.7.4.3=pyha847dfd_0\r\n  - urllib3=1.26.4=pyhd3eb1b0_0\r\n  - wheel=0.36.2=pyhd3eb1b0_0\r\n  - x264=1!157.20191217=h7b6447c_0\r\n  - xz=5.2.5=h7b6447c_0\r\n  - zlib=1.2.11=h7b6447c_3\r\n  - zstd=1.4.9=haebb681_0\r\n  - pip:\r\n    - click==8.0.1\r\n    - cycler==0.10.0\r\n    - datasets==1.8.0\r\n    - dill==0.3.4\r\n    - filelock==3.0.12\r\n    - fsspec==2021.6.0\r\n    - huggingface-hub==0.0.8\r\n    - joblib==1.0.1\r\n    - jsonpickle==2.0.0\r\n    - kiwisolver==1.3.1\r\n    - matplotlib==3.4.2\r\n    - multiprocess==0.70.12.2\r\n    - packaging==20.9\r\n    - pandas==1.2.4\r\n    - pyarrow==3.0.0\r\n    - pyparsing==2.4.7\r\n    - python-dateutil==2.8.1\r\n    - pytz==2021.1\r\n    - regex==2021.4.4\r\n    - sacremoses==0.0.45\r\n    - tokenizers==0.10.3\r\n    - tqdm==4.49.0\r\n    - transformers==4.6.1\r\n    - xxhash==2.0.2\r\nprefix: \/home\/mmajurski\/anaconda3\/envs\/round8\r\n```\r\n","comment_length":71,"text":"sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index \n ## Describe the bug\r\nThe built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].\r\n\r\nFor example:\r\nid = '56d1f453e7d4791d009025bd'\r\nanswers = {'text': ['Pure Land'], 'answer_start': [146]}\r\nHowever the actual text in context at location 146 is 'ure Land,'\r\nWhich is an off-by-one error from the correct answer.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\ndef check_context_answer_alignment(example):\r\n    for a_idx in range(len(example['answers']['text'])):\r\n        # check raw dataset for answer consistency between context and answer\r\n        answer_text = example['answers']['text'][a_idx]\r\n        a_st_idx = example['answers']['answer_start'][a_idx]\r\n        a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])\r\n        answer_text_from_context = example['context'][a_st_idx:a_end_idx]\r\n        if answer_text != answer_text_from_context:\r\n            #print(example['id'])\r\n            return False\r\n    return True\r\n\r\ndataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)\r\n\r\nstart_len = len(dataset)\r\ndataset = dataset.filter(check_context_answer_alignment,\r\n                                num_proc=1,\r\n                                keep_in_memory=True)\r\nend_len = len(dataset)\r\nprint('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))\r\n```\r\n\r\n## Expected results\r\nThis code should result in 0 rows being filtered out from the dataset.\r\n\r\n## Actual results\r\nThis filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.\r\n\r\nThis code will reproduce the problem and produce the following count:\r\n\"258 instances contain mis-alignment between the answer text and answer index.\"\r\n\r\n## Environment info\r\nSteps to rebuilt the Conda environment:\r\n```\r\n# create a virtual environment to stuff all these packages into\r\nconda create -n round8 python=3.8 -y\r\n\r\n# activate the virtual environment\r\nconda activate round8\r\n\r\n# install pytorch (best done through conda to handle cuda dependencies)\r\nconda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia\r\n\r\npip install jsonpickle transformers datasets matplotlib\r\n```\r\n\r\nOS: Ubuntu 20.04\r\nPython 3.8\r\n\r\nResult of `conda env export`:\r\n```\r\nname: round8\r\nchannels:\r\n  - pytorch-lts\r\n  - nvidia\r\n  - defaults\r\ndependencies:\r\n  - _libgcc_mutex=0.1=main\r\n  - _openmp_mutex=4.5=1_gnu\r\n  - blas=1.0=mkl\r\n  - brotlipy=0.7.0=py38h27cfd23_1003\r\n  - bzip2=1.0.8=h7b6447c_0\r\n  - ca-certificates=2021.5.25=h06a4308_1\r\n  - certifi=2021.5.30=py38h06a4308_0\r\n  - cffi=1.14.5=py38h261ae71_0\r\n  - chardet=4.0.0=py38h06a4308_1003\r\n  - cryptography=3.4.7=py38hd23ed53_0\r\n  - cudatoolkit=11.1.74=h6bb024c_0\r\n  - ffmpeg=4.2.2=h20bf706_0\r\n  - freetype=2.10.4=h5ab3b9f_0\r\n  - gmp=6.2.1=h2531618_2\r\n  - gnutls=3.6.15=he1e5248_0\r\n  - idna=2.10=pyhd3eb1b0_0\r\n  - intel-openmp=2021.2.0=h06a4308_610\r\n  - jpeg=9b=h024ee3a_2\r\n  - lame=3.100=h7b6447c_0\r\n  - lcms2=2.12=h3be6417_0\r\n  - ld_impl_linux-64=2.35.1=h7274673_9\r\n  - libffi=3.3=he6710b0_2\r\n  - libgcc-ng=9.3.0=h5101ec6_17\r\n  - libgomp=9.3.0=h5101ec6_17\r\n  - libidn2=2.3.1=h27cfd23_0\r\n  - libopus=1.3.1=h7b6447c_0\r\n  - libpng=1.6.37=hbc83047_0\r\n  - libstdcxx-ng=9.3.0=hd4cf53a_17\r\n  - libtasn1=4.16.0=h27cfd23_0\r\n  - libtiff=4.2.0=h85742a9_0\r\n  - libunistring=0.9.10=h27cfd23_0\r\n  - libuv=1.40.0=h7b6447c_0\r\n  - libvpx=1.7.0=h439df22_0\r\n  - libwebp-base=1.2.0=h27cfd23_0\r\n  - lz4-c=1.9.3=h2531618_0\r\n  - mkl=2021.2.0=h06a4308_296\r\n  - mkl-service=2.3.0=py38h27cfd23_1\r\n  - mkl_fft=1.3.0=py38h42c9631_2\r\n  - mkl_random=1.2.1=py38ha9443f7_2\r\n  - ncurses=6.2=he6710b0_1\r\n  - nettle=3.7.3=hbbd107a_1\r\n  - ninja=1.10.2=hff7bd54_1\r\n  - numpy=1.20.2=py38h2d18471_0\r\n  - numpy-base=1.20.2=py38hfae3a4d_0\r\n  - olefile=0.46=py_0\r\n  - openh264=2.1.0=hd408876_0\r\n  - openssl=1.1.1k=h27cfd23_0\r\n  - pillow=8.2.0=py38he98fc37_0\r\n  - pip=21.1.2=py38h06a4308_0\r\n  - pycparser=2.20=py_2\r\n  - pyopenssl=20.0.1=pyhd3eb1b0_1\r\n  - pysocks=1.7.1=py38h06a4308_0\r\n  - python=3.8.10=h12debd9_8\r\n  - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0\r\n  - readline=8.1=h27cfd23_0\r\n  - requests=2.25.1=pyhd3eb1b0_0\r\n  - setuptools=52.0.0=py38h06a4308_0\r\n  - six=1.16.0=pyhd3eb1b0_0\r\n  - sqlite=3.35.4=hdfb4753_0\r\n  - tk=8.6.10=hbc83047_0\r\n  - torchtext=0.9.1=py38\r\n  - torchvision=0.9.1=py38_cu111\r\n  - typing_extensions=3.7.4.3=pyha847dfd_0\r\n  - urllib3=1.26.4=pyhd3eb1b0_0\r\n  - wheel=0.36.2=pyhd3eb1b0_0\r\n  - x264=1!157.20191217=h7b6447c_0\r\n  - xz=5.2.5=h7b6447c_0\r\n  - zlib=1.2.11=h7b6447c_3\r\n  - zstd=1.4.9=haebb681_0\r\n  - pip:\r\n    - click==8.0.1\r\n    - cycler==0.10.0\r\n    - datasets==1.8.0\r\n    - dill==0.3.4\r\n    - filelock==3.0.12\r\n    - fsspec==2021.6.0\r\n    - huggingface-hub==0.0.8\r\n    - joblib==1.0.1\r\n    - jsonpickle==2.0.0\r\n    - kiwisolver==1.3.1\r\n    - matplotlib==3.4.2\r\n    - multiprocess==0.70.12.2\r\n    - packaging==20.9\r\n    - pandas==1.2.4\r\n    - pyarrow==3.0.0\r\n    - pyparsing==2.4.7\r\n    - python-dateutil==2.8.1\r\n    - pytz==2021.1\r\n    - regex==2021.4.4\r\n    - sacremoses==0.0.45\r\n    - tokenizers==0.10.3\r\n    - tqdm==4.49.0\r\n    - transformers==4.6.1\r\n    - xxhash==2.0.2\r\nprefix: \/home\/mmajurski\/anaconda3\/envs\/round8\r\n```\r\n \n Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so that all leading blank spaces in the source dataset are kept, and there is no misalignment between the answer text and the answer_start within the context.","embeddings":[-0.2330299318,-0.3295379579,-0.0441096425,0.3749362826,0.0878231078,-0.0763419643,0.1057570204,0.2279032022,-0.2927472591,0.1577830762,-0.0841630697,0.0450688601,0.3250361681,0.0685452819,-0.0926083177,0.1523635834,0.1756330729,0.0104246661,0.009945604,-0.1725040823,-0.2248985022,0.2991383374,-0.2980325818,0.03003061,-0.342564702,-0.0102489283,0.0364246443,0.1131834313,-0.1175084189,-0.4159388542,0.2125024199,-0.0936790556,0.0077113751,0.2638638318,-0.000116531,-0.1706253737,0.0728382915,-0.0973288417,-0.2682839036,0.0041923714,-0.0435589068,-0.1216914803,-0.1298896968,-0.1058420315,-0.2390315384,0.2717562318,-0.0673231781,-0.2866247594,0.6096699834,0.2443222255,0.1451372653,0.2883639336,0.0109265558,-0.064738296,0.1786869168,-0.1235733703,0.0756943524,0.119126901,0.1456331909,0.1079365462,-0.0346998237,0.4713858068,-0.1343523413,-0.0318910703,-0.0061442321,0.2904502153,0.206617564,-0.1278317124,0.0998568758,0.1311751604,0.2598810494,-0.2484471202,-0.424808681,-0.4569473565,-0.0358279571,-0.1224056184,0.3317508101,0.039453499,-0.0311524291,0.3647889495,-0.1365324855,0.0710276216,-0.0857497603,0.127139762,-0.0876931027,0.2383429706,-0.1219492704,0.1492218971,-0.2677045166,-0.1386347562,-0.0631168187,-0.1375988126,-0.105233863,0.4171854258,-0.5139366984,-0.1013480797,-0.1679380983,0.0907929763,0.2361780256,0.0694944859,-0.0699167773,0.0015248328,0.2659206092,0.0613521114,0.3417307138,0.2773298621,-0.1180535555,0.255628705,0.1020577699,0.1474464983,0.0414646193,0.1034675837,0.1996169537,-0.1841570288,-0.1082466468,-0.1547538042,0.1684667319,-0.1887482852,-0.5222857594,0.4338434935,-0.6319300532,0.2756719887,-0.0214355942,0.3482971787,-0.2216847241,0.2669170201,0.3325076103,0.244284451,-0.0650029629,-0.1851683259,-0.133084923,-0.0969801545,-0.0144069865,-0.0344590582,0.0072183558,-0.1293735057,0.2271303385,0.2961053848,0.2128195316,-0.2205923349,-0.3132048249,-0.1648734659,0.1410585493,0.0306854323,0.0271047894,0.1876549721,0.1873004138,-0.2884297669,0.073195301,0.0930062234,-0.2383058369,0.0768734515,-0.0992043242,0.2245198935,-0.1660035253,-0.056426052,0.0465995781,0.2456641197,0.2966586649,-0.2863129377,0.2683805823,-0.203826189,-0.3300075233,-0.0881249383,0.3650223017,0.2920378745,-0.1985461414,-0.0659254789,-0.0561406538,0.1563176215,0.0265871864,0.237526387,0.2730125487,0.2942620516,-0.4212450981,0.2674057782,0.1037338749,-0.8540112376,-0.5590248704,-0.1742199212,-0.1163506806,0.225110665,-0.1036156043,0.0728597566,0.4342785478,0.1543433517,0.2735286653,0.5391799212,0.0679247305,0.1189373881,-0.3740178347,0.0432915017,-0.0926679522,0.1235257834,-0.2477580458,-0.1743770838,0.0530980192,-0.1834742129,0.1845173985,0.1049054489,0.0700077787,0.242408067,0.3292447031,0.1207566783,0.1795453727,-0.5024951696,-0.4840292037,0.0933753848,-0.0992036015,0.0597215667,-0.0624961443,-0.1360670328,-0.3975332677,-0.0361760221,-0.3520284891,-0.3007717729,0.0909299403,0.0083287833,0.1742563844,0.1125237122,-0.1105441302,0.6274502277,-0.2027090788,0.2574426234,-0.0648542717,0.1838029921,-0.0369916596,0.0197878219,0.0257437229,0.4265458286,-0.0366344415,0.0865879729,-0.2453000844,0.5439850688,0.2354308665,0.1975178421,-0.2265350223,-0.0887751728,0.2265448421,-0.4291588366,-0.2648878694,0.2957112789,0.1549057662,0.1138918698,-0.3117320538,0.59599787,0.0686130524,0.0849598721,-0.2245444506,-0.0018041183,0.2800176144,-0.1242726669,-0.1135764197,-0.2309002727,0.2905323505,0.1432120651,0.5214198828,0.1039449498,-0.3891157508,0.1001307517,0.267216295,-0.1695472002,0.0794455707,0.1168767288,0.0412754938,-0.0822434872,0.024764752,0.2209540159,0.2899647057,0.2044537365,-0.3409023285,0.3199863434,-0.036653012,0.1461652517,0.2144389898,-0.1729850173,-0.0400366671,0.4994864166,0.3725070655,0.2427582741,-0.1843069196,0.1724185944,-0.3970791101,0.152523905,-0.5550450087,-0.021352414,-0.1496844441,-0.0735898316,-0.1993011385,-0.0192141831,-0.2774994969,-0.1124377027,0.248433575,0.1559061408,-0.0537631586,0.2769265175,0.0002657939,0.4594179392,0.0978769809,-0.1988540739,-0.0586240068,-0.0650393665,-0.1624031663,-0.0090979105,0.0156265758,0.1998775154,0.1458058804,-0.4826640189,-0.3008002043,-0.2854555845,-0.3453061879,0.2101686448,-0.0686363429,0.3613208234,0.1664168537,-0.0186989903,-0.3620541394,-0.2030521333,0.164502278,-0.0291347131,-0.4773875177,-0.0524751432,0.0556921959,-0.2053315192,-0.0412881449,-0.4563171864,-0.0989391729,-0.2709364295,0.0162285641,-0.1817271709,0.0823787078,0.2436575741,-0.1389108002,0.0348106995,-0.2918559611,0.1260592341,-0.5039397478,-0.0472881943,0.0939144045,0.0430192649,-0.3436332941,-0.2887289226,-0.232024312,-0.1749058217,-0.0350877568,-0.1375848353,-0.2258420736,-0.1793827713,0.2166627795,-0.2268270254,0.2435190976,0.2265461981,-0.1808743179,0.0880100653,-0.2281240225,-0.4454982877,0.1523829252,0.3971565962,0.2353447974,-0.1117967144,0.3300126195,-0.0246539097,0.6546520591,0.4053243101,0.226618126,0.1022702307,-0.2168853581,0.3332633674,-0.2054528892,-0.2187942415,0.312690258,0.1287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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2585","title":"sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index","comments":"If you are going to be altering the data cleaning from the source Squad dataset, here is one thing to consider.\r\nThere are occasional double spaces separating words which it might be nice to get rid of. \r\n\r\nEither way, thank you.","body":"## Describe the bug\r\nThe built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].\r\n\r\nFor example:\r\nid = '56d1f453e7d4791d009025bd'\r\nanswers = {'text': ['Pure Land'], 'answer_start': [146]}\r\nHowever the actual text in context at location 146 is 'ure Land,'\r\nWhich is an off-by-one error from the correct answer.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\ndef check_context_answer_alignment(example):\r\n    for a_idx in range(len(example['answers']['text'])):\r\n        # check raw dataset for answer consistency between context and answer\r\n        answer_text = example['answers']['text'][a_idx]\r\n        a_st_idx = example['answers']['answer_start'][a_idx]\r\n        a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])\r\n        answer_text_from_context = example['context'][a_st_idx:a_end_idx]\r\n        if answer_text != answer_text_from_context:\r\n            #print(example['id'])\r\n            return False\r\n    return True\r\n\r\ndataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)\r\n\r\nstart_len = len(dataset)\r\ndataset = dataset.filter(check_context_answer_alignment,\r\n                                num_proc=1,\r\n                                keep_in_memory=True)\r\nend_len = len(dataset)\r\nprint('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))\r\n```\r\n\r\n## Expected results\r\nThis code should result in 0 rows being filtered out from the dataset.\r\n\r\n## Actual results\r\nThis filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.\r\n\r\nThis code will reproduce the problem and produce the following count:\r\n\"258 instances contain mis-alignment between the answer text and answer index.\"\r\n\r\n## Environment info\r\nSteps to rebuilt the Conda environment:\r\n```\r\n# create a virtual environment to stuff all these packages into\r\nconda create -n round8 python=3.8 -y\r\n\r\n# activate the virtual environment\r\nconda activate round8\r\n\r\n# install pytorch (best done through conda to handle cuda dependencies)\r\nconda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia\r\n\r\npip install jsonpickle transformers datasets matplotlib\r\n```\r\n\r\nOS: Ubuntu 20.04\r\nPython 3.8\r\n\r\nResult of `conda env export`:\r\n```\r\nname: round8\r\nchannels:\r\n  - pytorch-lts\r\n  - nvidia\r\n  - defaults\r\ndependencies:\r\n  - _libgcc_mutex=0.1=main\r\n  - _openmp_mutex=4.5=1_gnu\r\n  - blas=1.0=mkl\r\n  - brotlipy=0.7.0=py38h27cfd23_1003\r\n  - bzip2=1.0.8=h7b6447c_0\r\n  - ca-certificates=2021.5.25=h06a4308_1\r\n  - certifi=2021.5.30=py38h06a4308_0\r\n  - cffi=1.14.5=py38h261ae71_0\r\n  - chardet=4.0.0=py38h06a4308_1003\r\n  - cryptography=3.4.7=py38hd23ed53_0\r\n  - cudatoolkit=11.1.74=h6bb024c_0\r\n  - ffmpeg=4.2.2=h20bf706_0\r\n  - freetype=2.10.4=h5ab3b9f_0\r\n  - gmp=6.2.1=h2531618_2\r\n  - gnutls=3.6.15=he1e5248_0\r\n  - idna=2.10=pyhd3eb1b0_0\r\n  - intel-openmp=2021.2.0=h06a4308_610\r\n  - jpeg=9b=h024ee3a_2\r\n  - lame=3.100=h7b6447c_0\r\n  - lcms2=2.12=h3be6417_0\r\n  - ld_impl_linux-64=2.35.1=h7274673_9\r\n  - libffi=3.3=he6710b0_2\r\n  - libgcc-ng=9.3.0=h5101ec6_17\r\n  - libgomp=9.3.0=h5101ec6_17\r\n  - libidn2=2.3.1=h27cfd23_0\r\n  - libopus=1.3.1=h7b6447c_0\r\n  - libpng=1.6.37=hbc83047_0\r\n  - libstdcxx-ng=9.3.0=hd4cf53a_17\r\n  - libtasn1=4.16.0=h27cfd23_0\r\n  - libtiff=4.2.0=h85742a9_0\r\n  - libunistring=0.9.10=h27cfd23_0\r\n  - libuv=1.40.0=h7b6447c_0\r\n  - libvpx=1.7.0=h439df22_0\r\n  - libwebp-base=1.2.0=h27cfd23_0\r\n  - lz4-c=1.9.3=h2531618_0\r\n  - mkl=2021.2.0=h06a4308_296\r\n  - mkl-service=2.3.0=py38h27cfd23_1\r\n  - mkl_fft=1.3.0=py38h42c9631_2\r\n  - mkl_random=1.2.1=py38ha9443f7_2\r\n  - ncurses=6.2=he6710b0_1\r\n  - nettle=3.7.3=hbbd107a_1\r\n  - ninja=1.10.2=hff7bd54_1\r\n  - numpy=1.20.2=py38h2d18471_0\r\n  - numpy-base=1.20.2=py38hfae3a4d_0\r\n  - olefile=0.46=py_0\r\n  - openh264=2.1.0=hd408876_0\r\n  - openssl=1.1.1k=h27cfd23_0\r\n  - pillow=8.2.0=py38he98fc37_0\r\n  - pip=21.1.2=py38h06a4308_0\r\n  - pycparser=2.20=py_2\r\n  - pyopenssl=20.0.1=pyhd3eb1b0_1\r\n  - pysocks=1.7.1=py38h06a4308_0\r\n  - python=3.8.10=h12debd9_8\r\n  - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0\r\n  - readline=8.1=h27cfd23_0\r\n  - requests=2.25.1=pyhd3eb1b0_0\r\n  - setuptools=52.0.0=py38h06a4308_0\r\n  - six=1.16.0=pyhd3eb1b0_0\r\n  - sqlite=3.35.4=hdfb4753_0\r\n  - tk=8.6.10=hbc83047_0\r\n  - torchtext=0.9.1=py38\r\n  - torchvision=0.9.1=py38_cu111\r\n  - typing_extensions=3.7.4.3=pyha847dfd_0\r\n  - urllib3=1.26.4=pyhd3eb1b0_0\r\n  - wheel=0.36.2=pyhd3eb1b0_0\r\n  - x264=1!157.20191217=h7b6447c_0\r\n  - xz=5.2.5=h7b6447c_0\r\n  - zlib=1.2.11=h7b6447c_3\r\n  - zstd=1.4.9=haebb681_0\r\n  - pip:\r\n    - click==8.0.1\r\n    - cycler==0.10.0\r\n    - datasets==1.8.0\r\n    - dill==0.3.4\r\n    - filelock==3.0.12\r\n    - fsspec==2021.6.0\r\n    - huggingface-hub==0.0.8\r\n    - joblib==1.0.1\r\n    - jsonpickle==2.0.0\r\n    - kiwisolver==1.3.1\r\n    - matplotlib==3.4.2\r\n    - multiprocess==0.70.12.2\r\n    - packaging==20.9\r\n    - pandas==1.2.4\r\n    - pyarrow==3.0.0\r\n    - pyparsing==2.4.7\r\n    - python-dateutil==2.8.1\r\n    - pytz==2021.1\r\n    - regex==2021.4.4\r\n    - sacremoses==0.0.45\r\n    - tokenizers==0.10.3\r\n    - tqdm==4.49.0\r\n    - transformers==4.6.1\r\n    - xxhash==2.0.2\r\nprefix: \/home\/mmajurski\/anaconda3\/envs\/round8\r\n```\r\n","comment_length":41,"text":"sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index \n ## Describe the bug\r\nThe built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].\r\n\r\nFor example:\r\nid = '56d1f453e7d4791d009025bd'\r\nanswers = {'text': ['Pure Land'], 'answer_start': [146]}\r\nHowever the actual text in context at location 146 is 'ure Land,'\r\nWhich is an off-by-one error from the correct answer.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nimport datasets\r\n\r\ndef check_context_answer_alignment(example):\r\n    for a_idx in range(len(example['answers']['text'])):\r\n        # check raw dataset for answer consistency between context and answer\r\n        answer_text = example['answers']['text'][a_idx]\r\n        a_st_idx = example['answers']['answer_start'][a_idx]\r\n        a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])\r\n        answer_text_from_context = example['context'][a_st_idx:a_end_idx]\r\n        if answer_text != answer_text_from_context:\r\n            #print(example['id'])\r\n            return False\r\n    return True\r\n\r\ndataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)\r\n\r\nstart_len = len(dataset)\r\ndataset = dataset.filter(check_context_answer_alignment,\r\n                                num_proc=1,\r\n                                keep_in_memory=True)\r\nend_len = len(dataset)\r\nprint('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))\r\n```\r\n\r\n## Expected results\r\nThis code should result in 0 rows being filtered out from the dataset.\r\n\r\n## Actual results\r\nThis filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.\r\n\r\nThis code will reproduce the problem and produce the following count:\r\n\"258 instances contain mis-alignment between the answer text and answer index.\"\r\n\r\n## Environment info\r\nSteps to rebuilt the Conda environment:\r\n```\r\n# create a virtual environment to stuff all these packages into\r\nconda create -n round8 python=3.8 -y\r\n\r\n# activate the virtual environment\r\nconda activate round8\r\n\r\n# install pytorch (best done through conda to handle cuda dependencies)\r\nconda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia\r\n\r\npip install jsonpickle transformers datasets matplotlib\r\n```\r\n\r\nOS: Ubuntu 20.04\r\nPython 3.8\r\n\r\nResult of `conda env export`:\r\n```\r\nname: round8\r\nchannels:\r\n  - pytorch-lts\r\n  - nvidia\r\n  - defaults\r\ndependencies:\r\n  - _libgcc_mutex=0.1=main\r\n  - _openmp_mutex=4.5=1_gnu\r\n  - blas=1.0=mkl\r\n  - brotlipy=0.7.0=py38h27cfd23_1003\r\n  - bzip2=1.0.8=h7b6447c_0\r\n  - ca-certificates=2021.5.25=h06a4308_1\r\n  - certifi=2021.5.30=py38h06a4308_0\r\n  - cffi=1.14.5=py38h261ae71_0\r\n  - chardet=4.0.0=py38h06a4308_1003\r\n  - cryptography=3.4.7=py38hd23ed53_0\r\n  - cudatoolkit=11.1.74=h6bb024c_0\r\n  - ffmpeg=4.2.2=h20bf706_0\r\n  - freetype=2.10.4=h5ab3b9f_0\r\n  - gmp=6.2.1=h2531618_2\r\n  - gnutls=3.6.15=he1e5248_0\r\n  - idna=2.10=pyhd3eb1b0_0\r\n  - intel-openmp=2021.2.0=h06a4308_610\r\n  - jpeg=9b=h024ee3a_2\r\n  - lame=3.100=h7b6447c_0\r\n  - lcms2=2.12=h3be6417_0\r\n  - ld_impl_linux-64=2.35.1=h7274673_9\r\n  - libffi=3.3=he6710b0_2\r\n  - libgcc-ng=9.3.0=h5101ec6_17\r\n  - libgomp=9.3.0=h5101ec6_17\r\n  - libidn2=2.3.1=h27cfd23_0\r\n  - libopus=1.3.1=h7b6447c_0\r\n  - libpng=1.6.37=hbc83047_0\r\n  - libstdcxx-ng=9.3.0=hd4cf53a_17\r\n  - libtasn1=4.16.0=h27cfd23_0\r\n  - libtiff=4.2.0=h85742a9_0\r\n  - libunistring=0.9.10=h27cfd23_0\r\n  - libuv=1.40.0=h7b6447c_0\r\n  - libvpx=1.7.0=h439df22_0\r\n  - libwebp-base=1.2.0=h27cfd23_0\r\n  - lz4-c=1.9.3=h2531618_0\r\n  - mkl=2021.2.0=h06a4308_296\r\n  - mkl-service=2.3.0=py38h27cfd23_1\r\n  - mkl_fft=1.3.0=py38h42c9631_2\r\n  - mkl_random=1.2.1=py38ha9443f7_2\r\n  - ncurses=6.2=he6710b0_1\r\n  - nettle=3.7.3=hbbd107a_1\r\n  - ninja=1.10.2=hff7bd54_1\r\n  - numpy=1.20.2=py38h2d18471_0\r\n  - numpy-base=1.20.2=py38hfae3a4d_0\r\n  - olefile=0.46=py_0\r\n  - openh264=2.1.0=hd408876_0\r\n  - openssl=1.1.1k=h27cfd23_0\r\n  - pillow=8.2.0=py38he98fc37_0\r\n  - pip=21.1.2=py38h06a4308_0\r\n  - pycparser=2.20=py_2\r\n  - pyopenssl=20.0.1=pyhd3eb1b0_1\r\n  - pysocks=1.7.1=py38h06a4308_0\r\n  - python=3.8.10=h12debd9_8\r\n  - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0\r\n  - readline=8.1=h27cfd23_0\r\n  - requests=2.25.1=pyhd3eb1b0_0\r\n  - setuptools=52.0.0=py38h06a4308_0\r\n  - six=1.16.0=pyhd3eb1b0_0\r\n  - sqlite=3.35.4=hdfb4753_0\r\n  - tk=8.6.10=hbc83047_0\r\n  - torchtext=0.9.1=py38\r\n  - torchvision=0.9.1=py38_cu111\r\n  - typing_extensions=3.7.4.3=pyha847dfd_0\r\n  - urllib3=1.26.4=pyhd3eb1b0_0\r\n  - wheel=0.36.2=pyhd3eb1b0_0\r\n  - x264=1!157.20191217=h7b6447c_0\r\n  - xz=5.2.5=h7b6447c_0\r\n  - zlib=1.2.11=h7b6447c_3\r\n  - zstd=1.4.9=haebb681_0\r\n  - pip:\r\n    - click==8.0.1\r\n    - cycler==0.10.0\r\n    - datasets==1.8.0\r\n    - dill==0.3.4\r\n    - filelock==3.0.12\r\n    - fsspec==2021.6.0\r\n    - huggingface-hub==0.0.8\r\n    - joblib==1.0.1\r\n    - jsonpickle==2.0.0\r\n    - kiwisolver==1.3.1\r\n    - matplotlib==3.4.2\r\n    - multiprocess==0.70.12.2\r\n    - packaging==20.9\r\n    - pandas==1.2.4\r\n    - pyarrow==3.0.0\r\n    - pyparsing==2.4.7\r\n    - python-dateutil==2.8.1\r\n    - pytz==2021.1\r\n    - regex==2021.4.4\r\n    - sacremoses==0.0.45\r\n    - tokenizers==0.10.3\r\n    - tqdm==4.49.0\r\n    - transformers==4.6.1\r\n    - xxhash==2.0.2\r\nprefix: \/home\/mmajurski\/anaconda3\/envs\/round8\r\n```\r\n \n If you are going to be altering the data cleaning from the source Squad dataset, here is one thing to consider.\r\nThere are occasional double spaces separating words which it might be nice to get rid of. \r\n\r\nEither way, thank you.","embeddings":[-0.2330299318,-0.3295379579,-0.0441096425,0.3749362826,0.0878231078,-0.0763419643,0.1057570204,0.2279032022,-0.2927472591,0.1577830762,-0.0841630697,0.0450688601,0.3250361681,0.0685452819,-0.0926083177,0.1523635834,0.1756330729,0.0104246661,0.009945604,-0.1725040823,-0.2248985022,0.2991383374,-0.2980325818,0.03003061,-0.342564702,-0.0102489283,0.0364246443,0.1131834313,-0.1175084189,-0.4159388542,0.2125024199,-0.0936790556,0.0077113751,0.2638638318,-0.000116531,-0.1706253737,0.0728382915,-0.0973288417,-0.2682839036,0.0041923714,-0.0435589068,-0.1216914803,-0.1298896968,-0.1058420315,-0.2390315384,0.2717562318,-0.0673231781,-0.2866247594,0.6096699834,0.2443222255,0.1451372653,0.2883639336,0.0109265558,-0.064738296,0.1786869168,-0.1235733703,0.0756943524,0.119126901,0.1456331909,0.1079365462,-0.0346998237,0.4713858068,-0.1343523413,-0.0318910703,-0.0061442321,0.2904502153,0.206617564,-0.1278317124,0.0998568758,0.1311751604,0.2598810494,-0.2484471202,-0.424808681,-0.4569473565,-0.0358279571,-0.1224056184,0.3317508101,0.039453499,-0.0311524291,0.3647889495,-0.1365324855,0.0710276216,-0.0857497603,0.127139762,-0.0876931027,0.2383429706,-0.1219492704,0.1492218971,-0.2677045166,-0.1386347562,-0.0631168187,-0.1375988126,-0.105233863,0.4171854258,-0.5139366984,-0.1013480797,-0.1679380983,0.0907929763,0.2361780256,0.0694944859,-0.0699167773,0.0015248328,0.2659206092,0.0613521114,0.3417307138,0.2773298621,-0.1180535555,0.255628705,0.1020577699,0.1474464983,0.0414646193,0.1034675837,0.1996169537,-0.1841570288,-0.1082466468,-0.1547538042,0.1684667319,-0.1887482852,-0.5222857594,0.4338434935,-0.6319300532,0.2756719887,-0.0214355942,0.3482971787,-0.2216847241,0.2669170201,0.3325076103,0.244284451,-0.0650029629,-0.1851683259,-0.133084923,-0.0969801545,-0.0144069865,-0.0344590582,0.0072183558,-0.1293735057,0.2271303385,0.2961053848,0.2128195316,-0.2205923349,-0.3132048249,-0.1648734659,0.1410585493,0.0306854323,0.0271047894,0.1876549721,0.1873004138,-0.2884297669,0.073195301,0.0930062234,-0.2383058369,0.0768734515,-0.0992043242,0.2245198935,-0.1660035253,-0.056426052,0.0465995781,0.2456641197,0.2966586649,-0.2863129377,0.2683805823,-0.203826189,-0.3300075233,-0.0881249383,0.3650223017,0.2920378745,-0.1985461414,-0.0659254789,-0.0561406538,0.1563176215,0.0265871864,0.237526387,0.2730125487,0.2942620516,-0.4212450981,0.2674057782,0.1037338749,-0.8540112376,-0.5590248704,-0.1742199212,-0.1163506806,0.225110665,-0.1036156043,0.0728597566,0.4342785478,0.1543433517,0.2735286653,0.5391799212,0.0679247305,0.1189373881,-0.3740178347,0.0432915017,-0.0926679522,0.1235257834,-0.2477580458,-0.1743770838,0.0530980192,-0.1834742129,0.1845173985,0.1049054489,0.0700077787,0.242408067,0.3292447031,0.1207566783,0.1795453727,-0.5024951696,-0.4840292037,0.0933753848,-0.0992036015,0.0597215667,-0.0624961443,-0.1360670328,-0.3975332677,-0.0361760221,-0.3520284891,-0.3007717729,0.0909299403,0.0083287833,0.1742563844,0.1125237122,-0.1105441302,0.6274502277,-0.2027090788,0.2574426234,-0.0648542717,0.1838029921,-0.0369916596,0.0197878219,0.0257437229,0.4265458286,-0.0366344415,0.0865879729,-0.2453000844,0.5439850688,0.2354308665,0.1975178421,-0.2265350223,-0.0887751728,0.2265448421,-0.4291588366,-0.2648878694,0.2957112789,0.1549057662,0.1138918698,-0.3117320538,0.59599787,0.0686130524,0.0849598721,-0.2245444506,-0.0018041183,0.2800176144,-0.1242726669,-0.1135764197,-0.2309002727,0.2905323505,0.1432120651,0.5214198828,0.1039449498,-0.3891157508,0.1001307517,0.267216295,-0.1695472002,0.0794455707,0.1168767288,0.0412754938,-0.0822434872,0.024764752,0.2209540159,0.2899647057,0.2044537365,-0.3409023285,0.3199863434,-0.036653012,0.1461652517,0.2144389898,-0.1729850173,-0.0400366671,0.4994864166,0.3725070655,0.2427582741,-0.1843069196,0.1724185944,-0.3970791101,0.152523905,-0.5550450087,-0.021352414,-0.1496844441,-0.0735898316,-0.1993011385,-0.0192141831,-0.2774994969,-0.1124377027,0.248433575,0.1559061408,-0.0537631586,0.2769265175,0.0002657939,0.4594179392,0.0978769809,-0.1988540739,-0.0586240068,-0.0650393665,-0.1624031663,-0.0090979105,0.0156265758,0.1998775154,0.1458058804,-0.4826640189,-0.3008002043,-0.2854555845,-0.3453061879,0.2101686448,-0.0686363429,0.3613208234,0.1664168537,-0.0186989903,-0.3620541394,-0.2030521333,0.164502278,-0.0291347131,-0.4773875177,-0.0524751432,0.0556921959,-0.2053315192,-0.0412881449,-0.4563171864,-0.0989391729,-0.2709364295,0.0162285641,-0.1817271709,0.0823787078,0.2436575741,-0.1389108002,0.0348106995,-0.2918559611,0.1260592341,-0.5039397478,-0.0472881943,0.0939144045,0.0430192649,-0.3436332941,-0.2887289226,-0.232024312,-0.1749058217,-0.0350877568,-0.1375848353,-0.2258420736,-0.1793827713,0.2166627795,-0.2268270254,0.2435190976,0.2265461981,-0.1808743179,0.0880100653,-0.2281240225,-0.4454982877,0.1523829252,0.3971565962,0.2353447974,-0.1117967144,0.3300126195,-0.0246539097,0.6546520591,0.4053243101,0.226618126,0.1022702307,-0.2168853581,0.3332633674,-0.2054528892,-0.2187942415,0.312690258,0.12877289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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2583","title":"Error iteration over IterableDataset using Torch DataLoader","comments":"Hi ! This is because you first need to format the dataset for pytorch:\r\n\r\n```python\r\n>>> import torch\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> torch_iterable_dataset = dataset.with_format(\"torch\")\r\n>>> assert isinstance(torch_iterable_dataset, torch.utils.data.IterableDataset)\r\n>>> dataloader = torch.utils.data.DataLoader(torch_iterable_dataset, batch_size=4)\r\n>>> next(iter(dataloader))\r\n{'id': tensor([0, 1, 2, 3]), 'text': ['Mtendere Village was inspired...]}\r\n```\r\n\r\nThis is because the pytorch dataloader expects a subclass of `torch.utils.data.IterableDataset`. Since you can't pass an arbitrary iterable to a pytorch dataloader, you first need to build an object that inherits from `torch.utils.data.IterableDataset` using `with_format(\"torch\")` for example.\r\n","body":"## Describe the bug\r\nI have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler. \r\n\r\nI am not sure if this is how it is meant to be used, but that's what seemed reasonable to me. \r\n\r\n## Steps to reproduce the bug\r\n\r\n1. Does not work.\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n>>> dataloader.sampler\r\n<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>\r\n>>> for batch in dataloader:\r\n...     print(batch)\r\n```\r\n\r\n2. Works.\r\n```python\r\nimport torch\r\nfrom torch.utils.data import Dataset, IterableDataset, DataLoader\r\nclass CustomIterableDataset(IterableDataset):\r\n  'Characterizes a dataset for PyTorch'\r\n  def __init__(self, data):\r\n        'Initialization'\r\n        self.data = data\r\n\r\n\r\n  def __iter__(self):\r\n        return iter(self.data)\r\n\r\n\r\ndata = list(range(12))\r\ndataset = CustomIterableDataset(data)\r\ndataloader = DataLoader(dataset, batch_size=4)\r\nprint(\"dataloader: \", dataloader.sampler)\r\nfor batch in dataloader:\r\n    print(batch)\r\n```\r\n\r\n## Expected results\r\nTo get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different.\r\ndataloader:  <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50>\r\ntensor([0, 1, 2, 3])\r\ntensor([4, 5, 6, 7])\r\ntensor([ 8,  9, 10, 11])\r\n\r\n## Actual results\r\n<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>\r\n\r\n...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 474, in _next_data\r\n    index = self._next_index()  # may raise StopIteration\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 427, in _next_index\r\n    return next(self._sampler_iter)  # may raise StopIteration\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/sampler.py\", line 227, in __iter__\r\n    for idx in self.sampler:\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/sampler.py\", line 67, in __iter__\r\n    return iter(range(len(self.data_source)))\r\nTypeError: object of type 'IterableDataset' has no len()\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: '1.8.1.dev0'\r\n- Platform: Linux\r\n- Python version: Python 3.6.8\r\n- PyArrow version: '3.0.0'\r\n","comment_length":93,"text":"Error iteration over IterableDataset using Torch DataLoader \n ## Describe the bug\r\nI have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler. \r\n\r\nI am not sure if this is how it is meant to be used, but that's what seemed reasonable to me. \r\n\r\n## Steps to reproduce the bug\r\n\r\n1. Does not work.\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n>>> dataloader.sampler\r\n<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>\r\n>>> for batch in dataloader:\r\n...     print(batch)\r\n```\r\n\r\n2. Works.\r\n```python\r\nimport torch\r\nfrom torch.utils.data import Dataset, IterableDataset, DataLoader\r\nclass CustomIterableDataset(IterableDataset):\r\n  'Characterizes a dataset for PyTorch'\r\n  def __init__(self, data):\r\n        'Initialization'\r\n        self.data = data\r\n\r\n\r\n  def __iter__(self):\r\n        return iter(self.data)\r\n\r\n\r\ndata = list(range(12))\r\ndataset = CustomIterableDataset(data)\r\ndataloader = DataLoader(dataset, batch_size=4)\r\nprint(\"dataloader: \", dataloader.sampler)\r\nfor batch in dataloader:\r\n    print(batch)\r\n```\r\n\r\n## Expected results\r\nTo get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different.\r\ndataloader:  <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50>\r\ntensor([0, 1, 2, 3])\r\ntensor([4, 5, 6, 7])\r\ntensor([ 8,  9, 10, 11])\r\n\r\n## Actual results\r\n<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>\r\n\r\n...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 474, in _next_data\r\n    index = self._next_index()  # may raise StopIteration\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 427, in _next_index\r\n    return next(self._sampler_iter)  # may raise StopIteration\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/sampler.py\", line 227, in __iter__\r\n    for idx in self.sampler:\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/sampler.py\", line 67, in __iter__\r\n    return iter(range(len(self.data_source)))\r\nTypeError: object of type 'IterableDataset' has no len()\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: '1.8.1.dev0'\r\n- Platform: Linux\r\n- Python version: Python 3.6.8\r\n- PyArrow version: '3.0.0'\r\n \n Hi ! This is because you first need to format the dataset for pytorch:\r\n\r\n```python\r\n>>> import torch\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> torch_iterable_dataset = dataset.with_format(\"torch\")\r\n>>> assert isinstance(torch_iterable_dataset, torch.utils.data.IterableDataset)\r\n>>> dataloader = torch.utils.data.DataLoader(torch_iterable_dataset, batch_size=4)\r\n>>> next(iter(dataloader))\r\n{'id': tensor([0, 1, 2, 3]), 'text': ['Mtendere Village was inspired...]}\r\n```\r\n\r\nThis is because the pytorch dataloader expects a subclass of `torch.utils.data.IterableDataset`. Since you can't pass an arbitrary iterable to a pytorch dataloader, you first need to build an object that inherits from `torch.utils.data.IterableDataset` using `with_format(\"torch\")` for example.\r\n","embeddings":[-0.1759884804,-0.3325825632,-0.0121410461,0.2365849912,0.1552752554,0.0204123762,0.4311739802,0.0018048278,-0.1689608544,0.2460096478,0.0963452384,0.2442091852,-0.3354809284,-0.4065969288,-0.2495775521,-0.2147274315,0.0861800462,-0.217220217,-0.2279188633,-0.0843941569,-0.1583958417,-0.0314952843,-0.2381762117,-0.0334465913,-0.0213509388,-0.0244186632,-0.120598048,0.0094267903,0.0826323181,-0.5168218017,0.4132375419,0.0784671381,0.5836470127,0.5928797722,-0.0001213014,0.0804407671,0.5695524812,-0.1488481462,-0.3406754136,0.0339830406,0.1853104383,0.1231119111,0.1892292798,-0.2503037155,0.0085637579,-0.1373459846,0.0413325764,-0.4429116547,0.0277451836,0.0911385715,0.127351135,0.1404467672,-0.2152099013,-0.1551946849,0.0873393342,0.0838435441,-0.0660060793,0.3676383197,0.6034711003,0.1462023854,-0.2805481553,0.1023533046,-0.0515720993,0.1446328014,0.2025340199,-0.2065145671,-0.1518067718,-0.1411313266,0.0165851172,0.137726143,0.4296510816,-0.3647227585,-0.2566857934,-0.2134337723,-0.0908371732,-0.0081759226,-0.0515622795,0.2455942184,-0.2517602444,-0.1019132212,-0.1053520739,0.2945753634,-0.2935186625,0.1506047249,0.1208930612,0.0607558563,-0.0605423823,0.4181459546,-0.0258095749,-0.0496773608,0.4104271531,-0.1466057748,0.1139799654,0.1791273206,-0.4098126292,0.1215738282,-0.074593015,0.0343862325,0.1090590954,0.2285431772,0.191299811,-0.0721809641,0.2148056626,-0.0758831427,0.2809829414,0.2104015499,0.1607042253,0.1309028268,-0.0484363362,0.1294634193,0.1207149625,-0.043157395,-0.0131197236,-0.2782546878,0.3315110207,0.2297443449,0.2427385151,0.0218726397,-0.2630295455,-0.0671552569,-0.377215147,-0.046477098,0.0921518356,0.031209385,-0.0663540661,-0.01823931,0.0260313656,0.2194151133,0.0019598796,-0.1363487989,-0.044967562,-0.0634313747,-0.2369847596,0.1653112769,0.0138125839,-0.5259135365,0.2178764939,0.0743197277,0.1240426004,0.0981908739,0.1407571584,-0.1538307667,0.087651819,-0.1886782199,0.1085830554,0.2937149107,0.0909665525,0.3701990843,0.1703479141,0.3358133435,-0.2139513344,-0.4311694205,-0.0041146064,0.1315057129,-0.1349411905,0.1484013498,-0.1661923081,0.0139729884,0.3592304885,-0.086299561,0.2563602626,-0.3309457302,0.0854897127,-0.3074098527,-0.1566838324,0.3201508522,-0.5023243427,-0.0094216177,0.0944096446,-0.1242311895,0.4520845413,0.1644668728,-0.380759865,0.3596851826,-0.4563367665,-0.0022490702,0.0037128867,-0.4484592378,-0.4353860021,0.2547225654,0.0600690469,0.29165712,0.1757151634,0.0443592407,0.4281608462,-0.3241616189,0.1788843125,0.0181607902,-0.2364029586,-0.1766932756,-0.3059003353,0.06542968,0.3222926259,0.1215381771,0.0787648037,0.2113111764,-0.0018342615,0.0364539027,0.2559021413,-0.162260294,0.1733245999,0.2712677717,-0.0221524425,0.1062509865,0.1877069771,-0.0639051124,-0.3826922774,0.1902539432,-0.0341444239,0.0252886638,0.0248268265,-0.0357971936,0.0746423528,0.1515569687,-0.4089162052,-0.1956710517,0.0207006149,0.3267149329,0.2401629686,-0.3410871029,0.051025603,0.177932322,-0.3679131567,-0.0905418396,-0.6530374289,-0.0157553479,0.1605352908,-0.2572544813,0.0066909054,0.0078652846,0.2089659721,-0.2063672841,-0.0949184895,0.2147931606,-0.1238057837,-0.1281567514,-0.1784879714,-0.0105119431,0.0613333322,-0.2036591768,-0.0033433426,0.3811857998,0.2206561118,-0.1643147618,0.1571412086,0.4330125153,-0.1378979385,0.4838735759,-0.0734657273,-0.0011852734,0.0547133498,0.2082166523,-0.4597417116,0.1447856128,0.2501415312,-0.062214449,0.4288068414,-0.1978160143,-0.3633216321,0.0964463502,-0.0651243404,-0.0165036712,-0.133232519,0.176212579,-0.2831240296,0.0837609991,0.1375372857,-0.0159248263,0.6980891824,0.0048870426,0.0211558826,-0.1067589521,0.1212168932,-0.0663993061,0.0172511786,0.2396637946,0.0811793506,0.1726081222,0.0910499394,-0.1229048222,-0.0504769608,-0.4383977354,-0.1312052011,0.1998901069,-0.0802580416,0.300724566,0.0954560488,-0.1390556544,-0.3806454539,-0.5877685547,0.0185129624,-0.2711995542,-0.2329483777,0.5140383244,0.0763324574,0.1100412235,0.0438049771,0.1240867674,0.3323763013,-0.1041971892,-0.0159296971,-0.0392114073,-0.3659599423,-0.0314605087,-0.0236697625,-0.6017323136,0.3704046309,0.1343916655,0.0404745713,-0.479201287,0.1423618495,0.0473591872,-0.0661919266,0.0706465095,0.4086326361,-0.0504630692,0.2938432693,-0.2023874968,0.3072182834,-0.0948523581,0.0008258498,0.0853473917,-0.093167305,-0.0026095277,0.1463578939,-0.0368866585,-0.2398698926,-0.2074588835,-0.0861339569,0.0731796995,-0.0081962924,0.1651799828,0.0242369957,0.0953033939,0.4552520216,-0.1504246593,-0.0639602095,-0.4166634679,0.1938693374,-0.3335467279,-0.2859089077,-0.2267332226,-0.2294795811,0.2497810721,0.3717745543,-0.3056227267,-0.1961064041,-0.0602116659,0.2252768129,-0.0817707479,-0.0425034091,0.2050268203,-0.1281113476,0.0889525265,-0.1457213461,-0.1033764184,0.0690963343,-0.2425730675,0.3543992937,0.2895650268,0.5595616102,0.1378503442,0.9526199102,0.4625449479,-0.1511872262,0.2041900307,0.0080109807,-0.1384082437,-0.0857749134,-0.3842646182,0.215283066,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2583","title":"Error iteration over IterableDataset using Torch DataLoader","comments":"Thank you for that and the example! \r\n\r\nWhat you said makes total sense; I just somehow missed that and assumed HF IterableDataset was a subclass of Torch IterableDataset. ","body":"## Describe the bug\r\nI have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler. \r\n\r\nI am not sure if this is how it is meant to be used, but that's what seemed reasonable to me. \r\n\r\n## Steps to reproduce the bug\r\n\r\n1. Does not work.\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n>>> dataloader.sampler\r\n<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>\r\n>>> for batch in dataloader:\r\n...     print(batch)\r\n```\r\n\r\n2. Works.\r\n```python\r\nimport torch\r\nfrom torch.utils.data import Dataset, IterableDataset, DataLoader\r\nclass CustomIterableDataset(IterableDataset):\r\n  'Characterizes a dataset for PyTorch'\r\n  def __init__(self, data):\r\n        'Initialization'\r\n        self.data = data\r\n\r\n\r\n  def __iter__(self):\r\n        return iter(self.data)\r\n\r\n\r\ndata = list(range(12))\r\ndataset = CustomIterableDataset(data)\r\ndataloader = DataLoader(dataset, batch_size=4)\r\nprint(\"dataloader: \", dataloader.sampler)\r\nfor batch in dataloader:\r\n    print(batch)\r\n```\r\n\r\n## Expected results\r\nTo get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different.\r\ndataloader:  <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50>\r\ntensor([0, 1, 2, 3])\r\ntensor([4, 5, 6, 7])\r\ntensor([ 8,  9, 10, 11])\r\n\r\n## Actual results\r\n<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>\r\n\r\n...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 474, in _next_data\r\n    index = self._next_index()  # may raise StopIteration\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 427, in _next_index\r\n    return next(self._sampler_iter)  # may raise StopIteration\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/sampler.py\", line 227, in __iter__\r\n    for idx in self.sampler:\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/sampler.py\", line 67, in __iter__\r\n    return iter(range(len(self.data_source)))\r\nTypeError: object of type 'IterableDataset' has no len()\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: '1.8.1.dev0'\r\n- Platform: Linux\r\n- Python version: Python 3.6.8\r\n- PyArrow version: '3.0.0'\r\n","comment_length":28,"text":"Error iteration over IterableDataset using Torch DataLoader \n ## Describe the bug\r\nI have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler. \r\n\r\nI am not sure if this is how it is meant to be used, but that's what seemed reasonable to me. \r\n\r\n## Steps to reproduce the bug\r\n\r\n1. Does not work.\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n>>> dataloader.sampler\r\n<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>\r\n>>> for batch in dataloader:\r\n...     print(batch)\r\n```\r\n\r\n2. Works.\r\n```python\r\nimport torch\r\nfrom torch.utils.data import Dataset, IterableDataset, DataLoader\r\nclass CustomIterableDataset(IterableDataset):\r\n  'Characterizes a dataset for PyTorch'\r\n  def __init__(self, data):\r\n        'Initialization'\r\n        self.data = data\r\n\r\n\r\n  def __iter__(self):\r\n        return iter(self.data)\r\n\r\n\r\ndata = list(range(12))\r\ndataset = CustomIterableDataset(data)\r\ndataloader = DataLoader(dataset, batch_size=4)\r\nprint(\"dataloader: \", dataloader.sampler)\r\nfor batch in dataloader:\r\n    print(batch)\r\n```\r\n\r\n## Expected results\r\nTo get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different.\r\ndataloader:  <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50>\r\ntensor([0, 1, 2, 3])\r\ntensor([4, 5, 6, 7])\r\ntensor([ 8,  9, 10, 11])\r\n\r\n## Actual results\r\n<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>\r\n\r\n...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 435, in __next__\r\n    data = self._next_data()\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 474, in _next_data\r\n    index = self._next_index()  # may raise StopIteration\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py\", line 427, in _next_index\r\n    return next(self._sampler_iter)  # may raise StopIteration\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/sampler.py\", line 227, in __iter__\r\n    for idx in self.sampler:\r\n  File \"\/data\/leshekha\/lib\/HFDatasets\/lib\/python3.6\/site-packages\/torch\/utils\/data\/sampler.py\", line 67, in __iter__\r\n    return iter(range(len(self.data_source)))\r\nTypeError: object of type 'IterableDataset' has no len()\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: '1.8.1.dev0'\r\n- Platform: Linux\r\n- Python version: Python 3.6.8\r\n- PyArrow version: '3.0.0'\r\n \n Thank you for that and the example! \r\n\r\nWhat you said makes total sense; I just somehow missed that and assumed HF IterableDataset was a subclass of Torch IterableDataset. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2573","title":"Finding right block-size with JSON loading difficult for user","comments":"This was actually a second error arising from a too small block-size in the json reader.\r\n\r\nFinding the right block size is difficult for the layman user","body":"As reported by @thomwolf, while loading a JSON Lines file with \"json\" loading script, he gets\r\n> json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)\r\n","comment_length":27,"text":"Finding right block-size with JSON loading difficult for user \n As reported by @thomwolf, while loading a JSON Lines file with \"json\" loading script, he gets\r\n> json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)\r\n \n This was actually a second error arising from a too small block-size in the json reader.\r\n\r\nFinding the right block size is difficult for the layman user","embeddings":[0.0956068933,0.0020218808,-0.2247490883,0.4068727493,0.1341332197,-0.0870383829,0.3515785933,0.3959694803,0.5053705573,0.4565078318,0.2659341097,-0.0984663814,0.0224605687,-0.063838549,-0.0379775129,-0.2568934858,-0.2618601322,0.2319747657,0.1568873078,0.3039900362,-0.1997620016,0.2665425837,0.1062276065,-0.1609105617,0.2236228734,0.0587515533,0.0914319232,0.0959857106,-0.0711931884,-0.4359497428,-0.0447092541,-0.1151688546,-0.0743276477,0.4498323202,-0.0000995917,0.0249896757,0.3473541737,-0.0728502572,-0.4421042204,-0.120011963,-0.060400039,-0.5030124187,0.20351623,-0.199337095,-0.021843465,-0.1310319453,-0.0668413788,-0.1065114066,0.462911278,0.1220876947,0.3715203702,-0.237700671,0.0456450135,0.0361532383,0.2381219566,0.0637214929,0.0411675163,0.3781586289,0.3516416848,0.5234601498,0.1095333919,0.0675432384,-0.0711688772,-0.0298221298,0.0829154477,-0.3186947703,0.2817250192,-0.0485040992,0.1941674054,0.2429829389,0.4865899086,0.4166166186,-0.0474563912,-0.3242163062,0.0322855152,0.1813371778,0.3077044785,0.0795376003,-0.253692627,-0.022642646,-0.2651890218,-0.0648075789,-0.026416013,-0.036925856,-0.0485683642,-0.0989516228,-0.1725890189,-0.0340950973,0.152447924,-0.1861773729,-0.0281526316,-0.1080389991,-0.3090918362,-0.0638663843,0.0473019257,-0.1721327156,0.1723929793,-0.4616991282,0.0737602338,0.0164623838,0.5297000408,0.1309640706,0.124232538,0.0838950947,0.1776370704,-0.0081726704,-0.0359089375,-0.195377633,0.1494318098,0.2763272822,0.1767874658,-0.0923806652,-0.2731758058,-0.2625056207,-0.2382466495,0.0424292795,-0.0853547454,-0.1323301643,-0.0086915521,0.2200216949,-0.429397583,-0.0551880151,0.0649292693,0.1727652103,0.0687273592,0.2640414536,0.2109944671,-0.0350077674,-0.1069791988,-0.0796667859,-0.1454554647,0.0986789018,0.1120595708,-0.1924844682,0.2174410969,0.1487836242,0.1483711451,-0.1516091973,-0.30999735,-0.3423232734,0.1462390721,0.0635312274,-0.1348585039,0.2650139928,0.186412096,-0.1065845564,-0.2369070202,-0.1228933558,-0.180056408,0.2556236088,-0.2207956612,-0.1268772036,0.0242798924,0.3463029563,0.024233982,0.14293167,-0.1664892137,-0.0688213333,0.0027225125,-0.1732430905,0.0776588246,-0.2282051593,0.044811815,-0.2076376379,-0.1221623197,0.1634411961,-0.3751145601,-0.1120071635,0.1969165355,-0.0536480919,0.0594989881,0.3059161901,-0.2070640475,0.2804148495,-0.0869016349,-0.1196012944,0.2914662957,0.1173439994,-0.2984392643,0.5185750127,-0.1299733669,0.2412544191,0.2093473673,-0.029225586,0.1271212846,0.0056954655,0.388454318,0.2863802016,0.1438802481,0.2596033812,-0.3090475798,-0.1199647635,0.0019169644,0.1428799927,-0.0248802826,-0.27548334,-0.0984636024,-0.0076827239,0.1241575181,-0.2261896878,-0.0808173344,0.2229843736,-0.0063510253,-0.1971470416,0.1822516471,0.2619439662,-0.1677093208,0.0242061354,-0.0506117046,-0.2076396495,0.0557606407,0.0121830124,-0.0724804997,-0.1452801079,0.1470714957,0.2062346935,0.3015975356,0.1180322543,0.123223044,-0.2187989354,0.10078381,0.0669476688,0.0789797679,0.0460177325,-0.2172989994,0.0409305245,-0.0568748116,-0.093319878,0.2606039941,-0.0812589452,-0.0284129344,-0.3079867065,-0.2069393396,0.0034053493,0.1463601738,-0.0012324867,-0.1660547256,-0.1716948301,-0.0142641552,0.452152431,0.1640970111,0.3425682187,0.0488318279,-0.1035275534,-0.0192071386,0.3329329193,-0.1527856439,0.093455568,-0.0145512316,-0.2997948229,0.401919961,0.0413827226,0.122253634,-0.0321630612,0.3996909857,0.554302454,-0.0444615781,-0.026263807,-0.1600777805,-0.0489269942,0.4573387802,-0.2258951366,0.0071738209,0.3564730287,0.1343528479,-0.1963520348,-0.105223082,0.1182256564,0.1299024075,0.1888304204,-0.0610684529,-0.1487724781,0.4033785462,-0.4765149951,0.1759030074,-0.0582790934,0.1923301816,0.2282369733,0.1666168869,-0.1296429485,-0.1703408808,-0.2919543982,-0.1598014683,0.1414598823,-0.2160116732,-0.0383913256,-0.1749043018,0.0171440672,0.1160070002,-0.1941159666,0.0634469017,-0.2764542699,0.186484009,0.0857999846,-0.3864562213,-0.2314625233,-0.1342249066,0.4629586041,-0.1204371005,0.388907522,-0.2953087091,-0.0841377974,-0.2262499034,0.2532871366,0.4552177787,-0.0919125453,0.0816128999,-0.274921149,0.0461798646,0.347112149,-0.0816960931,-0.0107905008,0.022841543,-0.0600496754,0.0560306832,0.4496861696,0.0836112052,0.030221235,-0.1500127167,-0.0744829252,-0.2961722612,0.2650219798,-0.0021039881,-0.1048985273,-0.3383951187,-0.2061034441,0.107868664,-0.3975123763,0.4412335753,0.2321575731,0.212052241,-0.3862463832,0.2198593765,0.1508169919,0.1889527887,0.3159148097,-0.2085716128,-0.4334502518,0.1112232432,-0.2329666913,-0.4228050709,0.1300275177,0.1339902282,0.0498610251,-0.2667621374,-0.4891366065,0.3003756106,-0.0521570146,-0.2767086625,-0.1889620572,-0.1978449821,0.2448020577,0.0081736157,-0.1918315291,0.0152293304,-0.2356634438,0.0061501791,0.1423393786,0.148025915,-0.1886839271,0.6826385856,-0.0103884991,0.0446234606,0.1303070486,0.0706444681,0.0657464042,0.3156469762,0.0240764227,-0.2853400409,0.3214404583,0.1970461458,0.143458724,-0.0817008466,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2569","title":"Weights of model checkpoint not initialized for RobertaModel for Bertscore","comments":"Hi @suzyahyah, thanks for reporting.\r\n\r\nThe message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:\r\n```\r\nSome weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.layer_norm.weight']\r\n- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\n```\r\n\r\nIn this case, this behavior IS expected and you can safely ignore the warning message.\r\n\r\nThe reason is that you are just using RoBERTa to get the contextual embeddings of the input sentences\/tokens, thus leaving away its head layer, whose weights are ignored.\r\n\r\nFeel free to reopen this issue if you need further explanations.","body":"When applying bertscore out of the box, \r\n\r\n```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```\r\n\r\nFollowing the typical usage from https:\/\/huggingface.co\/docs\/datasets\/loading_metrics.html\r\n\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('bertscore')\r\n\r\n# Example of typical usage\r\nfor batch in dataset:\r\n    inputs, references = batch\r\n    predictions = model(inputs)\r\n    metric.add_batch(predictions=predictions, references=references)\r\nscore = metric.compute(lang=\"en\")\r\n#score = metric.compute(model_type=\"roberta-large\") # gives the same error\r\n```\r\n\r\nI am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https:\/\/github.com\/Tiiiger\/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo.... \r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27\r\n- Python version:  3.9.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":167,"text":"Weights of model checkpoint not initialized for RobertaModel for Bertscore \n When applying bertscore out of the box, \r\n\r\n```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```\r\n\r\nFollowing the typical usage from https:\/\/huggingface.co\/docs\/datasets\/loading_metrics.html\r\n\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('bertscore')\r\n\r\n# Example of typical usage\r\nfor batch in dataset:\r\n    inputs, references = batch\r\n    predictions = model(inputs)\r\n    metric.add_batch(predictions=predictions, references=references)\r\nscore = metric.compute(lang=\"en\")\r\n#score = metric.compute(model_type=\"roberta-large\") # gives the same error\r\n```\r\n\r\nI am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https:\/\/github.com\/Tiiiger\/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo.... \r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27\r\n- Python version:  3.9.5\r\n- PyArrow version: 3.0.0\r\n \n Hi @suzyahyah, thanks for reporting.\r\n\r\nThe message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:\r\n```\r\nSome weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.layer_norm.weight']\r\n- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\n```\r\n\r\nIn this case, this behavior IS expected and you can safely ignore the warning message.\r\n\r\nThe reason is that you are just using RoBERTa to get the contextual embeddings of the input sentences\/tokens, thus leaving away its head layer, whose weights are ignored.\r\n\r\nFeel free to reopen this issue if you need further explanations.","embeddings":[-0.0595500432,-0.3645298481,0.0901097059,0.1289699376,0.4273766279,0.0748271346,0.2718206644,0.124234505,0.1251102686,0.1333975643,-0.1491384357,0.217213437,-0.1455290765,-0.0643486157,-0.0716609731,-0.2196490318,0.1432188153,-0.0499439314,-0.2094158232,-0.1764126867,-0.1302692592,0.1864649951,-0.2054327428,0.1243986264,-0.3585760295,0.1707725227,0.0284248386,0.3952339292,0.0566756651,-0.4421780407,0.4252820313,-0.0705891028,0.1829725206,0.487798959,-0.0001228714,0.0064606671,0.2213185281,-0.0540871173,-0.0313102044,-0.1773597002,0.0916281044,-0.2407545894,0.1299378872,-0.0846756846,0.0850726888,0.1098496169,-0.0971314088,-0.0917770565,0.4431142807,-0.0203818697,0.0755341873,0.6024700403,0.0763729885,-0.1494729072,0.0460663699,-0.1314279884,0.0313534476,0.7358220816,-0.153144151,-0.0208545029,-0.0985676721,0.3127400577,0.1029277667,0.3258488476,0.2433318794,-0.0050746533,0.0890485644,-0.0216680765,-0.3173093498,0.347205162,-0.0981717333,-0.1046690792,-0.2188250422,-0.2243639082,0.0887015238,-0.2474700511,0.1257462204,-0.0246987063,0.0381265357,0.1720036417,-0.2667013705,-0.0669068918,0.0676952899,-0.0849525183,0.1294219196,-0.1038843542,-0.3312368989,0.0494496226,0.5595440269,0.0194062963,-0.4946108758,-0.0332812555,0.0093517732,0.1292148232,-0.1981736571,0.0252225641,0.2562126815,0.3080791235,0.2015607953,-0.0441699661,0.0902203843,0.0475660749,-0.1666268557,0.0227408167,0.0656780973,0.3708826303,-0.1095082983,-0.0424451455,0.1951088607,0.4521815777,0.2514283657,-0.1259821802,-0.2047226727,-0.1200857311,0.0782651305,-0.1153678373,0.0622206256,-0.1526997685,-0.4366090596,-0.024324052,-0.0479732677,-0.2746985853,0.4581758082,0.2476866394,-0.0365002938,0.2026105672,-0.1451325715,0.3258635104,-0.1985165775,0.0236692689,-0.1916703731,0.0359917991,-0.5499864817,0.1129402071,0.1525399834,-0.514990449,0.3871522546,0.0121976295,0.4898635447,0.0477317236,-0.1363251656,-0.0647763461,0.0756391883,0.3032776117,-0.0555824786,0.3049114943,0.3483037055,0.248380214,-0.1436179429,-0.1127427444,-0.3222931623,-0.4240387082,0.3303536773,-0.0249223299,-0.2013020664,-0.0021148333,-0.6301630735,0.1443105489,0.0616411753,0.0386339538,-0.0829501227,0.1372282505,-0.1097322479,0.0338978805,0.461948365,0.5912770629,-0.4559357762,-0.3831556439,0.0175581947,-0.0373061523,0.2624933422,0.5404408574,-0.2662966847,0.2490768284,-0.1183156371,-0.1341705322,-0.0909342244,-0.2990626991,-0.1987127215,-0.1149462387,0.1860004514,0.0329382047,0.1031875983,-0.3880258799,-0.5773239136,-0.1748377681,0.077793479,-0.1614882648,0.0580865182,0.0355185121,-0.2055669576,-0.2308661193,0.0268766955,0.2811305523,-0.1402849257,0.0905096754,-0.2136240155,0.1375385672,-0.088408336,-0.1537056565,-0.0406620838,-0.0437063091,0.1233562157,-0.1413982064,0.0559836887,0.1983401328,-0.0795108303,0.2182419151,0.0872282758,0.0330903269,0.1745310426,0.0800559595,-0.2793905139,-0.099970907,-0.050696224,-0.0824540108,-0.0876182467,0.0734473541,0.0435826406,-0.0325288326,-0.4597765803,-0.1559244096,-0.3609873354,0.163539663,-0.1609194726,0.0514067635,-0.0361126959,0.0468638912,0.0299032778,0.3188659847,-0.0681626424,-0.2337530851,-0.0063475822,0.368624717,0.0399781242,-0.0959596038,0.2741053998,-0.0035358192,0.1728624254,-0.1739950478,-0.1187923774,-0.1149655432,0.0476638488,0.0078627998,-0.1623607129,0.3741194606,-0.039869532,0.1298767328,0.1223521829,-0.084031418,0.1379922032,0.0420225747,-0.0950801671,-0.2151756138,0.1294486225,0.1283426434,0.5055886507,-0.1493334323,-0.1057035476,-0.5590142608,-0.0101487963,0.023932267,-0.0259494651,0.222036168,-0.260264039,0.0751149952,-0.1623806953,-0.375482142,0.3970047235,0.0489646494,0.0447649397,-0.0641902685,0.0965383574,-0.1662309468,-0.1341766119,0.4033741653,-0.1383150816,0.0557984523,-0.0470858626,0.0068544578,-0.2336542755,0.0577766262,-0.0103694098,0.0481163561,-0.5006847978,0.2451440841,0.0958232656,0.2131485641,-0.2310560346,-0.6032778025,-0.2174278796,-0.1217876971,0.0403481983,-0.0738132745,0.0724710897,0.2759858668,0.3919827342,0.619857192,-0.0538355932,0.1658966094,-0.3506987393,0.0307397451,-0.0841733068,0.0006196868,0.0281954948,-0.2045597732,-0.1048332751,-0.3925685585,-0.0318240859,0.1536333114,-0.3445519805,0.1895877123,-0.3401435018,0.3981795311,0.3173835278,-0.0608042143,-0.1456557065,-0.2803340852,0.3773913085,-0.1592202485,-0.0017578057,-0.0181132909,-0.1346401572,-0.0219188705,-0.0361186415,0.0024108915,-0.0466626883,-0.1200052127,0.2488232106,0.2780738175,-0.1349141896,-0.0081026675,0.1877327263,0.309073925,-0.2970644832,-0.102199845,-0.1530170888,-0.6735306978,0.4321505129,-0.1006259322,-0.2623509169,-0.1679246724,0.0318279415,0.3910511732,-0.4273377955,-0.2776669264,-0.3598842025,-0.1445216686,-0.067840293,0.1398446113,0.3970988691,-0.0778832212,-0.1432484984,0.0223997086,-0.099999927,-0.1330249608,0.0972602144,-0.1907607168,0.3079788387,-0.3312959969,0.2309684902,-0.325683713,0.9833400846,0.208368659,-0.2381806374,0.5505979061,0.1054871976,0.3776525855,-0.178900972,-0.1699359417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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2569","title":"Weights of model checkpoint not initialized for RobertaModel for Bertscore","comments":"Hi @suzyahyah, I have created a Pull Request to filter out that warning message in this specific case, since the behavior is as expected and the warning message can only cause confusion for users (as in your case).","body":"When applying bertscore out of the box, \r\n\r\n```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```\r\n\r\nFollowing the typical usage from https:\/\/huggingface.co\/docs\/datasets\/loading_metrics.html\r\n\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('bertscore')\r\n\r\n# Example of typical usage\r\nfor batch in dataset:\r\n    inputs, references = batch\r\n    predictions = model(inputs)\r\n    metric.add_batch(predictions=predictions, references=references)\r\nscore = metric.compute(lang=\"en\")\r\n#score = metric.compute(model_type=\"roberta-large\") # gives the same error\r\n```\r\n\r\nI am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https:\/\/github.com\/Tiiiger\/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo.... \r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27\r\n- Python version:  3.9.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":38,"text":"Weights of model checkpoint not initialized for RobertaModel for Bertscore \n When applying bertscore out of the box, \r\n\r\n```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```\r\n\r\nFollowing the typical usage from https:\/\/huggingface.co\/docs\/datasets\/loading_metrics.html\r\n\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('bertscore')\r\n\r\n# Example of typical usage\r\nfor batch in dataset:\r\n    inputs, references = batch\r\n    predictions = model(inputs)\r\n    metric.add_batch(predictions=predictions, references=references)\r\nscore = metric.compute(lang=\"en\")\r\n#score = metric.compute(model_type=\"roberta-large\") # gives the same error\r\n```\r\n\r\nI am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https:\/\/github.com\/Tiiiger\/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo.... \r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27\r\n- Python version:  3.9.5\r\n- PyArrow version: 3.0.0\r\n \n Hi @suzyahyah, I have created a Pull Request to filter out that warning message in this specific case, since the behavior is as expected and the warning message can only cause confusion for users (as in your case).","embeddings":[-0.1506308615,-0.297534883,0.0790532753,0.0513515696,0.4647553563,0.0405767187,0.1979359239,0.190786466,0.1385852247,0.2084496319,-0.1303769946,0.2869918644,-0.1275676936,-0.086621359,-0.1238216907,-0.1949991286,0.1129312143,-0.0918190628,-0.2066622376,-0.1772622168,-0.168073222,0.1075783893,-0.2270771414,0.0877471119,-0.2647553086,0.1180240363,0.1158174053,0.3999868035,-0.0279442426,-0.549695909,0.4129525721,-0.1024811044,0.1832319349,0.432197988,-0.000118405,-0.0350618102,0.2312027663,-0.0642960742,-0.0272502974,-0.1799430996,0.0714151114,-0.21532619,0.1907900274,-0.0851066485,0.0962506235,0.110652335,-0.0638516322,-0.0977282301,0.2969880998,0.0147003299,0.1060960814,0.5801304579,0.091524452,-0.2033586353,0.0562141798,-0.2434053868,-0.0107169114,0.7996891737,-0.1194128245,0.0211138446,-0.1755490601,0.3603536487,0.0470004603,0.4131556749,0.2314981222,-0.0450551659,0.1458597034,-0.0140760988,-0.2861908376,0.363350749,-0.0495934747,-0.0992543772,-0.1434774399,-0.1704610288,0.1701110154,-0.3235532343,0.1518207192,-0.0207377486,-0.0049407082,0.1767668426,-0.1856806725,-0.0096387072,0.0392065085,-0.0255855788,0.1122339368,-0.1080647111,-0.3088824153,0.0715796649,0.5233258605,0.0219151527,-0.5161144137,0.0442516692,-0.0158390105,0.1050248444,-0.2215551883,0.0371879861,0.276848644,0.3556411266,0.2921352386,-0.051609721,0.1328827888,0.0495795757,-0.1677485108,0.0735968426,0.0619302392,0.3298578262,-0.0936576203,-0.1504183561,0.1854511499,0.4818689823,0.2026166916,-0.1468577385,-0.1522711217,-0.1516358703,0.1331294626,-0.0134107424,0.0515862331,-0.1503574103,-0.4884830117,0.0293980297,-0.0861961246,-0.2325775027,0.4540870488,0.2400120497,-0.0287968498,0.1914060861,-0.2195283026,0.3667712212,-0.1675243676,-0.0024104565,-0.2121832073,0.0200953875,-0.5994926691,0.0451913923,0.1670409292,-0.521114707,0.4407982528,0.0861022994,0.4616357088,0.0439056158,-0.1134123355,-0.1241905019,0.1050330102,0.3450675309,0.0275230482,0.2916050553,0.3321642876,0.2506280839,-0.1550834179,-0.0865121037,-0.3208598793,-0.4442728162,0.3166167736,0.0127649326,-0.12097352,-0.0538565628,-0.6307356954,0.1684771478,0.0612261593,0.0587103777,-0.0680129528,0.1115413606,-0.1867161691,-0.014588085,0.371406734,0.5238423944,-0.5235716701,-0.2953406274,0.0224866439,-0.0566582307,0.3146739006,0.4856605828,-0.2403754294,0.2253071368,-0.1106553748,-0.1308024079,-0.0051892768,-0.324064523,-0.1994791031,-0.055202812,0.1629431844,0.0029754476,0.1301812232,-0.4134738743,-0.5584423542,-0.1973029375,0.1586217284,-0.1974609792,0.0787228346,0.0256364979,-0.2331430912,-0.2044692934,0.0141701829,0.2407873869,-0.1170875207,0.0346441828,-0.1564632952,0.0461315364,-0.0578327477,-0.1170396432,-0.0120325265,-0.1053555831,0.1301748604,-0.1696342826,0.0152136404,0.1239441633,-0.0621881448,0.2335908562,0.1463427544,0.0846133754,0.2386511117,0.0692473352,-0.2949136198,-0.0895646289,-0.0579354838,-0.0117454203,-0.0491656289,0.0096058138,0.014750029,0.0431583896,-0.4249891639,-0.2600969076,-0.4469791353,0.1492612511,-0.0618759692,-0.0507430844,-0.0595060028,0.0249604285,0.0614042431,0.2770500779,-0.0201047286,-0.1134727448,-0.0162944812,0.3425474763,0.0704258457,-0.1281533688,0.2877139151,-0.0041894238,0.1435242444,-0.0778608173,-0.0890887976,-0.1550526321,0.0574499778,0.0166235436,-0.1131711453,0.3734732568,0.0200649891,0.0996995345,0.0735728964,-0.0686987117,0.1884991378,0.0305553917,0.0090344138,-0.2739720643,0.1552571803,0.1375312805,0.4346508682,-0.1644020975,-0.1481812298,-0.4836677313,0.0090099173,0.0718987063,0.0309538599,0.2066996843,-0.2137859315,0.0322789662,-0.1540057659,-0.3394297361,0.441845119,0.0757387802,0.13170816,-0.0668469593,0.0565903634,-0.2283736914,-0.0916003287,0.3643068075,-0.0837100744,0.1223224849,-0.0144600356,0.0050253039,-0.2743749321,0.1103991047,0.0533314422,0.1116011888,-0.4886286557,0.2111949772,0.0160638969,0.207506299,-0.2277429998,-0.5473071337,-0.1978655905,-0.1674134731,0.1108879074,0.0035506173,0.0155517273,0.245834291,0.3519870043,0.5763667226,-0.0105392141,0.1820797473,-0.3252362013,0.0379519351,-0.0824356228,0.0348154306,0.0212817378,-0.1395526379,-0.0369860195,-0.2842594087,-0.0685697123,0.1353874356,-0.3167825043,0.1629516482,-0.3225497305,0.4349209368,0.3414088786,-0.0739268288,-0.1379303783,-0.241278097,0.3552574217,-0.173766762,-0.0142197516,-0.045967672,-0.1685479283,-0.0038151995,-0.0415971838,-0.0552913882,-0.0487063453,-0.1575495154,0.1859256625,0.2891166508,-0.1052072197,-0.1152042001,0.1889691502,0.2362823486,-0.3005891442,-0.1001724005,-0.2634636164,-0.711925149,0.3826666772,-0.1397305578,-0.2567561269,-0.1022106484,0.0898858234,0.4123462141,-0.4059282541,-0.3205484748,-0.4146136343,-0.0838825405,-0.0104269674,0.1121117994,0.3206905425,-0.1259178519,-0.1657734066,-0.0424434617,-0.1663160324,-0.0597809553,0.0870635062,-0.2837340534,0.3143175542,-0.3372662663,0.2276594788,-0.275947541,0.9847343564,0.2884800732,-0.2326099277,0.6071634293,0.1287951469,0.3356271088,-0.2486676723,-0.1645040512,0.0805723518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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2561","title":"Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`","comments":"Hi ! I just tried to reproduce what you said:\r\n- create a local builder class\r\n- use `load_dataset`\r\n- update the builder class code\r\n- use `load_dataset` again (with or without `ignore_verifications=True`)\r\nAnd it creates a new cache, as expected.\r\n\r\nWhat modifications did you do to your builder's code ?","body":"## Describe the bug\r\nIf i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.\r\n\r\n## Steps to reproduce the bug\r\n\r\n- Create a local dataset builder class\r\n- load the local builder class file using `load_dataset` and let the cache build\r\n- update the file's content\r\n- The cache should rebuilt.\r\n\r\n## Expected results\r\n\r\nWith `ignore_verifications=True`, `load_dataset` should pick up existing cache.\r\n\r\n## Actual results\r\n\r\nCreates new cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0\r\n","comment_length":51,"text":"Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True` \n ## Describe the bug\r\nIf i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.\r\n\r\n## Steps to reproduce the bug\r\n\r\n- Create a local dataset builder class\r\n- load the local builder class file using `load_dataset` and let the cache build\r\n- update the file's content\r\n- The cache should rebuilt.\r\n\r\n## Expected results\r\n\r\nWith `ignore_verifications=True`, `load_dataset` should pick up existing cache.\r\n\r\n## Actual results\r\n\r\nCreates new cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0\r\n \n Hi ! I just tried to reproduce what you said:\r\n- create a local builder class\r\n- use `load_dataset`\r\n- update the builder class code\r\n- use `load_dataset` again (with or without `ignore_verifications=True`)\r\nAnd it creates a new cache, as expected.\r\n\r\nWhat modifications did you do to your builder's code ?","embeddings":[-0.380481869,0.4886573255,0.0326324254,0.1935642362,0.1446633488,0.1507260799,0.3192700744,0.3748850226,0.1290490031,0.2183320373,0.2149606794,0.3630369902,0.1198911443,-0.2817024291,0.0510923229,0.2790060341,0.1163431108,0.143495515,0.21445328,-0.0771563873,-0.028279366,-0.0800596401,-0.0401381962,-0.2102086246,-0.4505546987,0.1976117641,0.0528690033,0.0853829533,-0.0405516736,-0.466229558,0.3908791542,0.2441875637,-0.0562269986,0.2653215528,-0.0001090661,0.1760650277,0.3208662868,0.0526437834,-0.3978691399,0.1389088631,-0.3474245369,-0.4606624544,0.0223906413,-0.0884896964,-0.2164719403,-0.2490846813,-0.1407262981,-0.191591084,0.3035663664,0.0564859174,0.2780434191,0.0750867352,-0.1023399532,-0.0168681145,0.1199553385,-0.0507756919,-0.053615395,0.1609927416,0.2253709286,0.0361661166,-0.430483222,0.3174596727,-0.1896671653,0.2203765512,0.4303051829,-0.0973287672,0.16110304,-0.0093295556,0.377348423,0.0414983593,0.5871434808,-0.3299917579,-0.2986133099,-0.2611524761,-0.1944366395,-0.0155707281,0.2321196795,-0.183269605,-0.2992415428,0.1735045016,-0.4138687849,-0.2707409859,0.067833133,-0.1559703946,0.0528805628,-0.2511184216,0.0992153659,-0.0926114172,-0.089377813,0.1688511074,0.4349325597,-0.468609035,-0.0739733875,0.2702969313,-0.4989158809,0.0219631698,0.3845898807,0.3972643912,0.238409996,0.302289784,-0.1899261922,0.2409279048,0.1860504299,0.0968388021,-0.0826028883,0.3067308366,0.0410077795,-0.0109668709,0.2831624448,0.2031198889,-0.5477035046,-0.0137982024,0.0638632923,-0.2574612796,0.5983974338,-0.0898954123,0.0680672005,-0.328872472,-0.0316492282,0.1889811307,-0.0006035879,-0.1473223269,0.2239474058,0.2570385039,-0.1294848025,0.0850459188,-0.2500111759,-0.2127594501,-0.3465076685,-0.354465872,-0.3988960683,-0.2386138588,0.0201397929,0.3679382503,0.485526979,-0.5491024852,0.3118240833,0.2401132435,-0.061511606,0.0796848014,0.1753485054,-0.1361715347,0.1791896224,0.2202351242,-0.2889445424,0.0660595745,-0.0249982737,-0.1994285434,-0.0721063465,0.3408291936,-0.1144278422,-0.7981068492,0.0093575949,0.1745110154,-0.2340953797,0.0518296994,0.283639878,0.1155551523,0.4194816649,-0.3065300584,-0.1302641928,-0.0479084477,-0.2993509173,-0.1270203888,0.4868237972,0.6713590622,-0.4300766587,0.0946420655,-0.124225527,0.0880809203,0.0045686769,0.1541885585,-0.3215012252,0.4164039791,-0.2850262523,-0.4484735131,0.2019233555,-0.2974612415,-0.829376936,0.2752141654,0.1786656231,0.2141062915,0.1349737346,0.039904274,-0.0856212005,-0.1601605862,0.097990945,0.2460227013,-0.2004423738,0.0236593261,-0.4308620989,-0.1939272434,0.0640484244,0.0740330741,0.0089768032,0.2423089296,0.0550841652,0.1132432595,0.0285036881,0.008789924,0.134250015,0.383466959,0.1400841326,-0.1290516704,0.2051851153,-0.1118596196,-0.4544261098,0.4190022349,-0.2235322893,-0.1305979639,0.0498482175,-0.299634099,-0.1256818622,-0.0823088884,-0.4173101783,-0.2251382321,0.1377799064,0.1733632386,0.4545215666,-0.0684815571,-0.1343213469,0.2744274139,-0.171483323,0.159884885,-0.0228320528,-0.0699828118,-0.078765139,-0.0692961961,-0.3138266504,0.0410866849,0.1141755059,-0.0592819937,-0.2225729078,0.2792674899,0.0170241632,0.0943570212,0.0013861436,0.1983225644,0.0460142158,0.1652133614,0.1288751215,-0.1250752062,0.0781465024,-0.0983776748,-0.0527022667,0.0726588592,0.2021352798,0.1724301577,-0.2249980867,-0.3149552941,0.2511095107,-0.1476520598,0.0871010423,-0.0884525925,-0.0426745564,0.0656267852,0.4767363369,0.2285358906,-0.0164817311,0.168270871,0.4884268045,0.2000714242,-0.2914832234,0.0221241917,-0.1331483424,-0.2133834809,0.1711711138,0.3344317675,0.3634784222,0.1734696627,0.2099324018,0.0661090314,-0.0327125154,-0.1634075195,0.0613576658,-0.1118580773,-0.1488316953,0.4112389982,0.3244159222,0.1536159664,-0.3777528703,0.2912766635,0.1497383416,0.1518630385,-0.0133895529,0.1734291315,-0.4750730991,-0.111807391,-0.1549550593,0.1557514071,-0.3087697923,-0.1850283742,0.1412915736,0.1024342999,-0.0044620861,0.2299690992,-0.0596316159,0.314935118,0.0060207392,-0.5304799676,-0.3073922098,-0.0541435517,-0.4707733989,-0.0123460377,0.4609992206,-0.242698729,0.2919667661,-0.2242351323,-0.0838991329,-0.202625528,-0.2120319456,0.0953063741,0.1066370159,0.4123833776,-0.1788090318,-0.0082731778,-0.0438163728,0.0297020208,0.2912225723,-0.1716905236,-0.1785908192,-0.1113298386,-0.0883523896,0.1494503766,-0.2810606062,-0.2027480602,0.0114694666,-0.378338486,-0.1207697615,0.1415114999,0.0631860867,0.1967853606,-0.0582334176,0.1748851538,0.0205823891,0.1483393461,-0.2669385076,-0.6465246081,0.2283112556,-0.2430549413,-0.0668883547,0.0384100638,-0.3015871942,0.1177911907,-0.0815823972,-0.4913438261,-0.0105528599,-0.4558912516,0.3086244464,-0.0715983137,0.0444752313,0.4206596911,-0.002462599,-0.0336006358,-0.1908629388,-0.3150304258,0.1238770708,-0.1581401676,0.3299719989,0.0541929416,-0.0427867249,0.1322250515,0.5684074759,0.145355016,-0.0803417638,0.3159600496,0.1188294739,0.4034343362,-0.3024224937,-0.2068282366,0.0561797544,-0.0480517596,-0.2586320639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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2561","title":"Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`","comments":"Hi @lhoestq. Thanks for your reply. I just did minor modifications for which it should not regenerate cache (for e.g. Adding a print statement). Overall, regardless of cache miss, there should be an explicit option to allow reuse of existing cache if author knows cache shouldn't be affected.","body":"## Describe the bug\r\nIf i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.\r\n\r\n## Steps to reproduce the bug\r\n\r\n- Create a local dataset builder class\r\n- load the local builder class file using `load_dataset` and let the cache build\r\n- update the file's content\r\n- The cache should rebuilt.\r\n\r\n## Expected results\r\n\r\nWith `ignore_verifications=True`, `load_dataset` should pick up existing cache.\r\n\r\n## Actual results\r\n\r\nCreates new cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0\r\n","comment_length":48,"text":"Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True` \n ## Describe the bug\r\nIf i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.\r\n\r\n## Steps to reproduce the bug\r\n\r\n- Create a local dataset builder class\r\n- load the local builder class file using `load_dataset` and let the cache build\r\n- update the file's content\r\n- The cache should rebuilt.\r\n\r\n## Expected results\r\n\r\nWith `ignore_verifications=True`, `load_dataset` should pick up existing cache.\r\n\r\n## Actual results\r\n\r\nCreates new cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0\r\n \n Hi @lhoestq. Thanks for your reply. I just did minor modifications for which it should not regenerate cache (for e.g. Adding a print statement). Overall, regardless of cache miss, there should be an explicit option to allow reuse of existing cache if author knows cache shouldn't be affected.","embeddings":[-0.2966097295,0.4845605791,0.0497074835,0.1848862022,0.080392383,0.2186549455,0.2824616134,0.3745069206,0.1949127316,0.1779461205,0.2753896415,0.3544703126,0.0864098966,-0.2514214218,-0.0044403952,0.2905792594,0.0580530651,0.1453519464,0.1856721044,-0.0063897953,-0.0618961118,-0.059082225,-0.0472507104,-0.2655098736,-0.4217759669,0.1886301935,0.0811306983,-0.0028491432,-0.0397531688,-0.4942772985,0.4173816442,0.2595971823,0.0410107411,0.1719833463,-0.000109742,0.1775733232,0.2939718962,0.0204252359,-0.3915853202,0.1280866712,-0.3183541596,-0.3609194756,-0.015063501,-0.1168086305,-0.1470687985,-0.2058143914,-0.0759644806,-0.3138148487,0.3438763916,0.0339676887,0.2579464912,0.0727289319,-0.1777894199,0.0051544788,0.0632820651,-0.0600984395,-0.0318004042,0.1550073326,0.3051303923,0.046208445,-0.3910071254,0.4045751691,-0.2028275877,0.1022235006,0.4206958413,-0.2342661321,0.1239166856,-0.0326370671,0.3514985144,0.0883087069,0.6280643344,-0.3615947962,-0.2906777561,-0.2804444134,-0.1889223009,0.0089398455,0.1707501113,-0.2470255047,-0.1697717011,0.2825199962,-0.4102947116,-0.3994595408,0.0205434579,-0.1617592722,0.2046763301,-0.2736331522,0.0841637328,-0.0900348127,-0.1739295423,0.2221592069,0.4534497559,-0.4566788077,-0.0571203716,0.2458976954,-0.489816159,0.0414768793,0.3280723691,0.3956191838,0.2704196274,0.3395018578,-0.0873380825,0.2306830585,0.1428929865,0.1323721856,-0.0760943443,0.3461059034,0.044928655,-0.0047850083,0.234240368,0.2124478966,-0.4609407783,-0.0363115892,0.0972765535,-0.2108423263,0.5844724774,-0.0553591624,0.0181646664,-0.3203701377,-0.0643922389,0.2362914383,-0.035235174,-0.1552275121,0.1838333309,0.1808869988,-0.0278257858,0.2197253555,-0.1585133523,-0.187197119,-0.2385390699,-0.4174991846,-0.3815182745,-0.2056736797,0.025520239,0.3334385157,0.4631291926,-0.4986813962,0.3289692998,0.2453932315,-0.0809459016,0.0382729545,0.2030310184,-0.067565456,0.1907198876,0.2264155447,-0.3242384791,0.0466676243,0.0138496961,-0.1422075778,-0.0683883429,0.3774857223,-0.1055401862,-0.772066474,0.0577211417,0.1467430592,-0.230830729,0.0597771481,0.2950683534,0.0941423997,0.4354889095,-0.304712832,-0.0714970902,-0.039928697,-0.2986983955,-0.1711079329,0.4547204077,0.7276176214,-0.4251804352,0.0580194816,-0.0966184065,0.0903699026,-0.0047410023,0.1897814274,-0.2760145962,0.3597519994,-0.3368421793,-0.4499462843,0.1937788427,-0.3304957151,-0.7936922908,0.2364359796,0.2361168861,0.2683826983,0.1507766694,0.0912846029,-0.1303571761,-0.1721943468,0.0760541037,0.2031333297,-0.2156373709,-0.067044355,-0.4511630535,-0.2889775932,0.0535785966,0.0131588541,0.0885536969,0.3096219003,0.0585951172,0.1182934642,0.0537008829,-0.0029381225,0.1418864578,0.292199105,0.1424280852,-0.1862851828,0.1863689572,-0.1150948778,-0.4901274145,0.4268352985,-0.2512301505,-0.2352859676,0.0420520902,-0.3022159338,-0.1098713502,-0.1406947076,-0.3829514086,-0.2034128904,0.1235081479,0.2031829953,0.4170442224,-0.0460982583,-0.1830382198,0.3019635975,-0.2747035325,0.1761454195,-0.0656104535,-0.1344092637,-0.091384545,0.0057708807,-0.3230375946,0.0396357104,0.1474550366,-0.0976060852,-0.1709591001,0.2789711356,0.0086257644,0.1368511319,-0.0129127828,0.2477175295,0.0481240042,0.1332277358,0.0803824067,-0.195008412,0.0764137283,-0.0752483681,-0.0221899841,0.055542957,0.1095236242,0.0519843027,-0.2265179306,-0.3205174208,0.2170547247,-0.2078904808,0.0622809231,-0.1489880681,-0.071980387,0.0899278969,0.4432958663,0.1769195795,-0.0288119819,0.151440084,0.3761355877,0.2306368798,-0.2651818395,-0.0584282055,-0.0324974321,-0.2006670386,0.2052030712,0.4338606894,0.2664705515,0.1773048192,0.237342909,0.057374645,0.0024196065,-0.1575553268,0.0431730747,-0.1791941375,-0.1714890748,0.447245121,0.264668107,0.2256841362,-0.3657458127,0.3604898751,0.1917818487,0.1741558909,0.0068332399,0.1442461759,-0.4783364236,-0.1179984584,-0.1782153994,0.239092797,-0.2455631196,-0.2018035203,0.1303868145,0.1122721359,-0.0641460195,0.1965553313,-0.1686368585,0.3677161932,-0.0814805105,-0.5583733916,-0.3459394872,0.0020714966,-0.4155192673,0.0044738641,0.4792733788,-0.3283120394,0.3057935536,-0.2684009969,-0.1086743176,-0.2305951566,-0.2436314076,0.0833076164,0.1805217117,0.311242938,-0.266936183,-0.0569928139,-0.0704203323,0.1075663865,0.1130451784,-0.1787520796,-0.1501226276,-0.1828025877,-0.0643627346,0.1532535404,-0.3532607257,-0.2649860978,0.0208952688,-0.3403119147,-0.0538648665,0.0546617955,0.0128820492,0.2154653817,-0.1387061477,0.1285962611,0.0274688806,0.1715714484,-0.3066546321,-0.6944122314,0.2179216892,-0.1704295427,-0.0264715925,0.0731141716,-0.2889467478,0.1435308009,0.0199487656,-0.5505864024,-0.0304025579,-0.3796710968,0.2839161754,-0.1302035153,-0.0076424284,0.4433671832,0.0021885843,0.0139110675,-0.2255669087,-0.2300461531,0.1580529362,-0.1709071249,0.3343122602,0.0929654166,-0.1266642362,0.258610189,0.5497055054,0.2341137975,-0.0189248305,0.3620266616,0.2053526938,0.5025876164,-0.2435244918,-0.1163855791,0.0630784854,-0.1085820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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2561","title":"Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`","comments":"The cache is based on the hash of the dataset builder's code, so changing the code makes it recompute the cache.\r\n\r\nYou could still rename the cache directory of your previous computation to the new expected cache directory if you want to avoid having to recompute it and if you're sure that it would generate the exact same result.\r\n\r\nThe verifications are data integrity verifications: it checks the checksums of the downloaded files, as well as the size of the generated splits.","body":"## Describe the bug\r\nIf i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.\r\n\r\n## Steps to reproduce the bug\r\n\r\n- Create a local dataset builder class\r\n- load the local builder class file using `load_dataset` and let the cache build\r\n- update the file's content\r\n- The cache should rebuilt.\r\n\r\n## Expected results\r\n\r\nWith `ignore_verifications=True`, `load_dataset` should pick up existing cache.\r\n\r\n## Actual results\r\n\r\nCreates new cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0\r\n","comment_length":82,"text":"Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True` \n ## Describe the bug\r\nIf i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.\r\n\r\n## Steps to reproduce the bug\r\n\r\n- Create a local dataset builder class\r\n- load the local builder class file using `load_dataset` and let the cache build\r\n- update the file's content\r\n- The cache should rebuilt.\r\n\r\n## Expected results\r\n\r\nWith `ignore_verifications=True`, `load_dataset` should pick up existing cache.\r\n\r\n## Actual results\r\n\r\nCreates new cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0\r\n \n The cache is based on the hash of the dataset builder's code, so changing the code makes it recompute the cache.\r\n\r\nYou could still rename the cache directory of your previous computation to the new expected cache directory if you want to avoid having to recompute it and if you're sure that it would generate the exact same result.\r\n\r\nThe verifications are data integrity verifications: it checks the checksums of the downloaded files, as well as the size of the generated splits.","embeddings":[-0.3809878528,0.4386587143,-0.003196154,0.1750701964,0.0950203612,0.1552421749,0.2317519635,0.4199692309,0.1590616554,0.3092049956,0.1621158272,0.2830117941,0.1100006551,-0.2082076222,-0.032141652,0.2218123078,0.0682655275,0.1848935187,0.1504187733,-0.0182196461,-0.1042756811,-0.0340703353,-0.0095534287,-0.2419549674,-0.4294947982,0.0829604268,0.063556686,0.1601958722,-0.0879584178,-0.4699014127,0.3021024168,0.2475569993,-0.1526563764,0.2725529373,-0.0001049965,0.1780284494,0.3562346697,0.0568509437,-0.4711803198,0.2416318953,-0.3674046397,-0.4435736239,-0.1549166441,-0.1537539214,-0.0183799956,-0.1941011548,-0.1145722046,-0.2818766832,0.3508931994,0.0716103017,0.2827044129,0.1405942589,-0.2049485892,-0.0155738434,0.1999707669,-0.0148219187,-0.0281336438,0.1525065601,0.1852069646,0.0982399881,-0.4011557102,0.3540205956,-0.2052344829,0.166930899,0.3523953557,-0.1947120577,0.0473334119,-0.0936723351,0.3847611547,0.1260740608,0.5368218422,-0.2587720454,-0.3534723818,-0.2496431321,-0.2884817719,-0.0149809429,0.2838677168,-0.1221734807,-0.1945977658,0.1572081745,-0.4552613497,-0.2325942665,0.0808344558,-0.2229066193,0.0629747808,-0.2296004891,0.1435914338,-0.0874812603,0.0441175587,0.223488003,0.3453530073,-0.4724609554,-0.0402999818,0.2510861754,-0.4561106861,-0.0766494721,0.2587413788,0.3883616328,0.2604282498,0.3886110485,-0.1932558566,0.2456337363,0.1801903993,0.098462306,-0.1247398332,0.3354095221,-0.0490097664,0.0047542527,0.3091166914,0.2427242994,-0.3884593546,0.0066368589,0.1257502437,-0.2951217592,0.4921707511,0.0260522179,-0.057826031,-0.3234983683,-0.0804766789,0.1806787252,0.0233980939,-0.1273870617,0.166769281,0.1957849562,-0.0826733932,0.1365427077,-0.3365083337,-0.2401606888,-0.341868192,-0.3335377872,-0.3955495358,-0.1528071612,0.0255934075,0.3271663487,0.4925564229,-0.5260605216,0.3480464518,0.1541673839,-0.0543538705,0.099848561,0.2237308472,-0.2249774635,0.2071833611,0.3538979292,-0.2637627125,-0.0269211829,-0.0084566167,-0.0912471712,-0.1013355702,0.2589486539,-0.1230050176,-0.7289390564,0.118471168,0.212846145,-0.2772568762,0.0854331926,0.2786011398,0.0754414275,0.3407641351,-0.3217763901,-0.0556517057,-0.0820501521,-0.278036207,-0.129054755,0.44631055,0.7053080201,-0.4558100998,0.1331191659,-0.1671929061,-0.0553618968,0.0092204576,0.2276038826,-0.2356608957,0.3070384264,-0.3199964762,-0.2536692619,0.1878542304,-0.2249673903,-0.8255181313,0.2686665952,0.149260968,0.1901006699,0.2113611251,0.0449058972,0.0253551658,-0.2250094712,0.0002093963,0.2408092767,-0.2048729062,0.0776339844,-0.4715503156,-0.230891794,0.0494501889,0.0304169375,0.0650147498,0.1332587898,0.1275087893,0.1479833424,0.002820184,-0.0282347649,0.1279258579,0.2897245884,0.0725994706,-0.1606552303,0.2341433614,-0.1299551874,-0.4772045016,0.4268706739,-0.2430205494,-0.161271885,-0.0033574956,-0.367875576,-0.1315849721,-0.0959358439,-0.3227362037,-0.1761829257,0.1626995504,0.2217373401,0.4972623885,-0.0863663256,-0.092161499,0.2364337891,-0.2325900346,0.1563906223,-0.0437477678,-0.0619531497,-0.0965528041,-0.0037325129,-0.2672517896,0.0637132898,0.0612828881,-0.1438021511,-0.2119125128,0.3813095391,0.182417959,0.1483064294,0.1038815156,0.207360059,0.0454355367,0.0915351883,0.2094063312,-0.1386991888,0.0882934034,-0.0692195669,-0.1016848087,0.0941345692,-0.0026223233,0.1150359735,-0.097411871,-0.3273003995,0.2436901778,-0.0979226083,0.0482175723,-0.1521125287,-0.0224174112,0.1089246124,0.4705246091,0.2494598329,0.0959344208,0.1308138222,0.5666050315,0.0951122195,-0.3064001799,0.008478391,-0.0920882374,-0.2652546465,0.0858353749,0.4567349851,0.3705622852,0.1872846335,0.2971365452,0.0433766544,0.0250684544,-0.1981807351,0.0852613896,-0.0498640835,-0.2039026469,0.3712655306,0.2285541445,0.1648545414,-0.4026487768,0.2453194857,0.1885005534,0.1817783713,0.0076121073,0.210149616,-0.4117991328,-0.1393776834,-0.1930423081,0.155893892,-0.3065185547,-0.2943997979,0.1703382581,-0.0018876696,-0.1134918034,0.2163655162,-0.0438544005,0.3727186322,-0.0144591276,-0.5106043816,-0.3518974185,-0.0204889514,-0.421954304,0.0638473481,0.4755040109,-0.2497112304,0.364374578,-0.3301583529,-0.1467280984,-0.1775905192,-0.2530392408,0.0571762808,0.0023792256,0.2441037297,-0.1194614619,0.0094949184,0.090172559,0.073113054,0.1342111528,-0.1966596693,-0.2162160128,-0.1232861504,0.0012073183,0.1617399752,-0.405503273,-0.2719637454,0.0034306503,-0.3567607701,0.0014948995,0.1071014106,0.0538732335,0.1546714753,-0.1395678371,0.2075217217,0.0358731449,0.0946294367,-0.337197274,-0.5732303858,0.1985998154,-0.1797296554,-0.0840433538,0.0857976228,-0.2623572052,0.0923823118,0.0246700477,-0.5293719172,0.0089809969,-0.5315983295,0.2878866196,-0.1223090738,0.0137544731,0.4414839447,0.0198047347,-0.1030043513,-0.2068895102,-0.2004306614,0.0841571763,-0.2701410949,0.2128931284,-0.0614318289,-0.090494439,0.1922164559,0.6441383958,0.1705699861,-0.0261244494,0.2631525397,0.2355026901,0.5081669688,-0.3046405017,-0.1536633223,0.1751738042,-0.1124335676,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2561","title":"Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True`","comments":"Hi @apsdehal,\r\n\r\nIf you decide to follow @lhoestq's suggestion to rename the cache directory of your previous computation to the new expected cache directory, you can do the following to get the name of the new expected cache directory once #2500 is merged:\r\n```python\r\nfrom datasets import load_dataset_builder\r\ndataset_builder = load_dataset_builder(\"path\/to\/your\/dataset\")\r\nprint(dataset_builder.cache_dir)\r\n```\r\n\r\nThis way, you don't have to recompute the hash of the dataset script yourself each time you modify the script.","body":"## Describe the bug\r\nIf i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.\r\n\r\n## Steps to reproduce the bug\r\n\r\n- Create a local dataset builder class\r\n- load the local builder class file using `load_dataset` and let the cache build\r\n- update the file's content\r\n- The cache should rebuilt.\r\n\r\n## Expected results\r\n\r\nWith `ignore_verifications=True`, `load_dataset` should pick up existing cache.\r\n\r\n## Actual results\r\n\r\nCreates new cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0\r\n","comment_length":73,"text":"Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True` \n ## Describe the bug\r\nIf i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.\r\n\r\n## Steps to reproduce the bug\r\n\r\n- Create a local dataset builder class\r\n- load the local builder class file using `load_dataset` and let the cache build\r\n- update the file's content\r\n- The cache should rebuilt.\r\n\r\n## Expected results\r\n\r\nWith `ignore_verifications=True`, `load_dataset` should pick up existing cache.\r\n\r\n## Actual results\r\n\r\nCreates new cache.\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0\r\n \n Hi @apsdehal,\r\n\r\nIf you decide to follow @lhoestq's suggestion to rename the cache directory of your previous computation to the new expected cache directory, you can do the following to get the name of the new expected cache directory once #2500 is merged:\r\n```python\r\nfrom datasets import load_dataset_builder\r\ndataset_builder = load_dataset_builder(\"path\/to\/your\/dataset\")\r\nprint(dataset_builder.cache_dir)\r\n```\r\n\r\nThis way, you don't have to recompute the hash of the dataset script yourself each time you modify the script.","embeddings":[-0.4378628135,0.4782138169,-0.0076685501,0.1180528328,0.1569270641,0.1881317943,0.2960320413,0.4827200174,0.1750372797,0.3437815905,0.2252853215,0.3112469912,0.0643949583,-0.1951674372,0.0266977418,0.322676897,0.045707155,0.166235581,0.0950702727,0.005609889,-0.1577256471,-0.0480541214,-0.0432750061,-0.2891950309,-0.3779666424,0.1461428553,0.1288987696,0.1029422581,0.0418603197,-0.5005040765,0.3477988839,0.2545360923,-0.0790350735,0.3221059442,-0.0001039051,0.1450937688,0.3499033153,0.0292263068,-0.4508456588,0.1350848079,-0.3023414612,-0.3922794461,-0.0445121154,-0.1520197988,-0.1497523338,-0.1942988634,-0.0928564519,-0.3335602283,0.2368957698,0.0695878118,0.3009346724,0.1146359071,-0.174826473,-0.0558505058,0.1333578676,-0.0726914853,-0.0339046456,0.1525844485,0.171585083,-0.0266665537,-0.3792466223,0.4052736163,-0.2034496516,0.1334887892,0.399666816,-0.1477893144,0.0812321007,-0.0307958946,0.3842017055,0.0088948812,0.5154066086,-0.3620007336,-0.3648876846,-0.2112908363,-0.2311091572,-0.1044781134,0.2209356427,-0.1983426809,-0.2347516865,0.1736158878,-0.3927207589,-0.2276475728,0.0417950638,-0.180952549,0.0880759656,-0.2250020504,0.1053643301,-0.1372450739,-0.125476405,0.1452204734,0.3674782217,-0.394854635,-0.0215280261,0.228969425,-0.4612696171,-0.0239970554,0.3944355845,0.3140067756,0.2373757213,0.3474124372,-0.1095322147,0.2829091549,0.1750382781,0.0915098861,-0.1357051134,0.3506705761,-0.0340687074,-0.052787438,0.3768588603,0.2751902342,-0.4690761566,0.0056921476,0.106025368,-0.3259635866,0.5800098777,0.0720466673,0.0524250269,-0.3129971921,-0.0475726798,0.2453954369,0.0636419654,-0.0961804464,0.2122593969,0.2610956132,-0.1054353863,0.2311409265,-0.1733355671,-0.215301618,-0.3223363459,-0.2802629173,-0.4096826911,-0.1537312418,0.0119259618,0.3056171834,0.4829461873,-0.4689620733,0.316123575,0.1618570089,0.0118938787,0.1007125154,0.1903785169,-0.1322101802,0.2143111974,0.2632401288,-0.2132689357,0.0102520604,0.0313112512,-0.1175557598,-0.1058414653,0.223121658,-0.0317617171,-0.7341607809,0.0889080092,0.233043924,-0.2381561249,0.0474885292,0.2636314332,0.0336059257,0.3829751313,-0.2503570616,-0.1222589388,-0.0942947567,-0.3573611975,-0.175920859,0.4980244339,0.7052147985,-0.3996891379,0.1528285742,-0.115663141,0.0775155723,-0.066553548,0.1591843665,-0.2795010209,0.3339782953,-0.2976713479,-0.4289454818,0.1798465103,-0.3847131431,-0.788780272,0.2251588106,0.1457596868,0.149137944,0.1818509996,0.0396104641,-0.0169460047,-0.2184168547,0.1615031213,0.2312603593,-0.1634507626,0.0983477458,-0.4349821806,-0.2086125165,0.0089624533,0.0688338131,0.0870128646,0.169584766,0.056815207,0.0829610974,0.026787024,-0.0496538766,0.1402308345,0.3736862838,0.1132957041,-0.1159346849,0.220341593,-0.1655070335,-0.4990051389,0.4116998613,-0.1820967197,-0.1447318047,-0.0531154536,-0.322712332,-0.1338289082,-0.087515153,-0.3375990689,-0.1391991526,0.1884564161,0.2496445775,0.4460933805,-0.0771123692,-0.158167094,0.2838539183,-0.1640733927,0.0899734572,-0.1326985955,-0.1409479529,-0.0700131431,-0.0991641507,-0.1954044551,0.0574336536,0.1353019476,-0.0667103156,-0.26080966,0.3024924695,0.0793655366,0.1147842035,0.0711160004,0.2790294588,-0.0350753628,0.1858082116,0.1745343953,-0.0790572241,0.1426222324,-0.0384484343,-0.0413477905,0.1354292035,0.0463986173,0.1125914901,-0.1388845444,-0.3127244115,0.295370847,-0.1245715842,0.0633455738,-0.1481835991,-0.0057835588,0.0748842359,0.4566465914,0.1771675199,0.0018894294,0.2123999894,0.4804141819,0.1649549454,-0.2486862838,-0.0117339864,-0.09440431,-0.2098719925,0.0730080605,0.397223413,0.3072849214,0.2216350138,0.2192882299,0.0572420284,-0.0068486789,-0.1589208096,0.0911510214,-0.0753751621,-0.1550502926,0.3534370065,0.2896508873,0.1620254964,-0.3402825594,0.1717146039,0.1014906913,0.177421093,-0.0199414697,0.1480083019,-0.437425226,-0.1086441725,-0.174734205,0.205991596,-0.1812389791,-0.2643585503,0.0996612683,0.1013825685,-0.1185423583,0.216680184,-0.1225303784,0.3018139303,0.0229896735,-0.4956262708,-0.2863645554,-0.0362147503,-0.3606921732,0.031658262,0.4571745396,-0.2570876777,0.3414334357,-0.2753068507,-0.1161041185,-0.2035597265,-0.2270614058,0.0009948392,0.0311385747,0.3004502058,-0.1247740686,0.0048386138,0.0545157269,0.0616223998,0.1966441125,-0.2101594061,-0.161397323,-0.0959529504,-0.000732612,0.098420009,-0.3548962176,-0.3140829802,0.016634535,-0.4324187934,-0.0886578187,0.1247779801,0.0167996623,0.1820453554,-0.1176902428,0.1404753327,0.0436921827,0.1650148481,-0.2781701982,-0.6232032776,0.270997107,-0.2506586909,-0.0944469348,0.0615543984,-0.3288154304,0.1137563214,-0.0210148767,-0.4772715867,-0.0800592601,-0.5147484541,0.2753629386,-0.1654993743,0.031567663,0.4313963056,0.0792466849,-0.1137080565,-0.2364391834,-0.2883403301,0.0790760666,-0.2812061012,0.1944599748,0.0056791999,0.0140724061,0.18723315,0.5367400646,0.1950557381,-0.0902496651,0.2941389084,0.1639422178,0.4542673528,-0.3149476051,-0.2042661756,0.0530165359,-0.0757592767,-0.2786774337,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2559","title":"Memory usage consistently increases when processing a dataset with `.map`","comments":"Hi ! Can you share the function you pass to `map` ?\r\nI know you mentioned it would be hard to share some code but this would really help to understand what happened","body":"## Describe the bug\r\n\r\nI have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help.\r\n\r\n## Steps to reproduce the bug\r\n\r\nProviding code as it is would be hard. I can provide a MVP if that helps.\r\n\r\n## Expected results\r\n\r\nMemory usage should become consistent after some time following the launch of processing.\r\n\r\n## Actual results\r\n\r\nMemory usage keeps on increasing.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0","comment_length":33,"text":"Memory usage consistently increases when processing a dataset with `.map` \n ## Describe the bug\r\n\r\nI have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help.\r\n\r\n## Steps to reproduce the bug\r\n\r\nProviding code as it is would be hard. I can provide a MVP if that helps.\r\n\r\n## Expected results\r\n\r\nMemory usage should become consistent after some time following the launch of processing.\r\n\r\n## Actual results\r\n\r\nMemory usage keeps on increasing.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid\r\n- Python version: 3.7.7\r\n- PyArrow version: 3.0.0 \n Hi ! Can you share the function you pass to `map` ?\r\nI know you mentioned it would be hard to share some code but this would really help to understand what happened","embeddings":[-0.1570354253,-0.1291135401,0.0070893918,0.4211396873,0.1809348166,-0.0064084614,0.0589690395,0.1185609922,0.2151501626,0.1314368397,0.4036096632,0.5179601908,-0.1873755753,-0.0342993326,-0.0775477067,0.073951751,0.1007091478,0.0621853173,-0.002483153,0.0814324021,-0.2891268432,0.1485365182,-0.2638915777,-0.0169580318,-0.3848563731,-0.2375293523,0.0283594988,0.1595099121,0.2712178528,-0.4738775492,-0.1240217835,-0.0056957845,0.1005657837,0.5225373507,-0.0001255529,-0.0305608511,0.401930809,-0.0119665451,-0.0148503724,0.1246175542,-0.3412507474,-0.1495825499,0.0193182342,-0.0983853266,0.2345033735,-0.0282174107,-0.0144428136,-0.4091306925,0.2877306044,0.0745106563,0.1072863117,-0.0405011214,0.0402137116,0.2015887648,0.3487725258,0.3877718747,0.0770824477,0.1127612144,0.0478502326,-0.2627837956,-0.4136327207,0.2883907557,-0.0236985404,0.1897912771,0.5099977255,-0.0134917554,0.3311572373,-0.3816513121,0.4529604316,-0.1067861766,0.1417180002,-0.2396407723,0.230925858,-0.2007993907,-0.3240593374,-0.0723847225,0.257769227,0.1840465218,-0.3346029818,-0.0982146934,-0.2691874802,-0.2370991707,-0.1393745989,0.1706352383,0.1292566508,-0.3141852617,-0.0201004203,0.2519589067,0.3842785954,-0.1438483298,0.045506876,-0.3902648091,-0.0603136346,0.2051589787,-0.2177036256,0.3616625965,0.1598579139,-0.1390434057,0.186015591,-0.4799719453,0.0471167825,0.0493901037,-0.0707272589,0.3078666925,0.5049361587,0.0071358401,0.0339402556,0.3567737043,0.1445606202,0.0739017352,-0.4335692227,-0.09687379,-0.0254753511,-0.1504217684,0.5621683598,-0.4341062903,-0.0089524696,-0.0162021536,0.0838859826,0.1440406144,-0.2474454194,-0.1192666739,-0.1069318354,0.2773066759,0.1608290523,0.212488994,0.0798254684,-0.1200265735,-0.2981453836,0.1034978926,-0.0617954768,-0.2247256786,-0.4726648033,0.2819334269,-0.1137935072,-0.023724284,0.0799849033,0.0839325264,-0.1809536368,-0.4311408103,0.2190316468,-0.3097909391,0.3081334233,0.1337933093,-0.2712439299,0.1661079973,-0.2565067708,0.2414965183,0.0015335188,0.6213239431,-0.4938792288,-0.2463744134,0.0666981637,-0.0554476641,0.1610040069,0.2372991443,-0.289039582,0.0802726299,0.413513422,-0.1228782684,-0.0541271642,-0.1508701444,-0.3637880087,-0.3568014205,0.0744894594,0.5120196939,-0.2421150357,-0.0570950247,-0.0698018894,0.1361998171,0.3940269351,0.3394079208,-0.1452197582,0.2179701626,-0.2710808516,-0.0092691397,0.0535811782,-0.135399431,-0.4603150189,0.1626189798,0.0354248844,0.0441171899,-0.1676273495,0.0917235762,0.1136214286,-0.020611966,0.0810675174,0.2399548143,0.0672847927,0.2178075016,-0.3566509783,-0.3306137621,-0.1602676958,-0.0686361566,0.0161735695,-0.1525756121,0.0998775214,-0.3316319585,0.4156735837,-0.1726185232,0.1996031255,0.3873454928,0.32808882,0.0715711117,0.0820139423,-0.0371198021,0.0218242742,0.0685922205,-0.5524809957,-0.4697282314,-0.089287214,0.05952904,0.1769143194,0.2053637803,-0.095445618,-0.0883561969,-0.0736562163,-0.1179291904,-0.3036301136,-0.0247292574,0.1444119513,0.1526732743,0.0656351298,-0.0442605987,0.027166184,0.2399702519,0.0752367079,-0.2047433108,-0.201455757,0.0656852722,0.0661339164,0.0654207543,-0.261890471,0.1513354033,0.2842341065,0.106396459,-0.0741834193,0.0739029199,0.3849378228,-0.1497890353,0.2134182006,-0.0641799569,0.105747506,-0.0667208433,0.121331118,-0.2330114394,0.3269650042,0.3512523472,-0.0582070462,-0.1533294916,-0.0210708529,-0.0199612882,0.4386912584,-0.1539627463,-0.1473774612,0.087358512,0.2650532126,0.288436383,0.057037428,0.0676861703,0.483543694,0.1863622665,-0.0907853097,0.4215968847,-0.4808891118,-0.1382156312,0.2403400391,-0.0141240573,0.1993006617,0.0344419442,-0.1749187708,0.0550536364,0.0971427187,-0.0127408607,0.1064732373,0.2050014138,0.5473662615,0.1184342504,0.2693426311,-0.0106128324,-0.1087218076,-0.0634394959,0.0204938166,0.0222310498,-0.2989660501,0.2088721395,-0.2458423674,0.1614700109,-0.1152484119,0.1659799516,-0.2664535046,-0.0865114555,-0.1947995275,0.2522456348,-0.0238519162,0.0529238395,0.0373384468,0.1334961206,0.2425183505,-0.2201697379,-0.1335164756,-0.2635029554,-0.3734341562,-0.1866772175,0.3813974261,-0.18527399,0.3777766526,0.1496836841,0.1176155582,-0.2089557648,-0.1244721934,-0.0028930365,0.3083295524,0.2173012644,-0.0969915763,0.1768705547,-0.1049770266,0.0970579609,0.186447382,-0.4787376225,0.0666563064,0.1396715343,-0.119236283,-0.2064532787,-0.0172842238,0.0637788624,0.0498527773,-0.2209955454,0.2391790897,0.0881538615,0.3557169437,0.2330054939,0.4233998358,0.225534454,-0.2216945291,-0.2498881966,-0.1388605386,-0.0541365966,0.0314728022,-0.0852679312,-0.0235585552,0.0020438365,-0.0498107262,0.1298592687,0.3587283492,-0.6982625127,0.0301660262,-0.2623217106,0.2872711122,-0.1540242583,0.2327871621,0.1900490522,0.2522499859,0.0068761553,-0.1199147254,-0.3412647545,0.226945892,0.1021318734,0.2622027993,0.4573037624,0.2340842485,0.1723798066,0.6430386305,0.36811167,-0.1270593405,0.5097683072,-0.1093711257,0.1468427032,-0.1853081882,-0.0802948028,-0.1467505097,-0.3551236391,-0.101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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2554","title":"Multilabel metrics not supported","comments":"Hi @GuillemGSubies, thanks for reporting.\r\n\r\nI have made a PR to fix this issue and allow metrics to be computed also for multilabel classification problems.","body":"When I try to use a metric like F1 macro I get the following error:\r\n\r\n```\r\nTypeError: int() argument must be a string, a bytes-like object or a number, not 'list'\r\n```\r\nThere is an explicit casting here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075\/src\/datasets\/features.py#L274\r\n\r\nAnd looks like this is because here\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075\/metrics\/f1\/f1.py#L88\r\n\r\nthe features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work:\r\n\r\n```python\r\nclass F1(datasets.Metric):\r\n    def _info(self):\r\n        return datasets.MetricInfo(\r\n            description=_DESCRIPTION,\r\n            citation=_CITATION,\r\n            inputs_description=_KWARGS_DESCRIPTION,\r\n            features=datasets.Features(\r\n                {\r\n                    \"predictions\": datasets.Sequence(datasets.Value(\"int32\")),\r\n                    \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n                }\r\n            ),\r\n            reference_urls=[\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html\"],\r\n        )\r\n\r\n    def _compute(self, predictions, references, labels=None, pos_label=1, average=\"binary\", sample_weight=None):\r\n        return {\r\n            \"f1\": f1_score(\r\n                references,\r\n                predictions,\r\n                labels=labels,\r\n                pos_label=pos_label,\r\n                average=average,\r\n                sample_weight=sample_weight,\r\n            ),\r\n        }\r\n```\r\n","comment_length":25,"text":"Multilabel metrics not supported \n When I try to use a metric like F1 macro I get the following error:\r\n\r\n```\r\nTypeError: int() argument must be a string, a bytes-like object or a number, not 'list'\r\n```\r\nThere is an explicit casting here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075\/src\/datasets\/features.py#L274\r\n\r\nAnd looks like this is because here\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075\/metrics\/f1\/f1.py#L88\r\n\r\nthe features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work:\r\n\r\n```python\r\nclass F1(datasets.Metric):\r\n    def _info(self):\r\n        return datasets.MetricInfo(\r\n            description=_DESCRIPTION,\r\n            citation=_CITATION,\r\n            inputs_description=_KWARGS_DESCRIPTION,\r\n            features=datasets.Features(\r\n                {\r\n                    \"predictions\": datasets.Sequence(datasets.Value(\"int32\")),\r\n                    \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n                }\r\n            ),\r\n            reference_urls=[\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html\"],\r\n        )\r\n\r\n    def _compute(self, predictions, references, labels=None, pos_label=1, average=\"binary\", sample_weight=None):\r\n        return {\r\n            \"f1\": f1_score(\r\n                references,\r\n                predictions,\r\n                labels=labels,\r\n                pos_label=pos_label,\r\n                average=average,\r\n                sample_weight=sample_weight,\r\n            ),\r\n        }\r\n```\r\n \n Hi @GuillemGSubies, thanks for reporting.\r\n\r\nI have made a PR to fix this issue and allow metrics to be computed also for multilabel classification problems.","embeddings":[-0.2212646902,-0.1788480431,0.0044772709,0.3008992672,0.6725280881,-0.135990411,0.4806858599,-0.1023353487,0.2283084393,0.3166798353,-0.1240749434,0.2981927693,-0.2808501124,0.4046333432,-0.2508816123,-0.176120922,-0.2169739455,-0.3327276409,-0.0599899776,0.1059186533,-0.42456007,0.0359555557,-0.3928985894,-0.0474174023,-0.2053830773,0.0682160556,0.1161154285,-0.1135555729,-0.2787645161,-0.329521507,0.0360861979,-0.0185579155,-0.0064595272,0.4338566363,-0.0001161838,-0.1123573929,0.2313463986,0.0006338583,-0.1215105578,-0.2388671041,0.1006879508,-0.1241763309,0.0392229259,-0.2491005361,0.0151843484,0.0634786934,-0.3937759399,-0.1104588732,-0.0291239601,0.3288370967,0.1258651316,-0.062961854,0.4294290543,-0.0971220285,-0.1565203965,-0.015151497,-0.2259768844,0.2436192334,0.0067122462,0.1893280596,0.1993249655,0.367898792,0.0551728383,0.0967107713,0.3370931745,-0.0944951847,0.1595984548,-0.1680668592,-0.1602084935,0.2209853083,-0.061623808,-0.0668189675,-0.592457056,-0.0729389489,0.0992546678,-0.8592536449,0.1026922315,-0.1433411539,-0.0054159793,-0.1943722367,-0.4583276808,-0.1244826615,-0.1109633297,0.2019526213,-0.1810643077,0.4406156242,-0.1584268361,0.2374359667,0.3454503417,-0.1518542767,-0.2052839398,0.0081735235,0.05832633,0.205610618,-0.3568544388,0.1093110591,-0.1768293828,0.0013985224,0.1842876226,-0.1633948386,-0.2385578007,0.1422113329,-0.3320725262,0.0641800165,0.122553058,0.3998177052,0.2546080351,0.1608308256,0.072079584,0.1423228383,-0.1401439011,-0.156781882,-0.0698929578,-0.36560449,0.2567168474,0.3489768207,-0.2015082091,0.0135226008,-0.3946036994,-0.0448176153,-0.0712453648,0.0082791513,0.6896387935,0.091719225,-0.0318083167,0.2214342058,0.1153474003,0.4436763823,0.068796277,-0.2091442794,-0.0214124601,0.0479799248,-0.3339827061,0.048268538,0.1806904525,-0.0014864613,0.0539554358,0.1387739629,0.4493706822,-0.2276753336,-0.1483097225,-0.2939902842,-0.2085352242,0.1655087322,-0.0190413315,0.1512502879,0.3160668612,-0.2302957177,0.0357704349,0.0669233426,-0.4116387963,0.0382435285,-0.0666187927,0.0712282583,0.0483757332,0.1025643498,-0.2542997301,0.2213602811,-0.1325968355,-0.0665762275,-0.0036140094,-0.4038878083,-0.2503187954,-0.1125381142,0.2882920504,-0.1103660837,0.2029678673,-0.141621694,0.0057303826,0.1887963414,0.0668105632,0.119579576,-0.0640976131,0.03594134,0.0520436391,-0.0502258129,0.5578497052,-0.77246207,0.0221548453,-0.1110544279,0.1622603387,-0.1910859942,0.1772535294,0.0577876568,-0.0404196531,-0.2197519988,0.4865655899,0.0727670565,0.0616688654,-0.1428972483,-0.089566648,-0.1569128186,-0.0444326475,0.0377983116,0.3760761619,-0.0571965948,0.1041150764,0.380446434,-0.1173127294,-0.2153507024,0.0217813794,0.2393888831,-0.108428888,-0.2037583739,-0.021731142,-0.519529283,-0.2728665769,0.2056166083,0.0130411144,0.1000807732,0.3230245709,-0.1054260582,-0.2257852107,0.1438708901,0.0650469065,0.1493526995,0.0401068889,-0.0796515793,-0.0936166421,-0.0056920196,-0.131843403,-0.0627465919,0.1535237432,0.157718271,0.351640135,-0.0704767704,-0.0087629184,-0.08259812,0.0973036438,0.3517900109,0.434766084,0.1407412291,-0.0306357052,0.3947176039,0.2970744967,-0.5094220042,0.1806082875,0.3879396617,0.3607369959,-0.0730337054,-0.1506708413,-0.0504656769,0.2503890693,-0.2185509503,-0.1461922526,0.3504961729,-0.3115260899,0.3024228811,0.1430209726,0.3508873284,0.2961013317,0.1546096355,-0.2565431297,-0.3138019741,0.1629935205,-0.2991591692,-0.100536041,-0.109675996,-0.3435279727,-0.073240377,0.4491719604,0.2249610424,0.3339295983,0.1307243556,-0.045668751,0.1011583656,-0.2851280272,-0.4623366594,0.3690942228,0.0325323045,-0.0324040838,-0.0058949338,-0.2700992525,-0.1267909259,0.3215012848,0.0240250342,0.0201605055,0.0583690777,0.0777406767,0.012755651,0.0487495102,-0.0738748312,0.1537625641,0.0149455015,-0.3629771173,0.0582905561,-0.2453487366,-0.0463334993,-0.0935079902,-0.7585228682,-0.1761825681,-0.3346891999,-0.0403061807,0.2386613339,0.2290695608,0.2197218984,0.1551398933,0.2061389089,0.1241323799,0.1301218271,-0.0368105024,-0.1414488852,-0.1046102792,-0.0305022672,0.1273281425,-0.0723791197,0.0829007328,0.1874415874,0.2402452976,-0.2738409936,-0.5661293268,-0.0316678844,-0.0450724177,0.6306042075,0.2490514666,-0.359347105,-0.3242051303,0.2313190401,0.5897344947,-0.0427177288,0.2254867256,0.3594716191,-0.0859736204,-0.2206724435,-0.3616174459,0.3093791008,-0.0327621847,-0.2078435868,0.2410562336,0.214681536,-0.1038077176,0.3323223889,0.0637742728,0.1596950591,0.333335042,-0.0583828874,-0.2203381211,0.1170348898,0.1210966036,-0.2581457496,-0.2353103757,-0.0685076118,0.2108470351,0.1027471945,0.0835000724,-0.0877119899,-0.6157526374,0.0433085933,-0.1099518612,0.0962612256,0.353251636,-0.0236948617,-0.0236146413,-0.005031175,-0.295424372,-0.2439953089,0.1375765949,-0.2149140388,0.1514301598,-0.0051483507,0.1247481629,0.1719860137,0.1946258247,0.3539066315,0.0950792506,0.4159371555,0.017634863,0.2028158158,-0.1227415055,-0.496014297,0.2217462361,-0.0935750976,0.07301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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2554","title":"Multilabel metrics not supported","comments":"Sorry for reopening but I just noticed that the `_compute` method for the F1 metric is still not good enough for multilabel problems:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/92a3ee549705aa0a107c9fa5caf463b3b3da2616\/metrics\/f1\/f1.py#L115\r\n\r\nSomehow we should be able to change the parameter `average` at least","body":"When I try to use a metric like F1 macro I get the following error:\r\n\r\n```\r\nTypeError: int() argument must be a string, a bytes-like object or a number, not 'list'\r\n```\r\nThere is an explicit casting here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075\/src\/datasets\/features.py#L274\r\n\r\nAnd looks like this is because here\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075\/metrics\/f1\/f1.py#L88\r\n\r\nthe features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work:\r\n\r\n```python\r\nclass F1(datasets.Metric):\r\n    def _info(self):\r\n        return datasets.MetricInfo(\r\n            description=_DESCRIPTION,\r\n            citation=_CITATION,\r\n            inputs_description=_KWARGS_DESCRIPTION,\r\n            features=datasets.Features(\r\n                {\r\n                    \"predictions\": datasets.Sequence(datasets.Value(\"int32\")),\r\n                    \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n                }\r\n            ),\r\n            reference_urls=[\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html\"],\r\n        )\r\n\r\n    def _compute(self, predictions, references, labels=None, pos_label=1, average=\"binary\", sample_weight=None):\r\n        return {\r\n            \"f1\": f1_score(\r\n                references,\r\n                predictions,\r\n                labels=labels,\r\n                pos_label=pos_label,\r\n                average=average,\r\n                sample_weight=sample_weight,\r\n            ),\r\n        }\r\n```\r\n","comment_length":36,"text":"Multilabel metrics not supported \n When I try to use a metric like F1 macro I get the following error:\r\n\r\n```\r\nTypeError: int() argument must be a string, a bytes-like object or a number, not 'list'\r\n```\r\nThere is an explicit casting here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075\/src\/datasets\/features.py#L274\r\n\r\nAnd looks like this is because here\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075\/metrics\/f1\/f1.py#L88\r\n\r\nthe features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work:\r\n\r\n```python\r\nclass F1(datasets.Metric):\r\n    def _info(self):\r\n        return datasets.MetricInfo(\r\n            description=_DESCRIPTION,\r\n            citation=_CITATION,\r\n            inputs_description=_KWARGS_DESCRIPTION,\r\n            features=datasets.Features(\r\n                {\r\n                    \"predictions\": datasets.Sequence(datasets.Value(\"int32\")),\r\n                    \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n                }\r\n            ),\r\n            reference_urls=[\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.f1_score.html\"],\r\n        )\r\n\r\n    def _compute(self, predictions, references, labels=None, pos_label=1, average=\"binary\", sample_weight=None):\r\n        return {\r\n            \"f1\": f1_score(\r\n                references,\r\n                predictions,\r\n                labels=labels,\r\n                pos_label=pos_label,\r\n                average=average,\r\n                sample_weight=sample_weight,\r\n            ),\r\n        }\r\n```\r\n \n Sorry for reopening but I just noticed that the `_compute` method for the F1 metric is still not good enough for multilabel problems:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/92a3ee549705aa0a107c9fa5caf463b3b3da2616\/metrics\/f1\/f1.py#L115\r\n\r\nSomehow we should be able to change the parameter `average` at least","embeddings":[-0.3035680652,-0.1394236833,-0.0115919383,0.2726673782,0.5279552341,-0.1952756047,0.5079537034,-0.0628859401,0.2076368928,0.3886967301,-0.0487106368,0.2973473668,-0.2217356265,0.4341047108,-0.2341413051,-0.0707549006,-0.200794816,-0.3434456289,0.0283421446,0.0905188546,-0.5077167749,-0.0101844678,-0.3638970256,-0.1156722456,-0.2270053327,-0.0182497688,0.0979765654,-0.1754829288,-0.2296997905,-0.2378181368,-0.0170335136,0.0496578217,-0.0639616773,0.4505038857,-0.0001181712,-0.1012786403,0.2459929734,0.0301737096,-0.1078461185,-0.1841437072,0.1185205355,-0.1648151428,0.0086754616,-0.2204949111,0.06982591,0.0688105077,-0.3488445282,-0.1419554502,-0.0720500126,0.1873778105,0.1253160536,-0.0324134491,0.4248095751,-0.0607887246,-0.1471945345,-0.0582397245,-0.2666977644,0.1949736923,0.043495588,0.1868580282,0.2032805532,0.422010541,0.037661992,0.1651244313,0.3003547192,-0.0706610456,0.1455166638,-0.1510967761,-0.1603523791,0.3080407679,-0.0564502813,0.0158035047,-0.635895133,-0.048069723,0.0732048377,-0.9119991064,0.0503680743,-0.1811329871,-0.0580249429,-0.2335213125,-0.4666311145,-0.2487324625,-0.0932469144,0.1711519212,-0.2652578354,0.4843778908,-0.1763254255,0.2777332962,0.4350228012,-0.1122013181,-0.1716596037,0.0254646745,0.0770560578,0.1215124056,-0.376821667,0.140374288,-0.1167222038,-0.0248236507,0.1302328855,-0.0911493376,-0.1382235587,0.2172365636,-0.3093186319,0.0455195606,0.0791067034,0.4002377391,0.1860453188,0.0681049451,0.1556663215,0.1317169517,-0.2332745492,-0.1504370421,-0.067136839,-0.4312771559,0.2790439129,0.290759474,-0.1808716655,0.0184557084,-0.395763129,-0.069110781,-0.1570844054,-0.1049991474,0.613437891,0.0020763061,0.0024143613,0.2750537992,0.1013454571,0.3778010309,0.1015059352,-0.1680469364,-0.0518772416,0.0401669182,-0.2671671212,0.1157808751,0.1327092052,-0.0729621425,0.0199623257,0.0590318181,0.5086395144,-0.2617491782,-0.1435312778,-0.2925721109,-0.1798058003,0.1468335539,0.0079843979,0.1366337091,0.2490803152,-0.2459860444,0.0537578948,0.0275273416,-0.3923941553,-0.0230282601,-0.0181886107,0.0632863715,0.0652692169,0.1628028303,-0.2335101217,0.2275534421,-0.0998217314,-0.0122991269,-0.0417280458,-0.3783718944,-0.2402632684,-0.1465385109,0.2956342697,-0.0826121494,0.1374738216,-0.0433108881,-0.0983722806,0.2437232435,0.0620565899,0.1477964222,-0.0356191397,0.1494094282,0.0578727983,-0.1248839125,0.5698587894,-0.8263217807,-0.0543428548,-0.0653696805,0.1196307093,-0.1838829219,0.2207296342,0.1236061156,-0.0622566566,-0.2049982697,0.5292375684,0.0287634134,0.0766922086,-0.1654116511,-0.0452034883,-0.1814798564,0.0025854143,0.0323274136,0.3556848764,-0.0662944391,0.0673363283,0.3843099475,-0.128864333,-0.1662780195,-0.0372041054,0.2379474789,-0.0756335407,-0.2940786779,-0.0374398008,-0.4888573289,-0.2748550475,0.1924366355,0.0066636414,0.1104380712,0.3544664085,-0.1245582849,-0.1399169266,0.1633470356,0.0518831909,0.1366387606,0.0298617668,-0.0129007706,-0.1285599023,-0.0476547405,-0.118730709,-0.0693062842,0.1510059536,0.1082296744,0.3569384515,-0.102013588,0.0569275543,-0.0941231921,0.1031106189,0.3198626041,0.4424260557,0.1030012891,-0.0148116294,0.3644598126,0.2707090378,-0.4229551554,0.1699453592,0.3826807439,0.3953563571,-0.0745726377,-0.1939990371,-0.0692258403,0.2463510782,-0.2268754542,-0.1467498392,0.2875624001,-0.3185438514,0.3226970732,0.1468410045,0.3217119873,0.2566592991,0.2015048712,-0.2483807057,-0.3016522229,0.165836513,-0.2003807575,-0.0158548839,-0.145526275,-0.2880690396,0.0146934316,0.5453440547,0.1895278841,0.3373409212,0.1228359342,0.0022848533,0.0369544066,-0.2580645382,-0.4948505163,0.3334320486,0.0415717699,0.028438326,-0.0426700935,-0.2553107142,-0.1827679425,0.3511205912,0.0407595672,0.0417344198,0.0486370102,0.0792753249,0.0071958019,0.0633729994,-0.0573916249,0.1333844513,0.0825737193,-0.3026278615,0.0838804096,-0.2861490846,0.0715983137,-0.0622957423,-0.6351693869,-0.2106032521,-0.4214543104,-0.0522998795,0.2899976373,0.2163304836,0.2177073956,0.2216136456,0.2261159569,0.1186272576,0.0726354495,-0.0117893536,-0.1280404329,-0.1157172322,-0.0338572301,0.118290633,-0.0389686562,0.0646376386,0.118493177,0.2596382201,-0.3274493814,-0.5238174796,0.0096468823,-0.0023179813,0.6806579828,0.23966901,-0.3911102116,-0.2965575755,0.275867492,0.4702559114,-0.0445799083,0.1978139728,0.306175679,-0.1598010361,-0.2040161043,-0.3154521286,0.2829875946,-0.0351803936,-0.2298974991,0.309604615,0.203386277,-0.1084304154,0.245403856,0.0371381938,0.147974208,0.3892919123,-0.1050508022,-0.2885061502,0.0849882588,0.132137835,-0.212316975,-0.2019445747,-0.1721075177,0.2234297246,0.11494416,0.2029731125,-0.0886677876,-0.534219265,0.0833124667,-0.0871443078,0.1108505428,0.4103128314,0.0605780669,-0.0183368921,0.0032284709,-0.2754081786,-0.2222476304,0.1004622132,-0.1534579545,0.065196164,0.103480652,0.0315862931,0.1391022652,0.2355817258,0.3162106276,0.0807729587,0.4098400474,-0.0718539804,0.2187602967,-0.1353746355,-0.4852558374,0.2366565168,-0.0293111894,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2553","title":"load_dataset(\"web_nlg\") NonMatchingChecksumError","comments":"Hi ! Thanks for reporting. This is due to the WebNLG repository that got updated today.\r\nI just pushed a fix at #2558 - this shouldn't happen anymore in the future.","body":"Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('web_nlg', name=\"release_v3.0_en\", split=\"dev\")\r\n```\r\n\r\nGives\r\n\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/gitlab.com\/shimorina\/webnlg-dataset\/-\/archive\/master\/webnlg-dataset-master.zip']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: macOS-11.3.1-x86_64-i386-64bit\r\n- Python version: 3.9.4\r\n- PyArrow version: 3.0.0\r\n\r\nAlso tested on Linux, with python 3.6.8","comment_length":31,"text":"load_dataset(\"web_nlg\") NonMatchingChecksumError \n Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('web_nlg', name=\"release_v3.0_en\", split=\"dev\")\r\n```\r\n\r\nGives\r\n\r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/gitlab.com\/shimorina\/webnlg-dataset\/-\/archive\/master\/webnlg-dataset-master.zip']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: macOS-11.3.1-x86_64-i386-64bit\r\n- Python version: 3.9.4\r\n- PyArrow version: 3.0.0\r\n\r\nAlso tested on Linux, with python 3.6.8 \n Hi ! Thanks for reporting. This is due to the WebNLG repository that got updated today.\r\nI just pushed a fix at #2558 - this shouldn't happen anymore in the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2552","title":"Keys should be unique error on code_search_net","comments":"Two questions:\r\n- with `datasets-cli env` we don't have any information on the dataset script version used. Should we give access to this somehow? Either as a note in the Error message or as an argument with the name of the dataset to `datasets-cli env`?\r\n- I don't really understand why the id is duplicated in the code of `code_search_net`, how can I debug this actually?","body":"## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n","comment_length":66,"text":"Keys should be unique error on code_search_net \n ## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n \n Two questions:\r\n- with `datasets-cli env` we don't have any information on the dataset script version used. Should we give access to this somehow? Either as a note in the Error message or as an argument with the name of the dataset to `datasets-cli env`?\r\n- I don't really understand why the id is duplicated in the code of `code_search_net`, how can I debug this actually?","embeddings":[-0.0094925556,0.004909059,-0.1037806347,0.3735046387,0.0469307639,-0.0595812909,0.2355328649,0.2376770377,0.0580709688,0.0420985892,-0.0894128904,0.4072095454,-0.1577007771,0.1043384299,0.1628589183,0.0670307279,0.0143380752,0.1504513174,0.1202021763,-0.1288990974,-0.3279083371,0.2217665762,-0.2497897297,0.0505501106,-0.2336093187,-0.1389334798,-0.2191094309,0.2946093976,-0.1718128622,-0.6023648381,0.2365141064,0.2469079047,-0.304754287,0.348526001,-0.0001129588,0.1454983503,0.3868984282,-0.0112753259,-0.2460678816,-0.1964260042,-0.1086823568,-0.1141097546,0.0937169865,-0.3164001703,0.018459348,-0.1501702219,-0.1131840348,-0.5023266077,0.876992166,0.2101984024,0.2571647763,0.230317384,0.3121251166,-0.2214271277,0.2857811153,-0.1449810416,-0.0408077687,0.4033092856,0.1780676097,-0.050453268,-0.001569794,0.2535579801,0.149145022,-0.0922281891,0.4889210761,0.004278875,0.0523612984,-0.3802721798,0.1833044142,0.0842173025,0.1399863809,-0.2937673628,-0.2534074783,-0.2852416635,-0.0006022927,-0.2383116484,0.3581800461,0.1130280718,-0.1832094043,0.1009692475,-0.0234330706,-0.2443882823,-0.0209400747,-0.0669795573,-0.0024758936,0.000144258,-0.0406801328,0.1741480231,0.1436638087,-0.030949913,0.2942076325,-0.133569628,0.1088615656,0.2196424156,-0.4874308705,0.1088525727,-0.0417375639,-0.0666240379,0.2526279986,0.0501061976,0.1586542875,-0.1383236498,0.0582781099,0.0365461148,0.136211589,0.1176389456,0.024738526,0.2395918816,0.1972186118,-0.2188272476,-0.2711265981,-0.0564664528,-0.17859146,-0.1516632885,0.4401807189,0.0809084475,0.5045513511,0.0516365357,-0.1957731843,0.1798712909,-0.1808783412,-0.1719259471,0.0739718005,0.3315834403,0.1826923192,-0.3608222306,0.0812146813,0.2304950655,-0.1570223868,-0.2011741549,-0.3156377375,-0.1495875716,-0.0772534907,-0.1267662793,0.2154261917,-0.5755309463,0.2917330861,0.2776085734,-0.0764898807,-0.4185130596,-0.0106018735,0.0661275685,-0.21158427,0.1040804014,-0.0396964923,0.0929643214,-0.0853247195,-0.2381432056,-0.0866200179,0.2018012553,-0.21553424,-0.3841583431,-0.0113813402,0.270018816,0.0460587405,0.1273243129,0.0172670651,0.0344303735,0.2237819284,0.1911880821,0.0374225043,-0.0336898118,-0.0244738683,-0.3202982843,0.23071374,0.6258887649,-0.1388694644,-0.0994992405,-0.166160807,-0.0337914713,0.0866958424,0.3992448449,-0.2378602475,0.426987797,-0.3161387146,0.2103259116,0.7068001032,-0.2062661797,-0.3250540495,0.1362059861,-0.2523133457,-0.1497699767,0.1035354063,0.026996633,0.1862990856,0.095646143,0.1519941688,-0.0751006007,0.0733930171,-0.0979357809,-0.384691447,-0.3182408512,0.1399685591,-0.0788120851,0.1862515062,-0.1908337772,0.2699958682,-0.1556688398,0.0582116097,-0.1312351972,-0.1205604523,0.0026754646,0.2714601457,0.0408043377,0.0887514651,-0.0086452868,-0.4823821485,0.374763608,-0.3360308111,-0.2697573602,-0.1959292889,-0.3034653664,-0.3515293598,0.1438819766,-0.5467948318,0.0142956581,0.2060532719,-0.0663145036,0.2171556205,0.1020635888,-0.176940009,0.1594810337,0.0542384125,0.1100345775,-0.2068196833,0.2509876788,-0.473295927,-0.0428255834,0.0588917732,0.2304445505,0.3486605883,-0.126670599,-0.1688676924,0.444019258,-0.2193270922,0.0511581339,-0.0163343493,-0.0888499841,0.1398420334,-0.3815078437,0.2165362686,0.2500270009,0.1734208018,-0.1731620431,-0.0787188336,0.3603438139,-0.111679703,0.0948637575,-0.0413667299,-0.0870466456,0.0792675242,-0.1197792962,0.1631211042,-0.1041850671,0.2782212496,-0.3106930554,0.3890615702,0.1747398674,-0.2443796545,0.1206956282,0.2313815206,-0.040806178,0.1052942649,0.0120361065,0.032384526,-0.191202417,-0.2877425849,0.2358517349,0.3477755487,0.154177025,-0.0584747009,0.2757799625,-0.0113031315,-0.2352381796,0.3930318356,-0.1218933836,-0.1429179609,0.3976445794,0.1085420996,0.0489772335,-0.3879248798,-0.1217015237,0.1714469045,0.0767205209,-0.2829786837,0.0125734387,-0.1297802329,-0.0831947252,-0.456538856,0.5397939086,-0.1158734262,-0.1127381995,0.252034843,0.0170565713,0.0975915939,0.1204392239,-0.2307248563,0.1806775331,-0.0148847988,0.1146529019,-0.2355740368,-0.1124270409,-0.077190429,0.03742823,0.3936970532,-0.0605512783,0.5720286965,-0.1889272928,0.1064837798,-0.1775661409,-0.3476791382,-0.0131592071,-0.0041039702,0.1664582789,0.0505314544,-0.1058803722,-0.035335023,-0.378918618,0.2900122404,-0.3102478683,-0.4068109095,-0.0542965159,0.0398769937,-0.0646373257,-0.2149248123,-0.5273548961,-0.265047133,-0.2430540472,0.3169803917,-0.0962151885,0.1279311031,0.3379567564,-0.1227716357,-0.0101920851,-0.0059263171,0.2016517222,-0.3594234586,0.0480577499,0.0874655396,-0.0509008281,-0.353307426,-0.0688932985,-0.1448617578,0.1514542401,-0.0905499533,-0.3825456202,0.2584376633,-0.231079936,0.4560710192,-0.1607944965,0.3305143416,0.2242964655,0.2599559724,-0.1505051553,-0.185819298,-0.0531722978,0.1141134128,-0.125399828,0.0685934052,0.1404928416,0.2881910503,-0.3000931144,0.8568351269,0.6044374108,0.0024756135,0.2031872422,-0.173413977,0.3541770577,-0.2369024009,-0.4189771414,-0.2487502396,0.0740314499,0.024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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2552","title":"Keys should be unique error on code_search_net","comments":"Thanks for reporting. There was indeed an issue with the keys. The key was the addition of the file id and row id, which resulted in collisions. I just opened a PR to fix this at https:\/\/github.com\/huggingface\/datasets\/pull\/2555\r\n\r\nTo help users debug this kind of errors we could try to show a message like this\r\n```python\r\nDuplicateKeysError: both 42th and 1337th examples have the same keys `48`.\r\nPlease fix the dataset script at <path\/to\/the\/dataset\/script>\r\n```\r\n\r\nThis way users who what to look for if they want to debug this issue. I opened an issue to track this: https:\/\/github.com\/huggingface\/datasets\/issues\/2556","body":"## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n","comment_length":97,"text":"Keys should be unique error on code_search_net \n ## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n \n Thanks for reporting. There was indeed an issue with the keys. The key was the addition of the file id and row id, which resulted in collisions. I just opened a PR to fix this at https:\/\/github.com\/huggingface\/datasets\/pull\/2555\r\n\r\nTo help users debug this kind of errors we could try to show a message like this\r\n```python\r\nDuplicateKeysError: both 42th and 1337th examples have the same keys `48`.\r\nPlease fix the dataset script at <path\/to\/the\/dataset\/script>\r\n```\r\n\r\nThis way users who what to look for if they want to debug this issue. I opened an issue to track this: https:\/\/github.com\/huggingface\/datasets\/issues\/2556","embeddings":[-0.039390035,-0.0147919394,-0.092823565,0.3366979957,0.0873194709,-0.0139394747,0.2003903836,0.2712210715,0.0882736444,0.0667391494,-0.0861993954,0.3934495449,-0.1623921543,0.1329388171,0.1347277313,0.0915056542,-0.0428930745,0.1898689717,0.118189007,-0.056120418,-0.3183745444,0.2934969664,-0.222816214,0.0328287929,-0.2074050754,-0.1297896057,-0.1850280464,0.2394023091,-0.169512704,-0.6275754571,0.2506465316,0.2204239964,-0.3045646548,0.3702608347,-0.0001118734,0.1776437461,0.3890072703,-0.0119130826,-0.263148427,-0.1894607991,-0.0932743922,-0.0726644769,0.0986738652,-0.2597336173,-0.0612497963,-0.1425023079,-0.1062145233,-0.5259363651,0.8551874161,0.2249429375,0.261464715,0.1924865991,0.3430315554,-0.2581971586,0.2435848862,-0.1772041172,-0.0567812659,0.3766361773,0.2065399587,-0.0528299324,0.0148361363,0.2883768678,0.1508664638,-0.1099837124,0.4550983906,-0.0461321585,0.0562952235,-0.3445614576,0.202942282,0.0850430951,0.0933940634,-0.2972278595,-0.211820215,-0.2630234361,0.0436524637,-0.2451478392,0.3421130776,0.1394786388,-0.2177512348,0.1649510264,-0.0173711162,-0.258102119,0.004423562,-0.0469903834,-0.0124232657,-0.0174261965,-0.0472720303,0.1445003897,0.1904307753,-0.0294545386,0.2612523139,-0.0770370811,0.0319689102,0.1867050976,-0.5232821107,0.1069345474,-0.0528017282,-0.1080394983,0.2210678756,0.04155799,0.1781447381,-0.0987889767,0.0630774796,0.0046159402,0.1186117828,0.1113321632,0.0510718673,0.2479469478,0.2089866847,-0.1799351871,-0.2521626651,-0.0465332828,-0.2012174428,-0.1685853302,0.370346278,0.0584278665,0.5287957191,0.0775724277,-0.2316820472,0.1241981462,-0.1455936879,-0.1006391644,0.0610974915,0.3620432913,0.1859445423,-0.3567807674,0.1096722409,0.2256824821,-0.1423757821,-0.194125846,-0.3171714544,-0.106359385,-0.0811322033,-0.156812489,0.2419344336,-0.6133795381,0.2899116576,0.2416549325,-0.1092535332,-0.4836315811,0.0230332892,0.1235139221,-0.1988665313,0.0956891775,0.010164707,0.0778541639,-0.0203281436,-0.238665089,-0.0512514226,0.1840789467,-0.2536869943,-0.3633141816,-0.0695421621,0.2863248885,0.0670304596,0.0897065252,0.0178208277,0.036976248,0.2047014832,0.1793242544,0.0022217699,-0.0245714337,-0.0792566091,-0.2655107379,0.2568253875,0.635163486,-0.0922877565,-0.1067955345,-0.1737157553,-0.0022584435,0.0813440755,0.3821578324,-0.2429331839,0.3917122483,-0.3513783813,0.1783625484,0.713447988,-0.1958878785,-0.3543924987,0.1866781712,-0.2593115568,-0.218294844,0.0873140693,0.0251206383,0.1783084273,0.1153895631,0.157443732,-0.0760309249,0.0586169437,-0.0519526079,-0.3551417589,-0.3531219363,0.1054895669,-0.0788374171,0.1729315072,-0.1593489647,0.2709896266,-0.1526414305,0.1180687621,-0.1213864759,-0.0849593729,0.0823330656,0.2390115112,0.0496873073,0.0824484378,-0.0632929653,-0.4461555481,0.3696315587,-0.2972278595,-0.266858995,-0.239024654,-0.2505201697,-0.4130327106,0.1440363824,-0.5862923265,-0.0180854406,0.2047824413,-0.0346153788,0.2092174143,0.1034080163,-0.1269436926,0.1520022005,0.0593991727,0.0834335387,-0.1985911578,0.2522063851,-0.4453632534,-0.0550451502,0.0948074311,0.2252966017,0.325219512,-0.0994668528,-0.1627309918,0.417509675,-0.2410899103,0.0180140231,-0.0007237954,-0.1209466383,0.0787326172,-0.3930574059,0.1947408468,0.2712776065,0.178377375,-0.1665648371,-0.0573369339,0.3320143819,-0.0891046971,0.0955382586,-0.0527458787,-0.0315741859,0.1117780209,-0.1201968044,0.2202439606,-0.1167671606,0.2911606431,-0.3164441288,0.3656363487,0.1319552809,-0.2356605232,0.1314296573,0.3060259521,0.0220883489,0.1882434636,0.0140608279,0.0087792343,-0.155099526,-0.3161092103,0.2443338484,0.3074198365,0.1628214121,-0.1048359126,0.2712975144,0.0289769229,-0.2005146146,0.3751117587,-0.1769654602,-0.1046681628,0.3829626143,0.1048186272,0.0200045686,-0.4195503891,-0.1060770303,0.166590333,0.0904481038,-0.2432655543,0.024589492,-0.1573229134,-0.0550038442,-0.4926802218,0.5171210766,-0.1103608385,-0.1431573182,0.2413244843,0.0723625869,0.1362358183,0.1244721189,-0.2315515727,0.1443307847,0.0402568541,0.0933430716,-0.208307907,-0.1138941646,-0.0725762397,0.0338538177,0.37839064,-0.0770852938,0.5647823811,-0.1624939442,0.1164632887,-0.2422077358,-0.3452756703,-0.017885197,0.0072638318,0.1485368758,0.0805482119,-0.0375982709,-0.0214283541,-0.3726305664,0.2817128599,-0.3179251552,-0.4135041833,-0.0009355024,0.0158012602,-0.0357335135,-0.2129419148,-0.5386603475,-0.2469630688,-0.266546309,0.3176681697,-0.1062420309,0.0821354836,0.3633770347,-0.0593880489,-0.0168305058,-0.0493381135,0.1703391075,-0.3394080698,0.045024801,0.046050258,-0.0631044284,-0.3325830698,-0.0430321284,-0.1231541857,0.1562623382,-0.1182264686,-0.3490812778,0.2830144465,-0.2649712265,0.4167036414,-0.2116276324,0.2905671299,0.2058410645,0.256126225,-0.1482191086,-0.1712091118,-0.02909109,0.1058599502,-0.171593979,0.0603614785,0.0947430432,0.2958513498,-0.2571831346,0.8182141185,0.5736106038,-0.0000046855,0.1971295476,-0.2320547104,0.3561851382,-0.259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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2552","title":"Keys should be unique error on code_search_net","comments":"and are we sure there are not a lot of datasets which are now broken with this change?","body":"## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n","comment_length":18,"text":"Keys should be unique error on code_search_net \n ## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n \n and are we sure there are not a lot of datasets which are now broken with this change?","embeddings":[-0.0469085425,-0.0271531846,-0.1106117964,0.3490996659,0.081276536,-0.0156195574,0.1811806709,0.2770838141,0.0815910995,0.0544187576,-0.0599072017,0.3916929364,-0.1846611351,0.116615966,0.1564046592,0.078515403,-0.0312846079,0.1728187054,0.1106826067,-0.0542455949,-0.3256242573,0.2887380123,-0.2328311056,0.0389522165,-0.1848244071,-0.1620794386,-0.1911866963,0.2588772476,-0.1845095456,-0.6111863256,0.2418148369,0.2137483656,-0.3018578887,0.3558101058,-0.0001112483,0.1782946587,0.3963311613,-0.0183047689,-0.2428982258,-0.1785934716,-0.0976837426,-0.0869019106,0.0782905892,-0.2927312851,-0.0522992276,-0.1311941594,-0.1020006314,-0.5351581573,0.8553102612,0.2324268073,0.2680365145,0.1950211674,0.3377575576,-0.235691309,0.2538096011,-0.1840531081,-0.0478936695,0.3911312521,0.2176291645,-0.0469379611,-0.0018590186,0.3106984198,0.1507784575,-0.1226941347,0.4385913908,-0.0377838165,0.0561736748,-0.3310019672,0.1806346625,0.0725954771,0.1040767357,-0.3062256277,-0.2338497192,-0.2663578689,0.0390654281,-0.2513284683,0.3439498246,0.1335716099,-0.2220121175,0.1639823616,-0.0111537613,-0.2648167908,0.0115648899,-0.0475299433,-0.0154275643,0.0178863611,-0.0589888878,0.1368977427,0.160793066,-0.0466827862,0.2724993527,-0.0896123201,0.0467299111,0.1941236407,-0.5073847175,0.1113422439,-0.0263008662,-0.0870054588,0.2304593474,0.0269335285,0.1648990214,-0.1091128364,0.0596225448,0.0060264375,0.1255843341,0.1220989898,0.0224412084,0.244217068,0.2085755616,-0.1591237634,-0.2433698773,-0.0350925513,-0.1751270741,-0.191812858,0.3740758002,0.0837449282,0.5239519477,0.0817943066,-0.2396693677,0.13707757,-0.1299029738,-0.1012648121,0.0700322166,0.3665414751,0.160808742,-0.3450777531,0.116074048,0.185338378,-0.1395103931,-0.1890879124,-0.3252248168,-0.0816784799,-0.0858611017,-0.1678928435,0.2184514701,-0.5809813142,0.2865787446,0.2305636853,-0.0979512781,-0.475730598,0.0076656276,0.1049675941,-0.2080143392,0.0840548128,0.0011776964,0.059263289,-0.023182882,-0.2230984271,-0.0681596249,0.1733560413,-0.2561249435,-0.3439769447,-0.0812844634,0.2983009517,0.0687769279,0.0940390453,0.0188799016,0.0360048451,0.2115163952,0.1689544171,0.019319294,-0.0201480016,-0.0689401329,-0.2622361481,0.2715479732,0.6408420801,-0.0852002129,-0.1143703461,-0.1630569994,-0.0119723873,0.0820221528,0.3735702038,-0.2195251137,0.4098963439,-0.3701931834,0.1702483743,0.7158523202,-0.2098324448,-0.3514036536,0.1768588573,-0.2566369176,-0.1927757412,0.1062855944,0.017745195,0.171772033,0.1271534413,0.191995427,-0.074500829,0.0496413931,-0.0505873188,-0.3718301654,-0.3530538976,0.1030012369,-0.0865597725,0.1955659837,-0.1717545241,0.2626270652,-0.1302562356,0.1170928478,-0.1145149097,-0.0960906073,0.08498054,0.2491381764,0.0279336032,0.0814496204,-0.0725878999,-0.4511611462,0.3614913523,-0.2994681001,-0.2637138069,-0.2208357751,-0.2707116306,-0.3951714635,0.1444454044,-0.5617762804,-0.021909453,0.2265327275,-0.0335865505,0.2018054277,0.089362435,-0.1509428024,0.1715616882,0.0490317941,0.0780372918,-0.2100775987,0.2596832216,-0.4771119952,-0.0632256791,0.0932601988,0.2274794132,0.3199900389,-0.1160648391,-0.1683141291,0.4271498024,-0.2329930514,0.0158185381,-0.0029284244,-0.1300105751,0.0871503279,-0.3934369981,0.1725026667,0.2751096785,0.1673909575,-0.1346253902,-0.0697996989,0.3326274157,-0.098880671,0.0816605836,-0.0342422314,-0.0534674115,0.1030966714,-0.1120853573,0.2092016786,-0.1217363179,0.280827105,-0.3323273659,0.395688206,0.1194216758,-0.2414901406,0.1092133597,0.2924526334,0.0013845633,0.1975161284,0.0097058034,0.0288969744,-0.1784740537,-0.2953672111,0.2325143069,0.2991614342,0.1731813103,-0.108634688,0.2740420699,0.0366743244,-0.1799518317,0.3811586499,-0.1649849117,-0.1044396386,0.39415434,0.081823498,0.0342088789,-0.4060649276,-0.1442296058,0.1447810829,0.10745617,-0.2713996768,0.0040042065,-0.1440774053,-0.0816541612,-0.4855396152,0.5519646406,-0.0967328995,-0.1340057999,0.2336675078,0.0417370275,0.1260758787,0.1139730886,-0.2359859347,0.1488276422,0.0432489254,0.1060621962,-0.2104985863,-0.104381077,-0.0720650554,0.0447643474,0.3772628605,-0.0930300429,0.5541758537,-0.1986333728,0.0996911302,-0.2164834887,-0.3579094112,-0.0040377402,0.0208136421,0.149884209,0.1100701541,-0.0402450822,-0.0256814659,-0.3755349815,0.2560067475,-0.3044043183,-0.4428147972,-0.0225434508,0.0187568497,-0.0395334139,-0.2074314505,-0.5418492556,-0.2662756741,-0.2766748965,0.292663157,-0.0981774926,0.0814607516,0.3725579083,-0.0679178238,0.0038487245,-0.0269739237,0.1974369437,-0.3267024755,0.0312487204,0.0353545435,-0.0529341437,-0.3246124983,-0.057834886,-0.1475159973,0.173725903,-0.1259884685,-0.3620150685,0.2623095214,-0.2389128059,0.426699996,-0.2036780864,0.3028892279,0.2129474282,0.2573758662,-0.1596046388,-0.1698618531,-0.0314722806,0.0892744213,-0.167788744,0.0556728058,0.1018413827,0.304336071,-0.2619749606,0.8216831684,0.5829095244,-0.0059695388,0.2016521543,-0.2118443847,0.3733597398,-0.2440511435,-0.4355399013,-0.2459855527,0.1285446435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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2552","title":"Keys should be unique error on code_search_net","comments":"Thanks to the dummy data, we know for sure that most of them work as expected.\r\n`code_search_net` wasn't caught because the dummy data only have one dummy data file while the dataset script can actually load several of them using `os.listdir`. Let me take a look at all the other datasets that use `os.listdir` to see if the keys are alright","body":"## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n","comment_length":61,"text":"Keys should be unique error on code_search_net \n ## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n \n Thanks to the dummy data, we know for sure that most of them work as expected.\r\n`code_search_net` wasn't caught because the dummy data only have one dummy data file while the dataset script can actually load several of them using `os.listdir`. Let me take a look at all the other datasets that use `os.listdir` to see if the keys are alright","embeddings":[-0.0364270136,-0.0201952066,-0.0964384675,0.3276674449,0.0761770457,-0.013212272,0.2080996335,0.2912021279,0.0984992534,0.071184732,-0.067037195,0.3835592568,-0.1711656004,0.1363995969,0.1288103014,0.0837996826,-0.0438017175,0.1780875623,0.1199660823,-0.0594822355,-0.3179877698,0.2856626809,-0.234905839,0.0165575091,-0.2090522498,-0.137646243,-0.1760066003,0.2507343888,-0.1824110895,-0.5908303857,0.2711940408,0.2093391418,-0.2992361486,0.3508662581,-0.0001107919,0.1900058538,0.3981910348,-0.0256017838,-0.2827827632,-0.1791191101,-0.0728083253,-0.0612183549,0.0831817463,-0.2482592016,-0.0818561614,-0.1224978566,-0.1049753577,-0.5397647619,0.8384168148,0.2460178584,0.2726716101,0.2112665921,0.3223752081,-0.2598754168,0.2420286089,-0.1614331901,-0.0639911294,0.3828731775,0.1833221018,-0.0479908027,0.0057221493,0.2981964052,0.1404444277,-0.0875044614,0.4186807275,-0.0323041193,0.0468773171,-0.3087764084,0.2146259993,0.102823697,0.0884276927,-0.3128267825,-0.2070395052,-0.2669886947,0.0362550654,-0.2407446951,0.3180388212,0.1313280463,-0.202814877,0.1684758216,0.0087359957,-0.2333392948,0.0168732386,-0.035682261,-0.0120590134,0.0075637288,-0.0477772057,0.1442216039,0.1870479286,-0.0301870052,0.2658228576,-0.1093940288,0.019527711,0.1790449172,-0.5136678815,0.1230201051,-0.0518566482,-0.0984727517,0.2277046442,0.028717719,0.1698817313,-0.0687136874,0.0624927841,0.0053730593,0.0905160308,0.1125339046,0.0269704033,0.2809763849,0.2035713792,-0.1502015889,-0.2411468476,-0.0358261578,-0.1814642251,-0.1567694843,0.3382406533,0.0708147213,0.5329917669,0.0759846792,-0.2476930916,0.1277484149,-0.1235774383,-0.0753641129,0.0569920801,0.3604098856,0.1586510837,-0.3382246196,0.1139072999,0.2257108837,-0.1590736508,-0.2025012225,-0.3317087293,-0.119950287,-0.0704322308,-0.1687156856,0.2553776503,-0.5790083408,0.3142931163,0.2457666844,-0.1194907054,-0.4515373409,0.0783602819,0.1040028781,-0.1953116655,0.0890586823,0.0208360329,0.0583035946,0.0076981285,-0.2369559258,-0.0453378856,0.1677516699,-0.2531954348,-0.3557937145,-0.0943062007,0.3007490635,0.0646655783,0.0803303868,0.0169255044,0.055400297,0.1994582266,0.1568813622,-0.0127063803,-0.03325589,-0.1019226387,-0.2483245134,0.2735359669,0.6290325522,-0.1159738824,-0.0979010016,-0.1622074544,-0.0023760367,0.0797294006,0.3462894559,-0.2412421405,0.39027071,-0.3654875755,0.1659935266,0.6817964911,-0.2055112571,-0.3552485406,0.1524267942,-0.2477335483,-0.1710256934,0.0933934301,0.0315536559,0.1626765132,0.1522602588,0.1557519287,-0.066762425,0.057032384,-0.0515627451,-0.3554735482,-0.3471826017,0.1130477786,-0.067095235,0.1661296934,-0.1674634963,0.241747722,-0.1381936371,0.125456661,-0.112406373,-0.0802565515,0.0907338858,0.2145911753,0.0272686835,0.0985970348,-0.0643676594,-0.4726127684,0.3678174019,-0.2958661616,-0.237139076,-0.2157186568,-0.2840786278,-0.4142509103,0.1149873361,-0.5730288029,-0.0325938165,0.220072031,-0.0267939493,0.2193904966,0.0880645588,-0.120731473,0.1589354426,0.0497187562,0.0876840279,-0.2432176024,0.2517299056,-0.4583709538,-0.0632469282,0.1095043197,0.2277159989,0.3038507998,-0.1221485436,-0.1701619029,0.4219498336,-0.2227303982,0.0020727266,-0.0018699454,-0.1487248391,0.0674977824,-0.3830093145,0.1845069975,0.299370259,0.1757295728,-0.1566673368,-0.0986901745,0.3583337963,-0.0740240216,0.0907488316,-0.0510714389,-0.0368811749,0.1225299686,-0.1088033319,0.2126482427,-0.1271819621,0.2830677629,-0.3175019026,0.402438879,0.1329597384,-0.232151404,0.1211523637,0.2946249545,0.0163619891,0.1883885264,-0.0024340528,-0.021498641,-0.1830296367,-0.2807162106,0.2342668921,0.2953345776,0.1781710535,-0.1118414998,0.2570243478,0.0040008421,-0.187197119,0.3797668815,-0.1941545904,-0.1340707093,0.3846864104,0.0899585038,-0.0086621232,-0.4447206259,-0.1110007465,0.1650653332,0.1223477051,-0.2493936718,0.0003657769,-0.163993448,-0.0636476278,-0.4797675014,0.5345814824,-0.0994899422,-0.1488792896,0.2435329407,0.0811946914,0.1193659082,0.1278974265,-0.2404330522,0.149900198,0.0576753877,0.081865415,-0.2238828242,-0.1108079851,-0.0923991054,0.0561681278,0.3637707829,-0.0652652755,0.5341525674,-0.1901935488,0.0905751288,-0.2226313949,-0.3574887812,-0.0129970387,0.002620938,0.1580307782,0.1113686189,-0.0347031057,-0.0118298205,-0.3635375202,0.270929873,-0.3152554929,-0.4279221892,0.0140691744,0.0075032748,-0.0309336297,-0.2294654548,-0.5562415123,-0.2520560026,-0.2643299401,0.2726472616,-0.0757407993,0.0702347681,0.3859768212,-0.0329326019,-0.0093224272,-0.028510984,0.1671642959,-0.3440155685,0.0109188473,0.0526414216,-0.0584129617,-0.3240551949,-0.0440948233,-0.1682155877,0.1607696265,-0.1315598488,-0.3624717593,0.2376160473,-0.2448980063,0.4199641347,-0.2150039226,0.2762877047,0.2194638401,0.2579503059,-0.1546107978,-0.1946175992,-0.043108236,0.1191187724,-0.1772721857,0.0579537489,0.0751885027,0.2850073278,-0.2456805408,0.8134369254,0.5724945068,-0.0077527193,0.1956249475,-0.2384260297,0.3781944811,-0.2580958009,-0.4343471825,-0.2718248665,0.12006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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2552","title":"Keys should be unique error on code_search_net","comments":"I found one issue on `fever` (PR here: https:\/\/github.com\/huggingface\/datasets\/pull\/2557)\r\nAll the other ones seem fine :)","body":"## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n","comment_length":16,"text":"Keys should be unique error on code_search_net \n ## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n \n I found one issue on `fever` (PR here: https:\/\/github.com\/huggingface\/datasets\/pull\/2557)\r\nAll the other ones seem fine :)","embeddings":[-0.0202083215,0.0007684655,-0.0907565653,0.3496333361,0.085136801,-0.0248717666,0.1991802007,0.2581068277,0.0932296291,0.0736749247,-0.0927868485,0.3846124113,-0.1742851436,0.1145088226,0.1579982638,0.0978715718,-0.0322047956,0.1860354841,0.1146632507,-0.0663524717,-0.3220596611,0.2716599703,-0.2497789562,0.0057933927,-0.2198529243,-0.1277942508,-0.181460768,0.2327201515,-0.1582820863,-0.6227064729,0.2830570638,0.2367789745,-0.3231881559,0.3545233607,-0.0001138885,0.1849657595,0.3915029764,-0.0082440907,-0.272043705,-0.1919538826,-0.0973862335,-0.0876455605,0.1220966503,-0.2598312497,-0.0403411798,-0.126745373,-0.1003250331,-0.5317751765,0.8246197701,0.1936426014,0.2487318069,0.1887022704,0.3378750384,-0.2372305989,0.2439075857,-0.1666713655,-0.055227574,0.3877444565,0.1985958368,-0.0504906029,0.0121911727,0.2858553231,0.1549561769,-0.1102307737,0.4621378481,-0.052585803,0.0763141215,-0.3710743189,0.2049998045,0.089278467,0.0974366218,-0.2761585116,-0.2324383557,-0.2789843976,0.0560453385,-0.2132116556,0.3593420684,0.1221939251,-0.2076237649,0.1624036431,-0.0172570534,-0.2577531338,0.0126821715,-0.0440757908,0.0079860017,-0.0301998556,-0.0491084233,0.1541401595,0.1662726849,-0.03205375,0.2796732485,-0.0996795073,0.0411549285,0.2203778476,-0.557258606,0.1509339511,-0.0677143708,-0.097979486,0.2128643841,0.0290231053,0.1994989216,-0.0848766863,0.0650746226,0.0161665604,0.1034396291,0.096539937,0.0342402793,0.2327070087,0.2190907598,-0.1835646629,-0.251726985,-0.0357222743,-0.2129186839,-0.1737252921,0.4017063677,0.0611099601,0.5241869688,0.0679923967,-0.2271723598,0.1321724951,-0.1534138471,-0.100855045,0.0860638395,0.3410011828,0.1949203908,-0.3373978138,0.1137649566,0.2290328294,-0.1387767345,-0.2006437629,-0.3102109432,-0.121383667,-0.0552540682,-0.1454878449,0.2526148558,-0.6270567775,0.2642531097,0.2605431676,-0.1020764187,-0.4912848175,0.003810955,0.102369979,-0.2192972749,0.0943130627,0.0159664955,0.079572551,-0.0476267003,-0.2335376292,-0.0544433184,0.1780006588,-0.2537408769,-0.3525573909,-0.0227050148,0.2670414746,0.0590396114,0.1252042651,-0.0038168507,0.0386782661,0.2306306362,0.1851243079,-0.012323305,-0.0314189382,-0.0888888836,-0.2552238405,0.26879704,0.6540945768,-0.0965850651,-0.1106154993,-0.1760225296,-0.0000576481,0.1063128933,0.3705928326,-0.2221122235,0.4160015881,-0.3452946544,0.1597891599,0.7105045319,-0.1933838129,-0.3874007761,0.1839333773,-0.2476288974,-0.1824145019,0.106587939,0.0144339176,0.1848349273,0.1223945469,0.1634444296,-0.0733876675,0.0813080817,-0.0535241216,-0.3427564502,-0.3470592499,0.121237345,-0.0639005452,0.1830687076,-0.1579374671,0.272606492,-0.1373436153,0.0896514803,-0.1177653819,-0.063160114,0.0818895698,0.2199142277,0.0230338331,0.1041528732,-0.0576092042,-0.4630142152,0.381847471,-0.2959958017,-0.2730376422,-0.2498998493,-0.246673286,-0.3670990169,0.1595121622,-0.5803844333,0.0287356377,0.1856029034,-0.0443434604,0.2221629173,0.101912193,-0.1063280851,0.174161166,0.0609682389,0.0783875287,-0.20480676,0.2609229386,-0.4255307615,-0.0561136641,0.1025159657,0.2394187897,0.3366996348,-0.1111951694,-0.1904087812,0.4078956842,-0.2392886132,0.0221072398,-0.0348242894,-0.1301355064,0.0792379975,-0.387062341,0.1635849327,0.2781241834,0.1803104579,-0.1550774127,-0.0412182547,0.3161665499,-0.112228632,0.1176383421,-0.0673278421,-0.0462845862,0.1096145287,-0.1085916609,0.2148876637,-0.1273214817,0.3092455566,-0.322094202,0.4101284146,0.1456072778,-0.22843799,0.1148155704,0.2885634601,0.0129627753,0.1708480716,0.0142394491,0.0231226739,-0.1852747947,-0.3099054098,0.2503823042,0.307439208,0.1368938386,-0.1100538298,0.257715553,0.0272050723,-0.2018003911,0.3694401085,-0.1471651793,-0.1145038083,0.4094091654,0.1236055195,0.0418062434,-0.4395470321,-0.1031289399,0.2066828907,0.0979180336,-0.2481191754,0.0145452265,-0.1656813622,-0.0346917063,-0.4692341983,0.5736931562,-0.1025880724,-0.1179140359,0.2339111418,0.0687985197,0.1535279602,0.1181849316,-0.2238681316,0.1479697227,0.040018931,0.0913883373,-0.2332361639,-0.0914759412,-0.0854198113,0.004380466,0.4014088511,-0.1010481715,0.5399111509,-0.1814332008,0.079408668,-0.2130985856,-0.3361154199,-0.023754539,0.0386971571,0.1665667146,0.06583976,-0.0652712137,-0.0287215542,-0.3721289635,0.2697654366,-0.3284951448,-0.4169460833,0.0101507735,0.0055707325,-0.0413727947,-0.241752103,-0.5230997205,-0.2458373308,-0.2457344681,0.3262642622,-0.1252706051,0.0861840993,0.3265456855,-0.0873571783,-0.0341761373,-0.037580125,0.1786943078,-0.3457049727,0.04664151,0.0505956672,-0.0377124622,-0.3421707153,-0.058241915,-0.1246190444,0.1684557348,-0.0901200026,-0.3649651706,0.3040499389,-0.2496056408,0.4348332584,-0.216963917,0.2921915948,0.1888551712,0.252348423,-0.1235338822,-0.1784010381,-0.0185365994,0.1148375496,-0.1724200696,0.0444516055,0.1156713217,0.2916902006,-0.2708283961,0.8078584671,0.5670900941,0.0105675859,0.2099637091,-0.2143362761,0.3573082387,-0.2475783229,-0.4330516458,-0.265617162,0.1070937589,0.0813839361,0.09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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2552","title":"Keys should be unique error on code_search_net","comments":"Hi! Got same error when loading other dataset:\r\n```python3\r\nload_dataset('wikicorpus', 'raw_en')\r\n```\r\n\r\ntb:\r\n```pytb\r\n---------------------------------------------------------------------------\r\nDuplicatedKeysError                       Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1109                     example = self.info.features.encode_example(record)\r\n-> 1110                     writer.write(example, key)\r\n   1111             finally:\r\n\r\n\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    341             if self._check_duplicates:\r\n--> 342                 self.check_duplicate_keys()\r\n    343                 # Re-intializing to empty list for next batch\r\n\r\n\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in check_duplicate_keys(self)\r\n    352             if hash in tmp_record:\r\n--> 353                 raise DuplicatedKeysError(key)\r\n    354             else:\r\n\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 519\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\nVersion: datasets==1.11.0","body":"## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n","comment_length":91,"text":"Keys should be unique error on code_search_net \n ## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n \n Hi! Got same error when loading other dataset:\r\n```python3\r\nload_dataset('wikicorpus', 'raw_en')\r\n```\r\n\r\ntb:\r\n```pytb\r\n---------------------------------------------------------------------------\r\nDuplicatedKeysError                       Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n   1109                     example = self.info.features.encode_example(record)\r\n-> 1110                     writer.write(example, key)\r\n   1111             finally:\r\n\r\n\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n    341             if self._check_duplicates:\r\n--> 342                 self.check_duplicate_keys()\r\n    343                 # Re-intializing to empty list for next batch\r\n\r\n\/opt\/conda\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py in check_duplicate_keys(self)\r\n    352             if hash in tmp_record:\r\n--> 353                 raise DuplicatedKeysError(key)\r\n    354             else:\r\n\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 519\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\nVersion: datasets==1.11.0","embeddings":[-0.05065668,-0.0265310202,-0.0967089161,0.3376669586,0.0938270018,-0.0051850188,0.209637031,0.2723225057,0.0978665054,0.0846779868,-0.0767664909,0.3797584176,-0.1855772287,0.1312500238,0.1376769692,0.0704786927,-0.0451734737,0.1832596064,0.1311877966,-0.0342166089,-0.3247793019,0.277204752,-0.2256615758,0.010591249,-0.2162512988,-0.1412390471,-0.1852134913,0.2377099842,-0.1705391407,-0.6306680441,0.2620755136,0.2049673349,-0.2932028174,0.3494946063,-0.0001131814,0.1847909689,0.3866307437,-0.0220922306,-0.2522614896,-0.1984636784,-0.0995154232,-0.0947171897,0.0844924301,-0.2655925751,-0.0616117455,-0.1218316779,-0.0980843827,-0.563854754,0.8383651972,0.2186895013,0.2488029152,0.1780722141,0.3565167189,-0.2654960454,0.2333432734,-0.1770569235,-0.0573638491,0.3910858035,0.2137886882,-0.046179913,0.0189008489,0.2944657803,0.1549513191,-0.0945589989,0.4434221685,-0.0574329123,0.0748626143,-0.3529263735,0.2176122218,0.0829745308,0.1021128893,-0.2907671332,-0.224451527,-0.2823086083,0.0506774113,-0.2336997837,0.3289036155,0.1434268206,-0.2258563042,0.1656320989,-0.0085747242,-0.251427263,0.0117150955,-0.0579374619,0.0218853708,-0.0263175592,-0.0522210151,0.1410633177,0.1878550053,-0.0514880866,0.2746832967,-0.066602625,0.0352339335,0.1945719272,-0.5258197784,0.1267573237,-0.044437319,-0.1213004068,0.2348861694,0.0344490856,0.1818594038,-0.1160668433,0.0746419951,0.0022266628,0.0957321972,0.1307920367,0.014150287,0.2462746054,0.1825146526,-0.1548223495,-0.2489557713,-0.055968482,-0.1899846941,-0.1946251988,0.3649508655,0.0828335583,0.5289831758,0.086161226,-0.2361945361,0.1337899566,-0.1345017403,-0.0975798815,0.0642859116,0.3334595859,0.1980132908,-0.3291807473,0.1161269173,0.2278708071,-0.1273085922,-0.1955416352,-0.3080868721,-0.0857724398,-0.0730136856,-0.1487104446,0.2354016155,-0.6151371598,0.2906035185,0.2476571947,-0.1106656343,-0.5036480427,0.0178255811,0.103980951,-0.2419441491,0.078496024,0.0232211147,0.0666555315,-0.0222959407,-0.2303782851,-0.0598928668,0.1667026579,-0.2588890791,-0.375463903,-0.0666591451,0.2756346166,0.0991151854,0.1002610549,0.0241976697,0.0435334444,0.2254664153,0.1764665097,-0.0194035489,-0.0314910486,-0.0847402588,-0.2560476065,0.261962533,0.6545096636,-0.0967016146,-0.0910337567,-0.1727345586,0.0162480902,0.0978435725,0.3790159523,-0.2228413671,0.4225227237,-0.3517396152,0.1507118344,0.7040376067,-0.1903835386,-0.3690498769,0.1839616001,-0.25062567,-0.2029010057,0.1125364676,0.011254319,0.166931361,0.1460940689,0.1736392975,-0.0832218081,0.0437686481,-0.0466866717,-0.3396582007,-0.378565073,0.1158137321,-0.0809869766,0.1927709728,-0.1396760941,0.2820678353,-0.1008895338,0.106518738,-0.1311348677,-0.0840880349,0.085260585,0.2358700037,0.0379792601,0.0610436015,-0.0941919982,-0.4492209256,0.375194788,-0.2969653308,-0.2601015866,-0.2236411721,-0.2457150519,-0.4040378928,0.1304817796,-0.5598601699,-0.0123337079,0.1953626424,-0.026377935,0.2334782481,0.094678469,-0.1328272969,0.1824873984,0.0621087104,0.0786608309,-0.2175976783,0.2431551814,-0.4592080414,-0.0629118904,0.0995280519,0.2300848365,0.3229334354,-0.0937632397,-0.1757675558,0.4119703174,-0.2277362198,-0.0013415141,-0.000053395,-0.1339236647,0.0696881488,-0.3773822188,0.1881813556,0.2873413563,0.1758788228,-0.1463004947,-0.0467566364,0.3089581728,-0.0962026119,0.0982418284,-0.0553944409,-0.0379563756,0.1037116274,-0.1151415408,0.2339863181,-0.1282823086,0.3135376573,-0.3360981941,0.3928044736,0.1252299547,-0.2248101681,0.1092213914,0.2842074037,0.0226602666,0.1894483566,0.0273580495,0.0480376966,-0.1708833426,-0.3229432404,0.2553579807,0.3024672568,0.1463909,-0.13264139,0.2653288841,0.0214540493,-0.1830316931,0.3622650206,-0.1740483046,-0.1003461033,0.4041172564,0.0908254161,0.0271128844,-0.4047189355,-0.1392025799,0.1715613306,0.1138504818,-0.2378466725,0.0071048629,-0.1528921574,-0.0564409643,-0.4925183058,0.5608199239,-0.1072300524,-0.1246385574,0.2069615126,0.0719944164,0.1526001841,0.1005075648,-0.2353828847,0.1593100876,0.0551266931,0.1080132574,-0.1970785707,-0.0866711512,-0.0614470541,0.0214707665,0.3701880574,-0.1035158187,0.5540136099,-0.1882575303,0.1039422974,-0.2396972924,-0.3182742596,-0.0383764543,0.0301017147,0.1602738202,0.0871214345,-0.0271787085,-0.0237962883,-0.3870374262,0.2663693428,-0.2896523178,-0.4052238166,-0.0074309725,-0.0020919775,-0.0275115389,-0.2106699198,-0.5379372239,-0.2609393895,-0.2703044415,0.3129994869,-0.1199052706,0.0880830139,0.3569857478,-0.0480923764,-0.0172877945,-0.0212365892,0.1838794798,-0.3261013627,0.0197959747,0.033879146,-0.0585760958,-0.3170383871,-0.074226886,-0.1409898698,0.1756734997,-0.1075962782,-0.3542785645,0.286362946,-0.2775952816,0.4044704735,-0.2260819674,0.3047446311,0.1968455315,0.256400764,-0.1357321888,-0.1624037623,-0.0287843235,0.1043877974,-0.1742865741,0.074448064,0.1234032661,0.3143497109,-0.2439942062,0.8015075326,0.601149261,0.0021506308,0.2192438692,-0.2171153575,0.3666773736,-0.2702762485,-0.4478713572,-0.2633076906,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2552","title":"Keys should be unique error on code_search_net","comments":"The wikicorpus issue has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2844\r\n\r\nWe'll do a new release of `datasets` soon :)","body":"## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n","comment_length":17,"text":"Keys should be unique error on code_search_net \n ## Describe the bug\r\nLoading `code_search_net` seems not possible at the moment.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n>>> load_dataset('code_search_net')\r\nDownloading: 8.50kB [00:00, 3.09MB\/s]                                                                                                                                           \r\nDownloading: 19.1kB [00:00, 10.1MB\/s]                                                                                                                                           \r\nNo config specified, defaulting to: code_search_net\/all\r\nDownloading and preparing dataset code_search_net\/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to \/Users\/thomwolf\/.cache\/huggingface\/datasets\/code_search_net\/all\/1.0.0\/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...\r\nTraceback (most recent call last):         \r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/builder.py\", line 1067, in _prepare_split\r\n    writer.write(example, key)\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 343, in write\r\n    self.check_duplicate_keys()\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/datasets\/arrow_writer.py\", line 354, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 48\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.1.dev0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n \n The wikicorpus issue has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2844\r\n\r\nWe'll do a new release of `datasets` soon :)","embeddings":[-0.0742809549,-0.0351793021,-0.086682789,0.3581603765,0.0623259954,-0.0354929715,0.1684754789,0.2639017701,0.0927660689,0.1236139983,-0.0445139334,0.3784416318,-0.1519159526,0.1347224712,0.1137825847,0.0393825993,-0.0278353021,0.1998654604,0.1008107662,-0.0348957106,-0.348711282,0.3278391957,-0.2847043276,0.0620546378,-0.2731282413,-0.1773471981,-0.1778877974,0.200719893,-0.2140284181,-0.6042189598,0.2684114873,0.2027064562,-0.2295345366,0.379314661,-0.0001112176,0.1849371642,0.3967281282,-0.0099956645,-0.252895385,-0.1352210194,-0.0593605377,-0.0799274743,0.1142255887,-0.3115119338,-0.0552829355,-0.1050350145,-0.0662939399,-0.4870356917,0.8415837288,0.2476684451,0.2632397115,0.2291175723,0.3477338552,-0.2014206797,0.258848995,-0.2904579937,-0.082405217,0.3073558211,0.1880030036,-0.0712194666,-0.0259935986,0.3081966639,0.1307695657,-0.0733631998,0.3915862739,-0.0154980402,0.0570181794,-0.3279979229,0.2298202515,0.1004076973,0.1076005101,-0.2757329941,-0.1830676496,-0.2352993786,0.0456514582,-0.2553208768,0.3790766001,0.1081376746,-0.1849624813,0.1618368924,0.018458413,-0.1499607563,0.0437140539,-0.0016606788,-0.051175721,0.1737132818,-0.0323066302,0.1444085538,0.1939402521,-0.0547873341,0.3041885793,-0.0899312124,0.0066322144,0.1831382215,-0.5206641555,0.1190481037,-0.0558351502,-0.0297692921,0.188243255,-0.0209248234,0.1625713557,-0.0958015844,0.0999090895,0.0181384943,0.1590435803,0.0995517075,0.0560125671,0.262648195,0.2037771493,-0.1440712363,-0.2874125242,-0.0467572995,-0.1567611247,-0.1744732261,0.3424735665,0.0209654979,0.4984278679,0.0877819881,-0.1772675961,0.0851962641,-0.1482088268,-0.1240128204,0.0419483893,0.3618213236,0.1723600775,-0.2316883355,0.1341062337,0.2210865617,-0.1648888886,-0.0792284459,-0.3258473575,-0.0771105886,-0.0448642634,-0.150892809,0.2584308684,-0.5565131903,0.3257267475,0.216427207,-0.0575943179,-0.5052257776,0.066263631,0.1426427811,-0.2543682158,0.1114748195,0.0145561239,0.0953841284,-0.0699025765,-0.2688690126,-0.033451952,0.1893269569,-0.2687191963,-0.3343837559,-0.120107539,0.290027231,0.0964327157,0.0909281671,-0.0038086718,-0.0060241935,0.1928693354,0.1217826903,0.0052761976,-0.0563435927,-0.0965555757,-0.2477224767,0.2557833791,0.5845033526,-0.0346923061,-0.1073500365,-0.1453454643,-0.047122445,0.09786053,0.3257711828,-0.256755501,0.4472723305,-0.3528083265,0.2633313835,0.688100636,-0.244620651,-0.3804998696,0.0933755264,-0.2463257015,-0.1834537238,0.0795573816,0.0523541681,0.1828671992,0.1490884125,0.114357844,-0.0442781225,0.1039808318,-0.0017024446,-0.3555277586,-0.4046012759,0.1209371164,-0.0609739199,0.1574059725,-0.1855020672,0.2923003137,-0.0762466267,0.1631142497,-0.1323100775,-0.1166701168,0.0497569442,0.1757787764,0.0060181851,0.0691934004,-0.0537211634,-0.4638299644,0.3711787164,-0.3174013495,-0.2441738099,-0.2198593169,-0.2543005347,-0.4006926417,0.1687960774,-0.571015954,-0.0822550878,0.2377775311,-0.0140658766,0.1930306405,0.0721662194,-0.1365028918,0.1315037161,0.0369368494,0.0550393388,-0.203536734,0.2733279765,-0.4755397141,-0.0901231393,0.1020118743,0.2486847192,0.3709596694,-0.0731649026,-0.1867815852,0.398981601,-0.1945809573,0.0200651065,0.0209246557,-0.1216245145,0.058594767,-0.3596253693,0.1598382741,0.2624950409,0.1872991323,-0.2088916302,-0.0540271401,0.3151096404,-0.068915613,0.1212315932,-0.0756698549,-0.0245238524,0.1108407378,-0.1342621893,0.2704889178,-0.1226503253,0.2525312006,-0.3048864901,0.2955518365,0.1302850693,-0.1617019475,0.1204012707,0.296366334,-0.016450977,0.1959628314,-0.0138482805,0.0177933909,-0.1833081543,-0.2783831656,0.2625537217,0.2929568589,0.1389880627,-0.0898682252,0.2467435449,0.0580504611,-0.1775527,0.3787803352,-0.1610396206,-0.1312910765,0.3710986972,0.0966158137,-0.0010818283,-0.3934862316,-0.0888896585,0.1221456006,0.1192339733,-0.1838064343,-0.0478310101,-0.1479789168,-0.0141880084,-0.4854323864,0.55943048,-0.1174688265,-0.2091077268,0.2476030886,0.1138995141,0.1475590765,0.1345303059,-0.2055603117,0.1488521695,0.0309208147,0.0777810737,-0.24337551,-0.1421609074,-0.0806783065,0.0434354879,0.3722387254,-0.0059759771,0.5033736825,-0.1726587117,0.1110564321,-0.2709143162,-0.3622726202,-0.011248257,0.0630451441,0.1775150448,0.1069365516,0.0299363397,-0.049996499,-0.4720452726,0.2490351945,-0.323165834,-0.4180160761,-0.0479350127,0.0075530084,-0.0420717336,-0.2085278779,-0.5222279429,-0.2750418484,-0.2573629022,0.2600233853,-0.0206884667,0.0890500769,0.2367381603,-0.024525512,0.0394696817,-0.0720371976,0.1314702928,-0.3177736998,0.0144972801,0.0866977721,-0.0611512884,-0.3245949149,-0.0370558761,-0.1435474306,0.208547473,-0.1518795043,-0.4038965106,0.221521616,-0.2699886858,0.4851068556,-0.1768900305,0.3683383167,0.2399503887,0.2866680324,-0.1356308162,-0.1880393475,-0.0889881849,0.0879557952,-0.2125381529,0.0945468917,0.0932359025,0.2151426226,-0.3133164048,0.8754813671,0.541955471,0.0103153884,0.1690273732,-0.249479875,0.3023969829,-0.2638945282,-0.401509583,-0.1732810736,0.0919193178,0.10003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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2549","title":"Handling unlabeled datasets","comments":"Hi @nelson-liu,\r\n\r\nYou can pass the parameter `features` to `load_dataset`: https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/load.html#load_dataset\r\n\r\nIf you look at the code of the MNLI script you referred in your question (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/multi_nli\/multi_nli.py#L62-L77), you can see how the Features were originally specified. \r\n\r\nFeel free to use it as a template, customize it and pass it to `load_dataset` using the parameter `features`.","body":"Hi!\r\n\r\nIs there a way for datasets to produce unlabeled instances (e.g., the `ClassLabel` can be nullable).\r\n\r\nFor example, I want to use the MNLI dataset reader ( https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/multi_nli\/multi_nli.py ) on a file that doesn't have the `gold_label` field. I tried setting `\"label\": data.get(\"gold_label\")`, but got the following error:\r\n\r\n```\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 989, in _prepare_split\r\n    example = self.info.features.encode_example(record)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 953, in encode_example\r\n    return encode_nested_example(self, example)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 848, in encode_nested_example\r\n    k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 848, in <dictcomp>\r\n    k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 875, in encode_nested_example\r\n    return schema.encode_example(obj)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 653, in encode_example\r\n    if not -1 <= example_data < self.num_classes:\r\nTypeError: '<=' not supported between instances of 'int' and 'NoneType'\r\n```\r\n\r\nWhat's the proper way to handle reading unlabeled datasets, especially for downstream usage with Transformers?","comment_length":55,"text":"Handling unlabeled datasets \n Hi!\r\n\r\nIs there a way for datasets to produce unlabeled instances (e.g., the `ClassLabel` can be nullable).\r\n\r\nFor example, I want to use the MNLI dataset reader ( https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/multi_nli\/multi_nli.py ) on a file that doesn't have the `gold_label` field. I tried setting `\"label\": data.get(\"gold_label\")`, but got the following error:\r\n\r\n```\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 989, in _prepare_split\r\n    example = self.info.features.encode_example(record)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 953, in encode_example\r\n    return encode_nested_example(self, example)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 848, in encode_nested_example\r\n    k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 848, in <dictcomp>\r\n    k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 875, in encode_nested_example\r\n    return schema.encode_example(obj)\r\n  File \"\/home\/nfliu\/miniconda3\/envs\/debias\/lib\/python3.7\/site-packages\/datasets\/features.py\", line 653, in encode_example\r\n    if not -1 <= example_data < self.num_classes:\r\nTypeError: '<=' not supported between instances of 'int' and 'NoneType'\r\n```\r\n\r\nWhat's the proper way to handle reading unlabeled datasets, especially for downstream usage with Transformers? \n Hi @nelson-liu,\r\n\r\nYou can pass the parameter `features` to `load_dataset`: https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/load.html#load_dataset\r\n\r\nIf you look at the code of the MNLI script you referred in your question (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/multi_nli\/multi_nli.py#L62-L77), you can see how the Features were originally specified. \r\n\r\nFeel free to use it as a template, customize it and pass it to `load_dataset` using the parameter 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2548","title":"Field order issue in loading json","comments":"Hi @luyug, thanks for reporting.\r\n\r\nThe good news is that we fixed this issue only 9 days ago: #2507.\r\n\r\nThe patch is already in the master branch of our repository and it will be included in our next `datasets` release version 1.9.0.\r\n\r\nFeel free to reopen the issue if the problem persists.","body":"## Describe the bug\r\nThe `load_dataset` function expects columns in alphabetical order when loading json files.\r\n\r\nSimilar bug was previously reported for csv in #623 and fixed in #684.\r\n## Steps to reproduce the bug\r\n\r\nFor a json file `j.json`,\r\n```\r\n{\"c\":321, \"a\": 1, \"b\": 2}\r\n```\r\nRunning the following,\r\n```\r\nf= datasets.Features({'a': Value('int32'), 'b': Value('int32'), 'c': Value('int32')})\r\njson_data = datasets.load_dataset('json', data_files='j.json', features=f)\r\n```\r\n\r\n\r\n## Expected results\r\nA successful load.\r\n## Actual results\r\n```\r\nFile \"pyarrow\/table.pxi\", line 1409, in pyarrow.lib.Table.cast\r\nValueError: Target schema's field names are not matching the table's field names: ['c', 'a', 'b'], ['a', 'b', 'c']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyArrow version: 3.0.0\r\n\r\n","comment_length":52,"text":"Field order issue in loading json \n ## Describe the bug\r\nThe `load_dataset` function expects columns in alphabetical order when loading json files.\r\n\r\nSimilar bug was previously reported for csv in #623 and fixed in #684.\r\n## Steps to reproduce the bug\r\n\r\nFor a json file `j.json`,\r\n```\r\n{\"c\":321, \"a\": 1, \"b\": 2}\r\n```\r\nRunning the following,\r\n```\r\nf= datasets.Features({'a': Value('int32'), 'b': Value('int32'), 'c': Value('int32')})\r\njson_data = datasets.load_dataset('json', data_files='j.json', features=f)\r\n```\r\n\r\n\r\n## Expected results\r\nA successful load.\r\n## Actual results\r\n```\r\nFile \"pyarrow\/table.pxi\", line 1409, in pyarrow.lib.Table.cast\r\nValueError: Target schema's field names are not matching the table's field names: ['c', 'a', 'b'], ['a', 'b', 'c']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyArrow version: 3.0.0\r\n\r\n \n Hi @luyug, thanks for reporting.\r\n\r\nThe good news is that we fixed this issue only 9 days ago: #2507.\r\n\r\nThe patch is already in the master branch of our repository and it will be included in our next `datasets` release version 1.9.0.\r\n\r\nFeel free to reopen the issue if the problem persists.","embeddings":[0.1825091839,0.2316938639,-0.0159273576,0.2105989754,0.329020232,-0.0630036443,0.2235180438,0.4288536906,0.0090349196,0.0226684678,0.0754536316,0.6574710608,0.3253482878,-0.0124667594,-0.0245099887,-0.10777089,-0.0028557745,0.2404144555,-0.0928326845,0.0957933143,-0.3594990075,0.0710984692,-0.169530943,0.1843690276,0.1127448007,0.205004558,0.1421498358,0.1688929945,-0.1974498183,-0.4280341864,-0.1163161322,0.1603664458,0.0409124792,0.1538206637,-0.0001129423,-0.0930031985,0.2057088763,0.044096224,-0.1617198884,-0.4524683654,-0.1871806234,-0.2486564964,0.1546484977,-0.2271186858,-0.0396723188,-0.2239807397,-0.0603111573,0.055517558,0.1781316847,0.0954668894,0.191365093,0.1215380579,0.1931028664,0.1893153936,0.5152660608,-0.0020206263,-0.1111088321,0.2756317854,0.1847001314,-0.0094251689,-0.0758256093,0.0324886329,-0.1814019382,0.0318687186,0.1560178697,0.1483386159,-0.0425271988,0.087413609,-0.099468179,0.0486950763,0.5981535316,-0.4596160352,-0.2934252918,-0.200739786,0.1998674721,-0.2957478464,0.507579565,0.1562162042,0.2286590934,0.0513668843,0.0142116835,0.2856676579,-0.185043633,0.1023253724,-0.1568431407,0.1329457015,0.0515414216,-0.0219508745,-0.1376312226,-0.2529479861,0.3639647365,-0.2273322046,-0.1900892556,0.1349727213,-0.3493430018,0.0503967255,0.135819599,-0.0709700063,0.1523932964,0.1112184078,-0.0516666435,0.0156818945,0.0222560447,0.1730181277,0.2025149465,0.1798478961,0.0475179255,-0.0730152875,0.1967939883,0.0729970485,-0.062958777,-0.0940551385,-0.012366672,-0.195101589,-0.0103183212,0.0408960693,0.0609145984,-0.0840106681,-0.2918557525,0.0429290719,-0.124547407,-0.1908009052,-0.0035287847,0.3132716715,-0.0751944855,0.0949023589,0.0929289013,0.2363107204,0.0139913876,-0.0071011106,-0.0657398924,-0.1489697695,-0.0229369029,0.115148738,0.216383785,0.1122139841,0.1306640655,0.3509099782,0.0715359002,-0.0543669574,-0.047882814,-0.1265518814,-0.1324493438,0.4157491028,0.0547996126,0.1828192919,-0.0814390033,-0.1750667989,-0.2666810155,0.3741872311,-0.4962205291,-0.1093590036,-0.0579978302,0.2085763216,-0.1713588089,-0.1254500598,-0.6543222666,-0.1421352774,0.0294263437,-0.2914305627,-0.0671935529,-0.1449342221,0.1742371023,-0.2461764216,-0.1334021091,0.0312421825,-0.5555409789,0.0891386792,0.090237312,0.0520497598,-0.0067552254,-0.0432028584,-0.1329811662,0.1114545092,-0.0728556961,0.4425647259,0.0826909915,-0.2095859349,-0.3545516431,0.100312449,-0.18328996,0.1564613581,0.0873684362,0.0080047539,0.1473624557,0.0594875179,0.086240083,0.3296462893,0.1209139526,0.3840726018,-0.1493302286,-0.1144720092,0.1772043109,0.2748941481,-0.3706638515,0.0561122,0.1017419025,-0.3683663011,0.1644266546,-0.2329896688,-0.2976112962,0.2651005387,0.138519451,0.1046695411,0.292607367,0.0249064788,-0.4560880363,0.2608802319,0.0239530224,-0.0398318134,-0.5410388112,-0.1111306474,-0.022122426,0.0373545773,-0.2153449506,0.4330096543,0.1082401946,0.0037269872,-0.2064828724,-0.0993746668,-0.0000993488,0.0159663931,-0.1661820263,0.0472490974,-0.3866334856,0.454411447,-0.2604219317,-0.1414031982,0.1588269919,0.0324664339,0.2302994281,-0.2600081265,-0.145140484,0.2956563532,0.2853767574,0.0675241351,-0.2429714054,0.0512437634,-0.0149855288,0.0517478958,-0.1420660764,0.132409662,0.0144065954,-0.0093706083,-0.0859903172,0.7995302677,-0.0237492621,0.2128822505,0.0033282584,-0.3676089942,0.3820781112,0.0352012217,0.0164426453,-0.0694161803,0.3017965853,0.5649797916,-0.1108657792,0.2574886978,-0.360769093,0.1042413488,0.941155076,-0.125324294,-0.1056051776,0.0731294304,0.2090286911,0.0162601694,0.0963968784,-0.1628759503,0.3760441542,0.1892888546,-0.6180878282,-0.0649496317,0.0113626365,-0.1525395066,0.0734227449,-0.0244072117,0.2099510282,0.398982197,0.4225431979,0.0975220725,-0.389383018,-0.1733162105,0.1060144082,0.0876098499,-0.6017731428,0.0633966029,-0.3623118103,0.0237795599,-0.1168357581,-0.2730719745,-0.202742666,-0.0075551504,0.0238268878,0.265352428,-0.1859909743,0.2071102113,0.1384101808,0.0389135219,0.0704559311,-0.3349529803,-0.5142762065,-0.0779049844,-0.177746594,0.0132324751,0.1084139347,0.0567992218,0.0161678959,-0.1116787121,-0.0580410548,0.0624616817,-0.5082748532,-0.0208232459,-0.2801716924,0.0598618537,0.0490928814,0.2172631621,0.0719870105,-0.3592016399,0.2828599215,0.1679742932,-0.4280129969,0.5928493738,-0.1754595935,-0.2120300084,-0.1437628865,-0.4086635113,0.1344608366,-0.1835444719,0.257566154,0.2847779989,0.1752562523,-0.1098787487,-0.1036997512,-0.3340315521,-0.0910992697,0.0293778107,-0.0326463729,-0.0653820187,0.3305379152,-0.1414422691,-0.2782587409,-0.0305189118,-0.0579182319,-0.3105297685,0.0535399392,-0.3014820814,-0.0857230425,-0.0933257565,0.4113368094,0.061827153,-0.1718056351,0.1675982773,0.1254292279,-0.0224112235,-0.0237901509,-0.0857908353,-0.0717317909,0.3714417517,0.1108713374,-0.1400422752,0.6356081367,-0.0889731348,0.2890284061,-0.2067594528,0.0643243119,0.22384049,-0.1482941955,-0.0879818723,-0.2037138343,0.1653652936,-0.0510773435,-0.0864713416,0.22163262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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2547","title":"Dataset load_from_disk is too slow","comments":"Hi ! It looks like an issue with the virtual disk you are using.\r\n\r\nWe load datasets using memory mapping. In general it makes it possible to load very big files instantaneously since it doesn't have to read the file (it just assigns virtual memory to the file on disk).\r\nHowever there happens to be issues with virtual disks (for example on spot instances), for which memory mapping does a pass over the entire file, and this takes a while. We are discussing about this issue here: #2252 \r\n\r\nMemory mapping is something handled by the OS so we can't do much about it, though we're still trying to figure out what's causing this behavior exactly to see what we can do.","body":"@lhoestq \r\n## Describe the bug\r\nIt's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example). \r\n\r\n## Steps to reproduce the bug\r\nJust get the oscar in  spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset.\r\n\r\n## Expected results\r\nI expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time. \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Ubuntu 18\r\n- Python version: 3.8\r\n\r\n\r\nI've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model. ","comment_length":121,"text":"Dataset load_from_disk is too slow \n @lhoestq \r\n## Describe the bug\r\nIt's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example). \r\n\r\n## Steps to reproduce the bug\r\nJust get the oscar in  spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset.\r\n\r\n## Expected results\r\nI expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time. \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Ubuntu 18\r\n- Python version: 3.8\r\n\r\n\r\nI've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model.  \n Hi ! It looks like an issue with the virtual disk you are using.\r\n\r\nWe load datasets using memory mapping. In general it makes it possible to load very big files instantaneously since it doesn't have to read the file (it just assigns virtual memory to the file on disk).\r\nHowever there happens to be issues with virtual disks (for example on spot instances), for which memory mapping does a pass over the entire file, and this takes a while. We are discussing about this issue here: #2252 \r\n\r\nMemory mapping is something handled by the OS so we can't do much about it, though we're still trying to figure out what's causing this behavior exactly to see what we can 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2547","title":"Dataset load_from_disk is too slow","comments":"Okay, that's exactly my case, with spot instances... Therefore this isn't something we can change in any way to be able to load the dataset faster? I mean, what do you do internally at huggingface for being able to use spot instances with datasets efficiently?","body":"@lhoestq \r\n## Describe the bug\r\nIt's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example). \r\n\r\n## Steps to reproduce the bug\r\nJust get the oscar in  spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset.\r\n\r\n## Expected results\r\nI expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time. \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Ubuntu 18\r\n- Python version: 3.8\r\n\r\n\r\nI've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model. ","comment_length":45,"text":"Dataset load_from_disk is too slow \n @lhoestq \r\n## Describe the bug\r\nIt's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example). \r\n\r\n## Steps to reproduce the bug\r\nJust get the oscar in  spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset.\r\n\r\n## Expected results\r\nI expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time. \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Ubuntu 18\r\n- Python version: 3.8\r\n\r\n\r\nI've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model.  \n Okay, that's exactly my case, with spot instances... Therefore this isn't something we can change in any way to be able to load the dataset faster? I mean, what do you do internally at huggingface for being able to use spot instances with datasets efficiently?","embeddings":[-0.3196368515,-0.5587565303,-0.0620263182,0.516035676,0.232860297,0.0203786287,0.1246295944,0.1379356384,0.5566672683,0.1127059087,-0.0614803024,0.3583331406,-0.0153432656,0.2113305628,0.0768192783,-0.0829387158,0.2018542588,0.0174787268,0.0967767015,-0.0334709622,-0.0602658466,0.1847729385,-0.1777219623,-0.1801541001,-0.3432554603,-0.1481812596,0.0169770177,0.1757047623,-0.091173932,-0.3421683908,0.174323976,0.0779763013,0.1314018667,0.5717477202,-0.0001136536,-0.0459838435,0.1177239493,0.0748291686,-0.3427474797,0.0368377455,0.0366960578,-0.2794430852,0.152512759,-0.1323544234,-0.1332791746,0.1293530762,-0.0407350846,-0.3339373171,0.3590659499,-0.0157278776,0.1833483726,0.5802416205,-0.3182553649,-0.0626381412,-0.0680631995,0.0878693536,-0.2041863352,0.2897658646,0.4282694161,0.137509197,-0.1503990889,0.4081258178,-0.0401742682,0.1290538311,0.554825604,0.1066801548,-0.090983063,-0.1906510293,-0.1276057512,0.274615258,0.4216815233,-0.0359468348,-0.3770565987,-0.4575647116,0.0806800872,-0.3198552728,0.2432198226,0.1389402896,-0.0813618898,0.123814553,-0.1747931242,-0.1454377323,-0.0106413187,0.0115347812,0.3172792792,0.072094053,-0.1995429844,0.0143463109,0.4727319777,0.1806197912,-0.1624889672,-0.1308171749,0.1810790747,0.2825061083,-0.57771492,0.0114664305,0.0010847271,0.412504226,0.051838845,0.2784292996,0.3223172128,0.2358660251,-0.3069178164,0.0911345482,0.233955577,0.3614220321,-0.0564243272,-0.1345004886,0.3064207733,0.3840125203,-0.1135774478,0.0095585249,-0.2387743741,-0.1069194525,0.1238984764,-0.312969178,0.0145296305,-0.1244764552,-0.0202005524,-0.1086966917,0.1678449959,-0.1577461064,0.246166572,0.3766629696,-0.2775833607,0.5177403092,0.1202461943,-0.142570287,-0.4330539405,-0.1303923428,-0.1732846946,0.0804429352,-0.1950098425,0.2662882507,0.0701352134,-0.4665999115,0.2580045164,0.1210588962,0.2060399204,-0.2420329005,0.0148696592,-0.3456922174,-0.2548391819,0.1120524853,-0.0221589487,0.2393349111,0.003117749,0.0291967262,-0.3391521573,-0.1242933497,-0.3038776815,-0.5340563059,-0.1559813768,0.1528094262,-0.0333183035,0.0070495252,-0.6708773375,0.175240472,-0.0309536923,0.1613244861,-0.0681560263,0.0349203907,-0.4282566607,0.0129329655,0.4087439775,0.4829123616,-0.3364719748,-0.0092636868,-0.1738152951,0.0008567188,0.2438302189,0.6055168509,-0.2631828487,0.3598325253,-0.1960412264,0.0458980389,0.0975217521,-0.3626187742,-0.189652279,0.401699096,-0.0491989031,0.0704114214,0.1953025758,0.2154714763,0.0499428287,-0.0069148042,0.0264242832,0.4943832755,0.1099171937,0.3077587187,-0.3275250793,-0.2363768965,-0.0106486408,0.4756228328,-0.4628458619,-0.1358227432,-0.189200148,0.436730504,0.3493830264,-0.0706919506,-0.0973179042,0.2146663219,0.0543624461,0.1839344203,-0.0258474592,-0.0392578691,-0.3798598945,0.3749347329,0.2192986906,0.2074831128,0.0683715269,-0.1953056902,-0.2741436362,0.0775463134,-0.3143353462,0.015247494,-0.0685912594,0.0763565674,0.16917786,-0.0933396667,-0.3474593759,0.2462809086,-0.3357875049,0.0676223934,-0.4804140329,0.0079303021,0.0789154395,0.0794270784,-0.1203793883,0.0181700457,-0.0685081258,-0.0977360755,-0.091565758,0.3952661455,0.0936069041,0.4953532815,0.1984574646,0.1474683583,0.2836602628,0.1705910563,0.1966609508,-0.1005665436,0.332821548,-0.1249453798,-0.2328208238,0.3337990642,-0.1116134524,0.2934541702,0.148508504,-0.2852832675,0.1642889827,0.1510002911,-0.0583695024,-0.0630397275,0.4609430432,0.0204651263,0.6008419991,0.215366289,-0.4440368414,-0.043703191,0.4861674309,-0.0150728589,-0.0587383583,0.2730801105,0.0624162965,-0.3525634706,0.0695389658,-0.1610847116,0.2996737957,0.1361426562,-0.0543624014,0.0600378923,0.087023735,-0.1404526681,0.2030684352,-0.0107664689,0.3830636144,-0.0683272555,0.0322462134,-0.0401494056,-0.4450127482,-0.1755079329,-0.1217239648,0.1107884347,-0.2857523859,0.145692572,-0.0557505004,-0.0033409924,-0.2302478254,0.3559330106,-0.0930296034,-0.0899568051,-0.1406043768,0.3619558513,0.1214620918,0.0018907607,-0.043534968,0.4041738808,-0.034210328,-0.1786132604,-0.3653002083,-0.0047926516,-0.1858634949,-0.0485286415,0.2934470773,0.1590782553,0.2970312238,-0.2010054439,-0.1913919598,-0.2566900253,-0.0117398575,0.004149633,-0.0588130392,0.3551350534,0.0871948153,0.2114967257,-0.2618366182,-0.0664914399,0.2615098953,-0.294922173,-0.1493818164,-0.1307641417,-0.1245448589,0.1220935136,0.0281824488,-0.2825679779,-0.2752526104,-0.3055755496,0.3190359175,-0.0953012258,0.0536931753,-0.144734472,-0.0529215299,0.1388360262,-0.0915215611,0.1111416742,-0.1933532953,-0.6070045829,0.2168903053,0.0247757211,-0.2385846972,-0.1031488627,0.2235171795,0.1149047464,-0.1313179731,-0.5084902644,-0.3393250406,-0.3350628614,0.0989805162,-0.2253898531,0.152825743,0.1881372631,-0.1391521394,-0.0249838643,0.1779857278,-0.2847417593,-0.0286925212,0.0348592848,-0.0209886953,-0.0396118462,0.2691692412,-0.0004037227,0.468415916,0.1950828135,0.1571453214,0.4356394708,0.1224709377,0.1987567246,-0.403903991,-0.1974372566,0.0201035608,-0.2472994179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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2547","title":"Dataset load_from_disk is too slow","comments":"There are no solutions yet unfortunately.\r\nWe're still trying to figure out a way to make the loading instantaneous on such disks, I'll keep you posted","body":"@lhoestq \r\n## Describe the bug\r\nIt's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example). \r\n\r\n## Steps to reproduce the bug\r\nJust get the oscar in  spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset.\r\n\r\n## Expected results\r\nI expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time. \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Ubuntu 18\r\n- Python version: 3.8\r\n\r\n\r\nI've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model. ","comment_length":26,"text":"Dataset load_from_disk is too slow \n @lhoestq \r\n## Describe the bug\r\nIt's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example). \r\n\r\n## Steps to reproduce the bug\r\nJust get the oscar in  spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset.\r\n\r\n## Expected results\r\nI expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time. \r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.8.0\r\n- Platform: Ubuntu 18\r\n- Python version: 3.8\r\n\r\n\r\nI've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model.  \n There are no solutions yet unfortunately.\r\nWe're still trying to figure out a way to make the loading instantaneous on such disks, I'll keep you posted","embeddings":[-0.469478786,-0.2423017919,-0.1178942844,0.3941159248,0.2421814054,0.0529478155,0.1993055195,0.2657711208,0.4733243883,0.1050993577,0.0533883423,0.4494888484,-0.0244759563,0.0135968365,-0.0266761817,-0.013558601,0.2004590183,0.0515525267,0.2093926072,-0.1210182309,-0.166077584,0.2531619072,-0.1768193841,-0.2215415686,-0.2757839262,-0.1387374848,-0.0035660972,0.0403216183,-0.068663761,-0.3196170032,0.1828830093,0.1244310066,0.2284581363,0.4482245445,-0.00010555,-0.1314772964,0.133610636,0.0169427358,-0.3660498261,-0.1134866551,0.2358428538,-0.2445044667,0.1371787488,-0.2250014544,-0.0972655639,0.0251809154,-0.0460048281,-0.4642716348,0.2052056044,0.1093912497,0.2575610578,0.3051843643,-0.3067190647,-0.0636639223,-0.0051101944,0.0161650516,-0.234357208,0.2038806826,0.4900560379,0.1526582986,-0.1329111159,0.3433520794,-0.0540964566,0.0930550024,0.4101567566,0.007168029,0.0953570008,-0.2674491704,-0.011626713,0.2036076039,0.4939620197,-0.0521098524,-0.3432075679,-0.30284518,0.1475219429,-0.2496155351,0.2738335133,0.2413631976,-0.1156351566,0.1319803894,-0.0537608378,-0.187780425,-0.0633387938,0.0681013688,0.2679540813,0.2253524214,-0.0873984322,0.0108181862,0.4163218439,0.1147833914,0.2195016891,-0.2432810217,0.1509135514,0.374463439,-0.5127294064,-0.0124600101,-0.0313438661,0.2573702335,-0.0977262333,0.229055196,0.1536374092,0.2092105746,-0.1661462784,0.1606682539,0.1446455866,0.2459539473,-0.0865710378,-0.0402124599,0.2442095727,0.1976842284,-0.1823555082,-0.0017857405,-0.2736601233,-0.1554443985,0.2644675672,-0.2677514851,-0.1355288327,-0.1284794062,-0.0555531271,0.0388844013,0.1759007871,-0.1476335824,0.1911531687,0.3013442755,-0.2921019495,0.5728609562,0.0260151401,-0.0990992263,-0.3289968073,-0.0375294648,-0.1681725234,0.0735851973,-0.249655813,0.1538652331,0.1731598526,-0.2611287236,0.2046505958,0.0870450661,0.1814054102,-0.2219474912,0.2221283466,-0.4922517836,-0.115070492,0.1565223634,0.0439555049,0.2417901009,0.0218334626,-0.0940539241,-0.3755871356,0.1060028449,-0.3327483237,-0.4518424273,-0.2468843907,0.2598523498,0.0015027871,-0.040048454,-0.5704126954,0.1138264015,0.0026696075,0.0800517574,-0.1564571112,-0.0796247572,-0.2967770994,-0.0024338544,0.3181245327,0.3346172869,-0.4551331699,0.102175422,-0.2604034841,0.0749707818,0.3448600471,0.5159237981,-0.3132815063,0.2919723392,-0.1433934718,0.0827732757,0.2209132463,-0.2219853252,-0.2678492963,0.6216792464,0.0714554116,-0.0712878704,0.1130911037,0.2571855783,0.177236706,-0.0366671681,0.1337171644,0.5584762692,0.1512366235,0.2853977382,-0.3553659022,-0.2408134788,0.0228472352,0.5096572042,-0.2727359235,-0.1557220221,-0.1301405728,0.3853493333,0.3681561947,-0.1020006388,-0.0429258533,0.24489218,0.1422255635,0.0588854179,-0.0217141639,-0.0522179306,-0.209490642,0.3154984415,0.3402776122,0.105510138,0.0518874526,-0.1499329358,-0.2064654827,0.0269759055,-0.3376523852,0.0885742903,0.0651175827,0.0703162625,0.0686208978,-0.0590329617,-0.2992327809,0.2201518267,-0.3418131769,-0.1120349914,-0.2888448238,0.0647155195,-0.0210965723,0.0440994389,-0.0066615953,-0.1089289188,-0.015061412,-0.0243874881,-0.2034513652,0.3786915243,0.0924975276,0.5323041081,0.1840373278,-0.0338038206,0.1784894913,0.1857829392,0.1977801919,0.0105078947,0.3661213517,-0.1660588086,-0.1404550225,0.3742795587,-0.0605217814,0.2691720724,0.2077660859,-0.2657869756,0.3263538182,0.1359153837,-0.0913011804,-0.1102432013,0.4074093699,0.089459002,0.6007938385,0.1311140507,-0.3572582304,-0.0762512758,0.5789567828,0.0450012088,-0.0418994576,0.3076453805,0.0911244899,-0.3170955777,0.0501960255,0.1875695586,0.4310155809,0.1976147294,-0.0040011485,-0.0328989699,-0.0056909975,-0.2190225869,0.2751500607,-0.0797054172,0.498817414,0.0467041694,-0.0254617687,-0.006002218,-0.4627438784,-0.3295349777,-0.0532215647,0.1768090725,-0.0895202681,0.2188511938,-0.075361684,0.0838565305,-0.2535158098,0.2120685279,-0.0295272581,-0.1037035435,-0.1198300719,0.28434515,0.1204025373,0.0475351065,-0.1557546854,0.1716058105,-0.1020782739,-0.2200122178,-0.3387371898,-0.0934301838,-0.2868705392,0.0351008698,0.2899343073,0.0647917837,0.3139715791,-0.1468579024,-0.1171031594,-0.1601425558,-0.0196714327,-0.0257332213,-0.0159380417,0.1736114323,-0.0721044019,0.1581274718,-0.1802784801,-0.1895720959,0.1762481779,-0.2932653725,0.0095894625,-0.0883764476,-0.0860165283,-0.0133092999,-0.0503963567,-0.4648726583,-0.3632452488,-0.2891048789,0.0190057158,-0.0670994297,0.1013495997,-0.2616087496,0.0097601423,0.0619439259,0.1845126003,0.1032932326,-0.2362322658,-0.585478127,0.322601378,0.0049837912,-0.2711999714,0.0211331621,0.1947817206,0.2655994892,-0.0799174011,-0.538618207,-0.0461725965,-0.3027786314,0.0212117378,-0.3066812456,0.018114157,0.1055008546,-0.0077714929,-0.0813887194,0.0991656929,-0.169482708,-0.0149223125,-0.0797677115,0.083615467,-0.2301028073,0.2255464047,0.010092889,0.4175733924,0.055357825,0.0708941519,0.4242661297,0.243262589,0.1240621135,-0.3561916053,-0.1205060557,0.0345835239,-0.2498476803,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2543","title":"switching some low-level log.info's to log.debug?","comments":"Hi @stas00, thanks for pointing out this issue with logging.\r\n\r\nI agree that `datasets` can sometimes be too verbose... I can create a PR and we could discuss there the choice of the log levels for different parts of the code.","body":"In https:\/\/github.com\/huggingface\/transformers\/pull\/12276 we are now changing the examples to have `datasets` on the same log level as `transformers`, so that one setting can do a consistent logging across all involved components.\r\n\r\nThe trouble is that now we get a ton of these:\r\n\r\n```\r\n06\/23\/2021 12:15:31 - INFO - datasets.utils.filelock -   Lock 139627640431136 acquired on \/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock\r\n06\/23\/2021 12:15:31 - INFO - datasets.arrow_writer -   Done writing 50 examples in 12280 bytes \/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.\r\n06\/23\/2021 12:15:31 - INFO - datasets.arrow_dataset -   Set __getitem__(key) output type to python objects for no columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n06\/23\/2021 12:15:31 - INFO - datasets.utils.filelock -   Lock 139627640431136 released on \/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock\r\n```\r\n\r\nMay I suggest that these can be `log.debug` as it's no informative to the user.\r\n\r\nMore examples: these are not informative - too much information:\r\n```\r\n06\/23\/2021 12:14:26 - INFO - datasets.load -   Checking \/home\/stas\/.cache\/huggingface\/datasets\/downloads\/459933f1fe47711fad2f6ff8110014ff189120b45ad159ef5b8e90ea43a174fa.e23e7d1259a8c6274a82a42a8936dd1b87225302c6dc9b7261beb3bc2daac640.py for additional imports.\r\n06\/23\/2021 12:14:27 - INFO - datasets.builder -   Constructing Dataset for split train, validation, test, from \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a\r\n\r\n```\r\n\r\nWhile these are:\r\n```\r\n06\/23\/2021 12:14:27 - INFO - datasets.info -   Loading Dataset Infos from \/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a\r\n06\/23\/2021 12:14:27 - WARNING - datasets.builder -   Reusing dataset wmt16 (\/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)\r\n```\r\n\r\nI also realize that `transformers` examples don't have do use `info` for `datasets` to let the default `warning` keep logging to less noisy.\r\n\r\nBut I think currently the log levels are slightly misused and skewed by 1 level. Many `warnings` will better be `info`s and most `info`s be `debug`.\r\n\r\ne.g.:\r\n\r\n```\r\n06\/23\/2021 12:14:27 - WARNING - datasets.builder -   Reusing dataset wmt16 (\/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)\r\n```\r\n\r\nwhy is this a warning? it is informing me that the cache is used, there is nothing to be worried about. I'd have it as `info`.\r\n\r\nWarnings are typically something that's bordering error or the first thing to check when things don't work as expected.\r\n\r\ninfrequent info is there to inform of the different stages or important events.\r\n\r\nEverything else is debug.\r\n\r\nAt least the way I understand things. \r\n","comment_length":41,"text":"switching some low-level log.info's to log.debug? \n In https:\/\/github.com\/huggingface\/transformers\/pull\/12276 we are now changing the examples to have `datasets` on the same log level as `transformers`, so that one setting can do a consistent logging across all involved components.\r\n\r\nThe trouble is that now we get a ton of these:\r\n\r\n```\r\n06\/23\/2021 12:15:31 - INFO - datasets.utils.filelock -   Lock 139627640431136 acquired on \/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock\r\n06\/23\/2021 12:15:31 - INFO - datasets.arrow_writer -   Done writing 50 examples in 12280 bytes \/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.\r\n06\/23\/2021 12:15:31 - INFO - datasets.arrow_dataset -   Set __getitem__(key) output type to python objects for no columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n06\/23\/2021 12:15:31 - INFO - datasets.utils.filelock -   Lock 139627640431136 released on \/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock\r\n```\r\n\r\nMay I suggest that these can be `log.debug` as it's no informative to the user.\r\n\r\nMore examples: these are not informative - too much information:\r\n```\r\n06\/23\/2021 12:14:26 - INFO - datasets.load -   Checking \/home\/stas\/.cache\/huggingface\/datasets\/downloads\/459933f1fe47711fad2f6ff8110014ff189120b45ad159ef5b8e90ea43a174fa.e23e7d1259a8c6274a82a42a8936dd1b87225302c6dc9b7261beb3bc2daac640.py for additional imports.\r\n06\/23\/2021 12:14:27 - INFO - datasets.builder -   Constructing Dataset for split train, validation, test, from \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a\r\n\r\n```\r\n\r\nWhile these are:\r\n```\r\n06\/23\/2021 12:14:27 - INFO - datasets.info -   Loading Dataset Infos from \/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a\r\n06\/23\/2021 12:14:27 - WARNING - datasets.builder -   Reusing dataset wmt16 (\/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)\r\n```\r\n\r\nI also realize that `transformers` examples don't have do use `info` for `datasets` to let the default `warning` keep logging to less noisy.\r\n\r\nBut I think currently the log levels are slightly misused and skewed by 1 level. Many `warnings` will better be `info`s and most `info`s be `debug`.\r\n\r\ne.g.:\r\n\r\n```\r\n06\/23\/2021 12:14:27 - WARNING - datasets.builder -   Reusing dataset wmt16 (\/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)\r\n```\r\n\r\nwhy is this a warning? it is informing me that the cache is used, there is nothing to be worried about. I'd have it as `info`.\r\n\r\nWarnings are typically something that's bordering error or the first thing to check when things don't work as expected.\r\n\r\ninfrequent info is there to inform of the different stages or important events.\r\n\r\nEverything else is debug.\r\n\r\nAt least the way I understand things. \r\n \n Hi @stas00, thanks for pointing out this issue with logging.\r\n\r\nI agree that `datasets` can sometimes be too verbose... I can create a PR and we could discuss there the choice of the log levels for different parts of the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2542","title":"`datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa\/adversarialQA`","comments":"Hi @VictorSanh, thank you for reporting this issue with duplicated keys.\r\n\r\n- The issue with \"adversarial_qa\" was fixed 23 days ago: #2433. Current version of `datasets` (1.8.0) includes the patch.\r\n- I am investigating the issue with `drop`. I'll ping you to keep you informed.","body":"## Describe the bug\r\nFailure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"drop\")\r\nload_dataset(\"adversarial_qa\", \"adversarialQA\")\r\n```\r\n\r\n## Expected results\r\nThe examples keys should be unique.\r\n\r\n## Actual results\r\n```bash\r\n>>> load_dataset(\"drop\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset drop\/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to \/home\/hf\/.cache\/huggingface\/datasets\/drop\/default\/0.1.0\/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026...\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 992, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 409, in finalize\r\n    self.check_duplicate_keys()\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 349, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 28553293-d719-441b-8f00-ce3dc6df5398\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":45,"text":"`datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa\/adversarialQA` \n ## Describe the bug\r\nFailure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"drop\")\r\nload_dataset(\"adversarial_qa\", \"adversarialQA\")\r\n```\r\n\r\n## Expected results\r\nThe examples keys should be unique.\r\n\r\n## Actual results\r\n```bash\r\n>>> load_dataset(\"drop\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset drop\/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to \/home\/hf\/.cache\/huggingface\/datasets\/drop\/default\/0.1.0\/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026...\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 992, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 409, in finalize\r\n    self.check_duplicate_keys()\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 349, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 28553293-d719-441b-8f00-ce3dc6df5398\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n \n Hi @VictorSanh, thank you for reporting this issue with duplicated keys.\r\n\r\n- The issue with \"adversarial_qa\" was fixed 23 days ago: #2433. Current version of `datasets` (1.8.0) includes the patch.\r\n- I am investigating the issue with `drop`. I'll ping you to keep you informed.","embeddings":[-0.0905093253,-0.1160073131,0.00945515,0.470908016,0.0840874016,0.0286583472,0.3028091192,0.2397654057,0.1277973205,0.1814353019,-0.1439216137,0.5057473183,-0.048295714,0.1198933572,0.0266699288,-0.032785099,0.0012848077,0.0136723919,-0.2982798517,-0.0762103871,-0.2426401526,0.387050271,-0.2559149265,0.10335318,0.0598408356,-0.1042755619,-0.1572466195,0.3131027222,-0.0433670767,-0.2897949517,0.1407245398,0.0112546887,-0.2836559713,0.4620527029,-0.0001087736,-0.0179601684,0.1347592473,0.063810952,-0.4384645522,-0.1568871439,-0.2514858246,-0.0515594594,-0.0878485814,-0.2769972682,0.1303325295,-0.2024025172,-0.3193139136,-0.376553297,0.7059348822,0.0964307413,0.258453995,0.2994432449,0.0578671731,-0.2653636336,0.1903439015,0.03764534,-0.1994792819,0.2062627226,-0.0147559959,-0.0670088753,0.3541854918,0.160368681,-0.0145851849,-0.0506396517,0.2040350139,0.0678948611,0.0129833156,-0.1696084887,0.340441376,0.3239115477,0.2472787201,-0.5006279945,-0.2000300586,-0.2800831497,0.2844666243,-0.0895541832,0.1983643919,0.0411963165,-0.248807773,0.1426331103,0.066547215,0.1313625276,0.0587465577,-0.0792369768,0.1552489251,-0.0006441801,0.0497320481,0.0894895196,0.1573434621,-0.1723117828,-0.0185095444,-0.3647033572,0.0199675113,-0.1255145669,-0.5385659933,0.0129776504,-0.0629277751,0.0256692767,0.4364517331,0.0741026253,0.2214869559,-0.0984062999,-0.0482476018,0.0024116111,0.0399042964,0.3961126208,0.1762890518,0.0912107676,0.1504677236,0.1907208413,-0.2307083607,0.1641299576,0.0568992607,-0.1409792006,0.2863470614,0.1551881731,0.6009869576,-0.0693046823,-0.4248117208,0.2033160925,-0.1676324755,-0.124566488,-0.2327310294,0.1176542267,0.0935675427,-0.0219017286,-0.125678286,0.2016167492,-0.329765439,-0.1748307794,-0.2609870434,-0.0125090387,-0.0085719796,-0.0722294599,0.0838416889,-0.181197688,0.277915746,0.3087557852,-0.0046123383,-0.1379147619,0.2745801508,0.0929096639,-0.2666784823,0.0398336798,0.1705012321,0.0668969154,0.187929526,-0.3602023125,0.0600283071,0.1122215241,-0.0289515667,-0.1286867708,-0.1176909655,0.3158142269,0.0256931968,0.0972319841,-0.1766062826,0.1494122595,0.1119302288,0.0906810015,-0.0044025579,0.039072413,-0.1598896235,-0.4287461638,0.2847690582,0.7104182839,-0.3256116211,-0.1319526285,-0.1431197524,0.0301795825,0.1442250609,0.2293822169,-0.1759794354,0.2083805352,-0.3001039922,0.1173925847,0.0831043348,-0.342885524,-0.4917336702,0.2908804715,-0.3543904424,0.1900268048,0.1736855656,-0.0551680289,0.2153705508,-0.0025115306,0.011651733,-0.0165486876,-0.0907604471,-0.1353102922,-0.3101507425,-0.3366243541,0.1749132574,-0.2637753487,0.0284835584,0.0655985177,0.3573909402,-0.0704982579,0.2868715525,-0.0610246696,0.0467902683,0.0863300711,0.253734529,-0.0603276454,0.2205081582,-0.019394381,-0.5272825956,0.3890169859,-0.2856177986,0.0389644131,-0.1500831693,-0.2936635315,-0.5489760041,0.054138422,-0.4572040737,-0.2501839697,0.248770386,0.1914100796,-0.0284573697,0.1142127141,-0.064734377,0.2220266163,0.0449401326,0.0490150303,-0.4242556393,0.2599344254,-0.3817819953,0.052681651,0.083897166,0.2135799378,0.4154089987,-0.0532197431,-0.1252102405,0.3929850459,-0.0362856239,0.015139333,0.2010873556,-0.1390540749,0.2122402787,-0.1353643239,0.2662816644,0.107059367,0.183084175,-0.1398240626,-0.012119934,0.2761809826,-0.1850383729,0.1524389237,-0.0569083467,-0.0053153718,0.1371273845,-0.2301723808,0.1511038244,-0.2182911783,0.0741774589,-0.106757611,0.175869137,-0.0325058177,-0.1456296444,0.177807942,0.183447957,-0.1542737037,0.1718087494,-0.0479231402,0.0327788517,-0.0946388319,0.0258924644,0.5896530747,0.6832953095,0.1840275824,0.0930984914,0.1825769097,0.0101524424,-0.173652187,0.4018145204,-0.2306769639,-0.1879463941,0.3399558961,0.1338035613,0.0912758708,-0.3779127896,-0.0704192966,0.1394079626,0.2234371454,-0.2576568425,-0.0136110792,-0.4151098728,-0.0433590114,-0.1474149376,0.2553113997,-0.0163310636,-0.3288873732,0.1003978476,-0.1700997353,0.0378062688,0.2827537656,-0.080297403,0.2193176299,-0.0255129263,0.3116757572,-0.1784826368,0.0661221445,-0.2623886168,0.1238634214,0.0868652239,0.131130144,0.4407131076,-0.1406992227,-0.3182706237,-0.3026193976,-0.203552708,0.1379104704,-0.0104992753,0.3564635217,0.3850200176,0.0186122004,-0.1929819733,-0.357203126,0.2555490136,-0.0953965262,-0.4541634917,0.0896259025,-0.0987125114,0.0308767278,-0.1922013015,-0.5104909539,-0.1726352721,-0.2520100176,0.0773612335,-0.1438827962,0.1406255066,0.3311223984,-0.0303112864,0.1407981217,0.0260429103,0.0988141224,-0.4347645342,-0.1841461509,0.0602049045,-0.2762188315,-0.297478646,-0.0169336833,-0.1227035522,0.315713346,-0.1157890856,-0.4564087093,-0.3070779145,-0.2492566258,0.3409292698,-0.1840697676,0.2868571877,0.1675689518,0.1474985778,-0.1426562071,-0.2416717261,-0.0538782403,0.2804362774,-0.2117324024,0.040080253,0.1509509385,0.5182060599,0.0724675581,0.6133498549,0.5002933145,0.0882742405,0.2791177928,-0.0928221941,0.2891702354,-0.1708509326,-0.4779526591,-0.2943904102,0.1661699414,0.2061332017,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2542","title":"`datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa\/adversarialQA`","comments":"Hi @VictorSanh, the issue is already fixed and merged into master branch and will be included in our next release version 1.9.0.","body":"## Describe the bug\r\nFailure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"drop\")\r\nload_dataset(\"adversarial_qa\", \"adversarialQA\")\r\n```\r\n\r\n## Expected results\r\nThe examples keys should be unique.\r\n\r\n## Actual results\r\n```bash\r\n>>> load_dataset(\"drop\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset drop\/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to \/home\/hf\/.cache\/huggingface\/datasets\/drop\/default\/0.1.0\/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026...\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 992, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 409, in finalize\r\n    self.check_duplicate_keys()\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 349, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 28553293-d719-441b-8f00-ce3dc6df5398\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":22,"text":"`datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa\/adversarialQA` \n ## Describe the bug\r\nFailure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"drop\")\r\nload_dataset(\"adversarial_qa\", \"adversarialQA\")\r\n```\r\n\r\n## Expected results\r\nThe examples keys should be unique.\r\n\r\n## Actual results\r\n```bash\r\n>>> load_dataset(\"drop\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset drop\/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to \/home\/hf\/.cache\/huggingface\/datasets\/drop\/default\/0.1.0\/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026...\r\nTraceback (most recent call last):         \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 992, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 409, in finalize\r\n    self.check_duplicate_keys()\r\n  File \"\/home\/hf\/dev\/promptsource\/.venv\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 349, in check_duplicate_keys\r\n    raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 28553293-d719-441b-8f00-ce3dc6df5398\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n \n Hi @VictorSanh, the issue is already fixed and merged into master branch and will be included in our next release version 1.9.0.","embeddings":[-0.0905093253,-0.1160073131,0.00945515,0.470908016,0.0840874016,0.0286583472,0.3028091192,0.2397654057,0.1277973205,0.1814353019,-0.1439216137,0.5057473183,-0.048295714,0.1198933572,0.0266699288,-0.032785099,0.0012848077,0.0136723919,-0.2982798517,-0.0762103871,-0.2426401526,0.387050271,-0.2559149265,0.10335318,0.0598408356,-0.1042755619,-0.1572466195,0.3131027222,-0.0433670767,-0.2897949517,0.1407245398,0.0112546887,-0.2836559713,0.4620527029,-0.0001087736,-0.0179601684,0.1347592473,0.063810952,-0.4384645522,-0.1568871439,-0.2514858246,-0.0515594594,-0.0878485814,-0.2769972682,0.1303325295,-0.2024025172,-0.3193139136,-0.376553297,0.7059348822,0.0964307413,0.258453995,0.2994432449,0.0578671731,-0.2653636336,0.1903439015,0.03764534,-0.1994792819,0.2062627226,-0.0147559959,-0.0670088753,0.3541854918,0.160368681,-0.0145851849,-0.0506396517,0.2040350139,0.0678948611,0.0129833156,-0.1696084887,0.340441376,0.3239115477,0.2472787201,-0.5006279945,-0.2000300586,-0.2800831497,0.2844666243,-0.0895541832,0.1983643919,0.0411963165,-0.248807773,0.1426331103,0.066547215,0.1313625276,0.0587465577,-0.0792369768,0.1552489251,-0.0006441801,0.0497320481,0.0894895196,0.1573434621,-0.1723117828,-0.0185095444,-0.3647033572,0.0199675113,-0.1255145669,-0.5385659933,0.0129776504,-0.0629277751,0.0256692767,0.4364517331,0.0741026253,0.2214869559,-0.0984062999,-0.0482476018,0.0024116111,0.0399042964,0.3961126208,0.1762890518,0.0912107676,0.1504677236,0.1907208413,-0.2307083607,0.1641299576,0.0568992607,-0.1409792006,0.2863470614,0.1551881731,0.6009869576,-0.0693046823,-0.4248117208,0.2033160925,-0.1676324755,-0.124566488,-0.2327310294,0.1176542267,0.0935675427,-0.0219017286,-0.125678286,0.2016167492,-0.329765439,-0.1748307794,-0.2609870434,-0.0125090387,-0.0085719796,-0.0722294599,0.0838416889,-0.181197688,0.277915746,0.3087557852,-0.0046123383,-0.1379147619,0.2745801508,0.0929096639,-0.2666784823,0.0398336798,0.1705012321,0.0668969154,0.187929526,-0.3602023125,0.0600283071,0.1122215241,-0.0289515667,-0.1286867708,-0.1176909655,0.3158142269,0.0256931968,0.0972319841,-0.1766062826,0.1494122595,0.1119302288,0.0906810015,-0.0044025579,0.039072413,-0.1598896235,-0.4287461638,0.2847690582,0.7104182839,-0.3256116211,-0.1319526285,-0.1431197524,0.0301795825,0.1442250609,0.2293822169,-0.1759794354,0.2083805352,-0.3001039922,0.1173925847,0.0831043348,-0.342885524,-0.4917336702,0.2908804715,-0.3543904424,0.1900268048,0.1736855656,-0.0551680289,0.2153705508,-0.0025115306,0.011651733,-0.0165486876,-0.0907604471,-0.1353102922,-0.3101507425,-0.3366243541,0.1749132574,-0.2637753487,0.0284835584,0.0655985177,0.3573909402,-0.0704982579,0.2868715525,-0.0610246696,0.0467902683,0.0863300711,0.253734529,-0.0603276454,0.2205081582,-0.019394381,-0.5272825956,0.3890169859,-0.2856177986,0.0389644131,-0.1500831693,-0.2936635315,-0.5489760041,0.054138422,-0.4572040737,-0.2501839697,0.248770386,0.1914100796,-0.0284573697,0.1142127141,-0.064734377,0.2220266163,0.0449401326,0.0490150303,-0.4242556393,0.2599344254,-0.3817819953,0.052681651,0.083897166,0.2135799378,0.4154089987,-0.0532197431,-0.1252102405,0.3929850459,-0.0362856239,0.015139333,0.2010873556,-0.1390540749,0.2122402787,-0.1353643239,0.2662816644,0.107059367,0.183084175,-0.1398240626,-0.012119934,0.2761809826,-0.1850383729,0.1524389237,-0.0569083467,-0.0053153718,0.1371273845,-0.2301723808,0.1511038244,-0.2182911783,0.0741774589,-0.106757611,0.175869137,-0.0325058177,-0.1456296444,0.177807942,0.183447957,-0.1542737037,0.1718087494,-0.0479231402,0.0327788517,-0.0946388319,0.0258924644,0.5896530747,0.6832953095,0.1840275824,0.0930984914,0.1825769097,0.0101524424,-0.173652187,0.4018145204,-0.2306769639,-0.1879463941,0.3399558961,0.1338035613,0.0912758708,-0.3779127896,-0.0704192966,0.1394079626,0.2234371454,-0.2576568425,-0.0136110792,-0.4151098728,-0.0433590114,-0.1474149376,0.2553113997,-0.0163310636,-0.3288873732,0.1003978476,-0.1700997353,0.0378062688,0.2827537656,-0.080297403,0.2193176299,-0.0255129263,0.3116757572,-0.1784826368,0.0661221445,-0.2623886168,0.1238634214,0.0868652239,0.131130144,0.4407131076,-0.1406992227,-0.3182706237,-0.3026193976,-0.203552708,0.1379104704,-0.0104992753,0.3564635217,0.3850200176,0.0186122004,-0.1929819733,-0.357203126,0.2555490136,-0.0953965262,-0.4541634917,0.0896259025,-0.0987125114,0.0308767278,-0.1922013015,-0.5104909539,-0.1726352721,-0.2520100176,0.0773612335,-0.1438827962,0.1406255066,0.3311223984,-0.0303112864,0.1407981217,0.0260429103,0.0988141224,-0.4347645342,-0.1841461509,0.0602049045,-0.2762188315,-0.297478646,-0.0169336833,-0.1227035522,0.315713346,-0.1157890856,-0.4564087093,-0.3070779145,-0.2492566258,0.3409292698,-0.1840697676,0.2868571877,0.1675689518,0.1474985778,-0.1426562071,-0.2416717261,-0.0538782403,0.2804362774,-0.2117324024,0.040080253,0.1509509385,0.5182060599,0.0724675581,0.6133498549,0.5002933145,0.0882742405,0.2791177928,-0.0928221941,0.2891702354,-0.1708509326,-0.4779526591,-0.2943904102,0.1661699414,0.2061332017,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2538","title":"Loading partial dataset when debugging","comments":"Hi ! `load_dataset` downloads the full dataset once and caches it, so that subsequent calls to `load_dataset` just reloads the dataset from your disk.\r\nThen when you specify a `split` in `load_dataset`, it will just load the requested split from the disk. If your specified split is a sliced split (e.g. `\"train[:10]\"`), then it will load the 10 first rows of the train split that you have on disk.\r\n\r\nTherefore, as long as you don't delete your cache, all your calls to `load_dataset` will be very fast. Except the first call that downloads the dataset of course ^^","body":"I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits). \r\n\r\nEvery time I execute load_dataset  for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues.\r\n\r\nIs there a way to only load part of the dataset on load_dataset? This would really speed up my workflow.\r\nSomething like a debug mode would really help. Thanks!","comment_length":98,"text":"Loading partial dataset when debugging \n I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits). \r\n\r\nEvery time I execute load_dataset  for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues.\r\n\r\nIs there a way to only load part of the dataset on load_dataset? This would really speed up my workflow.\r\nSomething like a debug mode would really help. Thanks! \n Hi ! `load_dataset` downloads the full dataset once and caches it, so that subsequent calls to `load_dataset` just reloads the dataset from your disk.\r\nThen when you specify a `split` in `load_dataset`, it will just load the requested split from the disk. If your specified split is a sliced split (e.g. `\"train[:10]\"`), then it will load the 10 first rows of the train split that you have on disk.\r\n\r\nTherefore, as long as you don't delete your cache, all your calls to `load_dataset` will be very fast. Except the first call that downloads the dataset of course ^^","embeddings":[-0.3671891391,-0.1431681812,-0.0216082856,0.3622045815,-0.067530632,0.2756697237,0.5740375519,0.4168047011,0.2612759769,-0.0835103467,-0.082844846,0.1403204501,-0.1739207208,0.1820890456,0.0995866209,0.1087071076,-0.1426474005,0.3165223598,0.0734355226,-0.0784858689,0.1707005054,-0.26123932,-0.1748515218,-0.1787908077,-0.4388399422,0.0658321753,0.0724343061,0.1917069405,0.2921259999,-0.1966113895,0.3809176385,0.1237108037,0.0042912429,0.1477989405,-0.0001250374,0.0485777669,0.4530166984,-0.1177272201,-0.4413953125,0.0793153346,-0.237233296,-0.0172563251,0.4449100196,-0.0426382981,-0.0530504771,-0.187539041,0.0052164318,-0.3129993975,0.2829741836,-0.0120449858,0.0667552426,0.0641538724,-0.5506688356,0.1383746117,0.2196933031,-0.0400902294,0.0063698953,0.3839592934,0.4114600122,0.0257234722,-0.3189215958,0.143329367,-0.1303001493,0.2568044364,0.3651657701,-0.0175332408,0.016991362,-0.4844467342,0.277962625,0.3971960843,0.6139980555,0.0177978165,-0.324831605,-0.5615004301,0.0680839494,-0.1019032151,0.1404101104,-0.0329516903,-0.4404362738,-0.0019115366,-0.503777504,-0.1788008213,0.0656843781,0.1815218478,0.0742575899,-0.2963497937,0.0635769144,0.1199378818,0.4592187107,0.2257744819,0.3110461533,-0.0591473207,0.0867825672,0.1040572375,-0.3470100164,0.0552104115,-0.1715562642,-0.0937866569,-0.0360143371,0.5328012109,-0.0335619822,0.462310195,0.1792253256,0.0048949281,0.5818147659,-0.1044395044,0.1357749999,0.1604953557,0.3302040696,-0.3076428771,-0.0562027469,-0.0291394927,-0.4441228807,-0.1446470916,0.2911393344,-0.0331761204,-0.0609830953,-0.3301861286,-0.0984939113,-0.3457719386,0.1044795364,-0.027723629,0.2356433272,0.4508670866,0.0771400854,0.0577649698,-0.1905168742,0.0967730135,-0.2860884368,-0.0646092147,-0.1252984405,-0.179455772,-0.0533976667,0.1343159825,0.345648706,-0.3430443108,0.3036074042,-0.0918700993,0.4442437291,-0.1942007691,0.1545960009,-0.2542306483,-0.0301960651,0.1774717122,-0.0150245437,0.2808523476,0.1144268736,0.2564080954,-0.3054350317,0.4385035038,-0.114683114,-0.3874349892,0.0642130077,-0.0014648666,-0.3882314563,0.2442891449,-0.1981087327,0.2364187539,0.1910140961,-0.2338833213,-0.2300262004,0.0961127058,0.0001006437,-0.1139457971,0.6939179301,0.3801719248,-0.1435455978,-0.2851575911,-0.2555277944,0.0346329436,0.2755737603,0.2422008961,-0.4427700639,0.2324467003,-0.1369287521,-0.0750613958,0.5813103914,-0.0458763093,-0.3899812698,0.4592676461,-0.0660858899,0.2072698623,-0.0639574006,0.5403299332,0.2244986594,0.0629709959,0.1113066897,0.4323061109,-0.1434763521,0.1165145338,-0.2158845663,-0.1530449539,0.1313552409,0.4665034413,0.0372895077,0.1652179956,-0.0386094227,0.1996426433,0.1884550005,0.0003334567,-0.0666797683,0.1926486045,-0.1065419167,0.0639995784,0.0371609405,-0.2812865674,-0.5614965558,0.3774626255,0.1746428758,-0.3102475107,-0.0822079629,-0.0205233563,-0.101674065,-0.1576439291,-0.0180426165,-0.085049808,-0.1891314685,0.0147956302,-0.0793387517,-0.3969837427,-0.4567694068,0.1184231043,0.0288374275,-0.0162682217,-0.3463562131,0.1118151173,0.3637426794,-0.0564941429,-0.0430023111,-0.08845599,0.0026182537,-0.2663026452,-0.0660595968,0.3327568471,0.148797974,0.2904263139,0.045086097,0.1757802665,-0.0158164985,-0.0804262608,0.3634447455,-0.0561743379,0.0023397489,-0.1930642873,-0.19840464,0.3541494906,-0.4528671801,0.4073086679,0.0399317071,-0.2771592736,0.1374360621,-0.1565610468,-0.1314674616,-0.0885141939,0.4653721154,-0.2589767575,0.3783656657,0.2475851625,-0.0831934586,-0.1018943042,0.2595183253,-0.0359505638,0.004566073,0.2960415483,-0.46206671,-0.0278332178,-0.1357196867,-0.1868363023,0.0805441737,0.1411464661,0.1734262109,-0.2135947496,0.2454040945,-0.0822357908,0.1437816322,0.0757913962,0.105934225,0.1493422091,-0.1592341065,-0.1643154919,-0.1599853039,0.0717431307,0.158798337,0.1586150974,-0.2362281531,0.1022941917,-0.4182833135,-0.1048171669,0.0990508944,-0.0547475442,-0.0408968218,-0.122612536,0.0478475541,0.3270416558,0.0698409304,0.2051166296,-0.2745992839,0.1811829209,-0.1321081966,-0.3716071546,-0.2293088287,-0.3260007799,-0.0815040916,-0.0047446671,0.4853346646,0.1031744331,0.3148230612,-0.2068870813,-0.2574830949,0.1653002203,-0.0430504046,-0.0358235128,0.2538663745,0.2143922299,-0.1287669539,0.3431736529,0.1944276243,-0.362919867,0.0824215934,-0.1285372823,-0.0336526223,0.086115025,0.2274838239,0.3637343943,-0.0477724448,-0.134433195,-0.0298836417,-0.3539547622,0.3215943277,0.0293443948,0.1498514712,0.067358695,-0.1317503154,-0.1673803478,-0.0274369903,0.1491861194,0.0864903107,-0.3391231894,0.4030130506,0.1747459769,-0.0408624448,-0.0752274394,-0.0718040094,-0.1546743363,0.2917643487,-0.6640383601,-0.0668251142,-0.128480494,-0.0040828804,-0.1210261285,-0.2857667804,-0.002188307,-0.1174402833,0.0631621554,0.1598545462,-0.1683164537,-0.1388295293,-0.2689239085,0.0819728673,-0.0497700535,0.2759536803,-0.0506890528,0.8458833694,0.041929394,-0.1853019148,-0.1689160317,-0.1192442477,0.0638334975,-0.3244429231,-0.4014126956,-0.0687760115,-0.1970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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2538","title":"Loading partial dataset when debugging","comments":"Hi @reachtarunhere.\r\n\r\nBesides the above insights provided by @lhoestq and @thomwolf, there is also a Dataset feature in progress (I plan to finish it this week): #2249, which will allow you, when calling `load_dataset`, to pass the option to download\/preprocess\/cache only some specific split(s), which will definitely speed up your workflow.\r\n\r\nIf this feature is interesting for you, I can ping you once it will be merged into the master branch.","body":"I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits). \r\n\r\nEvery time I execute load_dataset  for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues.\r\n\r\nIs there a way to only load part of the dataset on load_dataset? This would really speed up my workflow.\r\nSomething like a debug mode would really help. Thanks!","comment_length":71,"text":"Loading partial dataset when debugging \n I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits). \r\n\r\nEvery time I execute load_dataset  for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues.\r\n\r\nIs there a way to only load part of the dataset on load_dataset? This would really speed up my workflow.\r\nSomething like a debug mode would really help. Thanks! \n Hi @reachtarunhere.\r\n\r\nBesides the above insights provided by @lhoestq and @thomwolf, there is also a Dataset feature in progress (I plan to finish it this week): #2249, which will allow you, when calling `load_dataset`, to pass the option to download\/preprocess\/cache only some specific split(s), which will definitely speed up your workflow.\r\n\r\nIf this feature is interesting for you, I can ping you once it will be merged into the master branch.","embeddings":[-0.4255480468,-0.1098934039,-0.0348530896,0.3032792509,-0.0704117268,0.282392472,0.5759145617,0.4311378002,0.3011431992,-0.0897593573,-0.0946438685,0.1700280756,-0.1675223559,0.2383288145,0.0475689396,0.0998504236,-0.1617254913,0.3242848217,0.0387713164,-0.0660673827,0.1287385225,-0.2229061127,-0.248351872,-0.2115873992,-0.4659939408,0.0293295104,0.0986263454,0.1705517471,0.3179525137,-0.1268362105,0.3389091194,0.137517035,-0.0084876288,0.1335718185,-0.0001241957,-0.0107220188,0.5006647706,-0.0931077823,-0.4362995028,0.0735286698,-0.237421155,-0.0477150455,0.4577589929,0.0274892692,-0.083421059,-0.1815837771,0.0253198538,-0.3546507955,0.2971426845,-0.0064251428,0.0737070292,0.1067952514,-0.4661458731,0.1442133933,0.1794302315,-0.050980635,-0.0700000003,0.3940604627,0.4568606913,-0.0344275609,-0.2619629502,0.1470095068,-0.0907033384,0.3317944407,0.3868725598,-0.0311696939,0.0120002786,-0.5471946001,0.2819919586,0.4399408996,0.6287299395,0.0329469703,-0.3415046036,-0.5828240514,0.0757654309,-0.0979445726,0.1490002275,0.0070247171,-0.4245784581,0.0065797223,-0.4864515066,-0.1778587997,0.0478371009,0.1568618119,0.0531837158,-0.2893976867,0.036896497,0.0943091363,0.3902567327,0.2198377252,0.3261693418,-0.057307262,0.0780214816,0.0870090798,-0.3868720233,0.0628545433,-0.1920491308,-0.0946867764,-0.0610498674,0.5311383009,-0.0742087141,0.4788445234,0.2084883004,-0.0358149149,0.5376525521,-0.150255248,0.1837615222,0.1738742143,0.3688533306,-0.3586819172,-0.0799363852,-0.0411484465,-0.3891741633,-0.1940121651,0.2670511305,-0.0223625023,-0.0176770724,-0.3543414772,-0.1577949077,-0.367539376,0.1221827939,-0.0502620265,0.2032260895,0.427698493,0.0881245658,0.0653591603,-0.1983948499,0.1510773003,-0.2707566917,-0.1073966995,-0.0971074551,-0.2272587568,-0.0123133911,0.1486579478,0.3650503159,-0.3609235585,0.2793487906,-0.0693353638,0.4252460003,-0.2300672531,0.1399095654,-0.266392529,-0.025323879,0.192095831,0.0380922072,0.2751621008,0.0690244585,0.2701106071,-0.3026752472,0.3971216977,-0.0496055931,-0.3965829909,0.0440241247,-0.0021049343,-0.3572974503,0.2418508828,-0.1939354539,0.2439127415,0.156740576,-0.149572894,-0.2970223129,0.0720491782,-0.0504864827,-0.0922596157,0.6279121041,0.3850626349,-0.1324379742,-0.1911819428,-0.2131745666,0.0428278968,0.2830062509,0.211183399,-0.3966250122,0.2519930005,-0.1373879462,-0.1186437383,0.5024479032,-0.0747006014,-0.3204850852,0.5063077211,-0.0870788023,0.180222556,-0.0622124895,0.4636337459,0.2160332948,0.04201442,0.0510071367,0.4863911867,-0.1479168683,0.1553367078,-0.2005395889,-0.1223475486,0.1038940474,0.4797152281,0.0456256084,0.1825736314,-0.050061103,0.1873490959,0.178916961,-0.0209913347,-0.07795652,0.1962694228,-0.117625609,0.0428444222,0.0597052202,-0.2848039865,-0.6154308319,0.3532468975,0.1503711194,-0.3225485086,-0.0631126836,-0.0078727929,-0.0616652928,-0.1172562689,-0.0140972454,-0.0845228136,-0.1754078418,0.0163250994,-0.0633150935,-0.4030498862,-0.4566090107,0.1725052595,0.0014144225,0.0076232208,-0.3340208232,0.114945747,0.3976435065,-0.0506278835,-0.0025283408,-0.0804589242,-0.0058342782,-0.2696807384,-0.0657005459,0.3011422455,0.1393207759,0.2979264855,0.1136752144,0.1450328827,-0.0313481092,-0.0681549385,0.3927516043,-0.0917418599,-0.0070267702,-0.2175112814,-0.1865833402,0.3615932167,-0.4406475723,0.4426867068,0.0334503911,-0.2793010473,0.1115421802,-0.1444292963,-0.1060400158,-0.0906952992,0.4702703059,-0.2627221346,0.3744698167,0.2515541315,-0.1180124953,-0.0431040414,0.2224398404,-0.0110001676,-0.0264835767,0.3000358641,-0.4567562938,-0.0279169902,-0.1657408774,-0.1882313937,0.1148747429,0.1626736075,0.1160588637,-0.2489050776,0.2345138341,-0.1317109764,0.1706822962,0.1135738492,0.1078627408,0.1801273376,-0.1570752263,-0.1743776351,-0.1415260434,0.1043536142,0.1491667479,0.1552515477,-0.2420474142,0.0409890823,-0.4336850047,-0.095436275,0.1087247506,0.0012945594,0.0000097473,-0.1684014052,0.0455468781,0.3233199418,0.0870681554,0.1428837925,-0.2267100811,0.1525973529,-0.1484452486,-0.3771046698,-0.2346262038,-0.274638176,-0.0331601575,0.0033810793,0.5305057764,0.1417619139,0.332596004,-0.2133327723,-0.2271145135,0.0889055207,-0.0519532859,-0.0518517308,0.2499420792,0.2526989579,-0.126336351,0.3071929514,0.2569523156,-0.3159406781,0.1098044291,-0.1088883951,0.0034073782,0.067696996,0.160555169,0.3011202514,0.0053965701,-0.1698033214,-0.0307083726,-0.3686235547,0.3665091991,0.0164294913,0.1151424572,0.0609630235,-0.1534951329,-0.2031879276,-0.055141151,0.1240792051,0.1300558448,-0.248816967,0.3999468684,0.1499376148,-0.0674016178,-0.0626190975,-0.0296015553,-0.1598857343,0.2536583245,-0.6322861314,-0.0330953076,-0.1697777808,0.0591506548,-0.0926251635,-0.3379983902,-0.0306049101,-0.1077454016,0.0659354776,0.1735351384,-0.1705049574,-0.1234268397,-0.3009801805,0.0332172327,0.0075061289,0.2793803811,-0.1313439757,0.7960772514,0.0527015403,-0.1776231378,-0.1590410769,-0.1315187067,0.0526378602,-0.3936482072,-0.3983528018,-0.0121327424,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2538","title":"Loading partial dataset when debugging","comments":"Thanks all for responding.\r\n\r\nHey @albertvillanova \r\n\r\nThanks. Yes, I would be interested.\r\n\r\n@lhoestq I think even if a small split is specified it loads up the full dataset from the disk (please correct me if this is not the case). Because it does seem to be slow to me even on subsequent calls. There is no repeated downloading so it seems that the cache is working.\r\n\r\nI am not aware of the streaming feature @thomwolf mentioned. So I might need to read up on it.","body":"I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits). \r\n\r\nEvery time I execute load_dataset  for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues.\r\n\r\nIs there a way to only load part of the dataset on load_dataset? This would really speed up my workflow.\r\nSomething like a debug mode would really help. Thanks!","comment_length":85,"text":"Loading partial dataset when debugging \n I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits). \r\n\r\nEvery time I execute load_dataset  for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues.\r\n\r\nIs there a way to only load part of the dataset on load_dataset? This would really speed up my workflow.\r\nSomething like a debug mode would really help. Thanks! \n Thanks all for responding.\r\n\r\nHey @albertvillanova \r\n\r\nThanks. Yes, I would be interested.\r\n\r\n@lhoestq I think even if a small split is specified it loads up the full dataset from the disk (please correct me if this is not the case). Because it does seem to be slow to me even on subsequent calls. There is no repeated downloading so it seems that the cache is working.\r\n\r\nI am not aware of the streaming feature @thomwolf mentioned. So I might need to read up on 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2532","title":"Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task","comments":"Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?","body":"[This colab notebook](https:\/\/colab.research.google.com\/drive\/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https:\/\/huggingface.co\/transformers\/custom_datasets.html#tok-ner).\r\n\r\nThe pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https:\/\/en.wikipedia.org\/wiki\/Kana_ligature) break the alignment of `return_offsets_mapping`:\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/122904371-db192700-d382-11eb-8917-1775db76db69.png)\r\n\r\nWithout the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https:\/\/colab.research.google.com\/drive\/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing)\r\n\r\nIt is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('\u30ff')` return '\u30b3\u30c8'.\r\n\r\nOne workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https:\/\/colab.research.google.com\/drive\/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`.\r\n\r\nI guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this.\r\n\r\np.s.\r\n**I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https:\/\/github.com\/huggingface\/datasets\/pull\/2466)**\r\n`get_dataset `is just a simple wrapping for `load_dataset`\r\nand the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained(\"xlm-roberta-large\")`","comment_length":18,"text":"Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task \n [This colab notebook](https:\/\/colab.research.google.com\/drive\/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https:\/\/huggingface.co\/transformers\/custom_datasets.html#tok-ner).\r\n\r\nThe pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https:\/\/en.wikipedia.org\/wiki\/Kana_ligature) break the alignment of `return_offsets_mapping`:\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/122904371-db192700-d382-11eb-8917-1775db76db69.png)\r\n\r\nWithout the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https:\/\/colab.research.google.com\/drive\/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing)\r\n\r\nIt is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('\u30ff')` return '\u30b3\u30c8'.\r\n\r\nOne workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https:\/\/colab.research.google.com\/drive\/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`.\r\n\r\nI guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this.\r\n\r\np.s.\r\n**I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https:\/\/github.com\/huggingface\/datasets\/pull\/2466)**\r\n`get_dataset `is just a simple wrapping for `load_dataset`\r\nand the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained(\"xlm-roberta-large\")` \n Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?","embeddings":[-0.1536964476,0.0790127367,0.0737818331,-0.0382318683,0.0921575204,-0.3246818185,-0.0899818391,0.1060319841,-0.461761117,0.1986739933,-0.2085373253,0.307420373,0.2766766548,0.0308846552,-0.2191910297,-0.1632778943,0.1479931921,0.2190573663,0.0056235306,0.0611228123,-0.1194189712,0.4571387172,-0.212335363,0.2143603265,-0.2866258919,-0.0706363171,0.125577569,-0.315648973,-0.1683979779,-0.3988147974,0.249714613,0.118910946,-0.0836713836,0.2043143213,-0.0001126969,-0.057501819,-0.0180353783,0.0142885102,0.178675279,-0.1964323819,0.128385514,-0.3769181371,-0.3356096447,0.1073889881,-0.0636701211,0.0396103747,0.2408124506,-0.0554189608,0.6431404352,0.4151496589,0.1455552727,-0.0134888468,0.2545183599,0.0607683174,-0.3109235764,0.2689674497,-0.095067516,0.0190099981,-0.1540722102,0.031468261,-0.0746515244,-0.0096175401,-0.0922699124,-0.4802750349,-0.0148855951,0.1148962528,0.3214075863,-0.4625909626,0.1870723367,0.0185327325,-0.2498886287,-0.0450737998,-0.1830774248,-0.3914948106,-0.1368259192,-0.2679938078,0.2000967115,-0.1542632878,-0.1181238517,0.1168286502,0.0120839812,0.1378460675,0.0297428202,-0.212039277,-0.2942862511,0.564735353,-0.0654119551,0.0426447168,-0.2047559917,-0.0101762712,0.3918432295,-0.042820774,0.0059831715,0.4373289049,-0.2852554321,-0.058525499,0.1970698237,-0.4548270106,-0.0079325698,-0.0356674455,0.1242711842,0.2060957551,0.0719589293,0.0609170049,-0.1630160064,0.4642789066,0.2114510983,0.2766360343,0.1968423277,0.1170869023,-0.0398001708,-0.0404408984,0.0627822056,-0.1251495481,-0.0498765483,0.2508600056,-0.3735534847,0.1039711758,-0.2315127552,0.4121437967,-0.0718666613,0.3000814319,0.0338656791,-0.0993585065,0.0393204316,0.2893413007,-0.3403167427,-0.1019151807,-0.0826219469,0.1086550057,-0.1952837557,0.0189833548,-0.2947455049,0.1247092485,-0.1966221482,0.1081779301,-0.0275508109,0.1280115843,0.3569866419,-0.0326926894,-0.0828055292,-0.0947526321,0.5368512273,0.2782027721,0.0261523295,0.1261869222,0.1154814288,-0.3598402143,-0.3748219907,-0.0433910713,-0.2157930881,0.0899901465,0.147903055,0.2012825161,-0.1073767021,0.1348217279,-0.3785095215,0.2858775556,0.3308013678,-0.2465245724,0.0029821259,-0.3679089248,-0.413536936,-0.1898316592,0.3192932606,-0.1442516297,-0.0017331068,-0.009171092,0.1106587127,0.1891391724,0.484011054,0.1339651644,0.3138591945,0.3631411195,-0.4917096794,0.4855646789,0.0987911001,-0.1336460561,-0.0881660357,-0.1546435505,-0.3981322944,0.257417798,-0.1778470278,-0.2853306532,0.1459316611,0.1648342311,0.1642516553,0.4675336182,-0.1345406771,0.0967916846,-0.0824026763,0.3093529642,-0.0516473278,-0.268320024,0.0125274332,-0.0348947681,-0.2307369262,-0.2382221967,-0.1597029567,-0.0885297135,0.1025192365,-0.0872082114,0.0438992865,-0.129617691,0.0238304473,-0.1528648138,0.042863179,-0.0116005866,-0.3619787693,0.2263039052,0.0064995773,-0.1665717661,-0.2440984845,0.1780437678,-0.1699587405,-0.1374195665,0.2298453599,0.0661637709,0.1778306663,0.3334915042,0.0553164519,0.299461484,0.1876499057,0.2598833144,-0.2379402965,0.1655212343,-0.1539325714,0.032883428,-0.1076619104,0.449572742,0.0576862879,-0.1159918755,-0.1262978613,0.2283831835,0.2532852888,-0.0442685932,-0.2839108407,0.0855725929,0.1471211314,0.1632165909,-0.3755307794,0.2840960324,-0.2077518404,-0.1224473789,0.5265046954,0.893276751,-0.0384244286,0.1267523468,-0.145782873,-0.0221706498,0.445866406,0.1181422994,-0.1546640098,-0.2694558501,0.0372086167,0.2975227833,-0.1064928845,0.0252682399,-0.0940389931,0.142189756,0.219361648,0.0666167215,-0.2200761288,0.1170537993,0.0044210218,-0.2848397195,0.173607558,0.0940917656,0.2308221906,0.0403839238,0.1598118842,0.2757774889,-0.2304855138,-0.162351653,0.1398642212,0.0159907062,0.1191512421,0.5081248879,0.1112573445,0.4917434454,-0.2569987476,-0.2506943941,-0.3280736804,0.0469812714,-0.1642935574,0.2328413278,-0.2001020163,-0.3381249607,-0.418884784,-0.1450555176,-0.1150102839,-0.5577634573,0.1381098479,0.0413245931,-0.2752411664,0.4234624803,0.1134367064,0.253870219,-0.1176146194,0.056621965,-0.2270076871,0.0496982858,-0.4241380692,-0.0032034696,0.1883004457,-0.227151975,0.1867785752,-0.3008562922,-0.0523730293,0.1612110883,-0.7381551266,0.1576875597,0.05999602,0.0300798248,0.26732862,-0.2804103792,-0.4084673822,-0.545712173,0.1107583344,0.0272976961,-0.3636149168,0.1239908785,-0.0817677304,-0.1428301781,-0.1644627154,-0.0236302614,0.0692033842,-0.2058781534,-0.0429567695,-0.4525324106,0.2052507252,0.237983197,-0.4658526182,-0.0950666294,-0.4946010411,0.0330812261,-0.1578765512,-0.0893625394,0.2096683681,-0.2081830204,-0.0607302301,-0.2306274623,-0.3089902699,0.4237656295,0.0377434194,-0.0328559466,-0.0219792239,0.0233350601,0.0698873624,-0.1160932183,-0.0024436321,0.0631492287,0.1390253603,-0.0318449587,0.0003424485,0.0977718607,0.0718512982,-0.2497506291,0.1135581806,-0.2141737193,0.0221480038,0.2703062892,0.2845879197,0.2309903651,-0.2108982056,0.0462130792,0.047887072,0.1470340341,-0.2481189519,-0.1872301251,0.1388610601,0.1633287668,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2532","title":"Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task","comments":"> Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?\r\n\r\nOh, I am sorry\r\nI would reopen the post on huggingface\/transformers","body":"[This colab notebook](https:\/\/colab.research.google.com\/drive\/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https:\/\/huggingface.co\/transformers\/custom_datasets.html#tok-ner).\r\n\r\nThe pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https:\/\/en.wikipedia.org\/wiki\/Kana_ligature) break the alignment of `return_offsets_mapping`:\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/122904371-db192700-d382-11eb-8917-1775db76db69.png)\r\n\r\nWithout the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https:\/\/colab.research.google.com\/drive\/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing)\r\n\r\nIt is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('\u30ff')` return '\u30b3\u30c8'.\r\n\r\nOne workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https:\/\/colab.research.google.com\/drive\/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`.\r\n\r\nI guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this.\r\n\r\np.s.\r\n**I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https:\/\/github.com\/huggingface\/datasets\/pull\/2466)**\r\n`get_dataset `is just a simple wrapping for `load_dataset`\r\nand the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained(\"xlm-roberta-large\")`","comment_length":30,"text":"Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task \n [This colab notebook](https:\/\/colab.research.google.com\/drive\/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https:\/\/huggingface.co\/transformers\/custom_datasets.html#tok-ner).\r\n\r\nThe pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https:\/\/en.wikipedia.org\/wiki\/Kana_ligature) break the alignment of `return_offsets_mapping`:\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/122904371-db192700-d382-11eb-8917-1775db76db69.png)\r\n\r\nWithout the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https:\/\/colab.research.google.com\/drive\/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing)\r\n\r\nIt is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('\u30ff')` return '\u30b3\u30c8'.\r\n\r\nOne workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https:\/\/colab.research.google.com\/drive\/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`.\r\n\r\nI guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this.\r\n\r\np.s.\r\n**I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https:\/\/github.com\/huggingface\/datasets\/pull\/2466)**\r\n`get_dataset `is just a simple wrapping for `load_dataset`\r\nand the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained(\"xlm-roberta-large\")` \n > Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?\r\n\r\nOh, I am sorry\r\nI would reopen the post on huggingface\/transformers","embeddings":[-0.1536964476,0.0790127367,0.0737818331,-0.0382318683,0.0921575204,-0.3246818185,-0.0899818391,0.1060319841,-0.461761117,0.1986739933,-0.2085373253,0.307420373,0.2766766548,0.0308846552,-0.2191910297,-0.1632778943,0.1479931921,0.2190573663,0.0056235306,0.0611228123,-0.1194189712,0.4571387172,-0.212335363,0.2143603265,-0.2866258919,-0.0706363171,0.125577569,-0.315648973,-0.1683979779,-0.3988147974,0.249714613,0.118910946,-0.0836713836,0.2043143213,-0.0001126969,-0.057501819,-0.0180353783,0.0142885102,0.178675279,-0.1964323819,0.128385514,-0.3769181371,-0.3356096447,0.1073889881,-0.0636701211,0.0396103747,0.2408124506,-0.0554189608,0.6431404352,0.4151496589,0.1455552727,-0.0134888468,0.2545183599,0.0607683174,-0.3109235764,0.2689674497,-0.095067516,0.0190099981,-0.1540722102,0.031468261,-0.0746515244,-0.0096175401,-0.0922699124,-0.4802750349,-0.0148855951,0.1148962528,0.3214075863,-0.4625909626,0.1870723367,0.0185327325,-0.2498886287,-0.0450737998,-0.1830774248,-0.3914948106,-0.1368259192,-0.2679938078,0.2000967115,-0.1542632878,-0.1181238517,0.1168286502,0.0120839812,0.1378460675,0.0297428202,-0.212039277,-0.2942862511,0.564735353,-0.0654119551,0.0426447168,-0.2047559917,-0.0101762712,0.3918432295,-0.042820774,0.0059831715,0.4373289049,-0.2852554321,-0.058525499,0.1970698237,-0.4548270106,-0.0079325698,-0.0356674455,0.1242711842,0.2060957551,0.0719589293,0.0609170049,-0.1630160064,0.4642789066,0.2114510983,0.2766360343,0.1968423277,0.1170869023,-0.0398001708,-0.0404408984,0.0627822056,-0.1251495481,-0.0498765483,0.2508600056,-0.3735534847,0.1039711758,-0.2315127552,0.4121437967,-0.0718666613,0.3000814319,0.0338656791,-0.0993585065,0.0393204316,0.2893413007,-0.3403167427,-0.1019151807,-0.0826219469,0.1086550057,-0.1952837557,0.0189833548,-0.2947455049,0.1247092485,-0.1966221482,0.1081779301,-0.0275508109,0.1280115843,0.3569866419,-0.0326926894,-0.0828055292,-0.0947526321,0.5368512273,0.2782027721,0.0261523295,0.1261869222,0.1154814288,-0.3598402143,-0.3748219907,-0.0433910713,-0.2157930881,0.0899901465,0.147903055,0.2012825161,-0.1073767021,0.1348217279,-0.3785095215,0.2858775556,0.3308013678,-0.2465245724,0.0029821259,-0.3679089248,-0.413536936,-0.1898316592,0.3192932606,-0.1442516297,-0.0017331068,-0.009171092,0.1106587127,0.1891391724,0.484011054,0.1339651644,0.3138591945,0.3631411195,-0.4917096794,0.4855646789,0.0987911001,-0.1336460561,-0.0881660357,-0.1546435505,-0.3981322944,0.257417798,-0.1778470278,-0.2853306532,0.1459316611,0.1648342311,0.1642516553,0.4675336182,-0.1345406771,0.0967916846,-0.0824026763,0.3093529642,-0.0516473278,-0.268320024,0.0125274332,-0.0348947681,-0.2307369262,-0.2382221967,-0.1597029567,-0.0885297135,0.1025192365,-0.0872082114,0.0438992865,-0.129617691,0.0238304473,-0.1528648138,0.042863179,-0.0116005866,-0.3619787693,0.2263039052,0.0064995773,-0.1665717661,-0.2440984845,0.1780437678,-0.1699587405,-0.1374195665,0.2298453599,0.0661637709,0.1778306663,0.3334915042,0.0553164519,0.299461484,0.1876499057,0.2598833144,-0.2379402965,0.1655212343,-0.1539325714,0.032883428,-0.1076619104,0.449572742,0.0576862879,-0.1159918755,-0.1262978613,0.2283831835,0.2532852888,-0.0442685932,-0.2839108407,0.0855725929,0.1471211314,0.1632165909,-0.3755307794,0.2840960324,-0.2077518404,-0.1224473789,0.5265046954,0.893276751,-0.0384244286,0.1267523468,-0.145782873,-0.0221706498,0.445866406,0.1181422994,-0.1546640098,-0.2694558501,0.0372086167,0.2975227833,-0.1064928845,0.0252682399,-0.0940389931,0.142189756,0.219361648,0.0666167215,-0.2200761288,0.1170537993,0.0044210218,-0.2848397195,0.173607558,0.0940917656,0.2308221906,0.0403839238,0.1598118842,0.2757774889,-0.2304855138,-0.162351653,0.1398642212,0.0159907062,0.1191512421,0.5081248879,0.1112573445,0.4917434454,-0.2569987476,-0.2506943941,-0.3280736804,0.0469812714,-0.1642935574,0.2328413278,-0.2001020163,-0.3381249607,-0.418884784,-0.1450555176,-0.1150102839,-0.5577634573,0.1381098479,0.0413245931,-0.2752411664,0.4234624803,0.1134367064,0.253870219,-0.1176146194,0.056621965,-0.2270076871,0.0496982858,-0.4241380692,-0.0032034696,0.1883004457,-0.227151975,0.1867785752,-0.3008562922,-0.0523730293,0.1612110883,-0.7381551266,0.1576875597,0.05999602,0.0300798248,0.26732862,-0.2804103792,-0.4084673822,-0.545712173,0.1107583344,0.0272976961,-0.3636149168,0.1239908785,-0.0817677304,-0.1428301781,-0.1644627154,-0.0236302614,0.0692033842,-0.2058781534,-0.0429567695,-0.4525324106,0.2052507252,0.237983197,-0.4658526182,-0.0950666294,-0.4946010411,0.0330812261,-0.1578765512,-0.0893625394,0.2096683681,-0.2081830204,-0.0607302301,-0.2306274623,-0.3089902699,0.4237656295,0.0377434194,-0.0328559466,-0.0219792239,0.0233350601,0.0698873624,-0.1160932183,-0.0024436321,0.0631492287,0.1390253603,-0.0318449587,0.0003424485,0.0977718607,0.0718512982,-0.2497506291,0.1135581806,-0.2141737193,0.0221480038,0.2703062892,0.2845879197,0.2309903651,-0.2108982056,0.0462130792,0.047887072,0.1470340341,-0.2481189519,-0.1872301251,0.1388610601,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2526","title":"Add COCO datasets","comments":"I'm currently adding it, the entire dataset is quite big around 30 GB so I add splits separately. You can take a look here https:\/\/huggingface.co\/datasets\/merve\/coco","body":"## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":25,"text":"Add COCO datasets \n ## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I'm currently adding it, the entire dataset is quite big around 30 GB so I add splits separately. You can take a look here 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2526","title":"Add COCO datasets","comments":"I talked to @lhoestq and it's best if I download this dataset through TensorFlow datasets instead, so I'll be implementing that one really soon.\r\n@NielsRogge ","body":"## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":25,"text":"Add COCO datasets \n ## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I talked to @lhoestq and it's best if I download this dataset through TensorFlow datasets instead, so I'll be implementing that one really soon.\r\n@NielsRogge 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2526","title":"Add COCO datasets","comments":"I started adding COCO, will be done tomorrow EOD\r\nmy work so far https:\/\/github.com\/merveenoyan\/datasets (my fork)","body":"## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":16,"text":"Add COCO datasets \n ## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I started adding COCO, will be done tomorrow EOD\r\nmy work so far https:\/\/github.com\/merveenoyan\/datasets (my fork)","embeddings":[-0.1830971241,-0.4954968691,-0.1293638796,0.1958058625,0.2031087875,0.0522426143,0.061166238,0.1078285351,-0.1408271492,0.2152936012,-0.5061153769,0.1604237705,-0.2287673801,0.4129444659,0.1122535467,-0.2273755819,0.0648131445,-0.0308302846,-0.3308288455,-0.0036851838,-0.0260930508,0.2202633321,0.1078369841,-0.0543191917,-0.3676821887,-0.2403667271,-0.414023906,-0.0104479883,-0.1629814208,-0.3388646245,0.1887264103,0.190489158,0.1099184379,0.4202794731,-0.000099335,0.0483939052,0.1232401803,-0.0476677753,-0.1006756127,-0.1377979368,-0.0299106203,-0.2224123925,0.076290004,-0.1409274936,-0.2448443919,0.0222036261,0.1469765007,-0.0342847481,0.4938249588,0.1551190913,0.3255771697,0.2972865105,0.145547986,-0.1597904414,-0.0705658868,0.4404633939,-0.228587836,0.2056517154,0.2494913191,0.1003657952,-0.151494503,0.5048561096,0.079723604,-0.0035876136,0.3867550492,0.1006958261,-0.171947062,-0.3873194754,0.0421301946,0.3258778155,0.2179569155,-0.2353211194,-0.3748445809,-0.4263024032,-0.0845584944,-0.3336862922,0.0193756185,0.2158463448,-0.1134589463,0.3053693771,-0.2778771222,-0.1418503821,-0.18736884,-0.031365905,0.0930575356,0.0581884682,-0.2006010115,-0.0816985145,0.1790593266,-0.11094594,-0.2917289436,-0.0758846998,0.0830258429,-0.0497785471,-0.1519256681,-0.1506875604,0.1640229523,0.2639547884,0.3488163948,0.1640653908,-0.0171573851,0.06954588,-0.2136377394,0.0535986051,0.0143902795,0.1392093301,-0.3029744923,0.0634488687,0.2123266906,0.2626653314,0.0744169429,-0.1468722522,0.062228743,0.2465628684,-0.3096370101,-0.1798336059,0.1158339083,-0.0739480779,-0.1272678375,-0.1404470354,0.1843511015,0.1304891557,0.1229877397,0.4018352926,-0.0523433536,0.1471991539,-0.012228854,0.1173824742,-0.0138409026,-0.0254161973,-0.2935403883,0.0786777735,-0.1961067915,0.1622936875,0.1732882559,-0.1381578594,0.1006384492,-0.3012259007,0.2386296093,0.1414854378,-0.1196795702,-0.1232067272,0.2070901096,0.1308246404,-0.1836972386,-0.137740165,0.1616330743,-0.0856748596,-0.2467428893,-0.2516529858,-0.1078790575,-0.2116127312,-0.1179377064,0.2365966439,-0.1990318149,-0.0947194844,-0.1724108458,0.544542253,-0.234418273,0.1846500188,-0.0166679081,0.3115614355,-0.2384574115,-0.1245720237,0.2357459813,0.4483696222,0.1596772075,-0.2710970342,-0.0084288828,-0.1290243566,-0.0757278204,0.0906270295,0.0097085433,0.0186355729,-0.1799825877,-0.0837752968,-0.2054136693,-0.2773387432,-0.307061106,0.0962429866,-0.148668468,0.1041126549,0.1342857629,0.2461723387,0.084889546,0.0331640728,-0.1690976471,0.3440744579,-0.1094757468,0.1186359376,0.0276047662,-0.3585336506,-0.0188236274,0.1936744452,0.1314886659,0.0119715929,-0.0150241842,-0.1629350334,0.1187624708,-0.1453720629,0.0959924832,0.0195973199,0.48751688,0.2449080646,-0.0597229376,-0.1964583844,-0.3888219595,0.120602943,0.2366469353,0.2952238619,-0.348541528,-0.3701237738,0.0477740876,0.1309761107,-0.0559434816,-0.3602466583,0.2725426555,0.2053338587,0.2676263452,0.0911464244,-0.1386466175,0.452614367,0.100691177,0.314014405,-0.1364488304,0.2385769337,-0.0795069486,0.1460513771,0.2589631379,0.1706639379,-0.126076445,-0.1393633485,0.1064372808,0.3833704591,-0.1321098059,0.2766015828,0.074078925,0.2560679317,0.3570300043,-0.4633083045,0.0365327857,-0.113815926,0.1421920359,0.0853779763,-0.3554255068,0.1984581947,-0.0238505043,0.170508936,0.2030125111,0.0338158794,0.2742365897,0.0357333161,-0.1630524397,-0.1870566159,-0.1097226366,-0.1212900206,0.4023305476,0.0331334211,-0.3532274663,0.0692909807,0.1202724054,0.0636259466,0.1472583115,0.2917274237,-0.2802788913,0.0227426291,0.1156259328,-0.0590889305,0.0322721303,0.3716903031,-0.0130216256,-0.0090416213,0.0520473756,-0.0267323647,0.1280230731,0.3033140004,-0.0149881002,0.0124245938,0.3296754956,-0.0602022111,-0.4379507005,-0.4200818241,-0.2544067204,-0.0152772442,0.0383735821,0.0605781861,-0.023643747,-0.3694733977,-0.0481651314,-0.0739736035,-0.1180633903,-0.0642254874,0.1111825183,-0.0970551595,-0.0621163882,0.0892138481,-0.2023911029,0.4370523393,-0.171784997,0.0638850853,-0.1405856311,-0.0270264521,-0.0530961193,0.2362477332,0.233524546,-0.1515629739,0.4964986145,-0.1305790246,0.4093131721,-0.0835265517,-0.6280469298,0.263561815,-0.2286240309,0.0520236492,0.1376831532,-0.0310421009,0.0110585857,-0.3714489937,0.2219210714,-0.2998407185,-0.1983090639,-0.1553590447,-0.1558996141,-0.0576361939,-0.2592270374,-0.1734856069,-0.336078912,-0.3955810368,0.5534715652,0.2611630559,0.0018901377,0.3988519311,0.1307338178,0.2327342182,-0.1429476291,0.1981041282,-0.073369965,-0.3765752614,0.1459179074,-0.2512547672,-0.1957679838,-0.2653811276,-0.3377858698,0.2177193761,-0.1815536171,-0.4793020487,-0.3075014651,-0.4294356406,0.1493200958,0.1583316922,0.0314869136,0.2436902225,0.0619677715,-0.1593917459,-0.0167858098,-0.1970130205,0.0444354415,-0.0462369397,-0.0363693535,-0.1812969297,0.3159364164,0.2815662324,0.3663443625,0.0090490934,-0.2829912901,0.4084844589,-0.0528133921,0.5416056514,-0.1923665702,-0.3140417933,0.309486419,-0.0949580744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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2526","title":"Add COCO datasets","comments":"Hi Merve @merveenoyan , thank you so much for your great contribution! May I ask about the current progress of your implementation? Cuz I see the pull request is still in progess here. Or can I just run the COCO scripts in your fork repo?","body":"## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":45,"text":"Add COCO datasets \n ## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hi Merve @merveenoyan , thank you so much for your great contribution! May I ask about the current progress of your implementation? Cuz I see the pull request is still in progess here. Or can I just run the COCO scripts in your fork repo?","embeddings":[-0.1481830031,-0.6077487469,-0.1135475338,0.1493356377,0.1304033995,-0.0674011856,0.0679602772,0.063296169,-0.1546791345,0.1748446524,-0.4082759321,0.1341961175,-0.3136057258,0.4300089777,0.1826982945,-0.2286393046,0.1053284332,-0.0457621515,-0.3508671522,-0.0315577202,0.0537075922,0.2814443111,0.0268767346,-0.088233009,-0.377543211,-0.1978283525,-0.4497271478,0.0032560299,-0.1510405838,-0.2792415619,0.2738299966,0.3187245131,0.0843590125,0.5230819583,-0.0000995643,-0.0296889879,0.1379313618,-0.0782710537,-0.0622387975,-0.217936188,0.014062928,-0.256179601,0.0391644388,-0.0787141994,-0.2754703164,0.1262145787,0.1198584139,0.0094128475,0.5431843996,0.0159367789,0.3012162149,0.2449459881,0.2217353284,-0.0502364263,-0.036648456,0.5251152515,-0.2470023632,0.2450256646,0.3528075516,0.0493275002,-0.1014832705,0.4669591188,0.0933720246,-0.121741876,0.4264151454,0.1051457822,-0.149135232,-0.4341969788,-0.0360566452,0.3108189106,0.1688047796,-0.2747212648,-0.3987088501,-0.4341090024,-0.1244250089,-0.3593810201,-0.0426133908,0.1849095821,-0.1470994651,0.293993175,-0.3316188157,-0.2106988132,-0.2084670961,-0.1049475968,0.0713270232,0.0734203905,-0.2273051441,-0.1382504106,0.2011782825,-0.1134697422,-0.3045860231,-0.0443577729,0.0938353091,0.0543673076,-0.1714622229,-0.2579430938,0.1306658238,0.2931527793,0.4161609411,0.2123474926,-0.0668383166,0.0804197416,-0.2645752728,0.0819366947,0.0888501406,0.2387815416,-0.2847462893,0.0691110417,0.1425421089,0.2687374353,0.118571952,-0.129788354,0.0076624914,0.2728764713,-0.3525584638,-0.1246637553,0.0613389462,-0.0896890461,-0.0402114615,-0.1297170222,0.1797961295,0.0584235564,0.1156146973,0.3689318299,0.0083716977,0.2148531377,0.0517581701,0.0389720537,0.0111040017,0.0656114817,-0.2631677091,0.0942652971,-0.2897315621,0.1236336157,0.1465768516,-0.1750878096,0.0286377743,-0.2999230623,0.3616319001,0.1826784313,-0.2376436591,-0.0926912129,0.2413560152,0.0876678154,-0.2589160204,-0.2434103489,0.1603566557,-0.1046101749,-0.2582579553,-0.1956784725,-0.0725444481,-0.1174806282,-0.0779488981,0.1891865432,-0.2737407982,-0.0974198878,-0.1909547448,0.573512733,-0.2586526275,0.256257385,0.013729156,0.2575101256,-0.2169082165,-0.1003692076,0.2846781313,0.4325504005,0.1330896914,-0.3012541533,0.0498379394,-0.1125649065,-0.1248687208,0.1056152135,0.0541270487,-0.1049089208,-0.1252392083,-0.06301561,-0.2883733809,-0.311663419,-0.2057278603,0.1275458485,-0.1209966019,0.1263123155,0.1058045998,0.179883346,0.0437839255,-0.047051087,-0.2059697807,0.3020094633,-0.1326427907,0.0581976175,-0.0430521332,-0.3348203897,-0.0334799513,0.2054642737,0.0934587792,-0.0320383012,-0.0922616944,-0.2087725401,0.1435790211,-0.0985138416,0.1123369113,-0.0450313948,0.5550230742,0.2901797891,-0.0077128354,-0.1250597686,-0.3824531436,0.1219376847,0.2920981944,0.3108231425,-0.3559426367,-0.3559834659,0.0741693601,0.1749686748,-0.0090883086,-0.3098276854,0.2370071709,0.0438642539,0.3547148407,0.1004908234,-0.2904187441,0.5034738779,0.0781238452,0.3102297187,-0.1096272469,0.305326879,-0.0617677309,0.1540939957,0.2581936717,0.2102325261,-0.1008664668,-0.2085846215,0.1764277816,0.3801365495,-0.1613645405,0.2803729773,0.0826823115,0.201095894,0.3894271851,-0.4596829712,0.1043278202,-0.1223584041,0.1844871193,0.0671713278,-0.3483916521,0.2585007548,-0.0859828517,0.1441822797,0.2027832419,-0.035824284,0.1810506582,0.0040032282,-0.2410807908,-0.1809638292,-0.0508643463,-0.1623945236,0.401132077,-0.0643250495,-0.3432492018,-0.0249733943,0.0887122378,0.0300047286,0.0950263739,0.3529081047,-0.2035713643,0.0836902857,0.0522248559,-0.0912984312,-0.0014962186,0.3463480175,-0.0757096037,0.0404844135,0.1623175144,0.0009144917,0.1543883383,0.3042554855,-0.0077410894,-0.0455982052,0.2752498388,-0.0205895808,-0.3908548355,-0.4086220562,-0.2820064127,-0.0789661407,0.0781169608,0.0664553046,0.0456572026,-0.4704908431,-0.0827601627,-0.1407465935,-0.1521462798,-0.0374850258,0.0647791401,-0.1707461029,0.0129944058,0.1213634536,-0.1557065099,0.4845149815,-0.2672514319,0.095896408,-0.2056938112,-0.0554570593,-0.0158634968,0.2406618595,0.1922277063,-0.212183252,0.4956508577,-0.1350808591,0.3783507645,0.0689359829,-0.6620024443,0.32065925,-0.275395602,-0.0210847724,0.0979948789,-0.0940935463,-0.0231038705,-0.387283206,0.2001825273,-0.3545336723,-0.280141741,-0.2451987118,-0.0701737702,-0.0438695885,-0.3365034759,-0.0336820632,-0.3725210726,-0.2874264717,0.6595184207,0.3265770078,0.0495902523,0.4228801131,0.0506725088,0.1926209778,-0.1730138063,0.0363692529,-0.0475680642,-0.4233732224,0.0811296627,-0.2325861305,-0.1572042555,-0.2389453053,-0.3657691181,0.1526512653,-0.2274912298,-0.4287040532,-0.2866928279,-0.4280322492,0.1511073112,0.2310552299,0.0554443672,0.2161157727,0.0266745724,-0.1340780556,-0.0404596291,-0.1747595668,0.0211972985,-0.0182434767,-0.0319071971,-0.0572591238,0.3264227808,0.3372630179,0.418954581,0.0541849397,-0.307515204,0.3088068664,0.004360429,0.5396020412,-0.1374633908,-0.2898799181,0.2960460782,-0.09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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2526","title":"Add COCO datasets","comments":"Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon. ","body":"## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":20,"text":"Add COCO datasets \n ## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2526","title":"Add COCO datasets","comments":"> Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon.\r\n\r\nIt's really nice of you!! I see you've commited another version just now","body":"## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":34,"text":"Add COCO datasets \n ## Adding a Dataset\r\n- **Name:** COCO\r\n- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.\r\n- **Paper + website:** https:\/\/cocodataset.org\/#home\r\n- **Data:** https:\/\/cocodataset.org\/#download\r\n- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n > Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon.\r\n\r\nIt's really nice of you!! I see you've commited another version just now","embeddings":[-0.1934701204,-0.5309681296,-0.1402341276,0.1794767827,0.2048457116,0.0255115815,0.1151223779,0.1192390993,-0.1104230583,0.2151785344,-0.4630050361,0.1605532318,-0.2300153077,0.4517650902,0.1464907974,-0.3006083071,0.0694584772,-0.0106533365,-0.3615532815,-0.0378415138,-0.0097517967,0.2497570813,0.1215396821,-0.0625084639,-0.3876415193,-0.1652375609,-0.4132882357,-0.0013871486,-0.1865250468,-0.2968485951,0.161062777,0.1904246807,0.106923379,0.3517815173,-0.0000972021,-0.0012930367,0.0781492889,-0.0638645664,-0.0840260983,-0.1386546791,0.0342686251,-0.2103820145,0.011723605,-0.1370432377,-0.2523168921,0.0639332905,0.1658106148,0.0030198228,0.4979831278,0.0893874839,0.3465308845,0.3033703566,0.1634634882,-0.1628329903,-0.044040069,0.4378757179,-0.2159177959,0.2004443407,0.2490647733,0.0342624113,-0.1394715309,0.5237408876,0.0728591159,-0.0841657594,0.4169035554,0.1073489264,-0.1173113212,-0.3659401834,0.003634068,0.3119284511,0.1654759645,-0.2524858117,-0.3245931864,-0.4091172516,-0.0982253477,-0.3041665852,-0.0264294837,0.2123916447,-0.1225989535,0.260406822,-0.2831291854,-0.1579482406,-0.1993269324,-0.0595798939,0.0980312228,0.0449953265,-0.1917615235,-0.1149512678,0.1540397853,-0.098374404,-0.3608998358,-0.0244672745,0.0559480637,-0.0364547223,-0.1278886199,-0.2112388611,0.1242532581,0.2326511592,0.3905368745,0.1669530571,-0.0058572725,0.0180086363,-0.1500649303,0.10686782,0.063243784,0.0980043784,-0.2145347893,0.0023213872,0.1126995981,0.2218938917,0.0600427054,-0.1525218189,0.0521927103,0.2615948915,-0.2969550788,-0.1609944105,0.0085978322,-0.0452071615,-0.1127480194,-0.137975648,0.1636565477,0.1402669251,0.0772225484,0.3996553719,-0.0253800508,0.156564787,0.0312231872,0.1360618919,0.0140316729,-0.048707407,-0.2638716996,0.0828928426,-0.2079493552,0.1466139108,0.1644577384,-0.1283705235,0.1246159822,-0.2959516048,0.2655276358,0.1528412551,-0.1272655427,-0.1178021431,0.2060530782,0.0976730809,-0.2000578493,-0.2064141482,0.1712490171,-0.1047813073,-0.2454761118,-0.2163650841,-0.0815372467,-0.1022265628,-0.1701678187,0.2517910898,-0.1652753949,-0.1246815398,-0.145549193,0.5694878101,-0.2560510039,0.1689445227,0.0010390163,0.2904418707,-0.2288542986,-0.1271140128,0.2319831848,0.4022485018,0.1482771188,-0.3218953907,0.0500177331,-0.1722915769,-0.1075197682,0.093864575,0.0469809398,-0.0164931435,-0.1639641374,-0.0149552505,-0.2472076714,-0.2590012252,-0.2225384265,0.09513437,-0.1887855381,0.0900440663,0.1342589259,0.312925458,0.0754884854,0.0050100139,-0.1578307599,0.3867312372,-0.1622313261,0.1322791129,0.0127433827,-0.3473218679,-0.0422685593,0.2088203281,0.13988702,0.0096483165,-0.0533990301,-0.1490580291,0.1575938612,-0.1392830461,0.0825397894,0.0249880571,0.4717233777,0.223451525,-0.0304204039,-0.2540601492,-0.3577676415,0.0758224204,0.175382033,0.3001264632,-0.2724626362,-0.3858940601,-0.0113309277,0.1238378957,0.0209342409,-0.3265001476,0.2931119502,0.1465921104,0.2337905765,0.0928664729,-0.1722550243,0.4682125747,0.1059876382,0.3398047686,-0.0923057646,0.2397872061,-0.0720510408,0.148538366,0.2338111848,0.1669134945,-0.1294005513,-0.1177363694,0.1702824384,0.3539483547,-0.1736133844,0.2719883621,0.1128240079,0.2677136362,0.3334775269,-0.4328163564,0.0004235121,-0.1250355542,0.1593155861,0.0901956111,-0.3029942214,0.2173176259,0.0276692417,0.122856684,0.2150999308,0.0494124033,0.2055405527,-0.026503697,-0.1527768373,-0.1938449442,-0.0426166318,-0.0791848227,0.2836700976,0.0284687504,-0.3563154042,0.038713485,0.1351415068,0.0661618263,0.1669453084,0.2859981656,-0.3097927868,0.0763632357,0.0687356442,-0.1018594801,-0.0047784317,0.3698250651,-0.0539709777,0.0253110602,0.0598289259,0.0024492107,0.1556711346,0.2341926545,-0.0040026056,0.0027280247,0.3238984346,-0.0778894424,-0.4543700516,-0.3752233982,-0.2943869233,-0.0894206911,0.0636471808,0.0913149789,0.080879949,-0.3989026546,-0.0719838515,-0.078182891,-0.1635679603,-0.042687729,0.1629776359,-0.0858713686,-0.0661076233,0.1006293297,-0.1332560629,0.4828721285,-0.1786891669,0.0908802599,-0.1301308721,-0.0365057923,-0.0031286387,0.262547344,0.1824093908,-0.1468693465,0.5306579471,-0.1585618109,0.3920664489,-0.0410469212,-0.6527221799,0.2404354811,-0.2054674923,0.0496834479,0.1159770042,-0.033940915,-0.0287514403,-0.3709395528,0.2120548785,-0.3028740883,-0.2398627847,-0.1930199414,-0.1115441099,-0.0319536552,-0.2858915329,-0.1110787094,-0.2816832662,-0.3570545614,0.5902017951,0.2784225643,0.0086703449,0.3503789604,0.10627345,0.1622226536,-0.2128297389,0.1786147654,-0.0526211709,-0.3745545745,0.0709420294,-0.2507502735,-0.1739176363,-0.3122421205,-0.3840383291,0.1812688857,-0.1918770969,-0.4488876462,-0.3188901842,-0.3832361996,0.1211920604,0.1752818972,0.016342327,0.208265692,0.0627986714,-0.1850941926,-0.0556527972,-0.1417640448,0.0730138123,-0.0259632189,-0.0340031944,-0.1953257173,0.2420343757,0.2464662939,0.3515484631,-0.0117136771,-0.263844043,0.3711579144,-0.0472952016,0.5277659893,-0.1379452944,-0.3134100735,0.3016724586,-0.09924305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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2522","title":"Documentation Mistakes in Dataset: emotion","comments":"Hi,\r\n\r\nthis issue has been already reported in the dataset repo (https:\/\/github.com\/dair-ai\/emotion_dataset\/issues\/2), so this is a bug on their side.","body":"As per documentation,\r\nDataset: emotion\r\nHomepage: https:\/\/github.com\/dair-ai\/emotion_dataset\r\n\r\nDataset: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/emotion\/emotion.py\r\n\r\nPermalink: https:\/\/huggingface.co\/datasets\/viewer\/?dataset=emotion\r\n\r\nEmotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper.\r\n\r\nBut when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust.","comment_length":20,"text":"Documentation Mistakes in Dataset: emotion \n As per documentation,\r\nDataset: emotion\r\nHomepage: https:\/\/github.com\/dair-ai\/emotion_dataset\r\n\r\nDataset: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/emotion\/emotion.py\r\n\r\nPermalink: https:\/\/huggingface.co\/datasets\/viewer\/?dataset=emotion\r\n\r\nEmotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper.\r\n\r\nBut when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust. \n Hi,\r\n\r\nthis issue has been already reported in the dataset repo (https:\/\/github.com\/dair-ai\/emotion_dataset\/issues\/2), so this is a bug on their side.","embeddings":[0.2212711871,-0.3492065668,-0.1087545753,0.5762394071,0.2677908242,0.1737760007,0.2942093909,0.1110398769,-0.1283250451,0.203823626,-0.1274428964,0.0429529399,-0.2023335248,-0.1131508723,-0.0055952733,-0.186274454,0.0835631117,-0.0872550756,0.3198854923,-0.1177928001,0.0335479043,0.1983209848,-0.0980703384,0.2302875668,-0.4554688334,-0.0563974157,-0.2686941922,-0.0515943803,-0.0469822958,-0.5062128901,0.0716762841,-0.0896961838,-0.0074797897,0.2895738184,-0.0001154651,-0.111664705,0.3260878623,0.0778146684,0.0454540886,-0.231089592,0.2143916786,-0.3071580231,0.3784171939,-0.0876959562,-0.0448352508,-0.5062558055,0.059291508,-0.0312779248,0.4184461236,0.4927144647,0.1667767763,0.178087756,0.2175596058,0.2025782615,0.4194192886,0.545627892,-0.0178259239,0.0204210337,-0.1378220469,-0.0215042494,-0.1851763129,0.3451380432,0.165079847,0.0712513551,0.1775846481,-0.091846481,0.199638024,-0.579117775,0.1523029506,0.1900897622,0.4783172607,-0.0171625223,-0.3321194351,-0.1146266535,0.0158168022,-0.1334289908,0.1938817799,0.2820130289,-0.1139772758,0.09297169,0.102289319,0.0965632573,-0.1609798819,0.4246109724,0.0660467595,0.1322462559,-0.3756153882,0.0194855109,0.0079664448,-0.2089993507,0.0384880938,0.143693611,-0.0376610756,0.2057614326,-0.1708817631,0.2282327861,-0.0167870335,0.0003168922,-0.0389231481,-0.2421786934,-0.1124494523,0.1332705915,-0.1147631854,-0.0613619089,0.6197059751,0.0495514646,0.1868034154,0.1237083226,0.293532759,0.090946354,-0.0030079882,-0.0017007591,-0.0367357954,0.0221608207,0.0139622083,-0.4305975437,-0.0058878432,0.0918471292,-0.3832752705,-0.0522363745,-0.2342670411,-0.3501877487,-0.0679655895,0.3387868404,0.0737476423,0.300462991,-0.0802049711,0.1375329792,0.017084064,-0.541508615,-0.1882719845,-0.0249801911,-0.2958142161,-0.0081739416,0.1116004735,-0.0923953354,0.6315552592,0.1559411436,-0.2107320428,-0.3750531375,0.088589035,-0.1992551237,0.3007443845,0.1824375391,-0.0359490551,0.2212837785,0.2664774358,-0.0924429819,0.0486649647,0.0915699974,-0.0351156369,-0.0881066769,-0.4956248403,0.0979886055,-0.2035675198,0.0199237596,-0.0390605964,0.6006979346,0.0803495347,0.1519803256,0.223384887,-0.0730353668,0.0122765219,-0.2965444326,0.3505527377,0.3677574098,-0.3522181213,-0.3982218504,-0.3678133786,0.0090231774,0.1439783573,0.4062836766,-0.3304085135,0.1271507442,-0.3378306329,0.2549834847,0.2418566793,-0.1866464168,-0.2345921695,0.112793766,0.357495904,0.0649622232,-0.0762086213,0.2588240504,0.1550677121,0.1859456897,0.0829654858,-0.2622185051,0.3079393506,-0.0407892577,-0.3400007188,-0.197105661,0.1795888096,0.0728721097,-0.0786690786,-0.0564080104,0.2922448516,-0.2469891012,0.6810661554,-0.0197623167,-0.0141569274,0.2730138302,0.1785178334,0.4840732813,0.1251228899,-0.0556451157,-0.1300527006,-0.1114680693,0.1443299949,0.4373609722,0.2046014816,-0.0555772036,-0.2629448473,-0.0983478725,-0.1430063695,-0.4279916584,0.1366409212,0.2002825737,-0.3659696579,0.2478677183,-0.3380771875,0.2200650275,-0.2423121184,0.0935569555,-0.2778650522,0.2316070795,-0.0960300267,0.017993493,0.1508451104,0.0809294209,0.0463765413,0.0718573779,-0.0797579885,0.2689031959,0.0939958096,0.1416842639,0.015278779,0.0105833048,0.3232316971,-0.4997332692,0.0140256248,0.158009991,-0.1684829742,0.2369734049,-0.1351102889,0.1746459752,0.272500664,0.0226436295,0.0001453864,0.1185611188,0.350269109,0.2929783165,-0.4297550917,-0.439863205,0.1170180514,-0.2998411059,0.1213404536,0.1156431139,-0.2244835347,0.2992221117,0.2973560691,0.0801387802,0.1543859988,0.2432106882,-0.5394952297,-0.3813195825,0.0901098847,0.0315185785,0.2323159128,0.2432172596,-0.1390015483,-0.1369745731,0.0364051759,-0.1281929165,0.2262079716,0.2362590432,0.1060448661,-0.0901912525,0.1861984283,0.0976965129,-0.3965107203,0.1131404415,-0.1362866014,0.2927363515,-0.4832673073,-0.1588353068,-0.1684350669,-0.3459651768,0.1443157345,-0.0566449314,-0.1863461137,-0.3293999732,-0.0384605601,-0.3649948537,-0.1907693446,-0.0067478972,-0.1385474503,0.0470802188,-0.0089543629,0.5758171678,-0.2279251367,-0.0529014245,-0.2607600689,0.0117808422,-0.0994350687,0.1297074854,0.0816469789,-0.259659797,-0.2590094805,-0.2978917956,-0.5375893116,0.2664554119,-0.2632860839,0.358553648,0.0612230599,-0.0123534529,-0.4086563587,-0.0889178887,0.2468182296,-0.2753388286,-0.1839739382,-0.1442348659,-0.0939528048,0.0186870303,0.1265518069,-0.5909531713,-0.2242352962,-0.1425719261,0.3636078537,-0.0673638657,0.1479123086,0.169193849,0.0336715467,0.1353575289,-0.3880838454,-0.0101905605,-0.4052891135,-0.2496473789,0.2451399863,-0.3715068996,-0.4380248785,0.1713705361,0.0093548661,0.146030888,-0.2629668117,-0.6767251492,0.0650976449,-0.2844429016,0.1649732143,-0.0391654707,0.2578712404,-0.1962353885,-0.0335553251,-0.1405586302,0.0131707154,-0.3025437593,-0.0472641811,-0.3038459718,0.4315596819,-0.2831240892,0.1189671978,0.0314503573,0.2606206536,0.4236908257,-0.1661621332,0.1330355704,-0.259968251,0.4982194602,0.1351463795,-0.2544529736,0.2596282661,-0.0836423784,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2522","title":"Documentation Mistakes in Dataset: emotion","comments":"The documentation has another bug in the dataset card [here](https:\/\/huggingface.co\/datasets\/emotion). \r\n\r\nIn the dataset summary **six** emotions are mentioned: *\"six basic emotions: anger, fear, joy, love, sadness, and surprise\"*, however, in the datafields section we have only **five**:\r\n```\r\nlabel: a classification label, with possible values including sadness (0), joy (1), love (2), anger (3), fear (4).\r\n```","body":"As per documentation,\r\nDataset: emotion\r\nHomepage: https:\/\/github.com\/dair-ai\/emotion_dataset\r\n\r\nDataset: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/emotion\/emotion.py\r\n\r\nPermalink: https:\/\/huggingface.co\/datasets\/viewer\/?dataset=emotion\r\n\r\nEmotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper.\r\n\r\nBut when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust.","comment_length":57,"text":"Documentation Mistakes in Dataset: emotion \n As per documentation,\r\nDataset: emotion\r\nHomepage: https:\/\/github.com\/dair-ai\/emotion_dataset\r\n\r\nDataset: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/emotion\/emotion.py\r\n\r\nPermalink: https:\/\/huggingface.co\/datasets\/viewer\/?dataset=emotion\r\n\r\nEmotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper.\r\n\r\nBut when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust. \n The documentation has another bug in the dataset card [here](https:\/\/huggingface.co\/datasets\/emotion). \r\n\r\nIn the dataset summary **six** emotions are mentioned: *\"six basic emotions: anger, fear, joy, love, sadness, and surprise\"*, however, in the datafields section we have only **five**:\r\n```\r\nlabel: a classification label, with possible values including sadness (0), joy (1), love (2), anger (3), fear (4).\r\n```","embeddings":[0.2278898358,-0.3761104047,-0.0679695234,0.559458673,0.2020365447,0.2261339873,0.1969560236,0.0225238781,0.0012619555,0.1313016266,-0.0691813901,0.0111189056,-0.2228643894,-0.0502622575,-0.0138233956,-0.2161301076,0.0691338405,-0.0917543992,0.3126777112,-0.2696833909,0.0065774722,0.1741949022,-0.1075703353,0.3312342465,-0.4914402962,-0.1694076657,-0.2645338774,-0.1125742644,-0.0838567391,-0.4627750814,0.0773080662,-0.1489901394,-0.1102660149,0.265615046,-0.0001172131,-0.1682924777,0.3496099114,0.0766922086,0.1515966952,-0.2617140412,0.1795948893,-0.3064340353,0.3491294682,-0.1322055757,-0.0499083661,-0.4883415401,0.1138828769,0.0986947715,0.4581675529,0.406891793,0.1307265311,0.1252243817,0.2503623366,0.2723953426,0.4812479615,0.5162388086,-0.0712072328,0.0651269108,-0.1372830123,-0.0309130028,-0.1807528436,0.463237226,0.2165532261,-0.0815217793,0.1990810782,-0.0434562266,0.219874993,-0.5311686397,0.1602468938,0.1802302748,0.2850949466,-0.0638985932,-0.4428116679,-0.0998122841,0.0253369547,-0.2860460281,0.2218652368,0.3302400112,-0.0758618191,0.0645094737,0.1092191264,0.0791156441,-0.1622501016,0.3589538038,0.100281246,0.2793100476,-0.3502037227,0.0411937721,-0.0028803686,-0.273841083,0.0054870313,0.0986312851,0.0087397611,0.3557098806,-0.1998452544,0.1306731105,-0.1507228911,0.1164679229,-0.0474217013,-0.1465781331,-0.0168622807,0.0994357765,-0.1982642114,-0.0319781788,0.6491283178,0.1379550546,0.1414261907,0.072365433,0.2075230628,0.1218810156,-0.0151571119,0.0612551011,-0.1285016239,0.1726149768,0.0895622,-0.3720334172,-0.0992072821,0.0802588537,-0.4401406944,0.1558768153,-0.1959200501,-0.3431527615,-0.0276568681,0.353000313,0.0095355762,0.1703326702,-0.113213025,0.1091175005,0.0928605273,-0.5085911155,-0.1914743483,0.03507163,-0.3806979656,-0.0458960459,-0.0186256263,-0.2053746134,0.6770132184,0.1465133429,-0.2249342203,-0.2549756169,0.0203732811,-0.2889786959,0.3356802762,0.2420586944,-0.2218084633,0.2563340068,0.2576767802,-0.0387507863,0.0023662318,0.0458545722,-0.1137370467,0.002437226,-0.4634218812,0.0898475274,-0.145665288,-0.0021218974,0.0308026671,0.6570588946,0.0548414849,0.1168634072,0.3303908408,-0.0518297851,0.0676664561,-0.2380173653,0.2713405192,0.2472933233,-0.3252942264,-0.4563797414,-0.3269239962,0.0317508206,0.1711357534,0.3313419521,-0.2914268374,0.1391987056,-0.2140191644,0.264631629,0.2556357384,-0.2909790874,-0.1948839575,0.065917559,0.4306678474,0.1617404521,-0.0688258186,0.2187496871,0.0870972499,0.1538556814,0.0773658156,-0.2183236927,0.1827375293,-0.0664437935,-0.3192428648,-0.3169690669,0.2524900436,0.1203930229,-0.065141052,-0.1005035713,0.2804677486,-0.1102930903,0.5481883287,0.0370081663,-0.0880785361,0.2336841226,0.0096493727,0.3732637465,0.056197498,-0.1309004128,-0.2360893637,-0.0932310373,0.0822874978,0.4233244956,0.2937693596,-0.1240419224,-0.2126408219,-0.1251939982,-0.1320444345,-0.4550388455,0.1354574412,0.0766673759,-0.2757888138,0.0697043017,-0.4738401175,0.1247914359,-0.3449214995,0.1077590063,-0.1872467101,0.0274993796,-0.1606874168,0.0560512021,0.0196067505,0.1620303094,0.0500946231,0.0117242765,0.0114396494,0.2905337512,0.1616409421,0.0381533056,-0.0267840773,0.1529183388,0.2848736644,-0.3941542208,-0.103001453,0.2044601142,-0.182417348,0.2483979911,-0.1493891627,0.2433140278,0.2147324383,0.0896873996,0.0906898007,0.0146306502,0.3298405111,0.31275931,-0.4836826622,-0.3788255155,0.1198706552,-0.3141622245,0.1814741045,0.1466916353,-0.4187917113,0.2675300837,0.3480523229,0.0624833144,0.2145915329,0.2947283387,-0.4955981672,-0.3514474332,0.0734770149,0.1325917542,0.2215260118,0.1777304113,-0.182829529,-0.0411256216,-0.0655629039,-0.0077312244,0.2787920535,0.2627869248,0.170111239,-0.0295680594,0.0757877976,0.2529313266,-0.2576183379,0.1173921824,-0.1043294072,0.1993349046,-0.4978722334,-0.0783391818,-0.1074453965,-0.3407883346,0.04449936,-0.0473348163,-0.1781683862,-0.246108219,-0.0990020037,-0.3316033185,-0.2231913507,0.0551879294,-0.1493961066,0.1439002752,-0.0501164272,0.5716134906,-0.2126542032,-0.0722906291,-0.2608469129,-0.0261839274,-0.0618445016,0.0212004818,0.0880753174,-0.2749095857,-0.2448915243,-0.1621079594,-0.5779834986,0.3178648055,-0.2971744835,0.3226926029,0.1571280956,0.0883028582,-0.4744961858,-0.2330656648,0.18789123,-0.284126848,-0.2534810305,-0.2032687962,-0.0802267715,0.0105869966,0.0498785116,-0.532214582,-0.2611315846,-0.0300936587,0.3929750621,-0.0322356448,0.2088370919,0.4098159969,-0.0724478066,0.1950921714,-0.4293953478,0.0638630912,-0.3973776996,-0.3433582485,0.2581242621,-0.2972024381,-0.4222040176,0.141506061,-0.0524155684,0.1839458793,-0.2734233439,-0.4647282958,0.0511505455,-0.1944264024,-0.017736027,-0.0410773195,0.3068296015,-0.150388658,-0.0550820082,-0.1177174002,0.0463657416,-0.3264190853,-0.0240100995,-0.2740052342,0.4890231192,-0.3161647916,0.1143299118,-0.0055919113,0.3012162447,0.3640303612,-0.0752407908,0.0941331908,-0.1751811355,0.4957688153,0.0734987035,-0.20488213,0.1901767701,-0.1140221581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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2516","title":"datasets.map pickle issue resulting in invalid mapping function","comments":"Hi ! `map` calls `__getstate__` using `dill` to hash your map function. This is used by the caching mechanism to recover previously computed results. That's why you don't see any `__setstate__` call.\r\n\r\nWhy do you change an attribute of your tokenizer when `__getstate__` is called ?","body":"I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n","comment_length":46,"text":"datasets.map pickle issue resulting in invalid mapping function \n I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n \n Hi ! `map` calls `__getstate__` using `dill` to hash your map function. This is used by the caching mechanism to recover previously computed results. That's why you don't see any `__setstate__` call.\r\n\r\nWhy do you change an attribute of your tokenizer when `__getstate__` is called ?","embeddings":[-0.2248874158,0.1931205094,0.1127529219,0.0287756212,0.0341960229,-0.3237702549,0.1385361105,0.195084691,0.3468089998,-0.100017868,0.1921808273,0.7253850698,-0.1929601282,0.2086696327,-0.0016190895,0.0595789626,0.0705189332,-0.0354076736,-0.0051377355,-0.064012669,-0.2173004895,0.0509161651,-0.3579756021,0.1536132991,-0.2765950263,-0.1768149883,0.0259390548,0.1186882928,-0.0299846679,-0.2711364627,0.2045458853,-0.0787939131,-0.0277055055,0.2411660254,-0.0001231694,-0.080115065,0.0161940679,-0.0863834247,-0.0041622892,-0.2175996751,-0.2230958939,-0.1239771247,-0.1235440746,-0.2294132262,0.0781571865,0.0024631347,-0.2965726852,-0.3813810945,0.6342011094,0.4318727851,0.1199069619,0.3648691177,-0.0678677708,-0.0346794352,-0.1355490088,0.3100190461,0.0528478138,0.22330302,0.1253270954,-0.3918119371,-0.0012149068,0.095629327,-0.1981257647,-0.216010198,0.1527506709,0.0591067895,0.1838099957,0.0106215868,-0.0086782463,0.0929949582,0.2169977278,-0.3573749661,-0.212029174,-0.3761959076,-0.0575140305,-0.1505009383,0.3402034342,-0.2168394774,0.131648913,0.1427595615,-0.0484839231,-0.1044859737,0.223940596,0.3518484533,-0.0390819162,0.2571777999,-0.016609231,0.3984128535,-0.0536790453,-0.1063232347,-0.1807260513,-0.1033443063,0.1016542092,0.4516169131,-0.0112113589,-0.2384910434,0.2105504125,-0.2123305202,-0.0243081972,0.2115104795,-0.1638644338,0.1225485504,-0.364127934,0.2640176713,0.4708405733,0.2212183774,0.1237124354,0.6363039017,0.1985704154,-0.1602922082,-0.3000450432,0.0745262355,0.3102485538,-0.101135999,-0.0373392925,0.1756477803,-0.1027164608,-0.1286521405,-0.0738535076,0.273021996,-0.7321617603,0.0083053699,0.1820911616,0.1216195971,-0.0009521256,-0.222410351,-0.0831374228,0.0777556822,-0.2158571482,0.1148085818,-0.118575193,0.1242495626,-0.3427777886,0.1104225591,-0.0432601124,0.0569451042,0.3175241649,0.1256694645,-0.1688387245,-0.059651278,0.1512505561,-0.2294484973,0.4016970098,-0.0197041202,-0.2360149175,0.0424856395,0.3069401979,-0.4420320988,-0.255575031,-0.059227135,-0.1526567638,-0.141263321,-0.0386343338,0.034725219,-0.2372962832,-0.0047180429,-0.3046909869,0.1028893217,0.3713427186,-0.2058525831,0.0988660082,-0.5015787482,-0.3563106656,-0.2076636106,0.0088023664,0.1774777621,-0.4386508763,-0.2434036136,-0.0980320275,0.1520785093,0.3357068598,0.1245676726,-0.2613388896,0.6007260084,-0.496250093,0.875687778,0.2869651318,-0.1486267298,-0.4010117352,0.0329538062,-0.1728947461,0.2320756316,-0.6788750887,-0.1038570404,0.1650154293,-0.0797584355,0.1138168424,0.3388640583,0.1549204737,-0.0953329578,-0.1282202452,0.0644310266,0.3268656433,-0.2837596238,0.0703485608,-0.0096958773,0.1624441892,-0.2167634517,0.2174033821,0.0343074575,0.1843425035,-0.1125897691,0.4559538662,0.1764437258,-0.1205102578,-0.3156250119,-0.1837683618,0.3253226578,-0.0280872509,-0.026879672,-0.1192952618,-0.0829102993,-0.1131267026,0.0560219474,-0.3399247825,-0.2433561981,0.0299025644,0.0243141092,0.2335766703,0.1753290445,-0.0835654512,-0.2479457259,-0.0506141856,0.0609713532,-0.055893112,0.0568838157,0.1251781583,-0.2686151862,-0.3178895414,0.167403996,0.4087355435,-0.0979360938,-0.1848382354,0.276186198,0.5985630751,-0.2991654873,-0.3962604403,-0.1816080213,-0.1041159853,0.1290626079,-0.2356887609,0.171751067,-0.002783909,-0.2309010923,-0.0256967079,0.5132235289,0.2476934642,0.3112365901,-0.4027119875,0.2516179383,-0.0123048415,-0.086805433,-0.1482175738,-0.337376684,-0.2627907693,0.0373323113,0.3305847347,-0.1344626099,-0.0364917815,0.0407440625,0.3557883203,-0.0735358521,0.3257069588,-0.0644305125,-0.1981063187,-0.1906806678,0.2980228662,0.3740843832,0.5166624188,-0.1092321575,-0.0572246984,0.0850376934,-0.0458905511,-0.0490012653,0.0168410391,-0.1722277254,-0.0378650054,0.1988481432,0.0485895835,-0.0998987854,-0.1812916398,0.009795065,0.0562791452,0.2922277153,-0.2386673987,0.0962414443,-0.5637153387,0.0709874779,-0.2885975242,-0.1303836107,0.0072157839,-0.4439607561,-0.2119714022,0.0941521823,-0.2360053658,0.218233943,-0.1610499918,-0.1597822011,-0.1504624039,-0.2889367342,0.0986514241,-0.2346133739,-0.2234230042,-0.0864400193,0.1288226098,0.1223812774,0.2256283313,0.3576847017,-0.4277692139,-0.34537521,-0.2861611247,0.3642108738,-0.1671608239,0.1216560602,0.3645741045,-0.210863471,-0.072285004,0.1510339975,0.3195462823,-0.1060249582,-0.1192033067,-0.1503362358,0.2773757279,0.024000911,-0.028316278,0.0144707328,-0.1706431657,-0.057648588,0.3715693355,-0.1595648676,0.0871455371,0.167441681,0.0440905541,0.2306474745,-0.1044093817,-0.260627687,-0.4392026067,-0.0985319316,0.1647433937,-0.196296975,-0.1257399321,-0.0366647914,-0.1095462143,0.1613349617,0.0417040139,-0.2359023392,0.0899801478,-0.0043371278,0.3883194923,-0.2089865357,0.1180841029,0.3752018213,0.1685070395,0.0596246868,-0.1573242694,-0.206185326,0.1814013869,0.3865940869,0.215642795,0.1895819902,0.2443961948,0.1597876698,0.8500338793,0.319329828,-0.4346615076,0.1205978617,-0.0580940507,-0.0644246265,-0.0762369111,-0.3137790561,-0.0662493855,-0.2433529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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2516","title":"datasets.map pickle issue resulting in invalid mapping function","comments":"@lhoestq because if I try to pickle my custom tokenizer (it contains a pure python pretokenization step in an otherwise rust backed tokenizer) I get\r\n\r\n> Exception: Error while attempting to pickle Tokenizer: Custom PreTokenizer cannot be serialized\r\n\r\nSo I remove the Custom PreTokenizer in `__getstate__` and then restore it in `__setstate__` (since it doesn't contain any state). This is what my `__getstate__`  \/ `__setstate__` looks like:\r\n\r\n    def __getstate__(self):\r\n        \"\"\"\r\n        Removes pre_tokenizer since it cannot be pickled\r\n        \"\"\"\r\n        logger.debug(\"Copy state dict\")\r\n        out = self.__dict__.copy()\r\n        logger.debug(\"Detaching pre_tokenizer\")\r\n        out['_tokenizer'].pre_tokenizer = tokenizers.pre_tokenizers.Sequence([]) \r\n        return out\r\n\r\n    def __setstate__(self, d):\r\n        \"\"\"\r\n        Reinstates pre_tokenizer\r\n        \"\"\"\r\n        logger.debug(\"Reattaching pre_tokenizer\")\r\n        self.__dict__ = d\r\n        self.backend_tokenizer.pre_tokenizer = self._pre_tokenizer()\r\n\r\nIf this is the case can you think of another way of avoiding my issue?","body":"I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n","comment_length":121,"text":"datasets.map pickle issue resulting in invalid mapping function \n I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n \n @lhoestq because if I try to pickle my custom tokenizer (it contains a pure python pretokenization step in an otherwise rust backed tokenizer) I get\r\n\r\n> Exception: Error while attempting to pickle Tokenizer: Custom PreTokenizer cannot be serialized\r\n\r\nSo I remove the Custom PreTokenizer in `__getstate__` and then restore it in `__setstate__` (since it doesn't contain any state). This is what my `__getstate__`  \/ `__setstate__` looks like:\r\n\r\n    def __getstate__(self):\r\n        \"\"\"\r\n        Removes pre_tokenizer since it cannot be pickled\r\n        \"\"\"\r\n        logger.debug(\"Copy state dict\")\r\n        out = self.__dict__.copy()\r\n        logger.debug(\"Detaching pre_tokenizer\")\r\n        out['_tokenizer'].pre_tokenizer = tokenizers.pre_tokenizers.Sequence([]) \r\n        return out\r\n\r\n    def __setstate__(self, d):\r\n        \"\"\"\r\n        Reinstates pre_tokenizer\r\n        \"\"\"\r\n        logger.debug(\"Reattaching pre_tokenizer\")\r\n        self.__dict__ = d\r\n        self.backend_tokenizer.pre_tokenizer = self._pre_tokenizer()\r\n\r\nIf this is the case can you think of another way of avoiding my issue?","embeddings":[-0.2248874158,0.1931205094,0.1127529219,0.0287756212,0.0341960229,-0.3237702549,0.1385361105,0.195084691,0.3468089998,-0.100017868,0.1921808273,0.7253850698,-0.1929601282,0.2086696327,-0.0016190895,0.0595789626,0.0705189332,-0.0354076736,-0.0051377355,-0.064012669,-0.2173004895,0.0509161651,-0.3579756021,0.1536132991,-0.2765950263,-0.1768149883,0.0259390548,0.1186882928,-0.0299846679,-0.2711364627,0.2045458853,-0.0787939131,-0.0277055055,0.2411660254,-0.0001231694,-0.080115065,0.0161940679,-0.0863834247,-0.0041622892,-0.2175996751,-0.2230958939,-0.1239771247,-0.1235440746,-0.2294132262,0.0781571865,0.0024631347,-0.2965726852,-0.3813810945,0.6342011094,0.4318727851,0.1199069619,0.3648691177,-0.0678677708,-0.0346794352,-0.1355490088,0.3100190461,0.0528478138,0.22330302,0.1253270954,-0.3918119371,-0.0012149068,0.095629327,-0.1981257647,-0.216010198,0.1527506709,0.0591067895,0.1838099957,0.0106215868,-0.0086782463,0.0929949582,0.2169977278,-0.3573749661,-0.212029174,-0.3761959076,-0.0575140305,-0.1505009383,0.3402034342,-0.2168394774,0.131648913,0.1427595615,-0.0484839231,-0.1044859737,0.223940596,0.3518484533,-0.0390819162,0.2571777999,-0.016609231,0.3984128535,-0.0536790453,-0.1063232347,-0.1807260513,-0.1033443063,0.1016542092,0.4516169131,-0.0112113589,-0.2384910434,0.2105504125,-0.2123305202,-0.0243081972,0.2115104795,-0.1638644338,0.1225485504,-0.364127934,0.2640176713,0.4708405733,0.2212183774,0.1237124354,0.6363039017,0.1985704154,-0.1602922082,-0.3000450432,0.0745262355,0.3102485538,-0.101135999,-0.0373392925,0.1756477803,-0.1027164608,-0.1286521405,-0.0738535076,0.273021996,-0.7321617603,0.0083053699,0.1820911616,0.1216195971,-0.0009521256,-0.222410351,-0.0831374228,0.0777556822,-0.2158571482,0.1148085818,-0.118575193,0.1242495626,-0.3427777886,0.1104225591,-0.0432601124,0.0569451042,0.3175241649,0.1256694645,-0.1688387245,-0.059651278,0.1512505561,-0.2294484973,0.4016970098,-0.0197041202,-0.2360149175,0.0424856395,0.3069401979,-0.4420320988,-0.255575031,-0.059227135,-0.1526567638,-0.141263321,-0.0386343338,0.034725219,-0.2372962832,-0.0047180429,-0.3046909869,0.1028893217,0.3713427186,-0.2058525831,0.0988660082,-0.5015787482,-0.3563106656,-0.2076636106,0.0088023664,0.1774777621,-0.4386508763,-0.2434036136,-0.0980320275,0.1520785093,0.3357068598,0.1245676726,-0.2613388896,0.6007260084,-0.496250093,0.875687778,0.2869651318,-0.1486267298,-0.4010117352,0.0329538062,-0.1728947461,0.2320756316,-0.6788750887,-0.1038570404,0.1650154293,-0.0797584355,0.1138168424,0.3388640583,0.1549204737,-0.0953329578,-0.1282202452,0.0644310266,0.3268656433,-0.2837596238,0.0703485608,-0.0096958773,0.1624441892,-0.2167634517,0.2174033821,0.0343074575,0.1843425035,-0.1125897691,0.4559538662,0.1764437258,-0.1205102578,-0.3156250119,-0.1837683618,0.3253226578,-0.0280872509,-0.026879672,-0.1192952618,-0.0829102993,-0.1131267026,0.0560219474,-0.3399247825,-0.2433561981,0.0299025644,0.0243141092,0.2335766703,0.1753290445,-0.0835654512,-0.2479457259,-0.0506141856,0.0609713532,-0.055893112,0.0568838157,0.1251781583,-0.2686151862,-0.3178895414,0.167403996,0.4087355435,-0.0979360938,-0.1848382354,0.276186198,0.5985630751,-0.2991654873,-0.3962604403,-0.1816080213,-0.1041159853,0.1290626079,-0.2356887609,0.171751067,-0.002783909,-0.2309010923,-0.0256967079,0.5132235289,0.2476934642,0.3112365901,-0.4027119875,0.2516179383,-0.0123048415,-0.086805433,-0.1482175738,-0.337376684,-0.2627907693,0.0373323113,0.3305847347,-0.1344626099,-0.0364917815,0.0407440625,0.3557883203,-0.0735358521,0.3257069588,-0.0644305125,-0.1981063187,-0.1906806678,0.2980228662,0.3740843832,0.5166624188,-0.1092321575,-0.0572246984,0.0850376934,-0.0458905511,-0.0490012653,0.0168410391,-0.1722277254,-0.0378650054,0.1988481432,0.0485895835,-0.0998987854,-0.1812916398,0.009795065,0.0562791452,0.2922277153,-0.2386673987,0.0962414443,-0.5637153387,0.0709874779,-0.2885975242,-0.1303836107,0.0072157839,-0.4439607561,-0.2119714022,0.0941521823,-0.2360053658,0.218233943,-0.1610499918,-0.1597822011,-0.1504624039,-0.2889367342,0.0986514241,-0.2346133739,-0.2234230042,-0.0864400193,0.1288226098,0.1223812774,0.2256283313,0.3576847017,-0.4277692139,-0.34537521,-0.2861611247,0.3642108738,-0.1671608239,0.1216560602,0.3645741045,-0.210863471,-0.072285004,0.1510339975,0.3195462823,-0.1060249582,-0.1192033067,-0.1503362358,0.2773757279,0.024000911,-0.028316278,0.0144707328,-0.1706431657,-0.057648588,0.3715693355,-0.1595648676,0.0871455371,0.167441681,0.0440905541,0.2306474745,-0.1044093817,-0.260627687,-0.4392026067,-0.0985319316,0.1647433937,-0.196296975,-0.1257399321,-0.0366647914,-0.1095462143,0.1613349617,0.0417040139,-0.2359023392,0.0899801478,-0.0043371278,0.3883194923,-0.2089865357,0.1180841029,0.3752018213,0.1685070395,0.0596246868,-0.1573242694,-0.206185326,0.1814013869,0.3865940869,0.215642795,0.1895819902,0.2443961948,0.1597876698,0.8500338793,0.319329828,-0.4346615076,0.1205978617,-0.0580940507,-0.0644246265,-0.0762369111,-0.3137790561,-0.0662493855,-0.24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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2516","title":"datasets.map pickle issue resulting in invalid mapping function","comments":"Actually, maybe I need to deep copy `self.__dict__`? That way `self` isn't modified. That was my intention and I thought it was working - I'll double-check after the weekend.","body":"I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n","comment_length":29,"text":"datasets.map pickle issue resulting in invalid mapping function \n I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n \n Actually, maybe I need to deep copy `self.__dict__`? That way `self` isn't modified. That was my intention and I thought it was working - I'll double-check after the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2516","title":"datasets.map pickle issue resulting in invalid mapping function","comments":"Doing a deep copy results in the warning:\r\n\r\n> 06\/20\/2021 16:02:15 - WARNING - datasets.fingerprint -   Parameter 'function'=<function tokenize_function at 0x7f1e95f05d40> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\n\r\n```\r\ndef __getstate__(self):\r\n    \"\"\"\r\n    Removes pre_tokenizer since it cannot be pickled\r\n    \"\"\"\r\n    logger.debug(\"Copy state dict\")\r\n    out = copy.deepcopy(self.__dict__)\r\n    logger.debug(\"Detaching pre_tokenizer\")\r\n    out['_tokenizer'].pre_tokenizer = tokenizers.pre_tokenizers.Sequence([]) \r\n    return out\r\n```","body":"I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n","comment_length":114,"text":"datasets.map pickle issue resulting in invalid mapping function \n I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n \n Doing a deep copy results in the warning:\r\n\r\n> 06\/20\/2021 16:02:15 - WARNING - datasets.fingerprint -   Parameter 'function'=<function tokenize_function at 0x7f1e95f05d40> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\n\r\n```\r\ndef __getstate__(self):\r\n    \"\"\"\r\n    Removes pre_tokenizer since it cannot be pickled\r\n    \"\"\"\r\n    logger.debug(\"Copy state dict\")\r\n    out = copy.deepcopy(self.__dict__)\r\n    logger.debug(\"Detaching pre_tokenizer\")\r\n    out['_tokenizer'].pre_tokenizer = tokenizers.pre_tokenizers.Sequence([]) \r\n    return out\r\n```","embeddings":[-0.2248874158,0.1931205094,0.1127529219,0.0287756212,0.0341960229,-0.3237702549,0.1385361105,0.195084691,0.3468089998,-0.100017868,0.1921808273,0.7253850698,-0.1929601282,0.2086696327,-0.0016190895,0.0595789626,0.0705189332,-0.0354076736,-0.0051377355,-0.064012669,-0.2173004895,0.0509161651,-0.3579756021,0.1536132991,-0.2765950263,-0.1768149883,0.0259390548,0.1186882928,-0.0299846679,-0.2711364627,0.2045458853,-0.0787939131,-0.0277055055,0.2411660254,-0.0001231694,-0.080115065,0.0161940679,-0.0863834247,-0.0041622892,-0.2175996751,-0.2230958939,-0.1239771247,-0.1235440746,-0.2294132262,0.0781571865,0.0024631347,-0.2965726852,-0.3813810945,0.6342011094,0.4318727851,0.1199069619,0.3648691177,-0.0678677708,-0.0346794352,-0.1355490088,0.3100190461,0.0528478138,0.22330302,0.1253270954,-0.3918119371,-0.0012149068,0.095629327,-0.1981257647,-0.216010198,0.1527506709,0.0591067895,0.1838099957,0.0106215868,-0.0086782463,0.0929949582,0.2169977278,-0.3573749661,-0.212029174,-0.3761959076,-0.0575140305,-0.1505009383,0.3402034342,-0.2168394774,0.131648913,0.1427595615,-0.0484839231,-0.1044859737,0.223940596,0.3518484533,-0.0390819162,0.2571777999,-0.016609231,0.3984128535,-0.0536790453,-0.1063232347,-0.1807260513,-0.1033443063,0.1016542092,0.4516169131,-0.0112113589,-0.2384910434,0.2105504125,-0.2123305202,-0.0243081972,0.2115104795,-0.1638644338,0.1225485504,-0.364127934,0.2640176713,0.4708405733,0.2212183774,0.1237124354,0.6363039017,0.1985704154,-0.1602922082,-0.3000450432,0.0745262355,0.3102485538,-0.101135999,-0.0373392925,0.1756477803,-0.1027164608,-0.1286521405,-0.0738535076,0.273021996,-0.7321617603,0.0083053699,0.1820911616,0.1216195971,-0.0009521256,-0.222410351,-0.0831374228,0.0777556822,-0.2158571482,0.1148085818,-0.118575193,0.1242495626,-0.3427777886,0.1104225591,-0.0432601124,0.0569451042,0.3175241649,0.1256694645,-0.1688387245,-0.059651278,0.1512505561,-0.2294484973,0.4016970098,-0.0197041202,-0.2360149175,0.0424856395,0.3069401979,-0.4420320988,-0.255575031,-0.059227135,-0.1526567638,-0.141263321,-0.0386343338,0.034725219,-0.2372962832,-0.0047180429,-0.3046909869,0.1028893217,0.3713427186,-0.2058525831,0.0988660082,-0.5015787482,-0.3563106656,-0.2076636106,0.0088023664,0.1774777621,-0.4386508763,-0.2434036136,-0.0980320275,0.1520785093,0.3357068598,0.1245676726,-0.2613388896,0.6007260084,-0.496250093,0.875687778,0.2869651318,-0.1486267298,-0.4010117352,0.0329538062,-0.1728947461,0.2320756316,-0.6788750887,-0.1038570404,0.1650154293,-0.0797584355,0.1138168424,0.3388640583,0.1549204737,-0.0953329578,-0.1282202452,0.0644310266,0.3268656433,-0.2837596238,0.0703485608,-0.0096958773,0.1624441892,-0.2167634517,0.2174033821,0.0343074575,0.1843425035,-0.1125897691,0.4559538662,0.1764437258,-0.1205102578,-0.3156250119,-0.1837683618,0.3253226578,-0.0280872509,-0.026879672,-0.1192952618,-0.0829102993,-0.1131267026,0.0560219474,-0.3399247825,-0.2433561981,0.0299025644,0.0243141092,0.2335766703,0.1753290445,-0.0835654512,-0.2479457259,-0.0506141856,0.0609713532,-0.055893112,0.0568838157,0.1251781583,-0.2686151862,-0.3178895414,0.167403996,0.4087355435,-0.0979360938,-0.1848382354,0.276186198,0.5985630751,-0.2991654873,-0.3962604403,-0.1816080213,-0.1041159853,0.1290626079,-0.2356887609,0.171751067,-0.002783909,-0.2309010923,-0.0256967079,0.5132235289,0.2476934642,0.3112365901,-0.4027119875,0.2516179383,-0.0123048415,-0.086805433,-0.1482175738,-0.337376684,-0.2627907693,0.0373323113,0.3305847347,-0.1344626099,-0.0364917815,0.0407440625,0.3557883203,-0.0735358521,0.3257069588,-0.0644305125,-0.1981063187,-0.1906806678,0.2980228662,0.3740843832,0.5166624188,-0.1092321575,-0.0572246984,0.0850376934,-0.0458905511,-0.0490012653,0.0168410391,-0.1722277254,-0.0378650054,0.1988481432,0.0485895835,-0.0998987854,-0.1812916398,0.009795065,0.0562791452,0.2922277153,-0.2386673987,0.0962414443,-0.5637153387,0.0709874779,-0.2885975242,-0.1303836107,0.0072157839,-0.4439607561,-0.2119714022,0.0941521823,-0.2360053658,0.218233943,-0.1610499918,-0.1597822011,-0.1504624039,-0.2889367342,0.0986514241,-0.2346133739,-0.2234230042,-0.0864400193,0.1288226098,0.1223812774,0.2256283313,0.3576847017,-0.4277692139,-0.34537521,-0.2861611247,0.3642108738,-0.1671608239,0.1216560602,0.3645741045,-0.210863471,-0.072285004,0.1510339975,0.3195462823,-0.1060249582,-0.1192033067,-0.1503362358,0.2773757279,0.024000911,-0.028316278,0.0144707328,-0.1706431657,-0.057648588,0.3715693355,-0.1595648676,0.0871455371,0.167441681,0.0440905541,0.2306474745,-0.1044093817,-0.260627687,-0.4392026067,-0.0985319316,0.1647433937,-0.196296975,-0.1257399321,-0.0366647914,-0.1095462143,0.1613349617,0.0417040139,-0.2359023392,0.0899801478,-0.0043371278,0.3883194923,-0.2089865357,0.1180841029,0.3752018213,0.1685070395,0.0596246868,-0.1573242694,-0.206185326,0.1814013869,0.3865940869,0.215642795,0.1895819902,0.2443961948,0.1597876698,0.8500338793,0.319329828,-0.4346615076,0.1205978617,-0.0580940507,-0.0644246265,-0.0762369111,-0.3137790561,-0.0662493855,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2516","title":"datasets.map pickle issue resulting in invalid mapping function","comments":"Looks like there is still an object that is not pickable in your `tokenize_function` function.\r\n\r\nYou can test if an object can be pickled and hashed by using \r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nHasher.hash(my_object)\r\n```\r\n\r\nUnder the hood it pickles the object to compute its hash, so it calls `__getstate__` when applicable.","body":"I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n","comment_length":52,"text":"datasets.map pickle issue resulting in invalid mapping function \n I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n \n Looks like there is still an object that is not pickable in your `tokenize_function` function.\r\n\r\nYou can test if an object can be pickled and hashed by using \r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nHasher.hash(my_object)\r\n```\r\n\r\nUnder the hood it pickles the object to compute its hash, so it calls `__getstate__` when applicable.","embeddings":[-0.2248874158,0.1931205094,0.1127529219,0.0287756212,0.0341960229,-0.3237702549,0.1385361105,0.195084691,0.3468089998,-0.100017868,0.1921808273,0.7253850698,-0.1929601282,0.2086696327,-0.0016190895,0.0595789626,0.0705189332,-0.0354076736,-0.0051377355,-0.064012669,-0.2173004895,0.0509161651,-0.3579756021,0.1536132991,-0.2765950263,-0.1768149883,0.0259390548,0.1186882928,-0.0299846679,-0.2711364627,0.2045458853,-0.0787939131,-0.0277055055,0.2411660254,-0.0001231694,-0.080115065,0.0161940679,-0.0863834247,-0.0041622892,-0.2175996751,-0.2230958939,-0.1239771247,-0.1235440746,-0.2294132262,0.0781571865,0.0024631347,-0.2965726852,-0.3813810945,0.6342011094,0.4318727851,0.1199069619,0.3648691177,-0.0678677708,-0.0346794352,-0.1355490088,0.3100190461,0.0528478138,0.22330302,0.1253270954,-0.3918119371,-0.0012149068,0.095629327,-0.1981257647,-0.216010198,0.1527506709,0.0591067895,0.1838099957,0.0106215868,-0.0086782463,0.0929949582,0.2169977278,-0.3573749661,-0.212029174,-0.3761959076,-0.0575140305,-0.1505009383,0.3402034342,-0.2168394774,0.131648913,0.1427595615,-0.0484839231,-0.1044859737,0.223940596,0.3518484533,-0.0390819162,0.2571777999,-0.016609231,0.3984128535,-0.0536790453,-0.1063232347,-0.1807260513,-0.1033443063,0.1016542092,0.4516169131,-0.0112113589,-0.2384910434,0.2105504125,-0.2123305202,-0.0243081972,0.2115104795,-0.1638644338,0.1225485504,-0.364127934,0.2640176713,0.4708405733,0.2212183774,0.1237124354,0.6363039017,0.1985704154,-0.1602922082,-0.3000450432,0.0745262355,0.3102485538,-0.101135999,-0.0373392925,0.1756477803,-0.1027164608,-0.1286521405,-0.0738535076,0.273021996,-0.7321617603,0.0083053699,0.1820911616,0.1216195971,-0.0009521256,-0.222410351,-0.0831374228,0.0777556822,-0.2158571482,0.1148085818,-0.118575193,0.1242495626,-0.3427777886,0.1104225591,-0.0432601124,0.0569451042,0.3175241649,0.1256694645,-0.1688387245,-0.059651278,0.1512505561,-0.2294484973,0.4016970098,-0.0197041202,-0.2360149175,0.0424856395,0.3069401979,-0.4420320988,-0.255575031,-0.059227135,-0.1526567638,-0.141263321,-0.0386343338,0.034725219,-0.2372962832,-0.0047180429,-0.3046909869,0.1028893217,0.3713427186,-0.2058525831,0.0988660082,-0.5015787482,-0.3563106656,-0.2076636106,0.0088023664,0.1774777621,-0.4386508763,-0.2434036136,-0.0980320275,0.1520785093,0.3357068598,0.1245676726,-0.2613388896,0.6007260084,-0.496250093,0.875687778,0.2869651318,-0.1486267298,-0.4010117352,0.0329538062,-0.1728947461,0.2320756316,-0.6788750887,-0.1038570404,0.1650154293,-0.0797584355,0.1138168424,0.3388640583,0.1549204737,-0.0953329578,-0.1282202452,0.0644310266,0.3268656433,-0.2837596238,0.0703485608,-0.0096958773,0.1624441892,-0.2167634517,0.2174033821,0.0343074575,0.1843425035,-0.1125897691,0.4559538662,0.1764437258,-0.1205102578,-0.3156250119,-0.1837683618,0.3253226578,-0.0280872509,-0.026879672,-0.1192952618,-0.0829102993,-0.1131267026,0.0560219474,-0.3399247825,-0.2433561981,0.0299025644,0.0243141092,0.2335766703,0.1753290445,-0.0835654512,-0.2479457259,-0.0506141856,0.0609713532,-0.055893112,0.0568838157,0.1251781583,-0.2686151862,-0.3178895414,0.167403996,0.4087355435,-0.0979360938,-0.1848382354,0.276186198,0.5985630751,-0.2991654873,-0.3962604403,-0.1816080213,-0.1041159853,0.1290626079,-0.2356887609,0.171751067,-0.002783909,-0.2309010923,-0.0256967079,0.5132235289,0.2476934642,0.3112365901,-0.4027119875,0.2516179383,-0.0123048415,-0.086805433,-0.1482175738,-0.337376684,-0.2627907693,0.0373323113,0.3305847347,-0.1344626099,-0.0364917815,0.0407440625,0.3557883203,-0.0735358521,0.3257069588,-0.0644305125,-0.1981063187,-0.1906806678,0.2980228662,0.3740843832,0.5166624188,-0.1092321575,-0.0572246984,0.0850376934,-0.0458905511,-0.0490012653,0.0168410391,-0.1722277254,-0.0378650054,0.1988481432,0.0485895835,-0.0998987854,-0.1812916398,0.009795065,0.0562791452,0.2922277153,-0.2386673987,0.0962414443,-0.5637153387,0.0709874779,-0.2885975242,-0.1303836107,0.0072157839,-0.4439607561,-0.2119714022,0.0941521823,-0.2360053658,0.218233943,-0.1610499918,-0.1597822011,-0.1504624039,-0.2889367342,0.0986514241,-0.2346133739,-0.2234230042,-0.0864400193,0.1288226098,0.1223812774,0.2256283313,0.3576847017,-0.4277692139,-0.34537521,-0.2861611247,0.3642108738,-0.1671608239,0.1216560602,0.3645741045,-0.210863471,-0.072285004,0.1510339975,0.3195462823,-0.1060249582,-0.1192033067,-0.1503362358,0.2773757279,0.024000911,-0.028316278,0.0144707328,-0.1706431657,-0.057648588,0.3715693355,-0.1595648676,0.0871455371,0.167441681,0.0440905541,0.2306474745,-0.1044093817,-0.260627687,-0.4392026067,-0.0985319316,0.1647433937,-0.196296975,-0.1257399321,-0.0366647914,-0.1095462143,0.1613349617,0.0417040139,-0.2359023392,0.0899801478,-0.0043371278,0.3883194923,-0.2089865357,0.1180841029,0.3752018213,0.1685070395,0.0596246868,-0.1573242694,-0.206185326,0.1814013869,0.3865940869,0.215642795,0.1895819902,0.2443961948,0.1597876698,0.8500338793,0.319329828,-0.4346615076,0.1205978617,-0.0580940507,-0.0644246265,-0.0762369111,-0.3137790561,-0.0662493855,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2516","title":"datasets.map pickle issue resulting in invalid mapping function","comments":"I figured it out, the problem is deep copy itself uses pickle (unless you implement `__deepcopy__`). So when I changed `__getstate__` it started throwing an error.\r\n\r\nI'm sure there's a better way of doing this, but in order to return the `__dict__` without the non-pikelable pre-tokeniser and without modifying self I removed the pre-tokenizers, did a deep copy and then re-generated it.\r\n\r\nIt does work -  although I noticed Hasher doesn't call `__hash__` if the object being hashed implements it which I feel it should? If it did I could return a hash of the tokenizers.json file instead.\r\n\r\n```\r\n    def __getstate__(self):\r\n        \"\"\"\r\n        Removes pre_tokenizer since it cannot be pickled\r\n        \"\"\"\r\n        logger.debug(\"Copy state dict\")\r\n        self.backend_tokenizer.pre_tokenizer = tokenizers.pre_tokenizers.Sequence([])\r\n        out = copy.deepcopy(self.__dict__)  #self.__dict__.copy()\r\n        self.backend_tokenizer.pre_tokenizer = self._pre_tokenizer()\r\n\r\n        return out\r\n```\r\n","body":"I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n","comment_length":126,"text":"datasets.map pickle issue resulting in invalid mapping function \n I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n \n I figured it out, the problem is deep copy itself uses pickle (unless you implement `__deepcopy__`). So when I changed `__getstate__` it started throwing an error.\r\n\r\nI'm sure there's a better way of doing this, but in order to return the `__dict__` without the non-pikelable pre-tokeniser and without modifying self I removed the pre-tokenizers, did a deep copy and then re-generated it.\r\n\r\nIt does work -  although I noticed Hasher doesn't call `__hash__` if the object being hashed implements it which I feel it should? If it did I could return a hash of the tokenizers.json file instead.\r\n\r\n```\r\n    def __getstate__(self):\r\n        \"\"\"\r\n        Removes pre_tokenizer since it cannot be pickled\r\n        \"\"\"\r\n        logger.debug(\"Copy state dict\")\r\n        self.backend_tokenizer.pre_tokenizer = tokenizers.pre_tokenizers.Sequence([])\r\n        out = copy.deepcopy(self.__dict__)  #self.__dict__.copy()\r\n        self.backend_tokenizer.pre_tokenizer = self._pre_tokenizer()\r\n\r\n        return out\r\n```\r\n","embeddings":[-0.2248874158,0.1931205094,0.1127529219,0.0287756212,0.0341960229,-0.3237702549,0.1385361105,0.195084691,0.3468089998,-0.100017868,0.1921808273,0.7253850698,-0.1929601282,0.2086696327,-0.0016190895,0.0595789626,0.0705189332,-0.0354076736,-0.0051377355,-0.064012669,-0.2173004895,0.0509161651,-0.3579756021,0.1536132991,-0.2765950263,-0.1768149883,0.0259390548,0.1186882928,-0.0299846679,-0.2711364627,0.2045458853,-0.0787939131,-0.0277055055,0.2411660254,-0.0001231694,-0.080115065,0.0161940679,-0.0863834247,-0.0041622892,-0.2175996751,-0.2230958939,-0.1239771247,-0.1235440746,-0.2294132262,0.0781571865,0.0024631347,-0.2965726852,-0.3813810945,0.6342011094,0.4318727851,0.1199069619,0.3648691177,-0.0678677708,-0.0346794352,-0.1355490088,0.3100190461,0.0528478138,0.22330302,0.1253270954,-0.3918119371,-0.0012149068,0.095629327,-0.1981257647,-0.216010198,0.1527506709,0.0591067895,0.1838099957,0.0106215868,-0.0086782463,0.0929949582,0.2169977278,-0.3573749661,-0.212029174,-0.3761959076,-0.0575140305,-0.1505009383,0.3402034342,-0.2168394774,0.131648913,0.1427595615,-0.0484839231,-0.1044859737,0.223940596,0.3518484533,-0.0390819162,0.2571777999,-0.016609231,0.3984128535,-0.0536790453,-0.1063232347,-0.1807260513,-0.1033443063,0.1016542092,0.4516169131,-0.0112113589,-0.2384910434,0.2105504125,-0.2123305202,-0.0243081972,0.2115104795,-0.1638644338,0.1225485504,-0.364127934,0.2640176713,0.4708405733,0.2212183774,0.1237124354,0.6363039017,0.1985704154,-0.1602922082,-0.3000450432,0.0745262355,0.3102485538,-0.101135999,-0.0373392925,0.1756477803,-0.1027164608,-0.1286521405,-0.0738535076,0.273021996,-0.7321617603,0.0083053699,0.1820911616,0.1216195971,-0.0009521256,-0.222410351,-0.0831374228,0.0777556822,-0.2158571482,0.1148085818,-0.118575193,0.1242495626,-0.3427777886,0.1104225591,-0.0432601124,0.0569451042,0.3175241649,0.1256694645,-0.1688387245,-0.059651278,0.1512505561,-0.2294484973,0.4016970098,-0.0197041202,-0.2360149175,0.0424856395,0.3069401979,-0.4420320988,-0.255575031,-0.059227135,-0.1526567638,-0.141263321,-0.0386343338,0.034725219,-0.2372962832,-0.0047180429,-0.3046909869,0.1028893217,0.3713427186,-0.2058525831,0.0988660082,-0.5015787482,-0.3563106656,-0.2076636106,0.0088023664,0.1774777621,-0.4386508763,-0.2434036136,-0.0980320275,0.1520785093,0.3357068598,0.1245676726,-0.2613388896,0.6007260084,-0.496250093,0.875687778,0.2869651318,-0.1486267298,-0.4010117352,0.0329538062,-0.1728947461,0.2320756316,-0.6788750887,-0.1038570404,0.1650154293,-0.0797584355,0.1138168424,0.3388640583,0.1549204737,-0.0953329578,-0.1282202452,0.0644310266,0.3268656433,-0.2837596238,0.0703485608,-0.0096958773,0.1624441892,-0.2167634517,0.2174033821,0.0343074575,0.1843425035,-0.1125897691,0.4559538662,0.1764437258,-0.1205102578,-0.3156250119,-0.1837683618,0.3253226578,-0.0280872509,-0.026879672,-0.1192952618,-0.0829102993,-0.1131267026,0.0560219474,-0.3399247825,-0.2433561981,0.0299025644,0.0243141092,0.2335766703,0.1753290445,-0.0835654512,-0.2479457259,-0.0506141856,0.0609713532,-0.055893112,0.0568838157,0.1251781583,-0.2686151862,-0.3178895414,0.167403996,0.4087355435,-0.0979360938,-0.1848382354,0.276186198,0.5985630751,-0.2991654873,-0.3962604403,-0.1816080213,-0.1041159853,0.1290626079,-0.2356887609,0.171751067,-0.002783909,-0.2309010923,-0.0256967079,0.5132235289,0.2476934642,0.3112365901,-0.4027119875,0.2516179383,-0.0123048415,-0.086805433,-0.1482175738,-0.337376684,-0.2627907693,0.0373323113,0.3305847347,-0.1344626099,-0.0364917815,0.0407440625,0.3557883203,-0.0735358521,0.3257069588,-0.0644305125,-0.1981063187,-0.1906806678,0.2980228662,0.3740843832,0.5166624188,-0.1092321575,-0.0572246984,0.0850376934,-0.0458905511,-0.0490012653,0.0168410391,-0.1722277254,-0.0378650054,0.1988481432,0.0485895835,-0.0998987854,-0.1812916398,0.009795065,0.0562791452,0.2922277153,-0.2386673987,0.0962414443,-0.5637153387,0.0709874779,-0.2885975242,-0.1303836107,0.0072157839,-0.4439607561,-0.2119714022,0.0941521823,-0.2360053658,0.218233943,-0.1610499918,-0.1597822011,-0.1504624039,-0.2889367342,0.0986514241,-0.2346133739,-0.2234230042,-0.0864400193,0.1288226098,0.1223812774,0.2256283313,0.3576847017,-0.4277692139,-0.34537521,-0.2861611247,0.3642108738,-0.1671608239,0.1216560602,0.3645741045,-0.210863471,-0.072285004,0.1510339975,0.3195462823,-0.1060249582,-0.1192033067,-0.1503362358,0.2773757279,0.024000911,-0.028316278,0.0144707328,-0.1706431657,-0.057648588,0.3715693355,-0.1595648676,0.0871455371,0.167441681,0.0440905541,0.2306474745,-0.1044093817,-0.260627687,-0.4392026067,-0.0985319316,0.1647433937,-0.196296975,-0.1257399321,-0.0366647914,-0.1095462143,0.1613349617,0.0417040139,-0.2359023392,0.0899801478,-0.0043371278,0.3883194923,-0.2089865357,0.1180841029,0.3752018213,0.1685070395,0.0596246868,-0.1573242694,-0.206185326,0.1814013869,0.3865940869,0.215642795,0.1895819902,0.2443961948,0.1597876698,0.8500338793,0.319329828,-0.4346615076,0.1205978617,-0.0580940507,-0.0644246265,-0.0762369111,-0.3137790561,-0.06624938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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2516","title":"datasets.map pickle issue resulting in invalid mapping function","comments":"I'm glad you figured something out :)\r\n\r\nRegarding hashing: we're not using hashing for the same purpose as the python `__hash__` purpose (which is in general for dictionary lookups). For example it is allowed for python hashing to not return the same hash across sessions, while our hashing must return the same hashes across sessions for the caching to work properly.","body":"I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n","comment_length":61,"text":"datasets.map pickle issue resulting in invalid mapping function \n I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` \/ `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.\r\n\r\nThe following reproduces the issue - most likely I'm missing something\r\n\r\nA simulated tokeniser which can be pickled\r\n\r\n```\r\nclass CustomTokenizer:\r\n    def __init__(self):\r\n        self.state = \"init\"\r\n\r\n    def __getstate__(self):\r\n        print(\"__getstate__ called\")\r\n        out = self.__dict__.copy()\r\n        self.state = \"pickled\"\r\n        return out\r\n    \r\n    def __setstate__(self, d):\r\n        print(\"__setstate__ called\")\r\n        self.__dict__ = d\r\n        self.state = \"restored\"\r\n\r\ntokenizer = CustomTokenizer()\r\n```\r\n\r\nTest that it actually works - prints \"__getstate__ called\" and \"__setstate__ called\"\r\n```\r\nimport pickle\r\nserialized = pickle.dumps(tokenizer)\r\nrestored = pickle.loads(serialized)\r\nassert restored.state == \"restored\"\r\n```\r\n\r\nSimulate a function that tokenises examples, when dataset.map is called, this function \r\n```\r\ndef tokenize_function(examples):\r\n    assert tokenizer.state == \"restored\" # this shouldn't fail but it does\r\n    output = tokenizer(examples)           # this will fail as tokenizer isn't really a tokenizer\r\n    return output\r\n```\r\n\r\nUse map to simulate tokenization\r\n```\r\nimport glob\r\nfrom datasets import load_dataset\r\n\r\nassert tokenizer.state == \"restored\"\r\ntrain_files = glob.glob('train*.csv')\r\nvalidation_files = glob.glob('validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n)\r\n```\r\n\r\nWhat's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ \/ __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?\r\n\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-22-a2aef4f74aaa> in <module>\r\n      8 tokenized_datasets = datasets.map(\r\n      9     tokenize_function,\r\n---> 10     batched=True,\r\n     11 )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    487                     desc=desc,\r\n    488                 )\r\n--> 489                 for k, dataset in self.items()\r\n    490             }\r\n    491         )\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1633                 fn_kwargs=fn_kwargs,\r\n   1634                 new_fingerprint=new_fingerprint,\r\n-> 1635                 desc=desc,\r\n   1636             )\r\n   1637         else:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    184         }\r\n    185         # apply actual function\r\n--> 186         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    187         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    188         # re-apply format to the output\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1961                                 indices,\r\n   1962                                 check_same_num_examples=len(input_dataset.list_indexes()) > 0,\r\n-> 1963                                 offset=offset,\r\n   1964                             )\r\n   1965                         except NumExamplesMismatch:\r\n\r\n~\/.pyenv\/versions\/3.7.6\/envs\/xxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1853                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1854             processed_inputs = (\r\n-> 1855                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1856             )\r\n   1857             if update_data is None:\r\n\r\n<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)\r\n      1 def tokenize_function(examples):\r\n----> 2     assert tokenizer.state == \"restored\"\r\n      3     tokenizer(examples)\r\n      4     return examples\r\n\r\n\r\n \n I'm glad you figured something out :)\r\n\r\nRegarding hashing: we're not using hashing for the same purpose as the python `__hash__` purpose (which is in general for dictionary lookups). For example it is allowed for python hashing to not return the same hash across sessions, while our hashing must return the same hashes across sessions for the caching to work 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2514","title":"Can datasets remove duplicated rows?","comments":"Hi ! For now this is probably the best option.\r\nWe might add a feature like this in the feature as well.\r\n\r\nDo you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\nOtherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some cases","body":"**Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno","comment_length":63,"text":"Can datasets remove duplicated rows? \n **Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno \n Hi ! For now this is probably the best option.\r\nWe might add a feature like this in the feature as well.\r\n\r\nDo you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\nOtherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2514","title":"Can datasets remove duplicated rows?","comments":"Yes, I'd like to work on this feature once I'm done with #2500, but first I have to do some research, and see if the implementation wouldn't be too complex.\r\n\r\nIn the meantime, maybe [this lib](https:\/\/github.com\/TomScheffers\/pyarrow_ops) can help. However, note that this lib operates directly on pyarrow tables and relies only on `hash` to find duplicates (e.g. `-1` and `-2` have the same hash in Python 3, so this lib will treat them as duplicates), which doesn't make much sense.","body":"**Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno","comment_length":80,"text":"Can datasets remove duplicated rows? \n **Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno \n Yes, I'd like to work on this feature once I'm done with #2500, but first I have to do some research, and see if the implementation wouldn't be too complex.\r\n\r\nIn the meantime, maybe [this lib](https:\/\/github.com\/TomScheffers\/pyarrow_ops) can help. However, note that this lib operates directly on pyarrow tables and relies only on `hash` to find duplicates (e.g. `-1` and `-2` have the same hash in Python 3, so this lib will treat them as duplicates), which doesn't make much sense.","embeddings":[0.104411073,0.0183886345,-0.1353909671,0.094954513,0.0331351422,0.1407132298,0.3450481594,0.2536354959,-0.6958328485,0.0018973832,-0.0936520845,0.3721427321,0.0341928788,0.1650433242,0.1942412257,0.0286316872,0.1286832243,0.2465856373,-0.1124954522,0.116292797,-0.1895906627,-0.2231096476,-0.3207571507,0.0351020806,-0.3403733075,0.0134709924,-0.3700193763,-0.0473617837,-0.2080128342,-0.2208465785,0.2427178174,0.5297170877,0.0183263551,0.2103604227,-0.000106261,-0.284894079,0.0180516597,0.1628575921,-0.3009908497,0.0122177238,-0.0974296927,-0.1236533374,0.1503576785,-0.0711361691,0.0399264731,-0.488966018,-0.2355798781,0.1284597963,0.1914304793,0.1222232431,0.2542186677,0.2170831412,-0.0721717402,0.1295604408,0.281085819,0.3251201808,-0.1984840035,0.4407833815,0.4495327473,-0.1497725546,0.3157269955,0.0626316667,-0.1624632478,0.145337224,0.1373353004,0.0009987081,-0.1338930428,-0.0835928544,0.0399319045,0.2834547162,0.5527540445,-0.6672785282,-0.3417749703,-0.3500338495,0.4291492999,-0.2890269756,-0.0532083958,0.0705096722,-0.0623901561,0.1691336781,-0.4275589287,-0.2888962924,-0.1135681942,-0.0553033948,-0.0859845281,-0.2437136024,0.2126117796,0.0046139881,0.1512090862,-0.0223211814,0.4632273018,-0.0744324103,0.2205946892,0.4448759556,-0.4444244206,-0.2212748528,-0.1027680039,0.1373488903,0.3310642242,0.1832380444,0.2757816017,0.0348050818,0.0536748171,0.2114180028,0.2253226489,-0.0003008748,-0.0716260001,0.1072478965,-0.0484820157,-0.171483025,-0.1903428733,0.1094561592,0.068816565,0.1245209798,0.422583878,-0.0973042846,0.2560529709,-0.1723722667,-0.3565198481,0.0375062786,-0.519122541,-0.2063179165,-0.0685030967,-0.1372722834,0.1091361493,-0.2522799373,-0.1279276907,0.0959540755,-0.3302261531,-0.2666727901,-0.0282258205,0.0720546618,-0.1864466667,-0.0160293803,0.3545638025,-0.2653107345,-0.1158275455,0.2977137864,0.1664303243,0.0528772399,0.2804608941,-0.0771169588,-0.0925080478,0.002487794,-0.1493972689,-0.2366427034,-0.1870187968,0.0621966645,-0.1377648115,0.5223926902,0.1237040982,-0.0923645198,-0.12273027,0.2812554836,-0.1491717845,-0.1853643209,-0.1511612087,0.1568148285,-0.0947597846,0.0086555341,0.1222714037,-0.1431024671,0.0737311468,-0.3464638591,-0.1126491502,0.06481307,-0.4670024514,-0.0562972575,-0.0684908032,-0.279932797,-0.1742832512,0.3257155716,-0.0579803474,0.1904354542,0.1309428364,0.097457625,0.0092130182,0.0978478566,-0.2693904638,0.030833723,0.0046320348,0.2481786311,-0.1455245912,0.2779869139,0.3345446885,-0.0485477149,0.0061093536,0.0208810754,-0.22293441,-0.401534915,-0.1754588336,-0.312045902,0.24020724,0.232738018,0.0118307928,-0.1296452135,0.1328344941,-0.3675982654,0.3119074404,-0.1483109146,-0.1385402828,-0.1938741207,0.4634333253,0.0824154094,0.1329257041,-0.3347445428,-0.4417125583,0.1908194274,-0.3296414018,-0.0624991357,-0.0673098788,-0.6913440228,0.0400846787,0.0445210971,-0.1122875512,0.2088786364,0.1710991412,-0.465197593,0.1041989923,-0.2003914863,-0.3963654041,0.1534633636,-0.22867468,0.214286834,-0.0842430815,0.489299655,0.0366029888,-0.1764849275,0.0690182224,0.4056182504,0.0476470105,0.0649266541,0.199491933,0.2082214355,-0.1004643589,-0.09535373,-0.0311935097,0.2300293446,0.1409668475,0.0056995028,-0.0579594113,0.1789648384,0.0268166959,0.0631047487,-0.3733065426,0.4113070071,-0.3832029104,-0.1537827253,0.0186023284,-0.1228374243,-0.0743722618,-0.150757432,0.1481102556,-0.1434396803,0.1585226655,0.3084122539,-0.1017345488,0.3747660816,-0.3540122211,0.2607422471,-0.1475556642,-0.277892679,0.1260588467,0.007866648,0.0648228303,-0.0456085727,0.2205125839,0.1937395185,0.2955671549,0.233207047,0.1198237985,0.2788223624,0.0400637686,0.0992880315,0.0858443528,-0.1198733002,0.0092498967,-0.1813764274,0.0222154818,0.2317917347,-0.362688899,0.143701449,0.2026194632,0.2773590982,-0.2206283063,-0.191067934,-0.5655443072,-0.4531567693,-0.0762024745,0.0453591384,-0.1780557483,0.0915171355,0.5150851011,0.1038604528,0.013792702,0.1103627086,-0.0746416897,0.3941479921,-0.2792817056,-0.0394747742,-0.1317638457,-0.1765998751,0.0203454494,0.2772711813,0.1195707992,0.1093560979,0.5857251883,0.0744352937,0.0695963129,-0.6109545827,-0.2983681858,-0.0311367474,0.0372670479,0.1495423168,0.085422568,-0.027060302,-0.3043860197,0.0325498171,-0.0417546108,-0.0985049233,-0.1936568767,-0.1042864472,0.1357042491,0.2669232488,-0.1313287169,-0.2455844283,-0.1122041568,-0.005250806,0.3367560804,-0.1136648431,0.162663877,-0.3023939133,0.016721664,-0.1321528405,-0.0836809278,-0.1281010956,-0.2123140395,-0.2869140804,0.0213606544,0.0162554774,-0.1465837061,-0.1533123404,0.1851413399,-0.200535804,0.4018159509,0.0114754131,-0.311875701,0.153409034,0.5076434016,0.0489151403,0.0520673953,0.35602355,0.3535315394,-0.0741010532,0.0246507879,0.0310232937,-0.1591763198,0.1633944958,0.0025987814,0.0769772753,0.138202548,0.2860575914,0.4721869826,0.2066885829,-0.095202677,0.1265106648,-0.0693728775,0.2181539088,0.0455579385,-0.3102408946,-0.1662701964,-0.3074214756,0.1874792874,0.4478248358,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2514","title":"Can datasets remove duplicated rows?","comments":"> Hi ! For now this is probably the best option.\r\n> We might add a feature like this in the feature as well.\r\n> \r\n> Do you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\n> Otherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some cases\r\n\r\nGreat if this is can be done. Thanks!!\r\n\r\nNot sure if you are asking me. In any case I don't know of any unfortunately :( in practice if data is really large we normally do it with spark (only for info. I understand this is not useful in developing this library..)","body":"**Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno","comment_length":119,"text":"Can datasets remove duplicated rows? \n **Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno \n > Hi ! For now this is probably the best option.\r\n> We might add a feature like this in the feature as well.\r\n> \r\n> Do you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\n> Otherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some cases\r\n\r\nGreat if this is can be done. Thanks!!\r\n\r\nNot sure if you are asking me. In any case I don't know of any unfortunately :( in practice if data is really large we normally do it with spark (only for info. I understand this is not useful in developing this library..)","embeddings":[0.1012984961,-0.1910532117,-0.1392705292,0.2864695787,0.0751885027,0.2036610991,0.2715580463,0.0986695886,-0.3335005939,-0.0163339805,-0.00081,0.2826299071,0.0401865058,0.1977713555,-0.0249465518,-0.0297985617,0.1026341692,0.13065283,-0.1036721319,0.0501254201,-0.0256591607,-0.1465695798,-0.270499438,-0.1782128066,-0.4471103847,-0.061712347,-0.4829657078,-0.0588315502,-0.1587790251,0.0186243448,0.2496173531,0.5069915652,0.0081406729,0.3449687064,-0.0001065691,-0.4029147923,-0.0071389847,0.1132893115,-0.1967679709,0.0577062555,-0.2064548135,-0.0378361829,0.0811134204,-0.0923915878,0.0455432497,-0.2710401416,-0.309235394,-0.2610732615,0.1535585672,0.0755504593,0.2574928105,-0.0642401427,-0.2274297774,0.0427543931,0.1963112056,0.5021438599,-0.140786618,0.2988500893,0.5304868817,-0.0445952229,0.1433535367,0.0672994182,-0.1113487929,0.1386645436,0.290704757,-0.1952319741,-0.2926008701,-0.1928344518,0.20410797,0.3246283233,0.8173855543,-0.491063118,-0.2823204696,-0.1588136703,0.4048905373,0.0392115638,-0.0883467644,0.0993426442,-0.0085499687,0.0695747882,-0.3531998694,-0.5799795389,-0.1640008539,-0.1828123778,0.0733937472,-0.4417580664,0.0995519087,-0.0448228754,0.1841219217,-0.0781926736,0.3813770711,-0.1632514894,0.1593037695,0.4425401986,-0.4141801298,-0.3620016873,-0.2003977299,-0.0943413004,0.3296689987,0.1987353414,0.3466756046,-0.0675841346,-0.1132863685,0.2721816897,0.3223301768,0.0195970554,-0.1558397412,0.011317079,0.1128345132,-0.1993379742,-0.2604490221,-0.0415460132,-0.0300454013,0.2280072421,0.3002118468,-0.0754928663,0.0775921121,-0.3900251091,-0.2964450717,0.0931489766,-0.2986606956,-0.1495645493,-0.0994157419,-0.1295012385,0.086814411,-0.1445708573,-0.0661547258,-0.1551336497,-0.2972076833,-0.227149263,-0.0256302543,0.1597307473,-0.223503679,-0.0066864784,0.3262166381,-0.2521714568,-0.0575285591,0.2420903593,0.1999292225,-0.1301860511,0.341953516,-0.284827441,0.0834751949,-0.0906566679,-0.2567923665,-0.2523036003,-0.2064595371,0.1417122781,-0.0916771069,0.4854812622,0.1296812594,-0.0932272449,-0.1475782394,0.2590628862,-0.0467090011,-0.1065523401,-0.27303496,0.2472009212,-0.1362901628,0.0149164218,0.1113040894,-0.0092505803,0.113448061,-0.2967493236,-0.0482333116,0.2049471736,-0.5000967979,0.0429928601,-0.1776574552,-0.294242382,-0.0986089185,0.4649655819,-0.0501374379,0.0564437509,0.0138209406,-0.0734155551,0.0800505951,0.0723868832,-0.164642632,0.3385246396,0.0167544726,0.1923589408,-0.140732497,0.4406460524,0.3836031556,-0.0312123038,0.1031772122,0.0156770442,-0.2498232871,-0.3883444071,-0.234484151,-0.4616602659,0.2157368213,0.0939397588,0.1487682909,-0.0323660076,0.1957757473,-0.0963867307,0.1839366704,-0.0687402487,-0.0946717411,-0.0008339873,0.448630482,0.1039017588,0.1751072258,-0.3972981274,-0.3710746765,0.0808521435,-0.1925429106,-0.1053730324,0.2508815825,-0.65753901,0.1710532457,-0.1175081357,-0.0220814086,0.1245761886,0.1292590648,-0.4144191146,0.0347934216,-0.3078157008,-0.2813328803,0.2298516035,-0.3113639057,0.0324754938,-0.2253491729,0.4157766104,0.1745212674,-0.0179054551,0.1903279573,0.3661206067,-0.0345994048,0.0753782913,-0.0093812048,0.1028031856,0.1476318687,-0.0036964882,0.187470898,0.2373096645,0.2151127607,-0.073116295,0.0110310037,0.3312472701,0.1400051415,-0.0228340421,-0.4782250226,0.1685675234,-0.5081922412,-0.1461952925,0.0462404415,-0.0886555314,0.0201870482,-0.1640032381,0.1715242714,-0.1282509118,0.1904507577,0.1697271615,-0.1296025068,0.3815738857,-0.4475439489,0.0641821921,-0.1406338811,-0.1185539067,0.03778027,0.0418273583,-0.018564444,-0.1150999144,0.3880048096,0.3194039762,0.3859788179,0.2076179981,0.162846908,0.2141689062,0.3135331571,0.0956354737,0.1016093567,-0.1003510654,0.0071907956,-0.1843217164,-0.1023382246,0.2157649696,-0.3473798633,0.0111862263,0.2527470589,0.2058586031,-0.0462606624,-0.2968865633,-0.5272918344,-0.6229697466,-0.10584905,0.2558296025,-0.17891334,0.1530619711,0.425755769,0.1220294461,0.1268965155,-0.0463796481,-0.1135757491,0.6114587784,-0.2947301567,-0.1177484468,-0.0578323454,-0.0651275218,-0.0194145516,0.2763374448,0.189846918,0.3241818845,0.7310884595,0.2817598581,0.1069163233,-0.6828422546,-0.3100990951,-0.1548416018,0.0220710784,0.2122166306,0.1046079472,0.0073591014,-0.1370043904,0.0865820646,-0.1891391426,-0.0357156247,-0.1473559737,-0.2665337026,0.2026568502,0.1801410615,-0.1981701106,-0.276049763,-0.00624335,-0.2233166397,0.345307678,-0.2187124044,0.1618303508,-0.4644885957,-0.0877307281,-0.070093058,0.179972589,0.0134258084,-0.2804414332,-0.2759269178,-0.0374962837,0.0590817742,-0.2052407712,-0.0942788497,0.2906415164,-0.0165990181,0.3591819108,-0.2501959801,-0.2154522091,0.0508950837,0.1491317302,0.0574434325,-0.0064027566,0.2411807626,0.2258652598,-0.0641691908,0.0476743691,-0.1351560056,-0.1079881191,0.0796849057,0.0920585915,0.2104487419,0.2346976846,0.1868864745,0.3986666799,0.4363937378,-0.0304672923,0.1008327827,0.1341678947,0.131381765,-0.0042360174,-0.3950912058,-0.0240605231,-0.281125366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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2514","title":"Can datasets remove duplicated rows?","comments":"Hello,\r\n\r\nI'm also interested in this feature.\r\nHas there been progress on this issue?\r\n\r\nCould we use a similar trick as above, but with a better hashing algorithm like SHA?\r\n\r\nWe could also use a [bloom filter](https:\/\/en.wikipedia.org\/wiki\/Bloom_filter), should we care a lot about collision in this case?","body":"**Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno","comment_length":47,"text":"Can datasets remove duplicated rows? \n **Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno \n Hello,\r\n\r\nI'm also interested in this feature.\r\nHas there been progress on this issue?\r\n\r\nCould we use a similar trick as above, but with a better hashing algorithm like SHA?\r\n\r\nWe could also use a [bloom filter](https:\/\/en.wikipedia.org\/wiki\/Bloom_filter), should we care a lot about collision in this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2514","title":"Can datasets remove duplicated rows?","comments":"For reference, we can get a solution fairly easily if we assume that we can hold in memory all unique values. \r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom itertools import cycle\r\nfrom functools import partial\r\n\r\nmemory = set()\r\ndef is_unique(elem:Any , column: str, memory: set) -> bool:\r\n    if elem[column] in memory:\r\n        return False\r\n    else:\r\n        memory.add(elem[column])\r\n        return True\r\n\r\n# Example dataset\r\nds = Dataset.from_dict({\"col1\" : [sent for i, sent in zip(range(10), cycle([\"apple\", \"orange\", \"pear\"]))],\r\n                                      \"col2\": [i % 5 for i in range(10)]})\r\n\r\n# Drop duplicates in `ds` on \"col1\"\r\nds2 = ds.filter(partial(is_unique, column=\"col1\", memory=memory))\r\n```\r\n\r\nOf course, we can improve the API so that we can introduce `Dataset.drop_duplicates`.\r\nFor the parallel version, we can use a shared memory set.","body":"**Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno","comment_length":117,"text":"Can datasets remove duplicated rows? \n **Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno \n For reference, we can get a solution fairly easily if we assume that we can hold in memory all unique values. \r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom itertools import cycle\r\nfrom functools import partial\r\n\r\nmemory = set()\r\ndef is_unique(elem:Any , column: str, memory: set) -> bool:\r\n    if elem[column] in memory:\r\n        return False\r\n    else:\r\n        memory.add(elem[column])\r\n        return True\r\n\r\n# Example dataset\r\nds = Dataset.from_dict({\"col1\" : [sent for i, sent in zip(range(10), cycle([\"apple\", \"orange\", \"pear\"]))],\r\n                                      \"col2\": [i % 5 for i in range(10)]})\r\n\r\n# Drop duplicates in `ds` on \"col1\"\r\nds2 = ds.filter(partial(is_unique, column=\"col1\", memory=memory))\r\n```\r\n\r\nOf course, we can improve the API so that we can introduce `Dataset.drop_duplicates`.\r\nFor the parallel version, we can use a shared memory 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2514","title":"Can datasets remove duplicated rows?","comments":"An approach that works assuming you can hold the all the unique document hashes in memory:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndef get_hash(example):\r\n    \"\"\"Get hash of content field.\"\"\"\r\n    return {\"hash\": hash(example[\"content\"])} # can use any hashing function here\r\n    \r\ndef check_uniques(example, uniques):\r\n    \"\"\"Check if current hash is still in set of unique hashes and remove if true.\"\"\"\r\n    if example[\"hash\"] in uniques:\r\n        uniques.remove(example[\"hash\"])\r\n        return True\r\n    else:\r\n        return False\r\n\r\nds = load_dataset(\"some_dataset\")\r\nds = ds.map(get_hash)\r\nuniques = set(ds.unique(\"hash\"))\r\nds_filter = ds.filter(check_uniques, fn_kwargs={\"uniques\": uniques})\r\n```\r\nIf the `uniques` could be stored in arrow then no additional memory would used at all but I don't know if this is possible.\r\n","body":"**Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno","comment_length":105,"text":"Can datasets remove duplicated rows? \n **Is your feature request related to a problem? Please describe.**\r\ni find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..\r\n\r\n\r\n**Describe the solution you'd like**\r\nhave a functionality of \" remove duplicated rows\"\r\n\r\n**Describe alternatives you've considered**\r\nconvert dataset to pandas, remove duplicate, and convert back...\r\n\r\n\r\n**Additional context**\r\nno \n An approach that works assuming you can hold the all the unique document hashes in memory:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndef get_hash(example):\r\n    \"\"\"Get hash of content field.\"\"\"\r\n    return {\"hash\": hash(example[\"content\"])} # can use any hashing function here\r\n    \r\ndef check_uniques(example, uniques):\r\n    \"\"\"Check if current hash is still in set of unique hashes and remove if true.\"\"\"\r\n    if example[\"hash\"] in uniques:\r\n        uniques.remove(example[\"hash\"])\r\n        return True\r\n    else:\r\n        return False\r\n\r\nds = load_dataset(\"some_dataset\")\r\nds = ds.map(get_hash)\r\nuniques = set(ds.unique(\"hash\"))\r\nds_filter = ds.filter(check_uniques, fn_kwargs={\"uniques\": uniques})\r\n```\r\nIf the `uniques` could be stored in arrow then no additional memory would used at all but I don't know if this is possible.\r\n","embeddings":[0.1261449903,-0.0498199724,-0.1053994149,0.2112491429,0.1417159885,0.3746891618,0.3247413933,0.1965273321,-0.2755319178,-0.1827451289,-0.0420246199,0.3148609996,-0.1531117857,0.0484707244,0.0363267921,0.0288591776,0.0412529372,0.036055088,-0.0045450586,-0.0276067611,-0.1966246665,-0.2204579562,-0.2598937452,-0.2703469992,-0.5386345387,-0.2057924122,-0.4531961381,-0.0423712619,-0.0952114835,-0.1666034013,0.2018258423,0.52654773,0.0193989333,0.3828200698,-0.0001092966,-0.3067096174,-0.0947595239,0.1381430179,-0.1937684715,-0.0548612587,-0.3981921971,-0.1445530504,-0.049995359,-0.1224653795,0.0363890156,-0.5011429787,-0.2548823357,-0.4400995672,0.1394600719,0.1003673151,0.1981304586,-0.0450377427,-0.1639250666,0.1757764965,0.0763428807,0.2950226963,-0.1750657707,0.3187934756,0.2092318833,-0.1993925422,0.2845774293,0.2516032159,-0.1373167485,0.1364164054,0.210017249,0.0167378262,-0.2134537697,-0.1328130066,0.2215414047,0.3434231877,0.5847854614,-0.5414519906,-0.4034639597,-0.3402245045,0.3673145175,-0.0258561224,-0.115136534,0.1642866582,-0.0529217683,0.0703152716,-0.4981235862,-0.3805653155,0.0003692086,-0.0809237659,0.066951558,-0.3899591863,0.2063949853,-0.0992162153,0.358288914,0.0353897624,0.3911629915,-0.2570682466,0.1614165902,0.5165131688,-0.3508859277,-0.1184653044,-0.2925266027,-0.1179226786,0.2304642349,-0.0150403781,0.4478669763,-0.0682113171,-0.0305137839,0.2626534104,0.2951313853,0.0590492301,-0.0733577088,0.3498743474,-0.1533007473,-0.1454315782,-0.2869285047,-0.0260607656,-0.0020258848,0.0910677984,0.5314266086,-0.1906805038,0.3284093738,-0.0535348766,-0.3242043853,0.168215096,-0.5680499077,-0.1051224768,-0.1321203709,-0.179527849,0.1994508356,-0.1247443184,0.0615183823,-0.0754744038,-0.2339508384,-0.066043824,-0.1155398414,0.2052506804,-0.2043918967,-0.0173669159,0.3584105074,-0.3459075987,-0.0796059594,0.2204957902,0.0256221686,-0.1542462856,0.3456593156,-0.1912165135,0.0948999748,-0.1210706607,-0.2101789266,-0.0988491848,-0.1125513613,0.1043056324,-0.096074976,0.3962144554,0.0686593503,0.0254265796,0.0531044006,0.2110591382,0.1331312507,-0.1520657837,-0.2677246928,0.236434117,0.0478814095,0.2267286927,0.196650371,-0.0894385353,-0.2204419822,-0.2262220085,-0.097638987,0.2936058044,-0.2888216376,-0.0975809768,-0.0776235759,-0.1001299545,-0.295930624,0.3554242551,0.0507837012,0.1774023622,0.011362941,0.0074281795,0.047048796,-0.3099355102,-0.2108721286,0.2002336532,0.1946757287,0.482313633,-0.2377370149,0.3267915249,0.3380859792,0.0963769555,0.1292691082,-0.1031271964,-0.2088990211,-0.3316685557,-0.2033809125,-0.3766828477,0.1321235895,-0.0190105792,0.2201158106,-0.0806149766,0.1101590246,-0.1228384078,0.2968446016,0.0277450271,-0.0695924833,0.0730373412,0.3441322148,-0.0273454115,0.202705428,-0.3975768983,-0.5348985195,0.2930077314,0.0477687903,-0.1512120813,0.147555843,-0.5930725336,0.1414064616,-0.0749246106,-0.0726198554,0.0470244735,0.1481486559,-0.4443629682,-0.0475459322,-0.2937520146,-0.3607999682,0.2687453032,-0.2815106511,0.1999357492,-0.2318865955,0.2075687498,0.222274363,-0.0090274783,0.0100139016,0.3330664635,-0.0335352011,0.0746714473,0.0399494618,0.1695877761,0.1136396527,0.0601735823,0.2029958963,0.3996711969,0.0935216919,-0.1716491729,0.0141592948,0.3009273112,0.1235131174,-0.0276988521,-0.3993589878,0.3146163225,-0.4298528135,-0.0575008541,0.0164186805,-0.0954096913,0.0018809461,-0.176852271,0.1380733699,-0.3050528467,0.1752087027,0.1254947186,-0.0406945869,0.3490583599,-0.21665591,0.1600874364,-0.2532988489,0.0127506675,0.0337242819,0.0206080619,0.0109078055,-0.0365497768,0.079020679,0.4144412577,0.3378670812,0.1490991563,0.3150014579,0.3108710647,0.2954062819,0.1106327027,0.1282185465,-0.0882220939,-0.0582973063,-0.0009177265,-0.1356182545,0.2068499625,-0.3065578043,0.2379710972,0.1844649166,0.2365675569,-0.1630847603,-0.1415455937,-0.5337043405,-0.3409025669,-0.050180275,0.1977475584,0.0156445932,0.1414390951,0.2753534615,0.1625239998,0.1454094201,0.0191320833,-0.3190606534,0.4629014432,-0.1990870386,0.0225880221,-0.0640287623,-0.1739008129,0.0884156525,0.2301791608,0.2323429883,0.204211548,0.621004045,0.2441425771,0.0679765195,-0.658003509,-0.0741540492,-0.0978678912,0.0288700443,0.22588633,-0.0046912394,-0.0354907028,-0.3405808508,0.1415921897,-0.106417127,-0.0755393952,-0.1343332976,-0.243973434,0.239606306,0.2124567032,0.0400987789,-0.2142073065,-0.0039959196,-0.0372929014,0.2396031767,-0.1559539139,0.1253541559,-0.3214847744,0.0561253205,-0.1556967795,0.1205299124,-0.0454231724,-0.2339591533,-0.3952290416,0.0404440761,0.1107518002,-0.1076601148,0.0403423533,0.1422195137,-0.1191192418,0.6033841968,-0.1170186996,-0.1445465088,0.1317227483,0.0857350752,0.0714639351,-0.0307247378,0.2304331362,0.2737774551,-0.0953337848,-0.0324648209,0.1405442655,-0.1327437907,0.0036135924,0.1288897991,0.0931657255,0.2433430403,0.3583599031,0.3802568913,0.513431251,-0.0166825354,0.0212008506,-0.0166837871,0.1962204874,0.0569188297,-0.5418906808,-0.3005375266,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2511","title":"Add C4","comments":"Update on this: I'm computing the checksums of the data files. It will be available soon","body":"## Adding a Dataset\r\n- **Name:** *C4*\r\n- **Description:** *https:\/\/github.com\/allenai\/allennlp\/discussions\/5056*\r\n- **Paper:** *https:\/\/arxiv.org\/abs\/1910.10683*\r\n- **Data:** *https:\/\/huggingface.co\/datasets\/allenai\/c4*\r\n- **Motivation:** *Used a lot for pretraining*\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nShould fix https:\/\/github.com\/huggingface\/datasets\/issues\/1710","comment_length":16,"text":"Add C4 \n ## Adding a Dataset\r\n- **Name:** *C4*\r\n- **Description:** *https:\/\/github.com\/allenai\/allennlp\/discussions\/5056*\r\n- **Paper:** *https:\/\/arxiv.org\/abs\/1910.10683*\r\n- **Data:** *https:\/\/huggingface.co\/datasets\/allenai\/c4*\r\n- **Motivation:** *Used a lot for pretraining*\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nShould fix https:\/\/github.com\/huggingface\/datasets\/issues\/1710 \n Update on this: I'm computing the checksums of the data files. It will be available soon","embeddings":[-0.2586114407,-0.2368931323,-0.2156255245,0.0744192898,0.2085535973,-0.0874803811,0.1077219546,0.3031297326,0.0643011406,0.3231354058,-0.2132708132,0.0666778013,-0.1542895585,0.4332842529,-0.0734491944,0.0308050662,0.0055632726,0.2454853207,-0.2516648471,-0.061238613,-0.1237060875,0.1600955725,0.1151486561,-0.3427098691,-0.2370680273,-0.0282374881,-0.0126363691,0.2399484366,-0.2678383291,-0.2220925242,0.1105905995,0.2579703927,-0.1301324666,0.3192816377,-0.0001002748,-0.0244540684,-0.0022872835,-0.1104863659,-0.0692491382,0.2499464452,-0.0230546407,-0.0328948461,-0.0620339401,-0.1048000455,-0.3629558384,0.0126546863,0.0622265562,-0.1381947398,0.3689251542,0.227187708,0.3632343411,0.3869170845,-0.0194074344,-0.1629914492,0.1113213673,0.0167693719,-0.0871038735,0.2593570054,0.2584525049,0.1209056899,-0.2002436519,0.2841012478,0.1847804785,0.0739731714,0.1756887287,0.0254423451,-0.0550467111,0.0629208535,0.0187196117,0.0853793174,0.1215298846,-0.2831244171,-0.3110687137,-0.1756326556,0.0741557702,-0.3809302449,0.1468225867,0.0236500967,0.008267899,-0.0215166695,-0.1706751734,0.0240884051,-0.1968894452,-0.0516155809,0.1005559191,0.0680278316,-0.2644309998,-0.2472828627,0.2497013658,0.0335759297,-0.3803047836,0.1121621057,-0.0670740008,-0.0589751825,-0.0907467976,-0.2901783288,0.0382392816,0.2546216547,0.3078463078,0.1262143254,0.1025282964,0.2484940588,-0.0234189685,0.1000332087,-0.1276970506,-0.078260839,0.035893105,-0.2585762441,0.3095819652,0.1944138557,-0.1259980649,-0.1424466521,0.0289675854,-0.1575796157,-0.0986094177,0.0520872958,-0.1677925587,-0.2475500852,-0.2214194685,0.0771839321,0.0888821557,0.0077095767,0.1176060364,0.5735186338,-0.0618299097,-0.1944669485,0.0046390933,0.2758075297,-0.1213817969,-0.1446335316,-0.3123088777,-0.0546745025,-0.2895224392,0.0227369461,0.3508206904,-0.0656067133,0.3327207863,0.0534064099,0.2864612639,0.1202825606,0.1082647294,0.0188669022,0.1733444482,0.0356118828,-0.0004558177,-0.2364005297,0.1209374517,-0.0725719705,-0.0417091288,-0.4723997414,0.0708002076,0.0765203163,-0.2188246995,0.3166131079,0.002436931,-0.0092682065,-0.0422471538,0.0561761521,-0.0712293535,-0.1864610016,0.0144609539,0.1856624931,-0.0902405903,-0.1852321327,0.1604566276,0.2504974306,0.0348879062,-0.1383182108,0.2225313634,-0.1801272184,0.0053034388,0.1121449545,0.0546575896,-0.1318762898,-0.206851691,0.0868494809,-0.0757228285,-0.1791615039,-0.2858584821,0.0691651702,-0.1840796471,-0.011854006,0.1742110401,0.1349700242,0.0025919797,-0.1063934565,-0.0471261404,0.0593735427,0.0134822046,0.1410415769,-0.2126968503,-0.2363345474,-0.1547973603,0.1216252521,0.1423418671,-0.1273562461,0.2024533153,-0.1252297759,0.3256566823,-0.3942752182,0.0615581498,0.1828734279,0.4015356302,0.021906985,-0.1932616085,-0.1827530116,-0.2574688494,0.0062637813,-0.0590291247,0.1451519877,0.0297113135,-0.3581786156,-0.0883436203,-0.2195280492,-0.1305889934,-0.0849122107,0.3844880164,0.2899780571,0.0879077539,-0.0343079455,0.0201949701,0.3061675727,-0.190151751,0.0976372734,-0.0789303705,0.1960108727,-0.1100703776,-0.1417631805,0.3407817483,0.1319140643,-0.0608002953,-0.1108487546,0.1443618685,0.3013299406,-0.0395784751,0.2133542299,0.2270402163,0.4914596379,0.0338227712,-0.158936128,0.0062979907,0.1531943828,-0.010462366,0.0646910369,-0.1888082325,0.5184321404,0.0301831048,-0.1605615616,0.2123947144,-0.1512403339,0.2263773531,-0.1737162322,-0.0606891066,-0.3493960798,0.1980843395,-0.0980962515,0.2594591081,-0.1316393167,-0.4100782275,0.0487894341,0.2338222265,0.1663601696,0.170386672,-0.0405135304,-0.0363341756,0.0802968368,0.1401967555,0.1761394441,0.213407591,0.5047037005,0.1331339777,0.0588973314,-0.1254735142,-0.1863913238,0.1383117884,-0.2588725984,-0.0642817765,0.2351440638,0.0219158251,-0.1701902002,-0.6001666188,0.0372480676,-0.249778524,-0.0394537561,-0.2543345094,-0.0752418265,-0.1240677088,-0.2492334992,-0.1242171004,0.0269252509,-0.1130862609,-0.1818078905,0.199516654,0.1633022428,-0.3851896822,0.0591588542,-0.2787411213,0.2953506112,0.0269858334,0.0085841501,0.0370016024,0.085481979,0.0408057943,0.2772705853,0.2907311916,0.0665358976,0.628795445,-0.3146470189,0.1830504537,-0.2187590152,-0.5036549568,0.1665034592,-0.1542676091,0.1319329143,0.2156217396,0.1723758429,-0.0999767333,-0.3396267891,0.1567200869,-0.3860035539,-0.3319838941,-0.046469871,-0.0542919151,0.0138935391,-0.3315369487,-0.1770873517,-0.0326982588,-0.2762327194,0.410189718,0.1199416295,0.013964761,0.1550645232,0.0994469449,0.1597021371,-0.3217945695,0.2671185434,-0.3387693465,-0.4841817319,0.2318953276,-0.3925537467,-0.4123530984,-0.192890808,-0.1043146774,0.3161990345,-0.0530861989,-0.2953405976,-0.2839132845,-0.1251228899,0.2562994063,0.0690907761,0.0794284791,0.2865327001,-0.0360680148,-0.3148214519,-0.2869904041,0.009124686,0.3432369232,-0.2602678835,0.0930616409,-0.3781878054,0.0193489045,0.0591104999,0.3250522316,0.180475384,-0.2283665389,0.3548689485,-0.1520234644,0.5535254478,0.0695398077,-0.1883889437,0.2312797308,-0.00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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2508","title":"Load Image Classification Dataset from Local ","comments":"Hi ! Is this folder structure a standard, a bit like imagenet ?\r\nIn this case maybe we can consider having a dataset loader for cifar-like, imagenet-like, squad-like, conll-like etc. datasets ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nmy_custom_cifar = load_dataset(\"cifar_like\", data_dir=\"path\/to\/data\/dir\")\r\n```\r\n\r\nLet me know what you think","body":"**Is your feature request related to a problem? Please describe.**\r\nYes - we would like to load an image classification dataset with datasets without having to write a custom data loader.\r\n\r\n**Describe the solution you'd like**\r\n\r\nGiven a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like \"cifar10\".\r\n\r\n**Describe alternatives you've considered**\r\n\r\nImplement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path)\r\n\r\nWrite custom data loader logic\r\n\r\n**Additional context**\r\n\r\nWe're training ViT on custom dataset\r\n","comment_length":48,"text":"Load Image Classification Dataset from Local  \n **Is your feature request related to a problem? Please describe.**\r\nYes - we would like to load an image classification dataset with datasets without having to write a custom data loader.\r\n\r\n**Describe the solution you'd like**\r\n\r\nGiven a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like \"cifar10\".\r\n\r\n**Describe alternatives you've considered**\r\n\r\nImplement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path)\r\n\r\nWrite custom data loader logic\r\n\r\n**Additional context**\r\n\r\nWe're training ViT on custom dataset\r\n \n Hi ! Is this folder structure a standard, a bit like imagenet ?\r\nIn this case maybe we can consider having a dataset loader for cifar-like, imagenet-like, squad-like, conll-like etc. datasets ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nmy_custom_cifar = load_dataset(\"cifar_like\", data_dir=\"path\/to\/data\/dir\")\r\n```\r\n\r\nLet me know what you 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2508","title":"Load Image Classification Dataset from Local ","comments":"@lhoestq I think we'll want a generic `image-folder` dataset (same as 'imagenet-like'). This is like `torchvision.datasets.ImageFolder`, and is something vision folks are used to seeing.","body":"**Is your feature request related to a problem? Please describe.**\r\nYes - we would like to load an image classification dataset with datasets without having to write a custom data loader.\r\n\r\n**Describe the solution you'd like**\r\n\r\nGiven a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like \"cifar10\".\r\n\r\n**Describe alternatives you've considered**\r\n\r\nImplement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path)\r\n\r\nWrite custom data loader logic\r\n\r\n**Additional context**\r\n\r\nWe're training ViT on custom dataset\r\n","comment_length":25,"text":"Load Image Classification Dataset from Local  \n **Is your feature request related to a problem? Please describe.**\r\nYes - we would like to load an image classification dataset with datasets without having to write a custom data loader.\r\n\r\n**Describe the solution you'd like**\r\n\r\nGiven a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like \"cifar10\".\r\n\r\n**Describe alternatives you've considered**\r\n\r\nImplement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path)\r\n\r\nWrite custom data loader logic\r\n\r\n**Additional context**\r\n\r\nWe're training ViT on custom dataset\r\n \n @lhoestq I think we'll want a generic `image-folder` dataset (same as 'imagenet-like'). This is like `torchvision.datasets.ImageFolder`, and is something vision folks are used to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2508","title":"Load Image Classification Dataset from Local ","comments":"Opening this back up, since I'm planning on tackling this. Already posted a quick version of it on my account on the hub.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('nateraw\/image-folder', data_files='PetImages\/')\r\n```","body":"**Is your feature request related to a problem? Please describe.**\r\nYes - we would like to load an image classification dataset with datasets without having to write a custom data loader.\r\n\r\n**Describe the solution you'd like**\r\n\r\nGiven a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like \"cifar10\".\r\n\r\n**Describe alternatives you've considered**\r\n\r\nImplement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path)\r\n\r\nWrite custom data loader logic\r\n\r\n**Additional context**\r\n\r\nWe're training ViT on custom dataset\r\n","comment_length":33,"text":"Load Image Classification Dataset from Local  \n **Is your feature request related to a problem? Please describe.**\r\nYes - we would like to load an image classification dataset with datasets without having to write a custom data loader.\r\n\r\n**Describe the solution you'd like**\r\n\r\nGiven a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like \"cifar10\".\r\n\r\n**Describe alternatives you've considered**\r\n\r\nImplement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path)\r\n\r\nWrite custom data loader logic\r\n\r\n**Additional context**\r\n\r\nWe're training ViT on custom dataset\r\n \n Opening this back up, since I'm planning on tackling this. Already posted a quick version of it on my account on the hub.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('nateraw\/image-folder', data_files='PetImages\/')\r\n```","embeddings":[-0.2027493119,-0.188690275,0.0131782666,0.4011635482,0.2593050301,-0.0279740077,0.1975970417,0.1451179683,0.3119898438,0.2857654095,-0.1935351789,0.1235475317,-0.3047415912,0.3270210922,0.0650774762,-0.2292608172,-0.2278257012,0.3378814161,-0.1562044919,-0.2475795001,-0.0562427118,-0.0963324755,0.1660833657,-0.0088663884,-0.3098389208,0.1793593615,-0.2453823984,0.5447191,-0.1185016185,-0.1016760245,0.4887914062,0.0333637074,0.3943757117,0.5124262571,-0.0001068552,0.087266162,-0.1294454187,-0.265117079,-0.0608895496,-0.2519886792,-0.0206751078,-0.1543317139,0.2418370992,-0.2121085674,-0.4997153878,-0.0551615655,0.0461996533,-0.3964985013,0.4658918977,0.1928687394,0.174645111,0.126873374,-0.342350632,0.02665288,-0.2240594476,0.4555782974,-0.1381314993,0.4050805867,0.4020667374,-0.2422726005,-0.0563127808,0.2717686594,0.0958084315,-0.0036633222,0.594912529,0.3227715492,-0.1611328721,-0.20971632,-0.185771212,0.1864373982,0.1650446355,-0.0789068192,-0.1229935661,-0.1892203987,0.1058700681,-0.1091682389,0.0645306259,0.1194690764,-0.4392844141,0.2350859642,-0.2582420409,-0.1784317493,-0.4480182528,0.0994268954,0.0262657702,-0.3858397901,-0.0895731077,-0.0566947088,0.128129527,-0.0233988855,-0.1673009396,0.0837891847,0.1429525465,0.4985156953,-0.1698465496,-0.2242092639,0.0119308187,0.0944930315,0.0747527033,-0.1614055783,0.1179353595,-0.0337839611,-0.453022927,0.0725452825,0.1935688406,0.1195869669,-0.0463378094,0.3049668074,-0.1034985706,0.0002569417,-0.4169119,-0.1191155016,-0.3012642562,-0.0344271734,-0.1744806916,-0.0468035527,-0.0193034932,-0.138956368,0.1612506807,-0.1624351442,0.1029184312,0.1908903569,0.1504067928,0.2424588352,-0.0482725091,-0.0031934674,0.1031282693,-0.1202344671,-0.2283467352,-0.0636385828,-0.1917521209,0.1401709765,-0.2598928511,0.1119391173,0.075955689,-0.2991689742,0.0413861088,-0.2150568664,0.472856164,0.2309122831,-0.0587947406,-0.156492576,0.3177806139,0.1721372455,-0.3297017217,-0.2200269848,0.3412006199,-0.1647662222,-0.2446691692,0.0908580944,-0.365121156,-0.2542339861,0.09029001,0.1549029052,0.0580705926,-0.1307823509,-0.3527176082,0.1504351497,-0.4175845683,0.0191522017,-0.1944582164,-0.007577274,-0.3421984911,-0.0516983457,0.347889632,0.4690683186,-0.3932281435,-0.1652914137,-0.1618795693,-0.1397892386,-0.0641402453,0.3136607707,-0.2297160625,0.3035108447,-0.1629567295,-0.0052741012,0.2236001641,-0.5785595775,-0.1155698746,0.2601819336,-0.1376008689,0.1651307046,-0.1817654222,0.4197821617,0.0482666679,-0.0234768186,-0.1105472967,0.7269153595,-0.2688494325,0.0874660611,0.0078729391,-0.3116492331,-0.0915126875,0.4174849391,0.018892169,0.2793212235,0.0595956147,-0.0981967524,0.0894353911,-0.258109659,0.0199274793,-0.0467378534,0.2255551368,0.2770264447,-0.0013278293,0.0582767464,-0.3241295815,0.4105852544,-0.0123326601,0.2917645574,-0.1126455665,-0.0683656409,-0.1458888203,-0.1702474952,-0.2722268105,-0.134783566,0.0470264331,0.0864298418,-0.0660315603,-0.158166945,-0.3154458106,0.3485401869,-0.4256828725,0.1370666921,0.1380571127,0.1380340308,0.1871838123,-0.0216990467,-0.1657732278,-0.0027624688,-0.1812498868,-0.1727612019,-0.1171171218,0.2002925724,0.0799686238,0.2456362247,0.2190037668,0.1401597708,0.2687151134,-0.2863899171,-0.0079384455,-0.0720431656,0.2825771272,0.0857939273,-0.2918455005,0.4547752142,-0.0203835387,0.222137019,0.1012874395,-0.1100744158,0.0155491764,-0.1211861894,0.0509902425,-0.0794550925,0.1232160181,0.4209697247,0.3136687577,0.129948929,-0.1777851433,-0.1941128075,-0.0812529027,0.0585995428,-0.0019886738,0.2639885545,0.0237173978,0.2941877246,0.02845034,-0.1595571935,0.42019099,0.1286082864,-0.1448091418,0.158753857,0.0780899003,0.0449383408,-0.0498137437,-0.0107468572,0.0640473589,-0.0586436056,-0.007914192,0.1536595225,-0.3867430389,-0.1319253445,-0.2363811433,0.0018710952,-0.0079262378,0.285233885,-0.1009811908,-0.3875078857,-0.1490825564,-0.1553316861,-0.1084499732,0.3473501205,0.0478672944,-0.1340536028,-0.0920879841,0.0323764384,0.064541012,0.7125998139,-0.02485203,-0.46919626,0.1037002727,-0.0467529036,-0.1814677566,0.1466980875,0.2156877518,-0.0644049793,0.360367775,-0.0669210032,0.0659147054,-0.2202038914,-0.2478675693,0.192679435,-0.0845230892,0.339830488,0.0223369244,0.2182110846,-0.0282246172,-0.1670151502,0.313624084,-0.1251924932,-0.2248562425,-0.3721477985,0.1539634317,0.3486576378,-0.2092300206,-0.3167017102,-0.5308420062,-0.3142337203,0.3089395761,0.4514374435,0.2063345164,0.2160308957,0.1925136894,0.0203400366,-0.2060065269,0.1167829633,0.0709908679,-0.522577107,0.0260534324,-0.2402145565,-0.2574028671,-0.2038243264,-0.1880463362,0.0893688425,0.0179092679,-0.3726289272,-0.3170896471,-0.0948065892,0.1927018315,-0.0982363448,-0.0089151198,0.1456410438,-0.076976262,-0.0927020982,-0.038080804,0.2784571946,0.1374171525,0.0226473361,-0.2067890167,0.1048831344,0.0421145819,-0.0949455202,0.3632555008,-0.1189904734,-0.1858229786,0.4987353683,-0.2188987881,0.2780492604,-0.5007695556,-0.4705104232,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2503","title":"SubjQA wrong boolean values in entries","comments":"@arnaudstiegler I have just checked that these mismatches are already present in the original dataset: https:\/\/github.com\/megagonlabs\/SubjQA\r\n\r\nWe are going to contact the dataset owners to report this.","body":"## Describe the bug\r\nSubjQA seems to have a boolean that's consistently wrong.\r\n\r\nIt defines:\r\n- question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective).\r\n- is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective)\r\n\r\nHowever, `is_ques_subjective` seems to have wrong values in the entire dataset.\r\n\r\nFor instance, in the example in the dataset card, we have:\r\n- \"question_subj_level\": 2\r\n- \"is_ques_subjective\": false\r\n\r\nHowever, according to the description, the question should be subjective since the `question_subj_level` is below 4\r\n","comment_length":27,"text":"SubjQA wrong boolean values in entries \n ## Describe the bug\r\nSubjQA seems to have a boolean that's consistently wrong.\r\n\r\nIt defines:\r\n- question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective).\r\n- is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective)\r\n\r\nHowever, `is_ques_subjective` seems to have wrong values in the entire dataset.\r\n\r\nFor instance, in the example in the dataset card, we have:\r\n- \"question_subj_level\": 2\r\n- \"is_ques_subjective\": false\r\n\r\nHowever, according to the description, the question should be subjective since the `question_subj_level` is below 4\r\n \n @arnaudstiegler I have just checked that these mismatches are already present in the original dataset: https:\/\/github.com\/megagonlabs\/SubjQA\r\n\r\nWe are going to contact the dataset owners to report this.","embeddings":[0.1235658228,0.0693046823,0.0270314571,0.1645201296,-0.3365960717,-0.0068990644,0.0865071937,0.045239836,-0.0927979127,0.1864392012,-0.0919599161,0.4202955663,0.2149700969,0.254910022,-0.218426764,0.1229733899,0.2286490202,0.1842408031,-0.1363601834,-0.1547209173,-0.4317789078,0.260576278,-0.5335964561,-0.0119052576,-0.1275137514,-0.2023657113,-0.1333579421,-0.1422060132,-0.2238854617,-0.2932470143,0.0730003268,-0.0335393548,-0.0811768249,0.1234094575,-0.0001093275,-0.2034583986,0.0526367277,0.0162325818,-0.2176798731,0.0769172758,-0.2617804706,-0.0417561233,-0.1978150308,0.0486691743,-0.3357685804,-0.4470198154,-0.2089022696,-0.268366456,0.3899082541,0.0091003245,0.1720302105,0.0610974617,-0.1662039608,0.2474562973,0.3539647162,-0.1808389723,-0.0492369607,0.2432310581,-0.0437768176,-0.046365045,0.1507534981,0.3816999495,0.0878882408,0.0792881027,0.1195269004,-0.1314198822,-0.0643833727,-0.2449347526,0.2853113115,0.2433769554,0.340031594,-0.1474963725,-0.4372366667,-0.0865620747,-0.0688515455,-0.1194220558,0.2729384899,0.0267802067,0.1324341595,-0.0465580709,0.1046249047,0.3983652294,0.1085541248,-0.0410750099,-0.1135641858,0.1411972493,-0.1243974566,-0.0282294825,-0.1896745563,-0.1182829961,0.0167922322,0.0029046785,-0.6352514625,0.1034262553,-0.4720294774,-0.0552292466,-0.0651546121,0.1520368755,-0.15406923,-0.3375006616,0.2958487868,-0.0503651798,0.2031734139,0.1381501257,0.5345699787,0.2554335892,0.3524950147,-0.2006354481,-0.0916452259,-0.2544087172,0.0428180024,0.0638490841,0.1985457242,-0.2788588107,-0.0028028965,0.0887786821,0.1084235609,-0.3041123748,-0.5812082291,0.1572570056,-0.4001148641,-0.045035664,0.134021163,0.0927854776,0.047653798,-0.2227297574,-0.0990667716,0.0209519267,-0.134592101,-0.4782003462,-0.1239519566,-0.0238467362,-0.1999012083,0.054493811,0.0194708202,-0.0180341434,-0.0020733154,0.3719302118,-0.1979906112,-0.2239182591,0.0091523537,-0.2884495854,0.2214837223,0.2032552809,-0.2496664226,0.2008807957,0.0493718758,-0.0521150827,-0.0147676887,0.3387775421,0.089169614,0.1819593757,-0.0369847305,0.1804388463,-0.0891753063,-0.0578743257,0.1410854757,0.3233660161,0.2153365165,0.0382418036,0.0631323233,-0.2067233622,-0.1559418738,-0.0916230232,-0.0364298038,0.1502397656,-0.6753096581,-0.1031776294,-0.0081572644,0.2379122674,0.0118778152,0.1806159019,0.191116184,0.2914659977,0.0767869577,-0.0861867368,-0.2743889093,0.1927633435,-0.4252072573,0.0999394506,0.422033906,-0.0812632442,-0.2345722467,0.2056286782,0.1705133617,0.0348280966,0.1862520278,0.0711834431,0.0980645046,-0.1092921942,-0.4336036146,-0.0682207122,-0.0198702402,0.2016846389,-0.1839958429,-0.070262529,0.0519549549,-0.4857279956,0.1369781941,0.1761033088,-0.2915922403,0.0750327855,0.457909286,-0.3810139298,0.2863347232,0.0377027057,0.1168815345,0.1942813396,-0.4058647156,0.0025864381,0.5612463355,-0.0935289487,-0.1990175396,0.1016951129,0.0479906648,-0.1811901033,0.1231813356,0.1443934739,-0.3442240059,-0.0312309042,-0.2829026282,-0.0618521273,0.1143131778,-0.0696996599,0.1398046911,-0.1327401847,-0.0771326274,0.0567477867,0.4595968127,0.3403603435,0.447440505,0.0692550167,-0.0982890725,0.1780465841,0.0816662908,-0.1003291011,-0.1397878826,-0.2158813775,-0.0932421163,-0.100940682,0.048600655,0.5878672004,-0.0976712927,0.345982492,0.1544420719,0.463313669,0.0811572522,-0.1465827227,-0.3019917905,0.2043401748,-0.0304879397,-0.1946274787,-0.172913,-0.4056322277,0.2356466651,0.0524048954,-0.089546673,0.247719571,0.1411375999,0.2954147458,0.3167260289,-0.1854985505,0.2649505734,-0.2104398161,-0.0202971678,-0.0652850494,0.2469478399,0.4416828156,0.1151769087,0.1954725832,-0.0981773287,-0.0625020713,-0.1693164855,-0.0454245768,0.2141800672,0.1679679155,-0.2550610304,-0.0083731012,0.0559946373,0.1583368033,-0.21402888,0.7436457276,-0.0561722852,0.0429213084,-0.3219944835,0.3840662241,-0.3165824413,-0.3022707999,0.0065395301,-0.526925981,0.0282171946,-0.2270455062,0.4689020813,-0.3912793398,-0.4067310393,0.3903204501,0.4209657609,0.6782056689,-0.1489887238,0.1956743896,-0.0283058733,-0.0563011095,-0.5697382689,0.0757160559,0.0366113819,-0.2367685884,0.0595968105,-0.1379311979,-0.1368680298,-0.0072096889,-0.2768760026,0.1192589402,0.0006518387,1.0724867582,-0.2695175707,-0.1467303783,-0.1190344021,0.1193541065,-0.1574742347,0.1590285003,-0.0539910309,0.0906694978,-0.1739587635,-0.1675078869,-0.1246683374,-0.35159567,0.0382580049,0.004620159,-0.1437669843,-0.2055474371,0.2674574852,0.4465821087,-0.1614125818,-0.3993704021,0.0713938922,-0.0173409935,-0.3348317742,-0.2631746233,0.3340443969,0.0522255749,-0.5183662176,-0.0925903469,-0.1297246367,0.4031534195,-0.2657470703,-0.3338995874,0.3003411591,0.0676516593,-0.2214169204,0.3181125522,-0.3968447447,-0.1473349184,-0.2791083753,-0.1210676059,-0.1548233926,-0.6240259409,0.1361133307,0.1735401303,0.4568222761,-0.0487644188,0.1495238245,0.1556363106,-0.0425291322,0.3412079811,0.1651685983,0.3119772077,0.3186435401,0.3356915116,0.083191663,-0.2547818124,0.3756683171,-0.027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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2503","title":"SubjQA wrong boolean values in entries","comments":"I have:\r\n- opened an issue in their repo: https:\/\/github.com\/megagonlabs\/SubjQA\/issues\/3\r\n- written an email to all the paper authors","body":"## Describe the bug\r\nSubjQA seems to have a boolean that's consistently wrong.\r\n\r\nIt defines:\r\n- question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective).\r\n- is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective)\r\n\r\nHowever, `is_ques_subjective` seems to have wrong values in the entire dataset.\r\n\r\nFor instance, in the example in the dataset card, we have:\r\n- \"question_subj_level\": 2\r\n- \"is_ques_subjective\": false\r\n\r\nHowever, according to the description, the question should be subjective since the `question_subj_level` is below 4\r\n","comment_length":19,"text":"SubjQA wrong boolean values in entries \n ## Describe the bug\r\nSubjQA seems to have a boolean that's consistently wrong.\r\n\r\nIt defines:\r\n- question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective).\r\n- is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective)\r\n\r\nHowever, `is_ques_subjective` seems to have wrong values in the entire dataset.\r\n\r\nFor instance, in the example in the dataset card, we have:\r\n- \"question_subj_level\": 2\r\n- \"is_ques_subjective\": false\r\n\r\nHowever, according to the description, the question should be subjective since the `question_subj_level` is below 4\r\n \n I have:\r\n- opened an issue in their repo: https:\/\/github.com\/megagonlabs\/SubjQA\/issues\/3\r\n- written an email to all the paper authors","embeddings":[0.1710738689,0.0587353781,0.0442122258,0.0991450697,-0.3185442686,-0.1043600217,0.0993457064,0.0244082045,-0.1466466784,0.2234863788,-0.0614027008,0.3840125203,0.2275162339,0.2279736251,-0.2728487849,0.1101417691,0.2479696721,0.166661188,-0.0700219274,-0.1800619364,-0.4520644546,0.2599630952,-0.6040433645,-0.0405316427,-0.1244438365,-0.2389922142,-0.1235726103,-0.1611758471,-0.3197330534,-0.2684791386,0.0228940286,-0.0024612597,-0.0706962943,0.1173533425,-0.0001112474,-0.1952677071,0.0751518682,0.0176677648,-0.2246204019,0.085610643,-0.1955979615,-0.0928517729,-0.2246859223,0.0400888659,-0.3261657059,-0.4419411123,-0.2020022124,-0.2190367281,0.3537075222,0.0255618338,0.1417027116,0.0158051699,-0.1125524268,0.2566840649,0.3405130208,-0.2426410466,-0.1025819257,0.2494196445,-0.0194552206,-0.0457550175,0.1129644662,0.3713188767,0.1074483916,0.0999766067,0.0808564126,-0.0649048761,-0.0014376957,-0.2529067099,0.2693755925,0.2289806306,0.3289312124,-0.1468628198,-0.4584599137,-0.0965828001,-0.0855324939,-0.0976805016,0.2790672183,0.0382844768,0.1127962172,-0.0264036488,0.1741399169,0.4021877646,0.0861006007,-0.0118232798,-0.11401885,0.117589727,-0.179119885,-0.0428106003,-0.1124674007,-0.1337602437,-0.0495318323,0.0108837923,-0.6033577323,0.1203535423,-0.4970486462,-0.0238896552,0.0368807875,0.114126496,-0.1481908709,-0.3702002168,0.3033159971,-0.053209506,0.2016269565,0.2120987773,0.5167773962,0.2139938921,0.37682423,-0.2153893858,-0.0588614717,-0.2195380032,0.0617492199,0.0186376739,0.1607973725,-0.2832615674,0.036087621,0.04941817,0.1375556737,-0.2750588357,-0.5347527266,0.1172761023,-0.4085183144,-0.0356437787,0.1520291716,0.0824728534,0.0522991531,-0.2182780951,-0.1242639795,-0.0142753702,-0.1809726954,-0.4663915634,-0.1026122496,-0.0126753598,-0.2421897352,0.0704667196,0.0095849838,-0.0051735188,-0.0326608047,0.3610464036,-0.1792062074,-0.2311148047,-0.045544751,-0.3192038238,0.2102465034,0.1566302478,-0.257568866,0.1769367307,0.0184607133,-0.0885849893,0.0113783451,0.3068144321,0.1314315349,0.2160139829,-0.0264996942,0.1529012471,-0.0488313958,-0.0194252003,0.1305797249,0.3427820802,0.2295735478,0.0766574964,0.097899422,-0.1246005967,-0.108446613,-0.120166406,-0.0074979821,0.0930765569,-0.5938064456,-0.0956555009,0.0028287093,0.2563048899,0.0060078651,0.1614622623,0.2352576256,0.3217531443,0.1038671359,-0.0696827844,-0.2798698545,0.1783294678,-0.4563191533,0.1051230207,0.4366212189,-0.1220444813,-0.221826598,0.2033574879,0.1472888291,0.0721242428,0.1484610587,0.060657151,0.091976963,-0.0978632793,-0.4295600951,-0.0188788548,-0.0461560562,0.1946666688,-0.1881421953,-0.0667494759,0.0751229078,-0.4497666657,0.1388593316,0.2089561969,-0.2969644368,0.0651323795,0.4925538003,-0.4521814287,0.2907610536,0.0284890067,0.1424675137,0.1890472323,-0.4601029158,0.0187790208,0.5386309624,-0.0874888152,-0.2166935951,0.0964654461,0.0762858465,-0.1828972846,0.1264407784,0.0942379162,-0.3206942976,0.0440112501,-0.2463474274,-0.0723955408,0.11738538,-0.053882312,0.1654940397,-0.1231289431,-0.0828999206,0.0102148503,0.5004791021,0.3310031891,0.4476000965,0.082400769,-0.0840768516,0.1700825393,0.0918709338,-0.0996949896,-0.1469714195,-0.2126724571,-0.1487464905,-0.045117598,0.0176735595,0.5685980916,-0.096134901,0.355956316,0.1158235297,0.4205283821,0.0681669116,-0.1510509849,-0.2987359464,0.1734165847,-0.0158575624,-0.2166254073,-0.2364974767,-0.3514859378,0.1823423803,0.0784165338,-0.1122974604,0.2293712199,0.214940846,0.2402786762,0.3250105083,-0.1460169107,0.307464391,-0.1975390613,0.0135957124,-0.0615344234,0.2345063239,0.3891462684,0.0415773876,0.1550029069,-0.1160536259,-0.0861490667,-0.1397906095,-0.031043807,0.1661272645,0.2036787719,-0.2768189907,-0.0534087159,0.0403561257,0.1367254257,-0.1804144233,0.7738114595,-0.0661959127,0.0629098415,-0.2841646075,0.4080078304,-0.3262366354,-0.3555271327,0.065151833,-0.5793977976,0.0429905877,-0.1876525283,0.4197408855,-0.3421962857,-0.371132195,0.3845454156,0.432146281,0.7168484926,-0.0835766047,0.197931245,0.0355790965,-0.0012880722,-0.5488743782,0.0704206377,-0.0014025839,-0.2150136977,0.0272914078,-0.1356779337,-0.0908275098,0.0397351198,-0.2662622929,0.1043313891,0.0013372216,1.0558440685,-0.2724154592,-0.1538788676,-0.101854004,0.1566333324,-0.0917654857,0.1692700982,-0.008325927,0.1373540461,-0.1299726665,-0.1773164272,-0.1217777207,-0.3256254196,0.1215997115,-0.0073376219,-0.1755313873,-0.2254958302,0.2760755122,0.4957951605,-0.099076882,-0.3769023418,0.0817377269,-0.0282826014,-0.2469940633,-0.2265727818,0.3488762975,0.0640418157,-0.501604259,-0.0953377411,-0.1688266695,0.4532333314,-0.2941656709,-0.2607148588,0.3030191064,0.1040576398,-0.2190982103,0.3839105368,-0.3634410799,-0.1291661412,-0.3180090487,-0.1227653474,-0.1539869308,-0.6412751079,0.1094340682,0.1728317142,0.494954139,0.0312018413,0.1230886132,0.215901345,-0.1157743186,0.4156973958,0.1808377504,0.296356976,0.3292664886,0.3150509894,0.1459970623,-0.3184501231,0.4076510072,-0.013140765,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2499","title":" Python Programming Puzzles","comments":"Thanks @VictorSanh!\r\nThere's also a [notebook](https:\/\/aka.ms\/python_puzzles) and [demo](https:\/\/aka.ms\/python_puzzles_study) available now to try out some of the puzzles","body":"## Adding a Dataset\r\n- **Name:** Python Programming Puzzles\r\n- **Description:** Programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis\r\n- **Paper:** https:\/\/arxiv.org\/pdf\/2106.05784.pdf\r\n- **Data:** https:\/\/github.com\/microsoft\/PythonProgrammingPuzzles ([Scrolling through the data](https:\/\/github.com\/microsoft\/PythonProgrammingPuzzles\/blob\/main\/problems\/README.md))\r\n- **Motivation:** Spans a large range of difficulty, problems, and domains. A useful resource for evaluation as we don't have a clear understanding of the abilities and skills of extremely large LMs.\r\n\r\nNote: it's a growing dataset (contributions are welcome), so we'll need careful versioning for this dataset.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":17,"text":" Python Programming Puzzles \n ## Adding a Dataset\r\n- **Name:** Python Programming Puzzles\r\n- **Description:** Programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis\r\n- **Paper:** https:\/\/arxiv.org\/pdf\/2106.05784.pdf\r\n- **Data:** https:\/\/github.com\/microsoft\/PythonProgrammingPuzzles ([Scrolling through the data](https:\/\/github.com\/microsoft\/PythonProgrammingPuzzles\/blob\/main\/problems\/README.md))\r\n- **Motivation:** Spans a large range of difficulty, problems, and domains. A useful resource for evaluation as we don't have a clear understanding of the abilities and skills of extremely large LMs.\r\n\r\nNote: it's a growing dataset (contributions are welcome), so we'll need careful versioning for this dataset.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Thanks @VictorSanh!\r\nThere's also a [notebook](https:\/\/aka.ms\/python_puzzles) and [demo](https:\/\/aka.ms\/python_puzzles_study) available now to try out some of the puzzles","embeddings":[-0.0690371692,-0.1561278403,-0.2759868503,-0.0527508967,-0.000647664,0.0701648518,0.0195636209,0.2562186122,0.0034628008,0.1276417226,0.0386161953,0.3095811307,-0.3876287043,0.3575746715,0.2331800461,-0.3480175138,-0.0425296463,-0.0044569364,-0.0990918577,0.0490014628,-0.1924676746,0.2509897351,-0.1473506838,-0.0799463317,0.0459989198,-0.2655206621,-0.1827941686,0.2118450552,-0.0962313265,-0.3995168209,-0.3597231209,0.0380528793,-0.1506151259,0.356187731,-0.0000946857,-0.4483577013,0.1580719501,-0.0328321941,-0.4988930523,-0.0523063503,0.1360356808,-0.2378710955,-0.1941977143,-0.1723545045,-0.1147182286,-0.0105924988,-0.0551272333,-0.3561468124,0.4866496921,0.2586369812,0.3744764626,0.2904421091,0.2213054895,-0.0379726924,0.1492575407,-0.1286346465,-0.157435745,0.2081690282,0.5029332042,-0.1821868271,-0.0104112653,0.0505567081,0.096982792,-0.1274922788,0.0570479035,-0.278098613,0.0993154868,-0.548532486,-0.1464281976,0.1003871411,0.1350398809,-0.1140143126,-0.254365921,-0.3878379762,-0.0214350503,-0.1134718135,-0.3032558262,0.2030613124,-0.2227848172,0.0181849226,0.0416184328,-0.1341142803,-0.0169906076,-0.0963678882,0.0912826732,0.2131258398,-0.0226303414,-0.1626652628,0.1081698835,-0.119375214,-0.1527066082,0.0314427391,0.1135204062,0.2347504348,-0.0868087187,-0.2609752119,0.2708579004,0.1239233166,0.0177956652,0.33556512,0.1480636001,-0.0060744844,0.3773305416,-0.1028960347,0.109333694,0.0928000659,-0.1637730598,-0.019672811,-0.0344462842,0.1125673279,-0.2922190726,0.0885627493,-0.0866945982,-0.1023688614,0.0563460588,0.1442159563,-0.0840299055,0.1820851117,-0.1716403663,0.2033602297,-0.2273284793,0.0299166795,-0.0007513291,0.0107504744,-0.1066906676,0.0260178782,-0.0303215552,0.0574970394,-0.2994653583,-0.0056301914,-0.1562254131,0.0581405722,-0.3145743012,-0.1795540005,0.3554589152,0.0870754495,0.041605141,0.1689746529,0.0125689367,-0.2826557755,0.306252867,-0.0045679612,0.1723783463,-0.017137466,-0.0784272552,-0.3076550066,0.0100729801,-0.2725691199,-0.2808988988,0.1333914995,0.2779684365,0.0449200533,-0.0949021652,0.3816742897,-0.1550905257,0.1056775898,0.2967669964,0.3964276612,-0.1516933888,0.3133289516,-0.0206085835,-0.1080102101,0.2293014824,-0.1770500839,0.2192471027,0.2873530388,-0.0541666485,0.0985069051,-0.0198064931,-0.0220546126,-0.3745416403,0.1586958319,0.1215590015,0.4654797316,-0.1846594065,0.0024684274,0.3773067296,-0.2076592296,0.1459478885,0.0320006981,-0.1530904472,0.0365185849,0.0253290199,0.2588229179,0.055617664,-0.0200537276,0.1840392202,0.314040482,-0.0406618565,-0.0831532478,-0.2736202776,0.0288454704,-0.1419500262,0.1271437854,0.1529227048,-0.1347588003,0.0323846154,-0.1064786389,0.1668428928,-0.1540444195,-0.2847520113,0.101653561,0.1968060136,0.1704759002,0.1061803475,-0.4948402345,-0.0954572186,-0.0782223791,0.2154520601,0.2092569023,0.1178890169,-0.067855604,-0.163056761,0.1954728961,0.0771314278,-0.2147099376,0.3580568433,-0.1830913872,0.1555978656,-0.1312407404,-0.1321453005,0.3327614069,0.0983534679,-0.0328270309,-0.293934077,0.0650842786,-0.1366574764,-0.132299751,0.1677355617,0.48086676,-0.0348925479,0.1765156537,0.1527649909,0.0091347564,0.168367222,-0.2324342728,0.081219174,0.3387724459,-0.156469658,-0.2968168855,0.1251401007,0.3188964725,-0.0220045634,0.1281387359,0.3337660134,0.3432987928,-0.0628278852,0.1198533103,0.3273600638,0.0607918687,-0.0467175134,-0.0157497115,-0.3352227509,-0.1780977398,0.4403584898,-0.0038231546,0.2370430529,-0.0717473552,-0.3317889571,0.1878476292,0.4561134279,-0.0291133486,0.2473886609,-0.1612132192,-0.1126268283,0.058572378,-0.2232492566,0.2160288095,0.0277732573,0.3220371604,0.2059086114,0.223083362,-0.2732074857,-0.2321784943,0.1780881286,0.0639737621,-0.2333123982,0.1899661273,0.1242356747,0.1144082993,-0.0533996299,-0.2826054096,-0.2626927197,-0.0810928941,-0.2464055866,0.2295690775,-0.1131106168,-0.1971317381,-0.2495608032,0.0127566662,-0.0888338089,-0.4756056666,0.1830380708,0.1165233776,-0.2463806719,-0.0390421152,0.041554641,0.2850763798,-0.2686403692,0.1312721819,0.1759013087,-0.2556347549,-0.0451754555,0.3335237205,0.1024491563,0.0451790653,0.7545125484,-0.4607816041,0.301704675,-0.1164506897,-0.5321436524,0.0517706983,-0.0727219954,0.6607948542,0.30450207,-0.0265441313,-0.0186404642,-0.170327276,0.0631332621,-0.1959264278,-0.0507183559,-0.1848328859,0.1850781292,-0.1605685204,-0.2132178396,-0.1860857308,-0.046531029,-0.3705588281,0.3902661502,0.2063408941,0.2195364237,0.0811281949,-0.0710448548,0.1906784475,0.0697576776,0.1549745053,-0.0232916307,-0.1519992501,0.0968977213,-0.3474819064,-0.2306865901,-0.1915353239,-0.4439184368,0.1147794649,0.0001647464,-0.0150546953,0.0378849432,-0.0818679109,0.215500921,-0.0263535175,0.3032610714,-0.2600440979,0.1974380761,-0.2345322967,-0.2070155442,-0.1931428015,0.1899802387,0.1369209886,0.0737718642,-0.175689131,0.0039855745,-0.0646066219,0.5416694283,0.1444898993,0.0965864584,0.1775265783,-0.0395968221,0.2368361354,-0.0356596075,-0.3107915521,0.0096796863,0.164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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"That\u2019s interesting thanks, let\u2019s see what we can do. Can you detail your last sentence? I\u2019m not sure I understand it well.","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":22,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n That\u2019s interesting thanks, let\u2019s see what we can do. Can you detail your last sentence? I\u2019m not sure I understand it well.","embeddings":[-0.3501352668,-0.2402974069,-0.0736680552,0.190357089,0.1027540565,0.147706598,0.2599887252,0.6868174076,-0.121130459,-0.0641201735,-0.356641978,0.2431406975,-0.1145989373,-0.0332216397,0.0663354844,-0.3101109862,0.0007658997,0.1003077179,-0.0678961575,-0.1308621615,-0.1019239053,-0.4241634309,0.0556780435,-0.2308925092,-0.1764650196,0.1433885545,-0.0729704648,0.0049904119,-0.0147783048,-0.3914083242,-0.016802527,0.2041123956,0.1916381568,0.2366560847,-0.0001087394,-0.0219625924,0.105563499,0.0298551396,-0.3094554543,0.2616169453,0.3262714446,-0.3157902956,0.0545597821,-0.2053410858,-0.4622154236,-0.2044653594,-0.0115542626,-0.30791834,0.043709524,0.0324950144,0.1543279141,0.2567405403,-0.3000166416,0.1878047138,0.3291626871,0.4852654338,-0.1752856523,0.0986196473,0.3001522422,0.0291677509,-0.5153069496,0.6179615855,-0.1722578853,-0.0325031802,0.177194804,0.0603692159,-0.2424562275,-0.1164502576,-0.0824211463,0.4060195386,0.032524813,-0.2445900589,-0.500759542,-0.4453507662,-0.0309528373,-0.5959249139,-0.100147076,0.1113878042,-0.2834424973,0.1466880888,-0.0680339932,0.0368809551,-0.1203642115,-0.09613619,-0.0547588803,0.2587930262,0.2410730571,-0.2225960046,0.0905139223,0.1961138248,0.4557999671,-0.1157797202,0.2760972977,-0.0045529366,-0.3522320092,-0.1666412801,-0.0236723572,-0.1867082566,-0.1529744416,-0.0668888763,0.1264795959,0.5531223416,-0.0911921859,-0.0197006557,-0.2372612804,0.0375605449,-0.1032606214,0.210592702,0.1966996491,-0.1186811998,0.0677641109,0.1936755478,-0.0780171901,-0.0493074879,-0.3120107949,0.142736569,-0.4815460443,-0.0842902958,-0.1862215847,0.0252111387,-0.1306553185,-0.1333502084,0.0409616493,0.3111973107,-0.2863078713,0.1948336661,0.1680843681,-0.1474616975,-0.2475613654,-0.2679266036,0.0511941016,0.1703957319,-0.2893526852,0.0069734068,0.2033197582,-0.2458306253,-0.0934309065,-0.070954971,0.074785918,0.388759762,0.020032078,-0.2280967683,0.4766450822,-0.0271022301,-0.0679627433,0.0136563703,0.181370452,0.5773720741,-0.279609412,0.1573994309,-0.1916829199,-0.2062162906,-0.1592074186,0.1019587144,0.0700449198,0.007335064,0.0668838844,0.3424280584,0.1331920326,0.0767204538,0.126472339,-0.249081552,-0.3356597424,-0.1178224832,0.3001018167,0.3032240868,-0.1989410818,-0.0473861061,0.4758732319,0.3441790342,0.4220882058,0.4858781695,-0.3000439703,0.1179692149,-0.0290246345,0.0072558247,0.3716585338,-0.369782567,-0.1297076046,0.3484440446,0.074912928,0.2708399892,0.1608398706,0.1948831677,0.4046298862,-0.0059370184,0.0729750544,0.395489186,0.0082202358,-0.0499474593,-0.4824648201,-0.1739059389,0.4433811307,0.3957262933,0.0574343726,-0.3865258992,-0.1188643202,-0.1047391221,0.4031055868,-0.3227175474,0.1457478553,0.108986631,-0.1720594317,0.153413415,0.0151736438,0.2190547287,-0.1121948287,0.2242095768,-0.3645497561,0.3796460927,0.0479122065,-0.1950641721,0.3231682479,-0.0361455195,-0.0039837821,-0.2554191351,0.0407075249,-0.0446182266,-0.2046026289,0.0253762789,0.0424456075,0.3719467223,-0.2666930556,0.1153119951,-0.2319298089,0.1106730998,0.052906137,-0.0528733134,-0.0534533113,0.2816495895,0.0650102496,-0.0430517569,-0.0791779757,0.1298173219,-0.1343417764,0.2028240263,-0.2158796489,0.1773772687,0.4185040295,-0.1259617507,0.2937958241,0.1701381952,-0.0248790234,-0.1208947748,-0.422383219,0.7661342025,0.4835484028,0.1557845175,-0.1161572859,-0.2186772972,-0.2221040428,0.148417592,0.0560706258,0.0844040662,0.5234335065,0.1368191689,0.1734043807,0.0809791461,-0.4177882671,0.3712826371,0.341157198,0.005134359,-0.1374358982,0.4363709092,-0.0785387158,-0.3662787378,0.3380372822,-0.5506458282,-0.005098918,0.233039096,0.2333679199,0.0235326774,-0.0177598242,-0.0314640813,0.0983767435,0.01055016,-0.0542775206,0.0126836607,0.2190360725,0.2533146739,-0.0732507408,-0.0944208428,-0.1324299425,0.0236582067,-0.1572207958,0.1237859577,-0.4127211571,-0.103166461,-0.1553760469,-0.3362688124,-0.0705448091,-0.1799101084,0.0300737731,0.1377863437,-0.0285042822,0.1710874438,0.0217946917,0.0469736978,0.057029631,-0.2233769894,-0.1739785969,-0.1969986111,-0.0307846274,0.0338535979,0.147559166,-0.032628864,0.3699895144,-0.006935156,0.1059629098,0.0763379633,-0.2765926421,0.0485948883,-0.1336117834,-0.2551215291,0.0496093929,-0.1258975714,-0.2280783504,-0.1208018288,0.0972726569,0.0017792792,0.0587237999,-0.1553657651,-0.0902638882,0.1817816049,-0.0192360058,-0.2255808413,-0.245959267,-0.3073492646,-0.0496062972,-0.1677562743,0.156537056,-0.2041864097,-0.0283644665,0.1308830678,0.2082248777,0.0456591472,0.0236809626,-0.4292769134,0.4179665148,-0.2306931019,-0.1688781232,-0.401109308,-0.0202502627,0.0000506992,0.3673450351,-0.4472309649,0.2142145932,-0.0647428632,0.1862570643,-0.2460256517,-0.1165185198,0.1445492953,0.2486154586,-0.0935690776,0.0080999294,0.0720040649,-0.1541757882,-0.2329793125,0.102216132,-0.0745993704,0.3041974008,0.3246068954,0.6840358973,-0.0617227219,-0.3457929492,0.1029266343,0.1235023215,0.1042312533,-0.0933369845,-0.2323143929,0.1221762151,-0.207471773,0.318169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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"Hi ! I just re-ran a quick benchmark and using `to_numpy()` seems to be faster now:\r\n\r\n```python\r\nimport pyarrow as pa  # I used pyarrow 3.0.0\r\nimport numpy as np\r\n\r\nn, max_length = 1_000, 512\r\nlow, high, size = 0, 2 << 16, (n, max_length)\r\n\r\ntable = pa.Table.from_pydict({\r\n    \"input_ids\": np.random.default_rng(42).integers(low=low, high=high, size=size).tolist()\r\n})\r\n\r\n\r\n%%timeit\r\n_ = table.to_pandas()[\"input_ids\"].to_numpy()\r\n# 1.44 ms \u00b1 80.1 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\r\n\r\n%%timeit\r\n_ = table[\"input_ids\"].to_pandas().to_numpy()\r\n# 461 \u00b5s \u00b1 14.2 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\r\n\r\n%%timeit\r\n_ = table[\"input_ids\"].to_numpy()\r\n# 317 \u00b5s \u00b1 5.06 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\r\n```\r\n\r\nCurrently the conversion from arrow to numpy is done in the NumpyArrowExtractor here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/d6d0ede9486ffad7944642ca9a326e058b676788\/src\/datasets\/formatting\/formatting.py#L143-L166\r\n\r\nLet's update the NumpyArrowExtractor to call `to_numpy` directly and see how our github benchmarks evolve ?__","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":150,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n Hi ! I just re-ran a quick benchmark and using `to_numpy()` seems to be faster now:\r\n\r\n```python\r\nimport pyarrow as pa  # I used pyarrow 3.0.0\r\nimport numpy as np\r\n\r\nn, max_length = 1_000, 512\r\nlow, high, size = 0, 2 << 16, (n, max_length)\r\n\r\ntable = pa.Table.from_pydict({\r\n    \"input_ids\": np.random.default_rng(42).integers(low=low, high=high, size=size).tolist()\r\n})\r\n\r\n\r\n%%timeit\r\n_ = table.to_pandas()[\"input_ids\"].to_numpy()\r\n# 1.44 ms \u00b1 80.1 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\r\n\r\n%%timeit\r\n_ = table[\"input_ids\"].to_pandas().to_numpy()\r\n# 461 \u00b5s \u00b1 14.2 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\r\n\r\n%%timeit\r\n_ = table[\"input_ids\"].to_numpy()\r\n# 317 \u00b5s \u00b1 5.06 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\r\n```\r\n\r\nCurrently the conversion from arrow to numpy is done in the NumpyArrowExtractor here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/d6d0ede9486ffad7944642ca9a326e058b676788\/src\/datasets\/formatting\/formatting.py#L143-L166\r\n\r\nLet's update the NumpyArrowExtractor to call `to_numpy` directly and see how our github benchmarks evolve 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"Sounds like a plan @lhoestq  If you create a PR I'll pick it up and try it out right away! ","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":20,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n Sounds like a plan @lhoestq  If you create a PR I'll pick it up and try it out right away! 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"I\u2019m not exactly sure how to read the graph but it seems that to_categorical take a lot of time here. Could you share more informations on the features\/stats of your datasets so we could maybe design a synthetic datasets that looks more similar for debugging testing?","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":46,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n I\u2019m not exactly sure how to read the graph but it seems that to_categorical take a lot of time here. Could you share more informations on the features\/stats of your datasets so we could maybe design a synthetic datasets that looks more similar for debugging testing?","embeddings":[-0.3501352668,-0.2402974069,-0.0736680552,0.190357089,0.1027540565,0.147706598,0.2599887252,0.6868174076,-0.121130459,-0.0641201735,-0.356641978,0.2431406975,-0.1145989373,-0.0332216397,0.0663354844,-0.3101109862,0.0007658997,0.1003077179,-0.0678961575,-0.1308621615,-0.1019239053,-0.4241634309,0.0556780435,-0.2308925092,-0.1764650196,0.1433885545,-0.0729704648,0.0049904119,-0.0147783048,-0.3914083242,-0.016802527,0.2041123956,0.1916381568,0.2366560847,-0.0001087394,-0.0219625924,0.105563499,0.0298551396,-0.3094554543,0.2616169453,0.3262714446,-0.3157902956,0.0545597821,-0.2053410858,-0.4622154236,-0.2044653594,-0.0115542626,-0.30791834,0.043709524,0.0324950144,0.1543279141,0.2567405403,-0.3000166416,0.1878047138,0.3291626871,0.4852654338,-0.1752856523,0.0986196473,0.3001522422,0.0291677509,-0.5153069496,0.6179615855,-0.1722578853,-0.0325031802,0.177194804,0.0603692159,-0.2424562275,-0.1164502576,-0.0824211463,0.4060195386,0.032524813,-0.2445900589,-0.500759542,-0.4453507662,-0.0309528373,-0.5959249139,-0.100147076,0.1113878042,-0.2834424973,0.1466880888,-0.0680339932,0.0368809551,-0.1203642115,-0.09613619,-0.0547588803,0.2587930262,0.2410730571,-0.2225960046,0.0905139223,0.1961138248,0.4557999671,-0.1157797202,0.2760972977,-0.0045529366,-0.3522320092,-0.1666412801,-0.0236723572,-0.1867082566,-0.1529744416,-0.0668888763,0.1264795959,0.5531223416,-0.0911921859,-0.0197006557,-0.2372612804,0.0375605449,-0.1032606214,0.210592702,0.1966996491,-0.1186811998,0.0677641109,0.1936755478,-0.0780171901,-0.0493074879,-0.3120107949,0.142736569,-0.4815460443,-0.0842902958,-0.1862215847,0.0252111387,-0.1306553185,-0.1333502084,0.0409616493,0.3111973107,-0.2863078713,0.1948336661,0.1680843681,-0.1474616975,-0.2475613654,-0.2679266036,0.0511941016,0.1703957319,-0.2893526852,0.0069734068,0.2033197582,-0.2458306253,-0.0934309065,-0.070954971,0.074785918,0.388759762,0.020032078,-0.2280967683,0.4766450822,-0.0271022301,-0.0679627433,0.0136563703,0.181370452,0.5773720741,-0.279609412,0.1573994309,-0.1916829199,-0.2062162906,-0.1592074186,0.1019587144,0.0700449198,0.007335064,0.0668838844,0.3424280584,0.1331920326,0.0767204538,0.126472339,-0.249081552,-0.3356597424,-0.1178224832,0.3001018167,0.3032240868,-0.1989410818,-0.0473861061,0.4758732319,0.3441790342,0.4220882058,0.4858781695,-0.3000439703,0.1179692149,-0.0290246345,0.0072558247,0.3716585338,-0.369782567,-0.1297076046,0.3484440446,0.074912928,0.2708399892,0.1608398706,0.1948831677,0.4046298862,-0.0059370184,0.0729750544,0.395489186,0.0082202358,-0.0499474593,-0.4824648201,-0.1739059389,0.4433811307,0.3957262933,0.0574343726,-0.3865258992,-0.1188643202,-0.1047391221,0.4031055868,-0.3227175474,0.1457478553,0.108986631,-0.1720594317,0.153413415,0.0151736438,0.2190547287,-0.1121948287,0.2242095768,-0.3645497561,0.3796460927,0.0479122065,-0.1950641721,0.3231682479,-0.0361455195,-0.0039837821,-0.2554191351,0.0407075249,-0.0446182266,-0.2046026289,0.0253762789,0.0424456075,0.3719467223,-0.2666930556,0.1153119951,-0.2319298089,0.1106730998,0.052906137,-0.0528733134,-0.0534533113,0.2816495895,0.0650102496,-0.0430517569,-0.0791779757,0.1298173219,-0.1343417764,0.2028240263,-0.2158796489,0.1773772687,0.4185040295,-0.1259617507,0.2937958241,0.1701381952,-0.0248790234,-0.1208947748,-0.422383219,0.7661342025,0.4835484028,0.1557845175,-0.1161572859,-0.2186772972,-0.2221040428,0.148417592,0.0560706258,0.0844040662,0.5234335065,0.1368191689,0.1734043807,0.0809791461,-0.4177882671,0.3712826371,0.341157198,0.005134359,-0.1374358982,0.4363709092,-0.0785387158,-0.3662787378,0.3380372822,-0.5506458282,-0.005098918,0.233039096,0.2333679199,0.0235326774,-0.0177598242,-0.0314640813,0.0983767435,0.01055016,-0.0542775206,0.0126836607,0.2190360725,0.2533146739,-0.0732507408,-0.0944208428,-0.1324299425,0.0236582067,-0.1572207958,0.1237859577,-0.4127211571,-0.103166461,-0.1553760469,-0.3362688124,-0.0705448091,-0.1799101084,0.0300737731,0.1377863437,-0.0285042822,0.1710874438,0.0217946917,0.0469736978,0.057029631,-0.2233769894,-0.1739785969,-0.1969986111,-0.0307846274,0.0338535979,0.147559166,-0.032628864,0.3699895144,-0.006935156,0.1059629098,0.0763379633,-0.2765926421,0.0485948883,-0.1336117834,-0.2551215291,0.0496093929,-0.1258975714,-0.2280783504,-0.1208018288,0.0972726569,0.0017792792,0.0587237999,-0.1553657651,-0.0902638882,0.1817816049,-0.0192360058,-0.2255808413,-0.245959267,-0.3073492646,-0.0496062972,-0.1677562743,0.156537056,-0.2041864097,-0.0283644665,0.1308830678,0.2082248777,0.0456591472,0.0236809626,-0.4292769134,0.4179665148,-0.2306931019,-0.1688781232,-0.401109308,-0.0202502627,0.0000506992,0.3673450351,-0.4472309649,0.2142145932,-0.0647428632,0.1862570643,-0.2460256517,-0.1165185198,0.1445492953,0.2486154586,-0.0935690776,0.0080999294,0.0720040649,-0.1541757882,-0.2329793125,0.102216132,-0.0745993704,0.3041974008,0.3246068954,0.6840358973,-0.0617227219,-0.3457929492,0.1029266343,0.1235023215,0.1042312533,-0.0933369845,-0.2323143929,0.1221762151,-0.207471773,0.318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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"> I\u2019m not exactly sure how to read the graph but it seems that to_categorical take a lot of time here. Could you share more informations on the features\/stats of your datasets so we could maybe design a synthetic datasets that looks more similar for debugging testing?\r\n\r\n@thomwolf starting from the top, each rectangle represents the cumulative amount of it takes to execute the method call. Therefore, format_batch in torch_formatter.py takes ~20 sec, and the largest portion of that call is taken by to_pandas call and the smaller portion (grey rectangle) by the other method invocation(s) in format_batch (series_to_numpy etc). \r\n\r\nFeatures of the dataset are BERT pre-training model input columns i.e:\r\n```\r\nf = Features({            \r\n \"input_ids\": Sequence(feature=Value(dtype=\"int32\")),           \r\n \"attention_mask\": Sequence(feature=Value(dtype=\"int8\")),            \r\n \"token_type_ids\": Sequence(feature=Value(dtype=\"int8\")),           \r\n \"labels\": Sequence(feature=Value(dtype=\"int32\")),           \r\n \"next_sentence_label\": Value(dtype=\"int8\")\r\n})\r\n```\r\n\r\nI'll work with @lhoestq till we get to the bottom of this one. \r\n ","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":140,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n > I\u2019m not exactly sure how to read the graph but it seems that to_categorical take a lot of time here. Could you share more informations on the features\/stats of your datasets so we could maybe design a synthetic datasets that looks more similar for debugging testing?\r\n\r\n@thomwolf starting from the top, each rectangle represents the cumulative amount of it takes to execute the method call. Therefore, format_batch in torch_formatter.py takes ~20 sec, and the largest portion of that call is taken by to_pandas call and the smaller portion (grey rectangle) by the other method invocation(s) in format_batch (series_to_numpy etc). \r\n\r\nFeatures of the dataset are BERT pre-training model input columns i.e:\r\n```\r\nf = Features({            \r\n \"input_ids\": Sequence(feature=Value(dtype=\"int32\")),           \r\n \"attention_mask\": Sequence(feature=Value(dtype=\"int8\")),            \r\n \"token_type_ids\": Sequence(feature=Value(dtype=\"int8\")),           \r\n \"labels\": Sequence(feature=Value(dtype=\"int32\")),           \r\n \"next_sentence_label\": Value(dtype=\"int8\")\r\n})\r\n```\r\n\r\nI'll work with @lhoestq till we get to the bottom of this one. \r\n 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"@lhoestq the proposed branch is faster, but overall training speedup is a few percentage points. I couldn't figure out how to include the GitHub branch into setup.py, so I couldn't start NVidia optimized Docker-based pre-training run.  But on bare metal, there is a slight improvement. I'll do some more performance traces. ","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":51,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n @lhoestq the proposed branch is faster, but overall training speedup is a few percentage points. I couldn't figure out how to include the GitHub branch into setup.py, so I couldn't start NVidia optimized Docker-based pre-training run.  But on bare metal, there is a slight improvement. I'll do some more performance traces. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"Hi @vblagoje, to install Datasets from @lhoestq PR reference #2505, you can use:\r\n```shell\r\npip install git+ssh:\/\/git@github.com\/huggingface\/datasets.git@refs\/pull\/2505\/head#egg=datasets\r\n```","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":18,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n Hi @vblagoje, to install Datasets from @lhoestq PR reference #2505, you can use:\r\n```shell\r\npip install git+ssh:\/\/git@github.com\/huggingface\/datasets.git@refs\/pull\/2505\/head#egg=datasets\r\n```","embeddings":[-0.3501352668,-0.2402974069,-0.0736680552,0.190357089,0.1027540565,0.147706598,0.2599887252,0.6868174076,-0.121130459,-0.0641201735,-0.356641978,0.2431406975,-0.1145989373,-0.0332216397,0.0663354844,-0.3101109862,0.0007658997,0.1003077179,-0.0678961575,-0.1308621615,-0.1019239053,-0.4241634309,0.0556780435,-0.2308925092,-0.1764650196,0.1433885545,-0.0729704648,0.0049904119,-0.0147783048,-0.3914083242,-0.016802527,0.2041123956,0.1916381568,0.2366560847,-0.0001087394,-0.0219625924,0.105563499,0.0298551396,-0.3094554543,0.2616169453,0.3262714446,-0.3157902956,0.0545597821,-0.2053410858,-0.4622154236,-0.2044653594,-0.0115542626,-0.30791834,0.043709524,0.0324950144,0.1543279141,0.2567405403,-0.3000166416,0.1878047138,0.3291626871,0.4852654338,-0.1752856523,0.0986196473,0.3001522422,0.0291677509,-0.5153069496,0.6179615855,-0.1722578853,-0.0325031802,0.177194804,0.0603692159,-0.2424562275,-0.1164502576,-0.0824211463,0.4060195386,0.032524813,-0.2445900589,-0.500759542,-0.4453507662,-0.0309528373,-0.5959249139,-0.100147076,0.1113878042,-0.2834424973,0.1466880888,-0.0680339932,0.0368809551,-0.1203642115,-0.09613619,-0.0547588803,0.2587930262,0.2410730571,-0.2225960046,0.0905139223,0.1961138248,0.4557999671,-0.1157797202,0.2760972977,-0.0045529366,-0.3522320092,-0.1666412801,-0.0236723572,-0.1867082566,-0.1529744416,-0.0668888763,0.1264795959,0.5531223416,-0.0911921859,-0.0197006557,-0.2372612804,0.0375605449,-0.1032606214,0.210592702,0.1966996491,-0.1186811998,0.0677641109,0.1936755478,-0.0780171901,-0.0493074879,-0.3120107949,0.142736569,-0.4815460443,-0.0842902958,-0.1862215847,0.0252111387,-0.1306553185,-0.1333502084,0.0409616493,0.3111973107,-0.2863078713,0.1948336661,0.1680843681,-0.1474616975,-0.2475613654,-0.2679266036,0.0511941016,0.1703957319,-0.2893526852,0.0069734068,0.2033197582,-0.2458306253,-0.0934309065,-0.070954971,0.074785918,0.388759762,0.020032078,-0.2280967683,0.4766450822,-0.0271022301,-0.0679627433,0.0136563703,0.181370452,0.5773720741,-0.279609412,0.1573994309,-0.1916829199,-0.2062162906,-0.1592074186,0.1019587144,0.0700449198,0.007335064,0.0668838844,0.3424280584,0.1331920326,0.0767204538,0.126472339,-0.249081552,-0.3356597424,-0.1178224832,0.3001018167,0.3032240868,-0.1989410818,-0.0473861061,0.4758732319,0.3441790342,0.4220882058,0.4858781695,-0.3000439703,0.1179692149,-0.0290246345,0.0072558247,0.3716585338,-0.369782567,-0.1297076046,0.3484440446,0.074912928,0.2708399892,0.1608398706,0.1948831677,0.4046298862,-0.0059370184,0.0729750544,0.395489186,0.0082202358,-0.0499474593,-0.4824648201,-0.1739059389,0.4433811307,0.3957262933,0.0574343726,-0.3865258992,-0.1188643202,-0.1047391221,0.4031055868,-0.3227175474,0.1457478553,0.108986631,-0.1720594317,0.153413415,0.0151736438,0.2190547287,-0.1121948287,0.2242095768,-0.3645497561,0.3796460927,0.0479122065,-0.1950641721,0.3231682479,-0.0361455195,-0.0039837821,-0.2554191351,0.0407075249,-0.0446182266,-0.2046026289,0.0253762789,0.0424456075,0.3719467223,-0.2666930556,0.1153119951,-0.2319298089,0.1106730998,0.052906137,-0.0528733134,-0.0534533113,0.2816495895,0.0650102496,-0.0430517569,-0.0791779757,0.1298173219,-0.1343417764,0.2028240263,-0.2158796489,0.1773772687,0.4185040295,-0.1259617507,0.2937958241,0.1701381952,-0.0248790234,-0.1208947748,-0.422383219,0.7661342025,0.4835484028,0.1557845175,-0.1161572859,-0.2186772972,-0.2221040428,0.148417592,0.0560706258,0.0844040662,0.5234335065,0.1368191689,0.1734043807,0.0809791461,-0.4177882671,0.3712826371,0.341157198,0.005134359,-0.1374358982,0.4363709092,-0.0785387158,-0.3662787378,0.3380372822,-0.5506458282,-0.005098918,0.233039096,0.2333679199,0.0235326774,-0.0177598242,-0.0314640813,0.0983767435,0.01055016,-0.0542775206,0.0126836607,0.2190360725,0.2533146739,-0.0732507408,-0.0944208428,-0.1324299425,0.0236582067,-0.1572207958,0.1237859577,-0.4127211571,-0.103166461,-0.1553760469,-0.3362688124,-0.0705448091,-0.1799101084,0.0300737731,0.1377863437,-0.0285042822,0.1710874438,0.0217946917,0.0469736978,0.057029631,-0.2233769894,-0.1739785969,-0.1969986111,-0.0307846274,0.0338535979,0.147559166,-0.032628864,0.3699895144,-0.006935156,0.1059629098,0.0763379633,-0.2765926421,0.0485948883,-0.1336117834,-0.2551215291,0.0496093929,-0.1258975714,-0.2280783504,-0.1208018288,0.0972726569,0.0017792792,0.0587237999,-0.1553657651,-0.0902638882,0.1817816049,-0.0192360058,-0.2255808413,-0.245959267,-0.3073492646,-0.0496062972,-0.1677562743,0.156537056,-0.2041864097,-0.0283644665,0.1308830678,0.2082248777,0.0456591472,0.0236809626,-0.4292769134,0.4179665148,-0.2306931019,-0.1688781232,-0.401109308,-0.0202502627,0.0000506992,0.3673450351,-0.4472309649,0.2142145932,-0.0647428632,0.1862570643,-0.2460256517,-0.1165185198,0.1445492953,0.2486154586,-0.0935690776,0.0080999294,0.0720040649,-0.1541757882,-0.2329793125,0.102216132,-0.0745993704,0.3041974008,0.3246068954,0.6840358973,-0.0617227219,-0.3457929492,0.10292663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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"Hey @albertvillanova yes thank you, I am aware, I can easily pull it from a terminal command line but then I can't automate docker image builds as dependencies are picked up from setup.py and for some reason setup.py doesn't accept this string format.","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":43,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n Hey @albertvillanova yes thank you, I am aware, I can easily pull it from a terminal command line but then I can't automate docker image builds as dependencies are picked up from setup.py and for some reason setup.py doesn't accept this string format.","embeddings":[-0.3501352668,-0.2402974069,-0.0736680552,0.190357089,0.1027540565,0.147706598,0.2599887252,0.6868174076,-0.121130459,-0.0641201735,-0.356641978,0.2431406975,-0.1145989373,-0.0332216397,0.0663354844,-0.3101109862,0.0007658997,0.1003077179,-0.0678961575,-0.1308621615,-0.1019239053,-0.4241634309,0.0556780435,-0.2308925092,-0.1764650196,0.1433885545,-0.0729704648,0.0049904119,-0.0147783048,-0.3914083242,-0.016802527,0.2041123956,0.1916381568,0.2366560847,-0.0001087394,-0.0219625924,0.105563499,0.0298551396,-0.3094554543,0.2616169453,0.3262714446,-0.3157902956,0.0545597821,-0.2053410858,-0.4622154236,-0.2044653594,-0.0115542626,-0.30791834,0.043709524,0.0324950144,0.1543279141,0.2567405403,-0.3000166416,0.1878047138,0.3291626871,0.4852654338,-0.1752856523,0.0986196473,0.3001522422,0.0291677509,-0.5153069496,0.6179615855,-0.1722578853,-0.0325031802,0.177194804,0.0603692159,-0.2424562275,-0.1164502576,-0.0824211463,0.4060195386,0.032524813,-0.2445900589,-0.500759542,-0.4453507662,-0.0309528373,-0.5959249139,-0.100147076,0.1113878042,-0.2834424973,0.1466880888,-0.0680339932,0.0368809551,-0.1203642115,-0.09613619,-0.0547588803,0.2587930262,0.2410730571,-0.2225960046,0.0905139223,0.1961138248,0.4557999671,-0.1157797202,0.2760972977,-0.0045529366,-0.3522320092,-0.1666412801,-0.0236723572,-0.1867082566,-0.1529744416,-0.0668888763,0.1264795959,0.5531223416,-0.0911921859,-0.0197006557,-0.2372612804,0.0375605449,-0.1032606214,0.210592702,0.1966996491,-0.1186811998,0.0677641109,0.1936755478,-0.0780171901,-0.0493074879,-0.3120107949,0.142736569,-0.4815460443,-0.0842902958,-0.1862215847,0.0252111387,-0.1306553185,-0.1333502084,0.0409616493,0.3111973107,-0.2863078713,0.1948336661,0.1680843681,-0.1474616975,-0.2475613654,-0.2679266036,0.0511941016,0.1703957319,-0.2893526852,0.0069734068,0.2033197582,-0.2458306253,-0.0934309065,-0.070954971,0.074785918,0.388759762,0.020032078,-0.2280967683,0.4766450822,-0.0271022301,-0.0679627433,0.0136563703,0.181370452,0.5773720741,-0.279609412,0.1573994309,-0.1916829199,-0.2062162906,-0.1592074186,0.1019587144,0.0700449198,0.007335064,0.0668838844,0.3424280584,0.1331920326,0.0767204538,0.126472339,-0.249081552,-0.3356597424,-0.1178224832,0.3001018167,0.3032240868,-0.1989410818,-0.0473861061,0.4758732319,0.3441790342,0.4220882058,0.4858781695,-0.3000439703,0.1179692149,-0.0290246345,0.0072558247,0.3716585338,-0.369782567,-0.1297076046,0.3484440446,0.074912928,0.2708399892,0.1608398706,0.1948831677,0.4046298862,-0.0059370184,0.0729750544,0.395489186,0.0082202358,-0.0499474593,-0.4824648201,-0.1739059389,0.4433811307,0.3957262933,0.0574343726,-0.3865258992,-0.1188643202,-0.1047391221,0.4031055868,-0.3227175474,0.1457478553,0.108986631,-0.1720594317,0.153413415,0.0151736438,0.2190547287,-0.1121948287,0.2242095768,-0.3645497561,0.3796460927,0.0479122065,-0.1950641721,0.3231682479,-0.0361455195,-0.0039837821,-0.2554191351,0.0407075249,-0.0446182266,-0.2046026289,0.0253762789,0.0424456075,0.3719467223,-0.2666930556,0.1153119951,-0.2319298089,0.1106730998,0.052906137,-0.0528733134,-0.0534533113,0.2816495895,0.0650102496,-0.0430517569,-0.0791779757,0.1298173219,-0.1343417764,0.2028240263,-0.2158796489,0.1773772687,0.4185040295,-0.1259617507,0.2937958241,0.1701381952,-0.0248790234,-0.1208947748,-0.422383219,0.7661342025,0.4835484028,0.1557845175,-0.1161572859,-0.2186772972,-0.2221040428,0.148417592,0.0560706258,0.0844040662,0.5234335065,0.1368191689,0.1734043807,0.0809791461,-0.4177882671,0.3712826371,0.341157198,0.005134359,-0.1374358982,0.4363709092,-0.0785387158,-0.3662787378,0.3380372822,-0.5506458282,-0.005098918,0.233039096,0.2333679199,0.0235326774,-0.0177598242,-0.0314640813,0.0983767435,0.01055016,-0.0542775206,0.0126836607,0.2190360725,0.2533146739,-0.0732507408,-0.0944208428,-0.1324299425,0.0236582067,-0.1572207958,0.1237859577,-0.4127211571,-0.103166461,-0.1553760469,-0.3362688124,-0.0705448091,-0.1799101084,0.0300737731,0.1377863437,-0.0285042822,0.1710874438,0.0217946917,0.0469736978,0.057029631,-0.2233769894,-0.1739785969,-0.1969986111,-0.0307846274,0.0338535979,0.147559166,-0.032628864,0.3699895144,-0.006935156,0.1059629098,0.0763379633,-0.2765926421,0.0485948883,-0.1336117834,-0.2551215291,0.0496093929,-0.1258975714,-0.2280783504,-0.1208018288,0.0972726569,0.0017792792,0.0587237999,-0.1553657651,-0.0902638882,0.1817816049,-0.0192360058,-0.2255808413,-0.245959267,-0.3073492646,-0.0496062972,-0.1677562743,0.156537056,-0.2041864097,-0.0283644665,0.1308830678,0.2082248777,0.0456591472,0.0236809626,-0.4292769134,0.4179665148,-0.2306931019,-0.1688781232,-0.401109308,-0.0202502627,0.0000506992,0.3673450351,-0.4472309649,0.2142145932,-0.0647428632,0.1862570643,-0.2460256517,-0.1165185198,0.1445492953,0.2486154586,-0.0935690776,0.0080999294,0.0720040649,-0.1541757882,-0.2329793125,0.102216132,-0.0745993704,0.3041974008,0.3246068954,0.6840358973,-0.0617227219,-0.3457929492,0.1029266343,0.1235023215,0.1042312533,-0.0933369845,-0.2323143929,0.1221762151,-0.207471773,0.3181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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"@vblagoje in that case, you can add this to your `setup.py`:\r\n```python\r\n    install_requires=[\r\n        \"datasets @ git+ssh:\/\/git@github.com\/huggingface\/datasets.git@refs\/pull\/2505\/head\",\r\n```","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":17,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n @vblagoje in that case, you can add this to your `setup.py`:\r\n```python\r\n    install_requires=[\r\n        \"datasets @ git+ssh:\/\/git@github.com\/huggingface\/datasets.git@refs\/pull\/2505\/head\",\r\n```","embeddings":[-0.3501352668,-0.2402974069,-0.0736680552,0.190357089,0.1027540565,0.147706598,0.2599887252,0.6868174076,-0.121130459,-0.0641201735,-0.356641978,0.2431406975,-0.1145989373,-0.0332216397,0.0663354844,-0.3101109862,0.0007658997,0.1003077179,-0.0678961575,-0.1308621615,-0.1019239053,-0.4241634309,0.0556780435,-0.2308925092,-0.1764650196,0.1433885545,-0.0729704648,0.0049904119,-0.0147783048,-0.3914083242,-0.016802527,0.2041123956,0.1916381568,0.2366560847,-0.0001087394,-0.0219625924,0.105563499,0.0298551396,-0.3094554543,0.2616169453,0.3262714446,-0.3157902956,0.0545597821,-0.2053410858,-0.4622154236,-0.2044653594,-0.0115542626,-0.30791834,0.043709524,0.0324950144,0.1543279141,0.2567405403,-0.3000166416,0.1878047138,0.3291626871,0.4852654338,-0.1752856523,0.0986196473,0.3001522422,0.0291677509,-0.5153069496,0.6179615855,-0.1722578853,-0.0325031802,0.177194804,0.0603692159,-0.2424562275,-0.1164502576,-0.0824211463,0.4060195386,0.032524813,-0.2445900589,-0.500759542,-0.4453507662,-0.0309528373,-0.5959249139,-0.100147076,0.1113878042,-0.2834424973,0.1466880888,-0.0680339932,0.0368809551,-0.1203642115,-0.09613619,-0.0547588803,0.2587930262,0.2410730571,-0.2225960046,0.0905139223,0.1961138248,0.4557999671,-0.1157797202,0.2760972977,-0.0045529366,-0.3522320092,-0.1666412801,-0.0236723572,-0.1867082566,-0.1529744416,-0.0668888763,0.1264795959,0.5531223416,-0.0911921859,-0.0197006557,-0.2372612804,0.0375605449,-0.1032606214,0.210592702,0.1966996491,-0.1186811998,0.0677641109,0.1936755478,-0.0780171901,-0.0493074879,-0.3120107949,0.142736569,-0.4815460443,-0.0842902958,-0.1862215847,0.0252111387,-0.1306553185,-0.1333502084,0.0409616493,0.3111973107,-0.2863078713,0.1948336661,0.1680843681,-0.1474616975,-0.2475613654,-0.2679266036,0.0511941016,0.1703957319,-0.2893526852,0.0069734068,0.2033197582,-0.2458306253,-0.0934309065,-0.070954971,0.074785918,0.388759762,0.020032078,-0.2280967683,0.4766450822,-0.0271022301,-0.0679627433,0.0136563703,0.181370452,0.5773720741,-0.279609412,0.1573994309,-0.1916829199,-0.2062162906,-0.1592074186,0.1019587144,0.0700449198,0.007335064,0.0668838844,0.3424280584,0.1331920326,0.0767204538,0.126472339,-0.249081552,-0.3356597424,-0.1178224832,0.3001018167,0.3032240868,-0.1989410818,-0.0473861061,0.4758732319,0.3441790342,0.4220882058,0.4858781695,-0.3000439703,0.1179692149,-0.0290246345,0.0072558247,0.3716585338,-0.369782567,-0.1297076046,0.3484440446,0.074912928,0.2708399892,0.1608398706,0.1948831677,0.4046298862,-0.0059370184,0.0729750544,0.395489186,0.0082202358,-0.0499474593,-0.4824648201,-0.1739059389,0.4433811307,0.3957262933,0.0574343726,-0.3865258992,-0.1188643202,-0.1047391221,0.4031055868,-0.3227175474,0.1457478553,0.108986631,-0.1720594317,0.153413415,0.0151736438,0.2190547287,-0.1121948287,0.2242095768,-0.3645497561,0.3796460927,0.0479122065,-0.1950641721,0.3231682479,-0.0361455195,-0.0039837821,-0.2554191351,0.0407075249,-0.0446182266,-0.2046026289,0.0253762789,0.0424456075,0.3719467223,-0.2666930556,0.1153119951,-0.2319298089,0.1106730998,0.052906137,-0.0528733134,-0.0534533113,0.2816495895,0.0650102496,-0.0430517569,-0.0791779757,0.1298173219,-0.1343417764,0.2028240263,-0.2158796489,0.1773772687,0.4185040295,-0.1259617507,0.2937958241,0.1701381952,-0.0248790234,-0.1208947748,-0.422383219,0.7661342025,0.4835484028,0.1557845175,-0.1161572859,-0.2186772972,-0.2221040428,0.148417592,0.0560706258,0.0844040662,0.5234335065,0.1368191689,0.1734043807,0.0809791461,-0.4177882671,0.3712826371,0.341157198,0.005134359,-0.1374358982,0.4363709092,-0.0785387158,-0.3662787378,0.3380372822,-0.5506458282,-0.005098918,0.233039096,0.2333679199,0.0235326774,-0.0177598242,-0.0314640813,0.0983767435,0.01055016,-0.0542775206,0.0126836607,0.2190360725,0.2533146739,-0.0732507408,-0.0944208428,-0.1324299425,0.0236582067,-0.1572207958,0.1237859577,-0.4127211571,-0.103166461,-0.1553760469,-0.3362688124,-0.0705448091,-0.1799101084,0.0300737731,0.1377863437,-0.0285042822,0.1710874438,0.0217946917,0.0469736978,0.057029631,-0.2233769894,-0.1739785969,-0.1969986111,-0.0307846274,0.0338535979,0.147559166,-0.032628864,0.3699895144,-0.006935156,0.1059629098,0.0763379633,-0.2765926421,0.0485948883,-0.1336117834,-0.2551215291,0.0496093929,-0.1258975714,-0.2280783504,-0.1208018288,0.0972726569,0.0017792792,0.0587237999,-0.1553657651,-0.0902638882,0.1817816049,-0.0192360058,-0.2255808413,-0.245959267,-0.3073492646,-0.0496062972,-0.1677562743,0.156537056,-0.2041864097,-0.0283644665,0.1308830678,0.2082248777,0.0456591472,0.0236809626,-0.4292769134,0.4179665148,-0.2306931019,-0.1688781232,-0.401109308,-0.0202502627,0.0000506992,0.3673450351,-0.4472309649,0.2142145932,-0.0647428632,0.1862570643,-0.2460256517,-0.1165185198,0.1445492953,0.2486154586,-0.0935690776,0.0080999294,0.0720040649,-0.1541757882,-0.2329793125,0.102216132,-0.0745993704,0.3041974008,0.3246068954,0.6840358973,-0.0617227219,-0.3457929492,0.1029266343,0.12350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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"@lhoestq @thomwolf @albertvillanova The new approach is definitely faster, dataloader now takes less than 3% cumulative time (pink rectangle two rectangles to the right of tensor.py backward invocation)\r\n\r\n![Screen Shot 2021-06-16 at 3 05 06 PM](https:\/\/user-images.githubusercontent.com\/458335\/122224432-19de4700-ce82-11eb-982f-d45d4bcc1e41.png)\r\n\r\nWhen we drill down into dataloader next invocation we get:\r\n\r\n![Screen Shot 2021-06-16 at 3 09 56 PM](https:\/\/user-images.githubusercontent.com\/458335\/122224976-a1c45100-ce82-11eb-8d40-59194740d616.png)\r\n\r\nAnd finally format_batch:\r\n\r\n![Screen Shot 2021-06-16 at 3 11 07 PM](https:\/\/user-images.githubusercontent.com\/458335\/122225132-cae4e180-ce82-11eb-8a16-967ab7c1c2aa.png)\r\n\r\n\r\nNot sure this could be further improved but this is definitely a decent step forward.\r\n\r\n","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":80,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n @lhoestq @thomwolf @albertvillanova The new approach is definitely faster, dataloader now takes less than 3% cumulative time (pink rectangle two rectangles to the right of tensor.py backward invocation)\r\n\r\n![Screen Shot 2021-06-16 at 3 05 06 PM](https:\/\/user-images.githubusercontent.com\/458335\/122224432-19de4700-ce82-11eb-982f-d45d4bcc1e41.png)\r\n\r\nWhen we drill down into dataloader next invocation we get:\r\n\r\n![Screen Shot 2021-06-16 at 3 09 56 PM](https:\/\/user-images.githubusercontent.com\/458335\/122224976-a1c45100-ce82-11eb-8d40-59194740d616.png)\r\n\r\nAnd finally format_batch:\r\n\r\n![Screen Shot 2021-06-16 at 3 11 07 PM](https:\/\/user-images.githubusercontent.com\/458335\/122225132-cae4e180-ce82-11eb-8a16-967ab7c1c2aa.png)\r\n\r\n\r\nNot sure this could be further improved but this is definitely a decent step forward.\r\n\r\n","embeddings":[-0.3501352668,-0.2402974069,-0.0736680552,0.190357089,0.1027540565,0.147706598,0.2599887252,0.6868174076,-0.121130459,-0.0641201735,-0.356641978,0.2431406975,-0.1145989373,-0.0332216397,0.0663354844,-0.3101109862,0.0007658997,0.1003077179,-0.0678961575,-0.1308621615,-0.1019239053,-0.4241634309,0.0556780435,-0.2308925092,-0.1764650196,0.1433885545,-0.0729704648,0.0049904119,-0.0147783048,-0.3914083242,-0.016802527,0.2041123956,0.1916381568,0.2366560847,-0.0001087394,-0.0219625924,0.105563499,0.0298551396,-0.3094554543,0.2616169453,0.3262714446,-0.3157902956,0.0545597821,-0.2053410858,-0.4622154236,-0.2044653594,-0.0115542626,-0.30791834,0.043709524,0.0324950144,0.1543279141,0.2567405403,-0.3000166416,0.1878047138,0.3291626871,0.4852654338,-0.1752856523,0.0986196473,0.3001522422,0.0291677509,-0.5153069496,0.6179615855,-0.1722578853,-0.0325031802,0.177194804,0.0603692159,-0.2424562275,-0.1164502576,-0.0824211463,0.4060195386,0.032524813,-0.2445900589,-0.500759542,-0.4453507662,-0.0309528373,-0.5959249139,-0.100147076,0.1113878042,-0.2834424973,0.1466880888,-0.0680339932,0.0368809551,-0.1203642115,-0.09613619,-0.0547588803,0.2587930262,0.2410730571,-0.2225960046,0.0905139223,0.1961138248,0.4557999671,-0.1157797202,0.2760972977,-0.0045529366,-0.3522320092,-0.1666412801,-0.0236723572,-0.1867082566,-0.1529744416,-0.0668888763,0.1264795959,0.5531223416,-0.0911921859,-0.0197006557,-0.2372612804,0.0375605449,-0.1032606214,0.210592702,0.1966996491,-0.1186811998,0.0677641109,0.1936755478,-0.0780171901,-0.0493074879,-0.3120107949,0.142736569,-0.4815460443,-0.0842902958,-0.1862215847,0.0252111387,-0.1306553185,-0.1333502084,0.0409616493,0.3111973107,-0.2863078713,0.1948336661,0.1680843681,-0.1474616975,-0.2475613654,-0.2679266036,0.0511941016,0.1703957319,-0.2893526852,0.0069734068,0.2033197582,-0.2458306253,-0.0934309065,-0.070954971,0.074785918,0.388759762,0.020032078,-0.2280967683,0.4766450822,-0.0271022301,-0.0679627433,0.0136563703,0.181370452,0.5773720741,-0.279609412,0.1573994309,-0.1916829199,-0.2062162906,-0.1592074186,0.1019587144,0.0700449198,0.007335064,0.0668838844,0.3424280584,0.1331920326,0.0767204538,0.126472339,-0.249081552,-0.3356597424,-0.1178224832,0.3001018167,0.3032240868,-0.1989410818,-0.0473861061,0.4758732319,0.3441790342,0.4220882058,0.4858781695,-0.3000439703,0.1179692149,-0.0290246345,0.0072558247,0.3716585338,-0.369782567,-0.1297076046,0.3484440446,0.074912928,0.2708399892,0.1608398706,0.1948831677,0.4046298862,-0.0059370184,0.0729750544,0.395489186,0.0082202358,-0.0499474593,-0.4824648201,-0.1739059389,0.4433811307,0.3957262933,0.0574343726,-0.3865258992,-0.1188643202,-0.1047391221,0.4031055868,-0.3227175474,0.1457478553,0.108986631,-0.1720594317,0.153413415,0.0151736438,0.2190547287,-0.1121948287,0.2242095768,-0.3645497561,0.3796460927,0.0479122065,-0.1950641721,0.3231682479,-0.0361455195,-0.0039837821,-0.2554191351,0.0407075249,-0.0446182266,-0.2046026289,0.0253762789,0.0424456075,0.3719467223,-0.2666930556,0.1153119951,-0.2319298089,0.1106730998,0.052906137,-0.0528733134,-0.0534533113,0.2816495895,0.0650102496,-0.0430517569,-0.0791779757,0.1298173219,-0.1343417764,0.2028240263,-0.2158796489,0.1773772687,0.4185040295,-0.1259617507,0.2937958241,0.1701381952,-0.0248790234,-0.1208947748,-0.422383219,0.7661342025,0.4835484028,0.1557845175,-0.1161572859,-0.2186772972,-0.2221040428,0.148417592,0.0560706258,0.0844040662,0.5234335065,0.1368191689,0.1734043807,0.0809791461,-0.4177882671,0.3712826371,0.341157198,0.005134359,-0.1374358982,0.4363709092,-0.0785387158,-0.3662787378,0.3380372822,-0.5506458282,-0.005098918,0.233039096,0.2333679199,0.0235326774,-0.0177598242,-0.0314640813,0.0983767435,0.01055016,-0.0542775206,0.0126836607,0.2190360725,0.2533146739,-0.0732507408,-0.0944208428,-0.1324299425,0.0236582067,-0.1572207958,0.1237859577,-0.4127211571,-0.103166461,-0.1553760469,-0.3362688124,-0.0705448091,-0.1799101084,0.0300737731,0.1377863437,-0.0285042822,0.1710874438,0.0217946917,0.0469736978,0.057029631,-0.2233769894,-0.1739785969,-0.1969986111,-0.0307846274,0.0338535979,0.147559166,-0.032628864,0.3699895144,-0.006935156,0.1059629098,0.0763379633,-0.2765926421,0.0485948883,-0.1336117834,-0.2551215291,0.0496093929,-0.1258975714,-0.2280783504,-0.1208018288,0.0972726569,0.0017792792,0.0587237999,-0.1553657651,-0.0902638882,0.1817816049,-0.0192360058,-0.2255808413,-0.245959267,-0.3073492646,-0.0496062972,-0.1677562743,0.156537056,-0.2041864097,-0.0283644665,0.1308830678,0.2082248777,0.0456591472,0.0236809626,-0.4292769134,0.4179665148,-0.2306931019,-0.1688781232,-0.401109308,-0.0202502627,0.0000506992,0.3673450351,-0.4472309649,0.2142145932,-0.0647428632,0.1862570643,-0.2460256517,-0.1165185198,0.1445492953,0.2486154586,-0.0935690776,0.0080999294,0.0720040649,-0.1541757882,-0.2329793125,0.102216132,-0.0745993704,0.3041974008,0.3246068954,0.6840358973,-0.0617227219,-0.3457929492,0.1029266343,0.1235023215,0.1042312533,-0.0933369845,-0.2323143929,0.1221762151,-0.2074717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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"> ```python\r\n> datasets @ git+ssh:\/\/git@github.com\/huggingface\/datasets.git@refs\/pull\/2505\/head\r\n> ```\r\n\r\n@albertvillanova how would I replace datasets dependency in https:\/\/github.com\/huggingface\/transformers\/blob\/master\/setup.py as the above approach is not working. ","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":24,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n > ```python\r\n> datasets @ git+ssh:\/\/git@github.com\/huggingface\/datasets.git@refs\/pull\/2505\/head\r\n> ```\r\n\r\n@albertvillanova how would I replace datasets dependency in https:\/\/github.com\/huggingface\/transformers\/blob\/master\/setup.py as the above approach is not working. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"@vblagoje I tested my proposed approach before posting it here and it worked for me. \r\n\r\nIs it not working in your case because of the SSH protocol? In that case you could try the same approach but using HTTPS:\r\n```\r\n\"datasets @ git+https:\/\/github.com\/huggingface\/datasets.git@refs\/pull\/2505\/head\",\r\n``` ","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":44,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n @vblagoje I tested my proposed approach before posting it here and it worked for me. \r\n\r\nIs it not working in your case because of the SSH protocol? In that case you could try the same approach but using HTTPS:\r\n```\r\n\"datasets @ git+https:\/\/github.com\/huggingface\/datasets.git@refs\/pull\/2505\/head\",\r\n``` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2498","title":"Improve torch formatting performance","comments":"@albertvillanova of course it works. Apologies. I needed to change datasets in all deps references , like [here](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/setup.py#L235) for example. ","body":"**Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n","comment_length":20,"text":"Improve torch formatting performance \n **Is your feature request related to a problem? Please describe.**\r\nIt would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors. \r\n\r\nA bit more background.  I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.\r\n\r\nThe current performance is about 30% slower than NVidia optimized BERT [examples](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded. \r\n\r\n**Describe the solution you'd like**\r\nUsing profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.\r\n![dataloader_next](https:\/\/user-images.githubusercontent.com\/458335\/121895543-59742a00-ccee-11eb-85fb-f07715e3f1f6.png)\r\n\r\nAs you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call. \r\n\r\nDigging a bit deeper into format_batch we can see the following profiler data:\r\n![torch_formatter](https:\/\/user-images.githubusercontent.com\/458335\/121895944-c7b8ec80-ccee-11eb-95d5-5875c5716c30.png)\r\n\r\nOnce again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion. \r\n\r\n**Describe alternatives you've considered**\r\nI am not familiar with pyarrow and have not yet considered the alternatives to the current approach. \r\n\r\nMost of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement.  Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%. \r\n\r\n \n @albertvillanova of course it works. Apologies. I needed to change datasets in all deps references , like [here](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/setup.py#L235) for example. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2481","title":"Delete extracted files to save disk space","comments":"My suggestion for this would be to have this enabled by default.\r\n\r\nPlus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:\r\n\r\n1. uncompress a handful of files via a generator enough to generate one arrow file\r\n2. process arrow file 1\r\n3. delete all the files that went in and aren't needed anymore.\r\n\r\nrinse and repeat.\r\n\r\n1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project\r\n2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing\r\n3. It would already include deleting temp files this issue is talking about\r\n\r\nI wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders.","body":"As discussed with @stas00 and @lhoestq, allowing the deletion of extracted files would save a great amount of disk space to typical user.","comment_length":164,"text":"Delete extracted files to save disk space \n As discussed with @stas00 and @lhoestq, allowing the deletion of extracted files would save a great amount of disk space to typical user. \n My suggestion for this would be to have this enabled by default.\r\n\r\nPlus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:\r\n\r\n1. uncompress a handful of files via a generator enough to generate one arrow file\r\n2. process arrow file 1\r\n3. delete all the files that went in and aren't needed anymore.\r\n\r\nrinse and repeat.\r\n\r\n1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project\r\n2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing\r\n3. It would already include deleting temp files this issue is talking about\r\n\r\nI wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders.","embeddings":[-0.1356213838,-0.3015790582,-0.1714526117,0.1553417295,-0.0760238245,-0.0403156914,-0.1220020875,0.5078596473,-0.0759534761,0.5109491944,0.1839041412,0.3984468281,-0.3721122444,0.2519610524,-0.1107841879,-0.1918504983,-0.0977600813,0.3879247606,0.0852558166,0.1664171815,-0.0113373147,-0.1601634026,0.0160193667,-0.1735659987,-0.1610468775,-0.2036674172,0.1742886603,0.0150632374,-0.3164533079,-0.3642852008,-0.0983547419,0.1170425713,0.1618454903,0.2395251989,-0.0001068214,-0.2105756253,0.1879487187,-0.0319870822,-0.3838619888,0.2532771826,-0.0644358918,-0.2182497084,0.020984346,0.0299099069,0.1138881817,-0.3589502871,-0.0091724787,-0.2793225348,0.6590781808,-0.1522957087,0.2111323476,0.1615836918,-0.3104902208,0.1768416911,0.0161841139,0.3898807466,-0.3480933607,0.1210870221,0.4206851125,0.1398861706,0.0320917815,0.3903601766,-0.061599277,-0.0733970255,0.5608956218,-0.2504017949,-0.0018344686,-0.6491248608,0.1009111628,0.0591418147,0.2640399337,-0.0302475076,-0.1682366282,-0.303260237,0.1826171428,-0.4667096436,0.1477618665,0.1348556429,-0.2945114374,0.0794928893,-0.115130432,-0.3061996996,-0.3989314437,-0.059657719,0.4848071337,-0.1565108895,0.045629777,-0.1460523605,0.3003849685,0.184118405,0.3356783092,-0.0832955539,0.0373022445,0.0891576409,-0.0273945481,-0.2479068786,-0.0708846524,-0.2019142658,-0.1583535224,0.2543638349,0.5093041062,0.2072617859,0.2530672252,0.0360715203,0.0824607238,0.1407410502,0.0225181095,-0.3135814369,0.2208501697,0.0810909495,0.1876424104,-0.035811048,-0.1597423255,0.0385523811,-0.1225860119,-0.0592423566,-0.0099593624,0.0264747832,-0.0054677497,-0.0316957384,0.0815773606,0.1349273622,0.0508202836,-0.0802242905,0.2607171237,0.583869338,-0.0523020402,0.2284559458,0.0538581572,-0.165893808,0.0019470691,0.0466800295,-0.1367949098,0.1242415383,0.2767364383,-0.1985018849,-0.0007487249,-0.1014563367,0.0445221774,-0.140229255,0.4040881395,0.1330535859,0.1663027108,0.1791575402,-0.1452494115,-0.2906928658,-0.1634166539,0.2401972413,-0.1942516714,0.317889899,-0.1339348406,-0.4168533087,-0.1584446579,0.2433993071,0.1234101728,0.3315380812,-0.3740736246,-0.0029831263,-0.185118258,-0.0062794401,0.1100404188,0.2283481508,0.1229907721,-0.1229147911,0.0011528463,0.099202618,-0.2082644999,0.0019111834,-0.3521500528,0.1476605386,0.2103511691,0.2112832814,-0.2174930274,0.1508672088,-0.0513635911,0.3056920767,0.3844228089,-0.0396411419,-0.3758777678,0.1250844002,-0.1171268672,0.0635380968,0.3967376947,0.1820128113,0.3838993907,-0.1808836758,-0.1668737382,0.3437715173,-0.0905222297,0.0229771491,-0.5567653179,-0.5417342782,-0.2692141235,0.0540842414,0.2500276864,-0.0277789179,0.1159667894,-0.0277544763,0.1581622213,-0.1157450154,0.2834402025,-0.0659279525,0.2879229784,-0.3269377947,-0.2581423521,0.1229908764,-0.2178051919,0.0017613814,-0.2269024849,-0.4157865942,0.0213951487,-0.3032647073,0.1413082033,-0.0749386176,-0.033028014,0.1762614995,0.1311375797,0.0012698655,0.0004972822,-0.3802503347,-0.517528832,-0.1215186268,-0.0330006853,0.0996584594,-0.2109696716,0.2710273564,-0.0107829068,-0.1132738292,0.098958835,0.0885714367,-0.2441337854,-0.2142184526,-0.0785041526,0.1750764996,-0.2076222003,0.4073339105,0.3072755039,0.3474683464,0.2859517038,-0.3495841622,0.2610292733,0.4026177227,0.0197046753,0.2463868707,-0.2008056343,0.1402627081,-0.1120508313,0.0014032872,-0.1213431656,0.0507838801,-0.145862475,-0.0369989164,-0.0701316074,-0.0479005501,-0.0976387262,0.1465107501,-0.1034493521,0.3378802836,0.2001923323,0.2742253542,0.4219549,0.1870205104,-0.1777384132,0.415522784,-0.0429303795,-0.3651118577,0.1717949361,0.2880438566,0.1948765218,0.1730131954,0.0711779967,-0.180332467,0.5252888799,-0.1838226616,0.4256459773,0.2466791272,0.0225341916,0.508084476,-0.1023883373,0.311640054,-0.2079672515,0.0337461568,0.0400211439,-0.0517089143,0.2694743574,-0.295173645,-0.1319106519,-0.0715440214,0.2251098454,-0.0056277905,0.0820553005,-0.0203735773,0.5460215211,0.2144937515,-0.6292132735,0.2050195485,0.0284267422,0.6209682226,-0.2706537545,0.1086053923,-0.4188214242,-0.0603151433,0.125739947,0.1639572978,0.3364751339,-0.025830403,0.3237527013,0.070252426,0.217561692,-0.3380711079,-0.1101963744,0.1077900827,0.1536597759,-0.2036513537,-0.0970041454,0.2288162112,-0.0879902467,-0.489382416,-0.2472160161,-0.4151543677,-0.0292705372,0.1077464595,0.1544803828,0.0633975565,-0.3697407842,-0.131983757,0.0087766601,-0.733098805,0.4366351664,-0.2078465968,0.1679955572,-0.0826826692,0.2305323035,-0.0202275682,0.1514763832,-0.0668018088,0.0914699063,-0.5894623399,0.0071841232,-0.1340795606,-0.2107977569,0.0536962077,-0.0734702572,0.0184512995,0.3070475459,-0.2912283838,-0.0223293565,-0.0389113612,0.1044672951,-0.1724076271,-0.0754538849,0.334551394,0.2662546337,-0.2806762755,0.1334557384,0.2682353854,0.0744224787,0.4424127936,0.1211573929,0.0608153977,-0.1510605812,0.3017262816,0.440004319,-0.1242219955,0.325374037,0.268820703,0.619294405,0.286211729,-0.2466650307,0.2488624454,0.1597568542,-0.0142295929,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2480","title":"Set download\/extracted paths configurable","comments":"For example to be able to send uncompressed and temp  build files to another volume\/partition, so that the user gets the minimal disk usage on their primary setup - and ends up with just the downloaded compressed data + arrow files, but outsourcing the huge files and building to another partition. e.g. on JZ there is a special partition for fast data, but it's also volatile, so only temp files should go there.\r\n\r\nThink of it as `TMPDIR` so we need the equivalent for `datasets`.","body":"As discussed with @stas00 and @lhoestq, setting these paths configurable may allow to overcome disk space limitation on different partitions\/drives.\r\n\r\nTODO:\r\n- [x] Set configurable extracted datasets path: #2487\r\n- [x] Set configurable downloaded datasets path: #2488\r\n- [ ] Set configurable \"incomplete\" datasets path?","comment_length":85,"text":"Set download\/extracted paths configurable \n As discussed with @stas00 and @lhoestq, setting these paths configurable may allow to overcome disk space limitation on different partitions\/drives.\r\n\r\nTODO:\r\n- [x] Set configurable extracted datasets path: #2487\r\n- [x] Set configurable downloaded datasets path: #2488\r\n- [ ] Set configurable \"incomplete\" datasets path? \n For example to be able to send uncompressed and temp  build files to another volume\/partition, so that the user gets the minimal disk usage on their primary setup - and ends up with just the downloaded compressed data + arrow files, but outsourcing the huge files and building to another partition. e.g. on JZ there is a special partition for fast data, but it's also volatile, so only temp files should go there.\r\n\r\nThink of it as `TMPDIR` so we need the equivalent for `datasets`.","embeddings":[-0.2962553203,-0.1055004969,-0.1821807027,0.189564541,0.1187052503,-0.0878328681,-0.0424844585,0.2916618586,0.0462339111,0.347949326,-0.1803242266,0.1907633692,0.0141955828,0.3997286856,0.0212996267,-0.0809282586,-0.2167209387,0.2052152604,-0.2652953565,0.180523932,-0.2859375775,0.0219762586,0.0442879908,-0.065782547,-0.0264658909,-0.1949851513,-0.02131119,0.0202082526,-0.1970957816,-0.2983090281,0.1801560968,0.2298398167,0.0550775453,0.3512578309,-0.0000983048,-0.0042582424,0.0631370991,-0.1732392907,-0.6294649839,-0.2513601482,-0.019107502,-0.5226925015,-0.0321825072,-0.2287234068,0.0223126914,-0.245350942,0.1677339673,-0.3536904454,0.1197797135,-0.0993938595,0.3681629896,0.2698152363,-0.043047931,-0.1813036501,0.2348441333,0.2331928313,-0.3291924894,0.0507573597,0.3433953226,0.2788313329,0.0131397964,0.0068487185,0.0295037478,0.3137277365,0.2971015275,-0.030737495,0.1122198477,-0.4712475836,0.0997477323,0.2625061274,0.5066245198,-0.3221046329,-0.3209204972,-0.4933105111,-0.0251851361,-0.1129073128,0.2811666727,0.1690110713,-0.304670006,0.1274890453,-0.3215426207,-0.133631438,-0.2272516489,0.2419533581,-0.0089452351,0.023959199,-0.1358814538,-0.0463341437,0.2854825556,0.1762559414,0.2132878006,-0.4044156969,0.1854973286,-0.0745382607,0.2001808286,-0.2847127914,-0.0808054656,-0.1120696664,0.0643399954,0.0092399204,0.0790888667,-0.0684436858,0.0642204136,0.0518128462,0.2564743459,0.0517005175,-0.2714928985,-0.0614267848,0.2406294793,-0.0381081663,0.217418924,0.048367843,-0.1444502622,-0.3023051322,-0.3724725246,-0.1259241402,-0.0751999244,0.0211896189,-0.3299500048,-0.3805194497,0.4152680039,0.0354148671,0.0559008196,-0.0144029129,0.134567529,-0.1691818684,-0.237179175,0.2818698287,-0.0938867629,0.0925750136,-0.0458540209,-0.0457006469,-0.2854702771,0.0869987905,0.2056019455,0.0078324061,0.1775724739,-0.1700742841,-0.0977415442,0.2256355882,0.4130826294,-0.0440710448,0.0689796582,0.3716663122,0.1851373017,0.1394669265,-0.2464462221,0.3634608686,-0.3581136763,0.2483163476,-0.2918246686,-0.440557152,0.0944593325,0.2806108296,-0.1643900573,0.000140954,-0.1923115402,0.0523219816,-0.0897785947,-0.0549220294,-0.0231637824,0.1035751551,-0.0189472828,-0.2156531066,0.1324109286,0.1738235056,-0.3945197165,-0.0616732948,-0.3099749684,-0.0832765102,0.1494045705,0.2036529183,-0.2687669396,0.1125361696,-0.356792897,0.1980066746,0.3827776611,-0.0618517846,-0.3516402543,0.1939809769,-0.117787607,-0.0740234852,0.2361405343,0.152056098,0.4000906348,-0.0394077897,-0.0989351124,0.5456370115,0.0877942368,0.0515716597,-0.2195086926,-0.6255843639,-0.0960066319,-0.0157759879,0.0799059793,-0.1631885767,0.2333318889,0.0486646704,0.0973784328,-0.0917536095,0.2353197038,0.0877411142,0.1764996648,-0.0881695747,-0.2954939604,0.0713954493,-0.3603202999,0.2684758008,-0.1432165056,-0.2140787393,0.0800394565,-0.0703865215,-0.0012989908,-0.2523118556,0.0203486439,-0.1723855585,0.25114429,0.1763208508,0.1582447588,-0.2860212326,-0.1124312952,-0.1914063394,0.082382448,0.0651015192,0.083324112,0.4587310553,-0.1591846347,-0.0013214529,-0.0579477064,0.1388753951,-0.2309408635,-0.3591414094,0.07074745,0.2254110128,0.0796133652,0.2203642428,0.2830663621,0.2405697554,0.2718525231,0.0907001793,0.2883929014,0.1396323442,-0.2050853968,0.1913954318,-0.3032184541,0.3291305304,-0.1139847264,-0.0407863706,0.1621512622,0.0199148115,0.1656925529,-0.0579242557,0.1096840054,-0.3439187407,-0.0135338139,0.0395466387,0.2599923611,0.2794912159,0.218991667,0.2041254342,0.3102349043,0.2133680433,0.0524346605,0.1946962029,0.010002315,-0.2498704046,-0.0461732782,0.5935592055,0.4426033497,0.4874846041,0.3771624863,-0.2000807077,0.0602378435,-0.3213655353,0.4303214848,-0.0018881229,-0.0261024106,0.1658359319,-0.2078157514,-0.0010186218,-0.1154893413,-0.1544919163,0.2019416541,-0.0175744742,0.0483082868,-0.389996171,-0.3329841197,-0.2143124044,-0.0565149374,0.0724727437,0.0167133603,-0.0889772624,0.348167181,0.1882576644,-0.3835696876,0.1427307427,-0.0516349748,0.3345633149,-0.2055127323,-0.2572612762,-0.0680400208,-0.0189592559,-0.001406703,0.240559727,0.2626760602,-0.0290705059,0.318559587,-0.0115838209,0.0144249005,-0.514113605,-0.1823756844,0.1400062144,0.1323257685,0.1149186715,0.0611002035,0.2755704522,0.2675017118,-0.18912597,0.0262238756,0.1039331481,-0.0953405648,-0.0886662304,0.0824930891,-0.099150762,-0.306763947,-0.2084642351,-0.2901556194,-0.6281812787,0.2831303179,-0.1043651327,0.2514548898,0.1861862391,0.4520158172,-0.0152569767,0.0524816662,-0.0490883254,-0.0354688093,-0.4996625781,0.2473182529,-0.4030629098,0.078699477,-0.1015755534,0.1316581219,0.1765712649,0.4386541843,-0.515838325,-0.0015856476,-0.2677957416,0.3502958119,0.2252785563,0.0487867184,0.4365338087,0.0645734668,-0.2161233872,-0.0506754592,-0.0251302719,0.1632097811,0.3058544993,0.1377157718,0.0263393484,-0.1690971702,0.1141312569,0.3385214806,-0.145069927,0.2844698727,0.3558307588,0.2150807381,0.5061594844,-0.2726740241,0.1611344665,0.280484587,0.0531855971,-0.175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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2474","title":"cache_dir parameter for load_from_disk ?","comments":"Hi ! `load_from_disk` doesn't move the data. If you specify a local path to your mounted drive, then the dataset is going to be loaded directly from the arrow file in this directory. The cache files that result from `map` operations are also stored in the same directory by default.\r\n\r\nHowever note than writing data to your google drive actually fills the VM's disk (see https:\/\/github.com\/huggingface\/datasets\/issues\/643)\r\n\r\nGiven that, I don't think that changing the cache directory changes anything.\r\n\r\nLet me know what you think","body":"**Is your feature request related to a problem? Please describe.**\r\nWhen using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:\r\n\r\n`\r\nfrom datasets import load_from_disk\r\n\r\nmyPreprocessedData = load_from_disk(\"\/content\/gdrive\/MyDrive\/ASR_data\/myPreprocessedData\")\r\n\r\n`\r\nI know that chaching on google drive could slow down learning. But at least it would run.\r\n\r\n**Describe the solution you'd like**\r\nAdd cache_Dir parameter to the load_from_disk function.\r\n\r\n**Describe alternatives you've considered**\r\nIt looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?\r\n","comment_length":84,"text":"cache_dir parameter for load_from_disk ? \n **Is your feature request related to a problem? Please describe.**\r\nWhen using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:\r\n\r\n`\r\nfrom datasets import load_from_disk\r\n\r\nmyPreprocessedData = load_from_disk(\"\/content\/gdrive\/MyDrive\/ASR_data\/myPreprocessedData\")\r\n\r\n`\r\nI know that chaching on google drive could slow down learning. But at least it would run.\r\n\r\n**Describe the solution you'd like**\r\nAdd cache_Dir parameter to the load_from_disk function.\r\n\r\n**Describe alternatives you've considered**\r\nIt looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?\r\n \n Hi ! `load_from_disk` doesn't move the data. If you specify a local path to your mounted drive, then the dataset is going to be loaded directly from the arrow file in this directory. The cache files that result from `map` operations are also stored in the same directory by default.\r\n\r\nHowever note than writing data to your google drive actually fills the VM's disk (see https:\/\/github.com\/huggingface\/datasets\/issues\/643)\r\n\r\nGiven that, I don't think that changing the cache directory changes anything.\r\n\r\nLet me know what you think","embeddings":[-0.2717247307,-0.12313582,-0.0702048838,0.1266331822,0.2155744135,-0.0214382932,0.2847643197,0.084847644,0.4296782315,0.3610470295,-0.1760807931,0.2474943846,-0.2383664548,0.2422399968,0.270316273,0.0099474564,0.0024952546,-0.0644657016,-0.0686381236,-0.1333422959,-0.1486033052,0.1585291773,0.0208627786,-0.2857365906,-0.0282743629,-0.1897913963,-0.0265019555,0.2757652402,0.3105606735,-0.3239333332,0.1548090726,0.1240978912,0.157219097,0.5559691191,-0.0001047829,-0.0235921051,-0.0222179685,-0.2570990622,-0.3680811822,-0.1126931608,-0.0655640736,-0.1430470794,0.0789605454,-0.0596486479,-0.4904256761,0.0890217945,-0.0152680352,-0.2515120208,0.1891818643,0.1155601069,0.2483702898,-0.0900903121,-0.3790552914,0.0660773069,0.0514307208,0.0461631231,-0.1904249787,0.5480613112,0.0846348852,0.1086061001,-0.145517841,0.4331534207,-0.0582385734,0.1626028866,0.530264616,0.3962347507,-0.0634314194,-0.1762126684,0.0431517214,-0.1202843711,0.5056242943,-0.2109804749,-0.2322986871,-0.2513057888,0.0228340011,-0.3922423124,0.384424001,0.1623055339,-0.1985076517,0.3777803779,-0.2268848866,-0.3963228464,-0.1039247438,0.0135895964,-0.0000064121,-0.1683011651,-0.2351841927,-0.0759985968,0.1762790829,0.1449361444,0.206283614,-0.0536264814,0.0520501025,0.3046586514,-0.0917593911,-0.1389120519,-0.0464858897,0.3314088881,0.0078661777,0.0147841228,0.4713613689,0.0797277391,-0.2129879445,0.1816708297,-0.067667596,0.3930065036,-0.2098642737,-0.0274156965,0.4752400815,0.1682695001,-0.2131383717,-0.0970623493,0.1519378722,-0.0439626947,0.2183887661,0.140013501,0.0364623815,-0.1857560575,0.2886313796,-0.2089163065,0.1319718659,0.0694612339,0.28249681,0.4558331966,-0.0666587055,-0.1313296705,0.1208478436,-0.2122956663,-0.1652946323,0.1883939356,-0.109368667,0.0455158725,-0.2290371507,0.489289701,0.2463846803,0.0902864486,0.2731122077,-0.2993309796,0.2071622461,0.0754927173,0.0755357817,-0.1780983359,0.3490899801,0.3921796679,-0.0062888535,0.0368461236,-0.0510139763,-0.0927096233,-0.363440901,0.2184134424,-0.4228341281,-0.4356271327,0.4194563925,0.1934923977,-0.185685724,0.0723155886,-0.5352765322,0.3024528325,-0.1795238107,-0.1689608097,-0.0451574512,0.0595604405,-0.5309442282,-0.3391389847,0.1563928425,0.2845223844,-0.1855960786,-0.1120542958,0.1516869664,0.1550040096,-0.137589395,0.3683568239,-0.2482610047,0.0201292392,-0.2403443307,-0.1525033116,0.200971067,-0.3625818491,-0.605230093,0.1620093286,-0.1262727827,-0.0310018901,-0.0377450697,0.1869264394,0.1384861767,-0.1977033615,0.3591814935,0.6201862693,0.0559349991,0.3013579547,-0.3125096262,-0.2830684781,-0.3325062394,0.2417350858,-0.0909443945,0.1030955315,0.1306126416,-0.2827852666,0.0508498624,-0.1940552145,0.0488463007,0.3934384882,0.3601915538,-0.2557527125,-0.0082358364,0.1270047426,-0.652000308,0.3312801719,-0.0341888703,-0.0623960271,-0.0443273447,-0.1631168574,0.1890245229,-0.1728112698,-0.0432419144,-0.0107518909,0.0655686334,0.0757776126,0.2253200263,-0.1849762201,-0.0131545244,0.328877598,0.0081908507,-0.067329213,0.1577627063,0.2264792174,0.0856376439,-0.1127577201,-0.1247871369,-0.0879629105,0.0816989914,-0.1688310802,-0.2173811197,0.3881791234,0.1821926832,0.4562583864,-0.0831858665,0.255866617,0.30075109,-0.0425577536,0.4923293293,0.0063057984,0.0130656706,0.1124818847,-0.3570446372,0.3859908283,-0.1907472908,0.1745758206,0.2347824723,-0.1141711324,0.19006975,-0.1285559833,0.1645834446,-0.1481104046,0.1359440982,0.3211123645,0.3957827389,0.1460084617,-0.1996143311,0.2674739063,0.3323667645,0.0968802571,0.1031925455,0.246134609,-0.3691287637,-0.2252021283,0.1397533119,0.197507143,0.5396437049,0.3424683511,0.3131107688,0.1506482363,0.1247832999,-0.0937287584,0.2106864601,-0.1117323339,0.1853049695,0.0762357116,0.190817982,-0.1487636566,-0.2165201157,0.0760200843,-0.0008258178,0.0549027845,-0.1907076687,0.143323943,0.0854379758,0.0519407131,-0.165815711,-0.0339375921,-0.076560393,0.0708651394,0.0929717124,0.4913678169,-0.1250120252,0.071845524,0.2910555005,0.393671155,-0.1227563918,-0.3675518036,-0.0487354584,-0.0331536904,-0.0719597563,0.0459643975,0.1463933885,-0.0009631031,0.2091947794,0.1547740102,-0.0044358699,-0.3165817857,-0.1744364053,0.1876914352,-0.0269155707,0.524433732,-0.057649862,0.0138232242,-0.0310174618,0.0415321067,-0.0137339924,-0.455465436,-0.1072462574,-0.3994989097,0.1093487367,0.2656004727,-0.3197873831,-0.0110392747,-0.3589963615,-0.5685352683,0.1637336612,0.2817828059,0.2383924723,0.265165031,0.0898809657,-0.0618730783,0.572666347,-0.0546644367,-0.3195027709,-0.3880082369,0.285887599,-0.3585863113,-0.0461908393,0.0663327053,-0.0486336984,0.0415387303,0.2487963736,-0.3152450025,-0.5569884777,-0.0941396281,0.0763135478,-0.1239234954,-0.0483976789,0.3200336695,-0.0705713704,-0.1355658621,-0.0175185837,-0.4911369383,0.0440961309,0.4789563715,0.0262698568,0.1413140446,0.0510880016,-0.0411006957,0.6494165659,-0.3143565357,0.0779204816,0.2143071592,0.2396313101,0.3123824894,-0.266377151,-0.1168241724,0.0004895034,0.0033940761,-0.550460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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2474","title":"cache_dir parameter for load_from_disk ?","comments":"Thanks for your answer! I am a little surprised since I just want to read the dataset.\r\n\r\nAfter debugging a bit, I noticed that the VM\u2019s disk fills up when the tables (generator) are converted to a list:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5ba149773d23369617563d752aca922081277ec2\/src\/datasets\/table.py#L850\r\n\r\nIf I try to iterate through the table\u2019s generator e.g.: \r\n\r\n`length = sum(1 for x in tables)`\r\n\r\nthe VM\u2019s disk fills up as well.\r\n\r\nI\u2019m running out of Ideas \ud83d\ude04 ","body":"**Is your feature request related to a problem? Please describe.**\r\nWhen using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:\r\n\r\n`\r\nfrom datasets import load_from_disk\r\n\r\nmyPreprocessedData = load_from_disk(\"\/content\/gdrive\/MyDrive\/ASR_data\/myPreprocessedData\")\r\n\r\n`\r\nI know that chaching on google drive could slow down learning. But at least it would run.\r\n\r\n**Describe the solution you'd like**\r\nAdd cache_Dir parameter to the load_from_disk function.\r\n\r\n**Describe alternatives you've considered**\r\nIt looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?\r\n","comment_length":69,"text":"cache_dir parameter for load_from_disk ? \n **Is your feature request related to a problem? Please describe.**\r\nWhen using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:\r\n\r\n`\r\nfrom datasets import load_from_disk\r\n\r\nmyPreprocessedData = load_from_disk(\"\/content\/gdrive\/MyDrive\/ASR_data\/myPreprocessedData\")\r\n\r\n`\r\nI know that chaching on google drive could slow down learning. But at least it would run.\r\n\r\n**Describe the solution you'd like**\r\nAdd cache_Dir parameter to the load_from_disk function.\r\n\r\n**Describe alternatives you've considered**\r\nIt looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?\r\n \n Thanks for your answer! I am a little surprised since I just want to read the dataset.\r\n\r\nAfter debugging a bit, I noticed that the VM\u2019s disk fills up when the tables (generator) are converted to a list:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5ba149773d23369617563d752aca922081277ec2\/src\/datasets\/table.py#L850\r\n\r\nIf I try to iterate through the table\u2019s generator e.g.: \r\n\r\n`length = sum(1 for x in tables)`\r\n\r\nthe VM\u2019s disk fills up as well.\r\n\r\nI\u2019m running out of Ideas \ud83d\ude04 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2474","title":"cache_dir parameter for load_from_disk ?","comments":"Indeed reading the data shouldn't increase the VM's disk. Not sure what google colab does under the hood for that to happen","body":"**Is your feature request related to a problem? Please describe.**\r\nWhen using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:\r\n\r\n`\r\nfrom datasets import load_from_disk\r\n\r\nmyPreprocessedData = load_from_disk(\"\/content\/gdrive\/MyDrive\/ASR_data\/myPreprocessedData\")\r\n\r\n`\r\nI know that chaching on google drive could slow down learning. But at least it would run.\r\n\r\n**Describe the solution you'd like**\r\nAdd cache_Dir parameter to the load_from_disk function.\r\n\r\n**Describe alternatives you've considered**\r\nIt looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?\r\n","comment_length":22,"text":"cache_dir parameter for load_from_disk ? \n **Is your feature request related to a problem? Please describe.**\r\nWhen using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:\r\n\r\n`\r\nfrom datasets import load_from_disk\r\n\r\nmyPreprocessedData = load_from_disk(\"\/content\/gdrive\/MyDrive\/ASR_data\/myPreprocessedData\")\r\n\r\n`\r\nI know that chaching on google drive could slow down learning. But at least it would run.\r\n\r\n**Describe the solution you'd like**\r\nAdd cache_Dir parameter to the load_from_disk function.\r\n\r\n**Describe alternatives you've considered**\r\nIt looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?\r\n \n Indeed reading the data shouldn't increase the VM's disk. Not sure what google colab does under the hood for that to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2474","title":"cache_dir parameter for load_from_disk ?","comments":"Apparently, Colab uses a local cache of the data files read\/written from Google Drive. See:\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/2087#issuecomment-860818457\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/1915#issuecomment-804234540\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/2147#issuecomment-885052636","body":"**Is your feature request related to a problem? Please describe.**\r\nWhen using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:\r\n\r\n`\r\nfrom datasets import load_from_disk\r\n\r\nmyPreprocessedData = load_from_disk(\"\/content\/gdrive\/MyDrive\/ASR_data\/myPreprocessedData\")\r\n\r\n`\r\nI know that chaching on google drive could slow down learning. But at least it would run.\r\n\r\n**Describe the solution you'd like**\r\nAdd cache_Dir parameter to the load_from_disk function.\r\n\r\n**Describe alternatives you've considered**\r\nIt looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?\r\n","comment_length":21,"text":"cache_dir parameter for load_from_disk ? \n **Is your feature request related to a problem? Please describe.**\r\nWhen using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:\r\n\r\n`\r\nfrom datasets import load_from_disk\r\n\r\nmyPreprocessedData = load_from_disk(\"\/content\/gdrive\/MyDrive\/ASR_data\/myPreprocessedData\")\r\n\r\n`\r\nI know that chaching on google drive could slow down learning. But at least it would run.\r\n\r\n**Describe the solution you'd like**\r\nAdd cache_Dir parameter to the load_from_disk function.\r\n\r\n**Describe alternatives you've considered**\r\nIt looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?\r\n \n Apparently, Colab uses a local cache of the data files read\/written from Google Drive. See:\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/2087#issuecomment-860818457\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/1915#issuecomment-804234540\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/2147#issuecomment-885052636","embeddings":[-0.3244540393,-0.1029559225,-0.0744115859,0.1464905888,0.1778117865,-0.0287655555,0.3205729723,0.0757735148,0.2688016593,0.4276999831,-0.0916890278,0.3176706135,-0.2790679634,0.2631689608,0.1338721216,0.1159451157,-0.0612634942,-0.0113297096,-0.0055351206,-0.147294119,-0.1522970349,0.1359369755,0.0048600659,-0.3714657426,-0.1582966745,-0.1370280534,-0.0462146513,0.1889029443,0.2744692266,-0.2572250664,0.2579567134,0.0803299844,0.2182693332,0.4198666513,-0.000106675,-0.1496877223,0.0011274879,-0.2742542326,-0.4748744071,-0.1342530996,-0.1666695774,-0.1006299853,0.1580016613,-0.0534663238,-0.5577460527,0.1957765669,-0.0002694694,-0.1378729492,0.1841362566,0.1151774079,0.2430312335,-0.2300930172,-0.432542026,0.0215187781,0.0231285449,0.0058162338,-0.2544879019,0.7005158067,0.2035986632,0.0490309708,-0.093618542,0.4356156588,-0.0506501794,0.1985402554,0.4544715285,0.3657146394,0.1149660945,-0.1835198551,0.0282936674,-0.1497601122,0.5139719844,-0.164507091,-0.1725474149,-0.2268379182,0.1583390832,-0.3892749846,0.3535244763,0.1632039994,-0.2536135614,0.3900915682,-0.1914844662,-0.3505886495,-0.1008558944,-0.010205457,0.0671565011,-0.0469055772,-0.1741707921,-0.1181009561,0.2294879258,0.1814557165,0.4381135702,-0.0491104238,0.0987410694,0.271850884,0.0087576574,-0.1776026934,-0.0921400562,0.3467929661,-0.083977744,-0.0057049743,0.4368867278,-0.0369181186,-0.114843905,0.2116931975,-0.1487443745,0.3390414715,-0.1729173958,-0.0624959245,0.3942640126,0.1852927804,-0.3253233731,-0.0321660526,-0.0053391452,0.0244645625,0.2546069324,0.178278476,-0.0792024061,-0.2264535874,0.1678865254,-0.2079105377,0.0702573806,0.1215865016,0.4086280167,0.394979775,-0.1458898485,-0.1479368061,0.0906420499,-0.3495952785,-0.1837414056,0.1633118987,-0.0860041976,0.0881531313,-0.2419553697,0.4661453366,0.3736924231,0.0702172443,0.2958607078,-0.2946588695,0.1999228299,0.040073052,0.0702145845,-0.3858602941,0.3285588324,0.3618240356,0.0868916512,-0.0747585446,-0.055969622,-0.1721423715,-0.3563911617,0.2618189454,-0.4217586517,-0.4688650966,0.4077794254,0.1980416924,-0.2596226633,0.0393983722,-0.5215078592,0.3150785267,-0.1936585903,-0.1158558801,-0.1249232441,0.0116892867,-0.6133230329,-0.3281109631,0.1120653823,0.3146484494,-0.2682584524,-0.0701208562,0.0310639497,0.1684191674,0.0097712735,0.2918018699,-0.2050191909,-0.0056923744,-0.2424409986,-0.2715183794,0.1311622858,-0.2902424932,-0.5318157673,0.1822556257,-0.083244063,-0.0458100028,0.0421363562,0.2137980759,0.1955586672,-0.2787418365,0.4660037458,0.7371929288,-0.0067821178,0.2190786451,-0.2931284606,-0.252523303,-0.4006675184,0.3051182032,-0.0640508384,0.1291933805,0.0639548972,-0.1899647713,0.0324765854,-0.1626486629,0.0316251256,0.3318890333,0.4517710507,-0.2042607665,-0.039381586,0.1868366748,-0.4851347804,0.3094934523,0.0939798653,-0.0561769083,0.0148591194,-0.1625466496,0.1125044078,-0.0706473067,0.0074772332,0.0853891373,0.0780241191,0.1085848361,0.1096860468,-0.2224677354,0.0809608102,0.4170772433,-0.0350446217,-0.1198413745,0.3130327463,0.2330861539,0.0751127899,-0.1608200222,0.0122438492,-0.1015792191,0.1588270515,-0.0738329068,-0.1699547172,0.2632190585,0.2919670343,0.3864577711,-0.0925174505,0.1992892027,0.2518652678,0.1269219816,0.4303394854,0.0017065806,0.0766183361,0.0865515769,-0.2119640708,0.4003140032,-0.2109985203,0.0953164846,0.2101730406,-0.0872490853,0.2390839159,-0.1367631853,0.1550589055,-0.2217127085,0.1565089822,0.2826496959,0.3504674733,0.1688726693,-0.1653012633,0.2906663418,0.2658109069,0.132778585,0.1020385921,0.1472922266,-0.2981210053,-0.2573698759,0.2224952877,0.3067610264,0.5491709113,0.3299583793,0.278300643,0.0947722122,0.0635619536,-0.1180249155,0.3042335212,-0.1371220499,0.2944245934,0.0220914856,0.1771909595,-0.2160727531,-0.1214740872,-0.0078250151,0.0626697913,0.0529534668,-0.1464785933,0.1529576033,0.028628232,-0.0564100742,-0.2047316432,-0.0718274266,0.0539105609,0.0610641539,0.0813521743,0.5613360405,-0.0858296901,0.0117593566,0.2957550287,0.48796013,-0.1534518898,-0.4310718179,-0.002335459,-0.0760231242,-0.1327655464,0.0875290781,0.053110294,0.0397902764,0.2417285442,0.1137178242,0.0321149044,-0.183123365,-0.1673665792,0.1913755834,-0.0430363677,0.5869830251,-0.0784534737,-0.0924053192,-0.13516967,-0.0315677449,-0.0672862455,-0.4143814743,0.0202174801,-0.4439909756,0.0948050171,0.2204948515,-0.2964598835,0.0223981719,-0.3755377233,-0.4353085756,0.0116950599,0.255219996,0.2514442801,0.2206457108,0.1056239679,-0.0356697887,0.7625158429,-0.0193726886,-0.3234817982,-0.3680900037,0.3583267331,-0.3416344225,0.0611049272,0.1858738661,-0.1024822444,0.0219532028,0.2454920113,-0.3768054247,-0.6603525281,0.010753518,0.012112678,-0.1667715162,-0.1437840313,0.1659485698,0.002278975,-0.1045520753,-0.0695091859,-0.4572409391,-0.0425137877,0.369946748,0.1500115097,0.072086893,0.0917719454,-0.0855112001,0.546043098,-0.4337559044,0.0450322516,0.1847637743,0.25758638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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2472","title":"Fix automatic generation of Zenodo DOI","comments":"I have received a reply from Zenodo support:\r\n> We are currently investigating and fixing this issue related to GitHub releases. As soon as we have solved it we will reach back to you.","body":"After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as \"Received\", instead of in green as \"Published\".\r\n\r\nI have contacted Zenodo support to fix this issue.\r\n\r\nTODO:\r\n- [x] Check with Zenodo to fix the issue\r\n- [x] Check BibTeX entry is right","comment_length":34,"text":"Fix automatic generation of Zenodo DOI \n After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as \"Received\", instead of in green as \"Published\".\r\n\r\nI have contacted Zenodo support to fix this issue.\r\n\r\nTODO:\r\n- [x] Check with Zenodo to fix the issue\r\n- [x] Check BibTeX entry is right \n I have received a reply from Zenodo support:\r\n> We are currently investigating and fixing this issue related to GitHub releases. As soon as we have solved it we will reach back to you.","embeddings":[-0.0967250019,0.3705003858,-0.0376106165,0.0805334821,0.1489466131,-0.1063510776,0.3863855004,0.4031184614,-0.061123658,0.2544853091,-0.0367732123,0.1469548494,0.2090731561,-0.0825828612,-0.1057561636,0.0061597624,-0.0564027242,0.347769171,-0.0588133708,-0.3109584451,-0.202006802,0.0611617602,-0.0399869643,0.1083547771,0.1429421604,-0.0178717673,-0.0885883048,0.0401961766,-0.6289506555,-0.294123888,0.2304198295,0.3536779583,0.0241569616,0.0977585763,-0.0001149367,-0.193783164,0.4348081052,-0.0376478955,0.1791505516,-0.3938967884,-0.4930344522,-0.3317319751,-0.1378981471,-0.2664651573,0.1761809289,-0.3223012388,0.1303076148,0.1256043911,0.0584312789,0.3948599398,0.1907943189,-0.0837685615,0.4714028239,-0.2440700382,0.1568886638,0.089191094,-0.0636926368,0.0680648983,-0.1046001986,0.2829131484,0.0303520281,0.2908119857,0.0494917929,0.1739325374,0.0865884349,0.1175321266,0.2991642058,-0.2475599647,0.1543980837,0.0806598887,0.7945279479,-0.1233866215,-0.1636312157,0.0783248544,0.2249083668,-0.2218073308,0.081119597,0.1100925356,0.1318532377,0.130947873,0.1229463443,-0.2656325102,-0.2755658031,0.0650090501,0.1293116957,-0.1051816493,-0.0930702239,0.0275286362,-0.0683570355,-0.1167767048,0.2643761635,-0.0598064698,-0.2564129531,0.2311538905,0.2473509312,-0.0809894577,-0.0505684763,0.0341484658,0.3572542667,0.0973298773,0.2150624245,-0.029652616,-0.0738931298,-0.1037192419,0.1122526824,0.0419797897,0.3595068455,-0.0530412607,0.199470371,0.0872092471,-0.0922934487,0.0737133175,0.0192253105,-0.0975429565,0.5840197206,-0.168579638,0.3791415691,-0.4698496461,-0.031587109,0.121334441,-0.0889860243,-0.1356124729,-0.4675282836,0.0189321321,-0.0387980044,0.2661347985,-0.1196072474,0.0638765544,-0.2146764696,-0.1119567603,-0.1042337269,-0.4499019384,0.0859547108,0.2072413117,-0.0272003449,-0.3636471331,0.220649913,0.2520163357,-0.2041303664,-0.5929424167,0.0520067625,0.2018798143,0.1980849802,0.2398324311,0.0729131177,0.2277602255,-0.071303986,-0.1430610567,-0.003468036,0.3119443953,-0.1970106065,-0.4158118367,-0.5545141697,0.1263848543,-0.0154378656,-0.0302750524,0.3378929794,0.172124058,0.2121184468,-0.1259371489,-0.1239725873,-0.0948559791,0.24657318,0.1058486253,0.1277978569,0.2760051489,0.1801678389,-0.059993919,-0.4540840387,0.07300511,0.3593138754,-0.028415475,-0.0059811133,0.3127602637,-0.0990879759,-0.3946625292,0.2607989311,-0.5165682435,-0.3449460864,-0.0686951727,-0.0866520852,0.0106102945,-0.0730016083,-0.1784451753,0.3146195412,0.264207691,-0.3067135215,-0.2928422987,-0.0682026073,-0.0304838419,-0.2615099847,-0.2276733965,-0.3169073462,-0.1217575297,0.6132693291,0.4346250892,0.1965690851,0.2121606916,0.2273948044,0.186016798,-0.055820398,0.105781585,0.5157321095,-0.2429505736,0.2426191866,-0.0186445955,-0.0445015281,0.1058372781,0.364911139,0.1284542531,-0.1423499137,-0.034027677,-0.3487661481,-0.1293665618,-0.1910822093,0.0236552507,0.1080503762,0.1056260392,-0.0852857828,0.1599137634,0.0961170495,-0.0619293377,-0.1601580381,0.1793097854,-0.0670498088,0.1715123653,0.0375265516,-0.0785583481,-0.0061084623,0.0678291842,0.082690306,0.1868734509,-0.212024644,0.2070410699,0.0062331068,0.0665713102,0.1753115058,0.3192522824,0.1791736037,-0.2357140481,-0.0851681307,0.1161766574,-0.2221634984,0.178564027,0.1205098927,0.2113172263,-0.2766640484,-0.2890143692,-0.3767161667,0.1957046986,0.5364695191,0.0458592996,-0.3070167005,-0.3863206208,-0.1415654272,-0.0293307249,0.1237045154,0.1486452818,-0.300234437,0.2740983069,0.5304563642,-0.0463285968,0.0184068996,-0.136000827,0.1112140492,-0.0769464672,0.1097098663,0.3504441679,0.1392980069,0.1322077364,-0.1204899102,0.1311260909,-0.2398717552,-0.1490615904,0.1420025676,0.0199940521,0.5988178253,0.2531802654,0.0380570255,-0.1625417471,-0.4330268204,0.2709622979,0.0133442227,0.4834909141,-0.1430612206,-0.5351665616,-0.0234868526,0.2057470381,-0.3697085083,-0.3503602445,-0.4329450727,-0.1239149496,0.0663367212,0.2066048384,0.1248129681,0.1780059934,0.057008408,0.0615401864,-0.0046262424,0.2375089973,-0.1140942425,0.07026954,0.0646879449,-0.0235295445,-0.1753820926,-0.1083829999,0.2719162405,-0.5216576457,0.1199310943,-0.5896314979,-0.0733040869,0.1976411194,0.1813601404,0.1604100168,-0.2260288149,0.1183457077,0.1115448996,-0.0442179404,0.0312312841,-0.2721320987,-0.1584836841,0.0709126964,-0.4817188978,0.2275580168,-0.0358135141,-0.4244445562,-0.0655133948,-0.0550049245,0.0293306299,0.1248127148,0.31581074,0.3227924407,0.0852842256,-0.1649546772,-0.1046718583,-0.2954024971,-0.102194421,-0.6679029465,0.2470011711,-0.3297753036,-0.3566850722,0.1468521804,0.1116567403,0.3824832737,-0.059308745,-0.2216844708,0.2715043426,0.0712309256,0.5275432467,0.1116093248,-0.225499317,0.5041925907,0.1181064844,-0.0133410562,-0.2628585994,0.2124114633,0.3426111042,0.01155819,0.2765508592,-0.0946914703,0.0140320119,-0.1651981324,0.8457219005,0.1296900958,-0.016075179,-0.0834944397,0.1961854994,0.4897732139,0.0004719502,-0.1514770389,0.0353651904,-0.0071054026,0.0440894179,0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2472","title":"Fix automatic generation of Zenodo DOI","comments":"Other repo maintainers had the same problem with Zenodo. \r\n\r\nThere is an open issue on their GitHub repo: zenodo\/zenodo#2181","body":"After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as \"Received\", instead of in green as \"Published\".\r\n\r\nI have contacted Zenodo support to fix this issue.\r\n\r\nTODO:\r\n- [x] Check with Zenodo to fix the issue\r\n- [x] Check BibTeX entry is right","comment_length":19,"text":"Fix automatic generation of Zenodo DOI \n After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as \"Received\", instead of in green as \"Published\".\r\n\r\nI have contacted Zenodo support to fix this issue.\r\n\r\nTODO:\r\n- [x] Check with Zenodo to fix the issue\r\n- [x] Check BibTeX entry is right \n Other repo maintainers had the same problem with Zenodo. \r\n\r\nThere is an open issue on their GitHub repo: zenodo\/zenodo#2181","embeddings":[-0.0932828709,0.394243449,-0.0347407646,0.099130474,0.1498733163,-0.1066873893,0.3806301951,0.3882210553,-0.0758683681,0.253555268,-0.064562887,0.2056420445,0.2068252861,-0.0944909677,-0.1203863472,0.016830558,-0.0314425714,0.3863615692,-0.057352256,-0.3248518109,-0.2133872807,0.0766483694,-0.0383451693,0.0802262947,0.1236057132,0.0087414347,-0.1001702398,0.0040567052,-0.6152876019,-0.2550566494,0.2330326736,0.3883582652,0.0097615132,0.1403077543,-0.0001156442,-0.2252505422,0.4270015359,-0.0163295865,0.1792460829,-0.3721206188,-0.5172886252,-0.3386047482,-0.1104393452,-0.2647010088,0.2235631049,-0.3678027391,0.115695253,0.1140099242,0.0215495154,0.3697971702,0.1875544637,-0.1051705927,0.4895671606,-0.217632398,0.1373074055,0.1001364216,-0.0828814432,0.0587363206,-0.1178573444,0.3063135147,0.0154910637,0.2852521241,0.0233926568,0.172807157,0.0930840448,0.1147537529,0.3039465249,-0.1714944541,0.1280236542,0.1230859533,0.832862258,-0.1432768703,-0.2120329738,0.0880180672,0.1983065903,-0.236638248,0.0740668997,0.078993395,0.1280183941,0.1463824213,0.1279241443,-0.2579166591,-0.253556639,0.0778847486,0.0788915157,-0.0996273234,-0.084779948,0.009448722,-0.0460835807,-0.0916435122,0.3670062423,-0.0872830674,-0.2454650104,0.1976322532,0.2415435463,-0.0673406124,-0.0352681242,0.1007865593,0.3323948979,0.1322206557,0.2674002647,-0.0176760405,-0.0796178356,-0.07322184,0.1397774816,-0.0086306017,0.3600791097,-0.1042005643,0.1855026037,0.0518748052,-0.0983477458,0.067417413,0.054464329,-0.1070985794,0.6239064932,-0.1588163525,0.3452750742,-0.4820899963,-0.0601069592,0.1509780437,-0.0923017487,-0.1525580734,-0.4643554091,-0.0169897508,-0.0030420616,0.3226550221,-0.072182335,0.0545920432,-0.2229958028,-0.168489635,-0.1057487652,-0.4463167489,0.0522426479,0.2065855414,-0.0011709336,-0.3521764874,0.197790578,0.2260470688,-0.1673873365,-0.5947030783,0.0487626828,0.1647951901,0.165528819,0.2422035933,0.0377056971,0.2358960509,-0.0561090186,-0.0592608117,-0.0253798738,0.2959363759,-0.1597511321,-0.3853194118,-0.5159863234,0.1229494661,-0.0173366182,-0.0714498237,0.3614366949,0.1926749647,0.2250159681,-0.1650751978,-0.1569584608,-0.0901965573,0.2544915378,0.124928683,0.1172455326,0.3184442818,0.0776741281,0.0068548811,-0.4972431064,0.0783776864,0.3269890547,-0.0679561794,0.0046435008,0.2849656045,-0.0645963103,-0.3945794106,0.2350950241,-0.4871272147,-0.3446820676,-0.0375059582,-0.0600487292,-0.0081504267,-0.0700566843,-0.1746236682,0.2466297299,0.2377924174,-0.3222362101,-0.2767680585,-0.1004822776,-0.0193046629,-0.2484045774,-0.2057971507,-0.2596994638,-0.1154619083,0.6102955341,0.4047065973,0.1696137041,0.2542264163,0.1998075843,0.2200543135,-0.0433983132,0.0964039788,0.4835833311,-0.2071894407,0.2367183715,-0.0505857952,-0.0197005216,0.0789463073,0.3896897435,0.0955225974,-0.0952427834,-0.0432568491,-0.3474522829,-0.1109543815,-0.1589937657,0.05873546,0.1061796546,0.078716293,-0.1146688089,0.1632086188,0.1062244549,-0.1158350781,-0.1601466984,0.1873285919,-0.0361526906,0.142281875,0.0558788218,-0.0835240185,0.0014843171,0.0762866288,0.0873074904,0.1970050782,-0.2366698086,0.2509304583,0.0188194551,0.1150829196,0.101737313,0.3152865767,0.1941678375,-0.224107787,-0.1002208665,0.1239513755,-0.2105144113,0.1670019776,0.0604422688,0.1879071295,-0.2931379676,-0.2764587998,-0.3872968554,0.2040327042,0.5019196868,0.0381810851,-0.3221302629,-0.4127164483,-0.1184822842,-0.0647520795,0.1155248284,0.1912376881,-0.2757062316,0.2671723962,0.4886540473,-0.0309058446,0.0030141713,-0.1926344633,0.0880844742,-0.0898323655,0.1228223294,0.3807592392,0.1672833413,0.149175033,-0.1024721861,0.0924102962,-0.2890713513,-0.1720481217,0.1176505461,0.0428197943,0.6119812131,0.2980555594,0.0219496656,-0.1812718213,-0.4736090899,0.2799232006,0.0439986289,0.4817019701,-0.1301573962,-0.5318512321,-0.048868563,0.2293630987,-0.3871013522,-0.3776044548,-0.3723509908,-0.1700176895,0.0392706692,0.2129530013,0.1064998358,0.1337770522,-0.007893987,0.0321994461,-0.010892381,0.239023149,-0.1423677057,0.0784383789,0.0735010058,-0.0318246074,-0.1976611763,-0.083522208,0.2792720199,-0.5059190392,0.1397278756,-0.5817276239,-0.0640512854,0.1704770923,0.2002034932,0.1675930768,-0.2041617483,0.1052956507,0.1108049899,-0.0587826036,0.0500783063,-0.2695665658,-0.1343619972,0.076053448,-0.4455569685,0.2318740338,-0.0432307497,-0.4657226205,-0.0397085659,-0.0698588639,0.0087665478,0.0806312487,0.2930549383,0.2758255303,0.1201167256,-0.1805644333,-0.098605305,-0.2380491346,-0.1330815405,-0.6524446011,0.2465555221,-0.3133287132,-0.3543634713,0.1500936747,0.1037111729,0.3814441562,-0.0061269663,-0.2047825754,0.2546283305,0.0792150795,0.4864209592,0.1491881609,-0.2074271888,0.5132312179,0.1229927093,-0.0085548116,-0.2754908502,0.2137739062,0.3333303034,-0.0036007545,0.290617913,-0.1034906805,0.0191383585,-0.1552757919,0.7816070318,0.0748125538,-0.0199385565,-0.0556436963,0.2394745946,0.5023627281,0.016449308,-0.1836204976,0.0851341262,-0.015605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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2472","title":"Fix automatic generation of Zenodo DOI","comments":"I have received the following request from Zenodo support:\r\n> Could you send us the link to the repository as well as the release tag?\r\n\r\nMy reply:\r\n> Sure, here it is:\r\n> - Link to the repository: https:\/\/github.com\/huggingface\/datasets\r\n> - Link to the repository at the release tag: https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.8.0\r\n> - Release tag: 1.8.0","body":"After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as \"Received\", instead of in green as \"Published\".\r\n\r\nI have contacted Zenodo support to fix this issue.\r\n\r\nTODO:\r\n- [x] Check with Zenodo to fix the issue\r\n- [x] Check BibTeX entry is right","comment_length":55,"text":"Fix automatic generation of Zenodo DOI \n After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as \"Received\", instead of in green as \"Published\".\r\n\r\nI have contacted Zenodo support to fix this issue.\r\n\r\nTODO:\r\n- [x] Check with Zenodo to fix the issue\r\n- [x] Check BibTeX entry is right \n I have received the following request from Zenodo support:\r\n> Could you send us the link to the repository as well as the release tag?\r\n\r\nMy reply:\r\n> Sure, here it is:\r\n> - Link to the repository: https:\/\/github.com\/huggingface\/datasets\r\n> - Link to the repository at the release tag: https:\/\/github.com\/huggingface\/datasets\/releases\/tag\/1.8.0\r\n> - Release tag: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2470","title":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`.","comments":"Hi ! It looks like the issue comes from pyarrow. What version of pyarrow are you using ? How did you install it ?","body":"## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n","comment_length":24,"text":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`. \n ## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n \n Hi ! It looks like the issue comes from pyarrow. What version of pyarrow are you using ? How did you install it 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2470","title":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`.","comments":"Thank you for the quick reply! I have `pyarrow==4.0.0`, and I am installing with `pip`. It's not one of my explicit dependencies, so I assume it came along with something else.","body":"## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n","comment_length":31,"text":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`. \n ## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n \n Thank you for the quick reply! I have `pyarrow==4.0.0`, and I am installing with `pip`. It's not one of my explicit dependencies, so I assume it came along with something else.","embeddings":[-0.4569717646,-0.2173907459,0.0228779204,0.4219461381,0.3113077581,-0.2264823467,0.2361344099,0.1525053084,0.0431262851,0.3643293679,0.5303300619,0.2979793251,-0.3346508443,-0.0465700552,0.047729928,-0.0265675541,0.3659133613,-0.2182355374,0.2511509061,0.3584368825,-0.2708789408,0.1627700925,-0.4191713631,0.2937121987,-0.2652947009,0.082243599,-0.1050226912,0.2865630984,-0.0974157006,-0.5258919597,-0.0305760354,-0.295525372,0.1912617087,0.4583674669,-0.0001182336,-0.0913553163,0.2139718682,0.0120125022,0.0930700898,-0.4430084825,-0.2259867936,-0.2528820932,-0.1637293845,-0.0408718698,0.3133925498,0.0046791183,-0.0663942918,-0.150278613,0.0759297609,0.7205310464,0.1591092348,0.214832738,0.2288711667,-0.1120013967,0.016339317,0.1341472566,-0.0305977836,0.061437469,0.4003654718,-0.2003950328,0.0249149706,-0.1673947722,0.092808485,-0.2669810057,0.305939585,-0.1227102131,0.1998042613,-0.6616144776,0.2974792719,-0.1724279821,-0.1635968983,-0.2878350317,-0.0509196632,-0.1632319391,-0.1430846751,-0.2230149657,0.177413553,-0.1671895236,-0.1571241021,0.0832860544,-0.2861917019,0.1699507236,0.0335625187,0.3086440563,-0.3192946315,0.4002259374,-0.0029774297,0.389618665,0.1262956411,-0.2813790739,-0.3420287967,0.1340495944,-0.0415284634,0.2674230039,-0.2257158607,-0.1663678735,0.1507905126,-0.1738581359,0.2904581726,-0.2406045794,-0.1656422466,0.2874025404,0.048612088,0.2133101672,0.2980216742,-0.0209573247,0.3021420836,0.3840674162,0.0499104597,-0.056064833,-0.102803044,-0.0400411636,0.2264666557,-0.1167617217,0.0892518535,0.1257736236,0.0740240142,-0.1534793824,-0.1198020428,0.4294593036,-0.2137423903,0.2773050666,0.3186097741,0.2963832617,0.3739162087,0.227028951,-0.2862907052,0.0338729285,-0.2570652664,-0.0871776044,-0.0442779921,-0.2006773353,-0.3708454072,0.0992647409,-0.005736412,0.239634648,0.1527641416,0.0450480506,0.2212321013,-0.539868176,0.1125220954,-0.2573070824,0.1339917481,0.0350833498,-0.2941997945,0.1408175528,0.1156675741,-0.1236832365,-0.1660993546,0.1671806574,-0.2124153674,-0.2457581609,-0.1474966705,0.1560774744,-0.0041650292,0.4375189543,-0.0617898293,0.3228711784,0.3464185596,-0.3285045028,-0.0258732624,-0.3166899085,-0.4711762369,-0.2692376971,-0.0478229038,0.2325534225,-0.1191353053,0.0298826676,0.2000778913,0.0202775728,0.0941302255,0.1629669219,-0.1979359239,-0.0177588277,-0.1911362857,0.5243681073,0.2797337174,-0.0643637404,-0.4906041324,0.1102769822,-0.2673051357,-0.1981518269,-0.4909549952,-0.1560129821,0.4023430943,-0.2073682845,0.4260299802,-0.0168935806,-0.1412145048,0.118385382,-0.306717515,0.094998017,0.1623565853,-0.1462418735,0.0806366652,-0.166501984,0.1831175238,-0.0708336085,0.0203282014,0.004628906,-0.0426518135,0.2887397707,0.2136349082,-0.0781941861,0.0351790115,-0.3048202991,-0.0683804154,0.2268817574,0.0347599015,-0.0888614431,-0.0282383524,0.0105143087,-0.0411652811,0.3073398471,0.1391114891,-0.1058630794,0.0216380805,-0.1246979013,0.0030890247,-0.0661532804,-0.0376062728,-0.0197845213,-0.1885098517,0.0246681049,-0.0632356256,-0.1272604764,-0.2010300606,-0.1646624357,-0.3887704015,0.1416743398,0.4239653945,-0.0064662574,-0.2309417278,0.5506316423,0.3362869918,-0.2484891564,-0.6358670592,-0.0847648457,0.3994875848,-0.1538935304,0.1634524167,0.0869446471,-0.0126662534,-0.1323807985,0.1911210567,0.2216383666,0.2824165821,0.1572643816,-0.0959971696,0.0425383486,0.1659151316,0.1229683161,0.0325946435,-0.199791953,-0.0025342163,0.1199669093,0.0561050922,-0.047854811,-0.0479735769,0.2361027151,0.2793250382,-0.0761121586,0.1238081977,0.0580437221,-0.1685209721,-0.0567469634,0.2826824784,-0.0136068668,0.6230338812,0.0613569953,-0.0287729017,0.0040426482,-0.3801935911,-0.0336282663,0.1335722506,0.0782550871,-0.1228369474,0.0503613725,0.2556922138,0.159722358,-0.1924543381,-0.3795136213,0.0199823845,0.3146329224,-0.4612223506,0.2040783465,-0.1188221872,0.2136378586,-0.0778881684,-0.1706882864,-0.1753237247,-0.3915620148,-0.0386055,0.1213032827,0.0050780186,0.1582536548,0.0887682661,-0.1417624056,0.04338919,0.3126336932,0.2620718777,-0.0042142225,-0.4652619362,-0.1256633401,0.3646590412,-0.1426314861,0.1807572693,0.1596735418,-0.4231134653,-0.1377694905,-0.554107368,0.0362168029,0.0458122902,0.2179297954,0.0650527328,0.0002729307,-0.1417898685,-0.0640055388,0.1627510339,-0.0710154772,-0.0511523709,0.1167618632,0.1763634235,-0.3484143615,-0.4004407525,0.0810818374,-0.2404452562,-0.2278887779,0.0398980491,0.0562176853,0.2899165154,0.033601027,0.3173662722,0.2378733456,0.0640233308,-0.2699205875,-0.2033462822,0.2120094001,-0.0509562194,-0.1475808471,-0.2852531075,-0.0752577856,0.1772031188,-0.022968391,0.2650331259,-0.2499954402,-0.0535250045,-0.0659198835,0.3023351431,-0.1044890657,-0.2844492197,0.3918755949,0.1078824848,0.0479716472,-0.0043216893,-0.3812083304,-0.0652099252,-0.2201593518,0.1879525185,-0.0040869415,0.5207585096,0.1579419822,0.5066372156,0.3318142593,-0.5369669795,0.1717587262,-0.2706901133,0.1213353053,-0.1566914618,-0.1280220896,0.3013108969,0.0409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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2470","title":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`.","comments":"Could you trying reinstalling pyarrow with pip ?\r\nI'm not sure why it would check in your multicurtural-sc directory for source files.","body":"## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n","comment_length":22,"text":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`. \n ## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n \n Could you trying reinstalling pyarrow with pip ?\r\nI'm not sure why it would check in your multicurtural-sc directory for source files.","embeddings":[-0.4569717646,-0.2173907459,0.0228779204,0.4219461381,0.3113077581,-0.2264823467,0.2361344099,0.1525053084,0.0431262851,0.3643293679,0.5303300619,0.2979793251,-0.3346508443,-0.0465700552,0.047729928,-0.0265675541,0.3659133613,-0.2182355374,0.2511509061,0.3584368825,-0.2708789408,0.1627700925,-0.4191713631,0.2937121987,-0.2652947009,0.082243599,-0.1050226912,0.2865630984,-0.0974157006,-0.5258919597,-0.0305760354,-0.295525372,0.1912617087,0.4583674669,-0.0001182336,-0.0913553163,0.2139718682,0.0120125022,0.0930700898,-0.4430084825,-0.2259867936,-0.2528820932,-0.1637293845,-0.0408718698,0.3133925498,0.0046791183,-0.0663942918,-0.150278613,0.0759297609,0.7205310464,0.1591092348,0.214832738,0.2288711667,-0.1120013967,0.016339317,0.1341472566,-0.0305977836,0.061437469,0.4003654718,-0.2003950328,0.0249149706,-0.1673947722,0.092808485,-0.2669810057,0.305939585,-0.1227102131,0.1998042613,-0.6616144776,0.2974792719,-0.1724279821,-0.1635968983,-0.2878350317,-0.0509196632,-0.1632319391,-0.1430846751,-0.2230149657,0.177413553,-0.1671895236,-0.1571241021,0.0832860544,-0.2861917019,0.1699507236,0.0335625187,0.3086440563,-0.3192946315,0.4002259374,-0.0029774297,0.389618665,0.1262956411,-0.2813790739,-0.3420287967,0.1340495944,-0.0415284634,0.2674230039,-0.2257158607,-0.1663678735,0.1507905126,-0.1738581359,0.2904581726,-0.2406045794,-0.1656422466,0.2874025404,0.048612088,0.2133101672,0.2980216742,-0.0209573247,0.3021420836,0.3840674162,0.0499104597,-0.056064833,-0.102803044,-0.0400411636,0.2264666557,-0.1167617217,0.0892518535,0.1257736236,0.0740240142,-0.1534793824,-0.1198020428,0.4294593036,-0.2137423903,0.2773050666,0.3186097741,0.2963832617,0.3739162087,0.227028951,-0.2862907052,0.0338729285,-0.2570652664,-0.0871776044,-0.0442779921,-0.2006773353,-0.3708454072,0.0992647409,-0.005736412,0.239634648,0.1527641416,0.0450480506,0.2212321013,-0.539868176,0.1125220954,-0.2573070824,0.1339917481,0.0350833498,-0.2941997945,0.1408175528,0.1156675741,-0.1236832365,-0.1660993546,0.1671806574,-0.2124153674,-0.2457581609,-0.1474966705,0.1560774744,-0.0041650292,0.4375189543,-0.0617898293,0.3228711784,0.3464185596,-0.3285045028,-0.0258732624,-0.3166899085,-0.4711762369,-0.2692376971,-0.0478229038,0.2325534225,-0.1191353053,0.0298826676,0.2000778913,0.0202775728,0.0941302255,0.1629669219,-0.1979359239,-0.0177588277,-0.1911362857,0.5243681073,0.2797337174,-0.0643637404,-0.4906041324,0.1102769822,-0.2673051357,-0.1981518269,-0.4909549952,-0.1560129821,0.4023430943,-0.2073682845,0.4260299802,-0.0168935806,-0.1412145048,0.118385382,-0.306717515,0.094998017,0.1623565853,-0.1462418735,0.0806366652,-0.166501984,0.1831175238,-0.0708336085,0.0203282014,0.004628906,-0.0426518135,0.2887397707,0.2136349082,-0.0781941861,0.0351790115,-0.3048202991,-0.0683804154,0.2268817574,0.0347599015,-0.0888614431,-0.0282383524,0.0105143087,-0.0411652811,0.3073398471,0.1391114891,-0.1058630794,0.0216380805,-0.1246979013,0.0030890247,-0.0661532804,-0.0376062728,-0.0197845213,-0.1885098517,0.0246681049,-0.0632356256,-0.1272604764,-0.2010300606,-0.1646624357,-0.3887704015,0.1416743398,0.4239653945,-0.0064662574,-0.2309417278,0.5506316423,0.3362869918,-0.2484891564,-0.6358670592,-0.0847648457,0.3994875848,-0.1538935304,0.1634524167,0.0869446471,-0.0126662534,-0.1323807985,0.1911210567,0.2216383666,0.2824165821,0.1572643816,-0.0959971696,0.0425383486,0.1659151316,0.1229683161,0.0325946435,-0.199791953,-0.0025342163,0.1199669093,0.0561050922,-0.047854811,-0.0479735769,0.2361027151,0.2793250382,-0.0761121586,0.1238081977,0.0580437221,-0.1685209721,-0.0567469634,0.2826824784,-0.0136068668,0.6230338812,0.0613569953,-0.0287729017,0.0040426482,-0.3801935911,-0.0336282663,0.1335722506,0.0782550871,-0.1228369474,0.0503613725,0.2556922138,0.159722358,-0.1924543381,-0.3795136213,0.0199823845,0.3146329224,-0.4612223506,0.2040783465,-0.1188221872,0.2136378586,-0.0778881684,-0.1706882864,-0.1753237247,-0.3915620148,-0.0386055,0.1213032827,0.0050780186,0.1582536548,0.0887682661,-0.1417624056,0.04338919,0.3126336932,0.2620718777,-0.0042142225,-0.4652619362,-0.1256633401,0.3646590412,-0.1426314861,0.1807572693,0.1596735418,-0.4231134653,-0.1377694905,-0.554107368,0.0362168029,0.0458122902,0.2179297954,0.0650527328,0.0002729307,-0.1417898685,-0.0640055388,0.1627510339,-0.0710154772,-0.0511523709,0.1167618632,0.1763634235,-0.3484143615,-0.4004407525,0.0810818374,-0.2404452562,-0.2278887779,0.0398980491,0.0562176853,0.2899165154,0.033601027,0.3173662722,0.2378733456,0.0640233308,-0.2699205875,-0.2033462822,0.2120094001,-0.0509562194,-0.1475808471,-0.2852531075,-0.0752577856,0.1772031188,-0.022968391,0.2650331259,-0.2499954402,-0.0535250045,-0.0659198835,0.3023351431,-0.1044890657,-0.2844492197,0.3918755949,0.1078824848,0.0479716472,-0.0043216893,-0.3812083304,-0.0652099252,-0.2201593518,0.1879525185,-0.0040869415,0.5207585096,0.1579419822,0.5066372156,0.3318142593,-0.5369669795,0.1717587262,-0.2706901133,0.1213353053,-0.1566914618,-0.1280220896,0.3013108969,0.040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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2470","title":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`.","comments":"Sure! I tried reinstalling to get latest. pip was mad because it looks like Datasets currently wants <4.0.0 (which is interesting, because apparently I ended up with 4.0.0 already?), but I gave it a shot anyway:\r\n\r\n```bash\r\n$ pip install --upgrade --force-reinstall pyarrow\r\nCollecting pyarrow\r\n  Downloading pyarrow-4.0.1-cp39-cp39-manylinux2014_x86_64.whl (21.9 MB)\r\n     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 21.9 MB 23.8 MB\/s\r\nCollecting numpy>=1.16.6\r\n  Using cached numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.4 MB)\r\nInstalling collected packages: numpy, pyarrow\r\n  Attempting uninstall: numpy\r\n    Found existing installation: numpy 1.20.3\r\n    Uninstalling numpy-1.20.3:\r\n      Successfully uninstalled numpy-1.20.3\r\n  Attempting uninstall: pyarrow\r\n    Found existing installation: pyarrow 3.0.0\r\n    Uninstalling pyarrow-3.0.0:\r\n      Successfully uninstalled pyarrow-3.0.0\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\ndatasets 1.8.0 requires pyarrow<4.0.0,>=1.0.0, but you have pyarrow 4.0.1 which is incompatible.\r\nSuccessfully installed numpy-1.20.3 pyarrow-4.0.1\r\n```\r\n\r\nTrying it, the same issue:\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121730226-3f470b80-caa4-11eb-85a5-684c44c816da.png)\r\n\r\nI tried installing `\"pyarrow<4.0.0\"`, which gave me 3.0.0. Running, still, same issue.\r\n\r\nI agree it's weird that pyarrow is checking the source code directory for its files. (There is no `pyarrow\/` directory there.) To me, that makes it seem like an issue with how pyarrow is called.\r\n\r\nOut of curiosity, I tried running this with fewer workers to see when the error arises:\r\n\r\n- 1: \u2705\r\n- 2: \u2705\r\n- 4: \u2705\r\n- 8: \u2705\r\n- 10: \u2705\r\n- 11: \u274c  \ud83e\udd14\r\n- 12: \u274c\r\n- 16: \u274c\r\n- 32: \u274c\r\n\r\nchecking my datasets:\r\n\r\n```python\r\n>>> datasets\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['text'],\r\n        num_rows: 389290\r\n    })\r\n    validation.sc: Dataset({\r\n        features: ['text'],\r\n        num_rows: 10  # \ud83e\udd14\r\n    })\r\n    validation.wvs: Dataset({\r\n        features: ['text'],\r\n        num_rows: 93928\r\n    })\r\n})\r\n```\r\n\r\nNew hypothesis: crash if `num_proc` > length of a dataset? \ud83d\ude05\r\n\r\nIf so, this might be totally my fault, as the caller. Could be a docs fix, or maybe this library could do a check to limit `num_proc` for this case?","body":"## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n","comment_length":305,"text":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`. \n ## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n \n Sure! I tried reinstalling to get latest. pip was mad because it looks like Datasets currently wants <4.0.0 (which is interesting, because apparently I ended up with 4.0.0 already?), but I gave it a shot anyway:\r\n\r\n```bash\r\n$ pip install --upgrade --force-reinstall pyarrow\r\nCollecting pyarrow\r\n  Downloading pyarrow-4.0.1-cp39-cp39-manylinux2014_x86_64.whl (21.9 MB)\r\n     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 21.9 MB 23.8 MB\/s\r\nCollecting numpy>=1.16.6\r\n  Using cached numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.4 MB)\r\nInstalling collected packages: numpy, pyarrow\r\n  Attempting uninstall: numpy\r\n    Found existing installation: numpy 1.20.3\r\n    Uninstalling numpy-1.20.3:\r\n      Successfully uninstalled numpy-1.20.3\r\n  Attempting uninstall: pyarrow\r\n    Found existing installation: pyarrow 3.0.0\r\n    Uninstalling pyarrow-3.0.0:\r\n      Successfully uninstalled pyarrow-3.0.0\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\ndatasets 1.8.0 requires pyarrow<4.0.0,>=1.0.0, but you have pyarrow 4.0.1 which is incompatible.\r\nSuccessfully installed numpy-1.20.3 pyarrow-4.0.1\r\n```\r\n\r\nTrying it, the same issue:\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121730226-3f470b80-caa4-11eb-85a5-684c44c816da.png)\r\n\r\nI tried installing `\"pyarrow<4.0.0\"`, which gave me 3.0.0. Running, still, same issue.\r\n\r\nI agree it's weird that pyarrow is checking the source code directory for its files. (There is no `pyarrow\/` directory there.) To me, that makes it seem like an issue with how pyarrow is called.\r\n\r\nOut of curiosity, I tried running this with fewer workers to see when the error arises:\r\n\r\n- 1: \u2705\r\n- 2: \u2705\r\n- 4: \u2705\r\n- 8: \u2705\r\n- 10: \u2705\r\n- 11: \u274c  \ud83e\udd14\r\n- 12: \u274c\r\n- 16: \u274c\r\n- 32: \u274c\r\n\r\nchecking my datasets:\r\n\r\n```python\r\n>>> datasets\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['text'],\r\n        num_rows: 389290\r\n    })\r\n    validation.sc: Dataset({\r\n        features: ['text'],\r\n        num_rows: 10  # \ud83e\udd14\r\n    })\r\n    validation.wvs: Dataset({\r\n        features: ['text'],\r\n        num_rows: 93928\r\n    })\r\n})\r\n```\r\n\r\nNew hypothesis: crash if `num_proc` > length of a dataset? \ud83d\ude05\r\n\r\nIf so, this might be totally my fault, as the caller. Could be a docs fix, or maybe this library could do a check to limit `num_proc` for this case?","embeddings":[-0.4569717646,-0.2173907459,0.0228779204,0.4219461381,0.3113077581,-0.2264823467,0.2361344099,0.1525053084,0.0431262851,0.3643293679,0.5303300619,0.2979793251,-0.3346508443,-0.0465700552,0.047729928,-0.0265675541,0.3659133613,-0.2182355374,0.2511509061,0.3584368825,-0.2708789408,0.1627700925,-0.4191713631,0.2937121987,-0.2652947009,0.082243599,-0.1050226912,0.2865630984,-0.0974157006,-0.5258919597,-0.0305760354,-0.295525372,0.1912617087,0.4583674669,-0.0001182336,-0.0913553163,0.2139718682,0.0120125022,0.0930700898,-0.4430084825,-0.2259867936,-0.2528820932,-0.1637293845,-0.0408718698,0.3133925498,0.0046791183,-0.0663942918,-0.150278613,0.0759297609,0.7205310464,0.1591092348,0.214832738,0.2288711667,-0.1120013967,0.016339317,0.1341472566,-0.0305977836,0.061437469,0.4003654718,-0.2003950328,0.0249149706,-0.1673947722,0.092808485,-0.2669810057,0.305939585,-0.1227102131,0.1998042613,-0.6616144776,0.2974792719,-0.1724279821,-0.1635968983,-0.2878350317,-0.0509196632,-0.1632319391,-0.1430846751,-0.2230149657,0.177413553,-0.1671895236,-0.1571241021,0.0832860544,-0.2861917019,0.1699507236,0.0335625187,0.3086440563,-0.3192946315,0.4002259374,-0.0029774297,0.389618665,0.1262956411,-0.2813790739,-0.3420287967,0.1340495944,-0.0415284634,0.2674230039,-0.2257158607,-0.1663678735,0.1507905126,-0.1738581359,0.2904581726,-0.2406045794,-0.1656422466,0.2874025404,0.048612088,0.2133101672,0.2980216742,-0.0209573247,0.3021420836,0.3840674162,0.0499104597,-0.056064833,-0.102803044,-0.0400411636,0.2264666557,-0.1167617217,0.0892518535,0.1257736236,0.0740240142,-0.1534793824,-0.1198020428,0.4294593036,-0.2137423903,0.2773050666,0.3186097741,0.2963832617,0.3739162087,0.227028951,-0.2862907052,0.0338729285,-0.2570652664,-0.0871776044,-0.0442779921,-0.2006773353,-0.3708454072,0.0992647409,-0.005736412,0.239634648,0.1527641416,0.0450480506,0.2212321013,-0.539868176,0.1125220954,-0.2573070824,0.1339917481,0.0350833498,-0.2941997945,0.1408175528,0.1156675741,-0.1236832365,-0.1660993546,0.1671806574,-0.2124153674,-0.2457581609,-0.1474966705,0.1560774744,-0.0041650292,0.4375189543,-0.0617898293,0.3228711784,0.3464185596,-0.3285045028,-0.0258732624,-0.3166899085,-0.4711762369,-0.2692376971,-0.0478229038,0.2325534225,-0.1191353053,0.0298826676,0.2000778913,0.0202775728,0.0941302255,0.1629669219,-0.1979359239,-0.0177588277,-0.1911362857,0.5243681073,0.2797337174,-0.0643637404,-0.4906041324,0.1102769822,-0.2673051357,-0.1981518269,-0.4909549952,-0.1560129821,0.4023430943,-0.2073682845,0.4260299802,-0.0168935806,-0.1412145048,0.118385382,-0.306717515,0.094998017,0.1623565853,-0.1462418735,0.0806366652,-0.166501984,0.1831175238,-0.0708336085,0.0203282014,0.004628906,-0.0426518135,0.2887397707,0.2136349082,-0.0781941861,0.0351790115,-0.3048202991,-0.0683804154,0.2268817574,0.0347599015,-0.0888614431,-0.0282383524,0.0105143087,-0.0411652811,0.3073398471,0.1391114891,-0.1058630794,0.0216380805,-0.1246979013,0.0030890247,-0.0661532804,-0.0376062728,-0.0197845213,-0.1885098517,0.0246681049,-0.0632356256,-0.1272604764,-0.2010300606,-0.1646624357,-0.3887704015,0.1416743398,0.4239653945,-0.0064662574,-0.2309417278,0.5506316423,0.3362869918,-0.2484891564,-0.6358670592,-0.0847648457,0.3994875848,-0.1538935304,0.1634524167,0.0869446471,-0.0126662534,-0.1323807985,0.1911210567,0.2216383666,0.2824165821,0.1572643816,-0.0959971696,0.0425383486,0.1659151316,0.1229683161,0.0325946435,-0.199791953,-0.0025342163,0.1199669093,0.0561050922,-0.047854811,-0.0479735769,0.2361027151,0.2793250382,-0.0761121586,0.1238081977,0.0580437221,-0.1685209721,-0.0567469634,0.2826824784,-0.0136068668,0.6230338812,0.0613569953,-0.0287729017,0.0040426482,-0.3801935911,-0.0336282663,0.1335722506,0.0782550871,-0.1228369474,0.0503613725,0.2556922138,0.159722358,-0.1924543381,-0.3795136213,0.0199823845,0.3146329224,-0.4612223506,0.2040783465,-0.1188221872,0.2136378586,-0.0778881684,-0.1706882864,-0.1753237247,-0.3915620148,-0.0386055,0.1213032827,0.0050780186,0.1582536548,0.0887682661,-0.1417624056,0.04338919,0.3126336932,0.2620718777,-0.0042142225,-0.4652619362,-0.1256633401,0.3646590412,-0.1426314861,0.1807572693,0.1596735418,-0.4231134653,-0.1377694905,-0.554107368,0.0362168029,0.0458122902,0.2179297954,0.0650527328,0.0002729307,-0.1417898685,-0.0640055388,0.1627510339,-0.0710154772,-0.0511523709,0.1167618632,0.1763634235,-0.3484143615,-0.4004407525,0.0810818374,-0.2404452562,-0.2278887779,0.0398980491,0.0562176853,0.2899165154,0.033601027,0.3173662722,0.2378733456,0.0640233308,-0.2699205875,-0.2033462822,0.2120094001,-0.0509562194,-0.1475808471,-0.2852531075,-0.0752577856,0.1772031188,-0.022968391,0.2650331259,-0.2499954402,-0.0535250045,-0.0659198835,0.3023351431,-0.1044890657,-0.2844492197,0.3918755949,0.1078824848,0.0479716472,-0.0043216893,-0.3812083304,-0.0652099252,-0.2201593518,0.1879525185,-0.0040869415,0.5207585096,0.1579419822,0.5066372156,0.3318142593,-0.5369669795,0.1717587262,-0.2706901133,0.1213353053,-0.1566914618,-0.1280220896,0.3013108969,0.0409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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2470","title":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`.","comments":"Good catch ! Not sure why it could raise such a weird issue from pyarrow though\r\nWe should definitely reduce num_proc to the length of the dataset if needed and log a warning.","body":"## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n","comment_length":33,"text":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`. \n ## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n \n Good catch ! Not sure why it could raise such a weird issue from pyarrow though\r\nWe should definitely reduce num_proc to the length of the dataset if needed and log a warning.","embeddings":[-0.4569717646,-0.2173907459,0.0228779204,0.4219461381,0.3113077581,-0.2264823467,0.2361344099,0.1525053084,0.0431262851,0.3643293679,0.5303300619,0.2979793251,-0.3346508443,-0.0465700552,0.047729928,-0.0265675541,0.3659133613,-0.2182355374,0.2511509061,0.3584368825,-0.2708789408,0.1627700925,-0.4191713631,0.2937121987,-0.2652947009,0.082243599,-0.1050226912,0.2865630984,-0.0974157006,-0.5258919597,-0.0305760354,-0.295525372,0.1912617087,0.4583674669,-0.0001182336,-0.0913553163,0.2139718682,0.0120125022,0.0930700898,-0.4430084825,-0.2259867936,-0.2528820932,-0.1637293845,-0.0408718698,0.3133925498,0.0046791183,-0.0663942918,-0.150278613,0.0759297609,0.7205310464,0.1591092348,0.214832738,0.2288711667,-0.1120013967,0.016339317,0.1341472566,-0.0305977836,0.061437469,0.4003654718,-0.2003950328,0.0249149706,-0.1673947722,0.092808485,-0.2669810057,0.305939585,-0.1227102131,0.1998042613,-0.6616144776,0.2974792719,-0.1724279821,-0.1635968983,-0.2878350317,-0.0509196632,-0.1632319391,-0.1430846751,-0.2230149657,0.177413553,-0.1671895236,-0.1571241021,0.0832860544,-0.2861917019,0.1699507236,0.0335625187,0.3086440563,-0.3192946315,0.4002259374,-0.0029774297,0.389618665,0.1262956411,-0.2813790739,-0.3420287967,0.1340495944,-0.0415284634,0.2674230039,-0.2257158607,-0.1663678735,0.1507905126,-0.1738581359,0.2904581726,-0.2406045794,-0.1656422466,0.2874025404,0.048612088,0.2133101672,0.2980216742,-0.0209573247,0.3021420836,0.3840674162,0.0499104597,-0.056064833,-0.102803044,-0.0400411636,0.2264666557,-0.1167617217,0.0892518535,0.1257736236,0.0740240142,-0.1534793824,-0.1198020428,0.4294593036,-0.2137423903,0.2773050666,0.3186097741,0.2963832617,0.3739162087,0.227028951,-0.2862907052,0.0338729285,-0.2570652664,-0.0871776044,-0.0442779921,-0.2006773353,-0.3708454072,0.0992647409,-0.005736412,0.239634648,0.1527641416,0.0450480506,0.2212321013,-0.539868176,0.1125220954,-0.2573070824,0.1339917481,0.0350833498,-0.2941997945,0.1408175528,0.1156675741,-0.1236832365,-0.1660993546,0.1671806574,-0.2124153674,-0.2457581609,-0.1474966705,0.1560774744,-0.0041650292,0.4375189543,-0.0617898293,0.3228711784,0.3464185596,-0.3285045028,-0.0258732624,-0.3166899085,-0.4711762369,-0.2692376971,-0.0478229038,0.2325534225,-0.1191353053,0.0298826676,0.2000778913,0.0202775728,0.0941302255,0.1629669219,-0.1979359239,-0.0177588277,-0.1911362857,0.5243681073,0.2797337174,-0.0643637404,-0.4906041324,0.1102769822,-0.2673051357,-0.1981518269,-0.4909549952,-0.1560129821,0.4023430943,-0.2073682845,0.4260299802,-0.0168935806,-0.1412145048,0.118385382,-0.306717515,0.094998017,0.1623565853,-0.1462418735,0.0806366652,-0.166501984,0.1831175238,-0.0708336085,0.0203282014,0.004628906,-0.0426518135,0.2887397707,0.2136349082,-0.0781941861,0.0351790115,-0.3048202991,-0.0683804154,0.2268817574,0.0347599015,-0.0888614431,-0.0282383524,0.0105143087,-0.0411652811,0.3073398471,0.1391114891,-0.1058630794,0.0216380805,-0.1246979013,0.0030890247,-0.0661532804,-0.0376062728,-0.0197845213,-0.1885098517,0.0246681049,-0.0632356256,-0.1272604764,-0.2010300606,-0.1646624357,-0.3887704015,0.1416743398,0.4239653945,-0.0064662574,-0.2309417278,0.5506316423,0.3362869918,-0.2484891564,-0.6358670592,-0.0847648457,0.3994875848,-0.1538935304,0.1634524167,0.0869446471,-0.0126662534,-0.1323807985,0.1911210567,0.2216383666,0.2824165821,0.1572643816,-0.0959971696,0.0425383486,0.1659151316,0.1229683161,0.0325946435,-0.199791953,-0.0025342163,0.1199669093,0.0561050922,-0.047854811,-0.0479735769,0.2361027151,0.2793250382,-0.0761121586,0.1238081977,0.0580437221,-0.1685209721,-0.0567469634,0.2826824784,-0.0136068668,0.6230338812,0.0613569953,-0.0287729017,0.0040426482,-0.3801935911,-0.0336282663,0.1335722506,0.0782550871,-0.1228369474,0.0503613725,0.2556922138,0.159722358,-0.1924543381,-0.3795136213,0.0199823845,0.3146329224,-0.4612223506,0.2040783465,-0.1188221872,0.2136378586,-0.0778881684,-0.1706882864,-0.1753237247,-0.3915620148,-0.0386055,0.1213032827,0.0050780186,0.1582536548,0.0887682661,-0.1417624056,0.04338919,0.3126336932,0.2620718777,-0.0042142225,-0.4652619362,-0.1256633401,0.3646590412,-0.1426314861,0.1807572693,0.1596735418,-0.4231134653,-0.1377694905,-0.554107368,0.0362168029,0.0458122902,0.2179297954,0.0650527328,0.0002729307,-0.1417898685,-0.0640055388,0.1627510339,-0.0710154772,-0.0511523709,0.1167618632,0.1763634235,-0.3484143615,-0.4004407525,0.0810818374,-0.2404452562,-0.2278887779,0.0398980491,0.0562176853,0.2899165154,0.033601027,0.3173662722,0.2378733456,0.0640233308,-0.2699205875,-0.2033462822,0.2120094001,-0.0509562194,-0.1475808471,-0.2852531075,-0.0752577856,0.1772031188,-0.022968391,0.2650331259,-0.2499954402,-0.0535250045,-0.0659198835,0.3023351431,-0.1044890657,-0.2844492197,0.3918755949,0.1078824848,0.0479716472,-0.0043216893,-0.3812083304,-0.0652099252,-0.2201593518,0.1879525185,-0.0040869415,0.5207585096,0.1579419822,0.5066372156,0.3318142593,-0.5369669795,0.1717587262,-0.2706901133,0.1213353053,-0.1566914618,-0.1280220896,0.3013108969,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2470","title":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`.","comments":"This has been fixed in #2566, thanks @connor-mccarthy !\r\nWe'll make a new release soon that includes the fix ;)","body":"## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n","comment_length":20,"text":"Crash when `num_proc` > dataset length for `map()` on a `datasets.Dataset`. \n ## Describe the bug\r\nCrash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.\r\n\r\nI believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# this function will be applied with map()\r\ndef tokenize_function(examples):\r\n    return tokenizer(\r\n        examples[\"text\"],\r\n        padding=PaddingStrategy.DO_NOT_PAD,\r\n        truncation=True,\r\n    )\r\n\r\n# data_files is a Dict[str, str] mapping name -> path\r\ndatasets = load_dataset(\"text\", data_files={...})  \r\n\r\n# this is where the error happens if num_proc = 16,\r\n# but is fine if num_proc = 1\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=num_workers,\r\n)\r\n```\r\n\r\n## Expected results\r\nThe `map()` function succeeds with `num_proc` > 1.\r\n\r\n## Actual results\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404271-a6cc5200-c910-11eb-8e27-5c893bd04042.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/1170062\/121404362-be0b3f80-c910-11eb-9117-658943029aef.png)\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.5\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes, but I think N\/A for this issue\r\n- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N\/A for this issue\r\n \n This has been fixed in #2566, thanks @connor-mccarthy !\r\nWe'll make a new release soon that includes the fix ;)","embeddings":[-0.4569717646,-0.2173907459,0.0228779204,0.4219461381,0.3113077581,-0.2264823467,0.2361344099,0.1525053084,0.0431262851,0.3643293679,0.5303300619,0.2979793251,-0.3346508443,-0.0465700552,0.047729928,-0.0265675541,0.3659133613,-0.2182355374,0.2511509061,0.3584368825,-0.2708789408,0.1627700925,-0.4191713631,0.2937121987,-0.2652947009,0.082243599,-0.1050226912,0.2865630984,-0.0974157006,-0.5258919597,-0.0305760354,-0.295525372,0.1912617087,0.4583674669,-0.0001182336,-0.0913553163,0.2139718682,0.0120125022,0.0930700898,-0.4430084825,-0.2259867936,-0.2528820932,-0.1637293845,-0.0408718698,0.3133925498,0.0046791183,-0.0663942918,-0.150278613,0.0759297609,0.7205310464,0.1591092348,0.214832738,0.2288711667,-0.1120013967,0.016339317,0.1341472566,-0.0305977836,0.061437469,0.4003654718,-0.2003950328,0.0249149706,-0.1673947722,0.092808485,-0.2669810057,0.305939585,-0.1227102131,0.1998042613,-0.6616144776,0.2974792719,-0.1724279821,-0.1635968983,-0.2878350317,-0.0509196632,-0.1632319391,-0.1430846751,-0.2230149657,0.177413553,-0.1671895236,-0.1571241021,0.0832860544,-0.2861917019,0.1699507236,0.0335625187,0.3086440563,-0.3192946315,0.4002259374,-0.0029774297,0.389618665,0.1262956411,-0.2813790739,-0.3420287967,0.1340495944,-0.0415284634,0.2674230039,-0.2257158607,-0.1663678735,0.1507905126,-0.1738581359,0.2904581726,-0.2406045794,-0.1656422466,0.2874025404,0.048612088,0.2133101672,0.2980216742,-0.0209573247,0.3021420836,0.3840674162,0.0499104597,-0.056064833,-0.102803044,-0.0400411636,0.2264666557,-0.1167617217,0.0892518535,0.1257736236,0.0740240142,-0.1534793824,-0.1198020428,0.4294593036,-0.2137423903,0.2773050666,0.3186097741,0.2963832617,0.3739162087,0.227028951,-0.2862907052,0.0338729285,-0.2570652664,-0.0871776044,-0.0442779921,-0.2006773353,-0.3708454072,0.0992647409,-0.005736412,0.239634648,0.1527641416,0.0450480506,0.2212321013,-0.539868176,0.1125220954,-0.2573070824,0.1339917481,0.0350833498,-0.2941997945,0.1408175528,0.1156675741,-0.1236832365,-0.1660993546,0.1671806574,-0.2124153674,-0.2457581609,-0.1474966705,0.1560774744,-0.0041650292,0.4375189543,-0.0617898293,0.3228711784,0.3464185596,-0.3285045028,-0.0258732624,-0.3166899085,-0.4711762369,-0.2692376971,-0.0478229038,0.2325534225,-0.1191353053,0.0298826676,0.2000778913,0.0202775728,0.0941302255,0.1629669219,-0.1979359239,-0.0177588277,-0.1911362857,0.5243681073,0.2797337174,-0.0643637404,-0.4906041324,0.1102769822,-0.2673051357,-0.1981518269,-0.4909549952,-0.1560129821,0.4023430943,-0.2073682845,0.4260299802,-0.0168935806,-0.1412145048,0.118385382,-0.306717515,0.094998017,0.1623565853,-0.1462418735,0.0806366652,-0.166501984,0.1831175238,-0.0708336085,0.0203282014,0.004628906,-0.0426518135,0.2887397707,0.2136349082,-0.0781941861,0.0351790115,-0.3048202991,-0.0683804154,0.2268817574,0.0347599015,-0.0888614431,-0.0282383524,0.0105143087,-0.0411652811,0.3073398471,0.1391114891,-0.1058630794,0.0216380805,-0.1246979013,0.0030890247,-0.0661532804,-0.0376062728,-0.0197845213,-0.1885098517,0.0246681049,-0.0632356256,-0.1272604764,-0.2010300606,-0.1646624357,-0.3887704015,0.1416743398,0.4239653945,-0.0064662574,-0.2309417278,0.5506316423,0.3362869918,-0.2484891564,-0.6358670592,-0.0847648457,0.3994875848,-0.1538935304,0.1634524167,0.0869446471,-0.0126662534,-0.1323807985,0.1911210567,0.2216383666,0.2824165821,0.1572643816,-0.0959971696,0.0425383486,0.1659151316,0.1229683161,0.0325946435,-0.199791953,-0.0025342163,0.1199669093,0.0561050922,-0.047854811,-0.0479735769,0.2361027151,0.2793250382,-0.0761121586,0.1238081977,0.0580437221,-0.1685209721,-0.0567469634,0.2826824784,-0.0136068668,0.6230338812,0.0613569953,-0.0287729017,0.0040426482,-0.3801935911,-0.0336282663,0.1335722506,0.0782550871,-0.1228369474,0.0503613725,0.2556922138,0.159722358,-0.1924543381,-0.3795136213,0.0199823845,0.3146329224,-0.4612223506,0.2040783465,-0.1188221872,0.2136378586,-0.0778881684,-0.1706882864,-0.1753237247,-0.3915620148,-0.0386055,0.1213032827,0.0050780186,0.1582536548,0.0887682661,-0.1417624056,0.04338919,0.3126336932,0.2620718777,-0.0042142225,-0.4652619362,-0.1256633401,0.3646590412,-0.1426314861,0.1807572693,0.1596735418,-0.4231134653,-0.1377694905,-0.554107368,0.0362168029,0.0458122902,0.2179297954,0.0650527328,0.0002729307,-0.1417898685,-0.0640055388,0.1627510339,-0.0710154772,-0.0511523709,0.1167618632,0.1763634235,-0.3484143615,-0.4004407525,0.0810818374,-0.2404452562,-0.2278887779,0.0398980491,0.0562176853,0.2899165154,0.033601027,0.3173662722,0.2378733456,0.0640233308,-0.2699205875,-0.2033462822,0.2120094001,-0.0509562194,-0.1475808471,-0.2852531075,-0.0752577856,0.1772031188,-0.022968391,0.2650331259,-0.2499954402,-0.0535250045,-0.0659198835,0.3023351431,-0.1044890657,-0.2844492197,0.3918755949,0.1078824848,0.0479716472,-0.0043216893,-0.3812083304,-0.0652099252,-0.2201593518,0.1879525185,-0.0040869415,0.5207585096,0.1579419822,0.5066372156,0.3318142593,-0.5369669795,0.1717587262,-0.2706901133,0.1213353053,-0.1566914618,-0.1280220896,0.3013108969,0.0409448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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2450","title":"BLUE file not found","comments":"Hi ! The `blue` metric doesn't exist, but the `bleu` metric does.\r\nYou can get the full list of metrics [here](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/metrics) or by running\r\n```python\r\nfrom datasets import list_metrics\r\n\r\nprint(list_metrics())\r\n```","body":"Hi, I'm having the following issue when I try to load the `blue` metric.\r\n\r\n```shell\r\nimport datasets\r\nmetric = datasets.load_metric('blue')\r\nTraceback (most recent call last):\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 621, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/metrics\/blue\/blue.py\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 332, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 621, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/metrics\/blue\/blue.py\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n  File \"<input>\", line 1, in <module>\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 605, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 343, in prepare_module\r\n    combined_path, github_file_path\r\nFileNotFoundError: Couldn't find file locally at blue\/blue.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/metrics\/blue\/blue.py.\r\nThe file is also not present on the master branch on github.\r\n```\r\nHere is dataset installed version info\r\n```shell\r\npip freeze | grep datasets\r\ndatasets==1.7.0\r\n```\r\n","comment_length":31,"text":"BLUE file not found \n Hi, I'm having the following issue when I try to load the `blue` metric.\r\n\r\n```shell\r\nimport datasets\r\nmetric = datasets.load_metric('blue')\r\nTraceback (most recent call last):\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 320, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 621, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/metrics\/blue\/blue.py\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 332, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 621, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/metrics\/blue\/blue.py\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n  File \"<input>\", line 1, in <module>\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 605, in load_metric\r\n    dataset=False,\r\n  File \"\/home\/irfan\/environments\/Perplexity_Transformers\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 343, in prepare_module\r\n    combined_path, github_file_path\r\nFileNotFoundError: Couldn't find file locally at blue\/blue.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.7.0\/metrics\/blue\/blue.py.\r\nThe file is also not present on the master branch on github.\r\n```\r\nHere is dataset installed version info\r\n```shell\r\npip freeze | grep datasets\r\ndatasets==1.7.0\r\n```\r\n \n Hi ! The `blue` metric doesn't exist, but the `bleu` metric does.\r\nYou can get the full list of metrics [here](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/metrics) or by running\r\n```python\r\nfrom datasets import list_metrics\r\n\r\nprint(list_metrics())\r\n```","embeddings":[-0.2038624436,-0.286285907,-0.0685010329,0.4027132094,0.3037265539,0.0655114725,0.2102440298,0.3386648595,0.0885978788,0.1891393214,-0.2280900776,-0.1502068639,0.0800003409,-0.3868101239,0.2086268514,-0.0838348791,-0.0976857319,0.2933502793,0.1404932737,-0.0119333351,-0.2906016111,0.279425174,-0.0793287903,0.1401927322,-0.2483473867,0.2007489204,0.0558202937,0.4576255977,-0.066158548,-0.4756872058,0.262820214,0.0613073483,0.0844941288,0.6237055659,-0.0001181479,0.1353184283,0.4002448022,-0.026030736,-0.1755044907,-0.7946474552,-0.2668886185,-0.3052861094,-0.1206793189,-0.1363381892,-0.0330706872,-0.0962109268,0.0631731302,-0.1688701361,0.0880968049,0.2695310712,0.1627939492,0.3915831447,0.0068830149,-0.0806269795,0.1766516715,-0.1143770814,0.1108325496,0.5380025506,0.1324937791,0.0339101963,0.1965762675,0.2750691473,0.0797883868,0.098599039,0.3210598528,-0.0133122467,0.5190857649,0.093614094,-0.0156915635,0.2679638863,0.2782544196,-0.0065161688,-0.5074590445,0.0587431863,-0.2388174832,-0.4378614128,0.3182871044,-0.0005305419,0.0924746692,0.040464472,-0.3045379519,-0.3758830428,-0.2266742438,0.008756551,-0.3165692091,0.271229744,-0.1296576858,-0.0393383913,0.131154418,0.035779655,-0.1490480006,-0.0241202097,-0.2086912543,0.1841868162,-0.2370862961,0.1590325236,-0.0566026047,-0.1240671426,0.1691560149,0.1326967925,0.2836335599,-0.0671531931,-0.1154061556,0.2826260924,0.1740510762,0.2684218884,0.0162829645,0.1422417462,0.3623938262,0.4090431035,-0.2026886195,-0.2172914594,-0.0469186157,-0.5095567107,0.2717924714,0.1654091477,0.2333503067,-0.3669013679,-0.546892941,-0.1752153039,0.2014846504,-0.138439551,0.2428230643,0.3890919685,-0.1542295218,0.1355956793,-0.0241192281,0.2843506038,-0.3726007044,-0.0479731485,-0.2461787909,0.270036608,-0.2791169882,0.1852990091,0.134738192,-0.1474324316,0.4780178666,-0.2440681159,0.1274051219,-0.2823776603,0.1358564347,-0.1307548732,-0.0632690564,0.3866440654,-0.0663409978,0.0235976931,0.0756518319,-0.1533665955,-0.1150935367,0.1922052354,-0.3756907582,-0.3689212203,0.0063163307,0.1795061976,-0.6024382114,-0.013512793,-0.1706515402,0.0045132828,0.0897604227,-0.5304581523,-0.1209513769,-0.0536715128,-0.3520642519,-0.1883587986,0.2899969816,0.5583953261,-0.2963094413,-0.3161924183,-0.2241495848,-0.0718351007,0.0118514877,0.1162731722,-0.1957620084,0.2485929281,-0.5058684945,-0.2216970921,0.6039164662,-0.9293052554,-0.3994351625,0.0354591422,-0.314542383,0.1001128778,0.1549662948,-0.0467824712,0.115826495,-0.2321648002,0.302493304,0.0525196567,0.074928388,-0.1573142558,-0.2148272693,-0.1992250383,0.1408916861,0.1859884113,0.3698249459,-0.0691337138,0.2987953722,-0.1159083545,-0.0255059134,0.0242809486,-0.0979231074,0.3807157874,0.0568256937,0.0048563476,0.0850338936,-0.4343167543,0.0962968469,0.2571773529,-0.1961857229,-0.2399356514,-0.0186847337,0.1108483896,-0.4768013954,-0.3033174574,-0.3182031512,-0.0944714919,0.0308126733,0.0620128363,0.0148465876,0.0749977529,-0.17083399,0.3170694709,-0.0074223857,0.0792751908,-0.0620083287,0.2012156248,0.0121677862,-0.0265175514,-0.0333781242,0.2669577599,0.3650774956,-0.1662282646,-0.1953881383,0.4600685835,0.0996977836,0.3213559985,0.2970384955,0.0997050256,0.3341647089,-0.215291068,0.0784406811,-0.0297695249,0.1145220175,-0.0374824256,0.0993940607,0.3468848467,0.0831461996,0.1389874369,0.1891477257,-0.2690497637,0.2665727139,-0.1116631404,0.0468978509,-0.120234102,0.4082636237,-0.0945784077,0.0844262168,-0.0353472419,0.0425563082,-0.1108863801,0.2558621466,-0.1055775434,0.0156317856,0.0747177899,-0.1970437914,-0.0989656746,-0.2834335864,0.2893654704,0.7424980998,0.2036165893,0.0498852096,-0.0030591253,0.0600026026,-0.135204643,0.1827728152,0.1364149898,0.0558149219,0.4532756209,0.1284675151,-0.1120860875,-0.1604457796,-0.2615599632,0.0380720012,-0.0798378214,-0.3087829351,0.2265219539,-0.1820993274,0.181409508,-0.0977322757,-0.0975359753,-0.0847241133,-0.2716193497,0.0139922788,0.1867242455,0.301818192,0.0267870892,-0.1049008816,0.1172481105,-0.0092865555,-0.2614940405,-0.0797163844,-0.226645276,-0.138090089,-0.0651665181,-0.0165860113,-0.0185929723,0.3383127153,-0.4342367053,0.2017265111,-0.2717138529,-0.3112030327,-0.0327798836,0.2021766156,0.5519148111,0.0779538527,0.0549889728,0.0839210004,0.2093093991,0.4240537882,-0.247879833,-0.1166461185,0.1270682067,-0.113442339,0.0935031623,-0.0398880206,-0.4275738299,-0.3291085064,-0.405400306,-0.1222823784,-0.1058298498,0.030580502,0.5178664327,0.0676998943,0.1980971694,0.1595279574,0.151336953,-0.1719416231,-0.5531238914,0.4830413461,-0.3444617689,-0.2105953395,0.3475134671,0.109351635,0.4056742191,0.047029946,-0.3599723279,-0.3414432108,0.1025691852,0.0229943469,-0.1240343228,0.1032207161,0.068411313,-0.1385262012,-0.0798841193,-0.168666631,-0.1995458752,0.0632746145,0.0649987981,0.6346903443,-0.1762986779,0.1323319823,-0.1719237566,0.6171426177,0.0703139454,0.0202168189,0.326261282,0.194279328,0.4952047467,-0.0125613613,-0.21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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2447","title":"dataset adversarial_qa has no answers in the \"test\" set","comments":"Hi ! I'm pretty sure that the answers are not made available for the test set on purpose because it is part of the DynaBench benchmark, for which you can submit your predictions on the website.\r\nIn any case we should mention this in the dataset card of this dataset.","body":"## Describe the bug\r\nWhen loading the adversarial_qa dataset the 'test' portion has no answers.  Only the 'train' and 'validation' portions do.  This occurs with all four of the configs ('adversarialQA', 'dbidaf', 'dbert', 'droberta')\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom   datasets import load_dataset\r\nexamples = load_dataset('adversarial_qa', 'adversarialQA', script_version=\"master\")['test']\r\nprint('Loaded {:,} examples'.format(len(examples)))\r\nhas_answers = 0\r\nfor e in examples:\r\n    if e['answers']['text']:\r\n        has_answers += 1\r\nprint('{:,} have answers'.format(has_answers))\r\n>>> Loaded 3,000 examples\r\n>>> 0 have answers\r\n\r\nexamples = load_dataset('adversarial_qa', 'adversarialQA', script_version=\"master\")['validation']\r\n<...code above...>\r\n>>> Loaded 3,000 examples\r\n>>> 3,000 have answers\r\n```\r\n\r\n## Expected results\r\nIf 'test' is a valid dataset, it should have answers. Also note that all of the 'train' and 'validation' sets have answers, there are no \"no answer\" questions with this set (not sure if this is correct or not).\r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.8.0-53-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyArrow version: 1.0.0\r\n\r\n","comment_length":50,"text":"dataset adversarial_qa has no answers in the \"test\" set \n ## Describe the bug\r\nWhen loading the adversarial_qa dataset the 'test' portion has no answers.  Only the 'train' and 'validation' portions do.  This occurs with all four of the configs ('adversarialQA', 'dbidaf', 'dbert', 'droberta')\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom   datasets import load_dataset\r\nexamples = load_dataset('adversarial_qa', 'adversarialQA', script_version=\"master\")['test']\r\nprint('Loaded {:,} examples'.format(len(examples)))\r\nhas_answers = 0\r\nfor e in examples:\r\n    if e['answers']['text']:\r\n        has_answers += 1\r\nprint('{:,} have answers'.format(has_answers))\r\n>>> Loaded 3,000 examples\r\n>>> 0 have answers\r\n\r\nexamples = load_dataset('adversarial_qa', 'adversarialQA', script_version=\"master\")['validation']\r\n<...code above...>\r\n>>> Loaded 3,000 examples\r\n>>> 3,000 have answers\r\n```\r\n\r\n## Expected results\r\nIf 'test' is a valid dataset, it should have answers. Also note that all of the 'train' and 'validation' sets have answers, there are no \"no answer\" questions with this set (not sure if this is correct or not).\r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.8.0-53-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyArrow version: 1.0.0\r\n\r\n \n Hi ! I'm pretty sure that the answers are not made available for the test set on purpose because it is part of the DynaBench benchmark, for which you can submit your predictions on the website.\r\nIn any case we should mention this in the dataset card of this dataset.","embeddings":[-0.1097810715,0.1781133115,-0.1390384734,0.3103133142,0.163477093,-0.0822487101,0.3094846606,0.4499256909,-0.0519624054,0.1181879789,0.1611241698,0.3690258563,-0.1847231686,-0.2479670346,0.0857571959,0.1090602502,-0.1185330972,0.0875692368,-0.072045058,-0.3029209673,-0.1661467552,0.0697546378,-0.1738792509,-0.1399212331,-0.2601901889,0.1874664277,-0.1425664574,0.0713289008,0.0379400924,-0.5859718919,0.284163028,-0.1142051965,0.0199329518,0.333371073,-0.0001112121,-0.0196807217,0.2017178386,-0.062374223,-0.4061582983,-0.1288897246,-0.5137016773,-0.258731097,0.1562203616,-0.2437217832,-0.2343089134,-0.2186056525,-0.1217001155,-0.2548487484,0.2813834548,0.5967983007,0.2503398359,0.1645575166,-0.2127765715,-0.181281805,0.0325633399,-0.1947725713,-0.1360811144,0.3340584934,0.0669581518,-0.2101397663,0.2344431132,0.1632981002,0.0818397924,-0.0216818526,-0.0547687039,-0.0577248409,0.2928401232,-0.2534742355,0.0799158737,0.2677954137,0.3713645339,-0.1823160201,-0.2502909899,-0.3610525429,0.2017528862,-0.0994583741,0.2094523758,0.2093106061,-0.2533089519,0.0622792356,-0.0272087287,0.2265873104,-0.0623358525,-0.0309756249,-0.252605617,0.4209857583,0.071873188,-0.1054385453,-0.057288602,0.155698061,0.0889472514,-0.3112669885,-0.0931109264,0.1401284486,-0.437082231,-0.0814287961,-0.0774305537,-0.0911607966,0.2894621193,0.1992064863,0.3563617766,-0.1585877538,0.1394703835,-0.1587534994,0.1482955068,0.4554486871,0.2018250078,0.2326283455,0.0324285403,0.1187070757,-0.0405059978,0.1136837304,-0.2118980885,-0.2287192792,0.130190894,0.2099892199,0.385589391,-0.2329800129,-0.3287391067,0.3438715339,-0.3541021347,-0.1151092127,-0.0645849332,0.3388816714,-0.3086433709,0.1680484265,-0.1507396251,0.2771680653,-0.2411506772,-0.2918148637,-0.2530305386,-0.1557367593,-0.3436105549,-0.1896779835,0.3385716081,0.2474781722,0.2008586377,0.0807726979,0.1476811916,-0.0245352928,0.2117058486,-0.3234573901,0.1355656236,0.3594354391,-0.1031559482,0.1419624388,0.155846864,-0.4854317009,0.078876555,0.5665237904,0.1898899525,-0.1312184483,0.0712206215,0.2513558865,-0.3031832278,0.0074214432,0.0121108666,0.3110601008,0.0177013874,-0.0367622524,0.3407376409,-0.3106961846,0.0375176482,-0.2748856246,0.6375951171,0.4450051785,-0.3981798887,0.1778471321,0.0053822226,-0.0573090203,-0.1676468551,-0.2092047036,0.0114109581,0.1053504422,-0.1846325696,-0.0219071824,0.2112338394,-0.3385699987,-0.4776214957,-0.1273833364,0.1589169949,0.2947568297,0.0232288837,-0.0901725516,0.1367347389,0.250972122,0.0836565495,0.1850374043,-0.0195128527,-0.2015656829,-0.4516374171,0.022746345,0.3778617084,0.369409591,0.2642970085,-0.0260876324,-0.1160885617,-0.3355936706,0.258883208,0.2518937588,-0.0911189467,0.1675364822,0.1315605342,-0.1300174147,0.0336127728,-0.0891024619,-0.4266718328,0.3443760872,0.0254590958,0.1381693631,0.1315917969,0.0282772034,-0.4881201982,0.0094749797,-0.3255443871,-0.5843749046,0.1927192807,0.2720829248,-0.0129175643,0.3838590384,-0.1999856383,0.2762494385,-0.0239834562,-0.0731474161,-0.180801928,0.2311245054,-0.0168623086,-0.1607399285,-0.0748773739,0.3288769126,-0.0815293714,0.0815682784,-0.1294564605,0.1215953976,0.0694558918,-0.3059769869,0.1249368936,0.4802087843,0.0908802971,-0.3299107254,-0.1332969666,0.2975678146,0.2645037472,-0.0471613258,-0.1427665651,0.3639020622,-0.0844002962,0.2512452602,-0.1064912006,0.1490966529,0.2662833631,-0.2230786979,-0.1498378217,0.0792866573,0.101217553,0.0388417765,0.3362998664,0.1632045805,-0.30365628,0.0747909546,0.3500984013,-0.0923981294,0.1034012362,0.0270148441,-0.1550431103,0.0407557152,0.2616890967,0.3052296937,0.663975358,0.2440157086,0.0889746249,0.0574707016,0.0073665157,-0.2085778862,0.1379159093,-0.2342082411,0.0052957698,0.4090867341,0.2028958201,0.0527467206,-0.0890481845,0.1290135384,-0.0421295464,0.1984057575,-0.2945136428,-0.0023095177,-0.175065726,0.1735696644,0.1295403391,-0.2093019634,-0.2406195253,-0.1649426967,0.2955542505,-0.3521289825,0.0030031323,0.3188256621,-0.2050446272,0.2272210419,-0.1827200055,0.0839221925,-0.2140140831,-0.1988140792,-0.4165837169,0.1731057316,-0.0749871582,-0.0569408685,0.2524851561,-0.2461756319,-0.1882236302,0.0628474876,-0.0934975147,0.0965294465,-0.0814576745,0.6559173465,-0.0215197261,0.07231085,-0.3762665391,-0.0415688604,0.3522254825,-0.0448904447,-0.0101421401,0.1523817182,0.240395084,0.0174525771,-0.0148715144,-0.7003239393,-0.3180599511,-0.209393099,0.0168716703,-0.0373114683,0.1344526559,0.1834020615,0.267659843,0.1186418608,-0.2419462055,-0.0092015984,-0.1599935293,-0.0506427437,0.3487632871,-0.1489490867,-0.5024710298,-0.1494236588,-0.2436115593,0.2617567778,-0.1332828999,-0.5106116533,-0.0782305673,-0.2623902559,0.3865682781,-0.189012453,-0.0559495389,0.3177070916,-0.1946865171,-0.0775459409,-0.2881661057,-0.0538055412,0.3896780312,-0.0496959127,0.1622001827,-0.2436951101,0.2100416571,0.0601756051,0.5309695005,0.1187317669,0.1414560527,0.2202576697,-0.2097058743,0.3259542584,-0.2176638842,-0.3961415589,0.028869614,0.244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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2447","title":"dataset adversarial_qa has no answers in the \"test\" set","comments":"Makes sense, but not intuitive for someone searching through the datasets.  Thanks for adding the note to clarify.","body":"## Describe the bug\r\nWhen loading the adversarial_qa dataset the 'test' portion has no answers.  Only the 'train' and 'validation' portions do.  This occurs with all four of the configs ('adversarialQA', 'dbidaf', 'dbert', 'droberta')\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom   datasets import load_dataset\r\nexamples = load_dataset('adversarial_qa', 'adversarialQA', script_version=\"master\")['test']\r\nprint('Loaded {:,} examples'.format(len(examples)))\r\nhas_answers = 0\r\nfor e in examples:\r\n    if e['answers']['text']:\r\n        has_answers += 1\r\nprint('{:,} have answers'.format(has_answers))\r\n>>> Loaded 3,000 examples\r\n>>> 0 have answers\r\n\r\nexamples = load_dataset('adversarial_qa', 'adversarialQA', script_version=\"master\")['validation']\r\n<...code above...>\r\n>>> Loaded 3,000 examples\r\n>>> 3,000 have answers\r\n```\r\n\r\n## Expected results\r\nIf 'test' is a valid dataset, it should have answers. Also note that all of the 'train' and 'validation' sets have answers, there are no \"no answer\" questions with this set (not sure if this is correct or not).\r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.8.0-53-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyArrow version: 1.0.0\r\n\r\n","comment_length":18,"text":"dataset adversarial_qa has no answers in the \"test\" set \n ## Describe the bug\r\nWhen loading the adversarial_qa dataset the 'test' portion has no answers.  Only the 'train' and 'validation' portions do.  This occurs with all four of the configs ('adversarialQA', 'dbidaf', 'dbert', 'droberta')\r\n\r\n## Steps to reproduce the bug\r\n```\r\nfrom   datasets import load_dataset\r\nexamples = load_dataset('adversarial_qa', 'adversarialQA', script_version=\"master\")['test']\r\nprint('Loaded {:,} examples'.format(len(examples)))\r\nhas_answers = 0\r\nfor e in examples:\r\n    if e['answers']['text']:\r\n        has_answers += 1\r\nprint('{:,} have answers'.format(has_answers))\r\n>>> Loaded 3,000 examples\r\n>>> 0 have answers\r\n\r\nexamples = load_dataset('adversarial_qa', 'adversarialQA', script_version=\"master\")['validation']\r\n<...code above...>\r\n>>> Loaded 3,000 examples\r\n>>> 3,000 have answers\r\n```\r\n\r\n## Expected results\r\nIf 'test' is a valid dataset, it should have answers. Also note that all of the 'train' and 'validation' sets have answers, there are no \"no answer\" questions with this set (not sure if this is correct or not).\r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.8.0-53-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyArrow version: 1.0.0\r\n\r\n \n Makes sense, but not intuitive for someone searching through the datasets.  Thanks for adding the note to clarify.","embeddings":[-0.0380188413,0.1775163263,-0.1093787625,0.3759938478,0.1982209831,-0.0062384722,0.3613942266,0.3695282042,-0.064131923,0.0352284424,0.1037120596,0.3792202771,-0.1990861148,-0.1972342879,0.0432114191,0.1114880666,-0.0155535378,0.1072037444,-0.0602785125,-0.3192011118,-0.2311211377,0.0949319154,-0.2330937833,-0.0949132293,-0.2921154499,0.1194490716,-0.2043208033,0.1561518759,0.0097583635,-0.5535634756,0.2813077569,-0.1492449194,-0.0167530496,0.4255777895,-0.0001118015,-0.0490550287,0.2256864905,-0.0351710208,-0.3814617097,-0.2443181276,-0.4445735216,-0.2766770124,0.1798815578,-0.2216132879,-0.1694054008,-0.2560254335,-0.1121231094,-0.1137276664,0.2730475664,0.5292611718,0.2409110069,0.0941973403,-0.1843172163,-0.133368358,0.1004744396,-0.094907403,-0.1246037483,0.3207915425,0.1239120662,-0.2727337778,0.2450588793,0.161215663,0.0185226779,-0.1245784909,0.0030624783,0.0022467026,0.2287279367,-0.2950288951,0.0905454755,0.2719071209,0.5253135562,-0.1579341441,-0.2285391539,-0.3373923302,0.1966067106,-0.0812611207,0.2187606841,0.2738299966,-0.1987095326,0.0751456246,-0.0344927274,0.2293820381,-0.0945535004,0.0515003391,-0.3328437805,0.3991541564,0.0483285598,-0.0972458422,-0.1221484467,0.1052148938,-0.0369532965,-0.2576110065,-0.0345442742,0.2256599665,-0.5387450457,-0.0272687823,0.0190763511,0.0055163186,0.2636825144,0.3198458254,0.2951920927,-0.2196024954,-0.0240470916,-0.1002113745,0.2141585946,0.3907952011,0.1931944937,0.2982254028,0.0379006118,0.0519817732,-0.0990941077,0.1056296602,-0.2550399899,-0.2089608759,0.2388080806,0.1443412602,0.4825346768,-0.2045682222,-0.2926563323,0.33776173,-0.4190512002,-0.14759776,-0.0480976924,0.4000511169,-0.2404103428,0.0922677219,-0.1325743198,0.2541822791,-0.2532403469,-0.2583611012,-0.2444072962,-0.1555507779,-0.3043295741,-0.2328317314,0.2940024734,0.0891265571,0.2044060081,0.1051169336,0.0744469836,0.0001031693,0.1703083217,-0.3443287909,0.1115709841,0.3897327185,-0.1513686329,0.2131369114,0.2146556675,-0.383913815,0.02497885,0.5199177265,0.0757908151,-0.0811048597,0.0184673406,0.2310703397,-0.2154954672,-0.0448467247,0.0854428932,0.3162041008,0.0786264464,-0.1287725717,0.2978664339,-0.3184589744,0.0218626391,-0.3275707662,0.5358551145,0.5741321445,-0.4068905711,0.1616363525,-0.0375148766,-0.054950811,-0.1443333924,-0.1478255391,0.0347904004,0.1809509993,-0.2410242856,0.0704634935,0.1228723824,-0.3233373165,-0.3895311654,-0.0886804685,0.189490363,0.3247493207,0.0354473218,-0.1182397828,-0.0009166035,0.1604258269,0.1369958073,0.1970087141,-0.0356904455,-0.1906371862,-0.382917881,0.0364628918,0.4099152982,0.3123600483,0.2516899705,-0.0237091016,-0.0720952228,-0.43276003,0.2436808944,0.2088194191,-0.1370542198,0.1104608774,0.2225861549,-0.0562018119,0.1033200324,-0.122828342,-0.4905353189,0.3253698051,0.1002145559,0.2054318339,0.0529075786,-0.0096935313,-0.4930993319,-0.0230460372,-0.3354252577,-0.5171920061,0.1825162619,0.251416862,-0.0514378697,0.3741511106,-0.2586688697,0.2646823525,0.0258375742,0.0281636659,-0.1718530804,0.2696374357,-0.0179110821,-0.0689149946,-0.1050148308,0.2524372339,0.0164250154,0.0803575516,-0.0995145738,0.1832994074,0.0995594263,-0.253220886,-0.0125661502,0.4138720632,0.107801415,-0.2240742296,-0.101709418,0.2977459133,0.3071589768,-0.1076306626,-0.1429254711,0.2557155192,-0.0801076889,0.3540757895,-0.0574544966,0.1349477023,0.2534440458,-0.2289259285,-0.1601988971,0.0503910258,0.1779779941,0.0509596504,0.4108891189,0.2816082239,-0.3163895905,0.1226554215,0.2962827384,-0.1855596006,0.0877043977,0.0665116683,-0.1073962599,0.1110722125,0.2065813094,0.3290757835,0.6542757154,0.2463446856,0.0147651481,0.0547763333,-0.0812356845,-0.2099500895,0.1697426289,-0.1730263829,0.096653007,0.4493786395,0.1986962557,0.1220147237,-0.1478346735,0.0132789714,-0.0728756264,0.2129718065,-0.4133994281,0.0900946483,-0.2010109127,0.1117902175,0.0624766946,-0.2461852133,-0.2787481248,-0.2188784778,0.2188399732,-0.440474391,-0.0339393541,0.2548559308,-0.2764028907,0.2695633173,-0.1428872794,0.0761199817,-0.2370153219,-0.1646420807,-0.4196374416,0.1350830942,0.018620966,-0.1070711389,0.2462532967,-0.2813128531,-0.2374907285,0.1074723601,-0.1896662116,0.1197578311,-0.1743261963,0.6320790052,0.0549536794,0.0948213637,-0.2963761389,-0.0584234372,0.3697791398,-0.0066816909,-0.0874721557,0.1727496982,0.2285975516,0.0186050907,-0.0421031266,-0.7604624033,-0.3359158635,-0.2818158567,-0.0193221159,0.0256294515,0.1912407279,0.3360476792,0.247378543,0.1308222115,-0.2239892334,0.0551474728,-0.1709394157,-0.0125009604,0.3971096873,-0.1424566507,-0.4635104537,-0.0660351589,-0.2137093693,0.2111829817,-0.0478711091,-0.4919604957,-0.0667051822,-0.2664311826,0.2749800086,-0.1996053755,0.0907574371,0.2340377569,-0.1649851054,-0.0545590706,-0.2723251283,-0.1440219879,0.3495979905,-0.09067256,0.1211499199,-0.1907510161,0.2533167601,-0.0005464607,0.4581643641,0.0876599401,0.1873094589,0.2734171748,-0.1752267182,0.4184911251,-0.232049495,-0.4264899194,0.0626349822,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2446","title":"`yelp_polarity` is broken","comments":"```\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/script_runner.py\", line 332, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 233, in <module>\r\n    configs = get_confs(option)\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 604, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 588, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 148, in get_confs\r\n    builder_cls = nlp.load.import_main_class(module_path[0], dataset=True)\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 85, in import_main_class\r\n    module = importlib.import_module(module_path)\r\nFile \"\/usr\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\nFile \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\r\nFile \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\r\nFile \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\nFile \"\/home\/sasha\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/yelp_polarity\/a770787b2526bdcbfc29ac2d9beb8e820fbc15a03afd3ebc4fb9d8529de57544\/yelp_polarity.py\", line 36, in <module>\r\n    from datasets.tasks import TextClassification\r\n```","body":"![image](https:\/\/user-images.githubusercontent.com\/22514219\/120828150-c4a35b00-c58e-11eb-8083-a537cee4dbb3.png)\r\n","comment_length":118,"text":"`yelp_polarity` is broken \n ![image](https:\/\/user-images.githubusercontent.com\/22514219\/120828150-c4a35b00-c58e-11eb-8083-a537cee4dbb3.png)\r\n \n ```\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/script_runner.py\", line 332, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 233, in <module>\r\n    configs = get_confs(option)\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 604, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 588, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 148, in get_confs\r\n    builder_cls = nlp.load.import_main_class(module_path[0], dataset=True)\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 85, in import_main_class\r\n    module = importlib.import_module(module_path)\r\nFile \"\/usr\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\nFile \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\r\nFile \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\r\nFile \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\nFile \"\/home\/sasha\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/yelp_polarity\/a770787b2526bdcbfc29ac2d9beb8e820fbc15a03afd3ebc4fb9d8529de57544\/yelp_polarity.py\", line 36, in <module>\r\n    from datasets.tasks import TextClassification\r\n```","embeddings":[-0.1024581045,-0.4164357781,-0.1597157121,0.1106378362,0.2700803578,-0.1342438161,0.0620417446,0.324896127,0.115024209,0.0208610073,-0.0733032897,0.0981361493,-0.0775282383,0.088160865,-0.0272907391,-0.0547005758,-0.0670888275,0.5007214546,-0.279864639,-0.1024006978,-0.187374115,0.3047941029,0.0469926558,0.1529836655,-0.0215161424,-0.0998178199,0.075682044,0.2807609439,-0.2198141068,-0.3285527229,0.0454352498,0.0923940316,-0.2174301147,0.1122374758,-0.0001101976,-0.0683468431,0.3514304161,-0.0494548529,0.0267474912,-0.3109245896,0.0556759872,-0.1162542105,0.0716132447,-0.175342679,-0.130285576,-0.2852269709,0.0069500348,-0.0522034727,0.5125470757,0.217505917,0.2951674759,0.0969505981,0.07746052,0.1119322181,0.0685760379,0.2308505774,-0.0519353822,0.0755407736,-0.1282691211,-0.4185512364,-0.0851896256,0.3796780705,0.0417252518,0.0093526682,0.1816565841,-0.2398160696,0.64311409,0.0964671522,0.0561751276,0.0915924609,-0.2606853843,0.0230086315,0.117477037,0.2456567436,-0.1648456007,-0.4188649952,0.0975314304,0.3891115189,-0.2444213927,-0.1872090697,-0.0527212434,-0.096131444,-0.2303685993,0.2770906687,-0.0000369342,0.7835253477,-0.001544368,0.1822980791,0.1811497062,0.0369461775,0.195466727,0.3975942433,-0.1270466596,0.0670382679,-0.0557206124,0.0359687619,0.2267304361,0.1506973207,0.0570467897,0.2722670436,0.3639073372,0.0971900225,0.2167173773,0.1387993097,0.0001440624,0.1953643113,0.0682745352,0.4745216668,0.2713787854,0.5279783607,0.1639027745,0.1432444304,0.1657558382,-0.4216600358,-0.0301527083,0.1145276353,0.0603313223,0.1303584427,-0.2250370532,0.0096477158,-0.0831861794,0.2637470663,0.202659294,0.2387269735,0.0568376854,0.2667765915,0.2185367197,0.0769397616,-0.0639017597,0.0330666006,-0.1990626603,0.3460360169,-0.3091924787,-0.0692949891,0.078894414,0.3844735622,0.5319616199,-0.1714345962,-0.2256480902,-0.0870869234,-0.02714861,-0.0683751851,0.1519236267,0.250631094,-0.0791389421,-0.0542153344,0.2363597304,0.0717259124,-0.0663518533,-0.1300756931,-0.0314719155,0.0940547511,-0.1985687315,0.3028070033,0.0391189456,0.0301341806,0.0850731954,0.0400215872,0.2629771531,-0.2812221646,0.1904451996,-0.2715656161,-0.2210510522,-0.1143209264,0.074341163,0.3664713204,-0.0416049883,-0.4093360305,-0.293621242,-0.1419834346,0.223440975,0.1826951951,0.0799330994,-0.0352958068,-0.2192550302,0.0191173293,0.3005329072,-0.3774944544,-0.1865869164,-0.1834850907,-0.1630088538,-0.0756063834,-0.0483941808,0.1819626689,-0.0024486179,0.1328393668,0.155842334,0.650113523,0.1210432053,0.1192088872,-0.4229591787,-0.0155736674,0.2117554098,-0.052793894,0.2383706421,-0.0355702266,0.0632933974,-0.1881220937,0.1184785962,-0.0154105201,0.2185810059,0.2496159971,0.5116145015,0.0674153715,0.086702086,0.0805798769,0.0377302803,-0.0798633546,-0.9023961425,0.1307978183,-0.3075226247,0.1495981961,-0.2495387644,-0.1359956414,-0.4057085216,-0.4762876928,0.2574538291,0.1651846766,0.1010157689,0.0820708126,0.1044291332,0.3060425222,-0.3019443452,0.0823413953,0.0181695465,0.2572535276,-0.2900827527,-0.0522765554,0.0459960885,0.2013465911,0.3136645854,-0.1686371714,-0.1792932898,0.3776599765,-0.5558848977,0.4288749993,-0.1506473124,-0.3552914262,0.22080037,-0.5869683027,-0.0280094035,0.1380987912,-0.0364759825,-0.0319529921,0.1685712636,0.2660727799,-0.1766497344,0.1643361598,0.1421674043,0.2842082977,0.1625176817,-0.0230881367,-0.1731597185,-0.3042883575,0.4437310696,-0.0175074041,0.1051840931,-0.3311535716,0.017173795,0.0191193298,0.4013838172,0.3117001057,0.1925226599,-0.0715919659,0.048675023,-0.1927369386,0.1532043964,0.2270621359,0.3311017752,0.1304068714,-0.2781845629,0.0966533795,-0.1665710211,-0.137836948,0.2107174098,0.0464989357,0.3183383942,0.3407025933,0.3252840936,-0.0894416347,-0.3340715766,-0.1943020225,-0.2050064951,0.228572458,0.0228807777,0.0516976751,-0.2642994523,-0.0561268441,0.0060938667,0.1965384483,-0.0209378339,-0.2515963614,0.1971224248,-0.0967067629,-0.2391358465,0.4282850623,0.1994766295,0.1254674941,0.2627290189,-0.1705452055,-0.3780843914,-0.2816615403,-0.0331926383,0.0982737243,-0.0743730143,-0.0480498262,0.4588577449,-0.1241153181,-0.0208083335,-0.2733244598,-0.466245681,0.0604558438,-0.1246377155,-0.0323550403,0.1386374533,0.2650431991,0.0291096382,0.0644889772,0.4058018327,-0.5469167829,-0.129997313,0.0779849812,-0.0843900666,0.0141160209,-0.3546925485,0.0162592232,-0.151968345,-0.4868755639,0.1228972673,0.4273002148,0.1107620597,0.1560126692,0.1129691377,-0.118295446,-0.1105833575,0.2593542337,-0.1041664407,0.3105356097,0.2750077248,-0.3885119557,-0.2793481052,-0.2813337445,-0.041482117,0.5379199982,0.0729843602,-0.2579219043,-0.0823838785,0.0530899949,-0.1429593712,-0.2245258987,-0.1798430383,0.295620054,0.0567408986,-0.1748363674,0.0133334519,0.1366025358,-0.0788140669,0.0510627814,0.2558814585,-0.0252816975,0.0437959507,0.0180152804,0.3831216991,-0.0396491699,-0.059311904,0.2597792149,0.0844619572,0.3718746603,0.0490069576,-0.3023099899,-0.0909235179,0.2394493073,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2444","title":"Sentence Boundaries missing in Dataset: xtreme \/ udpos","comments":"Hi,\r\n\r\nThis is a known issue. More info on this issue can be found in #2061. If you are looking for an open-source contribution, there are step-by-step instructions in the linked issue that you can follow to fix it.","body":"I was browsing through annotation guidelines, as suggested by the datasets introduction.\r\n\r\nThe guidlines saids \"There must be exactly one blank line after every sentence, including the last sentence in the file. Empty sentences are not allowed.\" in the [Sentence Boundaries and Comments section](https:\/\/universaldependencies.org\/format.html#sentence-boundaries-and-comments)\r\n\r\nBut the sentence boundaries seems not to be represented by huggingface datasets features well. I found out that multiple sentence are concatenated together as a 1D array, without any delimiter.\r\n\r\nPAN-x, which is another token classification subset from xtreme do represent the sentence boundary using a 2D array.\r\n\r\nYou may compare in PAN-x.en and udpos.English in the explorer:\r\n https:\/\/huggingface.co\/datasets\/viewer\/?dataset=xtreme","comment_length":39,"text":"Sentence Boundaries missing in Dataset: xtreme \/ udpos \n I was browsing through annotation guidelines, as suggested by the datasets introduction.\r\n\r\nThe guidlines saids \"There must be exactly one blank line after every sentence, including the last sentence in the file. Empty sentences are not allowed.\" in the [Sentence Boundaries and Comments section](https:\/\/universaldependencies.org\/format.html#sentence-boundaries-and-comments)\r\n\r\nBut the sentence boundaries seems not to be represented by huggingface datasets features well. I found out that multiple sentence are concatenated together as a 1D array, without any delimiter.\r\n\r\nPAN-x, which is another token classification subset from xtreme do represent the sentence boundary using a 2D array.\r\n\r\nYou may compare in PAN-x.en and udpos.English in the explorer:\r\n https:\/\/huggingface.co\/datasets\/viewer\/?dataset=xtreme \n Hi,\r\n\r\nThis is a known issue. More info on this issue can be found in #2061. If you are looking for an open-source contribution, there are step-by-step instructions in the linked issue that you can follow to fix 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2443","title":"Some tests hang on Windows","comments":"Hi ! That would be nice indeed to at least have a warning, since we don't handle the max path length limit.\r\nAlso if we could have an error instead of an infinite loop I'm sure windows users will appreciate that","body":"Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220.  This can be very tricky to debug, so I think now is a good time to address these issues\/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.\r\n","comment_length":41,"text":"Some tests hang on Windows \n Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220.  This can be very tricky to debug, so I think now is a good time to address these issues\/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.\r\n \n Hi ! That would be nice indeed to at least have a warning, since we don't handle the max path length limit.\r\nAlso if we could have an error instead of an infinite loop I'm sure windows users will appreciate 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2443","title":"Some tests hang on Windows","comments":"Unfortunately, I know this problem very well... \ud83d\ude05 \r\n\r\nI remember having proposed to throw an error instead of hanging in an infinite loop #2220: 60c7d1b6b71469599a27147a08100f594e7a3f84, 8c8ab60018b00463edf1eca500e434ff061546fc \r\nbut @lhoestq told me:\r\n> Note that the filelock module comes from this project that hasn't changed in years - while still being used by ten of thousands of projects:\r\nhttps:\/\/github.com\/benediktschmitt\/py-filelock\r\n> \r\n> Unless we have proper tests for this, I wouldn't recommend to change it\r\n\r\nI opened an Issue requesting a warning\/error at startup for that case: #2224","body":"Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220.  This can be very tricky to debug, so I think now is a good time to address these issues\/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.\r\n","comment_length":85,"text":"Some tests hang on Windows \n Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220.  This can be very tricky to debug, so I think now is a good time to address these issues\/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.\r\n \n Unfortunately, I know this problem very well... \ud83d\ude05 \r\n\r\nI remember having proposed to throw an error instead of hanging in an infinite loop #2220: 60c7d1b6b71469599a27147a08100f594e7a3f84, 8c8ab60018b00463edf1eca500e434ff061546fc \r\nbut @lhoestq told me:\r\n> Note that the filelock module comes from this project that hasn't changed in years - while still being used by ten of thousands of projects:\r\nhttps:\/\/github.com\/benediktschmitt\/py-filelock\r\n> \r\n> Unless we have proper tests for this, I wouldn't recommend to change it\r\n\r\nI opened an Issue requesting a warning\/error at startup for that case: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2443","title":"Some tests hang on Windows","comments":"@albertvillanova Thanks for additional info on this issue.\r\n\r\nYes, I think the best option is to throw an error instead of suppressing it in a loop. I've considered 2 more options, but I don't really like them:\r\n1. create a temporary file with a filename longer than 255 characters on import; if this fails, long paths are not enabled and raise a warning. I'm not sure about this approach because I don't like the idea of creating a temporary file on import for this purpose.\r\n2. check if long paths are enabled with [this code](https:\/\/stackoverflow.com\/a\/46546731\/14095927). As mentioned in the comment, this code relies on an undocumented function and Win10-specific.","body":"Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220.  This can be very tricky to debug, so I think now is a good time to address these issues\/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.\r\n","comment_length":109,"text":"Some tests hang on Windows \n Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220.  This can be very tricky to debug, so I think now is a good time to address these issues\/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.\r\n \n @albertvillanova Thanks for additional info on this issue.\r\n\r\nYes, I think the best option is to throw an error instead of suppressing it in a loop. I've considered 2 more options, but I don't really like them:\r\n1. create a temporary file with a filename longer than 255 characters on import; if this fails, long paths are not enabled and raise a warning. I'm not sure about this approach because I don't like the idea of creating a temporary file on import for this purpose.\r\n2. check if long paths are enabled with [this code](https:\/\/stackoverflow.com\/a\/46546731\/14095927). As mentioned in the comment, this code relies on an undocumented function and 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2441","title":"DuplicatedKeysError on personal dataset","comments":"Hi ! In your dataset script you must be yielding examples like\r\n```python\r\nfor line in file:\r\n    ...\r\n    yield key, {...}\r\n```\r\n\r\nSince `datasets` 1.7.0 we enforce the keys to be unique.\r\nHowever it looks like your examples generator creates duplicate keys: at least two examples have key 0.\r\n\r\nYou can fix that by making sure that your keys are unique.\r\n\r\nFor example if you use a counter to define the key of each example, make sure that your counter is not reset to 0 in during examples generation (between two open files for examples).\r\n\r\nLet me know if you have other questions :)","body":"## Describe the bug\r\nEver since today, I have been getting a DuplicatedKeysError while trying to load my dataset from my own script.\r\nError returned when running this line: `dataset = load_dataset('\/content\/drive\/MyDrive\/Thesis\/Datasets\/book_preprocessing\/goodreads_maharjan_trimmed_and_nered\/goodreadsnered.py')`\r\nNote that my script was working fine with earlier versions of the Datasets library. Cannot say with 100% certainty if I have been doing something wrong with my dataset script this whole time or if this is simply a bug with the new version of datasets.\r\n\r\n## Steps to reproduce the bug\r\nI cannot provide code to reproduce the error as I am working with my own dataset. I can however provide my script if requested.\r\n\r\n## Expected results\r\nFor my data to be loaded.\r\n\r\n## Actual results\r\n**DuplicatedKeysError** exception is raised\r\n```\r\nDownloading and preparing dataset good_reads_practice_dataset\/main_domain (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/good_reads_practice_dataset\/main_domain\/1.1.0\/64ff7c3fee2693afdddea75002eb6887d4fedc3d812ae3622128c8504ab21655...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nDuplicatedKeysError                       Traceback (most recent call last)\r\n\r\n<ipython-input-6-c342ea0dae9d> in <module>()\r\n----> 1 dataset = load_dataset('\/content\/drive\/MyDrive\/Thesis\/Datasets\/book_preprocessing\/goodreads_maharjan_trimmed_and_nered\/goodreadsnered.py')\r\n\r\n5 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs)\r\n    749         try_from_hf_gcs=try_from_hf_gcs,\r\n    750         base_path=base_path,\r\n--> 751         use_auth_token=use_auth_token,\r\n    752     )\r\n    753 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    573                     if not downloaded_from_gcs:\r\n    574                         self._download_and_prepare(\r\n--> 575                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    576                         )\r\n    577                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    650             try:\r\n    651                 # Prepare split will record examples associated to the split\r\n--> 652                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    653             except OSError as e:\r\n    654                 raise OSError(\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n    990                     writer.write(example, key)\r\n    991             finally:\r\n--> 992                 num_examples, num_bytes = writer.finalize()\r\n    993 \r\n    994         split_generator.split_info.num_examples = num_examples\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in finalize(self, close_stream)\r\n    407         # In case current_examples < writer_batch_size, but user uses finalize()\r\n    408         if self._check_duplicates:\r\n--> 409             self.check_duplicate_keys()\r\n    410             # Re-intializing to empty list for next batch\r\n    411             self.hkey_record = []\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in check_duplicate_keys(self)\r\n    347         for hash, key in self.hkey_record:\r\n    348             if hash in tmp_record:\r\n--> 349                 raise DuplicatedKeysError(key)\r\n    350             else:\r\n    351                 tmp_record.add(hash)\r\n\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 0\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.7.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.9\r\n- PyArrow version: 3.0.0\r\n","comment_length":104,"text":"DuplicatedKeysError on personal dataset \n ## Describe the bug\r\nEver since today, I have been getting a DuplicatedKeysError while trying to load my dataset from my own script.\r\nError returned when running this line: `dataset = load_dataset('\/content\/drive\/MyDrive\/Thesis\/Datasets\/book_preprocessing\/goodreads_maharjan_trimmed_and_nered\/goodreadsnered.py')`\r\nNote that my script was working fine with earlier versions of the Datasets library. Cannot say with 100% certainty if I have been doing something wrong with my dataset script this whole time or if this is simply a bug with the new version of datasets.\r\n\r\n## Steps to reproduce the bug\r\nI cannot provide code to reproduce the error as I am working with my own dataset. I can however provide my script if requested.\r\n\r\n## Expected results\r\nFor my data to be loaded.\r\n\r\n## Actual results\r\n**DuplicatedKeysError** exception is raised\r\n```\r\nDownloading and preparing dataset good_reads_practice_dataset\/main_domain (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/good_reads_practice_dataset\/main_domain\/1.1.0\/64ff7c3fee2693afdddea75002eb6887d4fedc3d812ae3622128c8504ab21655...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nDuplicatedKeysError                       Traceback (most recent call last)\r\n\r\n<ipython-input-6-c342ea0dae9d> in <module>()\r\n----> 1 dataset = load_dataset('\/content\/drive\/MyDrive\/Thesis\/Datasets\/book_preprocessing\/goodreads_maharjan_trimmed_and_nered\/goodreadsnered.py')\r\n\r\n5 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs)\r\n    749         try_from_hf_gcs=try_from_hf_gcs,\r\n    750         base_path=base_path,\r\n--> 751         use_auth_token=use_auth_token,\r\n    752     )\r\n    753 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    573                     if not downloaded_from_gcs:\r\n    574                         self._download_and_prepare(\r\n--> 575                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    576                         )\r\n    577                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    650             try:\r\n    651                 # Prepare split will record examples associated to the split\r\n--> 652                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    653             except OSError as e:\r\n    654                 raise OSError(\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n    990                     writer.write(example, key)\r\n    991             finally:\r\n--> 992                 num_examples, num_bytes = writer.finalize()\r\n    993 \r\n    994         split_generator.split_info.num_examples = num_examples\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in finalize(self, close_stream)\r\n    407         # In case current_examples < writer_batch_size, but user uses finalize()\r\n    408         if self._check_duplicates:\r\n--> 409             self.check_duplicate_keys()\r\n    410             # Re-intializing to empty list for next batch\r\n    411             self.hkey_record = []\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in check_duplicate_keys(self)\r\n    347         for hash, key in self.hkey_record:\r\n    348             if hash in tmp_record:\r\n--> 349                 raise DuplicatedKeysError(key)\r\n    350             else:\r\n    351                 tmp_record.add(hash)\r\n\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 0\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.7.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.7.9\r\n- PyArrow version: 3.0.0\r\n \n Hi ! In your dataset script you must be yielding examples like\r\n```python\r\nfor line in file:\r\n    ...\r\n    yield key, {...}\r\n```\r\n\r\nSince `datasets` 1.7.0 we enforce the keys to be unique.\r\nHowever it looks like your examples generator creates duplicate keys: at least two examples have key 0.\r\n\r\nYou can fix that by making sure that your keys are unique.\r\n\r\nFor example if you use a counter to define the key of each example, make sure that your counter is not reset to 0 in during examples generation (between two open files for examples).\r\n\r\nLet me know if you have other questions :)","embeddings":[-0.1864345074,-0.0565234087,0.0734844357,0.2193849683,0.1425481141,0.0292863455,0.5267676711,0.2207098007,0.0588233471,0.0872558504,-0.1790747494,0.2924934328,-0.1353885084,-0.1258903295,0.3225165606,0.1644579619,0.0292240959,-0.0048958994,-0.1378866285,0.0562093109,-0.1669512391,0.2724630833,-0.0154245673,0.4006582499,-0.1122406051,0.0180170108,-0.1549707204,0.3373055458,0.1574178636,-0.5524030328,0.3723440766,0.115193449,-0.0585309304,0.6388737559,-0.000114377,0.089326188,0.3678568304,0.0726804435,-0.3368042707,-0.2682757378,0.0349326618,-0.0251707342,0.1887518764,-0.3304275572,0.0402530059,-0.229095608,-0.0011710926,-0.3105666935,0.3904740512,0.2292785496,0.2183040529,0.0367066115,-0.0755213723,-0.1794561446,0.1490657926,0.3316625059,0.1221522838,0.2524907887,0.2090585381,-0.1218607128,0.093560718,0.0922568589,-0.2326767594,0.2715856433,0.4658198953,0.1381968111,-0.2277673036,-0.0389199071,0.2810682356,0.4387474358,0.4561787844,-0.5366321206,-0.2995787263,-0.4711150825,0.1770800203,-0.2411703467,0.1227191463,-0.0223870464,-0.1665583104,0.2151415795,-0.1649234295,-0.0753314644,-0.1069493145,0.0315973312,0.0011806944,-0.1223172769,-0.049361892,0.271197468,0.1206141412,-0.1859199107,-0.0420606621,-0.0807006657,-0.062629737,0.0479095578,-0.1217092127,-0.0121265231,0.0719313547,-0.0356171615,0.4212903976,0.1260746419,0.1623898894,0.0511264056,0.0721501485,0.0158360321,0.4798832834,0.0257830061,0.0838383511,0.253839016,0.2858423293,0.1228547394,-0.1708331406,-0.0057329764,-0.037885841,0.0616610274,0.5174378753,-0.0436327457,0.700039506,-0.2715612054,-0.2010089159,0.0989135578,0.0076636784,-0.1383434385,-0.0781496763,0.1666328758,0.0921997353,0.0152029637,-0.180369392,0.0483593643,-0.2964209914,-0.1243328527,-0.1058553755,0.090281494,0.0710753873,-0.030139178,0.32089746,-0.4846819341,0.1354746073,0.0996275842,0.022484228,-0.3461916447,0.1496575475,0.0179346725,-0.1550012827,0.0956206843,0.1365298182,0.372443825,0.0551547557,-0.2492426038,-0.1250035167,0.1580288708,-0.3686243892,-0.4084016979,-0.1084923893,0.223093614,-0.2670642436,-0.1035706699,-0.1620784849,0.055188071,0.3332821131,-0.1818750203,-0.0662663952,-0.2544150054,-0.4359380305,-0.2930590808,-0.0488225035,0.627838254,-0.3001243174,-0.0828407332,-0.5352852345,-0.1706262529,-0.2267044485,0.268750906,-0.3589932323,0.5027720928,-0.3484795094,0.2010123581,0.0668412521,-0.3456726968,-0.6580873132,0.5311324,-0.2746317685,0.327347517,0.0416119695,0.1276919842,0.4651711285,-0.1233073696,0.1120899841,-0.1775349528,0.0862104595,-0.1243482232,-0.0574253239,-0.1677972376,0.050897643,0.0288599618,0.0928659365,-0.2194238007,0.2983483076,0.0872548148,0.2812464535,-0.2383116633,-0.0349764228,0.1662206501,0.1789264679,0.0728690475,0.0283241384,-0.1941446811,-0.4341951609,0.3175955117,0.0040579163,-0.3278971016,-0.2090773284,-0.1817663461,-0.4485370219,0.0098392963,-0.5775942206,0.0009188339,0.0714324936,0.2051878422,-0.0812789798,0.0258612875,-0.1853131205,0.4073454738,0.1822107881,0.0808900222,-0.2352645993,0.0039496641,-0.0226734448,0.0041180314,0.0736214295,0.0747316927,0.3385287821,-0.0815312937,-0.1256201267,0.4060436487,0.064183481,0.1542711258,-0.187399596,-0.1152945533,0.1179354936,-0.154614687,0.3159890771,0.1982626766,0.2679871917,-0.1654774994,-0.3924897015,0.3851958513,0.0091077089,-0.1224951595,-0.1656302661,-0.0794710889,0.0632036924,-0.2009999901,-0.0268606432,-0.2052624524,0.2908895612,-0.2018519938,0.0799277723,0.1402326524,-0.1887188554,0.1398288161,0.1679102331,-0.1818193793,0.2790426016,0.1638421267,-0.0441890433,0.0209840108,-0.0386851579,0.2999103069,0.5103562474,0.153732866,-0.0037189524,0.2641640007,-0.1228415072,-0.0646803752,0.0696571469,-0.0417983532,0.0226826686,0.2188365459,0.0694565699,0.0686904863,-0.2538977861,0.0455449224,0.0083703725,0.3561425507,-0.4474920034,0.1444788277,-0.5289639831,-0.0410224162,-0.2909207344,0.1154117361,0.068720296,-0.1183312982,-0.0059037218,0.3284442425,0.0763666332,-0.0614555366,-0.2195770591,0.0737695545,-0.1509956568,-0.0838600397,0.0374967493,-0.1066306606,-0.457036078,0.0393652692,0.4354344904,-0.0755674616,0.4688960612,0.1013907269,-0.0867710486,-0.5481936932,-0.1828355342,-0.1221205518,-0.0135880848,0.1786353141,0.2338028997,0.0347757079,0.1136032194,-0.0659089908,0.0344041139,-0.1430894136,-0.2460508496,0.1748118997,-0.1032154411,-0.0170347877,-0.0606005527,-0.4004840553,-0.2146552801,0.0589309596,0.2180157304,-0.0278810523,-0.0025197237,0.1291808933,-0.0794784054,-0.0344787128,0.0746449903,-0.1346056461,-0.3363359571,-0.1720222831,0.2224801928,-0.14923127,-0.345107913,-0.0564711317,-0.0509004332,0.0318320617,-0.1824121773,-0.7195866704,-0.0192839764,-0.1149610728,0.2436561137,-0.2316603661,-0.1231254414,0.1163996905,0.0450868905,0.0219756514,-0.1702771932,-0.4202033281,-0.1165173352,-0.1416843086,0.0535265766,0.1716160625,0.5161118507,0.0215465147,0.3910152316,0.4786762893,-0.0518728308,0.1198170781,-0.0967197791,0.391258955,-0.1630542278,-0.5665510893,-0.0886632353,-0.1595169753,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2440","title":"Remove `extended` field from dataset tagger","comments":"The tagger also doesn't insert the value for the `size_categories` field automatically, so this should be fixed too","body":"## Describe the bug\r\nWhile working on #2435 I used the [dataset tagger](https:\/\/huggingface.co\/datasets\/tagging\/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included:\r\n\r\n```\r\ndataset_name = 'arcd'\r\n\r\n    @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\n    def test_changed_dataset_card(dataset_name):\r\n        card_path = repo_path \/ \"datasets\" \/ dataset_name \/ \"README.md\"\r\n        assert card_path.exists()\r\n        error_messages = []\r\n        try:\r\n            ReadMe.from_readme(card_path)\r\n        except Exception as readme_error:\r\n            error_messages.append(f\"The following issues have been found in the dataset cards:\\nREADME:\\n{readme_error}\")\r\n        try:\r\n            DatasetMetadata.from_readme(card_path)\r\n        except Exception as metadata_error:\r\n            error_messages.append(\r\n                f\"The following issues have been found in the dataset cards:\\nYAML tags:\\n{metadata_error}\"\r\n            )\r\n    \r\n        if error_messages:\r\n>           raise ValueError(\"\\n\".join(error_messages))\r\nE           ValueError: The following issues have been found in the dataset cards:\r\nE           YAML tags:\r\nE           __init__() got an unexpected keyword argument 'extended'\r\n\r\ntests\/test_dataset_cards.py:70: ValueError\r\n```\r\n\r\nConsider either removing this tag from the tagger or including it as part of the validation step in the CI.\r\n\r\ncc @yjernite ","comment_length":18,"text":"Remove `extended` field from dataset tagger \n ## Describe the bug\r\nWhile working on #2435 I used the [dataset tagger](https:\/\/huggingface.co\/datasets\/tagging\/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included:\r\n\r\n```\r\ndataset_name = 'arcd'\r\n\r\n    @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\n    def test_changed_dataset_card(dataset_name):\r\n        card_path = repo_path \/ \"datasets\" \/ dataset_name \/ \"README.md\"\r\n        assert card_path.exists()\r\n        error_messages = []\r\n        try:\r\n            ReadMe.from_readme(card_path)\r\n        except Exception as readme_error:\r\n            error_messages.append(f\"The following issues have been found in the dataset cards:\\nREADME:\\n{readme_error}\")\r\n        try:\r\n            DatasetMetadata.from_readme(card_path)\r\n        except Exception as metadata_error:\r\n            error_messages.append(\r\n                f\"The following issues have been found in the dataset cards:\\nYAML tags:\\n{metadata_error}\"\r\n            )\r\n    \r\n        if error_messages:\r\n>           raise ValueError(\"\\n\".join(error_messages))\r\nE           ValueError: The following issues have been found in the dataset cards:\r\nE           YAML tags:\r\nE           __init__() got an unexpected keyword argument 'extended'\r\n\r\ntests\/test_dataset_cards.py:70: ValueError\r\n```\r\n\r\nConsider either removing this tag from the tagger or including it as part of the validation step in the CI.\r\n\r\ncc @yjernite  \n The tagger also doesn't insert the value for the `size_categories` field automatically, so this should be fixed too","embeddings":[-0.0470579714,-0.0278825946,-0.0063311323,0.2230904996,0.3980685771,0.3846163154,0.3813194633,0.203803122,0.133192867,0.3070672452,0.3498389125,0.3464969993,-0.3376166523,0.0101224994,-0.1240958795,0.0616643392,0.1870365888,-0.0848196596,0.2828492224,0.1796520501,-0.2078076452,-0.1280446649,-0.015804451,-0.2396082878,-0.2903832495,0.0013581972,-0.1328539848,-0.0206578374,-0.2535718977,-0.6862009168,0.4132631421,0.0345202722,-0.0556321964,0.1361313462,-0.0001189538,-0.0852048844,0.1941103041,-0.0051044491,-0.1610985994,-0.294326067,0.0116299735,-0.0132540064,0.0771376863,-0.1406927407,0.2041279078,-0.2700171769,-0.2972790003,-0.2101826072,-0.0064122593,0.3029443622,0.1134225354,-0.1724954247,-0.2265181392,-0.2223748863,0.3100630343,0.1275651306,-0.1088298261,0.132264182,0.4971141517,0.3124465644,-0.0372097306,0.4784123003,-0.019173285,-0.0323127694,0.3561680019,-0.1500074863,0.3728245795,-0.3121429384,0.2381992489,-0.1095877811,0.3060843945,-0.4363087118,-0.1398069859,-0.6247736812,-0.0318108983,-0.483725518,0.3642436564,-0.1988351792,-0.0016445724,-0.1294550151,-0.2243828475,-0.2147236317,-0.0542467237,0.0448021181,-0.3654717207,0.4700638652,0.1729986668,0.0621889867,0.1544353217,-0.0747496262,-0.0489725061,0.085245356,-0.2398613393,0.0857923329,-0.0082028601,-0.0928957537,-0.0311395042,-0.0814366192,0.1322938055,0.3225845695,-0.2078666985,-0.0327617377,0.2405249625,0.1316328347,0.310002923,0.03076474,0.0436907746,0.3443269432,0.0164351966,0.1438977867,-0.0169156455,-0.1115257591,0.0808673874,-0.0741375759,0.4703764319,0.1669950783,0.2705390751,-0.2235886604,-0.2313901931,0.1506878585,-0.3144266307,0.1037450284,0.1048623845,0.1004370973,0.0077369781,-0.0336846113,-0.3246629536,0.3663532734,0.1444291621,0.0521876365,-0.0436124839,0.1409448832,-0.0900342688,-0.1279349923,0.3507840335,-0.0277270097,0.2396265715,0.1880385727,0.1474274248,0.0099657355,-0.1440269798,-0.1226906851,0.2804158926,0.3793877363,-0.0323032402,0.1165248752,0.2214988768,-0.2776072323,-0.1801417917,0.1413510591,-0.2764790952,-0.5645540357,-0.1676930487,0.2017687261,-0.0390372798,-0.0194359347,-0.2403202951,-0.0538733937,0.3568716943,-0.0461056493,0.1374583244,-0.3215439618,-0.0683542639,-0.0430002883,0.0962239876,0.300490737,-0.0930942744,-0.1085047349,-0.5604650378,0.2998743951,-0.0664228797,-0.2599172592,-0.3118373752,0.1593887359,-0.3636178672,0.2771801054,0.362359345,-0.5000998378,-0.2899017036,-0.0787229538,-0.1293275058,0.4150394201,0.0940617323,-0.1839195937,0.2706879973,-0.2614488006,-0.1139896661,-0.2425147444,-0.4982540011,-0.1073555425,-0.1978641599,-0.0011880208,0.3833704889,0.064986445,0.3685127497,-0.2640117407,0.1988302022,0.2539690435,0.0429284386,-0.1136466414,0.049541939,0.189852342,0.2255969793,-0.0586890019,0.1237284616,-0.2138512284,-0.4337544143,0.1700278819,-0.1862152517,0.0550416559,-0.4036817253,-0.4727565646,-0.337982893,0.0708444566,-0.2964731157,-0.2775361538,0.0362860039,-0.0333929248,-0.0529800169,-0.0850006193,0.0008828238,-0.1241720989,-0.3564129174,0.3598924279,0.0580932833,-0.1835439503,-0.1832258999,-0.0699560642,-0.2722539604,0.158375293,0.2830740213,-0.1735773236,-0.213320002,0.2277534455,0.3332911432,-0.0209884644,-0.2749373615,0.539573729,0.3640347123,0.0174277853,-0.1698105782,0.1185639724,-0.1406784505,-0.1380033791,-0.2306803763,0.0682132393,-0.2076488435,0.330758363,-0.0102807237,-0.1152118817,0.1514413655,0.0575256161,-0.1673299521,-0.3952556849,-0.1256975383,-0.2747526765,-0.0283359587,0.0621759482,0.1700967103,0.3082009554,0.4491210282,0.4563256502,-0.1045935601,0.1277947277,-0.1929861009,-0.0935542211,0.1554531008,0.3781324625,0.2799788415,0.1411229819,0.0164398514,0.3384657502,0.1400482208,-0.1087927446,0.4774755239,0.3182540536,0.1658418924,0.4758277833,0.3180497587,0.1766327769,-0.1152629033,-0.0490471795,0.100695841,0.2709631026,-0.294903785,0.2232638896,-0.4055165648,-0.3722479343,-0.0678367019,-0.4162577689,-0.3396762908,-0.5190808177,0.3021348715,0.1177894697,-0.3278955519,0.1806784123,-0.247670874,-0.0960876867,0.0339501053,-0.1522390842,-0.121889703,0.0645907149,-0.2581368983,-0.0085340953,0.1556768566,-0.4164269865,0.4039628506,-0.1176032275,0.1496205479,-0.3149619102,-0.7408567667,0.2484392524,-0.0770198628,0.1834637523,0.4292975664,-0.3148034513,0.0215790421,-0.2592755854,0.2269996107,0.0908662155,0.1376240551,0.0316740908,0.0850266889,0.1913524568,-0.3398470879,-0.539755106,-0.2005249709,-0.1603257805,-0.1500421762,0.1650024354,0.0840091854,0.4994533658,-0.064620696,-0.2609850466,-0.2369151264,0.0092040608,-0.0938684493,-0.0743312463,0.1962911189,-0.0398308374,-0.0746402442,0.3503736556,-0.139124766,0.5066404939,0.12604177,-0.2408871949,-0.1568250954,0.3411068916,0.3540205956,-0.0566255637,-0.3169088364,0.3661273122,0.2036691457,0.0364861041,-0.0601680465,0.1416758746,0.1864183247,-0.1816585511,0.2106060982,-0.3667785823,0.0976002589,0.3018325865,0.3059273362,-0.1678301394,-0.0497624427,0.2520911098,-0.2006797791,0.0213903189,-0.1348640472,-0.6103516817,-0.07212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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2440","title":"Remove `extended` field from dataset tagger","comments":"Thanks for reporting. Indeed the `extended` tag doesn't exist. Not sure why we had that in the tagger.\r\nThe repo of the tagger is here if someone wants to give this a try: https:\/\/github.com\/huggingface\/datasets-tagging\r\nOtherwise I can probably fix it next week","body":"## Describe the bug\r\nWhile working on #2435 I used the [dataset tagger](https:\/\/huggingface.co\/datasets\/tagging\/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included:\r\n\r\n```\r\ndataset_name = 'arcd'\r\n\r\n    @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\n    def test_changed_dataset_card(dataset_name):\r\n        card_path = repo_path \/ \"datasets\" \/ dataset_name \/ \"README.md\"\r\n        assert card_path.exists()\r\n        error_messages = []\r\n        try:\r\n            ReadMe.from_readme(card_path)\r\n        except Exception as readme_error:\r\n            error_messages.append(f\"The following issues have been found in the dataset cards:\\nREADME:\\n{readme_error}\")\r\n        try:\r\n            DatasetMetadata.from_readme(card_path)\r\n        except Exception as metadata_error:\r\n            error_messages.append(\r\n                f\"The following issues have been found in the dataset cards:\\nYAML tags:\\n{metadata_error}\"\r\n            )\r\n    \r\n        if error_messages:\r\n>           raise ValueError(\"\\n\".join(error_messages))\r\nE           ValueError: The following issues have been found in the dataset cards:\r\nE           YAML tags:\r\nE           __init__() got an unexpected keyword argument 'extended'\r\n\r\ntests\/test_dataset_cards.py:70: ValueError\r\n```\r\n\r\nConsider either removing this tag from the tagger or including it as part of the validation step in the CI.\r\n\r\ncc @yjernite ","comment_length":42,"text":"Remove `extended` field from dataset tagger \n ## Describe the bug\r\nWhile working on #2435 I used the [dataset tagger](https:\/\/huggingface.co\/datasets\/tagging\/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included:\r\n\r\n```\r\ndataset_name = 'arcd'\r\n\r\n    @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\n    def test_changed_dataset_card(dataset_name):\r\n        card_path = repo_path \/ \"datasets\" \/ dataset_name \/ \"README.md\"\r\n        assert card_path.exists()\r\n        error_messages = []\r\n        try:\r\n            ReadMe.from_readme(card_path)\r\n        except Exception as readme_error:\r\n            error_messages.append(f\"The following issues have been found in the dataset cards:\\nREADME:\\n{readme_error}\")\r\n        try:\r\n            DatasetMetadata.from_readme(card_path)\r\n        except Exception as metadata_error:\r\n            error_messages.append(\r\n                f\"The following issues have been found in the dataset cards:\\nYAML tags:\\n{metadata_error}\"\r\n            )\r\n    \r\n        if error_messages:\r\n>           raise ValueError(\"\\n\".join(error_messages))\r\nE           ValueError: The following issues have been found in the dataset cards:\r\nE           YAML tags:\r\nE           __init__() got an unexpected keyword argument 'extended'\r\n\r\ntests\/test_dataset_cards.py:70: ValueError\r\n```\r\n\r\nConsider either removing this tag from the tagger or including it as part of the validation step in the CI.\r\n\r\ncc @yjernite  \n Thanks for reporting. Indeed the `extended` tag doesn't exist. Not sure why we had that in the tagger.\r\nThe repo of the tagger is here if someone wants to give this a try: https:\/\/github.com\/huggingface\/datasets-tagging\r\nOtherwise I can probably fix it next week","embeddings":[0.0003140183,-0.0987946317,-0.0033113675,0.1912126392,0.4241888523,0.4057804346,0.3773061633,0.2321246117,0.1182753444,0.2808997929,0.3130059838,0.4294629097,-0.2918011546,0.0003099469,-0.0962812677,0.0519277453,0.2192724496,-0.0698820129,0.2905523479,0.1498899311,-0.186719507,-0.1042956784,-0.0217016246,-0.2240479439,-0.2687342763,-0.0041395938,-0.1522147208,-0.0004327623,-0.1924079955,-0.6609166265,0.3868137896,0.071211189,-0.0397313572,0.1408297569,-0.0001184678,-0.1125504524,0.2438776344,-0.0025301739,-0.1776151061,-0.2817538679,-0.0076940292,0.0383164957,0.1209352612,-0.1555958986,0.1453635097,-0.1953763962,-0.3327715695,-0.2186891437,0.1178630143,0.3451340199,0.1246328354,-0.0914979726,-0.2098382413,-0.1890344769,0.3394220471,0.0909312591,-0.0966884047,0.0902480483,0.5893580914,0.3097759187,0.0236048829,0.4658851326,-0.0466955267,-0.0535481274,0.3191026449,-0.1811038852,0.3747120798,-0.242455855,0.2049732953,-0.091123715,0.3223930895,-0.4056682885,-0.1556642652,-0.5504049063,-0.007573023,-0.5235106349,0.3982792199,-0.2334059775,-0.0243907496,-0.0912935212,-0.2531798184,-0.2212180197,-0.0475980751,0.0559128895,-0.3510925472,0.4075273275,0.1735143363,0.0653679967,0.1431018114,-0.0861212239,0.020751128,0.0846145377,-0.1744771153,0.0337960012,0.0334306881,-0.1315354258,-0.1024269983,-0.1374259442,0.1256537884,0.37399894,-0.2233776599,-0.0094907843,0.2022276223,0.145347923,0.343242377,-0.0394410305,0.0646753237,0.3392271698,0.0410912186,0.1974247396,-0.0468314588,-0.1100905985,0.0627237186,-0.0449791104,0.4439435005,0.1949736178,0.2984240651,-0.2926565707,-0.1970297247,0.0969917178,-0.3704855144,0.1451703757,0.1344015449,0.1267314255,0.05404374,-0.1025555655,-0.3116002679,0.3116007745,0.1424890906,-0.0113603882,-0.0340749249,0.153189078,-0.1016091183,-0.1060060188,0.4092148542,-0.0865495801,0.2645270228,0.1723595411,0.139774099,-0.0269341618,-0.0855332464,-0.0696397275,0.319128871,0.376521796,-0.0374684446,0.1472157985,0.2145492285,-0.2550094724,-0.1550869346,0.1358287483,-0.3036946654,-0.5740443468,-0.1560348868,0.1887000501,-0.0966336057,-0.0061226613,-0.194587782,-0.1367241293,0.3593397141,-0.0559064709,0.1324734092,-0.302239567,-0.1135979816,0.0077784802,0.1533000767,0.3194821179,-0.0298087504,-0.0656731501,-0.5532761216,0.2635629177,-0.1193991527,-0.2619855702,-0.3319779634,0.1100359559,-0.361199379,0.2333570719,0.278512001,-0.5548009872,-0.2303901762,-0.0377747454,-0.1724076718,0.4368152022,0.0398087837,-0.2358095646,0.2606691122,-0.3076125681,-0.1031483337,-0.2270416617,-0.472763896,-0.1627990901,-0.2732887864,0.0489266776,0.3657885492,0.0566968694,0.2494950742,-0.2655074,0.1501193792,0.2188585997,0.0700702593,-0.1165327206,0.066332899,0.2436310649,0.2496330291,-0.0021935413,0.1035938188,-0.1965005398,-0.4023182392,0.1305842251,-0.1783620119,0.0230578389,-0.48124066,-0.45917207,-0.3062449992,0.0376051329,-0.3534753621,-0.3354027569,0.0344671011,-0.0630825013,-0.0725845546,-0.0820656568,0.023466358,-0.1426420212,-0.3872588873,0.3070746958,0.0084980102,-0.1334138066,-0.1711049527,-0.04227373,-0.2389017195,0.1958137006,0.3363584578,-0.1897206604,-0.2188038081,0.2598940432,0.3583898842,-0.0879120901,-0.1984903067,0.5692500472,0.3428311348,-0.1001196802,-0.1322922409,0.138531357,-0.1558125615,-0.1278757155,-0.286218226,0.0895515457,-0.2382875681,0.3804147542,-0.0131929172,-0.0287683997,0.2370084226,0.0562688224,-0.2296647578,-0.429304719,-0.1230942458,-0.2335540205,-0.0564448833,0.0019826295,0.1389630288,0.3440558314,0.3959482014,0.4019793868,-0.1673779041,0.1014385596,-0.1910217106,-0.1044660136,0.1607940793,0.3864328563,0.2456977218,0.128486529,-0.0229314622,0.3320823908,0.1404837966,-0.110407874,0.4423740804,0.2740832269,0.1690316647,0.4438692629,0.2920733094,0.1619816571,-0.0570952408,-0.0997478217,0.1282672137,0.2701994479,-0.304061234,0.2250166982,-0.4346056879,-0.3808312714,-0.1063380837,-0.402180016,-0.3194605112,-0.5558412075,0.2902688682,0.0936841071,-0.3065008819,0.280662179,-0.1872084737,-0.1169866472,0.006309628,-0.1769814044,-0.1444192827,0.0145446789,-0.207355693,-0.0171891246,0.1901349276,-0.3280123472,0.3918944001,-0.0801410377,0.1511206329,-0.4546266198,-0.6928055286,0.2421106696,-0.1202883944,0.2134212404,0.4348098338,-0.3150678277,0.050928276,-0.2641372681,0.2383198291,-0.0089948224,0.1518588662,0.0307016578,0.0889758766,0.1576651633,-0.2906535566,-0.5436253548,-0.1608187854,-0.113581486,-0.1207598075,0.1297326535,0.0558198169,0.4889589548,-0.0584840141,-0.2023570389,-0.3087671399,-0.0078364406,-0.1023400053,-0.1442334056,0.1379031986,-0.0920272544,-0.1064266562,0.4192366302,-0.0954574198,0.5402063727,0.1076406017,-0.2199103832,-0.1565547138,0.3927101493,0.4241115451,-0.0569099598,-0.3841508031,0.3486684561,0.2105719,0.0443726331,-0.0648899153,0.1158821732,0.2074986249,-0.2082594186,0.2325932384,-0.3750937879,0.08650098,0.2408459336,0.3512165546,-0.1425565034,-0.0145412823,0.186694175,-0.2168249637,-0.0001610692,-0.1665429771,-0.5890359282,-0.1397545785,-0.17330621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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2434","title":"Extend QuestionAnsweringExtractive template to handle nested columns","comments":"this is also the case for the following datasets and configurations:\r\n\r\n* `mlqa` with config `mlqa-translate-train.ar`\r\n\r\n","body":"Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support \"flat\" features. We should extend the functionality to cover QA datasets like:\r\n\r\n* `iapp_wiki_qa_squad`\r\n* `parsinlu_reading_comprehension`\r\n\r\nwhere the nested features differ with those from `squad` and trigger an `ArrowNotImplementedError`:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-12-50e5b8f69c20> in <module>\r\n----> 1 ds.prepare_for_task(\"question-answering-extractive\")[0]\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in prepare_for_task(self, task)\r\n   1436         # We found a template so now flush `DatasetInfo` to skip the template update in `DatasetInfo.__post_init__`\r\n   1437         dataset.info.task_templates = None\r\n-> 1438         dataset = dataset.cast(features=template.features)\r\n   1439         return dataset\r\n   1440 \r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc)\r\n    977         format = self.format\r\n    978         dataset = self.with_format(\"arrow\")\r\n--> 979         dataset = dataset.map(\r\n    980             lambda t: t.cast(schema),\r\n    981             batched=True,\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1600 \r\n   1601         if num_proc is None or num_proc == 1:\r\n-> 1602             return self._map_single(\r\n   1603                 function=function,\r\n   1604                 with_indices=with_indices,\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    176         }\r\n    177         # apply actual function\r\n--> 178         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    179         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    180         # re-apply format to the output\r\n\r\n~\/git\/datasets\/src\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1940                         )  # Something simpler?\r\n   1941                         try:\r\n-> 1942                             batch = apply_function_on_filtered_inputs(\r\n   1943                                 batch,\r\n   1944                                 indices,\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1836                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1837             processed_inputs = (\r\n-> 1838                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1839             )\r\n   1840             if update_data is None:\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in <lambda>(t)\r\n    978         dataset = self.with_format(\"arrow\")\r\n    979         dataset = dataset.map(\r\n--> 980             lambda t: t.cast(schema),\r\n    981             batched=True,\r\n    982             batch_size=batch_size,\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.cast()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.ChunkedArray.cast()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/compute.py in cast(arr, target_type, safe)\r\n    241     else:\r\n    242         options = CastOptions.unsafe(target_type)\r\n--> 243     return call_function(\"cast\", [arr], options)\r\n    244 \r\n    245 \r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/_compute.pyx in pyarrow._compute.call_function()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/_compute.pyx in pyarrow._compute.Function.call()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: Unsupported cast from struct<answer_end: list<item: int32>, answer_start: list<item: int32>, text: list<item: string>> to struct using function cast_struct\r\n```","comment_length":16,"text":"Extend QuestionAnsweringExtractive template to handle nested columns \n Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support \"flat\" features. We should extend the functionality to cover QA datasets like:\r\n\r\n* `iapp_wiki_qa_squad`\r\n* `parsinlu_reading_comprehension`\r\n\r\nwhere the nested features differ with those from `squad` and trigger an `ArrowNotImplementedError`:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-12-50e5b8f69c20> in <module>\r\n----> 1 ds.prepare_for_task(\"question-answering-extractive\")[0]\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in prepare_for_task(self, task)\r\n   1436         # We found a template so now flush `DatasetInfo` to skip the template update in `DatasetInfo.__post_init__`\r\n   1437         dataset.info.task_templates = None\r\n-> 1438         dataset = dataset.cast(features=template.features)\r\n   1439         return dataset\r\n   1440 \r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc)\r\n    977         format = self.format\r\n    978         dataset = self.with_format(\"arrow\")\r\n--> 979         dataset = dataset.map(\r\n    980             lambda t: t.cast(schema),\r\n    981             batched=True,\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\r\n   1600 \r\n   1601         if num_proc is None or num_proc == 1:\r\n-> 1602             return self._map_single(\r\n   1603                 function=function,\r\n   1604                 with_indices=with_indices,\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    176         }\r\n    177         # apply actual function\r\n--> 178         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    179         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    180         # re-apply format to the output\r\n\r\n~\/git\/datasets\/src\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    395             # Call actual function\r\n    396 \r\n--> 397             out = func(self, *args, **kwargs)\r\n    398 \r\n    399             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)\r\n   1940                         )  # Something simpler?\r\n   1941                         try:\r\n-> 1942                             batch = apply_function_on_filtered_inputs(\r\n   1943                                 batch,\r\n   1944                                 indices,\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)\r\n   1836                 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n   1837             processed_inputs = (\r\n-> 1838                 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1839             )\r\n   1840             if update_data is None:\r\n\r\n~\/git\/datasets\/src\/datasets\/arrow_dataset.py in <lambda>(t)\r\n    978         dataset = self.with_format(\"arrow\")\r\n    979         dataset = dataset.map(\r\n--> 980             lambda t: t.cast(schema),\r\n    981             batched=True,\r\n    982             batch_size=batch_size,\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.cast()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.ChunkedArray.cast()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/compute.py in cast(arr, target_type, safe)\r\n    241     else:\r\n    242         options = CastOptions.unsafe(target_type)\r\n--> 243     return call_function(\"cast\", [arr], options)\r\n    244 \r\n    245 \r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/_compute.pyx in pyarrow._compute.call_function()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/_compute.pyx in pyarrow._compute.Function.call()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/miniconda3\/envs\/datasets\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: Unsupported cast from struct<answer_end: list<item: int32>, answer_start: list<item: int32>, text: list<item: string>> to struct using function cast_struct\r\n``` \n this is also the case for the following datasets and configurations:\r\n\r\n* `mlqa` with config `mlqa-translate-train.ar`\r\n\r\n","embeddings":[-0.4004985392,-0.3374337554,-0.0784154385,0.2124934494,0.0623010099,-0.0605397411,0.342718184,0.6537569761,0.2787890732,0.1644310951,-0.3850086629,0.8013219237,0.0938052833,0.1116523817,-0.22402969,-0.1465635002,0.0285621081,0.1905269176,-0.0690934658,0.1011490896,-0.317885071,0.193488434,-0.2012185156,0.1028063819,0.2194111347,-0.228253141,-0.0353386253,0.088482976,0.2185596377,-0.1091627553,0.353946507,0.2710413635,0.0640781745,-0.0168413743,-0.0001118251,-0.0238936115,-0.0758058652,-0.0531460196,-0.212666899,0.1347667426,-0.2212988734,-0.2578913867,0.0375700369,-0.3121807277,-0.1900653839,-0.2097728848,-0.0742548779,-0.0294896681,0.4723312259,-0.0135347294,0.1707757711,0.353399694,0.226946041,-0.1244799048,0.1267433614,-0.0933816358,-0.4431982636,-0.0782305524,0.5466852188,0.0300399885,0.0169388112,0.3652411997,-0.0343711115,0.107914716,0.023896955,-0.0528597124,0.3286340833,-0.1229999363,0.0111094778,0.4290266335,-0.0217081048,-0.1874396801,-0.2082417905,-0.3809464574,0.0123564163,-0.2188023478,-0.0093456274,-0.2322489768,-0.3328902423,0.1813619733,0.3615427911,-0.4655230343,-0.3431660831,-0.092115812,0.0644313693,0.3115049005,0.0014113832,-0.0799374655,-0.1401880085,-0.1510992944,0.1287179738,-0.0096265841,-0.0296482965,0.1576338708,-0.509965241,-0.4060970247,0.1071711034,-0.5747662783,0.1126487926,0.3861805201,0.4989239275,0.1735469252,0.0279558115,0.206945017,0.1508289874,0.1981992871,0.0859325454,0.1923662871,-0.0598652773,0.5967301726,-0.1092450023,-0.1933967024,-0.1167256311,-0.0851828158,0.1492389739,0.0838888288,0.5520023108,-0.0021331876,-0.3467601538,0.0427193008,0.0344929621,-0.0730466098,-0.2322002053,0.1289039552,0.0269288309,0.2737087011,-0.4161029756,0.3354622126,-0.107742779,0.0315742232,-0.0187874679,-0.1710878909,0.2539903522,0.3146185577,0.1120186672,0.1311676055,0.0525480881,-0.0430616252,0.4304729104,-0.0478942022,0.2082478702,0.0399069563,0.1974537373,0.1108006239,-0.2531925738,-0.285309881,0.222916618,-0.0126007618,-0.1032246649,0.3345327377,-0.1871690303,-0.2716550529,-0.2834213972,0.1354265809,-0.1475449204,-0.0186512191,-0.0152721088,0.3522073328,0.1309848577,-0.1189485714,0.2892568409,-0.1386577785,-0.1265509725,-0.2178006023,0.2230325192,-0.0989434645,-0.2481732666,0.0340819955,-0.2819128931,0.0077523724,-0.1541363746,0.1380896121,0.1155997887,0.1912062764,-0.0904866233,0.2903517187,0.1303349733,-0.4194324911,-0.1716729999,0.1422038227,-0.1727183312,0.4925249219,0.0901335031,-0.217970863,0.3718209267,-0.1345768869,-0.0932563394,0.1010200903,-0.0755134597,-0.262001574,-0.2281343192,-0.2768455446,0.0552995168,0.2245056331,-0.0404817089,-0.0239651147,-0.2805787623,-0.240359664,0.2465333492,-0.2161897421,0.0740369186,-0.272703737,0.6029760838,-0.2477521151,0.1103423685,-0.5407899022,-0.77259022,0.0796709806,-0.3421511948,0.1253489554,-0.2214034945,-0.1351489127,-0.2753249705,0.1095229983,-0.3861001432,-0.3508324027,0.1197643429,-0.1778397113,-0.1192595065,-0.333622247,-0.1127010956,-0.1888127774,0.1344316602,0.1812633574,-0.1693035066,0.0854024887,0.1900447011,0.0097132232,-0.0249612387,0.4596466124,-0.0569810867,0.0291838497,0.0498901382,0.098061271,0.0428169928,-0.0526704006,0.0180584732,-0.1237667128,-0.1701565981,-0.2258189619,0.0442458019,0.1731623262,0.0419866703,-0.0607020482,-0.1348133832,0.6354981065,0.2211730629,0.6569632888,-0.1559611559,-0.1290232688,0.0943490937,-0.049836684,-0.0629422441,-0.2428585291,-0.2287387401,0.2319350243,-0.0882754624,-0.4189498127,-0.3073641956,0.5817826986,0.0810655281,0.1374222338,0.0011099088,-0.1110069007,-0.0673702136,-0.0404732414,0.254290849,-0.2399120033,0.3141377866,0.3139423728,-0.065449588,0.0390544459,-0.0922980085,-0.0944457129,0.2009768635,-0.0393718854,-0.1034151614,0.4231419563,0.4579702318,-0.2617941499,-0.1843265146,-0.0396275111,-0.1588330716,-0.0572177731,-0.1092867255,0.0183848664,-0.0835384056,0.1203019843,-0.0117673762,0.1021931469,0.1570769697,-0.277908504,0.4337159395,0.216811195,-0.2702797651,0.3933640122,-0.0181811377,0.4547273219,0.0914212391,-0.2880818844,-0.3084332943,-0.3010666966,0.1893123835,0.2279930115,-0.1081851795,-0.1813700348,0.1264741421,0.130594328,0.202128455,-0.3754136562,-0.3695494533,0.0802297369,-0.1746841818,0.4358965456,-0.0005782454,0.2324522436,-0.0349891372,-0.4227176905,0.3027733266,0.3596452773,-0.1245285347,0.007156651,-0.0550786592,0.1573766172,-0.0598692596,-0.5525564551,0.1279469281,-0.459766537,0.300028652,-0.0259809047,0.2484274805,0.0255915448,0.1529374421,-0.1002791896,-0.3239284456,-0.1522279233,-0.1322710067,0.144474566,0.4026347399,-0.3067209721,0.0858300477,-0.3118074834,-0.2724570632,0.4823209047,0.1247967258,-0.0917277262,-0.1788227856,0.0560312644,0.5228121877,0.1135839745,-0.1982823759,0.2239517868,0.1521131694,-0.0610609092,0.0546885505,0.2073439658,0.3603225052,0.0601630099,-0.0624172501,-0.0270571057,0.2810737789,-0.1344600469,0.6102373004,0.1061382443,0.6012282968,0.3398057222,-0.0528019369,0.1166456267,-0.1236595362,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2431","title":"DuplicatedKeysError when trying to load adversarial_qa","comments":"Thanks for reporting !\r\n#2433 fixed the issue, thanks @mariosasko :)\r\n\r\nWe'll do a patch release soon of the library.\r\nIn the meantime, you can use the fixed version of adversarial_qa by adding `script_version=\"master\"` in `load_dataset`","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('adversarial_qa', 'adversarialQA')\r\n```\r\n\r\n## Expected results\r\nThe dataset should be loaded into memory\r\n\r\n## Actual results\r\n\r\n>DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4\r\n>Keys should be unique and deterministic in nature\r\n>\r\n>\r\n>During handling of the above exception, another exception occurred:\r\n>\r\n>DuplicatedKeysError                       Traceback (most recent call last)\r\n>\r\n>\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in check_duplicate_keys(self)\r\n>    347         for hash, key in self.hkey_record:\r\n>    348             if hash in tmp_record:\r\n>--> 349                 raise DuplicatedKeysError(key)\r\n>    350             else:\r\n>    351                 tmp_record.add(hash)\r\n>\r\n>DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4\r\n>Keys should be unique and deterministic in nature\r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":36,"text":"DuplicatedKeysError when trying to load adversarial_qa \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndataset = load_dataset('adversarial_qa', 'adversarialQA')\r\n```\r\n\r\n## Expected results\r\nThe dataset should be loaded into memory\r\n\r\n## Actual results\r\n\r\n>DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4\r\n>Keys should be unique and deterministic in nature\r\n>\r\n>\r\n>During handling of the above exception, another exception occurred:\r\n>\r\n>DuplicatedKeysError                       Traceback (most recent call last)\r\n>\r\n>\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in check_duplicate_keys(self)\r\n>    347         for hash, key in self.hkey_record:\r\n>    348             if hash in tmp_record:\r\n>--> 349                 raise DuplicatedKeysError(key)\r\n>    350             else:\r\n>    351                 tmp_record.add(hash)\r\n>\r\n>DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4\r\n>Keys should be unique and deterministic in nature\r\n\r\n## Environment info\r\n- `datasets` version: 1.7.0\r\n- Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n \n Thanks for reporting !\r\n#2433 fixed the issue, thanks @mariosasko :)\r\n\r\nWe'll do a patch release soon of the library.\r\nIn the meantime, you can use the fixed version of adversarial_qa by adding `script_version=\"master\"` in `load_dataset`","embeddings":[-0.1690554023,0.0245385617,0.0173124857,0.2362533659,0.2499980032,-0.2299038023,0.3607746065,0.223856926,0.0596654527,0.1077191457,0.1362492144,0.5518656969,-0.1071600392,-0.1683701426,0.1626174599,0.0517192595,-0.0379656665,0.1883038431,0.0392815843,0.0287615117,-0.3011764586,0.3779449761,-0.1012482494,0.3241074383,0.0359348692,-0.0679111406,-0.1033208892,0.1514307857,0.2137738913,-0.4177423418,0.0700682923,-0.0187095534,-0.1166656017,0.4811602533,-0.0001052441,0.144102633,0.2889031172,0.1035589874,-0.0913373306,-0.1600612849,0.0185088813,-0.1071981043,0.1912548542,-0.1924386919,0.1262396574,-0.4752852619,0.0131603889,-0.3006721735,0.5054063201,0.1201551408,0.3179467022,0.0903188139,0.2629330754,-0.171065256,0.1195176542,-0.1868845075,-0.1742420495,0.1196182147,-0.0303588919,0.0372036919,0.2526727319,0.2869953215,-0.0103847869,0.0183801148,0.4412295222,-0.0151753277,0.2555160224,0.1374571323,0.3210968971,0.3423620164,0.2900976837,-0.4888226092,-0.214271605,-0.0686696917,0.3486289978,-0.2374397665,0.236017555,0.027807869,-0.2709132135,-0.0538720042,0.3976077139,-0.1472533643,-0.0434323885,-0.1063056663,0.1602600515,0.1248760298,-0.0217962377,0.1693464369,0.3264658153,-0.1023193002,0.0459521711,0.0315127932,-0.2117156833,-0.0338133611,-0.4873888493,0.1138649359,-0.0784596279,-0.0931205899,0.2700706124,0.0484963879,0.512791276,-0.0115022063,0.0825756639,0.1235229596,0.1281139255,0.2494140565,0.1686249971,0.2489651293,0.2112814188,0.0933455601,-0.2349935323,0.024731908,-0.0834695995,-0.3852000535,0.6409913301,0.0559605286,0.3691559136,-0.0636514947,-0.248886019,0.1071039736,-0.3669061065,-0.0592118315,-0.2935501337,0.339161396,0.1958414912,0.0174902473,-0.1358401626,0.2525438666,-0.2060322464,0.2294732928,-0.2119791061,-0.1100280657,-0.061905086,-0.1172626242,0.0400002785,-0.1178540066,0.2964857817,-0.0017159585,-0.0026419838,-0.2703484595,0.3662492335,0.0210249461,-0.2416959405,0.2553783357,0.1975595951,0.1291869432,0.0458439291,-0.379288882,-0.1068110019,0.0993222892,-0.0052411021,-0.0512386039,-0.0127534429,0.2953703105,0.1497000754,0.0862279758,-0.0123280352,-0.0367017016,0.1766476929,-0.0752214566,0.0585838184,-0.0121802548,-0.1647233218,-0.442214638,0.0968423411,0.4755463004,-0.3256644011,-0.1278250664,-0.3280483186,-0.0849097446,-0.2783800066,0.2302599549,-0.2746446133,0.0413547792,-0.2059623748,0.0704587251,0.254255712,-0.4608035088,-0.4768517017,0.318764925,-0.3031131029,0.18343243,0.1363978684,0.1553700417,0.1448499858,0.0580859296,-0.0169934295,0.0008209471,0.1661281884,-0.1249247044,-0.2674152553,-0.3802102506,0.1170748994,-0.1609794647,0.0419294089,-0.2055484802,0.177867353,-0.4174796939,0.3148236275,-0.0983029604,0.0229328666,0.0728653371,0.2474905103,-0.0617426001,0.2550160289,0.024097519,-0.3038271964,0.251642257,-0.2327656895,-0.1694363505,-0.2557512224,-0.2266550213,-0.2787438333,0.1975636929,-0.5176190734,-0.166650936,0.259850353,0.2048997879,-0.1156930104,0.2234192938,-0.0729155019,0.0727873445,0.1837258041,0.0310812965,-0.2157280594,-0.1280266047,-0.2033466995,-0.00254299,0.1591006666,0.1641196609,0.105101198,-0.0211301092,-0.0315848105,0.3745127916,-0.2419859618,0.1823240966,-0.0071171322,-0.0134901097,0.0761555731,-0.3286634088,0.2699786425,0.1486452967,0.29554528,-0.1044862643,0.2927990854,0.2971185744,-0.0134968944,0.2014833391,-0.0366398916,0.0408822075,0.2538094521,-0.1656628549,0.0626064092,-0.0536196008,0.1130050868,0.0100470856,0.0348612666,-0.0021447081,-0.0330811813,0.2147905082,0.230107531,-0.0221389886,0.3043051064,0.0318967178,-0.2252117097,0.0949227586,-0.1188314036,0.5473522544,0.4136870205,0.1460587382,-0.3134827316,-0.0018698144,-0.1162209734,-0.2087380886,0.394338131,-0.1712438613,0.2710690796,0.2361287773,0.0873066187,0.021922851,-0.3811853826,0.0386912338,0.0817479938,0.2271606475,-0.1483618915,0.0967334434,-0.2366157621,0.2362318933,-0.1268054247,0.0108367437,-0.0239075106,-0.2116208225,0.0958054364,-0.1182431355,-0.0491660722,0.2551221848,-0.0901474953,0.1280748099,-0.1118554324,0.1302352399,0.0685867667,-0.2182480097,-0.3099152446,0.1367270499,0.2610578239,-0.0785213709,0.4030058086,0.0804523006,-0.0342302173,-0.1852927208,-0.191251263,-0.0399555415,-0.0497093648,0.2836412191,0.1351198554,-0.0747143477,-0.1941614002,-0.4077005386,0.2535795569,-0.2552897036,-0.176333636,0.3357872963,-0.0151262116,0.0049349866,-0.1734302789,-0.4460121393,-0.1923153996,-0.1484011561,0.2947089672,0.0645741895,0.1293051243,0.022704903,0.2246602774,0.0428250432,-0.1031477824,0.1216512248,-0.2890095711,0.1607942581,0.113841258,-0.1703122407,-0.3239609897,0.0529669784,-0.1017126292,0.360444665,-0.1304558516,-0.601433754,-0.2104379833,-0.2563964725,0.1294313669,-0.051540833,0.0978013575,0.2213895619,0.2180900574,-0.2045319378,0.0228927303,-0.1176154837,0.4647151232,-0.5940796137,-0.0129853617,0.0150222387,0.5313708782,-0.0693257302,0.5452752709,0.2173437327,0.0534125008,0.1459403783,-0.0911503434,0.3452010155,-0.1222963855,-0.2979429364,-0.295717895,-0.04432166,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2426","title":"Saving Graph\/Structured Data in Datasets","comments":"It should probably work out of the box to save structured data. If you want to show an example we can help you.","body":"Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help.","comment_length":23,"text":"Saving Graph\/Structured Data in Datasets \n Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help. \n It should probably work out of the box to save structured data. If you want to show an example we can help you.","embeddings":[-0.2739242315,0.0389155634,-0.0391023234,0.2492293268,0.1765464395,-0.1106182709,-0.0422585346,0.0588869825,0.0385465659,0.157548368,-0.1896000952,0.4191503823,-0.4267219007,0.6438033581,-0.1479446143,-0.2593741715,0.2712560892,0.0387915932,0.2738686502,0.0248014051,-0.1155969352,0.0541642085,0.0334124379,-0.0629185215,-0.2414860725,-0.483335495,-0.155989781,0.0525340214,-0.3321605921,-0.4602074623,0.193873778,-0.1048882827,0.3438137174,0.2096619457,-0.0001199866,-0.0553125031,0.1516453773,-0.1994683594,-0.0566304997,-0.2451012731,-0.0566121265,-0.2753434479,0.2017225623,-0.4001507759,-0.0335036851,-0.4654900134,0.0522395968,-0.2573404014,0.3798193038,0.1249340549,0.1100876033,-0.0983889103,0.357000947,0.1881575286,0.1299807578,0.5715721846,-0.1208103076,0.3607281148,0.013202833,0.1853478253,0.4580911994,-0.005127124,-0.1430881321,-0.1577080637,0.2900474668,-0.0249025803,-0.0308635589,-0.4098470807,-0.1116895378,0.1381860822,0.723748982,-0.4365120232,-0.233100906,-0.1412198097,-0.1959114671,-0.1156859398,-0.0175559595,0.152929157,-0.2070350796,0.2562131286,0.2139635235,-0.3798537552,-0.358709693,0.4734244347,-0.0559350103,0.2613530755,-0.0914311931,0.1273645312,-0.1421518177,-0.1118723825,0.0549218766,-0.1248368919,0.1320752054,0.0886373594,-0.0203796141,-0.0758737028,-0.4801715612,0.2287084609,0.0287253894,0.3036389649,0.0697164163,0.0168967787,0.3490336835,0.2395004481,0.475723654,-0.1442130953,0.0471447818,0.3832100034,0.4966682792,-0.3662723601,0.0406668074,-0.1784888506,-0.2933403552,0.1213971376,0.4435192943,-0.3187876046,0.5578758121,0.0079512931,-0.1359265149,0.0446229801,-0.1969697922,0.0967279375,-0.0216956679,0.3609926403,-0.2775350809,0.0732495338,-0.1412934065,0.5445889831,0.0454772003,0.2662028372,0.1382092535,0.0149851786,0.0218704604,0.0009210279,0.1089767292,-0.1986514181,0.0825165883,0.0825564787,-0.1388048232,-0.2052012384,0.0733292103,-0.3362773061,0.2886628807,0.1905325502,-0.0180045962,0.1952151805,-0.1086379811,-0.2577902675,-0.0838287324,0.2124571204,-0.2254009247,-0.5140501261,-0.5806310773,0.052874241,-0.2283154726,-0.2235742062,-0.323785007,0.1371755898,0.445918262,-0.0290160812,-0.0331984125,-0.1683663428,-0.1464661211,-0.5850507021,0.0567371398,-0.2350818515,-0.4784719348,0.0991271138,-0.3251682818,-0.0744251832,0.1291402578,0.4273463786,-0.0596012808,0.3404157162,0.0357297994,0.4138521254,0.698769629,-0.2777142227,-0.4148166776,-0.0819008127,0.4841585159,0.0040979031,-0.1289055198,0.3573928773,0.5263537169,-0.1022598296,-0.3043392599,0.0996801779,-0.0032445143,-0.0020724984,-0.1499859542,0.0159728248,0.3745208383,0.0442034192,-0.1373299807,-0.2539263368,0.2561991215,-0.2004583329,0.1385451257,0.0192221981,0.0950797871,0.1007085964,0.5383986831,0.5469770432,0.0907589272,-0.3336781263,-0.3127003908,0.0721707642,-0.2295341194,-0.308480233,-0.5126116872,-0.3048506677,-0.2286320329,0.2266468406,-0.1705741882,0.027937334,-0.0004071034,-0.1398181915,-0.0901462585,0.2064345628,-0.3856292069,0.0508762822,-0.0188888069,0.0089701163,0.1010847688,0.2701417804,-0.1949193776,-0.5788186789,0.0939568654,0.2997945845,-0.010896395,0.0467017516,-0.0175425373,0.3709216416,0.1792910844,-0.1432299167,-0.3749114573,0.1712652594,0.0279154982,-0.3871057034,0.0973690972,0.139295429,0.2226231247,0.0337360725,-0.13293989,0.2601615787,0.3219934702,-0.1201745346,-0.1562555879,0.1346736252,0.1626520753,0.119449079,-0.0708395168,-0.0142743047,-0.1685674936,-0.0778061152,0.0543080159,0.1200311333,-0.7086869478,0.0711341724,0.4323029518,-0.0024226049,0.5254663825,-0.059575066,-0.0401118435,-0.1502237916,0.2823343575,-0.03111214,0.2100045532,0.0562598929,-0.1707645208,-0.0801717415,-0.0489392206,-0.0759968981,0.0649271458,0.1203808561,0.3846667707,0.1639330387,0.1507395059,-0.034177877,-0.0859737024,0.2178903818,-0.1813958287,0.0015845292,-0.3497137129,0.1840836704,-0.1545985192,-0.4507990181,0.091239199,-0.2079921067,-0.1476707608,-0.231444791,-0.0228111409,0.0237650219,0.0121921031,0.1620066315,0.4222955406,0.2165821344,-0.1318423897,-0.3234273493,-0.2239544094,-0.3585410118,-0.1564082354,-0.011779882,0.0598242693,0.1675311774,0.4270932078,-0.039924264,0.2172190696,-0.3474119306,-0.2564814687,-0.0247166138,-0.063722536,0.0088332221,0.0550150499,0.3775037527,-0.1045713127,-0.1879948527,-0.0885133892,-0.1572542936,-0.1157544479,0.0610345379,-0.1081991792,-0.0642103553,0.109876208,-0.1394550204,-0.8664664626,-0.1614281684,0.3969891965,0.3282330036,0.061320819,-0.0835260227,-0.0021313094,-0.119987458,-0.1332860291,0.0077423435,-0.031291943,-0.3910387158,0.6375262141,-0.3082252741,-0.4324339628,0.2933846712,-0.0717979148,0.1257753819,-0.0882094726,0.1132861972,0.3267559707,-0.0515328199,0.4475869238,0.0574893318,-0.0259936173,0.2197533995,0.0480363369,0.1845579892,-0.1009557396,0.189058587,0.2827368379,0.1272770911,0.321865499,-0.0799065977,0.1303382814,0.0115075987,0.2465844899,0.0448854938,-0.0933634266,0.4398342669,-0.1336839944,0.3183667064,0.0108390311,0.0069366996,-0.0040597073,-0.16317524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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2426","title":"Saving Graph\/Structured Data in Datasets","comments":"An example of a toy dataset is like:\r\n```json\r\n[\r\n    {\r\n        \"name\": \"mike\",\r\n        \"friends\": [\r\n            \"tom\",\r\n            \"lily\"\r\n        ],\r\n        \"articles\": [\r\n            {\r\n                \"title\": \"aaaaa\",\r\n                \"reader\": [\r\n                    \"tom\",\r\n                    \"lucy\"\r\n                ]\r\n            }\r\n        ]\r\n    },\r\n    {\r\n        \"name\": \"tom\",\r\n        \"friends\": [\r\n            \"mike\",\r\n            \"bbb\"\r\n        ],\r\n        \"articles\": [\r\n            {\r\n                \"title\": \"xxxxx\",\r\n                \"reader\": [\r\n                    \"tom\",\r\n                    \"qqqq\"\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n```\r\nWe can use the friendship relation to build a directional graph, and a user node can be represented using the articles written by himself. And the relationship between articles can be built when the article has read by the same user.\r\nThis dataset can be used to model the heterogeneous relationship between users and articles, and this graph can be used to build recommendation systems to recommend articles to the user, or potential friends to the user.","body":"Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help.","comment_length":131,"text":"Saving Graph\/Structured Data in Datasets \n Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help. \n An example of a toy dataset is like:\r\n```json\r\n[\r\n    {\r\n        \"name\": \"mike\",\r\n        \"friends\": [\r\n            \"tom\",\r\n            \"lily\"\r\n        ],\r\n        \"articles\": [\r\n            {\r\n                \"title\": \"aaaaa\",\r\n                \"reader\": [\r\n                    \"tom\",\r\n                    \"lucy\"\r\n                ]\r\n            }\r\n        ]\r\n    },\r\n    {\r\n        \"name\": \"tom\",\r\n        \"friends\": [\r\n            \"mike\",\r\n            \"bbb\"\r\n        ],\r\n        \"articles\": [\r\n            {\r\n                \"title\": \"xxxxx\",\r\n                \"reader\": [\r\n                    \"tom\",\r\n                    \"qqqq\"\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n```\r\nWe can use the friendship relation to build a directional graph, and a user node can be represented using the articles written by himself. And the relationship between articles can be built when the article has read by the same user.\r\nThis dataset can be used to model the heterogeneous relationship between users and articles, and this graph can be used to build recommendation systems to recommend articles to the user, or potential friends to the user.","embeddings":[-0.2233352512,0.1035750657,-0.0550303981,0.2553001046,0.1664751619,-0.0883487985,-0.0420417003,0.0489242636,0.1316880137,0.0172995813,-0.1817377806,0.341371417,-0.4227148592,0.5882750154,-0.2004154623,-0.25589028,0.2201137543,-0.0427169427,0.3299941719,0.0772028118,-0.0972134098,-0.0045653326,0.1638395786,-0.0592396893,-0.245912984,-0.5715228319,-0.1594649255,0.0477374308,-0.3542378545,-0.4808264375,0.2615370452,-0.0621038005,0.2797881067,0.1629059613,-0.000119489,0.0557818934,0.1224136502,-0.1544818282,-0.0868340507,-0.3309611082,0.0056771506,-0.2981207371,0.2152486444,-0.3602895141,0.0141386576,-0.52231884,0.0453781597,-0.4032321274,0.4603859782,0.0864814296,0.1160716712,-0.1106244102,0.3321138024,0.2047654837,0.1417979449,0.6720956564,-0.1498085856,0.3692043722,0.0153676271,0.1761931777,0.4121541381,-0.0536443405,-0.1396135986,-0.1696149707,0.3264270425,0.0092136934,-0.025214877,-0.3577064574,-0.1132700592,0.142080605,0.5881964564,-0.3921891153,-0.2603586912,-0.2230053246,-0.2509318292,-0.0681216493,-0.0151373716,0.1142061576,-0.2154325545,0.2317511141,0.1675646752,-0.3765946925,-0.2405542284,0.5173786283,0.0040855436,0.224382177,-0.0817498192,0.1435154378,-0.1506551653,-0.2028892338,0.05542266,-0.239014551,0.1657639444,0.019779142,0.0126152048,-0.0969205722,-0.4118796587,0.1422938555,0.0904703215,0.2531909347,0.1131650805,0.0034745373,0.2498451173,0.212865442,0.5089201331,-0.1791360974,0.0563799664,0.3806176484,0.3717682362,-0.3154613674,0.0485287569,-0.1778391749,-0.2879604399,0.1798106581,0.4373398721,-0.3225421011,0.5189981461,0.0792660937,-0.1570601612,0.0459627546,-0.1656923741,0.1057720855,0.0260034595,0.3263950646,-0.2364593893,0.1145993993,-0.127290681,0.5873839259,0.0855612308,0.3219810128,0.1119706407,-0.0072313198,0.0196744576,0.0170335937,0.1154987887,-0.0898103714,0.0913378447,0.0445006564,-0.2310366333,-0.2699974775,0.0820463523,-0.3541987836,0.2971456945,0.1638869345,-0.0560777448,0.1963002831,-0.0513299592,-0.3526574969,-0.0780925602,0.123974517,-0.214168191,-0.4256947041,-0.5040130615,0.0581177212,-0.315015614,-0.2132231295,-0.3674656451,0.1815354526,0.4864902794,-0.0217237826,-0.1316982061,-0.1553574353,-0.212444514,-0.5415158868,0.0038440039,-0.1813927591,-0.4119336307,0.0449710228,-0.4088256359,0.0243230797,0.0672061667,0.4411496818,-0.082571201,0.3443979025,0.0977900997,0.406766504,0.6312229633,-0.2161991,-0.4147624373,-0.0524231456,0.3830607533,0.1183177084,-0.1405139416,0.3945300579,0.509745419,-0.1003322005,-0.2299243808,0.1021637768,-0.0321283638,0.0126858987,-0.1281439066,-0.0780200809,0.3725044131,0.0612571314,-0.1228097677,-0.2798149288,0.2493323237,-0.1987602264,0.0786968321,0.0687674433,0.1506728977,0.1583855301,0.5065504313,0.4946050644,-0.0140913855,-0.3567608297,-0.4202125072,0.0701819435,-0.1202671453,-0.3514761627,-0.5534553528,-0.3246555328,-0.2232934237,0.2171337903,-0.1879822016,0.1065857261,0.0359379686,-0.0916216373,-0.1437990367,0.1442363709,-0.2992870212,0.0262181088,0.0541749597,0.0288671777,0.0494713858,0.3267568052,-0.1388345212,-0.5361049771,0.1419084817,0.2938562334,0.0160471667,0.0654351041,0.020452043,0.3442263305,0.2059183568,-0.1587535143,-0.2881811559,0.2673157752,0.0207438711,-0.3377420306,0.0994936377,0.0964006558,0.2454944104,0.0149286073,-0.1673009694,0.3099408746,0.3228692114,-0.0099839615,-0.1581581235,0.1404611617,0.1189101934,0.1425235868,-0.0679236501,-0.0623697378,-0.2031425983,-0.0518230461,0.0549689941,0.1553440541,-0.6136972904,0.124884963,0.4467571676,0.1083694026,0.5621824265,0.0328808688,0.003314625,-0.1703310758,0.2184522599,0.0379648097,0.216180563,0.0516937338,-0.1855855584,-0.110531278,-0.0116996532,-0.0349002294,0.028251458,0.1544921845,0.4188004732,0.1511193663,0.2366502136,0.0117272949,-0.0266444236,0.1544203609,-0.1849400699,-0.0042223101,-0.3672721386,0.2306480855,-0.1960738152,-0.2586520016,0.0719429404,-0.2118722647,-0.1705953479,-0.2689159214,-0.0383747257,0.0383530669,-0.0827082321,0.1073316187,0.4270685017,0.1616069824,-0.1902111322,-0.3846328557,-0.1311557889,-0.4501526058,-0.1093700379,-0.0138796307,0.1302648187,0.1611512005,0.4358736575,-0.0209491886,0.2071597129,-0.2987848222,-0.2861085832,-0.1119458824,-0.0864021927,-0.0002219397,0.0333612598,0.3284339905,-0.1627060026,-0.2232958674,-0.0354165696,-0.0906159729,-0.1039472148,0.0554788858,-0.0491634831,-0.0825529769,0.1610196978,-0.1338107437,-0.8484687209,-0.165742889,0.4261323214,0.2923759818,0.0407998636,-0.0893886313,0.0410691537,-0.1693545878,-0.1445688754,-0.0015500413,-0.005745261,-0.3910816014,0.5569652915,-0.297108084,-0.3576735854,0.2122366726,-0.1462715864,0.0288633723,-0.0205753241,0.0949492902,0.4115853906,-0.0594883896,0.4241309762,0.1043307558,-0.1644164026,0.2474941015,0.1327968836,0.1801097244,-0.064238511,0.1386542022,0.3372526467,0.0933458805,0.2491908222,-0.1027858853,0.0881969109,0.0299030598,0.2436656952,0.055164326,-0.2277391851,0.4077163041,-0.0759844184,0.2586165071,-0.0077718794,-0.0284326859,-0.0658639818,-0.1733735502,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2426","title":"Saving Graph\/Structured Data in Datasets","comments":"Hi,\r\n\r\nyou can do the following to load this data into a `Dataset`:\r\n```python\r\nfrom datasets import Dataset\r\nexamples = [\r\n    {\r\n        \"name\": \"mike\",\r\n        \"friends\": [\r\n            \"tom\",\r\n            \"lily\"\r\n        ],\r\n        \"articles\": [\r\n            {\r\n                \"title\": \"aaaaa\",\r\n                \"reader\": [\r\n                    \"tom\",\r\n                    \"lucy\"\r\n                ]\r\n            }\r\n        ]\r\n    },\r\n    {\r\n        \"name\": \"tom\",\r\n        \"friends\": [\r\n            \"mike\",\r\n            \"bbb\"\r\n        ],\r\n        \"articles\": [\r\n            {\r\n                \"title\": \"xxxxx\",\r\n                \"reader\": [\r\n                    \"tom\",\r\n                    \"qqqq\"\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n\r\nkeys = examples[0].keys()\r\nvalues = [ex.values() for ex in examples]\r\ndataset = Dataset.from_dict({k: list(v) for k, v in zip(keys, zip(*values))})\r\n```\r\n\r\nLet us know if this works for you.","body":"Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help.","comment_length":93,"text":"Saving Graph\/Structured Data in Datasets \n Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help. \n Hi,\r\n\r\nyou can do the following to load this data into a `Dataset`:\r\n```python\r\nfrom datasets import Dataset\r\nexamples = [\r\n    {\r\n        \"name\": \"mike\",\r\n        \"friends\": [\r\n            \"tom\",\r\n            \"lily\"\r\n        ],\r\n        \"articles\": [\r\n            {\r\n                \"title\": \"aaaaa\",\r\n                \"reader\": [\r\n                    \"tom\",\r\n                    \"lucy\"\r\n                ]\r\n            }\r\n        ]\r\n    },\r\n    {\r\n        \"name\": \"tom\",\r\n        \"friends\": [\r\n            \"mike\",\r\n            \"bbb\"\r\n        ],\r\n        \"articles\": [\r\n            {\r\n                \"title\": \"xxxxx\",\r\n                \"reader\": [\r\n                    \"tom\",\r\n                    \"qqqq\"\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n\r\nkeys = examples[0].keys()\r\nvalues = [ex.values() for ex in examples]\r\ndataset = Dataset.from_dict({k: list(v) for k, v in zip(keys, zip(*values))})\r\n```\r\n\r\nLet us know if this works for you.","embeddings":[-0.2856687903,0.0489118472,-0.0369235054,0.2954734564,0.1761306524,-0.0925177932,0.0076426757,0.094109267,0.1752270609,0.0359997712,-0.2266489267,0.414647758,-0.4315076768,0.6326939464,-0.1680036932,-0.1835950762,0.2540252805,-0.0057012681,0.2815619409,0.067555666,-0.1143528074,0.0210009422,0.0942106321,-0.1012063399,-0.2402513772,-0.4843244255,-0.1383965611,0.1061426699,-0.2759398222,-0.5049470663,0.2628543377,-0.0757849887,0.3589752614,0.2296425849,-0.0001212197,0.0293496773,0.1541253775,-0.1582915485,-0.1681702733,-0.2760806382,-0.0338869095,-0.3862594068,0.2224015743,-0.352077812,0.0265035257,-0.5047537088,-0.0631149709,-0.438708365,0.4396774471,0.1020215526,0.1048100814,-0.0953225791,0.2954246104,0.189921692,0.0589852892,0.6107455492,-0.137282595,0.3834092319,0.0041535716,0.1410946846,0.4431878626,-0.0102145523,-0.2097631842,-0.1612381041,0.3201059997,0.0171319246,-0.0291725714,-0.3698993623,-0.1352078766,0.1660224795,0.5845979452,-0.3652295768,-0.2983254194,-0.3096181154,-0.228136152,-0.1148663908,0.0039704191,0.110199824,-0.198173821,0.2807420194,0.2201821208,-0.3308597505,-0.1807452291,0.4849873483,-0.0611000247,0.146174714,-0.1192958355,0.1419625729,-0.1227738187,-0.1803835034,0.0996391997,-0.2622883916,0.2115786076,0.05403658,-0.0296069365,-0.0414730161,-0.3642190099,0.124395512,0.0635046139,0.2624091208,0.1064799502,-0.013124737,0.2416505665,0.2026436925,0.4457499087,-0.2023298144,0.0707388073,0.4032110572,0.2888768613,-0.261646986,0.0439677425,-0.1640574038,-0.3500635922,0.0812721327,0.4830957651,-0.340732038,0.5467669368,0.0318036415,-0.1989638209,0.0567570068,-0.2073794156,0.0930052772,-0.0059705758,0.3341024816,-0.2197184861,0.1738167852,-0.1130866855,0.6057108045,0.0968959257,0.2678085268,0.0694801584,-0.0594241396,0.0333949141,0.0329499394,0.1419242173,-0.1907092333,0.045240175,0.0679358542,-0.1966766864,-0.3019955158,0.0097883483,-0.3076047003,0.3215050101,0.1786852032,-0.0007942825,0.2441357523,0.0554694794,-0.333433032,-0.0856918544,0.1601164341,-0.2456672341,-0.4013805389,-0.470400691,0.0464392267,-0.270311147,-0.2009264976,-0.423334837,0.1407182515,0.4591409564,-0.0270312373,-0.1629072279,-0.1851441264,-0.2272674441,-0.5391336679,0.0616365634,-0.130131498,-0.491052717,0.0238610953,-0.4131832123,0.033468686,0.0880380198,0.4129672348,-0.0644395277,0.3483416736,0.0359830782,0.3879745305,0.6230746508,-0.2450202405,-0.4455579817,-0.0123277232,0.4037216306,0.1489109099,-0.1765155643,0.3048385382,0.5396374464,-0.0855405629,-0.2140922397,0.1027064994,0.0024445693,0.0117912684,-0.1402405351,-0.045989152,0.4148258567,0.0751411691,-0.1731809676,-0.2115935981,0.1960125417,-0.2831936777,0.1302322745,0.0918998718,0.1341879368,0.1718764603,0.4094119668,0.5433160663,0.0042917985,-0.3294644058,-0.3844675124,0.1148533821,-0.1275493354,-0.3256943524,-0.5862291455,-0.2956007421,-0.2968480289,0.2654924691,-0.2253479362,0.1046174839,0.0233859792,-0.1460328102,-0.1592219323,0.1599626839,-0.3197219372,0.0339863636,0.0065495037,0.0329063013,0.0861336514,0.3561074734,-0.0928437412,-0.518142581,0.1240737587,0.2805194259,0.0706996918,0.1026900634,-0.0418438092,0.2740919292,0.2330722362,-0.2299735099,-0.3369737864,0.2312617749,-0.022167882,-0.3557398021,0.126083374,0.1292925179,0.2492378354,-0.0251101404,-0.1084318608,0.3343934715,0.27631706,0.0176386684,-0.2407128066,0.1901579052,0.1251969486,0.1463831514,-0.0831875801,-0.0966418684,-0.2315422595,-0.0299728718,0.0397298895,0.1298664361,-0.5606173277,0.07129246,0.3628565073,0.0552307628,0.4966156483,0.0064899344,-0.0281091835,-0.1260257065,0.234453842,0.0343133286,0.2439900935,0.0448081456,-0.1889482588,-0.1231997237,0.0325147659,-0.0325880311,0.0398155078,0.1706948131,0.4787935615,0.1793331206,0.1353459209,0.0705786571,-0.0077467966,0.1529106349,-0.1868605614,0.0215745121,-0.3932467997,0.2597412765,-0.1891775876,-0.3411652446,0.0717251301,-0.1798442006,-0.1124335229,-0.2484556437,-0.1268178672,0.0519933403,-0.0120929889,0.180280745,0.4102582932,0.1516159475,-0.1177892238,-0.4625420272,-0.1461651623,-0.4068709016,-0.108137019,-0.029054001,0.1182833463,0.129303202,0.3737321794,-0.0375159755,0.2248944044,-0.2988058329,-0.1950057596,-0.1388469785,-0.0749744102,-0.0545152053,-0.0024956635,0.3713651001,-0.1401944757,-0.1446201652,-0.0166386869,-0.1091725528,-0.069999665,0.0943960547,-0.0415677279,-0.0403397083,0.2053181529,-0.0593520068,-0.8583173156,-0.1564684808,0.3328521252,0.3435877562,0.0337750241,-0.0736743361,0.1039049029,-0.1537514925,-0.1172204241,-0.0354967192,-0.0155316768,-0.2680869699,0.6005049348,-0.2475970387,-0.3712285459,0.2166536897,-0.1438214034,0.0600721203,-0.0142243141,0.0875076354,0.4726675749,-0.0464403443,0.4293411076,0.0283014551,-0.1236563548,0.2986907065,0.1369947642,0.1880536079,-0.0151023027,0.176908493,0.2579629719,0.0381058529,0.163757652,-0.0598191656,0.1514721066,0.018788632,0.2478972524,0.1197220013,-0.1546406895,0.4252412021,-0.1102481782,0.2781702876,-0.0261385851,-0.1024712473,-0.1288133711,-0.150689438,0.044880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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2426","title":"Saving Graph\/Structured Data in Datasets","comments":"Thank you so much, and that works! I also have a question that if the dataset is very large, that cannot be loaded into the memory. How to create the Dataset?","body":"Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help.","comment_length":31,"text":"Saving Graph\/Structured Data in Datasets \n Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help. \n Thank you so much, and that works! I also have a question that if the dataset is very large, that cannot be loaded into the memory. How to create the Dataset?","embeddings":[-0.2723234296,0.0077559124,-0.0335844606,0.331335783,0.1474175602,-0.0770562068,-0.0660822317,0.0120472154,0.0752707794,0.1229880601,-0.173162654,0.313380152,-0.4319543242,0.5779833198,-0.0695673078,-0.2389570773,0.276214838,0.0760909691,0.2164649516,-0.0062684393,-0.1167420521,-0.0144838151,0.0624190047,-0.1413793266,-0.2844819725,-0.4980396032,-0.1976485997,0.1711293012,-0.4135592282,-0.498990953,0.2010753453,-0.0681336671,0.3138073087,0.2875531018,-0.0001202716,-0.0988923907,0.1517480463,-0.213938266,-0.0875948146,-0.2176559567,-0.0626532882,-0.3494538665,0.1856263429,-0.3744631708,0.0488128364,-0.461779207,0.0895982981,-0.2455245554,0.3802903295,0.2123028189,0.1083455458,-0.1020672917,0.3337271512,0.1387737095,0.2244872898,0.5536146164,-0.1602343321,0.3449639678,0.0203347094,0.184986949,0.3843227625,-0.1057037115,-0.1318714768,-0.1821804941,0.397329092,-0.0415188149,-0.0887694955,-0.4944135845,-0.0841982812,0.1948035955,0.7593106031,-0.4477229416,-0.2266944349,-0.1608174443,-0.2214411497,-0.0948323086,0.0108969165,0.2465351522,-0.2418276668,0.2171609253,0.1256919056,-0.4189335406,-0.3556168973,0.4478442967,-0.0639713556,0.2398026288,-0.0703294426,0.1305978894,-0.1145715863,-0.0866908953,0.0702921525,-0.1638026536,0.1005724594,0.1569358408,-0.0077922223,-0.0993697867,-0.4392795265,0.2444678545,0.131171748,0.2988360226,0.0962082222,-0.0442811921,0.3254297972,0.2030017674,0.4563657939,-0.1295352578,-0.0343310535,0.3509793282,0.4708310664,-0.4111912251,0.0793208852,-0.1461351365,-0.32575804,0.1213463098,0.4596614242,-0.3857610226,0.4507372975,0.018676294,-0.0674884617,0.0762663037,-0.1422474682,0.0838796049,-0.020079799,0.3746221066,-0.2849635184,0.0356439836,-0.1820646822,0.5128691792,-0.0016849629,0.2907336354,0.1076042131,0.0358643532,-0.0395977162,0.0730571374,0.1775100082,-0.15936023,0.1261921823,0.1004850268,-0.1680474877,-0.1745099723,0.1169634685,-0.3805246055,0.2957401872,0.2588338852,0.0160455797,0.1812125444,-0.150212273,-0.2076259255,-0.0921968967,0.2719935477,-0.1546025276,-0.539816916,-0.5330708027,0.0546012931,-0.2730056643,-0.1832983792,-0.3185444772,0.1531112045,0.4208410382,-0.0368215814,-0.0124009838,-0.1630356759,-0.1278664768,-0.6147217751,0.0856520161,-0.1255210489,-0.4851516187,0.1229011342,-0.3247629702,-0.0987713113,0.1090563759,0.4521107972,-0.07078702,0.3401065469,0.0563110188,0.3587877154,0.7113128901,-0.218078509,-0.4243231714,-0.070299536,0.4794392884,-0.0058620842,-0.057980042,0.3966946304,0.5118689537,-0.0480294339,-0.3228681684,0.130073607,-0.0515295602,-0.0404157937,-0.1537346542,-0.0078461068,0.3993906081,0.0581793077,-0.0624420084,-0.2450851649,0.2789312899,-0.1595264077,0.0747456029,-0.0010716429,0.1093669608,0.1044967324,0.4632634521,0.5171387196,0.0917439237,-0.2976774275,-0.3671291471,0.0860744566,-0.2413827032,-0.2384937704,-0.4375576377,-0.2809787095,-0.2324994802,0.184506461,-0.1535614282,0.0133439358,-0.0315263346,-0.1356746554,0.0234829839,0.159497261,-0.4104891121,0.0924570039,-0.0393071696,0.0162757635,0.0546815991,0.2408659607,-0.1573641151,-0.5781391859,0.1061069295,0.301720202,-0.0689623728,0.0416807383,0.0111880675,0.3606559932,0.2074419707,-0.1765874177,-0.3877708912,0.2146490812,0.0640983433,-0.3676768243,0.1983052939,0.0748228133,0.2533234954,-0.02154967,-0.1256420463,0.200494796,0.2751399875,-0.1084681824,-0.0767166615,0.0466155261,0.1586217582,0.1110260859,-0.0413988233,0.0030366348,-0.1252223104,-0.0301832072,0.1659463048,0.1786604971,-0.687571764,0.025208259,0.4318841398,-0.0037641011,0.4825694561,-0.0363643691,-0.1058223397,-0.1832434237,0.2555556893,-0.0549234636,0.2452308983,0.0651387647,-0.1634278744,-0.0654099584,-0.057419017,-0.1317062527,0.0382650271,0.1137054339,0.4122775495,0.183090955,0.1618249565,-0.0466056988,-0.1662721485,0.1843385845,-0.1508415788,0.0023883434,-0.3080722094,0.143203631,-0.0645182356,-0.4157723784,0.0653207153,-0.1595942676,-0.2017191797,-0.1290533543,0.0060436875,0.0526842847,0.0520017371,0.1046508104,0.411814183,0.2988615334,-0.1228376329,-0.2645308971,-0.2101124376,-0.3297555447,-0.1263043582,-0.0112475362,0.0675989836,0.1480524838,0.4714926481,-0.0406836756,0.2307069451,-0.3062478006,-0.1926319599,-0.0408775695,-0.1002945676,0.0242455732,0.0078032231,0.3974918425,-0.0643022507,-0.1873313934,-0.0652493984,-0.2046804577,-0.1442505866,0.0302599259,-0.1169730499,-0.0828620344,0.121373646,-0.1776868105,-0.8541564941,-0.1905872524,0.4202074707,0.3776107728,0.0883553177,-0.0926016644,0.044257585,-0.0691745207,-0.110158816,0.0476266779,-0.0483815484,-0.4029603899,0.6650779843,-0.3638497889,-0.4688949585,0.2904838324,-0.1085298881,0.1245299429,-0.0135353534,-0.0370056778,0.4082781076,-0.1297804862,0.402040571,0.0598957911,0.0274004787,0.2436969727,0.0090727108,0.1797289401,-0.0924161375,0.1197548881,0.2141741216,0.134723261,0.345688045,-0.0561615191,0.1580355465,-0.0328666791,0.3319851756,0.0610183068,-0.0342557207,0.4159286618,-0.1219007596,0.3614265025,-0.0465202145,0.0066815489,0.0552164651,-0.14596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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2426","title":"Saving Graph\/Structured Data in Datasets","comments":"If your dataset doesn't fit in memory, store it in a local file and load it from there. Check out [this chapter](https:\/\/huggingface.co\/docs\/datasets\/master\/loading_datasets.html#from-local-files) in the docs for more info.","body":"Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help.","comment_length":28,"text":"Saving Graph\/Structured Data in Datasets \n Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.\r\nAlthough I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. \r\n\r\nThank you very much for your help. \n If your dataset doesn't fit in memory, store it in a local file and load it from there. Check out [this chapter](https:\/\/huggingface.co\/docs\/datasets\/master\/loading_datasets.html#from-local-files) in the docs for more info.","embeddings":[-0.237810269,-0.1662292928,0.0330213755,0.3780060709,0.2134668827,-0.0944955647,-0.0819416642,0.0058013732,0.2306800038,0.1115748957,-0.3286620677,0.2727145553,-0.4143139124,0.7541984916,0.0344124287,-0.2124170363,0.2468240112,-0.0094740018,0.2130971253,0.0727865249,-0.1470599622,0.0766597688,0.122483775,-0.0655740127,-0.3424080908,-0.463511318,-0.144516468,0.2216348797,-0.2589560449,-0.4306832254,0.2377919406,-0.0232453737,0.3134662509,0.2755083144,-0.0001271783,0.0192323532,0.1439924836,-0.198069483,-0.1501704305,-0.2263995558,-0.0105654718,-0.2625780106,0.2062537819,-0.2558062971,-0.0249745864,-0.4207357168,0.0195486937,-0.2141057849,0.4344231188,0.0629910976,0.0510321781,0.0888584852,0.2963438928,0.1291370839,0.0012458344,0.7133287191,-0.0915173143,0.4353260696,-0.0811251178,0.1337190717,0.4062704146,0.0324031152,-0.1193139926,-0.1696986854,0.4198621213,0.0327906273,-0.1076439098,-0.2930718362,-0.0799694434,0.1958370507,0.5325228572,-0.3814711571,-0.2554703355,-0.4010131359,-0.1833397299,-0.1577882916,0.1092292666,0.101676859,-0.2182467729,0.2751451135,0.0796673223,-0.4316377342,-0.2243959159,0.462623328,-0.0305591356,0.1390717477,-0.218229875,0.1348426789,-0.009732022,-0.1697131693,-0.1591190845,-0.2166929692,0.1530637443,0.1046103239,0.0219988041,-0.0494470745,-0.3984912932,0.3054518104,0.1760906577,0.2698923647,0.0397170559,0.0487186201,0.1419565082,0.177365616,0.4741599858,-0.1392013729,0.0289493147,0.2929439843,0.4270748198,-0.1479544044,0.0825609341,-0.1716121733,-0.2451376319,0.1686770618,0.2907402217,-0.4451834857,0.5732555985,-0.0094405822,-0.1498821974,0.0264444333,-0.0496975146,0.0983386114,0.0392809436,0.4119528532,-0.2485650033,0.0763239861,-0.0822553113,0.567754209,0.0806292444,0.1816281974,0.0675828531,-0.1055876538,0.0586666875,0.1883953214,0.119540222,-0.304302156,0.1431476474,0.0493285544,-0.117508173,-0.2298802137,-0.1051741913,-0.2983853817,0.2503584325,0.1874824315,-0.034736421,0.2177912146,0.0264807232,-0.251060605,-0.0614526682,0.0711186901,-0.1817571968,-0.5472614169,-0.4872515202,-0.0172648542,-0.296346128,-0.1825623661,-0.494740665,0.2153961957,0.4036822021,-0.0304517038,-0.0772516876,-0.0381743051,-0.361159116,-0.4873213172,0.1917564422,-0.0130669074,-0.3687559068,-0.070466958,-0.2764284015,-0.06379278,0.0365636498,0.5089984536,-0.0615215823,0.3808539212,-0.0218580235,0.3172659874,0.6162870526,-0.2900633514,-0.3520003855,-0.0438315198,0.3447287977,0.1639666408,-0.1431290656,0.255654633,0.4974556565,-0.0588796884,-0.2075304687,0.0913296044,-0.0154418899,-0.0106452741,-0.1777820438,-0.0888601243,0.3596826494,0.0504076965,-0.1555024683,-0.1489335746,0.2017619014,-0.2562267482,0.1623859257,0.0518335551,0.1262307614,0.1539584398,0.4118930101,0.674677372,-0.0025748683,-0.380671829,-0.4766902924,0.1435331255,-0.0869005248,-0.2942626774,-0.4281311631,-0.3349537849,-0.2720730305,0.2078000903,-0.1555913836,0.0490857139,-0.0890747085,-0.1226063222,-0.0303374156,0.24661614,-0.4448167086,0.1691298634,-0.0641332716,0.1097605452,-0.0555861406,0.3028223515,-0.0643483549,-0.5284366012,0.1100298166,0.3356063664,-0.0236818772,0.0538898818,-0.0074759074,0.3950042427,0.1024578959,-0.1147257537,-0.2665483654,0.2249416262,0.0773112848,-0.3018085063,0.0808286816,0.0188892446,0.2364879698,0.0676601678,-0.1550954729,0.2655336857,0.2925465107,-0.034726575,-0.1800360978,0.0837549642,0.0568516552,0.17951864,-0.0535778515,-0.1041107699,-0.0837018713,-0.1460365802,0.1154142171,0.2012958825,-0.7476040721,0.0817903206,0.4663318098,-0.0182795506,0.4762844443,0.0598481223,-0.0826509669,-0.1684434116,0.2419523001,-0.1110014096,0.2505150735,0.0205011591,-0.1729886234,-0.0328332148,-0.0232802629,-0.0379643291,-0.0422997959,0.1455172747,0.3761143684,0.1378004253,0.1253286153,0.0087273847,-0.1385634691,0.2837544084,-0.2259045243,0.0164922141,-0.4621778131,0.1632553637,-0.1930512041,-0.4235275388,-0.006028852,-0.1084075347,-0.3135083914,-0.1574716419,-0.1068442985,0.1374977231,0.0040074326,0.1223513037,0.3831165135,0.3042136133,-0.1437426805,-0.2974486053,-0.239982143,-0.2823421657,-0.0940711275,-0.1037602574,0.1134150475,0.1871296316,0.4331095517,-0.1567296386,0.1698223501,-0.3087498844,-0.1628099084,-0.0224445332,-0.1605901271,0.0554261617,0.0662362576,0.3839928508,-0.1775969267,-0.1332483888,0.0893267021,-0.1924712062,-0.1230789125,0.0382372439,-0.0354033336,-0.0072129765,0.1521919072,0.0253635533,-0.7563423514,-0.1886426359,0.5028291941,0.2522119284,0.0238973498,-0.0296341702,-0.0192307364,-0.0403144583,-0.3316256702,0.0194334313,-0.0847092867,-0.4716016948,0.5566013455,-0.2912874818,-0.4345383644,0.2250980288,-0.0294178072,-0.0016179786,-0.1036294177,0.0356378816,0.2269741744,-0.0987163037,0.3692942262,0.0368262716,-0.0142815448,0.2845596075,-0.000279107,0.2400384396,-0.0237688515,0.0509027764,0.2991060019,0.1358183473,0.2495039254,-0.0381164216,0.1825693548,-0.029887015,0.3044150174,0.1194671169,-0.0898281857,0.3953668177,-0.1584452987,0.4451116621,-0.0311501641,-0.0932234302,0.0465212129,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2424","title":"load_from_disk and save_to_disk are not compatible with each other","comments":"Hi,\r\n\r\n`load_dataset` returns an instance of `DatasetDict` if `split` is not specified, so instead of `Dataset.load_from_disk`, use `DatasetDict.load_from_disk` to load the dataset from disk.","body":"## Describe the bug\r\nload_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"art\")\r\ndataset.save_to_disk(\"mydir\")\r\nd = Dataset.load_from_disk(\"mydir\")\r\n```\r\n\r\n## Expected results\r\nIt is expected that these two functions be the reverse of each other without more manipulation\r\n\r\n## Actual results\r\nFileNotFoundError: [Errno 2] No such file or directory: 'mydir\/art\/state.json'\r\n\r\n## Environment info\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n\r\n","comment_length":24,"text":"load_from_disk and save_to_disk are not compatible with each other \n ## Describe the bug\r\nload_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"art\")\r\ndataset.save_to_disk(\"mydir\")\r\nd = Dataset.load_from_disk(\"mydir\")\r\n```\r\n\r\n## Expected results\r\nIt is expected that these two functions be the reverse of each other without more manipulation\r\n\r\n## Actual results\r\nFileNotFoundError: [Errno 2] No such file or directory: 'mydir\/art\/state.json'\r\n\r\n## Environment info\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n\r\n \n Hi,\r\n\r\n`load_dataset` returns an instance of `DatasetDict` if `split` is not specified, so instead of `Dataset.load_from_disk`, use `DatasetDict.load_from_disk` to load the dataset from disk.","embeddings":[-0.1792232394,-0.225251615,-0.1031944528,0.443765372,0.1700460911,-0.0056649549,0.288071841,0.3563308418,0.3407506943,0.0764616281,0.0730309263,0.3753902614,0.0848360732,0.1554043442,-0.1350735724,-0.0391581059,0.2747603059,0.048006542,0.2421990782,-0.115025036,-0.2098629475,0.400549978,-0.203281939,-0.0734000653,-0.1446835399,-0.0062051648,-0.2334032953,0.3358605206,0.3330038488,0.0827944577,0.2947779894,-0.2153790295,0.0816993341,0.5532495975,-0.0001212123,0.0112388376,0.160898447,-0.229187578,-0.2692857683,-0.6934784651,-0.1458991766,-0.254847616,0.0668642372,-0.3095525503,0.1461135596,-0.0618361346,-0.0437009335,-0.2818159163,0.519669354,0.1374070048,0.1133457944,0.1389506608,0.1779476702,-0.2703320086,-0.0786266327,0.4976059198,0.0025216881,0.3422203362,0.1431116164,-0.139275983,0.1311556548,-0.0501510464,-0.3340208232,-0.1618638635,0.2924522758,0.2686187327,0.2871638238,-0.3989811838,-0.0131455446,0.1011754498,0.4063547254,-0.1671675295,-0.331186831,-0.3169541061,-0.3207164407,0.0708923116,0.4782576263,0.0573517419,-0.3660798967,0.1695609838,-0.1249224544,-0.3215165734,0.0082832025,0.3736847341,-0.04334566,-0.0029226099,-0.078749001,0.0695815757,0.2542456388,0.0742168278,0.3182521462,-0.4341986179,0.2093426138,0.0751498416,-0.1967533976,-0.0305962283,-0.1692512184,-0.4386776388,-0.1910796613,0.3895240724,0.2439085394,-0.1618518233,-0.4091942608,0.3818172216,0.4627968669,0.2550275624,0.1295374036,0.2542569339,0.2904804349,0.0670523569,-0.0025622717,0.0827606767,0.2443393469,-0.3209077418,0.3263378143,-0.168192938,0.3287460506,-0.1333357096,-0.3826584816,-0.0483474135,-0.2059504539,-0.096618399,-0.0288152806,0.1742832214,0.1753856093,0.3235180378,0.2330272198,0.3827495873,-0.1726453304,0.4203166366,-0.1407727301,-0.0053146444,0.1534060091,0.1977638453,0.137517795,-0.1364088207,0.1692527831,0.1127422452,-0.0249592569,-0.1497893631,-0.0999917462,-0.3721024394,0.1490969062,0.1833569258,0.3355745971,0.1456549317,0.1002760902,-0.361836344,-0.237632066,0.2797680795,-0.4703740776,-0.1679827422,0.084968619,0.1425002515,-0.2271607667,0.0532756262,-0.6181437969,-0.0054427949,0.2329247296,-0.3323367536,0.0348131061,-0.1326627731,-0.165564388,-0.4953819811,0.3575298786,0.2533137798,-0.3116635978,-0.0661654621,-0.1282549948,-0.1001225859,0.0401784927,0.3860417008,-0.0586670674,0.3588559926,-0.392334938,0.1302446872,0.551763773,-0.5543698668,-0.329724282,0.4475794137,-0.1710844934,-0.0719404668,0.0096968254,0.1479272544,0.2011791319,-0.1595356911,0.2984156311,0.4097066224,0.0290239155,-0.0377856418,-0.3559076488,-0.1730239838,0.1189952567,0.1487328559,-0.0316416919,0.1294300258,0.1749072373,0.0735625997,0.2986240983,0.0119523965,0.1110604629,0.37243855,0.3932012618,-0.0431029461,-0.1116136461,-0.139479652,-0.7380242348,0.215712145,0.2114938349,-0.3154179752,-0.0078514479,0.1417946965,-0.0605179667,-0.1606418788,-0.1623939276,0.1112134829,-0.0190429799,-0.0295296758,0.1480240673,0.0633510649,-0.2396693975,0.2358975261,0.2464085519,0.0769229084,-0.2499374896,0.6901504993,-0.0967807099,-0.3731459975,-0.091365397,-0.0424572788,0.3784249127,-0.0463225879,-0.1258752048,0.4283832014,0.0684175417,0.3490032256,-0.3699417412,-0.2295208722,0.1426036358,0.1124600768,0.0141563369,0.336281687,0.1857881546,-0.2280341089,-0.3808369637,0.1877964288,-0.2250871807,0.3004726171,0.1914522946,0.0634959638,0.1003570184,-0.1119061038,0.1852492094,-0.235955596,0.1871626377,-0.1902701706,0.2068027407,-0.2088867724,-0.1850789487,0.0922372937,0.3652824461,-0.053681381,0.2342032194,-0.0175956674,0.0251882598,-0.0746917427,-0.0590968728,0.4634986818,0.7871444225,0.0422297232,0.0795527399,-0.0054729604,0.0074960729,-0.2254068106,0.1815459877,0.0908007845,0.3721469045,0.413138926,0.0500583239,0.0782987699,0.0164352059,-0.1771486253,-0.1368957013,0.0040486008,-0.3926257789,0.0614943951,-0.1727619022,0.1391112208,-0.41207394,-0.0962932557,-0.1234484836,-0.4050314724,-0.2254335433,0.634055078,-0.0669438317,-0.1725351065,0.1766725183,0.0252850801,-0.1996213794,-0.2302349508,-0.2482907921,0.1143594161,-0.2080485374,-0.0911775306,0.2168107033,0.0659446642,0.2270480096,-0.184716627,-0.2007342279,-0.2244721353,0.0086451713,0.0563396327,-0.0481998771,0.174309954,0.0051670028,0.1690931022,0.1144624725,-0.3755486012,0.1989772469,-0.1584615707,-0.2648602128,0.1604645997,0.2826429904,-0.1355348974,-0.2202664614,-0.3173787594,-0.4011374414,-0.4135324955,0.1916930228,-0.0158063285,0.112751171,-0.3547134399,0.1864437312,0.0553877577,0.2779679298,-0.0414725766,-0.1246274933,-0.129309088,0.4934219718,-0.1611725539,-0.2432639301,0.0910674185,0.1499760598,0.0022744983,0.0416839868,-0.3326735795,-0.0748596862,-0.0855374411,0.1137662902,-0.2176433802,0.0636477023,0.2987585366,-0.0172817074,0.0534693897,-0.0840159729,-0.3136729002,0.1490674019,0.21666421,0.1762974709,-0.0757616907,0.3940104842,0.0701805875,0.3671786189,0.2821439207,0.0682801232,0.2314012498,0.1976292133,0.2039630115,-0.2118751109,-0.3651670516,-0.217256397,-0.1256068647,-0.3346657157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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2424","title":"load_from_disk and save_to_disk are not compatible with each other","comments":"Though I see a stream of issues open by people lost between datasets and datasets dicts so maybe there is here something that could be better in terms of UX. Could be better error handling or something else smarter to even avoid said errors but maybe we should think about this. Reopening to use this issue as a discussion place but feel free to open a new open if you prefer @lhoestq @albertvillanova ","body":"## Describe the bug\r\nload_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"art\")\r\ndataset.save_to_disk(\"mydir\")\r\nd = Dataset.load_from_disk(\"mydir\")\r\n```\r\n\r\n## Expected results\r\nIt is expected that these two functions be the reverse of each other without more manipulation\r\n\r\n## Actual results\r\nFileNotFoundError: [Errno 2] No such file or directory: 'mydir\/art\/state.json'\r\n\r\n## Environment info\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n\r\n","comment_length":73,"text":"load_from_disk and save_to_disk are not compatible with each other \n ## Describe the bug\r\nload_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"art\")\r\ndataset.save_to_disk(\"mydir\")\r\nd = Dataset.load_from_disk(\"mydir\")\r\n```\r\n\r\n## Expected results\r\nIt is expected that these two functions be the reverse of each other without more manipulation\r\n\r\n## Actual results\r\nFileNotFoundError: [Errno 2] No such file or directory: 'mydir\/art\/state.json'\r\n\r\n## Environment info\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n\r\n \n Though I see a stream of issues open by people lost between datasets and datasets dicts so maybe there is here something that could be better in terms of UX. Could be better error handling or something else smarter to even avoid said errors but maybe we should think about this. Reopening to use this issue as a discussion place but feel free to open a new open if you prefer @lhoestq @albertvillanova 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2424","title":"load_from_disk and save_to_disk are not compatible with each other","comments":"We should probably improve the error message indeed.\r\n\r\nAlso note that there exists a function `load_from_disk` that can load a Dataset or a DatasetDict. Under the hood it calls either `Dataset.load_from_disk` or `DatasetDict.load_from_disk`:\r\n\r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset_dict = load_from_disk(\"path\/to\/dataset\/dict\")\r\nsingle_dataset = load_from_disk(\"path\/to\/single\/dataset\")\r\n```","body":"## Describe the bug\r\nload_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"art\")\r\ndataset.save_to_disk(\"mydir\")\r\nd = Dataset.load_from_disk(\"mydir\")\r\n```\r\n\r\n## Expected results\r\nIt is expected that these two functions be the reverse of each other without more manipulation\r\n\r\n## Actual results\r\nFileNotFoundError: [Errno 2] No such file or directory: 'mydir\/art\/state.json'\r\n\r\n## Environment info\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n\r\n","comment_length":45,"text":"load_from_disk and save_to_disk are not compatible with each other \n ## Describe the bug\r\nload_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"art\")\r\ndataset.save_to_disk(\"mydir\")\r\nd = Dataset.load_from_disk(\"mydir\")\r\n```\r\n\r\n## Expected results\r\nIt is expected that these two functions be the reverse of each other without more manipulation\r\n\r\n## Actual results\r\nFileNotFoundError: [Errno 2] No such file or directory: 'mydir\/art\/state.json'\r\n\r\n## Environment info\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.10\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n\r\n \n We should probably improve the error message indeed.\r\n\r\nAlso note that there exists a function `load_from_disk` that can load a Dataset or a DatasetDict. Under the hood it calls either `Dataset.load_from_disk` or `DatasetDict.load_from_disk`:\r\n\r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset_dict = load_from_disk(\"path\/to\/dataset\/dict\")\r\nsingle_dataset = load_from_disk(\"path\/to\/single\/dataset\")\r\n```","embeddings":[-0.2114283144,-0.1918451488,-0.1080043539,0.4175554216,0.1788415313,0.0094063906,0.2517890036,0.3598573506,0.3484734297,0.1281708628,0.0906293541,0.3500769436,0.0675619841,0.1234149337,-0.1204770133,-0.0371128097,0.2396690845,0.0431480221,0.2499337792,-0.1075347662,-0.1955868453,0.3563712239,-0.1399710327,-0.0368440039,-0.1406249255,-0.0493298694,-0.1832460165,0.3529135883,0.329156965,0.0321098045,0.3073035479,-0.1563008726,0.0693301931,0.5561497808,-0.0001211894,0.0506671369,0.1738408804,-0.2001836896,-0.3015095294,-0.6945695281,-0.1187138036,-0.2993363142,0.1153070852,-0.3185425103,0.1460939348,-0.0933302566,-0.0410898775,-0.2919644415,0.5449647903,0.1292127073,0.1088067889,0.1360334903,0.2161679566,-0.2247788757,-0.0733537972,0.5043619871,0.0144879296,0.3683722615,0.1602687091,-0.122659348,0.1436018348,-0.007756067,-0.3498789668,-0.1579461545,0.2968854904,0.2928346992,0.3333088756,-0.4449427724,-0.0770189464,0.1268185824,0.3759043515,-0.1877381653,-0.3891636133,-0.3483689725,-0.2338569313,0.0552521795,0.4390164316,0.0421449281,-0.3749604523,0.1507019699,-0.0870305896,-0.3912684023,-0.0318068601,0.3521456718,-0.013760414,-0.0051348791,-0.0147847869,0.0822793916,0.2828969061,0.0774772167,0.3303999901,-0.4270054698,0.2116922587,0.0744733959,-0.241938442,-0.0240220483,-0.1862122864,-0.4937504232,-0.1886006147,0.3319095671,0.2741460502,-0.1271965653,-0.4449871182,0.3601480424,0.4342702627,0.2644770145,0.1294641793,0.2789503634,0.2962292135,0.0728928521,0.0235371199,0.0931862965,0.2209709436,-0.3597979248,0.2701231837,-0.0994902179,0.329267174,-0.1243443787,-0.4007611275,-0.0372810289,-0.1868371516,-0.1043993011,-0.0128229316,0.1663793325,0.1677876413,0.3757869601,0.2494017929,0.3480872214,-0.1479360014,0.4127962887,-0.109518148,0.0165517218,0.2048794925,0.2110491097,0.1036957279,-0.142040506,0.1754673123,0.0609239563,-0.0050596925,-0.1145134419,-0.0856750086,-0.3981600106,0.1377871335,0.1861119121,0.3335360587,0.166163668,0.0975963995,-0.3431741297,-0.2430256754,0.2610028684,-0.4517631233,-0.1967012286,0.0310486481,0.1310895234,-0.185141772,0.0582766943,-0.6198506355,-0.0169688426,0.1802481711,-0.3511140049,-0.0158939008,-0.1464461535,-0.1892220676,-0.4825013876,0.3719311953,0.2832886875,-0.3165066242,-0.0348567627,-0.0943608731,-0.1288660616,0.0539679341,0.4559873343,-0.0792085081,0.4309543073,-0.3708837926,0.0749579817,0.58112818,-0.633163929,-0.3446838856,0.4848427474,-0.1842655241,-0.0113264732,0.0165183768,0.15371494,0.2192757875,-0.156722486,0.3526002169,0.4051528871,0.0413565971,-0.0281782337,-0.3694812953,-0.1913498342,0.1292472333,0.1685738564,-0.0730328038,0.098166585,0.1295511127,0.0467545502,0.3483135402,0.001223473,0.0949436575,0.3625749946,0.4391660094,-0.0557367876,-0.1810932308,-0.0853820518,-0.7521965504,0.2291172594,0.1643960625,-0.263703227,-0.0664817318,0.1166827083,-0.0023818291,-0.1435466707,-0.1489107907,0.1193036884,-0.0247613564,-0.0315305926,0.1420332044,0.0465006046,-0.2370294929,0.2715092003,0.1500969678,0.0716424659,-0.1933504045,0.6443470716,-0.0932986811,-0.3494719565,-0.054322958,-0.0531428568,0.3440527916,-0.0708367154,-0.1461755186,0.4169158936,0.0370488241,0.3551664948,-0.3490378261,-0.2183392048,0.1753199697,0.1694666445,-0.0041925162,0.3294335604,0.1812501401,-0.2228418291,-0.4059947729,0.1439863294,-0.2043489814,0.3077221215,0.1737815589,0.0624669492,0.1120544747,-0.0566764772,0.1743007004,-0.2174956352,0.1451800615,-0.1665930003,0.2363595068,-0.1829375327,-0.1936642975,0.0557036027,0.3306387365,-0.0622398816,0.2126478255,0.0016045637,0.0850016102,-0.0840710625,-0.0744769275,0.4744531214,0.7585263252,0.0646188185,0.0331504419,-0.0072399406,-0.0344476923,-0.2042892575,0.199331075,0.0764113888,0.4182401597,0.4030751884,0.0133546088,0.114487879,0.0406402461,-0.2641814649,-0.1368008554,0.0475820154,-0.415517807,0.010881952,-0.1339762807,0.1105847731,-0.4369588196,-0.0678196177,-0.0970941931,-0.4311989844,-0.2040608376,0.6053654552,-0.1286749095,-0.1061858907,0.2125469893,0.020337332,-0.2196084112,-0.2715381086,-0.2055036873,0.0993707925,-0.1967610568,-0.1058103889,0.2419972718,0.0936895758,0.1949482709,-0.1839586198,-0.2100103498,-0.2731385827,-0.0082734302,0.0595859364,-0.0474168584,0.1841149777,0.0165117271,0.2255676687,0.1444560289,-0.4229511023,0.2088562548,-0.190148443,-0.2385510355,0.0926063582,0.2182631195,-0.1516928077,-0.2054022998,-0.3321664035,-0.4588548541,-0.4398188591,0.1718239784,-0.0543600917,0.1566059887,-0.3143665195,0.2442070991,0.0351690687,0.2324008942,0.0136316558,-0.0756383464,-0.1940948218,0.4806297421,-0.1420886219,-0.2508003414,0.0512710363,0.1974127591,0.0401312299,0.0472098663,-0.3657919466,-0.1209231243,-0.0864043906,0.0940967426,-0.2144783735,0.0691624358,0.2659473717,0.0285011437,0.0436960645,-0.0268319305,-0.3267620802,0.133175686,0.2165144533,0.1586675644,-0.0398017727,0.4220996201,0.0351590104,0.3567742705,0.2694926858,0.0391378067,0.1881526411,0.1792059094,0.2134340107,-0.18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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2415","title":"Cached dataset not loaded","comments":"It actually seems to happen all the time in above configuration:\r\n* the function `filter_by_duration` correctly loads cached processed dataset\r\n* the function `prepare_dataset` is always reexecuted\r\n\r\nI end up solving the issue by saving to disk my dataset at the end but I'm still wondering if it's a bug or limitation here.","body":"## Describe the bug\r\nI have a large dataset (common_voice, english) where I use several map and filter functions.\r\nSometimes my cached datasets after specific functions are not loaded.\r\nI always use the same arguments, same functions, no seed\u2026\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef filter_by_duration(batch):\r\n    return (\r\n        batch[\"duration\"] <= 10\r\n        and batch[\"duration\"] >= 1\r\n        and len(batch[\"target_text\"]) > 5\r\n    )\r\n\r\ndef prepare_dataset(batch):\r\n    batch[\"input_values\"] = processor(\r\n        batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]\r\n    ).input_values\r\n    with processor.as_target_processor():\r\n        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\r\n    return batch\r\n\r\ntrain_dataset = train_dataset.filter(\r\n    filter_by_duration,\r\n    remove_columns=[\"duration\"],\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# PROBLEM HERE -> below function is reexecuted and cache is not loaded\r\ntrain_dataset = train_dataset.map(\r\n    prepare_dataset,\r\n    remove_columns=train_dataset.column_names,\r\n    batch_size=training_args.per_device_train_batch_size,\r\n    batched=True,\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# Later in script\r\nset_caching_enabled(False)\r\n# apply map on trained model to eval\/test sets\r\n\r\n```\r\n\r\n## Expected results\r\nThe cached dataset should always be reloaded.\r\n\r\n## Actual results\r\nThe function is reexecuted.\r\n\r\nI have access to cached files `cache-xxxxx.arrow`.\r\nIs there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No","comment_length":53,"text":"Cached dataset not loaded \n ## Describe the bug\r\nI have a large dataset (common_voice, english) where I use several map and filter functions.\r\nSometimes my cached datasets after specific functions are not loaded.\r\nI always use the same arguments, same functions, no seed\u2026\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef filter_by_duration(batch):\r\n    return (\r\n        batch[\"duration\"] <= 10\r\n        and batch[\"duration\"] >= 1\r\n        and len(batch[\"target_text\"]) > 5\r\n    )\r\n\r\ndef prepare_dataset(batch):\r\n    batch[\"input_values\"] = processor(\r\n        batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]\r\n    ).input_values\r\n    with processor.as_target_processor():\r\n        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\r\n    return batch\r\n\r\ntrain_dataset = train_dataset.filter(\r\n    filter_by_duration,\r\n    remove_columns=[\"duration\"],\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# PROBLEM HERE -> below function is reexecuted and cache is not loaded\r\ntrain_dataset = train_dataset.map(\r\n    prepare_dataset,\r\n    remove_columns=train_dataset.column_names,\r\n    batch_size=training_args.per_device_train_batch_size,\r\n    batched=True,\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# Later in script\r\nset_caching_enabled(False)\r\n# apply map on trained model to eval\/test sets\r\n\r\n```\r\n\r\n## Expected results\r\nThe cached dataset should always be reloaded.\r\n\r\n## Actual results\r\nThe function is reexecuted.\r\n\r\nI have access to cached files `cache-xxxxx.arrow`.\r\nIs there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No \n It actually seems to happen all the time in above configuration:\r\n* the function `filter_by_duration` correctly loads cached processed dataset\r\n* the function `prepare_dataset` is always reexecuted\r\n\r\nI end up solving the issue by saving to disk my dataset at the end but I'm still wondering if it's a bug or limitation here.","embeddings":[-0.2978544831,-0.0423550606,-0.0758303478,0.4236162901,-0.0719461665,-0.0276887827,0.2326412499,0.3540042639,0.2089607567,-0.0697676763,0.0410520583,0.1886659861,-0.2849694192,-0.2252903432,0.1128703952,0.2975529432,0.0528036244,0.1180419326,-0.1090572625,-0.1540792584,-0.0150639126,0.2576181591,-0.0469234958,-0.093652606,-0.2029935271,0.1055824384,0.0262820609,0.1340813786,0.0921806395,-0.2740397155,0.5368282199,0.0920564905,0.1350365132,0.5165358186,-0.0001210673,0.1466437578,0.2859062552,-0.215739131,-0.2181520611,-0.1280438304,-0.2788822949,-0.0832798928,-0.1238182411,-0.2300582379,-0.0120474277,0.0122144446,-0.0481124632,-0.8536034226,0.459944576,0.0661035106,0.1565443277,-0.1820604801,-0.3796681762,0.1259405762,0.038267713,-0.07413809,0.0205731262,0.1491375864,0.1322319508,-0.0945808291,-0.0412483253,0.5044329762,-0.0829141736,-0.0581240319,0.0016792879,0.0538822822,0.2330061644,-0.1952100992,0.1459397674,0.1084957868,0.1963786185,-0.2124014497,-0.3501954079,-0.2727812529,-0.3075908422,-0.2135212123,0.3252165914,-0.1555833817,0.1468669474,0.1651790589,-0.6091327071,-0.0123855583,0.1913139969,-0.0265445951,-0.0197416823,-0.1129705459,-0.168192789,0.1520161331,0.1083762646,-0.0320031121,0.2745790482,-0.1657436788,-0.0053228564,0.4561800063,-0.0696609691,0.2084786743,0.3096645474,0.0638183728,-0.0398250967,0.2631065547,0.1751727313,0.1068111286,-0.0345857143,-0.0644117445,0.1306186318,0.2133267969,0.2404883653,0.2522965968,-0.049673751,-0.1146898791,-0.5343170762,0.0586863309,-0.1254806519,-0.2830507755,0.7806978822,0.1787222922,-0.0620118529,-0.401501447,0.064640604,0.041482646,-0.42515257,-0.3526496589,0.1482111812,0.4227842987,-0.22410357,0.2358968258,-0.0378687978,-0.1386456192,-0.2654689252,0.1546202749,-0.1668414921,-0.2038098127,-0.3884489834,0.1796002686,0.37313658,-0.4005204141,0.2701569796,0.0872643217,0.0604942627,-0.2748971879,0.165197596,-0.1340657026,0.5168511868,0.0657960027,0.0053642825,0.2182988822,0.1590651125,0.0378639959,-0.2242561281,0.1018682271,-0.5072760582,-0.1304601878,0.318675667,0.0764246434,-0.2396333665,0.3016883731,-0.209837392,0.2644234896,0.3779603243,-0.3355059624,-0.1166514009,-0.2069757432,-0.2666144669,-0.4785521328,0.4195537567,0.6090718508,-0.2946233451,-0.3102104068,-0.1382859498,-0.0281040333,-0.0599078014,0.0682887435,-0.2763515711,0.0992670506,-0.3678852618,0.0261640139,0.4927723706,-0.3572143912,-0.4876314104,0.0474483036,0.250859648,0.5972858667,-0.0802121386,-0.1463817358,0.1930467933,-0.2266284823,0.2826514542,0.1132326648,0.0213674568,-0.1429440379,-0.1665978581,-0.1039288044,0.1472658366,-0.1259729266,0.3338222206,0.1621620655,0.1865675449,-0.1245006174,0.1304099411,-0.0470252037,-0.0186559297,0.3194216788,-0.0883403495,-0.0465562865,0.1233384088,-0.2534306645,-0.3030246496,0.4161659777,-0.277041018,-0.5969229937,0.0289568231,0.0484691896,-0.207630232,-0.0645195767,-0.170820117,-0.2434182167,0.0316517092,0.2297227979,0.2284024656,-0.0210998245,-0.0545175895,0.5442660451,0.0102770561,-0.2740502059,-0.2433663309,0.1692317128,0.2576344907,-0.1716133356,-0.3055517972,0.0876919404,0.3064357936,-0.1491903067,-0.1570721865,0.1695891917,0.0594948232,0.2013689876,-0.2249587923,0.2929288149,-0.1600542963,-0.1222959012,0.1584830284,0.0284264144,-0.0416131653,-0.1636959612,-0.0239242911,0.3278394341,0.1689307094,0.0020596988,0.0516499132,-0.18602705,0.1895095557,-0.1054123193,-0.117535077,-0.2751552165,0.1547856033,-0.1886118948,0.4881473184,0.2394631356,0.0860364437,0.1719860733,0.4886368513,0.2141722739,0.1340205371,-0.0534790717,-0.3166265786,-0.1791449189,-0.1831711382,0.0051268237,0.162058875,-0.000577304,-0.0210616924,-0.023351742,0.0179936346,0.2533574998,0.0779890493,-0.2341278642,0.3470124602,0.1444575787,0.119849056,0.0359204039,-0.1744266897,0.0458373725,0.1211205721,-0.0410864949,-0.466869086,0.1364646703,-0.3292939067,0.2719795704,-0.2452030629,-0.0638307258,0.0286573898,-0.2246178985,-0.1919744313,0.3194694519,0.2880186141,0.2144105434,-0.3410937786,0.1146627292,-0.1894728988,-0.1603946239,-0.0100804102,-0.1131348908,-0.3489112854,-0.0086926008,0.1438347846,-0.2055016458,0.2075018138,-0.0255988836,-0.1401118487,-0.2677106261,-0.3595719039,0.0935123265,0.2070462853,0.3455573022,-0.2412908524,-0.0900674835,-0.20699431,-0.0637390614,0.1708731949,-0.2313913256,-0.1428841799,-0.0684183836,-0.0057264441,0.0865047798,0.0938635096,-0.4032764435,-0.3004500568,0.0697977245,-0.1072492748,-0.0847632661,0.0376866087,0.0733904243,-0.1441195458,-0.2160439491,-0.0327869318,-0.0495372526,-0.4220378101,-0.4178791642,0.2509204149,-0.2321704179,0.0369442664,-0.1274863183,-0.1213700399,-0.0014036332,0.5802029371,-0.3909652531,0.1654570103,-0.0096159568,0.0502640828,0.000515733,-0.1375783086,0.444417119,0.1967123896,0.1541717947,-0.2419928163,-0.411700666,0.0349578336,-0.0705297217,0.2873363793,0.1238329336,0.1701889038,-0.0201322362,1.0965393782,0.3184102774,-0.2103504837,0.0661234036,-0.0407362431,0.2642996013,-0.0109162964,-0.3952404857,-0.1654396057,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2415","title":"Cached dataset not loaded","comments":"Hi ! The hash used for caching `map` results is the fingerprint of the resulting dataset. It is computed using three things:\r\n- the old fingerprint of the dataset\r\n- the hash of the function\r\n- the hash of the other parameters passed to `map`\r\n\r\nYou can compute the hash of your function (or any python object) with\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nmy_func = lambda x: x + 1\r\nprint(Hasher.hash(my_func))\r\n```\r\n\r\nIf `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.","body":"## Describe the bug\r\nI have a large dataset (common_voice, english) where I use several map and filter functions.\r\nSometimes my cached datasets after specific functions are not loaded.\r\nI always use the same arguments, same functions, no seed\u2026\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef filter_by_duration(batch):\r\n    return (\r\n        batch[\"duration\"] <= 10\r\n        and batch[\"duration\"] >= 1\r\n        and len(batch[\"target_text\"]) > 5\r\n    )\r\n\r\ndef prepare_dataset(batch):\r\n    batch[\"input_values\"] = processor(\r\n        batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]\r\n    ).input_values\r\n    with processor.as_target_processor():\r\n        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\r\n    return batch\r\n\r\ntrain_dataset = train_dataset.filter(\r\n    filter_by_duration,\r\n    remove_columns=[\"duration\"],\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# PROBLEM HERE -> below function is reexecuted and cache is not loaded\r\ntrain_dataset = train_dataset.map(\r\n    prepare_dataset,\r\n    remove_columns=train_dataset.column_names,\r\n    batch_size=training_args.per_device_train_batch_size,\r\n    batched=True,\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# Later in script\r\nset_caching_enabled(False)\r\n# apply map on trained model to eval\/test sets\r\n\r\n```\r\n\r\n## Expected results\r\nThe cached dataset should always be reloaded.\r\n\r\n## Actual results\r\nThe function is reexecuted.\r\n\r\nI have access to cached files `cache-xxxxx.arrow`.\r\nIs there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No","comment_length":94,"text":"Cached dataset not loaded \n ## Describe the bug\r\nI have a large dataset (common_voice, english) where I use several map and filter functions.\r\nSometimes my cached datasets after specific functions are not loaded.\r\nI always use the same arguments, same functions, no seed\u2026\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef filter_by_duration(batch):\r\n    return (\r\n        batch[\"duration\"] <= 10\r\n        and batch[\"duration\"] >= 1\r\n        and len(batch[\"target_text\"]) > 5\r\n    )\r\n\r\ndef prepare_dataset(batch):\r\n    batch[\"input_values\"] = processor(\r\n        batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]\r\n    ).input_values\r\n    with processor.as_target_processor():\r\n        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\r\n    return batch\r\n\r\ntrain_dataset = train_dataset.filter(\r\n    filter_by_duration,\r\n    remove_columns=[\"duration\"],\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# PROBLEM HERE -> below function is reexecuted and cache is not loaded\r\ntrain_dataset = train_dataset.map(\r\n    prepare_dataset,\r\n    remove_columns=train_dataset.column_names,\r\n    batch_size=training_args.per_device_train_batch_size,\r\n    batched=True,\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# Later in script\r\nset_caching_enabled(False)\r\n# apply map on trained model to eval\/test sets\r\n\r\n```\r\n\r\n## Expected results\r\nThe cached dataset should always be reloaded.\r\n\r\n## Actual results\r\nThe function is reexecuted.\r\n\r\nI have access to cached files `cache-xxxxx.arrow`.\r\nIs there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No \n Hi ! The hash used for caching `map` results is the fingerprint of the resulting dataset. It is computed using three things:\r\n- the old fingerprint of the dataset\r\n- the hash of the function\r\n- the hash of the other parameters passed to `map`\r\n\r\nYou can compute the hash of your function (or any python object) with\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nmy_func = lambda x: x + 1\r\nprint(Hasher.hash(my_func))\r\n```\r\n\r\nIf `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2415","title":"Cached dataset not loaded","comments":"> If `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.\r\n\r\nYes I\u00a0think that was the issue.\r\n\r\nFor the hash of the function:\r\n* does it consider just the name or the actual code of the function\r\n* does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)","body":"## Describe the bug\r\nI have a large dataset (common_voice, english) where I use several map and filter functions.\r\nSometimes my cached datasets after specific functions are not loaded.\r\nI always use the same arguments, same functions, no seed\u2026\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef filter_by_duration(batch):\r\n    return (\r\n        batch[\"duration\"] <= 10\r\n        and batch[\"duration\"] >= 1\r\n        and len(batch[\"target_text\"]) > 5\r\n    )\r\n\r\ndef prepare_dataset(batch):\r\n    batch[\"input_values\"] = processor(\r\n        batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]\r\n    ).input_values\r\n    with processor.as_target_processor():\r\n        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\r\n    return batch\r\n\r\ntrain_dataset = train_dataset.filter(\r\n    filter_by_duration,\r\n    remove_columns=[\"duration\"],\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# PROBLEM HERE -> below function is reexecuted and cache is not loaded\r\ntrain_dataset = train_dataset.map(\r\n    prepare_dataset,\r\n    remove_columns=train_dataset.column_names,\r\n    batch_size=training_args.per_device_train_batch_size,\r\n    batched=True,\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# Later in script\r\nset_caching_enabled(False)\r\n# apply map on trained model to eval\/test sets\r\n\r\n```\r\n\r\n## Expected results\r\nThe cached dataset should always be reloaded.\r\n\r\n## Actual results\r\nThe function is reexecuted.\r\n\r\nI have access to cached files `cache-xxxxx.arrow`.\r\nIs there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No","comment_length":70,"text":"Cached dataset not loaded \n ## Describe the bug\r\nI have a large dataset (common_voice, english) where I use several map and filter functions.\r\nSometimes my cached datasets after specific functions are not loaded.\r\nI always use the same arguments, same functions, no seed\u2026\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef filter_by_duration(batch):\r\n    return (\r\n        batch[\"duration\"] <= 10\r\n        and batch[\"duration\"] >= 1\r\n        and len(batch[\"target_text\"]) > 5\r\n    )\r\n\r\ndef prepare_dataset(batch):\r\n    batch[\"input_values\"] = processor(\r\n        batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]\r\n    ).input_values\r\n    with processor.as_target_processor():\r\n        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\r\n    return batch\r\n\r\ntrain_dataset = train_dataset.filter(\r\n    filter_by_duration,\r\n    remove_columns=[\"duration\"],\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# PROBLEM HERE -> below function is reexecuted and cache is not loaded\r\ntrain_dataset = train_dataset.map(\r\n    prepare_dataset,\r\n    remove_columns=train_dataset.column_names,\r\n    batch_size=training_args.per_device_train_batch_size,\r\n    batched=True,\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# Later in script\r\nset_caching_enabled(False)\r\n# apply map on trained model to eval\/test sets\r\n\r\n```\r\n\r\n## Expected results\r\nThe cached dataset should always be reloaded.\r\n\r\n## Actual results\r\nThe function is reexecuted.\r\n\r\nI have access to cached files `cache-xxxxx.arrow`.\r\nIs there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No \n > If `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.\r\n\r\nYes I\u00a0think that was the issue.\r\n\r\nFor the hash of the function:\r\n* does it consider just the name or the actual code of the function\r\n* does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)","embeddings":[-0.2978544831,-0.0423550606,-0.0758303478,0.4236162901,-0.0719461665,-0.0276887827,0.2326412499,0.3540042639,0.2089607567,-0.0697676763,0.0410520583,0.1886659861,-0.2849694192,-0.2252903432,0.1128703952,0.2975529432,0.0528036244,0.1180419326,-0.1090572625,-0.1540792584,-0.0150639126,0.2576181591,-0.0469234958,-0.093652606,-0.2029935271,0.1055824384,0.0262820609,0.1340813786,0.0921806395,-0.2740397155,0.5368282199,0.0920564905,0.1350365132,0.5165358186,-0.0001210673,0.1466437578,0.2859062552,-0.215739131,-0.2181520611,-0.1280438304,-0.2788822949,-0.0832798928,-0.1238182411,-0.2300582379,-0.0120474277,0.0122144446,-0.0481124632,-0.8536034226,0.459944576,0.0661035106,0.1565443277,-0.1820604801,-0.3796681762,0.1259405762,0.038267713,-0.07413809,0.0205731262,0.1491375864,0.1322319508,-0.0945808291,-0.0412483253,0.5044329762,-0.0829141736,-0.0581240319,0.0016792879,0.0538822822,0.2330061644,-0.1952100992,0.1459397674,0.1084957868,0.1963786185,-0.2124014497,-0.3501954079,-0.2727812529,-0.3075908422,-0.2135212123,0.3252165914,-0.1555833817,0.1468669474,0.1651790589,-0.6091327071,-0.0123855583,0.1913139969,-0.0265445951,-0.0197416823,-0.1129705459,-0.168192789,0.1520161331,0.1083762646,-0.0320031121,0.2745790482,-0.1657436788,-0.0053228564,0.4561800063,-0.0696609691,0.2084786743,0.3096645474,0.0638183728,-0.0398250967,0.2631065547,0.1751727313,0.1068111286,-0.0345857143,-0.0644117445,0.1306186318,0.2133267969,0.2404883653,0.2522965968,-0.049673751,-0.1146898791,-0.5343170762,0.0586863309,-0.1254806519,-0.2830507755,0.7806978822,0.1787222922,-0.0620118529,-0.401501447,0.064640604,0.041482646,-0.42515257,-0.3526496589,0.1482111812,0.4227842987,-0.22410357,0.2358968258,-0.0378687978,-0.1386456192,-0.2654689252,0.1546202749,-0.1668414921,-0.2038098127,-0.3884489834,0.1796002686,0.37313658,-0.4005204141,0.2701569796,0.0872643217,0.0604942627,-0.2748971879,0.165197596,-0.1340657026,0.5168511868,0.0657960027,0.0053642825,0.2182988822,0.1590651125,0.0378639959,-0.2242561281,0.1018682271,-0.5072760582,-0.1304601878,0.318675667,0.0764246434,-0.2396333665,0.3016883731,-0.209837392,0.2644234896,0.3779603243,-0.3355059624,-0.1166514009,-0.2069757432,-0.2666144669,-0.4785521328,0.4195537567,0.6090718508,-0.2946233451,-0.3102104068,-0.1382859498,-0.0281040333,-0.0599078014,0.0682887435,-0.2763515711,0.0992670506,-0.3678852618,0.0261640139,0.4927723706,-0.3572143912,-0.4876314104,0.0474483036,0.250859648,0.5972858667,-0.0802121386,-0.1463817358,0.1930467933,-0.2266284823,0.2826514542,0.1132326648,0.0213674568,-0.1429440379,-0.1665978581,-0.1039288044,0.1472658366,-0.1259729266,0.3338222206,0.1621620655,0.1865675449,-0.1245006174,0.1304099411,-0.0470252037,-0.0186559297,0.3194216788,-0.0883403495,-0.0465562865,0.1233384088,-0.2534306645,-0.3030246496,0.4161659777,-0.277041018,-0.5969229937,0.0289568231,0.0484691896,-0.207630232,-0.0645195767,-0.170820117,-0.2434182167,0.0316517092,0.2297227979,0.2284024656,-0.0210998245,-0.0545175895,0.5442660451,0.0102770561,-0.2740502059,-0.2433663309,0.1692317128,0.2576344907,-0.1716133356,-0.3055517972,0.0876919404,0.3064357936,-0.1491903067,-0.1570721865,0.1695891917,0.0594948232,0.2013689876,-0.2249587923,0.2929288149,-0.1600542963,-0.1222959012,0.1584830284,0.0284264144,-0.0416131653,-0.1636959612,-0.0239242911,0.3278394341,0.1689307094,0.0020596988,0.0516499132,-0.18602705,0.1895095557,-0.1054123193,-0.117535077,-0.2751552165,0.1547856033,-0.1886118948,0.4881473184,0.2394631356,0.0860364437,0.1719860733,0.4886368513,0.2141722739,0.1340205371,-0.0534790717,-0.3166265786,-0.1791449189,-0.1831711382,0.0051268237,0.162058875,-0.000577304,-0.0210616924,-0.023351742,0.0179936346,0.2533574998,0.0779890493,-0.2341278642,0.3470124602,0.1444575787,0.119849056,0.0359204039,-0.1744266897,0.0458373725,0.1211205721,-0.0410864949,-0.466869086,0.1364646703,-0.3292939067,0.2719795704,-0.2452030629,-0.0638307258,0.0286573898,-0.2246178985,-0.1919744313,0.3194694519,0.2880186141,0.2144105434,-0.3410937786,0.1146627292,-0.1894728988,-0.1603946239,-0.0100804102,-0.1131348908,-0.3489112854,-0.0086926008,0.1438347846,-0.2055016458,0.2075018138,-0.0255988836,-0.1401118487,-0.2677106261,-0.3595719039,0.0935123265,0.2070462853,0.3455573022,-0.2412908524,-0.0900674835,-0.20699431,-0.0637390614,0.1708731949,-0.2313913256,-0.1428841799,-0.0684183836,-0.0057264441,0.0865047798,0.0938635096,-0.4032764435,-0.3004500568,0.0697977245,-0.1072492748,-0.0847632661,0.0376866087,0.0733904243,-0.1441195458,-0.2160439491,-0.0327869318,-0.0495372526,-0.4220378101,-0.4178791642,0.2509204149,-0.2321704179,0.0369442664,-0.1274863183,-0.1213700399,-0.0014036332,0.5802029371,-0.3909652531,0.1654570103,-0.0096159568,0.0502640828,0.000515733,-0.1375783086,0.444417119,0.1967123896,0.1541717947,-0.2419928163,-0.411700666,0.0349578336,-0.0705297217,0.2873363793,0.1238329336,0.1701889038,-0.0201322362,1.0965393782,0.3184102774,-0.2103504837,0.0661234036,-0.0407362431,0.2642996013,-0.0109162964,-0.3952404857,-0.1654396057,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2415","title":"Cached dataset not loaded","comments":"> does it consider just the name or the actual code of the function\r\n\r\nIt looks at the name and the actual code and all variables such as recursively. It uses `dill` to do so, which is based on `pickle`.\r\nBasically the hash is computed using the pickle bytes of your function (computed using `dill` to support most python objects).\r\n\r\n> does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)\r\n\r\nYes it does thanks to recursive pickling.","body":"## Describe the bug\r\nI have a large dataset (common_voice, english) where I use several map and filter functions.\r\nSometimes my cached datasets after specific functions are not loaded.\r\nI always use the same arguments, same functions, no seed\u2026\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef filter_by_duration(batch):\r\n    return (\r\n        batch[\"duration\"] <= 10\r\n        and batch[\"duration\"] >= 1\r\n        and len(batch[\"target_text\"]) > 5\r\n    )\r\n\r\ndef prepare_dataset(batch):\r\n    batch[\"input_values\"] = processor(\r\n        batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]\r\n    ).input_values\r\n    with processor.as_target_processor():\r\n        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\r\n    return batch\r\n\r\ntrain_dataset = train_dataset.filter(\r\n    filter_by_duration,\r\n    remove_columns=[\"duration\"],\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# PROBLEM HERE -> below function is reexecuted and cache is not loaded\r\ntrain_dataset = train_dataset.map(\r\n    prepare_dataset,\r\n    remove_columns=train_dataset.column_names,\r\n    batch_size=training_args.per_device_train_batch_size,\r\n    batched=True,\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# Later in script\r\nset_caching_enabled(False)\r\n# apply map on trained model to eval\/test sets\r\n\r\n```\r\n\r\n## Expected results\r\nThe cached dataset should always be reloaded.\r\n\r\n## Actual results\r\nThe function is reexecuted.\r\n\r\nI have access to cached files `cache-xxxxx.arrow`.\r\nIs there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No","comment_length":87,"text":"Cached dataset not loaded \n ## Describe the bug\r\nI have a large dataset (common_voice, english) where I use several map and filter functions.\r\nSometimes my cached datasets after specific functions are not loaded.\r\nI always use the same arguments, same functions, no seed\u2026\r\n\r\n## Steps to reproduce the bug\r\n```python\r\ndef filter_by_duration(batch):\r\n    return (\r\n        batch[\"duration\"] <= 10\r\n        and batch[\"duration\"] >= 1\r\n        and len(batch[\"target_text\"]) > 5\r\n    )\r\n\r\ndef prepare_dataset(batch):\r\n    batch[\"input_values\"] = processor(\r\n        batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0]\r\n    ).input_values\r\n    with processor.as_target_processor():\r\n        batch[\"labels\"] = processor(batch[\"target_text\"]).input_ids\r\n    return batch\r\n\r\ntrain_dataset = train_dataset.filter(\r\n    filter_by_duration,\r\n    remove_columns=[\"duration\"],\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# PROBLEM HERE -> below function is reexecuted and cache is not loaded\r\ntrain_dataset = train_dataset.map(\r\n    prepare_dataset,\r\n    remove_columns=train_dataset.column_names,\r\n    batch_size=training_args.per_device_train_batch_size,\r\n    batched=True,\r\n    num_proc=data_args.preprocessing_num_workers,\r\n)\r\n\r\n# Later in script\r\nset_caching_enabled(False)\r\n# apply map on trained model to eval\/test sets\r\n\r\n```\r\n\r\n## Expected results\r\nThe cached dataset should always be reloaded.\r\n\r\n## Actual results\r\nThe function is reexecuted.\r\n\r\nI have access to cached files `cache-xxxxx.arrow`.\r\nIs there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.5\r\n- PyTorch version (GPU?): 1.8.1+cu102 (True)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No \n > does it consider just the name or the actual code of the function\r\n\r\nIt looks at the name and the actual code and all variables such as recursively. It uses `dill` to do so, which is based on `pickle`.\r\nBasically the hash is computed using the pickle bytes of your function (computed using `dill` to support most python objects).\r\n\r\n> does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)\r\n\r\nYes it does thanks to recursive pickling.","embeddings":[-0.2978544831,-0.0423550606,-0.0758303478,0.4236162901,-0.0719461665,-0.0276887827,0.2326412499,0.3540042639,0.2089607567,-0.0697676763,0.0410520583,0.1886659861,-0.2849694192,-0.2252903432,0.1128703952,0.2975529432,0.0528036244,0.1180419326,-0.1090572625,-0.1540792584,-0.0150639126,0.2576181591,-0.0469234958,-0.093652606,-0.2029935271,0.1055824384,0.0262820609,0.1340813786,0.0921806395,-0.2740397155,0.5368282199,0.0920564905,0.1350365132,0.5165358186,-0.0001210673,0.1466437578,0.2859062552,-0.215739131,-0.2181520611,-0.1280438304,-0.2788822949,-0.0832798928,-0.1238182411,-0.2300582379,-0.0120474277,0.0122144446,-0.0481124632,-0.8536034226,0.459944576,0.0661035106,0.1565443277,-0.1820604801,-0.3796681762,0.1259405762,0.038267713,-0.07413809,0.0205731262,0.1491375864,0.1322319508,-0.0945808291,-0.0412483253,0.5044329762,-0.0829141736,-0.0581240319,0.0016792879,0.0538822822,0.2330061644,-0.1952100992,0.1459397674,0.1084957868,0.1963786185,-0.2124014497,-0.3501954079,-0.2727812529,-0.3075908422,-0.2135212123,0.3252165914,-0.1555833817,0.1468669474,0.1651790589,-0.6091327071,-0.0123855583,0.1913139969,-0.0265445951,-0.0197416823,-0.1129705459,-0.168192789,0.1520161331,0.1083762646,-0.0320031121,0.2745790482,-0.1657436788,-0.0053228564,0.4561800063,-0.0696609691,0.2084786743,0.3096645474,0.0638183728,-0.0398250967,0.2631065547,0.1751727313,0.1068111286,-0.0345857143,-0.0644117445,0.1306186318,0.2133267969,0.2404883653,0.2522965968,-0.049673751,-0.1146898791,-0.5343170762,0.0586863309,-0.1254806519,-0.2830507755,0.7806978822,0.1787222922,-0.0620118529,-0.401501447,0.064640604,0.041482646,-0.42515257,-0.3526496589,0.1482111812,0.4227842987,-0.22410357,0.2358968258,-0.0378687978,-0.1386456192,-0.2654689252,0.1546202749,-0.1668414921,-0.2038098127,-0.3884489834,0.1796002686,0.37313658,-0.4005204141,0.2701569796,0.0872643217,0.0604942627,-0.2748971879,0.165197596,-0.1340657026,0.5168511868,0.0657960027,0.0053642825,0.2182988822,0.1590651125,0.0378639959,-0.2242561281,0.1018682271,-0.5072760582,-0.1304601878,0.318675667,0.0764246434,-0.2396333665,0.3016883731,-0.209837392,0.2644234896,0.3779603243,-0.3355059624,-0.1166514009,-0.2069757432,-0.2666144669,-0.4785521328,0.4195537567,0.6090718508,-0.2946233451,-0.3102104068,-0.1382859498,-0.0281040333,-0.0599078014,0.0682887435,-0.2763515711,0.0992670506,-0.3678852618,0.0261640139,0.4927723706,-0.3572143912,-0.4876314104,0.0474483036,0.250859648,0.5972858667,-0.0802121386,-0.1463817358,0.1930467933,-0.2266284823,0.2826514542,0.1132326648,0.0213674568,-0.1429440379,-0.1665978581,-0.1039288044,0.1472658366,-0.1259729266,0.3338222206,0.1621620655,0.1865675449,-0.1245006174,0.1304099411,-0.0470252037,-0.0186559297,0.3194216788,-0.0883403495,-0.0465562865,0.1233384088,-0.2534306645,-0.3030246496,0.4161659777,-0.277041018,-0.5969229937,0.0289568231,0.0484691896,-0.207630232,-0.0645195767,-0.170820117,-0.2434182167,0.0316517092,0.2297227979,0.2284024656,-0.0210998245,-0.0545175895,0.5442660451,0.0102770561,-0.2740502059,-0.2433663309,0.1692317128,0.2576344907,-0.1716133356,-0.3055517972,0.0876919404,0.3064357936,-0.1491903067,-0.1570721865,0.1695891917,0.0594948232,0.2013689876,-0.2249587923,0.2929288149,-0.1600542963,-0.1222959012,0.1584830284,0.0284264144,-0.0416131653,-0.1636959612,-0.0239242911,0.3278394341,0.1689307094,0.0020596988,0.0516499132,-0.18602705,0.1895095557,-0.1054123193,-0.117535077,-0.2751552165,0.1547856033,-0.1886118948,0.4881473184,0.2394631356,0.0860364437,0.1719860733,0.4886368513,0.2141722739,0.1340205371,-0.0534790717,-0.3166265786,-0.1791449189,-0.1831711382,0.0051268237,0.162058875,-0.000577304,-0.0210616924,-0.023351742,0.0179936346,0.2533574998,0.0779890493,-0.2341278642,0.3470124602,0.1444575787,0.119849056,0.0359204039,-0.1744266897,0.0458373725,0.1211205721,-0.0410864949,-0.466869086,0.1364646703,-0.3292939067,0.2719795704,-0.2452030629,-0.0638307258,0.0286573898,-0.2246178985,-0.1919744313,0.3194694519,0.2880186141,0.2144105434,-0.3410937786,0.1146627292,-0.1894728988,-0.1603946239,-0.0100804102,-0.1131348908,-0.3489112854,-0.0086926008,0.1438347846,-0.2055016458,0.2075018138,-0.0255988836,-0.1401118487,-0.2677106261,-0.3595719039,0.0935123265,0.2070462853,0.3455573022,-0.2412908524,-0.0900674835,-0.20699431,-0.0637390614,0.1708731949,-0.2313913256,-0.1428841799,-0.0684183836,-0.0057264441,0.0865047798,0.0938635096,-0.4032764435,-0.3004500568,0.0697977245,-0.1072492748,-0.0847632661,0.0376866087,0.0733904243,-0.1441195458,-0.2160439491,-0.0327869318,-0.0495372526,-0.4220378101,-0.4178791642,0.2509204149,-0.2321704179,0.0369442664,-0.1274863183,-0.1213700399,-0.0014036332,0.5802029371,-0.3909652531,0.1654570103,-0.0096159568,0.0502640828,0.000515733,-0.1375783086,0.444417119,0.1967123896,0.1541717947,-0.2419928163,-0.411700666,0.0349578336,-0.0705297217,0.2873363793,0.1238329336,0.1701889038,-0.0201322362,1.0965393782,0.3184102774,-0.2103504837,0.0661234036,-0.0407362431,0.2642996013,-0.0109162964,-0.3952404857,-0.16543960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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2413","title":"AttributeError: 'DatasetInfo' object has no attribute 'task_templates'","comments":"Hi ! Can you try using a more up-to-date version ? We added the task_templates in `datasets` 1.7.0.\r\n\r\nIdeally when you're working on new datasets, you should install and use the local version of your fork of `datasets`. Here I think you tried to run the 1.7.0 tests with the 1.6.2 code","body":"## Describe the bug\r\nHello, \r\nI'm trying to add dataset and contribute, but test keep fail with below cli.\r\n` RUN_SLOW=1 pytest tests\/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>`\r\n\r\n## Steps to reproduce the bug\r\nIt seems like a bug when I see an error with the existing dataset, not the dataset I'm trying to add.\r\n\r\n` RUN_SLOW=1 pytest tests\/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<any_dataset>`\r\n\r\n\r\n## Expected results\r\nAll test passed\r\n\r\n## Actual results\r\n```\r\n                # check that dataset is not empty\r\n                self.parent.assertListEqual(sorted(dataset_builder.info.splits.keys()), sorted(dataset))\r\n                for split in dataset_builder.info.splits.keys():\r\n                    # check that loaded datset is not empty\r\n                    self.parent.assertTrue(len(dataset[split]) > 0)\r\n    \r\n                # check that we can cast features for each task template\r\n>               task_templates = dataset_builder.info.task_templates\r\nE               AttributeError: 'DatasetInfo' object has no attribute 'task_templates'\r\n\r\ntests\/test_dataset_common.py:175: AttributeError\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Darwin-20.4.0-x86_64-i386-64bit\r\n- Python version: 3.7.7\r\n- PyTorch version (GPU?): 1.7.0 (False)\r\n- Tensorflow version (GPU?): 2.3.0 (False)\r\n- Using GPU in script?: No\r\n- Using distributed or parallel set-up in script?: No\r\n","comment_length":52,"text":"AttributeError: 'DatasetInfo' object has no attribute 'task_templates' \n ## Describe the bug\r\nHello, \r\nI'm trying to add dataset and contribute, but test keep fail with below cli.\r\n` RUN_SLOW=1 pytest tests\/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>`\r\n\r\n## Steps to reproduce the bug\r\nIt seems like a bug when I see an error with the existing dataset, not the dataset I'm trying to add.\r\n\r\n` RUN_SLOW=1 pytest tests\/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<any_dataset>`\r\n\r\n\r\n## Expected results\r\nAll test passed\r\n\r\n## Actual results\r\n```\r\n                # check that dataset is not empty\r\n                self.parent.assertListEqual(sorted(dataset_builder.info.splits.keys()), sorted(dataset))\r\n                for split in dataset_builder.info.splits.keys():\r\n                    # check that loaded datset is not empty\r\n                    self.parent.assertTrue(len(dataset[split]) > 0)\r\n    \r\n                # check that we can cast features for each task template\r\n>               task_templates = dataset_builder.info.task_templates\r\nE               AttributeError: 'DatasetInfo' object has no attribute 'task_templates'\r\n\r\ntests\/test_dataset_common.py:175: AttributeError\r\n```\r\n\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.2\r\n- Platform: Darwin-20.4.0-x86_64-i386-64bit\r\n- Python version: 3.7.7\r\n- PyTorch version (GPU?): 1.7.0 (False)\r\n- Tensorflow version (GPU?): 2.3.0 (False)\r\n- Using GPU in script?: No\r\n- Using distributed or parallel set-up in script?: No\r\n \n Hi ! Can you try using a more up-to-date version ? We added the task_templates in `datasets` 1.7.0.\r\n\r\nIdeally when you're working on new datasets, you should install and use the local version of your fork of `datasets`. Here I think you tried to run the 1.7.0 tests with the 1.6.2 code","embeddings":[-0.4735528529,-0.2719739676,-0.0121506173,0.0597860627,0.2671819627,0.2102233022,0.2545935214,0.3851537704,0.0319131128,0.1861087233,0.0951910689,0.4420559108,-0.1509742588,0.0039889752,0.0563462004,-0.0786740929,-0.0314524733,0.1539635062,0.3197634816,0.0171360672,-0.2879475057,-0.1133656055,-0.0043047555,-0.1182009429,-0.1542956233,-0.171497196,-0.1579617858,0.0511678495,0.1129547581,-0.3455854356,0.4298336804,-0.1083598733,-0.0093474761,0.4918485284,-0.00012102,0.178600207,0.3251259029,-0.0006494033,-0.4968328774,-0.065669544,-0.391399622,-0.3287818432,0.0636085197,-0.1634951979,-0.2025725842,-0.3342350125,-0.0632523447,-0.3298051059,-0.086768806,0.4178372025,0.1655729413,0.5218763351,-0.1509704888,-0.4315092266,0.121765703,0.1786002666,-0.2803600729,0.170839712,0.6216207147,-0.1034466848,0.3462252319,0.0053155129,-0.0010401886,0.1662091613,0.1322756559,-0.1352190524,0.223420009,-0.3972377777,-0.0180807989,0.0762008876,0.5973716378,-0.2986184955,-0.4307505786,-0.1419692785,0.2230953574,0.0322043635,-0.0421730131,-0.1669620126,-0.2400552779,-0.0351164341,-0.0903396234,-0.1531327963,-0.1619226635,0.0752020255,0.1189873144,0.022189403,0.1850389838,0.111264661,-0.2130625248,0.1433154047,-0.09717796,-0.0581392869,-0.2552188039,0.1132180989,-0.2312996686,-0.2929350138,0.1680669487,-0.3805031776,0.2416609228,0.3471602798,-0.0348081961,-0.1617386937,0.1267192066,0.2000541091,0.030334333,0.2343725264,0.1062364429,0.2329974473,0.0663586408,-0.1103372946,0.0122648925,-0.1069707945,0.0525073558,-0.2436966449,0.2287586629,0.1440772712,0.6398686767,-0.0755982921,-0.560692668,-0.0253961179,-0.0251773056,0.0050571836,0.2622101605,0.4388638139,0.0128706601,-0.097531423,-0.1945374012,0.3225837648,-0.2628580034,-0.0823602229,-0.2578427196,0.2040020376,0.0043739043,-0.1395382732,0.322098285,-0.1710180491,0.2165559679,0.0552464873,0.1847632378,-0.0080519458,0.2094966918,-0.3163890839,-0.2044803649,0.3319312632,0.0183881335,0.3858396113,0.0564305186,-0.293910861,-0.1657142639,0.2179040313,-0.2973391414,-0.5190382004,-0.0173203126,0.2060691118,-0.0601036549,-0.1723547131,-0.35112831,-0.0557960756,0.1549577266,-0.1310038269,-0.1286191493,-0.3681014478,-0.1906939,-0.1142805442,0.2976214886,0.48236835,-0.3817064464,-0.0535848923,-0.0898585692,-0.1456292123,0.0348261669,-0.0019266937,-0.0912468433,0.3778104186,-0.2277224213,-0.0918507427,0.3079971969,-0.4803474844,-0.3461225927,0.3316580057,-0.0846224725,-0.0342730843,0.1829599142,0.0866437927,0.2815199792,-0.2343328148,0.000161327,0.1537230611,-0.2427243888,-0.0347398706,-0.2711488903,-0.0072603929,0.4636033475,0.2265042663,0.176244691,0.0114101162,0.2185559273,0.0250426698,0.064493753,-0.1237813979,0.1112319976,0.1950040609,0.7014116049,-0.1778082103,0.2300510108,-0.2532387674,-0.686537683,0.344271034,0.298545599,0.0529374108,-0.0783262998,0.0465380736,-0.4575315416,0.2122500539,-0.4423654974,-0.3045431077,-0.023945054,0.1752167642,-0.0368821435,-0.2862261534,-0.1815100312,0.1214267612,-0.3243822455,0.0647803023,0.1674654931,0.3153729439,-0.0987430438,-0.2384933978,-0.1168052033,0.0454538949,0.1586704254,-0.1916502714,-0.2262795568,0.1804823279,0.071852535,0.0765519291,0.2165350169,-0.0281479061,0.2404047698,-0.2461782694,-0.1484141648,0.1785614192,0.2710611224,-0.176605463,-0.0259797741,0.0694446266,0.0350021124,0.3100695014,-0.0453376472,-0.0502872467,0.3370392323,-0.0335725881,0.101717338,-0.0198310129,0.258048296,0.0984007344,0.4189310968,-0.2681364119,-0.301053375,0.1251331717,0.0404604636,0.0795261264,-0.0336546488,0.0976856053,-0.1843661219,0.210137248,0.1349643618,0.1800945401,0.6353419423,0.173872456,-0.1109515801,-0.1063899249,-0.1677987128,-0.0614228733,0.0778772384,-0.0424953401,0.2484363019,0.3933539987,0.2878506482,0.0008509708,-0.1763962656,-0.4252843559,0.3472664952,0.4965883493,-0.3807483613,-0.0114441672,0.042327635,-0.1184769869,-0.1318303198,-0.1406994015,0.0334356315,-0.3064957261,0.1057032496,0.1973102242,-0.014866285,0.2224334031,0.0229597576,0.0128619829,0.0781135038,-0.5534873605,-0.3724315464,-0.1585111469,-0.1578884572,0.0962258652,0.2058898956,-0.2709441483,0.4508914053,-0.0215346403,-0.0256652683,-0.2439593822,-0.2913550436,-0.0090347659,-0.072403416,0.6468446851,-0.0969461352,0.301179409,0.2462478578,-0.2688610554,0.4366424978,-0.0899263397,-0.2616486251,0.1155974641,-0.0554235242,-0.2192442715,-0.1361955702,-0.6507858634,-0.276322335,-0.2776764035,-0.1019040868,0.1167071089,0.0516929589,0.4311493337,0.2770000398,0.125761807,0.2852220535,0.2057918161,0.1320800483,0.0203058086,0.2052997798,-0.041547291,-0.3083122969,-0.0945478156,-0.1930867136,0.1220797598,0.2034581453,-0.1758288741,-0.3868483305,-0.2175997049,0.523040235,-0.1018581465,-0.1444369555,0.394279778,0.0856026635,0.0116369911,-0.1326035112,-0.1141730323,-0.0093503641,0.1062882841,0.1252449304,-0.1373667717,0.2916113138,-0.1365160644,0.6200549006,0.1993594617,-0.1321611255,0.2084064335,-0.3504512608,0.2625141442,-0.1612603813,-0.7129328251,0.2227699161,0.064211905,-0.330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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2407","title":".map() function got an unexpected keyword argument 'cache_file_name'","comments":"Hi @cindyxinyiwang,\r\nDid you try adding `.arrow` after `cache_file_name` argument? Here I think they're expecting something like that only for a cache file:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e08362256fb157c0b3038437fc0d7a0bbb50de5c\/src\/datasets\/arrow_dataset.py#L1556-L1558","body":"## Describe the bug\r\n\r\nI'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that \".map() function got an unexpected keyword argument 'cache_file_name'\". \r\n\r\nI believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function.\r\n\r\nHere is the code I use\r\n## Steps to reproduce the bug\r\n```datasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n   cache_file_name=\"my_tokenized_file\"\r\n)\r\n```\r\n\r\n## Actual results\r\n    tokenized_datasets = datasets.map(\r\nTypeError: map() got an unexpected keyword argument 'cache_file_name'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.6.2\r\n- Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10\r\n- Python version:3.8.5\r\n- PyArrow version:3.0.0\r\n","comment_length":24,"text":".map() function got an unexpected keyword argument 'cache_file_name' \n ## Describe the bug\r\n\r\nI'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that \".map() function got an unexpected keyword argument 'cache_file_name'\". \r\n\r\nI believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function.\r\n\r\nHere is the code I use\r\n## Steps to reproduce the bug\r\n```datasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n   cache_file_name=\"my_tokenized_file\"\r\n)\r\n```\r\n\r\n## Actual results\r\n    tokenized_datasets = datasets.map(\r\nTypeError: map() got an unexpected keyword argument 'cache_file_name'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.6.2\r\n- Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10\r\n- Python version:3.8.5\r\n- PyArrow version:3.0.0\r\n \n Hi @cindyxinyiwang,\r\nDid you try adding `.arrow` after `cache_file_name` argument? Here I think they're expecting something like that only for a cache file:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e08362256fb157c0b3038437fc0d7a0bbb50de5c\/src\/datasets\/arrow_dataset.py#L1556-L1558","embeddings":[0.1323000193,0.0689882264,0.0382361263,0.0000564463,0.0520310178,0.2963714898,0.1708019078,0.3389790654,-0.0635626316,-0.0203007851,0.1289214194,0.610024035,-0.2410923839,-0.5483763218,0.0488028489,0.090259932,0.3929358423,-0.0865352526,0.2103406191,0.0160696357,-0.3766417205,0.3897094429,-0.0564443506,0.2779166102,-0.4511603117,-0.2072949708,-0.0894433111,-0.0664880276,-0.0896062851,-0.391181618,0.2553226352,0.0901256204,-0.0756616443,0.489871949,-0.0001236526,-0.0626008809,0.0621398427,-0.164627403,-0.2050175667,-0.3376171291,-0.108075425,0.0543018244,-0.0680716634,-0.497097224,0.2129514813,-0.0833946764,0.1886314303,-0.6428586841,0.1455971301,0.172502622,0.1301087439,-0.1011054739,-0.2413280308,-0.1786019355,0.1032098681,-0.1168403178,-0.0114621865,0.2041410208,0.035358049,-0.2602057159,0.1032075211,0.3366818726,-0.3803628087,-0.0966832191,0.4977585375,0.0177516527,-0.2116977423,-0.3391501904,0.2500306368,-0.1566718221,0.1236489341,-0.4110817015,-0.3522394896,-0.1329432279,-0.3212546408,-0.1088350564,0.1613844782,-0.0994668975,-0.1431579143,-0.0172122009,0.0187122691,-0.2126890421,0.0077328901,0.1374987811,0.2252310216,0.1087041497,-0.1958861947,0.3618341386,0.3491643369,-0.0833931491,-0.2166385353,-0.3524973094,-0.0401295535,0.3894569576,-0.1926616281,-0.0108110849,0.0461867489,0.1170019284,0.0980422422,-0.3093523681,-0.0926846117,-0.145090729,0.0813978985,0.3073478937,0.2356880158,0.2836824954,0.3462310135,0.4869079292,-0.1531836241,-0.3088080585,-0.2916905582,-0.2059519738,0.0763019547,-0.1290071458,0.5090178847,-0.0678104758,0.2955312729,-0.1016577184,0.0983532891,0.0917264596,-0.3417958319,-0.0036549708,-0.0990243405,0.341468215,0.1448268741,-0.0943260938,-0.0087400302,0.2082662582,0.0484691709,-0.1415781528,-0.1375662535,0.0520832837,-0.253726095,0.2101465315,-0.1983944476,-0.0464085862,0.1395723373,0.0137630878,0.170556888,-0.1162838638,0.160295859,-0.0472937115,0.2373288572,-0.1563026309,-0.1153677031,0.4976298213,0.2821669877,-0.2278868705,-0.1183428764,0.0254225265,-0.5410766602,-0.3162823617,0.000062654,0.022127213,-0.0346530601,0.1262811273,-0.426648587,0.0120566403,0.5163833499,-0.2676182985,0.0308113955,-0.0304689668,-0.2301573753,-0.4813354611,0.020797763,0.3271371126,-0.0734873638,-0.0302486084,-0.0271823667,0.1944456547,-0.0033592696,0.1681511849,-0.0935864821,0.1899894774,-0.2116828561,0.7205870152,0.5101905465,-0.2484844923,-0.5775424838,0.273391515,-0.2147663087,0.013901134,-0.0861738399,0.0286193695,0.4520129263,-0.1365366876,0.437209785,-0.0731702372,-0.0409542918,0.1518031508,-0.2699029744,-0.2399378121,0.1261731833,-0.2583991587,-0.0506732762,0.1763263047,0.0805187747,0.0621099956,0.1262601614,0.097930178,0.234285295,0.0838978663,0.2890298367,0.0728417113,0.0257608369,-0.008435946,-0.2922082543,0.0839115307,0.196461454,-0.4200127721,-0.3298364878,-0.4760627747,-0.0561076403,0.0424852632,-0.1884646863,-0.0734005347,0.0064832517,0.0784215108,0.0639972761,0.1206236333,-0.0759832785,-0.0023852827,0.0435928106,-0.0542773716,0.0612000935,-0.2633388042,-0.0796586573,-0.1117172316,-0.2368734181,-0.1921242774,0.4008702934,-0.0590945631,-0.3225570917,0.4013506174,-0.0077051329,0.117150262,-0.0612250417,0.2972923815,-0.2990592122,-0.0420838594,0.0486955419,0.0069589787,0.0879930481,-0.0257945675,0.1187412217,0.1925860494,0.2606340051,0.1826837212,-0.2336699963,0.2960456014,-0.0235924106,0.0437992625,0.0012439389,-0.2449383885,-0.0402426831,-0.1408083737,0.3976156712,-0.1182780489,0.0518170111,0.0109181646,0.4879014492,0.125406608,0.1525567174,-0.0630090386,-0.1764896959,0.025393324,0.089999795,0.3629103899,0.5742313266,-0.0069505544,0.0451245978,-0.0071809585,0.1451582313,0.0854675472,0.0900083482,0.0456476398,-0.1075735614,-0.2344181985,-0.0852103606,0.240983218,0.0570673868,0.0389851555,0.1391420662,0.101846315,-0.1943262368,0.2980097234,-0.092138119,-0.0072416645,-0.0684707388,-0.0650317073,-0.2314721495,-0.4921033382,-0.1023306847,0.1390251219,0.0433198437,0.2613464892,-0.20452106,0.194872275,0.0464905873,-0.4844405353,-0.0153643424,-0.2388944328,-0.0431445278,-0.204358235,0.0273899734,-0.3609799147,0.421936214,-0.0387925506,-0.0085765989,-0.3726302087,-0.1466664672,0.1330472082,-0.1224432066,0.1531076729,0.1415273398,0.1062693521,-0.3267288804,-0.194094494,0.1713119298,-0.2614380121,0.0331206471,-0.078739509,0.1121828184,-0.2484862953,-0.1560818255,-0.0955816433,-0.0614487641,-0.1265228242,0.3240824342,-0.3173361421,0.0880131945,0.1432165205,-0.027240362,0.2221166342,-0.3109336495,-0.10956911,-0.3583908081,-0.4785100818,0.4354128838,-0.2844851911,-0.2151241452,0.0019907448,0.057266511,0.1640390307,0.2437956184,-0.2821912467,-0.2356435806,0.1157440618,0.2570863962,0.0183189958,-0.0090194829,0.2191235572,-0.0111932848,0.0872837752,-0.3030955493,-0.4302428663,0.1351031661,0.1022798121,0.3065890372,0.4003380239,0.2808996737,0.362971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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2407","title":".map() function got an unexpected keyword argument 'cache_file_name'","comments":"Hi ! `cache_file_name` is an argument of the `Dataset.map` method. Can you check that your `dataset` is indeed a `Dataset` object ?\r\n\r\nIf you loaded several splits, then it would actually be a `DatasetDict` (one dataset per split, in a dictionary).\r\nIn this case, since there are several datasets in the dict, the `DatasetDict.map` method requires a `cache_file_names` argument (with an 's'), so that you can provide one file name per split.","body":"## Describe the bug\r\n\r\nI'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that \".map() function got an unexpected keyword argument 'cache_file_name'\". \r\n\r\nI believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function.\r\n\r\nHere is the code I use\r\n## Steps to reproduce the bug\r\n```datasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n   cache_file_name=\"my_tokenized_file\"\r\n)\r\n```\r\n\r\n## Actual results\r\n    tokenized_datasets = datasets.map(\r\nTypeError: map() got an unexpected keyword argument 'cache_file_name'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.6.2\r\n- Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10\r\n- Python version:3.8.5\r\n- PyArrow version:3.0.0\r\n","comment_length":72,"text":".map() function got an unexpected keyword argument 'cache_file_name' \n ## Describe the bug\r\n\r\nI'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that \".map() function got an unexpected keyword argument 'cache_file_name'\". \r\n\r\nI believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function.\r\n\r\nHere is the code I use\r\n## Steps to reproduce the bug\r\n```datasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n   cache_file_name=\"my_tokenized_file\"\r\n)\r\n```\r\n\r\n## Actual results\r\n    tokenized_datasets = datasets.map(\r\nTypeError: map() got an unexpected keyword argument 'cache_file_name'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.6.2\r\n- Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10\r\n- Python version:3.8.5\r\n- PyArrow version:3.0.0\r\n \n Hi ! `cache_file_name` is an argument of the `Dataset.map` method. Can you check that your `dataset` is indeed a `Dataset` object ?\r\n\r\nIf you loaded several splits, then it would actually be a `DatasetDict` (one dataset per split, in a dictionary).\r\nIn this case, since there are several datasets in the dict, the `DatasetDict.map` method requires a `cache_file_names` argument (with an 's'), so that you can provide one file name per 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2407","title":".map() function got an unexpected keyword argument 'cache_file_name'","comments":"I think you are right. I used cache_file_names={data1: name1, data2: name2} and it works. Thank you!","body":"## Describe the bug\r\n\r\nI'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that \".map() function got an unexpected keyword argument 'cache_file_name'\". \r\n\r\nI believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function.\r\n\r\nHere is the code I use\r\n## Steps to reproduce the bug\r\n```datasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n   cache_file_name=\"my_tokenized_file\"\r\n)\r\n```\r\n\r\n## Actual results\r\n    tokenized_datasets = datasets.map(\r\nTypeError: map() got an unexpected keyword argument 'cache_file_name'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.6.2\r\n- Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10\r\n- Python version:3.8.5\r\n- PyArrow version:3.0.0\r\n","comment_length":16,"text":".map() function got an unexpected keyword argument 'cache_file_name' \n ## Describe the bug\r\n\r\nI'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that \".map() function got an unexpected keyword argument 'cache_file_name'\". \r\n\r\nI believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function.\r\n\r\nHere is the code I use\r\n## Steps to reproduce the bug\r\n```datasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n   cache_file_name=\"my_tokenized_file\"\r\n)\r\n```\r\n\r\n## Actual results\r\n    tokenized_datasets = datasets.map(\r\nTypeError: map() got an unexpected keyword argument 'cache_file_name'\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version:1.6.2\r\n- Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10\r\n- Python version:3.8.5\r\n- PyArrow version:3.0.0\r\n \n I think you are right. I used cache_file_names={data1: name1, data2: name2} and it works. Thank you!","embeddings":[0.1506065279,0.0931876749,0.046234984,-0.0068933293,0.049635455,0.2938355803,0.2111461759,0.3586550951,-0.06443993,0.0176359806,0.1194801331,0.6066251397,-0.2475739419,-0.4879621267,0.0684003457,0.1206603795,0.414899081,-0.0868131071,0.2050106525,0.0021265608,-0.3779777288,0.3605404794,-0.0720000491,0.2415003031,-0.4130324721,-0.1476259381,-0.1109034866,-0.0780590028,-0.0512373336,-0.3494436741,0.1783299595,0.1331271678,-0.089754872,0.4608037174,-0.0001248522,-0.0772017464,0.0131908813,-0.189556241,-0.1999052316,-0.3421218097,-0.1010760218,0.0225374438,-0.0683813095,-0.5165248513,0.2275047153,-0.1730200946,0.2001903653,-0.5802561641,0.1264273971,0.1558328122,0.1211594194,-0.1118961796,-0.2370792776,-0.1584720612,0.0761795342,-0.1248546541,-0.0106951464,0.1948423535,0.0108601814,-0.2778089643,0.1566221267,0.3239407837,-0.4211137295,-0.0880382657,0.4996004999,0.0565097071,-0.220529899,-0.3682149351,0.2468452156,-0.1557586044,0.160047695,-0.4097859859,-0.3636485338,-0.1235275194,-0.3060948849,-0.1057340354,0.1689180583,-0.0969173983,-0.1287402064,-0.0269522741,0.0185058676,-0.2359417677,0.0517394058,0.1558367908,0.2515682876,0.0878491476,-0.1667111367,0.3883554041,0.3064814508,-0.0860620886,-0.2235984504,-0.4085984528,-0.030748941,0.3572908044,-0.184774816,-0.0417935438,0.0571031123,0.0732770637,0.0752723292,-0.3570677042,-0.1044029444,-0.1761839241,0.1044635549,0.3414004147,0.239831984,0.2543738782,0.3329280317,0.4893091917,-0.1597922146,-0.3360438347,-0.311604172,-0.2010548264,0.0660401359,-0.096471779,0.5460054874,-0.0766859651,0.2803613842,-0.102068536,0.1100974455,0.0750942156,-0.3782733083,-0.012523056,-0.0800797418,0.3749354184,0.1374799162,-0.0799544752,-0.0131762614,0.1978539377,0.0574048348,-0.1682079285,-0.1408262104,0.088381663,-0.2228275836,0.2366519272,-0.1865341961,-0.0189995598,0.1354494244,0.0265366361,0.1726419181,-0.1520012319,0.2027388215,-0.0868520737,0.2472155839,-0.1467598379,-0.0666591749,0.5224140882,0.2376119941,-0.2446204424,-0.1467581391,0.0654154345,-0.519585371,-0.288936466,-0.000584065,0.0114896651,-0.0296990406,0.1388429105,-0.4297335446,0.0088157123,0.5321179032,-0.3010300696,-0.0069852872,-0.0502438992,-0.2498446405,-0.5160012841,0.0298702642,0.3006653488,-0.0805004016,-0.032352604,0.0187519081,0.2007806152,0.0075065377,0.1544819474,-0.0984018594,0.2479175478,-0.1984270662,0.6995722055,0.5317043662,-0.2337763011,-0.5732627511,0.2688948214,-0.1798194349,-0.0256475136,-0.0756236315,0.0339843743,0.4914753139,-0.1528477967,0.4469078183,-0.0536132418,-0.0290826,0.192588523,-0.2574965954,-0.2234037519,0.1693952978,-0.2530673146,-0.0420028828,0.1683845967,0.073596552,0.0956488624,0.100527972,0.0775981769,0.2457409054,0.0606872328,0.2730278373,0.0878141597,0.0292353295,0.0117293689,-0.2583968937,0.0582039654,0.2064500004,-0.449622184,-0.2803353667,-0.4923896492,-0.0269086044,0.0401753038,-0.1573172957,-0.0334996209,-0.003660334,0.0983634219,0.0660050213,0.1121909395,-0.0879581571,-0.0005152253,0.0636178255,-0.0778424814,0.0891707167,-0.2292373031,-0.0712287501,-0.128863588,-0.2181703746,-0.2020585835,0.4292916954,-0.0318069942,-0.2988354564,0.3882876039,-0.0271624662,0.0665292367,-0.0784718245,0.3237133622,-0.3152004182,-0.0091996025,0.0507583246,-0.0057599894,0.1366644204,-0.0144883664,0.0982632786,0.1785289943,0.3157641292,0.1690732986,-0.2228194475,0.3009205759,-0.0301989783,0.0125484364,0.0300274026,-0.2449702471,-0.0398891121,-0.1226134375,0.3339978755,-0.1022685394,-0.0005504786,0.0352188759,0.5029191375,0.1220408231,0.1732726097,-0.0695688948,-0.1503664851,-0.0012800422,0.0664687455,0.3170949221,0.5943683386,-0.0066189603,0.0173457023,-0.0088958219,0.1392169744,0.1031204611,0.0691981465,0.0365273133,-0.1297702789,-0.2433543503,-0.0627132431,0.2150774449,0.0854618847,0.0491771139,0.2067358196,0.1132831797,-0.2130065709,0.2950794399,-0.0789687857,0.0191499013,-0.075493522,-0.0520404354,-0.2320698649,-0.4821262956,-0.0861163065,0.2031510174,0.0416536294,0.2774496377,-0.1584031582,0.1630256176,-0.0084109399,-0.4518019557,0.0133335823,-0.2092440873,-0.043516513,-0.2183418721,0.0205727592,-0.3122446537,0.4293563962,-0.0221570786,-0.0626373515,-0.3932145834,-0.1321213841,0.1364067942,-0.132469818,0.1697255224,0.1360776573,0.1069907397,-0.284819752,-0.1963796169,0.1869968027,-0.2661243677,0.0500925705,-0.0728318393,0.1140383333,-0.2556231916,-0.1344592273,-0.0887681097,-0.0870846584,-0.0854689404,0.268052429,-0.3216689527,0.1308672875,0.0859511495,-0.0522363074,0.1807772517,-0.2963650227,-0.1283027381,-0.3490850627,-0.4505206943,0.4080706537,-0.2937375605,-0.2143178582,0.0160292219,0.0618532076,0.1160241738,0.237045154,-0.2521747947,-0.2652908862,0.1330054551,0.2280884087,0.0131181097,-0.0057230312,0.2380248308,0.0031640935,0.0866204724,-0.3071552813,-0.4675841033,0.1193784252,0.1409864128,0.3189295232,0.3793570101,0.2845683098,0.3596894145,0.6702024341,0.3310705125,-0.005253714,0.425998807,-0.1899456233,0.3477729857,-0.0295606814,-0.5100730658,-0.0894977972,-0.0939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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2400","title":"Concatenate several datasets with removed columns is not working.","comments":"Hi,\r\n\r\ndid you fill out the env info section manually or by copy-pasting the output of the `datasets-cli env` command?\r\n\r\nThis code should work without issues on 1.6.2 version (I'm working on master (1.6.2.dev0 version) and can't reproduce this error).","body":"## Describe the bug\r\n\r\nYou can't concatenate datasets when you removed columns before.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, concatenate_datasets\r\n\r\nwikiann= load_dataset(\"wikiann\",\"en\")\r\n\r\nwikiann[\"train\"] = wikiann[\"train\"].remove_columns([\"langs\",\"spans\"])\r\nwikiann[\"test\"] = wikiann[\"test\"].remove_columns([\"langs\",\"spans\"])\r\n\r\nassert wikiann[\"train\"].features.type == wikiann[\"test\"].features.type\r\n\r\nconcate = concatenate_datasets([wikiann[\"train\"],wikiann[\"test\"]])\r\n```\r\n\r\n## Expected results\r\nMerged dataset \r\n\r\n\r\n## Actual results\r\n```python\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=ClassLabel(num_classes=7, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], names_file=None, id=None), length=-1, id=None), 'langs': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'spans': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}\r\nwith type\r\nstruct<langs: list<item: string>, ner_tags: list<item: int64>, spans: list<item: string>, tokens: list<item: string>>\r\n\r\nbut expected something like\r\n{'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}\r\nwith type\r\nstruct<ner_tags: list<item: int64>, tokens: list<item: string>>\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: ~1.6.2~ 1.5.0\r\n- Platform: macos\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n","comment_length":40,"text":"Concatenate several datasets with removed columns is not working. \n ## Describe the bug\r\n\r\nYou can't concatenate datasets when you removed columns before.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, concatenate_datasets\r\n\r\nwikiann= load_dataset(\"wikiann\",\"en\")\r\n\r\nwikiann[\"train\"] = wikiann[\"train\"].remove_columns([\"langs\",\"spans\"])\r\nwikiann[\"test\"] = wikiann[\"test\"].remove_columns([\"langs\",\"spans\"])\r\n\r\nassert wikiann[\"train\"].features.type == wikiann[\"test\"].features.type\r\n\r\nconcate = concatenate_datasets([wikiann[\"train\"],wikiann[\"test\"]])\r\n```\r\n\r\n## Expected results\r\nMerged dataset \r\n\r\n\r\n## Actual results\r\n```python\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=ClassLabel(num_classes=7, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], names_file=None, id=None), length=-1, id=None), 'langs': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'spans': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}\r\nwith type\r\nstruct<langs: list<item: string>, ner_tags: list<item: int64>, spans: list<item: string>, tokens: list<item: string>>\r\n\r\nbut expected something like\r\n{'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}\r\nwith type\r\nstruct<ner_tags: list<item: int64>, tokens: list<item: string>>\r\n```\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: ~1.6.2~ 1.5.0\r\n- Platform: macos\r\n- Python version: 3.8.5\r\n- PyArrow version: 3.0.0\r\n \n Hi,\r\n\r\ndid you fill out the env info section manually or by copy-pasting the output of the `datasets-cli env` command?\r\n\r\nThis code should work without issues on 1.6.2 version (I'm working on master (1.6.2.dev0 version) and can't reproduce this error).","embeddings":[-0.0503957234,-0.1630990207,-0.0372494049,0.29215765,0.0712865591,0.1608157754,0.4275573492,0.3972988129,-0.128023386,0.0971976742,-0.2116857469,0.2830114663,0.1164146364,0.2401473969,-0.3315172791,-0.2897301912,0.3033925593,-0.0497661643,-0.1116106212,0.0089246044,-0.2896185219,0.1585718542,-0.5898987651,-0.0812414214,-0.2130750418,0.3191348612,-0.4302580953,0.0101843085,0.0158585962,-0.0107666077,0.2489322722,0.0348747857,0.0911828578,0.2840434313,-0.0001121243,-0.1066203564,0.2169155329,-0.098146677,-0.0510174669,-0.0768572837,-0.3660033047,-0.3289982677,0.0968496427,-0.1380754113,0.0926101133,-0.3934742808,-0.5519912243,-0.3150902092,-0.0737218633,0.3357467353,0.1743545085,0.1843100339,0.1966743767,0.0580199733,0.098164551,-0.1065447256,-0.1400445551,-0.3029312193,-0.0800745711,-0.2223571837,0.4281584024,0.2023564875,-0.1775318682,-0.2188138068,-0.1170001701,0.2738669217,-0.1349679083,-0.3314731121,-0.0674612671,0.1771031767,0.5298755169,-0.1704706401,-0.0786020607,-0.1111541465,0.1492639929,-0.3533147871,0.1071003452,0.2344943136,0.0967376977,0.0004615577,-0.0760485828,0.1861793846,-0.0712400973,0.1990367621,0.0029855049,0.3789429069,0.1802271307,0.0926468223,-0.000508545,-0.2979050875,0.2016520947,-0.451084435,-0.1082627848,0.100753814,-0.6059805155,-0.0505556278,-0.0591483302,-0.220883444,0.1962039024,0.0668276921,-0.3245195746,-0.084399648,0.1913082153,0.1033208817,0.1777590513,0.4177729487,-0.1163810045,0.2942094803,-0.2089640349,0.1199076697,-0.0970709324,0.083201699,0.0753655881,-0.0561545268,0.070139192,0.101112254,0.3909551799,-0.0969191641,-0.1147804409,0.1432804912,-0.3422627747,0.0044983653,-0.1969391853,-0.1263696402,-0.1667243391,0.5428573489,0.0377590321,0.3465742469,0.1597060263,-0.0662202612,-0.2639191151,0.0068230163,-0.0230933372,-0.349783808,-0.0136387125,-0.0729780421,0.0252169706,0.3798228204,0.0718706697,-0.0370393842,-0.2197403163,-0.1956977546,0.2782518864,-0.040623337,0.0026141929,-0.2228974551,0.4038158059,-0.1640163362,0.0380972587,-0.043440748,-0.5051544309,-0.1425156891,-0.6818566918,0.2404864877,0.0318961516,0.0104360897,-0.141025722,0.1947817057,0.1299948692,0.2286318243,-0.1718499511,-0.2043316811,0.0844891518,-0.1165866628,0.3006387353,0.1032018512,-0.2258979231,0.0896844864,0.2380647808,0.024186071,0.4148750305,0.1680238545,0.0080546904,0.0937932432,-0.3190907836,-0.0370072946,0.4073163569,-0.3568253219,-0.0753332525,0.0344850644,0.0019476183,0.396525234,0.0630723238,-0.1725126654,-0.0213235896,0.1155074462,0.3902072012,0.377129674,0.008525488,-0.0484941602,-0.0952870846,-0.3098079562,0.3817209899,-0.0649060011,0.0163189266,0.297275424,0.1458047479,-0.3483148217,0.375438869,-0.2316856831,-0.0468890034,0.3996683955,0.1350973696,0.0140624885,-0.0787636489,-0.0277048089,-0.622395575,0.0030916058,0.0060159098,0.1521741599,0.0274602231,-0.1527986079,-0.0295592137,-0.1535816789,-0.2728900611,0.0649764165,0.1627149731,0.1823699772,0.0888013095,-0.1181537658,-0.222929731,0.4120450318,-0.0862699673,0.0703940541,-0.3407647014,0.3083175719,-0.1723789126,0.032928057,0.1073514447,0.3806806207,0.3077317476,0.031149758,-0.1154230684,0.3030853868,-0.1770817786,-0.1594836861,-0.1415891051,-0.1729774475,0.031404905,-0.1056798846,-0.2074016482,-0.0851076618,-0.0073306849,-0.0969136134,-0.0933882594,0.1859098375,-0.0348238647,0.3872608542,0.0237940811,0.0162364934,0.2036125213,-0.1143120527,-0.0444548801,-0.1632085294,0.1956177205,0.0571594164,-0.2243569642,0.0947116464,-0.3509671688,0.101021491,0.4427131414,0.3745536506,-0.2229179591,-0.0197401363,-0.2566661835,-0.05037193,0.0802748725,-0.0437158383,0.5458464026,0.2856977582,-0.139158994,0.223729834,0.1402375251,-0.0241754893,0.1097457185,0.1719204485,0.0535752065,0.4272288382,0.3137719035,0.1490446478,-0.0310781747,-0.1394143701,0.0814510658,0.1154050753,-0.2603968382,0.0682633147,-0.4254438877,-0.3282284439,-0.0643066242,-0.4015084207,-0.1965873688,-0.5253537297,-0.2588239014,-0.0019736115,-0.2272165418,0.3588680029,-0.2451846302,-0.1407823861,0.1112647876,-0.3164879084,-0.1617892981,-0.0060373219,-0.0109855114,0.1099305451,-0.0284194648,-0.0724929869,-0.1141834706,-0.1195501536,-0.0290251151,-0.2793536782,-0.1802812666,0.0783889368,-0.410856694,-0.0059508011,0.0833566934,-0.1461045146,0.0428666398,-0.5514668822,0.2130687535,0.3447151184,-0.0989946499,0.353197813,0.1951990426,-0.0460549332,-0.0398075543,-0.4187425971,-0.1764497608,-0.3359154463,0.0367100723,0.0865780115,0.0953492224,0.2800819874,0.1913312525,-0.1775089651,0.087542586,0.0280540325,-0.1698313802,-0.172742635,0.1965539306,0.0289864335,-0.1954082847,0.037702404,-0.0466100387,0.1134588271,0.1409336329,-0.1158427373,-0.0908907801,-0.127644971,0.3326511085,-0.1209760234,0.2143722773,0.1329697669,0.1540289819,0.0231206939,0.078750819,0.028014645,0.0431594811,0.1299306899,0.3429623842,-0.2213283777,0.137257725,-0.1495714039,0.1375365853,0.3419882953,-0.0257243589,0.2826379538,0.0222580042,0.3406074941,-0.1352028996,-0.5328831673,-0.1271975487,-0.081422485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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2396","title":"strange datasets from OSCAR corpus","comments":"Hi ! Thanks for reporting\r\ncc @pjox is this an issue from the data ?\r\n\r\nAnyway we should at least mention that OSCAR could contain such contents in the dataset card, you're totally right @jerryIsHere ","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/119260850-4f876b80-bc07-11eb-8894-124302600643.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/119260875-675eef80-bc07-11eb-9da4-ee27567054ac.png)\r\nFrom the [official site ](https:\/\/oscar-corpus.com\/), the Yue Chinese dataset should have 2.2KB data.\r\n7 training instances is obviously not a right number.\r\nAs I can read Yue Chinese, I call tell the last instance is definitely not something that would appear on Common Crawl.\r\nAnd even if you don't read Yue Chinese, you can tell the first six instance are problematic.\r\n(It is embarrassing, as the 7 training instances look exactly like something from a pornographic novel or flitting messages in a chat of a dating app)\r\nIt might not be the problem of the huggingface\/datasets implementation, because when I tried to download the dataset from the official site, I found out that the zip file is corrupted.\r\nI will try to inform the host of OSCAR corpus later.\r\nAwy a remake about this dataset in huggingface\/datasets is needed, perhaps after the host of the dataset fixes the issue.\r\n\r\n> Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https:\/\/arxiv.org\/pdf\/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https:\/\/github.com\/oscar-corpus\/oscar-website\/issues) as well so that we can track it?\r\n\r\nThanks a lot, the new post is here:\r\nhttps:\/\/github.com\/oscar-corpus\/oscar-website\/issues\/11","comment_length":35,"text":"strange datasets from OSCAR corpus \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/119260850-4f876b80-bc07-11eb-8894-124302600643.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/119260875-675eef80-bc07-11eb-9da4-ee27567054ac.png)\r\nFrom the [official site ](https:\/\/oscar-corpus.com\/), the Yue Chinese dataset should have 2.2KB data.\r\n7 training instances is obviously not a right number.\r\nAs I can read Yue Chinese, I call tell the last instance is definitely not something that would appear on Common Crawl.\r\nAnd even if you don't read Yue Chinese, you can tell the first six instance are problematic.\r\n(It is embarrassing, as the 7 training instances look exactly like something from a pornographic novel or flitting messages in a chat of a dating app)\r\nIt might not be the problem of the huggingface\/datasets implementation, because when I tried to download the dataset from the official site, I found out that the zip file is corrupted.\r\nI will try to inform the host of OSCAR corpus later.\r\nAwy a remake about this dataset in huggingface\/datasets is needed, perhaps after the host of the dataset fixes the issue.\r\n\r\n> Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https:\/\/arxiv.org\/pdf\/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https:\/\/github.com\/oscar-corpus\/oscar-website\/issues) as well so that we can track it?\r\n\r\nThanks a lot, the new post is here:\r\nhttps:\/\/github.com\/oscar-corpus\/oscar-website\/issues\/11 \n Hi ! Thanks for reporting\r\ncc @pjox is this an issue from the data ?\r\n\r\nAnyway we should at least mention that OSCAR could contain such contents in the dataset card, you're totally right @jerryIsHere 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2396","title":"strange datasets from OSCAR corpus","comments":"Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https:\/\/arxiv.org\/pdf\/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https:\/\/github.com\/oscar-corpus\/oscar-website\/issues) as well so that we can track it?","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/119260850-4f876b80-bc07-11eb-8894-124302600643.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/119260875-675eef80-bc07-11eb-9da4-ee27567054ac.png)\r\nFrom the [official site ](https:\/\/oscar-corpus.com\/), the Yue Chinese dataset should have 2.2KB data.\r\n7 training instances is obviously not a right number.\r\nAs I can read Yue Chinese, I call tell the last instance is definitely not something that would appear on Common Crawl.\r\nAnd even if you don't read Yue Chinese, you can tell the first six instance are problematic.\r\n(It is embarrassing, as the 7 training instances look exactly like something from a pornographic novel or flitting messages in a chat of a dating app)\r\nIt might not be the problem of the huggingface\/datasets implementation, because when I tried to download the dataset from the official site, I found out that the zip file is corrupted.\r\nI will try to inform the host of OSCAR corpus later.\r\nAwy a remake about this dataset in huggingface\/datasets is needed, perhaps after the host of the dataset fixes the issue.\r\n\r\n> Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https:\/\/arxiv.org\/pdf\/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https:\/\/github.com\/oscar-corpus\/oscar-website\/issues) as well so that we can track it?\r\n\r\nThanks a lot, the new post is here:\r\nhttps:\/\/github.com\/oscar-corpus\/oscar-website\/issues\/11","comment_length":93,"text":"strange datasets from OSCAR corpus \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/119260850-4f876b80-bc07-11eb-8894-124302600643.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/119260875-675eef80-bc07-11eb-9da4-ee27567054ac.png)\r\nFrom the [official site ](https:\/\/oscar-corpus.com\/), the Yue Chinese dataset should have 2.2KB data.\r\n7 training instances is obviously not a right number.\r\nAs I can read Yue Chinese, I call tell the last instance is definitely not something that would appear on Common Crawl.\r\nAnd even if you don't read Yue Chinese, you can tell the first six instance are problematic.\r\n(It is embarrassing, as the 7 training instances look exactly like something from a pornographic novel or flitting messages in a chat of a dating app)\r\nIt might not be the problem of the huggingface\/datasets implementation, because when I tried to download the dataset from the official site, I found out that the zip file is corrupted.\r\nI will try to inform the host of OSCAR corpus later.\r\nAwy a remake about this dataset in huggingface\/datasets is needed, perhaps after the host of the dataset fixes the issue.\r\n\r\n> Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https:\/\/arxiv.org\/pdf\/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https:\/\/github.com\/oscar-corpus\/oscar-website\/issues) as well so that we can track it?\r\n\r\nThanks a lot, the new post is here:\r\nhttps:\/\/github.com\/oscar-corpus\/oscar-website\/issues\/11 \n Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https:\/\/arxiv.org\/pdf\/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https:\/\/github.com\/oscar-corpus\/oscar-website\/issues) as well so that we can track it?","embeddings":[0.2676993608,0.057094764,-0.0023013265,0.5770170689,0.1565007865,0.0609702878,-0.0124235256,0.2492677867,-0.2645931542,0.0486133806,-0.579642415,-0.1079981476,0.0431049913,-0.051175423,-0.0094940485,-0.301930815,0.110553138,0.0599582233,0.0344043113,-0.369253844,-0.1291408092,0.2438743412,-0.0991414487,0.0684380084,-0.0104771517,-0.1464854181,-0.2549023926,-0.0072285933,-0.3976078331,0.0282757394,0.1691304445,0.1257212758,0.0447246879,0.378584832,-0.0001098784,-0.1675397009,0.2241498679,-0.032616768,0.1237274259,-0.0597777031,0.1777801663,0.0427329913,-0.0099441046,0.0341181159,-0.2434695065,-0.2776968777,0.0040472257,0.1497946978,0.2936551273,0.410572499,0.2379986495,0.0630983114,-0.1171702296,-0.1336971223,0.2072796822,-0.0323365964,-0.2481697649,0.1810347885,0.466270566,0.0761901438,-0.1121683344,0.0576442331,0.0283341352,-0.101253666,0.2170051336,-0.1111785099,-0.2650502622,-0.4842260778,0.2932800949,0.5078215599,0.307980001,-0.0811569616,-0.2589426637,-0.2738285959,0.0988934413,-0.2739084661,0.2932356894,0.3606124222,0.0745738298,0.2289150208,-0.2088944763,0.1954556257,-0.1180684865,0.4085975289,-0.2678520679,-0.1681371033,-0.223840341,0.0197889302,0.0670833662,-0.0670693293,0.0395493396,-0.2790008485,0.0216593854,0.2727837563,-0.1698675603,-0.1353237629,0.0378287397,0.4480695426,0.1993468702,-0.1269877404,-0.0557646081,0.1898595542,-0.3269468844,-0.0471593998,0.3354088068,0.1453732997,-0.1572799683,-0.1377139837,0.1423854232,0.1614499837,0.0849004611,0.2207645029,-0.0777042508,-0.0842675045,-0.2245463133,-0.3745216727,0.0991557464,-0.3770408928,-0.2749187946,0.3003405631,-0.6244720817,0.2917198241,0.0357688703,0.1298946887,-0.1998814493,0.0667819455,-0.016715385,0.2029186487,-0.1194482073,-0.0397366621,-0.1921678632,0.2545002103,-0.217064783,0.2372613102,0.0401417799,-0.2705197036,0.2493536174,0.1856439114,0.3698456585,-0.1369044632,-0.0799858868,-0.4004633129,-0.0195614137,0.0929225683,-0.1745463461,0.1127903014,0.0614734329,-0.3261088133,-0.1222709864,-0.1741286218,-0.3078811765,-0.1431825906,0.0786640644,0.1830638647,-0.4335425794,0.0327018574,-0.3430885971,0.3751347363,0.0194555651,0.3101088107,0.3069826961,-0.0848392099,-0.2614415288,0.110479854,-0.1510209292,0.3771541417,-0.0065252073,-0.0835282505,-0.1661023796,0.0815068334,0.3079614937,0.2400632799,-0.0228678118,-0.0866745338,-0.2789590657,0.1016659811,-0.0555678383,-0.2338879108,-0.3346047997,0.2996109128,0.1844694763,0.2973280251,-0.1442996413,-0.1274283528,0.2322862893,-0.2734360099,0.2217368335,0.1740197837,0.0513248742,-0.027263226,-0.3290061653,-0.1187138036,-0.358600378,0.0662762225,0.130272001,0.0879968554,0.0359005406,0.0622565188,0.3574254215,0.026874952,0.0745497197,0.13262631,-0.1339467168,0.4023646414,0.1660578102,0.0027954392,0.0745985582,0.2480973452,-0.3026051223,0.5085480213,0.0180819575,-0.2257502228,-0.1997607797,-0.2386162728,-0.2820656598,-0.245531112,0.1467761993,-0.184961915,0.0019979251,0.3076104522,0.0463864841,0.0455984473,-0.2320450544,0.022844201,-0.5470091105,0.1783732176,0.0564264841,0.1313604116,0.1921941787,0.2265807688,0.0022634515,-0.1288478822,0.0188172571,0.0711189955,-0.3784909248,0.5216875076,0.0105420277,0.3390645385,0.4842693806,-0.3351532221,0.186759457,0.0962930098,0.205712527,-0.0595745817,-0.1192198545,0.4944604635,0.1703856438,-0.1687858999,0.0706616193,0.0164971575,0.283459574,-0.1336004138,-0.2227505445,-0.2548031509,0.424082756,0.1479568034,0.4548326135,0.1943281591,-0.4106912315,0.2208998203,0.0080121411,-0.0442878269,0.2007290721,0.3062259853,-0.1937873214,-0.0257208925,0.0227153599,0.2468966693,0.1487173736,0.0625743568,-0.0749768093,0.3186788559,-0.069458954,-0.3558135331,0.111846745,0.1962320656,0.4446726143,0.0999088213,0.3605704606,0.220251292,-0.7133963704,0.126008302,-0.0119821373,0.3289736509,-0.1542913318,0.2327584177,-0.1093545407,-0.3838168383,-0.425598979,-0.0526062325,-0.1197228208,-0.0765793249,0.1917120218,-0.0079616001,-0.0710900426,-0.0606718212,-0.2533044517,0.2126707733,-0.2307349592,0.3920391798,-0.097000666,-0.0001325482,-0.1199717075,0.1299186945,0.1205610186,0.0156219909,0.426179111,-0.2234457135,-0.144994095,-0.2055267841,-0.5277351737,0.3550101519,-0.037032783,-0.0828411877,-0.0709609762,-0.1230314299,-0.2948217392,-0.0460335873,0.0550760813,-0.1752235442,-0.1702314615,0.056853313,-0.1404699534,-0.0086305682,0.0384075195,-0.4563492537,-0.2169178724,-0.0600899011,0.2215566635,0.1342093796,0.1609433293,0.4600666165,-0.2288878113,0.140054822,0.1669894904,0.0807909295,-0.5246050954,-0.3567255139,0.3421804607,-0.3204010725,-0.4378070235,-0.20665133,-0.2681742311,-0.1136468872,0.0419772081,-0.3773085177,-0.5895934105,-0.2654289603,-0.1950018555,0.390293777,-0.3890113235,0.0661180988,-0.3487103283,-0.1124316305,0.0002544484,0.3013922572,-0.0172216762,0.0075142249,0.2562912703,-0.2389841676,0.5512993932,0.0627559274,0.3488258719,0.2001673132,0.0785247684,0.4007724524,0.1514521688,0.2562434077,-0.0319219492,-0.1340349764,0.3567915261,-0.1609951556,-0.011034623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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2391","title":"Missing original answers in kilt-TriviaQA","comments":"That could be useful indeed! Feel free to open a PR on the dataset card if you already have some code that runs, otherwise we'll take care of it soon :) ","body":"I previously opened an issue at https:\/\/github.com\/facebookresearch\/KILT\/issues\/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets\r\n\r\n## Describe the bug\r\nThe `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative answer which are accepted for the question.  \r\nHowever it'd be nice to know the original answer to the question (the only fields in `output` are `'answer', 'meta', 'provenance'`)\r\n\r\n## How to fix\r\nIt can be fixed by retrieving the original answer from the original TriviaQA (e.g. `trivia_qa['train'][0]['answer']['value']`), perhaps at the same place as here where one retrieves the questions https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md#loading-the-kilt-knowledge-source-and-task-data\r\n\r\ncc @yjernite who previously answered to an issue about KILT and TriviaQA :)\r\n","comment_length":31,"text":"Missing original answers in kilt-TriviaQA \n I previously opened an issue at https:\/\/github.com\/facebookresearch\/KILT\/issues\/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets\r\n\r\n## Describe the bug\r\nThe `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative answer which are accepted for the question.  \r\nHowever it'd be nice to know the original answer to the question (the only fields in `output` are `'answer', 'meta', 'provenance'`)\r\n\r\n## How to fix\r\nIt can be fixed by retrieving the original answer from the original TriviaQA (e.g. `trivia_qa['train'][0]['answer']['value']`), perhaps at the same place as here where one retrieves the questions https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md#loading-the-kilt-knowledge-source-and-task-data\r\n\r\ncc @yjernite who previously answered to an issue about KILT and TriviaQA :)\r\n \n That could be useful indeed! Feel free to open a PR on the dataset card if you already have some code that runs, otherwise we'll take care of it soon :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2391","title":"Missing original answers in kilt-TriviaQA","comments":"I can open a PR but there is 2 details to fix:\r\n- the name for the corresponding key (e.g. `original_answer`)\r\n- how to implement it: I\u2019m not sure what happens when you map `lambda x: {'input': ...}`\u00a0as it keeps the other keys (e.g. `output`) intact but here since we want to set a nested value (e.g. `x['output']['original_answer']`) I implemented it with a regular function (not lambda), see below\r\n\r\n```py\r\ndef add_original_answer(x, trivia_qa, triviaqa_map):\r\n    i = triviaqa_map[x['id']]\r\n    x['output']['original_answer'] = trivia_qa['validation'][i]['answer']['value']\r\n    return x\r\n```","body":"I previously opened an issue at https:\/\/github.com\/facebookresearch\/KILT\/issues\/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets\r\n\r\n## Describe the bug\r\nThe `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative answer which are accepted for the question.  \r\nHowever it'd be nice to know the original answer to the question (the only fields in `output` are `'answer', 'meta', 'provenance'`)\r\n\r\n## How to fix\r\nIt can be fixed by retrieving the original answer from the original TriviaQA (e.g. `trivia_qa['train'][0]['answer']['value']`), perhaps at the same place as here where one retrieves the questions https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md#loading-the-kilt-knowledge-source-and-task-data\r\n\r\ncc @yjernite who previously answered to an issue about KILT and TriviaQA :)\r\n","comment_length":84,"text":"Missing original answers in kilt-TriviaQA \n I previously opened an issue at https:\/\/github.com\/facebookresearch\/KILT\/issues\/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets\r\n\r\n## Describe the bug\r\nThe `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative answer which are accepted for the question.  \r\nHowever it'd be nice to know the original answer to the question (the only fields in `output` are `'answer', 'meta', 'provenance'`)\r\n\r\n## How to fix\r\nIt can be fixed by retrieving the original answer from the original TriviaQA (e.g. `trivia_qa['train'][0]['answer']['value']`), perhaps at the same place as here where one retrieves the questions https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md#loading-the-kilt-knowledge-source-and-task-data\r\n\r\ncc @yjernite who previously answered to an issue about KILT and TriviaQA :)\r\n \n I can open a PR but there is 2 details to fix:\r\n- the name for the corresponding key (e.g. `original_answer`)\r\n- how to implement it: I\u2019m not sure what happens when you map `lambda x: {'input': ...}`\u00a0as it keeps the other keys (e.g. `output`) intact but here since we want to set a nested value (e.g. `x['output']['original_answer']`) I implemented it with a regular function (not lambda), see below\r\n\r\n```py\r\ndef add_original_answer(x, trivia_qa, triviaqa_map):\r\n    i = triviaqa_map[x['id']]\r\n    x['output']['original_answer'] = trivia_qa['validation'][i]['answer']['value']\r\n    return x\r\n```","embeddings":[0.5155783296,-0.3537389636,0.0327610411,0.0395886675,-0.105386138,-0.0799774006,-0.0295903832,0.2565177381,0.085573785,0.1321024299,0.0442913473,0.5437101126,0.1936984211,0.4238483906,-0.1813199818,0.3287062645,0.0641255006,0.1050164923,0.1597800553,-0.2960401475,-0.3145171106,0.2330386192,-0.1170250624,0.1646067649,-0.1306224465,0.0120377354,-0.2015926093,0.21834369,0.2492164373,-0.3835995197,0.3044094443,-0.0027334217,-0.1335724294,-0.0683760867,-0.0001235717,-0.2483267486,0.0100177377,-0.2631385028,-0.4476844966,0.0135608371,-0.5491896272,0.0013693378,-0.2267566025,-0.2673223615,0.0246863365,0.0820418149,-0.0023390166,-0.181188643,0.2089129686,0.1528659314,0.0594027154,-0.1457809657,0.1589805633,-0.0494781211,0.0174915977,0.0762605891,-0.1484265029,0.1695866734,-0.1361159682,-0.0244756676,0.1385612339,0.2619105279,0.105464071,0.0110791102,-0.1540766358,-0.0243223608,0.10231556,-0.244240582,0.4118763208,0.1882626712,0.1229106709,-0.2038338333,-0.4319516122,-0.2443827987,0.1036714762,-0.018734375,-0.0171048008,0.0892703384,-0.2156305611,-0.2821560502,-0.00090461,-0.2265105546,-0.0220113378,0.1320800334,-0.055072777,0.3479349017,0.2165877968,0.1588949263,-0.1401011348,-0.0632673502,0.3329878449,-0.1604586393,-0.1640904397,0.3152612746,-0.3240547776,-0.1331782192,0.1900509894,0.0019526612,0.2189777344,-0.4318755567,-0.0086097261,0.1560154557,-0.2777463198,-0.0269539673,0.2008716017,0.2189187109,0.5501311421,-0.2920511663,-0.0707457289,0.2034399956,-0.1683296859,-0.1551692039,0.0187576879,0.1357354522,-0.0358962528,-0.0002326736,0.1213153899,-0.2009133399,-0.3044298589,0.3365494013,-0.5707329512,0.1031196564,-0.2290992439,0.1706618965,-0.1619618982,0.4370584786,-0.119920671,0.2700347304,-0.2904263437,-0.1402242482,-0.088785097,-0.0078456737,0.0948832482,0.2193794847,0.1299312264,-0.355532378,0.4627596736,0.3198022246,0.1934595704,-0.0461257696,-0.0313192494,-0.1281021386,0.0169504937,0.1240085885,0.1192691326,0.0273681041,0.2130442262,-0.2978673875,0.0743862987,0.3816873133,-0.3751229048,-0.3407919407,-0.1022927612,0.0808336139,-0.1557133645,-0.0852613896,-0.1102893203,0.3784490824,-0.0152195701,-0.1495497972,-0.0393542536,-0.3106988668,-0.1329434663,-0.3905701339,0.49024266,0.1699148417,-0.3541273773,0.3064566851,-0.2360414863,0.0928541571,-0.3066458702,-0.0370559171,-0.076302357,0.4838697016,-0.1619303674,0.0490621962,-0.2314925045,-0.4575146437,-0.3383847177,-0.3009569645,0.4830967486,0.4284089208,-0.0870445669,0.0214253701,0.0319995806,0.0131465476,0.0061029866,-0.1040413007,-0.1317987144,-0.214095667,-0.1762363464,-0.1944406778,-0.049631577,0.0566412881,0.0829337388,0.1037125587,0.283506453,0.2077239007,0.1905883551,0.0045289639,-0.1778160781,0.3082348406,0.3007472456,0.1162420735,0.1203731447,0.0259790253,-0.3274600208,0.1223377064,-0.6618936062,-0.0308636203,0.1369944066,-0.3331814706,-0.5493906736,0.3154106736,-0.1641771197,-0.6335079074,0.0482662804,-0.0674963593,0.2100677788,-0.0569680668,-0.0661991537,0.2212019414,-0.1994565427,-0.185448423,-0.2481945306,0.1694604307,-0.0260955114,-0.0751713812,-0.1915444434,0.5666322708,0.355817914,0.3527083099,0.0031313398,0.3100954294,0.2255421579,0.0509434752,0.4634577036,0.0723986775,0.225365907,0.082031548,-0.1245186478,0.2991172373,0.0801994801,0.0168047603,-0.3568072915,0.421233654,0.2299394757,0.0837266445,-0.1474848092,-0.0554867722,-0.2449639142,-0.2046156079,0.1883871853,-0.0484557226,-0.1712999642,-0.2022147179,-0.5047810674,-0.1734983027,-0.3660624325,0.4657456279,0.1779556125,0.0015858189,0.0422763675,-0.0474720486,-0.140778631,-0.0348974094,0.4081193805,-0.0663329139,0.3968331814,0.1243698671,-0.1609617919,-0.0058463388,-0.0208608415,0.036013227,0.1278193295,-0.0164298099,0.0743787959,0.4520234168,0.6341477036,0.0820695981,-0.0746798068,0.4535393715,-0.0603403263,0.0384703092,-0.1819293648,-0.1642486453,-0.4214442372,0.0934764221,0.0367738456,0.19537884,0.0722384825,-0.5293933153,0.3032277822,0.4172869325,-0.0395120159,0.2344897091,0.045224268,0.0601693951,-0.0857849643,-0.0692597851,-0.2800633609,-0.4454108179,-0.3175086677,0.0101327766,-0.4948932827,-0.211940378,0.1414636821,0.1508740634,-0.3360295892,-0.6223651767,-0.6542856097,-0.0059413896,-0.0045577791,0.5484629273,0.0428466164,0.3344559669,-0.6263720393,-0.4282755256,0.3463867903,0.1127221212,-0.3175826967,0.022467725,-0.0236435961,0.1038113162,0.0179196876,-0.5704700351,-0.0489381142,-0.0977127925,0.1817075014,0.0674074367,0.2584240735,0.102878049,0.0457065217,-0.2438624799,0.1191970482,-0.1969721168,-0.5478495359,0.0836739764,0.3846223652,-0.2477894425,-0.2864775956,-0.0700969845,-0.3900149167,0.2580229342,-0.0927009881,-0.4573445916,-0.6468146443,0.1500904858,0.5276089907,-0.1340635866,0.0861618742,0.1564029455,0.0242378414,0.0743472874,-0.1985616088,-0.1410773993,0.4161976576,0.3931500316,0.5974636674,0.3126388192,0.0723790601,0.3485159576,0.7757428288,0.2300544679,0.5763300061,0.1780343205,-0.3186384737,0.1277631819,-0.034983214,-0.2192827463,0.2881546319,-0.10394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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"Looks like there are multiple issues regarding this (#2386, #2322) and it's a WIP #2329. Currently these datasets are being loaded in-memory which is causing this issue. Quoting @mariosasko here for a quick fix:\r\n\r\n> set `keep_in_memory` to `False` when loading a dataset (`sst = load_dataset(\"sst\", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them)\r\n\r\n","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":69,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n Looks like there are multiple issues regarding this (#2386, #2322) and it's a WIP #2329. Currently these datasets are being loaded in-memory which is causing this issue. Quoting @mariosasko here for a quick fix:\r\n\r\n> set `keep_in_memory` to `False` when loading a dataset (`sst = load_dataset(\"sst\", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them)\r\n\r\n","embeddings":[-0.2158487439,0.0203107707,0.0422426425,0.1404154301,0.1821744591,0.0462308712,0.1409803778,0.3139572144,0.0551589616,-0.0644533038,-0.2359380573,-0.0579473414,-0.0247005746,-0.3315773904,-0.1081899628,0.0198047999,0.2144653797,0.0230753701,-0.1678417474,-0.0409687683,-0.1443585306,0.2673944831,-0.128291443,-0.228637591,-0.6212518811,0.1536585689,-0.0539875105,-0.0578289442,-0.1436334997,-0.3942032158,0.3681198657,0.2826853693,0.1136951819,0.4599291086,-0.00012043,0.1595689058,0.2031688243,0.0211555008,-0.4104935527,-0.0573553517,0.0354547277,-0.0839594007,0.0214983989,0.1640308499,-0.2790547013,-0.0306378007,0.1911177784,-0.5746431947,0.452419132,0.3432961404,0.160297215,0.5254045725,-0.1487574875,-0.0378798321,-0.0608341657,-0.0301156137,-0.332855463,0.0926357806,0.1314037144,0.0842170715,-0.2770285308,0.3524879217,0.097985439,-0.0218208209,0.16133672,0.0107811904,-0.1585707217,0.0202849843,0.2987552285,0.10706193,0.3787642717,-0.2141118944,-0.5825324059,-0.3364526331,-0.3538715541,0.0064531881,0.3440105021,-0.0655077994,0.1379141808,0.3956792057,-0.4331495464,-0.0684470385,0.0699945837,-0.0044340761,0.0611454509,0.2066850215,-0.1162460893,-0.0994941965,0.1231028289,0.149908945,0.1937531978,-0.1635281295,-0.109124884,0.2992784679,-0.3559890985,-0.0108128795,0.1666994542,0.0396621898,0.0110875499,0.1188242361,0.0640139356,0.1364050955,0.0925285518,-0.2549798191,-0.0728779137,0.5833615065,0.1947101653,0.2663045526,0.0184179097,0.0475953147,-0.1567291319,-0.096233882,-0.0447993651,-0.2194101065,0.2228741646,-0.0724640489,0.1600194871,-0.177988708,-0.0083685135,0.1688308865,0.0522615016,-0.1071201414,0.1425199807,0.297921747,-0.1997744441,0.2688975036,-0.0717916638,-0.1660918742,-0.2827946544,0.0425893366,-0.3636681736,-0.0625563711,-0.2846522033,0.2555699944,0.2676339149,-0.372418493,0.4367183745,0.1324850917,0.2268454731,0.0182676762,0.0358226374,-0.1124993712,0.3517661691,0.1498368829,-0.4831211865,0.4104645848,0.0979266092,0.1543205231,-0.2801340222,-0.034504652,-0.1624662131,-0.2589809299,0.1788692027,0.0967067927,-0.4759327471,-0.2851259112,0.1238623857,0.2492318451,0.3530565798,-0.1826398224,0.0576875657,-0.054647509,-0.3108603656,-0.0424191542,0.2072167397,0.6110782027,-0.0987188816,-0.2701899707,0.3160687387,0.0783183947,0.1078270078,0.2469794154,-0.1151805148,-0.2041630894,-0.0944092944,-0.0451543592,0.2127512693,-0.2799159884,-0.6564025283,0.0895091146,0.0316258781,0.2786871493,0.0627193525,-0.030414138,0.1169167235,-0.2188137472,-0.169888556,0.1760187596,0.0873951912,0.1103237867,-0.4886095524,-0.3197338879,0.0049685156,0.0149404025,0.1607397348,0.1425743401,-0.2066916376,0.1983790696,0.0647579879,0.1064668968,0.2382353395,0.4176746905,-0.0577855743,-0.2508044243,0.2393510789,-0.2066840678,-0.4679914415,0.3813743293,-0.0582001358,0.1504364312,0.0513900258,-0.0919354334,-0.314740777,-0.1900457591,-0.3224794865,-0.4129860103,0.0291994661,0.083561644,0.4664493203,-0.0627890825,-0.2708913088,0.4161113501,0.1776850522,0.2026192099,-0.1979149729,-0.0367069468,-0.0714992806,-0.1292344034,-0.3720748723,-0.0548154041,0.1507507265,-0.2703870535,-0.1646059304,0.4357369542,-0.0549746081,0.1264852285,-0.0163788572,0.2787240446,0.1310151666,0.2094838619,0.1261975765,0.1861446947,-0.1718526632,-0.0207077246,-0.0400916971,0.3744085133,0.1988709271,0.212100476,0.1096512601,-0.0578966551,-0.0523809791,0.020036336,-0.0271951109,-0.3291665018,0.2592810392,-0.0970329493,0.6289586425,0.1915283948,0.0682512671,0.2737976611,0.7349142432,0.1994312406,-0.2323375195,0.1495774388,-0.2541774213,-0.3552419245,0.1600754559,0.280841589,0.3655433357,0.0967631564,0.4016495049,0.1217315421,-0.0592810474,-0.0925822258,0.1318967044,-0.103768073,0.0365564413,-0.0743083283,0.0565556027,0.2631292343,-0.3260219693,0.136027202,0.0117203426,0.1084349826,-0.1194188669,0.2409161031,-0.3367220759,0.2784646749,-0.1354539096,-0.1432013363,-0.3787076771,-0.2035416663,-0.048936978,0.1152366698,-0.0025185787,0.3248536587,-0.2567089796,0.1713041961,-0.0361298993,-0.3038983345,-0.4968311787,-0.1464488059,-0.2653330266,-0.0522758998,-0.0077604922,-0.3781483173,-0.041796647,-0.1704156846,-0.2427740842,-0.1978735626,-0.329761982,0.1543189883,-0.072553426,-0.0289234985,-0.0718471333,0.0568940639,-0.1304771602,0.0907302946,0.1732173264,-0.3506852686,-0.1693695635,-0.0369205885,0.0841066614,0.1431051046,-0.2130721211,-0.2104904354,0.0077060619,-0.2791153491,0.1315926015,-0.1691376567,-0.0363107882,0.5730288625,0.0180480685,0.0493544452,-0.154198274,0.1935802996,-0.2817358673,-0.5460473299,0.4480652511,-0.0068342546,-0.3691204488,-0.1439013928,0.0137629816,0.1789387465,0.3921634257,-0.1815630645,-0.2465928942,-0.3960894048,0.0011066424,0.2109925449,0.0658638254,0.6789283156,-0.012012572,-0.0564476885,-0.2156410962,-0.1154806092,0.1403704733,-0.1744911224,0.321870178,-0.2863911688,0.1133488566,0.261436671,0.6822909117,0.2117644399,-0.0542893298,0.2297441661,0.2226077169,0.4835837185,-0.3701800406,-0.2817359567,-0.0303613804,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"Hi ! Since `datasets` 1.6.0 we no longer keep small datasets (<250MB) on disk and load them in RAM instead by default. This makes data processing and iterating on data faster. However datasets in RAM currently have no way to reload previous results from the cache (since nothing is written on disk). We are working on making the caching work for datasets in RAM.\r\n\r\nUntil then, I'd recommend passing `keep_in_memory=False` to the calls to `load_dataset` like here:\r\n\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/223943872e8c9c3fc11db3c6e93da07f5177423f\/examples\/pytorch\/language-modeling\/run_clm.py#L233\r\n\r\nThis way you say explicitly that you want your dataset to stay on the disk, and it will be able to recover previously computed results from the cache.","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":106,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n Hi ! Since `datasets` 1.6.0 we no longer keep small datasets (<250MB) on disk and load them in RAM instead by default. This makes data processing and iterating on data faster. However datasets in RAM currently have no way to reload previous results from the cache (since nothing is written on disk). We are working on making the caching work for datasets in RAM.\r\n\r\nUntil then, I'd recommend passing `keep_in_memory=False` to the calls to `load_dataset` like here:\r\n\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/223943872e8c9c3fc11db3c6e93da07f5177423f\/examples\/pytorch\/language-modeling\/run_clm.py#L233\r\n\r\nThis way you say explicitly that you want your dataset to stay on the disk, and it will be able to recover previously computed results from the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"OK, It doesn't look like we can use the proposed workaround - see https:\/\/github.com\/huggingface\/transformers\/issues\/11801\r\n\r\nCould you please add an env var for us to be able to turn off this unwanted in our situation behavior? It is really problematic for dev work, when one needs to restart the training very often and needs a quick startup time. Manual editing of standard scripts is not a practical option when one uses examples.\r\n\r\nThis could also be a problem for tests, which will be slower because of lack of cache, albeit usually we use tiny datasets there. I think we want caching for tests.\r\n\r\nThank you.","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":104,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n OK, It doesn't look like we can use the proposed workaround - see https:\/\/github.com\/huggingface\/transformers\/issues\/11801\r\n\r\nCould you please add an env var for us to be able to turn off this unwanted in our situation behavior? It is really problematic for dev work, when one needs to restart the training very often and needs a quick startup time. Manual editing of standard scripts is not a practical option when one uses examples.\r\n\r\nThis could also be a problem for tests, which will be slower because of lack of cache, albeit usually we use tiny datasets there. I think we want caching for tests.\r\n\r\nThank you.","embeddings":[-0.2158487439,0.0203107707,0.0422426425,0.1404154301,0.1821744591,0.0462308712,0.1409803778,0.3139572144,0.0551589616,-0.0644533038,-0.2359380573,-0.0579473414,-0.0247005746,-0.3315773904,-0.1081899628,0.0198047999,0.2144653797,0.0230753701,-0.1678417474,-0.0409687683,-0.1443585306,0.2673944831,-0.128291443,-0.228637591,-0.6212518811,0.1536585689,-0.0539875105,-0.0578289442,-0.1436334997,-0.3942032158,0.3681198657,0.2826853693,0.1136951819,0.4599291086,-0.00012043,0.1595689058,0.2031688243,0.0211555008,-0.4104935527,-0.0573553517,0.0354547277,-0.0839594007,0.0214983989,0.1640308499,-0.2790547013,-0.0306378007,0.1911177784,-0.5746431947,0.452419132,0.3432961404,0.160297215,0.5254045725,-0.1487574875,-0.0378798321,-0.0608341657,-0.0301156137,-0.332855463,0.0926357806,0.1314037144,0.0842170715,-0.2770285308,0.3524879217,0.097985439,-0.0218208209,0.16133672,0.0107811904,-0.1585707217,0.0202849843,0.2987552285,0.10706193,0.3787642717,-0.2141118944,-0.5825324059,-0.3364526331,-0.3538715541,0.0064531881,0.3440105021,-0.0655077994,0.1379141808,0.3956792057,-0.4331495464,-0.0684470385,0.0699945837,-0.0044340761,0.0611454509,0.2066850215,-0.1162460893,-0.0994941965,0.1231028289,0.149908945,0.1937531978,-0.1635281295,-0.109124884,0.2992784679,-0.3559890985,-0.0108128795,0.1666994542,0.0396621898,0.0110875499,0.1188242361,0.0640139356,0.1364050955,0.0925285518,-0.2549798191,-0.0728779137,0.5833615065,0.1947101653,0.2663045526,0.0184179097,0.0475953147,-0.1567291319,-0.096233882,-0.0447993651,-0.2194101065,0.2228741646,-0.0724640489,0.1600194871,-0.177988708,-0.0083685135,0.1688308865,0.0522615016,-0.1071201414,0.1425199807,0.297921747,-0.1997744441,0.2688975036,-0.0717916638,-0.1660918742,-0.2827946544,0.0425893366,-0.3636681736,-0.0625563711,-0.2846522033,0.2555699944,0.2676339149,-0.372418493,0.4367183745,0.1324850917,0.2268454731,0.0182676762,0.0358226374,-0.1124993712,0.3517661691,0.1498368829,-0.4831211865,0.4104645848,0.0979266092,0.1543205231,-0.2801340222,-0.034504652,-0.1624662131,-0.2589809299,0.1788692027,0.0967067927,-0.4759327471,-0.2851259112,0.1238623857,0.2492318451,0.3530565798,-0.1826398224,0.0576875657,-0.054647509,-0.3108603656,-0.0424191542,0.2072167397,0.6110782027,-0.0987188816,-0.2701899707,0.3160687387,0.0783183947,0.1078270078,0.2469794154,-0.1151805148,-0.2041630894,-0.0944092944,-0.0451543592,0.2127512693,-0.2799159884,-0.6564025283,0.0895091146,0.0316258781,0.2786871493,0.0627193525,-0.030414138,0.1169167235,-0.2188137472,-0.169888556,0.1760187596,0.0873951912,0.1103237867,-0.4886095524,-0.3197338879,0.0049685156,0.0149404025,0.1607397348,0.1425743401,-0.2066916376,0.1983790696,0.0647579879,0.1064668968,0.2382353395,0.4176746905,-0.0577855743,-0.2508044243,0.2393510789,-0.2066840678,-0.4679914415,0.3813743293,-0.0582001358,0.1504364312,0.0513900258,-0.0919354334,-0.314740777,-0.1900457591,-0.3224794865,-0.4129860103,0.0291994661,0.083561644,0.4664493203,-0.0627890825,-0.2708913088,0.4161113501,0.1776850522,0.2026192099,-0.1979149729,-0.0367069468,-0.0714992806,-0.1292344034,-0.3720748723,-0.0548154041,0.1507507265,-0.2703870535,-0.1646059304,0.4357369542,-0.0549746081,0.1264852285,-0.0163788572,0.2787240446,0.1310151666,0.2094838619,0.1261975765,0.1861446947,-0.1718526632,-0.0207077246,-0.0400916971,0.3744085133,0.1988709271,0.212100476,0.1096512601,-0.0578966551,-0.0523809791,0.020036336,-0.0271951109,-0.3291665018,0.2592810392,-0.0970329493,0.6289586425,0.1915283948,0.0682512671,0.2737976611,0.7349142432,0.1994312406,-0.2323375195,0.1495774388,-0.2541774213,-0.3552419245,0.1600754559,0.280841589,0.3655433357,0.0967631564,0.4016495049,0.1217315421,-0.0592810474,-0.0925822258,0.1318967044,-0.103768073,0.0365564413,-0.0743083283,0.0565556027,0.2631292343,-0.3260219693,0.136027202,0.0117203426,0.1084349826,-0.1194188669,0.2409161031,-0.3367220759,0.2784646749,-0.1354539096,-0.1432013363,-0.3787076771,-0.2035416663,-0.048936978,0.1152366698,-0.0025185787,0.3248536587,-0.2567089796,0.1713041961,-0.0361298993,-0.3038983345,-0.4968311787,-0.1464488059,-0.2653330266,-0.0522758998,-0.0077604922,-0.3781483173,-0.041796647,-0.1704156846,-0.2427740842,-0.1978735626,-0.329761982,0.1543189883,-0.072553426,-0.0289234985,-0.0718471333,0.0568940639,-0.1304771602,0.0907302946,0.1732173264,-0.3506852686,-0.1693695635,-0.0369205885,0.0841066614,0.1431051046,-0.2130721211,-0.2104904354,0.0077060619,-0.2791153491,0.1315926015,-0.1691376567,-0.0363107882,0.5730288625,0.0180480685,0.0493544452,-0.154198274,0.1935802996,-0.2817358673,-0.5460473299,0.4480652511,-0.0068342546,-0.3691204488,-0.1439013928,0.0137629816,0.1789387465,0.3921634257,-0.1815630645,-0.2465928942,-0.3960894048,0.0011066424,0.2109925449,0.0658638254,0.6789283156,-0.012012572,-0.0564476885,-0.2156410962,-0.1154806092,0.1403704733,-0.1744911224,0.321870178,-0.2863911688,0.1133488566,0.261436671,0.6822909117,0.2117644399,-0.0542893298,0.2297441661,0.2226077169,0.4835837185,-0.3701800406,-0.2817359567,-0.0303613804,-0.2276449054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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"Hi @stas00, \r\n\r\nYou are right: an env variable is needed to turn off this behavior. I am adding it.\r\n\r\nFor the moment there is a config parameter to turn off this behavior: `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None`\r\n\r\nYou can find this info in the docs:\r\n- in the docstring of the parameter `keep_in_memory` of the function [`load_datasets`](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/loading_methods.html#datasets.load_dataset):\r\n- in a Note in the docs about [Loading a Dataset](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#from-the-huggingface-hub)\r\n\r\n> The default in \ud83e\udd17Datasets is to memory-map the dataset on drive if its size is larger than datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES (default 250 MiB); otherwise, the dataset is copied in-memory. This behavior can be disabled by setting datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None, and in this case the dataset is not loaded in memory.","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":115,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n Hi @stas00, \r\n\r\nYou are right: an env variable is needed to turn off this behavior. I am adding it.\r\n\r\nFor the moment there is a config parameter to turn off this behavior: `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None`\r\n\r\nYou can find this info in the docs:\r\n- in the docstring of the parameter `keep_in_memory` of the function [`load_datasets`](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/loading_methods.html#datasets.load_dataset):\r\n- in a Note in the docs about [Loading a Dataset](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#from-the-huggingface-hub)\r\n\r\n> The default in \ud83e\udd17Datasets is to memory-map the dataset on drive if its size is larger than datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES (default 250 MiB); otherwise, the dataset is copied in-memory. This behavior can be disabled by setting datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None, and in this case the dataset is not loaded in memory.","embeddings":[-0.2158487439,0.0203107707,0.0422426425,0.1404154301,0.1821744591,0.0462308712,0.1409803778,0.3139572144,0.0551589616,-0.0644533038,-0.2359380573,-0.0579473414,-0.0247005746,-0.3315773904,-0.1081899628,0.0198047999,0.2144653797,0.0230753701,-0.1678417474,-0.0409687683,-0.1443585306,0.2673944831,-0.128291443,-0.228637591,-0.6212518811,0.1536585689,-0.0539875105,-0.0578289442,-0.1436334997,-0.3942032158,0.3681198657,0.2826853693,0.1136951819,0.4599291086,-0.00012043,0.1595689058,0.2031688243,0.0211555008,-0.4104935527,-0.0573553517,0.0354547277,-0.0839594007,0.0214983989,0.1640308499,-0.2790547013,-0.0306378007,0.1911177784,-0.5746431947,0.452419132,0.3432961404,0.160297215,0.5254045725,-0.1487574875,-0.0378798321,-0.0608341657,-0.0301156137,-0.332855463,0.0926357806,0.1314037144,0.0842170715,-0.2770285308,0.3524879217,0.097985439,-0.0218208209,0.16133672,0.0107811904,-0.1585707217,0.0202849843,0.2987552285,0.10706193,0.3787642717,-0.2141118944,-0.5825324059,-0.3364526331,-0.3538715541,0.0064531881,0.3440105021,-0.0655077994,0.1379141808,0.3956792057,-0.4331495464,-0.0684470385,0.0699945837,-0.0044340761,0.0611454509,0.2066850215,-0.1162460893,-0.0994941965,0.1231028289,0.149908945,0.1937531978,-0.1635281295,-0.109124884,0.2992784679,-0.3559890985,-0.0108128795,0.1666994542,0.0396621898,0.0110875499,0.1188242361,0.0640139356,0.1364050955,0.0925285518,-0.2549798191,-0.0728779137,0.5833615065,0.1947101653,0.2663045526,0.0184179097,0.0475953147,-0.1567291319,-0.096233882,-0.0447993651,-0.2194101065,0.2228741646,-0.0724640489,0.1600194871,-0.177988708,-0.0083685135,0.1688308865,0.0522615016,-0.1071201414,0.1425199807,0.297921747,-0.1997744441,0.2688975036,-0.0717916638,-0.1660918742,-0.2827946544,0.0425893366,-0.3636681736,-0.0625563711,-0.2846522033,0.2555699944,0.2676339149,-0.372418493,0.4367183745,0.1324850917,0.2268454731,0.0182676762,0.0358226374,-0.1124993712,0.3517661691,0.1498368829,-0.4831211865,0.4104645848,0.0979266092,0.1543205231,-0.2801340222,-0.034504652,-0.1624662131,-0.2589809299,0.1788692027,0.0967067927,-0.4759327471,-0.2851259112,0.1238623857,0.2492318451,0.3530565798,-0.1826398224,0.0576875657,-0.054647509,-0.3108603656,-0.0424191542,0.2072167397,0.6110782027,-0.0987188816,-0.2701899707,0.3160687387,0.0783183947,0.1078270078,0.2469794154,-0.1151805148,-0.2041630894,-0.0944092944,-0.0451543592,0.2127512693,-0.2799159884,-0.6564025283,0.0895091146,0.0316258781,0.2786871493,0.0627193525,-0.030414138,0.1169167235,-0.2188137472,-0.169888556,0.1760187596,0.0873951912,0.1103237867,-0.4886095524,-0.3197338879,0.0049685156,0.0149404025,0.1607397348,0.1425743401,-0.2066916376,0.1983790696,0.0647579879,0.1064668968,0.2382353395,0.4176746905,-0.0577855743,-0.2508044243,0.2393510789,-0.2066840678,-0.4679914415,0.3813743293,-0.0582001358,0.1504364312,0.0513900258,-0.0919354334,-0.314740777,-0.1900457591,-0.3224794865,-0.4129860103,0.0291994661,0.083561644,0.4664493203,-0.0627890825,-0.2708913088,0.4161113501,0.1776850522,0.2026192099,-0.1979149729,-0.0367069468,-0.0714992806,-0.1292344034,-0.3720748723,-0.0548154041,0.1507507265,-0.2703870535,-0.1646059304,0.4357369542,-0.0549746081,0.1264852285,-0.0163788572,0.2787240446,0.1310151666,0.2094838619,0.1261975765,0.1861446947,-0.1718526632,-0.0207077246,-0.0400916971,0.3744085133,0.1988709271,0.212100476,0.1096512601,-0.0578966551,-0.0523809791,0.020036336,-0.0271951109,-0.3291665018,0.2592810392,-0.0970329493,0.6289586425,0.1915283948,0.0682512671,0.2737976611,0.7349142432,0.1994312406,-0.2323375195,0.1495774388,-0.2541774213,-0.3552419245,0.1600754559,0.280841589,0.3655433357,0.0967631564,0.4016495049,0.1217315421,-0.0592810474,-0.0925822258,0.1318967044,-0.103768073,0.0365564413,-0.0743083283,0.0565556027,0.2631292343,-0.3260219693,0.136027202,0.0117203426,0.1084349826,-0.1194188669,0.2409161031,-0.3367220759,0.2784646749,-0.1354539096,-0.1432013363,-0.3787076771,-0.2035416663,-0.048936978,0.1152366698,-0.0025185787,0.3248536587,-0.2567089796,0.1713041961,-0.0361298993,-0.3038983345,-0.4968311787,-0.1464488059,-0.2653330266,-0.0522758998,-0.0077604922,-0.3781483173,-0.041796647,-0.1704156846,-0.2427740842,-0.1978735626,-0.329761982,0.1543189883,-0.072553426,-0.0289234985,-0.0718471333,0.0568940639,-0.1304771602,0.0907302946,0.1732173264,-0.3506852686,-0.1693695635,-0.0369205885,0.0841066614,0.1431051046,-0.2130721211,-0.2104904354,0.0077060619,-0.2791153491,0.1315926015,-0.1691376567,-0.0363107882,0.5730288625,0.0180480685,0.0493544452,-0.154198274,0.1935802996,-0.2817358673,-0.5460473299,0.4480652511,-0.0068342546,-0.3691204488,-0.1439013928,0.0137629816,0.1789387465,0.3921634257,-0.1815630645,-0.2465928942,-0.3960894048,0.0011066424,0.2109925449,0.0658638254,0.6789283156,-0.012012572,-0.0564476885,-0.2156410962,-0.1154806092,0.1403704733,-0.1744911224,0.321870178,-0.2863911688,0.1133488566,0.261436671,0.6822909117,0.2117644399,-0.0542893298,0.2297441661,0.2226077169,0.4835837185,-0.3701800406,-0.2817359567,-0.0303613804,-0.2276449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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"Yes, but this still requires one to edit the standard example scripts, so if I'm doing that already I just as well can add `keep_in_memory=False`.\r\n\r\nMay be the low hanging fruit is to add `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` env var to match the config, and if the user sets it to 0, then it'll be the same as `keep_in_memory=False` or `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0`?","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":58,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n Yes, but this still requires one to edit the standard example scripts, so if I'm doing that already I just as well can add `keep_in_memory=False`.\r\n\r\nMay be the low hanging fruit is to add `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` env var to match the config, and if the user sets it to 0, then it'll be the same as `keep_in_memory=False` or `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0`?","embeddings":[-0.2158487439,0.0203107707,0.0422426425,0.1404154301,0.1821744591,0.0462308712,0.1409803778,0.3139572144,0.0551589616,-0.0644533038,-0.2359380573,-0.0579473414,-0.0247005746,-0.3315773904,-0.1081899628,0.0198047999,0.2144653797,0.0230753701,-0.1678417474,-0.0409687683,-0.1443585306,0.2673944831,-0.128291443,-0.228637591,-0.6212518811,0.1536585689,-0.0539875105,-0.0578289442,-0.1436334997,-0.3942032158,0.3681198657,0.2826853693,0.1136951819,0.4599291086,-0.00012043,0.1595689058,0.2031688243,0.0211555008,-0.4104935527,-0.0573553517,0.0354547277,-0.0839594007,0.0214983989,0.1640308499,-0.2790547013,-0.0306378007,0.1911177784,-0.5746431947,0.452419132,0.3432961404,0.160297215,0.5254045725,-0.1487574875,-0.0378798321,-0.0608341657,-0.0301156137,-0.332855463,0.0926357806,0.1314037144,0.0842170715,-0.2770285308,0.3524879217,0.097985439,-0.0218208209,0.16133672,0.0107811904,-0.1585707217,0.0202849843,0.2987552285,0.10706193,0.3787642717,-0.2141118944,-0.5825324059,-0.3364526331,-0.3538715541,0.0064531881,0.3440105021,-0.0655077994,0.1379141808,0.3956792057,-0.4331495464,-0.0684470385,0.0699945837,-0.0044340761,0.0611454509,0.2066850215,-0.1162460893,-0.0994941965,0.1231028289,0.149908945,0.1937531978,-0.1635281295,-0.109124884,0.2992784679,-0.3559890985,-0.0108128795,0.1666994542,0.0396621898,0.0110875499,0.1188242361,0.0640139356,0.1364050955,0.0925285518,-0.2549798191,-0.0728779137,0.5833615065,0.1947101653,0.2663045526,0.0184179097,0.0475953147,-0.1567291319,-0.096233882,-0.0447993651,-0.2194101065,0.2228741646,-0.0724640489,0.1600194871,-0.177988708,-0.0083685135,0.1688308865,0.0522615016,-0.1071201414,0.1425199807,0.297921747,-0.1997744441,0.2688975036,-0.0717916638,-0.1660918742,-0.2827946544,0.0425893366,-0.3636681736,-0.0625563711,-0.2846522033,0.2555699944,0.2676339149,-0.372418493,0.4367183745,0.1324850917,0.2268454731,0.0182676762,0.0358226374,-0.1124993712,0.3517661691,0.1498368829,-0.4831211865,0.4104645848,0.0979266092,0.1543205231,-0.2801340222,-0.034504652,-0.1624662131,-0.2589809299,0.1788692027,0.0967067927,-0.4759327471,-0.2851259112,0.1238623857,0.2492318451,0.3530565798,-0.1826398224,0.0576875657,-0.054647509,-0.3108603656,-0.0424191542,0.2072167397,0.6110782027,-0.0987188816,-0.2701899707,0.3160687387,0.0783183947,0.1078270078,0.2469794154,-0.1151805148,-0.2041630894,-0.0944092944,-0.0451543592,0.2127512693,-0.2799159884,-0.6564025283,0.0895091146,0.0316258781,0.2786871493,0.0627193525,-0.030414138,0.1169167235,-0.2188137472,-0.169888556,0.1760187596,0.0873951912,0.1103237867,-0.4886095524,-0.3197338879,0.0049685156,0.0149404025,0.1607397348,0.1425743401,-0.2066916376,0.1983790696,0.0647579879,0.1064668968,0.2382353395,0.4176746905,-0.0577855743,-0.2508044243,0.2393510789,-0.2066840678,-0.4679914415,0.3813743293,-0.0582001358,0.1504364312,0.0513900258,-0.0919354334,-0.314740777,-0.1900457591,-0.3224794865,-0.4129860103,0.0291994661,0.083561644,0.4664493203,-0.0627890825,-0.2708913088,0.4161113501,0.1776850522,0.2026192099,-0.1979149729,-0.0367069468,-0.0714992806,-0.1292344034,-0.3720748723,-0.0548154041,0.1507507265,-0.2703870535,-0.1646059304,0.4357369542,-0.0549746081,0.1264852285,-0.0163788572,0.2787240446,0.1310151666,0.2094838619,0.1261975765,0.1861446947,-0.1718526632,-0.0207077246,-0.0400916971,0.3744085133,0.1988709271,0.212100476,0.1096512601,-0.0578966551,-0.0523809791,0.020036336,-0.0271951109,-0.3291665018,0.2592810392,-0.0970329493,0.6289586425,0.1915283948,0.0682512671,0.2737976611,0.7349142432,0.1994312406,-0.2323375195,0.1495774388,-0.2541774213,-0.3552419245,0.1600754559,0.280841589,0.3655433357,0.0967631564,0.4016495049,0.1217315421,-0.0592810474,-0.0925822258,0.1318967044,-0.103768073,0.0365564413,-0.0743083283,0.0565556027,0.2631292343,-0.3260219693,0.136027202,0.0117203426,0.1084349826,-0.1194188669,0.2409161031,-0.3367220759,0.2784646749,-0.1354539096,-0.1432013363,-0.3787076771,-0.2035416663,-0.048936978,0.1152366698,-0.0025185787,0.3248536587,-0.2567089796,0.1713041961,-0.0361298993,-0.3038983345,-0.4968311787,-0.1464488059,-0.2653330266,-0.0522758998,-0.0077604922,-0.3781483173,-0.041796647,-0.1704156846,-0.2427740842,-0.1978735626,-0.329761982,0.1543189883,-0.072553426,-0.0289234985,-0.0718471333,0.0568940639,-0.1304771602,0.0907302946,0.1732173264,-0.3506852686,-0.1693695635,-0.0369205885,0.0841066614,0.1431051046,-0.2130721211,-0.2104904354,0.0077060619,-0.2791153491,0.1315926015,-0.1691376567,-0.0363107882,0.5730288625,0.0180480685,0.0493544452,-0.154198274,0.1935802996,-0.2817358673,-0.5460473299,0.4480652511,-0.0068342546,-0.3691204488,-0.1439013928,0.0137629816,0.1789387465,0.3921634257,-0.1815630645,-0.2465928942,-0.3960894048,0.0011066424,0.2109925449,0.0658638254,0.6789283156,-0.012012572,-0.0564476885,-0.2156410962,-0.1154806092,0.1403704733,-0.1744911224,0.321870178,-0.2863911688,0.1133488566,0.261436671,0.6822909117,0.2117644399,-0.0542893298,0.2297441661,0.2226077169,0.4835837185,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"@stas00, however, for the moment, setting the value to `0` is equivalent to the opposite, i.e. `keep_in_memory=True`. This means the max size until which I load in memory is 0 bytes.\r\n\r\nTell me if this is logical\/convenient, or I should change it.","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":42,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n @stas00, however, for the moment, setting the value to `0` is equivalent to the opposite, i.e. `keep_in_memory=True`. This means the max size until which I load in memory is 0 bytes.\r\n\r\nTell me if this is logical\/convenient, or I should change it.","embeddings":[-0.2158487439,0.0203107707,0.0422426425,0.1404154301,0.1821744591,0.0462308712,0.1409803778,0.3139572144,0.0551589616,-0.0644533038,-0.2359380573,-0.0579473414,-0.0247005746,-0.3315773904,-0.1081899628,0.0198047999,0.2144653797,0.0230753701,-0.1678417474,-0.0409687683,-0.1443585306,0.2673944831,-0.128291443,-0.228637591,-0.6212518811,0.1536585689,-0.0539875105,-0.0578289442,-0.1436334997,-0.3942032158,0.3681198657,0.2826853693,0.1136951819,0.4599291086,-0.00012043,0.1595689058,0.2031688243,0.0211555008,-0.4104935527,-0.0573553517,0.0354547277,-0.0839594007,0.0214983989,0.1640308499,-0.2790547013,-0.0306378007,0.1911177784,-0.5746431947,0.452419132,0.3432961404,0.160297215,0.5254045725,-0.1487574875,-0.0378798321,-0.0608341657,-0.0301156137,-0.332855463,0.0926357806,0.1314037144,0.0842170715,-0.2770285308,0.3524879217,0.097985439,-0.0218208209,0.16133672,0.0107811904,-0.1585707217,0.0202849843,0.2987552285,0.10706193,0.3787642717,-0.2141118944,-0.5825324059,-0.3364526331,-0.3538715541,0.0064531881,0.3440105021,-0.0655077994,0.1379141808,0.3956792057,-0.4331495464,-0.0684470385,0.0699945837,-0.0044340761,0.0611454509,0.2066850215,-0.1162460893,-0.0994941965,0.1231028289,0.149908945,0.1937531978,-0.1635281295,-0.109124884,0.2992784679,-0.3559890985,-0.0108128795,0.1666994542,0.0396621898,0.0110875499,0.1188242361,0.0640139356,0.1364050955,0.0925285518,-0.2549798191,-0.0728779137,0.5833615065,0.1947101653,0.2663045526,0.0184179097,0.0475953147,-0.1567291319,-0.096233882,-0.0447993651,-0.2194101065,0.2228741646,-0.0724640489,0.1600194871,-0.177988708,-0.0083685135,0.1688308865,0.0522615016,-0.1071201414,0.1425199807,0.297921747,-0.1997744441,0.2688975036,-0.0717916638,-0.1660918742,-0.2827946544,0.0425893366,-0.3636681736,-0.0625563711,-0.2846522033,0.2555699944,0.2676339149,-0.372418493,0.4367183745,0.1324850917,0.2268454731,0.0182676762,0.0358226374,-0.1124993712,0.3517661691,0.1498368829,-0.4831211865,0.4104645848,0.0979266092,0.1543205231,-0.2801340222,-0.034504652,-0.1624662131,-0.2589809299,0.1788692027,0.0967067927,-0.4759327471,-0.2851259112,0.1238623857,0.2492318451,0.3530565798,-0.1826398224,0.0576875657,-0.054647509,-0.3108603656,-0.0424191542,0.2072167397,0.6110782027,-0.0987188816,-0.2701899707,0.3160687387,0.0783183947,0.1078270078,0.2469794154,-0.1151805148,-0.2041630894,-0.0944092944,-0.0451543592,0.2127512693,-0.2799159884,-0.6564025283,0.0895091146,0.0316258781,0.2786871493,0.0627193525,-0.030414138,0.1169167235,-0.2188137472,-0.169888556,0.1760187596,0.0873951912,0.1103237867,-0.4886095524,-0.3197338879,0.0049685156,0.0149404025,0.1607397348,0.1425743401,-0.2066916376,0.1983790696,0.0647579879,0.1064668968,0.2382353395,0.4176746905,-0.0577855743,-0.2508044243,0.2393510789,-0.2066840678,-0.4679914415,0.3813743293,-0.0582001358,0.1504364312,0.0513900258,-0.0919354334,-0.314740777,-0.1900457591,-0.3224794865,-0.4129860103,0.0291994661,0.083561644,0.4664493203,-0.0627890825,-0.2708913088,0.4161113501,0.1776850522,0.2026192099,-0.1979149729,-0.0367069468,-0.0714992806,-0.1292344034,-0.3720748723,-0.0548154041,0.1507507265,-0.2703870535,-0.1646059304,0.4357369542,-0.0549746081,0.1264852285,-0.0163788572,0.2787240446,0.1310151666,0.2094838619,0.1261975765,0.1861446947,-0.1718526632,-0.0207077246,-0.0400916971,0.3744085133,0.1988709271,0.212100476,0.1096512601,-0.0578966551,-0.0523809791,0.020036336,-0.0271951109,-0.3291665018,0.2592810392,-0.0970329493,0.6289586425,0.1915283948,0.0682512671,0.2737976611,0.7349142432,0.1994312406,-0.2323375195,0.1495774388,-0.2541774213,-0.3552419245,0.1600754559,0.280841589,0.3655433357,0.0967631564,0.4016495049,0.1217315421,-0.0592810474,-0.0925822258,0.1318967044,-0.103768073,0.0365564413,-0.0743083283,0.0565556027,0.2631292343,-0.3260219693,0.136027202,0.0117203426,0.1084349826,-0.1194188669,0.2409161031,-0.3367220759,0.2784646749,-0.1354539096,-0.1432013363,-0.3787076771,-0.2035416663,-0.048936978,0.1152366698,-0.0025185787,0.3248536587,-0.2567089796,0.1713041961,-0.0361298993,-0.3038983345,-0.4968311787,-0.1464488059,-0.2653330266,-0.0522758998,-0.0077604922,-0.3781483173,-0.041796647,-0.1704156846,-0.2427740842,-0.1978735626,-0.329761982,0.1543189883,-0.072553426,-0.0289234985,-0.0718471333,0.0568940639,-0.1304771602,0.0907302946,0.1732173264,-0.3506852686,-0.1693695635,-0.0369205885,0.0841066614,0.1431051046,-0.2130721211,-0.2104904354,0.0077060619,-0.2791153491,0.1315926015,-0.1691376567,-0.0363107882,0.5730288625,0.0180480685,0.0493544452,-0.154198274,0.1935802996,-0.2817358673,-0.5460473299,0.4480652511,-0.0068342546,-0.3691204488,-0.1439013928,0.0137629816,0.1789387465,0.3921634257,-0.1815630645,-0.2465928942,-0.3960894048,0.0011066424,0.2109925449,0.0658638254,0.6789283156,-0.012012572,-0.0564476885,-0.2156410962,-0.1154806092,0.1403704733,-0.1744911224,0.321870178,-0.2863911688,0.1133488566,0.261436671,0.6822909117,0.2117644399,-0.0542893298,0.2297441661,0.2226077169,0.4835837185,-0.3701800406,-0.2817359567,-0.0303613804,-0.2276449054,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"In my PR, to turn off current default bahavior, you should set env variable to one of: `{\"\", \"OFF\", \"NO\", \"FALSE\"}`.\r\n\r\nFor example:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=\r\n```","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":26,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n In my PR, to turn off current default bahavior, you should set env variable to one of: `{\"\", \"OFF\", \"NO\", \"FALSE\"}`.\r\n\r\nFor example:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=\r\n```","embeddings":[-0.2158487439,0.0203107707,0.0422426425,0.1404154301,0.1821744591,0.0462308712,0.1409803778,0.3139572144,0.0551589616,-0.0644533038,-0.2359380573,-0.0579473414,-0.0247005746,-0.3315773904,-0.1081899628,0.0198047999,0.2144653797,0.0230753701,-0.1678417474,-0.0409687683,-0.1443585306,0.2673944831,-0.128291443,-0.228637591,-0.6212518811,0.1536585689,-0.0539875105,-0.0578289442,-0.1436334997,-0.3942032158,0.3681198657,0.2826853693,0.1136951819,0.4599291086,-0.00012043,0.1595689058,0.2031688243,0.0211555008,-0.4104935527,-0.0573553517,0.0354547277,-0.0839594007,0.0214983989,0.1640308499,-0.2790547013,-0.0306378007,0.1911177784,-0.5746431947,0.452419132,0.3432961404,0.160297215,0.5254045725,-0.1487574875,-0.0378798321,-0.0608341657,-0.0301156137,-0.332855463,0.0926357806,0.1314037144,0.0842170715,-0.2770285308,0.3524879217,0.097985439,-0.0218208209,0.16133672,0.0107811904,-0.1585707217,0.0202849843,0.2987552285,0.10706193,0.3787642717,-0.2141118944,-0.5825324059,-0.3364526331,-0.3538715541,0.0064531881,0.3440105021,-0.0655077994,0.1379141808,0.3956792057,-0.4331495464,-0.0684470385,0.0699945837,-0.0044340761,0.0611454509,0.2066850215,-0.1162460893,-0.0994941965,0.1231028289,0.149908945,0.1937531978,-0.1635281295,-0.109124884,0.2992784679,-0.3559890985,-0.0108128795,0.1666994542,0.0396621898,0.0110875499,0.1188242361,0.0640139356,0.1364050955,0.0925285518,-0.2549798191,-0.0728779137,0.5833615065,0.1947101653,0.2663045526,0.0184179097,0.0475953147,-0.1567291319,-0.096233882,-0.0447993651,-0.2194101065,0.2228741646,-0.0724640489,0.1600194871,-0.177988708,-0.0083685135,0.1688308865,0.0522615016,-0.1071201414,0.1425199807,0.297921747,-0.1997744441,0.2688975036,-0.0717916638,-0.1660918742,-0.2827946544,0.0425893366,-0.3636681736,-0.0625563711,-0.2846522033,0.2555699944,0.2676339149,-0.372418493,0.4367183745,0.1324850917,0.2268454731,0.0182676762,0.0358226374,-0.1124993712,0.3517661691,0.1498368829,-0.4831211865,0.4104645848,0.0979266092,0.1543205231,-0.2801340222,-0.034504652,-0.1624662131,-0.2589809299,0.1788692027,0.0967067927,-0.4759327471,-0.2851259112,0.1238623857,0.2492318451,0.3530565798,-0.1826398224,0.0576875657,-0.054647509,-0.3108603656,-0.0424191542,0.2072167397,0.6110782027,-0.0987188816,-0.2701899707,0.3160687387,0.0783183947,0.1078270078,0.2469794154,-0.1151805148,-0.2041630894,-0.0944092944,-0.0451543592,0.2127512693,-0.2799159884,-0.6564025283,0.0895091146,0.0316258781,0.2786871493,0.0627193525,-0.030414138,0.1169167235,-0.2188137472,-0.169888556,0.1760187596,0.0873951912,0.1103237867,-0.4886095524,-0.3197338879,0.0049685156,0.0149404025,0.1607397348,0.1425743401,-0.2066916376,0.1983790696,0.0647579879,0.1064668968,0.2382353395,0.4176746905,-0.0577855743,-0.2508044243,0.2393510789,-0.2066840678,-0.4679914415,0.3813743293,-0.0582001358,0.1504364312,0.0513900258,-0.0919354334,-0.314740777,-0.1900457591,-0.3224794865,-0.4129860103,0.0291994661,0.083561644,0.4664493203,-0.0627890825,-0.2708913088,0.4161113501,0.1776850522,0.2026192099,-0.1979149729,-0.0367069468,-0.0714992806,-0.1292344034,-0.3720748723,-0.0548154041,0.1507507265,-0.2703870535,-0.1646059304,0.4357369542,-0.0549746081,0.1264852285,-0.0163788572,0.2787240446,0.1310151666,0.2094838619,0.1261975765,0.1861446947,-0.1718526632,-0.0207077246,-0.0400916971,0.3744085133,0.1988709271,0.212100476,0.1096512601,-0.0578966551,-0.0523809791,0.020036336,-0.0271951109,-0.3291665018,0.2592810392,-0.0970329493,0.6289586425,0.1915283948,0.0682512671,0.2737976611,0.7349142432,0.1994312406,-0.2323375195,0.1495774388,-0.2541774213,-0.3552419245,0.1600754559,0.280841589,0.3655433357,0.0967631564,0.4016495049,0.1217315421,-0.0592810474,-0.0925822258,0.1318967044,-0.103768073,0.0365564413,-0.0743083283,0.0565556027,0.2631292343,-0.3260219693,0.136027202,0.0117203426,0.1084349826,-0.1194188669,0.2409161031,-0.3367220759,0.2784646749,-0.1354539096,-0.1432013363,-0.3787076771,-0.2035416663,-0.048936978,0.1152366698,-0.0025185787,0.3248536587,-0.2567089796,0.1713041961,-0.0361298993,-0.3038983345,-0.4968311787,-0.1464488059,-0.2653330266,-0.0522758998,-0.0077604922,-0.3781483173,-0.041796647,-0.1704156846,-0.2427740842,-0.1978735626,-0.329761982,0.1543189883,-0.072553426,-0.0289234985,-0.0718471333,0.0568940639,-0.1304771602,0.0907302946,0.1732173264,-0.3506852686,-0.1693695635,-0.0369205885,0.0841066614,0.1431051046,-0.2130721211,-0.2104904354,0.0077060619,-0.2791153491,0.1315926015,-0.1691376567,-0.0363107882,0.5730288625,0.0180480685,0.0493544452,-0.154198274,0.1935802996,-0.2817358673,-0.5460473299,0.4480652511,-0.0068342546,-0.3691204488,-0.1439013928,0.0137629816,0.1789387465,0.3921634257,-0.1815630645,-0.2465928942,-0.3960894048,0.0011066424,0.2109925449,0.0658638254,0.6789283156,-0.012012572,-0.0564476885,-0.2156410962,-0.1154806092,0.1403704733,-0.1744911224,0.321870178,-0.2863911688,0.1133488566,0.261436671,0.6822909117,0.2117644399,-0.0542893298,0.2297441661,0.2226077169,0.48358371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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"IMHO, this behaviour is not very intuitive, as 0 is a normal quantity of bytes. So `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` to me reads as don't cache ever.\r\n\r\nAlso \"SIZE_IN_BYTES\" that can take one of `{\"\", \"OFF\", \"NO\", \"FALSE\"}` is also quite odd.\r\n\r\nI think supporting a very simple `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` that can accept any numerical value to match the name of the variable, requires minimal logic and is very straightforward. \r\n\r\nSo if you could adjust this logic - then `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` is all that's needed to not do in-memory datasets.\r\n\r\nDoes it make sense?","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":89,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n IMHO, this behaviour is not very intuitive, as 0 is a normal quantity of bytes. So `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` to me reads as don't cache ever.\r\n\r\nAlso \"SIZE_IN_BYTES\" that can take one of `{\"\", \"OFF\", \"NO\", \"FALSE\"}` is also quite odd.\r\n\r\nI think supporting a very simple `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` that can accept any numerical value to match the name of the variable, requires minimal logic and is very straightforward. \r\n\r\nSo if you could adjust this logic - then `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` is all that's needed to not do in-memory datasets.\r\n\r\nDoes it make 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"I understand your point @stas00, as I am not very convinced with current implementation.\r\n\r\nMy concern is: which numerical value should then pass a user who wants `keep_in_memory=True` by default, independently of dataset size? Currently it is `0` for this case.","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":41,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n I understand your point @stas00, as I am not very convinced with current implementation.\r\n\r\nMy concern is: which numerical value should then pass a user who wants `keep_in_memory=True` by default, independently of dataset size? Currently it is `0` for this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"That's a good question, and again the normal bytes can be used for that:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=1e12 # (~2**40)\r\n```\r\nSince it's unlikely that anybody will have more than 1TB RAM.\r\n\r\nIt's also silly that it uses BYTES and not MBYTES - that level of refinement doesn't seem to be of a practical use in this context.\r\n\r\nNot sure when it was added and if there are back-compat issues here, but perhaps it could be renamed `MAX_IN_MEMORY_DATASET_SIZE` and support 1M, 1G, 1T, etc. \r\n\r\nBut scientific notation is quite intuitive too, as each 000 zeros is the next M, G, T multiplier. Minus the discrepancy of 1024 vs 1000, which adds up. And it is easy to write down `1e12`, as compared to `1099511627776` (2**40).  (`1.1e12` is more exact).\r\n","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":127,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n That's a good question, and again the normal bytes can be used for that:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=1e12 # (~2**40)\r\n```\r\nSince it's unlikely that anybody will have more than 1TB RAM.\r\n\r\nIt's also silly that it uses BYTES and not MBYTES - that level of refinement doesn't seem to be of a practical use in this context.\r\n\r\nNot sure when it was added and if there are back-compat issues here, but perhaps it could be renamed `MAX_IN_MEMORY_DATASET_SIZE` and support 1M, 1G, 1T, etc. \r\n\r\nBut scientific notation is quite intuitive too, as each 000 zeros is the next M, G, T multiplier. Minus the discrepancy of 1024 vs 1000, which adds up. And it is easy to write down `1e12`, as compared to `1099511627776` (2**40).  (`1.1e12` is more exact).\r\n","embeddings":[-0.2158487439,0.0203107707,0.0422426425,0.1404154301,0.1821744591,0.0462308712,0.1409803778,0.3139572144,0.0551589616,-0.0644533038,-0.2359380573,-0.0579473414,-0.0247005746,-0.3315773904,-0.1081899628,0.0198047999,0.2144653797,0.0230753701,-0.1678417474,-0.0409687683,-0.1443585306,0.2673944831,-0.128291443,-0.228637591,-0.6212518811,0.1536585689,-0.0539875105,-0.0578289442,-0.1436334997,-0.3942032158,0.3681198657,0.2826853693,0.1136951819,0.4599291086,-0.00012043,0.1595689058,0.2031688243,0.0211555008,-0.4104935527,-0.0573553517,0.0354547277,-0.0839594007,0.0214983989,0.1640308499,-0.2790547013,-0.0306378007,0.1911177784,-0.5746431947,0.452419132,0.3432961404,0.160297215,0.5254045725,-0.1487574875,-0.0378798321,-0.0608341657,-0.0301156137,-0.332855463,0.0926357806,0.1314037144,0.0842170715,-0.2770285308,0.3524879217,0.097985439,-0.0218208209,0.16133672,0.0107811904,-0.1585707217,0.0202849843,0.2987552285,0.10706193,0.3787642717,-0.2141118944,-0.5825324059,-0.3364526331,-0.3538715541,0.0064531881,0.3440105021,-0.0655077994,0.1379141808,0.3956792057,-0.4331495464,-0.0684470385,0.0699945837,-0.0044340761,0.0611454509,0.2066850215,-0.1162460893,-0.0994941965,0.1231028289,0.149908945,0.1937531978,-0.1635281295,-0.109124884,0.2992784679,-0.3559890985,-0.0108128795,0.1666994542,0.0396621898,0.0110875499,0.1188242361,0.0640139356,0.1364050955,0.0925285518,-0.2549798191,-0.0728779137,0.5833615065,0.1947101653,0.2663045526,0.0184179097,0.0475953147,-0.1567291319,-0.096233882,-0.0447993651,-0.2194101065,0.2228741646,-0.0724640489,0.1600194871,-0.177988708,-0.0083685135,0.1688308865,0.0522615016,-0.1071201414,0.1425199807,0.297921747,-0.1997744441,0.2688975036,-0.0717916638,-0.1660918742,-0.2827946544,0.0425893366,-0.3636681736,-0.0625563711,-0.2846522033,0.2555699944,0.2676339149,-0.372418493,0.4367183745,0.1324850917,0.2268454731,0.0182676762,0.0358226374,-0.1124993712,0.3517661691,0.1498368829,-0.4831211865,0.4104645848,0.0979266092,0.1543205231,-0.2801340222,-0.034504652,-0.1624662131,-0.2589809299,0.1788692027,0.0967067927,-0.4759327471,-0.2851259112,0.1238623857,0.2492318451,0.3530565798,-0.1826398224,0.0576875657,-0.054647509,-0.3108603656,-0.0424191542,0.2072167397,0.6110782027,-0.0987188816,-0.2701899707,0.3160687387,0.0783183947,0.1078270078,0.2469794154,-0.1151805148,-0.2041630894,-0.0944092944,-0.0451543592,0.2127512693,-0.2799159884,-0.6564025283,0.0895091146,0.0316258781,0.2786871493,0.0627193525,-0.030414138,0.1169167235,-0.2188137472,-0.169888556,0.1760187596,0.0873951912,0.1103237867,-0.4886095524,-0.3197338879,0.0049685156,0.0149404025,0.1607397348,0.1425743401,-0.2066916376,0.1983790696,0.0647579879,0.1064668968,0.2382353395,0.4176746905,-0.0577855743,-0.2508044243,0.2393510789,-0.2066840678,-0.4679914415,0.3813743293,-0.0582001358,0.1504364312,0.0513900258,-0.0919354334,-0.314740777,-0.1900457591,-0.3224794865,-0.4129860103,0.0291994661,0.083561644,0.4664493203,-0.0627890825,-0.2708913088,0.4161113501,0.1776850522,0.2026192099,-0.1979149729,-0.0367069468,-0.0714992806,-0.1292344034,-0.3720748723,-0.0548154041,0.1507507265,-0.2703870535,-0.1646059304,0.4357369542,-0.0549746081,0.1264852285,-0.0163788572,0.2787240446,0.1310151666,0.2094838619,0.1261975765,0.1861446947,-0.1718526632,-0.0207077246,-0.0400916971,0.3744085133,0.1988709271,0.212100476,0.1096512601,-0.0578966551,-0.0523809791,0.020036336,-0.0271951109,-0.3291665018,0.2592810392,-0.0970329493,0.6289586425,0.1915283948,0.0682512671,0.2737976611,0.7349142432,0.1994312406,-0.2323375195,0.1495774388,-0.2541774213,-0.3552419245,0.1600754559,0.280841589,0.3655433357,0.0967631564,0.4016495049,0.1217315421,-0.0592810474,-0.0925822258,0.1318967044,-0.103768073,0.0365564413,-0.0743083283,0.0565556027,0.2631292343,-0.3260219693,0.136027202,0.0117203426,0.1084349826,-0.1194188669,0.2409161031,-0.3367220759,0.2784646749,-0.1354539096,-0.1432013363,-0.3787076771,-0.2035416663,-0.048936978,0.1152366698,-0.0025185787,0.3248536587,-0.2567089796,0.1713041961,-0.0361298993,-0.3038983345,-0.4968311787,-0.1464488059,-0.2653330266,-0.0522758998,-0.0077604922,-0.3781483173,-0.041796647,-0.1704156846,-0.2427740842,-0.1978735626,-0.329761982,0.1543189883,-0.072553426,-0.0289234985,-0.0718471333,0.0568940639,-0.1304771602,0.0907302946,0.1732173264,-0.3506852686,-0.1693695635,-0.0369205885,0.0841066614,0.1431051046,-0.2130721211,-0.2104904354,0.0077060619,-0.2791153491,0.1315926015,-0.1691376567,-0.0363107882,0.5730288625,0.0180480685,0.0493544452,-0.154198274,0.1935802996,-0.2817358673,-0.5460473299,0.4480652511,-0.0068342546,-0.3691204488,-0.1439013928,0.0137629816,0.1789387465,0.3921634257,-0.1815630645,-0.2465928942,-0.3960894048,0.0011066424,0.2109925449,0.0658638254,0.6789283156,-0.012012572,-0.0564476885,-0.2156410962,-0.1154806092,0.1403704733,-0.1744911224,0.321870178,-0.2863911688,0.1133488566,0.261436671,0.6822909117,0.2117644399,-0.0542893298,0.2297441661,0.2226077169,0.4835837185,-0.3701800406,-0.2817359567,-0.0303613804,-0.227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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2387","title":"datasets 1.6 ignores cache","comments":"Great! Thanks, @stas00.\r\n\r\nI am implementing your suggestion to turn off default value when set to `0`.\r\n\r\nFor the other suggestion (allowing different metric prefixes), I will discuss with @lhoestq to agree on its implementation.","body":"Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n","comment_length":35,"text":"datasets 1.6 ignores cache \n Moving from https:\/\/github.com\/huggingface\/transformers\/issues\/11801#issuecomment-845546612 \r\n\r\nQuoting @VictorSanh:\r\n\r\n> \r\n> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):\r\n> \r\n> > `{'train': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '\/home\/victor\/.cache\/huggingface\/datasets\/openwebtext10k\/plain_text\/1.0.0\/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b\/cache-97cf4c813e6469c6.arrow'}]}`\r\n> \r\n> while the same command with the latest version of datasets (actually starting at `1.6.0`) gives:\r\n> > `{'train': [], 'validation': []}`\r\n> \r\n\r\nI also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used.\r\n\r\nto reproduce:\r\n```\r\nUSE_TF=0 python  examples\/pytorch\/language-modeling\/run_clm.py \\\r\n    --model_name_or_path gpt2 \\\r\n    --dataset_name \"stas\/openwebtext-10k\" \\\r\n    --output_dir output_dir \\\r\n    --overwrite_output_dir \\\r\n    --do_train \\\r\n    --do_eval \\\r\n    --max_train_samples 1000 \\\r\n    --max_eval_samples 200 \\\r\n    --per_device_train_batch_size 4 \\\r\n    --per_device_eval_batch_size 4 \\\r\n    --num_train_epochs 1 \\\r\n    --warmup_steps 8 \\\r\n    --block_size 64 \\\r\n    --fp16 \\\r\n    --report_to none\r\n```\r\n\r\nthe first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run.\r\n\r\n@lhoestq \r\n \n Great! Thanks, @stas00.\r\n\r\nI am implementing your suggestion to turn off default value when set to `0`.\r\n\r\nFor the other suggestion (allowing different metric prefixes), I will discuss with @lhoestq to agree on its implementation.","embeddings":[-0.2158487439,0.0203107707,0.0422426425,0.1404154301,0.1821744591,0.0462308712,0.1409803778,0.3139572144,0.0551589616,-0.0644533038,-0.2359380573,-0.0579473414,-0.0247005746,-0.3315773904,-0.1081899628,0.0198047999,0.2144653797,0.0230753701,-0.1678417474,-0.0409687683,-0.1443585306,0.2673944831,-0.128291443,-0.228637591,-0.6212518811,0.1536585689,-0.0539875105,-0.0578289442,-0.1436334997,-0.3942032158,0.3681198657,0.2826853693,0.1136951819,0.4599291086,-0.00012043,0.1595689058,0.2031688243,0.0211555008,-0.4104935527,-0.0573553517,0.0354547277,-0.0839594007,0.0214983989,0.1640308499,-0.2790547013,-0.0306378007,0.1911177784,-0.5746431947,0.452419132,0.3432961404,0.160297215,0.5254045725,-0.1487574875,-0.0378798321,-0.0608341657,-0.0301156137,-0.332855463,0.0926357806,0.1314037144,0.0842170715,-0.2770285308,0.3524879217,0.097985439,-0.0218208209,0.16133672,0.0107811904,-0.1585707217,0.0202849843,0.2987552285,0.10706193,0.3787642717,-0.2141118944,-0.5825324059,-0.3364526331,-0.3538715541,0.0064531881,0.3440105021,-0.0655077994,0.1379141808,0.3956792057,-0.4331495464,-0.0684470385,0.0699945837,-0.0044340761,0.0611454509,0.2066850215,-0.1162460893,-0.0994941965,0.1231028289,0.149908945,0.1937531978,-0.1635281295,-0.109124884,0.2992784679,-0.3559890985,-0.0108128795,0.1666994542,0.0396621898,0.0110875499,0.1188242361,0.0640139356,0.1364050955,0.0925285518,-0.2549798191,-0.0728779137,0.5833615065,0.1947101653,0.2663045526,0.0184179097,0.0475953147,-0.1567291319,-0.096233882,-0.0447993651,-0.2194101065,0.2228741646,-0.0724640489,0.1600194871,-0.177988708,-0.0083685135,0.1688308865,0.0522615016,-0.1071201414,0.1425199807,0.297921747,-0.1997744441,0.2688975036,-0.0717916638,-0.1660918742,-0.2827946544,0.0425893366,-0.3636681736,-0.0625563711,-0.2846522033,0.2555699944,0.2676339149,-0.372418493,0.4367183745,0.1324850917,0.2268454731,0.0182676762,0.0358226374,-0.1124993712,0.3517661691,0.1498368829,-0.4831211865,0.4104645848,0.0979266092,0.1543205231,-0.2801340222,-0.034504652,-0.1624662131,-0.2589809299,0.1788692027,0.0967067927,-0.4759327471,-0.2851259112,0.1238623857,0.2492318451,0.3530565798,-0.1826398224,0.0576875657,-0.054647509,-0.3108603656,-0.0424191542,0.2072167397,0.6110782027,-0.0987188816,-0.2701899707,0.3160687387,0.0783183947,0.1078270078,0.2469794154,-0.1151805148,-0.2041630894,-0.0944092944,-0.0451543592,0.2127512693,-0.2799159884,-0.6564025283,0.0895091146,0.0316258781,0.2786871493,0.0627193525,-0.030414138,0.1169167235,-0.2188137472,-0.169888556,0.1760187596,0.0873951912,0.1103237867,-0.4886095524,-0.3197338879,0.0049685156,0.0149404025,0.1607397348,0.1425743401,-0.2066916376,0.1983790696,0.0647579879,0.1064668968,0.2382353395,0.4176746905,-0.0577855743,-0.2508044243,0.2393510789,-0.2066840678,-0.4679914415,0.3813743293,-0.0582001358,0.1504364312,0.0513900258,-0.0919354334,-0.314740777,-0.1900457591,-0.3224794865,-0.4129860103,0.0291994661,0.083561644,0.4664493203,-0.0627890825,-0.2708913088,0.4161113501,0.1776850522,0.2026192099,-0.1979149729,-0.0367069468,-0.0714992806,-0.1292344034,-0.3720748723,-0.0548154041,0.1507507265,-0.2703870535,-0.1646059304,0.4357369542,-0.0549746081,0.1264852285,-0.0163788572,0.2787240446,0.1310151666,0.2094838619,0.1261975765,0.1861446947,-0.1718526632,-0.0207077246,-0.0400916971,0.3744085133,0.1988709271,0.212100476,0.1096512601,-0.0578966551,-0.0523809791,0.020036336,-0.0271951109,-0.3291665018,0.2592810392,-0.0970329493,0.6289586425,0.1915283948,0.0682512671,0.2737976611,0.7349142432,0.1994312406,-0.2323375195,0.1495774388,-0.2541774213,-0.3552419245,0.1600754559,0.280841589,0.3655433357,0.0967631564,0.4016495049,0.1217315421,-0.0592810474,-0.0925822258,0.1318967044,-0.103768073,0.0365564413,-0.0743083283,0.0565556027,0.2631292343,-0.3260219693,0.136027202,0.0117203426,0.1084349826,-0.1194188669,0.2409161031,-0.3367220759,0.2784646749,-0.1354539096,-0.1432013363,-0.3787076771,-0.2035416663,-0.048936978,0.1152366698,-0.0025185787,0.3248536587,-0.2567089796,0.1713041961,-0.0361298993,-0.3038983345,-0.4968311787,-0.1464488059,-0.2653330266,-0.0522758998,-0.0077604922,-0.3781483173,-0.041796647,-0.1704156846,-0.2427740842,-0.1978735626,-0.329761982,0.1543189883,-0.072553426,-0.0289234985,-0.0718471333,0.0568940639,-0.1304771602,0.0907302946,0.1732173264,-0.3506852686,-0.1693695635,-0.0369205885,0.0841066614,0.1431051046,-0.2130721211,-0.2104904354,0.0077060619,-0.2791153491,0.1315926015,-0.1691376567,-0.0363107882,0.5730288625,0.0180480685,0.0493544452,-0.154198274,0.1935802996,-0.2817358673,-0.5460473299,0.4480652511,-0.0068342546,-0.3691204488,-0.1439013928,0.0137629816,0.1789387465,0.3921634257,-0.1815630645,-0.2465928942,-0.3960894048,0.0011066424,0.2109925449,0.0658638254,0.6789283156,-0.012012572,-0.0564476885,-0.2156410962,-0.1154806092,0.1403704733,-0.1744911224,0.321870178,-0.2863911688,0.1133488566,0.261436671,0.6822909117,0.2117644399,-0.0542893298,0.2297441661,0.2226077169,0.4835837185,-0.3701800406,-0.2817359567,-0.0303613804,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2377","title":"ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather","comments":"Hi ! This is because we are actually using the arrow streaming format. We plan to switch to the arrow IPC format.\r\nMore info at #1933 ","body":"## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom pyarrow import feather\r\n\r\ndataset = load_dataset('imdb', split='train')\r\ndataset.save_to_disk('dataset_dir')\r\ntable = feather.read_table('dataset_dir\/dataset.arrow')\r\n```\r\n\r\n## Expected results\r\nI expect that the saved dataset can be read by the official Apache Arrow methods.\r\n\r\n## Actual results\r\n```\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/pyarrow\/feather.py\", line 236, in read_table\r\n    reader.open(source, use_memory_map=memory_map)\r\n  File \"pyarrow\/feather.pxi\", line 67, in pyarrow.lib.FeatherReader.open\r\n  File \"pyarrow\/error.pxi\", line 123, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Not a Feather V1 or Arrow IPC file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets-1.6.2\r\n- Platform: Linux\r\n- Python version: 3.7\r\n- PyArrow version: 0.17.1, also 2.0.0\r\n","comment_length":26,"text":"ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather \n ## Describe the bug\r\nA clear and concise description of what the bug is.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nfrom pyarrow import feather\r\n\r\ndataset = load_dataset('imdb', split='train')\r\ndataset.save_to_disk('dataset_dir')\r\ntable = feather.read_table('dataset_dir\/dataset.arrow')\r\n```\r\n\r\n## Expected results\r\nI expect that the saved dataset can be read by the official Apache Arrow methods.\r\n\r\n## Actual results\r\n```\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/pyarrow\/feather.py\", line 236, in read_table\r\n    reader.open(source, use_memory_map=memory_map)\r\n  File \"pyarrow\/feather.pxi\", line 67, in pyarrow.lib.FeatherReader.open\r\n  File \"pyarrow\/error.pxi\", line 123, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Not a Feather V1 or Arrow IPC file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: datasets-1.6.2\r\n- Platform: Linux\r\n- Python version: 3.7\r\n- PyArrow version: 0.17.1, also 2.0.0\r\n \n Hi ! This is because we are actually using the arrow streaming format. We plan to switch to the arrow IPC format.\r\nMore info at #1933 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2373","title":"Loading dataset from local path","comments":"Version below works, checked again in the docs, and data_files should be a path.\r\n```\r\nds = datasets.load_dataset('my_script.py', \r\n                           data_files='\/data\/dir\/corpus.txt', \r\n                           cache_dir='.')\r\n```","body":"I'm trying to load a local dataset with the code below\r\n\r\n```\r\nds = datasets.load_dataset('my_script.py', \r\n                           data_files='corpus.txt', \r\n                           data_dir='\/data\/dir', \r\n                           cache_dir='.')\r\n```\r\nBut internally a BuilderConfig is created, which tries to use getmtime on the data_files string, without using data_dir. Is this a bug or am I not using the load_dataset correctly?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/bc61954083f74e6460688202e9f77dde2475319c\/src\/datasets\/builder.py#L153","comment_length":21,"text":"Loading dataset from local path \n I'm trying to load a local dataset with the code below\r\n\r\n```\r\nds = datasets.load_dataset('my_script.py', \r\n                           data_files='corpus.txt', \r\n                           data_dir='\/data\/dir', \r\n                           cache_dir='.')\r\n```\r\nBut internally a BuilderConfig is created, which tries to use getmtime on the data_files string, without using data_dir. Is this a bug or am I not using the load_dataset correctly?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/bc61954083f74e6460688202e9f77dde2475319c\/src\/datasets\/builder.py#L153 \n Version below works, checked again in the docs, and data_files should be a path.\r\n```\r\nds = datasets.load_dataset('my_script.py', \r\n                           data_files='\/data\/dir\/corpus.txt', \r\n                           cache_dir='.')\r\n```","embeddings":[-0.3634896576,0.2240853161,0.053444095,0.4514975846,0.1415300369,-0.1207066849,0.498796463,0.0649361387,0.1139449701,0.1279033422,0.1670618504,0.1164789125,-0.000026259,0.1618969291,0.2033519,0.2234757394,-0.000567297,0.0768432692,-0.1561119705,-0.0686451644,-0.2324312925,0.1196476519,-0.0156468209,0.1871809959,-0.3966724575,0.3161482811,0.0041334904,0.6595543027,0.020150587,-0.1594536304,0.4635256231,-0.0194137301,0.2626406848,0.6896225214,-0.0001233974,0.3195054829,0.4197114408,-0.1370545328,-0.4451933503,-0.3630022407,-0.3213688731,-0.1205844432,0.2730474472,-0.11777132,-0.3139307797,0.1684602052,0.1296423227,-0.4796307087,0.1251054704,0.3512626588,0.0864334106,0.1955178678,-0.4008831084,-0.0074883299,0.0875306055,0.4546239674,0.0039398121,0.2868144512,0.2027198225,-0.0509557985,0.0403102152,-0.1094479337,-0.114551954,0.1670668274,0.7290754914,0.2044696212,0.246295616,0.1195372492,0.1271799505,0.2097671032,0.8114512563,-0.2189270705,-0.3567315042,-0.3475878537,-0.295979619,-0.1321891546,0.2913793921,0.1959009916,-0.328309536,0.1550272554,-0.2826429009,-0.1759109199,-0.0385543704,0.3217872381,0.0216448698,0.2445595264,-0.1323623061,0.2320985943,-0.1640591025,0.0989941284,0.3219019771,-0.3972786367,-0.0250569135,0.2357768565,0.1988216937,0.3457135856,0.0492765792,0.4159120023,-0.040215753,0.0188087318,-0.048678197,0.0319599025,-0.0594865642,0.1328590661,0.4287555814,0.0450413488,0.1661290675,0.145797208,0.1568096727,0.0159146264,-0.1326692402,-0.1107086539,-0.0979724526,-0.4574611783,-0.1004288048,-0.1933715194,0.2295648158,-0.1493961513,0.0464507863,-0.2724032402,0.252602905,-0.0867495164,0.3000946343,0.3881671131,-0.1646872759,-0.1338105947,-0.0842716098,0.2251116484,-0.1790882349,0.0483147316,-0.358327806,-0.2650708258,-0.2043486834,0.027656192,-0.0473335683,-0.2438429892,0.283828944,-0.0513345227,0.1549018025,-0.1537651718,-0.1994173676,0.136416167,0.1271741986,0.3635897934,-0.2581698596,0.1701279432,0.2217472941,-0.3667539656,-0.2471632659,0.2983249128,-0.3065575659,-0.542529583,0.0149518829,0.0559000187,-0.212438792,0.0018625653,-0.0638151765,-0.1449575126,0.4413236678,0.011180127,-0.1396732777,-0.0297437217,-0.5766786337,-0.0687704608,0.2972009778,0.6269584894,-0.4744592309,-0.229979068,-0.0354942158,-0.1582820714,0.1261746436,0.0954372138,-0.5179555416,0.5660901666,-0.2751978636,-0.2663215995,0.5033833981,-0.449216783,-0.109479934,0.5727268457,-0.246774137,-0.1474408507,0.0393458232,0.1660744101,-0.1855107844,-0.1198162809,0.2940329611,0.4177986681,-0.0429040566,0.1091134325,-0.0920208097,-0.1166869253,0.1799337864,0.1157259643,-0.0208533984,0.2262751162,0.1564219147,-0.1143688932,0.160383895,-0.0202934071,0.093998827,0.2106742412,-0.2537521124,0.0658293813,0.1278431267,0.1546729654,-0.528696537,0.3985966146,-0.0480761528,-0.1569336355,0.0695543811,-0.0701723471,-0.1584976017,0.17531991,-0.4332988858,0.0715495795,-0.1169660091,0.2941356897,-0.0104163317,-0.1862038374,-0.262555927,0.0907969996,-0.0827613473,0.1009166092,-0.2276257128,-0.0032427341,0.0723079368,-0.013983231,-0.2208838165,0.1398984194,0.0464977883,-0.1580699682,-0.1367745697,0.3328722417,0.1175295264,0.4391644299,-0.1691031307,0.0723802596,0.0857858881,0.2635891438,0.1544679999,0.0564928278,0.1003271565,-0.1937914342,-0.129720971,0.2046453506,-0.1600632668,0.3911741376,-0.0227396917,-0.0961444601,0.177777648,0.0437285453,-0.1670854241,-0.0535982102,-0.0064443788,0.0601862855,0.4769275486,0.2809217572,-0.1788144112,0.0292527415,0.0615935959,-0.0224738009,-0.0784141272,0.0449133664,-0.1692564338,-0.1153314561,-0.0713187829,0.0733580515,0.3055044413,0.0455869548,0.1843348593,0.0199747384,0.071401298,-0.3972021639,0.1588449776,0.0964899808,0.0015545648,0.1022952124,-0.2346717864,-0.2873325646,-0.1957287788,0.2077186555,0.0872740969,-0.0018091075,-0.5557534695,0.2986184061,-0.3373382688,-0.2322006524,-0.3124395609,-0.5424563885,-0.1791621,-0.1586726308,-0.1613505185,0.1112186983,-0.0387292504,-0.0187358502,-0.0045743803,0.1716792434,-0.0236294903,-0.6744971871,-0.1274794489,-0.2660986781,-0.3435392082,-0.1125141382,0.4159903526,0.0857210457,-0.0353805013,-0.3524414897,-0.0573636666,-0.1474471837,0.2198313922,0.3596020639,0.0932917297,0.753444612,0.1883265227,0.0202108771,0.2376392633,0.0874288008,0.2951365113,0.1826603562,0.018356679,-0.0442107543,0.0366412774,-0.0859246105,-0.3417372704,-0.0414708368,-0.2050759196,-0.1807593256,0.1391971409,0.2741288543,0.1545012593,0.6428108811,0.1556607336,0.0804975107,-0.1419402212,0.1903131604,-0.122363098,-0.8258020878,0.0887810662,-0.145987466,-0.2825871706,-0.1199327782,-0.036819838,0.175004527,-0.1200867295,-0.3841398954,0.016501788,0.0389106274,0.1934177876,0.3129930496,-0.0268756375,0.1477226615,-0.1665030718,0.1391680688,-0.1151883155,-0.2201762944,0.304744184,-0.0463766195,0.0681138188,0.3982470036,0.2701755762,-0.349062413,0.6581444144,-0.0419468917,-0.135484308,0.2687523961,-0.3474255204,0.5696567297,-0.3677194118,-0.1921464354,0.1278744489,0.2734452486,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2363","title":"Trying to use metric.compute but get OSError","comments":"also, I test the function on some little data ,  get the same message:\r\n\r\n```\r\nPython 3.8.5 (default, Jan 27 2021, 15:41:15)\r\n[GCC 9.3.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric('accuracy')\r\n>>> metric.add_batch(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\n2021-05-15 16:39:17.240991: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\n>>> metric.compute()\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 112, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```","body":"I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n","comment_length":113,"text":"Trying to use metric.compute but get OSError \n I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n \n also, I test the function on some little data ,  get the same message:\r\n\r\n```\r\nPython 3.8.5 (default, Jan 27 2021, 15:41:15)\r\n[GCC 9.3.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric('accuracy')\r\n>>> metric.add_batch(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\n2021-05-15 16:39:17.240991: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\n>>> metric.compute()\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 112, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```","embeddings":[-0.3944184184,-0.2529711425,-0.0330115929,0.3631941974,0.253131628,-0.1011049151,0.2328529209,0.139267087,0.2106887102,0.6745797396,-0.0271198936,0.0674137399,-0.0561968796,0.0007951714,-0.1410534978,-0.1666642725,-0.1239938736,0.0129452525,0.0000074239,0.0780162588,-0.3373332918,0.0424939394,-0.2203928828,0.0718222335,-0.0541991033,-0.2079253197,0.206342876,0.042613849,-0.5113474727,-0.2387506664,0.2351529449,-0.3208845258,0.2170303017,0.8475168347,-0.0001149249,0.1326398402,0.0087183854,-0.1004110426,-0.0945497677,-0.276073128,0.2104712427,-0.2102968097,0.0614107549,-0.2894953787,0.1416248083,-0.025001768,-0.2049153298,-0.3347912133,0.2580504715,0.5588136911,0.1094384342,0.4805767834,0.0571895204,-0.1703307778,-0.374795258,-0.344244957,0.0392587148,0.670753479,-0.0942461342,-0.1250064075,-0.0927235708,-0.0397556424,0.2341285497,0.0877322108,0.206516698,-0.1326481998,0.8326205611,-0.2251918614,-0.1489875764,-0.0572964847,0.1004799753,0.0542257167,-0.3662271798,0.3212551177,-0.0640987307,-0.7123773694,-0.0083443522,-0.0554771312,-0.329859823,-0.1663537025,-0.3520748615,-0.0161977299,-0.4670949578,0.0650909245,-0.2273944765,0.2691727877,-0.1618948281,0.0761636347,0.314235419,0.3356452584,0.0720360652,-0.0285175629,0.1678747386,0.2304376811,-0.5341535807,-0.0079725357,-0.1267386079,-0.0935950652,-0.2341202796,-0.2478045374,0.3044139445,0.0229335669,0.2392277718,0.27803123,-0.1958615184,0.8154187799,0.0828209221,0.1414985061,0.0289000422,0.3671554923,-0.0761498585,-0.0763362721,-0.0666468665,-0.3928147554,0.3305636346,0.1671797335,-0.1789496094,-0.0470937341,-0.5103244781,-0.0825716555,-0.3074188232,-0.1921125054,0.3589552343,0.0782146156,-0.1096397415,0.1300759017,0.2878340185,0.190229252,-0.2090027928,-0.1163052544,-0.2376890033,0.325322479,-0.2651518881,0.1427433044,-0.2088134736,-0.2825702131,0.2108990848,-0.0339325406,0.2929007113,-0.372744292,0.1489905268,-0.1023257896,-0.0621559471,0.0755864009,0.1042392701,-0.2266150713,0.4715836942,-0.188901484,0.1012283415,0.0352442786,-0.6336891055,-0.4329087138,0.0577113368,0.2142496556,-0.1043527424,0.11758966,-0.2650551498,0.0452635139,0.3239228129,0.1826907694,-0.0352026895,-0.1126695797,-0.417783916,-0.147210747,0.2840802968,-0.2580549717,-0.245167166,0.0119533483,0.0527974665,-0.091761142,0.2312343568,0.238289997,-0.1830744445,0.6743611097,-0.0939318314,-0.2390212417,0.5787590742,-0.5383852124,-0.0357063636,-0.2172711045,-0.193100974,-0.1919911057,0.1511501074,-0.107356593,0.0182099994,0.0467908122,0.4387013316,0.0395708159,-0.0889608786,-0.3561971188,-0.1995178908,0.2851071954,0.2279101908,0.0419763997,0.4570365548,0.2853614986,0.1695158035,-0.0684723854,0.1887974292,-0.0180304926,-0.2879210114,-0.0030238016,0.0400323123,-0.3892459273,0.2894599736,-0.0779185146,0.2788540721,0.2235236019,-0.246622175,0.0826151818,0.3609803319,0.2236855477,-0.4406271875,0.2417325824,-0.1057087854,-0.0480109528,0.0733944699,-0.2125060558,0.2519195378,-0.0479268841,-0.0860952213,0.0504066125,-0.4933129251,-0.0235673264,-0.1894487143,0.0653727204,-0.1262327582,-0.0448605902,-0.1458986551,-0.0153140696,0.1543092728,0.0556869097,-0.2693291008,0.4298380017,0.2029634118,-0.1456071436,-0.0144350883,0.1559263766,-0.1474841088,0.1328604072,-0.0093299672,-0.1915466785,0.2004588246,-0.1141059697,0.0535784177,0.3843781948,0.1535725743,0.0171225872,-0.1076225862,0.0651368126,-0.0502192862,0.2021970153,0.0288014244,0.0270202309,0.4426712394,-0.3593238294,0.0580615848,-0.3788809478,0.3278771341,-0.4405206144,0.3899686038,-0.0393587872,0.0601105914,-0.0102301594,0.1284069866,-0.0726182908,-0.3743269444,-0.2551704347,0.4715960026,-0.0478855297,0.0405350626,0.0953617096,-0.0711544156,-0.0679310337,0.0304413363,0.3493910134,0.2070938945,0.0212720446,0.0449011773,-0.0521538779,-0.0465460382,-0.1306073666,-0.0280886311,0.3493729532,-0.225588873,0.1348799318,0.075287506,0.2183084935,-0.1993987262,-0.1545158476,-0.2003663927,-0.3204384446,0.0115698399,0.1411631107,0.3208896518,-0.1376408786,0.1372115463,0.1250700653,0.4607310593,0.2262209803,0.2846042216,-0.1636532545,0.0487440601,0.0333901569,-0.0979563445,-0.215823099,0.2466624081,-0.3523681462,0.0774699152,-0.0886008814,-0.1845653355,-0.1599787623,0.0074018487,0.2293597162,0.1024663746,-0.0420796461,-0.255674541,0.0428051874,0.2096294463,-0.2503049076,-0.3007627726,0.374556154,-0.058750622,-0.0117408587,-0.1317933202,-0.1412438005,-0.1016193479,-0.2883880734,-0.0360812098,0.2483190298,0.2062901258,0.2738169432,-0.005697303,0.3814315796,0.4019247591,-0.0543019064,-0.1986670345,-0.547450006,0.2148372084,-0.0925638452,-0.2276468575,0.0701643974,-0.0187872667,0.1677819192,0.1912883967,-0.2886455953,-0.673584044,0.1889113188,0.2871622443,-0.2609939575,0.1604726017,0.2804620564,-0.3490063846,-0.0287137385,-0.2814407945,-0.103891097,0.2245209068,0.0848220885,0.2112163156,-0.221072346,0.4646460712,0.0941406488,0.6489886642,0.0296453517,-0.1281051636,0.2985831499,0.1537393332,0.2447576225,-0.0038530349,-0.1338460147,0.0489935353,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2363","title":"Trying to use metric.compute but get OSError","comments":"Hi @hyusterr,\r\nIf you look at the example provided in `metrics\/accuracy.py`, it only does `metric.compute()` to calculate the accuracy. Here's an example:\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('accuracy')\r\noutput = metric.compute(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\nprint(output['accuracy'])  # 0.5\r\n```\r\n","body":"I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n","comment_length":44,"text":"Trying to use metric.compute but get OSError \n I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n \n Hi @hyusterr,\r\nIf you look at the example provided in `metrics\/accuracy.py`, it only does `metric.compute()` to calculate the accuracy. Here's an example:\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('accuracy')\r\noutput = metric.compute(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\nprint(output['accuracy'])  # 0.5\r\n```\r\n","embeddings":[-0.3944184184,-0.2529711425,-0.0330115929,0.3631941974,0.253131628,-0.1011049151,0.2328529209,0.139267087,0.2106887102,0.6745797396,-0.0271198936,0.0674137399,-0.0561968796,0.0007951714,-0.1410534978,-0.1666642725,-0.1239938736,0.0129452525,0.0000074239,0.0780162588,-0.3373332918,0.0424939394,-0.2203928828,0.0718222335,-0.0541991033,-0.2079253197,0.206342876,0.042613849,-0.5113474727,-0.2387506664,0.2351529449,-0.3208845258,0.2170303017,0.8475168347,-0.0001149249,0.1326398402,0.0087183854,-0.1004110426,-0.0945497677,-0.276073128,0.2104712427,-0.2102968097,0.0614107549,-0.2894953787,0.1416248083,-0.025001768,-0.2049153298,-0.3347912133,0.2580504715,0.5588136911,0.1094384342,0.4805767834,0.0571895204,-0.1703307778,-0.374795258,-0.344244957,0.0392587148,0.670753479,-0.0942461342,-0.1250064075,-0.0927235708,-0.0397556424,0.2341285497,0.0877322108,0.206516698,-0.1326481998,0.8326205611,-0.2251918614,-0.1489875764,-0.0572964847,0.1004799753,0.0542257167,-0.3662271798,0.3212551177,-0.0640987307,-0.7123773694,-0.0083443522,-0.0554771312,-0.329859823,-0.1663537025,-0.3520748615,-0.0161977299,-0.4670949578,0.0650909245,-0.2273944765,0.2691727877,-0.1618948281,0.0761636347,0.314235419,0.3356452584,0.0720360652,-0.0285175629,0.1678747386,0.2304376811,-0.5341535807,-0.0079725357,-0.1267386079,-0.0935950652,-0.2341202796,-0.2478045374,0.3044139445,0.0229335669,0.2392277718,0.27803123,-0.1958615184,0.8154187799,0.0828209221,0.1414985061,0.0289000422,0.3671554923,-0.0761498585,-0.0763362721,-0.0666468665,-0.3928147554,0.3305636346,0.1671797335,-0.1789496094,-0.0470937341,-0.5103244781,-0.0825716555,-0.3074188232,-0.1921125054,0.3589552343,0.0782146156,-0.1096397415,0.1300759017,0.2878340185,0.190229252,-0.2090027928,-0.1163052544,-0.2376890033,0.325322479,-0.2651518881,0.1427433044,-0.2088134736,-0.2825702131,0.2108990848,-0.0339325406,0.2929007113,-0.372744292,0.1489905268,-0.1023257896,-0.0621559471,0.0755864009,0.1042392701,-0.2266150713,0.4715836942,-0.188901484,0.1012283415,0.0352442786,-0.6336891055,-0.4329087138,0.0577113368,0.2142496556,-0.1043527424,0.11758966,-0.2650551498,0.0452635139,0.3239228129,0.1826907694,-0.0352026895,-0.1126695797,-0.417783916,-0.147210747,0.2840802968,-0.2580549717,-0.245167166,0.0119533483,0.0527974665,-0.091761142,0.2312343568,0.238289997,-0.1830744445,0.6743611097,-0.0939318314,-0.2390212417,0.5787590742,-0.5383852124,-0.0357063636,-0.2172711045,-0.193100974,-0.1919911057,0.1511501074,-0.107356593,0.0182099994,0.0467908122,0.4387013316,0.0395708159,-0.0889608786,-0.3561971188,-0.1995178908,0.2851071954,0.2279101908,0.0419763997,0.4570365548,0.2853614986,0.1695158035,-0.0684723854,0.1887974292,-0.0180304926,-0.2879210114,-0.0030238016,0.0400323123,-0.3892459273,0.2894599736,-0.0779185146,0.2788540721,0.2235236019,-0.246622175,0.0826151818,0.3609803319,0.2236855477,-0.4406271875,0.2417325824,-0.1057087854,-0.0480109528,0.0733944699,-0.2125060558,0.2519195378,-0.0479268841,-0.0860952213,0.0504066125,-0.4933129251,-0.0235673264,-0.1894487143,0.0653727204,-0.1262327582,-0.0448605902,-0.1458986551,-0.0153140696,0.1543092728,0.0556869097,-0.2693291008,0.4298380017,0.2029634118,-0.1456071436,-0.0144350883,0.1559263766,-0.1474841088,0.1328604072,-0.0093299672,-0.1915466785,0.2004588246,-0.1141059697,0.0535784177,0.3843781948,0.1535725743,0.0171225872,-0.1076225862,0.0651368126,-0.0502192862,0.2021970153,0.0288014244,0.0270202309,0.4426712394,-0.3593238294,0.0580615848,-0.3788809478,0.3278771341,-0.4405206144,0.3899686038,-0.0393587872,0.0601105914,-0.0102301594,0.1284069866,-0.0726182908,-0.3743269444,-0.2551704347,0.4715960026,-0.0478855297,0.0405350626,0.0953617096,-0.0711544156,-0.0679310337,0.0304413363,0.3493910134,0.2070938945,0.0212720446,0.0449011773,-0.0521538779,-0.0465460382,-0.1306073666,-0.0280886311,0.3493729532,-0.225588873,0.1348799318,0.075287506,0.2183084935,-0.1993987262,-0.1545158476,-0.2003663927,-0.3204384446,0.0115698399,0.1411631107,0.3208896518,-0.1376408786,0.1372115463,0.1250700653,0.4607310593,0.2262209803,0.2846042216,-0.1636532545,0.0487440601,0.0333901569,-0.0979563445,-0.215823099,0.2466624081,-0.3523681462,0.0774699152,-0.0886008814,-0.1845653355,-0.1599787623,0.0074018487,0.2293597162,0.1024663746,-0.0420796461,-0.255674541,0.0428051874,0.2096294463,-0.2503049076,-0.3007627726,0.374556154,-0.058750622,-0.0117408587,-0.1317933202,-0.1412438005,-0.1016193479,-0.2883880734,-0.0360812098,0.2483190298,0.2062901258,0.2738169432,-0.005697303,0.3814315796,0.4019247591,-0.0543019064,-0.1986670345,-0.547450006,0.2148372084,-0.0925638452,-0.2276468575,0.0701643974,-0.0187872667,0.1677819192,0.1912883967,-0.2886455953,-0.673584044,0.1889113188,0.2871622443,-0.2609939575,0.1604726017,0.2804620564,-0.3490063846,-0.0287137385,-0.2814407945,-0.103891097,0.2245209068,0.0848220885,0.2112163156,-0.221072346,0.4646460712,0.0941406488,0.6489886642,0.0296453517,-0.1281051636,0.2985831499,0.1537393332,0.2447576225,-0.0038530349,-0.1338460147,0.048993535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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2363","title":"Trying to use metric.compute but get OSError","comments":"I thought I can use Metric to collect predictions and references, this follows the step from huggingface's sample colab.\r\nBTW, I fix the problem by setting other cache_dir in load_metric, but I'm still wondering about the mechanism.","body":"I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n","comment_length":37,"text":"Trying to use metric.compute but get OSError \n I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n \n I thought I can use Metric to collect predictions and references, this follows the step from huggingface's sample colab.\r\nBTW, I fix the problem by setting other cache_dir in load_metric, but I'm still wondering about the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2363","title":"Trying to use metric.compute but get OSError","comments":"I tried this code on a colab notebook and it worked fine (with gpu enabled):\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('accuracy')\r\noutput = metric.add_batch(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\nfinal_score = metric.compute()\r\nprint(final_score)  # 0.5\r\n```\r\nAlso, in `load_metric`, I saw `cache_dir` is optional and it defaults to `~\/.datasets\/`","body":"I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n","comment_length":53,"text":"Trying to use metric.compute but get OSError \n I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n \n I tried this code on a colab notebook and it worked fine (with gpu enabled):\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('accuracy')\r\noutput = metric.add_batch(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\nfinal_score = metric.compute()\r\nprint(final_score)  # 0.5\r\n```\r\nAlso, in `load_metric`, I saw `cache_dir` is optional and it defaults to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2363","title":"Trying to use metric.compute but get OSError","comments":"Hi ! By default it caches the predictions and references used to compute the metric in `~\/.cache\/huggingface\/datasets\/metrics` (not `~\/.datasets\/`). Let me update the documentation @bhavitvyamalik .\r\n\r\nThe cache is used to store all the predictions and references passed to `add_batch` for example in order to compute the metric later when `compute` is called.\r\n\r\nI think the issue might come from the cache directory that is used by default. Can you check that you have the right permissions ? Otherwise feel free to set `cache_dir` to another location.","body":"I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n","comment_length":87,"text":"Trying to use metric.compute but get OSError \n I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError?\r\n\r\n```python\r\n195     for epoch in range(num_train_epochs):\r\n196         model.train()\r\n197         for step, batch in enumerate(train_loader):\r\n198             # print(batch['input_ids'].shape)\r\n199             outputs = model(**batch)\r\n200\r\n201             loss = outputs.loss\r\n202             loss \/= gradient_accumulation_steps\r\n203             accelerator.backward(loss)\r\n204\r\n205             predictions = outputs.logits.argmax(dim=-1)\r\n206             metric.add_batch(\r\n207                 predictions=accelerator.gather(predictions),\r\n208                 references=accelerator.gather(batch['labels'])\r\n209             )\r\n210             progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics})\r\n211\r\n212             if (step + 1) % 50 == 0 or step == len(train_loader) - 1:\r\n213                 train_metrics = metric.compute()\r\n```\r\n\r\nthe error message is as below:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_multi.py\", line 273, in <module>\r\n    main()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"run_multi.py\", line 213, in main\r\n    train_metrics = metric.compute()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 391, in compute\r\n    self._finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/metric.py\", line 342, in _finalize\r\n    self.writer.finalize()\r\n  File \"\/home\/yshuang\/.local\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 370, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```\r\n\r\n## Environment info\r\n<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->\r\n- `datasets` version: 1.6.1\r\n- Platform: Linux NAME=\"Ubuntu\" VERSION=\"20.04.1 LTS (Focal Fossa)\"\r\n- Python version: python3.8.5\r\n- PyArrow version: 4.0.0\r\n \n Hi ! By default it caches the predictions and references used to compute the metric in `~\/.cache\/huggingface\/datasets\/metrics` (not `~\/.datasets\/`). Let me update the documentation @bhavitvyamalik .\r\n\r\nThe cache is used to store all the predictions and references passed to `add_batch` for example in order to compute the metric later when `compute` is called.\r\n\r\nI think the issue might come from the cache directory that is used by default. Can you check that you have the right permissions ? Otherwise feel free to set `cache_dir` to another 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2356","title":"How to Add New Metrics Guide","comments":"Hi ! sorry for the late response \r\n\r\nIt would be fantastic to have a guide for adding metrics as well ! Currently we only have this template here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/templates\/new_metric_script.py\r\n\r\nWe can also include test utilities for metrics in the guide.\r\n\r\nWe have a pytest suite with commands that you can use to make sure your metric works as expected.\r\nIt has two useful commands:\r\n\r\n1. This commands tests the code in the `Examples:` desction of the docstring of the metric:\r\n```\r\npytest tests\/test_metric_common.py::LocalMetricTest::test_load_metric_<metric_name>\r\n```\r\nThis will run this code for example:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e0787aa2a781cc15a80f7597f56d1f12e23df4c9\/metrics\/accuracy\/accuracy.py#L40-L45\r\n\r\nMoreover this test is meant to be fast so users are free to add patches to the metric to avoid intensive computations.\r\nAnd example of intensive call patch can be found here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e0787aa2a781cc15a80f7597f56d1f12e23df4c9\/tests\/test_metric_common.py#L138-L151\r\n\r\n2. This test runs the same thing as 1. except that it doesn't use patches (the real metric is used):\r\n```\r\nRUN_SLOW=1 pytest tests\/test_metric_common.py::LocalMetricTest::test_load_metric_<metric_name>\r\n```\r\n\r\nFinally additional metric-specific tests can be added to `test_metric_common.py`.\r\n\r\nVoila :) Feel free to ping me if you have any question or if I can help\r\n","body":"**Is your feature request related to a problem? Please describe.**\r\nCurrently there is an absolutely fantastic guide for how to contribute a new dataset to the library. However, there isn't one for adding new metrics.\r\n\r\n**Describe the solution you'd like**\r\nI'd like for a guide in a similar style to the dataset guide for adding metrics. I believe many of the content in the dataset guide such as setup can be easily copied over with minimal changes. Also, from what I've seen with existing metrics, it shouldn't be as complicated, especially in documentation of the metric, mainly just citation and usage. The most complicated part I see would be in automated tests that run the new metrics, but y'all's test suite seem pretty comprehensive, so it might not be that hard.\r\n\r\n**Describe alternatives you've considered**\r\nOne alternative would be just not having the metrics be community generated and so would not need a step by step guide. New metrics would just be proposed as issues and the internal team would take care of them. However, I think it makes more sense to have a step by step guide for contributors to follow.\r\n\r\n**Additional context**\r\nI'd be happy to help with creating this guide as I am very interested in adding software engineering metrics to the library :nerd_face:, the part I would need guidance on would be testing.\r\n\r\nP.S. Love the library and community y'all have built! :hugs: \r\n","comment_length":176,"text":"How to Add New Metrics Guide \n **Is your feature request related to a problem? Please describe.**\r\nCurrently there is an absolutely fantastic guide for how to contribute a new dataset to the library. However, there isn't one for adding new metrics.\r\n\r\n**Describe the solution you'd like**\r\nI'd like for a guide in a similar style to the dataset guide for adding metrics. I believe many of the content in the dataset guide such as setup can be easily copied over with minimal changes. Also, from what I've seen with existing metrics, it shouldn't be as complicated, especially in documentation of the metric, mainly just citation and usage. The most complicated part I see would be in automated tests that run the new metrics, but y'all's test suite seem pretty comprehensive, so it might not be that hard.\r\n\r\n**Describe alternatives you've considered**\r\nOne alternative would be just not having the metrics be community generated and so would not need a step by step guide. New metrics would just be proposed as issues and the internal team would take care of them. However, I think it makes more sense to have a step by step guide for contributors to follow.\r\n\r\n**Additional context**\r\nI'd be happy to help with creating this guide as I am very interested in adding software engineering metrics to the library :nerd_face:, the part I would need guidance on would be testing.\r\n\r\nP.S. Love the library and community y'all have built! :hugs: \r\n \n Hi ! sorry for the late response \r\n\r\nIt would be fantastic to have a guide for adding metrics as well ! Currently we only have this template here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/templates\/new_metric_script.py\r\n\r\nWe can also include test utilities for metrics in the guide.\r\n\r\nWe have a pytest suite with commands that you can use to make sure your metric works as expected.\r\nIt has two useful commands:\r\n\r\n1. This commands tests the code in the `Examples:` desction of the docstring of the metric:\r\n```\r\npytest tests\/test_metric_common.py::LocalMetricTest::test_load_metric_<metric_name>\r\n```\r\nThis will run this code for example:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e0787aa2a781cc15a80f7597f56d1f12e23df4c9\/metrics\/accuracy\/accuracy.py#L40-L45\r\n\r\nMoreover this test is meant to be fast so users are free to add patches to the metric to avoid intensive computations.\r\nAnd example of intensive call patch can be found here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e0787aa2a781cc15a80f7597f56d1f12e23df4c9\/tests\/test_metric_common.py#L138-L151\r\n\r\n2. This test runs the same thing as 1. except that it doesn't use patches (the real metric is used):\r\n```\r\nRUN_SLOW=1 pytest tests\/test_metric_common.py::LocalMetricTest::test_load_metric_<metric_name>\r\n```\r\n\r\nFinally additional metric-specific tests can be added to `test_metric_common.py`.\r\n\r\nVoila :) Feel free to ping me if you have any question or if I can help\r\n","embeddings":[0.0184292234,0.009165708,-0.0002697915,-0.1558319926,0.0394446068,0.0882947743,-0.0663276687,0.0305395015,0.0264215395,0.0817607865,0.0160229094,0.2692242265,-0.1336126328,0.2791265249,0.252602607,-0.1617622674,-0.1102725938,-0.0691408813,0.1617088318,0.0204251409,-0.002773423,-0.021874046,0.329408586,0.0545105003,-0.1164637133,-0.0480870865,-0.1330723464,0.0665191337,-0.4833083153,-0.6584537029,0.1586213112,0.1352889538,-0.1188627034,0.4845243394,-0.0001103193,-0.3142381608,0.2092059851,-0.0670046434,-0.1198505983,-0.441141665,0.0299328081,-0.7234885097,0.1960804909,-0.0932002142,0.0312845409,-0.1347152293,-0.1554330438,0.1224784926,-0.0282175038,0.1885768771,0.213010639,0.0976783112,0.0443958864,-0.3083938956,-0.260330379,0.3114709854,-0.4239771366,0.5435327291,0.3079588115,0.1700689942,0.0951610059,-0.0022227019,0.0336410254,-0.0354855694,0.6798270941,-0.0897223577,0.0804402381,0.1071784347,-0.3254208863,0.3772416711,0.4795588553,-0.3143450618,-0.3093032837,-0.283180058,0.1683874726,-0.4429858625,-0.2639279366,-0.1353823096,-0.0871687531,-0.2493702322,-0.266956389,-0.4700167477,-0.2433303297,0.1533593982,0.1787814051,0.0073762131,0.0057962895,-0.1015962586,0.2997991443,0.1384744048,-0.4045351446,0.0882186815,-0.0804798678,0.3847275078,0.0626204833,-0.4114014506,0.2671383619,0.0210768189,0.4693697691,0.1305872798,0.3220776618,0.0416282825,-0.3315683007,0.1019887552,0.069723919,0.0302171819,0.2064802796,0.0259620585,0.0907073021,0.3396171331,0.4559905827,-0.2520832121,0.3968576193,-0.0956557021,0.0499762073,0.0685843676,0.1471230835,-0.3993322253,-0.0377423763,-0.3121259809,0.432828635,-0.4076386392,0.4414565265,0.0897256657,-0.0520035923,-0.2915807068,-0.3402695656,0.3253027797,-0.1487537324,0.2075919509,-0.0739264563,0.1344438344,-0.3010628521,0.5152187943,0.1827646196,-0.2941655815,0.2420096695,0.1666758358,0.1522644162,-0.199954167,0.0022773335,0.2404260784,0.0478422493,-0.0570169687,-0.123834759,0.1695913672,-0.1199385375,-0.2851220071,-0.3717520535,-0.0453540944,-0.3488799632,-0.4204467237,-0.2678246498,0.1413258314,-0.3109639585,-0.3429507613,-0.1060872748,0.4933302999,-0.3894296587,-0.1969149858,0.0484503955,0.4269002676,-0.2820628285,-0.3100942969,0.0637532398,0.357925266,-0.3396798968,-0.2324168235,-0.2341893762,0.0253998954,-0.1374936253,-0.0389815457,-0.0297508687,0.1589485407,0.0738943592,-0.1113869697,0.5824443102,-0.2802065611,0.2215571702,0.0327623673,-0.085318692,-0.2565297186,-0.1304361671,0.4572606385,0.0265267976,-0.2065899521,-0.2832887471,0.0571033023,-0.1942892671,-0.2472899556,-0.1541683972,-0.1237749606,0.2240421027,-0.1152999178,-0.0695195496,-0.0026222249,0.4326041341,-0.0192571636,-0.1784527302,-0.102321595,-0.2335809767,0.2795448005,0.52559793,-0.0219726469,0.2193109542,-0.2187476605,-0.1563498527,-0.0179751441,0.1284736246,-0.1166479439,0.3591010869,-0.1786890775,-0.4749299586,-0.0524333864,0.0522365682,0.066976361,0.0221097227,-0.3339243829,-0.042014949,0.0546156801,-0.1838302016,-0.0239482522,-0.3259169459,0.443764776,-0.0501364954,0.0161871444,0.0387043133,0.0317185409,0.0827146769,0.5306402445,0.2860185206,0.0981434435,-0.0662294701,0.1483388841,0.0292118378,0.0674835891,0.2121868581,0.6915304065,0.546990037,-0.0805040002,0.203546226,-0.2614835501,-0.0956279859,-0.1592144519,-0.2688032389,0.2466148138,-0.3072992265,0.1097352952,-0.1203326136,0.0513707474,-0.0616922006,-0.0289074238,-0.1241117194,-0.1857165992,0.1790335476,-0.0951611921,-0.0663760304,-0.0675027668,0.2252746373,0.2073013633,0.1898086518,-0.1084432602,0.0301645417,0.3718456924,0.1367536336,-0.1395901293,-0.1679075956,0.0820075125,0.1165526733,0.2425193489,0.0056011179,0.0916838497,-0.3783510327,-0.1168581396,-0.089568764,0.1801922768,-0.3604492545,-0.0228362791,0.0145989731,0.1239486039,0.0442001298,-0.0018293137,-0.1631917953,0.1606388688,-0.1625336409,-0.1156409532,0.0826383904,0.0563561097,-0.0095752887,-0.2589283288,-0.3223095238,-0.2484735399,0.2069886923,-0.0176496804,0.1311489791,-0.0120332576,0.1771413535,0.313678056,-0.3538408875,-0.0062791016,-0.1025248691,-0.2769832015,-0.0146990437,0.1558507681,0.0837280005,-0.0107058007,0.542370975,0.0823427513,0.3953062594,-0.4368837178,-0.8153743744,-0.0724791139,-0.1126619577,0.6125946641,0.0842719227,0.0896968171,0.1166645661,-0.2269663066,0.0211907309,-0.1636058241,0.0361229591,0.04617057,-0.3235256672,0.0530880876,-0.1868775785,-0.2285001278,-0.0836725086,-0.3801620603,0.1529833227,0.1903473437,0.0673877522,0.2270582765,0.0454426929,0.0681231618,-0.0045258016,0.2067427188,-0.0990071222,0.0303437915,0.2220388055,-0.3982443213,-0.1208576784,0.252918601,0.0172525626,0.2168854773,-0.1977648586,-0.2195447087,-0.7192444205,0.0633444116,0.3662299514,0.1567400992,0.3119517267,0.1860899627,0.031219136,0.0116891274,-0.0507835075,-0.5496695638,-0.0624050535,0.3355000317,0.2113073468,-0.1976547241,0.2261877358,0.1639116406,0.5142987967,0.1251865923,-0.1498404741,0.2382318825,0.0807622224,0.552067697,0.0971253142,-0.2344640195,0.083427839,0.11307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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2350","title":"`FaissIndex.save` throws error on GPU","comments":"Just in case, this is a workaround that I use in my code and it seems to do the job.\r\n\r\n```python\r\nif use_gpu_index:\r\n    data[\"train\"]._indexes[\"text_emb\"].faiss_index = faiss.index_gpu_to_cpu(data[\"train\"]._indexes[\"text_emb\"].faiss_index)\r\n```","body":"## Describe the bug\r\n\r\nAfter training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error.\r\n\r\n```\r\n  File \"index_wikipedia.py\", line 119, in <module>\r\n    data[\"train\"].save_faiss_index(\"text_emb\", index_save_path)\r\n  File \"\/home\/vlialin\/miniconda3\/envs\/cat\/lib\/python3.8\/site-packages\/datasets\/search.py\", line 470, in save_faiss_index\r\n    index.save(file)\r\n  File \"\/home\/vlialin\/miniconda3\/envs\/cat\/lib\/python3.8\/site-packages\/datasets\/search.py\", line 334, in save\r\n    faiss.write_index(index, str(file))\r\n  File \"\/home\/vlialin\/miniconda3\/envs\/cat\/lib\/python3.8\/site-packages\/faiss\/swigfaiss_avx2.py\", line 5654, in write_index\r\n    return _swigfaiss.write_index(*args)\r\nRuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at \/root\/miniconda3\/conda-bld\/faiss-pkg_1613235005464\/work\/faiss\/impl\/index_write.cpp:453: don't know how to serialize this type of index\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nAny dataset will do, I just selected a familiar one.\r\n\r\n```python\r\nimport numpy as np\r\nimport datasets\r\nINDEX_STR = \"OPQ16_128,IVF512,PQ32\"\r\nINDEX_SAVE_PATH = \"will_not_save.faiss\"\r\n\r\ndata = datasets.load_dataset(\"Fraser\/news-category-dataset\", split=f\"train[:10000]\")\r\n\r\ndef encode(item):\r\n    return {\"text_emb\": np.random.randn(768).astype(np.float32)}\r\n\r\ndata = data.map(encode)\r\n\r\ndata.add_faiss_index(column=\"text_emb\", string_factory=INDEX_STR, train_size=10_000, device=0)\r\ndata.save_faiss_index(\"text_emb\", INDEX_SAVE_PATH)\r\n```\r\n\r\n## Expected results\r\nSaving the index\r\n\r\n## Actual results\r\nError in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) ... don't know how to serialize this type of index\r\n\r\n## Environment info\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No\r\n\r\n\r\nI will be proposing a fix in a couple of minutes","comment_length":27,"text":"`FaissIndex.save` throws error on GPU \n ## Describe the bug\r\n\r\nAfter training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error.\r\n\r\n```\r\n  File \"index_wikipedia.py\", line 119, in <module>\r\n    data[\"train\"].save_faiss_index(\"text_emb\", index_save_path)\r\n  File \"\/home\/vlialin\/miniconda3\/envs\/cat\/lib\/python3.8\/site-packages\/datasets\/search.py\", line 470, in save_faiss_index\r\n    index.save(file)\r\n  File \"\/home\/vlialin\/miniconda3\/envs\/cat\/lib\/python3.8\/site-packages\/datasets\/search.py\", line 334, in save\r\n    faiss.write_index(index, str(file))\r\n  File \"\/home\/vlialin\/miniconda3\/envs\/cat\/lib\/python3.8\/site-packages\/faiss\/swigfaiss_avx2.py\", line 5654, in write_index\r\n    return _swigfaiss.write_index(*args)\r\nRuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at \/root\/miniconda3\/conda-bld\/faiss-pkg_1613235005464\/work\/faiss\/impl\/index_write.cpp:453: don't know how to serialize this type of index\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nAny dataset will do, I just selected a familiar one.\r\n\r\n```python\r\nimport numpy as np\r\nimport datasets\r\nINDEX_STR = \"OPQ16_128,IVF512,PQ32\"\r\nINDEX_SAVE_PATH = \"will_not_save.faiss\"\r\n\r\ndata = datasets.load_dataset(\"Fraser\/news-category-dataset\", split=f\"train[:10000]\")\r\n\r\ndef encode(item):\r\n    return {\"text_emb\": np.random.randn(768).astype(np.float32)}\r\n\r\ndata = data.map(encode)\r\n\r\ndata.add_faiss_index(column=\"text_emb\", string_factory=INDEX_STR, train_size=10_000, device=0)\r\ndata.save_faiss_index(\"text_emb\", INDEX_SAVE_PATH)\r\n```\r\n\r\n## Expected results\r\nSaving the index\r\n\r\n## Actual results\r\nError in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) ... don't know how to serialize this type of index\r\n\r\n## Environment info\r\n- `datasets` version: 1.6.2\r\n- Platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.8\r\n- PyTorch version (GPU?): 1.8.1+cu111 (True)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n- Using GPU in script?: Yes\r\n- Using distributed or parallel set-up in script?: No\r\n\r\n\r\nI will be proposing a fix in a couple of minutes \n Just in case, this is a workaround that I use in my code and it seems to do the job.\r\n\r\n```python\r\nif use_gpu_index:\r\n    data[\"train\"]._indexes[\"text_emb\"].faiss_index = faiss.index_gpu_to_cpu(data[\"train\"]._indexes[\"text_emb\"].faiss_index)\r\n```","embeddings":[-0.1138690859,0.2311160564,0.0493216403,0.2159428895,0.3592372239,0.1624383777,0.5158483982,0.5090635419,0.2508896887,0.2249036729,0.0669301525,0.2369603813,0.0509322956,-0.0680036098,-0.1380849034,-0.0389072336,0.2700771689,0.2628393769,0.157356739,-0.1693143994,-0.4407014251,0.0116727334,-0.0017445267,0.2576278448,-0.2485831231,-0.044677835,-0.0931610987,0.0168835968,-0.1002748013,-0.4397268891,0.0679610968,-0.2787279487,0.4836601615,0.3083126843,-0.0001119511,0.16159226,0.2866710722,-0.1724367291,-0.2263427526,0.1483052224,-0.0678200722,0.1392590106,0.0382678434,-0.2912577093,-0.1710164845,-0.2987514436,0.0940966308,-0.2719932199,-0.123602353,0.3052643836,0.2272423357,-0.1925358325,0.3092999756,-0.1131985635,0.1893387586,-0.0202897564,-0.1342300624,0.0498123802,-0.3920942843,0.3410638869,0.2851158679,0.3660792112,-0.0126446206,-0.4190097153,-0.2800433636,0.1759340912,0.60136801,-0.2439565659,0.116647087,-0.0416836329,0.1341054738,-0.2888173461,-0.3871852458,0.1896890849,0.2492049485,-0.493991971,-0.0477072038,0.0656199455,0.0472116917,0.2803214788,0.5671783686,-0.1080207601,-0.1454774588,-0.0482442752,0.1810242683,0.076971449,-0.045570109,0.2025454789,-0.090509668,-0.2889374197,-0.1879069954,-0.0037431747,0.0984284058,-0.0547393262,-0.3431196213,0.0062898733,-0.175707534,-0.4342462718,-0.0055380403,-0.0066519608,-0.4312520623,-0.008348275,0.2703360617,0.118852973,-0.3501147032,0.1605631411,0.009781952,-0.1619330496,0.1525248736,0.0218443051,-0.2132831514,-0.0922401622,-0.1168507561,0.1236494854,-0.1891160011,-0.0733328015,0.2162267417,-0.2217499167,-0.5828648806,0.3758404553,-0.3595431447,0.1377798021,0.0111374157,0.5140858293,0.0791313127,0.050104931,0.1652794629,0.2339881808,-0.1110544726,-0.1419420689,-0.2111119181,-0.1108440608,0.2596277893,0.2357220501,-0.1644803286,-0.3422324657,-0.0027447089,0.0669749305,-0.3403433263,-0.0236196872,0.1093347296,-0.2806960344,0.002856513,0.303132683,0.1009419486,0.0751547217,0.191299662,-0.3268490136,-0.1066069156,0.0402038284,0.0674366206,-0.1408487111,-0.3724992275,0.2187590897,0.2860943973,-0.1972838342,0.6523479223,0.0753616542,0.3555603027,0.1765325069,0.0953457132,-0.067618072,-0.0979846045,-0.3917908072,0.3624824286,0.1430670321,-0.1860331595,-0.0113125201,0.0957461894,0.0662762523,0.3802833259,0.5683287382,0.3160372674,0.2390820831,-0.2121520191,0.4203566611,0.272642225,-0.3779995739,-0.280284673,0.1330714524,-0.1357293874,-0.2128633261,0.2011710256,0.0591533929,0.1582609862,-0.0584350452,0.4598637223,0.1182971597,0.06503205,-0.108853817,-0.4900937974,-0.5541249514,0.2036344111,0.1098531485,0.0084758783,-0.1918828636,-0.1755236685,-0.5428344011,0.2811937928,-0.2432644367,-0.0737854317,0.0249400605,0.7233158946,0.1537608802,0.2423792332,-0.0366046019,0.1383715272,-0.0622231737,-0.1854722351,-0.0290439725,-0.1147089824,-0.2371757776,-0.0223276298,0.0183746554,0.0365044102,0.0494049825,0.1141124964,0.0131477229,-0.1199359447,0.3434326053,-0.2094563991,0.0398113243,-0.3076533973,0.0626216009,-0.2081642598,0.0722263381,-0.3703285158,-0.4066974223,-0.1425864846,0.1701209843,0.3474281132,-0.1863129437,0.0125181749,0.3988081813,0.0344950929,-0.1558338702,0.1012302041,0.0262103546,-0.0489243567,-0.2536347806,0.0307387523,0.5145238042,0.1880791783,0.1472197622,-0.0048773056,0.3355096877,0.1963059157,-0.0242392085,-0.0846971646,0.1306782216,0.4605296254,-0.0087657906,-0.0339583382,-0.2989700735,0.0284292139,0.0268369317,0.0555951558,-0.0418341942,-0.6681858897,0.2340365648,0.2718895376,0.00093151,0.5030539632,0.0890595317,-0.3418864012,0.0547037199,-0.0895656869,-0.5434659719,0.2345535606,0.1372830421,-0.1467340738,-0.1237027422,-0.33535707,-0.1771036536,0.4169137776,-0.2255945355,0.0904394537,0.1040292457,0.1014716923,0.0593896024,-0.1880995184,0.0337482095,-0.1696738899,0.0177561492,-0.3170367479,0.1562788039,-0.1312709153,0.013449572,-0.2444274873,-0.1842757612,-0.0321661569,0.0443149991,0.0538874753,0.1305157989,-0.1142224073,0.284424752,-0.3202932477,0.3281571269,0.4209784865,-0.1238969639,-0.0517931059,-0.0182623453,-0.3266088963,-0.0397244319,0.0600185879,-0.0189724937,0.1622944325,-0.082202144,-0.0453144275,-0.2157865018,-0.0934125483,0.0835173726,-0.1467777193,0.3859787583,-0.0344992802,0.087016359,0.0051547461,-0.03441412,0.1084697768,0.0458991528,-0.0604748949,-0.0019139997,-0.1769495606,0.031945616,-0.1569327712,-0.208674103,-0.1914224327,-0.2342451215,0.2827096581,-0.0373622552,0.2085962892,0.1510173678,0.0411782004,0.2148143202,0.1120457947,-0.0829849541,-0.3446111381,-0.1167994365,0.3281378448,-0.0233734585,-0.487514019,0.0882834867,0.0634188727,-0.1923591197,-0.0627999678,-0.0927179381,0.3474021852,-0.0381610282,0.0832146183,-0.1545877904,0.1555236429,0.2991626561,-0.0797351897,-0.0500682704,-0.1272290051,-0.1483065933,0.2376608849,0.0784068853,0.3545718789,-0.2086637467,0.3585048616,-0.0277999267,0.4486905336,0.2896327674,-0.3432827294,0.2418365777,-0.0633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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2347","title":"Add an API to access the language and pretty name of a dataset","comments":"Hi ! With @bhavitvyamalik we discussed about having something like\r\n```python\r\nfrom datasets import load_dataset_card\r\n\r\ndataset_card = load_dataset_card(\"squad\")\r\nprint(dataset_card.metadata.pretty_name)\r\n# Stanford Question Answering Dataset (SQuAD)\r\nprint(dataset_card.metadata.languages)\r\n# [\"en\"]\r\n\r\n```\r\nWhat do you think ?\r\n\r\nI don't know if you already have a way to load the model tags in `transformers` but we can agree on the API to have something consistent.\r\n\r\nAlso note that the pretty name would only be used to show users something prettier than a dataset id, but in the end the source of truth will stay the dataset id (here `squad`).","body":"It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts.","comment_length":95,"text":"Add an API to access the language and pretty name of a dataset \n It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts. \n Hi ! With @bhavitvyamalik we discussed about having something like\r\n```python\r\nfrom datasets import load_dataset_card\r\n\r\ndataset_card = load_dataset_card(\"squad\")\r\nprint(dataset_card.metadata.pretty_name)\r\n# Stanford Question Answering Dataset (SQuAD)\r\nprint(dataset_card.metadata.languages)\r\n# [\"en\"]\r\n\r\n```\r\nWhat do you think ?\r\n\r\nI don't know if you already have a way to load the model tags in `transformers` but we can agree on the API to have something consistent.\r\n\r\nAlso note that the pretty name would only be used to show users something prettier than a dataset id, but in the end the source of truth will stay the dataset id (here `squad`).","embeddings":[-0.188386336,0.1110786572,-0.1189651936,0.3318739533,0.3078963459,0.1275812835,0.2714886963,0.2705741525,-0.1630472392,0.2370295674,0.182367295,0.5307036042,-0.2200144529,0.3426440358,0.2076429725,-0.1973491013,-0.0799138099,-0.0694945529,0.0691232532,-0.068991445,-0.1761096567,-0.090749979,0.1633303612,0.1583644748,-0.0301870462,0.0261960234,-0.0522684716,-0.3547734916,-0.0490883105,-0.3833298385,0.0265071467,0.2685779333,0.2704098821,0.0535490401,-0.0001118512,-0.1514771879,0.428049475,-0.0854010656,-0.3574964106,-0.3493791521,-0.3073886037,-0.1295091808,0.1739201546,-0.3760478795,-0.1767501384,-0.3267531395,0.2022295594,-0.3540710807,0.2644023895,-0.0619541481,0.176770106,0.1459209621,-0.1946241856,-0.0098479623,0.1031239405,0.6372214556,-0.1374300569,0.0719209313,0.3668981791,-0.0315231904,0.0537887476,0.4254769981,0.1457024813,-0.099273853,0.1142932922,0.2550070584,0.0585154332,-0.2441239804,0.1967636049,0.308009088,0.6004468203,-0.3262732625,-0.5997585058,-0.3656826019,-0.022137465,0.0269224588,-0.1059647277,0.1007210612,0.0055736718,0.325719744,0.0648374185,-0.1178197637,-0.3247561455,0.1506285816,0.2871362567,0.673175633,0.0636098012,0.22268866,-0.2268857211,-0.05360654,0.2841743827,-0.2307621688,-0.0192712378,0.3466577232,0.0307829492,-0.2591355145,0.0764825791,-0.0677650273,0.0404178016,0.2686677575,0.0246033706,0.2520871162,-0.1839669943,0.057052467,0.1863696575,-0.0583458804,0.3656691611,0.3474362493,0.1395958662,-0.2905345857,0.3369954526,-0.1128106937,-0.4009593725,0.3998141885,0.1455465257,0.236240387,0.1147171482,0.0293195825,0.1392043382,0.0995041132,-0.0031157157,-0.193773061,0.0437826328,0.2090449929,-0.1029655933,0.4838320613,-0.0575311519,-0.0558884256,0.261975795,-0.2849515378,-0.0396051444,-0.0428765304,-0.1556304097,0.2016425729,0.1335633397,0.4709702134,0.1575273275,-0.2021083236,0.0211380981,0.0888663605,0.5216773152,-0.1072577387,0.1699405164,-0.1429651678,-0.1553065479,0.2478838414,0.0340855382,-0.331638068,-0.3818343282,0.0909754857,-0.1794744432,-0.0662017539,-0.2235243022,0.2019513249,-0.12367782,-0.0743874162,-0.3969196677,0.546120584,0.0621863715,-0.0336483493,0.094790183,0.0439010486,-0.080586791,-0.0177111849,-0.1008541659,0.4807066321,-0.3225237131,-0.3198370337,0.1414936781,-0.1929112971,-0.1637776345,-0.1636966318,-0.4127165377,0.278162241,0.0034137366,-0.0515932515,0.4448745847,-0.433339715,-0.0318218097,0.1604062766,-0.0531949773,-0.0830703527,0.2868572176,0.206754297,0.1231195852,0.0766081512,-0.0214541331,0.4316636026,-0.1471989006,0.0135139525,0.0924810171,-0.578383863,0.1879703403,0.4853284061,0.1501314491,-0.0893118456,0.0238716174,0.2388889939,0.4389698803,-0.1998252571,0.0331594422,-0.1284463406,0.2546330988,-0.0727186948,0.1066728234,-0.2864490449,-0.4631745815,0.0916081369,-0.1845386773,0.3253059089,-0.2072031498,-0.3501712084,0.1826668829,0.069185175,-0.1576730162,0.2429236025,0.1133722365,-0.0418601297,-0.1135338247,-0.1330611408,-0.1494903415,0.0675101578,-0.0991156325,-0.0889893919,-0.5047960877,-0.0932677686,0.0735899955,0.1328448653,-0.3385683298,0.0157211944,-0.1293935925,-0.154828459,-0.0818671733,0.0013924312,0.3518503904,0.1150767803,0.1976159066,0.6180114746,0.290583998,-0.2617429793,0.0504497923,0.3226174414,0.2168333828,0.0121784154,-0.3475636244,0.3583140075,-0.0680054799,0.2737100422,0.0967417136,-0.1490737051,0.4255134761,0.1746971607,-0.3774278164,-0.3048305213,-0.0601780042,0.1465276033,0.147524789,-0.0568139926,0.000165446,0.1434458047,0.5072767138,-0.08790344,0.0534009859,0.0310843028,-0.0818457156,-0.0111306952,0.0422716625,0.2109892517,0.2227065563,0.2407680154,-0.2791877389,-0.0600433983,0.283783406,-0.0428526103,0.2694918215,0.408303231,0.0491667688,-0.2313789427,-0.0485485569,0.1354128569,0.0925141424,-0.5166596174,0.1762725413,0.0856549963,-0.1000048593,0.270493418,0.1103067249,-0.3297911286,-0.3040924072,-0.356790185,-0.1466909945,-0.2314637899,0.0276809353,-0.4174096286,-0.2768644392,0.1285554767,-0.1056592539,0.2818367183,-0.2166183144,-0.2801747918,-0.080882065,-0.6389325857,-0.182870999,0.0282986108,0.1939239651,-0.1233168095,0.4811219871,-0.1316085905,0.2442051917,0.0948468819,-0.5168853998,0.2221330851,0.1477455497,0.1503617316,0.0017751112,-0.0010787621,0.3242390156,-0.0951399058,0.0896564275,-0.1282002032,0.2495244443,-0.0675789341,-0.0633373111,-0.247516349,-0.1216358691,-0.3174870312,-0.6008809209,-0.1647494882,0.0497582965,-0.0585563146,0.1887986958,0.3861448467,-0.131436184,0.2721607983,-0.166587919,-0.0442869253,-0.1468530446,-0.0880572498,0.1331970394,-0.10978356,-0.1724701673,-0.1961832345,-0.2997507751,0.2696593106,-0.2651053965,0.0918115377,-0.1628443152,-0.0597254261,0.0677622482,0.0137443012,0.0391947739,0.3031950891,0.1896317601,0.0316593982,0.138198033,-0.1563609689,0.1373777241,0.0796183944,0.0916441157,-0.0959923789,0.1144304425,-0.233294487,0.4291267693,-0.057969071,-0.2486724555,0.0941292346,0.1176648661,-0.0398776568,0.1980214566,-0.2120458484,-0.1962487549,-0.0698767677,-0.10648245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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2347","title":"Add an API to access the language and pretty name of a dataset","comments":"What dataset_info method are you talking about @julien-c ? In `huggingface_hub` I can only see `model_info`.","body":"It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts.","comment_length":16,"text":"Add an API to access the language and pretty name of a dataset \n It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts. \n What dataset_info method are you talking about @julien-c ? In `huggingface_hub` I can only see `model_info`.","embeddings":[-0.2759426534,-0.1491751969,-0.0873624384,0.4150775969,0.3421386778,0.1189722791,0.3897387385,0.3709770143,-0.0223603975,0.3746368587,0.0779034197,0.5090548396,-0.0601509586,0.3841827214,0.3461498022,-0.2023470253,-0.1172480658,0.0129679348,-0.0803340152,-0.1211486757,-0.1962136924,0.0521035045,0.1108366102,0.1241701245,-0.2669221163,0.0454564132,-0.0540004075,-0.1388418525,-0.0790893584,-0.3375242352,0.0865435675,0.2850354612,0.2838588655,0.0294624493,-0.0001076396,-0.0245080348,0.4776881635,-0.1515951008,-0.3618721366,-0.2217716128,-0.3296572268,-0.1733139902,0.2418285757,-0.4034040272,-0.1884512007,-0.2138314545,0.224421382,-0.1580801308,0.3432016373,0.0513024628,0.2172973156,0.3994908929,-0.102676332,-0.1373630464,0.04761355,0.6418023705,-0.2143192291,-0.0689423978,0.2645703852,0.1362203658,-0.1340632737,0.486395061,0.2200396359,-0.1540040821,0.0501422063,0.1936683506,0.0280212779,-0.1995519847,0.1938809752,0.298142463,0.525097549,-0.2898886502,-0.4724097848,-0.3455622494,-0.0765761286,0.0059020873,-0.0632784516,0.1038327143,-0.078157559,0.3310469985,-0.0015482671,-0.182741642,-0.3698796928,-0.0884884149,0.3872871697,0.6222913861,-0.0667792186,0.2156384438,-0.1351896524,-0.0614886768,0.2825260162,-0.1690822244,-0.1277253777,0.2868182361,-0.0393238179,-0.3108914495,0.2393340319,0.1192906275,0.0675930381,0.1376781464,0.1428592801,0.3573163748,-0.1809933037,0.0888535008,0.1385019571,-0.0307538137,0.3093574345,0.2264119238,0.2360432446,-0.021527959,0.3387371898,-0.2190620899,-0.3292751908,0.4511210024,0.0249057077,0.2304005325,0.1044783145,-0.0164831672,0.1443421096,-0.0354278199,0.2760775387,-0.2522013187,0.178229019,0.2969044745,-0.0513974875,0.3812257051,-0.1183512956,-0.1153667048,0.0954156965,-0.3546554446,-0.1189889982,-0.0383488499,-0.1936372966,0.2752449512,0.0766616315,0.3325634599,0.1666308194,-0.1525792032,0.0196104068,0.2120028734,0.3784129322,0.0297197718,0.1751953512,0.0140853906,-0.1419986784,0.1474352181,0.0596461184,-0.0874045566,-0.4299757481,-0.0136393746,-0.382239908,-0.3169518113,-0.2568475008,0.1982749104,-0.0872393548,0.0501367599,-0.3038825989,0.6148662567,-0.0784490779,-0.106677115,0.1258229613,0.1114142835,-0.1736579835,-0.0588228069,-0.0618404336,0.4257553816,-0.2679534853,-0.3829319477,-0.0307526831,-0.3184658289,-0.047787156,-0.10053844,-0.5002002716,0.2181773335,-0.0935139284,0.0518310741,0.4971801341,-0.3273154199,-0.0425875671,0.1693952829,-0.01667629,-0.0804448128,0.2692882121,0.1571595073,0.053384535,0.0325234272,-0.004432918,0.2725387812,-0.1532925218,0.0294047911,0.0630578995,-0.5404416919,0.054611031,0.3931621611,0.0242334809,0.0829235017,0.1160224527,0.2529343665,0.4412276149,-0.1962568462,0.0433011539,0.0485788845,0.242580533,-0.1425728947,0.1200500354,-0.2552195489,-0.5282456875,0.134233579,-0.2967631817,0.3904132843,-0.2090803534,-0.3232308626,0.1848082244,0.1415601373,-0.1126807183,0.0030877152,0.1554501951,0.0829647332,-0.0735130161,-0.2124800831,-0.2460973412,0.1851912141,-0.0752023384,-0.0761938393,-0.6570014358,-0.103659898,0.0326333903,0.0452943072,-0.2922037244,-0.0061010495,-0.0503631309,-0.2164345533,-0.1234729812,0.0844233334,0.295961678,0.1233161539,0.0977291688,0.5998771787,0.2218786627,-0.1326135248,-0.0450715944,0.0476418138,0.2004657686,0.029669065,-0.3212014735,0.4076057076,0.0848569646,0.1917535216,0.1073310822,-0.0558085777,0.3379788995,0.2143078893,-0.2994888425,-0.317826122,-0.0228854213,0.2004351169,0.2086059302,-0.0387231223,-0.0429479368,0.12272688,0.6142543554,-0.0771296024,-0.0394029319,0.1776136607,0.0620752759,-0.0580253229,0.2085754126,0.1728255749,0.2795028389,0.2470294684,-0.2898241282,0.0175849069,0.2250059992,-0.042623952,0.2176297903,0.2541165948,0.1007624194,-0.2975418866,-0.0693697482,0.0942350775,-0.0805080384,-0.5052796602,0.1470464617,0.1081088036,-0.1146179438,0.2134939879,0.0048204483,-0.1865454167,-0.2260833979,-0.2593690157,-0.2423830926,-0.2321575135,0.0639260337,-0.3443917036,-0.2731078863,0.167504862,-0.1685518026,0.4209560454,-0.1939249188,-0.0794449151,-0.327447623,-0.4993188977,-0.1878132522,0.0564041473,0.111486353,-0.0859057009,0.4333528876,-0.1752043664,0.2551008463,0.0451659784,-0.4706531763,0.3559008241,-0.0337875858,0.109446831,0.2159413844,0.1232699975,0.3101499677,-0.0756680965,0.1584525108,-0.209682107,0.118352443,-0.0982742012,-0.0985588655,-0.2147697955,-0.1783460081,-0.2231878638,-0.357454896,-0.3391286731,0.1538420618,-0.1468399763,0.2661872208,0.3840101659,-0.1879553944,0.3901928663,-0.1318159252,0.0069877296,-0.1764029115,-0.281493336,0.0871337429,-0.2605356872,-0.2236509025,-0.2518216968,-0.2001709491,0.3316057324,-0.1917027384,-0.0048782853,-0.1415728331,-0.3200156689,0.0396235846,0.0485229306,0.1005735472,0.5203320384,0.1714390367,-0.0038117054,0.0036428815,-0.3489682674,0.1289702058,0.185622558,0.0381870158,-0.0448597036,0.1224044189,-0.2703688741,0.3509935141,-0.1466285139,-0.1390264928,0.2506451309,-0.022420099,0.1232724488,-0.020252917,-0.175989911,-0.1975342631,-0.0261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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2345","title":"[Question] How to move and reuse preprocessed dataset? ","comments":"<s>Hi :) Can you share with us the code you used ?<\/s>\r\n\r\nEDIT: from https:\/\/github.com\/huggingface\/transformers\/issues\/11665#issuecomment-838348291 I understand you're using the run_clm.py script. Can you share your logs ?\r\n","body":"Hi, I am training a gpt-2 from scratch using run_clm.py.\r\n\r\nI want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),\r\n\r\nI tried to :\r\n\r\ncopy path_to_cache_dir\/datasets to new_cache_dir\/datasets\r\nset export HF_DATASETS_CACHE=\"new_cache_dir\/\"\r\nbut the program still re-preprocess the whole dataset without loading cache.\r\n\r\nI also tried to torch.save(lm_datasets, fw), but the saved file is only 14M.\r\n\r\nWhat is the proper way to do this?","comment_length":28,"text":"[Question] How to move and reuse preprocessed dataset?  \n Hi, I am training a gpt-2 from scratch using run_clm.py.\r\n\r\nI want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),\r\n\r\nI tried to :\r\n\r\ncopy path_to_cache_dir\/datasets to new_cache_dir\/datasets\r\nset export HF_DATASETS_CACHE=\"new_cache_dir\/\"\r\nbut the program still re-preprocess the whole dataset without loading cache.\r\n\r\nI also tried to torch.save(lm_datasets, fw), but the saved file is only 14M.\r\n\r\nWhat is the proper way to do this? \n <s>Hi :) Can you share with us the code you used ?<\/s>\r\n\r\nEDIT: from https:\/\/github.com\/huggingface\/transformers\/issues\/11665#issuecomment-838348291 I understand you're using the run_clm.py script. Can you share your logs ?\r\n","embeddings":[-0.1601244807,-0.326133579,0.0653828979,0.3396508694,0.2864599824,0.1715729535,0.1354581118,0.1812243313,-0.0809112191,-0.1558374763,-0.1377898008,0.0398773029,-0.168959111,0.0587198995,0.2985677719,-0.2673764825,0.1552250385,0.0188227296,-0.3201619685,0.0475619994,-0.0599813052,0.0041698474,-0.0590553656,-0.2007913291,-0.4416160882,-0.0313769318,-0.248014912,0.1666658074,0.1828492731,-0.1862577051,0.25329265,0.2420495898,0.3576463163,0.6828821898,-0.0001243633,0.1335567683,-0.2728155851,-0.2317272723,-0.2766866386,0.033128757,0.3927209675,-0.1214862019,-0.1350308955,-0.1829705387,-0.4399235547,-0.3136433661,-0.1345021874,-0.4278991222,0.7499090433,0.1695947349,0.0427683331,-0.2132157981,-0.2778227925,0.008503221,0.0278573241,0.0635983124,-0.0142037226,0.130135268,0.2992685139,0.0561226234,0.2704514265,0.0200520474,-0.1339302659,0.1400889754,0.1526925415,-0.2002475262,-0.2576467991,-0.0023741957,0.2037418187,-0.0177668817,0.3946386278,-0.5167760849,-0.0736816972,-0.3228442371,0.0029749095,-0.5202156305,-0.2885060012,0.1219025403,0.0930978507,0.4745025337,-0.42115587,-0.4792868793,-0.1270094216,-0.423194319,0.2090489119,0.1758548915,-0.0334332548,0.2210970074,-0.0002789216,0.3428712487,0.246133998,0.0327330157,-0.2068687677,0.2302808762,-0.0386438072,0.0416807681,-0.185777992,0.1895509958,-0.0354911052,0.3463308215,-0.0219855066,-0.2044128329,0.3041285872,-0.2163733989,-0.0310898088,0.3699341118,-0.238204971,0.369102031,0.0616072305,-0.2548559606,-0.3396961093,-0.2820080221,-0.3452650607,-0.0796073601,0.3917407095,0.3445879817,0.1377707124,-0.1579306573,0.0967462808,0.0325179249,0.030502595,-0.3261357546,-0.1352859437,0.19699651,0.0150934337,0.1261731684,0.2938515246,0.0826364085,-0.1128118411,-0.3416502774,-0.1811539978,0.1455423385,-0.3331011534,0.3481995463,0.0287016276,-0.014869987,0.2117708921,0.0153675238,0.1050676927,-0.1169733703,0.4738585651,-0.079364568,0.1513180733,0.2509043515,-0.1366546899,0.2494835854,0.2912724912,0.3461928368,-0.237950623,0.5287489295,-0.2154992372,-0.3498134613,0.4890762269,-0.0687368363,0.028009437,0.1787005514,-0.2850988507,0.0660907924,0.4531784952,0.0426065885,-0.0363986418,-0.2588700056,-0.1559437811,-0.2766834199,0.2264140844,0.154808268,-0.3293088377,-0.1583404541,0.3445459604,0.228521198,0.3519313633,0.386734128,-0.299557656,0.4961073995,-0.2172493935,-0.3410748541,0.4171503484,-0.3627423346,-0.0831839815,0.0610206202,-0.0586275943,0.0295937695,0.0395397469,0.0728784502,-0.1641704142,-0.3265634775,-0.3916639984,0.525267899,0.0657280236,0.0954927728,-0.3008169234,-0.2864235342,0.2404656112,-0.1850553602,0.0782072246,0.164751485,-0.1351279318,0.069657661,0.265425086,-0.1466569453,0.1503960043,0.211293295,0.1797007769,0.1057514101,-0.0425681919,0.0228968654,-0.4002119899,0.1264652163,-0.2417495549,-0.2840818465,0.3042552471,-0.1635408103,-0.2996501029,-0.1877946258,-0.2450942993,-0.2375672013,-0.0899938345,0.1676106304,0.4852058291,-0.2807754874,-0.2159505934,0.4022931755,-0.0248985793,0.0942259729,-0.0614655539,0.2730467618,-0.0532378256,-0.1499190778,-0.1567530632,-0.1414903849,0.3739397228,-0.3398053944,-0.1057071835,0.3445952237,0.0315981284,0.1384757161,0.0972635448,0.3569538295,-0.0082065947,-0.2292221934,0.2719107866,-0.0102203768,-0.0274838805,-0.0911909416,-0.3177952766,0.3725720644,0.0262950454,0.1716701537,0.0985738561,-0.2230465859,-0.1204782575,-0.2000549436,0.1363731623,0.2361083478,0.0083200103,0.160623163,0.3446005285,0.2063460797,-0.0403262451,0.2403444201,0.3612610102,0.0553271919,-0.0649843514,-0.1507966071,-0.228865236,-0.3063418269,0.0669332594,0.0615004003,0.043263942,0.0668413118,0.2860628664,0.2623815238,0.0553233512,-0.0461747982,0.0767881572,-0.2649209499,0.4027986526,0.0500800721,0.0191394705,0.0634204149,0.2220074236,0.4308409095,0.2273504883,0.4133346081,-0.1975356042,0.2897772193,-0.2299524993,0.1958617717,-0.1645824909,0.1251373291,0.0235758014,0.106869705,-0.2166393995,0.3630876243,0.3512009382,0.0673116222,-0.00158157,0.069393672,-0.0673962459,-0.2549570799,-0.0874466449,-0.2556751966,-0.0744591281,-0.0861398056,0.2902083993,-0.1208374947,0.2638791203,0.1115595102,-0.0741840675,-0.2830797732,0.2449823618,0.2341542393,0.0082205543,0.1375828236,-0.1480499357,0.3238554001,0.1407762319,0.25285092,-0.0139525691,-0.1429397762,-0.0660809055,-0.1644589752,-0.1860044897,0.1651820093,-0.0793921351,-0.2665188909,-0.1722411066,-0.0440107808,0.1693967134,0.1464495957,0.0157912131,-0.0370964408,0.04608858,-0.228190586,0.3308419287,0.1997729689,-0.1972014308,-0.7081502676,0.1870312095,-0.0855588317,-0.1360951811,0.0187754184,0.1196089908,0.2030193955,0.2234844118,-0.3637008369,-0.1337093264,-0.1479481161,0.0738007575,-0.0395294949,0.2716372609,0.4804903269,0.0577302165,0.0834795535,0.0219519809,-0.0803537294,0.3806947172,0.1315556765,0.1364147067,0.19788526,0.2309724987,0.1304398179,0.8657284975,0.1493235081,-0.0853927806,0.1692263782,0.0596273579,0.2112223059,-0.3018945754,-0.0521552004,0.0147803249,0.0333520807,-0.3499486446,0.170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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2345","title":"[Question] How to move and reuse preprocessed dataset? ","comments":"Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the same","body":"Hi, I am training a gpt-2 from scratch using run_clm.py.\r\n\r\nI want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),\r\n\r\nI tried to :\r\n\r\ncopy path_to_cache_dir\/datasets to new_cache_dir\/datasets\r\nset export HF_DATASETS_CACHE=\"new_cache_dir\/\"\r\nbut the program still re-preprocess the whole dataset without loading cache.\r\n\r\nI also tried to torch.save(lm_datasets, fw), but the saved file is only 14M.\r\n\r\nWhat is the proper way to do this?","comment_length":33,"text":"[Question] How to move and reuse preprocessed dataset?  \n Hi, I am training a gpt-2 from scratch using run_clm.py.\r\n\r\nI want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),\r\n\r\nI tried to :\r\n\r\ncopy path_to_cache_dir\/datasets to new_cache_dir\/datasets\r\nset export HF_DATASETS_CACHE=\"new_cache_dir\/\"\r\nbut the program still re-preprocess the whole dataset without loading cache.\r\n\r\nI also tried to torch.save(lm_datasets, fw), but the saved file is only 14M.\r\n\r\nWhat is the proper way to do this? \n Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2345","title":"[Question] How to move and reuse preprocessed dataset? ","comments":"> Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the same\r\n\r\nI only changed the `preprocessing_num_workers` maybe it is the problem~ I will try again~","body":"Hi, I am training a gpt-2 from scratch using run_clm.py.\r\n\r\nI want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),\r\n\r\nI tried to :\r\n\r\ncopy path_to_cache_dir\/datasets to new_cache_dir\/datasets\r\nset export HF_DATASETS_CACHE=\"new_cache_dir\/\"\r\nbut the program still re-preprocess the whole dataset without loading cache.\r\n\r\nI also tried to torch.save(lm_datasets, fw), but the saved file is only 14M.\r\n\r\nWhat is the proper way to do this?","comment_length":48,"text":"[Question] How to move and reuse preprocessed dataset?  \n Hi, I am training a gpt-2 from scratch using run_clm.py.\r\n\r\nI want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),\r\n\r\nI tried to :\r\n\r\ncopy path_to_cache_dir\/datasets to new_cache_dir\/datasets\r\nset export HF_DATASETS_CACHE=\"new_cache_dir\/\"\r\nbut the program still re-preprocess the whole dataset without loading cache.\r\n\r\nI also tried to torch.save(lm_datasets, fw), but the saved file is only 14M.\r\n\r\nWhat is the proper way to do this? \n > Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the same\r\n\r\nI only changed the `preprocessing_num_workers` maybe it is the problem~ I will try again~","embeddings":[-0.2302794009,-0.1823711097,0.0412170552,0.3404656649,0.1980711371,0.2086258978,-0.0375071019,0.2514610887,-0.1037038416,-0.2087676227,-0.0314362459,0.163025707,-0.1305736154,-0.0440304205,0.2582703829,-0.191184625,0.1755548716,-0.0452623554,-0.1915269196,-0.0165702756,-0.053558208,-0.0251102448,-0.0321363769,-0.2102221549,-0.3548283279,-0.1217458174,-0.2918241918,0.1653580219,0.1479510814,-0.2046285123,0.1572978795,0.1787934154,0.4092198908,0.5932112932,-0.0001262194,0.0605907179,-0.2513768375,-0.1923409402,-0.2729692161,-0.0186120551,0.3915597498,-0.1380922049,-0.1416184306,-0.1292648911,-0.416855067,-0.3540060818,-0.1318838298,-0.5027413964,0.6082751155,0.2023899108,0.0339937508,-0.4183064401,-0.3058912456,-0.0001165216,0.0776552707,-0.1023506001,0.0107616307,0.1995076388,0.3009852469,0.0655802563,0.2533354461,-0.0054526818,-0.191324845,0.2161302567,0.0771678165,-0.2489583045,-0.2419671565,-0.0063001793,0.2172265649,-0.0644972771,0.3854005635,-0.4955865145,-0.0214141812,-0.2836808264,0.0525718816,-0.6237027049,-0.2847483754,0.1535848528,0.0742787942,0.3391981721,-0.3694743216,-0.5197529793,-0.0286423266,-0.3435761034,0.2452744395,0.1527326554,-0.0363954119,0.2196631432,-0.0064444202,0.3711723387,0.3126406968,0.067528598,-0.2623577118,0.173351571,-0.0181058608,0.1303376704,-0.1184555069,0.3414288163,-0.0596794747,0.3688027859,0.0189884286,-0.2177861929,0.3481891453,-0.1931370795,-0.0610701963,0.2535793185,-0.1575440913,0.2674420476,0.0153020546,-0.30593732,-0.4410021901,-0.306471169,-0.3355246484,-0.1766227037,0.4219074249,0.3731341362,0.0536548346,-0.226765126,0.0651227906,0.1151025519,-0.0963107273,-0.3628315628,-0.1881166846,0.1191907451,-0.0267978217,0.061994154,0.2298413664,0.1135953516,-0.1300108284,-0.4259298742,-0.1784644574,0.1209823266,-0.3620635569,0.4047139883,0.0170489047,-0.1473718137,0.2812189162,0.1347008198,0.0576779209,-0.157856673,0.4618590176,-0.1600634158,0.2225682586,0.1569197327,-0.0888811126,0.2168241441,0.3071734011,0.4267371297,-0.2022321969,0.5157366395,-0.2954542935,-0.2999769151,0.5050640106,-0.0835787505,0.0763936043,0.2000036687,-0.141600132,0.0793812647,0.4280877113,0.0587629639,-0.0315423943,-0.2942147851,-0.1964375675,-0.2926189303,0.2295431942,0.0953661278,-0.327676177,-0.0593125261,0.3742058873,0.3352854848,0.3759862483,0.2220870554,-0.2464765012,0.4664205015,-0.2396627218,-0.3466240168,0.4473837018,-0.2679067552,-0.0157277305,0.0745586678,-0.0023964485,-0.0045465534,0.0178567097,0.0399054177,-0.1705620289,-0.2612144649,-0.3734619617,0.4028814733,0.0182685181,-0.0181437545,-0.3461932838,-0.1834283769,0.2144181877,-0.281565994,0.0642997101,0.1546642631,-0.0236602668,-0.0397349149,0.2138565183,-0.07063438,0.0812874883,0.2107388824,0.1581497192,0.1610248387,-0.08663515,0.0636222064,-0.2418338954,0.0950836539,-0.1858502328,-0.3736918569,0.426628679,-0.079285875,-0.2309802771,-0.2579778731,-0.2068706006,-0.1903482378,-0.0772554427,0.2529401481,0.4489644766,-0.2513560653,-0.103517361,0.4261999726,-0.1011625826,-0.006149414,0.0129419537,0.1810733825,-0.0196843203,-0.1941667199,-0.0644103885,-0.1135629416,0.4528129399,-0.2504135966,-0.0720244199,0.3093142807,0.0809951723,0.0377836116,0.0612075031,0.2628931403,-0.124869898,-0.1418714672,0.2970556319,-0.1096230149,-0.0812778026,-0.1403728276,-0.1864896864,0.3085307479,0.1251228601,0.1671189964,0.0939229652,-0.2757715583,-0.1386353374,-0.1613757759,0.2355558127,0.2098315358,0.0903167352,0.1427326202,0.3833736777,0.1253003031,-0.1511943042,0.3049738705,0.2918567061,0.1568562388,-0.065999724,-0.1570335031,-0.181794703,-0.3211259544,0.0839108229,0.0257231202,0.0624967143,0.0498829,0.3670087755,0.2906754017,0.0059052287,-0.084963195,0.0275960341,-0.3611502349,0.4245193601,0.0833941028,-0.0329412408,0.0688961223,0.2673463821,0.4997732639,0.2703295946,0.4532830119,-0.2201969177,0.2037452012,-0.3384391069,0.1429186314,-0.1421448737,0.1770968139,0.0565393642,0.0676854327,-0.2187446505,0.3874480724,0.3927195668,0.0559774712,-0.1153547391,0.0760849863,-0.1575406641,-0.1825377643,-0.0212741699,-0.3034434617,-0.1745171398,-0.0893428773,0.3128996789,-0.1144714206,0.3428845704,0.0956346393,-0.0854871348,-0.3158885539,0.3085270226,0.148235172,0.0799029842,0.1736142486,-0.1999173611,0.2825099528,0.0987995341,0.2641468346,-0.0171636902,-0.1196132898,-0.0731900632,-0.1901337057,-0.2272614092,0.1431858093,-0.02286979,-0.2841074765,-0.1119892299,0.0000451936,0.0224696305,0.2144094259,0.0258827228,-0.1833224446,0.0513681285,-0.2649686038,0.2935570776,0.2573852837,-0.257855773,-0.7665913701,0.1563871354,-0.1181642041,-0.0654659048,0.0767871588,0.2234165817,0.2946231067,0.2762686312,-0.4194144309,-0.0827117041,-0.0344308205,0.1499554515,-0.0958808959,0.2032488734,0.3651020229,0.0533620566,0.0602739044,-0.0092965188,-0.1047998443,0.3742393553,0.0774437636,0.206361562,0.215963617,0.2940153778,0.145108521,0.7868188024,0.195152089,-0.1160394549,0.2585864961,0.0802676603,0.2512440085,-0.3090752661,-0.0128970165,0.171986267,0.0376320556,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2344","title":"Is there a way to join multiple datasets in one?","comments":"Hi ! We don't have `join`\/`merge` on a certain column as in pandas.\r\nMaybe you can just use the [concatenate_datasets](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=concatenate#datasets.concatenate_datasets) function.\r\n","body":"**Is your feature request related to a problem? Please describe.**\nI need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2? \n\n**Describe the solution you'd like**\nId like to join them with a merge or join method, just like pandas dataframes. \n\n**Additional context**\nIf you want to extend an existing dataset with more data, for example for training a language model, you need that functionality. I've not found it in the documentation.","comment_length":21,"text":"Is there a way to join multiple datasets in one? \n **Is your feature request related to a problem? Please describe.**\nI need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2? \n\n**Describe the solution you'd like**\nId like to join them with a merge or join method, just like pandas dataframes. \n\n**Additional context**\nIf you want to extend an existing dataset with more data, for example for training a language model, you need that functionality. I've not found it in the documentation. \n Hi ! We don't have `join`\/`merge` on a certain column as in pandas.\r\nMaybe you can just use the [concatenate_datasets](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=concatenate#datasets.concatenate_datasets) function.\r\n","embeddings":[-0.4773362577,-0.6935091019,-0.0979438573,0.1795870066,0.1399442703,0.324791193,-0.2019497603,0.0913561285,0.0232805461,0.0314714238,-0.5830703974,-0.0147510488,0.2058378458,0.4802639186,0.0973033533,-0.3352275193,0.2061462551,0.0533265024,-0.0682545528,0.1907300651,-0.0057576806,0.1205627769,-0.1383992136,-0.0527564175,-0.4373386502,0.1436782777,-0.5212435722,-0.1436893493,-0.2237620801,0.1499155164,0.3690565825,0.3095445633,0.1582986861,0.4412491322,-0.0001130937,-0.2398640066,0.0831307396,0.091894336,-0.1158550158,-0.2447850555,-0.2960631847,-0.2401031852,0.1332097501,0.1241770536,-0.0557513945,-0.0315558836,0.0795208886,0.0654474273,0.0969701633,-0.1098946631,0.1444479525,0.0797101557,0.2500294149,-0.1734854579,-0.1637144983,0.511146009,0.0108181126,0.1524355412,0.4946920872,-0.0485214777,0.6609489918,-0.0064986385,0.0665581748,-0.0683701783,0.2285611778,0.100775443,-0.0508412197,-0.4745711088,-0.3607732952,0.3082880974,0.5458722711,-0.1255922467,-0.4932384789,-0.2368196696,0.2477696389,0.0213851891,-0.191336289,0.3288097382,0.2086051255,0.0240966771,-0.2064361274,-0.3917130828,0.0532743707,0.1583522111,-0.0697357133,0.1957956254,-0.0513200685,0.0908239409,0.0695555285,-0.2681914568,0.0352073982,-0.1660829335,-0.1516615003,0.1595116407,-0.6084676981,-0.3992604911,-0.2260915339,0.3628484607,0.4802445173,0.2024169266,-0.1985312402,0.0058766617,-0.4315240085,0.2300775647,0.2825003564,-0.2399666607,0.0240399688,-0.2077750862,0.3054992557,-0.1043953821,-0.1197053269,-0.0667931288,0.1038361266,-0.1081261933,-0.456207931,-0.2621081769,0.1290219277,-0.0245840736,0.0039546536,-0.319190383,0.1243011728,-0.070965834,-0.0413121134,0.0606067218,-0.1655995399,0.1167970821,-0.1396435946,0.2302725613,-0.1907328963,-0.2304217815,-0.0756720826,0.1119525135,0.0165523402,0.2893992066,-0.1712617874,-0.494278729,-0.2040009201,0.4133867621,0.4074558914,0.0469176136,-0.0839378759,-0.2842968106,-0.1569360793,0.0950027779,0.2700360417,-0.0443907417,0.0842060596,0.0237631667,-0.1463833451,-0.1203252599,-0.4403831959,-0.2885366082,-0.4382292032,0.0907670334,0.0465172119,-0.0341365039,-0.4737575352,0.4752351642,-0.2497937083,-0.0918000713,-0.0570735969,0.1923232675,-0.1775273681,-0.1139970347,0.1159601584,0.0173621904,-0.0776893198,-0.1454377472,0.1351715624,-0.0703127682,-0.3292372525,0.1861366332,-0.2383997291,0.1550084203,-0.1052752882,0.0049520712,0.3695624471,-0.4302903414,-0.0287413299,0.0589243174,0.1559230536,-0.1439162195,-0.0754961893,0.2700874507,0.043855112,0.1177537665,0.1722823381,0.4885490835,-0.1066983491,-0.1704289168,0.1633426249,-0.2834759057,0.208545506,0.0972488075,-0.031383235,-0.2148171514,0.0863160118,-0.166401282,0.2248512506,-0.0607077293,0.0528883524,0.2243683487,0.0630205795,0.2102553099,-0.0084778657,-0.4905330539,-0.4413351715,-0.0259323455,0.2855248153,0.084995456,0.2845337391,-0.2495342046,0.1780862808,-0.2793402374,-0.0916923732,-0.0079673845,0.0058748634,-0.0218319129,0.1856889576,-0.2826339304,-0.5492193699,0.685990572,0.1156111211,0.0149121145,-0.5255337358,0.7264195681,0.0302156247,0.0821208283,0.0499669015,0.2766754329,0.0578535758,-0.0626276135,0.2358025312,0.1307605207,-0.3658678532,0.2062020451,0.4405407906,0.1315921247,0.3730511367,-0.2377302498,-0.260142833,-0.3811036348,0.0679416433,-0.195644021,-0.360506475,0.2850862443,-0.0207605176,0.3033422232,0.0646377504,0.0476941578,0.0972117633,-0.0369289704,-0.1704229712,0.0372312292,-0.0251717437,-0.0196590051,-0.0002518992,0.1699335426,-0.397913456,0.0302122831,0.1181701124,-0.0063374308,-0.0161388051,-0.0998308435,-0.2815756202,0.0955167264,-0.0223459806,0.0471738093,0.4209526479,0.2107751369,-0.1005109251,0.0912071019,-0.0222610291,0.0151997237,0.0410538353,0.1977774501,-0.0018745521,0.1576871872,0.2379131317,0.0629280582,-0.0465944596,-0.0710174143,0.2590674758,-0.0271979924,-0.1693974882,-0.2094568163,-0.128433004,-0.3241541982,-0.0318349861,-0.3003270328,-0.4572021365,-0.2940026522,-0.1320739985,0.1283115149,0.1783221513,-0.0108237285,0.1779537946,0.5747698545,-0.2819282413,0.2220961452,-0.1566817611,0.2854074836,0.2318284512,0.13720119,0.1110639572,0.1105616465,0.3009609878,-0.0158740133,0.1589298397,-0.4940417409,-0.1534124017,0.0295919552,-0.2387657017,0.2145949751,0.0540904552,0.2275940627,-0.0427714847,-0.3297334909,0.305698663,0.175301984,-0.1237514392,-0.1903065741,0.0014500894,0.1199492887,-0.0652679875,0.0478746928,-0.3397681713,-0.2575618625,0.1969037205,-0.1347544789,0.0366297662,-0.2529940605,-0.1064912751,0.0301943403,-0.3341850042,-0.0353308916,-0.046087902,-0.4317243695,0.2534382939,-0.2205061316,-0.1305094659,0.1344530284,0.0573150516,0.0983774513,-0.3787458837,-0.0478381813,-0.3517192006,-0.2464513332,0.0267123915,0.0777891278,0.3056207299,0.0439632945,-0.0687061101,0.0510463901,0.0427715704,-0.1938399971,-0.1942002624,0.4216268957,0.2125367075,-0.1220898703,0.2178722471,-0.2056950927,0.1473842412,0.644236207,0.2356084585,-0.0413697995,0.0447314195,0.1844355911,-0.1091811806,-0.4813727736,-0.07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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2337","title":"NonMatchingChecksumError for web_of_science dataset","comments":"I've raised a PR for this. Should work with `dataset = load_dataset(\"web_of_science\", \"WOS11967\", ignore_verifications=True)`once it gets merged into the main branch. Thanks for reporting this! ","body":"NonMatchingChecksumError when trying to download the web_of_science dataset. \r\n\r\n>NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/data.mendeley.com\/datasets\/9rw3vkcfy4\/6\/files\/c9ea673d-5542-44c0-ab7b-f1311f7d61df\/WebOfScience.zip?dl=1']\r\n\r\nSetting `ignore_verfications=True` results in OSError.\r\n\r\n>OSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/37ab2c42f50d553c1d0ea432baca3e9e11fedea4aeec63a81e6b7e25dd10d4e7\/WOS5736\/X.txt'\r\n\r\n```python\r\ndataset = load_dataset('web_of_science', 'WOS5736')\r\n```\r\nThere are 3 data instances and they all don't work. 'WOS5736', 'WOS11967', 'WOS46985'\r\n\r\ndatasets 1.6.2\r\npython 3.7.10\r\nUbuntu 18.04.5 LTS","comment_length":25,"text":"NonMatchingChecksumError for web_of_science dataset \n NonMatchingChecksumError when trying to download the web_of_science dataset. \r\n\r\n>NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/data.mendeley.com\/datasets\/9rw3vkcfy4\/6\/files\/c9ea673d-5542-44c0-ab7b-f1311f7d61df\/WebOfScience.zip?dl=1']\r\n\r\nSetting `ignore_verfications=True` results in OSError.\r\n\r\n>OSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/37ab2c42f50d553c1d0ea432baca3e9e11fedea4aeec63a81e6b7e25dd10d4e7\/WOS5736\/X.txt'\r\n\r\n```python\r\ndataset = load_dataset('web_of_science', 'WOS5736')\r\n```\r\nThere are 3 data instances and they all don't work. 'WOS5736', 'WOS11967', 'WOS46985'\r\n\r\ndatasets 1.6.2\r\npython 3.7.10\r\nUbuntu 18.04.5 LTS \n I've raised a PR for this. Should work with `dataset = load_dataset(\"web_of_science\", \"WOS11967\", ignore_verifications=True)`once it gets merged into the main branch. Thanks for reporting this! 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2330","title":"Allow passing `desc` to `tqdm` in `Dataset.map()`","comments":"I think the user could pass the `desc` parameter to `map` so that it can be displayed in the tqdm progress bar, as suggested by @cccntu.\r\n\r\nWhen there's no multiprocessing, the `desc` of the progress bar could be the `desc` passed by the user.\r\nIn multiprocessing, we were already using a `desc` equal to `\"#\" + str(rank)`.\r\nWe can change it to be `(desc or \"\") + \"#\" + str(rank)` instead.\r\n\r\nIn the end, since both `desc` and `rank` could be None, we can have:\r\n```python\r\npbar_desc = (desc or \"\") + \"#\" + str(rank) if rank is not None else desc\r\n```\r\n\r\nFinally let's remember that if we add `desc` as a new parameter to `map`, we should add it to the `ignore_kwargs` list of the `@fingerprint_transform` decorator of `Dataset._map_single` since we don't want this parameter to affect the fingerprint of the resulting dataset.","body":"It's normal to have many `map()` calls, and some of them can take a few minutes,\r\nit would be nice to have a description on the progress bar.\r\n\r\nAlternative solution:\r\nPrint the description before\/after the `map()` call.","comment_length":145,"text":"Allow passing `desc` to `tqdm` in `Dataset.map()` \n It's normal to have many `map()` calls, and some of them can take a few minutes,\r\nit would be nice to have a description on the progress bar.\r\n\r\nAlternative solution:\r\nPrint the description before\/after the `map()` call. \n I think the user could pass the `desc` parameter to `map` so that it can be displayed in the tqdm progress bar, as suggested by @cccntu.\r\n\r\nWhen there's no multiprocessing, the `desc` of the progress bar could be the `desc` passed by the user.\r\nIn multiprocessing, we were already using a `desc` equal to `\"#\" + str(rank)`.\r\nWe can change it to be `(desc or \"\") + \"#\" + str(rank)` instead.\r\n\r\nIn the end, since both `desc` and `rank` could be None, we can have:\r\n```python\r\npbar_desc = (desc or \"\") + \"#\" + str(rank) if rank is not None else desc\r\n```\r\n\r\nFinally let's remember that if we add `desc` as a new parameter to `map`, we should add it to the `ignore_kwargs` list of the `@fingerprint_transform` decorator of `Dataset._map_single` since we don't want this parameter to affect the fingerprint of the resulting dataset.","embeddings":[-0.3543031216,0.0359196849,-0.0605332516,-0.169031918,0.3744505346,-0.1667570919,0.2543700635,0.2177440226,-0.3104316592,0.4162537158,0.2674728036,0.6729348302,-0.0182513837,0.0839984864,-0.1400039196,0.0010156956,-0.2245403677,0.2880353928,-0.1542477608,0.1733601391,-0.3427264392,-0.0123510053,-0.1521170139,0.333489269,-0.1336866617,-0.2490088791,0.0189019199,0.0414581671,-0.0060942615,-0.6088420153,-0.2313154191,0.4572477639,-0.1192472279,0.2423422784,-0.0001132663,0.0449169613,0.3025875092,-0.0090298932,0.0343071297,-0.0876949951,-0.2307193577,-0.2086026073,0.205450058,-0.5455300808,0.0810967162,0.0083245039,0.0779942423,-0.2727372646,0.2467460781,-0.2837902308,0.2267532796,0.3224823475,-0.4963641465,-0.1730308235,0.0051274141,-0.1487742811,-0.2355037779,-0.0538833328,0.4061657488,-0.0787890255,-0.3605757654,0.5466031432,0.0331227109,0.1048731655,0.0424411297,0.0684083477,0.9416398406,-0.3111875951,0.2162596583,0.5596245527,-0.0025465908,-0.403375566,-0.2936964333,-0.1252213269,0.1010691598,-0.2364679128,-0.1962261945,-0.1318239421,-0.4667769074,0.0572201423,-0.6661195159,0.2062962949,0.0636753291,-0.1756588966,-0.1616928428,0.1324810833,0.1472689211,0.175353691,0.048448775,-0.0012471844,-0.0412459597,0.0121267904,0.1333086044,0.0419924408,-0.2462373972,-0.2159461379,0.2609503269,-0.1506172419,0.0285712127,0.0838271677,-0.1659707427,0.2715797424,-0.1430589855,0.2127112597,0.1533040553,-0.0197043065,0.2840502262,-0.0085473321,0.0391802825,-0.3217777312,-0.1140473261,0.063157618,0.3379700184,-0.2410391718,0.5201619864,0.4561315179,-0.0073194918,0.0579646379,-0.0625929534,0.1070722118,0.2622315288,0.1972302049,-0.050163772,0.5713624954,0.2755984664,0.2373299748,0.0270602927,-0.1368020475,0.0655463561,-0.1194635928,-0.1515819579,-0.2082107812,-0.3167417049,0.1212709546,-0.3725419641,0.2893479168,0.0622703917,-0.0132815307,0.3908943534,0.0847775042,-0.0046318881,-0.1438905895,0.3979234993,0.3534492552,-0.0868191645,0.1720015407,0.142945081,-0.0032617503,-0.2947628498,0.4820659459,0.0177822802,-0.0298867673,-0.0325824879,0.1280913502,0.0457737334,0.2533397079,-0.1417144686,0.1408379674,0.0359459631,0.0297054537,0.258611083,-0.177757591,-0.4316973984,-0.3306638598,0.0106278807,0.1676989198,-0.1842898875,-0.2401033938,-0.1703087538,-0.0373635143,0.305379957,-0.0256110393,-0.2176831216,0.3224095404,-0.0214518029,0.3570397496,0.6694242954,-0.2494210154,-0.1231464222,0.469635278,-0.3237856627,-0.1044310108,0.1116032004,0.0702892765,0.3541651666,0.0041367575,-0.0440221578,-0.09036991,-0.1212184802,0.3055946529,-0.0965264514,-0.1311973631,0.225217849,0.0808431059,0.1800924391,0.043225605,-0.1003475562,-0.3944578767,0.4690576494,-0.0687007979,0.1408855915,0.105878599,0.036459282,-0.274223119,-0.2740654945,-0.1592959166,-0.1313504279,0.3375793397,-0.1488400847,0.0257428996,-0.237604931,-0.3887973726,-0.1905229688,0.2381239533,-0.0400184989,0.000591166,0.1616580188,-0.1433953643,-0.2247552127,-0.1466299444,-0.0102205034,-0.1418243498,-0.0116472915,-0.1284280717,0.2948251069,0.0363100208,0.1882728189,-0.1888583899,-0.0742232352,0.0934998617,0.1896006167,0.0957732946,-0.1555179656,0.4198977351,0.0185335502,0.1653473228,0.0407476798,0.126327157,-0.0886523649,-0.1433205456,-0.0579831526,0.1885076016,-0.0610125884,-0.06293156,0.0237915125,0.5794562101,0.0664438531,0.3983498812,-0.0122988941,0.1748764813,0.1880546212,-0.1832468957,-0.0905973166,-0.1429748237,-0.0115729617,0.4477988183,0.383444041,-0.1673359573,-0.2730872035,0.3041702211,0.5253994465,0.138740316,-0.0068682539,-0.0814467221,-0.1799041778,0.2821110785,0.1481162906,0.0180238858,0.1794286221,0.3364721835,0.1017204076,0.1056354865,-0.1593882143,-0.0237740036,0.0749181658,0.0301051624,-0.0055219666,0.016417196,-0.0589955263,-0.1441398263,-0.0818932801,-0.2558988333,0.0643959418,0.0688643083,-0.2571947575,-0.0724465474,-0.0504352711,-0.0811872184,0.0147793312,-0.192697987,-0.0432978719,-0.4102104008,0.16133596,0.0397835337,-0.2512671947,0.4082573056,-0.0590766892,-0.2016568184,0.1945892125,-0.0901442692,-0.0886031464,-0.3783821762,0.1800910234,0.0163422469,0.1017844379,0.0101125734,0.3469286561,0.0168993864,0.1499071568,-0.2127230465,0.0196143445,0.0517327711,-0.1476740092,-0.0338900983,0.0720474049,0.2288357466,-0.275609225,0.1529309005,0.245251745,-0.2540294528,-0.1178651676,0.0562493429,0.1432034671,-0.0320925787,-0.2721735835,0.1490704566,-0.1415087581,-0.3524087071,0.079158619,-0.3988301456,0.3791470826,-0.0064547686,-0.1370365322,0.0351493247,0.0223249495,-0.2152631879,-0.0719897747,-0.4446079731,0.0634605587,-0.1008327901,-0.073219806,-0.0186567698,0.0207427554,0.115023829,0.0150276115,-0.1133948267,-0.5253043175,-0.1396808326,0.4327143431,-0.2014661133,-0.1334905773,0.5469970703,0.1849723756,-0.26857391,-0.0150798149,-0.2984207869,0.0338325128,0.1508781612,0.0023235672,-0.2157335579,0.1579723954,-0.0173316039,0.7264468074,0.2910222113,-0.126720041,-0.114783816,-0.1962477267,0.1106468216,0.1317901909,-0.0290173069,-0.4755138755,-0.03745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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2327","title":"A syntax error in example","comments":"cc @beurkinger but I think this has been fixed internally and will soon be updated right ?","body":"![image](https:\/\/user-images.githubusercontent.com\/6883957\/117315905-b47a5c00-aeba-11eb-91eb-b2a4a0212a56.png)\r\n\r\nSorry to report with an image, I can't find the template source code of this snippet.","comment_length":17,"text":"A syntax error in example \n ![image](https:\/\/user-images.githubusercontent.com\/6883957\/117315905-b47a5c00-aeba-11eb-91eb-b2a4a0212a56.png)\r\n\r\nSorry to report with an image, I can't find the template source code of this snippet. \n cc @beurkinger but I think this has been fixed internally and will soon be updated right ?","embeddings":[0.0715632811,-0.4951067865,-0.1797281951,-0.1540903896,0.0774730146,-0.2150441259,0.1975012869,0.2825195789,-0.3641221225,0.1687918156,0.2558269501,0.3997045755,0.0529384203,0.0039085206,0.0124356346,-0.1999744922,0.0728849694,0.2567012608,0.0948015749,0.0897849798,-0.1415306926,0.4616273344,-0.2060655206,0.1898974925,-0.1933685988,-0.0379654169,0.0317138806,0.3373529017,-0.2273010314,-0.0470030457,0.0010915148,0.064178206,-0.1115034297,0.0319319181,-0.0000933617,0.0573273972,0.4510402381,-0.0477175824,-0.0617727712,-0.0962015763,-0.3083204925,0.1501756608,-0.0974037796,-0.2114107609,-0.2634438872,-0.0932573527,0.1575714946,0.225198999,0.6356444359,0.1518195719,0.4007506669,-0.0871496126,0.0372064859,-0.2963175476,0.1952328831,0.1279038936,-0.0493087508,-0.0032211577,0.3966778517,-0.0520781614,0.3011013269,0.7066950202,0.1491575241,-0.0617366955,0.1567130387,-0.0642567426,0.0901056379,-0.1462881416,0.1106479466,0.2388547957,0.1958262771,-0.004171296,-0.0273603406,0.1946362555,-0.2383274287,0.0582046025,0.104520224,0.1131511033,-0.0325616226,0.1628715843,-0.1777629256,-0.3388653398,-0.4302212298,0.0763217136,0.1298819184,0.1238824949,-0.2709282637,-0.0848228782,-0.0750402808,-0.0733413398,0.0690799356,-0.0947975218,-0.1755657643,0.032806363,0.3898117542,0.0336384363,0.2636396885,-0.1664782315,0.0907298997,0.2093675584,0.3645391166,0.1617857963,0.044568494,0.4841555059,-0.059862718,-0.2177202255,0.1092422828,0.0952872112,0.2170198262,0.2489142716,0.1988035738,-0.1722972095,-0.2023472637,-0.4475755095,-0.1721684784,-0.0195734091,0.1949792057,0.0756070614,-0.0213516075,-0.1340056956,0.0416855104,0.0311296862,-0.0521577597,0.2935717702,0.2862028182,-0.2618910074,0.2229367793,-0.0731547251,-0.0839980394,-0.2374912649,-0.3668646514,0.4344537556,-0.3073208928,-0.3818004131,-0.2056149691,-0.099942334,0.1797950417,0.0794072598,0.3844331205,-0.1954474896,0.2693597972,0.1235044524,0.1823216081,0.0405629426,-0.0613383763,-0.0378034748,0.1874403358,-0.140776962,0.0633979663,0.2403682172,-0.3574039936,0.0198715869,-0.3501662314,0.3257005811,0.0071030757,0.0871215984,-0.0015992568,0.1873692721,0.2495847344,-0.017472405,0.0723603144,-0.0797396749,0.0049851905,-0.1812770367,0.0313532576,0.1445374489,-0.1001026705,-0.0282105524,0.0006581482,-0.1491847038,0.1289750934,0.1001662761,-0.085537374,0.2190530449,-0.3449908793,0.3650205135,0.1883977503,-0.2218052745,0.2569124997,0.3305473626,-0.0898411795,-0.1762397736,-0.1385308057,-0.1838117838,-0.0027715589,-0.0424129516,-0.076133959,0.2300001532,0.0800398514,0.1793266833,-0.2126228958,0.0947306901,-0.1867510974,-0.0717876181,-0.0061205765,-0.0782548711,0.1166824177,0.3036964536,0.1652324796,-0.318279922,-0.0693618283,0.1438332051,0.4180040359,-0.1671893597,0.010069483,0.0409813747,0.1267256588,-0.1673319787,-0.2309223115,0.0167770237,-0.0212326106,0.1463430226,-0.2395848334,-0.0800207183,-0.1342269331,-0.3119918108,0.4072885513,0.0106123425,-0.016910756,-0.1834714264,0.0215902235,0.0133522497,-0.2049185336,-0.0322175324,0.025399331,0.049333401,-0.3071591258,-0.0733259171,0.0361094326,-0.2548871338,0.0245670862,-0.1094739214,-0.1592314988,0.4442049861,0.1413282901,0.0414529964,-0.0152209299,0.0102455113,-0.0901464671,-0.5474354029,-0.0892877877,0.1133851111,0.063894704,-0.0388867743,-0.2313878387,0.1792845875,0.1370069832,-0.0850085989,0.104477331,0.0620643087,0.0435256027,0.0470072515,-0.081009686,-0.1532190442,-0.0685969591,0.194492057,-0.0653938949,-0.3109587431,-0.3392188847,-0.088443853,0.203523308,0.0368342586,0.2094865739,0.0864068642,0.1118897274,-0.0480871908,0.1330356151,-0.1928911656,0.0090983538,0.1427801847,-0.0994561166,-0.0307848882,0.0792718157,-0.3007695675,0.3233331442,0.1179037541,0.1392183453,-0.0968128815,0.0771728456,-0.1826923937,-0.2567153573,-0.1586351693,-0.1180126369,-0.0927415043,-0.170383811,0.2097693831,-0.1907847524,-0.2285812646,0.0054413294,0.121594049,-0.0043110652,-0.2447490394,0.3674565554,-0.0179614741,-0.174718678,0.2753228247,0.1864567846,0.121570386,0.0565675907,0.2379210442,-0.2804912925,0.0258785095,-0.1376214027,0.3204863369,0.0677374303,-0.2399541438,0.1588524431,-0.1785077453,0.2150992751,-0.1911414862,-0.6027473807,0.2668872476,0.0793695301,0.1450598538,0.3221103549,0.2325113714,-0.0992604196,0.1294579506,0.3499355018,-0.0739874393,-0.2206541598,0.0096322596,0.0324231088,-0.0643796548,-0.4315524399,0.2253299654,0.1619851887,-0.2519227266,-0.1134442315,0.2032539546,0.2085518837,0.499602437,0.5264030695,0.1237835735,-0.1164513379,0.0399327874,0.0033893934,-0.315153867,0.1725258529,-0.3455632627,-0.2890867591,-0.1712598205,0.0984721258,0.1768655628,-0.1460026205,0.0523329452,-0.3894007802,0.0924828947,-0.0556530654,-0.0745255426,-0.0114935422,-0.0547323339,-0.0129131963,-0.4187718332,-0.5239934325,0.1694461256,0.1538664848,-0.3789800406,0.1989573538,0.0611085333,-0.087557666,0.2441907227,0.4768975377,0.0424322933,-0.0711891651,0.2790886164,0.0041550957,0.3523954153,-0.0233964939,0.1190907583,0.2066285461,0.363634944,-0.01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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2323","title":"load_dataset(\"timit_asr\") gives back duplicates of just one sample text","comments":"Thanks @ekeleshian for having reported.\r\n\r\nI am closing this issue once that you updated `datasets`. Feel free to reopen it if the problem persists.","body":"## Describe the bug\r\nWhen you look up on key [\"train\"] and then ['text'], you get back a list  with just one sentence duplicated 4620 times. Namely, the sentence \"Would such an act of refusal be useful?\". Similarly when you look up ['test'] and then ['text'], the list is one sentence repeated \"The bungalow was pleasantly situated near the shore.\" 1680 times. \r\n\r\nI tried to work around the issue by downgrading to datasets version 1.3.0, inspired by [this post](https:\/\/www.gitmemory.com\/issue\/huggingface\/datasets\/2052\/798904836) and removing the entire huggingface directory from ~\/.cache, but I still get the same issue.  \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\")\r\nprint(timit['train']['text'])\r\nprint(timit['test']['text'])\r\n```\r\n\r\n## Expected Result\r\nRows of diverse text, like how it is shown in the [wav2vec2.0 tutorial](https:\/\/colab.research.google.com\/github\/patrickvonplaten\/notebooks\/blob\/master\/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb)\r\n<img width=\"485\" alt=\"Screen Shot 2021-05-05 at 9 09 57 AM\" src=\"https:\/\/user-images.githubusercontent.com\/33647474\/117146094-d9b77f00-ad81-11eb-8306-f281850c127a.png\">\r\n\r\n\r\n## Actual results\r\nRows of repeated text.\r\n<img width=\"319\" alt=\"Screen Shot 2021-05-05 at 9 11 53 AM\" src=\"https:\/\/user-images.githubusercontent.com\/33647474\/117146231-f8b61100-ad81-11eb-834a-fc10410b0c9c.png\">\r\n\r\n\r\n## Versions\r\n- Datasets: 1.3.0\r\n- Python: 3.9.1\r\n- Platform: macOS-11.2.1-x86_64-i386-64bit}\r\n","comment_length":24,"text":"load_dataset(\"timit_asr\") gives back duplicates of just one sample text \n ## Describe the bug\r\nWhen you look up on key [\"train\"] and then ['text'], you get back a list  with just one sentence duplicated 4620 times. Namely, the sentence \"Would such an act of refusal be useful?\". Similarly when you look up ['test'] and then ['text'], the list is one sentence repeated \"The bungalow was pleasantly situated near the shore.\" 1680 times. \r\n\r\nI tried to work around the issue by downgrading to datasets version 1.3.0, inspired by [this post](https:\/\/www.gitmemory.com\/issue\/huggingface\/datasets\/2052\/798904836) and removing the entire huggingface directory from ~\/.cache, but I still get the same issue.  \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\")\r\nprint(timit['train']['text'])\r\nprint(timit['test']['text'])\r\n```\r\n\r\n## Expected Result\r\nRows of diverse text, like how it is shown in the [wav2vec2.0 tutorial](https:\/\/colab.research.google.com\/github\/patrickvonplaten\/notebooks\/blob\/master\/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb)\r\n<img width=\"485\" alt=\"Screen Shot 2021-05-05 at 9 09 57 AM\" src=\"https:\/\/user-images.githubusercontent.com\/33647474\/117146094-d9b77f00-ad81-11eb-8306-f281850c127a.png\">\r\n\r\n\r\n## Actual results\r\nRows of repeated text.\r\n<img width=\"319\" alt=\"Screen Shot 2021-05-05 at 9 11 53 AM\" src=\"https:\/\/user-images.githubusercontent.com\/33647474\/117146231-f8b61100-ad81-11eb-834a-fc10410b0c9c.png\">\r\n\r\n\r\n## Versions\r\n- Datasets: 1.3.0\r\n- Python: 3.9.1\r\n- Platform: macOS-11.2.1-x86_64-i386-64bit}\r\n \n Thanks @ekeleshian for having reported.\r\n\r\nI am closing this issue once that you updated `datasets`. Feel free to reopen it if the problem persists.","embeddings":[0.2821879089,-0.4890296757,0.0636294037,0.3835485876,0.1550767869,0.0093223024,0.2025510371,0.1971771866,-0.1960430145,0.0225637201,-0.0537860543,0.2588163614,-0.0607607588,-0.0019165596,0.1897579134,0.0341104642,0.1301131546,-0.0039515593,-0.0855849162,-0.2164427489,-0.0349717848,0.5018290877,-0.1255064309,0.039624732,-0.3864379525,0.0896656886,-0.2256180942,0.0954400674,0.0274894368,-0.4388808608,0.2015984505,0.2562795579,0.0076610618,0.1743332148,-0.0001294062,-0.1238207743,0.103335917,0.1966973394,-0.2948189676,-0.337613523,0.0371453874,-0.0082015544,0.476550132,0.0588161834,-0.1446308494,-0.1229952723,0.0461377911,-0.0911030397,0.5953748822,0.0530859642,0.0554759242,-0.0213040039,-0.2731775641,-0.1502211094,0.30012393,0.1408754438,0.0361843631,0.0638974905,0.2574379146,0.0740013942,0.0937062427,0.4073674381,-0.2614638805,0.0803650841,0.0201924555,0.2505540252,-0.3521604538,-0.2842356563,0.1567363143,0.3818914294,0.4144081473,-0.1949874014,-0.2438047528,-0.5332146287,0.161943242,0.0539523214,0.1597354859,-0.0827361718,-0.239992395,0.132328406,-0.272169441,-0.0274029933,-0.0044496772,0.0038483818,0.1031661034,-0.2842705548,-0.1285128742,0.1618483514,0.3656548262,-0.0383118913,-0.0753812864,-0.0201201346,-0.0413320735,0.1814562082,-0.4986139834,0.0300714653,0.0270303711,-0.0612340719,0.2511675954,0.0161169358,0.4327557385,-0.0579363778,-0.1301839948,-0.0773007572,0.41262725,0.3775301576,0.0727243051,-0.0954676718,0.0092438171,-0.1186076403,-0.2098524123,0.0403310657,-0.0111010782,0.1610854268,0.0967437252,-0.2585274279,0.3425212801,-0.3690470159,-0.4510843158,0.0706897229,-0.5093212724,-0.1209715977,-0.1602241993,0.093509011,0.1373853534,0.3813998401,0.1867179424,0.1462110281,-0.1727949232,-0.2743343115,-0.134309411,-0.1454474628,-0.0307364892,0.1082837433,0.1738090068,-0.5300574899,0.2907099128,0.3179664016,0.341807574,-0.4182767868,-0.1233289689,-0.0930299759,-0.1239289194,0.0292858966,-0.0926786661,0.4591899216,0.0831208751,-0.0351972915,0.0112058921,-0.0125043103,-0.2134313434,-0.1196761578,0.2937436998,-0.0058470955,-0.2225892097,-0.0508112013,0.0654349998,0.3292100132,0.0748777837,0.1093474776,-0.1791653037,-0.0059522255,-0.4267409444,-0.1410837471,0.0414114632,0.3480868042,-0.2709081471,-0.0908948332,0.2106882185,0.2735222876,-0.0548251383,0.5467896461,-0.0427984148,0.4276206791,-0.2642585337,0.0796886086,0.0182303637,-0.4098272324,-0.2936970294,0.2606933713,-0.1333964318,0.3572215736,0.2087152153,0.1109746322,0.2958289087,0.1419264823,0.1747303605,0.0112375775,0.3328603804,-0.1269817948,-0.2913201153,-0.2165751457,0.0697632954,0.0462378487,-0.3213202953,0.213571474,0.0712145939,0.0611044355,0.4559496045,0.0401258767,-0.0278414022,0.0761946365,0.1083892062,0.0714350268,0.3267050087,-0.057660155,-0.2763386369,0.0627625287,0.1535568237,0.0899405926,0.1892700791,-0.0697184727,-0.2410974801,-0.0870902613,-0.3644341528,-0.1309642196,-0.0653520897,0.3186425269,0.0336826257,0.0968320817,-0.070033446,0.487472713,-0.1397870034,0.0868068263,-0.3534024656,0.0399518311,0.1423211247,0.078276664,0.2095784694,0.3197647631,0.2262900472,0.1240757406,0.0834152624,0.2634825408,0.029740002,0.1141453683,-0.1574550569,-0.212614432,0.1984330416,-0.2226008475,-0.0833782852,0.1785469055,0.2592630982,-0.1271862537,-0.2556278706,0.195391506,0.1241352111,0.2335562855,-0.0585896485,0.021532936,0.0859504044,-0.0684443489,-0.1082612723,-0.2807025909,0.5420552492,-0.1498948187,0.218012318,0.1538831294,-0.3125824928,0.2360616326,0.2239007503,-0.0717561767,-0.0559593439,0.3721970022,-0.1574176699,-0.0650917515,-0.1354452372,0.0964777842,0.3051019311,0.1097943187,0.0447403416,0.027225472,-0.1308660805,0.045597259,0.2455358952,-0.0921415836,-0.0883161649,0.3082364798,0.3380037546,-0.0945692807,-0.598446548,0.1292411536,-0.0442171581,0.2760109007,-0.3640033603,0.012894542,-0.3676787019,0.0385611914,-0.3294149637,-0.026490096,-0.2005130351,-0.1136622652,0.188616544,0.3473803699,0.2270390242,0.0175411087,0.2276711464,0.3561970294,-0.0489334911,0.2410355657,-0.08919736,0.1284423023,-0.3455183506,-0.0424879417,-0.0230716765,0.0373932831,0.2573551238,-0.1349247098,-0.3074090779,-0.4002928138,-0.2227882594,0.253131032,-0.2650930285,0.410592556,0.3220893443,-0.0436666459,-0.4077951908,-0.2074154466,0.0193402227,-0.0297960863,-0.401899308,0.2117161304,-0.0361040346,0.0585928671,-0.0396243893,-0.5401884317,0.0536182858,0.050110504,0.5322161913,0.0152634261,0.0834391713,0.2894309461,0.0147456396,0.0821889639,-0.188985303,0.1407686025,-0.5706542134,-0.2644888163,0.1145198941,0.155229792,-0.2780033648,-0.2397743165,0.0120847588,-0.0123424381,-0.1511860639,-0.5954256654,-0.262006253,-0.1077622399,0.0324840359,-0.1540211886,0.3147455752,0.22699669,0.1243949309,0.0203050952,-0.216123566,0.0191078782,0.5199985504,0.1948654354,0.1195437759,0.107226871,0.1179140136,-0.0223576836,0.3257386088,0.8683472276,0.1731048524,0.2302144766,-0.1726848483,0.1653012335,-0.2504996061,-0.3475958109,-0.2155773193,-0.2688345611,0.0127172098,0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2322","title":"Calls to map are not cached.","comments":"I tried upgrading to `datasets==1.6.2` and downgrading to `1.6.0`. Both versions produce the same output.\r\n\r\nDowngrading to `1.5.0` works and produces the following output for me:\r\n\r\n```bash\r\nDownloading: 9.20kB [00:00, 3.94MB\/s]                   \r\nDownloading: 5.99kB [00:00, 3.29MB\/s]                   \r\nNo config specified, defaulting to: sst\/default\r\nDownloading and preparing dataset sst\/default (download: 6.83 MiB, generated: 3.73 MiB, post-processed: Unknown size, total: 10.56 MiB) to \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b...\r\n                                    Dataset sst downloaded and prepared to \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b. Subsequent calls will reuse this data.\r\nexecuted [0, 1]\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 94.83ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 92.75ba\/s]\r\nexecuted [0, 1]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 118.81ba\/s]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 123.06ba\/s]\r\nexecuted [0, 1]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 119.42ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 123.33ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\nexecuted [0, 1]\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-6079777aa097c8f8.arrow\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-2dc05c46f68eda6e.arrow\r\nexecuted [0, 1]\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-1ca347e7430b98f1.arrow\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-c0f1a73ce3ba40cd.arrow\r\nexecuted [0, 1]\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-832a1407bf1ac5b7.arrow\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-036316a259b773c4.arrow\r\n- Datasets: 1.5.0\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```","body":"## Describe the bug\r\nSomehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport datasets\r\ndatasets.set_caching_enabled(True)\r\nsst = datasets.load_dataset(\"sst\")\r\n\r\ndef foo(samples, i):\r\n    print(\"executed\", i[:10])\r\n    return samples\r\n\r\n# first call\r\nx = sst.map(foo, batched=True, with_indices=True,  num_proc=2)\r\n\r\nprint('\\n'*3, \"#\" * 30, '\\n'*3)\r\n\r\n# second call\r\ny = sst.map(foo, batched=True, with_indices=True, num_proc=2)\r\n\r\n# print version\r\nimport sys\r\nimport platform\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n\r\n## Actual results\r\nThis code prints the following output for me:\r\n```bash\r\nNo config specified, defaulting to: sst\/default\r\nReusing dataset sst (\/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 59.85ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 60.85ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 69.32ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 70.93ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 63.25ba\/s]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 57.69ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 58.10ba\/s]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 57.19ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 60.10ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 53.82ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 72.76ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 71.55ba\/s]\r\n\r\n- Datasets: 1.6.1\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```\r\n\r\n## Expected results\r\nCaching should work.\r\n\r\n","comment_length":387,"text":"Calls to map are not cached. \n ## Describe the bug\r\nSomehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport datasets\r\ndatasets.set_caching_enabled(True)\r\nsst = datasets.load_dataset(\"sst\")\r\n\r\ndef foo(samples, i):\r\n    print(\"executed\", i[:10])\r\n    return samples\r\n\r\n# first call\r\nx = sst.map(foo, batched=True, with_indices=True,  num_proc=2)\r\n\r\nprint('\\n'*3, \"#\" * 30, '\\n'*3)\r\n\r\n# second call\r\ny = sst.map(foo, batched=True, with_indices=True, num_proc=2)\r\n\r\n# print version\r\nimport sys\r\nimport platform\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n\r\n## Actual results\r\nThis code prints the following output for me:\r\n```bash\r\nNo config specified, defaulting to: sst\/default\r\nReusing dataset sst (\/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 59.85ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 60.85ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 69.32ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 70.93ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 63.25ba\/s]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 57.69ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 58.10ba\/s]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 57.19ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 60.10ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 53.82ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 72.76ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 71.55ba\/s]\r\n\r\n- Datasets: 1.6.1\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```\r\n\r\n## Expected results\r\nCaching should work.\r\n\r\n \n I tried upgrading to `datasets==1.6.2` and downgrading to `1.6.0`. Both versions produce the same output.\r\n\r\nDowngrading to `1.5.0` works and produces the following output for me:\r\n\r\n```bash\r\nDownloading: 9.20kB [00:00, 3.94MB\/s]                   \r\nDownloading: 5.99kB [00:00, 3.29MB\/s]                   \r\nNo config specified, defaulting to: sst\/default\r\nDownloading and preparing dataset sst\/default (download: 6.83 MiB, generated: 3.73 MiB, post-processed: Unknown size, total: 10.56 MiB) to \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b...\r\n                                    Dataset sst downloaded and prepared to \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b. Subsequent calls will reuse this data.\r\nexecuted [0, 1]\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 94.83ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 92.75ba\/s]\r\nexecuted [0, 1]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 118.81ba\/s]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 123.06ba\/s]\r\nexecuted [0, 1]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 119.42ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 123.33ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\nexecuted [0, 1]\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-6079777aa097c8f8.arrow\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-2dc05c46f68eda6e.arrow\r\nexecuted [0, 1]\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-1ca347e7430b98f1.arrow\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-c0f1a73ce3ba40cd.arrow\r\nexecuted [0, 1]\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-832a1407bf1ac5b7.arrow\r\nLoading cached processed dataset at \/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b\/cache-036316a259b773c4.arrow\r\n- Datasets: 1.5.0\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```","embeddings":[-0.165298298,-0.3959718347,-0.0088477768,0.1456610411,0.2062435001,-0.1019177139,0.300267756,0.125128895,0.395308286,0.0206203312,-0.0369784422,0.2428549081,0.1699713916,-0.2849351764,0.1880867928,0.2596046329,0.2713772058,-0.038533818,-0.2219654024,-0.1745347381,-0.191881597,0.1723637879,-0.3064852655,-0.2573413253,-0.2379022092,0.1117418483,-0.2603811026,-0.1230313405,0.4237754643,-0.3212160468,0.3784762025,-0.0580661073,0.0148669453,0.6959587932,-0.0001131878,-0.1605277658,0.1358755231,-0.1383192092,-0.0015481036,0.2133312672,-0.3389544785,-0.3100157976,-0.0045190621,-0.2349566966,-0.3307382464,0.0151236551,-0.0239326023,-0.8197697997,0.3728456795,0.2541063726,0.2951067388,0.1726136208,-0.0190224797,0.0742856041,0.0866029933,-0.0837912038,-0.2725945115,-0.0186919551,0.2995275855,-0.197439,-0.0282074399,0.3594809175,-0.1319518536,0.0735728815,0.3317604065,0.3265019655,0.2180390209,-0.1185917407,0.2567963302,-0.2258388698,0.3444477916,-0.2505610287,-0.3402968347,-0.2521976233,-0.4512898028,-0.0935974792,0.2305346429,-0.1816348881,-0.0175113846,0.0523241237,-0.3951409459,0.1461097598,0.2768736184,0.0239340328,0.0391482264,0.2585591078,-0.0857637897,0.106143713,0.0440758094,-0.1575079411,0.0185330994,-0.2476874739,-0.0233257357,0.3876006305,-0.4578020573,0.1309818476,0.4648628831,0.1431973726,-0.1110443696,-0.0129453996,0.1193559542,-0.0060807476,0.186362043,0.0010308042,0.0615783408,0.489025861,-0.0103559745,0.3551560938,-0.0375442989,-0.0719547942,-0.3230222762,-0.1323278099,0.2659285069,-0.1008695662,0.5175330639,0.1616133898,0.2005919665,-0.1302777082,0.1896832585,-0.0005333381,-0.1633596718,-0.1748707294,0.1617176831,0.3469945192,0.0869997665,-0.0365331583,-0.0770053044,-0.1087396294,-0.2207536846,-0.0763380229,-0.3961371481,-0.2438569218,-0.3070540428,0.1495587081,0.1974380314,-0.0326647386,0.1355968267,0.0273349602,-0.1005734578,-0.3355165124,0.0546230711,-0.1876348406,0.6559554338,0.1588800251,-0.2145817876,0.2533616722,0.3517921865,0.1798379868,-0.3027566373,0.2812997997,-0.4556754231,-0.0874435082,0.4142129421,0.1292607486,-0.0999099314,-0.0748003125,-0.1504984051,0.1253661066,0.7231861949,-0.1573019922,0.2668023705,-0.212902382,-0.4821093976,-0.3187292218,0.3494810164,0.4953725636,-0.1281026602,-0.1405409575,0.1062854603,0.3853788376,0.368337363,0.2536809444,-0.2394357473,0.1112239361,-0.2686232924,0.1886626929,0.2342705131,-0.3849431574,-0.6794064641,-0.0250380412,-0.0827804059,0.1595269293,0.0307165533,-0.0343516469,0.3004794419,-0.1870463789,0.1719581485,-0.0572245158,-0.0018519368,0.3272252679,-0.3206143677,-0.1073742956,0.0927475914,-0.115470849,-0.0071227793,0.1117239818,-0.0307562873,-0.1718794703,0.0220948569,-0.0848008692,0.0942297727,0.2804448307,0.1836043298,0.0743450895,0.1448784471,0.0220392961,-0.3660166264,0.3349871337,0.0531432256,-0.3603878915,0.0600706153,-0.1577845663,0.0208380874,0.0101065636,-0.0955998302,-0.3363130689,0.1041886955,0.3796642721,0.4487681389,-0.0369240418,-0.0252826866,0.1748471856,0.4177247584,-0.1355367303,-0.1835998148,-0.0341757238,-0.0137684839,-0.3760502636,-0.2427925766,0.0894411206,0.274984926,-0.0927510336,-0.249263674,0.288713038,0.0752177611,0.2719340324,-0.2303870916,0.0465388559,-0.1450292915,0.0545007698,0.1124541909,0.2869496942,0.0986518934,-0.1674384177,-0.0972822234,0.1450366974,0.1808003634,0.2196065933,-0.1466811597,-0.2114744335,-0.0159513969,-0.0938480347,0.0052856077,-0.1132846177,0.1361349821,-0.3531874716,0.2921642065,0.1115515679,0.0568295792,0.5075974464,0.6477879882,0.2599563897,0.0253585055,-0.1637759805,-0.1948089898,-0.1878933311,0.2220143676,0.0681649148,0.3480646014,0.1523018777,0.2674678266,-0.1897209287,0.181784898,-0.1461721808,0.176724419,-0.06012474,-0.2066122741,-0.0782416388,0.4010895789,-0.085679546,-0.1126346886,0.2867837846,0.0554506108,0.0140278377,-0.2849522233,0.2493801564,-0.2281689495,-0.0199214369,-0.0153332986,-0.0118861217,0.0360353887,-0.2648227811,-0.1972463578,0.216418758,0.1073125973,0.2796123028,-0.2293336987,0.0865707621,0.1439069062,-0.3094993234,-0.2070796043,-0.1622292995,-0.2965812981,-0.0212498065,-0.0704682246,-0.4137971997,0.3088332415,-0.1025654525,-0.0244361833,-0.1753551662,-0.0770955682,-0.1091737226,-0.0907441601,-0.1531941891,-0.194892019,-0.0244986359,-0.3243738711,0.1080334857,0.4173314869,-0.4949834049,-0.1076486185,-0.0787559971,0.1874899119,-0.0431405939,-0.2904832661,-0.0876899511,-0.014073424,-0.1836277992,0.1808856279,-0.0643402934,0.0390369818,0.1268297136,-0.0566194095,0.0236133356,-0.1371994764,-0.1245088875,-0.5041562319,-0.29039976,0.3763309121,-0.0780996829,-0.2742153406,0.0592228547,0.1451711059,0.0341395289,0.1656787694,-0.5678583384,-0.1953779012,-0.2156901658,0.3442631662,0.033339832,0.1807537973,0.5501797795,-0.0730526298,-0.0768213198,-0.2849739492,-0.3120509088,0.0548588224,0.1322661787,0.2266615331,0.1739227325,0.324097842,0.0204187445,0.7732428312,0.393682152,-0.0856743157,0.2908105254,0.0597865619,0.180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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2322","title":"Calls to map are not cached.","comments":"Hi,\r\n\r\nset `keep_in_memory` to False when loading a dataset (`sst = load_dataset(\"sst\", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them):\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/241a0b4a3a868778ee91e767ad406f9da7610df2\/src\/datasets\/arrow_dataset.py#L1718\r\n\r\n@albertvillanova It seems like this behavior was overlooked in #2182.\r\n\r\n","body":"## Describe the bug\r\nSomehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport datasets\r\ndatasets.set_caching_enabled(True)\r\nsst = datasets.load_dataset(\"sst\")\r\n\r\ndef foo(samples, i):\r\n    print(\"executed\", i[:10])\r\n    return samples\r\n\r\n# first call\r\nx = sst.map(foo, batched=True, with_indices=True,  num_proc=2)\r\n\r\nprint('\\n'*3, \"#\" * 30, '\\n'*3)\r\n\r\n# second call\r\ny = sst.map(foo, batched=True, with_indices=True, num_proc=2)\r\n\r\n# print version\r\nimport sys\r\nimport platform\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n\r\n## Actual results\r\nThis code prints the following output for me:\r\n```bash\r\nNo config specified, defaulting to: sst\/default\r\nReusing dataset sst (\/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 59.85ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 60.85ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 69.32ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 70.93ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 63.25ba\/s]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 57.69ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 58.10ba\/s]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 57.19ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 60.10ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 53.82ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 72.76ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 71.55ba\/s]\r\n\r\n- Datasets: 1.6.1\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```\r\n\r\n## Expected results\r\nCaching should work.\r\n\r\n","comment_length":46,"text":"Calls to map are not cached. \n ## Describe the bug\r\nSomehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport datasets\r\ndatasets.set_caching_enabled(True)\r\nsst = datasets.load_dataset(\"sst\")\r\n\r\ndef foo(samples, i):\r\n    print(\"executed\", i[:10])\r\n    return samples\r\n\r\n# first call\r\nx = sst.map(foo, batched=True, with_indices=True,  num_proc=2)\r\n\r\nprint('\\n'*3, \"#\" * 30, '\\n'*3)\r\n\r\n# second call\r\ny = sst.map(foo, batched=True, with_indices=True, num_proc=2)\r\n\r\n# print version\r\nimport sys\r\nimport platform\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n\r\n## Actual results\r\nThis code prints the following output for me:\r\n```bash\r\nNo config specified, defaulting to: sst\/default\r\nReusing dataset sst (\/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 59.85ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 60.85ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 69.32ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 70.93ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 63.25ba\/s]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 57.69ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 58.10ba\/s]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 57.19ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 60.10ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 53.82ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 72.76ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 71.55ba\/s]\r\n\r\n- Datasets: 1.6.1\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```\r\n\r\n## Expected results\r\nCaching should work.\r\n\r\n \n Hi,\r\n\r\nset `keep_in_memory` to False when loading a dataset (`sst = load_dataset(\"sst\", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them):\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/241a0b4a3a868778ee91e767ad406f9da7610df2\/src\/datasets\/arrow_dataset.py#L1718\r\n\r\n@albertvillanova It seems like this behavior was overlooked in #2182.\r\n\r\n","embeddings":[-0.165298298,-0.3959718347,-0.0088477768,0.1456610411,0.2062435001,-0.1019177139,0.300267756,0.125128895,0.395308286,0.0206203312,-0.0369784422,0.2428549081,0.1699713916,-0.2849351764,0.1880867928,0.2596046329,0.2713772058,-0.038533818,-0.2219654024,-0.1745347381,-0.191881597,0.1723637879,-0.3064852655,-0.2573413253,-0.2379022092,0.1117418483,-0.2603811026,-0.1230313405,0.4237754643,-0.3212160468,0.3784762025,-0.0580661073,0.0148669453,0.6959587932,-0.0001131878,-0.1605277658,0.1358755231,-0.1383192092,-0.0015481036,0.2133312672,-0.3389544785,-0.3100157976,-0.0045190621,-0.2349566966,-0.3307382464,0.0151236551,-0.0239326023,-0.8197697997,0.3728456795,0.2541063726,0.2951067388,0.1726136208,-0.0190224797,0.0742856041,0.0866029933,-0.0837912038,-0.2725945115,-0.0186919551,0.2995275855,-0.197439,-0.0282074399,0.3594809175,-0.1319518536,0.0735728815,0.3317604065,0.3265019655,0.2180390209,-0.1185917407,0.2567963302,-0.2258388698,0.3444477916,-0.2505610287,-0.3402968347,-0.2521976233,-0.4512898028,-0.0935974792,0.2305346429,-0.1816348881,-0.0175113846,0.0523241237,-0.3951409459,0.1461097598,0.2768736184,0.0239340328,0.0391482264,0.2585591078,-0.0857637897,0.106143713,0.0440758094,-0.1575079411,0.0185330994,-0.2476874739,-0.0233257357,0.3876006305,-0.4578020573,0.1309818476,0.4648628831,0.1431973726,-0.1110443696,-0.0129453996,0.1193559542,-0.0060807476,0.186362043,0.0010308042,0.0615783408,0.489025861,-0.0103559745,0.3551560938,-0.0375442989,-0.0719547942,-0.3230222762,-0.1323278099,0.2659285069,-0.1008695662,0.5175330639,0.1616133898,0.2005919665,-0.1302777082,0.1896832585,-0.0005333381,-0.1633596718,-0.1748707294,0.1617176831,0.3469945192,0.0869997665,-0.0365331583,-0.0770053044,-0.1087396294,-0.2207536846,-0.0763380229,-0.3961371481,-0.2438569218,-0.3070540428,0.1495587081,0.1974380314,-0.0326647386,0.1355968267,0.0273349602,-0.1005734578,-0.3355165124,0.0546230711,-0.1876348406,0.6559554338,0.1588800251,-0.2145817876,0.2533616722,0.3517921865,0.1798379868,-0.3027566373,0.2812997997,-0.4556754231,-0.0874435082,0.4142129421,0.1292607486,-0.0999099314,-0.0748003125,-0.1504984051,0.1253661066,0.7231861949,-0.1573019922,0.2668023705,-0.212902382,-0.4821093976,-0.3187292218,0.3494810164,0.4953725636,-0.1281026602,-0.1405409575,0.1062854603,0.3853788376,0.368337363,0.2536809444,-0.2394357473,0.1112239361,-0.2686232924,0.1886626929,0.2342705131,-0.3849431574,-0.6794064641,-0.0250380412,-0.0827804059,0.1595269293,0.0307165533,-0.0343516469,0.3004794419,-0.1870463789,0.1719581485,-0.0572245158,-0.0018519368,0.3272252679,-0.3206143677,-0.1073742956,0.0927475914,-0.115470849,-0.0071227793,0.1117239818,-0.0307562873,-0.1718794703,0.0220948569,-0.0848008692,0.0942297727,0.2804448307,0.1836043298,0.0743450895,0.1448784471,0.0220392961,-0.3660166264,0.3349871337,0.0531432256,-0.3603878915,0.0600706153,-0.1577845663,0.0208380874,0.0101065636,-0.0955998302,-0.3363130689,0.1041886955,0.3796642721,0.4487681389,-0.0369240418,-0.0252826866,0.1748471856,0.4177247584,-0.1355367303,-0.1835998148,-0.0341757238,-0.0137684839,-0.3760502636,-0.2427925766,0.0894411206,0.274984926,-0.0927510336,-0.249263674,0.288713038,0.0752177611,0.2719340324,-0.2303870916,0.0465388559,-0.1450292915,0.0545007698,0.1124541909,0.2869496942,0.0986518934,-0.1674384177,-0.0972822234,0.1450366974,0.1808003634,0.2196065933,-0.1466811597,-0.2114744335,-0.0159513969,-0.0938480347,0.0052856077,-0.1132846177,0.1361349821,-0.3531874716,0.2921642065,0.1115515679,0.0568295792,0.5075974464,0.6477879882,0.2599563897,0.0253585055,-0.1637759805,-0.1948089898,-0.1878933311,0.2220143676,0.0681649148,0.3480646014,0.1523018777,0.2674678266,-0.1897209287,0.181784898,-0.1461721808,0.176724419,-0.06012474,-0.2066122741,-0.0782416388,0.4010895789,-0.085679546,-0.1126346886,0.2867837846,0.0554506108,0.0140278377,-0.2849522233,0.2493801564,-0.2281689495,-0.0199214369,-0.0153332986,-0.0118861217,0.0360353887,-0.2648227811,-0.1972463578,0.216418758,0.1073125973,0.2796123028,-0.2293336987,0.0865707621,0.1439069062,-0.3094993234,-0.2070796043,-0.1622292995,-0.2965812981,-0.0212498065,-0.0704682246,-0.4137971997,0.3088332415,-0.1025654525,-0.0244361833,-0.1753551662,-0.0770955682,-0.1091737226,-0.0907441601,-0.1531941891,-0.194892019,-0.0244986359,-0.3243738711,0.1080334857,0.4173314869,-0.4949834049,-0.1076486185,-0.0787559971,0.1874899119,-0.0431405939,-0.2904832661,-0.0876899511,-0.014073424,-0.1836277992,0.1808856279,-0.0643402934,0.0390369818,0.1268297136,-0.0566194095,0.0236133356,-0.1371994764,-0.1245088875,-0.5041562319,-0.29039976,0.3763309121,-0.0780996829,-0.2742153406,0.0592228547,0.1451711059,0.0341395289,0.1656787694,-0.5678583384,-0.1953779012,-0.2156901658,0.3442631662,0.033339832,0.1807537973,0.5501797795,-0.0730526298,-0.0768213198,-0.2849739492,-0.3120509088,0.0548588224,0.1322661787,0.2266615331,0.1739227325,0.324097842,0.0204187445,0.7732428312,0.393682152,-0.0856743157,0.2908105254,0.0597865619,0.1808590144,-0.3642299771,-0.3403165042,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2322","title":"Calls to map are not cached.","comments":"Hi @villmow, thanks for reporting. \r\n\r\nAs @mariosasko has pointed out, we did not consider this case when introducing the feature of automatic in-memory for small datasets. This needs to be fixed.","body":"## Describe the bug\r\nSomehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport datasets\r\ndatasets.set_caching_enabled(True)\r\nsst = datasets.load_dataset(\"sst\")\r\n\r\ndef foo(samples, i):\r\n    print(\"executed\", i[:10])\r\n    return samples\r\n\r\n# first call\r\nx = sst.map(foo, batched=True, with_indices=True,  num_proc=2)\r\n\r\nprint('\\n'*3, \"#\" * 30, '\\n'*3)\r\n\r\n# second call\r\ny = sst.map(foo, batched=True, with_indices=True, num_proc=2)\r\n\r\n# print version\r\nimport sys\r\nimport platform\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n\r\n## Actual results\r\nThis code prints the following output for me:\r\n```bash\r\nNo config specified, defaulting to: sst\/default\r\nReusing dataset sst (\/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 59.85ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 60.85ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 69.32ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 70.93ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 63.25ba\/s]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 57.69ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 58.10ba\/s]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 57.19ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 60.10ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 53.82ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 72.76ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 71.55ba\/s]\r\n\r\n- Datasets: 1.6.1\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```\r\n\r\n## Expected results\r\nCaching should work.\r\n\r\n","comment_length":31,"text":"Calls to map are not cached. \n ## Describe the bug\r\nSomehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport datasets\r\ndatasets.set_caching_enabled(True)\r\nsst = datasets.load_dataset(\"sst\")\r\n\r\ndef foo(samples, i):\r\n    print(\"executed\", i[:10])\r\n    return samples\r\n\r\n# first call\r\nx = sst.map(foo, batched=True, with_indices=True,  num_proc=2)\r\n\r\nprint('\\n'*3, \"#\" * 30, '\\n'*3)\r\n\r\n# second call\r\ny = sst.map(foo, batched=True, with_indices=True, num_proc=2)\r\n\r\n# print version\r\nimport sys\r\nimport platform\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n\r\n## Actual results\r\nThis code prints the following output for me:\r\n```bash\r\nNo config specified, defaulting to: sst\/default\r\nReusing dataset sst (\/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 59.85ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 60.85ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 69.32ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 70.93ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 63.25ba\/s]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 57.69ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 58.10ba\/s]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 57.19ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 60.10ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 53.82ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 72.76ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 71.55ba\/s]\r\n\r\n- Datasets: 1.6.1\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```\r\n\r\n## Expected results\r\nCaching should work.\r\n\r\n \n Hi @villmow, thanks for reporting. \r\n\r\nAs @mariosasko has pointed out, we did not consider this case when introducing the feature of automatic in-memory for small datasets. This needs to be 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2322","title":"Calls to map are not cached.","comments":"Hi ! Currently a dataset that is in memory doesn't know doesn't know in which directory it has to read\/write cache files.\r\nOn the other hand, a dataset that loaded from the disk (via memory mapping) uses the directory from which the dataset is located to read\/write cache files.\r\n\r\nBecause of that, currently in-memory datasets simply don't use caching.\r\n\r\nMaybe a Dataset object could have a `cache_dir` that is set to the directory where the arrow files are created during `load_dataset` ?","body":"## Describe the bug\r\nSomehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport datasets\r\ndatasets.set_caching_enabled(True)\r\nsst = datasets.load_dataset(\"sst\")\r\n\r\ndef foo(samples, i):\r\n    print(\"executed\", i[:10])\r\n    return samples\r\n\r\n# first call\r\nx = sst.map(foo, batched=True, with_indices=True,  num_proc=2)\r\n\r\nprint('\\n'*3, \"#\" * 30, '\\n'*3)\r\n\r\n# second call\r\ny = sst.map(foo, batched=True, with_indices=True, num_proc=2)\r\n\r\n# print version\r\nimport sys\r\nimport platform\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n\r\n## Actual results\r\nThis code prints the following output for me:\r\n```bash\r\nNo config specified, defaulting to: sst\/default\r\nReusing dataset sst (\/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 59.85ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 60.85ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 69.32ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 70.93ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 63.25ba\/s]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 57.69ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 58.10ba\/s]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 57.19ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 60.10ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 53.82ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 72.76ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 71.55ba\/s]\r\n\r\n- Datasets: 1.6.1\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```\r\n\r\n## Expected results\r\nCaching should work.\r\n\r\n","comment_length":82,"text":"Calls to map are not cached. \n ## Describe the bug\r\nSomehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport datasets\r\ndatasets.set_caching_enabled(True)\r\nsst = datasets.load_dataset(\"sst\")\r\n\r\ndef foo(samples, i):\r\n    print(\"executed\", i[:10])\r\n    return samples\r\n\r\n# first call\r\nx = sst.map(foo, batched=True, with_indices=True,  num_proc=2)\r\n\r\nprint('\\n'*3, \"#\" * 30, '\\n'*3)\r\n\r\n# second call\r\ny = sst.map(foo, batched=True, with_indices=True, num_proc=2)\r\n\r\n# print version\r\nimport sys\r\nimport platform\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n\r\n## Actual results\r\nThis code prints the following output for me:\r\n```bash\r\nNo config specified, defaulting to: sst\/default\r\nReusing dataset sst (\/home\/johannes\/.cache\/huggingface\/datasets\/sst\/default\/1.0.0\/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 59.85ba\/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 60.85ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 69.32ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 70.93ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 63.25ba\/s]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 57.69ba\/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\n#0:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/5 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 58.10ba\/s]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5\/5 [00:00<00:00, 57.19ba\/s]\r\n#0:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/1 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 60.10ba\/s]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 53.82ba\/s]\r\n#0:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\n#1:   0%|          | 0\/2 [00:00<?, ?ba\/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 72.76ba\/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 71.55ba\/s]\r\n\r\n- Datasets: 1.6.1\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```\r\n\r\n## Expected results\r\nCaching should work.\r\n\r\n \n Hi ! Currently a dataset that is in memory doesn't know doesn't know in which directory it has to read\/write cache files.\r\nOn the other hand, a dataset that loaded from the disk (via memory mapping) uses the directory from which the dataset is located to read\/write cache files.\r\n\r\nBecause of that, currently in-memory datasets simply don't use caching.\r\n\r\nMaybe a Dataset object could have a `cache_dir` that is set to the directory where the arrow files are created during `load_dataset` ?","embeddings":[-0.165298298,-0.3959718347,-0.0088477768,0.1456610411,0.2062435001,-0.1019177139,0.300267756,0.125128895,0.395308286,0.0206203312,-0.0369784422,0.2428549081,0.1699713916,-0.2849351764,0.1880867928,0.2596046329,0.2713772058,-0.038533818,-0.2219654024,-0.1745347381,-0.191881597,0.1723637879,-0.3064852655,-0.2573413253,-0.2379022092,0.1117418483,-0.2603811026,-0.1230313405,0.4237754643,-0.3212160468,0.3784762025,-0.0580661073,0.0148669453,0.6959587932,-0.0001131878,-0.1605277658,0.1358755231,-0.1383192092,-0.0015481036,0.2133312672,-0.3389544785,-0.3100157976,-0.0045190621,-0.2349566966,-0.3307382464,0.0151236551,-0.0239326023,-0.8197697997,0.3728456795,0.2541063726,0.2951067388,0.1726136208,-0.0190224797,0.0742856041,0.0866029933,-0.0837912038,-0.2725945115,-0.0186919551,0.2995275855,-0.197439,-0.0282074399,0.3594809175,-0.1319518536,0.0735728815,0.3317604065,0.3265019655,0.2180390209,-0.1185917407,0.2567963302,-0.2258388698,0.3444477916,-0.2505610287,-0.3402968347,-0.2521976233,-0.4512898028,-0.0935974792,0.2305346429,-0.1816348881,-0.0175113846,0.0523241237,-0.3951409459,0.1461097598,0.2768736184,0.0239340328,0.0391482264,0.2585591078,-0.0857637897,0.106143713,0.0440758094,-0.1575079411,0.0185330994,-0.2476874739,-0.0233257357,0.3876006305,-0.4578020573,0.1309818476,0.4648628831,0.1431973726,-0.1110443696,-0.0129453996,0.1193559542,-0.0060807476,0.186362043,0.0010308042,0.0615783408,0.489025861,-0.0103559745,0.3551560938,-0.0375442989,-0.0719547942,-0.3230222762,-0.1323278099,0.2659285069,-0.1008695662,0.5175330639,0.1616133898,0.2005919665,-0.1302777082,0.1896832585,-0.0005333381,-0.1633596718,-0.1748707294,0.1617176831,0.3469945192,0.0869997665,-0.0365331583,-0.0770053044,-0.1087396294,-0.2207536846,-0.0763380229,-0.3961371481,-0.2438569218,-0.3070540428,0.1495587081,0.1974380314,-0.0326647386,0.1355968267,0.0273349602,-0.1005734578,-0.3355165124,0.0546230711,-0.1876348406,0.6559554338,0.1588800251,-0.2145817876,0.2533616722,0.3517921865,0.1798379868,-0.3027566373,0.2812997997,-0.4556754231,-0.0874435082,0.4142129421,0.1292607486,-0.0999099314,-0.0748003125,-0.1504984051,0.1253661066,0.7231861949,-0.1573019922,0.2668023705,-0.212902382,-0.4821093976,-0.3187292218,0.3494810164,0.4953725636,-0.1281026602,-0.1405409575,0.1062854603,0.3853788376,0.368337363,0.2536809444,-0.2394357473,0.1112239361,-0.2686232924,0.1886626929,0.2342705131,-0.3849431574,-0.6794064641,-0.0250380412,-0.0827804059,0.1595269293,0.0307165533,-0.0343516469,0.3004794419,-0.1870463789,0.1719581485,-0.0572245158,-0.0018519368,0.3272252679,-0.3206143677,-0.1073742956,0.0927475914,-0.115470849,-0.0071227793,0.1117239818,-0.0307562873,-0.1718794703,0.0220948569,-0.0848008692,0.0942297727,0.2804448307,0.1836043298,0.0743450895,0.1448784471,0.0220392961,-0.3660166264,0.3349871337,0.0531432256,-0.3603878915,0.0600706153,-0.1577845663,0.0208380874,0.0101065636,-0.0955998302,-0.3363130689,0.1041886955,0.3796642721,0.4487681389,-0.0369240418,-0.0252826866,0.1748471856,0.4177247584,-0.1355367303,-0.1835998148,-0.0341757238,-0.0137684839,-0.3760502636,-0.2427925766,0.0894411206,0.274984926,-0.0927510336,-0.249263674,0.288713038,0.0752177611,0.2719340324,-0.2303870916,0.0465388559,-0.1450292915,0.0545007698,0.1124541909,0.2869496942,0.0986518934,-0.1674384177,-0.0972822234,0.1450366974,0.1808003634,0.2196065933,-0.1466811597,-0.2114744335,-0.0159513969,-0.0938480347,0.0052856077,-0.1132846177,0.1361349821,-0.3531874716,0.2921642065,0.1115515679,0.0568295792,0.5075974464,0.6477879882,0.2599563897,0.0253585055,-0.1637759805,-0.1948089898,-0.1878933311,0.2220143676,0.0681649148,0.3480646014,0.1523018777,0.2674678266,-0.1897209287,0.181784898,-0.1461721808,0.176724419,-0.06012474,-0.2066122741,-0.0782416388,0.4010895789,-0.085679546,-0.1126346886,0.2867837846,0.0554506108,0.0140278377,-0.2849522233,0.2493801564,-0.2281689495,-0.0199214369,-0.0153332986,-0.0118861217,0.0360353887,-0.2648227811,-0.1972463578,0.216418758,0.1073125973,0.2796123028,-0.2293336987,0.0865707621,0.1439069062,-0.3094993234,-0.2070796043,-0.1622292995,-0.2965812981,-0.0212498065,-0.0704682246,-0.4137971997,0.3088332415,-0.1025654525,-0.0244361833,-0.1753551662,-0.0770955682,-0.1091737226,-0.0907441601,-0.1531941891,-0.194892019,-0.0244986359,-0.3243738711,0.1080334857,0.4173314869,-0.4949834049,-0.1076486185,-0.0787559971,0.1874899119,-0.0431405939,-0.2904832661,-0.0876899511,-0.014073424,-0.1836277992,0.1808856279,-0.0643402934,0.0390369818,0.1268297136,-0.0566194095,0.0236133356,-0.1371994764,-0.1245088875,-0.5041562319,-0.29039976,0.3763309121,-0.0780996829,-0.2742153406,0.0592228547,0.1451711059,0.0341395289,0.1656787694,-0.5678583384,-0.1953779012,-0.2156901658,0.3442631662,0.033339832,0.1807537973,0.5501797795,-0.0730526298,-0.0768213198,-0.2849739492,-0.3120509088,0.0548588224,0.1322661787,0.2266615331,0.1739227325,0.324097842,0.0204187445,0.7732428312,0.393682152,-0.0856743157,0.2908105254,0.0597865619,0.1808590144,-0.3642299771,-0.3403165042,0.0350094289,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2319","title":"UnicodeDecodeError for OSCAR (Afrikaans)","comments":"Thanks for reporting, @sgraaf.\r\n\r\nI am going to have a look at it. \r\n\r\nI guess the expected codec is \"UTF-8\". Normally, when no explicitly codec is passed, Python uses one which is platform-dependent. For Linux machines, the default codec is `utf_8`, which is OK. However for Windows machine, the default codec is `cp1252`, which causes the problem.","body":"## Describe the bug\r\nWhen loading the [OSCAR dataset](https:\/\/huggingface.co\/datasets\/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_af\")\r\n```\r\n\r\n## Expected results\r\nAnything but an error, really.\r\n\r\n## Actual results\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_af\")\r\nDownloading: 14.7kB [00:00, 4.91MB\/s]\r\nDownloading: 3.07MB [00:00, 32.6MB\/s]\r\nDownloading and preparing dataset oscar\/unshuffled_deduplicated_af (download: 62.93 MiB, generated: 163.38 MiB, post-processed: Unknown size, total: 226.32 MiB) to C:\\Users\\sgraaf\\.cache\\huggingface\\datasets\\oscar\\unshuffled_deduplicated_af\\1.0.0\\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464...\r\nDownloading: 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100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 66.0M\/66.0M [00:18<00:00, 3.50MB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\load.py\", line 745, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 979, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tqdm\\std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\Users\\sgraaf\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\oscar\\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464\\oscar.py\", line 359, in _generate_examples\r\n    for line in f:\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\encodings\\cp1252.py\", line 23, in decode\r\n    return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 7454: character maps to <undefined>\r\n```\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n- Datasets: 1.6.2\r\n- Python: 3.9.4 (tags\/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]\r\n- Platform: Windows-10-10.0.19041-SP0","comment_length":57,"text":"UnicodeDecodeError for OSCAR (Afrikaans) \n ## Describe the bug\r\nWhen loading the [OSCAR dataset](https:\/\/huggingface.co\/datasets\/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_af\")\r\n```\r\n\r\n## Expected results\r\nAnything but an error, really.\r\n\r\n## Actual results\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_af\")\r\nDownloading: 14.7kB [00:00, 4.91MB\/s]\r\nDownloading: 3.07MB [00:00, 32.6MB\/s]\r\nDownloading and preparing dataset oscar\/unshuffled_deduplicated_af (download: 62.93 MiB, generated: 163.38 MiB, post-processed: Unknown size, total: 226.32 MiB) to C:\\Users\\sgraaf\\.cache\\huggingface\\datasets\\oscar\\unshuffled_deduplicated_af\\1.0.0\\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 81.0\/81.0 [00:00<00:00, 40.5kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 66.0M\/66.0M [00:18<00:00, 3.50MB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\load.py\", line 745, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 979, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tqdm\\std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\Users\\sgraaf\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\oscar\\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464\\oscar.py\", line 359, in _generate_examples\r\n    for line in f:\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\encodings\\cp1252.py\", line 23, in decode\r\n    return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 7454: character maps to <undefined>\r\n```\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n- Datasets: 1.6.2\r\n- Python: 3.9.4 (tags\/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]\r\n- Platform: Windows-10-10.0.19041-SP0 \n Thanks for reporting, @sgraaf.\r\n\r\nI am going to have a look at it. \r\n\r\nI guess the expected codec is \"UTF-8\". Normally, when no explicitly codec is passed, Python uses one which is platform-dependent. For Linux machines, the default codec is `utf_8`, which is OK. However for Windows machine, the default codec is `cp1252`, which causes the problem.","embeddings":[-0.0694424137,-0.0689756423,-0.0686594769,0.5462196469,0.4998692274,0.0235804226,0.1604425609,0.1600644588,-0.2532077134,0.2616168559,0.108140856,0.0745659769,-0.1329873204,-0.1621266156,0.0694556683,-0.2411268204,0.0238732174,-0.0095882108,0.1490591168,-0.1138725877,-0.3042188883,0.3047537208,-0.146704644,0.1465251744,-0.338201195,0.1170730814,0.0047265538,0.2575016916,-0.1414587945,-0.3523659706,0.1280923933,0.0163177848,0.1069401726,0.2742300928,-0.0001185049,-0.0547297038,0.4611512125,-0.1315254271,-0.3736711144,0.0470520742,-0.2316299528,0.1640679091,-0.1074656174,-0.0671967864,0.1775648743,-0.2891407609,-0.0651192144,-0.5603585839,0.018415628,0.322922349,0.1736242771,0.1185969636,0.1850381345,0.0575322583,-0.1859926581,-0.1070656851,-0.0142301479,0.0994632468,0.2410674989,0.1616299301,-0.0901438296,0.1463269442,0.1150426418,0.0891946778,0.1299639195,0.0778747648,-0.1804778725,-0.2484130412,0.3013782501,0.1579844356,0.3460531831,-0.4334869385,-0.386044234,-0.3108896613,0.1961866468,-0.5193542242,0.540989697,-0.0237485357,-0.1159027442,0.2356258333,-0.0887801498,0.078484565,-0.1790719777,0.2531147897,-0.2060515881,0.0477679521,-0.2439979166,-0.00667615,-0.1567632258,-0.109207958,0.0756576806,-0.3706617951,-0.0559559315,0.3437899053,-0.179793492,0.1441420764,-0.0641237795,0.1609281451,0.2896220088,0.2038017958,0.1995994896,-0.0058476198,-0.3779214919,-0.0436835438,0.1444817781,0.3555548787,-0.0572909042,-0.0332700424,0.2502485216,0.3896280825,0.0751844347,-0.0033730227,-0.1319143623,-0.2046168447,0.2316856533,0.0210273936,0.5934262872,-0.2065752447,-0.2736486793,0.2963079214,-0.1085711718,-0.1333846599,-0.1332821995,0.1001211107,-0.1807520241,0.208304435,-0.1411847919,0.263507992,-0.0920619071,-0.3318838179,-0.1641348898,-0.2244988978,-0.0947454721,0.104287982,0.0092695877,0.0026700087,0.1869610399,0.0432528481,0.3022830784,-0.0885123089,-0.0725461096,-0.1705320179,-0.0138035817,0.0101132309,0.0544214398,0.2440924793,0.0441389903,-0.4594571888,0.0363978371,0.0950224996,-0.0368120633,-0.2857238352,-0.190805763,0.1487388909,-0.1962005198,-0.05188182,-0.2405536324,0.2764452696,0.1800576001,0.0541132875,0.2551351488,-0.0168271754,-0.0834420025,-0.1390443891,0.2472237051,0.5666908622,-0.3448715806,-0.0487917103,-0.1240096763,-0.2066462934,0.3429841101,0.2263717651,0.0529710837,-0.0220640656,-0.3649465442,0.1118094996,0.1237515584,-0.246365875,-0.543892324,0.1850887686,-0.0684071407,0.5069641471,0.1817364693,-0.1587031037,0.2452930361,-0.0106923943,0.2900948226,0.4672313631,-0.2506344914,-0.0112695359,-0.253282845,-0.2451152056,0.0314227976,0.2176154852,0.0890725926,0.0002360298,0.1373472661,0.2070339918,0.3857545555,-0.104228802,0.1944541037,0.1247579083,0.0118005583,0.3576553762,0.0679639205,-0.2294073999,-0.282086283,0.2640694678,-0.0024920979,0.1133578196,-0.5008912683,-0.1440782249,-0.3841666877,-0.133248657,-0.4603827298,-0.407979995,0.0958776325,0.2784141898,0.1699373871,0.5170188546,-0.355160147,0.5020518303,-0.0425743796,-0.0007125136,-0.5245204568,0.2013834864,0.1213725284,-0.2475564778,-0.1367651522,0.2605232596,-0.0419438966,-0.0601239018,-0.3732912242,0.4005674124,-0.0718277916,0.2017915398,0.0296432581,-0.0306066349,0.3568613827,-0.2730566859,-0.0263988581,0.2786277831,0.2903375924,-0.0451177098,0.1648098528,0.2245022357,0.0779714659,0.1424601823,0.0697221607,-0.0256782267,0.2891086042,0.1915865093,-0.0097916322,-0.216820851,0.2216823101,-0.2710436583,0.5030974746,0.0332432501,-0.2335601598,-0.0266738199,0.6612030268,-0.1373115629,0.126795128,0.26213485,-0.1895713508,0.1265211254,-0.1143813953,0.1996651441,0.383133918,-0.0028643475,-0.0537522845,0.154264167,0.2330895215,-0.1416677535,0.1646430194,-0.0629854426,0.2722617984,0.1404127479,-0.1023301706,0.0954846963,-0.4391815364,-0.2598067224,-0.1511851847,0.2690092325,-0.3030655384,0.0640029982,-0.3796649575,-0.0206939261,-0.1904437095,0.0154795432,-0.3886276186,-0.1819051802,-0.1342800707,-0.0216699317,0.0310603883,0.1142271087,-0.3544104993,-0.0076761735,0.0488115102,-0.1199690923,-0.2093363106,-0.1015667543,-0.3103099763,0.0438975021,0.3231027424,-0.0926567391,0.2462631166,-0.2765359282,-0.0418532155,-0.1076047346,-0.221381247,0.0839273483,-0.0488724895,0.0420714654,0.0048807901,-0.1037534922,-0.1238439083,-0.219120428,0.2420269549,-0.0446107425,-0.2791256309,-0.0322687142,0.2220712453,0.0287394859,0.0866416246,-0.136137262,-0.2833299041,-0.2109611034,0.1948475689,-0.0700791627,0.0065472955,-0.004164679,-0.1305136085,0.206185624,0.0590830483,0.0376083516,-0.3174690902,-0.157781139,0.4085605145,-0.310995847,-0.3512374461,-0.1355366409,-0.0830545723,-0.0184940137,-0.0540398359,-0.2928423882,-0.0980516225,-0.510127306,0.0637507737,-0.1237606332,-0.0457393974,0.119070597,-0.157507062,0.0576680489,-0.1093934625,-0.1019479036,-0.099363856,-0.2305915356,0.2241044194,0.0622040257,0.4960558414,0.0121934908,0.21265845,0.3057525158,0.2123214155,0.526016891,-0.0799332038,0.1849859506,-0.128806293,-0.1884320527,0.2252397984,-0.0842235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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2319","title":"UnicodeDecodeError for OSCAR (Afrikaans)","comments":"@sgraaf, I have just merged the fix in the master branch.\r\n\r\nYou can either:\r\n- install `datasets` from source code\r\n- wait until we make the next release of `datasets`\r\n- set the `utf-8` codec as your default instead of `cp1252`. This can be done by activating the Python [UTF-8 mode](https:\/\/www.python.org\/dev\/peps\/pep-0540) either by passing the command-line option `-X utf8` or by setting the environment variable `PYTHONUTF8=1`.","body":"## Describe the bug\r\nWhen loading the [OSCAR dataset](https:\/\/huggingface.co\/datasets\/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_af\")\r\n```\r\n\r\n## Expected results\r\nAnything but an error, really.\r\n\r\n## Actual results\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_af\")\r\nDownloading: 14.7kB [00:00, 4.91MB\/s]\r\nDownloading: 3.07MB [00:00, 32.6MB\/s]\r\nDownloading and preparing dataset oscar\/unshuffled_deduplicated_af (download: 62.93 MiB, generated: 163.38 MiB, post-processed: Unknown size, total: 226.32 MiB) to C:\\Users\\sgraaf\\.cache\\huggingface\\datasets\\oscar\\unshuffled_deduplicated_af\\1.0.0\\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 81.0\/81.0 [00:00<00:00, 40.5kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 66.0M\/66.0M [00:18<00:00, 3.50MB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\load.py\", line 745, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 979, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tqdm\\std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\Users\\sgraaf\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\oscar\\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464\\oscar.py\", line 359, in _generate_examples\r\n    for line in f:\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\encodings\\cp1252.py\", line 23, in decode\r\n    return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 7454: character maps to <undefined>\r\n```\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n- Datasets: 1.6.2\r\n- Python: 3.9.4 (tags\/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]\r\n- Platform: Windows-10-10.0.19041-SP0","comment_length":66,"text":"UnicodeDecodeError for OSCAR (Afrikaans) \n ## Describe the bug\r\nWhen loading the [OSCAR dataset](https:\/\/huggingface.co\/datasets\/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_af\")\r\n```\r\n\r\n## Expected results\r\nAnything but an error, really.\r\n\r\n## Actual results\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_af\")\r\nDownloading: 14.7kB [00:00, 4.91MB\/s]\r\nDownloading: 3.07MB [00:00, 32.6MB\/s]\r\nDownloading and preparing dataset oscar\/unshuffled_deduplicated_af (download: 62.93 MiB, generated: 163.38 MiB, post-processed: Unknown size, total: 226.32 MiB) to C:\\Users\\sgraaf\\.cache\\huggingface\\datasets\\oscar\\unshuffled_deduplicated_af\\1.0.0\\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 81.0\/81.0 [00:00<00:00, 40.5kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 66.0M\/66.0M [00:18<00:00, 3.50MB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\load.py\", line 745, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 652, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\datasets\\builder.py\", line 979, in _prepare_split\r\n    for key, record in utils.tqdm(\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tqdm\\std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\Users\\sgraaf\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\oscar\\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464\\oscar.py\", line 359, in _generate_examples\r\n    for line in f:\r\n  File \"C:\\Users\\sgraaf\\AppData\\Local\\Programs\\Python\\Python39\\lib\\encodings\\cp1252.py\", line 23, in decode\r\n    return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 7454: character maps to <undefined>\r\n```\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n- Datasets: 1.6.2\r\n- Python: 3.9.4 (tags\/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]\r\n- Platform: Windows-10-10.0.19041-SP0 \n @sgraaf, I have just merged the fix in the master branch.\r\n\r\nYou can either:\r\n- install `datasets` from source code\r\n- wait until we make the next release of `datasets`\r\n- set the `utf-8` codec as your default instead of `cp1252`. This can be done by activating the Python [UTF-8 mode](https:\/\/www.python.org\/dev\/peps\/pep-0540) either by passing the command-line option `-X utf8` or by setting the environment variable `PYTHONUTF8=1`.","embeddings":[-0.0694424137,-0.0689756423,-0.0686594769,0.5462196469,0.4998692274,0.0235804226,0.1604425609,0.1600644588,-0.2532077134,0.2616168559,0.108140856,0.0745659769,-0.1329873204,-0.1621266156,0.0694556683,-0.2411268204,0.0238732174,-0.0095882108,0.1490591168,-0.1138725877,-0.3042188883,0.3047537208,-0.146704644,0.1465251744,-0.338201195,0.1170730814,0.0047265538,0.2575016916,-0.1414587945,-0.3523659706,0.1280923933,0.0163177848,0.1069401726,0.2742300928,-0.0001185049,-0.0547297038,0.4611512125,-0.1315254271,-0.3736711144,0.0470520742,-0.2316299528,0.1640679091,-0.1074656174,-0.0671967864,0.1775648743,-0.2891407609,-0.0651192144,-0.5603585839,0.018415628,0.322922349,0.1736242771,0.1185969636,0.1850381345,0.0575322583,-0.1859926581,-0.1070656851,-0.0142301479,0.0994632468,0.2410674989,0.1616299301,-0.0901438296,0.1463269442,0.1150426418,0.0891946778,0.1299639195,0.0778747648,-0.1804778725,-0.2484130412,0.3013782501,0.1579844356,0.3460531831,-0.4334869385,-0.386044234,-0.3108896613,0.1961866468,-0.5193542242,0.540989697,-0.0237485357,-0.1159027442,0.2356258333,-0.0887801498,0.078484565,-0.1790719777,0.2531147897,-0.2060515881,0.0477679521,-0.2439979166,-0.00667615,-0.1567632258,-0.109207958,0.0756576806,-0.3706617951,-0.0559559315,0.3437899053,-0.179793492,0.1441420764,-0.0641237795,0.1609281451,0.2896220088,0.2038017958,0.1995994896,-0.0058476198,-0.3779214919,-0.0436835438,0.1444817781,0.3555548787,-0.0572909042,-0.0332700424,0.2502485216,0.3896280825,0.0751844347,-0.0033730227,-0.1319143623,-0.2046168447,0.2316856533,0.0210273936,0.5934262872,-0.2065752447,-0.2736486793,0.2963079214,-0.1085711718,-0.1333846599,-0.1332821995,0.1001211107,-0.1807520241,0.208304435,-0.1411847919,0.263507992,-0.0920619071,-0.3318838179,-0.1641348898,-0.2244988978,-0.0947454721,0.104287982,0.0092695877,0.0026700087,0.1869610399,0.0432528481,0.3022830784,-0.0885123089,-0.0725461096,-0.1705320179,-0.0138035817,0.0101132309,0.0544214398,0.2440924793,0.0441389903,-0.4594571888,0.0363978371,0.0950224996,-0.0368120633,-0.2857238352,-0.190805763,0.1487388909,-0.1962005198,-0.05188182,-0.2405536324,0.2764452696,0.1800576001,0.0541132875,0.2551351488,-0.0168271754,-0.0834420025,-0.1390443891,0.2472237051,0.5666908622,-0.3448715806,-0.0487917103,-0.1240096763,-0.2066462934,0.3429841101,0.2263717651,0.0529710837,-0.0220640656,-0.3649465442,0.1118094996,0.1237515584,-0.246365875,-0.543892324,0.1850887686,-0.0684071407,0.5069641471,0.1817364693,-0.1587031037,0.2452930361,-0.0106923943,0.2900948226,0.4672313631,-0.2506344914,-0.0112695359,-0.253282845,-0.2451152056,0.0314227976,0.2176154852,0.0890725926,0.0002360298,0.1373472661,0.2070339918,0.3857545555,-0.104228802,0.1944541037,0.1247579083,0.0118005583,0.3576553762,0.0679639205,-0.2294073999,-0.282086283,0.2640694678,-0.0024920979,0.1133578196,-0.5008912683,-0.1440782249,-0.3841666877,-0.133248657,-0.4603827298,-0.407979995,0.0958776325,0.2784141898,0.1699373871,0.5170188546,-0.355160147,0.5020518303,-0.0425743796,-0.0007125136,-0.5245204568,0.2013834864,0.1213725284,-0.2475564778,-0.1367651522,0.2605232596,-0.0419438966,-0.0601239018,-0.3732912242,0.4005674124,-0.0718277916,0.2017915398,0.0296432581,-0.0306066349,0.3568613827,-0.2730566859,-0.0263988581,0.2786277831,0.2903375924,-0.0451177098,0.1648098528,0.2245022357,0.0779714659,0.1424601823,0.0697221607,-0.0256782267,0.2891086042,0.1915865093,-0.0097916322,-0.216820851,0.2216823101,-0.2710436583,0.5030974746,0.0332432501,-0.2335601598,-0.0266738199,0.6612030268,-0.1373115629,0.126795128,0.26213485,-0.1895713508,0.1265211254,-0.1143813953,0.1996651441,0.383133918,-0.0028643475,-0.0537522845,0.154264167,0.2330895215,-0.1416677535,0.1646430194,-0.0629854426,0.2722617984,0.1404127479,-0.1023301706,0.0954846963,-0.4391815364,-0.2598067224,-0.1511851847,0.2690092325,-0.3030655384,0.0640029982,-0.3796649575,-0.0206939261,-0.1904437095,0.0154795432,-0.3886276186,-0.1819051802,-0.1342800707,-0.0216699317,0.0310603883,0.1142271087,-0.3544104993,-0.0076761735,0.0488115102,-0.1199690923,-0.2093363106,-0.1015667543,-0.3103099763,0.0438975021,0.3231027424,-0.0926567391,0.2462631166,-0.2765359282,-0.0418532155,-0.1076047346,-0.221381247,0.0839273483,-0.0488724895,0.0420714654,0.0048807901,-0.1037534922,-0.1238439083,-0.219120428,0.2420269549,-0.0446107425,-0.2791256309,-0.0322687142,0.2220712453,0.0287394859,0.0866416246,-0.136137262,-0.2833299041,-0.2109611034,0.1948475689,-0.0700791627,0.0065472955,-0.004164679,-0.1305136085,0.206185624,0.0590830483,0.0376083516,-0.3174690902,-0.157781139,0.4085605145,-0.310995847,-0.3512374461,-0.1355366409,-0.0830545723,-0.0184940137,-0.0540398359,-0.2928423882,-0.0980516225,-0.510127306,0.0637507737,-0.1237606332,-0.0457393974,0.119070597,-0.157507062,0.0576680489,-0.1093934625,-0.1019479036,-0.099363856,-0.2305915356,0.2241044194,0.0622040257,0.4960558414,0.0121934908,0.21265845,0.3057525158,0.2123214155,0.526016891,-0.0799332038,0.1849859506,-0.128806293,-0.1884320527,0.2252397984,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2318","title":"[api request] API to obtain \"dataset_module\" dynamic path?","comments":"Hi @richardliaw, \r\n\r\nFirst, thanks for the compliments.\r\n\r\nIn relation with your request, currently, the dynamic modules path is obtained this way:\r\n```python\r\nfrom datasets.load import init_dynamic_modules, MODULE_NAME_FOR_DYNAMIC_MODULES\r\n\r\ndynamic_modules_path = init_dynamic_modules(MODULE_NAME_FOR_DYNAMIC_MODULES)\r\n```\r\n\r\nLet me know if it is OK for you this way. \r\n\r\nI could set `MODULE_NAME_FOR_DYNAMIC_MODULES` as default value, so that you could instead obtain the path with:\r\n```\r\ndynamic_modules_path = datasets.load.init_dynamic_modules()\r\n```","body":"**Is your feature request related to a problem? Please describe.**\r\nA clear and concise description of what the problem is.\r\n\r\nThis is an awesome library. \r\n\r\nIt seems like the dynamic module path in this library has broken some of hyperparameter tuning functionality: https:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/34\r\n\r\nThis is because Ray will spawn new processes, and each process will load modules by path. However, we need to explicitly inform Ray to load the right modules, or else it will error upon import. \r\n\r\nI'd like an API to obtain the dynamic paths. This will allow us to support this functionality in this awesome library while being future proof.\r\n\r\n**Describe the solution you'd like**\r\nA clear and concise description of what you want to happen.\r\n\r\n`datasets.get_dynamic_paths -> List[str]` will be sufficient for my use case.\r\n\r\nBy offering this API, we will be able to address the following issues (by patching the ray integration sufficiently):\r\n\r\nhttps:\/\/github.com\/huggingface\/blog\/issues\/106\r\nhttps:\/\/github.com\/huggingface\/transformers\/issues\/11565\r\nhttps:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/34\r\nhttps:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/35\r\n\r\n","comment_length":63,"text":"[api request] API to obtain \"dataset_module\" dynamic path? \n **Is your feature request related to a problem? Please describe.**\r\nA clear and concise description of what the problem is.\r\n\r\nThis is an awesome library. \r\n\r\nIt seems like the dynamic module path in this library has broken some of hyperparameter tuning functionality: https:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/34\r\n\r\nThis is because Ray will spawn new processes, and each process will load modules by path. However, we need to explicitly inform Ray to load the right modules, or else it will error upon import. \r\n\r\nI'd like an API to obtain the dynamic paths. This will allow us to support this functionality in this awesome library while being future proof.\r\n\r\n**Describe the solution you'd like**\r\nA clear and concise description of what you want to happen.\r\n\r\n`datasets.get_dynamic_paths -> List[str]` will be sufficient for my use case.\r\n\r\nBy offering this API, we will be able to address the following issues (by patching the ray integration sufficiently):\r\n\r\nhttps:\/\/github.com\/huggingface\/blog\/issues\/106\r\nhttps:\/\/github.com\/huggingface\/transformers\/issues\/11565\r\nhttps:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/34\r\nhttps:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/35\r\n\r\n \n Hi @richardliaw, \r\n\r\nFirst, thanks for the compliments.\r\n\r\nIn relation with your request, currently, the dynamic modules path is obtained this way:\r\n```python\r\nfrom datasets.load import init_dynamic_modules, MODULE_NAME_FOR_DYNAMIC_MODULES\r\n\r\ndynamic_modules_path = init_dynamic_modules(MODULE_NAME_FOR_DYNAMIC_MODULES)\r\n```\r\n\r\nLet me know if it is OK for you this way. \r\n\r\nI could set `MODULE_NAME_FOR_DYNAMIC_MODULES` as default value, so that you could instead obtain the path with:\r\n```\r\ndynamic_modules_path = datasets.load.init_dynamic_modules()\r\n```","embeddings":[-0.1038029417,-0.2795985937,-0.1310288161,0.0527429543,0.3254469931,-0.2634874582,-0.0613957755,0.1563359052,-0.2127547264,0.3393896222,-0.0749454126,0.7778300047,-0.4725230038,0.3166542649,0.2836638987,-0.4223529398,-0.1075101271,0.0503048524,-0.2626203299,0.0036111402,-0.1428721696,0.2316277176,-0.084783338,0.2267727405,0.0757528618,-0.2109445781,-0.0228330232,0.0175177902,-0.138037622,-0.3746137917,0.2695262134,0.1497001052,0.2535261214,0.2199819386,-0.0001094077,0.0686421096,0.0977332592,-0.0621917918,-0.385055393,-0.5734552741,0.1486319751,-0.1373895407,0.1928843558,-0.2899366617,-0.0638167486,-0.274437964,-0.0536290333,-0.7661019564,0.291287601,0.063306205,0.2035726011,0.3836931884,-0.2115834653,0.0141160162,0.0111685703,0.2845671475,-0.1351316273,-0.1789131463,0.1712046117,0.1474252492,-0.0069576534,0.2718309462,-0.0836598128,0.1184935868,0.5701004267,0.092134282,-0.4181138277,-0.0629471838,0.1164077893,0.3929319978,0.4341738224,-0.4605739713,-0.3385897577,-0.3974283934,-0.2387160212,-0.1298523992,0.1684211493,0.1541239619,-0.2948129475,0.1381622702,0.0280635785,-0.1709858924,-0.2388433367,-0.0365590118,0.1353721321,0.0721849278,0.0338096544,0.0492584147,0.2138145566,-0.2562360764,0.2530487478,0.0001356122,0.1630683988,0.3501803577,-0.1332852989,0.0437952392,0.2593631446,0.0081867296,0.0949191153,0.1528411061,0.0803343207,0.07105726,-0.1307509691,-0.0033430359,0.1743541211,-0.0688316077,0.1667058021,0.266246289,0.0422718786,0.2100567371,0.0943904743,-0.0139658703,-0.0658002421,0.1338001788,-0.0473947525,-0.0295378156,0.0953877941,0.1512423158,-0.1286311597,-0.2674999237,-0.3199114501,-0.2002335787,0.3884600103,0.5135529637,-0.0828726888,0.0863124654,0.2151854038,-0.2800034285,0.1129065976,0.0486184992,-0.1234717444,0.1900316179,-0.1880220175,0.1826998442,0.1336837262,-0.1558955312,0.3612341881,-0.2448184192,0.2501977682,-0.0375415795,0.1097567528,0.211521998,0.2910254896,0.0866850987,-0.0058065658,-0.0145474803,-0.2215718031,0.1193017662,-0.4832944274,0.0427645445,-0.2613914013,-0.4978818297,0.0284053702,0.1506852806,-0.1773408055,-0.1017165482,-0.265321821,0.1563615948,-0.352812171,0.0960061997,-0.0530509278,0.0474222191,-0.2700793743,-0.1650848687,0.273958385,0.3499602973,-0.0629201978,-0.2511388361,0.0323060714,0.0200772565,-0.1705696881,-0.0503633469,-0.480982095,0.217200622,-0.1995638609,0.111890547,0.4243351817,-0.3662731946,0.1924604923,-0.0459742136,-0.059218064,-0.2813163102,0.0999210626,-0.0642255843,0.0895033553,0.0366246998,0.1906103939,0.4679450095,0.2107554525,-0.0519933738,0.0623168536,-0.4383768439,-0.1321052164,0.0569139458,0.0535727851,-0.098087281,0.042050872,-0.0495754778,0.5005158186,-0.0934973136,-0.0479240566,0.2800538242,0.2826190293,0.2624945641,0.2190205753,-0.4658496976,-0.1698426157,0.2003215104,-0.1806832254,0.004847005,-0.2005132735,-0.1883307993,0.0107190842,0.2374990433,0.0430157296,0.2332790941,0.1127445623,-0.0729937777,-0.0062701162,-0.0852662623,-0.4607096016,0.0859270841,-0.2912900746,0.1009320691,-0.2687898874,0.2666110992,0.1665558666,0.0113884602,-0.0941922814,0.306388706,0.0804970264,-0.1595148742,0.066316694,0.1853282154,0.1383262128,0.1945545971,-0.0541788638,0.4117592573,0.1305964142,-0.0497927703,0.1247769743,-0.1700545996,0.0635828003,0.3343411386,0.2617124915,0.4313472509,-0.3116960824,0.3364180326,0.2746247947,0.1848143488,0.2339006513,-0.0022998799,-0.3090560138,-0.0668614656,0.105562605,0.1788025945,0.023215035,-0.2177972645,-0.2678339779,0.1311531663,0.2675953507,0.0370154195,-0.0143411215,-0.0825007781,-0.1222535223,0.0992908403,0.1431242675,0.4640809298,0.2016625553,0.372367382,-0.0311848782,0.103514336,-0.1654464155,0.0827460364,0.508587718,0.5266926289,0.0420886427,-0.3552269638,-0.0315018892,0.0765445754,-0.1907278746,-0.364616096,-0.1555599123,-0.058780808,-0.3679342866,0.2199699581,-0.4546753764,-0.0473184288,-0.6218673587,-0.5643889308,0.0178714413,0.1321030408,-0.060395278,-0.3999856114,-0.1962894052,0.1978435069,0.0772585496,0.2225201875,-0.2102829069,-0.5583546162,-0.0730556175,-0.1039896309,-0.1112963036,-0.0039003547,0.1009964943,0.1162631139,0.4589595199,-0.0660966411,0.1545366645,-0.2830373645,-0.2296126336,0.420738101,0.0460110791,0.073922433,0.3897780776,0.0098355543,0.2122607678,-0.1580740362,0.076707907,-0.0547339618,-0.1132117584,-0.3992922306,0.0876093879,0.0643574595,-0.0876141563,-0.3364678025,-0.2850174308,-0.2404596359,0.1316914856,0.0220055655,0.3412692249,0.3866489828,0.0774681643,0.1976983249,-0.3972137272,0.3130909801,-0.0743631944,-0.0252564643,0.1658196896,-0.0637148693,-0.2479181886,0.1867426187,-0.1397674084,0.3825788796,0.2226330936,-0.174667418,-0.55777812,0.0760991797,0.3783728182,-0.115717873,0.1571434289,0.4352731705,0.3465403318,-0.0645105317,-0.0938565657,-0.6340901256,0.3788377643,-0.0431331247,0.1673584878,0.2510476112,0.3467082083,0.1777441055,0.7528575063,-0.181751579,-0.4236459732,0.0311472118,-0.1504568309,0.1973709315,0.2868965268,-0.2159771472,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2318","title":"[api request] API to obtain \"dataset_module\" dynamic path?","comments":"Hi @richardliaw, the feature is on the master branch and will be included in the next release in a couple of weeks.","body":"**Is your feature request related to a problem? Please describe.**\r\nA clear and concise description of what the problem is.\r\n\r\nThis is an awesome library. \r\n\r\nIt seems like the dynamic module path in this library has broken some of hyperparameter tuning functionality: https:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/34\r\n\r\nThis is because Ray will spawn new processes, and each process will load modules by path. However, we need to explicitly inform Ray to load the right modules, or else it will error upon import. \r\n\r\nI'd like an API to obtain the dynamic paths. This will allow us to support this functionality in this awesome library while being future proof.\r\n\r\n**Describe the solution you'd like**\r\nA clear and concise description of what you want to happen.\r\n\r\n`datasets.get_dynamic_paths -> List[str]` will be sufficient for my use case.\r\n\r\nBy offering this API, we will be able to address the following issues (by patching the ray integration sufficiently):\r\n\r\nhttps:\/\/github.com\/huggingface\/blog\/issues\/106\r\nhttps:\/\/github.com\/huggingface\/transformers\/issues\/11565\r\nhttps:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/34\r\nhttps:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/35\r\n\r\n","comment_length":22,"text":"[api request] API to obtain \"dataset_module\" dynamic path? \n **Is your feature request related to a problem? Please describe.**\r\nA clear and concise description of what the problem is.\r\n\r\nThis is an awesome library. \r\n\r\nIt seems like the dynamic module path in this library has broken some of hyperparameter tuning functionality: https:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/34\r\n\r\nThis is because Ray will spawn new processes, and each process will load modules by path. However, we need to explicitly inform Ray to load the right modules, or else it will error upon import. \r\n\r\nI'd like an API to obtain the dynamic paths. This will allow us to support this functionality in this awesome library while being future proof.\r\n\r\n**Describe the solution you'd like**\r\nA clear and concise description of what you want to happen.\r\n\r\n`datasets.get_dynamic_paths -> List[str]` will be sufficient for my use case.\r\n\r\nBy offering this API, we will be able to address the following issues (by patching the ray integration sufficiently):\r\n\r\nhttps:\/\/github.com\/huggingface\/blog\/issues\/106\r\nhttps:\/\/github.com\/huggingface\/transformers\/issues\/11565\r\nhttps:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/34\r\nhttps:\/\/discuss.huggingface.co\/t\/using-hyperparameter-search-in-trainer\/785\/35\r\n\r\n \n Hi @richardliaw, the feature is on the master branch and will be included in the next release in a couple of weeks.","embeddings":[-0.1270397156,-0.225305289,-0.1368040591,0.1205858663,0.2940106094,-0.3005486429,-0.0833771154,0.1747500896,-0.2136734873,0.3675940335,-0.0689021051,0.7768819332,-0.4621844292,0.3776905835,0.2999127507,-0.4458760321,-0.0975358039,0.0067398096,-0.322686404,0.0176408105,-0.1013591662,0.2357002795,-0.0494804457,0.2150381953,-0.0331281647,-0.2271060199,-0.0215991996,-0.0324077271,-0.2154179066,-0.3063216805,0.2938253284,0.0977493972,0.241946429,0.2549487054,-0.0001097169,0.0399271362,0.0869922489,-0.0253267381,-0.3892126083,-0.5852143764,0.086991176,-0.0527439713,0.2232045084,-0.2114178687,-0.0571547747,-0.2286361903,-0.0388008952,-0.7245185375,0.3127938807,0.0970972478,0.1863785684,0.3525506854,-0.2461271733,-0.0608689785,0.0263192523,0.3660175502,-0.1790616959,-0.22310628,0.2439689934,0.1758071482,-0.0902159587,0.2793928981,-0.0147101153,0.0854094401,0.5754789114,0.0873526558,-0.5218421817,-0.0078821545,0.1398223191,0.4570405781,0.4978515506,-0.4548654556,-0.3124949336,-0.3435848653,-0.2102581561,-0.061194744,0.2432989478,0.1618670225,-0.3192092776,0.1748104542,0.0064415107,-0.2058244348,-0.296787858,-0.0477677807,0.1724842042,0.0452364311,0.0128453113,0.0101400046,0.2376514375,-0.2335121781,0.2568751872,-0.0087721646,0.1329406351,0.3248983026,-0.1687449962,-0.0509651899,0.2447003424,0.0347744301,0.1226374879,0.1836777031,0.1095809937,0.1045258865,-0.2002750337,0.0051323455,0.2168016136,-0.0542629547,0.168360725,0.1642332524,0.043715205,0.1569670737,0.0752833635,0.0036233903,-0.0262984224,0.2485636324,-0.1683986038,-0.068460539,-0.0536364317,0.0930320621,-0.0980267897,-0.2727898359,-0.2284814715,-0.2464374006,0.402005285,0.5189792514,-0.0103534022,0.0032723539,0.1346749365,-0.3061783016,0.1540817767,0.0342514217,-0.132878527,0.1700687557,-0.1964595616,0.2172746956,0.1251200438,-0.1617827266,0.3757390678,-0.2147464454,0.2080125064,-0.0146880941,0.1403799355,0.2258154005,0.302125901,0.056196183,0.0005969711,-0.0795303434,-0.196061492,0.1319363117,-0.4790400267,0.0572812036,-0.2756034732,-0.5086496472,-0.0531340875,0.1233089715,-0.1517894715,-0.1602447033,-0.2275237292,0.250295788,-0.4091596305,0.0668224245,-0.0462653302,0.1205807403,-0.251566112,-0.1417647004,0.1624892354,0.319162637,-0.0573723167,-0.2667974234,0.0106131323,0.0205455888,-0.2142793238,0.0149703249,-0.4976260364,0.1595586538,-0.2514204681,0.2069936395,0.4655227959,-0.3496899009,0.1979121715,-0.0496314839,-0.120559372,-0.3511764109,0.1423155665,-0.0178619046,0.0505459346,0.0221116059,0.1919242144,0.4152484834,0.1833771169,-0.0652413368,0.1088487878,-0.5048277974,-0.191604346,0.0347432494,-0.0281982888,-0.111048907,0.1313598752,-0.0748344138,0.4719240963,-0.128629446,-0.0647891089,0.2573931813,0.2756946981,0.2501274049,0.1638350338,-0.5089278221,-0.1832372248,0.1345116049,-0.223482579,0.0207616147,-0.1253190935,-0.2170520872,0.0546782538,0.2268900424,0.1063441262,0.2143793106,0.0887199268,-0.0532281138,-0.019846499,-0.0994883627,-0.5061228871,-0.0065902886,-0.3069564402,0.1298153549,-0.2599502504,0.263589561,0.1946105659,0.0440865792,-0.044573117,0.2768558264,0.0326316915,-0.1502081454,0.0656056702,0.1314016879,0.1356819868,0.1755419225,0.0802517757,0.4977859557,0.1447063684,-0.050052885,0.1689353287,-0.222633332,0.016557388,0.2989408672,0.1698878706,0.3969588578,-0.2993741035,0.3082689941,0.274392724,0.171094507,0.2389397472,0.0241720192,-0.2681674063,-0.0689967498,0.1494846195,0.1469724178,0.0204165131,-0.1865819544,-0.2366634011,0.1510697305,0.3428978026,0.0796978697,0.022950815,-0.0155319609,-0.1506819576,0.0635261834,0.1780986041,0.4427559078,0.2015550584,0.3840312958,-0.0342352688,0.0657879636,-0.1435747892,0.0426152982,0.480145961,0.4844023287,0.047703024,-0.3410339952,-0.067403391,0.0669592395,-0.3018414676,-0.3176433742,-0.1428513527,-0.0518546775,-0.2832365334,0.1495362222,-0.4663021863,-0.0819149539,-0.657181561,-0.4793883264,-0.0096388683,0.1438778043,-0.0076334723,-0.4043323994,-0.2307163328,0.1738907099,0.0714429244,0.3288937211,-0.2518242002,-0.5264746547,-0.114265956,-0.0889327675,-0.1420903355,0.0081492029,0.1766569018,0.1147655919,0.4805981219,0.0131506929,0.2469646335,-0.3617047668,-0.3256879449,0.4542023242,0.0781988725,0.0940469131,0.3639710248,-0.0134570859,0.1510278285,-0.1486555189,0.0746757463,-0.0454936512,-0.123011969,-0.4061354697,0.1090995073,0.0852028355,-0.0579174198,-0.3914550841,-0.2709724903,-0.2069480121,0.1916268766,-0.028917376,0.3386306465,0.3339799345,0.0931618363,0.2643855214,-0.4949843884,0.2537980676,-0.1022889838,-0.0478170477,0.1019314602,-0.0608179532,-0.2543387413,0.1836875081,-0.1126869917,0.3660377264,0.1741805226,-0.2480429113,-0.5372393727,0.0946132615,0.4121119082,-0.0586607344,0.1167090386,0.4918664992,0.3178735375,-0.0698463693,-0.0744871646,-0.6162253022,0.3979557157,-0.0852422044,0.2072379142,0.2233785987,0.3112289906,0.1901506782,0.7600682974,-0.1596223712,-0.42568627,0.0601219386,-0.1740730554,0.2207672447,0.2339052558,-0.2157909572,-0.2629825175,-0.2709943056,-0.0954693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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2308","title":"Add COCO evaluation metrics","comments":"Hi @NielsRogge, \r\nI'd like to contribute these metrics to datasets. Let's start with `CocoEvaluator` first? Currently how are are you sending the ground truths and predictions in coco_evaluator?\r\n","body":"I'm currently working on adding Facebook AI's DETR model (end-to-end object detection with Transformers) to HuggingFace Transformers. The model is working fine, but regarding evaluation, I'm currently relying on external `CocoEvaluator` and `PanopticEvaluator` objects which are defined in the original repository ([here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py#L22) and [here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/panoptic_eval.py#L13) respectively). \r\n\r\nRunning these in a notebook gives you nice summaries like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/48327001\/116878842-326f0680-ac20-11eb-9061-d6da02193694.png)\r\n\r\nIt would be great if we could import these metrics from the Datasets library, something like this:\r\n\r\n```\r\nimport datasets\r\n\r\nmetric = datasets.load_metric('coco')\r\n\r\nfor model_input, gold_references in evaluation_dataset:\r\n    model_predictions = model(model_inputs)\r\n    metric.add_batch(predictions=model_predictions, references=gold_references)\r\n\r\nfinal_score = metric.compute()\r\n```\r\n\r\nI think this would be great for object detection and semantic\/panoptic segmentation in general, not just for DETR. Reproducing results of object detection papers would be way easier.\r\n\r\nHowever, object detection and panoptic segmentation evaluation is a bit more complex than accuracy (it's more like a summary of metrics at different thresholds rather than a single one). I'm not sure how to proceed here, but happy to help making this possible.\r\n\r\n\r\n\r\n","comment_length":28,"text":"Add COCO evaluation metrics \n I'm currently working on adding Facebook AI's DETR model (end-to-end object detection with Transformers) to HuggingFace Transformers. The model is working fine, but regarding evaluation, I'm currently relying on external `CocoEvaluator` and `PanopticEvaluator` objects which are defined in the original repository ([here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py#L22) and [here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/panoptic_eval.py#L13) respectively). \r\n\r\nRunning these in a notebook gives you nice summaries like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/48327001\/116878842-326f0680-ac20-11eb-9061-d6da02193694.png)\r\n\r\nIt would be great if we could import these metrics from the Datasets library, something like this:\r\n\r\n```\r\nimport datasets\r\n\r\nmetric = datasets.load_metric('coco')\r\n\r\nfor model_input, gold_references in evaluation_dataset:\r\n    model_predictions = model(model_inputs)\r\n    metric.add_batch(predictions=model_predictions, references=gold_references)\r\n\r\nfinal_score = metric.compute()\r\n```\r\n\r\nI think this would be great for object detection and semantic\/panoptic segmentation in general, not just for DETR. Reproducing results of object detection papers would be way easier.\r\n\r\nHowever, object detection and panoptic segmentation evaluation is a bit more complex than accuracy (it's more like a summary of metrics at different thresholds rather than a single one). I'm not sure how to proceed here, but happy to help making this possible.\r\n\r\n\r\n\r\n \n Hi @NielsRogge, \r\nI'd like to contribute these metrics to datasets. Let's start with `CocoEvaluator` first? Currently how are are you sending the ground truths and predictions in coco_evaluator?\r\n","embeddings":[-0.3099649251,-0.210986048,-0.0590808243,0.0364998765,0.1357953399,-0.1280122548,0.0327846818,-0.1446955502,-0.2019299865,0.1269806623,-0.6827290654,0.1239695549,-0.1395809948,0.1238021106,-0.2454466075,-0.0975500122,-0.0049184803,-0.0187490061,-0.217513442,0.1276486516,0.0637156069,0.1212180033,0.2470147163,0.1320974678,-0.2228360325,-0.1528722197,-0.2780018449,-0.3175072372,-0.1612353325,-0.3200707436,0.3088329732,0.4587270916,0.2178368121,0.3828972876,-0.0001260293,0.1676148772,0.0120117078,0.0352617018,-0.0486765914,-0.0712401196,0.0626677647,-0.3141658008,0.2967291176,-0.1566159576,0.2011528611,-0.0271209609,-0.1151306778,0.0279608723,0.3960064352,0.0947105587,0.0307992771,0.1865343302,-0.0129996138,-0.0139087019,-0.3567220569,0.3218263984,-0.3141763806,0.3093022108,0.0804275125,-0.1705961674,-0.1404986829,0.1835096776,0.3468079865,-0.1027476788,0.5674605966,0.0961440355,0.4028461576,-0.3214431107,-0.4800122976,0.1687449068,0.1818929464,-0.2496030927,-0.5749029517,-0.3565101624,-0.1467401981,-0.3485468328,-0.4368962348,-0.0716112927,-0.108432062,0.2087444514,-0.3374734819,-0.0290727485,-0.2903195024,-0.0355794653,-0.0136548253,0.3278526366,0.1791389734,-0.1927564144,0.388684839,-0.0898672342,0.3580983579,-0.2984927595,0.1656743586,0.0580153205,-0.2435355037,-0.2330769151,0.2841905653,0.1397185475,-0.0865409076,0.0263522509,0.291955173,0.0951316431,-0.2298988104,0.1196050867,0.1383743584,0.5930799842,0.078654632,0.6068129539,0.170524165,0.3567244709,0.0604417101,0.009807582,0.2361737043,0.1867909133,0.08795771,0.1499005407,-0.1823438704,-0.2842317522,-0.1777065545,0.1698988229,0.0837589502,-0.074615106,0.2970551252,0.4641896486,-0.1873854101,0.4703545272,-0.0868343562,-0.0211216658,0.0057172333,0.0514900386,-0.0179805923,0.3525495529,-0.2764952779,0.0843371525,0.1849103272,0.1202506199,-0.0409353636,-0.5112404823,0.7779881954,-0.3419728875,-0.1739482284,-0.3053329885,0.3040142059,0.2109246552,-0.0706052631,-0.2278837711,0.028767949,-0.2351464182,-0.2879236042,-0.2090793699,-0.0509464219,-0.2891754508,0.1410698146,0.0335556567,-0.5960389972,0.1051091179,-0.237392962,0.5540138483,-0.3729892075,0.1248563081,-0.08957766,-0.2057229578,-0.1545006633,-0.0026389603,0.282823503,0.2545711398,-0.1710279435,-0.2035298645,-0.096007891,-0.2274876982,-0.1596027762,0.2408809215,-0.0294255428,0.2013362944,0.1114363521,0.0861898661,0.1921635121,-0.7362812161,-0.1907059103,0.039125368,0.2540967762,0.2841348052,-0.0689087138,0.3182002902,0.1190395057,-0.1914613396,-0.1278437227,0.4185301661,-0.0923452452,-0.2677813768,-0.1036554724,0.0313858092,0.1740298271,0.1357136071,0.4682197869,-0.1663037837,0.101213932,0.0368285961,-0.207832247,-0.3178376257,-0.2059400678,-0.0450444967,0.3274961114,-0.0367590077,0.0985477045,-0.2202026844,-0.5051130652,0.1482058764,-0.1031731889,0.1890636832,0.1133558303,-0.3304752707,-0.0975143537,0.4347307086,-0.0014060313,-0.1480617523,0.02245887,-0.1518128365,0.2043616027,-0.026348481,-0.1034101248,0.1999194324,0.0833177567,0.2868762016,0.4424619973,0.3334579468,-0.057746239,0.1427787244,0.1259192526,0.4548034668,-0.0623297058,-0.0239386689,0.0574869961,0.3577261269,-0.0979781523,0.2656302154,0.2493121773,0.5448932052,0.1440551728,-0.3306070268,0.1368448138,-0.0180839933,0.0213611238,-0.0567232221,-0.013299495,0.7422036529,0.0734468102,0.4684187174,0.1045975983,-0.1195817664,0.042006027,0.0761806145,-0.2893473208,-0.3267185688,0.1725258678,-0.2551752925,0.1510015428,-0.118014209,-0.0232432783,-0.0939322412,-0.1533479691,-0.1365054548,0.1462494731,0.2674663067,0.0887184739,-0.092035003,-0.0518967509,-0.234629631,0.2595597506,0.0696014464,0.0040451973,0.2768526971,-0.1759034842,0.2066102177,0.1795294583,0.4463194609,-0.1659672856,0.2943893373,0.2906155288,-0.0403938219,-0.2838439047,-0.4341475666,-0.3130204678,-0.0064297346,-0.1635503918,0.3149732947,0.1163866445,-0.3425177336,0.1712343991,-0.4348503947,-0.2230145335,-0.5114440322,0.1025417224,0.0085555222,0.084088102,0.0869053304,0.0160094947,0.5051114559,-0.2759878039,0.1010564044,-0.0918443426,-0.3128788173,0.1251651943,-0.0227441937,0.2510174811,-0.0505835451,0.2167745084,0.06350407,0.3050380349,0.0786861703,-0.5782645941,0.161975652,0.0128238061,0.1595941633,-0.009425465,-0.5442686677,0.1438509077,-0.3647788167,0.1384721994,-0.1547429115,-0.068238616,-0.0656606033,-0.1959404051,-0.1478056759,-0.1425560862,0.0340810306,-0.4164380133,-0.1596177816,0.3661914468,0.1965493858,-0.0222530216,0.1161043867,0.0819729641,-0.0072670886,0.015481689,-0.1338333189,0.0023299209,0.0536471866,0.3982647359,-0.1423727721,-0.020068042,-0.242856279,-0.4220930934,-0.0924972445,-0.2059858441,-0.3302642405,-0.7189273834,-0.1237746179,0.1407007575,0.0311928671,-0.0348916277,0.0768194944,-0.2370531559,0.1992862374,-0.0613395236,-0.4014445245,0.0908249021,-0.0623569861,-0.083991617,-0.4966912866,0.4393984675,0.2411254495,0.380070895,-0.0167775396,-0.3829095066,0.0695053786,0.0900348425,0.3596771359,0.0528986715,-0.1561409533,0.188468486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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2308","title":"Add COCO evaluation metrics","comments":"Great!\r\n\r\nHere's a notebook that illustrates how I'm using `CocoEvaluator`: https:\/\/drive.google.com\/file\/d\/1VV92IlaUiuPOORXULIuAdtNbBWCTCnaj\/view?usp=sharing\r\n\r\nThe evaluation is near the end of the notebook.\r\n\r\n","body":"I'm currently working on adding Facebook AI's DETR model (end-to-end object detection with Transformers) to HuggingFace Transformers. The model is working fine, but regarding evaluation, I'm currently relying on external `CocoEvaluator` and `PanopticEvaluator` objects which are defined in the original repository ([here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py#L22) and [here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/panoptic_eval.py#L13) respectively). \r\n\r\nRunning these in a notebook gives you nice summaries like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/48327001\/116878842-326f0680-ac20-11eb-9061-d6da02193694.png)\r\n\r\nIt would be great if we could import these metrics from the Datasets library, something like this:\r\n\r\n```\r\nimport datasets\r\n\r\nmetric = datasets.load_metric('coco')\r\n\r\nfor model_input, gold_references in evaluation_dataset:\r\n    model_predictions = model(model_inputs)\r\n    metric.add_batch(predictions=model_predictions, references=gold_references)\r\n\r\nfinal_score = metric.compute()\r\n```\r\n\r\nI think this would be great for object detection and semantic\/panoptic segmentation in general, not just for DETR. Reproducing results of object detection papers would be way easier.\r\n\r\nHowever, object detection and panoptic segmentation evaluation is a bit more complex than accuracy (it's more like a summary of metrics at different thresholds rather than a single one). I'm not sure how to proceed here, but happy to help making this possible.\r\n\r\n\r\n\r\n","comment_length":20,"text":"Add COCO evaluation metrics \n I'm currently working on adding Facebook AI's DETR model (end-to-end object detection with Transformers) to HuggingFace Transformers. The model is working fine, but regarding evaluation, I'm currently relying on external `CocoEvaluator` and `PanopticEvaluator` objects which are defined in the original repository ([here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py#L22) and [here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/panoptic_eval.py#L13) respectively). \r\n\r\nRunning these in a notebook gives you nice summaries like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/48327001\/116878842-326f0680-ac20-11eb-9061-d6da02193694.png)\r\n\r\nIt would be great if we could import these metrics from the Datasets library, something like this:\r\n\r\n```\r\nimport datasets\r\n\r\nmetric = datasets.load_metric('coco')\r\n\r\nfor model_input, gold_references in evaluation_dataset:\r\n    model_predictions = model(model_inputs)\r\n    metric.add_batch(predictions=model_predictions, references=gold_references)\r\n\r\nfinal_score = metric.compute()\r\n```\r\n\r\nI think this would be great for object detection and semantic\/panoptic segmentation in general, not just for DETR. Reproducing results of object detection papers would be way easier.\r\n\r\nHowever, object detection and panoptic segmentation evaluation is a bit more complex than accuracy (it's more like a summary of metrics at different thresholds rather than a single one). I'm not sure how to proceed here, but happy to help making this possible.\r\n\r\n\r\n\r\n \n Great!\r\n\r\nHere's a notebook that illustrates how I'm using `CocoEvaluator`: https:\/\/drive.google.com\/file\/d\/1VV92IlaUiuPOORXULIuAdtNbBWCTCnaj\/view?usp=sharing\r\n\r\nThe evaluation is near the end of the notebook.\r\n\r\n","embeddings":[-0.2997457683,-0.2173083425,-0.0662911907,0.0201683957,0.1273829192,-0.1438953131,0.0299044643,-0.1443723142,-0.2041875869,0.134513706,-0.6726332307,0.1221146658,-0.1353891045,0.1291405559,-0.2434386015,-0.0863759667,-0.0150743863,-0.0097319195,-0.2172834873,0.1266033947,0.0592169836,0.1436478943,0.2437028587,0.1383200288,-0.2407882363,-0.1455112398,-0.2821148336,-0.3128210008,-0.152200982,-0.3235957623,0.2970020175,0.4498107135,0.195288673,0.3987880647,-0.000123575,0.1599732488,0.0171702933,0.0391538106,-0.0614747815,-0.0754960254,0.0772497281,-0.3173386157,0.2860774696,-0.1607625932,0.1821161509,-0.0290837213,-0.1013191044,0.0469154418,0.4056439698,0.100031741,0.050513804,0.1995001435,0.0012993686,-0.0145343486,-0.3424551785,0.3313701451,-0.3202508688,0.2981693149,0.083472468,-0.1853355765,-0.1613387167,0.199203074,0.3513002992,-0.093131505,0.568644166,0.0972017869,0.395811975,-0.3362832069,-0.4863196313,0.1939113289,0.1688250899,-0.2461128086,-0.5697387457,-0.3576303124,-0.16592668,-0.3212452233,-0.4314124286,-0.0633493662,-0.1420812011,0.2109694183,-0.3318120539,-0.0063184197,-0.2902599871,-0.0367846377,-0.0175289921,0.3369067013,0.1609748751,-0.2007407993,0.3905267715,-0.0829602629,0.3388564885,-0.2830789089,0.1836016178,0.0522666834,-0.2357368022,-0.239590764,0.2833957672,0.1345157325,-0.0850757062,0.0227248017,0.2996721864,0.1121045128,-0.2344082743,0.1268322021,0.1345121264,0.5766767263,0.060616944,0.5718221068,0.1867628545,0.3516945243,0.0615282729,0.0060395375,0.24470523,0.1921391636,0.0925656334,0.1304136068,-0.1720277667,-0.2655124962,-0.1750139445,0.1668262184,0.0971483886,-0.0691358447,0.2887948751,0.4693868756,-0.1852558553,0.4720817208,-0.0911772996,-0.0504620373,0.0083446885,0.0523504913,-0.0340195857,0.3280818462,-0.2931898832,0.0744849443,0.1807041764,0.1405318379,-0.0318055563,-0.5192122459,0.7789002657,-0.313687712,-0.1726381481,-0.2899074256,0.3224669695,0.2182406932,-0.0977120996,-0.2309614271,0.0274781007,-0.2426817864,-0.283683002,-0.2099500149,-0.0465671197,-0.2794578969,0.1206553802,0.0554584488,-0.5932693481,0.1008231863,-0.2202059776,0.572905004,-0.3765848875,0.1203671843,-0.0749170631,-0.1959534883,-0.1592183113,-0.012403016,0.2833305895,0.2463438511,-0.1743961424,-0.2132157981,-0.0936428979,-0.2362224162,-0.1588311791,0.2393621355,-0.0196399577,0.1804813296,0.1095646843,0.0924026668,0.185248211,-0.7222111821,-0.2145638615,0.048907049,0.2471457124,0.2894208729,-0.0734357759,0.3351033926,0.1267104,-0.189346835,-0.1360853314,0.4152053893,-0.0811438933,-0.2573272288,-0.1198692918,0.0354173258,0.1681862622,0.1451547891,0.433103621,-0.1665911525,0.1054945812,0.0371051319,-0.196060434,-0.320304662,-0.2076268047,-0.0496512391,0.3412632644,-0.0403319113,0.1081841141,-0.2308375984,-0.5144692063,0.1474981904,-0.1135462821,0.2028590292,0.122453101,-0.3373811841,-0.0957704484,0.4329608381,-0.0011986231,-0.1656338274,0.0466326438,-0.1520049572,0.2136487514,-0.0242753141,-0.1066238359,0.2016839087,0.0871169791,0.2898570895,0.4406019151,0.3309725523,-0.0673665702,0.1346359849,0.1199563444,0.4655914009,-0.0769188702,-0.0163238421,0.0534669049,0.3662233055,-0.0929570124,0.2508169115,0.2465990037,0.5548986197,0.1488536447,-0.3216001391,0.1336142272,-0.0017458077,0.0225114916,-0.0474049821,-0.0396965444,0.730409801,0.0812966004,0.463642776,0.1052093357,-0.1008718163,0.0464760251,0.0716416091,-0.3060565889,-0.3219819963,0.1666344404,-0.2491999418,0.1512070149,-0.0932580084,-0.0341611989,-0.0734885633,-0.1332815289,-0.1414821446,0.1587101221,0.2704225779,0.0616405271,-0.108017832,-0.0460405312,-0.2232921869,0.2471750826,0.0910435095,-0.0043952297,0.2587950528,-0.1916436404,0.1918939501,0.1756489724,0.4494325817,-0.1697312295,0.2736298144,0.2883634567,-0.0343804993,-0.2886705995,-0.4417698383,-0.328854233,-0.0141829066,-0.1479980499,0.3167216778,0.1225950569,-0.3476931453,0.1796490699,-0.436488688,-0.2220805287,-0.5205181837,0.1152007803,0.0132634863,0.0673807859,0.0899344906,0.0288928337,0.5286703706,-0.2699851096,0.1107042506,-0.1122965142,-0.3123542666,0.0993663445,0.0015263066,0.2481557727,-0.0478702746,0.2108082622,0.064555347,0.3195941746,0.0707352087,-0.5984122753,0.163905561,0.0002433452,0.16894871,-0.0078940392,-0.543690145,0.1415248364,-0.359223485,0.133017078,-0.180943653,-0.0755390897,-0.0621161759,-0.1881648302,-0.1498838812,-0.1533188075,0.0218634848,-0.407507062,-0.1856070459,0.3884068429,0.2024872005,-0.0216110572,0.1255498827,0.0721987411,0.0083553931,0.0168718901,-0.1339852065,-0.0112587996,0.0598986857,0.3984030187,-0.1504701227,-0.0178765357,-0.2467008978,-0.4240752459,-0.0942104906,-0.2257972658,-0.3218148947,-0.7311675549,-0.1432559639,0.1486822814,0.038619373,-0.0254496988,0.0705624372,-0.2284892648,0.1728915125,-0.0559927337,-0.3789964318,0.0764611885,-0.0758435726,-0.0935095847,-0.4966576099,0.4219006598,0.2515825033,0.3898653388,-0.0341858119,-0.392049551,0.0780420676,0.0776520148,0.3641143739,0.0493736491,-0.1420731992,0.1943820715,-0.19705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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2308","title":"Add COCO evaluation metrics","comments":"I went through the code you've [mentioned](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py) and I think there are 2 options on how we can go ahead:\r\n\r\n1) Implement how DETR people have done this (they're relying very heavily on the official implementation and they're focussing on torch dataset here. I feel ours should be something generic instead of pytorch specific.\r\n2) Do this [implementation](https:\/\/github.com\/cocodataset\/cocoapi\/blob\/ed842bffd41f6ff38707c4f0968d2cfd91088688\/PythonAPI\/pycocoEvalDemo.ipynb) where user can convert its output and ground truth annotation to pre-defined format and then feed it into our function to calculate metrics (looks very similar to you wanted above)\r\n\r\nIn my opinion, 2nd option looks very clean but I'm still figuring out how's it transforming the box co-ordinates of `coco_gt` which you've passed to `CocoEvaluator` (ground truth for evaluation). Since your model output was already converted to COCO api, I faced little problems there.","body":"I'm currently working on adding Facebook AI's DETR model (end-to-end object detection with Transformers) to HuggingFace Transformers. The model is working fine, but regarding evaluation, I'm currently relying on external `CocoEvaluator` and `PanopticEvaluator` objects which are defined in the original repository ([here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py#L22) and [here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/panoptic_eval.py#L13) respectively). \r\n\r\nRunning these in a notebook gives you nice summaries like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/48327001\/116878842-326f0680-ac20-11eb-9061-d6da02193694.png)\r\n\r\nIt would be great if we could import these metrics from the Datasets library, something like this:\r\n\r\n```\r\nimport datasets\r\n\r\nmetric = datasets.load_metric('coco')\r\n\r\nfor model_input, gold_references in evaluation_dataset:\r\n    model_predictions = model(model_inputs)\r\n    metric.add_batch(predictions=model_predictions, references=gold_references)\r\n\r\nfinal_score = metric.compute()\r\n```\r\n\r\nI think this would be great for object detection and semantic\/panoptic segmentation in general, not just for DETR. Reproducing results of object detection papers would be way easier.\r\n\r\nHowever, object detection and panoptic segmentation evaluation is a bit more complex than accuracy (it's more like a summary of metrics at different thresholds rather than a single one). I'm not sure how to proceed here, but happy to help making this possible.\r\n\r\n\r\n\r\n","comment_length":133,"text":"Add COCO evaluation metrics \n I'm currently working on adding Facebook AI's DETR model (end-to-end object detection with Transformers) to HuggingFace Transformers. The model is working fine, but regarding evaluation, I'm currently relying on external `CocoEvaluator` and `PanopticEvaluator` objects which are defined in the original repository ([here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py#L22) and [here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/panoptic_eval.py#L13) respectively). \r\n\r\nRunning these in a notebook gives you nice summaries like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/48327001\/116878842-326f0680-ac20-11eb-9061-d6da02193694.png)\r\n\r\nIt would be great if we could import these metrics from the Datasets library, something like this:\r\n\r\n```\r\nimport datasets\r\n\r\nmetric = datasets.load_metric('coco')\r\n\r\nfor model_input, gold_references in evaluation_dataset:\r\n    model_predictions = model(model_inputs)\r\n    metric.add_batch(predictions=model_predictions, references=gold_references)\r\n\r\nfinal_score = metric.compute()\r\n```\r\n\r\nI think this would be great for object detection and semantic\/panoptic segmentation in general, not just for DETR. Reproducing results of object detection papers would be way easier.\r\n\r\nHowever, object detection and panoptic segmentation evaluation is a bit more complex than accuracy (it's more like a summary of metrics at different thresholds rather than a single one). I'm not sure how to proceed here, but happy to help making this possible.\r\n\r\n\r\n\r\n \n I went through the code you've [mentioned](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py) and I think there are 2 options on how we can go ahead:\r\n\r\n1) Implement how DETR people have done this (they're relying very heavily on the official implementation and they're focussing on torch dataset here. I feel ours should be something generic instead of pytorch specific.\r\n2) Do this [implementation](https:\/\/github.com\/cocodataset\/cocoapi\/blob\/ed842bffd41f6ff38707c4f0968d2cfd91088688\/PythonAPI\/pycocoEvalDemo.ipynb) where user can convert its output and ground truth annotation to pre-defined format and then feed it into our function to calculate metrics (looks very similar to you wanted above)\r\n\r\nIn my opinion, 2nd option looks very clean but I'm still figuring out how's it transforming the box co-ordinates of `coco_gt` which you've passed to `CocoEvaluator` (ground truth for evaluation). Since your model output was already converted to COCO api, I faced little problems there.","embeddings":[-0.2762456536,-0.2085913718,-0.0654032603,0.0466305688,0.1744097322,-0.1518914849,0.0246486589,-0.1304698735,-0.1931971908,0.1273570806,-0.6784852743,0.121309571,-0.1210910976,0.1138263941,-0.2690066397,-0.0950239822,-0.0252741873,-0.01117709,-0.2147166431,0.1027091295,0.0485205166,0.178263396,0.2275513709,0.1101235598,-0.254538089,-0.1438167244,-0.2657630742,-0.2837206721,-0.1923244447,-0.3163909316,0.3075261712,0.4554100037,0.2024868876,0.4298277497,-0.0001223113,0.1656180918,0.0208981652,0.0250817984,-0.0517429598,-0.0965127945,0.0708127618,-0.3158518374,0.2736356556,-0.1634330601,0.1322087049,-0.030235678,-0.1248590201,0.0449080653,0.4284770191,0.1474260241,0.0711562485,0.1883551627,0.0393480249,-0.0047205747,-0.3366113007,0.3078396916,-0.3132876158,0.3043533266,0.059667293,-0.1810681075,-0.1572528481,0.223778367,0.3409006596,-0.0695617199,0.5765433311,0.1170461252,0.3985585272,-0.3481795788,-0.4635713398,0.1570440233,0.1507800668,-0.2379888147,-0.5474994183,-0.3472571671,-0.1965019703,-0.3565271199,-0.3971282542,-0.0950896218,-0.1564535499,0.1816256791,-0.3316313028,0.0121770641,-0.3044273555,-0.0409061536,0.0005170868,0.3494045436,0.1487829834,-0.1873195916,0.4057869911,-0.0739512742,0.3195967674,-0.2628585696,0.2078426033,0.0378848389,-0.2627312541,-0.2232255489,0.2751401961,0.1383701563,-0.07271377,0.0197973493,0.277000308,0.1162491813,-0.2116607279,0.116253823,0.1057365835,0.6059837341,0.0628726855,0.5722205639,0.1813307703,0.3665733635,0.0421056487,-0.0075578792,0.2369519323,0.1503079236,0.1127964556,0.1642229259,-0.1079887077,-0.2825087905,-0.1727300137,0.1893318892,0.1075053364,-0.0790875033,0.3042195141,0.4782568216,-0.1811585277,0.4461401999,-0.0655577332,-0.0462577306,-0.018244978,0.0664228797,-0.0574939139,0.3409990072,-0.3272207677,0.0691410154,0.1938776076,0.1533528864,-0.0313309766,-0.4772887826,0.7550008893,-0.3323637247,-0.1679587811,-0.302188158,0.3394156098,0.1996418089,-0.09726087,-0.191221267,0.0545454659,-0.2480310798,-0.2544415593,-0.2019887716,-0.0508468933,-0.2689056396,0.1182743087,0.0738486573,-0.5843091011,0.1008130983,-0.2262386978,0.5677810907,-0.3436725438,0.122991465,-0.059731476,-0.2086481154,-0.1660012752,-0.0124743292,0.3085112274,0.2649511993,-0.1796625555,-0.1830184162,-0.08096762,-0.2382341176,-0.1266950816,0.2226281017,-0.0105148107,0.2006147951,0.0851476267,0.1084154248,0.1714395881,-0.7114329338,-0.2342544049,0.0404349901,0.2236281931,0.2831639946,-0.0782680884,0.3078074157,0.1145276651,-0.1691482514,-0.1307482719,0.4090504944,-0.0740684122,-0.2533506751,-0.1601086408,0.0621848404,0.1803666949,0.1431153119,0.4445335865,-0.1584167629,0.0657703355,0.0254786722,-0.1756358147,-0.3219895959,-0.1698574573,-0.0325578898,0.3594143987,-0.023432564,0.1073376313,-0.2093330622,-0.5224545598,0.1673995852,-0.1160116866,0.2480333596,0.0971881971,-0.3190678954,-0.1431769878,0.4123532474,-0.049163118,-0.1736114025,0.069818534,-0.1345931441,0.2121600658,-0.0539133549,-0.0922610238,0.2222616971,0.0908731595,0.2821025252,0.4027760029,0.3636613786,-0.1111014411,0.129260987,0.1170272678,0.4381550252,0.0024813227,-0.0212981626,0.0223128274,0.3827012181,-0.0947424024,0.2323689014,0.2379073203,0.4944932461,0.1189072803,-0.3212482631,0.1284971535,0.0009626867,0.0421741977,-0.0464833528,-0.0077398974,0.7312797904,0.0620470941,0.4585793018,0.0933523029,-0.1022493839,0.0576270409,0.0553310104,-0.2785558403,-0.3295013309,0.1663157344,-0.2630715966,0.1256216168,-0.1224567592,-0.0244950354,-0.051908657,-0.1257094443,-0.1292593032,0.1346466094,0.281737864,0.0859710351,-0.0932163969,-0.0398116745,-0.2153113186,0.2656779289,0.0848730206,-0.0105626592,0.2854541838,-0.1838683784,0.1853946894,0.1745005697,0.4714135528,-0.1456243992,0.2780250311,0.2813168466,-0.0237598997,-0.3009242415,-0.4294913709,-0.2931693494,0.0122506646,-0.1763392687,0.3256577849,0.1141820624,-0.3490326405,0.1567971557,-0.4588406384,-0.2433834523,-0.5207465887,0.1033495888,0.0190505199,0.0706163645,0.127628088,0.0369858518,0.5012657046,-0.2499660105,0.0926333591,-0.120343402,-0.2922961414,0.0737061277,0.0129695507,0.2553323507,-0.0234923922,0.2086292505,0.0735825971,0.3236071169,0.0638396591,-0.5853328109,0.1565502584,-0.049568966,0.1875982732,-0.0077075786,-0.5228638649,0.1215294227,-0.3543279171,0.1721247137,-0.2101464123,-0.0749879554,-0.0530057326,-0.1912761033,-0.1462313235,-0.1577598602,0.0053714877,-0.4186987579,-0.1929976493,0.3601029813,0.2147980928,-0.0213449579,0.149769038,0.0865374133,0.0260949209,0.0550642833,-0.1355027854,-0.0529952385,0.0406923369,0.4118338525,-0.1769706011,-0.0537597276,-0.2154047787,-0.4016530812,-0.0702753589,-0.2474472076,-0.3203711212,-0.7439361811,-0.1434044391,0.1265176237,0.018342752,-0.0163928438,0.0766172707,-0.2344217002,0.1522072256,-0.0657699034,-0.4089482725,0.1194923073,-0.0777283534,-0.0890451148,-0.5065962672,0.4495178461,0.2294935733,0.3933584094,0.0016589224,-0.3807325065,0.1166252047,0.0910189077,0.3588932753,0.035747353,-0.1737159342,0.2155748159,-0.17940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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2308","title":"Add COCO evaluation metrics","comments":"Ok, thanks for the update.\r\n\r\nIndeed, the metrics API of Datasets is framework agnostic, so we can't rely on a PyTorch-only implementation.\r\n\r\n[This file](https:\/\/github.com\/cocodataset\/cocoapi\/blob\/ed842bffd41f6ff38707c4f0968d2cfd91088688\/PythonAPI\/pycocotools\/cocoeval.py) is probably want we need to implement.\r\n\r\n","body":"I'm currently working on adding Facebook AI's DETR model (end-to-end object detection with Transformers) to HuggingFace Transformers. The model is working fine, but regarding evaluation, I'm currently relying on external `CocoEvaluator` and `PanopticEvaluator` objects which are defined in the original repository ([here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py#L22) and [here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/panoptic_eval.py#L13) respectively). \r\n\r\nRunning these in a notebook gives you nice summaries like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/48327001\/116878842-326f0680-ac20-11eb-9061-d6da02193694.png)\r\n\r\nIt would be great if we could import these metrics from the Datasets library, something like this:\r\n\r\n```\r\nimport datasets\r\n\r\nmetric = datasets.load_metric('coco')\r\n\r\nfor model_input, gold_references in evaluation_dataset:\r\n    model_predictions = model(model_inputs)\r\n    metric.add_batch(predictions=model_predictions, references=gold_references)\r\n\r\nfinal_score = metric.compute()\r\n```\r\n\r\nI think this would be great for object detection and semantic\/panoptic segmentation in general, not just for DETR. Reproducing results of object detection papers would be way easier.\r\n\r\nHowever, object detection and panoptic segmentation evaluation is a bit more complex than accuracy (it's more like a summary of metrics at different thresholds rather than a single one). I'm not sure how to proceed here, but happy to help making this possible.\r\n\r\n\r\n\r\n","comment_length":31,"text":"Add COCO evaluation metrics \n I'm currently working on adding Facebook AI's DETR model (end-to-end object detection with Transformers) to HuggingFace Transformers. The model is working fine, but regarding evaluation, I'm currently relying on external `CocoEvaluator` and `PanopticEvaluator` objects which are defined in the original repository ([here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/coco_eval.py#L22) and [here](https:\/\/github.com\/facebookresearch\/detr\/blob\/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5\/datasets\/panoptic_eval.py#L13) respectively). \r\n\r\nRunning these in a notebook gives you nice summaries like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/48327001\/116878842-326f0680-ac20-11eb-9061-d6da02193694.png)\r\n\r\nIt would be great if we could import these metrics from the Datasets library, something like this:\r\n\r\n```\r\nimport datasets\r\n\r\nmetric = datasets.load_metric('coco')\r\n\r\nfor model_input, gold_references in evaluation_dataset:\r\n    model_predictions = model(model_inputs)\r\n    metric.add_batch(predictions=model_predictions, references=gold_references)\r\n\r\nfinal_score = metric.compute()\r\n```\r\n\r\nI think this would be great for object detection and semantic\/panoptic segmentation in general, not just for DETR. Reproducing results of object detection papers would be way easier.\r\n\r\nHowever, object detection and panoptic segmentation evaluation is a bit more complex than accuracy (it's more like a summary of metrics at different thresholds rather than a single one). I'm not sure how to proceed here, but happy to help making this possible.\r\n\r\n\r\n\r\n \n Ok, thanks for the update.\r\n\r\nIndeed, the metrics API of Datasets is framework agnostic, so we can't rely on a PyTorch-only implementation.\r\n\r\n[This file](https:\/\/github.com\/cocodataset\/cocoapi\/blob\/ed842bffd41f6ff38707c4f0968d2cfd91088688\/PythonAPI\/pycocotools\/cocoeval.py) is probably want we need to implement.\r\n\r\n","embeddings":[-0.2992970049,-0.2228718996,-0.0646218807,0.0151259722,0.1335908324,-0.1412784159,0.0264432374,-0.1363361776,-0.1954925209,0.1230499446,-0.6802905798,0.1259326339,-0.1547850221,0.1198722944,-0.2277210802,-0.0956800133,-0.0067188987,-0.0257028192,-0.206568256,0.1308602691,0.045062229,0.1559381485,0.2409396619,0.1375779659,-0.2349680364,-0.1268833131,-0.2791191638,-0.3148986101,-0.1528187543,-0.3324477971,0.3127663136,0.4507707655,0.2063035816,0.3868991137,-0.0001238944,0.1604953408,0.0293454695,0.0402950086,-0.0567350462,-0.0817935616,0.0817746967,-0.3192557096,0.2854306102,-0.1532307118,0.1690668762,-0.0341490284,-0.11895331,0.0350222848,0.4042483866,0.110640578,0.0524125993,0.1972407103,0.0121194068,-0.0126454048,-0.3311860859,0.3505337834,-0.3129050732,0.3000722826,0.0955421627,-0.1801473647,-0.1500790715,0.1911480427,0.3429262936,-0.0921719521,0.5646221042,0.094734095,0.3990193009,-0.3180494606,-0.4919623733,0.1922196746,0.1793985963,-0.2508137524,-0.5659953356,-0.3535729647,-0.1606238782,-0.3407167792,-0.4232775569,-0.0717138872,-0.1374007165,0.2017319947,-0.3214557171,-0.0216319673,-0.2929254472,-0.0346351229,-0.0127414381,0.3427121341,0.1697211266,-0.2110771686,0.3974739313,-0.0821206421,0.3321637213,-0.2659060657,0.1673607081,0.0565473512,-0.2368429601,-0.2462360114,0.2739785612,0.1305557489,-0.0841672197,0.0217711162,0.2886641026,0.1091312766,-0.2407827526,0.1194920912,0.1254581213,0.5836359262,0.0670502782,0.5859928131,0.1789158881,0.3602632582,0.0608827919,-0.0031642781,0.2547972798,0.1796337515,0.0882038474,0.1490274221,-0.1660265923,-0.2710515559,-0.1633441895,0.1672856361,0.1109902188,-0.0777895302,0.2944928706,0.4754862189,-0.1721894145,0.470359683,-0.0810871497,-0.0446160659,0.0116869276,0.038016744,-0.0310281925,0.3311302364,-0.2840276361,0.0755267441,0.1878756732,0.1341210902,-0.0454441309,-0.5317236781,0.7972844243,-0.3050076365,-0.1892516315,-0.2818904817,0.3368848562,0.2114264816,-0.1106370166,-0.2374489009,0.0274331123,-0.2493315488,-0.2812485099,-0.198466301,-0.0533035733,-0.2920435965,0.1029669642,0.051463183,-0.5960325599,0.1069889516,-0.2322064489,0.5729320645,-0.3790602386,0.1045930684,-0.0657642111,-0.2093566507,-0.1626275927,-0.007087301,0.2824583054,0.2480089068,-0.1690669358,-0.2179758549,-0.1003717855,-0.2434242368,-0.1601533294,0.2256184071,-0.025373321,0.1645831317,0.1001148745,0.0776345432,0.1797301024,-0.7268228531,-0.2085731477,0.0272011049,0.2609668076,0.2839909494,-0.0744253024,0.3155295551,0.1378124654,-0.176649332,-0.1323247701,0.4194304943,-0.0769564807,-0.2528687119,-0.1330254376,0.04196826,0.1788371056,0.1517906487,0.4420607388,-0.1673374772,0.1027848423,0.0223796275,-0.1959409565,-0.3134689927,-0.1980535686,-0.0567649007,0.3496710062,-0.0370448418,0.1241204813,-0.2290176004,-0.5125864744,0.1457272917,-0.1106102318,0.2121107131,0.1256605089,-0.331579417,-0.0971532613,0.4307197332,0.0061918478,-0.1665689349,0.041584827,-0.1597297192,0.2058889121,-0.0072757523,-0.1105474383,0.2088841498,0.0971468911,0.2919770777,0.4504459202,0.3401712477,-0.0702545047,0.1461795121,0.1189462095,0.461900115,-0.0725690126,-0.0189571343,0.0365773179,0.3616077006,-0.0942518041,0.267055124,0.2508037388,0.5370210409,0.1577999443,-0.3334538937,0.1407443285,-0.0011267818,0.0168407932,-0.0349564403,-0.0293977167,0.7423948646,0.0886614025,0.4548160136,0.0976241603,-0.1052287593,0.0497463532,0.070672594,-0.2982068658,-0.3200121224,0.1700922996,-0.2512491941,0.1522044092,-0.1100988314,-0.0280575752,-0.0896346048,-0.1401914209,-0.1429973096,0.1518868804,0.287394762,0.0649263859,-0.1160390601,-0.0440655425,-0.216845125,0.2574723661,0.0891266763,-0.0151801826,0.269939959,-0.1740833968,0.1887930185,0.1882830113,0.4376374483,-0.1654495001,0.2808382213,0.2957637906,-0.0424122177,-0.2989651859,-0.4278821945,-0.3164432645,-0.0064289253,-0.1498689502,0.3005524576,0.1229160354,-0.3501483798,0.1867867112,-0.4390273988,-0.2368394285,-0.5237368941,0.1158800051,0.0094083678,0.0711854696,0.1030559763,0.0307732392,0.5409748554,-0.2853987813,0.0967011452,-0.1340499669,-0.3083228469,0.0993752778,0.0013545768,0.2395742238,-0.0604021735,0.2097821832,0.081299834,0.334824115,0.0728450418,-0.5948604345,0.1543280631,0.0096929101,0.1603826582,-0.0054531153,-0.5465567708,0.1438091099,-0.3508621454,0.1390538365,-0.213007018,-0.0825375468,-0.0648088157,-0.1963885576,-0.1453529745,-0.1572336257,0.0272405203,-0.422347486,-0.1761989892,0.3842809498,0.2135610282,-0.0255784877,0.125026837,0.0594722554,-0.0020090819,0.0204948448,-0.1462385207,-0.0204305947,0.0450165421,0.4003348351,-0.1566701084,-0.0140681108,-0.2479526252,-0.4340081215,-0.1029934213,-0.2318248153,-0.3111585081,-0.7258253694,-0.1302177012,0.1542342007,0.0390790775,-0.0355025753,0.073373124,-0.2161287218,0.172402218,-0.0547054857,-0.3833325803,0.0750109255,-0.0586798415,-0.0996433944,-0.4895223677,0.4194797575,0.2412148565,0.3879185915,-0.0364152007,-0.3896676898,0.0873220563,0.076990582,0.3686606288,0.0446510874,-0.1522578746,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2301","title":"Unable to setup dev env on Windows","comments":"Hi @gchhablani, \r\n\r\nThere are some 3rd-party dependencies that require to build code in C. In this case, it is the library `python-Levenshtein`.\r\n\r\nOn Windows, in order to be able to build C code, you need to install at least `Microsoft C++ Build Tools` version 14. You can find more info here: https:\/\/visualstudio.microsoft.com\/visual-cpp-build-tools\/","body":"Hi\r\n\r\nI tried installing the `\".[dev]\"` version on Windows 10 after cloning.\r\n\r\nHere is the error I'm facing:\r\n\r\n```bat\r\n(env) C:\\testing\\datasets>pip install -e \".[dev]\"\r\nObtaining file:\/\/\/C:\/testing\/datasets\r\nRequirement already satisfied: numpy>=1.17 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (1.19.5)\r\nCollecting pyarrow>=0.17.1\r\n  Using cached pyarrow-4.0.0-cp37-cp37m-win_amd64.whl (13.3 MB)\r\nRequirement already satisfied: dill in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (0.3.1.1)\r\nCollecting pandas\r\n  Using cached pandas-1.2.4-cp37-cp37m-win_amd64.whl (9.1 MB)\r\nRequirement already satisfied: requests>=2.19.0 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (2.25.1)\r\nRequirement already satisfied: tqdm<4.50.0,>=4.27 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (4.49.0)\r\nRequirement already satisfied: xxhash in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (2.0.2)\r\nCollecting multiprocess\r\n  Using cached multiprocess-0.70.11.1-py37-none-any.whl (108 kB)\r\nRequirement already satisfied: fsspec in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (2021.4.0)\r\nCollecting huggingface_hub<0.1.0\r\n  Using cached huggingface_hub-0.0.8-py3-none-any.whl (34 kB)\r\nRequirement already satisfied: importlib_metadata in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (4.0.1)\r\nRequirement already satisfied: absl-py in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (0.12.0)\r\nRequirement already satisfied: pytest in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (6.2.3)\r\nCollecting pytest-xdist\r\n  Using cached pytest_xdist-2.2.1-py3-none-any.whl (37 kB)\r\nCollecting apache-beam>=2.24.0\r\n  Using cached apache_beam-2.29.0-cp37-cp37m-win_amd64.whl (3.7 MB)\r\nCollecting elasticsearch\r\n  Using cached elasticsearch-7.12.1-py2.py3-none-any.whl (339 kB)\r\nRequirement already satisfied: boto3==1.16.43 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (1.16.43)\r\nRequirement already satisfied: botocore==1.19.43 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (1.19.43)\r\nCollecting moto[s3]==1.3.16\r\n  Using cached moto-1.3.16-py2.py3-none-any.whl (879 kB)\r\nCollecting rarfile>=4.0\r\n  Using cached rarfile-4.0-py3-none-any.whl (28 kB)\r\nCollecting tensorflow>=2.3\r\n  Using cached tensorflow-2.4.1-cp37-cp37m-win_amd64.whl (370.7 MB)\r\nRequirement already satisfied: torch in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from 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c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from matplotlib->bert_score>=0.3.6->datasets==1.5.0.dev0) (8.2.0)\r\nRequirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from matplotlib->bert_score>=0.3.6->datasets==1.5.0.dev0) (0.10.0)\r\nRequirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from matplotlib->bert_score>=0.3.6->datasets==1.5.0.dev0) (1.3.1)\r\nCollecting multiprocess\r\n  Using cached multiprocess-0.70.11-py3-none-any.whl (98 kB)\r\n  Using cached multiprocess-0.70.10.zip (2.4 MB)\r\n  Using cached multiprocess-0.70.9-py3-none-any.whl\r\nRequirement already satisfied: joblib in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from nltk->datasets==1.5.0.dev0) (1.0.1)\r\nCollecting et-xmlfile\r\n  Using cached et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)\r\nRequirement already satisfied: pyzstd<0.15.0,>=0.14.4 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from py7zr->datasets==1.5.0.dev0) (0.14.4)\r\nCollecting pyppmd<0.13.0,>=0.12.1\r\n  Using cached pyppmd-0.12.1-cp37-cp37m-win_amd64.whl (32 kB)\r\nCollecting pycryptodome>=3.6.6\r\n  Using cached pycryptodome-3.10.1-cp35-abi3-win_amd64.whl (1.6 MB)\r\nCollecting bcj-cffi<0.6.0,>=0.5.1\r\n  Using cached bcj_cffi-0.5.1-cp37-cp37m-win_amd64.whl (21 kB)\r\nCollecting multivolumefile<0.3.0,>=0.2.0\r\n  Using cached multivolumefile-0.2.3-py3-none-any.whl (17 kB)\r\nRequirement already satisfied: iniconfig in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from pytest->datasets==1.5.0.dev0) (1.1.1)\r\nRequirement already satisfied: py>=1.8.2 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from pytest->datasets==1.5.0.dev0) (1.10.0)\r\nRequirement already satisfied: pluggy<1.0.0a1,>=0.12 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from pytest->datasets==1.5.0.dev0) (0.13.1)\r\nRequirement already satisfied: atomicwrites>=1.0 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from pytest->datasets==1.5.0.dev0) (1.4.0)\r\nRequirement already satisfied: colorama in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from pytest->datasets==1.5.0.dev0) (0.4.4)\r\nCollecting pytest-forked\r\n  Using cached pytest_forked-1.3.0-py2.py3-none-any.whl (4.7 kB)\r\nCollecting execnet>=1.1\r\n  Using cached execnet-1.8.0-py2.py3-none-any.whl (39 kB)\r\nRequirement already satisfied: apipkg>=1.4 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from execnet>=1.1->pytest-xdist->datasets==1.5.0.dev0) (1.5)\r\nCollecting portalocker==2.0.0\r\n  Using cached portalocker-2.0.0-py2.py3-none-any.whl (11 kB)\r\nRequirement already satisfied: scikit-learn>=0.21.3 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from seqeval->datasets==1.5.0.dev0) (0.24.2)\r\nRequirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from scikit-learn>=0.21.3->seqeval->datasets==1.5.0.dev0) (2.1.0)\r\nBuilding wheels for collected packages: python-Levenshtein\r\n  Building wheel for python-Levenshtein (setup.py) ... error\r\n  ERROR: Command errored out with exit status 1:\r\n   command: 'C:\\ProgramData\\Anaconda3\\envs\\env\\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d 'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-wheel-8jh7fm18'\r\n       cwd: C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\r\n  Complete output (27 lines):\r\n  running bdist_wheel\r\n  running build\r\n  running build_py\r\n  creating build\r\n  creating build\\lib.win-amd64-3.7\r\n  creating build\\lib.win-amd64-3.7\\Levenshtein\r\n  copying Levenshtein\\StringMatcher.py -> build\\lib.win-amd64-3.7\\Levenshtein\r\n  copying Levenshtein\\__init__.py -> build\\lib.win-amd64-3.7\\Levenshtein\r\n  running egg_info\r\n  writing python_Levenshtein.egg-info\\PKG-INFO\r\n  writing dependency_links to python_Levenshtein.egg-info\\dependency_links.txt\r\n  writing entry points to python_Levenshtein.egg-info\\entry_points.txt\r\n  writing namespace_packages to python_Levenshtein.egg-info\\namespace_packages.txt\r\n  writing requirements to python_Levenshtein.egg-info\\requires.txt\r\n  writing top-level names to python_Levenshtein.egg-info\\top_level.txt\r\n  reading manifest file 'python_Levenshtein.egg-info\\SOURCES.txt'\r\n  reading manifest template 'MANIFEST.in'\r\n  warning: no previously-included files matching '*pyc' found anywhere in distribution\r\n  warning: no previously-included files matching '*so' found anywhere in distribution\r\n  warning: no previously-included files matching '.project' found anywhere in distribution\r\n  warning: no previously-included files matching '.pydevproject' found anywhere in distribution\r\n  writing manifest file 'python_Levenshtein.egg-info\\SOURCES.txt'\r\n  copying Levenshtein\\_levenshtein.c -> build\\lib.win-amd64-3.7\\Levenshtein\r\n  copying Levenshtein\\_levenshtein.h -> build\\lib.win-amd64-3.7\\Levenshtein\r\n  running build_ext\r\n  building 'Levenshtein._levenshtein' extension\r\n  error: Microsoft Visual C++ 14.0 or greater is required. Get it with \"Microsoft C++ Build Tools\": https:\/\/visualstudio.microsoft.com\/visual-cpp-build-tools\/\r\n  ----------------------------------------\r\n  ERROR: Failed building wheel for python-Levenshtein\r\n  Running setup.py clean for python-Levenshtein\r\nFailed to build python-Levenshtein\r\nInstalling collected packages: python-Levenshtein, pytest-forked, pyppmd, pymongo, pyflakes, pydot, pycryptodome, pycodestyle, pyarrow, portalocker, pathspec, pandas, opt-einsum, oauth2client, nltk, mypy-extensions, multivolumefile, multiprocess, moto, mccabe, matplotlib, keras-preprocessing, huggingface-hub, hdfs, h5py, google-pasta, gast, flatbuffers, fastavro, execnet, et-xmlfile, entrypoints, crcmod, beautifulsoup4, bcj-cffi, avro-python3, astunparse, appdirs, zstandard, tldextract, tensorflow, sklearn, seqeval, sacrebleu, rouge-score, rarfile, pytest-xdist, py7zr, openpyxl, mwparserfromhell, lxml, langdetect, jiwer, isort, flake8, elasticsearch, datasets, conllu, bs4, black, bert-score, apache-beam\r\n    Running setup.py install for python-Levenshtein ... error\r\n    ERROR: Command errored out with exit status 1:\r\n     command: 'C:\\ProgramData\\Anaconda3\\envs\\env\\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-record-v7l7zitb\\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\\ProgramData\\Anaconda3\\envs\\env\\Include\\python-Levenshtein'\r\n         cwd: C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\r\n    Complete output (27 lines):\r\n    running install\r\n    running build\r\n    running build_py\r\n    creating build\r\n    creating build\\lib.win-amd64-3.7\r\n    creating build\\lib.win-amd64-3.7\\Levenshtein\r\n    copying Levenshtein\\StringMatcher.py -> build\\lib.win-amd64-3.7\\Levenshtein\r\n    copying Levenshtein\\__init__.py -> build\\lib.win-amd64-3.7\\Levenshtein\r\n    running egg_info\r\n    writing python_Levenshtein.egg-info\\PKG-INFO\r\n    writing dependency_links to python_Levenshtein.egg-info\\dependency_links.txt\r\n    writing entry points to python_Levenshtein.egg-info\\entry_points.txt\r\n    writing namespace_packages to python_Levenshtein.egg-info\\namespace_packages.txt\r\n    writing requirements to python_Levenshtein.egg-info\\requires.txt\r\n    writing top-level names to python_Levenshtein.egg-info\\top_level.txt\r\n    reading manifest file 'python_Levenshtein.egg-info\\SOURCES.txt'\r\n    reading manifest template 'MANIFEST.in'\r\n    warning: no previously-included files matching '*pyc' found anywhere in distribution\r\n    warning: no previously-included files matching '*so' found anywhere in distribution\r\n    warning: no previously-included files matching '.project' found anywhere in distribution\r\n    warning: no previously-included files matching '.pydevproject' found anywhere in distribution\r\n    writing manifest file 'python_Levenshtein.egg-info\\SOURCES.txt'\r\n    copying Levenshtein\\_levenshtein.c -> build\\lib.win-amd64-3.7\\Levenshtein\r\n    copying Levenshtein\\_levenshtein.h -> build\\lib.win-amd64-3.7\\Levenshtein\r\n    running build_ext\r\n    building 'Levenshtein._levenshtein' extension\r\n    error: Microsoft Visual C++ 14.0 or greater is required. Get it with \"Microsoft C++ Build Tools\": https:\/\/visualstudio.microsoft.com\/visual-cpp-build-tools\/\r\n    ----------------------------------------\r\nERROR: Command errored out with exit status 1: 'C:\\ProgramData\\Anaconda3\\envs\\env\\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-record-v7l7zitb\\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\\ProgramData\\Anaconda3\\envs\\env\\Include\\python-Levenshtein' Check the logs for full command output.\r\n```\r\n\r\nHere are conda and python versions:\r\n\r\n```bat\r\n(env) C:\\testing\\datasets>conda --version\r\nconda 4.9.2\r\n\r\n(env) C:\\testing\\datasets>python --version\r\nPython 3.7.10\r\n```\r\n\r\nPlease help me out. Thanks.","comment_length":52,"text":"Unable to setup dev env on Windows \n Hi\r\n\r\nI tried installing the `\".[dev]\"` version on Windows 10 after cloning.\r\n\r\nHere is the error I'm facing:\r\n\r\n```bat\r\n(env) C:\\testing\\datasets>pip install -e \".[dev]\"\r\nObtaining file:\/\/\/C:\/testing\/datasets\r\nRequirement already satisfied: numpy>=1.17 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (1.19.5)\r\nCollecting pyarrow>=0.17.1\r\n  Using cached pyarrow-4.0.0-cp37-cp37m-win_amd64.whl (13.3 MB)\r\nRequirement already satisfied: dill in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (0.3.1.1)\r\nCollecting pandas\r\n  Using cached pandas-1.2.4-cp37-cp37m-win_amd64.whl (9.1 MB)\r\nRequirement already satisfied: requests>=2.19.0 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (2.25.1)\r\nRequirement already satisfied: tqdm<4.50.0,>=4.27 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (4.49.0)\r\nRequirement already satisfied: xxhash in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (2.0.2)\r\nCollecting multiprocess\r\n  Using cached multiprocess-0.70.11.1-py37-none-any.whl (108 kB)\r\nRequirement already satisfied: fsspec in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (2021.4.0)\r\nCollecting huggingface_hub<0.1.0\r\n  Using cached huggingface_hub-0.0.8-py3-none-any.whl (34 kB)\r\nRequirement already satisfied: importlib_metadata in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (4.0.1)\r\nRequirement already satisfied: absl-py in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (0.12.0)\r\nRequirement already satisfied: pytest in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (6.2.3)\r\nCollecting pytest-xdist\r\n  Using cached pytest_xdist-2.2.1-py3-none-any.whl (37 kB)\r\nCollecting apache-beam>=2.24.0\r\n  Using cached apache_beam-2.29.0-cp37-cp37m-win_amd64.whl (3.7 MB)\r\nCollecting elasticsearch\r\n  Using cached elasticsearch-7.12.1-py2.py3-none-any.whl (339 kB)\r\nRequirement already satisfied: boto3==1.16.43 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (1.16.43)\r\nRequirement already satisfied: botocore==1.19.43 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (1.19.43)\r\nCollecting moto[s3]==1.3.16\r\n  Using cached moto-1.3.16-py2.py3-none-any.whl (879 kB)\r\nCollecting rarfile>=4.0\r\n  Using cached rarfile-4.0-py3-none-any.whl (28 kB)\r\nCollecting tensorflow>=2.3\r\n  Using cached tensorflow-2.4.1-cp37-cp37m-win_amd64.whl (370.7 MB)\r\nRequirement already satisfied: torch in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (1.8.1)\r\nRequirement already satisfied: transformers in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (4.5.1)\r\nCollecting bs4\r\n  Using cached bs4-0.0.1-py3-none-any.whl\r\nCollecting conllu\r\n  Using cached conllu-4.4-py2.py3-none-any.whl (15 kB)\r\nCollecting langdetect\r\n  Using cached langdetect-1.0.8-py3-none-any.whl\r\nCollecting lxml\r\n  Using cached lxml-4.6.3-cp37-cp37m-win_amd64.whl (3.5 MB)\r\nCollecting mwparserfromhell\r\n  Using cached mwparserfromhell-0.6-cp37-cp37m-win_amd64.whl (101 kB)\r\nCollecting nltk\r\n  Using cached nltk-3.6.2-py3-none-any.whl (1.5 MB)\r\nCollecting openpyxl\r\n  Using cached openpyxl-3.0.7-py2.py3-none-any.whl (243 kB)\r\nCollecting py7zr\r\n  Using cached py7zr-0.15.2-py3-none-any.whl (66 kB)\r\nCollecting tldextract\r\n  Using cached tldextract-3.1.0-py2.py3-none-any.whl (87 kB)\r\nCollecting zstandard\r\n  Using cached zstandard-0.15.2-cp37-cp37m-win_amd64.whl (582 kB)\r\nCollecting bert_score>=0.3.6\r\n  Using cached bert_score-0.3.9-py3-none-any.whl (59 kB)\r\nCollecting rouge_score\r\n  Using cached rouge_score-0.0.4-py2.py3-none-any.whl (22 kB)\r\nCollecting sacrebleu\r\n  Using cached sacrebleu-1.5.1-py3-none-any.whl (54 kB)\r\nRequirement already satisfied: scipy in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (1.6.3)\r\nCollecting seqeval\r\n  Using cached seqeval-1.2.2-py3-none-any.whl\r\nCollecting sklearn\r\n  Using cached sklearn-0.0-py2.py3-none-any.whl\r\nCollecting jiwer\r\n  Using cached jiwer-2.2.0-py3-none-any.whl (13 kB)\r\nRequirement already satisfied: toml>=0.10.1 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (0.10.2)\r\nRequirement already satisfied: requests_file>=1.5.1 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from datasets==1.5.0.dev0) (1.5.1)\r\nRequirement already satisfied: texttable>=1.6.3 in 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already satisfied: atomicwrites>=1.0 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from pytest->datasets==1.5.0.dev0) (1.4.0)\r\nRequirement already satisfied: colorama in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from pytest->datasets==1.5.0.dev0) (0.4.4)\r\nCollecting pytest-forked\r\n  Using cached pytest_forked-1.3.0-py2.py3-none-any.whl (4.7 kB)\r\nCollecting execnet>=1.1\r\n  Using cached execnet-1.8.0-py2.py3-none-any.whl (39 kB)\r\nRequirement already satisfied: apipkg>=1.4 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from execnet>=1.1->pytest-xdist->datasets==1.5.0.dev0) (1.5)\r\nCollecting portalocker==2.0.0\r\n  Using cached portalocker-2.0.0-py2.py3-none-any.whl (11 kB)\r\nRequirement already satisfied: scikit-learn>=0.21.3 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from seqeval->datasets==1.5.0.dev0) (0.24.2)\r\nRequirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\envs\\env\\lib\\site-packages (from scikit-learn>=0.21.3->seqeval->datasets==1.5.0.dev0) (2.1.0)\r\nBuilding wheels for collected packages: python-Levenshtein\r\n  Building wheel for python-Levenshtein (setup.py) ... error\r\n  ERROR: Command errored out with exit status 1:\r\n   command: 'C:\\ProgramData\\Anaconda3\\envs\\env\\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d 'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-wheel-8jh7fm18'\r\n       cwd: C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\r\n  Complete output (27 lines):\r\n  running bdist_wheel\r\n  running build\r\n  running build_py\r\n  creating build\r\n  creating build\\lib.win-amd64-3.7\r\n  creating build\\lib.win-amd64-3.7\\Levenshtein\r\n  copying Levenshtein\\StringMatcher.py -> build\\lib.win-amd64-3.7\\Levenshtein\r\n  copying Levenshtein\\__init__.py -> build\\lib.win-amd64-3.7\\Levenshtein\r\n  running egg_info\r\n  writing python_Levenshtein.egg-info\\PKG-INFO\r\n  writing dependency_links to python_Levenshtein.egg-info\\dependency_links.txt\r\n  writing entry points to python_Levenshtein.egg-info\\entry_points.txt\r\n  writing namespace_packages to python_Levenshtein.egg-info\\namespace_packages.txt\r\n  writing requirements to python_Levenshtein.egg-info\\requires.txt\r\n  writing top-level names to python_Levenshtein.egg-info\\top_level.txt\r\n  reading manifest file 'python_Levenshtein.egg-info\\SOURCES.txt'\r\n  reading manifest template 'MANIFEST.in'\r\n  warning: no previously-included files matching '*pyc' found anywhere in distribution\r\n  warning: no previously-included files matching '*so' found anywhere in distribution\r\n  warning: no previously-included files matching '.project' found anywhere in distribution\r\n  warning: no previously-included files matching '.pydevproject' found anywhere in distribution\r\n  writing manifest file 'python_Levenshtein.egg-info\\SOURCES.txt'\r\n  copying Levenshtein\\_levenshtein.c -> build\\lib.win-amd64-3.7\\Levenshtein\r\n  copying Levenshtein\\_levenshtein.h -> build\\lib.win-amd64-3.7\\Levenshtein\r\n  running build_ext\r\n  building 'Levenshtein._levenshtein' extension\r\n  error: Microsoft Visual C++ 14.0 or greater is required. Get it with \"Microsoft C++ Build Tools\": https:\/\/visualstudio.microsoft.com\/visual-cpp-build-tools\/\r\n  ----------------------------------------\r\n  ERROR: Failed building wheel for python-Levenshtein\r\n  Running setup.py clean for python-Levenshtein\r\nFailed to build python-Levenshtein\r\nInstalling collected packages: python-Levenshtein, pytest-forked, pyppmd, pymongo, pyflakes, pydot, pycryptodome, pycodestyle, pyarrow, portalocker, pathspec, pandas, opt-einsum, oauth2client, nltk, mypy-extensions, multivolumefile, multiprocess, moto, mccabe, matplotlib, keras-preprocessing, huggingface-hub, hdfs, h5py, google-pasta, gast, flatbuffers, fastavro, execnet, et-xmlfile, entrypoints, crcmod, beautifulsoup4, bcj-cffi, avro-python3, astunparse, appdirs, zstandard, tldextract, tensorflow, sklearn, seqeval, sacrebleu, rouge-score, rarfile, pytest-xdist, py7zr, openpyxl, mwparserfromhell, lxml, langdetect, jiwer, isort, flake8, elasticsearch, datasets, conllu, bs4, black, bert-score, apache-beam\r\n    Running setup.py install for python-Levenshtein ... error\r\n    ERROR: Command errored out with exit status 1:\r\n     command: 'C:\\ProgramData\\Anaconda3\\envs\\env\\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-record-v7l7zitb\\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\\ProgramData\\Anaconda3\\envs\\env\\Include\\python-Levenshtein'\r\n         cwd: C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\r\n    Complete output (27 lines):\r\n    running install\r\n    running build\r\n    running build_py\r\n    creating build\r\n    creating build\\lib.win-amd64-3.7\r\n    creating build\\lib.win-amd64-3.7\\Levenshtein\r\n    copying Levenshtein\\StringMatcher.py -> build\\lib.win-amd64-3.7\\Levenshtein\r\n    copying Levenshtein\\__init__.py -> build\\lib.win-amd64-3.7\\Levenshtein\r\n    running egg_info\r\n    writing python_Levenshtein.egg-info\\PKG-INFO\r\n    writing dependency_links to python_Levenshtein.egg-info\\dependency_links.txt\r\n    writing entry points to python_Levenshtein.egg-info\\entry_points.txt\r\n    writing namespace_packages to python_Levenshtein.egg-info\\namespace_packages.txt\r\n    writing requirements to python_Levenshtein.egg-info\\requires.txt\r\n    writing top-level names to python_Levenshtein.egg-info\\top_level.txt\r\n    reading manifest file 'python_Levenshtein.egg-info\\SOURCES.txt'\r\n    reading manifest template 'MANIFEST.in'\r\n    warning: no previously-included files matching '*pyc' found anywhere in distribution\r\n    warning: no previously-included files matching '*so' found anywhere in distribution\r\n    warning: no previously-included files matching '.project' found anywhere in distribution\r\n    warning: no previously-included files matching '.pydevproject' found anywhere in distribution\r\n    writing manifest file 'python_Levenshtein.egg-info\\SOURCES.txt'\r\n    copying Levenshtein\\_levenshtein.c -> build\\lib.win-amd64-3.7\\Levenshtein\r\n    copying Levenshtein\\_levenshtein.h -> build\\lib.win-amd64-3.7\\Levenshtein\r\n    running build_ext\r\n    building 'Levenshtein._levenshtein' extension\r\n    error: Microsoft Visual C++ 14.0 or greater is required. Get it with \"Microsoft C++ Build Tools\": https:\/\/visualstudio.microsoft.com\/visual-cpp-build-tools\/\r\n    ----------------------------------------\r\nERROR: Command errored out with exit status 1: 'C:\\ProgramData\\Anaconda3\\envs\\env\\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\VKC~1\\\\AppData\\\\Local\\\\Temp\\\\pip-install-ynt_dbm4\\\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\\\setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-record-v7l7zitb\\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\\ProgramData\\Anaconda3\\envs\\env\\Include\\python-Levenshtein' Check the logs for full command output.\r\n```\r\n\r\nHere are conda and python versions:\r\n\r\n```bat\r\n(env) C:\\testing\\datasets>conda --version\r\nconda 4.9.2\r\n\r\n(env) C:\\testing\\datasets>python --version\r\nPython 3.7.10\r\n```\r\n\r\nPlease help me out. Thanks. \n Hi @gchhablani, \r\n\r\nThere are some 3rd-party dependencies that require to build code in C. In this case, it is the library `python-Levenshtein`.\r\n\r\nOn Windows, in order to be able to build C code, you need to install at least `Microsoft C++ Build Tools` version 14. You can find more info here: https:\/\/visualstudio.microsoft.com\/visual-cpp-build-tools\/","embeddings":[-0.4121342301,-0.0855779797,-0.0977274776,-0.1387811452,0.3114477396,0.068374902,0.3448515236,0.0465872362,-0.1001304686,0.15623492,0.0433793887,0.2424268872,0.1581842005,0.2162972242,0.0545784347,0.0232140273,0.1360678375,0.4390758872,-0.2185967714,0.037439011,-0.0659957454,-0.0208863225,-0.1535535008,-0.0436114408,0.3134841323,-0.0264739394,-0.2292296886,0.1753225178,-0.3534286618,-0.3225588799,0.4030125737,-0.1064584628,0.4329041839,0.1327629685,-0.0001171767,0.2016099095,0.3223023117,-0.1553241611,-0.0909361094,0.0166196972,-0.0741046444,-0.4187884331,-0.0570751615,-0.1152198911,0.0013719843,-0.315310955,0.1467733234,-0.3042214811,0.0935275629,0.1765608937,0.1489830762,0.0158207696,0.3976289928,-0.0599443838,-0.0897259861,0.2228936255,-0.1047845185,0.2224471718,0.3808677495,-0.0767932087,0.2623265088,-0.0150638493,0.2122566849,0.0888135731,0.1605111212,0.0911397189,0.8259810209,-0.3334536552,-0.0124987392,-0.0583257899,0.4455650151,-0.1256540567,-0.2552556992,0.2472820431,0.1414016038,-0.325605303,0.261488229,0.3790609241,-0.3644766808,0.0435973518,-0.362721324,-0.2574132085,-0.4395217001,0.0149611542,0.1180061549,0.0826293603,0.1625450402,0.1449573189,-0.0660314187,0.0814976096,0.4332493246,0.0780942217,-0.1706448048,0.0194142777,-0.1333711296,-0.1515952498,-0.2876423001,0.3090680242,-0.0119880065,0.0120718013,-0.3012442887,-0.2072132975,0.2847516239,0.0740561634,-0.0211096983,0.3143170476,0.0671262816,-0.0794963837,0.1001376882,-0.1034927294,0.4024324119,0.0954257473,-0.220410049,-0.4841091931,-0.1915215552,0.502032876,0.0810901076,-0.1381357461,-0.2252849638,-0.0552830324,-0.278065145,0.1433484256,0.2262274027,0.247914955,-0.0905870795,-0.0975939855,0.0245632939,0.2494825125,-0.2053817958,0.111318931,0.1260858774,0.1531590074,-0.4578863382,-0.3628825843,0.2566353381,-0.2950874269,0.3838174641,0.2732485831,-0.0795328692,0.2728429437,-0.0912644342,0.190502882,0.1253395081,0.2146690041,0.192412436,0.1868555695,-0.0017933784,-0.2911956906,-0.0418691561,0.2853320539,0.0168284662,-0.2246380895,-0.3471564054,0.1082542017,-0.1117702425,0.017555505,0.0353535265,-0.0690591186,0.0887759179,0.1203753427,-0.0526402183,-0.1925052106,0.0195803028,-0.1289805025,0.3793908656,0.0687448531,-0.4063456953,0.2841972411,0.077341266,-0.1427752674,0.1688283831,0.032434091,-0.0016664289,0.1482707858,-0.2415356338,-0.2094384283,-0.1005012766,-0.4556683302,-0.1719743758,0.3552168608,-0.0029001641,-0.4598074555,0.2365486175,-0.120121628,0.0676399097,-0.1652117074,-0.088309899,0.2276965827,-0.035136126,-0.2145301253,-0.168868646,-0.2638670802,0.1783894897,-0.0220463779,-0.0101095643,-0.2324422002,-0.0198157672,-0.1845362335,-0.0388986133,-0.1567320377,0.2039210051,0.381216675,0.5579845905,-0.0816559568,-0.0684605464,0.0482342988,-0.1448994875,0.159021914,-0.4344110489,0.107544288,-0.0217336845,0.149328202,-0.0466755331,0.0853103474,-0.4496676028,-0.3711802661,0.116788134,0.0079455134,0.3306221664,-0.0743468627,0.1798881143,0.0693052262,0.0324366949,0.1059975326,-0.166411072,0.1343197078,-0.7459347248,-0.0168207139,-0.1043569595,0.1043144912,0.0355914086,-0.0769519955,0.0382833369,0.1590626389,-0.1584957093,-0.3072966635,-0.041364219,-0.3169023693,0.2125235647,-0.5566606522,0.2742674053,0.1649557948,0.0273893774,0.1721837968,-0.0810752809,-0.1246714965,-0.2803698778,-0.0341622308,-0.0767948776,0.297168076,0.3042367697,0.0494527295,0.0207218789,-0.1933784485,0.4338948429,0.575902164,0.0313213542,-0.3306666613,0.2104430646,0.0414407551,-0.1986906677,-0.0196556337,-0.142107293,-0.0658592954,-0.0807882249,0.0347912349,0.1587107182,-0.0406574272,0.5600038171,0.1366593093,-0.0128204282,0.2236149907,-0.2414536029,-0.0290230308,0.4613452852,-0.0718432441,-0.0432940163,0.1160499156,-0.3231308162,-0.1007453874,-0.0150394756,-0.2076589614,0.4360066056,0.3666128814,-0.414539963,-0.0872983187,-0.4251069129,0.0179413594,-0.3845640123,-0.5058633089,-0.1485284567,0.072969012,0.2087071687,-0.1159329861,0.2937576473,0.378765434,0.1709450483,-0.1817639172,-0.0483662486,-0.0111401537,-0.0018849734,-0.0123814195,-0.2988424599,0.0734532699,0.352761358,0.1322950125,0.4342385232,0.0266307555,0.0442592315,-0.1747365743,-0.3875649571,-0.0144538507,-0.1596354544,0.7915337682,0.1893676966,0.1281995326,-0.0219017398,-0.2373072803,0.2668535709,0.046285782,-0.2804477513,0.0047871685,-0.2273965627,-0.4810552001,-0.180219084,-0.3516542315,-0.1246601343,-0.0893754438,-0.107180655,-0.1341093034,-0.0714597255,0.4129341841,-0.0137822358,-0.0348017626,0.2470746636,-0.0075236536,-0.0635410845,0.111187987,0.2345679998,-0.4932157695,-0.4311946332,0.2324139327,0.0099825086,0.1128880084,0.1875332743,-0.2512127757,-0.1148042381,-0.1031353101,0.0084202457,-0.0645647049,0.2379448414,0.4715803862,0.2665568888,0.0352040157,-0.0425962657,-0.2322521061,0.1093985289,-0.0791745335,0.4247240722,0.2350768745,0.577147603,0.0135583226,0.4463897347,0.7228881121,0.0914104655,0.2535391748,-0.0413708538,0.3796830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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2300","title":"Add VoxPopuli","comments":"I'm happy to take this on:) One question: The original unlabelled data is stored unsegmented (see e.g. https:\/\/github.com\/facebookresearch\/voxpopuli\/blob\/main\/voxpopuli\/get_unlabelled_data.py#L30), but segmenting the audio in the dataset would require a dependency on something like soundfile or torchaudio. An alternative could be to provide the segments start and end times as a Sequence and then it's up to the user to perform the segmentation on-the-fly if they wish?","body":"## Adding a Dataset\r\n- **Name:** Voxpopuli\r\n- **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2101.00390\r\n- **Data:** https:\/\/github.com\/facebookresearch\/voxpopuli\r\n- **Motivation:** biggest unlabeled speech dataset\r\n\r\n**Note**: Since the dataset is so huge, we should only add the config `10k` in the beginning.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":65,"text":"Add VoxPopuli \n ## Adding a Dataset\r\n- **Name:** Voxpopuli\r\n- **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2101.00390\r\n- **Data:** https:\/\/github.com\/facebookresearch\/voxpopuli\r\n- **Motivation:** biggest unlabeled speech dataset\r\n\r\n**Note**: Since the dataset is so huge, we should only add the config `10k` in the beginning.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I'm happy to take this on:) One question: The original unlabelled data is stored unsegmented (see e.g. https:\/\/github.com\/facebookresearch\/voxpopuli\/blob\/main\/voxpopuli\/get_unlabelled_data.py#L30), but segmenting the audio in the dataset would require a dependency on something like soundfile or torchaudio. An alternative could be to provide the segments start and end times as a Sequence and then it's up to the user to perform the segmentation on-the-fly if they wish?","embeddings":[-0.2925267816,0.2328721285,-0.0486936197,-0.075351961,-0.1558787078,-0.1852182746,0.3809297681,0.2100731581,-0.0260843243,0.2548180521,-0.2955557704,0.0939441025,-0.5140134096,0.2282823771,-0.0125598451,-0.3366333544,0.0352492854,0.1592858881,0.1379971355,-0.164063409,-0.1692886204,-0.1792166829,-0.3667643964,-0.3199285865,-0.2815556526,-0.0437956937,-0.1714102328,-0.2002765089,-0.2472260892,-0.4485911429,-0.2518495023,0.0144170914,0.2051721662,0.7153629661,-0.0001053368,-0.2415970266,-0.0325996578,-0.2413592637,-0.1185308173,0.1629431397,-0.1493930668,-0.00040988,-0.5305423737,-0.1669961512,-0.2364865094,-0.1963237971,-0.0257730559,-0.3211324215,0.2954051197,0.1716529131,0.2048163861,-0.2917951643,-0.0625556633,-0.0694862232,0.2304438353,0.0651996434,-0.0031293798,-0.092455022,0.0819319785,0.1289016902,0.0632565841,0.4567219317,0.048035495,-0.2020329684,0.1953832358,-0.2308383435,-0.3671851754,-0.4931638539,-0.1287543923,0.5815055966,0.6085772514,-0.2704017162,-0.3155983984,-0.1887135506,0.017296046,-0.3431016505,-0.3455108106,0.3020210564,-0.1481228024,0.1379403323,-0.0139239151,-0.3403760493,-0.139805913,0.09456072,0.0610956997,0.3523587286,0.0075402851,-0.0270667337,-0.0055152192,-0.2872108817,-0.4808892012,0.0372110829,0.2388414741,0.4193492532,-0.0955717415,-0.3250354528,-0.1058607474,-0.1411993951,0.1988049746,0.0749901086,-0.0428450331,0.0520790927,0.1216937453,-0.0533867478,0.2828865647,0.0018168306,-0.0796325281,0.06189657,0.106637165,-0.3097324371,0.0792736784,0.07797914,-0.207936123,0.0881983042,-0.2041271627,0.0717201829,-0.0003148082,-0.0222755671,-0.036746826,0.0304692071,-0.3467599452,-0.0008884933,-0.0230709445,0.0554772355,-0.2168714851,-0.0929948613,-0.1684412062,0.2591876388,-0.0406800956,-0.0559704751,0.0351964869,0.1009159461,-0.1506544203,0.2815228701,0.222290054,-0.0228016302,-0.0549538657,0.1347343624,0.2096423507,0.0041599385,-0.1650160104,-0.0868010521,0.2816715837,-0.1880036741,-0.0206576772,0.0136978477,-0.4079439044,0.2230240554,-0.2369116098,-0.0792811513,0.2260556221,-0.2475363463,-0.3368155956,0.2681538761,-0.2152198553,0.0765840858,0.3830368519,0.4839094579,0.0821424276,-0.4656518102,0.1455120295,0.1029925793,-0.0668397248,-0.0582668446,0.2434944659,0.1746969968,-0.4136834145,-0.1219652221,0.1921569556,0.1991189867,0.0906349123,0.2032759637,-0.0010413016,0.1211841404,0.0335892662,0.3394368589,-0.0144694233,-0.2765723765,-0.2051727325,-0.003992503,-0.070061788,0.0412313417,0.0307181757,0.1075341478,0.7296260595,-0.2890873849,0.293964684,0.3556069434,-0.0669099241,-0.0297854375,-0.135075286,-0.3457042277,0.1745294929,0.2376138568,-0.0093147606,-0.0910741836,-0.2259638458,0.4240358472,0.279981941,-0.1462579668,0.2492959648,0.0866705701,0.4744336605,0.0259649679,0.0415912792,-0.5515688062,-0.0342338122,-0.0477552228,-0.0555826649,0.3149366677,0.0476558357,-0.4406020045,-0.0364435166,-0.3342244029,0.0223343838,-0.001787528,0.1680096388,0.0543685779,0.1334091276,0.0449331328,-0.5313402414,-0.3334075511,0.3088917732,-0.0396379344,-0.094154723,0.0056672106,0.0476123542,-0.0103824763,0.1713317186,0.411060065,0.203959614,-0.1203439832,0.3768965304,0.3011947274,0.09832187,-0.0104773622,-0.3251622915,0.1853755116,0.2171079665,-0.3233448565,0.1568763852,0.3292605579,0.1090728939,-0.223283127,-0.0851829723,0.2577805519,0.4536969066,0.1915163845,0.0335362069,-0.175367713,-0.1085515618,-0.0654786825,-0.2391389757,-0.1759951264,-0.066945523,0.0106972661,0.135772109,0.1745662391,-0.3150488734,0.1541749984,0.4628355205,-0.2876065373,0.1285879761,-0.0026041064,-0.3632547557,-0.2040610015,-0.016408056,-0.4502245784,0.0546335839,0.2087284923,0.2128758132,0.0812147558,0.3439451456,-0.0911908746,0.017957285,0.1442548782,-0.0896611065,0.3438565731,-0.0715648979,0.0123671293,-0.0352526866,-0.3124836981,-0.0005498257,-0.0457016751,0.0406434536,-0.1953156739,0.0758681744,-0.2181125283,0.0700938851,-0.6984212995,-0.1143324971,0.1414815634,0.3873268664,-0.1372511089,-0.1164514124,0.3872788846,-0.0124242669,0.6995114684,-0.0220749285,-0.1269961894,0.0451534875,-0.0926728398,0.1592124701,0.1802596897,-0.0271615721,0.2576533854,-0.0187917836,0.1747030169,0.0832940489,0.2450941056,-0.0755416676,0.2807094455,-0.0216200445,0.1031171903,0.0060365158,-0.034200836,0.2087468207,-0.3511863351,-0.1773940772,-0.0107193682,0.0613081343,-0.1447474808,-0.2861626446,0.2674386501,-0.0853765234,-0.2339542061,-0.6628136635,-0.4774103761,0.3154145181,0.3436959088,0.1062131599,0.0269194786,-0.1817506254,0.0004490287,-0.0913956165,0.1175121516,-0.194193244,-0.0850744992,0.3067539334,-0.3033916652,-0.1011050642,-0.2366933972,-0.1138288975,0.0029071958,0.025774572,-0.0672825202,0.3524060249,0.0338186473,-0.0124957217,-0.0293374248,-0.1199404076,0.1766284406,-0.0328032449,-0.0975781158,-0.0202307887,0.1687162369,-0.1106215864,0.1685243696,0.0209706128,-0.0062213643,-0.1654156148,0.1592139006,0.9956915975,0.4840340614,0.0175147466,-0.0846361965,-0.0097522605,0.0726347044,-0.1796179265,-0.0587085858,0.55327713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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2300","title":"Add VoxPopuli","comments":"Hey @jfainberg,\r\n\r\nThis sounds great! I think adding a dependency would not be a big problem, however automatically segmenting the data probably means that it would take a very long time to do:\r\n\r\n```python\r\ndataset = load_dataset(\"voxpopuli\", \"french\")\r\n```\r\n\r\n=> so as a start I think your option 2 is the way to go!","body":"## Adding a Dataset\r\n- **Name:** Voxpopuli\r\n- **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2101.00390\r\n- **Data:** https:\/\/github.com\/facebookresearch\/voxpopuli\r\n- **Motivation:** biggest unlabeled speech dataset\r\n\r\n**Note**: Since the dataset is so huge, we should only add the config `10k` in the beginning.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":54,"text":"Add VoxPopuli \n ## Adding a Dataset\r\n- **Name:** Voxpopuli\r\n- **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2101.00390\r\n- **Data:** https:\/\/github.com\/facebookresearch\/voxpopuli\r\n- **Motivation:** biggest unlabeled speech dataset\r\n\r\n**Note**: Since the dataset is so huge, we should only add the config `10k` in the beginning.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hey @jfainberg,\r\n\r\nThis sounds great! I think adding a dependency would not be a big problem, however automatically segmenting the data probably means that it would take a very long time to do:\r\n\r\n```python\r\ndataset = load_dataset(\"voxpopuli\", \"french\")\r\n```\r\n\r\n=> so as a start I think your option 2 is the way to go!","embeddings":[-0.3622044623,0.2060509473,-0.0382300317,0.0014168692,-0.1214067563,-0.1296067238,0.2858929932,0.3488551676,0.1810037941,0.2673174739,-0.1220442802,0.1734330058,-0.5791192651,0.248657003,0.1030062363,-0.1610254347,0.1628734767,0.2866100371,0.095686391,-0.1880006641,-0.1290652156,-0.0335456841,-0.2270617336,-0.2975462079,-0.3531153798,0.0726715773,-0.1824733466,-0.2084129304,-0.1638894826,-0.2925849855,-0.2777968347,0.0334550031,0.2236543447,0.5653854609,-0.0001063294,-0.1449197084,-0.0884732082,-0.3044509292,0.0654772148,-0.0746489689,-0.0837001726,-0.1278287917,-0.4064903855,-0.0930253044,-0.2304193527,-0.14254345,-0.0099482238,-0.2797588408,0.219080478,0.2948248982,0.2410483956,-0.0154664135,-0.0557157435,-0.1818623245,0.1919789463,0.0053661554,-0.0167084429,-0.1235181242,0.0366831571,0.0193053875,0.0953444839,0.5634723306,-0.0203446001,-0.2623254657,-0.0066587911,-0.0895410776,-0.12987113,-0.4109855294,-0.0241050459,0.4772322774,0.4497810006,-0.2047097981,-0.2180188149,-0.1389561445,0.0822957903,-0.2967887819,-0.1726078689,0.2062319666,-0.0013450619,0.155995369,0.1247609481,-0.3047011495,-0.0445498005,0.2276000381,-0.0649357587,0.4114827216,-0.0107397391,-0.1321664602,0.0144946594,-0.2984534502,-0.3767269552,0.2829953432,0.2045624703,0.2323184609,-0.1000517011,-0.3026239574,0.1004269496,-0.0496396273,0.1847789288,-0.0104825487,-0.1364692301,0.1110458747,0.097645916,0.0904042274,0.2405513525,-0.0651146472,-0.0079654716,-0.1032886803,0.1962272078,-0.2124027163,-0.1570116132,0.1893924028,-0.2619791031,0.115580909,-0.0177203119,0.0266590323,0.0966506302,-0.1736463159,0.017109666,0.2102119476,-0.1320615262,0.0434758067,-0.0822213665,0.3474678397,-0.1171204597,0.2644560337,-0.0594007261,0.3889173269,-0.1311837435,-0.2085464597,-0.033891499,0.1506564766,-0.0409684964,0.2504803836,0.182081297,0.1834037602,0.0814338103,0.0801912248,0.4036087692,0.0462034941,0.0749738216,-0.0386664495,0.1496947408,-0.2442623377,0.0992058143,-0.0477506556,-0.0776870847,-0.0466320142,-0.2552615106,-0.0784299299,0.2434822172,-0.2535855472,-0.2981429398,0.2990464866,-0.1131108627,-0.1176313832,0.2105854005,0.6592457294,-0.0265016109,-0.3424615562,-0.0631873459,0.0526253656,-0.1718238741,-0.0150967557,0.149996385,0.2983689904,-0.317938596,-0.198401913,0.0946157202,0.2202681154,0.0514327325,0.2936134636,-0.0016879287,0.0959733129,-0.0085592382,0.223138392,0.0867227092,-0.3522504866,-0.3762060404,0.0857585296,-0.0944087133,0.037445046,0.1689456701,0.205665946,0.5524084568,-0.2033003867,0.342663765,0.4188266098,-0.0198924486,-0.0120015536,-0.093754299,-0.3128604293,0.2254299968,0.3783738017,0.0043753078,0.0188300516,-0.266597122,0.3279888034,0.3670642376,-0.2761883736,0.1107815653,0.1414887607,0.4262054265,0.0683253035,-0.0993256941,-0.454015255,-0.0165982731,-0.0257257037,-0.0956509784,0.3194210231,-0.0702558458,-0.5392349958,0.0250341278,-0.3108381629,0.0048708576,0.0713820234,0.2480750084,0.1039580703,0.1168510467,0.0260644965,-0.4303718209,-0.031660486,0.1560540497,-0.1150336713,-0.1255701929,0.1169828102,-0.019477766,-0.1644623131,0.2081956714,0.2909441292,0.0755520463,0.0528743863,0.2668861747,0.0709270313,0.1357492059,-0.1132397428,-0.0462313034,0.0530095659,-0.0085947737,-0.384639442,0.0634869039,0.3921803832,-0.0621207058,-0.0536217839,0.0895175114,0.213598296,0.497148931,0.0607669316,-0.0248055328,-0.0618537515,0.0949490741,-0.0250037238,-0.1085492298,-0.2827538848,0.1236067936,0.1179459691,0.1139038429,0.1439710408,-0.433542192,0.0984679684,0.4523300827,-0.1298229843,0.385292381,0.048118487,-0.3084796667,-0.01942176,-0.0551850833,-0.3254057765,0.1220445335,0.3093282282,0.0621400326,-0.0152722774,0.2443618625,-0.0327318683,0.0469609983,0.0170565862,-0.198391661,0.2099432796,0.0601658225,-0.1522661448,-0.2575821877,-0.3199756145,-0.0662788898,-0.0185559131,0.097553961,-0.2175047547,0.0117299501,-0.0706099719,0.0489459261,-0.3854290545,-0.1949515641,0.1977466047,0.3800057173,-0.0833938792,-0.1135616079,0.2648079097,-0.0519088171,0.4458293021,-0.0833611116,-0.3190987706,0.1689811498,-0.1556269825,0.0674682334,0.1898882389,0.003277397,0.2206191272,0.0939170271,-0.0385639891,0.0312705263,0.1009206176,-0.1914321482,0.1785158366,-0.1373093128,0.0531251431,0.0208643079,0.0789514706,0.0419875495,-0.2782662809,-0.0537762046,-0.1046979651,0.1359056979,-0.0085710324,-0.2253914326,0.1661050171,0.0294249989,-0.3284982443,-0.7819321752,-0.564748764,0.2430546135,0.3136271536,0.1024924964,-0.0683986768,-0.1409730017,-0.0563666038,-0.1719581187,0.2470577806,-0.2032077312,0.0324627794,0.4612770379,-0.2707383037,-0.1865020841,-0.2920601368,-0.2375630289,0.0957440287,-0.1553489566,-0.2603883743,0.2003104389,-0.1632536799,0.0965487212,-0.0052643623,-0.026227301,0.3263149858,0.1320692152,-0.1316966712,-0.0994005501,0.1153095961,-0.0704140589,0.13906084,-0.0816311762,0.0134751415,-0.1746074855,0.031437844,0.6643480659,0.3344547451,0.0125743132,0.1750955582,-0.001384374,0.1119310111,-0.122755155,-0.1437574029,0.1638988107,-0.056109622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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2294","title":"Slow #0 when using map to tokenize.","comments":"Hi ! Have you tried other values for `preprocessing_num_workers` ? Is it always process 0 that is slower ?\r\nThere are no difference between process 0 and the others except that it processes the first shard of the dataset.","body":"Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=args.preprocessing_num_workers,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=not args.overwrite_cache,\r\n        )` to tokenize by multiprocessing. However, I have found that when `num_proc`>1\uff0cthe process _#0_ is much slower than others.\r\nIt looks like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/31714566\/116665555-81246280-a9cc-11eb-8a37-6e608ab310d0.png)\r\nIt takes more than 12 hours for #0, while others just about half an hour. Could anyone tell me it is normal or not, and is there any methods to speed up it?\r\n","comment_length":39,"text":"Slow #0 when using map to tokenize. \n Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=args.preprocessing_num_workers,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=not args.overwrite_cache,\r\n        )` to tokenize by multiprocessing. However, I have found that when `num_proc`>1\uff0cthe process _#0_ is much slower than others.\r\nIt looks like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/31714566\/116665555-81246280-a9cc-11eb-8a37-6e608ab310d0.png)\r\nIt takes more than 12 hours for #0, while others just about half an hour. Could anyone tell me it is normal or not, and is there any methods to speed up it?\r\n \n Hi ! Have you tried other values for `preprocessing_num_workers` ? Is it always process 0 that is slower ?\r\nThere are no difference between process 0 and the others except that it processes the first shard of the dataset.","embeddings":[-0.4360868633,-0.3340421319,-0.0331812017,-0.0555871613,0.0697014108,-0.1944787651,0.3812371194,0.2073157132,-0.2748001516,0.0517776236,0.3157570064,0.3878782392,-0.2385890484,-0.0081708841,-0.2220509499,0.1165851951,0.1717216223,0.1350863129,0.3698132932,-0.1361592263,-0.2459045947,-0.0289522111,-0.4537950158,0.1861464381,-0.5515934229,-0.0939204022,0.0168486442,0.0239229016,-0.1578884572,-0.332066685,-0.2777600884,0.0381636694,0.0004328316,0.3452011943,-0.0001291091,-0.2028736472,0.3343115449,0.3039593399,0.0683973506,-0.0113468543,0.1007517427,-0.4201109111,-0.0934073478,-0.0518576428,0.0113793248,0.0090428917,0.2106573284,-0.080221355,0.1935958713,0.0336161815,0.0578775331,0.3355197012,-0.4118835628,0.3002010286,-0.0285426844,0.1999465078,-0.0942220986,-0.0267875493,0.1064961553,-0.1693147868,0.1627754867,0.1288421005,-0.2492694855,-0.0105363671,0.2192675769,0.1379474849,0.5077482462,-0.4718617797,0.341483444,0.242095232,-0.1116657183,0.1791999191,-0.3060411811,-0.3064305782,0.0190430563,-0.4115245938,-0.002620022,0.0045570042,0.0472666025,-0.0208514314,-0.4781558216,0.1738860011,0.1423054338,0.0237278379,-0.2842411101,0.6366424561,0.0835530832,0.120980911,0.1686219275,-0.1988871247,-0.2149642408,-0.2544279397,0.1541729122,0.5229039788,-0.1893164515,-0.1005733088,-0.1133739948,-0.0243343841,-0.2357409447,-0.3004845083,-0.3913839161,0.3871785998,0.0365640409,0.1213994995,0.0062340447,-0.0791992918,0.0439269617,0.2379095554,0.2816818357,-0.4815149307,-0.2453261167,0.0616535023,-0.2930653095,-0.1480608732,-0.0174206942,0.1158762053,-0.21604608,-0.0180586483,-0.0678548515,0.0642534271,-0.3971650302,0.0320900157,0.3310677409,0.1310614496,-0.055100929,0.5344244838,-0.35351336,-0.0543107614,-0.512452662,0.0594564341,-0.0384577923,-0.1878851056,-0.2003136426,0.1468878388,-0.0343806148,0.1941287965,0.1312454641,0.0913665071,0.175832212,0.0764411837,0.553352952,-0.2733448148,0.2202516645,0.2896812558,-0.2941050529,0.4735400081,-0.0568000562,0.2583467662,-0.2132853121,0.4684457183,-0.513767004,-0.4455690384,0.1040069386,-0.0502087399,-0.0349195972,0.2365925312,-0.2229654044,0.3019234538,0.3209953606,-0.0065404489,-0.1902328581,-0.2581340671,-0.569391191,-0.0931628868,0.2414494753,0.2863372564,-0.128432557,0.0288027264,0.1292352378,0.2965081632,0.7083096504,0.3495642841,-0.1610260755,0.1975356489,-0.1121664122,0.5817081928,-0.0573083386,-0.0024948099,-0.1221966818,0.331412971,-0.3430331349,-0.1398524791,-0.0191545077,0.0702573508,0.2119109631,-0.1398646832,0.207643643,0.0084966999,-0.0263468977,0.3012176156,-0.4460811317,0.0836742297,0.2569980323,-0.0578046776,-0.1963777393,-0.1404869556,-0.3174522519,0.0300512332,0.0254321173,-0.0975899547,0.1209555939,0.0960759223,0.151537329,-0.1494010538,0.0074114809,0.0793573335,0.0448698588,0.1637542546,-0.1777203679,0.057131514,0.0228353757,-0.0804902911,0.1946886182,-0.0073325098,-0.0777315572,0.0006750442,-0.1042289138,-0.1330376565,-0.171391651,-0.0241134223,-0.0527573675,0.0256196428,0.0285961777,-0.0098101199,0.1717209369,-0.1269735247,0.0856135413,-0.1991586685,-0.3183270991,0.1766962707,0.293682754,-0.1260847151,0.0239189956,0.3493389785,0.339012146,0.0682437867,-0.0969691351,0.2577645183,0.3222962618,0.225922659,0.2996601462,-0.2068662196,-0.0924339071,-0.2335605621,0.2150356472,0.869019866,0.3758693337,0.2990359664,-0.0244135521,-0.2960143089,-0.0128675206,0.282496959,-0.0023054213,-0.0397009999,0.2367777079,0.3676816523,0.3466972709,0.2306513637,-0.0149930604,0.1968118399,0.136352554,-0.2129914761,-0.1881495118,0.0376621708,-0.034360297,-0.1988098323,0.1022654697,-0.5859146714,0.2442331463,-0.0352273881,0.1711592525,-0.0063750111,-0.1822370589,-0.0630133227,0.1467841268,0.1539622694,0.0412814133,-0.0272227563,0.1279747784,0.070094496,-0.0916842371,-0.1123032123,0.220798403,0.5185967088,-0.22422041,0.1264094859,-0.1815513223,0.3484581411,-0.0092580831,-0.1181666404,-0.1135653481,-0.2855651975,-0.0233045779,0.0805970505,0.1668619514,0.1096139997,-0.014418059,-0.0733010918,-0.1003982276,0.2277217209,-0.0665105879,-0.0254166946,-0.2751339674,-0.1501680911,0.3499274254,0.0754394457,0.0420038253,-0.0100306198,-0.3488819301,0.0729177147,-0.5219573975,-0.0330346897,0.0633742213,-0.0331862979,0.0179083683,0.0217051264,-0.5248930454,0.1516859829,0.1539483964,-0.1330841929,-0.2944366634,-0.0114098564,-0.2839923501,-0.23933658,-0.0805285797,0.1574743837,-0.0880601853,0.3033662438,0.1242295206,-0.1432494968,0.1995020509,-0.1756274104,-0.1001195386,0.1543434262,-0.0461929254,-0.3905456066,-0.0550690331,-0.2851268351,0.3271590769,-0.1026911139,0.0898140296,0.031749934,-0.0419975035,-0.0872765854,0.1167670116,0.0802402645,0.0108845262,-0.0897263885,0.0274961051,-0.0580018535,-0.0622239858,0.3971161842,0.1535115093,0.0334778503,0.1151597127,-0.3876470029,-0.1599521935,0.0064627104,-0.0498174839,0.0998841226,0.3346404731,0.0874356255,0.9783514738,0.1062373146,-0.2804869413,-0.1336958259,-0.1836688668,-0.2738057971,0.0565302372,0.0110868458,0.2926231027,0.3481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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2294","title":"Slow #0 when using map to tokenize.","comments":"Hi, I have found the reason of it. Before using the map function to tokenize the data, I concatenate the wikipedia and bookcorpus first, like this:\r\n```if args.dataset_name1 is not None:\r\n        dataset1 = load_dataset(args.dataset_name1, args.dataset_config_name1, split=\"train\")\r\n        dataset1 = dataset1.remove_columns('title')\r\n        if args.dataset_name2 is not None:\r\n            dataset2 = load_dataset(args.dataset_name2, args.dataset_config_name2,split=\"train\")\r\n            assert dataset1.features.type == dataset2.features.type, str(dataset1.features.type)+';'+str(dataset2.features.type)\r\n           datasets12 = concatenate_datasets([dataset1, dataset2], split='train')\r\n```\r\nWhen I just use one datasets, e.g. wikipedia, the problem seems no longer exist:\r\n![image](https:\/\/user-images.githubusercontent.com\/31714566\/116967059-13d24380-ace4-11eb-8d14-b7b9c9a275cc.png)\r\n\r\nBookcorpus has more row numbers than Wikipedia, however, it takes much more time to process each batch of wiki than that of bookcorpus. When we first concatenate two datasets and then use _map_ to process the concatenated datasets,  e.g. `num_proc=5`, process 0 has to process all of the wikipedia data, causing the problem that #0 takes a longer time to finish the job. \r\n\r\nThe problem is caused by the different characteristic of different datasets. One solution might be using _map_ first to process two datasets seperately, then concatenate the tokenized and processed datasets before input to the `Dataloader`.\r\n\r\n","body":"Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=args.preprocessing_num_workers,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=not args.overwrite_cache,\r\n        )` to tokenize by multiprocessing. However, I have found that when `num_proc`>1\uff0cthe process _#0_ is much slower than others.\r\nIt looks like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/31714566\/116665555-81246280-a9cc-11eb-8a37-6e608ab310d0.png)\r\nIt takes more than 12 hours for #0, while others just about half an hour. Could anyone tell me it is normal or not, and is there any methods to speed up it?\r\n","comment_length":172,"text":"Slow #0 when using map to tokenize. \n Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=args.preprocessing_num_workers,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=not args.overwrite_cache,\r\n        )` to tokenize by multiprocessing. However, I have found that when `num_proc`>1\uff0cthe process _#0_ is much slower than others.\r\nIt looks like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/31714566\/116665555-81246280-a9cc-11eb-8a37-6e608ab310d0.png)\r\nIt takes more than 12 hours for #0, while others just about half an hour. Could anyone tell me it is normal or not, and is there any methods to speed up it?\r\n \n Hi, I have found the reason of it. Before using the map function to tokenize the data, I concatenate the wikipedia and bookcorpus first, like this:\r\n```if args.dataset_name1 is not None:\r\n        dataset1 = load_dataset(args.dataset_name1, args.dataset_config_name1, split=\"train\")\r\n        dataset1 = dataset1.remove_columns('title')\r\n        if args.dataset_name2 is not None:\r\n            dataset2 = load_dataset(args.dataset_name2, args.dataset_config_name2,split=\"train\")\r\n            assert dataset1.features.type == dataset2.features.type, str(dataset1.features.type)+';'+str(dataset2.features.type)\r\n           datasets12 = concatenate_datasets([dataset1, dataset2], split='train')\r\n```\r\nWhen I just use one datasets, e.g. wikipedia, the problem seems no longer exist:\r\n![image](https:\/\/user-images.githubusercontent.com\/31714566\/116967059-13d24380-ace4-11eb-8d14-b7b9c9a275cc.png)\r\n\r\nBookcorpus has more row numbers than Wikipedia, however, it takes much more time to process each batch of wiki than that of bookcorpus. When we first concatenate two datasets and then use _map_ to process the concatenated datasets,  e.g. `num_proc=5`, process 0 has to process all of the wikipedia data, causing the problem that #0 takes a longer time to finish the job. \r\n\r\nThe problem is caused by the different characteristic of different datasets. One solution might be using _map_ first to process two datasets seperately, then concatenate the tokenized and processed datasets before input to the `Dataloader`.\r\n\r\n","embeddings":[-0.4216788113,-0.2463773936,-0.0001557727,0.0670946091,0.0309756473,-0.2085657269,0.4310260415,0.2237498313,-0.2545164824,0.0450726971,0.2334742099,0.3255723715,-0.141471222,0.0176063161,-0.2975897789,0.0944023654,0.1700220108,0.1978500187,0.3180062473,-0.106922403,-0.1142958179,-0.0325482376,-0.513414681,0.0937799811,-0.516996026,-0.094234772,-0.052355025,-0.008509526,-0.087839283,-0.3727582395,-0.1796108931,0.0533387512,0.0209058262,0.3229079843,-0.0001312324,-0.2167607844,0.321980685,0.251037389,-0.077723369,0.0198327024,0.0318119526,-0.3308933377,-0.077887468,-0.1234237701,0.0175319202,0.0237414055,0.0749674439,-0.2054030895,0.0913552269,0.0141805075,0.0364925042,0.3499347866,-0.3504897058,0.2594860792,-0.0747995898,0.2026159912,-0.0626254454,-0.0495990664,0.1012017727,-0.1707479507,0.1734009683,0.2813491523,-0.2406679839,-0.0495627448,0.1356945932,0.1603334695,0.4377095103,-0.4210774601,0.3177548945,0.2465801984,-0.0422619022,0.097834304,-0.3110384643,-0.2917485833,0.0804972127,-0.2755731344,-0.0260986835,0.0504077449,0.0243027862,-0.0064566298,-0.427964747,0.1010868847,0.1111115143,0.1455284357,-0.2755873501,0.5521758199,0.1067677513,0.2040685415,0.1689036191,-0.2546339631,-0.3334892988,-0.4314102232,0.1836215258,0.477684617,-0.287428081,-0.1189518645,-0.155375123,-0.2407781035,-0.1358047873,-0.3256503642,-0.4295946956,0.358568728,0.031497594,0.0367216021,0.1139290407,0.0111999372,0.1415977031,0.3352188766,0.2813811004,-0.3873303533,-0.2329002619,0.0947895348,-0.3194718063,0.0019109213,-0.1017474458,0.0573491007,-0.1384474933,-0.0624518916,-0.0863149762,0.0299601927,-0.4705274999,0.0324194096,0.3583807647,0.1112597138,-0.0891091153,0.556709826,-0.3564697802,-0.0558779985,-0.4742580652,0.1034273878,-0.0102328192,-0.1693373024,-0.2180451304,0.1857894063,0.0246342495,0.0998852178,0.0603745207,0.1137021706,0.1227449849,0.0288294069,0.5027255416,-0.393191725,0.2299547344,0.2137600332,-0.2416318357,0.5415733457,-0.0803955272,0.0517807752,-0.2654265463,0.4353518784,-0.5407435894,-0.4055198133,-0.0378012881,-0.0362009592,-0.0213706996,0.2882744372,-0.2898297608,0.3646245897,0.3560539782,-0.0592743903,-0.2194041908,-0.1098218486,-0.4298143387,-0.0662313327,0.1920270473,0.3820966482,-0.08586815,-0.0251053963,0.2118619382,0.364710182,0.7044425607,0.4722286463,-0.2808420956,0.1810291409,-0.1422220021,0.6345632076,0.1009041518,-0.2661673129,-0.1717864126,0.4077458084,-0.3184235096,-0.0377777442,0.0876825601,0.0555645339,0.1874470711,-0.1130772382,0.2441991717,0.1069103107,0.0766589344,0.3537717462,-0.4231739938,0.0233156718,0.3323550224,-0.0467140079,-0.2578987777,-0.1601985991,-0.2706966102,0.0933790952,0.073723577,-0.1934672594,0.1603425294,0.243669048,0.1488006413,-0.1971042007,0.0145643512,0.1307732165,-0.1473884583,0.1625458449,-0.0961497277,0.1292523295,-0.0469140559,-0.2002084106,0.1997577399,-0.1230454147,-0.1088268906,-0.0366090201,-0.1214845404,-0.1047692671,-0.1175522581,0.0085352669,0.0045816242,0.0854114592,0.0250084065,-0.0044681425,0.0385741219,-0.0387144461,0.1060211733,-0.0975360945,-0.2942665517,0.2391216606,0.2922899127,-0.1670206636,0.0466096327,0.3752070367,0.2724765241,0.0439519696,-0.0118864747,0.1884023398,0.3430142701,0.1958228052,0.2216542363,-0.1940376312,0.005526871,-0.2589847744,0.0628024861,0.8033874035,0.4351347685,0.3787606359,0.0590223223,-0.310072422,0.0016223151,0.1633840501,-0.0873767063,-0.1206291765,0.1915403157,0.3726432323,0.3853441179,0.3481548131,-0.0425087102,0.2032209188,0.2762518227,-0.1238583177,-0.1909551322,0.0339910053,-0.1010523364,-0.2211484015,0.1905425787,-0.5782694817,0.3371144533,0.0041400166,0.1818534732,-0.0196053367,-0.1397617459,-0.0293810517,0.208451733,0.1059032381,-0.0174112022,-0.0055940892,0.2592740357,0.0706541762,-0.0856020451,-0.2151544392,0.2720209062,0.4728945494,-0.173538819,0.1442514658,-0.1528486609,0.2518606484,-0.0047391434,-0.2206519246,-0.08354415,-0.2356707305,-0.0855709612,0.1491017044,0.1920591593,0.1763487011,0.0780320093,-0.0344592072,-0.0481045842,0.079381533,-0.0425899513,-0.0353820994,-0.2902143598,-0.1774063557,0.3210521638,0.0619787723,0.0119390367,0.0396472737,-0.311113894,-0.0545602813,-0.5426447392,-0.0045083473,0.0185830761,-0.1482398212,-0.00013115,-0.0861114264,-0.5322773457,0.0132138543,0.1309877932,0.1258892864,-0.313397795,0.1124276817,-0.2538447082,-0.3051471412,-0.123465769,0.1048952416,-0.0565843396,0.2436976582,0.1446895301,-0.1881213784,0.194577843,-0.0590647049,-0.0902348459,0.1419096142,-0.0224593785,-0.348313868,-0.0931343883,-0.212852478,0.2848571539,-0.0747310668,0.093404673,-0.0979233235,-0.0383979939,-0.0665078163,0.2167899758,-0.0050204648,-0.0108644944,-0.1919534802,0.1102564186,-0.1700980514,-0.0288522206,0.3901928663,0.2208699882,0.0089336941,0.186425671,-0.2493094802,-0.1036614627,-0.0369431302,-0.0451333262,0.0267364755,0.2068388909,0.138674438,0.8986776471,0.2649667263,-0.3478029668,-0.1407364905,-0.1692233533,-0.1681494415,0.0339967646,-0.0702737719,0.1933213323,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2294","title":"Slow #0 when using map to tokenize.","comments":"That makes sense ! You can indeed use `map` on both datasets separately and then concatenate.\r\nAnother option is to concatenate, then shuffle, and then `map`.","body":"Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=args.preprocessing_num_workers,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=not args.overwrite_cache,\r\n        )` to tokenize by multiprocessing. However, I have found that when `num_proc`>1\uff0cthe process _#0_ is much slower than others.\r\nIt looks like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/31714566\/116665555-81246280-a9cc-11eb-8a37-6e608ab310d0.png)\r\nIt takes more than 12 hours for #0, while others just about half an hour. Could anyone tell me it is normal or not, and is there any methods to speed up it?\r\n","comment_length":26,"text":"Slow #0 when using map to tokenize. \n Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=args.preprocessing_num_workers,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=not args.overwrite_cache,\r\n        )` to tokenize by multiprocessing. However, I have found that when `num_proc`>1\uff0cthe process _#0_ is much slower than others.\r\nIt looks like this:\r\n![image](https:\/\/user-images.githubusercontent.com\/31714566\/116665555-81246280-a9cc-11eb-8a37-6e608ab310d0.png)\r\nIt takes more than 12 hours for #0, while others just about half an hour. Could anyone tell me it is normal or not, and is there any methods to speed up it?\r\n \n That makes sense ! You can indeed use `map` on both datasets separately and then concatenate.\r\nAnother option is to concatenate, then shuffle, and then `map`.","embeddings":[-0.4388077557,-0.275254041,-0.0035829467,-0.0195050742,0.037655551,-0.1688462198,0.384619832,0.2470972836,-0.2695715129,0.0726477057,0.1870826185,0.3446471393,-0.1986020654,0.0340104513,-0.3061349988,0.0878567547,0.116552256,0.1894627661,0.2597584724,-0.0976634547,-0.1504402161,-0.0872968882,-0.4416112602,0.0910414532,-0.4734842181,-0.08449734,-0.0814721584,0.0456183404,-0.1439362466,-0.2998284101,-0.22646299,0.1087268293,-0.0366152525,0.3386248946,-0.0001279823,-0.232973814,0.2126871049,0.2498805076,0.0457996801,-0.0689349771,0.0412353016,-0.3254992962,-0.1309286058,-0.0187025275,-0.0309802908,-0.0179256815,0.1673597693,-0.1152555197,0.1905249357,-0.0400749259,0.0446927622,0.3407954872,-0.3702794313,0.2314551175,-0.0591440089,0.1832049042,-0.0711284131,-0.0657924339,0.1532546282,-0.2268456072,0.1455459893,0.1906764358,-0.2239545584,-0.0393581092,0.1851701438,0.1328697056,0.4181729555,-0.427369833,0.2814035416,0.2699203491,-0.1735964715,0.1514287293,-0.3362787962,-0.3355287313,0.0419164561,-0.3565230668,-0.0188186411,-0.0346324518,0.0667284355,-0.0074808202,-0.5250094533,0.1170422137,0.1661503464,0.0660812631,-0.3220513761,0.6341160536,0.1833461523,0.1864078939,0.1982421875,-0.2674390376,-0.325796634,-0.3126784563,0.1464710087,0.4829999804,-0.2674062848,-0.1194259301,-0.0261692889,-0.2011550963,-0.1995183825,-0.3869157434,-0.4060808718,0.3477786183,0.0074185971,0.0792026371,0.1178821549,0.0061186613,0.0867659748,0.3740830421,0.256185919,-0.4760053456,-0.2496020198,0.1057588756,-0.3221828341,-0.0576486103,-0.1828142852,0.0616205037,-0.2615204453,-0.0441681147,-0.1043375283,-0.0334302895,-0.4193731844,0.0477108434,0.3961853385,0.0800352916,0.0011543407,0.5042101145,-0.3501891494,0.0108959228,-0.4182539284,0.1281474382,-0.0084887184,-0.1850924343,-0.2621052563,0.1198893636,-0.0490981117,0.2026842833,0.0051389625,0.1334311366,0.1958023161,0.2225049287,0.4963684976,-0.2874108255,0.2397117168,0.2287209332,-0.1848647147,0.4846333861,-0.0675850436,0.1625760794,-0.2317890823,0.4496634603,-0.4836660624,-0.3581535518,0.0886901096,-0.0503153019,-0.0290916655,0.2750347555,-0.2045658231,0.4464249611,0.3693268895,-0.0993712246,-0.2557810247,-0.1525740325,-0.5478855968,-0.0989280045,0.2410939187,0.2521885633,-0.1584029645,-0.0110206315,0.1865359247,0.36947456,0.6669709682,0.5067364573,-0.2878709435,0.1310139894,-0.1607699543,0.6380207539,0.0407219082,-0.1091928631,-0.1088482216,0.3064481616,-0.4410433471,-0.0714501962,0.0128975352,-0.0292178765,0.1942563504,-0.1717973202,0.3794989586,0.0860753357,-0.1242235005,0.28827402,-0.4205456674,0.0442052595,0.3506424725,-0.083741039,-0.2194923162,-0.1622108221,-0.2516686916,-0.0266855694,0.0405645408,-0.1992124617,0.1669294089,0.0959291086,0.1650446355,-0.1816592366,-0.0439745672,0.1550924629,-0.0143420119,0.189425081,-0.1078041866,0.1165861934,-0.0158081129,-0.118895188,0.2822369337,-0.1358155012,-0.0231440477,0.0286241043,-0.1187128574,-0.1488008797,-0.1367196739,-0.0344713107,-0.0475839451,0.0045012212,0.0934303701,-0.0588060208,0.1493183523,-0.0622188859,0.1166628376,-0.1637578905,-0.2954397202,0.2489109933,0.2838457525,-0.1420406699,0.1078457758,0.3962527812,0.2024593502,0.0529389866,-0.0186137334,0.1946263313,0.3567746282,0.2995521426,0.2858372629,-0.3156941235,-0.0897186249,-0.2797776461,0.1507816315,0.8951528072,0.3780685961,0.3151248097,0.056814909,-0.2766118646,-0.004333124,0.2213652581,-0.0341794752,-0.0283556078,0.2033922672,0.3341964185,0.3051337004,0.2553846836,-0.0430152416,0.2804568112,0.2164853215,-0.09988828,-0.195873484,0.0009245285,-0.0914533958,-0.1556072384,0.1186439469,-0.4987288415,0.311635524,0.0389192775,0.249767676,0.001279352,-0.1723957956,-0.0137270037,0.1406293809,0.1262094676,-0.0237505399,-0.0348012596,0.2234736234,0.0336094499,-0.0484149568,-0.1124185473,0.3035728931,0.4295854867,-0.2028124034,0.0842254907,-0.1635441333,0.2961604595,-0.0375076123,-0.2037374824,-0.0970498621,-0.2546182871,0.0398239121,0.1681345105,0.0835213065,0.1886325032,0.0789038762,-0.1020120904,-0.1018423811,0.1730253696,0.0364545546,-0.0307044536,-0.2684114575,-0.1226452887,0.3741773069,0.0297645666,0.0512430444,0.0608648248,-0.3349949121,0.081458278,-0.5531005859,-0.0262767766,-0.0215872861,-0.1566880047,0.0560895838,-0.0829023719,-0.6097153425,0.0379511863,0.1915591359,0.0100364015,-0.3099003136,0.0293184351,-0.2165517062,-0.3161248863,-0.1767939627,0.1241433322,-0.0743780211,0.2454777658,0.1708730608,-0.2345411479,0.2101939917,-0.1212064549,-0.0949990898,0.1285719723,-0.1188468859,-0.4825815558,-0.078733772,-0.2240341902,0.3454927504,-0.0826594755,0.0813403726,-0.0067228428,-0.0524938256,-0.1030180603,0.2512447536,0.1546482295,0.0125445165,-0.0915634334,0.0958880857,-0.0917695835,0.0141655933,0.4137443304,0.1844600439,0.028602099,0.1182156801,-0.3019368351,-0.0392446704,0.0395163372,0.0175780077,0.0223320518,0.2818388343,0.1994910836,0.9364292026,0.1769333631,-0.2471613586,-0.1466753483,-0.1311273128,-0.1864523888,0.0355857946,-0.0280281082,0.2754463255,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2288","title":"Load_dataset for local CSV files","comments":"Hi,\r\n\r\nthis is not a standard CSV file (requires additional preprocessing) so I wouldn't label this as s bug. You could parse the examples with the regex module or the string API to extract the data, but the following approach is probably the easiest (once you load the data):\r\n```python\r\nimport ast\r\n# load the dataset and copy the features\r\ndef process(ex):\r\n    return {\"tokens\": ast.literal_eval(ex[\"tokens\"]), \"labels\": ast.literal_eval(ex[\"labels\"])}\r\ndataset = dataset.map(process, features=new_features)\r\n```\r\n","body":"The method load_dataset fails to correctly load a dataset from csv. \r\n\r\nMoreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings.\r\nrow example:\r\n```tokens  |  labels\r\n['I' , 'am', 'John']  |  ['PRON', 'AUX', 'PROPN' ] \r\n```\r\nThe method, loads each list as a string:  (i.g \"['I' , 'am', 'John']\").\r\nTo solve this issue, I copied the Datasets.Features, created Sequence types ( instead of Value)  and tried to cast the features type\r\n```\r\nnew_features['tokens'] = Sequence(feature=Value(dtype='string', id=None))\r\nnew_features['labels'] = Sequence(feature=ClassLabel(num_classes=len(tag2idx), names=list(unique_tags)))\r\ndataset = dataset.cast(new_features)\r\n```\r\nbut I got the following error \r\n```\r\nArrowNotImplementedError: Unsupported cast from string to list using function cast_list\r\n```\r\nMoreover, I tried to set feature parameter in load_dataset method, to my new_features, but this fails as well.\r\nHow can this be solved ?","comment_length":72,"text":"Load_dataset for local CSV files \n The method load_dataset fails to correctly load a dataset from csv. \r\n\r\nMoreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings.\r\nrow example:\r\n```tokens  |  labels\r\n['I' , 'am', 'John']  |  ['PRON', 'AUX', 'PROPN' ] \r\n```\r\nThe method, loads each list as a string:  (i.g \"['I' , 'am', 'John']\").\r\nTo solve this issue, I copied the Datasets.Features, created Sequence types ( instead of Value)  and tried to cast the features type\r\n```\r\nnew_features['tokens'] = Sequence(feature=Value(dtype='string', id=None))\r\nnew_features['labels'] = Sequence(feature=ClassLabel(num_classes=len(tag2idx), names=list(unique_tags)))\r\ndataset = dataset.cast(new_features)\r\n```\r\nbut I got the following error \r\n```\r\nArrowNotImplementedError: Unsupported cast from string to list using function cast_list\r\n```\r\nMoreover, I tried to set feature parameter in load_dataset method, to my new_features, but this fails as well.\r\nHow can this be solved ? \n Hi,\r\n\r\nthis is not a standard CSV file (requires additional preprocessing) so I wouldn't label this as s bug. You could parse the examples with the regex module or the string API to extract the data, but the following approach is probably the easiest (once you load the data):\r\n```python\r\nimport ast\r\n# load the dataset and copy the features\r\ndef process(ex):\r\n    return {\"tokens\": ast.literal_eval(ex[\"tokens\"]), \"labels\": ast.literal_eval(ex[\"labels\"])}\r\ndataset = dataset.map(process, features=new_features)\r\n```\r\n","embeddings":[-0.110432297,-0.0460996181,0.0017928476,0.0747474656,0.4480468929,0.0627773106,0.4552136958,0.219682008,0.299490124,-0.1329664886,0.206075266,0.4624345005,-0.0728465244,0.0797176287,-0.0546839014,-0.225862965,0.1463976949,0.121658057,-0.068167083,-0.0514059886,-0.2214132249,0.1172490567,-0.2457354516,0.0626452342,0.1473498046,0.067499727,0.0444802754,0.1194713861,0.1582894623,-0.4119055867,0.1597045362,-0.1485556513,0.3543725908,0.2177936286,-0.0001225974,-0.0308731589,0.2000939697,-0.0455260463,-0.1343298107,-0.4299833477,-0.3686624765,-0.4428124428,0.6035311222,-0.2337204367,0.0679722503,0.0058746655,-0.1318276227,-0.4414084256,0.3704528511,0.4917436838,0.1028463691,0.2604970336,-0.2128971964,0.0683712736,0.2531427145,0.172960192,-0.2683466673,0.0115083726,0.2413832992,0.2831138074,0.0202589948,-0.251404494,-0.3278875947,-0.1090300754,0.4502304494,0.1004333869,-0.1728934497,-0.2152026296,-0.0185692199,0.0967391878,0.6940967441,-0.0439019613,-0.3437823057,-0.1345560402,0.1029636487,-0.3265565336,0.0719097704,0.0394793227,-0.0936724469,0.2009950578,-0.2288368642,0.0179859232,-0.0210507773,0.2387349755,0.0580496825,-0.0684755296,-0.1830670089,0.2108053714,-0.0479418114,-0.2082449645,0.158677578,-0.2253193408,0.1418036073,0.307048142,-0.4954456389,0.1484595686,0.1250777394,-0.1021132842,-0.0821916088,0.2027681023,0.314424485,0.1013435349,-0.0861858726,0.2051599771,0.2729567289,0.2770818174,0.2978345454,0.0810159221,-0.0635052919,-0.0946139917,-0.1663808078,0.0363045745,-0.3185822666,-0.1923478991,0.3789396882,0.1587801576,0.1375382096,-0.3657591343,-0.362699002,0.2351652086,-0.2413615137,-0.0889683217,0.0760768577,0.283634156,-0.129787311,0.4884750247,0.1027978286,0.3484078944,0.1101016402,-0.1406168938,-0.161834538,-0.1079706252,-0.0891124681,-0.0602726191,0.0965371057,-0.3631424606,0.1043661162,-0.0351545699,0.0497357994,-0.1586531699,-0.2356229573,-0.0306247156,0.003865544,0.2680171132,0.0140141835,0.0956334621,0.3562788367,-0.04595403,-0.2184284329,0.3119084239,-0.4620773494,-0.2153678834,0.0045838011,-0.0065716007,-0.2278196365,-0.0331734493,-0.4317633212,0.0690495893,0.1968415082,0.1540489942,-0.1759973019,-0.4027112424,-0.2068364173,-0.4180796742,0.1779424399,0.3996336758,-0.5163905621,0.0751355812,0.0115570929,0.0245534964,0.1286622733,0.4650031328,-0.1831560582,0.296323359,-0.1501670331,0.3829417825,0.5047297478,0.099883981,-0.0535182878,0.5566630363,0.063063778,0.0640882254,0.1053316817,-0.0127036516,0.3253757954,-0.1700782776,0.0269855931,0.4483290911,-0.1635106355,-0.0150684919,-0.1051956415,-0.1559162289,0.2475551218,0.1033510715,-0.1184359044,0.3426096737,0.1636872888,-0.0604913868,0.0419215485,-0.3142814338,0.1542034149,0.2057690024,-0.0115860142,0.1584032178,-0.049541723,-0.0531680845,-0.636657536,-0.0379679389,0.2213625461,-0.1010948569,-0.3996529281,-0.0541028827,-0.3353928924,0.2223334759,-0.0879020244,0.5470007658,-0.1008092389,0.1392119527,-0.0799395666,0.0280992948,-0.0997856706,0.0700337514,-0.2976451218,0.0392469056,-0.2093299776,0.0670426711,0.2348161191,-0.1278736293,-0.0986958519,0.0919288248,0.3017797172,-0.0927237272,-0.2805355489,0.3237709701,0.0724094808,-0.1806598604,-0.4057722092,0.0707104579,0.1225665361,-0.4118911028,-0.0960494876,0.3675079346,0.298291862,-0.1217671931,-0.1948543936,0.5355858803,-0.1639039963,0.3591678143,-0.1003533378,0.0166162923,0.4018522501,0.0990034863,-0.2214927226,-0.0735374466,0.1187479869,0.1199110448,0.2753313482,0.0832990184,-0.4843612611,-0.107410118,0.2010634094,-0.1397026777,0.1424913108,0.3510163426,-0.0368599556,0.1023043171,0.0236881711,-0.2591193318,0.3383775949,-0.0355219319,-0.0439354815,-0.0970787778,-0.0985544994,-0.0578270108,0.1779477745,-0.03336142,0.1140087545,0.237608552,0.0508311465,-0.0402042083,-0.2066465169,-0.1779197305,0.1371901035,0.1496103406,-0.6657977104,0.1570387781,-0.4116671085,0.2593963742,-0.283072114,-0.0852635726,0.1355614513,-0.1846075803,-0.2917081416,0.2667333186,-0.1641355902,0.1261749268,-0.5663655996,-0.0178402979,0.2130135149,-0.6803790927,0.1811012328,0.0629809722,-0.1782786697,-0.076444611,0.3991116583,0.2297205627,0.0199138876,-0.0418583006,0.0285445899,-0.1528597027,-0.1876204312,0.1119922847,-0.1340637058,0.188344717,0.1034669206,0.060844101,-0.2700412869,-0.5033234954,0.2780170739,-0.1225630417,-0.1226960272,0.4148798585,-0.0341416374,-0.3602371514,-0.2041586041,-0.4714085758,-0.1735008657,-0.3078493476,0.1805062592,0.1036359519,-0.0178940371,0.0935265049,0.2594175637,0.0780491531,0.2604344487,0.043624308,-0.0324880071,0.0536315925,0.5690494776,-0.1156726182,-0.4092385471,0.0243679937,-0.202160418,-0.1955375969,0.3714095056,-0.1427942961,0.1118415743,-0.0826797634,0.2352448106,0.0215764083,-0.0280712508,0.3271402717,-0.030869728,0.0953569934,-0.1797703654,-0.2646719515,0.1653976887,0.2676552832,0.0848531201,0.5000003576,0.6963592172,-0.2381380945,0.4044570327,-0.0274050087,-0.1215998381,0.4540215731,-0.1748410761,0.0772479624,-0.1671363413,-0.2304948568,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2288","title":"Load_dataset for local CSV files","comments":"Hi,\r\n\r\nThanks for the reply.\r\nI  have already used  ```ast.literal_eval``` to evaluate the string into list, but I was getting another error:\r\n```\r\nArrowInvalid: Could not convert X with type str: tried to convert to int\r\n```\r\nWhy this happens ? Should labels be mapped to their ids and use int instead of str ?","body":"The method load_dataset fails to correctly load a dataset from csv. \r\n\r\nMoreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings.\r\nrow example:\r\n```tokens  |  labels\r\n['I' , 'am', 'John']  |  ['PRON', 'AUX', 'PROPN' ] \r\n```\r\nThe method, loads each list as a string:  (i.g \"['I' , 'am', 'John']\").\r\nTo solve this issue, I copied the Datasets.Features, created Sequence types ( instead of Value)  and tried to cast the features type\r\n```\r\nnew_features['tokens'] = Sequence(feature=Value(dtype='string', id=None))\r\nnew_features['labels'] = Sequence(feature=ClassLabel(num_classes=len(tag2idx), names=list(unique_tags)))\r\ndataset = dataset.cast(new_features)\r\n```\r\nbut I got the following error \r\n```\r\nArrowNotImplementedError: Unsupported cast from string to list using function cast_list\r\n```\r\nMoreover, I tried to set feature parameter in load_dataset method, to my new_features, but this fails as well.\r\nHow can this be solved ?","comment_length":55,"text":"Load_dataset for local CSV files \n The method load_dataset fails to correctly load a dataset from csv. \r\n\r\nMoreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings.\r\nrow example:\r\n```tokens  |  labels\r\n['I' , 'am', 'John']  |  ['PRON', 'AUX', 'PROPN' ] \r\n```\r\nThe method, loads each list as a string:  (i.g \"['I' , 'am', 'John']\").\r\nTo solve this issue, I copied the Datasets.Features, created Sequence types ( instead of Value)  and tried to cast the features type\r\n```\r\nnew_features['tokens'] = Sequence(feature=Value(dtype='string', id=None))\r\nnew_features['labels'] = Sequence(feature=ClassLabel(num_classes=len(tag2idx), names=list(unique_tags)))\r\ndataset = dataset.cast(new_features)\r\n```\r\nbut I got the following error \r\n```\r\nArrowNotImplementedError: Unsupported cast from string to list using function cast_list\r\n```\r\nMoreover, I tried to set feature parameter in load_dataset method, to my new_features, but this fails as well.\r\nHow can this be solved ? \n Hi,\r\n\r\nThanks for the reply.\r\nI  have already used  ```ast.literal_eval``` to evaluate the string into list, but I was getting another error:\r\n```\r\nArrowInvalid: Could not convert X with type str: tried to convert to int\r\n```\r\nWhy this happens ? Should labels be mapped to their ids and use int instead of str ?","embeddings":[-0.1745119542,-0.1091788337,-0.0231068395,0.0980234146,0.4885460138,0.0214575231,0.4982636869,0.2223207355,0.3661426306,-0.0579060018,0.0228598081,0.5268843174,-0.0411034487,0.1786160618,-0.0871043503,-0.1635926515,0.1504180282,0.1215566248,-0.0954685286,-0.054811608,-0.2822064757,0.1153573394,-0.2327804267,0.1028901264,0.0758110136,-0.0212439336,0.1522944868,0.0589686707,0.1551152021,-0.46901685,0.0427116603,-0.1593906432,0.4239130914,0.1603819579,-0.0001222864,-0.0723953396,0.2014042437,-0.0017853752,-0.1700443029,-0.2721410692,-0.3334534466,-0.331261456,0.5561583042,-0.2470588535,0.1309711039,-0.0750061497,-0.0999568924,-0.4044760168,0.2009782791,0.496066004,0.1088149324,0.1556666493,-0.0642046854,0.0743320733,0.3011790812,0.1582485288,-0.2453445643,0.0072984123,0.2836193144,0.4058943391,-0.0172992852,-0.1583520472,-0.2349089384,-0.1760405749,0.5206285119,0.0213500671,-0.1103872508,-0.2337289751,-0.0291656051,-0.0270526726,0.824239254,-0.0337931812,-0.33137393,0.0284710489,0.0084755486,-0.4012273252,-0.0091966204,-0.0444252566,-0.0706204623,0.1964198351,-0.2307465374,-0.0202314667,-0.1065665931,0.2094974965,0.1264789253,0.0024114156,-0.1604218334,0.252599299,-0.0231931992,-0.3424344659,0.1272237003,-0.1139903888,0.0617731921,0.2001869828,-0.5123823285,0.0699720904,-0.0475859046,-0.0426837727,-0.0511405244,0.1655862331,0.213074103,0.1673607081,-0.0533780903,0.1695119143,0.2711187899,0.4393441677,0.3845515549,0.1459635645,-0.0772828162,-0.2162695378,-0.1859956682,0.0367971063,-0.3532292843,-0.1677093655,0.4503811598,0.1206073463,0.0453948155,-0.472183913,-0.3626649082,0.1549423635,-0.2265360206,-0.0372204147,0.1171711385,0.376806885,-0.1251847893,0.5413228869,0.1200025603,0.2771107554,0.1804116219,-0.1838897914,-0.1071730107,-0.095461227,-0.145713225,-0.0957704335,0.038471695,-0.3362178206,0.1143923029,-0.0716412216,0.0394222774,-0.1666861027,-0.2015030086,-0.1003284752,0.1008358672,0.2000790834,-0.0727956071,0.1101015136,0.2909413874,-0.0541023985,-0.170156166,0.3316213489,-0.5061308742,-0.2791664302,-0.0096540516,0.0039627338,-0.0279720332,-0.0403443314,-0.3437798023,0.1190858558,0.1428308189,0.0864046663,-0.2007548809,-0.4404556453,-0.1076720208,-0.3787831068,0.096455805,0.2499113828,-0.5306060314,0.1216105968,0.0757080913,0.0141585395,0.2510149181,0.5593862534,-0.146987617,0.2511620224,-0.1406387985,0.3639690876,0.5061859488,-0.0752420351,-0.068733789,0.5408250093,0.1516232789,-0.1511905044,0.0947824046,0.1008558571,0.3450995982,-0.1787752211,0.0259270333,0.3236461878,-0.109979175,-0.0535836369,-0.2369864881,-0.0890310258,0.2300912738,0.1359630972,-0.1365004629,0.327465266,0.1866754591,0.0384798944,-0.0829609558,-0.301882267,0.1766661853,0.1386438608,-0.1012358144,0.1233697087,-0.0199299064,0.0147775253,-0.6875210404,-0.1371833086,0.1024215445,-0.0769350678,-0.2303434759,-0.0767372102,-0.3774006963,0.232375443,-0.0619983934,0.5937697887,-0.1051987484,0.1890155822,-0.090512082,-0.0568342432,-0.1481353045,-0.0236116908,-0.3030292392,0.0087987315,-0.1092748046,-0.134216845,0.1625541449,-0.1477347761,-0.1353454292,0.0716530308,0.3193821609,-0.058781907,-0.3308487833,0.3488802314,0.0969928429,-0.2780359983,-0.3008566499,0.1070826054,0.0893097818,-0.5350093842,-0.0993565544,0.264462769,0.3447467685,-0.156527102,-0.1530912966,0.4296925068,-0.2420479357,0.4259821475,-0.1141403764,-0.0087803118,0.3594492078,0.131830126,-0.2028185427,-0.0955797583,0.0416045077,0.0982768312,0.3094275296,0.1073539555,-0.5325565338,-0.0246956144,0.2461336553,-0.1355324388,0.1485410035,0.3439357579,0.0264561884,0.058364328,0.1440448761,-0.377235949,0.3147376776,0.0168545246,0.0069153598,-0.1343993247,-0.1218231022,-0.0391922072,0.244708553,-0.0561150871,0.0706914142,0.2142902464,0.033025153,-0.1653952301,-0.1804197729,-0.2334323823,0.0790087804,0.1729777008,-0.6239628792,0.0942308307,-0.3296726048,0.1743192524,-0.237088114,-0.2676620185,0.1135046855,-0.2016695589,-0.2089027762,0.2420663089,-0.120127514,0.1385365874,-0.403313905,0.0975712985,0.1901009232,-0.5237894654,-0.0100405533,0.0020129271,-0.2710900605,-0.0818773434,0.3792653382,0.2798444629,0.0619555041,-0.1255289763,0.152325511,-0.1678494662,-0.2552996874,0.0680264086,-0.0739110559,0.1182409003,0.0621922649,0.0708518252,-0.324840039,-0.3971909881,0.2296409458,-0.10517288,-0.1522288769,0.3554173708,-0.104435727,-0.3573527336,-0.1748083234,-0.5161355734,-0.2136065066,-0.4151930213,0.1933624744,0.139304474,0.0469273292,0.0435752273,0.2623166144,0.1231550351,0.3312580287,0.1154701263,-0.0740698054,0.0053718071,0.5278599858,-0.1268101931,-0.3519244492,0.0330701508,-0.1132661849,-0.0612165332,0.310936898,-0.0418369621,0.1912265122,-0.0278596263,0.2648984194,-0.0023115284,0.0812982619,0.2223408967,0.0311561543,0.0761905238,-0.1425583065,-0.2828026116,0.3175474405,0.1232309341,0.1157069057,0.5898655057,0.6031092405,-0.3257270455,0.3851868212,-0.0492634512,-0.1964689195,0.4677959681,-0.1519373357,0.1013601273,-0.231947571,-0.1796561927,-0.1222321317,-0.1774987131,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2285","title":"Help understanding how to build a dataset for language modeling as with the old TextDataset","comments":"\r\nI received an answer for this question on the HuggingFace Datasets forum by @lhoestq\r\n\r\nHi !\r\n\r\nIf you want to tokenize line by line, you can use this:\r\n\r\n```\r\nmax_seq_length = 512\r\nnum_proc = 4\r\n\r\ndef tokenize_function(examples):\r\n# Remove empty lines\r\nexamples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\nreturn tokenizer(\r\n    examples[\"text\"],\r\n    truncation=True,\r\n    max_length=max_seq_length,\r\n)\r\n\r\ntokenized_dataset = dataset.map(\r\ntokenize_function,\r\nbatched=True,\r\nnum_proc=num_proc,\r\nremove_columns=[\"text\"],\r\n)\r\n```\r\n\r\nThough the TextDataset was doing a different processing by concatenating all the texts and building blocks of size 512. If you need this behavior, then you must apply an additional map function after the tokenization:\r\n\r\n```\r\n# Main data processing function that will concatenate all texts from\r\n# our dataset and generate chunks of max_seq_length.\r\ndef group_texts(examples):\r\n# Concatenate all texts.\r\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\ntotal_length = len(concatenated_examples[list(examples.keys())[0]])\r\n# We drop the small remainder, we could add padding if the model supported it instead of this drop,\r\n# you can customize this part to your needs.\r\ntotal_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n# Split by chunks of max_len.\r\nresult = {\r\n    k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\r\n    for k, t in concatenated_examples.items()\r\n}\r\nreturn result\r\n\r\n# Note that with `batched=True`, this map processes 1,000 texts together,\r\n# so group_texts throws away a remainder for each of those groups of 1,000 texts.\r\n# You can adjust that batch_size here but a higher value might be slower to preprocess.\r\n\r\ntokenized_dataset = tokenized_dataset.map(\r\ngroup_texts,\r\nbatched=True,\r\nnum_proc=num_proc,\r\n)\r\n```\r\n\r\nThis code comes from the processing of the run_mlm.py example script of transformers\r\n\r\n","body":"Hello,\r\n\r\nI am trying to load a custom dataset that I will then use for language modeling. The dataset consists of a text file that has a whole document in each line, meaning that each line overpasses the normal 512 tokens limit of most tokenizers.\r\n\r\nI would like to understand what is the process to build a text dataset that tokenizes each line, having previously split the documents in the dataset into lines of a \"tokenizable\" size, as the old TextDataset class would do, where you only had to do the following, and a tokenized dataset without text loss would be available to pass to a DataCollator:\r\n\r\n```\r\nmodel_checkpoint = 'distilbert-base-uncased'\r\n\r\nfrom transformers import AutoTokenizer\r\ntokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\r\n\r\nfrom transformers import TextDataset\r\n\r\ndataset = TextDataset(\r\n    tokenizer=tokenizer,\r\n    file_path=\"path\/to\/text_file.txt\",\r\n    block_size=512,\r\n)\r\n```\r\n\r\nFor now, what I have is the following, which, of course, throws an error because each line is longer than the maximum block size in the tokenizer:\r\n\r\n```\r\nimport datasets\r\ndataset = datasets.load_dataset('path\/to\/text_file.txt')\r\n\r\nmodel_checkpoint = 'distilbert-base-uncased'\r\ntokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[\"text\"])\r\n\r\ntokenized_datasets = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=[\"text\"])\r\n\r\ntokenized_datasets\r\n```\r\n\r\nSo what would be the \"standard\" way of creating a dataset in the way it was done before?\r\n\r\nThank you very much for the help :))","comment_length":270,"text":"Help understanding how to build a dataset for language modeling as with the old TextDataset \n Hello,\r\n\r\nI am trying to load a custom dataset that I will then use for language modeling. The dataset consists of a text file that has a whole document in each line, meaning that each line overpasses the normal 512 tokens limit of most tokenizers.\r\n\r\nI would like to understand what is the process to build a text dataset that tokenizes each line, having previously split the documents in the dataset into lines of a \"tokenizable\" size, as the old TextDataset class would do, where you only had to do the following, and a tokenized dataset without text loss would be available to pass to a DataCollator:\r\n\r\n```\r\nmodel_checkpoint = 'distilbert-base-uncased'\r\n\r\nfrom transformers import AutoTokenizer\r\ntokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\r\n\r\nfrom transformers import TextDataset\r\n\r\ndataset = TextDataset(\r\n    tokenizer=tokenizer,\r\n    file_path=\"path\/to\/text_file.txt\",\r\n    block_size=512,\r\n)\r\n```\r\n\r\nFor now, what I have is the following, which, of course, throws an error because each line is longer than the maximum block size in the tokenizer:\r\n\r\n```\r\nimport datasets\r\ndataset = datasets.load_dataset('path\/to\/text_file.txt')\r\n\r\nmodel_checkpoint = 'distilbert-base-uncased'\r\ntokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[\"text\"])\r\n\r\ntokenized_datasets = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=[\"text\"])\r\n\r\ntokenized_datasets\r\n```\r\n\r\nSo what would be the \"standard\" way of creating a dataset in the way it was done before?\r\n\r\nThank you very much for the help :)) \n \r\nI received an answer for this question on the HuggingFace Datasets forum by @lhoestq\r\n\r\nHi !\r\n\r\nIf you want to tokenize line by line, you can use this:\r\n\r\n```\r\nmax_seq_length = 512\r\nnum_proc = 4\r\n\r\ndef tokenize_function(examples):\r\n# Remove empty lines\r\nexamples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\nreturn tokenizer(\r\n    examples[\"text\"],\r\n    truncation=True,\r\n    max_length=max_seq_length,\r\n)\r\n\r\ntokenized_dataset = dataset.map(\r\ntokenize_function,\r\nbatched=True,\r\nnum_proc=num_proc,\r\nremove_columns=[\"text\"],\r\n)\r\n```\r\n\r\nThough the TextDataset was doing a different processing by concatenating all the texts and building blocks of size 512. If you need this behavior, then you must apply an additional map function after the tokenization:\r\n\r\n```\r\n# Main data processing function that will concatenate all texts from\r\n# our dataset and generate chunks of max_seq_length.\r\ndef group_texts(examples):\r\n# Concatenate all texts.\r\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\ntotal_length = len(concatenated_examples[list(examples.keys())[0]])\r\n# We drop the small remainder, we could add padding if the model supported it instead of this drop,\r\n# you can customize this part to your needs.\r\ntotal_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n# Split by chunks of max_len.\r\nresult = {\r\n    k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\r\n    for k, t in concatenated_examples.items()\r\n}\r\nreturn result\r\n\r\n# Note that with `batched=True`, this map processes 1,000 texts together,\r\n# so group_texts throws away a remainder for each of those groups of 1,000 texts.\r\n# You can adjust that batch_size here but a higher value might be slower to preprocess.\r\n\r\ntokenized_dataset = tokenized_dataset.map(\r\ngroup_texts,\r\nbatched=True,\r\nnum_proc=num_proc,\r\n)\r\n```\r\n\r\nThis code comes from the processing of the run_mlm.py example script of transformers\r\n\r\n","embeddings":[-0.2392032892,-0.0001858806,0.0175925288,0.1512313336,0.1369385719,-0.1672839969,0.5160788298,0.0982798114,-0.0554774702,-0.1596171558,0.1894440204,-0.1941065639,-0.1646524668,0.1340248436,0.1435168535,-0.0841108859,0.1339856535,0.0562884994,0.1950121075,0.023583632,0.0756416768,0.0561196543,-0.0015379904,-0.0821405351,-0.5577692389,-0.1091072038,-0.1831939965,-0.1227100119,-0.3284448087,-0.4212741554,0.0153291784,0.3389095366,0.4065946937,0.4958460927,-0.0001238081,-0.2122489214,-0.177432999,-0.2544007003,-0.0525416359,-0.237539798,0.3744877279,-0.4061252177,0.107468687,-0.1326390505,0.0795988813,-0.2096719146,-0.0318121016,-0.2318027467,0.5191584826,0.4787013531,0.0087787909,0.0259202924,0.0743123814,0.1130469888,-0.3428885639,0.1746680588,-0.1214065105,0.1298532933,0.3205685914,-0.0565026291,-0.0402820073,-0.0930833444,0.0166849941,-0.3108217418,0.3316012919,0.1152971238,-0.1415722817,-0.4516028762,0.0755913779,0.2538356185,0.6707497239,-0.3457719982,-0.3893296719,-0.4439043105,-0.1682328582,0.0374632366,-0.0296423137,0.0730164275,-0.2541878819,0.1022670195,-0.1240605265,-0.294654727,-0.2216565907,0.0544245765,-0.3207271695,0.3257496655,-0.0336121321,0.0357048847,-0.1194261163,-0.2430410087,-0.059906356,-0.2204925269,0.2210455984,0.4883925915,-0.0837700814,-0.3066206872,-0.2297293544,-0.3395672441,0.5583885908,0.0868365467,-0.0990155861,0.0349288397,-0.2218428552,-0.0805967897,0.0350312404,0.3476090431,0.5046224594,-0.1744385362,0.0108416593,-0.169294998,-0.1438457072,-0.2762911618,-0.6274005175,0.117275551,-0.059924271,-0.0570774451,0.0392435677,-0.0520313345,0.2307766676,-0.0069928747,-0.2350392342,-0.0044798492,0.0452586971,0.0356492549,-0.0675020069,-0.0861181617,-0.2376567274,0.4212774634,-0.2759157717,-0.2418381125,0.0703430623,-0.1863947809,-0.1865321845,0.1939846724,0.1796313524,0.0179879908,0.3778651059,0.1087445989,-0.1177096665,-0.2375998646,0.1789949238,-0.2524816096,-0.0524344705,-0.0748906136,0.1091084182,0.2168307751,0.1097023636,-0.417730391,-0.2348013818,0.033199966,-0.0249291044,-0.3025587797,-0.0333838165,0.028493451,-0.192048341,-0.1472052634,-0.2405373156,0.6766546369,0.4798668027,0.0126460642,-0.0017274155,-0.0322487764,-0.1381386518,-0.0999684483,0.2362454236,0.4747399986,-0.7666448951,0.0063369167,0.0026421065,-0.0365215726,0.0965150073,0.3493333459,-0.1648329794,0.6068561077,-0.0613503121,0.5180051923,0.3503054678,0.130518645,-0.2049496472,0.1372380257,0.0292102527,0.1867638379,-0.107311368,-0.1562440097,0.5612369776,-0.2129071951,-0.0361982398,0.1182904318,-0.1476489156,-0.0669578165,0.0326253697,0.0857679993,0.2753346562,-0.1395994723,0.0697687194,-0.0398723632,-0.0444107465,0.3139216304,0.1966240555,-0.1594019681,0.2759898007,0.0362149328,0.2324165106,0.4073259532,-0.0153670628,-0.269325763,-0.0759757906,0.0567638688,0.2049039155,0.2655689716,-0.0570136979,-0.1916297227,-0.247473672,-0.1511307061,0.0207675304,-0.1977970451,-0.0181167405,0.0179733969,-0.1353481561,0.0234728567,-0.3133320808,0.1876451969,-0.2843897641,0.3427190185,-0.2325659841,-0.0005955913,0.2443991154,-0.1013842002,-0.1625291705,0.3830213845,-0.0273513813,-0.1313119382,0.0279479399,0.2366939336,0.3679019213,-0.1408003867,-0.2275568247,0.4600908458,0.3784703314,0.0309689473,0.457837671,-0.2016495168,0.019380128,-0.1889169216,0.0346812047,0.3599475324,0.209063679,0.2274591774,-0.0112377815,-0.0714983791,0.3128148019,0.0040353718,-0.0427536964,-0.0139180552,-0.015475913,0.1952089816,0.3493808806,0.0107706487,0.0465696417,0.3449261189,0.2432102263,-0.1532547027,0.1287156045,0.2910188138,-0.2863344848,-0.20529975,-0.1908234656,0.0046362383,0.0556731634,0.0476993956,0.3897702694,0.0664353073,-0.1433897018,-0.1299940348,0.2656420469,0.0605866201,-0.0630757362,0.310069561,-0.1549257338,0.1154171452,-0.2331500798,-0.4740202129,-0.1258013844,0.2036343068,-0.1853826642,0.1338459551,0.04872486,-0.0661096945,-0.3965555429,-0.1830105335,-0.371871978,0.0566233136,-0.0349516608,-0.1158750281,0.149379164,0.0335758068,0.2499835938,0.506326735,0.009573034,0.0583958924,0.2063226849,-0.2811831832,-0.25026232,-0.0346411243,0.0933179334,0.2416541576,0.1541722715,-0.087047115,0.0865254253,-0.0161855165,-0.5051578879,0.1515581161,-0.1692162603,0.4367188811,0.055425223,0.1744432747,0.0144784721,-0.0833800212,-0.2698356509,0.0895950049,-0.2077247351,-0.229113996,-0.11113929,-0.0045480668,0.0718693659,-0.1308140904,-0.3728190958,-0.0504081286,0.2722548544,0.1782402098,0.1835107654,-0.0798517168,-0.0299037285,0.2354943305,0.1032655314,0.0373405814,0.0435234234,-0.0512595996,0.2787410617,-0.2501344979,-0.2366040498,-0.0474127717,-0.1261128634,0.2636546195,0.1433220655,-0.2231925428,0.2140240073,-0.1526659727,0.0093198819,0.0154485377,-0.02381845,0.405169636,-0.0522558428,0.2788363993,-0.1214466766,0.018085232,0.0984245241,-0.2761836052,0.1969474107,-0.0060328697,0.6503129601,0.0369096696,0.6386395693,0.3684069216,-0.1325151026,0.0752433315,-0.0477322675,0.0422274098,-0.3112989068,-0.22771281,0.000606964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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2279","title":"Compatibility with Ubuntu 18 and GLIBC 2.27?","comments":"From the trace this seems like an error in the tokenizer library instead.\r\n\r\nDo you mind opening an issue at https:\/\/github.com\/huggingface\/tokenizers instead?","body":"## Describe the bug\r\nFor use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https:\/\/www.digitalocean.com\/community\/questions\/how-install-glibc-2-29-or-higher-in-ubuntu-18-04). \r\n\r\nI'm not sure if there is anything that can be done about this, but I'd like to confirm that using huggingface\/datasets requires either an upgrade to Ubuntu 19\/20 or a hand-rolled install of a higher version of GLIBC.\r\n\r\n## Steps to reproduce the bug\r\n1. clone the transformers repo\r\n2. move to examples\/pytorch\/language-modeling\r\n3. run example command:\r\n```python run_clm.py     --model_name_or_path gpt2     --dataset_name wikitext     --dataset_config_name wikitext-2-raw-v1     --do_train     --do_eval     --output_dir \/tmp\/test-clm```\r\n\r\n\r\n## Expected results\r\nAs described in the transformers repo.\r\n\r\n## Actual results\r\n```Traceback (most recent call last):\r\n  File \"run_clm.py\", line 34, in <module>\r\n    from transformers import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/__init__.py\", line 2487, in __getattr__\r\n    return super().__getattr__(name)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/file_utils.py\", line 1699, in __getattr__\r\n    module = self._get_module(self._class_to_module[name])\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/__init__.py\", line 2481, in _get_module\r\n    return importlib.import_module(\".\" + module_name, self.__name__)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/__init__.py\", line 19, in <module>\r\n    from . import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/layoutlm\/__init__.py\", line 23, in <module>\r\n    from .tokenization_layoutlm import LayoutLMTokenizer\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/layoutlm\/tokenization_layoutlm.py\", line 19, in <module>\r\n    from ..bert.tokenization_bert import BertTokenizer\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/bert\/tokenization_bert.py\", line 23, in <module>\r\n    from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils.py\", line 26, in <module>\r\n    from .tokenization_utils_base import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 68, in <module>\r\n    from tokenizers import AddedToken\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/tokenizers\/__init__.py\", line 79, in <module>\r\n    from .tokenizers import (\r\nImportError: \/lib\/x86_64-linux-gnu\/libm.so.6: version `GLIBC_2.29' not found (required by \/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/tokenizers\/tokenizers.cpython-37m-x86_64-linux-gnu.so)\r\n```\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```\r\n- Datasets: 1.6.1\r\n- Python: 3.7.10 (default, Feb 26 2021, 18:47:35) \r\n[GCC 7.3.0]\r\n- Platform: Linux-4.15.0-128-generic-x86_64-with-debian-buster-sid\r\n\r\n```\r\n","comment_length":22,"text":"Compatibility with Ubuntu 18 and GLIBC 2.27? \n ## Describe the bug\r\nFor use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https:\/\/www.digitalocean.com\/community\/questions\/how-install-glibc-2-29-or-higher-in-ubuntu-18-04). \r\n\r\nI'm not sure if there is anything that can be done about this, but I'd like to confirm that using huggingface\/datasets requires either an upgrade to Ubuntu 19\/20 or a hand-rolled install of a higher version of GLIBC.\r\n\r\n## Steps to reproduce the bug\r\n1. clone the transformers repo\r\n2. move to examples\/pytorch\/language-modeling\r\n3. run example command:\r\n```python run_clm.py     --model_name_or_path gpt2     --dataset_name wikitext     --dataset_config_name wikitext-2-raw-v1     --do_train     --do_eval     --output_dir \/tmp\/test-clm```\r\n\r\n\r\n## Expected results\r\nAs described in the transformers repo.\r\n\r\n## Actual results\r\n```Traceback (most recent call last):\r\n  File \"run_clm.py\", line 34, in <module>\r\n    from transformers import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/__init__.py\", line 2487, in __getattr__\r\n    return super().__getattr__(name)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/file_utils.py\", line 1699, in __getattr__\r\n    module = self._get_module(self._class_to_module[name])\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/__init__.py\", line 2481, in _get_module\r\n    return importlib.import_module(\".\" + module_name, self.__name__)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/__init__.py\", line 19, in <module>\r\n    from . import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/layoutlm\/__init__.py\", line 23, in <module>\r\n    from .tokenization_layoutlm import LayoutLMTokenizer\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/layoutlm\/tokenization_layoutlm.py\", line 19, in <module>\r\n    from ..bert.tokenization_bert import BertTokenizer\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/bert\/tokenization_bert.py\", line 23, in <module>\r\n    from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils.py\", line 26, in <module>\r\n    from .tokenization_utils_base import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 68, in <module>\r\n    from tokenizers import AddedToken\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/tokenizers\/__init__.py\", line 79, in <module>\r\n    from .tokenizers import (\r\nImportError: \/lib\/x86_64-linux-gnu\/libm.so.6: version `GLIBC_2.29' not found (required by \/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/tokenizers\/tokenizers.cpython-37m-x86_64-linux-gnu.so)\r\n```\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```\r\n- Datasets: 1.6.1\r\n- Python: 3.7.10 (default, Feb 26 2021, 18:47:35) \r\n[GCC 7.3.0]\r\n- Platform: Linux-4.15.0-128-generic-x86_64-with-debian-buster-sid\r\n\r\n```\r\n \n From the trace this seems like an error in the tokenizer library instead.\r\n\r\nDo you mind opening an issue at https:\/\/github.com\/huggingface\/tokenizers instead?","embeddings":[-0.142811805,-0.1723928154,0.1075327396,0.1302344054,0.0596383885,-0.0772048682,0.2908620238,0.2598784566,0.2259389609,0.0111382147,0.1740464121,0.2267897278,-0.2015923262,0.0207950752,0.0591229573,-0.2397232503,0.4002413452,0.1342248768,-0.3602030575,-0.0450363941,-0.1554612368,0.5982704759,-0.3146861196,-0.010957269,-0.4952140749,0.0392144546,-0.0519102439,0.1466761082,0.13439174,-0.1309381276,0.4062684178,-0.1249714494,-0.0909759551,0.754386127,-0.0001224395,0.0493884832,0.2803669572,-0.0356524996,-0.3091667891,-0.3431194127,0.4603341222,-0.2034225613,0.1984533519,0.0210759174,-0.1604524255,-0.0312387682,0.0431692265,-0.1770747751,0.306265533,0.2292727828,0.1635718495,0.6086139679,0.2872385085,-0.2450601906,0.1327299774,0.0671043396,-0.3019824922,0.1775183678,0.3917792141,0.0825865865,0.1014894694,0.3350143731,0.1052379236,-0.2016983181,0.4753361344,-0.0923256204,-0.2559214234,-0.2584765255,0.0923650339,0.1190377399,0.1131736115,-0.3634867966,-0.4058305025,-0.5498496294,-0.2388646007,0.2090977579,0.367434144,-0.2292598337,-0.0580108985,0.1378621012,-0.3331091702,-0.3827241659,-0.1197224706,-0.0331870615,-0.012718291,0.8859995604,-0.0173584167,0.1676160395,0.0610819794,0.0065577272,0.00012063,-0.1602582037,0.00854606,0.1647605896,-0.1325274855,-0.0888525993,0.053081993,-0.1233673468,0.0066031292,-0.1705616266,-0.2632810771,-0.1685592979,-0.1061792672,0.1360406727,0.0399863385,0.2783112526,-0.1579051763,0.4739059508,0.2186329961,0.2473488599,0.321674943,-0.1070386618,0.1026504189,-0.2637830079,-0.1310983449,0.1177573726,0.2092296034,-0.2898231447,-0.017997589,-0.0566714592,-0.0108650476,0.2226473093,0.0681747422,0.1660168469,-0.0328398757,0.3804472387,0.0928115323,0.2229461521,-0.1679702252,0.2382942885,-0.1164787933,-0.0974209011,-0.0144361146,-0.0716307014,0.1691595614,-0.0391608514,0.2538377941,0.2059005052,0.4203241169,0.0874161497,-0.1992437989,0.0457497984,0.0518945269,0.3462992609,-0.4304388165,0.3338389397,0.1684209108,-0.3088178933,-0.3119699657,-0.0098933876,-0.0638596117,-0.0914926156,-0.0469189622,0.0632655919,-0.2492465824,0.013064472,0.0124965664,0.1144580767,0.172650516,-0.4946889877,0.2769922614,-0.233783111,-0.2119734287,-0.0241523031,0.1076139584,0.0356387608,-0.2591195107,-0.4424846768,0.3240971267,-0.0839912519,-0.0312667713,0.243970871,-0.1142867506,-0.3914651573,-0.1942076236,-0.0358673967,-0.2203388661,-0.1526675969,-0.5307197571,0.0207097698,0.0859973133,0.2394689918,-0.0067258072,-0.0262469035,0.0615262613,-0.2693711817,-0.1275818497,0.3495996594,0.1197857261,0.0980074033,-0.4085882008,-0.3331645727,-0.0149042811,-0.0940913484,0.1942503303,-0.164009288,-0.1923215687,0.1174797788,0.4155255854,-0.2786252499,-0.0290605333,0.1464767158,0.5196980834,0.000521138,0.0618164539,-0.1114572659,-0.6179240942,0.4469006062,-0.2873841226,0.1566895992,-0.3138538003,-0.1721369773,-0.0007825637,0.1277842969,0.0836197957,-0.3957600594,-0.0320510939,0.0547227673,0.1871065795,0.0526270345,-0.1427202672,0.2477567494,0.0274521876,0.0078920322,-0.4396381378,0.3152775764,-0.0824749023,-0.1737359762,0.1070349589,0.2113476843,0.0512974896,-0.329780519,-0.1780619919,0.3240359724,-0.127081722,0.1900217682,-0.3105565012,0.0714245811,0.2983856797,-0.0419499762,-0.0758858919,0.3169097304,-0.007165622,0.0673383176,0.086399734,0.1811774969,0.0145425787,0.1595082581,0.2673688233,0.0799954161,0.0134186652,-0.0280264802,-0.0551781021,-0.205769375,0.053303048,-0.0545434989,-0.0722139925,0.1719166487,-0.1459628791,-0.0196434744,0.6664274335,0.0451898836,0.0067488635,0.1859145761,-0.1284980327,-0.104209736,0.1641649604,0.3101860285,0.2909154594,0.1102724299,-0.0396555513,0.2677046955,-0.0750832558,-0.1431828141,0.2155104727,0.096880801,0.4194385409,0.0191845484,0.0385328494,0.30566594,0.0614310354,-0.0603859276,-0.263718158,0.0914352238,-0.3121880293,0.4507681429,-0.0368034206,0.0784676224,-0.1586845964,-0.1577325314,-0.7852627635,-0.1883606911,0.0067408956,0.0399592854,-0.1980149597,0.3214106858,-0.0444885045,0.1915934533,-0.2949773371,-0.2638169527,-0.2137812376,0.0314920433,-0.5112403631,-0.0231350772,0.2785100937,0.1089708582,0.0357541703,-0.0728168488,-0.1063587368,0.0394180641,-0.5937504172,0.2073419094,-0.096671924,0.3363634646,0.0931136757,-0.1605436951,-0.1574152261,-0.2098421901,0.2231027633,-0.8244650364,-0.0332416594,-0.0670602322,-0.033060573,-0.2129868716,-0.310906589,0.0425588973,-0.1937351674,-0.2245925218,0.0641586706,0.0267934725,0.1504383236,0.6384630203,0.0632362068,0.0668419898,-0.368848443,-0.0192970764,0.0950938016,0.1094495058,0.264444232,0.0237147622,-0.464327395,-0.1210149899,-0.048923403,0.1750032455,0.1410595775,-0.3063699901,-0.2472435981,-0.4217748642,0.0800869763,0.193205893,-0.0237948094,0.4895001054,-0.2125200182,0.1190253422,-0.2503902018,-0.1644293964,0.1279211491,-0.0880082622,0.1465210468,0.0695360452,0.1001236588,0.1234058142,0.7045325637,0.4875889719,-0.0884720162,0.1642232537,0.1532943696,0.4174870849,-0.2423343807,-0.3420737088,-0.0094390912,0.0148862777,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2279","title":"Compatibility with Ubuntu 18 and GLIBC 2.27?","comments":"Hi @tginart, thanks for reporting.\r\n\r\nI think this issue is already open at `tokenizers` library: https:\/\/github.com\/huggingface\/tokenizers\/issues\/685","body":"## Describe the bug\r\nFor use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https:\/\/www.digitalocean.com\/community\/questions\/how-install-glibc-2-29-or-higher-in-ubuntu-18-04). \r\n\r\nI'm not sure if there is anything that can be done about this, but I'd like to confirm that using huggingface\/datasets requires either an upgrade to Ubuntu 19\/20 or a hand-rolled install of a higher version of GLIBC.\r\n\r\n## Steps to reproduce the bug\r\n1. clone the transformers repo\r\n2. move to examples\/pytorch\/language-modeling\r\n3. run example command:\r\n```python run_clm.py     --model_name_or_path gpt2     --dataset_name wikitext     --dataset_config_name wikitext-2-raw-v1     --do_train     --do_eval     --output_dir \/tmp\/test-clm```\r\n\r\n\r\n## Expected results\r\nAs described in the transformers repo.\r\n\r\n## Actual results\r\n```Traceback (most recent call last):\r\n  File \"run_clm.py\", line 34, in <module>\r\n    from transformers import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/__init__.py\", line 2487, in __getattr__\r\n    return super().__getattr__(name)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/file_utils.py\", line 1699, in __getattr__\r\n    module = self._get_module(self._class_to_module[name])\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/__init__.py\", line 2481, in _get_module\r\n    return importlib.import_module(\".\" + module_name, self.__name__)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/__init__.py\", line 19, in <module>\r\n    from . import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/layoutlm\/__init__.py\", line 23, in <module>\r\n    from .tokenization_layoutlm import LayoutLMTokenizer\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/layoutlm\/tokenization_layoutlm.py\", line 19, in <module>\r\n    from ..bert.tokenization_bert import BertTokenizer\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/bert\/tokenization_bert.py\", line 23, in <module>\r\n    from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils.py\", line 26, in <module>\r\n    from .tokenization_utils_base import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 68, in <module>\r\n    from tokenizers import AddedToken\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/tokenizers\/__init__.py\", line 79, in <module>\r\n    from .tokenizers import (\r\nImportError: \/lib\/x86_64-linux-gnu\/libm.so.6: version `GLIBC_2.29' not found (required by \/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/tokenizers\/tokenizers.cpython-37m-x86_64-linux-gnu.so)\r\n```\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```\r\n- Datasets: 1.6.1\r\n- Python: 3.7.10 (default, Feb 26 2021, 18:47:35) \r\n[GCC 7.3.0]\r\n- Platform: Linux-4.15.0-128-generic-x86_64-with-debian-buster-sid\r\n\r\n```\r\n","comment_length":16,"text":"Compatibility with Ubuntu 18 and GLIBC 2.27? \n ## Describe the bug\r\nFor use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https:\/\/www.digitalocean.com\/community\/questions\/how-install-glibc-2-29-or-higher-in-ubuntu-18-04). \r\n\r\nI'm not sure if there is anything that can be done about this, but I'd like to confirm that using huggingface\/datasets requires either an upgrade to Ubuntu 19\/20 or a hand-rolled install of a higher version of GLIBC.\r\n\r\n## Steps to reproduce the bug\r\n1. clone the transformers repo\r\n2. move to examples\/pytorch\/language-modeling\r\n3. run example command:\r\n```python run_clm.py     --model_name_or_path gpt2     --dataset_name wikitext     --dataset_config_name wikitext-2-raw-v1     --do_train     --do_eval     --output_dir \/tmp\/test-clm```\r\n\r\n\r\n## Expected results\r\nAs described in the transformers repo.\r\n\r\n## Actual results\r\n```Traceback (most recent call last):\r\n  File \"run_clm.py\", line 34, in <module>\r\n    from transformers import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/__init__.py\", line 2487, in __getattr__\r\n    return super().__getattr__(name)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/file_utils.py\", line 1699, in __getattr__\r\n    module = self._get_module(self._class_to_module[name])\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/__init__.py\", line 2481, in _get_module\r\n    return importlib.import_module(\".\" + module_name, self.__name__)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/__init__.py\", line 19, in <module>\r\n    from . import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/layoutlm\/__init__.py\", line 23, in <module>\r\n    from .tokenization_layoutlm import LayoutLMTokenizer\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/layoutlm\/tokenization_layoutlm.py\", line 19, in <module>\r\n    from ..bert.tokenization_bert import BertTokenizer\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/models\/bert\/tokenization_bert.py\", line 23, in <module>\r\n    from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils.py\", line 26, in <module>\r\n    from .tokenization_utils_base import (\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 68, in <module>\r\n    from tokenizers import AddedToken\r\n  File \"\/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/tokenizers\/__init__.py\", line 79, in <module>\r\n    from .tokenizers import (\r\nImportError: \/lib\/x86_64-linux-gnu\/libm.so.6: version `GLIBC_2.29' not found (required by \/home\/tginart\/anaconda3\/envs\/huggingface\/lib\/python3.7\/site-packages\/tokenizers\/tokenizers.cpython-37m-x86_64-linux-gnu.so)\r\n```\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```\r\n- Datasets: 1.6.1\r\n- Python: 3.7.10 (default, Feb 26 2021, 18:47:35) \r\n[GCC 7.3.0]\r\n- Platform: Linux-4.15.0-128-generic-x86_64-with-debian-buster-sid\r\n\r\n```\r\n \n Hi @tginart, thanks for reporting.\r\n\r\nI think this issue is already open at `tokenizers` library: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2278","title":"Loss result inGptNeoForCasual","comments":"Hi ! I think you might have to ask on the `transformers` repo on or the forum at https:\/\/discuss.huggingface.co\/\r\n\r\nClosing since it's not related to this library","body":"Is there any way you give the \" loss\" and \"logits\" results in the gpt neo api? ","comment_length":27,"text":"Loss result inGptNeoForCasual \n Is there any way you give the \" loss\" and \"logits\" results in the gpt neo api?  \n Hi ! I think you might have to ask on the `transformers` repo on or the forum at https:\/\/discuss.huggingface.co\/\r\n\r\nClosing since it's not related to this library","embeddings":[-0.1725184619,-0.5047546625,-0.0494229384,0.473176837,-0.0721403807,-0.3464438319,0.0108577479,0.0527497567,-0.4997354448,0.1422903687,-0.0995590985,-0.0388611034,0.100904882,0.2542966306,0.0646138266,-0.2345316112,-0.0951776728,0.298414439,-0.113592945,-0.2847176492,0.046722319,0.7516429424,-0.0423596501,0.4498414397,-0.1351107955,0.049132552,0.1681937128,-0.1435233206,-0.1015470177,-0.1026617214,0.0283567552,-0.2763161659,-0.1589080542,0.2016085386,-0.0001213429,-0.0264125504,0.2445292324,-0.0716523081,-0.1024020538,0.0959482342,0.2629530728,0.116295673,0.1316123009,-0.1478680223,-0.4186839163,-0.4525968432,-0.1050605103,-0.3300442994,0.2786361277,0.156503126,0.0447498858,0.4286803603,-0.2391529232,0.0296922661,0.1258453876,0.2987284064,-0.3401528001,-0.2796235979,0.0260950234,-0.3103776276,0.3510200679,0.2686969936,0.3863429129,-0.3036620319,0.0181093048,0.3710387051,0.1963018924,0.0109900814,0.0251023266,0.2208728939,0.2916791737,-0.1006681174,-0.1725571603,-0.3272690475,-0.076347895,-0.5108529329,-0.0946470425,0.1157223061,-0.2886104286,0.1027034521,-0.0897850245,0.1514853686,-0.3256933987,-0.1401705146,-0.1286067069,-0.0098543847,0.101522021,0.0668269917,0.2126813978,-0.0489526466,-0.4141369164,0.4112629294,-0.1428751051,-0.2921434939,-0.200718388,-0.5218161941,0.4599569738,0.2730282247,0.0312607288,-0.0208967198,0.1309318691,-0.2003554553,-0.2176462859,0.0279037207,0.137572065,-0.0961225256,0.4073675275,0.1480400711,0.1019013077,-0.0475528203,0.1873628795,-0.152867496,0.4590636492,0.3763070703,0.0797442868,0.1429805011,0.0296133645,-0.4953493178,-0.280303359,-0.2522744238,0.1631973982,0.1406692266,-0.1088396013,-0.1343104988,-0.0160850268,0.1368656904,0.4740391672,-0.1410940289,-0.2202246934,0.1094303131,-0.0504105948,0.0339513905,-0.4868873358,0.1971952766,0.1035983264,0.3923437893,0.0333321542,-0.3985877931,-0.2281475812,0.2114372551,-0.0236336533,-0.0581281483,0.431316793,0.2363340706,-0.2228953391,-0.2575081289,-0.1625892967,-0.2158327103,-0.3757222593,-0.2422768623,0.4703900218,0.1956524402,-0.167727679,0.1122743413,0.0742414594,-0.1150673479,0.0617525205,0.3946146965,-0.0748204216,-0.1125263274,0.228046447,-0.0367226638,-0.3630042672,-0.1009183377,0.147413373,0.201251477,-0.0140398229,-0.5118097067,0.2428103238,-0.364155829,-0.1003240347,0.6593738198,0.0223975312,-0.0343961418,-0.1571909338,0.066836521,0.1328684986,-0.129877761,-0.1844610423,-0.4084619284,-0.1652341485,-0.1120061278,0.0725027546,0.0194830596,0.0134648941,-0.1131599545,0.1517076939,0.2277487367,-0.1084786206,-0.0292873103,-0.1077157259,-0.1577295959,0.2575592995,0.077276364,0.0432767384,-0.0455071479,-0.0621432848,-0.0667331219,0.094162479,-0.1415484548,-0.0810557082,-0.2618052661,0.3792394102,-0.3635045588,0.2026861459,-0.2679693103,-0.2763743401,0.0226156209,-0.5987124443,0.4693303406,0.0762628838,-0.2173783481,0.1839211583,-0.0067559402,0.4335826039,0.1400045753,0.1324246228,0.1296623051,0.0034162169,0.2564793527,-0.0129541969,0.0517177582,0.0345509052,-0.0483000986,-0.3150790632,0.1414650828,0.2187710553,-0.2970020473,-0.0528162047,0.0064465385,0.2238237113,0.1665391624,0.0170899089,-0.013207946,-0.3755169809,0.4678097367,0.1800196171,0.8736052513,0.2860046923,-0.0812116936,-0.2098022401,0.3937200904,-0.1191533208,-0.1728307009,-0.1444349438,0.1783284396,0.4140585661,-0.0591887087,0.2585327625,-0.0206662919,-0.1965887547,-0.1870826781,-0.3010665178,0.1405651867,0.1782164425,-0.3403763175,-0.0610642657,-0.1732201576,-0.4480157495,-0.0855614915,0.5458114743,-0.1134902462,0.1407276392,0.1678895503,-0.163473323,0.2742599249,0.1547592133,-0.1610339582,0.5066561103,0.0889239088,0.4276523292,0.1344052404,-0.0072405036,-0.1800989658,0.270796001,0.1540868878,0.0585375801,0.1390319467,0.1704631597,0.0915151015,-0.2852141559,0.0512629338,-0.384313643,-0.2931557,-0.0383235775,-0.1311173439,0.1286748946,-0.4033598006,-0.2206862718,-0.1276967674,0.1210237071,-0.0528600886,0.2827812433,-0.0695349947,-0.2162185758,-0.2116764337,0.262898773,0.5175610185,-0.0787364468,0.5037275553,-0.4499675035,-0.0716039613,-0.2682846189,0.1174156219,-0.4420118332,-0.3176285326,0.3655708134,-0.2907857001,-0.0914599374,-0.0759961829,-0.1990365237,0.3708024025,0.0023099952,-0.3136318922,0.1398541629,0.0063221497,0.0689287037,0.0739045367,0.1504748613,-0.6341795325,-0.0714748725,-0.2168478519,-0.0190161038,0.1884606332,-0.4838357568,-0.059614066,-0.3014622927,-0.2533233464,0.5574224591,-0.0429198705,0.0389955826,0.8306107521,-0.5347166061,0.0162806753,0.089945823,0.2708842456,0.0525015965,-0.0101972176,-0.0211191736,-0.1664485037,-0.0324043334,-0.1454047263,0.5164799094,-0.063463375,0.0934325904,-0.2375232875,-0.3187120557,0.0204699021,-0.0377339609,-0.1212196723,0.0364743285,0.6025368571,0.0533900931,-0.1724061519,-0.0387756303,-0.1411359906,0.1813826114,0.1829167157,0.2384239882,-0.0252660289,0.1747956872,0.1527781337,0.2597800791,0.1726745963,-0.5284258127,0.0049804514,0.0444116071,-0.1989776343,0.1114135459,-0.061512202,-0.2533995509,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2276","title":"concatenate_datasets loads all the data into memory","comments":"Therefore, when I try to concatenate larger datasets (5x 35GB data sets) I also get an out of memory error, since over 90GB of swap space was used at the time of the crash:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nMemoryError                               Traceback (most recent call last)\r\n<ipython-input-6-9766d77530b9> in <module>\r\n     20         print(file_name)\r\n     21         cv_batch = load_from_disk(file_name)\r\n---> 22         cv_sampled_train = concatenate_datasets([cv_sampled_train, cv_batch])\r\n     23 \r\n     24 print(\"Saving to disk!\")\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\arrow_dataset.py in concatenate_datasets(dsets, info, split, axis)\r\n   2891 \r\n   2892     # Concatenate tables\r\n-> 2893     table = concat_tables([dset._data for dset in dsets if len(dset._data) > 0], axis=axis)\r\n   2894     table = update_metadata_with_features(table, None)\r\n   2895 \r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in concat_tables(tables, axis)\r\n    837     if len(tables) == 1:\r\n    838         return tables[0]\r\n--> 839     return ConcatenationTable.from_tables(tables, axis=axis)\r\n    840 \r\n    841 \r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in from_tables(cls, tables, axis)\r\n    697             return result\r\n    698 \r\n--> 699         blocks = to_blocks(tables[0])\r\n    700         for table in tables[1:]:\r\n    701             table_blocks = to_blocks(table)\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in to_blocks(table)\r\n    669                 return [[InMemoryTable(table)]]\r\n    670             elif isinstance(table, ConcatenationTable):\r\n--> 671                 return copy.deepcopy(table.blocks)\r\n    672             else:\r\n    673                 return [[table]]\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    151             copier = getattr(x, \"__deepcopy__\", None)\r\n    152             if copier is not None:\r\n--> 153                 y = copier(memo)\r\n    154             else:\r\n    155                 reductor = dispatch_table.get(cls)\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in __deepcopy__(self, memo)\r\n    143         # by adding it to the memo, self.table won't be copied\r\n    144         memo[id(self.table)] = self.table\r\n--> 145         return _deepcopy(self, memo)\r\n    146 \r\n    147     def __getstate__(self):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in _deepcopy(x, memo)\r\n     62     memo[id(x)] = result\r\n     63     for k, v in x.__dict__.items():\r\n---> 64         setattr(result, k, copy.deepcopy(v, memo))\r\n     65     return result\r\n     66 \r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    170                     y = x\r\n    171                 else:\r\n--> 172                     y = _reconstruct(x, memo, *rv)\r\n    173 \r\n    174     # If is its own copy, don't memoize.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)\r\n    262     if deep and args:\r\n    263         args = (deepcopy(arg, memo) for arg in args)\r\n--> 264     y = func(*args)\r\n    265     if deep:\r\n    266         memo[id(x)] = y\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <genexpr>(.0)\r\n    261     deep = memo is not None\r\n    262     if deep and args:\r\n--> 263         args = (deepcopy(arg, memo) for arg in args)\r\n    264     y = func(*args)\r\n    265     if deep:\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    170                     y = x\r\n    171                 else:\r\n--> 172                     y = _reconstruct(x, memo, *rv)\r\n    173 \r\n    174     # If is its own copy, don't memoize.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)\r\n    262     if deep and args:\r\n    263         args = (deepcopy(arg, memo) for arg in args)\r\n--> 264     y = func(*args)\r\n    265     if deep:\r\n    266         memo[id(x)] = y\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <genexpr>(.0)\r\n    261     deep = memo is not None\r\n    262     if deep and args:\r\n--> 263         args = (deepcopy(arg, memo) for arg in args)\r\n    264     y = func(*args)\r\n    265     if deep:\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_tuple(x, memo, deepcopy)\r\n    208 \r\n    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210     y = [deepcopy(a, memo) for a in x]\r\n    211     # We're not going to put the tuple in the memo, but it's still important we\r\n    212     # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <listcomp>(.0)\r\n    208 \r\n    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210     y = [deepcopy(a, memo) for a in x]\r\n    211     # We're not going to put the tuple in the memo, but it's still important we\r\n    212     # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_tuple(x, memo, deepcopy)\r\n    208 \r\n    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210     y = [deepcopy(a, memo) for a in x]\r\n    211     # We're not going to put the tuple in the memo, but it's still important we\r\n    212     # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <listcomp>(.0)\r\n    208 \r\n    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210     y = [deepcopy(a, memo) for a in x]\r\n    211     # We're not going to put the tuple in the memo, but it's still important we\r\n    212     # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    159                     reductor = getattr(x, \"__reduce_ex__\", None)\r\n    160                     if reductor is not None:\r\n--> 161                         rv = reductor(4)\r\n    162                     else:\r\n    163                         reductor = getattr(x, \"__reduce__\", None)\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyarrow\\io.pxi in pyarrow.lib.Buffer.__reduce_ex__()\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyarrow\\io.pxi in pyarrow.lib.Buffer.to_pybytes()\r\n\r\nMemoryError: \r\n\r\n```","body":"## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n","comment_length":1031,"text":"concatenate_datasets loads all the data into memory \n ## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n \n Therefore, when I try to concatenate larger datasets (5x 35GB data sets) I also get an out of memory error, since over 90GB of swap space was used at the time of the crash:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nMemoryError                               Traceback (most recent call last)\r\n<ipython-input-6-9766d77530b9> in <module>\r\n     20         print(file_name)\r\n     21         cv_batch = load_from_disk(file_name)\r\n---> 22         cv_sampled_train = concatenate_datasets([cv_sampled_train, cv_batch])\r\n     23 \r\n     24 print(\"Saving to disk!\")\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\arrow_dataset.py in concatenate_datasets(dsets, info, split, axis)\r\n   2891 \r\n   2892     # Concatenate tables\r\n-> 2893     table = concat_tables([dset._data for dset in dsets if len(dset._data) > 0], axis=axis)\r\n   2894     table = update_metadata_with_features(table, None)\r\n   2895 \r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in concat_tables(tables, axis)\r\n    837     if len(tables) == 1:\r\n    838         return tables[0]\r\n--> 839     return ConcatenationTable.from_tables(tables, axis=axis)\r\n    840 \r\n    841 \r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in from_tables(cls, tables, axis)\r\n    697             return result\r\n    698 \r\n--> 699         blocks = to_blocks(tables[0])\r\n    700         for table in tables[1:]:\r\n    701             table_blocks = to_blocks(table)\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in to_blocks(table)\r\n    669                 return [[InMemoryTable(table)]]\r\n    670             elif isinstance(table, ConcatenationTable):\r\n--> 671                 return copy.deepcopy(table.blocks)\r\n    672             else:\r\n    673                 return [[table]]\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    151             copier = getattr(x, \"__deepcopy__\", None)\r\n    152             if copier is not None:\r\n--> 153                 y = copier(memo)\r\n    154             else:\r\n    155                 reductor = dispatch_table.get(cls)\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in __deepcopy__(self, memo)\r\n    143         # by adding it to the memo, self.table won't be copied\r\n    144         memo[id(self.table)] = self.table\r\n--> 145         return _deepcopy(self, memo)\r\n    146 \r\n    147     def __getstate__(self):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in _deepcopy(x, memo)\r\n     62     memo[id(x)] = result\r\n     63     for k, v in x.__dict__.items():\r\n---> 64         setattr(result, k, copy.deepcopy(v, memo))\r\n     65     return result\r\n     66 \r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    170                     y = x\r\n    171                 else:\r\n--> 172                     y = _reconstruct(x, memo, *rv)\r\n    173 \r\n    174     # If is its own copy, don't memoize.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)\r\n    262     if deep and args:\r\n    263         args = (deepcopy(arg, memo) for arg in args)\r\n--> 264     y = func(*args)\r\n    265     if deep:\r\n    266         memo[id(x)] = y\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <genexpr>(.0)\r\n    261     deep = memo is not None\r\n    262     if deep and args:\r\n--> 263         args = (deepcopy(arg, memo) for arg in args)\r\n    264     y = func(*args)\r\n    265     if deep:\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    170                     y = x\r\n    171                 else:\r\n--> 172                     y = _reconstruct(x, memo, *rv)\r\n    173 \r\n    174     # If is its own copy, don't memoize.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)\r\n    262     if deep and args:\r\n    263         args = (deepcopy(arg, memo) for arg in args)\r\n--> 264     y = func(*args)\r\n    265     if deep:\r\n    266         memo[id(x)] = y\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <genexpr>(.0)\r\n    261     deep = memo is not None\r\n    262     if deep and args:\r\n--> 263         args = (deepcopy(arg, memo) for arg in args)\r\n    264     y = func(*args)\r\n    265     if deep:\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_tuple(x, memo, deepcopy)\r\n    208 \r\n    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210     y = [deepcopy(a, memo) for a in x]\r\n    211     # We're not going to put the tuple in the memo, but it's still important we\r\n    212     # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <listcomp>(.0)\r\n    208 \r\n    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210     y = [deepcopy(a, memo) for a in x]\r\n    211     # We're not going to put the tuple in the memo, but it's still important we\r\n    212     # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_tuple(x, memo, deepcopy)\r\n    208 \r\n    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210     y = [deepcopy(a, memo) for a in x]\r\n    211     # We're not going to put the tuple in the memo, but it's still important we\r\n    212     # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <listcomp>(.0)\r\n    208 \r\n    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210     y = [deepcopy(a, memo) for a in x]\r\n    211     # We're not going to put the tuple in the memo, but it's still important we\r\n    212     # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    144     copier = _deepcopy_dispatch.get(cls)\r\n    145     if copier is not None:\r\n--> 146         y = copier(x, memo)\r\n    147     else:\r\n    148         if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n    203     append = y.append\r\n    204     for a in x:\r\n--> 205         append(deepcopy(a, memo))\r\n    206     return y\r\n    207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n    159                     reductor = getattr(x, \"__reduce_ex__\", None)\r\n    160                     if reductor is not None:\r\n--> 161                         rv = reductor(4)\r\n    162                     else:\r\n    163                         reductor = getattr(x, \"__reduce__\", None)\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyarrow\\io.pxi in pyarrow.lib.Buffer.__reduce_ex__()\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyarrow\\io.pxi in pyarrow.lib.Buffer.to_pybytes()\r\n\r\nMemoryError: \r\n\r\n```","embeddings":[-0.2084973752,-0.1142902002,0.0484714955,0.4346452057,0.199329704,0.1706529409,-0.1373960376,0.2591967583,-0.1694815457,0.0429848805,0.0269325897,0.2109090835,0.0059097265,-0.2283868343,-0.1217163354,0.1358806938,0.2059148848,0.0483620204,-0.2932789326,-0.0278221704,-0.2548550665,0.2438369095,-0.3456242681,-0.3321818709,-0.3617053926,0.268399775,-0.288836211,0.4278264642,0.0539543182,0.0645484328,0.3253515661,-0.0345083736,0.0323292613,0.4721652567,-0.0001129602,-0.0248592626,0.1940784007,-0.1648728251,-0.3701837957,0.0790072531,-0.257725656,-0.4280099869,-0.1000079662,0.0440540612,0.4190388918,0.033717785,-0.1139069051,-0.7214249969,0.0311022121,0.1015497446,0.2013147473,0.0450461656,0.3114587069,-0.1664170623,-0.0396206118,0.0367016122,-0.1191298142,0.3378038704,0.1667080969,-0.2502472401,0.0814066082,0.0372247323,-0.2164464295,-0.1925283819,0.0617970489,0.2860530615,-0.2610839307,-0.4039141536,0.024543019,0.029022174,0.2679313123,-0.410095185,-0.042093657,-0.4409756958,0.036623124,-0.1752433777,0.2727940977,0.4027750194,-0.2462693602,0.293792367,-0.0894597396,0.0641691759,0.0416414775,0.0172061753,-0.2132966369,0.1253425181,-0.0891545489,0.0814755037,0.4102660716,-0.0002172078,0.29209885,-0.3168378174,-0.3524680436,0.0377512462,-0.5595110655,-0.0498269126,0.0755836442,-0.4589984715,0.2216620147,0.1062295064,0.1199405491,0.0183505844,0.212127164,-0.0187555477,0.3851090074,0.2461845875,-0.0689737573,0.4001848698,0.0538023338,-0.1622482836,0.302623421,0.0824345574,-0.029857846,-0.2151603252,0.2167406082,-0.2746641338,0.2562694252,-0.0793108121,-0.4918817282,0.2032411695,-0.1662214547,0.1221663281,-0.0328876488,0.1849793792,0.0313825198,0.5110003352,0.1733138263,0.1208142936,-0.0608106479,0.1380167902,-0.1753146946,-0.1013809815,-0.2131145895,0.3016291857,0.2818205357,-0.0465134867,0.0303951073,0.2547302544,0.1015235111,-0.2291039824,0.0861584991,-0.3414562643,0.0732797608,0.1529537141,0.0300752893,0.3761548698,0.1255546063,-0.0855323374,-0.0447999351,0.3257975876,-0.312535882,-0.1928355694,-0.2904739082,0.1899365634,0.0848515853,0.1171091199,-0.2278673947,0.2889300883,0.3921437562,0.2276145965,-0.0246095154,-0.0160901472,0.1302628517,-0.3428185284,0.3278464675,0.4563962221,-0.1643471569,0.2442180216,0.1593949497,0.2009991705,0.5149561167,0.3146007061,-0.0787222609,0.0898945928,-0.4410283864,-0.0834654197,0.1891031414,-0.1437196136,-0.263667345,0.2389305979,-0.250733614,0.3553431928,-0.0772488788,0.1755164564,-0.0911854059,0.1544353962,0.7130596638,0.4093846977,0.0317519046,0.1562935859,-0.3548541367,-0.0897677019,0.1346036643,-0.0315746814,-0.0920929909,0.2037222236,-0.0326738805,0.101136595,0.4664764702,-0.1077108309,0.153782919,0.4310880899,0.0536223911,-0.0714828223,-0.2064581066,0.0748017207,-0.5007076859,0.0443876721,0.0569930151,-0.2531939447,-0.0204966217,-0.0257828552,-0.0208263192,-0.0269897208,0.0041292487,0.0003340359,0.0507822856,0.2245372832,-0.1111161187,-0.05244793,-0.0241559371,0.8368053436,-0.1254532039,0.0507056601,-0.4194497764,0.1712570935,-0.0288675167,-0.2300039381,-0.0660286769,0.0523214936,0.1158133075,0.0188080091,-0.1096438766,0.5126361251,-0.1974750161,-0.052632004,-0.2070373297,-0.255222857,0.0165734403,-0.0363014676,0.0188739039,-0.2550829351,0.0981568024,-0.147838518,-0.0155783817,0.3320586979,-0.0144178234,0.0340214893,0.0131641245,-0.0984588116,-0.0694328323,-0.1997836083,0.1187121272,-0.187878564,0.1486685127,0.2488634884,0.2260706872,0.2236911505,-0.4840830564,-0.0810607523,0.2194009572,0.0873406306,0.2150361985,0.114058435,-0.3331508636,-0.1729366928,-0.0043807761,0.1619129777,0.7703347206,0.158680141,0.2190234959,0.027602585,-0.0670482293,-0.2215753347,0.2817302942,-0.0253939908,0.0702340901,0.2413841486,0.2463647574,-0.004090522,-0.2063647807,0.1619835943,0.1250704378,0.006698038,-0.2925804555,0.1093799099,-0.2098116875,0.0715502128,-0.3482342362,-0.3516216278,-0.0114572477,-0.4532111287,-0.3251947463,0.395349741,-0.0905613005,-0.1436620951,0.0558752455,0.2527936399,-0.0280837454,0.0283187088,0.0868103728,0.2992718816,-0.3061571419,0.0406388193,0.0782498419,-0.2633136213,0.0490673073,0.1617473811,-0.1413246542,0.0032180639,-0.0775131136,-0.0589703247,-0.0648287833,0.002731751,-0.1926749498,0.1106752828,0.0238208659,-0.3615097702,0.0093959169,0.4291680455,0.0770543218,0.4083506465,0.2699891925,-0.1056147888,-0.3779594004,-0.1318799257,-0.3104640245,-0.3968418539,0.0882834047,-0.0485187098,0.0247029588,0.3051521182,0.1884802282,-0.0642439499,0.0278732907,0.0880296603,-0.1925894171,-0.2163109332,0.4186181128,-0.0596594103,-0.2062426209,-0.1150502935,0.1457549334,-0.2462955415,0.2110211849,-0.4671842754,0.1191657484,-0.5144683123,0.036162667,-0.2598979175,0.0987710059,0.0204598419,0.0733611211,-0.07519418,-0.087361522,-0.0732144713,-0.1453312933,-0.0290519334,0.3612685502,-0.2521463335,-0.0733989552,0.2915198207,-0.0696369186,0.4133873582,0.0289635099,0.3018461764,0.1516514271,0.5536729693,-0.2386159748,-0.4298308492,-0.122861959,-0.1587635279,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2276","title":"concatenate_datasets loads all the data into memory","comments":"Hi ! this looks like an important issue. Let me try to reproduce this.\r\nCc @samsontmr this might be related to the memory issue you have in #2134 ","body":"## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n","comment_length":28,"text":"concatenate_datasets loads all the data into memory \n ## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n \n Hi ! this looks like an important issue. Let me try to reproduce this.\r\nCc @samsontmr this might be related to the memory issue you have in #2134 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2276","title":"concatenate_datasets loads all the data into memory","comments":"@lhoestq Just went to open a similar issue.\r\n\r\nIt seems like deep copying (tested on master) the dataset object writes the table's record batches (`dset._data._batches`) into RAM.\r\n\r\nTo find the bug, I modified the `_deepcopy` function in `table.py` as follows:\r\n```python\r\ndef _deepcopy(x, memo: dict):\r\n    \"\"\"deepcopy a regular class instance\"\"\"\r\n    import psutil # pip install this package\r\n    import time\r\n    cls = x.__class__\r\n    result = cls.__new__(cls)\r\n    memo[id(x)] = result\r\n    for k, v in x.__dict__.items():\r\n        print(\"=\"* 50)\r\n        print(\"Current memory:\", psutil.virtual_memory().percent)\r\n        print(f\"Saving object {k} with value {v}\")\r\n        setattr(result, k, copy.deepcopy(v, memo))\r\n        time.sleep(5)\r\n        print(\"Memory after copy:\", psutil.virtual_memory().percent)\r\n    return result\r\n```\r\nTest script:\r\n```python\r\nimport copy\r\nfrom datasets import load_dataset\r\nbk = load_dataset(\"bookcorpus\", split=\"train\")\r\nbk_copy = copy.deepcopy(bk)\r\n```","body":"## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n","comment_length":113,"text":"concatenate_datasets loads all the data into memory \n ## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n \n @lhoestq Just went to open a similar issue.\r\n\r\nIt seems like deep copying (tested on master) the dataset object writes the table's record batches (`dset._data._batches`) into RAM.\r\n\r\nTo find the bug, I modified the `_deepcopy` function in `table.py` as follows:\r\n```python\r\ndef _deepcopy(x, memo: dict):\r\n    \"\"\"deepcopy a regular class instance\"\"\"\r\n    import psutil # pip install this package\r\n    import time\r\n    cls = x.__class__\r\n    result = cls.__new__(cls)\r\n    memo[id(x)] = result\r\n    for k, v in x.__dict__.items():\r\n        print(\"=\"* 50)\r\n        print(\"Current memory:\", psutil.virtual_memory().percent)\r\n        print(f\"Saving object {k} with value {v}\")\r\n        setattr(result, k, copy.deepcopy(v, memo))\r\n        time.sleep(5)\r\n        print(\"Memory after copy:\", psutil.virtual_memory().percent)\r\n    return result\r\n```\r\nTest script:\r\n```python\r\nimport copy\r\nfrom datasets import load_dataset\r\nbk = load_dataset(\"bookcorpus\", split=\"train\")\r\nbk_copy = copy.deepcopy(bk)\r\n```","embeddings":[-0.1189593524,0.0833053589,0.1260726601,0.371997565,0.0171169862,0.113386929,-0.1137914583,0.3422017992,-0.3763219416,-0.0313398018,-0.0029891629,0.2833094299,0.1033000275,-0.1568289697,0.0146102719,0.4244330823,0.2601239979,0.134578377,-0.4625256956,-0.1483727992,-0.3425718844,-0.0025916975,-0.1786786169,-0.4195239842,-0.4456115961,0.3286666274,-0.3202904463,0.2377286553,-0.0560326912,0.128633216,0.355545193,0.1864214092,-0.0315982215,0.5348338485,-0.0001191608,0.0577579923,0.2564501762,-0.1635587364,-0.4494656026,0.1362714171,-0.3920058906,-0.3536658585,0.1314734966,0.0160404183,0.416490078,0.0663911626,-0.2298910618,-0.4634824991,-0.0232501589,0.0652262866,0.1593083292,0.1110112965,-0.0350349993,-0.1213506758,0.0089878831,0.1179197505,-0.1411336064,0.2453012168,-0.1013238281,-0.116763331,0.0576568209,0.1684359312,-0.35906896,-0.0874381438,-0.0095486175,0.2900185585,-0.1620877683,-0.2823731303,0.2129003853,0.0464695394,0.2146040499,-0.4365739226,-0.0415480658,-0.4749776125,-0.0196938086,-0.3048731089,0.0995297506,0.308213383,-0.1890996844,0.1121600196,-0.13280949,0.0581193231,0.0337472819,-0.073737599,-0.1217983887,-0.1695670187,0.0101737473,0.0271479599,0.4104741812,-0.0549752563,0.4511062503,-0.3960840404,-0.2956274152,0.0855898634,-0.5441936851,0.0141931763,0.1144322529,-0.2700991631,0.1956006885,0.2327721417,0.2149356455,0.0469081774,0.1379220337,-0.022849787,0.4365807772,0.1996461302,-0.1025633216,0.5009908676,0.1304700375,-0.1632460356,0.3409542143,0.110141784,-0.1132148653,-0.0781086311,0.3009509146,-0.3656769097,0.229241237,-0.1927221417,-0.3715546727,0.1397656053,0.0065254094,0.0778659061,-0.0496670604,0.3585070074,0.0671438053,0.2547057867,0.15389283,0.3614987135,-0.06985154,0.1949400306,-0.2237622738,-0.1218684241,-0.16035375,0.2349952906,0.366168052,0.0074927118,-0.0572899692,0.3013069332,0.0723042563,-0.1387106925,-0.0609996095,-0.3262606561,0.3241434395,0.2703713477,0.0587168597,0.2103704959,0.2266677767,-0.1304647774,-0.1423020065,0.4503260255,-0.2546396255,-0.0735558346,-0.069354929,0.10335695,0.0362258926,0.0443489477,-0.4758992195,0.3106041253,0.434776932,0.2327105105,0.0285809096,-0.1107441112,-0.1464839727,-0.3564536572,0.4472286999,0.4729628861,-0.0541462898,0.0487051979,0.0353441387,0.2649788857,0.5495262742,0.3402062953,0.0434603579,0.0879209563,-0.2372004837,0.0179053303,0.0983412564,-0.0005004104,-0.2484383583,0.1872132421,-0.2704435587,0.335116595,-0.0241550449,0.1016249433,0.1242662966,0.097319752,0.660245955,0.2771522999,0.0627577081,0.1526489705,-0.3004397154,-0.1258177757,0.0933524594,0.008635669,-0.1207739562,0.3070910871,-0.0019083718,-0.2292074412,0.386333853,0.0090980995,0.1405576915,0.2855954766,0.1549436897,-0.0899411812,-0.1746754348,0.2379614115,-0.3544645011,0.097163029,0.1835031509,-0.0397738367,-0.1856774241,-0.1383266896,0.134820208,0.2285040617,-0.2306197435,0.0922537223,0.0155475968,0.0989256203,-0.0327142216,0.1316239089,0.1348375231,0.6968744397,-0.1539064348,0.0587004051,-0.4625235796,0.1415293664,0.1405992806,-0.2519921958,-0.0621952377,0.2103710771,0.1967872679,0.0732989833,-0.1008108929,0.4235238731,-0.3193057775,-0.1450687796,-0.2693098783,0.0403387994,-0.0298414659,-0.0516968966,0.1415467411,-0.2254734337,0.2247688621,-0.1563021243,-0.056741938,0.450727582,0.0112392036,0.1874462962,-0.0045901737,-0.1919718832,0.0793231726,-0.1642476618,0.0901824981,-0.1587000936,0.1115505174,0.3809700608,0.1517383903,0.332141012,-0.3299784958,0.1686086655,0.19155626,-0.0641717762,0.2080635428,0.2489986569,-0.3479628861,-0.2832609713,-0.1409808546,0.3459080458,0.7657057047,0.0833002329,0.2235913277,-0.1126445308,-0.0013695674,-0.1795598119,0.3187161684,-0.0918825939,0.1735778749,0.2070209831,0.2243025899,-0.1472703516,-0.2065800428,0.4320197701,0.1452322006,-0.0747572333,-0.2772232294,0.0283994805,-0.3362882733,0.0518880039,-0.3733009398,-0.1222399995,0.0817801803,-0.3311992884,-0.2710944712,0.3929058015,-0.0935641974,-0.1096558943,-0.0498728529,0.3842968047,-0.0503481664,-0.1125858277,0.00265779,0.1787295491,-0.2788047791,-0.0525808372,-0.0056801997,-0.3215699792,0.0520076565,0.2059115171,-0.1652086973,0.0415372588,0.0537941642,0.026079135,-0.195922628,0.0513004847,-0.1727582514,0.1507132947,0.0790431798,-0.2274551392,-0.016985558,0.3729391992,0.0160092954,0.5513109565,0.2199639529,-0.2465394437,-0.1986986995,0.0650124103,-0.3557266295,-0.1634889692,0.2513190806,-0.1487260908,0.0252158549,0.1737631708,0.0827576742,-0.1229356602,0.0731968656,0.1564647704,-0.1991321892,-0.1466024965,0.422041297,-0.0023801727,-0.3424716592,-0.2232850343,0.0801083818,-0.3310308456,0.151091069,-0.5484544635,0.2285906076,-0.5363147259,0.2024652958,0.0190964006,0.0740161762,0.1360855848,0.1101197228,-0.0381845795,-0.1437601894,-0.2721313536,0.0334944762,0.006990639,0.0654356256,-0.0745998323,0.2158823609,0.2305157036,0.0186750889,0.3907527328,-0.0177374594,0.3915452063,0.1259574443,0.5700924397,-0.3393678367,-0.4471695721,-0.3153416514,-0.3729497492,-0.05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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2276","title":"concatenate_datasets loads all the data into memory","comments":"Thanks for the insights @mariosasko ! I'm working on a fix.\r\nSince this is a big issue I'll make a patch release as soon as this is fixed","body":"## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n","comment_length":28,"text":"concatenate_datasets loads all the data into memory \n ## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n \n Thanks for the insights @mariosasko ! I'm working on a fix.\r\nSince this is a big issue I'll make a patch release as soon as this is fixed","embeddings":[-0.1398513168,0.015114448,0.0576703213,0.3696739078,0.1839567572,0.1596759409,-0.033678297,0.2669216692,-0.2130460292,0.0089295153,0.0735274181,0.1821824461,0.1092261598,-0.2046645433,-0.1817605793,0.3304117918,0.2417273819,0.2320262641,-0.2873950303,-0.0829890594,-0.2531095147,0.2507131398,-0.311899215,-0.3932681084,-0.4115408361,0.2931381166,-0.3173240721,0.2868468463,0.0731343403,0.0163131412,0.3663010895,0.0684919283,-0.0108415475,0.4899155796,-0.0001141718,-0.0399469957,0.2374247015,-0.1666595042,-0.371073395,0.0382387303,-0.2115313262,-0.4239183962,0.0949019715,0.0163905304,0.3227258921,0.0276343487,-0.1392493546,-0.6195906401,-0.036331322,-0.0184868425,0.2055884451,-0.0736077577,0.0873234943,-0.0346819982,-0.1545480192,0.1501190811,-0.0562109314,0.2053209096,0.0462260507,-0.0868438259,0.1201179102,0.1036038622,-0.2861646712,-0.0662713721,0.0869725496,0.3886128962,-0.1644797772,-0.4153285623,0.0623523779,0.0289843343,0.2989785969,-0.3810659349,-0.0750826746,-0.4106537998,0.0723076537,-0.151874572,0.2585920095,0.5170176029,-0.2185684741,0.2292109579,-0.1242389977,-0.025238242,-0.016185388,-0.0290718637,-0.2198185027,0.1054319218,-0.126125142,0.0533120893,0.3340789378,0.0692817196,0.2617450953,-0.4625049829,-0.3334731758,0.1024646461,-0.5122957826,-0.0164026413,-0.0102712503,-0.3762557805,0.1318039745,0.2016307712,0.0878395736,0.0035140365,0.184177652,0.0197742544,0.3769460618,0.1765152961,0.0080639152,0.3474484384,0.1052436158,-0.2071619928,0.2690299451,0.0588003919,-0.0470570698,-0.2088435888,0.2501572967,-0.3447650969,0.1984031647,-0.1314506382,-0.4553020895,0.1100055501,-0.1005401239,0.0687937289,-0.0308024473,0.2494181991,-0.0282951146,0.4881114662,0.1856010258,0.3167209327,-0.0345314294,0.1992320865,-0.1725660563,-0.1550679654,-0.0954498351,0.2142083198,0.3256211579,-0.236500591,-0.0088531934,0.4181314409,0.0340125747,-0.1766606718,0.0817503259,-0.3128965497,0.2096921206,0.1971650124,0.1583949178,0.422079891,0.1548349708,-0.1589883715,-0.0694989264,0.4355956316,-0.287933737,-0.0658500716,-0.2847761214,0.1766027957,0.0320008993,0.1077617332,-0.2073271722,0.3298118412,0.4095009267,0.2896985114,-0.0911844745,0.0171630085,-0.0444950312,-0.3863945007,0.4746124744,0.374691695,-0.1228752956,0.1804333627,0.0796554461,0.2555893958,0.5120504498,0.3279236257,-0.0482980199,0.0531703196,-0.3730937541,0.0125809368,0.1686255187,-0.1546408236,-0.1727763116,0.3032649457,-0.2416497469,0.3287750483,-0.0439332575,0.169486925,-0.0733178183,0.1647847146,0.5839229226,0.465687722,0.1425248533,0.1275511533,-0.2993758321,-0.1339702904,0.1769673079,-0.1294547766,-0.225111872,0.3253209889,-0.0589603558,-0.1444763392,0.5275813937,-0.1006270573,0.2085408717,0.3714689314,0.150481984,-0.1359591931,-0.2112352699,0.194219932,-0.4309019148,0.0013527095,0.1675441265,-0.2653865218,-0.0621719025,-0.0578743853,0.1020050868,0.0258222912,-0.0636250824,0.0238730665,0.012160602,0.2641777694,-0.099379316,0.0258629303,-0.0086658709,0.8541288972,-0.033142563,0.0026922363,-0.2960159183,0.1831163168,0.0733396783,-0.26403597,-0.0746999756,0.0341198519,0.1943829954,0.0817593113,-0.0983499363,0.4858409762,-0.3071314394,-0.0535058901,-0.1756159216,-0.2325031608,-0.030167209,0.0834080726,0.0084746219,-0.1559773535,0.1944572628,-0.2200031728,-0.0202957094,0.3703812063,0.0729342252,0.1607596129,-0.0248842351,-0.0984202996,0.0550480001,-0.1947636753,0.1069363207,-0.2017446756,0.1457116306,0.2696819603,0.1671738029,0.3178861141,-0.469243139,0.0399977118,0.218240574,0.0924291983,0.1402144581,0.1689800322,-0.4341333807,-0.1735431701,-0.1096332297,0.18923226,0.7930139899,0.1422207355,0.2025080621,-0.081556946,0.0044435863,-0.2626176775,0.3425416946,-0.0330345668,0.0056893597,0.2706080377,0.2729274631,-0.1077963933,-0.1397445947,0.3924454451,0.0544332303,-0.0995829925,-0.2963497937,0.1051601619,-0.2472942919,0.1749789566,-0.3388817012,-0.2995420396,-0.0429706611,-0.3729386628,-0.3870545626,0.4966091514,-0.0050710961,-0.2058933675,-0.0000230926,0.3029479086,-0.0802884251,-0.1078599021,-0.0437195785,0.2978871167,-0.4031485617,0.0023266287,0.0329105556,-0.2414670736,0.0255984701,0.1616852582,-0.0746065453,0.0062471745,-0.0376013555,-0.1460683942,-0.1108777747,0.1193870306,-0.2633400559,0.144841373,0.0067907702,-0.3595245779,0.0139946369,0.4124944806,0.0607036278,0.4417824745,0.3276702166,-0.2216087729,-0.357884407,-0.0738942698,-0.2946987748,-0.2672674358,0.099407427,0.0303421058,0.0763324648,0.1941413432,0.0754337311,-0.164186433,0.0795483217,0.1035593972,-0.1700496078,-0.3065524995,0.5985969305,-0.0416410975,-0.3260031939,-0.0542533733,0.0742413849,-0.2196608633,0.1507494897,-0.5239768624,0.2077150792,-0.5243376493,0.0357964411,-0.2324332595,0.0915300697,-0.0867217407,0.0982720628,-0.0368086696,-0.1558751166,-0.1413527578,0.0125797698,0.0044497326,0.2630653977,-0.1881356984,-0.1222265065,0.1747363359,-0.1320964843,0.5040397644,-0.0255294964,0.3300635219,0.1965639591,0.5206709504,-0.2252723277,-0.4929818213,-0.2043385804,-0.2016645223,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2276","title":"concatenate_datasets loads all the data into memory","comments":"Hi @samsontmr @TaskManager91 the fix is on the master branch, feel free to install `datasets` from source and let us know if you still have issues","body":"## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n","comment_length":26,"text":"concatenate_datasets loads all the data into memory \n ## Describe the bug\r\nWhen I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.\r\n\r\nInterestingly, this happens when trying to save the new dataset to disk or concatenating it again.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/7063207\/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)\r\n\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import concatenate_datasets, load_from_disk\r\n\r\ntest_sampled_pro = load_from_disk(\"test_sampled_pro\")\r\nval_sampled_pro = load_from_disk(\"val_sampled_pro\")\r\n\r\nbig_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])\r\n\r\n# Loaded to memory\r\nbig_set.save_to_disk(\"big_set\")\r\n\r\n# Loaded to memory\r\nbig_set = concatenate_datasets([big_set, val_sampled_pro])\r\n```\r\n\r\n## Expected results\r\nThe data should be loaded into memory in batches and then saved directly to disk.\r\n\r\n## Actual results\r\nThe entire data set is loaded into the memory and then saved to the hard disk.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\n- Datasets: 1.6.1\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10\r\n```\r\n \n Hi @samsontmr @TaskManager91 the fix is on the master branch, feel free to install `datasets` from source and let us know if you still have issues","embeddings":[-0.170591861,0.0092170332,0.0292842202,0.3677366972,0.1755022407,0.1506508887,-0.0363330878,0.2557009161,-0.2184762359,0.0205546785,0.0756376907,0.2006986439,0.1137187034,-0.1913734525,-0.1878060699,0.3212848306,0.2241285145,0.2327459753,-0.3028806746,-0.0799712464,-0.2509787977,0.2532582581,-0.3152192235,-0.4041965008,-0.3926775455,0.2633895576,-0.324208647,0.2930183411,0.0551784709,0.0143002868,0.3530985415,0.0911309645,-0.006137263,0.5166870356,-0.0001119337,-0.0348054878,0.219003275,-0.1603055149,-0.3798380494,0.0134006506,-0.2082094103,-0.431055218,0.0528312698,-0.0045533706,0.3232043386,0.0165413711,-0.1144325137,-0.6221048832,-0.0218277741,-0.0040224274,0.2312553972,-0.0567462742,0.1109552085,-0.0584037527,-0.1406242996,0.1617460996,-0.0351833552,0.1927781105,0.0371735543,-0.0715287179,0.1079412773,0.079248555,-0.2629720271,-0.083159931,0.0752241835,0.3842883706,-0.1346914023,-0.4134614468,0.0804955363,0.0150397122,0.3004926145,-0.3991719782,-0.0938755423,-0.3797347844,0.0740216374,-0.1438546181,0.2679314315,0.4909189045,-0.2104186267,0.219563514,-0.1331172436,-0.036526829,-0.0239779484,-0.028899638,-0.2043293566,0.0931631997,-0.1354396194,0.0305273775,0.3242896497,0.0352999233,0.2702701688,-0.4690794647,-0.3298483789,0.1103057042,-0.513263464,-0.0276157558,-0.0030134087,-0.3875360489,0.1243834794,0.2151877582,0.0875878707,0.0227854196,0.184498772,0.0349109657,0.3635174632,0.1804081649,0.0184642617,0.3571968675,0.1284895092,-0.1866255999,0.2579454184,0.0578463078,-0.0656249374,-0.2233371288,0.2364633381,-0.3508513868,0.1808035523,-0.1380872279,-0.4598098695,0.1174360663,-0.0863997713,0.0496026762,-0.0094316872,0.2542226017,-0.0292180497,0.4726689756,0.1849807799,0.2863076925,-0.0344359241,0.201805681,-0.1823039949,-0.1430383474,-0.1071337834,0.208167702,0.318336159,-0.237367779,-0.0012824204,0.3865213394,0.03396466,-0.1422039121,0.0747801885,-0.3283980787,0.1817576289,0.1874959767,0.1599019468,0.385514915,0.1233887896,-0.1299316287,-0.0734612346,0.4197448492,-0.2636742592,-0.0535451323,-0.2766641974,0.2080066651,0.0323732533,0.0900532603,-0.1872912943,0.3255513608,0.3913214207,0.2665786147,-0.1022031605,0.0159123689,-0.0254238937,-0.4058591723,0.4793983996,0.3491703272,-0.1226651818,0.1716086864,0.0541150719,0.2214607894,0.5023226738,0.3170529008,-0.0411457643,0.0464436114,-0.3630587161,0.0208721384,0.172096312,-0.1713977009,-0.1892300695,0.2992790341,-0.232709378,0.305789113,-0.0190437678,0.1393660903,-0.0547649898,0.1674485207,0.5857989192,0.4550622106,0.1370738745,0.1111657768,-0.3130151033,-0.1439277977,0.1525891423,-0.1146862805,-0.2094824761,0.3083630502,-0.0232087485,-0.1329599023,0.5200488567,-0.1009353697,0.2115394026,0.374530077,0.174204886,-0.1261503696,-0.2095542401,0.176323697,-0.4116720557,-0.0128653375,0.1423327327,-0.2536487579,-0.0743055195,-0.058770448,0.0862646177,0.0054154764,-0.0461516567,0.0131203309,0.0468461588,0.2829904258,-0.0541425571,0.0100656962,-0.0051468834,0.8445510864,-0.0522520691,-0.0038746768,-0.2735114694,0.2024548203,0.0517794602,-0.2781853676,-0.0604393296,0.0601664297,0.1768258363,0.0623119436,-0.0969210938,0.5086948872,-0.3162319064,-0.0530032888,-0.128217712,-0.2386722118,-0.0197944585,0.0813364983,0.0303822011,-0.1581412107,0.1951512247,-0.2013555616,-0.0485713333,0.3627675772,0.0637389719,0.1470878571,0.0014080026,-0.1114584878,0.0617574602,-0.1842131168,0.1107689291,-0.1929574013,0.1656011343,0.2717431486,0.1882426143,0.2941269875,-0.4811876416,0.0245005563,0.2233474255,0.0795772821,0.1472449899,0.1627761126,-0.4270969927,-0.1794689596,-0.1047101915,0.1944568157,0.8014233112,0.1726982445,0.2021740973,-0.0856779665,-0.0116208401,-0.2446803749,0.3470253944,-0.0430199094,0.0150324805,0.2690575123,0.2735055089,-0.1009962186,-0.1482656449,0.3355564773,0.0742284507,-0.1128370687,-0.2773726285,0.0631775036,-0.2430235893,0.1607270092,-0.315087527,-0.2844523787,-0.0286442153,-0.3622499704,-0.3986128569,0.4999139607,0.0003713467,-0.1869661957,0.0068637296,0.2999919355,-0.0691002831,-0.0762333125,-0.0526445471,0.2924110591,-0.3996618092,0.0342250839,0.0327193178,-0.2350403666,0.059115652,0.1551668793,-0.063314639,-0.0152570223,-0.0391109101,-0.1443186551,-0.1046109498,0.1309558153,-0.2461868972,0.1443766654,0.0002403225,-0.3521909714,0.0151467007,0.386020869,0.0331956856,0.4261025488,0.317624867,-0.2234506309,-0.367921561,-0.089445807,-0.3102516234,-0.2938085794,0.0898835808,0.0360095501,0.0871712938,0.1884695143,0.0940233693,-0.1125219539,0.0752632767,0.1272690892,-0.1948869228,-0.3146141171,0.5944067836,-0.0795404613,-0.3260225058,-0.0507530794,0.0738560334,-0.2051508129,0.1580086946,-0.518168509,0.1775104702,-0.5287163258,0.0553962812,-0.2294151932,0.1287552863,-0.0822000131,0.1013927013,-0.0731060654,-0.1697247773,-0.1496968269,-0.0004368192,0.0265975446,0.2668580711,-0.1863807142,-0.1166978627,0.1746390909,-0.1422039568,0.5094496608,-0.0357053131,0.3200167716,0.1902724653,0.5234614611,-0.1986839771,-0.4719843864,-0.1932121515,-0.2005855143,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2275","title":"SNLI dataset has labels of -1 ","comments":"Hi @puzzler10, \r\nThose examples where `gold_label` field was empty, -1 label was alloted to it. In order to remove it you can filter the samples from train\/val\/test splits. Here's how you can drop those rows from the dataset:\r\n`dataset = load_dataset(\"snli\")`\r\n`dataset_test_filter = dataset['test'].filter(lambda example: example['label'] != -1)`\r\n\r\nI agree it should have been mentioned in the documentation. I'll raise a PR regarding the same. Thanks for pointing out!","body":"There are a number of rows with a label of -1 in the SNLI dataset. The dataset descriptions [here](https:\/\/nlp.stanford.edu\/projects\/snli\/) and [here](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/snli) don't list  -1 as a label possibility, and neither does the dataset viewer. As examples, see index 107 or 124 of the test set.\r\n\r\nIt isn't clear what these labels mean. I found a [line of code](https:\/\/github.com\/huggingface\/datasets\/blob\/80e59ef178d3bb2090d091bc32315c655eb0633d\/datasets\/snli\/snli.py#L94) that seems to put them in but it seems still unclear why they are there. The current workaround is to just drop the rows from any model being trained. \r\n\r\nPerhaps the documentation should be updated.","comment_length":69,"text":"SNLI dataset has labels of -1  \n There are a number of rows with a label of -1 in the SNLI dataset. The dataset descriptions [here](https:\/\/nlp.stanford.edu\/projects\/snli\/) and [here](https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/snli) don't list  -1 as a label possibility, and neither does the dataset viewer. As examples, see index 107 or 124 of the test set.\r\n\r\nIt isn't clear what these labels mean. I found a [line of code](https:\/\/github.com\/huggingface\/datasets\/blob\/80e59ef178d3bb2090d091bc32315c655eb0633d\/datasets\/snli\/snli.py#L94) that seems to put them in but it seems still unclear why they are there. The current workaround is to just drop the rows from any model being trained. \r\n\r\nPerhaps the documentation should be updated. \n Hi @puzzler10, \r\nThose examples where `gold_label` field was empty, -1 label was alloted to it. In order to remove it you can filter the samples from train\/val\/test splits. Here's how you can drop those rows from the dataset:\r\n`dataset = load_dataset(\"snli\")`\r\n`dataset_test_filter = dataset['test'].filter(lambda example: example['label'] != -1)`\r\n\r\nI agree it should have been mentioned in the documentation. I'll raise a PR regarding the same. Thanks for pointing out!","embeddings":[0.2989758253,-0.3761280775,-0.0328568369,0.2222822458,0.0694199353,0.0815882981,0.3312395513,0.1691942811,0.181328088,0.2444586903,-0.3545390666,0.4890278876,-0.1160793751,0.264321506,-0.0070854737,0.1549568772,0.2352825403,0.303981185,0.2841205299,-0.3298657238,-0.2717858553,0.0914113894,-0.405192703,0.2479602247,-0.3731481433,0.0205870774,0.1299512088,-0.0096964277,0.0335911103,-0.5129570961,0.0948818326,0.1173787862,-0.0772679076,-0.0724165887,-0.0001088527,-0.2403392494,0.0697496906,-0.047023192,-0.6223359704,-0.2529820502,-0.1181870475,0.2077092826,0.0751572996,0.0172909703,-0.0961224213,-0.052774895,0.0744386092,0.1242415681,-0.0809797347,0.3326574266,0.1794922054,0.3804579675,-0.1768749058,0.0707381293,0.3439856172,-0.1560855955,0.017882321,0.2107768506,-0.0133288531,0.0425255261,0.2260413021,0.5354082584,-0.0452154428,0.0348852649,0.3393327594,0.022858398,-0.0777820721,-0.4266029894,0.0421027653,0.3638975322,0.0810523927,-0.230555594,-0.3671469688,-0.2749627829,0.2244778425,-0.2685078979,0.0582703799,0.2668513954,0.0697489232,0.2614788413,-0.1460727155,-0.0509525016,-0.175192982,0.0005081995,-0.1057194099,0.6413038969,-0.1593694389,0.1289835721,0.5075032115,0.0728355199,-0.4432066679,0.0394593552,0.1568491906,0.4317371845,-0.569457233,-0.1717996895,-0.0282681026,0.1632059813,0.0481878258,0.2185510546,0.0437853001,0.0269279908,-0.0140581802,0.2916793823,-0.0694846883,0.1436824799,0.3065781593,-0.0466930643,0.211473763,0.0162288174,0.3600986302,0.0481924452,-0.2132719457,-0.139418155,0.1308576465,-0.2398141474,0.1998532116,-0.2626758516,-0.6473467946,0.2097320706,-0.3907648623,0.1524562687,0.086687237,0.0645216629,0.2203213722,-0.0598432161,-0.0791026875,0.0456990339,-0.1378145665,-0.4292081594,-0.1970970631,0.0903697908,-0.0988867879,-0.1167682186,0.2107839584,-0.3131540716,0.3315541744,0.0532458797,-0.2020506561,-0.0207850803,0.1787769049,0.1645315588,0.2473968714,0.5771651268,-0.3488188982,0.3330381811,0.0659814328,-0.196420148,-0.1017569751,-0.0393126309,-0.1723008603,-0.2989450991,-0.0818260387,0.1699967086,-0.1952711642,-0.1519026756,-0.0912291631,0.0878947675,-0.1778071076,0.1813961267,0.2566719353,-0.2120781988,-0.0585161708,-0.0010470236,0.3605412543,0.1575626135,-0.4851129651,-0.2034799308,0.1237736493,-0.4227319062,-0.0173462499,0.2736925185,0.0932890847,-0.1971938461,-0.0713968575,0.252091527,0.2139832377,-0.2968644202,-0.3827925324,0.0271746255,-0.3269345462,-0.1678779125,-0.1895414591,0.3081360459,-0.1457919627,0.1059518978,-0.0698814094,-0.2019356936,0.0900856256,-0.2835516632,-0.5512373447,-0.2125910074,0.2463142276,0.3127197921,-0.0492930077,0.0508235693,-0.1639029682,-0.0070770928,0.2720735669,0.0131315337,0.2313565612,0.1837443113,0.430899471,0.1810058206,0.0823249146,-0.0849987566,-0.3912090957,0.2321951538,0.0310569219,0.3658102751,0.1825841963,-0.2678924203,-0.2410059422,-0.2028992325,-0.2412320226,-0.2026890516,0.0869416073,0.0619796813,0.2216905057,-0.0590649284,-0.3432810307,0.0738289878,-0.3657287955,0.2240730226,-0.4974218607,-0.1394050717,0.1059826463,0.1703776419,-0.1773592383,0.2051088363,0.2067948431,0.0261948612,-0.0004640424,0.3204998672,-0.1998345405,-0.2124389261,0.230879724,0.4564017951,0.1108060181,-0.4229474962,-0.0603930503,0.3066322505,0.0346611701,0.0723259747,-0.3624300063,0.3699859083,-0.1780641973,0.1303417832,-0.157143116,0.1258876771,0.103882812,0.0623039193,-0.3292539716,-0.2804180086,-0.1012573317,-0.3951129913,0.3665444851,0.1523706019,-0.6327460408,0.1134551987,0.0347607322,-0.3291116953,-0.08874356,0.0729339942,-0.2424841821,-0.1196305528,0.3244645596,0.2411278337,0.1161471382,0.2301638573,0.1370828748,0.1427655667,-0.3513398767,-0.2686562538,0.0397718363,0.1523792148,-0.1068359688,-0.1208846271,0.1304067224,-0.0173292551,-0.3405174911,-0.2308853567,-0.0885479227,0.2723142207,-0.4020915031,0.0290394183,-0.2172335535,-0.1394995749,-0.3488059044,-0.0022709807,-0.2464655787,-0.2919360399,0.31280002,0.004745502,-0.203044042,0.1004573926,0.0316309668,0.5093470216,-0.1519939601,0.2992749512,-0.3002235591,-0.2652912438,-0.2074290961,0.0390334986,-0.2587574124,0.2907176912,0.3018003702,-0.1576564014,-0.333899647,-0.0033467682,-0.3023775518,0.0480288491,-0.1426198483,0.1002896801,0.1844928414,0.0393273644,-0.0511753,0.3166732192,0.0049948175,-0.2142882794,-0.1116836742,-0.0925555825,-0.0810804591,0.1272654086,-0.5270533562,-0.3587163091,-0.1944821924,-0.0670323074,0.2488209158,-0.199238494,0.0729883239,0.3046570718,-0.0580612198,0.30747962,-0.1491309702,0.1342582852,-0.1992041171,-0.4086114168,0.1899916232,0.0326557942,-0.2425484657,0.1930107474,0.2684447169,-0.0160765853,-0.2244117707,-0.3304808736,-0.0894588381,0.1758215427,0.0823932365,0.1061904654,-0.0795192942,-0.1110929474,0.0465240292,0.0067266622,-0.2253056914,0.0642919987,0.3514791429,-0.4254739881,0.2542331219,-0.1100141183,0.1659719795,0.010234803,0.2668569982,0.4718406796,0.0371448435,-0.1448502392,0.02754738,0.1412937492,-0.0483857915,0.174921751,0.1657844931,0.2737950683,-0.09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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2272","title":"Bug in Dataset.class_encode_column","comments":"This has been fixed in this commit: https:\/\/github.com\/huggingface\/datasets\/pull\/2254\/commits\/88676c930216cd4cc31741b99827b477d2b46cb6\r\n\r\nIt was introduced in #2246 : using map with `input_columns` doesn't return the other columns anymore","body":"## Describe the bug\r\n\r\nAll the rest of the columns except the one passed to `Dataset.class_encode_column` are discarded.\r\n\r\n## Expected results\r\n\r\nAll the original columns should be kept.\r\n\r\nThis needs regression tests.\r\n","comment_length":24,"text":"Bug in Dataset.class_encode_column \n ## Describe the bug\r\n\r\nAll the rest of the columns except the one passed to `Dataset.class_encode_column` are discarded.\r\n\r\n## Expected results\r\n\r\nAll the original columns should be kept.\r\n\r\nThis needs regression tests.\r\n \n This has been fixed in this commit: https:\/\/github.com\/huggingface\/datasets\/pull\/2254\/commits\/88676c930216cd4cc31741b99827b477d2b46cb6\r\n\r\nIt was introduced in #2246 : using map with `input_columns` doesn't return the other columns anymore","embeddings":[-0.0357847661,-0.1575217843,-0.0868108794,0.2376332283,0.5289875269,0.1175254658,0.5686455369,0.3704491556,0.2597455382,0.1947687119,-0.1332879066,0.54302001,0.0994263068,0.3341946304,0.0166783128,-0.1077984869,0.1042396128,0.1556778699,-0.5332588553,-0.2485715598,-0.3539150953,0.1785128117,-0.189593628,0.0363146365,-0.0856321976,0.2268483937,-0.0752511993,0.0718287379,-0.1968088746,0.0467561819,0.2146316022,-0.1659525633,-0.1237033755,0.077156879,-0.0000990271,0.0114663513,0.0859174654,-0.0091559635,-0.1566771418,0.0474112406,-0.1982406974,-0.0677877367,-0.0882979035,-0.2672233284,-0.1891156733,-0.2229386121,-0.1765221953,-0.2252686918,0.1340979189,0.0211990178,0.3522591293,0.2214994878,-0.1476594359,-0.0600136518,0.2655742168,0.217487067,-0.0632185414,-0.1774149835,0.0207797941,-0.0078307251,-0.0524007455,0.4466290176,0.0209640879,-0.1336341947,-0.0051338705,0.203453064,-0.0406310447,-0.0303325169,0.3821034133,0.1162569374,0.2409487814,-0.0864889845,-0.1750107706,-0.2274489552,0.0348636024,-0.2414517403,0.1153323129,-0.0931093097,0.1492735445,0.1759854406,-0.38226071,-0.0089526512,-0.0970892534,0.1939299107,-0.283518225,0.1710606366,-0.2821823657,0.0852564499,0.0107296398,0.0057511083,-0.2462015003,-0.1706468612,-0.0102349473,0.1799632013,-0.0588390231,-0.2139725536,0.0842067003,0.162787661,0.2372650951,-0.1169941127,-0.0369562693,0.2024584413,-0.0170683879,0.1216709465,0.3319074214,0.1637552977,0.1191615686,0.3312908411,0.2741263211,0.0048443577,0.0452919528,-0.1319956183,0.3326645195,0.2048241645,0.083530806,-0.113155812,0.0843298137,-0.2055879533,-0.1399455965,0.1899603307,0.0204549339,-0.0261583794,-0.0671980083,0.3910754621,0.0939425975,0.1522224694,0.0091073848,0.1897300631,0.0487884507,-0.1829747856,-0.4172065556,-0.0833830386,-0.1617066562,-0.1496147066,0.0571537018,-0.0215368681,0.3924635053,0.2146307975,-0.1008040458,-0.0021817882,-0.1540977359,-0.0079119075,0.4681972563,0.0567120984,-0.1498759538,0.2009750605,0.1821885258,-0.2646003664,0.0076709664,-0.0745368004,-0.1921508163,-0.0650472268,-0.1382558644,0.3105876446,0.0283279158,0.1095171571,-0.1155268252,0.2912951708,0.3388133943,-0.175012961,0.1155868396,-0.3287636936,-0.2149400711,-0.1349684894,0.1861959696,0.069545947,-0.2191279531,-0.2320273221,0.3701491952,-0.1202198938,0.0886877477,0.2754109204,-0.1826442331,-0.0655736551,-0.1955094934,0.2797731757,0.1806037277,-0.083446838,-0.4100649953,0.1806074828,0.097192347,0.0853633285,-0.1506409347,-0.0745889172,0.2699152827,-0.0252983179,0.0880026147,0.1082192063,0.0613477007,0.1780363172,-0.1506055444,-0.0585445203,0.1019947827,-0.1354887635,-0.0785999149,0.1987272352,0.1226853356,-0.1103497744,0.3551034033,-0.2854625881,0.165890336,0.2804603577,0.0421767086,-0.1347810328,0.0597593002,-0.2607401311,-0.4284385741,-0.0199495479,0.2042558193,-0.1423262358,-0.1081608459,-0.3205409646,-0.2251669466,-0.0325765759,-0.1822501272,0.0456575565,0.255636692,0.0194956847,0.019729564,0.0542693362,-0.2197064161,0.1615279764,-0.2340631187,0.0589018464,-0.4512899518,0.1625316143,0.0268572047,0.0533040054,-0.0592216887,0.2286079675,0.1907048225,-0.1196359992,0.0058782022,0.16816625,-0.200497523,0.1012768596,-0.2552154362,0.0251861475,0.2435627282,0.063422665,-0.007960083,-0.1525691003,0.1898518801,-0.0640749186,-0.3242453635,0.3277555704,0.1052784696,0.0502937287,0.0834908485,-0.0356507935,0.0753540844,-0.1134135425,-0.2598536313,-0.4904225469,-0.1066021249,-0.1074671745,-0.011583305,0.0517564677,-0.3236914575,0.1402301788,0.9188784361,-0.0623417795,-0.0147714987,-0.0939866975,-0.3445657492,0.0729151815,0.1259706467,0.3295140862,0.3965092301,0.2283821553,0.2659594119,0.0557789616,-0.1804527491,-0.0716970339,0.2371605188,0.0097744968,-0.0305779949,0.2169628143,0.2506073415,-0.0417708606,-0.5560484529,0.4048532844,0.0905832201,0.0257220827,-0.3956535459,0.0577441193,-0.5252432227,-0.0719339624,0.0748953447,-0.0868828818,0.1610769182,-0.4229637384,0.0589337647,0.0997522622,-0.4003640413,0.1637859195,-0.0271169152,-0.0133928992,0.0148535231,0.0539087318,-0.1009223387,-0.1556568444,-0.0931526572,0.1648465395,-0.0498673879,-0.1709077507,0.1399349868,-0.0500380546,-0.2620826364,-0.28654176,-0.4427244365,0.2523182929,-0.3048395813,0.0588300489,0.1076537743,0.1922371984,-0.3203525841,0.0516176634,0.3080891967,-0.1982468665,-0.3238245249,0.1872299761,0.1801666021,-0.1789220124,-0.3981729448,-0.4971632063,0.1300304234,-0.1350488067,0.1815885752,-0.1555291861,-0.063083142,0.0275862422,0.0029281843,0.0385403186,-0.3008101881,0.0293904543,-0.6573472023,-0.2217814475,0.3632721007,-0.2966222167,-0.5062741041,-0.1254555881,0.097908698,0.1011653394,0.0171907227,-0.3091311753,-0.3677557111,-0.0832215101,0.1842756569,-0.0637969449,0.2143393755,0.3514961302,-0.006168298,-0.1157656461,-0.1629763246,-0.1138976514,0.1560923457,0.2188880295,0.0149244191,-0.2122608274,0.4909074605,0.1053582579,0.2070960253,0.2912396789,0.0119976345,0.2866948545,-0.0825411975,0.5360159874,-0.1932152659,-0.2198107541,0.1119850427,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2267","title":"DatasetDict save load Failing test in 1.6 not in 1.5","comments":"I'm not able to reproduce this, do you think you can provide a code that creates a DatasetDict that has this issue when saving and reloading ?","body":"## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n","comment_length":27,"text":"DatasetDict save load Failing test in 1.6 not in 1.5 \n ## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n \n I'm not able to reproduce this, do you think you can provide a code that creates a DatasetDict that has this issue when saving and reloading ?","embeddings":[-0.1946798116,0.1140982956,-0.0127640385,0.284942925,0.064592123,-0.0614173673,0.1323396713,0.4565899968,0.4422373772,0.1792287529,0.2358967513,0.380851388,0.0545869619,-0.0561681502,-0.1504484713,-0.0622494444,0.2574026883,0.1362523735,0.047262188,0.1269330233,-0.1914962828,0.2465310097,-0.1496895254,-0.0032418445,-0.1895998418,0.1437868327,-0.2581939399,0.2942799032,-0.3553473651,-0.6636254787,0.2940472364,0.4080147743,0.3425835669,0.5095262527,-0.0001234102,0.0544177964,0.2841644287,-0.2096645385,-0.4484797716,-0.4640186727,-0.1993898153,0.0136942323,0.2473115027,-0.1567811072,0.174591288,-0.0732848793,-0.0321101137,-0.4220353365,0.1694841832,0.3417609334,0.1407151371,0.2266554832,0.2764903307,-0.2219138741,-0.2547798157,0.3302724957,-0.0204170793,0.48288396,-0.0186927021,0.0731682926,0.0414443091,0.0742254928,-0.0195427742,-0.1017797217,0.093925342,-0.0247307811,0.162043348,-0.0513432026,0.240814209,0.1979950964,0.6499521136,-0.2013998032,-0.5342013836,0.0352447331,-0.0611302778,0.1786230356,0.3324406445,-0.0698243231,0.0839635506,0.3244956732,-0.0997817516,-0.3653488159,-0.2351392955,-0.1203212589,0.0710190833,-0.2877634466,-0.0193996113,0.1131953076,-0.0397098362,0.0214464869,0.4245892167,-0.3535504639,-0.3489905298,-0.1019979045,-0.0459269546,-0.1144007817,-0.0653342977,-0.2706740201,0.113346532,-0.0720998496,0.2517627776,0.1316185147,-0.0391709432,0.0602462031,0.2115243673,0.2749886513,0.4198958874,0.2743373513,0.0357514173,0.0714356154,-0.2357528359,0.0838931948,-0.0404101573,-0.15166004,0.2088321447,-0.1358045042,0.5703294277,-0.4004871547,-0.3274163008,0.2602038383,-0.2743057013,-0.1729606539,-0.1039793417,0.2240318358,0.09327925,0.1972419173,0.1087378412,0.3317791224,0.0468955897,0.0277815927,-0.1801332235,0.0353045389,0.0400684513,0.011786541,0.0179779567,-0.0999579355,0.267460227,0.015178483,-0.4577178359,-0.1766552329,-0.0413619019,-0.0473274663,0.2514302135,0.0383211523,-0.2181576937,-0.056682542,0.2363063097,0.1262998134,-0.2239949852,0.2120421976,0.0718438476,-0.1589062363,-0.1686418802,0.1228035986,-0.3944003284,-0.2602534592,-0.2819331884,-0.0493995957,-0.083663933,-0.2920115888,-0.1123725697,-0.3287760615,-0.237355873,-0.1225714758,0.1428985149,0.4307113886,-0.5336277485,0.1942441016,0.1860763729,-0.0290210564,0.0966063365,-0.0230782609,0.0003097686,0.1380230188,-0.1793204844,0.1278733611,0.4110462368,-0.5454348922,-0.6159434319,0.307710886,-0.0049888324,0.1782058775,-0.1437655389,-0.0217472035,0.2127342075,-0.0530057736,0.0544378124,0.0589728355,0.0534363128,-0.0575243719,-0.4947072268,-0.3328706622,0.1166524291,-0.0132593643,-0.0556115769,0.091937542,0.1581820548,-0.2094933689,0.4844789207,-0.0858414844,-0.1812547147,0.1170089394,0.4515592456,-0.0738887712,0.0274728462,-0.2316350937,-0.5508966446,0.4312414229,0.0377286226,-0.0222507045,-0.0489411242,0.0583773702,-0.4379805923,0.1203429848,-0.3182476759,0.046511244,0.0106168631,0.078376852,-0.0866948366,-0.0088240383,-0.1149406061,0.140570417,-0.1519619673,0.0698583946,0.004358958,0.336599648,-0.2037377357,-0.2205159664,0.170929715,-0.0127892671,0.2898364365,-0.2678136826,-0.3201774955,0.4959192574,0.2282747328,0.0215910152,-0.0704142451,-0.3370976448,-0.0950707644,-0.0260207877,0.1928254664,0.4983442724,0.0574062653,0.0559969395,-0.2572018802,0.3138726652,0.0657231435,0.0568134151,0.0700780526,0.1781899929,0.2946604192,-0.024419995,-0.0170402769,-0.3440820575,0.2174295187,0.018817924,0.0102335596,0.0753727928,-0.1335324794,0.1482347995,0.25894925,0.0366390534,0.1828769594,0.0873260871,0.0021229947,-0.4102320969,0.0591992363,0.3296465278,0.6812970042,-0.0120210182,-0.0898986459,-0.0542867593,-0.0393865295,0.0392726399,0.1494913846,-0.0172953773,0.2711475194,0.1783984751,0.1901718229,0.0689663813,-0.2123125196,-0.1901509315,-0.0073718987,0.051064495,-0.3820844293,0.1315279007,-0.1379302591,0.5604397058,-0.204323709,-0.5258817077,-0.0548608974,-0.3896036744,-0.1108690277,0.3982457519,-0.1051947698,0.0420946889,-0.5863333344,0.042864982,-0.0554989949,-0.325607568,-0.1113469601,-0.1961999387,-0.1263737977,-0.1553942561,0.1137430668,0.1928962618,0.2179133743,-0.1094706878,-0.1682274193,-0.4347150326,-0.3339819312,0.1541959494,-0.1012643725,0.050694488,0.1850075871,-0.0659798011,0.1797925979,-0.0064853802,0.0098090507,-0.1555842906,-0.3324509263,0.1918925792,0.2623599172,-0.1795146763,0.0405300371,-0.4744684994,-0.317047894,-0.3201007545,0.2410695106,-0.0716331676,0.0295918006,0.0510351174,0.1749565452,0.152455315,0.0156619512,0.102222845,-0.0204779282,-0.1949142367,0.1171747521,-0.0141771985,-0.4500836134,0.1088241562,0.0357693024,0.1472648084,0.2089921087,-0.2778184414,-0.1442975402,0.0047041876,0.414567858,-0.0329649821,-0.2965220213,0.4994246364,0.335587889,0.0297882631,-0.1571239531,-0.0598418005,0.1395886838,0.0997576341,0.2127372026,-0.3571592867,0.4544912279,-0.3967370093,0.2623894513,-0.0561215617,-0.1933416575,0.2192241848,0.1355145276,0.3633723259,-0.4195737839,-0.2501191199,-0.0181212332,-0.2273002863,-0.14209079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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2267","title":"DatasetDict save load Failing test in 1.6 not in 1.5","comments":"Hi, I just ran into a similar error. Here is the minimal code to reproduce:\r\n```python\r\nfrom datasets import load_dataset, DatasetDict\r\nds = load_dataset('super_glue', 'multirc')\r\n\r\nds.save_to_disk('tempds')\r\n\r\nds = DatasetDict.load_from_disk('tempds')\r\n\r\n```\r\n\r\n```bash\r\nReusing dataset super_glue (\/home\/idahl\/.cache\/huggingface\/datasets\/super_glue\/multirc\/1.0.2\/2fb163bca9085c1deb906aff20f00c242227ff704a4e8c9cfdfe820be3abfc83)\r\nTraceback (most recent call last):\r\n  File \"\/home\/idahl\/eval-util-expl\/multirc\/tmp.py\", line 7, in <module>\r\n    ds = DatasetDict.load_from_disk('tempds')\r\n  File \"\/home\/idahl\/miniconda3\/envs\/eval-util-expl\/lib\/python3.9\/site-packages\/datasets\/dataset_dict.py\", line 710, in load_from_disk\r\n    dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/home\/idahl\/miniconda3\/envs\/eval-util-expl\/lib\/python3.9\/site-packages\/datasets\/arrow_dataset.py\", line 687, in load_from_disk\r\n    return Dataset(\r\n  File \"\/home\/idahl\/miniconda3\/envs\/eval-util-expl\/lib\/python3.9\/site-packages\/datasets\/arrow_dataset.py\", line 274, in __init__\r\n    raise ValueError(\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'answer': Value(dtype='string', id=None), 'idx': {'answer': Value(dtype='int32', id=None), 'paragraph': Value(dtype='int32', id=None), 'question': Value(dtype='int32', id=None)}, 'label': ClassLabel(num_classes=2, names=['False', 'True'], names_file=None, id=None), 'paragraph': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<answer: string, idx: struct<answer: int32, paragraph: int32, question: int32>, label: int64, paragraph: string, question: string>\r\n\r\nbut expected something like\r\n{'answer': Value(dtype='string', id=None), 'idx': {'paragraph': Value(dtype='int32', id=None), 'question': Value(dtype='int32', id=None), 'answer': Value(dtype='int32', id=None)}, 'label': Value(dtype='int64', id=None), 'paragraph': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<answer: string, idx: struct<paragraph: int32, question: int32, answer: int32>, label: int64, paragraph: string, question: string>\r\n\r\n```\r\n\r\nThe non-matching part seems to be\r\n`'label': ClassLabel(num_classes=2, names=['False', 'True'], names_file=None, id=None),`\r\nvs \r\n`'label': Value(dtype='int64', id=None),`\r\n\r\nAnd the order in the `<struct...` being different, which might cause the [features.type != inferred_features.type](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L274) condition to become true and raise this ValueError.\r\n\r\n\r\nI am using datasets version 1.6.2.\r\n\r\nEdit: can confirm, this works without error in version 1.5.0","body":"## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n","comment_length":226,"text":"DatasetDict save load Failing test in 1.6 not in 1.5 \n ## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n \n Hi, I just ran into a similar error. Here is the minimal code to reproduce:\r\n```python\r\nfrom datasets import load_dataset, DatasetDict\r\nds = load_dataset('super_glue', 'multirc')\r\n\r\nds.save_to_disk('tempds')\r\n\r\nds = DatasetDict.load_from_disk('tempds')\r\n\r\n```\r\n\r\n```bash\r\nReusing dataset super_glue (\/home\/idahl\/.cache\/huggingface\/datasets\/super_glue\/multirc\/1.0.2\/2fb163bca9085c1deb906aff20f00c242227ff704a4e8c9cfdfe820be3abfc83)\r\nTraceback (most recent call last):\r\n  File \"\/home\/idahl\/eval-util-expl\/multirc\/tmp.py\", line 7, in <module>\r\n    ds = DatasetDict.load_from_disk('tempds')\r\n  File \"\/home\/idahl\/miniconda3\/envs\/eval-util-expl\/lib\/python3.9\/site-packages\/datasets\/dataset_dict.py\", line 710, in load_from_disk\r\n    dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/home\/idahl\/miniconda3\/envs\/eval-util-expl\/lib\/python3.9\/site-packages\/datasets\/arrow_dataset.py\", line 687, in load_from_disk\r\n    return Dataset(\r\n  File \"\/home\/idahl\/miniconda3\/envs\/eval-util-expl\/lib\/python3.9\/site-packages\/datasets\/arrow_dataset.py\", line 274, in __init__\r\n    raise ValueError(\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'answer': Value(dtype='string', id=None), 'idx': {'answer': Value(dtype='int32', id=None), 'paragraph': Value(dtype='int32', id=None), 'question': Value(dtype='int32', id=None)}, 'label': ClassLabel(num_classes=2, names=['False', 'True'], names_file=None, id=None), 'paragraph': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<answer: string, idx: struct<answer: int32, paragraph: int32, question: int32>, label: int64, paragraph: string, question: string>\r\n\r\nbut expected something like\r\n{'answer': Value(dtype='string', id=None), 'idx': {'paragraph': Value(dtype='int32', id=None), 'question': Value(dtype='int32', id=None), 'answer': Value(dtype='int32', id=None)}, 'label': Value(dtype='int64', id=None), 'paragraph': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<answer: string, idx: struct<paragraph: int32, question: int32, answer: int32>, label: int64, paragraph: string, question: string>\r\n\r\n```\r\n\r\nThe non-matching part seems to be\r\n`'label': ClassLabel(num_classes=2, names=['False', 'True'], names_file=None, id=None),`\r\nvs \r\n`'label': Value(dtype='int64', id=None),`\r\n\r\nAnd the order in the `<struct...` being different, which might cause the [features.type != inferred_features.type](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L274) condition to become true and raise this ValueError.\r\n\r\n\r\nI am using datasets version 1.6.2.\r\n\r\nEdit: can confirm, this works without error in version 1.5.0","embeddings":[-0.1946798116,0.1140982956,-0.0127640385,0.284942925,0.064592123,-0.0614173673,0.1323396713,0.4565899968,0.4422373772,0.1792287529,0.2358967513,0.380851388,0.0545869619,-0.0561681502,-0.1504484713,-0.0622494444,0.2574026883,0.1362523735,0.047262188,0.1269330233,-0.1914962828,0.2465310097,-0.1496895254,-0.0032418445,-0.1895998418,0.1437868327,-0.2581939399,0.2942799032,-0.3553473651,-0.6636254787,0.2940472364,0.4080147743,0.3425835669,0.5095262527,-0.0001234102,0.0544177964,0.2841644287,-0.2096645385,-0.4484797716,-0.4640186727,-0.1993898153,0.0136942323,0.2473115027,-0.1567811072,0.174591288,-0.0732848793,-0.0321101137,-0.4220353365,0.1694841832,0.3417609334,0.1407151371,0.2266554832,0.2764903307,-0.2219138741,-0.2547798157,0.3302724957,-0.0204170793,0.48288396,-0.0186927021,0.0731682926,0.0414443091,0.0742254928,-0.0195427742,-0.1017797217,0.093925342,-0.0247307811,0.162043348,-0.0513432026,0.240814209,0.1979950964,0.6499521136,-0.2013998032,-0.5342013836,0.0352447331,-0.0611302778,0.1786230356,0.3324406445,-0.0698243231,0.0839635506,0.3244956732,-0.0997817516,-0.3653488159,-0.2351392955,-0.1203212589,0.0710190833,-0.2877634466,-0.0193996113,0.1131953076,-0.0397098362,0.0214464869,0.4245892167,-0.3535504639,-0.3489905298,-0.1019979045,-0.0459269546,-0.1144007817,-0.0653342977,-0.2706740201,0.113346532,-0.0720998496,0.2517627776,0.1316185147,-0.0391709432,0.0602462031,0.2115243673,0.2749886513,0.4198958874,0.2743373513,0.0357514173,0.0714356154,-0.2357528359,0.0838931948,-0.0404101573,-0.15166004,0.2088321447,-0.1358045042,0.5703294277,-0.4004871547,-0.3274163008,0.2602038383,-0.2743057013,-0.1729606539,-0.1039793417,0.2240318358,0.09327925,0.1972419173,0.1087378412,0.3317791224,0.0468955897,0.0277815927,-0.1801332235,0.0353045389,0.0400684513,0.011786541,0.0179779567,-0.0999579355,0.267460227,0.015178483,-0.4577178359,-0.1766552329,-0.0413619019,-0.0473274663,0.2514302135,0.0383211523,-0.2181576937,-0.056682542,0.2363063097,0.1262998134,-0.2239949852,0.2120421976,0.0718438476,-0.1589062363,-0.1686418802,0.1228035986,-0.3944003284,-0.2602534592,-0.2819331884,-0.0493995957,-0.083663933,-0.2920115888,-0.1123725697,-0.3287760615,-0.237355873,-0.1225714758,0.1428985149,0.4307113886,-0.5336277485,0.1942441016,0.1860763729,-0.0290210564,0.0966063365,-0.0230782609,0.0003097686,0.1380230188,-0.1793204844,0.1278733611,0.4110462368,-0.5454348922,-0.6159434319,0.307710886,-0.0049888324,0.1782058775,-0.1437655389,-0.0217472035,0.2127342075,-0.0530057736,0.0544378124,0.0589728355,0.0534363128,-0.0575243719,-0.4947072268,-0.3328706622,0.1166524291,-0.0132593643,-0.0556115769,0.091937542,0.1581820548,-0.2094933689,0.4844789207,-0.0858414844,-0.1812547147,0.1170089394,0.4515592456,-0.0738887712,0.0274728462,-0.2316350937,-0.5508966446,0.4312414229,0.0377286226,-0.0222507045,-0.0489411242,0.0583773702,-0.4379805923,0.1203429848,-0.3182476759,0.046511244,0.0106168631,0.078376852,-0.0866948366,-0.0088240383,-0.1149406061,0.140570417,-0.1519619673,0.0698583946,0.004358958,0.336599648,-0.2037377357,-0.2205159664,0.170929715,-0.0127892671,0.2898364365,-0.2678136826,-0.3201774955,0.4959192574,0.2282747328,0.0215910152,-0.0704142451,-0.3370976448,-0.0950707644,-0.0260207877,0.1928254664,0.4983442724,0.0574062653,0.0559969395,-0.2572018802,0.3138726652,0.0657231435,0.0568134151,0.0700780526,0.1781899929,0.2946604192,-0.024419995,-0.0170402769,-0.3440820575,0.2174295187,0.018817924,0.0102335596,0.0753727928,-0.1335324794,0.1482347995,0.25894925,0.0366390534,0.1828769594,0.0873260871,0.0021229947,-0.4102320969,0.0591992363,0.3296465278,0.6812970042,-0.0120210182,-0.0898986459,-0.0542867593,-0.0393865295,0.0392726399,0.1494913846,-0.0172953773,0.2711475194,0.1783984751,0.1901718229,0.0689663813,-0.2123125196,-0.1901509315,-0.0073718987,0.051064495,-0.3820844293,0.1315279007,-0.1379302591,0.5604397058,-0.204323709,-0.5258817077,-0.0548608974,-0.3896036744,-0.1108690277,0.3982457519,-0.1051947698,0.0420946889,-0.5863333344,0.042864982,-0.0554989949,-0.325607568,-0.1113469601,-0.1961999387,-0.1263737977,-0.1553942561,0.1137430668,0.1928962618,0.2179133743,-0.1094706878,-0.1682274193,-0.4347150326,-0.3339819312,0.1541959494,-0.1012643725,0.050694488,0.1850075871,-0.0659798011,0.1797925979,-0.0064853802,0.0098090507,-0.1555842906,-0.3324509263,0.1918925792,0.2623599172,-0.1795146763,0.0405300371,-0.4744684994,-0.317047894,-0.3201007545,0.2410695106,-0.0716331676,0.0295918006,0.0510351174,0.1749565452,0.152455315,0.0156619512,0.102222845,-0.0204779282,-0.1949142367,0.1171747521,-0.0141771985,-0.4500836134,0.1088241562,0.0357693024,0.1472648084,0.2089921087,-0.2778184414,-0.1442975402,0.0047041876,0.414567858,-0.0329649821,-0.2965220213,0.4994246364,0.335587889,0.0297882631,-0.1571239531,-0.0598418005,0.1395886838,0.0997576341,0.2127372026,-0.3571592867,0.4544912279,-0.3967370093,0.2623894513,-0.0561215617,-0.1933416575,0.2192241848,0.1355145276,0.3633723259,-0.4195737839,-0.2501191199,-0.0181212332,-0.2273002863,-0.1420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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2267","title":"DatasetDict save load Failing test in 1.6 not in 1.5","comments":"My current workaround is to remove the idx feature:\r\n\r\n```\r\n\r\nfrom datasets import load_dataset, DatasetDict, Value\r\nds = load_dataset('super_glue', 'multirc')\r\nds = ds.remove_columns('idx')\r\n\r\nds.save_to_disk('tempds')\r\n\r\nds = DatasetDict.load_from_disk('tempds')\r\n\r\n```\r\n\r\nworks.","body":"## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n","comment_length":29,"text":"DatasetDict save load Failing test in 1.6 not in 1.5 \n ## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n \n My current workaround is to remove the idx feature:\r\n\r\n```\r\n\r\nfrom datasets import load_dataset, DatasetDict, Value\r\nds = load_dataset('super_glue', 'multirc')\r\nds = ds.remove_columns('idx')\r\n\r\nds.save_to_disk('tempds')\r\n\r\nds = DatasetDict.load_from_disk('tempds')\r\n\r\n```\r\n\r\nworks.","embeddings":[-0.1946798116,0.1140982956,-0.0127640385,0.284942925,0.064592123,-0.0614173673,0.1323396713,0.4565899968,0.4422373772,0.1792287529,0.2358967513,0.380851388,0.0545869619,-0.0561681502,-0.1504484713,-0.0622494444,0.2574026883,0.1362523735,0.047262188,0.1269330233,-0.1914962828,0.2465310097,-0.1496895254,-0.0032418445,-0.1895998418,0.1437868327,-0.2581939399,0.2942799032,-0.3553473651,-0.6636254787,0.2940472364,0.4080147743,0.3425835669,0.5095262527,-0.0001234102,0.0544177964,0.2841644287,-0.2096645385,-0.4484797716,-0.4640186727,-0.1993898153,0.0136942323,0.2473115027,-0.1567811072,0.174591288,-0.0732848793,-0.0321101137,-0.4220353365,0.1694841832,0.3417609334,0.1407151371,0.2266554832,0.2764903307,-0.2219138741,-0.2547798157,0.3302724957,-0.0204170793,0.48288396,-0.0186927021,0.0731682926,0.0414443091,0.0742254928,-0.0195427742,-0.1017797217,0.093925342,-0.0247307811,0.162043348,-0.0513432026,0.240814209,0.1979950964,0.6499521136,-0.2013998032,-0.5342013836,0.0352447331,-0.0611302778,0.1786230356,0.3324406445,-0.0698243231,0.0839635506,0.3244956732,-0.0997817516,-0.3653488159,-0.2351392955,-0.1203212589,0.0710190833,-0.2877634466,-0.0193996113,0.1131953076,-0.0397098362,0.0214464869,0.4245892167,-0.3535504639,-0.3489905298,-0.1019979045,-0.0459269546,-0.1144007817,-0.0653342977,-0.2706740201,0.113346532,-0.0720998496,0.2517627776,0.1316185147,-0.0391709432,0.0602462031,0.2115243673,0.2749886513,0.4198958874,0.2743373513,0.0357514173,0.0714356154,-0.2357528359,0.0838931948,-0.0404101573,-0.15166004,0.2088321447,-0.1358045042,0.5703294277,-0.4004871547,-0.3274163008,0.2602038383,-0.2743057013,-0.1729606539,-0.1039793417,0.2240318358,0.09327925,0.1972419173,0.1087378412,0.3317791224,0.0468955897,0.0277815927,-0.1801332235,0.0353045389,0.0400684513,0.011786541,0.0179779567,-0.0999579355,0.267460227,0.015178483,-0.4577178359,-0.1766552329,-0.0413619019,-0.0473274663,0.2514302135,0.0383211523,-0.2181576937,-0.056682542,0.2363063097,0.1262998134,-0.2239949852,0.2120421976,0.0718438476,-0.1589062363,-0.1686418802,0.1228035986,-0.3944003284,-0.2602534592,-0.2819331884,-0.0493995957,-0.083663933,-0.2920115888,-0.1123725697,-0.3287760615,-0.237355873,-0.1225714758,0.1428985149,0.4307113886,-0.5336277485,0.1942441016,0.1860763729,-0.0290210564,0.0966063365,-0.0230782609,0.0003097686,0.1380230188,-0.1793204844,0.1278733611,0.4110462368,-0.5454348922,-0.6159434319,0.307710886,-0.0049888324,0.1782058775,-0.1437655389,-0.0217472035,0.2127342075,-0.0530057736,0.0544378124,0.0589728355,0.0534363128,-0.0575243719,-0.4947072268,-0.3328706622,0.1166524291,-0.0132593643,-0.0556115769,0.091937542,0.1581820548,-0.2094933689,0.4844789207,-0.0858414844,-0.1812547147,0.1170089394,0.4515592456,-0.0738887712,0.0274728462,-0.2316350937,-0.5508966446,0.4312414229,0.0377286226,-0.0222507045,-0.0489411242,0.0583773702,-0.4379805923,0.1203429848,-0.3182476759,0.046511244,0.0106168631,0.078376852,-0.0866948366,-0.0088240383,-0.1149406061,0.140570417,-0.1519619673,0.0698583946,0.004358958,0.336599648,-0.2037377357,-0.2205159664,0.170929715,-0.0127892671,0.2898364365,-0.2678136826,-0.3201774955,0.4959192574,0.2282747328,0.0215910152,-0.0704142451,-0.3370976448,-0.0950707644,-0.0260207877,0.1928254664,0.4983442724,0.0574062653,0.0559969395,-0.2572018802,0.3138726652,0.0657231435,0.0568134151,0.0700780526,0.1781899929,0.2946604192,-0.024419995,-0.0170402769,-0.3440820575,0.2174295187,0.018817924,0.0102335596,0.0753727928,-0.1335324794,0.1482347995,0.25894925,0.0366390534,0.1828769594,0.0873260871,0.0021229947,-0.4102320969,0.0591992363,0.3296465278,0.6812970042,-0.0120210182,-0.0898986459,-0.0542867593,-0.0393865295,0.0392726399,0.1494913846,-0.0172953773,0.2711475194,0.1783984751,0.1901718229,0.0689663813,-0.2123125196,-0.1901509315,-0.0073718987,0.051064495,-0.3820844293,0.1315279007,-0.1379302591,0.5604397058,-0.204323709,-0.5258817077,-0.0548608974,-0.3896036744,-0.1108690277,0.3982457519,-0.1051947698,0.0420946889,-0.5863333344,0.042864982,-0.0554989949,-0.325607568,-0.1113469601,-0.1961999387,-0.1263737977,-0.1553942561,0.1137430668,0.1928962618,0.2179133743,-0.1094706878,-0.1682274193,-0.4347150326,-0.3339819312,0.1541959494,-0.1012643725,0.050694488,0.1850075871,-0.0659798011,0.1797925979,-0.0064853802,0.0098090507,-0.1555842906,-0.3324509263,0.1918925792,0.2623599172,-0.1795146763,0.0405300371,-0.4744684994,-0.317047894,-0.3201007545,0.2410695106,-0.0716331676,0.0295918006,0.0510351174,0.1749565452,0.152455315,0.0156619512,0.102222845,-0.0204779282,-0.1949142367,0.1171747521,-0.0141771985,-0.4500836134,0.1088241562,0.0357693024,0.1472648084,0.2089921087,-0.2778184414,-0.1442975402,0.0047041876,0.414567858,-0.0329649821,-0.2965220213,0.4994246364,0.335587889,0.0297882631,-0.1571239531,-0.0598418005,0.1395886838,0.0997576341,0.2127372026,-0.3571592867,0.4544912279,-0.3967370093,0.2623894513,-0.0561215617,-0.1933416575,0.2192241848,0.1355145276,0.3633723259,-0.4195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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2267","title":"DatasetDict save load Failing test in 1.6 not in 1.5","comments":"It looks like this issue comes from the order of the fields in the 'idx' struct that is different for some reason.\r\nI'm looking into it. Note that as a workaround you can also flatten the nested features with `ds = ds.flatten()`","body":"## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n","comment_length":42,"text":"DatasetDict save load Failing test in 1.6 not in 1.5 \n ## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n \n It looks like this issue comes from the order of the fields in the 'idx' struct that is different for some reason.\r\nI'm looking into it. Note that as a workaround you can also flatten the nested features with `ds = ds.flatten()`","embeddings":[-0.1946798116,0.1140982956,-0.0127640385,0.284942925,0.064592123,-0.0614173673,0.1323396713,0.4565899968,0.4422373772,0.1792287529,0.2358967513,0.380851388,0.0545869619,-0.0561681502,-0.1504484713,-0.0622494444,0.2574026883,0.1362523735,0.047262188,0.1269330233,-0.1914962828,0.2465310097,-0.1496895254,-0.0032418445,-0.1895998418,0.1437868327,-0.2581939399,0.2942799032,-0.3553473651,-0.6636254787,0.2940472364,0.4080147743,0.3425835669,0.5095262527,-0.0001234102,0.0544177964,0.2841644287,-0.2096645385,-0.4484797716,-0.4640186727,-0.1993898153,0.0136942323,0.2473115027,-0.1567811072,0.174591288,-0.0732848793,-0.0321101137,-0.4220353365,0.1694841832,0.3417609334,0.1407151371,0.2266554832,0.2764903307,-0.2219138741,-0.2547798157,0.3302724957,-0.0204170793,0.48288396,-0.0186927021,0.0731682926,0.0414443091,0.0742254928,-0.0195427742,-0.1017797217,0.093925342,-0.0247307811,0.162043348,-0.0513432026,0.240814209,0.1979950964,0.6499521136,-0.2013998032,-0.5342013836,0.0352447331,-0.0611302778,0.1786230356,0.3324406445,-0.0698243231,0.0839635506,0.3244956732,-0.0997817516,-0.3653488159,-0.2351392955,-0.1203212589,0.0710190833,-0.2877634466,-0.0193996113,0.1131953076,-0.0397098362,0.0214464869,0.4245892167,-0.3535504639,-0.3489905298,-0.1019979045,-0.0459269546,-0.1144007817,-0.0653342977,-0.2706740201,0.113346532,-0.0720998496,0.2517627776,0.1316185147,-0.0391709432,0.0602462031,0.2115243673,0.2749886513,0.4198958874,0.2743373513,0.0357514173,0.0714356154,-0.2357528359,0.0838931948,-0.0404101573,-0.15166004,0.2088321447,-0.1358045042,0.5703294277,-0.4004871547,-0.3274163008,0.2602038383,-0.2743057013,-0.1729606539,-0.1039793417,0.2240318358,0.09327925,0.1972419173,0.1087378412,0.3317791224,0.0468955897,0.0277815927,-0.1801332235,0.0353045389,0.0400684513,0.011786541,0.0179779567,-0.0999579355,0.267460227,0.015178483,-0.4577178359,-0.1766552329,-0.0413619019,-0.0473274663,0.2514302135,0.0383211523,-0.2181576937,-0.056682542,0.2363063097,0.1262998134,-0.2239949852,0.2120421976,0.0718438476,-0.1589062363,-0.1686418802,0.1228035986,-0.3944003284,-0.2602534592,-0.2819331884,-0.0493995957,-0.083663933,-0.2920115888,-0.1123725697,-0.3287760615,-0.237355873,-0.1225714758,0.1428985149,0.4307113886,-0.5336277485,0.1942441016,0.1860763729,-0.0290210564,0.0966063365,-0.0230782609,0.0003097686,0.1380230188,-0.1793204844,0.1278733611,0.4110462368,-0.5454348922,-0.6159434319,0.307710886,-0.0049888324,0.1782058775,-0.1437655389,-0.0217472035,0.2127342075,-0.0530057736,0.0544378124,0.0589728355,0.0534363128,-0.0575243719,-0.4947072268,-0.3328706622,0.1166524291,-0.0132593643,-0.0556115769,0.091937542,0.1581820548,-0.2094933689,0.4844789207,-0.0858414844,-0.1812547147,0.1170089394,0.4515592456,-0.0738887712,0.0274728462,-0.2316350937,-0.5508966446,0.4312414229,0.0377286226,-0.0222507045,-0.0489411242,0.0583773702,-0.4379805923,0.1203429848,-0.3182476759,0.046511244,0.0106168631,0.078376852,-0.0866948366,-0.0088240383,-0.1149406061,0.140570417,-0.1519619673,0.0698583946,0.004358958,0.336599648,-0.2037377357,-0.2205159664,0.170929715,-0.0127892671,0.2898364365,-0.2678136826,-0.3201774955,0.4959192574,0.2282747328,0.0215910152,-0.0704142451,-0.3370976448,-0.0950707644,-0.0260207877,0.1928254664,0.4983442724,0.0574062653,0.0559969395,-0.2572018802,0.3138726652,0.0657231435,0.0568134151,0.0700780526,0.1781899929,0.2946604192,-0.024419995,-0.0170402769,-0.3440820575,0.2174295187,0.018817924,0.0102335596,0.0753727928,-0.1335324794,0.1482347995,0.25894925,0.0366390534,0.1828769594,0.0873260871,0.0021229947,-0.4102320969,0.0591992363,0.3296465278,0.6812970042,-0.0120210182,-0.0898986459,-0.0542867593,-0.0393865295,0.0392726399,0.1494913846,-0.0172953773,0.2711475194,0.1783984751,0.1901718229,0.0689663813,-0.2123125196,-0.1901509315,-0.0073718987,0.051064495,-0.3820844293,0.1315279007,-0.1379302591,0.5604397058,-0.204323709,-0.5258817077,-0.0548608974,-0.3896036744,-0.1108690277,0.3982457519,-0.1051947698,0.0420946889,-0.5863333344,0.042864982,-0.0554989949,-0.325607568,-0.1113469601,-0.1961999387,-0.1263737977,-0.1553942561,0.1137430668,0.1928962618,0.2179133743,-0.1094706878,-0.1682274193,-0.4347150326,-0.3339819312,0.1541959494,-0.1012643725,0.050694488,0.1850075871,-0.0659798011,0.1797925979,-0.0064853802,0.0098090507,-0.1555842906,-0.3324509263,0.1918925792,0.2623599172,-0.1795146763,0.0405300371,-0.4744684994,-0.317047894,-0.3201007545,0.2410695106,-0.0716331676,0.0295918006,0.0510351174,0.1749565452,0.152455315,0.0156619512,0.102222845,-0.0204779282,-0.1949142367,0.1171747521,-0.0141771985,-0.4500836134,0.1088241562,0.0357693024,0.1472648084,0.2089921087,-0.2778184414,-0.1442975402,0.0047041876,0.414567858,-0.0329649821,-0.2965220213,0.4994246364,0.335587889,0.0297882631,-0.1571239531,-0.0598418005,0.1395886838,0.0997576341,0.2127372026,-0.3571592867,0.4544912279,-0.3967370093,0.2623894513,-0.0561215617,-0.1933416575,0.2192241848,0.1355145276,0.3633723259,-0.4195737839,-0.2501191199,-0.0181212332,-0.2273002863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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2267","title":"DatasetDict save load Failing test in 1.6 not in 1.5","comments":"I just pushed a fix on `master`. We'll do a new release soon !\r\n\r\nThanks for reporting","body":"## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n","comment_length":17,"text":"DatasetDict save load Failing test in 1.6 not in 1.5 \n ## Describe the bug\r\n\r\nWe have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.\r\n\r\n\r\n\r\n\r\nDowngrading to `>1.6` -- fixes the problem.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\n### Load a dataset dict from jsonl \r\n\r\npath = '\/test\/foo'\r\n\r\nds_dict.save_to_disk(path)\r\n\r\nds_from_disk = DatasetDict.load_from_disk(path).  ## <-- this is where I see the error on 1.6\r\n```\r\n\r\n## Expected results\r\n\r\nUpgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.\r\n\r\n## Actual results\r\n```\r\n        # Infer features if None\r\n        inferred_features = Features.from_arrow_schema(arrow_table.schema)\r\n        if self.info.features is None:\r\n            self.info.features = inferred_features\r\n    \r\n        # Infer fingerprint if None\r\n    \r\n        if self._fingerprint is None:\r\n            self._fingerprint = generate_fingerprint(self)\r\n    \r\n        # Sanity checks\r\n    \r\n        assert self.features is not None, \"Features can't be None in a Dataset object\"\r\n        assert self._fingerprint is not None, \"Fingerprint can't be None in a Dataset object\"\r\n        if self.info.features.type != inferred_features.type:\r\n>           raise ValueError(\r\n                \"External features info don't match the dataset:\\nGot\\n{}\\nwith type\\n{}\\n\\nbut expected something like\\n{}\\nwith type\\n{}\".format(\r\n                    self.info.features, self.info.features.type, inferred_features, inferred_features.type\r\n                )\r\n            )\r\nE           ValueError: External features info don't match the dataset:\r\nE           Got\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>\r\nE           \r\nE           but expected something like\r\nE           {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}\r\nE           with type\r\nE           struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>\r\n\r\n..\/..\/..\/..\/..\/.virtualenvs\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:274: ValueError\r\n```\r\n## Versions\r\n- Datasets: 1.6.1\r\n- Python: 3.8.5 (default, Jan 26 2021, 10:01:04) \r\n[Clang 12.0.0 (clang-1200.0.32.2)]\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n\r\n```\r\n \n I just pushed a fix on `master`. We'll do a new release soon !\r\n\r\nThanks for reporting","embeddings":[-0.1946798116,0.1140982956,-0.0127640385,0.284942925,0.064592123,-0.0614173673,0.1323396713,0.4565899968,0.4422373772,0.1792287529,0.2358967513,0.380851388,0.0545869619,-0.0561681502,-0.1504484713,-0.0622494444,0.2574026883,0.1362523735,0.047262188,0.1269330233,-0.1914962828,0.2465310097,-0.1496895254,-0.0032418445,-0.1895998418,0.1437868327,-0.2581939399,0.2942799032,-0.3553473651,-0.6636254787,0.2940472364,0.4080147743,0.3425835669,0.5095262527,-0.0001234102,0.0544177964,0.2841644287,-0.2096645385,-0.4484797716,-0.4640186727,-0.1993898153,0.0136942323,0.2473115027,-0.1567811072,0.174591288,-0.0732848793,-0.0321101137,-0.4220353365,0.1694841832,0.3417609334,0.1407151371,0.2266554832,0.2764903307,-0.2219138741,-0.2547798157,0.3302724957,-0.0204170793,0.48288396,-0.0186927021,0.0731682926,0.0414443091,0.0742254928,-0.0195427742,-0.1017797217,0.093925342,-0.0247307811,0.162043348,-0.0513432026,0.240814209,0.1979950964,0.6499521136,-0.2013998032,-0.5342013836,0.0352447331,-0.0611302778,0.1786230356,0.3324406445,-0.0698243231,0.0839635506,0.3244956732,-0.0997817516,-0.3653488159,-0.2351392955,-0.1203212589,0.0710190833,-0.2877634466,-0.0193996113,0.1131953076,-0.0397098362,0.0214464869,0.4245892167,-0.3535504639,-0.3489905298,-0.1019979045,-0.0459269546,-0.1144007817,-0.0653342977,-0.2706740201,0.113346532,-0.0720998496,0.2517627776,0.1316185147,-0.0391709432,0.0602462031,0.2115243673,0.2749886513,0.4198958874,0.2743373513,0.0357514173,0.0714356154,-0.2357528359,0.0838931948,-0.0404101573,-0.15166004,0.2088321447,-0.1358045042,0.5703294277,-0.4004871547,-0.3274163008,0.2602038383,-0.2743057013,-0.1729606539,-0.1039793417,0.2240318358,0.09327925,0.1972419173,0.1087378412,0.3317791224,0.0468955897,0.0277815927,-0.1801332235,0.0353045389,0.0400684513,0.011786541,0.0179779567,-0.0999579355,0.267460227,0.015178483,-0.4577178359,-0.1766552329,-0.0413619019,-0.0473274663,0.2514302135,0.0383211523,-0.2181576937,-0.056682542,0.2363063097,0.1262998134,-0.2239949852,0.2120421976,0.0718438476,-0.1589062363,-0.1686418802,0.1228035986,-0.3944003284,-0.2602534592,-0.2819331884,-0.0493995957,-0.083663933,-0.2920115888,-0.1123725697,-0.3287760615,-0.237355873,-0.1225714758,0.1428985149,0.4307113886,-0.5336277485,0.1942441016,0.1860763729,-0.0290210564,0.0966063365,-0.0230782609,0.0003097686,0.1380230188,-0.1793204844,0.1278733611,0.4110462368,-0.5454348922,-0.6159434319,0.307710886,-0.0049888324,0.1782058775,-0.1437655389,-0.0217472035,0.2127342075,-0.0530057736,0.0544378124,0.0589728355,0.0534363128,-0.0575243719,-0.4947072268,-0.3328706622,0.1166524291,-0.0132593643,-0.0556115769,0.091937542,0.1581820548,-0.2094933689,0.4844789207,-0.0858414844,-0.1812547147,0.1170089394,0.4515592456,-0.0738887712,0.0274728462,-0.2316350937,-0.5508966446,0.4312414229,0.0377286226,-0.0222507045,-0.0489411242,0.0583773702,-0.4379805923,0.1203429848,-0.3182476759,0.046511244,0.0106168631,0.078376852,-0.0866948366,-0.0088240383,-0.1149406061,0.140570417,-0.1519619673,0.0698583946,0.004358958,0.336599648,-0.2037377357,-0.2205159664,0.170929715,-0.0127892671,0.2898364365,-0.2678136826,-0.3201774955,0.4959192574,0.2282747328,0.0215910152,-0.0704142451,-0.3370976448,-0.0950707644,-0.0260207877,0.1928254664,0.4983442724,0.0574062653,0.0559969395,-0.2572018802,0.3138726652,0.0657231435,0.0568134151,0.0700780526,0.1781899929,0.2946604192,-0.024419995,-0.0170402769,-0.3440820575,0.2174295187,0.018817924,0.0102335596,0.0753727928,-0.1335324794,0.1482347995,0.25894925,0.0366390534,0.1828769594,0.0873260871,0.0021229947,-0.4102320969,0.0591992363,0.3296465278,0.6812970042,-0.0120210182,-0.0898986459,-0.0542867593,-0.0393865295,0.0392726399,0.1494913846,-0.0172953773,0.2711475194,0.1783984751,0.1901718229,0.0689663813,-0.2123125196,-0.1901509315,-0.0073718987,0.051064495,-0.3820844293,0.1315279007,-0.1379302591,0.5604397058,-0.204323709,-0.5258817077,-0.0548608974,-0.3896036744,-0.1108690277,0.3982457519,-0.1051947698,0.0420946889,-0.5863333344,0.042864982,-0.0554989949,-0.325607568,-0.1113469601,-0.1961999387,-0.1263737977,-0.1553942561,0.1137430668,0.1928962618,0.2179133743,-0.1094706878,-0.1682274193,-0.4347150326,-0.3339819312,0.1541959494,-0.1012643725,0.050694488,0.1850075871,-0.0659798011,0.1797925979,-0.0064853802,0.0098090507,-0.1555842906,-0.3324509263,0.1918925792,0.2623599172,-0.1795146763,0.0405300371,-0.4744684994,-0.317047894,-0.3201007545,0.2410695106,-0.0716331676,0.0295918006,0.0510351174,0.1749565452,0.152455315,0.0156619512,0.102222845,-0.0204779282,-0.1949142367,0.1171747521,-0.0141771985,-0.4500836134,0.1088241562,0.0357693024,0.1472648084,0.2089921087,-0.2778184414,-0.1442975402,0.0047041876,0.414567858,-0.0329649821,-0.2965220213,0.4994246364,0.335587889,0.0297882631,-0.1571239531,-0.0598418005,0.1395886838,0.0997576341,0.2127372026,-0.3571592867,0.4544912279,-0.3967370093,0.2623894513,-0.0561215617,-0.1933416575,0.2192241848,0.1355145276,0.3633723259,-0.4195737839,-0.2501191199,-0.0181212332,-0.2273002863,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2262","title":"NewsPH NLI dataset script fails to access test data.","comments":"Thanks @bhavitvyamalik for the fix !\r\nThe fix will be available in the next release.\r\nIt's already available on the `master` branch. For now you can either install `datasets` from source or use `script_version=\"master\"` in `load_dataset` to use the fixed version of this dataset.","body":"In Newsph-NLI Dataset (#1192), it fails to access test data.\r\n\r\nAccording to the script below, the download manager will download the train data when trying to download the test data. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/2a2dd6316af2cc7fdf24e4779312e8ee0c7ed98b\/datasets\/newsph_nli\/newsph_nli.py#L71\r\n\r\nIf you download it according to the script above, you can see that train and test receive the same data as shown below.\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> newsph_nli = load_dataset(path=\".\/datasets\/newsph_nli.py\")\r\n>>> newsph_nli\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 420000\r\n    })\r\n    test: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 420000\r\n    })\r\n    validation: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 90000\r\n    })\r\n})\r\n>>> newsph_nli[\"train\"][0]\r\n{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',\r\n 'label': 1,\r\n 'premise': '\"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa,\" ayon kay Robredo sa inilabas nitong statement.'}\r\n>>> newsph_nli[\"test\"][0]\r\n{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',\r\n 'label': 1,\r\n 'premise': '\"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa,\" ayon kay Robredo sa inilabas nitong statement.'}\r\n```\r\n\r\nIn local, I modified the code of the source as below and got the correct result.\r\n```python\r\n71    test_path = os.path.join(download_path, \"test.csv\") \r\n```\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> newsph_nli = load_dataset(path=\".\/datasets\/newsph_nli.py\")\r\n>>> newsph_nli\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 420000\r\n    })\r\n    test: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 9000\r\n    })\r\n    validation: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 90000\r\n    })\r\n})\r\n>>> newsph_nli[\"train\"][0]\r\n{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',\r\n 'label': 1,\r\n 'premise': '\"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa,\" ayon kay Robredo sa inilabas nitong statement.'}\r\n>>> newsph_nli[\"test\"][0]\r\n{'hypothesis': '-- JAI (@JaiPaller) September 13, 2019',\r\n 'label': 1,\r\n 'premise': 'Pinag-iingat ng Konsulado ng Pilipinas sa Dubai ang publiko, partikular ang mga donor, laban sa mga scam na gumagamit ng mga charitable organization.'}\r\n```\r\n\r\nI don't have experience with open source pull requests, so I suggest that you reflect them in the source.\r\n\r\nThank you for reading :)","comment_length":44,"text":"NewsPH NLI dataset script fails to access test data. \n In Newsph-NLI Dataset (#1192), it fails to access test data.\r\n\r\nAccording to the script below, the download manager will download the train data when trying to download the test data. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/2a2dd6316af2cc7fdf24e4779312e8ee0c7ed98b\/datasets\/newsph_nli\/newsph_nli.py#L71\r\n\r\nIf you download it according to the script above, you can see that train and test receive the same data as shown below.\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> newsph_nli = load_dataset(path=\".\/datasets\/newsph_nli.py\")\r\n>>> newsph_nli\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 420000\r\n    })\r\n    test: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 420000\r\n    })\r\n    validation: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 90000\r\n    })\r\n})\r\n>>> newsph_nli[\"train\"][0]\r\n{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',\r\n 'label': 1,\r\n 'premise': '\"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa,\" ayon kay Robredo sa inilabas nitong statement.'}\r\n>>> newsph_nli[\"test\"][0]\r\n{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',\r\n 'label': 1,\r\n 'premise': '\"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa,\" ayon kay Robredo sa inilabas nitong statement.'}\r\n```\r\n\r\nIn local, I modified the code of the source as below and got the correct result.\r\n```python\r\n71    test_path = os.path.join(download_path, \"test.csv\") \r\n```\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> newsph_nli = load_dataset(path=\".\/datasets\/newsph_nli.py\")\r\n>>> newsph_nli\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 420000\r\n    })\r\n    test: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 9000\r\n    })\r\n    validation: Dataset({\r\n        features: ['premise', 'hypothesis', 'label'],\r\n        num_rows: 90000\r\n    })\r\n})\r\n>>> newsph_nli[\"train\"][0]\r\n{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',\r\n 'label': 1,\r\n 'premise': '\"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa,\" ayon kay Robredo sa inilabas nitong statement.'}\r\n>>> newsph_nli[\"test\"][0]\r\n{'hypothesis': '-- JAI (@JaiPaller) September 13, 2019',\r\n 'label': 1,\r\n 'premise': 'Pinag-iingat ng Konsulado ng Pilipinas sa Dubai ang publiko, partikular ang mga donor, laban sa mga scam na gumagamit ng mga charitable organization.'}\r\n```\r\n\r\nI don't have experience with open source pull requests, so I suggest that you reflect them in the source.\r\n\r\nThank you for reading :) \n Thanks @bhavitvyamalik for the fix !\r\nThe fix will be available in the next release.\r\nIt's already available on the `master` branch. For now you can either install `datasets` from source or use `script_version=\"master\"` in `load_dataset` to use the fixed version of this dataset.","embeddings":[-0.1607158035,0.2849110961,-0.126976192,0.1755043417,0.085902229,0.0402895994,0.2562074959,0.3658847213,0.0419336632,0.3072821796,0.0052477997,0.0433721282,0.0158911105,0.2191219032,0.177515775,-0.1738474071,-0.0040245214,0.0109901149,-0.1373095661,0.0521646887,-0.1775496453,0.0072262986,-0.1995907426,0.158338502,-0.0375777408,0.210018754,-0.2031048238,0.094160974,-0.1932428777,-0.6549795866,0.3090721965,0.1273378432,0.0832721815,0.2888227701,-0.0001082165,0.0894125178,0.2988508344,0.0091615925,-0.3292865455,-0.3701016903,-0.3256571591,-0.4836532176,0.0113816774,-0.3868236542,-0.1000621468,-0.1852469742,0.2667526305,0.109197624,0.2585923076,0.6185494065,0.2178416103,0.360341996,0.0827492103,-0.0977324769,0.1449719667,0.0725211352,-0.1622267962,0.1413548589,-0.0237706862,-0.1449209899,0.0799242333,0.1947094202,0.0518151969,0.1217470467,-0.187107414,0.1651480198,0.2658438981,-0.2221500427,-0.1103747487,0.2396497428,0.0484495461,-0.249437809,-0.2226448357,-0.0337351002,-0.0844576284,0.0159997903,0.1275511682,0.3240195215,-0.2225623131,0.1198315471,-0.304782033,-0.0031713874,-0.3851747811,0.395362854,-0.2153151929,0.21622172,0.0354196467,0.1695628762,0.1948063374,-0.1787180752,-0.1353119016,-0.1662853956,-0.1664285809,0.1104919314,0.0447671115,-0.0354129523,-0.0712503046,-0.3175800145,0.3023943007,0.1509393603,0.0641392469,0.0615024716,-0.0090623898,0.0035719338,0.1476139724,-0.1303728968,0.0290067215,0.3485050499,0.3061073124,0.2977993488,0.074188903,-0.0587520935,0.0372149087,-0.0911941156,0.1269004345,-0.0198108908,0.4139211178,-0.2075270712,-0.123758696,0.2419610322,-0.0031197101,-0.1290283054,-0.0752904266,0.4222076237,0.0038069515,0.0440091752,0.0618460029,0.3608467877,-0.035158217,-0.089692682,-0.2308822125,0.147298187,-0.3299790025,0.1613569409,0.6004884839,0.0971651897,0.2637774348,-0.0439442508,-0.195943445,-0.2917523086,0.1034094617,-0.2452872843,0.5001855493,0.1316228062,0.2623940408,0.1821569502,-0.0875076056,0.3129864931,0.0101262797,0.0853112414,-0.0186631531,-0.3564785719,0.2350665033,0.2354106456,-0.2452390343,-0.026149353,0.0424883924,0.0778634995,-0.1504396498,-0.3769740462,-0.0665062815,-0.1449884027,-0.2322539538,-0.1142747253,0.3140326738,0.4287765622,-0.0391391627,-0.0388351269,0.1627981365,-0.032857772,0.1943426877,0.0196333602,-0.3911494613,0.2049051523,-0.1807556301,0.0412888266,0.5587879419,-0.4500208795,-0.724973321,0.2535991371,-0.0212613605,0.053040307,0.1778284758,0.0625050664,-0.0860289708,-0.0278928783,-0.4274306595,0.3901510537,0.0546880439,0.222521916,0.0165248718,-0.1682703942,0.3594576418,0.2673202455,0.0448145457,-0.0441087037,-0.0306841321,0.4716745019,0.6009884477,0.0481058396,0.0090541067,0.0842781141,-0.0217513461,-0.1325216293,-0.0750061423,-0.0794651657,-0.0566266403,0.2953828871,0.0179411843,-0.017959062,-0.1477219313,-0.0005189619,-0.5050708652,-0.0385373309,-0.3645445406,-0.2266291231,0.2683858871,0.3846640289,0.3528345525,0.3000055254,-0.030288389,0.3504017889,-0.3305170238,-0.0496247225,-0.2497683465,0.2820423841,-0.2013546824,0.0091113858,-0.022729218,0.1534704417,0.1481286883,-0.0519124605,-0.0547741354,0.2898336053,0.1164437756,0.0854344741,-0.0616064072,0.0667308643,0.1511046588,-0.5583309531,0.271253109,0.3992883861,0.1664095074,-0.0469950549,-0.2413630188,0.2244085968,-0.029788319,0.1660888046,0.2944079936,-0.0828003511,0.2006493509,-0.0955019072,-0.2641208172,-0.1278713197,0.0308986194,-0.0712233111,-0.085645549,-0.0873884857,-0.0694778562,-0.0240070913,0.5533158183,-0.0228692107,-0.0859912261,-0.142210111,-0.1544724405,-0.0401732996,-0.0457055308,0.3417987823,0.3394460082,-0.0052406257,0.1332755834,0.2720524967,0.0106293047,-0.2517020702,0.111766167,0.125965625,0.1259822994,0.2157384753,0.0531439707,0.0178054795,-0.1025190577,0.0333006196,0.3690502644,0.1830672324,-0.2688636184,-0.019761784,-0.2633472979,-0.5984284282,-0.2817599177,-0.1618253291,0.0276547931,-0.4178620577,-0.0583133735,0.2076059729,0.0333635993,-0.1740947664,-0.3731132448,-0.0507754274,0.0232869387,-0.1845424771,-0.067758441,-0.111016877,-0.2914595008,0.0881278068,0.0988045037,0.2324982733,0.185199663,-0.3372068107,-0.1936274469,-0.4294444621,-0.0837894827,0.2052208036,0.038776271,0.3248253167,-0.0089297742,0.4082071781,0.127725631,0.0729600936,0.2689097524,-0.2258544266,-0.2140462101,0.3920098543,0.0003068156,-0.0243679658,-0.0420403257,-0.8750798106,-0.3868276775,-0.2710677683,-0.0702921376,-0.1641078442,0.1371508837,0.4277605116,0.0564923584,0.1112268642,-0.0629068166,0.1891739666,-0.2624030709,0.1213424057,0.1968318522,-0.4102002084,-0.6001107693,-0.0918635651,0.1056960821,0.1412737817,0.0692282766,-0.4383095801,-0.099446483,-0.2035561502,0.2111938596,0.065518029,-0.1395415962,0.0986180305,-0.1876580715,0.0152319307,-0.0599374846,0.0633013919,0.1874912083,0.0681978017,0.2779685557,0.1245734245,0.4347033203,0.1053015515,0.5339272022,0.2907461822,-0.0869296715,0.4084816575,-0.4260172844,0.0973081514,0.0522852205,-0.3037233949,0.1839884967,0.034941949,0.1958121359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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2256","title":"Running `datase.map` with `num_proc > 1` uses a lot of memory","comments":"Thanks for reporting ! We are working on this and we'll do a patch release very soon.","body":"## Describe the bug\r\nRunning `datase.map` with `num_proc > 1`  leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndstc8_datset = load_dataset(\"roskoN\/dstc8-reddit-corpus\", keep_in_memory=False)\r\n\r\n\r\ndef _prepare_sample(batch):\r\n    return {\"input_ids\": list(), \"attention_mask\": list()}\r\n\r\n\r\nfor split_name, dataset_split in list(dstc8_datset.items()):\r\n    print(f\"Processing {split_name}\")\r\n    encoded_dataset_split = dataset_split.map(\r\n        function=_prepare_sample,\r\n        batched=True,\r\n        num_proc=4,\r\n        remove_columns=dataset_split.column_names,\r\n        batch_size=10,\r\n        writer_batch_size=10,\r\n        keep_in_memory=False,\r\n    )\r\n    print(encoded_dataset_split)\r\n\r\n    path = f\".\/data\/encoded_{split_name}\"\r\n\r\n    encoded_dataset_split.save_to_disk(path)\r\n```\r\n\r\n## Expected results\r\nMemory usage should stay within reasonable boundaries.\r\n\r\n\r\n## Actual results\r\nThis is htop-output from running the provided script.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/8143425\/115954836-66954980-a4f3-11eb-8340-0153bdc3a475.png)\r\n\r\n## Versions\r\n```\r\n- Datasets: 1.6.0\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)\r\n[GCC 7.3.0]\r\n- Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10\r\n```\r\nRunning on WSL2\r\n","comment_length":17,"text":"Running `datase.map` with `num_proc > 1` uses a lot of memory \n ## Describe the bug\r\nRunning `datase.map` with `num_proc > 1`  leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndstc8_datset = load_dataset(\"roskoN\/dstc8-reddit-corpus\", keep_in_memory=False)\r\n\r\n\r\ndef _prepare_sample(batch):\r\n    return {\"input_ids\": list(), \"attention_mask\": list()}\r\n\r\n\r\nfor split_name, dataset_split in list(dstc8_datset.items()):\r\n    print(f\"Processing {split_name}\")\r\n    encoded_dataset_split = dataset_split.map(\r\n        function=_prepare_sample,\r\n        batched=True,\r\n        num_proc=4,\r\n        remove_columns=dataset_split.column_names,\r\n        batch_size=10,\r\n        writer_batch_size=10,\r\n        keep_in_memory=False,\r\n    )\r\n    print(encoded_dataset_split)\r\n\r\n    path = f\".\/data\/encoded_{split_name}\"\r\n\r\n    encoded_dataset_split.save_to_disk(path)\r\n```\r\n\r\n## Expected results\r\nMemory usage should stay within reasonable boundaries.\r\n\r\n\r\n## Actual results\r\nThis is htop-output from running the provided script.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/8143425\/115954836-66954980-a4f3-11eb-8340-0153bdc3a475.png)\r\n\r\n## Versions\r\n```\r\n- Datasets: 1.6.0\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)\r\n[GCC 7.3.0]\r\n- Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10\r\n```\r\nRunning on WSL2\r\n \n Thanks for reporting ! We are working on this and we'll do a patch release very soon.","embeddings":[-0.0754546002,-0.3018320203,-0.0413843729,0.3514220417,0.2028204054,0.0986170247,0.099690333,0.2842758894,0.2723552883,0.2235053182,0.1852624714,0.4316777885,-0.1457952112,-0.0435094424,-0.0000909037,0.1481419951,0.2869782448,0.0148507189,0.1028161347,0.0590187646,-0.4523685277,0.0157497246,-0.3991191387,-0.2898879349,-0.1479185075,0.1475247145,-0.0865113661,0.3391438127,0.2111929506,-0.5017675161,0.1326698512,-0.0852004588,0.1664270163,0.2540663183,-0.0001137038,-0.1059106588,0.1337454021,0.1001989022,-0.1821791977,0.1486949176,-0.312330991,-0.3803421557,-0.0605341941,-0.3717800379,0.0739793628,0.1645597368,-0.0146035962,-0.6698246598,0.218203634,0.3299291134,0.1970496178,-0.1352159828,-0.1267959476,0.1426888108,0.2613807321,0.2144849747,-0.0019844794,-0.2373128384,0.3164677024,0.1026870683,0.0636079609,0.4374418557,-0.2772944868,-0.031458009,-0.0467965528,0.1114465892,0.3358644247,-0.2466217428,0.3791429102,-0.1982060522,0.1761236638,-0.3240182698,0.1939211786,-0.1367563456,-0.2850831747,-0.4452186823,0.2263071686,0.0385255329,-0.1923348159,0.1316108555,-0.020382246,-0.0772100911,0.2251264602,-0.0430459939,-0.0519027896,0.1416192502,0.0057180547,0.1725652665,0.2679631412,-0.1135372818,-0.2476183027,-0.0225866605,-0.034672074,0.1145174801,-0.3829443753,-0.1603149325,0.0903514326,-0.1762013882,0.0510158837,-0.3032523692,0.105065465,0.1592099816,0.2167821974,0.1538217217,0.3814410865,0.0126948627,0.036227949,0.3134065866,0.2402250022,-0.1346106976,0.2550569773,0.057650283,0.171500355,-0.2757337689,0.3283064663,-0.0324563347,0.2573720813,-0.1818683892,-0.2745570242,0.2707969248,-0.0707862526,0.2562495172,0.0107705668,0.2392870039,0.2339676768,0.4569321871,-0.1290821284,-0.1677623093,-0.2951617241,-0.1780389547,-0.1612370014,-0.0624380484,-0.4973858595,0.0127899535,0.2001049221,-0.0157730225,-0.0672496557,0.0855232179,-0.100447394,-0.3198694289,0.0025576681,-0.1037027985,0.2624628842,0.2540854812,-0.2491627932,0.4018828571,-0.1385018826,-0.0014938404,-0.3082485199,0.4717456698,-0.3724940121,-0.2667652369,-0.1029589772,0.0955395624,0.1612251699,0.2892300487,-0.2312066108,0.2438077778,0.4335412681,0.0352739953,-0.0686667413,-0.1108095795,-0.6351304054,-0.2619717121,-0.0059178141,0.4431113899,-0.3248881996,0.2043526769,0.0911757201,0.061570663,0.5199885368,0.312188983,0.0134994769,-0.2704667449,0.0342344344,0.1604535282,0.2634508014,-0.3385006785,-0.8233773708,0.4366506338,-0.2258637249,-0.0950899646,0.1500333548,0.2552035451,0.3965847194,-0.1706774384,0.0508164763,0.2231238633,0.0575615577,0.6018776894,-0.4744095802,-0.2935760915,0.0439664386,-0.0241866279,-0.1145748198,-0.1487894803,0.0661745071,0.1963353306,0.4563109577,-0.0463559777,0.2246581763,0.1608531475,-0.1028402224,-0.0563536361,0.1254666001,-0.2527486086,-0.4038699269,0.122095786,0.0962012112,-0.1537615955,-0.0750262067,-0.1954279989,0.0505759828,0.0149783427,0.1189678013,-0.3708422184,0.0518171303,-0.0682420433,0.0159427635,-0.2603600323,0.0931864381,0.2902766168,0.1489875317,-0.0118339593,-0.2216127217,0.0043831752,-0.0359767675,-0.1321116835,-0.1899850667,0.112056978,0.1009918153,0.0459345132,-0.1223837584,0.2798475623,0.2700845599,-0.0917511955,-0.1688500196,0.0286816098,0.3951385319,-0.2478909791,0.3301876783,0.1503428668,0.2383357137,-0.1706946343,-0.1315209866,-0.0679994896,0.1871339828,0.2635293603,-0.3016976118,0.1257996261,0.2288258821,0.0110698612,0.233426705,-0.3601567745,0.0444171987,0.2666493952,0.0942667574,0.1847881675,0.0502932593,0.2274681032,0.3800434768,0.0071574715,-0.0674424171,0.2289534658,-0.4205145836,-0.2080101073,0.1267896742,-0.1767047048,0.4225719273,0.1683559418,0.0327091739,-0.0163709726,-0.0035293209,-0.0506971441,0.1758865863,0.087111719,-0.0744864345,-0.3462857008,0.2362720221,-0.135760501,-0.0663327277,-0.1010182872,0.0710001513,0.3797438443,-0.3099096715,0.0203586984,-0.1911893785,0.3776617348,0.0741064847,0.1036665365,0.1740548313,-0.1810127795,-0.0455531105,0.0940028802,0.010912885,0.0489136353,0.0440295264,0.133071959,0.0573309995,0.0461419038,-0.1617720425,-0.2432823479,-0.2543354332,-0.0349646062,0.2633143365,0.0199793596,0.35756585,0.0410643853,-0.1443333775,-0.0342577919,-0.1015553176,-0.0204920657,0.1299987137,-0.0371873863,-0.1992779672,-0.076335974,-0.0712473765,0.1608318388,-0.0548366718,-0.2768733203,-0.1413891464,0.3183433115,0.0626070201,-0.0981762037,-0.2886623442,-0.2188269645,-0.1263393611,-0.1334591806,0.1696945578,0.1252622306,0.4080451429,-0.281423986,0.2976653278,0.0794598535,0.121788308,-0.0141471475,-0.3536334038,-0.0541282296,0.0513544865,-0.0443673357,-0.1495989263,0.033932291,0.120275192,-0.1339851171,0.1744812578,-0.4615814388,0.1104643792,-0.4668396115,0.1116042063,0.0113620861,0.0800106376,0.5854110718,0.3444525599,-0.1051352322,0.0483481474,-0.2294131815,0.0313920677,-0.0876305178,0.0033826383,0.2333286107,0.4563247263,0.2585018575,0.1466429532,0.5971662402,-0.1571900547,0.0591799617,0.0931066796,-0.1019993052,-0.0068084695,-0.2207559496,0.1000622585,-0.1552474648,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2256","title":"Running `datase.map` with `num_proc > 1` uses a lot of memory","comments":"We did a patch release to fix this issue.\r\nIt should be fixed in the new version 1.6.1\r\n\r\nThanks again for reporting and for the details :)","body":"## Describe the bug\r\nRunning `datase.map` with `num_proc > 1`  leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndstc8_datset = load_dataset(\"roskoN\/dstc8-reddit-corpus\", keep_in_memory=False)\r\n\r\n\r\ndef _prepare_sample(batch):\r\n    return {\"input_ids\": list(), \"attention_mask\": list()}\r\n\r\n\r\nfor split_name, dataset_split in list(dstc8_datset.items()):\r\n    print(f\"Processing {split_name}\")\r\n    encoded_dataset_split = dataset_split.map(\r\n        function=_prepare_sample,\r\n        batched=True,\r\n        num_proc=4,\r\n        remove_columns=dataset_split.column_names,\r\n        batch_size=10,\r\n        writer_batch_size=10,\r\n        keep_in_memory=False,\r\n    )\r\n    print(encoded_dataset_split)\r\n\r\n    path = f\".\/data\/encoded_{split_name}\"\r\n\r\n    encoded_dataset_split.save_to_disk(path)\r\n```\r\n\r\n## Expected results\r\nMemory usage should stay within reasonable boundaries.\r\n\r\n\r\n## Actual results\r\nThis is htop-output from running the provided script.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/8143425\/115954836-66954980-a4f3-11eb-8340-0153bdc3a475.png)\r\n\r\n## Versions\r\n```\r\n- Datasets: 1.6.0\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)\r\n[GCC 7.3.0]\r\n- Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10\r\n```\r\nRunning on WSL2\r\n","comment_length":27,"text":"Running `datase.map` with `num_proc > 1` uses a lot of memory \n ## Describe the bug\r\nRunning `datase.map` with `num_proc > 1`  leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndstc8_datset = load_dataset(\"roskoN\/dstc8-reddit-corpus\", keep_in_memory=False)\r\n\r\n\r\ndef _prepare_sample(batch):\r\n    return {\"input_ids\": list(), \"attention_mask\": list()}\r\n\r\n\r\nfor split_name, dataset_split in list(dstc8_datset.items()):\r\n    print(f\"Processing {split_name}\")\r\n    encoded_dataset_split = dataset_split.map(\r\n        function=_prepare_sample,\r\n        batched=True,\r\n        num_proc=4,\r\n        remove_columns=dataset_split.column_names,\r\n        batch_size=10,\r\n        writer_batch_size=10,\r\n        keep_in_memory=False,\r\n    )\r\n    print(encoded_dataset_split)\r\n\r\n    path = f\".\/data\/encoded_{split_name}\"\r\n\r\n    encoded_dataset_split.save_to_disk(path)\r\n```\r\n\r\n## Expected results\r\nMemory usage should stay within reasonable boundaries.\r\n\r\n\r\n## Actual results\r\nThis is htop-output from running the provided script.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/8143425\/115954836-66954980-a4f3-11eb-8340-0153bdc3a475.png)\r\n\r\n## Versions\r\n```\r\n- Datasets: 1.6.0\r\n- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)\r\n[GCC 7.3.0]\r\n- Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10\r\n```\r\nRunning on WSL2\r\n \n We did a patch release to fix this issue.\r\nIt should be fixed in the new version 1.6.1\r\n\r\nThanks again for reporting and for the details :)","embeddings":[-0.0729513094,-0.2929819822,-0.042427361,0.3707073927,0.2069268823,0.0879345685,0.0891324282,0.2784173787,0.2625880837,0.2296098918,0.179768458,0.4309554696,-0.1489770412,-0.0277889445,-0.0165795069,0.1438741237,0.283003211,0.0198419057,0.0992945209,0.0716441944,-0.448911339,0.0283552222,-0.4113303423,-0.2822188139,-0.1598487943,0.1489114016,-0.0890552849,0.3382924199,0.2094049156,-0.516469419,0.1316249669,-0.0899930447,0.173614651,0.2649437785,-0.0001142843,-0.101619862,0.125217557,0.0971597806,-0.1957461089,0.1276341528,-0.3146615922,-0.3634471297,-0.0303165559,-0.3590409458,0.0726523474,0.1657172143,-0.0194390193,-0.6739723682,0.2170994878,0.3392878473,0.1941734254,-0.1366488934,-0.1196864545,0.1301043332,0.2364715487,0.2139768153,0.0010632157,-0.2308275849,0.322963804,0.1149242744,0.0747516453,0.4133724272,-0.2654496431,-0.0384335257,-0.0535099283,0.0997086763,0.352129519,-0.2720297873,0.3823864758,-0.1950076967,0.1814183444,-0.3327334523,0.1823688596,-0.1348747164,-0.2793178558,-0.4510781169,0.2298989743,0.0367099978,-0.1882492006,0.1366421878,-0.0380606093,-0.0894393548,0.2267846614,-0.0533736944,-0.0368233137,0.1144272089,0.0036473363,0.1740719527,0.2728739977,-0.111607343,-0.2295513302,-0.0282947272,-0.0394031405,0.1216240227,-0.3987645805,-0.157946229,0.0689608529,-0.1813658923,0.0463564768,-0.3080103993,0.1184394658,0.1578158587,0.2054126263,0.1522976756,0.3860761821,0.0093933782,0.0597691946,0.3085868657,0.2237630188,-0.1464236379,0.2582480013,0.0417789295,0.1672824472,-0.2809308171,0.3191932142,-0.0285182949,0.23656708,-0.1937867105,-0.2665585876,0.2586752474,-0.0610516109,0.2611827254,0.0222862605,0.2262551188,0.2448637784,0.4470606744,-0.1463556588,-0.160926789,-0.2841985822,-0.1833258569,-0.1512222886,-0.0574940294,-0.5046676397,0.0224807914,0.2060202956,-0.028394334,-0.0664362833,0.0933883861,-0.1053046659,-0.3432498574,0.012175451,-0.1125842556,0.2572804689,0.2620687485,-0.2398930192,0.3993423283,-0.1505595595,0.0162432101,-0.3013105094,0.4810847938,-0.3707550764,-0.2709364891,-0.0990084037,0.0852084234,0.1575821787,0.2947646379,-0.2420880646,0.2445336729,0.4286451042,0.0269888714,-0.0889528766,-0.121341981,-0.6368365288,-0.2681862414,0.0030347933,0.4324857593,-0.3264067769,0.2170574516,0.0809328929,0.0344491489,0.5263162851,0.310947299,0.0118526854,-0.2675107419,0.0316987745,0.1533008516,0.2658886909,-0.3270070851,-0.817191422,0.4371910393,-0.2157791704,-0.109528482,0.1383834928,0.2508608699,0.40468961,-0.1663723886,0.0347509049,0.2282755971,0.0608967543,0.5903041363,-0.4691990316,-0.303893894,0.0413282737,-0.0195751209,-0.1173685193,-0.1451271623,0.0748494491,0.1778643429,0.453548342,-0.058543466,0.2303673923,0.1774595976,-0.1110148206,-0.0563489832,0.1232414767,-0.2524731159,-0.4027755857,0.13394095,0.0832437128,-0.1526621878,-0.0804286152,-0.1751124114,0.0650854781,0.0063811243,0.12697725,-0.3695948124,0.0472450666,-0.078113012,0.0223031454,-0.2606379986,0.0992855877,0.2954145074,0.1608096063,-0.0029197617,-0.2119587362,0.0069003459,-0.0256237015,-0.1433457285,-0.1732806712,0.1090763211,0.112046659,0.0333948098,-0.1382002831,0.2837573588,0.2842199206,-0.0985985622,-0.1563302875,0.0272796862,0.4024820328,-0.2332773954,0.327091068,0.1425988823,0.2420417517,-0.1731173992,-0.1279882044,-0.0569084659,0.1828204393,0.2651683688,-0.2958455682,0.1233256534,0.2298754156,0.0121203372,0.2454758883,-0.36111781,0.0471434109,0.287186861,0.098257944,0.1909164488,0.0465373211,0.2114551216,0.3700146973,0.0057400702,-0.0826599076,0.2261688709,-0.4248207808,-0.2112520337,0.1328227967,-0.1818520278,0.4213223457,0.1641579717,0.0243873633,-0.0241139587,0.012031504,-0.0545138419,0.1692528725,0.0867379382,-0.0513383336,-0.367634356,0.2332722545,-0.1403825134,-0.0653906837,-0.1040041819,0.0889222696,0.3924361169,-0.2967655957,0.0110161183,-0.1935511976,0.400585711,0.0906683803,0.1055033356,0.1667192876,-0.18145518,-0.056737192,0.0982364044,0.0294012669,0.051611118,0.0656295717,0.1132944152,0.0556617863,0.0510638542,-0.1494139284,-0.2518106997,-0.2523031831,-0.0411089212,0.2673361897,0.0278153326,0.3511581421,0.0504587665,-0.155298993,-0.0317021236,-0.0926315561,-0.0218256712,0.1374751627,-0.034724433,-0.2257715315,-0.0832699984,-0.0728773773,0.1783143878,-0.0700538903,-0.2762166262,-0.1268468648,0.3321932256,0.061236538,-0.113702178,-0.2935315967,-0.2158631831,-0.1409101784,-0.1193530709,0.1708788723,0.1151362881,0.4196763933,-0.284565419,0.3068885803,0.0737346858,0.1142024025,-0.0205499046,-0.3569996059,-0.0585363396,0.0571643636,-0.0496483184,-0.1584663987,0.0290774722,0.1449582577,-0.1220377386,0.1793140471,-0.4542498887,0.118075192,-0.469053477,0.1137285158,0.0141398842,0.062865831,0.5845873952,0.3504353762,-0.0964849144,0.0529159978,-0.230626896,0.0342685841,-0.0814838186,-0.0005931606,0.2426695526,0.4532476664,0.2545185089,0.1341665834,0.6049479246,-0.1597821862,0.0588254035,0.0959706977,-0.1034300774,-0.0146367466,-0.2227414548,0.0980299488,-0.1607872099,-0.1319242567,0.4267861545,0.1794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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Hi ! Sorry to hear that. This may come from another issue then.\r\n\r\nFirst can we check if this latency comes from the dataset itself ?\r\nYou can try to load your dataset and benchmark the speed of querying random examples inside it ?\r\n```python\r\nimport time\r\nimport numpy as np\r\n\r\nfrom datasets import load_from_disk\r\n\r\ndataset = load_from_disk(...) # or from load_dataset...\r\n\r\n_start = time.time()\r\nn = 100\r\nfor i in np.random.default_rng(42).integers(0, len(dataset), size=n):\r\n    _ = dataset[i]\r\nprint(time.time() - _start)\r\n```\r\n\r\nIf we see a significant speed difference between your two datasets then it would mean that there's an issue somewhere","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":101,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Hi ! Sorry to hear that. This may come from another issue then.\r\n\r\nFirst can we check if this latency comes from the dataset itself ?\r\nYou can try to load your dataset and benchmark the speed of querying random examples inside it ?\r\n```python\r\nimport time\r\nimport numpy as np\r\n\r\nfrom datasets import load_from_disk\r\n\r\ndataset = load_from_disk(...) # or from load_dataset...\r\n\r\n_start = time.time()\r\nn = 100\r\nfor i in np.random.default_rng(42).integers(0, len(dataset), size=n):\r\n    _ = dataset[i]\r\nprint(time.time() - _start)\r\n```\r\n\r\nIf we see a significant speed difference between your two datasets then it would mean that there's an issue somewhere","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Hi @lhoestq, here is the result. I additionally measured time to `load_from_disk`:\r\n* 60GB\r\n```\r\nloading took:  22.618776321411133\r\nramdom indexing 100 times took: 0.10214924812316895\r\n```\r\n\r\n* 600GB\r\n```\r\nloading took:  1176.1764674186707\r\nramdom indexing 100 times took: 2.853600025177002\r\n```\r\n\r\nHmm.. I double checked that it's version 1.6.0. The difference seems quite big, could it be related to the running environment? \r\n","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":59,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Hi @lhoestq, here is the result. I additionally measured time to `load_from_disk`:\r\n* 60GB\r\n```\r\nloading took:  22.618776321411133\r\nramdom indexing 100 times took: 0.10214924812316895\r\n```\r\n\r\n* 600GB\r\n```\r\nloading took:  1176.1764674186707\r\nramdom indexing 100 times took: 2.853600025177002\r\n```\r\n\r\nHmm.. I double checked that it's version 1.6.0. The difference seems quite big, could it be related to the running environment? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"I'm surprised by the speed change. Can you give more details about your dataset ?\r\nThe speed depends on the number of batches in the arrow tables and the distribution of the lengths of the batches.\r\nYou can access the batches by doing `dataset.data.to_batches()` (use only for debugging) (it doesn't bring data in memory).\r\n\r\nAlso can you explain what parameters you used if you used `map` calls ?\r\nAlso if you have some code that reproduces the issue I'd be happy to investigate it.","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":84,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n I'm surprised by the speed change. Can you give more details about your dataset ?\r\nThe speed depends on the number of batches in the arrow tables and the distribution of the lengths of the batches.\r\nYou can access the batches by doing `dataset.data.to_batches()` (use only for debugging) (it doesn't bring data in memory).\r\n\r\nAlso can you explain what parameters you used if you used `map` calls ?\r\nAlso if you have some code that reproduces the issue I'd be happy to investigate it.","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.3324069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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Also if you could give us more info about your env like your OS, version of pyarrow and if you're using an HDD or a SSD","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":26,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Also if you could give us more info about your env like your OS, version of pyarrow and if you're using an HDD or a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Here are some details of my 600GB dataset. This is a dataset AFTER the `map` function and once I load this dataset, I do not use `map` anymore in the training. Regarding the distribution of the lengths, it is almost uniform (90% is 512 tokens, and 10% is randomly shorter than that -- typical setting for language modeling).\r\n```\r\nlen(batches):\r\n492763\r\n\r\nbatches[0]: \r\npyarrow.RecordBatch\r\nattention_mask: list<item: uint8>\r\n  child 0, item: uint8\r\ninput_ids: list<item: int16>\r\n  child 0, item: int16\r\nspecial_tokens_mask: list<item: uint8>\r\n  child 0, item: uint8\r\ntoken_type_ids: list<item: uint8>\r\n  child 0, item: uint8\r\n```\r\n\r\nHere the some parameters to `map` function just in case it is relevant:\r\n```\r\nnum_proc=1    # as multi processing is slower in my case\r\nload_from_cache_file=False\r\n```\r\n","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":118,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Here are some details of my 600GB dataset. This is a dataset AFTER the `map` function and once I load this dataset, I do not use `map` anymore in the training. Regarding the distribution of the lengths, it is almost uniform (90% is 512 tokens, and 10% is randomly shorter than that -- typical setting for language modeling).\r\n```\r\nlen(batches):\r\n492763\r\n\r\nbatches[0]: \r\npyarrow.RecordBatch\r\nattention_mask: list<item: uint8>\r\n  child 0, item: uint8\r\ninput_ids: list<item: int16>\r\n  child 0, item: int16\r\nspecial_tokens_mask: list<item: uint8>\r\n  child 0, item: uint8\r\ntoken_type_ids: list<item: uint8>\r\n  child 0, item: uint8\r\n```\r\n\r\nHere the some parameters to `map` function just in case it is relevant:\r\n```\r\nnum_proc=1    # as multi processing is slower in my case\r\nload_from_cache_file=False\r\n```\r\n","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.024000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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Regarding the environment, I am running the code on a cloud server. Here are some info:\r\n```\r\nUbuntu 18.04.5 LTS   # cat \/etc\/issue\r\npyarrow                 3.0.0  # pip list | grep pyarrow\r\n```\r\nThe data is stored in SSD and it is mounted to the machine via Network File System.\r\n\r\nIf you could point me to some of the commands to check the details of the environment, I would be happy to provide relevant information @lhoestq !","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":76,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Regarding the environment, I am running the code on a cloud server. Here are some info:\r\n```\r\nUbuntu 18.04.5 LTS   # cat \/etc\/issue\r\npyarrow                 3.0.0  # pip list | grep pyarrow\r\n```\r\nThe data is stored in SSD and it is mounted to the machine via Network File System.\r\n\r\nIf you could point me to some of the commands to check the details of the environment, I would be happy to provide relevant information @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"I am not sure how I could provide you with the reproducible code, since the problem only arises when the data is big. For the moment, I would share the part that I think is relevant. Feel free to ask me for more info.\r\n\r\n```python\r\nclass MyModel(pytorch_lightning.LightningModule)\r\n    def setup(self, stage):\r\n        self.dataset = datasets.load_from_disk(path)\r\n        self.dataset.set_format(\"torch\")\r\n\r\n    def train_dataloader(self):\r\n        collate_fn = transformers.DataCollatorForLanguageModeling(\r\n                tokenizer=transformers.ElectraTokenizerFast.from_pretrained(tok_path)\r\n        )\r\n        dataloader = torch.utils.DataLoader(\r\n                self.dataset,\r\n                batch_size=32,\r\n                collate_fn=collate_fn,\r\n                num_workers=8,\r\n                pin_memory=True,\r\n       )\r\n```","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":71,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n I am not sure how I could provide you with the reproducible code, since the problem only arises when the data is big. For the moment, I would share the part that I think is relevant. Feel free to ask me for more info.\r\n\r\n```python\r\nclass MyModel(pytorch_lightning.LightningModule)\r\n    def setup(self, stage):\r\n        self.dataset = datasets.load_from_disk(path)\r\n        self.dataset.set_format(\"torch\")\r\n\r\n    def train_dataloader(self):\r\n        collate_fn = transformers.DataCollatorForLanguageModeling(\r\n                tokenizer=transformers.ElectraTokenizerFast.from_pretrained(tok_path)\r\n        )\r\n        dataloader = torch.utils.DataLoader(\r\n                self.dataset,\r\n                batch_size=32,\r\n                collate_fn=collate_fn,\r\n                num_workers=8,\r\n                pin_memory=True,\r\n       )\r\n```","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.33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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Hi ! Sorry for the delay I haven't had a chance to take a look at this yet. Are you still experiencing this issue ?\r\nI'm asking because the latest patch release 1.6.2 fixed a few memory issues that could have lead to slow downs","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":45,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Hi ! Sorry for the delay I haven't had a chance to take a look at this yet. Are you still experiencing this issue ?\r\nI'm asking because the latest patch release 1.6.2 fixed a few memory issues that could have lead to slow 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Hi! I just ran the same code with different datasets (one is 60 GB and another 600 GB), and the latter runs much slower. ETA differs by 10x.","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":28,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Hi! I just ran the same code with different datasets (one is 60 GB and another 600 GB), and the latter runs much slower. ETA differs by 10x.","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.332406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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"@lhoestq and @hwijeen\r\n\r\nDespite upgrading to datasets 1.6.2, still experiencing extremely slow (2h00) loading for a 300Gb local dataset shard size 1.1Gb on local HDD (40Mb\/s read speed). This corresponds almost exactly to total data divided by reading speed implying that it reads the entire dataset at each load.\r\n\r\nStack details:\r\n=========\r\n\r\n> GCC version: Could not collect\r\n> Clang version: Could not collect\r\n> CMake version: Could not collect\r\n> \r\n> Python version: 3.7 (64-bit runtime)\r\n> Is CUDA available: True\r\n> CUDA runtime version: 10.2.89\r\n> GPU models and configuration: GPU 0: GeForce GTX 1050\r\n> Nvidia driver version: 457.63\r\n> cuDNN version: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\cudnn64_7.dll\r\n> HIP runtime version: N\/A\r\n> MIOpen runtime version: N\/A\r\n> \r\n> Versions of relevant libraries:\r\n> [pip3] datasets==1.6.2\r\n> [pip3] transformers==4.5.1\r\n> [pip3] numpy==1.19.1\r\n> [pip3] numpydoc==1.1.0\r\n> [pip3] pytorch-metric-learning==0.9.98\r\n> [pip3] torch==1.8.1\r\n> [pip3] torchaudio==0.8.1\r\n> [pip3] torchvision==0.2.2\r\n> [conda] blas                      2.16                        mkl    conda-forge\r\n> [conda] cudatoolkit               10.2.89              hb195166_8    conda-forge\r\n> [conda] libblas                   3.8.0                    16_mkl    conda-forge\r\n> [conda] libcblas                  3.8.0                    16_mkl    conda-forge\r\n> [conda] liblapack                 3.8.0                    16_mkl    conda-forge\r\n> [conda] liblapacke                3.8.0                    16_mkl    conda-forge\r\n> [conda] mkl                       2020.1                      216\r\n> [conda] numpy                     1.19.1           py37hae9e721_0    conda-forge\r\n> [conda] numpydoc                  1.1.0                      py_1    conda-forge\r\n> [conda] pytorch                   1.8.1           py3.7_cuda10.2_cudnn7_0    pytorch\r\n> [conda] pytorch-metric-learning   0.9.98             pyh39e3cac_0    metric-learning\r\n> [conda] torchaudio                0.8.1                      py37    pytorch\r\n> [conda] torchvision               0.2.2                      py_3    pytorch","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":227,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n @lhoestq and @hwijeen\r\n\r\nDespite upgrading to datasets 1.6.2, still experiencing extremely slow (2h00) loading for a 300Gb local dataset shard size 1.1Gb on local HDD (40Mb\/s read speed). This corresponds almost exactly to total data divided by reading speed implying that it reads the entire dataset at each load.\r\n\r\nStack details:\r\n=========\r\n\r\n> GCC version: Could not collect\r\n> Clang version: Could not collect\r\n> CMake version: Could not collect\r\n> \r\n> Python version: 3.7 (64-bit runtime)\r\n> Is CUDA available: True\r\n> CUDA runtime version: 10.2.89\r\n> GPU models and configuration: GPU 0: GeForce GTX 1050\r\n> Nvidia driver version: 457.63\r\n> cuDNN version: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\cudnn64_7.dll\r\n> HIP runtime version: N\/A\r\n> MIOpen runtime version: N\/A\r\n> \r\n> Versions of relevant libraries:\r\n> [pip3] datasets==1.6.2\r\n> [pip3] transformers==4.5.1\r\n> [pip3] numpy==1.19.1\r\n> [pip3] numpydoc==1.1.0\r\n> [pip3] pytorch-metric-learning==0.9.98\r\n> [pip3] torch==1.8.1\r\n> [pip3] torchaudio==0.8.1\r\n> [pip3] torchvision==0.2.2\r\n> [conda] blas                      2.16                        mkl    conda-forge\r\n> [conda] cudatoolkit               10.2.89              hb195166_8    conda-forge\r\n> [conda] libblas                   3.8.0                    16_mkl    conda-forge\r\n> [conda] libcblas                  3.8.0                    16_mkl    conda-forge\r\n> [conda] liblapack                 3.8.0                    16_mkl    conda-forge\r\n> [conda] liblapacke                3.8.0                    16_mkl    conda-forge\r\n> [conda] mkl                       2020.1                      216\r\n> [conda] numpy                     1.19.1           py37hae9e721_0    conda-forge\r\n> [conda] numpydoc                  1.1.0                      py_1    conda-forge\r\n> [conda] pytorch                   1.8.1           py3.7_cuda10.2_cudnn7_0    pytorch\r\n> [conda] pytorch-metric-learning   0.9.98             pyh39e3cac_0    metric-learning\r\n> [conda] torchaudio                0.8.1                      py37    pytorch\r\n> [conda] torchvision               0.2.2                      py_3    pytorch","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.332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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Hi @lhoestq thanks for the quick turn-around, actually the plain vanilla way, without an particular knack or fashion, I tried to look into the documentation for some alternative but couldn't find any\r\n\r\n> dataset = load_from_disk(dataset_path=os.path.join(datasets_dir,dataset_dir))","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":36,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Hi @lhoestq thanks for the quick turn-around, actually the plain vanilla way, without an particular knack or fashion, I tried to look into the documentation for some alternative but couldn't find any\r\n\r\n> dataset = load_from_disk(dataset_path=os.path.join(datasets_dir,dataset_dir))","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"I\u2019m facing the same issue when loading a 900GB dataset (stored via `save_to_disk`): `load_from_disk(path_to_dir)` takes 1.5 hours and htop consistently shows high IO rates > 120 M\/s.","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":27,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n I\u2019m facing the same issue when loading a 900GB dataset (stored via `save_to_disk`): `load_from_disk(path_to_dir)` takes 1.5 hours and htop consistently shows high IO rates > 120 M\/s.","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.33240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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"@tsproisl same here, smells like ~~teen spirit~~ intended generator inadvertently ending up iterator\r\n\r\n@lhoestq perhaps solution to detect bug location in code is to track its signature via HD read usage monitoring, option is to add tracking decorator on top each function and sequentially close all hatches from top to bottom, suggest PySmart https:\/\/pypi.org\/project\/pySMART\/ a Smartmontools implementation","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":57,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n @tsproisl same here, smells like ~~teen spirit~~ intended generator inadvertently ending up iterator\r\n\r\n@lhoestq perhaps solution to detect bug location in code is to track its signature via HD read usage monitoring, option is to add tracking decorator on top each function and sequentially close all hatches from top to bottom, suggest PySmart https:\/\/pypi.org\/project\/pySMART\/ a Smartmontools implementation","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.181683018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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"I wasn't able to reproduce this on a toy dataset of around 300GB:\r\n\r\n```python\r\nimport datasets as ds\r\n\r\ns = ds.load_dataset(\"squad\", split=\"train\")\r\ns4000 = ds.concatenate_datasets([s] * 4000)\r\nprint(ds.utils.size_str(s4000.data.nbytes))  # '295.48 GiB'\r\n\r\ns4000.save_to_disk(\"tmp\/squad_4000\")\r\n```\r\n\r\n```python\r\nimport psutil\r\nimport time\r\nfrom datasets import load_from_disk\r\n\r\ndisk = \"disk0\"  # You may have to change your disk here\r\niocnt1 = psutil.disk_io_counters(perdisk=True)[disk]\r\ntime1 = time.time()\r\n\r\ns4000_reloaded = load_from_disk(\"tmp\/squad_4000\")\r\n\r\ntime2 = time.time()\r\niocnt2 = psutil.disk_io_counters(perdisk=True)[disk]\r\n\r\nprint(f\"Blocks read {iocnt2.read_count - iocnt1.read_count}\")  # Blocks read 18\r\nprint(f\"Elapsed time: {time2 - time1:.02f}s\")  # Elapsed time: 14.60s\r\n```\r\n\r\nCould you run this on your side and tell me if how much time it takes ? Please run this when your machine is idle so that other processes don't interfere.\r\n\r\nI got these results on my macbook pro on datasets 1.6.2","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":130,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n I wasn't able to reproduce this on a toy dataset of around 300GB:\r\n\r\n```python\r\nimport datasets as ds\r\n\r\ns = ds.load_dataset(\"squad\", split=\"train\")\r\ns4000 = ds.concatenate_datasets([s] * 4000)\r\nprint(ds.utils.size_str(s4000.data.nbytes))  # '295.48 GiB'\r\n\r\ns4000.save_to_disk(\"tmp\/squad_4000\")\r\n```\r\n\r\n```python\r\nimport psutil\r\nimport time\r\nfrom datasets import load_from_disk\r\n\r\ndisk = \"disk0\"  # You may have to change your disk here\r\niocnt1 = psutil.disk_io_counters(perdisk=True)[disk]\r\ntime1 = time.time()\r\n\r\ns4000_reloaded = load_from_disk(\"tmp\/squad_4000\")\r\n\r\ntime2 = time.time()\r\niocnt2 = psutil.disk_io_counters(perdisk=True)[disk]\r\n\r\nprint(f\"Blocks read {iocnt2.read_count - iocnt1.read_count}\")  # Blocks read 18\r\nprint(f\"Elapsed time: {time2 - time1:.02f}s\")  # Elapsed time: 14.60s\r\n```\r\n\r\nCould you run this on your side and tell me if how much time it takes ? Please run this when your machine is idle so that other processes don't interfere.\r\n\r\nI got these results on my macbook pro on datasets 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Just tried on google colab and got ~1min for a 15GB dataset (only 200 times SQuAD), while it should be instantaneous. The time is spent reading the Apache Arrow table from the memory mapped file. This might come a virtual disk management issue. I'm trying to see if I can still speed it up on colab.","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":56,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Just tried on google colab and got ~1min for a 15GB dataset (only 200 times SQuAD), while it should be instantaneous. The time is spent reading the Apache Arrow table from the memory mapped file. This might come a virtual disk management issue. I'm trying to see if I can still speed it up on colab.","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.3324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"@lhoestq what is Google Colab's HD read speed, is it possible to introspect incl. make like SSD or HDD ?","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":20,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n @lhoestq what is Google Colab's HD read speed, is it possible to introspect incl. make like SSD or HDD ?","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.332406997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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"@lhoestq Thank you! The issue is getting more interesting. The second script is still running, but it's definitely taking much longer than 15 seconds.","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":24,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n @lhoestq Thank you! The issue is getting more interesting. The second script is still running, but it's definitely taking much longer than 15 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Okay, here\u2019s the ouput:\r\nBlocks read 158396\r\nElapsed time: 529.10s\r\n\r\nAlso using datasets 1.6.2. Do you have any ideas, how to pinpoint the problem?","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":24,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Okay, here\u2019s the ouput:\r\nBlocks read 158396\r\nElapsed time: 529.10s\r\n\r\nAlso using datasets 1.6.2. Do you have any ideas, how to pinpoint the problem?","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.33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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"@lhoestq, @tsproisl mmmh still writing on my side about 1h to go, thinking on it are your large datasets all monoblock unsharded ? mine is 335 times 1.18Gb shards.","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":29,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n @lhoestq, @tsproisl mmmh still writing on my side about 1h to go, thinking on it are your large datasets all monoblock unsharded ? mine is 335 times 1.18Gb shards.","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.332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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"The 529.10s was a bit too optimistic. I cancelled the reading process once before running it completely, therefore the harddrive cache probably did its work.\r\n\r\nHere are three consecutive runs\r\nFirst run (freshly written to disk):\r\nBlocks read 309702\r\nElapsed time: 1267.74s\r\nSecond run (immediately after):\r\nBlocks read 113944\r\nElapsed time: 417.55s\r\nThird run (immediately after):\r\nBlocks read 42518\r\nElapsed time: 199.19s\r\n","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":62,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n The 529.10s was a bit too optimistic. I cancelled the reading process once before running it completely, therefore the harddrive cache probably did its work.\r\n\r\nHere are three consecutive runs\r\nFirst run (freshly written to disk):\r\nBlocks read 309702\r\nElapsed time: 1267.74s\r\nSecond run (immediately after):\r\nBlocks read 113944\r\nElapsed time: 417.55s\r\nThird run (immediately after):\r\nBlocks read 42518\r\nElapsed time: 199.19s\r\n","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"@lhoestq \r\nFirst test\r\n> elapsed time: 11219.05s\r\n\r\nSecond test running bear with me, for Windows users slight trick to modify original \"disk0\" string:\r\n\r\nFirst find physical unit relevant key in dictionnary\r\n```\r\nimport psutil\r\npsutil.disk_io_counters(perdisk=True)\r\n```\r\n\r\n> {'PhysicalDrive0': sdiskio(read_count=18453286, write_count=4075333, read_bytes=479546467840, write_bytes=161590275072, read_time=20659, write_time=2464),\r\n>  'PhysicalDrive1': sdiskio(read_count=1495778, write_count=388781, read_bytes=548628622336, write_bytes=318234849280, read_time=426066, write_time=19085)}\r\n\r\nIn my case it's _PhysicalDrive1_\r\n\r\nThen insert relevant key's string as _disk_ variable\r\n\r\n```\r\npsutil.disk_io_counters()\r\ndisk = 'PhysicalDrive1'  # You may have to change your disk here\r\niocnt1 = psutil.disk_io_counters(perdisk=True)[disk]\r\ntime1 = time.time()\r\ns4000_reloaded = load_from_disk(\"your path here\")\r\ntime2 = time.time()\r\niocnt2 = psutil.disk_io_counters(perdisk=True)[disk]\r\nprint(f\"Blocks read {iocnt2.read_count - iocnt1.read_count}\")  # Blocks read 18\r\nprint(f\"Elapsed time: {time2 - time1:.02f}s\")  # Elapsed time: 14.60s\r\n```","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":115,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n @lhoestq \r\nFirst test\r\n> elapsed time: 11219.05s\r\n\r\nSecond test running bear with me, for Windows users slight trick to modify original \"disk0\" string:\r\n\r\nFirst find physical unit relevant key in dictionnary\r\n```\r\nimport psutil\r\npsutil.disk_io_counters(perdisk=True)\r\n```\r\n\r\n> {'PhysicalDrive0': sdiskio(read_count=18453286, write_count=4075333, read_bytes=479546467840, write_bytes=161590275072, read_time=20659, write_time=2464),\r\n>  'PhysicalDrive1': sdiskio(read_count=1495778, write_count=388781, read_bytes=548628622336, write_bytes=318234849280, read_time=426066, write_time=19085)}\r\n\r\nIn my case it's _PhysicalDrive1_\r\n\r\nThen insert relevant key's string as _disk_ variable\r\n\r\n```\r\npsutil.disk_io_counters()\r\ndisk = 'PhysicalDrive1'  # You may have to change your disk here\r\niocnt1 = psutil.disk_io_counters(perdisk=True)[disk]\r\ntime1 = time.time()\r\ns4000_reloaded = load_from_disk(\"your path here\")\r\ntime2 = time.time()\r\niocnt2 = psutil.disk_io_counters(perdisk=True)[disk]\r\nprint(f\"Blocks read {iocnt2.read_count - iocnt1.read_count}\")  # Blocks read 18\r\nprint(f\"Elapsed time: {time2 - time1:.02f}s\")  # Elapsed time: 14.60s\r\n```","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Unfortunately no. Thanks for running the benchmark though, it shows that you machine does a lot of read operations. This is not expected: in other machines it does almost no read operations which enables a very fast loading.\r\n\r\nI did some tests on google colab and have the same issue. The first time the dataset arrow file is memory mapped takes always a lot of time (time seems linear with respect to the dataset size). Reloading the dataset is then instantaneous since the arrow file has already been memory mapped.\r\n\r\nI also tried using the Arrow IPC file format (see #1933) instead of the current streaming format that we use but it didn't help.\r\n\r\nMemory mapping is handled by the OS and depends on the disk you're using, so I'm not sure we can do much about it. I'll continue to investigate anyway, because I still don't know why in some cases it would go through the entire file (high `Blocks read ` as in your tests) and in other cases it would do almost no reading.","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":177,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Unfortunately no. Thanks for running the benchmark though, it shows that you machine does a lot of read operations. This is not expected: in other machines it does almost no read operations which enables a very fast loading.\r\n\r\nI did some tests on google colab and have the same issue. The first time the dataset arrow file is memory mapped takes always a lot of time (time seems linear with respect to the dataset size). Reloading the dataset is then instantaneous since the arrow file has already been memory mapped.\r\n\r\nI also tried using the Arrow IPC file format (see #1933) instead of the current streaming format that we use but it didn't help.\r\n\r\nMemory mapping is handled by the OS and depends on the disk you're using, so I'm not sure we can do much about it. I'll continue to investigate anyway, because I still don't know why in some cases it would go through the entire file (high `Blocks read ` as in your tests) and in other cases it would do almost no reading.","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.33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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Just want to say that I am seeing the same issue. Dataset size if 268GB and it takes **3 hours** to load `load_from_disk`, using dataset version `1.9.0`. Filesystem underneath is `Lustre` ","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":31,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Just want to say that I am seeing the same issue. Dataset size if 268GB and it takes **3 hours** to load `load_from_disk`, using dataset version `1.9.0`. Filesystem underneath is `Lustre` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Hi @lhoestq, confirmed Windows issue, exact same code running on Linux OS total loading time about 3 minutes.","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":18,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Hi @lhoestq, confirmed Windows issue, exact same code running on Linux OS total loading time about 3 minutes.","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.33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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2252","title":"Slow dataloading with big datasets issue persists","comments":"Hmm that's different from what I got. I was on Ubuntu when reporting the initial issue.","body":"Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate.","comment_length":16,"text":"Slow dataloading with big datasets issue persists \n Hi,\r\n\r\nI reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).\r\nHowever, the problem seems to persist. Here is the profiled results:\r\n\r\n\r\n1) Running with 60GB\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  517.96         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nmodel_backward                     \t|  0.26144        \t|100            \t|  26.144         \t|  5.0475         \t|\r\nmodel_forward                      \t|  0.11123        \t|100            \t|  11.123         \t|  2.1474         \t|\r\nget_train_batch                    \t|  0.097121       \t|100            \t|  9.7121         \t|  1.8751         \t|\r\n```\r\n\r\n\r\n3) Running with 600GB, datasets==1.6.0\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  4563.2         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nget_train_batch                    \t|  5.1279         \t|100            \t|  512.79         \t|  11.237         \t|\r\nmodel_backward                     \t|  4.8394         \t|100            \t|  483.94         \t|  10.605         \t|\r\nmodel_forward                      \t|  0.12162        \t|100            \t|  12.162         \t|  0.26652        \t|\r\n```\r\n\r\nI see that `get_train_batch` lags when data is large. Could this be related to different issues?\r\nI would be happy to provide necessary information to investigate. \n Hmm that's different from what I got. I was on Ubuntu when reporting the initial issue.","embeddings":[-0.4040590525,0.2139611691,-0.134309724,0.1632939279,0.2269002348,-0.1808893532,0.2195484489,0.4007899463,0.1186829358,-0.0092018284,-0.3094537258,0.0660658404,0.1796627045,0.1480765939,-0.1164040118,0.1400319189,0.0100115351,0.0462014377,-0.1018281952,-0.0599145927,0.07170894,-0.1256888807,0.0162408222,-0.2149125636,-0.321829021,-0.035173133,0.2733658254,0.0361014716,-0.2680936158,-0.1831433326,0.2045997828,0.2419507354,0.0882460326,0.3962605298,-0.0001050314,-0.0951045752,0.3049376905,0.0726464018,-0.2473200262,0.1302319169,0.0542529263,-0.336699158,-0.1207264289,0.1083628237,-0.0350148529,0.1210587919,-0.2277355492,-0.0067853495,0.3558999598,0.1554210037,0.258639276,0.0054166568,-0.2216917872,-0.3251929879,0.1556022167,0.2288568765,-0.2061296552,0.316334933,0.4986956418,-0.1909870356,-0.5347768068,0.0420746282,-0.0375930667,0.1030172035,-0.2523976266,-0.064847365,0.1814326197,-0.0184340309,0.2288667262,0.2305987775,0.5177352428,0.2850895524,-0.3112405837,-0.283450067,-0.3231844306,-0.3884500265,0.209275797,-0.0874323472,-0.0877031907,0.0355130248,-0.2518314719,-0.202842623,0.0418954939,-0.069468081,-0.3781086206,-0.1201944426,-0.0730070472,-0.0856000185,0.1283757091,-0.2761492431,0.2117173225,-0.0886533707,0.1636075974,0.101612933,-0.5672925115,-0.0537961908,0.3513720036,0.0552260801,0.152962625,0.2054211497,0.1399331689,0.2763732672,0.0849820599,-0.2167821079,0.3756537437,0.2172034979,-0.207066685,-0.0734169632,0.4972681701,0.1715171486,-0.2257164568,0.0879854187,-0.145631671,-0.0804974213,0.0692875683,-0.2820042074,-0.170788154,-0.3726626635,-0.3188099563,0.252097398,0.1120118052,0.0482922681,0.1105851084,0.3219352365,-0.0813050419,0.4326556325,0.0139108989,-0.1857730746,-0.1517120451,-0.2132649273,-0.2128109038,-0.2773059905,-0.4498109221,0.1449789405,0.4436470568,0.0247756429,0.0010015079,0.2090330273,0.1274003536,-0.010391362,-0.2007555664,-0.3551882505,-0.2446888238,0.1974934787,-0.1794242114,0.3334898651,-0.1749725044,0.150991112,-0.1955755651,0.1619163901,-0.3553528786,-0.2452231944,0.3079734445,0.3025393784,-0.1045797467,-0.044610966,-0.4129046202,0.2642707825,-0.1100947186,0.0590913631,-0.1334376335,-0.0237634797,-0.1063722819,-0.0018752494,0.0808164775,0.2770811319,-0.3943965137,0.1528885067,-0.2211903185,0.1494279951,0.1306662112,0.3977420032,-0.3263542652,0.0876103118,-0.1106215045,0.0166786872,0.1702519059,-0.3009161949,-0.5397740602,0.399830848,-0.2098787725,0.1034381092,0.1209401488,-0.0182545036,0.3900322914,-0.2471283823,0.2662606239,0.5497125983,0.0612694584,0.1136698574,-0.3252187371,-0.1047572196,-0.033708971,0.5643473864,0.1143966317,-0.1104968041,0.1361210346,0.0966812074,0.1597174853,0.052768454,-0.0750206038,0.1345390379,0.1969802827,-0.0893592462,-0.0232280102,0.1839111,-0.4957300127,0.4001031518,-0.0450965427,-0.2439492494,0.3706954122,0.079882279,-0.1051796675,-0.1792069077,-0.1787629128,0.1509504467,0.1051254645,-0.1905723512,-0.0538962521,0.1902869493,0.1383858174,0.3167603016,-0.3021217287,-0.001923556,0.0116819637,0.1035133526,0.1402072906,-0.0813204572,0.1752838492,-0.0305479225,0.0584349036,-0.0661685467,-0.089132227,0.0895663351,0.1268130094,0.3413229883,-0.01244081,0.2423082441,0.1456023753,0.3362042606,0.1086204872,-0.0293664243,0.0543257706,-0.3189198077,-0.2518000305,0.4823022485,-0.0920576677,0.1507884413,0.116451405,-0.326992631,0.4123485386,-0.1484995186,0.255930841,0.2874782383,0.5536789298,0.0951023251,0.4025269747,0.2306759208,-0.2004444003,0.2244458497,0.2324167639,-0.0279092733,-0.3425456583,0.1649187654,-0.0515673012,-0.2804048061,0.0083010131,0.0587804243,0.552093327,0.1125910804,0.0406083763,-0.1931914985,0.4311437011,-0.305651933,0.276478231,-0.0137504013,0.081766732,0.4084943235,0.4574069381,-0.2098321319,-0.4086069167,-0.1135862768,0.3819160461,0.1469848007,-0.07280422,-0.1258427799,-0.203995809,-0.0578330941,-0.0548907034,-0.1194069013,-0.2367260009,-0.2680535913,0.0132276183,0.3044422567,-0.0600870922,0.0084868325,-0.2665054798,0.5148743987,0.0486832932,-0.0543113649,-0.4178192317,-0.0225873962,-0.215959236,0.1583644599,0.162519753,-0.2993919551,0.2113576233,0.1774643362,-0.0072441734,-0.0192260314,-0.3806327879,0.0831742585,0.2474463731,0.047911603,-0.234609589,0.0143209007,0.1991361976,0.0063428385,0.0695415363,-0.3881141245,-0.1075753048,0.0090646623,-0.0538933873,0.1783518642,0.0542122833,-0.2898902297,-0.1657841504,-0.4625604451,0.2143458873,-0.1899102479,0.00080242,0.2282609493,-0.1258067638,0.0544250906,0.0254087951,-0.1182290763,-0.2713723481,-0.5140330195,0.3919060528,-0.0605122671,-0.3088650703,-0.222576797,0.1292104721,-0.0127305621,0.27438429,-0.4091230333,0.1720881313,-0.2697909772,0.1982387453,0.0265607238,-0.0886877701,0.0365646072,-0.2912847102,-0.22062096,-0.0252424628,-0.0509685203,0.1468975991,0.2361966521,0.3168797791,-0.2697795033,0.3415651023,0.1393641233,0.4849933684,0.0297220852,-0.0464570634,0.3064985573,0.3087260723,0.1859564185,-0.2166093439,-0.1917747706,0.0240004919,-0.2419841737,-0.1816830188,0.3324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2250","title":"some issue in loading local txt file as Dataset for run_mlm.py","comments":"Hi,\r\n\r\n1. try\r\n    ```python\r\n    dataset = load_dataset(\"text\", data_files={\"train\": [\"a1.txt\", \"b1.txt\"], \"test\": [\"c1.txt\"]})\r\n    ```\r\n    instead.\r\n\r\n    Sadly, I can't reproduce the error on my machine. If the above code doesn't resolve the issue, try to update the library to the \r\n    newest version (`pip install datasets --upgrade`).\r\n\r\n2. https:\/\/github.com\/huggingface\/transformers\/blob\/3ed5e97ba04ce9b24b4a7161ea74572598a4c480\/examples\/pytorch\/language-modeling\/run_mlm.py#L258-L259\r\nThis is the original code. You'll have to modify the example source to work with multiple train files. To make it easier, let's say \"|\" will act as a delimiter between files:\r\n    ```python\r\n        if data_args.train_file is not None:\r\n            data_files[\"train\"] = data_args.train_file.split(\"|\")  # + .split(\"|\")\r\n    ```\r\n    Then call the script as follows (**dataset_name must be None**):\r\n    ```bash\r\n    python run_mlm.py [... other args] --train_file a1.txt|b1.txt\r\n    ```","body":"![image](https:\/\/user-images.githubusercontent.com\/14968123\/115773877-18cef300-a3c6-11eb-8e58-a9cbfd1001ec.png)\r\n\r\nfirst of all, I tried to load 3 .txt files as a dataset (sure that the directory and permission is OK.), I face with the below error.\r\n\r\n> FileNotFoundError: [Errno 2] No such file or directory: 'c'\r\n\r\nby removing one of the training .txt files It's fixed and although if I put all file as training it's ok\r\n![image](https:\/\/user-images.githubusercontent.com\/14968123\/115774207-867b1f00-a3c6-11eb-953b-905cfb112d25.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/14968123\/115774264-9b57b280-a3c6-11eb-9f36-7b109f0e5a31.png)\r\n\r\n\r\nafter this, my question is how could I use this defined Dataset for run_mlm.py for from scratch pretraining.\r\nby using  --train_file path_to_train_file just can use one .txt , .csv or, .json file. I tried to set my defined Dataset as --dataset_name but the below issue occurs.\r\n\r\n\r\n> Traceback (most recent call last):\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py\", line 336, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py\", line 621, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/dataset\/dataset.py\r\n\r\n> During handling of the above exception, another exception occurred:\r\n\r\n> Traceback (most recent call last):\r\n  File \"run_mlm.py\", line 486, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 242, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py\", line 719, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py\", line 347, in prepare_module\r\n    combined_path, github_file_path\r\nFileNotFoundError: Couldn't find file locally at dataset\/dataset.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.6.0\/datasets\/dataset\/dataset.py.\r\nThe file is also not present on the master branch on github.\r\n","comment_length":110,"text":"some issue in loading local txt file as Dataset for run_mlm.py \n ![image](https:\/\/user-images.githubusercontent.com\/14968123\/115773877-18cef300-a3c6-11eb-8e58-a9cbfd1001ec.png)\r\n\r\nfirst of all, I tried to load 3 .txt files as a dataset (sure that the directory and permission is OK.), I face with the below error.\r\n\r\n> FileNotFoundError: [Errno 2] No such file or directory: 'c'\r\n\r\nby removing one of the training .txt files It's fixed and although if I put all file as training it's ok\r\n![image](https:\/\/user-images.githubusercontent.com\/14968123\/115774207-867b1f00-a3c6-11eb-953b-905cfb112d25.png)\r\n![image](https:\/\/user-images.githubusercontent.com\/14968123\/115774264-9b57b280-a3c6-11eb-9f36-7b109f0e5a31.png)\r\n\r\n\r\nafter this, my question is how could I use this defined Dataset for run_mlm.py for from scratch pretraining.\r\nby using  --train_file path_to_train_file just can use one .txt , .csv or, .json file. I tried to set my defined Dataset as --dataset_name but the below issue occurs.\r\n\r\n\r\n> Traceback (most recent call last):\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py\", line 336, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py\", line 291, in cached_path\r\n    use_auth_token=download_config.use_auth_token,\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py\", line 621, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/dataset\/dataset.py\r\n\r\n> During handling of the above exception, another exception occurred:\r\n\r\n> Traceback (most recent call last):\r\n  File \"run_mlm.py\", line 486, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 242, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py\", line 719, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py\", line 347, in prepare_module\r\n    combined_path, github_file_path\r\nFileNotFoundError: Couldn't find file locally at dataset\/dataset.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.6.0\/datasets\/dataset\/dataset.py.\r\nThe file is also not present on the master branch on github.\r\n \n Hi,\r\n\r\n1. try\r\n    ```python\r\n    dataset = load_dataset(\"text\", data_files={\"train\": [\"a1.txt\", \"b1.txt\"], \"test\": [\"c1.txt\"]})\r\n    ```\r\n    instead.\r\n\r\n    Sadly, I can't reproduce the error on my machine. If the above code doesn't resolve the issue, try to update the library to the \r\n    newest version (`pip install datasets --upgrade`).\r\n\r\n2. https:\/\/github.com\/huggingface\/transformers\/blob\/3ed5e97ba04ce9b24b4a7161ea74572598a4c480\/examples\/pytorch\/language-modeling\/run_mlm.py#L258-L259\r\nThis is the original code. You'll have to modify the example source to work with multiple train files. To make it easier, let's say \"|\" will act as a delimiter between files:\r\n    ```python\r\n        if data_args.train_file is not None:\r\n            data_files[\"train\"] = data_args.train_file.split(\"|\")  # + .split(\"|\")\r\n    ```\r\n    Then call the script as follows (**dataset_name must be None**):\r\n    ```bash\r\n    python run_mlm.py [... other args] --train_file a1.txt|b1.txt\r\n    ```","embeddings":[-0.2187605202,-0.1667418778,0.0313900374,0.4202873111,0.4102846384,0.2086267918,0.3599502444,0.3321821988,0.0959176049,-0.0865045115,0.1432959884,0.1121851429,-0.313026011,0.1154056862,0.2711088955,-0.1855070144,-0.0543438829,0.1169156954,0.0061769388,-0.0527790189,-0.0572860017,0.2120701969,-0.0856259763,0.0367862619,-0.5357351303,-0.1124623269,-0.0895917416,0.5331773758,-0.1113560572,-0.4210807085,0.2054494172,-0.2190006226,0.5652353764,0.3786007762,-0.0001178408,0.1366185844,0.0195880178,-0.3046211302,-0.2785406709,-0.3674179018,-0.0473928452,-0.1517332792,0.3131980002,-0.2982816398,-0.2849579453,-0.2986627519,0.1183683649,-0.4172201753,0.5750330687,0.5418713689,0.1464993954,-0.1126833931,-0.1135130003,0.1661650389,0.1643294096,0.1989790797,0.0532440953,0.4563586712,0.235948056,-0.186613515,0.1223256141,0.0028428503,-0.0856467262,0.2835535109,0.4207870364,0.2291142344,0.2676959634,-0.3368067145,-0.0659586936,0.4293656945,0.5069271922,-0.4630822241,-0.1613177806,-0.2148654163,0.0965309665,-0.5334337354,0.1466606557,0.1705652922,-0.169303,0.1613130867,-0.0401136689,-0.1284488291,-0.2960370183,0.3532934189,0.1373363286,-0.004697883,-0.2373589724,0.1342300028,-0.0491270348,0.2122362554,0.1465490609,-0.1308979094,0.116060406,0.352633208,-0.2304416448,0.0151426448,-0.3056645989,-0.228691265,-0.104920052,-0.2218109965,0.0616082363,-0.1123377234,0.0158279967,0.1886262298,-0.0349827595,0.1783737838,-0.0147651555,0.2518513799,0.1695749611,-0.0863323212,-0.2277290672,-0.1104219034,-0.4364836216,-0.3626224995,-0.1716032326,0.186109066,-0.0098632099,-0.2603826821,-0.1868661791,-0.0135928411,-0.4629870653,-0.1196219325,0.050188873,0.4206337333,0.0471919477,-0.2701141834,0.1909436882,0.3114246428,-0.2982201278,-0.0205369294,-0.1354185492,0.2676131725,-0.1667536795,0.0067309653,0.2710702419,-0.0559694432,0.5489364862,-0.1468862593,0.0182242636,-0.1580575705,0.2986269891,-0.3071927726,0.1116289347,0.3208245039,-0.136329487,-0.114771083,0.2910399139,-0.0443102866,-0.1029344946,0.1777272969,-0.3631927967,-0.3632000387,0.0566768944,0.0550087988,-0.0763354525,0.0673879683,-0.0280140806,0.1393100917,0.3043964207,0.0021818108,-0.1248049214,-0.4227061868,-0.2112958878,-0.1633839011,0.3459704518,0.5008915663,-0.327234298,0.1389868855,-0.0614091307,0.0824354514,0.1293088347,0.1791763306,-0.4740315676,0.5258911252,-0.3562895656,0.2602771819,0.3359387815,-0.3661050797,0.2029674649,0.4296671152,-0.0621659532,-0.0405628383,0.1338306367,-0.0786804929,-0.1721276343,-0.0613215342,0.1770077497,0.224434793,-0.100840956,0.0733385086,-0.1971501857,-0.2577080429,-0.0527974516,0.1942562163,-0.0366877504,0.076797992,0.1875154078,-0.0004553866,0.1252678931,-0.267814666,0.120769605,0.3422435224,0.0662675872,0.1107200086,0.0467288233,0.1225750372,-0.3514122665,0.1639356613,-0.0799138099,-0.0736305416,-0.1430327296,-0.0644586608,-0.1137371659,-0.2062303871,-0.3944917619,-0.2405236363,0.0265963133,0.021793874,0.0545740873,0.0177385323,-0.3444501162,0.1442458928,-0.487413466,-0.0421785899,-0.0992305279,0.2021943778,0.0164213609,0.0035270979,0.0593575947,0.0558211654,0.0060588713,-0.32654953,-0.1184618026,0.3234590888,0.0528179258,0.0771333128,-0.1485342085,0.2100295573,0.1040451527,-0.0613660179,0.1945244819,0.0633509085,0.3194554448,-0.1345395893,-0.199500218,0.3969226778,0.0695660785,0.0165768638,0.1155702099,-0.2274149358,0.1046558321,-0.0991497263,-0.0072395988,-0.1597729623,0.2439163327,0.0610595495,0.1981470138,-0.1272440404,-0.3091569841,-0.2142795324,0.3216104507,0.254540652,0.0373857655,0.0955028683,-0.1733588725,0.0505982302,0.0459097587,0.2077760249,0.4979125857,0.0192748941,-0.2003566772,0.3725386858,0.0216428265,-0.2439142764,0.1191153452,-0.0843787342,0.3388143182,0.4956532717,-0.2445803583,-0.0676739663,-0.214152649,-0.0274478383,0.303414017,0.4202139974,-0.5494006276,0.2633036077,-0.3707825541,-0.1403633952,-0.4049875438,0.1483595371,-0.033233989,0.133253485,-0.3153639436,0.1717948765,0.248615548,0.0067794635,-0.1144893393,-0.0167532135,0.0440500714,-0.3137128651,0.09483096,-0.1302260309,-0.2783370316,-0.0179274641,0.5486425757,0.1192936152,0.0776710883,-0.1489702761,-0.3432919681,0.0173715539,-0.0160412099,0.1359623522,0.038618587,0.3947208822,-0.000171923,0.3190338016,-0.2006282955,-0.0312068965,0.1276067644,0.1451542974,-0.1676795632,-0.1626083255,0.004552512,-0.2500949204,-0.1370775998,-0.6858372688,-0.5413176417,-0.1693502814,0.0753475353,0.2003324926,0.194726333,0.1813874841,0.1762078702,0.2450316846,0.0561480038,0.1711566001,-0.0381061994,-0.4465243518,0.226453945,-0.2394654751,-0.2247730196,0.3580351472,-0.0442435667,0.021543989,0.1005166247,-0.5381856561,-0.1136736795,0.2504937351,-0.0290173553,-0.0123384399,-0.0333243571,0.2848323584,-0.0811643004,0.1176243722,-0.0744209588,-0.0750229433,0.3046918213,0.1528561562,0.2563612759,0.3320654631,0.531327486,-0.0368128642,0.7044878006,0.236127004,-0.2257723808,0.3754787147,-0.30376333,0.1068046167,-0.1427258849,-0.1135770157,0.2218452841,0.0835338235,-0.261366337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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2243","title":"Map is slow and processes batches one after another","comments":"Hi @villmow, thanks for reporting.\r\n\r\nCould you please try with the Datasets version 1.6? We released it yesterday and it fixes some issues about the processing speed. You can see the fix implemented by @lhoestq here: #2122.\r\n\r\nOnce you update Datasets, please confirm if the problem persists.","body":"## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot!","comment_length":47,"text":"Map is slow and processes batches one after another \n ## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot! \n Hi @villmow, thanks for reporting.\r\n\r\nCould you please try with the Datasets version 1.6? We released it yesterday and it fixes some issues about the processing speed. You can see the fix implemented by @lhoestq here: #2122.\r\n\r\nOnce you update Datasets, please confirm if the problem persists.","embeddings":[-0.3536394835,-0.4107639492,-0.0976410061,0.2570492923,-0.0076191481,0.1119764224,0.1884649545,0.3910495639,0.4355854094,-0.0132389804,0.2567101121,0.3540889323,0.0090388684,-0.1517525315,-0.1746495366,0.0698189065,0.1166413799,-0.1431869268,-0.0087070642,-0.1623785645,-0.3209817111,-0.087763235,-0.2003731877,0.0438098162,-0.1860580742,-0.2596023977,0.0139482338,0.0891774371,-0.0040700217,-0.3334619105,-0.0190602932,-0.0149467504,-0.1927878559,0.9497959614,-0.0001168566,-0.0805333108,0.2479249239,0.2797504961,0.1089458242,0.0850166529,-0.2313876301,-0.3576023281,0.0878629759,-0.2359792441,0.2497991621,-0.023537647,-0.002994285,-0.5800064802,0.2692072093,-0.0939939618,0.140329212,0.0948103815,-0.386369735,-0.0908237621,0.0266346149,0.005713955,0.0010937327,0.1683355421,0.5363388658,-0.2120462209,-0.1499995291,0.1861923039,-0.4690407515,0.300696224,0.0931109935,0.0692812651,0.5572910309,-0.561075449,0.2869141996,0.1746804267,0.0806175396,0.0566017814,-0.044365216,-0.401881963,-0.4251192212,0.0211951807,0.063075155,0.1158909574,-0.1820195317,-0.2456142008,-0.4943926632,0.016884638,0.2267080694,0.0516579226,0.0052897572,0.0966013446,0.1384706944,0.2925825417,0.3433318436,0.1091784909,0.0677273944,-0.305072695,0.0327481292,0.3052342236,-0.452498883,0.0874829516,0.4808760583,0.0541621819,0.0520278104,-0.3966451585,-0.142978698,0.027827343,0.0229985379,0.1050668061,0.3621997237,-0.006308923,-0.0987884104,0.2177391052,0.4536742568,-0.2545010448,-0.3706454635,0.2139307857,0.0409928337,-0.5764334202,0.1754998714,0.0035549165,0.0092065679,-0.0291023236,-0.0612536371,-0.0754595697,-0.1532066166,-0.1927638799,0.1660397798,0.3424512148,-0.1398621947,0.2019235492,-0.1730198562,-0.1404955387,-0.2200604081,0.2372357398,-0.1123882532,-0.3543464839,-0.3067362607,0.1507669836,0.1014041901,-0.2737501264,0.0847366825,0.3837851286,-0.3222764432,-0.1553747356,0.1739759445,-0.3959164023,0.0064279926,0.02302121,0.0425842926,0.6243890524,0.0900540799,0.2955989838,-0.2339507639,0.3710044324,-0.597797811,-0.1593910456,0.2099527568,0.0487274565,-0.0191689916,0.2650373578,-0.2873978615,0.0804849267,0.2995231748,-0.175204441,-0.2621099949,-0.1640048623,-0.3243804276,-0.309782505,-0.1098135337,0.3768040538,-0.0898602307,0.0984198675,-0.250891,0.1711792499,0.472150296,0.5991899967,-0.2346330732,0.4373857975,-0.2421700507,-0.0288157668,0.018366918,0.0336230397,-0.2913622558,0.7230955958,-0.3079105616,0.0267542675,-0.0977236032,0.2283003926,0.3933739364,-0.2332687676,0.269977957,0.3129475713,-0.0474728532,0.4035684764,-0.3195688128,-0.1058711931,0.1694390625,-0.0341540128,0.0220105406,0.0716377869,0.1143119931,-0.3669935763,0.3592448533,-0.0605864935,0.0328281857,0.2090796232,0.0447492786,-0.0553572476,-0.0184040796,0.0925919116,-0.0947196558,0.2243772,-0.0192848165,-0.3248773515,0.0324211195,0.0841892362,-0.0510729142,0.0634693652,-0.148111701,0.0944968536,-0.0638687611,0.1023333073,0.0228009783,-0.0569532141,0.2669195533,0.1030330062,0.1032134891,-0.2411195487,-0.0628770217,0.0730087459,0.0916984603,-0.0767954811,-0.0768885463,-0.2617317736,0.3339428306,-0.0912025124,-0.1781997085,0.1648807675,0.0898478627,0.3029913902,-0.0419128984,0.1013589427,0.1004371494,0.262160331,0.1643080562,0.0480548963,0.2535101473,-0.3618002236,-0.0108606694,0.1457659006,-0.0321851522,0.4392972291,-0.1151999235,-0.0318247154,-0.0059306752,-0.0788996592,0.2529892325,0.1770585775,0.2835728228,-0.0793612152,0.4447599351,0.2781111002,-0.0302263368,0.0132176057,0.4852698743,-0.0271275472,-0.1436029375,0.1334326118,-0.1315696836,-0.0328650586,-0.0420117863,0.1701156944,0.4768700898,0.0805022568,0.0264230464,-0.182655856,0.1206792891,-0.0661399812,0.1008415818,0.1482352167,0.4915498495,-0.045618128,0.3280079663,-0.0262983516,0.0204266366,-0.1683085412,0.2249910086,0.2838003635,-0.1066473126,0.1500249803,-0.0864218175,0.1899553239,-0.3173114359,-0.0530077182,0.0569251329,-0.2011275887,-0.1721757054,0.2013168633,0.0083301719,0.1018987522,0.1917135566,-0.2170332074,-0.0424208865,-0.105635047,-0.0220116004,-0.3020041585,-0.0366279744,-0.0245760642,0.3743671179,-0.0097525688,0.2599737346,0.1229065731,-0.4351427257,-0.0318548083,0.0146431364,-0.134053126,-0.1755057871,0.0385618508,-0.2328603119,0.2902824283,-0.071351856,-0.1962250173,0.2703505158,-0.6561154127,0.0064780097,-0.0568646416,-0.1580043286,-0.3678793013,-0.0483247302,-0.1501354724,-0.1791179329,-0.1662816256,0.3194873929,-0.1182533354,0.3504863977,-0.0193890464,0.1067479774,0.0565150231,0.1134434715,-0.1843942404,-0.1836296767,-0.2651648223,0.1830536276,0.0533219241,-0.0865589529,-0.2310346961,-0.0336551405,-0.103563711,0.2471540272,-0.391320765,0.1752493232,-0.5987238884,0.2207243592,0.0769206658,0.2120436877,0.3499593139,-0.1178209186,-0.0693313479,-0.1490348577,-0.2949303687,-0.0052095717,0.2313658893,-0.0322886072,0.1110155582,0.504396975,-0.0653442889,0.6194151044,0.4053158462,-0.060055986,0.0572079122,0.0808383003,-0.1393208802,-0.2665077448,-0.3724047542,0.0360680856,-0.3106940389,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2243","title":"Map is slow and processes batches one after another","comments":"Hi @albertvillanova, thanks for the reply. I just tried the new version and the problem still persists. \r\n\r\nDo I need to rebuild the saved dataset (which I load from disk) with the 1.6.0 version of datasets? My script loads this dataset and creates new datasets from it. I tried it without rebuilding.\r\n\r\nSee this short video of what happens. It does not create all processes at the same time:\r\n\r\nhttps:\/\/user-images.githubusercontent.com\/2743060\/115720139-0da3a500-a37d-11eb-833a-9bbacc70868d.mp4\r\n\r\n","body":"## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot!","comment_length":70,"text":"Map is slow and processes batches one after another \n ## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot! \n Hi @albertvillanova, thanks for the reply. I just tried the new version and the problem still persists. \r\n\r\nDo I need to rebuild the saved dataset (which I load from disk) with the 1.6.0 version of datasets? My script loads this dataset and creates new datasets from it. I tried it without rebuilding.\r\n\r\nSee this short video of what happens. It does not create all processes at the same time:\r\n\r\nhttps:\/\/user-images.githubusercontent.com\/2743060\/115720139-0da3a500-a37d-11eb-833a-9bbacc70868d.mp4\r\n\r\n","embeddings":[-0.3630149364,-0.3355692923,-0.0869320706,0.2914981842,0.0048044329,0.0850246921,0.207652986,0.3980056643,0.4348949194,-0.0089639695,0.2125943303,0.2832153738,-0.0250464026,-0.2042584866,-0.197465688,0.1578701138,0.1223637983,-0.0891835243,-0.0129013015,-0.1942545027,-0.3704572618,-0.0611239597,-0.1589694023,-0.0333990157,-0.203557536,-0.2246247679,0.0031420246,0.16731745,-0.0278627519,-0.3802036345,-0.0222847667,0.0346784741,-0.217319414,0.9670494795,-0.0001179558,-0.0346966349,0.2712252736,0.2329895794,0.0776725635,0.0469267331,-0.1714724004,-0.3223571479,0.1062251329,-0.192093581,0.2716529667,-0.0017312861,0.0206621755,-0.6094627976,0.2712439895,-0.0779175013,0.1323134154,0.1193444803,-0.4176820219,-0.1101857424,0.0098892516,0.0443693511,-0.0237345956,0.2211578041,0.469622016,-0.1314185411,-0.161554426,0.1670174003,-0.4216374755,0.2660012245,0.0706350505,0.0413721614,0.4938414395,-0.5665130019,0.3148688078,0.1981596947,0.0256941523,0.0241917334,-0.0996279344,-0.3593217731,-0.3677815795,0.0037667628,0.0352485627,0.1015741229,-0.1780721992,-0.2604908943,-0.510027349,-0.0142801199,0.2391904593,0.0101411361,0.0763001591,0.0058418438,0.1450753808,0.2875227332,0.352347672,0.1838088632,0.1186252385,-0.304667592,0.0057900851,0.2401407808,-0.4155762494,0.1519878656,0.4824928343,0.0279344805,0.0871175379,-0.4267273247,-0.089911215,0.0575835854,0.1102226451,0.0385423489,0.3332891166,-0.0898994356,-0.0239036288,0.1651181281,0.4020159245,-0.2371514887,-0.3967393339,0.1732116193,0.0284360144,-0.5089076757,0.1731099337,0.0160155334,0.0584516115,-0.0073794699,-0.0503870435,-0.0698949844,-0.1287699044,-0.2418865561,0.1157804355,0.3481166065,-0.0990552083,0.284419179,-0.1221458018,-0.1511015296,-0.3075758219,0.2642687559,-0.1004589126,-0.3427415788,-0.3130770326,0.1872937977,0.1027255654,-0.3253945112,0.1008893326,0.3663766086,-0.3238576651,-0.140540868,0.1640705168,-0.3921980262,0.037114948,0.0362593681,0.1015313864,0.5760555267,0.0617110655,0.3559787571,-0.2052507102,0.3554876149,-0.5027271509,-0.1576422155,0.1760053337,0.0561755635,-0.0610033758,0.2625415623,-0.230644688,0.077102907,0.3370042145,-0.2698493302,-0.3164979219,-0.1621470898,-0.2763829827,-0.3266085684,-0.1250043511,0.4855922461,-0.0969214812,0.0926420689,-0.2499944568,0.1078091785,0.4460745156,0.5792755485,-0.2000685632,0.3872777224,-0.223335743,-0.0858245641,0.1105750427,0.109143056,-0.3711589277,0.62790519,-0.2107079178,0.0252087936,-0.0302220359,0.2141833156,0.4351965487,-0.2300636619,0.189813152,0.2136393934,-0.0715662986,0.3919412196,-0.3567687869,-0.1234346107,0.1199619621,-0.068618618,0.0712119192,0.1128558591,0.1466659009,-0.3748902678,0.3419593573,-0.0813911706,0.0750169531,0.2327743322,0.0045928643,-0.0640248284,0.0254024249,0.0408823788,-0.1903897971,0.1950108856,-0.0402653478,-0.2926433086,0.0948730782,0.0526052527,-0.0963645726,0.0147375111,-0.1551770866,0.0935113654,-0.0817369744,0.2289909571,0.0444578193,-0.0817886963,0.1954099834,0.0643505752,0.1163239852,-0.2580953538,-0.1073692366,0.1122938022,0.0365022756,-0.1578089893,-0.0445042215,-0.2471504062,0.2896203101,-0.089943707,-0.1257686168,0.1543934196,0.0985815451,0.3515720069,-0.0858734176,0.0658476278,0.0880118981,0.3965590596,0.1759848893,0.0508555397,0.2638603151,-0.3270398974,0.0296379291,0.1832247823,0.0414749235,0.4627265632,-0.0223166067,-0.0116066085,-0.0076325648,-0.0596297979,0.2356954217,0.1006035656,0.2856350243,0.0174683537,0.465131402,0.2683877945,-0.0083425092,0.0978523716,0.5448834896,-0.0384720117,-0.145064041,0.1689181477,-0.0935998186,-0.0727545768,-0.1138985455,0.1575489193,0.4817344844,0.0790671259,0.0289869066,-0.1687620282,0.1559609026,-0.058006186,0.0718887746,0.1646239311,0.6082304716,0.0435824767,0.32114169,-0.0311715584,-0.0458855256,-0.1859902292,0.2890871763,0.2805094421,-0.129435271,0.1533901244,-0.0461231247,0.2741508484,-0.2902233005,-0.0880478173,0.0753923729,-0.2380780727,-0.1974068284,0.2284224778,-0.0029088338,0.1274891794,0.182867825,-0.1537574083,-0.0923857987,-0.0000270022,-0.0489275306,-0.3085848093,-0.0368410014,-0.0362476669,0.3555816114,-0.0531523712,0.2529759705,0.1141378954,-0.4489083588,-0.0389914289,-0.0068281908,-0.0905419141,-0.1739006042,-0.0252901316,-0.1881171018,0.2716783583,-0.029089747,-0.2099588066,0.265679419,-0.6992890835,-0.0694216564,-0.0357697308,-0.1853723973,-0.3714575171,-0.0621029101,-0.2339767069,-0.1610837579,-0.2183563262,0.2947669029,-0.0674247816,0.3748463988,-0.1149966195,0.09592437,0.0573428273,0.1013476327,-0.1709908843,-0.1748352498,-0.2321131527,0.1582455933,-0.0439944305,-0.1131773889,-0.247648403,-0.1134029776,-0.0751528069,0.3227633238,-0.3404302895,0.1396199167,-0.5815432072,0.172369644,0.0529167652,0.2146683037,0.4270578325,-0.0608473867,-0.0778912902,-0.1548994482,-0.3055674434,-0.0078540025,0.2052788138,-0.0036146445,0.0674420595,0.542740345,-0.0926426798,0.6695802212,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2243","title":"Map is slow and processes batches one after another","comments":"There can be a bit of delay between the creations of the processes but this delay should be the same for both your `map` calls. We should look into this.\r\nAlso if you hav some code that reproduces this issue on google colab that'd be really useful !\r\n\r\nRegarding the speed differences:\r\nThis looks like a similar issue as https:\/\/github.com\/huggingface\/datasets\/issues\/1992 who is experiencing the same speed differences between processes.\r\nThis is a known bug that we are investigating. As of now I've never managed to reproduce it on my machine so it's pretty hard for me to find where this issue comes from.\r\n","body":"## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot!","comment_length":103,"text":"Map is slow and processes batches one after another \n ## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot! \n There can be a bit of delay between the creations of the processes but this delay should be the same for both your `map` calls. We should look into this.\r\nAlso if you hav some code that reproduces this issue on google colab that'd be really useful !\r\n\r\nRegarding the speed differences:\r\nThis looks like a similar issue as https:\/\/github.com\/huggingface\/datasets\/issues\/1992 who is experiencing the same speed differences between processes.\r\nThis is a known bug that we are investigating. As of now I've never managed to reproduce it on my machine so it's pretty hard for me to find where this issue comes from.\r\n","embeddings":[-0.3194858134,-0.4774059355,-0.0747577175,0.3302206993,-0.0672015771,0.0412484743,0.2097329944,0.3263496161,0.4367236495,0.0293657854,0.241692856,0.3490332365,0.0190935712,-0.0388512351,-0.1365028918,0.0055769235,0.1733696014,-0.1191435233,0.0165218115,-0.1376280785,-0.2373458147,0.0043261829,-0.1967942417,-0.0159254391,-0.1699439734,-0.2258180082,-0.0241215285,0.0833455771,0.0180470161,-0.3501836061,0.0165381711,0.0091593005,-0.1990933716,0.9538552165,-0.0001199121,-0.0984314978,0.2415305674,0.3196500838,0.0868761763,0.0823288038,-0.2088344395,-0.3429727256,0.1231218204,-0.2194910794,0.181776464,0.0489583462,-0.0054669217,-0.478476882,0.2912492752,-0.1400743425,0.1226795465,0.1224082932,-0.4304054081,-0.1170066521,0.0643731058,0.0136844898,-0.0449960642,0.2221290916,0.5488747358,-0.1446338445,-0.1135171801,0.1787206531,-0.4208791554,0.3248113394,0.1550359428,0.1488950402,0.4124362469,-0.5443951488,0.2542956769,0.2439431697,0.0642348453,0.0644448474,-0.0699718073,-0.4524817467,-0.4557455182,-0.050525751,0.1526226699,0.1117550284,-0.2006653398,-0.2737897933,-0.5146392584,0.0925396383,0.2486539781,0.0496206582,-0.0354445167,0.0554922223,0.0845619068,0.298102051,0.3523456752,0.1011010781,0.0175358225,-0.359507978,0.0585528389,0.3016782999,-0.4295680523,0.0651175007,0.4697278142,0.0874668062,0.0563356057,-0.3640021086,-0.1127560064,-0.025833508,-0.0637136623,0.1232847795,0.3699847162,0.0635845363,-0.0990267172,0.1580916196,0.449812144,-0.2064523548,-0.3955384791,0.2026739419,0.099490352,-0.5509958863,0.1231858656,0.0072417259,-0.0061324919,-0.1270748079,-0.0732416138,-0.0996087193,-0.1479617655,-0.2157738209,0.1760329157,0.3118009865,-0.1720112413,0.1390254498,-0.1780219823,-0.1064369604,-0.2601937652,0.2096254528,-0.109958075,-0.3654551804,-0.316978395,0.2297674567,0.091924414,-0.2789958715,0.0632554516,0.4462325573,-0.2974608541,-0.1287810951,0.192067802,-0.4455226064,0.019282937,0.0177791025,0.0631104931,0.5827058554,0.0641357452,0.2910842896,-0.2870440185,0.352948457,-0.5255614519,-0.1047502756,0.247007072,0.0056428602,-0.0970230699,0.2554524839,-0.3724861443,0.1563412249,0.2468484342,-0.2550650239,-0.2419194132,-0.1594786346,-0.3996351361,-0.3362621963,-0.1038314849,0.4296418726,-0.1361075491,0.0145078488,-0.2160125673,0.1814296246,0.449703455,0.6606137156,-0.2419025302,0.405659467,-0.2201427519,-0.0463531241,-0.0111754192,0.0401429497,-0.2736548781,0.7009415627,-0.2806129754,0.0992984623,-0.0818606764,0.2187545002,0.3799466491,-0.245737955,0.2991667688,0.3218560517,-0.0316918343,0.4197546542,-0.3054518104,-0.1577080786,0.1585372537,-0.0863017142,-0.0191195291,0.1247162446,0.1028204411,-0.4522362649,0.3015806377,-0.0758424997,0.0183351822,0.218925029,0.1022760868,-0.0690943077,-0.0285861827,0.0735594779,-0.1101508141,0.2620704472,-0.0402679481,-0.2302188426,0.0483768396,0.0963158086,-0.0515501797,0.0079332693,-0.1040668786,0.093493022,-0.0941341966,0.1083738655,0.0511480682,-0.0304974932,0.2722037733,0.167321071,0.1698075831,-0.240647912,-0.0940886959,0.1516323239,0.1160434783,-0.1340617687,-0.0750783384,-0.2594904304,0.3234907091,-0.1187682226,-0.1830591112,0.1087518632,0.0736615136,0.3307231963,0.0041519548,0.1491952538,0.1749664396,0.283064276,0.2505117059,-0.0231627729,0.2347990423,-0.3815715909,-0.0548163727,0.2052121609,-0.0218360126,0.4643540382,-0.08364705,-0.0821953043,-0.0320651978,-0.0591447763,0.2360588312,0.1716521084,0.2913886011,-0.0511242673,0.4382725656,0.2802740932,-0.0629544333,0.0940982848,0.519520998,0.0014685434,-0.1753924787,0.1385180652,-0.1514688581,-0.0686138868,-0.0401510224,0.1637530774,0.5669997334,0.0724177659,0.0455683433,-0.1423933357,0.1140161902,-0.0465193503,0.0907405913,0.1230156347,0.4285607636,-0.07515122,0.3441388607,0.0090245297,0.0327259861,-0.1939031333,0.2061123252,0.2143487781,-0.0936268643,0.1492774636,-0.0833012834,0.1406968385,-0.3772099614,-0.0402711816,-0.0436655693,-0.1907573044,-0.217360422,0.2685177326,0.0243420787,0.053916961,0.2391970307,-0.1273527294,-0.1300688684,-0.100606665,-0.0242543276,-0.3237657547,-0.0682418197,-0.0410738252,0.3971996307,0.023496164,0.2353716493,0.0678013116,-0.4454363585,0.0030340324,-0.0445876271,-0.0715801045,-0.1857948899,0.1254251003,-0.2303630412,0.3192312121,-0.1090103835,-0.1898678392,0.3258904219,-0.6125127673,-0.0445981584,-0.1552238762,-0.1521812528,-0.3659999967,-0.0362102091,-0.1075744405,-0.1633157134,-0.1247296035,0.3783959448,-0.1027492285,0.3412996531,0.0129914852,0.0697473735,0.0358464234,0.1879738271,-0.0981814191,-0.2270577103,-0.2855250835,0.0979163498,-0.0202624034,-0.1072789356,-0.2148885131,-0.0279016811,-0.1364365667,0.249072507,-0.4367383122,0.0953364149,-0.6324816346,0.2248194814,0.112212576,0.1835850924,0.3638401926,-0.1243840903,-0.043315161,-0.1775204986,-0.4602483511,-0.0299754571,0.289239794,0.019463215,0.1156247705,0.4566768408,-0.1499717683,0.694293201,0.3712833524,-0.0745497346,0.0624874718,0.0717327595,-0.126137495,-0.3553828299,-0.3348673284,0.0974921957,-0.2912041247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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2243","title":"Map is slow and processes batches one after another","comments":"Upgrade to 1.6.1 solved my problem somehow. I did not change any of my code, but now it starts all processes around the same time.","body":"## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot!","comment_length":25,"text":"Map is slow and processes batches one after another \n ## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot! \n Upgrade to 1.6.1 solved my problem somehow. I did not change any of my code, but now it starts all processes around the same time.","embeddings":[-0.328520298,-0.4251143336,-0.0902183875,0.3159681857,0.017065281,0.090156652,0.2094428092,0.4447200894,0.4825794697,0.0649622902,0.2510423362,0.31609267,-0.0560902543,-0.3143905997,-0.2324120849,0.1299518049,0.1601340771,-0.1192271411,-0.0551301017,-0.2028851807,-0.3048771024,-0.0078666881,-0.1871987879,0.0967328474,-0.1404462755,-0.1534426957,-0.0539782532,0.1025865152,0.0483303927,-0.3943070173,0.0211049281,0.0591858663,-0.1813183576,0.8799492717,-0.000122085,0.0114948386,0.2477268279,0.2849186957,0.1633939743,-0.0141195599,-0.1903142631,-0.2765859067,0.2130437046,-0.1274119765,0.2456354648,0.0258935522,0.0164517108,-0.640250206,0.1821404994,-0.032140784,0.1067631543,0.0738543719,-0.3857874274,-0.0927315801,0.0135707865,-0.0621790886,0.018127799,0.236090228,0.508998394,-0.1530715078,-0.167480737,0.1986735314,-0.3881816566,0.3281824887,0.052534923,0.0307636634,0.6446434855,-0.5962138772,0.2733977437,0.1532078832,-0.0808097795,0.0810292587,-0.0253483243,-0.3553411067,-0.3839282691,0.0868265256,0.0888893381,0.1113566384,-0.1912720352,-0.1945640147,-0.4781170189,0.070794791,0.2309859097,0.0300617702,0.1173126921,0.1442994177,0.2024504691,0.3093335927,0.4033643007,0.2462476939,0.2411026657,-0.2332062125,0.0064064409,0.2732770443,-0.4459080398,0.1241496801,0.5200048685,0.0867764279,0.0475485101,-0.5806805491,-0.0912430659,0.0393422171,0.1242073253,0.0633936077,0.3477093577,-0.0904643461,-0.0357493237,0.2498846352,0.3441907465,-0.2958385944,-0.4390595257,0.2486191094,-0.0160911288,-0.5093580484,0.1471351683,0.0583608672,0.0214539897,-0.0482897162,0.0136080198,-0.1149354875,-0.1971804351,-0.2436129451,0.1031602472,0.3249608278,-0.1104527339,0.239444837,-0.1344807595,-0.1778879017,-0.2257106304,0.289318949,-0.1174995601,-0.2958429456,-0.3196435571,0.138715297,0.0025330682,-0.373770535,0.035556972,0.4177811444,-0.3017961085,-0.1876014322,0.1808341444,-0.3083860576,0.0056096427,0.0124672046,0.0029921175,0.5556014776,0.1374043971,0.5318370461,-0.2708052695,0.3144215345,-0.5807901621,-0.1458477825,0.1188044995,0.0506162941,-0.0251623224,0.2462059408,-0.1311655939,0.0963201597,0.2827989459,-0.329559505,-0.2998921871,-0.1696284413,-0.287966311,-0.2514474988,-0.1645826995,0.3703288734,0.0015978087,0.0699285194,-0.2103019953,0.0787313804,0.4496004581,0.4156296253,-0.207585305,0.3538426459,-0.2240495533,-0.062329974,0.132104829,0.0309717897,-0.4005418718,0.667723,-0.2599672675,-0.0095557217,-0.063424781,0.2496449798,0.4455076754,-0.1556868404,0.282045275,0.26270926,-0.0572414771,0.4519650042,-0.4122684896,-0.1481003761,0.0155626237,-0.0891285837,0.0221562926,0.1694601625,0.1445702612,-0.4743440747,0.3899276555,-0.0761157721,0.035759937,0.1839105785,-0.0694310591,-0.0629471987,0.0335050672,0.0339341462,-0.1193147749,0.2032585144,-0.04320715,-0.2309648842,0.1026155353,0.0101115936,-0.0872514844,-0.0492902808,-0.1813875735,0.0792013332,-0.0921608955,0.2140888721,-0.0339442566,-0.0652823299,0.1847362667,0.0358002447,0.1898382902,-0.2237684876,0.0226210859,0.0642823428,0.0673079491,-0.1615779996,-0.0262198243,-0.2895718813,0.3231557608,-0.1598036289,-0.2133892477,0.1712712497,0.0485898443,0.4096708298,-0.0624726862,-0.0313412324,0.0597195365,0.3930762112,0.1482026279,0.0926675573,0.1199301705,-0.310262084,0.0450549014,0.2158518285,0.0695550591,0.461722523,-0.0773711205,0.0262373984,0.0060173627,-0.0828284845,0.2806044817,0.0949622914,0.3291273713,0.0061603384,0.4355294704,0.3036996126,0.0188760124,0.0724018663,0.5454025269,-0.0023864422,-0.2093969584,0.1821486056,-0.0453135297,-0.0745837241,-0.0186945293,0.145009473,0.4704556167,0.0592017323,0.0415220857,-0.1976518631,0.1342905611,-0.0113459537,0.0264968798,0.1439185143,0.5454495549,-0.0772547647,0.3817724288,0.0127196899,-0.0481063426,-0.2288660705,0.1870306283,0.2764610648,-0.0724953115,0.2033232301,-0.0034867437,0.3172149062,-0.2773835361,-0.2160998434,0.0942140892,-0.1857625246,-0.2104178518,0.1738044173,0.0202491563,0.1362210959,0.1513073444,-0.0940554738,-0.0637962818,0.0024563335,0.0040113325,-0.3196435273,-0.0449066982,-0.0813286752,0.2629683018,-0.049146641,0.2252518833,0.150646612,-0.4977270663,0.0932104513,-0.0467607193,-0.0155137442,-0.1538104713,-0.1058646813,-0.2013936043,0.1916435808,-0.0757521987,-0.2173840702,0.2132003754,-0.630905211,-0.0781372637,-0.0248469114,-0.1241870075,-0.271828115,-0.1186771095,-0.1993278116,-0.2023314089,-0.1979486197,0.2467657775,-0.1272894144,0.3811453581,-0.2140100747,0.1172325164,0.0577185117,0.0452684984,-0.1253619492,-0.1413497925,-0.3023563921,0.1250578463,0.0086253742,-0.0639722943,-0.3012583852,-0.1058022976,-0.0266058855,0.3626348376,-0.3844183087,0.1069599986,-0.5251045227,0.1232716441,0.0808826983,0.1221255064,0.4194758534,0.0025602442,-0.0889196843,-0.1034303755,-0.225945279,0.0017521264,0.1917863339,-0.0445029251,0.0387105793,0.5795582533,-0.1471131295,0.6572436094,0.3843574822,-0.0609936826,-0.0111356899,-0.0183652584,-0.1962141842,-0.2524475157,-0.3930990398,0.0927753448,-0.2513141334,-0.3111329079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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2243","title":"Map is slow and processes batches one after another","comments":"Nice ! I'm glad this works now.\r\nClosing for now, but feel free to re-open if you experience this issue again.","body":"## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot!","comment_length":21,"text":"Map is slow and processes batches one after another \n ## Describe the bug\r\n\r\nI have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry. \r\n\r\nI process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.\r\n\r\npseudo code:\r\n```python\r\nds = datasets.load_from_disk(\"path\")\r\nnew_dataset = ds.map(work, batched=True, ...)  # fast uses all processes\r\nfinal_dataset = new_dataset.map(work2, batched=True, ...)  # slow starts one process after another\r\n```\r\n\r\n## Expected results\r\nSecond stage should be as fast as the first stage.\r\n\r\n## Versions\r\nPaste the output of the following code:\r\n- Datasets: 1.5.0\r\n- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)\r\n- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10    \r\n\r\nDo you guys have any idea? Thanks a lot! \n Nice ! I'm glad this works now.\r\nClosing for now, but feel free to re-open if you experience this issue again.","embeddings":[-0.3070743978,-0.4695039988,-0.092293784,0.2605443895,0.0237791054,0.0816692859,0.2060968131,0.3872794509,0.4719318151,0.020735044,0.2926188111,0.3568664789,0.0152331358,-0.172343865,-0.1459616125,0.0627288818,0.1288415045,-0.169583872,0.0138117224,-0.1798467487,-0.2991749048,-0.0734263286,-0.1795146763,0.0421460606,-0.1804162115,-0.2228629589,-0.0007136218,0.1077165678,0.0487654805,-0.2912753522,-0.0189660471,-0.0574136265,-0.217557162,0.954675138,-0.0001176089,-0.0876431689,0.2521365583,0.2681464255,0.147297591,0.0785760432,-0.2093132585,-0.3373840749,0.1125773117,-0.2512634993,0.2299133539,0.0075566242,0.0000873652,-0.5254435539,0.278719008,-0.1038165018,0.1410560757,0.0903238654,-0.3887793422,-0.0974326655,0.0356710441,-0.0157806017,-0.0041974084,0.1936461031,0.5403661728,-0.2044119835,-0.1470499635,0.165630877,-0.478243053,0.2883205414,0.1425646096,0.1045709103,0.579603076,-0.5662628412,0.2586008608,0.1645043492,0.0364158973,0.0676730499,0.0002429153,-0.4046181738,-0.4485390782,0.0004350235,0.0631367639,0.1191911995,-0.2013661861,-0.2541602552,-0.4854084253,0.060443718,0.2294803113,0.0657129213,-0.0226833243,0.1426103115,0.1721695662,0.2969888151,0.3728101552,0.1315265447,-0.0061089615,-0.2827489972,0.0329689942,0.2808256447,-0.4053996205,0.0805034265,0.5078934431,0.0328569859,0.0499742068,-0.354185909,-0.1188242659,-0.0054993713,0.0185323618,0.1302485466,0.3539407551,0.0411518216,-0.1375973225,0.1864664257,0.4516462684,-0.2629924417,-0.3996079564,0.1982381195,0.0668713972,-0.566060245,0.168046385,0.0080164503,0.0050520482,-0.0405716002,-0.0489652902,-0.0865609571,-0.1470603645,-0.1776747704,0.1613954008,0.3504637778,-0.1179773435,0.1744433045,-0.1617615074,-0.0972028673,-0.2360582799,0.2652748227,-0.1058576852,-0.3608027697,-0.3060231507,0.1375770718,0.0916159824,-0.223560378,0.1049929038,0.3826281726,-0.3034338951,-0.1787350923,0.1791311353,-0.3981319368,0.0234944597,0.0205251202,0.0504818633,0.5985868573,0.1087485179,0.2657260597,-0.2467353791,0.3976507485,-0.57773453,-0.1601953059,0.2145015895,0.0432265699,0.0021722559,0.2794611752,-0.2627557814,0.0550751425,0.3159916103,-0.1885834187,-0.2529248595,-0.1632301658,-0.2909041643,-0.3229499161,-0.1161959544,0.3648350835,-0.0555359013,0.0846190229,-0.2270486057,0.1989284754,0.4607825875,0.6413199902,-0.2464523613,0.4451713562,-0.2116091698,-0.0282693449,0.0078554638,0.0238374546,-0.2697035968,0.7397873998,-0.3511784077,0.0436996631,-0.0849282965,0.2499584705,0.3816208243,-0.2272116244,0.2943175733,0.3158895075,-0.0448896587,0.4030993581,-0.3237964213,-0.0970086679,0.1848305166,-0.0664784908,0.0195168704,0.0924740434,0.1116137728,-0.3846982121,0.3901068568,-0.0824221596,0.0202758014,0.2032290846,0.0583020747,-0.0374687687,-0.0450531319,0.146766901,-0.0363038629,0.2148035765,-0.0297569036,-0.3130533397,0.0280722175,0.0901294872,-0.0566683635,0.0405774973,-0.1481070369,0.0768140927,-0.069609493,0.1082078964,0.0066256733,-0.053109277,0.2858844399,0.0768032819,0.1393086016,-0.2579503953,-0.0606353693,0.0649239197,0.0832973793,-0.0895802304,-0.1150141731,-0.247845158,0.3522396386,-0.077013202,-0.1728483886,0.1359662265,0.0681286901,0.2770183682,-0.0817129314,0.1085997447,0.0848038718,0.2572034895,0.1620967239,0.0714403614,0.2367134541,-0.3935838342,0.0078216819,0.1139740646,-0.0127198147,0.4513719976,-0.1187484935,-0.0240847208,-0.0157214329,-0.1067117453,0.2500286996,0.1847309768,0.2829656899,-0.1002832726,0.4187237322,0.2549993992,-0.0326462835,0.0118966671,0.4661342502,-0.0203538276,-0.1252837181,0.1260020733,-0.1191269979,-0.0074430616,-0.0529824682,0.1171952114,0.4750802815,0.0915320888,0.0083646923,-0.1771562546,0.1136810407,-0.0731731281,0.079966262,0.1351906955,0.4936067164,-0.0609047115,0.3400739729,-0.0251336861,0.0226746611,-0.1699529886,0.2145279348,0.2423357219,-0.0909221172,0.1643041819,-0.0658701807,0.1948194802,-0.3574422002,-0.0718456432,0.0449446961,-0.1869829446,-0.1754426956,0.231722489,-0.0079136491,0.0853914693,0.2350057214,-0.2120740265,-0.0586857758,-0.121783942,0.0293068215,-0.3171491325,-0.0466824733,-0.0308324005,0.3746777475,-0.0422525331,0.2445873618,0.1350599974,-0.4562498331,-0.0100961933,0.0141648315,-0.1387724727,-0.1915162355,0.0532073863,-0.2786862254,0.2900403142,-0.0724834427,-0.2245515436,0.2720659077,-0.6565895677,0.007991367,-0.0480761863,-0.1245818883,-0.3801763952,-0.0833954886,-0.1291444749,-0.1720814258,-0.164166823,0.3399759233,-0.0990640596,0.33451882,-0.0075923009,0.0989273489,0.0189516041,0.1534696668,-0.1666975021,-0.1813574433,-0.2220367491,0.1597072631,0.0227446314,-0.0923017189,-0.2212070227,-0.0145056015,-0.1139128134,0.2172623575,-0.4146239161,0.1890673637,-0.5987946391,0.2085665315,0.0747189671,0.1890527457,0.3789047003,-0.1311908364,-0.0708762929,-0.1619340628,-0.3247306943,0.0190550517,0.2639814913,-0.0260156337,0.1003705859,0.4866039455,-0.0761096552,0.6302146912,0.3575815558,-0.0333175883,0.0737168267,0.0584787093,-0.1835186034,-0.2872954309,-0.3533495069,0.0379477702,-0.30341330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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2239","title":"Error loading wikihow dataset","comments":"Hi @odellus, thanks for reporting.\r\n\r\nThe `wikihow` dataset has 2 versions:\r\n- `all`: Consisting of the concatenation of all paragraphs as the articles and the bold lines as the reference summaries.\r\n- `sep`: Consisting of each paragraph and its summary.\r\n\r\nTherefore, in order to load it, you have to specify which version you would like, for example:\r\n```python\r\ndataset = load_dataset('wikihow', 'all')\r\n```\r\n\r\nPlease, tell me if this solves your problem.","body":"## Describe the bug\r\n\r\nWhen attempting to load wikihow into a dataset with\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\nI get the message:\r\n```\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\nat the end of a [full stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2).\r\n\r\n## Steps to reproduce the bug\r\n\r\nI have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https:\/\/huggingface.co\/datasets\/wikihow) says to use \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow')\r\n```\r\nto load the dataset. I do so and I get the message\r\n```\r\nAssertionError: The dataset wikihow with config all requires manual data.\r\n Please follow the manual download instructions:   You need to manually download two wikihow files. An overview of which files to download can be seen at https:\/\/github.com\/mahnazkoupaee\/WikiHow-Dataset.\r\n  You need to download the following two files manually:\r\n    1) https:\/\/ucsb.app.box.com\/s\/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path\/to\/folder>\/wikihowAll.csv\r\n    2) https:\/\/ucsb.app.box.com\/s\/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path\/to\/folder>\/wikihowSep.csv\r\n\r\n  The <path\/to\/folder> can e.g. be \"~\/manual_wikihow_data\".\r\n\r\n  Wikihow can then be loaded using the following command `datasets.load_dataset(\"wikihow\", data_dir=\"<path\/to\/folder>\")`.\r\n  .\r\n Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\nSo I create a directory `.\/wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory.\r\n\r\nThen I run \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\n\r\nthat's when I get the [stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2)\r\n\r\n## Expected results\r\nI expected it to load the downloaded files into a dataset.\r\n\r\n## Actual results\r\n```python\r\nUsing custom data configuration default-data_dir=.%2Fwikihow\r\nDownloading and preparing dataset wikihow\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/azureuser\/.cache\/huggingface\/datasets\/wikihow\/default-data_dir=.%2Fwikihow\/0.0.0\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2...                                                    ---------------------------------------------------------------------------\r\nAttributeError\r\nTraceback (most recent call last)\r\n<ipython-input-9-5e4d40142f30> in <module>\r\n----> 1 dataset = load_dataset('wikihow',data_dir='.\/wikihow')\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n745         try_from_hf_gcs=try_from_hf_gcs,\r\n746         base_path=base_path,--> \r\n747         use_auth_token=use_auth_token,\r\n748     )\r\n749 \r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n577                     if not downloaded_from_gcs:\r\n578                         self._download_and_prepare(                                                             -->\r\n579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs        \r\n580                         )                                                                                          \r\n581                     # Sync info\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n632         split_dict = SplitDict(dataset_name=self.name)\r\n633         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)                      -->\r\n634         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)                            \r\n635                                                                                                                     \r\n636         # Checksums verification\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikihow\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2\/wikihow.py in _split_generators(self, dl_manager)\r\n132\r\n133         path_to_manual_file = os.path.join(\r\n--> 134             os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename                        \r\n135         )                                                                                                           \r\n136\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n```\r\n- Datasets: 1.5.0\r\n- Python: 3.6.9 (default, Jan 26 2021, 15:33:00)                                      [GCC 8.4.0]\r\n- Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic\r\n```","comment_length":71,"text":"Error loading wikihow dataset \n ## Describe the bug\r\n\r\nWhen attempting to load wikihow into a dataset with\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\nI get the message:\r\n```\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\nat the end of a [full stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2).\r\n\r\n## Steps to reproduce the bug\r\n\r\nI have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https:\/\/huggingface.co\/datasets\/wikihow) says to use \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow')\r\n```\r\nto load the dataset. I do so and I get the message\r\n```\r\nAssertionError: The dataset wikihow with config all requires manual data.\r\n Please follow the manual download instructions:   You need to manually download two wikihow files. An overview of which files to download can be seen at https:\/\/github.com\/mahnazkoupaee\/WikiHow-Dataset.\r\n  You need to download the following two files manually:\r\n    1) https:\/\/ucsb.app.box.com\/s\/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path\/to\/folder>\/wikihowAll.csv\r\n    2) https:\/\/ucsb.app.box.com\/s\/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path\/to\/folder>\/wikihowSep.csv\r\n\r\n  The <path\/to\/folder> can e.g. be \"~\/manual_wikihow_data\".\r\n\r\n  Wikihow can then be loaded using the following command `datasets.load_dataset(\"wikihow\", data_dir=\"<path\/to\/folder>\")`.\r\n  .\r\n Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\nSo I create a directory `.\/wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory.\r\n\r\nThen I run \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\n\r\nthat's when I get the [stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2)\r\n\r\n## Expected results\r\nI expected it to load the downloaded files into a dataset.\r\n\r\n## Actual results\r\n```python\r\nUsing custom data configuration default-data_dir=.%2Fwikihow\r\nDownloading and preparing dataset wikihow\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/azureuser\/.cache\/huggingface\/datasets\/wikihow\/default-data_dir=.%2Fwikihow\/0.0.0\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2...                                                    ---------------------------------------------------------------------------\r\nAttributeError\r\nTraceback (most recent call last)\r\n<ipython-input-9-5e4d40142f30> in <module>\r\n----> 1 dataset = load_dataset('wikihow',data_dir='.\/wikihow')\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n745         try_from_hf_gcs=try_from_hf_gcs,\r\n746         base_path=base_path,--> \r\n747         use_auth_token=use_auth_token,\r\n748     )\r\n749 \r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n577                     if not downloaded_from_gcs:\r\n578                         self._download_and_prepare(                                                             -->\r\n579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs        \r\n580                         )                                                                                          \r\n581                     # Sync info\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n632         split_dict = SplitDict(dataset_name=self.name)\r\n633         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)                      -->\r\n634         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)                            \r\n635                                                                                                                     \r\n636         # Checksums verification\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikihow\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2\/wikihow.py in _split_generators(self, dl_manager)\r\n132\r\n133         path_to_manual_file = os.path.join(\r\n--> 134             os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename                        \r\n135         )                                                                                                           \r\n136\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n```\r\n- Datasets: 1.5.0\r\n- Python: 3.6.9 (default, Jan 26 2021, 15:33:00)                                      [GCC 8.4.0]\r\n- Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic\r\n``` \n Hi @odellus, thanks for reporting.\r\n\r\nThe `wikihow` dataset has 2 versions:\r\n- `all`: Consisting of the concatenation of all paragraphs as the articles and the bold lines as the reference summaries.\r\n- `sep`: Consisting of each paragraph and its summary.\r\n\r\nTherefore, in order to load it, you have to specify which version you would like, for example:\r\n```python\r\ndataset = load_dataset('wikihow', 'all')\r\n```\r\n\r\nPlease, tell me if this solves your problem.","embeddings":[-0.2274995297,0.370223105,0.0238096733,0.3987950683,0.2537099421,0.2747787535,0.4276534021,0.4391774237,0.2458434254,0.0935082212,0.216285497,0.3853774071,-0.0128944451,0.1863868237,0.1205022633,-0.2686142325,0.0110668847,0.1045249701,0.223709926,0.1890229732,-0.3448888063,0.0065287249,-0.2329775989,0.1040147245,-0.3400695622,0.0130667957,-0.0981442481,0.1813103855,-0.0533038154,-0.541544497,0.4757557511,-0.1185788065,0.2518153787,0.4039177299,-0.0001126044,0.1877029687,0.5060688257,0.0342030711,-0.442166388,-0.0944597572,-0.3161360323,-0.1228394881,0.2420690507,-0.1337201148,-0.0326552615,-0.0258759465,0.004085809,0.0358592607,0.2355376929,0.2652969062,0.1653248966,-0.1196185276,0.3745595217,-0.1360630244,0.1942486763,-0.0933305025,-0.0869166702,0.1622466892,0.0739811137,-0.2382172346,0.1115441099,0.20557262,-0.1832122952,0.0634829551,0.5792307854,-0.0081012985,0.2564974129,-0.332991004,0.2632296085,0.3839665055,0.8194904923,-0.2249974608,-0.2384388745,-0.2182331532,0.1727716476,0.1644721776,0.3875032365,0.3061026633,-0.3748465776,0.0360186175,-0.0377139971,-0.2557303309,-0.0580877513,0.2808242142,-0.294668287,0.4108183086,-0.0778242275,0.1745568216,-0.2258381695,-0.0642693043,0.2035174668,-0.32481125,-0.2376295626,0.3015760481,-0.1384118348,0.0768692344,0.1585353613,0.1928257942,0.0994419307,-0.0371334255,-0.100838013,-0.1908228844,0.1564052403,0.1384836882,0.4442620873,0.2339715958,-0.0133317849,0.2062765062,0.1207495183,0.3048851788,-0.1984937936,-0.1141341776,-0.201289773,-0.2741613984,0.1328350455,-0.2296912819,0.3544214666,-0.0338293388,-0.2546579242,0.2001613677,0.0116372267,0.0730370209,0.0347945094,0.4159566164,-0.2064811736,0.1446116865,0.2597591877,0.2927667499,0.1025156602,0.1234883666,-0.1005089432,0.0907599106,-0.0558772162,-0.0318382233,0.1500617266,-0.1930603236,0.2553026974,0.1958255172,0.125093013,-0.2846936584,-0.0093548056,0.2284626216,-0.2356490493,0.3494037092,0.0726296008,0.1431750059,0.1701949537,-0.6475993991,-0.1152566895,0.1633387059,-0.394199729,-0.3855848312,-0.2756711245,0.2184655666,-0.0403429158,0.0450250953,-0.419618398,-0.1785893738,0.2975123823,-0.2910596132,-0.2390632927,-0.0699154362,0.1259678304,-0.1256434321,0.356915772,0.4782387316,-0.3908683658,-0.0335086882,-0.0930062383,0.0438776985,-0.0572247431,-0.3101461828,-0.3554069996,0.4971389174,-0.288457185,-0.2874653041,0.2937496006,-0.4576177597,-0.2892730534,0.2900746465,0.0192084331,0.3332006037,-0.0229105316,-0.1692107767,-0.168219924,-0.0836994424,0.2327568531,0.2221877873,-0.0055906828,0.0550968647,-0.1940852553,-0.2364349663,0.1046095416,0.186486274,0.0511644967,0.1748686433,0.0437120758,0.0242446903,0.1256706417,-0.1626351625,-0.0451247096,0.1203386486,-0.0281695016,-0.0047448212,0.048327703,0.0023430884,-0.4610753655,0.3484283388,0.0433676019,0.1083413661,-0.1505929083,-0.0570079163,-0.2748941183,0.1563291699,-0.5284996033,-0.1772395521,0.16646716,0.0618430004,-0.149765268,-0.018513212,-0.124017477,0.0431065783,-0.3334240615,0.1222898439,-0.2070953697,0.1706795096,-0.1306974888,-0.0565901399,-0.0111799212,0.1489347219,0.0346692316,0.0848748386,-0.2042343467,0.3257358968,0.0568369739,0.1124035195,-0.0444702618,-0.0688852519,-0.037389446,-0.1064234599,0.0548359565,0.2269100845,0.2477425188,-0.0861583799,-0.092016995,0.230902642,0.0540100299,0.3870119154,0.0292328987,0.267403245,0.2057381421,-0.0143849896,-0.024044076,0.0058025098,0.1215947866,0.2089033723,0.0798321962,-0.0836231709,-0.1614168137,-0.1593151838,0.098150149,0.0621439032,0.0763312802,0.0264563933,-0.2656008005,0.0542387515,0.0102328975,0.2679121494,0.2297856659,0.0542744882,-0.2823029459,0.029405009,-0.0894653425,-0.0224468429,0.3617697656,0.1321124285,0.1846684366,0.3180979192,-0.1216696352,0.1428943425,-0.0601892583,-0.2058867961,-0.1090199202,0.2014214098,-0.4196126759,0.0011625303,0.0554270037,-0.12873137,-0.0488140583,-0.2332871109,-0.2695047557,-0.4261551201,-0.0986412242,0.0017243456,-0.1481575668,0.1272243708,-0.1493606865,0.1027415693,0.0276107434,-0.2686441839,-0.2308505625,-0.3650170863,-0.3169379532,-0.010910823,0.4290106595,0.014576084,0.1516437829,-0.3627824783,-0.041841913,-0.0665280297,-0.182035476,0.1500556469,0.0805360898,0.7029446959,0.1669664234,0.4550088048,0.0690727234,-0.4021815658,0.3672315776,0.0559912547,0.1430447549,0.2535715699,-0.0262755174,-0.233836621,0.0019271201,-0.326227963,-0.1746632308,-0.2757624686,-0.2295166552,0.3282880485,0.0036655893,0.5006689429,0.2981620729,0.1741961688,0.0721937418,0.293499738,-0.0413388088,-0.6339867711,0.3558411598,-0.1906136423,-0.2751670182,0.2133964598,-0.1321064532,0.2231930494,-0.0869277865,-0.5945403576,-0.0181757268,-0.0856766701,0.2774372101,-0.0212452579,0.174653545,0.0803521276,0.1810375452,0.0438931286,-0.16777201,0.0480528399,-0.1401127279,-0.1015582308,0.1961410195,0.1260029525,0.1382963806,-0.3169760108,0.6540639997,0.0909557492,0.1336974353,0.282076925,-0.2309667915,0.237269327,-0.2938368618,-0.366079241,0.142866239,-0.1669014841,-0.1178768277,0.21682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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2239","title":"Error loading wikihow dataset","comments":"Good call out. I did try that and that's when it told me to download the\ndataset. Don't believe I have tried it with local files. Will try first\nthing in the morning and get back to you.\n\nOn Mon, Apr 19, 2021, 11:17 PM Albert Villanova del Moral <\n***@***.***> wrote:\n\n> Hi @odellus <https:\/\/github.com\/odellus>, thanks for reporting.\n>\n> The wikihow dataset has 2 versions:\n>\n>    - all: Consisting of the concatenation of all paragraphs as the\n>    articles and the bold lines as the reference summaries.\n>    - sep: Consisting of each paragraph and its summary.\n>\n> Therefore, in order to load it, you have to specify which version you\n> would like, for example:\n>\n> dataset = load_dataset('wikihow', 'all')\n>\n> Please, tell me if this solves your problem.\n>\n> \u2014\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2239#issuecomment-823004146>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABDYI3HVRTBI2QT3BOG262DTJUL57ANCNFSM43GV5BZQ>\n> .\n>\n","body":"## Describe the bug\r\n\r\nWhen attempting to load wikihow into a dataset with\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\nI get the message:\r\n```\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\nat the end of a [full stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2).\r\n\r\n## Steps to reproduce the bug\r\n\r\nI have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https:\/\/huggingface.co\/datasets\/wikihow) says to use \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow')\r\n```\r\nto load the dataset. I do so and I get the message\r\n```\r\nAssertionError: The dataset wikihow with config all requires manual data.\r\n Please follow the manual download instructions:   You need to manually download two wikihow files. An overview of which files to download can be seen at https:\/\/github.com\/mahnazkoupaee\/WikiHow-Dataset.\r\n  You need to download the following two files manually:\r\n    1) https:\/\/ucsb.app.box.com\/s\/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path\/to\/folder>\/wikihowAll.csv\r\n    2) https:\/\/ucsb.app.box.com\/s\/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path\/to\/folder>\/wikihowSep.csv\r\n\r\n  The <path\/to\/folder> can e.g. be \"~\/manual_wikihow_data\".\r\n\r\n  Wikihow can then be loaded using the following command `datasets.load_dataset(\"wikihow\", data_dir=\"<path\/to\/folder>\")`.\r\n  .\r\n Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\nSo I create a directory `.\/wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory.\r\n\r\nThen I run \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\n\r\nthat's when I get the [stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2)\r\n\r\n## Expected results\r\nI expected it to load the downloaded files into a dataset.\r\n\r\n## Actual results\r\n```python\r\nUsing custom data configuration default-data_dir=.%2Fwikihow\r\nDownloading and preparing dataset wikihow\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/azureuser\/.cache\/huggingface\/datasets\/wikihow\/default-data_dir=.%2Fwikihow\/0.0.0\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2...                                                    ---------------------------------------------------------------------------\r\nAttributeError\r\nTraceback (most recent call last)\r\n<ipython-input-9-5e4d40142f30> in <module>\r\n----> 1 dataset = load_dataset('wikihow',data_dir='.\/wikihow')\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n745         try_from_hf_gcs=try_from_hf_gcs,\r\n746         base_path=base_path,--> \r\n747         use_auth_token=use_auth_token,\r\n748     )\r\n749 \r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n577                     if not downloaded_from_gcs:\r\n578                         self._download_and_prepare(                                                             -->\r\n579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs        \r\n580                         )                                                                                          \r\n581                     # Sync info\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n632         split_dict = SplitDict(dataset_name=self.name)\r\n633         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)                      -->\r\n634         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)                            \r\n635                                                                                                                     \r\n636         # Checksums verification\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikihow\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2\/wikihow.py in _split_generators(self, dl_manager)\r\n132\r\n133         path_to_manual_file = os.path.join(\r\n--> 134             os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename                        \r\n135         )                                                                                                           \r\n136\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n```\r\n- Datasets: 1.5.0\r\n- Python: 3.6.9 (default, Jan 26 2021, 15:33:00)                                      [GCC 8.4.0]\r\n- Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic\r\n```","comment_length":168,"text":"Error loading wikihow dataset \n ## Describe the bug\r\n\r\nWhen attempting to load wikihow into a dataset with\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\nI get the message:\r\n```\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\nat the end of a [full stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2).\r\n\r\n## Steps to reproduce the bug\r\n\r\nI have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https:\/\/huggingface.co\/datasets\/wikihow) says to use \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow')\r\n```\r\nto load the dataset. I do so and I get the message\r\n```\r\nAssertionError: The dataset wikihow with config all requires manual data.\r\n Please follow the manual download instructions:   You need to manually download two wikihow files. An overview of which files to download can be seen at https:\/\/github.com\/mahnazkoupaee\/WikiHow-Dataset.\r\n  You need to download the following two files manually:\r\n    1) https:\/\/ucsb.app.box.com\/s\/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path\/to\/folder>\/wikihowAll.csv\r\n    2) https:\/\/ucsb.app.box.com\/s\/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path\/to\/folder>\/wikihowSep.csv\r\n\r\n  The <path\/to\/folder> can e.g. be \"~\/manual_wikihow_data\".\r\n\r\n  Wikihow can then be loaded using the following command `datasets.load_dataset(\"wikihow\", data_dir=\"<path\/to\/folder>\")`.\r\n  .\r\n Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\nSo I create a directory `.\/wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory.\r\n\r\nThen I run \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\n\r\nthat's when I get the [stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2)\r\n\r\n## Expected results\r\nI expected it to load the downloaded files into a dataset.\r\n\r\n## Actual results\r\n```python\r\nUsing custom data configuration default-data_dir=.%2Fwikihow\r\nDownloading and preparing dataset wikihow\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/azureuser\/.cache\/huggingface\/datasets\/wikihow\/default-data_dir=.%2Fwikihow\/0.0.0\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2...                                                    ---------------------------------------------------------------------------\r\nAttributeError\r\nTraceback (most recent call last)\r\n<ipython-input-9-5e4d40142f30> in <module>\r\n----> 1 dataset = load_dataset('wikihow',data_dir='.\/wikihow')\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n745         try_from_hf_gcs=try_from_hf_gcs,\r\n746         base_path=base_path,--> \r\n747         use_auth_token=use_auth_token,\r\n748     )\r\n749 \r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n577                     if not downloaded_from_gcs:\r\n578                         self._download_and_prepare(                                                             -->\r\n579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs        \r\n580                         )                                                                                          \r\n581                     # Sync info\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n632         split_dict = SplitDict(dataset_name=self.name)\r\n633         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)                      -->\r\n634         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)                            \r\n635                                                                                                                     \r\n636         # Checksums verification\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikihow\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2\/wikihow.py in _split_generators(self, dl_manager)\r\n132\r\n133         path_to_manual_file = os.path.join(\r\n--> 134             os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename                        \r\n135         )                                                                                                           \r\n136\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n```\r\n- Datasets: 1.5.0\r\n- Python: 3.6.9 (default, Jan 26 2021, 15:33:00)                                      [GCC 8.4.0]\r\n- Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic\r\n``` \n Good call out. I did try that and that's when it told me to download the\ndataset. Don't believe I have tried it with local files. Will try first\nthing in the morning and get back to you.\n\nOn Mon, Apr 19, 2021, 11:17 PM Albert Villanova del Moral <\n***@***.***> wrote:\n\n> Hi @odellus <https:\/\/github.com\/odellus>, thanks for reporting.\n>\n> The wikihow dataset has 2 versions:\n>\n>    - all: Consisting of the concatenation of all paragraphs as the\n>    articles and the bold lines as the reference summaries.\n>    - sep: Consisting of each paragraph and its summary.\n>\n> Therefore, in order to load it, you have to specify which version you\n> would like, for example:\n>\n> dataset = load_dataset('wikihow', 'all')\n>\n> Please, tell me if this solves your problem.\n>\n> \u2014\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2239#issuecomment-823004146>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABDYI3HVRTBI2QT3BOG262DTJUL57ANCNFSM43GV5BZQ>\n> .\n>\n","embeddings":[-0.2274995297,0.370223105,0.0238096733,0.3987950683,0.2537099421,0.2747787535,0.4276534021,0.4391774237,0.2458434254,0.0935082212,0.216285497,0.3853774071,-0.0128944451,0.1863868237,0.1205022633,-0.2686142325,0.0110668847,0.1045249701,0.223709926,0.1890229732,-0.3448888063,0.0065287249,-0.2329775989,0.1040147245,-0.3400695622,0.0130667957,-0.0981442481,0.1813103855,-0.0533038154,-0.541544497,0.4757557511,-0.1185788065,0.2518153787,0.4039177299,-0.0001126044,0.1877029687,0.5060688257,0.0342030711,-0.442166388,-0.0944597572,-0.3161360323,-0.1228394881,0.2420690507,-0.1337201148,-0.0326552615,-0.0258759465,0.004085809,0.0358592607,0.2355376929,0.2652969062,0.1653248966,-0.1196185276,0.3745595217,-0.1360630244,0.1942486763,-0.0933305025,-0.0869166702,0.1622466892,0.0739811137,-0.2382172346,0.1115441099,0.20557262,-0.1832122952,0.0634829551,0.5792307854,-0.0081012985,0.2564974129,-0.332991004,0.2632296085,0.3839665055,0.8194904923,-0.2249974608,-0.2384388745,-0.2182331532,0.1727716476,0.1644721776,0.3875032365,0.3061026633,-0.3748465776,0.0360186175,-0.0377139971,-0.2557303309,-0.0580877513,0.2808242142,-0.294668287,0.4108183086,-0.0778242275,0.1745568216,-0.2258381695,-0.0642693043,0.2035174668,-0.32481125,-0.2376295626,0.3015760481,-0.1384118348,0.0768692344,0.1585353613,0.1928257942,0.0994419307,-0.0371334255,-0.100838013,-0.1908228844,0.1564052403,0.1384836882,0.4442620873,0.2339715958,-0.0133317849,0.2062765062,0.1207495183,0.3048851788,-0.1984937936,-0.1141341776,-0.201289773,-0.2741613984,0.1328350455,-0.2296912819,0.3544214666,-0.0338293388,-0.2546579242,0.2001613677,0.0116372267,0.0730370209,0.0347945094,0.4159566164,-0.2064811736,0.1446116865,0.2597591877,0.2927667499,0.1025156602,0.1234883666,-0.1005089432,0.0907599106,-0.0558772162,-0.0318382233,0.1500617266,-0.1930603236,0.2553026974,0.1958255172,0.125093013,-0.2846936584,-0.0093548056,0.2284626216,-0.2356490493,0.3494037092,0.0726296008,0.1431750059,0.1701949537,-0.6475993991,-0.1152566895,0.1633387059,-0.394199729,-0.3855848312,-0.2756711245,0.2184655666,-0.0403429158,0.0450250953,-0.419618398,-0.1785893738,0.2975123823,-0.2910596132,-0.2390632927,-0.0699154362,0.1259678304,-0.1256434321,0.356915772,0.4782387316,-0.3908683658,-0.0335086882,-0.0930062383,0.0438776985,-0.0572247431,-0.3101461828,-0.3554069996,0.4971389174,-0.288457185,-0.2874653041,0.2937496006,-0.4576177597,-0.2892730534,0.2900746465,0.0192084331,0.3332006037,-0.0229105316,-0.1692107767,-0.168219924,-0.0836994424,0.2327568531,0.2221877873,-0.0055906828,0.0550968647,-0.1940852553,-0.2364349663,0.1046095416,0.186486274,0.0511644967,0.1748686433,0.0437120758,0.0242446903,0.1256706417,-0.1626351625,-0.0451247096,0.1203386486,-0.0281695016,-0.0047448212,0.048327703,0.0023430884,-0.4610753655,0.3484283388,0.0433676019,0.1083413661,-0.1505929083,-0.0570079163,-0.2748941183,0.1563291699,-0.5284996033,-0.1772395521,0.16646716,0.0618430004,-0.149765268,-0.018513212,-0.124017477,0.0431065783,-0.3334240615,0.1222898439,-0.2070953697,0.1706795096,-0.1306974888,-0.0565901399,-0.0111799212,0.1489347219,0.0346692316,0.0848748386,-0.2042343467,0.3257358968,0.0568369739,0.1124035195,-0.0444702618,-0.0688852519,-0.037389446,-0.1064234599,0.0548359565,0.2269100845,0.2477425188,-0.0861583799,-0.092016995,0.230902642,0.0540100299,0.3870119154,0.0292328987,0.267403245,0.2057381421,-0.0143849896,-0.024044076,0.0058025098,0.1215947866,0.2089033723,0.0798321962,-0.0836231709,-0.1614168137,-0.1593151838,0.098150149,0.0621439032,0.0763312802,0.0264563933,-0.2656008005,0.0542387515,0.0102328975,0.2679121494,0.2297856659,0.0542744882,-0.2823029459,0.029405009,-0.0894653425,-0.0224468429,0.3617697656,0.1321124285,0.1846684366,0.3180979192,-0.1216696352,0.1428943425,-0.0601892583,-0.2058867961,-0.1090199202,0.2014214098,-0.4196126759,0.0011625303,0.0554270037,-0.12873137,-0.0488140583,-0.2332871109,-0.2695047557,-0.4261551201,-0.0986412242,0.0017243456,-0.1481575668,0.1272243708,-0.1493606865,0.1027415693,0.0276107434,-0.2686441839,-0.2308505625,-0.3650170863,-0.3169379532,-0.010910823,0.4290106595,0.014576084,0.1516437829,-0.3627824783,-0.041841913,-0.0665280297,-0.182035476,0.1500556469,0.0805360898,0.7029446959,0.1669664234,0.4550088048,0.0690727234,-0.4021815658,0.3672315776,0.0559912547,0.1430447549,0.2535715699,-0.0262755174,-0.233836621,0.0019271201,-0.326227963,-0.1746632308,-0.2757624686,-0.2295166552,0.3282880485,0.0036655893,0.5006689429,0.2981620729,0.1741961688,0.0721937418,0.293499738,-0.0413388088,-0.6339867711,0.3558411598,-0.1906136423,-0.2751670182,0.2133964598,-0.1321064532,0.2231930494,-0.0869277865,-0.5945403576,-0.0181757268,-0.0856766701,0.2774372101,-0.0212452579,0.174653545,0.0803521276,0.1810375452,0.0438931286,-0.16777201,0.0480528399,-0.1401127279,-0.1015582308,0.1961410195,0.1260029525,0.1382963806,-0.3169760108,0.6540639997,0.0909557492,0.1336974353,0.282076925,-0.2309667915,0.237269327,-0.2938368618,-0.366079241,0.142866239,-0.1669014841,-0.1178768277,0.2168298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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2239","title":"Error loading wikihow dataset","comments":"Hi @odellus, yes you are right.\r\n\r\nDue to the server where the `wikihow` dataset is hosted, the dataset can't be downloaded automatically by `huggingface` and you have to download it manually as you did.\r\n\r\nNevertheless, you have to specify which dataset version you would like to load anyway:\r\n```python\r\ndataset = load_dataset('wikihow', 'all', data_dir='.\/wikihow')\r\n```\r\nor\r\n```python\r\ndataset = load_dataset('wikihow', 'sep', data_dir='.\/wikihow')\r\n```\r\nI find that the instructions given by `huggingface` are not clear enough: I am going to fix this.\r\nPlease tell me if this eventually works for you.","body":"## Describe the bug\r\n\r\nWhen attempting to load wikihow into a dataset with\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\nI get the message:\r\n```\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\nat the end of a [full stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2).\r\n\r\n## Steps to reproduce the bug\r\n\r\nI have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https:\/\/huggingface.co\/datasets\/wikihow) says to use \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow')\r\n```\r\nto load the dataset. I do so and I get the message\r\n```\r\nAssertionError: The dataset wikihow with config all requires manual data.\r\n Please follow the manual download instructions:   You need to manually download two wikihow files. An overview of which files to download can be seen at https:\/\/github.com\/mahnazkoupaee\/WikiHow-Dataset.\r\n  You need to download the following two files manually:\r\n    1) https:\/\/ucsb.app.box.com\/s\/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path\/to\/folder>\/wikihowAll.csv\r\n    2) https:\/\/ucsb.app.box.com\/s\/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path\/to\/folder>\/wikihowSep.csv\r\n\r\n  The <path\/to\/folder> can e.g. be \"~\/manual_wikihow_data\".\r\n\r\n  Wikihow can then be loaded using the following command `datasets.load_dataset(\"wikihow\", data_dir=\"<path\/to\/folder>\")`.\r\n  .\r\n Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\nSo I create a directory `.\/wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory.\r\n\r\nThen I run \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\n\r\nthat's when I get the [stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2)\r\n\r\n## Expected results\r\nI expected it to load the downloaded files into a dataset.\r\n\r\n## Actual results\r\n```python\r\nUsing custom data configuration default-data_dir=.%2Fwikihow\r\nDownloading and preparing dataset wikihow\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/azureuser\/.cache\/huggingface\/datasets\/wikihow\/default-data_dir=.%2Fwikihow\/0.0.0\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2...                                                    ---------------------------------------------------------------------------\r\nAttributeError\r\nTraceback (most recent call last)\r\n<ipython-input-9-5e4d40142f30> in <module>\r\n----> 1 dataset = load_dataset('wikihow',data_dir='.\/wikihow')\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n745         try_from_hf_gcs=try_from_hf_gcs,\r\n746         base_path=base_path,--> \r\n747         use_auth_token=use_auth_token,\r\n748     )\r\n749 \r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n577                     if not downloaded_from_gcs:\r\n578                         self._download_and_prepare(                                                             -->\r\n579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs        \r\n580                         )                                                                                          \r\n581                     # Sync info\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n632         split_dict = SplitDict(dataset_name=self.name)\r\n633         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)                      -->\r\n634         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)                            \r\n635                                                                                                                     \r\n636         # Checksums verification\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikihow\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2\/wikihow.py in _split_generators(self, dl_manager)\r\n132\r\n133         path_to_manual_file = os.path.join(\r\n--> 134             os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename                        \r\n135         )                                                                                                           \r\n136\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n```\r\n- Datasets: 1.5.0\r\n- Python: 3.6.9 (default, Jan 26 2021, 15:33:00)                                      [GCC 8.4.0]\r\n- Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic\r\n```","comment_length":90,"text":"Error loading wikihow dataset \n ## Describe the bug\r\n\r\nWhen attempting to load wikihow into a dataset with\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\nI get the message:\r\n```\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\nat the end of a [full stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2).\r\n\r\n## Steps to reproduce the bug\r\n\r\nI have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https:\/\/huggingface.co\/datasets\/wikihow) says to use \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow')\r\n```\r\nto load the dataset. I do so and I get the message\r\n```\r\nAssertionError: The dataset wikihow with config all requires manual data.\r\n Please follow the manual download instructions:   You need to manually download two wikihow files. An overview of which files to download can be seen at https:\/\/github.com\/mahnazkoupaee\/WikiHow-Dataset.\r\n  You need to download the following two files manually:\r\n    1) https:\/\/ucsb.app.box.com\/s\/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path\/to\/folder>\/wikihowAll.csv\r\n    2) https:\/\/ucsb.app.box.com\/s\/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path\/to\/folder>\/wikihowSep.csv\r\n\r\n  The <path\/to\/folder> can e.g. be \"~\/manual_wikihow_data\".\r\n\r\n  Wikihow can then be loaded using the following command `datasets.load_dataset(\"wikihow\", data_dir=\"<path\/to\/folder>\")`.\r\n  .\r\n Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\nSo I create a directory `.\/wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory.\r\n\r\nThen I run \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wikihow', data_dir='.\/wikihow')\r\n```\r\n\r\nthat's when I get the [stack trace](https:\/\/gist.github.com\/odellus\/602c3b2de52f541d353b1022f320ffc2)\r\n\r\n## Expected results\r\nI expected it to load the downloaded files into a dataset.\r\n\r\n## Actual results\r\n```python\r\nUsing custom data configuration default-data_dir=.%2Fwikihow\r\nDownloading and preparing dataset wikihow\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/azureuser\/.cache\/huggingface\/datasets\/wikihow\/default-data_dir=.%2Fwikihow\/0.0.0\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2...                                                    ---------------------------------------------------------------------------\r\nAttributeError\r\nTraceback (most recent call last)\r\n<ipython-input-9-5e4d40142f30> in <module>\r\n----> 1 dataset = load_dataset('wikihow',data_dir='.\/wikihow')\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n745         try_from_hf_gcs=try_from_hf_gcs,\r\n746         base_path=base_path,--> \r\n747         use_auth_token=use_auth_token,\r\n748     )\r\n749 \r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n577                     if not downloaded_from_gcs:\r\n578                         self._download_and_prepare(                                                             -->\r\n579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs        \r\n580                         )                                                                                          \r\n581                     # Sync info\r\n~\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n632         split_dict = SplitDict(dataset_name=self.name)\r\n633         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)                      -->\r\n634         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)                            \r\n635                                                                                                                     \r\n636         # Checksums verification\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikihow\/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2\/wikihow.py in _split_generators(self, dl_manager)\r\n132\r\n133         path_to_manual_file = os.path.join(\r\n--> 134             os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename                        \r\n135         )                                                                                                           \r\n136\r\nAttributeError: 'BuilderConfig' object has no attribute 'filename'\r\n```\r\n## Versions\r\nPaste the output of the following code:\r\n```python\r\nimport datasets\r\nimport sys\r\nimport platform\r\n\r\nprint(f\"\"\"\r\n- Datasets: {datasets.__version__}\r\n- Python: {sys.version}\r\n- Platform: {platform.platform()}\r\n\"\"\")\r\n```\r\n```\r\n- Datasets: 1.5.0\r\n- Python: 3.6.9 (default, Jan 26 2021, 15:33:00)                                      [GCC 8.4.0]\r\n- Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic\r\n``` \n Hi @odellus, yes you are right.\r\n\r\nDue to the server where the `wikihow` dataset is hosted, the dataset can't be downloaded automatically by `huggingface` and you have to download it manually as you did.\r\n\r\nNevertheless, you have to specify which dataset version you would like to load anyway:\r\n```python\r\ndataset = load_dataset('wikihow', 'all', data_dir='.\/wikihow')\r\n```\r\nor\r\n```python\r\ndataset = load_dataset('wikihow', 'sep', data_dir='.\/wikihow')\r\n```\r\nI find that the instructions given by `huggingface` are not clear enough: I am going to fix this.\r\nPlease tell me if this eventually works for you.","embeddings":[-0.2274995297,0.370223105,0.0238096733,0.3987950683,0.2537099421,0.2747787535,0.4276534021,0.4391774237,0.2458434254,0.0935082212,0.216285497,0.3853774071,-0.0128944451,0.1863868237,0.1205022633,-0.2686142325,0.0110668847,0.1045249701,0.223709926,0.1890229732,-0.3448888063,0.0065287249,-0.2329775989,0.1040147245,-0.3400695622,0.0130667957,-0.0981442481,0.1813103855,-0.0533038154,-0.541544497,0.4757557511,-0.1185788065,0.2518153787,0.4039177299,-0.0001126044,0.1877029687,0.5060688257,0.0342030711,-0.442166388,-0.0944597572,-0.3161360323,-0.1228394881,0.2420690507,-0.1337201148,-0.0326552615,-0.0258759465,0.004085809,0.0358592607,0.2355376929,0.2652969062,0.1653248966,-0.1196185276,0.3745595217,-0.1360630244,0.1942486763,-0.0933305025,-0.0869166702,0.1622466892,0.0739811137,-0.2382172346,0.1115441099,0.20557262,-0.1832122952,0.0634829551,0.5792307854,-0.0081012985,0.2564974129,-0.332991004,0.2632296085,0.3839665055,0.8194904923,-0.2249974608,-0.2384388745,-0.2182331532,0.1727716476,0.1644721776,0.3875032365,0.3061026633,-0.3748465776,0.0360186175,-0.0377139971,-0.2557303309,-0.0580877513,0.2808242142,-0.294668287,0.4108183086,-0.0778242275,0.1745568216,-0.2258381695,-0.0642693043,0.2035174668,-0.32481125,-0.2376295626,0.3015760481,-0.1384118348,0.0768692344,0.1585353613,0.1928257942,0.0994419307,-0.0371334255,-0.100838013,-0.1908228844,0.1564052403,0.1384836882,0.4442620873,0.2339715958,-0.0133317849,0.2062765062,0.1207495183,0.3048851788,-0.1984937936,-0.1141341776,-0.201289773,-0.2741613984,0.1328350455,-0.2296912819,0.3544214666,-0.0338293388,-0.2546579242,0.2001613677,0.0116372267,0.0730370209,0.0347945094,0.4159566164,-0.2064811736,0.1446116865,0.2597591877,0.2927667499,0.1025156602,0.1234883666,-0.1005089432,0.0907599106,-0.0558772162,-0.0318382233,0.1500617266,-0.1930603236,0.2553026974,0.1958255172,0.125093013,-0.2846936584,-0.0093548056,0.2284626216,-0.2356490493,0.3494037092,0.0726296008,0.1431750059,0.1701949537,-0.6475993991,-0.1152566895,0.1633387059,-0.394199729,-0.3855848312,-0.2756711245,0.2184655666,-0.0403429158,0.0450250953,-0.419618398,-0.1785893738,0.2975123823,-0.2910596132,-0.2390632927,-0.0699154362,0.1259678304,-0.1256434321,0.356915772,0.4782387316,-0.3908683658,-0.0335086882,-0.0930062383,0.0438776985,-0.0572247431,-0.3101461828,-0.3554069996,0.4971389174,-0.288457185,-0.2874653041,0.2937496006,-0.4576177597,-0.2892730534,0.2900746465,0.0192084331,0.3332006037,-0.0229105316,-0.1692107767,-0.168219924,-0.0836994424,0.2327568531,0.2221877873,-0.0055906828,0.0550968647,-0.1940852553,-0.2364349663,0.1046095416,0.186486274,0.0511644967,0.1748686433,0.0437120758,0.0242446903,0.1256706417,-0.1626351625,-0.0451247096,0.1203386486,-0.0281695016,-0.0047448212,0.048327703,0.0023430884,-0.4610753655,0.3484283388,0.0433676019,0.1083413661,-0.1505929083,-0.0570079163,-0.2748941183,0.1563291699,-0.5284996033,-0.1772395521,0.16646716,0.0618430004,-0.149765268,-0.018513212,-0.124017477,0.0431065783,-0.3334240615,0.1222898439,-0.2070953697,0.1706795096,-0.1306974888,-0.0565901399,-0.0111799212,0.1489347219,0.0346692316,0.0848748386,-0.2042343467,0.3257358968,0.0568369739,0.1124035195,-0.0444702618,-0.0688852519,-0.037389446,-0.1064234599,0.0548359565,0.2269100845,0.2477425188,-0.0861583799,-0.092016995,0.230902642,0.0540100299,0.3870119154,0.0292328987,0.267403245,0.2057381421,-0.0143849896,-0.024044076,0.0058025098,0.1215947866,0.2089033723,0.0798321962,-0.0836231709,-0.1614168137,-0.1593151838,0.098150149,0.0621439032,0.0763312802,0.0264563933,-0.2656008005,0.0542387515,0.0102328975,0.2679121494,0.2297856659,0.0542744882,-0.2823029459,0.029405009,-0.0894653425,-0.0224468429,0.3617697656,0.1321124285,0.1846684366,0.3180979192,-0.1216696352,0.1428943425,-0.0601892583,-0.2058867961,-0.1090199202,0.2014214098,-0.4196126759,0.0011625303,0.0554270037,-0.12873137,-0.0488140583,-0.2332871109,-0.2695047557,-0.4261551201,-0.0986412242,0.0017243456,-0.1481575668,0.1272243708,-0.1493606865,0.1027415693,0.0276107434,-0.2686441839,-0.2308505625,-0.3650170863,-0.3169379532,-0.010910823,0.4290106595,0.014576084,0.1516437829,-0.3627824783,-0.041841913,-0.0665280297,-0.182035476,0.1500556469,0.0805360898,0.7029446959,0.1669664234,0.4550088048,0.0690727234,-0.4021815658,0.3672315776,0.0559912547,0.1430447549,0.2535715699,-0.0262755174,-0.233836621,0.0019271201,-0.326227963,-0.1746632308,-0.2757624686,-0.2295166552,0.3282880485,0.0036655893,0.5006689429,0.2981620729,0.1741961688,0.0721937418,0.293499738,-0.0413388088,-0.6339867711,0.3558411598,-0.1906136423,-0.2751670182,0.2133964598,-0.1321064532,0.2231930494,-0.0869277865,-0.5945403576,-0.0181757268,-0.0856766701,0.2774372101,-0.0212452579,0.174653545,0.0803521276,0.1810375452,0.0438931286,-0.16777201,0.0480528399,-0.1401127279,-0.1015582308,0.1961410195,0.1260029525,0.1382963806,-0.3169760108,0.6540639997,0.0909557492,0.1336974353,0.282076925,-0.2309667915,0.237269327,-0.2938368618,-0.366079241,0.142866239,-0.1669014841,-0.1178768277,0.216829806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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2237","title":"Update Dataset.dataset_size after transformed with map","comments":"@albertvillanova I would like to take this up. It would be great if you could point me as to how the dataset size is calculated in HF. Thanks!","body":"After loading a dataset, if we transform it by using `.map` its `dataset_size` attirbute is not updated.","comment_length":28,"text":"Update Dataset.dataset_size after transformed with map \n After loading a dataset, if we transform it by using `.map` its `dataset_size` attirbute is not updated. \n @albertvillanova I would like to take this up. It would be great if you could point me as to how the dataset size is calculated in HF. Thanks!","embeddings":[-0.2140057236,-0.3152703345,-0.122815989,0.151918903,0.0606090762,0.0196822062,0.2813269794,-0.1209019348,0.1866587102,0.1171898469,-0.1872747988,0.0038696581,0.380846709,0.1846315265,0.271148622,0.0805765986,0.2649388611,0.1283430755,-0.5549340844,-0.0820946544,-0.2858287394,-0.054799363,-0.3831388056,-0.339327246,0.0922732353,0.0161281005,-0.373599112,0.0223031156,-0.0533075258,-0.1949787438,0.0718323886,0.1569414437,0.2523393631,0.3328717947,-0.000118608,0.0030874119,0.060776107,-0.232634902,0.0674877316,0.0612015128,-0.3378659785,-0.1122008637,-0.3726660013,-0.131082654,0.0208221152,-0.1059127972,-0.2514362633,-0.1718560904,-0.0952340588,0.0019068832,0.126869157,0.0038471692,0.1123806685,-0.0862614885,0.0469178446,0.4319821894,0.3182981312,0.1597346365,-0.125337854,0.0477172844,-0.1166615114,0.4219639897,0.3868218064,-0.1872308403,0.6096593738,-0.2739829421,0.0580312908,-0.2759192884,0.25853163,-0.0472459495,0.6375643611,-0.0424682684,-0.1704120487,0.2470779419,-0.0291200113,0.2279429287,0.0077345422,-0.1512331367,0.2990824282,-0.0861105248,-0.4554169178,-0.4315140843,0.1040445045,-0.0273132809,-0.2427789867,0.1446979046,-0.1112796515,0.2554887235,-0.2085964233,-0.1983199716,-0.0714238435,-0.1080984697,-0.1298040152,0.2719272971,-0.1660077125,-0.2704578936,0.0243236981,0.1149551943,0.2199000716,-0.5036532879,-0.3162200153,-0.1402003467,-0.2914329171,0.0798045769,0.1231794283,0.2272793502,-0.0206621755,0.1592146009,-0.106652841,-0.1457856894,-0.1415425539,-0.1551551223,0.0363503546,-0.1320218295,0.1413971633,-0.3502228856,-0.1277054697,-0.2908428311,0.0824375153,-0.1878947318,0.0590823814,-0.1257807612,-0.0484554656,0.4238687456,-0.0765653327,0.2112364769,0.0476730317,-0.070592925,0.0623574853,-0.3581084013,-0.1582989246,-0.2173956931,-0.0436136425,-0.040028967,-0.2888955772,-0.0153968921,0.192159012,-0.0682718679,-0.0100626312,0.0613143258,-0.2472406179,-0.3080363274,0.5430862308,0.3725977242,-0.0346395187,-0.1020848155,-0.2056690007,0.0348296911,-0.2220863402,0.5323480368,-0.4249657094,-0.0220034309,-0.0164283272,0.1099156067,0.370747149,-0.142919302,-0.3712674379,0.4043624699,0.4439758658,-0.233330965,0.1766771525,-0.144543007,-0.3452298343,-0.1946550608,-0.0265192091,0.2341801673,-0.4227820039,-0.0769671574,0.1293610781,-0.0949333832,-0.0758656785,0.1869470328,-0.2029879838,0.2052725405,-0.2751541734,-0.0264795963,0.4559625387,0.153468594,-0.4716664255,0.2193999738,0.1093273908,-0.422233969,-0.1315321624,0.179771021,0.3205760121,-0.1876436174,-0.1563882232,0.2847704291,-0.151896745,-0.0051316624,-0.2183376849,-0.0154411839,0.2294495851,-0.272349447,0.3975720108,0.2733157873,0.4171893895,0.1851175278,0.2256759852,0.0936024413,0.2195407003,0.488866359,-0.1095969379,0.0856005922,0.3812716007,0.1324256659,-0.1740548611,-0.0752758533,-0.3204288483,-0.3319666684,0.6687418222,-0.0966060609,-0.3359660804,-0.0507548861,0.196964249,-0.0666560531,-0.0605126657,0.1799889356,0.0005359737,-0.4350314438,-0.0211310685,0.3384574652,-0.1690157652,-0.0516393818,-0.1935639381,0.4136397243,0.0334193856,0.1814790964,-0.2950874269,-0.2474314272,0.1697004884,-0.129627645,-0.5138614178,0.5568604469,0.0231895167,0.1645977944,0.3235615492,-0.0610960908,0.0794885308,-0.1397193819,0.4504624009,-0.211895898,0.2293570489,-0.0954312384,-0.0957633182,-0.0936714187,-0.0251375474,0.063264586,0.0550186671,-0.221438691,0.0508439057,0.0463702865,0.0053123394,-0.1153997034,-0.4647921622,0.0697127953,0.1271218359,0.0914869159,0.29965204,0.0864425153,0.5922946334,-0.1145482883,-0.2592746913,0.4422220886,-0.6018925905,-0.3079836965,0.0779067576,0.0987894163,0.4737581909,0.1595906168,0.3140643537,-0.0679319277,0.1276168078,0.1151645109,0.1253499538,-0.2597392201,0.2894563377,0.0159956608,0.3577781916,0.0495108292,-0.1046204194,0.1467944831,0.1935370415,0.000960484,-0.2141058296,0.0306788273,0.0610924847,-0.1605760008,-0.1327034384,-0.0776216462,-0.4361529052,-0.1390067041,-0.2273796648,-0.1222077459,0.2565586269,-0.0839830562,0.1252574474,-0.059715353,0.13939327,-0.0056539536,0.1181034297,0.0697114989,-0.2667838335,0.040638987,-0.1827833503,-0.2520087957,0.3343188167,-0.1925526261,0.0872487053,-0.2872765064,-0.5419437289,0.0671591088,0.0726977438,-0.1776107103,-0.0480507761,0.0545799658,-0.2404439151,0.5561314225,-0.0330331065,-0.2603099346,-0.1486041546,-0.0934144333,-0.269133836,0.1325567812,0.0069176992,-0.1663765758,-0.1284467578,-0.0060933586,-0.1103033349,-0.0678273439,0.0013865456,-0.0018110225,0.162728861,-0.1070974469,0.0917880684,-0.2905718088,-0.4420056641,-0.3465592563,0.2333111316,-0.1668551564,-0.2456224412,0.1360714436,-0.0397342369,0.0531751961,0.285584569,-0.4390060604,0.001907522,-0.2569933236,0.1336506903,-0.3816582561,0.4650990665,0.4649777412,-0.1931083053,-0.0217304453,-0.1601399332,-0.3421410322,0.0221876036,0.5710698962,0.4459757507,0.1922606379,0.4896537066,0.032642547,0.3656321466,0.035914652,-0.2391735464,0.3064498007,-0.018202303,0.2634079456,-0.3918576539,0.1460926384,0.1333952099,0.05889890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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2230","title":"Keys yielded while generating dataset are not being checked","comments":"Hi ! Indeed there's no verification on the uniqueness nor the types of the keys.\r\nDo you already have some ideas of what you would like to implement and how ?","body":"The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You!","comment_length":31,"text":"Keys yielded while generating dataset are not being checked \n The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You! \n Hi ! Indeed there's no verification on the uniqueness nor the types of the keys.\r\nDo you already have some ideas of what you would like to implement and how ?","embeddings":[0.0275365282,-0.2159314305,0.0291701369,0.4762756228,0.0684194565,-0.236362353,0.4168978631,0.0804130509,0.43124336,0.1339114755,0.1672184616,0.3502569795,-0.0206804313,0.2065166831,-0.0032002178,0.4027804732,0.0770776644,0.0507332645,-0.3874769807,-0.1746384501,-0.5882232189,0.0452296324,-0.1532139629,-0.0614637099,-0.1678526849,0.1618548632,-0.0681429282,-0.0138252173,-0.1972754598,-0.4447283149,0.2861754596,0.395427525,-0.1313844472,0.3610020876,-0.0001243995,-0.031345699,0.0754736513,-0.1916624457,-0.5255250931,0.0262024067,-0.1937693655,0.0950022191,-0.0176387988,-0.5688117146,-0.053775806,-0.1629761904,-0.0972570553,-0.4674580991,0.2114742845,0.2158976346,0.0896028355,0.4042153656,0.3006412983,0.2042028606,0.2016513646,0.6070529819,-0.2093221992,-0.3547791243,0.0443218388,0.3141504526,0.081910789,0.177616924,0.0606224015,-0.17011033,0.200685814,0.1464149356,-0.2246280462,-0.323405236,-0.0920045674,0.3267407119,0.3529065847,-0.1715489775,-0.3823952675,-0.3127988577,-0.0313100591,0.1885313839,0.4732134342,0.1420394927,-0.3297613263,0.0042914129,-0.2719512284,0.3002656102,-0.0179662202,-0.0041730828,0.258790642,0.1244735271,-0.0329472497,0.0638200045,0.2017904222,-0.1953783631,-0.1708811522,-0.4019609094,0.0959564373,0.3071315587,-0.1999058276,-0.2576290667,0.2660217881,0.194044441,0.4387064874,0.0169971455,-0.0074185338,0.0199898295,-0.1039318219,-0.1370512396,-0.0529770032,0.2784969211,0.1445692927,0.0533135198,0.4216781557,-0.0334113911,-0.1720716804,0.0693692267,-0.0535320565,0.0950190723,0.4306455255,0.2696842551,0.335506022,0.025251301,-0.1482149214,0.0974797383,-0.1257552505,-0.2656867802,0.2667299807,0.3143821061,-0.0797205865,0.0989429504,-0.1258902401,0.0694410652,-0.3220945597,-0.0343445353,-0.0853379816,-0.1496426165,-0.0802808627,-0.0657494068,0.364279598,-0.3379724324,0.0242795926,0.2694472075,-0.2059150934,-0.288422972,-0.1518755108,-0.0767865106,0.5315922499,0.2941078246,-0.1158165112,0.024007896,0.0686455518,-0.4160591662,-0.309743762,0.3407129049,-0.2412345409,-0.1555794626,0.2009787858,0.0987635627,-0.3604650795,-0.1261386573,-0.1306612045,-0.0645978823,0.3372377157,0.2071813345,0.0784945637,-0.1708253771,0.1355777979,-0.4528718889,0.2073252201,0.8914080262,-0.1023142859,-0.0063826209,-0.1526415646,-0.0278965682,0.1243735701,0.3553079963,-0.0161852706,0.3325898349,-0.3431203961,0.092856735,0.1716639251,-0.10762842,-0.2139098197,0.0121155484,0.1867583841,0.0967974141,0.5034436584,0.2218383104,0.2685669065,-0.0544432886,-0.2298940867,0.0698378459,0.0156069836,0.0602037869,-0.2132327706,-0.1308508813,0.0318849385,0.0194860678,-0.0369520262,0.2195641398,0.1360975951,-0.2552815676,0.0255509987,-0.2306274325,0.0509529673,-0.0563530996,0.1623330116,0.0917921513,0.0456483886,0.4361868799,-0.4905918837,0.2230523229,-0.0022498632,0.1985981464,-0.4391951263,-0.2743707597,-0.0583684258,0.2331923544,-0.3910266459,0.1591251194,0.0221546218,0.3127879202,0.3133720756,0.2550627887,-0.0731581673,0.0802614763,-0.0975416601,0.1954384744,-0.3723435104,0.0034481185,-0.071176298,0.068382673,-0.0470243059,0.264408648,0.103337653,-0.1461705863,-0.042986434,0.2023597211,0.2752099931,-0.0221164934,0.0455042571,0.3107895553,0.2325396389,-0.0491344407,-0.038399078,0.1895335317,0.1686585546,0.0750721842,-0.2943840027,0.8053556681,-0.2581625283,0.028535137,-0.0023022937,-0.0071980618,0.0217753276,-0.1347505897,-0.1365982592,-0.1884437501,-0.0732932538,-0.0163164865,0.1072857827,0.4089407623,-0.2961963713,0.157638669,0.7058497667,0.0941348001,0.2832787037,-0.0321235694,0.1921659261,-0.08089865,-0.2282799184,0.3368208706,0.1522255242,0.1965065747,0.1185858622,-0.3717745543,-0.0730491057,-0.1460594982,0.0427722856,-0.1891172081,-0.3544773459,0.3523737192,-0.3063002825,-0.1038096994,-0.5338432193,0.2326877713,0.1194282547,0.251612097,-0.5340343118,-0.2392493188,-0.1169456393,-0.0837991312,-0.228394866,-0.179580465,-0.0798715502,-0.3404607177,0.1645958573,0.0483226292,-0.0415060073,0.2251764238,-0.0276001878,0.177685082,0.0363353752,-0.5385163426,-0.4368271828,0.0511809215,-0.145413205,-0.0151514607,0.2481909692,-0.1409252733,0.3679054081,-0.3222305775,0.2253864855,-0.2745192051,-0.3014227152,0.0428631417,-0.1585089266,0.119652167,-0.0956903771,-0.0779617354,-0.0854469389,-0.0822699219,0.2423255742,-0.2479203939,-0.5795606375,0.2682214081,0.069850184,-0.187886402,-0.2038371116,-0.5165557861,-0.0041087638,0.0617712699,0.1320423484,-0.0713649914,0.1752530336,0.0461078286,0.1794478446,-0.1891895682,0.0000351128,-0.0443066023,-0.2693847418,-0.3226259351,0.6126976013,-0.0839342177,-0.2482605726,-0.0997935906,-0.1879334301,-0.2220841497,0.148715809,-0.3126063943,0.1589475423,-0.5614319444,0.6340059042,0.2777655721,0.2941862047,0.4636279643,0.096457094,0.0151649397,-0.231760785,0.0259325672,0.3365894854,-0.0143845584,0.2851168215,0.1721878797,0.2982102633,-0.2947055399,0.4157005548,0.3359111547,-0.1027194783,0.3535640538,-0.1526918858,0.3624245822,0.0208121259,-0.3244894147,-0.0466853455,0.197660163,0.144814193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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2230","title":"Keys yielded while generating dataset are not being checked","comments":"Hey @lhoestq, thank you so much for the opportunity.\r\nAlthough I haven't had much experience with the HF Datasets code, after a careful look at how the `ArrowWriter` functions, I think we can implement this as follows:\r\n\r\n1.  First, we would have to update the `ArrowWriter.write()` function here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fcd3c3c8e3b1d9a2f3686a496082e21f06591380\/src\/datasets\/arrow_writer.py#L296\r\nso that it accepts an additional argument `key` which would be appended along with the example here after hashing.\r\n\r\n2.  Then, we would need to create a `Hasher` class which will take the key as its input and return a hash for it (We might need to use some hash salt which can be passed to the ArrowWriter.writer() with value equal to the `split_name` for differentiating between same keys of different splits)\r\n\r\n    We can use the `hashlib.md5` function for hashing which will conert each key to its byte code before hashing (depending on the data type of the key) **Thus, the `key` type will be verified here**.\r\n\r\n3.  Now, we would have to edit this\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fcd3c3c8e3b1d9a2f3686a496082e21f06591380\/src\/datasets\/arrow_writer.py#L257\r\n so that it iterates over each `(hash, example)` pair (sorted according to hash). We can then simply **check whether each hash is different from the previous hash** (since they will be sorted)\r\n\r\nHowever, since I'm not very familiar with how the data is being written on disk in the form of a table, I might need some guidance for Step 3. \r\nPlease let me know your thought on this. Thanks!","body":"The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You!","comment_length":235,"text":"Keys yielded while generating dataset are not being checked \n The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You! \n Hey @lhoestq, thank you so much for the opportunity.\r\nAlthough I haven't had much experience with the HF Datasets code, after a careful look at how the `ArrowWriter` functions, I think we can implement this as follows:\r\n\r\n1.  First, we would have to update the `ArrowWriter.write()` function here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fcd3c3c8e3b1d9a2f3686a496082e21f06591380\/src\/datasets\/arrow_writer.py#L296\r\nso that it accepts an additional argument `key` which would be appended along with the example here after hashing.\r\n\r\n2.  Then, we would need to create a `Hasher` class which will take the key as its input and return a hash for it (We might need to use some hash salt which can be passed to the ArrowWriter.writer() with value equal to the `split_name` for differentiating between same keys of different splits)\r\n\r\n    We can use the `hashlib.md5` function for hashing which will conert each key to its byte code before hashing (depending on the data type of the key) **Thus, the `key` type will be verified here**.\r\n\r\n3.  Now, we would have to edit this\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fcd3c3c8e3b1d9a2f3686a496082e21f06591380\/src\/datasets\/arrow_writer.py#L257\r\n so that it iterates over each `(hash, example)` pair (sorted according to hash). We can then simply **check whether each hash is different from the previous hash** (since they will be sorted)\r\n\r\nHowever, since I'm not very familiar with how the data is being written on disk in the form of a table, I might need some guidance for Step 3. \r\nPlease let me know your thought on this. Thanks!","embeddings":[0.0275365282,-0.2159314305,0.0291701369,0.4762756228,0.0684194565,-0.236362353,0.4168978631,0.0804130509,0.43124336,0.1339114755,0.1672184616,0.3502569795,-0.0206804313,0.2065166831,-0.0032002178,0.4027804732,0.0770776644,0.0507332645,-0.3874769807,-0.1746384501,-0.5882232189,0.0452296324,-0.1532139629,-0.0614637099,-0.1678526849,0.1618548632,-0.0681429282,-0.0138252173,-0.1972754598,-0.4447283149,0.2861754596,0.395427525,-0.1313844472,0.3610020876,-0.0001243995,-0.031345699,0.0754736513,-0.1916624457,-0.5255250931,0.0262024067,-0.1937693655,0.0950022191,-0.0176387988,-0.5688117146,-0.053775806,-0.1629761904,-0.0972570553,-0.4674580991,0.2114742845,0.2158976346,0.0896028355,0.4042153656,0.3006412983,0.2042028606,0.2016513646,0.6070529819,-0.2093221992,-0.3547791243,0.0443218388,0.3141504526,0.081910789,0.177616924,0.0606224015,-0.17011033,0.200685814,0.1464149356,-0.2246280462,-0.323405236,-0.0920045674,0.3267407119,0.3529065847,-0.1715489775,-0.3823952675,-0.3127988577,-0.0313100591,0.1885313839,0.4732134342,0.1420394927,-0.3297613263,0.0042914129,-0.2719512284,0.3002656102,-0.0179662202,-0.0041730828,0.258790642,0.1244735271,-0.0329472497,0.0638200045,0.2017904222,-0.1953783631,-0.1708811522,-0.4019609094,0.0959564373,0.3071315587,-0.1999058276,-0.2576290667,0.2660217881,0.194044441,0.4387064874,0.0169971455,-0.0074185338,0.0199898295,-0.1039318219,-0.1370512396,-0.0529770032,0.2784969211,0.1445692927,0.0533135198,0.4216781557,-0.0334113911,-0.1720716804,0.0693692267,-0.0535320565,0.0950190723,0.4306455255,0.2696842551,0.335506022,0.025251301,-0.1482149214,0.0974797383,-0.1257552505,-0.2656867802,0.2667299807,0.3143821061,-0.0797205865,0.0989429504,-0.1258902401,0.0694410652,-0.3220945597,-0.0343445353,-0.0853379816,-0.1496426165,-0.0802808627,-0.0657494068,0.364279598,-0.3379724324,0.0242795926,0.2694472075,-0.2059150934,-0.288422972,-0.1518755108,-0.0767865106,0.5315922499,0.2941078246,-0.1158165112,0.024007896,0.0686455518,-0.4160591662,-0.309743762,0.3407129049,-0.2412345409,-0.1555794626,0.2009787858,0.0987635627,-0.3604650795,-0.1261386573,-0.1306612045,-0.0645978823,0.3372377157,0.2071813345,0.0784945637,-0.1708253771,0.1355777979,-0.4528718889,0.2073252201,0.8914080262,-0.1023142859,-0.0063826209,-0.1526415646,-0.0278965682,0.1243735701,0.3553079963,-0.0161852706,0.3325898349,-0.3431203961,0.092856735,0.1716639251,-0.10762842,-0.2139098197,0.0121155484,0.1867583841,0.0967974141,0.5034436584,0.2218383104,0.2685669065,-0.0544432886,-0.2298940867,0.0698378459,0.0156069836,0.0602037869,-0.2132327706,-0.1308508813,0.0318849385,0.0194860678,-0.0369520262,0.2195641398,0.1360975951,-0.2552815676,0.0255509987,-0.2306274325,0.0509529673,-0.0563530996,0.1623330116,0.0917921513,0.0456483886,0.4361868799,-0.4905918837,0.2230523229,-0.0022498632,0.1985981464,-0.4391951263,-0.2743707597,-0.0583684258,0.2331923544,-0.3910266459,0.1591251194,0.0221546218,0.3127879202,0.3133720756,0.2550627887,-0.0731581673,0.0802614763,-0.0975416601,0.1954384744,-0.3723435104,0.0034481185,-0.071176298,0.068382673,-0.0470243059,0.264408648,0.103337653,-0.1461705863,-0.042986434,0.2023597211,0.2752099931,-0.0221164934,0.0455042571,0.3107895553,0.2325396389,-0.0491344407,-0.038399078,0.1895335317,0.1686585546,0.0750721842,-0.2943840027,0.8053556681,-0.2581625283,0.028535137,-0.0023022937,-0.0071980618,0.0217753276,-0.1347505897,-0.1365982592,-0.1884437501,-0.0732932538,-0.0163164865,0.1072857827,0.4089407623,-0.2961963713,0.157638669,0.7058497667,0.0941348001,0.2832787037,-0.0321235694,0.1921659261,-0.08089865,-0.2282799184,0.3368208706,0.1522255242,0.1965065747,0.1185858622,-0.3717745543,-0.0730491057,-0.1460594982,0.0427722856,-0.1891172081,-0.3544773459,0.3523737192,-0.3063002825,-0.1038096994,-0.5338432193,0.2326877713,0.1194282547,0.251612097,-0.5340343118,-0.2392493188,-0.1169456393,-0.0837991312,-0.228394866,-0.179580465,-0.0798715502,-0.3404607177,0.1645958573,0.0483226292,-0.0415060073,0.2251764238,-0.0276001878,0.177685082,0.0363353752,-0.5385163426,-0.4368271828,0.0511809215,-0.145413205,-0.0151514607,0.2481909692,-0.1409252733,0.3679054081,-0.3222305775,0.2253864855,-0.2745192051,-0.3014227152,0.0428631417,-0.1585089266,0.119652167,-0.0956903771,-0.0779617354,-0.0854469389,-0.0822699219,0.2423255742,-0.2479203939,-0.5795606375,0.2682214081,0.069850184,-0.187886402,-0.2038371116,-0.5165557861,-0.0041087638,0.0617712699,0.1320423484,-0.0713649914,0.1752530336,0.0461078286,0.1794478446,-0.1891895682,0.0000351128,-0.0443066023,-0.2693847418,-0.3226259351,0.6126976013,-0.0839342177,-0.2482605726,-0.0997935906,-0.1879334301,-0.2220841497,0.148715809,-0.3126063943,0.1589475423,-0.5614319444,0.6340059042,0.2777655721,0.2941862047,0.4636279643,0.096457094,0.0151649397,-0.231760785,0.0259325672,0.3365894854,-0.0143845584,0.2851168215,0.1721878797,0.2982102633,-0.2947055399,0.4157005548,0.3359111547,-0.1027194783,0.3535640538,-0.1526918858,0.3624245822,0.0208121259,-0.3244894147,-0.0466853455,0.197660163,0.144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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2230","title":"Keys yielded while generating dataset are not being checked","comments":"Interesting !\r\nWe keep the dataset sorted in the order examples are generated by the builder (we expect the dataset builders to generate examples in deterministic order). Therefore I don't think we should shuffle the examples with the hashing. Let me know what you think.\r\nOther that that, I really like the idea of checking for keys duplicates in `write_examples_on_file` :)\r\n\r\nThis looks like a great plan ! Feel free to open a PR and ping me if you have questions or if I can help\r\n","body":"The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You!","comment_length":86,"text":"Keys yielded while generating dataset are not being checked \n The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You! \n Interesting !\r\nWe keep the dataset sorted in the order examples are generated by the builder (we expect the dataset builders to generate examples in deterministic order). Therefore I don't think we should shuffle the examples with the hashing. Let me know what you think.\r\nOther that that, I really like the idea of checking for keys duplicates in `write_examples_on_file` :)\r\n\r\nThis looks like a great plan ! Feel free to open a PR and ping me if you have questions or if I can help\r\n","embeddings":[0.0275365282,-0.2159314305,0.0291701369,0.4762756228,0.0684194565,-0.236362353,0.4168978631,0.0804130509,0.43124336,0.1339114755,0.1672184616,0.3502569795,-0.0206804313,0.2065166831,-0.0032002178,0.4027804732,0.0770776644,0.0507332645,-0.3874769807,-0.1746384501,-0.5882232189,0.0452296324,-0.1532139629,-0.0614637099,-0.1678526849,0.1618548632,-0.0681429282,-0.0138252173,-0.1972754598,-0.4447283149,0.2861754596,0.395427525,-0.1313844472,0.3610020876,-0.0001243995,-0.031345699,0.0754736513,-0.1916624457,-0.5255250931,0.0262024067,-0.1937693655,0.0950022191,-0.0176387988,-0.5688117146,-0.053775806,-0.1629761904,-0.0972570553,-0.4674580991,0.2114742845,0.2158976346,0.0896028355,0.4042153656,0.3006412983,0.2042028606,0.2016513646,0.6070529819,-0.2093221992,-0.3547791243,0.0443218388,0.3141504526,0.081910789,0.177616924,0.0606224015,-0.17011033,0.200685814,0.1464149356,-0.2246280462,-0.323405236,-0.0920045674,0.3267407119,0.3529065847,-0.1715489775,-0.3823952675,-0.3127988577,-0.0313100591,0.1885313839,0.4732134342,0.1420394927,-0.3297613263,0.0042914129,-0.2719512284,0.3002656102,-0.0179662202,-0.0041730828,0.258790642,0.1244735271,-0.0329472497,0.0638200045,0.2017904222,-0.1953783631,-0.1708811522,-0.4019609094,0.0959564373,0.3071315587,-0.1999058276,-0.2576290667,0.2660217881,0.194044441,0.4387064874,0.0169971455,-0.0074185338,0.0199898295,-0.1039318219,-0.1370512396,-0.0529770032,0.2784969211,0.1445692927,0.0533135198,0.4216781557,-0.0334113911,-0.1720716804,0.0693692267,-0.0535320565,0.0950190723,0.4306455255,0.2696842551,0.335506022,0.025251301,-0.1482149214,0.0974797383,-0.1257552505,-0.2656867802,0.2667299807,0.3143821061,-0.0797205865,0.0989429504,-0.1258902401,0.0694410652,-0.3220945597,-0.0343445353,-0.0853379816,-0.1496426165,-0.0802808627,-0.0657494068,0.364279598,-0.3379724324,0.0242795926,0.2694472075,-0.2059150934,-0.288422972,-0.1518755108,-0.0767865106,0.5315922499,0.2941078246,-0.1158165112,0.024007896,0.0686455518,-0.4160591662,-0.309743762,0.3407129049,-0.2412345409,-0.1555794626,0.2009787858,0.0987635627,-0.3604650795,-0.1261386573,-0.1306612045,-0.0645978823,0.3372377157,0.2071813345,0.0784945637,-0.1708253771,0.1355777979,-0.4528718889,0.2073252201,0.8914080262,-0.1023142859,-0.0063826209,-0.1526415646,-0.0278965682,0.1243735701,0.3553079963,-0.0161852706,0.3325898349,-0.3431203961,0.092856735,0.1716639251,-0.10762842,-0.2139098197,0.0121155484,0.1867583841,0.0967974141,0.5034436584,0.2218383104,0.2685669065,-0.0544432886,-0.2298940867,0.0698378459,0.0156069836,0.0602037869,-0.2132327706,-0.1308508813,0.0318849385,0.0194860678,-0.0369520262,0.2195641398,0.1360975951,-0.2552815676,0.0255509987,-0.2306274325,0.0509529673,-0.0563530996,0.1623330116,0.0917921513,0.0456483886,0.4361868799,-0.4905918837,0.2230523229,-0.0022498632,0.1985981464,-0.4391951263,-0.2743707597,-0.0583684258,0.2331923544,-0.3910266459,0.1591251194,0.0221546218,0.3127879202,0.3133720756,0.2550627887,-0.0731581673,0.0802614763,-0.0975416601,0.1954384744,-0.3723435104,0.0034481185,-0.071176298,0.068382673,-0.0470243059,0.264408648,0.103337653,-0.1461705863,-0.042986434,0.2023597211,0.2752099931,-0.0221164934,0.0455042571,0.3107895553,0.2325396389,-0.0491344407,-0.038399078,0.1895335317,0.1686585546,0.0750721842,-0.2943840027,0.8053556681,-0.2581625283,0.028535137,-0.0023022937,-0.0071980618,0.0217753276,-0.1347505897,-0.1365982592,-0.1884437501,-0.0732932538,-0.0163164865,0.1072857827,0.4089407623,-0.2961963713,0.157638669,0.7058497667,0.0941348001,0.2832787037,-0.0321235694,0.1921659261,-0.08089865,-0.2282799184,0.3368208706,0.1522255242,0.1965065747,0.1185858622,-0.3717745543,-0.0730491057,-0.1460594982,0.0427722856,-0.1891172081,-0.3544773459,0.3523737192,-0.3063002825,-0.1038096994,-0.5338432193,0.2326877713,0.1194282547,0.251612097,-0.5340343118,-0.2392493188,-0.1169456393,-0.0837991312,-0.228394866,-0.179580465,-0.0798715502,-0.3404607177,0.1645958573,0.0483226292,-0.0415060073,0.2251764238,-0.0276001878,0.177685082,0.0363353752,-0.5385163426,-0.4368271828,0.0511809215,-0.145413205,-0.0151514607,0.2481909692,-0.1409252733,0.3679054081,-0.3222305775,0.2253864855,-0.2745192051,-0.3014227152,0.0428631417,-0.1585089266,0.119652167,-0.0956903771,-0.0779617354,-0.0854469389,-0.0822699219,0.2423255742,-0.2479203939,-0.5795606375,0.2682214081,0.069850184,-0.187886402,-0.2038371116,-0.5165557861,-0.0041087638,0.0617712699,0.1320423484,-0.0713649914,0.1752530336,0.0461078286,0.1794478446,-0.1891895682,0.0000351128,-0.0443066023,-0.2693847418,-0.3226259351,0.6126976013,-0.0839342177,-0.2482605726,-0.0997935906,-0.1879334301,-0.2220841497,0.148715809,-0.3126063943,0.1589475423,-0.5614319444,0.6340059042,0.2777655721,0.2941862047,0.4636279643,0.096457094,0.0151649397,-0.231760785,0.0259325672,0.3365894854,-0.0143845584,0.2851168215,0.1721878797,0.2982102633,-0.2947055399,0.4157005548,0.3359111547,-0.1027194783,0.3535640538,-0.1526918858,0.3624245822,0.0208121259,-0.3244894147,-0.0466853455,0.197660163,0.14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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2230","title":"Keys yielded while generating dataset are not being checked","comments":"@lhoestq I'm glad you liked the idea!\r\nI think that since the keys will be unique and deterministic in the nature themselves, so even if we shuffle the examples according to the hash, a deterministic order would still be maintained (as the keys will always have the same hash, whenever the dataset is generated). \r\nAnd since, we are not dealing with time series data (which would require the data to be in original order), I don't think the order of examples would matter much, as long as the order is deterministic and constant for all users.\r\n\r\nI think that this is also what was originally envisioned as mentioned in the documentation here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/6775661b19d2ec339784f3d84553a3996a1d86c3\/src\/datasets\/builder.py#L973\r\n\r\nAlso, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\nLet me know your thoughts in it! I would be opening a PR soon :)","body":"The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You!","comment_length":171,"text":"Keys yielded while generating dataset are not being checked \n The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You! \n @lhoestq I'm glad you liked the idea!\r\nI think that since the keys will be unique and deterministic in the nature themselves, so even if we shuffle the examples according to the hash, a deterministic order would still be maintained (as the keys will always have the same hash, whenever the dataset is generated). \r\nAnd since, we are not dealing with time series data (which would require the data to be in original order), I don't think the order of examples would matter much, as long as the order is deterministic and constant for all users.\r\n\r\nI think that this is also what was originally envisioned as mentioned in the documentation here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/6775661b19d2ec339784f3d84553a3996a1d86c3\/src\/datasets\/builder.py#L973\r\n\r\nAlso, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\nLet me know your thoughts in it! I would be opening a PR soon :)","embeddings":[0.0275365282,-0.2159314305,0.0291701369,0.4762756228,0.0684194565,-0.236362353,0.4168978631,0.0804130509,0.43124336,0.1339114755,0.1672184616,0.3502569795,-0.0206804313,0.2065166831,-0.0032002178,0.4027804732,0.0770776644,0.0507332645,-0.3874769807,-0.1746384501,-0.5882232189,0.0452296324,-0.1532139629,-0.0614637099,-0.1678526849,0.1618548632,-0.0681429282,-0.0138252173,-0.1972754598,-0.4447283149,0.2861754596,0.395427525,-0.1313844472,0.3610020876,-0.0001243995,-0.031345699,0.0754736513,-0.1916624457,-0.5255250931,0.0262024067,-0.1937693655,0.0950022191,-0.0176387988,-0.5688117146,-0.053775806,-0.1629761904,-0.0972570553,-0.4674580991,0.2114742845,0.2158976346,0.0896028355,0.4042153656,0.3006412983,0.2042028606,0.2016513646,0.6070529819,-0.2093221992,-0.3547791243,0.0443218388,0.3141504526,0.081910789,0.177616924,0.0606224015,-0.17011033,0.200685814,0.1464149356,-0.2246280462,-0.323405236,-0.0920045674,0.3267407119,0.3529065847,-0.1715489775,-0.3823952675,-0.3127988577,-0.0313100591,0.1885313839,0.4732134342,0.1420394927,-0.3297613263,0.0042914129,-0.2719512284,0.3002656102,-0.0179662202,-0.0041730828,0.258790642,0.1244735271,-0.0329472497,0.0638200045,0.2017904222,-0.1953783631,-0.1708811522,-0.4019609094,0.0959564373,0.3071315587,-0.1999058276,-0.2576290667,0.2660217881,0.194044441,0.4387064874,0.0169971455,-0.0074185338,0.0199898295,-0.1039318219,-0.1370512396,-0.0529770032,0.2784969211,0.1445692927,0.0533135198,0.4216781557,-0.0334113911,-0.1720716804,0.0693692267,-0.0535320565,0.0950190723,0.4306455255,0.2696842551,0.335506022,0.025251301,-0.1482149214,0.0974797383,-0.1257552505,-0.2656867802,0.2667299807,0.3143821061,-0.0797205865,0.0989429504,-0.1258902401,0.0694410652,-0.3220945597,-0.0343445353,-0.0853379816,-0.1496426165,-0.0802808627,-0.0657494068,0.364279598,-0.3379724324,0.0242795926,0.2694472075,-0.2059150934,-0.288422972,-0.1518755108,-0.0767865106,0.5315922499,0.2941078246,-0.1158165112,0.024007896,0.0686455518,-0.4160591662,-0.309743762,0.3407129049,-0.2412345409,-0.1555794626,0.2009787858,0.0987635627,-0.3604650795,-0.1261386573,-0.1306612045,-0.0645978823,0.3372377157,0.2071813345,0.0784945637,-0.1708253771,0.1355777979,-0.4528718889,0.2073252201,0.8914080262,-0.1023142859,-0.0063826209,-0.1526415646,-0.0278965682,0.1243735701,0.3553079963,-0.0161852706,0.3325898349,-0.3431203961,0.092856735,0.1716639251,-0.10762842,-0.2139098197,0.0121155484,0.1867583841,0.0967974141,0.5034436584,0.2218383104,0.2685669065,-0.0544432886,-0.2298940867,0.0698378459,0.0156069836,0.0602037869,-0.2132327706,-0.1308508813,0.0318849385,0.0194860678,-0.0369520262,0.2195641398,0.1360975951,-0.2552815676,0.0255509987,-0.2306274325,0.0509529673,-0.0563530996,0.1623330116,0.0917921513,0.0456483886,0.4361868799,-0.4905918837,0.2230523229,-0.0022498632,0.1985981464,-0.4391951263,-0.2743707597,-0.0583684258,0.2331923544,-0.3910266459,0.1591251194,0.0221546218,0.3127879202,0.3133720756,0.2550627887,-0.0731581673,0.0802614763,-0.0975416601,0.1954384744,-0.3723435104,0.0034481185,-0.071176298,0.068382673,-0.0470243059,0.264408648,0.103337653,-0.1461705863,-0.042986434,0.2023597211,0.2752099931,-0.0221164934,0.0455042571,0.3107895553,0.2325396389,-0.0491344407,-0.038399078,0.1895335317,0.1686585546,0.0750721842,-0.2943840027,0.8053556681,-0.2581625283,0.028535137,-0.0023022937,-0.0071980618,0.0217753276,-0.1347505897,-0.1365982592,-0.1884437501,-0.0732932538,-0.0163164865,0.1072857827,0.4089407623,-0.2961963713,0.157638669,0.7058497667,0.0941348001,0.2832787037,-0.0321235694,0.1921659261,-0.08089865,-0.2282799184,0.3368208706,0.1522255242,0.1965065747,0.1185858622,-0.3717745543,-0.0730491057,-0.1460594982,0.0427722856,-0.1891172081,-0.3544773459,0.3523737192,-0.3063002825,-0.1038096994,-0.5338432193,0.2326877713,0.1194282547,0.251612097,-0.5340343118,-0.2392493188,-0.1169456393,-0.0837991312,-0.228394866,-0.179580465,-0.0798715502,-0.3404607177,0.1645958573,0.0483226292,-0.0415060073,0.2251764238,-0.0276001878,0.177685082,0.0363353752,-0.5385163426,-0.4368271828,0.0511809215,-0.145413205,-0.0151514607,0.2481909692,-0.1409252733,0.3679054081,-0.3222305775,0.2253864855,-0.2745192051,-0.3014227152,0.0428631417,-0.1585089266,0.119652167,-0.0956903771,-0.0779617354,-0.0854469389,-0.0822699219,0.2423255742,-0.2479203939,-0.5795606375,0.2682214081,0.069850184,-0.187886402,-0.2038371116,-0.5165557861,-0.0041087638,0.0617712699,0.1320423484,-0.0713649914,0.1752530336,0.0461078286,0.1794478446,-0.1891895682,0.0000351128,-0.0443066023,-0.2693847418,-0.3226259351,0.6126976013,-0.0839342177,-0.2482605726,-0.0997935906,-0.1879334301,-0.2220841497,0.148715809,-0.3126063943,0.1589475423,-0.5614319444,0.6340059042,0.2777655721,0.2941862047,0.4636279643,0.096457094,0.0151649397,-0.231760785,0.0259325672,0.3365894854,-0.0143845584,0.2851168215,0.1721878797,0.2982102633,-0.2947055399,0.4157005548,0.3359111547,-0.1027194783,0.3535640538,-0.1526918858,0.3624245822,0.0208121259,-0.3244894147,-0.0466853455,0.197660163,0.14481419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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2230","title":"Keys yielded while generating dataset are not being checked","comments":"When users load their own data, they expect the order to stay the same. I think that shuffling the data can make things inconvenient.\r\n\r\n> I think that this is also what was originally envisioned as mentioned in the documentation here:\r\n\r\nThis part was originally developed by tensorflow datasets, and tensorflow datasets indeed does the shuffling. However in this library this is probably not what we want in the general case. But if @albertvillanova and @thomwolf you have opinions on this please let us know.\r\n\r\n> Also, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\n\r\nMaybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch, but there might still be duplicates across batches. For 10 000 examples the hashes can just be stored as a python `set`.\r\n\r\nOtherwise if we want full deduplication, we need an extra tool that allows to temporarily save and query hashes that may need to use disk space rather than memory.","body":"The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You!","comment_length":224,"text":"Keys yielded while generating dataset are not being checked \n The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You! \n When users load their own data, they expect the order to stay the same. I think that shuffling the data can make things inconvenient.\r\n\r\n> I think that this is also what was originally envisioned as mentioned in the documentation here:\r\n\r\nThis part was originally developed by tensorflow datasets, and tensorflow datasets indeed does the shuffling. However in this library this is probably not what we want in the general case. But if @albertvillanova and @thomwolf you have opinions on this please let us know.\r\n\r\n> Also, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\n\r\nMaybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch, but there might still be duplicates across batches. For 10 000 examples the hashes can just be stored as a python `set`.\r\n\r\nOtherwise if we want full deduplication, we need an extra tool that allows to temporarily save and query hashes that may need to use disk space rather than memory.","embeddings":[0.0275365282,-0.2159314305,0.0291701369,0.4762756228,0.0684194565,-0.236362353,0.4168978631,0.0804130509,0.43124336,0.1339114755,0.1672184616,0.3502569795,-0.0206804313,0.2065166831,-0.0032002178,0.4027804732,0.0770776644,0.0507332645,-0.3874769807,-0.1746384501,-0.5882232189,0.0452296324,-0.1532139629,-0.0614637099,-0.1678526849,0.1618548632,-0.0681429282,-0.0138252173,-0.1972754598,-0.4447283149,0.2861754596,0.395427525,-0.1313844472,0.3610020876,-0.0001243995,-0.031345699,0.0754736513,-0.1916624457,-0.5255250931,0.0262024067,-0.1937693655,0.0950022191,-0.0176387988,-0.5688117146,-0.053775806,-0.1629761904,-0.0972570553,-0.4674580991,0.2114742845,0.2158976346,0.0896028355,0.4042153656,0.3006412983,0.2042028606,0.2016513646,0.6070529819,-0.2093221992,-0.3547791243,0.0443218388,0.3141504526,0.081910789,0.177616924,0.0606224015,-0.17011033,0.200685814,0.1464149356,-0.2246280462,-0.323405236,-0.0920045674,0.3267407119,0.3529065847,-0.1715489775,-0.3823952675,-0.3127988577,-0.0313100591,0.1885313839,0.4732134342,0.1420394927,-0.3297613263,0.0042914129,-0.2719512284,0.3002656102,-0.0179662202,-0.0041730828,0.258790642,0.1244735271,-0.0329472497,0.0638200045,0.2017904222,-0.1953783631,-0.1708811522,-0.4019609094,0.0959564373,0.3071315587,-0.1999058276,-0.2576290667,0.2660217881,0.194044441,0.4387064874,0.0169971455,-0.0074185338,0.0199898295,-0.1039318219,-0.1370512396,-0.0529770032,0.2784969211,0.1445692927,0.0533135198,0.4216781557,-0.0334113911,-0.1720716804,0.0693692267,-0.0535320565,0.0950190723,0.4306455255,0.2696842551,0.335506022,0.025251301,-0.1482149214,0.0974797383,-0.1257552505,-0.2656867802,0.2667299807,0.3143821061,-0.0797205865,0.0989429504,-0.1258902401,0.0694410652,-0.3220945597,-0.0343445353,-0.0853379816,-0.1496426165,-0.0802808627,-0.0657494068,0.364279598,-0.3379724324,0.0242795926,0.2694472075,-0.2059150934,-0.288422972,-0.1518755108,-0.0767865106,0.5315922499,0.2941078246,-0.1158165112,0.024007896,0.0686455518,-0.4160591662,-0.309743762,0.3407129049,-0.2412345409,-0.1555794626,0.2009787858,0.0987635627,-0.3604650795,-0.1261386573,-0.1306612045,-0.0645978823,0.3372377157,0.2071813345,0.0784945637,-0.1708253771,0.1355777979,-0.4528718889,0.2073252201,0.8914080262,-0.1023142859,-0.0063826209,-0.1526415646,-0.0278965682,0.1243735701,0.3553079963,-0.0161852706,0.3325898349,-0.3431203961,0.092856735,0.1716639251,-0.10762842,-0.2139098197,0.0121155484,0.1867583841,0.0967974141,0.5034436584,0.2218383104,0.2685669065,-0.0544432886,-0.2298940867,0.0698378459,0.0156069836,0.0602037869,-0.2132327706,-0.1308508813,0.0318849385,0.0194860678,-0.0369520262,0.2195641398,0.1360975951,-0.2552815676,0.0255509987,-0.2306274325,0.0509529673,-0.0563530996,0.1623330116,0.0917921513,0.0456483886,0.4361868799,-0.4905918837,0.2230523229,-0.0022498632,0.1985981464,-0.4391951263,-0.2743707597,-0.0583684258,0.2331923544,-0.3910266459,0.1591251194,0.0221546218,0.3127879202,0.3133720756,0.2550627887,-0.0731581673,0.0802614763,-0.0975416601,0.1954384744,-0.3723435104,0.0034481185,-0.071176298,0.068382673,-0.0470243059,0.264408648,0.103337653,-0.1461705863,-0.042986434,0.2023597211,0.2752099931,-0.0221164934,0.0455042571,0.3107895553,0.2325396389,-0.0491344407,-0.038399078,0.1895335317,0.1686585546,0.0750721842,-0.2943840027,0.8053556681,-0.2581625283,0.028535137,-0.0023022937,-0.0071980618,0.0217753276,-0.1347505897,-0.1365982592,-0.1884437501,-0.0732932538,-0.0163164865,0.1072857827,0.4089407623,-0.2961963713,0.157638669,0.7058497667,0.0941348001,0.2832787037,-0.0321235694,0.1921659261,-0.08089865,-0.2282799184,0.3368208706,0.1522255242,0.1965065747,0.1185858622,-0.3717745543,-0.0730491057,-0.1460594982,0.0427722856,-0.1891172081,-0.3544773459,0.3523737192,-0.3063002825,-0.1038096994,-0.5338432193,0.2326877713,0.1194282547,0.251612097,-0.5340343118,-0.2392493188,-0.1169456393,-0.0837991312,-0.228394866,-0.179580465,-0.0798715502,-0.3404607177,0.1645958573,0.0483226292,-0.0415060073,0.2251764238,-0.0276001878,0.177685082,0.0363353752,-0.5385163426,-0.4368271828,0.0511809215,-0.145413205,-0.0151514607,0.2481909692,-0.1409252733,0.3679054081,-0.3222305775,0.2253864855,-0.2745192051,-0.3014227152,0.0428631417,-0.1585089266,0.119652167,-0.0956903771,-0.0779617354,-0.0854469389,-0.0822699219,0.2423255742,-0.2479203939,-0.5795606375,0.2682214081,0.069850184,-0.187886402,-0.2038371116,-0.5165557861,-0.0041087638,0.0617712699,0.1320423484,-0.0713649914,0.1752530336,0.0461078286,0.1794478446,-0.1891895682,0.0000351128,-0.0443066023,-0.2693847418,-0.3226259351,0.6126976013,-0.0839342177,-0.2482605726,-0.0997935906,-0.1879334301,-0.2220841497,0.148715809,-0.3126063943,0.1589475423,-0.5614319444,0.6340059042,0.2777655721,0.2941862047,0.4636279643,0.096457094,0.0151649397,-0.231760785,0.0259325672,0.3365894854,-0.0143845584,0.2851168215,0.1721878797,0.2982102633,-0.2947055399,0.4157005548,0.3359111547,-0.1027194783,0.3535640538,-0.1526918858,0.3624245822,0.0208121259,-0.3244894147,-0.0466853455,0.197660163,0.144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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2230","title":"Keys yielded while generating dataset are not being checked","comments":"Yes I think we want to keep the original order by default and only shuffle when the user ask for it (for instance by calling `dataset.shuffle()`). That\u2019s how I had it in mind originally.","body":"The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You!","comment_length":34,"text":"Keys yielded while generating dataset are not being checked \n The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You! \n Yes I think we want to keep the original order by default and only shuffle when the user ask for it (for instance by calling `dataset.shuffle()`). That\u2019s how I had it in mind originally.","embeddings":[0.0275365282,-0.2159314305,0.0291701369,0.4762756228,0.0684194565,-0.236362353,0.4168978631,0.0804130509,0.43124336,0.1339114755,0.1672184616,0.3502569795,-0.0206804313,0.2065166831,-0.0032002178,0.4027804732,0.0770776644,0.0507332645,-0.3874769807,-0.1746384501,-0.5882232189,0.0452296324,-0.1532139629,-0.0614637099,-0.1678526849,0.1618548632,-0.0681429282,-0.0138252173,-0.1972754598,-0.4447283149,0.2861754596,0.395427525,-0.1313844472,0.3610020876,-0.0001243995,-0.031345699,0.0754736513,-0.1916624457,-0.5255250931,0.0262024067,-0.1937693655,0.0950022191,-0.0176387988,-0.5688117146,-0.053775806,-0.1629761904,-0.0972570553,-0.4674580991,0.2114742845,0.2158976346,0.0896028355,0.4042153656,0.3006412983,0.2042028606,0.2016513646,0.6070529819,-0.2093221992,-0.3547791243,0.0443218388,0.3141504526,0.081910789,0.177616924,0.0606224015,-0.17011033,0.200685814,0.1464149356,-0.2246280462,-0.323405236,-0.0920045674,0.3267407119,0.3529065847,-0.1715489775,-0.3823952675,-0.3127988577,-0.0313100591,0.1885313839,0.4732134342,0.1420394927,-0.3297613263,0.0042914129,-0.2719512284,0.3002656102,-0.0179662202,-0.0041730828,0.258790642,0.1244735271,-0.0329472497,0.0638200045,0.2017904222,-0.1953783631,-0.1708811522,-0.4019609094,0.0959564373,0.3071315587,-0.1999058276,-0.2576290667,0.2660217881,0.194044441,0.4387064874,0.0169971455,-0.0074185338,0.0199898295,-0.1039318219,-0.1370512396,-0.0529770032,0.2784969211,0.1445692927,0.0533135198,0.4216781557,-0.0334113911,-0.1720716804,0.0693692267,-0.0535320565,0.0950190723,0.4306455255,0.2696842551,0.335506022,0.025251301,-0.1482149214,0.0974797383,-0.1257552505,-0.2656867802,0.2667299807,0.3143821061,-0.0797205865,0.0989429504,-0.1258902401,0.0694410652,-0.3220945597,-0.0343445353,-0.0853379816,-0.1496426165,-0.0802808627,-0.0657494068,0.364279598,-0.3379724324,0.0242795926,0.2694472075,-0.2059150934,-0.288422972,-0.1518755108,-0.0767865106,0.5315922499,0.2941078246,-0.1158165112,0.024007896,0.0686455518,-0.4160591662,-0.309743762,0.3407129049,-0.2412345409,-0.1555794626,0.2009787858,0.0987635627,-0.3604650795,-0.1261386573,-0.1306612045,-0.0645978823,0.3372377157,0.2071813345,0.0784945637,-0.1708253771,0.1355777979,-0.4528718889,0.2073252201,0.8914080262,-0.1023142859,-0.0063826209,-0.1526415646,-0.0278965682,0.1243735701,0.3553079963,-0.0161852706,0.3325898349,-0.3431203961,0.092856735,0.1716639251,-0.10762842,-0.2139098197,0.0121155484,0.1867583841,0.0967974141,0.5034436584,0.2218383104,0.2685669065,-0.0544432886,-0.2298940867,0.0698378459,0.0156069836,0.0602037869,-0.2132327706,-0.1308508813,0.0318849385,0.0194860678,-0.0369520262,0.2195641398,0.1360975951,-0.2552815676,0.0255509987,-0.2306274325,0.0509529673,-0.0563530996,0.1623330116,0.0917921513,0.0456483886,0.4361868799,-0.4905918837,0.2230523229,-0.0022498632,0.1985981464,-0.4391951263,-0.2743707597,-0.0583684258,0.2331923544,-0.3910266459,0.1591251194,0.0221546218,0.3127879202,0.3133720756,0.2550627887,-0.0731581673,0.0802614763,-0.0975416601,0.1954384744,-0.3723435104,0.0034481185,-0.071176298,0.068382673,-0.0470243059,0.264408648,0.103337653,-0.1461705863,-0.042986434,0.2023597211,0.2752099931,-0.0221164934,0.0455042571,0.3107895553,0.2325396389,-0.0491344407,-0.038399078,0.1895335317,0.1686585546,0.0750721842,-0.2943840027,0.8053556681,-0.2581625283,0.028535137,-0.0023022937,-0.0071980618,0.0217753276,-0.1347505897,-0.1365982592,-0.1884437501,-0.0732932538,-0.0163164865,0.1072857827,0.4089407623,-0.2961963713,0.157638669,0.7058497667,0.0941348001,0.2832787037,-0.0321235694,0.1921659261,-0.08089865,-0.2282799184,0.3368208706,0.1522255242,0.1965065747,0.1185858622,-0.3717745543,-0.0730491057,-0.1460594982,0.0427722856,-0.1891172081,-0.3544773459,0.3523737192,-0.3063002825,-0.1038096994,-0.5338432193,0.2326877713,0.1194282547,0.251612097,-0.5340343118,-0.2392493188,-0.1169456393,-0.0837991312,-0.228394866,-0.179580465,-0.0798715502,-0.3404607177,0.1645958573,0.0483226292,-0.0415060073,0.2251764238,-0.0276001878,0.177685082,0.0363353752,-0.5385163426,-0.4368271828,0.0511809215,-0.145413205,-0.0151514607,0.2481909692,-0.1409252733,0.3679054081,-0.3222305775,0.2253864855,-0.2745192051,-0.3014227152,0.0428631417,-0.1585089266,0.119652167,-0.0956903771,-0.0779617354,-0.0854469389,-0.0822699219,0.2423255742,-0.2479203939,-0.5795606375,0.2682214081,0.069850184,-0.187886402,-0.2038371116,-0.5165557861,-0.0041087638,0.0617712699,0.1320423484,-0.0713649914,0.1752530336,0.0461078286,0.1794478446,-0.1891895682,0.0000351128,-0.0443066023,-0.2693847418,-0.3226259351,0.6126976013,-0.0839342177,-0.2482605726,-0.0997935906,-0.1879334301,-0.2220841497,0.148715809,-0.3126063943,0.1589475423,-0.5614319444,0.6340059042,0.2777655721,0.2941862047,0.4636279643,0.096457094,0.0151649397,-0.231760785,0.0259325672,0.3365894854,-0.0143845584,0.2851168215,0.1721878797,0.2982102633,-0.2947055399,0.4157005548,0.3359111547,-0.1027194783,0.3535640538,-0.1526918858,0.3624245822,0.0208121259,-0.3244894147,-0.0466853455,0.197660163,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2230","title":"Keys yielded while generating dataset are not being checked","comments":"Hey @lhoestq, I just had a more in-depth look at the original TFDS code about why the keys and hash were used in the first place.\r\n\r\nIn my opinion, the only use that the `hash(key)` serves is that it allows us to shuffle the examples in a deterministic order (as each example will always yield the same key and thus, the same hash on every system) so that the same dataset is generated for each user, irrespective of the order the examples are yielded by the dataset builder on different user systems.\r\n\r\nOtherwise, if we are not shuffling, then while yielding and writing the data, after getting the key and hashing it for an example, I can't quite see the use of the hash or the key. The hash will simply be generated for each example but not actually used anywhere?\r\n\r\n@lhoestq @thomwolf It would be great if you could explain a bit more about the usage of keys. Thanks!\r\n","body":"The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You!","comment_length":160,"text":"Keys yielded while generating dataset are not being checked \n The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You! \n Hey @lhoestq, I just had a more in-depth look at the original TFDS code about why the keys and hash were used in the first place.\r\n\r\nIn my opinion, the only use that the `hash(key)` serves is that it allows us to shuffle the examples in a deterministic order (as each example will always yield the same key and thus, the same hash on every system) so that the same dataset is generated for each user, irrespective of the order the examples are yielded by the dataset builder on different user systems.\r\n\r\nOtherwise, if we are not shuffling, then while yielding and writing the data, after getting the key and hashing it for an example, I can't quite see the use of the hash or the key. The hash will simply be generated for each example but not actually used anywhere?\r\n\r\n@lhoestq @thomwolf It would be great if you could explain a bit more about the usage of keys. Thanks!\r\n","embeddings":[0.0275365282,-0.2159314305,0.0291701369,0.4762756228,0.0684194565,-0.236362353,0.4168978631,0.0804130509,0.43124336,0.1339114755,0.1672184616,0.3502569795,-0.0206804313,0.2065166831,-0.0032002178,0.4027804732,0.0770776644,0.0507332645,-0.3874769807,-0.1746384501,-0.5882232189,0.0452296324,-0.1532139629,-0.0614637099,-0.1678526849,0.1618548632,-0.0681429282,-0.0138252173,-0.1972754598,-0.4447283149,0.2861754596,0.395427525,-0.1313844472,0.3610020876,-0.0001243995,-0.031345699,0.0754736513,-0.1916624457,-0.5255250931,0.0262024067,-0.1937693655,0.0950022191,-0.0176387988,-0.5688117146,-0.053775806,-0.1629761904,-0.0972570553,-0.4674580991,0.2114742845,0.2158976346,0.0896028355,0.4042153656,0.3006412983,0.2042028606,0.2016513646,0.6070529819,-0.2093221992,-0.3547791243,0.0443218388,0.3141504526,0.081910789,0.177616924,0.0606224015,-0.17011033,0.200685814,0.1464149356,-0.2246280462,-0.323405236,-0.0920045674,0.3267407119,0.3529065847,-0.1715489775,-0.3823952675,-0.3127988577,-0.0313100591,0.1885313839,0.4732134342,0.1420394927,-0.3297613263,0.0042914129,-0.2719512284,0.3002656102,-0.0179662202,-0.0041730828,0.258790642,0.1244735271,-0.0329472497,0.0638200045,0.2017904222,-0.1953783631,-0.1708811522,-0.4019609094,0.0959564373,0.3071315587,-0.1999058276,-0.2576290667,0.2660217881,0.194044441,0.4387064874,0.0169971455,-0.0074185338,0.0199898295,-0.1039318219,-0.1370512396,-0.0529770032,0.2784969211,0.1445692927,0.0533135198,0.4216781557,-0.0334113911,-0.1720716804,0.0693692267,-0.0535320565,0.0950190723,0.4306455255,0.2696842551,0.335506022,0.025251301,-0.1482149214,0.0974797383,-0.1257552505,-0.2656867802,0.2667299807,0.3143821061,-0.0797205865,0.0989429504,-0.1258902401,0.0694410652,-0.3220945597,-0.0343445353,-0.0853379816,-0.1496426165,-0.0802808627,-0.0657494068,0.364279598,-0.3379724324,0.0242795926,0.2694472075,-0.2059150934,-0.288422972,-0.1518755108,-0.0767865106,0.5315922499,0.2941078246,-0.1158165112,0.024007896,0.0686455518,-0.4160591662,-0.309743762,0.3407129049,-0.2412345409,-0.1555794626,0.2009787858,0.0987635627,-0.3604650795,-0.1261386573,-0.1306612045,-0.0645978823,0.3372377157,0.2071813345,0.0784945637,-0.1708253771,0.1355777979,-0.4528718889,0.2073252201,0.8914080262,-0.1023142859,-0.0063826209,-0.1526415646,-0.0278965682,0.1243735701,0.3553079963,-0.0161852706,0.3325898349,-0.3431203961,0.092856735,0.1716639251,-0.10762842,-0.2139098197,0.0121155484,0.1867583841,0.0967974141,0.5034436584,0.2218383104,0.2685669065,-0.0544432886,-0.2298940867,0.0698378459,0.0156069836,0.0602037869,-0.2132327706,-0.1308508813,0.0318849385,0.0194860678,-0.0369520262,0.2195641398,0.1360975951,-0.2552815676,0.0255509987,-0.2306274325,0.0509529673,-0.0563530996,0.1623330116,0.0917921513,0.0456483886,0.4361868799,-0.4905918837,0.2230523229,-0.0022498632,0.1985981464,-0.4391951263,-0.2743707597,-0.0583684258,0.2331923544,-0.3910266459,0.1591251194,0.0221546218,0.3127879202,0.3133720756,0.2550627887,-0.0731581673,0.0802614763,-0.0975416601,0.1954384744,-0.3723435104,0.0034481185,-0.071176298,0.068382673,-0.0470243059,0.264408648,0.103337653,-0.1461705863,-0.042986434,0.2023597211,0.2752099931,-0.0221164934,0.0455042571,0.3107895553,0.2325396389,-0.0491344407,-0.038399078,0.1895335317,0.1686585546,0.0750721842,-0.2943840027,0.8053556681,-0.2581625283,0.028535137,-0.0023022937,-0.0071980618,0.0217753276,-0.1347505897,-0.1365982592,-0.1884437501,-0.0732932538,-0.0163164865,0.1072857827,0.4089407623,-0.2961963713,0.157638669,0.7058497667,0.0941348001,0.2832787037,-0.0321235694,0.1921659261,-0.08089865,-0.2282799184,0.3368208706,0.1522255242,0.1965065747,0.1185858622,-0.3717745543,-0.0730491057,-0.1460594982,0.0427722856,-0.1891172081,-0.3544773459,0.3523737192,-0.3063002825,-0.1038096994,-0.5338432193,0.2326877713,0.1194282547,0.251612097,-0.5340343118,-0.2392493188,-0.1169456393,-0.0837991312,-0.228394866,-0.179580465,-0.0798715502,-0.3404607177,0.1645958573,0.0483226292,-0.0415060073,0.2251764238,-0.0276001878,0.177685082,0.0363353752,-0.5385163426,-0.4368271828,0.0511809215,-0.145413205,-0.0151514607,0.2481909692,-0.1409252733,0.3679054081,-0.3222305775,0.2253864855,-0.2745192051,-0.3014227152,0.0428631417,-0.1585089266,0.119652167,-0.0956903771,-0.0779617354,-0.0854469389,-0.0822699219,0.2423255742,-0.2479203939,-0.5795606375,0.2682214081,0.069850184,-0.187886402,-0.2038371116,-0.5165557861,-0.0041087638,0.0617712699,0.1320423484,-0.0713649914,0.1752530336,0.0461078286,0.1794478446,-0.1891895682,0.0000351128,-0.0443066023,-0.2693847418,-0.3226259351,0.6126976013,-0.0839342177,-0.2482605726,-0.0997935906,-0.1879334301,-0.2220841497,0.148715809,-0.3126063943,0.1589475423,-0.5614319444,0.6340059042,0.2777655721,0.2941862047,0.4636279643,0.096457094,0.0151649397,-0.231760785,0.0259325672,0.3365894854,-0.0143845584,0.2851168215,0.1721878797,0.2982102633,-0.2947055399,0.4157005548,0.3359111547,-0.1027194783,0.3535640538,-0.1526918858,0.3624245822,0.0208121259,-0.3244894147,-0.0466853455,0.197660163,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2230","title":"Keys yielded while generating dataset are not being checked","comments":"In `datasets` the keys are currently ignored.\r\nFor shuffling we don't use the keys. Instead we shuffle an array of indices. Since both the original order of the dataset and the indices shuffling are deterministic, then `dataset.shuffle` is deterministic as well.\r\nWe can use it to:\r\n1. detect duplicates\r\n2. verify that the generation order is indeed deterministic\r\n3. maybe more ?","body":"The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You!","comment_length":62,"text":"Keys yielded while generating dataset are not being checked \n The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You! \n In `datasets` the keys are currently ignored.\r\nFor shuffling we don't use the keys. Instead we shuffle an array of indices. Since both the original order of the dataset and the indices shuffling are deterministic, then `dataset.shuffle` is deterministic as well.\r\nWe can use it to:\r\n1. detect duplicates\r\n2. verify that the generation order is indeed deterministic\r\n3. maybe more 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2230","title":"Keys yielded while generating dataset are not being checked","comments":"Thanks a lot @lhoestq. I think I understand what we need to do now. The keys can indeed be used for detecting duplicates in generated examples as well as ensuring the order.\r\n\r\n> Maybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch,\r\n\r\nI think that checking for duplicates in every batch independently would be sufficient as the probability of collisions using something like `MD5` is very low. I would be opening a draft PR soon. It would be great to have your guidance. Thanks!","body":"The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You!","comment_length":119,"text":"Keys yielded while generating dataset are not being checked \n The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.\r\nCurrently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\nEven after having a tuple as key, the dataset is generated without any warning.\r\n\r\nAlso, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):\r\n```\r\n>>> import datasets\r\n>>> nik = datasets.load_dataset('anli')\r\nDownloading and preparing dataset anli\/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\anli\\plain_text\\0.1.0\\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...\r\n0 examples [00:00, ? examples\/s]1        {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: \"Rete filoviaria di Parma\" ) forms part of the public transport network of the city and \"comune\" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}\r\n2021-04-16 12:38:14.483968: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n1 examples [00:01,  1.87s\/ examples]1    {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Sharron Macready was a popular character through the 1980's.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': \"Bastedo didn't keep any pets because of her views on animal rights.\", 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}\r\n1        {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 \u2013 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage\/science fiction adventure series \"The Champions\". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}\r\n```\r\nHere also, the dataset was generated successfuly even hough it had same keys without any warning.\r\n\r\nThe reason appears to stem from here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L988\r\nHere, although it has access to every key, but it is not being checked and the example is written directly:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/src\/datasets\/builder.py#L992\r\n\r\nI would like to take this issue if you allow me. Thank You! \n Thanks a lot @lhoestq. I think I understand what we need to do now. The keys can indeed be used for detecting duplicates in generated examples as well as ensuring the order.\r\n\r\n> Maybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch,\r\n\r\nI think that checking for duplicates in every batch independently would be sufficient as the probability of collisions using something like `MD5` is very low. I would be opening a draft PR soon. It would be great to have your guidance. Thanks!","embeddings":[0.0275365282,-0.2159314305,0.0291701369,0.4762756228,0.0684194565,-0.236362353,0.4168978631,0.0804130509,0.43124336,0.1339114755,0.1672184616,0.3502569795,-0.0206804313,0.2065166831,-0.0032002178,0.4027804732,0.0770776644,0.0507332645,-0.3874769807,-0.1746384501,-0.5882232189,0.0452296324,-0.1532139629,-0.0614637099,-0.1678526849,0.1618548632,-0.0681429282,-0.0138252173,-0.1972754598,-0.4447283149,0.2861754596,0.395427525,-0.1313844472,0.3610020876,-0.0001243995,-0.031345699,0.0754736513,-0.1916624457,-0.5255250931,0.0262024067,-0.1937693655,0.0950022191,-0.0176387988,-0.5688117146,-0.053775806,-0.1629761904,-0.0972570553,-0.4674580991,0.2114742845,0.2158976346,0.0896028355,0.4042153656,0.3006412983,0.2042028606,0.2016513646,0.6070529819,-0.2093221992,-0.3547791243,0.0443218388,0.3141504526,0.081910789,0.177616924,0.0606224015,-0.17011033,0.200685814,0.1464149356,-0.2246280462,-0.323405236,-0.0920045674,0.3267407119,0.3529065847,-0.1715489775,-0.3823952675,-0.3127988577,-0.0313100591,0.1885313839,0.4732134342,0.1420394927,-0.3297613263,0.0042914129,-0.2719512284,0.3002656102,-0.0179662202,-0.0041730828,0.258790642,0.1244735271,-0.0329472497,0.0638200045,0.2017904222,-0.1953783631,-0.1708811522,-0.4019609094,0.0959564373,0.3071315587,-0.1999058276,-0.2576290667,0.2660217881,0.194044441,0.4387064874,0.0169971455,-0.0074185338,0.0199898295,-0.1039318219,-0.1370512396,-0.0529770032,0.2784969211,0.1445692927,0.0533135198,0.4216781557,-0.0334113911,-0.1720716804,0.0693692267,-0.0535320565,0.0950190723,0.4306455255,0.2696842551,0.335506022,0.025251301,-0.1482149214,0.0974797383,-0.1257552505,-0.2656867802,0.2667299807,0.3143821061,-0.0797205865,0.0989429504,-0.1258902401,0.0694410652,-0.3220945597,-0.0343445353,-0.0853379816,-0.1496426165,-0.0802808627,-0.0657494068,0.364279598,-0.3379724324,0.0242795926,0.2694472075,-0.2059150934,-0.288422972,-0.1518755108,-0.0767865106,0.5315922499,0.2941078246,-0.1158165112,0.024007896,0.0686455518,-0.4160591662,-0.309743762,0.3407129049,-0.2412345409,-0.1555794626,0.2009787858,0.0987635627,-0.3604650795,-0.1261386573,-0.1306612045,-0.0645978823,0.3372377157,0.2071813345,0.0784945637,-0.1708253771,0.1355777979,-0.4528718889,0.2073252201,0.8914080262,-0.1023142859,-0.0063826209,-0.1526415646,-0.0278965682,0.1243735701,0.3553079963,-0.0161852706,0.3325898349,-0.3431203961,0.092856735,0.1716639251,-0.10762842,-0.2139098197,0.0121155484,0.1867583841,0.0967974141,0.5034436584,0.2218383104,0.2685669065,-0.0544432886,-0.2298940867,0.0698378459,0.0156069836,0.0602037869,-0.2132327706,-0.1308508813,0.0318849385,0.0194860678,-0.0369520262,0.2195641398,0.1360975951,-0.2552815676,0.0255509987,-0.2306274325,0.0509529673,-0.0563530996,0.1623330116,0.0917921513,0.0456483886,0.4361868799,-0.4905918837,0.2230523229,-0.0022498632,0.1985981464,-0.4391951263,-0.2743707597,-0.0583684258,0.2331923544,-0.3910266459,0.1591251194,0.0221546218,0.3127879202,0.3133720756,0.2550627887,-0.0731581673,0.0802614763,-0.0975416601,0.1954384744,-0.3723435104,0.0034481185,-0.071176298,0.068382673,-0.0470243059,0.264408648,0.103337653,-0.1461705863,-0.042986434,0.2023597211,0.2752099931,-0.0221164934,0.0455042571,0.3107895553,0.2325396389,-0.0491344407,-0.038399078,0.1895335317,0.1686585546,0.0750721842,-0.2943840027,0.8053556681,-0.2581625283,0.028535137,-0.0023022937,-0.0071980618,0.0217753276,-0.1347505897,-0.1365982592,-0.1884437501,-0.0732932538,-0.0163164865,0.1072857827,0.4089407623,-0.2961963713,0.157638669,0.7058497667,0.0941348001,0.2832787037,-0.0321235694,0.1921659261,-0.08089865,-0.2282799184,0.3368208706,0.1522255242,0.1965065747,0.1185858622,-0.3717745543,-0.0730491057,-0.1460594982,0.0427722856,-0.1891172081,-0.3544773459,0.3523737192,-0.3063002825,-0.1038096994,-0.5338432193,0.2326877713,0.1194282547,0.251612097,-0.5340343118,-0.2392493188,-0.1169456393,-0.0837991312,-0.228394866,-0.179580465,-0.0798715502,-0.3404607177,0.1645958573,0.0483226292,-0.0415060073,0.2251764238,-0.0276001878,0.177685082,0.0363353752,-0.5385163426,-0.4368271828,0.0511809215,-0.145413205,-0.0151514607,0.2481909692,-0.1409252733,0.3679054081,-0.3222305775,0.2253864855,-0.2745192051,-0.3014227152,0.0428631417,-0.1585089266,0.119652167,-0.0956903771,-0.0779617354,-0.0854469389,-0.0822699219,0.2423255742,-0.2479203939,-0.5795606375,0.2682214081,0.069850184,-0.187886402,-0.2038371116,-0.5165557861,-0.0041087638,0.0617712699,0.1320423484,-0.0713649914,0.1752530336,0.0461078286,0.1794478446,-0.1891895682,0.0000351128,-0.0443066023,-0.2693847418,-0.3226259351,0.6126976013,-0.0839342177,-0.2482605726,-0.0997935906,-0.1879334301,-0.2220841497,0.148715809,-0.3126063943,0.1589475423,-0.5614319444,0.6340059042,0.2777655721,0.2941862047,0.4636279643,0.096457094,0.0151649397,-0.231760785,0.0259325672,0.3365894854,-0.0143845584,0.2851168215,0.1721878797,0.2982102633,-0.2947055399,0.4157005548,0.3359111547,-0.1027194783,0.3535640538,-0.1526918858,0.3624245822,0.0208121259,-0.3244894147,-0.0466853455,0.197660163,0.144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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2229","title":"`xnli` dataset creating a tuple key while yielding instead of `str` or `int`","comments":"Hi ! Sure sounds good. Also if you find other datasets that use tuples instead of str\/int, you can also fix them !\r\nthanks :)","body":"When using  `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\n\r\nSince, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset. \r\nI'm up for sending a fix for this, I think we can simply use `file_idx + \"_\" + row_idx` as a unique key instead of a tuple.","comment_length":25,"text":"`xnli` dataset creating a tuple key while yielding instead of `str` or `int` \n When using  `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\n\r\nSince, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset. \r\nI'm up for sending a fix for this, I think we can simply use `file_idx + \"_\" + row_idx` as a unique key instead of a tuple. \n Hi ! Sure sounds good. Also if you find other datasets that use tuples instead of str\/int, you can also fix them !\r\nthanks :)","embeddings":[-0.0539387055,0.0509747416,0.0451682322,0.1262666285,0.2513764799,0.0173006449,0.4449461997,0.2987630963,0.6008678675,0.2404715419,0.1092354357,0.4075706303,0.0195070487,0.2159906626,-0.0843307152,-0.0561311804,-0.0785400942,0.2352073789,-0.2244517952,-0.1137270257,-0.5031872392,0.121004805,0.0294014718,0.2606830299,-0.2010565847,0.0400427841,0.1937448382,0.1910863668,-0.0840927213,-0.252540797,0.4881781042,0.0085605886,0.0756866187,0.1829353571,-0.0001135974,0.0208701994,0.2497083247,-0.0734922364,-0.468265444,-0.2629375756,-0.2167611122,0.2286738306,-0.0234127901,-0.2467126846,-0.0012098321,-0.0569351614,-0.0673003197,-0.261190474,0.2860363722,0.1601759195,0.1450576633,0.4815440476,0.0250980984,-0.138239041,0.3516049385,0.2497404218,-0.1786918938,0.1878612489,0.1838114411,-0.2809710205,-0.0479889512,0.2159287632,0.206644848,0.1380616128,0.3269072473,0.2719631493,0.0823418498,-0.2623186409,-0.0092996899,0.3619779944,0.1439969987,-0.1113360003,-0.1411518604,0.0992559567,0.1987168491,-0.2580661774,0.1839321256,0.0912623852,-0.4491142333,-0.0272621475,0.1974137127,-0.1070303172,-0.2575809062,0.2337970138,0.0154427402,0.0173182525,0.0591291599,0.1151014492,0.0064529758,-0.3979426324,-0.1829753369,0.0894437879,0.2683078051,0.1556880623,-0.3238301873,-0.031098729,0.1199393868,-0.3682415187,0.3530477881,-0.0212667901,0.1273285896,0.0893297866,-0.3878022432,0.1040480211,-0.0136975339,0.0737677738,0.0256564636,0.0063200607,0.331030786,-0.1388094574,-0.053821817,0.0349739715,-0.0082260771,0.0409489498,-0.0041416218,-0.1950260252,0.4094110429,0.0185403768,-0.3756034076,0.203909114,0.0801996663,-0.0092292009,-0.0260075964,0.367501229,0.2517862022,0.1281282455,-0.032025855,0.1792397201,-0.2550680041,-0.4093573689,-0.2063461691,-0.0526213236,-0.0127909714,0.0976488143,0.2107612491,-0.3897715211,0.0392958447,0.3133696616,0.25810498,-0.0556731485,-0.0147759169,0.0536787212,-0.0691242367,0.2281856686,0.1762597859,0.129270792,0.2934645712,-0.6828520298,-0.0041344548,0.169910565,-0.5129545927,-0.3525079787,-0.2636779845,0.1806254387,0.1077662855,-0.0586749725,-0.3332086802,-0.0793849826,0.0376669317,0.2584780753,-0.0324632302,-0.3713117838,-0.0931690559,-0.3605386615,0.1431995332,0.3623300493,-0.2543599606,-0.0424474962,0.0729089081,-0.0691700354,0.1776707172,0.6391195059,-0.1121900827,0.1469333768,-0.2595930099,0.279083252,0.4829978645,-0.2314783931,-0.4842320085,0.4521036148,-0.1890470535,-0.0753735006,0.0764777213,0.3655757904,-0.0417426042,0.0079897316,0.2896290123,0.2973432541,-0.0450821184,0.0502691045,-0.3076674342,-0.2173958719,0.1462385952,0.2962858975,-0.0078178467,0.2304442376,-0.0029742457,0.1335355192,0.222263366,-0.3926811516,0.130096823,-0.0981249958,0.4231389761,0.1318524778,0.0693470463,0.1597889811,-0.5406081676,0.2111240923,-0.0602562279,0.1853376627,-0.1759155393,-0.2527042925,-0.2737975121,0.1418508142,-0.3421969414,0.2631456554,0.0014369828,-0.1117820516,-0.0338203795,-0.0560011975,-0.1530446261,0.1724900454,-0.3591039777,0.1286535114,-0.4930763245,0.3700057864,-0.0432023853,-0.0199131649,-0.0034728546,0.1393671334,-0.0881554857,-0.1522151828,-0.1518664956,0.2224430293,0.0088391555,0.0696530566,0.0315627344,0.1348724663,0.2902286649,-0.2376737893,0.0486095995,0.4267165363,0.079688929,-0.1021378264,-0.2944548726,0.6457003951,-0.2812168002,0.1006652564,0.068083182,0.1324638277,0.1876256913,-0.0304539204,-0.1810988188,-0.4469127655,0.1374972761,-0.0984272584,0.1333730817,-0.0339754373,-0.5189490914,0.1274168342,0.475038141,0.0838380754,0.1823892891,0.3433338404,-0.0326617137,0.0029704338,-0.2844299376,0.3413448036,0.3974466622,0.0594391115,-0.1956835389,-0.0011278983,-0.3301291764,-0.2890697122,0.0736719295,-0.3107881546,-0.0680122003,0.1784962118,0.0737751871,-0.0021064084,-0.3311927021,-0.0381827056,0.0670965463,0.3988737464,-0.4998731017,-0.2168361992,-0.2133771032,-0.6131717563,-0.2112469077,0.0100624897,0.1155965626,-0.3808224201,0.0488855802,0.1438613832,-0.0783905312,-0.0846129805,0.0677924603,0.3079422414,0.1276211441,-0.1672043353,-0.0977199301,-0.1523034275,-0.173420772,0.0324888304,0.1174714863,-0.0184826218,0.3863341212,-0.0920158625,0.0982084647,-0.4978992641,-0.3933636844,0.0216765832,-0.1079630181,0.1131493822,0.2016664892,0.4266102314,-0.1588403881,-0.3726124167,0.4755396247,-0.077461496,-0.3099055886,0.2457005084,-0.1300873607,-0.0226199105,-0.2289124876,-0.5481781363,0.0909843743,-0.3140561581,0.1613312066,0.0778830871,0.2737427354,0.1724705994,0.0965671316,0.0370361693,0.4510773122,-0.4143409729,-0.4166817963,-0.1695429236,0.3733316362,-0.339122355,-0.4973729551,-0.1566298753,-0.0387999229,0.1970476806,0.1855373234,-0.3948681951,0.0366139039,-0.1812382042,0.5950683951,-0.0583579578,0.0350835882,0.0124826701,-0.0552740805,-0.0447310694,-0.0964671671,0.1253857613,0.3055225313,0.1056367159,0.111787416,0.4784660637,0.4796535075,-0.044311326,0.6656321287,0.3739884198,-0.1023455635,0.4134171009,-0.2668378651,-0.0025653716,-0.2134555727,-0.109148778,-0.0565790012,0.0691796765,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2229","title":"`xnli` dataset creating a tuple key while yielding instead of `str` or `int`","comments":"@lhoestq I have sent a PR for fixing the issue. Would be great if you could have a look! Thanks!","body":"When using  `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\n\r\nSince, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset. \r\nI'm up for sending a fix for this, I think we can simply use `file_idx + \"_\" + row_idx` as a unique key instead of a tuple.","comment_length":20,"text":"`xnli` dataset creating a tuple key while yielding instead of `str` or `int` \n When using  `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/56346791aed417306d054d89bd693d6b7eab17f7\/datasets\/xnli\/xnli.py#L196\r\n\r\nSince, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset. \r\nI'm up for sending a fix for this, I think we can simply use `file_idx + \"_\" + row_idx` as a unique key instead of a tuple. \n @lhoestq I have sent a PR for fixing the issue. Would be great if you could have a look! Thanks!","embeddings":[-0.0702305734,0.0961900726,0.0778552964,0.1273570806,0.1900975257,0.0126102399,0.449765563,0.2994563282,0.6488567591,0.1779723167,0.0878629908,0.4414196014,0.0609022342,0.2075343877,-0.0426086709,-0.0038821478,-0.0590648577,0.2475437671,-0.270088613,-0.1440696567,-0.4942212999,0.1290112287,0.0590818264,0.2485807985,-0.1913190633,0.0619562566,0.2353725433,0.2271416336,-0.0578649268,-0.231456086,0.5725955963,0.0114073344,0.0351137668,0.2210238129,-0.0001182187,0.0057875635,0.235965848,-0.0812724978,-0.4586208165,-0.2777384222,-0.1937724501,0.2858184576,-0.0185022056,-0.1956772506,-0.0241901614,-0.0318657011,-0.0890224576,-0.2912680805,0.3045458198,0.1411118954,0.1012161225,0.5076861978,-0.0015598519,-0.1039695516,0.2827213705,0.3210789859,-0.1544620097,0.2094181031,0.1812248081,-0.2964422405,-0.0707189068,0.2409769893,0.2307791561,0.1223838925,0.3430539966,0.2623575032,0.0293429811,-0.2340623736,0.0456491373,0.3561387956,0.152802363,-0.158778891,-0.0808353052,0.1110712066,0.1855397373,-0.1912935376,0.2102704644,0.085092403,-0.4934516847,-0.0450517051,0.171436578,-0.1658021212,-0.2685093582,0.2046644688,0.0310249925,0.0256582052,0.0522603653,0.1541383266,-0.0537844896,-0.3798465729,-0.180950731,0.0566228628,0.2785687745,0.186706081,-0.3462030292,-0.0397396572,0.1426015645,-0.3174471557,0.4102703035,0.0238242261,0.0719214156,0.065637365,-0.4437951744,0.0565213561,0.0189705268,0.0064283973,0.0634887815,0.0258461908,0.3252578676,-0.1443240941,-0.0521679446,0.0622096881,0.0331528038,0.0548977889,-0.0047640242,-0.2382578254,0.4292405844,-0.0054675061,-0.3453379869,0.2238122076,0.1361610293,-0.0346963927,-0.0265237689,0.3213921487,0.2820681334,0.1355551928,-0.064050734,0.201431334,-0.2725867033,-0.4351241291,-0.1945147365,-0.06521862,-0.0201770738,0.1389552504,0.2019282579,-0.4108029306,0.025599461,0.3576527536,0.2412360311,-0.0624580346,-0.0854442045,0.0695406795,-0.0439071059,0.2065785527,0.1528370529,0.1327440888,0.2927932143,-0.6724526882,-0.0264560003,0.1488302201,-0.4627384841,-0.3408790231,-0.2876138985,0.1304148287,0.1075053364,-0.0338144116,-0.3273074925,-0.0331979506,0.0768313408,0.3027576804,-0.0145910513,-0.3540088832,-0.1421949714,-0.3390742242,0.1374310553,0.4490889013,-0.3026025891,-0.0341366641,0.1084006056,-0.039577052,0.1453768313,0.6637606025,-0.1132184714,0.1557540148,-0.2688517272,0.2169886976,0.4657373726,-0.2953676581,-0.4891296327,0.5142925382,-0.244742021,-0.0906946883,0.0965175927,0.3413774371,-0.0515389852,0.0072896979,0.280374229,0.2847390175,-0.0704721808,0.0136054931,-0.2741324902,-0.2110413462,0.1374817789,0.2579405606,0.0063828318,0.2574510872,-0.0397424847,0.1318580806,0.2551363111,-0.3740133047,0.1646961719,-0.1387715042,0.442397356,0.133719027,0.0754648671,0.1614752561,-0.5667373538,0.2011476904,-0.0271508321,0.1870721132,-0.2295346558,-0.2263766229,-0.3379972875,0.1709311455,-0.3567269146,0.2806505263,-0.0644999817,-0.1205384731,-0.0141854128,-0.0527514927,-0.119579725,0.217986241,-0.3730079234,0.1698513329,-0.513522923,0.3820358515,0.0151542323,-0.0033492115,-0.0094868299,0.1608085185,-0.0974757448,-0.1528729945,-0.1390003413,0.2045148015,-0.0318154059,0.0998232067,0.0785870627,0.1124184728,0.3132004738,-0.2474359721,0.0973865911,0.420461148,0.1018082201,-0.1078212857,-0.2806727886,0.6413202882,-0.265537709,0.1465586722,0.023377249,0.0997146517,0.1449086219,-0.0183355734,-0.156501472,-0.456420511,0.0755424201,-0.086596258,0.1723823696,-0.0415025093,-0.5107375383,0.1601004303,0.4665774107,0.0978005826,0.1503409594,0.3520168066,-0.0461252257,-0.0372990482,-0.2707844973,0.3472121358,0.4186730981,0.0066455025,-0.2218784839,0.0232526213,-0.3446961641,-0.2819954157,0.0617921017,-0.3365485668,-0.08019384,0.2138845026,0.0640868768,-0.0274222363,-0.3360664845,-0.0535807647,0.0640417561,0.35137555,-0.5140116215,-0.1834724396,-0.1738094836,-0.6247996092,-0.2197042555,0.0682751611,0.1395656765,-0.3413124979,0.0422627516,0.1681332439,-0.0272621587,-0.1199104786,0.0574179366,0.3083279431,0.1335732192,-0.1832105517,-0.0579218417,-0.1691828668,-0.1520417184,-0.0155690648,0.1158931032,-0.0518910177,0.3363802433,-0.0917729735,0.0576459654,-0.5386189222,-0.3667121828,0.0629966781,-0.1329158992,0.096696198,0.1896066815,0.4263233542,-0.1497290581,-0.3702141047,0.4545177221,-0.1328839958,-0.2982057333,0.210893482,-0.1885073185,0.0058833337,-0.1925978214,-0.5648372769,0.1293588877,-0.2554282844,0.1841124594,0.1010496616,0.2554558814,0.1573609859,0.0598342307,-0.0213592052,0.4327559769,-0.4605207741,-0.4601733088,-0.1789412349,0.3468564749,-0.3206701279,-0.4748046994,-0.1672707796,-0.0536935776,0.2448612005,0.1639297158,-0.3726163805,0.0362476744,-0.1461523473,0.6259244084,-0.0784577876,0.056387011,0.003346428,-0.0454089269,-0.0069139008,-0.1038952619,0.1286156476,0.3307235241,0.1364132166,0.1208571345,0.5113132,0.457803607,-0.0726824403,0.7307398319,0.3878905475,-0.0939111114,0.3839215636,-0.2607840598,0.0283578411,-0.1875331849,-0.0771837384,-0.0698270202,0.089350909,0.224711880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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2226","title":"Batched map fails when removing all columns","comments":"I found the problem. I called `set_format` on some columns before. This makes it crash. Here is a complete example to reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nsst = load_dataset(\"sst\")\r\nsst.set_format(\"torch\", columns=[\"label\"], output_all_columns=True)\r\nds = sst[\"train\"]\r\n\r\n# crashes\r\nds.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    remove_columns=ds.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=1,\r\n    batched=True,\r\n)\r\n```","body":"Hi @lhoestq ,\r\n\r\nI'm hijacking this issue, because I'm currently trying to do the approach you recommend:\r\n\r\n> Currently the optimal setup for single-column computations is probably to do something like\r\n> \r\n> ```python\r\n> result = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n> ```\r\n\r\nHere is my code: (see edit, in which I added a simplified version\r\n\r\n```\r\nThis is the error:\r\n```bash\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000\r\n```\r\nI wonder why this error occurs, when I delete every column? Can you give me a hint?\r\n\r\n### Edit:\r\nI preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error?  I can iterate over the\r\ncomplete dataset and print every sample before calling map. There seems to be no other problem with the dataset.\r\n\r\nI tried to simplify the code that crashes:\r\n\r\n```python\r\n# works\r\nlog.debug(dataset.column_names)\r\nlog.debug(dataset)\r\nfor i, sample in enumerate(dataset):\r\n    log.debug(i, sample)\r\n\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n)\r\n```\r\n\r\n```\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000\r\n```\r\n\r\nEdit2: \r\n\r\nMay this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:\r\n\r\n```python\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n    features=datasets.Features(\r\n        {\r\n              \"a\": datasets.Sequence(datasets.Value(\"int32\"))\r\n         }\r\n    )\r\n)\r\n```\r\n\r\n```\r\n File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1704, in _map_single\r\n    writer.write_batch(batch)\r\n  File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 312, in write_batch\r\n    col_type = schema.field(col).type if schema is not None else None\r\n  File \"pyarrow\/types.pxi\", line 1341, in pyarrow.lib.Schema.field\r\nKeyError: 'Column tokens does not exist in schema'\r\n```\r\n\r\n_Originally posted by @villmow in https:\/\/github.com\/huggingface\/datasets\/issues\/2193#issuecomment-820230874_","comment_length":49,"text":"Batched map fails when removing all columns \n Hi @lhoestq ,\r\n\r\nI'm hijacking this issue, because I'm currently trying to do the approach you recommend:\r\n\r\n> Currently the optimal setup for single-column computations is probably to do something like\r\n> \r\n> ```python\r\n> result = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n> ```\r\n\r\nHere is my code: (see edit, in which I added a simplified version\r\n\r\n```\r\nThis is the error:\r\n```bash\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000\r\n```\r\nI wonder why this error occurs, when I delete every column? Can you give me a hint?\r\n\r\n### Edit:\r\nI preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error?  I can iterate over the\r\ncomplete dataset and print every sample before calling map. There seems to be no other problem with the dataset.\r\n\r\nI tried to simplify the code that crashes:\r\n\r\n```python\r\n# works\r\nlog.debug(dataset.column_names)\r\nlog.debug(dataset)\r\nfor i, sample in enumerate(dataset):\r\n    log.debug(i, sample)\r\n\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n)\r\n```\r\n\r\n```\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000\r\n```\r\n\r\nEdit2: \r\n\r\nMay this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:\r\n\r\n```python\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n    features=datasets.Features(\r\n        {\r\n              \"a\": datasets.Sequence(datasets.Value(\"int32\"))\r\n         }\r\n    )\r\n)\r\n```\r\n\r\n```\r\n File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1704, in _map_single\r\n    writer.write_batch(batch)\r\n  File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 312, in write_batch\r\n    col_type = schema.field(col).type if schema is not None else None\r\n  File \"pyarrow\/types.pxi\", line 1341, in pyarrow.lib.Schema.field\r\nKeyError: 'Column tokens does not exist in schema'\r\n```\r\n\r\n_Originally posted by @villmow in https:\/\/github.com\/huggingface\/datasets\/issues\/2193#issuecomment-820230874_ \n I found the problem. I called `set_format` on some columns before. This makes it crash. Here is a complete example to reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nsst = load_dataset(\"sst\")\r\nsst.set_format(\"torch\", columns=[\"label\"], output_all_columns=True)\r\nds = sst[\"train\"]\r\n\r\n# crashes\r\nds.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    remove_columns=ds.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=1,\r\n    batched=True,\r\n)\r\n```","embeddings":[-0.1897988468,0.1186695099,0.0169567857,0.0345561393,0.2957544327,0.1997561753,0.7953292131,0.3469538689,0.2681218088,0.5083371997,0.1278548092,0.3986316621,-0.2221594751,-0.1497610509,-0.2561763227,-0.2258543968,0.180315271,0.1939368397,-0.1543451697,0.1138284206,-0.3259884417,-0.074356012,-0.5531980395,0.1137052774,0.0998652279,-0.281159699,-0.2459139675,-0.2607480586,0.0405273177,-0.3617533743,0.0900468752,-0.2502871752,0.1128109172,0.4839785695,-0.0001168795,0.0218302999,0.1716712415,-0.0334697515,-0.1326156855,0.0640774146,-0.1687964052,-0.1176634431,-0.0496759899,-0.3645480275,0.5358587503,-0.2108844817,-0.3315514624,-0.381175071,-0.0405727848,0.1007553786,0.169487834,0.1469649225,-0.0236559641,0.1243837774,0.1997407079,-0.0343382359,-0.1041169465,-0.008293177,0.4995136261,-0.4657591283,-0.0907840803,0.2961730361,-0.2830587029,-0.0230159666,0.1004072875,-0.0464141592,0.2637531757,-0.4090450108,0.1707249731,0.1164800003,-0.0180447102,-0.3283998966,-0.0548055284,-0.184510991,-0.1374211758,-0.5785285234,-0.1803824157,0.0719124228,-0.2919106185,-0.1150881946,-0.457372129,0.159992218,0.0326213799,0.1772422045,-0.0578017868,0.3028842211,0.151088357,0.5388099551,0.0138682658,-0.0603085943,-0.0003699868,-0.0276932251,0.0329861157,0.367708981,-0.493185699,-0.1327268332,-0.0320847854,-0.1082744449,0.2840714455,-0.2979654074,0.094810091,-0.0292264242,0.5917070508,0.1859064102,0.4440813661,0.2730583549,0.021104537,0.1322196424,0.0927797928,-0.053203471,0.0608418174,-0.0009549259,0.2914585471,-0.081994772,0.5020079613,0.2725434303,0.0259534437,-0.0072190715,-0.075207144,-0.158616364,-0.3384186327,0.204627499,-0.0911616161,0.0677755699,0.2737882435,0.0878871754,-0.3580185771,0.1353775859,-0.0966755003,-0.1938144863,-0.1133242175,0.1158341393,-0.3451522887,-0.1509786099,0.3529440463,0.1030584052,0.1164731979,-0.0047300588,-0.010748487,-0.1193328947,-0.2432302088,-0.1289957613,0.2258381397,0.02893872,0.1047094688,0.1680702269,0.2093173712,0.033626534,-0.0138567258,0.2573041618,-0.0536132939,-0.3020163178,-0.1343097836,0.1198306754,0.0918057412,0.2040550411,-0.3438605666,0.0384183526,0.4002182484,0.0612391569,0.0008474094,-0.290682137,0.220656991,-0.2849721313,-0.1069678515,0.2588521838,-0.5109861493,0.158715412,0.0789557025,0.0771709159,0.2378589064,0.2749264538,-0.2287099212,0.1179107651,-0.1785375029,0.287909776,0.1158813164,-0.121906437,-0.1291884631,0.2582710683,-0.1710102111,0.0061789071,-0.2403122038,-0.183501333,0.6115528941,-0.2941825986,0.1319783777,-0.0414869897,-0.3053473234,0.1779927462,-0.1582277268,-0.2711120248,-0.0471705981,-0.0894282609,0.1199706793,-0.01697715,0.302390933,-0.451308012,0.2286178917,-0.3265037835,0.339217186,0.4150714874,0.0918067545,-0.035193719,0.166398108,-0.1615534425,-0.7281162143,0.1981071383,0.1456974298,-0.0879978985,-0.3534245789,-0.2648589909,0.0765319467,0.1501424313,0.0226836484,0.1255517751,0.0711953044,-0.2024617195,0.0510789268,-0.3482041061,-0.2364073396,-0.2209415287,-0.1390867531,0.1733426154,-0.1894963533,-0.3441658318,-0.0279753543,-0.3571982086,-0.0204043388,0.1966486275,0.239356637,-0.1307834387,-0.0186508615,0.4590831697,-0.1979596764,-0.0776889548,-0.4247601926,-0.0810873657,-0.0454146862,-0.0406259447,-0.0342591852,-0.1599503905,0.142451182,-0.144739449,-0.3011455238,0.0029289648,-0.2381207794,0.5006266832,-0.1498275399,-0.0373831503,-0.0561359078,0.0231335517,-0.0771553442,-0.153137818,-0.1096757501,-0.0489469655,-0.2542680204,-0.0305233877,0.0260866899,-0.1773487478,0.221904546,-0.0419845395,0.0335538946,-0.0073624742,-0.2589181364,0.0027919528,0.339520216,-0.0899918675,0.4603516459,0.1365824491,0.0819970593,-0.0418755561,0.1916162968,0.0838154778,0.1106001586,0.2706128061,0.0925673023,0.0756221414,0.3986678123,0.0161472093,-0.2661878169,-0.351077348,0.1753147542,0.4957439005,-0.2044825107,-0.043800015,-0.0830236226,0.0995051414,0.2236653417,-0.1830222905,0.0713529959,-0.4394610822,-0.1476138383,0.1156395972,-0.1264729351,0.2542539537,-0.1595221162,-0.0608217195,0.1876654774,-0.0264898054,-0.1460788846,-0.1495200843,-0.1710772216,0.0146625154,-0.03508294,-0.144578144,0.0407584049,0.4789995551,-0.3435450494,-0.2255327553,-0.0635434538,-0.0063208234,-0.5026287436,-0.0688748881,0.3389750123,0.2059350759,-0.3035939336,-0.1560991108,0.0831781253,-0.1239539161,0.0785744712,0.2210019976,0.1313579977,-0.1047570184,-0.2336309254,0.1117874458,0.0731343701,-0.2843957543,0.1609170586,-0.1200424731,0.2389975786,-0.2521990538,0.2278801054,-0.2472717613,-0.0553630665,-0.40374735,0.0974972919,-0.010698528,0.1542848647,0.1120021343,-0.0112327524,0.0451950282,-0.0525667556,0.032991603,0.3330432177,-0.3355278969,0.046339605,-0.1026915088,0.135222882,-0.0032963625,0.232637465,0.3257702589,0.2052081823,0.0374394618,-0.0169250686,-0.164213717,0.0817854702,-0.0682317913,-0.003880508,0.0999058709,0.4984065294,0.2369377017,0.5979442,0.204280436,-0.0856824219,0.0948240235,-0.1327353269,0.0399422608,-0.1180508658,-0.3469358385,0.0949326307,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2226","title":"Batched map fails when removing all columns","comments":"Thanks for reporting and for providing this code to reproduce the issue, this is really helpful !","body":"Hi @lhoestq ,\r\n\r\nI'm hijacking this issue, because I'm currently trying to do the approach you recommend:\r\n\r\n> Currently the optimal setup for single-column computations is probably to do something like\r\n> \r\n> ```python\r\n> result = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n> ```\r\n\r\nHere is my code: (see edit, in which I added a simplified version\r\n\r\n```\r\nThis is the error:\r\n```bash\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000\r\n```\r\nI wonder why this error occurs, when I delete every column? Can you give me a hint?\r\n\r\n### Edit:\r\nI preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error?  I can iterate over the\r\ncomplete dataset and print every sample before calling map. There seems to be no other problem with the dataset.\r\n\r\nI tried to simplify the code that crashes:\r\n\r\n```python\r\n# works\r\nlog.debug(dataset.column_names)\r\nlog.debug(dataset)\r\nfor i, sample in enumerate(dataset):\r\n    log.debug(i, sample)\r\n\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n)\r\n```\r\n\r\n```\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000\r\n```\r\n\r\nEdit2: \r\n\r\nMay this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:\r\n\r\n```python\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n    features=datasets.Features(\r\n        {\r\n              \"a\": datasets.Sequence(datasets.Value(\"int32\"))\r\n         }\r\n    )\r\n)\r\n```\r\n\r\n```\r\n File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1704, in _map_single\r\n    writer.write_batch(batch)\r\n  File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 312, in write_batch\r\n    col_type = schema.field(col).type if schema is not None else None\r\n  File \"pyarrow\/types.pxi\", line 1341, in pyarrow.lib.Schema.field\r\nKeyError: 'Column tokens does not exist in schema'\r\n```\r\n\r\n_Originally posted by @villmow in https:\/\/github.com\/huggingface\/datasets\/issues\/2193#issuecomment-820230874_","comment_length":17,"text":"Batched map fails when removing all columns \n Hi @lhoestq ,\r\n\r\nI'm hijacking this issue, because I'm currently trying to do the approach you recommend:\r\n\r\n> Currently the optimal setup for single-column computations is probably to do something like\r\n> \r\n> ```python\r\n> result = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n> ```\r\n\r\nHere is my code: (see edit, in which I added a simplified version\r\n\r\n```\r\nThis is the error:\r\n```bash\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000\r\n```\r\nI wonder why this error occurs, when I delete every column? Can you give me a hint?\r\n\r\n### Edit:\r\nI preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error?  I can iterate over the\r\ncomplete dataset and print every sample before calling map. There seems to be no other problem with the dataset.\r\n\r\nI tried to simplify the code that crashes:\r\n\r\n```python\r\n# works\r\nlog.debug(dataset.column_names)\r\nlog.debug(dataset)\r\nfor i, sample in enumerate(dataset):\r\n    log.debug(i, sample)\r\n\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n)\r\n```\r\n\r\n```\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000\r\n```\r\n\r\nEdit2: \r\n\r\nMay this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:\r\n\r\n```python\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n    features=datasets.Features(\r\n        {\r\n              \"a\": datasets.Sequence(datasets.Value(\"int32\"))\r\n         }\r\n    )\r\n)\r\n```\r\n\r\n```\r\n File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1704, in _map_single\r\n    writer.write_batch(batch)\r\n  File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 312, in write_batch\r\n    col_type = schema.field(col).type if schema is not None else None\r\n  File \"pyarrow\/types.pxi\", line 1341, in pyarrow.lib.Schema.field\r\nKeyError: 'Column tokens does not exist in schema'\r\n```\r\n\r\n_Originally posted by @villmow in https:\/\/github.com\/huggingface\/datasets\/issues\/2193#issuecomment-820230874_ \n Thanks for reporting and for providing this code to reproduce the issue, this is really helpful !","embeddings":[-0.1897988468,0.1186695099,0.0169567857,0.0345561393,0.2957544327,0.1997561753,0.7953292131,0.3469538689,0.2681218088,0.5083371997,0.1278548092,0.3986316621,-0.2221594751,-0.1497610509,-0.2561763227,-0.2258543968,0.180315271,0.1939368397,-0.1543451697,0.1138284206,-0.3259884417,-0.074356012,-0.5531980395,0.1137052774,0.0998652279,-0.281159699,-0.2459139675,-0.2607480586,0.0405273177,-0.3617533743,0.0900468752,-0.2502871752,0.1128109172,0.4839785695,-0.0001168795,0.0218302999,0.1716712415,-0.0334697515,-0.1326156855,0.0640774146,-0.1687964052,-0.1176634431,-0.0496759899,-0.3645480275,0.5358587503,-0.2108844817,-0.3315514624,-0.381175071,-0.0405727848,0.1007553786,0.169487834,0.1469649225,-0.0236559641,0.1243837774,0.1997407079,-0.0343382359,-0.1041169465,-0.008293177,0.4995136261,-0.4657591283,-0.0907840803,0.2961730361,-0.2830587029,-0.0230159666,0.1004072875,-0.0464141592,0.2637531757,-0.4090450108,0.1707249731,0.1164800003,-0.0180447102,-0.3283998966,-0.0548055284,-0.184510991,-0.1374211758,-0.5785285234,-0.1803824157,0.0719124228,-0.2919106185,-0.1150881946,-0.457372129,0.159992218,0.0326213799,0.1772422045,-0.0578017868,0.3028842211,0.151088357,0.5388099551,0.0138682658,-0.0603085943,-0.0003699868,-0.0276932251,0.0329861157,0.367708981,-0.493185699,-0.1327268332,-0.0320847854,-0.1082744449,0.2840714455,-0.2979654074,0.094810091,-0.0292264242,0.5917070508,0.1859064102,0.4440813661,0.2730583549,0.021104537,0.1322196424,0.0927797928,-0.053203471,0.0608418174,-0.0009549259,0.2914585471,-0.081994772,0.5020079613,0.2725434303,0.0259534437,-0.0072190715,-0.075207144,-0.158616364,-0.3384186327,0.204627499,-0.0911616161,0.0677755699,0.2737882435,0.0878871754,-0.3580185771,0.1353775859,-0.0966755003,-0.1938144863,-0.1133242175,0.1158341393,-0.3451522887,-0.1509786099,0.3529440463,0.1030584052,0.1164731979,-0.0047300588,-0.010748487,-0.1193328947,-0.2432302088,-0.1289957613,0.2258381397,0.02893872,0.1047094688,0.1680702269,0.2093173712,0.033626534,-0.0138567258,0.2573041618,-0.0536132939,-0.3020163178,-0.1343097836,0.1198306754,0.0918057412,0.2040550411,-0.3438605666,0.0384183526,0.4002182484,0.0612391569,0.0008474094,-0.290682137,0.220656991,-0.2849721313,-0.1069678515,0.2588521838,-0.5109861493,0.158715412,0.0789557025,0.0771709159,0.2378589064,0.2749264538,-0.2287099212,0.1179107651,-0.1785375029,0.287909776,0.1158813164,-0.121906437,-0.1291884631,0.2582710683,-0.1710102111,0.0061789071,-0.2403122038,-0.183501333,0.6115528941,-0.2941825986,0.1319783777,-0.0414869897,-0.3053473234,0.1779927462,-0.1582277268,-0.2711120248,-0.0471705981,-0.0894282609,0.1199706793,-0.01697715,0.302390933,-0.451308012,0.2286178917,-0.3265037835,0.339217186,0.4150714874,0.0918067545,-0.035193719,0.166398108,-0.1615534425,-0.7281162143,0.1981071383,0.1456974298,-0.0879978985,-0.3534245789,-0.2648589909,0.0765319467,0.1501424313,0.0226836484,0.1255517751,0.0711953044,-0.2024617195,0.0510789268,-0.3482041061,-0.2364073396,-0.2209415287,-0.1390867531,0.1733426154,-0.1894963533,-0.3441658318,-0.0279753543,-0.3571982086,-0.0204043388,0.1966486275,0.239356637,-0.1307834387,-0.0186508615,0.4590831697,-0.1979596764,-0.0776889548,-0.4247601926,-0.0810873657,-0.0454146862,-0.0406259447,-0.0342591852,-0.1599503905,0.142451182,-0.144739449,-0.3011455238,0.0029289648,-0.2381207794,0.5006266832,-0.1498275399,-0.0373831503,-0.0561359078,0.0231335517,-0.0771553442,-0.153137818,-0.1096757501,-0.0489469655,-0.2542680204,-0.0305233877,0.0260866899,-0.1773487478,0.221904546,-0.0419845395,0.0335538946,-0.0073624742,-0.2589181364,0.0027919528,0.339520216,-0.0899918675,0.4603516459,0.1365824491,0.0819970593,-0.0418755561,0.1916162968,0.0838154778,0.1106001586,0.2706128061,0.0925673023,0.0756221414,0.3986678123,0.0161472093,-0.2661878169,-0.351077348,0.1753147542,0.4957439005,-0.2044825107,-0.043800015,-0.0830236226,0.0995051414,0.2236653417,-0.1830222905,0.0713529959,-0.4394610822,-0.1476138383,0.1156395972,-0.1264729351,0.2542539537,-0.1595221162,-0.0608217195,0.1876654774,-0.0264898054,-0.1460788846,-0.1495200843,-0.1710772216,0.0146625154,-0.03508294,-0.144578144,0.0407584049,0.4789995551,-0.3435450494,-0.2255327553,-0.0635434538,-0.0063208234,-0.5026287436,-0.0688748881,0.3389750123,0.2059350759,-0.3035939336,-0.1560991108,0.0831781253,-0.1239539161,0.0785744712,0.2210019976,0.1313579977,-0.1047570184,-0.2336309254,0.1117874458,0.0731343701,-0.2843957543,0.1609170586,-0.1200424731,0.2389975786,-0.2521990538,0.2278801054,-0.2472717613,-0.0553630665,-0.40374735,0.0974972919,-0.010698528,0.1542848647,0.1120021343,-0.0112327524,0.0451950282,-0.0525667556,0.032991603,0.3330432177,-0.3355278969,0.046339605,-0.1026915088,0.135222882,-0.0032963625,0.232637465,0.3257702589,0.2052081823,0.0374394618,-0.0169250686,-0.164213717,0.0817854702,-0.0682317913,-0.003880508,0.0999058709,0.4984065294,0.2369377017,0.5979442,0.204280436,-0.0856824219,0.0948240235,-0.1327353269,0.0399422608,-0.1180508658,-0.3469358385,0.0949326307,-0.3334673345,-0.0153741492,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2226","title":"Batched map fails when removing all columns","comments":"I merged a fix, it should work on `master` now :)\r\nWe'll do a new release soon !","body":"Hi @lhoestq ,\r\n\r\nI'm hijacking this issue, because I'm currently trying to do the approach you recommend:\r\n\r\n> Currently the optimal setup for single-column computations is probably to do something like\r\n> \r\n> ```python\r\n> result = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n> ```\r\n\r\nHere is my code: (see edit, in which I added a simplified version\r\n\r\n```\r\nThis is the error:\r\n```bash\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000\r\n```\r\nI wonder why this error occurs, when I delete every column? Can you give me a hint?\r\n\r\n### Edit:\r\nI preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error?  I can iterate over the\r\ncomplete dataset and print every sample before calling map. There seems to be no other problem with the dataset.\r\n\r\nI tried to simplify the code that crashes:\r\n\r\n```python\r\n# works\r\nlog.debug(dataset.column_names)\r\nlog.debug(dataset)\r\nfor i, sample in enumerate(dataset):\r\n    log.debug(i, sample)\r\n\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n)\r\n```\r\n\r\n```\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000\r\n```\r\n\r\nEdit2: \r\n\r\nMay this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:\r\n\r\n```python\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n    features=datasets.Features(\r\n        {\r\n              \"a\": datasets.Sequence(datasets.Value(\"int32\"))\r\n         }\r\n    )\r\n)\r\n```\r\n\r\n```\r\n File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1704, in _map_single\r\n    writer.write_batch(batch)\r\n  File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 312, in write_batch\r\n    col_type = schema.field(col).type if schema is not None else None\r\n  File \"pyarrow\/types.pxi\", line 1341, in pyarrow.lib.Schema.field\r\nKeyError: 'Column tokens does not exist in schema'\r\n```\r\n\r\n_Originally posted by @villmow in https:\/\/github.com\/huggingface\/datasets\/issues\/2193#issuecomment-820230874_","comment_length":18,"text":"Batched map fails when removing all columns \n Hi @lhoestq ,\r\n\r\nI'm hijacking this issue, because I'm currently trying to do the approach you recommend:\r\n\r\n> Currently the optimal setup for single-column computations is probably to do something like\r\n> \r\n> ```python\r\n> result = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n> ```\r\n\r\nHere is my code: (see edit, in which I added a simplified version\r\n\r\n```\r\nThis is the error:\r\n```bash\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000\r\n```\r\nI wonder why this error occurs, when I delete every column? Can you give me a hint?\r\n\r\n### Edit:\r\nI preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error?  I can iterate over the\r\ncomplete dataset and print every sample before calling map. There seems to be no other problem with the dataset.\r\n\r\nI tried to simplify the code that crashes:\r\n\r\n```python\r\n# works\r\nlog.debug(dataset.column_names)\r\nlog.debug(dataset)\r\nfor i, sample in enumerate(dataset):\r\n    log.debug(i, sample)\r\n\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n)\r\n```\r\n\r\n```\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000\r\n```\r\n\r\nEdit2: \r\n\r\nMay this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:\r\n\r\n```python\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n    features=datasets.Features(\r\n        {\r\n              \"a\": datasets.Sequence(datasets.Value(\"int32\"))\r\n         }\r\n    )\r\n)\r\n```\r\n\r\n```\r\n File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1704, in _map_single\r\n    writer.write_batch(batch)\r\n  File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 312, in write_batch\r\n    col_type = schema.field(col).type if schema is not None else None\r\n  File \"pyarrow\/types.pxi\", line 1341, in pyarrow.lib.Schema.field\r\nKeyError: 'Column tokens does not exist in schema'\r\n```\r\n\r\n_Originally posted by @villmow in https:\/\/github.com\/huggingface\/datasets\/issues\/2193#issuecomment-820230874_ \n I merged a fix, it should work on `master` now :)\r\nWe'll do a new release soon !","embeddings":[-0.1897988468,0.1186695099,0.0169567857,0.0345561393,0.2957544327,0.1997561753,0.7953292131,0.3469538689,0.2681218088,0.5083371997,0.1278548092,0.3986316621,-0.2221594751,-0.1497610509,-0.2561763227,-0.2258543968,0.180315271,0.1939368397,-0.1543451697,0.1138284206,-0.3259884417,-0.074356012,-0.5531980395,0.1137052774,0.0998652279,-0.281159699,-0.2459139675,-0.2607480586,0.0405273177,-0.3617533743,0.0900468752,-0.2502871752,0.1128109172,0.4839785695,-0.0001168795,0.0218302999,0.1716712415,-0.0334697515,-0.1326156855,0.0640774146,-0.1687964052,-0.1176634431,-0.0496759899,-0.3645480275,0.5358587503,-0.2108844817,-0.3315514624,-0.381175071,-0.0405727848,0.1007553786,0.169487834,0.1469649225,-0.0236559641,0.1243837774,0.1997407079,-0.0343382359,-0.1041169465,-0.008293177,0.4995136261,-0.4657591283,-0.0907840803,0.2961730361,-0.2830587029,-0.0230159666,0.1004072875,-0.0464141592,0.2637531757,-0.4090450108,0.1707249731,0.1164800003,-0.0180447102,-0.3283998966,-0.0548055284,-0.184510991,-0.1374211758,-0.5785285234,-0.1803824157,0.0719124228,-0.2919106185,-0.1150881946,-0.457372129,0.159992218,0.0326213799,0.1772422045,-0.0578017868,0.3028842211,0.151088357,0.5388099551,0.0138682658,-0.0603085943,-0.0003699868,-0.0276932251,0.0329861157,0.367708981,-0.493185699,-0.1327268332,-0.0320847854,-0.1082744449,0.2840714455,-0.2979654074,0.094810091,-0.0292264242,0.5917070508,0.1859064102,0.4440813661,0.2730583549,0.021104537,0.1322196424,0.0927797928,-0.053203471,0.0608418174,-0.0009549259,0.2914585471,-0.081994772,0.5020079613,0.2725434303,0.0259534437,-0.0072190715,-0.075207144,-0.158616364,-0.3384186327,0.204627499,-0.0911616161,0.0677755699,0.2737882435,0.0878871754,-0.3580185771,0.1353775859,-0.0966755003,-0.1938144863,-0.1133242175,0.1158341393,-0.3451522887,-0.1509786099,0.3529440463,0.1030584052,0.1164731979,-0.0047300588,-0.010748487,-0.1193328947,-0.2432302088,-0.1289957613,0.2258381397,0.02893872,0.1047094688,0.1680702269,0.2093173712,0.033626534,-0.0138567258,0.2573041618,-0.0536132939,-0.3020163178,-0.1343097836,0.1198306754,0.0918057412,0.2040550411,-0.3438605666,0.0384183526,0.4002182484,0.0612391569,0.0008474094,-0.290682137,0.220656991,-0.2849721313,-0.1069678515,0.2588521838,-0.5109861493,0.158715412,0.0789557025,0.0771709159,0.2378589064,0.2749264538,-0.2287099212,0.1179107651,-0.1785375029,0.287909776,0.1158813164,-0.121906437,-0.1291884631,0.2582710683,-0.1710102111,0.0061789071,-0.2403122038,-0.183501333,0.6115528941,-0.2941825986,0.1319783777,-0.0414869897,-0.3053473234,0.1779927462,-0.1582277268,-0.2711120248,-0.0471705981,-0.0894282609,0.1199706793,-0.01697715,0.302390933,-0.451308012,0.2286178917,-0.3265037835,0.339217186,0.4150714874,0.0918067545,-0.035193719,0.166398108,-0.1615534425,-0.7281162143,0.1981071383,0.1456974298,-0.0879978985,-0.3534245789,-0.2648589909,0.0765319467,0.1501424313,0.0226836484,0.1255517751,0.0711953044,-0.2024617195,0.0510789268,-0.3482041061,-0.2364073396,-0.2209415287,-0.1390867531,0.1733426154,-0.1894963533,-0.3441658318,-0.0279753543,-0.3571982086,-0.0204043388,0.1966486275,0.239356637,-0.1307834387,-0.0186508615,0.4590831697,-0.1979596764,-0.0776889548,-0.4247601926,-0.0810873657,-0.0454146862,-0.0406259447,-0.0342591852,-0.1599503905,0.142451182,-0.144739449,-0.3011455238,0.0029289648,-0.2381207794,0.5006266832,-0.1498275399,-0.0373831503,-0.0561359078,0.0231335517,-0.0771553442,-0.153137818,-0.1096757501,-0.0489469655,-0.2542680204,-0.0305233877,0.0260866899,-0.1773487478,0.221904546,-0.0419845395,0.0335538946,-0.0073624742,-0.2589181364,0.0027919528,0.339520216,-0.0899918675,0.4603516459,0.1365824491,0.0819970593,-0.0418755561,0.1916162968,0.0838154778,0.1106001586,0.2706128061,0.0925673023,0.0756221414,0.3986678123,0.0161472093,-0.2661878169,-0.351077348,0.1753147542,0.4957439005,-0.2044825107,-0.043800015,-0.0830236226,0.0995051414,0.2236653417,-0.1830222905,0.0713529959,-0.4394610822,-0.1476138383,0.1156395972,-0.1264729351,0.2542539537,-0.1595221162,-0.0608217195,0.1876654774,-0.0264898054,-0.1460788846,-0.1495200843,-0.1710772216,0.0146625154,-0.03508294,-0.144578144,0.0407584049,0.4789995551,-0.3435450494,-0.2255327553,-0.0635434538,-0.0063208234,-0.5026287436,-0.0688748881,0.3389750123,0.2059350759,-0.3035939336,-0.1560991108,0.0831781253,-0.1239539161,0.0785744712,0.2210019976,0.1313579977,-0.1047570184,-0.2336309254,0.1117874458,0.0731343701,-0.2843957543,0.1609170586,-0.1200424731,0.2389975786,-0.2521990538,0.2278801054,-0.2472717613,-0.0553630665,-0.40374735,0.0974972919,-0.010698528,0.1542848647,0.1120021343,-0.0112327524,0.0451950282,-0.0525667556,0.032991603,0.3330432177,-0.3355278969,0.046339605,-0.1026915088,0.135222882,-0.0032963625,0.232637465,0.3257702589,0.2052081823,0.0374394618,-0.0169250686,-0.164213717,0.0817854702,-0.0682317913,-0.003880508,0.0999058709,0.4984065294,0.2369377017,0.5979442,0.204280436,-0.0856824219,0.0948240235,-0.1327353269,0.0399422608,-0.1180508658,-0.3469358385,0.0949326307,-0.3334673345,-0.0153741492,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2218","title":"Duplicates in the LAMA dataset","comments":"Hi,\r\n\r\ncurrently the datasets API doesn't have a dedicated function to remove duplicate rows, but since the LAMA dataset is not too big (it fits in RAM), we can leverage pandas to help us remove duplicates:\r\n```python\r\n>>> from datasets import load_dataset, Dataset\r\n>>> dataset = load_dataset('lama', split='train')\r\n>>> dataset = Dataset.from_pandas(dataset.to_pandas().drop_duplicates(subset=...)) # specify a subset of the columns to consider in a list or use all of the columns if None\r\n```\r\n\r\nNote that the same can be achieved with the `Dataset.filter` method but this would requrie some extra work (filter function, speed?).","body":"I observed duplicates in the LAMA probing dataset, see a minimal code below. \r\n\r\n```\r\n>>> import datasets\r\n>>> dataset = datasets.load_dataset('lama')\r\nNo config specified, defaulting to: lama\/trex\r\nReusing dataset lama (\/home\/anam\/.cache\/huggingface\/datasets\/lama\/trex\/1.1.0\/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc)\r\n>>> train_dataset = dataset['train']\r\n>>> train_dataset[0]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n>>> train_dataset[1]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n```\r\n\r\nI checked the original data available at https:\/\/dl.fbaipublicfiles.com\/LAMA\/data.zip. This particular duplicated comes from:\r\n```\r\n{\"uuid\": \"40b2ed1c-0961-482e-844e-32596b6117c8\", \"obj_uri\": \"Q150\", \"obj_label\": \"French\", \"sub_uri\": \"Q441235\", \"sub_label\": \"Louis Jules Trochu\", \"predicate_id\": \"P103\", \"evidences\": [{\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}, {\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}]}\r\n``` \r\n\r\nWhat is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?  ","comment_length":94,"text":"Duplicates in the LAMA dataset \n I observed duplicates in the LAMA probing dataset, see a minimal code below. \r\n\r\n```\r\n>>> import datasets\r\n>>> dataset = datasets.load_dataset('lama')\r\nNo config specified, defaulting to: lama\/trex\r\nReusing dataset lama (\/home\/anam\/.cache\/huggingface\/datasets\/lama\/trex\/1.1.0\/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc)\r\n>>> train_dataset = dataset['train']\r\n>>> train_dataset[0]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n>>> train_dataset[1]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n```\r\n\r\nI checked the original data available at https:\/\/dl.fbaipublicfiles.com\/LAMA\/data.zip. This particular duplicated comes from:\r\n```\r\n{\"uuid\": \"40b2ed1c-0961-482e-844e-32596b6117c8\", \"obj_uri\": \"Q150\", \"obj_label\": \"French\", \"sub_uri\": \"Q441235\", \"sub_label\": \"Louis Jules Trochu\", \"predicate_id\": \"P103\", \"evidences\": [{\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}, {\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}]}\r\n``` \r\n\r\nWhat is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?   \n Hi,\r\n\r\ncurrently the datasets API doesn't have a dedicated function to remove duplicate rows, but since the LAMA dataset is not too big (it fits in RAM), we can leverage pandas to help us remove duplicates:\r\n```python\r\n>>> from datasets import load_dataset, Dataset\r\n>>> dataset = load_dataset('lama', split='train')\r\n>>> dataset = Dataset.from_pandas(dataset.to_pandas().drop_duplicates(subset=...)) # specify a subset of the columns to consider in a list or use all of the columns if None\r\n```\r\n\r\nNote that the same can be achieved with the `Dataset.filter` method but this would requrie some extra work (filter function, speed?).","embeddings":[0.261572361,-0.321171701,-0.0303781144,0.6542945504,0.3174540102,-0.1388740391,0.3127517998,0.3269729912,-0.5472849011,0.3365436196,-0.3454262018,0.3600648046,0.0819626823,-0.2813018262,0.1604101509,-0.1338147521,0.0254947934,-0.1661694944,-0.2045628428,-0.2063404322,-0.1825976968,0.3395481408,0.0372470431,0.0838601887,-0.140867278,0.1054256782,-0.1408223361,0.2696481645,0.0211215485,-0.2721820772,0.135984987,0.1195656359,-0.0423955284,0.498601675,-0.0001062553,0.0266553126,-0.1191423163,-0.0516993366,-0.1489317864,-0.0307918806,-0.0281007513,-0.0206731278,-0.0044797799,-0.2624208331,-0.3085130155,-0.0679863617,-0.0779155344,-0.110377565,0.4661053121,0.2096013427,0.2672528923,0.3489761353,-0.2831966579,-0.1589336097,0.222759366,-0.1836934388,-0.0866325945,0.4994784296,0.1833483577,0.6046422124,-0.0669347644,0.5685862303,0.0555699319,-0.0770117268,-0.280671984,-0.319239378,0.3182042837,-0.1115747392,0.3222734928,0.3282349706,-0.0091443872,-0.2400610596,-0.1954755038,-0.1231129169,-0.0279283728,-0.1791888773,0.0652221888,0.1354205757,0.0618838258,0.145541653,0.1373835504,0.0252608377,0.2321779728,-0.1204136834,-0.1461453438,-0.1684123576,-0.020073479,0.1156065762,0.1672807336,-0.1583189964,0.2028746605,-0.1564389169,-0.1728953719,0.049390059,-0.2232096195,-0.1347196251,0.0018672978,-0.1056643799,0.1160554811,0.5150147676,0.2339853346,0.3465538025,-0.1307944506,0.0239981376,0.154215306,0.0706345588,-0.0500430986,0.0645817295,-0.1759255826,0.0218920615,-0.316541791,0.0213840082,-0.3417175412,-0.1691877395,0.077304475,-0.0114144515,0.0543418713,-0.1062709838,-0.0838243812,-0.2307511121,-0.403701812,-0.1084920391,0.0605471805,0.1502104402,0.1372216493,0.2108773738,-0.3826012313,0.0159680881,-0.203060329,-0.1918776035,-0.3366514444,0.1416363716,-0.1806383282,0.0794324577,-0.0046100086,-0.722656846,0.4088579118,0.0900722593,-0.1415320188,-0.2692626417,0.4366317391,-0.2164307982,0.1583116204,0.0111389691,-0.1688192636,0.1273399889,-0.0186237115,-0.0218234546,-0.1080174148,0.0238687955,-0.1405326873,-0.0906960964,-0.1264307946,0.2938247025,0.1999355406,0.2575154901,0.3168151379,0.3918619454,-0.0317622386,0.0356455855,0.0930770412,0.0182362515,-0.3235778809,-0.1311048865,0.0814261809,0.2557787299,-0.0844862163,-0.0342722572,0.0129466234,0.0059579285,0.2059483379,0.5270101428,-0.1314258724,0.2728064358,-0.3161910474,0.3068459034,0.1753151566,-0.4883659184,-0.005071064,-0.1671351492,0.0342875831,0.1037544161,0.2280557156,0.0257393103,0.2874675095,-0.102095753,0.2206647247,-0.1189499497,0.2129841596,-0.1990242004,-0.2243700176,-0.0217814241,-0.04410078,-0.1115867645,-0.1224101856,-0.2407581955,0.023219645,-0.0264477897,0.1633731127,-0.087478511,-0.1633484215,-0.0287171733,0.0382602774,-0.0036900432,0.0643826351,0.0341986902,-0.1461330056,0.2015026361,-0.177235052,0.3269667625,0.0302657578,-0.1839377433,-0.2615669072,-0.0959582105,-0.2973302901,-0.1768952906,0.2863488495,0.2737017274,0.0176793355,0.1425696164,0.0079255672,0.1106468365,-0.0368708037,0.1908319294,-0.5088585019,0.0802482441,-0.1847524792,0.0603999868,0.1334294826,0.2846055627,0.1789463311,0.0156143932,0.2225701809,0.0092979688,0.312587589,0.027524583,0.2928505242,-0.1491547674,0.0233941413,-0.1707874835,-0.0926245153,0.0833111554,0.1865539849,-0.0546854064,-0.0379610248,0.4738602638,0.5329996943,-0.2722349763,0.1020209789,-0.1302971393,0.2794715166,-0.13579759,-0.0729421526,-0.492949456,0.0669534355,0.556897819,0.240272522,0.4626099169,-0.2420150191,0.089189589,0.1307507306,-0.1754649132,0.0280328002,-0.1890137345,-0.213729918,-0.111845158,0.1290919334,0.3554101884,0.0149280503,0.2737828195,0.5646850467,0.1218466386,-0.1524690241,-0.2303453982,0.1902780086,-0.0628233552,-0.1223017126,0.1247760057,0.1089129746,-0.0174058173,-0.7163102627,0.1091497615,0.0674972758,0.1782831848,-0.1040224731,0.0458259918,-0.5186646581,-0.2792091668,-0.3423459828,0.116162993,-0.2141042203,-0.2102277726,0.2235587537,0.0833830535,-0.0791669711,0.2216530442,-0.0587286018,0.4643632472,-0.1414313018,0.1901517808,-0.2832052708,0.0621855333,-0.2202775031,0.2213906348,0.1573350579,-0.0116416793,0.3668330908,-0.1453346759,-0.0091756685,-0.4053198099,-0.5439637899,0.1548528969,0.1620249599,0.1663461775,-0.0150466356,-0.2284674048,-0.0598770604,-0.2287738174,-0.092636548,0.2382824421,-0.418710351,-0.0588348731,-0.160943985,0.3222578466,-0.1648000032,-0.3569099605,0.1333421022,-0.1296956241,0.1131868586,0.094897002,0.2506995499,0.1955591291,-0.0962625518,0.1708019823,-0.4386238456,0.0114025883,-0.5250063539,-0.3347619176,0.1283046454,-0.1221257597,-0.0773989931,-0.1265262663,-0.4503448606,0.5347155333,-0.2043711841,-0.3808073103,-0.1040007174,0.0391999371,0.0052048257,0.3159893453,-0.072947599,-0.0524006747,0.2954608798,-0.31335783,-0.0572949871,0.005322624,0.0534760132,0.0567474142,0.1919592917,-0.3597275317,-0.0272062924,0.1268526465,0.176952526,0.4423536062,-0.0578040108,0.2711303532,0.1016453654,0.4149212241,-0.0350889452,-0.096319668,-0.1374595016,-0.1580803245,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2218","title":"Duplicates in the LAMA dataset","comments":"Oh, seems like my question wasn't specified well. I'm _not_ asking how to remove duplicates, but whether duplicates should be removed if I want to do the evaluation on the LAMA dataset as it was proposed in the original paper\/repository? In other words, will I get the same result if evaluate on the de-duplicated dataset loaded from HF's `datasets` as the results I'd get if I use the original data format and data processing script in https:\/\/github.com\/facebookresearch\/LAMA? ","body":"I observed duplicates in the LAMA probing dataset, see a minimal code below. \r\n\r\n```\r\n>>> import datasets\r\n>>> dataset = datasets.load_dataset('lama')\r\nNo config specified, defaulting to: lama\/trex\r\nReusing dataset lama (\/home\/anam\/.cache\/huggingface\/datasets\/lama\/trex\/1.1.0\/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc)\r\n>>> train_dataset = dataset['train']\r\n>>> train_dataset[0]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n>>> train_dataset[1]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n```\r\n\r\nI checked the original data available at https:\/\/dl.fbaipublicfiles.com\/LAMA\/data.zip. This particular duplicated comes from:\r\n```\r\n{\"uuid\": \"40b2ed1c-0961-482e-844e-32596b6117c8\", \"obj_uri\": \"Q150\", \"obj_label\": \"French\", \"sub_uri\": \"Q441235\", \"sub_label\": \"Louis Jules Trochu\", \"predicate_id\": \"P103\", \"evidences\": [{\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}, {\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}]}\r\n``` \r\n\r\nWhat is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?  ","comment_length":77,"text":"Duplicates in the LAMA dataset \n I observed duplicates in the LAMA probing dataset, see a minimal code below. \r\n\r\n```\r\n>>> import datasets\r\n>>> dataset = datasets.load_dataset('lama')\r\nNo config specified, defaulting to: lama\/trex\r\nReusing dataset lama (\/home\/anam\/.cache\/huggingface\/datasets\/lama\/trex\/1.1.0\/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc)\r\n>>> train_dataset = dataset['train']\r\n>>> train_dataset[0]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n>>> train_dataset[1]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n```\r\n\r\nI checked the original data available at https:\/\/dl.fbaipublicfiles.com\/LAMA\/data.zip. This particular duplicated comes from:\r\n```\r\n{\"uuid\": \"40b2ed1c-0961-482e-844e-32596b6117c8\", \"obj_uri\": \"Q150\", \"obj_label\": \"French\", \"sub_uri\": \"Q441235\", \"sub_label\": \"Louis Jules Trochu\", \"predicate_id\": \"P103\", \"evidences\": [{\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}, {\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}]}\r\n``` \r\n\r\nWhat is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?   \n Oh, seems like my question wasn't specified well. I'm _not_ asking how to remove duplicates, but whether duplicates should be removed if I want to do the evaluation on the LAMA dataset as it was proposed in the original paper\/repository? In other words, will I get the same result if evaluate on the de-duplicated dataset loaded from HF's `datasets` as the results I'd get if I use the original data format and data processing script in https:\/\/github.com\/facebookresearch\/LAMA? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2218","title":"Duplicates in the LAMA dataset","comments":"So it looks like the person who added LAMA to the library chose to have one item per piece of evidence rather than one per relation - and in this case, there are duplicate pieces of evidence for the target relation\r\n\r\nIf I understand correctly, to reproduce reported results, you would have to aggregate predictions for the several pieces of evidence provided for each relation (each unique `uuid`), but the original authors will know better \r\n\r\ncc @fabiopetroni ","body":"I observed duplicates in the LAMA probing dataset, see a minimal code below. \r\n\r\n```\r\n>>> import datasets\r\n>>> dataset = datasets.load_dataset('lama')\r\nNo config specified, defaulting to: lama\/trex\r\nReusing dataset lama (\/home\/anam\/.cache\/huggingface\/datasets\/lama\/trex\/1.1.0\/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc)\r\n>>> train_dataset = dataset['train']\r\n>>> train_dataset[0]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n>>> train_dataset[1]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n```\r\n\r\nI checked the original data available at https:\/\/dl.fbaipublicfiles.com\/LAMA\/data.zip. This particular duplicated comes from:\r\n```\r\n{\"uuid\": \"40b2ed1c-0961-482e-844e-32596b6117c8\", \"obj_uri\": \"Q150\", \"obj_label\": \"French\", \"sub_uri\": \"Q441235\", \"sub_label\": \"Louis Jules Trochu\", \"predicate_id\": \"P103\", \"evidences\": [{\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}, {\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}]}\r\n``` \r\n\r\nWhat is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?  ","comment_length":77,"text":"Duplicates in the LAMA dataset \n I observed duplicates in the LAMA probing dataset, see a minimal code below. \r\n\r\n```\r\n>>> import datasets\r\n>>> dataset = datasets.load_dataset('lama')\r\nNo config specified, defaulting to: lama\/trex\r\nReusing dataset lama (\/home\/anam\/.cache\/huggingface\/datasets\/lama\/trex\/1.1.0\/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc)\r\n>>> train_dataset = dataset['train']\r\n>>> train_dataset[0]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n>>> train_dataset[1]\r\n{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}\r\n```\r\n\r\nI checked the original data available at https:\/\/dl.fbaipublicfiles.com\/LAMA\/data.zip. This particular duplicated comes from:\r\n```\r\n{\"uuid\": \"40b2ed1c-0961-482e-844e-32596b6117c8\", \"obj_uri\": \"Q150\", \"obj_label\": \"French\", \"sub_uri\": \"Q441235\", \"sub_label\": \"Louis Jules Trochu\", \"predicate_id\": \"P103\", \"evidences\": [{\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}, {\"sub_surface\": \"Louis Jules Trochu\", \"obj_surface\": \"French\", \"masked_sentence\": \"Louis Jules Trochu ([lwi \\u0292yl t\\u0281\\u0254\\u0283y]; 12 March 1815 \\u2013 7 October 1896) was a [MASK] military leader and politician.\"}]}\r\n``` \r\n\r\nWhat is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?   \n So it looks like the person who added LAMA to the library chose to have one item per piece of evidence rather than one per relation - and in this case, there are duplicate pieces of evidence for the target relation\r\n\r\nIf I understand correctly, to reproduce reported results, you would have to aggregate predictions for the several pieces of evidence provided for each relation (each unique `uuid`), but the original authors will know better \r\n\r\ncc @fabiopetroni 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2214","title":"load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'","comments":"Hi @nsaphra, thanks for reporting.\r\n\r\nThis issue was fixed in `datasets` version 1.3.0. Could you please update `datasets` and tell me if the problem persists?\r\n```shell\r\npip install -U datasets\r\n```","body":"I'm having the same problem as [Notebooks issue 10](https:\/\/github.com\/huggingface\/notebooks\/issues\/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.\r\n\r\n```python\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric(\"glue\", \"sst2\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 502, in load_metric\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 66, in import_main_class\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1014, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 991, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 975, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 671, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 783, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/ns4008\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/glue\/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de\/glue.py\", line 105, in <module>\r\n    @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\r\nAttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'\r\n```","comment_length":31,"text":"load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' \n I'm having the same problem as [Notebooks issue 10](https:\/\/github.com\/huggingface\/notebooks\/issues\/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.\r\n\r\n```python\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric(\"glue\", \"sst2\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 502, in load_metric\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 66, in import_main_class\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1014, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 991, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 975, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 671, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 783, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/ns4008\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/glue\/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de\/glue.py\", line 105, in <module>\r\n    @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\r\nAttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'\r\n``` \n Hi @nsaphra, thanks for reporting.\r\n\r\nThis issue was fixed in `datasets` version 1.3.0. Could you please update `datasets` and tell me if the problem persists?\r\n```shell\r\npip install -U datasets\r\n```","embeddings":[-0.272324115,-0.2135577202,0.0193226859,0.1852925718,0.4195098579,0.0630552247,0.2729536891,0.1801064909,0.0756967589,-0.0524457283,-0.1940380335,0.1545963436,-0.0744487196,0.2974149585,0.0652143732,-0.0471376516,-0.0315006822,0.0150832096,-0.262601763,0.0296820384,-0.3187800348,0.293637991,-0.1672726125,-0.1764028966,-0.4021217525,-0.0105557591,0.0437473059,0.2441147864,-0.2862702608,-0.5002132654,0.205599755,0.0462033674,0.2963437438,0.3439409733,-0.0001162612,-0.0840861276,0.2146514505,-0.0279947501,-0.5544195771,-0.1524079293,0.0528011806,-0.2994415164,0.2512985468,-0.1053964123,-0.026472494,-0.1421283036,-0.0965214372,-0.2981289327,0.3209556639,0.2984568179,0.2062049806,0.6519038081,0.1126084998,-0.3246344924,0.0756547675,-0.1170940176,-0.1389452964,0.5830135942,0.2155656517,-0.2970149517,0.0055574444,0.1997525692,-0.0833818167,0.2960377336,0.5132633448,0.0072548999,0.0939969942,-0.0558887757,0.1015874147,0.059948612,0.484824419,-0.3377355635,-0.3453686535,-0.2876414061,0.157133773,-0.317251116,0.2258811444,-0.1266947538,0.0611915551,0.1738776267,-0.1425379813,-0.2746182978,0.027385395,0.2549823523,-0.0566361211,-0.1325753331,-0.3288052678,-0.0292661712,0.3993551731,0.0067997873,0.0061149034,0.1106405407,0.0018888788,0.2454485446,-0.2312647104,0.1604048014,0.2210600972,0.0638860911,0.1995056421,0.1169555858,0.1385359019,-0.1091200337,0.2827876508,0.1445126384,0.0451146699,0.455306083,0.4384199381,0.0501264371,0.2727783024,0.3831758201,0.2283397615,-0.1032075882,-0.0001541478,-0.4193750322,-0.1396079361,-0.1696378291,0.3145554066,-0.0437624045,-0.339749068,0.2789826691,0.225033164,-0.1100322455,0.0559781827,0.166669026,0.0389566608,0.0680720434,0.3797704279,0.1804562062,-0.2567597926,0.0695359856,-0.2539496422,0.019209398,-0.2204780877,0.2743342817,0.2387660295,-0.1765834093,0.2692468762,0.1394805461,0.0810193792,0.0131133115,-0.1423486918,0.1412217319,-0.2486168146,0.2955060303,0.013345235,0.1941344142,0.3053650856,-0.3389752507,-0.0738557056,-0.2483933419,-0.2457935363,-0.0913328826,-0.2465759516,0.1607254595,-0.3777996302,0.1018846259,-0.1548740715,0.0269365031,0.1158767045,0.0955258533,-0.0533686727,-0.0219097435,-0.4126269221,-0.0537653193,0.3398079276,0.407076776,-0.2471117377,-0.4057824016,0.1503967047,-0.1388975978,-0.2114148438,0.0392381735,0.0853885636,0.2430645525,-0.2263119221,0.0026322715,0.1370444,-0.5137932301,-0.4204660356,0.1396769583,-0.1487744749,0.0827302411,0.0750858337,-0.1714534611,0.1146221235,0.183054179,0.4504910111,0.0914153084,0.1167476848,-0.1548795253,-0.290825367,-0.1838073581,-0.2629747391,0.2544320524,0.1082778797,0.0519852117,0.175834924,0.1697672904,0.0678901598,-0.0798934996,0.011294866,0.4932676554,0.1543736309,-0.0364747122,0.1711865067,-0.2008412182,-0.5201351643,0.2156841606,-0.0183965508,0.0993434042,-0.0899934471,-0.0668857396,-0.374559015,-0.0463788509,-0.0753306672,-0.1765413284,0.0452711396,-0.0109367613,-0.1645413339,0.3287498355,-0.1420029849,0.2980311811,-0.3087519705,0.3473673463,-0.3626068532,0.2142395675,0.0971087739,-0.1683486402,0.1748615503,0.1577347219,0.1103116572,-0.1772711575,-0.0718013197,0.4855265319,0.1505092531,0.0629772693,-0.0285120029,0.2165285498,0.1915651858,-0.1421236694,-0.1667533368,-0.0733661801,0.035070125,-0.0022641413,-0.0186236035,0.1603875458,-0.1758860499,0.0595270544,0.1123987362,0.1688022166,0.048878897,-0.0778958648,-0.1880658865,-0.3839529753,0.479842335,-0.0180930253,0.3275276124,-0.0315788649,0.1737259477,-0.1127126664,0.3555789888,0.0096923541,0.0877412483,0.1162704453,-0.308801055,0.1370434165,-0.0036531514,0.011657536,0.4693245888,0.2006489336,-0.0045757908,0.2940479517,0.0089875711,-0.0964115486,0.12861377,0.0178792831,-0.1744787693,0.2901944816,-0.0639035255,-0.0595521629,-0.3733405769,0.1549218893,-0.0791442096,0.2749669254,-0.5363030434,-0.1757649183,-0.141107738,0.0296398327,0.0277284384,-0.0767671391,-0.2003962547,-0.2440668344,-0.0292488467,0.325810194,0.1955318749,0.3660383523,0.2152761966,0.4211014807,-0.0384670012,0.0846681148,-0.1243118271,-0.2874588072,-0.0785380751,0.0081650047,0.1657315195,-0.0335034579,0.1387250274,-0.6127305627,0.06443654,-0.236285001,-0.3688507378,0.0849087164,-0.0047380268,0.6359252334,0.3205825984,0.1494532824,-0.0961013958,0.0279980153,0.3684045076,0.0242663678,-0.2071206421,0.0681154877,-0.0857632682,-0.2336559445,-0.3043382466,-0.1047391817,0.0186322648,-0.503063798,0.3876099288,0.0402854495,-0.2336942852,0.3373192847,0.0795239285,0.2915133536,0.0523024015,0.2416902632,-0.261346221,-0.3416886032,0.2696045637,-0.2346160859,-0.4145871997,-0.037765462,0.1373154223,0.1808706522,-0.0452895761,-0.4627621174,-0.4108830392,-0.1143371314,0.1598947495,-0.1194815487,0.3886238635,0.1906858832,0.0357468687,0.0353719592,-0.3328506052,-0.1745150089,0.0547360964,-0.1960035712,0.0167503189,-0.2609972954,0.1186313927,0.0603463016,0.3715052307,0.2424533367,-0.0580785982,0.2844280005,-0.1521651447,0.6900770664,-0.1171550304,-0.5205394626,0.070948638,0.0032520145,0.20664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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2214","title":"load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'","comments":"There might be a bug in the conda version of `datasets` 1.2.1 where the datasets\/metric scripts are downloaded from `master` instead of the `1.2.1` repo.\r\n\r\nYou can try setting the env var `HF_SCRIPTS_VERSION=\"1.2.1\"` as a workaround. Let me know if that helps.","body":"I'm having the same problem as [Notebooks issue 10](https:\/\/github.com\/huggingface\/notebooks\/issues\/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.\r\n\r\n```python\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric(\"glue\", \"sst2\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 502, in load_metric\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 66, in import_main_class\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1014, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 991, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 975, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 671, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 783, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/ns4008\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/glue\/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de\/glue.py\", line 105, in <module>\r\n    @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\r\nAttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'\r\n```","comment_length":42,"text":"load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' \n I'm having the same problem as [Notebooks issue 10](https:\/\/github.com\/huggingface\/notebooks\/issues\/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.\r\n\r\n```python\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric(\"glue\", \"sst2\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 502, in load_metric\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 66, in import_main_class\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1014, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 991, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 975, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 671, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 783, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/ns4008\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/glue\/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de\/glue.py\", line 105, in <module>\r\n    @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\r\nAttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'\r\n``` \n There might be a bug in the conda version of `datasets` 1.2.1 where the datasets\/metric scripts are downloaded from `master` instead of the `1.2.1` repo.\r\n\r\nYou can try setting the env var `HF_SCRIPTS_VERSION=\"1.2.1\"` as a workaround. Let me know if that helps.","embeddings":[-0.272324115,-0.2135577202,0.0193226859,0.1852925718,0.4195098579,0.0630552247,0.2729536891,0.1801064909,0.0756967589,-0.0524457283,-0.1940380335,0.1545963436,-0.0744487196,0.2974149585,0.0652143732,-0.0471376516,-0.0315006822,0.0150832096,-0.262601763,0.0296820384,-0.3187800348,0.293637991,-0.1672726125,-0.1764028966,-0.4021217525,-0.0105557591,0.0437473059,0.2441147864,-0.2862702608,-0.5002132654,0.205599755,0.0462033674,0.2963437438,0.3439409733,-0.0001162612,-0.0840861276,0.2146514505,-0.0279947501,-0.5544195771,-0.1524079293,0.0528011806,-0.2994415164,0.2512985468,-0.1053964123,-0.026472494,-0.1421283036,-0.0965214372,-0.2981289327,0.3209556639,0.2984568179,0.2062049806,0.6519038081,0.1126084998,-0.3246344924,0.0756547675,-0.1170940176,-0.1389452964,0.5830135942,0.2155656517,-0.2970149517,0.0055574444,0.1997525692,-0.0833818167,0.2960377336,0.5132633448,0.0072548999,0.0939969942,-0.0558887757,0.1015874147,0.059948612,0.484824419,-0.3377355635,-0.3453686535,-0.2876414061,0.157133773,-0.317251116,0.2258811444,-0.1266947538,0.0611915551,0.1738776267,-0.1425379813,-0.2746182978,0.027385395,0.2549823523,-0.0566361211,-0.1325753331,-0.3288052678,-0.0292661712,0.3993551731,0.0067997873,0.0061149034,0.1106405407,0.0018888788,0.2454485446,-0.2312647104,0.1604048014,0.2210600972,0.0638860911,0.1995056421,0.1169555858,0.1385359019,-0.1091200337,0.2827876508,0.1445126384,0.0451146699,0.455306083,0.4384199381,0.0501264371,0.2727783024,0.3831758201,0.2283397615,-0.1032075882,-0.0001541478,-0.4193750322,-0.1396079361,-0.1696378291,0.3145554066,-0.0437624045,-0.339749068,0.2789826691,0.225033164,-0.1100322455,0.0559781827,0.166669026,0.0389566608,0.0680720434,0.3797704279,0.1804562062,-0.2567597926,0.0695359856,-0.2539496422,0.019209398,-0.2204780877,0.2743342817,0.2387660295,-0.1765834093,0.2692468762,0.1394805461,0.0810193792,0.0131133115,-0.1423486918,0.1412217319,-0.2486168146,0.2955060303,0.013345235,0.1941344142,0.3053650856,-0.3389752507,-0.0738557056,-0.2483933419,-0.2457935363,-0.0913328826,-0.2465759516,0.1607254595,-0.3777996302,0.1018846259,-0.1548740715,0.0269365031,0.1158767045,0.0955258533,-0.0533686727,-0.0219097435,-0.4126269221,-0.0537653193,0.3398079276,0.407076776,-0.2471117377,-0.4057824016,0.1503967047,-0.1388975978,-0.2114148438,0.0392381735,0.0853885636,0.2430645525,-0.2263119221,0.0026322715,0.1370444,-0.5137932301,-0.4204660356,0.1396769583,-0.1487744749,0.0827302411,0.0750858337,-0.1714534611,0.1146221235,0.183054179,0.4504910111,0.0914153084,0.1167476848,-0.1548795253,-0.290825367,-0.1838073581,-0.2629747391,0.2544320524,0.1082778797,0.0519852117,0.175834924,0.1697672904,0.0678901598,-0.0798934996,0.011294866,0.4932676554,0.1543736309,-0.0364747122,0.1711865067,-0.2008412182,-0.5201351643,0.2156841606,-0.0183965508,0.0993434042,-0.0899934471,-0.0668857396,-0.374559015,-0.0463788509,-0.0753306672,-0.1765413284,0.0452711396,-0.0109367613,-0.1645413339,0.3287498355,-0.1420029849,0.2980311811,-0.3087519705,0.3473673463,-0.3626068532,0.2142395675,0.0971087739,-0.1683486402,0.1748615503,0.1577347219,0.1103116572,-0.1772711575,-0.0718013197,0.4855265319,0.1505092531,0.0629772693,-0.0285120029,0.2165285498,0.1915651858,-0.1421236694,-0.1667533368,-0.0733661801,0.035070125,-0.0022641413,-0.0186236035,0.1603875458,-0.1758860499,0.0595270544,0.1123987362,0.1688022166,0.048878897,-0.0778958648,-0.1880658865,-0.3839529753,0.479842335,-0.0180930253,0.3275276124,-0.0315788649,0.1737259477,-0.1127126664,0.3555789888,0.0096923541,0.0877412483,0.1162704453,-0.308801055,0.1370434165,-0.0036531514,0.011657536,0.4693245888,0.2006489336,-0.0045757908,0.2940479517,0.0089875711,-0.0964115486,0.12861377,0.0178792831,-0.1744787693,0.2901944816,-0.0639035255,-0.0595521629,-0.3733405769,0.1549218893,-0.0791442096,0.2749669254,-0.5363030434,-0.1757649183,-0.141107738,0.0296398327,0.0277284384,-0.0767671391,-0.2003962547,-0.2440668344,-0.0292488467,0.325810194,0.1955318749,0.3660383523,0.2152761966,0.4211014807,-0.0384670012,0.0846681148,-0.1243118271,-0.2874588072,-0.0785380751,0.0081650047,0.1657315195,-0.0335034579,0.1387250274,-0.6127305627,0.06443654,-0.236285001,-0.3688507378,0.0849087164,-0.0047380268,0.6359252334,0.3205825984,0.1494532824,-0.0961013958,0.0279980153,0.3684045076,0.0242663678,-0.2071206421,0.0681154877,-0.0857632682,-0.2336559445,-0.3043382466,-0.1047391817,0.0186322648,-0.503063798,0.3876099288,0.0402854495,-0.2336942852,0.3373192847,0.0795239285,0.2915133536,0.0523024015,0.2416902632,-0.261346221,-0.3416886032,0.2696045637,-0.2346160859,-0.4145871997,-0.037765462,0.1373154223,0.1808706522,-0.0452895761,-0.4627621174,-0.4108830392,-0.1143371314,0.1598947495,-0.1194815487,0.3886238635,0.1906858832,0.0357468687,0.0353719592,-0.3328506052,-0.1745150089,0.0547360964,-0.1960035712,0.0167503189,-0.2609972954,0.1186313927,0.0603463016,0.3715052307,0.2424533367,-0.0580785982,0.2844280005,-0.1521651447,0.6900770664,-0.1171550304,-0.5205394626,0.070948638,0.0032520145,0.2066455036,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2214","title":"load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'","comments":"I just faced the same issue. I was using 1.2.1 from conda and received the same AttributeError complaining about 'add_start_docstrings'. Uninstalling the conda installed datasets and then installing the latest datasets (version 1.5.0) using pip install solved the issue for me. I don't like mixing up conda and pip installs in the same environments but this will have to do for now, until 1.5.0 is made available through conda.","body":"I'm having the same problem as [Notebooks issue 10](https:\/\/github.com\/huggingface\/notebooks\/issues\/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.\r\n\r\n```python\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric(\"glue\", \"sst2\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 502, in load_metric\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 66, in import_main_class\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1014, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 991, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 975, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 671, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 783, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/ns4008\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/glue\/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de\/glue.py\", line 105, in <module>\r\n    @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\r\nAttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'\r\n```","comment_length":69,"text":"load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' \n I'm having the same problem as [Notebooks issue 10](https:\/\/github.com\/huggingface\/notebooks\/issues\/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.\r\n\r\n```python\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric(\"glue\", \"sst2\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 502, in load_metric\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 66, in import_main_class\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1014, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 991, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 975, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 671, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 783, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/ns4008\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/glue\/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de\/glue.py\", line 105, in <module>\r\n    @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\r\nAttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'\r\n``` \n I just faced the same issue. I was using 1.2.1 from conda and received the same AttributeError complaining about 'add_start_docstrings'. Uninstalling the conda installed datasets and then installing the latest datasets (version 1.5.0) using pip install solved the issue for me. I don't like mixing up conda and pip installs in the same environments but this will have to do for now, until 1.5.0 is made available through conda.","embeddings":[-0.272324115,-0.2135577202,0.0193226859,0.1852925718,0.4195098579,0.0630552247,0.2729536891,0.1801064909,0.0756967589,-0.0524457283,-0.1940380335,0.1545963436,-0.0744487196,0.2974149585,0.0652143732,-0.0471376516,-0.0315006822,0.0150832096,-0.262601763,0.0296820384,-0.3187800348,0.293637991,-0.1672726125,-0.1764028966,-0.4021217525,-0.0105557591,0.0437473059,0.2441147864,-0.2862702608,-0.5002132654,0.205599755,0.0462033674,0.2963437438,0.3439409733,-0.0001162612,-0.0840861276,0.2146514505,-0.0279947501,-0.5544195771,-0.1524079293,0.0528011806,-0.2994415164,0.2512985468,-0.1053964123,-0.026472494,-0.1421283036,-0.0965214372,-0.2981289327,0.3209556639,0.2984568179,0.2062049806,0.6519038081,0.1126084998,-0.3246344924,0.0756547675,-0.1170940176,-0.1389452964,0.5830135942,0.2155656517,-0.2970149517,0.0055574444,0.1997525692,-0.0833818167,0.2960377336,0.5132633448,0.0072548999,0.0939969942,-0.0558887757,0.1015874147,0.059948612,0.484824419,-0.3377355635,-0.3453686535,-0.2876414061,0.157133773,-0.317251116,0.2258811444,-0.1266947538,0.0611915551,0.1738776267,-0.1425379813,-0.2746182978,0.027385395,0.2549823523,-0.0566361211,-0.1325753331,-0.3288052678,-0.0292661712,0.3993551731,0.0067997873,0.0061149034,0.1106405407,0.0018888788,0.2454485446,-0.2312647104,0.1604048014,0.2210600972,0.0638860911,0.1995056421,0.1169555858,0.1385359019,-0.1091200337,0.2827876508,0.1445126384,0.0451146699,0.455306083,0.4384199381,0.0501264371,0.2727783024,0.3831758201,0.2283397615,-0.1032075882,-0.0001541478,-0.4193750322,-0.1396079361,-0.1696378291,0.3145554066,-0.0437624045,-0.339749068,0.2789826691,0.225033164,-0.1100322455,0.0559781827,0.166669026,0.0389566608,0.0680720434,0.3797704279,0.1804562062,-0.2567597926,0.0695359856,-0.2539496422,0.019209398,-0.2204780877,0.2743342817,0.2387660295,-0.1765834093,0.2692468762,0.1394805461,0.0810193792,0.0131133115,-0.1423486918,0.1412217319,-0.2486168146,0.2955060303,0.013345235,0.1941344142,0.3053650856,-0.3389752507,-0.0738557056,-0.2483933419,-0.2457935363,-0.0913328826,-0.2465759516,0.1607254595,-0.3777996302,0.1018846259,-0.1548740715,0.0269365031,0.1158767045,0.0955258533,-0.0533686727,-0.0219097435,-0.4126269221,-0.0537653193,0.3398079276,0.407076776,-0.2471117377,-0.4057824016,0.1503967047,-0.1388975978,-0.2114148438,0.0392381735,0.0853885636,0.2430645525,-0.2263119221,0.0026322715,0.1370444,-0.5137932301,-0.4204660356,0.1396769583,-0.1487744749,0.0827302411,0.0750858337,-0.1714534611,0.1146221235,0.183054179,0.4504910111,0.0914153084,0.1167476848,-0.1548795253,-0.290825367,-0.1838073581,-0.2629747391,0.2544320524,0.1082778797,0.0519852117,0.175834924,0.1697672904,0.0678901598,-0.0798934996,0.011294866,0.4932676554,0.1543736309,-0.0364747122,0.1711865067,-0.2008412182,-0.5201351643,0.2156841606,-0.0183965508,0.0993434042,-0.0899934471,-0.0668857396,-0.374559015,-0.0463788509,-0.0753306672,-0.1765413284,0.0452711396,-0.0109367613,-0.1645413339,0.3287498355,-0.1420029849,0.2980311811,-0.3087519705,0.3473673463,-0.3626068532,0.2142395675,0.0971087739,-0.1683486402,0.1748615503,0.1577347219,0.1103116572,-0.1772711575,-0.0718013197,0.4855265319,0.1505092531,0.0629772693,-0.0285120029,0.2165285498,0.1915651858,-0.1421236694,-0.1667533368,-0.0733661801,0.035070125,-0.0022641413,-0.0186236035,0.1603875458,-0.1758860499,0.0595270544,0.1123987362,0.1688022166,0.048878897,-0.0778958648,-0.1880658865,-0.3839529753,0.479842335,-0.0180930253,0.3275276124,-0.0315788649,0.1737259477,-0.1127126664,0.3555789888,0.0096923541,0.0877412483,0.1162704453,-0.308801055,0.1370434165,-0.0036531514,0.011657536,0.4693245888,0.2006489336,-0.0045757908,0.2940479517,0.0089875711,-0.0964115486,0.12861377,0.0178792831,-0.1744787693,0.2901944816,-0.0639035255,-0.0595521629,-0.3733405769,0.1549218893,-0.0791442096,0.2749669254,-0.5363030434,-0.1757649183,-0.141107738,0.0296398327,0.0277284384,-0.0767671391,-0.2003962547,-0.2440668344,-0.0292488467,0.325810194,0.1955318749,0.3660383523,0.2152761966,0.4211014807,-0.0384670012,0.0846681148,-0.1243118271,-0.2874588072,-0.0785380751,0.0081650047,0.1657315195,-0.0335034579,0.1387250274,-0.6127305627,0.06443654,-0.236285001,-0.3688507378,0.0849087164,-0.0047380268,0.6359252334,0.3205825984,0.1494532824,-0.0961013958,0.0279980153,0.3684045076,0.0242663678,-0.2071206421,0.0681154877,-0.0857632682,-0.2336559445,-0.3043382466,-0.1047391817,0.0186322648,-0.503063798,0.3876099288,0.0402854495,-0.2336942852,0.3373192847,0.0795239285,0.2915133536,0.0523024015,0.2416902632,-0.261346221,-0.3416886032,0.2696045637,-0.2346160859,-0.4145871997,-0.037765462,0.1373154223,0.1808706522,-0.0452895761,-0.4627621174,-0.4108830392,-0.1143371314,0.1598947495,-0.1194815487,0.3886238635,0.1906858832,0.0357468687,0.0353719592,-0.3328506052,-0.1745150089,0.0547360964,-0.1960035712,0.0167503189,-0.2609972954,0.1186313927,0.0603463016,0.3715052307,0.2424533367,-0.0580785982,0.2844280005,-0.1521651447,0.6900770664,-0.1171550304,-0.5205394626,0.070948638,0.0032520145,0.2066455036,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2214","title":"load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'","comments":"Yep, seems to have fixed things! The conda package could really do with an update. Thanks!","body":"I'm having the same problem as [Notebooks issue 10](https:\/\/github.com\/huggingface\/notebooks\/issues\/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.\r\n\r\n```python\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric(\"glue\", \"sst2\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 502, in load_metric\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 66, in import_main_class\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1014, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 991, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 975, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 671, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 783, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/ns4008\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/glue\/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de\/glue.py\", line 105, in <module>\r\n    @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\r\nAttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'\r\n```","comment_length":16,"text":"load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' \n I'm having the same problem as [Notebooks issue 10](https:\/\/github.com\/huggingface\/notebooks\/issues\/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.\r\n\r\n```python\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric(\"glue\", \"sst2\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 502, in load_metric\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/site-packages\/datasets-1.2.1-py3.8.egg\/datasets\/load.py\", line 66, in import_main_class\r\n  File \"\/ext3\/miniconda3\/lib\/python3.8\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1014, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 991, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 975, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 671, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 783, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/ns4008\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/glue\/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de\/glue.py\", line 105, in <module>\r\n    @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\r\nAttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'\r\n``` \n Yep, seems to have fixed things! The conda package could really do with an update. Thanks!","embeddings":[-0.272324115,-0.2135577202,0.0193226859,0.1852925718,0.4195098579,0.0630552247,0.2729536891,0.1801064909,0.0756967589,-0.0524457283,-0.1940380335,0.1545963436,-0.0744487196,0.2974149585,0.0652143732,-0.0471376516,-0.0315006822,0.0150832096,-0.262601763,0.0296820384,-0.3187800348,0.293637991,-0.1672726125,-0.1764028966,-0.4021217525,-0.0105557591,0.0437473059,0.2441147864,-0.2862702608,-0.5002132654,0.205599755,0.0462033674,0.2963437438,0.3439409733,-0.0001162612,-0.0840861276,0.2146514505,-0.0279947501,-0.5544195771,-0.1524079293,0.0528011806,-0.2994415164,0.2512985468,-0.1053964123,-0.026472494,-0.1421283036,-0.0965214372,-0.2981289327,0.3209556639,0.2984568179,0.2062049806,0.6519038081,0.1126084998,-0.3246344924,0.0756547675,-0.1170940176,-0.1389452964,0.5830135942,0.2155656517,-0.2970149517,0.0055574444,0.1997525692,-0.0833818167,0.2960377336,0.5132633448,0.0072548999,0.0939969942,-0.0558887757,0.1015874147,0.059948612,0.484824419,-0.3377355635,-0.3453686535,-0.2876414061,0.157133773,-0.317251116,0.2258811444,-0.1266947538,0.0611915551,0.1738776267,-0.1425379813,-0.2746182978,0.027385395,0.2549823523,-0.0566361211,-0.1325753331,-0.3288052678,-0.0292661712,0.3993551731,0.0067997873,0.0061149034,0.1106405407,0.0018888788,0.2454485446,-0.2312647104,0.1604048014,0.2210600972,0.0638860911,0.1995056421,0.1169555858,0.1385359019,-0.1091200337,0.2827876508,0.1445126384,0.0451146699,0.455306083,0.4384199381,0.0501264371,0.2727783024,0.3831758201,0.2283397615,-0.1032075882,-0.0001541478,-0.4193750322,-0.1396079361,-0.1696378291,0.3145554066,-0.0437624045,-0.339749068,0.2789826691,0.225033164,-0.1100322455,0.0559781827,0.166669026,0.0389566608,0.0680720434,0.3797704279,0.1804562062,-0.2567597926,0.0695359856,-0.2539496422,0.019209398,-0.2204780877,0.2743342817,0.2387660295,-0.1765834093,0.2692468762,0.1394805461,0.0810193792,0.0131133115,-0.1423486918,0.1412217319,-0.2486168146,0.2955060303,0.013345235,0.1941344142,0.3053650856,-0.3389752507,-0.0738557056,-0.2483933419,-0.2457935363,-0.0913328826,-0.2465759516,0.1607254595,-0.3777996302,0.1018846259,-0.1548740715,0.0269365031,0.1158767045,0.0955258533,-0.0533686727,-0.0219097435,-0.4126269221,-0.0537653193,0.3398079276,0.407076776,-0.2471117377,-0.4057824016,0.1503967047,-0.1388975978,-0.2114148438,0.0392381735,0.0853885636,0.2430645525,-0.2263119221,0.0026322715,0.1370444,-0.5137932301,-0.4204660356,0.1396769583,-0.1487744749,0.0827302411,0.0750858337,-0.1714534611,0.1146221235,0.183054179,0.4504910111,0.0914153084,0.1167476848,-0.1548795253,-0.290825367,-0.1838073581,-0.2629747391,0.2544320524,0.1082778797,0.0519852117,0.175834924,0.1697672904,0.0678901598,-0.0798934996,0.011294866,0.4932676554,0.1543736309,-0.0364747122,0.1711865067,-0.2008412182,-0.5201351643,0.2156841606,-0.0183965508,0.0993434042,-0.0899934471,-0.0668857396,-0.374559015,-0.0463788509,-0.0753306672,-0.1765413284,0.0452711396,-0.0109367613,-0.1645413339,0.3287498355,-0.1420029849,0.2980311811,-0.3087519705,0.3473673463,-0.3626068532,0.2142395675,0.0971087739,-0.1683486402,0.1748615503,0.1577347219,0.1103116572,-0.1772711575,-0.0718013197,0.4855265319,0.1505092531,0.0629772693,-0.0285120029,0.2165285498,0.1915651858,-0.1421236694,-0.1667533368,-0.0733661801,0.035070125,-0.0022641413,-0.0186236035,0.1603875458,-0.1758860499,0.0595270544,0.1123987362,0.1688022166,0.048878897,-0.0778958648,-0.1880658865,-0.3839529753,0.479842335,-0.0180930253,0.3275276124,-0.0315788649,0.1737259477,-0.1127126664,0.3555789888,0.0096923541,0.0877412483,0.1162704453,-0.308801055,0.1370434165,-0.0036531514,0.011657536,0.4693245888,0.2006489336,-0.0045757908,0.2940479517,0.0089875711,-0.0964115486,0.12861377,0.0178792831,-0.1744787693,0.2901944816,-0.0639035255,-0.0595521629,-0.3733405769,0.1549218893,-0.0791442096,0.2749669254,-0.5363030434,-0.1757649183,-0.141107738,0.0296398327,0.0277284384,-0.0767671391,-0.2003962547,-0.2440668344,-0.0292488467,0.325810194,0.1955318749,0.3660383523,0.2152761966,0.4211014807,-0.0384670012,0.0846681148,-0.1243118271,-0.2874588072,-0.0785380751,0.0081650047,0.1657315195,-0.0335034579,0.1387250274,-0.6127305627,0.06443654,-0.236285001,-0.3688507378,0.0849087164,-0.0047380268,0.6359252334,0.3205825984,0.1494532824,-0.0961013958,0.0279980153,0.3684045076,0.0242663678,-0.2071206421,0.0681154877,-0.0857632682,-0.2336559445,-0.3043382466,-0.1047391817,0.0186322648,-0.503063798,0.3876099288,0.0402854495,-0.2336942852,0.3373192847,0.0795239285,0.2915133536,0.0523024015,0.2416902632,-0.261346221,-0.3416886032,0.2696045637,-0.2346160859,-0.4145871997,-0.037765462,0.1373154223,0.1808706522,-0.0452895761,-0.4627621174,-0.4108830392,-0.1143371314,0.1598947495,-0.1194815487,0.3886238635,0.1906858832,0.0357468687,0.0353719592,-0.3328506052,-0.1745150089,0.0547360964,-0.1960035712,0.0167503189,-0.2609972954,0.1186313927,0.0603463016,0.3715052307,0.2424533367,-0.0580785982,0.2844280005,-0.1521651447,0.6900770664,-0.1171550304,-0.5205394626,0.070948638,0.0032520145,0.2066455036,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2212","title":"Can't reach \"https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\" when trying to load fquad dataset","comments":"Hi ! Apparently the data are not available from this url anymore. We'll replace it with the new url when it's available","body":"I'm trying to load the [fquad dataset](https:\/\/huggingface.co\/datasets\/fquad) by running: \r\n\r\n```Python\r\nfquad = load_dataset(\"fquad\")\r\n```\r\n\r\nwhich produces the following error:\r\n\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset fquad\/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to \/root\/.cache\/huggingface\/datasets\/fquad\/default\/0.1.0\/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\r\n<ipython-input-48-a2721797e23b> in <module>()\r\n----> 1 fquad = load_dataset(\"fquad\")\r\n\r\n11 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    617 \r\n    618     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\r\n```\r\n\r\nDoes anyone know why that is and how to fix it? ","comment_length":22,"text":"Can't reach \"https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\" when trying to load fquad dataset \n I'm trying to load the [fquad dataset](https:\/\/huggingface.co\/datasets\/fquad) by running: \r\n\r\n```Python\r\nfquad = load_dataset(\"fquad\")\r\n```\r\n\r\nwhich produces the following error:\r\n\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset fquad\/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to \/root\/.cache\/huggingface\/datasets\/fquad\/default\/0.1.0\/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\r\n<ipython-input-48-a2721797e23b> in <module>()\r\n----> 1 fquad = load_dataset(\"fquad\")\r\n\r\n11 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    617 \r\n    618     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\r\n```\r\n\r\nDoes anyone know why that is and how to fix it?  \n Hi ! Apparently the data are not available from this url anymore. We'll replace it with the new url when it's available","embeddings":[-0.3520766795,0.1850519031,-0.1066456437,0.2455794364,0.3998158276,0.03360755,0.3876356781,0.2061039656,0.3057492673,0.1453926563,-0.2683598399,-0.1908080578,0.2827515602,0.0175514612,0.027606599,0.0714482665,-0.0717816278,-0.0440939777,-0.1596578062,0.0708816275,-0.3542164862,0.3704529405,-0.1904580444,0.1628842503,-0.3339352906,0.0633746386,0.0055798003,0.3590935767,-0.3199560642,-0.1079104543,0.286209017,0.0416278467,0.004151077,0.4852188826,-0.0001157215,0.1660642475,0.3586290479,-0.0317648053,-0.4298180938,-0.5632392168,-0.3045651317,-0.072570838,0.2153185606,-0.2087870389,-0.1616279483,-0.0290350895,0.1196179837,-0.4198773503,0.2603788972,0.4918957353,0.1842911541,-0.0472500958,0.2975516617,-0.1803262234,-0.0591369905,-0.1362888217,-0.0200078152,0.6450526714,-0.0140431896,-0.2045283467,-0.0508274771,0.0286189858,0.1182557642,0.1726587862,0.1585940719,0.2011903822,-0.3372362256,-0.1506722569,0.2118362486,0.0146889491,0.5337827802,-0.2849702537,-0.4723348618,-0.1233135611,-0.0491205491,-0.3767210245,0.5024482012,0.0925156698,-0.2628643513,0.1907122433,-0.4664973021,-0.2508746386,-0.1997162253,0.3678171635,-0.3860652447,0.2701286077,-0.1864978373,0.2325517088,0.2770298421,-0.1557939649,-0.2593966126,0.1098564789,0.1527526826,0.3186956048,-0.314109236,0.1022981256,-0.0440244749,-0.2830099761,0.1964606792,0.3815691173,-0.015078485,0.0076378272,-0.0969404653,0.164527759,0.4020113349,0.3189195991,-0.1120543703,0.2087809891,0.1873425245,0.6325322986,-0.1311301142,-0.1770190001,-0.1378113925,-0.081372045,-0.1658248156,-0.183514595,0.1537760198,-0.1985757202,-0.1576269418,0.1740346998,-0.2624880672,-0.0844819471,0.0354961865,0.3024871647,-0.0897951052,0.1091868505,0.0184984915,0.2455940992,-0.1006541178,0.1638454646,-0.1644030064,0.2479628474,-0.218215555,0.231559068,0.2416772842,0.1246844679,0.3092523515,-0.3974604607,0.0709448978,-0.0954510868,0.2211935818,-0.2454142272,-0.156402275,0.3248185813,0.1361620575,-0.019331608,0.1892515123,-0.0819873288,-0.120350033,-0.0591272786,-0.238079235,-0.2690150142,-0.1610825509,0.1693099141,-0.1822194904,0.0440686196,-0.3180564344,0.0211405642,-0.163014695,-0.2929230332,-0.02649194,0.1383045167,-0.1317382902,-0.1248099804,0.3689207137,0.5510037541,-0.2626015544,-0.0661597773,-0.2078201175,-0.0995471701,-0.0146748368,0.4244317114,-0.174391821,0.2035565674,-0.4347251058,0.2382106483,0.4783934355,-0.2472981364,-0.8428885341,0.427974999,-0.2450462729,0.1419134438,-0.091932185,0.0322814882,-0.0358391963,0.1951994896,0.6311691403,0.2926326096,-0.0661307499,-0.127931118,-0.0659748837,-0.3087247014,-0.0573887713,0.2728063464,-0.0072478638,0.173291862,0.2028902769,-0.11355827,0.203204602,0.1464847624,-0.1294753551,0.2984036505,0.0654904172,0.0501435176,-0.1333141923,-0.0529266,-0.6362524629,0.2412666529,-0.2172224224,-0.0310093351,-0.5615538359,0.2495313436,-0.4475069642,0.0381114259,-0.0719014481,0.0171304978,0.0995740071,0.0793238357,0.1675628722,0.1997745037,-0.2326557487,0.3575578332,0.0593538806,0.2285772711,-0.4177794456,0.3038277328,-0.068603389,-0.0128734708,-0.0401704386,0.0375095345,0.0876060575,-0.1907199472,-0.1723519415,0.3521072268,-0.0981610119,0.1596617848,0.3979538083,0.4166022539,0.1592304558,-0.0419741049,0.0967004448,-0.1133250967,0.0562817417,-0.0873886049,-0.1806227118,0.1824068427,0.1226353049,0.3023006022,0.140295893,0.033637926,0.365693301,0.1180057302,-0.0833135769,-0.1543994844,0.15414083,0.4664791822,0.2170068771,0.0557061583,-0.1387757808,-0.0494213514,0.3819881976,-0.2163211554,0.0133876475,0.2899906933,0.0413509086,-0.2723265886,0.1269237697,0.0862024873,0.4915738106,0.0586339347,0.1429887861,0.1387392431,-0.2469654232,-0.1638064682,0.0784770474,0.0087313019,0.0382009223,0.0595250651,0.1666518003,-0.0206933133,-0.1088888422,-0.3694197536,0.0472553745,0.1783639491,-0.1222572029,0.3422809839,-0.1195476055,-0.1523807496,0.1046303585,-0.3231037259,-0.2455987036,-0.4834875762,-0.1251465082,0.2941145301,0.0920701548,0.0488251857,-0.3312270641,0.0984709337,0.1856976151,-0.5195731521,-0.1338463426,-0.1513882279,-0.1231954619,0.0563571267,0.3074066341,-0.2006785274,0.2499470264,-0.2296645343,-0.100894548,-0.4725251794,-0.1700989753,0.0898433775,-0.0312449019,0.40792799,0.2447232157,0.3579797447,-0.0674479678,-0.0232894085,0.4561803341,0.0728968084,-0.0803323686,-0.0389703028,-0.1058985814,0.1410915405,0.054669302,-0.263803184,-0.2067806423,-0.3587554097,0.4501703084,0.0336139351,0.0696525946,0.1306530684,0.1599377245,0.2007167935,0.1608746797,-0.1496057361,-0.3580869138,-0.5675964355,0.4216380119,-0.2252491564,-0.062960811,0.0954560116,0.1260264516,0.4072681069,0.3108044267,-0.5964596272,-0.4336869121,-0.2441263199,0.2438689023,-0.0759851113,-0.2841826975,0.2059355229,-0.2813442647,-0.0455122963,0.0795237049,-0.3435887992,0.1072266921,0.0380031541,0.4218061566,0.1905358434,0.5441442132,-0.1006031409,0.596581161,0.0506761186,0.1118795276,0.3819426298,-0.1311579049,0.2393558174,-0.4368827343,-0.2892046869,0.1354883611,-0.0735363513,-0.31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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2212","title":"Can't reach \"https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\" when trying to load fquad dataset","comments":"I saw this on their website when we request to download the dataset:\r\n![image](https:\/\/user-images.githubusercontent.com\/19718818\/114879600-fa458680-9e1e-11eb-9e05-f0963d68ff0f.png)\r\n\r\nCan we still request them link for the dataset and make a PR? @lhoestq @yjernite ","body":"I'm trying to load the [fquad dataset](https:\/\/huggingface.co\/datasets\/fquad) by running: \r\n\r\n```Python\r\nfquad = load_dataset(\"fquad\")\r\n```\r\n\r\nwhich produces the following error:\r\n\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset fquad\/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to \/root\/.cache\/huggingface\/datasets\/fquad\/default\/0.1.0\/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\r\n<ipython-input-48-a2721797e23b> in <module>()\r\n----> 1 fquad = load_dataset(\"fquad\")\r\n\r\n11 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    617 \r\n    618     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\r\n```\r\n\r\nDoes anyone know why that is and how to fix it? ","comment_length":29,"text":"Can't reach \"https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\" when trying to load fquad dataset \n I'm trying to load the [fquad dataset](https:\/\/huggingface.co\/datasets\/fquad) by running: \r\n\r\n```Python\r\nfquad = load_dataset(\"fquad\")\r\n```\r\n\r\nwhich produces the following error:\r\n\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset fquad\/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to \/root\/.cache\/huggingface\/datasets\/fquad\/default\/0.1.0\/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\r\n<ipython-input-48-a2721797e23b> in <module>()\r\n----> 1 fquad = load_dataset(\"fquad\")\r\n\r\n11 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    617 \r\n    618     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\r\n```\r\n\r\nDoes anyone know why that is and how to fix it?  \n I saw this on their website when we request to download the dataset:\r\n![image](https:\/\/user-images.githubusercontent.com\/19718818\/114879600-fa458680-9e1e-11eb-9e05-f0963d68ff0f.png)\r\n\r\nCan we still request them link for the dataset and make a PR? @lhoestq @yjernite 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2212","title":"Can't reach \"https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\" when trying to load fquad dataset","comments":"I've contacted Martin (first author of the fquad paper) regarding a possible new url. Hopefully we can get one soon !","body":"I'm trying to load the [fquad dataset](https:\/\/huggingface.co\/datasets\/fquad) by running: \r\n\r\n```Python\r\nfquad = load_dataset(\"fquad\")\r\n```\r\n\r\nwhich produces the following error:\r\n\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset fquad\/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to \/root\/.cache\/huggingface\/datasets\/fquad\/default\/0.1.0\/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\r\n<ipython-input-48-a2721797e23b> in <module>()\r\n----> 1 fquad = load_dataset(\"fquad\")\r\n\r\n11 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    617 \r\n    618     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\r\n```\r\n\r\nDoes anyone know why that is and how to fix it? ","comment_length":21,"text":"Can't reach \"https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\" when trying to load fquad dataset \n I'm trying to load the [fquad dataset](https:\/\/huggingface.co\/datasets\/fquad) by running: \r\n\r\n```Python\r\nfquad = load_dataset(\"fquad\")\r\n```\r\n\r\nwhich produces the following error:\r\n\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset fquad\/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to \/root\/.cache\/huggingface\/datasets\/fquad\/default\/0.1.0\/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\r\n<ipython-input-48-a2721797e23b> in <module>()\r\n----> 1 fquad = load_dataset(\"fquad\")\r\n\r\n11 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    617 \r\n    618     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\r\n```\r\n\r\nDoes anyone know why that is and how to fix it?  \n I've contacted Martin (first author of the fquad paper) regarding a possible new url. Hopefully we can get one soon !","embeddings":[-0.3520766795,0.1850519031,-0.1066456437,0.2455794364,0.3998158276,0.03360755,0.3876356781,0.2061039656,0.3057492673,0.1453926563,-0.2683598399,-0.1908080578,0.2827515602,0.0175514612,0.027606599,0.0714482665,-0.0717816278,-0.0440939777,-0.1596578062,0.0708816275,-0.3542164862,0.3704529405,-0.1904580444,0.1628842503,-0.3339352906,0.0633746386,0.0055798003,0.3590935767,-0.3199560642,-0.1079104543,0.286209017,0.0416278467,0.004151077,0.4852188826,-0.0001157215,0.1660642475,0.3586290479,-0.0317648053,-0.4298180938,-0.5632392168,-0.3045651317,-0.072570838,0.2153185606,-0.2087870389,-0.1616279483,-0.0290350895,0.1196179837,-0.4198773503,0.2603788972,0.4918957353,0.1842911541,-0.0472500958,0.2975516617,-0.1803262234,-0.0591369905,-0.1362888217,-0.0200078152,0.6450526714,-0.0140431896,-0.2045283467,-0.0508274771,0.0286189858,0.1182557642,0.1726587862,0.1585940719,0.2011903822,-0.3372362256,-0.1506722569,0.2118362486,0.0146889491,0.5337827802,-0.2849702537,-0.4723348618,-0.1233135611,-0.0491205491,-0.3767210245,0.5024482012,0.0925156698,-0.2628643513,0.1907122433,-0.4664973021,-0.2508746386,-0.1997162253,0.3678171635,-0.3860652447,0.2701286077,-0.1864978373,0.2325517088,0.2770298421,-0.1557939649,-0.2593966126,0.1098564789,0.1527526826,0.3186956048,-0.314109236,0.1022981256,-0.0440244749,-0.2830099761,0.1964606792,0.3815691173,-0.015078485,0.0076378272,-0.0969404653,0.164527759,0.4020113349,0.3189195991,-0.1120543703,0.2087809891,0.1873425245,0.6325322986,-0.1311301142,-0.1770190001,-0.1378113925,-0.081372045,-0.1658248156,-0.183514595,0.1537760198,-0.1985757202,-0.1576269418,0.1740346998,-0.2624880672,-0.0844819471,0.0354961865,0.3024871647,-0.0897951052,0.1091868505,0.0184984915,0.2455940992,-0.1006541178,0.1638454646,-0.1644030064,0.2479628474,-0.218215555,0.231559068,0.2416772842,0.1246844679,0.3092523515,-0.3974604607,0.0709448978,-0.0954510868,0.2211935818,-0.2454142272,-0.156402275,0.3248185813,0.1361620575,-0.019331608,0.1892515123,-0.0819873288,-0.120350033,-0.0591272786,-0.238079235,-0.2690150142,-0.1610825509,0.1693099141,-0.1822194904,0.0440686196,-0.3180564344,0.0211405642,-0.163014695,-0.2929230332,-0.02649194,0.1383045167,-0.1317382902,-0.1248099804,0.3689207137,0.5510037541,-0.2626015544,-0.0661597773,-0.2078201175,-0.0995471701,-0.0146748368,0.4244317114,-0.174391821,0.2035565674,-0.4347251058,0.2382106483,0.4783934355,-0.2472981364,-0.8428885341,0.427974999,-0.2450462729,0.1419134438,-0.091932185,0.0322814882,-0.0358391963,0.1951994896,0.6311691403,0.2926326096,-0.0661307499,-0.127931118,-0.0659748837,-0.3087247014,-0.0573887713,0.2728063464,-0.0072478638,0.173291862,0.2028902769,-0.11355827,0.203204602,0.1464847624,-0.1294753551,0.2984036505,0.0654904172,0.0501435176,-0.1333141923,-0.0529266,-0.6362524629,0.2412666529,-0.2172224224,-0.0310093351,-0.5615538359,0.2495313436,-0.4475069642,0.0381114259,-0.0719014481,0.0171304978,0.0995740071,0.0793238357,0.1675628722,0.1997745037,-0.2326557487,0.3575578332,0.0593538806,0.2285772711,-0.4177794456,0.3038277328,-0.068603389,-0.0128734708,-0.0401704386,0.0375095345,0.0876060575,-0.1907199472,-0.1723519415,0.3521072268,-0.0981610119,0.1596617848,0.3979538083,0.4166022539,0.1592304558,-0.0419741049,0.0967004448,-0.1133250967,0.0562817417,-0.0873886049,-0.1806227118,0.1824068427,0.1226353049,0.3023006022,0.140295893,0.033637926,0.365693301,0.1180057302,-0.0833135769,-0.1543994844,0.15414083,0.4664791822,0.2170068771,0.0557061583,-0.1387757808,-0.0494213514,0.3819881976,-0.2163211554,0.0133876475,0.2899906933,0.0413509086,-0.2723265886,0.1269237697,0.0862024873,0.4915738106,0.0586339347,0.1429887861,0.1387392431,-0.2469654232,-0.1638064682,0.0784770474,0.0087313019,0.0382009223,0.0595250651,0.1666518003,-0.0206933133,-0.1088888422,-0.3694197536,0.0472553745,0.1783639491,-0.1222572029,0.3422809839,-0.1195476055,-0.1523807496,0.1046303585,-0.3231037259,-0.2455987036,-0.4834875762,-0.1251465082,0.2941145301,0.0920701548,0.0488251857,-0.3312270641,0.0984709337,0.1856976151,-0.5195731521,-0.1338463426,-0.1513882279,-0.1231954619,0.0563571267,0.3074066341,-0.2006785274,0.2499470264,-0.2296645343,-0.100894548,-0.4725251794,-0.1700989753,0.0898433775,-0.0312449019,0.40792799,0.2447232157,0.3579797447,-0.0674479678,-0.0232894085,0.4561803341,0.0728968084,-0.0803323686,-0.0389703028,-0.1058985814,0.1410915405,0.054669302,-0.263803184,-0.2067806423,-0.3587554097,0.4501703084,0.0336139351,0.0696525946,0.1306530684,0.1599377245,0.2007167935,0.1608746797,-0.1496057361,-0.3580869138,-0.5675964355,0.4216380119,-0.2252491564,-0.062960811,0.0954560116,0.1260264516,0.4072681069,0.3108044267,-0.5964596272,-0.4336869121,-0.2441263199,0.2438689023,-0.0759851113,-0.2841826975,0.2059355229,-0.2813442647,-0.0455122963,0.0795237049,-0.3435887992,0.1072266921,0.0380031541,0.4218061566,0.1905358434,0.5441442132,-0.1006031409,0.596581161,0.0506761186,0.1118795276,0.3819426298,-0.1311579049,0.2393558174,-0.4368827343,-0.2892046869,0.1354883611,-0.0735363513,-0.3199809194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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2212","title":"Can't reach \"https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\" when trying to load fquad dataset","comments":"They now made a website to force people who want to use the dataset for commercial purposes to seek a commercial license from them ...","body":"I'm trying to load the [fquad dataset](https:\/\/huggingface.co\/datasets\/fquad) by running: \r\n\r\n```Python\r\nfquad = load_dataset(\"fquad\")\r\n```\r\n\r\nwhich produces the following error:\r\n\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset fquad\/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to \/root\/.cache\/huggingface\/datasets\/fquad\/default\/0.1.0\/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\r\n<ipython-input-48-a2721797e23b> in <module>()\r\n----> 1 fquad = load_dataset(\"fquad\")\r\n\r\n11 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    617 \r\n    618     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\r\n```\r\n\r\nDoes anyone know why that is and how to fix it? ","comment_length":25,"text":"Can't reach \"https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\" when trying to load fquad dataset \n I'm trying to load the [fquad dataset](https:\/\/huggingface.co\/datasets\/fquad) by running: \r\n\r\n```Python\r\nfquad = load_dataset(\"fquad\")\r\n```\r\n\r\nwhich produces the following error:\r\n\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset fquad\/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to \/root\/.cache\/huggingface\/datasets\/fquad\/default\/0.1.0\/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n\r\n<ipython-input-48-a2721797e23b> in <module>()\r\n----> 1 fquad = load_dataset(\"fquad\")\r\n\r\n11 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n--> 616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    617 \r\n    618     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/illuin\/fquad\/train.json.zip\r\n```\r\n\r\nDoes anyone know why that is and how to fix it?  \n They now made a website to force people who want to use the dataset for commercial purposes to seek a commercial license from them ...","embeddings":[-0.3520766795,0.1850519031,-0.1066456437,0.2455794364,0.3998158276,0.03360755,0.3876356781,0.2061039656,0.3057492673,0.1453926563,-0.2683598399,-0.1908080578,0.2827515602,0.0175514612,0.027606599,0.0714482665,-0.0717816278,-0.0440939777,-0.1596578062,0.0708816275,-0.3542164862,0.3704529405,-0.1904580444,0.1628842503,-0.3339352906,0.0633746386,0.0055798003,0.3590935767,-0.3199560642,-0.1079104543,0.286209017,0.0416278467,0.004151077,0.4852188826,-0.0001157215,0.1660642475,0.3586290479,-0.0317648053,-0.4298180938,-0.5632392168,-0.3045651317,-0.072570838,0.2153185606,-0.2087870389,-0.1616279483,-0.0290350895,0.1196179837,-0.4198773503,0.2603788972,0.4918957353,0.1842911541,-0.0472500958,0.2975516617,-0.1803262234,-0.0591369905,-0.1362888217,-0.0200078152,0.6450526714,-0.0140431896,-0.2045283467,-0.0508274771,0.0286189858,0.1182557642,0.1726587862,0.1585940719,0.2011903822,-0.3372362256,-0.1506722569,0.2118362486,0.0146889491,0.5337827802,-0.2849702537,-0.4723348618,-0.1233135611,-0.0491205491,-0.3767210245,0.5024482012,0.0925156698,-0.2628643513,0.1907122433,-0.4664973021,-0.2508746386,-0.1997162253,0.3678171635,-0.3860652447,0.2701286077,-0.1864978373,0.2325517088,0.2770298421,-0.1557939649,-0.2593966126,0.1098564789,0.1527526826,0.3186956048,-0.314109236,0.1022981256,-0.0440244749,-0.2830099761,0.1964606792,0.3815691173,-0.015078485,0.0076378272,-0.0969404653,0.164527759,0.4020113349,0.3189195991,-0.1120543703,0.2087809891,0.1873425245,0.6325322986,-0.1311301142,-0.1770190001,-0.1378113925,-0.081372045,-0.1658248156,-0.183514595,0.1537760198,-0.1985757202,-0.1576269418,0.1740346998,-0.2624880672,-0.0844819471,0.0354961865,0.3024871647,-0.0897951052,0.1091868505,0.0184984915,0.2455940992,-0.1006541178,0.1638454646,-0.1644030064,0.2479628474,-0.218215555,0.231559068,0.2416772842,0.1246844679,0.3092523515,-0.3974604607,0.0709448978,-0.0954510868,0.2211935818,-0.2454142272,-0.156402275,0.3248185813,0.1361620575,-0.019331608,0.1892515123,-0.0819873288,-0.120350033,-0.0591272786,-0.238079235,-0.2690150142,-0.1610825509,0.1693099141,-0.1822194904,0.0440686196,-0.3180564344,0.0211405642,-0.163014695,-0.2929230332,-0.02649194,0.1383045167,-0.1317382902,-0.1248099804,0.3689207137,0.5510037541,-0.2626015544,-0.0661597773,-0.2078201175,-0.0995471701,-0.0146748368,0.4244317114,-0.174391821,0.2035565674,-0.4347251058,0.2382106483,0.4783934355,-0.2472981364,-0.8428885341,0.427974999,-0.2450462729,0.1419134438,-0.091932185,0.0322814882,-0.0358391963,0.1951994896,0.6311691403,0.2926326096,-0.0661307499,-0.127931118,-0.0659748837,-0.3087247014,-0.0573887713,0.2728063464,-0.0072478638,0.173291862,0.2028902769,-0.11355827,0.203204602,0.1464847624,-0.1294753551,0.2984036505,0.0654904172,0.0501435176,-0.1333141923,-0.0529266,-0.6362524629,0.2412666529,-0.2172224224,-0.0310093351,-0.5615538359,0.2495313436,-0.4475069642,0.0381114259,-0.0719014481,0.0171304978,0.0995740071,0.0793238357,0.1675628722,0.1997745037,-0.2326557487,0.3575578332,0.0593538806,0.2285772711,-0.4177794456,0.3038277328,-0.068603389,-0.0128734708,-0.0401704386,0.0375095345,0.0876060575,-0.1907199472,-0.1723519415,0.3521072268,-0.0981610119,0.1596617848,0.3979538083,0.4166022539,0.1592304558,-0.0419741049,0.0967004448,-0.1133250967,0.0562817417,-0.0873886049,-0.1806227118,0.1824068427,0.1226353049,0.3023006022,0.140295893,0.033637926,0.365693301,0.1180057302,-0.0833135769,-0.1543994844,0.15414083,0.4664791822,0.2170068771,0.0557061583,-0.1387757808,-0.0494213514,0.3819881976,-0.2163211554,0.0133876475,0.2899906933,0.0413509086,-0.2723265886,0.1269237697,0.0862024873,0.4915738106,0.0586339347,0.1429887861,0.1387392431,-0.2469654232,-0.1638064682,0.0784770474,0.0087313019,0.0382009223,0.0595250651,0.1666518003,-0.0206933133,-0.1088888422,-0.3694197536,0.0472553745,0.1783639491,-0.1222572029,0.3422809839,-0.1195476055,-0.1523807496,0.1046303585,-0.3231037259,-0.2455987036,-0.4834875762,-0.1251465082,0.2941145301,0.0920701548,0.0488251857,-0.3312270641,0.0984709337,0.1856976151,-0.5195731521,-0.1338463426,-0.1513882279,-0.1231954619,0.0563571267,0.3074066341,-0.2006785274,0.2499470264,-0.2296645343,-0.100894548,-0.4725251794,-0.1700989753,0.0898433775,-0.0312449019,0.40792799,0.2447232157,0.3579797447,-0.0674479678,-0.0232894085,0.4561803341,0.0728968084,-0.0803323686,-0.0389703028,-0.1058985814,0.1410915405,0.054669302,-0.263803184,-0.2067806423,-0.3587554097,0.4501703084,0.0336139351,0.0696525946,0.1306530684,0.1599377245,0.2007167935,0.1608746797,-0.1496057361,-0.3580869138,-0.5675964355,0.4216380119,-0.2252491564,-0.062960811,0.0954560116,0.1260264516,0.4072681069,0.3108044267,-0.5964596272,-0.4336869121,-0.2441263199,0.2438689023,-0.0759851113,-0.2841826975,0.2059355229,-0.2813442647,-0.0455122963,0.0795237049,-0.3435887992,0.1072266921,0.0380031541,0.4218061566,0.1905358434,0.5441442132,-0.1006031409,0.596581161,0.0506761186,0.1118795276,0.3819426298,-0.1311579049,0.2393558174,-0.4368827343,-0.2892046869,0.1354883611,-0.0735363513,-0.31998091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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2211","title":"Getting checksum error when trying to load lc_quad dataset","comments":"Hi,\r\n\r\nI've already opened a PR with the fix. If you are in a hurry, just build the project from source and run:\r\n```bash\r\ndatasets-cli test datasets\/lc_quad --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\n","body":"I'm having issues loading the [lc_quad](https:\/\/huggingface.co\/datasets\/fquad) dataset by running:\r\n\r\n```Python\r\nlc_quad = load_dataset(\"lc_quad\")\r\n```\r\n\r\nwhich is giving me the following error:\r\n\r\n``` \r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset lc_quad\/default (download: 3.69 MiB, generated: 19.77 MiB, post-processed: Unknown size, total: 23.46 MiB) to \/root\/.cache\/huggingface\/datasets\/lc_quad\/default\/2.0.0\/5a98fe174603f5dec6df07edf1c2b4d2317210d2ad61f5a393839bca4d64e5a7...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\r\n<ipython-input-42-404ace83f73c> in <module>()\r\n----> 1 lc_quad = load_dataset(\"lc_quad\")\r\n\r\n3 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/AskNowQA\/LC-QuAD2.0\/archive\/master.zip']\r\n```\r\n\r\nDoes anyone know why this could be and how I fix it? ","comment_length":31,"text":"Getting checksum error when trying to load lc_quad dataset \n I'm having issues loading the [lc_quad](https:\/\/huggingface.co\/datasets\/fquad) dataset by running:\r\n\r\n```Python\r\nlc_quad = load_dataset(\"lc_quad\")\r\n```\r\n\r\nwhich is giving me the following error:\r\n\r\n``` \r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset lc_quad\/default (download: 3.69 MiB, generated: 19.77 MiB, post-processed: Unknown size, total: 23.46 MiB) to \/root\/.cache\/huggingface\/datasets\/lc_quad\/default\/2.0.0\/5a98fe174603f5dec6df07edf1c2b4d2317210d2ad61f5a393839bca4d64e5a7...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n\r\n<ipython-input-42-404ace83f73c> in <module>()\r\n----> 1 lc_quad = load_dataset(\"lc_quad\")\r\n\r\n3 frames\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/AskNowQA\/LC-QuAD2.0\/archive\/master.zip']\r\n```\r\n\r\nDoes anyone know why this could be and how I fix it?  \n Hi,\r\n\r\nI've already opened a PR with the fix. If you are in a hurry, just build the project from source and run:\r\n```bash\r\ndatasets-cli test datasets\/lc_quad --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\n","embeddings":[-0.1586478055,0.0430786312,-0.0340241343,0.3563593924,0.2677668631,0.0147292903,0.0714035481,0.2469018549,0.3710919619,-0.0422729291,-0.1049166024,0.0677453876,-0.0377763398,0.0753146783,-0.2280279398,0.2225681841,0.0536352806,0.0427623615,-0.0752972588,0.0635507628,-0.2016302496,0.2370710671,-0.08427459,-0.2540154159,-0.2298655808,0.022695167,0.2053581625,0.4523075819,-0.284686029,-0.2342202663,0.2958003879,0.2990624905,0.177793175,0.3523058593,-0.0001155879,0.1017175913,0.3779894412,-0.189946264,-0.3943250775,-0.0775870606,-0.2616369724,-0.1055975109,0.1051906645,-0.1757149994,0.0675687641,0.3183248341,-0.1755350679,-0.1860815436,0.1201474518,0.0313655734,0.2525502741,0.6949155927,0.0769914463,0.0821324289,-0.0173101816,-0.0665880516,0.0181370638,0.6490388513,0.2040345371,-0.1024491787,-0.0439264029,0.1912636757,-0.0535609722,0.1119764149,0.171669215,-0.1965045333,-0.0742644519,-0.0029104268,0.2049059272,0.2949047983,0.4116024971,-0.354295969,-0.4142789841,-0.0578518435,0.0658886731,-0.19241032,0.3854560852,0.2196364552,-0.202465862,-0.1056818217,-0.3435885608,0.0057747285,0.1171856374,0.143759042,0.02749338,0.0133236069,0.011992489,0.0433482118,0.1741916984,-0.1444574744,-0.1835757196,-0.0199342594,-0.2033450156,0.2262380272,-0.4441525638,-0.0280742981,-0.0538369827,0.5078675747,0.161497131,0.5987932682,0.1114636809,0.2344520241,-0.1141757444,0.131139487,0.0641107485,0.1764589846,0.0813860819,0.2512382567,0.1025379524,0.5696989894,-0.112466,-0.0161850434,-0.1434008032,-0.067008093,0.020459095,0.0047425097,0.1683295965,-0.5566789508,-0.2990564108,0.4156864285,0.1240056381,-0.0489496663,0.4083915353,0.4536544979,-0.1148815006,0.0788542703,0.0255795624,0.0592117757,-0.2122116536,0.0015811836,-0.2094918638,0.095049344,-0.109906666,0.1747077703,0.3324213028,-0.2124649584,0.3945674002,-0.1254246235,0.554668963,-0.0824102387,0.0168444403,-0.0795143917,-0.2176980674,0.3631320894,-0.1268157661,0.0099507356,0.4028933644,-0.0756156445,-0.2388128042,-0.0873798952,-0.1864018887,-0.1896510571,-0.2478010505,0.1476690024,-0.4457603991,-0.0073195416,-0.1004635543,-0.431081146,0.3045098782,-0.3342050016,0.0233886093,-0.1273536235,-0.1298718005,-0.1921387166,0.2523182333,0.3476815522,-0.2570971847,-0.042105291,0.005207208,-0.2823810875,0.191449672,0.2836829424,-0.0853362828,0.018048035,-0.2128898054,-0.1154889539,0.0440893285,-0.3135988712,-0.6612861156,0.1054850817,0.2083634287,0.2534271777,-0.1629279107,0.0760661811,-0.1316621155,-0.0755772591,0.0276353955,-0.0193063058,0.0347143225,0.0147750927,-0.2605696619,-0.1714076847,0.1294886768,0.2703022659,-0.0975577682,-0.0796645805,0.0913568959,0.0269035045,0.195572868,-0.0815563351,-0.1271460056,0.1410221159,0.4066483974,0.0088428976,0.0124131655,-0.1226483509,-0.5203519464,0.4184086621,-0.1758986264,0.1054993346,-0.3010833561,0.0123756966,-0.3993290365,-0.0077080829,-0.2525680065,0.0424720533,0.0912322775,0.1547874808,0.1401317716,-0.018431779,-0.2219920009,0.1539941281,-0.402622968,0.1927557141,-0.4083478153,0.4389024079,0.1484152824,-0.0811729208,0.0649157166,0.2987393141,0.0440049544,0.0688854307,-0.1830139458,0.3952317238,0.3292908669,-0.0046892441,0.1685605198,0.2685255408,-0.0258053299,-0.1721272618,-0.0555889159,0.2175353318,0.1912800372,-0.0338720679,-0.0383775122,0.4533189535,-0.1471694559,0.0875585973,-0.0493916832,0.0198792871,0.2120854259,-0.037548665,-0.2479397207,-0.2896395624,0.3542730808,0.3663347065,0.2394928038,0.0791928396,-0.0366405286,-0.2723244131,-0.0387272686,0.0369359143,-0.0535523482,0.0033647083,0.2176830769,0.0531175286,0.1653119475,0.3118388951,0.3474906087,-0.0092862491,-0.0535392649,0.2015189081,-0.3057135344,-0.0120725799,-0.0608281977,0.0010351073,0.089135319,0.2957101464,0.0235628765,-0.0257526413,-0.3373190165,-0.1105120778,0.0253474694,0.3205064833,-0.3456208706,0.0741355494,-0.3163037002,-0.1975536197,-0.1722880751,-0.24867028,-0.4937612116,-0.4542938173,-0.1639761925,0.2879142463,0.0259698164,0.1399546862,-0.5169958472,0.1362466961,0.0900542811,-0.512042582,0.0927218571,-0.1727410704,-0.0236470792,0.0141025204,0.5645958185,0.020563338,0.4354997575,-0.1627611667,-0.027611088,-0.3428322673,-0.1681953967,-0.0328796282,-0.1144415289,0.2417946756,0.2336486727,0.2427727729,-0.1269131154,-0.21963498,0.3777884245,0.1318973303,-0.2311527729,0.1286144406,-0.1610604674,-0.1313485354,-0.0604946129,0.2755798995,-0.1098041609,-0.3019474447,-0.0892819986,0.175815016,0.092692107,-0.0439409241,-0.0829568282,0.1223537475,-0.0465585142,0.0810393393,-0.3157858253,-0.6449924707,0.3277232647,-0.0472498387,-0.3249155879,0.0022676487,-0.0587228388,0.4990760386,0.0536289699,-0.4397414625,-0.3454911113,-0.177422449,0.2491053939,0.0834853798,-0.0003843263,0.0818123147,-0.1677067727,0.0547439717,-0.0944872871,-0.252823323,-0.1147165969,-0.0507771038,0.5432264805,-0.1026443392,0.2152421921,-0.1030042171,0.6116770506,0.4520657063,-0.0781401247,0.3794070482,0.0450162813,0.4519749284,-0.1160534918,-0.335739255,0.0971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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2210","title":"dataloading slow when using HUGE dataset","comments":"Hi ! Yes this is an issue with `datasets<=1.5.0`\r\nThis issue has been fixed by #2122 , we'll do a new release soon :)\r\nFor now you can test it on the `master` branch.","body":"Hi,\r\n\r\nWhen I use datasets with 600GB data, the dataloading speed increases significantly. \r\nI am experimenting with two datasets, and one is about 60GB and the other 600GB.\r\nSimply speaking, my code uses `datasets.set_format(\"torch\")` function and let pytorch-lightning handle ddp training.\r\nWhen looking at the pytorch-lightning supported profile of two different runs, I see that fetching a batch(`get_train_batch`) consumes an unreasonable amount of time when data is large. What could be the cause?\r\n\r\n* 60GB data\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  200.33         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nrun_training_epoch                 \t|  71.994         \t|1              \t|  71.994         \t|  35.937         \t|\r\nrun_training_batch                 \t|  0.64373        \t|100            \t|  64.373         \t|  32.133         \t|\r\noptimizer_step_and_closure_0       \t|  0.64322        \t|100            \t|  64.322         \t|  32.108         \t|\r\ntraining_step_and_backward         \t|  0.61004        \t|100            \t|  61.004         \t|  30.452         \t|\r\nmodel_backward                     \t|  0.37552        \t|100            \t|  37.552         \t|  18.745         \t|\r\nmodel_forward                      \t|  0.22813        \t|100            \t|  22.813         \t|  11.387         \t|\r\ntraining_step                      \t|  0.22759        \t|100            \t|  22.759         \t|  11.361         \t|\r\nget_train_batch                    \t|  0.066385       \t|100            \t|  6.6385         \t|  3.3138         \t|\r\n```\r\n\r\n* 600GB data\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  3285.6         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nrun_training_epoch                 \t|  1397.9         \t|1              \t|  1397.9         \t|  42.546         \t|\r\nrun_training_batch                 \t|  7.2596         \t|100            \t|  725.96         \t|  22.095         \t|\r\noptimizer_step_and_closure_0       \t|  7.2589         \t|100            \t|  725.89         \t|  22.093         \t|\r\ntraining_step_and_backward         \t|  7.223          \t|100            \t|  722.3          \t|  21.984         \t|\r\nmodel_backward                     \t|  6.9662         \t|100            \t|  696.62         \t|  21.202         \t|\r\nget_train_batch                    \t|  6.322          \t|100            \t|  632.2          \t|  19.241         \t|\r\nmodel_forward                      \t|  0.24902        \t|100            \t|  24.902         \t|  0.75789        \t|\r\ntraining_step                      \t|  0.2485         \t|100            \t|  24.85          \t|  0.75633        \t|\r\n```\r\n","comment_length":34,"text":"dataloading slow when using HUGE dataset \n Hi,\r\n\r\nWhen I use datasets with 600GB data, the dataloading speed increases significantly. \r\nI am experimenting with two datasets, and one is about 60GB and the other 600GB.\r\nSimply speaking, my code uses `datasets.set_format(\"torch\")` function and let pytorch-lightning handle ddp training.\r\nWhen looking at the pytorch-lightning supported profile of two different runs, I see that fetching a batch(`get_train_batch`) consumes an unreasonable amount of time when data is large. What could be the cause?\r\n\r\n* 60GB data\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  200.33         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nrun_training_epoch                 \t|  71.994         \t|1              \t|  71.994         \t|  35.937         \t|\r\nrun_training_batch                 \t|  0.64373        \t|100            \t|  64.373         \t|  32.133         \t|\r\noptimizer_step_and_closure_0       \t|  0.64322        \t|100            \t|  64.322         \t|  32.108         \t|\r\ntraining_step_and_backward         \t|  0.61004        \t|100            \t|  61.004         \t|  30.452         \t|\r\nmodel_backward                     \t|  0.37552        \t|100            \t|  37.552         \t|  18.745         \t|\r\nmodel_forward                      \t|  0.22813        \t|100            \t|  22.813         \t|  11.387         \t|\r\ntraining_step                      \t|  0.22759        \t|100            \t|  22.759         \t|  11.361         \t|\r\nget_train_batch                    \t|  0.066385       \t|100            \t|  6.6385         \t|  3.3138         \t|\r\n```\r\n\r\n* 600GB data\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  3285.6         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nrun_training_epoch                 \t|  1397.9         \t|1              \t|  1397.9         \t|  42.546         \t|\r\nrun_training_batch                 \t|  7.2596         \t|100            \t|  725.96         \t|  22.095         \t|\r\noptimizer_step_and_closure_0       \t|  7.2589         \t|100            \t|  725.89         \t|  22.093         \t|\r\ntraining_step_and_backward         \t|  7.223          \t|100            \t|  722.3          \t|  21.984         \t|\r\nmodel_backward                     \t|  6.9662         \t|100            \t|  696.62         \t|  21.202         \t|\r\nget_train_batch                    \t|  6.322          \t|100            \t|  632.2          \t|  19.241         \t|\r\nmodel_forward                      \t|  0.24902        \t|100            \t|  24.902         \t|  0.75789        \t|\r\ntraining_step                      \t|  0.2485         \t|100            \t|  24.85          \t|  0.75633        \t|\r\n```\r\n \n Hi ! Yes this is an issue with `datasets<=1.5.0`\r\nThis issue has been fixed by #2122 , we'll do a new release soon :)\r\nFor now you can test it on the `master` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2210","title":"dataloading slow when using HUGE dataset","comments":"Hi, thank you for your answer. I did not realize that my issue stems from the same problem. ","body":"Hi,\r\n\r\nWhen I use datasets with 600GB data, the dataloading speed increases significantly. \r\nI am experimenting with two datasets, and one is about 60GB and the other 600GB.\r\nSimply speaking, my code uses `datasets.set_format(\"torch\")` function and let pytorch-lightning handle ddp training.\r\nWhen looking at the pytorch-lightning supported profile of two different runs, I see that fetching a batch(`get_train_batch`) consumes an unreasonable amount of time when data is large. What could be the cause?\r\n\r\n* 60GB data\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  200.33         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nrun_training_epoch                 \t|  71.994         \t|1              \t|  71.994         \t|  35.937         \t|\r\nrun_training_batch                 \t|  0.64373        \t|100            \t|  64.373         \t|  32.133         \t|\r\noptimizer_step_and_closure_0       \t|  0.64322        \t|100            \t|  64.322         \t|  32.108         \t|\r\ntraining_step_and_backward         \t|  0.61004        \t|100            \t|  61.004         \t|  30.452         \t|\r\nmodel_backward                     \t|  0.37552        \t|100            \t|  37.552         \t|  18.745         \t|\r\nmodel_forward                      \t|  0.22813        \t|100            \t|  22.813         \t|  11.387         \t|\r\ntraining_step                      \t|  0.22759        \t|100            \t|  22.759         \t|  11.361         \t|\r\nget_train_batch                    \t|  0.066385       \t|100            \t|  6.6385         \t|  3.3138         \t|\r\n```\r\n\r\n* 600GB data\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  3285.6         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nrun_training_epoch                 \t|  1397.9         \t|1              \t|  1397.9         \t|  42.546         \t|\r\nrun_training_batch                 \t|  7.2596         \t|100            \t|  725.96         \t|  22.095         \t|\r\noptimizer_step_and_closure_0       \t|  7.2589         \t|100            \t|  725.89         \t|  22.093         \t|\r\ntraining_step_and_backward         \t|  7.223          \t|100            \t|  722.3          \t|  21.984         \t|\r\nmodel_backward                     \t|  6.9662         \t|100            \t|  696.62         \t|  21.202         \t|\r\nget_train_batch                    \t|  6.322          \t|100            \t|  632.2          \t|  19.241         \t|\r\nmodel_forward                      \t|  0.24902        \t|100            \t|  24.902         \t|  0.75789        \t|\r\ntraining_step                      \t|  0.2485         \t|100            \t|  24.85          \t|  0.75633        \t|\r\n```\r\n","comment_length":18,"text":"dataloading slow when using HUGE dataset \n Hi,\r\n\r\nWhen I use datasets with 600GB data, the dataloading speed increases significantly. \r\nI am experimenting with two datasets, and one is about 60GB and the other 600GB.\r\nSimply speaking, my code uses `datasets.set_format(\"torch\")` function and let pytorch-lightning handle ddp training.\r\nWhen looking at the pytorch-lightning supported profile of two different runs, I see that fetching a batch(`get_train_batch`) consumes an unreasonable amount of time when data is large. What could be the cause?\r\n\r\n* 60GB data\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  200.33         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nrun_training_epoch                 \t|  71.994         \t|1              \t|  71.994         \t|  35.937         \t|\r\nrun_training_batch                 \t|  0.64373        \t|100            \t|  64.373         \t|  32.133         \t|\r\noptimizer_step_and_closure_0       \t|  0.64322        \t|100            \t|  64.322         \t|  32.108         \t|\r\ntraining_step_and_backward         \t|  0.61004        \t|100            \t|  61.004         \t|  30.452         \t|\r\nmodel_backward                     \t|  0.37552        \t|100            \t|  37.552         \t|  18.745         \t|\r\nmodel_forward                      \t|  0.22813        \t|100            \t|  22.813         \t|  11.387         \t|\r\ntraining_step                      \t|  0.22759        \t|100            \t|  22.759         \t|  11.361         \t|\r\nget_train_batch                    \t|  0.066385       \t|100            \t|  6.6385         \t|  3.3138         \t|\r\n```\r\n\r\n* 600GB data\r\n```\r\nAction                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nTotal                              \t|  -              \t|_              \t|  3285.6         \t|  100 %          \t|\r\n------------------------------------------------------------------------------------------------------------------------------------\r\nrun_training_epoch                 \t|  1397.9         \t|1              \t|  1397.9         \t|  42.546         \t|\r\nrun_training_batch                 \t|  7.2596         \t|100            \t|  725.96         \t|  22.095         \t|\r\noptimizer_step_and_closure_0       \t|  7.2589         \t|100            \t|  725.89         \t|  22.093         \t|\r\ntraining_step_and_backward         \t|  7.223          \t|100            \t|  722.3          \t|  21.984         \t|\r\nmodel_backward                     \t|  6.9662         \t|100            \t|  696.62         \t|  21.202         \t|\r\nget_train_batch                    \t|  6.322          \t|100            \t|  632.2          \t|  19.241         \t|\r\nmodel_forward                      \t|  0.24902        \t|100            \t|  24.902         \t|  0.75789        \t|\r\ntraining_step                      \t|  0.2485         \t|100            \t|  24.85          \t|  0.75633        \t|\r\n```\r\n \n Hi, thank you for your answer. I did not realize that my issue stems from the same problem. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2207","title":"making labels consistent across the datasets","comments":"Hi ! The ClassLabel feature type encodes the labels as integers.\r\nThe integer corresponds to the index of the label name in the `names` list of the ClassLabel.\r\nHere that means that the labels are 'entailment' (0), 'neutral' (1), 'contradiction' (2).\r\n\r\nYou can get the label names back by using `a.features['label'].int2str(i)`.\r\n","body":"Hi\r\nFor accessing the labels one can type \r\n```\r\n>>> a.features['label']\r\nClassLabel(num_classes=3, names=['entailment', 'neutral', 'contradiction'], names_file=None, id=None)\r\n```\r\nThe labels however are not consistent with the actual labels sometimes, for instance in case of XNLI, the actual labels are 0,1,2, but if one try to access as above they are entailment, neutral,contradiction,\r\nit would be great to have the labels consistent.\r\n\r\nthanks \r\n","comment_length":51,"text":"making labels consistent across the datasets \n Hi\r\nFor accessing the labels one can type \r\n```\r\n>>> a.features['label']\r\nClassLabel(num_classes=3, names=['entailment', 'neutral', 'contradiction'], names_file=None, id=None)\r\n```\r\nThe labels however are not consistent with the actual labels sometimes, for instance in case of XNLI, the actual labels are 0,1,2, but if one try to access as above they are entailment, neutral,contradiction,\r\nit would be great to have the labels consistent.\r\n\r\nthanks \r\n \n Hi ! The ClassLabel feature type encodes the labels as integers.\r\nThe integer corresponds to the index of the label name in the `names` list of the ClassLabel.\r\nHere that means that the labels are 'entailment' (0), 'neutral' (1), 'contradiction' (2).\r\n\r\nYou can get the label names back by using `a.features['label'].int2str(i)`.\r\n","embeddings":[0.0162600745,-0.1287364215,-0.0699902028,0.4029694796,0.3827131689,-0.1300277263,0.4260282815,0.0234175641,0.0852748901,0.2710852325,-0.2305020094,0.5326738358,-0.0153415753,0.4047870934,-0.307893008,0.0154723981,-0.1154663414,0.1015716866,0.1249587983,-0.3230445385,-0.2213249803,-0.1315994114,0.0152088413,0.334883064,-0.4379475415,-0.141682148,0.0278720055,-0.1909615248,0.0741544813,-0.4989104271,0.1208871752,0.3788155615,0.0031806116,0.1615455449,-0.0000959714,-0.2662477195,0.1202937737,0.0277738199,-0.0765430629,0.0507424362,-0.2216129452,-0.1730420589,0.0833134875,-0.4307701588,-0.2084742486,0.0238299482,-0.1198960394,-0.2397072762,-0.1590719521,-0.0710135326,0.2925275564,0.0040540034,0.128914088,0.1718682945,0.4010023177,-0.0080782948,0.0416165479,0.1494404376,0.3026364148,0.129017666,0.0510599241,0.5405575037,-0.035616409,-0.062250562,0.3284755647,0.1424699873,0.0399955511,-0.3465792239,0.0029805398,0.4912868738,0.584572196,-0.249494642,-0.4491060674,-0.1393752396,0.1123988628,-0.2547758222,-0.1436599791,0.0708718002,0.2149394006,0.042105861,0.0539676882,-0.0519852489,-0.2780491412,0.1498251706,0.0187883321,0.4695444107,0.0171505082,0.2028816044,-0.0887358561,-0.4577319026,0.0376456343,-0.0076666591,0.0038778041,0.2679313123,-0.1903167069,0.0004920989,-0.1286011934,0.0033978471,0.0820229799,0.1513404101,-0.2002418488,0.1516584754,-0.2954198718,0.1294176728,0.0067377677,0.3571250737,0.5226082802,0.2573030591,0.3497829437,-0.0255378429,-0.3429965973,-0.0779767856,-0.1289377362,0.117494978,0.3556327224,-0.0392704681,0.0160166845,-0.3400921524,-0.3314759433,0.0508840345,-0.1754248589,-0.0531823412,0.1985355914,0.2774687409,0.1758970618,0.3547516167,0.11419563,-0.0679105893,-0.129161194,-0.3238432109,-0.2257992774,-0.0322754085,-0.0664247125,-0.172146827,-0.0451963358,0.2057716101,0.2654462755,-0.1270953119,0.0177972671,-0.1221840158,-0.0213277731,-0.1247066259,0.013071551,-0.1685440987,-0.3194271922,0.0438333489,0.0006262753,-0.1108302698,-0.2505707741,-0.2797500491,-0.2588024437,0.0304975491,0.2304798216,0.3659622669,0.1701172888,-0.1080110818,0.3435677886,0.1185834035,-0.1111146957,-0.1016408578,0.1304856837,-0.3175900578,0.1881766468,-0.1673260629,-0.0616379194,0.0561994761,-0.3011079431,-0.1929347962,0.3234800994,-0.1957866251,-0.1605420858,0.0398119129,-0.0043440871,-0.1616312861,-0.0097828945,0.081334129,0.4260672331,-0.4890017807,-0.3455292881,-0.215180397,0.1158952788,-0.1208518222,0.2246493697,0.4517965615,0.092608735,0.0097813914,0.1628989875,0.0739813149,0.1442049444,-0.0268066023,-0.1295728534,-0.0836474597,0.0303375926,0.1009498239,-0.2006149888,-0.2347719818,0.1300798506,-0.0363650061,0.2824650407,-0.4692776203,0.0849545226,0.1094819158,0.4002465308,0.2273927182,-0.0841675252,-0.2679437995,-0.2897003591,0.2414544821,-0.0737488717,0.2531753778,0.2952412963,-0.4176500738,-0.0334187858,-0.114094846,-0.2889352143,-0.0800449848,0.3252169788,0.3039772511,-0.1158038303,-0.069671005,-0.1151366979,-0.0504070669,-0.1801474392,0.0385653004,-0.1042997018,-0.1267308742,0.0450599007,0.172812894,-0.1324340999,0.4198814332,0.2545403242,0.0150632067,-0.0720482096,0.2207140476,0.2466808558,-0.198717162,0.0628409237,0.3595350087,0.2418501973,-0.2225042135,0.04424062,0.1010803655,0.1455687732,0.1002046764,-0.1473449767,0.5054873228,-0.0140543981,0.0740434527,0.0558408797,0.0776267722,0.2117099166,-0.0607625097,-0.3135550618,-0.1297812462,0.0235370044,-0.0802810043,0.0891641453,0.1970622987,-0.6030899286,0.5452006459,0.6344435811,0.0285052974,0.1303443313,-0.2016640157,-0.2598124444,0.2631831467,0.137886703,0.1743709147,-0.091809541,0.3492028117,0.0007843775,-0.1269412488,-0.2725159824,-0.0351667553,0.032810647,-0.1560471207,0.1233802065,-0.066625379,0.1052386835,-0.1327948123,0.0184839554,-0.4001693428,-0.1800195128,-0.0406782813,-0.3130125701,0.1659746319,-0.1016380936,-0.2682479322,-0.3333453238,-0.4044164419,-0.3766966164,-0.4287503958,0.0814472511,-0.0757482573,-0.2525373995,0.2243506312,-0.0716915578,0.2579423189,0.0325993337,-0.0418787524,0.2593669593,-0.4869864881,-0.4448615611,0.1094455943,-0.0474651419,0.0781192034,0.2551839054,-0.1009775475,0.193041876,-0.0853098035,-0.4455027878,-0.0569377728,-0.1742358953,0.1129001975,0.1211117879,-0.0299212802,0.0745403618,0.0304516181,0.1996329427,-0.1402624696,0.0097622732,-0.0211205278,-0.0075489497,-0.3436497152,-0.0661106184,-0.6197527051,-0.3925741315,-0.2936830521,0.0688273609,-0.0980795622,0.095512405,0.060406141,-0.2549695671,0.0536748916,0.0824274421,0.5573016405,-0.296551615,-0.1041312292,0.1520818025,-0.1441338211,-0.1998024136,0.0779300928,-0.0933245122,0.1299971789,-0.0859044269,0.018262472,-0.3870120645,-0.096018739,0.2507855296,0.1855876893,0.2257487923,0.0180490837,0.2406856716,-0.1593569666,-0.1146705598,-0.1340183765,0.3696860373,0.0592280179,0.2220904082,0.0443476737,-0.170018658,-0.2130917609,0.0299039409,0.1449424475,-0.1307631284,0.0554137751,-0.0990678445,0.1191018224,0.282528013,-0.051853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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2206","title":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer","comments":"Hi,\r\n\r\nthe output of the tokenizers is treated specially in the lib to optimize the dataset size (see the code [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L138-L141)). It looks like that one of the values in a dictionary returned by the tokenizer is out of the assumed range.\r\nCan you please provide a minimal reproducible example for more help?","body":"I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it?","comment_length":53,"text":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer \n I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it? \n Hi,\r\n\r\nthe output of the tokenizers is treated specially in the lib to optimize the dataset size (see the code [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L138-L141)). It looks like that one of the values in a dictionary returned by the tokenizer is out of the assumed range.\r\nCan you please provide a minimal reproducible example for more help?","embeddings":[-0.2338283807,0.2921876907,-0.0588289574,0.0690962002,0.2734426856,-0.0860713869,0.18364425,0.3055635691,-0.5368508101,-0.1932197064,-0.0461710617,0.435470134,-0.0010597197,-0.2381744683,0.2536684275,-0.0995298922,0.0430259258,0.2507618368,0.2605471909,0.1317316443,-0.0321432874,0.1118264794,-0.1489618123,0.5977945924,-0.3350471854,-0.1270231009,0.0943686217,0.0601615049,-0.0006362203,-0.7611901164,0.0361957289,0.0767358392,0.257945925,0.1267624646,-0.000113376,0.082067512,0.3061074615,0.0346666314,-0.0352177434,0.0471045561,0.1980308145,-0.0341250151,0.2838692069,-0.2447410524,0.0418412797,-1.0216341019,-0.092064172,0.1748209447,0.4970366061,0.2085008621,0.2248863131,0.2016527355,0.420830071,0.0445980728,0.4089097083,0.0463489853,0.1187575832,0.2101746053,0.0648392066,-0.0744946226,-0.0082713105,-0.1188545451,-0.3061366975,0.2222594768,0.0819868073,0.0422179736,-0.0128988633,-0.0767085329,-0.0784930363,-0.0311290734,0.4616255462,-0.4693733454,-0.1958956271,-0.0923196748,0.0183173362,-0.416379869,0.1107923314,0.1142617688,-0.2171587646,0.0793027058,0.1194188371,-0.0217523016,-0.2067582905,0.1848450452,-0.0662417859,0.3541361094,0.030876739,0.2433264703,-0.0098536797,0.0171071067,0.2675454915,0.1362328231,-0.20636563,0.3336641192,-0.3007293344,0.0695281476,-0.0275461059,-0.2159134448,0.130602181,0.174859941,0.1709089279,0.104464978,0.1628208607,0.0762209892,-0.1299583167,0.1835837215,-0.162752375,0.2325523049,0.1585679799,-0.3149089813,0.0370789021,-0.1459881067,-0.3320124447,-0.2384871691,0.3329922855,0.1477688849,0.338866353,-0.0093660438,-0.1467920095,0.0947749093,-0.4526158869,-0.1450776309,0.0061007231,0.2455308884,0.2182272822,-0.227198109,-0.1537809521,0.2055501193,-0.1003332511,-0.2328752279,-0.1099831611,-0.0415037572,-0.0182201341,0.1874153167,0.2010739595,0.0286410004,0.2526602447,0.191785872,-0.0618622452,0.0152938822,0.3261609972,-0.1774689406,0.00584799,0.353584677,0.0630945936,0.3427803516,0.0158788543,-0.0727052465,-0.035782557,0.2693840265,-0.3565315902,-0.4401058257,-0.2793906927,0.1600381881,-0.1012900174,0.0013683235,-0.2858616412,-0.0320901535,0.4216962755,-0.3251367807,-0.1027349234,-0.3298664689,0.0337792262,-0.3652558625,0.3561752439,-0.0902508795,-0.56661731,0.1339240223,-0.0229863543,-0.1644707471,0.1516373754,0.3593240678,-0.3796893954,0.6116597056,-0.3605375588,0.6695796251,0.2312913537,-0.0378579535,-0.2667081356,-0.1009031981,-0.1092613116,0.0314189866,-0.1686679721,-0.1111759916,0.1504587829,-0.0762138218,-0.1399538964,0.0632124171,0.0288358945,0.0341261104,-0.1693430841,-0.0971364975,0.2992917001,0.1167512313,0.0325557068,-0.131849736,0.0156451128,-0.2472018301,0.0563441142,-0.3048322201,0.135184437,0.0759484395,0.2232836485,-0.0552587323,0.089476116,-0.1198254526,-0.2498248518,0.0050441306,-0.5045512915,-0.0529679134,-0.3933269978,-0.0097885858,-0.2112289518,0.0880161747,-0.1989298612,0.1071834713,0.1984060556,0.1060441881,0.0044009401,0.1505635381,0.0360580832,-0.1274581701,0.1176428646,0.1194809228,0.0944710746,0.2554818988,-0.2053661793,-0.614557445,0.12766774,-0.0362825505,0.2126549035,-0.0232843962,-0.0731346458,0.2727456093,-0.1284492314,0.0718912333,-0.2981536388,0.0743246377,0.0266837943,-0.4504473209,-0.1293709427,-0.0512205735,0.1151850969,-0.0185076017,0.4564298093,0.3656798005,0.2342091799,-0.0699911118,-0.1562936753,-0.2149705142,0.2021137327,0.032652162,0.2847647667,0.1573234648,0.0747532099,0.3221545815,0.1628095061,-0.0037151151,-0.0672149807,0.1072628424,0.3034688532,-0.1084505245,0.0763668269,0.0472967774,-0.1052669585,-0.1009280533,0.0544268526,-0.4296977818,0.2058745176,0.1628007293,-0.3397504389,0.0139675438,-0.073378697,-0.1370283961,0.1553959846,0.0631728098,0.3842323422,0.3814519346,0.2945330143,0.0729723871,-0.1637671739,0.017035896,0.2265655994,0.3600274324,-0.4554604888,-0.0783177093,-0.1630183607,-0.050847929,0.1702332795,-0.3216295242,-0.0486299023,-0.1523302495,0.0268299188,0.086485222,-0.0517295189,0.2023847401,0.0673771575,0.0459718928,0.1958207637,-0.0231608637,-0.3977649808,-0.2804777026,-0.2186540812,0.0824890435,0.3015551269,0.0403780341,0.2039325982,0.237473011,0.0278314594,-0.1771772653,-0.3392232656,0.0429519191,-0.0736969858,0.2346140146,0.0524714328,-0.0311724246,-0.080389455,-0.4751782417,0.2225087285,-0.236471504,-0.2954795361,0.1646377295,-0.1729840785,-0.0004738634,-0.0085444367,-0.0560015626,-0.2244289666,-0.1134307832,0.128232941,0.0839182213,0.1650997847,-0.2448323816,-0.0034249236,0.173871085,-0.203290984,-0.1970703602,-0.1134401187,0.045367118,0.351980567,-0.1946189553,-0.2235365659,-0.1290646791,-0.0890230834,0.3335036635,0.1064963043,-0.2684744,0.136640355,-0.0324387327,0.4266342223,-0.3216074407,-0.3929841518,0.3826549947,0.0959021151,-0.1160591617,0.0203609541,-0.00924493,0.0358997472,-0.1952968538,-0.1222014427,-0.0495722108,0.3201514781,-0.1264334768,0.8966103196,-0.2778320611,-0.1123260111,0.4765632749,-0.0585326925,-0.0492790081,-0.1197143942,-0.1173318997,-0.1053839251,-0.1219811961,0.1585762054,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2206","title":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer","comments":"Hi @yana-xuyan, thanks for reporting.\r\n\r\nAs clearly @mariosasko explained, `datasets` performs some optimizations in order to reduce the size of the dataset cache files. And one of them is storing the field `special_tokens_mask` as `int8`, which means that this field can only contain integers between `-128` to `127`. As your message error states, one of the values of this field is `50259`, and therefore it cannot be stored as an `int8`.\r\n\r\nMaybe we could implement a way to disable this optimization and allow using any integer value; although the size of the cache files would be much larger.","body":"I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it?","comment_length":98,"text":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer \n I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it? \n Hi @yana-xuyan, thanks for reporting.\r\n\r\nAs clearly @mariosasko explained, `datasets` performs some optimizations in order to reduce the size of the dataset cache files. And one of them is storing the field `special_tokens_mask` as `int8`, which means that this field can only contain integers between `-128` to `127`. As your message error states, one of the values of this field is `50259`, and therefore it cannot be stored as an `int8`.\r\n\r\nMaybe we could implement a way to disable this optimization and allow using any integer value; although the size of the cache files would be much larger.","embeddings":[-0.2338283807,0.2921876907,-0.0588289574,0.0690962002,0.2734426856,-0.0860713869,0.18364425,0.3055635691,-0.5368508101,-0.1932197064,-0.0461710617,0.435470134,-0.0010597197,-0.2381744683,0.2536684275,-0.0995298922,0.0430259258,0.2507618368,0.2605471909,0.1317316443,-0.0321432874,0.1118264794,-0.1489618123,0.5977945924,-0.3350471854,-0.1270231009,0.0943686217,0.0601615049,-0.0006362203,-0.7611901164,0.0361957289,0.0767358392,0.257945925,0.1267624646,-0.000113376,0.082067512,0.3061074615,0.0346666314,-0.0352177434,0.0471045561,0.1980308145,-0.0341250151,0.2838692069,-0.2447410524,0.0418412797,-1.0216341019,-0.092064172,0.1748209447,0.4970366061,0.2085008621,0.2248863131,0.2016527355,0.420830071,0.0445980728,0.4089097083,0.0463489853,0.1187575832,0.2101746053,0.0648392066,-0.0744946226,-0.0082713105,-0.1188545451,-0.3061366975,0.2222594768,0.0819868073,0.0422179736,-0.0128988633,-0.0767085329,-0.0784930363,-0.0311290734,0.4616255462,-0.4693733454,-0.1958956271,-0.0923196748,0.0183173362,-0.416379869,0.1107923314,0.1142617688,-0.2171587646,0.0793027058,0.1194188371,-0.0217523016,-0.2067582905,0.1848450452,-0.0662417859,0.3541361094,0.030876739,0.2433264703,-0.0098536797,0.0171071067,0.2675454915,0.1362328231,-0.20636563,0.3336641192,-0.3007293344,0.0695281476,-0.0275461059,-0.2159134448,0.130602181,0.174859941,0.1709089279,0.104464978,0.1628208607,0.0762209892,-0.1299583167,0.1835837215,-0.162752375,0.2325523049,0.1585679799,-0.3149089813,0.0370789021,-0.1459881067,-0.3320124447,-0.2384871691,0.3329922855,0.1477688849,0.338866353,-0.0093660438,-0.1467920095,0.0947749093,-0.4526158869,-0.1450776309,0.0061007231,0.2455308884,0.2182272822,-0.227198109,-0.1537809521,0.2055501193,-0.1003332511,-0.2328752279,-0.1099831611,-0.0415037572,-0.0182201341,0.1874153167,0.2010739595,0.0286410004,0.2526602447,0.191785872,-0.0618622452,0.0152938822,0.3261609972,-0.1774689406,0.00584799,0.353584677,0.0630945936,0.3427803516,0.0158788543,-0.0727052465,-0.035782557,0.2693840265,-0.3565315902,-0.4401058257,-0.2793906927,0.1600381881,-0.1012900174,0.0013683235,-0.2858616412,-0.0320901535,0.4216962755,-0.3251367807,-0.1027349234,-0.3298664689,0.0337792262,-0.3652558625,0.3561752439,-0.0902508795,-0.56661731,0.1339240223,-0.0229863543,-0.1644707471,0.1516373754,0.3593240678,-0.3796893954,0.6116597056,-0.3605375588,0.6695796251,0.2312913537,-0.0378579535,-0.2667081356,-0.1009031981,-0.1092613116,0.0314189866,-0.1686679721,-0.1111759916,0.1504587829,-0.0762138218,-0.1399538964,0.0632124171,0.0288358945,0.0341261104,-0.1693430841,-0.0971364975,0.2992917001,0.1167512313,0.0325557068,-0.131849736,0.0156451128,-0.2472018301,0.0563441142,-0.3048322201,0.135184437,0.0759484395,0.2232836485,-0.0552587323,0.089476116,-0.1198254526,-0.2498248518,0.0050441306,-0.5045512915,-0.0529679134,-0.3933269978,-0.0097885858,-0.2112289518,0.0880161747,-0.1989298612,0.1071834713,0.1984060556,0.1060441881,0.0044009401,0.1505635381,0.0360580832,-0.1274581701,0.1176428646,0.1194809228,0.0944710746,0.2554818988,-0.2053661793,-0.614557445,0.12766774,-0.0362825505,0.2126549035,-0.0232843962,-0.0731346458,0.2727456093,-0.1284492314,0.0718912333,-0.2981536388,0.0743246377,0.0266837943,-0.4504473209,-0.1293709427,-0.0512205735,0.1151850969,-0.0185076017,0.4564298093,0.3656798005,0.2342091799,-0.0699911118,-0.1562936753,-0.2149705142,0.2021137327,0.032652162,0.2847647667,0.1573234648,0.0747532099,0.3221545815,0.1628095061,-0.0037151151,-0.0672149807,0.1072628424,0.3034688532,-0.1084505245,0.0763668269,0.0472967774,-0.1052669585,-0.1009280533,0.0544268526,-0.4296977818,0.2058745176,0.1628007293,-0.3397504389,0.0139675438,-0.073378697,-0.1370283961,0.1553959846,0.0631728098,0.3842323422,0.3814519346,0.2945330143,0.0729723871,-0.1637671739,0.017035896,0.2265655994,0.3600274324,-0.4554604888,-0.0783177093,-0.1630183607,-0.050847929,0.1702332795,-0.3216295242,-0.0486299023,-0.1523302495,0.0268299188,0.086485222,-0.0517295189,0.2023847401,0.0673771575,0.0459718928,0.1958207637,-0.0231608637,-0.3977649808,-0.2804777026,-0.2186540812,0.0824890435,0.3015551269,0.0403780341,0.2039325982,0.237473011,0.0278314594,-0.1771772653,-0.3392232656,0.0429519191,-0.0736969858,0.2346140146,0.0524714328,-0.0311724246,-0.080389455,-0.4751782417,0.2225087285,-0.236471504,-0.2954795361,0.1646377295,-0.1729840785,-0.0004738634,-0.0085444367,-0.0560015626,-0.2244289666,-0.1134307832,0.128232941,0.0839182213,0.1650997847,-0.2448323816,-0.0034249236,0.173871085,-0.203290984,-0.1970703602,-0.1134401187,0.045367118,0.351980567,-0.1946189553,-0.2235365659,-0.1290646791,-0.0890230834,0.3335036635,0.1064963043,-0.2684744,0.136640355,-0.0324387327,0.4266342223,-0.3216074407,-0.3929841518,0.3826549947,0.0959021151,-0.1160591617,0.0203609541,-0.00924493,0.0358997472,-0.1952968538,-0.1222014427,-0.0495722108,0.3201514781,-0.1264334768,0.8966103196,-0.2778320611,-0.1123260111,0.4765632749,-0.0585326925,-0.0492790081,-0.1197143942,-0.1173318997,-0.1053839251,-0.1219811961,0.1585762054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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2206","title":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer","comments":"I'm facing same issue @mariosasko @albertvillanova \r\n\r\n```\r\nArrowInvalid: Integer value 50260 not in range: -128 to 127\r\n```\r\n\r\nTo reproduce:\r\n```python\r\nSPECIAL_TOKENS = ['<bos>','<eos>','<speaker1>','<speaker2>','<pad>']\r\nATTR_TO_SPECIAL_TOKEN = {\r\n    'bos_token': '<bos>', \r\n    'eos_token': '<eos>', \r\n    'pad_token': '<pad>',\r\n    'additional_special_tokens': ['<speaker1>', '<speaker2>']\r\n    }\r\n\r\ntokenizer = AutoTokenizer.from_pretrained(\"gpt2\", use_fast=False)\r\nnum_added_tokens =tokenizer.add_special_tokens(ATTR_TO_SPECIAL_TOKEN)\r\nvocab_size = len(self.tokenizer.encoder) + num_added_tokens\r\nvocab =tokenizer.get_vocab()\r\n\r\npad_index = tokenizer.pad_token_id\r\neos_index = tokenizer.eos_token_id\r\nbos_index = tokenizer.bos_token_id\r\nspeaker1_index = vocab[\"<speaker1>\"]\r\nspeaker2_index = vocab[\"<speaker2>\"]\r\n```\r\n\r\n```python\r\ntokenizer.decode(['50260'])\r\n'<speaker1>'\r\n```","body":"I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it?","comment_length":70,"text":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer \n I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it? \n I'm facing same issue @mariosasko @albertvillanova \r\n\r\n```\r\nArrowInvalid: Integer value 50260 not in range: -128 to 127\r\n```\r\n\r\nTo reproduce:\r\n```python\r\nSPECIAL_TOKENS = ['<bos>','<eos>','<speaker1>','<speaker2>','<pad>']\r\nATTR_TO_SPECIAL_TOKEN = {\r\n    'bos_token': '<bos>', \r\n    'eos_token': '<eos>', \r\n    'pad_token': '<pad>',\r\n    'additional_special_tokens': ['<speaker1>', '<speaker2>']\r\n    }\r\n\r\ntokenizer = AutoTokenizer.from_pretrained(\"gpt2\", use_fast=False)\r\nnum_added_tokens =tokenizer.add_special_tokens(ATTR_TO_SPECIAL_TOKEN)\r\nvocab_size = len(self.tokenizer.encoder) + num_added_tokens\r\nvocab =tokenizer.get_vocab()\r\n\r\npad_index = tokenizer.pad_token_id\r\neos_index = tokenizer.eos_token_id\r\nbos_index = tokenizer.bos_token_id\r\nspeaker1_index = vocab[\"<speaker1>\"]\r\nspeaker2_index = vocab[\"<speaker2>\"]\r\n```\r\n\r\n```python\r\ntokenizer.decode(['50260'])\r\n'<speaker1>'\r\n```","embeddings":[-0.2338283807,0.2921876907,-0.0588289574,0.0690962002,0.2734426856,-0.0860713869,0.18364425,0.3055635691,-0.5368508101,-0.1932197064,-0.0461710617,0.435470134,-0.0010597197,-0.2381744683,0.2536684275,-0.0995298922,0.0430259258,0.2507618368,0.2605471909,0.1317316443,-0.0321432874,0.1118264794,-0.1489618123,0.5977945924,-0.3350471854,-0.1270231009,0.0943686217,0.0601615049,-0.0006362203,-0.7611901164,0.0361957289,0.0767358392,0.257945925,0.1267624646,-0.000113376,0.082067512,0.3061074615,0.0346666314,-0.0352177434,0.0471045561,0.1980308145,-0.0341250151,0.2838692069,-0.2447410524,0.0418412797,-1.0216341019,-0.092064172,0.1748209447,0.4970366061,0.2085008621,0.2248863131,0.2016527355,0.420830071,0.0445980728,0.4089097083,0.0463489853,0.1187575832,0.2101746053,0.0648392066,-0.0744946226,-0.0082713105,-0.1188545451,-0.3061366975,0.2222594768,0.0819868073,0.0422179736,-0.0128988633,-0.0767085329,-0.0784930363,-0.0311290734,0.4616255462,-0.4693733454,-0.1958956271,-0.0923196748,0.0183173362,-0.416379869,0.1107923314,0.1142617688,-0.2171587646,0.0793027058,0.1194188371,-0.0217523016,-0.2067582905,0.1848450452,-0.0662417859,0.3541361094,0.030876739,0.2433264703,-0.0098536797,0.0171071067,0.2675454915,0.1362328231,-0.20636563,0.3336641192,-0.3007293344,0.0695281476,-0.0275461059,-0.2159134448,0.130602181,0.174859941,0.1709089279,0.104464978,0.1628208607,0.0762209892,-0.1299583167,0.1835837215,-0.162752375,0.2325523049,0.1585679799,-0.3149089813,0.0370789021,-0.1459881067,-0.3320124447,-0.2384871691,0.3329922855,0.1477688849,0.338866353,-0.0093660438,-0.1467920095,0.0947749093,-0.4526158869,-0.1450776309,0.0061007231,0.2455308884,0.2182272822,-0.227198109,-0.1537809521,0.2055501193,-0.1003332511,-0.2328752279,-0.1099831611,-0.0415037572,-0.0182201341,0.1874153167,0.2010739595,0.0286410004,0.2526602447,0.191785872,-0.0618622452,0.0152938822,0.3261609972,-0.1774689406,0.00584799,0.353584677,0.0630945936,0.3427803516,0.0158788543,-0.0727052465,-0.035782557,0.2693840265,-0.3565315902,-0.4401058257,-0.2793906927,0.1600381881,-0.1012900174,0.0013683235,-0.2858616412,-0.0320901535,0.4216962755,-0.3251367807,-0.1027349234,-0.3298664689,0.0337792262,-0.3652558625,0.3561752439,-0.0902508795,-0.56661731,0.1339240223,-0.0229863543,-0.1644707471,0.1516373754,0.3593240678,-0.3796893954,0.6116597056,-0.3605375588,0.6695796251,0.2312913537,-0.0378579535,-0.2667081356,-0.1009031981,-0.1092613116,0.0314189866,-0.1686679721,-0.1111759916,0.1504587829,-0.0762138218,-0.1399538964,0.0632124171,0.0288358945,0.0341261104,-0.1693430841,-0.0971364975,0.2992917001,0.1167512313,0.0325557068,-0.131849736,0.0156451128,-0.2472018301,0.0563441142,-0.3048322201,0.135184437,0.0759484395,0.2232836485,-0.0552587323,0.089476116,-0.1198254526,-0.2498248518,0.0050441306,-0.5045512915,-0.0529679134,-0.3933269978,-0.0097885858,-0.2112289518,0.0880161747,-0.1989298612,0.1071834713,0.1984060556,0.1060441881,0.0044009401,0.1505635381,0.0360580832,-0.1274581701,0.1176428646,0.1194809228,0.0944710746,0.2554818988,-0.2053661793,-0.614557445,0.12766774,-0.0362825505,0.2126549035,-0.0232843962,-0.0731346458,0.2727456093,-0.1284492314,0.0718912333,-0.2981536388,0.0743246377,0.0266837943,-0.4504473209,-0.1293709427,-0.0512205735,0.1151850969,-0.0185076017,0.4564298093,0.3656798005,0.2342091799,-0.0699911118,-0.1562936753,-0.2149705142,0.2021137327,0.032652162,0.2847647667,0.1573234648,0.0747532099,0.3221545815,0.1628095061,-0.0037151151,-0.0672149807,0.1072628424,0.3034688532,-0.1084505245,0.0763668269,0.0472967774,-0.1052669585,-0.1009280533,0.0544268526,-0.4296977818,0.2058745176,0.1628007293,-0.3397504389,0.0139675438,-0.073378697,-0.1370283961,0.1553959846,0.0631728098,0.3842323422,0.3814519346,0.2945330143,0.0729723871,-0.1637671739,0.017035896,0.2265655994,0.3600274324,-0.4554604888,-0.0783177093,-0.1630183607,-0.050847929,0.1702332795,-0.3216295242,-0.0486299023,-0.1523302495,0.0268299188,0.086485222,-0.0517295189,0.2023847401,0.0673771575,0.0459718928,0.1958207637,-0.0231608637,-0.3977649808,-0.2804777026,-0.2186540812,0.0824890435,0.3015551269,0.0403780341,0.2039325982,0.237473011,0.0278314594,-0.1771772653,-0.3392232656,0.0429519191,-0.0736969858,0.2346140146,0.0524714328,-0.0311724246,-0.080389455,-0.4751782417,0.2225087285,-0.236471504,-0.2954795361,0.1646377295,-0.1729840785,-0.0004738634,-0.0085444367,-0.0560015626,-0.2244289666,-0.1134307832,0.128232941,0.0839182213,0.1650997847,-0.2448323816,-0.0034249236,0.173871085,-0.203290984,-0.1970703602,-0.1134401187,0.045367118,0.351980567,-0.1946189553,-0.2235365659,-0.1290646791,-0.0890230834,0.3335036635,0.1064963043,-0.2684744,0.136640355,-0.0324387327,0.4266342223,-0.3216074407,-0.3929841518,0.3826549947,0.0959021151,-0.1160591617,0.0203609541,-0.00924493,0.0358997472,-0.1952968538,-0.1222014427,-0.0495722108,0.3201514781,-0.1264334768,0.8966103196,-0.2778320611,-0.1123260111,0.4765632749,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2206","title":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer","comments":"@mariosasko \r\nI am hitting this bug in the Bert tokenizer too.  I see that @albertvillanova labeled this as a bug back in April.  Has there been a fix released yet?\r\nWhat I did for now is to just disable the optimization in the HF library.  @yana-xuyan and @thomas-happify, is that what you did and did that work for you?\r\n\r\n","body":"I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it?","comment_length":59,"text":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer \n I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it? \n @mariosasko \r\nI am hitting this bug in the Bert tokenizer too.  I see that @albertvillanova labeled this as a bug back in April.  Has there been a fix released yet?\r\nWhat I did for now is to just disable the optimization in the HF library.  @yana-xuyan and @thomas-happify, is that what you did and did that work for you?\r\n\r\n","embeddings":[-0.2338283807,0.2921876907,-0.0588289574,0.0690962002,0.2734426856,-0.0860713869,0.18364425,0.3055635691,-0.5368508101,-0.1932197064,-0.0461710617,0.435470134,-0.0010597197,-0.2381744683,0.2536684275,-0.0995298922,0.0430259258,0.2507618368,0.2605471909,0.1317316443,-0.0321432874,0.1118264794,-0.1489618123,0.5977945924,-0.3350471854,-0.1270231009,0.0943686217,0.0601615049,-0.0006362203,-0.7611901164,0.0361957289,0.0767358392,0.257945925,0.1267624646,-0.000113376,0.082067512,0.3061074615,0.0346666314,-0.0352177434,0.0471045561,0.1980308145,-0.0341250151,0.2838692069,-0.2447410524,0.0418412797,-1.0216341019,-0.092064172,0.1748209447,0.4970366061,0.2085008621,0.2248863131,0.2016527355,0.420830071,0.0445980728,0.4089097083,0.0463489853,0.1187575832,0.2101746053,0.0648392066,-0.0744946226,-0.0082713105,-0.1188545451,-0.3061366975,0.2222594768,0.0819868073,0.0422179736,-0.0128988633,-0.0767085329,-0.0784930363,-0.0311290734,0.4616255462,-0.4693733454,-0.1958956271,-0.0923196748,0.0183173362,-0.416379869,0.1107923314,0.1142617688,-0.2171587646,0.0793027058,0.1194188371,-0.0217523016,-0.2067582905,0.1848450452,-0.0662417859,0.3541361094,0.030876739,0.2433264703,-0.0098536797,0.0171071067,0.2675454915,0.1362328231,-0.20636563,0.3336641192,-0.3007293344,0.0695281476,-0.0275461059,-0.2159134448,0.130602181,0.174859941,0.1709089279,0.104464978,0.1628208607,0.0762209892,-0.1299583167,0.1835837215,-0.162752375,0.2325523049,0.1585679799,-0.3149089813,0.0370789021,-0.1459881067,-0.3320124447,-0.2384871691,0.3329922855,0.1477688849,0.338866353,-0.0093660438,-0.1467920095,0.0947749093,-0.4526158869,-0.1450776309,0.0061007231,0.2455308884,0.2182272822,-0.227198109,-0.1537809521,0.2055501193,-0.1003332511,-0.2328752279,-0.1099831611,-0.0415037572,-0.0182201341,0.1874153167,0.2010739595,0.0286410004,0.2526602447,0.191785872,-0.0618622452,0.0152938822,0.3261609972,-0.1774689406,0.00584799,0.353584677,0.0630945936,0.3427803516,0.0158788543,-0.0727052465,-0.035782557,0.2693840265,-0.3565315902,-0.4401058257,-0.2793906927,0.1600381881,-0.1012900174,0.0013683235,-0.2858616412,-0.0320901535,0.4216962755,-0.3251367807,-0.1027349234,-0.3298664689,0.0337792262,-0.3652558625,0.3561752439,-0.0902508795,-0.56661731,0.1339240223,-0.0229863543,-0.1644707471,0.1516373754,0.3593240678,-0.3796893954,0.6116597056,-0.3605375588,0.6695796251,0.2312913537,-0.0378579535,-0.2667081356,-0.1009031981,-0.1092613116,0.0314189866,-0.1686679721,-0.1111759916,0.1504587829,-0.0762138218,-0.1399538964,0.0632124171,0.0288358945,0.0341261104,-0.1693430841,-0.0971364975,0.2992917001,0.1167512313,0.0325557068,-0.131849736,0.0156451128,-0.2472018301,0.0563441142,-0.3048322201,0.135184437,0.0759484395,0.2232836485,-0.0552587323,0.089476116,-0.1198254526,-0.2498248518,0.0050441306,-0.5045512915,-0.0529679134,-0.3933269978,-0.0097885858,-0.2112289518,0.0880161747,-0.1989298612,0.1071834713,0.1984060556,0.1060441881,0.0044009401,0.1505635381,0.0360580832,-0.1274581701,0.1176428646,0.1194809228,0.0944710746,0.2554818988,-0.2053661793,-0.614557445,0.12766774,-0.0362825505,0.2126549035,-0.0232843962,-0.0731346458,0.2727456093,-0.1284492314,0.0718912333,-0.2981536388,0.0743246377,0.0266837943,-0.4504473209,-0.1293709427,-0.0512205735,0.1151850969,-0.0185076017,0.4564298093,0.3656798005,0.2342091799,-0.0699911118,-0.1562936753,-0.2149705142,0.2021137327,0.032652162,0.2847647667,0.1573234648,0.0747532099,0.3221545815,0.1628095061,-0.0037151151,-0.0672149807,0.1072628424,0.3034688532,-0.1084505245,0.0763668269,0.0472967774,-0.1052669585,-0.1009280533,0.0544268526,-0.4296977818,0.2058745176,0.1628007293,-0.3397504389,0.0139675438,-0.073378697,-0.1370283961,0.1553959846,0.0631728098,0.3842323422,0.3814519346,0.2945330143,0.0729723871,-0.1637671739,0.017035896,0.2265655994,0.3600274324,-0.4554604888,-0.0783177093,-0.1630183607,-0.050847929,0.1702332795,-0.3216295242,-0.0486299023,-0.1523302495,0.0268299188,0.086485222,-0.0517295189,0.2023847401,0.0673771575,0.0459718928,0.1958207637,-0.0231608637,-0.3977649808,-0.2804777026,-0.2186540812,0.0824890435,0.3015551269,0.0403780341,0.2039325982,0.237473011,0.0278314594,-0.1771772653,-0.3392232656,0.0429519191,-0.0736969858,0.2346140146,0.0524714328,-0.0311724246,-0.080389455,-0.4751782417,0.2225087285,-0.236471504,-0.2954795361,0.1646377295,-0.1729840785,-0.0004738634,-0.0085444367,-0.0560015626,-0.2244289666,-0.1134307832,0.128232941,0.0839182213,0.1650997847,-0.2448323816,-0.0034249236,0.173871085,-0.203290984,-0.1970703602,-0.1134401187,0.045367118,0.351980567,-0.1946189553,-0.2235365659,-0.1290646791,-0.0890230834,0.3335036635,0.1064963043,-0.2684744,0.136640355,-0.0324387327,0.4266342223,-0.3216074407,-0.3929841518,0.3826549947,0.0959021151,-0.1160591617,0.0203609541,-0.00924493,0.0358997472,-0.1952968538,-0.1222014427,-0.0495722108,0.3201514781,-0.1264334768,0.8966103196,-0.2778320611,-0.1123260111,0.4765632749,-0.0585326925,-0.0492790081,-0.1197143942,-0.1173318997,-0.1053839251,-0.1219811961,0.15857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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2206","title":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer","comments":"Hi @gregg-ADP, \r\n\r\nThis is still a bug.\r\n\r\nAs @albertvillanova has suggested, maybe it's indeed worth adding a variable to `config.py` to have a way to disable this behavior.\r\n\r\nIn the meantime, this forced optimization can be disabled by specifying `features` (of the returned examples) in the `map` call:\r\n```python\r\nfrom datasets import *\r\n... # dataset init\r\nds.map(process_example, features=Features({\"special_tokens_mask\": Sequence(Value(\"int32\")), ... rest of the features}) \r\n```\r\n\r\ncc @lhoestq so he is also aware of this issue","body":"I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it?","comment_length":76,"text":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer \n I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it? \n Hi @gregg-ADP, \r\n\r\nThis is still a bug.\r\n\r\nAs @albertvillanova has suggested, maybe it's indeed worth adding a variable to `config.py` to have a way to disable this behavior.\r\n\r\nIn the meantime, this forced optimization can be disabled by specifying `features` (of the returned examples) in the `map` call:\r\n```python\r\nfrom datasets import *\r\n... # dataset init\r\nds.map(process_example, features=Features({\"special_tokens_mask\": Sequence(Value(\"int32\")), ... rest of the features}) \r\n```\r\n\r\ncc @lhoestq so he is also aware of this issue","embeddings":[-0.2338283807,0.2921876907,-0.0588289574,0.0690962002,0.2734426856,-0.0860713869,0.18364425,0.3055635691,-0.5368508101,-0.1932197064,-0.0461710617,0.435470134,-0.0010597197,-0.2381744683,0.2536684275,-0.0995298922,0.0430259258,0.2507618368,0.2605471909,0.1317316443,-0.0321432874,0.1118264794,-0.1489618123,0.5977945924,-0.3350471854,-0.1270231009,0.0943686217,0.0601615049,-0.0006362203,-0.7611901164,0.0361957289,0.0767358392,0.257945925,0.1267624646,-0.000113376,0.082067512,0.3061074615,0.0346666314,-0.0352177434,0.0471045561,0.1980308145,-0.0341250151,0.2838692069,-0.2447410524,0.0418412797,-1.0216341019,-0.092064172,0.1748209447,0.4970366061,0.2085008621,0.2248863131,0.2016527355,0.420830071,0.0445980728,0.4089097083,0.0463489853,0.1187575832,0.2101746053,0.0648392066,-0.0744946226,-0.0082713105,-0.1188545451,-0.3061366975,0.2222594768,0.0819868073,0.0422179736,-0.0128988633,-0.0767085329,-0.0784930363,-0.0311290734,0.4616255462,-0.4693733454,-0.1958956271,-0.0923196748,0.0183173362,-0.416379869,0.1107923314,0.1142617688,-0.2171587646,0.0793027058,0.1194188371,-0.0217523016,-0.2067582905,0.1848450452,-0.0662417859,0.3541361094,0.030876739,0.2433264703,-0.0098536797,0.0171071067,0.2675454915,0.1362328231,-0.20636563,0.3336641192,-0.3007293344,0.0695281476,-0.0275461059,-0.2159134448,0.130602181,0.174859941,0.1709089279,0.104464978,0.1628208607,0.0762209892,-0.1299583167,0.1835837215,-0.162752375,0.2325523049,0.1585679799,-0.3149089813,0.0370789021,-0.1459881067,-0.3320124447,-0.2384871691,0.3329922855,0.1477688849,0.338866353,-0.0093660438,-0.1467920095,0.0947749093,-0.4526158869,-0.1450776309,0.0061007231,0.2455308884,0.2182272822,-0.227198109,-0.1537809521,0.2055501193,-0.1003332511,-0.2328752279,-0.1099831611,-0.0415037572,-0.0182201341,0.1874153167,0.2010739595,0.0286410004,0.2526602447,0.191785872,-0.0618622452,0.0152938822,0.3261609972,-0.1774689406,0.00584799,0.353584677,0.0630945936,0.3427803516,0.0158788543,-0.0727052465,-0.035782557,0.2693840265,-0.3565315902,-0.4401058257,-0.2793906927,0.1600381881,-0.1012900174,0.0013683235,-0.2858616412,-0.0320901535,0.4216962755,-0.3251367807,-0.1027349234,-0.3298664689,0.0337792262,-0.3652558625,0.3561752439,-0.0902508795,-0.56661731,0.1339240223,-0.0229863543,-0.1644707471,0.1516373754,0.3593240678,-0.3796893954,0.6116597056,-0.3605375588,0.6695796251,0.2312913537,-0.0378579535,-0.2667081356,-0.1009031981,-0.1092613116,0.0314189866,-0.1686679721,-0.1111759916,0.1504587829,-0.0762138218,-0.1399538964,0.0632124171,0.0288358945,0.0341261104,-0.1693430841,-0.0971364975,0.2992917001,0.1167512313,0.0325557068,-0.131849736,0.0156451128,-0.2472018301,0.0563441142,-0.3048322201,0.135184437,0.0759484395,0.2232836485,-0.0552587323,0.089476116,-0.1198254526,-0.2498248518,0.0050441306,-0.5045512915,-0.0529679134,-0.3933269978,-0.0097885858,-0.2112289518,0.0880161747,-0.1989298612,0.1071834713,0.1984060556,0.1060441881,0.0044009401,0.1505635381,0.0360580832,-0.1274581701,0.1176428646,0.1194809228,0.0944710746,0.2554818988,-0.2053661793,-0.614557445,0.12766774,-0.0362825505,0.2126549035,-0.0232843962,-0.0731346458,0.2727456093,-0.1284492314,0.0718912333,-0.2981536388,0.0743246377,0.0266837943,-0.4504473209,-0.1293709427,-0.0512205735,0.1151850969,-0.0185076017,0.4564298093,0.3656798005,0.2342091799,-0.0699911118,-0.1562936753,-0.2149705142,0.2021137327,0.032652162,0.2847647667,0.1573234648,0.0747532099,0.3221545815,0.1628095061,-0.0037151151,-0.0672149807,0.1072628424,0.3034688532,-0.1084505245,0.0763668269,0.0472967774,-0.1052669585,-0.1009280533,0.0544268526,-0.4296977818,0.2058745176,0.1628007293,-0.3397504389,0.0139675438,-0.073378697,-0.1370283961,0.1553959846,0.0631728098,0.3842323422,0.3814519346,0.2945330143,0.0729723871,-0.1637671739,0.017035896,0.2265655994,0.3600274324,-0.4554604888,-0.0783177093,-0.1630183607,-0.050847929,0.1702332795,-0.3216295242,-0.0486299023,-0.1523302495,0.0268299188,0.086485222,-0.0517295189,0.2023847401,0.0673771575,0.0459718928,0.1958207637,-0.0231608637,-0.3977649808,-0.2804777026,-0.2186540812,0.0824890435,0.3015551269,0.0403780341,0.2039325982,0.237473011,0.0278314594,-0.1771772653,-0.3392232656,0.0429519191,-0.0736969858,0.2346140146,0.0524714328,-0.0311724246,-0.080389455,-0.4751782417,0.2225087285,-0.236471504,-0.2954795361,0.1646377295,-0.1729840785,-0.0004738634,-0.0085444367,-0.0560015626,-0.2244289666,-0.1134307832,0.128232941,0.0839182213,0.1650997847,-0.2448323816,-0.0034249236,0.173871085,-0.203290984,-0.1970703602,-0.1134401187,0.045367118,0.351980567,-0.1946189553,-0.2235365659,-0.1290646791,-0.0890230834,0.3335036635,0.1064963043,-0.2684744,0.136640355,-0.0324387327,0.4266342223,-0.3216074407,-0.3929841518,0.3826549947,0.0959021151,-0.1160591617,0.0203609541,-0.00924493,0.0358997472,-0.1952968538,-0.1222014427,-0.0495722108,0.3201514781,-0.1264334768,0.8966103196,-0.2778320611,-0.1123260111,0.4765632749,-0.0585326925,-0.0492790081,-0.1197143942,-0.1173318997,-0.1053839251,-0.1219811961,0.1585762054,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2206","title":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer","comments":"Thanks for the quick reply @mariosasko.  What I did was to changed the optimizer to use int32 instead of int8. \r\nWhat you're suggesting specifies the type for each feature explicitly without changing the HF code. This is definitely a better option.  However, we are hitting a new error later:\r\n```\r\n  File \"\/Users\/ccccc\/PycharmProjects\/aaaa-ml\/venv-source\/lib\/python3.8\/site-packages\/torch\/nn\/modules\/module.py\", line 1051, in _call_impl\r\n    return forward_call(*input, **kwargs)\r\nTypeError: forward() got an unexpected keyword argument 'pos'\r\n\r\n```\r\nWhere 'pos' is the name of a new feature we added.  Do you agree that your way of fixing the optimizer issue will not fix our new issue?  If not, I will continue with this optimizer fix until we resolve our other issue.\r\n","body":"I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it?","comment_length":111,"text":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer \n I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it? \n Thanks for the quick reply @mariosasko.  What I did was to changed the optimizer to use int32 instead of int8. \r\nWhat you're suggesting specifies the type for each feature explicitly without changing the HF code. This is definitely a better option.  However, we are hitting a new error later:\r\n```\r\n  File \"\/Users\/ccccc\/PycharmProjects\/aaaa-ml\/venv-source\/lib\/python3.8\/site-packages\/torch\/nn\/modules\/module.py\", line 1051, in _call_impl\r\n    return forward_call(*input, **kwargs)\r\nTypeError: forward() got an unexpected keyword argument 'pos'\r\n\r\n```\r\nWhere 'pos' is the name of a new feature we added.  Do you agree that your way of fixing the optimizer issue will not fix our new issue?  If not, I will continue with this optimizer fix until we resolve our other issue.\r\n","embeddings":[-0.2338283807,0.2921876907,-0.0588289574,0.0690962002,0.2734426856,-0.0860713869,0.18364425,0.3055635691,-0.5368508101,-0.1932197064,-0.0461710617,0.435470134,-0.0010597197,-0.2381744683,0.2536684275,-0.0995298922,0.0430259258,0.2507618368,0.2605471909,0.1317316443,-0.0321432874,0.1118264794,-0.1489618123,0.5977945924,-0.3350471854,-0.1270231009,0.0943686217,0.0601615049,-0.0006362203,-0.7611901164,0.0361957289,0.0767358392,0.257945925,0.1267624646,-0.000113376,0.082067512,0.3061074615,0.0346666314,-0.0352177434,0.0471045561,0.1980308145,-0.0341250151,0.2838692069,-0.2447410524,0.0418412797,-1.0216341019,-0.092064172,0.1748209447,0.4970366061,0.2085008621,0.2248863131,0.2016527355,0.420830071,0.0445980728,0.4089097083,0.0463489853,0.1187575832,0.2101746053,0.0648392066,-0.0744946226,-0.0082713105,-0.1188545451,-0.3061366975,0.2222594768,0.0819868073,0.0422179736,-0.0128988633,-0.0767085329,-0.0784930363,-0.0311290734,0.4616255462,-0.4693733454,-0.1958956271,-0.0923196748,0.0183173362,-0.416379869,0.1107923314,0.1142617688,-0.2171587646,0.0793027058,0.1194188371,-0.0217523016,-0.2067582905,0.1848450452,-0.0662417859,0.3541361094,0.030876739,0.2433264703,-0.0098536797,0.0171071067,0.2675454915,0.1362328231,-0.20636563,0.3336641192,-0.3007293344,0.0695281476,-0.0275461059,-0.2159134448,0.130602181,0.174859941,0.1709089279,0.104464978,0.1628208607,0.0762209892,-0.1299583167,0.1835837215,-0.162752375,0.2325523049,0.1585679799,-0.3149089813,0.0370789021,-0.1459881067,-0.3320124447,-0.2384871691,0.3329922855,0.1477688849,0.338866353,-0.0093660438,-0.1467920095,0.0947749093,-0.4526158869,-0.1450776309,0.0061007231,0.2455308884,0.2182272822,-0.227198109,-0.1537809521,0.2055501193,-0.1003332511,-0.2328752279,-0.1099831611,-0.0415037572,-0.0182201341,0.1874153167,0.2010739595,0.0286410004,0.2526602447,0.191785872,-0.0618622452,0.0152938822,0.3261609972,-0.1774689406,0.00584799,0.353584677,0.0630945936,0.3427803516,0.0158788543,-0.0727052465,-0.035782557,0.2693840265,-0.3565315902,-0.4401058257,-0.2793906927,0.1600381881,-0.1012900174,0.0013683235,-0.2858616412,-0.0320901535,0.4216962755,-0.3251367807,-0.1027349234,-0.3298664689,0.0337792262,-0.3652558625,0.3561752439,-0.0902508795,-0.56661731,0.1339240223,-0.0229863543,-0.1644707471,0.1516373754,0.3593240678,-0.3796893954,0.6116597056,-0.3605375588,0.6695796251,0.2312913537,-0.0378579535,-0.2667081356,-0.1009031981,-0.1092613116,0.0314189866,-0.1686679721,-0.1111759916,0.1504587829,-0.0762138218,-0.1399538964,0.0632124171,0.0288358945,0.0341261104,-0.1693430841,-0.0971364975,0.2992917001,0.1167512313,0.0325557068,-0.131849736,0.0156451128,-0.2472018301,0.0563441142,-0.3048322201,0.135184437,0.0759484395,0.2232836485,-0.0552587323,0.089476116,-0.1198254526,-0.2498248518,0.0050441306,-0.5045512915,-0.0529679134,-0.3933269978,-0.0097885858,-0.2112289518,0.0880161747,-0.1989298612,0.1071834713,0.1984060556,0.1060441881,0.0044009401,0.1505635381,0.0360580832,-0.1274581701,0.1176428646,0.1194809228,0.0944710746,0.2554818988,-0.2053661793,-0.614557445,0.12766774,-0.0362825505,0.2126549035,-0.0232843962,-0.0731346458,0.2727456093,-0.1284492314,0.0718912333,-0.2981536388,0.0743246377,0.0266837943,-0.4504473209,-0.1293709427,-0.0512205735,0.1151850969,-0.0185076017,0.4564298093,0.3656798005,0.2342091799,-0.0699911118,-0.1562936753,-0.2149705142,0.2021137327,0.032652162,0.2847647667,0.1573234648,0.0747532099,0.3221545815,0.1628095061,-0.0037151151,-0.0672149807,0.1072628424,0.3034688532,-0.1084505245,0.0763668269,0.0472967774,-0.1052669585,-0.1009280533,0.0544268526,-0.4296977818,0.2058745176,0.1628007293,-0.3397504389,0.0139675438,-0.073378697,-0.1370283961,0.1553959846,0.0631728098,0.3842323422,0.3814519346,0.2945330143,0.0729723871,-0.1637671739,0.017035896,0.2265655994,0.3600274324,-0.4554604888,-0.0783177093,-0.1630183607,-0.050847929,0.1702332795,-0.3216295242,-0.0486299023,-0.1523302495,0.0268299188,0.086485222,-0.0517295189,0.2023847401,0.0673771575,0.0459718928,0.1958207637,-0.0231608637,-0.3977649808,-0.2804777026,-0.2186540812,0.0824890435,0.3015551269,0.0403780341,0.2039325982,0.237473011,0.0278314594,-0.1771772653,-0.3392232656,0.0429519191,-0.0736969858,0.2346140146,0.0524714328,-0.0311724246,-0.080389455,-0.4751782417,0.2225087285,-0.236471504,-0.2954795361,0.1646377295,-0.1729840785,-0.0004738634,-0.0085444367,-0.0560015626,-0.2244289666,-0.1134307832,0.128232941,0.0839182213,0.1650997847,-0.2448323816,-0.0034249236,0.173871085,-0.203290984,-0.1970703602,-0.1134401187,0.045367118,0.351980567,-0.1946189553,-0.2235365659,-0.1290646791,-0.0890230834,0.3335036635,0.1064963043,-0.2684744,0.136640355,-0.0324387327,0.4266342223,-0.3216074407,-0.3929841518,0.3826549947,0.0959021151,-0.1160591617,0.0203609541,-0.00924493,0.0358997472,-0.1952968538,-0.1222014427,-0.0495722108,0.3201514781,-0.1264334768,0.8966103196,-0.2778320611,-0.1123260111,0.4765632749,-0.0585326925,-0.0492790081,-0.1197143942,-0.1173318997,-0.1053839251,-0.1219811961,0.1585762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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2206","title":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer","comments":"Hi @gwc4github,\r\n\r\nthe fix was merged a few minutes ago, and it doesn't require any changes on the user side (e.g. no need for specifying `features`). If you find time, feel free to install `datasets` from master with:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```\r\nand let us know if it works for your use case! ","body":"I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it?","comment_length":54,"text":"Got pyarrow error when loading a dataset while adding special tokens into the tokenizer \n I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1687, in _map_single\r\n    writer.write(example)\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 296, in write\r\n    self.write_on_file()\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 270, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\/array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 108, in __arrow_array__\r\n    out = out.cast(pa.list_(self.optimized_int_type))\r\n  File \"pyarrow\/array.pxi\", line 810, in pyarrow.lib.Array.cast\r\n  File \"\/home\/xuyan\/anaconda3\/envs\/convqa\/lib\/python3.7\/site-packages\/pyarrow\/compute.py\", line 281, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 465, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 294, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127\r\n\r\nDo you have any idea about it? \n Hi @gwc4github,\r\n\r\nthe fix was merged a few minutes ago, and it doesn't require any changes on the user side (e.g. no need for specifying `features`). If you find time, feel free to install `datasets` from master with:\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```\r\nand let us know if it works for your use case! 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2200","title":"_prepare_split will overwrite DatasetBuilder.info.features","comments":"Hi ! This might be related to #2153 \r\n\r\nYou're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\nI'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n\r\nEDIT: opened #2201","body":"Hi, here is my issue:\r\nI initialized a Csv datasetbuilder with specific features:\r\n```\r\ndef get_dataset_features(data_args):\r\n    features = {}\r\n    if data_args.text_features:\r\n        features.update({text_feature: hf_features.Value(\"string\") for text_feature in data_args.text_features.strip().split(\",\")})\r\n    if data_args.num_features:\r\n        features.update({text_feature: hf_features.Value(\"float32\") for text_feature in data_args.num_features.strip().split(\",\")})\r\n    if data_args.label_classes:\r\n        features[\"label\"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(\",\"))\r\n    else:\r\n        features[\"label\"] = hf_features.Value(\"float32\")\r\n    return hf_features.Features(features)\r\n\r\ndatasets = load_dataset(extension,\r\n                                data_files=data_files,\r\n                                sep=data_args.delimiter,\r\n                                header=data_args.header,\r\n                                column_names=data_args.column_names.split(\",\") if data_args.column_names else None,\r\n                                features=get_dataset_features(data_args=data_args))\r\n```\r\nThe `features` is printout as below before `builder_instance.as_dataset` is called:\r\n```\r\n{'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}\r\n````\r\n\r\nBut after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to:\r\n```\r\n{'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}\r\n```\r\n\r\nAfter digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`. \r\nBut `ArrowWriter` is initailized without passing `features`.\r\nSo my concern is:\r\nIt's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function?","comment_length":43,"text":"_prepare_split will overwrite DatasetBuilder.info.features \n Hi, here is my issue:\r\nI initialized a Csv datasetbuilder with specific features:\r\n```\r\ndef get_dataset_features(data_args):\r\n    features = {}\r\n    if data_args.text_features:\r\n        features.update({text_feature: hf_features.Value(\"string\") for text_feature in data_args.text_features.strip().split(\",\")})\r\n    if data_args.num_features:\r\n        features.update({text_feature: hf_features.Value(\"float32\") for text_feature in data_args.num_features.strip().split(\",\")})\r\n    if data_args.label_classes:\r\n        features[\"label\"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(\",\"))\r\n    else:\r\n        features[\"label\"] = hf_features.Value(\"float32\")\r\n    return hf_features.Features(features)\r\n\r\ndatasets = load_dataset(extension,\r\n                                data_files=data_files,\r\n                                sep=data_args.delimiter,\r\n                                header=data_args.header,\r\n                                column_names=data_args.column_names.split(\",\") if data_args.column_names else None,\r\n                                features=get_dataset_features(data_args=data_args))\r\n```\r\nThe `features` is printout as below before `builder_instance.as_dataset` is called:\r\n```\r\n{'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}\r\n````\r\n\r\nBut after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to:\r\n```\r\n{'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}\r\n```\r\n\r\nAfter digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`. \r\nBut `ArrowWriter` is initailized without passing `features`.\r\nSo my concern is:\r\nIt's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function? \n Hi ! This might be related to #2153 \r\n\r\nYou're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\nI'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n\r\nEDIT: opened #2201","embeddings":[-0.2406021953,-0.0382910147,-0.1077674851,0.1783376485,0.2982976437,0.2197924554,0.4546083808,0.2158689797,-0.3327544034,0.1149650663,0.1291757524,0.1715127975,0.0863348842,0.4873531759,-0.1215008423,0.0767409503,0.0831202567,0.2866099775,0.1698089838,-0.0732159466,-0.2161129415,0.103940621,-0.260414362,0.0780965611,-0.4611605406,0.0068310783,0.0738492385,0.1830849648,0.1115719378,-0.4215717316,0.1278963238,0.1931540817,-0.0024506638,0.0135782873,-0.0001165283,0.0921400413,0.0531196035,-0.0490681157,-0.3561661243,0.1506268084,-0.3260130882,-0.2654621005,-0.0111547243,-0.2360556424,-0.1424901187,-0.3270657361,-0.3870328367,0.1431457102,0.21410124,0.2266549468,0.1773981899,-0.2564507723,0.1165271029,0.1099403724,0.3127687573,0.1972708553,-0.0730632916,-0.2695540786,-0.294752568,0.1149955839,-0.1780861318,0.4079653323,-0.1879745275,-0.0244833566,0.1067840531,0.0501348674,-0.0720179081,-0.1327121854,0.0906457528,-0.3222067356,0.2972629964,0.0031607789,-0.0745959058,-0.5507095456,-0.1756774783,-0.3726785183,0.127729401,-0.167284295,0.1768467575,0.1145290211,-0.106956929,-0.3455787599,0.1335982829,-0.0906985849,-0.0378318988,0.3073068261,-0.4004644454,0.3126069605,-0.1145926118,0.0700759366,0.295794636,-0.2810826898,-0.2957600653,-0.1158553436,-0.1413079649,-0.1271789819,0.1206175536,-0.1135420054,-0.0204882305,0.0391933806,-0.183370471,0.1696168929,-0.0626039505,-0.0579128414,0.2710881531,0.3043972552,0.2000663131,0.1332727075,-0.0148484511,0.3680326641,-0.3029093444,-0.1120791882,0.3196536303,-0.1899855882,0.3789294064,0.3041920364,0.5231932402,-0.191226095,-0.2840594351,0.2589049637,0.3041548729,-0.047680594,-0.0207292493,0.1026611105,0.0128632467,0.2322100401,-0.2137506455,0.0731487572,-0.302153945,-0.1779461801,-0.2684747875,-0.2020440698,-0.0143179186,0.1958020329,-0.0162585042,-0.028942639,0.4025155902,0.1338067055,0.059362866,-0.5640695095,-0.0786658973,-0.141408965,0.245393455,0.2372240126,0.0063599204,0.297984153,0.345356226,-0.1086787507,0.0423199162,0.0928914696,-0.4464731514,-0.3818405867,0.0246932805,0.204917416,-0.038765531,0.2318276614,0.0056333705,0.0674563348,0.3656390905,-0.0792343542,-0.0102581428,-0.184376806,-0.4282066822,-0.0858234316,0.0689308494,0.3378699124,-0.3634487987,0.0270513836,-0.0374605916,-0.0010612832,0.2343657315,0.0017081597,-0.0472922921,0.2207314819,-0.2923869193,-0.1538804322,0.4493572712,0.0422428735,-0.2183953077,0.4702954888,-0.0512736104,0.1960454583,0.097857289,-0.1573392004,0.312523365,0.0569648445,-0.0447400771,0.0004948836,-0.1355825812,-0.0460937694,-0.2612141669,-0.2650542259,-0.008415495,-0.2596246004,0.0484578907,0.2520490289,-0.149836421,-0.2065731287,0.2138381004,0.1278960705,0.0759673044,0.238937363,-0.0274179019,0.4694034457,0.1447722316,-0.1496132612,-0.2516558468,-0.0256625861,-0.006287029,-0.226612851,-0.1490036845,-0.5273110271,-0.4477546513,-0.0709879026,-0.376218468,-0.264528662,0.0848521516,0.1711636782,-0.0287180431,-0.1455246806,0.0106613887,0.0743661597,-0.1461340338,0.1596860439,-0.0771052167,0.0408394523,-0.0278835911,0.0059103705,-0.0508349724,0.3137435317,0.2422779202,-0.1615428478,-0.3486573398,0.5029213428,0.129590854,0.0253561251,-0.3426887095,-0.3701828122,0.1229716316,-0.0126654766,-0.0751680434,-0.0887555107,0.0540345162,-0.0973859057,-0.0829878077,0.4442037046,-0.1351927668,0.2727766335,0.0633149743,-0.0458262675,0.1429138184,-0.0051077781,0.2960257828,-0.4752125442,-0.4301364422,-0.1807939857,-0.1933782995,0.1361899674,-0.0568668954,0.4161864221,0.7362260222,0.0912750587,-0.2430435121,-0.0015031998,-0.1585697085,-0.0849274471,0.1719686985,-0.1211564466,0.3886586726,0.0281732567,0.0056694192,0.0048922412,-0.0323953554,0.0279556587,0.2369142026,0.195208624,0.1960088462,0.2906233966,0.0994656309,-0.0508444123,-0.3068084419,0.2607759833,0.1012084484,0.1346651912,-0.3549932539,0.3007321954,-0.3476017416,0.1741188616,-0.3198778927,-0.0015288031,-0.0686357468,-0.4444669485,0.0085163433,0.3251644373,-0.04318358,0.1285805553,-0.1287238449,-0.1116976589,0.0461462475,-0.2226932645,0.1801130623,-0.0527548119,-0.3112846911,0.0221238621,-0.0914603621,0.1510246545,0.0681759343,0.0183007941,-0.2734510303,-0.2057698667,-0.3139711022,0.1297929287,-0.2200113833,0.1257251501,0.1559238434,-0.2314231992,0.3351610899,-0.3764411211,0.1105968058,0.1745934039,-0.030335892,-0.0731975138,0.1376205236,0.0059762136,-0.1190199405,-0.5209413767,0.0441726148,-0.2698732316,-0.0336812027,0.2164280266,0.0426517986,0.0496085621,0.011808469,-0.1766927093,0.179286629,0.0049384478,-0.4112112522,-0.2042838186,-0.0725068152,-0.1624400169,0.2003741264,0.031419199,-0.1869898736,0.0107457153,-0.0141782928,-0.3892456591,0.0628277361,-0.2297626436,0.1830192059,-0.0313399471,-0.0654165223,0.0715479031,0.3412663043,-0.0353141055,0.0644659922,-0.2030811757,0.2194034606,-0.0548892543,0.0521089919,-0.1899810135,0.3345891535,-0.1968087107,0.7819837332,-0.0370066687,0.1000483185,0.0815292224,-0.0416024178,0.2504915297,-0.2591479421,-0.3987992406,0.1645832807,-0.196344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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2200","title":"_prepare_split will overwrite DatasetBuilder.info.features","comments":"> Hi ! This might be related to #2153\r\n> \r\n> You're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\n> I'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n> \r\n> EDIT: opened #2201\r\n\r\nGlad to hear that! Thank you for your fix, I'm new to huggingface, it's a fantastic project \ud83d\ude01","body":"Hi, here is my issue:\r\nI initialized a Csv datasetbuilder with specific features:\r\n```\r\ndef get_dataset_features(data_args):\r\n    features = {}\r\n    if data_args.text_features:\r\n        features.update({text_feature: hf_features.Value(\"string\") for text_feature in data_args.text_features.strip().split(\",\")})\r\n    if data_args.num_features:\r\n        features.update({text_feature: hf_features.Value(\"float32\") for text_feature in data_args.num_features.strip().split(\",\")})\r\n    if data_args.label_classes:\r\n        features[\"label\"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(\",\"))\r\n    else:\r\n        features[\"label\"] = hf_features.Value(\"float32\")\r\n    return hf_features.Features(features)\r\n\r\ndatasets = load_dataset(extension,\r\n                                data_files=data_files,\r\n                                sep=data_args.delimiter,\r\n                                header=data_args.header,\r\n                                column_names=data_args.column_names.split(\",\") if data_args.column_names else None,\r\n                                features=get_dataset_features(data_args=data_args))\r\n```\r\nThe `features` is printout as below before `builder_instance.as_dataset` is called:\r\n```\r\n{'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}\r\n````\r\n\r\nBut after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to:\r\n```\r\n{'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}\r\n```\r\n\r\nAfter digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`. \r\nBut `ArrowWriter` is initailized without passing `features`.\r\nSo my concern is:\r\nIt's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function?","comment_length":67,"text":"_prepare_split will overwrite DatasetBuilder.info.features \n Hi, here is my issue:\r\nI initialized a Csv datasetbuilder with specific features:\r\n```\r\ndef get_dataset_features(data_args):\r\n    features = {}\r\n    if data_args.text_features:\r\n        features.update({text_feature: hf_features.Value(\"string\") for text_feature in data_args.text_features.strip().split(\",\")})\r\n    if data_args.num_features:\r\n        features.update({text_feature: hf_features.Value(\"float32\") for text_feature in data_args.num_features.strip().split(\",\")})\r\n    if data_args.label_classes:\r\n        features[\"label\"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(\",\"))\r\n    else:\r\n        features[\"label\"] = hf_features.Value(\"float32\")\r\n    return hf_features.Features(features)\r\n\r\ndatasets = load_dataset(extension,\r\n                                data_files=data_files,\r\n                                sep=data_args.delimiter,\r\n                                header=data_args.header,\r\n                                column_names=data_args.column_names.split(\",\") if data_args.column_names else None,\r\n                                features=get_dataset_features(data_args=data_args))\r\n```\r\nThe `features` is printout as below before `builder_instance.as_dataset` is called:\r\n```\r\n{'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}\r\n````\r\n\r\nBut after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to:\r\n```\r\n{'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}\r\n```\r\n\r\nAfter digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`. \r\nBut `ArrowWriter` is initailized without passing `features`.\r\nSo my concern is:\r\nIt's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function? \n > Hi ! This might be related to #2153\r\n> \r\n> You're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\n> I'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n> \r\n> EDIT: opened #2201\r\n\r\nGlad to hear that! Thank you for your fix, I'm new to huggingface, it's a fantastic project 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2196","title":"`load_dataset` caches two arrow files?","comments":"Hi ! Files that starts with `cache-*` are cached computation files, i.e. they are the cached results of map\/filter\/cast\/etc. operations. For example if you used `map` on your dataset to transform it, then the resulting dataset is going to be stored and cached in a `cache-*` file. These files are used to avoid having to load the dataset in RAM, even after many transforms","body":"Hi,\r\n\r\nI am using datasets to load large json file of 587G.\r\nI checked the cached folder and found that there are two arrow files created:\r\n* `cache-ed205e500a7dc44c.arrow` - 355G\r\n*  `json-train.arrow` - 582G\r\n\r\nWhy is the first file created?\r\nIf I delete it, would I still be able to `load_from_disk`?","comment_length":64,"text":"`load_dataset` caches two arrow files? \n Hi,\r\n\r\nI am using datasets to load large json file of 587G.\r\nI checked the cached folder and found that there are two arrow files created:\r\n* `cache-ed205e500a7dc44c.arrow` - 355G\r\n*  `json-train.arrow` - 582G\r\n\r\nWhy is the first file created?\r\nIf I delete it, would I still be able to `load_from_disk`? \n Hi ! Files that starts with `cache-*` are cached computation files, i.e. they are the cached results of map\/filter\/cast\/etc. operations. For example if you used `map` on your dataset to transform it, then the resulting dataset is going to be stored and cached in a `cache-*` file. These files are used to avoid having to load the dataset in RAM, even after many 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2196","title":"`load_dataset` caches two arrow files?","comments":"Thanks @lhoestq! Hmm.. that's strange because I specifically turned off auto caching, and saved mapped result, using `save_to_disk`, to another location. At this location, the following file is created:`355G\tcache-ed205e500a7dc44c.arrow`\r\n\r\nTo my observation, both `load_dataset` and `map` creates `cache-*` files, and I wonder what the `cache-*` file from `load_dataset` is for (as I believe the same information is stored in `json-train.arrow`.","body":"Hi,\r\n\r\nI am using datasets to load large json file of 587G.\r\nI checked the cached folder and found that there are two arrow files created:\r\n* `cache-ed205e500a7dc44c.arrow` - 355G\r\n*  `json-train.arrow` - 582G\r\n\r\nWhy is the first file created?\r\nIf I delete it, would I still be able to `load_from_disk`?","comment_length":61,"text":"`load_dataset` caches two arrow files? \n Hi,\r\n\r\nI am using datasets to load large json file of 587G.\r\nI checked the cached folder and found that there are two arrow files created:\r\n* `cache-ed205e500a7dc44c.arrow` - 355G\r\n*  `json-train.arrow` - 582G\r\n\r\nWhy is the first file created?\r\nIf I delete it, would I still be able to `load_from_disk`? \n Thanks @lhoestq! Hmm.. that's strange because I specifically turned off auto caching, and saved mapped result, using `save_to_disk`, to another location. At this location, the following file is created:`355G\tcache-ed205e500a7dc44c.arrow`\r\n\r\nTo my observation, both `load_dataset` and `map` creates `cache-*` files, and I wonder what the `cache-*` file from `load_dataset` is for (as I believe the same information is stored in `json-train.arrow`.","embeddings":[0.0172763765,-0.1464014351,-0.1149083897,0.6492552161,-0.0665469244,0.3106843829,0.2409403175,0.2578835487,0.3021347225,-0.2563412189,-0.0356172957,0.2787819207,0.1161758229,-0.4971514642,0.2449564934,0.1934699416,0.1645396501,0.0410898104,-0.1407982856,-0.102236554,-0.1449192762,0.1404834986,0.222994253,0.0501033925,-0.4586227834,-0.1467895508,0.0726128966,0.1264030486,-0.0452624112,-0.4037206173,0.3527000844,-0.0786419064,0.0307803452,0.4275515378,-0.0001186709,0.0302080251,0.3575187624,0.0993109867,-0.3851467669,-0.0286421832,-0.4544894695,-0.045694299,0.1494431496,-0.1819910556,0.380058825,-0.3371661901,-0.0662364289,-0.6853268743,0.4682787955,0.1548903733,0.179665491,-0.1966743469,-0.4313079417,0.0719178542,0.293679744,0.1138556749,-0.0295685101,-0.0345636867,-0.1491800249,0.2294983268,-0.035603445,0.1215262935,-0.1098485813,-0.0463744439,0.4478723705,0.1780072302,-0.1123236567,-0.2661146522,0.3580049574,0.053959474,0.8774031997,-0.3279373348,0.0023098926,-0.3756660819,-0.2317557335,-0.0114048133,0.2280352265,0.3111110628,0.1924596429,-0.0084913597,-0.201050505,-0.4259337783,0.0605824068,-0.1790846288,0.1664555967,-0.3395424187,-0.2024616152,0.3108737767,0.239737466,0.2673693299,0.2836093903,-0.4972212017,-0.3389441669,0.4030974209,-0.1985778362,0.0318644531,-0.0466743521,-0.0403825156,-0.03329641,0.29533723,0.4044393599,-0.2616675794,-0.1658657342,0.1207460389,0.2782330513,0.3537445664,0.2547233999,0.0268986337,-0.0461516008,-0.339974016,-0.0218213089,-0.208674103,-0.0490394048,-0.0859611183,0.4438293576,-0.4126614332,-0.0931672007,-0.0895797983,0.0455818772,0.0914719701,0.0405215658,-0.3477860391,0.0488015935,0.0804478601,0.182041809,0.2340861708,-0.233913824,0.0636636317,-0.0101311086,-0.0300818793,-0.1814797521,-0.0180175155,-0.1444652081,0.265342623,0.273581475,0.0721837953,0.2267827243,0.144211337,-0.2142865658,-0.0831868052,0.459084928,-0.2324572206,0.1928142756,0.4072645307,-0.163272813,0.4347712696,-0.060497269,0.1209551468,-0.1324381232,0.5263389349,-0.4890322983,-0.2337027639,0.1063235626,0.1635842174,-0.1018485352,0.2115263194,-0.6663516164,0.0656587705,0.323399514,-0.2094890773,0.2708799541,0.0823254585,-0.2421623766,-0.3829841912,-0.1247377023,0.3206803501,-0.3754127622,-0.1680775881,-0.1721181124,-0.1740692705,-0.2347567976,0.2553412616,-0.5216798782,0.3648763299,-0.4526238739,0.1768103391,0.5462340117,-0.3530583382,-0.4222028553,0.2921740115,-0.0394608714,0.1639520079,0.1800560951,0.2358596921,-0.129404977,-0.1869406253,-0.0265118349,-0.0481117629,0.1490021348,-0.252238214,-0.0990578309,-0.0420263819,0.0355101973,-0.0342949294,-0.2268188149,0.0001690536,0.0800405741,-0.0962591246,0.1690088212,0.1523696631,0.2140231878,0.2017194182,-0.1144552901,-0.2469071895,0.2298972309,0.1480555683,-0.7635045648,0.3079406023,-0.1594994515,-0.7619137168,-0.0747809261,-0.1076103002,-0.1010557115,-0.0893118978,-0.2672033906,-0.0377834477,-0.0343671031,0.12902309,0.0996729136,-0.189741984,-0.2920210958,0.5691560507,0.0448395796,-0.0608553216,-0.5005730391,0.0350248888,0.235583216,0.2694952488,-0.3725483716,-0.1414360106,0.0084840851,-0.1698793769,-0.0955605134,0.2989889085,0.1324427426,0.2216421515,0.1800314784,0.2426699847,0.0743947551,-0.0678143129,0.2297535837,-0.1207529604,0.1633173823,-0.3794222772,-0.285648793,0.0898486823,-0.154698506,0.0992564335,-0.1303553134,-0.2762314975,0.1194211543,0.0226738583,0.0369797982,-0.1994014829,0.0540000275,0.2509945035,0.3887884319,0.4738147259,0.1165863201,0.1842743903,0.3787924647,-0.208732456,-0.1766487062,0.1748841703,-0.3041078746,-0.4068354666,-0.1282897145,0.5813217759,0.6566796899,0.0224437937,0.2662492394,-0.0583369695,0.205867365,-0.1070435047,0.2185213268,-0.1296971589,0.3127490282,0.1337821037,0.031962011,-0.0801476166,-0.314345032,0.0470698923,0.223111093,0.0093092341,-0.1905515492,0.1201205775,-0.0449030362,0.1156192794,-0.2616874874,0.1045317799,-0.1441285312,-0.1079823747,-0.0595245697,0.1823108494,0.2603966594,-0.1355582327,-0.3985460103,0.521171093,-0.0771162659,-0.0677677765,-0.4286523759,-0.1353064775,0.002325743,0.0156934615,0.0473663174,-0.1873987019,0.0727301985,-0.074298352,0.2118711323,-0.2597361803,0.1914972216,0.0028847721,0.1121254787,-0.0621776842,-0.1794524193,-0.0543337725,-0.3448907435,0.0772262439,-0.136048317,-0.0512222014,-0.3175914884,-0.07461638,-0.1079362258,0.1040938348,-0.0770919472,-0.0732302144,0.047450684,0.015551934,0.2687015533,-0.0390584171,0.0824766755,-0.0717997998,-0.3182495534,0.2286307365,0.0234945621,0.0890922099,-0.5059283376,-0.6877738237,0.3267150521,-0.2294784486,0.0065221018,0.1465352327,0.025075784,0.1810816526,0.0638732538,-0.6293523312,0.1646163762,-0.1493868381,0.1181597039,-0.3938648701,-0.0835525468,0.1742088348,0.1690076292,0.0234810337,-0.2177424729,-0.0835142136,0.2013215125,0.0812743753,0.3253596425,-0.0374762565,0.1487569213,-0.1572584808,0.684986949,0.1634379178,0.2408478856,0.1585013419,0.3259359896,0.4771516621,-0.2095793933,0.0312422663,-0.1076795757,-0.150051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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2196","title":"`load_dataset` caches two arrow files?","comments":"This is a wrong report -- `cache-*` files are created only my `map`, not by `load_dataset`. ","body":"Hi,\r\n\r\nI am using datasets to load large json file of 587G.\r\nI checked the cached folder and found that there are two arrow files created:\r\n* `cache-ed205e500a7dc44c.arrow` - 355G\r\n*  `json-train.arrow` - 582G\r\n\r\nWhy is the first file created?\r\nIf I delete it, would I still be able to `load_from_disk`?","comment_length":16,"text":"`load_dataset` caches two arrow files? \n Hi,\r\n\r\nI am using datasets to load large json file of 587G.\r\nI checked the cached folder and found that there are two arrow files created:\r\n* `cache-ed205e500a7dc44c.arrow` - 355G\r\n*  `json-train.arrow` - 582G\r\n\r\nWhy is the first file created?\r\nIf I delete it, would I still be able to `load_from_disk`? \n This is a wrong report -- `cache-*` files are created only my `map`, not by `load_dataset`. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2195","title":"KeyError: '_indices_files' in `arrow_dataset.py`","comments":"Thanks @samsontmr this should be fixed on master now\r\n\r\nFeel free to reopen if you're still having issues","body":"After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset.\r\n\r\nTrace:\r\n```\r\nTraceback (most recent call last):\r\n  File \"load_data.py\", line 11, in <module>\r\n    dataset = load_from_disk(SRC)\r\n  File \"\/opt\/conda\/envs\/py38\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 784, in load_from_disk\r\n    return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/opt\/conda\/envs\/py38\/lib\/python3.8\/site-packages\/datasets\/dataset_dict.py\", line 692, in load_from_disk\r\n    dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/opt\/conda\/envs\/py38\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 634, in load_from_disk\r\n    if state[\"_indices_files\"]:\r\nKeyError: '_indices_files'\r\n```\r\n\r\nI believe this is the line causing the error since there may not be a `_indices_files` key in the older versions:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/b70141e3c5149430951773aaa0155555c5fb3e76\/src\/datasets\/arrow_dataset.py#L634\r\n\r\nMay I suggest using `state.get()` instead of directly indexing the dictionary?\r\n\r\n@lhoestq ","comment_length":18,"text":"KeyError: '_indices_files' in `arrow_dataset.py` \n After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset.\r\n\r\nTrace:\r\n```\r\nTraceback (most recent call last):\r\n  File \"load_data.py\", line 11, in <module>\r\n    dataset = load_from_disk(SRC)\r\n  File \"\/opt\/conda\/envs\/py38\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 784, in load_from_disk\r\n    return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/opt\/conda\/envs\/py38\/lib\/python3.8\/site-packages\/datasets\/dataset_dict.py\", line 692, in load_from_disk\r\n    dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n  File \"\/opt\/conda\/envs\/py38\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 634, in load_from_disk\r\n    if state[\"_indices_files\"]:\r\nKeyError: '_indices_files'\r\n```\r\n\r\nI believe this is the line causing the error since there may not be a `_indices_files` key in the older versions:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/b70141e3c5149430951773aaa0155555c5fb3e76\/src\/datasets\/arrow_dataset.py#L634\r\n\r\nMay I suggest using `state.get()` instead of directly indexing the dictionary?\r\n\r\n@lhoestq  \n Thanks @samsontmr this should be fixed on master now\r\n\r\nFeel free to reopen if you're still having issues","embeddings":[-0.3437005281,0.0625090078,-0.0619883277,0.6979857087,-0.0762992576,0.1504234225,0.1940686256,0.4931738079,0.5131545663,0.1480900198,0.0192217361,0.1839222014,-0.3918602765,0.0680304319,-0.1045260802,0.0790594593,0.0853407234,0.2412601411,-0.1544322819,-0.052290827,-0.2806541324,0.1007366776,-0.1309833378,0.211374253,-0.2218931764,0.1231649667,-0.0895246267,0.4890262485,-0.1853998005,-0.7146970034,0.4884228408,-0.0012378489,0.198797375,0.6145881414,-0.0001211874,0.2563541532,0.328388989,0.0076413802,-0.3190935552,-0.3326137662,-0.2397470772,-0.1572865993,0.3840711117,-0.1370294094,0.1061333418,-0.4474045634,-0.3572835922,-0.3314718306,0.2223311365,0.0783566535,0.1171720922,0.3026997149,0.2074174583,-0.1436059624,0.228953734,0.0077808006,0.1049866378,0.5003935099,0.1849455237,-0.2934077084,-0.115201056,0.1408634186,-0.1057001576,-0.0338394083,0.3557126224,-0.0116487294,0.5778528452,-0.1533564031,-0.0614438169,0.1703200489,0.7022152543,-0.145922184,-0.4514220357,-0.3354025185,-0.1894246638,-0.2096370906,0.2590704858,-0.2555756569,-0.2330313176,0.1550564617,0.1738686711,-0.1955357045,0.056436453,0.2905206382,-0.2734226584,0.3019711673,0.0345148742,-0.030322887,0.6314465404,-0.0803371742,0.0911605954,0.1656604558,-0.1702350974,0.0807329342,-0.2672142982,-0.038882982,0.1909446716,-0.3841376007,-0.2094092965,0.0528946966,-0.0255248528,0.1885953397,-0.1497028768,0.0399960838,0.6687847376,0.2489634603,0.1215692312,0.2297081053,0.2717272341,0.2171474695,-0.0350570753,0.0183774922,0.0184037704,-0.347192049,0.0448060893,0.119112514,0.3113000393,-0.3126894236,-0.0694165379,0.0593046881,-0.0624833927,-0.201540783,0.1370553374,0.3150915802,0.1558444947,0.5801064968,0.0374670699,0.1970073879,-0.0338273272,0.3713238239,-0.1358800232,-0.2581335306,-0.0995747894,-0.0013663978,0.1081545055,-0.5095399022,0.2359856814,-0.047541324,0.0749816075,0.0100532221,-0.1632342041,0.084071897,0.1596419215,0.3046089411,-0.1820002645,0.2101746202,0.3970351517,-0.3919407427,-0.1523308307,0.1238470376,-0.2913793623,-0.5782806873,-0.3056536615,0.1312686503,0.1295812875,0.0252597015,-0.2260646373,-0.1392920017,0.1114809364,0.0196710825,-0.0214505251,-0.0969825238,-0.0833036155,-0.1675645262,0.0159698743,0.3993794322,-0.5810675621,-0.0023690723,-0.2711961567,-0.1212873608,-0.2390404493,0.2816366851,-0.4063346982,0.2037551999,-0.3347817361,-0.1348029077,0.3043564558,-0.4426818788,-0.4974357784,0.3520651162,-0.0488600992,-0.1117116734,0.1396108866,-0.1120444015,0.1941486746,-0.0449751951,0.4687681496,0.0551270992,0.0712261647,-0.0960199609,-0.0715619698,-0.1324856579,0.03400857,0.3673783243,0.0757355466,-0.1486506313,-0.0234469827,-0.1910144389,0.3385557532,-0.033754088,-0.0971989632,0.31829229,0.2205266505,-0.0087158261,0.0638513342,-0.0000374727,-0.4078004658,0.4620512724,0.1059394255,-0.0443780683,-0.3058128953,0.0868807361,-0.2338584512,0.1178293079,-0.3985089958,0.0907850042,0.0071391193,-0.1555865258,-0.1538615823,-0.1076429859,-0.3897000253,-0.2656809688,0.0552962534,0.2186610997,-0.1987503469,0.2255682945,-0.1129231304,-0.0787864849,-0.0303891562,0.1103976592,-0.0472365431,-0.2692546546,-0.1994897723,0.3676323295,0.2034950703,0.0388879552,-0.1302076131,0.043396201,0.2617529631,-0.0059622438,0.3687925935,0.3228161037,0.0084586469,-0.1577733159,0.0768168792,0.4362381697,-0.1849120855,0.3767441213,-0.0632533655,0.0499109142,-0.0009461989,0.0831125975,0.1180704609,-0.5903175473,-0.0254220888,-0.1207477748,0.3981439769,-0.0711293966,-0.0318615586,0.011218044,0.2276003212,-0.0984686315,0.1662933677,0.001818024,-0.1907745749,0.0243224613,0.0431337841,0.2312740833,0.1958069205,0.137964353,-0.0030731107,0.0805642009,0.1951951087,-0.104629606,0.3564743698,0.0686343387,0.2952162921,0.2632842064,-0.2636420131,-0.1510628462,-0.2133145332,-0.0609140843,-0.1362898201,0.3755882978,-0.2155833691,-0.0201139748,-0.4201763272,-0.2314468026,-0.1256988645,-0.3376940191,-0.1507581621,-0.436881274,-0.088378273,0.2107362598,-0.2474728227,0.0550901107,-0.197035864,-0.1132625863,0.1789672524,-0.0340482183,0.060162805,-0.3995396197,-0.2059826851,-0.0277684163,0.496370852,-0.1698491424,-0.0224506948,0.0206515752,0.0296302382,-0.0853550285,-0.0005940204,0.1873171329,-0.1565771103,-0.0260392744,0.1472346932,0.3131296933,-0.0509984456,-0.209030956,0.1667183638,-0.1248663589,-0.2623379827,0.1546895355,0.0747201294,0.1215401292,-0.0419443063,-0.1364700198,-0.2812353671,-0.4659274518,0.1132291034,0.1329471916,0.3265402317,0.305771172,0.2569748461,0.2501551211,0.1969927549,-0.1252462119,-0.1024906486,-0.1609047353,0.0975885391,0.0345190391,-0.1683220714,-0.147705704,0.0333468765,0.1063582823,-0.1455202252,-0.5225371718,0.0735288337,0.0396972634,0.3337232172,-0.0432079136,0.1500978172,0.2738485038,0.2244729549,-0.1346719116,0.0823900551,-0.0716342703,-0.0528472476,0.1690415889,0.0709919184,0.0759250894,0.6219985485,-0.1741819829,0.9047240615,-0.0850608423,0.0983518362,0.3507145047,0.027119277,0.3714459538,-0.3195245266,-0.3450508714,-0.3971735537,-0.0128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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"Hi ! Yes we are working on making `filter` significantly faster. You can look at related PRs here: #2060 #2178 \r\n\r\nI think you can expect to have the fast version of `filter` available next week.\r\n\r\nWe'll make it only select one column, and we'll also make the overall filtering operation way faster by avoiding many arrow<->python conversions especially during writing.\r\n\r\nI'll let you know how it goes !","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":68,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n Hi ! Yes we are working on making `filter` significantly faster. You can look at related PRs here: #2060 #2178 \r\n\r\nI think you can expect to have the fast version of `filter` available next week.\r\n\r\nWe'll make it only select one column, and we'll also make the overall filtering operation way faster by avoiding many arrow<->python conversions especially during writing.\r\n\r\nI'll let you know how it goes 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"@lhoestq Thanks for the response\u2014 it's great to hear that we'll be getting a much faster `filter` method soon. However, my use case does also involve using `map` over a single column in order to pre-compute roughly uniformly sized batches, and right now that is also very slow. Is there any plan to make `map` faster for single column operations?\r\n\r\nIf that's not a priority for the maintainers right now, I could try my hand at adding the feature, but I can't guarantee I would do a good job given my lack of familiarity with pyarrow.","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":96,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n @lhoestq Thanks for the response\u2014 it's great to hear that we'll be getting a much faster `filter` method soon. However, my use case does also involve using `map` over a single column in order to pre-compute roughly uniformly sized batches, and right now that is also very slow. Is there any plan to make `map` faster for single column operations?\r\n\r\nIf that's not a priority for the maintainers right now, I could try my hand at adding the feature, but I can't guarantee I would do a good job given my lack of familiarity with pyarrow.","embeddings":[-0.1885621548,0.3007333875,-0.0062208734,-0.2915562987,0.1139059737,-0.125191018,0.377454102,0.6256682873,0.2875320315,0.0172812026,-0.0364471562,0.5041142702,0.0290438402,-0.1641895324,-0.0369139165,0.1709661037,0.1048239693,0.2638177872,0.2762956917,0.1285973787,-0.105499059,-0.230432421,-0.2212545425,-0.0562877022,0.0027704337,-0.1056099907,0.1589284986,-0.4839156866,-0.2446201891,-0.1703580618,0.2296721339,0.3419600129,-0.1645797342,0.1854377538,-0.0001265591,-0.015295011,0.0672803298,0.0046598678,-0.1326862276,-0.0347867832,0.1036187708,-0.2461567819,0.0807675719,-0.2282919735,0.1449373364,-0.0729279965,0.0921783447,-0.0480944179,-0.0118833063,-0.1497037262,0.0260400884,-0.1085724011,-0.2673380375,0.2866571844,0.393298775,0.1476784497,-0.0333684795,-0.1472428292,0.3618592322,-0.4993890524,-0.1028713882,0.5538045764,-0.4942291677,-0.0468575619,0.3240176439,0.0596823879,0.1114338115,-0.1639341563,0.2983200252,0.1498267651,0.1279602647,-0.2527841032,-0.2238488048,-0.2600064576,-0.0924418569,-0.1685850471,-0.0613482371,-0.0341120027,-0.395265609,-0.0476741418,-0.2808194458,-0.3235608637,-0.0451245233,0.3968591094,-0.4360876679,0.4768274724,0.2962536216,0.2747288644,0.3442022502,-0.1562515199,0.002886483,0.0076632174,0.4512862265,0.5650333166,-0.4838467538,-0.1833854765,0.0761876479,0.0020666937,0.1733239442,-0.2895022035,-0.1337257326,0.4674760103,0.3749387562,0.1282308549,0.3100210726,0.0715184212,-0.152591899,0.5785233378,0.449947834,-0.129087761,0.0587535352,0.1226570308,-0.0138666965,0.227632001,0.1756646484,-0.3141234815,-0.3155645132,-0.129306972,0.1225796044,-0.1538142413,-0.2330203354,-0.0025992324,0.0703268573,0.3658011854,0.2422051579,0.2920158505,-0.2204377204,-0.0425460264,-0.1804530472,0.0192145091,0.1090091318,0.0703660175,-0.0252906997,0.2317008525,0.1311382651,0.1572612971,-0.2627331614,0.022589691,-0.0427703559,0.1445612758,0.002202641,-0.1336308271,0.2234643698,0.3903025687,-0.1576905549,0.3427307308,0.1693411916,-0.1029923633,-0.3292446434,0.2667769492,-0.1952231824,-0.2769999504,0.1620929837,-0.042051293,-0.0549543053,0.2621858418,-0.1286166608,0.5985473394,0.3813329637,-0.2264772654,-0.161817044,-0.0694191754,-0.3165629208,-0.1783338338,0.2890984416,-0.107911706,-0.3547855914,-0.2313257307,-0.1878269315,0.3709045947,0.41609025,0.3849086165,-0.1198677272,0.1343068928,0.326631248,0.3321783543,0.5479459167,-0.1042645127,-0.5639656186,0.0655858293,-0.2416366339,0.0511492491,-0.132146135,0.3440311849,0.6694209576,0.0902323499,0.3183021545,0.1980740428,-0.1306650639,0.3279159963,-0.1836322248,-0.2531143427,0.1532042623,0.0489023924,0.0427230671,-0.1249033883,-0.0157539696,0.044599507,-0.012951361,-0.0185180809,0.1647890955,0.060226623,0.0655706674,-0.0913712457,0.1133537441,-0.2321182787,-0.2884664237,0.0555403233,0.3865182698,0.1459059417,-0.3229031563,-0.3966253698,-0.039270483,0.0619345754,0.4316823184,0.1690214723,-0.0822235197,-0.2904554009,0.3275698423,-0.2072603405,-0.1705351621,-0.1253021806,0.0233977586,0.0944539383,0.0706552267,-0.019744765,0.2438316643,-0.0827937871,-0.1178131625,-0.0614261217,0.2267122865,0.1529109329,0.1414231658,0.0943184793,0.1466474533,-0.0662407354,-0.17863442,0.5075376034,0.0700756013,0.2343178391,0.1177433059,-0.1211806536,0.1545407176,-0.1719129384,-0.5523522496,0.2861994505,0.1465551257,0.6651866436,0.0841302425,0.1220776737,0.1631964594,0.0618514493,-0.0704910606,-0.3236384392,0.0100724185,0.0261883866,-0.1350475997,0.1274299622,0.0202217549,0.2641682625,0.2843721807,-0.0682591498,0.1308690459,0.3047645986,-0.0523111336,-0.2207185328,0.2096385062,-0.1084976941,0.0057851104,0.1629600376,0.1217780188,-0.2879891992,-0.1651800722,0.0005724233,0.2164832354,0.179439202,-0.1415688396,-0.2166480571,0.259044528,-0.0143977152,-0.2989133596,-0.0106078032,0.0033571064,0.3582737446,-0.1539956331,-0.263288945,-0.2762300372,-0.0915408656,0.3394620717,-0.0426127873,0.0372456685,-0.309864819,0.307069093,0.1898735166,0.1026942208,0.2113774717,-0.1225055754,0.2939247191,-0.0268258285,-0.1728902757,-0.3328283131,-0.5393310785,0.0738206059,-0.0852858722,0.0609383211,0.3177292645,0.1754732579,0.3763103485,-0.3800204098,-0.223446101,-0.4325364828,0.0378246717,-0.0858775079,0.0967652947,0.0850851089,0.427698195,-0.3566629291,0.0667089149,-0.0191574004,-0.130415082,-0.0418006591,0.0976940244,0.0027200289,0.2611276507,-0.1275754124,0.2719992101,0.0730158091,-0.1125070751,-0.0697457045,-0.1818050444,0.3236169219,-0.4373997152,0.0072227512,-0.2794033587,-0.1387145519,-0.1494147927,-0.0410582535,0.195117414,0.4307050407,0.271338582,-0.0999782458,-0.2389670312,-0.1302749813,-0.1822680235,0.571095109,-0.2226636559,-0.0169394407,-0.204339087,0.0421478339,0.12186528,0.0817672759,0.4627899826,0.0375514291,0.0420409404,0.1029868349,-0.2466260791,-0.281311512,-0.0661424845,-0.2550459504,0.2800433934,0.56834656,0.3082821369,0.7266680598,0.0995223299,0.1849897355,-0.0433402807,-0.0169090759,-0.0830331743,0.0083542401,-0.242262274,-0.1618505865,-0.2642281055,0.0175665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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"Currently the optimal setup for single-column computations is probably to do something like\r\n```python\r\nresult = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n```\r\nThis has two advantages:\r\n- input_columns=\"my_col\" allows to only read the column \"my_col\"\r\n- remove_columns=dataset.column_names makes `map` only keep the output of your function `f`, and it drops the other columns of the dataset instead of keeping them.\r\n\r\nLet me know if it improves speed on your side.\r\n\r\nYou can also get more speed by using `batched=True` and setting `num_proc=` for multiprocessing","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":82,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n Currently the optimal setup for single-column computations is probably to do something like\r\n```python\r\nresult = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n```\r\nThis has two advantages:\r\n- input_columns=\"my_col\" allows to only read the column \"my_col\"\r\n- remove_columns=dataset.column_names makes `map` only keep the output of your function `f`, and it drops the other columns of the dataset instead of keeping them.\r\n\r\nLet me know if it improves speed on your side.\r\n\r\nYou can also get more speed by using `batched=True` and setting `num_proc=` for 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"Hi @lhoestq ,\r\n\r\nI'm hijacking this issue, because I'm currently trying to do the approach you recommend:\r\n\r\n> Currently the optimal setup for single-column computations is probably to do something like\r\n> \r\n> ```python\r\n> result = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n> ```\r\n\r\nHere is my code: (see edit, in which I added a simplified version\r\n\r\n```\r\nThis is the error:\r\n```bash\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000\r\n```\r\nI wonder why this error occurs, when I delete every column? Can you give me a hint?\r\n\r\n### Edit:\r\nI preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error?  I can iterate over the\r\ncomplete dataset and print every sample before calling map. There seems to be no other problem with the dataset.\r\n\r\nI tried to simplify the code that crashes:\r\n\r\n```python\r\n# works\r\nlog.debug(dataset.column_names)\r\nlog.debug(dataset)\r\nfor i, sample in enumerate(dataset):\r\n    log.debug(i, sample)\r\n\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n)\r\n```\r\n\r\n```\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000\r\n```\r\n\r\nEdit2: \r\n\r\nMay this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:\r\n\r\n```python\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n    features=datasets.Features(\r\n        {\r\n              \"a\": datasets.Sequence(datasets.Value(\"int32\"))\r\n         }\r\n    )\r\n)\r\n```\r\n\r\n```\r\n File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1704, in _map_single\r\n    writer.write_batch(batch)\r\n  File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 312, in write_batch\r\n    col_type = schema.field(col).type if schema is not None else None\r\n  File \"pyarrow\/types.pxi\", line 1341, in pyarrow.lib.Schema.field\r\nKeyError: 'Column tokens does not exist in schema'\r\n```","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":285,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n Hi @lhoestq ,\r\n\r\nI'm hijacking this issue, because I'm currently trying to do the approach you recommend:\r\n\r\n> Currently the optimal setup for single-column computations is probably to do something like\r\n> \r\n> ```python\r\n> result = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n> ```\r\n\r\nHere is my code: (see edit, in which I added a simplified version\r\n\r\n```\r\nThis is the error:\r\n```bash\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000\r\n```\r\nI wonder why this error occurs, when I delete every column? Can you give me a hint?\r\n\r\n### Edit:\r\nI preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error?  I can iterate over the\r\ncomplete dataset and print every sample before calling map. There seems to be no other problem with the dataset.\r\n\r\nI tried to simplify the code that crashes:\r\n\r\n```python\r\n# works\r\nlog.debug(dataset.column_names)\r\nlog.debug(dataset)\r\nfor i, sample in enumerate(dataset):\r\n    log.debug(i, sample)\r\n\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n)\r\n```\r\n\r\n```\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000\r\n```\r\n\r\nEdit2: \r\n\r\nMay this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:\r\n\r\n```python\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n    lambda x: {\"a\": list(range(20))},\r\n    input_columns=column,\r\n    remove_columns=dataset.column_names,\r\n    load_from_cache_file=False,\r\n    num_proc=num_workers,\r\n    batched=True,\r\n    features=datasets.Features(\r\n        {\r\n              \"a\": datasets.Sequence(datasets.Value(\"int32\"))\r\n         }\r\n    )\r\n)\r\n```\r\n\r\n```\r\n File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1704, in _map_single\r\n    writer.write_batch(batch)\r\n  File \"env\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 312, in write_batch\r\n    col_type = schema.field(col).type if schema is not None else None\r\n  File \"pyarrow\/types.pxi\", line 1341, in pyarrow.lib.Schema.field\r\nKeyError: 'Column tokens does not exist in schema'\r\n```","embeddings":[-0.1297885925,0.3438464403,0.0056276908,-0.2346831262,0.1094863191,-0.1253667325,0.395519793,0.5808492303,0.2243223041,0.0691065192,-0.0648775175,0.487503618,0.0244312882,-0.1736370772,-0.0233188793,0.2062173933,0.1099480912,0.3038486242,0.3059216738,0.0706640705,-0.0972660705,-0.196017921,-0.196914956,-0.0473396219,-0.0616178773,-0.0969334841,0.1700523049,-0.4098371267,-0.2656676769,-0.1343441457,0.3225792348,0.3406042457,-0.1604736745,0.1512905806,-0.0001253172,-0.0479153842,0.0150217032,-0.0417702869,-0.1860088855,-0.0878247544,0.1646131724,-0.21570611,0.1242413521,-0.2357680947,0.0668423474,-0.1099051833,0.0184353553,-0.0702940747,0.0177385099,-0.1491788179,0.037824627,-0.1153193414,-0.3119522929,0.2564554811,0.4294274449,0.1337286085,0.0135107469,-0.188696146,0.3076252043,-0.4169775546,-0.1078032851,0.5532121062,-0.4494970739,-0.0867570266,0.3468251824,0.0665670112,0.1131561846,-0.2048595995,0.2837946713,0.1231319457,0.1522666067,-0.2929821312,-0.2057834417,-0.2492104173,-0.0813231394,-0.2832041383,-0.076393187,0.0381773002,-0.4349862635,-0.056203261,-0.2655761242,-0.2840900421,-0.1205125153,0.4191443324,-0.5168408155,0.4453457296,0.2073737234,0.2835462093,0.1737061739,-0.1643485427,-0.0238912906,-0.0488698743,0.491484642,0.5992772579,-0.4381175041,-0.1678372473,0.0869320035,-0.0217091981,0.2280963212,-0.2405296862,-0.1810513586,0.4192118943,0.3674304485,0.1295282692,0.3315454721,0.0803944319,-0.0740255713,0.5757723451,0.4447900057,-0.0879587531,0.1487137228,0.0883329585,-0.0260100309,0.3440795839,0.2287721187,-0.2832647264,-0.200966239,-0.2048863471,0.0376366712,-0.0027047105,-0.2731019855,0.0661597475,0.0608097203,0.3648388684,0.2870669365,0.28793782,-0.1871757805,0.0238858033,-0.220812887,0.0010180156,0.055280339,0.1150854006,-0.0744161308,0.1614280641,0.2063453197,0.1675103307,-0.1636311561,0.0466469601,0.0027351519,0.0358745679,-0.059275195,-0.1801027209,0.2324701101,0.4378067553,-0.065989852,0.3828446567,0.2269887775,-0.2831005454,-0.3427932858,0.2607621253,-0.0827737451,-0.2477099746,0.1496957093,-0.0216771159,-0.096440427,0.3550901413,-0.1667314619,0.5624736547,0.3837341666,-0.1071810946,-0.1672737449,-0.1610994935,-0.2877018452,-0.1954727471,0.3321774006,-0.0849982426,-0.3995158374,-0.1915385127,-0.1742316931,0.2851774693,0.4178023338,0.3706712723,-0.0936105773,0.1220273376,0.3517781496,0.4231685996,0.5522978902,-0.0770867094,-0.5348977447,0.0360717662,-0.224387303,0.1220817044,-0.1777932346,0.3341093957,0.7191378474,0.1400877684,0.3616188765,0.2063519806,-0.1054341793,0.2566282451,-0.1739203483,-0.1875509322,0.1082142964,0.1010326296,0.0737975165,-0.1837998182,-0.0069080633,0.0134579642,0.0576997884,-0.0499883816,0.2077866495,0.0752976984,0.1167883947,-0.0806707665,0.0855278522,-0.2948940098,-0.3675554097,0.1026487499,0.4557942748,0.1674704254,-0.3501776755,-0.4428637624,-0.1378015727,0.0900194496,0.408793956,0.1375357062,-0.0490505621,-0.3041940629,0.2227518409,-0.1991464794,-0.2078983039,-0.1095270663,0.000580881,0.1515118182,0.0227787439,-0.0070037679,0.2466126382,-0.0959864631,-0.0374679118,-0.0547052398,0.2666029632,0.2137798518,0.1916841865,0.0870014504,0.1957302094,-0.117195189,-0.2693024278,0.4498797059,0.0647738278,0.201766178,0.0769662634,-0.1623327285,0.1554093212,-0.1814915091,-0.5328130722,0.3011566699,0.0802621767,0.6450633407,0.0309948269,0.1309301704,0.1760700792,0.0390137956,-0.1072938442,-0.3742184043,0.0231643748,0.0713966042,-0.1661333442,0.0815336928,-0.0277556721,0.2639753819,0.190789327,-0.0964990854,0.0930827558,0.262673378,-0.0679099038,-0.1828457117,0.226233691,-0.0757433847,0.020079948,0.1824525893,0.1405412853,-0.251835078,-0.1768807322,0.0360903814,0.1681702137,0.2130683362,-0.1704660207,-0.1802038252,0.2561203241,-0.0382318646,-0.3358976841,0.0600581691,0.0034637861,0.3546962142,-0.2224044949,-0.2182590067,-0.2511993051,-0.1353276521,0.3232792318,-0.0828273892,0.0066931015,-0.2888740599,0.2953870893,0.2205252796,0.1083198264,0.2351681292,-0.1334855705,0.3227449954,-0.0152808521,-0.1918017268,-0.2713219225,-0.4650273025,-0.0009278756,-0.0592176616,-0.0043337569,0.424478054,0.2421155274,0.390532434,-0.3245842457,-0.2451094389,-0.4025381804,0.0513647608,-0.1198890358,0.1941350847,0.109929733,0.4666310847,-0.3420136273,0.0523041189,-0.0229985379,-0.0488411263,-0.0397746973,0.0918895826,0.0137830339,0.2035859972,-0.1543302685,0.2484562695,0.0468144976,-0.1075429842,-0.0762664899,-0.0906670019,0.3686672151,-0.4598110914,-0.0609560199,-0.3338884711,-0.0207138788,-0.1050695851,-0.1179809868,0.2465784848,0.4201830029,0.2896746397,-0.1053839698,-0.1386381239,-0.1294739395,-0.1599316448,0.6129691005,-0.2647590637,-0.0381004252,-0.1784407198,0.0093415137,0.1334380805,0.1467615217,0.4467785656,0.0264446288,0.0575173497,0.0608379468,-0.2759607732,-0.2290013134,-0.1141933575,-0.2464484125,0.2201988995,0.5735800862,0.3240065277,0.7751105428,0.1520693004,0.1780719012,-0.051378455,-0.0341672599,-0.048240494,-0.0013013742,-0.2136194706,-0.1924018711,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"Hi ! Can you open a separate issue for that ?\r\nAlso if you could provide a google colab or a sample code to reproduce this issue that would be helpful.\r\nOn my side I was not able to reproduce this error.","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":42,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n Hi ! Can you open a separate issue for that ?\r\nAlso if you could provide a google colab or a sample code to reproduce this issue that would be helpful.\r\nOn my side I was not able to reproduce this error.","embeddings":[-0.0739275962,0.2622617483,0.010453444,-0.1460129768,0.1726150662,-0.0921144709,0.3422678113,0.5855397582,0.2409633398,0.0138788251,-0.0288185868,0.4858111739,0.0747995228,-0.2420258522,-0.0771973953,0.2121567279,0.0645875633,0.3524308801,0.3226831257,0.0753293633,-0.1231598556,-0.0958926827,-0.2396637797,-0.027308302,-0.1052388102,-0.0756350234,0.2402052283,-0.3865373731,-0.3200216889,-0.1982570887,0.3132258952,0.2609861195,-0.2287148386,0.1817971021,-0.0001276501,-0.0581163168,0.1082803756,-0.0010412417,-0.1381468177,-0.0250625256,0.1099642962,-0.1435803622,0.1595816016,-0.2177058607,0.060991589,-0.1062188968,0.0007782824,0.0064213825,0.0571450256,-0.0390551314,0.0349226594,-0.0823546201,-0.2686341107,0.2206536531,0.5313364863,0.1463045031,0.0038702819,-0.1021067426,0.3115445077,-0.3857913911,-0.1269279718,0.5586366057,-0.4722703993,-0.01489692,0.3759162426,0.0052528437,0.1003192887,-0.2585339546,0.3394195139,0.1346260309,0.2470730841,-0.252373606,-0.074480705,-0.2339121252,-0.0812008679,-0.242116794,0.0057814936,0.0697179139,-0.4549058974,-0.0236453619,-0.2457007021,-0.2589138448,-0.1095361933,0.3977832198,-0.5061330199,0.4292740226,0.1459528804,0.2955040932,0.1822728217,-0.2157247365,-0.0307177976,0.0070946617,0.385294944,0.5473134518,-0.5153126121,-0.1205781698,0.130554989,-0.0011697254,0.2056518495,-0.2471275777,-0.2166626006,0.3844571114,0.3991676569,0.1074465886,0.3933253586,0.1232143193,-0.0664458051,0.616337657,0.3975470662,-0.0870264173,0.0749649554,0.0728815496,-0.0070858588,0.3263713717,0.2278842181,-0.3896753788,-0.152371332,-0.2447281778,-0.0461859107,-0.0112265376,-0.2754251957,0.0753837675,0.1497301012,0.4403749406,0.2603336275,0.2501383126,-0.2040504664,0.0413907431,-0.2743712366,0.0689503774,0.0466026627,0.0728697851,-0.1434799582,0.1552459449,0.123694703,0.1516343951,-0.1084835753,0.0524867922,-0.0320192575,0.048827745,-0.0413984954,-0.2150832564,0.228081733,0.5221583247,-0.1386476308,0.4291599095,0.2292030752,-0.2557504475,-0.3305651546,0.2962751389,-0.2245358974,-0.2434123755,0.0945887342,-0.0493415967,-0.0717157423,0.3505977392,-0.112861231,0.5079697967,0.4323571324,-0.1694267094,-0.0945496261,-0.1912849247,-0.3159533441,-0.2156187296,0.2965351939,0.0022861755,-0.4213631749,-0.1952866614,-0.2165256441,0.2711444497,0.5304780006,0.4404331148,-0.124631308,0.0786661133,0.2929182649,0.4428164065,0.5207013488,-0.0072946679,-0.6128538251,0.1197995842,-0.2150262445,0.0924033374,-0.20398058,0.3250977099,0.6797481179,0.1578985155,0.3617906272,0.1648360789,-0.0221153032,0.2446768731,-0.1920106411,-0.0925857127,0.1381864846,0.0686163083,0.0109994439,-0.1415878683,0.0202317592,0.0064281835,-0.0117277279,-0.0229040515,0.1731658876,0.1595007777,0.1444867849,-0.0474596471,0.1431256384,-0.2540426254,-0.2715231478,0.0644922182,0.3877613842,0.1844186038,-0.3709639311,-0.3565759957,-0.2604984939,0.1445243061,0.3517885804,0.1155855134,-0.0966623873,-0.3039396107,0.1562776417,-0.1436887085,-0.1705742478,-0.0988533571,-0.0689976588,0.0802975222,0.0136118205,-0.0565731153,0.1670723706,-0.1067387238,-0.0188671555,-0.1260797679,0.2792257965,0.2390753031,0.0818327218,0.0696512014,0.2735039592,-0.1891265959,-0.2591295242,0.4771980047,0.0727245584,0.0756566003,0.1348820776,-0.1028706133,0.1460470408,-0.2113201916,-0.442625761,0.2642633021,0.125878945,0.6324936152,0.0156597514,0.1364632845,0.2505248487,0.0159851648,-0.075626336,-0.3630045652,0.0850607082,0.0069791414,-0.0781270936,0.0950096846,-0.0385916159,0.2597822249,0.2616235316,-0.0216930602,0.1133338213,0.365085125,-0.1223695502,-0.2129504681,0.1746501625,-0.1219652891,0.030689029,0.1632504463,0.1556094438,-0.209340632,-0.1445565969,-0.0418502688,0.1894768327,0.2225824594,-0.1069726273,-0.1777325273,0.2512494624,-0.0443398133,-0.3463544846,0.0572749078,0.0166495703,0.3730432689,-0.2785396874,-0.1976841539,-0.2901167274,-0.1320257038,0.304094553,-0.1016241536,-0.0022889264,-0.3022094965,0.3303010166,0.1749198735,0.1361000091,0.2405017763,-0.1292820424,0.3036357164,0.0538430288,-0.1591786146,-0.2672785819,-0.5161728263,-0.0140732713,-0.0943457559,0.0272433329,0.4950301051,0.2363532782,0.3811714947,-0.2868723571,-0.1996533573,-0.4601122141,0.0901708603,-0.1253234446,0.2369723916,0.0723383054,0.5490533113,-0.3248769045,0.0652616397,0.0097933291,-0.0948728323,-0.1028262749,0.181861043,-0.0713994503,0.1781483144,-0.1312668771,0.2205970287,0.0725918785,-0.1176746786,-0.1060684621,-0.116534695,0.3740614057,-0.3518956006,-0.0339260362,-0.2666487694,-0.1013841182,-0.1289845556,-0.1214599535,0.2334732413,0.4066542387,0.2464244366,-0.0668164641,-0.0993939638,-0.048872035,-0.1490194052,0.5926892757,-0.3425274789,0.0154618174,-0.2961544693,-0.0117681092,0.0977891088,0.0548851155,0.5126541853,-0.0307217874,0.0438489467,0.0353364907,-0.3410559297,-0.2484962046,-0.1060235426,-0.1593159586,0.2240360975,0.6323900223,0.2220179737,0.8341838121,0.1447575539,0.2216544896,0.011950735,-0.0426444821,0.026710676,-0.0763693005,-0.2541618943,-0.1907681227,-0.212671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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"@lhoestq Sorry I'm just responding now. I'm currently using your recommendation for the map on a single column, and I've gotten it to be fast enough to sort of work for my use case by just setting `num_proc=10`, although it's still quite slow. It's clear that it is still loading the entirety of each row into memory and then discarding everything except the selected column, instead of exploiting the columnar data format to only load the selected column.\r\n\r\nMy code is like this:\r\n```\r\n self.dataset = self.dataset.sort('num_tokens')\r\n batch_dataset = self.dataset.map(\r\n\tcompute_uniform_sized_batches,\r\n\tbatched=True, batch_size=10_000, num_proc=10, input_columns=['num_tokens'],\r\n\tremove_columns=get_columns_all_equal(self.dataset),\r\n\twith_indices=True,\r\n\tfn_kwargs=dict(max_size=tokens_per_batch)\r\n)\r\nself.batches = {\r\n\tname: list(zip(split['start'], split['length']))\r\n\tfor name, split in batch_dataset.items()\r\n}\r\n```\r\nI find that the processes with higher IDs take significantly longer to complete, presumably because the dataset is sorted by article length and they're loading the entire article text into memory, instead of just the 'num_tokens' column.\r\n\r\nI should note that my batching procedure would work best if I just used `batch_size=None` and loaded the whole column into memory at once, but I found that this was intolerably slow and gave me no progress information, so I'm using the less than ideal `batch_size=10_000`.","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":195,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n @lhoestq Sorry I'm just responding now. I'm currently using your recommendation for the map on a single column, and I've gotten it to be fast enough to sort of work for my use case by just setting `num_proc=10`, although it's still quite slow. It's clear that it is still loading the entirety of each row into memory and then discarding everything except the selected column, instead of exploiting the columnar data format to only load the selected column.\r\n\r\nMy code is like this:\r\n```\r\n self.dataset = self.dataset.sort('num_tokens')\r\n batch_dataset = self.dataset.map(\r\n\tcompute_uniform_sized_batches,\r\n\tbatched=True, batch_size=10_000, num_proc=10, input_columns=['num_tokens'],\r\n\tremove_columns=get_columns_all_equal(self.dataset),\r\n\twith_indices=True,\r\n\tfn_kwargs=dict(max_size=tokens_per_batch)\r\n)\r\nself.batches = {\r\n\tname: list(zip(split['start'], split['length']))\r\n\tfor name, split in batch_dataset.items()\r\n}\r\n```\r\nI find that the processes with higher IDs take significantly longer to complete, presumably because the dataset is sorted by article length and they're loading the entire article text into memory, instead of just the 'num_tokens' column.\r\n\r\nI should note that my batching procedure would work best if I just used `batch_size=None` and loaded the whole column into memory at once, but I found that this was intolerably slow and gave me no progress information, so I'm using the less than ideal `batch_size=10_000`.","embeddings":[-0.1100344881,0.2694859207,-0.0035479604,-0.2267886102,0.1348388344,-0.1574557573,0.3228950202,0.5779768229,0.2516382039,0.0171539988,-0.0084351329,0.5262537003,-0.0003768195,-0.2199472189,-0.0524406247,0.232875973,0.0863020569,0.2980628312,0.323153466,0.1050170362,-0.1054408401,-0.174614951,-0.1898676455,-0.0851241946,-0.080841817,-0.0479384959,0.2107251585,-0.3849202096,-0.2996502519,-0.199813664,0.2933183908,0.2998622954,-0.1688535213,0.1480011642,-0.0001249938,-0.0329319611,0.0593611151,-0.0150163192,-0.1549152732,-0.0841840953,0.1353837103,-0.2224764675,0.0945757329,-0.2144913375,0.050780829,-0.1102484018,0.0543325469,-0.0347234122,0.0267525464,-0.0667863414,0.0478332639,-0.08839605,-0.311880976,0.2385984659,0.4412196577,0.1660660058,0.0005605074,-0.1601057649,0.338162303,-0.3996120691,-0.1329923868,0.4940267503,-0.4622115195,-0.0765878409,0.3967831135,0.0319406912,0.0792658851,-0.2212283015,0.3087268472,0.1258919239,0.1974623054,-0.281825453,-0.0885776803,-0.2058185786,-0.0772382393,-0.2547152042,-0.0588703156,0.0190499406,-0.4656490982,-0.0028989639,-0.2404753566,-0.2685699463,-0.1122994125,0.4409021735,-0.5037668943,0.4319310188,0.2216345668,0.2682172656,0.2149718702,-0.2014353722,-0.0521682873,-0.0211346615,0.4715501964,0.5985535383,-0.4138296247,-0.1830009073,0.1064458266,0.0227847714,0.2341974825,-0.2586019635,-0.1823876202,0.4355725646,0.3492282629,0.1186510623,0.351398319,0.0338762328,-0.0562201031,0.5463657975,0.4081636667,-0.0972744748,0.1370310038,0.0992179886,0.0056041181,0.3168942928,0.2251746953,-0.3960950673,-0.2358844727,-0.1837448627,0.0351988114,-0.0079395445,-0.2091797292,0.0445251577,0.0754380748,0.4448008835,0.2787966132,0.3084744513,-0.2266307771,-0.0192539264,-0.235152185,0.0280104876,0.0535937063,0.0838450044,-0.0949299857,0.180284515,0.1423449516,0.1708377749,-0.1454723626,0.0148306256,-0.0590241738,0.1263824701,-0.0375047661,-0.1837397963,0.2449113876,0.4446518421,-0.149041757,0.3734372258,0.2086372077,-0.1996190995,-0.3181154132,0.3277811706,-0.1003847346,-0.2305619121,0.1103366688,-0.0251126606,-0.0854818076,0.3163959086,-0.0983737484,0.5966129303,0.3636236191,-0.1567471176,-0.1268096268,-0.1794048399,-0.3244422972,-0.2377982885,0.3155627251,-0.0559911542,-0.4163646698,-0.2329698056,-0.1773415655,0.3014839888,0.4253210723,0.3679703176,-0.1312419176,0.0589311086,0.3872956634,0.4368954301,0.5277869105,-0.0276141763,-0.6045199037,0.0641094819,-0.1901936978,0.0697050691,-0.1573721021,0.3717439175,0.6730760336,0.17122823,0.3508662283,0.2020464092,-0.043672543,0.2744268775,-0.1774673164,-0.2010703832,0.1675621867,0.0992528051,0.0320842452,-0.2219464779,-0.0036628288,0.0244350694,0.0568580665,0.0123090195,0.1843397766,0.0709385499,0.1305354238,-0.0961132273,0.1153456345,-0.2739713192,-0.2958954573,0.0815804675,0.384316206,0.1637853831,-0.3264375329,-0.3511686027,-0.181150943,0.0926743522,0.3969911933,0.0968651399,-0.0634365082,-0.3534477055,0.1967143863,-0.1489762217,-0.1934258789,-0.1466518492,0.0024978118,0.104288131,0.0114268316,-0.0093639866,0.2064909786,-0.1165341735,-0.0471429154,-0.1154092625,0.2352760136,0.2410050929,0.1672466546,0.0823833495,0.2372433841,-0.1647633761,-0.3285937905,0.4697247148,0.1023412496,0.1482241452,0.1163063049,-0.1425990164,0.1648341864,-0.1604898721,-0.5011028051,0.2884663641,0.1480735689,0.613444984,0.0310936701,0.1114454791,0.1854691356,0.0051052296,-0.0606541075,-0.3591530323,0.0357489549,0.097132057,-0.1129213646,0.1017361134,-0.0033517568,0.2966711223,0.1944944263,-0.0443623774,0.0852872208,0.3028745055,-0.127077952,-0.1970970631,0.2345538884,-0.1604048014,0.0320270136,0.1957661211,0.0991943032,-0.2818477154,-0.1702195704,0.0116474247,0.1980623305,0.1764254868,-0.1119158268,-0.1793755591,0.2269996107,-0.0561372489,-0.345241338,0.0782023072,-0.0096061574,0.3562093079,-0.2056916207,-0.2208713293,-0.2761364281,-0.1030508727,0.3162029684,-0.1026304811,-0.0073960093,-0.2565502524,0.3267335892,0.2095459253,0.133966729,0.2215714157,-0.1613433212,0.2975682616,0.0058617084,-0.1849832982,-0.2740886509,-0.5016184449,0.0176578164,-0.0662833825,0.0460664444,0.4536599815,0.2173223048,0.4591349065,-0.3362983763,-0.1795960516,-0.3963326812,0.0620957129,-0.125780344,0.2041710764,0.0657641962,0.5384756327,-0.3111215234,0.0434608497,-0.0108580422,-0.1486571133,-0.0590963587,0.1332432777,-0.0285292696,0.1866470128,-0.1014212742,0.213221401,0.0452919416,-0.1045640036,-0.0941851884,-0.0347685255,0.3498975039,-0.41978845,0.0379531607,-0.2923451662,-0.06746158,-0.1448165923,-0.1258461475,0.2819057703,0.433894366,0.2763317823,-0.1003727242,-0.1484422088,-0.0836175829,-0.1696136892,0.5442781448,-0.2675815225,-0.008610039,-0.2266428918,0.0212720837,0.1478111744,0.0819820985,0.5152600408,0.0026169308,0.0346801281,0.0704884082,-0.293710202,-0.2269754857,-0.0938408449,-0.1578365266,0.2495295107,0.5338845849,0.3260044158,0.7934350371,0.165519312,0.1323277801,-0.0513937585,-0.0149193248,-0.041881118,-0.0051874169,-0.1926889122,-0.1869391948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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"Hi @norabelrose ! I'm glad you managed to make this work on your side.\r\nRegarding memory usage, you can try to drop the columns that you don't want to use for your `map` for now.\r\n\r\nIn the future we'll try to find a way to not load unnecessary columns in memory in `map`. Currently the way it works is that it gets the batch as a python dict, then it updates it using the output of your mapping function, and finally it removes columns from `remove_columns`. Therefore for a moment some columns are loaded in memory even if you remove them or don't use them for your mapping function.\r\n\r\nIt would be nice to have a way to optimize memory for cases such as yours !","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":126,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n Hi @norabelrose ! I'm glad you managed to make this work on your side.\r\nRegarding memory usage, you can try to drop the columns that you don't want to use for your `map` for now.\r\n\r\nIn the future we'll try to find a way to not load unnecessary columns in memory in `map`. Currently the way it works is that it gets the batch as a python dict, then it updates it using the output of your mapping function, and finally it removes columns from `remove_columns`. Therefore for a moment some columns are loaded in memory even if you remove them or don't use them for your mapping function.\r\n\r\nIt would be nice to have a way to optimize memory for cases such as yours !","embeddings":[-0.1090221256,0.2856530845,-0.0173122119,-0.1360481232,0.1743156165,-0.084663257,0.276823163,0.5508022904,0.2953394353,-0.0280731265,-0.0430036187,0.4847539961,-0.0104954401,-0.2168293297,-0.0766484737,0.1780526191,0.0879167616,0.2981182933,0.2514612377,0.1253352016,-0.1527184248,-0.1559893638,-0.1923488528,-0.1331228912,-0.1440608799,-0.07994055,0.186787352,-0.3805733323,-0.2664017379,-0.2372178882,0.2627159357,0.3139121234,-0.1195456535,0.1400264651,-0.000123181,-0.0655137748,0.0379224308,0.0187601745,-0.1874784827,-0.0091844425,0.1147256866,-0.2367014587,0.1002808437,-0.2135514319,0.1023752615,-0.1358706057,-0.0096762376,-0.0671397895,0.0681037381,-0.0473486073,0.0690712109,-0.0756958723,-0.2891823649,0.2594843507,0.4704771042,0.1618333608,0.0502097532,-0.1637221426,0.3472459912,-0.3854161501,-0.192709133,0.5658636093,-0.4568247497,0.0031641799,0.3684160411,0.0001994617,0.1213868111,-0.2628812492,0.35361889,0.120161213,0.2966677547,-0.3108711541,-0.033721447,-0.2469123304,-0.0874592736,-0.2455392182,-0.0481368154,0.0224935692,-0.4655238986,-0.0642245263,-0.2485087365,-0.2779921591,-0.1007028446,0.3768830001,-0.412034899,0.4298382401,0.1846134812,0.2694784403,0.2300573289,-0.2425142676,-0.0344193503,-0.0763403475,0.3317642808,0.5829606652,-0.436930716,-0.1821168661,0.081949614,-0.0360533856,0.2144129574,-0.2783696949,-0.2595427632,0.3622379899,0.3828552961,0.0960842073,0.4457158446,0.1008039266,-0.0724330992,0.5937908888,0.3524028659,-0.1336074769,0.0922416672,0.0355100818,-0.0083410451,0.2875795066,0.30770123,-0.422405988,-0.146506682,-0.1777960509,-0.0036207726,-0.0233573094,-0.2148676962,0.0137573378,0.1195844784,0.462803036,0.2295093238,0.2768382728,-0.1977340877,-0.0248471405,-0.2141761929,0.0515227802,0.0254434757,0.0953226611,-0.1413809061,0.179748863,0.1237722933,0.2349213064,-0.0849079117,0.0241599642,-0.0277580358,0.0716111884,0.0253313668,-0.2372814268,0.2751714289,0.4669004977,-0.1456158608,0.3750057518,0.1949282438,-0.2103823274,-0.3377526104,0.2783410251,-0.1568898857,-0.2745813727,0.1174590066,-0.0101031,-0.0357580669,0.3091068566,-0.1921065599,0.5416850448,0.390511781,-0.1119554713,-0.140958339,-0.1778873354,-0.2912268341,-0.2638456225,0.330737561,0.0047742035,-0.4009059966,-0.2362161875,-0.2102873772,0.2777677476,0.4510690868,0.4538514912,-0.1082937941,0.1115136668,0.3450726867,0.3644188046,0.524923563,-0.1185906529,-0.5643758774,0.0911747441,-0.0897123888,0.1206969321,-0.1512248665,0.4033991098,0.6525670886,0.1655063033,0.3023563027,0.1939819157,-0.0593865886,0.3020563722,-0.154225558,-0.1881451011,0.1357928663,0.074010022,0.0358703509,-0.2453615069,-0.0491573364,0.0437317491,0.0210190117,-0.081031464,0.2167941034,0.0880567208,0.1659963876,-0.0188013967,0.1049728543,-0.251210928,-0.3391766846,0.0935987979,0.3977253735,0.1697494388,-0.3231752217,-0.3669006228,-0.1637741029,0.0814843327,0.3438125551,0.0508630276,-0.0270686317,-0.368604511,0.2010230273,-0.1962224245,-0.145414263,-0.140348047,-0.0127806105,0.0920539126,0.0132701211,-0.0675174594,0.1958551705,-0.0875226855,-0.015383983,-0.047894828,0.2411514074,0.2710720599,0.1745267212,0.0639069453,0.276348114,-0.1552943438,-0.2778624892,0.4768836498,0.0327292457,0.1264176071,0.1915228069,-0.1480120122,0.1535405517,-0.1665282249,-0.4134834111,0.2336407751,0.1651546955,0.6565909386,0.0086745974,0.133165881,0.1882093251,-0.0068121045,-0.0356929004,-0.3979204893,0.0318740234,0.021727547,-0.0778418183,0.1329749674,0.0074173394,0.1980525404,0.2051876485,-0.007843554,0.0878041536,0.3099796474,-0.2001462728,-0.2421217114,0.1981220096,-0.1498928368,0.0176193211,0.2220949829,0.0836346447,-0.255923301,-0.101659596,0.0087207844,0.2313448042,0.2254356146,-0.0668537766,-0.2380709052,0.1779941618,-0.0571115725,-0.306778729,0.0590055138,-0.0237057526,0.3478134871,-0.2445156276,-0.2061146051,-0.2547019422,-0.1980947107,0.34061113,-0.0816507787,0.0087110465,-0.2504313588,0.2866850793,0.1881872863,0.1070009843,0.2564488351,-0.1198146418,0.3113119602,0.029083034,-0.2222064137,-0.2729599178,-0.5268831849,-0.0290297773,-0.0305163953,0.1108557805,0.3845769167,0.3267253637,0.4251878262,-0.2760056257,-0.1865748167,-0.3498575091,0.0308897831,-0.1473956853,0.2165368795,0.0386563912,0.5493985415,-0.3426373005,0.0491896309,-0.0027287239,-0.1080833748,-0.0340949297,0.129964456,-0.0471408553,0.1786493808,-0.0442438684,0.1530379057,0.0591475293,-0.1323919296,-0.1048721001,-0.0440553986,0.3658530414,-0.3394891918,0.0361534506,-0.2583793402,-0.1067750528,-0.1361564994,-0.1385722011,0.2278966755,0.4098497033,0.2368452698,-0.0491841398,-0.0461336486,-0.0541709848,-0.0778101012,0.5534914136,-0.3383617699,0.0059875748,-0.242931217,-0.00154214,0.1468024552,0.1258050203,0.4377745986,0.0251519214,0.0017539191,0.0893941745,-0.2488978058,-0.2838338912,-0.1181722432,-0.1418951005,0.2551566362,0.5586386919,0.2956329584,0.8239872456,0.1558609158,0.2164775133,0.0134823853,0.0023206158,0.0195042118,-0.0187361687,-0.205739364,-0.2271466404,-0.2426349372,-0.0351009481,0.18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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"@lhoestq After looking through the source code, it looks like the following solution has at least some chance of working:\r\n- refactor `Dataset.map()` so that the `input_columns` parameter is implemented by using the `self.formatted_as()` context manager with `columns=input_columns`\r\n- change `Dataset._getitem()` so that it passes `self._data.drop(drop_columns)` to the `query_table()` function whenever `format_columns` is non-None and `output_all_columns` is False, instead of `self._data` itself","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":62,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n @lhoestq After looking through the source code, it looks like the following solution has at least some chance of working:\r\n- refactor `Dataset.map()` so that the `input_columns` parameter is implemented by using the `self.formatted_as()` context manager with `columns=input_columns`\r\n- change `Dataset._getitem()` so that it passes `self._data.drop(drop_columns)` to the `query_table()` function whenever `format_columns` is non-None and `output_all_columns` is False, instead of `self._data` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"Looks like a great direction :)\r\nNote that `query_table` doesn't bring data into memory. Only `format_table` does.\r\nAlso the dataset may already have a format with `columns=` already defined so we would need to define the formatted `input_dataset` like:\r\n```python\r\n# before the `map` main for loop\r\ninput_columns = input_columns if input_columns is not None else self.column_names\r\nif not self._output_all_columns:\r\n    columns = [col for col in input_columns if self._format_columns is None or col in self._format_columns]\r\n    input_dataset = self.with_format(\r\n        type=self._format_type,\r\n        columns=columns\r\n    )\r\nelse:\r\n    # in this case we could find a way to filter both format_columns and unformatted columns eventually\r\n    input_dataset = self\r\n# then input_dataset can be used in the main for loop of `map`\r\n```\r\n\r\nEDIT: oh and regarding streaming format versus file format for arrow, we plan to start using the file format #1933 at one point (though I'm not sure if it would improve performance)","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":148,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n Looks like a great direction :)\r\nNote that `query_table` doesn't bring data into memory. Only `format_table` does.\r\nAlso the dataset may already have a format with `columns=` already defined so we would need to define the formatted `input_dataset` like:\r\n```python\r\n# before the `map` main for loop\r\ninput_columns = input_columns if input_columns is not None else self.column_names\r\nif not self._output_all_columns:\r\n    columns = [col for col in input_columns if self._format_columns is None or col in self._format_columns]\r\n    input_dataset = self.with_format(\r\n        type=self._format_type,\r\n        columns=columns\r\n    )\r\nelse:\r\n    # in this case we could find a way to filter both format_columns and unformatted columns eventually\r\n    input_dataset = self\r\n# then input_dataset can be used in the main for loop of `map`\r\n```\r\n\r\nEDIT: oh and regarding streaming format versus file format for arrow, we plan to start using the file format #1933 at one point (though I'm not sure if it would improve performance)","embeddings":[-0.1205226481,0.2916710377,-0.0068580708,-0.1955762655,0.1112996936,-0.1071509719,0.2545422614,0.599486053,0.2043481618,-0.0285109952,-0.0340009406,0.4793538153,0.0217377339,-0.2131702602,0.0204914026,0.225736469,0.1094413996,0.354948014,0.2541983724,0.0966203585,-0.1538394839,-0.1982913762,-0.1836565286,-0.1087915748,-0.0737467185,-0.0132568274,0.1467769593,-0.3689957857,-0.3287634552,-0.1571341157,0.298129797,0.335116595,-0.1511103958,0.1153092608,-0.0001276158,-0.0219644252,-0.0039140563,-0.0525664724,-0.1830680817,-0.0591208749,0.1285026968,-0.2101277411,0.0795893446,-0.2305582464,0.0204804,-0.0886209607,0.0288893171,-0.0736887231,0.0148101766,-0.1144906878,0.0261801705,-0.0833349451,-0.3116624951,0.2302701473,0.4493208528,0.1998949051,0.0042839465,-0.1619109958,0.2894332707,-0.4011121392,-0.1172273755,0.5342761278,-0.4963223338,-0.0241851322,0.3617823124,0.0233744979,0.0134989619,-0.1979040802,0.3108482361,0.1128364354,0.225044027,-0.3431988657,-0.1086047068,-0.2292713672,-0.0838759914,-0.269415468,-0.0652150139,0.0585792288,-0.4512800276,-0.0089030201,-0.2625634074,-0.2576969862,-0.0991568193,0.4092383683,-0.5199321508,0.4321989715,0.1646626443,0.2750136256,0.1986683756,-0.219545126,-0.0431810357,-0.0297076963,0.3957521319,0.5969600677,-0.4020283818,-0.2055867016,0.1378192455,-0.0239012912,0.2302290201,-0.2287324965,-0.1678272933,0.3932457268,0.3833607137,0.1029802188,0.3581872582,0.006378524,-0.0522425734,0.6364861727,0.3577840924,-0.1339600235,0.1019664109,0.0639413446,-0.032211531,0.3444749415,0.2406603545,-0.3781087399,-0.1642122865,-0.1726717353,0.0016429852,-0.0048700697,-0.2048109621,0.050323423,0.075477168,0.4536677897,0.2588711381,0.2259848565,-0.1784389615,0.0110360645,-0.2188385576,0.0681120902,0.066275686,0.058855217,-0.0594325513,0.2203586847,0.1725183129,0.1975576878,-0.1392242461,0.0358778499,-0.0783822462,0.0963132307,-0.0028510378,-0.2358976901,0.3039046824,0.4720265269,-0.1210360602,0.3792858422,0.2375580519,-0.216723755,-0.347665906,0.3116398752,-0.1199537367,-0.1991361976,0.1178496629,-0.039899908,-0.1022518724,0.3331950009,-0.0838698894,0.6144601107,0.3624878526,-0.1243353635,-0.1075508147,-0.1345472783,-0.32340464,-0.2182026207,0.373061657,-0.037768282,-0.4412716627,-0.2263315171,-0.150397718,0.2870781124,0.401794672,0.3283833265,-0.1041111872,0.1448107213,0.3399391472,0.4092049301,0.5807511806,0.0020424912,-0.5462443829,0.0586960576,-0.1678364724,0.1042349115,-0.1550773829,0.3828926384,0.6717818975,0.1935446262,0.338942349,0.2051123232,-0.0929735601,0.2500671148,-0.0852625221,-0.1095489711,0.1974383742,0.1217210665,0.0077488422,-0.1483389735,-0.0093657142,0.0399956107,0.0263680834,-0.0251079723,0.1214189082,0.0646109208,0.1171696782,-0.0716163442,0.0811888129,-0.2945955396,-0.2906388342,0.0719172806,0.4683172405,0.1420728415,-0.3408866823,-0.3782500625,-0.1787259281,0.143328771,0.4013044834,0.1306817532,-0.0783435181,-0.3235485256,0.2239701152,-0.1672380716,-0.2348143458,-0.1474268734,-0.0294382703,0.0988392904,0.003334074,0.0025427374,0.2784744501,-0.0976318642,-0.032074526,-0.0948892683,0.2482024878,0.2585181594,0.2177055478,0.0754668862,0.2817989886,-0.1683757156,-0.2904419005,0.4979357719,0.0264392029,0.1684324294,0.1303661019,-0.20779562,0.1808964908,-0.1695896238,-0.4422305822,0.3461422622,0.1428615749,0.6261826158,0.0383623801,0.0879711881,0.1729707569,0.0151635632,-0.1029490754,-0.3706105947,0.0246630087,0.0675802454,-0.1296297461,0.1175779104,-0.0286361109,0.262838006,0.2400072962,-0.0757464692,0.1183066368,0.2568657994,-0.1232646853,-0.1520133466,0.1783413589,-0.1688497663,0.0163515192,0.1746796221,0.138066262,-0.2322896272,-0.1784208417,0.0018266228,0.2231835127,0.1911960244,-0.1504746377,-0.2071059197,0.1783090085,-0.0749041289,-0.3234237432,0.0882717744,-0.0030709892,0.3133063614,-0.2282298654,-0.2052068114,-0.255549252,-0.1922771633,0.3304744959,-0.1014569253,0.0395308957,-0.2535791099,0.3107281327,0.1738023013,0.0868997648,0.2332748622,-0.1845206171,0.3074072599,0.0328975953,-0.2708673775,-0.2649327815,-0.4880022407,0.0622917265,-0.0660041645,0.0105123185,0.434378624,0.2581012845,0.3665306568,-0.2448257804,-0.2507920265,-0.3883439302,0.0792067349,-0.136558488,0.2166884094,0.1166438758,0.5547269583,-0.3538579941,0.0443881303,-0.0130543523,-0.0474988818,-0.0637936145,0.1721643656,-0.0247787423,0.1852326244,-0.0968935937,0.2254283726,0.0812081844,-0.0963438004,-0.1229767874,-0.0644629002,0.3656049371,-0.3716361523,-0.0098516112,-0.2970950603,-0.1427563876,-0.1317302138,-0.1191973835,0.2455678284,0.4853591323,0.2650068104,-0.0503653735,-0.1151808277,-0.1063332558,-0.155970037,0.6325244904,-0.3378854692,0.0153188799,-0.2393226922,0.0202769823,0.1652296036,0.123026289,0.4506605566,-0.0520643629,0.0475989059,0.0480048731,-0.2972130179,-0.2780140638,-0.0886455327,-0.2216757834,0.2424536943,0.644724071,0.3033609986,0.7878698707,0.0838349983,0.1795894206,0.0335374475,-0.0619435869,0.0475945324,0.0101660993,-0.2111126184,-0.2400131226,-0.207214459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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"Good to know about `query_table` not bringing anything into memory. I was under the impression that it did because a while back I looked at my `map` operation in pdb and it looked like it was spending forever in line 93 of formatting.py, `return pa.concat_tables(....)`, although that was before the `fast_slice` interpolation search was implemented, so it may have had more to do with the slow ChunkedArray slice implementation than anything else.\r\n\r\nIf `query_table` is I\/O free then the fix may be as simple as just adding this to line 1779 of arrow_dataset.py:\r\n```python\r\n# Only load the columns we actually need\r\nif input_columns:\r\n  stack.enter_context(self.formatted_as(\r\n    self._format_type,\r\n    columns=input_columns,\r\n    output_all_columns=False,\r\n    **self._format_kwargs\r\n  ))\r\n```\r\nIt's not clear to me why the `[col for col in input_columns if self._format_columns is None or col in self._format_columns]` check would be necessary\u2014 it seems like either `input_columns` should simply temporarily override the `_format_columns` within the `map` operation, or we should throw an error if there are any conflicts. Currently it doesn't look like this case is checked for at all within `map`, but maybe I'm just missing it.","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":181,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n Good to know about `query_table` not bringing anything into memory. I was under the impression that it did because a while back I looked at my `map` operation in pdb and it looked like it was spending forever in line 93 of formatting.py, `return pa.concat_tables(....)`, although that was before the `fast_slice` interpolation search was implemented, so it may have had more to do with the slow ChunkedArray slice implementation than anything else.\r\n\r\nIf `query_table` is I\/O free then the fix may be as simple as just adding this to line 1779 of arrow_dataset.py:\r\n```python\r\n# Only load the columns we actually need\r\nif input_columns:\r\n  stack.enter_context(self.formatted_as(\r\n    self._format_type,\r\n    columns=input_columns,\r\n    output_all_columns=False,\r\n    **self._format_kwargs\r\n  ))\r\n```\r\nIt's not clear to me why the `[col for col in input_columns if self._format_columns is None or col in self._format_columns]` check would be necessary\u2014 it seems like either `input_columns` should simply temporarily override the `_format_columns` within the `map` operation, or we should throw an error if there are any conflicts. Currently it doesn't look like this case is checked for at all within `map`, but maybe I'm just missing it.","embeddings":[-0.1046057194,0.3224443197,-0.0158340186,-0.1899836212,0.0996766686,-0.0926468894,0.2255839109,0.5739011168,0.1345910579,-0.0051863473,-0.0915138796,0.457932353,0.0769863501,-0.2375071645,0.0071079414,0.256791234,0.0978264213,0.3570863008,0.3204873502,0.1195372492,-0.1944888085,-0.2011037469,-0.1671691835,-0.093112193,-0.1043966487,-0.0672499016,0.1529695094,-0.3571105897,-0.3311984241,-0.1571363956,0.2827627957,0.320168972,-0.2016059607,0.12886329,-0.000126842,-0.0501120053,0.0091096619,-0.0208219625,-0.1327055693,0.0189617556,0.204142198,-0.2184470594,0.0786347389,-0.2216875106,0.0527093895,-0.0613879487,0.0214829259,-0.0298988875,-0.0216247085,-0.0822018981,0.0331359431,-0.0527435802,-0.3226946592,0.211495012,0.4797375798,0.1571221501,0.0235043522,-0.2126512825,0.2892075479,-0.4368453622,-0.1270746738,0.5308563709,-0.5316395164,-0.0464950874,0.3427626789,0.049161043,0.0022185585,-0.2007324398,0.3583235145,0.0518088304,0.1605584472,-0.296064347,-0.0898218527,-0.2226341069,-0.0841382965,-0.2170465142,-0.066492714,0.0542102233,-0.456191659,-0.0182688907,-0.2613746524,-0.2506407201,-0.0523681492,0.3157967031,-0.5186735988,0.423607558,0.1705063879,0.2459537834,0.3076338768,-0.2111773044,-0.0427854173,0.0078033293,0.4094476998,0.5418298244,-0.3882897198,-0.1447073519,0.2166091651,-0.0046076714,0.2440500259,-0.2781878114,-0.1783222258,0.3794896901,0.4178121984,0.0922844037,0.3731509149,0.0572141968,-0.1314653903,0.6420698166,0.3668268919,-0.159287259,0.0897910744,0.082753852,0.0434356108,0.379861623,0.1911718845,-0.433431685,-0.1847686768,-0.1714393944,-0.0253485925,-0.0084876474,-0.1762731075,0.1174384728,0.0855485722,0.4891795516,0.2316110134,0.225787282,-0.1678627431,-0.0405790918,-0.2845214903,0.0734631494,0.0280695148,0.0345130339,-0.0512352437,0.2307008952,0.1601486057,0.1830994487,-0.1646120995,0.0152628208,-0.0722632706,0.1157179847,-0.0101245558,-0.2799978554,0.3596175015,0.5151501298,-0.1593509465,0.3464233577,0.21702604,-0.2327194214,-0.3488967121,0.3377682865,-0.132456243,-0.2263544947,0.1486907899,-0.0374891236,-0.0669678897,0.3339708149,-0.0396511108,0.6287926435,0.3945141435,-0.1595231593,-0.1377298385,-0.1415912062,-0.3631263077,-0.2324801534,0.4056436718,0.0062037702,-0.413715601,-0.266879946,-0.1262893677,0.3257215023,0.4485503733,0.4173039198,-0.0687523037,0.1465699822,0.3328170776,0.4406389892,0.4901854396,0.0109500466,-0.5700111389,0.0405080914,-0.1876828223,0.0902732089,-0.1475191861,0.3816785216,0.636492312,0.2377496064,0.3549273908,0.1779022068,-0.0855331644,0.2613480687,-0.1656879336,-0.1175130457,0.2443193942,0.1028595716,0.0680371821,-0.1726322323,-0.0305090565,0.0317019559,0.0234105289,0.0258355457,0.0950574651,0.0940840617,0.1272767931,-0.0584882908,0.0846121833,-0.2734279931,-0.2215220779,0.0759864673,0.4218752384,0.1808428466,-0.3522837162,-0.3539554179,-0.146749258,0.1585353613,0.3680203855,0.0825599059,-0.0650566891,-0.3180902302,0.1961794198,-0.1326883584,-0.226525411,-0.1426741481,-0.0524415523,0.0599178001,-0.0610491037,-0.0308259036,0.2068485469,-0.1080193147,-0.0747326314,-0.0408544727,0.1979223639,0.3113134205,0.1773786992,0.0573525243,0.2771246731,-0.2035668045,-0.2185436636,0.4961541891,0.0398581401,0.1094273478,0.1492642164,-0.2200749069,0.1322612911,-0.2009371817,-0.4113146961,0.3036414981,0.1208436266,0.6467670202,0.0456714258,0.0394136831,0.1866068095,0.0499052294,-0.0209066793,-0.346706599,0.0559415258,0.1037927717,-0.0900080651,0.185476467,0.0254997667,0.2736612856,0.2416229844,-0.0639337227,0.0869315565,0.2822878957,-0.0984455645,-0.2455402613,0.1920063645,-0.2486240715,0.0268249512,0.2137017101,0.1858732104,-0.2289258838,-0.1750144958,-0.0132448021,0.2093530297,0.2164669335,-0.185696736,-0.2287490219,0.1939637363,-0.051694721,-0.3180813193,0.1175254956,-0.0644953325,0.2998574376,-0.2273163348,-0.2105186582,-0.2503575683,-0.1601746827,0.377016753,-0.0750863627,-0.012997997,-0.2862330973,0.3427686095,0.1238389239,0.1124928966,0.2113857716,-0.2102439255,0.3423673511,0.0275171399,-0.2496317774,-0.2695373595,-0.5140575767,0.0300203655,-0.0644229054,-0.0016973639,0.4669359028,0.2170314342,0.3613241315,-0.2467996925,-0.1738606691,-0.4038601816,0.058160685,-0.1109809652,0.2604952157,0.1097898111,0.481426537,-0.3645493686,0.0615465492,-0.0480229072,-0.1158276647,-0.0675555468,0.1509262174,-0.0420305543,0.1865849793,-0.0737376511,0.2256578952,0.0664133132,-0.1036398038,-0.067420207,-0.1274869144,0.3572297692,-0.3141646981,-0.0175234694,-0.217801407,-0.1374755055,-0.0886398554,-0.1316664964,0.2906396687,0.4122536182,0.2820613086,-0.0256648976,-0.1306436658,-0.0831021518,-0.1489645243,0.5557096601,-0.3557236195,0.0762016997,-0.2389134169,0.0555393249,0.1153746843,0.1321066916,0.430894047,-0.0446397066,0.0358335897,0.0726099983,-0.3384998441,-0.2934176326,-0.0454604551,-0.1858828515,0.2047918439,0.5843628049,0.2935068607,0.7721477747,0.0733563155,0.2170889527,0.0129311299,-0.0168373156,0.0995332152,-0.067567043,-0.2521345615,-0.2696581185,-0.2066072673,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"`query_table` simply slices\/concatenates parts of the table. The actual data inside the table is not brought in memory.\r\nAlso I'm more in favor of declaring `input_dataset = self.with_format(...)` since `formatted_as` may update the dataset fingerprint of `self`, which is not expected when someone runs `map`.\r\n\r\n> It's not clear to me why the [col for col in input_columns if self._format_columns is None or col in self._format_columns] check would be necessary\u2014 it seems like either input_columns should simply temporarily override the _format_columns within the map operation, or we should throw an error if there are any conflicts. Currently it doesn't look like this case is checked for at all within map, but maybe I'm just missing it.\r\n\r\nActually yes we can just use input_columns. And we do need to add a check to make sure there are not conflicts or this could lead to confusing errors.","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":145,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n `query_table` simply slices\/concatenates parts of the table. The actual data inside the table is not brought in memory.\r\nAlso I'm more in favor of declaring `input_dataset = self.with_format(...)` since `formatted_as` may update the dataset fingerprint of `self`, which is not expected when someone runs `map`.\r\n\r\n> It's not clear to me why the [col for col in input_columns if self._format_columns is None or col in self._format_columns] check would be necessary\u2014 it seems like either input_columns should simply temporarily override the _format_columns within the map operation, or we should throw an error if there are any conflicts. Currently it doesn't look like this case is checked for at all within map, but maybe I'm just missing it.\r\n\r\nActually yes we can just use input_columns. And we do need to add a check to make sure there are not conflicts or this could lead to confusing errors.","embeddings":[-0.0905099362,0.2783811986,-0.0073054857,-0.1781735718,0.1408671141,-0.0961291492,0.3184326887,0.5706532598,0.1868227869,-0.0280762687,-0.0586419851,0.4846483767,0.0940666646,-0.2658150494,-0.0045916475,0.2481260002,0.100163646,0.3244252205,0.2597131133,0.0609557107,-0.1716758907,-0.2206242532,-0.2154829949,-0.0906187817,-0.0869437084,-0.0568239987,0.1601068228,-0.375895083,-0.3585896492,-0.1589115411,0.2664770782,0.3161299825,-0.2051470429,0.1349192262,-0.0001272232,-0.0201998558,0.0467341878,-0.037327908,-0.1995202601,-0.0664869398,0.1474398226,-0.1848529577,0.1072840989,-0.2459902614,0.059843298,-0.1259113997,0.0318901353,-0.0521755069,0.0090082446,-0.1114037856,0.0332686491,-0.0471821167,-0.3189130723,0.2509446144,0.4844817817,0.2142710388,-0.0029356712,-0.1754944623,0.2836517394,-0.3993281424,-0.1439495385,0.5009637475,-0.4719651043,-0.0450852662,0.3814987838,0.0277342685,0.0133877341,-0.197485283,0.3342888355,0.0723303556,0.2053318173,-0.3297653198,-0.0984181762,-0.2013444602,-0.0895540863,-0.2471139282,-0.0652233213,0.0728401765,-0.419649601,-0.006134755,-0.2666519284,-0.2152370214,-0.0831532106,0.3996171355,-0.4749021232,0.4343481064,0.1701668203,0.3172897398,0.1965943128,-0.1460077763,0.0056473636,-0.0809224248,0.4420045912,0.5432040691,-0.4257611334,-0.139276132,0.1378888637,0.019359678,0.2514255643,-0.2275911272,-0.1442237943,0.3652479053,0.3820867538,0.1383820772,0.3823768198,0.0552436523,-0.0678869411,0.5654723048,0.340166539,-0.1287790686,0.099251166,0.0866193026,0.0072611244,0.3771412671,0.2361530215,-0.397176981,-0.1501549929,-0.1441699862,-0.0033347334,-0.0359203704,-0.2364974469,0.0379734635,0.1146311089,0.4467284083,0.217115432,0.2219400555,-0.2395924926,0.0069658682,-0.2428150475,0.0167142022,0.0263271052,0.0361496843,-0.0885544717,0.1784718335,0.1263100952,0.1700144112,-0.1283931285,0.0416778587,-0.0802163482,0.1100666523,0.017849965,-0.2410555631,0.304330349,0.5060296655,-0.1270371079,0.4151405692,0.2337826341,-0.2551225126,-0.354177922,0.3156724274,-0.2056185156,-0.2452472001,0.1567534506,-0.0432886854,-0.0533226468,0.3144853413,-0.0819184855,0.5806527138,0.3763392568,-0.1514410079,-0.1035860702,-0.1623917818,-0.3125413656,-0.2653497756,0.3400167227,-0.0321212113,-0.4576417804,-0.2199665606,-0.1748510599,0.2500417233,0.4678746164,0.4039694667,-0.1157769188,0.1306847185,0.3230548203,0.4230688512,0.5786601305,-0.0044513447,-0.6051669121,0.0456339903,-0.1910752654,0.1302179098,-0.1258923858,0.3633518517,0.6630903482,0.1699456722,0.3502970338,0.208381623,-0.0832398906,0.2332906574,-0.1320047528,-0.0992477015,0.2240942717,0.0698581189,-0.0314692035,-0.1580111235,0.0432081893,-0.0146944486,0.012032832,-0.0307444688,0.1495976001,0.0352642536,0.1225036234,-0.0793454126,0.0968361646,-0.1701586097,-0.3151438236,0.0833316073,0.4757758677,0.1626962572,-0.3184887767,-0.4162273705,-0.1717945784,0.1589773893,0.3474315107,0.0717163458,-0.0883723497,-0.3062795699,0.1927574575,-0.1530602127,-0.2230236679,-0.1582785845,-0.0786756203,0.0918850824,-0.0018242518,-0.0461274311,0.2228624672,-0.0545991175,-0.0833940431,-0.0686212406,0.2227339447,0.2268548459,0.169095695,0.1006862223,0.3023964465,-0.2007376552,-0.3022830486,0.4918423891,0.0368731432,0.1524703801,0.1140558422,-0.2299974263,0.1563223302,-0.2239600271,-0.4133352041,0.2558535933,0.1995146126,0.6288716793,0.0527716987,0.0841597617,0.1763401031,0.0580587834,-0.0930086747,-0.3284853697,-0.0237520132,0.0519340262,-0.0684548616,0.1705927998,-0.0424999483,0.2811124325,0.3036043346,-0.0635403022,0.1217754781,0.2883030176,-0.1449168772,-0.220800966,0.2054200619,-0.1809400618,0.0339856185,0.1745779663,0.1639837474,-0.2133012861,-0.1876698881,-0.0083754892,0.199229762,0.2124735266,-0.1250440478,-0.2343129963,0.2074477971,-0.0626442581,-0.3354994357,0.0732227564,0.0212960802,0.2981348336,-0.2283177823,-0.1893731803,-0.2869470417,-0.1307526976,0.3210741282,-0.0955272242,0.0365262292,-0.2719177008,0.3166380525,0.1489036679,0.1104646474,0.2225836664,-0.0962288603,0.3324805498,0.0444538556,-0.2473369241,-0.2861946821,-0.4948458374,0.0235192161,-0.0699812695,0.0105353817,0.3992695808,0.2383036762,0.3589071631,-0.2722924352,-0.2329825312,-0.4402537048,0.0664399862,-0.1753516942,0.2203930467,0.1014787927,0.5082466602,-0.3530637026,0.0723143592,0.0008109203,-0.0886769667,-0.1040697321,0.1476087868,-0.0548230857,0.1584558636,-0.1324396282,0.2103565186,0.0645837039,-0.1025527269,-0.088569425,-0.1085603088,0.3564195335,-0.3576383591,-0.0122366296,-0.2859368622,-0.115928553,-0.0990879908,-0.1391461939,0.211953029,0.443318665,0.2500690818,-0.0770412982,-0.1258710325,-0.0833321437,-0.1012141854,0.6114694476,-0.364541024,-0.0142550459,-0.2090164423,0.0716235638,0.1647153497,0.1409079283,0.4514619112,-0.0423866212,0.0590075627,0.0534386039,-0.3214302361,-0.2903229892,-0.0478871129,-0.2004417479,0.2138630152,0.6448985338,0.309388876,0.8403735161,0.1130487919,0.1725102067,0.0337036103,-0.0510696247,0.0815417692,-0.0633322522,-0.2036994398,-0.2114545405,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2193","title":"Filtering\/mapping on one column is very slow","comments":"That sounds good to me! I just submitted a PR (#2246) implementing your approach. I also changed how `_query_table` handles Iterable keys since it still seemed like `pa.concat_tables` was taking a long time to create the table for each batch. Now my whole `map()` operation takes 1 min 46 seconds where it used to take somewhere on the order of 10 minutes.","body":"I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.","comment_length":62,"text":"Filtering\/mapping on one column is very slow \n I'm currently using the `wikipedia` dataset\u2014 I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.\r\n\r\nI want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that\u2014 I'm not very familiar with the pyarrow API.\r\n\r\nI know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.\r\n\r\nPS: This is definitely not a \"dataset request.\" I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. \n That sounds good to me! I just submitted a PR (#2246) implementing your approach. I also changed how `_query_table` handles Iterable keys since it still seemed like `pa.concat_tables` was taking a long time to create the table for each batch. Now my whole `map()` operation takes 1 min 46 seconds where it used to take somewhere on the order of 10 minutes.","embeddings":[-0.1737178862,0.330304265,-0.0151500572,-0.2352599353,0.0967264846,-0.1373569518,0.2451967895,0.6020462513,0.121574536,-0.0561254472,-0.0701213703,0.5131320357,0.0704808384,-0.2048014849,-0.0179261304,0.2067434043,0.0910430774,0.3320176601,0.3077172935,0.1418736279,-0.12450964,-0.1790834367,-0.1606733501,-0.0990440995,-0.0650921762,-0.0865211338,0.1429021358,-0.4043100178,-0.3323493898,-0.2287281901,0.3221342862,0.3873366117,-0.1767820269,0.1415216476,-0.0001248356,-0.0549316108,0.014044716,0.0137422355,-0.1824400872,-0.0295471847,0.1309021264,-0.2167410254,0.0755152702,-0.1730611324,0.0476248302,-0.1083694547,0.0306255519,-0.0478271581,0.0215574764,-0.0948138908,0.0412021503,-0.0476073287,-0.2681420147,0.2097008824,0.4645839036,0.1935361326,-0.0502106026,-0.1640952975,0.3533452749,-0.4462712109,-0.1411142349,0.5246965885,-0.4593464434,-0.0403555036,0.3705934584,0.0064206352,0.0693932325,-0.2104827464,0.2937604785,0.1523418128,0.1825716347,-0.3065946698,-0.1353577971,-0.2616470158,-0.0473709777,-0.2595102489,-0.060145285,0.045671083,-0.4410809577,-0.0086555965,-0.2520543635,-0.2057042569,-0.0832181573,0.3169912994,-0.4233549237,0.4046776891,0.2104633749,0.2437730879,0.2672819197,-0.2661036551,0.0122516463,0.0007127369,0.3663119674,0.5617972016,-0.4556772411,-0.1941936314,0.1578668803,-0.0031945738,0.3044346571,-0.243067503,-0.2232784033,0.4180018902,0.3920199871,0.0452779494,0.3689549565,0.0754629374,-0.0427866392,0.5948009491,0.3502455354,-0.09752886,0.0398624092,0.0979136527,0.0036888297,0.2870084643,0.2049080431,-0.3669087291,-0.1987364441,-0.1619669795,-0.0005876391,-0.0147799784,-0.169925496,0.022249965,0.1173086539,0.455984652,0.2418352216,0.2840332687,-0.2097379118,0.0206912067,-0.2422883809,0.0668707862,0.065980494,-0.0030266629,-0.1116452366,0.2152013183,0.1996005774,0.1780086756,-0.2033906281,0.0543159805,-0.0201782323,0.120637022,-0.0231195688,-0.2095009536,0.2548909187,0.4586892128,-0.1261474192,0.3494060338,0.2085407227,-0.2365360111,-0.3625700772,0.2813225985,-0.1292225868,-0.2616797686,0.1350033134,-0.0050355126,-0.0949928686,0.2969782948,-0.0937212929,0.5968532562,0.4027432203,-0.105794318,-0.1509811282,-0.1515808553,-0.303047806,-0.1957915276,0.3636776209,-0.0224481896,-0.4293302298,-0.1934003085,-0.130592674,0.3211943209,0.4375316501,0.4082219005,-0.1090678722,0.1430213749,0.369586736,0.3730922341,0.5056322217,0.0012932747,-0.5878244042,0.0985966027,-0.1475342661,0.077212885,-0.0818872824,0.3269074261,0.6758551002,0.1778357327,0.3605339825,0.1743607372,-0.0981857553,0.269028157,-0.1825914681,-0.1666394174,0.2102351338,0.1413112283,-0.0145347081,-0.1957734823,-0.0167291071,-0.0238866657,0.0183546543,-0.0340095572,0.1450583637,0.0538885221,0.1372446567,-0.0590700805,0.1375439316,-0.2296515107,-0.2415961921,0.0802753344,0.3720583916,0.2029228061,-0.394905448,-0.4120774269,-0.1733501405,0.1774894595,0.340257585,0.0905264392,-0.046565529,-0.3512777388,0.2362405062,-0.1481235325,-0.2082928121,-0.1503553391,-0.0387831889,0.0938901827,-0.0214913618,-0.0832755193,0.1942667216,-0.0556214489,-0.011073133,-0.0742620826,0.2472742945,0.25627473,0.158920303,0.0734024346,0.252461493,-0.1650274694,-0.1919422597,0.4814751446,0.0881001502,0.1320402473,0.1218481883,-0.2094859928,0.0983595178,-0.1908242404,-0.4711273611,0.3587895632,0.1109310985,0.683293283,0.0562724248,0.077021949,0.2420536578,0.0231919345,-0.0858704448,-0.3186715543,0.053463567,0.0744462162,-0.0931241438,0.134412393,0.0041623744,0.3699188828,0.2097867131,-0.048275914,0.0577067174,0.2880360782,-0.11212942,-0.2076292783,0.1912911087,-0.134046793,0.0256762952,0.1986670345,0.12500301,-0.2327360362,-0.1954657435,-0.0191463344,0.2358425856,0.1909601688,-0.1508255452,-0.213441506,0.2186042517,-0.0437209606,-0.3614638448,0.0391206034,-0.0381608158,0.3463041484,-0.2217038721,-0.1943615675,-0.2746430635,-0.1993744671,0.3418702781,-0.0573148467,0.0268589407,-0.2546793818,0.3636475205,0.147611931,0.1041288599,0.2501795888,-0.1866698563,0.2861469984,0.0559879802,-0.2362312078,-0.3087218106,-0.5034463406,0.0297077745,-0.0576346852,0.0336736441,0.3986271918,0.2478462309,0.3540845811,-0.2575275004,-0.235790506,-0.425932169,0.0515650474,-0.1574469656,0.2065596879,0.1195913553,0.5191636086,-0.3392128348,-0.0219038222,0.014687025,-0.1095936075,-0.0687238351,0.1560565531,-0.1134048998,0.1369801909,-0.0930724591,0.1554458141,0.0553491674,-0.1131982654,-0.0398740992,-0.0548620597,0.3683741987,-0.3320026994,0.0155543955,-0.3100392818,-0.1693540215,-0.1076868698,-0.1219562516,0.246271342,0.4157121778,0.2412174493,-0.0508850962,-0.1137241051,-0.0976725444,-0.1423163414,0.5603023767,-0.288372308,0.0036245259,-0.2501584888,0.0819394365,0.1499505639,0.1256531924,0.41136989,0.0233376138,0.0292191356,0.0727467611,-0.2747860849,-0.265996784,-0.1089745685,-0.1483224332,0.2204151005,0.5995185375,0.3056702018,0.8259323835,0.1461455673,0.2041177303,0.0113568408,-0.0393553376,0.0351681225,-0.0617045499,-0.2360727042,-0.2448732853,-0.2205196619,-0.073296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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2190","title":"News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs","comments":"Hi @anassalamah,\r\n\r\nCould you please try with this:\r\n```python\r\ntrain_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[98%:]')\r\n```","body":"I used load_dataset to load the news_commentary dataset for \"ar-en\" translation pairs but found translations from Arabic to Hindi.  \r\n\r\n```\r\ntrain_ds = load_dataset(\"news_commentary\", \"ar-en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", \"ar-en\", split='train[98%:]')\r\n\r\n# filtering out examples that are not ar-en translations but ar-hi\r\nval_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True)\r\n```\r\n\r\n* I'm fairly new to using datasets so I might be doing something wrong","comment_length":22,"text":"News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs \n I used load_dataset to load the news_commentary dataset for \"ar-en\" translation pairs but found translations from Arabic to Hindi.  \r\n\r\n```\r\ntrain_ds = load_dataset(\"news_commentary\", \"ar-en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", \"ar-en\", split='train[98%:]')\r\n\r\n# filtering out examples that are not ar-en translations but ar-hi\r\nval_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True)\r\n```\r\n\r\n* I'm fairly new to using datasets so I might be doing something wrong \n Hi @anassalamah,\r\n\r\nCould you please try with this:\r\n```python\r\ntrain_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[98%:]')\r\n```","embeddings":[-0.1603863835,0.0044462634,-0.2170423716,0.2492842227,-0.0537915379,0.08853852,0.3200059533,0.2293999642,-0.025391113,-0.235799253,-0.2461355627,0.1912741661,0.1839801073,0.2749135792,-0.0599651821,-0.3076826334,0.0750906318,-0.1572856307,-0.0141737703,-0.1957393885,-0.1186241284,0.4341784418,-0.185517773,0.0662630275,-0.0329696573,-0.0440256409,0.0081097521,-0.0337008461,0.1106164008,-0.1234146133,0.0804368034,0.2322270125,0.1669614017,0.0935731381,-0.000112882,0.0626912713,0.1198943853,-0.1591492891,-0.1555098444,-0.29826653,-0.1329162717,-0.2726258934,0.1110204533,-0.2844849527,-0.1342525035,-0.4882697463,-0.2545556724,-0.5316694975,0.5619793534,0.4927171171,0.2283712476,0.1886770427,-0.0293710399,-0.0626298636,0.0920654163,-0.1563192457,0.1393924356,0.3267943859,0.3788948655,0.233580485,-0.1417146772,0.4323812127,-0.2732176185,-0.1382027715,-0.3607785702,-0.0472382531,0.2317581177,-0.4424891472,0.2594132721,0.209234193,0.4308646619,-0.197879985,-0.3025541902,-0.1495145559,-0.124740459,0.0732997134,0.0783393085,0.1791186482,-0.2363340408,0.4208522141,0.1015166715,-0.1204006001,0.2136509866,0.3579363525,0.0695387945,0.0383974724,0.0634348392,0.1177145913,0.0055171782,-0.2202454656,0.1597228795,-0.1910922527,0.0891226754,0.3495159745,-0.3829525113,0.0538152009,-0.2302294374,-0.1596407443,-0.1328743845,-0.2417428195,-0.2591854632,0.2534895241,-0.1386710703,0.0434368029,0.0932815373,0.0334969424,0.2530211508,0.2435534298,-0.0637139454,-0.158026129,-0.1277534217,0.3061584532,-0.0908975825,-0.3678646386,-0.2495773733,-0.0187953915,0.010044408,-0.1621397883,-0.4117812812,0.1917952746,-0.540441215,-0.4620901346,-0.0832575038,0.1982965171,0.1635905951,0.1188690662,0.1320028305,0.3674795926,-0.1292291433,-0.3483611643,-0.2025290281,0.1246428639,-0.2258711755,-0.0809420794,0.1155193523,-0.1200007945,0.3928953409,0.0889490098,0.0468147174,-0.2268930525,-0.0558341555,-0.2146737576,-0.0459015965,-0.0501574203,0.1979777366,0.3317025602,0.2548564374,-0.2848145664,0.0236388408,0.3267103136,-0.4435573518,0.310405314,-0.1882556379,0.1667381823,-0.1077538133,-0.0085980082,-0.1697164774,0.6289420128,0.3114931583,-0.0870607495,-0.0063190805,-0.0197960045,-0.146292299,-0.0890332833,0.0914800093,0.268612206,-0.7350516915,-0.1571094692,0.2101731151,-0.0272703432,0.1780678034,0.4736939371,-0.2419959754,0.2404444814,-0.2692958415,-0.1279771924,0.35891819,-0.0183163602,-0.1526155323,0.0628183484,0.1232641637,0.1729451716,0.2040342242,0.1173776165,0.2221214473,-0.0477190502,0.2874537408,0.3555460572,0.182118699,0.0138756931,-0.1741799116,-0.1975986958,0.8043532968,0.1991026849,0.3475913703,0.0937266052,0.151637882,0.0677664429,0.4992110729,0.0608986244,0.0599411763,0.2085180134,-0.3930371106,-0.0078786854,0.0847025439,-0.0669237226,-0.1784451306,0.0225504972,-0.1481734514,0.2782815993,0.0313991494,0.0713068023,-0.5101252198,-0.2441058159,-0.228397429,0.1128571257,0.174093917,-0.0561848059,-0.2003445178,0.3978535533,0.0161955133,0.3494778872,-0.395316422,-0.0775607303,-0.4699580371,0.5394413471,0.1512416005,-0.2488870919,0.0358652435,0.1230579317,0.232458055,0.0670663863,-0.2001377642,0.033492513,0.2786709666,0.3482088149,-0.0507935137,-0.0064074779,0.2219891548,-0.5121762156,0.1271721572,0.3674887419,0.0434743389,-0.0011217565,0.0626947284,0.5724955201,-0.068531014,0.1539771706,-0.0435155146,0.0383259021,0.4436298907,0.0863988623,-0.189714998,-0.3766207993,0.150515601,0.1976629198,0.3643490076,0.4173545837,-0.1284819394,-0.0297192428,0.4801755846,0.0456367433,0.1539311409,0.2072928995,-0.0988580957,-0.10108006,-0.0438236892,0.1428357959,0.0806832388,0.2600463331,0.0762111992,-0.0822421461,0.1274817735,-0.1235573962,0.2154759467,-0.0048621707,0.2227825224,-0.0436737426,-0.0389345624,-0.1674813032,-0.3917497694,0.2549133599,0.091762282,0.2471168339,-0.3253046572,0.0379047133,-0.3931705654,-0.4345546663,-0.3691056073,-0.337320596,-0.1522034109,-0.2440908402,0.0691450164,-0.3370831907,-0.0516580306,0.273757726,0.0989735276,-0.1359253079,-0.0055580237,-0.1260436922,-0.0266693514,-0.281082809,-0.1618700176,0.1251725554,0.2314505726,0.0707543939,0.1313434243,-0.4163937867,-0.474951297,-0.119828105,-0.4053294361,-0.0478653796,-0.2889056802,0.1879488379,-0.1676564068,0.3506744206,-0.121194616,-0.3291274309,0.1718019843,0.3448718786,-0.173918128,0.0501891524,-0.1361021101,0.1287710518,0.0959296823,-0.7553024888,-0.4478923082,-0.18588081,-0.2732867599,-0.1029555127,0.137365818,-0.2782262862,0.1378390044,-0.0699487999,-0.0187814608,0.0390545577,-0.4745860696,-0.1095813587,0.2636788785,0.0078690508,-0.2437195331,-0.1009240299,-0.0027440989,0.1687009335,-0.1457637101,-0.1177996546,0.1816825122,-0.1753924638,0.2152718604,0.0845397934,0.0549144745,-0.0229640584,-0.071428068,0.065899387,-0.0468740687,-0.2789111137,0.1696225405,-0.0225339681,0.1660208255,0.0673169866,0.3073378503,-0.2233282328,0.4089117348,0.2515799999,-0.1983568519,0.4292526543,-0.0283505823,0.0025143488,-0.1306345463,-0.3173961639,-0.12879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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2190","title":"News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs","comments":"Hello @albertvillanova, \r\n\r\nThanks for the suggestion. I didn't know you could do that. however, it didn't resolve the issue\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/8571003\/114169966-ec819400-993a-11eb-8a67-930f9a9b2290.png)\r\n","body":"I used load_dataset to load the news_commentary dataset for \"ar-en\" translation pairs but found translations from Arabic to Hindi.  \r\n\r\n```\r\ntrain_ds = load_dataset(\"news_commentary\", \"ar-en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", \"ar-en\", split='train[98%:]')\r\n\r\n# filtering out examples that are not ar-en translations but ar-hi\r\nval_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True)\r\n```\r\n\r\n* I'm fairly new to using datasets so I might be doing something wrong","comment_length":20,"text":"News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs \n I used load_dataset to load the news_commentary dataset for \"ar-en\" translation pairs but found translations from Arabic to Hindi.  \r\n\r\n```\r\ntrain_ds = load_dataset(\"news_commentary\", \"ar-en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", \"ar-en\", split='train[98%:]')\r\n\r\n# filtering out examples that are not ar-en translations but ar-hi\r\nval_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True)\r\n```\r\n\r\n* I'm fairly new to using datasets so I might be doing something wrong \n Hello @albertvillanova, \r\n\r\nThanks for the suggestion. I didn't know you could do that. however, it didn't resolve the issue\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/8571003\/114169966-ec819400-993a-11eb-8a67-930f9a9b2290.png)\r\n","embeddings":[-0.2070920914,0.1082228944,-0.1750500053,0.325787425,-0.1173716411,0.1063093469,0.2994769812,0.2640418708,-0.0422107913,-0.2637794614,-0.2876567245,0.2302991599,0.2286696136,0.1873274744,-0.0158621632,-0.187350899,0.0602838621,-0.1162408143,-0.0766713321,-0.2078409791,-0.1206930727,0.4833289087,-0.1554276943,0.0874612927,-0.0198556017,-0.075730741,0.1117976755,-0.0085346177,0.0858545899,-0.0731238946,0.0822786689,0.2614829242,0.1084483415,0.0069688694,-0.000112457,0.0388533138,0.1481903791,-0.1804726124,-0.1509228647,-0.2352927923,-0.189953357,-0.1916122437,0.0494393408,-0.2331949621,-0.0486273542,-0.5036552548,-0.259227097,-0.6031265855,0.4965833724,0.4825668931,0.2310813069,0.1592324823,-0.080241695,-0.0408687107,0.113849245,-0.0614906475,0.1242374107,0.3094267547,0.4024916589,0.284763515,-0.2044501007,0.4041460156,-0.2282712162,-0.1140778661,-0.3524553478,-0.1065054387,0.1520601958,-0.3482451439,0.2825841308,0.2758645415,0.5080477595,-0.1678182334,-0.1993252486,-0.163642168,-0.1394638419,0.2181403637,0.1707418412,0.1603756845,-0.2545471489,0.4165382981,0.0235113278,-0.1333047599,0.2564955056,0.2740854621,0.0819528922,0.0065442226,0.0261745285,0.1473970413,-0.0265232809,-0.2402652949,0.2194836289,-0.1835362762,-0.0394179709,0.3342429698,-0.4208230078,0.0948475897,-0.2715766132,-0.1111835316,-0.0361876003,-0.2702115476,-0.2159671932,0.229037717,-0.2124342471,0.0176820308,0.1101568937,0.0204241443,0.2749475539,0.1409305483,-0.1145376712,-0.2219070792,-0.0550830439,0.3491464257,-0.0747398958,-0.3854444325,-0.3508569896,-0.0075297602,-0.0393993296,-0.1918295175,-0.4289531708,0.1837588251,-0.4606366456,-0.4549331665,-0.0740870312,0.1213783026,0.1983214766,0.0735761821,0.1456255168,0.3672660589,-0.1184895337,-0.5046819448,-0.1860050559,0.0585050359,-0.2199442685,-0.0350082293,0.1084139049,-0.038663514,0.3271984756,0.0896235108,0.0091534154,-0.2344892323,-0.067808263,-0.207544446,-0.0875821188,-0.0745326057,0.1588890702,0.3607528806,0.1669569463,-0.2650854588,0.0145604722,0.3440955579,-0.4482043982,0.3166036308,-0.207299605,0.135767594,-0.2083462775,-0.0145419855,-0.211677447,0.7168802023,0.2936292589,-0.1661977172,-0.0369467214,-0.0093934815,-0.1457163543,-0.0237391237,0.0767944381,0.336155355,-0.8569787145,-0.1413990408,0.2179490775,-0.0559222661,0.1649335325,0.4651129246,-0.252130419,0.2206267416,-0.2703158855,-0.0620368198,0.2639403343,0.1395991594,-0.1155772582,0.0304961447,0.1279390603,0.1764543802,0.1702403426,0.0675706938,0.2028357983,-0.0669738427,0.2557018101,0.3319889009,0.204748407,0.0191822704,-0.2515000105,-0.2176717073,0.7432216406,0.1392657906,0.3220184743,0.0856108293,0.2037649751,0.1042031348,0.4789544344,0.0701840892,0.1100987792,0.2135412693,-0.4879893363,0.0136606405,0.1181341186,-0.0065688314,-0.2372851819,0.052592013,-0.1922469139,0.2402575761,0.0976162478,0.0946607143,-0.5183297396,-0.2205010653,-0.1496953517,0.1412650645,0.1538337171,-0.0047622141,-0.3061053753,0.4099654555,0.0166393053,0.3181897402,-0.4055170715,-0.141106382,-0.5065098405,0.5023491383,0.2046554685,-0.1949003339,0.0360704511,0.0416821353,0.2079787701,0.0284688175,-0.2528675497,0.0272006933,0.190356642,0.4163331389,-0.0680365562,0.0285491943,0.1770003736,-0.5377523899,0.1077106446,0.270442754,-0.0166983884,-0.0243043397,0.0595322587,0.5245122313,-0.0873628557,0.1252810508,-0.1490870118,0.0368919112,0.4804258645,0.106395483,-0.1384664774,-0.3812029958,0.1789486408,0.2783130705,0.3480254412,0.5137357116,-0.0483732559,0.009833049,0.4501891434,0.1022343412,0.1360550225,0.2385698706,-0.1030573994,-0.1707395166,-0.037024498,0.1684930772,0.0658082291,0.1991298646,0.0308090486,-0.0914862007,0.1579202414,-0.1339720339,0.1961496025,-0.0373551548,0.3182069063,-0.014592614,-0.0755701885,-0.1697480381,-0.4137766659,0.2959269881,0.2264300138,0.2362146825,-0.3682640195,0.0184783004,-0.4315112829,-0.4144338667,-0.3770637512,-0.2337869108,-0.2051527649,-0.2250044793,0.1289830655,-0.3780675232,-0.0888224021,0.2565278709,0.1377760768,-0.0182585418,-0.0960767642,-0.0535562709,-0.1146815792,-0.2418716401,-0.2124231458,0.123713918,0.2201943398,0.0656659007,0.0410545357,-0.3897136152,-0.4834827483,-0.1878939718,-0.410392344,-0.0015650811,-0.2613317966,0.1828640848,-0.1427705139,0.3141559064,-0.0985723808,-0.2879113555,0.0858922899,0.3214446604,-0.1512869149,0.076855205,-0.1768757403,0.142793119,0.0454226062,-0.7031232119,-0.3522700667,-0.165628776,-0.2152815461,-0.1919798553,0.1147099882,-0.3081685901,0.0880977362,-0.0563028604,-0.0772619024,0.0004884935,-0.5082883239,-0.1679225266,0.2492828369,0.0321363918,-0.230691582,-0.1206715107,0.0442688651,0.1717723012,-0.1149341464,-0.1849770099,0.2866252661,-0.1248209924,0.2030000091,0.0720046535,-0.15070647,-0.0101025114,-0.1209734455,0.0821487382,-0.0942245722,-0.2967146933,0.2079440951,-0.0237842016,0.2100845128,0.0603336543,0.2478287071,-0.202966556,0.33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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2189","title":"save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object.","comments":"Hi ! We refactored save_to_disk in #2025 so this doesn't happen.\r\nFeel free to try it on master for now\r\nWe'll do a new release soon","body":"As you can see, it saves the entire dataset.\r\n\r\n@lhoestq \r\n\r\nYou can  check by going through the following example,\r\n\r\n```\r\nfrom datasets import load_from_disk,concatenate_datasets\r\n\r\nloaded_data=load_from_disk('\/home\/gsir059\/HNSW-ori\/my_knowledge_dataset')\r\nn=20\r\nkb_list=[loaded_data.shard(n, i, contiguous=True) for i in range(n)]\r\nfinal_dataset=concatenate_datasets([kb_list[1],kb_list[2]])\r\nfinal_dataset.save_to_disk('\/home\/gsir059\/haha\/k.arrow')\r\n```","comment_length":26,"text":"save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object. \n As you can see, it saves the entire dataset.\r\n\r\n@lhoestq \r\n\r\nYou can  check by going through the following example,\r\n\r\n```\r\nfrom datasets import load_from_disk,concatenate_datasets\r\n\r\nloaded_data=load_from_disk('\/home\/gsir059\/HNSW-ori\/my_knowledge_dataset')\r\nn=20\r\nkb_list=[loaded_data.shard(n, i, contiguous=True) for i in range(n)]\r\nfinal_dataset=concatenate_datasets([kb_list[1],kb_list[2]])\r\nfinal_dataset.save_to_disk('\/home\/gsir059\/haha\/k.arrow')\r\n``` \n Hi ! We refactored save_to_disk in #2025 so this doesn't happen.\r\nFeel free to try it on master for now\r\nWe'll do a new release soon","embeddings":[-0.2544762492,0.0369030796,-0.0747913346,0.0235322844,0.2022773474,0.2077016681,0.3147445321,0.3745852709,-0.0703066215,0.2908636332,-0.1010363847,0.1949494332,0.0023286699,0.2046323568,-0.0519777238,0.1872817427,0.3261983991,0.2545649409,-0.202490136,-0.1318568736,-0.3687126338,0.3375607431,-0.0871662647,-0.3545360267,-0.2599395514,0.1303224117,-0.3702034056,0.1801269501,0.0867946893,-0.0932735503,0.1130666807,0.0303959753,0.1794944555,0.1613758355,-0.0001024196,-0.0183926225,0.0984345675,-0.2301510423,-0.5061350465,0.0002731246,-0.0454339124,-0.4297147691,-0.0318013839,-0.2701205909,0.0554985516,0.0306889862,-0.0264874194,-0.3492635787,0.1595520228,0.1346326768,0.309262991,-0.0615131408,0.2263928801,-0.1241282299,0.0925930217,-0.061951369,-0.1597601026,-0.0174898487,-0.3068611026,0.0766253173,0.1838666946,0.2538936436,-0.0520645045,-0.1528071463,0.102972433,0.3315379024,0.2000031471,-0.3587841392,0.1949589849,0.0508012176,0.3223497868,-0.4228012264,-0.3215260804,-0.1421272755,-0.0708394945,-0.4146020412,0.0688096136,0.2725207508,-0.0372716561,0.1376816183,-0.2104525864,-0.0943455771,-0.069819577,0.0858969092,-0.0970790237,-0.0578370914,-0.2149947584,0.0483856872,0.4175906181,-0.2034444958,0.0756108984,-0.4940950274,-0.0186775941,-0.0991517976,-0.371789217,-0.0662881136,-0.0382048488,-0.1302770078,0.1177785024,0.1654810011,0.1567116082,0.0172697473,0.2839658558,0.1399288625,0.1291773468,0.1396843344,0.2109784186,0.068145901,0.2035749704,-0.1210986599,0.0115744416,0.0075942227,0.1597272307,-0.0621643066,0.3084388375,-0.1679115593,0.2425519675,-0.0319233574,-0.0998061746,0.2859278023,0.2349762172,0.065560393,-0.2424149215,0.3184429109,0.0408475026,0.1560940892,-0.0597217456,0.3190596104,0.1788872778,0.0181819517,-0.3325875998,-0.1384913027,0.0419947393,0.1650955081,0.1061084345,-0.1681782007,0.2190976292,-0.1321576238,0.1250537783,0.0135273319,-0.0349260196,-0.2764335275,0.3223589957,0.2634705901,0.3512492776,-0.0020948618,-0.0024642614,-0.3695912659,-0.1427434683,0.2944760919,-0.0911261812,-0.1671527624,-0.2289693654,0.3325921893,-0.0890039057,0.158902213,-0.3130002916,0.1394113004,0.3160459995,0.0520765781,-0.1731426567,-0.1179567501,-0.1283720881,-0.5556748509,0.377820909,0.1488147974,-0.0492435843,0.141371876,-0.0439600162,-0.0754638687,0.0834014639,0.1271142513,0.0235083513,0.0756958425,-0.3151823878,0.4141362607,0.3489595652,-0.0966070369,-0.3720923662,0.16935651,-0.0126689365,-0.0344887562,0.0872744471,0.1582108438,0.2718800902,-0.0721340999,0.248126477,0.2817783058,0.0836539865,0.0335968472,-0.3789056838,0.1018801481,-0.042809505,-0.1003163457,-0.2233697176,0.2506659329,0.0871279091,-0.1296687871,0.3815090358,-0.2515321672,0.2696840763,0.438035965,0.2545845807,-0.2948097885,-0.1404696703,0.125088796,-0.6262158751,0.1282477081,0.2535210252,-0.1669909507,0.0085176928,-0.1840660721,-0.0659888312,0.1249769032,-0.2429518849,0.0508549958,0.2963756621,0.2682271004,0.1049840152,-0.0379334837,-0.1391397417,0.1948613375,-0.0677439421,0.0089129601,-0.256256789,0.2754266262,-0.166095227,-0.3440093994,0.0040625175,0.0395993441,0.1255230457,-0.0486763008,-0.0014976056,0.5335175395,-0.1285197139,0.064571552,-0.0740877315,-0.0528816469,-0.033474654,0.0302071832,0.0748106986,0.0701766685,0.1700238585,-0.0647116527,-0.3401039243,0.3452366889,0.1025321856,0.2119351029,0.2155987918,-0.0051220949,0.1724714935,-0.1435659975,0.0114306547,-0.2770319581,-0.0996472463,0.1160653681,0.1534866542,-0.0418848917,-0.1850672215,0.2772960663,0.5342244506,0.0047868043,0.2684757113,0.1592027992,-0.3228473365,-0.2200796157,-0.0512727015,0.2949929833,0.5205633044,0.2487683296,0.2373423725,-0.0739356056,-0.0226968527,-0.1503005177,0.3239525259,-0.0092433114,0.1937918961,0.4255512059,0.0722199753,-0.1734611094,-0.2458447516,0.216474548,-0.0804639757,0.0885643438,-0.228697747,-0.1394912004,-0.1880013943,0.1280589253,-0.1593550593,-0.1150401607,-0.1817211807,-0.5245651007,-0.132096678,0.4993870854,-0.1842717975,-0.0487550646,0.1641299576,0.0706619993,-0.0039135288,-0.1958074868,-0.0752318576,0.0643517524,-0.1250968724,0.2130450606,-0.0566554144,-0.181535989,0.2067794353,-0.109968923,0.1588941365,-0.3810654283,-0.0727515593,0.0060042944,-0.1633942723,0.0072642621,0.0531139337,0.2406397462,-0.0669149756,-0.2053179592,0.157913655,0.0342975482,-0.2009773403,0.322337389,0.1404600888,-0.1537690461,-0.1958235949,-0.2867220938,-0.1278878003,-0.3667713702,0.2408263385,0.0425303727,0.1054977402,0.0470765643,0.3343971968,0.0507683307,0.1508998424,-0.1325421482,-0.2667574883,-0.4190298021,0.5335767269,-0.3335801363,-0.4781666398,0.2454314977,-0.0832561553,0.0287268851,0.0784360543,-0.4200796187,-0.051572755,-0.0873609185,0.041065041,-0.0841630474,0.2444756329,0.290635258,0.1483316123,-0.1550348252,-0.1538979858,-0.2125998288,0.257129252,0.1114040986,0.3442560136,-0.1954375505,0.1338014156,0.1407623738,0.1544995606,0.1809569895,0.1047078893,0.3695558608,0.101584442,0.5001899004,-0.3050990701,-0.2166190594,-0.1263912469,-0.1631068587,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2188","title":"Duplicate data in Timit dataset","comments":"Hi ! Thanks for reporting\r\nIf I recall correctly this has been recently fixed #1995\r\nCan you try to upgrade your local version of `datasets` ?\r\n```\r\npip install --upgrade datasets\r\n```","body":"I ran a simple code to list all texts in Timit dataset and the texts were all the same.\r\nIs this dataset corrupted?\r\n**Code:**\r\ntimit = load_dataset(\"timit_asr\")\r\nprint(*timit['train']['text'], sep='\\n')\r\n**Result:**\r\nWould such an act of refusal be useful?\r\nWould such an act of refusal be useful?\r\nWould such an act of refusal be useful?\r\nWould such an act of refusal be useful?\r\n...\r\n...\r\nWould such an act of refusal be useful?","comment_length":32,"text":"Duplicate data in Timit dataset \n I ran a simple code to list all texts in Timit dataset and the texts were all the same.\r\nIs this dataset corrupted?\r\n**Code:**\r\ntimit = load_dataset(\"timit_asr\")\r\nprint(*timit['train']['text'], sep='\\n')\r\n**Result:**\r\nWould such an act of refusal be useful?\r\nWould such an act of refusal be useful?\r\nWould such an act of refusal be useful?\r\nWould such an act of refusal be useful?\r\n...\r\n...\r\nWould such an act of refusal be useful? \n Hi ! Thanks for reporting\r\nIf I recall correctly this has been recently fixed #1995\r\nCan you try to upgrade your local version of `datasets` ?\r\n```\r\npip install --upgrade datasets\r\n```","embeddings":[-0.0085601602,-0.3116270602,-0.0845909119,0.6408435106,0.2972834706,0.1798605472,0.2127219588,0.3483661413,-0.3550944924,0.0640646145,-0.1848578006,0.3607736826,-0.0370692685,0.1377495974,0.1514657885,0.1737731546,-0.0231208708,-0.0077550919,-0.289678067,-0.2847210169,-0.0019159283,0.1685191691,-0.0928682387,0.0616810247,-0.0929964259,0.2439683527,-0.1059634238,-0.1654433757,-0.0110924039,-0.2916881442,0.039128527,0.1391637921,-0.1956939995,0.4862783551,-0.0001211682,0.1176715791,0.141956076,0.0384408645,-0.2202286571,-0.2915332615,-0.1174837649,-0.1514540017,0.2768204212,-0.093014434,-0.1665944755,0.0084072202,0.0813450813,-0.1515229493,0.3422286212,0.1550573856,0.1326091737,0.1251025796,-0.0319642946,0.1261979192,0.2245519608,0.1391940564,0.0429698601,0.1158418208,0.351942867,0.365419805,0.4090771675,0.1617848128,-0.2078804225,0.2010411173,-0.3571011424,0.0607093535,0.0050468207,-0.1050854921,-0.0295727327,0.3857053518,0.8181933165,-0.3492590189,-0.1290716231,0.0266013034,-0.0413215868,-0.1824618429,0.1129803434,0.1279145926,-0.2183217853,0.161939308,-0.1877855808,0.1523911208,0.041346591,0.1306083202,0.081719704,-0.3430477679,-0.0316619277,0.0668796524,-0.0958121046,0.0588132851,0.12808685,-0.1315314174,-0.0388975516,-0.0530780815,-0.0954163894,-0.0338919684,-0.5064440966,-0.1193039939,0.172377333,0.2862872481,0.4710802138,0.0988899469,-0.2946043015,-0.0915646106,0.475292325,-0.06704925,-0.1020936668,-0.0530314967,-0.1388146281,-0.2779713571,-0.396797061,-0.026198633,0.0038941933,0.0036781437,0.2771374285,-0.0767455623,0.3120041192,-0.2282845825,-0.5888941884,-0.0935210958,-0.4488693178,-0.0916466266,-0.1306605637,-0.1027447209,0.0775114745,0.0874411389,0.0335715637,0.0318364874,-0.2102265209,-0.1265277565,-0.1079942286,-0.306145221,-0.0174625721,-0.077755779,-0.009289748,-0.3747313321,0.1959086359,0.29171893,-0.0306807812,-0.2282073945,0.0096422089,-0.1862995327,-0.0141302561,0.0944495574,0.0140463412,0.3920698464,0.2078878731,-0.0813206136,0.0554698072,0.2834269106,-0.2466738671,-0.1098549291,0.113766335,0.1410531104,-0.0766588002,-0.1108217612,-0.1702792197,0.1028332189,0.2403246611,-0.1323535144,0.03633729,0.0477540791,-0.218952477,-0.0610455163,-0.18714948,0.343459785,-0.5317342877,0.0615807585,-0.0243831296,0.0334308296,0.0333389305,0.3163551986,-0.1876560152,0.369735986,-0.2389821559,-0.152196452,0.1076035202,-0.2316105068,-0.3582291901,0.0533435643,-0.0566046461,-0.0179928467,0.0473609604,0.2656922042,0.1468071342,0.0304086693,0.0650881603,-0.2567756176,0.2930332124,-0.3015697896,-0.4290743172,-0.06268657,0.4001121819,-0.0568456091,-0.2479376495,0.0383779816,0.073491253,0.0613337196,0.3726989031,0.2081892043,-0.1574118286,0.2859969735,0.2329321504,0.3830018938,0.2396134436,-0.1696216166,0.2872486711,-0.0753625929,0.1877301931,0.2143067569,0.2333484143,-0.2413323671,-0.1373885572,-0.0262933951,-0.3445990086,0.1045035645,0.1936203688,0.2245903313,-0.3319903016,0.1008642688,-0.097961545,0.6168999672,-0.3779688478,0.0188989546,-0.1586885154,0.1941652894,-0.1101722419,0.1503379047,0.0451617874,0.0427666083,0.0110732764,0.0053405897,-0.1109578982,0.1506658643,0.1766404063,-0.1716476828,-0.2193176895,-0.3255206048,0.2579401433,-0.5105289817,-0.2207472622,0.5468989611,0.2125024647,-0.1158464402,-0.0344049409,-0.0573118851,-0.222957477,-0.0068441378,-0.0107683185,0.1831531972,0.0709391758,-0.1089939922,0.0113869375,-0.2626825273,0.3656535149,0.1338628232,0.1802449822,0.3277192414,-0.0700456724,0.1865557283,0.0942412168,-0.0637165084,0.0107965237,0.0091217747,-0.0198521186,0.3328455687,-0.1506571621,0.1400274634,0.2939414978,0.1768110245,-0.0199784096,0.1302333921,0.029967472,-0.0383365676,0.2424828559,0.179170996,-0.1587680727,0.2704196572,-0.0308715347,-0.0532163829,-0.5526506305,0.2271169424,0.2784294486,0.3408263326,-0.2932356596,-0.0568935461,-0.3629149199,-0.1330238432,-0.5408290625,-0.0371635184,-0.2454103529,-0.1514032781,0.0793837905,0.0795682669,0.2793279588,0.1364261955,-0.0461639129,-0.1637203842,-0.0556844063,0.0606978349,-0.1150866449,0.097170651,-0.3198128343,0.0955677778,0.225795716,0.1798101664,0.238954559,-0.3659195304,0.0553943254,-0.343419522,-0.312902838,0.1037420407,-0.1895084232,0.3461570144,0.1730522215,0.1027155668,-0.3056551516,-0.2064390182,0.0690842494,-0.1115272939,-0.3633112013,0.0304775815,0.0058882791,-0.1495912969,-0.0017719446,-0.6204960346,-0.2918468714,0.062333867,0.3842011392,0.0756763145,0.1663363874,0.3559519947,-0.0517888367,-0.0216832906,0.2291805744,0.2381192893,-0.5113918781,-0.2132319957,-0.0249327552,0.1379188448,-0.2106077671,-0.1239940599,0.0099257389,0.109306246,0.0942362323,-0.5886982679,0.0314737521,-0.0134940101,-0.1855831742,0.0318211131,-0.0464713387,0.2385500818,0.0799124837,-0.0116049452,-0.0772038549,0.0090622306,0.0986079127,-0.1817134172,0.3093614876,0.0258249231,0.1898767948,0.0985339135,0.2369150072,0.63784796,0.0301537644,0.2011714876,-0.2357314974,0.495657295,-0.0320334621,-0.4854995012,-0.2749164402,-0.1550837308,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"An educated guess: does this refer to the fact that depending on the custom column names in the dataset files (csv in this case), there is a dataset loader being created? and this dataset loader - using the \"custom data configuration\" is used among all jobs running using this particular csv files? (thinking out loud here...)\r\n\r\nIf this is the case, it may be ok for my use case (have to think about it more), still a bit surprising given that datasets caching is disabled (or so I hope) by the lines I pasted above. ","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":95,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n An educated guess: does this refer to the fact that depending on the custom column names in the dataset files (csv in this case), there is a dataset loader being created? and this dataset loader - using the \"custom data configuration\" is used among all jobs running using this particular csv files? (thinking out loud here...)\r\n\r\nIf this is the case, it may be ok for my use case (have to think about it more), still a bit surprising given that datasets caching is disabled (or so I hope) by the lines I pasted above. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"Hi ! Currently disabling the caching means that all the dataset transform like `map`, `filter` etc. ignore the cache: it doesn't write nor read processed cache files.\r\nHowever `load_dataset` reuses datasets that have already been prepared: it does reload prepared dataset files.\r\n\r\nIndeed from the documentation:\r\n> datasets.set_caching_enabled(boolean: bool)\r\n\r\n> When applying transforms on a dataset, the data are stored in cache files. The caching mechanism allows to reload an existing cache file if it\u2019s already been computed.\r\n> Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated after each transform.\r\n> If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets. More precisely, if the caching is disabled:\r\n> - cache files are always recreated\r\n> - cache files are written to a temporary directory that is deleted when session closes\r\n> - cache files are named using a random hash instead of the dataset fingerprint - use datasets.Dataset.save_to_disk() to save a transformed dataset or it will be deleted when session closes\r\n> - caching doesn\u2019t affect datasets.load_dataset(). If you want to regenerate a dataset from scratch you should use the download_mode parameter in datasets.load_dataset().","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":202,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n Hi ! Currently disabling the caching means that all the dataset transform like `map`, `filter` etc. ignore the cache: it doesn't write nor read processed cache files.\r\nHowever `load_dataset` reuses datasets that have already been prepared: it does reload prepared dataset files.\r\n\r\nIndeed from the documentation:\r\n> datasets.set_caching_enabled(boolean: bool)\r\n\r\n> When applying transforms on a dataset, the data are stored in cache files. The caching mechanism allows to reload an existing cache file if it\u2019s already been computed.\r\n> Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated after each transform.\r\n> If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets. More precisely, if the caching is disabled:\r\n> - cache files are always recreated\r\n> - cache files are written to a temporary directory that is deleted when session closes\r\n> - cache files are named using a random hash instead of the dataset fingerprint - use datasets.Dataset.save_to_disk() to save a transformed dataset or it will be deleted when session closes\r\n> - caching doesn\u2019t affect datasets.load_dataset(). If you want to regenerate a dataset from scratch you should use the download_mode parameter in datasets.load_dataset().","embeddings":[-0.0997084454,0.0613426492,-0.0944522992,0.1375739574,0.2868357897,0.1569391489,0.2946323752,0.0576292314,0.2189560533,-0.2274291068,0.0563382395,0.1891585737,-0.1538448781,-0.1349174827,-0.0155598177,0.4577814043,0.1715121865,0.0599948317,-0.1155785844,0.0047335713,-0.0356277116,-0.0026572377,-0.2127188146,0.0402786806,-0.4570401311,-0.1324051023,0.1229298636,-0.0716498047,-0.0130503355,-0.5536743402,0.4886825979,0.3283195496,0.0741478354,0.2518526912,-0.0001118015,0.0727999434,0.2476903051,-0.0178316571,-0.2822506726,-0.003201406,-0.5275136232,-0.3997315764,0.2077168673,-0.1217703149,0.0088245841,-0.2282435447,0.0214830358,-0.7317454815,0.3986606002,0.2624377608,0.2537710965,-0.1417690814,-0.3225730658,0.0698815659,0.0294989012,0.1056568027,-0.0790080056,-0.1197099462,0.1755229682,0.00115097,-0.1297950745,0.3401147127,-0.1820656359,0.2184822261,0.5376929641,-0.0251180753,-0.3009690344,-0.3138095438,0.3533349931,-0.0475770123,0.8824431896,-0.3004612327,-0.2132212669,-0.3719502687,-0.1303938329,-0.107222043,0.2515766323,-0.0626551956,0.1068924516,0.1626930237,-0.5890986919,-0.3423182666,-0.0354443565,-0.1216804236,-0.1883628964,-0.0987214446,-0.3428514898,0.1247541532,-0.0124882208,0.0298062265,0.5160682201,-0.4871827662,-0.0295811631,0.2198566943,-0.4583883584,0.0142551577,0.1422318816,0.4166750908,-0.0496474281,0.220106706,0.1075856835,0.0910666659,-0.1975068301,0.0559330285,0.3907082677,0.382031858,0.0675789565,0.1710542142,0.0845614672,-0.2150832415,-0.1033617258,-0.1998728067,0.1032040641,-0.3857403398,0.5634826422,-0.0266759973,0.2618582845,-0.294590503,-0.3204870224,0.0342997648,0.0142830722,-0.2044890523,0.1050424129,0.1328129321,-0.0418563187,0.1020384952,-0.1786978096,-0.0918801203,-0.1994803548,-0.2397157699,-0.2297836989,-0.1517885029,-0.3549550474,0.0677673593,0.2348688841,-0.3219405413,0.0271079615,0.3597248495,0.0614182539,-0.2675002515,0.1516847908,-0.0396935157,0.3635239005,0.3102005124,-0.3273587525,0.2836120129,0.3906999528,-0.0564260893,-0.1284382939,0.382796973,-0.4262167811,-0.3516856134,0.1925227195,0.1590859294,-0.4444234371,-0.1102425605,-0.2061909288,0.0902894586,0.4188187122,-0.0466482677,0.1753661036,-0.1439128071,-0.130047664,-0.2852768302,0.1402714401,0.4280160069,-0.5497478247,-0.0674493983,-0.2498912662,-0.0562166758,0.1099889353,0.2396276444,-0.4815126359,0.2011151314,-0.2263178527,-0.1987856477,0.1428757459,-0.121588923,-0.4337772429,0.1803838015,0.2499530017,0.4296301901,0.0207902826,0.078282021,0.0647751093,-0.0365483053,-0.1067260504,-0.1406600326,0.061826434,-0.245730862,-0.1365717649,-0.2602417171,0.3213842511,-0.1354840398,0.1037089154,0.2238005251,0.0367317945,0.0181506891,0.0311799441,-0.0368100591,0.1045531631,0.3294175267,-0.0188392252,0.103748925,0.1263507605,0.080727458,-0.4650685489,0.3508911133,0.1209326386,-0.4675593674,-0.14911443,-0.1868676096,-0.1537768841,-0.121195741,-0.4467225373,-0.1915006787,0.0879468173,-0.0069218948,-0.0161998272,-0.0809430704,-0.2429459542,0.4812088311,-0.2107368857,-0.1352239698,-0.2489376366,0.141337797,0.0420287326,0.2810896337,-0.2694530785,0.054643441,0.1063114479,0.0800701752,0.0314386189,0.2419422418,0.0877580643,0.4027651846,-0.0050890767,0.3685146272,0.0253670849,-0.1032399833,0.2767234445,-0.0941211581,0.1489203721,-0.0821669027,-0.1542397439,0.2779749334,-0.2706847191,0.2121043652,-0.2125898302,-0.2112362087,0.124009423,-0.0346608013,-0.0884961858,-0.1701375246,-0.0698364154,0.0679313987,0.1711432636,0.3167246878,-0.0430301689,0.0704462975,0.3305062056,0.0603819676,-0.1028052941,0.0156823378,-0.1570168436,-0.3179258704,0.0985313952,0.4447603226,0.4994432926,0.1275836676,0.066603303,-0.0098827332,0.0787229836,-0.1767649353,0.249533385,-0.0649069324,0.0468865484,0.1298066676,0.0407715552,-0.0312101245,-0.3414979577,0.11968337,0.2410701066,0.1237346604,-0.4768824279,0.3093511462,-0.446213007,-0.0721967071,-0.139826566,0.1368020177,-0.2670971155,-0.4040007889,-0.1407969892,0.0495771766,0.2279472202,0.121240437,-0.2031304836,0.3428511024,-0.1336136758,-0.022177333,-0.1573786438,-0.1400097758,-0.1562621593,0.0989416838,0.1103478596,-0.3031523824,0.2201781422,-0.0197522976,0.1069621742,-0.3282643855,0.0002519308,0.0028273987,0.0342083722,0.1573209912,-0.2203421295,-0.0943113267,-0.1018785164,0.0736058652,-0.0442598574,0.0304602515,0.1118764803,-0.1155054942,-0.0266527645,0.0439222828,-0.059039779,-0.2237265259,-0.2553488612,-0.1147144139,-0.1969298869,-0.0365621708,-0.1048907787,0.5499766469,-0.0062835664,-0.0027899391,0.0241824035,-0.0475359708,-0.5478751063,-0.4771881998,0.2433176488,-0.0103913406,-0.0927400067,0.1800292134,0.1821801513,0.3579396904,0.209037587,-0.5898662806,0.071619831,-0.1588022709,0.0233939569,0.029422611,-0.0425002202,0.2604376376,0.1229924932,-0.0197214708,-0.1496986896,-0.0818621293,-0.1456715614,-0.1006152257,0.2926935852,-0.014061884,-0.0258151423,0.1166400015,0.8728512526,0.3354340494,-0.174358353,0.1497428715,0.0036005187,0.6802711487,-0.0236994382,-0.27216884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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"Thank you for the clarification. \r\n\r\nThis is a bit confusing. On one hand, it says that cache files are always recreated and written to a temporary directory that is removed; on the other hand the last bullet point makes me think that since the default according to the docs for `download_mode (Optional datasets.GenerateMode) \u2013 select the download\/generate mode - Default to REUSE_DATASET_IF_EXISTS` => it almost sounds that it could reload prepared dataset files. Where are these files stored? I guess not in the temporary directory that is removed... \r\n\r\nI find this type of api design error-prone. When I see as a programmer `datasets.set_caching_enabled(False)` I expect no reuse of anything in the cache. ","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":112,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n Thank you for the clarification. \r\n\r\nThis is a bit confusing. On one hand, it says that cache files are always recreated and written to a temporary directory that is removed; on the other hand the last bullet point makes me think that since the default according to the docs for `download_mode (Optional datasets.GenerateMode) \u2013 select the download\/generate mode - Default to REUSE_DATASET_IF_EXISTS` => it almost sounds that it could reload prepared dataset files. Where are these files stored? I guess not in the temporary directory that is removed... \r\n\r\nI find this type of api design error-prone. When I see as a programmer `datasets.set_caching_enabled(False)` I expect no reuse of anything in the cache. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"It would be nice if the documentation elaborated on all the possible values for `download_mode` and\/or a link to `datasets.GenerateMode`. \r\nThis info here:\r\n```\r\n    \"\"\"`Enum` for how to treat pre-existing downloads and data.\r\n    The default mode is `REUSE_DATASET_IF_EXISTS`, which will reuse both\r\n    raw downloads and the prepared dataset if they exist.\r\n    The generations modes:\r\n    |                                    | Downloads | Dataset |\r\n    | -----------------------------------|-----------|---------|\r\n    | `REUSE_DATASET_IF_EXISTS` (default)| Reuse     | Reuse   |\r\n    | `REUSE_CACHE_IF_EXISTS`            | Reuse     | Fresh   |\r\n    | `FORCE_REDOWNLOAD`                 | Fresh     | Fresh   |\r\n```","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":84,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n It would be nice if the documentation elaborated on all the possible values for `download_mode` and\/or a link to `datasets.GenerateMode`. \r\nThis info here:\r\n```\r\n    \"\"\"`Enum` for how to treat pre-existing downloads and data.\r\n    The default mode is `REUSE_DATASET_IF_EXISTS`, which will reuse both\r\n    raw downloads and the prepared dataset if they exist.\r\n    The generations modes:\r\n    |                                    | Downloads | Dataset |\r\n    | -----------------------------------|-----------|---------|\r\n    | `REUSE_DATASET_IF_EXISTS` (default)| Reuse     | Reuse   |\r\n    | `REUSE_CACHE_IF_EXISTS`            | Reuse     | Fresh   |\r\n    | `FORCE_REDOWNLOAD`                 | Fresh     | Fresh   |\r\n```","embeddings":[-0.132575646,0.0042611659,-0.1003257781,0.1450196654,0.2611149251,0.0885635614,0.1497801691,-0.0054396302,0.0523649976,-0.2209995091,-0.0105340136,-0.0093214773,-0.0788026974,0.0148025285,-0.0911452025,0.4515508115,0.1717670411,0.0544777438,-0.2703152001,-0.0126867108,-0.0795434117,-0.0508989282,-0.1528280079,-0.0177352671,-0.32577914,-0.0017379133,0.0608107559,-0.0484852456,-0.2403380722,-0.6755395532,0.5190625191,0.4494843185,0.2083913535,0.1146773845,-0.0001168151,0.0489492677,0.3039731085,-0.1338789612,-0.2875078917,-0.0103915669,-0.545085609,-0.2912785709,0.1124305576,-0.1120601073,0.0395753756,-0.1538888067,0.0811221823,-0.5785891414,0.3715526164,0.3695131838,0.1818338633,-0.1832768321,-0.2001060545,0.0893235952,0.141222015,0.2437101752,-0.1237794831,0.037241336,0.322075516,0.1247805357,-0.018199617,0.2879489958,-0.0560561381,0.2960295379,0.565648675,0.0093442956,0.1636774093,-0.4565844536,0.2388889641,0.0250146352,1.1493660212,-0.2332352996,-0.2873546481,-0.3733200729,-0.1109540015,-0.3186913729,0.2588652074,0.0140755,-0.0272201523,0.1969658136,-0.6656895876,-0.2369510233,-0.1510466486,-0.1439849883,-0.1140457541,0.0083224606,-0.3288093209,0.0601481088,0.0228604544,-0.044869937,0.5436966419,-0.4406124651,0.0177962817,0.2235455811,-0.3665752411,-0.135038197,0.1707547605,0.3412370086,0.0559608191,0.2809710801,0.004347065,-0.1090332493,-0.1632771045,0.079607442,0.4099765122,0.1847312152,0.153617993,-0.1211039722,0.1026657671,-0.0653599501,-0.0127923414,-0.1522945166,0.0578929633,-0.3024713397,0.5569894314,0.0259583648,0.2239093632,-0.2749815285,-0.3039260209,-0.0038776384,0.014193425,-0.2060308754,0.0928864032,0.1861323118,-0.0754157305,0.0621719472,-0.2922911942,0.0188443791,-0.159011811,-0.3619897962,-0.1578511894,-0.1793255508,-0.3269338608,0.0204795729,0.2394786775,-0.2574568391,0.1005438492,0.4393438399,0.0420365855,-0.2138965577,0.1889699399,0.0254874099,0.2010563165,0.4977919161,-0.2865719199,0.1973752677,0.2685185969,0.0111293374,-0.1918101013,0.4145025015,-0.3691080213,-0.5318116546,0.1474086493,0.139004916,-0.506051302,-0.1422891319,-0.0900613442,0.1105869412,0.3779514432,-0.0056704073,0.1612610966,-0.1432572454,-0.1368408501,-0.3684626222,0.0003973177,0.4416205287,-0.611340344,-0.0170365162,-0.3705367744,-0.2274078131,0.1591456085,0.1475977004,-0.3789155781,0.1754211485,-0.1863431185,-0.2089995593,0.1826339066,-0.1241744831,-0.3571071923,0.1453820467,0.3975531161,0.2618071437,0.0976000652,0.1303774863,0.1016698256,-0.1206594333,-0.1181316376,-0.0377236307,-0.0049879448,-0.2503035367,-0.0772855729,-0.3503139913,0.1276770532,-0.1639273614,0.0155469934,0.1499371231,0.0802270621,-0.1014925092,-0.0588772818,-0.0537634157,0.0857708976,0.1736115813,0.1347942948,0.1471327841,0.0282387193,0.1696176082,-0.6066907644,0.3852394223,0.0662049577,-0.3480160236,-0.0871552899,-0.211859405,-0.2996636629,-0.0250247512,-0.3529517949,-0.2120274156,0.0458297171,-0.0506738834,0.1571803093,-0.1518154591,-0.2582426965,0.3438586891,-0.2423756868,0.0148016233,-0.1462842226,0.1495354921,-0.0488027707,0.2019355744,-0.2094453871,-0.0156090166,0.1156178489,0.0086224116,0.0717082545,0.4083191454,0.1737720817,0.4718440175,-0.1032984555,0.4203929007,0.1377690732,0.0736767352,0.2135801762,-0.1835939586,0.1024537906,0.0142307077,-0.2718121707,0.3134011626,-0.1931469142,0.0768746585,-0.1140330881,-0.2736133039,0.1306158602,0.0810394958,-0.0460012183,-0.2751426995,-0.0808304176,0.1798795462,0.0037942186,0.3058403432,-0.0288724918,0.2879081964,0.2200863957,-0.2195736766,-0.0610304438,-0.0124928104,0.0235614646,-0.4724953473,0.1849091053,0.4928191006,0.555147469,0.0222601853,0.2973548174,0.043381393,-0.0461560786,-0.2960595489,0.3671460152,-0.1907936782,0.0465036333,0.1567800939,-0.0506649427,-0.1499492526,-0.3461315334,0.1256599128,0.3300360441,0.3210259676,-0.4505416453,0.1189449579,-0.4110972881,-0.0787225813,-0.0761827454,0.1549040824,-0.2310667783,-0.3646591306,-0.1081455722,0.1242578849,0.1314709485,-0.0163651314,-0.1997042,0.2560541332,-0.1282802969,0.0514581017,-0.1284702718,-0.1792527735,-0.1449583173,0.065004088,0.0717630014,-0.3010616899,0.258638978,-0.1313634813,0.094339475,-0.2228563428,-0.0122406129,0.0962457359,0.0774576664,0.2347013056,-0.131571576,0.0296801832,0.1202929989,0.1396782696,-0.081918329,-0.0028210666,-0.0017424873,-0.2095082849,-0.0061241915,0.1070558205,-0.1420656741,-0.3035123646,-0.2983780503,-0.0950419977,-0.2578513622,0.0004816628,-0.0052524256,0.5572144389,-0.0099968771,0.0066042226,0.0939478353,0.015162576,-0.5945007205,-0.4954152405,0.2906995714,-0.1030748338,-0.0394016244,0.2776832581,0.0995816216,0.3611543477,0.2926027477,-0.5287021995,-0.0356636159,-0.1569282264,0.1076141,0.1552859247,0.0289056674,0.2721232176,0.0868229419,-0.0061669108,-0.1301562637,0.0074913995,-0.1374365091,-0.0472735874,0.2741394937,0.0605774149,0.0238111783,0.0636950731,0.9581979513,0.4418125749,-0.0832928121,0.1660141349,0.0828534365,0.6536433697,0.088015765,-0.0483274721,0.0106890174,-0.22499555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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"I have another question. Assuming that I understood correctly and there is reuse of datasets files when caching is disabled (!), I'm guessing there is a directory that is created based on some information on the dataset file. I'm interested in the situation where I'm loading a (custom) dataset from local disk. What information is used to create the directory\/filenames where the files are stored?\r\n\r\nI'm concerned about the following scenario: if I have a file, let's say `train.csv` at path `the_path`, run once, the dataset is prepared, some models are run, etc. Now let's say there is an issue and I recreate `train.csv` at the same path `the_path`. Is there enough information in the temporary name\/hash to *not* reload the *old* prepared dataset (e.g., timestamp of the file)? Or is it going to reload the *old* prepared file? ","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":139,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n I have another question. Assuming that I understood correctly and there is reuse of datasets files when caching is disabled (!), I'm guessing there is a directory that is created based on some information on the dataset file. I'm interested in the situation where I'm loading a (custom) dataset from local disk. What information is used to create the directory\/filenames where the files are stored?\r\n\r\nI'm concerned about the following scenario: if I have a file, let's say `train.csv` at path `the_path`, run once, the dataset is prepared, some models are run, etc. Now let's say there is an issue and I recreate `train.csv` at the same path `the_path`. Is there enough information in the temporary name\/hash to *not* reload the *old* prepared dataset (e.g., timestamp of the file)? Or is it going to reload the *old* prepared file? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"Thanks for the feedback, we'll work in improving this aspect of the documentation.\r\n\r\n> Where are these files stored? I guess not in the temporary directory that is removed...\r\n\r\nWe're using the Arrow file format to load datasets. Therefore each time you load a dataset, it is prepared as an arrow file on your disk. By default the file is located in the ~\/.cache\/huggingface\/datasets\/<dataset_name>\/<config_id>\/<version> directory.\r\n\r\n> What information is used to create the directory\/filenames where the files are stored?\r\n\r\nThe config_id contains a hash that takes into account:\r\n- the dataset loader used and its source code (e.g. the \"csv\" loader)\r\n- the arguments passed to the loader (e.g. the csv delimiter)\r\n- metadata of the local data files if any (e.g. their timestamps)\r\n\r\n> I'm concerned about the following scenario: if I have a file, let's say train.csv at path the_path, run once, the dataset is prepared, some models are run, etc. Now let's say there is an issue and I recreate train.csv at the same path the_path. Is there enough information in the temporary name\/hash to not reload the old prepared dataset (e.g., timestamp of the file)? Or is it going to reload the old prepared file?\r\n\r\nYes the timestamp of the local csv file is taken into account. If you edit your csv file, the config_id will change and loading the dataset will create a new arrow file.","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":231,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n Thanks for the feedback, we'll work in improving this aspect of the documentation.\r\n\r\n> Where are these files stored? I guess not in the temporary directory that is removed...\r\n\r\nWe're using the Arrow file format to load datasets. Therefore each time you load a dataset, it is prepared as an arrow file on your disk. By default the file is located in the ~\/.cache\/huggingface\/datasets\/<dataset_name>\/<config_id>\/<version> directory.\r\n\r\n> What information is used to create the directory\/filenames where the files are stored?\r\n\r\nThe config_id contains a hash that takes into account:\r\n- the dataset loader used and its source code (e.g. the \"csv\" loader)\r\n- the arguments passed to the loader (e.g. the csv delimiter)\r\n- metadata of the local data files if any (e.g. their timestamps)\r\n\r\n> I'm concerned about the following scenario: if I have a file, let's say train.csv at path the_path, run once, the dataset is prepared, some models are run, etc. Now let's say there is an issue and I recreate train.csv at the same path the_path. Is there enough information in the temporary name\/hash to not reload the old prepared dataset (e.g., timestamp of the file)? Or is it going to reload the old prepared file?\r\n\r\nYes the timestamp of the local csv file is taken into account. If you edit your csv file, the config_id will change and loading the dataset will create a new arrow file.","embeddings":[0.0426528901,0.1128463596,-0.0747745112,0.3651132286,0.187513724,0.16579099,0.3618480265,-0.0763975754,0.1920935363,-0.145717144,0.1244352832,0.0901360363,-0.269436568,-0.1203824431,0.0926404372,0.3132811189,0.1218126342,0.0018566946,-0.2674063146,-0.0612732209,-0.0238487758,0.0139037659,-0.0476134755,0.0775936395,-0.5502688885,-0.1778058559,-0.0284800045,0.1388774067,-0.04224471,-0.5476482511,0.4293833077,0.2526207268,0.1022150964,0.4431468546,-0.0001136524,0.1564302742,0.1653490961,-0.0595049635,-0.3835014999,-0.0884687677,-0.4759688675,-0.2783870995,0.2284000963,-0.1791343987,0.0559503101,-0.1754128486,0.0664183646,-0.7208995819,0.4306547642,0.3034115732,0.2095777094,-0.1128296256,-0.3428081572,0.16692698,0.1212248057,0.1176112965,-0.0647921115,0.0958380178,0.0171205495,0.0299993977,-0.1908132881,0.3489200175,0.0284836553,0.0995286107,0.6798650026,0.1164378077,-0.3021637797,-0.2816374302,0.1710201502,-0.0168927088,0.8757190704,-0.3772922456,-0.3937720656,-0.4215785861,-0.0722437426,-0.2910638452,0.3705019653,-0.0349943116,0.0338183269,0.1965864748,-0.4137375951,-0.2978313863,-0.1873830855,-0.1798488945,-0.0950306356,-0.2104989588,-0.3281470239,0.0431772061,0.0529060252,0.0894156471,0.3412467837,-0.4796886146,0.0544818491,0.3145650029,-0.3024866879,0.0585052669,0.0669266209,0.3536751568,-0.1032308489,0.1801586002,0.3130632043,-0.0729674026,-0.2701882124,0.1462639421,0.3341298699,0.3940928578,-0.0065452317,0.0667705387,0.2119108289,-0.1026698872,-0.1689470112,-0.2856127322,-0.0797221437,-0.4382008612,0.3683880568,0.000018508,0.182117328,-0.2402320653,-0.0969628394,0.0762550831,0.064712882,-0.1605023444,0.2484934181,0.1427246332,-0.0343203582,0.0168877076,-0.1806506366,-0.1179083884,-0.1633675843,-0.0465737991,-0.1743831933,-0.1365133673,-0.3817654252,0.1212826893,0.2318619043,-0.2882330716,0.172854811,0.2895810008,0.1174064279,-0.1914015263,0.1421865225,-0.099582918,0.3003712893,0.3959777057,-0.3659523129,0.2547310293,0.2532998025,0.0470304228,-0.238289535,0.2351591587,-0.5508418083,-0.5308260322,0.1095277816,0.1173829436,-0.418703407,-0.0184019562,-0.2455897331,-0.1156271249,0.3187911212,0.062400654,0.2418775707,-0.2037982196,-0.1772338003,-0.3270478845,0.0544245467,0.5265339613,-0.6201192737,0.0139235519,-0.1013620198,-0.056978371,-0.0566182621,0.4388082325,-0.564440012,0.2282956541,-0.2668422163,-0.0418815315,0.2101627588,-0.2359636277,-0.2370516509,0.0387334414,0.0486462861,0.3653502166,0.1788061261,0.06647484,0.0901674479,-0.13607499,-0.0383855477,-0.0327425189,0.0925370231,-0.2657940686,-0.1053954437,-0.2953509092,0.0831988603,-0.2103252411,-0.0497050881,-0.013660498,0.0565380715,-0.1861411333,-0.0096127726,-0.1338639259,0.0811727941,0.4035136104,0.0486800149,0.284064889,0.095466435,0.2152553946,-0.5267915726,0.423304379,0.1852604151,-0.4941213131,-0.2006000727,-0.2615771294,-0.0718733817,-0.0862336755,-0.4411376417,-0.1706931144,0.0341377407,0.0446686447,0.1159935743,-0.0514420792,-0.3322877586,0.4101415873,-0.4049244225,-0.0417120084,-0.3104243279,0.084500432,0.0234978925,0.2354417592,-0.3102506399,0.1405351907,0.0299967621,-0.06298998,0.0427050665,0.36036098,0.1344787329,0.4914358556,0.1914765388,0.519780755,0.0840321481,-0.0144377733,0.3538705409,-0.1910290867,0.1165380031,0.0307780337,-0.3368668258,0.3540522158,-0.3387058377,0.1638696492,-0.0121469386,-0.2903886735,0.1131076068,0.0169298779,-0.0755761564,-0.0768131614,-0.0301896594,0.0985094756,0.254365176,0.4224379957,-0.2281018943,0.1754329354,0.3237643838,-0.0215610415,-0.261690706,0.0235331543,-0.1400740743,-0.2476538271,0.1388082355,0.4800477028,0.5014015436,0.1201579794,0.2383859158,-0.0533888675,0.0216731839,-0.2430947423,0.2449923307,-0.0331893004,0.083022736,0.1908868253,-0.0131332744,0.0383751877,-0.2990606129,-0.0289780442,0.1093659773,0.0501198731,-0.4698907733,0.3693457246,-0.3929538131,-0.0820747241,-0.3823751807,0.0865272582,-0.227621913,-0.2523648739,-0.0142976884,0.0020540985,0.0541889369,0.0533842295,-0.2774982452,0.4074819684,-0.1261828691,-0.0919074938,-0.1295308024,-0.1039870307,-0.1594325751,0.0164022036,0.2174411863,-0.1975784004,0.0960609838,-0.0307019521,0.0838793591,-0.3365899026,0.1275617778,0.0149003174,0.0186424032,0.2037233859,-0.1277354062,0.0353868119,-0.1253300309,-0.0770093277,-0.1005413979,0.0471430346,0.0399333239,-0.1819608957,0.1469050795,0.1284316927,-0.1161590591,-0.2377016544,-0.3192602694,-0.132194519,0.0855249316,0.0390010215,0.0027544945,0.5872468352,-0.0016312777,0.1230578125,-0.0244505946,0.0663004667,-0.441637069,-0.6039819121,0.3160150647,-0.1407755613,-0.0384192504,0.1388538927,0.0660286695,0.2734756768,0.1964789331,-0.6239000559,-0.1205821261,-0.0345267914,0.1573712379,0.1162360385,0.0700723305,0.309738785,0.0374815576,-0.0466117337,-0.150802061,0.0029912887,0.0816945359,-0.0783292428,0.2549778819,0.1132200956,-0.0473794565,0.0169874933,1.1582586765,0.2377635986,-0.0377980545,0.0956422314,0.0073740934,0.7471949458,-0.0396334566,-0.0848495737,-0.0874110982,-0.2571058571,-0.475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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"Thank you for all your clarifications, really helpful! \r\n\r\nIf you have the bandwidth, please do revisit the api wrt cache disabling. Anywhere in the computer stack  (hardware included) where you disable the cache, one assumes there is no caching that happens. ","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":41,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n Thank you for all your clarifications, really helpful! \r\n\r\nIf you have the bandwidth, please do revisit the api wrt cache disabling. Anywhere in the computer stack  (hardware included) where you disable the cache, one assumes there is no caching that happens. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"I have another question about caching, this time in the case where FORCE_REDOWNLOAD is used to load the dataset, the datasets cache is one directory as defined by HF_HOME and there are multiple concurrent jobs running in a cluster using the same local dataset (i.e., same local files in the cluster). Does anything in the naming convention and\/or file access\/locking that you're using prevent race conditions between the concurrent jobs on the caching of the local dataset they all use?\r\n\r\nI noticed some errors (can provide more details if helpful) in load_dataset\/prepare_split that lead to my question above. \r\n\r\nLet me know if my question is clear, I can elaborate more if needed @lhoestq  Thank you!","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":115,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n I have another question about caching, this time in the case where FORCE_REDOWNLOAD is used to load the dataset, the datasets cache is one directory as defined by HF_HOME and there are multiple concurrent jobs running in a cluster using the same local dataset (i.e., same local files in the cluster). Does anything in the naming convention and\/or file access\/locking that you're using prevent race conditions between the concurrent jobs on the caching of the local dataset they all use?\r\n\r\nI noticed some errors (can provide more details if helpful) in load_dataset\/prepare_split that lead to my question above. \r\n\r\nLet me know if my question is clear, I can elaborate more if needed @lhoestq  Thank you!","embeddings":[-0.1976588219,0.0997744873,-0.1261471659,0.2399428338,0.0289438497,0.0434310474,0.4564794302,0.0984457806,0.4220702648,-0.2127026469,-0.02498319,-0.0400107726,-0.0365918837,0.0134087205,-0.1342043132,0.4030806124,0.2076878548,-0.0396345407,-0.024214901,-0.1413462311,-0.1500361115,-0.0289533883,-0.0475723408,0.0606783032,-0.5617077947,-0.0671415403,0.0013730753,0.1623043418,0.1725726724,-0.4235329926,0.3519337773,0.5294938087,0.0670450926,0.4914126098,-0.000113091,-0.0403809808,0.1958641857,-0.0477524549,-0.2622745633,-0.1576190591,-0.5350163579,-0.4387889802,0.1720770895,-0.0988316759,0.0574336424,-0.1514247209,0.0411747545,-0.7131090164,0.4519171119,0.1427449733,0.2041774243,-0.1091207042,-0.4887529612,0.0160306338,0.0962762758,0.3353848159,0.0161248278,0.1014839038,0.2383863479,-0.3198069036,-0.3291200399,0.2688158154,-0.0138629032,0.3965907693,0.3954433203,0.0086095035,-0.2763716578,-0.2745757997,0.1655485034,0.0608960241,0.7798292041,-0.2836916447,-0.3395363092,-0.4090892673,-0.1475395113,-0.179307729,0.269965589,0.0716021284,0.0429681391,0.084578447,-0.3964806497,-0.2547957301,0.0036561524,-0.2672636509,-0.0383914709,-0.0404894166,-0.1567974687,0.08330708,-0.0747073144,0.0514648147,0.6233451962,-0.5045340657,0.1790409088,0.1976583749,-0.461689353,0.0237274785,-0.0364603847,0.391843915,-0.0540191829,-0.0245083682,-0.1536819339,0.0867669061,-0.1915480793,0.1589517742,0.4464426339,0.1503168792,0.1327717155,0.2022628486,0.2322850674,-0.3030024469,-0.3496911526,-0.0695606917,0.0665362924,-0.4245012701,0.2996263802,0.0340163559,0.1059436426,-0.3955444396,-0.2909142077,0.0781235546,0.1319535226,-0.278221339,0.0458954424,0.1907658726,0.0533931516,0.0282913912,-0.251444906,0.083957538,-0.3625871241,-0.2256822139,-0.2220804542,-0.1252384633,-0.2524294257,0.1785207093,0.2062439173,-0.2326714993,0.0402708203,0.3950311542,0.0962525681,-0.1698834002,0.1404356211,-0.1678614914,0.2335447967,0.3442668915,-0.2546127737,0.2355287969,0.3324483335,0.0308906045,-0.1443060786,0.2100905776,-0.4729039967,-0.4184192717,0.1001290902,0.1033481881,-0.4051647186,-0.024724545,-0.1482537091,0.0726757571,0.5164402723,0.0497851707,0.0943618119,-0.2699440718,-0.2512399554,-0.1872328371,-0.0906452164,0.5964114666,-0.6261852384,0.0243644491,-0.1429244578,-0.0997800827,0.0721887052,0.3109852076,-0.4317257702,0.1771506667,-0.2471140921,-0.0300256945,0.156193763,-0.2819254994,-0.2977422774,0.2853240967,-0.0048165028,0.2871124446,0.1105008647,-0.0748473853,0.0774108246,-0.2064444572,0.0731695145,0.0241682958,-0.0004380147,-0.1278619617,-0.2169781923,-0.2795419097,0.1265176088,-0.0591096692,0.1558092833,0.1283837259,0.1315277815,-0.0641909987,0.1397758573,-0.042271547,0.1320656836,0.3305953741,-0.0562164523,0.2266031504,0.1514285058,0.2036530674,-0.6013004184,0.4022013545,0.0565555915,-0.4018244743,-0.0155669907,-0.19565323,-0.1518979967,-0.1913250834,-0.4529682398,-0.0661183074,0.0631238446,-0.1243939474,-0.0742722526,-0.2170352936,-0.2007468343,0.8151642084,-0.3109962046,-0.031359341,-0.0666529536,0.2341920882,-0.065775536,0.2016776949,-0.1721449941,0.0347544812,0.2445912063,-0.0093639335,-0.0629991591,0.3307782412,0.2265687883,0.5461758375,0.1839093864,0.2227417678,0.1407313198,0.1049825251,0.3516898155,-0.2002376169,0.1083542332,-0.1767371595,-0.2546150982,0.3825460374,-0.3531452715,0.2602759302,-0.1692652553,-0.2800016105,0.0722134933,-0.0253565293,0.057882674,-0.0620063394,0.1634283662,-0.0544249155,0.2642069757,0.215580821,-0.1668545306,0.1455173194,0.198378399,0.0810150802,-0.1046497077,-0.1036524251,0.0311687831,-0.3026696742,0.1909436136,0.4321049452,0.6589485407,0.0841457546,0.1966480017,-0.0270573683,-0.0880918503,-0.2778379321,0.1220179349,-0.0720074773,-0.0023052332,0.2029411942,0.0612138882,-0.1524753571,-0.3158774972,-0.0474574044,0.289884001,0.0772339255,-0.4448517859,0.1399446577,-0.2746499181,0.0384695977,-0.1693123728,0.1227735728,-0.3491450548,-0.3374187946,-0.0173502117,0.1677834988,0.0397741683,0.023976896,-0.3611675203,0.3172857463,-0.3596293926,-0.0370979793,-0.1784277409,-0.3712448776,-0.1486219168,0.0270703863,0.2050141543,0.0677456036,0.2444338351,0.0925387964,-0.1358068585,-0.1985537261,0.2229715288,-0.0905946866,-0.0714234337,0.1459326744,-0.3722260296,-0.0143959671,-0.041953478,-0.0836177021,0.001237568,-0.0441604704,0.0252002217,-0.2760803103,0.076716952,0.1379478723,-0.0329541266,-0.126061365,-0.3585487604,-0.297344327,-0.1416451782,-0.0665722191,-0.0133502455,0.381219238,-0.1233073846,-0.109098427,0.2232977748,-0.2107465267,-0.5925027132,-0.4445326626,0.0657245293,-0.1588762552,-0.1897361875,-0.0171294678,0.1173106879,0.1880111396,0.1741952747,-0.5063966513,0.0306788776,-0.0522000492,0.1078686342,0.1149188355,0.0119206095,0.2383215278,0.0561734252,0.0068713585,-0.1866166741,0.0011006157,0.0650862604,-0.0169898253,0.215507552,0.031028172,-0.1636013985,-0.1278615892,0.9876939058,0.2820958197,-0.0200459696,0.2478423119,0.2097233236,0.5658015609,0.0727661252,-0.2682198286,-0.0198029596,-0.32165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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"I got another error that convinces me there is a race condition (one of the test files had zero samples at prediction time). I think it comes down to the fact that the `config_id` above (used in the naming for the cache) has no information on who's touching the data. If I have 2 concurrent jobs, both loading the same dataset and forcing redownload, they may step on each other foot\/caching of the dataset. ","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":74,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n I got another error that convinces me there is a race condition (one of the test files had zero samples at prediction time). I think it comes down to the fact that the `config_id` above (used in the naming for the cache) has no information on who's touching the data. If I have 2 concurrent jobs, both loading the same dataset and forcing redownload, they may step on each other foot\/caching of the dataset. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"We're using a locking mechanism to prevent two processes from writing at the same time. The locking is based on the `filelock` module.\r\nAlso directories that are being written use a suffix \".incomplete\" so that reading is not possible on a dataset being written.\r\n\r\nDo you think you could provide a simple code to reproduce the race condition you experienced ?","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":61,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n We're using a locking mechanism to prevent two processes from writing at the same time. The locking is based on the `filelock` module.\r\nAlso directories that are being written use a suffix \".incomplete\" so that reading is not possible on a dataset being written.\r\n\r\nDo you think you could provide a simple code to reproduce the race condition you experienced ?","embeddings":[-0.196753189,0.1176110059,-0.0897247791,0.355255723,0.1577601433,0.1552672088,0.1879122555,0.1608638912,0.3102245629,-0.1881488115,0.1910189688,0.0179731119,-0.2734490633,0.0268451404,-0.2476358712,0.4049519598,0.1267285794,-0.0034802724,-0.1405780464,0.0179454051,0.0459334515,0.0447995812,-0.1037570462,-0.0012473409,-0.476983726,-0.2035485059,0.0959886834,0.1866528988,-0.0399648771,-0.5564548969,0.3019807339,0.4502322674,0.0678038895,0.5293136835,-0.000115094,-0.127984643,0.2920048237,-0.0531719662,-0.2578074932,-0.072666049,-0.5385324359,-0.4082248807,0.2471218407,-0.146911934,-0.0855540335,-0.0813922212,0.1438263208,-0.8633799553,0.5533196926,0.2262484878,0.1847568601,0.0490379669,-0.3496796489,0.0953061506,0.0084356172,0.0291794706,-0.0481614172,0.0714802593,0.4345098138,-0.2140529752,-0.1860693246,0.3216883838,-0.2358110845,0.2497453392,0.4829368591,0.0574803166,-0.1865171492,-0.2884808779,0.1342118382,0.0174988154,0.7710599303,-0.4393549562,-0.1225506067,-0.3344056606,-0.0687558502,-0.3336839378,0.3206148446,-0.0728345737,-0.0339725092,0.2561246455,-0.472356528,-0.0451729968,0.0078581031,-0.1658639163,0.0376529098,-0.0919709876,-0.1253417432,0.0373961031,-0.1179847047,0.1216066778,0.5495470166,-0.5747789741,0.0110015748,0.2291449308,-0.5444290638,-0.0410546325,0.0324345529,0.4762230217,-0.1241324171,0.1248387769,-0.0110148611,0.0557427444,-0.0400535017,0.1130376309,0.3680425584,0.2238933593,0.121543318,0.2314200252,0.2183250636,-0.1693474203,-0.2075107992,-0.0547740422,0.0730549321,-0.3701848686,0.5901768804,0.0753738359,0.1406158656,-0.3174619675,-0.3990462422,0.2899316251,-0.0197662972,-0.0911198407,0.1224696338,0.1468481123,-0.0552359708,-0.2533983588,-0.0159429125,-0.0094256988,-0.2785454988,-0.1450182348,-0.1850530058,-0.1182996035,-0.3526631296,-0.0679666027,0.1608492732,-0.1622107476,-0.0802540854,0.4727271199,-0.029606238,-0.1480693519,0.2859733105,-0.1099013165,0.0946576819,0.2131463587,-0.1637274921,0.3256706893,0.4162673056,-0.0792467371,-0.1719822437,0.3135366142,-0.3567288816,-0.2460056245,0.0298705455,0.1221244261,-0.5536221266,-0.0403471068,0.0136223137,0.0955767781,0.4750459194,0.0389860831,0.2495601326,-0.1382685006,-0.2923187017,-0.1906098276,-0.0443615206,0.731172502,-0.5330130458,0.1285101622,-0.1396491379,-0.0286400672,0.1445684135,0.2841942608,-0.2962820232,0.1323597878,-0.2115534991,-0.131062746,0.0573135279,-0.1184853688,-0.4381595552,0.174296245,-0.1678357869,0.4191295505,0.2183451355,0.302973032,0.0468535759,-0.0431203954,0.0522445664,-0.0761143863,0.0460267179,-0.2244427949,-0.1492369473,-0.3316139579,0.221999228,-0.1010166705,0.0453742519,0.0104914447,0.0069762175,-0.1455114931,0.1469559371,-0.0052159191,0.1594039649,0.3471348882,0.0670275539,0.2349253297,0.0162089616,-0.139325738,-0.4302188456,0.3824398816,0.1700233966,-0.3273444176,-0.0764160827,-0.1682446301,-0.274194032,-0.1506723911,-0.4196013212,-0.0648655742,0.0559284091,-0.1632685661,-0.0997350514,0.0063885967,-0.0581865646,0.7144823074,-0.3550771773,-0.1178708747,-0.1787383705,0.1654230952,-0.0695138872,-0.0650652945,-0.1282577813,0.016238397,0.1677399576,0.088434346,0.0401568003,0.1977181882,0.1437592059,0.4981746078,-0.0173988808,0.4018269181,0.0288784131,0.0421065651,0.1149366274,0.0580227301,0.1100393608,-0.0902630389,-0.2157793194,0.4076112211,-0.3188075125,0.0771591738,-0.2222145051,-0.1766803712,0.0708627626,-0.1201454252,0.0369205289,-0.053223297,0.326100409,0.0933621824,0.0394372642,0.2987610996,-0.1140406504,0.1542569548,0.3554931581,0.1300945282,0.1081680655,-0.0364470668,0.0031142693,-0.1081881672,0.0884905532,0.3828621507,0.4414940476,0.1244364381,0.1364682615,0.0507013127,-0.0579844825,-0.2591862381,0.3113619685,-0.1765068322,-0.0299759656,0.2807720006,0.0883563086,-0.1850343645,-0.2827694416,-0.1310247183,0.1508902758,0.2686749697,-0.414002955,0.189224571,-0.4363931417,-0.006558218,-0.2460869253,0.1756597012,-0.1134246811,-0.2515414357,0.0480956212,0.3083199263,0.1618435681,-0.0087755835,-0.363363117,0.1862072349,-0.0862537995,-0.0292842239,-0.1167898998,-0.2754992545,-0.1771163046,0.0540667474,0.2507048845,0.0493864864,0.2407139838,0.0357820429,-0.0555945002,-0.1726720184,0.0869023576,-0.1923737973,0.0038501304,0.3356839418,-0.1077344865,0.0945145786,-0.0795742273,-0.0116854757,-0.147387445,0.0740206838,-0.0783890486,-0.125515759,0.0772518516,0.0360645577,-0.1096629277,-0.1280042827,-0.308237344,-0.1841048002,-0.0727252215,-0.0917507634,0.01081663,0.4160496593,0.0477247052,0.0082806358,0.0806196406,-0.0969172567,-0.4676979482,-0.3725499511,0.1579973996,0.0714701563,-0.1243049502,0.0383487493,0.1377870291,0.1091660932,0.0639515966,-0.4974830449,-0.0647069365,-0.2150494456,0.1265069991,0.0788128972,-0.0567570329,0.3888295591,0.1091576368,-0.034947034,-0.2198407203,0.1228038892,0.0552483983,-0.2224344611,0.1876205802,-0.0696273968,0.1301476955,0.0980787948,0.9211750031,0.4421302378,-0.1490120441,0.1426526755,0.0367858633,0.5512974858,0.0714660138,-0.2881068885,-0.2727802992,-0.20127560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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2187","title":"Question (potential issue?) related to datasets caching","comments":"I can provide details about the code I'm running (it's really-really close to some official samples from the huggingface transformers examples, I can point to the exact sample file, I kept a record of that). I can also describe in which conditions this race occurs (I'm convinced it has to do with forcing the redownloading of the dataset, I've been running hundreds of experiments before and didn't have a problem before I forced the redownload). I also can provide samples of the different stack errors I get and some details about the level of concurrency of jobs I was running. I can also try to imagine how the race manifests (I'm fairly sure that it's a combo of one job cleaning up and another job being in the middle of the run).\r\n\r\nHowever, I have to cleanup all this to make sure I'm no spilling any info I shouldn't be spilling. I'll try to do it by the end of the week, if you think all this is helpful. \r\n\r\nFor now, I have a workaround. Don't use forcing redownloading. And to be ultra careful (although I don't think this is a problem), I run a series of jobs that will prepare the datasets and I know there is no concurrency wrt the dataset. Once that's done (and I believe even having multiple jobs loading the datasets at the same time doesn't create problems, as long as REUSE_DATASET_IF_EXISTS is the policy for loading the dataset, so the filelock mechanism you're using is working in that scenario), the prepared datasets will be reused, no race possible in any way. \r\n\r\nThanks for all the details you provided, it helped me understand the underlying implementation and coming up with workarounds when I ran into issues. ","body":"I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!","comment_length":291,"text":"Question (potential issue?) related to datasets caching \n I thought I had disabled datasets caching in my code, as follows:\r\n```\r\nfrom datasets import set_caching_enabled\r\n...\r\ndef main():\r\n\r\n    # disable caching in datasets\r\n    set_caching_enabled(False)\r\n```\r\nHowever, in my log files I see messages like the following:\r\n\r\n```\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Using custom data configuration default-888a87931cbc5877\r\n04\/07\/2021 18:34:42 - WARNING - datasets.builder -   Reusing dataset csv (xxxx\/cache-transformers\/datasets\/csv\/default-888a87931cbc5877\/0.0.0\/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93\r\n```\r\nCan you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! \n I can provide details about the code I'm running (it's really-really close to some official samples from the huggingface transformers examples, I can point to the exact sample file, I kept a record of that). I can also describe in which conditions this race occurs (I'm convinced it has to do with forcing the redownloading of the dataset, I've been running hundreds of experiments before and didn't have a problem before I forced the redownload). I also can provide samples of the different stack errors I get and some details about the level of concurrency of jobs I was running. I can also try to imagine how the race manifests (I'm fairly sure that it's a combo of one job cleaning up and another job being in the middle of the run).\r\n\r\nHowever, I have to cleanup all this to make sure I'm no spilling any info I shouldn't be spilling. I'll try to do it by the end of the week, if you think all this is helpful. \r\n\r\nFor now, I have a workaround. Don't use forcing redownloading. And to be ultra careful (although I don't think this is a problem), I run a series of jobs that will prepare the datasets and I know there is no concurrency wrt the dataset. Once that's done (and I believe even having multiple jobs loading the datasets at the same time doesn't create problems, as long as REUSE_DATASET_IF_EXISTS is the policy for loading the dataset, so the filelock mechanism you're using is working in that scenario), the prepared datasets will be reused, no race possible in any way. \r\n\r\nThanks for all the details you provided, it helped me understand the underlying implementation and coming up with workarounds when I ran into issues. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2185","title":".map() and distributed training","comments":"Hi, one workaround would be to save the mapped(tokenized in your case) file using `save_to_disk`, and having each process load this file using `load_from_disk`. This is what I am doing, and in this case, I turn off the ability to automatically load from the cache.\r\n\r\nAlso, multiprocessing the map function seems to be slower at the moment (#1992), hope this helps you.","body":"Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters.","comment_length":62,"text":".map() and distributed training \n Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters. \n Hi, one workaround would be to save the mapped(tokenized in your case) file using `save_to_disk`, and having each process load this file using `load_from_disk`. This is what I am doing, and in this case, I turn off the ability to automatically load from the cache.\r\n\r\nAlso, multiprocessing the map function seems to be slower at the moment (#1992), hope this helps 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2185","title":".map() and distributed training","comments":"Thanks @hwijeen for the workaround, feels a bit prototypical but it works! (it seems files are written twice then though)\r\n\r\n(I haven't observed slowness using multiprocessed map function but I could be wrong)","body":"Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters.","comment_length":33,"text":".map() and distributed training \n Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters. \n Thanks @hwijeen for the workaround, feels a bit prototypical but it works! (it seems files are written twice then though)\r\n\r\n(I haven't observed slowness using multiprocessed map function but I could be wrong)","embeddings":[-0.2537618279,-0.2207167447,0.1177767888,0.0745745301,0.2116166502,-0.1097597107,0.5170732141,0.0102053266,0.2801567912,0.0237633772,0.1822478175,0.3434494138,-0.2290642709,-0.5855442286,0.0941431373,0.0615340844,0.1861155033,0.0366594605,-0.0377356932,-0.2008032501,-0.1529330909,0.0759166479,-0.1542706788,0.1170513257,-0.7540709376,-0.1523153037,-0.1834535897,0.1027398556,0.2253180742,-0.3499087691,0.2342278361,0.2213348448,0.4013623595,0.5336914062,-0.0001272091,0.0312626697,0.1878611445,-0.1983309686,0.0397178754,-0.0476766787,0.1641900688,0.0050253472,0.1936113387,-0.2953366041,0.1310725659,-0.1476733088,0.3262221217,-0.4418162107,0.4140013158,-0.1077297255,-0.0018774115,0.0943896994,-0.5714957118,0.1398764253,-0.2214399129,0.1234803572,0.1711140275,0.2180822492,0.3029266596,-0.3416551054,-0.3789148629,0.1582272202,-0.1466236264,0.1340594739,0.4135627449,0.0366018042,-0.0789881125,-0.3487006128,-0.075750038,-0.1218584478,0.1076876521,-0.0743067637,-0.1881792545,-0.4665382504,-0.2733976543,-0.0016580709,-0.0024628954,0.116243735,-0.0233727768,-0.1197138727,-0.5124091506,-0.0625246316,0.0552944317,0.1326544136,-0.2732613087,0.4564465284,-0.0254738089,0.3163393438,0.1419928521,0.0543378443,0.0837460086,-0.1719923168,0.1296369135,0.3715832233,0.0275528189,0.0141743347,0.0489273742,-0.3223410249,-0.0232889,0.0356097072,0.2321069092,0.2598097324,-0.0479859076,0.265766263,0.4007704556,0.0927509293,-0.1391413957,0.6415418983,0.0301112514,-0.2004719824,-0.6418218613,-0.1724125892,-0.3519452512,-0.0627355203,0.3335886896,0.0427098982,-0.2098451257,0.0722271129,0.4604047239,-0.2394168377,-0.0065294988,-0.1325436831,0.0212120153,0.2508155704,0.1287937164,0.437037617,-0.0555269644,0.1014266536,-0.190160498,0.1589486897,-0.0596041419,-0.3029897511,-0.3049105704,0.316068083,0.1618765295,0.1877589226,0.4239696264,-0.1001108065,0.289442122,-0.1613698006,0.3981889188,-0.1120462343,0.6585771441,0.1044589132,-0.2513783276,0.2223655581,0.1862223744,0.3770996928,-0.1535631716,0.335416913,-0.5022829771,-0.4383485317,0.4798748195,-0.0732970536,-0.0095938472,0.0692022368,-0.1767223477,0.0427591875,0.5391663909,-0.2560784817,0.134424001,-0.3575458229,-0.2090658844,-0.1776281595,0.2568801343,0.4551616609,0.3137147129,-0.341816932,0.1241682246,0.3162250221,0.2081830502,0.2290944606,-0.6239327788,0.3898105621,-0.2199796587,0.2747520506,0.3872870505,-0.3571222723,-0.1435228735,0.2945982218,-0.0989739373,0.0233595725,-0.1570941806,-0.0249821246,0.4641161859,-0.0252317898,0.1258820295,0.2964549661,-0.216262728,0.2001035213,-0.261736244,0.0169189703,0.0804381073,-0.0070854248,0.1947211921,0.0033709346,-0.0128012626,0.1196192652,0.2070769817,0.0102299741,0.2283787429,0.0252541639,-0.3850671053,0.0924091339,0.165482685,0.0665195286,-0.1504980773,0.1814241707,-0.0332348272,-0.3959296942,-0.1602503955,-0.0522115901,0.1628748178,-0.2076937854,-0.3108922839,-0.1402078569,-0.1141550615,0.1380293667,-0.0407926142,-0.2170617431,-0.3000540435,-0.0936286971,0.1410432905,-0.0588696413,0.2177682668,0.1037803516,0.1914524436,-0.1203961521,-0.4317591786,-0.1487240344,0.1081416756,-0.2924800515,-0.1076147482,0.3776341677,0.0455733202,0.2080194801,-0.1105848923,0.3216450512,0.2376917005,0.066899173,0.1610087454,0.1279921681,-0.1245717034,-0.1512021422,-0.142078042,0.3465422094,0.128989622,0.1471359581,-0.0155485664,0.0163485743,0.0418233871,0.0829828158,-0.2396878004,-0.0824884102,-0.3200368583,-0.1921786666,0.2612736225,0.1546629518,0.0379151292,0.0008637675,0.3312492073,0.0261272565,0.0083661247,-0.1484815776,-0.1806361079,0.0400650427,0.1552491784,0.1572336107,0.3422781229,-0.0142571228,0.3932816386,-0.1144815236,0.1034137383,0.06225916,-0.0817419663,-0.0038878322,0.2324126065,0.15785712,-0.0431561358,0.183517918,-0.2471131682,-0.104632467,0.3485348523,-0.2459665388,0.0869296491,0.2329536527,-0.0123324441,0.1808802485,-0.5816081762,0.0918158442,0.0024792906,0.0669043064,-0.1619982123,0.2604064047,-0.0950392038,0.4181236029,-0.0660849512,0.2435841858,-0.1192760319,-0.2782994807,0.1805499345,-0.15312244,-0.012159599,-0.1923269033,0.1284361035,-0.2399803996,-0.0226501599,0.0775606632,-0.0577873811,-0.1429297924,0.1827362925,0.1133465022,0.2074275613,-0.5552704334,-0.2969094515,-0.0320735425,-0.378795594,-0.1486826241,0.1686326563,-0.4979081154,0.1192707866,-0.055137407,0.245264411,-0.1717564315,-0.1245706826,-0.3170382977,-0.2305708379,-0.0193637144,0.133881554,-0.0549433865,0.1418401748,0.0033795778,-0.1053651124,-0.2393789291,-0.1764801741,-0.2918813229,0.0651777461,-0.5251649618,0.3886308968,-0.1867223978,-0.0471369028,-0.109347865,-0.0461086854,-0.0654087663,0.6399704814,-0.334898591,0.0144050214,0.3095326722,-0.010113867,-0.0965969041,0.1116476655,0.5424685478,0.1066478863,0.1266251504,-0.0549990423,0.2443153262,0.2560414374,0.1710574329,0.0513743982,0.1595277488,-0.1193900257,0.1637128294,1.2782388926,0.1522191167,-0.0518915094,0.0262432639,0.0314820632,-0.0140471822,0.0686671063,-0.1058131158,0.0613840669,-0.3951035738,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2185","title":".map() and distributed training","comments":"To my understanding, files are written twice anyhow(one after load_dataset, another aftet map). It's just that you now have it at the location where you can see, whereas it was secretlely saved at caching folder(.cache\/huggingface\/datasets by default)! Correct me if I'm wrong!","body":"Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters.","comment_length":42,"text":".map() and distributed training \n Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters. \n To my understanding, files are written twice anyhow(one after load_dataset, another aftet map). It's just that you now have it at the location where you can see, whereas it was secretlely saved at caching folder(.cache\/huggingface\/datasets by default)! Correct me if I'm wrong!","embeddings":[-0.2537618279,-0.2207167447,0.1177767888,0.0745745301,0.2116166502,-0.1097597107,0.5170732141,0.0102053266,0.2801567912,0.0237633772,0.1822478175,0.3434494138,-0.2290642709,-0.5855442286,0.0941431373,0.0615340844,0.1861155033,0.0366594605,-0.0377356932,-0.2008032501,-0.1529330909,0.0759166479,-0.1542706788,0.1170513257,-0.7540709376,-0.1523153037,-0.1834535897,0.1027398556,0.2253180742,-0.3499087691,0.2342278361,0.2213348448,0.4013623595,0.5336914062,-0.0001272091,0.0312626697,0.1878611445,-0.1983309686,0.0397178754,-0.0476766787,0.1641900688,0.0050253472,0.1936113387,-0.2953366041,0.1310725659,-0.1476733088,0.3262221217,-0.4418162107,0.4140013158,-0.1077297255,-0.0018774115,0.0943896994,-0.5714957118,0.1398764253,-0.2214399129,0.1234803572,0.1711140275,0.2180822492,0.3029266596,-0.3416551054,-0.3789148629,0.1582272202,-0.1466236264,0.1340594739,0.4135627449,0.0366018042,-0.0789881125,-0.3487006128,-0.075750038,-0.1218584478,0.1076876521,-0.0743067637,-0.1881792545,-0.4665382504,-0.2733976543,-0.0016580709,-0.0024628954,0.116243735,-0.0233727768,-0.1197138727,-0.5124091506,-0.0625246316,0.0552944317,0.1326544136,-0.2732613087,0.4564465284,-0.0254738089,0.3163393438,0.1419928521,0.0543378443,0.0837460086,-0.1719923168,0.1296369135,0.3715832233,0.0275528189,0.0141743347,0.0489273742,-0.3223410249,-0.0232889,0.0356097072,0.2321069092,0.2598097324,-0.0479859076,0.265766263,0.4007704556,0.0927509293,-0.1391413957,0.6415418983,0.0301112514,-0.2004719824,-0.6418218613,-0.1724125892,-0.3519452512,-0.0627355203,0.3335886896,0.0427098982,-0.2098451257,0.0722271129,0.4604047239,-0.2394168377,-0.0065294988,-0.1325436831,0.0212120153,0.2508155704,0.1287937164,0.437037617,-0.0555269644,0.1014266536,-0.190160498,0.1589486897,-0.0596041419,-0.3029897511,-0.3049105704,0.316068083,0.1618765295,0.1877589226,0.4239696264,-0.1001108065,0.289442122,-0.1613698006,0.3981889188,-0.1120462343,0.6585771441,0.1044589132,-0.2513783276,0.2223655581,0.1862223744,0.3770996928,-0.1535631716,0.335416913,-0.5022829771,-0.4383485317,0.4798748195,-0.0732970536,-0.0095938472,0.0692022368,-0.1767223477,0.0427591875,0.5391663909,-0.2560784817,0.134424001,-0.3575458229,-0.2090658844,-0.1776281595,0.2568801343,0.4551616609,0.3137147129,-0.341816932,0.1241682246,0.3162250221,0.2081830502,0.2290944606,-0.6239327788,0.3898105621,-0.2199796587,0.2747520506,0.3872870505,-0.3571222723,-0.1435228735,0.2945982218,-0.0989739373,0.0233595725,-0.1570941806,-0.0249821246,0.4641161859,-0.0252317898,0.1258820295,0.2964549661,-0.216262728,0.2001035213,-0.261736244,0.0169189703,0.0804381073,-0.0070854248,0.1947211921,0.0033709346,-0.0128012626,0.1196192652,0.2070769817,0.0102299741,0.2283787429,0.0252541639,-0.3850671053,0.0924091339,0.165482685,0.0665195286,-0.1504980773,0.1814241707,-0.0332348272,-0.3959296942,-0.1602503955,-0.0522115901,0.1628748178,-0.2076937854,-0.3108922839,-0.1402078569,-0.1141550615,0.1380293667,-0.0407926142,-0.2170617431,-0.3000540435,-0.0936286971,0.1410432905,-0.0588696413,0.2177682668,0.1037803516,0.1914524436,-0.1203961521,-0.4317591786,-0.1487240344,0.1081416756,-0.2924800515,-0.1076147482,0.3776341677,0.0455733202,0.2080194801,-0.1105848923,0.3216450512,0.2376917005,0.066899173,0.1610087454,0.1279921681,-0.1245717034,-0.1512021422,-0.142078042,0.3465422094,0.128989622,0.1471359581,-0.0155485664,0.0163485743,0.0418233871,0.0829828158,-0.2396878004,-0.0824884102,-0.3200368583,-0.1921786666,0.2612736225,0.1546629518,0.0379151292,0.0008637675,0.3312492073,0.0261272565,0.0083661247,-0.1484815776,-0.1806361079,0.0400650427,0.1552491784,0.1572336107,0.3422781229,-0.0142571228,0.3932816386,-0.1144815236,0.1034137383,0.06225916,-0.0817419663,-0.0038878322,0.2324126065,0.15785712,-0.0431561358,0.183517918,-0.2471131682,-0.104632467,0.3485348523,-0.2459665388,0.0869296491,0.2329536527,-0.0123324441,0.1808802485,-0.5816081762,0.0918158442,0.0024792906,0.0669043064,-0.1619982123,0.2604064047,-0.0950392038,0.4181236029,-0.0660849512,0.2435841858,-0.1192760319,-0.2782994807,0.1805499345,-0.15312244,-0.012159599,-0.1923269033,0.1284361035,-0.2399803996,-0.0226501599,0.0775606632,-0.0577873811,-0.1429297924,0.1827362925,0.1133465022,0.2074275613,-0.5552704334,-0.2969094515,-0.0320735425,-0.378795594,-0.1486826241,0.1686326563,-0.4979081154,0.1192707866,-0.055137407,0.245264411,-0.1717564315,-0.1245706826,-0.3170382977,-0.2305708379,-0.0193637144,0.133881554,-0.0549433865,0.1418401748,0.0033795778,-0.1053651124,-0.2393789291,-0.1764801741,-0.2918813229,0.0651777461,-0.5251649618,0.3886308968,-0.1867223978,-0.0471369028,-0.109347865,-0.0461086854,-0.0654087663,0.6399704814,-0.334898591,0.0144050214,0.3095326722,-0.010113867,-0.0965969041,0.1116476655,0.5424685478,0.1066478863,0.1266251504,-0.0549990423,0.2443153262,0.2560414374,0.1710574329,0.0513743982,0.1595277488,-0.1193900257,0.1637128294,1.2782388926,0.1522191167,-0.0518915094,0.0262432639,0.0314820632,-0.0140471822,0.0686671063,-0.1058131158,0.0613840669,-0.3951035738,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2185","title":".map() and distributed training","comments":"So to answer my initial question, I was just doing something stupid as I was not re-giving the `preprocessing_num_workers` arguments when launching the distributed training (and it was then set to `None`). I initially thought the hash was computed only with the `tokenize_function` but it's all arguments. Thanks @lhoestq for clarifying!","body":"Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters.","comment_length":51,"text":".map() and distributed training \n Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters. \n So to answer my initial question, I was just doing something stupid as I was not re-giving the `preprocessing_num_workers` arguments when launching the distributed training (and it was then set to `None`). I initially thought the hash was computed only with the `tokenize_function` but it's all arguments. Thanks @lhoestq for clarifying!","embeddings":[-0.2537618279,-0.2207167447,0.1177767888,0.0745745301,0.2116166502,-0.1097597107,0.5170732141,0.0102053266,0.2801567912,0.0237633772,0.1822478175,0.3434494138,-0.2290642709,-0.5855442286,0.0941431373,0.0615340844,0.1861155033,0.0366594605,-0.0377356932,-0.2008032501,-0.1529330909,0.0759166479,-0.1542706788,0.1170513257,-0.7540709376,-0.1523153037,-0.1834535897,0.1027398556,0.2253180742,-0.3499087691,0.2342278361,0.2213348448,0.4013623595,0.5336914062,-0.0001272091,0.0312626697,0.1878611445,-0.1983309686,0.0397178754,-0.0476766787,0.1641900688,0.0050253472,0.1936113387,-0.2953366041,0.1310725659,-0.1476733088,0.3262221217,-0.4418162107,0.4140013158,-0.1077297255,-0.0018774115,0.0943896994,-0.5714957118,0.1398764253,-0.2214399129,0.1234803572,0.1711140275,0.2180822492,0.3029266596,-0.3416551054,-0.3789148629,0.1582272202,-0.1466236264,0.1340594739,0.4135627449,0.0366018042,-0.0789881125,-0.3487006128,-0.075750038,-0.1218584478,0.1076876521,-0.0743067637,-0.1881792545,-0.4665382504,-0.2733976543,-0.0016580709,-0.0024628954,0.116243735,-0.0233727768,-0.1197138727,-0.5124091506,-0.0625246316,0.0552944317,0.1326544136,-0.2732613087,0.4564465284,-0.0254738089,0.3163393438,0.1419928521,0.0543378443,0.0837460086,-0.1719923168,0.1296369135,0.3715832233,0.0275528189,0.0141743347,0.0489273742,-0.3223410249,-0.0232889,0.0356097072,0.2321069092,0.2598097324,-0.0479859076,0.265766263,0.4007704556,0.0927509293,-0.1391413957,0.6415418983,0.0301112514,-0.2004719824,-0.6418218613,-0.1724125892,-0.3519452512,-0.0627355203,0.3335886896,0.0427098982,-0.2098451257,0.0722271129,0.4604047239,-0.2394168377,-0.0065294988,-0.1325436831,0.0212120153,0.2508155704,0.1287937164,0.437037617,-0.0555269644,0.1014266536,-0.190160498,0.1589486897,-0.0596041419,-0.3029897511,-0.3049105704,0.316068083,0.1618765295,0.1877589226,0.4239696264,-0.1001108065,0.289442122,-0.1613698006,0.3981889188,-0.1120462343,0.6585771441,0.1044589132,-0.2513783276,0.2223655581,0.1862223744,0.3770996928,-0.1535631716,0.335416913,-0.5022829771,-0.4383485317,0.4798748195,-0.0732970536,-0.0095938472,0.0692022368,-0.1767223477,0.0427591875,0.5391663909,-0.2560784817,0.134424001,-0.3575458229,-0.2090658844,-0.1776281595,0.2568801343,0.4551616609,0.3137147129,-0.341816932,0.1241682246,0.3162250221,0.2081830502,0.2290944606,-0.6239327788,0.3898105621,-0.2199796587,0.2747520506,0.3872870505,-0.3571222723,-0.1435228735,0.2945982218,-0.0989739373,0.0233595725,-0.1570941806,-0.0249821246,0.4641161859,-0.0252317898,0.1258820295,0.2964549661,-0.216262728,0.2001035213,-0.261736244,0.0169189703,0.0804381073,-0.0070854248,0.1947211921,0.0033709346,-0.0128012626,0.1196192652,0.2070769817,0.0102299741,0.2283787429,0.0252541639,-0.3850671053,0.0924091339,0.165482685,0.0665195286,-0.1504980773,0.1814241707,-0.0332348272,-0.3959296942,-0.1602503955,-0.0522115901,0.1628748178,-0.2076937854,-0.3108922839,-0.1402078569,-0.1141550615,0.1380293667,-0.0407926142,-0.2170617431,-0.3000540435,-0.0936286971,0.1410432905,-0.0588696413,0.2177682668,0.1037803516,0.1914524436,-0.1203961521,-0.4317591786,-0.1487240344,0.1081416756,-0.2924800515,-0.1076147482,0.3776341677,0.0455733202,0.2080194801,-0.1105848923,0.3216450512,0.2376917005,0.066899173,0.1610087454,0.1279921681,-0.1245717034,-0.1512021422,-0.142078042,0.3465422094,0.128989622,0.1471359581,-0.0155485664,0.0163485743,0.0418233871,0.0829828158,-0.2396878004,-0.0824884102,-0.3200368583,-0.1921786666,0.2612736225,0.1546629518,0.0379151292,0.0008637675,0.3312492073,0.0261272565,0.0083661247,-0.1484815776,-0.1806361079,0.0400650427,0.1552491784,0.1572336107,0.3422781229,-0.0142571228,0.3932816386,-0.1144815236,0.1034137383,0.06225916,-0.0817419663,-0.0038878322,0.2324126065,0.15785712,-0.0431561358,0.183517918,-0.2471131682,-0.104632467,0.3485348523,-0.2459665388,0.0869296491,0.2329536527,-0.0123324441,0.1808802485,-0.5816081762,0.0918158442,0.0024792906,0.0669043064,-0.1619982123,0.2604064047,-0.0950392038,0.4181236029,-0.0660849512,0.2435841858,-0.1192760319,-0.2782994807,0.1805499345,-0.15312244,-0.012159599,-0.1923269033,0.1284361035,-0.2399803996,-0.0226501599,0.0775606632,-0.0577873811,-0.1429297924,0.1827362925,0.1133465022,0.2074275613,-0.5552704334,-0.2969094515,-0.0320735425,-0.378795594,-0.1486826241,0.1686326563,-0.4979081154,0.1192707866,-0.055137407,0.245264411,-0.1717564315,-0.1245706826,-0.3170382977,-0.2305708379,-0.0193637144,0.133881554,-0.0549433865,0.1418401748,0.0033795778,-0.1053651124,-0.2393789291,-0.1764801741,-0.2918813229,0.0651777461,-0.5251649618,0.3886308968,-0.1867223978,-0.0471369028,-0.109347865,-0.0461086854,-0.0654087663,0.6399704814,-0.334898591,0.0144050214,0.3095326722,-0.010113867,-0.0965969041,0.1116476655,0.5424685478,0.1066478863,0.1266251504,-0.0549990423,0.2443153262,0.2560414374,0.1710574329,0.0513743982,0.1595277488,-0.1193900257,0.1637128294,1.2782388926,0.1522191167,-0.0518915094,0.0262432639,0.0314820632,-0.0140471822,0.0686671063,-0.1058131158,0.0613840669,-0.3951035738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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2185","title":".map() and distributed training","comments":"This cache process isn't really consistent. I just changed `per_device_train_batch_size` of training script and now it rebuilding the dataset cache!!!! Why?","body":"Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters.","comment_length":21,"text":".map() and distributed training \n Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters. \n This cache process isn't really consistent. I just changed `per_device_train_batch_size` of training script and now it rebuilding the dataset cache!!!! Why?","embeddings":[-0.2537618279,-0.2207167447,0.1177767888,0.0745745301,0.2116166502,-0.1097597107,0.5170732141,0.0102053266,0.2801567912,0.0237633772,0.1822478175,0.3434494138,-0.2290642709,-0.5855442286,0.0941431373,0.0615340844,0.1861155033,0.0366594605,-0.0377356932,-0.2008032501,-0.1529330909,0.0759166479,-0.1542706788,0.1170513257,-0.7540709376,-0.1523153037,-0.1834535897,0.1027398556,0.2253180742,-0.3499087691,0.2342278361,0.2213348448,0.4013623595,0.5336914062,-0.0001272091,0.0312626697,0.1878611445,-0.1983309686,0.0397178754,-0.0476766787,0.1641900688,0.0050253472,0.1936113387,-0.2953366041,0.1310725659,-0.1476733088,0.3262221217,-0.4418162107,0.4140013158,-0.1077297255,-0.0018774115,0.0943896994,-0.5714957118,0.1398764253,-0.2214399129,0.1234803572,0.1711140275,0.2180822492,0.3029266596,-0.3416551054,-0.3789148629,0.1582272202,-0.1466236264,0.1340594739,0.4135627449,0.0366018042,-0.0789881125,-0.3487006128,-0.075750038,-0.1218584478,0.1076876521,-0.0743067637,-0.1881792545,-0.4665382504,-0.2733976543,-0.0016580709,-0.0024628954,0.116243735,-0.0233727768,-0.1197138727,-0.5124091506,-0.0625246316,0.0552944317,0.1326544136,-0.2732613087,0.4564465284,-0.0254738089,0.3163393438,0.1419928521,0.0543378443,0.0837460086,-0.1719923168,0.1296369135,0.3715832233,0.0275528189,0.0141743347,0.0489273742,-0.3223410249,-0.0232889,0.0356097072,0.2321069092,0.2598097324,-0.0479859076,0.265766263,0.4007704556,0.0927509293,-0.1391413957,0.6415418983,0.0301112514,-0.2004719824,-0.6418218613,-0.1724125892,-0.3519452512,-0.0627355203,0.3335886896,0.0427098982,-0.2098451257,0.0722271129,0.4604047239,-0.2394168377,-0.0065294988,-0.1325436831,0.0212120153,0.2508155704,0.1287937164,0.437037617,-0.0555269644,0.1014266536,-0.190160498,0.1589486897,-0.0596041419,-0.3029897511,-0.3049105704,0.316068083,0.1618765295,0.1877589226,0.4239696264,-0.1001108065,0.289442122,-0.1613698006,0.3981889188,-0.1120462343,0.6585771441,0.1044589132,-0.2513783276,0.2223655581,0.1862223744,0.3770996928,-0.1535631716,0.335416913,-0.5022829771,-0.4383485317,0.4798748195,-0.0732970536,-0.0095938472,0.0692022368,-0.1767223477,0.0427591875,0.5391663909,-0.2560784817,0.134424001,-0.3575458229,-0.2090658844,-0.1776281595,0.2568801343,0.4551616609,0.3137147129,-0.341816932,0.1241682246,0.3162250221,0.2081830502,0.2290944606,-0.6239327788,0.3898105621,-0.2199796587,0.2747520506,0.3872870505,-0.3571222723,-0.1435228735,0.2945982218,-0.0989739373,0.0233595725,-0.1570941806,-0.0249821246,0.4641161859,-0.0252317898,0.1258820295,0.2964549661,-0.216262728,0.2001035213,-0.261736244,0.0169189703,0.0804381073,-0.0070854248,0.1947211921,0.0033709346,-0.0128012626,0.1196192652,0.2070769817,0.0102299741,0.2283787429,0.0252541639,-0.3850671053,0.0924091339,0.165482685,0.0665195286,-0.1504980773,0.1814241707,-0.0332348272,-0.3959296942,-0.1602503955,-0.0522115901,0.1628748178,-0.2076937854,-0.3108922839,-0.1402078569,-0.1141550615,0.1380293667,-0.0407926142,-0.2170617431,-0.3000540435,-0.0936286971,0.1410432905,-0.0588696413,0.2177682668,0.1037803516,0.1914524436,-0.1203961521,-0.4317591786,-0.1487240344,0.1081416756,-0.2924800515,-0.1076147482,0.3776341677,0.0455733202,0.2080194801,-0.1105848923,0.3216450512,0.2376917005,0.066899173,0.1610087454,0.1279921681,-0.1245717034,-0.1512021422,-0.142078042,0.3465422094,0.128989622,0.1471359581,-0.0155485664,0.0163485743,0.0418233871,0.0829828158,-0.2396878004,-0.0824884102,-0.3200368583,-0.1921786666,0.2612736225,0.1546629518,0.0379151292,0.0008637675,0.3312492073,0.0261272565,0.0083661247,-0.1484815776,-0.1806361079,0.0400650427,0.1552491784,0.1572336107,0.3422781229,-0.0142571228,0.3932816386,-0.1144815236,0.1034137383,0.06225916,-0.0817419663,-0.0038878322,0.2324126065,0.15785712,-0.0431561358,0.183517918,-0.2471131682,-0.104632467,0.3485348523,-0.2459665388,0.0869296491,0.2329536527,-0.0123324441,0.1808802485,-0.5816081762,0.0918158442,0.0024792906,0.0669043064,-0.1619982123,0.2604064047,-0.0950392038,0.4181236029,-0.0660849512,0.2435841858,-0.1192760319,-0.2782994807,0.1805499345,-0.15312244,-0.012159599,-0.1923269033,0.1284361035,-0.2399803996,-0.0226501599,0.0775606632,-0.0577873811,-0.1429297924,0.1827362925,0.1133465022,0.2074275613,-0.5552704334,-0.2969094515,-0.0320735425,-0.378795594,-0.1486826241,0.1686326563,-0.4979081154,0.1192707866,-0.055137407,0.245264411,-0.1717564315,-0.1245706826,-0.3170382977,-0.2305708379,-0.0193637144,0.133881554,-0.0549433865,0.1418401748,0.0033795778,-0.1053651124,-0.2393789291,-0.1764801741,-0.2918813229,0.0651777461,-0.5251649618,0.3886308968,-0.1867223978,-0.0471369028,-0.109347865,-0.0461086854,-0.0654087663,0.6399704814,-0.334898591,0.0144050214,0.3095326722,-0.010113867,-0.0965969041,0.1116476655,0.5424685478,0.1066478863,0.1266251504,-0.0549990423,0.2443153262,0.2560414374,0.1710574329,0.0513743982,0.1595277488,-0.1193900257,0.1637128294,1.2782388926,0.1522191167,-0.0518915094,0.0262432639,0.0314820632,-0.0140471822,0.0686671063,-0.1058131158,0.0613840669,-0.3951035738,-0.130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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2185","title":".map() and distributed training","comments":"Hi ! A `map` function is recomputed if the code changes or if any of the variables it uses changes. Can you check that your function doesn't use `per_device_train_batch_size` or any variable that contains `per_device_train_batch_size` ?","body":"Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters.","comment_length":36,"text":".map() and distributed training \n Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters. \n Hi ! A `map` function is recomputed if the code changes or if any of the variables it uses changes. Can you check that your function doesn't use `per_device_train_batch_size` or any variable that contains `per_device_train_batch_size` ?","embeddings":[-0.2537618279,-0.2207167447,0.1177767888,0.0745745301,0.2116166502,-0.1097597107,0.5170732141,0.0102053266,0.2801567912,0.0237633772,0.1822478175,0.3434494138,-0.2290642709,-0.5855442286,0.0941431373,0.0615340844,0.1861155033,0.0366594605,-0.0377356932,-0.2008032501,-0.1529330909,0.0759166479,-0.1542706788,0.1170513257,-0.7540709376,-0.1523153037,-0.1834535897,0.1027398556,0.2253180742,-0.3499087691,0.2342278361,0.2213348448,0.4013623595,0.5336914062,-0.0001272091,0.0312626697,0.1878611445,-0.1983309686,0.0397178754,-0.0476766787,0.1641900688,0.0050253472,0.1936113387,-0.2953366041,0.1310725659,-0.1476733088,0.3262221217,-0.4418162107,0.4140013158,-0.1077297255,-0.0018774115,0.0943896994,-0.5714957118,0.1398764253,-0.2214399129,0.1234803572,0.1711140275,0.2180822492,0.3029266596,-0.3416551054,-0.3789148629,0.1582272202,-0.1466236264,0.1340594739,0.4135627449,0.0366018042,-0.0789881125,-0.3487006128,-0.075750038,-0.1218584478,0.1076876521,-0.0743067637,-0.1881792545,-0.4665382504,-0.2733976543,-0.0016580709,-0.0024628954,0.116243735,-0.0233727768,-0.1197138727,-0.5124091506,-0.0625246316,0.0552944317,0.1326544136,-0.2732613087,0.4564465284,-0.0254738089,0.3163393438,0.1419928521,0.0543378443,0.0837460086,-0.1719923168,0.1296369135,0.3715832233,0.0275528189,0.0141743347,0.0489273742,-0.3223410249,-0.0232889,0.0356097072,0.2321069092,0.2598097324,-0.0479859076,0.265766263,0.4007704556,0.0927509293,-0.1391413957,0.6415418983,0.0301112514,-0.2004719824,-0.6418218613,-0.1724125892,-0.3519452512,-0.0627355203,0.3335886896,0.0427098982,-0.2098451257,0.0722271129,0.4604047239,-0.2394168377,-0.0065294988,-0.1325436831,0.0212120153,0.2508155704,0.1287937164,0.437037617,-0.0555269644,0.1014266536,-0.190160498,0.1589486897,-0.0596041419,-0.3029897511,-0.3049105704,0.316068083,0.1618765295,0.1877589226,0.4239696264,-0.1001108065,0.289442122,-0.1613698006,0.3981889188,-0.1120462343,0.6585771441,0.1044589132,-0.2513783276,0.2223655581,0.1862223744,0.3770996928,-0.1535631716,0.335416913,-0.5022829771,-0.4383485317,0.4798748195,-0.0732970536,-0.0095938472,0.0692022368,-0.1767223477,0.0427591875,0.5391663909,-0.2560784817,0.134424001,-0.3575458229,-0.2090658844,-0.1776281595,0.2568801343,0.4551616609,0.3137147129,-0.341816932,0.1241682246,0.3162250221,0.2081830502,0.2290944606,-0.6239327788,0.3898105621,-0.2199796587,0.2747520506,0.3872870505,-0.3571222723,-0.1435228735,0.2945982218,-0.0989739373,0.0233595725,-0.1570941806,-0.0249821246,0.4641161859,-0.0252317898,0.1258820295,0.2964549661,-0.216262728,0.2001035213,-0.261736244,0.0169189703,0.0804381073,-0.0070854248,0.1947211921,0.0033709346,-0.0128012626,0.1196192652,0.2070769817,0.0102299741,0.2283787429,0.0252541639,-0.3850671053,0.0924091339,0.165482685,0.0665195286,-0.1504980773,0.1814241707,-0.0332348272,-0.3959296942,-0.1602503955,-0.0522115901,0.1628748178,-0.2076937854,-0.3108922839,-0.1402078569,-0.1141550615,0.1380293667,-0.0407926142,-0.2170617431,-0.3000540435,-0.0936286971,0.1410432905,-0.0588696413,0.2177682668,0.1037803516,0.1914524436,-0.1203961521,-0.4317591786,-0.1487240344,0.1081416756,-0.2924800515,-0.1076147482,0.3776341677,0.0455733202,0.2080194801,-0.1105848923,0.3216450512,0.2376917005,0.066899173,0.1610087454,0.1279921681,-0.1245717034,-0.1512021422,-0.142078042,0.3465422094,0.128989622,0.1471359581,-0.0155485664,0.0163485743,0.0418233871,0.0829828158,-0.2396878004,-0.0824884102,-0.3200368583,-0.1921786666,0.2612736225,0.1546629518,0.0379151292,0.0008637675,0.3312492073,0.0261272565,0.0083661247,-0.1484815776,-0.1806361079,0.0400650427,0.1552491784,0.1572336107,0.3422781229,-0.0142571228,0.3932816386,-0.1144815236,0.1034137383,0.06225916,-0.0817419663,-0.0038878322,0.2324126065,0.15785712,-0.0431561358,0.183517918,-0.2471131682,-0.104632467,0.3485348523,-0.2459665388,0.0869296491,0.2329536527,-0.0123324441,0.1808802485,-0.5816081762,0.0918158442,0.0024792906,0.0669043064,-0.1619982123,0.2604064047,-0.0950392038,0.4181236029,-0.0660849512,0.2435841858,-0.1192760319,-0.2782994807,0.1805499345,-0.15312244,-0.012159599,-0.1923269033,0.1284361035,-0.2399803996,-0.0226501599,0.0775606632,-0.0577873811,-0.1429297924,0.1827362925,0.1133465022,0.2074275613,-0.5552704334,-0.2969094515,-0.0320735425,-0.378795594,-0.1486826241,0.1686326563,-0.4979081154,0.1192707866,-0.055137407,0.245264411,-0.1717564315,-0.1245706826,-0.3170382977,-0.2305708379,-0.0193637144,0.133881554,-0.0549433865,0.1418401748,0.0033795778,-0.1053651124,-0.2393789291,-0.1764801741,-0.2918813229,0.0651777461,-0.5251649618,0.3886308968,-0.1867223978,-0.0471369028,-0.109347865,-0.0461086854,-0.0654087663,0.6399704814,-0.334898591,0.0144050214,0.3095326722,-0.010113867,-0.0965969041,0.1116476655,0.5424685478,0.1066478863,0.1266251504,-0.0549990423,0.2443153262,0.2560414374,0.1710574329,0.0513743982,0.1595277488,-0.1193900257,0.1637128294,1.2782388926,0.1522191167,-0.0518915094,0.0262432639,0.0314820632,-0.0140471822,0.0686671063,-0.1058131158,0.0613840669,-0.3951035738,-0.130812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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2185","title":".map() and distributed training","comments":"My code is actually a transformer's example for training t5, I modified a bit:\r\n\r\nhttps:\/\/github.com\/puraminy\/transformers\/blob\/4b40877132eedb566043f83de8f1d29a84d71430\/examples\/flax\/language-modeling\/run_t5_mlm_flax.py#L614\r\n\r\nNo, it doesn't use `per_device_train_batch_size`. I remember it worked for several times and then for no reason or various reasons like the above it started to build the cache again, as if it had an expiration date (maybe), or maybe I had changed the code! \r\n\r\nSo, to get rid of these problems I saved cache with a name (was forced to not use multiple_processes, because otherwise it generates multiple files) and then I load it from this cache file. ","body":"Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters.","comment_length":94,"text":".map() and distributed training \n Hi,\r\nI have a question regarding distributed training and the `.map` call on a dataset.\r\n\r\nI have a local dataset \"my_custom_dataset\" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.\r\n`dataset` is then tokenized:\r\n```python\r\ndatasets = load_from_disk(dataset_path=my_path)\r\n\r\n[...]\r\n\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name])\r\n\r\nlogger.info(\"Mapping dataset to tokenized dataset.\")\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=preprocessing_num_workers,\r\n    remove_columns=column_names,\r\n    load_from_cache_file=True,\r\n)\r\n```\r\nI am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path\/train` (there is only a train split).\r\nWhen I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.\r\n\r\nEverything so far was done by launching a **single process script**.\r\nI now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. \r\n\r\nI tried adding the `cache_file_name` argument: `cache_file_name={\"train\": my_path\/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.\r\n\r\n**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.\r\n\r\n- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_clm.py) in my case)\r\n- I am using 1.5.0 version of datasets if that matters. \n My code is actually a transformer's example for training t5, I modified a bit:\r\n\r\nhttps:\/\/github.com\/puraminy\/transformers\/blob\/4b40877132eedb566043f83de8f1d29a84d71430\/examples\/flax\/language-modeling\/run_t5_mlm_flax.py#L614\r\n\r\nNo, it doesn't use `per_device_train_batch_size`. I remember it worked for several times and then for no reason or various reasons like the above it started to build the cache again, as if it had an expiration date (maybe), or maybe I had changed the code! \r\n\r\nSo, to get rid of these problems I saved cache with a name (was forced to not use multiple_processes, because otherwise it generates multiple files) and then I load it from this cache file. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2181","title":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)","comments":"Hi ! Can you try to increase the block size ? For example\r\n```python\r\nblock_size_10MB = 10<<20\r\nload_dataset(\"json\", ..., block_size=block_size_10MB)\r\n```\r\nThe block size corresponds to how much bytes to process at a time from the input stream.\r\nThis will determine multi-threading granularity as well as the size of individual chunks in the dataset.\r\n\r\nYou can also try with bigger block sizes if needed","body":"Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!","comment_length":64,"text":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) \n Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! \n Hi ! Can you try to increase the block size ? For example\r\n```python\r\nblock_size_10MB = 10<<20\r\nload_dataset(\"json\", ..., block_size=block_size_10MB)\r\n```\r\nThe block size corresponds to how much bytes to process at a time from the input stream.\r\nThis will determine multi-threading granularity as well as the size of individual chunks in the dataset.\r\n\r\nYou can also try with bigger block sizes if needed","embeddings":[0.016556887,0.0126864687,-0.0075106705,0.3409382999,0.1604535431,-0.2647139132,0.193632707,0.5859469771,-0.1621605307,-0.0815067366,0.1654730737,0.1441630423,-0.0150832608,0.0145624503,-0.1295208484,-0.1911917031,0.033368215,0.0782742128,-0.0815535039,0.1355070025,-0.0368864611,0.2328725159,-0.0935274884,0.2323912382,-0.0870280042,-0.0781508908,0.1980634928,0.3374814093,-0.3348842263,-0.4495861232,0.1612249315,-0.2951855063,0.0062585096,0.3340712488,-0.0001156308,0.0471057519,0.2495900393,0.109179385,0.0187429283,-0.2191216499,0.2808351219,-0.3987070322,0.228011921,-0.1674175858,0.1295851022,-0.4462138414,-0.3999225199,0.3737807572,0.4277296364,-0.125555113,0.1651013941,0.091210708,0.2657488286,0.2669147551,0.4368439317,-0.0652506948,0.027398346,0.5041721463,0.3052019775,-0.2382042259,-0.3354803026,-0.1370317638,-0.249592483,0.1381146312,0.3779326081,-0.1172010303,0.076280944,0.0589680932,-0.0799430832,0.0377216749,0.2263211757,-0.4086019993,-0.1033591554,-0.0781874731,-0.0856484845,-0.1830741614,0.3079182208,0.3882312179,-0.2103946954,0.0698304623,-0.1914447248,-0.1000016332,-0.1991208196,0.1786511391,-0.3580315709,-0.1259495318,-0.0024537381,0.3322300911,0.2310532629,-0.007610932,-0.035575673,0.0311062783,-0.2734938562,-0.056598112,-0.2094626725,0.0166391041,0.0015142764,-0.3171832263,0.1906490326,0.2260884643,0.1960860789,-0.0655944422,0.2463149428,0.2574926019,0.6111616492,0.059475515,-0.3522540629,-0.1677471697,0.2054002136,0.237815097,-0.0194872171,0.0609876625,-0.0592074208,-0.5538618565,0.0594820604,-0.2798270881,0.259775579,-0.2160245329,-0.2103198022,0.1333221942,-0.7950463891,0.0500115268,0.1662532538,0.2496998012,0.0272156876,0.2363475859,0.0485518463,0.1581959277,0.0089593437,-0.1194209307,-0.0078674201,0.1263681799,-0.152511999,0.0385965146,0.1579921395,-0.1335816085,-0.0064160214,0.1193348318,0.2173471451,-0.2116392553,0.1862066239,0.0166174266,0.0477700792,0.3288215995,-0.0516762882,0.1116407961,-0.0548598059,-0.0126550142,-0.2687699497,0.4798909724,-0.0626880005,-0.3448120952,-0.1742749214,0.100412406,-0.2606137395,0.2655539811,0.0049829497,0.0375169143,0.1504191756,-0.3691319823,0.2161603868,-0.131151095,0.1778304875,-0.3457722068,0.1031698063,-0.0813739225,-0.686923027,0.017364502,0.2560487986,0.1754170209,-0.094436802,0.5446502566,-0.415045321,0.1447616071,-0.045954112,0.1615658551,0.1449172199,0.160628736,-0.6060854793,0.3728240132,-0.2745345831,0.057643231,0.0307355803,-0.0146053843,-0.1264383495,-0.0241248291,-0.0301004108,0.3515685797,0.0832778588,0.2309474498,-0.3654409647,-0.2369496226,0.1233631745,0.3634355962,-0.1145457625,-0.4116987586,-0.0493318588,-0.1916890144,0.055319462,-0.1157415733,0.1517809778,0.4218284488,0.135918498,-0.1193930358,0.102686882,0.1130747423,-0.4654299319,0.021460088,0.0717728287,-0.0631102473,-0.3461884856,-0.0881970525,-0.030149851,0.2487495095,0.0945114121,0.2060095221,0.0340390354,-0.1731155217,0.0721888989,-0.0024395175,-0.0986388773,-0.413590461,0.1334338039,0.0734615549,-0.0101108309,0.390627265,0.0637331009,-0.293379277,0.0692314729,0.1497817785,-0.0336900316,-0.1447110027,-0.0944388956,0.184556067,-0.0829884708,0.0026114385,-0.2447776794,0.1293199956,0.2801520824,-0.3557440937,0.0277640875,0.1032500342,0.0765697882,-0.0654545948,0.2241742462,0.4178517759,-0.328894347,0.203213647,0.0963192433,-0.2922596335,0.0688130632,-0.0792855769,-0.0184273757,0.1651138216,0.2211024761,0.3922338784,0.2243866175,0.1382108033,-0.1602222621,-0.119418323,0.398599565,-0.1834302247,-0.0815003961,0.152345404,-0.1335780174,-0.0331332907,0.0964195654,-0.1294155121,0.1355017573,0.2478066981,-0.1427013129,-0.0006218139,0.0272530131,-0.1798278987,0.2222775072,0.197826013,0.1286401451,0.2018653899,0.525388062,-0.1262486577,-0.2340527326,-0.2239226252,0.24420017,0.1961286515,-0.2908231318,0.1298102885,-0.0687725171,0.0243006777,-0.0847825631,-0.679587245,-0.3061884344,-0.0921970904,0.0006510711,0.4704107642,-0.330901593,0.0706555918,0.1599271297,0.3159408569,0.0793664604,-0.1885332614,-0.044373747,-0.3438287079,-0.2535299659,-0.0260646604,0.4630136192,0.1464138925,0.0704189911,0.3701916039,0.0221871901,-0.0610447712,-0.4288595021,0.218477577,-0.0467988625,0.0778331682,0.0034065631,0.1291554272,-0.247211799,-0.2504459023,0.3068675399,0.0360249728,-0.205761373,0.327116698,-0.1920830458,0.0744263157,-0.1781024784,0.1151923761,0.2128263414,-0.5418013334,0.3973288238,-0.1855192035,0.0039909063,-0.2795964479,0.2635209858,0.0960103422,0.0679152757,-0.0404972062,0.0594006777,-0.4201915264,0.1961016506,-0.0403325036,-0.1881746352,0.0418398418,0.1097168624,0.1519098282,0.1650311649,-0.6058769226,-0.0128622605,-0.0775761679,0.5512671471,-0.34305498,-0.2375435829,0.1149812415,0.1875803918,-0.0166976135,0.1244238466,-0.1434655637,0.0150683811,-0.007390561,0.2049880326,-0.1010244936,0.5279217958,0.1304708272,0.7018238306,-0.1654545516,-0.1430081576,0.3313239813,0.2419932485,0.0773653984,0.0481638126,-0.0284133386,-0.1179990172,-0.1809873581,-0.082166642,-0.0068381014,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2181","title":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)","comments":"Hi @lhoestq! Thank you for your prompt reply.\r\nI have experimented with (10<<20, 10<<28, 10<<30, 10<<33, 10<<34), since my machine has 192G of memory, but it's either the above-mentioned error or processed killed because of OOM.\r\n\r\nCould you give me a bit of background on why block size needs to be exactly calibrated?\r\nTo my understanding, small block sized should run just fine despite its slowness..\r\n\r\n\r\n","body":"Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!","comment_length":66,"text":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) \n Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! \n Hi @lhoestq! Thank you for your prompt reply.\r\nI have experimented with (10<<20, 10<<28, 10<<30, 10<<33, 10<<34), since my machine has 192G of memory, but it's either the above-mentioned error or processed killed because of OOM.\r\n\r\nCould you give me a bit of background on why block size needs to be exactly calibrated?\r\nTo my understanding, small block sized should run just fine despite its slowness..\r\n\r\n\r\n","embeddings":[0.016556887,0.0126864687,-0.0075106705,0.3409382999,0.1604535431,-0.2647139132,0.193632707,0.5859469771,-0.1621605307,-0.0815067366,0.1654730737,0.1441630423,-0.0150832608,0.0145624503,-0.1295208484,-0.1911917031,0.033368215,0.0782742128,-0.0815535039,0.1355070025,-0.0368864611,0.2328725159,-0.0935274884,0.2323912382,-0.0870280042,-0.0781508908,0.1980634928,0.3374814093,-0.3348842263,-0.4495861232,0.1612249315,-0.2951855063,0.0062585096,0.3340712488,-0.0001156308,0.0471057519,0.2495900393,0.109179385,0.0187429283,-0.2191216499,0.2808351219,-0.3987070322,0.228011921,-0.1674175858,0.1295851022,-0.4462138414,-0.3999225199,0.3737807572,0.4277296364,-0.125555113,0.1651013941,0.091210708,0.2657488286,0.2669147551,0.4368439317,-0.0652506948,0.027398346,0.5041721463,0.3052019775,-0.2382042259,-0.3354803026,-0.1370317638,-0.249592483,0.1381146312,0.3779326081,-0.1172010303,0.076280944,0.0589680932,-0.0799430832,0.0377216749,0.2263211757,-0.4086019993,-0.1033591554,-0.0781874731,-0.0856484845,-0.1830741614,0.3079182208,0.3882312179,-0.2103946954,0.0698304623,-0.1914447248,-0.1000016332,-0.1991208196,0.1786511391,-0.3580315709,-0.1259495318,-0.0024537381,0.3322300911,0.2310532629,-0.007610932,-0.035575673,0.0311062783,-0.2734938562,-0.056598112,-0.2094626725,0.0166391041,0.0015142764,-0.3171832263,0.1906490326,0.2260884643,0.1960860789,-0.0655944422,0.2463149428,0.2574926019,0.6111616492,0.059475515,-0.3522540629,-0.1677471697,0.2054002136,0.237815097,-0.0194872171,0.0609876625,-0.0592074208,-0.5538618565,0.0594820604,-0.2798270881,0.259775579,-0.2160245329,-0.2103198022,0.1333221942,-0.7950463891,0.0500115268,0.1662532538,0.2496998012,0.0272156876,0.2363475859,0.0485518463,0.1581959277,0.0089593437,-0.1194209307,-0.0078674201,0.1263681799,-0.152511999,0.0385965146,0.1579921395,-0.1335816085,-0.0064160214,0.1193348318,0.2173471451,-0.2116392553,0.1862066239,0.0166174266,0.0477700792,0.3288215995,-0.0516762882,0.1116407961,-0.0548598059,-0.0126550142,-0.2687699497,0.4798909724,-0.0626880005,-0.3448120952,-0.1742749214,0.100412406,-0.2606137395,0.2655539811,0.0049829497,0.0375169143,0.1504191756,-0.3691319823,0.2161603868,-0.131151095,0.1778304875,-0.3457722068,0.1031698063,-0.0813739225,-0.686923027,0.017364502,0.2560487986,0.1754170209,-0.094436802,0.5446502566,-0.415045321,0.1447616071,-0.045954112,0.1615658551,0.1449172199,0.160628736,-0.6060854793,0.3728240132,-0.2745345831,0.057643231,0.0307355803,-0.0146053843,-0.1264383495,-0.0241248291,-0.0301004108,0.3515685797,0.0832778588,0.2309474498,-0.3654409647,-0.2369496226,0.1233631745,0.3634355962,-0.1145457625,-0.4116987586,-0.0493318588,-0.1916890144,0.055319462,-0.1157415733,0.1517809778,0.4218284488,0.135918498,-0.1193930358,0.102686882,0.1130747423,-0.4654299319,0.021460088,0.0717728287,-0.0631102473,-0.3461884856,-0.0881970525,-0.030149851,0.2487495095,0.0945114121,0.2060095221,0.0340390354,-0.1731155217,0.0721888989,-0.0024395175,-0.0986388773,-0.413590461,0.1334338039,0.0734615549,-0.0101108309,0.390627265,0.0637331009,-0.293379277,0.0692314729,0.1497817785,-0.0336900316,-0.1447110027,-0.0944388956,0.184556067,-0.0829884708,0.0026114385,-0.2447776794,0.1293199956,0.2801520824,-0.3557440937,0.0277640875,0.1032500342,0.0765697882,-0.0654545948,0.2241742462,0.4178517759,-0.328894347,0.203213647,0.0963192433,-0.2922596335,0.0688130632,-0.0792855769,-0.0184273757,0.1651138216,0.2211024761,0.3922338784,0.2243866175,0.1382108033,-0.1602222621,-0.119418323,0.398599565,-0.1834302247,-0.0815003961,0.152345404,-0.1335780174,-0.0331332907,0.0964195654,-0.1294155121,0.1355017573,0.2478066981,-0.1427013129,-0.0006218139,0.0272530131,-0.1798278987,0.2222775072,0.197826013,0.1286401451,0.2018653899,0.525388062,-0.1262486577,-0.2340527326,-0.2239226252,0.24420017,0.1961286515,-0.2908231318,0.1298102885,-0.0687725171,0.0243006777,-0.0847825631,-0.679587245,-0.3061884344,-0.0921970904,0.0006510711,0.4704107642,-0.330901593,0.0706555918,0.1599271297,0.3159408569,0.0793664604,-0.1885332614,-0.044373747,-0.3438287079,-0.2535299659,-0.0260646604,0.4630136192,0.1464138925,0.0704189911,0.3701916039,0.0221871901,-0.0610447712,-0.4288595021,0.218477577,-0.0467988625,0.0778331682,0.0034065631,0.1291554272,-0.247211799,-0.2504459023,0.3068675399,0.0360249728,-0.205761373,0.327116698,-0.1920830458,0.0744263157,-0.1781024784,0.1151923761,0.2128263414,-0.5418013334,0.3973288238,-0.1855192035,0.0039909063,-0.2795964479,0.2635209858,0.0960103422,0.0679152757,-0.0404972062,0.0594006777,-0.4201915264,0.1961016506,-0.0403325036,-0.1881746352,0.0418398418,0.1097168624,0.1519098282,0.1650311649,-0.6058769226,-0.0128622605,-0.0775761679,0.5512671471,-0.34305498,-0.2375435829,0.1149812415,0.1875803918,-0.0166976135,0.1244238466,-0.1434655637,0.0150683811,-0.007390561,0.2049880326,-0.1010244936,0.5279217958,0.1304708272,0.7018238306,-0.1654545516,-0.1430081576,0.3313239813,0.2419932485,0.0773653984,0.0481638126,-0.0284133386,-0.1179990172,-0.1809873581,-0.08216664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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2181","title":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)","comments":"We're using the JSON loader of pyarrow. It parses the file chunk by chunk to load the dataset.\r\nThis issue happens when there's no delimiter in one chunk of data. For json line, the delimiter is the end of line.\r\nSo with a big value for chunk_size this should have worked unless you have one extremely long line in your file.\r\n\r\nAlso what version of pyarrow are you using ?\r\n\r\nFInally I wonder if it could be an issue on pyarrow's side when using big json files. (I haven't tested big json files like yours)","body":"Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!","comment_length":95,"text":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) \n Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! \n We're using the JSON loader of pyarrow. It parses the file chunk by chunk to load the dataset.\r\nThis issue happens when there's no delimiter in one chunk of data. For json line, the delimiter is the end of line.\r\nSo with a big value for chunk_size this should have worked unless you have one extremely long line in your file.\r\n\r\nAlso what version of pyarrow are you using ?\r\n\r\nFInally I wonder if it could be an issue on pyarrow's side when using big json files. (I haven't tested big json files like yours)","embeddings":[0.016556887,0.0126864687,-0.0075106705,0.3409382999,0.1604535431,-0.2647139132,0.193632707,0.5859469771,-0.1621605307,-0.0815067366,0.1654730737,0.1441630423,-0.0150832608,0.0145624503,-0.1295208484,-0.1911917031,0.033368215,0.0782742128,-0.0815535039,0.1355070025,-0.0368864611,0.2328725159,-0.0935274884,0.2323912382,-0.0870280042,-0.0781508908,0.1980634928,0.3374814093,-0.3348842263,-0.4495861232,0.1612249315,-0.2951855063,0.0062585096,0.3340712488,-0.0001156308,0.0471057519,0.2495900393,0.109179385,0.0187429283,-0.2191216499,0.2808351219,-0.3987070322,0.228011921,-0.1674175858,0.1295851022,-0.4462138414,-0.3999225199,0.3737807572,0.4277296364,-0.125555113,0.1651013941,0.091210708,0.2657488286,0.2669147551,0.4368439317,-0.0652506948,0.027398346,0.5041721463,0.3052019775,-0.2382042259,-0.3354803026,-0.1370317638,-0.249592483,0.1381146312,0.3779326081,-0.1172010303,0.076280944,0.0589680932,-0.0799430832,0.0377216749,0.2263211757,-0.4086019993,-0.1033591554,-0.0781874731,-0.0856484845,-0.1830741614,0.3079182208,0.3882312179,-0.2103946954,0.0698304623,-0.1914447248,-0.1000016332,-0.1991208196,0.1786511391,-0.3580315709,-0.1259495318,-0.0024537381,0.3322300911,0.2310532629,-0.007610932,-0.035575673,0.0311062783,-0.2734938562,-0.056598112,-0.2094626725,0.0166391041,0.0015142764,-0.3171832263,0.1906490326,0.2260884643,0.1960860789,-0.0655944422,0.2463149428,0.2574926019,0.6111616492,0.059475515,-0.3522540629,-0.1677471697,0.2054002136,0.237815097,-0.0194872171,0.0609876625,-0.0592074208,-0.5538618565,0.0594820604,-0.2798270881,0.259775579,-0.2160245329,-0.2103198022,0.1333221942,-0.7950463891,0.0500115268,0.1662532538,0.2496998012,0.0272156876,0.2363475859,0.0485518463,0.1581959277,0.0089593437,-0.1194209307,-0.0078674201,0.1263681799,-0.152511999,0.0385965146,0.1579921395,-0.1335816085,-0.0064160214,0.1193348318,0.2173471451,-0.2116392553,0.1862066239,0.0166174266,0.0477700792,0.3288215995,-0.0516762882,0.1116407961,-0.0548598059,-0.0126550142,-0.2687699497,0.4798909724,-0.0626880005,-0.3448120952,-0.1742749214,0.100412406,-0.2606137395,0.2655539811,0.0049829497,0.0375169143,0.1504191756,-0.3691319823,0.2161603868,-0.131151095,0.1778304875,-0.3457722068,0.1031698063,-0.0813739225,-0.686923027,0.017364502,0.2560487986,0.1754170209,-0.094436802,0.5446502566,-0.415045321,0.1447616071,-0.045954112,0.1615658551,0.1449172199,0.160628736,-0.6060854793,0.3728240132,-0.2745345831,0.057643231,0.0307355803,-0.0146053843,-0.1264383495,-0.0241248291,-0.0301004108,0.3515685797,0.0832778588,0.2309474498,-0.3654409647,-0.2369496226,0.1233631745,0.3634355962,-0.1145457625,-0.4116987586,-0.0493318588,-0.1916890144,0.055319462,-0.1157415733,0.1517809778,0.4218284488,0.135918498,-0.1193930358,0.102686882,0.1130747423,-0.4654299319,0.021460088,0.0717728287,-0.0631102473,-0.3461884856,-0.0881970525,-0.030149851,0.2487495095,0.0945114121,0.2060095221,0.0340390354,-0.1731155217,0.0721888989,-0.0024395175,-0.0986388773,-0.413590461,0.1334338039,0.0734615549,-0.0101108309,0.390627265,0.0637331009,-0.293379277,0.0692314729,0.1497817785,-0.0336900316,-0.1447110027,-0.0944388956,0.184556067,-0.0829884708,0.0026114385,-0.2447776794,0.1293199956,0.2801520824,-0.3557440937,0.0277640875,0.1032500342,0.0765697882,-0.0654545948,0.2241742462,0.4178517759,-0.328894347,0.203213647,0.0963192433,-0.2922596335,0.0688130632,-0.0792855769,-0.0184273757,0.1651138216,0.2211024761,0.3922338784,0.2243866175,0.1382108033,-0.1602222621,-0.119418323,0.398599565,-0.1834302247,-0.0815003961,0.152345404,-0.1335780174,-0.0331332907,0.0964195654,-0.1294155121,0.1355017573,0.2478066981,-0.1427013129,-0.0006218139,0.0272530131,-0.1798278987,0.2222775072,0.197826013,0.1286401451,0.2018653899,0.525388062,-0.1262486577,-0.2340527326,-0.2239226252,0.24420017,0.1961286515,-0.2908231318,0.1298102885,-0.0687725171,0.0243006777,-0.0847825631,-0.679587245,-0.3061884344,-0.0921970904,0.0006510711,0.4704107642,-0.330901593,0.0706555918,0.1599271297,0.3159408569,0.0793664604,-0.1885332614,-0.044373747,-0.3438287079,-0.2535299659,-0.0260646604,0.4630136192,0.1464138925,0.0704189911,0.3701916039,0.0221871901,-0.0610447712,-0.4288595021,0.218477577,-0.0467988625,0.0778331682,0.0034065631,0.1291554272,-0.247211799,-0.2504459023,0.3068675399,0.0360249728,-0.205761373,0.327116698,-0.1920830458,0.0744263157,-0.1781024784,0.1151923761,0.2128263414,-0.5418013334,0.3973288238,-0.1855192035,0.0039909063,-0.2795964479,0.2635209858,0.0960103422,0.0679152757,-0.0404972062,0.0594006777,-0.4201915264,0.1961016506,-0.0403325036,-0.1881746352,0.0418398418,0.1097168624,0.1519098282,0.1650311649,-0.6058769226,-0.0128622605,-0.0775761679,0.5512671471,-0.34305498,-0.2375435829,0.1149812415,0.1875803918,-0.0166976135,0.1244238466,-0.1434655637,0.0150683811,-0.007390561,0.2049880326,-0.1010244936,0.5279217958,0.1304708272,0.7018238306,-0.1654545516,-0.1430081576,0.3313239813,0.2419932485,0.0773653984,0.0481638126,-0.0284133386,-0.1179990172,-0.1809873581,-0.082166642,-0.0068381014,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2181","title":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)","comments":"I'm using `pyarrow==3.0.0` with `datasets==1.5.0`.\r\n\r\nYour point totally makes sense. I will check if my jsonl file contains an extremely long file and let you know. \r\n\r\nHere are some different error messages that I got when tweaking `block_size`. I also suspect that this is related to the pyarrow... but I guess it would be wonderful if datasesets could give a clear guide on how to play with large datasets! (I am suddenly experiencing various issue when working with large datasets.. e.g. #1992 )\r\n```python\r\n    return paj.ReadOptions(use_threads=self.use_threads, block_size=self.block_size)\r\n  File \"pyarrow\/_json.pyx\", line 56, in pyarrow._json.ReadOptions.__init__\r\n  File \"pyarrow\/_json.pyx\", line 81, in pyarrow._json.ReadOptions.block_size.__set__\r\nOverflowError: value too large to convert to int32_t\r\n```\r\n\r\n```python\r\n\r\nline 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Exceeded maximum rows\r\n```","body":"Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!","comment_length":137,"text":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) \n Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! \n I'm using `pyarrow==3.0.0` with `datasets==1.5.0`.\r\n\r\nYour point totally makes sense. I will check if my jsonl file contains an extremely long file and let you know. \r\n\r\nHere are some different error messages that I got when tweaking `block_size`. I also suspect that this is related to the pyarrow... but I guess it would be wonderful if datasesets could give a clear guide on how to play with large datasets! (I am suddenly experiencing various issue when working with large datasets.. e.g. #1992 )\r\n```python\r\n    return paj.ReadOptions(use_threads=self.use_threads, block_size=self.block_size)\r\n  File \"pyarrow\/_json.pyx\", line 56, in pyarrow._json.ReadOptions.__init__\r\n  File \"pyarrow\/_json.pyx\", line 81, in pyarrow._json.ReadOptions.block_size.__set__\r\nOverflowError: value too large to convert to int32_t\r\n```\r\n\r\n```python\r\n\r\nline 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Exceeded maximum rows\r\n```","embeddings":[0.016556887,0.0126864687,-0.0075106705,0.3409382999,0.1604535431,-0.2647139132,0.193632707,0.5859469771,-0.1621605307,-0.0815067366,0.1654730737,0.1441630423,-0.0150832608,0.0145624503,-0.1295208484,-0.1911917031,0.033368215,0.0782742128,-0.0815535039,0.1355070025,-0.0368864611,0.2328725159,-0.0935274884,0.2323912382,-0.0870280042,-0.0781508908,0.1980634928,0.3374814093,-0.3348842263,-0.4495861232,0.1612249315,-0.2951855063,0.0062585096,0.3340712488,-0.0001156308,0.0471057519,0.2495900393,0.109179385,0.0187429283,-0.2191216499,0.2808351219,-0.3987070322,0.228011921,-0.1674175858,0.1295851022,-0.4462138414,-0.3999225199,0.3737807572,0.4277296364,-0.125555113,0.1651013941,0.091210708,0.2657488286,0.2669147551,0.4368439317,-0.0652506948,0.027398346,0.5041721463,0.3052019775,-0.2382042259,-0.3354803026,-0.1370317638,-0.249592483,0.1381146312,0.3779326081,-0.1172010303,0.076280944,0.0589680932,-0.0799430832,0.0377216749,0.2263211757,-0.4086019993,-0.1033591554,-0.0781874731,-0.0856484845,-0.1830741614,0.3079182208,0.3882312179,-0.2103946954,0.0698304623,-0.1914447248,-0.1000016332,-0.1991208196,0.1786511391,-0.3580315709,-0.1259495318,-0.0024537381,0.3322300911,0.2310532629,-0.007610932,-0.035575673,0.0311062783,-0.2734938562,-0.056598112,-0.2094626725,0.0166391041,0.0015142764,-0.3171832263,0.1906490326,0.2260884643,0.1960860789,-0.0655944422,0.2463149428,0.2574926019,0.6111616492,0.059475515,-0.3522540629,-0.1677471697,0.2054002136,0.237815097,-0.0194872171,0.0609876625,-0.0592074208,-0.5538618565,0.0594820604,-0.2798270881,0.259775579,-0.2160245329,-0.2103198022,0.1333221942,-0.7950463891,0.0500115268,0.1662532538,0.2496998012,0.0272156876,0.2363475859,0.0485518463,0.1581959277,0.0089593437,-0.1194209307,-0.0078674201,0.1263681799,-0.152511999,0.0385965146,0.1579921395,-0.1335816085,-0.0064160214,0.1193348318,0.2173471451,-0.2116392553,0.1862066239,0.0166174266,0.0477700792,0.3288215995,-0.0516762882,0.1116407961,-0.0548598059,-0.0126550142,-0.2687699497,0.4798909724,-0.0626880005,-0.3448120952,-0.1742749214,0.100412406,-0.2606137395,0.2655539811,0.0049829497,0.0375169143,0.1504191756,-0.3691319823,0.2161603868,-0.131151095,0.1778304875,-0.3457722068,0.1031698063,-0.0813739225,-0.686923027,0.017364502,0.2560487986,0.1754170209,-0.094436802,0.5446502566,-0.415045321,0.1447616071,-0.045954112,0.1615658551,0.1449172199,0.160628736,-0.6060854793,0.3728240132,-0.2745345831,0.057643231,0.0307355803,-0.0146053843,-0.1264383495,-0.0241248291,-0.0301004108,0.3515685797,0.0832778588,0.2309474498,-0.3654409647,-0.2369496226,0.1233631745,0.3634355962,-0.1145457625,-0.4116987586,-0.0493318588,-0.1916890144,0.055319462,-0.1157415733,0.1517809778,0.4218284488,0.135918498,-0.1193930358,0.102686882,0.1130747423,-0.4654299319,0.021460088,0.0717728287,-0.0631102473,-0.3461884856,-0.0881970525,-0.030149851,0.2487495095,0.0945114121,0.2060095221,0.0340390354,-0.1731155217,0.0721888989,-0.0024395175,-0.0986388773,-0.413590461,0.1334338039,0.0734615549,-0.0101108309,0.390627265,0.0637331009,-0.293379277,0.0692314729,0.1497817785,-0.0336900316,-0.1447110027,-0.0944388956,0.184556067,-0.0829884708,0.0026114385,-0.2447776794,0.1293199956,0.2801520824,-0.3557440937,0.0277640875,0.1032500342,0.0765697882,-0.0654545948,0.2241742462,0.4178517759,-0.328894347,0.203213647,0.0963192433,-0.2922596335,0.0688130632,-0.0792855769,-0.0184273757,0.1651138216,0.2211024761,0.3922338784,0.2243866175,0.1382108033,-0.1602222621,-0.119418323,0.398599565,-0.1834302247,-0.0815003961,0.152345404,-0.1335780174,-0.0331332907,0.0964195654,-0.1294155121,0.1355017573,0.2478066981,-0.1427013129,-0.0006218139,0.0272530131,-0.1798278987,0.2222775072,0.197826013,0.1286401451,0.2018653899,0.525388062,-0.1262486577,-0.2340527326,-0.2239226252,0.24420017,0.1961286515,-0.2908231318,0.1298102885,-0.0687725171,0.0243006777,-0.0847825631,-0.679587245,-0.3061884344,-0.0921970904,0.0006510711,0.4704107642,-0.330901593,0.0706555918,0.1599271297,0.3159408569,0.0793664604,-0.1885332614,-0.044373747,-0.3438287079,-0.2535299659,-0.0260646604,0.4630136192,0.1464138925,0.0704189911,0.3701916039,0.0221871901,-0.0610447712,-0.4288595021,0.218477577,-0.0467988625,0.0778331682,0.0034065631,0.1291554272,-0.247211799,-0.2504459023,0.3068675399,0.0360249728,-0.205761373,0.327116698,-0.1920830458,0.0744263157,-0.1781024784,0.1151923761,0.2128263414,-0.5418013334,0.3973288238,-0.1855192035,0.0039909063,-0.2795964479,0.2635209858,0.0960103422,0.0679152757,-0.0404972062,0.0594006777,-0.4201915264,0.1961016506,-0.0403325036,-0.1881746352,0.0418398418,0.1097168624,0.1519098282,0.1650311649,-0.6058769226,-0.0128622605,-0.0775761679,0.5512671471,-0.34305498,-0.2375435829,0.1149812415,0.1875803918,-0.0166976135,0.1244238466,-0.1434655637,0.0150683811,-0.007390561,0.2049880326,-0.1010244936,0.5279217958,0.1304708272,0.7018238306,-0.1654545516,-0.1430081576,0.3313239813,0.2419932485,0.0773653984,0.0481638126,-0.0284133386,-0.1179990172,-0.1809873581,-0.082166642,-0.006838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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2181","title":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)","comments":"I am getting the same error. When I tweak the block_size, I also find:\r\n`OverflowError: value too large to convert to int32_t`\r\nand \r\n`pyarrow.lib.ArrowInvalid: Exceeded maximum rows`\r\n","body":"Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!","comment_length":27,"text":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) \n Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! \n I am getting the same error. When I tweak the block_size, I also find:\r\n`OverflowError: value too large to convert to int32_t`\r\nand \r\n`pyarrow.lib.ArrowInvalid: Exceeded maximum rows`\r\n","embeddings":[0.016556887,0.0126864687,-0.0075106705,0.3409382999,0.1604535431,-0.2647139132,0.193632707,0.5859469771,-0.1621605307,-0.0815067366,0.1654730737,0.1441630423,-0.0150832608,0.0145624503,-0.1295208484,-0.1911917031,0.033368215,0.0782742128,-0.0815535039,0.1355070025,-0.0368864611,0.2328725159,-0.0935274884,0.2323912382,-0.0870280042,-0.0781508908,0.1980634928,0.3374814093,-0.3348842263,-0.4495861232,0.1612249315,-0.2951855063,0.0062585096,0.3340712488,-0.0001156308,0.0471057519,0.2495900393,0.109179385,0.0187429283,-0.2191216499,0.2808351219,-0.3987070322,0.228011921,-0.1674175858,0.1295851022,-0.4462138414,-0.3999225199,0.3737807572,0.4277296364,-0.125555113,0.1651013941,0.091210708,0.2657488286,0.2669147551,0.4368439317,-0.0652506948,0.027398346,0.5041721463,0.3052019775,-0.2382042259,-0.3354803026,-0.1370317638,-0.249592483,0.1381146312,0.3779326081,-0.1172010303,0.076280944,0.0589680932,-0.0799430832,0.0377216749,0.2263211757,-0.4086019993,-0.1033591554,-0.0781874731,-0.0856484845,-0.1830741614,0.3079182208,0.3882312179,-0.2103946954,0.0698304623,-0.1914447248,-0.1000016332,-0.1991208196,0.1786511391,-0.3580315709,-0.1259495318,-0.0024537381,0.3322300911,0.2310532629,-0.007610932,-0.035575673,0.0311062783,-0.2734938562,-0.056598112,-0.2094626725,0.0166391041,0.0015142764,-0.3171832263,0.1906490326,0.2260884643,0.1960860789,-0.0655944422,0.2463149428,0.2574926019,0.6111616492,0.059475515,-0.3522540629,-0.1677471697,0.2054002136,0.237815097,-0.0194872171,0.0609876625,-0.0592074208,-0.5538618565,0.0594820604,-0.2798270881,0.259775579,-0.2160245329,-0.2103198022,0.1333221942,-0.7950463891,0.0500115268,0.1662532538,0.2496998012,0.0272156876,0.2363475859,0.0485518463,0.1581959277,0.0089593437,-0.1194209307,-0.0078674201,0.1263681799,-0.152511999,0.0385965146,0.1579921395,-0.1335816085,-0.0064160214,0.1193348318,0.2173471451,-0.2116392553,0.1862066239,0.0166174266,0.0477700792,0.3288215995,-0.0516762882,0.1116407961,-0.0548598059,-0.0126550142,-0.2687699497,0.4798909724,-0.0626880005,-0.3448120952,-0.1742749214,0.100412406,-0.2606137395,0.2655539811,0.0049829497,0.0375169143,0.1504191756,-0.3691319823,0.2161603868,-0.131151095,0.1778304875,-0.3457722068,0.1031698063,-0.0813739225,-0.686923027,0.017364502,0.2560487986,0.1754170209,-0.094436802,0.5446502566,-0.415045321,0.1447616071,-0.045954112,0.1615658551,0.1449172199,0.160628736,-0.6060854793,0.3728240132,-0.2745345831,0.057643231,0.0307355803,-0.0146053843,-0.1264383495,-0.0241248291,-0.0301004108,0.3515685797,0.0832778588,0.2309474498,-0.3654409647,-0.2369496226,0.1233631745,0.3634355962,-0.1145457625,-0.4116987586,-0.0493318588,-0.1916890144,0.055319462,-0.1157415733,0.1517809778,0.4218284488,0.135918498,-0.1193930358,0.102686882,0.1130747423,-0.4654299319,0.021460088,0.0717728287,-0.0631102473,-0.3461884856,-0.0881970525,-0.030149851,0.2487495095,0.0945114121,0.2060095221,0.0340390354,-0.1731155217,0.0721888989,-0.0024395175,-0.0986388773,-0.413590461,0.1334338039,0.0734615549,-0.0101108309,0.390627265,0.0637331009,-0.293379277,0.0692314729,0.1497817785,-0.0336900316,-0.1447110027,-0.0944388956,0.184556067,-0.0829884708,0.0026114385,-0.2447776794,0.1293199956,0.2801520824,-0.3557440937,0.0277640875,0.1032500342,0.0765697882,-0.0654545948,0.2241742462,0.4178517759,-0.328894347,0.203213647,0.0963192433,-0.2922596335,0.0688130632,-0.0792855769,-0.0184273757,0.1651138216,0.2211024761,0.3922338784,0.2243866175,0.1382108033,-0.1602222621,-0.119418323,0.398599565,-0.1834302247,-0.0815003961,0.152345404,-0.1335780174,-0.0331332907,0.0964195654,-0.1294155121,0.1355017573,0.2478066981,-0.1427013129,-0.0006218139,0.0272530131,-0.1798278987,0.2222775072,0.197826013,0.1286401451,0.2018653899,0.525388062,-0.1262486577,-0.2340527326,-0.2239226252,0.24420017,0.1961286515,-0.2908231318,0.1298102885,-0.0687725171,0.0243006777,-0.0847825631,-0.679587245,-0.3061884344,-0.0921970904,0.0006510711,0.4704107642,-0.330901593,0.0706555918,0.1599271297,0.3159408569,0.0793664604,-0.1885332614,-0.044373747,-0.3438287079,-0.2535299659,-0.0260646604,0.4630136192,0.1464138925,0.0704189911,0.3701916039,0.0221871901,-0.0610447712,-0.4288595021,0.218477577,-0.0467988625,0.0778331682,0.0034065631,0.1291554272,-0.247211799,-0.2504459023,0.3068675399,0.0360249728,-0.205761373,0.327116698,-0.1920830458,0.0744263157,-0.1781024784,0.1151923761,0.2128263414,-0.5418013334,0.3973288238,-0.1855192035,0.0039909063,-0.2795964479,0.2635209858,0.0960103422,0.0679152757,-0.0404972062,0.0594006777,-0.4201915264,0.1961016506,-0.0403325036,-0.1881746352,0.0418398418,0.1097168624,0.1519098282,0.1650311649,-0.6058769226,-0.0128622605,-0.0775761679,0.5512671471,-0.34305498,-0.2375435829,0.1149812415,0.1875803918,-0.0166976135,0.1244238466,-0.1434655637,0.0150683811,-0.007390561,0.2049880326,-0.1010244936,0.5279217958,0.1304708272,0.7018238306,-0.1654545516,-0.1430081576,0.3313239813,0.2419932485,0.0773653984,0.0481638126,-0.0284133386,-0.1179990172,-0.1809873581,-0.082166642,-0.00683810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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2181","title":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)","comments":"I made more tests. I used a smaller dataset and I was getting the same error, which means that it was not necessarily linked to the dataset size. To make both my smaller and larger datasets work, I got rid of lists with the json file. I had the following data format:\r\n```python\r\n[\r\n  {'key': \"a\", 'value': ['one', 'two', 'three']},\r\n  {'key': \"b\", 'value': ['four', 'five', 'six']}\r\n]\r\n```\r\nI changed to:\r\n\r\n```python\r\n  {'key': \"a\", 'value': 'one\\ntwo\\nthree'},\r\n  {'key': \"b\", 'value': 'four\\nfive\\nsix']}\r\n```\r\nand that worked!\r\n\r\nI used the following to reformat my json file:\r\n```python\r\nwith open(file_name, \"w\", encoding=\"utf-8\") as f:\r\n    for item in list_:\r\n        f.write(json.dumps(item) + \"\\n\")\r\n```\r\nThis works with `block_size_10MB = 10 << 20` or without specifying `block_size`.","body":"Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!","comment_length":120,"text":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) \n Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! \n I made more tests. I used a smaller dataset and I was getting the same error, which means that it was not necessarily linked to the dataset size. To make both my smaller and larger datasets work, I got rid of lists with the json file. I had the following data format:\r\n```python\r\n[\r\n  {'key': \"a\", 'value': ['one', 'two', 'three']},\r\n  {'key': \"b\", 'value': ['four', 'five', 'six']}\r\n]\r\n```\r\nI changed to:\r\n\r\n```python\r\n  {'key': \"a\", 'value': 'one\\ntwo\\nthree'},\r\n  {'key': \"b\", 'value': 'four\\nfive\\nsix']}\r\n```\r\nand that worked!\r\n\r\nI used the following to reformat my json file:\r\n```python\r\nwith open(file_name, \"w\", encoding=\"utf-8\") as f:\r\n    for item in list_:\r\n        f.write(json.dumps(item) + \"\\n\")\r\n```\r\nThis works with `block_size_10MB = 10 << 20` or without specifying `block_size`.","embeddings":[0.016556887,0.0126864687,-0.0075106705,0.3409382999,0.1604535431,-0.2647139132,0.193632707,0.5859469771,-0.1621605307,-0.0815067366,0.1654730737,0.1441630423,-0.0150832608,0.0145624503,-0.1295208484,-0.1911917031,0.033368215,0.0782742128,-0.0815535039,0.1355070025,-0.0368864611,0.2328725159,-0.0935274884,0.2323912382,-0.0870280042,-0.0781508908,0.1980634928,0.3374814093,-0.3348842263,-0.4495861232,0.1612249315,-0.2951855063,0.0062585096,0.3340712488,-0.0001156308,0.0471057519,0.2495900393,0.109179385,0.0187429283,-0.2191216499,0.2808351219,-0.3987070322,0.228011921,-0.1674175858,0.1295851022,-0.4462138414,-0.3999225199,0.3737807572,0.4277296364,-0.125555113,0.1651013941,0.091210708,0.2657488286,0.2669147551,0.4368439317,-0.0652506948,0.027398346,0.5041721463,0.3052019775,-0.2382042259,-0.3354803026,-0.1370317638,-0.249592483,0.1381146312,0.3779326081,-0.1172010303,0.076280944,0.0589680932,-0.0799430832,0.0377216749,0.2263211757,-0.4086019993,-0.1033591554,-0.0781874731,-0.0856484845,-0.1830741614,0.3079182208,0.3882312179,-0.2103946954,0.0698304623,-0.1914447248,-0.1000016332,-0.1991208196,0.1786511391,-0.3580315709,-0.1259495318,-0.0024537381,0.3322300911,0.2310532629,-0.007610932,-0.035575673,0.0311062783,-0.2734938562,-0.056598112,-0.2094626725,0.0166391041,0.0015142764,-0.3171832263,0.1906490326,0.2260884643,0.1960860789,-0.0655944422,0.2463149428,0.2574926019,0.6111616492,0.059475515,-0.3522540629,-0.1677471697,0.2054002136,0.237815097,-0.0194872171,0.0609876625,-0.0592074208,-0.5538618565,0.0594820604,-0.2798270881,0.259775579,-0.2160245329,-0.2103198022,0.1333221942,-0.7950463891,0.0500115268,0.1662532538,0.2496998012,0.0272156876,0.2363475859,0.0485518463,0.1581959277,0.0089593437,-0.1194209307,-0.0078674201,0.1263681799,-0.152511999,0.0385965146,0.1579921395,-0.1335816085,-0.0064160214,0.1193348318,0.2173471451,-0.2116392553,0.1862066239,0.0166174266,0.0477700792,0.3288215995,-0.0516762882,0.1116407961,-0.0548598059,-0.0126550142,-0.2687699497,0.4798909724,-0.0626880005,-0.3448120952,-0.1742749214,0.100412406,-0.2606137395,0.2655539811,0.0049829497,0.0375169143,0.1504191756,-0.3691319823,0.2161603868,-0.131151095,0.1778304875,-0.3457722068,0.1031698063,-0.0813739225,-0.686923027,0.017364502,0.2560487986,0.1754170209,-0.094436802,0.5446502566,-0.415045321,0.1447616071,-0.045954112,0.1615658551,0.1449172199,0.160628736,-0.6060854793,0.3728240132,-0.2745345831,0.057643231,0.0307355803,-0.0146053843,-0.1264383495,-0.0241248291,-0.0301004108,0.3515685797,0.0832778588,0.2309474498,-0.3654409647,-0.2369496226,0.1233631745,0.3634355962,-0.1145457625,-0.4116987586,-0.0493318588,-0.1916890144,0.055319462,-0.1157415733,0.1517809778,0.4218284488,0.135918498,-0.1193930358,0.102686882,0.1130747423,-0.4654299319,0.021460088,0.0717728287,-0.0631102473,-0.3461884856,-0.0881970525,-0.030149851,0.2487495095,0.0945114121,0.2060095221,0.0340390354,-0.1731155217,0.0721888989,-0.0024395175,-0.0986388773,-0.413590461,0.1334338039,0.0734615549,-0.0101108309,0.390627265,0.0637331009,-0.293379277,0.0692314729,0.1497817785,-0.0336900316,-0.1447110027,-0.0944388956,0.184556067,-0.0829884708,0.0026114385,-0.2447776794,0.1293199956,0.2801520824,-0.3557440937,0.0277640875,0.1032500342,0.0765697882,-0.0654545948,0.2241742462,0.4178517759,-0.328894347,0.203213647,0.0963192433,-0.2922596335,0.0688130632,-0.0792855769,-0.0184273757,0.1651138216,0.2211024761,0.3922338784,0.2243866175,0.1382108033,-0.1602222621,-0.119418323,0.398599565,-0.1834302247,-0.0815003961,0.152345404,-0.1335780174,-0.0331332907,0.0964195654,-0.1294155121,0.1355017573,0.2478066981,-0.1427013129,-0.0006218139,0.0272530131,-0.1798278987,0.2222775072,0.197826013,0.1286401451,0.2018653899,0.525388062,-0.1262486577,-0.2340527326,-0.2239226252,0.24420017,0.1961286515,-0.2908231318,0.1298102885,-0.0687725171,0.0243006777,-0.0847825631,-0.679587245,-0.3061884344,-0.0921970904,0.0006510711,0.4704107642,-0.330901593,0.0706555918,0.1599271297,0.3159408569,0.0793664604,-0.1885332614,-0.044373747,-0.3438287079,-0.2535299659,-0.0260646604,0.4630136192,0.1464138925,0.0704189911,0.3701916039,0.0221871901,-0.0610447712,-0.4288595021,0.218477577,-0.0467988625,0.0778331682,0.0034065631,0.1291554272,-0.247211799,-0.2504459023,0.3068675399,0.0360249728,-0.205761373,0.327116698,-0.1920830458,0.0744263157,-0.1781024784,0.1151923761,0.2128263414,-0.5418013334,0.3973288238,-0.1855192035,0.0039909063,-0.2795964479,0.2635209858,0.0960103422,0.0679152757,-0.0404972062,0.0594006777,-0.4201915264,0.1961016506,-0.0403325036,-0.1881746352,0.0418398418,0.1097168624,0.1519098282,0.1650311649,-0.6058769226,-0.0128622605,-0.0775761679,0.5512671471,-0.34305498,-0.2375435829,0.1149812415,0.1875803918,-0.0166976135,0.1244238466,-0.1434655637,0.0150683811,-0.007390561,0.2049880326,-0.1010244936,0.5279217958,0.1304708272,0.7018238306,-0.1654545516,-0.1430081576,0.3313239813,0.2419932485,0.0773653984,0.0481638126,-0.0284133386,-0.1179990172,-0.1809873581,-0.082166642,-0.0068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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2181","title":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)","comments":"Thanks @hwijeen for reporting and thanks @jpilaul for pointing this out.\r\n\r\nIndeed, those are different JSON-like formats:\r\n- the first one is the **standard JSON** format: all the file content is JSON-valid, thus all content is either a JSON object (between curly brackets `{...}`) or a JSON array (between square brackets `[...]`)\r\n- the second one is called **JSON Lines**: the entire file content is not JSON-valid, but only every line (newline-delimited) is JSON-valid\r\n\r\nCurrently PyArrow only supports **JSON Lines** format: \r\n- https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.json.read_json.html\r\n  > Currently only the line-delimited JSON format is supported.\r\n- https:\/\/arrow.apache.org\/docs\/python\/json.html\r\n  > Arrow supports reading columnar data from line-delimited JSON files.","body":"Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!","comment_length":104,"text":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) \n Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! \n Thanks @hwijeen for reporting and thanks @jpilaul for pointing this out.\r\n\r\nIndeed, those are different JSON-like formats:\r\n- the first one is the **standard JSON** format: all the file content is JSON-valid, thus all content is either a JSON object (between curly brackets `{...}`) or a JSON array (between square brackets `[...]`)\r\n- the second one is called **JSON Lines**: the entire file content is not JSON-valid, but only every line (newline-delimited) is JSON-valid\r\n\r\nCurrently PyArrow only supports **JSON Lines** format: \r\n- https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.json.read_json.html\r\n  > Currently only the line-delimited JSON format is supported.\r\n- https:\/\/arrow.apache.org\/docs\/python\/json.html\r\n  > Arrow supports reading columnar data from line-delimited JSON 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2181","title":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)","comments":"Thanks @albertvillanova for your explanation, it is helpful to know (maybe add to docs?)!\r\nHowever, the problem I described above happened when I was dealing with jsonl files \ud83d\ude3f\r\nAlthough I did not thoroughly inspect, I suspect the cause was the one extremely long document in my case.","body":"Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!","comment_length":48,"text":"Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) \n Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.\r\nWhen loading a huge json file of 500GB, pyarrow complains as follows:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1027, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/user\/.pyenv\/versions\/3.7.9\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/app\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/json\/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641\/json.py\", line 83, in _generate_tables\r\n    parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 247, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\nWhen using only a small portion of the sample file, say first 100 lines, it works perfectly well..\r\n\r\nI see that it is the error from pyarrow, but could you give me a hint or possible solutions?\r\n#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! \n Thanks @albertvillanova for your explanation, it is helpful to know (maybe add to docs?)!\r\nHowever, the problem I described above happened when I was dealing with jsonl files \ud83d\ude3f\r\nAlthough I did not thoroughly inspect, I suspect the cause was the one extremely long document in my case.","embeddings":[0.016556887,0.0126864687,-0.0075106705,0.3409382999,0.1604535431,-0.2647139132,0.193632707,0.5859469771,-0.1621605307,-0.0815067366,0.1654730737,0.1441630423,-0.0150832608,0.0145624503,-0.1295208484,-0.1911917031,0.033368215,0.0782742128,-0.0815535039,0.1355070025,-0.0368864611,0.2328725159,-0.0935274884,0.2323912382,-0.0870280042,-0.0781508908,0.1980634928,0.3374814093,-0.3348842263,-0.4495861232,0.1612249315,-0.2951855063,0.0062585096,0.3340712488,-0.0001156308,0.0471057519,0.2495900393,0.109179385,0.0187429283,-0.2191216499,0.2808351219,-0.3987070322,0.228011921,-0.1674175858,0.1295851022,-0.4462138414,-0.3999225199,0.3737807572,0.4277296364,-0.125555113,0.1651013941,0.091210708,0.2657488286,0.2669147551,0.4368439317,-0.0652506948,0.027398346,0.5041721463,0.3052019775,-0.2382042259,-0.3354803026,-0.1370317638,-0.249592483,0.1381146312,0.3779326081,-0.1172010303,0.076280944,0.0589680932,-0.0799430832,0.0377216749,0.2263211757,-0.4086019993,-0.1033591554,-0.0781874731,-0.0856484845,-0.1830741614,0.3079182208,0.3882312179,-0.2103946954,0.0698304623,-0.1914447248,-0.1000016332,-0.1991208196,0.1786511391,-0.3580315709,-0.1259495318,-0.0024537381,0.3322300911,0.2310532629,-0.007610932,-0.035575673,0.0311062783,-0.2734938562,-0.056598112,-0.2094626725,0.0166391041,0.0015142764,-0.3171832263,0.1906490326,0.2260884643,0.1960860789,-0.0655944422,0.2463149428,0.2574926019,0.6111616492,0.059475515,-0.3522540629,-0.1677471697,0.2054002136,0.237815097,-0.0194872171,0.0609876625,-0.0592074208,-0.5538618565,0.0594820604,-0.2798270881,0.259775579,-0.2160245329,-0.2103198022,0.1333221942,-0.7950463891,0.0500115268,0.1662532538,0.2496998012,0.0272156876,0.2363475859,0.0485518463,0.1581959277,0.0089593437,-0.1194209307,-0.0078674201,0.1263681799,-0.152511999,0.0385965146,0.1579921395,-0.1335816085,-0.0064160214,0.1193348318,0.2173471451,-0.2116392553,0.1862066239,0.0166174266,0.0477700792,0.3288215995,-0.0516762882,0.1116407961,-0.0548598059,-0.0126550142,-0.2687699497,0.4798909724,-0.0626880005,-0.3448120952,-0.1742749214,0.100412406,-0.2606137395,0.2655539811,0.0049829497,0.0375169143,0.1504191756,-0.3691319823,0.2161603868,-0.131151095,0.1778304875,-0.3457722068,0.1031698063,-0.0813739225,-0.686923027,0.017364502,0.2560487986,0.1754170209,-0.094436802,0.5446502566,-0.415045321,0.1447616071,-0.045954112,0.1615658551,0.1449172199,0.160628736,-0.6060854793,0.3728240132,-0.2745345831,0.057643231,0.0307355803,-0.0146053843,-0.1264383495,-0.0241248291,-0.0301004108,0.3515685797,0.0832778588,0.2309474498,-0.3654409647,-0.2369496226,0.1233631745,0.3634355962,-0.1145457625,-0.4116987586,-0.0493318588,-0.1916890144,0.055319462,-0.1157415733,0.1517809778,0.4218284488,0.135918498,-0.1193930358,0.102686882,0.1130747423,-0.4654299319,0.021460088,0.0717728287,-0.0631102473,-0.3461884856,-0.0881970525,-0.030149851,0.2487495095,0.0945114121,0.2060095221,0.0340390354,-0.1731155217,0.0721888989,-0.0024395175,-0.0986388773,-0.413590461,0.1334338039,0.0734615549,-0.0101108309,0.390627265,0.0637331009,-0.293379277,0.0692314729,0.1497817785,-0.0336900316,-0.1447110027,-0.0944388956,0.184556067,-0.0829884708,0.0026114385,-0.2447776794,0.1293199956,0.2801520824,-0.3557440937,0.0277640875,0.1032500342,0.0765697882,-0.0654545948,0.2241742462,0.4178517759,-0.328894347,0.203213647,0.0963192433,-0.2922596335,0.0688130632,-0.0792855769,-0.0184273757,0.1651138216,0.2211024761,0.3922338784,0.2243866175,0.1382108033,-0.1602222621,-0.119418323,0.398599565,-0.1834302247,-0.0815003961,0.152345404,-0.1335780174,-0.0331332907,0.0964195654,-0.1294155121,0.1355017573,0.2478066981,-0.1427013129,-0.0006218139,0.0272530131,-0.1798278987,0.2222775072,0.197826013,0.1286401451,0.2018653899,0.525388062,-0.1262486577,-0.2340527326,-0.2239226252,0.24420017,0.1961286515,-0.2908231318,0.1298102885,-0.0687725171,0.0243006777,-0.0847825631,-0.679587245,-0.3061884344,-0.0921970904,0.0006510711,0.4704107642,-0.330901593,0.0706555918,0.1599271297,0.3159408569,0.0793664604,-0.1885332614,-0.044373747,-0.3438287079,-0.2535299659,-0.0260646604,0.4630136192,0.1464138925,0.0704189911,0.3701916039,0.0221871901,-0.0610447712,-0.4288595021,0.218477577,-0.0467988625,0.0778331682,0.0034065631,0.1291554272,-0.247211799,-0.2504459023,0.3068675399,0.0360249728,-0.205761373,0.327116698,-0.1920830458,0.0744263157,-0.1781024784,0.1151923761,0.2128263414,-0.5418013334,0.3973288238,-0.1855192035,0.0039909063,-0.2795964479,0.2635209858,0.0960103422,0.0679152757,-0.0404972062,0.0594006777,-0.4201915264,0.1961016506,-0.0403325036,-0.1881746352,0.0418398418,0.1097168624,0.1519098282,0.1650311649,-0.6058769226,-0.0128622605,-0.0775761679,0.5512671471,-0.34305498,-0.2375435829,0.1149812415,0.1875803918,-0.0166976135,0.1244238466,-0.1434655637,0.0150683811,-0.007390561,0.2049880326,-0.1010244936,0.5279217958,0.1304708272,0.7018238306,-0.1654545516,-0.1430081576,0.3313239813,0.2419932485,0.0773653984,0.0481638126,-0.0284133386,-0.1179990172,-0.1809873581,-0.082166642,-0.0068381014,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2176","title":"Converting a Value to a ClassLabel","comments":"Hi @nelson-liu!\r\nHere is what I do to convert a string to class label:\r\n\r\n```python\r\nfrom datasets import load_dataset, features\r\n\r\n\r\ndset = load_dataset(...)\r\ncol_name = \"the string column name\"\r\n\r\nclass_names  = dset.unique(col_name)\r\nclass_feature = features.ClassLabel(names=sorted(class_names))\r\ndset = dset.map(lambda str_value: {col_name: class_feature.str2int(str_value)}, input_columns=col_name)\r\n\r\ndset = dset.cast(features.Features({\r\n    ...\r\n    col_name: class_feature\r\n})\r\n```\r\n","body":"Hi!\r\n\r\nIn the docs for `cast`, it's noted that `For non-trivial conversion, e.g. string <-> ClassLabel you should use map() to update the Dataset.`\r\n\r\nWould it be possible to have an example  that demonstrates such a string <-> ClassLabel conversion using `map`? Thanks!","comment_length":50,"text":"Converting a Value to a ClassLabel \n Hi!\r\n\r\nIn the docs for `cast`, it's noted that `For non-trivial conversion, e.g. string <-> ClassLabel you should use map() to update the Dataset.`\r\n\r\nWould it be possible to have an example  that demonstrates such a string <-> ClassLabel conversion using `map`? Thanks! \n Hi @nelson-liu!\r\nHere is what I do to convert a string to class label:\r\n\r\n```python\r\nfrom datasets import load_dataset, features\r\n\r\n\r\ndset = load_dataset(...)\r\ncol_name = \"the string column name\"\r\n\r\nclass_names  = dset.unique(col_name)\r\nclass_feature = features.ClassLabel(names=sorted(class_names))\r\ndset = dset.map(lambda str_value: {col_name: class_feature.str2int(str_value)}, input_columns=col_name)\r\n\r\ndset = dset.cast(features.Features({\r\n    ...\r\n    col_name: class_feature\r\n})\r\n```\r\n","embeddings":[-0.0255655907,-0.1936047971,0.0500335023,0.0593827851,0.6273636222,0.2115247846,0.2829759717,0.0898772702,0.0759942457,-0.0568674728,0.1029048711,0.6814887524,-0.0339189284,0.115833059,-0.063829571,-0.2042801529,0.1750881374,0.1827311069,-0.1002876386,-0.056887541,-0.3199301064,-0.0221358184,-0.4753821194,0.1178039089,-0.0183532331,-0.0131625459,-0.0521399416,-0.1745000482,-0.0095149893,-0.2616071403,-0.0025995478,-0.2266906947,-0.0235290825,0.4040020704,-0.0001230374,-0.325283885,0.1713490933,0.1276385337,-0.1027286276,-0.0012642887,-0.5446994901,0.1509112716,-0.0493944585,-0.3124809265,-0.0679486319,0.4585186839,0.0102069946,-0.3518183827,-0.0797238797,-0.0385747217,0.0643964559,-0.2685542405,0.0433487855,0.1502618194,0.1173128039,-0.0639188141,0.1979943216,0.1961912811,0.5107662082,0.3812748194,-0.0233184416,0.2310218215,-0.2420650274,-0.4479733109,0.2709723711,0.1409620345,0.0147539843,-0.3441451192,0.2141891122,0.0697048306,0.5089139938,-0.2787736952,-0.2406190187,-0.0289247558,0.2034195364,-0.1926503479,-0.1329814643,0.0453148559,0.1556026638,0.0525094084,-0.5872043371,-0.0947453901,0.0096688913,0.0064496761,-0.04198296,0.3577371538,-0.0681064501,0.2042177618,-0.183602497,-0.3301814795,-0.0656691194,-0.1423177719,0.053425204,0.451168865,0.0285003148,-0.1622573733,-0.1803563833,0.308008492,-0.3923470676,-0.4623276889,-0.0825618654,0.2044612765,-0.1535297781,0.2360501885,0.1304843426,-0.0718885139,0.6325418353,0.2619615495,-0.0377869047,-0.3972043097,-0.3506874442,0.0470989533,-0.1964601278,-0.0970517769,0.3145183623,0.2756204009,-0.0859175399,-0.208613649,0.060328491,-0.0629566833,-0.6144182086,0.1692011654,0.0577347204,0.102768749,0.159675926,0.15300861,0.3831623495,0.0127394684,0.3372881114,-0.5151320696,0.0256724358,0.1687627882,-0.0594784804,-0.2546179891,-0.2046683133,0.1303680539,-0.1657950729,0.1104661152,-0.1394721568,-0.2411892265,-0.1913698465,0.3533140719,0.1423953176,0.0091943825,-0.3899411559,0.0919626057,0.362169534,-0.3446409106,-0.3618513048,0.4646984339,-0.3172389567,0.105220139,0.2430622429,0.0626072958,0.3431931734,-0.2344117314,-0.0157063436,0.1214720458,0.2205845118,-0.323260963,0.2305161804,-0.3888455629,-0.2717774212,-0.1573465765,0.1096925884,0.1466993243,-0.5220822692,-0.172278434,0.220476523,0.1450605094,-0.2246677876,-0.303283751,-0.0844213963,0.4340304732,0.1755695194,0.0679722875,0.743145287,-0.4040966332,-0.1180322319,0.2816625834,-0.044224117,-0.5518223047,-0.1832592934,0.4364493787,0.1886939406,-0.1759246737,0.1285496354,0.1844903529,-0.2581253946,0.1220353022,0.0849035829,0.0374753475,0.1188872978,-0.1132329702,-0.204303816,0.4207707644,0.1227368861,0.4091431797,-0.0281125456,-0.2878473997,0.3498987257,0.0733438507,-0.0331092849,0.0556454845,-0.1548636705,-0.233484447,0.1583824158,0.0418301709,0.3316720724,-0.0388476215,0.2497375906,-0.2951244116,-0.0320845544,-0.0257879458,0.237743035,0.2743281126,0.0450926907,-0.2332085818,0.057657294,-0.3211953342,0.0445083641,-0.2223040015,-0.1409013718,0.2288301438,-0.0443542078,-0.2813130021,0.1206276938,-0.1339090914,-0.5128315687,-0.1144197211,0.2241096944,0.0126015656,-0.1962092072,0.3364670575,0.0232319161,-0.1875057369,-0.1845719963,0.2407033145,0.192868799,-0.2362461537,0.2976700068,0.1409193724,0.1962014437,-0.2853609324,-0.3096443117,0.2476768792,-0.0560093597,0.2904866934,-0.0988103375,0.2705557048,-0.028501587,0.0112132011,-0.0903075114,0.1164753959,-0.4181664586,-0.2635629475,0.1799531579,0.0261154603,-0.2478145808,0.2360076606,0.1281989664,-0.5026358962,0.2705974281,0.1162729636,-0.2514476478,0.3145844638,-0.0029368745,0.1560785621,0.1207884029,-0.0455898792,0.1869260818,0.11836081,-0.0162551049,0.1488968283,0.3097302616,0.0846782476,0.1536530405,-0.380091697,0.0024057084,0.1814293265,0.2393162549,-0.0628101677,0.1377518922,0.0139249545,-0.3364920318,0.0362231247,0.0175071452,0.225125134,-0.1867703199,-0.1059157103,0.1685806066,-0.3640716374,-0.1875962168,-0.0140826553,0.06448742,0.1266998947,-0.2037019283,-0.0895199254,0.1548196673,-0.4496595562,0.098924078,-0.2649113536,-0.142023325,-0.0725619942,-0.0077444417,-0.0715042129,0.0643922165,-0.142561093,0.185032472,-0.104815796,-0.5802630186,-0.0710165054,-0.2839055955,-0.1934550405,0.3171650171,-0.2148442715,-0.4116519094,0.1804641038,0.1438295096,-0.3101681173,-0.0612025261,0.1244389638,0.0559946187,-0.154782474,-0.1652976722,-0.0611049905,-0.0006229725,-0.0131550021,0.1271002442,-0.1438927799,-0.1116666272,-0.2295333743,0.1373449564,0.4809709489,0.2863416672,0.1686650068,0.1029805243,-0.1149902642,0.0788241178,0.0629107952,0.0188949015,0.0958622918,-0.0502325632,-0.1277494729,-0.040573366,0.009583707,-0.0468978845,-0.0986372828,0.2942809463,0.0259362422,0.1530192196,0.2664952874,0.3972101808,0.0180965643,-0.0478277467,-0.3397721052,-0.0846751928,0.0018226623,-0.0003129006,0.2170868665,0.4192286134,0.0019848177,0.1187851056,-0.1266331971,-0.3776563704,-0.2086678445,-0.210274443,-0.1063516438,-0.0071304459,-0.0226576813,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2175","title":"dataset.search_batch() function outputs all -1 indices sometime.","comments":"Actually, I found the answer [here](https:\/\/github.com\/facebookresearch\/faiss\/wiki\/FAQ#what-does-it-mean-when-a-search-returns--1-ids). \r\n\r\nSo we have to do some modifications to the code for instances where the index doesn't retrieve any IDs.","body":"I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n ","comment_length":25,"text":"dataset.search_batch() function outputs all -1 indices sometime. \n I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n  \n Actually, I found the answer [here](https:\/\/github.com\/facebookresearch\/faiss\/wiki\/FAQ#what-does-it-mean-when-a-search-returns--1-ids). \r\n\r\nSo we have to do some modifications to the code for instances where the index doesn't retrieve any IDs.","embeddings":[0.0112970611,-0.3721057475,-0.1019985154,0.0192688778,0.2021879554,-0.0966877714,0.2803093195,0.2968439758,0.1391415298,0.4209129214,-0.2324287444,-0.2434533834,0.1058326885,-0.033898145,-0.1621748805,0.1081455201,0.2741164267,0.3812984526,-0.0525572002,-0.5196725726,-0.3187375963,0.1191092208,-0.0284683872,0.3729512095,-0.2878592014,0.2902801335,-0.0275247227,-0.0893982723,-0.2744064629,-0.4506147206,0.5258822441,-0.3038473129,0.4114505351,0.2260930985,-0.0001312564,0.1222993061,0.3880340755,-0.0330385678,-0.0978071392,-0.1585950553,-0.0568627752,0.1535648406,0.0832173079,-0.0496736802,0.0529693365,-0.293507278,0.2583491504,-0.0760006383,-0.0392876305,0.2710106373,0.0165874027,-0.1001899764,-0.2166370302,-0.0440472066,0.6340439916,-0.316478014,0.0808619559,-0.143078208,0.4359707832,-0.0289208461,0.1490160525,0.1251526773,0.0537862107,-0.0263432525,-0.2640728354,0.1519919336,0.6509360671,-0.347019285,0.1948388517,0.2429174185,0.2132443488,-0.0165178366,-0.3425406516,0.1010914296,0.1615769565,0.0643818378,-0.0493507236,0.1699866951,-0.1023728475,0.2492177188,-0.0735962987,0.3226192296,-0.2642556131,0.1283892244,0.0921010301,0.3048032522,0.0520673916,0.1365374923,0.1266665608,0.1793742031,0.2614558935,0.1650856435,0.1424205452,0.2667565942,-0.545294106,-0.0396536961,0.3013203442,-0.2635917068,0.1599813849,-0.3341228962,-0.295882225,0.0122053837,-0.1906159222,-0.0811451674,0.1348101348,0.0300397296,-0.0409570709,0.0879250839,0.2758273184,-0.4434856772,0.0225716718,0.05646386,0.1405127645,0.0172800384,-0.0684079379,0.1226767525,0.0707820579,-0.2924739122,-0.3751615286,-0.092208527,-0.2851830423,0.2287971377,-0.2117733508,0.1873225123,0.3979750276,0.0808830336,-0.0434568711,0.0965683982,-0.1920486689,-0.0721826628,-0.1490160227,-0.1684361994,-0.1290246695,0.0257082675,0.1512941122,-0.4258549809,-0.1803211719,0.0613467805,-0.1417217553,0.1943153292,0.1223837063,-0.1732915044,0.4533530176,0.5615368485,-0.1166198105,0.3323795497,0.1357719451,0.0466702506,-0.0244340524,0.0082993126,0.0602010712,-0.5894426703,0.1787384748,0.0659676343,0.0158018582,0.3439203501,0.3202601373,0.3334341943,-0.2957113981,0.3365435004,-0.0159025975,-0.4066883028,0.0313610584,-0.0263190251,0.2536776662,0.1740864664,-0.0535256527,0.1447275877,0.0188022442,-0.2671530545,0.10422948,0.3047020435,0.0776820034,0.2836076021,-0.4879200757,0.1991422325,0.1316751391,-0.2821635902,-0.3218086958,0.056944754,-0.2449695468,-0.6043919325,0.0168585777,0.2755216658,0.2623558939,0.202829197,0.2860118151,0.1991926134,-0.0223771054,-0.1068089604,-0.4190152586,-0.0749165043,0.262950778,-0.0806498677,0.2959195077,0.2607781291,0.0897124112,-0.7707069516,0.3443847895,0.078310065,0.097485669,-0.0759550408,0.3600547612,0.0039185164,0.717531085,-0.0647906512,-0.0875052363,0.1142708212,-0.3557227254,0.0644310415,0.0200779531,-0.2087035775,-0.0145462397,-0.0329615809,0.060866762,0.2663134634,-0.0613433495,-0.1442528665,0.2852407098,-0.1506960839,-0.2166908532,0.0279039238,-0.1892898977,-0.1727768183,-0.4619542658,0.2869715989,-0.0044568004,-0.2153081298,0.0080345124,-0.0235068556,0.3108555675,-0.0657237023,0.0083285766,0.1663467586,-0.2015215904,-0.2778989971,0.7204379439,0.1057652831,-0.2275300771,-0.4191123843,0.1112755015,0.474425137,0.1263320744,-0.1979949772,0.1549974382,0.2241897285,-0.2468896806,0.4735146165,-0.122557722,-0.001431486,0.0749269798,0.142181322,-0.1148776785,-0.2041202635,0.1019529179,-0.1453208774,-0.1148640439,0.0365648121,-0.251742065,0.180736661,-0.1566976607,-0.4020548463,0.0373723991,0.2453040183,0.0468486212,-0.0122243585,0.123898834,-0.2407312244,0.3852651417,0.1465954036,-0.1858467013,-0.2387987822,-0.1886484921,-0.1494654566,0.0910928547,0.1543564796,-0.24721466,0.1434974521,0.3117801845,-0.0742414072,-0.3294375837,-0.2253194153,-0.3847643137,0.2618565857,-0.2814090848,-0.0455774181,0.0504941493,-0.027146671,-0.3396145701,-0.0936510414,-0.0362692289,-0.076493375,0.2038690597,-0.3289221823,-0.0500363447,0.0073315878,-0.1228785291,0.2336872816,0.0564926751,0.3149169087,-0.6124581099,0.0611079,-0.3827643991,-0.2278803438,-0.5138926506,0.306232959,-0.0604019165,-0.0381238796,-0.5279856324,-0.5108689666,0.1429802775,0.0286729913,-0.1098232344,-0.0350111946,0.3234111667,0.0188210737,-0.2142340839,0.3020818233,-0.0022866556,-0.0831172541,-0.1791341454,0.1730704904,-0.2697455883,0.3766937852,-0.2327563167,-0.2679131627,-0.0313489214,-0.0388202593,0.1331948042,-0.2206423879,0.12472675,0.0276666228,0.1237579361,-0.1222937331,-0.0160318147,-0.0337288529,-0.2221769094,-0.2021137774,0.2577733994,0.3072982728,-0.3648935258,-0.2410871536,-0.1581915617,-0.5619202256,0.4506982565,-0.135999456,-0.1375068575,0.0701930895,0.0001695525,0.0629063025,0.441385746,0.1164398417,-0.069123663,-0.0520515628,-0.2459105402,-0.0521875471,0.3002716005,-0.0251200367,0.2634778321,0.0242301766,-0.0936778486,-0.2716232836,0.7259996533,0.1539808065,-0.071441479,-0.0122895008,0.1312197149,0.1544188857,0.1646025777,0.0283248667,0.2517497838,0.2088071257,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2175","title":"dataset.search_batch() function outputs all -1 indices sometime.","comments":"@lhoestq  @patrickvonplaten \r\n\r\nI also found another short bug in the retrieval part.   Especially, when retrieving documents. If Faiss returns the -1 as the index, the retriever will always use the last element in the dataset.\r\n\r\nplease check [def get_doc_dicts function](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L222)\r\n\r\n\r\nDoes the use of the HNSW guarantee to retrieve valid indexes always? \r\n\r\n","body":"I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n ","comment_length":52,"text":"dataset.search_batch() function outputs all -1 indices sometime. \n I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n  \n @lhoestq  @patrickvonplaten \r\n\r\nI also found another short bug in the retrieval part.   Especially, when retrieving documents. If Faiss returns the -1 as the index, the retriever will always use the last element in the dataset.\r\n\r\nplease check [def get_doc_dicts function](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L222)\r\n\r\n\r\nDoes the use of the HNSW guarantee to retrieve valid indexes always? \r\n\r\n","embeddings":[0.015852442,-0.2806913555,-0.0632928833,0.0849646404,0.0853546485,-0.1038461328,0.2369085401,0.302310586,0.105233565,0.3437609076,-0.2517732382,-0.215343684,0.1649906933,-0.2617835999,-0.1169081405,0.0578847006,0.2875569463,0.3696747422,-0.0135725662,-0.5771781802,-0.4364783466,0.0454011261,-0.1087439135,0.359913975,-0.2922560573,0.1714099944,-0.0935413465,-0.0432227775,-0.2799686193,-0.4463257194,0.5693718195,-0.2051214129,0.4998085797,0.1934945583,-0.0001310245,0.0607319064,0.3989880085,-0.0673881844,-0.117808044,-0.1064763665,-0.1419556737,0.0830873996,0.0018900594,-0.0588475429,0.0330884568,-0.2911155522,0.2227630317,-0.1077727899,-0.0043290067,0.3318609297,0.0216136575,0.0569206215,-0.173826471,0.0023263954,0.7081764936,-0.3659300208,0.0985162109,-0.0367784202,0.48124367,-0.0528162047,0.1526664197,0.1807488054,0.0019854784,0.0009164838,-0.3093779683,0.2561921775,0.6104996204,-0.2700276077,0.1526672244,0.3558495343,0.3618957698,0.0523847118,-0.406441927,0.0502593704,0.1637898088,0.0432621539,-0.0340016484,0.2212761492,-0.0714895874,0.2482031435,0.0166672189,0.3187111318,-0.3073361218,0.2191102803,0.0984985083,0.2952780128,0.045627635,0.0873526782,0.1593000591,0.2445999533,0.3256033659,0.0117048277,0.0640360117,0.2494962066,-0.4395670891,0.0244554505,0.2559680939,-0.1571497023,0.1326080412,-0.3659854531,-0.2643686235,-0.0438166745,-0.1686107218,-0.0382474698,0.2108128071,0.098118037,-0.1500431895,0.1708924472,0.3238219619,-0.3653721213,0.0207301285,0.0379847735,0.1482292414,0.0402758159,-0.0610235743,0.0817494988,0.0378922187,-0.3919434249,-0.3166395426,-0.0747113004,-0.2071287632,0.1259294152,-0.3126271069,0.181250006,0.325061202,0.1952412128,-0.0645725131,0.1933427453,-0.2017333806,0.0103716953,-0.196739316,-0.1962479651,-0.1573488712,0.082716845,0.0814234093,-0.3686146736,-0.1698286235,0.0812144578,-0.0815586746,0.1601490825,0.0442280285,-0.2015522122,0.4831160605,0.4733822942,-0.1933667213,0.2601576149,0.0769247487,0.0277600214,-0.0702724233,0.0068190293,0.0162304454,-0.5032625198,0.166158244,0.0640356243,0.1187813208,0.3264333308,0.3128567636,0.251963079,-0.1443451345,0.3916178942,0.0385279916,-0.5134455562,0.0387769341,-0.0768570602,0.195645988,0.1660141647,0.0098022418,0.1663014144,0.0855415612,-0.1749097705,0.0766915604,0.3393226862,0.0318907797,0.3072890937,-0.5202445388,0.2322781831,0.1017867401,-0.4268246889,-0.4370739162,0.0217671208,-0.0934108943,-0.4778693914,0.0489125997,0.2361738682,0.4215180576,0.0948141888,0.1668855101,0.2142251134,-0.0300276577,-0.1694520712,-0.5196028948,-0.0608971231,0.1508563161,-0.1074654385,0.4080958366,0.305515945,0.0542486794,-0.6813327074,0.4600915015,0.0416386276,0.0762428343,-0.0491395555,0.3157004416,0.0517031178,0.6557026505,-0.0099551706,-0.0952553526,0.1157822162,-0.3426854312,0.099101387,0.0702580735,-0.1577310711,0.0223703794,-0.0416803434,-0.0441979878,0.2016412467,-0.0683345348,-0.0571705922,0.2374501228,-0.0488145016,-0.1406459659,0.0389309078,-0.2022221386,-0.1388723701,-0.5094869733,0.3016099036,0.0281648319,-0.1289514303,0.0058402522,0.0292830504,0.2807450891,-0.0501279756,-0.0498349071,0.0834944844,-0.1135855839,-0.2807566524,0.5495071411,0.1666018218,-0.1528518647,-0.5171636939,0.2230280787,0.515527904,0.1301402003,-0.2427323759,0.1156460494,0.2706033885,-0.2726711035,0.3828047514,-0.1083431393,-0.0549600907,0.0667175129,0.0663514361,-0.1498882324,-0.2030669451,0.0003471868,-0.2384688407,-0.0611852445,-0.0635788962,-0.2246356755,0.0834550411,-0.0093103051,-0.3920833766,0.0029282621,0.2973248065,0.0369533524,-0.0580252632,0.0672341213,-0.2013530284,0.3888042271,0.1604969949,-0.1281766891,-0.20559448,-0.220016703,-0.1835314184,0.0934477225,0.1413725913,-0.2600218654,0.2491225153,0.1945117414,-0.0841558129,-0.2929005027,-0.1100271419,-0.4757437706,0.1957865506,-0.256444633,-0.0163515359,0.0148865031,-0.0253463387,-0.4278128147,-0.1020294353,0.0343128778,-0.1144874915,0.1415252835,-0.2802917063,-0.0328773819,0.0512912758,-0.2847647965,0.1777866781,-0.0399978012,0.3042396605,-0.5487369895,0.066704534,-0.462308228,-0.2274498343,-0.4807326198,0.2997175455,0.0594249144,-0.025593726,-0.5072212219,-0.416665107,0.13378793,0.1072622463,-0.0663938969,0.000182708,0.2799879909,0.0276467688,-0.1641700566,0.3296678066,0.0342072994,-0.11002177,-0.098552607,0.1459679157,-0.2561155558,0.2692086101,-0.1441011876,-0.3244903386,-0.0767010301,-0.0238161776,0.0231356528,-0.2285491526,0.0347294435,0.0748743042,0.0923198536,-0.1048884764,-0.0713524669,0.0395773128,-0.2044111043,-0.1953291893,0.3142088652,0.335285753,-0.4002545178,-0.351967901,-0.2729283273,-0.5548422337,0.4818032384,-0.1490300447,-0.092409797,0.0499774329,0.0470276959,0.1055967361,0.4930113852,0.1570604444,-0.1823891848,-0.0377443954,-0.3242684007,0.0488731004,0.3383647203,0.0189887714,0.3005694151,0.1381306946,-0.1576920152,-0.3023749292,0.7045295238,0.2232642472,-0.0359439254,0.0347887501,0.2212922722,0.2890367806,0.1963255107,-0.005157379,0.179197818,0.1580584049,-0.3494434953,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2175","title":"dataset.search_batch() function outputs all -1 indices sometime.","comments":"Hi !\r\nNo it happens sometimes to return -1, especially if your dataset is small.\r\nIf your dataset is big enough it shouldn't happen in my experience.\r\n\r\nIdeally we should ignore all the -1 that are returned. It should be possible to change that in RAG's code ","body":"I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n ","comment_length":47,"text":"dataset.search_batch() function outputs all -1 indices sometime. \n I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n  \n Hi !\r\nNo it happens sometimes to return -1, especially if your dataset is small.\r\nIf your dataset is big enough it shouldn't happen in my experience.\r\n\r\nIdeally we should ignore all the -1 that are returned. It should be possible to change that in RAG's code 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2175","title":"dataset.search_batch() function outputs all -1 indices sometime.","comments":"I also checked with some indexes it returns more -1s. Specially with IVF\nwhen nprobr is very low. It doesn't happen when using HNSW though. But at\nthe moment if it happens, dataset will always return the last element.\nMaybe we should change it to repeat the most last valid retrieved doc id.\nWhat do you think?\n\nOn Wed, Apr 7, 2021, 21:09 Quentin Lhoest ***@***.***> wrote:\n\n> Hi !\n> No it happens sometimes to return -1, especially if your dataset is small.\n> If your dataset is big enough it shouldn't happen.\n>\n> Ideally we should ignore all the -1 that are returned. It should be\n> possible to change that in RAG's code\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2175#issuecomment-814746509>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AEA4FGTENOTLBEZTXEO2RS3THQOMPANCNFSM42PRVYDA>\n> .\n>\n","body":"I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n ","comment_length":150,"text":"dataset.search_batch() function outputs all -1 indices sometime. \n I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n  \n I also checked with some indexes it returns more -1s. Specially with IVF\nwhen nprobr is very low. It doesn't happen when using HNSW though. But at\nthe moment if it happens, dataset will always return the last element.\nMaybe we should change it to repeat the most last valid retrieved doc id.\nWhat do you think?\n\nOn Wed, Apr 7, 2021, 21:09 Quentin Lhoest ***@***.***> wrote:\n\n> Hi !\n> No it happens sometimes to return -1, especially if your dataset is small.\n> If your dataset is big enough it shouldn't happen.\n>\n> Ideally we should ignore all the -1 that are returned. It should be\n> possible to change that in RAG's code\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2175#issuecomment-814746509>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AEA4FGTENOTLBEZTXEO2RS3THQOMPANCNFSM42PRVYDA>\n> .\n>\n","embeddings":[-0.0352195762,-0.3799373209,-0.0478167236,0.0425724685,0.1534609348,-0.0822973475,0.2974610329,0.3594945967,0.1545493454,0.4000560939,-0.2719640434,-0.1355304122,0.1801845729,-0.1162477732,-0.1042535901,0.0176872201,0.2510522008,0.411700666,-0.0238933545,-0.565504849,-0.3472929001,0.0554176494,-0.1621703207,0.3566616476,-0.2797409892,0.2079564035,-0.0260151736,-0.1103101596,-0.256462276,-0.4766809642,0.5943052769,-0.2659983039,0.4845439494,0.1558329761,-0.0001315398,0.0446879305,0.3873681426,-0.0088379504,-0.1438093483,-0.1818877459,-0.1090587005,0.1446238309,0.043235112,-0.0554544628,0.0236524157,-0.2460289001,0.2300864756,-0.1483960003,-0.0233964622,0.2602907419,0.0044122054,0.1053060442,-0.1652548313,0.041457992,0.6535948515,-0.2939594686,0.0850197971,-0.0507773273,0.4610896111,-0.0346091203,0.0753294975,0.1881507784,-0.001329746,0.0161234234,-0.2380205691,0.2958970666,0.5326974988,-0.2921397388,0.2075950205,0.3135941327,0.4642091095,-0.0597214401,-0.3771156371,0.0416517034,0.1993481219,-0.0407039151,-0.0111020561,0.1670993567,-0.0956297889,0.2709483504,-0.04237305,0.3521004915,-0.3264372051,0.2192137539,0.0522135571,0.2759846747,0.0092749791,0.1088937595,0.2060456723,0.2290040255,0.2474695891,0.0632587597,0.0785056502,0.2596684694,-0.5432364941,-0.0447989218,0.2311130315,-0.1491761059,0.168373853,-0.3305642009,-0.2783356011,-0.0451584458,-0.1775639057,-0.1095416769,0.2284875512,0.0602514334,-0.0660796091,0.1812396348,0.2954419553,-0.4274535179,0.0121696023,0.0389140509,0.1758261919,0.0083056372,-0.0358683094,0.0813368633,0.0619083866,-0.3679016829,-0.3316133022,-0.1007401869,-0.2808183134,0.1629868299,-0.2730956376,0.1317038685,0.3634670377,0.146359548,-0.0804848224,0.109670341,-0.2068977803,-0.0622215904,-0.1117540151,-0.1769572496,-0.2272161841,0.0936456844,0.1030369103,-0.4674134552,-0.2122441828,0.1181561574,-0.1083407924,0.1680172682,0.0629030019,-0.1599711627,0.4342661798,0.5060639977,-0.1651233733,0.3369477689,0.1049577147,0.0215298515,-0.0737526789,-0.0155770238,0.0040881461,-0.5431575179,0.1411886364,0.0422844291,0.086526081,0.2734597325,0.2544466853,0.2392120212,-0.1934601814,0.3121080399,0.0012689596,-0.4304847717,-0.0292749032,-0.018434424,0.2137634903,0.2421736568,-0.0626737326,0.2080489844,-0.0002855672,-0.1212485656,0.1199433059,0.3533531725,0.0338832662,0.302108109,-0.5125042796,0.2115641385,0.0529593378,-0.4102325737,-0.4800129235,0.0852876529,-0.2135843337,-0.4950402677,0.0450820029,0.2997730374,0.3912861049,0.1524201483,0.1798935682,0.219024986,-0.0094275316,-0.1238508746,-0.473811537,-0.0203025304,0.1659637392,-0.0641442537,0.2935773134,0.298910439,0.0591122285,-0.6651149392,0.5013080835,0.043033544,0.065996252,-0.1130268052,0.3581508398,0.0074451803,0.6426554918,-0.0502744801,-0.1780302674,0.0940999091,-0.2164572924,0.0910365358,0.030363787,-0.1974472106,-0.0065674917,-0.076018557,-0.0133750765,0.2669549882,-0.1085205749,-0.0845133141,0.2347446084,-0.1179971248,-0.2005395442,0.1396357268,-0.1923646331,-0.116550453,-0.5885253549,0.2917744815,0.0471416079,-0.1216971278,0.0123132244,-0.0259520076,0.2452685088,-0.0673241243,-0.014078211,0.0787069723,-0.1622734964,-0.2337723523,0.6361717582,0.0879751518,-0.1430283189,-0.4997425377,0.1686100215,0.422323674,0.1080616117,-0.1674082726,0.0855955258,0.2818098962,-0.2499457151,0.3955381513,-0.1710406095,0.0001389042,0.0027462172,0.109056823,-0.1679994464,-0.2835789025,0.0963128433,-0.1664516926,0.0276063457,0.0002169652,-0.2364962101,0.1579594463,0.0416519232,-0.4263933599,-0.0353489667,0.318325907,0.0457656831,-0.0429300852,0.0815228671,-0.181658417,0.3200334609,0.2101315409,-0.1190136001,-0.1937002242,-0.2115126401,-0.1423906386,0.1488105804,0.1308148801,-0.2229634821,0.219523862,0.2386860996,-0.0670403913,-0.3496905267,-0.1729846001,-0.4460815489,0.2864308655,-0.2928532362,-0.0258862637,-0.0175163746,0.0072540739,-0.39909181,-0.1714131981,0.0129362922,-0.095446676,0.1556204557,-0.296256125,0.0372462012,0.0285777561,-0.1710748076,0.2711132765,-0.0011822265,0.3199294508,-0.5916768312,0.0757883191,-0.4536616802,-0.2365648597,-0.4173086882,0.2427259088,0.0915850624,-0.0534466542,-0.4779871702,-0.4758246839,0.0625490919,0.0882033035,-0.0828444362,-0.0033015423,0.2853538692,0.0243894327,-0.1791019738,0.2776325643,-0.0406959765,-0.0596278124,-0.1343443692,0.1894851327,-0.2842754424,0.3387922943,-0.1425896436,-0.3792662621,-0.0355894528,-0.048461888,0.0492439345,-0.3014895022,0.084129557,0.1359289885,0.0926205143,-0.1223763973,-0.0579570457,-0.0589886941,-0.1991788,-0.2659586668,0.276599884,0.3525114059,-0.3270429075,-0.3649878502,-0.2087554485,-0.4985119998,0.4951010942,-0.214250505,-0.0898166597,0.049526196,0.0754159689,0.0916238055,0.4582168758,0.0985497013,-0.1285074949,-0.0332209542,-0.2222563326,-0.006869453,0.2888504863,0.0057800626,0.2680354118,0.1182754189,-0.0085810991,-0.2806569934,0.6924853325,0.2454247773,-0.1094336212,0.0033003264,0.1748466045,0.1834654957,0.0850424543,-0.051114887,0.1842916459,0.124213554,-0.360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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2175","title":"dataset.search_batch() function outputs all -1 indices sometime.","comments":"That would be an easy way to workaround this issue. Feel free to open a PR on `transformers` and ping me ! :)","body":"I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n ","comment_length":23,"text":"dataset.search_batch() function outputs all -1 indices sometime. \n I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n  \n That would be an easy way to workaround this issue. Feel free to open a PR on `transformers` and ping me ! :)","embeddings":[-0.0255081374,-0.43428877,-0.0677888989,0.0737091675,0.2557210028,-0.086884737,0.3684980273,0.2831262648,0.0772832334,0.4528709054,-0.2223181874,-0.1984312534,0.0448379964,-0.063660197,-0.1256999224,-0.0068469043,0.28163445,0.4138695896,-0.1538365483,-0.5060524344,-0.2846685946,0.0919231623,-0.0996468067,0.3383357823,-0.2817484736,0.2458025515,0.0199471842,-0.1887715161,-0.2253594995,-0.4320185483,0.5846902728,-0.3023687601,0.4834949374,0.2854872942,-0.0001320533,0.1387141198,0.4092568159,-0.0608325861,-0.0931295976,-0.1343977004,-0.1120465994,0.1853378564,0.0772110745,-0.0690410882,0.0472479463,-0.2181160152,0.2536381185,-0.0957304761,0.0374494381,0.2091830373,0.0222841147,0.0297923461,-0.2128657401,-0.0140263839,0.6179968715,-0.2804864943,0.0285131484,-0.1167524979,0.4519714713,-0.0117919669,0.1072011068,0.1190893203,0.0261775609,-0.0273777936,-0.2742781341,0.1751958728,0.6338202953,-0.3051923811,0.1454397738,0.2770226002,0.3461788893,-0.0626919419,-0.3898919225,0.113210842,0.129207179,0.0541254319,-0.0549930856,0.1394111216,-0.1022167653,0.2307329923,-0.0631995201,0.3316141963,-0.3248741031,0.0969753265,0.0660300031,0.4078654051,0.0728807971,0.118746601,0.1552821845,0.2217082083,0.3452387452,0.1692684144,0.138444528,0.2564408183,-0.5704571009,-0.0328681879,0.2476677895,-0.3040004373,0.0914045572,-0.3667513728,-0.2984798849,-0.0011986484,-0.1310694665,-0.0730982348,0.1572881192,0.0958087891,-0.1555739641,0.19059591,0.289927572,-0.4025508165,0.0473156571,0.0571678989,0.133002311,-0.0341476277,-0.0552361384,0.163936168,0.0571805947,-0.3014554381,-0.3065543473,-0.187353,-0.2294137776,0.177884087,-0.2336438745,0.1199391782,0.3631218076,0.103890717,-0.1139714867,0.128608495,-0.2179741114,0.0080415085,-0.1513518244,-0.1766064316,-0.1628477275,0.0268835928,0.1277550906,-0.3845344484,-0.2226812243,0.0798276961,-0.0499852449,0.1883440316,0.0910435617,-0.1283472031,0.3765000999,0.5422126651,-0.1805920154,0.3457104862,0.1473206729,0.064840287,-0.0523054376,0.0569305569,0.0623312034,-0.5635715723,0.2131013423,0.0429791138,-0.0099195102,0.3326792121,0.2444730401,0.2408253849,-0.2267525345,0.3575468659,-0.0646775663,-0.4154815972,0.0750563741,-0.0482546538,0.2719373107,0.1569995582,-0.075921081,0.1987424791,0.0016797713,-0.2054322809,0.1476551443,0.3099027276,0.0314142443,0.2916763723,-0.4581266642,0.1396230012,0.098017633,-0.3492688537,-0.4021477401,-0.0138706183,-0.2424459308,-0.6214386821,0.057207074,0.2899440825,0.3333925307,0.2120535523,0.1955876499,0.2302583605,-0.0019288264,-0.0502748601,-0.434353888,-0.0875273794,0.2455367744,-0.0673291385,0.3384167552,0.3327716887,0.0542202145,-0.6487197876,0.3746267259,0.0277143046,0.1122425422,-0.0425458848,0.3574846685,-0.0490810797,0.6802942753,-0.0569766685,-0.0994707271,0.1098419875,-0.4044047594,0.1155566126,-0.0753009692,-0.1674618721,-0.0443620533,-0.0288728736,0.0516013429,0.2574236095,-0.0835033953,-0.135890305,0.2232373357,-0.1738039851,-0.2003886998,0.0807734653,-0.0530159362,-0.185188964,-0.5225940943,0.2937067449,0.0043019275,-0.2040100396,-0.0028494082,-0.0113272816,0.2704211771,-0.1043976992,-0.0470289476,0.1162900105,-0.2604333758,-0.1867754757,0.6034044027,0.0949822813,-0.2119152099,-0.5101861954,0.1308421791,0.5453127623,0.1332515329,-0.1913255304,0.1168153957,0.280608505,-0.2984347939,0.4549020231,-0.1223077849,0.0325306058,0.0806694478,0.0950451866,-0.129133448,-0.1698790491,0.0585401244,-0.1538615376,-0.1018987522,0.0576151647,-0.1809526682,0.0724459067,-0.0708554685,-0.3715221584,0.0288233273,0.237229839,0.0213536881,0.0044943471,0.1027630717,-0.1742998809,0.3418673277,0.1591805518,-0.1636027843,-0.2321522981,-0.1992494911,-0.1335535496,0.0652231798,0.1495561004,-0.2263540924,0.1603021175,0.2735206187,-0.1297739893,-0.2528555691,-0.2256877571,-0.3778072298,0.292773813,-0.2615428567,0.0061667948,-0.0009528858,0.0838354155,-0.4358869493,-0.0872685313,-0.0372384042,-0.0413084663,0.1497715116,-0.2632548809,-0.0305020865,0.0376622565,-0.1437921822,0.2031647116,0.0334750637,0.2562789321,-0.5662103891,0.0405999459,-0.4012899101,-0.2267560363,-0.5071386099,0.2500789762,-0.0428261831,-0.0016554183,-0.4777206182,-0.3937184811,0.1553553641,0.0370763019,-0.0991733,-0.0453234911,0.2388147116,0.0361723267,-0.1780598909,0.2443041503,-0.001067506,-0.0549965166,-0.1276286095,0.2173933536,-0.3013999462,0.328219533,-0.2105446607,-0.3242880106,-0.0426462144,-0.090696454,0.0754310787,-0.2990777194,0.0941386297,0.0747863278,0.128862083,-0.1666218042,-0.0098447958,-0.0191105604,-0.1045617387,-0.1794235855,0.338285774,0.3333112001,-0.3282365203,-0.3087780774,-0.2162129283,-0.4937134087,0.5283079743,-0.0928187445,-0.0157318972,0.0018385454,-0.0691943467,0.0996076465,0.4600244164,0.1996829659,-0.0514195599,-0.038545128,-0.2258525044,-0.0831207037,0.3009174466,0.0581529327,0.3111126125,0.0482883938,-0.0592646599,-0.273632735,0.7122138739,0.1361288577,-0.0920717418,-0.0344325975,0.1491027772,0.1136701852,0.159275949,0.0019229664,0.1652656645,0.22888349,-0.4103986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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2175","title":"dataset.search_batch() function outputs all -1 indices sometime.","comments":"Sure.  Will push everything together with RAG end to end. :) thanks a lot.\n\nOn Wed, Apr 7, 2021, 21:16 Quentin Lhoest ***@***.***> wrote:\n\n> That would be an easy way to workaround this issue. Feel free to open a PR\n> on transformers and ping me ! :)\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2175#issuecomment-814752589>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AEA4FGWLROCGARKN7WOJYSTTHQPH5ANCNFSM42PRVYDA>\n> .\n>\n","body":"I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n ","comment_length":82,"text":"dataset.search_batch() function outputs all -1 indices sometime. \n I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, \"IVF65536_HNSW32,Flat\")**.\r\n\r\nDuring the retrieval phase exactly in [this line of retrieval_rag.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/models\/rag\/retrieval_rag.py#L231) an error issue when all retrieved indices are -1.  Please refer to the screenshot of a PID worker. \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/113782387-37a67600-9786-11eb-9c29-acad661a9648.png)\r\n\r\n\r\nHere, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?\r\n\r\nIs this a problem of the index, where the faiss can't find any similar vector?\r\nIs there documentation on the output index being -1?\r\n\r\n@lhoestq \r\n  \n Sure.  Will push everything together with RAG end to end. :) thanks a lot.\n\nOn Wed, Apr 7, 2021, 21:16 Quentin Lhoest ***@***.***> wrote:\n\n> That would be an easy way to workaround this issue. Feel free to open a PR\n> on transformers and ping me ! :)\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2175#issuecomment-814752589>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AEA4FGWLROCGARKN7WOJYSTTHQPH5ANCNFSM42PRVYDA>\n> .\n>\n","embeddings":[-0.0283135884,-0.4459782839,-0.0621183515,0.1084139571,0.292105943,-0.0717789158,0.3549713194,0.3072511852,0.0994754583,0.437363416,-0.2883694172,-0.1778719276,0.0475873761,-0.0405465253,-0.118691586,0.0098077962,0.2470591068,0.4063918591,-0.1565123051,-0.5071401,-0.2862778902,0.1293407232,-0.0813589767,0.3280839324,-0.3517280519,0.2273842394,0.0281685721,-0.139819786,-0.2862049341,-0.4498890936,0.6027758718,-0.2830130458,0.4530468285,0.2541905642,-0.0001301647,0.1176812127,0.4395870864,-0.0651393086,-0.151789546,-0.1811907887,-0.0812221095,0.2138450742,0.0726928785,-0.0371777378,0.0060919323,-0.2177123427,0.2247651815,-0.1663434207,0.0418865085,0.1865682751,0.0233882628,0.1017680019,-0.1806958616,-0.0772505105,0.599283874,-0.2709929645,0.0486720465,-0.0523496829,0.456910044,0.0019479635,0.092516318,0.1397999376,0.0461416878,-0.0342070013,-0.2073286623,0.1863569766,0.5528726578,-0.2722924352,0.1710795015,0.2689873576,0.3746112585,-0.0904797465,-0.4090772271,0.0736160576,0.1362152398,0.0564786717,-0.0152638722,0.1417938769,-0.11472141,0.258849293,-0.10165108,0.3451104462,-0.3150436282,0.1087806076,0.0793952867,0.3879116178,-0.0039707585,0.1147775874,0.1775084287,0.2319929898,0.2189952284,0.1565219313,0.14337641,0.2508933246,-0.5799228549,-0.0268856771,0.2490704209,-0.2496403754,0.1563377976,-0.2913855016,-0.3163481653,0.00535121,-0.1451295167,-0.1174501553,0.1691085994,0.1386014223,-0.0916091129,0.1230222657,0.2601278722,-0.3778172135,0.0464000925,0.0165737811,0.1478918493,-0.0251178313,-0.1337244511,0.1508807987,0.069909282,-0.3211227953,-0.3362391889,-0.1822969168,-0.2384527177,0.156142503,-0.1856631786,0.1956372261,0.3701727986,0.0615759417,-0.1258408278,0.1341767162,-0.1884582341,-0.0083691636,-0.1735410243,-0.1935446411,-0.2100252956,0.0248548873,0.1446507871,-0.408821106,-0.1844087988,0.1028482765,-0.0403670706,0.2042436898,0.0240630526,-0.1293121874,0.381536752,0.5448859334,-0.1957634091,0.3435320258,0.1752250791,0.0462620445,-0.0807228684,0.0104634566,0.0561413988,-0.5754235387,0.1869510561,0.0365568846,-0.0247184113,0.2832264006,0.2651228607,0.2736901343,-0.2275352329,0.3701698482,-0.0708191022,-0.3711774945,0.04962001,-0.0310726147,0.2711126506,0.1841756701,-0.1032493189,0.17016913,-0.001558539,-0.2113306969,0.1576528698,0.355874896,0.0396361016,0.2429683954,-0.4880305827,0.2115790546,0.0413329266,-0.3464721441,-0.4149978459,0.0347531475,-0.2874720395,-0.5506218672,0.0943671912,0.2539378405,0.2913822234,0.2167929858,0.221115917,0.1762186736,-0.0184136033,-0.0822382942,-0.4449312091,-0.1094930619,0.1820261031,-0.0668349788,0.2814899087,0.301538825,0.0567572825,-0.6469029784,0.4162867665,-0.0154198837,0.1333107352,-0.0414281078,0.3924898803,0.0089213466,0.6352488399,-0.0670065507,-0.1997414529,0.1173175722,-0.3723261058,0.1516669691,-0.1041777655,-0.2065367252,-0.0788395107,-0.0319018774,0.0432047695,0.2024579644,-0.0909036323,-0.1343251765,0.2712657154,-0.1845749021,-0.2099106908,0.1425682306,-0.0775697306,-0.16261518,-0.5799489617,0.302626133,0.0197168775,-0.1899344325,0.0344842225,0.0379857793,0.2533740699,-0.1179477647,-0.0377238281,0.1215903759,-0.2508451939,-0.1928158849,0.6424354315,0.0720804334,-0.1853332669,-0.5010784268,0.1635936052,0.4550594389,0.1374626607,-0.1593974382,0.0422651842,0.3477858305,-0.3275406957,0.4250102639,-0.1345338076,0.035993889,0.0596402772,0.0846265033,-0.1595563889,-0.2088905871,0.0791566595,-0.145915702,-0.032150086,0.0721165463,-0.1810097545,0.1149869263,-0.0065552909,-0.3482501507,-0.0046556154,0.2404882163,-0.0307829194,0.0100295199,0.07758867,-0.1530270427,0.3684752285,0.1996846646,-0.1538051069,-0.2296267748,-0.1705859303,-0.1761599332,0.1038918048,0.1313128024,-0.2240185589,0.1574562341,0.2851550877,-0.1116567329,-0.2977469563,-0.1965698749,-0.4191932678,0.2730156183,-0.2794298828,-0.0117355324,-0.0167913362,0.0561585575,-0.451954931,-0.118975684,-0.084305644,-0.0798701793,0.143268466,-0.2832213342,-0.0262453835,0.0633004606,-0.1149929687,0.2100354284,0.0624083057,0.233408764,-0.6038302183,0.0384087674,-0.4086795151,-0.2158354968,-0.4646937549,0.250713408,0.0166676193,0.0315372348,-0.4577505291,-0.421887219,0.0902834013,0.0586623549,-0.1387365311,-0.0181439128,0.2579153776,0.0200823043,-0.2282142788,0.2372665852,0.0528728962,-0.0917406157,-0.1654131562,0.19085069,-0.2420116514,0.3424965739,-0.1977430731,-0.36896047,-0.012199576,-0.1239932403,0.1559957266,-0.3126060963,0.0653308406,0.1334224492,0.1033990234,-0.1387468874,-0.0690943077,-0.0376080275,-0.1779902577,-0.1865139157,0.3142649829,0.3371855915,-0.3545959592,-0.3330664933,-0.1831586212,-0.4907527864,0.5181811452,-0.1064134985,-0.0649667308,-0.0219290555,-0.0372577161,0.1350492388,0.4749009609,0.1957831234,-0.0861116499,-0.0533937924,-0.2279218733,-0.1095679924,0.3319778442,0.0107251741,0.2729150951,0.060941264,-0.0408687852,-0.2583755255,0.7406029105,0.2493864,-0.0772702396,-0.0038316017,0.1467562765,0.1696023941,0.1052189171,-0.0348764062,0.2000178993,0.1997401714,-0.3633256555,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2170","title":"Wikipedia historic dumps are deleted but hf\/datasets hardcodes dump date","comments":"It seems that this can be fixed from user's end by including a `date` argument, like this:\r\n\r\n`dataset = datasets.load_dataset('wikipedia', '20200501.en', date='20210420')`\r\n\r\nYou can get available dates from [here](https:\/\/dumps.wikimedia.org\/enwiki\/).\r\n\r\nThis is not a proper fix however as all the files will still have '20200501' in their file names.","body":"Wikimedia does not keep all historical dumps. For example, as of today https:\/\/dumps.wikimedia.org\/kowiki\/ only provides\r\n\r\n```\r\n20201220\/                                          02-Feb-2021 01:36                   -\r\n20210101\/                                          21-Feb-2021 01:26                   -\r\n20210120\/                                          02-Mar-2021 01:25                   -\r\n20210201\/                                          21-Mar-2021 01:26                   -\r\n20210220\/                                          02-Apr-2021 01:26                   -\r\n20210301\/                                          03-Mar-2021 08:10                   -\r\n20210320\/                                          21-Mar-2021 18:13                   -\r\n20210401\/                                          03-Apr-2021 10:08                   -\r\nlatest\/                                            03-Apr-2021 10:08                   -\r\n```\r\n\r\nHowever, the wikipedia dataset provided in the library, only supports the following configs, none of which are applicable anymore when disregarding the cached datasets:\r\n\r\n```\r\nValueError: BuilderConfig 20210401.ko not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']\r\n```\r\n\r\nThe cached datasets:\r\n\r\n```\r\n% aws s3 --no-sign-request --endpoint-url https:\/\/storage.googleapis.com ls s3:\/\/huggingface-nlp\/cache\/datasets\/wikipedia\/\r\n                           PRE 20200501.de\/\r\n                           PRE 20200501.en\/\r\n                           PRE 20200501.fr\/\r\n                           PRE 20200501.frr\/\r\n                           PRE 20200501.it\/\r\n                           PRE 20200501.simple\/\r\n```","comment_length":48,"text":"Wikipedia historic dumps are deleted but hf\/datasets hardcodes dump date \n Wikimedia does not keep all historical dumps. For example, as of today https:\/\/dumps.wikimedia.org\/kowiki\/ only provides\r\n\r\n```\r\n20201220\/                                          02-Feb-2021 01:36                   -\r\n20210101\/                                          21-Feb-2021 01:26                   -\r\n20210120\/                                          02-Mar-2021 01:25                   -\r\n20210201\/                                          21-Mar-2021 01:26                   -\r\n20210220\/                                          02-Apr-2021 01:26                   -\r\n20210301\/                                          03-Mar-2021 08:10                   -\r\n20210320\/                                          21-Mar-2021 18:13                   -\r\n20210401\/                                          03-Apr-2021 10:08                   -\r\nlatest\/                                            03-Apr-2021 10:08                   -\r\n```\r\n\r\nHowever, the wikipedia dataset provided in the library, only supports the following configs, none of which are applicable anymore when disregarding the cached datasets:\r\n\r\n```\r\nValueError: BuilderConfig 20210401.ko not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']\r\n```\r\n\r\nThe cached datasets:\r\n\r\n```\r\n% aws s3 --no-sign-request --endpoint-url https:\/\/storage.googleapis.com ls s3:\/\/huggingface-nlp\/cache\/datasets\/wikipedia\/\r\n                           PRE 20200501.de\/\r\n                           PRE 20200501.en\/\r\n                           PRE 20200501.fr\/\r\n                           PRE 20200501.frr\/\r\n                           PRE 20200501.it\/\r\n                           PRE 20200501.simple\/\r\n``` \n It seems that this can be fixed from user's end by including a `date` argument, like this:\r\n\r\n`dataset = datasets.load_dataset('wikipedia', '20200501.en', date='20210420')`\r\n\r\nYou can get available dates from [here](https:\/\/dumps.wikimedia.org\/enwiki\/).\r\n\r\nThis is not a proper fix however as all the files will still have '20200501' in their file names.","embeddings":[-0.0480471887,0.339981854,-0.0221922565,0.0315804072,-0.3193795681,0.156512931,0.3078280389,0.5392638445,0.176421985,0.1010856703,-0.0753563866,0.0723977834,0.2024516314,-0.2449931204,-0.1113701835,-0.182833761,0.0309121516,0.0772673413,-0.1100769192,-0.2934595644,-0.1943483204,0.1416632831,-0.297540009,-0.1001333967,-0.1351926029,0.1282684058,-0.1212271452,-0.1731771827,-0.236763224,-0.2825134099,0.2279289067,0.2149960697,0.2150947005,0.2445408404,-0.0001126101,-0.0224174578,0.4396784306,-0.0729151815,-0.6998178959,0.3375677466,-0.4934145212,-0.2081644088,-0.2113407701,-0.2803381681,0.0249623079,0.0599011295,0.1523051113,-0.0141651891,0.2861972451,0.1448759735,0.2360973507,-0.0227914248,0.2254752666,-0.168529287,0.3714103401,0.313983798,-0.1705524623,-0.1458384842,-0.3366081715,-0.0145526333,-0.08039698,0.5216310024,-0.0483497046,-0.2389984727,0.1012657657,-0.1834843755,-0.1664302051,-0.1672370434,0.4476556182,0.3567122519,0.5580341816,-0.1420494467,-0.1736138016,0.0477034599,-0.2691888511,-0.3119944036,0.3322288692,0.1879585236,-0.0743197575,0.086736396,0.0434375294,-0.1867388338,-0.1235563383,0.4326101243,-0.2945711315,0.6903668046,0.0475939922,-0.0200513583,-0.2650398314,-0.0862197578,-0.0129311718,-0.316567719,0.0335615203,0.3412547708,0.1046115011,-0.1992751062,-0.0340665504,0.1706593335,0.1952961534,-0.2298509181,-0.1247055903,0.1728707105,0.1436997503,-0.084455736,0.2835518122,-0.2561607063,0.2181194872,0.14792867,0.4882195592,0.3513464332,-0.199255392,-0.0234843548,0.3428494632,0.0529373288,-0.0747671202,-0.1216740608,0.1743231118,-0.3396600187,0.1949049085,0.1505921632,0.1067213863,-0.3127460182,-0.3135461807,0.3275847137,-0.1723534465,0.0550002493,-0.0030665204,-0.1820926815,-0.1246303394,-0.1386279911,-0.1526300758,-0.0057441792,0.1183300242,0.1284084022,0.2885450423,-0.1358129829,0.3161966801,0.2317119241,-0.0991327614,0.1145829186,-0.1741470397,0.0152740525,0.2022375613,0.3797014952,0.0382675529,0.1678479016,-0.1536102742,-0.2450581938,-0.3076083064,-0.0909029543,-0.2692331076,0.003493526,-0.3159883618,0.2036642581,0.1309011728,0.023593137,0.0376099013,0.1419020146,0.1953950673,-0.2002358288,0.0092894314,-0.0318655558,-0.0295625664,-0.1204090193,0.2662154734,0.1527356058,-0.1783032268,-0.1148426086,-0.0741789192,0.3705844283,0.1859222949,0.1107274964,-0.0983730704,0.0929739103,-0.1122706607,-0.2385077775,0.2569867074,-0.0488516539,-0.2477300316,-0.142287612,0.5185168386,0.1292577237,-0.0351007208,-0.1889841408,0.5140385032,-0.2660749555,0.0054178634,0.1362960041,0.1500758231,-0.0966951251,-0.1010837778,-0.332380712,0.0940822363,-0.1349024326,0.5809412599,-0.0463306271,0.3364216387,0.6471557617,0.377738893,0.0675285086,0.2076520026,0.3722668886,-0.1182639599,-0.0895943046,0.2840391397,-0.2195201367,-0.3384561539,0.2157478929,-0.1119534969,0.1423028857,0.0572796129,-0.2991670668,-0.2290911525,0.0036074684,-0.1578158438,-0.1922062188,0.2709792554,0.1160458103,0.2696571946,0.3821341693,0.0707488135,-0.2911677361,-0.0803748667,0.0224439856,-0.1743042469,0.2483406663,-0.3143912852,0.0948141441,0.0104760071,0.1629978865,0.2624762356,0.0467962511,0.1376544833,-0.1132883802,0.1075474173,0.1248564869,-0.0343472473,0.0228770245,0.2571222782,-0.2377163917,0.1016275734,0.3862405419,-0.0660331622,-0.1089660376,-0.3348828554,0.2010368705,0.3396961689,-0.0081818094,-0.096274212,-0.0002625089,0.2850736976,-0.0728488266,0.1175118685,-0.4912272096,-0.1852969378,0.2930184901,-0.1906876117,0.0877466351,-0.094600752,0.2002782524,0.6361352801,-0.0283581223,0.0360681862,0.3035472631,-0.3020724654,-0.3853172362,0.3101589084,-0.1874304563,-0.3706717193,0.1790653914,0.2985033989,-0.18427293,-0.191159457,-0.100862518,0.3368108273,0.0546802357,0.2021879703,0.0554028116,0.3110338151,0.2944017947,-0.3105643094,0.1880522519,-0.1699729711,0.085625492,-0.001388579,-0.0112569062,-0.5644822717,0.0784604028,0.1482223868,-0.0970541537,-0.5503886342,-0.6236146092,0.2578138113,0.0797467902,-0.121780701,0.1836832017,-0.2991448939,0.2259609252,0.1115831584,0.2293134332,-0.359590143,-0.3663398325,-0.1580598056,0.0799806118,0.0267514233,-0.1627448797,0.2104156166,-0.1402296871,-0.5127974749,-0.4433472753,-0.6245263815,0.3214212656,-0.0778710693,-0.0595676601,0.1482465565,0.2876193821,0.0446178094,-0.3598255515,-0.0445629284,-0.0845200792,-0.0961177647,-0.1044833139,-0.3406780362,0.240445748,-0.0595720522,0.1670760661,-0.1340950131,-0.1953468472,-0.1555752754,0.4140326381,0.0772166699,0.2195165008,-0.0850676894,-0.3220025897,0.0055803121,0.208600238,-0.4126555026,-0.3447987735,0.5001904368,-0.3294726312,-0.3419498801,0.169386819,-0.0701691285,-0.0787318423,0.0163808577,-0.4505988955,0.1796373427,0.0271939728,0.3788494468,0.3891842067,-0.0260350276,0.2755755782,0.0348579735,-0.0446544662,-0.0865289196,0.0076209502,-0.111386463,-0.0580644794,0.3612657189,0.1532097161,0.0982783735,0.046685338,0.9649457932,0.129607752,0.175260812,0.2703441381,0.0933707729,0.422911495,-0.296841979,0.031347923,0.0888592005,0.0506782047,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2166","title":"Regarding Test Sets for the GEM datasets","comments":"Hi @vyraun ! The test references for CommonGen are not publicly available: you can reach out to the original dataset authors if you would like to ask for them, but we will not be releasing them as part of GEM (March 31st was the release date for the test set inputs, references are incidentally released for some of the test sets but shouldn't really be used for benchmark submissions)\r\n\r\ncc @sebastiangehrmann","body":"@yjernite Hi, are the test sets for the GEM datasets scheduled to be [added soon](https:\/\/gem-benchmark.com\/shared_task)? \r\n\r\ne.g.\r\n\r\n```\r\nfrom datasets import load_dataset\r\nDATASET_NAME=\"common_gen\"\r\ndata = load_dataset(\"gem\", DATASET_NAME)\r\n```\r\n\r\nThe test set doesn't have the target or references.\r\n\r\n```\r\ndata['test'][0]\r\n{'concept_set_id': 0, 'concepts': ['drill', 'field', 'run', 'team'], 'gem_id': 'common_gen-test-0', 'gem_parent_id': 'common_gen-test-0', 'references': [], 'target': ''}\r\n```\r\n\r\n","comment_length":71,"text":"Regarding Test Sets for the GEM datasets \n @yjernite Hi, are the test sets for the GEM datasets scheduled to be [added soon](https:\/\/gem-benchmark.com\/shared_task)? \r\n\r\ne.g.\r\n\r\n```\r\nfrom datasets import load_dataset\r\nDATASET_NAME=\"common_gen\"\r\ndata = load_dataset(\"gem\", DATASET_NAME)\r\n```\r\n\r\nThe test set doesn't have the target or references.\r\n\r\n```\r\ndata['test'][0]\r\n{'concept_set_id': 0, 'concepts': ['drill', 'field', 'run', 'team'], 'gem_id': 'common_gen-test-0', 'gem_parent_id': 'common_gen-test-0', 'references': [], 'target': ''}\r\n```\r\n\r\n \n Hi @vyraun ! The test references for CommonGen are not publicly available: you can reach out to the original dataset authors if you would like to ask for them, but we will not be releasing them as part of GEM (March 31st was the release date for the test set inputs, references are incidentally released for some of the test sets but shouldn't really be used for benchmark submissions)\r\n\r\ncc @sebastiangehrmann","embeddings":[-0.3199131787,-0.0961244851,-0.1973158568,0.1844352782,-0.0919602215,0.118466258,0.2837320268,0.3940259218,-0.1052865312,-0.0161253773,0.1892587394,0.2699712515,-0.3344815075,0.1294022053,-0.0042844992,0.2062070072,0.0137714744,0.0532648787,-0.1366805881,-0.1827852875,-0.0030422248,0.2782809138,-0.1043493971,0.0674216449,-0.4564504027,-0.2105980515,-0.1491971314,0.127491951,-0.0292934906,-0.2702152729,0.3068476915,0.1725850701,-0.1328514069,0.4753216803,-0.0001048454,-0.092475757,0.2723983228,-0.1354853958,-0.2651570439,-0.3887256682,-0.3688611686,-0.0995736867,-0.0375091918,-0.1074555814,-0.1079825461,-0.0469908938,-0.1061154976,-0.4838304222,0.0354261696,0.2401734143,0.2840694189,0.3282464445,-0.0739153102,-0.2256850451,0.1541750729,-0.2887515128,-0.2577032745,0.2372087538,0.0459526889,0.0304730348,0.2049218714,0.1084306687,0.0687660798,0.1535712928,0.1378137618,0.1191932037,-0.0138435252,-0.373249799,-0.1442776918,0.2480443418,0.6280167699,-0.3502284884,-0.481459111,-0.1563065499,-0.2806761265,-0.0516629964,0.023211265,0.2364029884,-0.1203913689,0.1719749123,-0.3285424411,-0.3881807923,0.0009274193,-0.2865280509,0.1206907853,0.0589674823,-0.0700026527,-0.1194067672,-0.0599666685,-0.0071771275,-0.1204124317,-0.1025010645,-0.1917668432,0.0383693352,-0.3621035516,-0.4651756883,-0.175957039,-0.1125507876,0.3332552314,0.4292494655,0.5199062228,0.1588800102,-0.1818336248,0.1502227336,0.3433420062,0.1893132925,0.3401162326,-0.1188675836,0.1506562531,-0.0951440632,-0.1557407379,0.2906402647,-0.0817789659,-0.1565802395,-0.0089309104,0.1021690294,0.0856286511,-0.1682278216,-0.2093770653,0.1630986035,0.2056977898,-0.260371536,-0.1541120708,0.1218300983,-0.0569119677,-0.1605733037,-0.3958009481,0.1040320247,-0.118862398,-0.3285265565,-0.1978198588,0.0562764294,0.0054480582,0.0675543547,0.1492599696,-0.2012851387,0.4375038743,-0.0745779499,0.2316796184,-0.0567061342,0.3254747093,-0.0189853609,0.0689285025,0.2251051366,-0.0693525597,0.2178825587,-0.0323822051,-0.1107460484,-0.3100337088,0.3561433256,-0.129467085,-0.1646644026,0.3178631961,0.2424749583,-0.6210737228,-0.2433426082,-0.3021156192,0.4153852463,-0.0981868654,-0.204107523,-0.0203391463,-0.1193988323,0.068235524,-0.2143627405,0.30456388,0.1103176698,-0.1111432463,-0.0800725073,0.2217071205,-0.0407881364,0.0517827086,0.0540543795,-0.0427649952,-0.2430043966,-0.0572339632,0.173601687,0.3769561052,-0.1589796394,-0.1349399835,-0.0331564583,-0.2171144187,-0.1006632671,0.1498170048,-0.0805038661,0.1672935635,-0.1374400407,-0.1856816113,0.121845156,-0.2028912157,0.0184544958,-0.4238097966,-0.2053615302,0.3437220752,-0.0682144165,0.0103603704,-0.2939474583,0.0078066834,0.0155284181,0.1362079531,0.1302019209,-0.1227962673,0.210458681,0.1868480742,-0.3276837766,0.0944227278,-0.0963011757,-0.199502781,0.3398899734,0.3615368009,0.162085548,0.6675425172,-0.130902335,-0.3988662064,0.1048480421,-0.1167538464,-0.2448287904,0.1276991069,0.0224810317,0.0640118048,-0.0409484543,-0.2648723722,-0.1205672026,-0.2782312632,-0.0588954277,0.1076681316,0.4514991045,-0.1003340185,-0.0928502679,0.1537717283,0.0597490519,-0.059230525,-0.1679751277,-0.044983279,0.2149646729,0.2345026881,0.3967015743,0.5926070809,0.4832264483,0.1302341968,-0.4008479714,0.0054555545,-0.142151922,-0.0121017983,0.0782554522,-0.0040949523,0.5423492193,-0.0047594677,-0.0244002175,0.1844103038,-0.0400171652,0.0160118099,0.022838803,-0.1425108165,0.0788014531,0.006102806,-0.2962937653,-0.1734932065,0.0747587383,-0.1455050558,0.1093975306,0.3203474283,0.0163721777,-0.0421183705,0.0658583194,-0.0669589639,-0.352091074,-0.0452134982,0.2316674739,0.2718405426,0.2748383582,0.5183141828,-0.1675246209,0.0718117133,0.1177208573,0.2747019231,0.1620261371,0.3612320721,0.0641383231,0.0139861517,-0.0303531978,0.0787399039,0.0004979396,0.254319191,-0.0126081267,-0.265535146,-0.1824519783,-0.0529922061,0.4955043197,-0.1487344205,-0.2569100559,-0.2974275351,-0.1817663759,0.1917511225,-0.2066358626,0.118453294,0.1440537721,-0.1022746935,0.1683882326,-0.3601239324,-0.0104556242,-0.362883091,-0.2907291055,-0.0691317543,0.2497561425,0.0202234685,-0.1943970025,0.5272140503,-0.0249321256,-0.0333685093,0.0251066126,-0.3380875587,-0.0206466988,-0.2334231883,0.6151625514,-0.0213523917,0.1114930809,-0.1227341518,-0.0504871979,0.1510163397,-0.367094487,-0.2025052458,-0.4389244616,0.0394201949,0.1674714088,0.0141842114,-0.7287330627,-0.313970834,-0.136432901,0.0277883224,0.078133367,0.0207945723,0.0937430039,-0.0762842596,-0.0136159062,0.2463950664,0.1230628118,-0.1112482697,-0.2469578534,0.0709656775,-0.2565820813,-0.2834366262,0.0082686692,-0.2342488468,0.5369318128,-0.2553298175,-0.4300388098,0.1072039083,0.138503328,0.1700685471,0.3131311834,-0.3208166063,-0.0004081299,0.0782382041,-0.2634627521,-0.2218330652,-0.2145446092,0.0192224532,0.1301267594,0.0001412471,0.2200954109,-0.0504713729,0.1129408255,1.2106736898,0.3273599744,-0.2195055038,0.0636019483,-0.1927153468,0.0751804858,-0.0221974738,-0.125181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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2165","title":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset","comments":"Hi,\r\n\r\na HF dataset can be converted to a Torch Dataset with a simple wrapper as follows:\r\n```python\r\nfrom torch.utils.data import Dataset\r\n \r\nclass HFDataset(Dataset):\r\n    def __init__(self, dset):\r\n        self.dset = dset\r\n\r\n    def __getitem__(self, idx):\r\n        return self.dset[idx]\r\n\r\n    def __len__(self):\r\n        return len(self.dset)\r\n\r\ntrain_ds = HFDataset(train_ds)\r\n```\r\n@lhoestq Since the Arrow Dataset already provides `__getitem__` and `__len__`, I think we could use the [virtual subclass](https:\/\/docs.python.org\/3\/library\/abc.html#abc.ABCMeta.register) mechanism from the `abc` module to elegantly solve this issue. This mechanism would allow the Arrow Dataset to be used in place of the Torch Dataset because the `isinstance(instance of Arrow Dataset, TorchDataset)` check would return True (DeepSpeed has this check [here](https:\/\/github.com\/microsoft\/DeepSpeed\/blob\/ab5534fc4c0f8ca21ada321f9730d723aa31288b\/deepspeed\/runtime\/engine.py#L823)).\r\n\r\nAnd it requires a minimal change in the `arrow_dataset.py` file:\r\n```python\r\nif config.TORCH_AVAILABLE:\r\n    from torch.utils.data import Dataset as TorchDataset\r\n    TorchDataset.register(Dataset)\r\n```","body":"Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n","comment_length":124,"text":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset \n Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n \n Hi,\r\n\r\na HF dataset can be converted to a Torch Dataset with a simple wrapper as follows:\r\n```python\r\nfrom torch.utils.data import Dataset\r\n \r\nclass HFDataset(Dataset):\r\n    def __init__(self, dset):\r\n        self.dset = dset\r\n\r\n    def __getitem__(self, idx):\r\n        return self.dset[idx]\r\n\r\n    def __len__(self):\r\n        return len(self.dset)\r\n\r\ntrain_ds = HFDataset(train_ds)\r\n```\r\n@lhoestq Since the Arrow Dataset already provides `__getitem__` and `__len__`, I think we could use the [virtual subclass](https:\/\/docs.python.org\/3\/library\/abc.html#abc.ABCMeta.register) mechanism from the `abc` module to elegantly solve this issue. This mechanism would allow the Arrow Dataset to be used in place of the Torch Dataset because the `isinstance(instance of Arrow Dataset, TorchDataset)` check would return True (DeepSpeed has this check [here](https:\/\/github.com\/microsoft\/DeepSpeed\/blob\/ab5534fc4c0f8ca21ada321f9730d723aa31288b\/deepspeed\/runtime\/engine.py#L823)).\r\n\r\nAnd it requires a minimal change in the `arrow_dataset.py` file:\r\n```python\r\nif config.TORCH_AVAILABLE:\r\n    from torch.utils.data import Dataset as TorchDataset\r\n    TorchDataset.register(Dataset)\r\n```","embeddings":[-0.2578476369,-0.2652207911,0.092290394,0.3382236362,0.1922441572,0.2227143943,-0.0302729923,0.3457017541,-0.1513580829,-0.1886630952,-0.3486473262,0.3248988092,-0.1942691356,-0.1420536041,0.1868406981,-0.2680391371,0.1673274487,-0.0773859099,-0.2466962934,-0.1544105113,-0.1386658102,-0.1202884912,0.0764644146,0.0747125149,0.0671005175,0.0934039727,0.0947778746,0.1692148447,-0.0237060338,-0.3378914595,0.2977361679,0.0648861304,0.5751037598,0.2561476827,-0.0001273232,0.276031971,0.0935578793,-0.0755770281,0.0312795937,-0.0670314357,-0.0523281954,-0.0347759612,0.1930178553,-0.1016476229,-0.3808221221,-0.5978340507,-0.1727228761,-0.5317306519,0.0468852669,0.4796395302,0.0522026569,0.2359526902,-0.0159182791,0.0726409778,0.2887389064,-0.0978339612,-0.1821518838,0.1489152312,0.0003680434,0.1105434671,-0.1440718323,0.1899665147,-0.1628380269,-0.0048651923,0.4622430801,-0.1983766258,-0.3828516006,-0.1405878067,-0.111037761,0.4482490122,0.178531304,-0.3304377496,-0.1622341275,0.0913779661,-0.0402276292,-0.1471044719,-0.3329949081,0.1591297686,0.0133263832,0.1457206905,0.0341933519,-0.0348534435,-0.1720402837,0.0566778407,0.1703047007,0.2567848563,0.1148063466,0.2005096525,0.1122348979,-0.3005607724,0.4763327241,-0.2886344492,0.0199535806,-0.1253504753,-0.1023310423,-0.0023473741,-0.3257407248,-0.0770284161,0.4265949726,0.1055244431,0.3500558436,0.3115786612,-0.1508314162,0.0488586538,-0.0783415884,-0.1055723429,0.1002681479,0.2584201097,-0.0067850319,-0.2417301983,0.0126299374,0.2741792202,-0.0550226197,-0.3046224415,0.0989658833,-0.0002228799,-0.1225692928,-0.1470832229,-0.2946826518,-0.0963819325,-0.3344509602,0.0045026164,0.1658029407,0.1950969696,-0.0069162836,0.1559909135,0.4160038233,0.5736421943,-0.2732748389,-0.0442828089,0.0392627008,0.1037559137,-0.0204075892,-0.2133430839,-0.0619503036,-0.1895148009,0.0589849092,-0.0272746123,-0.1381994635,0.2087249905,0.4373679757,-0.1368232667,0.2677766681,0.0341860503,0.0437844358,0.010727806,0.3737203777,0.4464449286,-0.2962799966,0.2520726621,-0.3542585075,-0.2294124216,-0.029993346,0.0291095488,0.1518912315,-0.3381064236,-0.2596776485,0.3441300392,-0.0471921749,0.0570967868,0.0422882177,-0.4522698224,-0.3920982778,-0.1631955504,0.2866369784,0.2642970383,-0.4543376863,-0.1082593873,0.0669646636,-0.1349182129,0.1149821132,0.2226646841,-0.2385523468,-0.0212371852,-0.3799104691,-0.1190298796,0.5021675825,-0.3953184485,0.050411053,0.0612166077,0.137086466,-0.0883586109,0.0613233782,0.0608317517,-0.1293748021,-0.1211572438,0.177742213,0.1931716949,-0.1514971703,-0.2278325409,-0.1285722256,-0.1981902272,0.3369712532,-0.0489436053,0.2017852515,0.2132306546,-0.1483509839,-0.3161423504,0.2712277472,-0.0189265776,0.1627959609,0.2168645859,-0.2954394817,0.1737281829,-0.051365871,0.1118234918,0.0711634085,0.130041793,0.0647766069,0.0738745108,0.1080077663,0.0362750329,-0.1997978538,0.1803220958,-0.113247104,0.0170497075,-0.0658317581,-0.0846983716,-0.1495205015,-0.2479619533,-0.0476644002,0.2230951041,-0.4051263928,0.1576890349,-0.5375214219,0.2642347515,0.1351727545,0.1516149044,-0.0461898148,0.2652033567,0.0887988359,0.0306286942,-0.1722036302,0.2192764282,0.0369970165,-0.0704309493,0.2098776251,0.2918401062,0.1083689183,-0.9760195017,0.4055995643,0.4391723871,0.1616518945,-0.0263196845,-0.0654944032,0.737255156,0.2774629295,0.3000271916,-0.0249516424,0.013337099,0.0203084964,0.1796132028,-0.2465766966,0.0484425835,-0.1590321511,0.064657785,0.335036546,-0.067366749,-0.260027945,0.072083801,-0.0637842268,-0.0697551519,0.0892389715,0.2696089745,-0.5429385304,-0.0200525057,0.2840873897,-0.1175016835,0.2478155345,0.1257991046,0.2453291118,0.0192358196,-0.1548355669,0.116677314,0.3136957884,0.1029244885,0.3733651638,-0.0712378472,0.0262523629,-0.060589917,-0.2755173743,-0.0274274237,-0.1287209988,-0.1086189672,-0.4903784692,0.2309375554,-0.3518230617,0.0477069952,-0.3123164177,-0.1259673834,0.0694135576,-0.1767664701,-0.1042157784,-0.0295040123,0.1408050656,0.1130039543,-0.415553093,-0.2949005365,0.1695128828,-0.414301157,0.0517658107,-0.1680516899,-0.1829601973,-0.0821089074,0.1017923206,-0.0141242957,0.3234539926,0.2352633625,-0.0364735238,-0.4555365443,-0.1237668544,0.0857319236,-0.1682258546,0.1182565764,0.166331172,0.0459988341,0.2455699295,0.0315160081,0.0844996497,0.0641371161,-0.0313020274,0.0945117623,-0.4060159624,0.1735185236,0.3860553205,-0.1754043847,-0.3225271702,-0.2034178078,-0.4023958445,0.0576415658,0.0536065735,-0.0176006444,0.3570226431,0.1686804444,0.2799854577,-0.0048589534,-0.1254453808,-0.0701352805,0.3763884008,-0.0555457734,-0.2689588964,-0.1432141811,-0.3228366673,0.2073334157,0.3074100316,-0.3161610961,0.0621641614,-0.2311657071,0.3600255251,0.0136123896,-0.0250633042,0.3019002378,-0.1703789532,0.1023959517,-0.1909873933,-0.2149832547,-0.0230281558,-0.2790466547,0.295226723,0.3544196188,0.4846105278,-0.1506832093,1.0412967205,-0.0465684608,-0.3697224557,0.3973759711,-0.1264249682,0.1498175263,-0.18332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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2165","title":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset","comments":"Interesting ! Thanks for sharing this @mariosasko . I like the idea\r\nThis looks like something we should add IMO","body":"Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n","comment_length":20,"text":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset \n Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n \n Interesting ! Thanks for sharing this @mariosasko . I like the idea\r\nThis looks like something we should add IMO","embeddings":[-0.2704315186,-0.3552313447,0.0578338169,0.3274024427,0.1308025718,0.2287478447,-0.0746707842,0.375287354,-0.1519821286,-0.2421408743,-0.3885174096,0.3905744851,-0.2419558316,0.0159070753,0.1749463826,-0.30655393,0.2215385139,0.0879765153,-0.2604885101,-0.1844500154,-0.1150370091,-0.0907219425,0.062386252,0.1066241488,0.0754081309,0.0218765084,0.0603702664,0.136170283,-0.0912094861,-0.3727336824,0.1620187461,0.0805812404,0.6023117304,0.2542231381,-0.000129465,0.1214095578,0.0471356772,-0.1353145391,0.1333484501,-0.064884387,-0.1909479499,-0.0160525888,0.1982826144,-0.1445407718,-0.3125005066,-0.5852587819,-0.0864551291,-0.3302887976,0.1317971796,0.5674702525,0.0448211171,0.2532970011,0.0411301218,0.0413687751,0.2709915936,-0.1340613514,-0.1894199848,0.051447209,-0.0449580252,0.1564192921,-0.1868779957,0.1760749966,-0.1330002695,0.026119912,0.3555385172,-0.3080777824,-0.3637715876,-0.1833691448,-0.0535963401,0.5436592698,0.2123097479,-0.2470743954,-0.0536052287,0.1764544398,-0.0406476855,-0.1832195371,-0.4047416747,0.2438120395,-0.034479212,0.1389831454,-0.0193724483,-0.2209188342,-0.1599256545,0.0496588573,0.2337450832,0.3325103819,0.0863249451,0.172097981,0.219557181,-0.3176527023,0.4350479543,-0.2300881743,0.0136854481,-0.1905343384,-0.0844793469,-0.0536000393,-0.367852509,-0.0870001391,0.4450832009,0.0914297029,0.1940545291,0.3110405505,-0.1877269894,0.0876536444,-0.1655006707,-0.2114872187,0.1320845187,0.2368789464,0.0151883224,-0.2638655305,-0.0350546204,0.2685378194,-0.1065920591,-0.4033831954,0.0268643498,0.0132904379,-0.1553608179,-0.1946214139,-0.178907603,-0.2201947719,-0.2744254768,-0.0415353402,0.0255338755,0.2432051748,-0.0197861511,0.0324828252,0.245116666,0.6499621868,-0.1770289391,-0.1200614497,0.023189323,0.1186215729,-0.0104452195,-0.178273648,-0.090177238,-0.1836858094,0.0831105039,-0.0311975162,-0.2837878168,0.1042146087,0.3755451441,-0.1094037741,0.2886730433,0.0350889266,0.1142901108,0.0078265797,0.278511256,0.411947608,-0.3310035467,0.2717525959,-0.5120928288,-0.1923474967,-0.1678929627,0.0259820148,0.1766342074,-0.3390814066,-0.4178642333,0.3362242877,0.0006454258,0.1393365562,0.0111863548,-0.4340932369,-0.3783961535,-0.1909058392,0.1955821961,0.261905849,-0.3799597919,-0.1437560171,0.1453683078,-0.1773046702,0.2109769136,0.3144453764,-0.2082760036,0.0645736456,-0.4396536946,-0.0454900004,0.6284146905,-0.3011912107,0.1494977623,0.1173237562,0.1322222054,-0.2706713676,0.116647616,-0.0181617606,-0.078445673,-0.0447085649,0.1449571401,0.2285384387,-0.13093701,-0.1571481675,-0.1241616905,-0.307241261,0.486638695,-0.0557226315,0.2174060345,0.2946627736,-0.1126179099,-0.2794396281,0.3269390762,-0.068717517,0.1383547783,0.2432400733,-0.3034929335,0.0261795148,-0.0794050321,0.1094832942,0.1647337675,-0.0427010991,0.0162464995,0.0033794018,0.0795040429,0.0811147019,-0.2249483317,0.1649547964,-0.1156474873,0.0749071389,-0.081822224,-0.0752734914,-0.1724797487,-0.1560641527,-0.0423299819,0.2044191957,-0.3709309101,0.1121349409,-0.5357997417,0.2550240457,0.0991600826,0.1160597727,0.0869624913,0.2077864558,0.0999309719,0.0034452002,-0.131325379,0.1819621921,0.0038002152,0.0547716357,0.3907215595,0.3828898668,0.0789416879,-1.0408308506,0.374701798,0.4230888784,0.1125190854,-0.1140040681,-0.0838940293,0.7048979998,0.198375091,0.2045114785,-0.0233915877,-0.0807897225,0.0893388912,0.1275876164,-0.2039730698,0.0450539328,-0.0958149061,-0.0324646607,0.2899934947,-0.0298492406,-0.3910634518,-0.0265778545,0.0611635745,0.0086546754,0.09781041,0.3565205038,-0.5009670854,-0.1333936006,0.2598287165,-0.326651305,0.1245565265,0.1496166587,0.166812256,0.0315002911,-0.0519470535,0.0357631817,0.2676283121,-0.0245042816,0.4564778209,0.0383480191,0.0988226682,-0.0827441067,-0.3403329551,0.0135738216,-0.1481552124,-0.0426874049,-0.4770848453,0.1296864003,-0.3740543127,-0.1077135056,-0.3232433498,0.016059909,0.0922948197,-0.0635075942,-0.1585211158,-0.0373835191,0.2656473517,0.1264510155,-0.4192354977,-0.2399662435,0.2190296352,-0.262365669,-0.0459454991,-0.1887426227,-0.079747051,-0.082025215,0.0914017707,0.0161286127,0.2941834629,0.1412696093,0.1444798708,-0.4242689312,-0.1420470774,0.002584188,-0.0993426144,0.0697790384,0.159506321,0.1411956102,0.2244947702,0.0567411259,0.04318434,0.0411016792,-0.040708743,0.1077585667,-0.4161676168,0.1413841844,0.4955780804,-0.1301068515,-0.3275340199,-0.2123824954,-0.3685065806,0.0493371673,0.1444416344,-0.1609321982,0.3607040048,0.1027723551,0.1735191792,-0.0248907655,-0.0694721416,-0.0942184925,0.3464500308,-0.077798374,-0.2930299938,-0.1745948941,-0.2663951218,0.1151683778,0.1990528703,-0.344843328,0.1793664396,-0.1567104161,0.3103542924,-0.0030000366,-0.0515287928,0.3110346496,-0.1472959667,0.1452459246,-0.1825834811,-0.2098716795,-0.0008980338,-0.1738931835,0.3357551396,0.4362910092,0.456735611,-0.2110413313,0.9687743783,-0.0148856388,-0.3842717409,0.5177223682,-0.1633149981,0.1034211069,-0.191820398,-0.2534873188,-0.0386898778,-0.0802293047,0.3667734265,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2165","title":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset","comments":"@mariosasko \r\nThx for your code!\r\nIt perfectly works with a small modification for HF NLP dataset:\r\n```\r\noriginal_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds = HFDataset(train_ds['train']) # needs splitting\r\n```","body":"Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n","comment_length":28,"text":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset \n Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n \n @mariosasko \r\nThx for your code!\r\nIt perfectly works with a small modification for HF NLP dataset:\r\n```\r\noriginal_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds = HFDataset(train_ds['train']) # needs splitting\r\n```","embeddings":[-0.2263557464,-0.3335738182,0.070314914,0.3271583915,0.1331761032,0.1971441209,-0.0642952695,0.3733654618,-0.1391186118,-0.2662570179,-0.3984828591,0.3849843442,-0.179449752,-0.009580628,0.1554363668,-0.300897032,0.1781212389,0.076728873,-0.2033017427,-0.2192837894,-0.0984729603,-0.0496377274,0.0211220514,0.125883013,0.0060907789,0.0596893653,0.0468233041,0.2068376541,-0.0479721501,-0.3290063739,0.1856233627,0.0352230929,0.5887832046,0.2546223998,-0.0001306744,0.1232334152,0.0475138538,-0.1539012492,0.1102606431,-0.1059945002,-0.1251382828,-0.037439622,0.1923864782,-0.1235969514,-0.3527246416,-0.5381259322,-0.0982355997,-0.290035665,0.1821317077,0.5682997108,0.0439835005,0.2380842417,-0.0001738147,0.0502141267,0.2654594481,-0.1158183888,-0.1378005445,0.0770626739,-0.0951834396,0.1144737378,-0.2686012983,0.1428055316,-0.1503308266,0.072083056,0.3074949682,-0.3364239931,-0.4187689722,-0.1907426119,-0.0029426359,0.5164994001,0.1975763738,-0.2164663225,-0.0486005694,0.1461015195,-0.1083505079,-0.1705802828,-0.36707443,0.2305695266,-0.0336455964,0.1331619173,0.037428882,-0.1993561387,-0.1586773843,0.0975329578,0.2056356072,0.3252075911,0.0735005364,0.1826860458,0.2181069851,-0.3050098419,0.3931107819,-0.2096302658,-0.032205753,-0.1784204692,-0.1522565931,-0.0226949342,-0.3895271122,-0.0581793077,0.4548136294,0.0893317983,0.1379813701,0.3401524723,-0.1559869051,0.0358810574,-0.1179903522,-0.1961131692,0.1375550181,0.2798586786,-0.0167926978,-0.3020294011,-0.0787237957,0.233344838,-0.0840417594,-0.412138164,0.053519275,0.0179046523,-0.1774987131,-0.2150649875,-0.2046911567,-0.2606694996,-0.2944220603,-0.0563340746,0.0382383801,0.2656323016,0.0401877016,0.0500871055,0.1952546835,0.6915662289,-0.2502996325,-0.1751054674,0.0112741599,0.1056316867,-0.0220319144,-0.182876125,-0.1040798649,-0.2147393376,0.1593292803,-0.0057802135,-0.2855864167,0.1035448611,0.3521379828,-0.1735322922,0.2845810354,0.0553727895,0.1202842668,0.0789878294,0.3318808675,0.3952591419,-0.3046818376,0.2600634694,-0.5100749135,-0.2010753751,-0.1981972903,0.0106506571,0.1503171921,-0.2748128176,-0.4242849052,0.3067432344,0.0271716546,0.1364007741,0.0167120453,-0.4317055047,-0.3108105063,-0.1870919764,0.2145445198,0.2237268388,-0.3245928586,-0.1132323965,0.1227968559,-0.1630419791,0.2324233502,0.3118992746,-0.2270777225,0.0936800241,-0.4663161039,0.0080991257,0.6656390429,-0.3257720172,0.1022588164,0.1635378003,0.0983478501,-0.2311872244,0.1086594462,-0.0553934127,-0.0797104612,-0.0385858119,0.1303543001,0.2308624685,-0.094621554,-0.1622785926,-0.1658529043,-0.2639196813,0.5142816901,-0.0872293562,0.2348762602,0.3476688564,-0.0908901617,-0.2052481771,0.3716835678,-0.0414164327,0.1036917642,0.2142174244,-0.3256297112,0.0535456091,-0.073021993,0.0921149254,0.1350721419,-0.0252536591,0.0498598292,-0.033963386,0.0881131068,0.1063458622,-0.2813718021,0.1484549791,-0.1486712545,0.0176728163,-0.1103216186,-0.0677547008,-0.2025362998,-0.1761354953,-0.0231417269,0.286152482,-0.3305152357,0.12785092,-0.4923216701,0.2535890639,0.0675133988,0.1056316346,0.1362594068,0.1969215572,0.1536073983,-0.0305043459,-0.1565905809,0.2011708468,0.0490445942,0.0091487216,0.359028995,0.2326785326,0.0966187045,-1.0042270422,0.3522100151,0.4355178177,0.1550198644,-0.1452267319,-0.0021778455,0.6890761852,0.2010133117,0.2365346253,-0.0610618405,-0.0508690514,0.0942555219,0.1049328148,-0.1251616329,0.0396533199,-0.0850838497,-0.0466094166,0.2758455575,-0.0639292672,-0.3997496367,-0.0149441082,0.1547486633,-0.0097320555,0.0751002729,0.3563731611,-0.5196492672,-0.1247848123,0.3244453073,-0.3547014892,0.1424794346,0.1628667712,0.1763049215,0.0010702891,-0.0438614339,0.0199416392,0.2627765834,0.0312216226,0.4762645066,0.0328029841,0.1253219992,-0.0937413201,-0.320377022,0.0578687899,-0.1390546262,-0.0352983363,-0.4970809519,0.1807901859,-0.4068142176,-0.1239830256,-0.2987782061,-0.0224426761,0.0396812484,-0.109131597,-0.2386263758,0.0302582346,0.3040401042,0.0953090936,-0.417355597,-0.3343822062,0.219783932,-0.2575615644,-0.0490855873,-0.1521484107,-0.1422960311,-0.0792501494,0.0874339938,0.0642262474,0.2967342436,0.1606131494,0.117771633,-0.3894567788,-0.1165060624,0.0104811285,-0.1332972944,0.0998919457,0.1726034433,0.1334909797,0.1722554117,0.0761121884,0.0592288338,0.0346762761,-0.0320955515,0.1489344388,-0.3590313792,0.1663262397,0.4372859299,-0.1095225364,-0.2783699036,-0.2167597115,-0.3826257586,0.0475437827,0.1732869744,-0.1151548102,0.3042676449,0.1316343397,0.211764127,-0.0758673996,-0.121579662,-0.0538332798,0.360645473,-0.0776255131,-0.3281585276,-0.1559724212,-0.2494729608,0.0884207562,0.2179971188,-0.3521410227,0.1593417078,-0.1564198434,0.3416546881,-0.0553478934,-0.0349266231,0.3035986125,-0.1843936294,0.1471060514,-0.2114264071,-0.2488058656,0.061330162,-0.1483648866,0.3629367054,0.3649741709,0.4517152905,-0.1608110219,0.9838785529,-0.0303140003,-0.3359048367,0.5201300979,-0.1261212975,0.086239405,-0.2030792385,-0.2672263384,-0.02986584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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2165","title":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset","comments":"@lhoestq Sadly, from Python 3.7 onwards `torch.utils.data.Dataset` doesn't support the virtual subclass mechanism due to `typing.Generic` type no longer having `abc.ABCMeta` as its metaclass.\r\n\r\nWith that in mind, another option is to remove a direct type check (`isinstance(dataset, torch.utils.data.Dataset)`) in `deepspeed.initalize` and to rewrite the checks in a manner similar to `torch.utils.data.DataLoader` ([link](https:\/\/github.com\/pytorch\/pytorch\/blob\/b80c6f863f2327c712c478f67c248b94d66b65ac\/torch\/utils\/data\/dataloader.py#L197-L239)). This is exactly why the `DataLoader` works with arbitrary objects that provide `__getitem__` and `__len__` (and in our case, the `ArrowDataset`). By doing so, their code wouldn't be any stricter in comparison to the `DataLoader`.\r\n\r\nSo if you agree, I can open an issue in their repo and fix this if they like the idea.","body":"Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n","comment_length":108,"text":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset \n Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n \n @lhoestq Sadly, from Python 3.7 onwards `torch.utils.data.Dataset` doesn't support the virtual subclass mechanism due to `typing.Generic` type no longer having `abc.ABCMeta` as its metaclass.\r\n\r\nWith that in mind, another option is to remove a direct type check (`isinstance(dataset, torch.utils.data.Dataset)`) in `deepspeed.initalize` and to rewrite the checks in a manner similar to `torch.utils.data.DataLoader` ([link](https:\/\/github.com\/pytorch\/pytorch\/blob\/b80c6f863f2327c712c478f67c248b94d66b65ac\/torch\/utils\/data\/dataloader.py#L197-L239)). This is exactly why the `DataLoader` works with arbitrary objects that provide `__getitem__` and `__len__` (and in our case, the `ArrowDataset`). By doing so, their code wouldn't be any stricter in comparison to the `DataLoader`.\r\n\r\nSo if you agree, I can open an issue in their repo and fix this if they like the idea.","embeddings":[-0.1920574605,-0.2704025805,0.0988816097,0.3232178986,0.2382786572,0.1883175522,-0.045659896,0.3633685112,-0.1526031047,-0.2291758657,-0.2917279899,0.3924194276,-0.2503360212,-0.1537752897,0.1516395509,-0.2407059222,0.181158945,0.0032430561,-0.269872725,-0.199164629,-0.1094516814,-0.1029918268,0.0290850047,0.0508622639,0.0944085643,0.1040174365,0.0816430524,0.1808154732,-0.1128340736,-0.3632012308,0.3348354399,0.0631520003,0.568320632,0.3380843997,-0.0001285925,0.2495380491,0.1516090184,-0.0954126567,-0.048618298,0.0058680433,-0.0310990922,-0.1078799888,0.2537959218,-0.0793687776,-0.2585174739,-0.6241518259,-0.1227698997,-0.5026624203,0.0664021,0.4283711314,0.0463510714,0.3282434344,0.0120554427,0.0952218473,0.2861973643,-0.0377576649,-0.1647085696,0.1939546615,0.058613088,0.1633573174,-0.1833067387,0.0865228325,-0.1490781903,0.0236566886,0.4681111276,-0.2537768781,-0.3934955001,-0.1979160607,-0.1612958759,0.5067217946,0.2559040487,-0.3469001651,-0.2240120173,0.0779629573,-0.0618266314,-0.1476538777,-0.217859596,0.1044944972,-0.0763134807,0.0751041695,0.0757466853,0.0165292565,-0.1876958758,0.1010662317,0.1476613879,0.1680387855,0.1117822528,0.1688792557,0.1994540989,-0.3436609209,0.3780586123,-0.2171828002,0.0021823584,-0.1452053636,-0.1031779498,-0.1081254631,-0.3531705737,-0.1528615355,0.383035928,0.1301053762,0.3453951478,0.3318163455,-0.2277630717,0.0814984962,-0.0249896515,-0.1177566424,0.1623562276,0.2724095583,0.0154545484,-0.1962260902,0.0820427164,0.2925750017,-0.1423644423,-0.3170528114,0.1186282784,0.0623504221,-0.1232669055,-0.1359723061,-0.1813091934,-0.1504846066,-0.2349579036,0.0214676932,0.1157316044,0.1644117832,-0.0010171535,0.218443051,0.3691661656,0.5446549654,-0.2553099394,-0.1770908087,0.0780243725,0.1107357293,-0.0750111192,-0.2217584997,0.0215201508,-0.1792351902,0.0170285013,-0.0673136413,-0.1304965019,0.3081122637,0.2849660218,-0.1399293393,0.2640922964,0.066293478,-0.0842040703,0.0411605984,0.3640844822,0.4290799797,-0.3253016174,0.2448960245,-0.3367206454,-0.1767986864,-0.1608814746,0.0041704481,0.1324421465,-0.3356076777,-0.3940821886,0.1987404376,0.0049035777,0.0119573334,0.0823145136,-0.5586115122,-0.2367926985,-0.1953954101,0.2141402662,0.2105928212,-0.4652603567,-0.1148305535,-0.032348454,-0.1110006422,0.1935072243,0.307397604,-0.2178398818,-0.02567886,-0.3970097005,-0.0204563402,0.5086792111,-0.2867502272,0.0247857943,0.1531435698,0.1048132852,-0.0957697034,0.1215051711,0.0464643911,-0.0526720993,-0.1397288442,0.1875391155,0.1678770036,-0.1229540184,-0.2729192674,-0.1068385616,-0.2577292621,0.3619751334,-0.0371052884,0.1300515831,0.21104303,-0.1213669851,-0.2831217051,0.2862468362,-0.0242851339,0.1068466604,0.1502738148,-0.201292932,0.1783106625,0.0183900129,0.0233058948,0.0052240025,0.1541561186,0.0099737821,0.0297068898,-0.0348640531,0.0375255942,-0.1254970282,0.1646582633,-0.0355101675,0.0571016632,-0.0778035596,-0.0694773495,-0.2363839,-0.2742439806,-0.1225562841,0.0787640288,-0.4409392476,0.178393513,-0.5549850464,0.1552928835,0.1712520868,0.118306607,-0.0533446819,0.2489914149,0.0549134687,-0.0293417964,-0.1652685553,0.179641068,0.123894982,-0.0634167492,0.2238185704,0.2895556986,0.0666508451,-0.8759514093,0.3386188447,0.4776403606,0.1758344322,-0.0606287606,-0.0306877978,0.6746484637,0.269923985,0.2855539024,-0.0329919308,-0.009948181,0.0626338199,0.2108589262,-0.2279220968,0.0115737105,-0.1417211294,0.178337425,0.2537435591,0.038325455,-0.243582055,0.0231189635,0.0286006462,-0.1075292602,0.1185367703,0.3567152619,-0.5336498618,-0.017127106,0.2257860303,-0.1542957425,0.2203960866,0.079782635,0.2486361116,-0.0573246554,-0.1059990004,0.0514930636,0.3088961244,0.0781626776,0.3587369621,-0.0323414095,0.0559454672,-0.055926349,-0.3572993875,0.0482363254,-0.1054936945,-0.0915639922,-0.511428237,0.2086586803,-0.4489869177,0.0558845885,-0.3178323209,-0.11460834,-0.0138481874,-0.1688977927,-0.1546970457,0.0197159052,0.1207129732,0.1362329274,-0.4001930952,-0.1381525248,0.2074002773,-0.4670451283,0.0483384393,-0.1286058426,-0.2392186224,-0.11042054,0.1444949061,-0.1327437758,0.2613075376,0.2039724141,0.0211454481,-0.4411420822,-0.1653214991,0.084304221,-0.165503338,0.0724387318,0.2261061519,0.0766591281,0.3106871247,-0.0076395953,0.0699216053,-0.0379846878,-0.0956487656,0.1708254069,-0.3793530464,0.2229391485,0.3711147308,-0.1360011548,-0.3164051175,-0.2379695773,-0.3310390413,0.0463848375,0.1217815578,-0.0089485813,0.3633452654,0.1398113519,0.2525959611,0.0753093213,-0.0746526346,-0.0394742712,0.4306192398,0.0210851952,-0.309174329,-0.1776057482,-0.3541427255,0.1667936444,0.3370664716,-0.3112230897,0.0742642134,-0.2507444024,0.3438512087,0.0475702249,-0.0304733571,0.3099016547,-0.1462945938,0.0725222453,-0.2030642629,-0.1782543659,-0.018077448,-0.2516228259,0.3121638298,0.3920455873,0.4929171503,-0.1153860688,1.0432752371,-0.1220421493,-0.5147123337,0.4142384827,-0.1147439703,0.1429576278,-0.1226013973,-0.2998675406,-0.085366033,-0.2901656628,0.293903619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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2165","title":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset","comments":"That makes sense ! Feel free to open an issue on their repo and discuss this idea","body":"Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n","comment_length":17,"text":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset \n Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n \n That makes sense ! Feel free to open an issue on their repo and discuss this idea","embeddings":[-0.2620015442,-0.305977881,0.0482731722,0.3386891484,0.1174888611,0.2266668677,-0.0971872956,0.3648378253,-0.1662661433,-0.218519181,-0.3497868478,0.394482851,-0.2521584034,-0.0014390136,0.1679954678,-0.2934633195,0.2071099281,0.0787692592,-0.2426628768,-0.2180524319,-0.1317133754,-0.0721179247,0.0561505035,0.1099010855,0.0453758053,0.0387697108,0.0631345063,0.1353642642,-0.1149804741,-0.3563358188,0.2166966498,0.0383347869,0.5877937675,0.2521918118,-0.0001278631,0.1164884567,0.0586811751,-0.1147667915,0.1068665981,-0.0929902941,-0.174374029,-0.0208212119,0.2010273784,-0.1319154799,-0.307541728,-0.5764225125,-0.0690180138,-0.3266124725,0.1039493755,0.5637236238,0.0582137741,0.2589324415,0.0555239804,0.0262063984,0.3165854514,-0.147557795,-0.1901795268,0.0691053122,-0.0712911859,0.1284115613,-0.1900322437,0.1756014824,-0.1578796208,0.0448201932,0.3596791625,-0.3286312819,-0.3591727018,-0.1928064525,-0.0682306364,0.5270514488,0.2203394622,-0.2135134488,-0.0389554873,0.1802363545,-0.0732751936,-0.1967220008,-0.3787529469,0.198263824,-0.0162514597,0.1270776987,-0.0345080607,-0.2056979984,-0.1423495859,0.071584031,0.1724095792,0.3733929396,0.0766004547,0.1745888442,0.2233617008,-0.3262197375,0.4041501284,-0.2009280473,0.0032965213,-0.1929715872,-0.1247138083,-0.0328153223,-0.3565469682,-0.0698654428,0.4570572376,0.1322319508,0.2172506899,0.308917284,-0.1965114176,0.0849978849,-0.1674623042,-0.2068859786,0.1313789189,0.2410633713,0.0171281621,-0.270380646,-0.016132243,0.2711999416,-0.1041060314,-0.3959648609,0.0039959676,0.0003850678,-0.1091186702,-0.2102986723,-0.1964522302,-0.2079259306,-0.268462956,-0.050248567,0.0188779403,0.2163887322,-0.0120289233,0.0506806783,0.2428289056,0.6414881349,-0.2044611126,-0.097200729,0.0029477645,0.0924842507,-0.0101870429,-0.1769014001,-0.0899683461,-0.149987042,0.1072871983,-0.0319974124,-0.2831275761,0.1549769044,0.3588211536,-0.097734794,0.2597135603,0.0583751425,0.0769039541,0.0361856818,0.2901774347,0.4265041947,-0.3359179497,0.2660285532,-0.5100514293,-0.1835344583,-0.1684627682,0.0424546376,0.1579413116,-0.3772470057,-0.4059728384,0.3280169666,-0.0173317287,0.1169557199,0.061781045,-0.4518920183,-0.337308079,-0.1928305179,0.1911066025,0.2245470732,-0.3925149441,-0.1291614622,0.1234330535,-0.1684450209,0.2170879543,0.3301728964,-0.2159967273,0.0313050635,-0.4144066274,-0.0111348229,0.6295618415,-0.2943707407,0.1210268736,0.0977080241,0.1029670089,-0.2821547985,0.1250987202,-0.0195885003,-0.1065179259,-0.0370885618,0.1584386826,0.2269485444,-0.1056133434,-0.1747419834,-0.1187010482,-0.320887357,0.4772762954,-0.066561155,0.2013028115,0.281778127,-0.1068628952,-0.2452245504,0.3179291785,-0.072518222,0.1154307649,0.2461644113,-0.2807471454,0.0252963305,-0.0772584677,0.1072368175,0.1306121349,-0.0304074716,0.0351776928,0.0028217356,0.0903309956,0.069700934,-0.2287643403,0.1778737903,-0.109227404,0.0447425656,-0.0674027279,-0.0622881912,-0.1602267325,-0.1794353276,-0.0604325496,0.1831260026,-0.3872933984,0.1212605238,-0.5793866515,0.2193060666,0.0925740972,0.1212818176,0.0703063309,0.2091723382,0.1279124767,-0.0046756081,-0.1303329319,0.187411204,-0.022134142,0.0734135732,0.3958806992,0.385607183,0.0828461125,-1.0582391024,0.3952005804,0.4471340179,0.1119966432,-0.0961246639,-0.0695654154,0.6733705401,0.1918676794,0.2059757859,-0.0288748946,-0.0767917708,0.1104539037,0.1275686473,-0.2036572844,0.0659687892,-0.0860861093,-0.0166577548,0.3008368611,-0.0184975974,-0.3902417719,-0.025535984,0.1084865928,0.0096155014,0.0751611888,0.3450949192,-0.5225219727,-0.1504500955,0.2817334235,-0.314238131,0.1200883761,0.1677169204,0.2351465672,0.0198727716,-0.0947349891,-0.0013714206,0.2785582244,-0.0029330628,0.4571766257,0.0140758725,0.0845476538,-0.0685095936,-0.3553752005,0.0135916043,-0.1594937146,-0.0283026639,-0.4566035569,0.1471638829,-0.4094292521,-0.1325123608,-0.2963459492,-0.0165236592,0.0800621137,-0.0948295295,-0.15250732,-0.0411940999,0.2538252771,0.0888529047,-0.4279693067,-0.2591484189,0.2284206301,-0.2501128316,-0.0765269697,-0.1794051677,-0.1035007462,-0.0624710321,0.0933521017,0.0121554593,0.2956854999,0.1202921867,0.1410046667,-0.4283608794,-0.1182050705,0.0054063243,-0.1314729303,0.0446436219,0.1953770965,0.1535257101,0.191829294,0.0539567173,0.0574833304,0.0092918808,-0.0366963334,0.1131214499,-0.4322033525,0.1673461497,0.4726242721,-0.1572847366,-0.3136512339,-0.218298316,-0.3373828232,0.0825211182,0.1418906897,-0.1249805465,0.3699382544,0.1498870701,0.1845361292,-0.0177838095,-0.0911418125,-0.0760118142,0.344949007,-0.0636730939,-0.2971725762,-0.1309769303,-0.2379546762,0.1214394495,0.2287795842,-0.3549236953,0.1684113592,-0.1870066673,0.3227963746,0.0330061279,-0.0170318428,0.3250388801,-0.1784799695,0.1134302318,-0.1828920245,-0.2321367115,-0.0109383613,-0.1593890637,0.3865181208,0.4134480357,0.4659550786,-0.2001300901,0.9793554544,-0.0141021693,-0.3777912557,0.5141610503,-0.1381682456,0.1083688661,-0.1941893399,-0.2245259285,-0.0368074775,-0.100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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2165","title":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset","comments":"@y-rokutan Hi, now if you install `deepspeed` from master (this feature will be available in the next official release), the code should work without subclassing. Let us know if you still have any issues.","body":"Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n","comment_length":34,"text":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset \n Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n \n @y-rokutan Hi, now if you install `deepspeed` from master (this feature will be available in the next official release), the code should work without subclassing. Let us know if you still have any issues.","embeddings":[-0.2635490596,-0.3762305975,0.0858724415,0.3691848218,0.1816754192,0.3435536921,-0.0518239327,0.4010279179,-0.1650431603,-0.258449614,-0.3508144617,0.3204566538,-0.2445640415,0.0142735196,0.1966751963,-0.3255868852,0.2107604295,0.0282448214,-0.2117948681,-0.2563457787,-0.2183745801,-0.0660058558,0.0091975173,0.1386984736,-0.0008858729,0.0312691592,0.0924518183,0.1584639698,-0.1116146445,-0.366057694,0.2941603065,0.0659787208,0.6609077454,0.2473991662,-0.000130302,0.1516997963,0.0485581644,-0.0997549444,0.1597805619,-0.0868282244,-0.2239148766,0.0278999675,0.2070776224,-0.1289735138,-0.4146131575,-0.6126383543,-0.0347185321,-0.3231691718,0.1267071068,0.452029109,0.0357436016,0.1441907734,0.0045099747,0.0439588875,0.2477651834,-0.1934699863,-0.1854258925,0.0936603993,-0.0743704736,0.1410143077,-0.2250965089,0.0856280103,-0.1529570222,0.0256859586,0.4744634628,-0.305649519,-0.3226049542,-0.2647331357,-0.0733929202,0.5432499051,0.2157951146,-0.2298208177,-0.1046039164,0.1822216362,-0.055059012,-0.2311099917,-0.3596684635,0.2333776355,0.0170585625,0.130522728,0.0112073291,-0.1299144477,-0.1863081157,0.0875596553,0.2806100547,0.3632269502,0.0851095244,0.1512035578,0.2155201882,-0.2419120222,0.3436709046,-0.2262788415,0.0070464769,-0.1812538952,-0.1346868873,-0.0533127859,-0.3148994446,-0.0849068016,0.378131032,0.1288087815,0.174003005,0.3770180345,-0.1272423118,0.0730314553,-0.1347578317,-0.1246929839,0.2171281427,0.2373773903,0.0385422036,-0.2505725026,-0.0205379482,0.2715170383,-0.1502734572,-0.2897736728,-0.0468763784,0.0514920764,-0.1515406519,-0.176536873,-0.1665547341,-0.1864985526,-0.3177583218,-0.0514321253,0.0475088358,0.2343060076,0.0550113358,0.0473903492,0.2683138549,0.6924920082,-0.2033870816,-0.1185874566,0.0322334804,0.1174828932,-0.0959676951,-0.1980102062,-0.0800251067,-0.1564632803,0.0190549064,0.0263228212,-0.3015548289,0.1301091909,0.3758248091,-0.0996755585,0.2288138419,0.0459041074,0.0318006389,0.073764205,0.3789966106,0.4413585663,-0.3008752167,0.2715044022,-0.4328229725,-0.2426431626,-0.1156899855,-0.0130986301,0.1889711767,-0.2756026387,-0.4165625274,0.3720363975,0.0163513198,0.1343854517,0.010878602,-0.4601106048,-0.4295722246,-0.1702985466,0.2251150608,0.1835807115,-0.4052045047,-0.1438096017,0.062004108,-0.1478810906,0.2841463983,0.2974840403,-0.1768792123,0.016512746,-0.3843535483,-0.0178858098,0.5604600906,-0.2282059491,0.1074003354,0.1166640818,0.1041468829,-0.219372645,0.1730726361,0.0408265479,-0.0429625809,-0.0621203855,0.122775875,0.222984612,-0.121819593,-0.2111681253,-0.104038842,-0.2291263938,0.4387815595,-0.0603312403,0.2165647894,0.3418134451,-0.1543974578,-0.2581012845,0.3750075698,-0.0382846296,0.1416975558,0.2198246866,-0.2698795497,0.1213159412,-0.0675660819,0.0889883339,0.2008627802,0.0510114655,0.0607858002,0.0659811869,0.1656821668,0.0506542847,-0.1664160639,0.1074292883,-0.1352767646,0.0021145626,-0.1062234044,-0.0797208324,-0.2741826177,-0.2691041529,-0.090402782,0.2340280414,-0.4500222206,0.1428385675,-0.5754755735,0.161221534,0.1305392385,0.1429700106,0.0562027805,0.1802558452,0.0934509635,-0.0646034479,-0.1435524672,0.2007333338,0.048501309,-0.0976667851,0.3783784807,0.2502427399,0.0699367747,-1.0497536659,0.3431996405,0.4532094896,0.0923509821,-0.1317552924,-0.0381977931,0.6848375201,0.2695319653,0.2214422524,-0.0369539931,-0.0438535325,0.0043841512,0.1337317079,-0.2001353651,0.0169994719,-0.1230770424,-0.0709825307,0.2662944794,-0.0146301854,-0.3610474467,-0.0365428105,0.0466169044,-0.0357978307,0.1359192878,0.3326887786,-0.5043021441,-0.0583159514,0.2491913438,-0.1609594822,0.1241280884,0.1438811123,0.2088501453,0.0072885808,-0.101140283,0.0282054152,0.2873184085,0.0236929804,0.377846688,-0.0637540445,-0.0188888684,-0.0708828643,-0.3746263683,0.0985848084,-0.1449115276,-0.0693301633,-0.4895896316,0.1309631765,-0.4484529495,-0.071146965,-0.2224132419,-0.0177480634,0.0441772714,-0.049512472,-0.1779140681,-0.0210448932,0.2825018764,0.1720361263,-0.4338253736,-0.2607576847,0.2005131692,-0.2989144623,0.0215104632,-0.143920064,-0.1155608967,-0.0867560953,0.129149735,-0.0038978767,0.2609847784,0.1594065875,0.0566514991,-0.2899437845,-0.1649594158,-0.02743572,-0.2137408108,0.1000065282,0.1556865275,0.1725544333,0.1556279212,0.0278074201,0.1050844342,0.0160122514,-0.0390340053,0.0990422294,-0.3585253656,0.1676232368,0.4255625308,-0.1689360291,-0.2158085406,-0.235413447,-0.3735868931,0.030824298,0.1254537106,-0.1326868385,0.2924011052,0.0698174909,0.2185610384,0.0046675834,-0.0764476508,-0.0743279159,0.3837733269,-0.0212695897,-0.3230701983,-0.1505278647,-0.2576108873,0.106747739,0.154496491,-0.315159291,0.1952720284,-0.2176488936,0.2480791807,0.0706432834,-0.1014247611,0.3441767693,-0.1542800218,0.141318664,-0.1726019233,-0.2886224389,0.0019523023,-0.1891165525,0.3890924156,0.3815199733,0.5699663162,-0.186128214,0.9883284569,-0.0373085476,-0.3312836289,0.5028914809,-0.0849152431,0.0929300338,-0.1694969386,-0.3107759058,-0.0327687375,-0.120906047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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2165","title":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset","comments":"Worth mentioning that any function that expects a `torch..Dataset` (like `torch..DataLoader`) will fail a mypy-esque typecheck if a `datasets.Dataset` is passed, even though it implements the interface correctly (I think). The virtual subclass idea was a good one- I wonder if there's another workaround given the Generic issue. What we're really talking about is something similar to the structural subtyping semantics that `typing.Protocol` defines. If `torch..DataLoader` accepted anything that supports `__getitem__` and `__len__` methods this would be much easier. Not sure if there's a way to do this without the wrapper from the perspective of `datasets`.","body":"Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n","comment_length":96,"text":"How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset \n Hi,\r\n\r\nI'm trying to pretraine deep-speed model using HF arxiv dataset like:\r\n```\r\ntrain_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds.set_format(\r\n        type=\"torch\",\r\n        columns=[\"input_ids\", \"attention_mask\", \"global_attention_mask\", \"labels\"],\r\n    )\r\nengine, _, _, _ = deepspeed.initialize(\r\n    args=args,\r\n    model=model,\r\n    model_parameters=[p for p in model.parameters() if p.requires_grad],\r\n    training_data=train_ds)\r\n```\r\nbut deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?\r\n \n Worth mentioning that any function that expects a `torch..Dataset` (like `torch..DataLoader`) will fail a mypy-esque typecheck if a `datasets.Dataset` is passed, even though it implements the interface correctly (I think). The virtual subclass idea was a good one- I wonder if there's another workaround given the Generic issue. What we're really talking about is something similar to the structural subtyping semantics that `typing.Protocol` defines. If `torch..DataLoader` accepted anything that supports `__getitem__` and `__len__` methods this would be much easier. Not sure if there's a way to do this without the wrapper from the perspective of 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2162","title":"visualization for cc100 is broken ","comments":"This looks like an issue with the cc100 dataset itself but not sure\r\nDid you try loading cc100 on your machine ?","body":"Hi\r\nvisualization through dataset viewer for cc100 is broken\r\nhttps:\/\/huggingface.co\/datasets\/viewer\/\r\n\r\nthanks a lot\r\n","comment_length":22,"text":"visualization for cc100 is broken  \n Hi\r\nvisualization through dataset viewer for cc100 is broken\r\nhttps:\/\/huggingface.co\/datasets\/viewer\/\r\n\r\nthanks a lot\r\n \n This looks like an issue with the cc100 dataset itself but not sure\r\nDid you try loading cc100 on your machine ?","embeddings":[-0.5656793714,-0.2004439533,-0.0850355178,0.1293200999,0.2095071673,-0.0013308327,0.1641536355,0.1368134022,-0.0659237504,0.4231900573,-0.0523652881,0.2376998067,0.1289270371,0.4269973636,-0.0176101644,-0.2411688119,0.0532902479,0.4060421884,-0.3985911012,0.0212525502,0.043411959,0.0523336455,-0.1739806384,-0.0693603009,-0.1812584847,-0.2919968665,-0.0957440063,0.1488842666,-0.2506378889,-0.4322847128,0.060996592,0.1807223707,0.1114192307,0.3614214361,-0.0000948288,0.0520825982,0.3273299932,0.1721242219,-0.1750353873,0.1617924869,-0.3436121643,-0.0873224363,0.2742396593,-0.0548348837,-0.1783528179,0.1911072731,-0.0204064362,-0.0307972282,0.3275450766,-0.2472634166,0.4222187698,0.2349870056,0.0398907512,-0.3050333858,-0.1605335921,0.1931785196,-0.113192156,0.1625883579,0.1607860476,-0.0157570317,-0.0894084945,0.2910957634,0.1937401295,-0.006556401,0.0340785794,-0.1106692553,-0.0367662013,-0.2360448241,0.247420311,0.0474843569,0.4210410118,0.0793646798,0.148647204,0.1815606654,-0.0695406497,-0.6380495429,0.1570040882,0.1009481773,0.0964078605,0.2430051565,-0.3762997985,-0.0677687675,-0.2017343193,-0.0138401808,-0.2421446592,-0.1251512617,-0.2139435709,0.0061438913,0.0715346336,0.0346728414,-0.0787408054,0.2379066795,-0.3031328917,-0.0922413692,-0.2987854779,-0.0159615185,0.1302254945,0.4907619655,0.0583911687,0.1188843995,0.3125003278,0.195605576,0.2119922638,0.2065149695,0.1031227857,0.0831065848,-0.2976173162,0.1005259529,0.4791635871,0.3510072231,-0.0793915838,-0.259262532,-0.116581358,-0.2494165301,-0.1120600998,-0.0014789108,0.0683400854,-0.194302693,-0.2914254069,0.0996225923,0.0628406182,0.183207646,0.0965739191,0.4411553144,0.0086648585,0.1357439011,0.0215744972,0.1259842366,-0.1545628309,-0.2653722465,-0.2535617352,-0.0937746614,-0.2249134183,-0.0452786833,0.1392562985,-0.3511639833,0.1486142576,0.2843308151,0.24389036,-0.0997054949,-0.0786505267,0.0231300183,-0.0841180608,0.2223770916,0.092014499,-0.0371528268,0.1359326839,-0.0663182363,0.3401287496,-0.1389588863,-0.1046525538,-0.1058119312,-0.3097951114,0.3111568689,-0.0393325984,0.0318809152,-0.1348148137,0.0415361747,0.0054447893,-0.0063716546,-0.1271549761,0.0690330788,-0.0485788174,-0.1065335572,0.2176987976,0.2176955491,-0.5703334212,0.0275736544,-0.1413705796,-0.1999806762,0.0043481258,0.1873580962,-0.1815028191,-0.0876335129,-0.1811662763,-0.0983159542,-0.1626347303,-0.2828272283,-0.3876275718,0.1866451055,-0.0017750179,-0.0798946992,-0.1160927862,-0.2375925779,0.2893235385,-0.0153164174,-0.3430396616,-0.2110937834,0.0176128596,-0.0127845118,-0.2409080863,-0.2167172283,0.0838877037,-0.0764978975,0.147901848,0.1105542108,0.1725352705,-0.2935748994,0.2661367953,-0.1485100538,0.1161036119,0.0912747309,0.2704190314,-0.1600845456,-0.1128767356,-0.0416106172,-0.0674103796,0.0204777047,0.0606135689,0.271689415,0.039632272,-0.0088672508,-0.2549166679,-0.0335104018,-0.3341198862,-0.1676502228,0.3803723454,0.092168957,-0.2315768003,0.0005186008,-0.0421158932,0.1758345664,0.0126305791,0.0483755507,0.1761740148,0.3433649838,-0.275255084,-0.063771978,0.1673242152,-0.0719604343,0.2455675006,-0.1013275832,-0.1643158644,0.4407145083,-0.1486031413,0.4009417593,0.1041888744,-0.1629094034,0.3613379896,-0.6228360534,0.110934779,-0.0870218277,0.0760375559,0.1592278779,-0.0845578164,-0.096132867,0.0773143694,-0.0738332346,-0.0045655984,0.1905115545,0.2280788422,0.003077413,-0.0194355231,-0.2421627194,0.1882217675,0.2159336805,0.3058051169,-0.1830739081,-0.3681041896,0.1584586501,0.1430668533,0.1536124945,0.0109324045,-0.0728268772,-0.3498611748,0.0213345792,0.3850712478,0.0080434829,-0.1321379095,0.1849578768,-0.0770443752,0.0816132873,-0.1488431245,-0.0276763439,0.0297795273,-0.0575451069,0.3187336326,0.0401709229,0.0173402485,-0.1541346163,-0.5369300246,0.0395917781,0.0058500143,0.2060564309,-0.2513286173,-0.0886813924,-0.3233671188,-0.1130416617,0.2310751677,0.0394948013,0.0922844857,-0.2377570421,0.0139981238,0.1969210356,-0.1181684583,0.2624606788,0.0012004502,0.213672623,0.0281767324,0.5410407782,-0.2618633509,-0.1442385167,-0.1840663254,0.2897135019,0.0871278495,-0.0790610313,0.1842187643,-0.081015341,0.2908611596,-0.1730745733,-0.3851382732,0.2910663486,-0.1283418387,0.1904563755,-0.0026750055,0.2665259242,-0.3870860636,-0.0203630459,0.1880902201,-0.0413361788,-0.0606841072,-0.0031808945,-0.0159594659,-0.3020131588,-0.2241060138,0.075142391,-0.1735683084,-0.3436998725,0.0384506099,0.1464444995,0.0640092716,0.174707979,0.2289257199,-0.097976543,0.148127839,-0.0595277585,-0.3003802299,-0.5974050164,0.1632573009,-0.5660870671,-0.4630771577,0.1409201324,0.1243423894,0.4092950523,0.0419113412,-0.374995172,0.1733655035,-0.0801885501,-0.0343607329,0.1711721718,-0.1160889268,0.1028820798,0.1523438841,-0.2272927761,-0.1422841102,-0.2734931707,-0.0979778916,-0.1062644497,0.1159405336,-0.2867873311,0.0807190239,0.1047687456,0.4419476986,0.1123191491,-0.0741317198,0.4239270389,-0.0862234309,0.5601354241,-0.1611284167,-0.3377251327,0.1469471008,-0.150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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2162","title":"visualization for cc100 is broken ","comments":"Hi\nloading works fine, but the viewer only is broken\nthanks\n\nOn Wed, Apr 7, 2021 at 12:17 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> This looks like an issue with the cc100 dataset itself but not sure\n> Did you try loading cc100 on your machine ?\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2162#issuecomment-814793809>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMRUO33JSOYGT6RETWLTHQWNLANCNFSM42IUOR6Q>\n> .\n>\n","body":"Hi\r\nvisualization through dataset viewer for cc100 is broken\r\nhttps:\/\/huggingface.co\/datasets\/viewer\/\r\n\r\nthanks a lot\r\n","comment_length":80,"text":"visualization for cc100 is broken  \n Hi\r\nvisualization through dataset viewer for cc100 is broken\r\nhttps:\/\/huggingface.co\/datasets\/viewer\/\r\n\r\nthanks a lot\r\n \n Hi\nloading works fine, but the viewer only is broken\nthanks\n\nOn Wed, Apr 7, 2021 at 12:17 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> This looks like an issue with the cc100 dataset itself but not sure\n> Did you try loading cc100 on your machine ?\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2162#issuecomment-814793809>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMRUO33JSOYGT6RETWLTHQWNLANCNFSM42IUOR6Q>\n> .\n>\n","embeddings":[-0.4892333746,-0.25718835,-0.0154404212,0.1678069085,0.2279232144,0.036741484,0.1436953247,0.0804585591,-0.0384785756,0.3180071712,-0.1425651461,0.2456283122,0.141941458,0.4538696408,0.0949068367,-0.320658803,0.1359129101,0.3763961196,-0.4416952729,0.0868928432,0.0558291823,0.103545703,-0.1063319966,-0.1049200669,-0.1652607024,-0.2434975803,-0.1958783269,0.2551199198,-0.2148875594,-0.4934449494,0.0909606665,0.2749294937,0.1034732461,0.3986742496,-0.0001022205,0.049955342,0.3971211314,0.1768862605,-0.2313628942,-0.0155357495,-0.3341015279,-0.0857342929,0.3056445122,0.0706033558,-0.0899706483,0.1896803677,-0.0461142957,-0.0780905783,0.4632003605,-0.3263448775,0.3314780593,0.1898190677,0.2163901627,-0.23562482,-0.2317939252,0.3152169585,-0.1130151227,0.3244764805,0.1339665651,0.0188412573,-0.1388449818,0.281008184,0.1435005665,-0.0581777468,0.2358365953,-0.1443402469,-0.1305725127,-0.2075549364,0.131921187,0.1367862076,0.4491756856,-0.0257950295,0.0039736619,0.1525943726,-0.1334011406,-0.7048443556,0.2510020733,0.0759367272,0.1342730075,0.2552893162,-0.4646982849,-0.0914586186,-0.1806110889,-0.0053140786,-0.2083681375,-0.2731879056,-0.2704397142,0.0497461669,0.084737502,0.0378440917,-0.1317539513,0.2829217315,-0.3070276678,-0.0304717589,-0.332672745,-0.0233380254,0.0595650822,0.608674407,0.1661481112,0.1431820542,0.262011677,0.1786367744,0.1625328362,0.1637374759,0.1870710552,0.1150897145,-0.2575576305,0.004103967,0.3975895941,0.3946669102,-0.0249199551,-0.2377850562,-0.1014918461,-0.1884737164,-0.2739528716,-0.052520074,0.0503012761,-0.2563163936,-0.283516854,0.0952253789,-0.0298537444,0.2155107856,0.1024071574,0.4667984545,-0.0222323146,0.1667989045,-0.0565112494,0.0894866958,-0.1122331619,-0.2835863531,-0.1714645773,-0.1027710214,-0.2645992339,0.0969906002,0.1056806892,-0.5895859599,0.1090206057,0.3119139075,0.2893698215,-0.1508764178,-0.2520390153,0.0349118412,-0.1531484574,0.2180142552,-0.0182143059,-0.0133201024,0.1382204294,-0.0576597825,0.295964092,-0.1241456196,-0.1790524572,-0.1323901862,-0.2980608642,0.1988736987,-0.2029173374,0.0301478654,-0.2107234746,0.1286042333,-0.0801802799,-0.0655269474,-0.1545435488,0.1827645004,-0.0919286162,-0.0699687004,0.2726301849,0.3414158523,-0.6863186955,0.0113491025,-0.2106210291,-0.214264214,-0.1314727962,0.222207129,-0.1535880864,-0.2164105475,-0.1873219162,-0.1039628461,-0.379512012,-0.3386236429,-0.3993170261,0.2244603336,0.0147255166,0.1020725742,-0.1624567062,-0.3465771675,0.2197372764,-0.1073613912,-0.4080575109,-0.3395632505,0.0483307168,-0.090697363,-0.2202285975,-0.2207838148,-0.0248326976,-0.1567666829,0.0327054337,0.0524218418,0.2398315221,-0.2631926239,0.2512972951,-0.1058086976,0.0842958912,0.1203728765,0.2442514449,-0.035289973,-0.0389813371,-0.0316407159,-0.1632596105,0.093528159,0.1100458354,0.1650076658,0.0794903114,0.0177719798,-0.2871534228,0.0784739479,-0.2831757069,-0.2864531875,0.229266569,-0.0078505753,-0.220486477,-0.0568295754,-0.1291802078,0.2829442918,-0.0350263044,0.0403471477,0.0812886879,0.3608579338,-0.2117709816,0.0663953274,0.1169618443,0.0159762464,0.2351304442,-0.1477355957,-0.182675764,0.486557126,-0.135331735,0.493232578,0.0639591962,-0.1806655824,0.4445699751,-0.686383605,0.1042585969,-0.1436603665,0.0319808349,0.1143553928,-0.1996471435,-0.0951006711,-0.0258608237,-0.0602661707,-0.004764393,0.3002632558,0.175249055,0.0208751317,-0.1129434332,-0.2815241814,0.3239423037,0.1032822952,0.3746682107,-0.1104339957,-0.3645000756,0.094450295,0.0589414947,0.0663930848,-0.1505609751,-0.0212418884,-0.4246389866,0.058792945,0.4740256369,0.0202400573,-0.1344058961,0.1091591269,-0.1784779876,0.1640637815,-0.1147888899,-0.0054770997,0.0562664084,0.0153738195,0.3779697716,-0.05947439,-0.0090441294,-0.0412558727,-0.4889648259,-0.0065910919,-0.036040578,0.1884359568,-0.3148462474,-0.0544636995,-0.2563505173,-0.1135910228,0.1614991277,-0.0664468929,-0.0294059757,-0.2248456478,-0.1075395644,0.2443359047,-0.088251017,0.2095916569,0.0373778045,0.2259053886,-0.0769371539,0.5092431307,-0.383086592,-0.0880490392,-0.1897036582,0.207261771,0.151835829,-0.1588685811,0.1316144913,-0.0541120805,0.2230404466,-0.1354923695,-0.4229271412,0.351303339,-0.2789322436,0.2825937569,0.0180322565,0.1744456142,-0.5256007314,0.0149826678,0.2019056976,-0.0165836718,-0.0259550773,-0.0310120787,0.0307590403,-0.2149371505,-0.1770557761,0.1357313395,-0.0972959399,-0.2872205079,0.2448734641,0.1140967384,0.0695603862,0.299531132,0.1981417537,-0.00537088,0.0894727111,-0.1570617557,-0.261768043,-0.6501930356,0.1145755798,-0.580360055,-0.4522836208,0.1640467793,0.183376506,0.4095659554,0.0223093852,-0.4418850243,0.20043239,-0.0492386557,-0.0774395615,0.1694942713,-0.2371670902,-0.0520734861,0.1274531931,-0.1467647105,-0.1221009195,-0.2460592538,-0.2049303055,-0.1848915219,0.1114116535,-0.2083913535,0.1352379024,0.1887238622,0.4692262709,0.3177906871,0.0179005563,0.3638769984,-0.0256513823,0.6060584188,-0.0940382704,-0.3954617381,0.1610117108,-0.2635265291,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2161","title":"any possibility to download part of large datasets only?","comments":"oh, great, really awesome feature to have, thank you very much for the great, fabulous work","body":"Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks","comment_length":16,"text":"any possibility to download part of large datasets only? \n Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks \n oh, great, really awesome feature to have, thank you very much for the great, fabulous work","embeddings":[-0.4569222927,-0.4417874813,-0.1896090657,0.1484071463,0.1038997695,0.1881608218,-0.2670145929,0.3661693335,0.0632605478,0.4351860881,-0.4470095634,-0.1280029565,-0.1039806157,0.3638656139,0.19023633,-0.0035727338,-0.1473829001,0.2523769736,-0.2892186642,-0.1919610351,-0.0600638241,-0.1989702284,-0.2322041541,-0.2747715414,0.1167170703,0.0897206813,-0.0433795117,-0.1757752746,-0.5081452131,0.0985448956,0.141211316,0.2492479384,0.1027179956,0.0950109512,-0.0001107031,-0.4209389687,0.2668626904,-0.0625784174,-0.0843795389,-0.0519629829,-0.2210000753,0.1284930706,-0.2507516444,-0.3328807056,-0.0196087305,0.2339521646,0.1992979199,0.052536834,0.1239218563,0.1056275144,0.1930296421,0.1519487053,-0.0723707303,-0.1902453154,0.4425436258,0.0775688216,-0.081824325,0.0459746085,0.5918859243,0.2943479419,0.1667646766,0.0849437714,0.1034169719,0.2473489344,-0.1054589748,-0.2039708793,-0.0984312892,-0.6417449117,0.233724162,0.6659565568,0.6920181513,0.0556284226,-0.1506160647,-0.1202960387,0.0236584172,-0.1743654162,-0.2018057406,0.6123729944,-0.3871223927,0.2348982692,-0.4387528002,-0.2116997689,-0.12346486,0.2909560204,-0.3018239737,0.2000410855,-0.1299531013,-0.1054302454,0.407800734,0.0596905127,0.1735424995,0.1433303207,-0.1963621229,0.3420022428,-0.1567712128,-0.536655128,-0.1837792397,0.2887184918,0.5101740956,0.2040085793,0.1140416861,0.1894533783,-0.0184952524,-0.101585336,0.5733489394,-0.112977244,-0.3923663497,0.0809294656,0.3171570301,-0.0752295479,-0.0010931335,-0.0670251325,0.0463561118,0.1651315689,-0.5736999512,0.0215226673,-0.1362490803,-0.4492550492,0.0948014408,-0.281965673,0.3471706808,0.0344671607,-0.0805370361,0.1907563657,-0.0205787905,0.0423795208,-0.4645516276,-0.1681990176,-0.0162997488,-0.1974528283,-0.0918180943,0.0224358495,0.0416284464,-0.0880819559,0.3229663074,-0.1877465099,0.2957335114,-0.1981063187,0.3834774196,0.2267553657,-0.1125748008,-0.247535646,-0.0029422953,0.198540315,0.0700587556,0.1578546464,-0.2934513986,0.5020813346,-0.1349240392,0.3260570765,-0.2535348535,-0.2201768011,-0.0583021417,0.1515716463,-0.2562196553,-0.1333431751,-0.2288068384,0.1576193869,-0.1931798905,0.0939430147,0.079911761,0.1832103282,-0.0579355024,-0.0438142307,0.0031955966,-0.0112947142,-0.1513257176,-0.0708584413,-0.1550827473,-0.2979604602,0.3592858016,0.1999580413,-0.1971529722,-0.2521312237,-0.1948246062,-0.1017807499,0.2613486648,0.0787256286,-0.5193734169,0.2332689762,0.0885039642,-0.2687841952,0.0295890868,0.1556360126,0.5315999389,-0.0201996379,-0.262334317,0.5017807484,0.0501082316,0.0913201496,-0.0916647464,-0.2254313082,-0.1961180717,0.2768270373,0.2638245523,0.3479859829,0.4208673835,-0.0123440474,0.5024280548,-0.1851747334,0.2358278185,0.0584069751,0.2728781402,-0.1755978167,-0.0143288821,-0.4393552542,-0.1206988469,0.1615196615,-0.0966031253,-0.2584325671,0.3596479297,-0.1486888677,0.0447295383,-0.3190504313,0.2947393954,-0.0419411473,-0.0287704561,-0.2965403795,0.409374088,-0.0676431283,-0.3393349648,-0.2134828418,-0.0733152181,0.0736941621,0.1995432824,-0.0110144354,0.3500831127,0.2485636324,0.2441171408,-0.181439355,0.0056323633,-0.0823098347,0.0218137093,0.1309656054,0.0395607837,0.1180215552,0.0199108571,-0.0270482022,0.3809484243,-0.113807179,0.0639724284,0.1126640588,0.1333455592,0.0193930604,-0.4595884085,0.345484674,-0.2954245508,0.0264134519,0.16872558,-0.0491245538,0.3897067606,-0.2340516895,0.1279972494,0.0550441071,0.3604438603,0.163096264,0.0675400347,-0.0170681085,-0.2695915401,-0.1503736973,0.0879044235,-0.0890912414,-0.1837015003,0.2362305224,-0.2942356765,0.1283306181,0.1056605652,0.1332110316,0.2384630591,0.2614440024,0.419123292,-0.0596985407,0.5338898301,-0.0798916295,0.0344999954,-0.1112052947,-0.2185872793,0.0271370411,0.1192569062,-0.2720867693,-0.1960972995,0.1185508817,0.1599407047,0.1313765645,0.1610589474,-0.5328859687,-0.1614165753,-0.5867491364,0.201430887,0.1765574366,0.0589453951,-0.0102747884,0.1366430372,0.3728674352,-0.1443712711,-0.1607402563,-0.0089779543,0.4862968624,-0.1067721546,0.356928736,-0.1209159717,0.0533811711,0.2583846748,0.252482444,0.0410920382,0.0816703141,0.2867903113,0.1602697074,-0.1303244531,-0.4161487818,-0.0071792677,0.089368321,0.3147806525,-0.1491581202,-0.0932761505,0.2668730617,-0.0285225995,0.1035330892,-0.2054284811,-0.1214779317,-0.2091581225,-0.144128561,0.0973671749,0.0649711415,-0.2281395048,0.0051542521,-0.5068331957,-0.3248071373,0.5314695835,0.3946836591,0.2537349463,-0.1494753212,-0.1242601499,-0.251788795,-0.3573119938,-0.4232786,-0.1491891146,-0.78258425,0.4113961458,-0.2570545971,-0.3438847661,-0.0755715668,0.1506991684,-0.1608594209,0.4088255167,-0.247415781,0.0621273853,-0.2387616783,0.02575019,0.1554755718,0.1434172243,-0.1240387261,-0.7096162438,-0.1004810557,0.1014398709,0.3139617145,-0.2614524961,0.250983268,0.3713076413,-0.0902180597,-0.1903648376,0.0399543159,0.5214747787,-0.0268349163,0.2538589537,0.13070409,-0.0290280152,0.0504805818,-0.0862841979,0.0495517105,0.3566077054,-0.0647659302,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2161","title":"any possibility to download part of large datasets only?","comments":"We'll work on dataset streaming soon. This should allow you to only load the examples you need ;)","body":"Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks","comment_length":18,"text":"any possibility to download part of large datasets only? \n Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks \n We'll work on dataset streaming soon. This should allow you to only load the examples you need ;)","embeddings":[-0.4160075784,-0.4227997363,-0.0912121311,0.2210884541,0.1178921759,0.208614096,-0.2158917636,0.3746378422,0.1075313836,0.3137337267,-0.2778739035,-0.2264849246,-0.0971202999,0.3623839021,0.2786155045,-0.1379799843,-0.1310558468,0.2409262955,-0.195934087,-0.1360583901,0.0351950452,-0.3711940944,-0.1557517797,-0.2762488127,0.1072821841,0.0065381951,-0.0016220268,-0.152801916,-0.3216457367,0.1749350876,0.1286829263,0.0949424505,0.2968363166,0.0225311723,-0.0001144941,-0.3012693226,0.3300118446,-0.2018281966,-0.2040800303,-0.0629979521,-0.3263174295,0.163317278,-0.2283700705,-0.2755313218,0.0555490777,0.1558898836,0.1559820175,-0.021303568,0.3270632327,0.1292208731,0.1475346684,0.0527245738,-0.1914350986,-0.0762383267,0.2807136178,0.1402224898,0.0194406081,0.0566957854,0.6661752462,0.300422132,0.1056198776,0.0831306353,0.0118729435,0.1913021952,-0.0942525864,-0.3307495117,-0.1941795945,-0.623032093,0.2782781124,0.8252424598,0.6387521029,0.0939345956,-0.1760771275,-0.2948754728,0.1539803594,-0.1587239504,-0.246307373,0.629265964,-0.3267938793,0.1728767008,-0.540204823,-0.2325883061,-0.1865912229,0.2556458712,-0.2171286941,0.0882261023,-0.079015933,-0.0391912796,0.3143946528,-0.0377393067,0.311180383,-0.0938632265,-0.2239779532,0.1800582856,-0.2369107753,-0.3743753433,-0.3241960108,0.3473267555,0.3826083839,0.146750167,0.2207317203,0.196091488,0.0698590428,0.0267213043,0.424017787,-0.1731639504,-0.2814107835,0.2035138011,0.2531688809,0.0371988602,0.0458770916,-0.0351084732,-0.1991999596,0.2605749965,-0.5001295209,-0.0200261753,-0.3018489778,-0.421323806,-0.0149688032,-0.264572084,0.1118759811,0.1028684527,-0.0745217651,0.1771867275,-0.0060785618,0.2028246224,-0.3606562912,0.0311386921,0.07468009,-0.50466609,-0.0934187546,-0.0063180411,0.0433680825,0.049343653,0.3772119284,-0.2916145325,0.379822284,-0.1353830248,0.2860985696,0.1216544881,-0.023139311,-0.1652133018,-0.0564624742,0.1320164204,0.1193649247,0.1957210898,-0.0756725222,0.4238185585,-0.1083752736,0.3343409002,-0.1200220659,-0.2665345371,0.1457662731,0.1189228818,-0.3860332072,-0.080825299,-0.5248143673,0.2487920821,-0.1913020462,-0.0301384795,0.0101986537,0.0602893978,-0.0674410462,-0.0665501058,0.2375759333,0.0933558792,-0.2284577638,-0.0791617408,-0.15456523,-0.1223678142,0.3714396358,0.2702901363,-0.2721643746,-0.2225671113,-0.1437160969,-0.205364868,0.245246917,0.0532231517,-0.4416381121,0.4437435567,0.0718872845,-0.0778924227,0.0095832935,0.1884521246,0.6336510777,0.0795476735,-0.2656627893,0.6198097467,-0.1294934899,0.101855278,-0.0497492477,-0.2907518148,-0.1316679418,0.2972227037,0.2475744635,0.391423285,0.1908108443,0.0140925888,0.3742015064,-0.203521505,0.2755431235,0.1488898546,0.1048043296,-0.121955663,-0.1044193357,-0.332970947,-0.2455965877,0.2019154578,0.1670384556,-0.5572571754,0.3659934402,-0.1457132399,0.0362955369,-0.3327440023,0.3766084015,0.0140642934,-0.1043354869,-0.3237961829,0.3023703098,-0.2443415821,-0.3178130388,-0.0696160868,-0.1488308907,0.0794271454,0.0792413726,0.1160821617,0.4168473184,0.1795063615,0.2777090371,-0.3217895031,-0.0120968772,-0.0677321255,0.0181381609,0.1762458533,0.0013726536,0.2247814983,0.1153095365,-0.0137184346,0.2783602178,-0.197149381,0.0457342044,0.2255697697,0.1472244412,-0.0683518946,-0.4450111091,0.3316784799,-0.2376499176,0.1782503575,0.1220900714,0.0174170621,0.2728701234,-0.1368841976,0.0763017237,0.1350092739,0.450481683,0.0766145438,0.2161708474,-0.0247611105,-0.4326867461,-0.2301254869,-0.0211267062,-0.263863951,-0.2799152434,0.2115961909,-0.413223654,0.0817955658,0.1887237579,0.207456246,0.0813945904,0.2328840345,0.3495491147,-0.082832031,0.6517193317,-0.1666290164,-0.0071176989,-0.0003751305,-0.1914367229,-0.1143666804,0.05064594,-0.2767645121,-0.0760612339,0.1437413245,0.2027590871,0.0762582123,-0.0086085144,-0.3400782645,-0.244123891,-0.6714405417,0.0762961507,0.1519193649,0.0577860847,0.0029210374,0.0193789694,0.4107501209,-0.0264356062,-0.0868318379,-0.0315064378,0.5220625997,-0.0951065347,0.2493910491,-0.1078799069,-0.0365835465,0.0866006464,0.2252970189,0.1121644601,0.1787572801,0.2441754937,-0.0232837871,-0.08037965,-0.3961844742,0.0118816802,-0.0368463323,0.3093235791,-0.0959338173,-0.1772044152,0.4233986437,-0.1610391736,-0.017416887,-0.2303730249,-0.1522228271,-0.0341773704,0.0224791374,0.0635860935,0.268858701,-0.1699955165,0.1525307298,-0.4189006686,-0.346806109,0.3461508155,0.1341480911,0.1633359194,-0.2418619841,-0.0921135768,-0.2322889268,-0.2644003332,-0.3277679384,0.0947527215,-0.8488109708,0.41301018,-0.1486281306,-0.3536370397,-0.0303917248,0.0645904839,-0.2282583863,0.5403364897,-0.3521283269,0.1787143648,-0.2025313079,-0.119592838,0.1085025817,0.0318410844,-0.2324289829,-0.7003283501,0.000242744,0.0069476576,0.2122762352,-0.1871311963,0.1414143294,0.3855127692,-0.0612121001,0.0452300757,0.1133166179,0.6742675304,0.1244411543,0.1787571311,0.0296519063,0.0931019261,-0.0170451719,0.0397601128,0.0937465951,0.3169905841,-0.0764262006,-0.1842021048,0.17324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2161","title":"any possibility to download part of large datasets only?","comments":"thanks a lot Quentin, this would be really really a great feature to have\n\nOn Wed, Apr 7, 2021 at 12:14 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> We'll work on dataset streaming soon. This should allow you to only load\n> the examples you need ;)\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2161#issuecomment-814791922>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMROD62QAKIJMAKWISTTHQWBVANCNFSM42IUI5JQ>\n> .\n>\n","body":"Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks","comment_length":79,"text":"any possibility to download part of large datasets only? \n Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks \n thanks a lot Quentin, this would be really really a great feature to have\n\nOn Wed, Apr 7, 2021 at 12:14 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> We'll work on dataset streaming soon. This should allow you to only load\n> the examples you need ;)\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2161#issuecomment-814791922>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMROD62QAKIJMAKWISTTHQWBVANCNFSM42IUI5JQ>\n> .\n>\n","embeddings":[-0.4333571494,-0.4534873664,-0.0487325303,0.2667760849,0.1081289425,0.2431801558,-0.1623352021,0.4673766494,0.0734750777,0.3738210499,-0.3642461896,-0.2017757744,-0.0959115475,0.457791537,0.3195568621,-0.1472721994,-0.1357191503,0.2194820344,-0.2238364071,-0.1688051075,0.017404547,-0.2593170106,-0.1123769209,-0.2532622814,0.0350642912,0.0898537561,-0.0617154092,-0.0844494626,-0.3761279285,0.1701032072,0.1983957291,0.2140855193,0.2366961986,-0.0048213038,-0.0001184534,-0.2889572978,0.2554901242,-0.1432120502,-0.1864690632,-0.0577484518,-0.1817248613,0.1063679606,-0.2372541577,-0.262868762,-0.0091608325,0.1348176152,0.0690670013,-0.0218835771,0.3579902649,0.0345511064,0.1137142628,0.1012044027,-0.1499210149,-0.1075810269,0.272598505,0.1650012732,-0.0292274952,0.060077142,0.5326678753,0.2669931352,0.1294612437,0.117367059,-0.0027756188,0.2834877372,-0.0907342881,-0.1894118488,-0.2640944421,-0.6127950549,0.2104036659,0.8935748935,0.5572637916,0.0032649203,-0.2365484685,-0.4016392529,0.1302787066,-0.3426574171,-0.1672671884,0.5966566801,-0.3093205392,0.192076847,-0.5190604329,-0.1575798392,-0.1254054755,0.2723096907,-0.2245434374,-0.0139281359,-0.1768504083,-0.0458901934,0.4784290195,-0.0008327375,0.1448497325,-0.0473284982,-0.1877050549,0.2302681953,-0.2143140286,-0.4565867186,-0.2157699317,0.2906162441,0.5468568802,0.2475865185,0.0848186165,0.1489576548,-0.017101923,-0.0635389686,0.491554141,-0.2148909718,-0.2800322473,0.1818933934,0.3234026134,0.083050549,0.1106132641,-0.0376580507,-0.0844548568,0.245326519,-0.5971102715,-0.0122542037,-0.2203571945,-0.4416057169,0.0325089097,-0.2539963424,0.1385335475,0.1137556285,-0.0686253831,0.1652995795,0.0227542017,0.1592404246,-0.3400886655,0.0705235898,-0.0038737166,-0.4409328699,-0.132912755,-0.0432235599,0.0712560043,0.1238532141,0.4013691545,-0.3420198262,0.3170058131,-0.1057771742,0.3391904235,0.1396165043,-0.1107201129,-0.1596134454,-0.0881129429,0.1561319083,0.0169006623,0.2920125127,-0.0551254116,0.4835698307,-0.1643662751,0.2821950614,-0.1610147059,-0.2401708961,0.1469040513,0.0407236703,-0.3738723397,-0.0618707091,-0.5082563758,0.358671546,-0.1699185371,0.0181915406,-0.0189901385,0.1638083309,-0.2160223573,-0.0025573042,0.2322248369,0.2576268911,-0.2118474096,-0.1752518415,-0.1312912852,-0.139586404,0.3125015497,0.3773928583,-0.2835659087,-0.2201153636,-0.2215556204,-0.0606914833,0.1919154823,0.0454829633,-0.4724436104,0.3615725636,0.0099886172,-0.030708842,0.0478569455,0.1477380991,0.6136130691,0.0301964954,-0.1679208726,0.5325431228,-0.08927086,0.0808937028,-0.049649179,-0.3678531349,-0.1579562426,0.2755120993,0.1951163858,0.4164232314,0.1727066934,-0.0402595215,0.4430029988,-0.2201928347,0.3368228376,0.1818815023,0.1613423824,-0.0847179294,-0.1551973224,-0.4076611102,-0.2964693308,0.2118466496,0.132121712,-0.4998221397,0.3217774034,-0.2148119807,0.0395307168,-0.3210993707,0.4084578753,0.005683789,-0.1502961963,-0.3171791434,0.3548722863,-0.1546446234,-0.3655125201,-0.0756156072,-0.1111734882,0.1478280872,0.0036148818,0.1395067871,0.4277284741,0.1841936409,0.2695959508,-0.2078377455,0.0122560803,-0.1331464052,0.0429858752,0.1440230161,-0.0245181285,0.1365618855,0.093528308,0.0826350451,0.3256608546,-0.0734846964,0.0104416581,0.0894771665,0.1080784276,-0.0458220914,-0.493114531,0.4346589446,-0.2044020891,0.1076002344,0.0549512468,0.0469650067,0.2766539752,-0.1762056351,0.0548627973,0.0512986891,0.4707574844,0.0086868182,0.1812570542,-0.0498716682,-0.4779136181,-0.152316913,0.0950541571,-0.18972978,-0.2867540419,0.2613023818,-0.3662053645,0.1668248773,0.1373097152,0.1499001682,0.1633578241,0.1919973493,0.3767030835,-0.0624540746,0.6231998205,-0.1187390387,-0.0556434728,-0.0444185175,-0.2490230203,-0.0204355139,0.0649898201,-0.1989006847,-0.133872211,0.1765610427,0.1989263147,0.1194389164,-0.0303290859,-0.34018448,-0.3156105876,-0.5664642453,0.0017028174,0.1305923015,-0.0309436936,0.0081284959,0.0766007081,0.4942077994,-0.0850624666,-0.1224118844,0.0192583464,0.5008317828,-0.1121080294,0.2183012664,-0.1350676566,0.0262115765,0.1769272685,0.1761347502,0.0995416939,0.1548149586,0.2831908166,0.0096908715,-0.1914437711,-0.402951777,-0.0166045222,0.0456123985,0.1962953359,-0.0449833237,-0.0982212797,0.392893374,-0.127578482,0.0537221506,-0.1555530727,-0.1446164101,-0.1366891265,-0.0164341182,0.056850113,0.3071407378,-0.2456603795,0.1228187382,-0.3954452872,-0.3361863494,0.519174993,0.2037495226,0.1333261281,-0.1553138196,-0.12507613,-0.193160221,-0.3779405951,-0.3520568311,-0.0067362194,-0.8144291043,0.3607504666,-0.1228069142,-0.3429990411,-0.0961320922,0.040607363,-0.2655555606,0.4734797776,-0.4235666096,-0.0079286043,-0.2472912967,0.0260360315,0.130742535,0.0601334423,-0.0914825872,-0.7272815108,0.013625415,0.0473928005,0.0997406766,-0.1694025844,0.1886521727,0.3197810948,-0.0790273175,0.0955648646,0.0600277446,0.7942323685,0.200170368,0.2377908826,0.0253898948,0.0094836866,0.1083216667,-0.0896689743,0.0220876019,0.3123306632,-0.0865291134,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2161","title":"any possibility to download part of large datasets only?","comments":"Is streaming completed? On the 1.8.0 docs it is mentioned (https:\/\/huggingface.co\/docs\/datasets\/dataset_streaming.html), but when following the example I get the following error:\r\n\r\n```\r\n>>> dataset2 = load_dataset(\"amazon_us_reviews\", \"Pet_Products_v1_00\", split='train', streaming=True)\r\n\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-21-1eedab26cff1> in <module>()\r\n----> 1 en_dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)\r\n    339                 if value is not None:\r\n    340                     if not hasattr(builder_config, key):\r\n--> 341                         raise ValueError(f\"BuilderConfig {builder_config} doesn't have a '{key}' key.\")\r\n    342                     setattr(builder_config, key, value)\r\n    343 \r\n\r\nValueError: BuilderConfig OscarConfig(name='unshuffled_deduplicated_en', version=1.0.0, data_dir=None, data_files=None, description='Unshuffled and deduplicated, English OSCAR dataset') doesn't have a 'streaming' key.\r\n```\r\n\r\nUPDATE: Managed to get streaming working by building from source and installing the additional `datasets[streaming]` package:\r\n\r\n```\r\n!pip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n!pip install datasets[streaming]\r\n```","body":"Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks","comment_length":123,"text":"any possibility to download part of large datasets only? \n Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks \n Is streaming completed? On the 1.8.0 docs it is mentioned (https:\/\/huggingface.co\/docs\/datasets\/dataset_streaming.html), but when following the example I get the following error:\r\n\r\n```\r\n>>> dataset2 = load_dataset(\"amazon_us_reviews\", \"Pet_Products_v1_00\", split='train', streaming=True)\r\n\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-21-1eedab26cff1> in <module>()\r\n----> 1 en_dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)\r\n    339                 if value is not None:\r\n    340                     if not hasattr(builder_config, key):\r\n--> 341                         raise ValueError(f\"BuilderConfig {builder_config} doesn't have a '{key}' key.\")\r\n    342                     setattr(builder_config, key, value)\r\n    343 \r\n\r\nValueError: BuilderConfig OscarConfig(name='unshuffled_deduplicated_en', version=1.0.0, data_dir=None, data_files=None, description='Unshuffled and deduplicated, English OSCAR dataset') doesn't have a 'streaming' key.\r\n```\r\n\r\nUPDATE: Managed to get streaming working by building from source and installing the additional `datasets[streaming]` package:\r\n\r\n```\r\n!pip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n!pip install datasets[streaming]\r\n```","embeddings":[-0.4476095736,-0.3885074854,0.0095246909,0.370123446,0.1067445353,0.1929703951,-0.0340346843,0.5148774385,0.0819868594,0.2324690074,-0.4534536302,-0.1990633309,-0.1784165353,0.4000229239,0.3254315257,-0.1704499125,-0.1151958555,0.1878741086,-0.0883976594,-0.1764554977,-0.0659911856,-0.0661999807,-0.1601547897,-0.1373348683,0.0878684372,-0.0127935819,-0.0002598203,-0.0606895387,-0.39527753,-0.0482899174,0.2791904211,0.1899072081,0.1114025861,0.1363454163,-0.0001268418,-0.1690050513,0.3633389175,-0.1690449715,-0.3607497513,-0.1897484213,-0.1767892838,0.086456649,-0.0258887466,-0.1968064457,0.0642866492,0.0536655597,0.0196072031,-0.020212071,0.4206509292,0.0790272653,0.0756611899,0.2862719297,0.106442757,0.1084069982,0.1896714866,0.1896163821,0.0424217843,0.1744011492,0.263699919,0.2539024949,0.0544782504,0.2674317658,0.0258967616,0.3122105598,0.0494102202,-0.183249861,-0.1826818734,-0.668645978,0.1587405503,0.6345419288,0.2820595503,-0.0176110677,-0.5097762346,-0.4816657305,0.1284909695,-0.6643292904,-0.068087779,0.6299393773,-0.4362098575,0.0515867434,-0.6224960089,-0.2577052712,-0.2082426399,0.3043964505,-0.195134297,0.1637154073,-0.2123502642,0.1319192797,0.264487505,-0.0100984648,0.0959196463,-0.2319997996,-0.0489966199,0.3651181459,-0.1721380055,-0.3230677545,-0.130076468,0.282348156,0.3912600279,0.2919591069,-0.0171136837,0.1264038086,0.1318633109,-0.0820960104,0.5853521824,-0.1074894518,-0.3332640231,0.2814321518,0.3189247549,0.3265119493,0.135591343,-0.0895507261,-0.1213611066,0.2551878393,-0.3615022004,-0.0669895336,0.0641150624,-0.3408076465,-0.0459385924,-0.1280491054,0.0305283554,-0.0864463896,0.0000250813,0.226681754,-0.0999816954,0.2417342663,-0.2276192307,0.0628773347,-0.0823991746,-0.2827228606,-0.0952995569,0.0019261892,0.1059893444,0.1242706925,0.2675984502,-0.5731204152,0.3885329366,-0.1137729064,0.5026563406,0.0964687839,-0.1554783434,-0.1604901403,0.0389188565,0.3139612079,0.0650518611,0.4057741761,0.0124905873,0.2547515333,-0.2002497166,0.2239141315,-0.1098953262,-0.4199577868,0.0124073839,0.0053586732,-0.3765225708,-0.040492624,-0.397038579,0.2838448584,0.0285333991,0.106795311,-0.0624929741,0.1174524948,-0.091144003,-0.0263898205,0.3707153797,0.3494339287,-0.0923917964,-0.2054376155,-0.1573995799,-0.0737660825,0.2902519405,0.4422823787,-0.3034178317,-0.1268008649,-0.2115474045,-0.259855032,0.3551361859,-0.085635215,-0.352812171,0.3833649755,0.1783619076,0.1554275453,0.1417041719,-0.0104912426,0.6427505016,0.0784268603,0.0167188384,0.496611774,-0.1728039831,-0.0436564982,-0.0887445584,-0.3199162781,-0.1930649579,0.3292202353,0.1675110012,0.3437730968,-0.0655097291,0.0158571266,0.4304685295,-0.0927524418,0.4422924221,0.1339258403,0.2232974321,0.0517347157,-0.2251435518,-0.4198052585,-0.390024364,0.2023004889,0.2302104235,-0.3973640203,0.1322660744,-0.1938214302,-0.0703584552,-0.2523659766,0.1331303269,-0.1378821582,-0.1556593478,-0.1328205913,0.3140981197,-0.1343347132,-0.5509424806,-0.0244986136,-0.0420947075,0.2612622678,-0.1101006791,0.2821855545,0.1781553775,-0.0102132494,0.2728771865,-0.1592219174,-0.0508325063,-0.1912722141,-0.048191987,0.216403842,-0.0373281613,0.0216384586,-0.1655334532,0.0964832753,0.3940747976,-0.002895595,-0.0280518048,0.1597703993,0.0947019085,-0.0528160967,-0.288987875,0.5896885991,-0.0793756694,0.1561117172,0.2797580659,0.0629937351,0.2583141625,0.024599582,-0.1124245524,-0.0111213,0.346318692,-0.129313767,0.3581708372,-0.1327632219,-0.5335001349,-0.2308386415,0.3453401625,-0.1733855456,-0.3991067111,0.2580744624,-0.2973130941,0.1224728078,0.1615042388,0.0103752734,0.0803766623,0.110991329,0.3299811184,0.0556583405,0.4505049288,-0.0324967541,0.0974358246,0.1050534099,-0.2600086629,0.0505798906,0.0576691367,-0.1339040548,-0.323603034,-0.029413078,0.1493064016,0.2342587411,-0.1711518914,-0.1821264625,-0.2360212654,-0.4625363052,-0.1564715952,-0.1158612967,-0.1308811605,-0.0992546082,-0.0840450302,0.6108608246,0.0186103415,0.0107477754,-0.0253544003,0.1656914949,0.0254362002,0.2290917486,-0.224337846,0.0105298162,0.155006811,0.0568729527,0.139343068,-0.0375017673,0.3074813783,-0.0599760674,-0.2525286078,-0.2569149137,-0.0906152427,0.0830261782,0.0150333764,-0.0439837649,0.0417102128,0.5142203569,-0.180791989,-0.1234094873,0.0148858782,-0.0761067271,-0.1766090542,-0.0104610734,-0.0691785663,0.229768768,-0.0946160257,0.0419856496,-0.4150296152,-0.3144565821,0.5148342848,0.376521498,0.2471687347,0.0415063798,-0.1072001234,-0.1911929697,-0.4565024674,-0.1922227144,0.0175258256,-0.8641309142,0.370621711,-0.048012685,-0.2978565097,0.0169300493,0.0122651653,-0.1107021272,0.3640224338,-0.385035485,0.1171999052,-0.2212017477,0.206400007,0.0881161764,0.1745103002,0.0010338839,-0.5461443663,0.1023331657,0.0984678641,0.0187489185,-0.2533030212,0.2585525215,0.2878212035,-0.1134539098,0.3037965596,0.010864621,1.0486694574,0.3577305079,0.1942342073,0.115386501,-0.0145859458,0.1851329356,-0.1911917329,-0.1458620876,0.3457380831,-0.078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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2161","title":"any possibility to download part of large datasets only?","comments":"Hi ! Streaming is available on `master` only right now. We'll make a new release 1.9.0 on Monday :)","body":"Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks","comment_length":19,"text":"any possibility to download part of large datasets only? \n Hi\r\nSome of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled\/unshuffled data without going through first downloading the whole data then sampling? thanks \n Hi ! Streaming is available on `master` only right now. We'll make a new release 1.9.0 on Monday :)","embeddings":[-0.4722626507,-0.4255569279,-0.1131871417,0.1344200224,0.0880462602,0.1738644838,-0.3542895317,0.3757156134,-0.0502581708,0.3286562264,-0.4129242003,-0.227931425,-0.1485811472,0.3793706298,0.2008761317,-0.0660857111,-0.1095422283,0.2192226052,-0.1483973712,-0.113596186,0.0163903777,-0.2493553013,-0.2375249416,-0.3178961575,0.2018005103,0.0277642515,-0.0485128127,-0.2904225588,-0.5257316232,0.0757009089,0.1052192748,0.1233388856,0.213425979,0.1093875691,-0.000115171,-0.3466500938,0.416819036,-0.1254801452,-0.0490663573,-0.1248012558,-0.3066554964,0.130421102,-0.1911244839,-0.1625910848,0.0535930432,0.1501369327,0.2008933574,0.069564037,0.1805625111,0.1309281439,0.1482643336,0.0635441318,-0.1145861596,-0.0245387964,0.2895706594,0.067812182,-0.0297182202,0.0065403683,0.5009749532,0.3647523224,0.1164343059,0.0436584316,0.0524160825,0.1226052493,0.0101258187,-0.3156893551,-0.1707394719,-0.6554211974,0.3526365161,0.7226455212,0.6469684839,0.101222679,-0.2085970789,-0.0435767472,0.17564255,-0.2512713373,-0.1427433491,0.6370520592,-0.3258793354,0.1487766057,-0.5430160165,-0.3155020773,-0.1687672138,0.304164201,-0.2599721551,0.3214052022,-0.0001990961,-0.0499977507,0.360345006,-0.002015355,0.2264091223,0.0228020065,-0.3124683499,0.2853650451,-0.2960832715,-0.3794879317,-0.3436775804,0.2594523728,0.2998431623,0.0891993567,0.1823874265,0.2357151657,0.0800661519,0.0151364719,0.5451431274,-0.107617259,-0.323325932,0.0730135068,0.3837715685,0.0101135392,0.1688045412,-0.0248710178,-0.0028995492,0.2587714791,-0.5076771379,-0.0003627476,-0.2615731955,-0.4713282585,0.1559984088,-0.2777479291,0.1362636089,0.0707588866,-0.0810322016,0.107066147,-0.0906696469,0.241054073,-0.4281466305,-0.0736106336,0.0975691974,-0.322091043,-0.1091440171,-0.0105769159,0.0280527826,-0.0787927359,0.2643789351,-0.5082525611,0.3371801674,-0.0795179009,0.3361353576,0.1806773543,-0.189269647,-0.1488175243,-0.0888085067,0.2145675272,0.104994379,0.2417758852,-0.2071612328,0.5045181513,-0.0368065313,0.2993345857,-0.1207159311,-0.283534795,0.1389861703,0.1309602708,-0.2674580514,-0.062985599,-0.3869692683,0.2854522467,-0.1779265255,0.0000469603,-0.0142693501,0.0776571557,-0.0435365401,-0.1150779128,0.2379499823,-0.1307851672,-0.2373149991,0.0068901898,-0.1620619893,-0.1714998037,0.4258080125,0.3534020483,-0.1707287431,-0.3960533142,-0.0673998818,-0.1546263099,0.2148637474,0.0437496714,-0.538692832,0.3985548913,0.131785959,-0.2564815879,0.0821295381,0.152552709,0.6344279647,0.1193681955,-0.2234910131,0.5501548648,-0.0589089245,0.0575012118,-0.095140256,-0.3214320838,-0.2185790986,0.1635003984,0.3424953818,0.4317515194,0.2950881124,0.0950777158,0.4195162654,-0.0896089002,0.2856127918,0.1713019013,0.1827346236,-0.1341881752,-0.0874409676,-0.3088537753,-0.0879281759,0.1430228949,0.1239087284,-0.3638298512,0.4067093432,-0.1922576278,0.088481389,-0.2914109528,0.4060345888,-0.0655182824,-0.0696879029,-0.178422913,0.2960861921,-0.2208670527,-0.3730580509,-0.2006771713,-0.0802235082,0.0889411271,0.0760953501,0.0453677736,0.2278527915,0.1880146265,0.3012546897,-0.3396292031,0.018282637,-0.0757414475,0.032717552,0.2482907921,-0.0751573667,0.1401787251,0.0556409135,0.0482327528,0.4031480849,-0.1497116387,-0.0283271428,0.3423365057,0.1293026507,-0.0366101377,-0.3519751728,0.2771193087,-0.2177254111,0.1233278662,0.1699346602,-0.002215513,0.3464900255,-0.0757288709,0.0770131797,0.1272966266,0.3411634564,0.037285123,0.0722674429,-0.1566409618,-0.369949609,-0.257492274,0.0630850568,-0.1521055996,-0.26229617,0.1761300564,-0.3542080522,0.0377749689,0.1432314068,0.1573666632,0.0434849299,0.1828321368,0.5009159446,-0.0189324152,0.5529599786,-0.1913037747,0.0323056504,0.0135213435,-0.367751658,0.0108494833,0.0776748508,-0.3239637613,-0.1185696498,0.2415394038,0.2291006446,0.1548428237,0.1494725496,-0.4640162587,-0.3105860651,-0.5512290001,0.1515311897,0.0502730459,-0.0246748868,-0.0118748834,0.0819526687,0.4645318687,0.0342576206,-0.1236094907,0.0287054069,0.3785234392,-0.1320492923,0.4409327209,-0.1768302023,-0.0413004272,0.2356285155,0.2211539149,0.0218223985,0.0270522088,0.1783610731,0.0761639476,-0.0646819621,-0.3325956166,-0.063948743,-0.0094431862,0.2617275417,-0.1962979883,-0.1334728152,0.3212757707,-0.0662328526,0.0288736112,-0.2167477161,-0.3117702305,-0.1400713623,-0.0669214502,-0.0051100315,0.1507525146,-0.1814942956,0.1428144574,-0.4396058321,-0.3790428042,0.3784822226,0.2214270681,0.1600247025,-0.3005808592,-0.121088028,-0.187777549,-0.2930058539,-0.4380383193,0.0462037064,-0.8105823994,0.322945267,-0.1686301529,-0.3520539105,0.1457237601,0.2076426595,-0.1211160719,0.4795018733,-0.4310851395,0.2508367002,-0.11923296,-0.1019102409,0.187668398,-0.0328274444,-0.2147152424,-0.6559681296,-0.0396956056,0.0308427792,0.2113589942,-0.3204505146,0.3215064704,0.3877242208,-0.0777190253,0.0027181855,0.1230207086,0.6648045182,0.0610291958,0.3236869872,0.0348335579,0.1735765487,0.039022468,-0.0626723468,0.0875246301,0.3505371809,-0.0765626058,-0.180175066,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2160","title":"data_args.preprocessing_num_workers almost freezes ","comments":"Hi.\r\nI cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks\r\n\r\n```\r\n#3:  11%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                                                                                                  | 172\/1583 [00:46<06:21,  3.70ba\/s]\r\n#4:   9%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f                                                                                                                                    | 143\/1583 [00:46<07:46,  3.09ba\/s]\r\n#7:   6%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                                                                                                                                          | 98\/1583 [00:45<11:34,  2.14ba\/s]\r\n#5:   8%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d                                                                                                                                      | 124\/1583 [00:46<09:03,  2.68ba\/s]\r\n#6:   7%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  \r\n```","body":"Hi @lhoestq \r\n\r\nI am running this code from huggingface transformers https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py \r\n\r\nto speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during  tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up.\r\n\r\nthanks","comment_length":71,"text":"data_args.preprocessing_num_workers almost freezes  \n Hi @lhoestq \r\n\r\nI am running this code from huggingface transformers https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py \r\n\r\nto speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during  tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up.\r\n\r\nthanks \n Hi.\r\nI cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks\r\n\r\n```\r\n#3:  11%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                                                                                                  | 172\/1583 [00:46<06:21,  3.70ba\/s]\r\n#4:   9%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f                                                                                                                                    | 143\/1583 [00:46<07:46,  3.09ba\/s]\r\n#7:   6%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                                                                                                                                          | 98\/1583 [00:45<11:34,  2.14ba\/s]\r\n#5:   8%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d                                                                                                                                      | 124\/1583 [00:46<09:03,  2.68ba\/s]\r\n#6:   7%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  \r\n```","embeddings":[-0.251410991,-0.2479194999,-0.1668762267,0.0695675686,0.1297327876,-0.1807381213,0.4334701002,0.0990605354,-0.3538162112,0.2440417558,0.0638783947,0.239945367,0.0941158682,-0.1330185235,-0.0470905453,0.1516224444,0.1583945155,0.01122397,-0.1086994112,0.1517171264,-0.3039160669,0.2973909676,-0.1818717718,-0.1495001316,-0.3720234931,0.0712001994,0.1070430055,-0.0386771485,0.1266263425,-0.3786482513,-0.3722394109,0.2621736825,-0.1717153788,0.3977265656,-0.0001282334,0.0551564693,0.3310023546,0.334130466,-0.0993837044,0.1854704767,0.5246136189,-0.3683948219,0.0892792493,0.0601530075,-0.0127817662,0.0405185968,-0.0577857569,-0.1024643332,0.4945966899,-0.0235326365,0.0106404126,0.5192914009,-0.1219599321,0.2335590869,-0.3205538392,0.1153099611,-0.0788028166,0.1173704341,0.4010337293,0.197347939,0.3235229254,0.1297678053,-0.1178333163,-0.0455494262,-0.0672251657,0.2181202024,0.4664070308,-0.646307528,0.254032135,0.1169221625,-0.0574613065,0.1287365109,-0.0659624562,-0.2802684307,-0.1109629199,-0.5052868128,0.0478969365,-0.286601454,-0.0035855249,-0.0845630616,-0.1638436168,-0.03434553,0.0380558297,0.0745908245,0.2919778228,0.402382344,-0.2579991817,0.1412986517,0.4859220386,0.0520768389,-0.1439908743,-0.2433384508,0.1748287976,0.1739303768,-0.3967978656,0.094879739,-0.2360816002,0.1659679711,-0.1208465323,0.0911727697,-0.1102677658,0.51345402,0.4665889442,-0.2004278302,-0.1314332783,-0.1684507728,0.0231272895,-0.3938961029,0.5149444938,0.0773059353,0.012577232,0.0659774691,-0.242899552,-0.5354476571,0.1613685787,0.1407512277,0.0415939949,-0.1196804419,0.209046483,0.080072321,0.1907866448,-0.2290394753,0.3454236686,0.3752199113,-0.2789570987,0.1992174536,-0.2575200498,0.0450633168,-0.5872555375,-0.0557829179,0.0518546924,-0.2311387658,-0.0153393801,0.1887843162,0.2071906477,-0.2020408809,0.196659416,0.2144078612,0.6740496755,-0.422600776,0.3631923199,-0.1636646241,-0.0096500507,0.2447361499,-0.0727551579,0.3624317944,0.0352872498,0.3586993515,-0.3110885918,0.0150666256,0.0074116229,-0.2275886089,0.1634588242,-0.0480351113,-0.0323655978,0.2433685958,0.0056008692,0.1432507187,0.5009929538,0.38757056,0.0300536565,-0.1195357144,-0.4238312244,0.0253996365,0.2834200859,0.4679894447,0.5233025551,-0.0402477235,-0.0364370383,-0.0225381311,0.7288458943,0.2094704658,-0.0243388563,-0.0221082605,-0.0677255467,0.1443639994,-0.0810829997,0.072325848,0.0567137636,0.2270413637,-0.224065423,-0.0860366151,0.2414156049,0.0726291463,0.1347102076,-0.0877699628,0.1700471938,0.1761258692,0.2823870778,0.1734233946,-0.2352311164,0.014629432,-0.0128331911,0.0171705708,-0.3240709007,-0.0673821121,-0.5180609226,-0.0416161567,0.0703363642,0.005442726,0.1118422002,0.1537818164,-0.0834670961,0.0560844056,0.327490896,0.0175311863,-0.1221803427,-0.037940491,-0.3193322718,0.2669684887,0.0784632713,-0.0842257962,0.0320651904,0.0909706578,-0.1867544204,-0.0062119798,-0.1380517185,-0.0347193703,0.1920596659,-0.0670048296,0.0034454467,-0.083128348,0.0475848839,0.2688089609,-0.2574956715,0.0044197752,0.0832877159,-0.1455596387,-0.1100080982,0.0941949785,0.0614338331,-0.3610614836,0.0165602807,0.2966277599,0.1466055065,0.3110172749,-0.4479559958,0.5583019853,0.2145093381,0.1290580332,0.0892316625,0.0092792502,0.021773586,-0.1451040804,0.4335660338,0.4299760759,-0.2997783124,0.390612781,0.1070610359,-0.1821053326,-0.0560044833,0.2489263564,-0.2439727336,-0.0261756238,0.454087466,-0.0477517769,0.2301163375,0.22236453,-0.1836317331,0.2125936747,0.123734735,0.0111783529,-0.1703724861,0.0403767228,-0.0734566823,-0.1334791332,-0.2499898225,-0.6115149856,0.1777780205,-0.0069288849,-0.0365603045,-0.0478599966,0.0042380751,-0.206107989,0.2798293531,0.4372163415,0.186759606,0.1711970568,-0.1949297041,-0.0732352957,-0.1825201064,-0.129712075,0.1186935604,0.1672266275,-0.1895805597,0.1366368383,-0.2387172282,0.2938965261,-0.166299969,-0.1849084646,-0.4397236109,-0.0257224683,-0.1499881893,0.0983165428,0.010332007,0.1029523015,0.0242338125,0.08819855,-0.1002230793,0.0622529797,-0.1612755358,0.0371611714,-0.0491185896,-0.245304063,0.2989142239,0.1687112451,-0.0080339629,0.1353286952,-0.3070052266,-0.0110725723,-0.2749910653,0.1400469542,0.1708554476,0.3440091908,-0.0735172853,0.1897306591,0.0009131254,0.0276050251,0.100685887,-0.1594691724,-0.0642061532,0.0215204302,-0.1502985209,-0.124828808,-0.1320841461,0.0152242919,-0.2438078076,-0.1898180544,0.1589920819,-0.0782173648,-0.0424328186,0.1317870468,-0.0138681279,0.1339338869,0.259021163,0.1452918202,-0.1662133634,-0.0018491204,0.3186675012,-0.102850832,-0.1215235814,-0.3235512972,-0.1072703972,0.1314612776,-0.2445207685,-0.060911905,-0.015192505,-0.5067573786,-0.242324397,-0.1723922938,0.0972394496,0.4248436689,-0.0066832025,-0.0099411821,0.1922746897,-0.226909861,0.257029742,-0.0619243681,-0.3097364902,0.1396402568,0.470805198,-0.2967545688,0.6741126776,0.0749658123,-0.1954611242,0.050569091,0.0368689932,-0.1507929116,-0.0553347506,-0.0852852687,0.1318517178,0.2249401212,-0.0552713312,0.3454117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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2158","title":"viewer \"fake_news_english\" error","comments":"Thanks for reporting !\r\nThe viewer doesn't have all the dependencies of the datasets. We may add openpyxl to be able to show this dataset properly","body":"When I visit the [Huggingface - viewer](https:\/\/huggingface.co\/datasets\/viewer\/) web site, under the dataset \"fake_news_english\" I've got this error:\r\n\r\n> ImportError: To be able to use this dataset, you need to install the following dependencies['openpyxl'] using 'pip install # noqa: requires this pandas optional dependency for reading xlsx files' for instance'\r\n\r\nas well as the error Traceback.\r\n\r\n","comment_length":26,"text":"viewer \"fake_news_english\" error \n When I visit the [Huggingface - viewer](https:\/\/huggingface.co\/datasets\/viewer\/) web site, under the dataset \"fake_news_english\" I've got this error:\r\n\r\n> ImportError: To be able to use this dataset, you need to install the following dependencies['openpyxl'] using 'pip install # noqa: requires this pandas optional dependency for reading xlsx files' for instance'\r\n\r\nas well as the error Traceback.\r\n\r\n \n Thanks for reporting !\r\nThe viewer doesn't have all the dependencies of the datasets. We may add openpyxl to be able to show this dataset properly","embeddings":[-0.1493438631,-0.1812810451,0.0336430967,0.341632545,0.2343087941,0.2863430381,-0.0157952346,0.2244071364,0.1654276997,0.0734670162,-0.2225701958,-0.1259506196,-0.03064215,0.3286091685,-0.0807032138,-0.209744215,0.2249317169,0.1918756962,-0.0383199081,-0.2154147625,-0.1752157807,0.2497071475,-0.2327596545,0.033299882,-0.1657814384,-0.005650437,0.0485558584,-0.1095098481,-0.2481057048,-0.2244533449,0.2573483884,-0.0757726133,0.0078952471,0.4936810136,-0.0001168755,0.0112852957,0.2621284723,0.0009628056,-0.0758979172,-0.1726921201,0.1580370665,-0.5534417033,0.2592311502,0.005135417,-0.1263031811,-0.6198988557,0.1344301105,-0.0443131849,0.4444801807,0.4118565917,0.2116182446,0.4163538218,0.4214429855,0.0597534105,-0.1707170755,0.0774004906,-0.0333689302,0.2485256046,-0.112039946,0.2506457567,0.0464389957,0.3587521315,-0.0722557977,-0.1672137231,-0.1182158664,-0.2060662955,-0.0668232441,-0.3877575099,0.065018788,0.2956514955,0.2338842452,-0.1602156162,-0.1942190379,-0.1710206121,-0.0075114551,0.1529851109,0.234055236,0.3493386805,0.0310773924,0.2444141656,0.089173153,-0.3955403268,-0.0207314249,0.1518016458,-0.2865380049,0.271138519,-0.2161859572,0.1463052034,0.2850150168,-0.1231927201,0.1981233209,-0.0080020959,-0.0066175866,0.168779552,-0.1542187631,0.0125708319,0.0683273897,0.4823487401,-0.1209615543,-0.4073605537,-0.4050123692,0.0087040886,-0.2241734564,0.2380542606,0.0841645971,0.0210722256,-0.0141955065,0.2033031434,0.1795813739,0.3211095035,0.1527701914,0.0679236874,0.0083028544,-0.2380101979,-0.4805679023,-0.3320239782,0.3121840954,-0.2570629716,-0.3029878438,0.2578764558,-0.0671019778,-0.0249174535,0.0111848582,0.182961151,-0.1553817987,0.4950966239,0.2343169153,0.1126600429,-0.2211113721,-0.4079149067,-0.1736008525,0.2763749361,0.0510919429,-0.1620607972,-0.032807745,-0.6860463023,0.2591982484,0.1957404912,0.1480438709,0.047656849,-0.1888779849,-0.0919522792,-0.2200558782,0.3047617674,0.1786770523,0.131814003,0.553268671,-0.3143092096,-0.1588373482,0.0255600773,-0.0247138031,-0.1193820387,-0.3907499313,0.1378650069,-0.3698265254,-0.0951169133,0.1383287907,0.1952489018,-0.2921114266,-0.2234259546,-0.1163565814,0.0132624386,-0.0202204604,0.0533876978,0.0504159555,0.5927078128,-0.4545469284,-0.3469185829,0.0944002643,-0.2169104517,0.0935402066,0.1010093316,-0.0120336916,-0.0668989122,-0.1673156321,-0.3382666707,0.2603876591,-0.4653259516,-0.2659332752,0.3473155797,0.0669156909,0.2950734198,0.1359106153,-0.1164171621,-0.2745509148,0.2345121801,-0.4540861249,0.0552111603,0.0520400256,-0.0751473382,-0.2216024101,-0.2808434665,0.2575274706,0.3178930283,0.1873325706,0.0692965537,-0.0543510877,-0.1554214954,0.2374704778,0.1147569418,0.0038577621,0.1038566008,0.1040420681,0.3543663621,0.054034166,0.002796411,0.0732586458,-0.0716021359,-0.0064702518,0.290582031,-0.1062062085,-0.2331238538,-0.3494341373,0.0760537684,-0.1288511604,-0.3132869899,0.0632410869,0.1411925107,-0.0140754459,0.5048753619,-0.1575295925,0.27174595,-0.1571386158,0.1240423918,-0.5752680898,0.1061153635,-0.2694675624,0.1400140375,0.1747436374,0.0970163867,-0.0346790664,-0.0112214023,0.0091679152,0.1916556209,-0.151748389,0.2544415593,0.1007500738,0.0872532651,0.250277102,-0.4632273316,0.1977060437,0.1617235839,0.1071821675,0.1465789974,-0.0417563878,0.1484865248,0.053547699,0.0759226158,-0.0791275725,0.4016434252,0.3749584258,0.2591935396,-0.0877757892,-0.372130245,0.4974481463,-0.1468615532,0.3775921464,-0.1414720416,-0.2833206952,-0.0737213716,0.1207566187,0.0512172654,0.1060101315,0.2926082015,-0.2328044027,0.1253170669,0.2561503351,-0.0175857991,0.1818636805,0.0954405665,-0.1969885677,0.2725213766,0.0246928614,-0.239864707,0.1188213229,0.0407809541,-0.1291139424,-0.0179597847,-0.1501010209,-0.0485614575,-0.5117405057,0.124787122,0.0088861641,0.2497189641,-0.3337169588,0.0810149387,-0.1943718195,-0.3811690807,-0.209766373,-0.4099612236,-0.2709551156,-0.422414571,-0.0119396104,0.105178073,-0.0280012209,0.3039633632,-0.018855961,0.1006590724,-0.0498096198,0.0785682201,-0.2276708633,-0.165293023,-0.0761513337,0.1163732186,0.1672727913,0.0746080056,0.1345999092,-0.5980010629,0.1774813384,-0.4021346271,-0.3951074481,0.1637867838,-0.3234314024,0.4101296961,0.2941055596,0.1234956756,-0.0674271658,-0.0877377763,0.2502646148,-0.1842281669,0.1840976924,0.039619837,-0.1432317793,-0.1686982214,0.0873312205,-0.1414393485,-0.2506596744,-0.3166332245,0.2217575312,0.1017183363,0.1098301411,-0.0413943306,0.1959975064,0.2456980497,-0.09875191,0.2361411005,-0.2863528132,-0.5247047544,0.3783111572,-0.1568012238,-0.3085812926,0.2651654482,0.0860078037,0.1951528192,-0.4093189836,-0.733242929,-0.2308117151,-0.1923142374,-0.0415656306,0.033482153,0.1754434854,0.1946000755,0.0358054265,0.0847483799,-0.0624023117,-0.050844159,-0.2867293656,-0.3271091878,0.0163104553,-0.0000910936,0.5346378088,-0.0564330071,0.2596888244,0.4661255777,0.1804060042,0.3968822956,0.1061628312,0.788567245,0.0033063213,-0.493596822,-0.0231896807,0.1583252102,0.220878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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2153","title":"load_dataset ignoring features","comments":"Nice question which helped me a lot! I have wasted a lot of time to the `DatasetDict` creation from a csv file. Hope the document of this module add some simple examples.","body":"First of all, I'm sorry if it is a repeated issue or the changes are already in master, I searched and I didn't find anything. \r\n\r\nI'm using datasets 1.5.0\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/37592763\/113114369-8f376580-920b-11eb-900d-94365b59f04b.png)\r\n\r\nAs you can see, when I load the dataset, the ClassLabels are ignored, I have to cast the dataset in order to make it work.\r\n\r\nCode to reproduce:\r\n\r\n```python\r\nimport datasets\r\ndata_location = \"\/data\/prueba_multiclase\"\r\nfeatures = datasets.Features(\r\n        {\"texto\": datasets.Value(\"string\"), \"label\": datasets.features.ClassLabel(names=[\"false\", \"true\"])}\r\n    )\r\ndataset = datasets.load_dataset(\r\n        \"csv\", data_files=data_location, delimiter=\"\\t\", features=features\r\n    )\r\n```\r\n\r\nDataset I used:\r\n\r\n\r\n[prueba_multiclase.zip](https:\/\/github.com\/huggingface\/datasets\/files\/6235022\/prueba_multiclase.zip) (it has to be unzipped)\r\n\r\n\r\nThank you! \u2764\ufe0f \r\n","comment_length":32,"text":"load_dataset ignoring features \n First of all, I'm sorry if it is a repeated issue or the changes are already in master, I searched and I didn't find anything. \r\n\r\nI'm using datasets 1.5.0\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/37592763\/113114369-8f376580-920b-11eb-900d-94365b59f04b.png)\r\n\r\nAs you can see, when I load the dataset, the ClassLabels are ignored, I have to cast the dataset in order to make it work.\r\n\r\nCode to reproduce:\r\n\r\n```python\r\nimport datasets\r\ndata_location = \"\/data\/prueba_multiclase\"\r\nfeatures = datasets.Features(\r\n        {\"texto\": datasets.Value(\"string\"), \"label\": datasets.features.ClassLabel(names=[\"false\", \"true\"])}\r\n    )\r\ndataset = datasets.load_dataset(\r\n        \"csv\", data_files=data_location, delimiter=\"\\t\", features=features\r\n    )\r\n```\r\n\r\nDataset I used:\r\n\r\n\r\n[prueba_multiclase.zip](https:\/\/github.com\/huggingface\/datasets\/files\/6235022\/prueba_multiclase.zip) (it has to be unzipped)\r\n\r\n\r\nThank you! \u2764\ufe0f \r\n \n Nice question which helped me a lot! I have wasted a lot of time to the `DatasetDict` creation from a csv file. Hope the document of this module add some simple examples.","embeddings":[-0.0856140181,-0.0304919872,0.0129400603,0.2842156291,0.4256821573,0.2531645894,0.6406395435,-0.0539474487,0.2234636098,0.0533237867,0.1543203592,0.3363256156,-0.1144230142,0.4599338472,-0.1519141644,-0.0312218927,0.063283965,0.1389202476,0.0868092924,-0.1416218579,-0.3790285289,0.0661422163,-0.3810799122,-0.0748045146,-0.199522078,0.2117881924,0.0234554317,0.07192076,-0.0297584608,-0.5245155096,0.413390696,0.2789027691,0.3400850296,0.251696527,-0.0001132447,-0.0907421485,0.0439932868,-0.1335797906,-0.1930736899,-0.1258175671,-0.4377946556,-0.3049478531,0.293554306,-0.3018359244,-0.3062906265,0.1707061231,-0.2648938298,-0.2134493887,-0.0529288314,0.1210677773,0.1923326999,0.0092524923,-0.0871038511,0.1758142114,0.0143903447,-0.0617420748,-0.325831145,0.2043394744,0.1856566221,0.2550998926,-0.0153446216,0.2552683055,-0.214363575,0.0363501497,0.5101963282,-0.1277292073,-0.0145440605,-0.3282902539,0.0238243286,0.1738422066,0.7974794507,-0.2552067935,-0.4845220149,-0.2741535008,0.1363139153,-0.4022521377,0.1647414267,0.0906636193,0.2678411901,0.1801911443,-0.4022525847,-0.0693216324,-0.1144453436,0.0422427021,0.031348858,-0.0237456132,-0.1550961435,0.1390875131,-0.1776063144,-0.0167391114,0.3612679541,-0.2586158216,-0.0753731653,0.2356112748,-0.2841072381,0.1693637818,0.091674529,0.1357545108,0.0742881224,0.1346576959,0.0195800122,0.2739371359,-0.3180241883,0.1293332875,0.1421793848,-0.0020369068,0.5196121335,0.1570585668,0.198042199,0.0485058203,-0.2741855383,0.0649611875,-0.156292364,-0.0674709305,0.2910913527,0.0448109359,0.1363649368,-0.3723415136,-0.1389972121,0.0318643376,-0.1400588751,-0.0162491165,0.1177361235,0.2474844903,0.0247120373,0.4246523976,0.2354547828,0.2400788367,-0.2873142064,-0.3280520141,-0.2301762104,-0.1649012864,-0.0516043603,0.0536998697,0.4024191797,0.172232613,0.1889072657,-0.0935541242,-0.1197241247,-0.1503179371,-0.1773230135,0.0644035339,-0.0126002934,0.2159597725,-0.1039229706,0.1599552333,0.1865864992,-0.2099940628,0.0202711225,0.1871026605,-0.2052912414,-0.0912482366,0.1930449158,0.1692913622,-0.3488231599,-0.0992196351,-0.4686614871,0.1708803028,-0.0383038186,-0.0585591123,-0.0010499486,-0.4762669206,-0.074391745,-0.2339225709,0.3303196728,0.4801045656,-0.4956296384,-0.1252076775,0.303334415,-0.1631097049,-0.0423816964,-0.1286754161,-0.0879168361,0.1719543934,-0.095555529,-0.3004548252,0.2273970246,-0.1882335097,-0.3485089242,-0.1945337504,0.0981055051,0.0058674407,0.1647294462,0.3072187304,-0.0137548149,0.0016859525,-0.098152332,0.4130121768,0.0815085173,-0.016189836,-0.1648076773,-0.0039023247,0.0153039144,0.327521801,-0.0128150014,0.2236014903,0.1535398513,0.0070145875,0.1234481335,-0.2657747567,0.2417103946,0.3734304309,0.0262524337,0.2091947347,0.0803009719,-0.0996135399,-0.4244221449,0.3336114585,0.1969466656,-0.0301368739,0.0143269869,-0.1335237324,0.0164890569,-0.0420469604,-0.599829495,-0.0444379859,0.0043741786,0.108144179,-0.1282560676,-0.0561452545,-0.0211245511,0.2936041355,-0.2329749018,0.2568300068,-0.3175535798,0.0984747335,0.1104398668,-0.0933684781,-0.1049847901,0.2762140632,0.3593247235,-0.2134838402,-0.139570713,0.252053529,0.2794742584,-0.2395739555,-0.003348226,0.0503881983,0.1536212564,-0.38654387,0.0303776786,0.0651876405,0.2595449984,-0.1391369104,-0.4149834514,0.4254066348,-0.1629067808,0.1436124742,0.0709132105,-0.0759841576,0.3986670077,-0.0515849888,-0.1249036938,-0.0875488296,0.0120539544,-0.0065012444,0.2523196042,0.2596165538,-0.4297820628,0.1778017133,0.3382619917,0.023081027,-0.0686432719,-0.0502457023,-0.177865088,0.0848932713,0.2156614512,0.0609677508,0.2490087152,0.2434667051,-0.0681903735,0.0026713256,-0.0539208427,-0.0883627459,0.2646454871,-0.0272719078,-0.0585923605,-0.0459214188,0.1281882077,0.0731413141,-0.2991208434,-0.1569283456,0.0864324719,-0.0542883426,-0.5587561131,-0.0162466988,-0.1895411015,0.0446084775,-0.1800445765,-0.1181470081,-0.2130714059,-0.2718034983,-0.1732316613,0.382527411,0.0305418987,0.0627364963,-0.4193843305,0.107765615,0.0894372389,-0.5412775874,0.2205042988,0.1355538666,-0.4152530134,-0.0298727639,-0.1084800959,0.2368005067,0.2161876708,-0.1324639916,0.1083870828,-0.2184620649,-0.2653663456,0.0387863144,-0.246820569,0.0459491313,0.0089157,-0.0679905936,-0.055145558,0.1420685053,0.3785454929,0.0763236508,0.0598126762,0.2437888384,0.207072407,-0.2385058254,-0.2769645751,-0.5045168996,-0.1614888161,-0.3960846364,0.041375909,0.0946876779,-0.251645565,0.138907209,0.057367716,0.1830513179,0.2809784114,0.1540104747,-0.0310248919,-0.235582009,0.3494182825,-0.1501153409,-0.2869130671,0.1117159054,-0.0587879494,-0.0539898053,0.0010019642,-0.3663876951,0.088975668,-0.0896046609,0.3901561499,0.2198274881,0.1605475247,0.3285789788,0.2460654676,0.1037363186,-0.2389996946,-0.3663343489,0.0832588673,-0.0620273054,0.191442579,-0.0777534172,0.110681124,-0.2450100929,0.3476117253,-0.0138874138,0.0028704084,0.4750258625,-0.0642099977,0.2776764333,-0.155767858,-0.2877364457,0.0507540666,-0.2303261608,0.011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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2148","title":"Add configurable options to `seqeval` metric","comments":"Hi @marrodion. \r\n\r\nThanks for pointing this out. It would be great to incorporate this metric-specific enhancement.\r\n\r\nAnother possibility would be to require the user to input the scheme as a string `mode=\"strict\", scheme=\"IOB2\"` and then dynamically import the corresponding module using Python `importlib`:\r\n```python\r\nif scheme:\r\n    scheme = importlib.import_module(f\"seqeval.scheme.{scheme}\")\r\n```\r\n\r\nFeel free to create a Pull Request to make this contribution.","body":"Right now `load_metric(\"seqeval\")` only works in the default mode of evaluation (equivalent to conll evaluation).\r\n\r\nHowever, seqeval library [supports](https:\/\/github.com\/chakki-works\/seqeval#support-features) different evaluation schemes (IOB1, IOB2, etc.), which can be plugged in just by supporting additional kwargs in `Seqeval._compute`\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/85cf7ff920c90ca2e12bedca12b36d2a043c3da2\/metrics\/seqeval\/seqeval.py#L109\r\n\r\nThings that would be relevant are, for example, supporting `mode=\"strict\", scheme=IOB2` to count only full entity match as a true positive and omit partial matches.\r\n\r\nThe only problem I see is that the spirit of `metrics` seems to not require additional imports from user. `seqeval` only supports schemes as objects, without any string aliases. \r\n\r\nIt can be solved naively with mapping like `{\"IOB2\": seqeval.scheme.IOB2}`. Or just left as is and require user to explicitly import scheme from `seqeval` if he wants to configure it past the default implementation.\r\n\r\nIf that makes sense, I am happy to implement the change.","comment_length":61,"text":"Add configurable options to `seqeval` metric \n Right now `load_metric(\"seqeval\")` only works in the default mode of evaluation (equivalent to conll evaluation).\r\n\r\nHowever, seqeval library [supports](https:\/\/github.com\/chakki-works\/seqeval#support-features) different evaluation schemes (IOB1, IOB2, etc.), which can be plugged in just by supporting additional kwargs in `Seqeval._compute`\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/85cf7ff920c90ca2e12bedca12b36d2a043c3da2\/metrics\/seqeval\/seqeval.py#L109\r\n\r\nThings that would be relevant are, for example, supporting `mode=\"strict\", scheme=IOB2` to count only full entity match as a true positive and omit partial matches.\r\n\r\nThe only problem I see is that the spirit of `metrics` seems to not require additional imports from user. `seqeval` only supports schemes as objects, without any string aliases. \r\n\r\nIt can be solved naively with mapping like `{\"IOB2\": seqeval.scheme.IOB2}`. Or just left as is and require user to explicitly import scheme from `seqeval` if he wants to configure it past the default implementation.\r\n\r\nIf that makes sense, I am happy to implement the change. \n Hi @marrodion. \r\n\r\nThanks for pointing this out. It would be great to incorporate this metric-specific enhancement.\r\n\r\nAnother possibility would be to require the user to input the scheme as a string `mode=\"strict\", scheme=\"IOB2\"` and then dynamically import the corresponding module using Python `importlib`:\r\n```python\r\nif scheme:\r\n    scheme = importlib.import_module(f\"seqeval.scheme.{scheme}\")\r\n```\r\n\r\nFeel free to create a Pull Request to make this contribution.","embeddings":[-0.4404146075,0.1902088523,-0.0845154896,-0.1625581235,0.0749471933,-0.1645829231,0.1490213871,0.249832496,-0.0836082622,0.3530944288,-0.4670089781,0.2486553341,-0.0323203169,0.2693849504,0.0718923658,0.3165939152,-0.2818853259,-0.0426247083,0.0006639335,0.1087301672,-0.552981019,-0.0117782867,-0.1030309796,-0.15207313,0.0609822944,0.2906382382,0.0560519472,-0.022627607,-0.1254418641,-0.5280546546,0.0175919458,0.4460298717,-0.2212209105,-0.0601568706,-0.0001078836,-0.1581130475,0.1689354926,-0.2260208726,-0.209033519,-0.0844341889,-0.3633291125,-0.291166991,0.306224227,-0.2748051286,0.0388805196,0.2660091817,-0.0165568199,-0.1161660179,0.0370270275,0.1991532445,0.1833565831,0.1182956547,-0.1218209118,-0.0621905066,-0.209774971,-0.0393513143,-0.1454713047,0.3663655221,0.082575649,0.226062566,-0.1475075483,0.0548125058,-0.0825342089,-0.242446214,0.2761701941,-0.1193725616,0.6835107803,-0.0490721017,-0.0101480577,0.3530786335,0.2921997607,-0.1381694674,-0.3030595481,-0.1382803172,0.0993463695,-0.6759464741,0.1466280818,-0.096086137,-0.1030868888,-0.0826961473,-0.1479647011,-0.033458773,-0.3022654653,0.0747025311,-0.2574523985,0.3939167857,0.1031625718,-0.0649499893,0.3541351259,-0.0873658583,-0.2809019983,0.1270287186,-0.0665891171,0.1656541824,-0.7408687472,-0.1477435529,-0.0183482822,-0.1815031767,0.2883767486,0.1682549715,0.1724751145,0.4368014336,0.2517397404,0.3541308939,-0.021023307,0.343251586,0.3131861091,-0.0112347333,0.2474581152,0.0248375814,0.3714899421,-0.0004124012,0.0090165222,-0.659924984,0.0679211393,0.1159389913,-0.1485631764,-0.2398644835,-0.4179399014,-0.1582226902,-0.0586348698,-0.1113006771,0.5018102527,0.123510316,-0.1155994385,-0.0895082802,0.0192277562,0.4378791749,-0.1458706856,-0.0979548097,0.016352592,0.0411669351,0.204576686,0.0621869266,0.2487519979,-0.3678162396,0.1303182095,-0.0490685068,0.4444544017,-0.1659912616,0.1460129023,0.1006266922,-0.2069736719,0.0788970739,-0.1332622319,-0.421855092,0.0916285068,-0.2899059653,-0.4019014537,-0.0714191794,-0.1759268045,-0.4768915176,0.0616531745,0.2252954692,-0.5246810317,-0.0130113326,-0.008132752,0.8326738477,-0.3221261501,0.0141438488,-0.0476214364,0.1276525706,-0.4847742617,-0.0889536738,0.5480530858,0.3216397464,-0.22203134,-0.2398311794,-0.2551134527,-0.0794141144,-0.2599944174,-0.2022042722,-0.0602394193,0.0837334469,0.0311896801,-0.0284743086,0.7662084699,-0.4222574532,-0.0210452862,-0.0803242028,0.1119456962,-0.2272862643,0.1323760301,-0.0373109914,0.3576788902,0.0049101464,0.0843078047,0.2205335498,0.1616144478,-0.261389941,-0.2668775916,-0.2711868584,-0.2316744179,0.1784901768,0.3956591189,-0.0351171643,0.320366621,-0.0065064873,-0.2160911262,-0.0357711166,0.1151763126,0.0618478023,0.2012492418,-0.1130905524,0.0918779001,-0.1234125197,0.060049627,0.2182962447,-0.0904197395,0.0480323285,0.121668376,-0.1127960607,-0.4679356515,0.0559176914,-0.1898509711,0.055529885,0.2089476287,-0.3269338012,-0.0342498571,0.1159047559,0.0020938292,0.0913385451,0.2793194056,0.0862990469,-0.0139891878,-0.0003465335,0.211473912,0.0567865744,0.2087517381,0.4165763259,0.1913335472,0.2593639791,-0.109164238,0.4110058844,-0.050290972,0.1841282696,-0.0150859347,0.5559138656,0.0683776066,0.0065893224,0.0519290306,-0.051652614,0.0611868091,-0.0641518161,-0.2367987633,0.5868170857,-0.0794756785,0.2356154472,-0.3424955904,0.0648828372,0.1319375932,-0.095331423,-0.552742064,-0.3062807024,0.1004475355,-0.0132671436,0.2669738233,0.0566559285,0.0582371354,-0.3772333264,-0.1556093544,0.1259270161,0.1413586736,0.1581708342,0.0661022961,-0.2052513212,0.0382201746,-0.4662923217,0.2903058827,0.215023607,0.0154534616,-0.0470344312,-0.2343880236,-0.0794960707,0.3340309262,0.1572770625,0.0158218406,0.2130637616,0.3205015659,-0.2190813273,-0.3135822415,-0.0800573677,0.0409122705,-0.042243503,-0.3572460115,-0.094319433,-0.1978915185,0.2450003624,0.1717701554,0.0460357703,-0.202575013,-0.4122638106,0.5190328956,0.2028549761,-0.1720626652,0.1802853495,-0.1151275709,0.3317414224,-0.0443053022,-0.4356972575,-0.1409579813,-0.1817716956,0.0940515399,0.0956959128,0.0057353685,0.1029757261,0.3749670088,-0.0675891861,0.2991303205,0.0355553702,-0.6364486814,-0.0239804611,0.2268261462,0.5722032785,0.1552384496,-0.4056130052,-0.0692542568,-0.0509045385,0.2231383026,0.2037607431,-0.0061180177,0.4136137068,-0.0609643981,0.2502992749,-0.2219782174,0.0178008229,0.1000242978,-0.5327192545,0.2453567982,0.0594392419,-0.0078855697,0.0686219111,0.1769714504,0.0562076718,0.1112414449,0.1771620363,-0.1424997002,0.1927740872,0.4930687547,-0.2048598081,-0.0341397859,0.0772614628,-0.278519243,0.4551796317,0.0798746943,-0.1620344967,-0.5353206992,0.3236708641,0.2179788947,-0.0605301745,-0.4171498716,0.0311844852,0.1809620559,0.0233632885,-0.0555088222,-0.1814023256,0.2989302874,0.1583503783,0.1338731498,-0.0115191266,0.216440931,0.1655124575,0.0562133566,-0.0208813697,0.2980598509,0.0986942351,0.0352460034,0.2259937972,0.1511306763,-0.0841050372,0.1994831413,0.34873119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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2146","title":"Dataset file size on disk is very large with 3D Array","comments":"Hi ! In the arrow file we store all the integers as uint8.\r\nSo your arrow file should weigh around `height x width x n_channels x n_images` bytes.\r\n\r\nWhat feature type do your TFDS dataset have ?\r\n\r\nIf it uses a `tfds.features.Image` type, then what is stored is the encoded data (as png or jpg for example). Since these encodings are made for compression, the resulting tfrecord is smaller that the arrow file.\r\n\r\nWe are working on adding a similar feature in `datasets`: the ability to store the encoded data instead of the raw integers for images, but also for audio data. This way, arrow files will have similar sizes as tfrecords for images.","body":"Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n","comment_length":114,"text":"Dataset file size on disk is very large with 3D Array \n Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n \n Hi ! In the arrow file we store all the integers as uint8.\r\nSo your arrow file should weigh around `height x width x n_channels x n_images` bytes.\r\n\r\nWhat feature type do your TFDS dataset have ?\r\n\r\nIf it uses a `tfds.features.Image` type, then what is stored is the encoded data (as png or jpg for example). Since these encodings are made for compression, the resulting tfrecord is smaller that the arrow file.\r\n\r\nWe are working on adding a similar feature in `datasets`: the ability to store the encoded data instead of the raw integers for images, but also for audio data. This way, arrow files will have similar sizes as tfrecords for 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2146","title":"Dataset file size on disk is very large with 3D Array","comments":"Thanks for the prompt response. You're right about the encoding, I have the `tfds.features.Image` feature type you mentioned.\r\nHowever, as described in the `dataset_info.json`, my dataset is made of 1479 (224x224x3) images. 1479 x 224 x 224 x 3 = 222630912 bytes which is far from the actual size 520803408 bytes. \r\n\r\nAnyway I look forward to the Image feature type in `datasets`. ","body":"Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n","comment_length":62,"text":"Dataset file size on disk is very large with 3D Array \n Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n \n Thanks for the prompt response. You're right about the encoding, I have the `tfds.features.Image` feature type you mentioned.\r\nHowever, as described in the `dataset_info.json`, my dataset is made of 1479 (224x224x3) images. 1479 x 224 x 224 x 3 = 222630912 bytes which is far from the actual size 520803408 bytes. \r\n\r\nAnyway I look forward to the Image feature type in `datasets`. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2146","title":"Dataset file size on disk is very large with 3D Array","comments":"@lhoestq I changed the data structure so I have a  2D Array feature type instead of a 3D Array by grouping the two last dimensions ( a 224x672 2D Array instead of a 224x224x3 3D Array). The file size is now  223973964 bytes, nearly half the previous size! Which is around of what I would expect.\r\nI found similar behavior in existing `datasets` collection, when comparing black and white vs color image, for example MNIST vs CIFAR. ","body":"Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n","comment_length":77,"text":"Dataset file size on disk is very large with 3D Array \n Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n \n @lhoestq I changed the data structure so I have a  2D Array feature type instead of a 3D Array by grouping the two last dimensions ( a 224x672 2D Array instead of a 224x224x3 3D Array). The file size is now  223973964 bytes, nearly half the previous size! Which is around of what I would expect.\r\nI found similar behavior in existing `datasets` collection, when comparing black and white vs color image, for example MNIST vs CIFAR. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2146","title":"Dataset file size on disk is very large with 3D Array","comments":"Interesting !\r\nThis may be because of the offsets that are stored with the array data.\r\n\r\nCurrently the offsets are stored even if the `shape` of the arrays is fixed. This was needed because of some issues with pyarrow a few months ago. I think these issues have been addressed now, so we can probably try to remove them to make the file lighter.\r\n\r\nIdeally in your case the floats data should be 220 MB for both Array2D and Array3D","body":"Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n","comment_length":80,"text":"Dataset file size on disk is very large with 3D Array \n Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n \n Interesting !\r\nThis may be because of the offsets that are stored with the array data.\r\n\r\nCurrently the offsets are stored even if the `shape` of the arrays is fixed. This was needed because of some issues with pyarrow a few months ago. I think these issues have been addressed now, so we can probably try to remove them to make the file lighter.\r\n\r\nIdeally in your case the floats data should be 220 MB for both Array2D and Array3D","embeddings":[-0.1452738494,-0.1132628769,-0.1544179171,0.4201557934,0.2135634124,0.1139131263,0.5066809654,0.2730783522,0.0205232408,0.0358475372,-0.1937043518,0.0874153972,-0.1694584191,0.3150443435,0.0677503124,0.1006881595,-0.0745178536,0.2513983548,-0.1334785819,-0.0857200548,-0.0097005265,0.0191179775,-0.0179097727,-0.1838068217,-0.0615620911,-0.1613830328,0.0913496315,-0.0241849087,-0.0830982178,0.0797271058,0.2449781299,-0.0880117193,0.1781996787,0.5363877416,-0.0001173844,0.0502765737,0.108294405,-0.1655321717,0.0791868642,0.2893508673,-0.0925560445,-0.4095061719,-0.0746671557,-0.3576083779,0.0769588351,-0.3249549866,-0.1521175504,-0.1535450816,0.1848900914,0.0791001096,0.2110456824,0.1533048898,-0.0271132421,0.415045768,0.0993782207,0.5338887572,-0.1533458233,0.1268558055,-0.1435388923,0.4372014105,0.084297426,0.0795503631,0.1613585651,0.2877249718,0.5040504932,-0.055517599,0.2392112762,-0.246191889,0.1298937201,0.4001127183,0.8583656549,-0.2007374316,-0.1842802763,-0.3255720437,-0.1083301753,-0.0323392935,0.0225410033,0.3800633848,-0.049889937,0.021931123,-0.6522023082,-0.1392243952,-0.2269416898,0.1455018222,-0.2136535794,-0.4809675813,-0.1380903423,0.1503390074,-0.1890008897,-0.2658364773,0.2607783675,-0.3955922127,0.0734376535,-0.0997146145,-0.2659658492,-0.2101803869,-0.2905137539,-0.3803426623,0.3530271351,-0.1538060755,-0.0108280405,-0.151993528,-0.364893049,-0.1477414966,0.1039847285,0.3860372305,-0.4768467844,-0.138597995,-0.1418677419,-0.2950907052,-0.0408817492,-0.0522949062,-0.2465380579,-0.0907576829,0.1986332238,-0.4835402966,0.0639369488,-0.0618666336,0.0446808524,0.0238069706,0.0864812881,-0.0952553973,-0.075162895,0.1316531152,0.1410508007,0.135276109,-0.1400613934,-0.039203234,-0.1859151274,0.0416380167,-0.1229669973,-0.1046110019,0.0626311079,-0.081755273,0.1144735962,0.1505685151,0.052291356,-0.1904567331,0.0903884694,0.2001003176,0.2624739408,-0.3473196626,0.4073754847,0.3550085127,0.1589064002,0.1983184069,-0.2854207754,0.1398651004,-0.1997080892,0.4944943488,-0.5467603207,-0.0453600101,-0.4173412025,0.0906217396,-0.2234345526,-0.0543203168,-0.5728366375,0.0097432947,0.3395531476,-0.1186216325,0.0678242967,-0.2173455358,-0.3785401583,-0.294359833,-0.0371542424,0.0312655084,-0.7563121319,0.2612313628,-0.1514395922,0.0817364231,0.1952205747,0.5952161551,-0.0899516866,0.4252856076,-0.4255605638,-0.0484716371,0.0430041216,-0.0384001322,-0.6611084342,0.487341702,0.0706712455,0.0485031083,0.1124144495,0.320182234,0.1636859328,-0.016375415,-0.2228286564,0.3266060352,0.0910482332,0.0426690839,-0.1908326745,-0.3811230659,0.0882390067,0.0459806919,-0.1374954879,-0.1405356973,0.1314450502,0.0834871456,0.2107224613,-0.1292201728,0.0360603482,0.3705856502,0.2096632719,-0.2556730807,-0.0215890892,0.1423658431,-0.6317534447,0.1833813041,0.2472616285,-0.4363888502,0.1557535678,-0.1044431329,0.0382063761,-0.2296464294,-0.0895897746,0.2013011128,0.0237026345,-0.0005763058,-0.0091966204,-0.0104556819,-0.0085912012,-0.147752732,0.1148846447,-0.1881847829,-0.4114223123,0.356153816,0.213565439,-0.0350419208,-0.1056866199,-0.078861475,-0.3569892943,-0.341083467,-0.1865350902,0.0800963417,0.2511079609,0.1212477982,-0.0773310214,0.4837380052,0.2234310806,0.0761163831,0.3378689587,-0.1210732833,0.0945777297,-0.2206157446,-0.2020530403,0.216520682,-0.1918247491,0.0659026727,0.0202879235,-0.3593147099,0.2857810557,0.1333052814,0.1050533727,-0.2576690316,-0.0769441724,0.1929026395,0.3467417359,0.2029425353,0.1076560989,0.0467540659,0.6398187876,-0.1667219102,0.1070185304,0.364521265,-0.1265423596,-0.0783273652,0.1058712453,0.2452514023,0.5257766247,0.217031315,0.045964893,-0.0413736738,0.4925529659,-0.1443800777,0.0811916888,0.0678964555,0.5235673785,0.2810914516,0.1602386236,0.0027567402,-0.284455806,-0.1133067086,0.2043940723,0.2465661764,-0.303352654,0.1999263167,-0.1631348729,-0.2681356966,-0.0948000997,0.1902986467,-0.060111925,0.1248413771,-0.3277061582,-0.1944479644,0.0251019541,-0.0797675923,0.1439219266,0.0847972631,0.2937849462,-0.0679315031,0.2701685727,0.3433182836,-0.0878251344,0.0354879275,0.4142909348,-0.1485609859,0.3000029325,-0.1635025293,0.2096656561,-0.138071239,-0.2516806722,0.1204532534,0.0605554953,0.0706452951,-0.109574303,0.2740660906,-0.1995595098,0.2018909901,0.0286068246,0.0493995771,-0.2603569627,0.0002532348,-0.0481878854,-0.1286520362,-0.2174704075,0.0934461653,-0.2839459479,-0.050991375,0.2078645676,0.1816901416,0.2575588822,0.3705631793,0.1723432094,0.0412281081,0.1493278593,-0.0243310146,-0.1940806955,-0.4591633081,0.1459610909,-0.0282914434,-0.3725241125,0.1432911903,0.0114656771,0.192036286,0.2045514584,-0.6480360627,-0.0394913256,-0.293095082,0.0378666818,-0.17624484,0.1218835488,0.0200794712,-0.2159727663,0.0826531723,-0.0400286093,-0.1227325797,-0.0801262707,0.1514735818,0.1209952906,0.127742365,0.5383259654,0.2677577734,0.410494417,0.1294866651,-0.3016063273,0.1248357072,-0.1209927276,0.3700109422,-0.2481317222,0.0517883822,0.3423662782,0.1794074625,-0.201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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2146","title":"Dataset file size on disk is very large with 3D Array","comments":"Yeah for sure, can you be a bit more specific about where the offset is stored in the code base ? And any reference to pyarrow issues if you have some. I would be very interested in contributing to `datasets` by trying to fix this issue. ","body":"Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n","comment_length":46,"text":"Dataset file size on disk is very large with 3D Array \n Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n \n Yeah for sure, can you be a bit more specific about where the offset is stored in the code base ? And any reference to pyarrow issues if you have some. I would be very interested in contributing to `datasets` by trying to fix this issue. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2146","title":"Dataset file size on disk is very large with 3D Array","comments":"Pyarrow has two types of lists: variable length lists and fixed size lists.\r\nCurrently we store the ArrayXD data as variable length lists. They take more disk space because they must store both actual data and offsets.\r\nIn the `datasets` code this is done here:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/dbac87c8a083f806467f5afc4ec9b401a7e4c15c\/src\/datasets\/features.py#L346-L352\r\n\r\nTo use a fixed length list, one should use the `list_size` argument of `pyarrow.list_()`.\r\nI believe this would work directly modulo some changes in the numpy conversion here:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/dbac87c8a083f806467f5afc4ec9b401a7e4c15c\/src\/datasets\/features.py#L381-L395","body":"Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n","comment_length":75,"text":"Dataset file size on disk is very large with 3D Array \n Hi, \r\n\r\nI have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. \r\n\r\nThe actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. \r\n\r\n`{\r\n    \"description\": \"\",\r\n    \"citation\": \"\",\r\n    \"homepage\": \"\",\r\n    \"license\": \"\",\r\n    \"features\": {\r\n        \"image\": {\r\n            \"shape\": [224, 224, 3],\r\n            \"dtype\": \"uint8\",\r\n            \"id\": null,\r\n            \"_type\": \"Array3D\",\r\n        }\r\n    },\r\n    \"post_processed\": null,\r\n    \"supervised_keys\": null,\r\n    \"builder_name\": \"shot_type_image_dataset\",\r\n    \"config_name\": \"default\",\r\n    \"version\": {\r\n        \"version_str\": \"0.0.0\",\r\n        \"description\": null,\r\n        \"major\": 0,\r\n        \"minor\": 0,\r\n        \"patch\": 0,\r\n    },\r\n    \"splits\": {\r\n        \"train\": {\r\n            \"name\": \"train\",\r\n            \"num_bytes\": 520803408,\r\n            \"num_examples\": 1479,\r\n            \"dataset_name\": \"shot_type_image_dataset\",\r\n        }\r\n    },\r\n    \"download_checksums\": {\r\n        \"\": {\r\n            \"num_bytes\": 16940447118,\r\n            \"checksum\": \"5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03\",\r\n        }\r\n    },\r\n    \"download_size\": 16940447118,\r\n    \"post_processing_size\": null,\r\n    \"dataset_size\": 520803408,\r\n    \"size_in_bytes\": 17461250526,\r\n}`\r\n\r\nI have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.\r\n\r\nI am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.\r\n\r\nThis might be a problem for large dataset. \r\n\r\nThanks for your help. \r\n \n Pyarrow has two types of lists: variable length lists and fixed size lists.\r\nCurrently we store the ArrayXD data as variable length lists. They take more disk space because they must store both actual data and offsets.\r\nIn the `datasets` code this is done here:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/dbac87c8a083f806467f5afc4ec9b401a7e4c15c\/src\/datasets\/features.py#L346-L352\r\n\r\nTo use a fixed length list, one should use the `list_size` argument of `pyarrow.list_()`.\r\nI believe this would work directly modulo some changes in the numpy conversion here:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/dbac87c8a083f806467f5afc4ec9b401a7e4c15c\/src\/datasets\/features.py#L381-L395","embeddings":[-0.1452738494,-0.1132628769,-0.1544179171,0.4201557934,0.2135634124,0.1139131263,0.5066809654,0.2730783522,0.0205232408,0.0358475372,-0.1937043518,0.0874153972,-0.1694584191,0.3150443435,0.0677503124,0.1006881595,-0.0745178536,0.2513983548,-0.1334785819,-0.0857200548,-0.0097005265,0.0191179775,-0.0179097727,-0.1838068217,-0.0615620911,-0.1613830328,0.0913496315,-0.0241849087,-0.0830982178,0.0797271058,0.2449781299,-0.0880117193,0.1781996787,0.5363877416,-0.0001173844,0.0502765737,0.108294405,-0.1655321717,0.0791868642,0.2893508673,-0.0925560445,-0.4095061719,-0.0746671557,-0.3576083779,0.0769588351,-0.3249549866,-0.1521175504,-0.1535450816,0.1848900914,0.0791001096,0.2110456824,0.1533048898,-0.0271132421,0.415045768,0.0993782207,0.5338887572,-0.1533458233,0.1268558055,-0.1435388923,0.4372014105,0.084297426,0.0795503631,0.1613585651,0.2877249718,0.5040504932,-0.055517599,0.2392112762,-0.246191889,0.1298937201,0.4001127183,0.8583656549,-0.2007374316,-0.1842802763,-0.3255720437,-0.1083301753,-0.0323392935,0.0225410033,0.3800633848,-0.049889937,0.021931123,-0.6522023082,-0.1392243952,-0.2269416898,0.1455018222,-0.2136535794,-0.4809675813,-0.1380903423,0.1503390074,-0.1890008897,-0.2658364773,0.2607783675,-0.3955922127,0.0734376535,-0.0997146145,-0.2659658492,-0.2101803869,-0.2905137539,-0.3803426623,0.3530271351,-0.1538060755,-0.0108280405,-0.151993528,-0.364893049,-0.1477414966,0.1039847285,0.3860372305,-0.4768467844,-0.138597995,-0.1418677419,-0.2950907052,-0.0408817492,-0.0522949062,-0.2465380579,-0.0907576829,0.1986332238,-0.4835402966,0.0639369488,-0.0618666336,0.0446808524,0.0238069706,0.0864812881,-0.0952553973,-0.075162895,0.1316531152,0.1410508007,0.135276109,-0.1400613934,-0.039203234,-0.1859151274,0.0416380167,-0.1229669973,-0.1046110019,0.0626311079,-0.081755273,0.1144735962,0.1505685151,0.052291356,-0.1904567331,0.0903884694,0.2001003176,0.2624739408,-0.3473196626,0.4073754847,0.3550085127,0.1589064002,0.1983184069,-0.2854207754,0.1398651004,-0.1997080892,0.4944943488,-0.5467603207,-0.0453600101,-0.4173412025,0.0906217396,-0.2234345526,-0.0543203168,-0.5728366375,0.0097432947,0.3395531476,-0.1186216325,0.0678242967,-0.2173455358,-0.3785401583,-0.294359833,-0.0371542424,0.0312655084,-0.7563121319,0.2612313628,-0.1514395922,0.0817364231,0.1952205747,0.5952161551,-0.0899516866,0.4252856076,-0.4255605638,-0.0484716371,0.0430041216,-0.0384001322,-0.6611084342,0.487341702,0.0706712455,0.0485031083,0.1124144495,0.320182234,0.1636859328,-0.016375415,-0.2228286564,0.3266060352,0.0910482332,0.0426690839,-0.1908326745,-0.3811230659,0.0882390067,0.0459806919,-0.1374954879,-0.1405356973,0.1314450502,0.0834871456,0.2107224613,-0.1292201728,0.0360603482,0.3705856502,0.2096632719,-0.2556730807,-0.0215890892,0.1423658431,-0.6317534447,0.1833813041,0.2472616285,-0.4363888502,0.1557535678,-0.1044431329,0.0382063761,-0.2296464294,-0.0895897746,0.2013011128,0.0237026345,-0.0005763058,-0.0091966204,-0.0104556819,-0.0085912012,-0.147752732,0.1148846447,-0.1881847829,-0.4114223123,0.356153816,0.213565439,-0.0350419208,-0.1056866199,-0.078861475,-0.3569892943,-0.341083467,-0.1865350902,0.0800963417,0.2511079609,0.1212477982,-0.0773310214,0.4837380052,0.2234310806,0.0761163831,0.3378689587,-0.1210732833,0.0945777297,-0.2206157446,-0.2020530403,0.216520682,-0.1918247491,0.0659026727,0.0202879235,-0.3593147099,0.2857810557,0.1333052814,0.1050533727,-0.2576690316,-0.0769441724,0.1929026395,0.3467417359,0.2029425353,0.1076560989,0.0467540659,0.6398187876,-0.1667219102,0.1070185304,0.364521265,-0.1265423596,-0.0783273652,0.1058712453,0.2452514023,0.5257766247,0.217031315,0.045964893,-0.0413736738,0.4925529659,-0.1443800777,0.0811916888,0.0678964555,0.5235673785,0.2810914516,0.1602386236,0.0027567402,-0.284455806,-0.1133067086,0.2043940723,0.2465661764,-0.303352654,0.1999263167,-0.1631348729,-0.2681356966,-0.0948000997,0.1902986467,-0.060111925,0.1248413771,-0.3277061582,-0.1944479644,0.0251019541,-0.0797675923,0.1439219266,0.0847972631,0.2937849462,-0.0679315031,0.2701685727,0.3433182836,-0.0878251344,0.0354879275,0.4142909348,-0.1485609859,0.3000029325,-0.1635025293,0.2096656561,-0.138071239,-0.2516806722,0.1204532534,0.0605554953,0.0706452951,-0.109574303,0.2740660906,-0.1995595098,0.2018909901,0.0286068246,0.0493995771,-0.2603569627,0.0002532348,-0.0481878854,-0.1286520362,-0.2174704075,0.0934461653,-0.2839459479,-0.050991375,0.2078645676,0.1816901416,0.2575588822,0.3705631793,0.1723432094,0.0412281081,0.1493278593,-0.0243310146,-0.1940806955,-0.4591633081,0.1459610909,-0.0282914434,-0.3725241125,0.1432911903,0.0114656771,0.192036286,0.2045514584,-0.6480360627,-0.0394913256,-0.293095082,0.0378666818,-0.17624484,0.1218835488,0.0200794712,-0.2159727663,0.0826531723,-0.0400286093,-0.1227325797,-0.0801262707,0.1514735818,0.1209952906,0.127742365,0.5383259654,0.2677577734,0.410494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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2144","title":"Loading wikipedia 20200501.en throws pyarrow related error","comments":"That's how I loaded the dataset\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n```","body":"**Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n","comment_length":17,"text":"Loading wikipedia 20200501.en throws pyarrow related error \n **Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n \n That's how I loaded the dataset\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n```","embeddings":[-0.1041540205,0.3526462018,0.0340582058,0.3633103967,0.2745778561,0.1802078485,0.2762243748,0.4623250365,-0.04348813,-0.113784872,0.0247374158,0.236021921,0.1520678252,-0.1178016663,0.1841091067,-0.1683138758,0.0038767338,0.0068803728,0.0847920552,0.1328444481,-0.2001205236,0.1146743447,-0.1824860871,0.1555170566,-0.5857800245,0.0881166533,-0.0660176575,0.0540414415,0.0833243653,-0.613804698,0.3904819489,-0.0500538088,0.2010909319,0.2121308595,-0.0001204597,0.0749956965,0.4643647373,-0.0041334573,-0.2960134745,-0.0811445788,0.186580047,-0.0205053128,0.3180885315,-0.3607697487,0.1008782089,-0.2803391814,0.2242598385,0.0471996665,0.1832180023,0.0086322986,0.168285802,-0.1830526292,0.3375393748,0.0306443255,0.7112432122,-0.1374686658,0.0815054998,0.4424607456,0.1225971282,-0.1607152224,-0.186434865,0.23629798,-0.2502577007,0.1203090623,0.2671839893,-0.0770430341,0.3848769963,-0.2004953474,0.3921535015,0.2679577172,0.7762554288,-0.4092206359,0.1730330735,-0.2313123494,-0.0328712203,-0.0229594614,0.0938252583,0.2307374179,-0.27676934,0.0545302071,0.1571857184,-0.1188708618,-0.2609159052,0.301315248,-0.0642476678,0.1997214258,0.1399375498,0.3910686672,0.0163352489,-0.2268839478,0.0019353561,0.0632643551,0.1672586203,0.2208643556,-0.3931137919,0.107257776,-0.0683028772,0.1139047593,0.3778621256,-0.1693618745,-0.021596102,0.0659415349,0.2391710877,0.2064648122,0.4192265868,0.1241850182,-0.3237407506,-0.2987220883,0.2721662819,0.1988998652,-0.254214257,-0.0364414603,-0.0612123869,-0.1137952879,0.1385727972,-0.1709839702,0.4291738868,-0.2699913085,-0.220579192,0.1195769534,-0.1446794569,-0.0715681612,-0.1552301943,0.3867201805,-0.0916787088,0.2424993664,0.0672103316,0.0622379445,-0.1821437031,-0.1878142953,0.0057542627,0.1186774671,-0.2124670595,0.1797935814,0.2939305305,-0.1538039595,0.2583542168,0.1435221881,-0.1506101638,-0.1206758171,0.1700648069,0.0130382087,-0.2326727659,0.309833169,0.0075005512,0.2468717843,-0.0328951664,-0.1449694484,-0.1790345013,0.3182585537,-0.2848282754,-0.5152549744,-0.262249738,0.1209049523,-0.0116632255,0.0844510719,0.1616870016,0.0576540641,0.4413489699,-0.5136592984,-0.1003230512,-0.1257850081,-0.1855283529,-0.4527531266,0.1706373543,0.3363130093,-0.5569394231,0.0798316821,-0.0507644974,0.0824038908,0.2173671722,0.4450024068,-0.2298555672,0.1757664233,-0.1654862761,0.1924015433,0.1095912755,-0.3121576309,-0.5411019325,0.2140131146,-0.0612866841,0.1959047168,0.0259571318,0.2906033099,0.0093369419,0.1717624664,0.4052648544,0.2492511719,-0.0467647091,0.0787921399,-0.4085267782,-0.3879698217,0.3385632038,0.2016654164,-0.0047450308,-0.1988982856,0.1968246847,0.3221473992,0.2584798336,-0.2035795897,0.1287960708,0.0218389891,0.0705177039,-0.0129466308,0.2327663898,-0.2238298804,-0.4262207448,0.11144609,-0.3431405723,-0.0235893708,-0.3043866754,-0.361228615,-0.5231873989,0.0663694814,-0.171464175,-0.2400977015,0.115050301,-0.0613334365,0.0953495279,0.2569484115,0.1027859375,-0.0896757692,-0.2042666227,-0.0175576173,-0.4030902386,0.240516305,-0.308798492,-0.1786494255,-0.0221906919,0.2451284826,0.324608773,0.1002937332,0.0431751907,0.1558428705,0.1467771679,0.0931873098,-0.1324361414,-0.0177001171,0.2272260636,-0.2668777108,0.2040083557,0.1240440756,0.1868998408,-0.221017614,0.05095274,0.1322418749,-0.0019998513,0.1718758643,-0.0822376162,-0.0712248981,0.1570295691,-0.0034186426,0.3967843354,-0.1355208606,0.2599032819,0.4711607099,0.0430019759,0.1037084162,-0.0057747457,0.0103615662,0.1143461317,-0.0471609905,0.1531215012,0.1461279243,-0.3416619301,-0.2851566672,0.2188828588,-0.1442351341,0.0782735273,0.0937128961,0.0280694515,0.1398488432,-0.0689809099,-0.195103392,0.1316027343,-0.1178800836,0.4590720534,0.310067445,0.3028900325,-0.029343931,-0.3765672147,0.0593944713,0.0831138715,0.379365176,-0.3040897548,0.0794889852,-0.171332255,-0.243459627,-0.0772669688,-0.013044945,-0.2915118039,-0.317315042,0.0815705061,0.5144311786,0.0328679904,0.0099860048,-0.0540035702,-0.0480350479,0.0796879381,-0.1308909357,-0.0505061187,-0.7057309747,-0.3859112859,0.0051475652,0.2574861944,-0.1339800954,0.2502458096,-0.0254044235,-0.1927474737,-0.3766916692,-0.5513484478,-0.0036723248,-0.1104055196,0.3178443015,0.0176331513,0.3957713842,0.0727890357,-0.2152879387,0.2103869319,0.0130584994,-0.0248649139,0.2813790739,-0.3631691337,0.0844142213,-0.0089278743,-0.2284815609,-0.2832888961,-0.1489331275,0.1993967593,0.0630166978,0.1870315522,0.2383005023,0.0628918335,0.1012383327,0.2758947015,0.0676462352,-0.3717182577,-0.1608327031,0.3060976565,-0.2184316963,-0.2773063481,0.1912308037,0.002722593,0.1241865531,0.2216507792,-0.5899142623,0.0072065173,0.0857219473,0.3271362782,0.031964194,-0.1462098956,0.2367205918,0.0211273748,0.0012416313,0.062059287,0.0056531909,-0.1206239983,-0.1537077129,0.166241616,0.1140279919,0.2094682604,0.1647175252,0.7836135626,0.0867718309,0.0424960814,0.4263213873,-0.188306883,0.0635278597,-0.1462810636,-0.0966464877,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2144","title":"Loading wikipedia 20200501.en throws pyarrow related error","comments":"Hi ! It looks like the arrow file in the folder\r\n`\/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted.\r\n\r\nCan you take a look and check that it's 18.3GB ?\r\n\r\nIf not, then maybe you need to redownload it:\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache', download_mode=\"force_redownload\")\r\n```","body":"**Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n","comment_length":46,"text":"Loading wikipedia 20200501.en throws pyarrow related error \n **Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n \n Hi ! It looks like the arrow file in the folder\r\n`\/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted.\r\n\r\nCan you take a look and check that it's 18.3GB ?\r\n\r\nIf not, then maybe you need to redownload it:\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache', download_mode=\"force_redownload\")\r\n```","embeddings":[-0.1041540205,0.3526462018,0.0340582058,0.3633103967,0.2745778561,0.1802078485,0.2762243748,0.4623250365,-0.04348813,-0.113784872,0.0247374158,0.236021921,0.1520678252,-0.1178016663,0.1841091067,-0.1683138758,0.0038767338,0.0068803728,0.0847920552,0.1328444481,-0.2001205236,0.1146743447,-0.1824860871,0.1555170566,-0.5857800245,0.0881166533,-0.0660176575,0.0540414415,0.0833243653,-0.613804698,0.3904819489,-0.0500538088,0.2010909319,0.2121308595,-0.0001204597,0.0749956965,0.4643647373,-0.0041334573,-0.2960134745,-0.0811445788,0.186580047,-0.0205053128,0.3180885315,-0.3607697487,0.1008782089,-0.2803391814,0.2242598385,0.0471996665,0.1832180023,0.0086322986,0.168285802,-0.1830526292,0.3375393748,0.0306443255,0.7112432122,-0.1374686658,0.0815054998,0.4424607456,0.1225971282,-0.1607152224,-0.186434865,0.23629798,-0.2502577007,0.1203090623,0.2671839893,-0.0770430341,0.3848769963,-0.2004953474,0.3921535015,0.2679577172,0.7762554288,-0.4092206359,0.1730330735,-0.2313123494,-0.0328712203,-0.0229594614,0.0938252583,0.2307374179,-0.27676934,0.0545302071,0.1571857184,-0.1188708618,-0.2609159052,0.301315248,-0.0642476678,0.1997214258,0.1399375498,0.3910686672,0.0163352489,-0.2268839478,0.0019353561,0.0632643551,0.1672586203,0.2208643556,-0.3931137919,0.107257776,-0.0683028772,0.1139047593,0.3778621256,-0.1693618745,-0.021596102,0.0659415349,0.2391710877,0.2064648122,0.4192265868,0.1241850182,-0.3237407506,-0.2987220883,0.2721662819,0.1988998652,-0.254214257,-0.0364414603,-0.0612123869,-0.1137952879,0.1385727972,-0.1709839702,0.4291738868,-0.2699913085,-0.220579192,0.1195769534,-0.1446794569,-0.0715681612,-0.1552301943,0.3867201805,-0.0916787088,0.2424993664,0.0672103316,0.0622379445,-0.1821437031,-0.1878142953,0.0057542627,0.1186774671,-0.2124670595,0.1797935814,0.2939305305,-0.1538039595,0.2583542168,0.1435221881,-0.1506101638,-0.1206758171,0.1700648069,0.0130382087,-0.2326727659,0.309833169,0.0075005512,0.2468717843,-0.0328951664,-0.1449694484,-0.1790345013,0.3182585537,-0.2848282754,-0.5152549744,-0.262249738,0.1209049523,-0.0116632255,0.0844510719,0.1616870016,0.0576540641,0.4413489699,-0.5136592984,-0.1003230512,-0.1257850081,-0.1855283529,-0.4527531266,0.1706373543,0.3363130093,-0.5569394231,0.0798316821,-0.0507644974,0.0824038908,0.2173671722,0.4450024068,-0.2298555672,0.1757664233,-0.1654862761,0.1924015433,0.1095912755,-0.3121576309,-0.5411019325,0.2140131146,-0.0612866841,0.1959047168,0.0259571318,0.2906033099,0.0093369419,0.1717624664,0.4052648544,0.2492511719,-0.0467647091,0.0787921399,-0.4085267782,-0.3879698217,0.3385632038,0.2016654164,-0.0047450308,-0.1988982856,0.1968246847,0.3221473992,0.2584798336,-0.2035795897,0.1287960708,0.0218389891,0.0705177039,-0.0129466308,0.2327663898,-0.2238298804,-0.4262207448,0.11144609,-0.3431405723,-0.0235893708,-0.3043866754,-0.361228615,-0.5231873989,0.0663694814,-0.171464175,-0.2400977015,0.115050301,-0.0613334365,0.0953495279,0.2569484115,0.1027859375,-0.0896757692,-0.2042666227,-0.0175576173,-0.4030902386,0.240516305,-0.308798492,-0.1786494255,-0.0221906919,0.2451284826,0.324608773,0.1002937332,0.0431751907,0.1558428705,0.1467771679,0.0931873098,-0.1324361414,-0.0177001171,0.2272260636,-0.2668777108,0.2040083557,0.1240440756,0.1868998408,-0.221017614,0.05095274,0.1322418749,-0.0019998513,0.1718758643,-0.0822376162,-0.0712248981,0.1570295691,-0.0034186426,0.3967843354,-0.1355208606,0.2599032819,0.4711607099,0.0430019759,0.1037084162,-0.0057747457,0.0103615662,0.1143461317,-0.0471609905,0.1531215012,0.1461279243,-0.3416619301,-0.2851566672,0.2188828588,-0.1442351341,0.0782735273,0.0937128961,0.0280694515,0.1398488432,-0.0689809099,-0.195103392,0.1316027343,-0.1178800836,0.4590720534,0.310067445,0.3028900325,-0.029343931,-0.3765672147,0.0593944713,0.0831138715,0.379365176,-0.3040897548,0.0794889852,-0.171332255,-0.243459627,-0.0772669688,-0.013044945,-0.2915118039,-0.317315042,0.0815705061,0.5144311786,0.0328679904,0.0099860048,-0.0540035702,-0.0480350479,0.0796879381,-0.1308909357,-0.0505061187,-0.7057309747,-0.3859112859,0.0051475652,0.2574861944,-0.1339800954,0.2502458096,-0.0254044235,-0.1927474737,-0.3766916692,-0.5513484478,-0.0036723248,-0.1104055196,0.3178443015,0.0176331513,0.3957713842,0.0727890357,-0.2152879387,0.2103869319,0.0130584994,-0.0248649139,0.2813790739,-0.3631691337,0.0844142213,-0.0089278743,-0.2284815609,-0.2832888961,-0.1489331275,0.1993967593,0.0630166978,0.1870315522,0.2383005023,0.0628918335,0.1012383327,0.2758947015,0.0676462352,-0.3717182577,-0.1608327031,0.3060976565,-0.2184316963,-0.2773063481,0.1912308037,0.002722593,0.1241865531,0.2216507792,-0.5899142623,0.0072065173,0.0857219473,0.3271362782,0.031964194,-0.1462098956,0.2367205918,0.0211273748,0.0012416313,0.062059287,0.0056531909,-0.1206239983,-0.1537077129,0.166241616,0.1140279919,0.2094682604,0.1647175252,0.7836135626,0.0867718309,0.0424960814,0.4263213873,-0.188306883,0.0635278597,-0.1462810636,-0.0966464877,0.0314848572,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2144","title":"Loading wikipedia 20200501.en throws pyarrow related error","comments":"> Hi ! It looks like the arrow file in the folder\r\n> `\/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted.\r\n> \r\n> Can you take a look and check that it's 18.3GB ?\r\n> \r\n> If not, then maybe you need to redownload it:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache', download_mode=\"force_redownload\")\r\n> ```\r\n\r\nHi Ihoestq, thanks for the reply! Actually i think my issue is i couldn't download the dataset beyond 10.7G. It feels like the whole dataset is split into different volumes and after the first one was downloaded it crashed before proceeding to the next one. I did try 'force_redownload' mode but still got the same issue.","body":"**Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n","comment_length":113,"text":"Loading wikipedia 20200501.en throws pyarrow related error \n **Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n \n > Hi ! It looks like the arrow file in the folder\r\n> `\/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted.\r\n> \r\n> Can you take a look and check that it's 18.3GB ?\r\n> \r\n> If not, then maybe you need to redownload it:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache', download_mode=\"force_redownload\")\r\n> ```\r\n\r\nHi Ihoestq, thanks for the reply! Actually i think my issue is i couldn't download the dataset beyond 10.7G. It feels like the whole dataset is split into different volumes and after the first one was downloaded it crashed before proceeding to the next one. I did try 'force_redownload' mode but still got the same issue.","embeddings":[-0.1041540205,0.3526462018,0.0340582058,0.3633103967,0.2745778561,0.1802078485,0.2762243748,0.4623250365,-0.04348813,-0.113784872,0.0247374158,0.236021921,0.1520678252,-0.1178016663,0.1841091067,-0.1683138758,0.0038767338,0.0068803728,0.0847920552,0.1328444481,-0.2001205236,0.1146743447,-0.1824860871,0.1555170566,-0.5857800245,0.0881166533,-0.0660176575,0.0540414415,0.0833243653,-0.613804698,0.3904819489,-0.0500538088,0.2010909319,0.2121308595,-0.0001204597,0.0749956965,0.4643647373,-0.0041334573,-0.2960134745,-0.0811445788,0.186580047,-0.0205053128,0.3180885315,-0.3607697487,0.1008782089,-0.2803391814,0.2242598385,0.0471996665,0.1832180023,0.0086322986,0.168285802,-0.1830526292,0.3375393748,0.0306443255,0.7112432122,-0.1374686658,0.0815054998,0.4424607456,0.1225971282,-0.1607152224,-0.186434865,0.23629798,-0.2502577007,0.1203090623,0.2671839893,-0.0770430341,0.3848769963,-0.2004953474,0.3921535015,0.2679577172,0.7762554288,-0.4092206359,0.1730330735,-0.2313123494,-0.0328712203,-0.0229594614,0.0938252583,0.2307374179,-0.27676934,0.0545302071,0.1571857184,-0.1188708618,-0.2609159052,0.301315248,-0.0642476678,0.1997214258,0.1399375498,0.3910686672,0.0163352489,-0.2268839478,0.0019353561,0.0632643551,0.1672586203,0.2208643556,-0.3931137919,0.107257776,-0.0683028772,0.1139047593,0.3778621256,-0.1693618745,-0.021596102,0.0659415349,0.2391710877,0.2064648122,0.4192265868,0.1241850182,-0.3237407506,-0.2987220883,0.2721662819,0.1988998652,-0.254214257,-0.0364414603,-0.0612123869,-0.1137952879,0.1385727972,-0.1709839702,0.4291738868,-0.2699913085,-0.220579192,0.1195769534,-0.1446794569,-0.0715681612,-0.1552301943,0.3867201805,-0.0916787088,0.2424993664,0.0672103316,0.0622379445,-0.1821437031,-0.1878142953,0.0057542627,0.1186774671,-0.2124670595,0.1797935814,0.2939305305,-0.1538039595,0.2583542168,0.1435221881,-0.1506101638,-0.1206758171,0.1700648069,0.0130382087,-0.2326727659,0.309833169,0.0075005512,0.2468717843,-0.0328951664,-0.1449694484,-0.1790345013,0.3182585537,-0.2848282754,-0.5152549744,-0.262249738,0.1209049523,-0.0116632255,0.0844510719,0.1616870016,0.0576540641,0.4413489699,-0.5136592984,-0.1003230512,-0.1257850081,-0.1855283529,-0.4527531266,0.1706373543,0.3363130093,-0.5569394231,0.0798316821,-0.0507644974,0.0824038908,0.2173671722,0.4450024068,-0.2298555672,0.1757664233,-0.1654862761,0.1924015433,0.1095912755,-0.3121576309,-0.5411019325,0.2140131146,-0.0612866841,0.1959047168,0.0259571318,0.2906033099,0.0093369419,0.1717624664,0.4052648544,0.2492511719,-0.0467647091,0.0787921399,-0.4085267782,-0.3879698217,0.3385632038,0.2016654164,-0.0047450308,-0.1988982856,0.1968246847,0.3221473992,0.2584798336,-0.2035795897,0.1287960708,0.0218389891,0.0705177039,-0.0129466308,0.2327663898,-0.2238298804,-0.4262207448,0.11144609,-0.3431405723,-0.0235893708,-0.3043866754,-0.361228615,-0.5231873989,0.0663694814,-0.171464175,-0.2400977015,0.115050301,-0.0613334365,0.0953495279,0.2569484115,0.1027859375,-0.0896757692,-0.2042666227,-0.0175576173,-0.4030902386,0.240516305,-0.308798492,-0.1786494255,-0.0221906919,0.2451284826,0.324608773,0.1002937332,0.0431751907,0.1558428705,0.1467771679,0.0931873098,-0.1324361414,-0.0177001171,0.2272260636,-0.2668777108,0.2040083557,0.1240440756,0.1868998408,-0.221017614,0.05095274,0.1322418749,-0.0019998513,0.1718758643,-0.0822376162,-0.0712248981,0.1570295691,-0.0034186426,0.3967843354,-0.1355208606,0.2599032819,0.4711607099,0.0430019759,0.1037084162,-0.0057747457,0.0103615662,0.1143461317,-0.0471609905,0.1531215012,0.1461279243,-0.3416619301,-0.2851566672,0.2188828588,-0.1442351341,0.0782735273,0.0937128961,0.0280694515,0.1398488432,-0.0689809099,-0.195103392,0.1316027343,-0.1178800836,0.4590720534,0.310067445,0.3028900325,-0.029343931,-0.3765672147,0.0593944713,0.0831138715,0.379365176,-0.3040897548,0.0794889852,-0.171332255,-0.243459627,-0.0772669688,-0.013044945,-0.2915118039,-0.317315042,0.0815705061,0.5144311786,0.0328679904,0.0099860048,-0.0540035702,-0.0480350479,0.0796879381,-0.1308909357,-0.0505061187,-0.7057309747,-0.3859112859,0.0051475652,0.2574861944,-0.1339800954,0.2502458096,-0.0254044235,-0.1927474737,-0.3766916692,-0.5513484478,-0.0036723248,-0.1104055196,0.3178443015,0.0176331513,0.3957713842,0.0727890357,-0.2152879387,0.2103869319,0.0130584994,-0.0248649139,0.2813790739,-0.3631691337,0.0844142213,-0.0089278743,-0.2284815609,-0.2832888961,-0.1489331275,0.1993967593,0.0630166978,0.1870315522,0.2383005023,0.0628918335,0.1012383327,0.2758947015,0.0676462352,-0.3717182577,-0.1608327031,0.3060976565,-0.2184316963,-0.2773063481,0.1912308037,0.002722593,0.1241865531,0.2216507792,-0.5899142623,0.0072065173,0.0857219473,0.3271362782,0.031964194,-0.1462098956,0.2367205918,0.0211273748,0.0012416313,0.062059287,0.0056531909,-0.1206239983,-0.1537077129,0.166241616,0.1140279919,0.2094682604,0.1647175252,0.7836135626,0.0867718309,0.0424960814,0.4263213873,-0.188306883,0.0635278597,-0.1462810636,-0.0966464877,0.0314848572,-0.3452987075,-0.2316510081,0.1231539845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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2144","title":"Loading wikipedia 20200501.en throws pyarrow related error","comments":"I just tried on my side and got no issues.\r\nWhen downloading the dataset again, did it crash at 10.7GB as well ?","body":"**Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n","comment_length":23,"text":"Loading wikipedia 20200501.en throws pyarrow related error \n **Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n \n I just tried on my side and got no issues.\r\nWhen downloading the dataset again, did it crash at 10.7GB as well ?","embeddings":[-0.1041540205,0.3526462018,0.0340582058,0.3633103967,0.2745778561,0.1802078485,0.2762243748,0.4623250365,-0.04348813,-0.113784872,0.0247374158,0.236021921,0.1520678252,-0.1178016663,0.1841091067,-0.1683138758,0.0038767338,0.0068803728,0.0847920552,0.1328444481,-0.2001205236,0.1146743447,-0.1824860871,0.1555170566,-0.5857800245,0.0881166533,-0.0660176575,0.0540414415,0.0833243653,-0.613804698,0.3904819489,-0.0500538088,0.2010909319,0.2121308595,-0.0001204597,0.0749956965,0.4643647373,-0.0041334573,-0.2960134745,-0.0811445788,0.186580047,-0.0205053128,0.3180885315,-0.3607697487,0.1008782089,-0.2803391814,0.2242598385,0.0471996665,0.1832180023,0.0086322986,0.168285802,-0.1830526292,0.3375393748,0.0306443255,0.7112432122,-0.1374686658,0.0815054998,0.4424607456,0.1225971282,-0.1607152224,-0.186434865,0.23629798,-0.2502577007,0.1203090623,0.2671839893,-0.0770430341,0.3848769963,-0.2004953474,0.3921535015,0.2679577172,0.7762554288,-0.4092206359,0.1730330735,-0.2313123494,-0.0328712203,-0.0229594614,0.0938252583,0.2307374179,-0.27676934,0.0545302071,0.1571857184,-0.1188708618,-0.2609159052,0.301315248,-0.0642476678,0.1997214258,0.1399375498,0.3910686672,0.0163352489,-0.2268839478,0.0019353561,0.0632643551,0.1672586203,0.2208643556,-0.3931137919,0.107257776,-0.0683028772,0.1139047593,0.3778621256,-0.1693618745,-0.021596102,0.0659415349,0.2391710877,0.2064648122,0.4192265868,0.1241850182,-0.3237407506,-0.2987220883,0.2721662819,0.1988998652,-0.254214257,-0.0364414603,-0.0612123869,-0.1137952879,0.1385727972,-0.1709839702,0.4291738868,-0.2699913085,-0.220579192,0.1195769534,-0.1446794569,-0.0715681612,-0.1552301943,0.3867201805,-0.0916787088,0.2424993664,0.0672103316,0.0622379445,-0.1821437031,-0.1878142953,0.0057542627,0.1186774671,-0.2124670595,0.1797935814,0.2939305305,-0.1538039595,0.2583542168,0.1435221881,-0.1506101638,-0.1206758171,0.1700648069,0.0130382087,-0.2326727659,0.309833169,0.0075005512,0.2468717843,-0.0328951664,-0.1449694484,-0.1790345013,0.3182585537,-0.2848282754,-0.5152549744,-0.262249738,0.1209049523,-0.0116632255,0.0844510719,0.1616870016,0.0576540641,0.4413489699,-0.5136592984,-0.1003230512,-0.1257850081,-0.1855283529,-0.4527531266,0.1706373543,0.3363130093,-0.5569394231,0.0798316821,-0.0507644974,0.0824038908,0.2173671722,0.4450024068,-0.2298555672,0.1757664233,-0.1654862761,0.1924015433,0.1095912755,-0.3121576309,-0.5411019325,0.2140131146,-0.0612866841,0.1959047168,0.0259571318,0.2906033099,0.0093369419,0.1717624664,0.4052648544,0.2492511719,-0.0467647091,0.0787921399,-0.4085267782,-0.3879698217,0.3385632038,0.2016654164,-0.0047450308,-0.1988982856,0.1968246847,0.3221473992,0.2584798336,-0.2035795897,0.1287960708,0.0218389891,0.0705177039,-0.0129466308,0.2327663898,-0.2238298804,-0.4262207448,0.11144609,-0.3431405723,-0.0235893708,-0.3043866754,-0.361228615,-0.5231873989,0.0663694814,-0.171464175,-0.2400977015,0.115050301,-0.0613334365,0.0953495279,0.2569484115,0.1027859375,-0.0896757692,-0.2042666227,-0.0175576173,-0.4030902386,0.240516305,-0.308798492,-0.1786494255,-0.0221906919,0.2451284826,0.324608773,0.1002937332,0.0431751907,0.1558428705,0.1467771679,0.0931873098,-0.1324361414,-0.0177001171,0.2272260636,-0.2668777108,0.2040083557,0.1240440756,0.1868998408,-0.221017614,0.05095274,0.1322418749,-0.0019998513,0.1718758643,-0.0822376162,-0.0712248981,0.1570295691,-0.0034186426,0.3967843354,-0.1355208606,0.2599032819,0.4711607099,0.0430019759,0.1037084162,-0.0057747457,0.0103615662,0.1143461317,-0.0471609905,0.1531215012,0.1461279243,-0.3416619301,-0.2851566672,0.2188828588,-0.1442351341,0.0782735273,0.0937128961,0.0280694515,0.1398488432,-0.0689809099,-0.195103392,0.1316027343,-0.1178800836,0.4590720534,0.310067445,0.3028900325,-0.029343931,-0.3765672147,0.0593944713,0.0831138715,0.379365176,-0.3040897548,0.0794889852,-0.171332255,-0.243459627,-0.0772669688,-0.013044945,-0.2915118039,-0.317315042,0.0815705061,0.5144311786,0.0328679904,0.0099860048,-0.0540035702,-0.0480350479,0.0796879381,-0.1308909357,-0.0505061187,-0.7057309747,-0.3859112859,0.0051475652,0.2574861944,-0.1339800954,0.2502458096,-0.0254044235,-0.1927474737,-0.3766916692,-0.5513484478,-0.0036723248,-0.1104055196,0.3178443015,0.0176331513,0.3957713842,0.0727890357,-0.2152879387,0.2103869319,0.0130584994,-0.0248649139,0.2813790739,-0.3631691337,0.0844142213,-0.0089278743,-0.2284815609,-0.2832888961,-0.1489331275,0.1993967593,0.0630166978,0.1870315522,0.2383005023,0.0628918335,0.1012383327,0.2758947015,0.0676462352,-0.3717182577,-0.1608327031,0.3060976565,-0.2184316963,-0.2773063481,0.1912308037,0.002722593,0.1241865531,0.2216507792,-0.5899142623,0.0072065173,0.0857219473,0.3271362782,0.031964194,-0.1462098956,0.2367205918,0.0211273748,0.0012416313,0.062059287,0.0056531909,-0.1206239983,-0.1537077129,0.166241616,0.1140279919,0.2094682604,0.1647175252,0.7836135626,0.0867718309,0.0424960814,0.4263213873,-0.188306883,0.0635278597,-0.1462810636,-0.0966464877,0.0314848572,-0.3452987075,-0.2316510081,0.1231539845,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2144","title":"Loading wikipedia 20200501.en throws pyarrow related error","comments":"> I just tried on my side and got no issues.\r\n> When downloading the dataset again, did it crash at 10.7GB as well ?\r\n\r\nYes i have tried it multiple times on different machines. I am wondering if you could share the screenshot of your dependency versions and i will try to make them the same as yours?","body":"**Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n","comment_length":59,"text":"Loading wikipedia 20200501.en throws pyarrow related error \n **Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n \n > I just tried on my side and got no issues.\r\n> When downloading the dataset again, did it crash at 10.7GB as well ?\r\n\r\nYes i have tried it multiple times on different machines. I am wondering if you could share the screenshot of your dependency versions and i will try to make them the same as yours?","embeddings":[-0.1041540205,0.3526462018,0.0340582058,0.3633103967,0.2745778561,0.1802078485,0.2762243748,0.4623250365,-0.04348813,-0.113784872,0.0247374158,0.236021921,0.1520678252,-0.1178016663,0.1841091067,-0.1683138758,0.0038767338,0.0068803728,0.0847920552,0.1328444481,-0.2001205236,0.1146743447,-0.1824860871,0.1555170566,-0.5857800245,0.0881166533,-0.0660176575,0.0540414415,0.0833243653,-0.613804698,0.3904819489,-0.0500538088,0.2010909319,0.2121308595,-0.0001204597,0.0749956965,0.4643647373,-0.0041334573,-0.2960134745,-0.0811445788,0.186580047,-0.0205053128,0.3180885315,-0.3607697487,0.1008782089,-0.2803391814,0.2242598385,0.0471996665,0.1832180023,0.0086322986,0.168285802,-0.1830526292,0.3375393748,0.0306443255,0.7112432122,-0.1374686658,0.0815054998,0.4424607456,0.1225971282,-0.1607152224,-0.186434865,0.23629798,-0.2502577007,0.1203090623,0.2671839893,-0.0770430341,0.3848769963,-0.2004953474,0.3921535015,0.2679577172,0.7762554288,-0.4092206359,0.1730330735,-0.2313123494,-0.0328712203,-0.0229594614,0.0938252583,0.2307374179,-0.27676934,0.0545302071,0.1571857184,-0.1188708618,-0.2609159052,0.301315248,-0.0642476678,0.1997214258,0.1399375498,0.3910686672,0.0163352489,-0.2268839478,0.0019353561,0.0632643551,0.1672586203,0.2208643556,-0.3931137919,0.107257776,-0.0683028772,0.1139047593,0.3778621256,-0.1693618745,-0.021596102,0.0659415349,0.2391710877,0.2064648122,0.4192265868,0.1241850182,-0.3237407506,-0.2987220883,0.2721662819,0.1988998652,-0.254214257,-0.0364414603,-0.0612123869,-0.1137952879,0.1385727972,-0.1709839702,0.4291738868,-0.2699913085,-0.220579192,0.1195769534,-0.1446794569,-0.0715681612,-0.1552301943,0.3867201805,-0.0916787088,0.2424993664,0.0672103316,0.0622379445,-0.1821437031,-0.1878142953,0.0057542627,0.1186774671,-0.2124670595,0.1797935814,0.2939305305,-0.1538039595,0.2583542168,0.1435221881,-0.1506101638,-0.1206758171,0.1700648069,0.0130382087,-0.2326727659,0.309833169,0.0075005512,0.2468717843,-0.0328951664,-0.1449694484,-0.1790345013,0.3182585537,-0.2848282754,-0.5152549744,-0.262249738,0.1209049523,-0.0116632255,0.0844510719,0.1616870016,0.0576540641,0.4413489699,-0.5136592984,-0.1003230512,-0.1257850081,-0.1855283529,-0.4527531266,0.1706373543,0.3363130093,-0.5569394231,0.0798316821,-0.0507644974,0.0824038908,0.2173671722,0.4450024068,-0.2298555672,0.1757664233,-0.1654862761,0.1924015433,0.1095912755,-0.3121576309,-0.5411019325,0.2140131146,-0.0612866841,0.1959047168,0.0259571318,0.2906033099,0.0093369419,0.1717624664,0.4052648544,0.2492511719,-0.0467647091,0.0787921399,-0.4085267782,-0.3879698217,0.3385632038,0.2016654164,-0.0047450308,-0.1988982856,0.1968246847,0.3221473992,0.2584798336,-0.2035795897,0.1287960708,0.0218389891,0.0705177039,-0.0129466308,0.2327663898,-0.2238298804,-0.4262207448,0.11144609,-0.3431405723,-0.0235893708,-0.3043866754,-0.361228615,-0.5231873989,0.0663694814,-0.171464175,-0.2400977015,0.115050301,-0.0613334365,0.0953495279,0.2569484115,0.1027859375,-0.0896757692,-0.2042666227,-0.0175576173,-0.4030902386,0.240516305,-0.308798492,-0.1786494255,-0.0221906919,0.2451284826,0.324608773,0.1002937332,0.0431751907,0.1558428705,0.1467771679,0.0931873098,-0.1324361414,-0.0177001171,0.2272260636,-0.2668777108,0.2040083557,0.1240440756,0.1868998408,-0.221017614,0.05095274,0.1322418749,-0.0019998513,0.1718758643,-0.0822376162,-0.0712248981,0.1570295691,-0.0034186426,0.3967843354,-0.1355208606,0.2599032819,0.4711607099,0.0430019759,0.1037084162,-0.0057747457,0.0103615662,0.1143461317,-0.0471609905,0.1531215012,0.1461279243,-0.3416619301,-0.2851566672,0.2188828588,-0.1442351341,0.0782735273,0.0937128961,0.0280694515,0.1398488432,-0.0689809099,-0.195103392,0.1316027343,-0.1178800836,0.4590720534,0.310067445,0.3028900325,-0.029343931,-0.3765672147,0.0593944713,0.0831138715,0.379365176,-0.3040897548,0.0794889852,-0.171332255,-0.243459627,-0.0772669688,-0.013044945,-0.2915118039,-0.317315042,0.0815705061,0.5144311786,0.0328679904,0.0099860048,-0.0540035702,-0.0480350479,0.0796879381,-0.1308909357,-0.0505061187,-0.7057309747,-0.3859112859,0.0051475652,0.2574861944,-0.1339800954,0.2502458096,-0.0254044235,-0.1927474737,-0.3766916692,-0.5513484478,-0.0036723248,-0.1104055196,0.3178443015,0.0176331513,0.3957713842,0.0727890357,-0.2152879387,0.2103869319,0.0130584994,-0.0248649139,0.2813790739,-0.3631691337,0.0844142213,-0.0089278743,-0.2284815609,-0.2832888961,-0.1489331275,0.1993967593,0.0630166978,0.1870315522,0.2383005023,0.0628918335,0.1012383327,0.2758947015,0.0676462352,-0.3717182577,-0.1608327031,0.3060976565,-0.2184316963,-0.2773063481,0.1912308037,0.002722593,0.1241865531,0.2216507792,-0.5899142623,0.0072065173,0.0857219473,0.3271362782,0.031964194,-0.1462098956,0.2367205918,0.0211273748,0.0012416313,0.062059287,0.0056531909,-0.1206239983,-0.1537077129,0.166241616,0.1140279919,0.2094682604,0.1647175252,0.7836135626,0.0867718309,0.0424960814,0.4263213873,-0.188306883,0.0635278597,-0.1462810636,-0.0966464877,0.0314848572,-0.3452987075,-0.2316510081,0.1231539845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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2144","title":"Loading wikipedia 20200501.en throws pyarrow related error","comments":"I tried using `datasets` from `master` on macos with python 3.7.2\r\nI also have `requests==2.23.0` and `tqdm==4.45.0`.","body":"**Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n","comment_length":17,"text":"Loading wikipedia 20200501.en throws pyarrow related error \n **Problem description**\r\nI am getting the following error when trying to load wikipedia\/20200501.en dataset.\r\n\r\n**Error log**\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14.6k\/14.6k [00:00<00:00, 5.41MB\/s]\r\nDownloading:  59%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                                              | 10.7G\/18.3G [11:30<08:08, 15.5MB\/s]\r\nDataset wikipedia downloaded and prepared to \/usr\/local\/workspace\/NAS_NLP\/cache\/wikipedia\/20200501.en\/1.0.0\/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.\r\nTraceback (most recent call last):\r\n  File \"load_wiki.py\", line 2, in <module>\r\n    ds = load_dataset('wikipedia', '20200501.en', cache_dir='\/usr\/local\/workspace\/NAS_NLP\/cache')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 751, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 746, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 763, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py\", line 835, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 324, in read_table\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 544, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Expected to be able to read 9176784 bytes for message body, got 4918712\r\n\r\n**Detailed version info**\r\ndatasets==1.5.0\r\n  - dataclasses [required: Any, installed: 0.8]\r\n  - dill [required: Any, installed: 0.3.3]\r\n  - fsspec [required: Any, installed: 0.8.7]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n  - huggingface-hub [required: <0.1.0, installed: 0.0.7]\r\n    - filelock [required: Any, installed: 3.0.12]\r\n    - importlib-metadata [required: Any, installed: 1.7.0]\r\n      - zipp [required: >=0.5, installed: 3.1.0]\r\n    - requests [required: Any, installed: 2.24.0]\r\n      - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n      - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n      - idna [required: >=2.5,<3, installed: 2.6]\r\n      - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n    - tqdm [required: Any, installed: 4.49.0]\r\n  - importlib-metadata [required: Any, installed: 1.7.0]\r\n    - zipp [required: >=0.5, installed: 3.1.0]\r\n  - multiprocess [required: Any, installed: 0.70.11.1]\r\n    - dill [required: >=0.3.3, installed: 0.3.3]\r\n  - numpy [required: >=1.17, installed: 1.17.0]\r\n  - pandas [required: Any, installed: 1.1.5]\r\n    - numpy [required: >=1.15.4, installed: 1.17.0]\r\n    - python-dateutil [required: >=2.7.3, installed: 2.8.0]\r\n      - six [required: >=1.5, installed: 1.15.0]\r\n    - pytz [required: >=2017.2, installed: 2020.1]\r\n  - pyarrow [required: >=0.17.1, installed: 3.0.0]\r\n    - numpy [required: >=1.16.6, installed: 1.17.0]\r\n  - requests [required: >=2.19.0, installed: 2.24.0]\r\n    - certifi [required: >=2017.4.17, installed: 2020.6.20]\r\n    - chardet [required: >=3.0.2,<4, installed: 3.0.4]\r\n    - idna [required: >=2.5,<3, installed: 2.6]\r\n    - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]\r\n  - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]\r\n  - xxhash [required: Any, installed: 2.0.0]\r\n \n I tried using `datasets` from `master` on macos with python 3.7.2\r\nI also have `requests==2.23.0` and `tqdm==4.45.0`.","embeddings":[-0.1041540205,0.3526462018,0.0340582058,0.3633103967,0.2745778561,0.1802078485,0.2762243748,0.4623250365,-0.04348813,-0.113784872,0.0247374158,0.236021921,0.1520678252,-0.1178016663,0.1841091067,-0.1683138758,0.0038767338,0.0068803728,0.0847920552,0.1328444481,-0.2001205236,0.1146743447,-0.1824860871,0.1555170566,-0.5857800245,0.0881166533,-0.0660176575,0.0540414415,0.0833243653,-0.613804698,0.3904819489,-0.0500538088,0.2010909319,0.2121308595,-0.0001204597,0.0749956965,0.4643647373,-0.0041334573,-0.2960134745,-0.0811445788,0.186580047,-0.0205053128,0.3180885315,-0.3607697487,0.1008782089,-0.2803391814,0.2242598385,0.0471996665,0.1832180023,0.0086322986,0.168285802,-0.1830526292,0.3375393748,0.0306443255,0.7112432122,-0.1374686658,0.0815054998,0.4424607456,0.1225971282,-0.1607152224,-0.186434865,0.23629798,-0.2502577007,0.1203090623,0.2671839893,-0.0770430341,0.3848769963,-0.2004953474,0.3921535015,0.2679577172,0.7762554288,-0.4092206359,0.1730330735,-0.2313123494,-0.0328712203,-0.0229594614,0.0938252583,0.2307374179,-0.27676934,0.0545302071,0.1571857184,-0.1188708618,-0.2609159052,0.301315248,-0.0642476678,0.1997214258,0.1399375498,0.3910686672,0.0163352489,-0.2268839478,0.0019353561,0.0632643551,0.1672586203,0.2208643556,-0.3931137919,0.107257776,-0.0683028772,0.1139047593,0.3778621256,-0.1693618745,-0.021596102,0.0659415349,0.2391710877,0.2064648122,0.4192265868,0.1241850182,-0.3237407506,-0.2987220883,0.2721662819,0.1988998652,-0.254214257,-0.0364414603,-0.0612123869,-0.1137952879,0.1385727972,-0.1709839702,0.4291738868,-0.2699913085,-0.220579192,0.1195769534,-0.1446794569,-0.0715681612,-0.1552301943,0.3867201805,-0.0916787088,0.2424993664,0.0672103316,0.0622379445,-0.1821437031,-0.1878142953,0.0057542627,0.1186774671,-0.2124670595,0.1797935814,0.2939305305,-0.1538039595,0.2583542168,0.1435221881,-0.1506101638,-0.1206758171,0.1700648069,0.0130382087,-0.2326727659,0.309833169,0.0075005512,0.2468717843,-0.0328951664,-0.1449694484,-0.1790345013,0.3182585537,-0.2848282754,-0.5152549744,-0.262249738,0.1209049523,-0.0116632255,0.0844510719,0.1616870016,0.0576540641,0.4413489699,-0.5136592984,-0.1003230512,-0.1257850081,-0.1855283529,-0.4527531266,0.1706373543,0.3363130093,-0.5569394231,0.0798316821,-0.0507644974,0.0824038908,0.2173671722,0.4450024068,-0.2298555672,0.1757664233,-0.1654862761,0.1924015433,0.1095912755,-0.3121576309,-0.5411019325,0.2140131146,-0.0612866841,0.1959047168,0.0259571318,0.2906033099,0.0093369419,0.1717624664,0.4052648544,0.2492511719,-0.0467647091,0.0787921399,-0.4085267782,-0.3879698217,0.3385632038,0.2016654164,-0.0047450308,-0.1988982856,0.1968246847,0.3221473992,0.2584798336,-0.2035795897,0.1287960708,0.0218389891,0.0705177039,-0.0129466308,0.2327663898,-0.2238298804,-0.4262207448,0.11144609,-0.3431405723,-0.0235893708,-0.3043866754,-0.361228615,-0.5231873989,0.0663694814,-0.171464175,-0.2400977015,0.115050301,-0.0613334365,0.0953495279,0.2569484115,0.1027859375,-0.0896757692,-0.2042666227,-0.0175576173,-0.4030902386,0.240516305,-0.308798492,-0.1786494255,-0.0221906919,0.2451284826,0.324608773,0.1002937332,0.0431751907,0.1558428705,0.1467771679,0.0931873098,-0.1324361414,-0.0177001171,0.2272260636,-0.2668777108,0.2040083557,0.1240440756,0.1868998408,-0.221017614,0.05095274,0.1322418749,-0.0019998513,0.1718758643,-0.0822376162,-0.0712248981,0.1570295691,-0.0034186426,0.3967843354,-0.1355208606,0.2599032819,0.4711607099,0.0430019759,0.1037084162,-0.0057747457,0.0103615662,0.1143461317,-0.0471609905,0.1531215012,0.1461279243,-0.3416619301,-0.2851566672,0.2188828588,-0.1442351341,0.0782735273,0.0937128961,0.0280694515,0.1398488432,-0.0689809099,-0.195103392,0.1316027343,-0.1178800836,0.4590720534,0.310067445,0.3028900325,-0.029343931,-0.3765672147,0.0593944713,0.0831138715,0.379365176,-0.3040897548,0.0794889852,-0.171332255,-0.243459627,-0.0772669688,-0.013044945,-0.2915118039,-0.317315042,0.0815705061,0.5144311786,0.0328679904,0.0099860048,-0.0540035702,-0.0480350479,0.0796879381,-0.1308909357,-0.0505061187,-0.7057309747,-0.3859112859,0.0051475652,0.2574861944,-0.1339800954,0.2502458096,-0.0254044235,-0.1927474737,-0.3766916692,-0.5513484478,-0.0036723248,-0.1104055196,0.3178443015,0.0176331513,0.3957713842,0.0727890357,-0.2152879387,0.2103869319,0.0130584994,-0.0248649139,0.2813790739,-0.3631691337,0.0844142213,-0.0089278743,-0.2284815609,-0.2832888961,-0.1489331275,0.1993967593,0.0630166978,0.1870315522,0.2383005023,0.0628918335,0.1012383327,0.2758947015,0.0676462352,-0.3717182577,-0.1608327031,0.3060976565,-0.2184316963,-0.2773063481,0.1912308037,0.002722593,0.1241865531,0.2216507792,-0.5899142623,0.0072065173,0.0857219473,0.3271362782,0.031964194,-0.1462098956,0.2367205918,0.0211273748,0.0012416313,0.062059287,0.0056531909,-0.1206239983,-0.1537077129,0.166241616,0.1140279919,0.2094682604,0.1647175252,0.7836135626,0.0867718309,0.0424960814,0.4263213873,-0.188306883,0.0635278597,-0.1462810636,-0.0966464877,0.0314848572,-0.3452987075,-0.2316510081,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2139","title":"TypeError when using save_to_disk in a dataset loaded with ReadInstruction split","comments":"Hi !\r\nI think this has been fixed recently on `master`.\r\nCan you try again by installing `datasets` from `master` ?\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```","body":"Hi,\r\n\r\nLoading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`.\r\n\r\nHere is the minimal reproducible example:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom datasets import ReadInstruction\r\n\r\ndata_1 = load_dataset(\r\n    \"wikiann\",\r\n    \"en\",\r\n    split=\"validation\",\r\n)\r\n\r\ndata_1.save_to_disk(\"temporary_path_1\")\r\n\r\nprint(\"Save with regular split works.\")\r\n\r\ndata_2 = load_dataset(\r\n    \"wikiann\",\r\n    \"en\",\r\n    split=ReadInstruction(\"validation\", to=50, unit=\"%\"),\r\n)\r\n\r\ndata_2.save_to_disk(\"temporary_path_2\")\r\n```\r\n\r\nand the corresponding output:\r\n\r\n```\r\nReusing dataset wikiann (\/xxxxx\/.cache\/huggingface\/datasets\/wikiann\/en\/1.1.0\/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)\r\nSave with regular split works.\r\nReusing dataset wikiann (\/xxxxx\/.cache\/huggingface\/datasets\/wikiann\/en\/1.1.0\/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)\r\nTraceback (most recent call last):\r\n  File \"bug.py\", line 20, in <module>\r\n    data_2.save_to_disk(\"temporary_path_2\")\r\n  File \"\/xxxxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 645, in save_to_disk\r\n    json.dump(state, state_file, indent=2, sort_keys=True)\r\n  File \"\/usr\/lib\/python3.7\/json\/__init__.py\", line 179, in dump\r\n    for chunk in iterable:\r\n  File \"\/usr\/lib\/python3.7\/json\/encoder.py\", line 431, in _iterencode\r\n    yield from _iterencode_dict(o, _current_indent_level)\r\n  File \"\/usr\/lib\/python3.7\/json\/encoder.py\", line 405, in _iterencode_dict\r\n    yield from chunks\r\n  File \"\/usr\/lib\/python3.7\/json\/encoder.py\", line 438, in _iterencode\r\n    o = _default(o)\r\n  File \"\/usr\/lib\/python3.7\/json\/encoder.py\", line 179, in default\r\n    raise TypeError(f'Object of type {o.__class__.__name__} '\r\nTypeError: Object of type ReadInstruction is not JSON serializable\r\n```\r\n\r\nLet me know if there is some misuse from my end.\r\n\r\nThanks in advance.\r\n ","comment_length":26,"text":"TypeError when using save_to_disk in a dataset loaded with ReadInstruction split \n Hi,\r\n\r\nLoading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`.\r\n\r\nHere is the minimal reproducible example:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom datasets import ReadInstruction\r\n\r\ndata_1 = load_dataset(\r\n    \"wikiann\",\r\n    \"en\",\r\n    split=\"validation\",\r\n)\r\n\r\ndata_1.save_to_disk(\"temporary_path_1\")\r\n\r\nprint(\"Save with regular split works.\")\r\n\r\ndata_2 = load_dataset(\r\n    \"wikiann\",\r\n    \"en\",\r\n    split=ReadInstruction(\"validation\", to=50, unit=\"%\"),\r\n)\r\n\r\ndata_2.save_to_disk(\"temporary_path_2\")\r\n```\r\n\r\nand the corresponding output:\r\n\r\n```\r\nReusing dataset wikiann (\/xxxxx\/.cache\/huggingface\/datasets\/wikiann\/en\/1.1.0\/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)\r\nSave with regular split works.\r\nReusing dataset wikiann (\/xxxxx\/.cache\/huggingface\/datasets\/wikiann\/en\/1.1.0\/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)\r\nTraceback (most recent call last):\r\n  File \"bug.py\", line 20, in <module>\r\n    data_2.save_to_disk(\"temporary_path_2\")\r\n  File \"\/xxxxx\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 645, in save_to_disk\r\n    json.dump(state, state_file, indent=2, sort_keys=True)\r\n  File \"\/usr\/lib\/python3.7\/json\/__init__.py\", line 179, in dump\r\n    for chunk in iterable:\r\n  File \"\/usr\/lib\/python3.7\/json\/encoder.py\", line 431, in _iterencode\r\n    yield from _iterencode_dict(o, _current_indent_level)\r\n  File \"\/usr\/lib\/python3.7\/json\/encoder.py\", line 405, in _iterencode_dict\r\n    yield from chunks\r\n  File \"\/usr\/lib\/python3.7\/json\/encoder.py\", line 438, in _iterencode\r\n    o = _default(o)\r\n  File \"\/usr\/lib\/python3.7\/json\/encoder.py\", line 179, in default\r\n    raise TypeError(f'Object of type {o.__class__.__name__} '\r\nTypeError: Object of type ReadInstruction is not JSON serializable\r\n```\r\n\r\nLet me know if there is some misuse from my end.\r\n\r\nThanks in advance.\r\n  \n Hi !\r\nI think this has been fixed recently on `master`.\r\nCan you try again by installing `datasets` from `master` ?\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git\r\n```","embeddings":[-0.1317065656,0.2105600834,0.0438051894,0.3900926709,0.3082846701,0.2542080283,0.4115675688,0.2183050513,0.1559664458,0.0927028954,-0.1810030639,0.3821820319,-0.1831880361,0.4692062438,-0.3343833089,-0.2261848152,0.1314814836,0.0324933976,0.0247836336,0.1516534537,-0.2917743325,0.2401537746,-0.2096995413,-0.0722319856,-0.1023984179,-0.2237329334,-0.0900937691,0.1784162223,-0.0479017384,-0.270075351,0.4981570244,-0.0863716081,0.2484552562,0.4595005214,-0.0001193325,0.2449737489,0.2752628922,-0.1217553765,-0.4869103432,-0.1442823708,-0.112406753,-0.3307709694,0.2289606631,-0.2849002481,0.2563339472,0.032430876,-0.096720852,-0.2834960222,0.5513250828,-0.1404765546,0.1310997903,0.2201003879,0.2116705775,-0.0764774606,-0.0196526162,0.818102777,-0.3116717935,0.1208766252,-0.1681930125,0.1594806761,0.1860604286,0.139774695,0.077550739,-0.1289093494,0.1089108884,-0.0839564577,0.0414062925,-0.1637930125,-0.0721092597,0.0359598212,0.6240739226,-0.2325592786,-0.5413184166,-0.5269168019,-0.0988926366,0.1037318781,0.1883218139,0.0515774116,0.1023936495,0.1159397885,-0.0749133602,-0.1888535172,-0.1452777237,-0.0556888357,-0.0028690889,-0.3264967203,-0.2233644128,0.3309184015,0.2039163411,-0.0945509523,0.1549262106,-0.2039530426,-0.092710495,-0.0095436256,-0.2591286004,-0.2582912147,-0.463026464,-0.1182582676,0.0588776506,0.2336980402,0.108112976,-0.1332108825,-0.2215162218,0.1171837002,0.6883972287,0.1997386664,0.1489496082,0.402256608,0.029157605,0.1394936591,0.0054768277,-0.0856491104,0.166863516,-0.07627327,0.3958076239,0.0007771892,0.2863453031,-0.1103602871,-0.5084636807,-0.023641672,-0.3658467531,-0.2045274526,-0.0995762274,0.112592347,0.2994084954,0.2819698453,0.1736928523,0.4408884645,-0.0008400108,-0.095121637,-0.0727198273,-0.0292542335,0.0549812205,0.2533734143,0.0195076615,-0.0273443554,0.0528054126,0.0067487173,-0.4714187086,-0.2150871903,0.1913157701,-0.2646383345,0.0638191029,0.2851462364,0.2803843021,0.1533473432,0.1773774326,-0.3570280969,-0.3106738031,0.1204815879,-0.0965005457,-0.0488858037,-0.0437003672,0.0947650969,-0.1267966479,0.0659134686,-0.5947527885,-0.0187632609,0.4436030686,-0.2635980844,-0.0719171688,-0.2288510203,-0.0116031589,-0.3403199315,0.0138408691,0.4004660845,-0.3379040956,-0.0681683049,-0.2169623524,-0.2334198356,0.1664637476,0.4936624169,-0.0835335925,0.516941011,-0.1955942512,0.3982065916,0.1584657282,-0.2379556447,-0.2000245303,0.5022702217,-0.1037771031,0.1734888703,-0.0952558294,-0.0241522361,0.4080353379,-0.173525393,0.1406641603,0.0812345371,-0.0405478813,-0.0777483135,-0.194061473,-0.2555298805,0.1296404302,0.0126433447,-0.1006332636,0.1342628598,0.1260567456,0.1880754828,0.3111828566,-0.0541514978,0.2493341416,0.3054505289,0.117798306,-0.1352878213,-0.1075367704,-0.0221280865,-0.3029607236,-0.0044431854,0.2780956626,-0.2112738043,-0.0934908092,-0.1717114598,-0.2598276436,0.230335027,-0.2763422132,0.2911271751,0.0529794879,0.1208057478,-0.0248321537,0.2489490956,-0.0919569507,0.1896861494,-0.035242334,0.1281069666,-0.4389435053,0.4238277376,-0.0409120433,-0.3451372385,0.0302613918,0.0569884367,0.2687718272,-0.134433791,-0.0043099276,0.3361161947,0.2367794812,0.13005279,-0.2204691917,-0.0598820932,0.2161560059,-0.0719890445,-0.1192336306,0.2150163651,0.3863373101,-0.1784895062,-0.1886806488,0.3041788638,-0.0705867633,0.1873155981,0.0063082632,0.112623103,0.0241007935,-0.002370018,-0.0940756425,-0.4081260264,0.0147502823,0.0237155166,-0.1132685915,-0.2551979721,-0.0995129123,0.1117107123,0.4493326843,-0.2675612867,0.2195772082,0.0182680245,-0.1571053565,-0.0029315124,-0.0543791577,0.4611753821,0.5188760757,-0.0728522763,0.2165549845,-0.1663918197,0.0560957193,-0.0409083143,0.1319316179,0.1509232819,0.3151162267,0.2516278028,0.0549508184,0.0588581562,-0.0803973228,0.149934113,-0.0055816928,0.1412248909,-0.404068172,0.1511809379,-0.2129691243,0.3331795335,-0.1819794029,-0.2257498801,0.0959367529,-0.6485622525,-0.4150827229,0.4897378385,-0.1861340553,-0.039807003,0.0067072879,-0.0427006856,-0.0165362135,-0.0674778298,-0.124694787,0.1129419208,-0.2868056893,-0.0729240328,0.2463184297,-0.2035422623,0.2206840366,-0.1972499937,0.0126172695,-0.2052572817,0.0368248969,0.1174176559,-0.1562771499,0.1773216873,0.1652359366,0.2820571661,0.0242906269,-0.2725773752,0.0964507759,0.1477789581,-0.1811760366,0.1133565828,-0.0025246169,-0.0518953949,-0.1257267296,-0.158946678,-0.3489079773,-0.1871428788,0.5054351687,-0.1241575256,-0.2554841042,-0.1415614784,0.1180824488,0.1391178817,0.2809080482,-0.2997899354,-0.1871420145,-0.129202038,0.3373076916,-0.0742208287,-0.1497434825,0.1376183778,0.04738646,-0.2108744681,-0.0256522689,-0.4338174462,0.2235617787,-0.1067928895,0.041715987,-0.1858791709,0.0072100954,0.2149301469,0.1670225263,0.119503133,-0.0201800372,-0.4066403508,0.2743183672,-0.1029174104,0.2617074251,-0.3179399371,0.3655718863,0.2267260849,-0.0936277136,0.1042671725,0.1052585691,0.4074470103,0.4023137391,0.22925232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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2135","title":"en language data from MLQA dataset is missing","comments":"Hi ! Indeed only the languages of the `translate-train` data are included...\r\nI can't find a link to download the english train set on https:\/\/github.com\/facebookresearch\/MLQA though, do you know where we can download it ?","body":"Hi\r\nI need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq  thank you for your help to fix this issue. ","comment_length":35,"text":"en language data from MLQA dataset is missing \n Hi\r\nI need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq  thank you for your help to fix this issue.  \n Hi ! Indeed only the languages of the `translate-train` data are included...\r\nI can't find a link to download the english train set on https:\/\/github.com\/facebookresearch\/MLQA though, do you know where we can download it ?","embeddings":[-0.0344628803,0.2046830654,-0.227187261,0.2308054119,0.0578236319,0.2997930646,-0.0395332575,-0.0461162217,-0.1498138011,0.1264657974,0.195892483,-0.1498139054,0.067390956,0.4813955724,0.2251041383,-0.2650109828,-0.009786183,-0.079442285,-0.1342740804,-0.4665370584,0.0198140983,0.5020956993,-0.0956803113,-0.031468194,-0.1739675552,0.1743480265,-0.3577691317,0.0911980495,-0.1458940953,0.0571778044,0.1609995961,-0.3259171247,0.2097357213,-0.1647292376,-0.0001091284,0.0488843881,0.0214317422,-0.2748510242,-0.0866407752,-0.3655617535,-0.119143106,-0.2956379354,-0.1769669056,-0.1298706681,-0.1838501245,-0.0736344382,0.1559998691,-0.2765039802,0.1190437302,0.3667834997,0.1788885742,-0.1254622489,0.1327679604,-0.2215016633,0.0960634872,-0.0015868166,0.1220990717,0.0806107,0.3105128109,0.0426357538,0.1654581577,0.5170339942,0.3657609224,-0.1951818019,-0.2743358314,0.0918550566,0.4387052655,-0.6130290627,0.2459109873,0.2323862463,0.3963029981,0.0642605722,-0.2204280496,-0.0049439119,0.2352886647,0.1712478101,0.0973937288,0.4704558551,-0.331916362,0.2780916095,-0.1069712043,-0.5191504955,-0.0931159258,0.2702248693,-0.1711803973,0.2614015341,0.0409468375,-0.0682276338,-0.0980315283,0.0551038012,0.1683304608,0.1132869795,-0.2043648809,0.2074112594,-0.0596287251,-0.0694435984,-0.1071814075,-0.0628479496,0.0442221314,-0.3017544746,-0.4466921389,0.1175957918,-0.098255001,0.0766113847,0.2756772339,-0.0042863861,0.0451873802,-0.1117994413,-0.0182722826,-0.4221897721,-0.0167216752,-0.1126443073,-0.2181791067,0.0549062714,-0.6116184592,0.2076267749,-0.1856248081,-0.3717593849,-0.2271015197,-0.0178472102,-0.548188448,-0.1329703033,-0.0842325613,0.1708070636,0.1391259432,0.1097304523,0.0387191661,0.0876052156,-0.2127081901,-0.5654237866,-0.1886694729,0.216659233,-0.2004490644,-0.2025626153,-0.0060594459,0.2342913002,0.4591828883,-0.1983031332,0.0367990211,0.0089186076,0.1423790157,-0.152129814,0.1630709767,0.1273078769,0.2819876373,0.1840767115,-0.0005702196,-0.2114921212,0.0147197312,0.2187458277,-0.3707552552,-0.1261291802,-0.3416649103,0.2081024051,-0.0157137103,-0.2832198441,0.2268716097,0.7006776929,-0.0515589416,0.017304562,0.2035379857,0.0312999897,-0.078900829,0.0856770352,0.200035125,0.2012119293,-0.601254046,-0.4723479152,0.132443592,-0.1496263295,0.2162726969,0.3347105086,-0.308137536,-0.0056157857,-0.2851573527,0.1473447233,0.3123139739,-0.090883866,-0.3621456027,0.0711625069,0.0378513075,-0.1875449121,-0.1637816429,0.2593780756,-0.0118397549,0.1117783114,0.2610759437,0.4686023593,-0.1223036051,-0.1898304373,-0.1053931713,-0.2279411852,0.622326076,0.2268796265,0.212640658,-0.1230190992,0.1040074155,0.1111715212,0.2606935799,-0.0795060396,0.1433201581,0.5985336304,-0.1020627618,0.0215906184,0.2536551654,-0.0001963689,0.0188051555,-0.0132762641,-0.6309732199,0.2035327554,0.0095851244,0.021999117,-0.0432276987,-0.1058528498,-0.1966770589,-0.2617295086,0.1333910227,-0.2965782881,-0.0784632564,0.4289409816,0.0495193824,-0.0088376524,-0.2400574237,-0.131760627,-0.0592113137,0.4036954343,-0.2992990315,0.0492517166,0.2209753692,0.4827614725,0.0691576824,-0.0927278921,-0.0542097725,-0.0900359303,-0.0186838806,0.0758046508,0.2721095383,0.1493699998,0.5323989391,-0.2613608241,0.2088598758,0.1365316361,-0.0400511809,0.0231652334,-0.0972541869,0.2465907186,0.3171747923,-0.021942256,0.0535087921,0.344753921,0.3326198161,-0.0062588919,-0.0123571251,-0.3073703945,0.3201687634,-0.072664842,0.136526227,-0.0757598355,-0.1469404548,0.2850211263,0.7387043238,0.1240657568,0.2098559886,0.084957771,-0.4218397439,-0.2090277374,0.297154665,0.0460442305,0.0749297887,0.1877487451,0.3617949188,-0.0734828711,0.4398923516,-0.140120551,0.2000077516,0.1594542116,0.1363021135,-0.2569343448,0.0658930317,-0.0054348838,-0.4006048739,0.1530468166,0.2428847998,-0.0181932673,0.1239670739,0.1371142566,-0.3840941191,-0.8181413412,-0.1210189834,-0.3285419047,-0.2786876559,-0.1606533974,0.1709367633,-0.7855653167,0.0042886627,0.2437331825,0.3373645544,-0.014211446,-0.1161951944,0.1168831736,-0.0756036043,-0.1245467365,-0.4825126827,0.1762280315,-0.0289322622,0.1055137739,0.0067073326,-0.1609124243,0.0697346777,-0.2360586375,-0.48951298,0.1627566963,-0.0336750783,0.1449293941,-0.0722268894,-0.0020543311,-0.4595686197,0.0561483987,0.0869889036,0.1313925833,-0.3589528501,-0.0264959875,-0.1333763897,-0.0539586432,-0.0296468325,-0.4481564164,-0.7942317724,0.0470998026,-0.0254445542,0.0235762578,0.0455225408,-0.0960215256,-0.181181848,-0.0012225162,-0.0862498581,-0.0588547997,-0.4134362042,-0.1808990538,0.1929139197,-0.089289166,-0.4240905344,0.3333979249,0.0575139672,0.1615899056,-0.2290339321,-0.4955675602,-0.2501125634,0.1038496494,0.0756998435,-0.2734216452,-0.0491298884,0.3173699081,-0.1497465223,-0.0121672777,-0.0257461332,-0.0596243441,0.0275728069,0.2583023906,0.4377896786,-0.4849637151,0.3810780644,0.2894515693,0.3349755108,0.3354417384,0.1928781867,0.1851141006,0.0049765268,0.204833433,0.0775878057,-0.093962878,0.4627104402,0.0153496601,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2135","title":"en language data from MLQA dataset is missing","comments":"Hi @lhoestq \r\nthank you very much for coming back to me, now I see, you are right, in the link you sent I see split of  {split}-context-{context_language}-question-{question_language}.json with context_language=question_language=en, TFDS most probably has extracted english ones from these files as en language files, but translate-train\/test do not have en indeed. thanks a lot for the great explanations","body":"Hi\r\nI need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq  thank you for your help to fix this issue. ","comment_length":57,"text":"en language data from MLQA dataset is missing \n Hi\r\nI need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq  thank you for your help to fix this issue.  \n Hi @lhoestq \r\nthank you very much for coming back to me, now I see, you are right, in the link you sent I see split of  {split}-context-{context_language}-question-{question_language}.json with context_language=question_language=en, TFDS most probably has extracted english ones from these files as en language files, but translate-train\/test do not have en indeed. thanks a lot for the great explanations","embeddings":[-0.0408197865,-0.0362202525,-0.1773771346,0.3228131235,0.1323038191,0.2861721516,0.0952832699,0.1073280647,-0.2343607247,0.1195361987,0.0948156416,0.0513932146,0.1680396497,0.523902595,0.206296578,-0.401420325,-0.0716602728,0.0019192108,-0.1760003865,-0.3533481658,0.057527937,0.4546353221,-0.1336801648,0.097742781,-0.283597976,0.0972493514,-0.4078720808,0.208615616,0.1086850464,0.0607025623,0.2069737017,-0.3061185181,0.1648241729,0.0024818643,-0.0001026441,0.0339449085,0.0562221073,-0.3316811323,-0.1245716438,-0.3011199534,-0.2936724722,-0.1166031733,-0.1913672239,-0.2669698298,-0.2100294083,-0.1600869596,0.0102598798,-0.4025422931,0.294521451,0.3969225585,0.2244403213,0.0597102493,0.1115079373,-0.0750962645,0.1472627819,0.0179583561,0.1538949758,0.0909608826,0.1666130871,0.0342720151,0.1306973696,0.5047555566,0.2061388046,-0.1100770161,-0.2966980338,0.1319010258,0.2486949563,-0.6032835245,0.2411035001,0.3763130307,0.5104178786,-0.0404776447,-0.2418508083,-0.1448254138,-0.0136252465,0.0993714705,0.1884403527,0.2149871439,-0.1208995357,0.2852535546,-0.0185189284,-0.312222898,-0.0564261451,0.1563021839,-0.4507113099,0.2781139314,0.0598659143,-0.0272461213,-0.207635656,0.0720339715,0.214170292,0.0857241899,-0.2254954129,0.1790015846,-0.2212730348,-0.2123450488,0.0013374971,-0.2044260204,-0.0787948668,-0.1716720015,-0.5002161264,0.0687911063,-0.075840354,0.0553399287,0.1506575495,0.1425784379,0.0742118508,-0.0000407521,-0.0345675312,-0.5035304427,-0.1269602627,-0.1095474735,-0.0571455173,-0.047424417,-0.5842799544,0.2645859122,-0.0489339381,-0.2018536329,-0.2747574449,-0.0557357557,-0.6679561138,-0.2636979818,-0.1630070359,0.0584547967,0.207579568,0.1428805441,0.0831876993,0.131198138,-0.3226513565,-0.4529734254,-0.2833188772,0.2836948633,-0.0452450551,-0.1650166959,-0.0661564469,0.2656957805,0.5278736949,-0.2556885183,-0.0366106443,-0.0110005038,0.074529916,-0.1067346185,0.0769132599,0.088959977,0.1962748915,0.0763683543,0.0843277425,-0.1846405268,0.0152574796,0.2290404737,-0.4823003709,-0.0396410599,-0.1821142286,0.2690197527,-0.1023227274,-0.2360186428,0.1503035277,0.6440026164,0.119322069,-0.0179714523,0.1329053193,-0.0403433889,-0.1953152269,0.0868195295,0.2072445899,0.2232121378,-0.7090595365,-0.2352009565,0.2311965525,-0.1255780458,0.0649191663,0.2192748338,-0.1816485524,0.2974399328,-0.2996007204,0.3631656766,0.1245925054,-0.3740579188,-0.118547298,0.2619829774,0.0106617613,-0.1634265929,-0.1058031917,-0.1710813642,0.160108164,0.0318514332,0.0292906221,0.3716142774,-0.2197672874,-0.1256002933,-0.1732638627,-0.1923583448,0.5057458282,0.1941702515,0.1641436815,-0.1853484511,0.045119103,0.1144302264,0.2556923032,-0.2323063165,0.0895541832,0.4553655088,-0.1292886883,-0.0670070052,0.2314843684,-0.1167257577,-0.240120694,0.0309543479,-0.503665328,0.2067003399,-0.0675953329,0.0471132062,-0.1141039729,-0.1690893024,-0.1501901448,-0.266376555,0.2185669839,-0.128269881,-0.1501293778,0.5255807638,0.1210810244,-0.0323449001,0.0362350829,-0.108423762,-0.1963132471,0.4880462587,-0.1885199696,0.0312878825,0.1449316591,0.3960081935,0.1007948294,-0.2371941954,-0.1993572116,0.0358269177,0.01040811,0.2304920107,0.0124625033,-0.0573116876,0.4358989298,-0.2884270847,-0.0393668376,0.1249902397,0.0571261086,-0.0100623779,-0.1664896756,0.3173385859,0.1417885125,-0.0154546611,0.0820578039,0.1560006589,0.2744921148,-0.2671564519,0.1516212076,-0.2489779443,0.1365277469,-0.1008696184,-0.0263799652,-0.0152852172,-0.0500319637,0.3457328677,0.9597117901,-0.0387768224,0.0324349031,0.1088812649,-0.2438545823,-0.2485950589,0.3327144086,0.2219971269,0.3681366146,0.2550665736,0.27862221,-0.059629485,0.3849787116,-0.2170824558,0.2478822172,0.1016413942,0.1226856858,-0.0602648593,0.0111213792,-0.1178099215,-0.3725441396,0.2336734086,0.2865144312,0.2004962564,-0.0297941826,0.2682924867,-0.5310765505,-0.6335401535,-0.0419786051,-0.2432370484,-0.1427946985,-0.2481586635,0.272582382,-0.5574778914,-0.1085469127,0.2909506559,0.4410870671,-0.0919117853,-0.1788231879,0.1353015155,-0.2354326993,-0.2749875486,-0.567044735,0.2050751746,0.0931925327,0.1703082919,0.1206256896,-0.244024083,0.0810178593,-0.0437348634,-0.4981174469,0.1700586826,-0.2002723217,0.281632632,-0.0635293499,0.0004735475,-0.4595284462,-0.0440799706,0.0705253184,0.1709949672,-0.3872296214,0.0093745571,-0.0262725689,-0.0521978997,-0.157628715,-0.7814698219,-0.5482575297,-0.0636083931,-0.1323074549,-0.1232254654,0.0307012759,-0.022132799,-0.1314075291,0.0421980061,0.0472832769,-0.1079865843,-0.3893261254,-0.1267874241,0.1431223899,-0.1554432809,-0.3047045767,0.2366571873,0.0481761917,0.4225658774,-0.2196065784,-0.3472971618,-0.1204656363,0.056391757,0.1077346206,-0.3534255326,-0.1192000061,0.5090583563,-0.017927574,-0.0089256978,-0.1515680403,0.0382350422,0.1233563051,0.2782582045,0.3899996281,-0.4099954367,0.599019289,0.3242695332,0.2691341341,0.1265043616,0.1426517665,0.1432933211,-0.0658831447,0.1140460372,-0.0823448151,-0.0694597214,0.4828374386,0.0637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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2135","title":"en language data from MLQA dataset is missing","comments":"I close the ticket, since I do not see any en existing, they have trained on \"SQuAD V1.1\" instead. Thanks. ","body":"Hi\r\nI need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq  thank you for your help to fix this issue. ","comment_length":20,"text":"en language data from MLQA dataset is missing \n Hi\r\nI need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq  thank you for your help to fix this issue.  \n I close the ticket, since I do not see any en existing, they have trained on \"SQuAD V1.1\" instead. Thanks. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2134","title":"Saving large in-memory datasets with save_to_disk crashes because of pickling","comments":"Hi !\r\nIndeed `save_to_disk` doesn't call pickle anymore. Though the `OverflowError` can still appear for in-memory datasets bigger than 4GB. This happens when doing this for example:\r\n```python\r\nimport pyarrow as pa\r\nimport pickle\r\n\r\narr = pa.array([0] * ((4 * 8 << 30) \/\/ 64))\r\ntable = pa.Table.from_arrays([a], names=[\"foo\"])\r\npickle.dumps(table)  # fails with an OverflowError\r\npickle.dumps(table, 4)  # works !\r\n```\r\nWe'll do the change to use `protocol=4`.\r\n\r\nMoreover I've also seen other users complain about this error\r\n```\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```\r\n\r\nIt looks like something related to the 4GB limit as well but I'm not able to reproduce on my side.\r\nDo you think you can provide a script that reproduces the issue ?\r\nHow big is your dataset ? (number of bytes, number of rows)\r\n\r\n","body":"Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```","comment_length":134,"text":"Saving large in-memory datasets with save_to_disk crashes because of pickling \n Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n``` \n Hi !\r\nIndeed `save_to_disk` doesn't call pickle anymore. Though the `OverflowError` can still appear for in-memory datasets bigger than 4GB. This happens when doing this for example:\r\n```python\r\nimport pyarrow as pa\r\nimport pickle\r\n\r\narr = pa.array([0] * ((4 * 8 << 30) \/\/ 64))\r\ntable = pa.Table.from_arrays([a], names=[\"foo\"])\r\npickle.dumps(table)  # fails with an OverflowError\r\npickle.dumps(table, 4)  # works !\r\n```\r\nWe'll do the change to use `protocol=4`.\r\n\r\nMoreover I've also seen other users complain about this error\r\n```\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```\r\n\r\nIt looks like something related to the 4GB limit as well but I'm not able to reproduce on my side.\r\nDo you think you can provide a script that reproduces the issue ?\r\nHow big is your dataset ? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2134","title":"Saving large in-memory datasets with save_to_disk crashes because of pickling","comments":"Hi!\r\nSo I've managed to created a minimum working (well technically crashing) example for the multiprocessing case, I create a huge list of zeros, like in your example, and then I try to .map(None, num_proc=2) over it, which then crashes, here's the code:\r\n\r\n```python\r\nfrom datasets import  Dataset\r\n\r\nif __name__ == '__main__':\r\n    ton_of_zeroes = [0] * ((12 * 8 << 30) \/\/ 64)\r\n    large_dataset = Dataset.from_dict({'col': ton_of_zeroes})\r\n    print(\"Start\")\r\n    large_dataset.map(function=None, num_proc=2)\r\n    print(\"Done - should not print\")\r\n```\r\n\r\nThe amount of zeros could probably be reduced, I haven't tried to minimize it to find the breaking point, I just increased it from your code (which by quick glance I assumed tried to allocate over 4 GiB)\r\n\r\nRunning this results in the following traceback:\r\n\r\n```\r\nParameter 'indices'=[        0         1         2 ... 805306365 805306366 805306367] of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\nTraceback (most recent call last):\r\n  File \".\/crash_multiproc_pickle.py\", line 7, in <module>\r\n    large_dataset.map(function=None, num_proc=2)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```\r\n\r\nMy datasets usually have hundreds of thousands to low millions of rows, with each row containing a list of 10 strings and list of vectors of different length (the strings tokenized), which in the worst case have 10\\*512\\*8 = 40960 bytes (but usually it is much smaller, as the vectors tend to be shorter. I need these groups of text lines to create training data for the Inverse Cloze Task.\r\n\r\nAnyway I don't think my particular dataset is relevant, as the tiny script I created also manages to crash.\r\nBut I think the issue is the same as the save_to_disk, from the traceback it seems that in multiprocessing, it tries to use dill to return the result of the map workers, which tries to pickle the data and can't do it, probably because it's again using the older pickle protocol. That's my guess anyway.","body":"Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```","comment_length":832,"text":"Saving large in-memory datasets with save_to_disk crashes because of pickling \n Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n``` \n Hi!\r\nSo I've managed to created a minimum working (well technically crashing) example for the multiprocessing case, I create a huge list of zeros, like in your example, and then I try to .map(None, num_proc=2) over it, which then crashes, here's the code:\r\n\r\n```python\r\nfrom datasets import  Dataset\r\n\r\nif __name__ == '__main__':\r\n    ton_of_zeroes = [0] * ((12 * 8 << 30) \/\/ 64)\r\n    large_dataset = Dataset.from_dict({'col': ton_of_zeroes})\r\n    print(\"Start\")\r\n    large_dataset.map(function=None, num_proc=2)\r\n    print(\"Done - should not print\")\r\n```\r\n\r\nThe amount of zeros could probably be reduced, I haven't tried to minimize it to find the breaking point, I just increased it from your code (which by quick glance I assumed tried to allocate over 4 GiB)\r\n\r\nRunning this results in the following traceback:\r\n\r\n```\r\nParameter 'indices'=[        0         1         2 ... 805306365 805306366 805306367] of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\nTraceback (most recent call last):\r\n  File \".\/crash_multiproc_pickle.py\", line 7, in <module>\r\n    large_dataset.map(function=None, num_proc=2)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```\r\n\r\nMy datasets usually have hundreds of thousands to low millions of rows, with each row containing a list of 10 strings and list of vectors of different length (the strings tokenized), which in the worst case have 10\\*512\\*8 = 40960 bytes (but usually it is much smaller, as the vectors tend to be shorter. I need these groups of text lines to create training data for the Inverse Cloze Task.\r\n\r\nAnyway I don't think my particular dataset is relevant, as the tiny script I created also manages to crash.\r\nBut I think the issue is the same as the save_to_disk, from the traceback it seems that in multiprocessing, it tries to use dill to return the result of the map workers, which tries to pickle the data and can't do it, probably because it's again using the older pickle protocol. That's my guess anyway.","embeddings":[-0.3168887198,0.0995949134,0.1197799519,0.3708881736,0.2521232963,0.0021294134,-0.2834976017,0.4639680088,0.1905564368,0.0964653715,0.0808833018,0.5146647096,-0.3989095688,0.2905986607,-0.1574802846,-0.0833722576,0.220017001,-0.1045619994,-0.2615798116,0.1710371524,-0.2519762218,-0.1009903997,0.0205409992,-0.1659641564,-0.1712375283,-0.3141442537,-0.0271552056,0.3956106007,-0.3193687201,-0.3452962339,-0.1409765035,-0.2231153846,0.2281501889,0.3274106383,-0.0001218502,0.0387924016,0.1374743134,-0.1475669891,-0.496276319,0.0046155262,0.0253546927,-0.5504588485,-0.1945044249,-0.2504110038,0.3338277936,-0.0910536945,0.1185293049,-0.3753315806,0.2385226488,0.2061813921,0.140699327,0.3183521926,0.2884148061,-0.0236258283,0.0810795277,0.2532385886,-0.2371630371,0.2104443759,0.2143962979,0.0940100402,-0.0697052926,-0.1063089967,-0.0489088744,-0.2752487361,0.0091197919,-0.0715226382,-0.357534498,-0.2352924496,0.1131324992,0.135972783,0.2058551759,-0.4716520309,-0.6115646958,-0.4440332353,-0.097816743,-0.272595644,0.0673024952,0.2560089231,-0.0856662393,-0.0091383085,-0.1487092227,-0.5428615808,0.0650518984,0.0935430601,0.3128340542,-0.26405707,-0.2599284947,0.2104803622,0.4827492833,-0.2832579911,-0.2492699474,-0.1989862472,0.0249465033,-0.0148134846,-0.0100060645,-0.1636580229,-0.0667285696,-0.0531579033,0.2876333594,0.1288197488,-0.0264490116,0.1699509919,-0.3184286654,-0.0102004604,0.3466800749,-0.0542850979,-0.2104748338,0.327796936,0.3184904456,0.0511623994,0.1151701286,0.086256519,-0.0244318526,-0.1946864277,0.0468381457,-0.1393448114,0.1426373422,-0.1221712157,-0.0298002008,0.1717314422,-0.1409705281,0.2041668147,-0.0327623338,0.2195823342,-0.0259777587,0.0620772541,-0.2864434123,0.0794292018,-0.1583689749,0.0751292333,-0.1170247421,0.1987030208,0.0240919162,0.235345006,0.1321487278,0.0247917902,0.2072657198,-0.0151788006,-0.2567573786,0.1097202972,0.0652008727,-0.404794693,0.1266951561,0.2791752815,-0.0430324003,0.1116224751,0.0867824629,0.0422736853,-0.240793094,0.1412682682,0.0091912337,-0.2104313523,-0.0454823412,0.0081623318,-0.1934388876,-0.1259142905,-0.5878509283,0.073908776,0.3553926349,-0.1766408086,-0.126213342,-0.2640080154,-0.1701362729,-0.3441281915,0.0026655586,0.3127559721,-0.3485505283,0.1430537701,-0.0181174353,0.1768348813,0.504804194,0.4281790257,0.0170925166,-0.0940089971,-0.2584463358,0.4258436561,0.2039240748,-0.0345929265,-0.4821711779,0.1800985634,0.0055722166,-0.0328227244,-0.1284407824,0.1374112666,0.027889898,-0.0201430563,-0.0864936188,0.1751217991,0.0206469875,0.1888284534,-0.323286593,-0.4281207919,-0.0569952503,0.0229820739,-0.0687384903,-0.0665232986,0.0438066088,-0.2526403069,0.1586155146,-0.1642065495,0.2267966717,0.3692833185,0.3107089102,-0.1413410604,-0.2410432696,-0.2540671825,-0.3209947348,0.2904059291,0.1346235275,-0.2128845602,-0.2942925692,0.0248158015,-0.0160081368,0.0344790183,-0.0461660139,0.392995894,-0.0561229847,-0.0432412252,0.2098881602,0.1974905282,-0.0971104875,-0.1669189483,-0.219002679,0.1228189394,-0.2471029162,0.1366456747,-0.0582225323,-0.4469140768,0.0568948835,-0.0252148286,0.0113012502,-0.1903934628,-0.0567970425,0.1131048501,0.2439712137,-0.0361532383,-0.0844455659,0.1641011685,0.0477742292,0.0527436025,0.182499662,-0.0172294062,0.280044049,0.0196366422,-0.1887224317,0.3480973244,0.0926225036,0.1415981203,0.341621846,-0.1152978465,0.0515543148,0.072982952,0.2104120553,-0.172123149,-0.1512315273,0.1688516289,0.355353266,-0.0358184911,-0.356580317,0.0416285731,0.5232881308,0.0071559884,0.4139182568,0.2888955176,-0.0300788376,-0.1796032488,0.0495374165,0.1489823759,0.4825516343,0.0719781369,0.2031986713,-0.0497339629,0.0984952301,-0.0927228108,0.0490241759,0.1893742085,0.2143327147,0.2653184831,0.1862361878,-0.0225452539,-0.3985636234,0.0068510873,-0.0103281774,0.2490284443,-0.1407552063,0.1229116619,-0.3488911688,-0.0930573866,-0.1756005138,0.0690052435,-0.0824647918,-0.3389725685,-0.2977801859,0.398057729,-0.379188478,0.0952738151,-0.0824063346,0.2396454364,0.1588449031,0.0193319712,0.0811564326,0.1053368673,-0.1719753444,-0.0753005743,0.3932111859,-0.178361997,0.4449950159,0.2942570746,-0.2515664995,-0.5642057657,-0.0096309939,0.0972327441,-0.0446406193,0.2913724482,0.1569111049,0.2594354451,-0.0637068897,0.1674998999,0.0285899006,0.1679051667,-0.2048674971,-0.0965486988,0.1249711663,0.1148413345,0.0120950313,-0.1214811131,-0.3409324288,-0.3965244889,0.4174043536,-0.0325719304,0.1087350845,0.2555639148,0.3698806167,0.3338328898,0.1646701694,0.0131674772,-0.079559043,-0.200690195,0.2692923546,0.0735173449,-0.2851146758,-0.1685407311,0.1163950637,-0.1761887819,0.1963485479,-0.296160996,0.2146085501,-0.5010900497,0.1750914454,-0.073869504,0.1984625012,0.3098811805,0.0670666397,-0.0232707076,0.0926834792,-0.0622301623,-0.0249271542,0.3186881542,0.213870734,0.0005777985,0.2953265607,0.2987188399,0.658223927,0.1857357174,-0.2299649119,0.5270459056,0.1381633133,0.2322872728,-0.2867571712,-0.1393384933,0.0083172759,-0.383877933,-0.15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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2134","title":"Saving large in-memory datasets with save_to_disk crashes because of pickling","comments":"I just merged a fix #2150 that allows to pickle tables bigger than 4GiB\r\nFeel free to try it on the `master` branch !","body":"Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```","comment_length":24,"text":"Saving large in-memory datasets with save_to_disk crashes because of pickling \n Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n``` \n I just merged a fix #2150 that allows to pickle tables bigger than 4GiB\r\nFeel free to try it on the `master` branch !","embeddings":[-0.3168887198,0.0995949134,0.1197799519,0.3708881736,0.2521232963,0.0021294134,-0.2834976017,0.4639680088,0.1905564368,0.0964653715,0.0808833018,0.5146647096,-0.3989095688,0.2905986607,-0.1574802846,-0.0833722576,0.220017001,-0.1045619994,-0.2615798116,0.1710371524,-0.2519762218,-0.1009903997,0.0205409992,-0.1659641564,-0.1712375283,-0.3141442537,-0.0271552056,0.3956106007,-0.3193687201,-0.3452962339,-0.1409765035,-0.2231153846,0.2281501889,0.3274106383,-0.0001218502,0.0387924016,0.1374743134,-0.1475669891,-0.496276319,0.0046155262,0.0253546927,-0.5504588485,-0.1945044249,-0.2504110038,0.3338277936,-0.0910536945,0.1185293049,-0.3753315806,0.2385226488,0.2061813921,0.140699327,0.3183521926,0.2884148061,-0.0236258283,0.0810795277,0.2532385886,-0.2371630371,0.2104443759,0.2143962979,0.0940100402,-0.0697052926,-0.1063089967,-0.0489088744,-0.2752487361,0.0091197919,-0.0715226382,-0.357534498,-0.2352924496,0.1131324992,0.135972783,0.2058551759,-0.4716520309,-0.6115646958,-0.4440332353,-0.097816743,-0.272595644,0.0673024952,0.2560089231,-0.0856662393,-0.0091383085,-0.1487092227,-0.5428615808,0.0650518984,0.0935430601,0.3128340542,-0.26405707,-0.2599284947,0.2104803622,0.4827492833,-0.2832579911,-0.2492699474,-0.1989862472,0.0249465033,-0.0148134846,-0.0100060645,-0.1636580229,-0.0667285696,-0.0531579033,0.2876333594,0.1288197488,-0.0264490116,0.1699509919,-0.3184286654,-0.0102004604,0.3466800749,-0.0542850979,-0.2104748338,0.327796936,0.3184904456,0.0511623994,0.1151701286,0.086256519,-0.0244318526,-0.1946864277,0.0468381457,-0.1393448114,0.1426373422,-0.1221712157,-0.0298002008,0.1717314422,-0.1409705281,0.2041668147,-0.0327623338,0.2195823342,-0.0259777587,0.0620772541,-0.2864434123,0.0794292018,-0.1583689749,0.0751292333,-0.1170247421,0.1987030208,0.0240919162,0.235345006,0.1321487278,0.0247917902,0.2072657198,-0.0151788006,-0.2567573786,0.1097202972,0.0652008727,-0.404794693,0.1266951561,0.2791752815,-0.0430324003,0.1116224751,0.0867824629,0.0422736853,-0.240793094,0.1412682682,0.0091912337,-0.2104313523,-0.0454823412,0.0081623318,-0.1934388876,-0.1259142905,-0.5878509283,0.073908776,0.3553926349,-0.1766408086,-0.126213342,-0.2640080154,-0.1701362729,-0.3441281915,0.0026655586,0.3127559721,-0.3485505283,0.1430537701,-0.0181174353,0.1768348813,0.504804194,0.4281790257,0.0170925166,-0.0940089971,-0.2584463358,0.4258436561,0.2039240748,-0.0345929265,-0.4821711779,0.1800985634,0.0055722166,-0.0328227244,-0.1284407824,0.1374112666,0.027889898,-0.0201430563,-0.0864936188,0.1751217991,0.0206469875,0.1888284534,-0.323286593,-0.4281207919,-0.0569952503,0.0229820739,-0.0687384903,-0.0665232986,0.0438066088,-0.2526403069,0.1586155146,-0.1642065495,0.2267966717,0.3692833185,0.3107089102,-0.1413410604,-0.2410432696,-0.2540671825,-0.3209947348,0.2904059291,0.1346235275,-0.2128845602,-0.2942925692,0.0248158015,-0.0160081368,0.0344790183,-0.0461660139,0.392995894,-0.0561229847,-0.0432412252,0.2098881602,0.1974905282,-0.0971104875,-0.1669189483,-0.219002679,0.1228189394,-0.2471029162,0.1366456747,-0.0582225323,-0.4469140768,0.0568948835,-0.0252148286,0.0113012502,-0.1903934628,-0.0567970425,0.1131048501,0.2439712137,-0.0361532383,-0.0844455659,0.1641011685,0.0477742292,0.0527436025,0.182499662,-0.0172294062,0.280044049,0.0196366422,-0.1887224317,0.3480973244,0.0926225036,0.1415981203,0.341621846,-0.1152978465,0.0515543148,0.072982952,0.2104120553,-0.172123149,-0.1512315273,0.1688516289,0.355353266,-0.0358184911,-0.356580317,0.0416285731,0.5232881308,0.0071559884,0.4139182568,0.2888955176,-0.0300788376,-0.1796032488,0.0495374165,0.1489823759,0.4825516343,0.0719781369,0.2031986713,-0.0497339629,0.0984952301,-0.0927228108,0.0490241759,0.1893742085,0.2143327147,0.2653184831,0.1862361878,-0.0225452539,-0.3985636234,0.0068510873,-0.0103281774,0.2490284443,-0.1407552063,0.1229116619,-0.3488911688,-0.0930573866,-0.1756005138,0.0690052435,-0.0824647918,-0.3389725685,-0.2977801859,0.398057729,-0.379188478,0.0952738151,-0.0824063346,0.2396454364,0.1588449031,0.0193319712,0.0811564326,0.1053368673,-0.1719753444,-0.0753005743,0.3932111859,-0.178361997,0.4449950159,0.2942570746,-0.2515664995,-0.5642057657,-0.0096309939,0.0972327441,-0.0446406193,0.2913724482,0.1569111049,0.2594354451,-0.0637068897,0.1674998999,0.0285899006,0.1679051667,-0.2048674971,-0.0965486988,0.1249711663,0.1148413345,0.0120950313,-0.1214811131,-0.3409324288,-0.3965244889,0.4174043536,-0.0325719304,0.1087350845,0.2555639148,0.3698806167,0.3338328898,0.1646701694,0.0131674772,-0.079559043,-0.200690195,0.2692923546,0.0735173449,-0.2851146758,-0.1685407311,0.1163950637,-0.1761887819,0.1963485479,-0.296160996,0.2146085501,-0.5010900497,0.1750914454,-0.073869504,0.1984625012,0.3098811805,0.0670666397,-0.0232707076,0.0926834792,-0.0622301623,-0.0249271542,0.3186881542,0.213870734,0.0005777985,0.2953265607,0.2987188399,0.658223927,0.1857357174,-0.2299649119,0.5270459056,0.1381633133,0.2322872728,-0.2867571712,-0.1393384933,0.0083172759,-0.383877933,-0.15290057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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2134","title":"Saving large in-memory datasets with save_to_disk crashes because of pickling","comments":"awesome! I started getting this error as well when I tried to tokenize with a longer sequence length","body":"Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```","comment_length":18,"text":"Saving large in-memory datasets with save_to_disk crashes because of pickling \n Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n``` \n awesome! I started getting this error as well when I tried to tokenize with a longer sequence 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2134","title":"Saving large in-memory datasets with save_to_disk crashes because of pickling","comments":"@prokopCerny does this fix work for you? I found that with the latest master, my container with 500GB RAM starts crashing when I try to map a large dataset using `num_proc`.\r\n\r\n@lhoestq would it be possible to implement some logic to keep the individual cache files small (say below 100mb)? I find this helps with loading large datasets, but the \"hack\" I was using (increasing `num_proc` to a large number) doesn't work anymore with the latest master; my container crashes even with `num_proc=200` now","body":"Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```","comment_length":84,"text":"Saving large in-memory datasets with save_to_disk crashes because of pickling \n Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n``` \n @prokopCerny does this fix work for you? I found that with the latest master, my container with 500GB RAM starts crashing when I try to map a large dataset using `num_proc`.\r\n\r\n@lhoestq would it be possible to implement some logic to keep the individual cache files small (say below 100mb)? I find this helps with loading large datasets, but the \"hack\" I was using (increasing `num_proc` to a large number) doesn't work anymore with the latest master; my container crashes even with `num_proc=200` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2134","title":"Saving large in-memory datasets with save_to_disk crashes because of pickling","comments":"Closing since the original issue was fixed in #2150 \r\nFeel free to reopen if you are still experiencing it.\r\nFor the other problems, please open separate issues","body":"Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n```","comment_length":27,"text":"Saving large in-memory datasets with save_to_disk crashes because of pickling \n Using Datasets 1.5.0 on Python 3.7.\r\nRecently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.\r\n\r\n So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. \r\n\r\nWhen I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 80, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 75, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 60, in tokenize_and_chunkify\r\n    contexts_dataset.save_to_disk(chunked_path)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 457, in save_to_disk\r\n    self = pickle.loads(pickle.dumps(self))\r\nOverflowError: cannot serialize a bytes object larger than 4 GiB\r\n```\r\nFrom what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.\r\n\r\nTo save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.\r\n\r\nAdditional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 94, in <module>\r\n    main()\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 89, in main\r\n    tokenize_and_chunkify(config)\r\n  File \".\/tokenize_and_chunkify_in_memory.py\", line 67, in tokenize_and_chunkify\r\n    contexts_dataset.map(function=None, cache_file_name=str(output_dir_path \/ \"tmp.arrow\"), writer_batch_size=50000, num_proc=config.threads)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1485, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 657, in get\r\n    raise self._value\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/pool.py\", line 431, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 454, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 437, in dump\r\n    self.save(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 662, in save_reduce\r\n    save(state)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/cernypro\/dev\/envs\/huggingface_gpu\/lib\/python3.7\/site-packages\/dill\/_dill.py\", line 941, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 859, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 885, in _batch_setitems\r\n    save(v)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 846, in _batch_appends\r\n    save(tmp[0])\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 789, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 819, in save_list\r\n    self._batch_appends(obj)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 843, in _batch_appends\r\n    save(x)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 549, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 638, in save_reduce\r\n    save(args)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 774, in save_tuple\r\n    save(element)\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 504, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/mnt\/appl\/software\/Python\/3.7.4-GCCcore-8.3.0\/lib\/python3.7\/pickle.py\", line 732, in save_bytes\r\n    self._write_large_bytes(BINBYTES + pack(\"<I\", n), obj)\r\nstruct.error: 'I' format requires 0 <= number <= 4294967295\r\n``` \n Closing since the original issue was fixed in #2150 \r\nFeel free to reopen if you are still experiencing it.\r\nFor the other problems, please open separate issues","embeddings":[-0.3168887198,0.0995949134,0.1197799519,0.3708881736,0.2521232963,0.0021294134,-0.2834976017,0.4639680088,0.1905564368,0.0964653715,0.0808833018,0.5146647096,-0.3989095688,0.2905986607,-0.1574802846,-0.0833722576,0.220017001,-0.1045619994,-0.2615798116,0.1710371524,-0.2519762218,-0.1009903997,0.0205409992,-0.1659641564,-0.1712375283,-0.3141442537,-0.0271552056,0.3956106007,-0.3193687201,-0.3452962339,-0.1409765035,-0.2231153846,0.2281501889,0.3274106383,-0.0001218502,0.0387924016,0.1374743134,-0.1475669891,-0.496276319,0.0046155262,0.0253546927,-0.5504588485,-0.1945044249,-0.2504110038,0.3338277936,-0.0910536945,0.1185293049,-0.3753315806,0.2385226488,0.2061813921,0.140699327,0.3183521926,0.2884148061,-0.0236258283,0.0810795277,0.2532385886,-0.2371630371,0.2104443759,0.2143962979,0.0940100402,-0.0697052926,-0.1063089967,-0.0489088744,-0.2752487361,0.0091197919,-0.0715226382,-0.357534498,-0.2352924496,0.1131324992,0.135972783,0.2058551759,-0.4716520309,-0.6115646958,-0.4440332353,-0.097816743,-0.272595644,0.0673024952,0.2560089231,-0.0856662393,-0.0091383085,-0.1487092227,-0.5428615808,0.0650518984,0.0935430601,0.3128340542,-0.26405707,-0.2599284947,0.2104803622,0.4827492833,-0.2832579911,-0.2492699474,-0.1989862472,0.0249465033,-0.0148134846,-0.0100060645,-0.1636580229,-0.0667285696,-0.0531579033,0.2876333594,0.1288197488,-0.0264490116,0.1699509919,-0.3184286654,-0.0102004604,0.3466800749,-0.0542850979,-0.2104748338,0.327796936,0.3184904456,0.0511623994,0.1151701286,0.086256519,-0.0244318526,-0.1946864277,0.0468381457,-0.1393448114,0.1426373422,-0.1221712157,-0.0298002008,0.1717314422,-0.1409705281,0.2041668147,-0.0327623338,0.2195823342,-0.0259777587,0.0620772541,-0.2864434123,0.0794292018,-0.1583689749,0.0751292333,-0.1170247421,0.1987030208,0.0240919162,0.235345006,0.1321487278,0.0247917902,0.2072657198,-0.0151788006,-0.2567573786,0.1097202972,0.0652008727,-0.404794693,0.1266951561,0.2791752815,-0.0430324003,0.1116224751,0.0867824629,0.0422736853,-0.240793094,0.1412682682,0.0091912337,-0.2104313523,-0.0454823412,0.0081623318,-0.1934388876,-0.1259142905,-0.5878509283,0.073908776,0.3553926349,-0.1766408086,-0.126213342,-0.2640080154,-0.1701362729,-0.3441281915,0.0026655586,0.3127559721,-0.3485505283,0.1430537701,-0.0181174353,0.1768348813,0.504804194,0.4281790257,0.0170925166,-0.0940089971,-0.2584463358,0.4258436561,0.2039240748,-0.0345929265,-0.4821711779,0.1800985634,0.0055722166,-0.0328227244,-0.1284407824,0.1374112666,0.027889898,-0.0201430563,-0.0864936188,0.1751217991,0.0206469875,0.1888284534,-0.323286593,-0.4281207919,-0.0569952503,0.0229820739,-0.0687384903,-0.0665232986,0.0438066088,-0.2526403069,0.1586155146,-0.1642065495,0.2267966717,0.3692833185,0.3107089102,-0.1413410604,-0.2410432696,-0.2540671825,-0.3209947348,0.2904059291,0.1346235275,-0.2128845602,-0.2942925692,0.0248158015,-0.0160081368,0.0344790183,-0.0461660139,0.392995894,-0.0561229847,-0.0432412252,0.2098881602,0.1974905282,-0.0971104875,-0.1669189483,-0.219002679,0.1228189394,-0.2471029162,0.1366456747,-0.0582225323,-0.4469140768,0.0568948835,-0.0252148286,0.0113012502,-0.1903934628,-0.0567970425,0.1131048501,0.2439712137,-0.0361532383,-0.0844455659,0.1641011685,0.0477742292,0.0527436025,0.182499662,-0.0172294062,0.280044049,0.0196366422,-0.1887224317,0.3480973244,0.0926225036,0.1415981203,0.341621846,-0.1152978465,0.0515543148,0.072982952,0.2104120553,-0.172123149,-0.1512315273,0.1688516289,0.355353266,-0.0358184911,-0.356580317,0.0416285731,0.5232881308,0.0071559884,0.4139182568,0.2888955176,-0.0300788376,-0.1796032488,0.0495374165,0.1489823759,0.4825516343,0.0719781369,0.2031986713,-0.0497339629,0.0984952301,-0.0927228108,0.0490241759,0.1893742085,0.2143327147,0.2653184831,0.1862361878,-0.0225452539,-0.3985636234,0.0068510873,-0.0103281774,0.2490284443,-0.1407552063,0.1229116619,-0.3488911688,-0.0930573866,-0.1756005138,0.0690052435,-0.0824647918,-0.3389725685,-0.2977801859,0.398057729,-0.379188478,0.0952738151,-0.0824063346,0.2396454364,0.1588449031,0.0193319712,0.0811564326,0.1053368673,-0.1719753444,-0.0753005743,0.3932111859,-0.178361997,0.4449950159,0.2942570746,-0.2515664995,-0.5642057657,-0.0096309939,0.0972327441,-0.0446406193,0.2913724482,0.1569111049,0.2594354451,-0.0637068897,0.1674998999,0.0285899006,0.1679051667,-0.2048674971,-0.0965486988,0.1249711663,0.1148413345,0.0120950313,-0.1214811131,-0.3409324288,-0.3965244889,0.4174043536,-0.0325719304,0.1087350845,0.2555639148,0.3698806167,0.3338328898,0.1646701694,0.0131674772,-0.079559043,-0.200690195,0.2692923546,0.0735173449,-0.2851146758,-0.1685407311,0.1163950637,-0.1761887819,0.1963485479,-0.296160996,0.2146085501,-0.5010900497,0.1750914454,-0.073869504,0.1984625012,0.3098811805,0.0670666397,-0.0232707076,0.0926834792,-0.0622301623,-0.0249271542,0.3186881542,0.213870734,0.0005777985,0.2953265607,0.2987188399,0.658223927,0.1857357174,-0.2299649119,0.5270459056,0.1381633133,0.2322872728,-0.2867571712,-0.1393384933,0.0083172759,-0.383877933,-0.152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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2133","title":"bug in mlqa dataset ","comments":"If you print those questions, you get readable texts:\r\n```python\r\n>>> questions = [\r\n...     \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631?\",\r\n...     \"\\u0643\\u0645 \\u0645\\u0631\\u0629 \\u064a\\u062a\\u0645 \\u0646\\u0634\\u0631\\u0647\\u0627 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n...     \"\\u0645\\u0627 \\u0647\\u064a \\u0627\\u0644\\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u064a\\u0648\\u0645\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n...     \"\\u0643\\u0645 \\u0639\\u062f\\u062f \\u0627\\u0644\\u0627\\u0648\\u0631\\u0627\\u0642 \\u0627\\u0644\\u0627\\u062e\\u0628\\u0627\\u0631\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0627\\u0644\\u062a\\u064a \\u0648\\u062c\\u062f\\u062a \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n...     \"\\u0641\\u064a \\u0627\\u064a \\u0633\\u0646\\u0629 \\u0628\\u062f\\u0627\\u062a \\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u0637\\u0627\\u0644\\u0628 \\u0627\\u0644\\u062d\\u0633 \\u0627\\u0644\\u0633\\u0644\\u064a\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\"\r\n... ]\r\n>>> print(questions)\r\n['\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?', '\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?', '\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?', '\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?', '\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?']\r\n```\r\nI don't think we can change this","body":"Hi \r\nLooking into MLQA dataset for langauge \"ar\":\r\n\r\n```\r\n \"question\": [\r\n    \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631?\",\r\n    \"\\u0643\\u0645 \\u0645\\u0631\\u0629 \\u064a\\u062a\\u0645 \\u0646\\u0634\\u0631\\u0647\\u0627 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0645\\u0627 \\u0647\\u064a \\u0627\\u0644\\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u064a\\u0648\\u0645\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0643\\u0645 \\u0639\\u062f\\u062f \\u0627\\u0644\\u0627\\u0648\\u0631\\u0627\\u0642 \\u0627\\u0644\\u0627\\u062e\\u0628\\u0627\\u0631\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0627\\u0644\\u062a\\u064a \\u0648\\u062c\\u062f\\u062a \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0641\\u064a \\u0627\\u064a \\u0633\\u0646\\u0629 \\u0628\\u062f\\u0627\\u062a \\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u0637\\u0627\\u0644\\u0628 \\u0627\\u0644\\u062d\\u0633 \\u0627\\u0644\\u0633\\u0644\\u064a\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\"\r\n  ]\r\n```\r\n\r\nthe questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq \r\n","comment_length":111,"text":"bug in mlqa dataset  \n Hi \r\nLooking into MLQA dataset for langauge \"ar\":\r\n\r\n```\r\n \"question\": [\r\n    \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631?\",\r\n    \"\\u0643\\u0645 \\u0645\\u0631\\u0629 \\u064a\\u062a\\u0645 \\u0646\\u0634\\u0631\\u0647\\u0627 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0645\\u0627 \\u0647\\u064a \\u0627\\u0644\\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u064a\\u0648\\u0645\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0643\\u0645 \\u0639\\u062f\\u062f \\u0627\\u0644\\u0627\\u0648\\u0631\\u0627\\u0642 \\u0627\\u0644\\u0627\\u062e\\u0628\\u0627\\u0631\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0627\\u0644\\u062a\\u064a \\u0648\\u062c\\u062f\\u062a \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0641\\u064a \\u0627\\u064a \\u0633\\u0646\\u0629 \\u0628\\u062f\\u0627\\u062a \\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u0637\\u0627\\u0644\\u0628 \\u0627\\u0644\\u062d\\u0633 \\u0627\\u0644\\u0633\\u0644\\u064a\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\"\r\n  ]\r\n```\r\n\r\nthe questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq \r\n \n If you print those questions, you get readable texts:\r\n```python\r\n>>> questions = [\r\n...     \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631?\",\r\n...     \"\\u0643\\u0645 \\u0645\\u0631\\u0629 \\u064a\\u062a\\u0645 \\u0646\\u0634\\u0631\\u0647\\u0627 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n...     \"\\u0645\\u0627 \\u0647\\u064a \\u0627\\u0644\\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u064a\\u0648\\u0645\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n...     \"\\u0643\\u0645 \\u0639\\u062f\\u062f \\u0627\\u0644\\u0627\\u0648\\u0631\\u0627\\u0642 \\u0627\\u0644\\u0627\\u062e\\u0628\\u0627\\u0631\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0627\\u0644\\u062a\\u064a \\u0648\\u062c\\u062f\\u062a \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n...     \"\\u0641\\u064a \\u0627\\u064a \\u0633\\u0646\\u0629 \\u0628\\u062f\\u0627\\u062a \\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u0637\\u0627\\u0644\\u0628 \\u0627\\u0644\\u062d\\u0633 \\u0627\\u0644\\u0633\\u0644\\u064a\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\"\r\n... ]\r\n>>> print(questions)\r\n['\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?', '\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?', '\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?', '\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?', '\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?']\r\n```\r\nI don't think we can change this","embeddings":[-0.115853101,-0.231567964,-0.2734039724,0.2112123966,0.3321486115,0.0711947754,0.3524691463,0.1213381737,-0.3227720559,0.2254540175,0.0618873164,0.2817641795,0.2518624663,0.2561215162,0.1166762114,-0.047785569,0.1292088628,0.179456532,0.0600869805,-0.197704494,-0.196472913,0.2453739792,-0.1337222606,0.0539799668,-0.0679920763,-0.033509817,-0.0207405165,0.220893696,-0.0425398685,0.0830165371,-0.1433827132,-0.2492368817,-0.1639159769,0.005175015,-0.0001021291,-0.1083842143,0.1797213703,-0.0530055612,-0.2548291981,0.3303485215,-0.4433495402,0.0542143062,-0.3197640181,-0.3245649338,-0.18316935,-0.1904883683,-0.05527585,-0.5454843044,0.3158854544,0.3931446373,0.3555622101,0.1169286147,-0.0093527725,-0.0591307953,0.2337930202,-0.0075344178,0.0532882288,-0.0125398654,0.0099399295,-0.0157065429,0.0644993559,0.5903977156,0.1528967172,-0.0744416192,-0.2841548622,0.0947690308,0.2282186002,-0.1410573274,0.3744464219,0.2255229354,0.2068464309,-0.2394515574,-0.1113861203,0.070110254,0.03870086,-0.1902514845,-0.0511132963,0.2683143914,0.0487997532,0.0880704224,0.1475322396,0.1784594804,-0.1978724748,0.0273552109,-0.4308957458,0.4159484506,-0.1553923637,-0.0334863551,-0.1144851521,-0.2438467145,-0.1090199426,0.1160906479,-0.1431100965,0.1030091941,-0.1794525683,-0.188223511,-0.1247729361,0.2676885426,0.013106185,-0.0319190025,0.1373087317,0.1452646405,0.2267717719,-0.0552490279,-0.1274060756,-0.010375008,-0.1002315283,-0.115046382,0.2245224118,-0.1293273121,-0.0769749582,0.015334459,0.0609565601,-0.2191800922,-0.1487826705,0.2285619527,0.1345374286,-0.1410000473,-0.3242001534,0.056123469,-0.3493770361,0.0548069552,-0.1677003652,0.391261518,0.2288828194,-0.0240960065,-0.139952004,-0.0753454566,-0.1917124987,-0.3327361643,-0.3898272812,0.1255715787,0.1248719692,-0.27682212,-0.0159336664,0.3214123547,0.215539068,-0.0244428106,0.1260242909,0.0829229057,0.0949713215,-0.1912811249,0.2312819362,0.2930064201,-0.1015833616,-0.0493383966,-0.0328686349,-0.0564297847,0.0095267622,0.1452571899,0.0762135983,0.0079340953,-0.3933087587,0.3797929287,0.395275563,-0.0949567109,0.1754642427,0.2335368693,0.2187478989,0.0496398285,0.1180229709,-0.1062783301,-0.0219002888,-0.1518765986,0.063460812,0.0898392871,-0.526848197,0.0284028426,-0.3689579368,-0.0933342502,0.3024438322,0.3060186207,0.0991310477,-0.0708538219,-0.170061931,0.3025590479,0.1694825292,-0.0779909417,-0.3400383294,0.2042782009,-0.159998998,-0.1875737309,0.2839224935,0.2088894844,-0.1037751511,-0.0227849204,0.1018831581,0.1511971503,-0.1055432111,0.0276853014,-0.123123616,-0.1423805505,0.1725804061,0.0685132965,-0.2583670616,-0.1137779951,0.2595126629,0.024177283,0.3915384114,-0.0147941019,-0.0749591962,0.0135183893,0.3583312035,-0.4317682683,0.162547648,-0.0722043589,-0.3222065568,-0.219033882,-0.609854579,0.0809970871,-0.233429864,-0.1778639853,-0.1760995686,-0.1031846404,0.0356507711,0.0966448039,0.3250931799,0.1980949938,-0.0805992335,0.2429176867,0.0244634617,-0.0335509703,-0.0382368453,-0.1474969685,-0.3175995946,0.2270222902,-0.1953133941,0.0706109107,0.1879199296,0.3434305489,0.1390678883,-0.0944974348,0.1414386928,0.0136982501,0.095292449,0.2897173464,-0.1747563481,0.1883360296,0.2945405543,-0.2585287094,0.0214189049,0.3033981323,0.3103998601,0.0042769727,-0.1195691824,0.3716930151,0.0615129918,-0.0408308692,0.0971447751,0.029447915,0.4241673052,-0.0936098993,0.1024363488,-0.5371354222,0.168956995,0.1346676648,0.2095032185,0.0624080636,-0.5084814429,0.439779222,0.4208018482,-0.0966070369,0.0676753074,-0.1444756836,-0.1639984995,0.0293982271,0.0471223593,0.1910907626,0.2587710917,0.2780264914,0.0747869611,0.0024714572,-0.0050052307,-0.4039029181,0.34732306,-0.0582332052,0.0497225896,0.2490378618,0.1140516698,-0.0766907632,-0.3842312396,0.1208591983,-0.0701796636,0.0866600648,-0.2455856353,0.1962551326,-0.3665637672,-0.5910026431,-0.3121281564,-0.2207443565,0.1649776548,-0.2732044756,0.28374663,-0.1319197714,-0.2716562748,0.2348254919,0.1886444241,0.3562226892,0.238636449,0.2746743858,-0.310839653,-0.0177151002,-0.1842351109,0.310754627,0.1267085671,0.2490748763,0.1786777973,-0.2989786267,-0.1730295867,-0.2467732877,-0.4817385077,0.089297086,-0.032913927,0.3612640798,0.0369145088,-0.0061595929,-0.1015347168,-0.0593845882,-0.0475925393,0.1554066539,-0.321716845,-0.099974297,-0.1066943109,-0.0564780273,-0.2586625516,-0.5629889369,-0.2710545957,-0.1909809411,-0.0850012749,-0.0118034007,0.2423169762,-0.1813126653,-0.0354815312,0.1197240576,0.090256907,-0.0801457763,-0.3614607751,0.2991599441,0.361500293,-0.153798148,-0.4440942407,-0.1383514553,-0.1057202816,0.2217350155,-0.3010010421,-0.3994612396,-0.0869593993,-0.140109688,-0.2230976373,-0.0174844526,-0.073769547,0.2164241225,-0.1130887046,-0.2328870147,-0.0172807127,-0.1985118836,0.1154638603,0.130371049,0.169789657,-0.1722306162,0.5826883912,0.0520810261,0.1483297944,0.1584417224,-0.1299767792,0.171134308,-0.1682365537,0.1268296391,-0.0480326973,-0.045324862,0.3635538816,0.1533102244,-0.0188578218,0.3690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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2133","title":"bug in mlqa dataset ","comments":"Hi @dorost1234.\r\n\r\nIn Python 3, strings are sequences of Unicode _code points_. Unicode is a specification that maps all characters (and emoji symbols) with its unique representation in terms of code points. That is what you see: Unicode code points (represented by a \\u escaped sequence of 16-bit hex values).\r\n\r\nCharacters are usually represented (on screen and papers) with a graphical element called _glyph_. That is what you would like to see: glyphs. But Python does not care about glyphs: that is the job of the GUI or the terminal; glyphs are what you get with the `print` function (if your terminal is properly configured to display those glyphs).\r\n\r\nYou have more detailed information about Unicode in the Python documentation: https:\/\/docs.python.org\/3\/howto\/unicode.html","body":"Hi \r\nLooking into MLQA dataset for langauge \"ar\":\r\n\r\n```\r\n \"question\": [\r\n    \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631?\",\r\n    \"\\u0643\\u0645 \\u0645\\u0631\\u0629 \\u064a\\u062a\\u0645 \\u0646\\u0634\\u0631\\u0647\\u0627 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0645\\u0627 \\u0647\\u064a \\u0627\\u0644\\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u064a\\u0648\\u0645\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0643\\u0645 \\u0639\\u062f\\u062f \\u0627\\u0644\\u0627\\u0648\\u0631\\u0627\\u0642 \\u0627\\u0644\\u0627\\u062e\\u0628\\u0627\\u0631\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0627\\u0644\\u062a\\u064a \\u0648\\u062c\\u062f\\u062a \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0641\\u064a \\u0627\\u064a \\u0633\\u0646\\u0629 \\u0628\\u062f\\u0627\\u062a \\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u0637\\u0627\\u0644\\u0628 \\u0627\\u0644\\u062d\\u0633 \\u0627\\u0644\\u0633\\u0644\\u064a\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\"\r\n  ]\r\n```\r\n\r\nthe questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq \r\n","comment_length":121,"text":"bug in mlqa dataset  \n Hi \r\nLooking into MLQA dataset for langauge \"ar\":\r\n\r\n```\r\n \"question\": [\r\n    \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631?\",\r\n    \"\\u0643\\u0645 \\u0645\\u0631\\u0629 \\u064a\\u062a\\u0645 \\u0646\\u0634\\u0631\\u0647\\u0627 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0645\\u0627 \\u0647\\u064a \\u0627\\u0644\\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u064a\\u0648\\u0645\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0643\\u0645 \\u0639\\u062f\\u062f \\u0627\\u0644\\u0627\\u0648\\u0631\\u0627\\u0642 \\u0627\\u0644\\u0627\\u062e\\u0628\\u0627\\u0631\\u064a\\u0629 \\u0644\\u0644\\u0637\\u0644\\u0627\\u0628 \\u0627\\u0644\\u062a\\u064a \\u0648\\u062c\\u062f\\u062a \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\",\r\n    \"\\u0641\\u064a \\u0627\\u064a \\u0633\\u0646\\u0629 \\u0628\\u062f\\u0627\\u062a \\u0648\\u0631\\u0642\\u0629 \\u0627\\u0644\\u0637\\u0627\\u0644\\u0628 \\u0627\\u0644\\u062d\\u0633 \\u0627\\u0644\\u0633\\u0644\\u064a\\u0645 \\u0628\\u0627\\u0644\\u0646\\u0634\\u0631 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u0645?\"\r\n  ]\r\n```\r\n\r\nthe questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq \r\n \n Hi @dorost1234.\r\n\r\nIn Python 3, strings are sequences of Unicode _code points_. Unicode is a specification that maps all characters (and emoji symbols) with its unique representation in terms of code points. That is what you see: Unicode code points (represented by a \\u escaped sequence of 16-bit hex values).\r\n\r\nCharacters are usually represented (on screen and papers) with a graphical element called _glyph_. That is what you would like to see: glyphs. But Python does not care about glyphs: that is the job of the GUI or the terminal; glyphs are what you get with the `print` function (if your terminal is properly configured to display those glyphs).\r\n\r\nYou have more detailed information about Unicode in the Python documentation: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2132","title":"TydiQA dataset is mixed and is not split per language ","comments":"You can filter the languages this way:\r\n```python\r\ntydiqa_en = tydiqa_dataset.filter(lambda x: x[\"language\"] == \"english\")\r\n```\r\n\r\nOtherwise maybe we can have one configuration per language ?\r\nWhat do you think of this for example ?\r\n\r\n```python\r\nload_dataset(\"tydiqa\", \"primary_task.en\")\r\n```","body":"Hi @lhoestq \r\nCurrently TydiQA is mixed and user can only access the whole training set of all languages:\r\nhttps:\/\/www.tensorflow.org\/datasets\/catalog\/tydi_qa\r\n\r\nfor using this dataset, one need to train\/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have  them split and I appreciate your help on this. \r\n\r\nMeanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot ","comment_length":39,"text":"TydiQA dataset is mixed and is not split per language  \n Hi @lhoestq \r\nCurrently TydiQA is mixed and user can only access the whole training set of all languages:\r\nhttps:\/\/www.tensorflow.org\/datasets\/catalog\/tydi_qa\r\n\r\nfor using this dataset, one need to train\/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have  them split and I appreciate your help on this. \r\n\r\nMeanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot  \n You can filter the languages this way:\r\n```python\r\ntydiqa_en = tydiqa_dataset.filter(lambda x: x[\"language\"] == \"english\")\r\n```\r\n\r\nOtherwise maybe we can have one configuration per language ?\r\nWhat do you think of this for example ?\r\n\r\n```python\r\nload_dataset(\"tydiqa\", \"primary_task.en\")\r\n```","embeddings":[-0.2592062056,-0.23528862,-0.204411611,0.2665453851,0.2771573961,-0.025829006,0.3412586749,0.3332071304,-0.1514983028,0.077681914,-0.3343148828,-0.0462720059,-0.0167529695,0.4214190245,0.0419181399,-0.320132941,-0.1105380207,0.0581458621,-0.1372380257,-0.009822676,0.023834575,0.1549571157,-0.1817224771,0.1316394061,0.0915518329,-0.1736210138,-0.0809654817,-0.0455806702,0.0590337105,-0.0585781932,0.4464208782,0.1073087528,0.267031461,0.3341659904,-0.0001084638,0.1922285259,-0.0415637456,-0.3396548927,0.1110021845,-0.2360355109,-0.1122633591,-0.3174516559,-0.0924162939,-0.204316467,-0.5365697145,-0.1827822179,0.0295533817,-0.3825767338,0.3870956004,0.2981826663,0.1465835422,0.1663268059,-0.1849585027,0.2282588482,-0.0572569855,0.0546517596,-0.1057092249,-0.0530425459,0.5951901674,-0.0774741173,0.3307021856,0.2777237296,0.1899045557,0.0749691203,-0.4010953307,0.1116975546,0.0430990271,-0.5291082263,0.0033926866,0.5172391534,0.4501475692,0.0541579202,-0.3280532956,-0.1335923374,-0.0303184725,-0.2640711069,0.1254836172,0.3814415932,-0.1881920695,0.2486717999,0.3558285534,0.0850683898,-0.262557447,0.1491750926,-0.2938213944,0.3037575781,-0.0313543119,0.0603938252,-0.0548035987,-0.1423953772,0.2361643016,0.2435273081,0.0689521581,0.256490767,-0.4733611643,-0.2544720471,-0.1434990466,-0.3753200769,-0.1192742288,-0.3847779334,0.137922585,0.3340659738,-0.1463015974,-0.0021057045,0.2769569755,-0.0824466422,0.2340909988,0.3656898141,0.2035248131,-0.1067949831,0.0105769364,0.0757092983,-0.1920621395,-0.3306031525,-0.4287641644,0.2712708414,0.174067229,0.0394181311,-0.3159442842,-0.1879136562,-0.1263174713,-0.2216869295,0.1088807583,0.3437946141,-0.0275603309,-0.009170562,0.0795595348,0.4542489052,-0.3114559054,-0.3753986061,-0.1421310157,-0.053627532,-0.1073865518,-0.1681628376,0.0842008591,0.2148738205,-0.0248488355,0.0667255297,-0.0025112557,-0.1574177444,0.3517777622,-0.2013829499,0.117537111,0.0731887966,0.1900308579,0.0227768905,0.2191588134,-0.2975347042,-0.3487254381,0.0580839403,-0.278357178,-0.2406381369,0.0743742287,0.1674489826,0.3175940514,0.1445011795,0.0269591846,0.7229220271,0.3423041105,0.0413441323,-0.0289358981,0.0486030392,-0.2848916948,-0.0555126928,0.2881773114,0.162882939,-0.5802620053,-0.1281797439,0.2130147964,-0.322067529,0.215567112,0.2149673998,-0.1866123378,0.3718908727,0.0974325612,-0.2031816691,0.7471084595,-0.2007604241,-0.3531040549,0.039042335,0.1203382835,0.1161096543,0.0331046879,0.0693071336,0.1348564476,-0.0257895254,0.205305934,0.5336025357,-0.2365159988,0.0297370832,0.0447351895,-0.0627163351,0.3733536303,0.4188765585,-0.0572330654,-0.2378853709,-0.0126547432,0.2349274904,0.382068783,-0.1548735797,0.0942421779,0.0616843626,-0.303639859,0.1535947621,0.0307177715,-0.2157306373,-0.2534970939,0.2257353961,-0.3984822035,0.2190700918,0.0171267856,-0.1806215197,0.1356500089,-0.1984942108,-0.3050163984,-0.2456896752,0.1831280589,-0.1110467687,0.0207428113,0.1629618406,-0.1032495424,0.0985127985,-0.1514897197,-0.0800129473,-0.1922066063,0.0206570756,0.0212977212,-0.0716993809,0.0058197477,0.2450117022,-0.1134706959,-0.2013849914,-0.0574636124,-0.0935559794,0.1642187089,0.1159625202,-0.1913137138,-0.2085589916,0.3557848334,-0.0733127221,-0.0767932683,0.0469789654,0.0040056356,-0.1576796919,0.0584184006,0.5854184031,-0.0236305781,0.2771928012,0.0699686334,-0.0590587445,0.3816142678,-0.1984386891,-0.0212117136,-0.2191328704,0.2769998014,-0.1963724792,0.0008180983,-0.0419192724,-0.441222012,0.1670796573,0.4789676368,0.1411519647,0.2277944386,0.0006079229,0.305113405,0.1892092675,-0.0941263661,0.2189483792,0.1907681823,0.0479791425,0.2750668824,-0.1851740032,0.3741773963,-0.1362421513,0.0320604295,-0.158335954,0.0158972535,0.0188290253,-0.0313223079,-0.1062834114,-0.013290707,0.1975892037,0.1503423303,0.128862083,-0.022720214,0.2100903392,-0.4768428802,-0.3273683488,-0.2023428231,-0.307257086,0.0733935237,0.2087882161,0.2061761469,-0.4034985602,-0.1359766126,0.2662344277,0.2984233797,-0.0250988193,-0.1683412343,-0.1758296937,0.0169531498,-0.2763130963,-0.216508761,0.1792732626,0.2920338809,-0.0906245112,0.2981304228,-0.3847173154,-0.2344311476,0.0706419796,-0.2707393765,0.093471922,-0.2213926166,0.0547199436,-0.1595893353,0.1746494174,0.0280353669,-0.1556295156,0.3326012492,0.2598111033,-0.1141361222,0.1519023627,-0.0054758387,0.1311878562,0.0371142253,-0.6562100053,-0.7368292212,-0.1958166808,-0.2545368671,-0.1273786575,0.3844645321,-0.4101475775,0.0553293005,-0.1220950484,0.0513957217,0.1855765134,-0.0182751603,-0.157880187,0.1308661252,-0.3448569477,-0.3513146937,-0.0799213648,-0.0584392808,0.6852425337,0.0431133397,-0.1299108565,0.3011080325,-0.1743428707,0.055523973,-0.131667763,-0.0169700161,0.2986245751,-0.0941570997,0.1243958324,-0.0248790327,0.2955364585,0.1937161684,-0.2547276914,0.2401013076,0.0134681333,0.3823543489,0.1917834431,0.711796701,0.2415472418,-0.0094934255,0.2683726251,0.0604744852,-0.2061232775,-0.096832782,-0.3948800862,0.012679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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2132","title":"TydiQA dataset is mixed and is not split per language ","comments":"Hi\nthank you very much for the great response, this will be really wonderful\nto have one configuration per language, as one need the dataset in majority\nof case per language for cross-lingual evaluations.\nThis becomes also then more close to TFDS format, which is separated per\nlanguage https:\/\/www.tensorflow.org\/datasets\/catalog\/tydi_qa which will be\nreally awesome to have.\nthanks\n\nOn Mon, Mar 29, 2021 at 6:17 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> You can filter the languages this way:\n>\n> tydiqa_en = tydiqa_dataset.filter(lambda x: x[\"language\"] == \"english\")\n>\n> Otherwise maybe we can have one configuration per language ?\n> What do you think of this for example ?\n>\n> load_dataset(\"tydiqa\", \"primary_task.en\")\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2132#issuecomment-809516799>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMXPW2PWSQ2RHG73O7TTGCY4LANCNFSM4Z7ER7IA>\n> .\n>\n","body":"Hi @lhoestq \r\nCurrently TydiQA is mixed and user can only access the whole training set of all languages:\r\nhttps:\/\/www.tensorflow.org\/datasets\/catalog\/tydi_qa\r\n\r\nfor using this dataset, one need to train\/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have  them split and I appreciate your help on this. \r\n\r\nMeanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot ","comment_length":145,"text":"TydiQA dataset is mixed and is not split per language  \n Hi @lhoestq \r\nCurrently TydiQA is mixed and user can only access the whole training set of all languages:\r\nhttps:\/\/www.tensorflow.org\/datasets\/catalog\/tydi_qa\r\n\r\nfor using this dataset, one need to train\/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have  them split and I appreciate your help on this. \r\n\r\nMeanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot  \n Hi\nthank you very much for the great response, this will be really wonderful\nto have one configuration per language, as one need the dataset in majority\nof case per language for cross-lingual evaluations.\nThis becomes also then more close to TFDS format, which is separated per\nlanguage https:\/\/www.tensorflow.org\/datasets\/catalog\/tydi_qa which will be\nreally awesome to have.\nthanks\n\nOn Mon, Mar 29, 2021 at 6:17 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> You can filter the languages this way:\n>\n> tydiqa_en = tydiqa_dataset.filter(lambda x: x[\"language\"] == \"english\")\n>\n> Otherwise maybe we can have one configuration per language ?\n> What do you think of this for example ?\n>\n> load_dataset(\"tydiqa\", \"primary_task.en\")\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2132#issuecomment-809516799>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMXPW2PWSQ2RHG73O7TTGCY4LANCNFSM4Z7ER7IA>\n> .\n>\n","embeddings":[-0.3552147746,-0.232304275,-0.1865600795,0.2470623255,0.3051548004,-0.0820614621,0.3825841844,0.3362080455,-0.191450879,0.1315774769,-0.4605718851,-0.0628384873,0.0637698621,0.4531888664,0.0351658799,-0.2893125117,-0.1692789942,0.0985133275,-0.2443104386,0.0038089035,0.1467833221,0.2375836372,-0.1056776047,0.0857928395,0.0535366237,-0.2327951342,-0.0800041556,-0.0570415594,0.0407805629,-0.0867752507,0.4129017889,0.2202050239,0.2382610142,0.3516655266,-0.000102664,0.2214549333,-0.0919180363,-0.2966122925,0.0862295106,-0.1834036857,-0.101301223,-0.2273289114,-0.1310020983,-0.1961812079,-0.5325117707,-0.1905819923,-0.0044926647,-0.3459679782,0.3396964371,0.2731972039,0.1980174333,0.2754566371,-0.1090018749,0.1592267305,-0.0758001879,0.0541526936,-0.1506026238,-0.0746902749,0.4563406408,-0.0522275232,0.1966013759,0.273450762,0.2479817718,0.0726507157,-0.3480714858,0.0870823935,-0.0185377765,-0.4929688573,0.0308897756,0.6588577628,0.4845636785,-0.0275688395,-0.3650477529,-0.1043434516,-0.1176581979,-0.245856598,0.1216570064,0.2802472413,-0.1396408975,0.3281907439,0.3242917359,0.1832720935,-0.2296533287,0.1559876651,-0.3659547269,0.2008359134,-0.0907996818,-0.0041711116,0.0995028615,-0.1770900637,0.0782897547,0.2008456886,0.0825700238,0.194704935,-0.5239602923,-0.3435084224,0.0030070683,-0.4299471676,0.021633314,-0.3731372952,0.1823283583,0.3100450933,-0.2238860279,-0.0205531996,0.1694973707,0.0432292968,0.3170821965,0.2966723442,0.1683046371,-0.0715260655,0.1160192788,0.0968123525,-0.1079168543,-0.2469825447,-0.5589663386,0.1996992677,0.086108759,0.061725568,-0.2949924767,-0.26252985,-0.0720699802,-0.1945292056,0.1462904215,0.315251112,-0.0117469188,-0.0516727678,0.0411455706,0.338816911,-0.3357954621,-0.3760806024,-0.2025689334,0.0154270008,-0.0782455131,-0.1298018396,0.0722822621,0.153594017,-0.0297240801,0.0980604738,-0.0132704731,-0.0353594497,0.2344280034,-0.1937999129,0.1276607811,0.0803777575,0.1863416731,0.006922184,0.1699039042,-0.3082532585,-0.2829098105,-0.0149562228,-0.1393337846,-0.1989016235,-0.0089012599,0.2219691277,0.1782563478,0.1121172681,0.0467124507,0.8153547049,0.2904577851,0.0116438242,-0.0301055796,0.030306533,-0.3385447264,-0.0141155692,0.3378117979,0.1740821898,-0.6436671019,-0.0770445913,0.3162026405,-0.341324091,0.1643741578,0.2579717338,-0.1845357269,0.2711555958,0.0388609543,0.0341985077,0.5991305709,-0.1869712472,-0.3114596307,0.068309322,0.067710191,0.0123742167,0.029267801,-0.0617258921,0.2344316989,-0.0872586668,0.0991235226,0.4348345995,-0.2404679954,0.029494239,-0.0098550096,-0.1206749305,0.2951943278,0.3530770838,-0.1855426133,-0.2928940654,0.0624755621,0.2671692371,0.3282123804,-0.2255352587,0.0736841857,0.1006432101,-0.267234534,0.0892196447,0.0731190518,-0.2335533947,-0.3995292783,0.2664976418,-0.3235734701,0.2553372085,0.0927843228,-0.2169161737,0.1256585419,-0.2715001404,-0.1884758621,-0.2257441878,0.2386907786,-0.0153274126,0.0980970263,0.1489165127,-0.1053274199,-0.0331549011,-0.2175190002,-0.1276916862,-0.2352512479,-0.0042178808,0.0115162125,-0.0245697871,0.0336272828,0.205608651,-0.1945265383,-0.2773687541,-0.0686696544,-0.0554685481,0.0843357369,0.1762463599,-0.1867500693,-0.0557047464,0.360755831,-0.0796791762,-0.1604560316,0.0065563787,-0.0439492241,-0.1347127557,-0.0142064327,0.5713962317,-0.0082733352,0.153434068,0.1384377629,-0.1113999635,0.4390425086,-0.2118981183,-0.0669085383,-0.3066830039,0.3075168133,-0.2405388951,-0.0250526965,-0.0206583682,-0.4667216241,0.293887794,0.5241797566,0.1195422187,0.2275018096,0.0260458216,0.3398803473,0.1909879148,-0.0613884032,0.2351474315,0.1668780297,0.1093578115,0.3604397178,-0.211672008,0.3253709078,-0.1727220714,0.0418736897,-0.2282512337,-0.00065654,0.1424652338,0.012185324,-0.0922971666,-0.1998010129,0.1816362441,0.153148219,0.138164863,0.0138187101,0.1529121101,-0.5816357732,-0.3761355281,-0.2104390562,-0.2378638238,0.0063176104,0.2482803017,0.2964659631,-0.3051173091,-0.1677930057,0.3162819445,0.3392682374,0.0744111836,-0.1750604659,-0.0538644753,-0.071854502,-0.1794320494,-0.2444062233,0.2388399094,0.3472923338,-0.0135217048,0.3145675957,-0.3366048634,-0.2414300144,0.0362312235,-0.3904020488,0.1810480058,-0.2720422745,0.0022113458,-0.146835506,0.1355131716,-0.0168852434,-0.1380896121,0.2735020518,0.1713932008,-0.2211097032,0.0685836151,-0.0055789077,0.1013226211,-0.1264633685,-0.6710653901,-0.5951109529,-0.2512134314,-0.1333300173,-0.245185107,0.3593440354,-0.2196395695,0.0570242219,-0.0083577083,0.0164688993,0.154420957,-0.089319922,-0.1075412259,0.1558358818,-0.3837592006,-0.3826019168,-0.1815673411,-0.0794025734,0.6028256416,-0.0109306956,-0.1639606804,0.1444200724,-0.217967838,0.0313021056,-0.1359931976,-0.0464085154,0.3886911273,-0.1523509026,0.0700265169,-0.0642261431,0.210116908,0.2466238439,-0.1676119715,0.1926054358,0.0213303845,0.3507079482,0.2853187323,0.6870272756,0.2225359082,-0.0186998509,0.2611910403,0.0376181938,-0.0973070636,-0.1640946716,-0.3305539787,0.1045776755,0.279066771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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2131","title":"When training with Multi-Node Multi-GPU the worker 2 has TypeError: 'NoneType' object","comments":"Hi ! Thanks for reporting\r\nI was able to reproduce this issue. This was caused by missing split infos if a worker reloads the cache of the other worker.\r\n\r\nI just opened https:\/\/github.com\/huggingface\/datasets\/pull\/2137 to fix this issue","body":"\bversion: 1.5.0\r\nmet a very strange error, I am training large scale language model, and need train on 2 machines(workers).\r\nAnd sometimes I will get this error `TypeError: 'NoneType' object is not iterable`\r\nThis is traceback\r\n```\r\n\r\n71 | \u00a0 | Traceback (most recent call last):\r\n-- | -- | --\r\n72 | \u00a0 | File \"run_gpt.py\", line 316, in <module>\r\n73 | \u00a0 | main()\r\n74 | \u00a0 | File \"run_gpt.py\", line 222, in main\r\n75 | \u00a0 | delimiter=\"\\t\", column_names=[\"input_ids\", \"attention_mask\", \"chinese_ref\"])\r\n76 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 747, in load_dataset\r\n77 | \u00a0 | use_auth_token=use_auth_token,\r\n78 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 513, in download_and_prepare\r\n79 | \u00a0 | self.download_post_processing_resources(dl_manager)\r\n80 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 673, in download_post_processing_resources\r\n81 | \u00a0 | for split in self.info.splits:\r\n82 | \u00a0 | TypeError: 'NoneType' object is not iterable\r\n83 | \u00a0 | WARNING:datasets.builder:Reusing dataset csv (\/usr\/local\/app\/.cache\/huggingface\/datasets\/csv\/default-1c257ebd48e225e7\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2)\r\n84 | \u00a0 | Traceback (most recent call last):\r\n85 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/runpy.py\", line 193, in _run_module_as_main\r\n86 | \u00a0 | \"__main__\", mod_spec)\r\n87 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/runpy.py\", line 85, in _run_code\r\n88 | \u00a0 | exec(code, run_globals)\r\n89 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/torch\/distributed\/launch.py\", line 340, in <module>\r\n90 | \u00a0 | main()\r\n91 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/torch\/distributed\/launch.py\", line 326, in main\r\n92 | \u00a0 | sigkill_handler(signal.SIGTERM, None)  # not coming back\r\n93 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/torch\/distributed\/launch.py\", line 301, in sigkill_handler\r\n94 | \u00a0 | raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)\r\n\r\n```\r\nOn worker 1 it loads the dataset well, however on worker 2 will get this error. \r\nAnd I will meet this error from time to time, sometimes it just goes well.","comment_length":37,"text":"When training with Multi-Node Multi-GPU the worker 2 has TypeError: 'NoneType' object \n \bversion: 1.5.0\r\nmet a very strange error, I am training large scale language model, and need train on 2 machines(workers).\r\nAnd sometimes I will get this error `TypeError: 'NoneType' object is not iterable`\r\nThis is traceback\r\n```\r\n\r\n71 | \u00a0 | Traceback (most recent call last):\r\n-- | -- | --\r\n72 | \u00a0 | File \"run_gpt.py\", line 316, in <module>\r\n73 | \u00a0 | main()\r\n74 | \u00a0 | File \"run_gpt.py\", line 222, in main\r\n75 | \u00a0 | delimiter=\"\\t\", column_names=[\"input_ids\", \"attention_mask\", \"chinese_ref\"])\r\n76 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 747, in load_dataset\r\n77 | \u00a0 | use_auth_token=use_auth_token,\r\n78 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 513, in download_and_prepare\r\n79 | \u00a0 | self.download_post_processing_resources(dl_manager)\r\n80 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 673, in download_post_processing_resources\r\n81 | \u00a0 | for split in self.info.splits:\r\n82 | \u00a0 | TypeError: 'NoneType' object is not iterable\r\n83 | \u00a0 | WARNING:datasets.builder:Reusing dataset csv (\/usr\/local\/app\/.cache\/huggingface\/datasets\/csv\/default-1c257ebd48e225e7\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2)\r\n84 | \u00a0 | Traceback (most recent call last):\r\n85 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/runpy.py\", line 193, in _run_module_as_main\r\n86 | \u00a0 | \"__main__\", mod_spec)\r\n87 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/runpy.py\", line 85, in _run_code\r\n88 | \u00a0 | exec(code, run_globals)\r\n89 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/torch\/distributed\/launch.py\", line 340, in <module>\r\n90 | \u00a0 | main()\r\n91 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/torch\/distributed\/launch.py\", line 326, in main\r\n92 | \u00a0 | sigkill_handler(signal.SIGTERM, None)  # not coming back\r\n93 | \u00a0 | File \"\/data\/miniconda3\/lib\/python3.7\/site-packages\/torch\/distributed\/launch.py\", line 301, in sigkill_handler\r\n94 | \u00a0 | raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)\r\n\r\n```\r\nOn worker 1 it loads the dataset well, however on worker 2 will get this error. \r\nAnd I will meet this error from time to time, sometimes it just goes well. \n Hi ! Thanks for reporting\r\nI was able to reproduce this issue. This was caused by missing split infos if a worker reloads the cache of the other worker.\r\n\r\nI just opened https:\/\/github.com\/huggingface\/datasets\/pull\/2137 to fix this issue","embeddings":[-0.1806102991,-0.4468090534,0.0129236523,0.5495145917,0.1124854609,-0.0091340337,0.5815147161,0.2967011034,0.1129247844,0.2367109209,0.3380283117,0.0173764732,-0.1162154898,0.1675734371,-0.042410925,-0.2821591198,-0.1764215678,0.0434676446,-0.227805078,-0.214592278,-0.3433728218,0.1396662146,-0.1313170046,0.1889922619,-0.5063354373,-0.4234542847,-0.1063818559,-0.0327261165,0.2665303946,-0.2152226418,0.2046148032,-0.3352004588,0.1856167316,0.8994958401,-0.0001186402,0.148360461,0.1208232865,0.0624540038,-0.144217357,-0.4792484939,0.083634451,-0.3793367445,0.0438947678,-0.372520566,-0.1640225798,-0.1153089702,0.2508283257,0.0683696643,0.038129054,0.2097055912,0.1652571112,0.3379758,0.1185292006,-0.0910865888,-0.1230920553,-0.078175433,-0.124581486,-0.0570696816,-0.1463324875,0.2151985615,0.1408524811,0.3001521528,0.0600437447,0.0522123091,-0.0947405323,-0.0429644659,0.5682382584,-0.4400096834,0.0403892882,0.3434166312,0.0855879858,0.0078646038,-0.2014046609,-0.1323599219,0.1542741805,-0.118224524,0.1847367287,0.3629119694,-0.0531053208,0.1500600725,-0.1300462335,-0.0676265731,-0.2308019549,0.0534002073,-0.1115095243,0.0901557282,-0.1316496879,0.4046879113,0.1569740921,0.0425376371,0.1446679085,-0.1151811853,0.1561760455,0.0373051763,-0.4768414795,-0.2667486668,0.0841662511,-0.1505966187,-0.1545930207,-0.1207949668,0.1211863682,-0.1792515069,-0.1328291744,0.3402727544,0.2215323895,-0.208968699,-0.0280376002,0.0667775273,0.1572731435,-0.1440431923,-0.3448980153,0.0656591803,-0.123423703,-0.3975611329,0.0672159195,0.3313922286,0.550983429,0.0814335942,-0.2558076084,-0.3254729509,-0.4844892621,0.0634808987,0.1591971517,0.071599789,-0.0215618368,0.7668600082,-0.1380043477,0.1425333768,-0.2015482634,-0.4433032274,-0.150910601,0.2117178291,-0.2316481024,0.0988812521,-0.0608816147,0.0209043287,-0.0537675731,0.0669301525,0.006623521,-0.0675481558,0.2549972832,-0.6412526369,0.0265079904,0.2047789544,0.1866111457,0.1211187914,0.1911760271,-0.0848483518,-0.1786593199,0.0570020229,-0.4990594983,-0.264444977,-0.0435095765,0.071315363,0.1389947832,0.2218963653,0.0635314956,0.151714012,0.5338413119,-0.0446067266,0.041408807,-0.3242377341,-0.3369706869,-0.3063197136,-0.3215333521,0.4540395737,0.0853606761,-0.0651732013,0.2063797265,-0.058066532,0.3713686466,0.4545201957,0.0005992663,0.0837388784,-0.1234111786,0.0138952779,0.2660521865,-0.2399116904,0.0215655714,0.4763100743,-0.4028788805,0.0788712651,0.3922178149,-0.0496553518,-0.2771501541,-0.0372722521,0.1895166337,0.3423955739,-0.0628310069,0.009341618,-0.2271394581,-0.0792751983,0.4815947115,-0.1569288224,0.3270474672,0.0703963637,-0.0276751965,-0.1134375632,0.2275328934,-0.3470682502,-0.0631333143,0.1020137817,0.2154045105,-0.1462944895,0.0686946362,-0.1687207073,-0.2463460565,0.0542072281,-0.1364753246,0.1516544819,0.2225850374,-0.0803485885,0.2349827886,-0.2266496718,-0.2233921885,0.0553607643,0.0752304047,0.3720036149,-0.2636727989,0.1632019877,0.0245809779,0.0021455172,-0.3904486895,-0.2759417295,-0.1010340154,0.2078496218,-0.0291660819,-0.1070364043,-0.0839110985,0.2642500401,0.4416565597,-0.2129094154,0.2406531572,0.1404104829,-0.1080904603,0.0494443811,-0.0524048693,0.1803079396,0.35804829,-0.1872611344,-0.1409421414,0.2008781582,0.134699285,-0.1484245509,0.3699091673,0.4953466356,0.090521425,0.2097523212,0.0449271016,0.078612186,0.1215231866,0.0240474232,-0.0164274201,0.0827938691,0.0056439694,0.1275957525,-0.1125174537,0.0920489132,-0.6476395726,0.0956622809,0.8060963154,0.1173804998,0.4062262475,0.0086445129,0.1291960925,0.0277588628,-0.154481113,-0.0285173319,0.4229156077,-0.0422861688,0.2465559989,-0.0392972976,-0.0626192093,-0.1413778216,0.0522282161,-0.1825522035,0.7166270614,0.2830475867,0.132720679,-0.0022643518,-0.0056484104,-0.2538818419,0.2736298442,0.450527519,-0.3334290981,0.3502019644,-0.3673314154,-0.0159904975,-0.2581938505,0.2661478817,-0.0938294679,-0.0245775245,-0.2472632974,0.1864033341,0.1503834277,0.1223219782,0.35891819,0.1457659602,0.1071996093,0.2317183763,0.2299328446,0.0111648263,-0.2177335769,-0.072553508,0.2922357321,-0.3358941078,0.0090650646,-0.1226599514,-0.5176730752,-0.351428628,-0.0638254508,0.0656745881,0.013568298,0.0029365411,0.2864256203,0.0785256252,-0.0120970001,-0.0619992651,0.1552059203,-0.4553328753,-0.1128283218,0.2476546913,-0.3855648041,0.1096370816,-0.1286381036,-0.2846000791,-0.2985165715,-0.1682437807,0.6335537434,0.036101602,0.4326667488,0.1627968699,0.1140319705,0.1608608812,0.1367166787,0.0956066176,-0.1563305557,-0.4536629319,0.1469080001,0.0463184677,-0.2639672458,-0.0813129917,0.0779389441,0.3259366453,0.1940153837,-0.301644206,-0.2473767698,0.0698908642,-0.0073892139,-0.0802942067,0.043606136,0.3106846511,-0.081747666,-0.0795649514,0.1049370244,0.1663469374,0.1308562309,0.2061449587,0.2597747743,0.215860039,0.2530972958,0.2540295124,0.535112977,0.2206321359,-0.0674460903,0.2258820832,-0.1522954851,-0.004480199,0.1564436704,-0.3594617546,0.1473370194,-0.2330273092,-0.1332301199,-0.046654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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2130","title":"wikiann dataset is missing columns ","comments":"Here please find TFDS format of this dataset: https:\/\/www.tensorflow.org\/datasets\/catalog\/wikiann\r\nwhere there is a span column, this is really necessary to be able to use the data, and I appreciate your help @lhoestq ","body":"Hi\r\nWikiann dataset needs to have \"spans\" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq ","comment_length":32,"text":"wikiann dataset is missing columns  \n Hi\r\nWikiann dataset needs to have \"spans\" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq  \n Here please find TFDS format of this dataset: https:\/\/www.tensorflow.org\/datasets\/catalog\/wikiann\r\nwhere there is a span column, this is really necessary to be able to use the data, and I appreciate your help @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2130","title":"wikiann dataset is missing columns ","comments":"Hi !\r\nApparently you can get the spans from the NER tags using `tags_to_spans` defined here:\r\n\r\nhttps:\/\/github.com\/tensorflow\/datasets\/blob\/c7096bd38e86ed240b8b2c11ecab9893715a7d55\/tensorflow_datasets\/text\/wikiann\/wikiann.py#L81-L126\r\n\r\nIt would be nice to include the `spans` field in this dataset as in TFDS. This could be a good first issue for new contributors !\r\n\r\nThe objective is to use `tags_to_spans` in the `_generate_examples` method [here](https:\/\/github.com\/huggingface\/nlp\/blob\/c98e4b8f23e3770c401c6d9326e243e1ffd599ec\/datasets\/wikiann\/wikiann.py#L292-L316) to create he `spans` for each example.","body":"Hi\r\nWikiann dataset needs to have \"spans\" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq ","comment_length":61,"text":"wikiann dataset is missing columns  \n Hi\r\nWikiann dataset needs to have \"spans\" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq  \n Hi !\r\nApparently you can get the spans from the NER tags using `tags_to_spans` defined here:\r\n\r\nhttps:\/\/github.com\/tensorflow\/datasets\/blob\/c7096bd38e86ed240b8b2c11ecab9893715a7d55\/tensorflow_datasets\/text\/wikiann\/wikiann.py#L81-L126\r\n\r\nIt would be nice to include the `spans` field in this dataset as in TFDS. This could be a good first issue for new contributors !\r\n\r\nThe objective is to use `tags_to_spans` in the `_generate_examples` method [here](https:\/\/github.com\/huggingface\/nlp\/blob\/c98e4b8f23e3770c401c6d9326e243e1ffd599ec\/datasets\/wikiann\/wikiann.py#L292-L316) to create he `spans` for each example.","embeddings":[0.0096025355,-0.3400487602,-0.0416718014,0.2219899446,0.2957082987,0.1506409198,0.3469608426,0.0389459059,0.0920513347,0.2753715515,0.0485684238,-0.045065973,-0.0080623208,0.3632449508,0.3476263881,-0.7035677433,0.0311781894,0.2775577009,-0.1697649062,0.0125467908,-0.2386454344,0.4350678325,-0.3131059706,-0.1572514474,-0.4427957535,-0.0176231563,-0.2585735023,-0.0887654722,-0.0000620789,-0.2098566294,0.3393801153,-0.2286444157,0.2572216094,-0.1111909077,-0.0001127702,0.0135668544,0.0615054667,-0.0787709877,0.0453730971,-0.0841397271,0.0922175422,-0.2578555644,-0.0348374099,-0.2561403215,-0.3284046948,-0.0753948241,-0.1517588496,0.0951740965,-0.0247506388,0.2180269063,0.1504639238,0.7661395073,0.1667824239,-0.0779862031,0.3054935932,-0.1118118763,-0.3342805505,-0.2091901451,0.1571556777,-0.1502291113,0.0648353174,0.4155132771,0.1755719334,-0.210781768,0.382522136,0.3127261996,-0.3683208525,-0.4562546313,0.0013717967,0.7677554488,0.4328103364,0.0056352643,-0.0264454279,-0.4336354136,0.01052956,-0.0954040512,0.0216443855,0.1007573307,-0.1240713,0.2177965641,-0.0346526355,-0.0619390868,-0.2247723192,0.1798864007,-0.2414661199,0.2327037901,0.0405296348,-0.0485336147,0.0854124948,-0.0204809867,-0.2247584909,0.1801456213,0.1715926826,0.1254052967,-0.429428637,-0.0337392278,-0.0181058794,0.3602000475,0.2100777924,0.0503829122,-0.0640601814,-0.2628415525,0.056887351,0.1270198673,-0.1555838138,0.0523052365,0.0083033936,0.1369297951,-0.0462952778,0.2109606564,0.0836109295,-0.1855955273,-0.0599287488,0.0971124768,-0.3002229333,-0.3053974211,-0.1432881206,0.0434895977,0.0042301407,0.1945303231,0.0694304854,0.1005565748,0.0134357084,0.2938938439,-0.0275055505,0.4606292546,-0.0773592144,0.2148902118,0.0255656186,-0.0853917748,-0.3643148839,0.1905200779,0.0240063369,0.1809857786,0.2539698482,-0.3256501555,0.471021086,-0.0390254408,-0.0434678458,0.1570568681,-0.052610606,0.0993080512,-0.1652287543,0.4395032525,-0.0349980034,0.0834425688,0.1697856337,-0.1075861007,-0.0663325265,-0.0548300222,-0.2166231573,-0.0040973453,-0.2454872876,0.1436573416,-0.1251898706,-0.2313668728,-0.0372504257,0.2354412079,0.1080498323,-0.2084845752,0.0712388083,0.0248907413,-0.3170247674,-0.1683148593,0.5056077838,0.0399223454,-0.3781881928,-0.3986588418,0.2939234674,-0.1754312366,0.1552590728,0.2094493359,0.0846953318,0.1171685159,-0.1244228855,0.6250929236,0.1958319098,-0.2356544137,-0.1009486169,-0.1965232342,0.1829894483,0.2725662589,0.0235283356,-0.0120192813,0.4229515195,0.1425526589,-0.04571734,0.3367981911,-0.2082446963,-0.0657014027,-0.1000476927,-0.1101307869,0.2190688848,0.1781906635,0.184169203,0.1768701673,-0.2432332784,0.2072175443,0.1569544971,-0.3025969267,0.1999224126,0.2825665772,0.022987444,0.2774751782,0.1182470098,-0.2388511896,-0.6610696316,0.2167615891,-0.0592281856,0.4719746411,0.2986933887,-0.2056801617,-0.1835254431,-0.1310995668,0.0069148391,-0.3616106808,0.108855322,-0.0447663888,0.0705667883,0.2840746045,-0.2449754775,-0.1201994866,-0.4714967906,0.2484924793,-0.5372623205,0.3105761111,-0.0796099827,0.0625527874,-0.0304808728,0.4805233181,-0.0285154581,0.0097297132,0.1863795072,0.0043972102,-0.2089272439,0.2935680747,-0.1489443332,0.3889605105,0.2140198648,-0.3447876275,-0.1804744601,-0.0957653672,-0.0777004063,-0.2883735895,-0.0209821425,0.2886338234,0.0869845748,0.4630931914,0.1234813854,0.3386545479,-0.0108269025,-0.1775801182,-0.0193868633,-0.2788067162,0.1496568769,-0.5367226601,0.1365720183,-0.0645934567,-0.4196057022,0.2042813897,0.427975446,0.0633509979,0.0718592405,0.0658705533,-0.4009689987,0.1018072888,0.0327859856,0.0492319576,0.1975305676,0.1843510568,0.3715714514,0.0923902094,-0.0590708666,-0.2055073678,0.2382936627,-0.0001002185,0.1313720047,0.2200181633,-0.0017095954,0.0597875081,-0.1700548679,0.1733988523,-0.2498636097,0.3950412869,-0.3663571775,-0.0438934714,-0.1436811239,-0.2902813852,0.0288748071,-0.0873045996,-0.141522035,-0.4789790809,0.0346712805,-0.0348249972,0.0401363261,0.171226576,-0.1327504516,0.2457701415,-0.11718072,0.1424688846,-0.4899621308,-0.1282466203,-0.17699866,0.148713395,-0.0496284142,-0.0152700916,0.0834247991,-0.2558934987,0.060629528,-0.2867175341,-0.5384836793,0.1535821855,-0.2546754777,-0.0277708229,0.2747757733,0.6557905078,-0.2294668406,-0.2044860721,0.256631881,-0.3007084727,-0.1779071093,0.1082173362,-0.2639544606,0.1515257061,-0.251955837,-0.2615916729,-0.2821849585,-0.2204409391,0.1645997465,-0.0915771127,-0.012906163,0.236612767,-0.0878046602,0.2831338942,-0.1692287624,0.0539389476,-0.1425020844,-0.2312063873,0.4616045058,-0.2854109108,-0.2608576119,0.080700174,-0.3057962358,0.5293775797,0.0693496987,-0.3885041177,-0.1409018785,0.1390135735,0.1803540736,0.0790945366,0.0125414105,0.4569828808,-0.3402540088,0.1417889595,-0.3257477283,0.316938132,-0.0234829076,-0.2708432972,0.142745629,-0.159111172,0.1861771345,0.1096415371,0.2882342935,0.2257869989,0.0538421944,0.2514748573,-0.4117173851,0.2570097744,0.0497902818,-0.1021385714,0.3323720992,-0.02468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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2130","title":"wikiann dataset is missing columns ","comments":"Hi @lhoestq \r\nthank you very much for the help, it would be very nice to have it included, here is the full code, one need to also convert tags to string first:\r\n\r\n```\r\nimport datasets \r\nfrom datasets import load_dataset\r\n\r\ndef tags_to_spans(tags):\r\n  \"\"\"Convert tags to spans.\"\"\"\r\n  spans = set()\r\n  span_start = 0\r\n  span_end = 0\r\n  active_conll_tag = None\r\n  for index, string_tag in enumerate(tags):\r\n    # Actual BIO tag.\r\n    bio_tag = string_tag[0]\r\n    assert bio_tag in [\"B\", \"I\", \"O\"], \"Invalid Tag\"\r\n    conll_tag = string_tag[2:]\r\n    if bio_tag == \"O\":\r\n      # The span has ended.\r\n      if active_conll_tag:\r\n        spans.add((active_conll_tag, (span_start, span_end)))\r\n      active_conll_tag = None\r\n      # We don't care about tags we are\r\n      # told to ignore, so we do nothing.\r\n      continue\r\n    elif bio_tag == \"B\":\r\n      # We are entering a new span; reset indices and active tag to new span.\r\n      if active_conll_tag:\r\n        spans.add((active_conll_tag, (span_start, span_end)))\r\n      active_conll_tag = conll_tag\r\n      span_start = index\r\n      span_end = index\r\n    elif bio_tag == \"I\" and conll_tag == active_conll_tag:\r\n      # We're inside a span.\r\n      span_end += 1\r\n    else:\r\n      # This is the case the bio label is an \"I\", but either:\r\n      # 1) the span hasn't started - i.e. an ill formed span.\r\n      # 2) We have IOB1 tagging scheme.\r\n      # We'll process the previous span if it exists, but also include this\r\n      # span. This is important, because otherwise, a model may get a perfect\r\n      # F1 score whilst still including false positive ill-formed spans.\r\n      if active_conll_tag:\r\n        spans.add((active_conll_tag, (span_start, span_end)))\r\n      active_conll_tag = conll_tag\r\n      span_start = index\r\n      span_end = index\r\n  # Last token might have been a part of a valid span.\r\n  if active_conll_tag:\r\n    spans.add((active_conll_tag, (span_start, span_end)))\r\n  # Return sorted list of spans\r\n  return sorted(list(spans), key=lambda x: x[1][0])\r\n\r\ndataset = load_dataset('wikiann', 'en', split=\"train\")\r\nner_tags = {\r\n   0:\"O\",\r\n   1:\"B-PER\",\r\n   2:\"I-PER\",\r\n   3:\"B-ORG\",\r\n   4:\"I-ORG\",\r\n   5:\"B-LOC\",\r\n   6:\"I-LOC\"\r\n}\r\n\r\ndef get_spans(tokens, tags):\r\n  \"\"\"Convert tags to textspans.\"\"\"\r\n  spans = tags_to_spans(tags)\r\n  text_spans = [\r\n      x[0] + \": \" + \" \".join([tokens[i]\r\n                              for i in range(x[1][0], x[1][1] + 1)])\r\n      for x in spans\r\n  ]\r\n  if not text_spans:\r\n    text_spans = [\"None\"]\r\n  return text_spans\r\n\r\n\r\nfor i, d in enumerate(dataset):\r\n   tokens = d['tokens']\r\n   tags = d['ner_tags']\r\n   tags = [ner_tags[i] for i in tags]\r\n   spans = get_spans(tokens, tags)\r\n   print(\"spans \", spans)\r\n   print(d)\r\n   if i > 10:\r\n     break; \r\n```\r\nI am not sure how to contribute to the repository and how things work, could you let me know how one can access the datasets to be able to contribute to the repository? Maybe I could do it then\r\nthanks \r\n","body":"Hi\r\nWikiann dataset needs to have \"spans\" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq ","comment_length":402,"text":"wikiann dataset is missing columns  \n Hi\r\nWikiann dataset needs to have \"spans\" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq  \n Hi @lhoestq \r\nthank you very much for the help, it would be very nice to have it included, here is the full code, one need to also convert tags to string first:\r\n\r\n```\r\nimport datasets \r\nfrom datasets import load_dataset\r\n\r\ndef tags_to_spans(tags):\r\n  \"\"\"Convert tags to spans.\"\"\"\r\n  spans = set()\r\n  span_start = 0\r\n  span_end = 0\r\n  active_conll_tag = None\r\n  for index, string_tag in enumerate(tags):\r\n    # Actual BIO tag.\r\n    bio_tag = string_tag[0]\r\n    assert bio_tag in [\"B\", \"I\", \"O\"], \"Invalid Tag\"\r\n    conll_tag = string_tag[2:]\r\n    if bio_tag == \"O\":\r\n      # The span has ended.\r\n      if active_conll_tag:\r\n        spans.add((active_conll_tag, (span_start, span_end)))\r\n      active_conll_tag = None\r\n      # We don't care about tags we are\r\n      # told to ignore, so we do nothing.\r\n      continue\r\n    elif bio_tag == \"B\":\r\n      # We are entering a new span; reset indices and active tag to new span.\r\n      if active_conll_tag:\r\n        spans.add((active_conll_tag, (span_start, span_end)))\r\n      active_conll_tag = conll_tag\r\n      span_start = index\r\n      span_end = index\r\n    elif bio_tag == \"I\" and conll_tag == active_conll_tag:\r\n      # We're inside a span.\r\n      span_end += 1\r\n    else:\r\n      # This is the case the bio label is an \"I\", but either:\r\n      # 1) the span hasn't started - i.e. an ill formed span.\r\n      # 2) We have IOB1 tagging scheme.\r\n      # We'll process the previous span if it exists, but also include this\r\n      # span. This is important, because otherwise, a model may get a perfect\r\n      # F1 score whilst still including false positive ill-formed spans.\r\n      if active_conll_tag:\r\n        spans.add((active_conll_tag, (span_start, span_end)))\r\n      active_conll_tag = conll_tag\r\n      span_start = index\r\n      span_end = index\r\n  # Last token might have been a part of a valid span.\r\n  if active_conll_tag:\r\n    spans.add((active_conll_tag, (span_start, span_end)))\r\n  # Return sorted list of spans\r\n  return sorted(list(spans), key=lambda x: x[1][0])\r\n\r\ndataset = load_dataset('wikiann', 'en', split=\"train\")\r\nner_tags = {\r\n   0:\"O\",\r\n   1:\"B-PER\",\r\n   2:\"I-PER\",\r\n   3:\"B-ORG\",\r\n   4:\"I-ORG\",\r\n   5:\"B-LOC\",\r\n   6:\"I-LOC\"\r\n}\r\n\r\ndef get_spans(tokens, tags):\r\n  \"\"\"Convert tags to textspans.\"\"\"\r\n  spans = tags_to_spans(tags)\r\n  text_spans = [\r\n      x[0] + \": \" + \" \".join([tokens[i]\r\n                              for i in range(x[1][0], x[1][1] + 1)])\r\n      for x in spans\r\n  ]\r\n  if not text_spans:\r\n    text_spans = [\"None\"]\r\n  return text_spans\r\n\r\n\r\nfor i, d in enumerate(dataset):\r\n   tokens = d['tokens']\r\n   tags = d['ner_tags']\r\n   tags = [ner_tags[i] for i in tags]\r\n   spans = get_spans(tokens, tags)\r\n   print(\"spans \", spans)\r\n   print(d)\r\n   if i > 10:\r\n     break; \r\n```\r\nI am not sure how to contribute to the repository and how things work, could you let me know how one can access the datasets to be able to contribute to the repository? Maybe I could do it then\r\nthanks \r\n","embeddings":[0.0544689596,-0.3270129263,-0.0403905883,0.1326312274,0.2911989093,0.2451720685,0.4067163765,0.1009387821,0.3828304708,0.1556164026,-0.0446803346,-0.2178338468,0.022738019,0.5038781166,0.3199461699,-0.5506708026,0.1787430644,0.2836467326,0.0272963587,-0.1717296094,-0.3373773396,0.4151010215,-0.3747096658,-0.0913586766,-0.4198635519,-0.0521617867,-0.1558331102,0.0584633909,-0.2034338564,-0.4034020305,0.329411298,-0.2170119286,0.3202043772,-0.1154671088,-0.000112541,-0.1020316333,0.1146142855,-0.074127838,-0.0352271535,0.1029127836,-0.002470843,-0.3005408645,-0.0322350711,-0.2677301466,-0.2919104695,-0.127156347,-0.3608503342,-0.0884254649,-0.1699058861,0.1173611358,0.159419924,0.4930120409,0.187277481,-0.0216508061,0.3861594796,-0.3597493768,-0.2000508308,-0.2237435728,0.2682072818,-0.1672688574,0.2621860504,0.5828677416,-0.0686727986,-0.2373126149,0.3806412518,0.1863372028,-0.0965012759,-0.3542980254,0.1478399783,0.4884112477,0.4684244394,-0.1065146923,-0.1449523121,-0.2883477807,0.0657660663,-0.3216191232,0.0381794125,0.1077186316,0.0494384505,0.2737787962,-0.0201080497,0.0279782601,-0.0424054191,0.0508821867,-0.210271731,0.5055118203,0.0122651393,-0.0037946987,0.0270377286,0.0542170182,-0.2672324479,0.0604036003,-0.000239622,0.1808323115,-0.4676630497,-0.0149633326,-0.0637844354,0.7178283334,0.3234871626,0.1278939843,-0.2642259598,-0.4074225724,0.2040248364,0.0630208775,0.0163024049,0.2194528282,0.017754212,0.1384577304,-0.1093440801,0.2411258668,0.1371101886,-0.1718849093,0.1250827461,0.0112145869,0.0479666665,-0.0958264023,-0.0406199582,-0.1761170626,-0.1931806505,0.1283470541,-0.0858135149,0.0573211499,0.1037845463,0.3675775528,0.0357893631,0.4057350755,-0.018197991,0.2109326571,0.0414993502,-0.184901908,-0.3456234634,0.172377035,-0.0278714001,0.0412089787,0.206149891,-0.2425575107,0.4308030903,-0.0141541502,-0.1144159213,0.1334284395,-0.1852980107,-0.1513625234,0.0604233481,0.4951552749,-0.1300824881,0.2043958306,0.1434557885,-0.2034413368,-0.0840799585,0.0595366172,-0.2339164466,0.0201573782,-0.4020797014,0.1664364785,-0.1108929515,-0.2205124497,-0.0506509095,0.1065469459,0.0239960384,-0.2974378765,0.1126538292,-0.0470756963,-0.2015538961,-0.030695213,0.5745875239,0.189858973,-0.1930187941,-0.4148733914,0.436081022,-0.0263606999,0.2025561035,0.2573499382,0.2017432451,0.1720208228,-0.1324974298,0.4924700558,0.0134954182,-0.40783301,-0.2425951064,-0.3458082676,0.2205595076,0.3580059409,0.1147091091,0.0673441067,0.3221626282,0.0256251562,0.0146008562,0.3263651431,-0.0976219475,-0.020421287,-0.2326065004,-0.1400739849,0.236929372,0.1449745744,0.0888518617,0.0245976467,-0.2037452757,-0.005946245,0.2381316125,-0.316313684,0.1725292206,0.2288783789,0.08282125,0.2735074461,0.232669726,-0.3583384752,-0.5895218849,0.2272499949,0.1954203993,0.255638063,0.1291144043,-0.2900441289,-0.3728681803,-0.0594472177,-0.1254445165,-0.3232939243,0.1320796162,0.0530789942,0.0084884148,0.1862318367,-0.2948439717,-0.1074061468,-0.2357054651,0.2480612695,-0.3208344877,0.2400354594,-0.1686615497,-0.0328182504,0.0763440505,0.5403302312,0.2565369308,0.1897570938,0.2228338718,0.0916352421,-0.1450525075,0.0279494468,-0.0130796339,0.3257299066,0.1200422198,-0.3045971096,-0.1943529397,-0.1095432118,0.170019567,-0.2002994716,0.0100841383,0.0964758247,-0.0876425877,0.3575112224,-0.0103016002,0.2266384512,0.1442184597,-0.317783922,-0.0587051809,-0.2331504822,0.1952025294,-0.5777381063,0.1849351972,-0.1640563011,-0.2464762926,0.1813760549,0.4493895471,-0.098596096,0.0781156868,-0.1623220593,-0.4065583348,0.1908221841,0.0960349143,0.0082345149,0.2017858922,0.2465304881,0.1954315603,-0.087123096,-0.0235711709,-0.0434327573,0.2000274062,-0.0140677243,0.0008669352,0.2576184273,0.042649895,0.083630316,-0.1842699945,0.1168388054,-0.3363680243,0.2314849198,-0.3505465984,-0.0540646389,-0.0917514116,-0.1673776954,0.0889806449,-0.4040912092,-0.0554510579,-0.3870818913,0.1263013333,-0.2790803611,0.0139314625,0.2027008981,-0.2238692641,0.3222596943,-0.0865766481,0.0373751707,-0.5950977206,-0.1842696071,-0.174240604,0.0743246302,-0.1024547964,0.1173359379,0.0428954512,-0.4117192328,-0.0802081302,-0.2833993435,-0.4494692981,0.1166326553,-0.2666516304,0.1625020057,0.3171647787,0.4674129486,-0.0920891464,-0.1513972878,0.3166525364,-0.1866428107,-0.0389574729,0.1505758762,-0.0790561214,0.0194667708,-0.1821569353,-0.1631705165,-0.3570208848,-0.0780605748,0.1331015676,-0.0158120357,-0.139848724,0.1193088666,-0.0854908153,0.1214030683,-0.1633446962,0.155112505,-0.1893157959,-0.3184945583,0.3573021889,-0.0028754638,-0.2122382373,0.0864414126,-0.2347598672,0.3610833287,-0.0930034667,-0.3724879026,-0.1330963075,0.2365102023,0.1786486953,0.0681660175,0.0832075551,0.4093667865,-0.1339916289,0.1313051432,-0.3668015599,0.0353037491,-0.1806097031,-0.4232722819,0.2840663493,-0.3843836486,0.2557320893,0.0164650436,0.1918068528,0.206547454,0.2342860401,0.2982036173,-0.2027377784,0.2933222651,-0.0803180486,-0.315286845,0.286696285,-0.1873613596,0.12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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2130","title":"wikiann dataset is missing columns ","comments":"Cool ! Let me give you some context:\r\n\r\n#### Contribution guide\r\n\r\nYou can find the contribution guide here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/CONTRIBUTING.md\r\n\r\nIt explains how to set up your dev environment in a few steps.\r\n\r\n#### Dataset loading\r\n\r\nEach Dataset is defined by a Table that have many rows (one row = one example) and columns (one column = one feature).\r\nTo change how a dataset is constructed, you have to modify its dataset script that you can find here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wikiann\/wikiann.py\r\n\r\nIt includes everything needed to load the WikiANN dataset.\r\nYou can load locally a modified version of `wikiann.py` with `load_dataset(\"path\/to\/wikiann.py\")`.\r\n\r\n#### Define a new column\r\n\r\nEach column has a name and a type. You can see how the features of WikiANN are defined here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c98e4b8f23e3770c401c6d9326e243e1ffd599ec\/datasets\/wikiann\/wikiann.py#L245-L263\r\n\r\nIdeally we would have one additional feature \"spans\":\r\n```python\r\n        \"spans\": datasets.Sequence(datasets.Value(\"string\")),\r\n```\r\n\r\n#### Compute the content of each row\r\n\r\nTo build the WikiANN rows, the _generate_examples method from [here](https:\/\/github.com\/huggingface\/nlp\/blob\/c98e4b8f23e3770c401c6d9326e243e1ffd599ec\/datasets\/wikiann\/wikiann.py#L292-L316) is used. This function `yield` one python dictionary for each example:\r\n```python\r\nyield guid_index, {\"tokens\": tokens, \"ner_tags\": ner_tags, \"langs\": langs}\r\n```\r\n\r\nThe objective would be to return instead something like\r\n```python\r\nspans = spans = get_spans(tokens, tags)\r\nyield guid_index, {\"tokens\": tokens, \"ner_tags\": ner_tags, \"langs\": langs, \"spans\": spans}\r\n```\r\n\r\nLet me know if you have questions !","body":"Hi\r\nWikiann dataset needs to have \"spans\" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq ","comment_length":208,"text":"wikiann dataset is missing columns  \n Hi\r\nWikiann dataset needs to have \"spans\" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq  \n Cool ! Let me give you some context:\r\n\r\n#### Contribution guide\r\n\r\nYou can find the contribution guide here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/CONTRIBUTING.md\r\n\r\nIt explains how to set up your dev environment in a few steps.\r\n\r\n#### Dataset loading\r\n\r\nEach Dataset is defined by a Table that have many rows (one row = one example) and columns (one column = one feature).\r\nTo change how a dataset is constructed, you have to modify its dataset script that you can find here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wikiann\/wikiann.py\r\n\r\nIt includes everything needed to load the WikiANN dataset.\r\nYou can load locally a modified version of `wikiann.py` with `load_dataset(\"path\/to\/wikiann.py\")`.\r\n\r\n#### Define a new column\r\n\r\nEach column has a name and a type. You can see how the features of WikiANN are defined here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c98e4b8f23e3770c401c6d9326e243e1ffd599ec\/datasets\/wikiann\/wikiann.py#L245-L263\r\n\r\nIdeally we would have one additional feature \"spans\":\r\n```python\r\n        \"spans\": datasets.Sequence(datasets.Value(\"string\")),\r\n```\r\n\r\n#### Compute the content of each row\r\n\r\nTo build the WikiANN rows, the _generate_examples method from [here](https:\/\/github.com\/huggingface\/nlp\/blob\/c98e4b8f23e3770c401c6d9326e243e1ffd599ec\/datasets\/wikiann\/wikiann.py#L292-L316) is used. This function `yield` one python dictionary for each example:\r\n```python\r\nyield guid_index, {\"tokens\": tokens, \"ner_tags\": ner_tags, \"langs\": langs}\r\n```\r\n\r\nThe objective would be to return instead something like\r\n```python\r\nspans = spans = get_spans(tokens, tags)\r\nyield guid_index, {\"tokens\": tokens, \"ner_tags\": ner_tags, \"langs\": langs, \"spans\": spans}\r\n```\r\n\r\nLet me know if you have questions !","embeddings":[0.00569537,-0.2647256255,-0.0400679149,0.0995709822,0.3907708824,0.108981356,0.2883937061,0.1041567475,0.0132615454,0.1453700364,-0.0328590088,-0.012727851,0.0221513528,0.4286503494,0.2620583177,-0.7255477905,0.1374138445,0.2676771879,-0.2057022899,0.064583838,-0.3263520598,0.3927432895,-0.3106453419,-0.1321281493,-0.3484145701,0.0134390527,-0.2690922916,0.1206341535,-0.094850786,-0.2926417589,0.3413374126,-0.2243309915,0.1851857305,-0.0907957479,-0.0001031631,-0.0195615683,0.1880478561,-0.0933684632,-0.1129946187,0.104285799,0.0963925868,-0.3275562525,-0.0058890199,-0.3028342426,-0.3155614734,-0.1220751405,-0.314163208,-0.0753199756,0.0279854368,0.0826015249,0.258515358,0.7911584973,0.3671272695,-0.1308148801,0.1523732394,-0.1912457943,-0.2992332876,-0.1442314386,0.1775325537,-0.1432342082,0.0846133232,0.4101109803,-0.0642807782,-0.1549720764,0.3905907869,0.1574980766,-0.3582746983,-0.2313543558,0.1976914704,0.5012369752,0.4747832119,-0.1316248775,-0.1059161797,-0.3632461429,0.0720394626,0.013684826,0.1230837405,0.1361771077,0.0144161973,0.2254358381,-0.0582174473,0.0265036747,-0.0693991557,0.1189300045,-0.2114753872,0.2074497342,-0.1068044305,0.0620140657,0.0106154885,-0.0684678257,-0.2683206797,0.1802330613,-0.0499178059,0.1471552402,-0.3356831372,-0.004959424,0.0384217538,0.6451608539,0.3114064932,0.160973534,-0.1400149614,-0.1313830912,0.1271444261,0.0326535068,0.0230206288,0.1219923571,-0.0616221689,0.0901369378,-0.1017376259,0.4083092511,0.1303039044,-0.2530061603,0.0655365959,-0.0008754515,-0.2312635183,-0.2843500972,0.0263554864,-0.0653640851,0.0867279246,0.0201112106,0.2051498741,0.1149983779,0.0867084563,0.4324637055,0.0214245394,0.4151434004,-0.0994242132,0.2552137077,-0.0415621698,-0.1355647743,-0.372338891,0.1453407109,-0.0185190216,0.1431327164,0.2540236712,-0.2808769643,0.5359506011,0.0414320454,0.0351255909,0.0959896296,-0.1034573913,0.0935248286,-0.219346121,0.3652416766,-0.0746772513,0.0697429031,0.2229989767,0.0003277576,-0.1197451428,-0.1145025715,-0.1494786739,-0.1088829413,-0.2668517232,0.2236600816,-0.1043155044,-0.1133723333,-0.0867222175,0.0667773783,0.0291008446,-0.3127827644,0.1017027423,0.0485590585,-0.0921484828,-0.2088678628,0.6047569513,0.1654423475,-0.1434135288,-0.3574486077,0.2727653384,-0.0880977288,0.1259169877,0.1465769857,0.0717699602,-0.0579953566,-0.1217666268,0.3233641386,-0.033621598,-0.1268147528,-0.2349395156,-0.1781380624,0.1438904107,0.2943347096,0.1185503453,-0.0268246569,0.2524349988,0.0249335952,-0.0260239821,0.2677815557,-0.0522507429,0.0685677305,-0.1652991623,-0.2542303503,-0.1006456688,0.114961639,0.0070869769,0.0217571016,-0.1243227646,0.0791169778,0.1917811185,-0.2824204862,0.2072332799,0.2506262958,0.0283801816,0.3811194897,0.1086606234,-0.1813731343,-0.55864501,0.1994146258,0.1036452651,0.218346417,0.1075798646,-0.3085534275,-0.2711724937,-0.0610955432,-0.1522563845,-0.3668555617,0.1992108822,0.0398298502,0.2403293103,0.2699042261,-0.2106195092,0.019899223,-0.1637937129,0.1998686343,-0.5162340403,0.2422401458,-0.144382,0.1127644554,0.2207106501,0.5053961873,0.1098286957,0.095648095,0.2538810372,0.2179433852,-0.2460688949,0.2443443537,-0.0961793214,0.1683004946,0.203624621,-0.2858159542,0.0485560559,-0.0938987285,0.12423978,-0.1547206193,-0.0726822913,0.3829087913,-0.1319556385,0.3680707216,0.0671642795,0.3307341933,0.1689669788,-0.2372763753,-0.1055499837,-0.2722427845,0.1091720164,-0.4910502434,0.1773588657,-0.1322771311,-0.3371235132,0.1953537762,0.2848120928,0.025427267,0.1182907894,-0.2042376548,-0.4753547609,0.1389717013,0.1840975881,0.0435797796,0.2743403912,0.3023234904,0.2259216607,0.0136573724,-0.073548831,-0.1543085277,0.1365187764,0.0112465713,-0.0455773249,0.1225967035,0.0138392234,-0.0227478985,-0.1749592274,0.1751216054,-0.3515152335,0.2264763713,-0.2688479722,-0.1025939211,-0.1479930282,-0.1891606003,0.087436609,-0.2197569311,-0.0882473812,-0.3651939034,-0.041730389,-0.1194724292,-0.0319992974,0.1705795676,-0.2978868186,0.1058899164,-0.1315856725,-0.0018019387,-0.4364799857,-0.1045141891,-0.1432973146,0.189244628,-0.0223207641,0.0549125224,0.1327008456,-0.2095164806,0.0066544134,-0.3827700615,-0.4155872762,0.0275884233,-0.35132581,0.0488559753,0.311540097,0.5177799463,-0.1577759236,-0.2482329756,0.4025249779,-0.2532039881,-0.0400607102,0.1358628124,-0.0422104783,0.0660645738,-0.1633740366,-0.1545830518,-0.1375987232,-0.2262219787,0.2925009727,-0.0385330878,-0.1350613236,0.0658270046,0.0675549135,0.2127572298,-0.2418706715,0.033891391,-0.2137432247,-0.3011052012,0.3603340983,-0.2365947664,-0.3118934631,0.114138253,-0.2673543096,0.4330877662,0.0052811247,-0.3415950835,-0.2719323933,0.0759830549,0.1883362532,0.0712454543,0.1267052144,0.4980594814,-0.0780174583,0.0473269336,-0.3099205792,0.0694152638,-0.1727192849,-0.3517266214,0.2287162542,-0.2808218896,0.2558266521,-0.0000867576,0.2596426904,0.2716817856,0.3824597597,0.3410160542,-0.2625616193,0.4241220951,-0.0869308338,-0.2107981443,0.3139961362,-0.2213087976,0.33670982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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2129","title":"How to train BERT model with next sentence prediction?","comments":"Hi !\r\nWe're not using `TextDatasetForNextSentencePrediction` in `datasets`.\r\nAlthough you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.","body":"Hello.\r\n\r\nI'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction \r\nlike ` TextDatasetForNextSentencePrediction` of `huggingface\/transformers` ?\r\n","comment_length":25,"text":"How to train BERT model with next sentence prediction? \n Hello.\r\n\r\nI'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction \r\nlike ` TextDatasetForNextSentencePrediction` of `huggingface\/transformers` ?\r\n \n Hi !\r\nWe're not using `TextDatasetForNextSentencePrediction` in `datasets`.\r\nAlthough you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.","embeddings":[0.1917076111,-0.4076450765,-0.0147695839,-0.2003295273,0.0005590364,-0.2783757746,0.1422066987,-0.0127167599,-0.025662059,0.1647019535,0.1556011438,0.0493725613,-0.154809311,0.0617535748,0.3285053372,-0.6952239275,0.1574005932,0.2188371867,0.009745351,-0.1505313516,-0.1178788096,0.0806166455,-0.4232550859,0.3682828546,-0.2839281857,0.2008214891,-0.3334325254,-0.3163951933,0.0460604019,-0.3753812909,0.4582475722,0.4070344269,0.4587988555,0.2150741071,-0.0001232347,-0.2639684081,0.2025311142,-0.2711344659,0.1549269408,-0.397015512,-0.2493353486,-0.0373750441,0.224298805,-0.1815945953,-0.2838987112,0.5994516015,0.3069365919,-0.1343758255,0.5240787268,0.1173760816,0.0644793734,-0.2960521281,0.2634472549,-0.0121575696,0.1068005115,0.0945525542,-0.0285038985,-0.1371493936,0.5605081916,-0.3304388225,-0.214719072,0.1836271137,0.0088032726,0.0330856815,0.1647519916,0.2331249118,0.08448603,-0.3325142264,-0.2616679966,0.4053135216,0.6561758518,-0.2457776666,-0.5032140017,-0.1677221358,0.2668515742,0.0886337683,0.0660710782,-0.2455876619,-0.0742204636,0.172976777,0.0470753126,-0.3753949106,-0.4167921245,-0.0373331085,-0.1894734949,0.4580947161,-0.0336559638,-0.2123973519,0.2601470351,-0.2106651068,0.0755168945,-0.0940305889,0.1235339269,0.4406594336,-0.2064272463,-0.3648631275,-0.4254557788,0.1489193439,0.0206151064,-0.1328348368,-0.1927832663,-0.0155303888,-0.3786577582,-0.1238597631,0.3669160306,0.2585484982,-0.1486679614,0.156735003,-0.2529693544,0.0401985459,-0.2351615131,-0.187681824,-0.1457653791,0.0405989587,0.2635551393,-0.0009324501,-0.2121559978,-0.2638917267,0.0923204944,-0.0122005958,-0.2975632548,-0.0437658429,-0.0768808052,-0.0495994948,-0.2629685998,0.3475267887,-0.0341194905,0.0840811431,0.004310641,-0.0952290446,0.1146304458,-0.2163175344,-0.2744835913,0.5209566951,0.0122855129,0.0309622642,0.1323937774,-0.1471810341,-0.0324883945,0.0797838643,-0.3151405454,-0.4305484891,-0.0083031971,-0.1275520623,-0.3998718262,0.0512893051,-0.1620687842,0.4676387012,-0.0828430876,-0.0061797379,-0.2025858015,-0.0096685551,-0.0974933654,-0.039380189,-0.0925439224,-0.3396705389,0.1031367257,0.5064285994,-0.1084639728,-0.16338332,0.1413177997,0.1575701982,-0.3678383231,0.1389539242,0.1589907259,0.1643917114,-0.4927240908,-0.2593483627,0.0213332176,0.1455704421,0.1135781407,-0.0017803777,0.2418296635,0.2236623317,0.0471895337,0.1285944134,-0.121634312,-0.1538394094,0.0056323283,0.0265370309,0.1224368885,0.3793030977,-0.1333853304,-0.0062227282,0.0107168267,-0.0767609626,-0.1099487543,0.1562724113,-0.1232269108,-0.1303879321,0.2724360824,-0.1773424745,-0.1098015755,-0.0045532607,0.1601713747,0.1707350761,-0.273924619,0.5601226687,0.1560769379,-0.0312377177,0.0705626458,-0.0506209098,0.1408510208,0.3964399397,0.0488680042,-0.3296991587,-0.2331066579,0.025221467,0.0732033923,-0.1738704741,0.0573339611,0.156717062,0.0682236925,0.1022693142,-0.0619812123,-0.153783083,-0.0262000691,0.007966659,-0.1567399949,0.104198277,-0.3908598423,0.1572053879,0.1015610397,0.078009814,-0.1400816143,0.2063272297,-0.0807496905,-0.0708810985,0.0169515051,0.1848720312,0.0469684489,0.0842282698,0.1061468348,0.1018844172,0.2673927546,0.1256760955,-0.0165784508,-0.2433182001,0.0560218543,-0.3695042729,-0.0651000887,0.0415278189,-0.1241791919,-0.0720176026,-0.1598884165,0.0651435107,0.4588318765,0.0063851457,-0.0904362947,-0.021364117,-0.1394985318,0.0529068187,-0.0706688091,-0.3643370271,0.1267165989,0.0481702015,0.252730161,-0.086599566,-0.2399302125,-0.479178071,0.2228339761,-0.174281925,0.1773670614,0.6320177913,-0.0266932193,0.0455350094,-0.1115405187,-0.2695039809,-0.0333991498,0.074216187,-0.0677747875,-0.1553356498,-0.0478103161,0.0303329155,-0.0855010897,-0.0484032221,0.1988034844,-0.1507501453,-0.256457597,0.1240442544,0.208206296,-0.3337570131,-0.1325201541,-0.0418708101,-0.079084821,-0.0052340426,-0.3815504014,-0.3519111574,-0.0259710513,-0.433439374,-0.0323948599,0.2949759364,-0.0653684363,-0.4361508191,0.1682783961,-0.0546021946,0.0482203513,0.3473086357,0.1947134584,0.2138714343,-0.2305919975,-0.1608908921,-0.0018192065,0.0149620892,-0.1995003819,-0.1814264208,-0.3212387562,-0.002616164,0.0561572872,-0.1347346455,-0.2250508368,0.3262950182,-0.1361160427,0.0319761075,0.2602560222,-0.1232942268,-0.147617504,0.0506936051,-0.0717645884,-0.0051311892,-0.0586234108,-0.1770613492,0.2005548477,0.1248717755,0.268186748,-0.0115823569,-0.2322618067,0.3484328091,0.2574885786,0.1877579093,-0.0506631993,-0.0934298486,-0.0130336666,0.1530901492,-0.335332036,-0.1277411133,0.1583463997,-0.6027779579,0.1883777082,0.1695082486,0.0336447842,-0.3637563288,-0.0378945097,-0.1281221509,-0.3695198596,-0.0321028531,-0.2308461517,0.0703089014,-0.1127632186,-0.0981119722,-0.1799512953,-0.0321077332,0.1058504805,0.1223225966,-0.1785277575,-0.0347802527,0.3163150549,-0.0391908661,0.3434461951,0.059299998,0.0938187838,-0.2042819262,0.4010342062,0.1032167375,-0.29291448,0.3866353631,0.2231460512,-0.1186935306,-0.0734554529,0.0201253314,0.02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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2129","title":"How to train BERT model with next sentence prediction?","comments":"Thanks.\r\n\r\nDo you mean that `TextDatasetForNextSentencePrediction.create_exapmles_from_document` can be applied to dataset object other than `TextDatasetForNextSentencePrediction`  e.g. a `Dataset` object which is  loaded by  `datasets.load_dataset`?","body":"Hello.\r\n\r\nI'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction \r\nlike ` TextDatasetForNextSentencePrediction` of `huggingface\/transformers` ?\r\n","comment_length":24,"text":"How to train BERT model with next sentence prediction? \n Hello.\r\n\r\nI'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction \r\nlike ` TextDatasetForNextSentencePrediction` of `huggingface\/transformers` ?\r\n \n Thanks.\r\n\r\nDo you mean that `TextDatasetForNextSentencePrediction.create_exapmles_from_document` can be applied to dataset object other than `TextDatasetForNextSentencePrediction`  e.g. a `Dataset` object which is  loaded by  `datasets.load_dataset`?","embeddings":[0.1056432575,-0.4607722163,0.0155634182,-0.0898428783,0.0474332199,-0.2899057567,0.1532109827,-0.0051718443,0.0640042797,0.1214186475,0.1853307784,0.1281926483,-0.15475142,0.1282212734,0.3845975101,-0.6245176792,0.1314673722,0.2932183444,-0.0514884144,-0.2433327734,-0.1696441323,0.073376745,-0.3516555727,0.3405378163,-0.2001281977,0.2702140808,-0.2841590941,-0.2478072792,0.0087696882,-0.3340138495,0.4047503173,0.3907679617,0.4258323312,0.1643717438,-0.0001291847,-0.2329467982,0.114739798,-0.327388674,0.1137860715,-0.2843592763,-0.3555471599,-0.1060970351,0.26434201,-0.2460024804,-0.2422992587,0.5384497046,0.3581981957,-0.3058199584,0.5242074728,0.2908453941,0.014402139,-0.2546208799,0.3124649823,-0.020818809,0.234868899,0.1671464443,-0.0251937862,-0.221622318,0.5485693812,-0.2986629307,-0.1138822734,0.1442739964,-0.001834902,0.0014028711,0.2522639632,0.2281707823,0.1782483011,-0.4217782915,-0.1953303516,0.3760996461,0.8002337813,-0.3221176863,-0.4739646316,-0.2419865429,0.337128818,0.0954880714,0.125540942,-0.3030329049,-0.1238534153,0.1631256491,0.0612535663,-0.5166053772,-0.4674112201,-0.0005794692,-0.1490326375,0.4557164311,0.0035350297,-0.1510754377,0.2814887762,-0.1666588485,0.0924607813,-0.0972436219,0.0533188321,0.4821875989,-0.1632685363,-0.3454045653,-0.4761663675,0.0677624866,0.052934207,-0.0809000805,-0.1527076811,-0.0065810508,-0.3215615153,-0.0709301978,0.3516069651,0.2880739272,-0.0382891856,0.1914600134,-0.2848033309,0.019716531,-0.2681785524,-0.1537960172,-0.2019642442,0.0399128795,0.2256612331,-0.0506861508,-0.2289211899,-0.2590069175,0.0584818088,0.0120561328,-0.3375009,-0.0117463842,-0.0488630719,-0.0032535426,-0.1972683519,0.365483284,0.0605770722,0.1042166278,-0.0432598777,-0.1118823513,0.1542611718,-0.1464899629,-0.3213078082,0.5611475706,0.0152224051,0.0879947469,0.1360875666,-0.257285893,-0.0913072154,0.0353684165,-0.2435012609,-0.4149817526,-0.0002142072,-0.0694689378,-0.3622319698,0.0641431585,-0.1449044347,0.4078865647,-0.1863521188,0.0402226113,-0.1744345874,-0.0844530538,-0.1815864444,-0.0750281587,-0.0413931832,-0.2907975614,0.0477495193,0.594668448,-0.0108278953,-0.2039385736,0.2527200878,0.0989334062,-0.4636226594,0.1473245323,0.1507641822,0.233998239,-0.5984503031,-0.3204489648,-0.1350890547,-0.0203734282,0.0243378524,-0.1360595226,0.2206615806,0.3552303612,0.0420456268,0.1495957226,-0.1622886956,-0.225841105,-0.0467957594,0.0574617088,0.1385168433,0.3321613967,0.0013421765,0.0143065415,0.1212003529,-0.0726614147,-0.1335150599,0.1792034209,-0.0856366977,-0.1414667517,0.3205605745,-0.1384384483,-0.2136109173,0.0385105684,0.2292429209,0.2022790164,-0.2451477349,0.5747007728,0.1156553701,-0.1170086786,0.1535997987,0.0178294871,0.0713092312,0.4100022316,0.0239023808,-0.3240316808,-0.3736844361,-0.0477182567,0.0286675133,-0.1072706431,0.1274763793,0.1054651141,0.012155191,0.1328706145,-0.0856349394,-0.121808812,-0.0996610671,0.0531059392,-0.1422735006,0.0766886249,-0.3936302066,0.1868783385,0.1223072857,0.1092066094,-0.1749433726,0.1587536484,0.0218498018,-0.0378621854,-0.0522038043,0.1195597053,-0.0094299642,0.1290657222,0.067081295,0.0998976976,0.2876978219,0.1083296165,-0.0522969216,-0.0948836952,0.0758774877,-0.3679954112,-0.0043352665,0.083757773,-0.0230586398,-0.08894182,-0.086956501,0.0916647315,0.5599464774,-0.0662254244,0.0157278627,-0.0259141997,-0.1526126862,0.0673014149,-0.0987571776,-0.477316767,0.0501062199,0.1455092132,0.3399935663,-0.1226238757,-0.1897749305,-0.4507785141,0.2656005323,-0.1263488084,0.2417237163,0.6629160643,-0.0783080757,-0.0234727152,-0.1915446222,-0.3000428677,0.0701903924,0.0617020093,-0.052704934,-0.0462213382,0.0219264328,0.0213555023,0.0432414412,-0.0637597293,0.2537965477,-0.0874788687,-0.3057594597,0.0475675277,0.136055544,-0.3302097321,-0.1117590368,-0.0098609962,-0.1607448608,0.037585672,-0.2359712571,-0.2540568411,-0.1496952325,-0.4255619943,-0.081215702,0.3417153955,-0.0035799034,-0.3079102933,0.1087584645,-0.0709210187,0.0714120269,0.4477214813,0.1611574143,0.1817821711,-0.2483633906,-0.1266447902,-0.0054879463,-0.0562205277,-0.1853322238,-0.1140364558,-0.273488462,-0.0050244643,0.0911660194,-0.1832859963,-0.2373598218,0.2947241068,-0.100845322,0.0405861288,0.3184700906,-0.230072543,-0.1163907796,-0.0433314592,-0.0805316716,-0.0763998181,-0.1201311275,-0.3259272277,0.1578608304,0.1687268019,0.2795046866,-0.0727304071,-0.2464631945,0.2916783392,0.2228225321,0.2209516019,0.0110416273,-0.1808915585,-0.0432179756,0.1967174113,-0.2912327349,-0.0663998947,0.1601238847,-0.5610364079,0.1195918098,0.0951546729,0.0408797301,-0.4551675022,-0.0261343662,-0.1833859682,-0.2468456775,-0.1003962308,-0.2364137918,0.0273860488,-0.2666942179,-0.0847500861,-0.0711287037,0.156466648,0.1362821311,0.1602434218,-0.1897275001,-0.1324950904,0.4403705895,-0.0629497617,0.2673963904,-0.0151512064,0.0811432227,-0.2939181924,0.3493477106,0.0586958528,-0.2686465681,0.48464939,0.2132169306,-0.0882057771,-0.1651723087,-0.0161853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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2129","title":"How to train BERT model with next sentence prediction?","comments":"It would probably require a bit of tweaking, but you can apply it to a dataset, yes.\r\nThis should give you a new dataset with sentence pairs you can train a model on.\r\n\r\nYou can find the documentation about dataset processing here:\r\nhttps:\/\/huggingface.co\/docs\/datasets\/processing.html#processing-data-with-map","body":"Hello.\r\n\r\nI'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction \r\nlike ` TextDatasetForNextSentencePrediction` of `huggingface\/transformers` ?\r\n","comment_length":43,"text":"How to train BERT model with next sentence prediction? \n Hello.\r\n\r\nI'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction \r\nlike ` TextDatasetForNextSentencePrediction` of `huggingface\/transformers` ?\r\n \n It would probably require a bit of tweaking, but you can apply it to a dataset, yes.\r\nThis should give you a new dataset with sentence pairs you can train a model on.\r\n\r\nYou can find the documentation about dataset processing here:\r\nhttps:\/\/huggingface.co\/docs\/datasets\/processing.html#processing-data-with-map","embeddings":[0.2216068804,-0.4078709483,0.0245913751,-0.1242590845,0.0343485698,-0.1844581366,0.090413101,0.022120554,-0.0510081947,0.0038792929,0.0329256169,0.0937431753,-0.188250035,0.1736756414,0.340739429,-0.6513742805,0.1297619492,0.1553862095,-0.0457976721,-0.0744871125,-0.0303964727,0.1138738766,-0.2975053489,0.3866290748,-0.2856276035,0.167577818,-0.3479522169,-0.1773992926,0.0666725039,-0.2951296866,0.4142337739,0.4732449651,0.4460688531,0.2521967292,-0.0001230383,-0.2152750939,0.1302850842,-0.317743361,0.2301827669,-0.4261970222,-0.2667488754,0.0393679813,0.2027443647,-0.1260904372,-0.3113471568,0.5966160893,0.2558301985,-0.2117690146,0.6537800431,0.0716565251,0.0451868847,-0.1689057946,0.2814650238,-0.0095189353,0.1461712867,0.1682046354,0.051208362,-0.075174652,0.6243174672,-0.3600681424,-0.229109928,0.1985415518,0.0063695475,-0.0540356338,0.2797682583,0.2602438331,-0.0144231571,-0.3843044043,-0.2895087898,0.4200867116,0.5511063933,-0.3132858574,-0.4524233639,-0.2447676212,0.2225971371,0.1461891383,0.0692787766,-0.2382965386,-0.1358793378,0.217027247,0.0773830041,-0.4006276727,-0.3453338146,-0.031248413,-0.105140999,0.3863505423,-0.0517031439,-0.1975738704,0.3232114017,-0.1545153111,-0.038774088,-0.0306088217,0.0835926607,0.5048011541,-0.2043062598,-0.4376601279,-0.3445557356,0.2067991197,0.0553344972,-0.1207953691,-0.2273675501,0.0705550388,-0.4263335764,-0.1015718356,0.4228085577,0.2061456591,-0.0779852644,0.165561229,-0.2637424171,0.032218229,-0.2298651189,-0.1523415446,-0.0986558348,0.0267325249,0.0893845111,-0.0812669396,-0.2630622089,-0.2469582409,0.1177515984,-0.0168839917,-0.2530955076,-0.0311104003,-0.0024541211,-0.0078826984,-0.2393765599,0.3082728386,0.0032434908,0.1589819491,-0.0273883268,-0.0793853626,0.1286915392,-0.2796353102,-0.2976554036,0.521655798,-0.0006238387,0.0101393722,0.1354135424,-0.1380978823,-0.0575402379,0.0933877677,-0.3288878202,-0.3122949898,-0.1033507437,-0.1362433732,-0.4028102756,0.1046491563,-0.0766845122,0.4250044525,-0.0973897427,-0.01419181,-0.2520745397,-0.028616054,-0.1406524479,-0.0588148199,-0.1818667501,-0.315163821,0.1049203873,0.533912003,-0.0480680913,-0.2282016426,0.0822864249,0.2302295715,-0.409604907,0.1246807352,0.1977958977,0.2521820068,-0.4910140336,-0.3078060448,0.073643513,0.1320023239,0.0859720558,0.0172373969,0.1739121825,0.1844780445,0.0657802522,0.1815827936,-0.1284930259,-0.1241852939,0.0365569219,0.0787436441,0.0417461954,0.4049219191,-0.1343845278,0.0324973091,0.0313700065,-0.1705056578,-0.0235561207,0.1146536097,-0.1572643518,-0.1915020794,0.1869989634,-0.1793524176,-0.0607713647,-0.0713655651,0.0958517864,0.2079959661,-0.3159270287,0.5599302053,0.1924262047,-0.0377030969,0.0784757808,0.0126629313,0.0805470347,0.491473645,-0.0041995738,-0.346273303,-0.1972341686,0.0738095418,-0.043400038,-0.0861883387,0.0072639431,0.0966252461,0.023781158,0.096205242,-0.0591682643,-0.2067830861,-0.0411678143,-0.1293233037,-0.1154093295,0.0758733824,-0.374376595,0.1479420513,0.1461742371,0.1246055812,-0.1697932929,0.2056616843,-0.0149310539,-0.0443587676,-0.0572474971,0.1798478514,0.0025718522,0.0809474736,0.1336023957,0.1328283846,0.1827095449,0.199707374,0.0009612282,-0.1804948598,0.1244325712,-0.3619657159,-0.1211431101,0.0214708112,-0.1050131246,-0.129161045,-0.1315614134,0.1197164729,0.4141704142,0.0427773632,-0.1037675217,0.0138473157,-0.1520467401,0.0279948413,-0.0964471102,-0.3477111459,0.0861943439,0.0540065132,0.258962065,-0.0506213121,-0.1999686807,-0.3999041319,0.2041917741,-0.0999379456,0.0958664268,0.6327774525,-0.1422082782,0.0661653429,-0.130759865,-0.2718037069,-0.014897678,0.1047483012,-0.0653653517,-0.091326572,-0.0169510506,0.0341923162,-0.1671380699,0.0311099086,0.1905682385,-0.2052601129,-0.2006826103,0.1428112537,0.209374696,-0.2070282102,-0.1485776454,-0.019425666,-0.1435665786,0.0612083487,-0.3151902854,-0.4285087585,-0.1279743612,-0.4911929071,-0.1034004763,0.2582746744,-0.0149435019,-0.3871529102,0.1514899135,-0.0499115773,0.1510235369,0.2841986716,0.194639042,0.1277811825,-0.2070503831,-0.2238790989,0.0237685461,0.0494989716,-0.0592209361,-0.1773704588,-0.2605481744,-0.0906507373,-0.0399954878,-0.0956390277,-0.3120402396,0.2897357643,-0.2499188334,0.1297583282,0.2941756546,-0.0759723112,-0.2079925537,0.0435942486,-0.0186340138,-0.0096070059,-0.0767076239,-0.2290391922,0.2426138222,0.2458166778,0.1850925982,-0.0338907242,-0.2169891149,0.3477907479,0.3525303006,0.1444896609,-0.0369680151,-0.1149627641,0.0262422953,0.1613724828,-0.3884990513,-0.164185375,0.1081548929,-0.5920629501,0.1830576956,0.1576792449,-0.0129233748,-0.3662762344,0.0132029438,-0.1370257884,-0.3882237673,-0.0276667308,-0.3468875289,0.096284017,-0.0810795054,-0.0687437728,-0.115794912,0.045665957,0.115896225,0.143484056,-0.1767230183,0.0795535147,0.3418131769,-0.0882980153,0.3423989415,0.0596097447,0.1252828985,-0.1889260858,0.4627276659,0.1235032305,-0.2810504138,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2128","title":"Dialogue action slot name and value are reversed in MultiWoZ 2.2","comments":"Hi\r\nGood catch ! Thanks for reporting\r\n\r\nIf you are interested in contributing, feel free to open a PR to fix this :) ","body":"Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial!\r\n\r\nI spot an error that the order of Dialogue action slot names and values are reversed.\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/649b2c469779bc4221e1b6969aa2496d63eb5953\/datasets\/multi_woz_v22\/multi_woz_v22.py#L251-L262","comment_length":23,"text":"Dialogue action slot name and value are reversed in MultiWoZ 2.2 \n Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial!\r\n\r\nI spot an error that the order of Dialogue action slot names and values are reversed.\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/649b2c469779bc4221e1b6969aa2496d63eb5953\/datasets\/multi_woz_v22\/multi_woz_v22.py#L251-L262 \n Hi\r\nGood catch ! Thanks for reporting\r\n\r\nIf you are interested in contributing, feel free to open a PR to fix this :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2123","title":"Problem downloading GEM wiki_auto_asset_turk dataset","comments":"Hi,\r\n\r\nsadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with:\r\n```bash\r\npip install git+https:\/\/github.com\/huggingface\/datasets\r\n``` ","body":"@yjernite \r\n\r\n### Summary\r\n\r\nI am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.\r\n\r\n### Steps to reproduce\r\nCode snippet:\r\n\r\nfrom datasets import load_dataset\r\n#dataset = load_dataset('gem', 'web_nlg_en')\r\ndataset = load_dataset('gem', 'wiki_auto_asset_turk')\r\n\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nI expect the dataset to start downloading (download bar appears and progresses toward 100%)\r\n\r\n**Actual behavior:**\r\nInstead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:\r\n\r\nDownloading: 36.6kB [00:00, 37.2MB\/s]\r\nDownloading: 41.7kB [00:00, ?B\/s]\r\nDownloading and preparing dataset gem\/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\\Users\\sfmil\\.cache\\huggingface\\datasets\\gem\\wiki_auto_asset_turk\\1.0.0\\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...\r\n\r\n### Is this a regression?\r\nNo, it was the first time I was trying to download this dataset (same for the other ones).\r\n\r\n### Debug info\r\n- Python version: Python 3.8.2\r\n- OS version: Windows 10 Family","comment_length":26,"text":"Problem downloading GEM wiki_auto_asset_turk dataset \n @yjernite \r\n\r\n### Summary\r\n\r\nI am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.\r\n\r\n### Steps to reproduce\r\nCode snippet:\r\n\r\nfrom datasets import load_dataset\r\n#dataset = load_dataset('gem', 'web_nlg_en')\r\ndataset = load_dataset('gem', 'wiki_auto_asset_turk')\r\n\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nI expect the dataset to start downloading (download bar appears and progresses toward 100%)\r\n\r\n**Actual behavior:**\r\nInstead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:\r\n\r\nDownloading: 36.6kB [00:00, 37.2MB\/s]\r\nDownloading: 41.7kB [00:00, ?B\/s]\r\nDownloading and preparing dataset gem\/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\\Users\\sfmil\\.cache\\huggingface\\datasets\\gem\\wiki_auto_asset_turk\\1.0.0\\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...\r\n\r\n### Is this a regression?\r\nNo, it was the first time I was trying to download this dataset (same for the other ones).\r\n\r\n### Debug info\r\n- Python version: Python 3.8.2\r\n- OS version: Windows 10 Family \n Hi,\r\n\r\nsadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with:\r\n```bash\r\npip install git+https:\/\/github.com\/huggingface\/datasets\r\n``` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2123","title":"Problem downloading GEM wiki_auto_asset_turk dataset","comments":"Is there an error message ?\r\nWhat stacktrace do you get if you interrupt the execution of the program while downloading ?","body":"@yjernite \r\n\r\n### Summary\r\n\r\nI am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.\r\n\r\n### Steps to reproduce\r\nCode snippet:\r\n\r\nfrom datasets import load_dataset\r\n#dataset = load_dataset('gem', 'web_nlg_en')\r\ndataset = load_dataset('gem', 'wiki_auto_asset_turk')\r\n\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nI expect the dataset to start downloading (download bar appears and progresses toward 100%)\r\n\r\n**Actual behavior:**\r\nInstead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:\r\n\r\nDownloading: 36.6kB [00:00, 37.2MB\/s]\r\nDownloading: 41.7kB [00:00, ?B\/s]\r\nDownloading and preparing dataset gem\/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\\Users\\sfmil\\.cache\\huggingface\\datasets\\gem\\wiki_auto_asset_turk\\1.0.0\\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...\r\n\r\n### Is this a regression?\r\nNo, it was the first time I was trying to download this dataset (same for the other ones).\r\n\r\n### Debug info\r\n- Python version: Python 3.8.2\r\n- OS version: Windows 10 Family","comment_length":22,"text":"Problem downloading GEM wiki_auto_asset_turk dataset \n @yjernite \r\n\r\n### Summary\r\n\r\nI am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.\r\n\r\n### Steps to reproduce\r\nCode snippet:\r\n\r\nfrom datasets import load_dataset\r\n#dataset = load_dataset('gem', 'web_nlg_en')\r\ndataset = load_dataset('gem', 'wiki_auto_asset_turk')\r\n\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nI expect the dataset to start downloading (download bar appears and progresses toward 100%)\r\n\r\n**Actual behavior:**\r\nInstead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:\r\n\r\nDownloading: 36.6kB [00:00, 37.2MB\/s]\r\nDownloading: 41.7kB [00:00, ?B\/s]\r\nDownloading and preparing dataset gem\/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\\Users\\sfmil\\.cache\\huggingface\\datasets\\gem\\wiki_auto_asset_turk\\1.0.0\\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...\r\n\r\n### Is this a regression?\r\nNo, it was the first time I was trying to download this dataset (same for the other ones).\r\n\r\n### Debug info\r\n- Python version: Python 3.8.2\r\n- OS version: Windows 10 Family \n Is there an error message ?\r\nWhat stacktrace do you get if you interrupt the execution of the program while downloading ?","embeddings":[-0.1321628094,0.0142390393,-0.0815602541,0.4399939477,0.2992451489,0.140218094,0.3794731498,0.147788018,0.2320196778,-0.0426291004,0.2344708741,0.1440670043,0.1074127555,-0.0218766388,-0.1961918324,-0.141847834,-0.0774908215,-0.0699517354,0.1331875622,-0.1573004574,-0.0862878039,0.1746144444,-0.1601432264,-0.0402904339,-0.2822526991,0.0316642597,0.1252023727,0.0306358933,-0.262068212,-0.1533477008,0.5731248856,0.0543964766,0.0777289867,0.4127718508,-0.0001159479,-0.0370518938,0.4838816524,-0.080642283,-0.0932808295,-0.4343478084,-0.2929488719,-0.428958118,0.1403427571,-0.3042384386,0.2589861751,0.3699575067,-0.0471938103,0.1229425743,0.1223153099,0.0604695305,0.2033275515,0.2653098702,0.0952295139,0.0126955928,0.2613676488,-0.5051375031,-0.1550694555,0.0339410566,-0.2403400242,0.2069715708,0.0613532253,0.1425447017,-0.1477518827,0.4220528305,0.0742655471,0.1051860005,-0.0462688878,-0.6357185245,0.3205806911,0.4154242277,0.785974741,-0.0836304799,-0.3444286585,-0.0804104954,0.0373599268,0.0171118677,0.1368553638,0.4656033814,-0.4767647684,0.0495452657,-0.3980374336,0.0067694043,-0.040577624,0.1948126405,-0.3115484118,0.1401275843,-0.1546680778,0.0738654211,-0.0989530906,0.0863773525,-0.2981093824,-0.3427293301,-0.1150037423,0.2818672061,-0.4654174447,0.115643926,0.131799683,0.0213572085,0.2442520708,0.2795456648,0.0694085807,0.2185829431,-0.1733071506,0.1026939377,0.2011168897,0.2909263372,-0.1033421382,-0.3722575903,0.2547006309,0.1148330793,0.0021217789,0.0427103639,-0.1206836402,0.0035349797,0.2396795452,-0.2869566679,0.2340714931,-0.1200394854,-0.2040838152,0.3872778714,0.031422168,-0.0665427744,-0.1857517809,0.2559370995,-0.1681705415,-0.0881124362,-0.1446923018,0.3099090159,-0.1549852788,0.1047627032,-0.0853241384,-0.1981688738,-0.0362772048,-0.1300715506,0.2148918211,-0.2713961005,0.3452538252,0.124105826,-0.1539851576,-0.1573804617,-0.0081057372,-0.1100425795,0.1143710092,0.3390209377,0.1603041887,0.8072764874,-0.0744100437,-0.1066129878,-0.0066189403,0.0822717994,-0.2562863529,-0.2304996699,-0.0900202468,0.157515049,-0.4614281058,-0.1936807632,-0.0424200147,0.2153875083,0.1626544297,-0.1899282336,-0.0849236324,-0.0099527137,0.0580629371,-0.251806289,0.4788047373,0.5534905791,-0.3083427846,-0.0215373598,-0.1111752093,-0.0194274858,0.4509563446,0.0034095438,0.0026833799,-0.034290757,-0.0746415108,0.3815183342,0.3053486347,0.0375183113,-0.6943234801,0.2703822851,-0.1899664253,0.2698354423,-0.0633267462,0.1415518224,0.132953316,0.181542486,-0.3257965446,0.2547839582,0.0073143984,-0.1606895924,-0.3478211462,-0.5174865723,0.1066831276,0.0958976746,0.143538624,-0.0442353822,0.1080907136,-0.0246796273,0.3150688112,0.2980012894,0.2028721124,0.1548967808,0.1308111548,-0.2210355848,-0.0933985561,-0.1403895915,-0.0056310981,0.3707253635,0.5322477221,0.06015772,-0.0091592455,0.0486918725,-0.3059341609,-0.0659251139,-0.2911716104,-0.0881628916,0.0756877288,0.0847407654,0.2108381987,0.3549518585,0.0124003189,0.0866913199,-0.0655509457,0.0727596283,0.0445239395,0.2100453377,0.0401301458,-0.1469335556,-0.2157851309,-0.1121001095,-0.035180036,0.0463373177,-0.2612461746,0.1182314754,-0.232616365,0.0806273818,-0.0174806062,-0.1799469739,0.0855987445,0.0720395893,-0.077047281,0.0010036328,0.2271564752,-0.020828791,0.064842023,0.0480485633,0.2259175926,0.3803313076,-0.0249135811,0.1595377773,0.341937989,-0.0640395507,0.0453701466,0.0332495421,0.3400067091,-0.1862511784,-0.1197081655,-0.167440787,-0.1324003637,0.1907773018,0.5834674835,0.0761657432,0.0721691549,0.2146659344,-0.2984449863,0.0452684946,0.0092086466,0.5815960765,0.3240529597,0.1171023101,0.2098080367,-0.3470689952,0.0677618086,-0.2248481065,0.0345480777,0.384731859,0.0652724132,0.4256387651,0.1201915368,0.1514938176,-0.0837181211,-0.1162302792,0.1635286361,0.1416743696,-0.0068451497,-0.0428998396,-0.232544288,0.1540494859,0.059819527,-0.4542242885,-0.1608757824,-0.2700364888,-0.1119742841,-0.0115425466,0.0120171644,-0.0320087597,-0.1950570345,-0.1726177037,-0.0271611176,-0.3007733524,-0.3105888963,-0.1425126046,-0.3204442263,-0.0535465032,0.1348847449,-0.0596891083,0.3072903156,-0.1035958678,0.0955520645,-0.0590402149,-0.1923726052,0.1506723613,0.0209468324,0.3641676307,-0.1945985258,0.4104298651,-0.1434186101,-0.1419550478,0.1534850895,-0.0161835942,0.0091253445,0.237668395,0.1352460831,-0.1796499193,-0.1143886149,-0.3453753889,-0.3516607583,-0.3800868094,-0.0870520249,0.159319222,-0.0395010971,0.0151041318,0.1947156936,0.1441307217,0.0985892713,-0.0364617966,-0.0853472054,-0.3460047543,0.7302365303,-0.1546864063,-0.3846496642,0.296200335,-0.0790098533,0.2984314859,0.1366481334,-0.5572382808,0.2780165374,-0.2417080104,0.2090036273,0.1866083741,0.0398764201,0.1015410498,-0.2553366125,0.0548728704,-0.126894623,-0.0496451035,0.008050113,0.0881617814,0.313269943,0.0028204548,0.3143161237,0.021727303,0.909424305,0.280085355,-0.1164816618,0.3772396743,0.0508962311,0.0764647126,0.0019647577,-0.1339125037,-0.1527619213,-0.2287874669,0.0307079833,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2123","title":"Problem downloading GEM wiki_auto_asset_turk dataset","comments":"Sorry for the long time since my last comment, I tried again and don't seem to have the problem anymore, thanks for your support!","body":"@yjernite \r\n\r\n### Summary\r\n\r\nI am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.\r\n\r\n### Steps to reproduce\r\nCode snippet:\r\n\r\nfrom datasets import load_dataset\r\n#dataset = load_dataset('gem', 'web_nlg_en')\r\ndataset = load_dataset('gem', 'wiki_auto_asset_turk')\r\n\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nI expect the dataset to start downloading (download bar appears and progresses toward 100%)\r\n\r\n**Actual behavior:**\r\nInstead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:\r\n\r\nDownloading: 36.6kB [00:00, 37.2MB\/s]\r\nDownloading: 41.7kB [00:00, ?B\/s]\r\nDownloading and preparing dataset gem\/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\\Users\\sfmil\\.cache\\huggingface\\datasets\\gem\\wiki_auto_asset_turk\\1.0.0\\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...\r\n\r\n### Is this a regression?\r\nNo, it was the first time I was trying to download this dataset (same for the other ones).\r\n\r\n### Debug info\r\n- Python version: Python 3.8.2\r\n- OS version: Windows 10 Family","comment_length":24,"text":"Problem downloading GEM wiki_auto_asset_turk dataset \n @yjernite \r\n\r\n### Summary\r\n\r\nI am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.\r\n\r\n### Steps to reproduce\r\nCode snippet:\r\n\r\nfrom datasets import load_dataset\r\n#dataset = load_dataset('gem', 'web_nlg_en')\r\ndataset = load_dataset('gem', 'wiki_auto_asset_turk')\r\n\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nI expect the dataset to start downloading (download bar appears and progresses toward 100%)\r\n\r\n**Actual behavior:**\r\nInstead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:\r\n\r\nDownloading: 36.6kB [00:00, 37.2MB\/s]\r\nDownloading: 41.7kB [00:00, ?B\/s]\r\nDownloading and preparing dataset gem\/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\\Users\\sfmil\\.cache\\huggingface\\datasets\\gem\\wiki_auto_asset_turk\\1.0.0\\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...\r\n\r\n### Is this a regression?\r\nNo, it was the first time I was trying to download this dataset (same for the other ones).\r\n\r\n### Debug info\r\n- Python version: Python 3.8.2\r\n- OS version: Windows 10 Family \n Sorry for the long time since my last comment, I tried again and don't seem to have the problem anymore, thanks for your support!","embeddings":[-0.0690356866,-0.0099978196,-0.0715510249,0.4162681401,0.2822793126,0.1222887412,0.3851531148,0.2134170383,0.2334703952,-0.1133927181,0.2428507507,0.1628344953,0.1382036656,-0.0105028385,-0.1870101988,-0.1910438836,-0.0316886716,-0.0682897791,0.1373938471,-0.2212913483,-0.0542819127,0.1533599347,-0.1293253452,-0.0846192688,-0.2987632453,0.1394868642,0.1094797105,-0.085387446,-0.3324198425,-0.0843059495,0.5183299184,0.0374567769,0.0902131796,0.2967351973,-0.000117695,-0.1262214333,0.4400681555,-0.080070436,-0.0858739391,-0.4032157362,-0.2881535888,-0.458510071,0.1159151644,-0.2507407665,0.1574537903,0.3613402545,-0.0828236639,0.2039815336,0.0602639616,0.0538335852,0.2015053034,0.2203440219,0.1119733229,0.0012682587,0.1720883697,-0.5136726499,-0.1961065084,-0.015647037,-0.2056452334,0.1159265116,0.0759868249,0.1538500339,-0.1160210818,0.4487346113,0.1005097404,0.1362582892,-0.0333033577,-0.5621496439,0.3449827433,0.3804026544,0.8352507949,-0.0441537835,-0.3848358095,-0.0902389884,0.0297753345,0.077689372,0.101225704,0.4795999527,-0.4089401364,0.0576068014,-0.4534149766,0.0004345138,-0.0540678427,0.2307631969,-0.3505893946,0.2053674459,-0.1281363964,0.0540480874,-0.0512708426,-0.022917768,-0.3819190264,-0.3187372983,-0.1858948916,0.3288645744,-0.3765308857,0.0876472816,0.081802547,0.1013025567,0.3065651655,0.3332847059,0.0595947914,0.2026231885,-0.2427235991,0.0974041671,0.1333460063,0.3499379158,-0.0496277288,-0.3339032233,0.233652994,0.0831762031,-0.0409998335,0.0889140368,-0.1327002347,-0.0273617897,0.2532709241,-0.2916349769,0.1138650849,-0.1210354045,-0.1329976618,0.3446778953,0.1328086406,-0.103574574,-0.1729872823,0.2109665573,-0.2614233792,-0.074850291,-0.1549175531,0.3760821223,-0.1342865229,0.1245543286,-0.0551476106,-0.1946308017,-0.032064341,-0.1308616251,0.2359174937,-0.2384796739,0.3978219926,0.1198453903,-0.2069476247,-0.1694868803,-0.0482036918,-0.0695440844,0.0892040208,0.302932173,0.1519700736,0.8524777889,-0.1055618376,-0.1533081383,0.0277879871,0.0271231532,-0.2666284442,-0.2013563663,-0.1391534954,0.1449596882,-0.4829969108,-0.2651830316,-0.0555663481,0.2311142832,0.2233172655,-0.1439942271,-0.0843165517,0.0087370137,0.0678068474,-0.2231290042,0.4462706745,0.5631440282,-0.2154604197,-0.0273493417,-0.1023787484,0.082902953,0.5172902942,-0.0132263666,0.0177302659,-0.1962208897,-0.0552291609,0.3503416181,0.2345134169,0.0634287074,-0.66987288,0.2986477911,-0.1121719927,0.2659198046,0.035139747,0.1727237254,0.0882032365,0.2288590372,-0.2764081359,0.3082546294,0.074830249,-0.1855424196,-0.3515208066,-0.4687909484,0.0907577798,0.0779029503,0.1690832973,0.0095519163,0.066642113,-0.0545492247,0.3368754685,0.2913600802,0.2231932431,0.0540956371,0.1401033401,-0.2684960663,-0.0352166593,-0.1048107073,-0.0164095256,0.4072797298,0.5592125654,0.0616346374,0.0100326911,0.097896859,-0.2644705772,-0.1222736537,-0.3213399649,-0.0197784025,0.0524631739,0.0943089351,0.2169118226,0.2984938622,0.0374979712,0.0401896127,-0.0420678146,0.052352611,0.0416194201,0.1940546185,0.067439422,-0.133195743,-0.1795913577,-0.1060785726,0.0163922012,0.1452955157,-0.2291959822,-0.0028151597,-0.2187502235,0.0633663014,-0.0434074067,-0.0914289057,0.1379369795,0.0981813371,-0.0766848847,0.032803338,0.1698219776,-0.0115782395,-0.0029624163,0.0853825659,0.2800171673,0.375519067,-0.0150109995,0.2399068624,0.3355819285,-0.0406278931,0.0454286821,-0.014503235,0.3255586624,-0.2656667829,-0.1476519108,-0.0680989102,-0.0925351679,0.2774904072,0.5297239423,0.1324802637,0.1280038059,0.2106357217,-0.3184182644,0.0189467445,-0.0144940224,0.5863617659,0.2611171901,0.0961605534,0.2073136568,-0.4086148739,0.0570140183,-0.2168998271,0.0457976833,0.32647264,0.0644427836,0.3982263505,0.101974763,0.1328803003,-0.0371610709,-0.0382136144,0.1255006641,0.1326801181,-0.041086629,-0.085897699,-0.2936616242,0.2567718327,0.0599694885,-0.4399902225,-0.1302776337,-0.2698121965,-0.1121220961,-0.0198086612,0.0327046216,-0.0770640299,-0.124431923,-0.2014309764,-0.0588909648,-0.3738242686,-0.3677837849,-0.1252192706,-0.3382562697,-0.0427056365,0.1071572006,-0.0983289182,0.2873528898,-0.1204516813,0.0573703423,-0.0958856121,-0.1930486262,0.039848987,-0.0043083508,0.3839710355,-0.2377865762,0.4564390182,-0.1663805097,-0.0597173534,0.1663670242,0.0243512075,0.0365823619,0.2220618278,0.1051800102,-0.2187347859,-0.098590076,-0.2855588496,-0.3799356818,-0.3414305151,-0.0858326033,0.2054734975,-0.0430646613,0.0882582217,0.2635269463,0.034782052,0.1744258255,0.0250118729,-0.0449167714,-0.2868692577,0.8346586823,-0.0881904364,-0.4069334865,0.2835735381,-0.0147732254,0.314887464,0.1019528434,-0.5072258115,0.2176256478,-0.2097766548,0.1955391765,0.2795872688,0.0657097325,0.0610905699,-0.3151111901,0.0881062672,-0.1394304186,-0.0146766808,-0.0487380736,0.1244137511,0.2875226438,0.0122674014,0.2843146026,0.0379387587,0.8390620947,0.2417924404,-0.1325186193,0.4327153563,0.03403043,0.0223410707,0.0224244557,-0.1650138348,-0.2018046081,-0.2494834065,0.15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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2123","title":"Problem downloading GEM wiki_auto_asset_turk dataset","comments":"Great ! I'm closing the issue then. Feel free to re-open if you experience this issue again","body":"@yjernite \r\n\r\n### Summary\r\n\r\nI am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.\r\n\r\n### Steps to reproduce\r\nCode snippet:\r\n\r\nfrom datasets import load_dataset\r\n#dataset = load_dataset('gem', 'web_nlg_en')\r\ndataset = load_dataset('gem', 'wiki_auto_asset_turk')\r\n\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nI expect the dataset to start downloading (download bar appears and progresses toward 100%)\r\n\r\n**Actual behavior:**\r\nInstead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:\r\n\r\nDownloading: 36.6kB [00:00, 37.2MB\/s]\r\nDownloading: 41.7kB [00:00, ?B\/s]\r\nDownloading and preparing dataset gem\/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\\Users\\sfmil\\.cache\\huggingface\\datasets\\gem\\wiki_auto_asset_turk\\1.0.0\\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...\r\n\r\n### Is this a regression?\r\nNo, it was the first time I was trying to download this dataset (same for the other ones).\r\n\r\n### Debug info\r\n- Python version: Python 3.8.2\r\n- OS version: Windows 10 Family","comment_length":17,"text":"Problem downloading GEM wiki_auto_asset_turk dataset \n @yjernite \r\n\r\n### Summary\r\n\r\nI am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.\r\n\r\n### Steps to reproduce\r\nCode snippet:\r\n\r\nfrom datasets import load_dataset\r\n#dataset = load_dataset('gem', 'web_nlg_en')\r\ndataset = load_dataset('gem', 'wiki_auto_asset_turk')\r\n\r\n```\r\n\r\n**Expected behavior:**\r\n\r\nI expect the dataset to start downloading (download bar appears and progresses toward 100%)\r\n\r\n**Actual behavior:**\r\nInstead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:\r\n\r\nDownloading: 36.6kB [00:00, 37.2MB\/s]\r\nDownloading: 41.7kB [00:00, ?B\/s]\r\nDownloading and preparing dataset gem\/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\\Users\\sfmil\\.cache\\huggingface\\datasets\\gem\\wiki_auto_asset_turk\\1.0.0\\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...\r\n\r\n### Is this a regression?\r\nNo, it was the first time I was trying to download this dataset (same for the other ones).\r\n\r\n### Debug info\r\n- Python version: Python 3.8.2\r\n- OS version: Windows 10 Family \n Great ! I'm closing the issue then. Feel free to re-open if you experience this issue again","embeddings":[-0.0635910705,0.0054957317,-0.0699880645,0.3975265026,0.2704818249,0.1117716953,0.4051198661,0.2125456631,0.2219942659,-0.0980705991,0.2423038036,0.1652891636,0.1437678784,-0.0094379121,-0.1849551201,-0.1691983789,-0.0209271722,-0.0780066624,0.1397191435,-0.2287252843,-0.0718829259,0.1454975754,-0.1185270548,-0.0883480906,-0.2948794365,0.146615684,0.0971317291,-0.0787394345,-0.3126651645,-0.0990360752,0.5155933499,0.056869749,0.0788812563,0.2951326072,-0.0001170882,-0.1244743913,0.4406700134,-0.0718017891,-0.1148906499,-0.3813538849,-0.2894219756,-0.4607839882,0.1017347574,-0.2550836205,0.1578007936,0.3514332175,-0.0695673302,0.1909139305,0.0411403365,0.034796942,0.2039115578,0.2345373333,0.0773691535,-0.0092191705,0.1685435772,-0.508143127,-0.2009909451,-0.0149710234,-0.1961533278,0.113062039,0.0518401638,0.1712566763,-0.1112728342,0.4450575709,0.1092763469,0.1475444883,-0.0390336178,-0.5593597293,0.3386213183,0.3914212286,0.8110873699,-0.0349254459,-0.3909861147,-0.1163943782,0.0144400205,0.0883416012,0.109456338,0.473174572,-0.4042176604,0.0580026209,-0.4430180192,0.0162063446,-0.0424059331,0.240130052,-0.3565360904,0.2099107653,-0.1218152046,0.0495125279,-0.0501043424,-0.0193964932,-0.3825842738,-0.3250644207,-0.1779280454,0.3222630024,-0.3595634401,0.0945246145,0.1082110479,0.0944507793,0.3044976592,0.3384501636,0.0650235713,0.2179397345,-0.2423131913,0.0910515338,0.125758633,0.3379720151,-0.0297967251,-0.3428627849,0.2374131829,0.0910969079,-0.0390360728,0.0894546658,-0.1421337873,-0.0136255389,0.240049243,-0.299739182,0.1066754982,-0.1061817706,-0.1355123669,0.3545142412,0.1436535269,-0.1008923575,-0.1819547713,0.2096240073,-0.2602635324,-0.079368107,-0.1583039761,0.3832471371,-0.1480674297,0.1088962555,-0.0646661595,-0.2023756504,-0.0430512875,-0.1350317597,0.2424610555,-0.2153110206,0.3975866735,0.108227618,-0.2019285262,-0.1622778475,-0.0482724346,-0.060051363,0.1039723456,0.3016758561,0.1376928836,0.8678787947,-0.0983777121,-0.148656249,0.0211304724,0.0338245668,-0.2486865222,-0.1927576214,-0.1243552044,0.1496052444,-0.5011072755,-0.2612002194,-0.047611203,0.2438677847,0.2210102677,-0.1569941491,-0.0882685781,0.0083466312,0.0572759919,-0.2188308388,0.4434719384,0.5722365975,-0.2253334671,-0.0457061864,-0.111920245,0.0897270963,0.508541584,-0.0067210724,0.0091117164,-0.1866528094,-0.0492388383,0.3607643843,0.2355579436,0.0613823123,-0.6746481061,0.2866029143,-0.126722008,0.3046183586,0.0336515307,0.1798470914,0.0886608064,0.2086722702,-0.2679759562,0.3060261905,0.0844401345,-0.1812129766,-0.3593185246,-0.4738073051,0.0832657292,0.0717399344,0.1719872355,0.0067714499,0.0662724376,-0.0591569953,0.3414898813,0.277741313,0.2246892005,0.0503941961,0.1414612681,-0.2738792896,-0.0399673544,-0.0855868533,-0.0241376627,0.4271655381,0.5506248474,0.0638864413,0.0144227007,0.0955625251,-0.246964097,-0.1217616498,-0.3224138319,-0.044006791,0.05783673,0.1223813221,0.2030841559,0.3038417101,0.0479569882,0.0396658629,-0.0411104038,0.0564445034,0.0390907601,0.1793064624,0.087945044,-0.1351893246,-0.2017707825,-0.098815985,0.0059030135,0.139186129,-0.2159442008,0.0058312127,-0.2344039381,0.0820512027,-0.0509567596,-0.0871933699,0.1415044367,0.1149088368,-0.0709475204,0.0311205629,0.1603158563,-0.0156809967,-0.0311794616,0.1139838845,0.2691166401,0.381483078,-0.0116239432,0.2220475525,0.3370935321,-0.0468466207,0.0317519717,-0.0229212679,0.3230012059,-0.2710412145,-0.139449358,-0.0685373172,-0.0948330015,0.3044227362,0.5473522544,0.1282820106,0.1361958981,0.2058171481,-0.3219820559,0.0160424374,-0.0090268776,0.5947281122,0.2657072842,0.1015208885,0.2187125534,-0.3983244002,0.0488169231,-0.2300374359,0.03764477,0.3252567947,0.0534546152,0.4030189514,0.1133934855,0.1385077983,-0.0495172776,-0.0279438421,0.1278281361,0.1014389098,-0.0418631062,-0.0687827766,-0.2825423777,0.26626122,0.0600420386,-0.4334226847,-0.1350178272,-0.2884692252,-0.1005189791,-0.0144825391,0.0014506166,-0.0770191774,-0.1364600062,-0.1926987916,-0.0802036226,-0.3923891783,-0.349755615,-0.1259525269,-0.352196008,-0.0385328606,0.1013768688,-0.0939426199,0.2879138887,-0.1168455109,0.065668799,-0.0893291533,-0.2068766207,0.044809252,-0.0092045255,0.3843480647,-0.2553695142,0.4414508939,-0.1614165008,-0.0581801832,0.1734229028,0.0283342786,0.0432912186,0.2223571241,0.108708933,-0.1992360502,-0.0968783945,-0.2927434146,-0.3652944863,-0.3451603055,-0.0720007494,0.1937093735,-0.0490679853,0.0892691463,0.2577719986,0.0275494736,0.1637058556,0.0274684522,-0.0524254963,-0.2859866023,0.8228676915,-0.0953495875,-0.4027893543,0.2715852261,-0.0302079618,0.31057024,0.1123722121,-0.50491786,0.2296973914,-0.2130983472,0.2032733411,0.2825913131,0.055016689,0.0714829266,-0.3109722733,0.0758317932,-0.1627503783,-0.0042988164,-0.0300587881,0.1257300377,0.2862550318,-0.0014703412,0.2728950679,0.0501534902,0.8432106376,0.2358387709,-0.1333760172,0.4208791852,0.0450754985,0.0392548442,0.0224322733,-0.1539186984,-0.1987058669,-0.2561722398,0.147011324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2116","title":"Creating custom dataset results in error while calling the map() function","comments":"Hi,\r\n\r\nthe `_data` attribute is missing due to `MyDataset.__init__` not calling the parent `__init__`. However, I don't think it's a good idea to subclass the `datasets.Dataset` class (e.g. it's kind of dangerous to override `datasets.Dataset.__getitem__`). Instead, it's better to follow the \"association over inheritance\" approach with a simple wrapper class that delegates calls to a wrapped `Dataset` (map, etc.). Btw, the library offers the `datasets.Dataset.from_pandas` class method to directly create a `datasets.Dataset` from the dataframe.","body":"calling `map()` of `datasets` library results into an error while defining a Custom dataset.\r\nReproducible example:\r\n```\r\nimport datasets\r\nclass MyDataset(datasets.Dataset):\r\n\r\n    def __init__(self, sentences):\r\n        \"Initialization\"\r\n        self.samples = sentences\r\n\r\n    def __len__(self):\r\n        \"Denotes the total number of samples\"\r\n        return len(self.samples)\r\n\r\n    def __getitem__(self, index):\r\n        \"Generates one sample of data\"\r\n        # Select sample\r\n        # Load data and get label\r\n        samples = self.samples[index]\r\n\r\n        return samples\r\n\r\ndef preprocess_function_train(examples):\r\n        inputs = examples\r\n        labels = [example+tokenizer.eos_token for example in examples ]\r\n        inputs = tokenizer(inputs, max_length=30, padding=True, truncation=True)\r\n        labels = tokenizer(labels, max_length=30, padding=True, truncation=True)\r\n        model_inputs = inputs\r\n        model_inputs[\"labels\"] = labels[\"input_ids\"]\r\n        print(\"about to return\")\r\n        return model_inputs\r\n\r\n\r\n##train[\"sentence\"] is dataframe column\r\ntrain_dataset = MyDataset(train['sentence'].values.tolist())\r\ntrain_dataset = train_dataset.map(\r\n            preprocess_function,\r\n            batched = True,\r\n            batch_size=32\r\n        )\r\n```\r\n\r\nStack trace of error:\r\n```\r\nTraceback (most recent call last):\r\n  File \"dir\/train_generate.py\", line 362, in <module>\r\n    main()\r\n  File \"dir\/train_generate.py\", line 245, in main\r\n    train_dataset = train_dataset.map(\r\n  File \"anaconda_dir\/anaconda3\/envs\/env1\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1244, in map\r\n    return self._map_single(\r\n  File \"anaconda_dir\/anaconda3\/envs\/env1\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 149, in wrapper\r\n    unformatted_columns = set(self.column_names) - set(self._format_columns or [])\r\n  File \"anaconda_dir\/anaconda3\/envs\/env1\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 526, in column_names\r\n    return self._data.column_names\r\nAttributeError: 'MyDataset' object has no attribute '_data'\r\n```","comment_length":75,"text":"Creating custom dataset results in error while calling the map() function \n calling `map()` of `datasets` library results into an error while defining a Custom dataset.\r\nReproducible example:\r\n```\r\nimport datasets\r\nclass MyDataset(datasets.Dataset):\r\n\r\n    def __init__(self, sentences):\r\n        \"Initialization\"\r\n        self.samples = sentences\r\n\r\n    def __len__(self):\r\n        \"Denotes the total number of samples\"\r\n        return len(self.samples)\r\n\r\n    def __getitem__(self, index):\r\n        \"Generates one sample of data\"\r\n        # Select sample\r\n        # Load data and get label\r\n        samples = self.samples[index]\r\n\r\n        return samples\r\n\r\ndef preprocess_function_train(examples):\r\n        inputs = examples\r\n        labels = [example+tokenizer.eos_token for example in examples ]\r\n        inputs = tokenizer(inputs, max_length=30, padding=True, truncation=True)\r\n        labels = tokenizer(labels, max_length=30, padding=True, truncation=True)\r\n        model_inputs = inputs\r\n        model_inputs[\"labels\"] = labels[\"input_ids\"]\r\n        print(\"about to return\")\r\n        return model_inputs\r\n\r\n\r\n##train[\"sentence\"] is dataframe column\r\ntrain_dataset = MyDataset(train['sentence'].values.tolist())\r\ntrain_dataset = train_dataset.map(\r\n            preprocess_function,\r\n            batched = True,\r\n            batch_size=32\r\n        )\r\n```\r\n\r\nStack trace of error:\r\n```\r\nTraceback (most recent call last):\r\n  File \"dir\/train_generate.py\", line 362, in <module>\r\n    main()\r\n  File \"dir\/train_generate.py\", line 245, in main\r\n    train_dataset = train_dataset.map(\r\n  File \"anaconda_dir\/anaconda3\/envs\/env1\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1244, in map\r\n    return self._map_single(\r\n  File \"anaconda_dir\/anaconda3\/envs\/env1\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 149, in wrapper\r\n    unformatted_columns = set(self.column_names) - set(self._format_columns or [])\r\n  File \"anaconda_dir\/anaconda3\/envs\/env1\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 526, in column_names\r\n    return self._data.column_names\r\nAttributeError: 'MyDataset' object has no attribute '_data'\r\n``` \n Hi,\r\n\r\nthe `_data` attribute is missing due to `MyDataset.__init__` not calling the parent `__init__`. However, I don't think it's a good idea to subclass the `datasets.Dataset` class (e.g. it's kind of dangerous to override `datasets.Dataset.__getitem__`). Instead, it's better to follow the \"association over inheritance\" approach with a simple wrapper class that delegates calls to a wrapped `Dataset` (map, etc.). Btw, the library offers the `datasets.Dataset.from_pandas` class method to directly create a `datasets.Dataset` from the dataframe.","embeddings":[-0.3095269501,0.191983223,-0.0280814078,0.0809183568,0.232263878,0.0244533066,0.406316489,0.373221457,0.2232667953,0.0579666048,0.1070924476,0.4760219455,-0.3742123246,-0.0511050224,0.078491807,-0.008936286,0.0559965968,0.1856331676,-0.1835915893,-0.0389979072,-0.3738663197,0.1936639845,-0.1824554652,0.2054496109,-0.210869208,-0.2884715199,0.1117464006,-0.0803328604,-0.2930240929,-0.3859240413,0.3439577818,-0.0687114596,0.0053722714,0.7015194893,-0.0001045329,0.0541853383,0.1606344581,-0.0544360206,-0.0714643225,-0.2946879864,-0.1300773621,-0.0597293377,0.0653227791,-0.3553414047,-0.0708623379,-0.2266567498,-0.220487982,-0.5003935099,0.2376445383,0.5857280493,0.3092918396,0.0065489239,0.0157839805,-0.2425520867,0.2406726778,0.2789570093,-0.1627862453,0.1254620999,0.1019261479,-0.4344261885,0.0211201198,0.2214974761,-0.0519024022,0.0171142984,0.2251113802,-0.0729819164,0.0247415639,-0.3594280183,0.0332762487,0.130114764,0.4118058085,-0.5375848413,-0.3002355099,0.0617003068,0.017713232,-0.1704155803,0.256087929,0.0747603178,-0.1573248357,0.0529475473,-0.0775137767,0.1310314834,-0.0968102962,0.3694398403,0.1306232065,0.1370378733,-0.163695991,0.2666483521,0.1102192923,-0.4297789037,0.0232802052,-0.1080652922,0.079526633,0.4080006778,-0.3664742112,-0.1668650508,0.0558801256,-0.4467190802,0.1431356817,-0.1520475298,0.0226492062,-0.1222045124,0.1436309367,0.1354880631,0.2312283814,0.1579550654,-0.020108683,0.4038410485,-0.0876074061,-0.0010177116,-0.2695802748,-0.0837880298,0.0295201782,-0.226069212,0.0857030228,0.0621499643,0.1651797742,0.0209620092,0.0974718556,0.0965921432,-0.3174827993,0.1343469769,0.1440456808,0.2185225636,0.1541198641,-0.1662794203,-0.0625314713,0.1299290508,-0.1169798896,0.203919217,-0.2468061596,0.1485862881,-0.3834022582,0.0072864708,0.1762004644,0.1544377357,0.2860071361,-0.0002727853,0.0227019284,-0.1487195492,0.1852497607,-0.348821491,0.1049800441,0.1605466306,-0.0992633328,0.2123647481,0.2893146574,-0.0225479286,-0.1482504755,0.0764975175,-0.1301563233,-0.3638644218,-0.1915854365,0.2496528625,0.0830784664,0.2395719886,-0.1176466122,0.2286229581,0.4339629114,-0.1881231517,0.0269856546,-0.1968439668,-0.2700251341,-0.352468878,-0.0846340805,0.1618772894,-0.3914586306,0.1544375569,-0.1199159697,-0.0528848283,-0.0214935802,0.1791034937,-0.3845655322,0.521454215,-0.1856967807,0.3085446954,0.475150764,-0.1975558698,-0.4616967142,0.1067754626,-0.0429601111,-0.0831896737,-0.3642078936,0.0197023433,0.3551739156,-0.0662748963,0.1959029734,0.016236145,-0.0600998737,0.1576967537,0.0483646989,-0.0516046844,0.1196902916,0.0165812187,-0.013093696,0.1287436187,0.2245815694,-0.1051992998,-0.0357201546,-0.2099777907,0.0927982405,0.3010170758,0.3053904772,0.1411412805,-0.1029464677,-0.1510809064,-0.2661797106,0.1586987823,0.1626496464,0.1728419065,-0.2033346444,-0.1110057309,-0.2070168555,0.0860201716,-0.1815240979,-0.0441401824,0.2680633068,-0.0587690622,0.1048195809,0.0061802985,-0.0609137565,0.0378706604,-0.3639878333,0.1085462272,-0.1308040172,-0.0567317382,0.0285437759,-0.203426227,0.0002930372,0.1345686615,0.1134953424,0.0211040508,-0.2943387628,0.3483093679,0.2806219757,-0.1980163604,-0.0684035048,0.0162229929,-0.1527741551,-0.2838914394,0.0488851033,0.1119783223,0.1651587188,-0.1241091639,0.002693682,0.159896329,0.0852550641,0.0486392602,-0.0865831822,0.183762297,0.2777926326,0.1556942165,-0.1324805766,-0.2694280148,-0.1678481102,0.0698685795,0.3245127201,0.016435599,0.0377558321,-0.2219960243,0.438349247,0.1324268579,0.1742410362,0.1287521273,-0.0500493273,-0.0606957451,0.0421922058,0.3934545517,0.5440600514,0.0357974358,-0.1568249613,0.0857430696,-0.156833455,0.0600520298,0.0551590733,0.0921577364,0.3318344653,-0.0541455001,0.041653946,0.0244366173,-0.1346431226,-0.3162122965,0.0016258216,0.2799021006,-0.1794581413,-0.0567138083,-0.0656723306,-0.0579748079,-0.0733893514,-0.2275720686,-0.0409509949,-0.1828249693,-0.2411832511,-0.0484186821,-0.0376831666,0.266205579,0.0047602723,-0.0348585099,0.1677853316,-0.3207717836,0.321235925,-0.3943755031,-0.1202824786,0.1123848632,0.0908779502,-0.0423453748,0.3318918347,0.1308334172,0.0404118188,-0.2614420056,-0.1993706971,0.0841476321,-0.0970380679,0.1033655852,0.3699523807,0.0143367508,-0.2100001723,-0.180920884,0.3269987106,-0.1559433192,-0.0598935224,0.0247111749,-0.1132064238,-0.1555879265,-0.1792828292,-0.3093281388,-0.2378805727,-0.2599627078,0.0836842656,-0.0193547867,0.1814557165,0.1999303401,0.2797604799,0.3256443739,-0.0174704157,-0.1835035235,-0.2639644444,-0.2820212245,0.1595141888,-0.5192967057,-0.1890835762,0.1598889977,-0.1196864843,0.4478085041,0.312395066,-0.3665910363,-0.0338880382,-0.0988793522,0.1386392117,-0.0980389714,-0.1112381741,0.4257833958,0.1343908161,-0.0249691941,-0.2975027859,-0.435723722,0.2185882926,-0.0120279333,0.1079024076,0.187114343,0.6236602664,-0.0216526594,0.7203986049,0.3328924477,-0.3126322329,0.2853400409,-0.1934003085,0.2419656664,-0.2737648189,-0.368257463,-0.0074806199,0.0095230909,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2106","title":"WMT19 Dataset for Kazakh-English is not formatted correctly","comments":"Hi ! Thanks for reporting\r\n\r\nBy looking at the raw `news-commentary-v14.en-kk.tsv` file, it looks like there are at least 17 lines with this issue.\r\nMoreover these issues are not always the same:\r\n- L97 is only `kk` text and must be appended at the end of the `kk` text of the **next** line\r\n- L2897 is only `kk` text and must be appended at the end of the `kk` text of the **previous** line\r\n- L1247 and L1248 are only `kk` texts and must be inserted at the **beginning** of the `kk` text of the next line\r\n- (and there are many others)\r\n\r\nIt would be nice to have a corrected version of this file ! The file is available in the `wmt\/news-commentary` repository on the Datasets Hub here:\r\nhttps:\/\/huggingface.co\/datasets\/wmt\/news-commentary\/tree\/main\/v14\/training\r\n\r\nThen maybe we can notify the WMT authors and host the corrected version somewhere","body":"In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error.\r\n\r\nThe News Commentary v14 parallel data set for kk-en from http:\/\/www.statmt.org\/wmt19\/translation-task.html has a bug here:\r\n\r\n> Line 94. The Swiss National Bank, for its part, has been battling with the deflationary effects of the franc\u2019s dramatic appreciation over the past few years.\t\u0428\u0432\u0435\u0439\u0446\u0430\u0440\u0438\u044f\u043d\u044b\u04a3 \u04b0\u043b\u0442\u0442\u044b\u049b \u0431\u0430\u043d\u043a\u0456 \u04e9\u0437 \u0442\u0430\u0440\u0430\u043f\u044b\u043d\u0430\u043d, \u0441\u043e\u04a3\u0493\u044b \u0431\u0456\u0440\u043d\u0435\u0448\u0435 \u0436\u044b\u043b \u0456\u0448\u0456\u043d\u0434\u0435 \u0444\u0440\u0430\u043d\u043a \u049b\u04b1\u043d\u044b\u043d\u044b\u04a3 \u049b\u0430\u0442\u0442\u044b \u04e9\u0441\u0443\u0456\u043d\u0456\u04a3 \u0434\u0435\u0444\u043b\u044f\u0446\u0438\u044f\u043b\u044b\u049b \u04d9\u0441\u0435\u0440\u0456\u043c\u0435\u043d \u043a\u04af\u0440\u0435\u0441\u0456\u043f \u043a\u0435\u043b\u0435\u0434\u0456.\r\n> \r\n> Line 95. \u0414\u0435\u0444\u043b\u044f\u0446\u0438\u044f\u043b\u044b\u049b \u043a\u04af\u0448\u0442\u0435\u0440 2008 \u0436\u044b\u043b\u044b \u0442\u0435\u0440\u0435\u04a3 \u0436\u04d9\u043d\u0435 \u04b1\u0437\u0430\u049b\u049b\u0430 \u0441\u043e\u0437\u044b\u043b\u0493\u0430\u043d \u0436\u0430\u04bb\u0430\u043d\u0434\u044b\u049b \u0434\u0430\u0493\u0434\u0430\u0440\u044b\u0441\u049b\u0430 \u0431\u0430\u0439\u043b\u0430\u043d\u044b\u0441\u0442\u044b \u043e\u0440\u044b\u043d \u0430\u043b\u0493\u0430\u043d \u0456\u0440\u0456 \u044d\u043a\u043e\u043d\u043e\u043c\u0438\u043a\u0430\u043b\u044b\u049b \u0436\u04d9\u043d\u0435 \u049b\u0430\u0440\u0436\u044b\u043b\u044b\u049b \u043e\u0440\u044b\u043d \u0430\u043b\u043c\u0430\u0441\u0443\u043b\u0430\u0440\u0434\u044b\u04a3 \u0430\u0440\u049b\u0430\u0441\u044b\u043d\u0434\u0430 \u0431\u043e\u0441\u0430\u0442\u044b\u043b\u0434\u044b.  \u0416\u0435\u043a\u0435 \u049b\u0430\u0440\u044b\u0437 \u049b\u0430\u0440\u0430\u0436\u0430\u0442\u044b \u04af\u043b\u0435\u0441\u0456\u043d\u0456\u04a3 \u049b\u044b\u0441\u049b\u0430\u0440\u0443\u044b \u043e\u0440\u0442\u0430\u043b\u044b\u049b \u0431\u0430\u043d\u043a\u0442\u0456\u04a3 \u0440\u0435\u0444\u043b\u044f\u0446\u0438\u044f\u0493\u0430 \u0436\u04b1\u043c\u0441\u0430\u043b\u0493\u0430\u043d \u043a\u04af\u0448-\u0436\u0456\u0433\u0435\u0440\u0456\u043d\u0435 \u0442\u04b1\u0440\u0430\u049b\u0442\u044b \u0441\u043e\u049b\u049b\u0430\u043d \u049b\u0430\u0440\u0441\u044b \u0436\u0435\u043b\u0434\u0435\u0439 \u0431\u043e\u043b\u0434\u044b.\r\n> \r\n> Line 96. The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate.\t2009 \u0436\u044b\u043b\u044b, \u0430\u043b\u0434\u044b\u04a3\u0493\u044b \u049b\u0430\u0442\u0430\u0440\u043b\u044b \u044d\u043a\u043e\u043d\u043e\u043c\u0438\u043a\u0430\u043b\u0430\u0440\u0434\u044b\u04a3 \u0448\u0430\u043c\u0430\u043c\u0435\u043d \u04af\u0448\u0442\u0435\u043d \u0431\u0456\u0440\u0456 \u0431\u0430\u0493\u0430\u043d\u044b\u04a3 \u0442\u04e9\u043c\u0435\u043d\u0434\u0435\u0443\u0456\u043d \u043a\u04e9\u0440\u0441\u0435\u0442\u0442\u0456, \u0431\u04b1\u043b \u0441\u043e\u0493\u044b\u0441\u0442\u0430\u043d \u043a\u0435\u0439\u0456\u043d\u0433\u0456 \u0436\u043e\u0493\u0430\u0440\u044b \u0434\u0435\u04a3\u0433\u0435\u0439 \u0431\u043e\u043b\u0434\u044b.\r\n\r\nAs you can see, line 95 has only the Kazakh translation which should be part of line 96. This causes all of the following English-Kazakh translation pairs to be one off rendering ALL of those translations incorrect. This issue was not fixed when the dataset was imported to Huggingface. By running this code \r\n\r\n```\r\nimport datasets\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wmt19', 'kk-en')\r\nfor key in dataset['train']['translation']:\r\n    if 'The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008.' in key['kk']:\r\n        print(key['en'])\r\n        print(key['kk'])\r\n        break\r\n```\r\nwe get: \r\n> 2009 \u0436\u044b\u043b\u044b, \u0430\u043b\u0434\u044b\u04a3\u0493\u044b \u049b\u0430\u0442\u0430\u0440\u043b\u044b \u044d\u043a\u043e\u043d\u043e\u043c\u0438\u043a\u0430\u043b\u0430\u0440\u0434\u044b\u04a3 \u0448\u0430\u043c\u0430\u043c\u0435\u043d \u04af\u0448\u0442\u0435\u043d \u0431\u0456\u0440\u0456 \u0431\u0430\u0493\u0430\u043d\u044b\u04a3 \u0442\u04e9\u043c\u0435\u043d\u0434\u0435\u0443\u0456\u043d \u043a\u04e9\u0440\u0441\u0435\u0442\u0442\u0456, \u0431\u04b1\u043b \u0441\u043e\u0493\u044b\u0441\u0442\u0430\u043d \u043a\u0435\u0439\u0456\u043d\u0433\u0456 \u0436\u043e\u0493\u0430\u0440\u044b \u0434\u0435\u04a3\u0433\u0435\u0439 \u0431\u043e\u043b\u0434\u044b.\r\n> The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate.\r\n\r\nwhich shows that the issue still persists in the Huggingface dataset. The Kazakh sentence matches up to the next English sentence in the dataset instead of the current one.\r\n\r\nPlease let me know if there's you have any ideas to fix this one-off error from the dataset or if this can be fixed by Huggingface.","comment_length":144,"text":"WMT19 Dataset for Kazakh-English is not formatted correctly \n In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error.\r\n\r\nThe News Commentary v14 parallel data set for kk-en from http:\/\/www.statmt.org\/wmt19\/translation-task.html has a bug here:\r\n\r\n> Line 94. The Swiss National Bank, for its part, has been battling with the deflationary effects of the franc\u2019s dramatic appreciation over the past few years.\t\u0428\u0432\u0435\u0439\u0446\u0430\u0440\u0438\u044f\u043d\u044b\u04a3 \u04b0\u043b\u0442\u0442\u044b\u049b \u0431\u0430\u043d\u043a\u0456 \u04e9\u0437 \u0442\u0430\u0440\u0430\u043f\u044b\u043d\u0430\u043d, \u0441\u043e\u04a3\u0493\u044b \u0431\u0456\u0440\u043d\u0435\u0448\u0435 \u0436\u044b\u043b \u0456\u0448\u0456\u043d\u0434\u0435 \u0444\u0440\u0430\u043d\u043a \u049b\u04b1\u043d\u044b\u043d\u044b\u04a3 \u049b\u0430\u0442\u0442\u044b \u04e9\u0441\u0443\u0456\u043d\u0456\u04a3 \u0434\u0435\u0444\u043b\u044f\u0446\u0438\u044f\u043b\u044b\u049b \u04d9\u0441\u0435\u0440\u0456\u043c\u0435\u043d \u043a\u04af\u0440\u0435\u0441\u0456\u043f \u043a\u0435\u043b\u0435\u0434\u0456.\r\n> \r\n> Line 95. \u0414\u0435\u0444\u043b\u044f\u0446\u0438\u044f\u043b\u044b\u049b \u043a\u04af\u0448\u0442\u0435\u0440 2008 \u0436\u044b\u043b\u044b \u0442\u0435\u0440\u0435\u04a3 \u0436\u04d9\u043d\u0435 \u04b1\u0437\u0430\u049b\u049b\u0430 \u0441\u043e\u0437\u044b\u043b\u0493\u0430\u043d \u0436\u0430\u04bb\u0430\u043d\u0434\u044b\u049b \u0434\u0430\u0493\u0434\u0430\u0440\u044b\u0441\u049b\u0430 \u0431\u0430\u0439\u043b\u0430\u043d\u044b\u0441\u0442\u044b \u043e\u0440\u044b\u043d \u0430\u043b\u0493\u0430\u043d \u0456\u0440\u0456 \u044d\u043a\u043e\u043d\u043e\u043c\u0438\u043a\u0430\u043b\u044b\u049b \u0436\u04d9\u043d\u0435 \u049b\u0430\u0440\u0436\u044b\u043b\u044b\u049b \u043e\u0440\u044b\u043d \u0430\u043b\u043c\u0430\u0441\u0443\u043b\u0430\u0440\u0434\u044b\u04a3 \u0430\u0440\u049b\u0430\u0441\u044b\u043d\u0434\u0430 \u0431\u043e\u0441\u0430\u0442\u044b\u043b\u0434\u044b.  \u0416\u0435\u043a\u0435 \u049b\u0430\u0440\u044b\u0437 \u049b\u0430\u0440\u0430\u0436\u0430\u0442\u044b \u04af\u043b\u0435\u0441\u0456\u043d\u0456\u04a3 \u049b\u044b\u0441\u049b\u0430\u0440\u0443\u044b \u043e\u0440\u0442\u0430\u043b\u044b\u049b \u0431\u0430\u043d\u043a\u0442\u0456\u04a3 \u0440\u0435\u0444\u043b\u044f\u0446\u0438\u044f\u0493\u0430 \u0436\u04b1\u043c\u0441\u0430\u043b\u0493\u0430\u043d \u043a\u04af\u0448-\u0436\u0456\u0433\u0435\u0440\u0456\u043d\u0435 \u0442\u04b1\u0440\u0430\u049b\u0442\u044b \u0441\u043e\u049b\u049b\u0430\u043d \u049b\u0430\u0440\u0441\u044b \u0436\u0435\u043b\u0434\u0435\u0439 \u0431\u043e\u043b\u0434\u044b.\r\n> \r\n> Line 96. The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate.\t2009 \u0436\u044b\u043b\u044b, \u0430\u043b\u0434\u044b\u04a3\u0493\u044b \u049b\u0430\u0442\u0430\u0440\u043b\u044b \u044d\u043a\u043e\u043d\u043e\u043c\u0438\u043a\u0430\u043b\u0430\u0440\u0434\u044b\u04a3 \u0448\u0430\u043c\u0430\u043c\u0435\u043d \u04af\u0448\u0442\u0435\u043d \u0431\u0456\u0440\u0456 \u0431\u0430\u0493\u0430\u043d\u044b\u04a3 \u0442\u04e9\u043c\u0435\u043d\u0434\u0435\u0443\u0456\u043d \u043a\u04e9\u0440\u0441\u0435\u0442\u0442\u0456, \u0431\u04b1\u043b \u0441\u043e\u0493\u044b\u0441\u0442\u0430\u043d \u043a\u0435\u0439\u0456\u043d\u0433\u0456 \u0436\u043e\u0493\u0430\u0440\u044b \u0434\u0435\u04a3\u0433\u0435\u0439 \u0431\u043e\u043b\u0434\u044b.\r\n\r\nAs you can see, line 95 has only the Kazakh translation which should be part of line 96. This causes all of the following English-Kazakh translation pairs to be one off rendering ALL of those translations incorrect. This issue was not fixed when the dataset was imported to Huggingface. By running this code \r\n\r\n```\r\nimport datasets\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('wmt19', 'kk-en')\r\nfor key in dataset['train']['translation']:\r\n    if 'The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008.' in key['kk']:\r\n        print(key['en'])\r\n        print(key['kk'])\r\n        break\r\n```\r\nwe get: \r\n> 2009 \u0436\u044b\u043b\u044b, \u0430\u043b\u0434\u044b\u04a3\u0493\u044b \u049b\u0430\u0442\u0430\u0440\u043b\u044b \u044d\u043a\u043e\u043d\u043e\u043c\u0438\u043a\u0430\u043b\u0430\u0440\u0434\u044b\u04a3 \u0448\u0430\u043c\u0430\u043c\u0435\u043d \u04af\u0448\u0442\u0435\u043d \u0431\u0456\u0440\u0456 \u0431\u0430\u0493\u0430\u043d\u044b\u04a3 \u0442\u04e9\u043c\u0435\u043d\u0434\u0435\u0443\u0456\u043d \u043a\u04e9\u0440\u0441\u0435\u0442\u0442\u0456, \u0431\u04b1\u043b \u0441\u043e\u0493\u044b\u0441\u0442\u0430\u043d \u043a\u0435\u0439\u0456\u043d\u0433\u0456 \u0436\u043e\u0493\u0430\u0440\u044b \u0434\u0435\u04a3\u0433\u0435\u0439 \u0431\u043e\u043b\u0434\u044b.\r\n> The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate.\r\n\r\nwhich shows that the issue still persists in the Huggingface dataset. The Kazakh sentence matches up to the next English sentence in the dataset instead of the current one.\r\n\r\nPlease let me know if there's you have any ideas to fix this one-off error from the dataset or if this can be fixed by Huggingface. \n Hi ! Thanks for reporting\r\n\r\nBy looking at the raw `news-commentary-v14.en-kk.tsv` file, it looks like there are at least 17 lines with this issue.\r\nMoreover these issues are not always the same:\r\n- L97 is only `kk` text and must be appended at the end of the `kk` text of the **next** line\r\n- L2897 is only `kk` text and must be appended at the end of the `kk` text of the **previous** line\r\n- L1247 and L1248 are only `kk` texts and must be inserted at the **beginning** of the `kk` text of the next line\r\n- (and there are many others)\r\n\r\nIt would be nice to have a corrected version of this file ! The file is available in the `wmt\/news-commentary` repository on the Datasets Hub here:\r\nhttps:\/\/huggingface.co\/datasets\/wmt\/news-commentary\/tree\/main\/v14\/training\r\n\r\nThen maybe we can notify the WMT authors and host the corrected version somewhere","embeddings":[-0.0953304172,-0.5491961241,-0.0432180017,0.3162058294,-0.0860267431,0.0107725346,0.1837910563,0.1722095013,-0.1140662581,0.2022086978,0.1289513409,0.0987930223,0.111726664,0.5228421092,0.0502476022,-0.2106284201,0.1143199801,-0.1941502392,-0.2592694759,-0.1341278553,-0.0660601184,0.1056311578,-0.4930419326,-0.0548006594,-0.4117452502,-0.1151644886,-0.1502326727,-0.0785620958,-0.0658805519,-0.1956119835,0.0703047737,0.1335619986,-0.1053127944,0.3510219157,-0.0001112676,-0.2417628765,0.021564262,-0.0297251698,-0.1629369408,0.0762894899,-0.1257774383,-0.8057081103,-0.2112897336,-0.120443292,-0.1037973389,-0.4211285114,-0.1573449224,0.0498358682,0.3065554798,0.1048358008,0.1853925437,0.2881683111,-0.0026125675,-0.1187297106,-0.1859125048,-0.0203668773,-0.1585624367,0.0198849589,0.1100702509,0.1526906639,-0.1194509044,0.6587665081,0.0118426559,-0.1108202636,-0.0299412534,-0.1155029833,-0.1476062089,0.0271584671,0.0776109397,0.4503267705,0.4413164258,0.017042812,-0.0518957898,-0.1849430799,0.0404484235,-0.3243750036,0.0702300817,0.1509529352,-0.2087913752,0.1687709689,0.0222084727,0.3100071847,0.0986339226,0.0540111065,-0.2007593811,0.467933476,-0.1476940811,0.0270631034,0.0542254187,-0.0414870754,-0.1118217632,-0.0609524846,-0.0135244671,-0.1418372095,-0.1481644362,-0.0013134108,-0.0952405259,-0.2198383361,0.1539940387,-0.5207996368,-0.1159045249,0.355996877,-0.2307448834,-0.177794233,0.192759797,0.5753376484,0.0576573908,-0.3404280543,0.1400430948,0.1638718247,0.0051132953,-0.0042761825,0.0858228281,-0.4291135669,-0.2851946354,0.1375285685,-0.0554422997,-0.0388974287,-0.0930311009,0.1223476455,0.1957934797,-0.1440604776,-0.2310918123,0.0197817087,-0.1138918549,0.4291629791,-0.1612132788,0.0644564778,-0.1776590496,-0.169715777,-0.1797194481,-0.1399149001,-0.3185177445,-0.0326874964,-0.1918263137,0.1269253343,0.2599645555,0.2328584194,0.0105895195,-0.081656985,-0.3358832598,-0.0744915381,0.4750349224,0.2353508472,-0.109406732,0.1894775331,-0.0672534183,0.0855240226,0.001310652,0.0184608791,-0.3052257597,0.2370880991,-0.0127146421,0.1222734153,-0.128480792,-0.0958907977,-0.2741920352,0.5493567586,0.0236123577,-0.0164982509,0.1953630298,-0.1072928831,-0.3907093108,-0.1250798106,0.2603446841,0.1920642853,-0.3559532166,-0.0284667704,0.1010944322,-0.0517998189,0.4939495623,0.4126811922,0.1056014895,0.1440576911,-0.0203153882,0.1787227392,-0.0787365213,0.0409197286,-0.3128335774,0.0134839611,-0.2729091942,0.4639530182,-0.0156172393,0.0288684107,0.0604276918,0.2915114164,0.1621890217,0.0457521379,0.0671483427,0.2363936752,-0.2866164744,-0.3670729399,0.6842613816,-0.1920403242,0.1620819867,-0.3465865254,0.0237741433,-0.1346558183,0.5904834867,0.2387703955,0.1202825904,-0.1334968954,-0.0776724368,0.0483449437,0.2563450634,0.152576074,-0.2615077794,0.206906423,0.199422285,0.4913658798,0.2391413897,0.1307504177,-0.1787830442,-0.1840566248,-0.2560290694,-0.3216755092,0.213231191,0.0867892504,-0.4696191549,0.3506502509,-0.0635577142,-0.0447053984,-0.0672241002,-0.026002178,-0.2125884295,-0.0297508147,-0.0089499792,-0.02854947,-0.1508794725,0.2967285514,0.2445807904,0.1812208593,-0.2409955561,0.0913004875,0.0638272762,0.6336416602,-0.1121875346,-0.2415217608,0.2419113517,-0.7164649963,0.035252668,0.118126601,-0.0593292303,-0.0951408893,0.1239964887,0.43100667,-0.0151632149,0.0634539649,-0.1755182892,0.0894917697,0.2513158023,-0.0591865927,-0.0728715137,-0.2587556541,0.1687140316,-0.2381840944,-0.2176354825,-0.0330536291,-0.143403545,0.3936029971,0.5164348483,0.2517271936,0.226430878,-0.103132844,-0.4107159078,-0.1604110152,0.3795114756,0.2175553739,-0.0869412795,0.0945360139,0.172304675,0.2995283604,0.1139071137,-0.1324412972,0.1295514703,0.0206169207,0.0954602361,0.2805342674,-0.1022091731,0.020655809,-0.2630800605,0.518856883,-0.1302155107,0.0445324443,-0.1824714243,-0.1719665676,-0.4352489412,-0.086605221,-0.5954187512,-0.3802389503,-0.1560970545,-0.4191438556,0.0219246428,-0.2171105295,-0.2821983993,0.2730413079,0.5708176494,0.1382266283,-0.3288728595,0.6834566593,-0.3291601241,-0.5311251879,-0.1904894859,0.0764606073,0.233076781,0.5193153024,0.1068043858,-0.4199673235,-0.1401586831,-0.057606373,-0.6339119077,0.1419069469,-0.369179219,-0.0652037039,-0.0582939722,0.319121182,-0.251332283,-0.1517112255,0.2821587324,-0.1380905062,-0.0785943791,0.374920845,-0.204396829,-0.1959827691,-0.1805733442,-0.440983057,-0.3408245146,-0.1562552303,0.2199539244,-0.3337595165,-0.2653497756,0.4110278487,0.235519737,0.0143299084,-0.3028132915,0.1374218166,-0.2295391858,-0.12518242,0.0368715338,-0.0791391358,-0.487685591,0.1958709657,0.1955722123,-0.1193501353,-0.0972853601,-0.1942091733,0.4939127564,-0.006794889,-0.1669482887,0.184014231,0.1353168041,0.0118209524,-0.0623589419,-0.0028104091,-0.1753420234,-0.236891821,-0.0616775267,-0.0132810017,0.1650728434,-0.2205608785,0.2217827439,0.3598546982,0.2768031061,0.1467605531,0.2321696281,-0.078348428,-0.0849826857,0.3804539442,-0.007025497,-0.1966290176,-0.0072077503,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2105","title":"Request to remove S2ORC dataset","comments":"Hello @kyleclo! Currently, we are getting the data from your bucket, so if you remove it the HF script won't work anymore :) \r\n\r\nUntil you solve things on your end, @lhoestq suggested we just return a warning message when people try to load that dataset from HF. What would you like it to say?","body":"Hi!  I was wondering if it's possible to remove [S2ORC](https:\/\/huggingface.co\/datasets\/s2orc) from hosting on Huggingface's platform?  Unfortunately, there are some legal considerations about how we make this data available.  Happy to add back to Huggingface's platform once we work out those hurdles!  Thanks!","comment_length":54,"text":"Request to remove S2ORC dataset \n Hi!  I was wondering if it's possible to remove [S2ORC](https:\/\/huggingface.co\/datasets\/s2orc) from hosting on Huggingface's platform?  Unfortunately, there are some legal considerations about how we make this data available.  Happy to add back to Huggingface's platform once we work out those hurdles!  Thanks! \n Hello @kyleclo! Currently, we are getting the data from your bucket, so if you remove it the HF script won't work anymore :) \r\n\r\nUntil you solve things on your end, @lhoestq suggested we just return a warning message when people try to load that dataset from HF. What would you like it to say?","embeddings":[0.2497458011,-0.3549620509,-0.0154878516,0.2169867009,0.0509789921,-0.0599792711,0.0153631736,0.1701468229,0.3005094528,0.0649961978,-0.3151451349,-0.2419163585,-0.397151947,0.367079407,-0.2082139105,0.1481842548,-0.1210899502,0.0473451391,-0.2688441575,-0.0216256119,0.1051725298,0.2462723106,-0.0533281751,0.3302411735,-0.2204811424,-0.1824883968,0.0003759059,0.2880859673,-0.168196097,-0.3303515613,0.3342998028,0.3446072936,0.1943356246,0.4782934189,-0.0001191036,-0.2259052545,-0.0449481197,0.0634203553,-0.336304158,-0.0503856689,-0.0639070496,0.0830901861,-0.0390450619,0.0706025958,-0.1767835617,-0.1328932345,0.0946238115,-0.1127751172,0.45149526,0.2237595469,0.1472906172,0.5189114809,-0.2679886222,0.235634774,0.0838877112,0.5844472647,-0.0968283489,0.3613194525,-0.1507354081,-0.1344151646,0.0206344444,0.3544667661,-0.4820709229,-0.0808606297,0.3423905373,-0.2815232277,-0.3463054299,-0.2894932926,0.1905046552,0.3609244525,0.5013285875,-0.1849517077,-0.008570591,0.032625623,0.0586836264,0.0704151616,0.093675971,0.1089070514,0.0631737262,0.453869462,-0.2132694721,-0.5096263289,-0.2386959493,-0.1701668203,-0.191897139,0.2741118371,-0.2331748307,-0.0442752056,0.0756487027,-0.0128305592,0.1244937256,-0.1404350102,-0.1270954162,0.2118843794,-0.1232205629,-0.3587535918,-0.1462815851,0.097842671,0.3382321596,-0.0054843603,-0.033905372,-0.1031496674,-0.3579530716,0.1639176607,0.2696815729,-0.106899187,0.1650609672,0.080244422,0.2007359564,0.3710959852,0.2687785029,0.1058006957,0.2009716928,0.1437756717,-0.2677750289,0.0206930619,0.4212612212,-0.4505495429,-0.4954606593,0.3764736354,-0.0396209918,-0.125484556,0.1502397209,0.2772763073,0.1018729731,-0.0195235908,0.1454119831,0.116527833,-0.1844519079,-0.3579524755,-0.1602798849,0.2095674723,-0.251229465,0.1203959957,0.1802686155,-0.1312956214,0.1287759393,0.1143668145,0.6399711967,0.2406097054,-0.1666058302,-0.1466241181,0.2603840828,0.1203874052,-0.1811207086,0.0802044198,0.1370403469,0.0961927846,-0.2488334775,0.0890592933,-0.3099267483,-0.2802248597,-0.516005218,0.0420542955,-0.1513456851,0.0481919311,-0.3567976356,-0.051567588,-0.3598996103,0.1025721878,0.2436718196,0.1525050253,-0.4501981139,-0.0034856186,-0.1981196105,0.5347214937,-0.066969648,-0.0042566424,0.3785064518,-0.3493422866,0.0128762685,0.404050231,-0.2383314222,-0.253508985,-0.4391379058,-0.1414205581,-0.5946813822,-0.1744572818,0.0992880538,0.5375522971,-0.2992700934,0.2132284194,0.0099565657,-0.2031260878,0.0113221304,-0.5564859509,-0.0973526761,-0.0895495191,0.2681993246,-0.2259251326,-0.2278825641,-0.3242280185,0.0612322576,-0.1026290804,0.1750382781,0.3118291497,0.0767643154,-0.0738639906,0.6009222269,0.0521623716,0.0797494277,0.3716990948,0.2853856087,0.2455003411,-0.2253010124,0.0560515188,-0.1960527152,-0.0647549108,-0.0250650346,-0.0749502406,0.1551264226,-0.2883827984,-0.1902813762,0.0183652174,0.027183542,0.1615642458,-0.0629756227,-0.3182079494,-0.0179348737,-0.111243628,-0.3682821989,0.6763768792,-0.1507049054,0.1577686369,-0.1715857834,0.1568666399,0.0588740036,0.280924648,0.2297971547,0.0875786468,0.2233127654,-0.0150029752,0.1429278851,0.2585816085,-0.1537299752,0.0822614133,0.2234179676,0.4290006459,0.1444981545,-0.1048828661,0.4809744954,-0.082741186,-0.0540455692,0.2035284787,0.087127015,0.2614385188,-0.3357444704,0.0526356287,-0.3771407902,-0.1790440381,0.1647438109,-0.0615302585,-0.1390989125,0.0128756389,0.1143247783,0.1538396627,-0.2277926952,-0.0845726058,-0.3620781302,-0.1090864539,-0.1644118428,-0.2193997502,-0.2350513041,0.2299925387,-0.0151846111,-0.0578251369,0.4176206291,-0.0115158409,0.2619350255,0.128081888,-0.1451253593,0.2411237061,0.3163654208,-0.0746850371,0.2493267804,0.0866191313,-0.0355136953,-0.0647578165,0.1031338647,0.0849600285,-0.3730011284,-0.2814788222,-0.3263089061,-0.0815616027,-0.3990690708,0.1705252379,-0.1877942681,-0.3982465565,0.031280417,0.4227204025,-0.6007093191,0.0725847334,0.3227221966,0.2935101986,-0.1004199386,-0.0541537106,-0.290679872,0.3760780692,-0.3606549501,0.0042782482,-0.157555148,0.155975014,-0.0230977293,0.048961062,-0.059231896,-0.0755986795,0.591833353,0.0483928882,-0.1953922212,-0.2420415431,-0.0286112633,0.2571062744,0.0222353246,-0.2735895216,0.3110335767,0.4664096534,-0.1958955973,0.0888095424,-0.230655998,-0.3152903318,-0.0752920508,-0.0221924447,-0.0101125613,0.1665061712,-0.1660592556,0.3509397209,-0.1194058433,-0.1206385791,-0.1005800813,-0.2498231232,0.1017661914,0.1561323553,-0.2423310131,-0.0377894007,-0.4389007986,-0.2412038893,-0.4783966541,-0.7775785327,0.1322237998,0.1551061273,0.0619400218,0.3001137376,0.2907038629,0.0243347287,-0.2453390807,-0.4676840603,-0.2666758597,0.1108360961,0.0299536344,-0.0711925402,-0.0387910604,0.1316688657,-0.0325359255,-0.0444163457,0.020417789,-0.1365660727,-0.2444277853,-0.0075051161,0.2517967522,0.0325369388,0.1261018366,0.0227919519,0.4973379076,0.1448776573,0.0841678232,0.4559576213,0.1717983335,0.3662770689,0.0237877294,-0.2425237447,0.1033528522,0.1416413337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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2105","title":"Request to remove S2ORC dataset","comments":"Hi @kyleclo, as of today, you have not removed your bucket data yet, and therefore HuggingFace can download it from there.\r\n\r\nIs it OK? Are you planning to eventually delete it? Thank you.","body":"Hi!  I was wondering if it's possible to remove [S2ORC](https:\/\/huggingface.co\/datasets\/s2orc) from hosting on Huggingface's platform?  Unfortunately, there are some legal considerations about how we make this data available.  Happy to add back to Huggingface's platform once we work out those hurdles!  Thanks!","comment_length":33,"text":"Request to remove S2ORC dataset \n Hi!  I was wondering if it's possible to remove [S2ORC](https:\/\/huggingface.co\/datasets\/s2orc) from hosting on Huggingface's platform?  Unfortunately, there are some legal considerations about how we make this data available.  Happy to add back to Huggingface's platform once we work out those hurdles!  Thanks! \n Hi @kyleclo, as of today, you have not removed your bucket data yet, and therefore HuggingFace can download it from there.\r\n\r\nIs it OK? Are you planning to eventually delete it? Thank 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2105","title":"Request to remove S2ORC dataset","comments":"Hi!  Sorry I missed @yjernite 's previous message, thanks for responding!  \r\n\r\nIs there an option where we can keep our data in our bucket, but the HF script no longer pulls data from it?  ","body":"Hi!  I was wondering if it's possible to remove [S2ORC](https:\/\/huggingface.co\/datasets\/s2orc) from hosting on Huggingface's platform?  Unfortunately, there are some legal considerations about how we make this data available.  Happy to add back to Huggingface's platform once we work out those hurdles!  Thanks!","comment_length":34,"text":"Request to remove S2ORC dataset \n Hi!  I was wondering if it's possible to remove [S2ORC](https:\/\/huggingface.co\/datasets\/s2orc) from hosting on Huggingface's platform?  Unfortunately, there are some legal considerations about how we make this data available.  Happy to add back to Huggingface's platform once we work out those hurdles!  Thanks! \n Hi!  Sorry I missed @yjernite 's previous message, thanks for responding!  \r\n\r\nIs there an option where we can keep our data in our bucket, but the HF script no longer pulls data from it?  ","embeddings":[0.3147089779,-0.2077655643,-0.0239039473,0.3282617927,-0.0140945502,-0.1829030514,0.0175194405,0.172574982,0.1771008372,0.1621481776,-0.4038794339,-0.2786833048,-0.3127873242,0.457226783,-0.1433838755,0.1945003718,-0.0702701509,0.0524483249,-0.3202078044,0.0324457549,0.1301748008,0.2261826545,-0.0884683505,0.0661727116,-0.1380365342,-0.1128825396,-0.0782067031,0.1449866146,-0.1255226731,0.0694023669,0.2610510886,0.4705429077,0.0800844952,0.4339930117,-0.0001245844,-0.3209076226,-0.1540060341,0.0056156814,-0.4018520713,-0.1130016148,-0.0782959759,0.2641175985,-0.090856187,0.1397795975,-0.0534056127,-0.1262888759,0.0841633454,-0.1527345777,0.3187084496,0.2342592031,0.1314422786,0.575772047,-0.4303703308,0.2741442323,0.1232632101,0.6085684896,-0.1516879201,0.3492450416,-0.1199225709,-0.1097447276,0.2139885128,0.3302992582,-0.4357930124,-0.0924803093,0.249126032,-0.1535654664,-0.39856866,-0.2252966166,0.4142615497,0.2827500403,0.4330858886,-0.2104133517,-0.090478234,0.1022289321,0.0474308133,0.0494886525,0.0116874781,0.2144204527,0.0960223377,0.428937614,-0.3170785904,-0.6390438676,-0.2341393828,-0.1140615866,-0.3001761734,0.3237962425,-0.297346741,-0.064186208,-0.0738193691,0.0914316252,0.1797328889,-0.2322839797,0.012712026,0.1589960158,-0.0431557819,-0.3171835244,-0.2178788185,0.3126013875,0.2448574007,0.0445581377,0.0243876893,-0.1624384373,-0.2845410109,0.2050913423,0.3105307519,-0.0921661034,0.0419713147,0.0940728635,0.277145952,0.2518097162,0.2529099584,0.0715969577,0.1932775676,0.2897895277,-0.3893837929,-0.1564794332,0.5164502859,-0.4300911725,-0.416610688,0.3595436811,-0.055288177,-0.0619812571,0.1564075798,0.3035776615,0.1137095541,-0.0069999732,0.0698101297,0.0436593108,-0.2103589326,-0.3135282099,-0.1677012593,0.1802435517,-0.0791733786,-0.0170090403,0.2489594072,-0.2133324295,0.1953548342,0.1686762571,0.57807827,0.2631825507,-0.1107336059,-0.2234827727,0.3362011313,0.1756037772,-0.1483076066,0.1314199865,0.0225152355,0.1181295216,-0.2510817945,0.0298500694,-0.2922266126,-0.1787335873,-0.4848286808,0.0030082706,-0.1213938668,0.0565310046,-0.3228920698,-0.0896095932,-0.2625923753,0.1147754863,0.1649736762,0.0599419586,-0.3381242156,-0.0675013363,-0.1339311302,0.3447757065,-0.1291263402,-0.0032780236,0.4492904246,-0.217882663,-0.0202052947,0.389357239,-0.1809060127,-0.2190599293,-0.331469655,-0.1568554342,-0.4964418709,-0.2505916357,-0.0249097198,0.3402804732,-0.1744554937,0.0753001571,0.1196339279,-0.2354543209,0.2151425481,-0.457449317,-0.2506250739,-0.0161332916,0.188545838,-0.2797206342,-0.1101487204,-0.3190248013,-0.0062155803,-0.1179453284,0.1130622849,0.1365055293,-0.0230848268,-0.0962608829,0.4757193923,0.0390383042,0.276992023,0.3343988955,0.2557878494,0.1921966225,-0.2453435063,-0.0499248132,-0.2992227674,-0.095937632,-0.1304721832,-0.1254461706,0.1675016731,-0.3209268153,-0.0511224084,-0.0252981614,0.2593214214,0.1686456054,-0.0502269901,-0.289798528,0.005914236,-0.1839488447,-0.4556969404,0.622061491,0.0663529411,0.2031348497,-0.2550747693,0.2425263375,0.0820942149,0.1792575419,0.2667661905,0.1372851431,0.1339348108,-0.0780462846,0.1209661216,0.2498843968,-0.1223325953,0.1720343828,0.2978731096,0.5357654095,0.1379655898,0.0415779874,0.4445880055,-0.1042024791,-0.0114139263,0.1728285253,-0.044643905,0.3055931628,-0.2436221987,0.2162426114,-0.1580538303,-0.1398379803,0.2460268885,-0.1375356913,-0.1633789986,-0.1040517762,0.2351144254,0.0534564406,-0.129294008,-0.027631931,-0.3138299584,0.08439219,-0.0928236246,-0.114094764,-0.2346712351,0.1019420847,-0.2157959044,-0.157847032,0.4094680846,-0.0664004236,0.2941377461,0.1521456391,-0.1029986367,0.1443632841,0.4886132181,-0.1357595325,0.2988614738,0.1198166832,-0.1439885348,-0.0977470353,0.2450703681,0.1141307279,-0.4201129675,-0.1339052022,-0.2944412231,-0.0589345358,-0.2316478938,0.1991477609,-0.3803668022,-0.3432620764,0.0363623835,0.3859191835,-0.5940880775,-0.0211536158,0.2378588766,0.4233310521,-0.0320284627,-0.0436418802,-0.2210730314,0.4132110476,-0.5426178575,0.020808218,-0.197937429,0.0864612684,0.0401306972,0.0360178836,0.0378568955,0.0495851785,0.6460420489,0.1960494369,-0.2760607302,-0.2868872285,-0.0752945095,0.2934106588,0.0080150366,-0.2756493986,0.2361773998,0.4233388305,-0.354983896,0.2639247775,-0.2653572559,-0.4017896056,-0.115709424,-0.0299876258,-0.0388156325,0.2117659897,-0.1225452349,0.3960286379,-0.0757073984,-0.1922045201,-0.0743346885,-0.1557215452,0.1037826389,0.0387075059,-0.3879113495,-0.04676985,-0.4429834187,-0.2824510336,-0.4912324846,-0.778198719,0.1207469106,0.0699068308,0.057207875,0.332026571,0.3459808826,0.111493811,-0.1548401415,-0.3589918911,-0.2789942622,0.1449712664,0.0791931823,-0.1249559447,0.0872814357,0.3909487128,-0.0427657627,-0.0010109162,-0.0317496695,-0.1366344392,-0.2140945196,0.2423254251,0.1992513686,0.0455872454,0.0257471818,0.0106926076,0.6046019793,0.1010861024,0.1844915003,0.3397406042,0.1942436397,0.3856178522,-0.0550438724,-0.232566908,0.2094053626,0.1510695219,-0.35104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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2104","title":"Trouble loading wiki_movies","comments":"Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`.\r\n\r\nTo use `wiki_movies`, please update `datasets` with\r\n```\r\npip install --upgrade datasets\r\n```","body":"Hello,\r\nI am trying to load_dataset(\"wiki_movies\") and it gives me this error - \r\n\r\n`FileNotFoundError: Couldn't find file locally at wiki_movies\/wiki_movies.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/wiki_movies\/wiki_movies.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/wiki_movies\/wiki_movies.py`\r\n\r\nTrying to do `python run_mlm.py \\\r\n    --model_name_or_path roberta-base \\\r\n    --dataset_name wiki_movies \\` also gives the same error. \r\n\r\nIs this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. \r\nThank you!","comment_length":27,"text":"Trouble loading wiki_movies \n Hello,\r\nI am trying to load_dataset(\"wiki_movies\") and it gives me this error - \r\n\r\n`FileNotFoundError: Couldn't find file locally at wiki_movies\/wiki_movies.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/wiki_movies\/wiki_movies.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/wiki_movies\/wiki_movies.py`\r\n\r\nTrying to do `python run_mlm.py \\\r\n    --model_name_or_path roberta-base \\\r\n    --dataset_name wiki_movies \\` also gives the same error. \r\n\r\nIs this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. \r\nThank you! \n Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`.\r\n\r\nTo use `wiki_movies`, please update `datasets` with\r\n```\r\npip install --upgrade datasets\r\n```","embeddings":[-0.2073984444,-0.0135140801,-0.0442202128,0.3873903155,0.2891761363,0.1963276118,0.1652412564,0.2904251814,0.1623347551,-0.0591131747,-0.0651294068,0.0398705192,0.0390818752,0.0095692184,0.2180415839,-0.1336186975,0.1319327056,-0.0677647516,0.0409930721,-0.1519562751,-0.2609515786,0.1905121803,-0.0947827175,0.0493639857,-0.3012830019,-0.0213264003,-0.0860880464,0.3671931624,-0.1554056555,-0.3068653345,0.4555034339,-0.1563687027,0.6124148369,0.557012558,-0.0001154108,0.0793064833,0.4557094872,-0.0438040532,-0.3138126731,-0.2339858413,0.1990428716,-0.3066937029,0.3091194332,-0.0718859509,-0.1147370338,-0.0357826762,0.1456821859,-0.1668719202,0.1003157943,0.0856170207,0.1734515131,0.2701095939,0.2892472148,-0.0911118761,0.2961936891,0.1140339896,-0.0202871021,0.4139375389,0.2665452063,-0.4807064235,0.2214951515,0.3279593587,-0.3136359453,-0.0704162121,0.5433495641,0.1534255147,0.3824139535,-0.3687499762,0.1887979656,0.3565346599,0.6607331038,-0.082890965,-0.3993507028,-0.0977442786,0.1088015437,-0.1376087964,0.2221833766,0.2584237158,-0.0588011406,0.1871618778,0.0967815742,-0.3336417079,0.0515212454,0.5347654819,0.0972616374,0.495184958,-0.0458648615,0.0977662057,0.1348397285,-0.2122950405,-0.1636189222,0.0070710653,-0.0641391799,0.4242524803,0.0684447214,0.103847377,-0.0089857206,0.4857098162,-0.1439783871,-0.0411232486,-0.1150691286,-0.0414399244,0.0586496219,0.2267344296,0.3234931529,0.0552307591,-0.0866382122,0.0497813858,0.2694105804,0.4145142436,-0.1821436435,-0.1986727118,-0.1851852089,-0.0572147965,-0.3604752421,-0.2458050251,0.3958001733,-0.1175649837,-0.2839041054,-0.0130766714,-0.2200167477,-0.2205563337,0.2549139559,0.554427743,-0.2136487961,0.1867852509,0.0356269404,0.2033433467,-0.2397906929,0.1961438656,-0.1105699241,0.191212222,-0.1960520893,0.2319417596,0.3138676286,-0.0700656101,0.4374935925,-0.1168764308,-0.001596784,-0.1212561131,-0.0064617116,0.0378491059,-0.1349067241,0.4637263417,-0.0092822723,0.2072140574,0.3247037232,-0.310964942,-0.1152983084,0.0831781477,-0.2376967818,-0.3491517007,-0.1643438041,0.0872072205,0.1324012727,-0.1991852224,-0.2350740582,0.0310069155,0.0281784106,-0.5631130338,-0.0473254807,0.1131903678,0.0118427556,-0.2234303057,0.3480658531,0.6888164282,-0.2556487918,-0.0585722364,-0.3802088201,-0.0084375693,-0.0683746338,0.1990648955,-0.2756039798,0.0593011007,-0.3056888878,-0.0649878308,0.1098074764,-0.5922428966,-0.2333725989,0.2845813334,-0.1365546733,0.1409637332,0.125161007,0.1688186377,-0.3357461393,-0.1660052687,0.1106749028,-0.051254347,0.0735550523,-0.2340956926,-0.1769212335,-0.1521123648,0.1346911639,0.1320302784,0.0286435653,0.0403858274,0.0866599083,-0.0284465738,0.2409082204,0.0489289574,-0.1485192627,0.1628978252,0.3491697609,0.4564087987,-0.0384004116,-0.0741909742,-0.4570272863,0.1895824671,-0.107014969,0.0173462518,-0.1545140743,-0.0961494073,-0.06145107,0.0410896279,-0.1808400601,-0.2253644913,0.0850974694,0.054778289,-0.0529993176,0.1655847877,-0.27090469,-0.1925833076,-0.0947699547,0.2471811026,-0.4027302563,0.251999259,-0.1455648541,-0.0060172165,-0.0319152623,0.2065127194,0.2627585232,-0.1470631659,0.1542284042,0.390866667,-0.0781015381,0.2508268654,0.2339447886,0.1645369977,0.1641920507,-0.0635677576,0.2368172109,0.1799629778,0.2339200824,0.0259684641,-0.1443175822,0.3370316625,-0.0091855526,0.2501546443,0.218484953,0.0082469713,0.1893139035,0.0529132225,-0.0398360416,-0.2208691835,0.432536304,0.2068346143,0.1125894114,-0.0895571187,-0.2570354342,-0.0425548479,0.0272043329,-0.0885862857,0.2019982934,0.0528827906,-0.3966023326,0.1287508011,-0.0062545682,0.1457238793,0.3508633375,0.1889484227,-0.1748357713,-0.0642648563,-0.0122196469,-0.2063001543,0.2619258463,0.1753786355,0.0702292025,0.1796754152,-0.0203876663,0.0231949333,-0.2697029114,0.0314484648,-0.1116849259,0.1491054446,-0.2536031604,0.1608959585,-0.2967224717,-0.0686194301,-0.2333277017,-0.4196031392,-0.3267813027,-0.2818979323,-0.0361126885,0.3151589632,0.2061970383,0.0726836622,0.0288040824,-0.018881822,-0.3148033917,-0.1647440791,-0.1038463637,-0.3200178146,-0.2616140246,-0.0129883084,0.5495477915,-0.0611363761,0.1579234898,-0.1074337065,-0.0375607982,-0.2795412838,-0.5420339704,-0.0647832081,-0.1012944356,0.5056604743,0.0170397907,0.3776248395,-0.1283347607,-0.3390266299,0.3770352006,-0.0025069732,0.1115655228,-0.1031463221,-0.0545268431,-0.3354249597,0.0182406269,-0.4875997305,-0.4276797473,-0.1854010522,0.1409364045,0.2385593951,-0.1262493134,0.1926994622,0.1057868078,0.1727087349,-0.1761257499,0.027730396,-0.0571366362,-0.3297139108,0.3998129666,-0.226129353,-0.2458426505,0.4008429348,0.0212226678,0.0199608039,-0.296489954,-0.5815371871,-0.1569053829,0.1353253424,0.067797929,0.1800185144,0.1077848375,0.1994774491,-0.0731825605,0.0334883668,0.0757279992,-0.1539250761,-0.2185354829,-0.1357319504,0.2919263542,-0.1059441492,0.486774981,-0.4204573929,0.7676123977,0.0365934484,0.0802361146,0.3636780679,-0.1858739853,0.4716041684,-0.0990008041,-0.4413351417,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2104","title":"Trouble loading wiki_movies","comments":"Thanks a lot! That solved it and I was able to upload a model trained on it as well :)","body":"Hello,\r\nI am trying to load_dataset(\"wiki_movies\") and it gives me this error - \r\n\r\n`FileNotFoundError: Couldn't find file locally at wiki_movies\/wiki_movies.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/wiki_movies\/wiki_movies.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/wiki_movies\/wiki_movies.py`\r\n\r\nTrying to do `python run_mlm.py \\\r\n    --model_name_or_path roberta-base \\\r\n    --dataset_name wiki_movies \\` also gives the same error. \r\n\r\nIs this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. \r\nThank you!","comment_length":20,"text":"Trouble loading wiki_movies \n Hello,\r\nI am trying to load_dataset(\"wiki_movies\") and it gives me this error - \r\n\r\n`FileNotFoundError: Couldn't find file locally at wiki_movies\/wiki_movies.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/wiki_movies\/wiki_movies.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/wiki_movies\/wiki_movies.py`\r\n\r\nTrying to do `python run_mlm.py \\\r\n    --model_name_or_path roberta-base \\\r\n    --dataset_name wiki_movies \\` also gives the same error. \r\n\r\nIs this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. \r\nThank you! \n Thanks a lot! That solved it and I was able to upload a model trained on it as well :)","embeddings":[-0.1121565476,-0.0139938025,0.0073024975,0.4026295841,0.3210339546,0.2078264952,0.2428797483,0.2546978891,0.1591462195,-0.1293400973,-0.0809662715,-0.0636573881,-0.0174834076,0.0371454656,0.2689795792,-0.1464951634,0.1440912783,-0.0804251581,0.0386452563,-0.1759168506,-0.2859064937,0.1560275704,-0.0958821923,0.0523040965,-0.3731435835,-0.0070697526,-0.1327134371,0.4059880972,-0.1415062249,-0.2147176713,0.4609825909,-0.1746772528,0.615390718,0.5836373568,-0.0001164071,0.1489440203,0.359936893,-0.1114915237,-0.2162447274,-0.1964496374,0.2064691633,-0.2007347345,0.2710044682,-0.0929706842,-0.1247370839,0.0024904485,0.1638142616,-0.1582214087,0.0603854433,0.166710943,0.1763141602,0.2521987855,0.217990756,-0.0762792006,0.2325390875,0.1530012637,-0.0561034381,0.4803478718,0.1946611851,-0.5227861404,0.1577254832,0.2466911525,-0.2417711616,-0.1198350862,0.5773373842,0.1589505821,0.3579044044,-0.3634532988,0.1996272802,0.4087169766,0.6135014892,0.0077126753,-0.370562017,-0.1067453921,0.1170774624,-0.0947812945,0.2538561225,0.3591564596,-0.0323338993,0.2213998437,0.0957057402,-0.2623707354,-0.0076847128,0.4480513036,0.0597298406,0.4561125338,-0.0188436452,0.0658945441,0.1543425322,-0.1914173961,-0.2707585394,-0.0120477099,-0.037583001,0.4495453835,0.0333939567,0.1286818385,-0.0111189717,0.4703658819,-0.1006703675,-0.0471908003,-0.0657706112,-0.0687851161,0.0245423317,0.1941131949,0.2931633592,0.0672160462,-0.1521092057,-0.0033023122,0.2521961629,0.4258029759,-0.280762732,-0.2049828321,-0.25954476,-0.048532486,-0.4100686014,-0.3608543575,0.378241986,-0.1459192783,-0.3006827831,0.0537187345,-0.2405789495,-0.2009180188,0.2333174646,0.5762203932,-0.1611888111,0.1728943437,0.0266179629,0.1784474105,-0.253541559,0.1513384879,-0.1137235165,0.2067685872,-0.2304682285,0.2217813432,0.2740677893,-0.0368960947,0.4466458261,-0.1666414291,0.0131379003,-0.1406337619,0.0264505632,-0.005662269,-0.1238665432,0.4525800347,-0.0446489863,0.1515550613,0.3736090958,-0.3536576033,-0.1087918207,0.0837685466,-0.1776937395,-0.3517285287,-0.1060338616,0.0890505984,0.0701194853,-0.193263337,-0.2120414525,0.1135182306,0.0000123633,-0.4642557204,-0.0614666604,0.1810542345,-0.0781361982,-0.2276006937,0.3975328803,0.7473238707,-0.2878182828,-0.0496014506,-0.2907510102,0.0037929702,-0.0579738282,0.2702949643,-0.2691882551,0.151519835,-0.3390530944,0.0829209238,0.0662628189,-0.4536378086,-0.2166564614,0.3275752366,-0.1736964434,0.1387213767,0.059236344,0.1650346369,-0.3982459903,-0.1746533513,0.1784097999,0.0247272737,0.0772709846,-0.2549577653,-0.2193595171,-0.1762289256,0.1955959201,0.2131317854,0.0416980609,0.0317923725,0.0563543476,-0.0642138794,0.2531671822,-0.019083675,-0.203308776,0.1463198215,0.3620998263,0.3864982128,-0.0049206833,-0.0095542707,-0.4130249619,0.2022417784,-0.1039938107,0.0395719111,-0.0981931686,-0.07035245,-0.0420112349,0.0717680156,-0.2699703574,-0.2291836739,0.0739770234,0.0106790503,-0.1434487253,0.2013347596,-0.3167356849,-0.2031098753,-0.2150320858,0.2404493541,-0.3909479082,0.2344633788,-0.1204902753,0.0242043771,-0.0409120955,0.1739754528,0.2111233771,-0.1363126636,0.1331696361,0.3344074786,-0.0879498795,0.1992669553,0.3177417517,0.2289922982,0.171147123,-0.0794308186,0.1990139633,0.2143538892,0.2429057956,-0.029391164,-0.1889046133,0.3995218277,-0.0126831708,0.2303565294,0.24644126,0.0190832987,0.1926739961,0.0369023979,-0.0532949306,-0.1471385807,0.4825780094,0.249484852,0.1442347616,-0.0129474057,-0.2999362946,-0.1178653836,-0.0263981614,-0.1227505654,0.2149269432,0.0591586344,-0.4093381763,0.0853517577,-0.0433144458,0.1072097197,0.3932321668,0.1262845546,-0.1545924991,-0.0733814016,-0.003325501,-0.2250157893,0.2733381987,0.1382026374,0.0763287544,0.2013794333,0.0129279839,0.0100938091,-0.3816268146,0.0092583671,-0.077999495,0.0810074881,-0.2232536972,0.1762468219,-0.1877779067,-0.0294050667,-0.2262083292,-0.3067170978,-0.3279341161,-0.2259712368,-0.0376599059,0.2222719938,0.1373322308,0.0279576629,-0.0798412561,0.0734919384,-0.2874613702,-0.1831968576,-0.0358819626,-0.2942383885,-0.2293563038,-0.0159935523,0.6218863726,-0.1350497454,0.1590105891,-0.0959247127,-0.0893320516,-0.213015005,-0.5159598589,-0.0486749336,-0.0142011419,0.4615418017,-0.0363291353,0.4115782678,-0.1496347636,-0.348587662,0.4001303017,0.0615613274,0.1341527253,-0.054527875,-0.0358310677,-0.2908650935,-0.0004587636,-0.4532815814,-0.3932527006,-0.1345376372,0.1750051379,0.218378216,-0.1383410841,0.2596439421,0.0735045075,0.2045798451,-0.2176378965,-0.005353678,-0.0445678681,-0.3735512495,0.4692232013,-0.1874055713,-0.3353832364,0.401188612,0.005090809,-0.0372040942,-0.3107057512,-0.593129158,-0.0952707753,0.1236285716,0.0605258867,0.2066353858,0.0428930148,0.186994493,-0.1618367285,0.0479859821,0.0944118649,-0.1230252236,-0.1039203629,-0.0901140198,0.3017452657,-0.1561519206,0.4466749132,-0.4208746552,0.714972496,0.0078862729,0.0581380278,0.3880026937,-0.1477459669,0.4323696494,-0.1417575926,-0.3912588954,-0.019208448,-0.2129064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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2103","title":"citation, homepage, and license fields of `dataset_info.json` are duplicated many times","comments":"Thanks for reporting :)\r\nMaybe we can concatenate fields only if they are different.\r\n\r\nCurrently this is done here:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/349ac4398a3bcae6356f14c5754483383a60e8a4\/src\/datasets\/info.py#L180-L196\r\n\r\nThis can be a good first contribution to the library.\r\nPlease comment if you'd like to improve this and open a PR :)","body":"This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation.\r\n\r\nExample result:\r\n```\r\n\"citation\": \"@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n\r\n```\r\n\r\n@lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times.","comment_length":43,"text":"citation, homepage, and license fields of `dataset_info.json` are duplicated many times \n This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation.\r\n\r\nExample result:\r\n```\r\n\"citation\": \"@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n    url    = {https:\/\/dumps.wikimedia.org}\\n}\\n\\n@ONLINE {wikidump,\\n    author = {Wikimedia Foundation},\\n    title  = {Wikimedia Downloads},\\n\r\n```\r\n\r\n@lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times. \n Thanks for reporting :)\r\nMaybe we can concatenate fields only if they are different.\r\n\r\nCurrently this is done here:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/349ac4398a3bcae6356f14c5754483383a60e8a4\/src\/datasets\/info.py#L180-L196\r\n\r\nThis can be a good first contribution to the library.\r\nPlease comment if you'd like to improve this and open a PR :)","embeddings":[0.1381235868,0.0136456965,-0.0630835369,0.3942780793,-0.0054810438,0.0727092922,0.2289254814,0.3846517801,-0.0693545863,0.0431411639,0.0773394853,0.6607301235,0.4834605753,-0.1113678068,0.0986291617,0.2540152669,0.0726539865,0.0371466614,0.1630943567,-0.0247602761,-0.1442041695,0.3476740122,-0.0284530371,0.0256110393,-0.2232681066,-0.0213754456,-0.2590197325,0.2612617314,-0.0107875718,-0.2939233184,-0.2302542329,0.3371426463,-0.3443260789,0.0409678519,-0.000113471,-0.1048205495,0.093062371,0.0950631872,-0.1159348041,-0.1097441167,-0.331269294,0.0067826402,0.1233062446,-0.3301422596,0.2031037509,-0.0964053869,-0.1437792182,0.101548247,0.3578597307,0.0314734094,0.1344466507,-0.3486812413,-0.0364137962,0.211799413,0.1670621932,0.5066618919,0.2788807154,-0.0225377176,0.0567879193,0.1372627169,-0.0372865982,0.3509612978,-0.0174391065,0.0689737424,-0.0889396742,0.0198654644,0.3704961836,-0.2638970912,0.4029415846,0.2036248744,0.1998370588,-0.2152676433,-0.064980261,-0.1508311033,0.2594078779,0.043362163,0.0116458386,0.1248427853,0.291568011,0.0952090696,-0.1523608714,-0.1289327741,0.0156486388,-0.2076678574,-0.1042188406,0.1388810277,0.0545036197,0.2547236085,0.4554472864,-0.175698638,-0.0094513241,-0.3732994497,-0.3630179763,-0.1488604546,-0.1156102791,-0.2586490512,-0.0468389168,-0.0697722435,0.1238707379,0.1188213527,0.1158270091,0.2533144355,0.0044774348,0.1921118647,0.4902763367,-0.1692830473,-0.016740907,0.1683241874,0.1627899259,-0.1561909616,-0.3621469438,0.28835392,0.2324282974,0.250054121,-0.2877439857,-0.2357924581,0.2499600947,-0.1283691972,-0.1634404063,0.164468646,-0.2285562903,-0.2335813791,-0.0743250772,-0.0127040986,0.2380670011,0.0311072487,-0.0816188157,-0.0761941373,0.0614849664,-0.1274145097,-0.1334026605,-0.0573373772,0.0249821022,0.0447401442,0.2108948976,0.195805952,0.2852685153,0.4387192726,-0.2492441833,-0.650668025,0.407578975,0.0235326048,0.0609327853,0.2766374946,-0.0646025911,0.2921264768,0.0561475046,-0.0985993445,-0.184970662,0.2049913704,-0.5609199405,-0.170074448,-0.0984943584,0.1963616312,0.069088906,0.0584161617,0.069195658,0.04308974,0.1906209588,0.0887872651,-0.0559138395,0.0977033228,-0.2419963181,-0.0013050016,-0.2179880887,0.4913587272,-0.2344311029,0.0301890373,-0.1352132708,0.1047382727,0.1158543229,0.3253320158,-0.300350666,0.4413999021,-0.1408083737,0.1248408854,0.1913560927,-0.2989719808,-0.1260942519,0.4549366236,-0.2909891903,-0.1740697026,0.1084959358,0.0151913567,0.1984761506,0.0299537629,0.2903322875,-0.1629746556,0.2847070098,0.1826170683,-0.107877031,0.0077407528,0.1597626656,0.1104981229,-0.5287647843,-0.1930352598,0.0745646432,0.1035661548,-0.0100893853,-0.1794373989,-0.1288513839,0.4532905221,-0.0080440436,-0.1855049878,0.2804617882,0.2361408919,-0.1617327482,-0.0047418061,-0.2121648937,-0.2374351174,0.0126130339,-0.3103972077,0.1363417208,0.057701651,-0.2258719057,-0.0005589699,0.1718885899,0.2167414129,-0.1872630268,-0.0663008541,0.034436278,-0.0065423255,-0.2294784039,-0.0580725074,-0.3690659106,-0.2251571864,-0.1929299533,0.2902039289,0.1001267433,0.0516589992,0.4500667751,-0.0769634619,-0.0083697289,0.1213895679,0.4090743363,0.2413639724,0.3329517841,0.2167107612,0.5112675428,0.1091349646,0.0661497861,0.0320899449,0.037671186,-0.3805708587,-0.3177200258,0.2866712511,0.0399540626,0.0024847279,-0.0771473721,0.0277876277,0.4658515155,0.1128012985,0.0328102298,-0.4600546956,-0.0184919033,0.6355430484,-0.2225127816,0.4609511197,0.0620354526,0.5856894851,0.4512516558,-0.3411588073,-0.128510341,-0.0126765436,-0.1237115413,-0.4660646021,-0.0280859452,0.3222884834,0.2815458179,0.1424501687,-0.074334234,0.1308515668,0.1566407979,-0.2218484879,0.3593808711,0.0051235263,0.0784253031,0.0761669502,0.2985602617,-0.076149419,-0.4390314519,-0.2105133981,0.5419505835,0.1828189194,-0.3398180008,-0.0741727725,-0.5753316879,0.2566515207,-0.208587423,0.0790688396,-0.2199468911,-0.3306314349,0.1915864795,0.5351461172,-0.1917527169,0.0248043165,0.2189728767,0.4054920971,-0.0478357635,0.0378665105,-0.2421166599,-0.2526839077,-0.0021718435,0.0007377458,0.1682926267,-0.1667028368,0.0985403582,-0.000967806,-0.0618157461,-0.6569470763,-0.385278821,0.2029548883,-0.2176854908,-0.0880903304,0.0604702905,-0.0229235943,-0.4931525886,-0.1360338181,-0.2491198331,0.4120309651,-0.3168639839,0.1458394229,-0.2303530276,-0.1695064455,-0.3084942997,-0.2869493365,0.0622559264,0.0428432189,0.2375161201,0.0092667229,0.2903403938,0.0147328591,-0.2407885045,-0.0194590893,-0.1292811632,0.1680930108,-0.6054062247,-0.3265309334,-0.1008286327,-0.1986559182,-0.0840509832,-0.0753735378,0.298283875,-0.0494761169,-0.1083335951,-0.5357647538,-0.1331084222,0.1364542544,0.0310637057,-0.2595835924,-0.026640201,0.322006464,0.3555791378,-0.2242719382,0.0075758635,-0.0670364723,0.220254451,0.3102791607,0.2186113447,-0.1807077676,-0.1428261697,-0.2503644526,0.4967601299,0.6802615523,-0.0872247666,-0.0812431723,0.0648198426,0.1408363879,0.0672508702,0.0875329748,-0.1258482635,-0.088083759,0.0161327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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2099","title":"load_from_disk takes a long time to load local dataset","comments":"Hi !\r\nCan you share more information about the features of your dataset ? You can get them by printing `my_dataset.features`\r\nCan you also share the code of your `map` function ?","body":"I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :)","comment_length":32,"text":"load_from_disk takes a long time to load local dataset \n I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :) \n Hi !\r\nCan you share more information about the features of your dataset ? You can get them by printing `my_dataset.features`\r\nCan you also share the code of your `map` function 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2099","title":"load_from_disk takes a long time to load local dataset","comments":"It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a list of integers. The `text` column is removed during tokenization.\r\n\r\n```\r\ndef add_len_and_seq(example):\r\n    end_idx = example['input_ids'].index(SEP)\r\n    example['actual_len'] = end_idx-1\r\n    seq_len = len(example['input_ids'])\r\n    \r\n\r\n    example['seq'] = [PAD_ID] + [np.uint8(example['some_integer'])]*(end_idx-1) + [PAD_ID]*(seq_len-end_idx)\r\n    \r\n    return example\r\n```\r\n","body":"I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :)","comment_length":51,"text":"load_from_disk takes a long time to load local dataset \n I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :) \n It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a list of integers. The `text` column is removed during tokenization.\r\n\r\n```\r\ndef add_len_and_seq(example):\r\n    end_idx = example['input_ids'].index(SEP)\r\n    example['actual_len'] = end_idx-1\r\n    seq_len = len(example['input_ids'])\r\n    \r\n\r\n    example['seq'] = [PAD_ID] + [np.uint8(example['some_integer'])]*(end_idx-1) + [PAD_ID]*(seq_len-end_idx)\r\n    \r\n    return example\r\n```\r\n","embeddings":[-0.2723700404,-0.1468664557,-0.0388626195,0.1874405742,0.4000378251,0.0453446023,0.4641442895,0.2294565886,0.6530973911,-0.0394133255,0.2031130493,0.5122695565,-0.0241835993,-0.1652990133,0.1699289083,0.1053498611,0.3435818553,0.1828202009,0.5284671783,-0.2227723897,-0.2088466734,-0.0600212328,-0.0657849684,-0.0276850443,-0.2205491662,-0.0452075787,-0.0313016213,0.0227651168,-0.1343157291,-0.3194649816,0.0928841531,0.0193027686,0.2287545651,0.2781813145,-0.0001281752,-0.1722031683,0.0046071191,0.1535199732,-0.1430971771,0.2643969953,0.4690803885,-0.5940458179,0.2383883148,-0.35034284,0.0618379153,-0.2827260792,-0.3401739299,-0.2147907913,-0.0075521097,-0.1195013151,0.0158577468,0.361687541,-0.333750397,0.1539883912,-0.0743322521,0.1938998699,-0.1434206069,0.1258124262,0.4729337394,-0.1927229464,0.0029671367,0.0226153564,-0.2509937584,-0.0965019763,0.3857630193,0.0741025284,0.1951773018,0.0483410358,0.0506101027,0.1477310807,0.5389365554,-0.0009465857,-0.0577600747,-0.3210890889,-0.0554644354,-0.3969578147,0.3212765455,0.0301397387,-0.2767955363,0.0759613663,-0.1086108908,-0.0788263232,0.0298109204,0.2188173085,-0.2157344967,0.0422307178,0.0866941735,0.1933327317,0.3188702464,-0.108380802,-0.098751843,-0.0084894449,0.1952891648,0.4358851314,-0.32837376,-0.2487675399,0.0326809287,-0.0019629954,-0.1444237828,0.1964237988,0.0889205411,0.2106192708,-0.1054590121,0.0051103127,0.2394284308,0.4608980417,-0.2296869457,0.1211880669,0.0778399855,-0.3174993396,-0.1782213151,-0.0916159153,-0.2083225548,0.0359108225,0.4978763759,-0.5051928759,-0.2428243905,-0.1187323183,-0.0430596881,0.2367561013,0.3178033829,0.0038013018,0.2405851185,0.2986124158,0.0302921738,0.5268796086,-0.0790837556,0.1430350542,-0.2173659503,-0.0234914441,-0.0706594512,-0.2602777779,-0.1074772254,0.1086402237,-0.0289179366,0.3335449994,-0.0739851072,0.1510807425,-0.1349657774,0.0091545433,-0.0671710148,-0.1628105342,0.2305504978,0.207768634,-0.165542528,0.2659240365,0.2583853602,-0.3331991136,-0.4613665044,0.2853639424,-0.1705411524,-0.4137839377,-0.3964413404,-0.0067317081,-0.2626550198,-0.0938732624,-0.4485415518,-0.040241316,0.1792416871,0.0432296805,-0.0430105366,-0.2162176222,-0.1451558769,-0.1301585585,0.0579306632,0.0728645623,-0.2945594192,0.1127770171,-0.2678593099,0.3557194471,0.3770870268,0.7180876136,-0.2919610441,0.4202105999,-0.2172201127,0.2997386754,0.5369105339,-0.1884544194,-0.4042335749,0.6187259555,-0.1343872249,0.0445357151,0.0361252315,0.4975116253,0.2584006786,-0.0394756347,0.1837880462,0.6960242987,0.1099874303,0.2276614457,-0.4064319432,-0.0267873649,0.219917953,0.3522608578,-0.3312678635,0.1514369547,0.0653631762,0.0905347392,0.2092308849,0.061622601,-0.1017940342,0.2813984752,0.2582951486,0.1643486321,0.0434327312,0.2720756233,-0.1440769434,0.2002818286,0.5107887983,-0.0763214156,-0.0500876233,-0.2480376959,-0.3892806172,0.2931813002,-0.1443336755,0.3294907212,-0.1960772425,0.0404949859,0.130680263,0.0196183752,-0.1077524498,-0.273044616,-0.2539159358,-0.031821534,-0.0143755488,0.0497076549,0.1867201775,-0.0047953371,-0.4482590258,0.1168485582,0.0564297922,0.0313600414,-0.2220863551,0.2688148916,0.2077786177,0.0680243,-0.0495331548,0.2058762163,0.1093481556,0.0337096527,0.2628691196,0.0712591782,0.1975321621,-0.2217781097,-0.1914296001,0.5089946389,-0.0855100527,0.248140797,-0.2729073763,-0.0396678485,0.4773667157,0.1242774874,0.0985501483,-0.025013186,0.2052785456,0.2778957188,0.2757321,0.1117201671,-0.1666743606,0.271894753,0.2947527468,-0.2841126323,-0.03043513,0.2060741037,0.0274871588,-0.3105518222,-0.0116950702,-0.1333327889,0.2517815828,0.1176275611,0.1077013463,-0.3004761338,-0.0617363006,-0.0989977047,0.17740722,-0.0865197703,-0.1013619453,0.1303023398,0.0194883365,-0.0826523304,-0.3771759868,-0.0525450744,0.0686625987,0.4366832376,-0.2205613852,0.2747827768,-0.0571823306,0.1997994334,-0.0164392851,-0.301741153,0.0251299702,-0.3157307804,-0.2038689554,0.2908666134,-0.0447209217,-0.1348048449,-0.068859227,0.2550409436,0.2128171325,-0.4541468024,-0.2794964612,-0.3061749935,-0.1113633513,-0.1972857565,0.3697757125,0.0489766374,0.3008130193,-0.0138004106,-0.1955286711,-0.2244669497,-0.341160208,-0.1097940505,-0.0475998111,0.2139697522,-0.1861353517,0.3008183837,-0.5481994748,-0.0654698014,0.3417084217,-0.2881565392,-0.1716946065,0.3768146932,-0.0064205378,-0.1605555713,0.1009441391,0.1047660187,-0.1269279718,-0.1898217648,0.2512330711,0.0706498772,0.1390314847,-0.2185506821,0.0698025078,0.021884976,0.4788020253,-0.2052250654,-0.1983879507,-0.1957680881,0.5628789067,0.0449668951,-0.183082208,-0.1186386943,0.0367398113,-0.0935700461,0.0155285075,-0.5713308454,0.1604190171,-0.3806986213,0.4317447245,-0.1833308488,0.0472297631,0.1662706286,-0.2633866966,0.0482754558,0.1891607046,0.020515671,0.1094460562,0.0715712905,0.0302696917,-0.0074167978,0.7050042152,-0.1910776794,0.5004677176,-0.2420652956,-0.0491507463,0.3664191365,-0.1120703742,0.2564029992,-0.3437128961,-0.2357252389,-0.240497857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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2099","title":"load_from_disk takes a long time to load local dataset","comments":"Is `PAD_ID` a python integer ? You need all the integers in `example['seq']` to have the same type.\r\nDoes this work if you remove the `np.uint8` and use python integers instead ?","body":"I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :)","comment_length":32,"text":"load_from_disk takes a long time to load local dataset \n I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :) \n Is `PAD_ID` a python integer ? You need all the integers in `example['seq']` to have the same type.\r\nDoes this work if you remove the `np.uint8` and use python integers instead 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2099","title":"load_from_disk takes a long time to load local dataset","comments":"yup I casted it to `np.uint8` outside the function where it was defined. It was originally using python integers.","body":"I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :)","comment_length":19,"text":"load_from_disk takes a long time to load local dataset \n I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :) \n yup I casted it to `np.uint8` outside the function where it was defined. It was originally using python 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2099","title":"load_from_disk takes a long time to load local dataset","comments":"Strangely, even when I manually created `np.arrays` of specific `dtypes`, the types in the final `dataset_info.json` that gets written are still `int64`.\r\n\r\nUpdate: I tried creating lists of `int8`s and got the same result.","body":"I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :)","comment_length":34,"text":"load_from_disk takes a long time to load local dataset \n I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :) \n Strangely, even when I manually created `np.arrays` of specific `dtypes`, the types in the final `dataset_info.json` that gets written are still `int64`.\r\n\r\nUpdate: I tried creating lists of `int8`s and got the same result.","embeddings":[-0.2173988074,-0.1876725256,-0.0520239398,0.336670965,0.3299890161,0.0349005423,0.4153964818,0.2823090255,0.6786063313,-0.1099481285,0.1326237321,0.5752293468,0.194253251,-0.0471497588,0.1443952918,0.1174114272,0.3587273955,0.2722390592,0.3763953149,-0.1764423251,-0.3060879707,0.0475458466,-0.0370804928,-0.0319931991,-0.0833014771,-0.0650950223,0.0567429177,0.0116351899,-0.1999146342,-0.3068855703,0.1065616608,-0.0948057771,0.2616828084,0.2380930036,-0.0001292553,-0.1857509911,0.1267102659,0.0981938243,-0.2422356755,0.324352026,0.4163019657,-0.4820283353,0.2607607245,-0.3481028974,0.1100002602,-0.4955370724,-0.271995604,-0.3215941489,-0.0316408873,-0.1341709346,0.0246905722,0.467775166,-0.2241472602,0.1327084005,-0.201158911,0.4392254055,-0.2193626016,-0.0071726125,0.4425097108,0.0582930297,-0.0485814847,-0.0865372121,-0.1406695396,-0.0158314817,0.4063599408,0.0615013614,0.2055451274,0.1228796989,0.0694571212,0.172819525,0.620713532,0.0334614404,-0.0707622766,-0.2485562265,-0.1489282101,-0.2460744828,0.4324035048,0.0362544432,-0.2357409298,0.1717061549,-0.1762757003,-0.0015281078,-0.073338151,0.2504836023,-0.1308272183,-0.1616676152,-0.0110073313,0.1591522545,0.3527541161,-0.196405679,0.077416636,-0.0155625092,0.0091212317,0.3035268784,-0.3383488953,-0.2830924392,0.0515062213,-0.0079683717,-0.0780863836,0.0634728298,0.162239328,0.2495290041,-0.2331532538,-0.047210671,0.2891207337,0.360879004,-0.0328744687,0.0068867933,0.0910084769,-0.3389004469,0.0694476366,0.0200346243,-0.1676364541,0.0830196068,0.3784140646,-0.496773541,-0.2162392288,-0.1500123739,-0.0208427552,0.240024671,0.3636525869,0.0160096996,0.1731543243,0.1814385355,0.0771478713,0.6115540862,-0.0976982936,0.0728253201,-0.1629833132,-0.1739878207,-0.019771032,-0.3106483221,-0.1181267276,0.085008882,0.1496679187,0.3727173507,-0.0459691957,0.1671038717,-0.2715291381,0.015793955,-0.0969670117,-0.113113232,0.312556833,0.2298932523,-0.0987831131,0.255743295,0.1460681409,-0.2891290188,-0.4497122467,0.3425271809,-0.1358734816,-0.2943195701,-0.4542927444,-0.0250244699,-0.2671667635,-0.0196941737,-0.4391439259,-0.1076664925,0.1381552815,0.0255165212,-0.0122429384,-0.2078366429,-0.0829693824,-0.1118213981,-0.0885614827,0.0849554241,-0.4510560334,0.1749621928,-0.2742944062,0.2847523987,0.2883858085,0.6702421308,-0.2067277133,0.2299301922,-0.1860372871,0.3189185262,0.4110121131,-0.0977737606,-0.3115602434,0.7315465808,-0.0117100887,-0.1459036469,0.1338101327,0.5266262293,0.2496895939,-0.0425945669,0.1365237534,0.6743009686,0.1748803407,0.2215825468,-0.2907084227,-0.0866582021,0.048170846,0.4137925208,-0.4890213609,0.1240891442,0.1250769049,-0.054476399,0.1471737921,0.0456148833,-0.0502899215,0.2346315384,0.2530584335,-0.0990096927,0.0564774163,0.3052899241,-0.3008289337,0.1053384021,0.3949753046,-0.180567354,0.0343267322,-0.2042903155,-0.2784738243,0.3042504489,-0.0289720707,0.5546165705,-0.1815521121,0.0827285722,0.0816990957,-0.1209331155,-0.1571292728,-0.1332882643,-0.2426025718,-0.1102127433,-0.1819319129,0.0081924116,0.2339276224,-0.0504297167,-0.30652529,-0.0196361449,0.0269085113,-0.0384555683,-0.1630454808,0.2365633547,0.2002563924,0.0035289566,-0.0308959708,0.1877446175,0.151982531,0.0807134062,0.3181470931,0.0528730489,0.1747149825,-0.2780156136,-0.1761330813,0.503749907,-0.189139083,0.2216318548,-0.2732480764,-0.078467682,0.4578700662,0.1897336245,0.0803913251,-0.0869936049,0.1122872159,0.3219029903,0.1343201846,0.2910538316,-0.2332690954,0.3427419066,0.4129641652,-0.3111322522,-0.0754261836,0.2076277435,0.0162483081,-0.3657492101,-0.0054653161,-0.0687501132,0.3628416061,0.0558839776,0.0507821552,-0.369697839,-0.0517441444,-0.1846944243,0.1200352162,-0.0936766416,-0.0346512869,0.0992259383,-0.0428062268,-0.2816371322,-0.3656868935,0.0651431158,0.1108409688,0.388858825,-0.3100927174,0.2180454433,-0.1968772113,0.0692615584,0.0168185998,-0.2861744165,0.1109502688,-0.1754750162,-0.2048086971,0.3162035346,-0.0743373856,-0.1524071544,0.064966999,0.423001796,0.2276905626,-0.4015130699,-0.3833144307,-0.2376029491,-0.1568235606,-0.1752423644,0.3804137707,0.1047094241,0.1703634411,-0.1451798677,-0.1550821364,-0.2253527939,-0.1828177124,-0.1096083447,0.0458928607,0.2592884302,-0.1274664849,0.3801020384,-0.5672895908,0.0562553778,0.2641807497,-0.2876508832,-0.285835743,0.3660439849,-0.1474811584,-0.3140183687,-0.0337165073,0.03936892,-0.0902423635,-0.2155958563,0.3713660538,0.0776870474,0.1806075871,-0.2034236491,0.1504123211,0.007472916,0.4369546175,-0.2281594872,-0.2046726346,-0.2325005084,0.6616532207,-0.061055664,-0.2485224307,-0.0557904243,0.1784061342,-0.1016363725,-0.0587272793,-0.5192955136,0.1694786549,-0.4142033458,0.3584964871,-0.1935166419,0.0395521745,0.1161186844,-0.293694526,0.0407142825,0.1232160851,-0.1552179754,0.2262769789,0.1684099436,0.1085288152,0.0971095338,0.667519629,-0.2841599584,0.2335277349,-0.1284044534,-0.2436570525,0.3138743639,0.0045588366,0.2500695586,-0.4183475971,-0.0997267738,-0.2140558362,0.1322830468,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2099","title":"load_from_disk takes a long time to load local dataset","comments":"Yes this is a known issue: #625 \r\nWe're working on making the precision kept for numpy :)\r\nTo specify the precision of the integers, currently one needs to specify the output features with `.map(..., features=output_features)`","body":"I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :)","comment_length":35,"text":"load_from_disk takes a long time to load local dataset \n I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :) \n Yes this is a known issue: #625 \r\nWe're working on making the precision kept for numpy :)\r\nTo specify the precision of the integers, currently one needs to specify the output features with `.map(..., features=output_features)`","embeddings":[-0.2853317261,-0.2187879235,-0.0280981176,0.2345814109,0.3253434598,0.0638445765,0.4536391497,0.2049428672,0.6538368464,-0.0338119492,0.1011138856,0.4731715918,0.1274633706,-0.169902727,0.0634809881,0.1007766947,0.3845754564,0.170215264,0.4007259607,-0.2280999869,-0.3928019702,-0.0222928356,-0.0956596732,-0.0267841909,-0.2128368318,-0.1019784436,-0.0361412168,0.0544555523,-0.1071031392,-0.2511247993,0.021519443,-0.1343869418,0.3368290067,0.2338037044,-0.0001270739,-0.1998752505,0.0475257188,0.1346090436,-0.2297703028,0.3513155878,0.4877164662,-0.6189042926,0.216591239,-0.3899850249,0.1582983881,-0.1895425618,-0.3014677465,-0.266580075,-0.0368053429,-0.1214371175,0.0450730026,0.5076544881,-0.2321638167,0.1393104643,-0.2855154872,0.3035943508,-0.2053806335,0.0653144121,0.4982056916,0.054926306,-0.0345793851,-0.0517828688,-0.1683321148,-0.0302100033,0.3090568185,0.107944496,0.4078367651,0.1171322837,-0.0080201579,0.1199575067,0.4965890944,0.0335557647,-0.0846852586,-0.3417033255,-0.1112953573,-0.4144537449,0.3293813467,0.0911913514,-0.3490361571,0.1724072993,-0.0470476486,-0.0798033103,0.0139332637,0.2245960385,-0.1558129638,-0.0913809836,0.0197384842,0.1317343712,0.2276474237,-0.1148111597,0.0845651254,0.0103159174,0.2136578709,0.42485255,-0.4246657193,-0.2353165001,0.0208281279,0.014246136,-0.1653255224,0.0102034872,0.1822424978,0.2515753806,-0.1862856001,0.0219252128,0.3129630089,0.4280665815,-0.1733574569,0.1863619834,0.0927741602,-0.243365854,0.1412958801,0.0152633199,-0.1005442068,0.0422579832,0.5480375886,-0.4681058228,-0.1633123904,-0.132016778,-0.142205447,0.2277147174,0.4887236655,-0.0417849496,0.1561329663,0.1455444992,0.0806359574,0.5697749257,-0.0562968105,0.1194836274,-0.1807684451,-0.1578468531,-0.0843190402,-0.3356249034,-0.0562649444,0.1903012842,0.0402992442,0.3683851361,-0.0683654025,0.1541199237,-0.1981162876,0.0685257316,-0.1171715334,-0.1645004898,0.2333038598,0.1856643856,-0.1380418688,0.255790025,0.1359824538,-0.1931857169,-0.4076722562,0.4262596071,-0.1646172255,-0.340272218,-0.4790644348,0.009015671,-0.1866096854,0.0037994366,-0.3539022505,-0.1691920608,0.2202159166,0.0695998296,-0.0052030212,-0.2440938503,-0.1140649393,-0.1563969254,-0.0493967533,0.0769275203,-0.3092054427,0.1904052049,-0.1717406362,0.3061513603,0.3065825105,0.7243385315,-0.1892374307,0.216844812,-0.2830536664,0.237182647,0.4602162838,-0.0460090525,-0.3574863672,0.6271573305,-0.1066113636,-0.078496851,0.0641870126,0.5633755922,0.2529529035,-0.1901667863,0.1485000402,0.6897596717,0.10922122,0.199160859,-0.4090479612,-0.0733782426,0.0952199027,0.4398112893,-0.407392621,0.1320135444,0.0775019005,-0.0325040147,0.2144829333,0.1083314866,-0.0670487806,0.2884332538,0.2247227728,-0.0826955587,-0.0157324541,0.2709610164,-0.179816857,0.1594817191,0.4164332449,-0.1629320979,-0.0383521691,-0.1796811223,-0.3229567111,0.3180896342,-0.0788329244,0.5116457343,-0.1500653028,0.0236002747,0.1203980818,-0.1022779197,-0.07345853,-0.017423816,-0.2825340629,-0.0966027305,-0.1130622104,0.0154976984,0.1525216252,-0.0754139274,-0.3999119997,0.0115298182,0.0648076534,0.0145667084,-0.2211715579,0.3539468348,0.1323072165,-0.0865555406,-0.1187740117,0.266340524,0.1861677915,0.0791090503,0.3964467347,0.0986074582,0.2052937597,-0.2505775988,-0.1120992154,0.4844498038,-0.1962548494,0.1562964618,-0.2818818986,-0.0745494142,0.4282444119,0.1892055869,0.149519816,-0.0909317881,0.1781179309,0.3216135204,0.2176568657,0.2503772378,-0.2243136317,0.2397245318,0.2951150537,-0.3496774435,0.0404902436,0.2224862128,0.0427819677,-0.2908237875,-0.0233579762,-0.1991864443,0.357840091,0.0737689435,0.065910399,-0.2941621542,-0.0975086167,-0.1970733702,0.0982190073,-0.0139931822,-0.0982626826,0.0808211043,-0.0495457463,-0.1687764525,-0.2680055499,0.1539381295,0.0446598753,0.3528857529,-0.3059108555,0.0973189697,-0.0127622513,0.2521743774,-0.0604741015,-0.3014185727,0.0552328452,-0.2555603087,-0.2118463367,0.2482574731,-0.047424823,-0.1437081099,-0.0814494416,0.3157687485,0.2143785655,-0.3621745706,-0.3067261875,-0.2365983725,-0.1183376387,-0.1699169874,0.3378154635,0.0872206688,0.1963418722,-0.0735865384,-0.1557127684,-0.1935285479,-0.0977740362,-0.1680152863,0.0816521496,0.1938207895,-0.1836411655,0.3560246825,-0.5311698914,0.0350948758,0.2957322896,-0.2823811769,-0.164279148,0.3820283711,-0.0902661309,-0.3041514158,0.0562240109,0.1490205973,-0.2280776203,-0.226206407,0.1455477625,0.0563633256,0.1158922613,-0.280510664,0.090152964,-0.0288886279,0.46661219,-0.1933833063,-0.2513419092,-0.18547602,0.6473256946,0.0334665217,-0.2793290317,-0.1435446143,0.0893830135,-0.1167957261,-0.0809702426,-0.4640571177,0.1544277817,-0.3944261968,0.5134144425,-0.2122781277,0.1099839881,0.1466535926,-0.3103833199,0.0481796786,0.2114723176,-0.0594053343,0.1511201859,0.1554528773,0.1205791458,0.0782571137,0.6237229705,-0.2136434317,0.2885495424,-0.1777602434,-0.1728207916,0.3498382866,0.0695884153,0.241609022,-0.3530932367,-0.1464719772,-0.2309068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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2099","title":"load_from_disk takes a long time to load local dataset","comments":"Do you know what step is taking forever in the code ?\r\nWhat happens if you interrupt the execution of the dataset loading ?","body":"I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :)","comment_length":24,"text":"load_from_disk takes a long time to load local dataset \n I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :) \n Do you know what step is taking forever in the code ?\r\nWhat happens if you interrupt the execution of the dataset loading ?","embeddings":[-0.357709229,-0.1911456436,-0.0542891771,0.2522047162,0.3178031147,0.0741005987,0.4124632478,0.2019445449,0.6933247447,0.0321035273,0.1932916492,0.4987606406,0.1199500635,-0.0854377076,0.078265138,0.1477905661,0.3398175836,0.2383556068,0.4402107,-0.1339953244,-0.3000718951,-0.0153765325,-0.1427359879,0.0422020368,-0.1994234174,-0.1508752704,-0.0522637442,0.0502910502,-0.1458295584,-0.2906326354,0.0190012082,-0.0184636898,0.2739640474,0.2816301286,-0.0001262479,-0.2672604918,0.0696517602,0.1940072477,-0.2356897742,0.3061158955,0.3798666894,-0.4438560903,0.2077921778,-0.3478523493,0.1490973085,-0.2778462768,-0.2573793828,-0.3251244724,-0.0373226218,-0.1680577695,0.045437146,0.4464398324,-0.3012800813,0.0890896022,-0.1744759232,0.3106313646,-0.1221053749,0.0339281447,0.5823899508,-0.0583966412,-0.0453900956,-0.0751692504,-0.1739264131,-0.1305466592,0.4071652889,0.0562382899,0.2787828743,0.0998648778,0.0840034708,0.1261284649,0.6219447255,-0.0203819778,0.0500371382,-0.2161280066,-0.0790367424,-0.3577191234,0.4000557661,0.003064764,-0.3239591122,0.1873583347,-0.0897821486,-0.0954797268,-0.0011594529,0.2260670811,-0.1863961369,-0.1411526501,0.0256379247,0.1344275326,0.2987030745,-0.1170691326,0.0555922054,-0.0563122779,0.1497805715,0.4445002675,-0.417232424,-0.191189155,0.0042201998,0.1244223416,-0.1765966713,0.1036820933,0.1432673037,0.214681834,-0.1279767752,-0.0154735511,0.2563345432,0.3761742711,-0.1128492653,0.09652026,0.1039664671,-0.3212371469,-0.0395908877,-0.0354100689,-0.2497454733,-0.0189914219,0.4814788699,-0.4471083283,-0.200149402,-0.132312119,-0.0559440106,0.2049459964,0.4125044048,-0.0235757884,0.2181765288,0.2371752858,0.0863840953,0.4874051809,-0.0957277715,-0.0333782248,-0.2088415474,-0.1472740769,-0.0681420043,-0.2910850942,-0.1855145991,0.1931467503,0.0448805355,0.2586331069,-0.1528833807,0.2015702128,-0.2130119652,0.0637814105,-0.1322868764,-0.155823037,0.254134655,0.1592623591,-0.1326764524,0.2026861757,0.1348429918,-0.3092466295,-0.405815959,0.4017782211,-0.1136583388,-0.3545094728,-0.4384785593,0.013739787,-0.2659834325,-0.0327593312,-0.3687290847,-0.0985771567,0.0929255858,0.0433784761,-0.0409642234,-0.1871865094,-0.0954578146,-0.0469432361,0.0055030761,0.1939486116,-0.4462917149,0.2134213448,-0.2279573083,0.2241958082,0.2977640927,0.641292274,-0.2757306695,0.3365720809,-0.2637676299,0.2700733542,0.4910587966,-0.0406596847,-0.3431300819,0.7155482769,-0.1504546255,-0.1908347011,0.0214608889,0.5547387004,0.2817492187,-0.0781407282,0.0928895995,0.6829154491,0.0984284952,0.1808472872,-0.3708581924,-0.0509107038,0.0724437535,0.4867711961,-0.4286133945,0.1816755533,0.1529930085,-0.0166825186,0.1958255768,0.057199575,-0.0741308257,0.2589164674,0.231020391,0.0226330291,0.0371011607,0.2178665698,-0.2101085633,0.1258466542,0.527991116,-0.2242015153,-0.0082037067,-0.1891955137,-0.3130001128,0.3344785273,-0.1324252784,0.6150864959,-0.1887344867,0.0111453244,0.0530636646,-0.1579793096,-0.2006955445,-0.0270678494,-0.2793725133,-0.0978188068,-0.121064581,0.0068969922,0.1456569582,-0.0332153738,-0.4086243212,-0.028823968,-0.0118711898,0.0069377348,-0.1949533373,0.3073679209,0.1759618521,0.049708467,0.0049637621,0.1439480633,0.0891773999,0.0135565326,0.3173201382,-0.0389472879,0.2369768173,-0.2791323364,-0.0835857391,0.4249715805,-0.2261408567,0.2390493751,-0.2577944994,-0.1333370656,0.3874959648,0.0980762988,0.1315007359,0.0421084613,0.2405784279,0.3197242022,0.284244746,0.2978320718,-0.293325454,0.2334150076,0.281560719,-0.3991186023,-0.1306752861,0.1706736535,0.0838242322,-0.2651279569,-0.0376284048,-0.1081735119,0.292342335,0.12961483,0.044421263,-0.3026949465,-0.0511693098,-0.1725466698,0.1334510893,-0.0285677481,-0.0655025467,0.0821654499,-0.0790793151,-0.1506348103,-0.335841924,0.0008955905,0.1101568267,0.4040704072,-0.3022896051,0.2084807903,-0.1015755013,0.0667805225,-0.0115138814,-0.2169559598,0.1370065659,-0.1746398956,-0.2419863492,0.2149674147,-0.0095614661,-0.1359001547,-0.0398203433,0.4075907767,0.1740256548,-0.4136068225,-0.3883339167,-0.2927636206,-0.1515942216,-0.1457689553,0.4902339578,0.1144228652,0.2129697204,-0.0206116792,-0.1446669251,-0.2113123983,-0.1823829263,-0.1195882559,0.0484520197,0.1704612076,-0.1940094233,0.3333062232,-0.544780314,-0.0393551663,0.2153694779,-0.2854782641,-0.266269207,0.3533629179,-0.1288697422,-0.331546694,0.0084265741,0.0862202495,-0.1368310004,-0.2057427615,0.2223112881,-0.0022178309,0.1104766503,-0.2818082571,0.0997146294,0.0512808561,0.4467150569,-0.2579179108,-0.1771113724,-0.2561301887,0.6002789736,0.0168119725,-0.2479852885,-0.0719557405,0.10865134,-0.1167465374,-0.045904845,-0.5427120924,0.2715320587,-0.4648799002,0.4532858431,-0.263738066,0.039822679,0.0943992287,-0.2558395565,0.0357978009,0.234397158,-0.1071772575,0.1661345363,0.0742164031,0.1016753688,0.054877568,0.6813262701,-0.222206369,0.3173042238,-0.2216823548,-0.1420523971,0.3213466406,0.0146830436,0.2703052163,-0.3492729366,-0.1600702554,-0.2527992725,0.1392287314,-0.360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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2099","title":"load_from_disk takes a long time to load local dataset","comments":"After a synchronous discussion, we found that the cache file sizes have an enormous effect on the loading speed: smaller cache files result in faster load times. `num_proc` controls the number of cache files that are being written and is inversely proportional to the individual file size. In other words, increase `num_proc` for smaller cache files :)\r\n\r\nMaybe this can be highlighted somewhere in the docs.","body":"I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :)","comment_length":66,"text":"load_from_disk takes a long time to load local dataset \n I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).\r\n\r\nDoes anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?\r\n\r\nTagging @lhoestq since you seem to be working on these issues and PRs :) \n After a synchronous discussion, we found that the cache file sizes have an enormous effect on the loading speed: smaller cache files result in faster load times. `num_proc` controls the number of cache files that are being written and is inversely proportional to the individual file size. In other words, increase `num_proc` for smaller cache files :)\r\n\r\nMaybe this can be highlighted somewhere in the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2092","title":"How to disable making arrow tables in load_dataset ?","comments":"Hi ! We plan to add streaming features in the future.\r\n\r\nThis should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead.\r\nWhat do you think about this ?\r\n\r\nIf you have ideas or suggestions of what you expect from such features as a user, feel free to share them, this is really valuable to us !","body":"Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?","comment_length":84,"text":"How to disable making arrow tables in load_dataset ? \n Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? \n Hi ! We plan to add streaming features in the future.\r\n\r\nThis should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead.\r\nWhat do you think about this ?\r\n\r\nIf you have ideas or suggestions of what you expect from such features as a user, feel free to share them, this is really valuable to us 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2092","title":"How to disable making arrow tables in load_dataset ?","comments":"People mainly want this feature either because it takes too much time too make arrow tables, or they occupy too much memory on the disk. I think both the problem can be solved if we provide arrow tables themselves on datasets hub. Can we do this currently @lhoestq ? \r\n","body":"Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?","comment_length":49,"text":"How to disable making arrow tables in load_dataset ? \n Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? \n People mainly want this feature either because it takes too much time too make arrow tables, or they occupy too much memory on the disk. I think both the problem can be solved if we provide arrow tables themselves on datasets hub. Can we do this currently @lhoestq ? \r\n","embeddings":[-0.2959380448,-0.2490315884,-0.1036210507,0.2489057779,-0.0450339466,0.1605256647,0.2928253114,-0.0211192481,0.3396778107,0.2951470315,0.0326353945,0.4835270643,0.0262511838,0.0174946953,0.2436440885,0.1563296914,-0.0142451841,0.5176954269,-0.3697742522,-0.0229949504,-0.0617189966,-0.3304600716,-0.0252592657,0.1437551677,-0.2534678578,-0.1641295403,0.1912166774,-0.2878650427,-0.404660821,-0.2799391448,0.3027493656,0.3371615708,-0.1649561822,0.1134256497,-0.0001224674,0.0932132602,0.1214200929,0.2131106704,-0.4440474808,0.0841318667,-0.2974019051,-0.0556284562,0.4793878496,-0.0023389403,0.2543599308,-0.2447424382,-0.2770701349,-0.3397575617,0.1714115143,-0.1323938221,0.1215376779,0.112274453,-0.1933120042,0.0961996689,0.454631269,0.3035830259,-0.5730573535,0.1401541531,-0.038998995,0.0041109356,-0.2913177013,-0.1048902199,-0.0712384135,-0.130702123,0.3765466511,-0.10007447,-0.2564789355,-0.3019478619,0.0996722877,0.2157416642,0.7043825984,-0.5096978545,-0.1869640201,-0.296082139,0.3445961475,0.1056356579,0.2261914611,0.2963042259,-0.1142078191,0.1142809018,-0.2742856145,-0.673818469,-0.2642534375,-0.1397593468,0.3414140046,-0.1845974624,0.0385366976,0.0644926801,0.1961012483,-0.0430389866,0.7297346592,-0.2825857401,-0.3581550419,0.1335631609,-0.2341220379,-0.2815178037,-0.2018962651,0.4096239805,0.290076673,0.2482919395,0.4759663343,-0.0250483006,0.0451112874,-0.0968000963,-0.1799183935,0.1840306669,0.3575602174,0.0966903418,0.2082593292,-0.0555774756,0.2637524903,0.052723296,0.0418464057,-0.3272048235,-0.0190531276,0.0066924822,-0.0198303405,0.0030468088,-0.007712564,-0.0944463387,0.1107688099,0.1129938141,0.3573161364,-0.0435708389,0.2807615101,0.0208206773,-0.1791737676,0.3096635938,-0.1984789371,-0.2092533857,-0.0486997217,-0.0780898333,-0.1887566298,0.4118489325,0.3620302975,0.083716251,-0.2157825083,0.4662021995,-0.1323503703,0.0752661526,0.2060665637,-0.2312607169,0.0327525847,0.4121606648,0.0306291562,-0.1248455942,-0.1179491505,0.1336194873,-0.1240875646,0.4790473878,-0.2371688932,-0.3656114042,0.0523756742,0.0470530055,-0.092440933,0.0502534062,-0.8543148041,0.1679319441,-0.1389351785,0.3042337,0.3795031607,0.0627918616,-0.1190134808,-0.1159868836,0.0628859475,0.4255441725,-0.2484682351,0.090476267,-0.0120127928,-0.2226300836,-0.2038796246,-0.0825044364,-0.4281786978,0.287289232,-0.0954106823,-0.0719880536,0.4668298066,-0.024769485,-0.1460669786,-0.0146440296,0.0894162953,-0.1040178314,0.1017408445,0.0415880531,-0.0115556549,-0.0248694289,-0.2259537578,-0.0121472236,0.1868849099,-0.2042139024,0.1919275522,-0.3240942955,0.059901882,-0.0337832235,-0.2282293141,0.2141553611,0.2180252075,-0.1546841264,-0.0180260744,-0.1164072081,-0.0072901039,0.0910575092,0.0413847864,-0.0866690725,-0.0808133706,0.0877005234,-0.6414068341,0.2503461242,-0.1015685126,-0.0174977668,0.0042331922,-0.2572941184,0.1323525161,0.2630307078,-0.1018625572,0.5300439596,-0.0298506264,-0.7860901356,0.0059396685,-0.2782944739,-0.2895979285,0.0892977566,-0.4312280118,0.2539480329,-0.3105936348,0.2821172476,0.316422224,0.0983440205,0.0581024401,0.1112550497,-0.0316416584,0.3322236836,-0.1496320963,0.1671593785,0.1781448573,0.1764755994,0.1676577181,0.3872046173,0.1097668856,-0.3977028728,0.4344574511,-0.4769638777,-0.0125671364,0.0138704302,0.0165406018,0.1664523482,-0.3843301535,0.3188412488,-0.0439417958,-0.0056265057,-0.0875653252,0.2539410293,-0.1913870871,-0.2431895435,0.0255522653,0.3106911778,0.3294464946,0.3639852405,0.1657052934,-0.1471230537,-0.0390839428,0.0423742421,0.1576631069,0.1884934306,0.0106430501,-0.1029715091,0.1735065728,0.0350404494,0.241239816,0.1518500894,0.0659835339,0.0166904256,-0.1517919749,-0.2184060365,0.2604802549,0.0894291848,0.3960984945,0.0850111246,-0.0699949786,-0.0542901345,-0.4305777252,-0.0012596905,-0.2288176864,-0.1033506691,-0.3287900686,-0.1780311316,-0.2584324181,0.0095932512,0.0861177444,0.0227590278,-0.4254294336,0.0443890058,0.2645111084,-0.1380120665,0.0785575062,-0.0195887592,-0.2097582966,0.4630798697,-0.1646036953,-0.0274950657,-0.5118434429,-0.024354402,0.243795976,0.0163122471,-0.0275832489,0.1487127393,0.2704418302,-0.0155711165,0.2265708596,-0.3180449605,0.1024793983,0.1528233439,-0.0561017171,-0.1438629329,0.2807646692,0.1074606329,-0.1540966034,0.2022063881,-0.1312025338,-0.1807152331,-0.0840286463,0.0891090631,-0.3830259442,0.2847309411,-0.0689710751,0.1236855239,0.0513851829,-0.1602576077,0.2237327546,0.0258761458,0.1076872125,0.147577107,0.1839425117,-0.0231058877,-0.0198115297,0.0666629896,0.0129089206,-0.3925453424,0.4017270803,-0.2407953739,-0.0834747702,0.2055182159,0.1197904944,0.2135044187,0.3010386825,-0.5540850759,0.3444393277,-0.3526318371,0.0488325916,-0.2269196957,0.0578687191,0.1938934475,0.2327788323,0.1075320542,-0.2064687461,0.0343337543,0.2128235698,-0.0164831784,-0.209298417,0.0607978925,0.2286076397,-0.3945918381,0.3263339698,-0.1310033649,0.1730514467,0.1884269118,0.0671583116,0.4778157175,-0.2602343857,0.2625123262,-0.4720084667,0.05131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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2092","title":"How to disable making arrow tables in load_dataset ?","comments":"@lhoestq I think the ```try_from_hf_gcs``` provide the same functionality. What all datasets are available on HF GCS? Are all the datasets on huggingFace datasets hub are made available on GCS, automatically?","body":"Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?","comment_length":31,"text":"How to disable making arrow tables in load_dataset ? \n Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? \n @lhoestq I think the ```try_from_hf_gcs``` provide the same functionality. What all datasets are available on HF GCS? Are all the datasets on huggingFace datasets hub are made available on GCS, automatically?","embeddings":[-0.0699815601,-0.5376306772,-0.0606945418,0.2699545324,0.0311884806,0.1857694387,0.204721123,-0.0738599524,0.5251614451,0.1915540397,-0.2083578557,0.2142614573,0.1429023594,0.0653284416,0.2521748543,0.0714230388,0.0697012991,0.3191800117,-0.4851082563,-0.2686434388,-0.1109206453,-0.03626002,-0.1380767375,0.1288930029,-0.2874598503,-0.2463818341,-0.0120837195,-0.0107512623,-0.2599879205,-0.2313734889,0.3290418684,0.3906885684,-0.0985376686,0.2785464227,-0.0001202594,0.0522382483,-0.0483481288,0.0375811793,-0.4667463899,-0.0595739074,-0.0433042981,0.0232827868,0.5407465696,0.1272079498,0.0793076903,-0.2443219572,-0.2729644477,-0.3654932976,0.3282220662,-0.0181431919,0.1577931494,0.3644492924,-0.2300797403,-0.131116733,0.2805165648,0.1233369708,-0.4778993428,0.2537664473,0.0540299267,0.0632021576,-0.161352694,-0.0243520178,-0.1481767595,-0.0582384244,0.3744199872,-0.1106753796,-0.2596371472,-0.4817552865,0.1657986343,0.4390707314,0.781811893,-0.3694945574,-0.0524643138,-0.2837283015,0.1551827043,0.1223525554,0.1964025348,0.3899795413,-0.0861202106,0.1196514368,-0.1751600057,-0.515737474,-0.2267815024,-0.2266264409,0.3075189292,-0.1318724006,-0.1225428358,0.0585406087,0.4478563666,0.1124921814,0.3323293626,-0.1756272614,-0.4155189395,0.3108769059,-0.413418144,-0.2898392677,-0.1153087616,0.4277424812,0.3985426128,0.2306174189,0.317887336,0.1235973686,0.0537714101,-0.0411233418,-0.0586042218,0.1582379788,0.1617207527,0.0768049657,0.0983530208,-0.036856506,0.1641530693,-0.0357238688,0.0670006722,-0.3181801736,-0.2331126928,-0.0434866585,-0.0704027712,-0.1852671802,-0.073716417,0.0096376333,0.3312657475,0.0439499468,0.3386299908,0.271124959,0.3304340243,-0.0882129744,-0.1544590443,0.3299454153,-0.2381676435,-0.2385379225,-0.1456321627,-0.1315277219,-0.2117994279,0.4393602908,0.2759017348,-0.0501812212,-0.1849318743,0.4688886106,-0.0440242849,0.1317591965,-0.0492257662,-0.303842783,0.2460277081,0.4969675541,-0.0509323329,-0.0564777702,0.1598425508,0.2020442188,-0.2483125627,0.1833151579,-0.3329205215,-0.3900697231,-0.0035929398,0.032222718,0.0412290543,-0.0404381827,-0.8469280601,0.3460439742,-0.3431281447,0.3712806106,0.4440469444,0.1190370098,-0.0005574742,-0.1731143743,0.0333195813,0.5034125447,-0.1700749546,-0.0085305916,0.1776849329,-0.3054769635,-0.1037433594,0.0439892076,-0.3630769551,0.0695024729,-0.1272325665,-0.0857857466,0.3792748153,0.0228773598,-0.052542299,0.0845265687,0.1702493131,-0.0148179904,0.0711665228,-0.1773425341,-0.0218242295,0.0199649539,-0.4476978481,0.1567911506,0.2190752327,-0.1982150972,0.0974611491,-0.2478875816,0.0965178534,-0.0324610323,-0.1310712397,0.2336816341,0.2506481409,-0.5099535584,0.135522902,-0.1414466202,-0.1844237447,0.1786037683,0.0470607132,-0.0133712832,-0.0411119349,0.17425403,-0.7623881102,0.3165916204,-0.1788858622,0.1900544316,0.0594747365,-0.1816143692,0.1207243353,0.2537692785,-0.102853626,0.3356228769,-0.0349904932,-0.5231173038,0.0846730992,-0.3067087829,-0.4564953744,0.3038044572,-0.3575839102,0.3665586114,-0.1958850771,0.315084964,0.3337168992,0.0993265584,0.0923652723,-0.0198599678,0.106133312,0.0853918791,-0.1777103841,0.2503392696,0.1215639114,0.3644580245,0.2029536664,0.3083251417,0.0576008298,-0.4684823155,0.323420614,-0.3931081295,0.0375391282,0.1042507961,0.0386784077,0.3395082355,-0.2522573471,0.3089871407,0.1465860456,-0.066014573,-0.0955755487,0.1360981166,-0.1097238511,-0.1193592921,0.1630503088,0.1692170948,0.3703482449,0.364467144,-0.0822132975,0.0035113879,0.0637509376,-0.1441782415,-0.0282288883,0.1420616806,-0.098956883,-0.2244120836,0.3118466437,-0.2024132013,0.0964982882,0.193342939,0.1441769898,0.0530510619,-0.2020272613,-0.2763196826,0.2760865986,0.0468645208,0.3984256387,-0.0255295113,-0.0146948826,-0.0302966777,-0.6330464482,0.1473524868,-0.2354974449,-0.1206886396,-0.3019895554,0.0783719271,-0.3228674233,0.0418340079,0.1446887255,0.0071426434,-0.567517519,0.0022964124,0.2356795669,-0.040466506,0.0701785386,-0.0669188276,-0.3137353063,0.6049495935,-0.2892213166,-0.0028113348,-0.5583313704,0.0597380102,0.0854417458,0.0307134129,0.0690543875,0.2096919268,0.341091603,0.0348372832,0.0712579191,-0.2587764263,0.1375205517,0.3671420217,-0.1972143054,-0.0893801972,0.0204221793,0.1433391422,-0.2664320171,0.2388625592,-0.1036956236,-0.4493598044,-0.0378563739,0.1693824083,-0.4139093161,0.172244221,-0.032829769,0.0564120971,-0.086694181,-0.1806505471,0.2011324614,-0.0130633609,0.2047112137,0.3151851892,0.0498370714,-0.0383823663,-0.1923555136,0.1486049592,-0.1679401547,-0.4587257504,0.3519231081,-0.1713169068,-0.1477936953,0.2070529163,0.0551617034,0.1059929654,0.1161226481,-0.609770298,0.2893597484,-0.2824665606,0.0281240512,-0.0854872093,0.0346316695,0.3091964722,-0.0576828122,0.0163572039,-0.2320910543,0.0540945902,0.1564190984,0.0065894662,-0.0831755027,0.0299194176,0.0104064625,-0.3959492445,0.4441452622,-0.038936194,0.1681032479,0.3979781568,0.1006596386,0.4846961796,-0.3332364559,0.1686082333,-0.3442380726,-0.084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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2092","title":"How to disable making arrow tables in load_dataset ?","comments":"Only datasets like wikipedia, wiki40b, wiki_dpr and natural questions are available already processed on the HF google storage. This is used to download directly the arrow file instead of building it from the original data files.","body":"Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?","comment_length":36,"text":"How to disable making arrow tables in load_dataset ? \n Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? \n Only datasets like wikipedia, wiki40b, wiki_dpr and natural questions are available already processed on the HF google storage. This is used to download directly the arrow file instead of building it from the original data files.","embeddings":[-0.1666766554,-0.0997227728,-0.0602355674,0.251595974,-0.046882987,0.1143143401,0.2708075941,0.0048356182,0.4697786868,0.1632679105,-0.0370749906,0.1923091859,0.1889043152,0.0169300884,0.2863861322,0.1164424717,-0.0252678543,0.4686415792,-0.3328589797,-0.1243106127,-0.1999843121,-0.2028210759,-0.0752780363,0.0748854652,-0.0713145509,-0.2044893652,0.1787602305,-0.1432494372,-0.3065359592,-0.3528852165,0.1729183197,0.3800322115,-0.1642081887,0.1244354025,-0.0001261445,0.094289951,0.0329206325,0.0948778763,-0.5260359049,0.006027014,-0.1746383309,0.0031732447,0.4379302263,-0.0186206643,0.2819115818,-0.4082455337,-0.1863089353,-0.3450863361,0.3139982522,0.0053225714,0.0905807093,-0.0716364905,-0.0604900382,0.2094389796,0.5697895288,0.2733406425,-0.4536009133,0.2838582098,-0.160365209,-0.0454603545,-0.233036831,-0.019829955,-0.1649195403,-0.0176153965,0.3427163661,-0.0658057034,-0.2182617188,-0.4648956954,0.3253163993,0.4471846819,0.8690113425,-0.4102916121,-0.0729228109,-0.1844525039,0.2027647644,0.0973021239,0.2856376767,0.3990594149,-0.1066478416,0.0758944452,-0.2127400041,-0.6784489155,-0.25742957,-0.0711820871,0.2603639662,-0.1153429598,0.0183343701,0.050158713,0.3017888069,0.0081159929,0.3686649203,-0.223840639,-0.3648709059,0.272334367,-0.1842785031,-0.2159967422,-0.3739869595,0.1744879186,0.4409631193,0.1009864882,0.4295786917,0.0503217205,0.2170754522,-0.071750775,-0.1252764016,0.1128390431,0.1504207999,0.0533880554,0.2999628484,0.0077718552,0.1263809949,-0.0047516157,0.1571422666,-0.2876321971,-0.0752578303,-0.1376900822,-0.0675592944,-0.1553011984,0.0428053699,0.0308059659,-0.0968296155,-0.0888143331,0.1404576004,0.1764500588,0.231339559,-0.0716202036,-0.0928623602,0.3906411231,-0.2245062888,-0.1648521125,0.065603666,-0.1498602033,-0.1348668039,0.4797789156,0.4003249407,0.0539176986,-0.1964550912,0.47218135,-0.2374716252,0.0166779142,0.3698478937,-0.344773978,0.1794475317,0.5079669952,0.1643345654,-0.1345516741,-0.0287734866,0.0166882053,-0.1465750039,0.379989922,-0.3486206234,-0.2767961323,-0.0916240662,0.0192897059,0.1560793966,0.1055180803,-0.8544896841,0.2341753542,-0.0627288297,0.192218557,0.3423011899,0.1384256929,-0.147481814,-0.1924449801,0.1250506341,0.4684499502,-0.3885135949,0.0869876742,0.0318108052,-0.1592184603,-0.1226311997,0.0867344886,-0.3584826887,0.256198436,-0.1413198113,-0.0886124223,0.4231479168,0.1055300534,-0.19324103,0.2495382875,0.1716930568,-0.0479312763,0.1314125806,-0.0283833425,0.0007526541,0.0692844614,-0.3884218335,0.1332307309,0.3127720952,-0.148754105,0.1245481968,-0.2645056248,0.1171085909,-0.1291519403,-0.1417371631,0.3576332033,0.3157827854,-0.2228238136,0.1151502952,-0.1869778186,-0.0566203855,0.2460997701,0.0988696292,0.0628359094,-0.0105710886,0.2677644491,-0.7945275903,0.1788479835,-0.3360014558,0.0576645248,-0.0180710051,-0.2592436671,0.0695411116,0.1732881069,-0.0863894597,0.2867399454,-0.0377667658,-0.5865476131,0.022451492,-0.0337138176,-0.133320421,0.0592200272,-0.4370775223,0.2424857765,-0.2334551513,0.3700748682,0.2047596872,0.1187113523,0.0710401684,-0.0198318381,0.0175126679,0.2195648104,-0.1529205143,0.1186263859,0.1463334411,0.2047602236,0.1609807163,0.3847929239,0.0475857146,-0.5845116973,0.3888181746,-0.3807119727,0.0493073575,-0.0686550662,0.0091466028,0.2706066668,-0.0396314114,0.3539585471,0.1418545991,-0.0684293434,-0.0405804627,0.1539785266,-0.0198241435,-0.3033726215,0.0530690961,0.2990089953,0.4224357605,0.3638781905,-0.0066055553,-0.1219093576,0.1077943668,-0.0360347591,0.2045954913,0.3079280853,-0.044127509,-0.3254676461,0.0753500983,-0.1600909531,0.1006897688,0.1284569651,0.1205157787,0.0344251357,-0.1513556838,-0.2663257122,0.329082489,-0.0247675776,0.4524427056,0.1972550601,0.0525163971,-0.0429168604,-0.5053237081,0.1169964001,-0.2084615827,-0.0675217137,-0.3271872103,0.0067364187,-0.23215051,-0.0429422073,0.0692347512,0.197429508,-0.5449183583,-0.0646310076,0.2237851471,-0.0641596019,0.1264277846,-0.1384565234,-0.2107720226,0.3652667701,-0.0992822796,-0.1764004529,-0.6031213403,-0.0247923918,0.1110709682,-0.0146484962,0.0877328217,0.1183421239,0.2117766887,0.0654745623,-0.0797165409,-0.4650186002,-0.1034691408,0.2821801007,-0.0597591959,-0.1261900067,0.1421791911,0.2849062085,-0.1957388818,0.1819579452,-0.1306826174,-0.2314673364,-0.1515339464,0.2393718362,-0.3711264729,0.4133032262,-0.0417639241,-0.0285002366,0.1013329104,-0.0524804294,0.1647360772,0.140779227,0.2111324966,0.1314134151,0.169273898,-0.0328297131,0.0723700151,0.0704223588,-0.1246540397,-0.2096573561,0.4650502801,-0.2878260016,-0.1776955724,0.2696314454,0.0248889253,0.0481998138,0.3943352699,-0.7647732496,0.4628793895,-0.2994994223,0.0385507531,-0.3009708524,-0.0657135546,0.177926302,0.1902365685,0.0707082599,-0.2031704187,0.1442662925,0.3033227623,-0.0519685261,-0.1278569102,0.2401959449,0.3711486161,-0.4483270049,0.6514479518,-0.2132665366,0.2048831135,0.3713408113,0.0653752908,0.4804442525,-0.3940525651,0.3682645559,-0.3533893824,0.0440669544,0.21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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2092","title":"How to disable making arrow tables in load_dataset ?","comments":"@lhoestq How can we make sure that the data we upload on HuggingFace hub is available in form of preprocessed arrow files ?","body":"Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?","comment_length":23,"text":"How to disable making arrow tables in load_dataset ? \n Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? \n @lhoestq How can we make sure that the data we upload on HuggingFace hub is available in form of preprocessed arrow files ?","embeddings":[-0.1100975871,-0.4545698762,-0.1245342419,0.3275228739,0.138504684,0.0666502416,0.1080085412,0.0051873387,0.2907291651,0.2163161486,-0.0762575269,0.3311557472,0.0758255497,0.2674562335,0.3114099205,0.0753525868,-0.0126066515,0.3374112546,-0.4707742035,-0.0729587302,-0.0226511937,-0.1312700659,-0.0280798543,0.1429725587,-0.3697292507,-0.1202766299,0.1659023911,0.1318922639,-0.4114736021,-0.3004578054,0.2799259424,0.3918724656,-0.2110333443,0.2840105295,-0.0001185283,0.0347914472,0.0148414318,0.0469427444,-0.42345348,-0.0630001873,-0.1052796096,-0.0900127366,0.552696228,0.1363579482,0.1049978584,-0.4011049569,-0.2245007008,-0.1469555646,0.4965695739,-0.1221488342,0.1708035469,0.400201261,-0.0367949791,0.0012566323,0.2102968395,0.3903617859,-0.490552336,0.2777503729,0.1035267413,0.042191416,-0.2085119486,-0.0491254888,-0.0048570805,-0.1373191476,0.3756073117,-0.0360606983,-0.1985394508,-0.4639984667,0.0708648711,0.3484270573,0.5639098287,-0.3297149539,-0.1446353048,-0.3594789207,0.2402592599,-0.0216076244,0.3293654323,0.225537464,-0.1578767896,0.0673475042,-0.2663391531,-0.5470802188,-0.3333836794,-0.218575567,0.5047805309,-0.076915063,-0.1086540893,-0.0129904263,0.2491814792,-0.0777414218,0.2917518914,-0.0805463344,-0.2887803316,0.2161269933,-0.1914640665,-0.3093226552,-0.281237334,0.4735992253,0.3961885571,0.400200367,0.5239634514,0.0959406942,0.072010994,-0.1174758673,-0.0317182876,0.1587304324,0.193554014,-0.1155328155,0.2119951695,0.1582829058,0.1720876396,0.009821374,0.0601810478,-0.3296580017,-0.1955471039,-0.0224679429,0.1544831693,-0.1553844362,0.0939445496,-0.1374528259,0.2613368034,0.0638690218,0.3560952544,0.0569743812,0.180691883,-0.0295460783,-0.1791046262,0.379006207,-0.248083204,-0.2792924047,-0.1047427654,-0.1694387048,-0.1806078106,0.4586046338,0.4090617895,-0.1122873053,-0.0887114778,0.4487025738,0.0572253019,-0.0153427804,0.0484907404,-0.1698323339,0.1490182281,0.4378680885,-0.0290969741,-0.0705789328,-0.0994414911,0.1250169873,-0.1465258449,0.2650176883,-0.3646680713,-0.4418751597,0.0444759578,0.0543172583,-0.0444458537,0.0182588138,-0.7932122946,0.1292459667,-0.2953799963,0.3496223092,0.2732408345,0.1834332496,-0.0424465351,-0.2014659047,0.1859554499,0.352290839,-0.1842111498,0.0959440693,0.1387863308,-0.3303410709,-0.1780183613,0.1179890856,-0.3667709529,0.242792204,-0.1544751376,0.0416446663,0.4212193191,-0.0907354727,-0.0077271443,-0.0763670057,-0.0343230106,0.1033494473,0.1471956819,-0.1076357365,-0.0380745903,-0.1216002479,-0.2622415721,0.1754321456,0.1581322998,-0.1894269288,0.089052707,-0.2200370729,-0.0470202528,0.0155239971,-0.3854585588,0.1361887455,0.0694885924,-0.3777515888,0.0612149648,-0.1447183937,-0.106374152,0.07156609,-0.0054898555,0.0905893818,-0.0275947712,0.2464648336,-0.8245880008,0.261398077,-0.1207316667,-0.0144083798,-0.1286077648,-0.1991339326,0.0065702735,0.196771428,-0.1613360196,0.4198437035,0.0400856398,-0.6630686522,0.123702541,-0.2078924626,-0.4388538003,0.1259002537,-0.3588649333,0.4169155061,-0.5347799063,0.1409382224,0.2798046768,0.002394771,0.10170836,0.0857685208,-0.005962065,0.1202664897,-0.0833942071,0.208268553,0.2024602443,0.4575892687,0.3043973446,0.4158650339,0.1502268165,-0.4250659347,0.2253432125,-0.4385340214,-0.0400626548,-0.0068691913,-0.0001241759,0.2602646947,-0.4774878025,0.2487118244,0.071763739,-0.0799846649,-0.0640240014,0.1609707922,-0.2656231523,-0.2145772427,0.1559644043,0.1631298959,0.3208202124,0.2711210847,0.0731402189,-0.0346236266,0.0183139648,-0.105354175,-0.0562522002,0.1356912255,0.0144982468,-0.0940969512,0.1921983659,-0.1028650925,0.1302126795,0.1553015262,0.1930710524,0.0749020875,-0.1782563627,-0.3413229883,0.2432800531,0.1592385769,0.340529263,-0.0173164885,-0.0734461918,-0.0706315041,-0.5441478491,0.1775170416,-0.3389026225,-0.1754332185,-0.3400018513,-0.0805465057,-0.2896670997,0.0255501829,0.0797001049,-0.2292761207,-0.5078178048,0.0496624038,0.322514981,-0.0190664548,-0.0947642475,-0.0793125555,-0.058397565,0.5088121295,-0.227946803,0.0185197163,-0.5136077404,0.1025357172,0.1898605227,0.0411525331,0.1310687214,0.115196839,0.3711613715,-0.0504336767,0.1480102986,-0.2743229568,0.0571415238,0.2185662389,-0.1748706698,-0.0808990002,0.0650817528,0.2000825256,-0.0712435842,0.1652932465,0.0119652422,-0.2896600962,-0.1578966379,0.098006919,-0.2179299444,0.2611680329,-0.1466182917,0.078511335,0.084781535,-0.2372642457,0.3560937643,0.0727703571,0.1659401357,0.1821744889,0.1853740215,0.0225035213,-0.2359180599,0.133266151,-0.0792166218,-0.4334353805,0.4475567937,-0.233407557,-0.2612316906,0.3169552684,0.129692778,0.2379167527,0.1163240746,-0.5254436135,0.1258656383,-0.1705875099,0.0319621451,-0.1600883752,0.0561917908,0.3184776008,0.0864020437,-0.0027333333,-0.1439156085,0.1006568968,0.3076089025,-0.0458948351,-0.0421247296,0.0426631048,0.0149013232,-0.4100801647,0.4363572598,-0.0640627742,0.2218597829,0.3528681397,0.1127993464,0.396458149,-0.2960090041,0.2807895243,-0.5286573172,-0.0807221904,0.2156338394,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2092","title":"How to disable making arrow tables in load_dataset ?","comments":"We're still working on this :) This will be available soon\r\nUsers will be able to put their processed arrow files on the Hub","body":"Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?","comment_length":24,"text":"How to disable making arrow tables in load_dataset ? \n Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? \n We're still working on this :) This will be available soon\r\nUsers will be able to put their processed arrow files on the Hub","embeddings":[-0.3103685677,-0.2093830109,-0.1464321464,0.2652353942,0.0345696248,0.0666943863,0.2737096548,0.1132889166,0.3428294659,0.2253294438,0.0225788374,0.5258630514,0.0204506256,0.1348215342,0.2148839384,0.2104867846,-0.0567545183,0.3835603595,-0.370223552,-0.0217931662,-0.0132307783,-0.242534712,-0.0369567052,0.1654260159,-0.352792412,-0.1516540945,0.2050920129,-0.0799770728,-0.3733310401,-0.3553424776,0.2808741033,0.3713862002,-0.2569585443,0.2140728086,-0.0001159839,0.0817523003,0.1164755374,0.1294047385,-0.3511275947,0.0271335766,-0.2709644437,-0.0506317429,0.5330007672,0.048189234,0.1740213484,-0.3140257001,-0.326105535,-0.2373576313,0.3075525463,-0.1084174141,0.1901614219,0.1338144392,-0.1258650869,0.0214221459,0.3372371793,0.2644141614,-0.5166210532,0.1766559333,0.0029492094,0.0186856538,-0.2683089375,-0.0692397505,-0.0806634501,-0.1670561135,0.2791842818,-0.1535366327,-0.1141778752,-0.4275369346,0.0803016573,0.2599158585,0.7489739656,-0.3426682949,-0.1013403311,-0.2271207571,0.2399844527,0.046319887,0.2915193141,0.2484015524,-0.1438123435,0.0492912047,-0.3220917583,-0.580715239,-0.3312959075,-0.2287511379,0.3389687538,-0.101069957,-0.0031777222,-0.0043580965,0.2739900947,-0.032246463,0.6454591155,-0.0957823172,-0.4226688147,0.1684318185,-0.2579934299,-0.345099777,-0.2567856312,0.3940750957,0.2697523832,0.3345091939,0.5258702636,0.0830111206,0.1021843106,-0.0601601824,-0.0829810873,0.1526259184,0.3308385909,-0.0539739765,0.2798469663,-0.029062232,0.0941072702,0.030074412,0.0683990493,-0.457054168,-0.1736430973,0.0321198478,-0.0016698214,-0.1858097911,0.0548646078,-0.1981121302,0.1823126525,0.0743919834,0.3349526525,0.0447428524,0.2945014238,0.0740870833,-0.1831943095,0.2993355393,-0.2095319033,-0.1715037823,-0.0472710803,-0.2076269537,-0.2439629138,0.3211621642,0.3855163157,0.0219374523,-0.1519086957,0.4505899847,-0.063513115,0.0171689428,0.2573388219,-0.1818991452,0.1704493314,0.5423360467,0.0090393126,-0.1620635241,-0.1104332358,0.0282398332,-0.0701754913,0.4509628415,-0.417442441,-0.3716728389,0.0600933656,0.1057705209,-0.0788251087,-0.0629551485,-0.771576345,0.0448148362,-0.1329560578,0.3094379008,0.2648726702,0.1242686138,-0.0766548067,-0.2044762373,0.0895226151,0.2795028687,-0.3330899775,0.1844024807,-0.0560634099,-0.2037720829,-0.17547144,-0.0012652776,-0.4258303642,0.2005177587,-0.1304040402,0.0118652526,0.5192417502,-0.0667947084,-0.0070453533,0.0726575479,0.0436769091,-0.0900366902,0.1525606513,-0.0635976642,-0.0286896247,-0.1447896361,-0.2948603332,0.0517011844,0.237039566,-0.1687286794,0.1082211956,-0.2682799995,0.094784379,-0.091040425,-0.2780421078,0.121010989,0.230222553,-0.2342467159,0.1505142897,-0.1324554533,-0.1091565043,0.1529869139,0.0997963771,-0.096534647,-0.002666326,0.1729294509,-0.64591223,0.2446274161,-0.1348222941,-0.0035569465,-0.0143185882,-0.2581925988,0.0553368852,0.1292538792,-0.1676240861,0.3968893588,0.0644265562,-0.759450078,-0.0210416224,-0.2730541825,-0.3172904551,0.0633029118,-0.3434702456,0.2090676874,-0.3996973634,0.1945395172,0.2741888165,0.0175567735,0.0969060883,0.0465892106,-0.0056083496,0.1921326816,-0.1821298599,0.2162335664,0.2495952696,0.3024962544,0.2750135064,0.3957771957,0.0631694719,-0.4625014663,0.4302769601,-0.4933479428,-0.0508707277,-0.0299595408,0.0575819127,0.2281270474,-0.438893646,0.2713938653,0.0143624665,-0.064069286,-0.0025123826,0.1378728896,-0.1525087059,-0.1967370063,0.069967024,0.2555822134,0.2578304112,0.2911311388,0.1528061181,-0.0244936161,0.0216730591,0.0796902329,-0.0018178378,0.1429063529,0.0162566788,-0.1354296207,0.1111126095,0.0136045255,0.2229940146,0.1735732108,0.0848785788,-0.0212465655,-0.1328105032,-0.3586786687,0.3063268363,0.1005614549,0.4162053466,0.0529248305,-0.0220041536,-0.1683461368,-0.5321386456,0.102372326,-0.200342223,-0.1142188236,-0.3129298091,-0.105699636,-0.3163559735,-0.0081692068,0.0785483122,-0.1067342833,-0.4908870757,-0.0413646847,0.344756484,-0.0384743065,-0.0207313877,-0.0190737359,-0.2208305597,0.3961042762,-0.1212590188,0.0660755485,-0.6435341835,-0.0223674718,0.2465204448,0.1031156704,0.0695400611,0.1105958,0.2626501918,0.0976542607,0.1982641667,-0.2104328275,0.0442119911,0.1528565884,-0.0880778879,-0.1403047889,0.1137235165,0.1261760592,-0.0997866467,0.1234928444,-0.1222463474,-0.2544909716,-0.120075278,0.168158412,-0.2586185038,0.2979022861,-0.1329230666,-0.0157442782,0.1073456928,-0.2231793404,0.1770680249,0.1241577491,0.1861981899,0.1440699846,0.2328467518,-0.0193372183,-0.0556620844,-0.0799492672,-0.0262091141,-0.3828001022,0.4329149127,-0.3228119612,-0.1916915327,0.34599787,0.2078003883,0.3050369024,0.1848863661,-0.4744650722,0.3793217838,-0.2185674608,0.0129554383,-0.2385337055,-0.0208479501,0.2446891665,0.1760331392,-0.0418691449,-0.1761095077,0.2295515239,0.2544288337,0.0437615439,-0.0548274145,0.0251075812,0.0771376565,-0.3237439692,0.356377393,-0.1245106086,0.1490378678,0.2294834852,0.0269236248,0.3668178618,-0.306694299,0.3087341189,-0.4477266669,-0.0415461659,0.14450018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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2089","title":"Add documentaton for dataset README.md files","comments":"Hi ! We are using the [datasets-tagging app](https:\/\/github.com\/huggingface\/datasets-tagging) to select the tags to add.\r\n\r\nWe are also adding the full list of tags in #2107 \r\nThis covers multilinguality, language_creators, licenses, size_categories and task_categories.\r\n\r\nIn general if you want to add a tag that doesn't exist (for example for a custom license) you must make it start with `other-` and then a custom tag name.\r\n\r\nedit (@theo-m) if you ever find yourself resorting to adding an `other-*` tag, please do ping us somewhere so we can think about adding it to the \"official\" list :)","body":"Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip","comment_length":94,"text":"Add documentaton for dataset README.md files \n Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip \n Hi ! We are using the [datasets-tagging app](https:\/\/github.com\/huggingface\/datasets-tagging) to select the tags to add.\r\n\r\nWe are also adding the full list of tags in #2107 \r\nThis covers multilinguality, language_creators, licenses, size_categories and task_categories.\r\n\r\nIn general if you want to add a tag that doesn't exist (for example for a custom license) you must make it start with `other-` and then a custom tag name.\r\n\r\nedit (@theo-m) if you ever find yourself resorting to adding an `other-*` tag, please do ping us somewhere so we can think about adding it to the \"official\" list :)","embeddings":[-0.2009669244,0.252024591,-0.1304934472,0.0798938423,0.1809331179,0.3571422994,0.3119640648,0.1849832982,0.1644606739,-0.016346449,-0.0213761125,0.3338293135,-0.0963025689,0.1983852983,0.1711067706,0.0399823152,0.1146339625,-0.0630787686,0.3094624579,-0.1812989861,-0.2011339366,-0.0329795852,-0.057861574,-0.2001816034,-0.3019789159,-0.0922157615,-0.2026932538,0.0341942944,-0.3977000415,-0.607642889,0.0248835068,0.3334518075,0.1742963344,0.0735345557,-0.000107235,-0.1596610099,0.1788149178,-0.239566654,-0.0191534813,-0.1502264142,-0.2650319934,-0.4221037328,-0.1440393329,-0.0700992867,0.0782822296,-0.2990449965,-0.0470196195,-0.1426072866,0.0189226512,0.4477118552,0.2050608993,-0.2106184363,0.3308573961,-0.2609883249,0.4178492427,0.2113643438,-0.1037406772,0.3762668073,0.452617228,0.0936253443,0.0395131744,0.1774764657,-0.1239313483,0.179583773,0.3749469519,-0.045122914,0.3577607572,-0.5386645198,0.2289720774,0.3996181786,1.153159976,-0.2931374013,-0.310759902,-0.4575535357,0.1056198329,-0.1025070623,0.1988972574,0.3195011318,0.0421364456,0.1530991048,0.1238834932,-0.34485659,-0.2380984873,0.1258865893,0.1208824664,0.4693534076,-0.0495393053,-0.0461795665,0.2738636136,-0.0765385404,0.0914462432,-0.00860688,-0.2317645997,0.1363527626,0.1594582349,-0.0646353066,-0.2846120894,-0.105354555,0.2494053841,-0.0171731189,-0.0379194431,0.0125727346,-0.1057558358,0.3409814835,0.1243555397,-0.1768715531,0.165177092,0.2110916972,0.1014955044,-0.2915710509,0.3328114152,-0.1448082477,-0.159210965,-0.1529749483,0.0148062557,-0.0020573905,-0.1087305993,-0.2182008326,0.0389909446,-0.02366082,0.0868689343,-0.1800802499,0.0146845635,0.162394017,0.1409135461,-0.2257521302,-0.2854691744,0.1749260426,0.0696320757,-0.1272712797,-0.0241921861,0.2270143479,-0.1285205483,0.1915755123,0.301363349,-0.0326029696,0.2947966158,0.2256808728,-0.1369580477,-0.1198657006,0.2090932876,-0.0301327631,0.2764083445,0.3786623478,-0.0219721943,0.0751835331,-0.0591326617,-0.4303908944,-0.2476119399,0.1332641542,-0.4022579193,-0.2395637184,-0.4142950773,0.2273344398,-0.1181674078,-0.0451684557,-0.2261714041,0.2805584669,0.0936193168,-0.1459254622,0.2434444577,0.2106729597,-0.3568451703,-0.2805345356,0.0597083196,0.436062783,-0.4021865726,-0.0059407167,-0.0924031287,-0.0220785905,-0.1453588158,-0.1148246452,-0.2623159289,0.4330624044,-0.3410653472,0.2678823769,0.4367043674,-0.4631901383,-0.2488020062,0.1513609141,0.0247621946,-0.2738645375,0.1768344641,0.0510142669,0.5288446546,-0.1293236464,-0.2133076787,-0.0302911382,-0.1984529942,-0.1061873287,-0.1198968068,0.0688978583,0.0974699929,0.092744723,0.093556799,-0.0396653973,0.4201057553,0.1274108291,-0.0038211634,-0.07197579,0.1842219979,0.3900684714,0.2178639024,0.2241284251,-0.03491541,-0.0805127546,-0.4012998343,0.1436464041,0.1019424722,0.2396580577,0.0087045608,-0.2792170942,-0.3434494734,-0.1810734421,-0.3264996111,-0.3358734548,0.1371124834,0.0958833694,-0.2006122172,0.1670179516,-0.1382451653,0.2745993137,0.0647682399,0.1290216297,-0.212790072,-0.0356389806,-0.2517776787,-0.0058522774,0.226232335,0.0662213042,0.2643677592,0.0570805259,-0.0368257388,0.2464054525,0.1964491904,0.2589336932,0.2621414661,0.540453732,0.410502255,-0.2865335941,0.3115128577,-0.3376363814,0.1178432927,-0.2898557484,-0.3992738426,0.0765101612,-0.1542877853,-0.1191549525,-0.1171760187,-0.050810542,0.1809277833,0.0152329504,-0.1221300662,-0.4397419691,-0.1711908132,0.0106513444,0.0177947488,0.000757023,0.200720951,0.1873478293,0.6095921993,-0.1133483797,0.1476428509,0.0923634395,-0.1258233786,-0.0289077107,0.1834839582,0.2011351883,0.1320294738,0.2399761528,0.2614587843,0.0891629234,-0.0447775535,-0.2854482532,0.1424058974,-0.0416227803,-0.0293980613,0.2343936116,-0.2252787352,-0.0814030692,-0.2844702899,-0.1078715771,0.1900336295,0.3333036304,-0.2733850181,-0.2253767103,-0.2778774798,-0.0811996087,-0.2023283094,-0.2605310977,-0.4937746227,-0.3111183345,0.0932645053,0.2138171345,0.0015652273,-0.00290539,-0.2456213981,-0.0235087667,-0.2265373617,0.1571208835,-0.2096743882,-0.2361392081,-0.1060385182,0.0858783573,0.4665773809,0.4596540332,0.5857223272,-0.2945281565,0.2909271419,-0.3250324726,-0.4925007522,0.1065503433,-0.1111655533,0.3061677217,0.0278481282,0.0064205467,-0.033051338,-0.0431219079,0.0325089544,0.0170207079,-0.0860795304,-0.1726716608,-0.0578184798,-0.1768981367,-0.0446688682,-0.5428530574,-0.1846921444,-0.3722652793,0.0926657468,-0.1413791776,0.1481264681,0.108059302,0.0608188137,0.2076724619,-0.1641061604,0.2128446847,-0.2528631985,-0.0471615717,0.483152777,-0.3762440383,-0.2860786617,0.5221699476,0.0618429184,0.3283829689,-0.22356686,-0.2752349675,0.1938800961,0.2008592933,0.0614607818,-0.0204016268,-0.0826197192,0.2799642682,0.2153713554,0.0446460471,-0.2033184618,-0.2075478435,-0.1618234217,-0.0651315823,-0.012049268,0.0391638502,0.1518172324,-0.0432340801,0.4195065796,0.0300061014,0.184286207,0.3928280175,-0.0128651299,0.3791911602,0.047049623,-0.3830392659,0.197731629,0.0401779152,0.1801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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2089","title":"Add documentaton for dataset README.md files","comments":"@lhoestq hmm - ok thanks for the answer.\r\nTo be honest I am not sure if this issue can be closed now.\r\nI just wanted to point out that this should either be documented or linked in the documentation.\r\nIf you feel like it is (will be) please just close this.","body":"Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip","comment_length":51,"text":"Add documentaton for dataset README.md files \n Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip \n @lhoestq hmm - ok thanks for the answer.\r\nTo be honest I am not sure if this issue can be closed now.\r\nI just wanted to point out that this should either be documented or linked in the documentation.\r\nIf you feel like it is (will be) please just close this.","embeddings":[-0.1834505647,0.251365006,-0.108965151,0.1974366605,0.1107171848,0.3312231898,0.3745901287,0.1719959974,0.0320847295,0.0473188944,0.0283438452,0.2382667959,-0.06312792,0.0927071273,0.0448256098,0.0325517729,0.0677399784,-0.0201820545,0.2282102108,-0.2717784047,-0.1881774515,-0.0120634753,-0.1926741004,-0.2600643933,-0.2601344883,-0.1061909795,-0.1686320007,0.1679512262,-0.4175153971,-0.5190830827,0.0170215424,0.2592081428,0.1794570684,0.069258146,-0.000108684,-0.2377832085,0.2274885327,-0.2222405076,0.0124146575,-0.0039696,-0.256370604,-0.392083168,-0.2011699975,-0.1843143404,0.0813640505,-0.1434149742,-0.1106911227,-0.2170637399,0.1881171465,0.5700099468,0.2062799335,-0.2507901192,0.3001177907,-0.2143563777,0.4646461606,0.2964088321,-0.1445172429,0.3467390537,0.5063992143,0.116429463,0.0515992343,0.1678493321,-0.1461413056,0.1065743566,0.311891824,-0.1332371533,0.4417340159,-0.5099037886,0.2496395558,0.3756162524,1.1862678528,-0.1897372007,-0.1440565884,-0.3056923747,0.0748555884,-0.0499141552,0.1617724895,0.3897077143,0.0141748274,0.144161731,0.138372466,-0.3804753423,-0.3181429207,0.0608467199,0.1116281077,0.419542253,-0.0779646263,-0.075421229,0.1478106678,-0.0849926621,0.2784859538,0.0570362695,-0.2141937315,0.225955084,0.189255476,-0.1122400016,-0.3635398746,-0.1978414059,0.2118561715,-0.1735544801,-0.0536954589,0.0306305848,-0.0166816674,0.2824446261,0.1840374321,-0.098953709,0.1554633826,0.2676413357,0.1074184477,-0.2280970961,0.2706859708,-0.0682267174,-0.2281811833,-0.1300102174,-0.0250221174,0.041349709,-0.0668682903,-0.345576942,0.0765684023,-0.0438406542,0.0707652122,-0.148353368,0.0131454086,0.2365357429,0.0193656068,-0.1580260992,-0.2276223749,0.109149605,0.0131610446,-0.1187187806,0.0190704297,0.2393930852,-0.1226379648,0.190498367,0.267563045,0.0570891798,0.206442818,0.2571180761,-0.0690384209,-0.2132615745,0.2207864374,-0.0350578055,0.3134301901,0.3436415792,-0.0439306572,0.0869112089,-0.0894113556,-0.3784480393,-0.1885696352,0.1912267357,-0.5235507488,-0.2824635208,-0.3953041732,0.205935955,-0.0752783492,0.0405865051,-0.1238837391,0.2208991647,0.1222835556,-0.1311614513,0.1706708819,0.1315923631,-0.379452616,-0.2931358516,-0.0147849862,0.4112755954,-0.3530879319,0.0920699909,-0.1462333351,0.0608904026,-0.0973646492,0.0338928849,-0.3202523887,0.3123343289,-0.3375391662,0.1994763911,0.4865379035,-0.6444005966,-0.2490532696,0.1680991501,0.0996946618,-0.337928623,0.1998403221,-0.076159656,0.5245919228,-0.2472617328,-0.1961391121,-0.1009456366,-0.1206154674,-0.044988554,-0.2056626827,0.0024414766,-0.0279927906,0.1433908641,0.0930473357,-0.0085915076,0.5141079426,0.111548692,0.11130023,-0.1130470037,0.2350051701,0.5121799707,0.278008312,0.0733763576,-0.0036751251,0.0601929538,-0.3458781242,0.1016534045,0.0381818712,0.2979979217,0.0225258414,-0.2697308958,-0.3078657687,-0.2098493576,-0.3070385754,-0.3466337323,0.1509182602,0.072135374,-0.2899815142,0.1742564142,-0.0583876967,0.258552283,-0.022553971,-0.0229609944,-0.1868529618,0.0147313429,-0.1795284599,0.0714114159,0.3635661602,0.1179975122,0.2934602201,0.0673181117,-0.0512963347,0.1727052331,0.1681962311,0.1867890209,0.1970174909,0.5089138746,0.348587513,-0.3183594644,0.3216242194,-0.3319413066,0.1059602946,-0.2947618067,-0.3823413551,0.093760021,-0.0875143111,-0.1562456191,-0.096652925,-0.0550427102,0.1559996605,-0.0430997014,-0.1213439107,-0.418656379,-0.2139490098,0.1382642686,0.0492486134,0.0433533676,0.1525081694,0.2591985166,0.6951568127,-0.089364782,0.1233370453,0.0477645323,-0.044406157,-0.0458785295,0.2343299687,0.1793375909,0.2504524589,0.1950840801,0.2688598633,0.1685174257,-0.025638463,-0.2154762894,0.1143739894,-0.1564902067,0.0548340157,0.1859874725,-0.2092626542,-0.0657521039,-0.2408761233,0.0019681288,0.1768082976,0.3061312735,-0.2752851844,-0.3088805974,-0.2675393224,-0.010409792,-0.2429445237,-0.1491997838,-0.5167268515,-0.174337998,0.0638108775,0.1178996637,0.0370092876,-0.0429781489,-0.1899590045,0.1676542461,-0.2003879398,0.2505880892,-0.1858588606,-0.2149976641,-0.2299165875,0.0794585422,0.4636722505,0.5456963181,0.5315335393,-0.2386776805,0.2786902189,-0.3564065397,-0.440682143,0.0858712047,-0.0464326851,0.3145121932,-0.0619887598,0.1028822437,-0.0427765138,0.0008728108,-0.0199087579,0.1111815944,-0.1103436574,-0.2667787075,0.0028240881,-0.3291390836,-0.0390195288,-0.594435513,-0.1559260488,-0.3857374489,0.0730233863,-0.1942426115,0.2018292248,0.0708701611,-0.0117161777,0.1006474048,-0.1449375451,0.1740794182,-0.3098455966,-0.1001522988,0.4769842029,-0.4734630883,-0.4342747331,0.5254812837,0.0934798867,0.3927616477,-0.1414426416,-0.3136377335,0.2251268327,0.1927728355,-0.2009271532,-0.1818776131,-0.0200747941,0.3267469704,0.2113699168,0.0663215294,-0.2078844458,-0.2461215407,-0.0968589932,-0.0086201597,0.0727901161,-0.0197774451,0.0684674531,-0.0048043313,0.3320693374,-0.0051236353,0.2867011428,0.3341181278,-0.0068125003,0.3818561733,0.1176935434,-0.3456970453,0.2654700279,0.0527135134,0.142698735,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2089","title":"Add documentaton for dataset README.md files","comments":"We're still working on the validation+documentation in this.\r\nFeel free to keep this issue open till we've added them","body":"Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip","comment_length":19,"text":"Add documentaton for dataset README.md files \n Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip \n We're still working on the validation+documentation in this.\r\nFeel free to keep this issue open till we've added them","embeddings":[-0.2152332067,0.2649821639,-0.1258544773,0.1683345139,0.153197214,0.2933406234,0.2924147546,0.1951218694,0.031965781,0.020962853,0.0500942841,0.2049097121,-0.0792760625,0.1006224751,-0.0031886012,0.0226522125,0.0487911105,-0.0505709611,0.209790498,-0.2532515228,-0.1110622212,-0.0721068233,-0.1680813283,-0.2169418484,-0.279617697,-0.1498723179,-0.121067971,0.1117399558,-0.5551061034,-0.5625538826,-0.0569595918,0.3236434758,0.2056470215,0.0757121667,-0.0001088561,-0.2372320294,0.3131054044,-0.1969533563,0.0418356284,-0.0134433964,-0.3186578751,-0.3487761617,-0.1418905705,-0.1790460944,0.0734013245,-0.1668996066,-0.0970635936,-0.1242037937,0.2178792953,0.6139640212,0.192051664,-0.176824674,0.3337362409,-0.1620907336,0.3486065865,0.2870318294,-0.1387577951,0.3151708543,0.4389590025,0.1328053623,-0.0011085592,0.172968775,-0.0888033286,0.0742474571,0.354427278,-0.1740669608,0.4313996136,-0.5642960072,0.1373408437,0.4447307587,1.1757175922,-0.2466039658,-0.147630468,-0.2829697132,0.0595339239,0.0118933395,0.2021713555,0.3936532438,0.0201638862,0.1482875943,0.0523763299,-0.2962444723,-0.3464110196,0.027329538,0.1638356596,0.444943279,-0.0405867808,-0.1621433347,0.2406282723,-0.0741209388,0.2830766141,-0.00571978,-0.2207933366,0.2731741071,0.1728255004,-0.1457782239,-0.3448441029,-0.1132450327,0.2412940264,-0.1358947456,-0.0598471463,0.0647012591,-0.0455052815,0.3457221389,0.199695304,-0.0847962797,0.184991315,0.2489361912,0.113955982,-0.192776233,0.2991896272,-0.0795426071,-0.149065882,-0.1413848996,0.0583407022,0.1044747978,-0.1232852116,-0.3599949479,0.0938883796,0.0048555108,0.1318528652,-0.2013231069,0.0663122311,0.1965319812,0.0225523729,-0.1681499779,-0.3468319476,0.0708110183,0.0511130691,-0.1352705657,0.0246487856,0.2435915619,-0.1412160397,0.1586894989,0.2875902355,-0.0040694489,0.2766801417,0.1819750816,-0.062001776,-0.2075318396,0.2298635095,-0.0740521848,0.3065574765,0.3483856022,-0.0645222738,-0.0704309493,-0.1015463248,-0.3342269659,-0.2269043475,0.1176794767,-0.3748679459,-0.3299014866,-0.3605083525,0.2099002898,-0.0848126188,0.0780488551,-0.0824628845,0.2596985996,0.1449100226,-0.0913847238,0.1895816177,0.1342038512,-0.2440786362,-0.2446256876,-0.0487653241,0.3932786286,-0.3049049675,0.1307745278,-0.1182967275,-0.0363224708,-0.1880007982,0.0069952076,-0.3300700784,0.3603716195,-0.2602694035,0.2314064205,0.5233443975,-0.6178893447,-0.190892309,0.2019526362,0.1188919842,-0.3653545976,0.1996655613,-0.1225311011,0.5625225306,-0.3021692634,-0.2760796547,-0.0837178305,-0.2096390724,-0.0862350911,-0.2159940302,0.065836899,0.0004535391,0.0925549418,0.087124981,-0.1019032076,0.4954625666,0.149652034,0.0810179487,-0.1325366795,0.1871227175,0.4300885499,0.3101040721,0.0051652817,-0.0187908895,0.0297723208,-0.3159400821,0.0068745865,0.0792017356,0.3121462464,0.0602492094,-0.2735976875,-0.309137702,-0.2166574746,-0.3294516206,-0.3489198983,0.1617864966,-0.0128998496,-0.1812311262,0.1981271356,-0.146534577,0.2803251147,-0.0659631118,0.046155367,-0.1705372483,-0.0243125334,-0.2345082313,0.0765738711,0.2834572196,0.1128891408,0.2357717752,0.059747085,-0.0370491147,0.2769049108,0.2859248221,0.1337736696,0.1228956655,0.5454466939,0.406909287,-0.3052136898,0.3498113751,-0.3611955941,0.0050803572,-0.2370350063,-0.3916619122,0.0973380879,-0.2199732363,-0.1376938224,-0.091186896,-0.0909057707,0.1451142579,-0.0685637593,-0.2191044688,-0.4312099218,-0.2434509695,0.0687407479,0.0123265199,0.0651024878,0.0938151777,0.15814282,0.6090510488,-0.1135696471,0.133507818,0.073922351,0.0357706137,-0.1065810472,0.2230447829,0.2026791722,0.1815531105,0.137719512,0.2089840025,0.1837079674,-0.1069417372,-0.2314970493,0.1740404367,-0.1743568182,0.0433527939,0.1960575879,-0.3303851485,-0.0107408874,-0.2964419723,-0.0297243763,0.1803777963,0.3155517578,-0.2707270086,-0.2726546228,-0.2162563354,-0.0204582606,-0.1964751929,-0.1118424535,-0.4751445949,-0.2135225832,0.1043400839,0.1421674192,0.0377768651,-0.0222376026,-0.2111313641,0.0961314291,-0.174366191,0.219769612,-0.2013042569,-0.229246676,-0.1875761449,0.098502405,0.4579515457,0.5772249699,0.4862652719,-0.24764736,0.2650541067,-0.2878770232,-0.5216217041,0.073495537,-0.0353743546,0.299456507,-0.0513310991,0.0952728689,-0.0173348971,0.0269205794,-0.0814487487,0.0998441353,-0.1886216104,-0.2019718736,-0.0158067979,-0.3107983172,-0.028933933,-0.5464140177,-0.1762472689,-0.3826165199,0.0350002348,-0.2176812738,0.1791503876,0.0694593266,0.0290386882,0.089963913,-0.2129889578,0.2034207135,-0.2511434853,-0.0605329312,0.4775668979,-0.4183160663,-0.4114857316,0.4373288155,0.0720372126,0.363257587,-0.1737964302,-0.2805134654,0.1150470078,0.1619207412,-0.1244618818,-0.0841160268,-0.0052799494,0.3449583352,0.2725786567,0.0719382241,-0.1632326096,-0.2096580118,-0.1451490223,0.02962064,0.1314594299,-0.0244175438,0.067877017,0.0100661647,0.3052534759,0.0540943593,0.1991354972,0.3418984413,0.0613150336,0.4321850538,0.1601080745,-0.3594634533,0.3082561493,0.048932787,0.1297257841,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2089","title":"Add documentaton for dataset README.md files","comments":"Hi ! There's the tagging app at https:\/\/huggingface.co\/datasets\/tagging\/ that you can use.\r\nIt shows the list of all the tags you can use.\r\n\r\nIt is based on all the tag sets defined in this folder:\r\nhttps:\/\/github.com\/huggingface\/datasets\/tree\/master\/src\/datasets\/utils\/resources","body":"Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip","comment_length":36,"text":"Add documentaton for dataset README.md files \n Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip \n Hi ! There's the tagging app at https:\/\/huggingface.co\/datasets\/tagging\/ that you can use.\r\nIt shows the list of all the tags you can use.\r\n\r\nIt is based on all the tag sets defined in this folder:\r\nhttps:\/\/github.com\/huggingface\/datasets\/tree\/master\/src\/datasets\/utils\/resources","embeddings":[-0.286616981,0.0945327431,-0.1588486731,0.1930643469,0.2768509686,0.315613687,0.2661206424,0.1931910217,0.171658054,0.0233468805,-0.1272895783,0.2412937731,-0.1264384389,0.359706521,0.0914905146,0.0267632809,0.0860838294,-0.1028402001,0.1592577845,-0.1845795959,-0.1691451818,0.0174296387,-0.044866249,-0.1641505063,-0.3286385238,-0.1695875823,-0.1266560256,0.0994561017,-0.4146160781,-0.5925245285,-0.0502110906,0.3110641241,0.2041491121,0.1158077419,-0.0001010503,-0.2147537619,0.2052169591,-0.2352301031,-0.013936406,-0.1126761436,-0.1768777668,-0.3279271722,-0.0601527914,-0.1067197546,-0.0597188883,-0.2925773561,-0.0841798484,-0.1904508024,0.1979566365,0.4205386937,0.2686114609,-0.1103500277,0.340637058,-0.2250534147,0.2706674039,0.1677974612,-0.1239818856,0.4374400079,0.4900670052,0.1472700536,-0.0278129727,0.250887543,-0.0373196527,0.1873492599,0.4723614454,-0.0820315927,0.2423785776,-0.5768037438,0.2497014999,0.3870515823,1.039131403,-0.2780817747,-0.2529759705,-0.3692144752,0.0836822018,-0.1449427456,0.1967424452,0.3172575235,0.0246470422,0.1599288136,-0.0215232354,-0.3635124862,-0.2490009964,0.030807713,0.1805025488,0.4041182697,-0.1161129922,-0.1012218967,0.2309455723,-0.1453734934,-0.0280741658,0.1179569662,-0.1853637695,0.1704542786,0.125797376,-0.0564186536,-0.324847132,-0.0684511289,0.2850894034,-0.0259751752,-0.0902743563,0.0960413814,-0.0556011684,0.2861751914,0.1512073129,-0.1017969623,0.1857317537,0.176115647,0.1215500161,-0.1716192961,0.2133102119,-0.1562376469,-0.153006956,-0.0906531587,-0.0275148321,-0.0823393092,-0.1527571231,-0.2826740742,0.0200317372,-0.11338792,0.0969016626,-0.1256388575,0.0243133307,0.2854116261,0.0740623772,-0.2125341147,-0.2671316862,0.1896766275,-0.0061659305,-0.1419605017,-0.0466414765,0.1665447801,-0.1256685704,0.207286194,0.2524135411,-0.1257540733,0.3280339837,0.1352378428,-0.0758281052,-0.1853524148,0.1354757547,0.0137874149,0.2163782716,0.2859224081,-0.0209440663,0.0056272959,-0.0394663066,-0.4077000618,-0.2394081503,-0.0369075313,-0.4110156298,-0.2267906815,-0.3655214608,0.2676398754,-0.124319613,-0.0459319018,-0.2027338445,0.3219774961,0.0812157765,0.0450323857,0.166280359,0.2537733912,-0.3481631279,-0.2576192319,0.0302518811,0.4434049428,-0.2620563209,-0.1156657487,0.0625194162,-0.0872343853,-0.1335525066,0.0185211655,-0.257881403,0.2984237671,-0.2528005838,0.2648495734,0.3774803281,-0.4607772231,-0.1682922393,0.0759339035,0.0210004933,-0.3429535329,0.1542671919,-0.0457942262,0.4741377234,-0.1783985943,-0.1736360937,0.0215551723,-0.1356056333,0.038315203,-0.1707610786,-0.0267440602,0.042084042,0.1477921456,0.0901245028,-0.0927910879,0.3923928142,0.0840129331,-0.0065961652,-0.1246610805,0.1666934341,0.3540766239,0.1807696372,0.1878831238,-0.0094583705,-0.1003722772,-0.4468117654,0.1082814559,0.078050144,0.2980458438,-0.0181669407,-0.3064984083,-0.3663927019,-0.2408527881,-0.3977145255,-0.3615729809,0.1890882999,0.0303558763,-0.1249261945,0.1889827847,-0.1747094691,0.2096574157,-0.0263159256,0.125870347,-0.2828810215,-0.054106608,-0.1897651404,-0.0121795414,0.3533417583,0.0889376253,0.2029140145,-0.0041824896,-0.0547653288,0.3221335709,0.1733797193,0.2728250623,0.2341203839,0.5424048305,0.3943711519,-0.3049960136,0.2806555331,-0.3187286556,0.0982841775,-0.241307959,-0.4253418148,0.1217970327,-0.2108707279,-0.0855719894,-0.087822713,-0.0431888029,0.2023260891,0.0257894844,-0.1415568739,-0.4092408419,-0.1996077299,-0.0929376036,0.1340217888,-0.0142621389,0.0702899694,0.1593573391,0.6423447132,-0.0103725214,0.1053344235,0.1113668531,-0.116008088,0.0188492369,0.2301196009,0.1541118771,0.0834338441,0.2618065774,0.2813137174,0.1955121905,0.0121777561,-0.2820138931,0.1582181454,-0.0850909278,-0.008879411,0.1671547741,-0.2094474286,-0.1137016043,-0.2982536256,-0.0130098648,0.1025971174,0.2986978889,-0.297252208,-0.2102067471,-0.3044349849,-0.1905401647,-0.2461327612,-0.2486096472,-0.5396837592,-0.2466231585,0.0836437494,0.0687134564,0.018367378,0.057609871,-0.1129017919,0.0185980666,-0.2259149253,0.2494397908,-0.1671553552,-0.218542859,-0.1116598696,0.1529919058,0.5170536637,0.4568577409,0.641466558,-0.3583681285,0.2536119521,-0.2470322996,-0.4476350546,0.1278040558,-0.0985560715,0.2760167718,0.0628417954,0.0869512632,-0.0324171707,0.0335605703,0.1297655404,-0.0260875169,-0.1214939356,-0.2069978714,-0.0215773471,-0.2263179123,-0.0622157939,-0.5958678722,-0.211692065,-0.4526795745,0.1733249873,-0.1179511845,0.1648702621,0.1894071698,-0.0055302889,0.2414371222,-0.2219726145,0.2568546832,-0.2677108049,-0.1366797835,0.3523485065,-0.4366490543,-0.3181603551,0.5248668194,0.2030135989,0.407084018,-0.1904558986,-0.2272369862,0.0399037413,0.1460729837,-0.005459764,0.0456531234,0.0450852737,0.2071583271,0.1842418611,0.0126624154,-0.1487964541,-0.1339661032,-0.1094261259,-0.1532999575,-0.0452630222,0.000242649,0.1260112971,-0.0166641101,0.4119056165,0.2007798254,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2089","title":"Add documentaton for dataset README.md files","comments":"I don't think so. Feel free to take a look at the tags of other models (example [here](https:\/\/huggingface.co\/bert-base-uncased\/blob\/main\/README.md)). But we should definitely have some docs or an app to write the tags. Feel free to open an issue in the `transformers` repo or in the `huggingface_hub` repo so we can discuss this","body":"Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip","comment_length":52,"text":"Add documentaton for dataset README.md files \n Hi,\r\nthe dataset README files have special headers.\r\nSomehow a documenation of the allowed values and tags is missing.\r\nCould you add that?\r\n\r\nJust to give some concrete questions that should be answered imo:\r\n- which values can be passted to multilinguality?\r\n- what should be passed to language_creators?\r\n- which values should licenses have? What do I say when it is a custom license? Should I add a link?\r\n- how should I choose size_categories ? What are valid ranges?\r\n- what are valid task_categories?\r\n\r\nThanks\r\nPhilip \n I don't think so. Feel free to take a look at the tags of other models (example [here](https:\/\/huggingface.co\/bert-base-uncased\/blob\/main\/README.md)). But we should definitely have some docs or an app to write the tags. Feel free to open an issue in the `transformers` repo or in the `huggingface_hub` repo so we can discuss this","embeddings":[-0.1301450878,-0.0352435075,-0.0532184169,0.2471045256,0.2942425311,0.2559415102,0.4152002931,0.1293420494,0.0928785503,-0.0484237932,-0.1729551107,0.0700884908,-0.1718711555,0.3322659135,0.1102287322,-0.0799867585,0.0745821446,-0.064012073,0.0829297826,-0.3425408304,-0.1205999777,0.0473311469,0.0086057996,-0.1590167731,-0.3415005803,-0.0647564381,-0.1081090271,0.087882258,-0.3661057651,-0.4418179095,0.0343780592,0.36452806,0.2753328681,0.2000294924,-0.0001122496,-0.0820678696,0.201798141,-0.2937220037,0.0805850103,-0.0695166364,-0.1306844801,-0.2235603034,0.0002540752,-0.1659983844,-0.0443721972,-0.1104560047,-0.0838512406,0.0729084164,0.4370497465,0.37518695,0.148133561,0.1568712592,0.3579421341,-0.0753630251,0.3906325996,0.5329559445,-0.1721142977,0.3704756498,0.4002628922,0.1344771534,-0.0086024338,0.3429555893,0.0102651846,0.0815118849,0.5320785642,0.0614764616,0.4577286839,-0.4999290407,0.0746051297,0.4968686402,0.8872467279,-0.2778629363,-0.3920515776,-0.4509535432,0.0511733517,-0.0022098804,0.1275162399,0.2292139977,-0.0223254487,0.2023370564,-0.0359625295,-0.3970157206,-0.2950106859,-0.0164486282,0.2132585198,0.5303966999,-0.0830988958,-0.0483889282,0.2028928697,-0.0750717819,0.0379065275,-0.0209300052,-0.133031413,0.180884257,0.18294397,-0.2720577121,-0.4023817778,-0.0091049857,0.2835808098,-0.1288718879,-0.0929570571,0.0104748737,-0.0570272692,0.2156037092,0.2461791337,-0.0706653595,-0.1041215435,0.2844152153,0.1489333659,-0.0632366166,0.350486666,-0.1537154317,-0.1432397068,0.0320519693,-0.1625291705,-0.0585426688,-0.1197234988,-0.2173473984,0.1657661945,-0.1894990802,0.2245896459,-0.1191413254,0.0865387097,0.3314216733,0.0674898252,-0.1350482404,-0.2403621227,0.0849950463,0.0512669496,-0.1340712458,-0.0295302644,0.0856456608,-0.1304166615,0.2566341758,0.2308344841,-0.045377247,0.2571469545,0.0018116845,0.1141917408,-0.0951160043,0.0790747926,0.0553013124,0.1696841568,0.3754540682,-0.219862327,0.0422180071,-0.0593294054,-0.3838603199,-0.2995629907,0.0680775791,-0.3602797985,-0.2845461965,-0.3764235675,0.1332672536,-0.1712684035,0.0672326908,-0.2517314553,0.292639941,0.1982022375,-0.0982505456,0.2711409032,0.2577008009,-0.2646192312,-0.1525433958,0.0543294661,0.5442987084,-0.1550184637,-0.2414232641,-0.0121600637,-0.0153322872,-0.2538617551,0.1582325399,-0.3104732335,0.288032949,-0.2125798464,0.1954707801,0.3075235188,-0.5969015956,-0.2878676057,0.1899215728,0.04410135,-0.243017897,0.2752088904,-0.0098639047,0.4363812506,-0.2221133113,-0.2216964513,-0.1164819226,-0.1454751641,0.0300425999,-0.1788652986,-0.1824240685,0.180166468,0.1770681292,0.1427143812,-0.0056527299,0.2813097835,0.3177366853,0.1807597876,-0.0894367993,0.1050366908,0.3970932066,0.3476620018,0.0344510376,0.0576082245,-0.0956659093,-0.6226606965,0.0369949825,-0.0407796726,0.3914708495,-0.0753991529,-0.3238521218,-0.264056772,-0.2537076473,-0.3115502596,-0.4422731102,0.0766931549,-0.0490222834,-0.2323368639,0.1121200547,-0.2760218084,0.3250449002,0.084157981,0.176702261,-0.4231535792,0.0879205242,-0.3023082614,0.0981676504,0.2412701547,0.1098993495,0.127358675,-0.0951983631,0.0094879605,0.2334367931,-0.0210516434,0.2175308615,0.0982459635,0.6624143124,0.5612775087,-0.3053041995,0.1880624741,-0.2835327983,0.0984657779,-0.1810097694,-0.5105965734,0.0683180094,-0.1871127784,-0.057043232,-0.0027717671,-0.1004650444,0.1907692105,0.0320719443,-0.2853167951,-0.3556480706,-0.2345433235,0.0964967459,0.23932302,0.0928759575,0.1901282668,0.0627860054,0.750420332,-0.1258005053,0.0917745605,0.1957152486,-0.2276316434,-0.0212666411,0.1258779466,0.122194767,0.1449053735,0.2134449035,0.0604290143,0.1615928411,0.0986356139,-0.1737308502,0.1242914572,0.0372124575,-0.0700257421,0.242862463,-0.2385572791,0.011652953,-0.2925586998,-0.0238551814,0.1365642846,0.2298060954,-0.3926616013,-0.168823719,0.0505814403,0.0385569893,-0.2868267894,-0.2530362606,-0.6583180428,-0.1110902429,0.0961049572,0.0289084744,0.0561192855,0.0624089018,0.0627806485,0.1970192492,-0.1762809157,0.2620811462,-0.3391105831,-0.2260823995,-0.0866138414,0.0383844711,0.4431889653,0.3795891702,0.3404743075,-0.3237490654,0.2406792492,-0.1147800907,-0.6005818248,0.2257779539,-0.1384498924,0.3139522076,0.1184751168,0.0575810559,0.1074348912,-0.1111849323,0.1358054876,-0.0334656984,-0.1298608184,-0.2549876869,-0.0332795344,-0.2619003356,-0.1209807396,-0.4445782006,-0.2484944314,-0.3826474845,0.3122243583,-0.1325089335,0.0555029251,0.307759285,-0.0077625881,0.1976738274,-0.2474431694,0.1154809222,-0.1699935347,-0.090065755,0.4744247496,-0.3464311361,-0.2834531367,0.2812028229,0.0528321378,0.25548172,-0.2843570411,-0.2434411198,0.0428086631,0.093488425,-0.2509506941,0.0276176929,0.0766631067,0.2747790217,0.2590764165,0.1170005798,-0.1130938604,-0.1745864153,-0.0401857384,0.0621444434,-0.0020896541,-0.104607448,0.1465646178,-0.0757131651,0.3623982966,0.0772003904,0.0615587085,0.33162269,0.0571857095,0.4331573546,0.0660251081,-0.3546023369,0.2062720358,0.0067272028,0.08007779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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2083","title":"`concatenate_datasets` throws error when changing the order of datasets to concatenate","comments":"Hi,\r\n\r\nthis bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit:\r\n```python\r\ncommon_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'])\r\n\r\n``` \r\nThe order is important because the resulting dataset inherits the schema metadata of the first dataset passed to the `concatenate_datasets(...)` function (`pa.concat_tables` [docs](https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.concat_tables.html)). I'll try to fix this ASAP.","body":"Hey, \r\n\r\nI played around with the `concatenate_datasets(...)` function: https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets\r\n\r\nand noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO.\r\n\r\nHere is a google colab to reproduce the error: https:\/\/colab.research.google.com\/drive\/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing","comment_length":70,"text":"`concatenate_datasets` throws error when changing the order of datasets to concatenate \n Hey, \r\n\r\nI played around with the `concatenate_datasets(...)` function: https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets\r\n\r\nand noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO.\r\n\r\nHere is a google colab to reproduce the error: https:\/\/colab.research.google.com\/drive\/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing \n Hi,\r\n\r\nthis bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit:\r\n```python\r\ncommon_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'])\r\n\r\n``` \r\nThe order is important because the resulting dataset inherits the schema metadata of the first dataset passed to the `concatenate_datasets(...)` function (`pa.concat_tables` [docs](https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.concat_tables.html)). I'll try to fix this ASAP.","embeddings":[-0.0796854198,-0.0035570161,0.0493244082,0.0962445959,0.4001432955,0.2031105757,0.1476463228,0.1685884148,-0.4686336219,0.0390344746,-0.11334382,0.2235859036,0.1865087897,0.2279952466,-0.1524720341,-0.3764883876,0.1803440005,-0.0335420966,-0.0318521857,0.1589729786,-0.3306152225,0.2782516181,-0.4217684269,0.1194007322,-0.2770402431,0.1655754745,-0.2876584828,0.2142007649,-0.131747961,-0.308303684,0.221818909,0.2089252919,0.0268243812,0.4889546037,-0.0001056023,-0.0131241502,0.1394525766,0.0269066729,-0.2604879141,-0.2272506654,-0.225127086,-0.1086767837,-0.1623241156,0.0379752778,0.1469448805,-0.159701854,-0.2429727167,-0.1502541155,0.2837979496,0.2774026096,0.2379924804,0.1755374074,0.2426477224,-0.3864704967,0.1160053238,-0.1271754503,-0.0323286094,0.0827695578,-0.157835111,-0.2114221156,0.1623141021,0.2935610712,-0.2205399424,-0.3700946271,0.0482722595,0.1449314654,-0.0490996353,-0.135188058,-0.0003450025,0.072688289,0.4189139605,-0.4847950041,-0.3530631959,-0.2878331244,0.3797289729,-0.3848426044,0.2335145026,0.1079680622,0.1808571815,0.1601840109,-0.1117425859,-0.0052864044,0.0029117123,0.0073696179,-0.0543385968,0.0794908032,0.0577444658,0.0121798953,0.1555245072,-0.3029306531,0.0764301568,-0.1139460281,-0.2723210752,0.1129819676,-0.4819879532,0.0012335911,0.0431282371,-0.252556473,0.3593414724,0.1304217428,-0.0217060558,-0.0283695441,-0.0975560844,0.1175317317,0.253303647,0.3586049676,0.0956618488,0.2279179692,0.0909649283,0.2217277437,0.1781282425,0.022759283,0.122976847,-0.293256104,-0.039441742,0.1017413363,0.305955708,0.0601080246,-0.1682273895,0.1103920862,-0.1277956516,0.0942963511,0.045383051,0.1752948463,0.0419019386,0.3478609622,0.2925291657,0.3554245532,0.1251151711,0.0136122657,-0.1590159833,-0.2540304959,-0.1026407182,0.29680264,-0.037919201,-0.11697115,-0.020783022,0.3439497948,0.1724196076,0.1211991161,-0.2369533777,0.0161234085,-0.1319067776,0.2203481644,0.0439352281,-0.0071234931,0.0624270923,-0.007491855,-0.1447689682,-0.0189630631,-0.2858016491,-0.5416709781,-0.4975184798,0.248180896,0.1760655344,-0.0887838677,0.0011430816,0.2109565288,0.3790776432,-0.2144869119,-0.0100913104,-0.1311472058,-0.3337848186,-0.1783127338,-0.0306516513,0.2728123367,-0.0783873945,0.0389477611,0.1670252681,0.0284016803,-0.135316208,0.4119367599,-0.1325293928,-0.119503662,-0.1640453488,0.2925847769,0.2148224711,-0.3234607577,-0.1954002827,0.015626844,-0.1306155324,0.0493506454,0.0488129668,-0.1344640404,-0.193211019,-0.1346464753,0.3389673233,0.3616016805,0.017334627,0.0530788675,-0.0639780313,-0.0736650974,0.2254837155,-0.1554727852,-0.4454149008,0.2084553838,-0.2820552289,-0.1806510389,0.3597200513,-0.2028734535,-0.0249193776,0.3321020007,0.1902955174,0.1028186008,0.2281092852,0.0330319628,-0.4944085479,0.0692005306,0.0779701546,0.1120494828,-0.2732640207,-0.3051612675,-0.1832832396,-0.0261381604,-0.3229482174,0.1075261682,0.1899009496,0.0190829579,0.1013582349,-0.1390779912,-0.1406923831,0.3601429164,-0.0401884206,0.1133174747,-0.4093932509,0.294631362,-0.2757107615,-0.0057807006,0.0960211456,0.2850590646,0.1517305523,0.0952609628,-0.2058046609,0.6714096069,-0.2294558883,-0.2981728613,0.0129514113,-0.0409915037,0.0063927756,0.0604904629,-0.101959236,-0.0107201189,-0.1760645807,-0.0578090511,-0.2104922235,0.3796176016,0.0027714288,0.2878172398,-0.0005501803,0.1273317039,0.2977004349,0.0236182511,-0.052281227,-0.3162916303,0.1110669598,0.0720413923,0.1083640754,0.0913331062,-0.5487784147,0.1539600044,0.6155299544,-0.0723493472,-0.0204856396,0.0756000876,-0.0469926298,-0.0043916823,0.0246623587,-0.034298718,0.5151794553,0.1922239363,-0.1451512128,0.3800708354,-0.1407290846,-0.1236649305,0.1264637858,0.0085585769,0.1879565418,0.3748966157,0.4192982614,0.2545351386,-0.2852300704,-0.2111072242,0.0020570953,0.0120813008,-0.2482442856,0.0661968216,-0.2989223599,-0.1473240107,-0.2143440694,-0.4559225738,-0.534398973,-0.36468032,0.1637233794,0.0951002836,-0.2306734174,0.320912689,-0.0733216628,0.0126334261,0.3799387217,-0.080790706,-0.2083370984,-0.030446792,0.0225907359,-0.0015236454,0.0968098938,-0.1379451752,0.0062935026,-0.0825955048,-0.0205822196,-0.271705091,-0.2621474564,0.0251964014,-0.3797262311,-0.0711877495,0.2013302743,-0.0451544262,0.1132562235,-0.4360528588,0.4303462803,0.1572064757,-0.2147203684,0.6075904369,0.1684282124,-0.1878268272,-0.1536263376,-0.407448113,-0.1293826252,-0.4146422744,0.2266934216,-0.2033040971,-0.0041574929,0.5315075517,0.1389883757,-0.2513957918,-0.1662716866,0.1982160062,-0.3319247365,-0.22686176,0.1182051748,-0.2356241345,-0.3592353165,-0.0796587616,0.1398949027,0.0986564979,0.063547574,-0.1372637153,-0.1302847415,-0.1049028412,0.2873049974,-0.3098780513,0.0758379847,0.3729993701,0.3206865489,-0.1161200628,-0.1279148608,-0.1826058179,-0.0058533968,0.2448066026,0.2611472607,-0.1807264835,0.3100435436,-0.0188209228,0.3491805494,0.2283605039,-0.0057375743,0.1428749263,0.040973112,0.3439367414,-0.2610262334,-0.303193748,-0.1533000022,-0.2464200407,0.22554954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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2080","title":"Multidimensional arrays in a Dataset","comments":"Hi !\r\n\r\nThis is actually supported ! but not yet in `from_pandas`.\r\nYou can use `from_dict` for now instead:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n    'bbox': [\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n}\r\ndataset = Dataset.from_dict(dataset)\r\n```\r\n\r\nThis will work but to use it with the torch formatter you must specify the `Array2D` feature type in order to tell the shape:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n    'bbox': [\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n}\r\ndataset = Dataset.from_dict(dataset, features=Features({\r\n    \"bbox\": Array2D(shape=(3, 4), dtype=\"int64\"),\r\n    \"input_ids\": Value(\"int64\")\r\n}))\r\ndataset.set_format(\"torch\")\r\nprint(dataset[0]['bbox'])\r\n# tensor([[1, 2, 3, 4],\r\n#         [1, 2, 3, 4],\r\n#         [1, 2, 3, 4]])\r\n```\r\nIf you don't specify the `Array2D` feature type, then the inferred type will be Sequence(Sequence(Value(\"int64\"))) and therefore the torch formatter will return list of tensors","body":"Hi,\r\n\r\nI'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row.\r\n\r\nThe following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`)\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = pd.DataFrame({\r\n    'bbox': [\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n})\r\ndataset = Dataset.from_pandas(dataset)\r\n```\r\n\r\nSince I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists.\r\n\r\n```\r\nimport torch\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\n\r\ndataset = pd.DataFrame({\r\n    'bbox': [\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]]\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n})\r\ndataset = Dataset.from_pandas(dataset)\r\n\r\ndef test(examples):\r\n    return {'bbbox': torch.Tensor(examples['bbox'])}\r\ndataset = dataset.map(test)\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n\r\ndataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True)\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n\r\ndef test2(examples):\r\n    return {'bbbox': torch.stack(examples['bbox'])}\r\ndataset = dataset.map(test2)\r\n\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n```\r\n\r\nIs is possible to support n-D arrays\/tensors in datasets? \r\nIt seems that it can also be useful for this [feature request](https:\/\/github.com\/huggingface\/datasets\/issues\/263).","comment_length":165,"text":"Multidimensional arrays in a Dataset \n Hi,\r\n\r\nI'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row.\r\n\r\nThe following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`)\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = pd.DataFrame({\r\n    'bbox': [\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n})\r\ndataset = Dataset.from_pandas(dataset)\r\n```\r\n\r\nSince I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists.\r\n\r\n```\r\nimport torch\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\n\r\ndataset = pd.DataFrame({\r\n    'bbox': [\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]]\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n})\r\ndataset = Dataset.from_pandas(dataset)\r\n\r\ndef test(examples):\r\n    return {'bbbox': torch.Tensor(examples['bbox'])}\r\ndataset = dataset.map(test)\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n\r\ndataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True)\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n\r\ndef test2(examples):\r\n    return {'bbbox': torch.stack(examples['bbox'])}\r\ndataset = dataset.map(test2)\r\n\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n```\r\n\r\nIs is possible to support n-D arrays\/tensors in datasets? \r\nIt seems that it can also be useful for this [feature request](https:\/\/github.com\/huggingface\/datasets\/issues\/263). \n Hi !\r\n\r\nThis is actually supported ! but not yet in `from_pandas`.\r\nYou can use `from_dict` for now instead:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n    'bbox': [\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n}\r\ndataset = Dataset.from_dict(dataset)\r\n```\r\n\r\nThis will work but to use it with the torch formatter you must specify the `Array2D` feature type in order to tell the shape:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n    'bbox': [\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n}\r\ndataset = Dataset.from_dict(dataset, features=Features({\r\n    \"bbox\": Array2D(shape=(3, 4), dtype=\"int64\"),\r\n    \"input_ids\": Value(\"int64\")\r\n}))\r\ndataset.set_format(\"torch\")\r\nprint(dataset[0]['bbox'])\r\n# tensor([[1, 2, 3, 4],\r\n#         [1, 2, 3, 4],\r\n#         [1, 2, 3, 4]])\r\n```\r\nIf you don't specify the `Array2D` feature type, then the inferred type will be Sequence(Sequence(Value(\"int64\"))) and therefore the torch formatter will return list of 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2080","title":"Multidimensional arrays in a Dataset","comments":"Thanks for the explanation. \r\nWith my original DataFrame, I did\r\n```\r\ndataset = dataset.to_dict(\"list\")\r\n```\r\nand then the rest of the transformation from dictionary works just fine.","body":"Hi,\r\n\r\nI'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row.\r\n\r\nThe following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`)\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = pd.DataFrame({\r\n    'bbox': [\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n})\r\ndataset = Dataset.from_pandas(dataset)\r\n```\r\n\r\nSince I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists.\r\n\r\n```\r\nimport torch\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\n\r\ndataset = pd.DataFrame({\r\n    'bbox': [\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]]\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n})\r\ndataset = Dataset.from_pandas(dataset)\r\n\r\ndef test(examples):\r\n    return {'bbbox': torch.Tensor(examples['bbox'])}\r\ndataset = dataset.map(test)\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n\r\ndataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True)\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n\r\ndef test2(examples):\r\n    return {'bbbox': torch.stack(examples['bbox'])}\r\ndataset = dataset.map(test2)\r\n\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n```\r\n\r\nIs is possible to support n-D arrays\/tensors in datasets? \r\nIt seems that it can also be useful for this [feature request](https:\/\/github.com\/huggingface\/datasets\/issues\/263).","comment_length":27,"text":"Multidimensional arrays in a Dataset \n Hi,\r\n\r\nI'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row.\r\n\r\nThe following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`)\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = pd.DataFrame({\r\n    'bbox': [\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n        np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n})\r\ndataset = Dataset.from_pandas(dataset)\r\n```\r\n\r\nSince I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists.\r\n\r\n```\r\nimport torch\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\n\r\ndataset = pd.DataFrame({\r\n    'bbox': [\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]],\r\n        [[1,2,3,4],[1,2,3,4],[1,2,3,4]]\r\n    ],\r\n    'input_ids': [1, 2, 3, 4]\r\n})\r\ndataset = Dataset.from_pandas(dataset)\r\n\r\ndef test(examples):\r\n    return {'bbbox': torch.Tensor(examples['bbox'])}\r\ndataset = dataset.map(test)\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n\r\ndataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True)\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n\r\ndef test2(examples):\r\n    return {'bbbox': torch.stack(examples['bbox'])}\r\ndataset = dataset.map(test2)\r\n\r\nprint(dataset[0]['bbox'])\r\nprint(dataset[0]['bbbox'])\r\n```\r\n\r\nIs is possible to support n-D arrays\/tensors in datasets? \r\nIt seems that it can also be useful for this [feature request](https:\/\/github.com\/huggingface\/datasets\/issues\/263). \n Thanks for the explanation. \r\nWith my original DataFrame, I did\r\n```\r\ndataset = dataset.to_dict(\"list\")\r\n```\r\nand then the rest of the transformation from dictionary works just 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2078","title":"MemoryError when computing WER metric","comments":"Hi ! Thanks for reporting.\r\nWe're indeed using `jiwer` to compute the WER.\r\n\r\nMaybe instead of calling `jiwer.wer` once for all the preditions\/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible.\r\n\r\nCurrently the code to compute the WER is defined here:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/349ac4398a3bcae6356f14c5754483383a60e8a4\/metrics\/wer\/wer.py#L93-L94","body":"Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n","comment_length":56,"text":"MemoryError when computing WER metric \n Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n \n Hi ! Thanks for reporting.\r\nWe're indeed using `jiwer` to compute the WER.\r\n\r\nMaybe instead of calling `jiwer.wer` once for all the preditions\/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible.\r\n\r\nCurrently the code to compute the WER is defined here:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/349ac4398a3bcae6356f14c5754483383a60e8a4\/metrics\/wer\/wer.py#L93-L94","embeddings":[0.1249791905,-0.2007298619,0.0587182119,0.3256509304,0.433393985,0.0737894103,-0.2180431932,0.2950879037,0.0288314931,0.4309869111,0.0965305641,-0.0946346819,-0.3058875501,-0.5752207041,-0.1799835265,-0.3347723186,-0.1305915564,-0.0603135042,-0.0939678624,0.267347157,-0.2414007336,0.2181451321,-0.3488659561,0.122934103,-0.0717325732,-0.0110546872,-0.1759239286,0.1331792176,-0.2212716192,-0.5667486191,0.3025929928,-0.3079388142,0.1904908121,0.3594332933,-0.000114279,0.0282555725,0.19010216,-0.178447023,-0.0109599112,0.1174560264,-0.1258537024,-0.1865432262,-0.1319401711,-0.0092576966,-0.0329881646,0.0375669524,0.1080688611,-0.2880086005,0.0632087141,0.2449464798,0.1992428005,-0.0609460808,0.2961238921,-0.2295078933,-0.1241091788,-0.1905775517,0.1977030337,0.3163279891,-0.2703140378,-0.1776358634,-0.2714266181,0.2580875754,-0.0427771099,0.2143146247,0.0936119035,0.1157601327,0.0853944719,0.0110054342,0.0847661495,-0.2555573285,0.2671687901,-0.3263044655,-0.0301647987,0.2048876584,-0.1614335924,-0.4275716245,0.1660306156,0.1187261567,-0.5047883391,0.0975702256,-0.2040266842,-0.1204812452,-0.394231081,0.1254809946,-0.0058269789,0.0910312086,-0.1965167075,0.1057423353,0.3368060887,0.1566442996,0.0604361072,0.2069692314,-0.1233543605,0.0125260502,-0.3743089437,-0.0881424993,-0.0584868453,-0.232619822,0.1580233574,-0.569531858,0.582660079,-0.0841271803,0.3524422348,0.4458707869,-0.2218324989,0.8173162937,-0.0972185805,0.0790163055,0.041408278,0.0784390643,-0.1152928844,-0.2438932359,-0.0898666158,-0.3839600384,0.2813354135,0.1884756237,-0.1235135794,-0.1992423385,-0.4400852621,0.0639662519,-0.0409351289,0.1174827218,0.1261956394,0.318808198,0.1651479155,0.489371866,0.3132435679,0.0720733255,-0.1783270389,0.0621534623,-0.2334481925,0.2319099456,-0.2242501229,0.0308982152,0.1252744943,0.2297951728,0.3616980016,-0.2322330773,0.2106768191,-0.2574244142,0.2766292393,-0.1106605157,0.0838679075,0.2026334405,-0.0995929539,-0.0087277889,0.229771018,0.2736266553,0.0345627628,0.1165985167,-0.212802887,-0.2432083488,0.3005083799,0.1580395401,-0.058649797,0.0679567158,-0.064557381,-0.0057821609,0.2805483043,-0.0548926443,-0.3401754797,0.0868661329,-0.7389823794,-0.2600598931,0.1528928429,0.0060362252,-0.1756756008,0.0054638353,0.2558612823,-0.0120836552,0.2422092408,0.0330554321,0.0680655092,0.1206671,-0.2044021338,-0.1056600213,0.3498846292,-0.8443564773,-0.1511173397,-0.0499686413,-0.1328816861,-0.0256584417,0.2559601665,-0.2146238387,0.1497683972,0.1131592616,0.3785301447,-0.2864328027,-0.1795535982,0.0241758507,-0.1898522526,-0.1139877066,0.179043591,-0.1413308531,0.3943657875,0.0892044976,0.1655692011,0.1407639533,0.2155860364,-0.027472429,0.1473459303,0.2670481503,0.2488005161,-0.6484080553,0.3060636222,-0.0628642514,0.1538829207,0.1872498542,-0.242123276,0.0660027862,0.4159049988,0.429313302,-0.1300992519,0.016114207,0.0612439737,-0.1678278446,0.2202239931,-0.0615801029,0.306425035,-0.2538882494,-0.0041135349,0.0725794286,-0.3613118827,-0.0806768984,-0.0067587825,-0.2327689081,-0.1088713482,-0.2220413536,0.2936443686,0.1907931715,0.2006856948,0.1718473136,-0.2917720675,0.350572139,0.1063389406,-0.0170480106,0.1874941736,0.1884122491,-0.1544378251,-0.4010769427,0.2174313664,-0.0282920189,0.2030489743,-0.118592605,0.2363728136,-0.125006333,0.0056329728,0.3543412387,0.0527254231,0.3139891028,0.1135732532,0.2762680948,0.1866981685,0.1431760937,0.3140142262,-0.4340707362,-0.2543807924,-0.389414072,0.3616285622,-0.3148328066,0.3210273087,0.1509761661,-0.143353641,0.4951443672,-0.1373810619,-0.1780490279,-0.0775060281,-0.3291467428,0.4842241108,0.0247815214,0.0627912804,0.1173731685,-0.0368315242,0.1472533494,0.1195161045,0.2113156915,-0.1094565839,-0.0626852959,0.2112149298,-0.4670906663,0.1594258845,0.1257491559,0.1019963622,0.3387947083,-0.2200201005,-0.1152894199,0.267416209,0.2180289924,-0.1320870072,-0.0435180217,-0.025865024,-0.2954534888,-0.204206109,0.3511810899,0.1040427685,0.1294223517,-0.0459422618,0.2327170968,0.2416962981,0.4276043475,0.2114255726,-0.0255014356,-0.181180343,0.0590603203,0.0218610112,-0.049709592,0.1463518292,0.0862486511,-0.0498690605,-0.0435718745,-0.1804246157,-0.0071563218,-0.0013990207,0.4104417562,0.2205947489,0.0727371052,-0.2366647571,-0.0322218463,0.1743239164,-0.2374216616,-0.038494654,0.5531134009,-0.0587114953,0.0892808437,-0.0565565489,-0.1715920269,-0.093634598,-0.396466732,-0.0207021777,0.0413863994,0.1471429765,-0.1181743443,0.1409462243,0.2246731222,0.1708787829,0.0079454714,-0.2062764615,-0.3394733369,-0.0268059596,-0.2306408435,-0.1545539051,0.0618929826,0.0176213887,0.6852975488,0.1380208135,-0.1405830383,-0.4560725093,0.0017372953,-0.2098908722,-0.030375788,-0.01608417,0.2644729018,-0.0626578182,-0.1569625586,-0.284212321,-0.0542895645,0.2124326974,0.1187750623,-0.0273834895,0.0317650139,0.0322173722,0.2830829322,0.5209565759,0.395329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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2078","title":"MemoryError when computing WER metric","comments":"Hi,\r\n\r\nI've just pushed a pull request that is related to this issue https:\/\/github.com\/huggingface\/datasets\/pull\/2169. It's not iterative, but it should avoid memory errors. It's based on the editdistance python library. An iterative implementation should be as easy as storing scores and words stepwise and dividing at the end.  ","body":"Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n","comment_length":48,"text":"MemoryError when computing WER metric \n Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n \n Hi,\r\n\r\nI've just pushed a pull request that is related to this issue https:\/\/github.com\/huggingface\/datasets\/pull\/2169. It's not iterative, but it should avoid memory errors. It's based on the editdistance python library. An iterative implementation should be as easy as storing scores and words stepwise and dividing at the end.  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2078","title":"MemoryError when computing WER metric","comments":"I see, this was solved by other thread. Ok, let me know if you want to switch the implementation for any reason :)","body":"Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n","comment_length":23,"text":"MemoryError when computing WER metric \n Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n \n I see, this was solved by other thread. Ok, let me know if you want to switch the implementation for any reason :)","embeddings":[0.1249791905,-0.2007298619,0.0587182119,0.3256509304,0.433393985,0.0737894103,-0.2180431932,0.2950879037,0.0288314931,0.4309869111,0.0965305641,-0.0946346819,-0.3058875501,-0.5752207041,-0.1799835265,-0.3347723186,-0.1305915564,-0.0603135042,-0.0939678624,0.267347157,-0.2414007336,0.2181451321,-0.3488659561,0.122934103,-0.0717325732,-0.0110546872,-0.1759239286,0.1331792176,-0.2212716192,-0.5667486191,0.3025929928,-0.3079388142,0.1904908121,0.3594332933,-0.000114279,0.0282555725,0.19010216,-0.178447023,-0.0109599112,0.1174560264,-0.1258537024,-0.1865432262,-0.1319401711,-0.0092576966,-0.0329881646,0.0375669524,0.1080688611,-0.2880086005,0.0632087141,0.2449464798,0.1992428005,-0.0609460808,0.2961238921,-0.2295078933,-0.1241091788,-0.1905775517,0.1977030337,0.3163279891,-0.2703140378,-0.1776358634,-0.2714266181,0.2580875754,-0.0427771099,0.2143146247,0.0936119035,0.1157601327,0.0853944719,0.0110054342,0.0847661495,-0.2555573285,0.2671687901,-0.3263044655,-0.0301647987,0.2048876584,-0.1614335924,-0.4275716245,0.1660306156,0.1187261567,-0.5047883391,0.0975702256,-0.2040266842,-0.1204812452,-0.394231081,0.1254809946,-0.0058269789,0.0910312086,-0.1965167075,0.1057423353,0.3368060887,0.1566442996,0.0604361072,0.2069692314,-0.1233543605,0.0125260502,-0.3743089437,-0.0881424993,-0.0584868453,-0.232619822,0.1580233574,-0.569531858,0.582660079,-0.0841271803,0.3524422348,0.4458707869,-0.2218324989,0.8173162937,-0.0972185805,0.0790163055,0.041408278,0.0784390643,-0.1152928844,-0.2438932359,-0.0898666158,-0.3839600384,0.2813354135,0.1884756237,-0.1235135794,-0.1992423385,-0.4400852621,0.0639662519,-0.0409351289,0.1174827218,0.1261956394,0.318808198,0.1651479155,0.489371866,0.3132435679,0.0720733255,-0.1783270389,0.0621534623,-0.2334481925,0.2319099456,-0.2242501229,0.0308982152,0.1252744943,0.2297951728,0.3616980016,-0.2322330773,0.2106768191,-0.2574244142,0.2766292393,-0.1106605157,0.0838679075,0.2026334405,-0.0995929539,-0.0087277889,0.229771018,0.2736266553,0.0345627628,0.1165985167,-0.212802887,-0.2432083488,0.3005083799,0.1580395401,-0.058649797,0.0679567158,-0.064557381,-0.0057821609,0.2805483043,-0.0548926443,-0.3401754797,0.0868661329,-0.7389823794,-0.2600598931,0.1528928429,0.0060362252,-0.1756756008,0.0054638353,0.2558612823,-0.0120836552,0.2422092408,0.0330554321,0.0680655092,0.1206671,-0.2044021338,-0.1056600213,0.3498846292,-0.8443564773,-0.1511173397,-0.0499686413,-0.1328816861,-0.0256584417,0.2559601665,-0.2146238387,0.1497683972,0.1131592616,0.3785301447,-0.2864328027,-0.1795535982,0.0241758507,-0.1898522526,-0.1139877066,0.179043591,-0.1413308531,0.3943657875,0.0892044976,0.1655692011,0.1407639533,0.2155860364,-0.027472429,0.1473459303,0.2670481503,0.2488005161,-0.6484080553,0.3060636222,-0.0628642514,0.1538829207,0.1872498542,-0.242123276,0.0660027862,0.4159049988,0.429313302,-0.1300992519,0.016114207,0.0612439737,-0.1678278446,0.2202239931,-0.0615801029,0.306425035,-0.2538882494,-0.0041135349,0.0725794286,-0.3613118827,-0.0806768984,-0.0067587825,-0.2327689081,-0.1088713482,-0.2220413536,0.2936443686,0.1907931715,0.2006856948,0.1718473136,-0.2917720675,0.350572139,0.1063389406,-0.0170480106,0.1874941736,0.1884122491,-0.1544378251,-0.4010769427,0.2174313664,-0.0282920189,0.2030489743,-0.118592605,0.2363728136,-0.125006333,0.0056329728,0.3543412387,0.0527254231,0.3139891028,0.1135732532,0.2762680948,0.1866981685,0.1431760937,0.3140142262,-0.4340707362,-0.2543807924,-0.389414072,0.3616285622,-0.3148328066,0.3210273087,0.1509761661,-0.143353641,0.4951443672,-0.1373810619,-0.1780490279,-0.0775060281,-0.3291467428,0.4842241108,0.0247815214,0.0627912804,0.1173731685,-0.0368315242,0.1472533494,0.1195161045,0.2113156915,-0.1094565839,-0.0626852959,0.2112149298,-0.4670906663,0.1594258845,0.1257491559,0.1019963622,0.3387947083,-0.2200201005,-0.1152894199,0.267416209,0.2180289924,-0.1320870072,-0.0435180217,-0.025865024,-0.2954534888,-0.204206109,0.3511810899,0.1040427685,0.1294223517,-0.0459422618,0.2327170968,0.2416962981,0.4276043475,0.2114255726,-0.0255014356,-0.181180343,0.0590603203,0.0218610112,-0.049709592,0.1463518292,0.0862486511,-0.0498690605,-0.0435718745,-0.1804246157,-0.0071563218,-0.0013990207,0.4104417562,0.2205947489,0.0727371052,-0.2366647571,-0.0322218463,0.1743239164,-0.2374216616,-0.038494654,0.5531134009,-0.0587114953,0.0892808437,-0.0565565489,-0.1715920269,-0.093634598,-0.396466732,-0.0207021777,0.0413863994,0.1471429765,-0.1181743443,0.1409462243,0.2246731222,0.1708787829,0.0079454714,-0.2062764615,-0.3394733369,-0.0268059596,-0.2306408435,-0.1545539051,0.0618929826,0.0176213887,0.6852975488,0.1380208135,-0.1405830383,-0.4560725093,0.0017372953,-0.2098908722,-0.030375788,-0.01608417,0.2644729018,-0.0626578182,-0.1569625586,-0.284212321,-0.0542895645,0.2124326974,0.1187750623,-0.0273834895,0.0317650139,0.0322173722,0.2830829322,0.5209565759,0.3953298926,0.1029953957,0.2838952839,0.192132026,0.0036794737,0.0900348872,-0.1075095385,0.1794606596,-0.1067251489,-0.1737584621,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2078","title":"MemoryError when computing WER metric","comments":"Thanks for diving into this anyway ^^'\r\nAs you said this actually got solved a few days ago","body":"Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n","comment_length":18,"text":"MemoryError when computing WER metric \n Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n \n Thanks for diving into this anyway ^^'\r\nAs you said this actually got solved a few days 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2078","title":"MemoryError when computing WER metric","comments":"Someone created an issue https:\/\/github.com\/jitsi\/jiwer\/issues\/40 at jiwer which shows that this is still a problem in the current version. Would be curious to figure out how this can be fixed by jiwer... :) I assume that it runs of out memory because it's trying to compute the WER over (too many) test samples?","body":"Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n","comment_length":53,"text":"MemoryError when computing WER metric \n Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n \n Someone created an issue https:\/\/github.com\/jitsi\/jiwer\/issues\/40 at jiwer which shows that this is still a problem in the current version. Would be curious to figure out how this can be fixed by jiwer... :) I assume that it runs of out memory because it's trying to compute the WER over (too many) test 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2078","title":"MemoryError when computing WER metric","comments":"Hi !\r\n\r\nIt's computed iteratively so not sure what could go wrong\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8afd0ba8c27800a55ea69d9fcd702dc97d9c16d8\/metrics\/wer\/wer.py#L100-L106\r\n\r\n@NiklasHoltmeyer what version of `datasets` are you running ?\r\n","body":"Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n","comment_length":22,"text":"MemoryError when computing WER metric \n Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n \n Hi !\r\n\r\nIt's computed iteratively so not sure what could go wrong\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8afd0ba8c27800a55ea69d9fcd702dc97d9c16d8\/metrics\/wer\/wer.py#L100-L106\r\n\r\n@NiklasHoltmeyer what version of `datasets` are you running ?\r\n","embeddings":[0.1249791905,-0.2007298619,0.0587182119,0.3256509304,0.433393985,0.0737894103,-0.2180431932,0.2950879037,0.0288314931,0.4309869111,0.0965305641,-0.0946346819,-0.3058875501,-0.5752207041,-0.1799835265,-0.3347723186,-0.1305915564,-0.0603135042,-0.0939678624,0.267347157,-0.2414007336,0.2181451321,-0.3488659561,0.122934103,-0.0717325732,-0.0110546872,-0.1759239286,0.1331792176,-0.2212716192,-0.5667486191,0.3025929928,-0.3079388142,0.1904908121,0.3594332933,-0.000114279,0.0282555725,0.19010216,-0.178447023,-0.0109599112,0.1174560264,-0.1258537024,-0.1865432262,-0.1319401711,-0.0092576966,-0.0329881646,0.0375669524,0.1080688611,-0.2880086005,0.0632087141,0.2449464798,0.1992428005,-0.0609460808,0.2961238921,-0.2295078933,-0.1241091788,-0.1905775517,0.1977030337,0.3163279891,-0.2703140378,-0.1776358634,-0.2714266181,0.2580875754,-0.0427771099,0.2143146247,0.0936119035,0.1157601327,0.0853944719,0.0110054342,0.0847661495,-0.2555573285,0.2671687901,-0.3263044655,-0.0301647987,0.2048876584,-0.1614335924,-0.4275716245,0.1660306156,0.1187261567,-0.5047883391,0.0975702256,-0.2040266842,-0.1204812452,-0.394231081,0.1254809946,-0.0058269789,0.0910312086,-0.1965167075,0.1057423353,0.3368060887,0.1566442996,0.0604361072,0.2069692314,-0.1233543605,0.0125260502,-0.3743089437,-0.0881424993,-0.0584868453,-0.232619822,0.1580233574,-0.569531858,0.582660079,-0.0841271803,0.3524422348,0.4458707869,-0.2218324989,0.8173162937,-0.0972185805,0.0790163055,0.041408278,0.0784390643,-0.1152928844,-0.2438932359,-0.0898666158,-0.3839600384,0.2813354135,0.1884756237,-0.1235135794,-0.1992423385,-0.4400852621,0.0639662519,-0.0409351289,0.1174827218,0.1261956394,0.318808198,0.1651479155,0.489371866,0.3132435679,0.0720733255,-0.1783270389,0.0621534623,-0.2334481925,0.2319099456,-0.2242501229,0.0308982152,0.1252744943,0.2297951728,0.3616980016,-0.2322330773,0.2106768191,-0.2574244142,0.2766292393,-0.1106605157,0.0838679075,0.2026334405,-0.0995929539,-0.0087277889,0.229771018,0.2736266553,0.0345627628,0.1165985167,-0.212802887,-0.2432083488,0.3005083799,0.1580395401,-0.058649797,0.0679567158,-0.064557381,-0.0057821609,0.2805483043,-0.0548926443,-0.3401754797,0.0868661329,-0.7389823794,-0.2600598931,0.1528928429,0.0060362252,-0.1756756008,0.0054638353,0.2558612823,-0.0120836552,0.2422092408,0.0330554321,0.0680655092,0.1206671,-0.2044021338,-0.1056600213,0.3498846292,-0.8443564773,-0.1511173397,-0.0499686413,-0.1328816861,-0.0256584417,0.2559601665,-0.2146238387,0.1497683972,0.1131592616,0.3785301447,-0.2864328027,-0.1795535982,0.0241758507,-0.1898522526,-0.1139877066,0.179043591,-0.1413308531,0.3943657875,0.0892044976,0.1655692011,0.1407639533,0.2155860364,-0.027472429,0.1473459303,0.2670481503,0.2488005161,-0.6484080553,0.3060636222,-0.0628642514,0.1538829207,0.1872498542,-0.242123276,0.0660027862,0.4159049988,0.429313302,-0.1300992519,0.016114207,0.0612439737,-0.1678278446,0.2202239931,-0.0615801029,0.306425035,-0.2538882494,-0.0041135349,0.0725794286,-0.3613118827,-0.0806768984,-0.0067587825,-0.2327689081,-0.1088713482,-0.2220413536,0.2936443686,0.1907931715,0.2006856948,0.1718473136,-0.2917720675,0.350572139,0.1063389406,-0.0170480106,0.1874941736,0.1884122491,-0.1544378251,-0.4010769427,0.2174313664,-0.0282920189,0.2030489743,-0.118592605,0.2363728136,-0.125006333,0.0056329728,0.3543412387,0.0527254231,0.3139891028,0.1135732532,0.2762680948,0.1866981685,0.1431760937,0.3140142262,-0.4340707362,-0.2543807924,-0.389414072,0.3616285622,-0.3148328066,0.3210273087,0.1509761661,-0.143353641,0.4951443672,-0.1373810619,-0.1780490279,-0.0775060281,-0.3291467428,0.4842241108,0.0247815214,0.0627912804,0.1173731685,-0.0368315242,0.1472533494,0.1195161045,0.2113156915,-0.1094565839,-0.0626852959,0.2112149298,-0.4670906663,0.1594258845,0.1257491559,0.1019963622,0.3387947083,-0.2200201005,-0.1152894199,0.267416209,0.2180289924,-0.1320870072,-0.0435180217,-0.025865024,-0.2954534888,-0.204206109,0.3511810899,0.1040427685,0.1294223517,-0.0459422618,0.2327170968,0.2416962981,0.4276043475,0.2114255726,-0.0255014356,-0.181180343,0.0590603203,0.0218610112,-0.049709592,0.1463518292,0.0862486511,-0.0498690605,-0.0435718745,-0.1804246157,-0.0071563218,-0.0013990207,0.4104417562,0.2205947489,0.0727371052,-0.2366647571,-0.0322218463,0.1743239164,-0.2374216616,-0.038494654,0.5531134009,-0.0587114953,0.0892808437,-0.0565565489,-0.1715920269,-0.093634598,-0.396466732,-0.0207021777,0.0413863994,0.1471429765,-0.1181743443,0.1409462243,0.2246731222,0.1708787829,0.0079454714,-0.2062764615,-0.3394733369,-0.0268059596,-0.2306408435,-0.1545539051,0.0618929826,0.0176213887,0.6852975488,0.1380208135,-0.1405830383,-0.4560725093,0.0017372953,-0.2098908722,-0.030375788,-0.01608417,0.2644729018,-0.0626578182,-0.1569625586,-0.284212321,-0.0542895645,0.2124326974,0.1187750623,-0.0273834895,0.0317650139,0.0322173722,0.2830829322,0.5209565759,0.3953298926,0.1029953957,0.2838952839,0.192132026,0.0036794737,0.0900348872,-0.1075095385,0.1794606596,-0.1067251489,-0.1737584621,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2078","title":"MemoryError when computing WER metric","comments":"One possible explanation might be that it is the user who is passing all the sentences in a single element to `wer.compute`?\r\n\r\nAs current implementation iterates over the elements of `predictions` and `references`, this can be problematic if `predictions` and `references` contain a single huge element each. \r\n\r\nThis could be the case, for example, with a single string with all sentences:\r\n```python\r\nresult[\"predicted\"] = \"One sentence. Other sentence.\"\r\n```\r\nor with a __double__ nested list of sentence lists\r\n```python\r\nresult[\"predicted\"] = [[ [\"One sentence.\"], [\"Other sentence\"] ]]\r\n```\r\n\r\nThe user should check the dimensions of the data structure passed to `predictions` and `references`.","body":"Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n","comment_length":103,"text":"MemoryError when computing WER metric \n Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n \n One possible explanation might be that it is the user who is passing all the sentences in a single element to `wer.compute`?\r\n\r\nAs current implementation iterates over the elements of `predictions` and `references`, this can be problematic if `predictions` and `references` contain a single huge element each. \r\n\r\nThis could be the case, for example, with a single string with all sentences:\r\n```python\r\nresult[\"predicted\"] = \"One sentence. Other sentence.\"\r\n```\r\nor with a __double__ nested list of sentence lists\r\n```python\r\nresult[\"predicted\"] = [[ [\"One sentence.\"], [\"Other sentence\"] ]]\r\n```\r\n\r\nThe user should check the dimensions of the data structure passed to `predictions` and `references`.","embeddings":[0.1249791905,-0.2007298619,0.0587182119,0.3256509304,0.433393985,0.0737894103,-0.2180431932,0.2950879037,0.0288314931,0.4309869111,0.0965305641,-0.0946346819,-0.3058875501,-0.5752207041,-0.1799835265,-0.3347723186,-0.1305915564,-0.0603135042,-0.0939678624,0.267347157,-0.2414007336,0.2181451321,-0.3488659561,0.122934103,-0.0717325732,-0.0110546872,-0.1759239286,0.1331792176,-0.2212716192,-0.5667486191,0.3025929928,-0.3079388142,0.1904908121,0.3594332933,-0.000114279,0.0282555725,0.19010216,-0.178447023,-0.0109599112,0.1174560264,-0.1258537024,-0.1865432262,-0.1319401711,-0.0092576966,-0.0329881646,0.0375669524,0.1080688611,-0.2880086005,0.0632087141,0.2449464798,0.1992428005,-0.0609460808,0.2961238921,-0.2295078933,-0.1241091788,-0.1905775517,0.1977030337,0.3163279891,-0.2703140378,-0.1776358634,-0.2714266181,0.2580875754,-0.0427771099,0.2143146247,0.0936119035,0.1157601327,0.0853944719,0.0110054342,0.0847661495,-0.2555573285,0.2671687901,-0.3263044655,-0.0301647987,0.2048876584,-0.1614335924,-0.4275716245,0.1660306156,0.1187261567,-0.5047883391,0.0975702256,-0.2040266842,-0.1204812452,-0.394231081,0.1254809946,-0.0058269789,0.0910312086,-0.1965167075,0.1057423353,0.3368060887,0.1566442996,0.0604361072,0.2069692314,-0.1233543605,0.0125260502,-0.3743089437,-0.0881424993,-0.0584868453,-0.232619822,0.1580233574,-0.569531858,0.582660079,-0.0841271803,0.3524422348,0.4458707869,-0.2218324989,0.8173162937,-0.0972185805,0.0790163055,0.041408278,0.0784390643,-0.1152928844,-0.2438932359,-0.0898666158,-0.3839600384,0.2813354135,0.1884756237,-0.1235135794,-0.1992423385,-0.4400852621,0.0639662519,-0.0409351289,0.1174827218,0.1261956394,0.318808198,0.1651479155,0.489371866,0.3132435679,0.0720733255,-0.1783270389,0.0621534623,-0.2334481925,0.2319099456,-0.2242501229,0.0308982152,0.1252744943,0.2297951728,0.3616980016,-0.2322330773,0.2106768191,-0.2574244142,0.2766292393,-0.1106605157,0.0838679075,0.2026334405,-0.0995929539,-0.0087277889,0.229771018,0.2736266553,0.0345627628,0.1165985167,-0.212802887,-0.2432083488,0.3005083799,0.1580395401,-0.058649797,0.0679567158,-0.064557381,-0.0057821609,0.2805483043,-0.0548926443,-0.3401754797,0.0868661329,-0.7389823794,-0.2600598931,0.1528928429,0.0060362252,-0.1756756008,0.0054638353,0.2558612823,-0.0120836552,0.2422092408,0.0330554321,0.0680655092,0.1206671,-0.2044021338,-0.1056600213,0.3498846292,-0.8443564773,-0.1511173397,-0.0499686413,-0.1328816861,-0.0256584417,0.2559601665,-0.2146238387,0.1497683972,0.1131592616,0.3785301447,-0.2864328027,-0.1795535982,0.0241758507,-0.1898522526,-0.1139877066,0.179043591,-0.1413308531,0.3943657875,0.0892044976,0.1655692011,0.1407639533,0.2155860364,-0.027472429,0.1473459303,0.2670481503,0.2488005161,-0.6484080553,0.3060636222,-0.0628642514,0.1538829207,0.1872498542,-0.242123276,0.0660027862,0.4159049988,0.429313302,-0.1300992519,0.016114207,0.0612439737,-0.1678278446,0.2202239931,-0.0615801029,0.306425035,-0.2538882494,-0.0041135349,0.0725794286,-0.3613118827,-0.0806768984,-0.0067587825,-0.2327689081,-0.1088713482,-0.2220413536,0.2936443686,0.1907931715,0.2006856948,0.1718473136,-0.2917720675,0.350572139,0.1063389406,-0.0170480106,0.1874941736,0.1884122491,-0.1544378251,-0.4010769427,0.2174313664,-0.0282920189,0.2030489743,-0.118592605,0.2363728136,-0.125006333,0.0056329728,0.3543412387,0.0527254231,0.3139891028,0.1135732532,0.2762680948,0.1866981685,0.1431760937,0.3140142262,-0.4340707362,-0.2543807924,-0.389414072,0.3616285622,-0.3148328066,0.3210273087,0.1509761661,-0.143353641,0.4951443672,-0.1373810619,-0.1780490279,-0.0775060281,-0.3291467428,0.4842241108,0.0247815214,0.0627912804,0.1173731685,-0.0368315242,0.1472533494,0.1195161045,0.2113156915,-0.1094565839,-0.0626852959,0.2112149298,-0.4670906663,0.1594258845,0.1257491559,0.1019963622,0.3387947083,-0.2200201005,-0.1152894199,0.267416209,0.2180289924,-0.1320870072,-0.0435180217,-0.025865024,-0.2954534888,-0.204206109,0.3511810899,0.1040427685,0.1294223517,-0.0459422618,0.2327170968,0.2416962981,0.4276043475,0.2114255726,-0.0255014356,-0.181180343,0.0590603203,0.0218610112,-0.049709592,0.1463518292,0.0862486511,-0.0498690605,-0.0435718745,-0.1804246157,-0.0071563218,-0.0013990207,0.4104417562,0.2205947489,0.0727371052,-0.2366647571,-0.0322218463,0.1743239164,-0.2374216616,-0.038494654,0.5531134009,-0.0587114953,0.0892808437,-0.0565565489,-0.1715920269,-0.093634598,-0.396466732,-0.0207021777,0.0413863994,0.1471429765,-0.1181743443,0.1409462243,0.2246731222,0.1708787829,0.0079454714,-0.2062764615,-0.3394733369,-0.0268059596,-0.2306408435,-0.1545539051,0.0618929826,0.0176213887,0.6852975488,0.1380208135,-0.1405830383,-0.4560725093,0.0017372953,-0.2098908722,-0.030375788,-0.01608417,0.2644729018,-0.0626578182,-0.1569625586,-0.284212321,-0.0542895645,0.2124326974,0.1187750623,-0.0273834895,0.0317650139,0.0322173722,0.2830829322,0.5209565759,0.3953298926,0.1029953957,0.2838952839,0.192132026,0.0036794737,0.0900348872,-0.1075095385,0.1794606596,-0.1067251489,-0.173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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2078","title":"MemoryError when computing WER metric","comments":"Hi all,\r\n\r\nin my case I was using and older version of datasets and, as @albertvillanova points out, passing the full list of sentences for the metric calculation. The problem was in the way jiwer implements WER, as it tries to compute WER for the full list at once instead of doing it element-wise. I think that with the latest implementation of datasets, or by using the alternative WER function that I've contributed on this [pull request](https:\/\/github.com\/huggingface\/datasets\/pull\/2169) there shouldn't be memory errors.","body":"Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n","comment_length":82,"text":"MemoryError when computing WER metric \n Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n \n Hi all,\r\n\r\nin my case I was using and older version of datasets and, as @albertvillanova points out, passing the full list of sentences for the metric calculation. The problem was in the way jiwer implements WER, as it tries to compute WER for the full list at once instead of doing it element-wise. I think that with the latest implementation of datasets, or by using the alternative WER function that I've contributed on this [pull request](https:\/\/github.com\/huggingface\/datasets\/pull\/2169) there shouldn't be memory 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2078","title":"MemoryError when computing WER metric","comments":"@lhoestq i was using Datasets==1.5.0 with 1.6.1 it worked (atleast the first run) but 1.5.0 is not compatible with my preprocessing. i cant save my dataset to a parquet file while using the latest datasets version\r\n\r\n-> \r\n```\r\n  File \"..\/preprocess_dataset.py\", line 132, in <module>\r\n    pq.write_table(train_dataset.data, f'{resampled_data_dir}\/{data_args.dataset_config_name}.train.parquet')\r\n  File \"\/usr\/local\/lib\/python3.8\/dist-packages\/pyarrow\/parquet.py\", line 1674, in write_table\r\n    writer.write_table(table, row_group_size=row_group_size)\r\n  File \"\/usr\/local\/lib\/python3.8\/dist-packages\/pyarrow\/parquet.py\", line 588, in write_table\r\n    self.writer.write_table(table, row_group_size=row_group_size)\r\nTypeError: Argument 'table' has incorrect type (expected pyarrow.lib.Table, got ConcatenationTable)\r\n``` \r\n\r\nif i do \r\n```\r\nimport pyarrow.parquet as pq\r\n...\r\n...\r\npq.write_table(train_dataset.data, 'train.parquet')\r\npq.write_table(eval_dataset.data, 'eval.parquet')\r\n```\r\n\r\nwhile using 1.6.1. and its working with 1.5.0\r\n","body":"Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n","comment_length":96,"text":"MemoryError when computing WER metric \n Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:\r\n\r\n```\r\nwer = load_metric(\"wer\")\r\nprint(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n```\r\n\r\nHowever, I receive the following exception:\r\n\r\n`Traceback (most recent call last):\r\n  File \"\/home\/diego\/IpGlobal\/wav2vec\/test_wav2vec.py\", line 51, in <module>\r\n    print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 403, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/diego\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/wer\/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281\/wer.py\", line 94, in _compute\r\n    return wer(references, predictions)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 81, in wer\r\n    truth, hypothesis, truth_transform, hypothesis_transform, **kwargs\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 192, in compute_measures\r\n    H, S, D, I = _get_operation_counts(truth, hypothesis)\r\n  File \"\/home\/diego\/miniconda3\/envs\/wav2vec3.6\/lib\/python3.6\/site-packages\/jiwer\/measures.py\", line 273, in _get_operation_counts\r\n    editops = Levenshtein.editops(source_string, destination_string)\r\nMemoryError`\r\n\r\nMy system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.\r\n\r\n\r\n\r\n \n @lhoestq i was using Datasets==1.5.0 with 1.6.1 it worked (atleast the first run) but 1.5.0 is not compatible with my preprocessing. i cant save my dataset to a parquet file while using the latest datasets version\r\n\r\n-> \r\n```\r\n  File \"..\/preprocess_dataset.py\", line 132, in <module>\r\n    pq.write_table(train_dataset.data, f'{resampled_data_dir}\/{data_args.dataset_config_name}.train.parquet')\r\n  File \"\/usr\/local\/lib\/python3.8\/dist-packages\/pyarrow\/parquet.py\", line 1674, in write_table\r\n    writer.write_table(table, row_group_size=row_group_size)\r\n  File \"\/usr\/local\/lib\/python3.8\/dist-packages\/pyarrow\/parquet.py\", line 588, in write_table\r\n    self.writer.write_table(table, row_group_size=row_group_size)\r\nTypeError: Argument 'table' has incorrect type (expected pyarrow.lib.Table, got ConcatenationTable)\r\n``` \r\n\r\nif i do \r\n```\r\nimport pyarrow.parquet as pq\r\n...\r\n...\r\npq.write_table(train_dataset.data, 'train.parquet')\r\npq.write_table(eval_dataset.data, 'eval.parquet')\r\n```\r\n\r\nwhile using 1.6.1. and its working with 1.5.0\r\n","embeddings":[0.1249791905,-0.2007298619,0.0587182119,0.3256509304,0.433393985,0.0737894103,-0.2180431932,0.2950879037,0.0288314931,0.4309869111,0.0965305641,-0.0946346819,-0.3058875501,-0.5752207041,-0.1799835265,-0.3347723186,-0.1305915564,-0.0603135042,-0.0939678624,0.267347157,-0.2414007336,0.2181451321,-0.3488659561,0.122934103,-0.0717325732,-0.0110546872,-0.1759239286,0.1331792176,-0.2212716192,-0.5667486191,0.3025929928,-0.3079388142,0.1904908121,0.3594332933,-0.000114279,0.0282555725,0.19010216,-0.178447023,-0.0109599112,0.1174560264,-0.1258537024,-0.1865432262,-0.1319401711,-0.0092576966,-0.0329881646,0.0375669524,0.1080688611,-0.2880086005,0.0632087141,0.2449464798,0.1992428005,-0.0609460808,0.2961238921,-0.2295078933,-0.1241091788,-0.1905775517,0.1977030337,0.3163279891,-0.2703140378,-0.1776358634,-0.2714266181,0.2580875754,-0.0427771099,0.2143146247,0.0936119035,0.1157601327,0.0853944719,0.0110054342,0.0847661495,-0.2555573285,0.2671687901,-0.3263044655,-0.0301647987,0.2048876584,-0.1614335924,-0.4275716245,0.1660306156,0.1187261567,-0.5047883391,0.0975702256,-0.2040266842,-0.1204812452,-0.394231081,0.1254809946,-0.0058269789,0.0910312086,-0.1965167075,0.1057423353,0.3368060887,0.1566442996,0.0604361072,0.2069692314,-0.1233543605,0.0125260502,-0.3743089437,-0.0881424993,-0.0584868453,-0.232619822,0.1580233574,-0.569531858,0.582660079,-0.0841271803,0.3524422348,0.4458707869,-0.2218324989,0.8173162937,-0.0972185805,0.0790163055,0.041408278,0.0784390643,-0.1152928844,-0.2438932359,-0.0898666158,-0.3839600384,0.2813354135,0.1884756237,-0.1235135794,-0.1992423385,-0.4400852621,0.0639662519,-0.0409351289,0.1174827218,0.1261956394,0.318808198,0.1651479155,0.489371866,0.3132435679,0.0720733255,-0.1783270389,0.0621534623,-0.2334481925,0.2319099456,-0.2242501229,0.0308982152,0.1252744943,0.2297951728,0.3616980016,-0.2322330773,0.2106768191,-0.2574244142,0.2766292393,-0.1106605157,0.0838679075,0.2026334405,-0.0995929539,-0.0087277889,0.229771018,0.2736266553,0.0345627628,0.1165985167,-0.212802887,-0.2432083488,0.3005083799,0.1580395401,-0.058649797,0.0679567158,-0.064557381,-0.0057821609,0.2805483043,-0.0548926443,-0.3401754797,0.0868661329,-0.7389823794,-0.2600598931,0.1528928429,0.0060362252,-0.1756756008,0.0054638353,0.2558612823,-0.0120836552,0.2422092408,0.0330554321,0.0680655092,0.1206671,-0.2044021338,-0.1056600213,0.3498846292,-0.8443564773,-0.1511173397,-0.0499686413,-0.1328816861,-0.0256584417,0.2559601665,-0.2146238387,0.1497683972,0.1131592616,0.3785301447,-0.2864328027,-0.1795535982,0.0241758507,-0.1898522526,-0.1139877066,0.179043591,-0.1413308531,0.3943657875,0.0892044976,0.1655692011,0.1407639533,0.2155860364,-0.027472429,0.1473459303,0.2670481503,0.2488005161,-0.6484080553,0.3060636222,-0.0628642514,0.1538829207,0.1872498542,-0.242123276,0.0660027862,0.4159049988,0.429313302,-0.1300992519,0.016114207,0.0612439737,-0.1678278446,0.2202239931,-0.0615801029,0.306425035,-0.2538882494,-0.0041135349,0.0725794286,-0.3613118827,-0.0806768984,-0.0067587825,-0.2327689081,-0.1088713482,-0.2220413536,0.2936443686,0.1907931715,0.2006856948,0.1718473136,-0.2917720675,0.350572139,0.1063389406,-0.0170480106,0.1874941736,0.1884122491,-0.1544378251,-0.4010769427,0.2174313664,-0.0282920189,0.2030489743,-0.118592605,0.2363728136,-0.125006333,0.0056329728,0.3543412387,0.0527254231,0.3139891028,0.1135732532,0.2762680948,0.1866981685,0.1431760937,0.3140142262,-0.4340707362,-0.2543807924,-0.389414072,0.3616285622,-0.3148328066,0.3210273087,0.1509761661,-0.143353641,0.4951443672,-0.1373810619,-0.1780490279,-0.0775060281,-0.3291467428,0.4842241108,0.0247815214,0.0627912804,0.1173731685,-0.0368315242,0.1472533494,0.1195161045,0.2113156915,-0.1094565839,-0.0626852959,0.2112149298,-0.4670906663,0.1594258845,0.1257491559,0.1019963622,0.3387947083,-0.2200201005,-0.1152894199,0.267416209,0.2180289924,-0.1320870072,-0.0435180217,-0.025865024,-0.2954534888,-0.204206109,0.3511810899,0.1040427685,0.1294223517,-0.0459422618,0.2327170968,0.2416962981,0.4276043475,0.2114255726,-0.0255014356,-0.181180343,0.0590603203,0.0218610112,-0.049709592,0.1463518292,0.0862486511,-0.0498690605,-0.0435718745,-0.1804246157,-0.0071563218,-0.0013990207,0.4104417562,0.2205947489,0.0727371052,-0.2366647571,-0.0322218463,0.1743239164,-0.2374216616,-0.038494654,0.5531134009,-0.0587114953,0.0892808437,-0.0565565489,-0.1715920269,-0.093634598,-0.396466732,-0.0207021777,0.0413863994,0.1471429765,-0.1181743443,0.1409462243,0.2246731222,0.1708787829,0.0079454714,-0.2062764615,-0.3394733369,-0.0268059596,-0.2306408435,-0.1545539051,0.0618929826,0.0176213887,0.6852975488,0.1380208135,-0.1405830383,-0.4560725093,0.0017372953,-0.2098908722,-0.030375788,-0.01608417,0.2644729018,-0.0626578182,-0.1569625586,-0.284212321,-0.0542895645,0.2124326974,0.1187750623,-0.0273834895,0.0317650139,0.0322173722,0.2830829322,0.5209565759,0.3953298926,0.1029953957,0.2838952839,0.192132026,0.0036794737,0.0900348872,-0.1075095385,0.1794606596,-0.1067251489,-0.1737584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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2076","title":"Issue: Dataset download error","comments":"Hi @XuhuiZhou, thanks for reporting this issue. \r\n\r\nIndeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.","body":"The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link?","comment_length":32,"text":"Issue: Dataset download error \n The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link? \n Hi @XuhuiZhou, thanks for reporting this issue. \r\n\r\nIndeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.","embeddings":[-0.2337625921,0.1458084732,0.0028066062,-0.0309512708,-0.0274386685,0.0824488178,0.3759451807,0.2059576809,0.1537598073,0.0144035025,0.1848477721,-0.1723960787,0.233960107,0.2969171107,0.1645151675,-0.0703357011,0.112443462,0.039021574,-0.2078589052,0.0084466971,-0.3404218256,0.2083994001,-0.3360630274,0.0776542872,-0.1339364201,0.1082167551,-0.2342879176,0.204613924,0.0053033242,-0.5016649365,0.1856248826,0.2496987134,0.2551788092,0.2303705812,-0.0001261118,-0.0265719853,0.4359161556,-0.1366342902,-0.2173595279,0.099079974,-0.4538171589,-0.223021552,-0.1618050784,-0.330869019,0.2659609914,-0.1933172345,0.1802410781,0.3238401711,0.1627966315,0.5249042511,0.0954555199,0.2440396845,0.383268863,-0.017859133,0.0295927692,-0.1398885399,0.0056165019,0.2431164831,0.16717875,-0.1312993467,0.1934746653,0.22652632,-0.0200180989,0.0947979912,0.2447730601,0.0861292556,0.2219021469,-0.4831433594,0.2752977014,0.0740563795,0.9506683946,-0.3367395699,-0.395308286,0.0342546217,0.1537052989,-0.0347731821,0.2187415063,-0.0993440971,-0.0766191855,0.1927044094,-0.0719738603,-0.4454037249,-0.2641430497,0.2948839962,-0.0705403015,0.6482892632,0.2004719526,0.1531991214,0.1943139136,-0.0314076692,0.3032341003,0.1202326268,-0.1972546726,-0.0020126104,0.2240179926,-0.2257652134,-0.1425874531,0.1321183443,-0.0379861146,0.0900656357,-0.0158376172,-0.0804638788,-0.1313908845,0.0318026841,0.2977488637,-0.0259650797,-0.0639488399,-0.135734573,0.5959560871,0.4367472529,0.1359974444,-0.0484655611,0.2467383593,-0.5086395144,-0.0841770098,0.1293987483,0.3410683274,-0.0649032518,-0.240212664,-0.3356491625,0.0078357346,-0.1144976616,-0.1429990828,0.2463043928,-0.0248055141,0.0981618837,0.0490982644,0.1763306558,-0.0579827689,-0.2141543925,0.0657005459,0.1828427166,0.0734454319,0.0571352467,0.1977758557,0.2320502847,-0.0572202317,-0.0442006141,-0.3850712776,-0.213274017,-0.2779664695,0.0655522272,0.0705886409,-0.005324062,-0.0922143757,0.117328763,-0.0756690428,-0.1958306432,-0.1742605716,0.300824374,-0.4043316245,-0.1922464222,-0.1143398136,-0.0011621449,-0.2243248671,-0.3667369783,0.1075007841,0.020169178,-0.3128478825,-0.3899010122,-0.0386796892,-0.0872283652,-0.3737506866,-0.1814860851,0.1254688054,0.7528218031,-0.5478048325,-0.2115307301,-0.2793195844,-0.2170637697,0.0229437053,0.2886830568,-0.132589668,0.3553591073,-0.4054445326,0.1295455247,0.5153886676,-0.417206198,-0.7371981144,0.144679144,-0.0059493012,-0.1772410125,0.1121795177,0.185042128,0.1248529702,-0.2103365809,-0.4866012335,0.1159009188,-0.0161943007,0.0633499995,-0.1106215268,-0.5575144291,0.3404843807,0.0558741838,0.2634509802,0.23671031,0.461234808,-0.0207831375,0.4065787792,-0.0609179251,0.1000960618,0.3868402541,0.4776051342,0.0447238311,0.2310122401,0.0881084874,-0.413458854,0.1487097889,0.1035044715,0.0112760682,-0.3565673232,-0.0582169667,-0.4483054876,-0.1214280277,-0.2669527829,0.0149108171,-0.0321032815,0.0315877348,0.0605464466,0.0501000136,-0.1429137588,0.0495217741,-0.1035647839,-0.0192484818,0.0885358974,0.5486960411,-0.1757840812,-0.0942082703,0.1593255997,0.1271205097,0.2328132838,-0.2303649634,-0.1200081632,0.4031567574,-0.2489540577,0.4772265255,0.3295185268,0.307207197,0.1701678187,-0.4089676738,0.2946333289,0.4525774121,0.0123852631,0.0576634742,-0.1450582743,-0.1638094187,0.0704360977,0.2487749904,0.0597915947,0.0034618124,0.1770109385,-0.2544127405,0.0570459329,-0.1840036511,-0.3074975908,-0.1961553991,-0.0975589827,-0.2141680121,-0.0272730198,0.1944456697,0.548995018,-0.0981293172,0.2854127288,0.3537480831,-0.0131097604,-0.105992727,-0.1862697303,0.635764122,0.347435385,-0.0694110394,-0.1281581074,0.1623578817,0.2249321193,-0.1026633754,0.2864802778,0.1101239994,-0.2034563124,0.1419662237,-0.0807323828,-0.1086298153,-0.0259588137,0.0884374306,0.148977533,0.3354448676,-0.3748236597,-0.0133951707,-0.3511465788,-0.4899004996,-0.3558903933,0.4411506355,-0.2762396336,-0.306037426,-0.0208473057,0.3651280403,-0.1554075927,0.120349355,0.1482290775,0.3038351536,0.0550497174,-0.0216290802,-0.3250444531,-0.0932601988,-0.1645984352,-0.0629142374,0.367408365,0.0479612648,0.0950696021,-0.5916244388,-0.0839566216,-1.030079484,-0.0702777207,0.0633477494,0.1601599604,0.1411256939,0.2158608586,0.3985548615,0.1617077589,-0.041331239,0.1102820709,-0.2546848059,0.149924174,-0.2434190363,-0.1488886178,0.1329917908,0.0021189197,-0.1931252033,-0.5508337021,-0.1271964759,0.1204171851,-0.066043064,0.0839750245,-0.1132252291,0.2934363484,-0.2033239156,0.2035319656,-0.311720103,0.1289834827,0.3097099662,0.4342479408,-0.0575692765,-0.3968016803,0.2718307972,0.1163857132,-0.0004378126,-0.0436475314,-0.5142811537,-0.1939069182,0.0119605642,0.2472350001,-0.0632775873,0.3359144032,0.1272754073,0.1365230978,0.0328033231,0.007582542,-0.2230256647,-0.1242373288,0.3777466118,0.520142436,0.5347796082,0.4276016355,-0.1535900384,0.2914156318,-0.1811151057,0.0489291362,0.3592682481,-0.3136166632,0.2962454259,0.1019999981,-0.2116543204,0.159325853,-0.1649796963,0.0663417205,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2076","title":"Issue: Dataset download error","comments":"It would be nice to update the urls indeed !\r\n\r\nTo do this, you just need to replace the urls in `iwslt2017.py` and then update the dataset_infos.json file with\r\n```\r\ndatasets-cli test .\/datasets\/iwslt2017 --all_configs --save_infos --ignore_verifications\r\n```","body":"The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link?","comment_length":37,"text":"Issue: Dataset download error \n The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link? \n It would be nice to update the urls indeed !\r\n\r\nTo do this, you just need to replace the urls in `iwslt2017.py` and then update the dataset_infos.json file with\r\n```\r\ndatasets-cli test .\/datasets\/iwslt2017 --all_configs --save_infos --ignore_verifications\r\n```","embeddings":[-0.2658804059,0.1200055629,-0.0836954564,-0.1060316935,-0.0561511554,0.0131992185,0.1135270521,0.3138656914,0.1883039474,-0.066574432,0.0962729752,0.0473739319,0.2630379796,0.2957364917,0.0410313308,0.0087639913,0.0977707878,-0.012059615,-0.283359617,0.0477566421,-0.4010069668,0.185447976,-0.3126529157,-0.0935631618,-0.0601683185,0.1259154081,-0.3016757965,0.1154684275,-0.028102709,-0.6263319254,0.272007972,0.3913546503,0.316203177,0.1880385131,-0.0001158188,-0.1218974292,0.3769318461,-0.136982739,-0.2984445393,0.0435839929,-0.6287513971,-0.3450363576,-0.1360737532,-0.2700529695,0.22668235,-0.3958939314,0.0692494288,0.2054876685,0.1902896464,0.5130621195,0.1835779846,0.3192787766,0.2641099989,-0.0636398941,-0.059832979,-0.0225952119,0.0009521026,0.0705709308,0.2730740309,-0.1185739487,0.2492121458,0.2715386748,-0.0152353542,0.010092359,0.2788783312,-0.0789464712,0.2402883321,-0.4742940366,0.181489706,-0.0224352125,0.8869271278,-0.3021595776,-0.4625476003,0.1310071498,0.0809325352,-0.0163424946,0.0607640631,-0.079737246,-0.1218894497,0.237042442,-0.172701776,-0.4722522497,-0.2464633584,0.3339924812,0.1414072663,0.6231669188,0.1615864336,0.1888065934,0.1574316919,-0.1041223854,0.2951930165,0.0304806884,-0.2320180684,0.0254935026,-0.007402264,-0.2957542837,-0.0889699832,0.0468946323,0.0084476089,0.2330870628,-0.0686246678,0.0362836011,-0.1587981135,-0.0047766604,0.2110501528,0.0406269357,0.1961813718,-0.0088387365,0.4371440411,0.6046849489,0.1306504011,-0.0418736488,0.2024896741,-0.3794595599,-0.2645076215,0.1908553988,0.2263668478,-0.1044933274,-0.1037598774,-0.2227149606,0.0441980623,-0.1268343776,-0.0476008616,0.2771447599,-0.1958618313,0.2930210531,0.1614924967,0.1268990338,-0.022056086,-0.2848353088,-0.0018294775,0.0769977197,0.0430149138,0.1160066724,0.1961963475,0.3151423633,0.1433610618,-0.0962003246,-0.3094979525,-0.1295165867,-0.1571160406,0.2209687084,0.1312751323,0.0491552278,-0.1215953007,0.2386569381,-0.0439963043,-0.0227940101,-0.1235121638,0.2856395245,-0.2518401444,-0.2053989768,-0.1939584911,0.0960125476,-0.1884029508,-0.3877756894,0.0536033362,-0.0562488288,-0.2581742704,-0.3503612876,0.014182834,-0.0462336726,-0.3027746379,-0.1621436626,0.1841401756,0.5909867287,-0.456173867,-0.1229094043,-0.1829642206,-0.2061738223,0.0710778534,0.1980037689,-0.1920287311,0.1715581268,-0.1458818614,0.0955499709,0.5259150863,-0.4413202703,-0.5953449607,0.2981191576,0.0802383572,-0.1355934739,0.0588275492,0.2166193724,0.0263025165,-0.2233210504,-0.5292845368,0.1446314603,-0.0034890403,0.1147148758,-0.2648130655,-0.5547558069,0.2628459632,0.0557124242,0.0934379548,0.1474309415,0.2381829172,0.1280943602,0.5918655396,-0.088500008,0.1206206903,0.2447381616,0.4602724612,0.218875289,0.2180595845,0.1307167709,-0.3153165877,0.1767422557,0.10156174,0.0456397422,-0.3460792005,-0.1567668319,-0.4196773767,-0.0620038807,-0.274181664,-0.2161063701,0.0659201071,0.1583601385,0.1244073957,-0.1213255227,-0.2550233006,0.169154942,-0.2095880359,-0.0120715657,-0.0166058764,0.4373683929,-0.1722989827,0.0374265909,0.0834373087,0.0616313852,0.180836767,-0.2504228652,-0.2579286098,0.6097425818,-0.2146878242,0.2997120023,0.4298552871,0.1492438912,0.1207442582,-0.3250736296,0.2012681216,0.4072022438,-0.00894388,0.1707870364,-0.0672066137,-0.1860921532,-0.0213031191,0.4060423672,0.0932738408,-0.0004359893,0.1702741832,-0.2249174416,-0.0809182078,-0.2327186316,-0.2935729325,-0.1963057667,-0.258728534,-0.1721239239,0.0489456989,0.2477435172,0.4090799391,-0.0953668058,0.0576708876,0.3085025251,0.0654459223,-0.0970147103,-0.1172316298,0.6027164459,0.3352910876,-0.0526720621,-0.2476411462,0.1453818977,0.216224879,-0.0910260081,0.2975569069,0.13904351,-0.0856266543,0.0401939154,-0.048189871,-0.0874652043,0.0182938781,-0.0237715133,0.1500088274,0.3771407902,-0.287627548,-0.0323111676,-0.3990878165,-0.5441100001,-0.2596680522,0.2256843001,-0.1787061989,-0.3382890522,0.0340355709,0.2766568363,-0.1662930101,0.2912934124,0.0830879062,0.2072326839,-0.0298417266,0.1257413179,-0.2831582427,-0.1041707322,-0.1987837255,-0.0051610041,0.3439479768,0.0879269093,0.1258616,-0.5627509952,-0.1183171272,-0.8395566344,-0.0598167367,-0.1080491394,0.1796332449,0.1123823896,0.2555810213,0.4747670889,0.2410743684,-0.1111321226,0.225404188,-0.1684920192,0.231316179,-0.3595496416,-0.0888079032,0.052810587,-0.031606216,-0.3374100924,-0.4721329808,-0.2592510581,0.099806428,-0.088309899,0.1336279213,-0.0836938545,0.3151398897,-0.2561671734,0.0332767144,-0.2438776344,0.0764196813,0.1267900318,0.3804925382,-0.1830556542,-0.4039416015,0.1734005958,0.1466891617,0.1630404294,-0.1210628375,-0.4460037649,-0.2127233148,0.0524972901,0.1790835857,-0.0879116654,0.3145512342,0.0866703689,0.0773892254,-0.0600989312,-0.0604950711,-0.1283682138,-0.142270565,0.2520179451,0.6086521149,0.3990364373,0.3653602898,-0.014084368,0.275329113,-0.0019119915,-0.0331550017,0.2885213792,-0.2751230896,0.2634461522,0.1812659353,-0.3473004699,0.0599865802,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2076","title":"Issue: Dataset download error","comments":"Is this a command to update my local files or fix the file Github repo in general? (I am not so familiar with the datasets-cli command here)\r\n\r\nI also took a brief look at the **Sharing your dataset** section, looks like I could fix that locally and push it to the repo? I guess we are \"canonical\" category?","body":"The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link?","comment_length":58,"text":"Issue: Dataset download error \n The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link? \n Is this a command to update my local files or fix the file Github repo in general? (I am not so familiar with the datasets-cli command here)\r\n\r\nI also took a brief look at the **Sharing your dataset** section, looks like I could fix that locally and push it to the repo? I guess we are \"canonical\" category?","embeddings":[-0.328745544,0.2278453708,0.017168263,-0.1485101581,-0.0278213564,0.1295360625,0.2159447074,0.4029399455,0.1252267361,-0.0533371381,0.1292182505,-0.1483832747,0.3661812544,0.1626039147,0.0930708423,0.0371344499,0.0882588997,0.1164590269,-0.1444546878,-0.1393209249,-0.2735645771,0.0519362874,-0.1136716679,0.0818874985,-0.0942528695,0.0077920412,-0.2059002221,0.1530778706,-0.1278963983,-0.4773188829,0.3734855652,0.3872337639,0.2581133246,0.1217223108,-0.0001211941,0.0353706032,0.4054268897,-0.0881666243,-0.3705869913,0.0765684843,-0.5739338994,-0.4313016832,-0.0611455031,-0.2580585182,0.1887960732,-0.1298538148,0.2098872662,0.0811502114,0.1162736267,0.4262779951,0.1302455664,0.2230622321,0.3262282014,-0.0737565309,0.1489093006,0.1681558192,-0.0132277319,0.3721017838,0.4372789264,-0.1092656925,0.0860342085,0.1534002274,-0.0840912983,0.2124538869,0.3541404605,0.049540963,0.4574348629,-0.404850632,0.2234413922,0.032490734,0.8967596889,-0.4227740765,-0.3420685828,0.087257579,0.0095371474,-0.0898228288,0.1586155295,0.0859672278,-0.2002848536,0.2778534293,-0.1804689765,-0.3604149222,-0.1591273397,0.3106373549,-0.0873087049,0.5873485804,0.1010470092,0.2082229257,0.1031948328,-0.057155557,-0.127402395,0.0459690653,-0.2225729376,0.0741773844,0.1182245016,-0.2077798098,-0.1212854013,-0.1025085747,0.1066649109,0.1532485634,-0.1984604448,-0.0369431339,-0.1734641045,0.0718769804,0.403215915,-0.0381648429,0.0457206406,0.1548082232,0.5666858554,0.4230882525,0.0112662762,-0.0179061182,0.1470717341,-0.2979692519,-0.0977271646,0.1550770104,0.4336481392,-0.3121049702,-0.2366575152,-0.1028786153,0.1271014214,-0.1144597977,-0.0768235996,0.1278249919,-0.1613834947,0.2357040942,-0.1247183383,0.0475263856,-0.1026306227,-0.3002571762,0.0427250899,-0.0334792435,0.1329357326,0.1183083653,0.0120964209,0.0708040446,0.0517747104,-0.0831964761,-0.0121604884,-0.179164499,-0.2284108847,0.1644670367,-0.1286881268,0.2666129172,-0.2245610952,0.2676091492,0.1448450983,-0.3232682049,-0.2828286588,0.2915723324,-0.288251549,-0.3567067087,-0.3281010687,0.0365198739,-0.0712554827,-0.1758402437,-0.0324564986,-0.2105956227,-0.1574168056,-0.2907342017,0.0741475895,0.0098834308,-0.1292327195,-0.1871705204,0.1953063756,0.5042909384,-0.6066138744,-0.1614392996,-0.2313724458,-0.081650801,0.2087162137,0.3378128409,-0.2769352496,0.1699398756,-0.1460845619,0.0277961008,0.5927787423,-0.5152544379,-0.6871725321,0.2041898519,-0.0130712017,-0.2299176455,-0.0214091223,0.2520213723,0.1231497452,-0.3452990651,-0.4743498862,0.0846300796,-0.1290565133,0.0995862931,-0.2955790758,-0.5416894555,0.1625074148,0.1816478223,0.0469318554,0.1821604222,0.3992836177,0.1766407937,0.3272173405,0.0588194765,0.2011160702,0.2115599811,0.5126587749,0.1314488947,0.1533075869,0.2232039869,-0.2943217158,0.146966368,0.2489130944,0.0949624106,-0.3220586181,-0.2608608007,-0.4123025239,-0.1020810902,-0.3485299647,-0.0909804702,-0.0179825611,0.1807579845,0.1164785549,-0.1103476584,-0.3459634185,0.3093793094,-0.476324141,0.0745256543,0.2337314487,0.5755878091,-0.1695272624,-0.019681856,0.0520269014,0.1301290393,0.0639668852,-0.2785018384,-0.2143508196,0.5593360662,-0.3127242923,0.234146744,0.4399033189,0.3117654622,0.1940223128,-0.3202221096,0.1119112819,0.2882171273,-0.022600593,0.1490286142,-0.1741977036,-0.1989568919,0.0576519929,0.1708124727,-0.0655332804,0.0031271211,0.2749859393,-0.0832780227,-0.1410722733,-0.1622516066,-0.2921606898,-0.0250303149,-0.0415077321,-0.045363903,-0.0604689419,0.1785668433,0.5440226793,-0.1238421276,0.1165739074,0.3003477454,0.0952580795,-0.0596884862,-0.1099886373,0.5564720035,0.3104226589,-0.1512852013,-0.1697158962,0.1790992916,0.0578599684,-0.0373099893,0.0465996191,0.1925277859,-0.1200713366,0.0109616425,-0.046825774,-0.0372149833,0.1695201844,0.1583189517,0.1474125087,0.4839606285,-0.2462186962,-0.0133018671,-0.3664867878,-0.5353518724,-0.3251509666,0.3189966977,-0.379027307,-0.1859087646,-0.042106688,0.1787430197,-0.210716486,0.19015719,0.2043239027,0.3172184527,-0.0788311958,0.00094445,-0.1719424427,-0.2316943556,-0.3306145072,-0.0519320183,0.4443805516,-0.0417089462,0.1766371727,-0.6509737372,-0.1097551808,-1.0360774994,-0.016830612,-0.0461155251,0.2316710204,0.1153671294,0.1593004912,0.4537458718,-0.0163831022,-0.170742467,0.2607427239,-0.0401942916,0.2149461806,-0.3174853027,-0.1729994863,0.0167532824,0.04561349,-0.1350577325,-0.5641142726,-0.3411818147,0.1600964665,0.0241009574,0.1449770927,-0.0509621575,0.043548774,-0.1906140596,0.1358193606,-0.2904105186,0.1560662836,-0.010705228,0.2633077204,-0.1443635225,-0.4014081657,0.1329776198,0.3028902113,0.0468480401,0.0664788932,-0.5709301829,-0.2596457303,0.0302447546,0.4149636924,0.1201115176,0.2456101626,0.1748134196,-0.0011636629,0.0383690484,0.0454277806,-0.1914303899,-0.0953515768,0.5123045444,0.3417057693,0.4293990731,0.3754684925,-0.1085308641,0.5380147696,0.062819846,-0.1203672141,0.2591626644,-0.1626125574,0.3061215878,-0.0002962946,-0.2931819856,0.1956028342,-0.15505604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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2076","title":"Issue: Dataset download error","comments":"This command will update your local file. Then you can open a Pull Request to push your fix to the github repo :)\r\nAnd yes you are right, it is a \"canonical\" dataset, i.e. a dataset script defined in this github repo (as opposed to dataset repositories of users on the huggingface hub)","body":"The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link?","comment_length":53,"text":"Issue: Dataset download error \n The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link? \n This command will update your local file. Then you can open a Pull Request to push your fix to the github repo :)\r\nAnd yes you are right, it is a \"canonical\" dataset, i.e. a dataset script defined in this github repo (as opposed to dataset repositories of users on the huggingface hub)","embeddings":[-0.2335668504,-0.1188599244,0.0189633165,-0.1395551413,0.0161116607,-0.005983714,0.1757637858,0.3258337975,0.2067858279,-0.0491480418,0.1204306707,-0.0448961146,0.2096992731,0.4580940306,0.1885541528,-0.0856994539,0.07323239,0.004608932,-0.3236835599,-0.0383639783,-0.2702765465,0.3223570287,-0.1850292981,0.1354490221,-0.2389193773,0.1353986859,-0.1885152608,0.1983837634,-0.1805908531,-0.4625661671,0.5045471191,0.2400262505,0.1559289396,0.1923993081,-0.0001092647,-0.0245884806,0.3951172531,-0.1717740744,-0.1087753251,-0.0114078615,-0.3153648376,-0.3022530079,0.0101289153,-0.2516593337,0.0398821309,-0.1055716202,0.1627428532,0.2556882501,0.2135183364,0.4070446789,0.2583003044,0.4219934642,0.4020578265,-0.1656321436,-0.09692467,0.0451835729,-0.1839631051,0.0846094936,0.1392523199,-0.1658406854,-0.0079474412,0.2628327608,0.0380655825,0.1774068475,0.3683722913,0.0332367308,0.2770965099,-0.4773376286,0.1777479351,0.2426344305,0.7822639942,-0.3499058485,-0.2700644732,0.0836930573,0.0555038229,-0.0859580487,0.1442579627,0.0836822465,-0.1838019937,0.2145691812,-0.0270690061,-0.1974052936,-0.1474059224,0.1989337206,-0.1147592813,0.4739906788,-0.0418312289,0.1100740507,0.218718797,-0.1721976846,-0.0383893065,0.2162341475,-0.1291910708,0.0995903909,0.0271943081,-0.1612711847,-0.039502915,0.1277526915,0.2365034521,0.1215084642,-0.1918831617,0.0103196688,-0.1954933703,-0.0749077797,0.3661405444,-0.0246856436,0.0377587527,-0.2054487765,0.6392711401,0.4878350198,0.1346796453,0.0405800529,0.1530724019,-0.311966449,-0.2728495598,-0.0209970269,0.4147314429,-0.235813722,-0.1946694851,-0.2294030637,0.2842105329,0.0485092849,-0.1242199019,0.2408177108,-0.06433101,0.2211938202,-0.0133276777,0.0414089859,-0.1770207137,-0.2442883253,-0.1393059492,0.0375979915,0.1275270581,0.026943218,0.1333570182,-0.0171654243,0.2050207108,-0.0505627915,-0.0681749955,-0.0954978541,-0.3666022718,0.2655593157,-0.0367649235,0.2211721539,-0.1885928959,0.2225810289,0.1322175115,-0.1558461338,-0.2449506819,0.1167248785,-0.2422583848,-0.3335548937,-0.3620155454,0.1275477707,-0.3240920007,-0.2706543803,0.0901475251,0.028584715,-0.2450877875,-0.1080999672,0.0043523996,-0.0430625863,-0.3726972044,-0.1773117781,0.2803132236,0.5215454102,-0.416731149,-0.2620277703,-0.1202760562,-0.2433588952,0.0799776986,0.345772177,-0.1807958931,0.1630963683,-0.2910603881,0.2494163811,0.2408754826,-0.356361419,-0.6736316681,-0.024149796,-0.0234209839,-0.1064473614,-0.0478005894,0.1957676709,0.0992479548,-0.2680750489,-0.3319655061,0.1364902109,0.0773571059,0.1863158643,-0.2293026149,-0.5873643756,0.1502218395,0.0775780752,0.0790321082,-0.0348252915,0.2630714476,0.2338417321,0.3538371623,-0.0224072766,0.0953424126,0.2815830708,0.4012528956,0.1791230738,0.2152360529,0.0869182572,-0.2827004492,0.1946865767,0.1992518157,0.263484776,-0.2397794127,-0.212899074,-0.5051876307,-0.0997637138,-0.3482337296,-0.2533221245,0.1373305619,0.0607018694,0.0376957692,-0.0402494632,-0.3664126694,0.3527960777,-0.2735735178,-0.0182049051,-0.1620049626,0.4413309097,-0.1737802625,-0.0358938128,0.2160104811,0.2136527896,0.11474704,-0.2147005945,-0.1133193001,0.3855250478,-0.2043389082,0.2543190122,0.2713074088,0.2959888875,0.1429627985,-0.4405148327,0.0782660767,0.2604020834,0.0026073689,0.1207899675,-0.1412735581,-0.0389373526,0.0335713923,0.1048457995,-0.0179276429,0.1726455688,0.4351980686,-0.1481435448,-0.1388987154,-0.2231837809,-0.202028051,-0.279030323,-0.1909111589,-0.1220412105,-0.0943429023,0.1953204423,0.6526303887,-0.081134215,0.1934058368,0.3059241772,-0.0880405605,-0.0834919214,-0.0656971186,0.2408764064,0.2677809894,0.0272595603,-0.1545904875,0.1892936826,0.0176765937,-0.073094666,0.1090161726,0.0896600485,-0.1755732894,0.0301620252,-0.0732033774,-0.08097101,-0.2738899887,0.2919788659,-0.0533328727,0.3131765723,-0.2786055803,-0.1210931838,-0.3761145473,-0.4734442532,-0.2801196873,0.1665584743,-0.3854687512,-0.3420085609,0.0037457533,0.2556138337,-0.1119044721,0.2181049883,0.1516188085,0.3672245145,-0.100423947,0.1979857683,-0.2305040956,-0.231559962,-0.2434086353,0.0562481172,0.303778857,0.1399769634,0.3024443388,-0.6965792775,-0.0006317458,-0.9145677686,-0.2800679803,0.1137308925,0.0784535185,0.2336857915,0.3152012527,0.4718111753,0.0362509415,-0.092990987,0.1916047335,-0.3334913254,0.0445958674,-0.2865602374,-0.202850014,0.0358545929,-0.0149767259,-0.1837147176,-0.4896522462,-0.3156398237,0.2744005322,-0.0282386187,0.0308955275,0.0294818673,0.0008582678,-0.0627072379,0.0112187136,-0.2304227203,-0.1335806251,0.1109158695,0.4065054059,-0.1313940436,-0.5269101262,0.2307729423,0.1508883685,0.1147906408,-0.2585181892,-0.5553159118,-0.3104572296,-0.0462882854,0.4160473943,0.1070752367,0.3148148954,0.2358893752,-0.0479348749,-0.0593311265,-0.0556549393,-0.3613809049,-0.0467392765,0.3273712993,0.3528597951,0.3838159442,0.2802631259,-0.0130923865,0.4280107021,0.1091012806,-0.0421830937,0.232493788,-0.3416447639,0.3144183159,-0.10110531,-0.312794596,0.2138321996,-0.213012695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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2076","title":"Issue: Dataset download error","comments":"Hi, thanks for the answer. \r\n\r\nI gave a try to the problem today. But I encountered an upload error: \r\n\r\n```\r\ngit push -u origin fix_link_iwslt\r\nEnter passphrase for key '\/home2\/xuhuizh\/.ssh\/id_rsa': \r\nERROR: Permission to huggingface\/datasets.git denied to XuhuiZhou.\r\nfatal: Could not read from remote repository.\r\n\r\nPlease make sure you have the correct access rights\r\nand the repository exists.\r\n```\r\n\r\nAny insight here? \r\n\r\nBy the way, when I run the datasets-cli command, it shows the following error, but does not seem to be the error coming from `iwslt.py`\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home2\/xuhuizh\/anaconda3\/envs\/UMT\/bin\/datasets-cli\", line 33, in <module>\r\n    sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/commands\/datasets_cli.py\", line 35, in main\r\n    service.run()\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/commands\/test.py\", line 141, in run\r\n    try_from_hf_gcs=False,\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/builder.py\", line 639, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/utils\/info_utils.py\", line 32, in verify_checksums\r\n    raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https:\/\/wit3.fbk.eu\/archive\/2017-01-trnmted\/\/texts\/DeEnItNlRo\/DeEnItNlRo\/DeEnItNlRo-DeEnItNlRo.tgz'}\r\n```","body":"The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link?","comment_length":148,"text":"Issue: Dataset download error \n The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link? \n Hi, thanks for the answer. \r\n\r\nI gave a try to the problem today. But I encountered an upload error: \r\n\r\n```\r\ngit push -u origin fix_link_iwslt\r\nEnter passphrase for key '\/home2\/xuhuizh\/.ssh\/id_rsa': \r\nERROR: Permission to huggingface\/datasets.git denied to XuhuiZhou.\r\nfatal: Could not read from remote repository.\r\n\r\nPlease make sure you have the correct access rights\r\nand the repository exists.\r\n```\r\n\r\nAny insight here? \r\n\r\nBy the way, when I run the datasets-cli command, it shows the following error, but does not seem to be the error coming from `iwslt.py`\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home2\/xuhuizh\/anaconda3\/envs\/UMT\/bin\/datasets-cli\", line 33, in <module>\r\n    sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/commands\/datasets_cli.py\", line 35, in main\r\n    service.run()\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/commands\/test.py\", line 141, in run\r\n    try_from_hf_gcs=False,\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/builder.py\", line 639, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/xuhuizh\/projects\/datasets\/src\/datasets\/utils\/info_utils.py\", line 32, in verify_checksums\r\n    raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https:\/\/wit3.fbk.eu\/archive\/2017-01-trnmted\/\/texts\/DeEnItNlRo\/DeEnItNlRo\/DeEnItNlRo-DeEnItNlRo.tgz'}\r\n```","embeddings":[-0.1731770486,-0.0103908228,0.0573096722,-0.0393557921,0.0538161732,-0.0124162808,0.2562997043,0.3127626777,0.1133866832,0.0201736651,0.031347841,0.0189829245,0.3240020573,0.2409658432,0.1655391902,0.0832946524,0.0935480818,0.0070209429,-0.2977551818,-0.0181837156,-0.2464222908,0.2069954872,-0.1871381104,0.0984047502,-0.0664139017,0.0939154625,-0.1437421888,0.132330969,0.0711327344,-0.5863393545,0.2837122679,0.1516173184,0.2967727184,0.2796620727,-0.0001216275,-0.0298803672,0.2723470628,-0.133409366,-0.3635321259,-0.0276102722,-0.4098815024,-0.4128105938,-0.2503136992,-0.2697912157,0.1252630949,-0.0470432676,0.052626878,0.1782296002,0.2551392615,0.4470214844,0.1154646128,0.3542398214,0.4006434083,-0.0996041298,-0.0763279796,0.0624176264,-0.0808924809,0.1945292354,0.1765618473,0.004309325,0.319073379,0.1249282211,0.0774350762,0.0930948779,0.2438849509,-0.0995643437,0.4032182395,-0.391949445,0.2226319909,0.0467114076,0.757717669,-0.2985826135,-0.5799194574,-0.0442384295,0.1599040031,0.0499051549,0.1854637265,0.0469910651,-0.2484175116,0.3061733842,-0.1514507979,-0.4920485318,-0.2695293725,0.3524078131,0.0779909343,0.4447513223,0.0607923344,0.1950392127,0.2497365922,-0.1434856057,0.0307610016,0.1108072475,-0.2411747128,0.1607265174,0.0114447465,-0.101246573,-0.1041230708,0.1892344803,0.1351170391,0.0979463086,-0.229634434,-0.0681871325,-0.0995049998,-0.0101151504,0.2546986639,0.0938820541,-0.0022864302,0.0234885979,0.4250120819,0.4284123778,0.205971241,-0.0637580827,0.0849416628,-0.3227720857,-0.3013033867,0.0541344583,0.2840809226,-0.1511059105,-0.2094755173,-0.2937165499,0.0399769582,-0.0105951065,-0.0702962652,0.220625788,-0.1020833254,0.1546022892,0.0166574493,0.1494405866,-0.0292690303,-0.3486822844,0.0202518329,-0.005921334,-0.0080564497,0.1573093385,0.1737334579,-0.0985193104,0.0062291725,-0.0080840718,-0.2337122411,-0.1763401628,-0.2984265983,0.1085456312,0.1047854647,0.1262549013,-0.135873273,0.2610064745,0.034558475,-0.1750120968,-0.0659154877,0.1226122901,-0.3552239239,-0.2412035614,-0.247345522,0.0090103764,-0.2274768203,-0.1725842357,-0.151414454,-0.0946407542,-0.126471132,-0.3580670059,-0.0076441127,0.0192548428,-0.2257637233,-0.1544501334,0.2990473807,0.5962198377,-0.5039988756,-0.3450402021,-0.2770167589,-0.2029024065,0.0789278671,0.319047451,-0.2443762571,0.2259961218,-0.2795182168,0.128381446,0.3324233592,-0.5260116458,-0.7665931582,0.2068858594,0.0146962292,-0.0245410148,0.0172463097,0.0817185864,0.0900460705,-0.2895280719,-0.6715387106,0.0571655147,0.0781765506,0.1101767048,-0.174841702,-0.5463120937,0.1403319836,-0.0209613889,0.0476417989,0.2466131002,0.452853024,0.162594974,0.484161675,-0.106832318,0.2486445159,0.1819880009,0.5103002787,0.1815202534,0.1283855289,0.0267958213,-0.3259159923,0.259906143,0.2652192116,0.1192675605,-0.4695916474,-0.0714321285,-0.383805126,-0.0778345019,-0.3433720767,-0.1025640815,-0.0172441565,0.2139872462,0.0695284158,-0.0191354621,-0.1408530772,0.2331992835,-0.1202415079,-0.0135404021,-0.0989577547,0.3463067412,-0.1621232182,-0.0224540047,-0.0325786434,0.1884357184,0.2230120599,-0.2281770557,-0.141573146,0.4947054088,-0.4156080186,0.322922051,0.2500270009,0.1136658341,0.1753347516,-0.3674814105,0.1324027032,0.2234054357,0.023047762,0.1755135059,-0.1077930257,-0.2079482675,0.0823868066,0.2169461399,0.0122453496,-0.0019036907,0.2869502604,-0.1655271649,-0.0504661202,-0.1969952881,-0.1162137166,-0.1363363564,-0.1499486119,-0.1951585263,-0.0224002544,0.1898770034,0.4447519183,-0.0751990378,0.1647149622,0.3001323044,0.0432810113,0.0643934384,-0.0550909862,0.6107308865,0.2715999782,-0.1495793611,-0.2571927607,0.0710750371,0.2623735964,-0.1553240865,0.191364482,0.0627828464,-0.0459873453,0.0877594873,-0.0473504849,-0.0395073555,0.0910843536,0.2150114328,0.1679970324,0.4387922287,-0.4453455806,-0.0061937873,-0.429361552,-0.6389654279,-0.2003344744,0.250191927,-0.2366262525,-0.1858728379,-0.1833362132,0.2103633136,-0.0788792372,0.2053449303,0.0379035957,0.3174077868,-0.0843602195,0.1659031957,-0.2603982687,-0.0483416431,-0.206170246,-0.0613751225,0.2968954444,-0.0703743771,0.2665001154,-0.5192921162,-0.1063686758,-0.938561976,-0.0400097258,0.0445348062,0.0885354728,0.2453931123,0.1265654713,0.4747102857,0.1492189318,-0.059593495,0.2381046712,-0.1594686359,0.1200468764,-0.1929836571,-0.0851439834,0.0652761981,-0.1378386766,-0.1064785644,-0.5346980691,-0.2964923978,0.358810842,-0.0660284981,0.1081246287,0.0974272117,0.2043614835,-0.0727187395,-0.0373493396,-0.170776248,0.0235799383,0.0401086658,0.2624898851,-0.0981030911,-0.4878210425,0.1330920905,0.3627158403,0.2413807064,-0.0405476578,-0.4037993252,-0.0293230526,0.02823947,0.5150356293,-0.0052890922,0.2320146263,0.0692331716,-0.0297776442,0.073668994,-0.0076382211,-0.158682555,-0.0632468909,0.2388671339,0.4697849452,0.5023777485,0.5303525329,-0.0160536561,0.3157682717,0.0231780373,0.033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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2076","title":"Issue: Dataset download error","comments":"Hi ! To create a PR on this repo your must fork it and create a branch on your fork. See how to fork the repo [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md#start-by-preparing-your-environment).\r\nAnd to make the command work without the `ExpectedMoreDownloadedFiles` error, you just need to use the `--ignore_verifications` flag.","body":"The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link?","comment_length":45,"text":"Issue: Dataset download error \n The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link? \n Hi ! To create a PR on this repo your must fork it and create a branch on your fork. See how to fork the repo [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md#start-by-preparing-your-environment).\r\nAnd to make the command work without the `ExpectedMoreDownloadedFiles` error, you just need to use the `--ignore_verifications` flag.","embeddings":[-0.2560881972,-0.0967695266,0.0178538598,-0.0659613013,0.0944884494,0.049594406,0.1920959204,0.2611156106,0.1636176109,0.0419375785,0.0050670132,-0.0133628454,0.2525415123,0.3941254914,0.3025112152,-0.1754232049,0.152458638,0.0719700158,-0.2172753215,0.0543560348,-0.2055191845,0.2804819345,-0.2303613126,0.0209608916,-0.1193833053,0.1293283403,-0.3126569688,0.1732152849,-0.1787016839,-0.6029583812,0.3105776906,0.3934975564,0.2743170857,0.2377393097,-0.0001235105,-0.0232407935,0.3910424709,-0.1918268353,-0.2590373456,-0.0267316513,-0.3742640615,-0.4201498628,-0.1484021991,-0.1779622287,0.0797267258,-0.2863840461,0.1079144999,0.3710989058,0.2191396654,0.3876252472,0.1255221516,0.4391294718,0.5360848308,-0.1845331043,-0.0708419606,0.1065436676,-0.1627684236,0.2750478685,0.178085506,-0.080706276,0.2044587582,0.173540324,-0.0209426358,0.0642212927,0.3660503328,0.1444919556,0.3109253347,-0.4145330489,0.1908102334,0.2390266955,0.7586012483,-0.338901639,-0.4380357563,-0.0868122056,0.16902937,-0.270378083,0.1488966495,-0.036151588,-0.1700431556,0.2979440391,-0.16484496,-0.4038984478,-0.2858358324,0.3131330013,0.0762056261,0.4441383183,0.0129923113,0.1372981668,0.4096088111,-0.1014984697,-0.0474601611,0.1721398234,-0.2839389443,0.0506905243,0.1691611707,-0.2852407992,-0.1591252536,0.0787700936,0.300927341,0.1759157628,-0.3208157718,-0.1262310594,-0.1952875853,-0.0971674025,0.2797228694,0.088927716,0.0287298113,-0.0519122779,0.4055681825,0.6368993521,0.2258510143,-0.0737120435,0.151320681,-0.3014290929,-0.2974278927,-0.0874256492,0.2769135237,-0.1878219247,-0.0803302974,-0.2514245212,0.1449325234,-0.1607227921,-0.086113669,0.3290611804,-0.1138228402,0.1629012227,0.0208368599,0.1700348705,-0.1408061832,-0.2607530653,-0.0238272939,0.0364172868,0.0059565594,0.1546955854,0.1064969152,0.0981587544,0.180533424,-0.0965210497,-0.0971262306,-0.1832295507,-0.3297604024,0.1840197444,0.0529415272,0.0353848338,-0.1530470252,0.1169521585,0.0272814836,-0.1550368071,-0.2040176541,0.2259788811,-0.1315045208,-0.3452872336,-0.1903003156,0.0012859319,-0.2778307199,-0.4454151988,-0.0313395187,0.0521886945,-0.2458992153,-0.2138424069,0.0153320953,0.076546602,-0.3249950111,-0.1181462556,0.2416535616,0.7265816331,-0.5139055252,-0.2789271772,-0.0764444843,-0.3277512491,0.224521786,0.251598984,-0.1615429223,0.2488374412,-0.3000688255,0.0010975152,0.3311366141,-0.3804425299,-0.6101977825,0.1879918128,-0.0540633611,-0.0505580269,0.112440519,0.1294066608,0.1397242546,-0.3284981251,-0.5146924257,-0.0157630797,-0.0232061427,0.030654937,-0.1765310317,-0.6106555462,0.2020420879,-0.0040537803,0.0648262873,0.2028805614,0.2194609642,0.2528126836,0.4670443237,-0.0675518289,0.0977601483,0.2156460285,0.4710688889,0.2730847299,0.0670088977,0.0601324365,-0.1762896627,0.1627109051,0.1490887552,0.2279654741,-0.2123599499,-0.061800994,-0.5251754522,-0.1285540313,-0.3235305548,-0.2717590332,-0.0287415627,0.0901924446,0.2446545511,-0.1121621206,-0.2586700916,0.2478964031,-0.2294490486,0.1092184335,-0.1207645014,0.4380150437,-0.2130049467,-0.0119133778,-0.0282821488,0.1945421696,0.2381058931,-0.321310252,-0.0859922245,0.4823783338,-0.3050897121,0.2762858272,0.3618417084,0.2052758634,0.2600658238,-0.4229335785,0.2289719582,0.123264052,-0.0746655166,0.1577857435,-0.1887511164,-0.0927077085,-0.0088550942,0.2620516121,-0.0418127105,-0.0275799446,0.3348996639,-0.1963994205,-0.0883994475,-0.2597973347,-0.2072836459,-0.2496730536,-0.2829404771,-0.2605138421,0.0171166938,0.2126549631,0.4123401046,-0.1385304332,0.0295032654,0.3590830863,0.0673985854,-0.0065640658,-0.2185413986,0.4872529209,0.3576416671,0.0202488285,-0.1737889498,0.1996552944,0.1690061092,-0.0750790164,0.2454595119,0.1035990864,-0.1709520072,0.058602009,-0.0797947794,-0.0475222506,-0.0519351587,0.1176466644,0.1086500436,0.3397515118,-0.2460732609,-0.0674613863,-0.3342811167,-0.6024009585,-0.3408093154,0.1691476405,-0.3219607174,-0.1643619686,-0.0301357284,0.2658126354,-0.0377430804,0.2160297632,0.1099042818,0.2593465447,-0.0848574564,0.1422858089,-0.238899067,0.0188314728,-0.1592909396,-0.0287546888,0.3545594215,0.0627913177,0.2899083495,-0.5861014128,-0.2462389469,-1.0009710789,-0.1878997684,0.1199490428,0.0032070307,0.1540724933,0.4157357812,0.3913275599,0.2890338004,0.0603233017,0.278177321,-0.2399691492,0.1530865133,-0.2830749154,-0.152023837,0.0930634364,-0.059289556,-0.2569691837,-0.4589641392,-0.2208829075,0.2960768044,0.1360130608,0.1107346714,0.0468280315,0.2436873317,-0.0483041219,0.0023817073,-0.2082563192,0.0096366294,0.0344335288,0.2957965136,-0.1325061172,-0.4118843377,0.2555496991,0.2001850307,0.0281182639,-0.2047903687,-0.5103381276,-0.3684630096,-0.0034458237,0.4062130451,0.0548579693,0.46388188,0.1891883016,0.062508747,-0.0122242803,-0.0108426567,-0.367806524,-0.0895677879,0.1972306073,0.5582021475,0.4164224267,0.4434440136,-0.1451857984,0.5577757955,0.087642312,0.009520161,0.2989994287,-0.3636427522,0.4048640132,-0.0420439765,-0.3322061896,0.1826124936,-0.1945263445,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2076","title":"Issue: Dataset download error","comments":"Hi @XuhuiZhou,\r\n\r\nAs @lhoestq has well explained, you need to fork HF's repository, create a feature branch in your fork, push your changes to it and then open a Pull Request to HF's upstream repository. This is so because at HuggingFace Datasets we follow a development model called \"Fork and Pull Model\". You can find more information here:\r\n- [Understanding the GitHub flow](https:\/\/guides.github.com\/introduction\/flow\/)\r\n- [Forking Projects](https:\/\/guides.github.com\/activities\/forking\/)\r\n\r\nAlternatively, if you find all these steps too complicated, you can use the GitHub official command line tool: [GitHub CLI](https:\/\/cli.github.com\/). Once installed, in order to create a Pull Request, you only need to use this command:\r\n```shell\r\ngh pr create --web\r\n```\r\nThis utility will automatically create the fork, push your changes and open a Pull Request, under the hood.","body":"The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link?","comment_length":126,"text":"Issue: Dataset download error \n The download link in `iwslt2017.py` file does not seem to work anymore.\r\n\r\nFor example, `FileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnted\/texts\/zh\/en\/zh-en.tgz`\r\n\r\nWould be nice if we could modify it script and use the new downloadable link? \n Hi @XuhuiZhou,\r\n\r\nAs @lhoestq has well explained, you need to fork HF's repository, create a feature branch in your fork, push your changes to it and then open a Pull Request to HF's upstream repository. This is so because at HuggingFace Datasets we follow a development model called \"Fork and Pull Model\". You can find more information here:\r\n- [Understanding the GitHub flow](https:\/\/guides.github.com\/introduction\/flow\/)\r\n- [Forking Projects](https:\/\/guides.github.com\/activities\/forking\/)\r\n\r\nAlternatively, if you find all these steps too complicated, you can use the GitHub official command line tool: [GitHub CLI](https:\/\/cli.github.com\/). Once installed, in order to create a Pull Request, you only need to use this command:\r\n```shell\r\ngh pr create --web\r\n```\r\nThis utility will automatically create the fork, push your changes and open a Pull Request, under the hood.","embeddings":[-0.2110829651,-0.2354261875,0.0539522693,-0.120353587,-0.0160378627,0.0573175848,0.0736910924,0.3318694532,0.2753102779,0.0360611007,-0.1463584155,-0.1400417089,0.3278672099,0.3584648371,0.2050697505,-0.164364472,0.1016285419,0.0192579348,-0.3486397266,-0.0259078201,-0.2046322525,0.1603539288,-0.0323321708,0.0914419442,-0.0280473791,0.019089058,-0.293988049,0.3911278546,-0.0363712646,-0.472815603,0.4034941792,0.3701826334,0.2042797953,0.3489311934,-0.0001184653,0.0142525993,0.2367955893,-0.0410921276,-0.2115555257,-0.1077309176,-0.2645911574,-0.4390313923,-0.1316624433,-0.1381315887,0.034405373,-0.0912179872,0.1589486897,0.3369330764,0.3802199364,0.2255799621,0.1258405149,0.4230258167,0.4710550904,-0.2185387313,0.0862675235,0.0614392348,-0.1282148063,0.2936409712,0.0898409784,-0.0591585785,0.036488492,0.2152909189,0.0781656206,0.2497442812,0.4219130576,0.0737006068,0.1608163565,-0.3957279325,0.1415680498,0.2385166436,0.6311095953,-0.3547596037,-0.3796705306,-0.0760816336,0.0333204195,-0.1144907176,0.1499064118,0.0264801923,-0.1659982949,0.3294635713,0.0786603838,-0.5474177003,-0.2395004928,0.231194675,0.1810732931,0.2853419185,0.0017947436,0.0789149925,0.379914701,-0.132351324,-0.1323436052,0.1575139761,-0.2554055154,0.1031677052,0.1107840315,-0.2356530577,-0.110346429,-0.0550629981,0.4048175812,0.0613307059,-0.3543204963,-0.04447468,-0.2573795617,0.0111754378,0.174470976,-0.0149969887,-0.1784735471,-0.0599963218,0.4562728703,0.4776796997,0.1987167001,-0.1451387405,0.2203816921,-0.2268173546,-0.3670242727,-0.2010921836,0.4068787694,-0.2196868807,-0.0714218169,-0.304386586,0.1304107904,-0.0501970984,-0.1943059415,0.4044190347,-0.121839188,0.0369838066,0.0639911965,0.2810359597,-0.1574193239,-0.2196881771,-0.1134860739,-0.0748006925,0.1971755177,0.1814006269,0.0033577275,-0.0108487029,0.0622827858,0.1343208253,-0.1932731718,-0.1267478317,-0.2192617208,0.2691918314,-0.0305070169,0.2095509768,-0.0249657277,0.2104760557,0.1229593828,-0.11837174,-0.1643955708,-0.025780892,-0.2253971547,-0.2972949743,-0.3480557799,0.0438442752,-0.0791406706,-0.3015474677,-0.0541525371,0.0778256878,-0.3137895167,-0.1640633941,0.0472615808,0.1616930962,-0.2903907001,-0.1199439019,0.3145063221,0.6965306997,-0.2458405495,-0.3773680925,-0.1598867178,-0.1359545439,0.0721240714,0.3017199039,-0.1499699354,0.1195338294,-0.2713197172,0.1623042077,0.2666556835,-0.4356101751,-0.5098182559,0.2222330719,-0.0900063813,-0.0561342798,0.0360271595,0.0909888521,0.1352198273,-0.2080696225,-0.5479161739,0.0284518823,0.0575360544,0.0603860132,-0.2171857506,-0.5456910133,0.0474578962,0.0427253917,0.1120493263,0.348154068,0.4164614975,0.1177427024,0.3645390868,-0.1829752922,0.1338173151,0.2897298336,0.5038339496,0.4074414074,0.0071039628,0.0301901512,-0.2982338071,0.1431921422,0.0423161201,0.254090935,-0.1266067922,-0.1244278774,-0.4942826331,-0.0951791778,-0.2824363112,-0.1889820695,0.0047092726,0.067904003,0.0674922094,-0.0355721414,-0.1386614889,0.3957072198,-0.2146778256,0.1661385596,-0.1394138038,0.5787001848,-0.160461396,0.0581317246,0.1075868532,0.1332895905,0.2444794476,-0.2259410769,-0.1514311284,0.4178691208,-0.421497345,0.2410955578,0.5301553607,0.2047100812,0.2471736968,-0.5035048723,0.3256246746,0.1582865864,-0.052460853,0.1165192947,-0.0072110454,-0.0388913155,0.0617169142,0.224024564,0.0515305176,0.063894242,0.4288527966,-0.2204475105,-0.024872506,-0.1123149991,-0.2210939229,-0.4116818607,-0.0536614023,-0.312276423,-0.1100313067,0.2835019827,0.5371803045,-0.1335303336,0.1254914105,0.4350464046,-0.0412507504,0.0413592607,0.0192504972,0.2674684823,0.0542728119,0.0737496838,-0.1289021671,0.2507378757,0.0435523689,0.0266730916,0.1505316496,0.1345540583,-0.0677167922,-0.0425433218,-0.0737454891,-0.1119700521,-0.0348199792,0.0337275825,-0.0966105834,0.2112507522,-0.3681170046,-0.0346296318,-0.3610076606,-0.6220450401,-0.3655466437,0.1438074112,-0.4196515679,-0.2589598596,-0.0270658415,0.3142710328,-0.1000511646,0.1556216925,0.1236823946,0.1750087142,-0.1223044842,0.2079287022,-0.2587126493,-0.0562148727,-0.1604387611,-0.0437365137,0.267429173,0.2079910338,0.3205575943,-0.5224847198,-0.1317167729,-0.9016187191,-0.114495188,0.2193331718,0.0459594578,0.1146957725,0.3226823211,0.4915757775,0.1684923619,0.0363832712,0.2897359431,-0.1280543506,0.0844142139,-0.271086067,-0.1807758361,0.1145590991,0.0175795574,-0.0776854381,-0.5018087029,-0.3658218682,0.3381377757,0.0024151437,0.0508712232,0.2050856948,0.1885677725,0.0024425085,0.1367033124,-0.1255963445,0.0093972124,0.056289494,0.3124879003,-0.2125174254,-0.3424602151,0.3461087048,0.2088090777,-0.062254671,-0.245833382,-0.5297685266,-0.1404603124,-0.0356180146,0.5182726979,0.0156707373,0.4950365126,0.1784035861,-0.0216535311,-0.0080255382,-0.0368843488,-0.223123759,0.0537452884,0.2179126143,0.3834924102,0.4108876288,0.4933067858,-0.1012102738,0.6489822268,0.1351569891,0.0992670879,0.3816055059,-0.2987954617,0.3904784322,-0.1469760239,-0.2986433804,-0.0016428845,-0.1519547552,0.2234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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2075","title":"ConnectionError: Couldn't reach common_voice.py","comments":"Hi @LifaSun, thanks for reporting this issue.\r\n\r\nSometimes, GitHub has some connectivity problems. Could you confirm that the problem persists?","body":"When I run: \r\nfrom datasets import load_dataset, load_metric\r\n\r\ncommon_voice_train = load_dataset(\"common_voice\", \"zh-CN\", split=\"train+validation\")\r\ncommon_voice_test = load_dataset(\"common_voice\", \"zh-CN\", split=\"test\")\r\n\r\nGot:\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nVersion:\r\n1.4.1\r\n\r\nThanks!  @lhoestq @LysandreJik @thomwolf ","comment_length":20,"text":"ConnectionError: Couldn't reach common_voice.py \n When I run: \r\nfrom datasets import load_dataset, load_metric\r\n\r\ncommon_voice_train = load_dataset(\"common_voice\", \"zh-CN\", split=\"train+validation\")\r\ncommon_voice_test = load_dataset(\"common_voice\", \"zh-CN\", split=\"test\")\r\n\r\nGot:\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/common_voice\/common_voice.py\r\n\r\nVersion:\r\n1.4.1\r\n\r\nThanks!  @lhoestq @LysandreJik @thomwolf  \n Hi @LifaSun, thanks for reporting this issue.\r\n\r\nSometimes, GitHub has some connectivity problems. Could you confirm that the problem persists?","embeddings":[-0.4876464605,-0.0846986622,-0.0791777521,-0.0048034391,0.3903559744,0.0129868183,0.2704308629,0.3016326427,-0.063188158,0.3089751303,-0.2126752585,-0.1347054243,0.1226182878,0.0170099232,-0.0094278194,-0.0931782797,-0.0044788229,0.00298292,0.192533344,0.0247693788,-0.0387781002,0.0858430639,-0.1760057956,0.3123496771,-0.1892662793,-0.0507797785,0.0589113794,0.0770447105,-0.2132197171,-0.3560753465,0.4676538706,0.0277777296,-0.041264724,0.8190293312,-0.0001130046,0.1451916546,0.4414004385,-0.068325147,-0.3076119423,-0.4945730567,-0.0904799625,-0.0165243447,0.1303874403,0.1618437022,-0.1614775807,0.0518424325,0.0687628835,-0.2308126092,0.3591995537,0.3560591638,0.2153366059,0.2006513178,0.3548300266,-0.1868588775,-0.052108977,-0.1892620027,-0.0218943432,0.8155437112,0.0865337104,-0.22358796,0.2252188474,0.1362261027,0.1147135571,-0.0424558148,-0.0971720517,-0.0446065627,0.1151468083,-0.3021116853,0.0465990752,0.2485738397,0.4080207646,-0.4108532369,-0.3393287957,-0.0096714012,0.0688005537,-0.1919004619,0.1794370115,0.0755963624,-0.3747929335,0.08968997,-0.4395814538,-0.3170163929,-0.1687384248,0.2219960839,-0.0388029628,0.0705616921,-0.111549288,0.0763533041,0.178044945,0.0002032817,-0.3434875906,0.1713796258,0.0020537416,0.405492574,-0.5099363327,-0.0270293746,-0.2446025461,-0.3834081292,0.3409876525,-0.0012129704,-0.0660155118,-0.0444582701,-0.2184315771,0.1957034618,0.3238674998,0.2319249064,-0.0145540545,0.227080971,0.4110629559,0.2189212292,-0.0950866938,-0.0442090929,-0.3507368565,-0.3648319244,0.1944305003,-0.013700786,0.380974561,-0.302343756,-0.2657470405,0.095067665,-0.2045124918,-0.0274013709,-0.0486893654,0.3798324764,-0.1350037754,0.0382551141,-0.1236829087,0.4221954346,-0.118559815,0.0815408006,-0.2166409343,-0.0903094262,-0.1128263101,0.097669512,0.0493132956,-0.3984252512,0.2074008137,0.0379754826,0.3572260141,-0.3074057102,-0.0891105384,-0.0856576413,-0.5720186234,0.1642294973,0.0533686914,0.2146516442,0.2993554473,-0.1587567925,-0.1170123294,-0.1049671918,-0.3046244383,-0.471655488,0.0482198186,0.2257326394,-0.0102702016,0.0569618605,-0.4090845883,-0.1771777272,0.1486499459,-0.2936156392,-0.0880606547,-0.301613301,0.0597571023,-0.1316051185,0.4870406091,0.0713867545,0.0527719595,-0.1694486588,-0.0351202115,-0.1018608809,0.0548310429,0.526974082,-0.2484565973,0.0601397119,-0.2221782804,0.156096682,0.521710515,-0.652610898,-0.4879722595,0.4022220075,-0.235869348,0.0671707466,-0.0939288139,-0.2874566615,0.0392743908,-0.0323440395,0.5161759853,0.1174638942,-0.0979949012,-0.1968774498,0.0407909006,-0.2047234774,0.1706810594,0.0211102925,-0.0097485371,0.0787399635,0.1623610556,-0.0269786585,0.416403383,0.10372857,0.009008836,0.2248878032,0.3108515441,-0.2277763933,-0.1555938274,-0.1596635133,0.1518708616,0.1177366078,0.2797800601,0.3692600131,-0.1291464716,-0.1252070367,-0.4446752965,-0.0257855784,-0.2310210466,0.0622798614,0.056808766,0.0320310742,0.0646314397,0.219354555,-0.1840764135,0.1622597575,-0.2816715837,0.1097330898,-0.1320050806,0.4692251086,-0.2606515586,-0.0348062143,0.2211777568,0.2767660618,0.0456460901,-0.0788386613,-0.2147000879,0.3152984679,-0.3178600073,0.2351754904,0.1205977276,0.283167541,0.1175950915,-0.591106534,0.0591872707,0.1485545635,-0.0232406184,0.0424575247,0.3778440356,0.2386407107,0.1728033572,0.4496591091,0.0726335719,0.08587493,0.290910095,0.24765715,-0.134275645,0.1453574449,0.1616977602,-0.1719187647,0.3192304075,-0.1830841154,-0.340532124,-0.1651495099,0.3829872012,-0.0832233503,0.107893385,0.1136161461,-0.1390830427,-0.1129106358,-0.0132415881,-0.0086587472,0.2383928448,0.0911889374,0.0106042596,0.3226593733,-0.2620108128,0.0036352507,0.0549353063,0.0583234318,0.1743072122,0.2589446008,0.02267267,-0.0217205696,-0.0456663743,-0.2580975294,-0.209335506,0.1478906423,-0.1808155328,0.1480846554,-0.086663872,0.0132794818,-0.0805373564,-0.3049543798,-0.5015799999,-0.2510821521,-0.1252114028,0.1089010015,0.3503382802,0.2614883184,-0.0398212261,-0.1008044556,0.1907769144,-0.2764284909,0.0604965203,-0.2182856798,-0.2243892103,0.070605509,0.0844244957,-0.312320143,0.0233451128,-0.2295905203,-0.1374640465,-0.0782050639,-0.1175296307,0.0083509637,-0.2455486953,0.3056176305,0.1816340238,0.3986811042,0.2330412269,-0.0065053413,0.4217598736,-0.2188811749,-0.0219951048,0.0442473516,-0.0634039417,0.0226563644,0.1767362803,-0.2832997441,-0.4118851721,-0.3967009187,0.2375123203,-0.1698881388,0.050104972,0.2355161756,0.123425521,-0.0473635271,0.0705346987,0.3045687377,-0.1642432511,-0.3654202819,0.0484303348,-0.0856717601,-0.1601704806,0.1718711555,0.2391327024,0.5762704611,-0.0725358129,-0.4448835552,-0.0168504268,-0.0534744635,0.3242331147,0.1021109298,-0.0072238008,-0.0170469321,-0.0905972868,-0.057672061,0.140351668,-0.2352853864,-0.0629813671,0.2043684125,0.3051237464,0.1238506883,0.5398365259,-0.1348452121,0.6648232937,0.2927260399,0.1564437598,0.187933147,-0.2086391002,0.341358602,-0.2027694732,-0.4419165254,0.1032795236,-0.17082946,-0.0757673755,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2070","title":"ArrowInvalid issue for squad v2 dataset","comments":"Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column.\r\n\r\nIndeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a batch.\r\n\r\nHowever it seems like `tokenized_examples` doesn't have the same number of elements in each field. One field seems to have `1180` elements while `candidate_attention_mask` only has `1178`.","body":"Hello, I am using the huggingface official question answering example notebook (https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb). \r\n\r\nIn the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error:\r\n\r\n`ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178`\r\n\r\nMy code is as follows:\r\n\r\n```\r\ndef generate_candidate_questions(examples):\r\n  val_questions = examples[\"question\"]\r\n  candididate_questions = random.sample(datasets[\"train\"][\"question\"], len(val_questions))\r\n  candididate_questions = [x[:max_length] for x in candididate_questions]\r\n  return candididate_questions\r\n\r\ndef prepare_validation_features(examples, use_mixing=False):\r\n  pad_on_right = tokenizer.padding_side == \"right\"\r\n  tokenized_examples = tokenizer(\r\n      examples[\"question\" if pad_on_right else \"context\"],\r\n      examples[\"context\" if pad_on_right else \"question\"],\r\n      truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n      max_length=max_length,\r\n      stride=doc_stride,\r\n      return_overflowing_tokens=True,\r\n      return_offsets_mapping=True,\r\n      padding=\"max_length\",\r\n  )\r\n  if use_mixing:\r\n    candidate_questions = generate_candidate_questions(examples)\r\n    tokenized_candidates = tokenizer(\r\n        candidate_questions if pad_on_right else examples[\"context\"],\r\n        examples[\"context\"] if pad_on_right else candidate_questions,\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=max_length,\r\n        stride=doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n  sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n  tokenized_examples[\"example_id\"] = []\r\n\r\n  if use_mixing:\r\n    tokenized_examples[\"candidate_input_ids\"] = tokenized_candidates[\"input_ids\"]\r\n    tokenized_examples[\"candidate_attention_mask\"] = tokenized_candidates[\"attention_mask\"]\r\n    tokenized_examples[\"candidate_token_type_ids\"] = tokenized_candidates[\"token_type_ids\"]\r\n\r\n  for i in range(len(tokenized_examples[\"input_ids\"])):\r\n      sequence_ids = tokenized_examples.sequence_ids(i)\r\n      context_index = 1 if pad_on_right else 0\r\n\r\n      sample_index = sample_mapping[i]\r\n      tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n      tokenized_examples[\"offset_mapping\"][i] = [\r\n          (o if sequence_ids[k] == context_index else None)\r\n          for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n      ]\r\n\r\n  return tokenized_examples\r\n\r\n\r\n\r\nvalidation_features = datasets[\"validation\"].map(\r\n    lambda xs: prepare_validation_features(xs, True),\r\n    batched=True,\r\n    remove_columns=datasets[\"validation\"].column_names\r\n)\r\n```\r\n\r\nI guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help!","comment_length":80,"text":"ArrowInvalid issue for squad v2 dataset \n Hello, I am using the huggingface official question answering example notebook (https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb). \r\n\r\nIn the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error:\r\n\r\n`ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178`\r\n\r\nMy code is as follows:\r\n\r\n```\r\ndef generate_candidate_questions(examples):\r\n  val_questions = examples[\"question\"]\r\n  candididate_questions = random.sample(datasets[\"train\"][\"question\"], len(val_questions))\r\n  candididate_questions = [x[:max_length] for x in candididate_questions]\r\n  return candididate_questions\r\n\r\ndef prepare_validation_features(examples, use_mixing=False):\r\n  pad_on_right = tokenizer.padding_side == \"right\"\r\n  tokenized_examples = tokenizer(\r\n      examples[\"question\" if pad_on_right else \"context\"],\r\n      examples[\"context\" if pad_on_right else \"question\"],\r\n      truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n      max_length=max_length,\r\n      stride=doc_stride,\r\n      return_overflowing_tokens=True,\r\n      return_offsets_mapping=True,\r\n      padding=\"max_length\",\r\n  )\r\n  if use_mixing:\r\n    candidate_questions = generate_candidate_questions(examples)\r\n    tokenized_candidates = tokenizer(\r\n        candidate_questions if pad_on_right else examples[\"context\"],\r\n        examples[\"context\"] if pad_on_right else candidate_questions,\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=max_length,\r\n        stride=doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n  sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n  tokenized_examples[\"example_id\"] = []\r\n\r\n  if use_mixing:\r\n    tokenized_examples[\"candidate_input_ids\"] = tokenized_candidates[\"input_ids\"]\r\n    tokenized_examples[\"candidate_attention_mask\"] = tokenized_candidates[\"attention_mask\"]\r\n    tokenized_examples[\"candidate_token_type_ids\"] = tokenized_candidates[\"token_type_ids\"]\r\n\r\n  for i in range(len(tokenized_examples[\"input_ids\"])):\r\n      sequence_ids = tokenized_examples.sequence_ids(i)\r\n      context_index = 1 if pad_on_right else 0\r\n\r\n      sample_index = sample_mapping[i]\r\n      tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n      tokenized_examples[\"offset_mapping\"][i] = [\r\n          (o if sequence_ids[k] == context_index else None)\r\n          for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n      ]\r\n\r\n  return tokenized_examples\r\n\r\n\r\n\r\nvalidation_features = datasets[\"validation\"].map(\r\n    lambda xs: prepare_validation_features(xs, True),\r\n    batched=True,\r\n    remove_columns=datasets[\"validation\"].column_names\r\n)\r\n```\r\n\r\nI guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help! \n Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column.\r\n\r\nIndeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a batch.\r\n\r\nHowever it seems like `tokenized_examples` doesn't have the same number of elements in each field. One field seems to have `1180` elements while `candidate_attention_mask` only has `1178`.","embeddings":[0.195086509,-0.5012049079,0.0041139694,0.2735462487,0.2830952704,-0.0630852804,0.2918868959,0.12445537,-0.3849657178,0.1932278574,0.0684070587,0.2712973952,0.1175046489,-0.2701620162,-0.0708334744,-0.1942008138,0.043876484,-0.0185051747,0.1663554609,-0.0736735165,-0.1644646674,0.2084969878,-0.4730278254,0.3151913881,-0.3681923151,-0.2792748511,-0.0652289987,-0.1413509101,-0.2085033357,-0.7724220157,0.189392671,-0.2526650727,0.2286590785,0.2269305289,-0.0001293044,-0.0708507299,0.0546478927,-0.1082279235,-0.2221769392,0.0503468625,0.2006007582,-0.1157533154,0.0235010628,-0.1506015658,0.0862615928,-0.2532844245,-0.2258586138,-0.2837268412,0.5048593879,0.2183131427,0.0398420133,0.3087803721,0.13261576,-0.0180970859,0.0254218541,0.0207152404,-0.1003998369,-0.0611900054,0.2177817076,-0.016631728,0.110686101,0.6158602238,0.2363804728,0.1203650907,0.3287784159,-0.0844715908,0.0512515418,-0.2424855679,0.1621574163,0.1987718046,0.120321475,-0.080656372,-0.2992402017,-0.3082882464,0.2235186994,-0.0175583772,0.1416762769,-0.1600324064,0.2267239243,0.0799518377,-0.0211595092,0.276373595,-0.2309037298,0.1246564016,0.1555744708,0.0581968427,-0.1273341775,0.3357895613,0.0843684226,-0.4302313924,-0.3450859487,0.1799298525,-0.1760932505,0.384899199,-0.4720772803,-0.1346978843,-0.178399533,-0.2347410917,0.3301493824,-0.1297595501,0.2012562305,-0.0882847235,0.3551928401,0.0218711421,0.0719589442,0.3336797357,0.308562547,0.4262860119,-0.2180073559,-0.3491986692,0.120548971,-0.1206212938,-0.2547729611,-0.3665161133,0.2209726423,0.1167232394,0.384670794,-0.0516374893,-0.3939847946,0.4682804048,-0.7649683952,0.081198208,-0.0058187293,0.178189069,0.2198238224,-0.0031698421,-0.1204943955,0.2113592923,-0.0259241145,-0.2856942415,-0.020511305,-0.07508035,-0.1858078688,0.0906661525,0.057143975,0.1531473994,0.3249381185,-0.1028737649,0.3799371123,-0.3787389398,0.1879871935,-0.1844678521,0.3455336094,-0.0727417469,-0.3098551929,0.1424631774,0.0584285632,-0.1654934138,-0.0539139695,-0.1027214974,-0.1874339432,-0.1261960715,-0.2425878048,-0.0208732765,0.0811207071,0.1629774123,0.104338415,0.1319513619,0.2459521294,-0.0968066007,0.3539926112,-0.3905323744,0.120577842,-0.0145222489,-0.0205828305,0.1708712578,-0.3565444648,-0.1542259753,0.5501691699,0.2950061858,-0.2008676827,0.3871436417,-0.1549215466,0.0752360523,-0.1555736959,0.5609897971,0.2675245106,-0.7531769872,-0.3551869392,-0.0503066555,-0.2858443856,-0.0522272214,0.0802324414,-0.1308106929,0.3515367806,0.0559620075,0.050204888,-0.0221526362,-0.0017574559,0.0674414933,-0.1703104973,-0.0660700426,0.0601951964,-0.056051977,-0.2504400015,-0.2083390653,-0.1153802574,-0.1653351635,0.0465347208,-0.0787110776,0.2557053864,0.3074446619,0.3064408898,0.0371078067,0.2982430458,-0.0424509309,-0.5195571184,-0.0673793852,-0.086148344,-0.0555269644,-0.0934732854,-0.3999192715,-0.483473599,0.1228377223,-0.2551138699,-0.3626616597,-0.0108572375,0.0558655784,0.1518115997,-0.3138305545,-0.064778775,0.0877950713,-0.0671913549,0.2688457966,-0.1233368739,-0.016010385,-0.0595269911,-0.1805099845,0.045282837,0.1954571307,0.3556151688,-0.0670107231,-0.1062885895,0.3517386019,0.2318141907,-0.1074745283,0.1140647829,-0.1387194693,0.1696459502,-0.2511654496,-0.0463707261,0.1581393927,0.0831789002,0.0153888222,-0.1450790912,0.4966517091,0.0857706517,0.3637129366,-0.3584550321,0.0083901044,0.0568754524,0.1508820653,-0.4269770682,-0.0256515313,-0.1253533959,0.1449754983,0.4017035365,-0.1017795429,-0.0515564755,0.0492357202,0.3358851075,0.0354063287,-0.0763477013,-0.024279274,-0.0925268009,0.1731872112,0.263124764,-0.0448205955,0.2035182714,0.1293767393,-0.2184268087,-0.0190938301,-0.0147033511,0.111966379,0.055592116,0.0647543073,0.11833217,0.2495093197,0.3302026987,-0.0305676535,-0.1890271753,0.0396373719,0.1306311786,0.0940906778,-0.3066777289,0.2154724896,-0.1222217977,0.1913228929,-0.0042545311,-0.2016801387,-0.0127335032,-0.1387368143,-0.110694252,0.1354757994,0.050706517,0.3411769569,0.2539761662,0.3478592634,0.4629756212,0.025127247,-0.0785275102,-0.0609102733,-0.1276552975,-0.1625013798,0.0096559869,-0.1742608249,0.0441797562,0.2778018713,-0.0506546758,-0.460667938,-0.6184928417,0.1887655109,-0.1437838376,0.1473682672,0.2346592396,0.1375778764,-0.552395761,-0.1461130083,0.2825928032,0.1565440595,-0.3298376203,0.0078809075,-0.1075720564,-0.2112471759,0.0480222479,-0.2935303152,-0.1629592478,-0.0616582334,0.579547286,-0.3441344798,-0.1095499247,0.2096663713,0.2039573491,0.0286259688,-0.3302955925,-0.1491159499,-0.272418201,0.1424410641,0.3052252829,-0.3198955059,-0.0532762818,-0.0678489506,-0.1944743246,0.2294441611,0.0854576379,-0.0846457332,-0.3280028999,0.0363932662,0.3106918931,-0.2562968433,0.1329609752,0.4908977747,-0.1166251227,0.206622526,-0.0954710543,-0.2646959126,0.3713108003,0.1710574478,0.2116571069,-0.1192346439,0.5886774659,0.0093836384,0.8307531476,0.3969449103,0.0631243512,0.0407918356,-0.1729646176,0.008927281,-0.1118541881,-0.1739912778,0.2700004578,-0.1890034974,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2068","title":"PyTorch not available error on SageMaker GPU docker though it is installed ","comments":"Hey @sivakhno,\r\n\r\nhow does your `requirements.txt` look like to install the `datasets` library and which version of it are you running? Can you try to install `datasets>=1.4.0`","body":"I get en error when running data loading using SageMaker SDK\r\n\r\n```\r\n  File \"main.py\", line 34, in <module>\r\n    run_training()\r\n  File \"main.py\", line 25, in run_training\r\n    dm.setup('fit')\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/pytorch_lightning\/core\/datamodule.py\", line 92, in wrapped_fn\r\n    return fn(*args, **kwargs)\r\n  File \"\/opt\/ml\/code\/data_module.py\", line 103, in setup\r\n    self.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 995, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\nFile \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/__init__.py\", line 114, in get_formatter\r\n    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\r\nValueError: PyTorch needs to be installed to be able to return PyTorch tensors.\r\n```\r\n\r\nwhen trying to execute dataset loading using this notebook  https:\/\/github.com\/PyTorchLightning\/pytorch-lightning\/blob\/master\/notebooks\/04-transformers-text-classification.ipynb, specifically lines \r\n\r\n```\r\nself.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]\r\nself.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n```\r\n\r\nThe SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com\/pytorch-training:1.4.0-gpu-py3 .\r\n\r\nBy running container interactively I have checked that torch loading completes successfully by executing `https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/config.py#L39`. \r\n\r\nAlso as a first line in the data loading module I have \r\n\r\n```\r\nimport os\r\nos.environ[\"USE_TF\"] = \"0\" \r\nos.environ[\"USE_TORCH\"] = \"1\" \r\n````\r\n\r\nBut unfortunately the error stills persists. Any suggestions would be appreciated as I am stack.\r\nMany Thanks! \r\n\r\n","comment_length":27,"text":"PyTorch not available error on SageMaker GPU docker though it is installed  \n I get en error when running data loading using SageMaker SDK\r\n\r\n```\r\n  File \"main.py\", line 34, in <module>\r\n    run_training()\r\n  File \"main.py\", line 25, in run_training\r\n    dm.setup('fit')\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/pytorch_lightning\/core\/datamodule.py\", line 92, in wrapped_fn\r\n    return fn(*args, **kwargs)\r\n  File \"\/opt\/ml\/code\/data_module.py\", line 103, in setup\r\n    self.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 995, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\nFile \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/__init__.py\", line 114, in get_formatter\r\n    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\r\nValueError: PyTorch needs to be installed to be able to return PyTorch tensors.\r\n```\r\n\r\nwhen trying to execute dataset loading using this notebook  https:\/\/github.com\/PyTorchLightning\/pytorch-lightning\/blob\/master\/notebooks\/04-transformers-text-classification.ipynb, specifically lines \r\n\r\n```\r\nself.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]\r\nself.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n```\r\n\r\nThe SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com\/pytorch-training:1.4.0-gpu-py3 .\r\n\r\nBy running container interactively I have checked that torch loading completes successfully by executing `https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/config.py#L39`. \r\n\r\nAlso as a first line in the data loading module I have \r\n\r\n```\r\nimport os\r\nos.environ[\"USE_TF\"] = \"0\" \r\nos.environ[\"USE_TORCH\"] = \"1\" \r\n````\r\n\r\nBut unfortunately the error stills persists. Any suggestions would be appreciated as I am stack.\r\nMany Thanks! \r\n\r\n \n Hey @sivakhno,\r\n\r\nhow does your `requirements.txt` look like to install the `datasets` library and which version of it are you running? Can you try to install 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2068","title":"PyTorch not available error on SageMaker GPU docker though it is installed ","comments":"Hi @philschmid - thanks for suggestion. I am using `datasets==1.4.1`. \r\nI have also tried using `torch=1.6.0` (docker `763104351884.dkr.ecr.eu-central-1.amazonaws.com\/pytorch-training:1.6.0-gpu-py3 `), but the error is the same. ","body":"I get en error when running data loading using SageMaker SDK\r\n\r\n```\r\n  File \"main.py\", line 34, in <module>\r\n    run_training()\r\n  File \"main.py\", line 25, in run_training\r\n    dm.setup('fit')\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/pytorch_lightning\/core\/datamodule.py\", line 92, in wrapped_fn\r\n    return fn(*args, **kwargs)\r\n  File \"\/opt\/ml\/code\/data_module.py\", line 103, in setup\r\n    self.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 995, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\nFile \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/__init__.py\", line 114, in get_formatter\r\n    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\r\nValueError: PyTorch needs to be installed to be able to return PyTorch tensors.\r\n```\r\n\r\nwhen trying to execute dataset loading using this notebook  https:\/\/github.com\/PyTorchLightning\/pytorch-lightning\/blob\/master\/notebooks\/04-transformers-text-classification.ipynb, specifically lines \r\n\r\n```\r\nself.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]\r\nself.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n```\r\n\r\nThe SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com\/pytorch-training:1.4.0-gpu-py3 .\r\n\r\nBy running container interactively I have checked that torch loading completes successfully by executing `https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/config.py#L39`. \r\n\r\nAlso as a first line in the data loading module I have \r\n\r\n```\r\nimport os\r\nos.environ[\"USE_TF\"] = \"0\" \r\nos.environ[\"USE_TORCH\"] = \"1\" \r\n````\r\n\r\nBut unfortunately the error stills persists. Any suggestions would be appreciated as I am stack.\r\nMany Thanks! \r\n\r\n","comment_length":25,"text":"PyTorch not available error on SageMaker GPU docker though it is installed  \n I get en error when running data loading using SageMaker SDK\r\n\r\n```\r\n  File \"main.py\", line 34, in <module>\r\n    run_training()\r\n  File \"main.py\", line 25, in run_training\r\n    dm.setup('fit')\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/pytorch_lightning\/core\/datamodule.py\", line 92, in wrapped_fn\r\n    return fn(*args, **kwargs)\r\n  File \"\/opt\/ml\/code\/data_module.py\", line 103, in setup\r\n    self.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 995, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\nFile \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/__init__.py\", line 114, in get_formatter\r\n    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\r\nValueError: PyTorch needs to be installed to be able to return PyTorch tensors.\r\n```\r\n\r\nwhen trying to execute dataset loading using this notebook  https:\/\/github.com\/PyTorchLightning\/pytorch-lightning\/blob\/master\/notebooks\/04-transformers-text-classification.ipynb, specifically lines \r\n\r\n```\r\nself.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]\r\nself.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n```\r\n\r\nThe SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com\/pytorch-training:1.4.0-gpu-py3 .\r\n\r\nBy running container interactively I have checked that torch loading completes successfully by executing `https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/config.py#L39`. \r\n\r\nAlso as a first line in the data loading module I have \r\n\r\n```\r\nimport os\r\nos.environ[\"USE_TF\"] = \"0\" \r\nos.environ[\"USE_TORCH\"] = \"1\" \r\n````\r\n\r\nBut unfortunately the error stills persists. Any suggestions would be appreciated as I am stack.\r\nMany Thanks! \r\n\r\n \n Hi @philschmid - thanks for suggestion. I am using `datasets==1.4.1`. \r\nI have also tried using `torch=1.6.0` (docker `763104351884.dkr.ecr.eu-central-1.amazonaws.com\/pytorch-training:1.6.0-gpu-py3 `), but the error is the same. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2068","title":"PyTorch not available error on SageMaker GPU docker though it is installed ","comments":"Could paste the code you use the start your training job and the fine-tuning script you run? ","body":"I get en error when running data loading using SageMaker SDK\r\n\r\n```\r\n  File \"main.py\", line 34, in <module>\r\n    run_training()\r\n  File \"main.py\", line 25, in run_training\r\n    dm.setup('fit')\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/pytorch_lightning\/core\/datamodule.py\", line 92, in wrapped_fn\r\n    return fn(*args, **kwargs)\r\n  File \"\/opt\/ml\/code\/data_module.py\", line 103, in setup\r\n    self.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 995, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\nFile \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/__init__.py\", line 114, in get_formatter\r\n    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\r\nValueError: PyTorch needs to be installed to be able to return PyTorch tensors.\r\n```\r\n\r\nwhen trying to execute dataset loading using this notebook  https:\/\/github.com\/PyTorchLightning\/pytorch-lightning\/blob\/master\/notebooks\/04-transformers-text-classification.ipynb, specifically lines \r\n\r\n```\r\nself.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]\r\nself.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n```\r\n\r\nThe SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com\/pytorch-training:1.4.0-gpu-py3 .\r\n\r\nBy running container interactively I have checked that torch loading completes successfully by executing `https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/config.py#L39`. \r\n\r\nAlso as a first line in the data loading module I have \r\n\r\n```\r\nimport os\r\nos.environ[\"USE_TF\"] = \"0\" \r\nos.environ[\"USE_TORCH\"] = \"1\" \r\n````\r\n\r\nBut unfortunately the error stills persists. Any suggestions would be appreciated as I am stack.\r\nMany Thanks! \r\n\r\n","comment_length":17,"text":"PyTorch not available error on SageMaker GPU docker though it is installed  \n I get en error when running data loading using SageMaker SDK\r\n\r\n```\r\n  File \"main.py\", line 34, in <module>\r\n    run_training()\r\n  File \"main.py\", line 25, in run_training\r\n    dm.setup('fit')\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/pytorch_lightning\/core\/datamodule.py\", line 92, in wrapped_fn\r\n    return fn(*args, **kwargs)\r\n  File \"\/opt\/ml\/code\/data_module.py\", line 103, in setup\r\n    self.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 995, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\nFile \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/formatting\/__init__.py\", line 114, in get_formatter\r\n    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\r\nValueError: PyTorch needs to be installed to be able to return PyTorch tensors.\r\n```\r\n\r\nwhen trying to execute dataset loading using this notebook  https:\/\/github.com\/PyTorchLightning\/pytorch-lightning\/blob\/master\/notebooks\/04-transformers-text-classification.ipynb, specifically lines \r\n\r\n```\r\nself.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]\r\nself.dataset[split].set_format(type=\"torch\", columns=self.columns)\r\n```\r\n\r\nThe SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com\/pytorch-training:1.4.0-gpu-py3 .\r\n\r\nBy running container interactively I have checked that torch loading completes successfully by executing `https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/config.py#L39`. \r\n\r\nAlso as a first line in the data loading module I have \r\n\r\n```\r\nimport os\r\nos.environ[\"USE_TF\"] = \"0\" \r\nos.environ[\"USE_TORCH\"] = \"1\" \r\n````\r\n\r\nBut unfortunately the error stills persists. Any suggestions would be appreciated as I am stack.\r\nMany Thanks! \r\n\r\n \n Could paste the code you use the start your training job and the fine-tuning script you run? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2067","title":"Multiprocessing windows error","comments":"Hi ! Thanks for reporting.\r\nThis looks like a bug, could you try to provide a minimal code example that reproduces the issue ? This would be very helpful !\r\n\r\nOtherwise I can try to run the wav2vec2 code above on my side but probably not this week..","body":"As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ","comment_length":48,"text":"Multiprocessing windows error \n As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop  \n Hi ! Thanks for reporting.\r\nThis looks like a bug, could you try to provide a minimal code example that reproduces the issue ? This would be very helpful !\r\n\r\nOtherwise I can try to run the wav2vec2 code above on my side but probably not this week..","embeddings":[-0.0581421144,-0.4536787868,-0.0355622135,0.2569919527,0.0860946625,-0.0037736383,0.115531303,-0.0629601926,0.0408037528,0.3191868663,0.2538127005,0.0701293051,-0.3072563708,-0.0425456725,-0.2219943404,-0.2463472039,0.1892075837,-0.1294742376,0.3075393438,0.3353129625,-0.425796777,0.5416621566,-0.4026214778,0.1973485947,-0.414326489,0.0059316708,-0.258384347,0.4944570363,0.3894542158,-0.3567263484,0.0712473169,-0.3931779563,0.1509012282,0.1528387666,-0.0001224974,-0.1242067292,0.0399249382,-0.0401795469,0.410685569,0.2325061411,0.2327505797,0.1160329655,-0.0896714181,-0.0001363825,0.1803089231,0.317689836,0.0230255742,-0.0436639488,0.3788876235,0.2494235188,0.1086351946,0.2697453499,0.0584818125,-0.0172719918,-0.3373928964,-0.4087221026,-0.0356067978,0.1879889816,0.4800273478,-0.3880698979,0.2081042826,0.229860425,-0.2476220727,0.0222452357,-0.3043947816,0.0047380412,0.3776167333,-0.5156354904,0.0704574734,-0.08932776,-0.1066430882,-0.1918105632,0.3175110221,0.0956236348,0.1241043136,-0.8489111066,0.1194221899,0.2649233639,-0.3856474757,0.0965462103,-0.0331685618,0.0699983388,-0.060277734,-0.1735407412,0.1558316201,-0.05035761,0.0599201359,0.165252164,0.1297031045,0.3123299479,-0.2853071392,0.2992669642,-0.0785920322,0.0563823245,-0.4582304358,-0.0107892044,0.0245604292,0.2138339579,-0.3155740499,-0.2193693519,-0.0722816586,0.0577091873,0.210329175,0.1877655536,0.2185833901,-0.1882567406,0.0442293398,-0.5686359406,0.1675219238,-0.5050089359,-0.1424953043,-0.0811683536,-0.0080247419,-0.5607304573,0.3573493659,0.3995010257,0.2302154154,-0.1080729291,-0.3386083841,0.2101215869,-0.04086392,0.2901071012,0.273465097,0.2971855104,0.3146837354,0.7152168751,-0.0177826546,0.2513940036,-0.5292391181,0.0686728582,0.0788345113,0.1238428429,-0.2765547633,0.1074580178,0.2124366015,0.0187721364,0.1902923584,0.1390351951,0.2973748744,-0.368814528,-0.0184524059,-0.2097483873,-0.1706739515,0.2196747065,0.2305232435,0.1968380958,-0.0093255816,0.0610076077,0.0238536336,0.2254341245,-0.3823579848,-0.3544293344,-0.1269885302,0.0252814814,0.397954613,0.4406096637,0.3450522423,-0.1068729162,0.3525731564,-0.3299072385,-0.124025993,-0.4204719961,-0.3616985977,-0.34269014,-0.0801263303,0.2953493595,0.030153187,0.3120937049,0.3005546927,-0.0583335087,0.1898305267,0.3420273364,0.2321760505,-0.1541697383,-0.1427909285,-0.0512975156,-0.2075472027,-0.2447627336,0.0287267622,0.1045083106,-0.0876488164,-0.0770462602,0.2426429093,0.0912932903,-0.1405437142,-0.3693986237,0.4593514204,-0.2429709136,-0.1423679143,0.2521110475,-0.1832213998,0.0826770887,-0.0361781567,0.0155518427,0.0554710738,-0.0393529236,-0.0699979812,-0.0750164688,0.2342378497,0.1212345883,0.0071897889,0.2943185866,0.2643739283,-0.0255832206,0.047279533,-0.3173382282,0.02500326,0.0944315717,-0.1837330759,-0.0593166575,0.4418401718,0.1487971097,0.1914493144,0.2850689292,-0.1577903032,-0.2866767049,0.0075830286,-0.0888098776,0.0314149782,-0.2019582391,0.0416018069,0.5415998101,0.0924480632,-0.2050208151,-0.0775554553,-0.0432454906,-0.3279131949,-0.3441634178,-0.0958048925,0.3061342537,0.2324241102,0.2131822407,0.0931091383,0.2249252349,0.178348273,-0.1250432134,-0.1788385957,-0.0189846084,0.1657315642,-0.3093577623,0.0945758149,0.0233976655,0.1686377674,-0.2740843892,0.4795754552,0.1112647653,-0.0392507128,0.24645257,-0.1647881866,0.1857769489,0.1239148453,0.0868051723,0.250082314,0.2957986593,0.3540994227,0.0737764016,-0.0943075716,-0.3773719072,-0.3285286129,0.063435398,0.1519417465,0.2393576354,-0.0259907767,0.058411438,-0.1990371644,0.201797083,-0.0167576745,-0.4524724483,0.736939311,0.0708688125,-0.1586373597,0.3013827205,-0.3818297684,-0.1029828191,0.2564709783,0.0558761545,0.0142505569,0.1514872015,-0.0793216005,-0.0701853782,0.0156658385,-0.0630172342,-0.1121387333,0.0245234929,-0.3366858065,0.1437121034,-0.0703526139,0.5119808912,-0.3930772543,-0.4185869694,-0.0158641767,-0.1448655725,0.0737106055,0.4412177503,-0.1272929609,-0.0961341336,0.0165484846,0.0042984234,0.1229744181,0.2859156728,0.3148186505,-0.0421822332,-0.1014562547,-0.0997362509,0.2983363867,-0.1647490561,0.2087989748,0.3532303572,-0.0372239389,0.0849438533,0.1645260006,-0.0095089255,0.0576119013,0.2622989118,0.0440347157,0.0375002697,0.176767081,-0.4125063121,0.0998600572,-0.3187246919,0.1915216148,0.2610322237,0.1713414192,-0.3974964321,-0.2762928605,-0.0283086598,-0.4276906848,-0.1810949445,0.055199068,-0.1846828759,0.2561765015,-0.2547455132,0.0900386646,0.0847056285,0.2642419636,-0.2037704289,-0.1433807611,0.1716799438,-0.08444231,-0.2580580413,-0.1759235412,0.250746727,0.1663268954,0.0340598263,-0.2300753742,-0.1844059527,-0.3176006377,0.0196092445,0.0322731808,-0.0572806187,0.2107921094,0.3846412003,0.110975489,-0.0915515348,0.0241961833,-0.1118701622,0.1226555035,0.0250537358,0.1420998275,0.2201239318,0.0712735504,-0.169597134,0.5418444872,0.5851689577,0.1231955811,0.3024074733,0.1154460981,0.0167698357,0.0343535766,-0.1813274175,0.0432581604,-0.4538233876,-0.1846060753,0.1852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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2067","title":"Multiprocessing windows error","comments":"```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\n\r\n\r\nupdated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4)\r\n\r\n```","body":"As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ","comment_length":22,"text":"Multiprocessing windows error \n As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop  \n ```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\n\r\n\r\nupdated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4)\r\n\r\n```","embeddings":[-0.2164214551,-0.269531697,-0.0307765733,0.1850229353,0.1784022748,0.1376417726,0.0575687103,-0.0025023981,0.0087179644,0.2766421437,0.1489748359,0.1610600799,-0.2854522765,-0.1023178324,-0.1237755269,-0.1828478575,0.1945973486,-0.1632987708,0.263887167,0.3403266966,-0.4150704443,0.4792796075,-0.3459638655,0.0734494776,-0.5422175527,-0.1041582078,-0.2976150811,0.4396467209,0.197288841,-0.4517998695,-0.013877756,-0.1838330626,0.2181247473,0.1776028126,-0.0001206921,-0.1065645739,0.0575244762,-0.0216337312,0.3331870735,0.2294842601,0.0989151821,0.0286370553,-0.0890319198,-0.0855964497,0.1740061492,0.2978885472,0.096328333,-0.1282736808,0.5274698138,0.3825234473,0.0758575127,0.2272967398,0.0876476318,0.007820514,-0.1878419071,-0.2888141274,0.0203558411,0.2024262398,0.5406011939,-0.3069016933,0.0876099318,0.2608896792,-0.3369736075,0.0058557997,-0.3788694441,-0.0015098701,0.3409759402,-0.4972144961,0.1355955601,-0.0681021661,-0.0130132353,-0.321980238,0.1528369337,-0.021674145,0.1863062382,-0.8767055869,-0.0115937479,0.1989576966,-0.314476192,0.0741171166,-0.1529564261,-0.0594550893,-0.0408123471,-0.1191436052,0.183025524,0.0230448544,0.0427067503,0.1673840731,0.2412383705,0.2289174944,-0.1954458654,0.2736682594,-0.1021791324,0.13654311,-0.5750361681,-0.0034187443,-0.0358424,0.1198296323,-0.2478046715,-0.3653281629,-0.0101330867,0.066942513,0.2934780419,0.1694190651,0.2630363405,-0.154176116,0.0505522303,-0.5396240354,0.233746469,-0.497073561,-0.1793611497,-0.0397479534,-0.039352864,-0.5704105496,0.3287129104,0.4325676262,0.2949965894,-0.0771086738,-0.3808996379,0.1982300878,-0.0343120545,0.1575937867,0.1864148825,0.2853849232,0.3009817004,0.6022768021,-0.1089114398,0.243097499,-0.5615463257,-0.024918871,0.0065897619,0.0263321586,-0.3680235744,0.1488280743,0.2685838044,0.0536361784,0.1249185503,0.1504223496,0.1372532994,-0.3701722026,-0.1397931427,-0.2361291796,-0.2652803361,0.2284741998,0.2142871171,0.2383062541,-0.0474647135,-0.0238052923,0.0262911543,0.3022709191,-0.3516969085,-0.3986416459,-0.1266380697,0.0315956064,0.3112643957,0.2649718225,0.0864211544,0.1411970854,0.4162356555,-0.2684273422,-0.1504580826,-0.3968690038,-0.2766779661,-0.3078669608,-0.0656391382,0.4170382023,-0.1365273446,0.290032804,0.2960905731,0.0314433426,0.1945922971,0.3655435443,0.1552812606,-0.0813851133,-0.0505696312,0.0832357258,-0.0205600634,-0.2881668806,-0.1992242336,0.176038146,-0.0738317147,-0.0978386626,0.3155908883,0.1629916131,0.0818682835,-0.2875578105,0.4907436669,-0.1638596207,-0.1147925854,0.219432503,-0.1701681465,0.1143033877,0.0020510694,0.0597540326,0.0055045085,-0.100123167,-0.0747044161,-0.0821861401,0.3725978732,0.0493526608,0.0329319201,0.4288325608,0.1251131445,0.042679064,-0.0380034633,-0.4412008226,-0.1410391778,0.0364274196,-0.1454827189,0.1202213094,0.215491876,0.0123201339,0.1436545104,0.2799672484,-0.1707317233,-0.3652132154,0.0262440089,-0.0855276436,0.0470418036,-0.2536361516,-0.0740335807,0.5049322844,-0.0410295837,-0.0658523813,-0.118306987,-0.035705775,-0.2768333554,-0.4343212843,-0.0377741158,0.3438779712,0.2160584927,0.1489761472,0.0480393767,0.3426544666,0.1863054037,0.0081386408,-0.1519266367,-0.0605586432,0.2119040787,-0.3094341457,0.0922355205,0.0435259901,0.1680229157,-0.1974246055,0.3308628798,0.0512520261,0.0455577262,0.3031246662,-0.1308645457,0.1274566352,0.13500835,0.1218287945,0.1568627208,0.1545986831,0.2793993652,0.0749835894,-0.0271780044,-0.2646307051,-0.3050138354,0.0248108041,0.2788725495,0.1574839652,0.0075597251,0.0476800613,-0.3225958943,0.1222753748,-0.0220541358,-0.3254919946,0.7709748745,0.1396939605,-0.0917925388,0.2921946347,-0.43721506,-0.1092860624,0.2887381017,0.0958150402,0.018291682,0.121315375,-0.0725876763,-0.0066961017,-0.1947628707,-0.0800314918,0.0012708978,0.1616831422,-0.3943112493,0.0567448884,-0.1457272321,0.3488927186,-0.3866231441,-0.4130340815,-0.0436930172,-0.2089831978,-0.0044999304,0.3486006856,-0.008753649,0.0981045291,0.0509685092,-0.1500322372,0.1428934932,0.1455201507,0.2662494481,-0.0906187147,-0.1254091561,-0.1048674658,0.3953677416,-0.1487078816,0.2262792736,0.253669858,0.0533809736,-0.0227702446,0.1229572371,-0.1032215282,0.0013769236,0.3677129447,0.0344865471,0.0714260712,0.0728884935,-0.3751188815,0.1372556537,-0.3081077933,0.1805383712,0.1420270801,0.1769648045,-0.428165257,-0.1545358449,-0.2390204221,-0.4491527379,-0.2015045434,0.0472735427,-0.2694447339,0.2583689988,-0.260084033,0.1403761804,0.1228197441,0.3062795401,-0.1006057486,-0.1949616075,0.2093833685,-0.0533713996,-0.2659558058,-0.2152358145,0.0953186676,0.149229303,-0.0089385174,-0.0756138712,-0.2012024671,-0.300093174,-0.1328931153,-0.0041212174,-0.0031261484,0.3379204571,0.5044857264,0.1537652314,-0.0895865709,0.0280563515,-0.1914943606,0.0816868246,0.1449466348,0.1434945613,0.1560181379,0.1827497333,-0.1249647066,0.4865584373,0.6543411016,-0.0066320677,0.3675019145,0.0008984012,0.0191048346,0.0103926109,-0.2558258474,0.0708779246,-0.4434515536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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2067","title":"Multiprocessing windows error","comments":"\r\n\r\n\r\n\r\n\r\nI was able to copy some of the shell \r\nThis is repeating every half second\r\nWin 10, Anaconda with python 3.8, datasets installed from main branche\r\n```\r\n\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 287, in _fixup_main_from_path\r\n    _check_not_importing_main()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 116, in spawn_main\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 134, in _check_not_importing_main\r\n    main_content = runpy.run_path(main_path,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 265, in run_path\r\n    exitcode = _main(fd, parent_sentinel)\r\n    raise RuntimeError('''\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 125, in _main\r\nRuntimeError:\r\n        An attempt has been made to start a new process before the\r\n        current process has finished its bootstrapping phase.\r\n\r\n        This probably means that you are not using fork to start your\r\n        child processes and you have forgotten to use the proper idiom\r\n        in the main module:\r\n\r\n            if __name__ == '__main__':\r\n                freeze_support()\r\n                ...\r\n\r\n        The \"freeze_support()\" line can be omitted if the program\r\n        is not going to be frozen to produce an executable.    return _run_module_code(code, init_globals, run_name,\r\n    prepare(preparation_data)\r\n\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 97, in _run_module_code\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 236, in prepare\r\n    _run_code(code, mod_globals, init_globals,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 87, in _run_code\r\n    _fixup_main_from_path(data['init_main_from_path'])\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 287, in _fixup_main_from_path\r\n    exec(code, run_globals)\r\n  File \"F:\\Codes\\Python Apps\\asr\\test.py\", line 6, in <module>\r\n    updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4)\r\n    main_content = runpy.run_path(main_path,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1370, in map\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 265, in run_path\r\n    with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool:\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\context.py\", line 119, in Pool\r\n    return _run_module_code(code, init_globals, run_name,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 97, in _run_module_code\r\n    _run_code(code, mod_globals, init_globals,\r\n    return Pool(processes, initializer, initargs, maxtasksperchild,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 87, in _run_code\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 212, in __init__\r\n    exec(code, run_globals)\r\n  File \"F:\\Codes\\Python Apps\\asr\\test.py\", line 6, in <module>\r\n    self._repopulate_pool()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 303, in _repopulate_pool\r\n    updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1370, in map\r\n    return self._repopulate_pool_static(self._ctx, self.Process,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 326, in _repopulate_pool_static\r\n    with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool:\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\context.py\", line 119, in Pool\r\n    w.start()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\process.py\", line 121, in start\r\n    return Pool(processes, initializer, initargs, maxtasksperchild,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 212, in __init__\r\n    self._popen = self._Popen(self)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\context.py\", line 327, in _Popen\r\n    self._repopulate_pool()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 303, in _repopulate_pool\r\n    return Popen(process_obj)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\popen_spawn_win32.py\", line 45, in __init__\r\n    return self._repopulate_pool_static(self._ctx, self.Process,\r\n    prep_data = spawn.get_preparation_data(process_obj._name)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 326, in _repopulate_pool_static\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 154, in get_preparation_data\r\n    _check_not_importing_main()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 134, in _check_not_importing_main\r\n    w.start()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\process.py\", line 121, in start\r\n    raise RuntimeError('''\r\nRuntimeError:\r\n        An attempt has been made to start a new process before the\r\n        current process has finished its bootstrapping phase.\r\n\r\n        This probably means that you are not using fork to start your\r\n        child processes and you have forgotten to use the proper idiom\r\n        in the main module:\r\n\r\n            if __name__ == '__main__':\r\n                freeze_support()\r\n                ...\r\n```","body":"As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ","comment_length":433,"text":"Multiprocessing windows error \n As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop  \n \r\n\r\n\r\n\r\n\r\nI was able to copy some of the shell \r\nThis is repeating every half second\r\nWin 10, Anaconda with python 3.8, datasets installed from main branche\r\n```\r\n\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 287, in _fixup_main_from_path\r\n    _check_not_importing_main()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 116, in spawn_main\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 134, in _check_not_importing_main\r\n    main_content = runpy.run_path(main_path,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 265, in run_path\r\n    exitcode = _main(fd, parent_sentinel)\r\n    raise RuntimeError('''\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 125, in _main\r\nRuntimeError:\r\n        An attempt has been made to start a new process before the\r\n        current process has finished its bootstrapping phase.\r\n\r\n        This probably means that you are not using fork to start your\r\n        child processes and you have forgotten to use the proper idiom\r\n        in the main module:\r\n\r\n            if __name__ == '__main__':\r\n                freeze_support()\r\n                ...\r\n\r\n        The \"freeze_support()\" line can be omitted if the program\r\n        is not going to be frozen to produce an executable.    return _run_module_code(code, init_globals, run_name,\r\n    prepare(preparation_data)\r\n\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 97, in _run_module_code\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 236, in prepare\r\n    _run_code(code, mod_globals, init_globals,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 87, in _run_code\r\n    _fixup_main_from_path(data['init_main_from_path'])\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 287, in _fixup_main_from_path\r\n    exec(code, run_globals)\r\n  File \"F:\\Codes\\Python Apps\\asr\\test.py\", line 6, in <module>\r\n    updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4)\r\n    main_content = runpy.run_path(main_path,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1370, in map\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 265, in run_path\r\n    with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool:\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\context.py\", line 119, in Pool\r\n    return _run_module_code(code, init_globals, run_name,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 97, in _run_module_code\r\n    _run_code(code, mod_globals, init_globals,\r\n    return Pool(processes, initializer, initargs, maxtasksperchild,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 87, in _run_code\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 212, in __init__\r\n    exec(code, run_globals)\r\n  File \"F:\\Codes\\Python Apps\\asr\\test.py\", line 6, in <module>\r\n    self._repopulate_pool()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 303, in _repopulate_pool\r\n    updated_dataset = dataset.map(lambda example: {'sentence1': 'My sentence: ' + example['sentence1']}, num_proc=4)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1370, in map\r\n    return self._repopulate_pool_static(self._ctx, self.Process,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 326, in _repopulate_pool_static\r\n    with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool:\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\context.py\", line 119, in Pool\r\n    w.start()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\process.py\", line 121, in start\r\n    return Pool(processes, initializer, initargs, maxtasksperchild,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 212, in __init__\r\n    self._popen = self._Popen(self)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\context.py\", line 327, in _Popen\r\n    self._repopulate_pool()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 303, in _repopulate_pool\r\n    return Popen(process_obj)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\popen_spawn_win32.py\", line 45, in __init__\r\n    return self._repopulate_pool_static(self._ctx, self.Process,\r\n    prep_data = spawn.get_preparation_data(process_obj._name)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\pool.py\", line 326, in _repopulate_pool_static\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 154, in get_preparation_data\r\n    _check_not_importing_main()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 134, in _check_not_importing_main\r\n    w.start()\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\process.py\", line 121, in start\r\n    raise RuntimeError('''\r\nRuntimeError:\r\n        An attempt has been made to start a new process before the\r\n        current process has finished its bootstrapping phase.\r\n\r\n        This probably means that you are not using fork to start your\r\n        child processes and you have forgotten to use the proper idiom\r\n        in the main module:\r\n\r\n            if __name__ == '__main__':\r\n                freeze_support()\r\n                ...\r\n```","embeddings":[-0.1881467849,-0.3144050539,-0.0391355641,0.1985245347,0.1112000048,0.004422768,0.1473799646,0.0522424132,0.007206195,0.1542068422,0.0380617343,-0.0369985774,-0.2301331311,0.0049480656,-0.1641259491,-0.2017735839,0.1229714751,-0.1477968991,0.108270444,0.3176008463,-0.4681925774,0.4709626138,-0.2818864286,0.1857171208,-0.198204428,-0.0535663292,-0.2812374532,0.5518501401,0.2289519757,-0.5014718771,0.1033398733,-0.3695967793,0.2053667456,0.192868039,-0.0001191628,-0.0957157239,0.1240097806,0.0172286984,0.2794227004,0.2258308828,0.2599862516,0.0434033126,0.0091181993,-0.022887405,0.2111760527,0.2333109677,0.0288525671,-0.1462767869,0.475712508,0.1996723413,0.0683800951,0.1587361097,0.1223477423,-0.0179201271,-0.3447393179,-0.2424110025,-0.0074872836,0.2551131546,0.4874388874,-0.3183261156,0.2209866792,0.1477992982,-0.2789092064,0.0836960822,-0.4995912611,0.0385394022,0.6404622197,-0.5573272109,0.0123135289,0.0039335908,-0.1371411383,-0.1722593457,0.1412511766,-0.0714389086,0.2048701644,-0.8496664762,-0.0566585362,0.2238097042,-0.4533593059,0.0700570643,0.0376135893,0.1382519752,0.0211229026,-0.1229700223,0.1787132472,0.0480568931,0.0241068453,0.2137415856,0.2611123621,0.2568986416,-0.2901437283,0.3060054183,-0.1656566411,0.1110760197,-0.559320569,0.0435666554,-0.0875910893,0.1395756155,-0.26038903,-0.0911267251,-0.1450668722,-0.0010928861,0.2437376082,0.1224617213,0.16437985,-0.2249252349,0.016275337,-0.5754626393,0.0762996003,-0.3193160892,-0.0192709044,-0.0631263629,-0.0396311693,-0.5433811545,0.1638689786,0.4754507244,0.3127381802,-0.0908295736,-0.4087652862,0.0046493872,0.054897096,0.1591065675,0.3263029754,0.3785496056,0.2375260741,0.649328053,0.116466403,0.3008677363,-0.4983567894,0.2498873323,0.105526261,-0.009915052,-0.2646825314,0.2840289176,0.201582551,-0.1050174385,0.1919124722,0.0913679674,0.2648686171,-0.1933916658,-0.0548236333,-0.1775243133,-0.1875754297,0.2483474612,0.1039774194,0.2727549374,0.0782229826,0.0614007749,-0.0588395223,0.1217043996,-0.2332015634,-0.3323019743,-0.2908734977,0.0487952456,0.3455986083,0.4107144475,0.3590714931,-0.1826985478,0.3697884083,-0.1141108572,-0.1149107367,-0.3369792104,-0.3605934381,-0.2536899149,0.0559893548,0.3577569723,-0.0117116673,0.2625139952,0.1336178035,-0.023401374,0.1730084717,0.2555250823,0.1529283077,-0.0441390127,-0.1599845588,-0.1845404953,-0.2531806231,-0.4692437053,0.2813512981,-0.0531997643,-0.0947239995,0.0144146662,0.2635256648,-0.0924054384,0.0841419771,-0.3598734736,0.4103763103,-0.1397632509,-0.0000294809,0.156400606,-0.2134256065,-0.0189389195,-0.2104984671,0.1580802798,-0.1415789723,-0.1717151105,-0.1469049156,-0.0887722746,0.2672272325,0.13230443,0.002933617,0.2626314759,0.3290075064,-0.0504727885,0.070511654,-0.2354250848,-0.1410974711,0.1449600756,-0.0926912799,0.0614887998,0.2999998927,0.2150601894,0.184471339,0.2884706557,-0.2682780325,-0.3507547081,0.0538341627,-0.0101501597,0.1759853065,-0.1773452163,0.013145634,0.5986759067,-0.1233620197,0.0136165908,-0.2358869612,-0.1150441468,-0.3619886339,-0.2789753377,-0.010339723,0.3257572055,0.1760945618,0.0544384234,0.1365503967,0.2462071925,0.1490508914,-0.1596979201,-0.1565377712,-0.1665803939,0.1694262624,-0.2053367943,0.1545628011,0.0625823438,0.0926790237,-0.2570307553,0.4300040007,0.0516269915,-0.0339030623,0.3756663203,-0.0890954062,0.1873241514,0.1525298506,-0.0126634585,0.2188524008,0.1479418576,0.3459525406,0.1692016423,0.1437898129,-0.4511528611,-0.1519183069,0.1961424947,0.2103529871,0.0790235922,-0.1222661883,0.1141883358,-0.1815057397,0.1832985282,-0.0153749026,-0.1870618463,0.7362310886,0.0474283323,-0.1104163975,0.2805086076,-0.4284941554,-0.0939547271,0.3900580108,0.0612931587,0.2331193388,0.1693288684,-0.1249577552,-0.0459342524,-0.0291668698,-0.2392556816,-0.0362743028,0.0800577775,-0.3402797282,0.061263185,-0.0924087912,0.4969219267,-0.4114156961,-0.4017821848,0.0227532499,-0.1363874376,-0.0535736457,0.4672952294,-0.1314401478,0.0236187,-0.0362695865,-0.0113865444,0.0388440676,0.3347727656,0.156779781,-0.0217868797,-0.0881472006,-0.0752070248,0.3987684548,-0.0621618815,0.2075349689,0.2854368985,-0.0574674681,0.2356499135,0.0421252362,-0.0356818102,0.0262664538,0.3805266321,0.0301337205,0.1732631922,0.1848597229,-0.5782219172,0.1626747847,-0.163552314,0.1181733534,0.1189880297,0.0275810603,-0.4057868719,-0.1938906163,-0.0397473946,-0.4883069992,-0.296564281,0.0322543196,-0.0325806886,0.1934853643,-0.0064605447,0.1003118232,0.1495274305,0.0736577809,-0.1381520033,-0.1220574453,0.0768527016,-0.0614772812,-0.2239359319,-0.048726093,0.0344485268,0.0997959003,0.0538104735,-0.2458163649,-0.1084734052,-0.2988756597,-0.1147123873,0.0434826389,0.0423186161,0.21405904,0.4646483362,0.245115608,-0.1603371054,0.11727079,0.0354307257,0.1138501689,0.0141474232,0.1571184695,0.1290081888,0.0388406776,-0.2746428251,0.5560711026,0.7195617557,0.0079728663,0.2117627263,0.1725878119,0.1281331182,-0.0888722166,-0.2836923003,0.0189995691,-0.3320361376,-0.047518421,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2067","title":"Multiprocessing windows error","comments":"Thanks this is really helpful !\r\nI'll try to reproduce on my side and come back to you","body":"As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ","comment_length":18,"text":"Multiprocessing windows error \n As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop  \n Thanks this is really helpful !\r\nI'll try to reproduce on my side and come back to you","embeddings":[-0.1506284475,-0.4665443301,-0.085092485,0.231307283,0.0865275562,0.0101603009,0.0336137787,-0.0307324585,0.0230884105,0.2842899561,0.1215858534,0.0667377338,-0.3062893748,0.0160491895,-0.1941297948,-0.1727220565,0.0984337926,-0.1465362012,0.2517451346,0.3242737353,-0.4602844119,0.486584723,-0.3101291656,0.1724273115,-0.4106292725,-0.0460810959,-0.2003931105,0.4842282832,0.2549553514,-0.3877053261,-0.0000801438,-0.3113578856,0.1509665549,0.1404424161,-0.0001155468,-0.1447120905,0.0140570253,-0.0155945476,0.3914166689,0.2356814146,0.1747708768,0.0154116098,-0.0930722207,-0.027862424,0.132762447,0.2751011848,0.0475338399,0.0036513577,0.403698951,0.3019194007,0.1590741128,0.2588334978,0.1020681933,0.0435885414,-0.3204174638,-0.389621377,-0.0509017669,0.2563922703,0.5162356496,-0.2866722345,0.1748640537,0.2624700963,-0.2407959998,0.0373887308,-0.316942066,-0.0094364276,0.431633532,-0.5044822693,0.0530499406,-0.0857313499,-0.0860016197,-0.1723192185,0.3172115386,0.1498873085,0.1057220623,-0.8734208941,0.0373722538,0.2236952931,-0.3608475327,0.0636084974,-0.0768172219,0.000486433,-0.0733843148,-0.2057019621,0.106475234,-0.0420243628,0.0588759296,0.162166357,0.1567879617,0.2581148148,-0.2660805583,0.3123065829,-0.0895460024,0.0824717656,-0.5067335367,-0.0130569059,0.0244877208,0.2107514292,-0.281976223,-0.2238519341,-0.0398795083,0.1608051062,0.2515883148,0.181238845,0.2087535411,-0.1637963802,0.0577949211,-0.5973331332,0.2277550101,-0.4697573483,-0.1418823451,-0.0908495039,0.0590582006,-0.5761107206,0.3017511666,0.4158447385,0.1878656745,-0.1029671282,-0.3051148355,0.2279174775,0.0387915261,0.244416669,0.2676872015,0.3037410676,0.3295402229,0.67036587,0.0046987813,0.1355355531,-0.5587133169,0.1353852004,0.0535593033,0.0962220579,-0.3082456291,0.1239422709,0.2694671154,0.0077629364,0.164147377,0.1423988342,0.2745949924,-0.3172977865,-0.0431899056,-0.2245990038,-0.1705032289,0.2402047515,0.2170298845,0.1954387873,-0.0491465889,0.0771684945,0.0033321951,0.2177007794,-0.3714387119,-0.3710877895,-0.0780069754,0.0910405517,0.4564350545,0.4124155641,0.2775190175,-0.0414841846,0.2997341156,-0.2898950875,-0.1395444274,-0.4417795241,-0.2753013372,-0.3028675616,-0.0770642534,0.2549555302,-0.013891736,0.3129976094,0.3275535107,-0.0539206825,0.1966925859,0.3646476269,0.2357976139,-0.2295782119,-0.076528579,-0.01541859,-0.1692055315,-0.2164342701,-0.0256465394,0.1248811483,-0.0502498113,-0.1582021564,0.2369344383,0.1396849602,-0.0421049297,-0.325414449,0.4733939469,-0.1873985678,-0.138039127,0.2417535782,-0.2508323193,0.1379858553,-0.0901914462,0.074640967,0.078596428,-0.1308210194,-0.0758600682,-0.0913820043,0.2532756925,0.1211822405,-0.0288552847,0.3372828662,0.1698221266,-0.0188710131,0.0452157408,-0.3747882247,0.0022795128,0.0269336868,-0.2805185616,0.0384882465,0.3604878485,0.1296922415,0.1333177537,0.2604677081,-0.1930642724,-0.2732436955,0.0716792122,-0.0973805264,0.0637126565,-0.201838389,-0.0118380021,0.4987510443,0.0421448089,-0.1541185826,-0.1427766979,-0.0749818385,-0.3986949027,-0.3253808618,-0.0215554293,0.3518841863,0.2070241123,0.1487410814,0.1229823679,0.2170087397,0.0977165103,-0.1447271258,-0.1434539706,-0.0267221518,0.2210915238,-0.2565096021,0.091993168,0.0154685685,0.1554853916,-0.2237295657,0.4834907055,0.0780260339,-0.0517863892,0.2152548432,-0.1356084943,0.1757023036,0.1661110371,0.0268721301,0.2828970253,0.2699865103,0.3601727188,0.1210275888,-0.0984187275,-0.3358142972,-0.3411460519,0.0787207037,0.2047636211,0.1856312752,-0.0494339094,0.0344514064,-0.1975595951,0.1480878592,-0.0296658818,-0.4081707597,0.6608490944,0.1290354431,-0.1238795221,0.2897005975,-0.416824162,-0.1286766082,0.2691907287,0.0189799685,-0.0485235676,0.1837819517,-0.1021908894,-0.1305869967,-0.0627082437,-0.0959941,-0.0835921988,0.0905502811,-0.3112027049,0.0754139945,-0.0683130026,0.4818834662,-0.3881742954,-0.4849456847,-0.0363850668,-0.201094985,0.0933721662,0.3769926727,-0.0660397336,-0.0369546078,0.063978903,-0.0753771067,0.1353933662,0.3412621617,0.2241446227,-0.0824958757,-0.0757005289,-0.0427121148,0.3543700874,-0.1073642969,0.2451773137,0.3000584543,-0.008039441,0.1237863973,0.1547088772,-0.0708740205,0.0497369952,0.254612118,-0.0004117277,0.1000178233,0.1898938566,-0.4014510214,0.0981199816,-0.3271220922,0.2163941264,0.2593770325,0.1389705986,-0.3894452155,-0.2654180527,-0.1531652808,-0.4650588632,-0.1933446229,0.0158451255,-0.1655590534,0.2833057344,-0.2524057925,0.0624522232,0.1090325117,0.3357043862,-0.1297424138,-0.1685179323,0.2200897932,-0.032310348,-0.3071311414,-0.2082278132,0.2030312866,0.2279906869,0.0628904179,-0.247068435,-0.1562498361,-0.3293305337,-0.0154413581,-0.018792307,-0.0382837914,0.2496671826,0.3877030313,0.1706475317,-0.1693151891,0.1369193047,-0.0877079442,0.03437078,0.0354771949,0.1656462699,0.2234891057,0.0942002535,-0.1743258387,0.4798260927,0.5712582469,0.0682216212,0.2840141654,0.1166627631,-0.0279757325,0.0197366383,-0.1986924112,0.104167968,-0.4029532671,-0.1182696894,0.16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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2067","title":"Multiprocessing windows error","comments":"if __name__ == '__main__':\r\n\r\n\r\nThis line before calling the map function stops the error but the script still repeats endless","body":"As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ","comment_length":20,"text":"Multiprocessing windows error \n As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop  \n if __name__ == '__main__':\r\n\r\n\r\nThis line before calling the map function stops the error but the script still repeats endless","embeddings":[-0.1108912677,-0.4392870367,-0.0885387287,0.2090211362,0.0622631758,0.0388560519,0.0055700028,0.03431236,0.1305547059,0.3507117927,0.1845920682,0.1130338311,-0.2703493237,-0.0625594631,-0.1630701721,-0.0991831198,0.095834516,-0.0558623001,0.2845197916,0.3060237467,-0.5200814605,0.4720445573,-0.3290190399,0.1505489945,-0.3321788013,-0.0268693548,-0.1273716986,0.4326402247,0.277725637,-0.4478860795,-0.0330367759,-0.309070617,0.1182406545,0.1131975502,-0.0001193809,-0.1579200476,-0.0142006651,-0.0612700544,0.4552111626,0.1623017192,0.1796985269,0.0962326601,-0.1407280117,-0.1364838779,0.213292256,0.3244244158,0.118041724,-0.0536647364,0.4028441608,0.2655664682,0.1118752733,0.2055491209,0.0939192697,0.0754237399,-0.2695282996,-0.3726022243,-0.0564221442,0.2890223861,0.5033324361,-0.3820000887,0.1414164007,0.3143700957,-0.3030370176,0.0375135019,-0.2931313515,-0.064066574,0.5305408239,-0.5780696273,0.0976736173,-0.1105160043,-0.170950681,-0.2201832086,0.3771393299,0.1610389054,0.0507414714,-0.8099094629,0.0275954138,0.1364703476,-0.4156236649,0.0305882376,-0.1491321474,0.0135831144,-0.0268383697,-0.1467842609,0.0975081772,0.0018120224,0.0674111247,0.222169742,0.2512000203,0.2676575184,-0.3172388971,0.3002196252,-0.0685031116,0.116116181,-0.4423087239,-0.0357514955,0.0773455799,0.1887768209,-0.2333673686,-0.2691735029,0.0109956246,0.1386380941,0.2498514652,0.213896811,0.2139116228,-0.1912622303,0.1267627478,-0.6191260219,0.2186712474,-0.4347941577,-0.1045144573,-0.0480078869,0.0685980618,-0.5401844382,0.3463864326,0.4573223293,0.2797254324,-0.1079146639,-0.2896052599,0.295633167,0.0119032739,0.2019830793,0.257234782,0.3027789295,0.3510761857,0.6872230172,-0.0302588232,0.0749516934,-0.5382590294,0.1709015071,0.1013110653,0.1404786259,-0.3260935545,0.1511106044,0.2556617558,0.0663330182,0.1399945617,0.0715279505,0.2646604478,-0.3194342554,-0.1016467512,-0.2185526639,-0.0239290278,0.2193690985,0.1559507251,0.2279539108,-0.0514835007,0.075115785,-0.0280017797,0.2288478315,-0.3791945875,-0.3146420419,0.0056251031,0.0608852096,0.474270761,0.4800147712,0.3062348068,-0.0977224931,0.2995284498,-0.3837092221,-0.0615888424,-0.440713644,-0.3253913522,-0.3349105418,-0.1095616743,0.3342874646,0.0165768471,0.3336035907,0.3500510156,-0.0059903609,0.1059108749,0.2597663403,0.2196130902,-0.2469085008,-0.1390855163,-0.0079351738,-0.168292582,-0.1990410984,-0.0274671968,0.1558074504,-0.0895251781,-0.1395351291,0.2032585442,0.0898367316,0.0090118442,-0.3770282269,0.4743012786,-0.2610336542,-0.1601289511,0.2233112007,-0.1887081712,0.1628859192,-0.0973653793,-0.0018645076,0.047680404,-0.0739371479,-0.0460991897,-0.0943077579,0.2538553774,0.1462079287,-0.0373493657,0.2930855453,0.1426821798,-0.0440980606,0.0412538275,-0.3803648651,-0.0369581431,0.0419527739,-0.2586917281,-0.0318134427,0.2738764286,0.1100662202,0.1533838063,0.2823152542,-0.1785801053,-0.1833353639,0.0399044976,-0.080184482,0.057030566,-0.2264947146,-0.0417506993,0.370016247,0.0136441039,-0.1833051294,-0.1689893007,-0.1147308126,-0.3820264339,-0.3083172739,-0.0627655685,0.3502779007,0.2317863703,0.097002767,0.0890680775,0.234830305,0.1195787042,-0.147467643,-0.1531673819,0.0274254475,0.1904621869,-0.2588332593,0.0580979772,0.0713124573,0.184904173,-0.2433962226,0.4806243479,0.063830331,-0.0317932963,0.2943502963,-0.1405332088,0.1632416248,0.1101213619,-0.0063846633,0.2178630531,0.1491163671,0.3250453472,0.1516055465,-0.0632991567,-0.3150221705,-0.2845452726,0.0527499691,0.2794163823,0.1230376959,-0.0862115994,-0.0113490885,-0.1456184238,0.1935890317,-0.0329517536,-0.4224088192,0.6393153071,0.0958269164,-0.1360435188,0.2696197927,-0.4196719825,-0.1150873452,0.241999805,0.0387356803,0.0164453946,0.1887708604,-0.1815696061,-0.1324128062,-0.0871769264,-0.197066322,-0.0816427469,0.0346397236,-0.2595418394,0.0612711944,-0.0489790514,0.4968196452,-0.3601051569,-0.4657548964,-0.0427572653,-0.1918397695,0.0447609946,0.4225370288,-0.0910363048,0.0003684022,0.048364386,-0.0752312392,0.1253618598,0.2083099633,0.1887212098,-0.1300217807,-0.0176637694,-0.0903121531,0.4238033593,-0.2149709314,0.2365716547,0.2388014346,-0.036035765,0.0988385081,0.1630550921,-0.0131028984,0.024331525,0.1573209167,0.054088667,0.172326535,0.1641872972,-0.3335534334,0.094628416,-0.425177753,0.2039519548,0.249865666,0.1348346174,-0.4796755016,-0.3031045198,-0.1587507129,-0.3429673612,-0.2128161788,0.0812863335,-0.1794169247,0.3070078194,-0.3042918444,0.1231354997,0.0839877576,0.2612291873,-0.1630327404,-0.2192539573,0.2409317642,0.0028167977,-0.2471591383,-0.1566565037,0.2460523993,0.197476849,0.0351051688,-0.1808267981,-0.1518914253,-0.33172068,0.0068318718,0.0114257615,-0.008176974,0.2596694529,0.4391315281,0.1996572465,-0.1285470724,0.1151487827,-0.1072459146,0.0388023481,0.0945961848,0.1217528582,0.3355996013,0.1242963001,-0.1919824928,0.3964086771,0.585052073,0.0669824779,0.2981480956,0.0712781623,0.0723011494,-0.0084353928,-0.1640690714,0.0700911209,-0.3994195461,-0.1546176821,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2067","title":"Multiprocessing windows error","comments":"Indeed you needed `if __name__ == '__main__'` since accoding to [this stackoverflow post](https:\/\/stackoverflow.com\/a\/18205006):\r\n\r\n> On Windows the subprocesses will import (i.e. execute) the main module at start. You need to insert an if __name__ == '__main__': guard in the main module to avoid creating subprocesses recursively.\r\n\r\nRegarding the hanging issue, can you try to update `dill` and `multiprocess` ?","body":"As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ","comment_length":59,"text":"Multiprocessing windows error \n As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop  \n Indeed you needed `if __name__ == '__main__'` since accoding to [this stackoverflow post](https:\/\/stackoverflow.com\/a\/18205006):\r\n\r\n> On Windows the subprocesses will import (i.e. execute) the main module at start. You need to insert an if __name__ == '__main__': guard in the main module to avoid creating subprocesses recursively.\r\n\r\nRegarding the hanging issue, can you try to update `dill` and `multiprocess` ?","embeddings":[-0.1351817697,-0.4226135314,-0.1194850281,-0.0176143181,0.1080636531,-0.0960128754,0.1320640445,0.0308473576,-0.1013921574,0.3602203131,-0.0551886074,0.0946900994,-0.3736594021,-0.1421171576,-0.1579376906,-0.0196444057,0.0329828709,-0.1128270403,0.0478783175,0.0904548019,-0.3394441903,0.4024178982,-0.2713930011,0.0268041342,-0.401966542,0.0796891004,-0.0617421083,0.5093019009,0.019131951,-0.3639236689,-0.1021172404,-0.2195554376,0.2386392504,0.1321731061,-0.0001034106,-0.1818677485,0.0205885172,0.0046904827,0.1855369657,0.0788146481,0.362146616,-0.2269083709,0.0714554638,-0.0414677821,0.0445211679,0.2184216082,0.005528091,0.0762189701,0.4799880087,0.2293442637,0.263673991,0.0895196125,0.1886885166,0.1408014446,-0.1524417549,-0.2853358388,-0.1197071671,0.4174712598,0.3994277716,-0.1610610336,0.0792940259,0.2322094887,-0.1770654768,0.0859544873,-0.2321711183,-0.000966591,0.4042548835,-0.4059965312,0.0156400278,0.0012499293,-0.09211573,-0.1074041873,0.2277501673,0.1052797064,0.2289254218,-0.8124302626,0.0166804157,0.0472573861,-0.3250298202,0.080849044,0.1410831511,-0.1242963299,-0.0220248494,-0.2145822495,-0.0428274125,0.100537844,0.1382362247,0.0571483411,0.1464475691,0.0077999323,-0.2372009009,0.3869692683,-0.0115062576,0.085008584,-0.4147729576,0.077057384,0.1552436501,0.0051671388,-0.2343978584,-0.2477191985,-0.0266916826,0.1380921006,0.2967424393,0.1935619116,0.1310121119,-0.0973500684,0.1096473336,-0.5587246418,0.3567598462,-0.4095540345,-0.0658391863,0.1163014695,-0.0664674267,-0.4812475145,0.2111390829,0.3640954792,0.1387633979,-0.1785726696,-0.2412886322,0.2611695826,0.0761051923,0.1619967818,0.1748350412,0.3731553853,0.2703039944,0.6323959827,0.1211332902,0.0395691097,-0.5594881773,0.3015013039,0.0585801527,0.0765402988,-0.1999404132,-0.0367091857,0.2190437019,0.0480022505,0.1093272939,0.0806696862,0.1975702196,-0.1294086725,-0.0299188029,-0.1190906018,-0.0193335675,0.1967867166,0.1208981425,0.2237978429,-0.0420531817,0.0469095223,-0.1270228773,0.2816563249,-0.1704918593,-0.2169582993,-0.0966705903,0.2064185292,0.3183296323,0.3302809894,0.4069041312,0.0202212799,0.0787565857,-0.3521757424,-0.212965861,-0.4262780547,-0.1532217413,-0.2204384953,0.1979792714,0.2015244067,-0.2252460271,0.2377127111,0.1215576977,-0.1078297123,0.2043155879,0.254185617,0.2908347547,-0.2798776627,0.0065354672,-0.0297299959,-0.0913808048,-0.2077922672,-0.0343788154,0.0867681205,-0.0955197811,-0.1293618232,0.0654301494,0.1055273861,0.1556668133,-0.1546861231,0.3835849762,0.0252294131,0.062547259,0.1670588851,-0.3813206553,0.1540871114,-0.1082117036,0.1325490475,-0.0153022446,-0.3098196983,-0.1858509481,-0.0440183766,0.3126030564,0.1123026311,-0.0583191514,0.3177084625,0.2139800936,-0.0429484509,0.0330179296,-0.2787873745,-0.0473936722,-0.0326261669,-0.2836299241,0.1109803393,0.2854698896,0.1276585013,0.083592169,0.2665581107,-0.1575900018,-0.2924698889,0.2016206682,0.0617833026,0.0584755093,-0.0908382758,-0.1190522909,0.2270743549,0.1003558263,-0.1943213344,-0.1213511899,-0.1139202788,-0.4869252741,-0.2864394188,0.0616920441,0.404365927,0.0870122164,0.0715784803,0.0119792195,0.2805937529,0.1202455461,-0.1503616422,-0.0562342592,-0.1117319986,0.1633470953,-0.166598618,0.0865739584,0.1966273487,0.1060523614,-0.1940512657,0.3674074709,0.0948762819,0.1114334911,0.2885196805,-0.0115702366,0.165648222,0.1555183828,0.098912783,0.1268939972,0.0978220478,0.4008514881,0.1141287312,0.1021068692,-0.2333516181,-0.2867882848,0.1027944684,0.2684311271,0.0992106199,-0.0008464996,0.1202717423,-0.1331630945,0.0946435481,-0.0441561006,-0.4050086141,0.3301173449,0.1766394973,0.0809020922,0.2493042052,-0.4532828033,-0.1189093217,0.2047943473,-0.1069699898,-0.1498229504,0.2540160716,-0.1136460304,-0.1780838966,-0.1086232215,-0.1908424199,-0.1463781744,0.1059681475,-0.1225523576,-0.1124032363,-0.2060617059,0.5869614482,-0.3379522264,-0.5343268514,-0.2172742486,-0.2400305569,0.2002085447,0.295699954,-0.1551198959,0.1767859161,0.1703995466,-0.1477064937,0.1539740562,0.2446304411,0.1054776385,-0.196619302,-0.0911579654,0.0645607635,0.3895747662,0.0536355153,0.2719883621,0.2185650766,-0.0839993656,0.1159341112,0.0179363005,-0.1176390424,0.006587489,0.2527399063,0.0523972698,0.0870604962,0.2333325297,-0.4007569551,0.1690364182,-0.414524883,0.1989921033,0.2155409604,0.1596940607,-0.2973793745,-0.3184919655,-0.3417880833,-0.4226312339,-0.4237927794,-0.004604165,-0.1088418216,0.1892410517,-0.305377841,0.0938827246,0.1626902968,0.4764080644,0.1099185124,-0.109791182,0.3985269964,0.1149214655,-0.423284322,-0.3036082685,0.073973611,0.0862476006,0.0005403945,-0.3487333953,-0.0938106179,-0.2605395019,-0.0402700678,-0.0411944725,0.0168380681,0.1922750175,0.320517689,0.2199819982,-0.1974911094,0.1253982931,-0.1087216958,0.0102563053,0.0217464101,0.0642498359,0.1324173957,-0.0082423287,-0.3252888024,0.5187214613,0.2988110781,0.0430265293,0.2415017337,0.1495644003,0.0738498792,-0.1125651523,-0.1149780378,0.2159844488,-0.1812377572,-0.1233823076,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2067","title":"Multiprocessing windows error","comments":"```\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 791, in move\r\n    os.rename(src, real_dst)\r\nFileExistsError: [WinError 183] Eine Datei kann nicht erstellt werden, wenn sie bereits vorhanden ist: 'D:\\\\huggingfacecache\\\\common_voice\\\\de\\\\6.1.0\\\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\\\tmpx9fl_jg8' -> 'D:\\\\huggingfacecache\\\\common_voice\\\\de\\\\6.1.0\\\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\\\cache-9b4f203a63742dfc.arrow'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 116, in spawn_main\r\n    exitcode = _main(fd, parent_sentinel)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 125, in _main\r\n    prepare(preparation_data)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 236, in prepare\r\n    _fixup_main_from_path(data['init_main_from_path'])\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 287, in _fixup_main_from_path\r\n    main_content = runpy.run_path(main_path,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 265, in run_path\r\n    return _run_module_code(code, init_globals, run_name,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 97, in _run_module_code\r\n    _run_code(code, mod_globals, init_globals,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 87, in _run_code\r\n    exec(code, run_globals)\r\n  File \"F:\\Codes\\Python Apps\\asr\\cvtrain.py\", line 243, in <module>\r\n    common_voice_train = common_voice_train.map(remove_special_characters, remove_columns=[\"sentence\"])\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1339, in map\r\n    return self._map_single(\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 203, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1646, in _map_single\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 805, in move\r\n    copy_function(src, real_dst)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 435, in copy2\r\n    copyfile(src, dst, follow_symlinks=follow_symlinks)\r\n  0%|                                                                                                                                                                                                 | 0\/27771 [00:00<?, ?ex\/s] \r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 264, in copyfile\r\n    with open(src, 'rb') as fsrc, open(dst, 'wb') as fdst:\r\nOSError: [Errno 22] Invalid argument: 'D:\\\\huggingfacecache\\\\common_voice\\\\de\\\\6.1.0\\\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\\\cache-9b4f203a63742dfc.arrow'\r\n```\r\n\r\nI was adding freeze support before calling the mapping function like this\r\nif __name__ == '__main__':\r\n    freeze_support()\r\n   dataset.map(....)","body":"As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ","comment_length":224,"text":"Multiprocessing windows error \n As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop  \n ```\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 791, in move\r\n    os.rename(src, real_dst)\r\nFileExistsError: [WinError 183] Eine Datei kann nicht erstellt werden, wenn sie bereits vorhanden ist: 'D:\\\\huggingfacecache\\\\common_voice\\\\de\\\\6.1.0\\\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\\\tmpx9fl_jg8' -> 'D:\\\\huggingfacecache\\\\common_voice\\\\de\\\\6.1.0\\\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\\\cache-9b4f203a63742dfc.arrow'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 116, in spawn_main\r\n    exitcode = _main(fd, parent_sentinel)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 125, in _main\r\n    prepare(preparation_data)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 236, in prepare\r\n    _fixup_main_from_path(data['init_main_from_path'])\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\multiprocess\\spawn.py\", line 287, in _fixup_main_from_path\r\n    main_content = runpy.run_path(main_path,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 265, in run_path\r\n    return _run_module_code(code, init_globals, run_name,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 97, in _run_module_code\r\n    _run_code(code, mod_globals, init_globals,\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\runpy.py\", line 87, in _run_code\r\n    exec(code, run_globals)\r\n  File \"F:\\Codes\\Python Apps\\asr\\cvtrain.py\", line 243, in <module>\r\n    common_voice_train = common_voice_train.map(remove_special_characters, remove_columns=[\"sentence\"])\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1339, in map\r\n    return self._map_single(\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 203, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\site-packages\\datasets\\arrow_dataset.py\", line 1646, in _map_single\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 805, in move\r\n    copy_function(src, real_dst)\r\n  File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 435, in copy2\r\n    copyfile(src, dst, follow_symlinks=follow_symlinks)\r\n  0%|                                                                                                                                                                                                 | 0\/27771 [00:00<?, ?ex\/s] \r\n File \"C:\\Users\\flozi\\anaconda3\\envs\\wav2vec\\lib\\shutil.py\", line 264, in copyfile\r\n    with open(src, 'rb') as fsrc, open(dst, 'wb') as fdst:\r\nOSError: [Errno 22] Invalid argument: 'D:\\\\huggingfacecache\\\\common_voice\\\\de\\\\6.1.0\\\\0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f\\\\cache-9b4f203a63742dfc.arrow'\r\n```\r\n\r\nI was adding freeze support before calling the mapping function like this\r\nif __name__ == '__main__':\r\n    freeze_support()\r\n   dataset.map(....)","embeddings":[-0.0126367873,-0.3874335885,-0.0540233664,0.2022801787,0.1353472173,0.0187555365,0.0908477604,0.0778494179,0.0393029898,0.2612574995,0.0116539327,-0.0717005804,-0.3001478612,-0.0775017887,-0.1249796599,-0.2181285769,0.1993367821,-0.1225006506,0.269847393,0.356908977,-0.4530968964,0.5776935816,-0.3369621933,0.2170217484,-0.3738832772,-0.0177008342,-0.2501499355,0.632183075,0.3016950786,-0.3766628504,-0.0409930572,-0.3141252398,0.1125467271,0.1129684299,-0.0001192791,-0.089968428,-0.0525463074,-0.0609052889,0.3652586341,0.290307045,0.2686345875,0.0707848147,-0.147267893,0.0154838562,0.2896389961,0.2888123691,-0.006915614,-0.1151354834,0.5103618503,0.3108975887,0.0964280143,0.2305839509,0.2101578414,0.0053207423,-0.2566919029,-0.3280828595,-0.0426160879,0.3265691698,0.4258087873,-0.3297048509,0.1285290271,0.2043698281,-0.2732006311,-0.0346861966,-0.4689787924,0.0665710866,0.4387367368,-0.5151545405,0.1164577082,-0.0351320766,-0.2860221863,-0.1260883212,0.1404029429,-0.029868219,0.1666518003,-0.7686854601,0.0550998002,0.1580188274,-0.3607051671,0.091051884,-0.1080025062,0.0767953098,-0.0104954215,-0.1862132698,0.2111445963,-0.0008120959,-0.019244967,0.1576218009,0.2552622259,0.2031299174,-0.392275095,0.3448496461,-0.0837166831,0.0803355053,-0.5561103225,0.0755068734,0.0039692852,0.3153910041,-0.2706433535,-0.2075476348,-0.0714274347,0.0356827155,0.2324625999,0.1222752929,0.0230680108,-0.0048259669,0.0517110117,-0.6401047707,0.1803151071,-0.2331951559,-0.0265000183,-0.0146057038,-0.0706128255,-0.6683466434,0.1515746415,0.4515000284,0.2356954962,-0.1143412441,-0.3852737844,0.0835883915,-0.0305759497,0.2230826914,0.3593811095,0.3545972109,0.3032065928,0.7514006495,0.0042986241,0.2004503459,-0.4899940193,0.1166705862,0.0720064417,0.1323166192,-0.2918378115,0.3349353671,0.2156770825,-0.0239379443,0.1369723827,0.164311111,0.360123992,-0.3316320777,-0.1411790848,-0.1307917535,-0.1664626002,0.2033270001,0.0766125321,0.1889309138,0.0378992856,0.0553929545,0.0216193609,0.0089605767,-0.2776421905,-0.4142327309,-0.1573722363,0.0547512919,0.3095566034,0.4490513206,0.3361923993,-0.040479254,0.3349382579,-0.2400988936,-0.0812296048,-0.3279943466,-0.380537957,-0.301134795,0.0936458185,0.3241679072,-0.0066859694,0.2289615721,0.3274364471,-0.0684341341,0.1032435969,0.297083497,0.2495614588,-0.1760049015,-0.1911648363,-0.0364766307,-0.2446795106,-0.3650830388,0.1048916802,0.0467591211,-0.0803728402,0.0639581904,0.3371140957,-0.1018789113,-0.0524265952,-0.3592816889,0.4140973091,-0.2576809227,-0.0918970332,0.1771696806,-0.2155274004,0.0601212196,-0.203685835,0.0210249219,-0.1643568724,-0.0989763066,-0.0998637974,-0.0078449547,0.2219620347,0.097081393,0.0769435093,0.2889820933,0.1434745491,0.0259143934,0.087331906,-0.2388994545,-0.2275289297,0.191991359,-0.1981401294,0.0689499155,0.2805078626,0.1310913265,0.1273567975,0.2248363644,-0.2467898726,-0.3968261182,0.0477636456,-0.0143680833,0.186863929,-0.1970892102,0.0084711136,0.5062981844,0.0165170114,-0.0949578583,-0.2039612979,-0.169604525,-0.3601671159,-0.3072246611,-0.1549814045,0.3238282204,0.1839459389,0.0657491758,0.1309625357,0.3159964383,0.0797458068,-0.0409716405,-0.1492735893,0.0090151094,0.0980657861,-0.1574138105,0.1081504896,-0.0386535116,0.1315318495,-0.2199793458,0.4834600985,0.1523741633,-0.0931472704,0.4009242058,-0.180802539,0.1260690838,0.1215776429,0.1076726019,0.2794555128,0.1402688473,0.4186382592,0.0721666068,0.0782997832,-0.4159421921,-0.2257855833,0.0954073593,0.1177244633,0.0903874934,-0.0744839162,0.0460262522,-0.1072593927,0.104970485,-0.0948022157,-0.3587927222,0.7520030737,0.0278983396,-0.0459603108,0.3025048077,-0.335475266,-0.1355493367,0.2962962985,0.0279772859,0.1241544336,0.2167376727,-0.1388795674,-0.0001352541,-0.0964124799,-0.1687215865,-0.044995565,-0.0071348264,-0.406647414,-0.024322018,-0.0248642135,0.5111103058,-0.3909213841,-0.4135290086,-0.1359335482,-0.2026254684,-0.0086053135,0.415025264,-0.0883477032,0.0252901986,0.0009805209,-0.0015359793,0.0286414064,0.3189967275,0.0660698041,0.0243099462,-0.1014132276,-0.0852872804,0.3036764562,-0.1688354462,0.1168232188,0.228347227,0.0036367669,0.0986431539,0.0210669655,0.0300963633,-0.0424238406,0.300599277,0.118265748,0.1036484092,0.1613984108,-0.4868730605,0.2238768786,-0.3242425323,0.1113819033,0.1531891227,0.09359321,-0.3151869476,-0.2749288082,-0.0205562767,-0.439335227,-0.2900476754,0.1368467957,-0.2023085356,0.1643991917,-0.1601554155,-0.0626424402,0.1890598089,0.1449099779,0.0051386734,-0.1338879764,0.1356221586,-0.0922913924,-0.2108957916,-0.0406019278,0.0750097632,0.1215820089,0.0531441942,-0.2295716852,-0.0980330259,-0.4399691522,-0.0351897888,-0.0250896756,-0.0186105464,0.1120939031,0.4357006252,0.2380365133,-0.1481883973,0.0748033896,-0.1058745235,0.1633775085,0.0396495052,0.119326964,0.1462286711,0.0730898976,-0.1429154277,0.4496085346,0.630186975,0.154060021,0.3096239567,0.0698949695,0.0727033913,0.0026802786,-0.2179089934,0.1445668042,-0.377972573,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2067","title":"Multiprocessing windows error","comments":"Usually OSError of an arrow file on windows means that the file is currently opened as a dataset object, so you can't overwrite it until the dataset object falls out of scope.\r\nCan you make sure that there's no dataset object that loaded the `cache-9b4f203a63742dfc.arrow` file ?","body":"As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ","comment_length":47,"text":"Multiprocessing windows error \n As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop  \n Usually OSError of an arrow file on windows means that the file is currently opened as a dataset object, so you can't overwrite it until the dataset object falls out of scope.\r\nCan you make sure that there's no dataset object that loaded the `cache-9b4f203a63742dfc.arrow` file ?","embeddings":[-0.3012990952,-0.2888678908,-0.0838766396,0.198555544,0.02432587,-0.052532386,0.0652546212,-0.0146976653,0.0849000886,0.2829809785,0.1703787446,0.2638420165,-0.3119595647,-0.1078653112,-0.328858465,-0.1583811939,0.0069893231,-0.0020367806,0.138228938,0.2635188103,-0.4521364272,0.4412011504,-0.2616143525,0.2926626205,-0.3932662308,-0.2115527093,-0.1386809349,0.4438162744,0.0661770478,-0.4786926508,0.0005648832,-0.4143380225,0.1744173318,0.1870656013,-0.0001159315,-0.0684330389,0.0426297598,0.04080908,0.2628766,0.208753258,0.2227461338,-0.0434499197,0.0806638524,-0.0629374161,0.3600696623,0.1284508407,-0.0207276102,-0.1225399077,0.4598210752,0.4392023385,0.1687345356,0.2901833653,0.1781140119,0.0979067758,-0.1573561281,-0.335421145,-0.1023714244,0.2453910857,0.3822478056,-0.2205765843,0.0241906587,0.2357566059,-0.1961411834,0.1414800137,-0.3374696076,-0.0767130777,0.4196671546,-0.4534091055,0.1091992185,-0.0303223785,0.0826913714,-0.1416197419,0.2035350949,0.0713245869,0.0753627419,-0.842799902,0.0727443546,0.2241643965,-0.3516501784,0.0234747157,0.0954195634,-0.0722953007,-0.1213669553,-0.085589312,0.0745182112,0.0074092401,0.022639405,0.2146469653,0.2442722321,0.2144128978,-0.0997226313,0.3688184619,-0.1694298387,0.0775763169,-0.4725194275,0.1255840659,-0.1103662252,0.1066867039,-0.2916961908,-0.0352253467,0.137009114,0.1670446992,0.1998490542,0.2073924094,0.2034582496,-0.0674649626,0.092772834,-0.5522864461,0.1139974147,-0.4261636734,-0.166234985,-0.0775045827,-0.0583077446,-0.6734814048,0.3865687847,0.3823788166,0.2839415967,-0.1032035127,-0.3083171546,0.1988817006,-0.0128980838,0.1473649293,0.2252822518,0.2256570458,0.3258136213,0.7511101961,0.0178365782,0.1931528449,-0.5615142584,0.1034065038,0.1231035963,0.0790546387,-0.3985133469,0.1447613835,0.1824887693,0.0066033914,0.1923017204,-0.0137633383,0.2352599502,-0.278847903,0.0069424268,-0.2292852998,-0.0583152622,0.2340582013,0.1257648319,0.2475094944,0.0495033227,0.0967292115,0.0479145609,0.326302737,-0.4066798389,-0.4346692264,-0.2120714933,0.0855837092,0.3318754435,0.3249842227,0.1921969801,-0.1262155175,0.2943273485,-0.2313537151,0.0385075994,-0.4561121464,-0.2042520195,-0.3638229668,-0.0964917466,0.2022486031,-0.1977696419,0.2708090544,0.1846260577,-0.0608924963,0.15179272,0.4130903184,0.1046212092,-0.2259478271,-0.1728733927,0.0734241605,0.0210492555,-0.1163441539,-0.1803096086,0.0592478588,-0.1359141916,-0.1248631477,0.1888771802,0.0812422633,-0.0329829268,-0.1442915201,0.2504725754,-0.1793409288,-0.0492131077,0.136493057,-0.2602326572,0.1588729471,-0.2608008683,0.0828104541,0.0153116994,-0.2696191967,0.0008563873,-0.3050094545,0.3133668303,0.0978256389,-0.0198541898,0.3342060149,0.2149971128,-0.0492851287,0.1161437631,-0.2854295075,-0.0591478571,0.0361086018,-0.1702705175,-0.0094009656,0.1469446719,0.1502004415,0.1828629076,0.3816410899,-0.2632250488,-0.2783358693,0.0617177077,-0.0859493241,0.0882493556,-0.2227866054,-0.0987012982,0.4120551646,0.0145449629,-0.0910349116,-0.0943504795,-0.0471290424,-0.3684729934,-0.396433562,-0.1642622203,0.2239807993,0.0859246626,0.1844966263,-0.0255651437,0.2649831772,0.074529551,-0.1738922,-0.1268713325,0.0038351624,0.1243378818,-0.4475932717,0.1477015615,0.1227588281,0.0794961601,-0.1409550309,0.4905478954,0.1705703586,-0.0011535034,0.2986797392,-0.1256642938,0.2619835436,0.1233756617,0.1530236453,0.1067121178,0.1765255928,0.5011745095,-0.0110435979,0.0172143895,-0.3096614778,-0.2230342627,0.0612099022,0.303337425,0.1709257215,0.0834095478,0.0470923036,-0.3043580055,0.2167911977,-0.024833573,-0.2864579558,0.6092163324,0.0939816087,-0.0480900072,0.231730327,-0.373400718,-0.1023523882,0.2643330991,0.0958670825,0.2297342718,0.1650208235,-0.1404709816,-0.0639905632,-0.1730157733,-0.2523163557,-0.1724818051,0.094815582,-0.3047887087,0.1145616844,-0.1076311916,0.4711627066,-0.4364013672,-0.5715088248,-0.021258451,-0.2287167162,0.0943652615,0.3082365692,-0.0678597838,0.0081791058,-0.1076450124,-0.042923402,0.1949378103,0.2007215172,0.2071800828,-0.1450234354,-0.1535374522,-0.052669093,0.3419601321,-0.1540523469,0.2248845845,0.2756938338,0.1928737164,-0.0250498727,0.1833356768,-0.0884312987,0.1462684423,0.0871223658,0.0022804325,0.1489719748,0.0244928058,-0.4572962224,0.1740276217,-0.4738200903,0.0965530649,0.1864628494,0.1273257881,-0.2730242312,-0.1548935622,-0.1657605022,-0.4591580331,-0.3735136688,0.1161698848,-0.1351014525,0.3259915709,-0.1724887937,0.1658078432,0.1888148636,0.2840820253,-0.1159976423,-0.0464286767,0.2811430097,0.0274788402,-0.2755481303,-0.2080998421,0.1801069677,0.1271849424,0.0716013238,-0.06430424,-0.2595281899,-0.2846136093,-0.0832406133,0.1008598059,-0.1380191892,0.2387013435,0.477081567,0.2313629687,-0.1933699101,0.1174181923,-0.0919038802,0.0458456948,-0.070402056,0.1200022548,0.2052814215,0.2294620723,-0.2155586034,0.6325454116,0.458214879,0.0307927225,0.368042618,0.1240982339,0.0656026006,0.0024617233,-0.1606244743,0.0912006646,-0.4899176955,-0.0612865463,0.1534350663,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2067","title":"Multiprocessing windows error","comments":"Now I understand\r\nThe error occures because the script got restarted in another thread, so the object is already loaded.\r\nStill don't have an idea why a new thread starts the whole script again","body":"As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop ","comment_length":34,"text":"Multiprocessing windows error \n As described here https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2\r\n\r\nWhen using the num_proc argument on windows the whole Python environment crashes and hanging in loop.\r\nFor example at the map_to_array part.\r\nAn error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop  \n Now I understand\r\nThe error occures because the script got restarted in another thread, so the object is already loaded.\r\nStill don't have an idea why a new thread starts the whole script again","embeddings":[-0.1712803096,-0.6625725627,-0.015667554,0.3150129318,-0.0103678983,-0.0258934647,0.1337396502,-0.1321054697,0.0881273896,0.2671475112,0.232795313,0.0681709498,-0.1253809929,-0.0673753694,-0.1413185745,-0.0939045995,0.0912751779,-0.0172677543,0.2123175263,0.2924873829,-0.4489221871,0.4319563508,-0.2962608933,0.1619097292,-0.42137146,-0.0308511462,-0.2437839359,0.5600068569,0.3595537543,-0.4632327557,-0.0308159143,-0.3616602719,0.2421137989,0.1394914687,-0.0001222365,-0.1207989976,0.0044936901,0.0334821828,0.4292647541,0.2617112398,0.256551981,0.1222397462,0.0499286354,-0.0862630606,0.2329550385,0.3592978716,0.1269486696,-0.0144656608,0.4354672134,0.2034126818,0.0813900158,0.3555282354,-0.0857627019,0.0056748446,-0.3969982862,-0.5251868367,0.0046940357,0.296761781,0.5274708271,-0.3028681874,0.0461270064,0.1945869923,-0.2724509835,0.1417519748,-0.2901106179,-0.0503193475,0.4512547553,-0.4748883247,0.1035405248,-0.0433434583,-0.0536869615,-0.1352439821,0.3585698307,0.0834635943,0.0728166699,-0.7990478277,0.0103992308,0.190216586,-0.2553431988,0.096899651,0.0283484794,0.0178157985,0.0180599336,-0.2019286454,0.2141691148,-0.0824945942,0.0619110763,0.2800361514,0.1466712654,0.5134991407,-0.1706375331,0.2777177691,-0.087668404,0.0011055532,-0.3932269812,0.0370178968,0.1774582118,0.277544111,-0.252810955,-0.0979533121,-0.0176566932,0.1252793372,0.1939222962,0.1167160645,0.2955619395,-0.2059355825,0.0939263403,-0.7193553448,0.2374643087,-0.3886641264,-0.3085561097,-0.1262356341,0.0120177334,-0.4726979434,0.5541794896,0.4347582161,0.3090134859,0.0277839955,-0.3281398714,0.2083690166,-0.0347872265,0.0783002228,0.1989098787,0.2392332107,0.2733900845,0.7265440822,-0.0037451158,0.0854377598,-0.6531360745,0.1251110286,0.0120789027,0.0753920078,-0.2927777171,0.2110494971,0.1864272803,-0.0895856246,0.2464871556,0.2471140325,0.2002475709,-0.4891739488,-0.0408615172,-0.1518585533,-0.1792335659,0.2111970633,0.3016980588,0.1857576966,0.0291875117,0.1140998751,-0.0160736237,0.2811371088,-0.4462313354,-0.4116550088,-0.1033919603,0.0195803791,0.4211145341,0.4560446143,0.2546276152,-0.1894743592,0.2538910806,-0.2814007998,-0.0544106737,-0.461558193,-0.3425110877,-0.3588590324,-0.2177269459,0.389501065,0.0493825972,0.2522602975,0.3038569987,-0.1281827688,0.0314975567,0.2952198088,0.1101751328,-0.0572135672,-0.2617501616,-0.0374993123,-0.300983578,-0.2548570931,-0.0252919346,0.1373521835,-0.1725201309,0.0516206399,0.2739418447,0.1959233433,-0.0675699785,-0.2892933786,0.4228897095,-0.3635500968,-0.1916923821,0.1980202496,-0.1281657219,0.222415328,-0.1405159533,-0.0704592764,0.0285913814,-0.0140975127,0.0600583218,-0.0747787729,0.3166647851,0.0692889243,-0.1556330472,0.2202437371,0.1203227118,0.0767462179,0.1401014179,-0.2823578119,-0.152595073,0.0815182775,-0.1464046538,-0.0648122951,0.4011059701,0.0777115673,0.1414192915,0.2770750821,-0.1633775085,-0.2156054378,-0.0240562093,0.0627138317,-0.112335965,-0.2868498266,-0.0649836585,0.5646058917,-0.0967248604,-0.1501529515,-0.1792104989,-0.0841849223,-0.350661397,-0.2286669761,-0.0900260657,0.3097996712,0.3073635995,0.0568663478,0.1322549284,0.2861675024,0.169241786,-0.1924744695,-0.1999479383,-0.0042844173,0.112461172,-0.2249925286,0.1471295357,-0.0961142406,0.186507225,-0.3468338251,0.5800670981,0.0672759786,0.0132123213,0.1750359684,-0.0966913775,0.0544390194,0.0918219611,-0.0746489167,0.1807294786,0.1711861193,0.3868533075,0.2444775552,-0.0928145573,-0.2572655976,-0.4158343375,0.0337533802,0.2161758393,0.1949465573,0.0153190996,-0.0202917568,-0.1501761377,0.1898733526,-0.1834151298,-0.433026731,0.7109690905,0.0500399172,-0.2200763524,0.3194894791,-0.3909804821,-0.1599504948,0.1458370537,0.070384644,0.2738213837,0.1810692698,-0.1337866336,-0.0707348585,-0.1608113647,-0.1408390254,-0.1102713794,0.031002637,-0.3158797026,0.1546570361,-0.055671297,0.5288811922,-0.571741879,-0.2965098321,0.10394492,-0.1431491524,-0.0262530204,0.4722512364,0.0434093922,-0.1602646112,0.1058794633,-0.0084899422,0.0533195026,0.4678297937,0.2351025343,-0.1078584865,-0.0809373334,-0.1481265724,0.2720526755,-0.2653984129,0.2656965256,0.2764616311,0.0217561033,0.0874209255,0.1147224978,-0.075492315,0.0789229274,0.2230233848,0.036299713,0.1391550303,0.248261258,-0.3195886016,0.073961243,-0.3315235078,0.1612979174,0.1816623658,0.0405378379,-0.4710936248,-0.1950660646,-0.1901166737,-0.2966616154,-0.0453175604,0.0324400067,-0.1622371078,0.2462160885,-0.294074446,0.0460797958,0.0580279827,0.3004622161,-0.1297500879,-0.1775104254,0.1552203894,-0.2279863954,-0.1865616292,-0.1770904809,0.2402932197,0.0929943472,0.0529624596,-0.2480436116,-0.3508509398,-0.4391510785,-0.031079337,0.0163289234,-0.0975261778,0.2750748098,0.3872764409,0.1705066115,-0.1009671763,0.0555064976,-0.2020672709,-0.0402283557,-0.0234500598,0.0423740745,0.2111396044,0.2805841267,-0.1063534915,0.3829604983,0.4770336747,0.0414049625,0.2799215019,0.0125130145,0.0776947513,-0.0477388985,-0.2113067061,0.003753881,-0.470631361,-0.2145147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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Hi ! Thanks for reporting.\r\n\r\nCurrently there's no way to specify this.\r\n\r\nWhen loading\/processing a dataset, the arrow file is written using a temporary file. Then once writing is finished, it's moved to the cache directory (using `shutil.move` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1646))\r\n\r\nThat means it keeps the permissions specified by the `tempfile.NamedTemporaryFile` object, i.e. `-rw-------` instead of `-rw-r--r--`. Improving this could be a nice first contribution to the library :)","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":67,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Hi ! Thanks for reporting.\r\n\r\nCurrently there's no way to specify this.\r\n\r\nWhen loading\/processing a dataset, the arrow file is written using a temporary file. Then once writing is finished, it's moved to the cache directory (using `shutil.move` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1646))\r\n\r\nThat means it keeps the permissions specified by the `tempfile.NamedTemporaryFile` object, i.e. `-rw-------` instead of `-rw-r--r--`. Improving this could be a nice first contribution to the library :)","embeddings":[-0.1139019504,0.2815682292,-0.0789349675,0.1177367494,-0.0259675644,0.0905662552,0.4060142338,0.1146640405,-0.0934797376,-0.0789992288,-0.1739809513,0.1220881864,-0.0590267405,-0.2090671808,-0.0211602096,0.0670156926,0.1375491321,0.1199803948,-0.0209175125,-0.0775220543,-0.0809950233,-0.0633883476,-0.0703818351,-0.0224659387,-0.1490499079,-0.2274232358,0.2636191547,0.2165788859,0.0505269207,-0.2489475012,0.3933263719,0.5603933334,0.0788718909,0.0368106626,-0.000116711,-0.1258149594,0.0032831449,-0.1000813544,0.0103589203,0.1562263221,0.0038824517,-0.1965801418,0.0558968522,-0.1056093276,-0.0717570186,-0.0097516607,0.0705216452,-0.6625335813,0.5320053697,0.0911226645,0.1612340957,-0.0026611446,-0.1905544251,0.2374723703,0.3361723721,0.0495368987,-0.2999814749,0.1237215474,0.2532830834,-0.1594290137,-0.0995529592,0.148567304,-0.2521521151,0.1400251538,0.2718129456,-0.0997622386,-0.4176008403,-0.4772850871,0.0809204727,0.2960629463,0.747620523,-0.2350284159,-0.6028381586,-0.3080027103,0.0544243641,0.0413428061,0.4234346449,0.3541356623,0.0877567008,0.2651182115,-0.2606980503,-0.0438046753,-0.2725637257,0.070776321,0.35934484,-0.0616410412,-0.0856513306,0.1499899328,0.1002019048,-0.111253649,0.1271584183,0.1506175697,-0.337290138,0.474403739,-0.3153336346,0.1050160602,-0.2302905917,0.2877424955,0.1954170316,0.4209030867,0.2329907119,0.2070785314,-0.0053031011,0.1705385149,0.1138963699,0.20385167,0.3360154629,0.3591210842,0.5324926376,-0.2701568305,-0.024704719,-0.0427581742,0.0312547907,-0.3672650158,0.103521429,0.1941137612,0.0301708225,-0.2266283631,0.204646647,0.0779337958,-0.1002264842,-0.167516008,0.0820592269,0.2720398009,0.0798468813,0.0128088081,0.065899387,0.1747447997,0.0094335834,-0.215938881,0.0131437788,-0.3183414638,-0.3558564484,0.4290776551,0.40105474,-0.291205287,0.0979927853,0.1806641221,0.1176939309,-0.1855770797,0.4365372062,-0.1271778941,0.4656579792,0.4169456959,0.1208031625,0.3317728937,0.0265738927,0.0928413346,-0.1925934702,0.2829546332,-0.2726329565,-0.2166964263,0.175218001,-0.0269472282,-0.1224512458,0.2985428274,-0.4154493511,0.0384297632,0.502469182,-0.1370154321,0.5040848255,0.2393234074,-0.0870369673,-0.4691597521,-0.1199874654,0.3807287514,-0.2105481327,-0.0402141921,-0.1333297491,-0.0546319708,-0.0819173157,0.4235098064,-0.109972313,0.0455709472,-0.1004369631,0.2615795732,0.5675195456,-0.1785275191,-0.5578048229,0.0242959373,0.2172668874,-0.1352314651,0.1179342046,0.0860395804,0.1229751408,-0.1273262948,-0.284052819,0.3851826191,0.2767724395,0.2349369973,0.1342643797,-0.0928195268,0.0077565159,-0.0463661291,-0.0722496733,0.0897894725,0.375887692,-0.3563419282,0.2464521676,-0.2243757546,0.2806276977,0.4626378715,0.1498912126,-0.0122647164,-0.0047416734,-0.0646034554,-0.5048742294,0.2847756445,-0.1048576757,-0.3897142708,-0.0608885214,-0.4528693557,0.1028999016,-0.1895225346,-0.2636232674,-0.1018768474,0.017187167,-0.1585030556,0.0539396107,-0.3411321044,0.0777298659,0.3728661537,0.1256472021,0.0926500782,-0.3170206845,0.1152751297,0.0476624407,-0.1084339693,-0.2096910626,0.0605445951,0.1726613939,-0.002027127,-0.2862120271,0.3028354645,0.1127103344,0.1189708412,-0.0751321688,0.3147709668,-0.0050380905,-0.0476806946,0.1310374737,-0.2222432345,-0.0586357638,-0.0701221153,-0.2442379892,0.4377450943,-0.1195915714,0.0303490069,-0.2249271423,0.1831503659,0.132362932,0.046571523,-0.2125594616,-0.1815370768,-0.0345945694,0.1969378144,0.3852101266,0.1386466175,0.0139447292,0.0297811031,0.4034456313,0.2228404135,0.1470018327,-0.0202917978,-0.2516340911,0.0367880724,0.3316293061,0.3446901441,0.2373872399,0.0239808597,0.2902217507,-0.2486601919,0.3055889606,-0.113760747,0.02035111,-0.0909836739,0.1459841877,-0.1794549972,-0.109683834,-0.0330922529,-0.1341073364,0.2339397073,0.0153258545,0.0891915709,-0.3397918642,-0.1028057933,-0.2307841927,-0.1499902457,0.2217230201,-0.1383294165,-0.5088286996,-0.0281061325,-0.2192546576,0.2135237455,-0.0701654404,0.1367037147,-0.356069088,0.091114141,-0.0506913625,-0.2864060402,-0.2728375793,0.1188561991,0.0805146396,-0.0211581737,0.2400399595,-0.517811358,0.2112212926,0.1159661859,0.2316272706,-0.3298773468,0.0041569145,-0.1636846215,0.0082725715,0.0884402767,-0.2788843811,0.1476421654,-0.1584482789,-0.0314575508,-0.1058922186,-0.3152564168,-0.2138490826,0.0034107615,-0.0378890149,0.1163538545,-0.2580218315,-0.1951401532,-0.1801881343,-0.18530038,0.6918123364,0.1287395656,0.2276194692,0.1614701301,-0.1822317243,-0.2958165407,-0.2885788977,0.183935985,-0.3551091552,-0.4615796208,0.3765580654,-0.4756702781,-0.3748242259,0.158117339,0.082302928,0.1495977789,0.42214185,-0.0201894753,-0.0683132187,-0.2880268097,0.2520219088,-0.2304074317,0.1999935061,0.0781799331,0.0989564955,0.1633312851,-0.043246828,-0.1822208613,0.1520093828,-0.029681135,-0.2216472775,0.1875087768,0.0989704803,0.0829665884,0.8199104667,0.4076658487,0.2582357228,0.3022812009,0.3216724098,0.6892610192,0.0174009334,-0.0783379301,0.1767946184,-0.0483368449,0.0242172834,0.0791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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Hi @lhoestq,\r\nI looked into this and yes you're right. The `NamedTemporaryFile` is always created with mode 0600, which prevents group from reading the file. Should we change the permissions of `tmp_file.name` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1871) and [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1590), post creation to 0644 inorder for group and others to read it?","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":47,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Hi @lhoestq,\r\nI looked into this and yes you're right. The `NamedTemporaryFile` is always created with mode 0600, which prevents group from reading the file. Should we change the permissions of `tmp_file.name` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1871) and [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1590), post creation to 0644 inorder for group and others to read it?","embeddings":[0.0629384518,0.0359809436,-0.1101711914,0.2581417561,0.0601995401,0.1329313815,0.3463633358,0.0934354737,-0.2962673604,-0.0303029157,-0.2803182304,-0.0526039489,0.0845718682,-0.3134352565,-0.0208834391,0.0789132565,0.09932556,0.0465859957,0.0940522999,-0.0247985981,-0.1166854352,-0.0544997044,-0.0450048149,0.0221961923,-0.3177739978,-0.1033814847,0.1908843368,0.2698512673,0.1508690715,-0.200258255,0.2497221529,0.4694798887,0.0598714575,0.3494140506,-0.000114599,-0.1124707162,0.0119852358,-0.1353016943,0.118162021,0.1928916574,-0.0182461236,-0.22133407,-0.1113025844,-0.152967304,-0.16108495,0.0837538466,0.1846528947,-0.5527175665,0.5385577083,0.1245617867,0.1720823199,0.0057427944,-0.1951499581,0.2009535134,0.2407549173,0.0036829012,-0.3262526393,0.0302172042,0.2792116404,-0.0454495847,0.002083211,0.1100866273,-0.3180331886,0.053196229,0.2149992287,-0.0566222593,-0.3709004819,-0.5180937052,0.094792597,0.1781882793,0.6756849289,-0.1113857925,-0.5353290439,-0.2812231779,-0.0895615816,0.0903991908,0.4896016717,0.3181558549,0.0230461117,0.3016812205,-0.3137831986,0.0671202093,-0.3229381442,-0.04458021,0.2822045982,-0.0871294439,-0.173117578,0.0809838921,-0.0632286146,-0.1068553478,0.0429089703,0.0462721847,-0.2184241563,0.3654839694,-0.1981413662,0.1555486619,-0.1800216883,0.2678591907,0.111198917,0.1944088042,0.2835121751,0.1963796318,0.0309714507,0.1938633919,0.0246972349,0.1463092864,0.287943095,0.3549540639,0.5183432102,-0.3496189415,-0.1995034665,-0.0610197224,-0.0284248274,-0.3210827112,0.0414985754,0.158202067,-0.0354944766,-0.2328118831,0.2620952129,0.0351260155,-0.0712618753,-0.1606621891,0.0406592898,0.1971991211,0.0480622053,-0.0866670161,0.0407198146,0.1288432777,-0.0269408561,-0.2982856631,-0.0283729099,-0.4603434205,-0.3541779816,0.474407047,0.3691009879,-0.2717590034,0.0936808288,0.0842931941,0.1707918346,-0.2367326766,0.3412822783,-0.1334002465,0.5103940964,0.4080188274,0.2221882343,0.3522750735,0.0669590682,0.0441810973,-0.1179875359,0.2986825407,-0.3013738692,-0.2637017667,0.3343161047,0.0149558317,-0.0801657885,0.3795684874,-0.2512296438,0.0185795836,0.681684494,-0.170981437,0.351564616,0.2320223898,-0.2374744564,-0.5314830542,-0.151695773,0.3780290782,-0.1693877429,0.0742439628,0.0357906483,-0.0674911365,-0.1018869132,0.4217514396,0.0083622066,0.0582154803,-0.119858779,0.2898958921,0.4725685716,-0.1939911097,-0.5428640246,0.1267787516,0.1299711466,-0.0664729849,0.3089483082,0.0464779623,-0.0427411087,-0.2918027639,-0.1804221272,0.1915933341,0.212032631,0.1901742071,-0.0247668047,0.0432117768,-0.0482288934,-0.0622350574,-0.1300258487,0.1985943466,0.3295985162,-0.3414554,0.2142969221,-0.1713816971,0.2108123153,0.4186030328,0.073629804,0.0669039264,0.0291220397,-0.016330624,-0.4610424638,0.2555752993,-0.0218510367,-0.4010992348,0.0054408996,-0.4914402664,0.1290147752,-0.3363108933,-0.3406080008,-0.0647975355,0.0632672161,0.0183589105,0.0761864036,-0.3664520681,0.2044519037,0.5146685243,0.1338807195,0.0282470174,-0.2692756355,0.0394070074,-0.1339569539,-0.086526975,-0.2505769134,0.0464060009,0.2739406228,-0.0465336554,-0.2661620677,0.297026515,0.0823474601,0.1673611104,-0.1172355711,0.3938716948,-0.0788085014,0.0712931827,0.0673542693,-0.2324420363,-0.0185895637,-0.1873426586,-0.2011199892,0.3971303105,-0.0731996968,-0.0277610347,-0.2225701064,0.019858025,0.1111690477,0.0375960991,-0.2298998088,-0.1336543262,0.0004847185,0.2611473203,0.3052661121,0.1462403834,0.0192765314,0.0581715442,0.395688951,0.1736739129,0.0108145904,-0.1291410178,-0.1472670883,0.0067958757,0.2061491311,0.1864343435,0.2714275122,0.0716433153,0.3254535794,-0.1534226835,0.3050518036,-0.2041756362,0.0301484074,0.0434623472,0.086762324,-0.1232263371,-0.1194540709,-0.0113524795,0.0347760841,0.3009331822,0.1134383753,0.046524711,-0.4610220492,-0.0870063603,-0.0742906407,-0.2454039752,0.1902792454,0.0319869779,-0.5370312929,-0.0665605441,-0.2675870955,0.2792454958,-0.0333689936,0.0761397183,-0.2946603298,0.1234034449,-0.0175063461,-0.1909071207,-0.3536759317,0.1222712398,0.0023418423,0.0004374341,0.2619783878,-0.6254097819,0.24114573,-0.0882334188,0.2667477727,-0.0968482494,0.0500756912,-0.1185679957,-0.1074600145,0.1476898193,-0.4817633629,0.1109736115,-0.1808289587,0.0987089127,0.0289317183,-0.2333098054,-0.2578202486,-0.016946882,0.0593120754,-0.0710202307,-0.2332138419,-0.2273849845,-0.1899101883,-0.1511593461,0.5195321441,0.105715476,0.269163996,0.224483043,-0.3414484859,-0.2507658303,-0.2969976962,0.2749943137,-0.4282775223,-0.5996938348,0.3007461131,-0.5475546718,-0.4464110732,0.1092202589,0.2504780889,0.1346750706,0.3845810592,-0.0407423377,-0.1219737604,-0.1917245835,0.3223770261,-0.2072091848,0.1761509627,0.0659391582,0.083215557,0.1708462089,-0.0917485803,-0.1216966584,0.0654681548,-0.0577644072,-0.1907241046,0.2162450254,0.1568779945,0.1997010559,0.7097824812,0.5631089807,0.3239501417,0.2527132332,0.2194504589,0.6763728857,0.031847883,-0.1741032749,0.2044819891,-0.0473355539,-0.0900380015,0.2030479163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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Good idea :) we could even update the permissions after the file has been moved by shutil.move [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1899) and [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1646) actually.\r\nApparently they set the default 0600 for temporary files for security reasons, so let's update the umask only after the file has been moved","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":45,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Good idea :) we could even update the permissions after the file has been moved by shutil.move [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1899) and [here](https:\/\/github.com\/huggingface\/datasets\/blob\/f6b8251eb975f66a568356d2a40d86442c03beb9\/src\/datasets\/arrow_dataset.py#L1646) actually.\r\nApparently they set the default 0600 for temporary files for security reasons, so let's update the umask only after the file has been moved","embeddings":[-0.1486676335,0.140438199,-0.077205576,0.0294155963,0.129579559,0.0463582389,0.3478874564,0.0346235968,-0.1299446672,0.0200762283,-0.1389166564,-0.067659311,0.018578738,-0.2712926567,-0.0381213203,0.1840073764,0.1872666627,-0.050572902,0.0260022245,-0.150437355,-0.1744749844,-0.2032019049,-0.121594049,-0.0883418694,-0.2847404182,-0.1473378539,0.1421106309,0.290712595,-0.026803337,-0.205343917,0.3806231916,0.6655226946,0.1290059835,0.1411438435,-0.0001143174,-0.1695264429,0.0144900102,-0.0989307687,-0.0480888598,0.1656078994,0.0056180959,-0.176836744,-0.1670405269,-0.1249091774,-0.1164288819,0.0679443851,0.1516608298,-0.6368479729,0.4671244025,0.2360779941,0.1751862019,-0.0875462815,-0.1900076568,0.0938937366,0.2960259318,0.0340903811,-0.1558020562,0.1395667493,0.3170785904,-0.2480278313,-0.0745830759,0.0521345474,-0.2701604962,0.0413465984,0.1690549552,-0.1223843694,-0.4245992899,-0.5232907534,0.1400272995,0.1401761323,0.5876787901,-0.1320253164,-0.6810224056,-0.1792589724,0.0988089591,0.1577606797,0.3485298753,0.3710069954,0.1510733515,0.3128534853,-0.4745202065,0.0269012675,-0.1794065833,0.0649565756,0.2594903708,0.0227286164,-0.0561285093,0.1227333471,0.0393806435,-0.1470490396,0.226629734,0.1350258142,-0.2991216183,0.4194127321,-0.2455161363,0.1774930209,-0.2231111079,0.4376940727,0.1799850315,0.4181913137,0.0872389749,0.2030546665,0.0778266937,0.1625780314,-0.0534402765,0.2625301182,0.3795915246,0.4361659884,0.5410408974,-0.1719222367,-0.1775008738,-0.0319096595,0.0393198729,-0.3863134086,0.0216778889,0.3015040159,0.0488694981,-0.1432594061,0.1021598727,0.0534757152,-0.145302102,-0.148937434,0.0250715576,0.3009731174,0.0430658981,-0.0739550292,0.1384394169,0.2581042051,-0.0287790261,-0.2274983227,-0.0411532447,-0.3239300549,-0.2817668319,0.4088188112,0.3677934706,-0.1878886074,0.101187408,0.2275787145,0.1257278919,-0.1951033175,0.4349660277,-0.0672740266,0.438270092,0.4045004845,0.1662745625,0.2871085405,0.117904298,-0.0030374888,-0.0721817687,0.2393576801,-0.2409156114,-0.2144767195,0.3211545944,0.0223250538,-0.1168767363,0.2934509516,-0.2296114564,-0.0007313314,0.4634190798,-0.0361244567,0.4591250718,0.207006678,-0.2314863503,-0.3873574734,-0.0402342789,0.4332177639,0.0299061686,-0.0863910094,-0.1236816794,0.0183941908,-0.0481883101,0.3993483782,-0.0471750498,0.0548309982,-0.1530556828,0.1871629655,0.4613814354,-0.258734405,-0.6373534203,0.0726479664,0.1465536952,-0.0786797106,0.2618924081,0.08661706,0.0377138332,-0.1949888021,0.0043648044,0.342358321,0.2405193001,0.2537207305,0.0663498864,-0.0077340221,-0.0602349527,0.0200081933,0.0013894761,0.2903935015,0.3742437065,-0.3105019629,0.2526362836,-0.1824452728,0.2496407777,0.5188255906,0.1698431671,-0.0004984581,-0.0231630951,0.0068137473,-0.5052887797,0.2801291645,-0.0990816429,-0.3432284296,-0.0170614012,-0.5464772582,0.0339361951,-0.295126915,-0.2967860699,-0.1955869794,0.0566845685,-0.0701138452,0.0690510795,-0.252803117,0.1593259871,0.3845067322,0.1605415046,0.0566413701,-0.1457927227,0.1346704215,-0.081649743,-0.1207492873,-0.180744797,-0.0492200963,0.3001846969,-0.0570606478,-0.2775063515,0.3385224938,0.17911686,0.0763240308,0.0040688557,0.254802525,0.0230381414,0.2091920376,0.0859539658,-0.2223036736,-0.1074581072,-0.0814243183,-0.2597409487,0.4529503584,-0.099596858,-0.0642129555,-0.1837929487,0.2674842179,0.1773672551,-0.0096661076,-0.1697296798,-0.1619027853,0.0502426103,0.2686875165,0.3828038871,0.191071853,0.0564034171,0.0289090741,0.4421550632,0.228987962,0.1401608735,-0.0511930883,-0.1317163408,0.0894537792,0.2256492972,0.3671813607,0.2941921651,0.0511363149,0.2728003263,-0.1256541908,0.3981505334,-0.1889785826,-0.0232524648,-0.0589449815,0.0303115062,-0.1392395198,-0.1011135727,-0.0179827027,-0.1365193874,0.3469208479,0.0543535613,0.1654342413,-0.3396596611,-0.1209827587,-0.1977423728,-0.1341227293,0.2395082861,-0.0243767239,-0.5376529694,-0.0739468113,-0.1817751527,0.2030472904,-0.0811516717,0.2060426027,-0.1902074516,0.038556084,-0.0144935763,-0.0730997398,-0.2904838026,0.0984546393,0.105922468,-0.0069641774,0.2305039614,-0.6279553771,0.1780643314,0.0020359608,0.2553761601,-0.4001020491,-0.034234155,-0.2547353506,-0.0081992056,0.0870362893,-0.3297148347,0.0861911476,-0.1502718925,0.0583186597,0.0083366567,-0.2916869819,-0.20983845,0.0311511327,0.0012752177,0.0132982181,-0.2869879305,-0.1387992948,-0.1922626793,-0.2366529107,0.6767053604,0.1732081324,0.121654667,-0.0473201387,-0.1707835495,-0.4097970426,-0.2292842269,0.1703742146,-0.3344255984,-0.5908139944,0.3133073747,-0.4503074586,-0.4157241285,0.0389999114,0.1510161012,0.0554660074,0.4662570357,-0.0203363318,-0.2331549823,-0.3058477044,0.2212012559,-0.2192827314,0.1822481155,0.0141008068,0.0414113104,0.1408321112,0.0244252291,-0.1305058599,0.1217963621,-0.018032698,-0.3016529679,0.0822101235,0.2010877728,0.204924643,0.580342114,0.4103990197,0.2980840206,0.2216827571,0.1889456064,0.6551346779,0.0210199095,-0.2211459577,0.2425189167,-0.0396676734,-0.0471641086,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Would it be possible to actually set the umask based on a user provided argument? For example, a popular usecase my team has is using a shared file-system for processing datasets. This may involve writing\/deleting other files, or changing filenames, which a -rw-r--r-- wouldn't fix. ","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":45,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Would it be possible to actually set the umask based on a user provided argument? For example, a popular usecase my team has is using a shared file-system for processing datasets. This may involve writing\/deleting other files, or changing filenames, which a -rw-r--r-- wouldn't fix. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Note that you can get the cache files of a dataset with the `cache_files` attributes.\r\nThen you can `chmod` those files and all the other cache files in the same directory.\r\n\r\nMoreover we can probably keep the same permissions after each transform. This way you just need to set the permissions once after doing `load_dataset` for example, and then all the new transformed cached files will have the same permissions.\r\nWhat do you think ?","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":75,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Note that you can get the cache files of a dataset with the `cache_files` attributes.\r\nThen you can `chmod` those files and all the other cache files in the same directory.\r\n\r\nMoreover we can probably keep the same permissions after each transform. This way you just need to set the permissions once after doing `load_dataset` for example, and then all the new transformed cached files will have the same permissions.\r\nWhat do you think ?","embeddings":[-0.1194682121,0.259423852,-0.0967040062,0.030385362,0.0231789872,0.108349517,0.3391306102,0.2117253691,-0.3362343907,-0.0368237682,-0.144387126,0.0220604017,0.0109385578,-0.2525072694,-0.0301325992,0.0743881837,0.2278102189,0.1208136603,-0.1184287965,-0.0778083727,-0.075373821,-0.1556305587,-0.0645339414,-0.0688157603,-0.2214254439,-0.1528554559,0.2344744802,0.1463683099,0.0678269491,-0.0460343957,0.2212159932,0.5300709009,0.1394367814,0.1297320575,-0.0001113997,-0.1518128812,-0.0184238739,-0.0894035697,0.0535904691,0.1944102198,-0.1325050443,-0.2093675733,-0.2764068842,-0.1594099402,-0.1122246161,0.1167044714,0.1642095745,-0.6036273837,0.5433728695,0.1404612362,0.1918493062,-0.1789263189,-0.4104811549,0.0826761946,0.2436183691,0.0785933211,-0.1785174906,0.04801514,0.2731805742,-0.0799815059,-0.0498960316,0.1404462755,-0.2539242208,0.0236761626,0.2357445508,-0.1408498287,-0.2738702297,-0.5522377491,0.1436683536,0.0834262669,0.6017299891,-0.1232601777,-0.511813879,-0.2026664019,0.1050677076,0.2066523582,0.271225214,0.3691605031,0.0936826393,0.2965431809,-0.567749083,-0.0496130064,-0.1733717471,-0.0445344932,0.2027281225,-0.1169199646,-0.0359124318,0.0578542314,-0.0220860094,-0.0612771027,0.2435199022,0.1687997431,-0.3648124933,0.3007421792,-0.1860245764,0.081230022,-0.2588999867,0.4488408566,0.1628779918,0.3240204751,0.0920307189,0.1952226907,0.0709580034,0.1408430487,-0.0613670163,0.2960296273,0.2489762455,0.4393759072,0.4983150661,-0.3928606808,-0.2331755012,0.0259574745,-0.0032002854,-0.388525337,0.0423042811,0.3014153838,0.0058666081,-0.1941059083,0.2318097502,0.0230727643,0.027775785,-0.2067477554,0.0348299928,0.2914712131,0.1373826563,-0.0485934094,0.0312974676,0.1531578898,0.0877905637,-0.4073995352,-0.0142287789,-0.3188688457,-0.420042038,0.4248704016,0.4544428289,-0.1531492621,-0.0572805852,0.2649716735,0.2661159337,-0.1907425821,0.2379843742,-0.0216703843,0.4578373134,0.3296433687,0.1938187033,0.311994493,0.0720115304,0.0607616752,-0.146786198,0.292378366,-0.29846403,-0.1055828929,0.3784654438,0.0557939261,-0.0133023085,0.3157940209,-0.3977424204,0.0588390194,0.5510915518,-0.1779379696,0.3704768121,0.2052333355,-0.0273762494,-0.3665219545,-0.1580839753,0.3250620067,0.0082274694,-0.0332447477,0.017076645,0.0294689406,-0.0730763301,0.2770831287,-0.0774066001,0.0894968137,-0.1327400208,0.1082334742,0.5130734444,0.0005703575,-0.5620501637,0.1342485696,0.1925625503,-0.1734322906,0.2454901934,0.0096967714,0.118533656,-0.2128259689,-0.2583186328,0.3564736545,0.1773575544,0.2536076307,0.1109802723,0.0733582154,0.041068811,-0.1329737753,0.1154408455,0.4333033264,0.2992612422,-0.1824632734,0.1617095321,-0.3766427338,0.2751658261,0.45553267,-0.0365321115,-0.1015515253,-0.0176632442,-0.0073524737,-0.3946579099,0.247298032,-0.3183889985,-0.3896824121,0.075565286,-0.4126992226,0.0492651425,-0.3918960989,-0.1642128825,-0.0987653658,0.1022050977,0.0285070986,0.0124798473,-0.3670446575,0.1314273328,0.4621594548,0.2361068577,-0.0110711921,-0.1629889905,0.1359280497,-0.0919627696,-0.0392834507,-0.2335417867,-0.0225048736,0.3091940582,-0.0741982162,-0.2938147783,0.2166342139,0.0186285283,0.1711794883,-0.1068189964,0.3129374087,0.0382642783,0.0255691111,0.1322211623,-0.2872108519,-0.1348640919,-0.1101088226,-0.1841524839,0.3122933209,-0.1572696865,-0.0030589013,-0.1704038084,0.2214102,0.1716569662,-0.0735706985,-0.1987816989,-0.1661064476,-0.088059999,0.1540699452,0.4002358317,0.1994902194,0.1589418948,0.068356894,0.3487052917,0.1581217945,0.0729849041,-0.0398593061,-0.1238119975,0.1162960455,0.1139309257,0.4215901494,0.2496374995,0.0624098107,0.338224709,-0.1299830824,0.3225905001,-0.1342461705,-0.011377262,-0.1550781578,0.072602883,-0.125195846,-0.1960162669,0.0252934471,0.0131512424,0.2432511747,0.1521943659,0.0736545175,-0.3208759427,-0.0531048663,-0.2478597462,-0.1940211803,0.2186927348,-0.0531890206,-0.4666417539,0.0351025276,-0.2137262672,0.1207758933,-0.0420896076,0.2620489597,-0.2179088444,0.1883021593,-0.0940762311,-0.0955028534,-0.2950898707,0.2740035057,-0.0162446368,0.0140992003,0.2692720592,-0.7202327251,0.0822767466,-0.1037915945,0.3351433277,-0.3325995207,-0.0013951976,-0.1677365005,-0.0583965369,0.0400000736,-0.5119997263,0.0790724754,-0.1321319342,0.0055728839,-0.1503351927,-0.238878563,-0.1751628965,0.0638330132,-0.008444095,-0.0630751848,-0.2691434622,-0.1105901375,-0.0748643503,-0.1529547572,0.4172171056,0.0242529884,0.2210449427,0.0712548941,-0.2961513698,-0.4592242241,-0.1708663702,0.3224810362,-0.3507023752,-0.466373086,0.3746478558,-0.4652484655,-0.3919489086,0.0089758979,0.1525062919,0.095647268,0.5313209891,0.0081075681,0.0065638674,-0.2156218439,0.1642853767,-0.2448116094,0.1427258551,0.0633396283,0.1203003153,0.1232927889,-0.0262694508,-0.1419418752,0.0039663757,0.0027323298,-0.1979462057,0.055900313,-0.0234035719,0.2471247315,0.6841848493,0.4743024707,0.2400143892,0.2036267966,0.338916868,0.6224419475,0.1208772957,-0.1956527084,0.146668762,-0.0395012163,-0.1463398933,0.071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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"This means we'll check the permissions of other `cache_files` already created for a dataset before setting permissions for new  `cache_files`?","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":20,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n This means we'll check the permissions of other `cache_files` already created for a dataset before setting permissions for new  `cache_files`?","embeddings":[-0.142857343,0.2471917123,-0.1365763098,0.0218382087,-0.0611331612,0.1519794762,0.2972180843,0.174941346,-0.2172032446,-0.021546457,-0.0839366764,-0.058681149,0.0317838453,-0.2856924236,-0.0319773704,0.1332599372,0.1821877956,0.0443536714,0.0965484157,-0.0609316044,-0.1018133834,-0.1174136102,-0.0272719245,-0.0965083688,-0.2872996032,-0.1700498164,0.2464344651,0.1395107508,-0.0033842588,-0.1678146273,0.3220139146,0.5283101797,0.1039889827,0.1232832149,-0.0001113227,-0.1744422317,0.033192683,-0.0960740373,0.0774690434,0.1945365965,-0.1794525534,-0.1781274825,-0.1543626934,-0.144608736,-0.0952903852,0.1648488343,0.1669419259,-0.6869980693,0.4415377676,0.189167425,0.2091258317,-0.2013516277,-0.3777410388,0.0495412871,0.2510105371,0.0257300995,-0.151621744,0.129445523,0.2156837583,-0.1824462116,-0.032410115,0.0404493399,-0.3017315865,0.1421568543,0.1594826132,-0.1164247617,-0.3029307723,-0.5466194749,0.1151201949,0.1016687006,0.7427783608,-0.1304640025,-0.522177577,-0.2247487158,0.0260399971,0.1467635334,0.3395380974,0.3094368577,0.0320562236,0.2644794881,-0.4822494686,-0.0603268854,-0.1234009415,-0.014905205,0.2107096463,-0.0578127392,-0.0543184206,0.0511019975,0.0089813937,-0.0314753205,0.169854641,0.1223381236,-0.3839349151,0.3322783113,-0.2173950076,0.0476983376,-0.2045626938,0.4771674573,0.2469502836,0.3256938159,0.1847619265,0.1895772666,0.0483525209,0.2100984305,-0.0137557806,0.2042704076,0.3664555848,0.3078047335,0.5489116311,-0.3686142564,-0.150949046,-0.0088529363,0.0877646878,-0.3914700747,0.1254261732,0.2992861867,0.0144491643,-0.1791375428,0.1573959142,0.1027249396,-0.0705528408,-0.2997865081,0.0422843881,0.254150629,0.0851157978,-0.0763174742,-0.0722942278,0.1465340704,0.0324665457,-0.3402569294,-0.0242126584,-0.2963759005,-0.3305017352,0.4524023235,0.4746803343,-0.216855064,0.0492036045,0.2484591752,0.1645347327,-0.1960693151,0.3496295214,-0.0917858779,0.3922555447,0.325597018,0.236175105,0.315621227,0.0267675649,0.0272481088,-0.1347603351,0.2677808404,-0.3485305309,-0.1394203454,0.2724356353,0.0698515624,-0.0414474979,0.2683043778,-0.4000046253,0.0963903144,0.58205688,-0.1821672469,0.3664318919,0.3113206923,-0.1842417121,-0.3967323303,-0.1390602887,0.3455355763,-0.0855741948,-0.020813765,-0.0343694091,0.024369942,-0.0582221448,0.2858800888,-0.0335437842,0.2149337977,-0.1570042819,0.1900594383,0.462510258,-0.0394868851,-0.5458711982,0.1002073511,0.2019240707,-0.1777585447,0.1742074937,0.0081294021,0.0909030214,-0.1267573535,-0.1123052686,0.3109281957,0.1437157989,0.2210419029,0.120558545,0.0834582895,0.0209560115,-0.1079564393,0.0776221231,0.3490343988,0.3411437869,-0.2767296433,0.1331041008,-0.254473567,0.2185061276,0.4260055721,0.074725084,-0.1456937045,-0.0268901233,-0.0790520906,-0.4902147949,0.2971678674,-0.1627685428,-0.4536584318,0.106226638,-0.4373446703,0.0376185998,-0.3469263613,-0.1941640526,-0.0228366554,0.1004707813,0.0246154983,0.0370799601,-0.3169473708,0.1327251047,0.4763798118,0.2424373031,-0.0093253832,-0.0706985295,0.1099776477,-0.0890513435,-0.0683467612,-0.2553044856,-0.0874614194,0.2582812309,-0.0127727296,-0.2527157962,0.2583009303,0.1220441982,0.0432484299,-0.0386163369,0.2850624919,0.0263188332,0.1067762077,0.0334019624,-0.2647302747,-0.0848738402,-0.1350855082,-0.2590285242,0.3348577619,-0.0561041124,-0.0997390896,-0.1969343424,0.1580215991,0.1715060472,-0.0287488885,-0.0890948251,-0.1804487705,-0.0196946412,0.146156162,0.4113472402,0.1597367972,0.0770034865,-0.0062580802,0.3378331661,0.1522029936,0.1228649393,-0.051602833,-0.0356426761,0.0973333195,0.1813939214,0.4060631096,0.2397594899,0.0390439592,0.3714919388,-0.2152391225,0.2886893153,-0.1711799353,-0.0337363221,-0.1191084757,-0.0279642604,-0.0900122896,-0.1263743192,-0.0320602246,-0.0565780178,0.2918662131,0.1202108338,0.0907791182,-0.3121079504,-0.1080121771,-0.2118527591,-0.2145947516,0.2602289915,-0.0251871645,-0.5517680645,-0.0475474298,-0.1702072769,0.1539954096,0.0121373646,0.1869329214,-0.3203655183,0.1947370172,-0.0745554492,-0.1296607405,-0.2249746919,0.2378225029,0.0894279927,0.0461001061,0.3092416227,-0.6353923082,0.1701682955,-0.0190933682,0.2957183123,-0.3063868582,0.0594555065,-0.1494595557,0.0051053748,0.1052008495,-0.5079748034,0.0896257013,-0.1639073491,0.0149555402,-0.1765128672,-0.1853563935,-0.2112885416,-0.0408041999,-0.0131543204,-0.0053919931,-0.2753220797,-0.0776922852,-0.0943378881,-0.1151461899,0.4051733911,0.1105337515,0.2217614055,0.0120439706,-0.3030836284,-0.3747799397,-0.2446158826,0.2633528113,-0.3676407039,-0.6070088148,0.359326303,-0.4767056704,-0.3456923366,0.0433665141,0.1331198215,0.0991720632,0.4297140837,-0.0041101635,-0.0211545713,-0.2280497402,0.2210166603,-0.1964038312,0.1139001474,0.0318868272,0.0017948297,0.1376152188,-0.0344894566,-0.106222406,0.0473002046,0.0065845279,-0.2572190166,0.1131489351,0.0360500067,0.1607574373,0.7411164045,0.4388709068,0.3008575439,0.3324706554,0.3114252687,0.6650049686,0.0971945599,-0.1134444103,0.1356713772,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"> This way you just need to set the permissions once after doing load_dataset for example, and then all the new transformed cached files will have the same permissions.\r\n\r\nI was referring to this. Ensuring that newly generated `cache_files` have the same permissions","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":43,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n > This way you just need to set the permissions once after doing load_dataset for example, and then all the new transformed cached files will have the same permissions.\r\n\r\nI was referring to this. Ensuring that newly generated `cache_files` have the same permissions","embeddings":[-0.1649939716,0.2504474223,-0.1311805099,0.0018534489,0.0164031461,0.069685705,0.3859352171,0.2012920529,-0.243175894,-0.0268525351,-0.0832427293,0.0317552835,0.0088323886,-0.2032843828,-0.0566226169,0.1244930476,0.2251376659,0.1058571711,-0.0469120219,-0.0759035721,-0.1004975885,-0.1561995745,0.0020295053,-0.1382747442,-0.2803068757,-0.1393663138,0.250901103,0.1854483038,0.0243440717,-0.1551291049,0.2401857376,0.5586883426,0.1195890307,0.1278124452,-0.000108509,-0.1533883512,-0.055117704,-0.0804115534,0.0271396842,0.1929069906,-0.0747349039,-0.1969085336,-0.1917303503,-0.1530253291,-0.1357583404,0.1554865688,0.100497894,-0.6505795717,0.5274168849,0.1168165579,0.2193976641,-0.209947139,-0.3844199479,0.0381097049,0.2251027673,0.0479847714,-0.1684113145,0.1200009957,0.2247462869,-0.1187588498,-0.0439551845,0.1332410127,-0.3048710525,0.1042669639,0.2058614343,-0.1382554322,-0.2443340719,-0.5563910604,0.046993196,0.0875775963,0.6264379621,-0.0997860432,-0.5162678361,-0.2456747442,0.1034486592,0.1492293626,0.2983026206,0.3047206998,0.0544698052,0.2399952114,-0.5317008495,-0.0625439957,-0.1593406498,-0.0585576221,0.2357821465,-0.1048958525,0.0082516326,0.0236698687,0.0207114555,-0.0796308666,0.1798670888,0.1895848215,-0.4009330273,0.3293815851,-0.2059290409,0.0583290905,-0.2278135568,0.4405922294,0.2491425574,0.3186540604,0.1585353911,0.2033318281,0.0782409832,0.138376072,-0.1031730548,0.25676018,0.3083620071,0.3042286038,0.5483266115,-0.373014003,-0.2216498405,0.0326330364,0.0325833224,-0.3678637743,0.0904005691,0.3038929403,0.0947401226,-0.1761719137,0.2136986703,0.1021986604,-0.0048243995,-0.2351537645,-0.0421869718,0.2320284396,0.15459764,-0.0539756417,0.0452964231,0.1512020975,-0.0480053052,-0.3640183508,-0.0102021722,-0.3659335077,-0.3953460753,0.4914129972,0.4919768572,-0.1580433846,-0.0312158372,0.2863592505,0.1892482638,-0.1733047068,0.2945377827,0.009412948,0.4202327132,0.2606206536,0.1965859681,0.297606498,0.0516441613,0.0267492365,-0.0783434138,0.3222174048,-0.3108763099,-0.1091556996,0.2779992223,0.093814522,-0.0476204678,0.2909790277,-0.3671762347,0.0775539577,0.5478352904,-0.1628122181,0.3683684468,0.2572859228,-0.0955010355,-0.3553991914,-0.1884086132,0.3907552361,-0.0281885117,-0.0231803469,0.0068803262,-0.0012331039,-0.1035846323,0.2831072211,-0.0647817925,0.1235592961,-0.1371367723,0.1607354879,0.4129779041,0.0167185403,-0.4896352291,0.1158401445,0.1546936035,-0.1152524054,0.2207864225,0.0094192261,0.1001000702,-0.1719110906,-0.1612109691,0.3214648962,0.2198984027,0.2970988452,0.0704512745,0.0779512376,0.0667019188,-0.1811559796,0.0924618393,0.4182465374,0.2940609157,-0.2295071781,0.1312010437,-0.3995591402,0.2576647699,0.3549225032,0.056103576,-0.1388367414,-0.0227873698,0.0151936188,-0.465315789,0.2769156098,-0.2263011783,-0.3846693933,0.1097714528,-0.4639790356,0.0545065328,-0.357956171,-0.1813165545,-0.0714326426,0.1170308292,-0.0007731954,0.0085544297,-0.3308463991,0.1423318684,0.475835681,0.2152563483,0.010602708,-0.1697903126,0.0889068022,-0.0840100795,-0.0551139712,-0.2312562168,-0.0318296291,0.341377914,-0.0363165475,-0.2875853777,0.2420509309,0.0710614175,0.1021632701,-0.0845881253,0.2678926289,0.0363490619,0.126285091,0.116907008,-0.3277269602,-0.1060258076,-0.1014627218,-0.1738274693,0.3757101893,-0.0945548564,-0.0556380637,-0.2235654444,0.1533218473,0.1444268823,-0.0640033036,-0.1271198392,-0.1657225639,-0.0189023614,0.1657900959,0.327701658,0.2167519033,0.1122761443,0.1225076765,0.2859781981,0.1796833277,0.0327649675,-0.0168508645,-0.0244936328,0.1001113653,0.1447474509,0.4372525811,0.2620206773,0.0575689003,0.2727523148,-0.1311240494,0.3154355884,-0.1805612892,-0.0081166187,-0.1601167321,0.0520053692,-0.0672347024,-0.1780880988,-0.0127130188,-0.0466869473,0.2545972168,0.1635733247,0.0634753853,-0.3407387137,-0.0851239786,-0.220859766,-0.1682960391,0.2248603106,-0.0677297488,-0.5411041379,-0.0004616114,-0.1686425656,0.1575246304,-0.0167004056,0.2290778011,-0.2502934039,0.230942741,-0.0517680682,-0.1068639457,-0.2852395177,0.1904079318,0.0318781622,0.0391563587,0.2648197412,-0.6819695234,0.108217001,-0.049818676,0.3214016855,-0.3362497389,-0.00658532,-0.1964795291,-0.0312058963,0.0932099745,-0.5233697891,0.110667862,-0.0999888703,-0.0006877767,-0.1943660229,-0.2517957091,-0.2066126317,-0.0055068177,-0.0349692963,-0.0534206629,-0.2905823588,-0.1511854231,-0.0614063665,-0.1431301534,0.4335410297,0.0851116404,0.2298046649,0.0896163732,-0.2578107119,-0.481002599,-0.161527887,0.329818964,-0.4195415676,-0.5174462199,0.3248214126,-0.5282965899,-0.3527770936,0.0235857219,0.1081352681,0.0974141732,0.5158916116,0.0116876289,-0.0420574248,-0.1728509516,0.2145496309,-0.2988652885,0.0996037349,0.0261344928,0.0606896952,0.0976809114,-0.0486990474,-0.1222708896,0.0462080315,-0.0357472785,-0.2109108269,0.0705680549,0.015196871,0.1574347317,0.6721852422,0.395839572,0.271814853,0.2819131017,0.2598074675,0.6446481943,0.0928580835,-0.1120511666,0.0937767923,-0.0434907377,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Yes exactly\r\n\r\nI imagine users can first do `load_dataset`, then chmod on the arrow files. After that all the new cache files could have the same permissions as the first arrow files. Opinions on this ?","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":36,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Yes exactly\r\n\r\nI imagine users can first do `load_dataset`, then chmod on the arrow files. After that all the new cache files could have the same permissions as the first arrow files. Opinions on this ?","embeddings":[-0.0982318446,0.2663797736,-0.1286433786,0.0853287876,-0.1320201755,0.0446737185,0.3324171603,0.118530646,-0.2356887311,-0.0621478073,-0.0989212915,0.0370316058,0.0195826124,-0.2461390197,-0.0805585906,0.0872824639,0.1227616966,0.1072847396,-0.0332508013,-0.0952983871,-0.0240278449,-0.1715578735,-0.0351817384,-0.0332676768,-0.1896342188,-0.2290271521,0.2739097178,0.2133919001,0.0309158172,-0.2010471672,0.2349330038,0.5016699433,0.1013555303,0.2010825276,-0.0001147357,-0.2658905089,0.0338738635,-0.0575639419,0.0078436742,0.2669466734,-0.1161731705,-0.2100120932,-0.0704279616,-0.1335719526,0.0365238823,0.0545726605,0.091035977,-0.7105931044,0.5204786658,0.0122761745,0.1791727096,-0.1938915104,-0.4023864865,0.083286427,0.2860995233,0.0430654585,-0.1606021523,0.1730280221,0.3103099167,-0.1361086369,-0.0524351932,0.1056952253,-0.2200270742,0.0986945555,0.2257885933,-0.1704335958,-0.2636166513,-0.4594815671,0.0759378374,0.1365863681,0.8266537786,-0.1198400781,-0.4820735455,-0.2638094723,0.0733138099,0.1983567327,0.2936440408,0.3246242404,0.0016637691,0.2001912594,-0.3582293093,-0.1222776547,-0.1994502544,-0.0233813897,0.2468711138,-0.1406525075,-0.0500772372,0.0535528772,0.0249775331,-0.0849356428,0.2674473822,0.0949541032,-0.3414618969,0.3800834417,-0.1853994131,0.0996082351,-0.2771460712,0.4416387975,0.1848989874,0.367099911,0.3062287569,0.261547029,0.0208358727,0.1910760105,0.014087867,0.2300602198,0.3232108951,0.3352376819,0.4978227019,-0.4137336612,-0.127027601,0.0657259524,-0.0633114055,-0.4195164442,0.0475073755,0.2454414964,-0.0278086159,-0.2097613066,0.222966671,0.1323930621,-0.0605982095,-0.2762775719,0.0271370858,0.3039213717,0.0922554731,0.0124805961,-0.0268436596,0.153289035,0.0331942029,-0.3798217475,0.0337653942,-0.3871083856,-0.4200714529,0.4929867983,0.4044268727,-0.1765277535,-0.033105135,0.2085524648,0.2425050139,-0.1636571139,0.3366356492,-0.0425914712,0.379229635,0.2863856852,0.1798949838,0.363432616,0.0198319405,0.0766787007,-0.1069900841,0.3586049974,-0.3342375159,-0.1467377394,0.2806135416,0.0304297805,-0.0477084741,0.2889646292,-0.4568911493,0.0635594353,0.4904056191,-0.1834812313,0.4105270505,0.2586520612,-0.0667117164,-0.4665744901,-0.203553617,0.4040682316,-0.1568007618,-0.043655809,-0.0511367545,0.0621061735,-0.1280885935,0.3088611662,-0.1244794205,0.114096649,-0.1328294575,0.1697129458,0.6214051247,-0.0554199554,-0.5478649735,0.1586862952,0.1892972887,-0.1027346328,0.2174239904,-0.0242303237,0.0259667523,-0.1259121299,-0.2156150937,0.2760068476,0.2407997102,0.1559189409,0.1018859372,0.0270283762,-0.0300635956,-0.0831490383,0.0225224774,0.2714837492,0.4265298843,-0.4358933568,0.2547696829,-0.296164602,0.243110463,0.4442699552,0.085216172,-0.0715317428,-0.0012013061,-0.0202656426,-0.5487953424,0.2893896103,-0.1768640429,-0.5273402333,0.0809141025,-0.4074394405,0.0392736867,-0.2897551358,-0.1810069829,0.0466867089,0.0411264412,0.0338834487,-0.0887254253,-0.4143950939,0.1162468866,0.3877355754,0.2359033525,0.031515725,-0.0698557869,0.0841894448,-0.071255438,-0.0374090448,-0.2687806189,-0.04930567,0.1768609732,0.0349990427,-0.2530092895,0.2679135203,0.0601470843,0.1441573054,-0.0802506134,0.2444082946,0.0304318555,-0.0141182709,0.1684853435,-0.2584220171,-0.0873372331,-0.1304801553,-0.2503659427,0.3609991074,-0.1785717607,-0.0334620401,-0.1110743806,0.1398726702,0.0843084604,0.0162244067,-0.1475671977,-0.1299829036,0.0527595319,0.2098969668,0.4378208816,0.2361806631,0.0789472386,0.1187857091,0.2683714926,0.1318301409,0.1181724072,-0.073609218,-0.1377052218,0.1421776861,0.247188136,0.36562711,0.2177818716,0.0397991464,0.3672138453,-0.2019691765,0.3553775251,-0.1610699892,-0.046028547,-0.1480808258,0.1355393976,-0.0648089275,-0.1560852081,-0.0596935451,0.0181031991,0.2383271307,0.1332809776,0.0081427488,-0.3435696363,-0.1004173458,-0.1508623809,-0.1511526555,0.1755269915,-0.1299466491,-0.4723000824,0.0583783053,-0.2219809443,0.1132966131,-0.0233516917,0.1973372549,-0.3970792592,0.2917776108,-0.128702566,-0.1223498359,-0.2481480837,0.230194509,0.0850458816,-0.001760689,0.3133168817,-0.609719336,0.0888756588,0.0885057524,0.3788987398,-0.2676569521,0.042918358,-0.1726459563,0.0873144269,-0.0265730564,-0.5216035247,0.0375708565,-0.2170250714,-0.0558556765,-0.1870139837,-0.2340089679,-0.2217162997,-0.0772934034,-0.0286511667,-0.0113656241,-0.2143590897,-0.0855834559,-0.1366393864,-0.1832230389,0.5226250887,0.1414613575,0.2560490966,-0.0023058013,-0.2259359956,-0.4002258182,-0.2243372947,0.3215575516,-0.3546554148,-0.4346234798,0.3281576633,-0.5130246282,-0.3416810632,0.0300765783,0.1240739971,0.1058700457,0.5141780972,-0.0260713454,0.08564087,-0.2186697721,0.201466918,-0.3087972999,0.1535314769,-0.0030912021,0.1196375713,0.1466685832,-0.0533184335,-0.0471319593,0.0780136362,-0.08128272,-0.2787156105,0.1123440564,-0.0189688616,0.1544212699,0.8165989518,0.4180293977,0.3073640168,0.2578543723,0.355474174,0.6551366448,0.1428945512,-0.0472472981,0.1153945178,-0.0610897467,-0.07311565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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Sounds nice but I feel this is a sub-part of the approach mentioned by @siddk. Instead of letting the user set new permissions by itself first and then making sure newly generated files have same permissions why don't we ask the user initially only what they want? What are your thoughts?","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":51,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Sounds nice but I feel this is a sub-part of the approach mentioned by @siddk. Instead of letting the user set new permissions by itself first and then making sure newly generated files have same permissions why don't we ask the user initially only what they want? What are your thoughts?","embeddings":[-0.0215067733,0.3034674823,-0.1613317132,-0.0614497289,-0.0943001211,0.0116148414,0.359359175,0.2102664709,-0.2881747484,0.0673978999,0.0218295306,-0.1237791181,0.0104278307,-0.2228574008,-0.14883551,0.1429995596,0.1225395575,0.1753851026,0.0643005967,-0.085233666,-0.0920050368,-0.25709185,0.0005783064,-0.0861270055,-0.3042495549,-0.1460853517,0.1376358569,0.1973124892,-0.0369805433,-0.1767524332,0.1454704255,0.5835268497,0.0604470633,0.1700236797,-0.000113813,-0.2299204469,0.0330866985,-0.0436646901,0.0165126044,0.2122360617,-0.073548831,-0.1644747406,-0.1943162978,-0.2433009744,-0.0462049842,0.1997121722,0.1979092062,-0.6541274786,0.5026413202,-0.036366798,0.1695914268,-0.2603195608,-0.4581024647,0.1149789691,0.2927057147,0.15541327,-0.1459863037,0.1088739336,0.4278711677,-0.2223556638,-0.0490458384,0.1052149683,-0.3066333234,0.1089935899,0.1439869106,-0.1189896241,-0.2343177795,-0.5785056949,0.0060195769,0.1186374128,0.7419643998,-0.041439373,-0.500002563,-0.3280445933,0.1157064736,0.2075068206,0.2222390324,0.3163419366,-0.1446903646,0.263974309,-0.4496850073,-0.093929708,-0.1393081397,-0.049711898,0.2192329317,-0.1179769263,-0.0371810645,0.0456820801,-0.035000667,0.017656872,0.2272628993,0.1144418865,-0.3335204422,0.3299391866,-0.1112532988,-0.0360920504,-0.2102335095,0.4718672931,0.2461711168,0.3557560146,0.2872901857,0.2251413018,0.0457360595,0.1456659436,0.0792336091,0.1384641081,0.3433903158,0.1938130111,0.5769106746,-0.3486528993,-0.0621921681,0.0978375822,0.0803735331,-0.309866786,0.1034736335,0.3815611601,-0.0208681747,-0.1177322268,0.1676379442,0.1382079273,-0.1400250793,-0.4070581198,-0.0386475138,0.2055515051,0.0458725207,-0.078172043,-0.005032972,0.1522562951,0.0363857076,-0.3549081385,0.0399276763,-0.3292353153,-0.3953119218,0.4818220139,0.5079624057,-0.210538581,-0.0186098106,0.2744808197,0.1505053788,-0.1056959406,0.3306500316,-0.0605823994,0.3992083967,0.2370217294,0.165595293,0.3706308901,-0.0237126276,-0.0111304512,-0.1217286736,0.3696787953,-0.3321189582,-0.1372836083,0.3728598356,0.054056108,-0.0034280377,0.3053071499,-0.3055223525,0.0975191444,0.4749915004,-0.1878505945,0.2645231187,0.3264573216,-0.1508256793,-0.4486918449,-0.1300678998,0.4278678894,-0.0520535856,0.0365230031,-0.0196552537,0.0565940328,-0.1223031953,0.2681515217,-0.0935709327,0.3671370745,-0.1649925113,0.1947631538,0.4293022752,-0.0106864683,-0.4389024675,0.1946795881,0.1696154028,-0.035028778,0.2052502036,0.0265082084,0.0568236969,-0.2115881741,-0.1429784149,0.2417724133,0.0068370011,0.2508014739,0.0217115916,-0.0139128566,0.0808936432,-0.1215712577,0.0580402538,0.3386098444,0.4151690602,-0.3177106977,0.2092598528,-0.3718044758,0.2121628076,0.2398813665,0.1287965626,-0.1351441592,-0.1405808926,0.0094823986,-0.4876556396,0.3215155005,-0.1577846259,-0.3858752251,0.145815447,-0.3815568984,0.1196946427,-0.3553343713,-0.0348144807,-0.0107425814,0.0641673431,0.011809337,-0.1238074079,-0.3839871883,0.0663139001,0.4213301837,0.1884029955,0.010893099,-0.0326096453,0.0793724954,-0.1622633934,0.0366548821,-0.3233988285,-0.1027956232,0.1893297732,-0.0289433897,-0.1541258991,0.2733415961,0.0848938525,0.0598437302,-0.0764382258,0.283999294,0.0712548941,0.1836898476,0.0505548939,-0.2241450548,-0.0589557067,-0.2423047572,-0.3031650186,0.3828921914,-0.0749819502,-0.0996832997,-0.1992581189,0.0980178714,-0.0098716058,-0.1272235513,-0.1304626912,-0.099872984,-0.0674129054,0.2601042688,0.2791475356,0.2810331881,0.0791931972,0.0599673539,0.3185290396,0.1296101063,0.0791056901,-0.0605271421,0.0566118956,0.0924589634,0.1974955946,0.3689728379,0.2394682914,0.0554052033,0.2686427832,-0.1833655983,0.3721331656,-0.1642819792,-0.0501156189,-0.0609723777,0.0270876717,-0.0607792512,-0.1656572372,0.0488088503,0.0602557845,0.2263970077,0.0412445255,-0.0455979444,-0.2876110375,-0.1677026451,-0.1197688505,-0.2105101496,0.2882496715,-0.0389637016,-0.4841929078,0.0286956802,-0.1127680317,0.1295776814,-0.0789717212,0.1243952364,-0.2907033861,0.343616128,-0.1635691375,-0.0660114139,-0.1650728583,0.2653517127,0.0996403098,0.0267295688,0.3398630619,-0.6474868655,0.2354015261,0.0241679084,0.3293393552,-0.2896651626,-0.0847147554,-0.1201487482,-0.0619772561,0.1082119122,-0.4897520542,0.0002001086,-0.1599302441,-0.024095282,-0.272926569,-0.1890534014,-0.169342339,-0.1547238082,0.0760243684,0.0209451504,-0.3150222898,-0.115810059,-0.0677887201,-0.1333333552,0.4151948392,0.1257451326,0.2871500552,0.0431081839,-0.2571903169,-0.4677893519,-0.2548931539,0.4332953095,-0.2978619933,-0.573590219,0.2667667568,-0.366728276,-0.2950362563,-0.0547699742,0.0534234121,0.0496604182,0.5260625482,-0.0280429497,-0.0710068494,-0.1727811247,0.2378222793,-0.3201941848,0.1760142893,0.0507704318,0.0443122573,0.1043993905,-0.005994197,-0.063017644,0.0995541736,-0.0724110752,-0.3326378763,0.210569337,0.054405693,0.2172205001,0.8299294114,0.3929793239,0.3720266521,0.2671792805,0.3564840853,0.6644089222,0.1048224345,-0.0725023672,0.1425319463,-0.0364810452,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Yes sounds good. Should this be a parameter in `load_dataset` ? Or an env variable ? Or use the value of `os.umask` ?","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":23,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Yes sounds good. Should this be a parameter in `load_dataset` ? Or an env variable ? Or use the value of `os.umask` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Ideally it should be a parameter in `load_dataset` but I'm not sure how important it is for the users (considering only important things should go into `load_dataset` parameters)","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":28,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Ideally it should be a parameter in `load_dataset` but I'm not sure how important it is for the users (considering only important things should go into `load_dataset` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"I think it's fairly important; for context, our team uses a shared file-system where many folks run experiments based on datasets that are cached by other users.\r\n\r\nFor example, I might start a training run, downloading a dataset. Then, a couple of days later, a collaborator using the same repository might want to use the same dataset on the same shared filesystem, but won't be able to under the default permissions.\r\n\r\nBeing able to specify directly in the top-level `load_dataset()` call seems important, but an equally valid option would be to just inherit from the running user's `umask` (this should probably be the default anyway).\r\n\r\nSo basically, argument that takes a custom set of permissions, and by default, use the running user's umask!","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":123,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n I think it's fairly important; for context, our team uses a shared file-system where many folks run experiments based on datasets that are cached by other users.\r\n\r\nFor example, I might start a training run, downloading a dataset. Then, a couple of days later, a collaborator using the same repository might want to use the same dataset on the same shared filesystem, but won't be able to under the default permissions.\r\n\r\nBeing able to specify directly in the top-level `load_dataset()` call seems important, but an equally valid option would be to just inherit from the running user's `umask` (this should probably be the default anyway).\r\n\r\nSo basically, argument that takes a custom set of permissions, and by default, use the running user's umask!","embeddings":[-0.1335193068,0.2638700008,-0.0887797624,-0.0136506241,-0.0483198538,0.1047770232,0.3552216589,0.1191613078,-0.1658600122,0.0378849208,-0.106523864,-0.0248673074,-0.0191920474,-0.3137134314,-0.1179799512,0.1778150201,0.1263326705,0.0858631954,0.0602515973,-0.108570978,-0.1009776145,-0.1389106661,0.0054339189,-0.1248307154,-0.2158824205,-0.1946879774,0.2352733314,0.2853993177,0.0203850195,-0.0933629945,0.3288930357,0.5935889482,0.108140178,0.1202231944,-0.0001126702,-0.1727904677,-0.0196036771,-0.1369560361,-0.0687135532,0.1222948506,-0.0050359312,-0.1052324176,-0.080206342,-0.1873364002,-0.0884159729,0.1841935962,0.1439650208,-0.7255420685,0.4894111156,0.095662944,0.1734694839,-0.1860495359,-0.3902597427,0.0528155975,0.1976868361,0.0250731856,-0.1162096336,0.1660548002,0.3335852027,-0.1733828038,-0.0796371624,-0.0047566737,-0.3949322701,0.0934961587,0.1791542917,-0.168472603,-0.2764598429,-0.4620153606,0.091608122,0.159108296,0.6701866984,-0.1889321804,-0.4884358943,-0.2769802809,0.0945892707,0.2630771101,0.3322439194,0.2956201732,-0.070246771,0.294301033,-0.4962939024,0.0249761268,-0.1143071577,0.0711589605,0.2440434396,-0.0776517168,-0.0221331306,0.0635323748,0.0589097142,-0.0742979869,0.1298757792,0.0513516665,-0.2814569473,0.3380032778,-0.144187659,0.0683853924,-0.1546314061,0.3203953505,0.2561030388,0.3386801183,0.2594955266,0.2057443708,0.0260845218,0.2732971311,0.0364703201,0.1471388489,0.3354699612,0.2981513143,0.5131576657,-0.3563105166,-0.0789603442,0.0803651586,0.0015175763,-0.3296959102,0.0532517582,0.2754203975,-0.0348209627,-0.1684566885,0.1972961426,0.043387752,-0.1375850886,-0.2175005972,0.0006882965,0.2218636721,0.143580094,-0.0419343412,0.050697498,0.1568997353,-0.0298380684,-0.3474836349,-0.0023438695,-0.3836150765,-0.3437673748,0.4538429081,0.502584815,-0.1503937244,0.0259815305,0.2286189497,0.1914967,-0.176214993,0.3555212319,-0.0703046024,0.369527638,0.2953026295,0.310430944,0.3790474832,0.1347994059,-0.0384794399,-0.1857957095,0.2925340235,-0.3189618886,-0.1988264769,0.3306159377,0.0252362601,-0.129405424,0.3491237462,-0.435300976,0.0539279692,0.4892047346,-0.1339069307,0.3949003816,0.2284995466,-0.1604258567,-0.4301351607,-0.097062245,0.3187615871,-0.0068246857,-0.0861124545,-0.0970914438,-0.0044264956,-0.1046250388,0.334502399,-0.0875778571,0.1819878221,-0.0739462227,0.1962854713,0.4308474958,-0.1192560419,-0.5625243187,0.085646227,0.2081537545,0.0200194307,0.1798261851,0.0332902782,0.0928128958,-0.1795361489,-0.013073747,0.3748737872,0.1747640222,0.2336510718,0.1346783638,-0.0211188998,-0.0107621932,-0.0737503245,0.016223602,0.2922054827,0.3899533749,-0.2811438739,0.233219713,-0.3469516337,0.2264998257,0.3875041902,0.0886686966,-0.0321269333,-0.0775921419,-0.0125407958,-0.4649496973,0.3139138222,-0.019517919,-0.3743110895,0.0781528056,-0.4427528679,0.1527733952,-0.3058959544,-0.078541331,-0.0590090863,0.0684402958,-0.0169347655,-0.0626724809,-0.2871268094,0.1386516839,0.397677809,0.1569042951,-0.0005136139,-0.1833364666,0.1158879325,-0.0207362417,-0.0901093408,-0.2096593976,-0.0049833176,0.2107448727,0.0538748764,-0.2475261539,0.3181375861,0.1360021085,0.0878940597,-0.2036231458,0.3133203089,-0.0595914982,0.143978402,0.0800896585,-0.2660570145,-0.0818465874,-0.1320270598,-0.2821069062,0.4214651585,-0.1982382387,-0.0984292552,-0.2274123132,0.1985201389,0.1358624548,-0.0531105921,-0.1689100564,-0.1018535942,0.0592443943,0.2333414853,0.4352440834,0.2416633219,0.0863659605,-0.0734826997,0.2969388664,0.1667918116,0.1391642541,-0.0248610619,-0.1173865423,0.071324572,0.2680752277,0.3193115592,0.2627224326,0.0815075636,0.3029861748,-0.1794038266,0.3617700934,-0.1280270666,-0.0533135124,-0.063828893,-0.0096192937,-0.1516234279,-0.2004453838,0.0383140184,-0.0102994144,0.2832241654,0.0139391981,0.0310584754,-0.3533719182,-0.0851112679,-0.1569666117,-0.1439567208,0.3133104742,-0.0409684479,-0.5186011195,-0.0337253846,-0.2119198143,0.2901796699,-0.0679176375,0.2043979615,-0.2976181209,0.272285521,-0.1464714557,-0.210093379,-0.1833209246,0.1580687612,0.049779065,0.0190461595,0.3156298101,-0.599206388,0.1461791694,-0.0014800217,0.258728683,-0.1778861582,0.071714893,-0.1856780797,0.0394682437,0.0848226175,-0.4404526949,0.0827055499,-0.1666044593,-0.0552184246,-0.1495183706,-0.2587300539,-0.1490297318,0.0165517759,0.0491509512,0.0126490491,-0.2662484646,-0.0743446648,-0.1646046489,-0.1609423608,0.506388247,0.1258137375,0.2180757672,0.0177706312,-0.2047579288,-0.443353653,-0.2076893151,0.3096786439,-0.3285373151,-0.5994923711,0.3129225075,-0.439291507,-0.2962847054,-0.0024911431,0.117717512,0.077165626,0.508112669,0.0141370408,-0.099913016,-0.1493712813,0.2239403427,-0.226396963,0.1315018386,0.0092313997,0.080677405,0.1833283007,-0.0216768179,-0.1074555442,0.0599435456,-0.0806834474,-0.3560809195,0.1849569231,0.044839479,0.2210550457,0.688346684,0.4353192449,0.3087536693,0.1947027594,0.3311053216,0.6988666654,0.0714703426,-0.096798569,0.1601403505,-0.0599365234,-0.1391473413,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Maybe let's start by defaulting to the user's umask !\r\nDo you want to give it a try @bhavitvyamalik ?","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":20,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Maybe let's start by defaulting to the user's umask !\r\nDo you want to give it a try @bhavitvyamalik 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Yeah sure! Instead of using default `0o644` should I first extract umask of current user and then use `os.umask` on it? We can do it inside `Dataset` class so that all folders\/files created during the call use running user's umask\r\n\r\n","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":40,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Yeah sure! Instead of using default `0o644` should I first extract umask of current user and then use `os.umask` on it? We can do it inside `Dataset` class so that all folders\/files created during the call use running user's umask\r\n\r\n","embeddings":[-0.2242726237,0.2261134684,-0.0817525238,0.0094758645,0.0471359864,0.0149757005,0.2634158432,0.0719240457,-0.1914955974,0.0407302789,-0.1341527104,0.0863360539,0.0493934341,-0.3142187595,-0.116745472,0.1358978152,0.1529258937,-0.0015896432,-0.0006147567,-0.0963858441,-0.2239349782,-0.0862397924,-0.1034872457,-0.0336775966,-0.3453719318,-0.2179553211,0.2119508833,0.2314933091,-0.088236481,-0.1905014664,0.2426152229,0.6062232256,0.180053249,0.1983390749,-0.0001155414,-0.2169194967,0.0884057879,-0.0958579928,0.0502289869,0.2252657861,0.013323619,-0.0652768463,-0.1900797188,-0.1833731681,-0.2362517565,0.1567798853,0.1003571898,-0.7182676792,0.3611292243,0.2002629042,0.1723333001,-0.2232726365,-0.2373960316,0.0888243765,0.2409683764,0.1156111211,-0.0989544019,0.0126231266,0.3320398033,-0.1668721884,-0.0690127909,0.0343173519,-0.2808453739,0.0031792175,0.1546429545,-0.0779458731,-0.2983679771,-0.5014804602,0.1238657981,0.1186875179,0.7641479969,-0.1171290502,-0.5640271306,-0.0496227294,0.1295876354,0.2901589274,0.3258309066,0.305441618,-0.0033054377,0.3387885094,-0.556910336,0.0916791111,-0.2294591665,0.1012438983,0.1967002898,0.0366982259,-0.0670412257,0.1275733858,0.0062289634,-0.1861501485,0.2320861965,0.0242003761,-0.3480373025,0.3791960776,-0.0957939103,-0.0125216506,-0.1537094861,0.5009945631,0.2390560806,0.2913291156,0.1713189781,0.274330616,0.050613381,0.1730332375,-0.0316947699,0.1815221906,0.4626689255,0.2795345783,0.5615982413,-0.2898006737,-0.1274800003,0.0201109517,0.0461159162,-0.3072869778,0.0547322407,0.2786273658,0.0398364998,-0.174011156,0.2315386385,0.0633214638,-0.1945544779,-0.197217226,0.0998361036,0.3442124426,0.0919306949,-0.0721913651,0.0562526621,0.1736780405,-0.0946675763,-0.356892854,-0.0090116914,-0.3280628324,-0.2786850035,0.3977695405,0.4268735349,-0.0590404123,0.048290953,0.2228619903,0.1398226917,-0.161517486,0.4451647699,-0.104907997,0.4017587006,0.3126306534,0.2725572884,0.2442822158,0.0619794242,0.0393465497,-0.1138395071,0.336044848,-0.3299659491,-0.1983573437,0.3492939174,0.0604472309,-0.0421798714,0.2782869935,-0.2858790755,0.0682794452,0.4992926717,-0.0701061413,0.3984587491,0.195801869,-0.2189560235,-0.4167913795,-0.0710380599,0.2780780196,-0.0480387099,-0.0400205776,-0.1446887553,-0.0054468689,0.0121445721,0.330481708,-0.0306329019,0.2323587388,-0.1623285711,0.1956809014,0.4786145389,-0.1494109333,-0.6601572037,0.0933339521,0.1913601309,-0.1107339188,0.1814989895,0.0880109221,-0.0511975065,-0.1090390608,0.0170315672,0.2962633073,0.1734892279,0.2679624557,0.0782138109,0.0145116327,-0.1027067155,0.0111187836,0.0351286866,0.2638350129,0.3305090368,-0.3714771569,0.2493853122,-0.3268905878,0.2354728878,0.4526799023,0.2369099408,-0.1237699613,0.0088803181,0.0077164839,-0.3304775655,0.2219384611,-0.1120770723,-0.3366325796,0.0353856198,-0.5332360268,0.1161451787,-0.2586063445,-0.1835006028,-0.1028007269,0.0589737743,-0.0131905703,0.0431489311,-0.2665457428,0.1984104961,0.2996811867,0.2209105939,0.0467694215,-0.1186486408,0.0867330134,-0.0954204723,-0.1309914589,-0.2401005775,-0.0371330455,0.2725090086,-0.0368210115,-0.2458095402,0.3138571382,0.2480554283,-0.0437143184,-0.2244311124,0.2249997556,-0.0020516347,0.079135552,0.0931482315,-0.1511032581,-0.0838271007,-0.1197993234,-0.2827210724,0.5314025879,-0.091330409,-0.1963916123,-0.2848781645,0.2199092954,0.1736144274,-0.0392812863,-0.1375671029,-0.1251328737,0.0259200186,0.2324845195,0.387634933,0.1938304454,0.1323322803,0.0504521653,0.3756450713,0.1494712234,0.1309051216,-0.0374019146,-0.0467793383,0.0487797484,0.2571569979,0.2776413858,0.379956305,0.0739180669,0.2769946754,-0.1387668848,0.3776808679,-0.1041438803,-0.0102290418,-0.0151650468,-0.0896696225,-0.1636366695,-0.219245851,-0.0727818832,-0.022523215,0.2986067235,0.0683608279,0.1479007453,-0.3589763641,-0.1564719528,-0.1277645528,-0.2656018734,0.3322516978,0.0047389707,-0.4301035702,-0.0467858799,-0.1731005311,0.2626761496,-0.0556140803,0.2959812284,-0.1558345556,0.2158390582,-0.0157291833,-0.0303070098,-0.1898551434,0.1892218292,0.0821965858,0.0232586712,0.2347505093,-0.6215863824,0.1095520407,-0.0536568686,0.2752892673,-0.388805747,-0.0542002879,-0.1635511816,0.0965677276,0.0827460662,-0.3341892064,-0.0747675076,-0.2251954824,-0.0046242233,-0.0955513343,-0.3325315714,-0.2238927782,0.0089835487,0.0110308044,-0.0332578756,-0.2029950619,-0.0610420071,-0.1555436999,-0.1267533749,0.4859426618,0.1549123973,0.2311938405,-0.2037691623,-0.2212675959,-0.3293208182,-0.1553503722,0.1997089237,-0.346811384,-0.6803945899,0.3059538603,-0.5067756176,-0.3965727985,0.0117284013,0.1094526798,0.1193613037,0.5079334378,0.0697839335,-0.2112737894,-0.1897013038,0.2861461937,-0.2172395885,0.1488739252,-0.0102196513,0.0184298791,0.1693856865,-0.0138924997,-0.1542496085,0.0458573811,-0.0391508415,-0.2779386044,0.1855635941,0.1891299635,0.1423265189,0.5497563481,0.4713593721,0.3665600419,0.2696023583,0.2861098051,0.6051007509,0.078364104,-0.1019397676,0.1641366333,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"You can get the umask using `os.umask` and then I guess you can just use `os.chmod` as in your previous PR, but with the right permissions depending on the umask.","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":30,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n You can get the umask using `os.umask` and then I guess you can just use `os.chmod` as in your previous PR, but with the right permissions depending on the umask.","embeddings":[-0.1857263744,0.1737921834,-0.1075274125,-0.1851043403,-0.0081702275,0.0839048028,0.3565381169,0.0757277459,-0.1108319834,0.1052552685,-0.2269385457,0.1543242037,0.1039311737,-0.2934491336,-0.0649807751,0.2179357857,0.0900316015,-0.0351480022,-0.0519519933,-0.008974527,-0.139015764,-0.1062896103,-0.0973075032,-0.022077186,-0.2167216241,-0.1977164894,0.1695958227,0.3388963938,-0.0508378111,-0.2708571553,0.1947233826,0.5540147424,0.0244192667,0.1041323543,-0.0001054582,-0.1182964668,0.0259445105,-0.0766808167,0.0837031528,0.2544776201,0.0616562963,-0.0599539205,-0.2419357747,-0.1721672565,-0.2474578321,0.1950842589,0.1271020174,-0.5841297507,0.3706359565,0.2619087398,0.2632213831,0.0041867811,-0.2159233093,0.0827071518,0.2322053611,0.0394572988,-0.1844671965,0.004054938,0.3705589175,-0.0817732215,-0.029460717,0.0447111353,-0.1966822892,0.07993792,0.1410067081,-0.0492858887,-0.2201645672,-0.4966886044,0.171047762,0.0686404631,0.737410903,-0.0505575724,-0.5258098245,0.0515309945,0.1633486897,0.268779397,0.3548974693,0.3101957142,-0.0543667637,0.3213352561,-0.4402199388,0.1428738385,-0.2138585299,0.0231555253,0.2160202712,0.1043673605,-0.0138586164,0.1185606271,0.1312624663,-0.1723819524,0.1273884475,0.2220983505,-0.2841736376,0.4184722304,-0.0803543478,0.0213506687,-0.179577142,0.4277115166,0.1930829436,0.1829823107,0.2210577428,0.2631050944,0.0399355628,0.2481009811,-0.1107326448,0.087615408,0.3066399097,0.3309390843,0.600415349,-0.2753514349,-0.1773978174,0.03613941,-0.0040830355,-0.4007755816,-0.0075756093,0.2617320418,0.1033153832,-0.2411533147,0.2197194248,-0.0143741863,-0.0503947176,-0.1352396458,0.0554776005,0.4130481184,0.2159236968,-0.2010885477,0.1329979897,0.1610405743,0.0002650758,-0.2362119555,-0.009621718,-0.2826401293,-0.3536853194,0.3084272146,0.4239220321,0.0281935409,-0.0059308391,0.2135394067,0.1640729159,-0.2162478566,0.3558654785,0.0013643519,0.4887394607,0.3780561388,0.2298045456,0.1242796332,0.0713280812,0.0070001739,-0.1356545389,0.375425607,-0.1685845703,-0.1559192836,0.2985610366,0.1440183669,0.0517648198,0.2353738695,-0.1760039479,0.0034030655,0.4222274721,0.019279385,0.4730708301,0.263438344,-0.1811551601,-0.3831002712,-0.0662586316,0.1607260108,-0.1245954037,-0.0031760992,-0.2033124715,-0.1225336045,-0.0638828427,0.3225443661,-0.0349490419,0.1460774988,-0.1530025601,0.1799973994,0.4943962097,-0.105420202,-0.5366950631,0.0179225653,0.1420943141,-0.1575104743,0.2553617656,0.0699409097,-0.0862172246,-0.1205488741,-0.0840222538,0.3386438191,0.2401405275,0.1952783763,0.0759221613,0.0618522577,-0.1156338155,0.0749076232,0.0716744363,0.3632016182,0.3213571906,-0.3289202154,0.2820542455,-0.3049741089,0.2371451855,0.4326992035,0.2295791209,-0.0457228646,0.0391492844,-0.0006195487,-0.2805831134,0.2985621691,-0.2028404325,-0.2408683002,0.0562757328,-0.5901844501,0.0555274002,-0.2122088671,-0.1397847235,-0.1616668701,0.158700794,0.0242923088,0.1034163311,-0.3360012472,0.2234393954,0.274657011,0.2683152556,-0.0121355113,-0.0398344696,0.0267179031,-0.2164893746,-0.1205914617,-0.1934047192,0.0145761492,0.18436566,-0.0178955551,-0.2853236794,0.353510052,0.1852293611,0.0203632955,-0.2001335323,0.1085040718,-0.1056202352,0.1050096005,0.0359733067,-0.186685428,-0.1000032648,-0.0848841146,-0.2744738758,0.3847475946,-0.0844669119,-0.1805409193,-0.2337636054,0.2074716985,0.1745804399,-0.0609582812,-0.085184738,-0.1573993266,-0.0265763216,0.1765721738,0.318298012,0.144673124,0.1370994747,0.0576272644,0.2981226444,0.1863227487,0.0654523596,-0.0301004052,-0.013126622,0.1375167668,0.2089313567,0.2619199753,0.3403193653,0.1240618601,0.3443061709,-0.0757716522,0.3184883595,-0.0739094466,0.0338750035,-0.111021325,-0.125604108,-0.1951065212,-0.1830316037,-0.090452686,-0.136238113,0.2723574936,0.0790062174,0.1283605695,-0.3007153571,-0.170316115,-0.1353120208,-0.2051401287,0.351392597,0.0678389221,-0.5054529905,-0.0896584615,-0.051605396,0.2065724134,-0.1915788352,0.3344317973,-0.1828148216,0.1299615949,0.0263543371,-0.0370130427,-0.2518053055,0.1204160973,0.1502980739,0.1103513986,0.1937009394,-0.5752273798,0.1569736749,-0.0448526442,0.225627169,-0.3547329009,0.0017738494,-0.1311611831,-0.0715568066,-0.0430169702,-0.3177586496,-0.0399482287,-0.1978435218,-0.09189073,-0.0097182319,-0.3843921423,-0.3132214844,-0.0307987873,0.1463947743,-0.0677189454,-0.3414189517,-0.0593514219,-0.1389716715,-0.2776058018,0.4889319241,0.1193414181,0.2346739471,-0.0731223822,-0.330290705,-0.2986045778,-0.0375004373,0.1694009453,-0.4259344637,-0.5024003386,0.3395012021,-0.5373857021,-0.4263425767,0.1487544179,0.2579756975,0.1109817699,0.4555156827,0.0572565794,-0.1329280585,-0.1570355445,0.211027652,-0.2334004343,0.1151978225,0.0440225862,0.0399837866,0.0330163501,-0.0285793152,-0.1677332073,0.0434171669,-0.0440920554,-0.2562850416,0.029993454,0.1130884588,0.185866341,0.5306411386,0.2862323225,0.3638160527,0.2622100413,0.217113167,0.64016819,0.0089031262,-0.0704265684,0.1620161682,0.0473679267,-0.0726466253,0.20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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"FWIW, we have this issue with other caches - e.g. `transformers` model files. So probably will need to backport this into `transformers` as well.\r\n\r\nthanks @thomwolf for the pointer.","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":29,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n FWIW, we have this issue with other caches - e.g. `transformers` model files. So probably will need to backport this into `transformers` as well.\r\n\r\nthanks @thomwolf for the pointer.","embeddings":[-0.1207443476,0.2110619843,-0.0841049924,0.0468187705,-0.0028637,0.1459375322,0.3854053617,0.1391602904,-0.2011681795,-0.0585675202,-0.1354780197,-0.0176231824,0.1156202182,-0.2905223966,-0.0399506688,0.1175884604,0.1337425113,0.0035101185,-0.0366873182,-0.0839240626,-0.0998415574,-0.0752499774,-0.0850521475,-0.0726410747,-0.3615028858,-0.1329764724,0.2650974691,0.2255142927,0.0090469653,-0.1246050224,0.3047429919,0.4333624244,0.0784689039,0.1299982369,-0.0001106508,-0.1399454772,0.0487940088,-0.0642532706,0.0905670524,0.2714790702,-0.1321593374,-0.0761748701,-0.1686177701,-0.1069586277,-0.1106696054,0.2972267866,0.1226569265,-0.5791894197,0.4974912405,0.1974837929,0.2274942696,-0.0538505316,-0.3413310051,0.0540440641,0.292683512,0.0809753537,-0.2243646085,0.0552525185,0.2692474425,-0.1958608627,-0.0183214154,0.0551859252,-0.2950374782,0.1153828874,0.1548876911,-0.121912688,-0.2390939146,-0.5055351257,0.1950537115,0.0620003827,0.7401837707,-0.0504377522,-0.4978570938,-0.1324620992,0.0188213438,0.2531050146,0.4132610261,0.339654386,-0.0014014319,0.2719789445,-0.5397583246,0.0072858073,-0.1382894665,-0.0447920263,0.1522361636,-0.0007977514,-0.0132668167,0.0848157778,-0.0201911423,-0.0922665223,0.090726532,0.0848192573,-0.3772098124,0.3344877064,-0.1749615073,0.08187107,-0.177461639,0.4131331146,0.1411212683,0.2401570976,0.2163678855,0.2027886808,0.0941766724,0.2004716247,-0.0032466499,0.1715729535,0.3708486855,0.3696019351,0.5744590759,-0.3903861344,-0.1783677936,0.0079541774,0.0443068855,-0.32673648,0.1036925837,0.2564816773,0.018813489,-0.2269121408,0.1517628729,0.1165376753,-0.0508614704,-0.2100030929,0.0428451113,0.2555075586,0.1683595628,-0.1430663168,0.0302899703,0.1445005834,0.033597365,-0.4030351341,-0.0297992416,-0.342314899,-0.4116488695,0.3594991267,0.4779577553,-0.1639427245,-0.0407774895,0.222055167,0.106182754,-0.247854054,0.2698282003,-0.038396921,0.4771815538,0.3560424149,0.1411651522,0.3438854814,0.0771397278,-0.0027546473,-0.0662941262,0.3606114089,-0.2789525092,-0.0623124391,0.3091772497,0.0600036979,-0.0259723179,0.2341597825,-0.3002974391,0.0653690845,0.6256068349,-0.1201736033,0.4150093198,0.3077589273,-0.1959592104,-0.3834025562,-0.1492616832,0.3160617948,-0.1199639887,-0.0305181965,-0.0605188683,-0.0397669449,-0.0460440218,0.2801170945,-0.0383947827,0.1791476458,-0.0919910967,0.1846491396,0.4149959981,-0.0262843464,-0.5820008516,0.1069188416,0.1509170234,-0.162860617,0.131841585,-0.0172963869,0.0163426846,-0.0730525032,-0.1057644933,0.3387136161,0.1975997537,0.2817466259,0.0935706571,0.1124939024,0.0462467633,-0.0897384584,0.1349194348,0.4027235806,0.2795726955,-0.2379645109,0.173605606,-0.2297310829,0.300124824,0.4829814732,0.0882581174,-0.1560760438,0.0094194477,-0.0881859511,-0.4463413358,0.3059563637,-0.1769651771,-0.3649234474,0.0936228931,-0.386436373,0.0234633759,-0.2765288055,-0.2104593515,-0.0824699402,0.0991922617,0.0023034241,-0.0160406232,-0.3887529969,0.2274432927,0.3831131756,0.3045082986,-0.0300990697,-0.0952215046,0.1234670952,-0.1274682879,-0.0749267191,-0.2616179883,-0.0406631567,0.292234391,0.0081635015,-0.3218149245,0.2308075279,0.1162156835,0.0567136817,-0.0873666108,0.2493588179,-0.0352321453,0.0807025135,0.04281931,-0.1840926707,-0.0539492406,-0.1325837523,-0.2853808701,0.2740264237,-0.0370531045,-0.065147616,-0.2375504524,0.2129855603,0.1421752423,-0.0171996634,-0.0711244345,-0.1782414019,-0.0635961741,0.1644781828,0.3153994083,0.2228892446,0.1195312962,0.0457767583,0.4026435614,0.2261164486,0.1156601459,-0.0225620512,-0.1593435258,0.0662745684,0.1717029065,0.3607619107,0.2226926237,0.0145552959,0.3567924798,-0.2121688128,0.2847136259,-0.1805050224,0.0212035645,-0.0329974815,-0.0282044876,-0.150320068,-0.0930583924,0.0097617758,-0.1050756276,0.3722705543,0.167938292,0.0914387628,-0.3406541646,-0.1041124314,-0.2076177597,-0.1616516113,0.2695324123,-0.0192013569,-0.6123397946,-0.0851016343,-0.1596945822,0.1629980206,-0.0465835556,0.1571589708,-0.2691048682,0.2040271908,-0.0234534331,-0.1180556118,-0.3213480413,0.1613807082,0.0332060531,0.0537653603,0.2560738325,-0.6495655179,0.0917300135,0.0269913189,0.3185979426,-0.2326247543,0.0595846288,-0.146228075,-0.0715482086,0.039310541,-0.5172543526,0.1141471863,-0.1451077312,0.0379493348,-0.0977479443,-0.2473940104,-0.2224614471,0.0385755375,0.0551554561,-0.1070951447,-0.3245078623,-0.0422688909,-0.0262890328,-0.1569161266,0.434792906,0.0526706651,0.1734728366,0.1191670671,-0.3077769876,-0.3521289229,-0.2285962403,0.2458472103,-0.4041653275,-0.5424076319,0.3612726033,-0.4819200039,-0.4319618046,0.0786874145,0.2185782641,0.0797881484,0.4152204692,0.0437998883,0.08299537,-0.2696126699,0.1614716202,-0.1929752082,0.0766387656,0.0515767932,0.0490494967,0.1112638786,-0.0858579054,-0.1944163442,0.101161994,0.0200057719,-0.2241318375,0.0323726051,-0.0041683228,0.1705233306,0.6693534851,0.3900607228,0.2593128383,0.266666919,0.3642522693,0.5709922314,0.0490256846,-0.1328871846,0.1190948635,-0.0469398797,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2065","title":"Only user permission of saved cache files, not group","comments":"Hi @stas00,\r\nFor this should we use the same umask code in the respective model directory inside `TRANSFORMERS_CACHE`?","body":"Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions?","comment_length":18,"text":"Only user permission of saved cache files, not group \n Hello,\r\n\r\nIt seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? \n Hi @stas00,\r\nFor this should we use the same umask code in the respective model directory inside `TRANSFORMERS_CACHE`?","embeddings":[-0.2081566155,0.1117953211,-0.0612362847,0.0407365672,-0.0061203358,0.1279416531,0.3894425333,0.0571595915,-0.2127809525,-0.0419883057,-0.1921856999,-0.123159267,0.1095725074,-0.3343217373,-0.0680713058,0.1472350657,0.1823889464,-0.0031142761,-0.0365220085,-0.1263749003,-0.1284440309,-0.0820216462,-0.0372605622,-0.0927570686,-0.4490078986,-0.1368952245,0.2483736128,0.1558468193,-0.0073176953,-0.0654333159,0.2889894247,0.5932964087,0.2336144149,0.1448652744,-0.0001169936,-0.1180792525,0.0391271897,-0.1211671084,0.0457089245,0.2500948608,0.0250783712,0.0906369463,-0.1391555667,-0.0956835151,-0.1904146224,0.3318998516,0.1194253191,-0.5785183907,0.5322913527,0.1401566565,0.130934,-0.0141243609,-0.3408102095,0.10062664,0.2094195932,0.1773287505,-0.1400357336,0.0186299924,0.3194201589,-0.2108982801,-0.0711339787,0.0976831466,-0.2137307972,0.0867346451,0.1340913922,-0.0724966973,-0.1330477446,-0.5008397102,0.1076873764,0.0051327101,0.7109176517,-0.1423660368,-0.612354219,-0.0560642667,-0.0337141119,0.384621948,0.3383271694,0.2856696844,-0.0450287275,0.3755660057,-0.5367846489,0.0684916824,-0.2287129462,-0.0421604998,0.1386980861,0.2050118148,-0.0096681016,0.0889965668,-0.0827852711,-0.0961588174,0.2455018461,-0.0053520827,-0.3007267118,0.3485356867,-0.1820778102,-0.0426302142,-0.2202080637,0.3356885612,0.1667661667,0.3436241448,0.1889274865,0.1737207174,0.1336089373,0.2200419158,0.0078025744,0.234630838,0.3791342378,0.5106111169,0.5242033005,-0.30716452,-0.2008763999,-0.0352666862,0.0575917177,-0.2570674419,0.0307384301,0.3571114838,-0.0152630061,-0.188887924,0.2196753174,-0.034931723,-0.066640012,-0.2599709928,0.0711568817,0.3590391278,0.1452923268,-0.0390979014,0.0123907421,0.1415419281,-0.0037328633,-0.3380565047,-0.0130785946,-0.3680552244,-0.3254832923,0.4648858905,0.4442012906,0.08915288,0.0396748893,0.1346452236,0.2065699846,-0.2064386606,0.3750051856,0.0013635495,0.4003739655,0.4353657663,0.2459950745,0.336381644,0.0151282893,-0.0809630826,-0.1440072656,0.3134813607,-0.2428768277,-0.1931257248,0.4719362259,0.0360613652,-0.043691285,0.2789411545,-0.2668830156,0.0263302345,0.6654939055,-0.0902685896,0.4075945318,0.3725253344,-0.1105863452,-0.4050094783,-0.0672455877,0.3489086032,-0.0102784438,-0.1288803965,-0.0141267609,-0.0060507483,-0.0985238478,0.463062048,-0.0963642597,0.1696590483,-0.0943508521,0.0977954343,0.5256636143,-0.1732740849,-0.6490622163,0.1500491053,0.2032510191,-0.1029816791,0.2669632137,0.0139924325,-0.0650034547,-0.1346264035,-0.0769748762,0.3790937066,0.1322385818,0.2626502812,0.0344484486,0.0248263404,0.0166534409,-0.0300011933,0.1266818941,0.3690103889,0.2432256788,-0.193096146,0.3039462566,-0.229432568,0.2638662457,0.4840605855,0.1777195781,-0.1601688862,0.0862397403,-0.0189624559,-0.4171614349,0.2549883723,-0.3125730455,-0.3193022609,-0.0076533998,-0.5207284689,0.0762457103,-0.244481802,-0.206301704,-0.2149771005,0.0228075814,-0.0447027534,0.1049694568,-0.411161691,0.1110285223,0.3464643359,0.311540693,0.0495175533,-0.193200022,0.1817408949,-0.1896053404,-0.0689440891,-0.337669462,-0.1110593602,0.2050257325,-0.0192736387,-0.2887729108,0.2957594991,0.1430360079,0.0078316079,-0.1420380324,0.3261239529,0.0783728734,0.0470811464,0.0594101362,-0.1094659939,-0.12912862,-0.1017894223,-0.3648785949,0.3784070909,-0.0848443881,-0.0770208091,-0.1734049618,0.2051728368,0.1564763188,0.0075013209,-0.1363317221,-0.1656583101,-0.0612607673,0.1625006646,0.4034815729,0.2103193402,0.2471065968,-0.0259754378,0.3793479502,0.1497155577,0.0658812821,-0.065851137,-0.1965869516,0.0703303218,0.1888887286,0.3214994371,0.2710371017,0.0138437944,0.2941034734,-0.2110759318,0.4143128991,-0.1599639803,0.010028325,0.0432761908,-0.0896273553,-0.131138429,-0.170338273,-0.0357452147,-0.0115990462,0.2849645317,0.0954615474,0.0764548853,-0.3082891703,-0.0691173673,-0.0060016289,-0.1206072494,0.2113471776,0.0088370368,-0.5343105793,0.0134322289,-0.1519223005,0.1806573272,-0.045663856,0.2692695558,-0.0677061602,0.1702588797,-0.0167088509,0.0004538158,-0.3200584948,0.138548553,0.1185337976,0.0039669154,0.2043606043,-0.6563039422,-0.0275075492,0.046050474,0.3301205039,-0.2155572623,-0.0556217991,-0.1608067155,0.0905814245,0.0782435313,-0.4964037538,0.0002072454,-0.108616285,-0.065435417,-0.0726546645,-0.2998086214,-0.1591831595,0.0295738354,-0.0222705938,-0.0988710895,-0.222018227,-0.0480080284,-0.1937407702,-0.1297093183,0.4764782786,0.1165347323,0.1581726521,-0.0076772133,-0.2429285645,-0.4943922758,-0.1973128766,0.2132521272,-0.247784391,-0.5221064091,0.4046732783,-0.5165218115,-0.4042021632,-0.0378913768,0.1206363961,0.1027639732,0.4209198952,0.1223703325,0.0109216245,-0.2580068409,0.0667786002,-0.1446611732,0.0787593052,-0.0300868824,0.0193842519,0.19900617,0.0284438841,-0.099014394,0.0424443819,-0.0189030934,-0.1615502685,-0.0023801303,0.1194666922,0.1578582376,0.5748952627,0.515283227,0.3354964554,0.1676900536,0.4023922384,0.4540117979,0.1008544788,-0.0585556477,0.040979851,0.020249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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2061","title":"Cannot load udpos subsets from xtreme dataset using load_dataset()","comments":"@lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n\r\nThe bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.","body":"Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n","comment_length":35,"text":"Cannot load udpos subsets from xtreme dataset using load_dataset() \n Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n \n @lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n\r\nThe bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.","embeddings":[-0.3464027941,-0.0752857924,0.002613897,0.4443909824,0.3713300824,0.069119066,0.2405683845,0.0394754633,0.5101771355,0.2061247975,-0.3589054644,0.0527050309,0.0860960111,0.0808662176,0.1144125015,-0.3151043355,-0.0602493994,0.0173558537,-0.2503674328,-0.065504171,-0.4105169475,-0.0623261929,-0.3046581447,0.1095427871,-0.1711033434,0.2064580172,0.0141455419,0.1647338569,-0.3150216639,-0.4950218201,0.5633596182,0.0180328377,0.5800744891,0.221322,-0.0001253432,0.1060385182,0.4861793816,-0.0613055825,-0.3452345729,-0.4049583972,-0.4189888239,-0.1315970272,-0.0690613464,-0.0964650735,0.1045276895,-0.0043987469,-0.0823340118,-0.2585622072,0.3321028948,0.3678756952,0.0941703171,0.3720282614,0.061857868,-0.2211653441,0.4757196605,-0.0422705263,-0.1489076465,0.1563735753,0.4271000326,-0.3246812224,0.1801449507,0.1246253327,-0.209139958,0.2249173075,-0.1637178361,0.0560058244,0.1821350753,-0.2583509088,0.2391248494,0.1349655539,0.8412337303,-0.1512385607,-0.4147819579,0.0182388742,-0.1014063731,-0.3554817438,0.1830140948,0.0008575391,-0.0578870513,0.0788415372,-0.0767996833,-0.0602417439,0.0503758267,0.289073348,-0.1434578896,0.2910360396,0.0541204214,0.1338610649,0.3239519,-0.1089203432,0.4901404083,0.0979419872,-0.0730714723,0.2410603017,-0.3231164515,0.2865316868,-0.0756045282,-0.3037876189,0.2389643937,-0.1045979336,-0.1012069955,0.0039554867,-0.1443649828,0.3295553327,0.4467023611,0.258592695,0.3614312708,0.2899973094,0.1072534993,0.1587685496,-0.0565737747,-0.0633865967,-0.4474610388,-0.2160627842,0.0857923925,-0.0271296799,0.1571703404,-0.4506189227,-0.5827982426,0.0448348261,-0.1629013866,-0.217883721,0.05834952,0.2688898146,0.1545642018,0.4297795296,0.0458623022,0.4458411336,-0.4962795973,-0.2659801245,-0.0544239134,0.1687626541,-0.4189635813,0.0585133731,0.1439427137,0.0052871024,0.2148580551,0.0167755205,0.2656741142,-0.2452537715,0.1288290769,-0.2358862758,-0.244421348,0.3540515602,0.3105243742,0.1218978986,0.2711307406,-0.3746761978,-0.0785986483,0.2891557217,-0.2225109637,-0.2350116372,0.0909441113,0.0328545943,-0.1796912402,0.0550205037,-1.0357831717,-0.0764572024,0.0851031467,0.1069326326,-0.0155084543,-0.275529623,-0.1712114811,0.0025955918,0.2848057449,0.5355801582,-0.2868715227,-0.0464064963,-0.1408394873,-0.0400896706,0.3355934024,0.3613086641,-0.3357942998,0.0737545416,-0.1279850602,0.1057119593,0.1982139647,-0.5150758028,-0.4803656042,0.1640977561,0.0509281084,-0.0161880385,-0.0857606456,-0.3274516165,0.2393174767,0.0357203931,0.1953124404,0.2589243054,0.0580973849,-0.0908872336,-0.1751016676,-0.0985951722,0.136113286,0.4271915853,0.2911248505,0.1453995854,0.2614878714,0.4392369092,0.0436218232,0.0165753514,0.0131826354,0.3216635585,0.0333599299,0.0902014673,0.0362407826,-0.0593443327,-0.4222900867,0.2021975517,-0.0930997282,-0.0453706384,0.0975539014,0.1812404096,-0.3897111714,0.1230222583,-0.3247447312,-0.3197046518,-0.0942896307,0.0866035298,-0.0964135006,-0.0818081424,-0.1698002815,0.0806680992,-0.0715484098,0.1103202999,-0.3754514456,0.4191193879,0.049578242,-0.0648903623,0.0807236657,0.2063002884,0.2299803942,-0.1260796189,-0.1753814518,0.1877421886,0.6032566428,-0.1791830808,0.1357364804,-0.0441033766,0.3042578995,-0.3885697424,0.1962021738,-0.0273815524,0.1187217534,0.1130647287,0.0598304868,0.254188925,-0.0359747075,0.1840643734,-0.1281909943,0.1224722341,0.3874266744,0.0022266286,0.0613593124,-0.2924954891,0.5102493167,0.3250283897,0.3184416592,0.0524797477,-0.1459858865,-0.2242728472,-0.1109304726,-0.1516300291,0.0816720501,0.1515698433,-0.2236988097,0.0444757342,0.2037227601,0.2373357564,0.32943362,0.0214671995,0.0758472681,-0.0468033701,0.0237630997,0.0701770708,-0.0097976169,-0.0718164816,-0.1015756652,0.0644405559,0.0008363276,-0.2057575434,-0.2248116881,0.0729394481,0.2377972454,0.4230693877,-0.300316304,0.0473215431,-0.4231781065,-0.1595294625,0.1389964968,-0.0802792013,-0.0387310572,-0.1483372748,-0.3189133108,0.2778151631,0.3382665813,0.1288223416,0.1079095602,-0.239862293,-0.0247332957,-0.2022278309,0.1674776077,-0.1916826367,-0.1363240331,-0.1142772362,0.2343533635,0.1731068641,0.0974104404,-0.3408343494,-0.0454138368,-0.2319974899,-0.0555951707,0.2320308834,-0.0026652913,0.30958727,-0.0770000219,0.14246355,-0.3586760759,0.0364723913,0.3661396801,0.0212173313,-0.1678794324,0.2677277923,-0.0123514775,-0.1251045763,0.0236814786,-0.14731206,-0.299810648,-0.2093851715,0.0171216857,0.0482656993,0.0239049792,-0.0471153446,0.0395985246,0.0380500816,0.1774878949,-0.1749292463,-0.340343833,-0.4319575131,0.4691960514,-0.2710248232,-0.2475002557,0.0816968828,0.0473560803,0.2014110088,0.3418824375,-0.4603409767,-0.0737863109,-0.1173928976,0.1646256745,0.0758609176,0.1864943802,0.0469675176,-0.1311487854,0.1585203111,-0.0770411864,-0.3395670652,-0.1058110222,0.2425631136,0.2578392923,-0.1052164957,0.4483567774,-0.2508384287,0.2393931001,-0.0129460888,0.2891106904,0.5426401496,-0.0893333852,0.1529127359,-0.3217939138,-0.3143526018,-0.099711448,-0.1948753744,-0.13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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2061","title":"Cannot load udpos subsets from xtreme dataset using load_dataset()","comments":"Hi ! Thanks for reporting @adzcodez \r\n\r\n\r\n> @lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n> \r\n> The bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.\r\n\r\nYou're right: \"_\" should be added to the list of labels, and the examples must be sequences of tokens, not singles tokens.\r\n","body":"Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n","comment_length":66,"text":"Cannot load udpos subsets from xtreme dataset using load_dataset() \n Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n \n Hi ! Thanks for reporting @adzcodez \r\n\r\n\r\n> @lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n> \r\n> The bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.\r\n\r\nYou're right: \"_\" should be added to the list of labels, and the examples must be sequences of tokens, not singles tokens.\r\n","embeddings":[-0.3464027941,-0.0752857924,0.002613897,0.4443909824,0.3713300824,0.069119066,0.2405683845,0.0394754633,0.5101771355,0.2061247975,-0.3589054644,0.0527050309,0.0860960111,0.0808662176,0.1144125015,-0.3151043355,-0.0602493994,0.0173558537,-0.2503674328,-0.065504171,-0.4105169475,-0.0623261929,-0.3046581447,0.1095427871,-0.1711033434,0.2064580172,0.0141455419,0.1647338569,-0.3150216639,-0.4950218201,0.5633596182,0.0180328377,0.5800744891,0.221322,-0.0001253432,0.1060385182,0.4861793816,-0.0613055825,-0.3452345729,-0.4049583972,-0.4189888239,-0.1315970272,-0.0690613464,-0.0964650735,0.1045276895,-0.0043987469,-0.0823340118,-0.2585622072,0.3321028948,0.3678756952,0.0941703171,0.3720282614,0.061857868,-0.2211653441,0.4757196605,-0.0422705263,-0.1489076465,0.1563735753,0.4271000326,-0.3246812224,0.1801449507,0.1246253327,-0.209139958,0.2249173075,-0.1637178361,0.0560058244,0.1821350753,-0.2583509088,0.2391248494,0.1349655539,0.8412337303,-0.1512385607,-0.4147819579,0.0182388742,-0.1014063731,-0.3554817438,0.1830140948,0.0008575391,-0.0578870513,0.0788415372,-0.0767996833,-0.0602417439,0.0503758267,0.289073348,-0.1434578896,0.2910360396,0.0541204214,0.1338610649,0.3239519,-0.1089203432,0.4901404083,0.0979419872,-0.0730714723,0.2410603017,-0.3231164515,0.2865316868,-0.0756045282,-0.3037876189,0.2389643937,-0.1045979336,-0.1012069955,0.0039554867,-0.1443649828,0.3295553327,0.4467023611,0.258592695,0.3614312708,0.2899973094,0.1072534993,0.1587685496,-0.0565737747,-0.0633865967,-0.4474610388,-0.2160627842,0.0857923925,-0.0271296799,0.1571703404,-0.4506189227,-0.5827982426,0.0448348261,-0.1629013866,-0.217883721,0.05834952,0.2688898146,0.1545642018,0.4297795296,0.0458623022,0.4458411336,-0.4962795973,-0.2659801245,-0.0544239134,0.1687626541,-0.4189635813,0.0585133731,0.1439427137,0.0052871024,0.2148580551,0.0167755205,0.2656741142,-0.2452537715,0.1288290769,-0.2358862758,-0.244421348,0.3540515602,0.3105243742,0.1218978986,0.2711307406,-0.3746761978,-0.0785986483,0.2891557217,-0.2225109637,-0.2350116372,0.0909441113,0.0328545943,-0.1796912402,0.0550205037,-1.0357831717,-0.0764572024,0.0851031467,0.1069326326,-0.0155084543,-0.275529623,-0.1712114811,0.0025955918,0.2848057449,0.5355801582,-0.2868715227,-0.0464064963,-0.1408394873,-0.0400896706,0.3355934024,0.3613086641,-0.3357942998,0.0737545416,-0.1279850602,0.1057119593,0.1982139647,-0.5150758028,-0.4803656042,0.1640977561,0.0509281084,-0.0161880385,-0.0857606456,-0.3274516165,0.2393174767,0.0357203931,0.1953124404,0.2589243054,0.0580973849,-0.0908872336,-0.1751016676,-0.0985951722,0.136113286,0.4271915853,0.2911248505,0.1453995854,0.2614878714,0.4392369092,0.0436218232,0.0165753514,0.0131826354,0.3216635585,0.0333599299,0.0902014673,0.0362407826,-0.0593443327,-0.4222900867,0.2021975517,-0.0930997282,-0.0453706384,0.0975539014,0.1812404096,-0.3897111714,0.1230222583,-0.3247447312,-0.3197046518,-0.0942896307,0.0866035298,-0.0964135006,-0.0818081424,-0.1698002815,0.0806680992,-0.0715484098,0.1103202999,-0.3754514456,0.4191193879,0.049578242,-0.0648903623,0.0807236657,0.2063002884,0.2299803942,-0.1260796189,-0.1753814518,0.1877421886,0.6032566428,-0.1791830808,0.1357364804,-0.0441033766,0.3042578995,-0.3885697424,0.1962021738,-0.0273815524,0.1187217534,0.1130647287,0.0598304868,0.254188925,-0.0359747075,0.1840643734,-0.1281909943,0.1224722341,0.3874266744,0.0022266286,0.0613593124,-0.2924954891,0.5102493167,0.3250283897,0.3184416592,0.0524797477,-0.1459858865,-0.2242728472,-0.1109304726,-0.1516300291,0.0816720501,0.1515698433,-0.2236988097,0.0444757342,0.2037227601,0.2373357564,0.32943362,0.0214671995,0.0758472681,-0.0468033701,0.0237630997,0.0701770708,-0.0097976169,-0.0718164816,-0.1015756652,0.0644405559,0.0008363276,-0.2057575434,-0.2248116881,0.0729394481,0.2377972454,0.4230693877,-0.300316304,0.0473215431,-0.4231781065,-0.1595294625,0.1389964968,-0.0802792013,-0.0387310572,-0.1483372748,-0.3189133108,0.2778151631,0.3382665813,0.1288223416,0.1079095602,-0.239862293,-0.0247332957,-0.2022278309,0.1674776077,-0.1916826367,-0.1363240331,-0.1142772362,0.2343533635,0.1731068641,0.0974104404,-0.3408343494,-0.0454138368,-0.2319974899,-0.0555951707,0.2320308834,-0.0026652913,0.30958727,-0.0770000219,0.14246355,-0.3586760759,0.0364723913,0.3661396801,0.0212173313,-0.1678794324,0.2677277923,-0.0123514775,-0.1251045763,0.0236814786,-0.14731206,-0.299810648,-0.2093851715,0.0171216857,0.0482656993,0.0239049792,-0.0471153446,0.0395985246,0.0380500816,0.1774878949,-0.1749292463,-0.340343833,-0.4319575131,0.4691960514,-0.2710248232,-0.2475002557,0.0816968828,0.0473560803,0.2014110088,0.3418824375,-0.4603409767,-0.0737863109,-0.1173928976,0.1646256745,0.0758609176,0.1864943802,0.0469675176,-0.1311487854,0.1585203111,-0.0770411864,-0.3395670652,-0.1058110222,0.2425631136,0.2578392923,-0.1052164957,0.4483567774,-0.2508384287,0.2393931001,-0.0129460888,0.2891106904,0.5426401496,-0.0893333852,0.1529127359,-0.3217939138,-0.3143526018,-0.099711448,-0.1948753744,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2061","title":"Cannot load udpos subsets from xtreme dataset using load_dataset()","comments":"@lhoestq Can you please label this issue with the \"good first issue\" label? I'm not sure I'll find time to fix this.\r\n\r\nTo resolve it, the user should:\r\n1. add `\"_\"` to the list of labels\r\n2. transform the udpos subset to the conll format (I think the preprocessing logic can be borrowed from [the original repo](https:\/\/github.com\/google-research\/xtreme\/blob\/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de\/utils_preprocess.py#L187-L204))\r\n3. update the dummy data\r\n4. update the dataset info\r\n5. [optional] add info about the data fields structure of the udpos subset to the dataset readme","body":"Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n","comment_length":84,"text":"Cannot load udpos subsets from xtreme dataset using load_dataset() \n Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n \n @lhoestq Can you please label this issue with the \"good first issue\" label? I'm not sure I'll find time to fix this.\r\n\r\nTo resolve it, the user should:\r\n1. add `\"_\"` to the list of labels\r\n2. transform the udpos subset to the conll format (I think the preprocessing logic can be borrowed from [the original repo](https:\/\/github.com\/google-research\/xtreme\/blob\/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de\/utils_preprocess.py#L187-L204))\r\n3. update the dummy data\r\n4. update the dataset info\r\n5. [optional] add info about the data fields structure of the udpos subset to the dataset readme","embeddings":[-0.3464027941,-0.0752857924,0.002613897,0.4443909824,0.3713300824,0.069119066,0.2405683845,0.0394754633,0.5101771355,0.2061247975,-0.3589054644,0.0527050309,0.0860960111,0.0808662176,0.1144125015,-0.3151043355,-0.0602493994,0.0173558537,-0.2503674328,-0.065504171,-0.4105169475,-0.0623261929,-0.3046581447,0.1095427871,-0.1711033434,0.2064580172,0.0141455419,0.1647338569,-0.3150216639,-0.4950218201,0.5633596182,0.0180328377,0.5800744891,0.221322,-0.0001253432,0.1060385182,0.4861793816,-0.0613055825,-0.3452345729,-0.4049583972,-0.4189888239,-0.1315970272,-0.0690613464,-0.0964650735,0.1045276895,-0.0043987469,-0.0823340118,-0.2585622072,0.3321028948,0.3678756952,0.0941703171,0.3720282614,0.061857868,-0.2211653441,0.4757196605,-0.0422705263,-0.1489076465,0.1563735753,0.4271000326,-0.3246812224,0.1801449507,0.1246253327,-0.209139958,0.2249173075,-0.1637178361,0.0560058244,0.1821350753,-0.2583509088,0.2391248494,0.1349655539,0.8412337303,-0.1512385607,-0.4147819579,0.0182388742,-0.1014063731,-0.3554817438,0.1830140948,0.0008575391,-0.0578870513,0.0788415372,-0.0767996833,-0.0602417439,0.0503758267,0.289073348,-0.1434578896,0.2910360396,0.0541204214,0.1338610649,0.3239519,-0.1089203432,0.4901404083,0.0979419872,-0.0730714723,0.2410603017,-0.3231164515,0.2865316868,-0.0756045282,-0.3037876189,0.2389643937,-0.1045979336,-0.1012069955,0.0039554867,-0.1443649828,0.3295553327,0.4467023611,0.258592695,0.3614312708,0.2899973094,0.1072534993,0.1587685496,-0.0565737747,-0.0633865967,-0.4474610388,-0.2160627842,0.0857923925,-0.0271296799,0.1571703404,-0.4506189227,-0.5827982426,0.0448348261,-0.1629013866,-0.217883721,0.05834952,0.2688898146,0.1545642018,0.4297795296,0.0458623022,0.4458411336,-0.4962795973,-0.2659801245,-0.0544239134,0.1687626541,-0.4189635813,0.0585133731,0.1439427137,0.0052871024,0.2148580551,0.0167755205,0.2656741142,-0.2452537715,0.1288290769,-0.2358862758,-0.244421348,0.3540515602,0.3105243742,0.1218978986,0.2711307406,-0.3746761978,-0.0785986483,0.2891557217,-0.2225109637,-0.2350116372,0.0909441113,0.0328545943,-0.1796912402,0.0550205037,-1.0357831717,-0.0764572024,0.0851031467,0.1069326326,-0.0155084543,-0.275529623,-0.1712114811,0.0025955918,0.2848057449,0.5355801582,-0.2868715227,-0.0464064963,-0.1408394873,-0.0400896706,0.3355934024,0.3613086641,-0.3357942998,0.0737545416,-0.1279850602,0.1057119593,0.1982139647,-0.5150758028,-0.4803656042,0.1640977561,0.0509281084,-0.0161880385,-0.0857606456,-0.3274516165,0.2393174767,0.0357203931,0.1953124404,0.2589243054,0.0580973849,-0.0908872336,-0.1751016676,-0.0985951722,0.136113286,0.4271915853,0.2911248505,0.1453995854,0.2614878714,0.4392369092,0.0436218232,0.0165753514,0.0131826354,0.3216635585,0.0333599299,0.0902014673,0.0362407826,-0.0593443327,-0.4222900867,0.2021975517,-0.0930997282,-0.0453706384,0.0975539014,0.1812404096,-0.3897111714,0.1230222583,-0.3247447312,-0.3197046518,-0.0942896307,0.0866035298,-0.0964135006,-0.0818081424,-0.1698002815,0.0806680992,-0.0715484098,0.1103202999,-0.3754514456,0.4191193879,0.049578242,-0.0648903623,0.0807236657,0.2063002884,0.2299803942,-0.1260796189,-0.1753814518,0.1877421886,0.6032566428,-0.1791830808,0.1357364804,-0.0441033766,0.3042578995,-0.3885697424,0.1962021738,-0.0273815524,0.1187217534,0.1130647287,0.0598304868,0.254188925,-0.0359747075,0.1840643734,-0.1281909943,0.1224722341,0.3874266744,0.0022266286,0.0613593124,-0.2924954891,0.5102493167,0.3250283897,0.3184416592,0.0524797477,-0.1459858865,-0.2242728472,-0.1109304726,-0.1516300291,0.0816720501,0.1515698433,-0.2236988097,0.0444757342,0.2037227601,0.2373357564,0.32943362,0.0214671995,0.0758472681,-0.0468033701,0.0237630997,0.0701770708,-0.0097976169,-0.0718164816,-0.1015756652,0.0644405559,0.0008363276,-0.2057575434,-0.2248116881,0.0729394481,0.2377972454,0.4230693877,-0.300316304,0.0473215431,-0.4231781065,-0.1595294625,0.1389964968,-0.0802792013,-0.0387310572,-0.1483372748,-0.3189133108,0.2778151631,0.3382665813,0.1288223416,0.1079095602,-0.239862293,-0.0247332957,-0.2022278309,0.1674776077,-0.1916826367,-0.1363240331,-0.1142772362,0.2343533635,0.1731068641,0.0974104404,-0.3408343494,-0.0454138368,-0.2319974899,-0.0555951707,0.2320308834,-0.0026652913,0.30958727,-0.0770000219,0.14246355,-0.3586760759,0.0364723913,0.3661396801,0.0212173313,-0.1678794324,0.2677277923,-0.0123514775,-0.1251045763,0.0236814786,-0.14731206,-0.299810648,-0.2093851715,0.0171216857,0.0482656993,0.0239049792,-0.0471153446,0.0395985246,0.0380500816,0.1774878949,-0.1749292463,-0.340343833,-0.4319575131,0.4691960514,-0.2710248232,-0.2475002557,0.0816968828,0.0473560803,0.2014110088,0.3418824375,-0.4603409767,-0.0737863109,-0.1173928976,0.1646256745,0.0758609176,0.1864943802,0.0469675176,-0.1311487854,0.1585203111,-0.0770411864,-0.3395670652,-0.1058110222,0.2425631136,0.2578392923,-0.1052164957,0.4483567774,-0.2508384287,0.2393931001,-0.0129460888,0.2891106904,0.5426401496,-0.0893333852,0.1529127359,-0.3217939138,-0.3143526018,-0.099711448,-0.1948753744,-0.138990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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2061","title":"Cannot load udpos subsets from xtreme dataset using load_dataset()","comments":"I tried fixing this issue, but its working fine in the dev version : \"1.6.2.dev0\"\r\n\r\nI think somebody already fixed it. ","body":"Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n","comment_length":21,"text":"Cannot load udpos subsets from xtreme dataset using load_dataset() \n Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n \n I tried fixing this issue, but its working fine in the dev version : \"1.6.2.dev0\"\r\n\r\nI think somebody already fixed it. ","embeddings":[-0.3464027941,-0.0752857924,0.002613897,0.4443909824,0.3713300824,0.069119066,0.2405683845,0.0394754633,0.5101771355,0.2061247975,-0.3589054644,0.0527050309,0.0860960111,0.0808662176,0.1144125015,-0.3151043355,-0.0602493994,0.0173558537,-0.2503674328,-0.065504171,-0.4105169475,-0.0623261929,-0.3046581447,0.1095427871,-0.1711033434,0.2064580172,0.0141455419,0.1647338569,-0.3150216639,-0.4950218201,0.5633596182,0.0180328377,0.5800744891,0.221322,-0.0001253432,0.1060385182,0.4861793816,-0.0613055825,-0.3452345729,-0.4049583972,-0.4189888239,-0.1315970272,-0.0690613464,-0.0964650735,0.1045276895,-0.0043987469,-0.0823340118,-0.2585622072,0.3321028948,0.3678756952,0.0941703171,0.3720282614,0.061857868,-0.2211653441,0.4757196605,-0.0422705263,-0.1489076465,0.1563735753,0.4271000326,-0.3246812224,0.1801449507,0.1246253327,-0.209139958,0.2249173075,-0.1637178361,0.0560058244,0.1821350753,-0.2583509088,0.2391248494,0.1349655539,0.8412337303,-0.1512385607,-0.4147819579,0.0182388742,-0.1014063731,-0.3554817438,0.1830140948,0.0008575391,-0.0578870513,0.0788415372,-0.0767996833,-0.0602417439,0.0503758267,0.289073348,-0.1434578896,0.2910360396,0.0541204214,0.1338610649,0.3239519,-0.1089203432,0.4901404083,0.0979419872,-0.0730714723,0.2410603017,-0.3231164515,0.2865316868,-0.0756045282,-0.3037876189,0.2389643937,-0.1045979336,-0.1012069955,0.0039554867,-0.1443649828,0.3295553327,0.4467023611,0.258592695,0.3614312708,0.2899973094,0.1072534993,0.1587685496,-0.0565737747,-0.0633865967,-0.4474610388,-0.2160627842,0.0857923925,-0.0271296799,0.1571703404,-0.4506189227,-0.5827982426,0.0448348261,-0.1629013866,-0.217883721,0.05834952,0.2688898146,0.1545642018,0.4297795296,0.0458623022,0.4458411336,-0.4962795973,-0.2659801245,-0.0544239134,0.1687626541,-0.4189635813,0.0585133731,0.1439427137,0.0052871024,0.2148580551,0.0167755205,0.2656741142,-0.2452537715,0.1288290769,-0.2358862758,-0.244421348,0.3540515602,0.3105243742,0.1218978986,0.2711307406,-0.3746761978,-0.0785986483,0.2891557217,-0.2225109637,-0.2350116372,0.0909441113,0.0328545943,-0.1796912402,0.0550205037,-1.0357831717,-0.0764572024,0.0851031467,0.1069326326,-0.0155084543,-0.275529623,-0.1712114811,0.0025955918,0.2848057449,0.5355801582,-0.2868715227,-0.0464064963,-0.1408394873,-0.0400896706,0.3355934024,0.3613086641,-0.3357942998,0.0737545416,-0.1279850602,0.1057119593,0.1982139647,-0.5150758028,-0.4803656042,0.1640977561,0.0509281084,-0.0161880385,-0.0857606456,-0.3274516165,0.2393174767,0.0357203931,0.1953124404,0.2589243054,0.0580973849,-0.0908872336,-0.1751016676,-0.0985951722,0.136113286,0.4271915853,0.2911248505,0.1453995854,0.2614878714,0.4392369092,0.0436218232,0.0165753514,0.0131826354,0.3216635585,0.0333599299,0.0902014673,0.0362407826,-0.0593443327,-0.4222900867,0.2021975517,-0.0930997282,-0.0453706384,0.0975539014,0.1812404096,-0.3897111714,0.1230222583,-0.3247447312,-0.3197046518,-0.0942896307,0.0866035298,-0.0964135006,-0.0818081424,-0.1698002815,0.0806680992,-0.0715484098,0.1103202999,-0.3754514456,0.4191193879,0.049578242,-0.0648903623,0.0807236657,0.2063002884,0.2299803942,-0.1260796189,-0.1753814518,0.1877421886,0.6032566428,-0.1791830808,0.1357364804,-0.0441033766,0.3042578995,-0.3885697424,0.1962021738,-0.0273815524,0.1187217534,0.1130647287,0.0598304868,0.254188925,-0.0359747075,0.1840643734,-0.1281909943,0.1224722341,0.3874266744,0.0022266286,0.0613593124,-0.2924954891,0.5102493167,0.3250283897,0.3184416592,0.0524797477,-0.1459858865,-0.2242728472,-0.1109304726,-0.1516300291,0.0816720501,0.1515698433,-0.2236988097,0.0444757342,0.2037227601,0.2373357564,0.32943362,0.0214671995,0.0758472681,-0.0468033701,0.0237630997,0.0701770708,-0.0097976169,-0.0718164816,-0.1015756652,0.0644405559,0.0008363276,-0.2057575434,-0.2248116881,0.0729394481,0.2377972454,0.4230693877,-0.300316304,0.0473215431,-0.4231781065,-0.1595294625,0.1389964968,-0.0802792013,-0.0387310572,-0.1483372748,-0.3189133108,0.2778151631,0.3382665813,0.1288223416,0.1079095602,-0.239862293,-0.0247332957,-0.2022278309,0.1674776077,-0.1916826367,-0.1363240331,-0.1142772362,0.2343533635,0.1731068641,0.0974104404,-0.3408343494,-0.0454138368,-0.2319974899,-0.0555951707,0.2320308834,-0.0026652913,0.30958727,-0.0770000219,0.14246355,-0.3586760759,0.0364723913,0.3661396801,0.0212173313,-0.1678794324,0.2677277923,-0.0123514775,-0.1251045763,0.0236814786,-0.14731206,-0.299810648,-0.2093851715,0.0171216857,0.0482656993,0.0239049792,-0.0471153446,0.0395985246,0.0380500816,0.1774878949,-0.1749292463,-0.340343833,-0.4319575131,0.4691960514,-0.2710248232,-0.2475002557,0.0816968828,0.0473560803,0.2014110088,0.3418824375,-0.4603409767,-0.0737863109,-0.1173928976,0.1646256745,0.0758609176,0.1864943802,0.0469675176,-0.1311487854,0.1585203111,-0.0770411864,-0.3395670652,-0.1058110222,0.2425631136,0.2578392923,-0.1052164957,0.4483567774,-0.2508384287,0.2393931001,-0.0129460888,0.2891106904,0.5426401496,-0.0893333852,0.1529127359,-0.3217939138,-0.3143526018,-0.099711448,-0.1948753744,-0.1389905959,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2061","title":"Cannot load udpos subsets from xtreme dataset using load_dataset()","comments":"Hi,\r\n\r\nafter #2326, the lines with pos tags equal to `\"_\"` are filtered out when generating the dataset, so this fixes the KeyError described above. However, the udpos subset should be in the conll format i.e. it should yield sequences of tokens and not single tokens, so it would be great to see this fixed (feel free to borrow the logic from [here](https:\/\/github.com\/google-research\/xtreme\/blob\/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de\/utils_preprocess.py#L187-L204) if you decide to work on this). ","body":"Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n","comment_length":70,"text":"Cannot load udpos subsets from xtreme dataset using load_dataset() \n Hello, \r\n\r\nI am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error. \r\n\r\nReprex is: \r\n\r\n`from datasets import load_dataset `\r\n`dataset = load_dataset('xtreme', 'udpos.English')`\r\n\r\nThe error is: \r\n`KeyError: '_'`\r\n\r\nThe full traceback is: \r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-5-7181359ea09d> in <module>\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('xtreme', 'udpos.English')\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    738 \r\n    739     # Download and prepare data\r\n--> 740     builder_instance.download_and_prepare(\r\n    741         download_config=download_config,\r\n    742         download_mode=download_mode,\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n    576                             logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n    577                     if not downloaded_from_gcs:\r\n--> 578                         self._download_and_prepare(\r\n    579                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    580                         )\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    654             try:\r\n    655                 # Prepare split will record examples associated to the split\r\n--> 656                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    657             except OSError as e:\r\n    658                 raise OSError(\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\builder.py in _prepare_split(self, split_generator)\r\n    977                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    978             ):\r\n--> 979                 example = self.info.features.encode_example(record)\r\n    980                 writer.write(example)\r\n    981         finally:\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example)\r\n    946     def encode_example(self, example):\r\n    947         example = cast_to_python_objects(example)\r\n--> 948         return encode_nested_example(self, example)\r\n    949 \r\n    950     def encode_batch(self, batch):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    840     # Nested structures: we allow dict, list\/tuples, sequences\r\n    841     if isinstance(schema, dict):\r\n--> 842         return {\r\n    843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in <dictcomp>(.0)\r\n    841     if isinstance(schema, dict):\r\n    842         return {\r\n--> 843             k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n    844         }\r\n    845     elif isinstance(schema, (list, tuple)):\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_nested_example(schema, obj)\r\n    868     # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n    869     elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):\r\n--> 870         return schema.encode_example(obj)\r\n    871     # Other object should be directly convertible to a native Arrow type (like Translation and Translation)\r\n    872     return obj\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in encode_example(self, example_data)\r\n    647         # If a string is given, convert to associated integer\r\n    648         if isinstance(example_data, str):\r\n--> 649             example_data = self.str2int(example_data)\r\n    650 \r\n    651         # Allowing -1 to mean no label.\r\n\r\n~\\Anaconda3\\envs\\mlenv\\lib\\site-packages\\datasets\\features.py in str2int(self, values)\r\n    605                 if value not in self._str2int:\r\n    606                     value = value.strip()\r\n--> 607                 output.append(self._str2int[str(value)])\r\n    608             else:\r\n    609                 # No names provided, try to integerize\r\n\r\nKeyError: '_'\r\n\r\n \n Hi,\r\n\r\nafter #2326, the lines with pos tags equal to `\"_\"` are filtered out when generating the dataset, so this fixes the KeyError described above. However, the udpos subset should be in the conll format i.e. it should yield sequences of tokens and not single tokens, so it would be great to see this fixed (feel free to borrow the logic from [here](https:\/\/github.com\/google-research\/xtreme\/blob\/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de\/utils_preprocess.py#L187-L204) if you decide to work on this). 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2059","title":"Error while following docs to load the `ted_talks_iwslt` dataset","comments":"This has been fixed in #2064  by @mariosasko (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)","body":"I am currently trying to load the `ted_talks_iwslt` dataset into google colab.\r\n\r\nThe [docs](https:\/\/huggingface.co\/datasets\/ted_talks_iwslt) mention the following way of doing so.\r\n\r\n```python\r\ndataset = load_dataset(\"ted_talks_iwslt\", language_pair=(\"it\", \"pl\"), year=\"2014\")\r\n```\r\n\r\nExecuting it results in the error attached below.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-7dcc67154ef9> in <module>()\r\n----> 1 dataset = load_dataset(\"ted_talks_iwslt\", language_pair=(\"it\", \"pl\"), year=\"2014\")\r\n\r\n4 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    730         hash=hash,\r\n    731         features=features,\r\n--> 732         **config_kwargs,\r\n    733     )\r\n    734 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in __init__(self, writer_batch_size, *args, **kwargs)\r\n    927 \r\n    928     def __init__(self, *args, writer_batch_size=None, **kwargs):\r\n--> 929         super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n    930         # Batch size used by the ArrowWriter\r\n    931         # It defines the number of samples that are kept in memory before writing them\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)\r\n    241             name,\r\n    242             custom_features=features,\r\n--> 243             **config_kwargs,\r\n    244         )\r\n    245 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)\r\n    337             if \"version\" not in config_kwargs and hasattr(self, \"VERSION\") and self.VERSION:\r\n    338                 config_kwargs[\"version\"] = self.VERSION\r\n--> 339             builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs)\r\n    340 \r\n    341         # otherwise use the config_kwargs to overwrite the attributes\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/ted_talks_iwslt\/024d06b1376b361e59245c5878ab8acf9a7576d765f2d0077f61751158e60914\/ted_talks_iwslt.py in __init__(self, language_pair, year, **kwargs)\r\n    219             description=description,\r\n    220             version=datasets.Version(\"1.1.0\", \"\"),\r\n--> 221             **kwargs,\r\n    222         )\r\n    223 \r\n\r\nTypeError: __init__() got multiple values for keyword argument 'version'\r\n```\r\n\r\nHow to resolve this? \r\n\r\nPS: Thanks a lot @huggingface team for creating this great library!","comment_length":28,"text":"Error while following docs to load the `ted_talks_iwslt` dataset \n I am currently trying to load the `ted_talks_iwslt` dataset into google colab.\r\n\r\nThe [docs](https:\/\/huggingface.co\/datasets\/ted_talks_iwslt) mention the following way of doing so.\r\n\r\n```python\r\ndataset = load_dataset(\"ted_talks_iwslt\", language_pair=(\"it\", \"pl\"), year=\"2014\")\r\n```\r\n\r\nExecuting it results in the error attached below.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-6-7dcc67154ef9> in <module>()\r\n----> 1 dataset = load_dataset(\"ted_talks_iwslt\", language_pair=(\"it\", \"pl\"), year=\"2014\")\r\n\r\n4 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)\r\n    730         hash=hash,\r\n    731         features=features,\r\n--> 732         **config_kwargs,\r\n    733     )\r\n    734 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in __init__(self, writer_batch_size, *args, **kwargs)\r\n    927 \r\n    928     def __init__(self, *args, writer_batch_size=None, **kwargs):\r\n--> 929         super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n    930         # Batch size used by the ArrowWriter\r\n    931         # It defines the number of samples that are kept in memory before writing them\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)\r\n    241             name,\r\n    242             custom_features=features,\r\n--> 243             **config_kwargs,\r\n    244         )\r\n    245 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)\r\n    337             if \"version\" not in config_kwargs and hasattr(self, \"VERSION\") and self.VERSION:\r\n    338                 config_kwargs[\"version\"] = self.VERSION\r\n--> 339             builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs)\r\n    340 \r\n    341         # otherwise use the config_kwargs to overwrite the attributes\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/ted_talks_iwslt\/024d06b1376b361e59245c5878ab8acf9a7576d765f2d0077f61751158e60914\/ted_talks_iwslt.py in __init__(self, language_pair, year, **kwargs)\r\n    219             description=description,\r\n    220             version=datasets.Version(\"1.1.0\", \"\"),\r\n--> 221             **kwargs,\r\n    222         )\r\n    223 \r\n\r\nTypeError: __init__() got multiple values for keyword argument 'version'\r\n```\r\n\r\nHow to resolve this? \r\n\r\nPS: Thanks a lot @huggingface team for creating this great library! \n This has been fixed in #2064  by @mariosasko (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)","embeddings":[-0.2140100002,0.1284443438,0.0485115461,0.2001313567,0.0312912539,0.0933724046,0.6712156534,0.1944082826,0.1241025403,-0.0619180724,-0.0495757982,0.3381051421,-0.291054666,0.4190407395,0.077388525,-0.2890882194,0.0997464731,0.0731823891,-0.0559581928,0.206050396,-0.3254210353,0.2124046534,-0.4152063429,0.0127494466,-0.2546612024,-0.2034210265,0.0468139276,0.2763831913,-0.2661168575,-0.3130564094,0.388687551,-0.0194335897,0.4227880836,0.4655911326,-0.0001143111,0.1071715355,0.51889956,-0.1366393417,-0.5111317039,-0.3482480347,-0.42827788,0.0564358458,0.192046836,-0.1413850635,0.0406109989,0.1922591478,0.1550243199,0.0707044527,0.2410453558,0.401366204,0.1814022809,0.3804128468,0.2313805819,-0.2351676375,0.1545953453,0.0248502288,-0.0978888422,0.5647636056,-0.0470754243,-0.1141025722,0.1286965609,0.3489504457,-0.1531593651,0.1483006477,0.2271824032,0.0371534787,-0.206946224,-0.2533878982,0.1699537337,0.1438006461,0.6555977464,-0.2468865812,-0.2574800253,-0.0335899629,0.0099552674,-0.257073462,0.2009013444,0.1602668911,-0.0814757422,0.0440074243,-0.0826552436,-0.0206362251,-0.2317146063,0.2486288399,-0.1192691177,0.3788589239,-0.1296508908,0.2292333394,0.1909642965,-0.1613063663,0.4198672473,0.0673462003,-0.00037431,0.1236649305,-0.172006011,0.0920586661,0.0743063763,0.2679724097,0.0050417609,-0.1803118438,-0.0461912714,-0.1731351018,0.0700950995,0.3650493324,0.4140588045,0.1718422025,0.0364552327,0.2923616469,0.2413597256,0.3500970602,-0.0186878219,0.0057673766,-0.2672667205,-0.1643571705,0.1300109178,0.0049816333,0.4590969682,-0.0423844941,-0.4038477242,-0.0003768379,-0.3010182083,0.0824715495,0.0890631452,0.3704461157,0.0278564673,-0.0001119386,0.2095947862,0.1817014366,-0.1293827146,-0.0431667268,-0.1425672174,0.4191194475,-0.1319151819,0.1497374773,0.2187040746,0.0767599866,0.1410082281,-0.1379396319,0.2220699191,0.0446452945,-0.0268260315,-0.1082442328,-0.1772271544,0.2855977118,0.1986786872,0.1157576293,0.271702975,-0.4462215602,-0.2021680623,0.1938804984,-0.3336772621,-0.438097626,-0.2477481961,0.1399913877,-0.1422355026,-0.080782041,-0.6888030171,0.0970536843,0.3203808963,-0.2366490066,-0.1176822484,-0.3462983072,-0.4640255272,-0.1770429164,0.1038890705,0.6105265617,-0.4028030932,-0.103244558,-0.2107320875,0.1306753308,0.1703797132,0.2542798221,-0.3040802181,0.4380046427,-0.1264621168,0.1686676145,0.0272127017,-0.2167752981,-0.2442434728,0.0688678175,0.1441135108,0.1390661299,-0.1006631553,-0.1111619696,0.1963772774,-0.1156174466,0.4870840609,0.2835547328,-0.1532050222,0.0493033864,-0.1728231311,-0.1591173261,0.2652175725,0.2218355387,0.1602453738,0.1063035578,0.0204085279,0.1632985771,-0.0760866478,-0.1615734845,-0.1027200222,0.10231518,0.3372314274,0.0457199253,-0.0576756485,0.070242539,-0.6290019155,0.1177144274,-0.0899862126,0.4876350462,-0.2378153205,-0.0781501308,-0.375954181,0.2315167785,-0.2586623132,0.0211970247,0.1027925834,0.1375456005,-0.3405932784,0.1095857993,0.0024863463,0.3937729895,-0.1800981611,-0.0502063073,-0.5546491146,0.3506638408,-0.2476859093,-0.2609458864,0.2360201627,0.0128415721,0.1512277275,-0.0852371752,-0.1042425856,0.220842585,0.0838627964,0.1782544851,-0.0415431447,0.0498291962,0.0866228566,-0.2470028996,-0.0502538495,0.2800141275,0.30624336,-0.02226023,0.0072829183,0.3388239443,0.2145283222,0.0617963001,-0.1064285338,0.0869064629,0.1529307961,-0.0321946591,-0.01544616,-0.109802112,0.0940818042,0.3077631593,-0.217350319,-0.006536785,-0.3673067391,0.1065693945,0.3472861946,-0.0406758636,0.1063068956,0.2004859149,-0.0458071828,-0.2589944899,0.1528945863,-0.0988076031,0.3416545689,-0.0652151853,-0.1908445209,-0.020092275,0.0983544439,0.0000412357,0.1111444309,0.1305607706,0.2045361847,-0.021802593,0.1338376552,0.0469109789,-0.1636053473,-0.1726353616,0.0007486735,0.489151746,-0.4590349793,0.3445876539,-0.0838145465,-0.3980570436,-0.1177790537,-0.021256553,-0.09370967,-0.3758860826,-0.1497988552,0.476954937,-0.016330108,0.0647049546,0.3318704069,0.3093937635,0.3434869051,-0.1801780015,-0.091370672,-0.366915822,-0.2936855257,0.0322301835,0.4665715098,0.1150845885,0.2103034109,-0.2565609515,-0.0744037926,-0.1555275321,-0.3767433167,0.1977673918,-0.2820949256,0.5574057698,0.1927935779,0.4036362469,-0.4021496177,-0.417080164,0.2165425271,-0.2159273028,0.0228553899,0.0600701869,-0.2575754821,-0.1695455909,-0.0780540779,-0.2154356837,-0.388065964,-0.3334845304,0.0411791652,0.0234914459,0.1087777913,0.4082966149,0.1039955169,0.0826147273,0.500186801,-0.1201792136,-0.2204819471,-0.1896117479,0.3713559806,-0.2679738402,-0.2996506691,0.0574339963,-0.1118457392,-0.0345388837,-0.12770015,-0.3979893625,-0.1679548174,-0.0796003416,0.0343476385,0.0870103762,0.0046829493,0.0903062001,0.0409919173,0.0165092535,-0.1444610506,-0.3971961141,-0.0447067805,0.286280632,0.1896523237,0.3199491799,0.1527796835,-0.2465851158,0.4933204651,0.1270400584,-0.1835066378,0.3492406607,0.0250619091,0.1754009724,-0.3715402186,-0.4368455708,0.2376172245,-0.1879225224,-0.2280799001,0.2979317009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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2056","title":"issue with opus100\/en-fr dataset ","comments":"@lhoestq  I also deleted the cache and redownload the file and still the same issue, I appreciate any help on this. thanks ","body":"Hi\r\nI am running run_mlm.py code of huggingface repo with opus100\/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? \r\n\r\nThanks a lot  @lhoestq for your help in advance.\r\n\r\n`\r\nthread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', \/__w\/tokenizers\/tokenizers\/tokenizers\/src\/tokenizer\/normalizer.rs:382:21\r\nnote: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\r\n 63%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                   | 626\/1000 [00:27<00:16, 22.69ba\/s]\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm.py\", line 550, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 412, in main\r\n    in zip(data_args.dataset_name, data_args.dataset_config_name)]\r\n  File \"run_mlm.py\", line 411, in <listcomp>\r\n    logger) for dataset_name, dataset_config_name\\\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 96, in get_tokenized_dataset\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in map\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1309, in map\r\n    update_data=update_data,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 204, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1574, in _map_single\r\n    batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1490, in apply_function_on_filtered_inputs\r\n    function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 89, in tokenize_function\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2347, in __call__\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2532, in batch_encode_plus\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_fast.py\", line 384, in _batch_encode_plus\r\n    is_pretokenized=is_split_into_words,\r\npyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617\r\n\r\n`","comment_length":22,"text":"issue with opus100\/en-fr dataset  \n Hi\r\nI am running run_mlm.py code of huggingface repo with opus100\/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? \r\n\r\nThanks a lot  @lhoestq for your help in advance.\r\n\r\n`\r\nthread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', \/__w\/tokenizers\/tokenizers\/tokenizers\/src\/tokenizer\/normalizer.rs:382:21\r\nnote: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\r\n 63%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                   | 626\/1000 [00:27<00:16, 22.69ba\/s]\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm.py\", line 550, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 412, in main\r\n    in zip(data_args.dataset_name, data_args.dataset_config_name)]\r\n  File \"run_mlm.py\", line 411, in <listcomp>\r\n    logger) for dataset_name, dataset_config_name\\\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 96, in get_tokenized_dataset\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in map\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1309, in map\r\n    update_data=update_data,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 204, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1574, in _map_single\r\n    batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1490, in apply_function_on_filtered_inputs\r\n    function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 89, in tokenize_function\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2347, in __call__\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2532, in batch_encode_plus\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_fast.py\", line 384, in _batch_encode_plus\r\n    is_pretokenized=is_split_into_words,\r\npyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617\r\n\r\n` \n @lhoestq  I also deleted the cache and redownload the file and still the same issue, I appreciate any help on this. thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2056","title":"issue with opus100\/en-fr dataset ","comments":"Here please find the minimal code to reproduce the issue @lhoestq  note this only happens with MT5TokenizerFast\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import MT5TokenizerFast\r\n\r\ndef get_tokenized_dataset(dataset_name, dataset_config_name, tokenizer):\r\n    datasets = load_dataset(dataset_name, dataset_config_name, script_version=\"master\")\r\n    column_names = datasets[\"train\"].column_names\r\n    text_column_name = \"translation\"\r\n    def process_dataset(datasets):\r\n        def process_function(examples):\r\n            lang = \"fr\"\r\n            return {\"src_texts\": [example[lang] for example in examples[text_column_name]]}\r\n        datasets = datasets.map(\r\n            process_function,\r\n            batched=True,\r\n            num_proc=None,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=True,\r\n        )\r\n        return datasets\r\n    datasets = process_dataset(datasets)\r\n    text_column_name = \"src_texts\"\r\n    column_names = [\"src_texts\"]\r\n    def tokenize_function(examples):\r\n            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n    tokenized_datasets = datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=None,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=True\r\n    )\r\n\r\nif __name__ == \"__main__\":\r\n     tokenizer_kwargs = {\r\n        \"cache_dir\": None,\r\n        \"use_fast\": True,\r\n        \"revision\": \"main\",\r\n        \"use_auth_token\": None\r\n     }\r\n     tokenizer = MT5TokenizerFast.from_pretrained(\"google\/mt5-small\", **tokenizer_kwargs)\r\n     get_tokenized_dataset(dataset_name=\"opus100\", dataset_config_name=\"en-fr\", tokenizer=tokenizer)\r\n~     \r\n```","body":"Hi\r\nI am running run_mlm.py code of huggingface repo with opus100\/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? \r\n\r\nThanks a lot  @lhoestq for your help in advance.\r\n\r\n`\r\nthread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', \/__w\/tokenizers\/tokenizers\/tokenizers\/src\/tokenizer\/normalizer.rs:382:21\r\nnote: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\r\n 63%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                   | 626\/1000 [00:27<00:16, 22.69ba\/s]\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm.py\", line 550, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 412, in main\r\n    in zip(data_args.dataset_name, data_args.dataset_config_name)]\r\n  File \"run_mlm.py\", line 411, in <listcomp>\r\n    logger) for dataset_name, dataset_config_name\\\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 96, in get_tokenized_dataset\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in map\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1309, in map\r\n    update_data=update_data,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 204, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1574, in _map_single\r\n    batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1490, in apply_function_on_filtered_inputs\r\n    function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 89, in tokenize_function\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2347, in __call__\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2532, in batch_encode_plus\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_fast.py\", line 384, in _batch_encode_plus\r\n    is_pretokenized=is_split_into_words,\r\npyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617\r\n\r\n`","comment_length":114,"text":"issue with opus100\/en-fr dataset  \n Hi\r\nI am running run_mlm.py code of huggingface repo with opus100\/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? \r\n\r\nThanks a lot  @lhoestq for your help in advance.\r\n\r\n`\r\nthread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', \/__w\/tokenizers\/tokenizers\/tokenizers\/src\/tokenizer\/normalizer.rs:382:21\r\nnote: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\r\n 63%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                   | 626\/1000 [00:27<00:16, 22.69ba\/s]\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm.py\", line 550, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 412, in main\r\n    in zip(data_args.dataset_name, data_args.dataset_config_name)]\r\n  File \"run_mlm.py\", line 411, in <listcomp>\r\n    logger) for dataset_name, dataset_config_name\\\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 96, in get_tokenized_dataset\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in map\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1309, in map\r\n    update_data=update_data,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 204, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1574, in _map_single\r\n    batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1490, in apply_function_on_filtered_inputs\r\n    function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 89, in tokenize_function\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2347, in __call__\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2532, in batch_encode_plus\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_fast.py\", line 384, in _batch_encode_plus\r\n    is_pretokenized=is_split_into_words,\r\npyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617\r\n\r\n` \n Here please find the minimal code to reproduce the issue @lhoestq  note this only happens with MT5TokenizerFast\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import MT5TokenizerFast\r\n\r\ndef get_tokenized_dataset(dataset_name, dataset_config_name, tokenizer):\r\n    datasets = load_dataset(dataset_name, dataset_config_name, script_version=\"master\")\r\n    column_names = datasets[\"train\"].column_names\r\n    text_column_name = \"translation\"\r\n    def process_dataset(datasets):\r\n        def process_function(examples):\r\n            lang = \"fr\"\r\n            return {\"src_texts\": [example[lang] for example in examples[text_column_name]]}\r\n        datasets = datasets.map(\r\n            process_function,\r\n            batched=True,\r\n            num_proc=None,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=True,\r\n        )\r\n        return datasets\r\n    datasets = process_dataset(datasets)\r\n    text_column_name = \"src_texts\"\r\n    column_names = [\"src_texts\"]\r\n    def tokenize_function(examples):\r\n            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n    tokenized_datasets = datasets.map(\r\n            tokenize_function,\r\n            batched=True,\r\n            num_proc=None,\r\n            remove_columns=column_names,\r\n            load_from_cache_file=True\r\n    )\r\n\r\nif __name__ == \"__main__\":\r\n     tokenizer_kwargs = {\r\n        \"cache_dir\": None,\r\n        \"use_fast\": True,\r\n        \"revision\": \"main\",\r\n        \"use_auth_token\": None\r\n     }\r\n     tokenizer = MT5TokenizerFast.from_pretrained(\"google\/mt5-small\", **tokenizer_kwargs)\r\n     get_tokenized_dataset(dataset_name=\"opus100\", dataset_config_name=\"en-fr\", tokenizer=tokenizer)\r\n~     \r\n```","embeddings":[-0.4117364585,-0.1673511267,-0.0156450551,0.3972483575,0.1182573065,0.0823763385,0.1922526211,0.2819981277,-0.1431498528,0.2579930723,-0.2424349785,-0.0248958599,0.1322234273,0.3834213614,-0.2399135977,-0.1725855172,-0.0782720894,0.1208091751,-0.4165076911,-0.1202893779,-0.4559483826,0.2238832414,-0.1563926637,0.2297671288,0.3186606765,-0.0702556372,0.1518447101,0.057933744,-0.2905824184,-0.3938674927,-0.1512016654,-0.2992527187,0.3983563483,0.2486596107,-0.0001240568,0.0139625799,0.1579867601,-0.0384621657,0.0809596032,-0.0592175759,0.1000378951,0.1473319381,-0.0287124142,-0.3051408231,0.0371817499,-0.2251016945,-0.3708876371,-0.2680359483,0.2880217433,0.3753131926,0.0867760256,0.5228812695,0.3373613358,-0.1462075114,0.1830143183,-0.0422982462,0.0586619824,-0.0693615079,0.323479563,-0.2280785143,0.3684239984,0.2993505597,-0.1102045476,-0.136514172,0.2203771174,-0.2563655972,0.6804661155,-0.5535476804,0.011115835,0.3440338969,0.073990345,0.0230665654,-0.0722477585,0.0284326747,-0.0147417244,-0.8122330308,-0.0916823149,0.1136502177,-0.0426637977,0.1667119116,-0.3281251788,0.056635309,0.0846511796,0.180134967,0.0343027003,0.3327208459,0.0061036893,0.2988551259,0.2502726614,-0.229519859,-0.409422785,0.2705802023,-0.108532019,0.2871657014,-0.4670937657,-0.0439887866,0.0700741261,-0.1362367272,0.4946710467,0.2132567763,-0.5131159425,-0.162680462,0.4100387394,-0.0067464188,0.2849927545,0.269594878,-0.0111154756,0.023031868,0.202965647,0.0215273388,0.1223784089,-0.0572648272,-0.0405006334,-0.2940114141,-0.2561855912,-0.0725328997,0.1384642124,-0.489279747,-0.2409026027,0.3881003857,-0.3146696985,-0.0595293082,0.5197177529,0.3339307308,0.196629256,-0.1151291132,-0.0928354487,0.2790851295,-0.3406359553,0.0021832027,-0.0180696789,-0.0317981578,-0.0366431437,0.0159903839,-0.0337793902,-0.6434463263,0.2684477866,0.1619419903,0.5190970302,-0.4269457459,0.1198232174,-0.2199243009,-0.0168505087,0.2775986493,-0.0571954623,0.1540126652,0.3632331789,0.0032506017,-0.0590387098,0.0531924404,-0.4613539279,-0.4059747458,-0.3444899619,0.0495101698,-0.1387339532,0.4477998018,0.1756897718,-0.0166503843,0.4423442781,-0.1872699559,0.2043519616,-0.182628423,-0.3020606339,0.0518628433,0.3899740875,0.1399208009,0.0265579876,0.1484417319,0.2194242626,0.2127541155,0.3668286502,0.5186986923,-0.2727029324,-0.0015196375,-0.4783477783,0.2312484533,0.0005177835,-0.4624033868,-0.2426678091,-0.1247510314,-0.2872537076,-0.2944654524,0.0553656332,-0.1781386286,0.6758924723,-0.0452677011,0.2189907879,0.0732169747,0.0459526889,-0.1863196194,-0.2554853857,0.0077219098,0.3513787687,-0.1988129765,0.0082609039,-0.0232906919,-0.0493819155,-0.1482340842,0.1101337969,-0.060543824,0.1323255599,0.2601273656,0.2107945085,0.0742594004,0.2487873733,-0.0611242391,0.1535781622,-0.0508140847,-0.4693951607,0.251824528,-0.0783604905,0.0079228571,-0.3655864894,0.0975491628,-0.1030970439,-0.2655682862,-0.0031700695,-0.2116549015,0.1113093868,0.1097497866,0.1644181311,-0.1983311623,-0.0019175176,0.2205998451,-0.3111978769,0.3991585374,-0.2442423105,-0.0184909273,0.0290290676,0.3571317792,0.3908420801,-0.2759011984,-0.1644442379,0.2347953171,0.0223703906,-0.2906487584,-0.2894754708,0.3155043125,0.1678450257,-0.3867717683,0.0024176578,-0.282569766,0.0304201841,-0.0328209475,0.1935184002,0.527335763,-0.1704324037,0.3984831274,-0.2610982358,0.1100407392,0.0254600924,0.1451858878,0.0004749658,-0.1778210849,0.3125909269,0.3107431531,0.1120001227,0.0675271749,-0.1241336092,-0.0501553342,0.2982763052,0.0116719827,0.1062054485,0.0498672538,-0.0823500603,-0.0183439478,0.1508110166,-0.2998584211,0.1713975817,0.1358766407,-0.0547967032,0.0122387363,-0.0049690604,-0.0121079898,0.2557174265,0.0212531257,0.3840557337,0.192031607,0.0013486212,-0.0890289843,-0.1562478244,-0.105488345,0.1233065203,0.4478662312,-0.2748400867,0.0627049878,-0.0854327828,0.0197419319,-0.1227129921,-0.3330768645,-0.6193659306,-0.1888411939,0.1114291176,0.0365404487,0.0309601035,0.0561556891,0.1487271488,-0.070354946,0.2286869437,0.1457809955,0.169361338,-0.0629823133,-0.1823603064,-0.1482490748,0.1953943819,-0.0163381845,0.0471094511,0.1365008801,-0.0967713669,-0.0325959511,-0.5894052386,0.310652107,-0.0568139292,0.3301675618,0.0491235182,0.1147355363,-0.0633864552,-0.1362303346,0.1913006306,0.099512808,-0.2120435089,0.0822472572,-0.0835589767,0.2379554212,-0.1326675117,-0.095053643,-0.2327533215,-0.4111004472,0.2914522886,-0.1913591027,0.0587136559,0.2794517279,-0.1564147621,-0.0205895007,0.0797978863,-0.1391308457,-0.3224676251,0.1024730131,0.347124368,-0.2841886282,-0.2615577579,-0.2328037471,0.1744868457,0.2109635621,-0.1036836728,-0.4139459133,-0.021609962,-0.1247051358,0.0797610208,-0.2371442914,0.1470896006,0.3746415079,-0.1469144076,-0.0013900505,-0.1065231264,-0.1248332486,0.1823372692,-0.0900530741,0.0056254733,-0.0408166088,0.744967103,-0.1414622217,0.5462422371,0.1166381612,0.046352122,0.3334827721,-0.185360834,0.3638515472,-0.092579186,-0.3035498559,0.2539817989,0.25159740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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2056","title":"issue with opus100\/en-fr dataset ","comments":"as per https:\/\/github.com\/huggingface\/tokenizers\/issues\/626 this looks like to be the tokenizer bug, I therefore, reported it there https:\/\/github.com\/huggingface\/tokenizers\/issues\/626 and  I am closing this one.","body":"Hi\r\nI am running run_mlm.py code of huggingface repo with opus100\/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? \r\n\r\nThanks a lot  @lhoestq for your help in advance.\r\n\r\n`\r\nthread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', \/__w\/tokenizers\/tokenizers\/tokenizers\/src\/tokenizer\/normalizer.rs:382:21\r\nnote: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\r\n 63%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                   | 626\/1000 [00:27<00:16, 22.69ba\/s]\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm.py\", line 550, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 412, in main\r\n    in zip(data_args.dataset_name, data_args.dataset_config_name)]\r\n  File \"run_mlm.py\", line 411, in <listcomp>\r\n    logger) for dataset_name, dataset_config_name\\\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 96, in get_tokenized_dataset\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in map\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1309, in map\r\n    update_data=update_data,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 204, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1574, in _map_single\r\n    batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1490, in apply_function_on_filtered_inputs\r\n    function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 89, in tokenize_function\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2347, in __call__\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2532, in batch_encode_plus\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_fast.py\", line 384, in _batch_encode_plus\r\n    is_pretokenized=is_split_into_words,\r\npyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617\r\n\r\n`","comment_length":23,"text":"issue with opus100\/en-fr dataset  \n Hi\r\nI am running run_mlm.py code of huggingface repo with opus100\/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this? \r\n\r\nThanks a lot  @lhoestq for your help in advance.\r\n\r\n`\r\nthread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', \/__w\/tokenizers\/tokenizers\/tokenizers\/src\/tokenizer\/normalizer.rs:382:21\r\nnote: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\r\n 63%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a                                   | 626\/1000 [00:27<00:16, 22.69ba\/s]\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm.py\", line 550, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 412, in main\r\n    in zip(data_args.dataset_name, data_args.dataset_config_name)]\r\n  File \"run_mlm.py\", line 411, in <listcomp>\r\n    logger) for dataset_name, dataset_config_name\\\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 96, in get_tokenized_dataset\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in map\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py\", line 448, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1309, in map\r\n    update_data=update_data,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 204, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py\", line 337, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1574, in _map_single\r\n    batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1490, in apply_function_on_filtered_inputs\r\n    function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/user\/dara\/dev\/codes\/seq2seq\/data\/tokenize_datasets.py\", line 89, in tokenize_function\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2347, in __call__\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_base.py\", line 2532, in batch_encode_plus\r\n    **kwargs,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/transformers\/tokenization_utils_fast.py\", line 384, in _batch_encode_plus\r\n    is_pretokenized=is_split_into_words,\r\npyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617\r\n\r\n` \n as per https:\/\/github.com\/huggingface\/tokenizers\/issues\/626 this looks like to be the tokenizer bug, I therefore, reported it there https:\/\/github.com\/huggingface\/tokenizers\/issues\/626 and  I am closing this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2055","title":"is there a way to override a dataset object saved with save_to_disk?","comments":"I tried this way, but when there is a mapping process to the dataset, it again uses a random cache name. atm, I am trying to use the following method by setting an exact cache file,\r\n\r\n```\r\n            dataset_with_embedding =csv_dataset.map(\r\n                partial(self.embed, ctx_encoder=ctx_encoder, ctx_tokenizer=self.context_tokenizer),\r\n                batched=True,\r\n                batch_size=1,\r\n                features=new_features,\r\n                cache_file_name=cache_arrow_path,\r\n                load_from_cache_file=False\r\n            )\r\n```\r\nSo here we set a cache_file_name , after this it uses the same  file name when saving again and again. ","body":"At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file.  Is there a way to override such an object? ","comment_length":69,"text":"is there a way to override a dataset object saved with save_to_disk? \n At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file.  Is there a way to override such an object?  \n I tried this way, but when there is a mapping process to the dataset, it again uses a random cache name. atm, I am trying to use the following method by setting an exact cache file,\r\n\r\n```\r\n            dataset_with_embedding =csv_dataset.map(\r\n                partial(self.embed, ctx_encoder=ctx_encoder, ctx_tokenizer=self.context_tokenizer),\r\n                batched=True,\r\n                batch_size=1,\r\n                features=new_features,\r\n                cache_file_name=cache_arrow_path,\r\n                load_from_cache_file=False\r\n            )\r\n```\r\nSo here we set a cache_file_name , after this it uses the same  file name when saving again and again. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2055","title":"is there a way to override a dataset object saved with save_to_disk?","comments":"I'm not sure I understand your issue, can you elaborate ?\r\n\r\n`cache_file_name` is indeed an argument you can set to specify the cache file that will be used for the processed dataset. By default the file is named with something like `cache-<fingerprint>.arrow` where the fingerprint is a hash.","body":"At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file.  Is there a way to override such an object? ","comment_length":48,"text":"is there a way to override a dataset object saved with save_to_disk? \n At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file.  Is there a way to override such an object?  \n I'm not sure I understand your issue, can you elaborate ?\r\n\r\n`cache_file_name` is indeed an argument you can set to specify the cache file that will be used for the processed dataset. By default the file is named with something like `cache-<fingerprint>.arrow` where the fingerprint is a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2055","title":"is there a way to override a dataset object saved with save_to_disk?","comments":"Let's say I am updating a set of embedding in a dataset that is around 40GB inside a training loop every 500 steps (Ex: calculating the embeddings in updated ctx_encoder in RAG and saving it to the passage path).  So when we use **dataset_object.save_to_disk('passage_path_directory')** it will save the new dataset object every time with a random file name, especially when we do some transformations to dataset objects such as map or shards. This way, we keep collecting unwanted files that will eventually eat up all the disk space. \r\n\r\nBut if we can save the dataset object every time by a single name like **data_shard_1.arrow**, it will automatically remove the previous file and save the new one in the same directory.  I found the above-mentioned code snippet useful to complete this task. \r\n\r\nIs this clear?","body":"At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file.  Is there a way to override such an object? ","comment_length":134,"text":"is there a way to override a dataset object saved with save_to_disk? \n At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file.  Is there a way to override such an object?  \n Let's say I am updating a set of embedding in a dataset that is around 40GB inside a training loop every 500 steps (Ex: calculating the embeddings in updated ctx_encoder in RAG and saving it to the passage path).  So when we use **dataset_object.save_to_disk('passage_path_directory')** it will save the new dataset object every time with a random file name, especially when we do some transformations to dataset objects such as map or shards. This way, we keep collecting unwanted files that will eventually eat up all the disk space. \r\n\r\nBut if we can save the dataset object every time by a single name like **data_shard_1.arrow**, it will automatically remove the previous file and save the new one in the same directory.  I found the above-mentioned code snippet useful to complete this task. \r\n\r\nIs this clear?","embeddings":[0.0457346216,-0.0365211032,-0.0065601123,-0.0663145259,0.2652109563,0.1467555016,0.0534220524,0.1336881816,-0.1323261559,0.3330439031,0.3283967674,0.4801915288,-0.3888702691,0.0933786184,0.2319840342,0.0744830072,0.373470217,-0.0670178831,-0.1254188418,0.1810304821,-0.4179126322,0.0441506915,0.2109159231,0.0159829482,-0.0820500329,-0.0264369417,0.0004691452,-0.2508091927,0.0576554537,-0.4308080077,0.2283994853,0.2565983236,0.1199135557,0.1386187524,-0.0001212558,-0.0695586503,0.0095337871,-0.0692437291,-0.416901648,0.0994594991,-0.0609362386,-0.4046192169,-0.0431455038,-0.4321476221,0.3878163993,-0.2496526539,0.2778483927,-0.1676830202,0.1013099253,-0.1304500103,0.0757567957,-0.0730952471,0.1910556108,0.2092887312,0.1304677725,0.2837990224,-0.1073035821,0.2409989238,0.0536172539,0.2485469431,0.0044527454,0.104701452,-0.0398981124,-0.0903648436,0.7107020617,0.0397537798,-0.0830013826,-0.0689411908,0.063255161,0.1232702956,0.5087640882,-0.533945322,-0.1315351725,-0.1749764681,0.1265869588,-0.3090947866,0.2692341506,-0.2114600688,-0.0695016161,0.0209068414,-0.0852169693,-0.6508269906,-0.198544085,0.2556485534,0.2591593564,-0.5391643643,-0.0664123595,0.2288216203,0.316231966,0.0076818229,0.3161955178,-0.3927267194,-0.2238267362,-0.3384256661,-0.1280718744,-0.1810763776,-0.3935975134,0.2291760147,0.0554537289,0.3497772813,0.4361371398,0.1356812268,-0.0677442178,0.2203283459,0.1018314883,0.0233845469,-0.0405252948,0.1268221289,0.0779919401,-0.0195702258,-0.169791624,-0.0301521216,0.2458382398,-0.3112148345,0.3921233118,-0.2665874958,0.257268995,0.058126498,0.0073820548,-0.0774414167,-0.0419784375,0.1095786989,-0.0056771566,-0.1309606433,0.235304296,-0.1221491098,-0.1005398855,0.1382056624,0.1775675714,0.1451976746,0.0803287625,-0.1761173606,0.104832679,0.4112702906,0.0389890447,0.172429949,-0.0452261455,-0.2201778442,0.0412365906,-0.0717496648,0.1835631281,0.0025440357,0.4037235081,-0.098399803,-0.1946253181,0.1945430189,-0.1654605716,-0.1861855239,-0.2288786173,0.2491870373,-0.239801228,-0.279717207,-0.0215783864,0.0262750406,-0.2249813229,-0.1048920155,-0.5177105069,0.1623329073,-0.1310679317,-0.2600252628,0.2126580477,0.2772542238,-0.2532300949,-0.2626474202,-0.000172325,0.1168229133,-0.338301003,-0.0473852791,-0.1328247637,-0.0268523376,-0.3620640635,-0.1325851083,-0.2609158754,-0.0106197828,-0.295156002,-0.0211456101,0.3409298062,-0.1320462674,-0.5908213258,-0.2278280854,-0.1508217603,-0.005291455,-0.0067438367,0.5044074059,0.1441428661,-0.0576039106,-0.5045726895,0.053438548,0.0619736947,-0.350050658,-0.0606923327,-0.1441928595,-0.197633028,-0.1717526466,-0.2981700301,0.2197761685,-0.0556042753,0.0251110438,0.2167973518,0.0732120574,0.2401830405,0.4485553801,0.4341867566,-0.1154959351,-0.1688873321,-0.1425728649,-0.3905547261,-0.0319552533,0.5924943686,-0.4588957131,0.1132578477,-0.2073935866,0.174704805,0.123046875,-0.1968377233,0.2352232784,-0.0548228249,0.3122776449,-0.0037066585,-0.1566853523,-0.3619379103,0.0674391091,-0.172998786,0.1804399937,-0.1348822415,0.1464636177,0.1023183763,-0.0458523631,-0.3633918464,-0.1533870846,-0.1064044684,-0.096509032,-0.0751480162,0.4044415355,-0.246415019,0.1191925928,0.1726580709,0.4314657152,-0.0619406402,-0.3676759899,0.2172438353,0.2357064933,0.0893714577,0.1228102148,-0.3586910963,0.1210450679,-0.0270180535,0.1744958609,-0.061239928,0.1384723485,0.0513607152,-0.0509707928,-0.3274543285,-0.2260819077,-0.3494554758,0.184198454,0.4219741821,-0.0683638528,-0.1296262443,0.1065360755,0.6030349731,-0.101148963,0.0626432374,0.1921167225,-0.2957580686,-0.0801921189,-0.2568456233,0.8007151484,0.3792867064,0.0745852366,0.1722947806,-0.2163551897,0.0766077787,-0.0620703399,0.1834325045,0.3528274894,0.2313379943,0.0689827651,0.2171487808,-0.0842011049,-0.2327295542,0.0229159687,0.0310181547,-0.004507483,-0.0311258566,0.0636650845,-0.0542579889,0.0626263544,-0.4437845647,0.0974419639,-0.2583097219,-0.1972875297,0.0239639804,0.3298319578,-0.0633206964,0.180943951,0.2926631868,0.4197939634,-0.264410466,-0.3293955326,0.0235056039,0.2038994282,-0.1538181454,-0.1272312403,0.1778415293,-0.3315117061,0.3227912188,0.134487927,0.1641263515,-0.7020509839,0.0724505633,-0.1511764824,0.2363016009,0.1226458549,-0.0113487924,0.0670083091,-0.2285551131,0.068781279,-0.0212822631,0.020753067,0.0384541973,-0.0363367721,0.0566614121,0.2914115191,0.1204237416,0.2116761059,-0.1690319031,-0.1292986423,0.189424634,-0.0924684107,-0.0127827143,-0.1523361206,0.2509376407,0.0716323406,-0.0205147211,-0.3927164078,0.0094660949,-0.6643105149,0.3599241078,-0.0630418807,0.0037833641,-0.0244873203,-0.0377796106,-0.0436001047,0.2727342248,-0.3989458382,-0.2452392876,-0.039836105,0.1406462044,-0.076299116,-0.1881086528,0.1733016074,0.4887489676,-0.020689059,0.0275857393,-0.4749938846,0.2654614449,0.1800307184,0.3496613503,0.2071923167,0.1719932705,0.1153355539,0.3271223307,-0.0671866015,-0.1364271194,0.4031299353,0.1584939659,0.3615847528,0.0697800592,-0.0579105429,-0.1681996137,-0.21034234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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2054","title":"Could not find file for ZEST dataset","comments":"This has been fixed in #2057 by @matt-peters (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)","body":"I am trying to use zest dataset from Allen AI using below code in colab,\r\n```\r\n!pip install -q datasets\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"zest\")\r\n```\r\n\r\nI am getting the following error,\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset zest\/default (download: 5.53 MiB, generated: 19.96 MiB, post-processed: Unknown size, total: 25.48 MiB) to \/root\/.cache\/huggingface\/datasets\/zest\/default\/0.0.0\/1f7a230fbfc964d979bbca0f0130fbab3259fce547ee758ad8aa4f9c9bec6cca...\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-6-18dbbc1a4b8a> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 dataset = load_dataset(\"zest\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    612             )\r\n    613         elif response is not None and response.status_code == 404:\r\n--> 614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/ai2-datasets.s3-us-west-2.amazonaws.com\/zest\/zest.zip\r\n```","comment_length":28,"text":"Could not find file for ZEST dataset \n I am trying to use zest dataset from Allen AI using below code in colab,\r\n```\r\n!pip install -q datasets\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"zest\")\r\n```\r\n\r\nI am getting the following error,\r\n```\r\nUsing custom data configuration default\r\n\r\nDownloading and preparing dataset zest\/default (download: 5.53 MiB, generated: 19.96 MiB, post-processed: Unknown size, total: 25.48 MiB) to \/root\/.cache\/huggingface\/datasets\/zest\/default\/0.0.0\/1f7a230fbfc964d979bbca0f0130fbab3259fce547ee758ad8aa4f9c9bec6cca...\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-6-18dbbc1a4b8a> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 dataset = load_dataset(\"zest\")\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)\r\n    612             )\r\n    613         elif response is not None and response.status_code == 404:\r\n--> 614             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n    615         _raise_if_offline_mode_is_enabled(f\"Tried to reach {url}\")\r\n    616         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/ai2-datasets.s3-us-west-2.amazonaws.com\/zest\/zest.zip\r\n``` \n This has been fixed in #2057 by @matt-peters (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)","embeddings":[-0.5492244363,-0.1562176496,-0.0973048881,0.3641074598,0.3012053668,0.0931077302,0.0318725109,0.2230132669,0.2383637726,0.4310354888,-0.2034218013,-0.0097504938,-0.1162817851,0.1218978167,-0.0843498036,0.2447763383,-0.2308540195,0.4342668355,0.4354298413,0.0809467658,-0.1463077515,0.3227610588,-0.0329578854,0.0134566315,-0.2296866775,-0.0266707186,0.064969182,0.2269898206,-0.2469374985,-0.450075388,0.5318314433,-0.2040969729,0.382145524,0.4862642884,-0.0001192676,0.076782912,0.2923074663,-0.1723966599,-0.277204752,-0.6352200508,-0.5189527273,-0.1069162339,0.0227837767,-0.1745985448,-0.4987584949,0.1419637799,0.0905801207,0.0844901279,0.1318611056,0.4710632861,0.1323474646,-0.0255525988,0.4266806841,-0.2614115179,0.6230580807,0.0675981194,-0.1357329488,0.0985978022,0.2885711491,0.2700979114,0.4061024189,0.1312184632,-0.0209040325,0.1605392992,0.1259364933,0.0051018451,0.2155117989,-0.3926919401,0.2901311219,0.2027785182,0.7724493742,-0.2937853634,-0.3040547073,0.1115150675,0.0811822861,-0.4664172828,0.2170495391,0.1507623792,-0.2802621722,0.2095118612,0.1901822388,-0.0583263561,-0.1123306453,0.3451277912,0.0879818723,0.1269708425,-0.1504745185,0.005552602,-0.0237789806,-0.1501269788,0.2711737752,-0.0389018208,-0.0841390938,0.1122513935,-0.2754395604,-0.1604266167,0.0166549496,0.2436979711,-0.0223717783,0.1306643784,0.0592908077,-0.2001150995,0.1021633819,0.1904091984,0.0622776076,0.1541700661,-0.0759224519,-0.3409619927,0.2246865183,0.1661467701,-0.2793195844,-0.2687019706,-0.1667175591,-0.2494900674,-0.1447491348,-0.0926700681,0.1913476735,-0.350455761,-0.4722189307,-0.2292265892,-0.5416941047,0.087120004,0.1921856552,0.3942376375,0.0877431333,-0.2509027421,-0.099341929,-0.0406720936,-0.2872309387,-0.0326353163,-0.1425810456,0.2985761166,-0.1021685675,0.0024052823,0.2607377768,0.0807706863,0.5792786479,-0.2843334973,0.0084049255,-0.0233256873,0.2975989282,-0.2074875236,0.0521377176,0.4288754463,0.380862385,0.0161638167,-0.0982530788,-0.444539398,-0.3056356609,0.3296527863,-0.439458847,-0.4657526314,-0.1354969889,0.0743892714,-0.4352447391,0.003523994,0.0006512358,-0.2347811162,0.1604177505,-0.3929885924,-0.0217237957,-0.3515498638,-0.043433331,-0.2112887949,0.2502272725,0.3778166175,-0.6014804244,0.0128424959,-0.3805820644,0.1550828367,0.1199296936,0.1989078075,-0.1685970575,0.5266979933,-0.2713928223,-0.0915017128,0.6319549084,-0.6368858814,-0.8415307999,-0.1662150472,-0.0623471439,-0.0491294153,0.2330861986,0.2953896523,0.1769868433,-0.2069680542,0.0053665191,0.0280054733,-0.1691209078,-0.113520205,-0.1612265706,-0.1857514232,-0.3377084732,0.145174399,-0.0424749926,-0.1901915222,0.3948430717,-0.0299163144,-0.0283179618,0.0357074253,0.0041886875,0.133307308,0.5193724036,0.1891492605,-0.0042397128,0.0231180303,-0.308227241,0.0448477343,-0.1094072685,0.0647506788,-0.3085492551,-0.0404104739,-0.2715916932,-0.3036621809,-0.4424655139,-0.2636521459,0.0211440884,-0.0341807157,0.3390513957,0.0827218741,-0.040954344,0.0463739112,-0.0391621701,0.037134897,-0.1816708893,0.1381236762,-0.1515363753,-0.3019536138,-0.0527058132,0.2655154169,-0.0792637393,0.005150808,-0.1573840231,0.1158519909,0.1705407947,-0.1826977134,0.2029107809,0.2453830838,0.2108663321,-0.2708515823,0.1889864504,-0.0082362387,0.1406192929,0.0224563219,-0.026514288,0.1814837456,0.0642991066,-0.0926171616,0.0142035084,-0.1576364487,0.3949759603,-0.0413950495,0.0071036331,-0.0691644773,0.102370508,0.2601845264,0.2686906457,0.0029978487,-0.1557581872,0.0182827525,0.5315015316,0.1142820269,0.0069914851,0.0530432053,-0.1327366233,0.0963900611,0.2903290093,0.4729301333,0.604775846,0.3320865035,-0.2014874071,-0.1512369961,0.0997540131,-0.1310831904,0.0955172479,0.1010877788,0.2135856599,0.2487532645,0.0912274346,0.0835678726,-0.0502954423,-0.5824819207,-0.0593077838,0.3502303362,-0.1561137587,-0.1299252361,-0.16298531,-0.4231378138,0.0121324444,-0.2939905822,0.1531610489,-0.0411801115,-0.0124833537,0.279330492,0.3121997118,0.1506228,-0.1241767779,0.1046569273,0.0333119668,-0.5073771477,-0.1087347716,-0.0898247883,-0.1168642789,0.0260922201,0.429665029,0.0990311503,0.2851288617,-0.4056546986,0.1447036564,-0.2727582157,-0.1465431601,0.2022347003,0.049581822,0.3368256986,0.1884136349,0.1252973676,-0.2552151382,-0.0274087675,0.2641089261,-0.0612297095,-0.1408555359,-0.0980735049,-0.2689775229,-0.0554559156,0.0529270358,-0.3438365757,-0.56391114,-0.2585717142,-0.1811029166,0.1891873181,0.1829727888,0.0982800871,0.1670468599,0.1794397831,0.0341231078,-0.0652257949,0.0789482594,-0.5655246973,0.4193027318,-0.5766721964,-0.1247311234,0.4705350399,-0.1488472223,0.5684301853,0.0205997638,-0.2560326755,-0.0891146585,-0.1598949581,0.445884198,-0.1885035634,0.0396770313,0.3315299451,-0.0147490259,0.043048095,0.0007880731,-0.0024127138,-0.0621490963,0.0407078937,0.6619494557,0.192100808,0.4910691381,-0.239226386,0.5676385164,0.0756407902,-0.044208888,0.4262981713,-0.1783228964,0.221961543,-0.5061756968,-0.1453301609,0.164827913,0.185512051,-0.1260237843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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2052","title":"Timit_asr dataset repeats examples","comments":"Hi,\r\n\r\nthis was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: \r\n```bash\r\npip install git+https:\/\/github.com\/huggingface\/datasets\r\n```","body":"Summary\r\n\r\nWhen loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same\r\nSteps to reproduce\r\n\r\nAs an example, on this code there is the text from the training part:\r\n\r\nCode snippet:\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\ntimit['train']['text']\r\n#['Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n```\r\nThe same behavior happens for other columns\r\n\r\nExpected behavior:\r\n\r\nDifferent info on the actual timit_asr dataset\r\n\r\nActual behavior:\r\n\r\nWhen loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same. I've checked datasets 1.3 and the rows are different\r\nDebug info\r\n\r\n    Streamlit version: (get it with $ streamlit version)\r\n    Python version: Python 3.6.12\r\n    Using Conda? PipEnv? PyEnv? Pex? Using pip\r\n    OS version: Centos-release-7-9.2009.1.el7.centos.x86_64\r\n\r\nAdditional information\r\n\r\nYou can check the same behavior on https:\/\/huggingface.co\/datasets\/viewer\/?dataset=timit_asr","comment_length":32,"text":"Timit_asr dataset repeats examples \n Summary\r\n\r\nWhen loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same\r\nSteps to reproduce\r\n\r\nAs an example, on this code there is the text from the training part:\r\n\r\nCode snippet:\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\ntimit['train']['text']\r\n#['Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n# 'Would such an act of refusal be useful?',\r\n```\r\nThe same behavior happens for other columns\r\n\r\nExpected behavior:\r\n\r\nDifferent info on the actual timit_asr dataset\r\n\r\nActual behavior:\r\n\r\nWhen loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same. I've checked datasets 1.3 and the rows are different\r\nDebug info\r\n\r\n    Streamlit version: (get it with $ streamlit version)\r\n    Python version: Python 3.6.12\r\n    Using Conda? PipEnv? PyEnv? Pex? Using pip\r\n    OS version: Centos-release-7-9.2009.1.el7.centos.x86_64\r\n\r\nAdditional information\r\n\r\nYou can check the same behavior on https:\/\/huggingface.co\/datasets\/viewer\/?dataset=timit_asr \n Hi,\r\n\r\nthis was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: \r\n```bash\r\npip install git+https:\/\/github.com\/huggingface\/datasets\r\n```","embeddings":[0.0693238303,-0.2360813767,0.0143211801,0.3875417411,0.2564877272,-0.052774936,0.3894462585,0.1509235948,-0.3530937731,0.2145144641,0.0362559035,0.3838868439,-0.1914107949,0.3149206638,0.1428200752,0.0897241086,-0.0645085797,0.1746298671,-0.5109459758,-0.207854107,0.1559592038,0.107349582,-0.0441997871,-0.0219411999,-0.1925637275,0.2516241968,0.034533523,-0.2753400803,0.0288738497,-0.6021385193,0.2152353972,0.0859404504,0.1602355838,0.5223380923,-0.0001203386,0.085062556,-0.1006255597,-0.0585892051,-0.4437145889,-0.3315133452,-0.129373461,-0.0098590283,0.4994364381,-0.1294307709,-0.353879869,-0.2658421397,0.0228594225,0.1678988934,0.1387133151,0.1977487206,0.1442998946,0.2076290697,-0.4852289855,-0.0598838851,0.1789754033,0.0927969217,-0.0897974819,0.1834605187,-0.1145897433,0.34348014,-0.0861930922,0.2969873846,0.0472152121,0.3001303971,-0.0325891189,0.0343293697,0.0803429931,-0.1385795176,0.0530866496,0.3074523211,0.6112798452,-0.1911728531,-0.006164331,-0.2379683256,0.2809107602,-0.0089171771,0.1315224618,0.2282369733,-0.1578891575,0.1716043204,-0.4439931214,0.1640815884,-0.2220607996,0.1022906825,-0.0723651275,-0.4805187881,-0.2431519479,0.0890064687,-0.1993243694,-0.1923929602,0.2014489323,-0.2141873837,0.1594441235,0.0345593467,-0.2622407675,0.1092169285,-0.0398223922,0.2607025504,0.0437209159,0.2176585197,0.7037342191,0.195019111,-0.1651889831,-0.1209520847,0.1111265793,0.214263007,0.1403140426,-0.1036118343,0.0006801457,0.1406616271,-0.3247894347,0.0161448438,-0.0948205218,0.3506808877,0.2983540297,-0.4594764709,0.2485127151,-0.2413159907,-0.4751442373,0.1753272116,-0.4329754412,-0.1862203479,-0.0301720351,0.1282126009,-0.032817442,0.2887885273,-0.0671522915,-0.1436191946,0.033126764,-0.142973125,-0.1733697951,-0.2447229028,-0.2997201979,0.3173596859,0.1830929518,-0.5795408487,0.1755428463,0.5316569805,-0.0979854912,-0.0599521585,0.1499044001,0.0484337136,0.0605391487,0.0527878292,-0.0916916057,0.2154544741,0.0453242026,0.1959704161,-0.0045511969,0.0992062241,0.0060083386,0.0848728716,0.5086863041,0.1952257603,-0.325463444,-0.1416299641,-0.1361305118,0.1503886133,-0.1309700757,-0.1011544988,0.0790347829,-0.2211146057,-0.1790559888,-0.1774930656,0.0675057545,0.3432037234,-0.3481624424,-0.1797856688,0.3411158323,0.067322433,0.1209600568,0.0170693621,-0.0397707447,0.2547914088,-0.1584244668,-0.0625496134,0.0198324639,-0.1838140935,-0.2675595284,0.1368087679,-0.0317496583,0.4677166343,0.0180712417,-0.0457966253,0.325183183,0.3720587194,0.0871371999,-0.0469075069,0.4046121836,-0.0370944291,-0.4191308022,-0.0683913678,0.1653861851,0.1985431314,-0.173589766,0.2753488421,0.1103103012,-0.0650227144,0.3484919071,-0.1165023744,-0.1479747742,-0.1333269775,0.0910538957,0.155309394,0.1657625288,-0.3785127699,-0.1361991912,-0.0131421974,0.2551571131,0.19182688,0.3550348878,-0.0333180465,-0.3664958477,-0.2809584439,-0.39504987,-0.0378554352,0.102124989,0.5243572593,-0.0206522215,0.2404852659,0.0254026055,0.654519558,-0.4834011793,0.1554483622,-0.1168727428,0.1829820573,0.0746080354,-0.0083817737,0.0238861367,0.0853050053,0.181991592,-0.1432335526,0.119768247,0.3222537637,0.0608440898,-0.0229393691,-0.2227678597,-0.1932323426,0.2595642805,-0.1983759105,-0.1534409821,0.1877188385,0.1179831624,0.0357431434,-0.0955214202,0.3096632957,0.0103773084,0.148306787,0.2213652581,0.0738518089,0.021175174,-0.0808992386,-0.3685730994,-0.4152880311,0.4115943313,-0.0445591882,0.0660966039,0.173598513,-0.413744837,0.179787755,0.0957723185,-0.1290572584,-0.1319271475,0.1978547126,-0.1585223675,0.2480879724,0.0489423648,0.3009958267,0.2230157256,0.1953908056,-0.0019308525,0.0747675821,-0.2692965269,-0.1935736835,0.1433221847,0.0193449222,-0.2603229284,0.4037386477,0.2191151977,-0.0176813658,-0.6143211722,-0.0884906352,0.2171187997,0.2974383533,-0.2661319673,0.0853498876,-0.4890751839,-0.0450068302,-0.3011750877,-0.1606367528,0.1040866077,0.0812572762,0.039384041,0.208057642,0.1990811676,0.2026453763,-0.3972111642,0.2981900573,-0.0682340711,0.4391578436,-0.0972708389,0.2479812652,-0.3348937333,0.0424616002,-0.085272558,0.1215661615,0.2688836455,-0.4862579703,0.0993539914,-0.3178024888,-0.329951942,0.3918776512,-0.1489201188,0.4757384062,0.2216872126,0.0783465505,-0.1970855892,-0.2506314814,0.0102321412,-0.1400298178,-0.2091223747,0.1264417022,-0.0564196296,0.0704466626,-0.1707552373,-0.5320759416,0.1326203495,-0.1759246141,0.4266978502,0.192959249,0.1590533257,0.5182472467,0.0276148356,0.3334203064,0.1154283062,0.2547902167,-0.4598690867,-0.2551384568,0.2225894332,-0.0588355176,-0.1310429275,-0.1529302299,0.0204576701,0.0693665817,0.2738563716,-0.6682428122,-0.0594674423,-0.2347604781,0.0669735521,0.0098983468,0.0657857433,0.1793263555,-0.0859024301,-0.0352972932,-0.1392109543,-0.0137345372,0.2026993185,-0.0850526467,-0.0505669229,0.0524007902,0.1572902948,0.0933310315,0.1990322769,0.4609867632,-0.1091711521,0.2948640883,-0.0948017314,0.5107052922,-0.0797246322,-0.17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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2050","title":"Build custom dataset to fine-tune Wav2Vec2","comments":"Sure you can use the json loader\r\n```python\r\ndata_files = {\"train\": \"path\/to\/your\/train_data.json\", \"test\": \"path\/to\/your\/test_data.json\"}\r\ntrain_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\ntest_dataset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n```\r\n\r\nYou just need to make sure that the data contain the paths to the audio files.\r\nIf not, feel free to use `.map()` to add them.","body":"Thank you for your recent tutorial on how to finetune Wav2Vec2 on a custom dataset. The example you gave here (https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2) was on the CommonVoice dataset. However, what if I want to load my own dataset?  I have a manifest (transcript and their audio files) in a JSON file. \r\n","comment_length":51,"text":"Build custom dataset to fine-tune Wav2Vec2 \n Thank you for your recent tutorial on how to finetune Wav2Vec2 on a custom dataset. The example you gave here (https:\/\/huggingface.co\/blog\/fine-tune-xlsr-wav2vec2) was on the CommonVoice dataset. However, what if I want to load my own dataset?  I have a manifest (transcript and their audio files) in a JSON file. \r\n \n Sure you can use the json loader\r\n```python\r\ndata_files = {\"train\": \"path\/to\/your\/train_data.json\", \"test\": \"path\/to\/your\/test_data.json\"}\r\ntrain_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\ntest_dataset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n```\r\n\r\nYou just need to make sure that the data contain the paths to the audio files.\r\nIf not, feel free to use `.map()` to add them.","embeddings":[-0.0861028954,0.0252731182,0.0131383371,0.0565956719,-0.0154160764,-0.0016975594,0.040185865,0.1672806442,0.1113969013,-0.0323929042,-0.2139553726,0.4168869257,-0.2158389539,0.1142557338,-0.0266242605,0.0259716772,-0.1856243163,0.225793615,0.1270139068,-0.0297930297,-0.2303095013,0.150539577,-0.0891870037,-0.0707921013,-0.1212103069,0.1165201217,0.177677691,0.4099347293,-0.1358955055,-0.3398261666,0.407466501,0.3265336156,0.1645614952,0.3329025209,-0.0000988554,-0.1569031477,-0.0146500124,-0.2888993621,0.0687238723,0.1292687804,-0.4952058494,-0.1590586156,-0.1031423062,-0.0598484576,-0.2974746227,-0.1860620081,-0.298794359,-0.2663181126,0.7216199636,0.3141840994,0.2168492973,-0.3120064139,0.0287783369,-0.0162665453,0.0748966634,0.0478535108,0.066221267,0.4144843817,0.2814677954,0.0652268752,0.2580654919,0.043904271,-0.3099857569,0.1127453893,0.265550971,-0.0027987545,-0.4111703336,-0.2162590772,-0.0957747549,0.577278614,0.4842572808,-0.5012128353,-0.1985921413,-0.189050734,0.1999863088,-0.4806697965,-0.0414562412,0.0458245836,-0.1702143699,0.3052021563,-0.0256954655,-0.3619791865,-0.2076643556,-0.0906062201,0.0454348624,0.0527963378,-0.1664787829,-0.0589325055,0.042361144,-0.051307749,-0.1957069039,0.0393166207,-0.2192359418,0.3246355355,-0.4265645444,-0.3958183825,-0.095357798,-0.4423437715,0.0504589006,0.0338856839,0.3694809675,0.143151626,-0.1174625009,0.1512011141,0.0218686815,0.0106418319,0.0032325401,-0.1732013226,0.0986957997,-0.432667017,-0.0350537896,0.0264260657,-0.2180433124,-0.0432572737,-0.3427793086,0.0566503368,0.148542583,-0.3140284121,-0.3048003018,-0.0890865549,-0.1365513057,0.0110783745,0.0665753931,0.4089030921,0.1010563821,0.0541140921,-0.056967169,0.3980821073,0.1612128168,0.0188260544,0.0439640395,0.2589755356,0.3334704638,0.1825697869,0.3748436272,0.0000860344,0.4036771059,-0.1982594281,0.2077446282,-0.1387869567,-0.0327320136,0.133154735,-0.2826277316,-0.0561921336,-0.0048104748,0.1839497387,-0.1019115523,0.0315981098,-0.3335483372,-0.0936334953,-0.3694884479,-0.3046224117,0.1110639796,0.2222231477,-0.2240485251,-0.1587861031,-0.1389620304,0.0635609925,-0.1052223668,-0.2892745733,-0.2854251862,-0.0336191878,-0.26550439,-0.1619248539,0.1819533408,-0.0987349972,-0.6757566929,0.0897569954,-0.1216203794,0.1931489855,0.0001066574,0.2764821649,-0.0473640747,0.5055429339,-0.0960018933,0.3563430011,0.2618568242,-0.4331692457,-0.2342766672,0.3278563023,-0.1937007606,0.1395905763,0.1119498685,-0.0538505726,0.4180216789,-0.3144515455,0.2169784904,0.4147839248,0.1870000362,0.0101187285,0.1476110518,-0.0495184697,0.1854101121,0.0495967306,-0.3701095283,-0.0695197806,-0.1135778651,-0.0335274599,0.2687246799,-0.1552427709,0.0629094094,0.18053855,0.2452691048,-0.2102152556,0.0133582,0.1091393232,0.0447729416,0.1026234776,-0.1104324013,0.005972906,0.34248209,0.0717757791,-0.2754268646,-0.2076176256,-0.3448870778,-0.3608686328,0.185911119,0.1474171728,0.2212572396,-0.4752925038,-0.4122073054,0.0807687119,-0.0943108499,-0.1427309811,-0.2458337843,-0.0367690213,0.3501383066,0.007016798,0.3941415846,0.3947726488,0.1325919777,0.1408108473,-0.0319033936,0.4642842114,0.2063316107,0.3702318966,0.1435026079,0.2380604744,-0.1249080598,-0.4643808603,0.3657501638,0.3537258804,0.2512151301,-0.076651141,-0.3008253574,0.3731018305,0.2522552609,0.3634337187,-0.0481414311,-0.1168700159,0.3635115325,0.1261614561,-0.2628050745,0.1092672423,-0.091446951,-0.0808728561,0.4064539373,-0.2065781206,-0.0683862567,0.0845374316,0.2053621113,-0.0750657693,0.0809962749,0.1536124796,-0.4257684648,-0.1188572049,-0.1134339124,0.1789563149,0.4203054011,0.3598633707,-0.0708565563,-0.0662555024,-0.0526872054,-0.1265423447,0.1080329344,-0.1157122329,-0.0939975083,0.3089731336,0.1058522537,-0.1834134459,-0.1501322389,-0.0604770221,-0.3132826686,0.0747417882,-0.3948696256,-0.4067824483,-0.1029475257,0.3815223277,-0.1875569969,-0.354523629,0.0532795675,0.1806257963,0.2497491241,0.0836810991,-0.0780070722,0.0876738206,0.0497489795,0.4142825902,0.0394403823,-0.3689314127,0.2387041748,-0.0507123694,0.078801319,0.2575671971,0.1206863075,0.2905208766,0.113808386,0.2241352201,0.091978021,0.040780127,0.1982498318,0.0474724695,-0.1298720986,0.1506040394,0.0200970359,0.2760104239,0.3697803617,-0.1345028281,-0.1777005047,0.1584922373,-0.0070836954,-0.0365135446,0.0327608846,0.0971892178,0.1346780211,-0.5730341077,-0.2228899002,-0.401925534,0.3425261378,0.1432172358,0.0825379714,-0.2697078288,0.0670059398,0.059599556,0.0927966088,0.316563338,-0.2320633829,-0.2677368522,0.477547735,-0.2591694891,-0.0345867649,0.1361819655,-0.105293721,0.3692614138,-0.1506731361,-0.0477362685,0.2083643675,0.0922414288,-0.1593067646,0.1981938481,0.0033950168,0.299620986,0.0201970898,-0.0316830464,-0.2138721496,-0.1847884208,0.139983505,0.3487140834,0.0934473425,0.2376148254,0.0113632325,-0.6527879238,0.5451339483,0.2003678381,0.1047428548,0.0255910363,-0.1037690639,0.0688263476,-0.114126429,0.0598314591,0.1120026708,0.063936159,-0.1524635255,0.4134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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"I think faiss automatically sets the number of threads to use to build the index.\r\nCan you check how many CPU cores are being used when you build the index in `use_own_knowleldge_dataset` as compared to this script ? Are there other programs running (maybe for rank>0) ?","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":47,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n I think faiss automatically sets the number of threads to use to build the index.\r\nCan you check how many CPU cores are being used when you build the index in `use_own_knowleldge_dataset` as compared to this script ? Are there other programs running (maybe for rank>0) ?","embeddings":[-0.4988238811,-0.2682781518,-0.0245007072,0.1282283813,0.0594396777,0.2077840716,0.1214630976,0.4241297543,0.291741699,0.2924969196,-0.1188249737,0.1883148551,0.1372130662,0.085820809,-0.1362560391,0.1750429869,0.2370326221,0.0853344873,0.2607980072,-0.1344140023,-0.3274796903,-0.3090646267,-0.1589119136,-0.2578627467,-0.4006919265,-0.0958186537,-0.2436220646,-0.0820504501,0.1982473135,-0.4729171097,-0.137532711,0.1398431212,0.3402583897,0.2582926452,-0.0001220497,0.0556767285,0.2280078083,0.0654628575,-0.2229975015,0.6411964297,0.3400724828,-0.0694884807,-0.1751104295,-0.3107712865,-0.1455549747,-0.1110416427,0.0810796022,-0.0469295718,-0.166365087,0.1943010539,0.0266239122,-0.0582869202,0.0522551611,-0.0154001648,0.4025111794,-0.0755912513,-0.0691769868,0.3174717128,0.1635129154,-0.0722376183,0.0997427031,0.4481079876,0.12064147,-0.2267771959,0.1089045852,0.2051612586,0.7838166952,0.0224396084,0.0652679056,0.152669698,0.0153403804,-0.0905528963,-0.4430085719,-0.1029038355,0.4257141352,-0.6180593967,-0.0674013048,-0.0837419853,-0.1014744639,0.0538599454,0.1745852083,-0.030230185,0.0363178104,0.0474507846,0.1748995632,0.1653574407,0.3874710798,-0.0599023178,0.5584340096,0.0424474142,-0.1168545187,0.0448825061,-0.0920769498,0.0121963527,-0.6050299406,0.0880849659,-0.0309161283,-0.099331513,-0.0926872119,0.0178016424,-0.3694860637,0.0061654514,0.2365601063,0.0033363802,-0.183065027,-0.0902583003,-0.5061043501,0.055881232,0.083901681,0.106286943,-0.3780879974,0.0992346108,-0.0033380785,-0.0043100738,-0.3259801865,-0.2981352806,-0.2288905531,0.2228072584,-0.1811424345,0.0771879777,-0.403968364,-0.25227651,0.2050724775,0.416221559,-0.1177439839,-0.0761364475,0.0108397733,-0.0346156918,-0.2344279587,0.1761687398,-0.2220168859,0.2306603193,-0.0319639184,0.5045982003,-0.0291060489,-0.2213940918,-0.0890693069,-0.0217307452,0.083963491,-0.0078422474,-0.044163648,-0.2998936474,0.2466678023,0.0749230385,-0.2632145882,-0.0676850677,-0.0939525291,0.1211105511,-0.2278231531,0.2215083539,-0.2173948735,-0.3651023209,0.0271579791,0.0193311907,0.3086776733,-0.0630925894,0.420822531,0.1143972278,0.1730186641,0.1613348126,0.159176752,-0.1314434856,-0.2680214942,0.0831866488,0.2267838418,0.3627386689,0.0937216207,0.0347714052,0.2980125546,0.3901303113,0.2124055773,0.3999934494,-0.1159487739,0.6762604713,-0.320004195,0.0842921436,0.2451203614,-0.2553785443,-0.2924265265,0.1869049817,-0.1663640887,-0.0773261935,0.3296633363,0.518270731,0.3120296896,0.0870154649,0.3410930037,0.3062556088,0.0090267127,-0.0312473625,-0.4050686955,-0.0582714975,0.2639195919,0.380407393,-0.3354525268,0.10174077,-0.0401814356,-0.3298804164,0.6009740233,-0.4761057198,-0.1217618585,0.0680584759,0.3615735471,0.3393865526,0.2328698933,0.2761101127,0.1495419145,0.1626259685,0.0960143954,0.0987884179,0.1701551676,-0.4183505476,0.2156298608,-0.0257499516,-0.2181926966,0.1078814194,-0.0609779805,0.1500251442,-0.0456768312,-0.1998734474,-0.1568868458,0.5427840352,-0.6157974005,0.0279460251,-0.2488968521,-0.1560835391,0.0070153689,-0.2134255767,-0.0342439711,0.142958805,0.052496288,-0.3868663311,-0.0538331419,0.1028612107,0.1266249865,-0.187676087,0.5111077428,-0.1136960238,-0.0370367952,0.2962219417,0.044952888,-0.1512648761,0.1090166271,-0.1937616616,-0.0018785683,0.4852037132,0.3487839997,0.2458340824,-0.0214935225,-0.2332086414,0.0470289811,-0.0406502709,-0.0793870017,0.2275258452,0.0799438357,0.1302532405,0.409172982,-0.0333352648,-0.3332441449,0.2324638963,0.1857769042,-0.2031303048,-0.4314085543,0.2710421979,0.3516993821,-0.0368976481,-0.3497084081,-0.5220082998,0.2360264212,0.3185990751,0.1883933693,-0.071653828,-0.0276507791,-0.2472812831,0.1691094786,0.0565532297,-0.048372224,0.1000481471,0.1766420156,-0.2145446539,-0.2067899555,-0.2552964985,-0.1363964677,0.4286457598,-0.0443389192,0.3094405532,0.1777624935,0.2771865427,-0.1247245297,-0.1249763295,0.2887524962,0.0088405078,0.0416105874,-0.1089353785,-0.0362386964,0.1931093037,-0.0469494499,0.3649187982,0.1957939118,-0.3250905871,0.0198445637,-0.0984703004,-0.121494934,-0.0789786056,0.1210055202,-0.1505499333,0.1174001396,0.0439472869,-0.0525806546,-0.1851366758,-0.0862713903,-0.1575880647,-0.0794765055,-0.2974514365,-0.1662347317,-0.2460688353,-0.1250380278,-0.119644545,0.0043304516,0.0005504017,-0.0106870206,-0.0404876322,-0.2826742828,-0.1588367969,0.060794808,-0.2936334312,-0.2167021781,-0.1739186198,0.2281414121,-0.0243999027,0.0930592716,-0.1156688854,0.0667516664,0.1222407445,-0.0957010388,-0.1543302834,-0.0534614287,-0.2331429124,0.2917095423,0.1491725743,0.0468316413,-0.0794368014,-0.4122965932,-0.0143970978,0.4969037771,-0.5218712687,-0.0407988019,-0.3583908975,0.2859594226,-0.315094173,0.4582990408,0.0567070097,-0.2462490499,-0.1380138248,0.3644826114,0.1745104492,0.3875301182,-0.0617133379,0.0549775884,-0.087917462,0.2675451636,-0.1149751395,0.5162414312,0.327231437,-0.2136980891,-0.0478191786,-0.2623966038,0.1159517244,0.0527700186,-0.1398820877,0.2965247333,0.0834778845,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"Hi,\r\n I am running the add_faiss_index during the training process of the RAG from the master process (rank 0). But at the exact moment, I do not run any other process since I do it in every 5000 training steps. \r\n \r\n I think what you say is correct. It depends on the number of CPU cores. I did an experiment to compare the time taken to finish the add_faiss_index process on use_own_knowleldge_dataset.py vs the training loop thing.  The training loop thing takes 40 mins more. It might be natural right? \r\n \r\n \r\n at the moment it uses around 40 cores of a 96 core machine (I am fine-tuning the entire process).  ","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":108,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n Hi,\r\n I am running the add_faiss_index during the training process of the RAG from the master process (rank 0). But at the exact moment, I do not run any other process since I do it in every 5000 training steps. \r\n \r\n I think what you say is correct. It depends on the number of CPU cores. I did an experiment to compare the time taken to finish the add_faiss_index process on use_own_knowleldge_dataset.py vs the training loop thing.  The training loop thing takes 40 mins more. It might be natural right? \r\n \r\n \r\n at the moment it uses around 40 cores of a 96 core machine (I am fine-tuning the entire process).  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"Can you try to set the number of threads manually ?\r\nIf you set the same number of threads for both the `use_own_knowledge_dataset.py` and RAG training, it should take the same amount of time.\r\nYou can see how to set the number of thread in the faiss wiki: https:\/\/github.com\/facebookresearch\/faiss\/wiki\/Threads-and-asynchronous-calls","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":49,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n Can you try to set the number of threads manually ?\r\nIf you set the same number of threads for both the `use_own_knowledge_dataset.py` and RAG training, it should take the same amount of time.\r\nYou can see how to set the number of thread in the faiss wiki: https:\/\/github.com\/facebookresearch\/faiss\/wiki\/Threads-and-asynchronous-calls","embeddings":[-0.4988238811,-0.2682781518,-0.0245007072,0.1282283813,0.0594396777,0.2077840716,0.1214630976,0.4241297543,0.291741699,0.2924969196,-0.1188249737,0.1883148551,0.1372130662,0.085820809,-0.1362560391,0.1750429869,0.2370326221,0.0853344873,0.2607980072,-0.1344140023,-0.3274796903,-0.3090646267,-0.1589119136,-0.2578627467,-0.4006919265,-0.0958186537,-0.2436220646,-0.0820504501,0.1982473135,-0.4729171097,-0.137532711,0.1398431212,0.3402583897,0.2582926452,-0.0001220497,0.0556767285,0.2280078083,0.0654628575,-0.2229975015,0.6411964297,0.3400724828,-0.0694884807,-0.1751104295,-0.3107712865,-0.1455549747,-0.1110416427,0.0810796022,-0.0469295718,-0.166365087,0.1943010539,0.0266239122,-0.0582869202,0.0522551611,-0.0154001648,0.4025111794,-0.0755912513,-0.0691769868,0.3174717128,0.1635129154,-0.0722376183,0.0997427031,0.4481079876,0.12064147,-0.2267771959,0.1089045852,0.2051612586,0.7838166952,0.0224396084,0.0652679056,0.152669698,0.0153403804,-0.0905528963,-0.4430085719,-0.1029038355,0.4257141352,-0.6180593967,-0.0674013048,-0.0837419853,-0.1014744639,0.0538599454,0.1745852083,-0.030230185,0.0363178104,0.0474507846,0.1748995632,0.1653574407,0.3874710798,-0.0599023178,0.5584340096,0.0424474142,-0.1168545187,0.0448825061,-0.0920769498,0.0121963527,-0.6050299406,0.0880849659,-0.0309161283,-0.099331513,-0.0926872119,0.0178016424,-0.3694860637,0.0061654514,0.2365601063,0.0033363802,-0.183065027,-0.0902583003,-0.5061043501,0.055881232,0.083901681,0.106286943,-0.3780879974,0.0992346108,-0.0033380785,-0.0043100738,-0.3259801865,-0.2981352806,-0.2288905531,0.2228072584,-0.1811424345,0.0771879777,-0.403968364,-0.25227651,0.2050724775,0.416221559,-0.1177439839,-0.0761364475,0.0108397733,-0.0346156918,-0.2344279587,0.1761687398,-0.2220168859,0.2306603193,-0.0319639184,0.5045982003,-0.0291060489,-0.2213940918,-0.0890693069,-0.0217307452,0.083963491,-0.0078422474,-0.044163648,-0.2998936474,0.2466678023,0.0749230385,-0.2632145882,-0.0676850677,-0.0939525291,0.1211105511,-0.2278231531,0.2215083539,-0.2173948735,-0.3651023209,0.0271579791,0.0193311907,0.3086776733,-0.0630925894,0.420822531,0.1143972278,0.1730186641,0.1613348126,0.159176752,-0.1314434856,-0.2680214942,0.0831866488,0.2267838418,0.3627386689,0.0937216207,0.0347714052,0.2980125546,0.3901303113,0.2124055773,0.3999934494,-0.1159487739,0.6762604713,-0.320004195,0.0842921436,0.2451203614,-0.2553785443,-0.2924265265,0.1869049817,-0.1663640887,-0.0773261935,0.3296633363,0.518270731,0.3120296896,0.0870154649,0.3410930037,0.3062556088,0.0090267127,-0.0312473625,-0.4050686955,-0.0582714975,0.2639195919,0.380407393,-0.3354525268,0.10174077,-0.0401814356,-0.3298804164,0.6009740233,-0.4761057198,-0.1217618585,0.0680584759,0.3615735471,0.3393865526,0.2328698933,0.2761101127,0.1495419145,0.1626259685,0.0960143954,0.0987884179,0.1701551676,-0.4183505476,0.2156298608,-0.0257499516,-0.2181926966,0.1078814194,-0.0609779805,0.1500251442,-0.0456768312,-0.1998734474,-0.1568868458,0.5427840352,-0.6157974005,0.0279460251,-0.2488968521,-0.1560835391,0.0070153689,-0.2134255767,-0.0342439711,0.142958805,0.052496288,-0.3868663311,-0.0538331419,0.1028612107,0.1266249865,-0.187676087,0.5111077428,-0.1136960238,-0.0370367952,0.2962219417,0.044952888,-0.1512648761,0.1090166271,-0.1937616616,-0.0018785683,0.4852037132,0.3487839997,0.2458340824,-0.0214935225,-0.2332086414,0.0470289811,-0.0406502709,-0.0793870017,0.2275258452,0.0799438357,0.1302532405,0.409172982,-0.0333352648,-0.3332441449,0.2324638963,0.1857769042,-0.2031303048,-0.4314085543,0.2710421979,0.3516993821,-0.0368976481,-0.3497084081,-0.5220082998,0.2360264212,0.3185990751,0.1883933693,-0.071653828,-0.0276507791,-0.2472812831,0.1691094786,0.0565532297,-0.048372224,0.1000481471,0.1766420156,-0.2145446539,-0.2067899555,-0.2552964985,-0.1363964677,0.4286457598,-0.0443389192,0.3094405532,0.1777624935,0.2771865427,-0.1247245297,-0.1249763295,0.2887524962,0.0088405078,0.0416105874,-0.1089353785,-0.0362386964,0.1931093037,-0.0469494499,0.3649187982,0.1957939118,-0.3250905871,0.0198445637,-0.0984703004,-0.121494934,-0.0789786056,0.1210055202,-0.1505499333,0.1174001396,0.0439472869,-0.0525806546,-0.1851366758,-0.0862713903,-0.1575880647,-0.0794765055,-0.2974514365,-0.1662347317,-0.2460688353,-0.1250380278,-0.119644545,0.0043304516,0.0005504017,-0.0106870206,-0.0404876322,-0.2826742828,-0.1588367969,0.060794808,-0.2936334312,-0.2167021781,-0.1739186198,0.2281414121,-0.0243999027,0.0930592716,-0.1156688854,0.0667516664,0.1222407445,-0.0957010388,-0.1543302834,-0.0534614287,-0.2331429124,0.2917095423,0.1491725743,0.0468316413,-0.0794368014,-0.4122965932,-0.0143970978,0.4969037771,-0.5218712687,-0.0407988019,-0.3583908975,0.2859594226,-0.315094173,0.4582990408,0.0567070097,-0.2462490499,-0.1380138248,0.3644826114,0.1745104492,0.3875301182,-0.0617133379,0.0549775884,-0.087917462,0.2675451636,-0.1149751395,0.5162414312,0.327231437,-0.2136980891,-0.0478191786,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"Ok, I will report the details too soon. I am the first one on the list and currently add_index being computed for the 3rd time in the loop. Actually seems like the time is taken to complete each interaction is the same, but around 1 hour more compared to running it without the training loop. A the moment this takes 5hrs and 30 mins.  If there is any way to faster the process, an end-to-end rag will be perfect. So I will also try out with different thread numbers too.  \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/111453464-798c5f80-8778-11eb-86d0-19d212f58e38.png)\r\n","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":91,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n Ok, I will report the details too soon. I am the first one on the list and currently add_index being computed for the 3rd time in the loop. Actually seems like the time is taken to complete each interaction is the same, but around 1 hour more compared to running it without the training loop. A the moment this takes 5hrs and 30 mins.  If there is any way to faster the process, an end-to-end rag will be perfect. So I will also try out with different thread numbers too.  \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/111453464-798c5f80-8778-11eb-86d0-19d212f58e38.png)\r\n","embeddings":[-0.4988238811,-0.2682781518,-0.0245007072,0.1282283813,0.0594396777,0.2077840716,0.1214630976,0.4241297543,0.291741699,0.2924969196,-0.1188249737,0.1883148551,0.1372130662,0.085820809,-0.1362560391,0.1750429869,0.2370326221,0.0853344873,0.2607980072,-0.1344140023,-0.3274796903,-0.3090646267,-0.1589119136,-0.2578627467,-0.4006919265,-0.0958186537,-0.2436220646,-0.0820504501,0.1982473135,-0.4729171097,-0.137532711,0.1398431212,0.3402583897,0.2582926452,-0.0001220497,0.0556767285,0.2280078083,0.0654628575,-0.2229975015,0.6411964297,0.3400724828,-0.0694884807,-0.1751104295,-0.3107712865,-0.1455549747,-0.1110416427,0.0810796022,-0.0469295718,-0.166365087,0.1943010539,0.0266239122,-0.0582869202,0.0522551611,-0.0154001648,0.4025111794,-0.0755912513,-0.0691769868,0.3174717128,0.1635129154,-0.0722376183,0.0997427031,0.4481079876,0.12064147,-0.2267771959,0.1089045852,0.2051612586,0.7838166952,0.0224396084,0.0652679056,0.152669698,0.0153403804,-0.0905528963,-0.4430085719,-0.1029038355,0.4257141352,-0.6180593967,-0.0674013048,-0.0837419853,-0.1014744639,0.0538599454,0.1745852083,-0.030230185,0.0363178104,0.0474507846,0.1748995632,0.1653574407,0.3874710798,-0.0599023178,0.5584340096,0.0424474142,-0.1168545187,0.0448825061,-0.0920769498,0.0121963527,-0.6050299406,0.0880849659,-0.0309161283,-0.099331513,-0.0926872119,0.0178016424,-0.3694860637,0.0061654514,0.2365601063,0.0033363802,-0.183065027,-0.0902583003,-0.5061043501,0.055881232,0.083901681,0.106286943,-0.3780879974,0.0992346108,-0.0033380785,-0.0043100738,-0.3259801865,-0.2981352806,-0.2288905531,0.2228072584,-0.1811424345,0.0771879777,-0.403968364,-0.25227651,0.2050724775,0.416221559,-0.1177439839,-0.0761364475,0.0108397733,-0.0346156918,-0.2344279587,0.1761687398,-0.2220168859,0.2306603193,-0.0319639184,0.5045982003,-0.0291060489,-0.2213940918,-0.0890693069,-0.0217307452,0.083963491,-0.0078422474,-0.044163648,-0.2998936474,0.2466678023,0.0749230385,-0.2632145882,-0.0676850677,-0.0939525291,0.1211105511,-0.2278231531,0.2215083539,-0.2173948735,-0.3651023209,0.0271579791,0.0193311907,0.3086776733,-0.0630925894,0.420822531,0.1143972278,0.1730186641,0.1613348126,0.159176752,-0.1314434856,-0.2680214942,0.0831866488,0.2267838418,0.3627386689,0.0937216207,0.0347714052,0.2980125546,0.3901303113,0.2124055773,0.3999934494,-0.1159487739,0.6762604713,-0.320004195,0.0842921436,0.2451203614,-0.2553785443,-0.2924265265,0.1869049817,-0.1663640887,-0.0773261935,0.3296633363,0.518270731,0.3120296896,0.0870154649,0.3410930037,0.3062556088,0.0090267127,-0.0312473625,-0.4050686955,-0.0582714975,0.2639195919,0.380407393,-0.3354525268,0.10174077,-0.0401814356,-0.3298804164,0.6009740233,-0.4761057198,-0.1217618585,0.0680584759,0.3615735471,0.3393865526,0.2328698933,0.2761101127,0.1495419145,0.1626259685,0.0960143954,0.0987884179,0.1701551676,-0.4183505476,0.2156298608,-0.0257499516,-0.2181926966,0.1078814194,-0.0609779805,0.1500251442,-0.0456768312,-0.1998734474,-0.1568868458,0.5427840352,-0.6157974005,0.0279460251,-0.2488968521,-0.1560835391,0.0070153689,-0.2134255767,-0.0342439711,0.142958805,0.052496288,-0.3868663311,-0.0538331419,0.1028612107,0.1266249865,-0.187676087,0.5111077428,-0.1136960238,-0.0370367952,0.2962219417,0.044952888,-0.1512648761,0.1090166271,-0.1937616616,-0.0018785683,0.4852037132,0.3487839997,0.2458340824,-0.0214935225,-0.2332086414,0.0470289811,-0.0406502709,-0.0793870017,0.2275258452,0.0799438357,0.1302532405,0.409172982,-0.0333352648,-0.3332441449,0.2324638963,0.1857769042,-0.2031303048,-0.4314085543,0.2710421979,0.3516993821,-0.0368976481,-0.3497084081,-0.5220082998,0.2360264212,0.3185990751,0.1883933693,-0.071653828,-0.0276507791,-0.2472812831,0.1691094786,0.0565532297,-0.048372224,0.1000481471,0.1766420156,-0.2145446539,-0.2067899555,-0.2552964985,-0.1363964677,0.4286457598,-0.0443389192,0.3094405532,0.1777624935,0.2771865427,-0.1247245297,-0.1249763295,0.2887524962,0.0088405078,0.0416105874,-0.1089353785,-0.0362386964,0.1931093037,-0.0469494499,0.3649187982,0.1957939118,-0.3250905871,0.0198445637,-0.0984703004,-0.121494934,-0.0789786056,0.1210055202,-0.1505499333,0.1174001396,0.0439472869,-0.0525806546,-0.1851366758,-0.0862713903,-0.1575880647,-0.0794765055,-0.2974514365,-0.1662347317,-0.2460688353,-0.1250380278,-0.119644545,0.0043304516,0.0005504017,-0.0106870206,-0.0404876322,-0.2826742828,-0.1588367969,0.060794808,-0.2936334312,-0.2167021781,-0.1739186198,0.2281414121,-0.0243999027,0.0930592716,-0.1156688854,0.0667516664,0.1222407445,-0.0957010388,-0.1543302834,-0.0534614287,-0.2331429124,0.2917095423,0.1491725743,0.0468316413,-0.0794368014,-0.4122965932,-0.0143970978,0.4969037771,-0.5218712687,-0.0407988019,-0.3583908975,0.2859594226,-0.315094173,0.4582990408,0.0567070097,-0.2462490499,-0.1380138248,0.3644826114,0.1745104492,0.3875301182,-0.0617133379,0.0549775884,-0.087917462,0.2675451636,-0.1149751395,0.5162414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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"@lhoestq  on a different note, I read about using Faiss-GPU, but the documentation says we should use it when the dataset has the ability to fit into the GPU memory. Although this might work, in the long-term this is not that practical for me.\r\n\r\nhttps:\/\/github.com\/matsui528\/faiss_tips","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":45,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n @lhoestq  on a different note, I read about using Faiss-GPU, but the documentation says we should use it when the dataset has the ability to fit into the GPU memory. Although this might work, in the long-term this is not that practical for me.\r\n\r\nhttps:\/\/github.com\/matsui528\/faiss_tips","embeddings":[-0.4988238811,-0.2682781518,-0.0245007072,0.1282283813,0.0594396777,0.2077840716,0.1214630976,0.4241297543,0.291741699,0.2924969196,-0.1188249737,0.1883148551,0.1372130662,0.085820809,-0.1362560391,0.1750429869,0.2370326221,0.0853344873,0.2607980072,-0.1344140023,-0.3274796903,-0.3090646267,-0.1589119136,-0.2578627467,-0.4006919265,-0.0958186537,-0.2436220646,-0.0820504501,0.1982473135,-0.4729171097,-0.137532711,0.1398431212,0.3402583897,0.2582926452,-0.0001220497,0.0556767285,0.2280078083,0.0654628575,-0.2229975015,0.6411964297,0.3400724828,-0.0694884807,-0.1751104295,-0.3107712865,-0.1455549747,-0.1110416427,0.0810796022,-0.0469295718,-0.166365087,0.1943010539,0.0266239122,-0.0582869202,0.0522551611,-0.0154001648,0.4025111794,-0.0755912513,-0.0691769868,0.3174717128,0.1635129154,-0.0722376183,0.0997427031,0.4481079876,0.12064147,-0.2267771959,0.1089045852,0.2051612586,0.7838166952,0.0224396084,0.0652679056,0.152669698,0.0153403804,-0.0905528963,-0.4430085719,-0.1029038355,0.4257141352,-0.6180593967,-0.0674013048,-0.0837419853,-0.1014744639,0.0538599454,0.1745852083,-0.030230185,0.0363178104,0.0474507846,0.1748995632,0.1653574407,0.3874710798,-0.0599023178,0.5584340096,0.0424474142,-0.1168545187,0.0448825061,-0.0920769498,0.0121963527,-0.6050299406,0.0880849659,-0.0309161283,-0.099331513,-0.0926872119,0.0178016424,-0.3694860637,0.0061654514,0.2365601063,0.0033363802,-0.183065027,-0.0902583003,-0.5061043501,0.055881232,0.083901681,0.106286943,-0.3780879974,0.0992346108,-0.0033380785,-0.0043100738,-0.3259801865,-0.2981352806,-0.2288905531,0.2228072584,-0.1811424345,0.0771879777,-0.403968364,-0.25227651,0.2050724775,0.416221559,-0.1177439839,-0.0761364475,0.0108397733,-0.0346156918,-0.2344279587,0.1761687398,-0.2220168859,0.2306603193,-0.0319639184,0.5045982003,-0.0291060489,-0.2213940918,-0.0890693069,-0.0217307452,0.083963491,-0.0078422474,-0.044163648,-0.2998936474,0.2466678023,0.0749230385,-0.2632145882,-0.0676850677,-0.0939525291,0.1211105511,-0.2278231531,0.2215083539,-0.2173948735,-0.3651023209,0.0271579791,0.0193311907,0.3086776733,-0.0630925894,0.420822531,0.1143972278,0.1730186641,0.1613348126,0.159176752,-0.1314434856,-0.2680214942,0.0831866488,0.2267838418,0.3627386689,0.0937216207,0.0347714052,0.2980125546,0.3901303113,0.2124055773,0.3999934494,-0.1159487739,0.6762604713,-0.320004195,0.0842921436,0.2451203614,-0.2553785443,-0.2924265265,0.1869049817,-0.1663640887,-0.0773261935,0.3296633363,0.518270731,0.3120296896,0.0870154649,0.3410930037,0.3062556088,0.0090267127,-0.0312473625,-0.4050686955,-0.0582714975,0.2639195919,0.380407393,-0.3354525268,0.10174077,-0.0401814356,-0.3298804164,0.6009740233,-0.4761057198,-0.1217618585,0.0680584759,0.3615735471,0.3393865526,0.2328698933,0.2761101127,0.1495419145,0.1626259685,0.0960143954,0.0987884179,0.1701551676,-0.4183505476,0.2156298608,-0.0257499516,-0.2181926966,0.1078814194,-0.0609779805,0.1500251442,-0.0456768312,-0.1998734474,-0.1568868458,0.5427840352,-0.6157974005,0.0279460251,-0.2488968521,-0.1560835391,0.0070153689,-0.2134255767,-0.0342439711,0.142958805,0.052496288,-0.3868663311,-0.0538331419,0.1028612107,0.1266249865,-0.187676087,0.5111077428,-0.1136960238,-0.0370367952,0.2962219417,0.044952888,-0.1512648761,0.1090166271,-0.1937616616,-0.0018785683,0.4852037132,0.3487839997,0.2458340824,-0.0214935225,-0.2332086414,0.0470289811,-0.0406502709,-0.0793870017,0.2275258452,0.0799438357,0.1302532405,0.409172982,-0.0333352648,-0.3332441449,0.2324638963,0.1857769042,-0.2031303048,-0.4314085543,0.2710421979,0.3516993821,-0.0368976481,-0.3497084081,-0.5220082998,0.2360264212,0.3185990751,0.1883933693,-0.071653828,-0.0276507791,-0.2472812831,0.1691094786,0.0565532297,-0.048372224,0.1000481471,0.1766420156,-0.2145446539,-0.2067899555,-0.2552964985,-0.1363964677,0.4286457598,-0.0443389192,0.3094405532,0.1777624935,0.2771865427,-0.1247245297,-0.1249763295,0.2887524962,0.0088405078,0.0416105874,-0.1089353785,-0.0362386964,0.1931093037,-0.0469494499,0.3649187982,0.1957939118,-0.3250905871,0.0198445637,-0.0984703004,-0.121494934,-0.0789786056,0.1210055202,-0.1505499333,0.1174001396,0.0439472869,-0.0525806546,-0.1851366758,-0.0862713903,-0.1575880647,-0.0794765055,-0.2974514365,-0.1662347317,-0.2460688353,-0.1250380278,-0.119644545,0.0043304516,0.0005504017,-0.0106870206,-0.0404876322,-0.2826742828,-0.1588367969,0.060794808,-0.2936334312,-0.2167021781,-0.1739186198,0.2281414121,-0.0243999027,0.0930592716,-0.1156688854,0.0667516664,0.1222407445,-0.0957010388,-0.1543302834,-0.0534614287,-0.2331429124,0.2917095423,0.1491725743,0.0468316413,-0.0794368014,-0.4122965932,-0.0143970978,0.4969037771,-0.5218712687,-0.0407988019,-0.3583908975,0.2859594226,-0.315094173,0.4582990408,0.0567070097,-0.2462490499,-0.1380138248,0.3644826114,0.1745104492,0.3875301182,-0.0617133379,0.0549775884,-0.087917462,0.2675451636,-0.1149751395,0.5162414312,0.327231437,-0.2136980891,-0.0478191786,-0.2623966038,0.1159517244,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"@lhoestq \r\n\r\nHi,  I executed the **use_own_dataset.py** script independently and ask a few of my friends to run their programs in the HPC machine at the same time.  \r\n\r\n Once there are so many other processes are running the add_index function gets slows down naturally.  So basically the speed of the add_index depends entirely on the number of CPU processes.  Then I set the number of threads as you have mentioned and got actually the same time for RAG training and independat running.  So you are correct! :) \r\n\r\n \r\n Then I added this [issue in Faiss repostiary](https:\/\/github.com\/facebookresearch\/faiss\/issues\/1767). I got an answer saying our current **IndexHNSWFlat** can get slow for 30 million vectors and it would be better to use alternatives. What do you think?","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":121,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n @lhoestq \r\n\r\nHi,  I executed the **use_own_dataset.py** script independently and ask a few of my friends to run their programs in the HPC machine at the same time.  \r\n\r\n Once there are so many other processes are running the add_index function gets slows down naturally.  So basically the speed of the add_index depends entirely on the number of CPU processes.  Then I set the number of threads as you have mentioned and got actually the same time for RAG training and independat running.  So you are correct! :) \r\n\r\n \r\n Then I added this [issue in Faiss repostiary](https:\/\/github.com\/facebookresearch\/faiss\/issues\/1767). I got an answer saying our current **IndexHNSWFlat** can get slow for 30 million vectors and it would be better to use alternatives. What do you think?","embeddings":[-0.4988238811,-0.2682781518,-0.0245007072,0.1282283813,0.0594396777,0.2077840716,0.1214630976,0.4241297543,0.291741699,0.2924969196,-0.1188249737,0.1883148551,0.1372130662,0.085820809,-0.1362560391,0.1750429869,0.2370326221,0.0853344873,0.2607980072,-0.1344140023,-0.3274796903,-0.3090646267,-0.1589119136,-0.2578627467,-0.4006919265,-0.0958186537,-0.2436220646,-0.0820504501,0.1982473135,-0.4729171097,-0.137532711,0.1398431212,0.3402583897,0.2582926452,-0.0001220497,0.0556767285,0.2280078083,0.0654628575,-0.2229975015,0.6411964297,0.3400724828,-0.0694884807,-0.1751104295,-0.3107712865,-0.1455549747,-0.1110416427,0.0810796022,-0.0469295718,-0.166365087,0.1943010539,0.0266239122,-0.0582869202,0.0522551611,-0.0154001648,0.4025111794,-0.0755912513,-0.0691769868,0.3174717128,0.1635129154,-0.0722376183,0.0997427031,0.4481079876,0.12064147,-0.2267771959,0.1089045852,0.2051612586,0.7838166952,0.0224396084,0.0652679056,0.152669698,0.0153403804,-0.0905528963,-0.4430085719,-0.1029038355,0.4257141352,-0.6180593967,-0.0674013048,-0.0837419853,-0.1014744639,0.0538599454,0.1745852083,-0.030230185,0.0363178104,0.0474507846,0.1748995632,0.1653574407,0.3874710798,-0.0599023178,0.5584340096,0.0424474142,-0.1168545187,0.0448825061,-0.0920769498,0.0121963527,-0.6050299406,0.0880849659,-0.0309161283,-0.099331513,-0.0926872119,0.0178016424,-0.3694860637,0.0061654514,0.2365601063,0.0033363802,-0.183065027,-0.0902583003,-0.5061043501,0.055881232,0.083901681,0.106286943,-0.3780879974,0.0992346108,-0.0033380785,-0.0043100738,-0.3259801865,-0.2981352806,-0.2288905531,0.2228072584,-0.1811424345,0.0771879777,-0.403968364,-0.25227651,0.2050724775,0.416221559,-0.1177439839,-0.0761364475,0.0108397733,-0.0346156918,-0.2344279587,0.1761687398,-0.2220168859,0.2306603193,-0.0319639184,0.5045982003,-0.0291060489,-0.2213940918,-0.0890693069,-0.0217307452,0.083963491,-0.0078422474,-0.044163648,-0.2998936474,0.2466678023,0.0749230385,-0.2632145882,-0.0676850677,-0.0939525291,0.1211105511,-0.2278231531,0.2215083539,-0.2173948735,-0.3651023209,0.0271579791,0.0193311907,0.3086776733,-0.0630925894,0.420822531,0.1143972278,0.1730186641,0.1613348126,0.159176752,-0.1314434856,-0.2680214942,0.0831866488,0.2267838418,0.3627386689,0.0937216207,0.0347714052,0.2980125546,0.3901303113,0.2124055773,0.3999934494,-0.1159487739,0.6762604713,-0.320004195,0.0842921436,0.2451203614,-0.2553785443,-0.2924265265,0.1869049817,-0.1663640887,-0.0773261935,0.3296633363,0.518270731,0.3120296896,0.0870154649,0.3410930037,0.3062556088,0.0090267127,-0.0312473625,-0.4050686955,-0.0582714975,0.2639195919,0.380407393,-0.3354525268,0.10174077,-0.0401814356,-0.3298804164,0.6009740233,-0.4761057198,-0.1217618585,0.0680584759,0.3615735471,0.3393865526,0.2328698933,0.2761101127,0.1495419145,0.1626259685,0.0960143954,0.0987884179,0.1701551676,-0.4183505476,0.2156298608,-0.0257499516,-0.2181926966,0.1078814194,-0.0609779805,0.1500251442,-0.0456768312,-0.1998734474,-0.1568868458,0.5427840352,-0.6157974005,0.0279460251,-0.2488968521,-0.1560835391,0.0070153689,-0.2134255767,-0.0342439711,0.142958805,0.052496288,-0.3868663311,-0.0538331419,0.1028612107,0.1266249865,-0.187676087,0.5111077428,-0.1136960238,-0.0370367952,0.2962219417,0.044952888,-0.1512648761,0.1090166271,-0.1937616616,-0.0018785683,0.4852037132,0.3487839997,0.2458340824,-0.0214935225,-0.2332086414,0.0470289811,-0.0406502709,-0.0793870017,0.2275258452,0.0799438357,0.1302532405,0.409172982,-0.0333352648,-0.3332441449,0.2324638963,0.1857769042,-0.2031303048,-0.4314085543,0.2710421979,0.3516993821,-0.0368976481,-0.3497084081,-0.5220082998,0.2360264212,0.3185990751,0.1883933693,-0.071653828,-0.0276507791,-0.2472812831,0.1691094786,0.0565532297,-0.048372224,0.1000481471,0.1766420156,-0.2145446539,-0.2067899555,-0.2552964985,-0.1363964677,0.4286457598,-0.0443389192,0.3094405532,0.1777624935,0.2771865427,-0.1247245297,-0.1249763295,0.2887524962,0.0088405078,0.0416105874,-0.1089353785,-0.0362386964,0.1931093037,-0.0469494499,0.3649187982,0.1957939118,-0.3250905871,0.0198445637,-0.0984703004,-0.121494934,-0.0789786056,0.1210055202,-0.1505499333,0.1174001396,0.0439472869,-0.0525806546,-0.1851366758,-0.0862713903,-0.1575880647,-0.0794765055,-0.2974514365,-0.1662347317,-0.2460688353,-0.1250380278,-0.119644545,0.0043304516,0.0005504017,-0.0106870206,-0.0404876322,-0.2826742828,-0.1588367969,0.060794808,-0.2936334312,-0.2167021781,-0.1739186198,0.2281414121,-0.0243999027,0.0930592716,-0.1156688854,0.0667516664,0.1222407445,-0.0957010388,-0.1543302834,-0.0534614287,-0.2331429124,0.2917095423,0.1491725743,0.0468316413,-0.0794368014,-0.4122965932,-0.0143970978,0.4969037771,-0.5218712687,-0.0407988019,-0.3583908975,0.2859594226,-0.315094173,0.4582990408,0.0567070097,-0.2462490499,-0.1380138248,0.3644826114,0.1745104492,0.3875301182,-0.0617133379,0.0549775884,-0.087917462,0.2675451636,-0.1149751395,0.5162414312,0.327231437,-0.2136980891,-0.0478191786,-0.2623966038,0.1159517244,0.0527700186,-0.1398820877,0.2965247333,0.08347788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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"It's a matter of tradeoffs.\r\nHSNW is fast at query time but takes some time to build.\r\nA flat index is flat to build but is \"slow\" at query time.\r\nAn IVF index is probably a good choice for you: fast building and fast queries (but still slower queries than HSNW).\r\n\r\nNote that for an IVF index you would need to have an `nprobe` parameter (number of cells to visit for one query, there are `nlist` in total) that is not too small in order to have good retrieval accuracy, but not too big otherwise the queries will take too much time. From the faiss documentation:\r\n> The nprobe parameter is always a way of adjusting the tradeoff between speed and accuracy of the result. Setting nprobe = nlist gives the same result as the brute-force search (but slower).\r\n\r\nFrom my experience with indexes on DPR embeddings, setting nprobe around 1\/4 of nlist gives really good retrieval accuracy and there's no need to have a value higher than that (or you would need to brute-force in order to see a difference).","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":181,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n It's a matter of tradeoffs.\r\nHSNW is fast at query time but takes some time to build.\r\nA flat index is flat to build but is \"slow\" at query time.\r\nAn IVF index is probably a good choice for you: fast building and fast queries (but still slower queries than HSNW).\r\n\r\nNote that for an IVF index you would need to have an `nprobe` parameter (number of cells to visit for one query, there are `nlist` in total) that is not too small in order to have good retrieval accuracy, but not too big otherwise the queries will take too much time. From the faiss documentation:\r\n> The nprobe parameter is always a way of adjusting the tradeoff between speed and accuracy of the result. Setting nprobe = nlist gives the same result as the brute-force search (but slower).\r\n\r\nFrom my experience with indexes on DPR embeddings, setting nprobe around 1\/4 of nlist gives really good retrieval accuracy and there's no need to have a value higher than that (or you would need to brute-force in order to see a difference).","embeddings":[-0.4988238811,-0.2682781518,-0.0245007072,0.1282283813,0.0594396777,0.2077840716,0.1214630976,0.4241297543,0.291741699,0.2924969196,-0.1188249737,0.1883148551,0.1372130662,0.085820809,-0.1362560391,0.1750429869,0.2370326221,0.0853344873,0.2607980072,-0.1344140023,-0.3274796903,-0.3090646267,-0.1589119136,-0.2578627467,-0.4006919265,-0.0958186537,-0.2436220646,-0.0820504501,0.1982473135,-0.4729171097,-0.137532711,0.1398431212,0.3402583897,0.2582926452,-0.0001220497,0.0556767285,0.2280078083,0.0654628575,-0.2229975015,0.6411964297,0.3400724828,-0.0694884807,-0.1751104295,-0.3107712865,-0.1455549747,-0.1110416427,0.0810796022,-0.0469295718,-0.166365087,0.1943010539,0.0266239122,-0.0582869202,0.0522551611,-0.0154001648,0.4025111794,-0.0755912513,-0.0691769868,0.3174717128,0.1635129154,-0.0722376183,0.0997427031,0.4481079876,0.12064147,-0.2267771959,0.1089045852,0.2051612586,0.7838166952,0.0224396084,0.0652679056,0.152669698,0.0153403804,-0.0905528963,-0.4430085719,-0.1029038355,0.4257141352,-0.6180593967,-0.0674013048,-0.0837419853,-0.1014744639,0.0538599454,0.1745852083,-0.030230185,0.0363178104,0.0474507846,0.1748995632,0.1653574407,0.3874710798,-0.0599023178,0.5584340096,0.0424474142,-0.1168545187,0.0448825061,-0.0920769498,0.0121963527,-0.6050299406,0.0880849659,-0.0309161283,-0.099331513,-0.0926872119,0.0178016424,-0.3694860637,0.0061654514,0.2365601063,0.0033363802,-0.183065027,-0.0902583003,-0.5061043501,0.055881232,0.083901681,0.106286943,-0.3780879974,0.0992346108,-0.0033380785,-0.0043100738,-0.3259801865,-0.2981352806,-0.2288905531,0.2228072584,-0.1811424345,0.0771879777,-0.403968364,-0.25227651,0.2050724775,0.416221559,-0.1177439839,-0.0761364475,0.0108397733,-0.0346156918,-0.2344279587,0.1761687398,-0.2220168859,0.2306603193,-0.0319639184,0.5045982003,-0.0291060489,-0.2213940918,-0.0890693069,-0.0217307452,0.083963491,-0.0078422474,-0.044163648,-0.2998936474,0.2466678023,0.0749230385,-0.2632145882,-0.0676850677,-0.0939525291,0.1211105511,-0.2278231531,0.2215083539,-0.2173948735,-0.3651023209,0.0271579791,0.0193311907,0.3086776733,-0.0630925894,0.420822531,0.1143972278,0.1730186641,0.1613348126,0.159176752,-0.1314434856,-0.2680214942,0.0831866488,0.2267838418,0.3627386689,0.0937216207,0.0347714052,0.2980125546,0.3901303113,0.2124055773,0.3999934494,-0.1159487739,0.6762604713,-0.320004195,0.0842921436,0.2451203614,-0.2553785443,-0.2924265265,0.1869049817,-0.1663640887,-0.0773261935,0.3296633363,0.518270731,0.3120296896,0.0870154649,0.3410930037,0.3062556088,0.0090267127,-0.0312473625,-0.4050686955,-0.0582714975,0.2639195919,0.380407393,-0.3354525268,0.10174077,-0.0401814356,-0.3298804164,0.6009740233,-0.4761057198,-0.1217618585,0.0680584759,0.3615735471,0.3393865526,0.2328698933,0.2761101127,0.1495419145,0.1626259685,0.0960143954,0.0987884179,0.1701551676,-0.4183505476,0.2156298608,-0.0257499516,-0.2181926966,0.1078814194,-0.0609779805,0.1500251442,-0.0456768312,-0.1998734474,-0.1568868458,0.5427840352,-0.6157974005,0.0279460251,-0.2488968521,-0.1560835391,0.0070153689,-0.2134255767,-0.0342439711,0.142958805,0.052496288,-0.3868663311,-0.0538331419,0.1028612107,0.1266249865,-0.187676087,0.5111077428,-0.1136960238,-0.0370367952,0.2962219417,0.044952888,-0.1512648761,0.1090166271,-0.1937616616,-0.0018785683,0.4852037132,0.3487839997,0.2458340824,-0.0214935225,-0.2332086414,0.0470289811,-0.0406502709,-0.0793870017,0.2275258452,0.0799438357,0.1302532405,0.409172982,-0.0333352648,-0.3332441449,0.2324638963,0.1857769042,-0.2031303048,-0.4314085543,0.2710421979,0.3516993821,-0.0368976481,-0.3497084081,-0.5220082998,0.2360264212,0.3185990751,0.1883933693,-0.071653828,-0.0276507791,-0.2472812831,0.1691094786,0.0565532297,-0.048372224,0.1000481471,0.1766420156,-0.2145446539,-0.2067899555,-0.2552964985,-0.1363964677,0.4286457598,-0.0443389192,0.3094405532,0.1777624935,0.2771865427,-0.1247245297,-0.1249763295,0.2887524962,0.0088405078,0.0416105874,-0.1089353785,-0.0362386964,0.1931093037,-0.0469494499,0.3649187982,0.1957939118,-0.3250905871,0.0198445637,-0.0984703004,-0.121494934,-0.0789786056,0.1210055202,-0.1505499333,0.1174001396,0.0439472869,-0.0525806546,-0.1851366758,-0.0862713903,-0.1575880647,-0.0794765055,-0.2974514365,-0.1662347317,-0.2460688353,-0.1250380278,-0.119644545,0.0043304516,0.0005504017,-0.0106870206,-0.0404876322,-0.2826742828,-0.1588367969,0.060794808,-0.2936334312,-0.2167021781,-0.1739186198,0.2281414121,-0.0243999027,0.0930592716,-0.1156688854,0.0667516664,0.1222407445,-0.0957010388,-0.1543302834,-0.0534614287,-0.2331429124,0.2917095423,0.1491725743,0.0468316413,-0.0794368014,-0.4122965932,-0.0143970978,0.4969037771,-0.5218712687,-0.0407988019,-0.3583908975,0.2859594226,-0.315094173,0.4582990408,0.0567070097,-0.2462490499,-0.1380138248,0.3644826114,0.1745104492,0.3875301182,-0.0617133379,0.0549775884,-0.087917462,0.2675451636,-0.1149751395,0.5162414312,0.327231437,-0.2136980891,-0.0478191786,-0.2623966038,0.1159517244,0.0527700186,-0.1398820877,0.2965247333,0.08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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"@lhoestq \r\n\r\nThanks a lot for sharing all this prior knowledge. \r\n\r\nJust asking what would be a good nlist of parameters for 30 million embeddings?","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":24,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n @lhoestq \r\n\r\nThanks a lot for sharing all this prior knowledge. \r\n\r\nJust asking what would be a good nlist of parameters for 30 million embeddings?","embeddings":[-0.4988238811,-0.2682781518,-0.0245007072,0.1282283813,0.0594396777,0.2077840716,0.1214630976,0.4241297543,0.291741699,0.2924969196,-0.1188249737,0.1883148551,0.1372130662,0.085820809,-0.1362560391,0.1750429869,0.2370326221,0.0853344873,0.2607980072,-0.1344140023,-0.3274796903,-0.3090646267,-0.1589119136,-0.2578627467,-0.4006919265,-0.0958186537,-0.2436220646,-0.0820504501,0.1982473135,-0.4729171097,-0.137532711,0.1398431212,0.3402583897,0.2582926452,-0.0001220497,0.0556767285,0.2280078083,0.0654628575,-0.2229975015,0.6411964297,0.3400724828,-0.0694884807,-0.1751104295,-0.3107712865,-0.1455549747,-0.1110416427,0.0810796022,-0.0469295718,-0.166365087,0.1943010539,0.0266239122,-0.0582869202,0.0522551611,-0.0154001648,0.4025111794,-0.0755912513,-0.0691769868,0.3174717128,0.1635129154,-0.0722376183,0.0997427031,0.4481079876,0.12064147,-0.2267771959,0.1089045852,0.2051612586,0.7838166952,0.0224396084,0.0652679056,0.152669698,0.0153403804,-0.0905528963,-0.4430085719,-0.1029038355,0.4257141352,-0.6180593967,-0.0674013048,-0.0837419853,-0.1014744639,0.0538599454,0.1745852083,-0.030230185,0.0363178104,0.0474507846,0.1748995632,0.1653574407,0.3874710798,-0.0599023178,0.5584340096,0.0424474142,-0.1168545187,0.0448825061,-0.0920769498,0.0121963527,-0.6050299406,0.0880849659,-0.0309161283,-0.099331513,-0.0926872119,0.0178016424,-0.3694860637,0.0061654514,0.2365601063,0.0033363802,-0.183065027,-0.0902583003,-0.5061043501,0.055881232,0.083901681,0.106286943,-0.3780879974,0.0992346108,-0.0033380785,-0.0043100738,-0.3259801865,-0.2981352806,-0.2288905531,0.2228072584,-0.1811424345,0.0771879777,-0.403968364,-0.25227651,0.2050724775,0.416221559,-0.1177439839,-0.0761364475,0.0108397733,-0.0346156918,-0.2344279587,0.1761687398,-0.2220168859,0.2306603193,-0.0319639184,0.5045982003,-0.0291060489,-0.2213940918,-0.0890693069,-0.0217307452,0.083963491,-0.0078422474,-0.044163648,-0.2998936474,0.2466678023,0.0749230385,-0.2632145882,-0.0676850677,-0.0939525291,0.1211105511,-0.2278231531,0.2215083539,-0.2173948735,-0.3651023209,0.0271579791,0.0193311907,0.3086776733,-0.0630925894,0.420822531,0.1143972278,0.1730186641,0.1613348126,0.159176752,-0.1314434856,-0.2680214942,0.0831866488,0.2267838418,0.3627386689,0.0937216207,0.0347714052,0.2980125546,0.3901303113,0.2124055773,0.3999934494,-0.1159487739,0.6762604713,-0.320004195,0.0842921436,0.2451203614,-0.2553785443,-0.2924265265,0.1869049817,-0.1663640887,-0.0773261935,0.3296633363,0.518270731,0.3120296896,0.0870154649,0.3410930037,0.3062556088,0.0090267127,-0.0312473625,-0.4050686955,-0.0582714975,0.2639195919,0.380407393,-0.3354525268,0.10174077,-0.0401814356,-0.3298804164,0.6009740233,-0.4761057198,-0.1217618585,0.0680584759,0.3615735471,0.3393865526,0.2328698933,0.2761101127,0.1495419145,0.1626259685,0.0960143954,0.0987884179,0.1701551676,-0.4183505476,0.2156298608,-0.0257499516,-0.2181926966,0.1078814194,-0.0609779805,0.1500251442,-0.0456768312,-0.1998734474,-0.1568868458,0.5427840352,-0.6157974005,0.0279460251,-0.2488968521,-0.1560835391,0.0070153689,-0.2134255767,-0.0342439711,0.142958805,0.052496288,-0.3868663311,-0.0538331419,0.1028612107,0.1266249865,-0.187676087,0.5111077428,-0.1136960238,-0.0370367952,0.2962219417,0.044952888,-0.1512648761,0.1090166271,-0.1937616616,-0.0018785683,0.4852037132,0.3487839997,0.2458340824,-0.0214935225,-0.2332086414,0.0470289811,-0.0406502709,-0.0793870017,0.2275258452,0.0799438357,0.1302532405,0.409172982,-0.0333352648,-0.3332441449,0.2324638963,0.1857769042,-0.2031303048,-0.4314085543,0.2710421979,0.3516993821,-0.0368976481,-0.3497084081,-0.5220082998,0.2360264212,0.3185990751,0.1883933693,-0.071653828,-0.0276507791,-0.2472812831,0.1691094786,0.0565532297,-0.048372224,0.1000481471,0.1766420156,-0.2145446539,-0.2067899555,-0.2552964985,-0.1363964677,0.4286457598,-0.0443389192,0.3094405532,0.1777624935,0.2771865427,-0.1247245297,-0.1249763295,0.2887524962,0.0088405078,0.0416105874,-0.1089353785,-0.0362386964,0.1931093037,-0.0469494499,0.3649187982,0.1957939118,-0.3250905871,0.0198445637,-0.0984703004,-0.121494934,-0.0789786056,0.1210055202,-0.1505499333,0.1174001396,0.0439472869,-0.0525806546,-0.1851366758,-0.0862713903,-0.1575880647,-0.0794765055,-0.2974514365,-0.1662347317,-0.2460688353,-0.1250380278,-0.119644545,0.0043304516,0.0005504017,-0.0106870206,-0.0404876322,-0.2826742828,-0.1588367969,0.060794808,-0.2936334312,-0.2167021781,-0.1739186198,0.2281414121,-0.0243999027,0.0930592716,-0.1156688854,0.0667516664,0.1222407445,-0.0957010388,-0.1543302834,-0.0534614287,-0.2331429124,0.2917095423,0.1491725743,0.0468316413,-0.0794368014,-0.4122965932,-0.0143970978,0.4969037771,-0.5218712687,-0.0407988019,-0.3583908975,0.2859594226,-0.315094173,0.4582990408,0.0567070097,-0.2462490499,-0.1380138248,0.3644826114,0.1745104492,0.3875301182,-0.0617133379,0.0549775884,-0.087917462,0.2675451636,-0.1149751395,0.5162414312,0.327231437,-0.2136980891,-0.0478191786,-0.2623966038,0.1159517244,0.0527700186,-0.1398820877,0.2965247333,0.083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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"When IVF is used alone, nlist should be between `4*sqrt(n)` and `16*sqrt(n)`.\r\nFor more details take a look at [this section of the Faiss wiki](https:\/\/github.com\/facebookresearch\/faiss\/wiki\/Guidelines-to-choose-an-index#how-big-is-the-dataset)","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":25,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n When IVF is used alone, nlist should be between `4*sqrt(n)` and `16*sqrt(n)`.\r\nFor more details take a look at [this section of the Faiss wiki](https:\/\/github.com\/facebookresearch\/faiss\/wiki\/Guidelines-to-choose-an-index#how-big-is-the-dataset)","embeddings":[-0.4988238811,-0.2682781518,-0.0245007072,0.1282283813,0.0594396777,0.2077840716,0.1214630976,0.4241297543,0.291741699,0.2924969196,-0.1188249737,0.1883148551,0.1372130662,0.085820809,-0.1362560391,0.1750429869,0.2370326221,0.0853344873,0.2607980072,-0.1344140023,-0.3274796903,-0.3090646267,-0.1589119136,-0.2578627467,-0.4006919265,-0.0958186537,-0.2436220646,-0.0820504501,0.1982473135,-0.4729171097,-0.137532711,0.1398431212,0.3402583897,0.2582926452,-0.0001220497,0.0556767285,0.2280078083,0.0654628575,-0.2229975015,0.6411964297,0.3400724828,-0.0694884807,-0.1751104295,-0.3107712865,-0.1455549747,-0.1110416427,0.0810796022,-0.0469295718,-0.166365087,0.1943010539,0.0266239122,-0.0582869202,0.0522551611,-0.0154001648,0.4025111794,-0.0755912513,-0.0691769868,0.3174717128,0.1635129154,-0.0722376183,0.0997427031,0.4481079876,0.12064147,-0.2267771959,0.1089045852,0.2051612586,0.7838166952,0.0224396084,0.0652679056,0.152669698,0.0153403804,-0.0905528963,-0.4430085719,-0.1029038355,0.4257141352,-0.6180593967,-0.0674013048,-0.0837419853,-0.1014744639,0.0538599454,0.1745852083,-0.030230185,0.0363178104,0.0474507846,0.1748995632,0.1653574407,0.3874710798,-0.0599023178,0.5584340096,0.0424474142,-0.1168545187,0.0448825061,-0.0920769498,0.0121963527,-0.6050299406,0.0880849659,-0.0309161283,-0.099331513,-0.0926872119,0.0178016424,-0.3694860637,0.0061654514,0.2365601063,0.0033363802,-0.183065027,-0.0902583003,-0.5061043501,0.055881232,0.083901681,0.106286943,-0.3780879974,0.0992346108,-0.0033380785,-0.0043100738,-0.3259801865,-0.2981352806,-0.2288905531,0.2228072584,-0.1811424345,0.0771879777,-0.403968364,-0.25227651,0.2050724775,0.416221559,-0.1177439839,-0.0761364475,0.0108397733,-0.0346156918,-0.2344279587,0.1761687398,-0.2220168859,0.2306603193,-0.0319639184,0.5045982003,-0.0291060489,-0.2213940918,-0.0890693069,-0.0217307452,0.083963491,-0.0078422474,-0.044163648,-0.2998936474,0.2466678023,0.0749230385,-0.2632145882,-0.0676850677,-0.0939525291,0.1211105511,-0.2278231531,0.2215083539,-0.2173948735,-0.3651023209,0.0271579791,0.0193311907,0.3086776733,-0.0630925894,0.420822531,0.1143972278,0.1730186641,0.1613348126,0.159176752,-0.1314434856,-0.2680214942,0.0831866488,0.2267838418,0.3627386689,0.0937216207,0.0347714052,0.2980125546,0.3901303113,0.2124055773,0.3999934494,-0.1159487739,0.6762604713,-0.320004195,0.0842921436,0.2451203614,-0.2553785443,-0.2924265265,0.1869049817,-0.1663640887,-0.0773261935,0.3296633363,0.518270731,0.3120296896,0.0870154649,0.3410930037,0.3062556088,0.0090267127,-0.0312473625,-0.4050686955,-0.0582714975,0.2639195919,0.380407393,-0.3354525268,0.10174077,-0.0401814356,-0.3298804164,0.6009740233,-0.4761057198,-0.1217618585,0.0680584759,0.3615735471,0.3393865526,0.2328698933,0.2761101127,0.1495419145,0.1626259685,0.0960143954,0.0987884179,0.1701551676,-0.4183505476,0.2156298608,-0.0257499516,-0.2181926966,0.1078814194,-0.0609779805,0.1500251442,-0.0456768312,-0.1998734474,-0.1568868458,0.5427840352,-0.6157974005,0.0279460251,-0.2488968521,-0.1560835391,0.0070153689,-0.2134255767,-0.0342439711,0.142958805,0.052496288,-0.3868663311,-0.0538331419,0.1028612107,0.1266249865,-0.187676087,0.5111077428,-0.1136960238,-0.0370367952,0.2962219417,0.044952888,-0.1512648761,0.1090166271,-0.1937616616,-0.0018785683,0.4852037132,0.3487839997,0.2458340824,-0.0214935225,-0.2332086414,0.0470289811,-0.0406502709,-0.0793870017,0.2275258452,0.0799438357,0.1302532405,0.409172982,-0.0333352648,-0.3332441449,0.2324638963,0.1857769042,-0.2031303048,-0.4314085543,0.2710421979,0.3516993821,-0.0368976481,-0.3497084081,-0.5220082998,0.2360264212,0.3185990751,0.1883933693,-0.071653828,-0.0276507791,-0.2472812831,0.1691094786,0.0565532297,-0.048372224,0.1000481471,0.1766420156,-0.2145446539,-0.2067899555,-0.2552964985,-0.1363964677,0.4286457598,-0.0443389192,0.3094405532,0.1777624935,0.2771865427,-0.1247245297,-0.1249763295,0.2887524962,0.0088405078,0.0416105874,-0.1089353785,-0.0362386964,0.1931093037,-0.0469494499,0.3649187982,0.1957939118,-0.3250905871,0.0198445637,-0.0984703004,-0.121494934,-0.0789786056,0.1210055202,-0.1505499333,0.1174001396,0.0439472869,-0.0525806546,-0.1851366758,-0.0862713903,-0.1575880647,-0.0794765055,-0.2974514365,-0.1662347317,-0.2460688353,-0.1250380278,-0.119644545,0.0043304516,0.0005504017,-0.0106870206,-0.0404876322,-0.2826742828,-0.1588367969,0.060794808,-0.2936334312,-0.2167021781,-0.1739186198,0.2281414121,-0.0243999027,0.0930592716,-0.1156688854,0.0667516664,0.1222407445,-0.0957010388,-0.1543302834,-0.0534614287,-0.2331429124,0.2917095423,0.1491725743,0.0468316413,-0.0794368014,-0.4122965932,-0.0143970978,0.4969037771,-0.5218712687,-0.0407988019,-0.3583908975,0.2859594226,-0.315094173,0.4582990408,0.0567070097,-0.2462490499,-0.1380138248,0.3644826114,0.1745104492,0.3875301182,-0.0617133379,0.0549775884,-0.087917462,0.2675451636,-0.1149751395,0.5162414312,0.327231437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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2046","title":"add_faisis_index  gets very slow when doing it interatively  ","comments":"@lhoestq  Thanks a lot for the help you have given to solve this issue. As per my experiments, IVF index suits well for my case and it is a lot faster. The use of this can make the entire RAG end-to-end trainable lot faster.  So I will close this issue. Will do the final PR soon. ","body":"As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n","comment_length":56,"text":"add_faisis_index  gets very slow when doing it interatively   \n As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag\/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal?   Any way to make this process faster? \r\n\r\n@lhoestq \r\n\r\n```\r\n   def training_step(self, batch, batch_idx) -> Dict:\r\n\r\n    \r\n        if (not batch_idx==0) and (batch_idx%5==0):\r\n\r\n            print(\"******************************************************\")\r\n            ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder\r\n            model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance  #this will be load in the CPU\r\n            model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff\r\n\r\n\r\n            list_of_gpus = ['cuda:2','cuda:3']\r\n            c_dir='\/custom\/cache\/dir'\r\n\r\n            kb_dataset = load_dataset(\"csv\", data_files=[self.custom_config.csv_path], split=\"train\", delimiter=\"\\t\", column_names=[\"title\", \"text\"],cache_dir=c_dir) \r\n\r\n            print(kb_dataset)\r\n\r\n      \r\n            n=len(list_of_gpus) #nunber of dedicated GPUs\r\n            kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]\r\n\r\n            #kb_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/haha-dir')\r\n\r\n\r\n            print(self.trainer.global_rank)\r\n            dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])\r\n            output = [None for _ in list_of_gpus]\r\n\r\n            #self.trainer.accelerator_connector.accelerator.barrier(\"embedding_process\")\r\n            dist.all_gather_object(output, dataset_shards)\r\n            \r\n\r\n            #This creation and re-initlaization of the new index\r\n            if (self.trainer.global_rank==0):  #saving will be done in the main process \r\n           \r\n                combined_dataset = concatenate_datasets(output)\r\n    \r\n                passages_path =self.config.passages_path\r\n\r\n                logger.info(\"saving the dataset with  \")\r\n                #combined_dataset.save_to_disk('\/hpc\/gsir059\/MY-Test\/RAY\/transformers\/examples\/research_projects\/rag\/MY-Passage')\r\n                combined_dataset.save_to_disk(passages_path)\r\n                logger.info(\"Add faiss index to the dataset that consist of embeddings\") \r\n\r\n    \r\n                embedding_dataset=combined_dataset\r\n                index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)\r\n                embedding_dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n\r\n                embedding_dataset.get_index(\"embeddings\").save(self.config.index_path)\r\n\r\n \n @lhoestq  Thanks a lot for the help you have given to solve this issue. As per my experiments, IVF index suits well for my case and it is a lot faster. The use of this can make the entire RAG end-to-end trainable lot faster.  So I will close this issue. Will do the final PR soon. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2040","title":"ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk","comments":"Hi ! To help me understand the situation, can you print the values of  `load_from_disk(PATH_DATA_CLS_A)['train']._indices_data_files` and `load_from_disk(PATH_DATA_CLS_B)['train']._indices_data_files` ?\r\nThey should both have a path to an arrow file\r\n\r\nAlso note that from #2025 concatenating datasets will no longer have such restrictions.","body":"Hi there,\r\n\r\nI am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):\r\n```python\r\nconcatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])\r\n```\r\nYielding the following error:\r\n```python\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\nBeen trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...\r\n\r\n`load_from_disk(PATH_DATA_CLS_A)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 785\r\n})\r\n```\r\n`load_from_disk(PATH_DATA_CLS_B)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 3341\r\n})\r\n```","comment_length":41,"text":"ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk \n Hi there,\r\n\r\nI am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):\r\n```python\r\nconcatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])\r\n```\r\nYielding the following error:\r\n```python\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\nBeen trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...\r\n\r\n`load_from_disk(PATH_DATA_CLS_A)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 785\r\n})\r\n```\r\n`load_from_disk(PATH_DATA_CLS_B)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 3341\r\n})\r\n``` \n Hi ! To help me understand the situation, can you print the values of  `load_from_disk(PATH_DATA_CLS_A)['train']._indices_data_files` and `load_from_disk(PATH_DATA_CLS_B)['train']._indices_data_files` ?\r\nThey should both have a path to an arrow file\r\n\r\nAlso note that from #2025 concatenating datasets will no longer have such restrictions.","embeddings":[-0.0112980641,-0.0258976668,-0.0449595004,0.5074425936,0.1715030521,0.1827167124,0.0579556674,0.1286477149,-0.062890619,0.1336227506,0.0703736618,0.2955010831,-0.0843805,-0.1025009677,-0.325756371,-0.1238994524,0.2124682367,-0.0417845137,-0.1874171495,-0.1163776815,-0.433898896,0.3035929203,-0.0941289663,0.0441321991,-0.3788501918,-0.1216486916,-0.3139139712,0.3639658391,-0.1314383149,-0.1400639564,0.3840758502,-0.0434939414,0.1431432962,0.4993310869,-0.000123913,-0.0518494174,-0.0255158506,-0.1264899373,-0.3373901546,-0.3795106709,-0.2954404652,-0.2493540496,0.1174825877,-0.3515410125,0.3774068356,-0.1962019503,-0.1029743999,-0.72650069,0.2809568048,0.2295499593,0.0761976615,0.1972938329,0.1317069829,-0.0478940718,0.0319104157,0.0735818446,0.1537163258,0.2029614002,-0.0550396815,0.023870077,0.1942694187,-0.0005485428,-0.2328488678,-0.118594557,0.1362632364,0.4979306757,0.0554191284,-0.5291479826,-0.0690567121,0.0863210261,0.4802346826,-0.3763854802,-0.3781012595,-0.1759452671,0.0070042545,-0.3268713355,0.1010299474,0.3545840085,0.0001945458,0.111808531,-0.0617500842,-0.0342379436,-0.2941899002,0.072594814,-0.0851020068,0.0358606912,-0.085884653,0.1781402677,0.4493532777,0.0479511246,0.3216394186,-0.4869163632,0.0636960641,0.1922716051,-0.1096954197,0.1452182978,-0.1640024483,-0.5908123851,0.0066751731,-0.0109491264,0.1836389899,-0.1348267496,-0.175842151,0.302200824,0.2534691989,0.3053895533,-0.0141317127,0.4288300574,-0.0103183277,-0.1330448687,0.0501374006,-0.0258551724,-0.0293559134,-0.422658354,0.0796900839,-0.0718142763,0.1062273234,0.0129516535,-0.3877182007,0.1803555936,-0.2840075195,-0.1573118418,0.097162649,-0.0178047977,0.0777068138,0.3530093431,0.3876343668,0.3053706586,0.0820669904,0.2978774905,-0.1446357071,0.1967317462,0.0749064609,0.083897993,0.1068450958,0.0016405154,0.0119908359,0.1067731008,0.0797290355,-0.1220689043,0.0271952283,-0.3416927755,0.2821960449,0.1566369385,0.0981630832,0.3480808735,0.2592440546,-0.2748633921,-0.1870557368,0.3995105326,-0.357647866,-0.1869686842,-0.1376323253,0.0494735204,-0.1045715734,0.1410053968,-0.4229938686,-0.0059976471,0.3018890619,0.3051957488,-0.1290160865,-0.0568313226,-0.1296139359,-0.4591296613,0.341227144,0.0073847673,-0.4581022561,0.0097240172,0.2903791368,0.1048898995,0.1965613365,0.4308505058,-0.1441585571,0.3636823595,-0.3045046329,0.0102365799,0.4004561603,-0.4094220102,-0.1038569584,0.1716967374,-0.1796122342,-0.0410915762,0.3645077944,0.027034644,0.0063065663,0.209405601,0.7220623493,0.242559135,0.0746658593,-0.1657408774,-0.0952380598,-0.2326027751,0.3555584252,-0.2086943984,-0.133979246,-0.0259477682,-0.2588166296,-0.5167700052,0.4240078032,-0.2052067667,0.0640659034,0.4364206791,0.3568811715,-0.1233663261,-0.1038491875,0.1537686735,-0.1934182346,0.1686595827,-0.0148310009,-0.222709313,-0.2647582591,-0.0220493209,-0.2106941342,0.1269541979,-0.0709804296,0.0814448446,-0.0153741334,0.0325087346,0.0056665661,-0.1054476202,-0.3481857479,0.5563704371,-0.163145721,0.0219019782,-0.2541760206,0.4569391906,-0.1578050554,-0.0263907127,-0.0453710034,0.0369337983,0.2729869187,0.1677791625,0.0227815844,0.4717352688,-0.0430073328,0.0950465053,0.0893382505,0.0808452666,0.1716275215,0.0057399063,0.1627613157,-0.0579361059,0.1558342874,-0.1728034914,-0.0909185782,0.4870926142,-0.3344883025,0.4485846162,0.1146494597,0.0512755178,0.1062385887,0.0439708121,-0.0784747005,-0.2369313389,0.0169220511,0.0849660113,0.2192247063,0.2170421481,-0.3525520861,0.0700837746,0.1829123944,0.0754989311,-0.0589676462,-0.0108339787,-0.1080609784,-0.0159977395,-0.1095021665,0.3902995288,0.5840013623,0.1719545275,0.1649895161,-0.1167301685,-0.13713108,-0.1706053317,0.3417837322,0.1398366243,0.2364146113,0.1728817374,-0.0857251808,-0.0756203309,-0.0717299432,-0.0175315533,-0.1918958873,0.0328854062,-0.5536921024,-0.0458903313,-0.2176486552,-0.1098990589,-0.4481921196,-0.3460378349,-0.0974932611,-0.5419760346,-0.2592168748,0.4547411799,-0.1169457287,-0.0527836606,-0.2844993174,0.1456625015,0.0052279974,0.067862317,0.1234738752,0.0104368366,-0.078316763,-0.0318523794,-0.1368813068,0.0180019811,-0.0136504984,-0.0726327598,0.2563133538,-0.2595613599,0.066964522,-0.1467585117,-0.1533612609,0.0849603042,0.0322470106,0.315906316,-0.2281421572,-0.6266650558,0.0580363348,0.5674738884,-0.1389603317,0.0900544003,0.2620217204,-0.18418549,0.0017389727,-0.2506844997,-0.4523783922,-0.2502115369,0.0298633371,-0.1747046113,0.2491324097,0.1712756604,0.2696705759,0.1090817451,0.3577369452,0.1941004395,-0.1135379747,-0.2557307482,0.4422734082,-0.0536296591,-0.1642774045,0.0017761585,0.008876658,0.0754111558,0.1504588127,-0.3661916852,0.018025646,-0.2361494601,0.1363372058,-0.1147485897,0.3011988699,0.1340018809,0.4595842361,-0.0462786779,-0.258654505,-0.273722291,0.2683783472,0.1589276046,0.3044026494,-0.0123012429,0.2090227306,0.0731703639,0.3439159095,0.3640868664,0.1217375472,0.2549828589,0.1836441308,0.5346710086,0.0144409453,-0.4406895638,-0.4077027738,-0.001152494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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2040","title":"ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk","comments":"Sure, thanks for the fast reply!\r\n\r\nFor dataset A: `[{'filename': 'drive\/MyDrive\/data_target_task\/dataset_a\/train\/cache-4797266bf4db1eb7.arrow'}]`\r\nFor dataset B: `[]`\r\n\r\nNo clue why for B it returns nothing. `PATH_DATA_CLS_B` is exactly the same in `save_to_disk` and `load_from_disk`... Also I can verify that the folder physically exists under 'drive\/MyDrive\/data_target_task\/dataset_b\/'","body":"Hi there,\r\n\r\nI am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):\r\n```python\r\nconcatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])\r\n```\r\nYielding the following error:\r\n```python\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\nBeen trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...\r\n\r\n`load_from_disk(PATH_DATA_CLS_A)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 785\r\n})\r\n```\r\n`load_from_disk(PATH_DATA_CLS_B)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 3341\r\n})\r\n```","comment_length":43,"text":"ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk \n Hi there,\r\n\r\nI am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):\r\n```python\r\nconcatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])\r\n```\r\nYielding the following error:\r\n```python\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\nBeen trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...\r\n\r\n`load_from_disk(PATH_DATA_CLS_A)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 785\r\n})\r\n```\r\n`load_from_disk(PATH_DATA_CLS_B)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 3341\r\n})\r\n``` \n Sure, thanks for the fast reply!\r\n\r\nFor dataset A: `[{'filename': 'drive\/MyDrive\/data_target_task\/dataset_a\/train\/cache-4797266bf4db1eb7.arrow'}]`\r\nFor dataset B: `[]`\r\n\r\nNo clue why for B it returns nothing. `PATH_DATA_CLS_B` is exactly the same in `save_to_disk` and `load_from_disk`... Also I can verify that the folder physically exists under 'drive\/MyDrive\/data_target_task\/dataset_b\/'","embeddings":[-0.0077575627,-0.0073159016,-0.0188266747,0.5390487909,0.2280756831,0.1842921823,0.0531079397,0.130966872,-0.0564404465,0.1484978646,0.062885955,0.2342555374,-0.0325047113,-0.1666058749,-0.3268118501,-0.0597667173,0.2327091545,0.0071995803,-0.0789073482,-0.1194586605,-0.422042191,0.2971969545,-0.1387291849,-0.0585789718,-0.3954503536,-0.0667551979,-0.4116065502,0.4386834204,-0.1577688307,-0.1015263945,0.5081785321,-0.0862840116,0.1224367991,0.6370720267,-0.0001226115,-0.0308201071,-0.032479845,-0.2020162344,-0.2702163756,-0.4532000124,-0.2112329751,-0.2356748879,-0.0447952561,-0.3365784883,0.2640665174,-0.0735307634,-0.1124737114,-0.7267848849,0.1495839357,0.2615768313,0.0758540258,0.2023937106,0.0412549563,-0.011546365,-0.0034611092,0.1429173648,0.1469982415,0.1667424291,-0.0944767669,-0.0606658533,0.2073462307,-0.0751989707,-0.3042544127,0.0131352898,0.1142961979,0.4793724716,-0.0599415526,-0.4340187013,-0.0781594962,-0.0228613894,0.4604327679,-0.369572401,-0.3713535666,-0.1061630473,0.0483915769,-0.2898160219,0.1351525187,0.3806920946,-0.0266378783,0.1082230732,-0.2135585099,0.0090284878,-0.252833426,-0.0439741537,-0.1308439374,-0.0517339781,-0.068910569,0.1527519077,0.42635566,0.1246833652,0.3369889557,-0.5939996839,0.0247724019,0.2136669159,-0.2035794705,0.0758955702,-0.0231927112,-0.4214418828,0.0178550277,-0.0495417453,0.2194831818,-0.1294461191,-0.1592091769,0.2647929192,0.1793381274,0.4705998302,-0.0306805484,0.4814172685,0.0561166964,-0.1248716637,-0.0164853912,0.0220423788,-0.0107339174,-0.2809270322,0.17250669,-0.0376351178,0.0512935445,0.0172459036,-0.4902620018,0.172274068,-0.2627789676,-0.1579497606,0.1375524551,-0.0358217433,0.0247514136,0.3958359361,0.3578871191,0.3385949135,0.0773075074,0.3362582922,-0.175747022,0.2379208058,0.0614542402,0.1129006669,0.2048764229,-0.0268174615,-0.0324295498,0.0669087991,0.0196863823,-0.0630932227,0.007189549,-0.4055666625,0.2116440386,0.1468408108,0.1655864418,0.3385901451,0.3134979904,-0.3476326466,-0.1363859773,0.3720194697,-0.390168786,-0.1624980271,-0.0608660802,0.0683365762,-0.1990797222,0.0822419822,-0.4669093788,0.0059260186,0.2804733515,0.3146547973,-0.2105138451,-0.0311501529,-0.2271650136,-0.4472225904,0.3581000268,0.0410957895,-0.4393667579,0.0318099149,0.265956223,0.1168274358,0.2134186924,0.4657851458,-0.0183206759,0.4720543623,-0.3376360536,-0.0125897452,0.3418802023,-0.4551288486,-0.1726371199,0.2068525404,-0.171176374,-0.0108986041,0.3100948036,0.0450184084,-0.0124492357,0.2387021035,0.7105070353,0.3106862605,-0.036810305,-0.0591208488,-0.1282616407,-0.1791668236,0.3601859212,-0.1305136085,-0.071739614,0.1258464158,-0.2418709695,-0.4316968918,0.3587989807,-0.2525398135,0.0563765168,0.4634277523,0.3992080986,-0.1521523148,-0.1093364879,0.0702472553,-0.1755597442,0.2409662008,0.0380816013,-0.141144231,-0.2892437577,0.0492358245,-0.1995348483,0.0720840171,-0.1083417609,0.0309189446,0.0110549619,0.1900498271,0.0202374849,-0.128693521,-0.2456143945,0.641156733,-0.0821437463,-0.0126171969,-0.1710728258,0.562954247,-0.2242329568,-0.0949676037,-0.0774531215,-0.0047572316,0.2522273958,0.1173365116,0.0192381889,0.4581305087,-0.0293524768,0.0652101263,0.1646009833,-0.0015374377,0.1587028652,0.0926408395,0.1691076308,-0.0322373174,0.2578332722,-0.2327931523,-0.1162926629,0.3702292144,-0.3233274519,0.4082520306,0.1217195764,-0.0517719053,0.1363519728,0.0588621609,0.0029764769,-0.1529113352,0.0995619074,0.1531849355,0.2225988209,0.2118729651,-0.3046705425,-0.0127987899,0.2571274936,0.0371879302,0.0053212987,0.0151513517,-0.0527290627,-0.1384252459,-0.2007956654,0.3541591763,0.7381997108,0.1167203337,0.1836723089,-0.0615902022,-0.156595096,-0.1144140214,0.2958693802,0.138366282,0.188490063,0.2836687863,0.0051927832,-0.0268551148,-0.1313907206,0.0052786744,-0.1450089365,0.0549790338,-0.5187076926,-0.0354224518,-0.1969224513,-0.0688647479,-0.3841024637,-0.3000256419,-0.0957658738,-0.5155733228,-0.2982620597,0.4832041562,0.034070272,-0.1017188281,-0.216841206,0.1896806955,0.0425127409,-0.0658033416,0.0900249109,-0.0020249798,-0.1174458414,-0.0193233937,-0.1849119067,-0.0644801632,0.0272129346,-0.1759793311,0.2759288847,-0.3246739805,0.0726150647,-0.1426634938,-0.2029588223,0.173454985,0.077978313,0.2803866863,-0.2035324872,-0.5924379826,0.1457236707,0.5013505816,-0.186918214,0.1658065021,0.2946106195,-0.2110977173,-0.0700031146,-0.2169883549,-0.3874444067,-0.2615768015,-0.0783747956,-0.196580708,0.175164789,0.0926072896,0.1931917667,0.1270978153,0.3630620837,0.1643963158,-0.1537372172,-0.367639035,0.44455567,0.0054613114,-0.1883690804,-0.0298494119,0.0075802496,0.1189812571,0.1703369021,-0.3710669279,-0.0209495798,-0.2660009265,0.1324578971,-0.0986753255,0.3205017149,0.1483410448,0.3702240288,-0.0279010814,-0.2777628005,-0.3389345407,0.1872110963,0.2447299063,0.3925762773,-0.0807114989,0.2742601335,0.1444842368,0.3211856186,0.3186561167,0.0706983656,0.3618190289,0.2049423158,0.5604659319,-0.04103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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2040","title":"ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk","comments":"In the next release you'll be able to concatenate any kinds of dataset (either from memory or from disk).\r\n\r\nFor now I'd suggest you to flatten the indices of the A and B datasets. This will remove the indices mapping and you will be able to concatenate them. You can flatten the indices with\r\n```python\r\ndataset = dataset.flatten_indices()\r\n```","body":"Hi there,\r\n\r\nI am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):\r\n```python\r\nconcatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])\r\n```\r\nYielding the following error:\r\n```python\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\nBeen trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...\r\n\r\n`load_from_disk(PATH_DATA_CLS_A)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 785\r\n})\r\n```\r\n`load_from_disk(PATH_DATA_CLS_B)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 3341\r\n})\r\n```","comment_length":59,"text":"ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk \n Hi there,\r\n\r\nI am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):\r\n```python\r\nconcatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])\r\n```\r\nYielding the following error:\r\n```python\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\nBeen trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...\r\n\r\n`load_from_disk(PATH_DATA_CLS_A)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 785\r\n})\r\n```\r\n`load_from_disk(PATH_DATA_CLS_B)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 3341\r\n})\r\n``` \n In the next release you'll be able to concatenate any kinds of dataset (either from memory or from disk).\r\n\r\nFor now I'd suggest you to flatten the indices of the A and B datasets. This will remove the indices mapping and you will be able to concatenate them. You can flatten the indices with\r\n```python\r\ndataset = dataset.flatten_indices()\r\n```","embeddings":[-0.0903852209,-0.0803103447,-0.0217147637,0.4296606481,0.2122907937,0.2667319477,0.0407163389,0.1795164198,-0.0718934461,0.174695462,-0.0009916488,0.1995578706,-0.0881771892,-0.0326921009,-0.3439054191,-0.0558386669,0.1930517405,0.0187105779,-0.1041580364,0.0098615009,-0.4442354441,0.243652001,-0.155220598,-0.0359293222,-0.311473012,-0.0647420511,-0.3580109477,0.3418426216,-0.1153114215,-0.1206123084,0.5508387685,-0.0477537625,0.0869863778,0.5565166473,-0.0001167422,-0.0058714054,-0.0398115367,-0.159683913,-0.2412981391,-0.4698682725,-0.2993323505,-0.2401568294,-0.0213559829,-0.2968689799,0.208661586,-0.1860080361,-0.1097184047,-0.7185208797,0.2358155102,0.2194238901,0.131616354,0.1606049538,0.1590324193,-0.0537228845,0.0060946266,0.0604138039,0.1772609204,0.1795203984,-0.1299273819,-0.0358634815,0.2642757595,-0.0068642069,-0.3868632019,-0.0542209633,0.1337354779,0.4457705021,0.0942109674,-0.4561156034,-0.1477254331,0.0752305239,0.4200157225,-0.3262958825,-0.3272228241,-0.091366224,0.0971419364,-0.3858567774,0.1183445603,0.3693805337,0.0639820844,0.1274228245,-0.2128302157,-0.0889138207,-0.1895785928,0.0065884353,-0.115011625,0.0218899827,-0.0210954417,0.1646567136,0.5099681616,-0.0325126573,0.4155707657,-0.5363280773,0.0476031303,0.1897673905,-0.199121505,0.0359905288,-0.0857117847,-0.546063602,0.0706862956,-0.1810738593,0.1162746027,-0.1160381213,-0.1602855325,0.3259332776,0.1867987663,0.4233537316,0.0459591895,0.5026447773,0.0342941098,-0.0802171454,0.0644280985,0.0524058603,0.0719278976,-0.3668627441,0.0957351327,0.0153415883,0.1143159419,0.0008162334,-0.4245216846,0.1277352124,-0.2855323851,-0.0848076418,0.0569595098,-0.0795739964,0.0466366075,0.3995075226,0.3896420598,0.3132678866,0.0783832967,0.2504098117,-0.1776385903,0.176717788,0.1420002282,0.1240313724,0.152634263,-0.0639882907,-0.0817171186,0.1919679046,-0.0037851273,0.0142077599,0.0226849988,-0.359480828,0.1906329244,0.1651127189,0.1070034131,0.2670373321,0.3068104684,-0.3512372673,-0.1848161668,0.3993564546,-0.4019362032,-0.1600684375,-0.1483420432,0.1177690029,-0.1106535122,0.0929520577,-0.4305930138,0.1036647558,0.2892502248,0.2214403301,-0.2550332248,-0.1051570699,-0.2563718557,-0.3879457414,0.3073952496,-0.0501087978,-0.431759268,-0.0085132178,0.2673155665,0.1099048629,0.2351316512,0.4662023783,-0.0782097131,0.3340336978,-0.2627853453,-0.0979297161,0.1698978543,-0.3320308328,-0.1988217384,0.1342197955,-0.1535136253,0.03503225,0.1987936944,0.0435760245,0.0285198595,0.2498156577,0.7739896774,0.3166610599,-0.0380970016,-0.1191873774,-0.1531618983,-0.2666220963,0.4661263227,-0.1315011382,-0.1416645795,0.0988497511,-0.3725159168,-0.397574544,0.3472753465,-0.2832825184,0.0636298358,0.4213288426,0.3263689578,-0.1817166209,-0.1018906087,0.024623448,-0.2054289579,0.1895036548,-0.0335666239,-0.1018203795,-0.2266622931,-0.0587005801,-0.1673020273,0.0187402908,-0.0538976155,0.141298905,0.0392029732,0.0141676487,-0.0718826056,-0.1022611409,-0.2673949003,0.5654734969,-0.090332523,0.0383026525,-0.2548471689,0.4797309637,-0.1687238365,-0.0423460342,-0.0257890597,-0.007185135,0.2499664873,0.098609969,-0.0060409666,0.4919343293,-0.0936736017,0.0119200563,0.0779165924,0.0159203801,0.227192685,0.0178498216,0.0769848973,0.0056219194,0.1291652322,-0.1870341152,-0.0795801282,0.4245119989,-0.3667976558,0.3369196951,0.0314631686,0.0869895667,0.2149218321,-0.0200444143,-0.0327523798,-0.2353004366,0.0180129632,0.0077934843,0.0598469116,0.1597383618,-0.3271050751,-0.0502549335,0.1370064467,0.0435917601,-0.008159495,0.0476348028,-0.0792988464,-0.0775588676,-0.1422454268,0.2626615167,0.595733881,0.205141291,0.0933410451,0.0301323123,-0.1562049389,-0.0462851711,0.3124066889,0.1212166324,0.1940273345,0.2194419503,-0.0138920229,-0.0226647984,-0.0724640936,0.0575230457,-0.1133960783,0.1180530414,-0.5014936328,-0.0962722301,-0.3015748262,-0.1021734476,-0.3828296959,-0.3004683256,-0.1973970681,-0.4838761389,-0.2615421116,0.4586673677,-0.0590238944,0.038206771,-0.1270175278,0.1683130711,0.0720048472,0.0265385341,0.1558901817,-0.0216734577,-0.0583191738,0.0444997177,-0.0778574124,-0.0622468777,0.0591107383,-0.1217503026,0.2177443206,-0.2410200834,0.0385862365,-0.150095433,-0.1827576756,0.0993428677,0.0969478637,0.2907016277,-0.2663249373,-0.5855751038,0.1232848167,0.5215249062,-0.1266570985,0.1730052233,0.2626032531,-0.182980448,-0.0798323452,-0.2237952054,-0.3680772781,-0.2371441722,-0.0798078775,-0.2592636049,0.2472724766,0.0990316272,0.2649503946,0.0820237175,0.3654113412,0.1007734388,-0.1446047574,-0.287836194,0.3886061311,-0.0084413933,-0.1790605932,0.0065309382,-0.0050052297,0.086275056,0.1664551497,-0.3701839447,-0.0095428824,-0.1804279238,0.096353583,-0.1326226592,0.2878193855,0.1116801575,0.4846867621,-0.053226389,-0.1380140185,-0.3144214749,0.1558962464,0.1922058761,0.3816485405,-0.0430138633,0.2722981274,0.0688322559,0.2513549626,0.3474906385,0.1060320064,0.2202700973,0.268048048,0.467143923,-0.0679680854,-0.5600995421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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2040","title":"ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk","comments":"Indeed this works. Not the most elegant solution, but it does the trick. Thanks a lot! ","body":"Hi there,\r\n\r\nI am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):\r\n```python\r\nconcatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])\r\n```\r\nYielding the following error:\r\n```python\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\nBeen trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...\r\n\r\n`load_from_disk(PATH_DATA_CLS_A)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 785\r\n})\r\n```\r\n`load_from_disk(PATH_DATA_CLS_B)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 3341\r\n})\r\n```","comment_length":16,"text":"ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk \n Hi there,\r\n\r\nI am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):\r\n```python\r\nconcatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])\r\n```\r\nYielding the following error:\r\n```python\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\nBeen trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...\r\n\r\n`load_from_disk(PATH_DATA_CLS_A)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 785\r\n})\r\n```\r\n`load_from_disk(PATH_DATA_CLS_B)['train']` yields:\r\n```python\r\nDataset({\r\n    features: ['labels', 'text'],\r\n    num_rows: 3341\r\n})\r\n``` \n Indeed this works. Not the most elegant solution, but it does the trick. Thanks a lot! 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2038","title":"outdated dataset_infos.json might fail verifications","comments":"Hi ! Thanks for reporting.\r\n\r\nTo update the dataset_infos.json you can run:\r\n```\r\ndatasets-cli test .\/datasets\/doc2dial --all_configs --save_infos --ignore_verifications\r\n```","body":"The [doc2dial\/dataset_infos.json](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/doc2dial\/dataset_infos.json) is outdated. It would fail data_loader when verifying download checksum etc..\r\n\r\nCould you please update this file or point me how to update this file?\r\n\r\nThank you.","comment_length":20,"text":"outdated dataset_infos.json might fail verifications \n The [doc2dial\/dataset_infos.json](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/doc2dial\/dataset_infos.json) is outdated. It would fail data_loader when verifying download checksum etc..\r\n\r\nCould you please update this file or point me how to update this file?\r\n\r\nThank you. \n Hi ! Thanks for reporting.\r\n\r\nTo update the dataset_infos.json you can run:\r\n```\r\ndatasets-cli test .\/datasets\/doc2dial --all_configs --save_infos --ignore_verifications\r\n```","embeddings":[-0.120092757,0.1984136701,-0.111874871,0.1850017458,0.1130970418,0.2163039148,0.1035631448,0.4946838021,0.2069722265,-0.0703191087,0.0729325563,0.0472202599,0.1981547624,0.2417432368,-0.068925254,-0.0911237523,-0.0267710928,0.2664070427,0.0681155771,0.0885860845,-0.0266231392,0.1407285482,-0.1923577338,-0.1282282919,-0.2190597206,-0.0406580307,0.0387163423,0.1655928046,-0.5414218307,-0.4992533326,0.4304624498,0.4793901742,0.0819710493,0.3855613768,-0.0001081328,-0.0376769081,0.5466258526,-0.029058706,-0.4618392289,-0.0519545451,-0.3342080414,-0.1855138689,-0.141147241,-0.0835442767,-0.0152585106,-0.3255074024,-0.1239994019,0.0789585784,0.2135807127,0.1726304293,0.2469211668,0.4211307168,0.5330948234,-0.1848715693,0.1094438359,-0.0424621068,0.1855426133,0.4435175955,0.3252074718,0.0316635482,0.2927803397,0.3482064903,-0.0668817461,-0.1015633717,0.1929742694,-0.1665298045,0.1315116435,-0.1064366922,0.1657461971,0.1777226031,0.6516272426,-0.3433517218,-0.3737469912,-0.0835310742,-0.1550707519,-0.1994565874,0.391864717,-0.2893846333,0.2753103673,0.2195625752,-0.1972355843,-0.3501672745,-0.0167721212,0.055279512,-0.0843022764,-0.1005989462,-0.1842762381,-0.1455242485,0.1457502395,-0.1432099789,-0.1317905039,-0.0046239425,-0.3207756579,0.0597509742,-0.0772146285,-0.299791187,-0.2040339261,0.1048173681,0.3750511706,0.2149078548,0.0941932574,0.0497511104,-0.3412804008,0.1715231836,0.1495141983,0.1182381883,0.4304278493,-0.0208122265,0.3718360066,0.6826687455,0.1144597232,-0.0479870811,-0.0042079687,-0.2481483668,0.0243405681,0.0238256902,0.3393543065,-0.4905768633,-0.0651530549,0.1865612864,-0.0159931201,-0.055738017,0.2356281877,0.3187642992,-0.3612932265,0.1625730842,0.1662807018,-0.0296565257,0.044611685,-0.3122760952,-0.0763921142,0.0261264704,-0.0284057055,0.215308249,0.3751752377,-0.3757915497,0.4739308059,0.0353367962,-0.1602192819,0.0439420529,-0.074450843,-0.1207977086,-0.2017621845,0.2391945273,-0.0254514012,0.0504769906,-0.0973938107,-0.0127743306,0.0273161884,-0.1816865206,-0.3045568764,-0.3762091398,-0.2405503392,0.2019519657,-0.023463089,-0.1118202433,-0.2365120947,-0.1028553918,0.1793441027,-0.4997892678,-0.0455499254,-0.0730096623,-0.155411467,-0.0220387727,0.192650035,0.3321076632,-0.0709209517,0.0900682062,0.0667346194,-0.3115293384,-0.2241204828,0.2014637589,-0.0882985443,0.1940482706,-0.2010300308,0.0603739433,-0.0404514968,-0.6165745854,-0.3859308362,0.1060364693,0.223071456,-0.093243517,-0.1347037405,-0.2367650419,0.2366414517,-0.295226872,-0.1073620096,0.0489997827,0.105088301,-0.0239576884,-0.3267243803,-0.2293679863,-0.0333115645,0.1030370221,-0.0429917984,0.0195009224,0.0344419479,0.4565271735,0.1423131675,-0.0409456752,0.0469592325,0.2812905312,0.4166460633,0.0335949063,-0.0447669327,-0.0408999324,-0.5700752139,0.2099355757,0.1407439113,-0.1254222691,-0.1250814945,-0.1638533324,-0.2609325349,-0.0597878806,-0.065704219,-0.0178748127,0.1555882245,0.1592798233,0.3321816325,0.1094373241,-0.1191442758,0.2130118906,-0.5373790264,0.1782788485,-0.2622483075,0.1779333204,-0.0304578319,0.1253200918,0.3239291906,-0.0993104726,0.0879661664,-0.1290887594,-0.0831435993,0.5027294755,0.0093866559,0.1357946843,0.2471980602,0.3728539348,0.0543974303,-0.0852174759,0.0808365643,-0.165267691,-0.0419656858,0.0343083777,-0.26733163,0.0326821022,-0.0849874839,-0.0673847646,0.2248898298,-0.0741193071,0.3140821457,-0.0297689978,-0.0388831608,-0.2751832306,-0.1777119637,-0.0751736611,0.3607105315,-0.1430140436,-0.0626577437,0.1559810489,0.4095159471,-0.0206291452,-0.0906753987,0.1984057724,0.0815259218,-0.2291123271,-0.0816606358,0.2641119063,0.2345742136,0.2276795805,-0.014012428,0.1894861758,0.0711768642,-0.0561925173,0.1673857868,-0.1691849679,-0.0097601917,0.4036227465,0.1256836802,0.0108988751,-0.265037179,0.0454338603,0.2030114681,0.516420424,-0.3069397211,-0.0780188665,-0.2033129483,-0.0223651901,-0.0027797318,-0.3437184393,-0.3578514755,-0.1349753141,0.0679993033,0.4726887941,0.1156928837,0.1248631254,-0.0538580865,0.2224537879,0.0925403833,-0.1651447713,-0.187139973,0.0658758953,-0.0979108512,0.0556772798,0.3926015794,-0.0635674596,0.3531554639,-0.524425745,-0.1744995117,-0.5462660193,-0.4836139679,0.2196639627,-0.2057814747,0.2577606738,0.2761024833,0.317279458,0.1261567175,-0.0000820585,0.155358687,-0.2760593295,-0.354603678,-0.2557950914,-0.0191665124,-0.1092161834,-0.1351924241,-0.3698330522,0.1012540907,-0.2100868821,0.1020710245,0.0008240548,0.0267242696,0.2857055366,0.081451714,0.1861861497,-0.1705827713,0.206465587,-0.2983559966,-0.7436742187,0.0950695127,-0.1993618757,-0.228820622,0.0111966496,0.2518720627,0.2966251969,-0.0640645251,-0.4512532353,-0.3920636177,-0.0903876573,0.1593727618,0.0165096372,-0.1554335654,0.395457536,-0.0997767374,-0.1225270405,-0.192751348,-0.3537536561,0.1185755879,-0.0744083971,0.3598248363,-0.0353286564,0.2783931494,-0.0069323485,0.5527372956,0.0637156218,-0.3731845021,0.2554654777,0.036172159,0.5047958493,-0.1203782111,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"Dear @lhoestq for wikipedia dataset I also get the same error, I greatly appreciate if you could have a look into this dataset as well. Below please find the command to reproduce the error:\r\n\r\n```\r\ndataset = load_dataset(\"wikipedia\", \"20200501.bg\")\r\nprint(dataset)\r\n```\r\n\r\nYour library is my only chance to be able training  the models at scale and I am grateful for your help.\r\n\r\n","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":62,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n Dear @lhoestq for wikipedia dataset I also get the same error, I greatly appreciate if you could have a look into this dataset as well. Below please find the command to reproduce the error:\r\n\r\n```\r\ndataset = load_dataset(\"wikipedia\", \"20200501.bg\")\r\nprint(dataset)\r\n```\r\n\r\nYour library is my only chance to be able training  the models at scale and I am grateful for your help.\r\n\r\n","embeddings":[-0.2544980347,-0.0779204071,-0.1537582129,0.4308663607,0.3997030556,0.3504618704,0.1368506402,0.5331582427,0.1893276423,0.0235845186,-0.1773036569,-0.0942173079,0.0944036767,0.0111841084,-0.0297050718,-0.4591732025,-0.0672903061,0.0295370072,-0.1352714151,-0.1168936566,-0.2519630194,0.1256146282,-0.0791749284,-0.0490285978,-0.0387454666,-0.1828399897,-0.095835425,-0.2073058486,-0.3507466614,-0.240864411,0.3545782268,0.0192575995,0.3101119697,0.152997002,-0.0001114368,-0.0509645268,0.313116461,-0.2357945591,-0.2936119437,-0.2888573706,-0.0720896423,-0.3261856437,-0.0904391855,-0.420031637,0.1274462193,0.0466828533,0.080671154,-0.1318009049,0.0757578686,0.299225539,0.2468712181,-0.1426034421,0.313434124,-0.1457377374,0.3651962578,-0.3777080178,-0.0280337762,0.3395861685,0.2187208384,0.0713426769,0.2154791504,0.2220428586,-0.2341407239,0.0134992879,0.0482247174,-0.291223824,0.222205475,-0.6297016144,0.5206420422,0.5344284177,0.8290324211,-0.1283516139,-0.0874669701,0.0723921135,-0.0260593966,-0.031374447,-0.0557402261,0.4819797575,-0.1893264949,0.0975982994,0.1261651665,-0.1254302114,-0.1567192525,0.4228464365,-0.1241363883,0.4435888529,-0.0527197532,0.2112598121,-0.0216665938,-0.102701664,-0.2636416852,-0.0696592405,-0.0961601064,0.2933215201,-0.2793983221,0.0196358114,-0.1615019292,0.0556470677,0.3511354923,-0.1503831744,-0.2346212864,-0.0450777449,0.0960106328,0.1575745195,0.1801504046,0.1637832075,0.0620483644,-0.200006485,0.2782812417,0.3079036772,-0.0842119008,-0.0761709884,-0.2100430429,-0.2230561525,-0.2947570086,0.0944294706,0.0875293911,-0.2031713277,-0.1779466271,0.0071135005,-0.1443609297,-0.0194389597,-0.202394858,0.3628205657,-0.1501425803,0.4312385917,0.1779012084,0.3288222253,-0.1035727784,-0.2613099515,-0.0173523482,0.3065485656,-0.1393195242,-0.1036603302,0.2187829465,-0.1099155918,0.2648515999,-0.089371942,-0.1031381935,-0.1764115244,-0.1536911428,-0.3695675731,-0.1791034937,0.230408892,0.1619764566,0.2839277089,0.1476140618,-0.244747147,-0.2305807471,0.1207451969,-0.3596149981,-0.1681548953,-0.2051547766,0.1944684684,0.0610982925,-0.0546504743,-0.3160067499,0.2876641154,0.3030638993,-0.1423988342,0.1180728897,-0.0075459145,-0.0232539773,-0.225190118,0.1790115982,0.4908005595,-0.399959296,0.091897428,-0.0238504577,-0.0337120444,0.1897819787,0.1842971891,-0.1779527366,0.2565417886,-0.0978842974,0.0381600037,0.2101310194,-0.3214419484,-0.5776352286,0.186053887,0.113410078,-0.1670030951,0.0994797125,0.0160005782,0.2750306129,-0.0493875705,0.052454181,0.3956748247,0.1368381381,-0.1056447476,-0.3274506032,-0.5078234673,0.3264816701,0.1633179337,0.4547148943,-0.1322700828,0.2759852409,0.2917218208,0.3191742897,-0.1915477812,0.1246789992,0.2333606035,-0.0089483308,-0.0445753597,-0.002562576,-0.3310278654,-0.3097580373,0.1477600932,0.1029136032,0.1654645205,-0.1609553099,0.029683331,-0.3161896169,-0.0888073742,-0.1953119785,0.10326875,0.1995639354,-0.1052765027,0.1195931062,0.4854590893,0.1265164316,-0.0994593799,-0.4156903923,-0.0948526934,-0.17893444,0.0923319757,-0.2174285501,0.2044921666,0.0336807854,0.0258089639,0.1796839386,0.0730525404,-0.1607236117,-0.0110613015,-0.0784521401,-0.1035443768,0.1354628503,0.116963841,0.1331282407,-0.417375356,0.1920297742,0.1084797084,0.3297463953,-0.1357724667,0.1178161129,0.1222370416,0.0399733894,0.3655338883,0.1234308109,0.1498314887,0.4555997252,0.0305262543,0.1263544261,-0.0049747,0.3271189332,0.1556606591,0.0127488235,-0.1329593062,-0.02927793,-0.1135142446,0.3166727722,-0.0845338032,0.2671483159,0.2927592099,-0.0765190423,-0.004859813,-0.0978270099,0.1533981711,0.0522076748,0.0699049756,0.1065892354,-0.0998440012,0.359649688,-0.1293927282,0.2643093467,0.1755426973,0.1863330156,0.209135592,0.1350800842,-0.0111756697,-0.1882829219,-0.1296795756,0.0699935928,0.3717179298,0.0691837072,0.165945366,-0.0484345779,-0.1418599933,0.0841909274,-0.0523921847,-0.2509426475,-0.2163173407,-0.1256383955,-0.0857288986,0.091583252,-0.0001049751,-0.3266668916,-0.0721021518,0.0621662624,-0.3203505874,-0.1605682075,-0.2981233299,-0.3684496582,0.0767874196,0.3871294558,0.1112902835,0.2952094376,-0.3326290548,-0.1918789148,-0.0899458826,-0.2681111097,-0.0143140424,-0.1569705009,0.2413951159,-0.0241171494,0.7152194381,-0.1799962372,-0.251421392,0.1846487224,-0.0070200525,0.0658744872,0.2497714758,-0.1534786075,-0.0429239906,0.0845356733,-0.4856536686,-0.5155764222,-0.2696566582,0.0125194602,0.048517894,0.0878061801,0.1686406732,0.1924842894,0.0680263713,0.121187754,0.1809071153,-0.1900433302,-0.1518690884,0.4496890306,-0.0274131726,-0.4299626648,0.1881868094,0.0109940656,0.3149316907,0.0079274345,-0.6572358608,0.1441125572,-0.2763589025,0.2499436736,-0.0219936892,0.183476612,0.114343822,-0.2136970013,0.0479722433,0.0449956916,0.1059101671,-0.0406209044,-0.2095253319,0.2556217909,0.0299457368,0.2961854935,0.1644209623,0.6888408661,0.0548558161,0.1658407599,0.4367816746,0.2385823429,0.2310316861,-0.2314289063,-0.3645718992,0.0605744347,-0.2394915372,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"Hi @dorost1234,\r\nTry installing this library first, `pip install 'apache-beam[gcp]' --use-feature=2020-resolver` followed by loading dataset like this using beam runner.\r\n\r\n`dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')`\r\n\r\n I also read in error stack trace that:\r\n\r\n> Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc.\r\n\r\nWorked perfectly fine after this (Ignore these warnings)\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/19718818\/110908410-c7e2ce00-8334-11eb-8d10-7354359e9ec3.png)\r\n\r\n","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":83,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n Hi @dorost1234,\r\nTry installing this library first, `pip install 'apache-beam[gcp]' --use-feature=2020-resolver` followed by loading dataset like this using beam runner.\r\n\r\n`dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')`\r\n\r\n I also read in error stack trace that:\r\n\r\n> Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc.\r\n\r\nWorked perfectly fine after this (Ignore these warnings)\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/19718818\/110908410-c7e2ce00-8334-11eb-8d10-7354359e9ec3.png)\r\n\r\n","embeddings":[-0.2544980347,-0.0779204071,-0.1537582129,0.4308663607,0.3997030556,0.3504618704,0.1368506402,0.5331582427,0.1893276423,0.0235845186,-0.1773036569,-0.0942173079,0.0944036767,0.0111841084,-0.0297050718,-0.4591732025,-0.0672903061,0.0295370072,-0.1352714151,-0.1168936566,-0.2519630194,0.1256146282,-0.0791749284,-0.0490285978,-0.0387454666,-0.1828399897,-0.095835425,-0.2073058486,-0.3507466614,-0.240864411,0.3545782268,0.0192575995,0.3101119697,0.152997002,-0.0001114368,-0.0509645268,0.313116461,-0.2357945591,-0.2936119437,-0.2888573706,-0.0720896423,-0.3261856437,-0.0904391855,-0.420031637,0.1274462193,0.0466828533,0.080671154,-0.1318009049,0.0757578686,0.299225539,0.2468712181,-0.1426034421,0.313434124,-0.1457377374,0.3651962578,-0.3777080178,-0.0280337762,0.3395861685,0.2187208384,0.0713426769,0.2154791504,0.2220428586,-0.2341407239,0.0134992879,0.0482247174,-0.291223824,0.222205475,-0.6297016144,0.5206420422,0.5344284177,0.8290324211,-0.1283516139,-0.0874669701,0.0723921135,-0.0260593966,-0.031374447,-0.0557402261,0.4819797575,-0.1893264949,0.0975982994,0.1261651665,-0.1254302114,-0.1567192525,0.4228464365,-0.1241363883,0.4435888529,-0.0527197532,0.2112598121,-0.0216665938,-0.102701664,-0.2636416852,-0.0696592405,-0.0961601064,0.2933215201,-0.2793983221,0.0196358114,-0.1615019292,0.0556470677,0.3511354923,-0.1503831744,-0.2346212864,-0.0450777449,0.0960106328,0.1575745195,0.1801504046,0.1637832075,0.0620483644,-0.200006485,0.2782812417,0.3079036772,-0.0842119008,-0.0761709884,-0.2100430429,-0.2230561525,-0.2947570086,0.0944294706,0.0875293911,-0.2031713277,-0.1779466271,0.0071135005,-0.1443609297,-0.0194389597,-0.202394858,0.3628205657,-0.1501425803,0.4312385917,0.1779012084,0.3288222253,-0.1035727784,-0.2613099515,-0.0173523482,0.3065485656,-0.1393195242,-0.1036603302,0.2187829465,-0.1099155918,0.2648515999,-0.089371942,-0.1031381935,-0.1764115244,-0.1536911428,-0.3695675731,-0.1791034937,0.230408892,0.1619764566,0.2839277089,0.1476140618,-0.244747147,-0.2305807471,0.1207451969,-0.3596149981,-0.1681548953,-0.2051547766,0.1944684684,0.0610982925,-0.0546504743,-0.3160067499,0.2876641154,0.3030638993,-0.1423988342,0.1180728897,-0.0075459145,-0.0232539773,-0.225190118,0.1790115982,0.4908005595,-0.399959296,0.091897428,-0.0238504577,-0.0337120444,0.1897819787,0.1842971891,-0.1779527366,0.2565417886,-0.0978842974,0.0381600037,0.2101310194,-0.3214419484,-0.5776352286,0.186053887,0.113410078,-0.1670030951,0.0994797125,0.0160005782,0.2750306129,-0.0493875705,0.052454181,0.3956748247,0.1368381381,-0.1056447476,-0.3274506032,-0.5078234673,0.3264816701,0.1633179337,0.4547148943,-0.1322700828,0.2759852409,0.2917218208,0.3191742897,-0.1915477812,0.1246789992,0.2333606035,-0.0089483308,-0.0445753597,-0.002562576,-0.3310278654,-0.3097580373,0.1477600932,0.1029136032,0.1654645205,-0.1609553099,0.029683331,-0.3161896169,-0.0888073742,-0.1953119785,0.10326875,0.1995639354,-0.1052765027,0.1195931062,0.4854590893,0.1265164316,-0.0994593799,-0.4156903923,-0.0948526934,-0.17893444,0.0923319757,-0.2174285501,0.2044921666,0.0336807854,0.0258089639,0.1796839386,0.0730525404,-0.1607236117,-0.0110613015,-0.0784521401,-0.1035443768,0.1354628503,0.116963841,0.1331282407,-0.417375356,0.1920297742,0.1084797084,0.3297463953,-0.1357724667,0.1178161129,0.1222370416,0.0399733894,0.3655338883,0.1234308109,0.1498314887,0.4555997252,0.0305262543,0.1263544261,-0.0049747,0.3271189332,0.1556606591,0.0127488235,-0.1329593062,-0.02927793,-0.1135142446,0.3166727722,-0.0845338032,0.2671483159,0.2927592099,-0.0765190423,-0.004859813,-0.0978270099,0.1533981711,0.0522076748,0.0699049756,0.1065892354,-0.0998440012,0.359649688,-0.1293927282,0.2643093467,0.1755426973,0.1863330156,0.209135592,0.1350800842,-0.0111756697,-0.1882829219,-0.1296795756,0.0699935928,0.3717179298,0.0691837072,0.165945366,-0.0484345779,-0.1418599933,0.0841909274,-0.0523921847,-0.2509426475,-0.2163173407,-0.1256383955,-0.0857288986,0.091583252,-0.0001049751,-0.3266668916,-0.0721021518,0.0621662624,-0.3203505874,-0.1605682075,-0.2981233299,-0.3684496582,0.0767874196,0.3871294558,0.1112902835,0.2952094376,-0.3326290548,-0.1918789148,-0.0899458826,-0.2681111097,-0.0143140424,-0.1569705009,0.2413951159,-0.0241171494,0.7152194381,-0.1799962372,-0.251421392,0.1846487224,-0.0070200525,0.0658744872,0.2497714758,-0.1534786075,-0.0429239906,0.0845356733,-0.4856536686,-0.5155764222,-0.2696566582,0.0125194602,0.048517894,0.0878061801,0.1686406732,0.1924842894,0.0680263713,0.121187754,0.1809071153,-0.1900433302,-0.1518690884,0.4496890306,-0.0274131726,-0.4299626648,0.1881868094,0.0109940656,0.3149316907,0.0079274345,-0.6572358608,0.1441125572,-0.2763589025,0.2499436736,-0.0219936892,0.183476612,0.114343822,-0.2136970013,0.0479722433,0.0449956916,0.1059101671,-0.0406209044,-0.2095253319,0.2556217909,0.0299457368,0.2961854935,0.1644209623,0.6888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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"For wikipedia dataset, looks like the files it's looking for are no longer available. For `bg`, I checked [here](https:\/\/dumps.wikimedia.org\/bgwiki\/). For this I think `dataset_infos.json` for this dataset has to made again? You'll have to load this dataset also using beam runner.\r\n\r\n","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":41,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n For wikipedia dataset, looks like the files it's looking for are no longer available. For `bg`, I checked [here](https:\/\/dumps.wikimedia.org\/bgwiki\/). For this I think `dataset_infos.json` for this dataset has to made again? You'll have to load this dataset also using beam runner.\r\n\r\n","embeddings":[-0.2544980347,-0.0779204071,-0.1537582129,0.4308663607,0.3997030556,0.3504618704,0.1368506402,0.5331582427,0.1893276423,0.0235845186,-0.1773036569,-0.0942173079,0.0944036767,0.0111841084,-0.0297050718,-0.4591732025,-0.0672903061,0.0295370072,-0.1352714151,-0.1168936566,-0.2519630194,0.1256146282,-0.0791749284,-0.0490285978,-0.0387454666,-0.1828399897,-0.095835425,-0.2073058486,-0.3507466614,-0.240864411,0.3545782268,0.0192575995,0.3101119697,0.152997002,-0.0001114368,-0.0509645268,0.313116461,-0.2357945591,-0.2936119437,-0.2888573706,-0.0720896423,-0.3261856437,-0.0904391855,-0.420031637,0.1274462193,0.0466828533,0.080671154,-0.1318009049,0.0757578686,0.299225539,0.2468712181,-0.1426034421,0.313434124,-0.1457377374,0.3651962578,-0.3777080178,-0.0280337762,0.3395861685,0.2187208384,0.0713426769,0.2154791504,0.2220428586,-0.2341407239,0.0134992879,0.0482247174,-0.291223824,0.222205475,-0.6297016144,0.5206420422,0.5344284177,0.8290324211,-0.1283516139,-0.0874669701,0.0723921135,-0.0260593966,-0.031374447,-0.0557402261,0.4819797575,-0.1893264949,0.0975982994,0.1261651665,-0.1254302114,-0.1567192525,0.4228464365,-0.1241363883,0.4435888529,-0.0527197532,0.2112598121,-0.0216665938,-0.102701664,-0.2636416852,-0.0696592405,-0.0961601064,0.2933215201,-0.2793983221,0.0196358114,-0.1615019292,0.0556470677,0.3511354923,-0.1503831744,-0.2346212864,-0.0450777449,0.0960106328,0.1575745195,0.1801504046,0.1637832075,0.0620483644,-0.200006485,0.2782812417,0.3079036772,-0.0842119008,-0.0761709884,-0.2100430429,-0.2230561525,-0.2947570086,0.0944294706,0.0875293911,-0.2031713277,-0.1779466271,0.0071135005,-0.1443609297,-0.0194389597,-0.202394858,0.3628205657,-0.1501425803,0.4312385917,0.1779012084,0.3288222253,-0.1035727784,-0.2613099515,-0.0173523482,0.3065485656,-0.1393195242,-0.1036603302,0.2187829465,-0.1099155918,0.2648515999,-0.089371942,-0.1031381935,-0.1764115244,-0.1536911428,-0.3695675731,-0.1791034937,0.230408892,0.1619764566,0.2839277089,0.1476140618,-0.244747147,-0.2305807471,0.1207451969,-0.3596149981,-0.1681548953,-0.2051547766,0.1944684684,0.0610982925,-0.0546504743,-0.3160067499,0.2876641154,0.3030638993,-0.1423988342,0.1180728897,-0.0075459145,-0.0232539773,-0.225190118,0.1790115982,0.4908005595,-0.399959296,0.091897428,-0.0238504577,-0.0337120444,0.1897819787,0.1842971891,-0.1779527366,0.2565417886,-0.0978842974,0.0381600037,0.2101310194,-0.3214419484,-0.5776352286,0.186053887,0.113410078,-0.1670030951,0.0994797125,0.0160005782,0.2750306129,-0.0493875705,0.052454181,0.3956748247,0.1368381381,-0.1056447476,-0.3274506032,-0.5078234673,0.3264816701,0.1633179337,0.4547148943,-0.1322700828,0.2759852409,0.2917218208,0.3191742897,-0.1915477812,0.1246789992,0.2333606035,-0.0089483308,-0.0445753597,-0.002562576,-0.3310278654,-0.3097580373,0.1477600932,0.1029136032,0.1654645205,-0.1609553099,0.029683331,-0.3161896169,-0.0888073742,-0.1953119785,0.10326875,0.1995639354,-0.1052765027,0.1195931062,0.4854590893,0.1265164316,-0.0994593799,-0.4156903923,-0.0948526934,-0.17893444,0.0923319757,-0.2174285501,0.2044921666,0.0336807854,0.0258089639,0.1796839386,0.0730525404,-0.1607236117,-0.0110613015,-0.0784521401,-0.1035443768,0.1354628503,0.116963841,0.1331282407,-0.417375356,0.1920297742,0.1084797084,0.3297463953,-0.1357724667,0.1178161129,0.1222370416,0.0399733894,0.3655338883,0.1234308109,0.1498314887,0.4555997252,0.0305262543,0.1263544261,-0.0049747,0.3271189332,0.1556606591,0.0127488235,-0.1329593062,-0.02927793,-0.1135142446,0.3166727722,-0.0845338032,0.2671483159,0.2927592099,-0.0765190423,-0.004859813,-0.0978270099,0.1533981711,0.0522076748,0.0699049756,0.1065892354,-0.0998440012,0.359649688,-0.1293927282,0.2643093467,0.1755426973,0.1863330156,0.209135592,0.1350800842,-0.0111756697,-0.1882829219,-0.1296795756,0.0699935928,0.3717179298,0.0691837072,0.165945366,-0.0484345779,-0.1418599933,0.0841909274,-0.0523921847,-0.2509426475,-0.2163173407,-0.1256383955,-0.0857288986,0.091583252,-0.0001049751,-0.3266668916,-0.0721021518,0.0621662624,-0.3203505874,-0.1605682075,-0.2981233299,-0.3684496582,0.0767874196,0.3871294558,0.1112902835,0.2952094376,-0.3326290548,-0.1918789148,-0.0899458826,-0.2681111097,-0.0143140424,-0.1569705009,0.2413951159,-0.0241171494,0.7152194381,-0.1799962372,-0.251421392,0.1846487224,-0.0070200525,0.0658744872,0.2497714758,-0.1534786075,-0.0429239906,0.0845356733,-0.4856536686,-0.5155764222,-0.2696566582,0.0125194602,0.048517894,0.0878061801,0.1686406732,0.1924842894,0.0680263713,0.121187754,0.1809071153,-0.1900433302,-0.1518690884,0.4496890306,-0.0274131726,-0.4299626648,0.1881868094,0.0109940656,0.3149316907,0.0079274345,-0.6572358608,0.1441125572,-0.2763589025,0.2499436736,-0.0219936892,0.183476612,0.114343822,-0.2136970013,0.0479722433,0.0449956916,0.1059101671,-0.0406209044,-0.2095253319,0.2556217909,0.0299457368,0.2961854935,0.1644209623,0.6888408661,0.0548558161,0.1658407599,0.4367816746,0.2385823429,0.2310316861,-0.2314289063,-0.3645718992,0.0605744347,-0.239491537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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"Hello @dorost1234,\r\n\r\nIndeed, Wikipedia datasets need a lot of preprocessing and this is done using Apache Beam. That is the reason why it is required that you install Apache Beam in order to preform this preprocessing.\r\n\r\nFor some specific default parameters (English Wikipedia), Hugging Face has already preprocessed the dataset for you (and it is stored in the cloud). That is the reason why you do not get the error for English: the preprocessing is already done by HF and you just get the preprocessed dataset; Apache Beam is not required in that case.","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":94,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n Hello @dorost1234,\r\n\r\nIndeed, Wikipedia datasets need a lot of preprocessing and this is done using Apache Beam. That is the reason why it is required that you install Apache Beam in order to preform this preprocessing.\r\n\r\nFor some specific default parameters (English Wikipedia), Hugging Face has already preprocessed the dataset for you (and it is stored in the cloud). That is the reason why you do not get the error for English: the preprocessing is already done by HF and you just get the preprocessed dataset; Apache Beam is not required in that case.","embeddings":[-0.2544980347,-0.0779204071,-0.1537582129,0.4308663607,0.3997030556,0.3504618704,0.1368506402,0.5331582427,0.1893276423,0.0235845186,-0.1773036569,-0.0942173079,0.0944036767,0.0111841084,-0.0297050718,-0.4591732025,-0.0672903061,0.0295370072,-0.1352714151,-0.1168936566,-0.2519630194,0.1256146282,-0.0791749284,-0.0490285978,-0.0387454666,-0.1828399897,-0.095835425,-0.2073058486,-0.3507466614,-0.240864411,0.3545782268,0.0192575995,0.3101119697,0.152997002,-0.0001114368,-0.0509645268,0.313116461,-0.2357945591,-0.2936119437,-0.2888573706,-0.0720896423,-0.3261856437,-0.0904391855,-0.420031637,0.1274462193,0.0466828533,0.080671154,-0.1318009049,0.0757578686,0.299225539,0.2468712181,-0.1426034421,0.313434124,-0.1457377374,0.3651962578,-0.3777080178,-0.0280337762,0.3395861685,0.2187208384,0.0713426769,0.2154791504,0.2220428586,-0.2341407239,0.0134992879,0.0482247174,-0.291223824,0.222205475,-0.6297016144,0.5206420422,0.5344284177,0.8290324211,-0.1283516139,-0.0874669701,0.0723921135,-0.0260593966,-0.031374447,-0.0557402261,0.4819797575,-0.1893264949,0.0975982994,0.1261651665,-0.1254302114,-0.1567192525,0.4228464365,-0.1241363883,0.4435888529,-0.0527197532,0.2112598121,-0.0216665938,-0.102701664,-0.2636416852,-0.0696592405,-0.0961601064,0.2933215201,-0.2793983221,0.0196358114,-0.1615019292,0.0556470677,0.3511354923,-0.1503831744,-0.2346212864,-0.0450777449,0.0960106328,0.1575745195,0.1801504046,0.1637832075,0.0620483644,-0.200006485,0.2782812417,0.3079036772,-0.0842119008,-0.0761709884,-0.2100430429,-0.2230561525,-0.2947570086,0.0944294706,0.0875293911,-0.2031713277,-0.1779466271,0.0071135005,-0.1443609297,-0.0194389597,-0.202394858,0.3628205657,-0.1501425803,0.4312385917,0.1779012084,0.3288222253,-0.1035727784,-0.2613099515,-0.0173523482,0.3065485656,-0.1393195242,-0.1036603302,0.2187829465,-0.1099155918,0.2648515999,-0.089371942,-0.1031381935,-0.1764115244,-0.1536911428,-0.3695675731,-0.1791034937,0.230408892,0.1619764566,0.2839277089,0.1476140618,-0.244747147,-0.2305807471,0.1207451969,-0.3596149981,-0.1681548953,-0.2051547766,0.1944684684,0.0610982925,-0.0546504743,-0.3160067499,0.2876641154,0.3030638993,-0.1423988342,0.1180728897,-0.0075459145,-0.0232539773,-0.225190118,0.1790115982,0.4908005595,-0.399959296,0.091897428,-0.0238504577,-0.0337120444,0.1897819787,0.1842971891,-0.1779527366,0.2565417886,-0.0978842974,0.0381600037,0.2101310194,-0.3214419484,-0.5776352286,0.186053887,0.113410078,-0.1670030951,0.0994797125,0.0160005782,0.2750306129,-0.0493875705,0.052454181,0.3956748247,0.1368381381,-0.1056447476,-0.3274506032,-0.5078234673,0.3264816701,0.1633179337,0.4547148943,-0.1322700828,0.2759852409,0.2917218208,0.3191742897,-0.1915477812,0.1246789992,0.2333606035,-0.0089483308,-0.0445753597,-0.002562576,-0.3310278654,-0.3097580373,0.1477600932,0.1029136032,0.1654645205,-0.1609553099,0.029683331,-0.3161896169,-0.0888073742,-0.1953119785,0.10326875,0.1995639354,-0.1052765027,0.1195931062,0.4854590893,0.1265164316,-0.0994593799,-0.4156903923,-0.0948526934,-0.17893444,0.0923319757,-0.2174285501,0.2044921666,0.0336807854,0.0258089639,0.1796839386,0.0730525404,-0.1607236117,-0.0110613015,-0.0784521401,-0.1035443768,0.1354628503,0.116963841,0.1331282407,-0.417375356,0.1920297742,0.1084797084,0.3297463953,-0.1357724667,0.1178161129,0.1222370416,0.0399733894,0.3655338883,0.1234308109,0.1498314887,0.4555997252,0.0305262543,0.1263544261,-0.0049747,0.3271189332,0.1556606591,0.0127488235,-0.1329593062,-0.02927793,-0.1135142446,0.3166727722,-0.0845338032,0.2671483159,0.2927592099,-0.0765190423,-0.004859813,-0.0978270099,0.1533981711,0.0522076748,0.0699049756,0.1065892354,-0.0998440012,0.359649688,-0.1293927282,0.2643093467,0.1755426973,0.1863330156,0.209135592,0.1350800842,-0.0111756697,-0.1882829219,-0.1296795756,0.0699935928,0.3717179298,0.0691837072,0.165945366,-0.0484345779,-0.1418599933,0.0841909274,-0.0523921847,-0.2509426475,-0.2163173407,-0.1256383955,-0.0857288986,0.091583252,-0.0001049751,-0.3266668916,-0.0721021518,0.0621662624,-0.3203505874,-0.1605682075,-0.2981233299,-0.3684496582,0.0767874196,0.3871294558,0.1112902835,0.2952094376,-0.3326290548,-0.1918789148,-0.0899458826,-0.2681111097,-0.0143140424,-0.1569705009,0.2413951159,-0.0241171494,0.7152194381,-0.1799962372,-0.251421392,0.1846487224,-0.0070200525,0.0658744872,0.2497714758,-0.1534786075,-0.0429239906,0.0845356733,-0.4856536686,-0.5155764222,-0.2696566582,0.0125194602,0.048517894,0.0878061801,0.1686406732,0.1924842894,0.0680263713,0.121187754,0.1809071153,-0.1900433302,-0.1518690884,0.4496890306,-0.0274131726,-0.4299626648,0.1881868094,0.0109940656,0.3149316907,0.0079274345,-0.6572358608,0.1441125572,-0.2763589025,0.2499436736,-0.0219936892,0.183476612,0.114343822,-0.2136970013,0.0479722433,0.0449956916,0.1059101671,-0.0406209044,-0.2095253319,0.2556217909,0.0299457368,0.2961854935,0.1644209623,0.6888408661,0.0548558161,0.1658407599,0.4367816746,0.2385823429,0.2310316861,-0.2314289063,-0.3645718992,0.0605744347,-0.2394915372,-0.03100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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"Hi\nI really appreciate if huggingface can kindly provide preprocessed\ndatasets, processing these datasets require sufficiently large resources\nand I do not have unfortunately access to, and perhaps many others too.\nthanks\n\nOn Fri, Mar 12, 2021 at 9:04 AM Albert Villanova del Moral <\n***@***.***> wrote:\n\n> Hello @dorost1234 <https:\/\/github.com\/dorost1234>,\n>\n> Indeed, Wikipedia datasets need a lot of preprocessing and this is done\n> using Apache Beam. That is the reason why it is required that you install\n> Apache Beam in order to preform this preprocessing.\n>\n> For some specific default parameters (English Wikipedia), Hugging Face has\n> already preprocessed the dataset for you (and it is stored in the cloud).\n> That is the reason why you do not get the error for English: the\n> preprocessing is already done by HF and you just get the preprocessed\n> dataset; Apache Beam is not required in that case.\n>\n> \u2014\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2035#issuecomment-797310899>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMXACFQZAGMK4VGXRETTDHDI3ANCNFSM4ZA5R2UA>\n> .\n>\n","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":185,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n Hi\nI really appreciate if huggingface can kindly provide preprocessed\ndatasets, processing these datasets require sufficiently large resources\nand I do not have unfortunately access to, and perhaps many others too.\nthanks\n\nOn Fri, Mar 12, 2021 at 9:04 AM Albert Villanova del Moral <\n***@***.***> wrote:\n\n> Hello @dorost1234 <https:\/\/github.com\/dorost1234>,\n>\n> Indeed, Wikipedia datasets need a lot of preprocessing and this is done\n> using Apache Beam. That is the reason why it is required that you install\n> Apache Beam in order to preform this preprocessing.\n>\n> For some specific default parameters (English Wikipedia), Hugging Face has\n> already preprocessed the dataset for you (and it is stored in the cloud).\n> That is the reason why you do not get the error for English: the\n> preprocessing is already done by HF and you just get the preprocessed\n> dataset; Apache Beam is not required in that case.\n>\n> \u2014\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2035#issuecomment-797310899>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMXACFQZAGMK4VGXRETTDHDI3ANCNFSM4ZA5R2UA>\n> .\n>\n","embeddings":[-0.2544980347,-0.0779204071,-0.1537582129,0.4308663607,0.3997030556,0.3504618704,0.1368506402,0.5331582427,0.1893276423,0.0235845186,-0.1773036569,-0.0942173079,0.0944036767,0.0111841084,-0.0297050718,-0.4591732025,-0.0672903061,0.0295370072,-0.1352714151,-0.1168936566,-0.2519630194,0.1256146282,-0.0791749284,-0.0490285978,-0.0387454666,-0.1828399897,-0.095835425,-0.2073058486,-0.3507466614,-0.240864411,0.3545782268,0.0192575995,0.3101119697,0.152997002,-0.0001114368,-0.0509645268,0.313116461,-0.2357945591,-0.2936119437,-0.2888573706,-0.0720896423,-0.3261856437,-0.0904391855,-0.420031637,0.1274462193,0.0466828533,0.080671154,-0.1318009049,0.0757578686,0.299225539,0.2468712181,-0.1426034421,0.313434124,-0.1457377374,0.3651962578,-0.3777080178,-0.0280337762,0.3395861685,0.2187208384,0.0713426769,0.2154791504,0.2220428586,-0.2341407239,0.0134992879,0.0482247174,-0.291223824,0.222205475,-0.6297016144,0.5206420422,0.5344284177,0.8290324211,-0.1283516139,-0.0874669701,0.0723921135,-0.0260593966,-0.031374447,-0.0557402261,0.4819797575,-0.1893264949,0.0975982994,0.1261651665,-0.1254302114,-0.1567192525,0.4228464365,-0.1241363883,0.4435888529,-0.0527197532,0.2112598121,-0.0216665938,-0.102701664,-0.2636416852,-0.0696592405,-0.0961601064,0.2933215201,-0.2793983221,0.0196358114,-0.1615019292,0.0556470677,0.3511354923,-0.1503831744,-0.2346212864,-0.0450777449,0.0960106328,0.1575745195,0.1801504046,0.1637832075,0.0620483644,-0.200006485,0.2782812417,0.3079036772,-0.0842119008,-0.0761709884,-0.2100430429,-0.2230561525,-0.2947570086,0.0944294706,0.0875293911,-0.2031713277,-0.1779466271,0.0071135005,-0.1443609297,-0.0194389597,-0.202394858,0.3628205657,-0.1501425803,0.4312385917,0.1779012084,0.3288222253,-0.1035727784,-0.2613099515,-0.0173523482,0.3065485656,-0.1393195242,-0.1036603302,0.2187829465,-0.1099155918,0.2648515999,-0.089371942,-0.1031381935,-0.1764115244,-0.1536911428,-0.3695675731,-0.1791034937,0.230408892,0.1619764566,0.2839277089,0.1476140618,-0.244747147,-0.2305807471,0.1207451969,-0.3596149981,-0.1681548953,-0.2051547766,0.1944684684,0.0610982925,-0.0546504743,-0.3160067499,0.2876641154,0.3030638993,-0.1423988342,0.1180728897,-0.0075459145,-0.0232539773,-0.225190118,0.1790115982,0.4908005595,-0.399959296,0.091897428,-0.0238504577,-0.0337120444,0.1897819787,0.1842971891,-0.1779527366,0.2565417886,-0.0978842974,0.0381600037,0.2101310194,-0.3214419484,-0.5776352286,0.186053887,0.113410078,-0.1670030951,0.0994797125,0.0160005782,0.2750306129,-0.0493875705,0.052454181,0.3956748247,0.1368381381,-0.1056447476,-0.3274506032,-0.5078234673,0.3264816701,0.1633179337,0.4547148943,-0.1322700828,0.2759852409,0.2917218208,0.3191742897,-0.1915477812,0.1246789992,0.2333606035,-0.0089483308,-0.0445753597,-0.002562576,-0.3310278654,-0.3097580373,0.1477600932,0.1029136032,0.1654645205,-0.1609553099,0.029683331,-0.3161896169,-0.0888073742,-0.1953119785,0.10326875,0.1995639354,-0.1052765027,0.1195931062,0.4854590893,0.1265164316,-0.0994593799,-0.4156903923,-0.0948526934,-0.17893444,0.0923319757,-0.2174285501,0.2044921666,0.0336807854,0.0258089639,0.1796839386,0.0730525404,-0.1607236117,-0.0110613015,-0.0784521401,-0.1035443768,0.1354628503,0.116963841,0.1331282407,-0.417375356,0.1920297742,0.1084797084,0.3297463953,-0.1357724667,0.1178161129,0.1222370416,0.0399733894,0.3655338883,0.1234308109,0.1498314887,0.4555997252,0.0305262543,0.1263544261,-0.0049747,0.3271189332,0.1556606591,0.0127488235,-0.1329593062,-0.02927793,-0.1135142446,0.3166727722,-0.0845338032,0.2671483159,0.2927592099,-0.0765190423,-0.004859813,-0.0978270099,0.1533981711,0.0522076748,0.0699049756,0.1065892354,-0.0998440012,0.359649688,-0.1293927282,0.2643093467,0.1755426973,0.1863330156,0.209135592,0.1350800842,-0.0111756697,-0.1882829219,-0.1296795756,0.0699935928,0.3717179298,0.0691837072,0.165945366,-0.0484345779,-0.1418599933,0.0841909274,-0.0523921847,-0.2509426475,-0.2163173407,-0.1256383955,-0.0857288986,0.091583252,-0.0001049751,-0.3266668916,-0.0721021518,0.0621662624,-0.3203505874,-0.1605682075,-0.2981233299,-0.3684496582,0.0767874196,0.3871294558,0.1112902835,0.2952094376,-0.3326290548,-0.1918789148,-0.0899458826,-0.2681111097,-0.0143140424,-0.1569705009,0.2413951159,-0.0241171494,0.7152194381,-0.1799962372,-0.251421392,0.1846487224,-0.0070200525,0.0658744872,0.2497714758,-0.1534786075,-0.0429239906,0.0845356733,-0.4856536686,-0.5155764222,-0.2696566582,0.0125194602,0.048517894,0.0878061801,0.1686406732,0.1924842894,0.0680263713,0.121187754,0.1809071153,-0.1900433302,-0.1518690884,0.4496890306,-0.0274131726,-0.4299626648,0.1881868094,0.0109940656,0.3149316907,0.0079274345,-0.6572358608,0.1441125572,-0.2763589025,0.2499436736,-0.0219936892,0.183476612,0.114343822,-0.2136970013,0.0479722433,0.0449956916,0.1059101671,-0.0406209044,-0.2095253319,0.2556217909,0.0299457368,0.2961854935,0.1644209623,0.6888408661,0.0548558161,0.1658407599,0.4367816746,0.2385823429,0.2310316861,-0.2314289063,-0.3645718992,0.0605744347,-0.2394915372,-0.0310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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"Hi everyone\r\nthanks for the helpful pointers, I did it as @bhavitvyamalik suggested, for me this freezes on this command for several hours, \r\n\r\n`Downloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/users\/dara\/cache\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\n`\r\n\r\nDo you know how long this takes? Any specific requirements the machine should have? like very large memory or so? @lhoestq \r\n\r\nthanks \r\n\r\n\r\n","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":65,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n Hi everyone\r\nthanks for the helpful pointers, I did it as @bhavitvyamalik suggested, for me this freezes on this command for several hours, \r\n\r\n`Downloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/users\/dara\/cache\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\n`\r\n\r\nDo you know how long this takes? Any specific requirements the machine should have? like very large memory or so? @lhoestq \r\n\r\nthanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"HI @dorost1234, \r\nThe dataset size is 631.84 MiB so depending on your internet speed it'll take some time. You can monitor your internet speed meanwhile to see if it's downloading the dataset or not (use `nload` if you're using linux\/mac to monitor the same). In my case it took around 3-4 mins. Since they haven't used `download_and_extract` here that's why there's no download progress bar.","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":65,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n HI @dorost1234, \r\nThe dataset size is 631.84 MiB so depending on your internet speed it'll take some time. You can monitor your internet speed meanwhile to see if it's downloading the dataset or not (use `nload` if you're using linux\/mac to monitor the same). In my case it took around 3-4 mins. Since they haven't used `download_and_extract` here that's why there's no download progress 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"Hi\r\nthanks, my internet speed should be good, but this really freezes for me, this is how I try to get this dataset:\r\n\r\n`from datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')`\r\n\r\nthe output I see if different also from what you see after writing  this command:\r\n\r\n`Downloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/users\/dara\/cache\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...`\r\n\r\ndo you have any idea why it might get freezed? anything I am missing @lhoestq @bhavitvyamalik. Do I need maybe to set anything special for apache-beam? \r\n\r\nthanks a lot \r\n\r\nOn Tue, Mar 16, 2021 at 9:03 AM Bhavitvya Malik ***@***.***>\r\nwrote:\r\n\r\n> HI @dorost1234 <https:\/\/github.com\/dorost1234>,\r\n> The dataset size is 631.84 MiB so depending on your internet speed it'll\r\n> take some time. You can monitor your internet speed meanwhile to see if\r\n> it's downloading the dataset or not (use nload if you're using linux\/mac\r\n> to monitor the same). In my case it took around 3-4 mins. Since they\r\n> haven't used download_and_extract here that's why there's no download\r\n> progress bar.\r\n>\r\n> \u2014\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2035#issuecomment-800044303>,\r\n> or unsubscribe\r\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMQIHNNLM2LGG6QKZ73TD4GDJANCNFSM4ZA5R2UA>\r\n> .\r\n>\r\n","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":212,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n Hi\r\nthanks, my internet speed should be good, but this really freezes for me, this is how I try to get this dataset:\r\n\r\n`from datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')`\r\n\r\nthe output I see if different also from what you see after writing  this command:\r\n\r\n`Downloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/users\/dara\/cache\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...`\r\n\r\ndo you have any idea why it might get freezed? anything I am missing @lhoestq @bhavitvyamalik. Do I need maybe to set anything special for apache-beam? \r\n\r\nthanks a lot \r\n\r\nOn Tue, Mar 16, 2021 at 9:03 AM Bhavitvya Malik ***@***.***>\r\nwrote:\r\n\r\n> HI @dorost1234 <https:\/\/github.com\/dorost1234>,\r\n> The dataset size is 631.84 MiB so depending on your internet speed it'll\r\n> take some time. You can monitor your internet speed meanwhile to see if\r\n> it's downloading the dataset or not (use nload if you're using linux\/mac\r\n> to monitor the same). In my case it took around 3-4 mins. Since they\r\n> haven't used download_and_extract here that's why there's no download\r\n> progress bar.\r\n>\r\n> \u2014\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2035#issuecomment-800044303>,\r\n> or unsubscribe\r\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMQIHNNLM2LGG6QKZ73TD4GDJANCNFSM4ZA5R2UA>\r\n> .\r\n>\r\n","embeddings":[-0.2544980347,-0.0779204071,-0.1537582129,0.4308663607,0.3997030556,0.3504618704,0.1368506402,0.5331582427,0.1893276423,0.0235845186,-0.1773036569,-0.0942173079,0.0944036767,0.0111841084,-0.0297050718,-0.4591732025,-0.0672903061,0.0295370072,-0.1352714151,-0.1168936566,-0.2519630194,0.1256146282,-0.0791749284,-0.0490285978,-0.0387454666,-0.1828399897,-0.095835425,-0.2073058486,-0.3507466614,-0.240864411,0.3545782268,0.0192575995,0.3101119697,0.152997002,-0.0001114368,-0.0509645268,0.313116461,-0.2357945591,-0.2936119437,-0.2888573706,-0.0720896423,-0.3261856437,-0.0904391855,-0.420031637,0.1274462193,0.0466828533,0.080671154,-0.1318009049,0.0757578686,0.299225539,0.2468712181,-0.1426034421,0.313434124,-0.1457377374,0.3651962578,-0.3777080178,-0.0280337762,0.3395861685,0.2187208384,0.0713426769,0.2154791504,0.2220428586,-0.2341407239,0.0134992879,0.0482247174,-0.291223824,0.222205475,-0.6297016144,0.5206420422,0.5344284177,0.8290324211,-0.1283516139,-0.0874669701,0.0723921135,-0.0260593966,-0.031374447,-0.0557402261,0.4819797575,-0.1893264949,0.0975982994,0.1261651665,-0.1254302114,-0.1567192525,0.4228464365,-0.1241363883,0.4435888529,-0.0527197532,0.2112598121,-0.0216665938,-0.102701664,-0.2636416852,-0.0696592405,-0.0961601064,0.2933215201,-0.2793983221,0.0196358114,-0.1615019292,0.0556470677,0.3511354923,-0.1503831744,-0.2346212864,-0.0450777449,0.0960106328,0.1575745195,0.1801504046,0.1637832075,0.0620483644,-0.200006485,0.2782812417,0.3079036772,-0.0842119008,-0.0761709884,-0.2100430429,-0.2230561525,-0.2947570086,0.0944294706,0.0875293911,-0.2031713277,-0.1779466271,0.0071135005,-0.1443609297,-0.0194389597,-0.202394858,0.3628205657,-0.1501425803,0.4312385917,0.1779012084,0.3288222253,-0.1035727784,-0.2613099515,-0.0173523482,0.3065485656,-0.1393195242,-0.1036603302,0.2187829465,-0.1099155918,0.2648515999,-0.089371942,-0.1031381935,-0.1764115244,-0.1536911428,-0.3695675731,-0.1791034937,0.230408892,0.1619764566,0.2839277089,0.1476140618,-0.244747147,-0.2305807471,0.1207451969,-0.3596149981,-0.1681548953,-0.2051547766,0.1944684684,0.0610982925,-0.0546504743,-0.3160067499,0.2876641154,0.3030638993,-0.1423988342,0.1180728897,-0.0075459145,-0.0232539773,-0.225190118,0.1790115982,0.4908005595,-0.399959296,0.091897428,-0.0238504577,-0.0337120444,0.1897819787,0.1842971891,-0.1779527366,0.2565417886,-0.0978842974,0.0381600037,0.2101310194,-0.3214419484,-0.5776352286,0.186053887,0.113410078,-0.1670030951,0.0994797125,0.0160005782,0.2750306129,-0.0493875705,0.052454181,0.3956748247,0.1368381381,-0.1056447476,-0.3274506032,-0.5078234673,0.3264816701,0.1633179337,0.4547148943,-0.1322700828,0.2759852409,0.2917218208,0.3191742897,-0.1915477812,0.1246789992,0.2333606035,-0.0089483308,-0.0445753597,-0.002562576,-0.3310278654,-0.3097580373,0.1477600932,0.1029136032,0.1654645205,-0.1609553099,0.029683331,-0.3161896169,-0.0888073742,-0.1953119785,0.10326875,0.1995639354,-0.1052765027,0.1195931062,0.4854590893,0.1265164316,-0.0994593799,-0.4156903923,-0.0948526934,-0.17893444,0.0923319757,-0.2174285501,0.2044921666,0.0336807854,0.0258089639,0.1796839386,0.0730525404,-0.1607236117,-0.0110613015,-0.0784521401,-0.1035443768,0.1354628503,0.116963841,0.1331282407,-0.417375356,0.1920297742,0.1084797084,0.3297463953,-0.1357724667,0.1178161129,0.1222370416,0.0399733894,0.3655338883,0.1234308109,0.1498314887,0.4555997252,0.0305262543,0.1263544261,-0.0049747,0.3271189332,0.1556606591,0.0127488235,-0.1329593062,-0.02927793,-0.1135142446,0.3166727722,-0.0845338032,0.2671483159,0.2927592099,-0.0765190423,-0.004859813,-0.0978270099,0.1533981711,0.0522076748,0.0699049756,0.1065892354,-0.0998440012,0.359649688,-0.1293927282,0.2643093467,0.1755426973,0.1863330156,0.209135592,0.1350800842,-0.0111756697,-0.1882829219,-0.1296795756,0.0699935928,0.3717179298,0.0691837072,0.165945366,-0.0484345779,-0.1418599933,0.0841909274,-0.0523921847,-0.2509426475,-0.2163173407,-0.1256383955,-0.0857288986,0.091583252,-0.0001049751,-0.3266668916,-0.0721021518,0.0621662624,-0.3203505874,-0.1605682075,-0.2981233299,-0.3684496582,0.0767874196,0.3871294558,0.1112902835,0.2952094376,-0.3326290548,-0.1918789148,-0.0899458826,-0.2681111097,-0.0143140424,-0.1569705009,0.2413951159,-0.0241171494,0.7152194381,-0.1799962372,-0.251421392,0.1846487224,-0.0070200525,0.0658744872,0.2497714758,-0.1534786075,-0.0429239906,0.0845356733,-0.4856536686,-0.5155764222,-0.2696566582,0.0125194602,0.048517894,0.0878061801,0.1686406732,0.1924842894,0.0680263713,0.121187754,0.1809071153,-0.1900433302,-0.1518690884,0.4496890306,-0.0274131726,-0.4299626648,0.1881868094,0.0109940656,0.3149316907,0.0079274345,-0.6572358608,0.1441125572,-0.2763589025,0.2499436736,-0.0219936892,0.183476612,0.114343822,-0.2136970013,0.0479722433,0.0449956916,0.1059101671,-0.0406209044,-0.2095253319,0.2556217909,0.0299457368,0.2961854935,0.1644209623,0.6888408661,0.0548558161,0.1658407599,0.4367816746,0.2385823429,0.2310316861,-0.2314289063,-0.3645718992,0.0605744347,-0.2394915372,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"I tried this on another machine (followed the same procedure I've mentioned above). This is what it shows (during the freeze period) for me:\r\n```\r\n>>> dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')\r\nDownloading: 5.26kB [00:00, 1.23MB\/s]                                                                                                                                    \r\nDownloading: 1.40kB [00:00, 327kB\/s]                                                                                                                                     \r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bhavitvya\/.cache\/huggingface\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nWARNING:apache_beam.internal.gcp.auth:Unable to find default credentials to use: The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials. See https:\/\/developers.google.com\/accounts\/docs\/application-default-credentials for more information.\r\nConnecting anonymously.\r\nWARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\r\n```\r\nAfter around 10 minutes, here's the loading of dataset:\r\n```\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:16<00:00, 16.42s\/sources]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.12sources\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.14sources\/s]\r\nDataset wiki40b downloaded and prepared to \/home\/bhavitvya\/.cache\/huggingface\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f. Subsequent calls will reuse this data.\r\n```","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":156,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n I tried this on another machine (followed the same procedure I've mentioned above). This is what it shows (during the freeze period) for me:\r\n```\r\n>>> dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')\r\nDownloading: 5.26kB [00:00, 1.23MB\/s]                                                                                                                                    \r\nDownloading: 1.40kB [00:00, 327kB\/s]                                                                                                                                     \r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bhavitvya\/.cache\/huggingface\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nWARNING:apache_beam.internal.gcp.auth:Unable to find default credentials to use: The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials. See https:\/\/developers.google.com\/accounts\/docs\/application-default-credentials for more information.\r\nConnecting anonymously.\r\nWARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\r\n```\r\nAfter around 10 minutes, here's the loading of dataset:\r\n```\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:16<00:00, 16.42s\/sources]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.12sources\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.14sources\/s]\r\nDataset wiki40b downloaded and prepared to \/home\/bhavitvya\/.cache\/huggingface\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f. Subsequent calls will reuse this data.\r\n```","embeddings":[-0.2544980347,-0.0779204071,-0.1537582129,0.4308663607,0.3997030556,0.3504618704,0.1368506402,0.5331582427,0.1893276423,0.0235845186,-0.1773036569,-0.0942173079,0.0944036767,0.0111841084,-0.0297050718,-0.4591732025,-0.0672903061,0.0295370072,-0.1352714151,-0.1168936566,-0.2519630194,0.1256146282,-0.0791749284,-0.0490285978,-0.0387454666,-0.1828399897,-0.095835425,-0.2073058486,-0.3507466614,-0.240864411,0.3545782268,0.0192575995,0.3101119697,0.152997002,-0.0001114368,-0.0509645268,0.313116461,-0.2357945591,-0.2936119437,-0.2888573706,-0.0720896423,-0.3261856437,-0.0904391855,-0.420031637,0.1274462193,0.0466828533,0.080671154,-0.1318009049,0.0757578686,0.299225539,0.2468712181,-0.1426034421,0.313434124,-0.1457377374,0.3651962578,-0.3777080178,-0.0280337762,0.3395861685,0.2187208384,0.0713426769,0.2154791504,0.2220428586,-0.2341407239,0.0134992879,0.0482247174,-0.291223824,0.222205475,-0.6297016144,0.5206420422,0.5344284177,0.8290324211,-0.1283516139,-0.0874669701,0.0723921135,-0.0260593966,-0.031374447,-0.0557402261,0.4819797575,-0.1893264949,0.0975982994,0.1261651665,-0.1254302114,-0.1567192525,0.4228464365,-0.1241363883,0.4435888529,-0.0527197532,0.2112598121,-0.0216665938,-0.102701664,-0.2636416852,-0.0696592405,-0.0961601064,0.2933215201,-0.2793983221,0.0196358114,-0.1615019292,0.0556470677,0.3511354923,-0.1503831744,-0.2346212864,-0.0450777449,0.0960106328,0.1575745195,0.1801504046,0.1637832075,0.0620483644,-0.200006485,0.2782812417,0.3079036772,-0.0842119008,-0.0761709884,-0.2100430429,-0.2230561525,-0.2947570086,0.0944294706,0.0875293911,-0.2031713277,-0.1779466271,0.0071135005,-0.1443609297,-0.0194389597,-0.202394858,0.3628205657,-0.1501425803,0.4312385917,0.1779012084,0.3288222253,-0.1035727784,-0.2613099515,-0.0173523482,0.3065485656,-0.1393195242,-0.1036603302,0.2187829465,-0.1099155918,0.2648515999,-0.089371942,-0.1031381935,-0.1764115244,-0.1536911428,-0.3695675731,-0.1791034937,0.230408892,0.1619764566,0.2839277089,0.1476140618,-0.244747147,-0.2305807471,0.1207451969,-0.3596149981,-0.1681548953,-0.2051547766,0.1944684684,0.0610982925,-0.0546504743,-0.3160067499,0.2876641154,0.3030638993,-0.1423988342,0.1180728897,-0.0075459145,-0.0232539773,-0.225190118,0.1790115982,0.4908005595,-0.399959296,0.091897428,-0.0238504577,-0.0337120444,0.1897819787,0.1842971891,-0.1779527366,0.2565417886,-0.0978842974,0.0381600037,0.2101310194,-0.3214419484,-0.5776352286,0.186053887,0.113410078,-0.1670030951,0.0994797125,0.0160005782,0.2750306129,-0.0493875705,0.052454181,0.3956748247,0.1368381381,-0.1056447476,-0.3274506032,-0.5078234673,0.3264816701,0.1633179337,0.4547148943,-0.1322700828,0.2759852409,0.2917218208,0.3191742897,-0.1915477812,0.1246789992,0.2333606035,-0.0089483308,-0.0445753597,-0.002562576,-0.3310278654,-0.3097580373,0.1477600932,0.1029136032,0.1654645205,-0.1609553099,0.029683331,-0.3161896169,-0.0888073742,-0.1953119785,0.10326875,0.1995639354,-0.1052765027,0.1195931062,0.4854590893,0.1265164316,-0.0994593799,-0.4156903923,-0.0948526934,-0.17893444,0.0923319757,-0.2174285501,0.2044921666,0.0336807854,0.0258089639,0.1796839386,0.0730525404,-0.1607236117,-0.0110613015,-0.0784521401,-0.1035443768,0.1354628503,0.116963841,0.1331282407,-0.417375356,0.1920297742,0.1084797084,0.3297463953,-0.1357724667,0.1178161129,0.1222370416,0.0399733894,0.3655338883,0.1234308109,0.1498314887,0.4555997252,0.0305262543,0.1263544261,-0.0049747,0.3271189332,0.1556606591,0.0127488235,-0.1329593062,-0.02927793,-0.1135142446,0.3166727722,-0.0845338032,0.2671483159,0.2927592099,-0.0765190423,-0.004859813,-0.0978270099,0.1533981711,0.0522076748,0.0699049756,0.1065892354,-0.0998440012,0.359649688,-0.1293927282,0.2643093467,0.1755426973,0.1863330156,0.209135592,0.1350800842,-0.0111756697,-0.1882829219,-0.1296795756,0.0699935928,0.3717179298,0.0691837072,0.165945366,-0.0484345779,-0.1418599933,0.0841909274,-0.0523921847,-0.2509426475,-0.2163173407,-0.1256383955,-0.0857288986,0.091583252,-0.0001049751,-0.3266668916,-0.0721021518,0.0621662624,-0.3203505874,-0.1605682075,-0.2981233299,-0.3684496582,0.0767874196,0.3871294558,0.1112902835,0.2952094376,-0.3326290548,-0.1918789148,-0.0899458826,-0.2681111097,-0.0143140424,-0.1569705009,0.2413951159,-0.0241171494,0.7152194381,-0.1799962372,-0.251421392,0.1846487224,-0.0070200525,0.0658744872,0.2497714758,-0.1534786075,-0.0429239906,0.0845356733,-0.4856536686,-0.5155764222,-0.2696566582,0.0125194602,0.048517894,0.0878061801,0.1686406732,0.1924842894,0.0680263713,0.121187754,0.1809071153,-0.1900433302,-0.1518690884,0.4496890306,-0.0274131726,-0.4299626648,0.1881868094,0.0109940656,0.3149316907,0.0079274345,-0.6572358608,0.1441125572,-0.2763589025,0.2499436736,-0.0219936892,0.183476612,0.114343822,-0.2136970013,0.0479722433,0.0449956916,0.1059101671,-0.0406209044,-0.2095253319,0.2556217909,0.0299457368,0.2961854935,0.1644209623,0.6888408661,0.0548558161,0.1658407599,0.4367816746,0.2385823429,0.2310316861,-0.2314289063,-0.3645718992,0.0605744347,-0.2394915372,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2035","title":"wiki40b\/wikipedia for almost all languages cannot be downloaded","comments":"Hi\r\nI honestly also now tried on another machine and nothing shows up after\r\nhours of waiting. Are you sure you have not set any specific setting? maybe\r\ngoogle cloud which seems it is used here, needs some credential setting?\r\nthanks for any suggestions on this\r\n\r\nOn Tue, Mar 16, 2021 at 10:02 AM Bhavitvya Malik ***@***.***>\r\nwrote:\r\n\r\n> I tried this on another machine (followed the same procedure I've\r\n> mentioned above). This is what it shows (during the freeze period) for me:\r\n>\r\n> >>> dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')\r\n> Downloading: 5.26kB [00:00, 1.23MB\/s]\r\n> Downloading: 1.40kB [00:00, 327kB\/s]\r\n> Downloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bhavitvya\/.cache\/huggingface\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\n> WARNING:apache_beam.internal.gcp.auth:Unable to find default credentials to use: The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials. See https:\/\/developers.google.com\/accounts\/docs\/application-default-credentials for more information.\r\n> Connecting anonymously.\r\n> WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\r\n>\r\n> After around 10 minutes, here's the loading of dataset:\r\n>\r\n> 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:16<00:00, 16.42s\/sources]\r\n> 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.12sources\/s]\r\n> 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.14sources\/s]\r\n> Dataset wiki40b downloaded and prepared to \/home\/bhavitvya\/.cache\/huggingface\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f. Subsequent calls will reuse this data.\r\n>\r\n> \u2014\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2035#issuecomment-800081772>,\r\n> or unsubscribe\r\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMX6A2ZTRZUIIZVFRCDTD4NC3ANCNFSM4ZA5R2UA>\r\n> .\r\n>\r\n","body":"Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n```","comment_length":259,"text":"wiki40b\/wikipedia for almost all languages cannot be downloaded \n Hi\r\nI am trying to download the data as below:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\")\r\nprint(dataset)\r\n```\r\n\r\nI am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.\r\n\r\nI really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. \r\n\r\nthank you very much.\r\n\r\n```\r\n(fast) dara@vgne046:\/user\/dara\/dev\/codes\/seq2seq$ python test_data.py\r\nDownloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp\/dara\/cache_home_2\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nTraceback (most recent call last):\r\n  File \"test_data.py\", line 3, in <module>\r\n    dataset = load_dataset(\"wiki40b\", \"cs\")\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 579, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1105, in _download_and_prepare\r\n    import apache_beam as beam\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/__init__.py\", line 96, in <module>\r\n    from apache_beam import io\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/__init__.py\", line 23, in <module>\r\n    from apache_beam.io.avroio import *\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/apache_beam-2.28.0-py3.7-linux-x86_64.egg\/apache_beam\/io\/avroio.py\", line 55, in <module>\r\n    import avro\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 668, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 638, in _load_backward_compatible\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 34, in <module>\r\n  File \"\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/__init__.py\", line 30, in LoadResource\r\nNotADirectoryError: [Errno 20] Not a directory: '\/user\/dara\/libs\/anaconda3\/envs\/fast\/lib\/python3.7\/site-packages\/avro_python3-1.9.2.1-py3.7.egg\/avro\/VERSION.txt'\r\n``` \n Hi\r\nI honestly also now tried on another machine and nothing shows up after\r\nhours of waiting. Are you sure you have not set any specific setting? maybe\r\ngoogle cloud which seems it is used here, needs some credential setting?\r\nthanks for any suggestions on this\r\n\r\nOn Tue, Mar 16, 2021 at 10:02 AM Bhavitvya Malik ***@***.***>\r\nwrote:\r\n\r\n> I tried this on another machine (followed the same procedure I've\r\n> mentioned above). This is what it shows (during the freeze period) for me:\r\n>\r\n> >>> dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')\r\n> Downloading: 5.26kB [00:00, 1.23MB\/s]\r\n> Downloading: 1.40kB [00:00, 327kB\/s]\r\n> Downloading and preparing dataset wiki40b\/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bhavitvya\/.cache\/huggingface\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\n> WARNING:apache_beam.internal.gcp.auth:Unable to find default credentials to use: The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials. See https:\/\/developers.google.com\/accounts\/docs\/application-default-credentials for more information.\r\n> Connecting anonymously.\r\n> WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\r\n>\r\n> After around 10 minutes, here's the loading of dataset:\r\n>\r\n> 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:16<00:00, 16.42s\/sources]\r\n> 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.12sources\/s]\r\n> 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.14sources\/s]\r\n> Dataset wiki40b downloaded and prepared to \/home\/bhavitvya\/.cache\/huggingface\/datasets\/wiki40b\/cs\/1.1.0\/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f. Subsequent calls will reuse this data.\r\n>\r\n> \u2014\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/2035#issuecomment-800081772>,\r\n> or unsubscribe\r\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMX6A2ZTRZUIIZVFRCDTD4NC3ANCNFSM4ZA5R2UA>\r\n> .\r\n>\r\n","embeddings":[-0.2544980347,-0.0779204071,-0.1537582129,0.4308663607,0.3997030556,0.3504618704,0.1368506402,0.5331582427,0.1893276423,0.0235845186,-0.1773036569,-0.0942173079,0.0944036767,0.0111841084,-0.0297050718,-0.4591732025,-0.0672903061,0.0295370072,-0.1352714151,-0.1168936566,-0.2519630194,0.1256146282,-0.0791749284,-0.0490285978,-0.0387454666,-0.1828399897,-0.095835425,-0.2073058486,-0.3507466614,-0.240864411,0.3545782268,0.0192575995,0.3101119697,0.152997002,-0.0001114368,-0.0509645268,0.313116461,-0.2357945591,-0.2936119437,-0.2888573706,-0.0720896423,-0.3261856437,-0.0904391855,-0.420031637,0.1274462193,0.0466828533,0.080671154,-0.1318009049,0.0757578686,0.299225539,0.2468712181,-0.1426034421,0.313434124,-0.1457377374,0.3651962578,-0.3777080178,-0.0280337762,0.3395861685,0.2187208384,0.0713426769,0.2154791504,0.2220428586,-0.2341407239,0.0134992879,0.0482247174,-0.291223824,0.222205475,-0.6297016144,0.5206420422,0.5344284177,0.8290324211,-0.1283516139,-0.0874669701,0.0723921135,-0.0260593966,-0.031374447,-0.0557402261,0.4819797575,-0.1893264949,0.0975982994,0.1261651665,-0.1254302114,-0.1567192525,0.4228464365,-0.1241363883,0.4435888529,-0.0527197532,0.2112598121,-0.0216665938,-0.102701664,-0.2636416852,-0.0696592405,-0.0961601064,0.2933215201,-0.2793983221,0.0196358114,-0.1615019292,0.0556470677,0.3511354923,-0.1503831744,-0.2346212864,-0.0450777449,0.0960106328,0.1575745195,0.1801504046,0.1637832075,0.0620483644,-0.200006485,0.2782812417,0.3079036772,-0.0842119008,-0.0761709884,-0.2100430429,-0.2230561525,-0.2947570086,0.0944294706,0.0875293911,-0.2031713277,-0.1779466271,0.0071135005,-0.1443609297,-0.0194389597,-0.202394858,0.3628205657,-0.1501425803,0.4312385917,0.1779012084,0.3288222253,-0.1035727784,-0.2613099515,-0.0173523482,0.3065485656,-0.1393195242,-0.1036603302,0.2187829465,-0.1099155918,0.2648515999,-0.089371942,-0.1031381935,-0.1764115244,-0.1536911428,-0.3695675731,-0.1791034937,0.230408892,0.1619764566,0.2839277089,0.1476140618,-0.244747147,-0.2305807471,0.1207451969,-0.3596149981,-0.1681548953,-0.2051547766,0.1944684684,0.0610982925,-0.0546504743,-0.3160067499,0.2876641154,0.3030638993,-0.1423988342,0.1180728897,-0.0075459145,-0.0232539773,-0.225190118,0.1790115982,0.4908005595,-0.399959296,0.091897428,-0.0238504577,-0.0337120444,0.1897819787,0.1842971891,-0.1779527366,0.2565417886,-0.0978842974,0.0381600037,0.2101310194,-0.3214419484,-0.5776352286,0.186053887,0.113410078,-0.1670030951,0.0994797125,0.0160005782,0.2750306129,-0.0493875705,0.052454181,0.3956748247,0.1368381381,-0.1056447476,-0.3274506032,-0.5078234673,0.3264816701,0.1633179337,0.4547148943,-0.1322700828,0.2759852409,0.2917218208,0.3191742897,-0.1915477812,0.1246789992,0.2333606035,-0.0089483308,-0.0445753597,-0.002562576,-0.3310278654,-0.3097580373,0.1477600932,0.1029136032,0.1654645205,-0.1609553099,0.029683331,-0.3161896169,-0.0888073742,-0.1953119785,0.10326875,0.1995639354,-0.1052765027,0.1195931062,0.4854590893,0.1265164316,-0.0994593799,-0.4156903923,-0.0948526934,-0.17893444,0.0923319757,-0.2174285501,0.2044921666,0.0336807854,0.0258089639,0.1796839386,0.0730525404,-0.1607236117,-0.0110613015,-0.0784521401,-0.1035443768,0.1354628503,0.116963841,0.1331282407,-0.417375356,0.1920297742,0.1084797084,0.3297463953,-0.1357724667,0.1178161129,0.1222370416,0.0399733894,0.3655338883,0.1234308109,0.1498314887,0.4555997252,0.0305262543,0.1263544261,-0.0049747,0.3271189332,0.1556606591,0.0127488235,-0.1329593062,-0.02927793,-0.1135142446,0.3166727722,-0.0845338032,0.2671483159,0.2927592099,-0.0765190423,-0.004859813,-0.0978270099,0.1533981711,0.0522076748,0.0699049756,0.1065892354,-0.0998440012,0.359649688,-0.1293927282,0.2643093467,0.1755426973,0.1863330156,0.209135592,0.1350800842,-0.0111756697,-0.1882829219,-0.1296795756,0.0699935928,0.3717179298,0.0691837072,0.165945366,-0.0484345779,-0.1418599933,0.0841909274,-0.0523921847,-0.2509426475,-0.2163173407,-0.1256383955,-0.0857288986,0.091583252,-0.0001049751,-0.3266668916,-0.0721021518,0.0621662624,-0.3203505874,-0.1605682075,-0.2981233299,-0.3684496582,0.0767874196,0.3871294558,0.1112902835,0.2952094376,-0.3326290548,-0.1918789148,-0.0899458826,-0.2681111097,-0.0143140424,-0.1569705009,0.2413951159,-0.0241171494,0.7152194381,-0.1799962372,-0.251421392,0.1846487224,-0.0070200525,0.0658744872,0.2497714758,-0.1534786075,-0.0429239906,0.0845356733,-0.4856536686,-0.5155764222,-0.2696566582,0.0125194602,0.048517894,0.0878061801,0.1686406732,0.1924842894,0.0680263713,0.121187754,0.1809071153,-0.1900433302,-0.1518690884,0.4496890306,-0.0274131726,-0.4299626648,0.1881868094,0.0109940656,0.3149316907,0.0079274345,-0.6572358608,0.1441125572,-0.2763589025,0.2499436736,-0.0219936892,0.183476612,0.114343822,-0.2136970013,0.0479722433,0.0449956916,0.1059101671,-0.0406209044,-0.2095253319,0.2556217909,0.0299457368,0.2961854935,0.1644209623,0.6888408661,0.0548558161,0.1658407599,0.4367816746,0.2385823429,0.2310316861,-0.2314289063,-0.3645718992,0.0605744347,-0.2394915372,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2031","title":"wikipedia.py generator that extracts XML doesn't release memory","comments":"Hi @miyamonz \r\nThanks for investigating this issue, good job !\r\nIt would be awesome to integrate your fix in the library, could you open a pull request ?","body":"I tried downloading Japanese wikipedia, but it always failed because of out of memory maybe.\r\n\r\nI found that the generator function that extracts XML data in wikipedia.py doesn't release memory in the loop.\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/13a5b7db992ad5cf77895e4c0f76595314390418\/datasets\/wikipedia\/wikipedia.py#L464-L502\r\n\r\n`root.clear()` intend to clear memory, but it doesn't.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/13a5b7db992ad5cf77895e4c0f76595314390418\/datasets\/wikipedia\/wikipedia.py#L490\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/13a5b7db992ad5cf77895e4c0f76595314390418\/datasets\/wikipedia\/wikipedia.py#L494\r\nI replaced them with `elem.clear()`, then it seems to work correctly.\r\n\r\nhere is the notebook to reproduce it.\r\nhttps:\/\/gist.github.com\/miyamonz\/dc06117302b6e85fa51cbf46dde6bb51#file-xtract_content-ipynb","comment_length":28,"text":"wikipedia.py generator that extracts XML doesn't release memory \n I tried downloading Japanese wikipedia, but it always failed because of out of memory maybe.\r\n\r\nI found that the generator function that extracts XML data in wikipedia.py doesn't release memory in the loop.\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/13a5b7db992ad5cf77895e4c0f76595314390418\/datasets\/wikipedia\/wikipedia.py#L464-L502\r\n\r\n`root.clear()` intend to clear memory, but it doesn't.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/13a5b7db992ad5cf77895e4c0f76595314390418\/datasets\/wikipedia\/wikipedia.py#L490\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/13a5b7db992ad5cf77895e4c0f76595314390418\/datasets\/wikipedia\/wikipedia.py#L494\r\nI replaced them with `elem.clear()`, then it seems to work correctly.\r\n\r\nhere is the notebook to reproduce it.\r\nhttps:\/\/gist.github.com\/miyamonz\/dc06117302b6e85fa51cbf46dde6bb51#file-xtract_content-ipynb \n Hi @miyamonz \r\nThanks for investigating this issue, good job !\r\nIt would be awesome to integrate your fix in the library, could you open a pull request ?","embeddings":[0.218652904,-0.1109624952,-0.0523667894,0.6246852279,0.3160352409,0.0942655355,-0.2117367834,0.3332336545,0.2093814313,0.2579506338,0.0060537024,0.1517625153,0.2327899486,-0.1265527904,-0.2175300568,-0.2103440166,0.0437293872,0.0743473619,0.0371215492,-0.2225494236,-0.2259665877,0.1241694018,-0.2827935219,-0.12147104,-0.1757008135,0.1507509947,0.0834618583,-0.1925340891,-0.1768444926,-0.3868060112,0.142742753,0.0021264469,-0.1252807826,0.2270927429,-0.0001113045,-0.1786878556,0.5870485306,-0.0175461024,-0.1438791752,0.1073924974,-0.2037125081,-0.0739126429,-0.1006892473,-0.2878377438,0.1415049881,-0.0300183408,0.1694545895,-0.2743773758,0.3588359654,-0.0555356257,0.2296685129,-0.0995117724,0.4134443104,-0.0468071289,0.7186948657,0.2662892342,-0.0335875936,0.029499257,0.1483324766,-0.1202112958,0.0261039324,0.404242605,-0.1569174826,0.0165875256,0.2526498139,-0.1493367255,0.0439883471,-0.5066655278,0.462076813,0.3427121341,0.5755248666,-0.149921447,0.1062823087,0.0310698599,-0.103971675,-0.1315815896,0.2247127742,0.4001595676,-0.6533283591,-0.0683356151,0.310906291,-0.4820676148,-0.1790922135,0.2063481659,-0.0007484592,0.3183763027,-0.0073742974,0.0634122789,0.2590226829,-0.0018349971,-0.1167000756,-0.0434845239,-0.0742314756,0.2187654525,0.0305326488,0.1633389443,0.1844055355,-0.1822960973,0.2779972553,-0.4200806022,-0.4094339311,0.0066289743,0.2780645192,-0.0407775752,0.3894085586,0.0270341001,-0.0170564633,0.1133421734,0.3600495756,0.2115766406,-0.349632591,-0.0846013352,0.130389154,0.0520469174,0.0831234306,-0.1065111905,0.0191854648,-0.0676592961,0.1701021045,0.2386854142,-0.2658776343,-0.0708776414,-0.2080523819,0.4120154381,-0.0438275561,0.372759521,0.1869971752,0.0853901058,-0.4100779891,-0.311619401,-0.1378388852,0.2522204816,-0.2821925879,0.0472872071,0.0010046506,-0.1443132162,0.2116989046,0.1731064916,-0.1932032406,-0.4035503566,0.0540767089,-0.1906531304,0.2073850334,0.2648632824,0.0696727708,0.2217064202,-0.1199448183,-0.0872631669,-0.0428388752,0.2829099,-0.0179263931,-0.0642531589,0.000170208,0.2416815013,0.0659806058,0.1953769028,0.0082763946,-0.1624775529,0.5027115345,0.1204016134,0.0157041252,0.0061148582,-0.0120037021,-0.2563004196,0.1782667786,0.4500468075,0.115413323,0.1249703392,-0.1744931787,0.133442983,0.4826317132,0.1633789986,0.0041837408,0.4243835807,-0.1227871999,0.2591704428,-0.0239813644,-0.2639952004,-0.3197956979,-0.0183573831,0.1209944561,0.2170036286,-0.0394850187,0.027487237,0.1240423471,0.3383145034,0.0136304786,0.2789423168,0.0746846274,0.1292003542,-0.4539269507,-0.227173686,0.2193467021,-0.3085442483,0.3155327439,-0.0395956486,0.1276439577,0.4033349752,0.6220804453,-0.2563405931,0.3680494428,0.1581970751,0.3149207532,0.0550059788,0.0325528122,-0.1054977551,-0.121082373,0.0815544575,-0.2072693706,0.3825250268,-0.0645792186,-0.1309960932,-0.1074616387,0.0873796344,-0.2546545267,-0.3456029594,0.2049910873,0.1016071886,0.3502486348,0.2771036327,0.131781891,-0.4485380948,-0.1533707082,0.1642220616,-0.7328389287,0.0174813066,-0.164858073,-0.085667558,-0.1884648651,0.0739084408,-0.074687399,0.0146213006,-0.1174387038,-0.1440369487,0.2105429918,0.1954229027,-0.2769904435,-0.0807574838,0.2259412706,-0.4512328207,0.1940934062,0.4587644041,0.1753469408,-0.0962863714,0.1605661511,-0.1155879945,-0.0212012045,0.045373708,0.1233118102,0.0445288047,0.2474220991,0.1230115145,0.159300819,-0.1362757236,0.1121829897,0.5882779956,-0.0906394497,0.0201329291,-0.1109917685,-0.052671887,0.448946476,0.2313284576,0.1596060991,0.0479361117,-0.3074405491,-0.3058307171,0.0191244688,0.0920182243,-0.1586149633,-0.0036709667,0.1324584484,-0.0507996678,0.3378245831,-0.2958723903,0.4203760922,-0.0014915981,0.1611881405,0.2169839293,0.1296709478,-0.0276828781,-0.3015906215,0.2943036258,-0.0072424943,0.3758419454,0.1096902117,-0.2038994581,0.0588484295,-0.4482650757,0.0137910014,-0.1235477105,-0.2024699301,-0.3298225403,0.1109552011,0.2990667522,-0.2745769322,-0.1786494851,0.0398271568,-0.2857567668,0.0882399678,-0.0641841069,-0.1608580053,-0.3678180277,-0.1747340411,0.0267358031,0.2860378325,-0.0619838387,0.0603972748,0.0625112429,-0.2241583019,-0.2190746814,-0.1240182966,0.2660339773,-0.0900258049,0.0177640691,-0.2295581549,0.4114502966,0.1463361233,-0.1430453509,0.2513380051,-0.3257459104,0.015834108,0.2487401962,0.1499990225,0.1744736135,-0.1233965084,-0.4725928009,0.095373489,-0.4844042063,0.216541484,-0.071549505,0.1014145464,0.6841795444,0.0560019203,-0.0651491731,-0.0548735559,0.1761159748,-0.3081732094,-0.1929737628,0.2051042914,-0.1673519909,-0.3377017677,0.0364821218,-0.0153450109,0.3164588213,0.3823059201,-0.2814798951,0.281981945,-0.1632010192,0.3446781039,0.0712391883,0.2762082517,0.1284642816,-0.0342609622,-0.0964972302,0.0976973027,0.4960945249,-0.0526958145,-0.0706965923,0.0776841268,0.1368727386,0.0494218767,0.0495294519,0.4761870503,0.1436742991,0.5873883367,0.3695218265,0.117123574,0.0994822308,-0.0463386811,-0.1920556873,0.007617882,-0.2097374946,-0.2204042077,0.14378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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2029","title":"Loading a faiss index KeyError","comments":"In your code `dataset2` doesn't contain the \"embeddings\" column, since it is created from the pandas DataFrame with columns \"text\" and \"label\".\r\n\r\nTherefore when you call `dataset2[embeddings_name]`, you get a `KeyError`.\r\n\r\nIf you want the \"embeddings\" column back, you can create `dataset2` with\r\n```python\r\ndataset2 = load_from_disk(dataset_filename)\r\n```\r\nwhere `dataset_filename` is the place where you saved you dataset with the embeddings in the first place.","body":"I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.\r\n\r\nThe basic steps are:\r\n\r\n1. Create a dataset (dataset1)\r\n2. Create an embeddings column using DPR\r\n3. Add a faiss index to the dataset\r\n4. Save faiss index to a file\r\n5. Create a new dataset (dataset2) with the same text and label information as dataset1\r\n6. Try to load the faiss index from file to dataset2\r\n7. Get `KeyError: \"Column embeddings not in the dataset\"`\r\n\r\nI've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing\r\n\r\nUbuntu Version\r\nVERSION=\"18.04.5 LTS (Bionic Beaver)\"\r\n\r\ndatasets==1.4.1\r\nfaiss==1.5.3\r\nfaiss-gpu==1.7.0\r\ntorch==1.8.0+cu101\r\ntransformers==4.3.3\r\n\r\nNVIDIA-SMI 460.56\r\nDriver Version: 460.32.03\r\nCUDA Version: 11.2    \r\nTesla K80           \r\n\r\nI was basically following the steps here: https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index\r\n\r\nI included the exact code from the documentation at the end of the notebook to show that they don't work either.\r\n","comment_length":65,"text":"Loading a faiss index KeyError \n I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.\r\n\r\nThe basic steps are:\r\n\r\n1. Create a dataset (dataset1)\r\n2. Create an embeddings column using DPR\r\n3. Add a faiss index to the dataset\r\n4. Save faiss index to a file\r\n5. Create a new dataset (dataset2) with the same text and label information as dataset1\r\n6. Try to load the faiss index from file to dataset2\r\n7. Get `KeyError: \"Column embeddings not in the dataset\"`\r\n\r\nI've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing\r\n\r\nUbuntu Version\r\nVERSION=\"18.04.5 LTS (Bionic Beaver)\"\r\n\r\ndatasets==1.4.1\r\nfaiss==1.5.3\r\nfaiss-gpu==1.7.0\r\ntorch==1.8.0+cu101\r\ntransformers==4.3.3\r\n\r\nNVIDIA-SMI 460.56\r\nDriver Version: 460.32.03\r\nCUDA Version: 11.2    \r\nTesla K80           \r\n\r\nI was basically following the steps here: https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index\r\n\r\nI included the exact code from the documentation at the end of the notebook to show that they don't work either.\r\n \n In your code `dataset2` doesn't contain the \"embeddings\" column, since it is created from the pandas DataFrame with columns \"text\" and \"label\".\r\n\r\nTherefore when you call `dataset2[embeddings_name]`, you get a `KeyError`.\r\n\r\nIf you want the \"embeddings\" column back, you can create `dataset2` with\r\n```python\r\ndataset2 = load_from_disk(dataset_filename)\r\n```\r\nwhere `dataset_filename` is the place where you saved you dataset with the embeddings in the first place.","embeddings":[0.0550739542,-0.6190827489,0.0702500269,0.3613130152,0.1554000527,0.2710704207,0.3453339934,0.0510576218,0.5391324162,0.2844024301,-0.0778134242,0.1564425826,0.4091768861,-0.0636667162,-0.0600452609,-0.0365858525,0.2299646586,0.2709702551,0.2771186829,-0.1172366887,-0.2132829577,0.1254137605,-0.2415112406,0.239650771,-0.2419934869,0.1532631963,-0.2360691279,0.020749405,-0.1963155121,-0.4184430838,0.321844846,-0.2817219794,0.6558502913,0.1962961704,-0.0001232087,0.0766335726,0.3582345247,-0.1224535927,-0.2154051512,0.0617960207,0.1387543678,0.0691665038,0.2078349441,-0.0417269841,-0.0147014875,-0.6515750289,0.0266365558,-0.1597283334,0.2974438667,-0.0055642687,0.0943733007,0.0098311165,0.0809608251,-0.3012819588,0.0691149384,-0.3261920512,-0.0540437624,0.2427324355,0.0452901497,0.0076503479,0.0668617189,0.29880476,-0.0401020646,0.0219106451,0.4202647209,0.2766120732,0.3658125699,-0.1659394056,0.1298123002,0.1871755868,0.3916547894,-0.1082114428,-0.4308947623,-0.1739238501,0.2771785855,-0.1161144003,0.2110203952,-0.3297830224,-0.0157120544,0.2792980671,0.4298000336,-0.243448332,0.1159293428,0.0830414221,-0.0895649642,-0.0029288968,-0.1924940199,0.0390617587,0.1796053648,-0.1281749308,-0.307997942,-0.1144421324,0.1000975594,0.1209597439,-0.3051662743,0.1096482873,0.1938894242,0.0081687309,-0.1355560273,0.0080531584,-0.2509580255,-0.0373694599,0.0572971366,0.379947722,-0.164004311,0.0147695262,0.090032205,0.0611759201,0.0604663938,0.2328324318,0.0334695764,-0.2108221501,-0.1764001697,-0.1549174488,-0.4953107834,-0.3186217248,-0.0450263172,-0.3158306479,-0.4681292772,0.3127001822,-0.4021078646,-0.1081995666,-0.0031759485,0.575340271,0.2059751302,-0.1731270552,0.2817462683,0.3410767615,-0.2425622195,0.2558035553,-0.1618468463,0.1206276342,-0.1269558668,0.374479413,0.0527705029,-0.4389058053,0.2052887082,0.0444829464,0.2107004672,0.102874659,-0.4216088951,0.0001691421,0.2830238938,0.2812859416,-0.0830779523,0.0920130461,0.3403278589,-0.3423228264,-0.1909656823,0.0451865792,-0.1148211733,-0.0747320205,-0.178488344,0.0911493599,-0.0704007819,-0.089724198,0.174400419,0.0561904423,0.1586268246,-0.2581044436,0.0801398531,0.025198523,-0.1384505183,-0.1799932271,0.4784166813,0.3956215382,-0.7722764015,-0.3988278806,0.1143974215,0.16897735,-0.1344381273,0.2855871916,-0.0892437398,0.0623679198,-0.2319801748,0.4219353795,0.1115486994,-0.1278712451,-0.1927447021,0.0685717985,0.0679323524,0.0048264419,0.2271308601,-0.1617614329,0.3247577846,0.1531335115,0.3888324201,0.071337685,0.0399592631,-0.3257410824,-0.1789891124,-0.3758673668,0.0991174653,0.1026760042,-0.0961196348,-0.0742361397,-0.1836107671,-0.3227061033,0.0883146152,-0.2380028069,-0.0485619232,0.0400439128,0.3023085296,0.4282283783,0.4427762628,0.0368306786,-0.0115634566,0.2662013471,0.0324873514,0.0951553211,-0.2985555232,-0.0519876368,-0.2356908619,0.0464012995,-0.0661984682,-0.20133847,-0.0659089163,-0.009110312,-0.0246781707,0.0475275218,-0.3220712543,0.0777285993,-0.1947692037,0.2195265889,-0.4618268013,0.4788576961,-0.2620447278,-0.2415874451,0.0462658219,0.3040081859,0.186638236,-0.1725841314,0.1663331091,0.094939366,-0.0937456936,-0.1992226392,0.1190167964,-0.1917669028,0.159357354,-0.1571003348,0.0574439988,-0.0669224784,0.192354843,0.1220769957,-0.1792988032,0.4870289862,0.0392018817,0.2792657614,-0.1853858978,-0.1539820135,0.1131258532,-0.0026398764,0.0101032462,-0.3382643461,-0.0889248773,-0.0046553123,-0.1199177504,-0.0626572818,0.0268098097,0.1231286377,0.0174136776,0.0285699107,-0.1038769111,0.053535033,-0.3347164094,0.0676233172,0.0471458174,-0.3681941032,0.3542415798,0.289367795,-0.1669884175,-0.246678561,-0.2466314435,-0.2522492409,0.1304650158,-0.0527902134,-0.2380274534,0.0619876496,-0.1329466254,0.0930444598,-0.2039635777,-0.0393128879,0.007351818,0.1990103424,-0.5885115862,0.0874701738,-0.3268994689,-0.0848519132,-0.0089322384,-0.1183215305,-0.0350329727,-0.3714100122,0.1818681508,-0.1842870414,-0.0339048915,0.2288198322,0.0108241513,0.1986064464,-0.0158729255,0.0349431261,-0.1994726807,-0.1922690272,-0.2766529322,-0.0750846788,-0.0261261296,0.2121435702,0.2464039624,-0.0174932294,-0.1035188213,-0.1036008671,-0.2901809812,0.2608532906,-0.2184689045,0.0960317403,-0.1443014145,0.1334666908,-0.1150142848,-0.2030875385,0.3162400126,-0.0712855309,-0.2233364731,-0.0748690814,0.1445922405,-0.0172571,-0.1171412542,-0.3439745307,0.0865703896,-0.2017683089,-0.0519000329,-0.1715556085,-0.0757853314,-0.3403173089,0.388887614,0.1098029464,-0.1206581891,0.0835879892,-0.2783555686,0.0760336444,0.3620343804,-0.0495057367,-0.229421407,0.16086182,0.0534705631,-0.2114243507,-0.073970668,-0.3018965423,-0.5255084038,-0.0137849767,0.2082932889,0.2294152379,0.4104632437,0.1461436599,-0.0166889336,-0.0821893066,-0.0818065554,-0.3491121233,0.0339904241,-0.1274582595,0.1863522828,-0.1580067128,0.5168377757,-0.2627623081,0.4241446257,0.4182263613,-0.3344832957,0.401863575,-0.1026427299,0.4811010957,-0.1938903332,-0.1900715828,0.1305034757,0.052109967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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2029","title":"Loading a faiss index KeyError","comments":"Ok in that case HF should fix their misleading example at https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index \r\n\r\nI copy-pasted it here.\r\n\r\n> When you are done with your queries you can save your index on disk:\r\n> \r\n> ```python\r\n> ds_with_embeddings.save_faiss_index('embeddings', 'my_index.faiss')\r\n> ```\r\n> Then reload it later:\r\n> \r\n> ```python\r\n> ds = load_dataset('crime_and_punish', split='train[:100]')\r\n> ds.load_faiss_index('embeddings', 'my_index.faiss')\r\n> ```","body":"I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.\r\n\r\nThe basic steps are:\r\n\r\n1. Create a dataset (dataset1)\r\n2. Create an embeddings column using DPR\r\n3. Add a faiss index to the dataset\r\n4. Save faiss index to a file\r\n5. Create a new dataset (dataset2) with the same text and label information as dataset1\r\n6. Try to load the faiss index from file to dataset2\r\n7. Get `KeyError: \"Column embeddings not in the dataset\"`\r\n\r\nI've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing\r\n\r\nUbuntu Version\r\nVERSION=\"18.04.5 LTS (Bionic Beaver)\"\r\n\r\ndatasets==1.4.1\r\nfaiss==1.5.3\r\nfaiss-gpu==1.7.0\r\ntorch==1.8.0+cu101\r\ntransformers==4.3.3\r\n\r\nNVIDIA-SMI 460.56\r\nDriver Version: 460.32.03\r\nCUDA Version: 11.2    \r\nTesla K80           \r\n\r\nI was basically following the steps here: https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index\r\n\r\nI included the exact code from the documentation at the end of the notebook to show that they don't work either.\r\n","comment_length":57,"text":"Loading a faiss index KeyError \n I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.\r\n\r\nThe basic steps are:\r\n\r\n1. Create a dataset (dataset1)\r\n2. Create an embeddings column using DPR\r\n3. Add a faiss index to the dataset\r\n4. Save faiss index to a file\r\n5. Create a new dataset (dataset2) with the same text and label information as dataset1\r\n6. Try to load the faiss index from file to dataset2\r\n7. Get `KeyError: \"Column embeddings not in the dataset\"`\r\n\r\nI've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing\r\n\r\nUbuntu Version\r\nVERSION=\"18.04.5 LTS (Bionic Beaver)\"\r\n\r\ndatasets==1.4.1\r\nfaiss==1.5.3\r\nfaiss-gpu==1.7.0\r\ntorch==1.8.0+cu101\r\ntransformers==4.3.3\r\n\r\nNVIDIA-SMI 460.56\r\nDriver Version: 460.32.03\r\nCUDA Version: 11.2    \r\nTesla K80           \r\n\r\nI was basically following the steps here: https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index\r\n\r\nI included the exact code from the documentation at the end of the notebook to show that they don't work either.\r\n \n Ok in that case HF should fix their misleading example at https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index \r\n\r\nI copy-pasted it here.\r\n\r\n> When you are done with your queries you can save your index on disk:\r\n> \r\n> ```python\r\n> ds_with_embeddings.save_faiss_index('embeddings', 'my_index.faiss')\r\n> ```\r\n> Then reload it later:\r\n> \r\n> ```python\r\n> ds = load_dataset('crime_and_punish', split='train[:100]')\r\n> ds.load_faiss_index('embeddings', 'my_index.faiss')\r\n> ```","embeddings":[0.0172792971,-0.5772305727,0.0631230474,0.279512614,0.0801492929,0.2722066641,0.3179306686,0.1032484248,0.5430009365,0.2702875733,-0.1154732332,0.1119506285,0.4107095003,-0.1057186723,-0.0582559854,-0.0193880815,0.2288171202,0.2564585805,0.2251714319,-0.133942306,-0.2051008195,0.0973475128,-0.2735082805,0.2104548216,-0.2178766876,0.1518886089,-0.2278769314,0.0568944402,-0.1768641174,-0.4319815338,0.3245584965,-0.2239339501,0.6598060727,0.1977226138,-0.0001215702,0.1218788475,0.3807426989,-0.1595098823,-0.2513687015,0.1151010543,0.0764162019,0.0272239614,0.2132130265,-0.0726772174,0.0061008078,-0.6095624566,0.0533289872,-0.1794200838,0.2412755638,0.0245589074,0.1126011312,0.0576791242,0.0422044955,-0.3755269051,0.0992318392,-0.3127266467,-0.0681922734,0.2848208547,-0.0071604131,0.0023000252,0.0063478462,0.3573625684,-0.0200696494,0.019340964,0.40046224,0.2284757048,0.3043691218,-0.104426235,0.1567376405,0.1700628698,0.3159224689,-0.154877454,-0.3962084949,-0.1216680035,0.1943065971,-0.126398623,0.1970370412,-0.3063472509,0.0235137101,0.3119217753,0.3952243328,-0.1900372207,0.1177399307,0.0820095688,-0.0645475909,-0.0513839945,-0.210855037,0.0240792986,0.1902793646,-0.0865309462,-0.282009393,-0.1559734195,0.0747636557,0.082327269,-0.3504092395,0.1680515409,0.2065456957,0.0209433213,-0.0723240972,-0.0248584077,-0.2064311802,0.0152599523,0.1569410264,0.3611029983,-0.2030455619,-0.017811818,0.0448900573,0.0191625301,0.1014064252,0.2769632638,-0.0010159569,-0.2169072479,-0.1131690145,-0.1501648128,-0.4902278185,-0.3367262781,-0.0414405912,-0.348993659,-0.4786964655,0.3067212999,-0.4552730024,-0.130434528,0.018645715,0.6528446674,0.1860147119,-0.2571901679,0.252235204,0.3296818435,-0.3448681235,0.2203894705,-0.1981097162,0.1131346524,-0.1160796732,0.4127857387,0.1097563803,-0.4600390792,0.1754471064,0.0120851984,0.2221800536,0.0842839256,-0.3430346251,-0.0307907071,0.3162079155,0.2977688015,-0.0658048019,0.0719670281,0.3783288896,-0.3262055516,-0.2240067273,0.0465687253,-0.126117155,-0.1155303195,-0.1683064103,0.114999406,-0.0447194502,-0.0695156902,0.235982269,0.0991610661,0.1431575119,-0.2634614706,0.0815166011,0.0393043198,-0.1817218661,-0.1781526506,0.4668316245,0.4261373878,-0.7533151507,-0.3583073318,0.1044595167,0.224639833,-0.1304668635,0.2801527381,-0.1336553246,0.0512032956,-0.2663244009,0.4776728451,0.160535723,-0.1500926614,-0.2101146579,0.1152872294,0.1037184149,0.0163553115,0.1906678379,-0.1538437605,0.3397403955,0.1171113402,0.3372926116,-0.0242131148,0.0364694707,-0.3472739458,-0.2551965415,-0.3373095393,0.0823269412,0.0959324986,-0.0580283813,-0.1127974764,-0.1097772121,-0.3149940968,0.1116435379,-0.2194283158,-0.0689909086,0.1341983676,0.2931415141,0.39780882,0.4509916306,0.0653036982,-0.0793156028,0.2604404688,0.0285848882,0.0608433485,-0.1846120656,-0.1017718986,-0.2828640342,0.0477874689,-0.1088200212,-0.2191662639,-0.0282452758,-0.0028138554,-0.0579029992,0.0574611612,-0.3144201934,0.1071905494,-0.2037627101,0.1530472189,-0.4385951161,0.4663409591,-0.3173539937,-0.2310287952,0.0460032001,0.2543338537,0.1745008379,-0.1636387557,0.1295262575,0.1026977822,-0.0566196516,-0.2618732154,0.1746410429,-0.1655095518,0.1427622139,-0.2291909605,0.0832302794,-0.0419446938,0.2045514137,0.1157635376,-0.2281857133,0.4575839937,0.052060809,0.2677704096,-0.147359997,-0.2205034792,0.1386884302,0.0298014004,-0.0034481036,-0.3238343894,-0.1025569364,0.0377517603,-0.1164529398,-0.0653850213,0.0181211494,0.1922570169,0.0198896974,0.0316699035,-0.1030548885,0.0811387077,-0.2599281073,0.0253882762,0.0923055783,-0.4249454141,0.3604758382,0.2861751616,-0.1327142715,-0.2303947806,-0.2657188177,-0.2874986827,0.067374818,-0.0804419145,-0.2273133397,0.1274018884,-0.0742557272,0.0862707794,-0.2223926038,-0.0440521315,0.0122665884,0.14903754,-0.5887740254,0.1396591216,-0.3880037963,-0.0942457318,0.029119445,-0.1383638084,-0.0869042873,-0.38944754,0.2312373668,-0.1868146658,-0.0542590059,0.2351478785,-0.0808565244,0.182911247,0.0158675853,-0.0098379692,-0.1690037996,-0.1615710407,-0.3648664951,-0.0854422674,-0.0241525564,0.2970384359,0.2358442545,-0.0316454992,-0.1209298298,-0.1339874715,-0.3524387479,0.2762563825,-0.143265456,0.1291477531,-0.1065488905,0.0773718283,-0.1703564078,-0.1709821969,0.3147136867,-0.0222135,-0.1975483745,-0.0149076078,0.1242613047,0.0355618633,-0.0651528388,-0.3328302205,0.1047071815,-0.2776853442,-0.1136709005,-0.1886593103,-0.0535951145,-0.3046745062,0.3891735375,0.1195937842,-0.1083313227,0.0964702517,-0.2930319011,0.1472296715,0.3678099513,-0.1009715348,-0.2994407117,0.1863138974,0.0289020743,-0.2174456269,-0.0439000092,-0.3964253068,-0.4838353395,-0.0262634344,0.223999843,0.2083174735,0.3773666024,0.1585734785,-0.0878758579,-0.0857687965,-0.0988790467,-0.3516177237,0.0867616683,-0.1840332896,0.1607315242,-0.1917382479,0.5027089119,-0.2363250405,0.4415203929,0.4734849036,-0.3138492107,0.4359805882,-0.0560271367,0.502671957,-0.2558838427,-0.1605994105,0.1289680749,0.0415621363,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2029","title":"Loading a faiss index KeyError","comments":"Hi !\r\n\r\nThe code of the example is valid.\r\nAn index is a search engine, it's not considered a column of a dataset.\r\nWhen you do `ds.load_faiss_index(\"embeddings\", 'my_index.faiss')`, it attaches an index named \"embeddings\" to the dataset but it doesn't re-add the \"embeddings\" column. You can list the indexes of a dataset by using `ds.list_indexes()`.\r\n\r\nIf I understand correctly by reading this example you thought that it was re-adding the \"embeddings\" column.\r\nThis looks misleading indeed, and we should add a note to make it more explicit that it doesn't store the column that was used to build the index.\r\n\r\nFeel free to open a PR to suggest an improvement on the documentation if you want to contribute :)","body":"I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.\r\n\r\nThe basic steps are:\r\n\r\n1. Create a dataset (dataset1)\r\n2. Create an embeddings column using DPR\r\n3. Add a faiss index to the dataset\r\n4. Save faiss index to a file\r\n5. Create a new dataset (dataset2) with the same text and label information as dataset1\r\n6. Try to load the faiss index from file to dataset2\r\n7. Get `KeyError: \"Column embeddings not in the dataset\"`\r\n\r\nI've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing\r\n\r\nUbuntu Version\r\nVERSION=\"18.04.5 LTS (Bionic Beaver)\"\r\n\r\ndatasets==1.4.1\r\nfaiss==1.5.3\r\nfaiss-gpu==1.7.0\r\ntorch==1.8.0+cu101\r\ntransformers==4.3.3\r\n\r\nNVIDIA-SMI 460.56\r\nDriver Version: 460.32.03\r\nCUDA Version: 11.2    \r\nTesla K80           \r\n\r\nI was basically following the steps here: https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index\r\n\r\nI included the exact code from the documentation at the end of the notebook to show that they don't work either.\r\n","comment_length":119,"text":"Loading a faiss index KeyError \n I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.\r\n\r\nThe basic steps are:\r\n\r\n1. Create a dataset (dataset1)\r\n2. Create an embeddings column using DPR\r\n3. Add a faiss index to the dataset\r\n4. Save faiss index to a file\r\n5. Create a new dataset (dataset2) with the same text and label information as dataset1\r\n6. Try to load the faiss index from file to dataset2\r\n7. Get `KeyError: \"Column embeddings not in the dataset\"`\r\n\r\nI've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing\r\n\r\nUbuntu Version\r\nVERSION=\"18.04.5 LTS (Bionic Beaver)\"\r\n\r\ndatasets==1.4.1\r\nfaiss==1.5.3\r\nfaiss-gpu==1.7.0\r\ntorch==1.8.0+cu101\r\ntransformers==4.3.3\r\n\r\nNVIDIA-SMI 460.56\r\nDriver Version: 460.32.03\r\nCUDA Version: 11.2    \r\nTesla K80           \r\n\r\nI was basically following the steps here: https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index\r\n\r\nI included the exact code from the documentation at the end of the notebook to show that they don't work either.\r\n \n Hi !\r\n\r\nThe code of the example is valid.\r\nAn index is a search engine, it's not considered a column of a dataset.\r\nWhen you do `ds.load_faiss_index(\"embeddings\", 'my_index.faiss')`, it attaches an index named \"embeddings\" to the dataset but it doesn't re-add the \"embeddings\" column. You can list the indexes of a dataset by using `ds.list_indexes()`.\r\n\r\nIf I understand correctly by reading this example you thought that it was re-adding the \"embeddings\" column.\r\nThis looks misleading indeed, and we should add a note to make it more explicit that it doesn't store the column that was used to build the index.\r\n\r\nFeel free to open a PR to suggest an improvement on the documentation if you want to contribute :)","embeddings":[0.139708057,-0.5335465074,0.0514714457,0.3196974695,0.1264748573,0.2750567496,0.4119959772,-0.0404785536,0.6586285233,0.2073548138,-0.0624137037,0.153858155,0.3866050839,-0.0489014611,0.0130298138,-0.0173613261,0.3034028709,0.2208625972,0.2414616048,-0.1565983891,-0.1813698411,0.0717174187,-0.2770503163,0.1112730652,-0.1968314648,0.1754428148,-0.2499136031,-0.0327954814,-0.2255384624,-0.3927151859,0.3298938274,-0.2455042154,0.6396087408,0.1909313798,-0.0001244103,0.1136664003,0.3821778893,-0.1372379363,-0.2595387101,0.1243394017,-0.0407922491,0.0283891633,0.2259729803,-0.0741742477,0.0443998501,-0.6045323014,0.0306181759,-0.2664104402,0.2041671723,0.0784025118,0.0811231956,0.0266717058,0.0594932884,-0.3501586318,0.1453064531,-0.29447034,-0.1072397977,0.2723374963,0.0363665484,0.0591787286,0.0967705175,0.2234830856,-0.0297007617,-0.0044607348,0.3816767037,0.3206753731,0.3233527839,-0.1499414593,0.1832972765,0.1396442652,0.4778426588,-0.0867141932,-0.3786843717,-0.1274588257,0.2133221924,-0.1693466753,0.1781800389,-0.3018350303,0.0494873561,0.2833381593,0.3947203457,-0.2305176258,0.1729187667,0.1110191867,-0.1058646441,0.0296114553,-0.2333698124,0.0812688395,0.1254786402,-0.0815552846,-0.2309127748,-0.1110212654,0.1203840896,0.1144591793,-0.3478138745,0.2244480401,0.210499987,0.0192310531,-0.1170734689,0.0355053283,-0.1175126806,0.0451515131,0.0825698897,0.3602949083,-0.161278978,0.0425950997,0.0905499384,-0.0014759696,0.0315709524,0.2060628831,0.0346700773,-0.2354734838,-0.1273182929,-0.0511647947,-0.4601175785,-0.3972397447,-0.0220848061,-0.293967247,-0.4682563245,0.3269699216,-0.3949016035,-0.1495504081,0.0359078683,0.6100538373,0.2423648089,-0.2410033941,0.2574202418,0.3329061866,-0.2494074851,0.2505556643,-0.2155791223,0.100160785,-0.179163456,0.4160974026,0.0883165598,-0.4362990558,0.2058899403,-0.0156852864,0.1153184846,0.0160861574,-0.4422804117,-0.0107994024,0.3289083242,0.3100945652,-0.11815501,0.06469886,0.2906962931,-0.3939503729,-0.1781438142,0.0867635831,-0.1132606044,-0.0243502967,-0.1908863187,0.0711474419,-0.1113985702,-0.1381538957,0.2029106319,0.1236761436,0.1540800482,-0.2610228956,0.1042369753,0.0324439295,-0.1694502234,-0.1880511492,0.5441301465,0.390566051,-0.754373908,-0.3546563387,0.0210219808,0.1953683048,-0.0898331255,0.2088247091,-0.0703034028,0.1384410709,-0.2801352143,0.4364034235,0.2500101924,-0.0299273785,-0.1367249489,-0.0278813988,0.0822835788,0.011289943,0.1462400109,-0.04641141,0.3089675605,0.2303739935,0.2861495614,0.0061393036,0.0597964413,-0.3615947366,-0.1948387325,-0.2727174759,0.1160120293,0.0804308429,-0.0372421294,-0.0280089863,-0.0962934196,-0.3618800044,0.0644499883,-0.1609582454,-0.0479053222,0.0518079177,0.319955796,0.42494753,0.4439717829,0.0635534972,-0.1428378075,0.2731236815,0.1668573618,0.0024764196,-0.1946393996,-0.0410471819,-0.3104975522,0.1002364233,-0.0861708149,-0.1901261508,-0.0936026499,0.0434049889,-0.0393041335,0.0348566957,-0.3543615341,0.0917701945,-0.1389931142,0.1869313419,-0.4618312716,0.4687990248,-0.244671911,-0.2399786562,-0.0660559684,0.2928885221,0.0953243077,-0.23050116,0.1591469049,0.1184905693,0.0150072752,-0.2839363813,0.1743018031,-0.175525561,0.1507220715,-0.212949276,0.1087340266,-0.0652895123,0.256676048,0.0690950677,-0.239273876,0.4237160385,0.0181186292,0.2957782745,-0.1987942308,-0.21147798,0.1270289868,0.041851487,-0.0545729175,-0.301696986,-0.1867242754,-0.0259514283,-0.1224479675,-0.0458274446,0.0013797299,0.1479832083,-0.0317740999,0.0533249378,-0.0762059763,0.0467843376,-0.3594025671,0.0175261647,0.0633010194,-0.4014998972,0.4099878669,0.267688185,-0.042484086,-0.2847337723,-0.158041507,-0.292766422,0.0362489186,-0.0372612216,-0.2339008451,0.1330188066,-0.0564494058,0.0249525327,-0.2709622383,0.0384881869,0.1010742933,0.1226191595,-0.5553826094,0.0918593928,-0.3217408955,-0.0228082351,0.0247970279,-0.1072799042,-0.0440434925,-0.3912600279,0.1531036347,-0.1146237552,-0.0169351585,0.2143235952,-0.0619997643,0.218171373,-0.0247203466,-0.0401436351,-0.187653169,-0.2124548405,-0.3034582734,-0.0988075808,-0.0032459972,0.295403868,0.2241867334,-0.0202666204,-0.07088162,-0.1617212147,-0.3294917643,0.2559995651,-0.1673352122,0.1325889826,-0.1250998527,0.1196718812,-0.1969907135,-0.1260314584,0.2635339797,-0.0487390421,-0.1794922203,-0.0537561104,0.1822658777,0.0249118358,-0.1008231267,-0.4248007536,0.1342135072,-0.1854077578,-0.0751737133,-0.1683256179,-0.0700722784,-0.2950831652,0.3934319615,0.1232633367,-0.0822989047,0.0814593434,-0.3621648252,0.1432950199,0.3559267223,-0.0511646233,-0.30620417,0.2269723713,-0.023869615,-0.2126529813,-0.0232424326,-0.300165087,-0.4479035735,0.0247274693,0.148649171,0.3076381087,0.4144200683,0.1959233135,-0.1259261519,-0.0382230207,-0.1248020902,-0.420760721,0.1242525429,-0.0117373336,0.1973400861,-0.0983321816,0.5074976683,-0.3051088452,0.4946763515,0.3491691351,-0.4038631916,0.449875176,-0.1538039297,0.5353226066,-0.2270278633,-0.187646836,0.097731173,0.0977133512,-0.074256718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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2029","title":"Loading a faiss index KeyError","comments":"> If I understand correctly by reading this example you thought that it was re-adding the \"embeddings\" column.\r\nYes. I was trying to use the dataset in RAG and it complained that the dataset didn't have the right columns. No problems when loading the dataset with `load_from_disk` and then doing `load_faiss_index`\r\n\r\nWhat I learned was\r\n1. column and index are different\r\n2. loading the index does not create a column\r\n3. the column is not needed to be able to use the index\r\n4. RAG needs both the embeddings column and the index\r\n\r\nIf I can come up with a way to articulate this in the right spot in the docs, I'll open a PR","body":"I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.\r\n\r\nThe basic steps are:\r\n\r\n1. Create a dataset (dataset1)\r\n2. Create an embeddings column using DPR\r\n3. Add a faiss index to the dataset\r\n4. Save faiss index to a file\r\n5. Create a new dataset (dataset2) with the same text and label information as dataset1\r\n6. Try to load the faiss index from file to dataset2\r\n7. Get `KeyError: \"Column embeddings not in the dataset\"`\r\n\r\nI've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing\r\n\r\nUbuntu Version\r\nVERSION=\"18.04.5 LTS (Bionic Beaver)\"\r\n\r\ndatasets==1.4.1\r\nfaiss==1.5.3\r\nfaiss-gpu==1.7.0\r\ntorch==1.8.0+cu101\r\ntransformers==4.3.3\r\n\r\nNVIDIA-SMI 460.56\r\nDriver Version: 460.32.03\r\nCUDA Version: 11.2    \r\nTesla K80           \r\n\r\nI was basically following the steps here: https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index\r\n\r\nI included the exact code from the documentation at the end of the notebook to show that they don't work either.\r\n","comment_length":115,"text":"Loading a faiss index KeyError \n I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.\r\n\r\nThe basic steps are:\r\n\r\n1. Create a dataset (dataset1)\r\n2. Create an embeddings column using DPR\r\n3. Add a faiss index to the dataset\r\n4. Save faiss index to a file\r\n5. Create a new dataset (dataset2) with the same text and label information as dataset1\r\n6. Try to load the faiss index from file to dataset2\r\n7. Get `KeyError: \"Column embeddings not in the dataset\"`\r\n\r\nI've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing\r\n\r\nUbuntu Version\r\nVERSION=\"18.04.5 LTS (Bionic Beaver)\"\r\n\r\ndatasets==1.4.1\r\nfaiss==1.5.3\r\nfaiss-gpu==1.7.0\r\ntorch==1.8.0+cu101\r\ntransformers==4.3.3\r\n\r\nNVIDIA-SMI 460.56\r\nDriver Version: 460.32.03\r\nCUDA Version: 11.2    \r\nTesla K80           \r\n\r\nI was basically following the steps here: https:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html#adding-a-faiss-index\r\n\r\nI included the exact code from the documentation at the end of the notebook to show that they don't work either.\r\n \n > If I understand correctly by reading this example you thought that it was re-adding the \"embeddings\" column.\r\nYes. I was trying to use the dataset in RAG and it complained that the dataset didn't have the right columns. No problems when loading the dataset with `load_from_disk` and then doing `load_faiss_index`\r\n\r\nWhat I learned was\r\n1. column and index are different\r\n2. loading the index does not create a column\r\n3. the column is not needed to be able to use the index\r\n4. RAG needs both the embeddings column and the index\r\n\r\nIf I can come up with a way to articulate this in the right spot in the docs, I'll open a PR","embeddings":[0.1141662821,-0.6093646288,0.0622365586,0.3725110888,0.1373017132,0.2862834334,0.4066281021,0.0279657878,0.5614460111,0.2074248642,-0.0576396435,0.1514220387,0.4737912714,-0.0607365854,-0.052989319,-0.0416151509,0.2702905834,0.2431628108,0.2737343609,-0.1626692861,-0.2025646716,0.1247658506,-0.2621251643,0.1718708277,-0.248210907,0.2487478256,-0.2778123617,0.0308961533,-0.2404202968,-0.3826129138,0.3425858915,-0.2952020168,0.6600481868,0.1492620856,-0.0001231916,0.1020782292,0.3273308277,-0.1871940792,-0.2122855037,0.0915357992,0.1122795045,0.0143894553,0.2014682144,-0.0870565027,-0.0057215164,-0.6249670982,-0.0121625978,-0.1644823253,0.1824403107,0.0332699306,0.1068647653,-0.0065372856,0.0351543799,-0.378389746,0.205112651,-0.333899796,-0.1093248278,0.3227841258,0.091817826,0.0659408346,0.054192394,0.1877554655,-0.0682540908,0.1206275746,0.4152822495,0.2805346549,0.3271452487,-0.094155848,0.1916977316,0.1926267892,0.4542701542,-0.0401832089,-0.3339141309,-0.1143841222,0.2563905418,-0.1585264355,0.2307870388,-0.2589789927,0.0183759909,0.3088291585,0.3913377821,-0.2083024085,0.14921166,0.1125609502,-0.0701969862,-0.0499437191,-0.2336436063,0.1062823236,0.1187618524,-0.0412242934,-0.2293090522,-0.1664850116,0.069802843,0.0867944583,-0.3313302994,0.1300160289,0.1641416997,0.0661594048,-0.1091429666,0.0547850318,-0.1893745512,-0.0264874715,0.1374494433,0.3345857263,-0.1803767979,0.0246970616,0.044420246,-0.0005222475,-0.0113884984,0.2225162685,0.0441291928,-0.2677798569,-0.0962475091,-0.1068619564,-0.5135743618,-0.3039865196,-0.1129447892,-0.3393259943,-0.4211431146,0.2222638577,-0.4232880473,-0.1412899494,-0.0298155472,0.5755088925,0.275249064,-0.1636072695,0.2179998308,0.3787249327,-0.2972567379,0.1781837791,-0.1884079576,0.087363176,-0.179817751,0.4388460517,0.1041527614,-0.4247675538,0.2150660455,0.0994253233,0.0897854567,0.0637221336,-0.3770472109,-0.0550754666,0.3408403993,0.2761136889,-0.0871317983,0.0847101957,0.3560982049,-0.2921209633,-0.2050967067,0.059205655,-0.1236091405,-0.1076332703,-0.1479506642,0.0917600542,-0.0937497392,-0.058144372,0.199640885,0.0405034423,0.1504989862,-0.3299962282,0.0449836962,-0.0093830032,-0.1618902683,-0.2245410234,0.447907865,0.3724362254,-0.7759336829,-0.3368355334,0.0550395437,0.1556860805,-0.0823373571,0.2427782863,-0.1244228259,0.1349112391,-0.270999074,0.4014906883,0.1220537797,-0.0901549235,-0.1708913743,0.0448942482,0.0930247009,0.0285857841,0.2380019128,-0.0986707658,0.3254885972,0.1497478038,0.3354112506,-0.0001988137,0.0043552821,-0.3671826124,-0.1630478948,-0.3921634853,0.0697727874,0.0956226438,-0.1325723231,-0.0251712631,-0.1350774169,-0.4143718481,0.0810390562,-0.2481820732,-0.048055999,0.0244530439,0.2674900591,0.3455544114,0.4671978056,0.0824592113,-0.1673379987,0.2564873695,0.1291600615,0.0423053987,-0.1225517914,-0.0157379657,-0.2794288397,-0.0127059286,-0.0496821292,-0.1818123311,-0.0790601149,0.0145893861,-0.0118765989,-0.0757844374,-0.33210814,0.1303437799,-0.257668376,0.2069558501,-0.5300257206,0.4520913363,-0.2802708149,-0.2228663564,0.0329993255,0.2488915771,0.14724347,-0.2128631026,0.1438020319,0.1014066562,-0.0003674161,-0.2585800588,0.1111174598,-0.1829434633,0.1620029062,-0.1446011513,0.0851277485,-0.1179838702,0.2549681664,0.0650633052,-0.2244727015,0.4591362774,-0.0234917179,0.3052208424,-0.202988565,-0.2119348198,0.1173565015,-0.0178667922,-0.0267293882,-0.3300513327,-0.1173002943,0.0439026356,-0.1680182666,-0.0541885011,0.0857990757,0.1069037989,-0.0282339547,0.0522947609,-0.1552650481,0.0112996986,-0.2901861072,0.0189379416,0.0887711123,-0.3720996976,0.4891066849,0.2894639075,-0.1515061259,-0.2688131332,-0.2140420377,-0.3313252926,0.0595771186,-0.0840370655,-0.2100378275,0.1587175876,-0.1157231256,0.0546810143,-0.224160701,0.0081050992,0.0759624913,0.157697618,-0.5991175175,0.0572370812,-0.3049148917,-0.036601983,0.0308595318,-0.1012304276,0.0415180661,-0.3487308025,0.1815759391,-0.1190846041,-0.0377943367,0.1511288434,-0.0626414046,0.2203113884,-0.0816774219,0.0488701053,-0.1951137781,-0.2207871825,-0.334548831,-0.0690884665,-0.0555741191,0.301159054,0.1825916618,-0.0411623158,-0.1298975945,-0.1396417469,-0.2877671123,0.3002024293,-0.1688946486,0.1524758488,-0.1127929464,0.1133968979,-0.1385278404,-0.1531698406,0.3262204826,-0.0357740298,-0.1949459612,-0.0704273805,0.0943008736,-0.0298892073,-0.0883331448,-0.4785193503,0.1902282983,-0.2006299049,-0.141656056,-0.1947053671,-0.0839786455,-0.4174040556,0.4110864103,0.0925738513,-0.0698832646,0.0942998752,-0.3801019788,0.069809489,0.3063396513,-0.0815023631,-0.2894349396,0.1465222538,0.0096548581,-0.2156970799,0.0390084982,-0.3196030557,-0.5543977618,0.0217474271,0.159510687,0.2703954875,0.4036834836,0.156766817,-0.1057035625,-0.0533162802,-0.1179203019,-0.4052333236,0.0328005999,-0.0535211489,0.2113653421,-0.1147602126,0.486948967,-0.3206720352,0.522379458,0.381057322,-0.3383180499,0.4276521206,-0.1127688363,0.5202954412,-0.2422707379,-0.1422119439,0.1987451464,0.05869478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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2026","title":"KeyError on using map after renaming a column","comments":"Hi,\r\n\r\nActually, the error occurs due to these two lines:\r\n```python\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n```\r\n`Dataset.rename_column` doesn't update the `_format_columns` attribute, previously defined by `Dataset.set_format`, with a new column name which is why this new column is missing in the output.","body":"Hi,\r\n\r\nI'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function.\r\n\r\nHere is what I try:\r\n\r\n```python\r\ntransform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])])\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    print(examples)\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n        else:\r\n            images.append(examples[\"image\"][example_idx].permute(2,0,1))\r\n        labels.append(examples[\"label\"][example_idx])\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('cifar10')\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n\r\nfeatures = datasets.Features({\r\n            \"image\": datasets.Array3D(shape=(3,32,32),dtype=\"float32\"),\r\n            \"label\": datasets.features.ClassLabel(names=[\r\n                            \"airplane\",\r\n                            \"automobile\",\r\n                            \"bird\",\r\n                            \"cat\",\r\n                            \"deer\",\r\n                            \"dog\",\r\n                            \"frog\",\r\n                            \"horse\",\r\n                            \"ship\",\r\n                            \"truck\",\r\n                        ]),\r\n        })\r\ntrain_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n```\r\nThe error:\r\n```python\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-54-bf29672c53ee> in <module>()\r\n     14                         ]),\r\n     15         })\r\n---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1287         test_inputs = self[:2] if batched else self[0]\r\n   1288         test_indices = [0, 1] if batched else 0\r\n-> 1289         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1290         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1291 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1258             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1259             processed_inputs = (\r\n-> 1260                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1261             )\r\n   1262             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n<ipython-input-52-b4dccbafb70d> in prepare_features(examples)\r\n      3     labels = []\r\n      4     print(examples)\r\n----> 5     for example_idx, example in enumerate(examples[\"image\"]):\r\n      6         if transform is not None:\r\n      7             images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n\r\nKeyError: 'image'\r\n```\r\n\r\nThe print statement inside returns this:\r\n```python\r\n{'label': tensor([6, 9])}\r\n```\r\nApparently, both `img` and `image` do not exist after renaming. \r\n\r\nNote that this code works fine with `img` everywhere.\r\n\r\nNotebook: https:\/\/colab.research.google.com\/drive\/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing\r\n\r\n","comment_length":42,"text":"KeyError on using map after renaming a column \n Hi,\r\n\r\nI'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function.\r\n\r\nHere is what I try:\r\n\r\n```python\r\ntransform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])])\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    print(examples)\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n        else:\r\n            images.append(examples[\"image\"][example_idx].permute(2,0,1))\r\n        labels.append(examples[\"label\"][example_idx])\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('cifar10')\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n\r\nfeatures = datasets.Features({\r\n            \"image\": datasets.Array3D(shape=(3,32,32),dtype=\"float32\"),\r\n            \"label\": datasets.features.ClassLabel(names=[\r\n                            \"airplane\",\r\n                            \"automobile\",\r\n                            \"bird\",\r\n                            \"cat\",\r\n                            \"deer\",\r\n                            \"dog\",\r\n                            \"frog\",\r\n                            \"horse\",\r\n                            \"ship\",\r\n                            \"truck\",\r\n                        ]),\r\n        })\r\ntrain_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n```\r\nThe error:\r\n```python\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-54-bf29672c53ee> in <module>()\r\n     14                         ]),\r\n     15         })\r\n---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1287         test_inputs = self[:2] if batched else self[0]\r\n   1288         test_indices = [0, 1] if batched else 0\r\n-> 1289         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1290         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1291 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1258             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1259             processed_inputs = (\r\n-> 1260                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1261             )\r\n   1262             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n<ipython-input-52-b4dccbafb70d> in prepare_features(examples)\r\n      3     labels = []\r\n      4     print(examples)\r\n----> 5     for example_idx, example in enumerate(examples[\"image\"]):\r\n      6         if transform is not None:\r\n      7             images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n\r\nKeyError: 'image'\r\n```\r\n\r\nThe print statement inside returns this:\r\n```python\r\n{'label': tensor([6, 9])}\r\n```\r\nApparently, both `img` and `image` do not exist after renaming. \r\n\r\nNote that this code works fine with `img` everywhere.\r\n\r\nNotebook: https:\/\/colab.research.google.com\/drive\/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing\r\n\r\n \n Hi,\r\n\r\nActually, the error occurs due to these two lines:\r\n```python\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n```\r\n`Dataset.rename_column` doesn't update the `_format_columns` attribute, previously defined by `Dataset.set_format`, with a new column name which is why this new column is missing in the output.","embeddings":[0.0343904048,0.0061999829,-0.0635800064,-0.334228158,0.4808440804,0.2616392076,0.6042662859,0.237331897,0.1370912194,0.0865909159,0.054035522,0.5270302892,-0.1558949649,0.2994630933,-0.1945005655,-0.0881165639,0.4133820236,0.0935034528,-0.1156333089,0.1972025782,-0.4435485899,0.0497060306,-0.3014574051,0.2807840407,-0.3147513866,-0.1778963804,0.054533463,-0.1938546598,-0.2473179102,-0.274669081,-0.0492155105,0.0971030518,-0.0223661978,0.6005281806,-0.0001189421,0.0826987103,0.188707307,-0.0865515545,0.082985498,-0.2960751951,-0.1819153279,-0.0115416646,-0.2230614275,-0.5142738819,0.0406857617,-0.0636158809,-0.077172786,-0.2671773434,0.1024493352,0.2466998845,0.1638297886,-0.1138828397,0.2209231108,-0.0220486131,0.0439468622,0.2382541597,-0.1926510036,0.0769321546,0.0491049998,-0.3582004011,0.2290986776,0.693173945,-0.2142525613,-0.0826261491,0.2492942363,0.1485666484,0.4132107496,-0.3595764339,0.3614650071,-0.1366176903,0.2379055619,-0.1512385756,-0.1043806374,-0.054232765,-0.0018236447,-0.2537844181,-0.0232361704,-0.3196378648,0.0776960477,-0.2296992242,-0.3305271864,-0.1850074828,0.1285365671,0.2078439891,0.1231135502,-0.097198315,-0.0296082962,0.4492817819,0.1049034894,-0.322670877,-0.1246059462,0.0269131735,0.098084107,0.2211732715,-0.2241782248,-0.0697751865,0.105466947,-0.0227153953,-0.2865684628,-0.6728146672,0.017100919,-0.1154988408,0.1804634184,0.0785013214,0.106362313,0.2072906345,-0.087925978,0.3839033544,0.0437416993,0.0499913432,-0.3903314173,-0.2533300221,0.1843829304,-0.3990157247,0.3597591519,0.2472612262,0.3776059747,0.0936776847,0.4118515253,-0.0127613107,0.1394363791,-0.0965100452,-0.0235801451,0.3582357764,0.2444261461,-0.0504651479,0.1044974774,0.1474120915,-0.0377255827,0.2744560242,-0.0399699099,0.1520807594,-0.5921468139,-0.10537678,-0.1591845006,0.0644563138,0.0273747109,-0.1856066436,0.1200411022,-0.1029791012,-0.017414419,-0.062561661,0.4089117348,0.1914608777,-0.3518828154,0.1143235788,0.2287517935,-0.2352549285,-0.1231607944,0.2178232223,-0.6010237336,-0.1866197884,-0.1935406029,0.0911867172,0.2030768991,0.1996438056,-0.2752969265,-0.1346438378,0.5062403679,-0.4040708244,0.082678318,-0.2427093983,-0.2949000895,-0.2364552915,-0.1690340787,-0.0339538306,-0.0462761037,-0.0256844573,-0.1759432405,0.3576537967,0.0155100189,-0.0486620031,-0.1009822637,0.2132537961,-0.0342827775,0.1602661163,0.4271647036,-0.3145627677,-0.2063587606,-0.0152603928,-0.2203991115,-0.1616605967,-0.0920220613,0.093030706,0.2039043605,-0.0727139935,0.4399832785,-0.154413715,-0.0898718163,0.178547591,0.1311589926,-0.0462149791,0.1775305122,-0.0791230053,0.1281124204,0.208673045,0.1111345366,0.0116959661,-0.0227944031,-0.2068924159,0.0939341784,0.0991031379,0.2192526907,-0.0245531406,0.0360569097,0.0803689882,-0.3255301416,0.167881459,0.1803426594,0.141612947,-0.2925769389,-0.1196023375,-0.2550840676,-0.1336407512,-0.176066801,0.0316427574,0.0552044027,-0.054000631,-0.0344205536,-0.1491251588,-0.1648832858,-0.1527555883,0.0497404523,0.1093318611,-0.3486706614,-0.0184663273,-0.2110998482,-0.1441620439,-0.3850237131,0.0132771088,0.1881341487,-0.0540964752,-0.2381078899,0.3495651782,0.1393090487,0.0440969206,-0.3154114485,0.1756070554,0.0911011919,-0.0334517248,-0.0013047188,0.1042696312,0.1814650595,-0.0348044224,0.1123570204,0.2557912469,-0.2581357658,0.3806613088,-0.2395886481,0.1288557351,-0.0841588005,-0.1766913235,-0.2373664528,-0.393781811,-0.143782109,-0.2950774133,-0.2323311865,-0.0712196752,0.3053756952,-0.2079800516,0.5871576667,-0.0409827009,0.0240916926,-0.0057099815,0.0615631565,0.289617002,0.0114168618,0.1064895242,0.297829479,-0.009614123,-0.2233593762,0.2672612965,0.1708517224,0.2767239213,0.2040939629,0.2167057693,0.244703874,0.0480267294,0.1485542208,-0.0613893084,0.0164967366,-0.2999012768,0.3099987805,0.2844228446,-0.2336353511,0.0998230577,-0.0660420731,0.0700871125,0.2823909223,-0.1527176499,0.0562228933,-0.2700430751,-0.094888635,0.3865874112,0.0920764729,0.4332132339,-0.1842424124,-0.3789016604,0.2417967469,-0.35465464,-0.0365538523,-0.4494333863,0.030626379,-0.0325060226,0.0856916159,-0.4606234431,0.0817554891,-0.1647819877,-0.1355617791,-0.1211330071,-0.5227620006,0.1218558326,-0.4426466227,0.0148037989,0.2468884289,0.2771109343,-0.2926217616,-0.1771504432,0.4542585015,-0.2178657502,-0.1494309604,0.125867188,0.0121634537,-0.039333865,-0.1180340871,-0.1904156059,-0.0265278462,-0.1234948337,0.1169085056,-0.2646969259,0.0163139403,0.5068838596,0.2759175897,-0.2442613244,0.1430361718,-0.0489320122,-0.1584293842,-0.3822782338,0.208315134,-0.0868597105,0.0838408098,0.0047291089,-0.1143030971,-0.0037284025,0.0410409383,-0.1813217849,-0.292372793,-0.0811148286,0.252065897,0.1551319957,0.1015874445,0.2861323655,0.4163930118,-0.1233831346,0.0155468863,-0.2297620475,-0.0407741778,0.4496167004,0.2750443816,0.2839065492,0.4489243031,-0.0161301196,0.2931136191,-0.1320103854,-0.1058140993,0.4552730322,-0.3281630576,0.097139366,-0.427151382,-0.4983298779,0.021225499,-0.3456521928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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2026","title":"KeyError on using map after renaming a column","comments":"Hi @mariosasko,\n\nThanks for opening a PR on this :)\nWhy does the old name also disappear?","body":"Hi,\r\n\r\nI'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function.\r\n\r\nHere is what I try:\r\n\r\n```python\r\ntransform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])])\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    print(examples)\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n        else:\r\n            images.append(examples[\"image\"][example_idx].permute(2,0,1))\r\n        labels.append(examples[\"label\"][example_idx])\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('cifar10')\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n\r\nfeatures = datasets.Features({\r\n            \"image\": datasets.Array3D(shape=(3,32,32),dtype=\"float32\"),\r\n            \"label\": datasets.features.ClassLabel(names=[\r\n                            \"airplane\",\r\n                            \"automobile\",\r\n                            \"bird\",\r\n                            \"cat\",\r\n                            \"deer\",\r\n                            \"dog\",\r\n                            \"frog\",\r\n                            \"horse\",\r\n                            \"ship\",\r\n                            \"truck\",\r\n                        ]),\r\n        })\r\ntrain_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n```\r\nThe error:\r\n```python\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-54-bf29672c53ee> in <module>()\r\n     14                         ]),\r\n     15         })\r\n---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1287         test_inputs = self[:2] if batched else self[0]\r\n   1288         test_indices = [0, 1] if batched else 0\r\n-> 1289         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1290         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1291 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1258             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1259             processed_inputs = (\r\n-> 1260                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1261             )\r\n   1262             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n<ipython-input-52-b4dccbafb70d> in prepare_features(examples)\r\n      3     labels = []\r\n      4     print(examples)\r\n----> 5     for example_idx, example in enumerate(examples[\"image\"]):\r\n      6         if transform is not None:\r\n      7             images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n\r\nKeyError: 'image'\r\n```\r\n\r\nThe print statement inside returns this:\r\n```python\r\n{'label': tensor([6, 9])}\r\n```\r\nApparently, both `img` and `image` do not exist after renaming. \r\n\r\nNote that this code works fine with `img` everywhere.\r\n\r\nNotebook: https:\/\/colab.research.google.com\/drive\/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing\r\n\r\n","comment_length":17,"text":"KeyError on using map after renaming a column \n Hi,\r\n\r\nI'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function.\r\n\r\nHere is what I try:\r\n\r\n```python\r\ntransform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])])\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    print(examples)\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n        else:\r\n            images.append(examples[\"image\"][example_idx].permute(2,0,1))\r\n        labels.append(examples[\"label\"][example_idx])\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('cifar10')\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n\r\nfeatures = datasets.Features({\r\n            \"image\": datasets.Array3D(shape=(3,32,32),dtype=\"float32\"),\r\n            \"label\": datasets.features.ClassLabel(names=[\r\n                            \"airplane\",\r\n                            \"automobile\",\r\n                            \"bird\",\r\n                            \"cat\",\r\n                            \"deer\",\r\n                            \"dog\",\r\n                            \"frog\",\r\n                            \"horse\",\r\n                            \"ship\",\r\n                            \"truck\",\r\n                        ]),\r\n        })\r\ntrain_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n```\r\nThe error:\r\n```python\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-54-bf29672c53ee> in <module>()\r\n     14                         ]),\r\n     15         })\r\n---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1287         test_inputs = self[:2] if batched else self[0]\r\n   1288         test_indices = [0, 1] if batched else 0\r\n-> 1289         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1290         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1291 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1258             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1259             processed_inputs = (\r\n-> 1260                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1261             )\r\n   1262             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n<ipython-input-52-b4dccbafb70d> in prepare_features(examples)\r\n      3     labels = []\r\n      4     print(examples)\r\n----> 5     for example_idx, example in enumerate(examples[\"image\"]):\r\n      6         if transform is not None:\r\n      7             images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n\r\nKeyError: 'image'\r\n```\r\n\r\nThe print statement inside returns this:\r\n```python\r\n{'label': tensor([6, 9])}\r\n```\r\nApparently, both `img` and `image` do not exist after renaming. \r\n\r\nNote that this code works fine with `img` everywhere.\r\n\r\nNotebook: https:\/\/colab.research.google.com\/drive\/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing\r\n\r\n \n Hi @mariosasko,\n\nThanks for opening a PR on this :)\nWhy does the old name also disappear?","embeddings":[0.0343904048,0.0061999829,-0.0635800064,-0.334228158,0.4808440804,0.2616392076,0.6042662859,0.237331897,0.1370912194,0.0865909159,0.054035522,0.5270302892,-0.1558949649,0.2994630933,-0.1945005655,-0.0881165639,0.4133820236,0.0935034528,-0.1156333089,0.1972025782,-0.4435485899,0.0497060306,-0.3014574051,0.2807840407,-0.3147513866,-0.1778963804,0.054533463,-0.1938546598,-0.2473179102,-0.274669081,-0.0492155105,0.0971030518,-0.0223661978,0.6005281806,-0.0001189421,0.0826987103,0.188707307,-0.0865515545,0.082985498,-0.2960751951,-0.1819153279,-0.0115416646,-0.2230614275,-0.5142738819,0.0406857617,-0.0636158809,-0.077172786,-0.2671773434,0.1024493352,0.2466998845,0.1638297886,-0.1138828397,0.2209231108,-0.0220486131,0.0439468622,0.2382541597,-0.1926510036,0.0769321546,0.0491049998,-0.3582004011,0.2290986776,0.693173945,-0.2142525613,-0.0826261491,0.2492942363,0.1485666484,0.4132107496,-0.3595764339,0.3614650071,-0.1366176903,0.2379055619,-0.1512385756,-0.1043806374,-0.054232765,-0.0018236447,-0.2537844181,-0.0232361704,-0.3196378648,0.0776960477,-0.2296992242,-0.3305271864,-0.1850074828,0.1285365671,0.2078439891,0.1231135502,-0.097198315,-0.0296082962,0.4492817819,0.1049034894,-0.322670877,-0.1246059462,0.0269131735,0.098084107,0.2211732715,-0.2241782248,-0.0697751865,0.105466947,-0.0227153953,-0.2865684628,-0.6728146672,0.017100919,-0.1154988408,0.1804634184,0.0785013214,0.106362313,0.2072906345,-0.087925978,0.3839033544,0.0437416993,0.0499913432,-0.3903314173,-0.2533300221,0.1843829304,-0.3990157247,0.3597591519,0.2472612262,0.3776059747,0.0936776847,0.4118515253,-0.0127613107,0.1394363791,-0.0965100452,-0.0235801451,0.3582357764,0.2444261461,-0.0504651479,0.1044974774,0.1474120915,-0.0377255827,0.2744560242,-0.0399699099,0.1520807594,-0.5921468139,-0.10537678,-0.1591845006,0.0644563138,0.0273747109,-0.1856066436,0.1200411022,-0.1029791012,-0.017414419,-0.062561661,0.4089117348,0.1914608777,-0.3518828154,0.1143235788,0.2287517935,-0.2352549285,-0.1231607944,0.2178232223,-0.6010237336,-0.1866197884,-0.1935406029,0.0911867172,0.2030768991,0.1996438056,-0.2752969265,-0.1346438378,0.5062403679,-0.4040708244,0.082678318,-0.2427093983,-0.2949000895,-0.2364552915,-0.1690340787,-0.0339538306,-0.0462761037,-0.0256844573,-0.1759432405,0.3576537967,0.0155100189,-0.0486620031,-0.1009822637,0.2132537961,-0.0342827775,0.1602661163,0.4271647036,-0.3145627677,-0.2063587606,-0.0152603928,-0.2203991115,-0.1616605967,-0.0920220613,0.093030706,0.2039043605,-0.0727139935,0.4399832785,-0.154413715,-0.0898718163,0.178547591,0.1311589926,-0.0462149791,0.1775305122,-0.0791230053,0.1281124204,0.208673045,0.1111345366,0.0116959661,-0.0227944031,-0.2068924159,0.0939341784,0.0991031379,0.2192526907,-0.0245531406,0.0360569097,0.0803689882,-0.3255301416,0.167881459,0.1803426594,0.141612947,-0.2925769389,-0.1196023375,-0.2550840676,-0.1336407512,-0.176066801,0.0316427574,0.0552044027,-0.054000631,-0.0344205536,-0.1491251588,-0.1648832858,-0.1527555883,0.0497404523,0.1093318611,-0.3486706614,-0.0184663273,-0.2110998482,-0.1441620439,-0.3850237131,0.0132771088,0.1881341487,-0.0540964752,-0.2381078899,0.3495651782,0.1393090487,0.0440969206,-0.3154114485,0.1756070554,0.0911011919,-0.0334517248,-0.0013047188,0.1042696312,0.1814650595,-0.0348044224,0.1123570204,0.2557912469,-0.2581357658,0.3806613088,-0.2395886481,0.1288557351,-0.0841588005,-0.1766913235,-0.2373664528,-0.393781811,-0.143782109,-0.2950774133,-0.2323311865,-0.0712196752,0.3053756952,-0.2079800516,0.5871576667,-0.0409827009,0.0240916926,-0.0057099815,0.0615631565,0.289617002,0.0114168618,0.1064895242,0.297829479,-0.009614123,-0.2233593762,0.2672612965,0.1708517224,0.2767239213,0.2040939629,0.2167057693,0.244703874,0.0480267294,0.1485542208,-0.0613893084,0.0164967366,-0.2999012768,0.3099987805,0.2844228446,-0.2336353511,0.0998230577,-0.0660420731,0.0700871125,0.2823909223,-0.1527176499,0.0562228933,-0.2700430751,-0.094888635,0.3865874112,0.0920764729,0.4332132339,-0.1842424124,-0.3789016604,0.2417967469,-0.35465464,-0.0365538523,-0.4494333863,0.030626379,-0.0325060226,0.0856916159,-0.4606234431,0.0817554891,-0.1647819877,-0.1355617791,-0.1211330071,-0.5227620006,0.1218558326,-0.4426466227,0.0148037989,0.2468884289,0.2771109343,-0.2926217616,-0.1771504432,0.4542585015,-0.2178657502,-0.1494309604,0.125867188,0.0121634537,-0.039333865,-0.1180340871,-0.1904156059,-0.0265278462,-0.1234948337,0.1169085056,-0.2646969259,0.0163139403,0.5068838596,0.2759175897,-0.2442613244,0.1430361718,-0.0489320122,-0.1584293842,-0.3822782338,0.208315134,-0.0868597105,0.0838408098,0.0047291089,-0.1143030971,-0.0037284025,0.0410409383,-0.1813217849,-0.292372793,-0.0811148286,0.252065897,0.1551319957,0.1015874445,0.2861323655,0.4163930118,-0.1233831346,0.0155468863,-0.2297620475,-0.0407741778,0.4496167004,0.2750443816,0.2839065492,0.4489243031,-0.0161301196,0.2931136191,-0.1320103854,-0.1058140993,0.4552730322,-0.3281630576,0.097139366,-0.427151382,-0.4983298779,0.021225499,-0.3456521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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2026","title":"KeyError on using map after renaming a column","comments":"I just merged a @mariosasko 's PR that fixes this issue.\r\nIf it happens again, feel free to re-open :)","body":"Hi,\r\n\r\nI'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function.\r\n\r\nHere is what I try:\r\n\r\n```python\r\ntransform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])])\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    print(examples)\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n        else:\r\n            images.append(examples[\"image\"][example_idx].permute(2,0,1))\r\n        labels.append(examples[\"label\"][example_idx])\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('cifar10')\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n\r\nfeatures = datasets.Features({\r\n            \"image\": datasets.Array3D(shape=(3,32,32),dtype=\"float32\"),\r\n            \"label\": datasets.features.ClassLabel(names=[\r\n                            \"airplane\",\r\n                            \"automobile\",\r\n                            \"bird\",\r\n                            \"cat\",\r\n                            \"deer\",\r\n                            \"dog\",\r\n                            \"frog\",\r\n                            \"horse\",\r\n                            \"ship\",\r\n                            \"truck\",\r\n                        ]),\r\n        })\r\ntrain_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n```\r\nThe error:\r\n```python\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-54-bf29672c53ee> in <module>()\r\n     14                         ]),\r\n     15         })\r\n---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1287         test_inputs = self[:2] if batched else self[0]\r\n   1288         test_indices = [0, 1] if batched else 0\r\n-> 1289         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1290         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1291 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1258             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1259             processed_inputs = (\r\n-> 1260                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1261             )\r\n   1262             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n<ipython-input-52-b4dccbafb70d> in prepare_features(examples)\r\n      3     labels = []\r\n      4     print(examples)\r\n----> 5     for example_idx, example in enumerate(examples[\"image\"]):\r\n      6         if transform is not None:\r\n      7             images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n\r\nKeyError: 'image'\r\n```\r\n\r\nThe print statement inside returns this:\r\n```python\r\n{'label': tensor([6, 9])}\r\n```\r\nApparently, both `img` and `image` do not exist after renaming. \r\n\r\nNote that this code works fine with `img` everywhere.\r\n\r\nNotebook: https:\/\/colab.research.google.com\/drive\/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing\r\n\r\n","comment_length":20,"text":"KeyError on using map after renaming a column \n Hi,\r\n\r\nI'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function.\r\n\r\nHere is what I try:\r\n\r\n```python\r\ntransform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])])\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    print(examples)\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n        else:\r\n            images.append(examples[\"image\"][example_idx].permute(2,0,1))\r\n        labels.append(examples[\"label\"][example_idx])\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('cifar10')\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n\r\nfeatures = datasets.Features({\r\n            \"image\": datasets.Array3D(shape=(3,32,32),dtype=\"float32\"),\r\n            \"label\": datasets.features.ClassLabel(names=[\r\n                            \"airplane\",\r\n                            \"automobile\",\r\n                            \"bird\",\r\n                            \"cat\",\r\n                            \"deer\",\r\n                            \"dog\",\r\n                            \"frog\",\r\n                            \"horse\",\r\n                            \"ship\",\r\n                            \"truck\",\r\n                        ]),\r\n        })\r\ntrain_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n```\r\nThe error:\r\n```python\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-54-bf29672c53ee> in <module>()\r\n     14                         ]),\r\n     15         })\r\n---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1287         test_inputs = self[:2] if batched else self[0]\r\n   1288         test_indices = [0, 1] if batched else 0\r\n-> 1289         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1290         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1291 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1258             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1259             processed_inputs = (\r\n-> 1260                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1261             )\r\n   1262             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n<ipython-input-52-b4dccbafb70d> in prepare_features(examples)\r\n      3     labels = []\r\n      4     print(examples)\r\n----> 5     for example_idx, example in enumerate(examples[\"image\"]):\r\n      6         if transform is not None:\r\n      7             images.append(transform(examples[\"image\"][example_idx].permute(2,0,1)))\r\n\r\nKeyError: 'image'\r\n```\r\n\r\nThe print statement inside returns this:\r\n```python\r\n{'label': tensor([6, 9])}\r\n```\r\nApparently, both `img` and `image` do not exist after renaming. \r\n\r\nNote that this code works fine with `img` everywhere.\r\n\r\nNotebook: https:\/\/colab.research.google.com\/drive\/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing\r\n\r\n \n I just merged a @mariosasko 's PR that fixes this issue.\r\nIf it happens again, feel free to re-open :)","embeddings":[0.0343904048,0.0061999829,-0.0635800064,-0.334228158,0.4808440804,0.2616392076,0.6042662859,0.237331897,0.1370912194,0.0865909159,0.054035522,0.5270302892,-0.1558949649,0.2994630933,-0.1945005655,-0.0881165639,0.4133820236,0.0935034528,-0.1156333089,0.1972025782,-0.4435485899,0.0497060306,-0.3014574051,0.2807840407,-0.3147513866,-0.1778963804,0.054533463,-0.1938546598,-0.2473179102,-0.274669081,-0.0492155105,0.0971030518,-0.0223661978,0.6005281806,-0.0001189421,0.0826987103,0.188707307,-0.0865515545,0.082985498,-0.2960751951,-0.1819153279,-0.0115416646,-0.2230614275,-0.5142738819,0.0406857617,-0.0636158809,-0.077172786,-0.2671773434,0.1024493352,0.2466998845,0.1638297886,-0.1138828397,0.2209231108,-0.0220486131,0.0439468622,0.2382541597,-0.1926510036,0.0769321546,0.0491049998,-0.3582004011,0.2290986776,0.693173945,-0.2142525613,-0.0826261491,0.2492942363,0.1485666484,0.4132107496,-0.3595764339,0.3614650071,-0.1366176903,0.2379055619,-0.1512385756,-0.1043806374,-0.054232765,-0.0018236447,-0.2537844181,-0.0232361704,-0.3196378648,0.0776960477,-0.2296992242,-0.3305271864,-0.1850074828,0.1285365671,0.2078439891,0.1231135502,-0.097198315,-0.0296082962,0.4492817819,0.1049034894,-0.322670877,-0.1246059462,0.0269131735,0.098084107,0.2211732715,-0.2241782248,-0.0697751865,0.105466947,-0.0227153953,-0.2865684628,-0.6728146672,0.017100919,-0.1154988408,0.1804634184,0.0785013214,0.106362313,0.2072906345,-0.087925978,0.3839033544,0.0437416993,0.0499913432,-0.3903314173,-0.2533300221,0.1843829304,-0.3990157247,0.3597591519,0.2472612262,0.3776059747,0.0936776847,0.4118515253,-0.0127613107,0.1394363791,-0.0965100452,-0.0235801451,0.3582357764,0.2444261461,-0.0504651479,0.1044974774,0.1474120915,-0.0377255827,0.2744560242,-0.0399699099,0.1520807594,-0.5921468139,-0.10537678,-0.1591845006,0.0644563138,0.0273747109,-0.1856066436,0.1200411022,-0.1029791012,-0.017414419,-0.062561661,0.4089117348,0.1914608777,-0.3518828154,0.1143235788,0.2287517935,-0.2352549285,-0.1231607944,0.2178232223,-0.6010237336,-0.1866197884,-0.1935406029,0.0911867172,0.2030768991,0.1996438056,-0.2752969265,-0.1346438378,0.5062403679,-0.4040708244,0.082678318,-0.2427093983,-0.2949000895,-0.2364552915,-0.1690340787,-0.0339538306,-0.0462761037,-0.0256844573,-0.1759432405,0.3576537967,0.0155100189,-0.0486620031,-0.1009822637,0.2132537961,-0.0342827775,0.1602661163,0.4271647036,-0.3145627677,-0.2063587606,-0.0152603928,-0.2203991115,-0.1616605967,-0.0920220613,0.093030706,0.2039043605,-0.0727139935,0.4399832785,-0.154413715,-0.0898718163,0.178547591,0.1311589926,-0.0462149791,0.1775305122,-0.0791230053,0.1281124204,0.208673045,0.1111345366,0.0116959661,-0.0227944031,-0.2068924159,0.0939341784,0.0991031379,0.2192526907,-0.0245531406,0.0360569097,0.0803689882,-0.3255301416,0.167881459,0.1803426594,0.141612947,-0.2925769389,-0.1196023375,-0.2550840676,-0.1336407512,-0.176066801,0.0316427574,0.0552044027,-0.054000631,-0.0344205536,-0.1491251588,-0.1648832858,-0.1527555883,0.0497404523,0.1093318611,-0.3486706614,-0.0184663273,-0.2110998482,-0.1441620439,-0.3850237131,0.0132771088,0.1881341487,-0.0540964752,-0.2381078899,0.3495651782,0.1393090487,0.0440969206,-0.3154114485,0.1756070554,0.0911011919,-0.0334517248,-0.0013047188,0.1042696312,0.1814650595,-0.0348044224,0.1123570204,0.2557912469,-0.2581357658,0.3806613088,-0.2395886481,0.1288557351,-0.0841588005,-0.1766913235,-0.2373664528,-0.393781811,-0.143782109,-0.2950774133,-0.2323311865,-0.0712196752,0.3053756952,-0.2079800516,0.5871576667,-0.0409827009,0.0240916926,-0.0057099815,0.0615631565,0.289617002,0.0114168618,0.1064895242,0.297829479,-0.009614123,-0.2233593762,0.2672612965,0.1708517224,0.2767239213,0.2040939629,0.2167057693,0.244703874,0.0480267294,0.1485542208,-0.0613893084,0.0164967366,-0.2999012768,0.3099987805,0.2844228446,-0.2336353511,0.0998230577,-0.0660420731,0.0700871125,0.2823909223,-0.1527176499,0.0562228933,-0.2700430751,-0.094888635,0.3865874112,0.0920764729,0.4332132339,-0.1842424124,-0.3789016604,0.2417967469,-0.35465464,-0.0365538523,-0.4494333863,0.030626379,-0.0325060226,0.0856916159,-0.4606234431,0.0817554891,-0.1647819877,-0.1355617791,-0.1211330071,-0.5227620006,0.1218558326,-0.4426466227,0.0148037989,0.2468884289,0.2771109343,-0.2926217616,-0.1771504432,0.4542585015,-0.2178657502,-0.1494309604,0.125867188,0.0121634537,-0.039333865,-0.1180340871,-0.1904156059,-0.0265278462,-0.1234948337,0.1169085056,-0.2646969259,0.0163139403,0.5068838596,0.2759175897,-0.2442613244,0.1430361718,-0.0489320122,-0.1584293842,-0.3822782338,0.208315134,-0.0868597105,0.0838408098,0.0047291089,-0.1143030971,-0.0037284025,0.0410409383,-0.1813217849,-0.292372793,-0.0811148286,0.252065897,0.1551319957,0.1015874445,0.2861323655,0.4163930118,-0.1233831346,0.0155468863,-0.2297620475,-0.0407741778,0.4496167004,0.2750443816,0.2839065492,0.4489243031,-0.0161301196,0.2931136191,-0.1320103854,-0.1058140993,0.4552730322,-0.3281630576,0.097139366,-0.427151382,-0.4983298779,0.021225499,-0.3456521928,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2022","title":"ValueError when rename_column on splitted dataset","comments":"Hi,\r\n\r\nThis is a bug so thanks for reporting it. `Dataset.__setstate__`  is the problem, which is called when `Dataset.rename_column` tries to copy the dataset with `copy.deepcopy(self)`. This only happens if the `split` arg in `load_dataset` was defined as `ReadInstruction`.\r\n\r\nTo overcome this issue, use the named splits API (for now):\r\n```python\r\ntrain_ds, test_ds = load_dataset(\r\n    path='csv',               \r\n    delimiter='\\t',          \r\n    data_files=text_files,    \r\n    split=['train[:90%]', 'train[-10%:]'],\r\n)\r\n\r\ntrain_ds = train_ds.rename_column('sentence', 'text')\r\n```","body":"Hi there,\r\nI am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so:\r\n\r\n```python\r\nsplit = {\r\n    'train': ReadInstruction('train', to=90, unit='%'),\r\n    'test': ReadInstruction('train', from_=-10, unit='%')\r\n}\r\n\r\ndataset = load_dataset(\r\n    path='csv',               # use 'text' loading script to load from local txt-files\r\n    delimiter='\\t',           # xxx\r\n    data_files=text_files,    # list of paths to local text files\r\n    split=split,              # xxx\r\n)\r\n\r\ndataset\r\n```\r\n\r\nPart of output:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'sentiment'],\r\n        num_rows: 900\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'sentiment'],\r\n        num_rows: 100\r\n    })\r\n})\r\n```\r\nAfterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however:\r\n```python\r\ndataset['train'].rename_column('sentence', 'text')\r\n```\r\n```python\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/splits.py in __init__(self, name)\r\n    353         for split_name in split_names_from_instruction:\r\n    354             if not re.match(_split_re, split_name):\r\n--> 355                 raise ValueError(f\"Split name should match '{_split_re}'' but got '{split_name}'.\")\r\n    356 \r\n    357     def __str__(self):\r\n\r\nValueError: Split name should match '^\\w+(\\.\\w+)*$'' but got 'ReadInstruction('.\r\n```\r\nIn particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split.\r\n\r\nThanks in advance! :)","comment_length":66,"text":"ValueError when rename_column on splitted dataset \n Hi there,\r\nI am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so:\r\n\r\n```python\r\nsplit = {\r\n    'train': ReadInstruction('train', to=90, unit='%'),\r\n    'test': ReadInstruction('train', from_=-10, unit='%')\r\n}\r\n\r\ndataset = load_dataset(\r\n    path='csv',               # use 'text' loading script to load from local txt-files\r\n    delimiter='\\t',           # xxx\r\n    data_files=text_files,    # list of paths to local text files\r\n    split=split,              # xxx\r\n)\r\n\r\ndataset\r\n```\r\n\r\nPart of output:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'sentiment'],\r\n        num_rows: 900\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'sentiment'],\r\n        num_rows: 100\r\n    })\r\n})\r\n```\r\nAfterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however:\r\n```python\r\ndataset['train'].rename_column('sentence', 'text')\r\n```\r\n```python\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/splits.py in __init__(self, name)\r\n    353         for split_name in split_names_from_instruction:\r\n    354             if not re.match(_split_re, split_name):\r\n--> 355                 raise ValueError(f\"Split name should match '{_split_re}'' but got '{split_name}'.\")\r\n    356 \r\n    357     def __str__(self):\r\n\r\nValueError: Split name should match '^\\w+(\\.\\w+)*$'' but got 'ReadInstruction('.\r\n```\r\nIn particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split.\r\n\r\nThanks in advance! :) \n Hi,\r\n\r\nThis is a bug so thanks for reporting it. `Dataset.__setstate__`  is the problem, which is called when `Dataset.rename_column` tries to copy the dataset with `copy.deepcopy(self)`. This only happens if the `split` arg in `load_dataset` was defined as `ReadInstruction`.\r\n\r\nTo overcome this issue, use the named splits API (for now):\r\n```python\r\ntrain_ds, test_ds = load_dataset(\r\n    path='csv',               \r\n    delimiter='\\t',          \r\n    data_files=text_files,    \r\n    split=['train[:90%]', 'train[-10%:]'],\r\n)\r\n\r\ntrain_ds = train_ds.rename_column('sentence', 'text')\r\n```","embeddings":[-0.0835750848,0.2197992057,-0.0348290578,-0.0423846953,0.4208770096,0.0730622262,0.6438042521,0.417984277,-0.0225529727,0.3396532834,-0.0866872147,0.3978035748,-0.0521849729,0.3644353151,-0.2562588155,-0.2477057576,0.1044141725,-0.0795424059,0.1267364323,0.2697291374,-0.3300528526,0.0778078511,-0.3932777643,0.126068756,-0.0781336427,0.0623800941,0.0325193703,0.095376946,0.0282755606,-0.3610774279,0.1441161931,-0.0355102234,0.1899649948,0.5556836724,-0.0001162362,0.123281084,0.2225487083,-0.1280173063,-0.1891635507,-0.4528475702,-0.0052785026,-0.1680362076,0.0017363135,-0.350676477,0.1707240045,0.2284773737,-0.2309949547,-0.2584788203,0.1006070673,0.3693120182,0.1076899767,0.0965298414,-0.0107203871,0.1191240698,0.1085148677,0.02124759,-0.3047722876,0.0899827629,-0.0761238411,-0.1792219281,-0.1137903929,0.2317518592,-0.0711766481,0.1480419189,-0.0235446021,0.3106743395,0.0345892012,-0.046964284,0.0888525695,0.0671797469,0.2144692689,-0.2401781976,-0.4639912844,-0.4442960322,0.1637722403,-0.3250167668,0.2570495903,-0.1877467483,0.1232365146,0.1840747148,-0.1398658454,0.0523615777,-0.1373051703,-0.1406392157,-0.0923041031,0.0893618017,-0.0323616415,0.4094693363,-0.1002934575,-0.1114295572,0.1749086529,0.0513332188,-0.0785202086,-0.1311808378,-0.5019623637,0.0278138947,-0.2302477956,-0.2595581114,-0.320128262,-0.1200023293,0.015836617,-0.2517315745,0.2726792395,0.2164970785,-0.0362976082,0.1996526569,0.1300997585,0.4851779044,0.2359965295,0.1402986795,-0.2038473189,-0.0915264264,0.0607342832,-0.2988728285,0.1496177316,0.1908584237,0.1388384253,0.1012246609,-0.422322154,-0.0214158427,-0.299931258,-0.1886160672,-0.030533433,0.2214560509,0.2032395601,0.4568883777,0.1693875492,0.0574802384,-0.0006995207,-0.1406515241,-0.0371253602,0.1793324798,-0.301969558,0.1505992264,0.0399702266,0.1964562237,0.0814438388,-0.2706573308,-0.0208249222,-0.299153924,-0.0203204229,-0.1991917193,0.0952113345,0.2918279171,-0.0835521519,-0.107908383,0.116414018,-0.4457986653,0.0352158584,0.2579235137,-0.2536048889,-0.320792675,0.1397257596,0.1286002249,0.0399009176,-0.0527537614,0.0482414179,-0.0229599494,0.6056432724,-0.1394455284,0.1351458579,-0.3096872866,-0.2267056406,-0.0160398446,-0.0332796611,0.1000444666,-0.5333275795,-0.1184836403,0.0636194199,0.1941967756,0.1769787073,0.1755306721,0.1830014735,0.1583334655,0.1335396022,0.266903609,0.2967285514,-0.3958649337,0.0288950782,0.2807647586,-0.3185531199,-0.1601911634,0.3707808554,-0.2173383236,0.1207509637,-0.1076937914,0.1341798902,0.0113364346,-0.0657366589,0.0960765854,-0.0378147103,-0.0701453909,0.3307214677,-0.018021524,0.0188636724,-0.1157904789,-0.1568621844,-0.2933054864,0.3243606389,-0.0264651477,0.1164182797,0.1300329119,0.1057565063,0.5420783758,0.1200013235,-0.0832299888,-0.2253147513,0.0283834282,0.3180817366,-0.1683246642,-0.3165953457,-0.1709285378,-0.432600975,0.0489438772,-0.0752222762,0.047204908,0.0313136242,0.2542936206,-0.1051330194,-0.1217908859,-0.4117276967,0.2951657772,-0.5749081969,0.2464329451,-0.3180003464,0.0775999576,0.0061799488,-0.3219508827,-0.2502307892,0.1552675813,0.1714720279,-0.0112890517,-0.2374314368,0.6657509208,0.34775123,0.0463436544,-0.3765339553,-0.2769818604,-0.1085001081,0.2417751104,-0.2358049601,-0.1330698282,0.2457468659,-0.0412518121,-0.0628126115,0.2938631177,-0.4202154875,0.5500609875,-0.0109366123,-0.1532364488,0.002068257,-0.2575076222,-0.1037362888,-0.3930054307,-0.1505555212,-0.3311613202,0.1140188873,-0.196557343,-0.1869239062,-0.2932314277,0.6310689449,-0.0536831878,-0.1708038598,0.0077965646,0.1357934773,-0.0466479398,-0.0799038783,0.6231101751,0.4926960766,0.0294919666,-0.1018566489,0.2511084676,0.1871850491,-0.2857350409,0.2048228085,0.2681159675,0.1419734806,0.4111416042,0.2456434667,0.005155154,-0.2268374413,-0.1838648766,0.3968054354,0.1195372418,-0.4919120967,0.0235623717,-0.164911896,0.2419187725,-0.2057008147,-0.226712659,0.1853089333,-0.544261992,-0.2349176556,0.3472461104,-0.3372885883,0.3051025867,-0.1589921862,-0.3950490952,0.0922147259,-0.1661286056,-0.0719222948,-0.1782116592,-0.0885189027,-0.0458369814,-0.126146242,-0.0972286686,0.0410267226,-0.1662219167,-0.0943965763,-0.1365423203,-0.2923829257,-0.0274039879,-0.4529346526,-0.0093845455,0.400508523,0.0366891734,0.1800103635,-0.4637690783,0.2288455218,0.0775950626,-0.0209740959,0.0346015543,0.2929466963,0.0061281142,-0.1640412956,-0.669023633,-0.2725984752,-0.3042469621,0.1338291615,-0.2729791403,-0.1675990075,0.0596722402,-0.1618382335,0.0142350011,0.1734488904,0.1514361352,-0.0215064287,0.0119573008,0.150385648,0.0146297365,0.1776951849,0.1265307963,0.1827682853,-0.1011585221,0.0592221878,-0.1364729106,-0.0564785674,-0.0916979313,0.3380533755,0.0930299312,-0.0017823929,0.2639890909,0.2979951799,-0.0363329686,-0.1717988253,-0.2675010264,0.246560514,0.0576315522,0.1198230162,0.0803304985,0.6318507195,0.2055416703,0.1752791852,-0.150716871,-0.0701785162,0.0658943951,-0.0693151802,0.0188845173,-0.190381512,-0.0143460678,-0.0719314367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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2022","title":"ValueError when rename_column on splitted dataset","comments":"This has been fixed in #2043 , thanks @mariosasko \r\nThe fix is available on master and we'll do a new release soon :)\r\n\r\nfeel free to re-open if you still have issues","body":"Hi there,\r\nI am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so:\r\n\r\n```python\r\nsplit = {\r\n    'train': ReadInstruction('train', to=90, unit='%'),\r\n    'test': ReadInstruction('train', from_=-10, unit='%')\r\n}\r\n\r\ndataset = load_dataset(\r\n    path='csv',               # use 'text' loading script to load from local txt-files\r\n    delimiter='\\t',           # xxx\r\n    data_files=text_files,    # list of paths to local text files\r\n    split=split,              # xxx\r\n)\r\n\r\ndataset\r\n```\r\n\r\nPart of output:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'sentiment'],\r\n        num_rows: 900\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'sentiment'],\r\n        num_rows: 100\r\n    })\r\n})\r\n```\r\nAfterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however:\r\n```python\r\ndataset['train'].rename_column('sentence', 'text')\r\n```\r\n```python\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/splits.py in __init__(self, name)\r\n    353         for split_name in split_names_from_instruction:\r\n    354             if not re.match(_split_re, split_name):\r\n--> 355                 raise ValueError(f\"Split name should match '{_split_re}'' but got '{split_name}'.\")\r\n    356 \r\n    357     def __str__(self):\r\n\r\nValueError: Split name should match '^\\w+(\\.\\w+)*$'' but got 'ReadInstruction('.\r\n```\r\nIn particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split.\r\n\r\nThanks in advance! :)","comment_length":32,"text":"ValueError when rename_column on splitted dataset \n Hi there,\r\nI am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so:\r\n\r\n```python\r\nsplit = {\r\n    'train': ReadInstruction('train', to=90, unit='%'),\r\n    'test': ReadInstruction('train', from_=-10, unit='%')\r\n}\r\n\r\ndataset = load_dataset(\r\n    path='csv',               # use 'text' loading script to load from local txt-files\r\n    delimiter='\\t',           # xxx\r\n    data_files=text_files,    # list of paths to local text files\r\n    split=split,              # xxx\r\n)\r\n\r\ndataset\r\n```\r\n\r\nPart of output:\r\n```python\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['sentence', 'sentiment'],\r\n        num_rows: 900\r\n    })\r\n    test: Dataset({\r\n        features: ['sentence', 'sentiment'],\r\n        num_rows: 100\r\n    })\r\n})\r\n```\r\nAfterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however:\r\n```python\r\ndataset['train'].rename_column('sentence', 'text')\r\n```\r\n```python\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/splits.py in __init__(self, name)\r\n    353         for split_name in split_names_from_instruction:\r\n    354             if not re.match(_split_re, split_name):\r\n--> 355                 raise ValueError(f\"Split name should match '{_split_re}'' but got '{split_name}'.\")\r\n    356 \r\n    357     def __str__(self):\r\n\r\nValueError: Split name should match '^\\w+(\\.\\w+)*$'' but got 'ReadInstruction('.\r\n```\r\nIn particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split.\r\n\r\nThanks in advance! :) \n This has been fixed in #2043 , thanks @mariosasko \r\nThe fix is available on master and we'll do a new release soon :)\r\n\r\nfeel free to re-open if you still have issues","embeddings":[-0.0835750848,0.2197992057,-0.0348290578,-0.0423846953,0.4208770096,0.0730622262,0.6438042521,0.417984277,-0.0225529727,0.3396532834,-0.0866872147,0.3978035748,-0.0521849729,0.3644353151,-0.2562588155,-0.2477057576,0.1044141725,-0.0795424059,0.1267364323,0.2697291374,-0.3300528526,0.0778078511,-0.3932777643,0.126068756,-0.0781336427,0.0623800941,0.0325193703,0.095376946,0.0282755606,-0.3610774279,0.1441161931,-0.0355102234,0.1899649948,0.5556836724,-0.0001162362,0.123281084,0.2225487083,-0.1280173063,-0.1891635507,-0.4528475702,-0.0052785026,-0.1680362076,0.0017363135,-0.350676477,0.1707240045,0.2284773737,-0.2309949547,-0.2584788203,0.1006070673,0.3693120182,0.1076899767,0.0965298414,-0.0107203871,0.1191240698,0.1085148677,0.02124759,-0.3047722876,0.0899827629,-0.0761238411,-0.1792219281,-0.1137903929,0.2317518592,-0.0711766481,0.1480419189,-0.0235446021,0.3106743395,0.0345892012,-0.046964284,0.0888525695,0.0671797469,0.2144692689,-0.2401781976,-0.4639912844,-0.4442960322,0.1637722403,-0.3250167668,0.2570495903,-0.1877467483,0.1232365146,0.1840747148,-0.1398658454,0.0523615777,-0.1373051703,-0.1406392157,-0.0923041031,0.0893618017,-0.0323616415,0.4094693363,-0.1002934575,-0.1114295572,0.1749086529,0.0513332188,-0.0785202086,-0.1311808378,-0.5019623637,0.0278138947,-0.2302477956,-0.2595581114,-0.320128262,-0.1200023293,0.015836617,-0.2517315745,0.2726792395,0.2164970785,-0.0362976082,0.1996526569,0.1300997585,0.4851779044,0.2359965295,0.1402986795,-0.2038473189,-0.0915264264,0.0607342832,-0.2988728285,0.1496177316,0.1908584237,0.1388384253,0.1012246609,-0.422322154,-0.0214158427,-0.299931258,-0.1886160672,-0.030533433,0.2214560509,0.2032395601,0.4568883777,0.1693875492,0.0574802384,-0.0006995207,-0.1406515241,-0.0371253602,0.1793324798,-0.301969558,0.1505992264,0.0399702266,0.1964562237,0.0814438388,-0.2706573308,-0.0208249222,-0.299153924,-0.0203204229,-0.1991917193,0.0952113345,0.2918279171,-0.0835521519,-0.107908383,0.116414018,-0.4457986653,0.0352158584,0.2579235137,-0.2536048889,-0.320792675,0.1397257596,0.1286002249,0.0399009176,-0.0527537614,0.0482414179,-0.0229599494,0.6056432724,-0.1394455284,0.1351458579,-0.3096872866,-0.2267056406,-0.0160398446,-0.0332796611,0.1000444666,-0.5333275795,-0.1184836403,0.0636194199,0.1941967756,0.1769787073,0.1755306721,0.1830014735,0.1583334655,0.1335396022,0.266903609,0.2967285514,-0.3958649337,0.0288950782,0.2807647586,-0.3185531199,-0.1601911634,0.3707808554,-0.2173383236,0.1207509637,-0.1076937914,0.1341798902,0.0113364346,-0.0657366589,0.0960765854,-0.0378147103,-0.0701453909,0.3307214677,-0.018021524,0.0188636724,-0.1157904789,-0.1568621844,-0.2933054864,0.3243606389,-0.0264651477,0.1164182797,0.1300329119,0.1057565063,0.5420783758,0.1200013235,-0.0832299888,-0.2253147513,0.0283834282,0.3180817366,-0.1683246642,-0.3165953457,-0.1709285378,-0.432600975,0.0489438772,-0.0752222762,0.047204908,0.0313136242,0.2542936206,-0.1051330194,-0.1217908859,-0.4117276967,0.2951657772,-0.5749081969,0.2464329451,-0.3180003464,0.0775999576,0.0061799488,-0.3219508827,-0.2502307892,0.1552675813,0.1714720279,-0.0112890517,-0.2374314368,0.6657509208,0.34775123,0.0463436544,-0.3765339553,-0.2769818604,-0.1085001081,0.2417751104,-0.2358049601,-0.1330698282,0.2457468659,-0.0412518121,-0.0628126115,0.2938631177,-0.4202154875,0.5500609875,-0.0109366123,-0.1532364488,0.002068257,-0.2575076222,-0.1037362888,-0.3930054307,-0.1505555212,-0.3311613202,0.1140188873,-0.196557343,-0.1869239062,-0.2932314277,0.6310689449,-0.0536831878,-0.1708038598,0.0077965646,0.1357934773,-0.0466479398,-0.0799038783,0.6231101751,0.4926960766,0.0294919666,-0.1018566489,0.2511084676,0.1871850491,-0.2857350409,0.2048228085,0.2681159675,0.1419734806,0.4111416042,0.2456434667,0.005155154,-0.2268374413,-0.1838648766,0.3968054354,0.1195372418,-0.4919120967,0.0235623717,-0.164911896,0.2419187725,-0.2057008147,-0.226712659,0.1853089333,-0.544261992,-0.2349176556,0.3472461104,-0.3372885883,0.3051025867,-0.1589921862,-0.3950490952,0.0922147259,-0.1661286056,-0.0719222948,-0.1782116592,-0.0885189027,-0.0458369814,-0.126146242,-0.0972286686,0.0410267226,-0.1662219167,-0.0943965763,-0.1365423203,-0.2923829257,-0.0274039879,-0.4529346526,-0.0093845455,0.400508523,0.0366891734,0.1800103635,-0.4637690783,0.2288455218,0.0775950626,-0.0209740959,0.0346015543,0.2929466963,0.0061281142,-0.1640412956,-0.669023633,-0.2725984752,-0.3042469621,0.1338291615,-0.2729791403,-0.1675990075,0.0596722402,-0.1618382335,0.0142350011,0.1734488904,0.1514361352,-0.0215064287,0.0119573008,0.150385648,0.0146297365,0.1776951849,0.1265307963,0.1827682853,-0.1011585221,0.0592221878,-0.1364729106,-0.0564785674,-0.0916979313,0.3380533755,0.0930299312,-0.0017823929,0.2639890909,0.2979951799,-0.0363329686,-0.1717988253,-0.2675010264,0.246560514,0.0576315522,0.1198230162,0.0803304985,0.6318507195,0.2055416703,0.1752791852,-0.150716871,-0.0701785162,0.0658943951,-0.0693151802,0.0188845173,-0.190381512,-0.0143460678,-0.0719314367,-0.3799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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2021","title":"Interactively doing  save_to_disk and load_from_disk corrupts the datasets object?","comments":"Hi,\r\n\r\nCan you give us a minimal reproducible example? This [part](https:\/\/huggingface.co\/docs\/datasets\/master\/processing.html#controling-the-cache-behavior) of the docs explains how to control caching.","body":" dataset_info.json file saved after using  save_to_disk gets corrupted as follows. \r\n \r\n \r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/110568474-ed969880-81b7-11eb-832f-2e5129656016.png)\r\n\r\nIs there a way to disable the cache that will save to \/tmp\/huggiface\/datastes ? \r\nI have a feeling there is a serious issue with cashing.","comment_length":19,"text":"Interactively doing  save_to_disk and load_from_disk corrupts the datasets object? \n  dataset_info.json file saved after using  save_to_disk gets corrupted as follows. \r\n \r\n \r\n![image](https:\/\/user-images.githubusercontent.com\/16892570\/110568474-ed969880-81b7-11eb-832f-2e5129656016.png)\r\n\r\nIs there a way to disable the cache that will save to \/tmp\/huggiface\/datastes ? \r\nI have a feeling there is a serious issue with cashing. \n Hi,\r\n\r\nCan you give us a minimal reproducible example? This [part](https:\/\/huggingface.co\/docs\/datasets\/master\/processing.html#controling-the-cache-behavior) of the docs explains how to control caching.","embeddings":[-0.0748554915,-0.1469714046,0.0561726168,0.775801003,0.2905217409,0.3213843107,-0.2317185104,0.1311905235,0.1942945272,0.1316702813,-0.150269568,0.066835992,0.2389940321,0.2033745795,0.1379984468,0.2394488454,0.4092518687,-0.102217637,-0.2969146073,-0.0024894476,-0.0571397915,0.201320827,0.2392439842,-0.0475083664,-0.370827347,-0.1665406972,0.0865426436,0.3370094299,-0.0457871743,-0.3307515681,0.1703151166,0.2500181794,-0.2806940675,0.3997516632,-0.0001322348,-0.278096348,0.0654913113,-0.1120894477,-0.4807301462,0.3449938595,-0.2146836668,0.1056199148,0.1832559109,-0.051474601,0.018076079,0.0305722896,-0.0448686555,-0.2771779299,0.4946137369,-0.141809091,0.0099722333,0.2939893603,0.0540788174,0.170543164,-0.0891119391,0.352006942,-0.1730331928,0.247376591,-0.0567087792,-0.0422368832,-0.1335352063,0.3271785676,-0.1942922324,-0.221531406,0.4750732183,0.1670784801,-0.2261087745,-0.1015610918,0.1416803747,0.0300045144,0.2404650599,-0.4413738251,-0.4901424646,-0.409457624,-0.1626343131,-0.2622802854,0.4829567075,0.1149294302,0.0237637423,0.2698065937,-0.6267800331,-0.3822417259,-0.0752123967,0.1046850234,0.2587689459,-0.6426105499,-0.2962203026,-0.0202892181,0.0960970968,-0.0269153994,-0.2518183887,-0.2842938304,-0.4094386399,0.2043643147,-0.2804290354,-0.1706220955,-0.0365125388,0.6992857456,0.3096770644,0.0266730599,0.1539710015,-0.0268790461,-0.3828503788,0.0570360385,0.4450926185,0.1841095984,-0.1510336846,-0.3038631976,0.1937832385,0.2487015128,0.1934034079,-0.1279371232,0.6235292554,0.0654036105,0.3158103824,-0.2794091403,0.1027026176,-0.426775068,-0.158187449,0.2238548249,0.1136852205,0.1270822883,0.1518256515,0.2743179798,-0.2032476068,0.2388757765,-0.1741434634,0.2308161557,-0.1112838238,-0.1644691378,-0.2411801964,0.0200549606,-0.1028403789,0.4697910845,0.3572986126,-0.2158576995,0.1555408388,0.1488798708,-0.2194773406,-0.2177105844,-0.1413962096,-0.1730891615,0.2110491693,0.2001749575,-0.0784013942,0.261449188,0.0442139916,0.1813435405,-0.1044088379,-0.016748948,-0.3025808632,-0.1653333157,0.2371432632,-0.0093313269,-0.3137509823,-0.0204300024,-0.3833115101,-0.0406767949,0.373403877,-0.0606607869,0.2270976603,0.3761088252,-0.4323396385,-0.2126269937,0.1186989844,0.7272849083,-0.0777010769,-0.1809046268,0.3380313516,-0.0677055046,-0.0976461768,0.4955670834,-0.0461681187,0.1240394264,-0.1751255095,-0.0000537494,0.0921350494,-0.4036758244,-0.5119621754,-0.0031919228,0.2435677648,0.1814943403,0.0818844736,0.170397684,0.0276783817,-0.2080336809,-0.3021430373,0.2457524091,0.0571978651,0.1007413268,-0.2472885996,-0.2150123417,0.0834205523,-0.0444382764,-0.3568450809,0.4231317639,-0.0431624055,-0.0345389694,0.0292218924,-0.004833607,0.2021774203,0.3284560442,0.2372113466,0.1297816485,0.0238769706,0.0728912726,-0.4457373619,0.1206498072,0.0823695213,-0.2579562664,-0.0335220397,-0.2511535585,-0.0211948436,0.1351121068,-0.078266114,0.1193073615,-0.0983167291,-0.0323054828,0.2460309565,0.171704784,-0.2977082431,0.8645343781,0.06487602,0.2302663475,-0.5460054874,-0.0653682947,0.1276176572,-0.0912412032,-0.3207089603,-0.145103693,0.1751980335,-0.212664187,-0.0814768299,0.6701141,-0.0175038632,0.4155429006,0.0389863402,0.350451082,0.2606140971,0.1764128357,-0.0385825448,-0.011305158,0.1953107566,0.0030963675,-0.2079170346,0.2323743105,0.0401252396,-0.0596024506,-0.05956278,-0.1137470901,0.1619164348,-0.1846334636,0.0205509271,-0.3181634247,-0.0380241275,-0.2064877599,0.2917069495,-0.0238008369,-0.3807862103,0.1157953665,0.2447306663,0.0247230623,0.0320941396,-0.0001367864,-0.0198976006,-0.3366125524,0.0410211496,0.2540482879,0.5070416927,-0.0893044025,0.0645957738,0.0287317503,0.0835928917,-0.0630104914,0.1469515264,0.0372381955,0.07628382,0.1165311262,0.0270344745,-0.0039836271,-0.4554965198,0.4673797786,-0.1203905419,0.1310746521,-0.4288453162,0.3027190268,-0.0754572377,-0.1827237159,-0.0489479601,0.0194734093,-0.3586302698,-0.2553177178,0.1200568601,0.7634333372,0.0096856477,-0.0343590416,-0.0973856151,0.6595603228,-0.1374249607,0.1637319922,-0.5104157925,0.0852933675,-0.1150370985,-0.2006239742,0.1317938268,-0.2283476293,0.3360621631,-0.2017661035,-0.1169518903,-0.3274325728,0.1613985896,0.1672955453,0.152726993,0.1604951024,-0.030452121,0.1365007907,-0.0491777658,0.2691923082,0.0262319092,-0.2377795428,-0.2840476334,0.0405837484,0.1341718137,0.038881477,-0.1139045432,0.3498009145,-0.0329399891,-0.0541743338,0.2562649548,-0.1604389846,0.0446980335,0.1442906708,0.0514641702,-0.1174157262,-0.2412376255,0.09536919,-0.3460167646,-0.8206087351,0.3271396756,0.0187050141,-0.2429233193,0.1773413271,0.4334367812,-0.0686252937,0.2557433546,-0.4300456047,-0.3236347139,-0.1416156739,-0.1264070123,-0.2315827906,0.0207725018,0.111052677,0.107680954,0.0291786287,-0.0460481755,-0.2006755471,0.2317097783,-0.0316279344,0.4652905166,-0.1587823778,0.2857140899,0.1445428133,0.1652269661,0.0474158451,0.0871285126,0.5161062479,0.1988771558,0.7607293129,-0.0900925919,-0.1815169305,-0.341968298,-0.287255615,-0.14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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2012","title":"No upstream branch","comments":"What's the issue exactly ?\r\n\r\nGiven an `upstream` remote repository with url `https:\/\/github.com\/huggingface\/datasets.git`, you can totally rebase from `upstream\/master`.\r\n\r\nIt's mentioned at the beginning how to add the `upstream` remote repository\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/987df6b4e9e20fc0c92bc9df48137d170756fd7b\/ADD_NEW_DATASET.md#L10-L14","body":"Feels like the documentation on adding a new dataset is outdated?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/987df6b4e9e20fc0c92bc9df48137d170756fd7b\/ADD_NEW_DATASET.md#L49-L54\r\n\r\nThere is no upstream branch on remote. ","comment_length":32,"text":"No upstream branch \n Feels like the documentation on adding a new dataset is outdated?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/987df6b4e9e20fc0c92bc9df48137d170756fd7b\/ADD_NEW_DATASET.md#L49-L54\r\n\r\nThere is no upstream branch on remote.  \n What's the issue exactly ?\r\n\r\nGiven an `upstream` remote repository with url `https:\/\/github.com\/huggingface\/datasets.git`, you can totally rebase from `upstream\/master`.\r\n\r\nIt's mentioned at the beginning how to add the `upstream` remote repository\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/987df6b4e9e20fc0c92bc9df48137d170756fd7b\/ADD_NEW_DATASET.md#L10-L14","embeddings":[-0.0541970842,-0.342902869,-0.0709576011,-0.2234128565,0.1322251409,0.0014155612,0.1212346554,0.0149749089,-0.4329250455,0.1663282961,0.0103836032,-0.0504560061,0.1446346939,0.2027385086,0.0767325982,-0.0622126684,0.2159233242,-0.1112824976,0.0094948607,-0.3877199292,-0.0959463716,0.0407857597,0.0525698029,-0.4642565846,-0.0445150174,0.0981033295,-0.1298272461,0.0079100905,-0.4901851416,-0.4163923562,0.3651945889,-0.0329049341,-0.0429880321,0.476279676,-0.0001129406,-0.0150297079,0.3386003077,0.0055787019,-0.1953437775,-0.2580168545,-0.431886524,-0.4034749866,-0.0033092254,0.2223095149,-0.1875630021,0.0652669817,0.1741567552,0.0976460204,0.1949372739,0.1445427239,0.2238371819,0.2049379796,0.2905164659,-0.4031758904,0.1369868666,0.3619071245,-0.2284917831,0.6680122018,0.2545554936,0.4873504639,0.0701367334,0.14298594,0.3143943548,-0.1501743644,0.2716187537,0.0007992763,0.0826397389,-0.1987708509,0.0030304641,0.1926567405,0.6094884872,0.122026749,-0.5760812759,-0.1504706144,-0.0158565044,-0.2908196449,0.0060971137,0.1517653316,-0.0127051836,0.1186981946,-0.279786855,-0.5576728582,-0.1758411378,0.3202894926,0.2198292464,0.283773452,-0.158690542,-0.0596121699,0.1148347929,0.158334896,-0.304923892,0.2595874071,-0.1861921698,0.0499552265,0.0524859428,-0.1112526357,0.017377466,0.1793416291,0.1147997528,0.4388834834,-0.2054879665,-0.2199703455,-0.2314783484,0.0753109008,0.2165307552,0.0756217539,-0.0312717557,-0.079086341,0.1986977309,0.0602077693,0.3548210561,-0.2259039581,0.0818692148,-0.0484484732,-0.3386690617,-0.1433110684,0.2231083512,-0.3168765008,0.1877762079,-0.2154599726,-0.0260353778,-0.0105401417,-0.0132227642,-0.0294341352,0.0110923816,-0.0049992367,-0.1991396695,0.2391584814,-0.1727277935,-0.0976906419,-0.2451280355,0.0471845642,-0.3421871364,-0.0774903446,-0.0103982724,-0.5276147127,0.4298349321,0.2547621727,0.2393214256,0.2254777998,-0.4373151064,0.1930025369,-0.0207110047,0.2717285752,-0.0286174472,-0.0238103941,-0.0957150683,0.0014506789,-0.0622847714,-0.3066673875,-0.2370884866,-0.3535678983,-0.3002385795,0.1684078276,-0.2077749521,-0.0839427635,-0.4613353312,0.2800756693,-0.2034398466,-0.1949636489,0.2160711884,0.311763078,-0.1525012851,-0.2019260973,0.3219577074,0.3567749262,0.1260176301,-0.2448609173,-0.1671650261,0.0270011984,0.0185945444,0.231768325,-0.196821481,0.0661111996,-0.1416386366,-0.3127050102,0.1076084748,-0.3901762068,-0.2116607726,0.1747912019,-0.1828833818,-0.2197279781,-0.031187484,0.1089667529,0.0628408641,-0.120844841,-0.152158618,-0.0259998683,-0.2195287794,-0.3192836642,-0.148110047,-0.1362634301,-0.4844519496,-0.084295705,0.0573401675,0.1630784124,0.3549596071,-0.0026099316,-0.1355063021,0.0839673728,0.0962489396,0.4097358286,0.6342180371,0.2227144986,-0.148292616,0.0710422769,-0.4192636013,0.1008315533,0.1169343814,0.3873185813,0.1521229893,-0.0850232095,-0.044359196,-0.1968233734,-0.0004862121,-0.4259001911,0.0244929101,0.1884888411,0.213853538,0.2234975249,-0.2566771209,0.095883742,-0.0294915326,0.1528879702,-0.6297127008,0.1088110358,-0.1664846092,0.0939432904,0.0237173997,0.2124968767,0.0070326482,-0.2777433097,0.0966232345,0.3237044215,-0.2774696052,0.2817159891,0.3697200418,0.1944118738,0.3049992621,-0.1333852112,-0.0943499804,-0.1449738592,-0.0889540166,0.1931201816,-0.2182801962,0.0946670026,0.0110948728,-0.0022163887,0.2311751992,0.1143167168,0.2847479582,0.0765318498,-0.051417511,-0.3194243312,0.1716019809,-0.0226421971,0.244502753,-0.0863726661,-0.1692863256,0.2999427319,0.11817801,-0.1917345077,-0.2504562438,0.2126444876,0.0553066768,-0.034940321,0.1509420872,0.3782607019,0.2094811201,0.3140182197,0.2590820789,0.2057402134,0.2297553718,-0.2771490812,0.0332849883,-0.0133661879,-0.0976354778,0.0102001075,0.2610309124,0.0893155113,-0.2038084418,0.261734277,-0.1053256765,0.2662078142,0.039454136,0.1391785294,-0.2544880807,-0.1987805665,-0.2773379087,-0.4256397486,-0.4906124175,-0.2860661149,0.050332889,0.2227463573,0.0436121225,0.0481662527,0.180276975,0.0652741268,-0.3773147464,0.074153997,-0.0229747146,-0.2716953158,-0.0949644372,0.1281139851,0.0593441203,-0.1130554304,0.537050724,-0.321662873,-0.0148123214,-0.551178515,-0.7157526612,0.1464820504,-0.1888093501,0.2367566079,0.3718587458,-0.0906212851,-0.0535561927,-0.121469602,0.1372005194,-0.3795280755,-0.2920962572,-0.2372146398,-0.1862315983,-0.0549148843,-0.1697185636,-0.5078220367,0.0070132078,-0.3114489913,0.6385708451,-0.1654120982,-0.059781149,0.3390489817,0.0503287353,-0.1282752007,-0.2300447226,0.0783430934,-0.1004794165,-0.3781207502,0.1260284483,-0.3046660423,-0.3719869554,0.3863863051,0.1393262148,0.4151358902,-0.1058664173,-0.5360229015,0.0873725712,-0.1337156445,0.1259727925,0.2594906986,0.3358611763,0.4246498942,-0.297468096,-0.0998068675,-0.1367073655,-0.1937779188,0.025569981,0.0290752817,0.3333816528,-0.1351306438,0.6006179452,-0.0499070957,0.6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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2012","title":"No upstream branch","comments":"~~What difference is there with the default `origin` remote that is set when you clone the repo?~~ I've just understood that this applies to **forks** of the repo \ud83e\udd21 ","body":"Feels like the documentation on adding a new dataset is outdated?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/987df6b4e9e20fc0c92bc9df48137d170756fd7b\/ADD_NEW_DATASET.md#L49-L54\r\n\r\nThere is no upstream branch on remote. ","comment_length":29,"text":"No upstream branch \n Feels like the documentation on adding a new dataset is outdated?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/987df6b4e9e20fc0c92bc9df48137d170756fd7b\/ADD_NEW_DATASET.md#L49-L54\r\n\r\nThere is no upstream branch on remote.  \n ~~What difference is there with the default `origin` remote that is set when you clone the repo?~~ I've just understood that this applies to **forks** of the repo \ud83e\udd21 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2010","title":"Local testing fails","comments":"I'm not able to reproduce on my side.\r\nCan you provide the full stacktrace please ?\r\nWhat version of `python` and `dill` do you have ? Which OS are you using ?","body":"I'm following the CI setup as described in \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8eee4fa9e133fe873a7993ba746d32ca2b687551\/.circleci\/config.yml#L16-L19\r\n\r\nin a new conda environment, at commit https:\/\/github.com\/huggingface\/datasets\/commit\/4de6dbf84e93dad97e1000120d6628c88954e5d4\r\n\r\nand getting\r\n\r\n```\r\nFAILED tests\/test_caching.py::RecurseDumpTest::test_dump_ipython_function - TypeError: an integer is required (got type bytes)\r\n1 failed, 2321 passed, 5109 skipped, 10 warnings in 124.32s (0:02:04)\r\n```\r\n\r\nSeems like a discrepancy with CI, perhaps a lib version that's not controlled? \r\nTried with `pyarrow=={1.0.0,0.17.1,2.0.0}`","comment_length":32,"text":"Local testing fails \n I'm following the CI setup as described in \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8eee4fa9e133fe873a7993ba746d32ca2b687551\/.circleci\/config.yml#L16-L19\r\n\r\nin a new conda environment, at commit https:\/\/github.com\/huggingface\/datasets\/commit\/4de6dbf84e93dad97e1000120d6628c88954e5d4\r\n\r\nand getting\r\n\r\n```\r\nFAILED tests\/test_caching.py::RecurseDumpTest::test_dump_ipython_function - TypeError: an integer is required (got type bytes)\r\n1 failed, 2321 passed, 5109 skipped, 10 warnings in 124.32s (0:02:04)\r\n```\r\n\r\nSeems like a discrepancy with CI, perhaps a lib version that's not controlled? \r\nTried with `pyarrow=={1.0.0,0.17.1,2.0.0}` \n I'm not able to reproduce on my side.\r\nCan you provide the full stacktrace please ?\r\nWhat version of `python` and `dill` do you have ? Which OS are you using ?","embeddings":[-0.1566001624,0.0945401862,0.0029507412,0.0519755632,-0.1364501864,-0.259108901,0.4086020291,0.2240652144,-0.1059585363,0.2676886618,-0.0058867261,0.0788329914,-0.153766185,0.5032173395,-0.2350831777,0.1064348221,0.029259108,0.151907742,-0.2006684542,0.0791208073,-0.0376023613,0.2799378037,-0.2265824378,-0.0447771065,-0.3562315404,-0.034193404,-0.2187789977,0.1896177977,-0.1608967483,-0.3254478276,0.6411889791,0.0324766263,0.0277306344,0.7641415,-0.0001287131,0.0393472388,0.4674041271,-0.1851536632,-0.3570637405,-0.2890094817,0.352075994,-0.0747951642,0.4789135456,-0.1217302904,-0.0535692163,0.3681681156,-0.3571668565,-0.2829234302,0.337782383,0.2963617146,0.0819578841,0.5929397941,-0.1577762514,-0.0510140397,-0.0935970247,0.6183962226,-0.2803871334,0.6484384537,0.4351875484,-0.1534273028,-0.1185101122,-0.0808705688,0.1125618145,0.1081115827,-0.1265399009,0.0491170213,-0.1212580875,-0.2817679346,0.1882838756,-0.0434961244,0.427869767,-0.334849149,-0.3669345677,0.2918598652,-0.1322660148,-0.3039441109,0.4314469695,-0.0946971774,-0.4797498882,0.2464614213,-0.5746465921,0.1803811342,-0.095984593,0.1889845878,-0.3290427327,0.0139242448,-0.0037794746,0.0756979659,-0.0833096504,-0.0759193376,0.0775504485,0.218075335,0.1124901474,0.2105472386,0.0192041602,-0.1825955212,0.0042889011,0.2666103542,0.2472054362,0.4240624905,-0.182477057,-0.122575298,0.0941482261,0.1986427158,0.0297927018,0.414953351,0.1767910719,0.3287169933,0.4472732842,-0.0314272344,-0.0349344239,0.21398063,0.2701272964,-0.2194721401,0.0874325112,-0.0599674396,0.1236558408,-0.4884465933,-0.2430199832,0.3342878819,-0.2255838662,0.0130314277,0.2921376526,0.237573728,0.0871120691,0.1962379962,0.1621374488,0.4079699218,-0.1178594828,0.3706064224,-0.1039627865,-0.1124639362,-0.1322607696,0.190343529,0.5385653377,-0.0471802279,0.4167149365,-0.0070279688,0.5418527126,-0.0736422986,0.0300187226,-0.0224932767,0.1987535506,0.5158859491,-0.2314249128,-0.0430944599,0.2318384051,-0.1680154502,-0.2934478521,0.1876366138,-0.036553476,-0.360126704,-0.1811524779,-0.0328332819,-0.2402826995,0.0049480121,0.4045207202,-0.4238608778,0.1729899198,0.015730679,0.0126330955,-0.2405943274,0.0043097562,0.0955928937,0.325524956,0.5624173284,-0.1283846796,-0.040545214,0.0909309685,0.0560348853,0.3275008798,0.322997272,-0.2174919546,-0.079765968,-0.1228102669,-0.1518611461,0.3863773048,-0.5328124762,-0.2762471735,0.3100045621,0.1289285719,-0.1696591526,-0.1427436322,-0.208109349,0.1203560159,0.1268339306,0.3403026164,0.0339691974,-0.1404906511,-0.0252439156,-0.3712669313,-0.2300162911,0.0015914919,0.0133343441,0.0256101005,0.1600136161,0.1667650193,-0.4279124141,0.1311002374,-0.0518474355,-0.1038099676,0.0527333058,0.4886046052,-0.4241816401,0.0636517629,0.2276821285,-0.1836992353,0.3035302758,-0.0784523413,0.313644439,-0.1373235136,-0.0588411503,-0.5334228873,0.4065414369,-0.0188787188,-0.1267283708,-0.1220251769,0.0448220596,0.3527234793,0.1135549918,-0.1742255986,0.0604515374,-0.1182366535,0.104523845,0.3406917155,-0.0194027256,-0.4063432217,-0.4013935924,-0.1491001397,-0.0286082122,0.0115148136,-0.2519133389,-0.3854123652,0.2479814291,0.0569710359,-0.2244205177,0.2305828482,0.0816854611,0.2954339981,-0.0162090566,0.0367697999,0.0003966364,-0.1849222481,-0.0339136943,0.0098309023,0.3970498443,-0.0316920765,0.169982478,-0.1750234216,0.1441203803,0.4002425969,0.0310115479,-0.1585004479,-0.2736608684,0.3045320809,-0.0049121412,0.5264109969,0.0516441762,-0.1131985411,-0.019890476,-0.237845093,-0.1569035947,0.0667518228,0.3204009533,0.2629225552,-0.013487855,0.2638959587,0.0033946426,0.503872633,-0.1240240857,0.1816550493,0.1384320855,-0.2771652043,-0.3122504652,0.0401483029,-0.1818047613,-0.096017167,0.2377836555,0.2507990301,-0.0702297986,-0.2175699323,-0.2124915868,-0.1106227636,0.3328876495,-0.2270733416,0.2456769198,-0.1492201388,0.1999557167,-0.1773367077,-0.2198433727,-0.1061055735,-0.2637876868,-0.0378574058,0.2395730466,-0.0407100022,0.2350499034,-0.3171925545,0.4599715471,0.1577052921,-0.6352958083,-0.2629722655,-0.1444387585,-0.3395789862,-0.0776634365,0.3256201744,-0.3727446198,0.1843812317,-0.4821203649,0.012367812,0.1615027338,-0.4731438756,0.2949295938,0.135427624,0.5952253342,-0.0459183864,0.1508339494,-0.1922258884,-0.3344004452,0.4108138978,-0.319222331,-0.3365814984,0.0547794253,0.007266148,-0.16813609,-0.5058836341,-0.3063298166,-0.3148087859,-0.0533497594,0.1918007135,0.0501124673,0.1443279386,0.3339980245,0.4101510048,-0.0044366512,0.2073384672,0.0506999753,-0.0100285169,-0.4861396253,0.2371548414,-0.3061432242,-0.3615093827,-0.1987366676,-0.0529225543,0.3845674992,0.184077844,-0.5582749844,-0.2909577489,-0.1777923852,0.454624474,0.1030155793,-0.0468172766,0.2920683026,0.0422036573,0.0365013853,-0.0270703416,-0.3199698031,0.2881191969,0.0237831213,0.3060825765,-0.0239138398,0.1803099811,-0.0305550229,0.7351323962,0.1958531886,-0.240850091,0.3919067085,0.1805097163,0.3743961453,-0.4311841428,-0.2628128529,0.2902439833,0.0368955061,-0.446859747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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2010","title":"Local testing fails","comments":"```\r\nco_filename = '<ipython-input-2-e0383a102aae>', returned_obj = [0]\r\n                                                                                                                                                                       \r\n    def create_ipython_func(co_filename, returned_obj):\r\n        def func():\r\n            return returned_obj\r\n     \r\n        code = func.__code__\r\n>       code = CodeType(*[getattr(code, k) if k != \"co_filename\" else co_filename for k in code_args])\r\nE       TypeError: an integer is required (got type bytes)\r\n\r\ntests\/test_caching.py:152: TypeError\r\n```\r\n\r\nPython 3.8.8 \r\ndill==0.3.1.1\r\n","body":"I'm following the CI setup as described in \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8eee4fa9e133fe873a7993ba746d32ca2b687551\/.circleci\/config.yml#L16-L19\r\n\r\nin a new conda environment, at commit https:\/\/github.com\/huggingface\/datasets\/commit\/4de6dbf84e93dad97e1000120d6628c88954e5d4\r\n\r\nand getting\r\n\r\n```\r\nFAILED tests\/test_caching.py::RecurseDumpTest::test_dump_ipython_function - TypeError: an integer is required (got type bytes)\r\n1 failed, 2321 passed, 5109 skipped, 10 warnings in 124.32s (0:02:04)\r\n```\r\n\r\nSeems like a discrepancy with CI, perhaps a lib version that's not controlled? \r\nTried with `pyarrow=={1.0.0,0.17.1,2.0.0}`","comment_length":47,"text":"Local testing fails \n I'm following the CI setup as described in \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8eee4fa9e133fe873a7993ba746d32ca2b687551\/.circleci\/config.yml#L16-L19\r\n\r\nin a new conda environment, at commit https:\/\/github.com\/huggingface\/datasets\/commit\/4de6dbf84e93dad97e1000120d6628c88954e5d4\r\n\r\nand getting\r\n\r\n```\r\nFAILED tests\/test_caching.py::RecurseDumpTest::test_dump_ipython_function - TypeError: an integer is required (got type bytes)\r\n1 failed, 2321 passed, 5109 skipped, 10 warnings in 124.32s (0:02:04)\r\n```\r\n\r\nSeems like a discrepancy with CI, perhaps a lib version that's not controlled? \r\nTried with `pyarrow=={1.0.0,0.17.1,2.0.0}` \n ```\r\nco_filename = '<ipython-input-2-e0383a102aae>', returned_obj = [0]\r\n                                                                                                                                                                       \r\n    def create_ipython_func(co_filename, returned_obj):\r\n        def func():\r\n            return returned_obj\r\n     \r\n        code = func.__code__\r\n>       code = CodeType(*[getattr(code, k) if k != \"co_filename\" else co_filename for k in code_args])\r\nE       TypeError: an integer is required (got type bytes)\r\n\r\ntests\/test_caching.py:152: TypeError\r\n```\r\n\r\nPython 3.8.8 \r\ndill==0.3.1.1\r\n","embeddings":[-0.1595425755,0.0836868957,0.0080741448,0.0541945584,-0.0605914593,-0.2532820702,0.4322317541,0.3210858405,0.0665906072,0.2115702927,-0.0241569411,0.1039160192,-0.1923717856,0.4952004254,-0.2158735842,0.1359183192,0.0552796796,0.1934275776,-0.1318421066,0.0838604644,-0.095966205,0.2499605566,-0.2564118505,-0.04104276,-0.3022986948,-0.0245991461,-0.148721233,0.1830328107,-0.1826664209,-0.3603295684,0.6797194481,0.0199894588,-0.0009315464,0.6896492839,-0.00012576,0.0270796269,0.5052758455,-0.1809416413,-0.4039542675,-0.3320510089,0.1519881189,0.0037361854,0.3448631763,-0.1304060519,-0.0594432652,0.4255079925,-0.269272536,-0.3886942267,0.2537177503,0.3457771242,0.0861626714,0.6625935435,-0.1529203802,-0.0738562047,-0.0913394094,0.7564921379,-0.2964876592,0.6160543561,0.3982497454,-0.1529932469,-0.0890129432,-0.0370634012,0.1238623559,0.2078801244,-0.1936955452,0.1046222895,-0.1856215894,-0.3124096692,0.127361849,-0.0407238081,0.3077149093,-0.2930814624,-0.4210140109,0.2640115619,-0.1962429136,-0.4216315448,0.4477261603,-0.0321058594,-0.5543996096,0.1441136599,-0.6164290905,0.3599109054,-0.0956348106,0.1937013716,-0.2613513172,-0.0860241354,-0.1219527274,0.1039446294,-0.068969138,-0.1067535803,-0.0950204208,0.1158439144,0.0893242508,0.2673538625,0.0417058803,-0.2120662332,0.074565962,0.2886244953,0.1956859827,0.4594943821,-0.2210506797,-0.0970078409,0.0751854256,0.1858525425,0.0422291197,0.3725644946,0.1567064077,0.3691079617,0.40904212,0.0981971771,-0.1236698702,0.1423254311,0.2234022915,-0.2243603319,0.1463966519,0.0168841872,0.1898580343,-0.4522972107,-0.281761229,0.305057317,-0.1572923958,0.0335101858,0.3083818853,0.2298793048,0.0924803242,0.1790280342,0.1544673294,0.316252321,-0.217780754,0.2494838536,-0.0718299225,-0.0928375646,-0.1575595587,0.2292731553,0.5492322445,0.0396385491,0.405988425,0.0170017499,0.5969008803,-0.0530274436,0.084572956,-0.0820266455,0.2296279371,0.461274296,-0.2623299658,0.0007759786,0.2538804114,-0.1570122689,-0.309691608,0.0716572851,0.0254918672,-0.3389891982,-0.1903701574,-0.0041225697,-0.236270383,0.0440888926,0.3062964976,-0.4115735888,0.2350123227,-0.0339255668,0.066669412,-0.297963053,0.0334088169,0.0907244608,0.294031769,0.5816891789,0.0320189446,-0.0587128475,0.0872225538,0.1703658998,0.3222624958,0.3197820783,-0.2711986601,-0.080106847,-0.2009895593,-0.2122240365,0.4604290128,-0.5281284451,-0.2205269933,0.3383586407,0.1630102247,-0.0469934419,-0.024037499,-0.177936852,0.1230152026,0.0250453204,0.3560113013,0.02011583,-0.1439755708,-0.0507279299,-0.3050992787,-0.2096912265,-0.0326529033,-0.052001711,-0.0125007462,0.1478217989,0.1682515889,-0.3553977609,0.1316869557,-0.0926364511,-0.1003679484,-0.0265650917,0.4984470904,-0.436465919,-0.0033532269,0.1779424399,-0.1466665268,0.3157603443,-0.0652014092,0.3325464427,-0.1267909557,-0.0786102414,-0.4938027263,0.3182767332,-0.0920939893,-0.1416630447,-0.0879943743,0.1350090951,0.3590696454,0.1158088967,-0.2030635178,0.1976779103,-0.1674304605,0.0674079508,0.1798962653,-0.1173199266,-0.331875056,-0.3938655853,-0.1756162345,-0.0437940694,-0.0576129481,-0.2317662388,-0.3719640672,0.2077530324,0.0961100161,-0.1659799218,0.1020836383,0.0658373535,0.2748291492,0.0711638778,0.0056704115,0.0138983317,-0.1340230554,0.0324418806,-0.0586631671,0.5032771826,0.0161019936,0.1436363161,-0.1191534922,0.0993645117,0.3589843512,0.0462640114,-0.2223187238,-0.2488673031,0.3026212454,0.0815824345,0.4269850552,0.1282005608,-0.1495231241,0.0208277684,0.0208050832,-0.16235511,0.0347993001,0.2654781342,0.2746232152,-0.0285593122,0.2743022442,-0.0487582088,0.6123738885,-0.1288354844,0.1622859985,0.0833441317,-0.2782640755,-0.2611792386,0.0942928717,-0.1272592396,-0.1881013811,0.1684157401,0.2399555296,-0.0283272844,-0.2438034415,-0.2251516283,-0.1123573557,0.2600666583,-0.3167208731,0.3092109561,-0.1657240987,0.1626483798,-0.2243668437,-0.1949146241,0.0339774676,-0.2156327367,-0.1114063933,0.3362267911,-0.0429508388,0.2492925078,-0.3598666489,0.3789109588,0.1270722896,-0.6568129063,-0.2761151493,-0.0493920036,-0.358027786,-0.0783266053,0.3884337544,-0.3923020363,0.2230754346,-0.4847135246,-0.0083078006,0.1642359793,-0.4976350963,0.2448467165,0.1052220985,0.5596272945,0.0088488068,0.0886570513,-0.2389024943,-0.4123895466,0.4063134193,-0.2631421983,-0.3811514676,0.0973349065,-0.0332760774,-0.2352034003,-0.5069959164,-0.3434551358,-0.2640545368,-0.0690315664,0.3037874699,0.0882631391,0.1101689935,0.4585253894,0.408048898,-0.0020045231,0.1282039881,0.0467161201,-0.0791560635,-0.5806210637,0.2987908721,-0.3213188052,-0.306871295,-0.239809677,-0.1218192056,0.4034667313,0.2295228094,-0.4456574023,-0.1923150271,-0.191101715,0.4425347149,0.1964614242,0.0090472912,0.2945613563,0.0079896888,0.0029951094,-0.0171324536,-0.2513278127,0.2703384757,0.1097452715,0.316788733,0.0037349085,0.2698606253,0.0379693881,0.6234934926,0.3126415014,-0.2371502519,0.3645984828,0.1278417557,0.4174308479,-0.5137651563,-0.3205949366,0.3046328425,-0.02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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2010","title":"Local testing fails","comments":"I managed to reproduce. This comes from the CodeType init signature that is different in python 3.8.8\r\nI opened a PR to fix this test\r\nThanks !","body":"I'm following the CI setup as described in \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8eee4fa9e133fe873a7993ba746d32ca2b687551\/.circleci\/config.yml#L16-L19\r\n\r\nin a new conda environment, at commit https:\/\/github.com\/huggingface\/datasets\/commit\/4de6dbf84e93dad97e1000120d6628c88954e5d4\r\n\r\nand getting\r\n\r\n```\r\nFAILED tests\/test_caching.py::RecurseDumpTest::test_dump_ipython_function - TypeError: an integer is required (got type bytes)\r\n1 failed, 2321 passed, 5109 skipped, 10 warnings in 124.32s (0:02:04)\r\n```\r\n\r\nSeems like a discrepancy with CI, perhaps a lib version that's not controlled? \r\nTried with `pyarrow=={1.0.0,0.17.1,2.0.0}`","comment_length":27,"text":"Local testing fails \n I'm following the CI setup as described in \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/8eee4fa9e133fe873a7993ba746d32ca2b687551\/.circleci\/config.yml#L16-L19\r\n\r\nin a new conda environment, at commit https:\/\/github.com\/huggingface\/datasets\/commit\/4de6dbf84e93dad97e1000120d6628c88954e5d4\r\n\r\nand getting\r\n\r\n```\r\nFAILED tests\/test_caching.py::RecurseDumpTest::test_dump_ipython_function - TypeError: an integer is required (got type bytes)\r\n1 failed, 2321 passed, 5109 skipped, 10 warnings in 124.32s (0:02:04)\r\n```\r\n\r\nSeems like a discrepancy with CI, perhaps a lib version that's not controlled? \r\nTried with `pyarrow=={1.0.0,0.17.1,2.0.0}` \n I managed to reproduce. This comes from the CodeType init signature that is different in python 3.8.8\r\nI opened a PR to fix this test\r\nThanks !","embeddings":[-0.2129509598,0.0823646188,0.0238413643,0.0946619436,0.0496156551,-0.1908244491,0.3812552691,0.3104431927,0.0505841784,0.2253711671,0.1375240833,0.1101146713,-0.1860496998,0.6575943232,-0.1123831272,0.1562343091,0.0674784482,0.2533706129,-0.0266020261,0.131237939,-0.1200583279,0.2680254579,-0.2432248741,-0.0653258264,-0.2336260676,-0.0076859454,-0.1573687196,0.2269874364,-0.1869493574,-0.5005517006,0.7258083224,-0.0596951246,-0.01177736,0.6850973368,-0.0001276803,0.0460287258,0.5900765061,-0.1311247945,-0.2848140895,-0.2090544403,0.1225332916,-0.009903796,0.4425723255,-0.0584858656,-0.0636177137,0.4232131541,-0.3844325244,-0.450411737,0.3081777394,0.3412259221,0.0856561214,0.6891777515,-0.1833498776,-0.0647268742,-0.1557070017,0.7216380835,-0.2735095322,0.5735644698,0.4503455758,-0.1201026291,-0.0678781569,-0.1077148542,0.1148411334,0.1880001724,-0.1224890649,0.0224849395,-0.0936857313,-0.1882404387,0.1383006722,-0.0737014711,0.3249828219,-0.2547633648,-0.3429161608,0.1858745962,-0.1495690942,-0.5330841541,0.5146340728,-0.1113534197,-0.476841867,0.1915977299,-0.5451625586,0.2967489064,-0.0748954862,0.1553913951,-0.3125766516,-0.083272025,-0.1046864837,0.110724628,-0.0891967937,-0.0674922615,0.0989183336,0.1787144542,-0.0076315664,0.2872788608,0.1070478484,-0.2111832947,0.014469808,0.3994742036,0.1787104905,0.4556684494,-0.2514183819,-0.0727530271,0.122231327,0.1218675002,-0.0088822292,0.3863516748,0.2146190107,0.230663076,0.3758414984,0.0232177433,-0.0810358673,0.1415196359,0.2208197266,-0.2816311717,0.2822886109,-0.0321859568,0.1462036818,-0.4389745295,-0.0922711268,0.2229402214,-0.0682022572,0.0167919975,0.2327680588,0.2272420973,0.1824134588,0.1523486078,0.0944300443,0.4298304319,-0.1778352708,0.2726736963,-0.0876457244,-0.1687568575,-0.1537641287,0.1956510395,0.4986808002,-0.1537046432,0.4561303854,-0.0057980446,0.6063702703,-0.0310987569,0.0206634328,-0.027587628,0.2237470895,0.4527343512,-0.2669318318,-0.0231731255,0.3080402613,-0.2273074985,-0.3217340708,0.0499487519,0.1005844027,-0.4410426617,-0.2197590023,-0.0188295841,-0.283090204,-0.0106937038,0.272988081,-0.4864016771,0.2079430223,0.0007704758,0.0649352446,-0.2876134515,0.1068172902,0.1075926796,0.2090523392,0.5335060358,0.0195477121,-0.0479588173,0.0798538178,0.0793181211,0.285722822,0.3176197708,-0.267046392,-0.0958705693,-0.199275896,-0.1947425008,0.4257667959,-0.5156852007,-0.1774297804,0.3740007281,0.1563418657,-0.1985573322,-0.0494492166,-0.2684140801,0.0657720938,0.098622635,0.240212962,0.0567436665,-0.1665887833,0.060193494,-0.3022293746,-0.2904709876,-0.0210461076,0.0338267833,0.0249781422,0.176968202,0.199806124,-0.4017611742,0.1179298162,-0.0926960185,-0.1164355874,0.0205856804,0.5448556542,-0.3864399791,-0.0036130815,0.1999371499,-0.0327697322,0.3577795327,-0.0780031607,0.2045546025,-0.1400953829,-0.0209076274,-0.5408804417,0.3800971806,-0.0991014838,-0.0528328083,-0.1179859787,0.0609114096,0.3830114007,0.1435604393,-0.2402081192,0.0653643534,-0.0774748623,0.094665207,0.2909987569,-0.0861129239,-0.3287919462,-0.4251502156,-0.15633443,-0.0233165193,0.0048968778,-0.2242097408,-0.4006009698,0.2171485126,0.088674739,-0.1416569501,0.074613139,0.0953619629,0.2319166362,0.0282800142,-0.0058214879,0.0119786272,-0.1869169474,-0.0353843607,0.0147935646,0.4722611308,0.0298198331,0.0833989531,-0.1591881812,0.1093278974,0.353066206,0.0234856755,-0.1824235171,-0.3159005642,0.2667004168,0.0317160487,0.588888526,0.1291597039,-0.1410611421,-0.1075278744,-0.0508211851,-0.1599721909,-0.0017898654,0.1870503128,0.2271011174,-0.0158884823,0.2266928256,-0.1101670861,0.553195715,-0.1585794538,0.074934043,0.1816506088,-0.2237858772,-0.3028706908,0.1822277009,-0.1517623514,-0.0119571825,0.1467459798,0.2263767421,-0.0592967048,-0.2383322716,-0.2199627608,-0.0692370012,0.1834665239,-0.3558005095,0.240716666,-0.1439508647,0.0936436579,-0.3681105375,-0.2125537544,0.0038098046,-0.253749311,-0.1256662607,0.2570477128,0.0167053044,0.2124892622,-0.3055720925,0.4417043328,0.1103474349,-0.6165693402,-0.230748862,-0.0421803705,-0.2874609232,-0.1102833077,0.3440142572,-0.4251949191,0.1442624032,-0.5436748266,-0.0106434077,0.2099768966,-0.5716857314,0.3610416055,0.1101282462,0.5630653501,0.0908894315,0.1359783709,-0.0833356082,-0.32854563,0.3907449543,-0.2811042964,-0.320284456,0.1116458178,-0.0592874885,-0.2386706769,-0.4834084809,-0.224175185,-0.1732220352,-0.0486699156,0.2961143851,0.0813507736,0.0692149922,0.4372489452,0.3239246309,-0.0071029454,0.0879161656,0.0492147356,-0.0323043391,-0.6007462144,0.2518290579,-0.2372844368,-0.2854530513,-0.1597947329,-0.0864265561,0.4862286747,0.0981935784,-0.3998621404,-0.1802986115,-0.1212024838,0.4978191555,0.1771852523,-0.0901314989,0.3163525462,0.0246620309,0.0493826084,-0.0573718548,-0.2262752652,0.1844124347,-0.004375041,0.3145067394,0.017887475,0.2293648869,-0.0737022236,0.6722061038,0.2346595973,-0.2608801723,0.3040290177,0.0912792236,0.3293498755,-0.4512397051,-0.2017839253,0.2601115406,0.024302505,-0.4762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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2009","title":"Ambiguous documentation","comments":"Hi @theo-m !\r\n\r\nA few lines above this line, you'll find that the `_split_generators` method returns a list of `SplitGenerator`s objects:\r\n\r\n```python\r\ndatasets.SplitGenerator(\r\n    name=datasets.Split.VALIDATION,\r\n    # These kwargs will be passed to _generate_examples\r\n    gen_kwargs={\r\n        \"filepath\": os.path.join(data_dir, \"dev.jsonl\"),\r\n        \"split\": \"dev\",\r\n    },\r\n),\r\n```\r\n\r\nNotice the `gen_kwargs` argument passed to the constructor of `SplitGenerator`: this dict will be unpacked as keyword arguments to pass to the `_generat_examples` method (in this case the `filepath` and `split` arguments).\r\n\r\nLet me know if that helps!","body":"https:\/\/github.com\/huggingface\/datasets\/blob\/2ac9a0d24a091989f869af55f9f6411b37ff5188\/templates\/new_dataset_script.py#L156-L158\r\n\r\nLooking at the template, I find this documentation line to be confusing, the method parameters don't include the `gen_kwargs` so I'm unclear where they're coming from.\r\n\r\nHappy to push a PR with a clearer statement when I understand the meaning.","comment_length":79,"text":"Ambiguous documentation \n https:\/\/github.com\/huggingface\/datasets\/blob\/2ac9a0d24a091989f869af55f9f6411b37ff5188\/templates\/new_dataset_script.py#L156-L158\r\n\r\nLooking at the template, I find this documentation line to be confusing, the method parameters don't include the `gen_kwargs` so I'm unclear where they're coming from.\r\n\r\nHappy to push a PR with a clearer statement when I understand the meaning. \n Hi @theo-m !\r\n\r\nA few lines above this line, you'll find that the `_split_generators` method returns a list of `SplitGenerator`s objects:\r\n\r\n```python\r\ndatasets.SplitGenerator(\r\n    name=datasets.Split.VALIDATION,\r\n    # These kwargs will be passed to _generate_examples\r\n    gen_kwargs={\r\n        \"filepath\": os.path.join(data_dir, \"dev.jsonl\"),\r\n        \"split\": \"dev\",\r\n    },\r\n),\r\n```\r\n\r\nNotice the `gen_kwargs` argument passed to the constructor of `SplitGenerator`: this dict will be unpacked as keyword arguments to pass to the `_generat_examples` method (in this case the `filepath` and `split` arguments).\r\n\r\nLet me know if that helps!","embeddings":[0.0630255416,-0.0559889339,-0.0537045784,0.1070300266,0.0235678162,0.1693243831,0.3407925665,0.1025089547,-0.1445971131,-0.1650757343,0.0825752988,0.3592206538,0.0475475378,0.0229658056,0.1957679987,-0.226720497,0.0967702419,0.1418141127,-0.0160175748,-0.1553122252,-0.3404794335,0.260173142,-0.3286114335,0.3037527204,-0.2405357361,-0.2140244842,-0.1296740472,0.3539068997,-0.4259344935,-0.2638630569,0.2149847448,0.1732774228,-0.1561237723,0.1987597942,-0.0001124477,0.0061698449,0.294578284,-0.1892530769,-0.1790917218,0.0210828166,-0.3679366112,-0.177504614,0.0289063752,-0.3533598781,0.1910025924,-0.3274487853,0.0365873501,-0.1565573066,0.5122282505,0.1903434098,0.1555772275,0.0230273176,0.1395213306,-0.1815897673,-0.1611030996,0.5714946985,-0.2454582304,-0.1139202565,0.0408839472,0.1185335889,0.0628223196,0.2183078676,0.0905427784,-0.1116909012,0.4655440748,-0.0411249548,-0.2104089409,-0.5389917493,0.097147584,0.2854478657,0.5018543601,-0.2640081048,-0.2595461011,-0.2530981004,-0.1830708086,-0.1483088136,-0.1307505369,0.347219348,-0.3724952936,-0.0102221146,-0.3185660541,-0.2991984189,-0.26646173,-0.3025457561,0.1488544345,0.3738004565,-0.1506491452,0.1400251687,0.0473176725,0.1209765524,-0.0652601048,-0.227573663,0.0284532961,0.4920312762,0.0334739983,-0.1478964984,0.2251163274,0.0053131371,0.4009979963,0.2251070142,-0.0026085726,-0.2663418651,0.0731081888,0.1978611946,0.358804673,-0.2031565905,0.3699664474,0.0002882908,0.0691666305,0.0147317173,0.226358518,-0.1669269949,0.1684111953,-0.3830891252,0.0449414216,0.0379318297,0.3937673271,-0.1123331413,-0.0958527774,0.0414182171,-0.1373912543,-0.3935705125,0.0558243245,0.0493346043,0.2171518803,-0.0029187831,-0.089061588,-0.1021661758,-0.1244849116,-0.2616605163,-0.2409771383,0.2836477757,-0.1866672635,0.1743055582,-0.1942203939,-0.2025459409,0.3773424625,0.2009484172,0.2653453946,0.1425868273,0.1070011929,0.1838041246,0.1337120235,0.2818725109,-0.0353092216,0.1737410277,0.220831573,0.0379706286,-0.302809,0.0211159531,-0.4155864716,-0.3842844665,0.045171719,0.105499588,-0.2765238583,0.1617227197,-0.2013297826,0.3348644376,0.0546006337,-0.1171721071,0.0920717791,0.0104708672,-0.0390683413,-0.5046525598,0.312372297,0.4569101334,-0.4068639278,-0.1470859945,-0.1976564378,-0.2394910455,-0.0142202331,0.0178308152,-0.3050229847,0.4071798325,-0.2098042667,0.4111057222,0.5502249599,-0.0264273565,0.2617900968,0.4143196642,0.2440982014,0.0400957167,0.1967811733,-0.3653977811,0.1281254292,-0.1726318449,-0.2287610918,0.1094249412,-0.120420076,-0.2198890299,0.0926813558,0.0025510988,-0.1228934303,-0.1769018918,-0.115951471,0.0135553312,0.0102812005,0.1956638843,0.4094674885,-0.2382920235,0.1461001784,0.3196187913,0.3091386259,-0.1995504797,0.222786501,0.0575036407,-0.6098825932,-0.0888738409,0.0193511471,0.1056629047,-0.1684192419,-0.2631372213,-0.3937975764,0.0714038163,-0.2420980334,-0.2368248403,0.1145801023,-0.0982949883,0.2783874869,-0.1827614307,-0.3041557968,-0.0179167967,-0.0206736829,0.0775979906,-0.6391506195,0.1906316578,-0.0363016762,0.1559221745,-0.0530466586,0.0459443703,0.0631185621,-0.2324765623,0.1714751869,0.4587950706,0.4186858833,-0.0333929174,-0.0633999854,0.3129434288,-0.0341993682,-0.0261276308,0.3035819829,-0.0439402387,0.1650856137,0.0573666841,-0.3358438015,0.4323942661,-0.2407272309,0.3330581188,0.2003154308,0.0207136869,0.1567843109,0.1918630004,-0.1606838405,-0.113787815,-0.2559685409,0.206464842,0.3144348562,0.0809791163,-0.2916631997,0.4554891586,0.5633227825,-0.3260163963,-0.2243342996,0.0192822777,0.0195236579,-0.3728529811,-0.0673384592,0.3394281268,0.5309390426,0.024987286,0.1743120849,0.0886852294,-0.267105937,-0.226940006,0.4196886718,0.0794181749,0.0610033311,0.065126434,-0.5002262592,-0.265781343,-0.3489812315,-0.1541882008,0.2562901676,0.2185079604,-0.367577225,0.2802488208,0.0644766912,0.1209354028,-0.262529254,-0.1141392887,-0.2750662565,-0.5612815619,0.042691756,-0.0299975779,-0.0898886174,0.0450083874,0.0352852643,0.2285885364,-0.0247040503,0.1770927012,-0.3000349402,-0.1925522536,-0.0262379795,0.1083843559,-0.0044304915,0.0198434778,0.2194134891,-0.1660426855,0.1192978993,-0.266951263,-0.3701042235,0.2968113124,-0.4268538654,0.2854997516,0.7126973271,0.3422035873,0.0494979918,-0.2332897037,0.0504644662,-0.1468913406,-0.2825329006,-0.2010700405,-0.1071046591,0.072487399,-0.3547919393,-0.5272268653,0.0341721885,-0.2080563158,0.2492889166,-0.0573888198,0.2517394722,0.2855532169,0.2062866837,0.2444442958,0.0874435753,0.1648581028,-0.1609761119,-0.3166238368,0.2856165767,-0.4865406454,-0.1536630541,0.1032462344,-0.0922755897,0.4634689391,-0.0369105339,-0.106406264,-0.3701996505,-0.1186551675,0.0424704887,0.0186975393,0.3841692209,0.1690154821,-0.0273057893,0.0256089401,-0.3647847772,-0.1000140235,0.0551919639,-0.3473075926,0.1823521256,0.0608928725,0.4155501425,-0.0704485551,0.6626817584,0.3334254026,0.0407076627,-0.0579508692,-0.1177552864,0.4817964137,-0.019930752,-0.1661497205,0.0748713687,0.1015366763,-0.1329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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2009","title":"Ambiguous documentation","comments":"Oh ok I hadn't made the connection between those two, will offer a tweak to the comment and the template then - thanks!","body":"https:\/\/github.com\/huggingface\/datasets\/blob\/2ac9a0d24a091989f869af55f9f6411b37ff5188\/templates\/new_dataset_script.py#L156-L158\r\n\r\nLooking at the template, I find this documentation line to be confusing, the method parameters don't include the `gen_kwargs` so I'm unclear where they're coming from.\r\n\r\nHappy to push a PR with a clearer statement when I understand the meaning.","comment_length":23,"text":"Ambiguous documentation \n https:\/\/github.com\/huggingface\/datasets\/blob\/2ac9a0d24a091989f869af55f9f6411b37ff5188\/templates\/new_dataset_script.py#L156-L158\r\n\r\nLooking at the template, I find this documentation line to be confusing, the method parameters don't include the `gen_kwargs` so I'm unclear where they're coming from.\r\n\r\nHappy to push a PR with a clearer statement when I understand the meaning. \n Oh ok I hadn't made the connection between those two, will offer a tweak to the comment and the template then - thanks!","embeddings":[0.0907537043,-0.1784818918,-0.0821813196,-0.1347765923,0.2026104182,0.1043046713,0.4321020842,0.0742146522,-0.0925442725,-0.1297511011,0.1296102107,0.2071546614,0.0149755012,0.1009712443,0.3126719594,0.0319476649,0.2020220309,0.0814612806,-0.1270157397,-0.1860020608,-0.3509488404,0.1204457358,-0.2026405036,0.2474186122,-0.3214331269,-0.0715385154,-0.0957001448,0.3153885305,-0.380150795,-0.3472014368,0.3936366141,0.2135883868,-0.1184881628,0.1507874429,-0.0001064841,0.0313349441,0.4308875203,-0.1848325431,-0.1925987154,0.2388115823,-0.2063976526,-0.2435216606,0.0402131043,-0.3196239471,0.0919546187,-0.2830057442,0.1082213372,-0.0474153124,0.3947280943,0.2065674663,0.2524823844,0.0181180071,0.0514019579,-0.2937716544,-0.0214617737,0.3528165221,-0.2551085949,0.1114547998,0.1892898679,-0.0178190805,-0.2224143296,0.3152564764,0.0603582934,-0.2014649063,0.5262850523,-0.0914600566,0.1253803372,-0.3355511725,0.2306743711,0.1955230981,0.6530069709,-0.2880720198,-0.3390621841,-0.1587803215,-0.0159975514,-0.1725635082,-0.1106833965,0.1456159502,-0.4438188076,0.0985820666,-0.3290769756,-0.2995276451,-0.3381352425,-0.1893777251,0.1041536853,0.3985577524,-0.1467222124,0.0287834778,-0.079810366,-0.0637106225,0.0606430843,-0.0514689386,-0.0620313957,0.4795161188,0.0475221723,-0.0571064651,0.3913280666,0.3072203398,0.4849086404,0.3319509327,0.0647529662,-0.1249570772,0.0482135676,0.1219810545,0.1688570827,-0.0952121541,0.2625269592,-0.1981397867,0.1932673156,0.0716884732,0.219352603,-0.1851039827,0.1651634425,-0.4140134454,0.096027039,-0.1382640302,0.3235099018,-0.2193565518,-0.0478596203,0.0212953109,0.1541705281,-0.2329805791,0.0033720983,0.1460006982,0.0977548808,-0.0294088554,-0.0267413203,-0.0824776515,-0.1639438123,-0.0177769605,-0.33675161,0.2282175273,-0.2819629014,0.2159061134,-0.2383446097,-0.1050835401,0.3639842868,0.2932413518,0.2600688338,0.3762291372,-0.1156295836,0.2062501162,0.1441384554,0.2639551163,-0.30725196,0.1014636084,0.15148139,0.097640872,-0.21210967,0.0222522859,-0.3375568688,-0.4068917036,0.033155825,0.1558642685,-0.2776870728,-0.0517162718,-0.0528339557,0.4435343444,0.1255674064,-0.1862482131,0.043944709,0.0011063695,-0.0643742234,-0.4972919822,0.3103567958,0.4556650817,-0.300209254,-0.201537326,-0.0556298494,-0.258897543,0.1801887602,0.1800517738,-0.2338027507,0.115781568,-0.2990383506,0.2452955097,0.4009967446,-0.0305048618,0.1852113158,0.2886868417,0.2408436984,-0.0456033088,0.2281843424,-0.1671770811,-0.1202177703,-0.1961425096,-0.1287868172,-0.0771344081,0.0493371077,-0.1397655308,-0.1034402624,-0.0711304918,-0.0705191121,-0.1175256222,-0.1683060378,0.0547597893,0.0953352898,0.101554729,0.393232435,-0.2345935851,0.1285251677,0.3467071354,0.4189898372,-0.1288628429,0.0794210732,0.0565179512,-0.6715270877,-0.0466049649,0.0131097082,0.2423960418,-0.1882204562,-0.2485075593,-0.4149152637,0.0028945433,-0.0466381311,-0.2650403976,0.149155423,-0.0113790995,0.1515475214,-0.1366390139,-0.1981600374,-0.0785095468,-0.0282414369,0.011772505,-0.4073155522,0.126412645,-0.0308604874,0.0549484864,0.0660938174,0.1211642995,0.1455517113,0.0127390949,0.2302755415,0.3799113333,0.2193588614,0.0544941276,0.1166277677,0.367143631,-0.1192397103,0.0965395421,0.2640690506,-0.0831126943,0.1438301951,-0.0361757167,-0.3096537292,0.2935355604,0.1455044299,0.1645067036,0.1982659847,-0.0875326321,0.1234214157,0.1111753657,-0.1437384039,-0.1031609476,-0.2327030599,0.1511912048,0.4141843617,0.094940789,-0.3783423901,0.4364842176,0.2460189909,-0.2534933388,-0.1884205341,0.1434516311,0.0920640752,-0.4229130745,-0.0424715467,-0.0089846468,0.4792378545,0.1818936318,0.2370540947,0.1093299687,-0.2840774655,-0.3740889728,0.2590414286,-0.0067262975,-0.0551690087,0.02403065,-0.1259056628,-0.2680931985,-0.2931367159,-0.0103911245,0.0646330044,0.2418514639,-0.4246813059,0.1512791514,0.0138725247,-0.0488583334,-0.2073214501,-0.132053405,-0.367715776,-0.5768344402,0.0648537576,0.0012631109,-0.2023102194,0.0999623239,-0.1299651265,0.3149814904,0.0695011169,0.2884173691,-0.3500846326,-0.2509894371,0.0356564596,0.1521712393,-0.0046584504,-0.1069710329,0.3328808546,-0.1114894897,0.0681734979,-0.438057214,-0.5030035973,0.1767327338,-0.3395259678,0.3017337918,0.5701349378,0.4036961198,0.0565251261,-0.0830231905,0.0947981849,-0.1777533293,-0.2436972857,-0.2531808615,-0.1625373214,0.0825258419,-0.3432886004,-0.3473718464,-0.0341793224,-0.2782125473,0.2322392166,0.0811353996,0.1493464857,0.4569232464,0.1185975596,0.1602661461,0.0463690646,0.1483184695,-0.2061444074,-0.3614016771,0.3522810936,-0.3719358444,-0.2803904116,0.0186990555,0.0436760001,0.4063167274,-0.0261209644,-0.2084701061,-0.4375233948,-0.0988153741,0.2255115807,0.0795594454,0.463264823,0.1754227877,-0.1288135052,-0.1690650284,-0.3697242439,-0.1438215226,0.0056055249,-0.5320705771,0.1350747496,0.116126202,0.2016551346,-0.1373723596,0.6183996201,0.1834114045,-0.0393332243,0.0863244534,-0.235701263,0.6174334884,-0.1425574422,-0.0519838259,0.1715782732,0.045517414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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2007","title":"How to not load huggingface datasets into memory ","comments":"So maybe a summary here: \r\nIf I could fit a large model with batch_size = X into memory, is there a way I could train this model for huge datasets with keeping setting the same? thanks ","body":"Hi\r\nI am running this example from transformers library version 4.3.3:\r\n(Here is the full documentation https:\/\/github.com\/huggingface\/transformers\/issues\/8771 but the running command should work out of the box)\r\n\r\n USE_TF=0  deepspeed  run_seq2seq.py --model_name_or_path google\/mt5-base --dataset_name wmt16 --dataset_config_name ro-en --source_prefix \"translate English to Romanian: \" --task translation_en_to_ro   --output_dir \/test\/test_large  --do_train --do_eval --predict_with_generate  --max_train_samples 500   --max_val_samples 500  --max_source_length 128 --max_target_length 128 --sortish_sampler --per_device_train_batch_size 8   --val_max_target_length 128 --deepspeed ds_config.json --num_train_epochs 1 --eval_steps 25000 --warmup_steps 500 --overwrite_output_dir\r\n\r\n(Here please find the script: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/run_seq2seq.py)\r\n\r\nIf you do not pass max_train_samples in above command to load the full dataset, then I get memory issue on a gpu with 24 GigBytes of memory.\r\n \r\nI need to train large-scale mt5 model on large-scale datasets of wikipedia (multiple of them concatenated or other datasets in multiple languages like OPUS), could you help me how I can avoid loading the full data into memory? to make the scripts not related to data size? \r\n\r\nIn above example, I was hoping the script could work without relying on dataset size, so I can still train the model without subsampling training set.\r\n\r\nthank you so much @lhoestq for your great help in advance\r\n\r\n\r\n","comment_length":36,"text":"How to not load huggingface datasets into memory  \n Hi\r\nI am running this example from transformers library version 4.3.3:\r\n(Here is the full documentation https:\/\/github.com\/huggingface\/transformers\/issues\/8771 but the running command should work out of the box)\r\n\r\n USE_TF=0  deepspeed  run_seq2seq.py --model_name_or_path google\/mt5-base --dataset_name wmt16 --dataset_config_name ro-en --source_prefix \"translate English to Romanian: \" --task translation_en_to_ro   --output_dir \/test\/test_large  --do_train --do_eval --predict_with_generate  --max_train_samples 500   --max_val_samples 500  --max_source_length 128 --max_target_length 128 --sortish_sampler --per_device_train_batch_size 8   --val_max_target_length 128 --deepspeed ds_config.json --num_train_epochs 1 --eval_steps 25000 --warmup_steps 500 --overwrite_output_dir\r\n\r\n(Here please find the script: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/run_seq2seq.py)\r\n\r\nIf you do not pass max_train_samples in above command to load the full dataset, then I get memory issue on a gpu with 24 GigBytes of memory.\r\n \r\nI need to train large-scale mt5 model on large-scale datasets of wikipedia (multiple of them concatenated or other datasets in multiple languages like OPUS), could you help me how I can avoid loading the full data into memory? to make the scripts not related to data size? \r\n\r\nIn above example, I was hoping the script could work without relying on dataset size, so I can still train the model without subsampling training set.\r\n\r\nthank you so much @lhoestq for your great help in advance\r\n\r\n\r\n \n So maybe a summary here: \r\nIf I could fit a large model with batch_size = X into memory, is there a way I could train this model for huge datasets with keeping setting the same? thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2007","title":"How to not load huggingface datasets into memory ","comments":"The `datastets` library doesn't load datasets into memory. Therefore you can load a dataset that is terabytes big without filling up your RAM.\r\n\r\nThe only thing that's loaded into memory during training is the batch used in the training step.\r\nSo as long as your model works with batch_size = X, then you can load an even bigger dataset and it will work as well with the same batch_size.\r\n\r\nNote that you still have to take into account that some batches take more memory than others, depending on the texts lengths. If it works for a batch with batch_size = X and with texts of maximum length, then it will work for all batches.\r\n\r\nIn your case I guess that there are a few long sentences in the dataset. For those long sentences you get a memory error on your GPU because they're too long. By passing `max_train_samples` you may have taken a subset of the dataset that only contain short sentences. That's probably why in your case it worked only when you set `max_train_samples`.\r\nI'd suggest you to reduce the batch size so that the batches with long sentences can be loaded on the GPU.\r\n\r\nLet me know if that helps or if you have other questions","body":"Hi\r\nI am running this example from transformers library version 4.3.3:\r\n(Here is the full documentation https:\/\/github.com\/huggingface\/transformers\/issues\/8771 but the running command should work out of the box)\r\n\r\n USE_TF=0  deepspeed  run_seq2seq.py --model_name_or_path google\/mt5-base --dataset_name wmt16 --dataset_config_name ro-en --source_prefix \"translate English to Romanian: \" --task translation_en_to_ro   --output_dir \/test\/test_large  --do_train --do_eval --predict_with_generate  --max_train_samples 500   --max_val_samples 500  --max_source_length 128 --max_target_length 128 --sortish_sampler --per_device_train_batch_size 8   --val_max_target_length 128 --deepspeed ds_config.json --num_train_epochs 1 --eval_steps 25000 --warmup_steps 500 --overwrite_output_dir\r\n\r\n(Here please find the script: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/run_seq2seq.py)\r\n\r\nIf you do not pass max_train_samples in above command to load the full dataset, then I get memory issue on a gpu with 24 GigBytes of memory.\r\n \r\nI need to train large-scale mt5 model on large-scale datasets of wikipedia (multiple of them concatenated or other datasets in multiple languages like OPUS), could you help me how I can avoid loading the full data into memory? to make the scripts not related to data size? \r\n\r\nIn above example, I was hoping the script could work without relying on dataset size, so I can still train the model without subsampling training set.\r\n\r\nthank you so much @lhoestq for your great help in advance\r\n\r\n\r\n","comment_length":208,"text":"How to not load huggingface datasets into memory  \n Hi\r\nI am running this example from transformers library version 4.3.3:\r\n(Here is the full documentation https:\/\/github.com\/huggingface\/transformers\/issues\/8771 but the running command should work out of the box)\r\n\r\n USE_TF=0  deepspeed  run_seq2seq.py --model_name_or_path google\/mt5-base --dataset_name wmt16 --dataset_config_name ro-en --source_prefix \"translate English to Romanian: \" --task translation_en_to_ro   --output_dir \/test\/test_large  --do_train --do_eval --predict_with_generate  --max_train_samples 500   --max_val_samples 500  --max_source_length 128 --max_target_length 128 --sortish_sampler --per_device_train_batch_size 8   --val_max_target_length 128 --deepspeed ds_config.json --num_train_epochs 1 --eval_steps 25000 --warmup_steps 500 --overwrite_output_dir\r\n\r\n(Here please find the script: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/run_seq2seq.py)\r\n\r\nIf you do not pass max_train_samples in above command to load the full dataset, then I get memory issue on a gpu with 24 GigBytes of memory.\r\n \r\nI need to train large-scale mt5 model on large-scale datasets of wikipedia (multiple of them concatenated or other datasets in multiple languages like OPUS), could you help me how I can avoid loading the full data into memory? to make the scripts not related to data size? \r\n\r\nIn above example, I was hoping the script could work without relying on dataset size, so I can still train the model without subsampling training set.\r\n\r\nthank you so much @lhoestq for your great help in advance\r\n\r\n\r\n \n The `datastets` library doesn't load datasets into memory. Therefore you can load a dataset that is terabytes big without filling up your RAM.\r\n\r\nThe only thing that's loaded into memory during training is the batch used in the training step.\r\nSo as long as your model works with batch_size = X, then you can load an even bigger dataset and it will work as well with the same batch_size.\r\n\r\nNote that you still have to take into account that some batches take more memory than others, depending on the texts lengths. If it works for a batch with batch_size = X and with texts of maximum length, then it will work for all batches.\r\n\r\nIn your case I guess that there are a few long sentences in the dataset. For those long sentences you get a memory error on your GPU because they're too long. By passing `max_train_samples` you may have taken a subset of the dataset that only contain short sentences. That's probably why in your case it worked only when you set `max_train_samples`.\r\nI'd suggest you to reduce the batch size so that the batches with long sentences can be loaded on the GPU.\r\n\r\nLet me know if that helps or if you have other questions","embeddings":[-0.1518766731,-0.5147151947,0.0332163535,0.4974522889,0.5210765004,0.0139152762,0.1180271581,0.2357333153,0.4079456329,0.1879961044,0.0076194578,-0.202884227,-0.2850468755,0.3407030404,0.0886425823,-0.103249006,0.0532818362,0.086624898,-0.5714523196,0.052350603,-0.2737412453,-0.0161269214,0.0159723274,-0.3641580939,-0.3886540532,0.1600887328,0.0624867231,0.1186249182,-0.0098638106,-0.0714208186,0.1428127885,-0.0393951349,0.1770171374,0.4372489452,-0.0001269822,0.1079645008,0.1380944401,-0.3148227036,-0.0306409597,-0.0322296172,-0.00419241,-0.2059285641,0.2809613943,-0.1789403409,-0.150394097,0.1292396337,-0.0043970579,-0.1768301129,0.6732686162,0.0866890699,0.0589532889,0.1535408795,-0.2452387959,0.1667440385,0.157634601,0.1976086199,0.0586231574,0.1164000556,-0.104916431,0.1034323648,0.0304610971,0.3367860019,-0.248097226,-0.1271037906,0.412345171,0.0033178255,0.0176058318,-0.4697835445,0.1020778939,0.2507305443,0.4073416591,-0.2239231765,-0.2138442248,-0.2794689834,-0.3462489545,-0.2446087301,0.1370269954,0.3081847727,-0.143369481,0.211253196,-0.5356808305,-0.3474308848,-0.1170952469,-0.0391612761,0.0610946529,-0.1411525458,-0.1147686169,0.118608132,0.4361445904,-0.1669552028,-0.3358583152,-0.0003861996,0.1707512885,0.2230681926,-0.5019465685,-0.3163248301,-0.0657733232,-0.1057263538,0.4721098542,0.1215846986,-0.0796092525,0.0936994851,0.3299092054,0.0091587044,0.2849020362,0.1574461758,-0.3003250659,-0.0072965627,0.2969490886,0.1490688771,0.1434296668,-0.1105317473,-0.0333796069,-0.0507383198,-0.2047497332,-0.259154737,0.2845853567,-0.2782020271,0.0794593021,-0.0892999843,-0.0343100466,0.170126304,-0.0699241608,0.5180566907,-0.0827048868,0.3102358878,-0.0071106786,0.131597057,-0.4338736534,0.0145770172,-0.1941797286,0.2515988946,-0.2240199149,0.3322900236,0.411667794,0.0499283858,0.379703939,-0.0956689641,0.0674586296,-0.0763127133,0.0796634704,-0.3235810399,0.1239142492,0.4826919138,0.0355792567,0.1951072961,0.0725165308,0.1766030341,-0.2136323899,0.396887064,-0.230730623,-0.2883965969,0.363097012,0.0053726593,-0.4209285676,0.2904184163,-0.5321798325,0.2664295435,0.1223289296,0.2021841109,0.0557638854,0.078967303,-0.3984458148,-0.0862805545,0.4873942137,0.8010213971,-0.1476057023,-0.3931111991,0.1329436004,-0.1004578918,0.1959773898,0.5725108385,-0.0142801609,0.0148478178,-0.0144559685,-0.2140351981,0.0709257945,-0.1415775716,-0.354434818,0.2842701674,-0.170895949,0.1742833555,0.2594866455,0.245840773,-0.0926848352,0.1357045472,-0.1018791869,0.3744672239,0.0681137666,0.2252355516,-0.1271794438,-0.5379964113,0.2040785849,0.2169702202,0.2022631615,0.0872878507,-0.1350940913,0.6384108663,0.0238448363,-0.1233225539,0.150255397,0.5214672089,-0.029772684,-0.0962148979,-0.0108517697,-0.0647933111,-0.5006047487,0.1989262551,-0.0586789697,0.0444423407,-0.1048355103,0.0006818472,0.0573625341,-0.1273258924,0.0957001373,-0.1771862358,-0.1211929917,-0.2916912436,0.1514546126,-0.1347030401,-0.0903468654,0.1488622576,-0.0584070943,0.2972849607,-0.6091175079,-0.1088676304,0.1102921367,-0.1234868392,0.0259958319,0.1248041317,-0.0882156715,-0.0845849365,0.1067788377,0.2415024489,0.1124199778,0.0178392511,-0.2222641259,0.6307321787,0.2434305549,-0.3127706051,0.120512858,0.0697668567,0.1251268089,0.0252608154,-0.1785954386,0.1950393319,-0.1331377178,0.3484589159,0.0058566616,-0.2301434278,-0.0358800404,-0.2495764047,-0.1238652468,-0.3175210655,0.2297707349,0.3747276664,0.3159516454,0.3822626472,-0.1301639974,-0.6248890162,0.4318443835,-0.0019924701,0.0073690708,0.1488064826,-0.4452287853,-0.0800923407,-0.0596907996,-0.374764502,0.2052786648,0.2200757861,-0.0135828219,0.1306917071,0.4082516134,-0.051337,0.1627495438,0.3493756354,0.1488740146,-0.0548449643,-0.0312599577,-0.1541599631,-0.1904212981,0.2100085169,0.027010059,-0.2555870712,-0.4284456074,0.054600928,-0.1863786578,-0.1533458978,-0.19714351,0.2054142356,-0.4946786165,0.1187362373,-0.0151108671,0.1751280576,-0.0662437603,0.1509770453,0.1996802539,0.4412361085,0.1918815821,-0.4602290988,-0.0057205306,0.0038936462,0.0292233676,-0.0567871071,0.156275332,-0.1057111621,0.2035379261,0.0814274028,-0.2594136894,0.0631288737,-0.1128867716,0.2416180372,-0.1343260258,0.3047715425,-0.0908702239,0.2014537007,0.1846033186,0.0711597651,-0.1057425439,-0.1495919079,-0.0326353051,0.1421228945,0.0993307829,0.3976770639,-0.3058061004,-0.206270352,-0.0010525942,-0.3736867607,0.4215454757,0.1011799723,-0.0064921095,0.3571302295,0.2769917846,0.0848625675,-0.1952385604,0.295612067,-0.0905101672,-0.2000130266,0.4355374277,0.0201416388,-0.2268256694,0.2055979371,-0.0464503802,0.195723027,0.2565194964,-0.6173763275,-0.0449292623,-0.245066151,-0.3239588737,-0.0527185872,0.2297547013,0.2915318608,-0.3330810666,0.061943084,0.041418232,-0.1222939864,0.0963732973,-0.04288489,0.2026363611,0.2002973109,0.5743064284,-0.0909149274,0.9312599301,0.1241061613,0.1804883778,0.0755283907,-0.0374021158,0.2525525987,-0.1314361542,0.0153975999,-0.1186559275,-0.005834965,-0.1430701613,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2005","title":"Setting to torch format not working with torchvision and MNIST","comments":"Adding to the previous information, I think `torch.utils.data.DataLoader` is doing some conversion. \r\nWhat I tried:\r\n```python\r\ntrain_dataset = load_dataset('mnist')\r\n```\r\nI don't use any `map` or `set_format` or any `transform`. I use this directly, and try to load batches using the `DataLoader` with batch size 2, I get an output like this for the `image`:\r\n\r\n```\r\n[[tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor...\r\n```\r\nFor `label`, it works fine:\r\n```\r\ntensor([7, 6])\r\n```\r\nNote that I didn't specify conversion to torch tensors anywhere.\r\n\r\nBasically, there are two problems here:\r\n1. `dataset.map` doesn't return tensor type objects, even though it uses the transforms, the grayscale conversion in transform was done, but the output was lists only.\r\n2.  The `DataLoader` performs its own conversion, which may be not desired.\r\n\r\nI understand that we can't change `DataLoader` because it is a torch functionality, however, is there a way we can handle image data to allow using it with torch `DataLoader` and `torchvision` properly?\r\n\r\nI think if the `image` was a torch tensor (N,H,W,C), or  a list of torch tensors (H,W,C), before it is passed to `DataLoader`, then we might not face this issue. ","body":"Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.","comment_length":202,"text":"Setting to torch format not working with torchvision and MNIST \n Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. \n Adding to the previous information, I think `torch.utils.data.DataLoader` is doing some conversion. \r\nWhat I tried:\r\n```python\r\ntrain_dataset = load_dataset('mnist')\r\n```\r\nI don't use any `map` or `set_format` or any `transform`. I use this directly, and try to load batches using the `DataLoader` with batch size 2, I get an output like this for the `image`:\r\n\r\n```\r\n[[tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor...\r\n```\r\nFor `label`, it works fine:\r\n```\r\ntensor([7, 6])\r\n```\r\nNote that I didn't specify conversion to torch tensors anywhere.\r\n\r\nBasically, there are two problems here:\r\n1. `dataset.map` doesn't return tensor type objects, even though it uses the transforms, the grayscale conversion in transform was done, but the output was lists only.\r\n2.  The `DataLoader` performs its own conversion, which may be not desired.\r\n\r\nI understand that we can't change `DataLoader` because it is a torch functionality, however, is there a way we can handle image data to allow using it with torch `DataLoader` and `torchvision` properly?\r\n\r\nI think if the `image` was a torch tensor (N,H,W,C), or  a list of torch tensors (H,W,C), before it is passed to `DataLoader`, then we might not face this issue. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2005","title":"Setting to torch format not working with torchvision and MNIST","comments":"What's the feature types of your new dataset after `.map` ?\r\n\r\nCan you try with adding `features=` in the `.map` call in order to set the \"image\" feature type to `Array2D` ?\r\nThe default feature type is lists of lists, we've not implemented shape verification to use ArrayXD instead of nested lists yet","body":"Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.","comment_length":53,"text":"Setting to torch format not working with torchvision and MNIST \n Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. \n What's the feature types of your new dataset after `.map` ?\r\n\r\nCan you try with adding `features=` in the `.map` call in order to set the \"image\" feature type to `Array2D` ?\r\nThe default feature type is lists of lists, we've not implemented shape verification to use ArrayXD instead of nested lists yet","embeddings":[-0.12737526,-0.3511648178,-0.017932741,0.3536089063,0.4760023057,0.0887141973,0.7330293059,0.3804347813,0.0655255616,-0.0380423479,-0.1111534312,0.3819508553,-0.2329687476,-0.3339346349,0.0351093672,-0.56149441,0.2225798965,-0.0998275802,-0.2807984054,-0.0613472909,-0.243133992,0.0354893506,-0.2184970975,-0.1084961668,-0.547935307,0.0440959707,-0.0195557699,-0.0765685439,-0.06229949,-0.1391803324,0.1223469973,-0.1744965166,0.4444530904,0.7012936473,-0.0001277011,0.0703018233,0.3642728925,-0.1737781912,0.0499992296,-0.1610135287,0.0400467664,-0.2573829293,0.0727764145,-0.0946834609,-0.2234939188,-0.1708452553,0.0959103853,-0.17902942,-0.0281423349,0.4191688895,0.077129975,0.2790103853,0.2466612458,0.1386305541,0.2278117537,0.5182864666,-0.2734378576,-0.0561165325,0.2261500657,0.37547791,0.1240043715,0.5647351146,-0.2913345993,-0.0362333432,0.266944766,0.1290054172,-0.1014554724,-0.3140054941,0.0491575636,0.089462921,0.4219441712,-0.1494393945,-0.0807789043,-0.1436640322,-0.143978551,-0.1273805052,0.0405908599,0.2803191841,-0.0718406066,-0.1872386485,-0.5392424464,0.3052915633,-0.1486232579,0.2131595016,-0.2805242538,-0.0034206891,-0.1381631345,0.2928087115,-0.0471190289,-0.112712875,0.1930810809,-0.1234148592,0.1300757527,-0.0098258834,-0.1436316669,-0.0534907505,-0.3895993233,-0.3150125444,-0.2001479119,-0.1602650434,0.1664855033,0.1392757297,-0.1163957268,0.1280712485,0.1826325953,0.2680338025,0.105782643,0.3207025826,-0.0858922377,-0.2895810604,-0.0469189361,0.0877334401,-0.2962270379,-0.435752362,0.1545339972,0.3252966106,0.2452212572,0.0687427595,0.1298554242,-0.3239412606,-0.1089225262,-0.0095866034,0.075225018,0.285038203,-0.1186793,0.24390468,0.4804830253,0.3972030282,-0.1538181752,0.0695624724,0.0250724964,0.0315209553,-0.440495193,-0.2856986523,0.1534098238,-0.1177733615,0.100070186,0.0374316685,0.0464155935,0.1006969139,0.0734966248,-0.1858698428,0.6246291399,0.147647351,-0.1944740117,0.2767927051,0.2709063888,0.5297806263,-0.3042984307,0.2565316558,-0.5095701218,0.0652529001,-0.0230914187,-0.0145437596,0.2186551988,-0.020721972,-0.2121624202,0.0158288926,0.5923122168,-0.0836780667,0.2734875381,-0.57916224,0.0949639827,-0.1913348734,0.034152355,0.0636695474,-0.2047151774,-0.0523584187,0.3863441348,0.0919497311,0.3154585958,0.1602817774,0.0510833338,0.0798699334,-0.2253530473,-0.2350505739,0.3363234699,-0.3768565059,0.0920383558,-0.0176931173,0.0938790813,-0.0638038367,0.1120190248,0.3361779451,0.0326004587,-0.128001526,0.0294485986,0.1963148117,-0.1661822349,0.1402871758,0.0773598477,0.1913028061,0.1933745444,0.0350063629,-0.0156836379,0.2076539844,-0.2216719985,0.1775767207,-0.0542802997,-0.0747133866,0.0711769462,0.0403783172,-0.3698907197,-0.0121967075,-0.0474696793,0.1864667684,-0.23310031,-0.048721496,0.0236351732,0.0822172239,0.0582793653,0.047925096,0.0049781557,-0.1919564456,-0.1822061539,-0.1390272975,-0.0032419825,0.0922108144,-0.2094767392,-0.2617242932,-0.0523435548,-0.1232490763,0.1783037037,0.0588379279,-0.3590371013,0.0510254167,0.019273432,-0.3653303087,-0.2013600469,0.1733924001,0.2676690519,-0.0996206775,-0.0422345139,0.1916035116,0.0632886291,0.1146331578,-0.5482739806,0.0834660977,0.1778419167,-0.2469699979,0.2043911219,0.2162791938,0.1485340595,-0.2755032778,-0.1960345507,0.4387866855,0.1604105234,0.315912962,-0.3300254345,0.116101861,-0.0726469457,-0.0394018255,-0.1906339079,-0.0408082679,-0.277281642,0.0730238482,-0.1318593472,0.0129822334,-0.4569184184,-0.0449360423,0.2962621152,0.0887641758,0.1758723557,-0.0012164807,-0.399846226,0.2133122981,0.1771268845,-0.6013535261,0.2978383601,-0.0043616337,0.1892076284,-0.1050189212,0.0955833048,-0.1625430584,0.2484440655,0.1140429005,0.090748094,-0.1994652301,0.1418524235,-0.1079259515,0.0268462822,0.0263701156,-0.1406289488,-0.2336931378,-0.3589504957,0.2713825405,-0.1661832929,-0.1345185637,-0.4018543959,-0.2621856332,0.0360565595,-0.0740050003,-0.0823338255,0.1507581174,0.1855677366,0.190434888,0.26341784,-0.1818942726,0.3743162453,-0.4130297601,-0.0353545919,-0.0750095248,-0.2867960632,-0.0991648287,0.0896557719,-0.5234113932,0.0914847627,-0.2289899141,-0.0931907445,-0.2501759529,0.0135480622,0.1418226808,-0.2678500712,-0.2850030065,0.3085908592,0.1939281374,-0.142482236,0.040662054,0.2663511038,0.0103072627,0.0469011813,0.1537833363,-0.0896200389,0.1165602952,-0.063298367,-0.1767405272,-0.0966183841,-0.0750613436,-0.0704168677,-0.0217595734,0.0633770004,0.3462581038,0.3721836209,-0.0898904651,-0.052042447,0.0356094316,0.06296473,-0.3976880014,0.5225032568,-0.2261542529,-0.3608259261,0.1192016751,0.0238555167,0.0651473776,0.4862691164,0.0605229028,0.0497316308,0.0117824115,-0.0789448172,0.2525928319,0.2211969346,0.3510203063,-0.091124773,0.0191658437,-0.1647072583,0.0154163484,-0.1018712595,-0.0632551014,0.608886838,0.185083881,0.2399424464,0.0992583558,0.6996648312,-0.0611373,-0.7380955219,0.2168067545,-0.2425099015,0.1921664476,-0.1181217954,-0.3539586067,0.3988561332,-0.2094196975,0.0643741786,-0.0859671235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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2005","title":"Setting to torch format not working with torchvision and MNIST","comments":"Hi @lhoestq\r\n\r\nRaw feature types are like this:\r\n```\r\nImage:\r\n<class 'list'> 60000  #(type, len)\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'int'>\r\nLabel:\r\n<class 'list'> 60000\r\n<class 'int'>\r\n```\r\nInside the `prepare_feature` method with batch size 100000 , after processing, they are like this:\r\n\r\nInside Prepare Train Features\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter map, the feature type are like this:\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'float'>\r\nLabel:\r\n<class 'list'> 60000\r\n<class 'int'>\r\n```\r\n\r\nAfter dataloader with batch size 2, the batch features are like this:\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n<hr>\r\n\r\nWhen I was setting the format of `train_dataset` to 'torch' after mapping  - \r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nCorresponding DataLoader batch:\r\n```\r\nFrom DataLoader batch features\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nI will check with features and get back.\r\n\r\n\r\n\r\n","body":"Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.","comment_length":213,"text":"Setting to torch format not working with torchvision and MNIST \n Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. \n Hi @lhoestq\r\n\r\nRaw feature types are like this:\r\n```\r\nImage:\r\n<class 'list'> 60000  #(type, len)\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'int'>\r\nLabel:\r\n<class 'list'> 60000\r\n<class 'int'>\r\n```\r\nInside the `prepare_feature` method with batch size 100000 , after processing, they are like this:\r\n\r\nInside Prepare Train Features\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter map, the feature type are like this:\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'float'>\r\nLabel:\r\n<class 'list'> 60000\r\n<class 'int'>\r\n```\r\n\r\nAfter dataloader with batch size 2, the batch features are like this:\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n<hr>\r\n\r\nWhen I was setting the format of `train_dataset` to 'torch' after mapping  - \r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nCorresponding DataLoader batch:\r\n```\r\nFrom DataLoader batch features\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nI will check with features and get back.\r\n\r\n\r\n\r\n","embeddings":[-0.12737526,-0.3511648178,-0.017932741,0.3536089063,0.4760023057,0.0887141973,0.7330293059,0.3804347813,0.0655255616,-0.0380423479,-0.1111534312,0.3819508553,-0.2329687476,-0.3339346349,0.0351093672,-0.56149441,0.2225798965,-0.0998275802,-0.2807984054,-0.0613472909,-0.243133992,0.0354893506,-0.2184970975,-0.1084961668,-0.547935307,0.0440959707,-0.0195557699,-0.0765685439,-0.06229949,-0.1391803324,0.1223469973,-0.1744965166,0.4444530904,0.7012936473,-0.0001277011,0.0703018233,0.3642728925,-0.1737781912,0.0499992296,-0.1610135287,0.0400467664,-0.2573829293,0.0727764145,-0.0946834609,-0.2234939188,-0.1708452553,0.0959103853,-0.17902942,-0.0281423349,0.4191688895,0.077129975,0.2790103853,0.2466612458,0.1386305541,0.2278117537,0.5182864666,-0.2734378576,-0.0561165325,0.2261500657,0.37547791,0.1240043715,0.5647351146,-0.2913345993,-0.0362333432,0.266944766,0.1290054172,-0.1014554724,-0.3140054941,0.0491575636,0.089462921,0.4219441712,-0.1494393945,-0.0807789043,-0.1436640322,-0.143978551,-0.1273805052,0.0405908599,0.2803191841,-0.0718406066,-0.1872386485,-0.5392424464,0.3052915633,-0.1486232579,0.2131595016,-0.2805242538,-0.0034206891,-0.1381631345,0.2928087115,-0.0471190289,-0.112712875,0.1930810809,-0.1234148592,0.1300757527,-0.0098258834,-0.1436316669,-0.0534907505,-0.3895993233,-0.3150125444,-0.2001479119,-0.1602650434,0.1664855033,0.1392757297,-0.1163957268,0.1280712485,0.1826325953,0.2680338025,0.105782643,0.3207025826,-0.0858922377,-0.2895810604,-0.0469189361,0.0877334401,-0.2962270379,-0.435752362,0.1545339972,0.3252966106,0.2452212572,0.0687427595,0.1298554242,-0.3239412606,-0.1089225262,-0.0095866034,0.075225018,0.285038203,-0.1186793,0.24390468,0.4804830253,0.3972030282,-0.1538181752,0.0695624724,0.0250724964,0.0315209553,-0.440495193,-0.2856986523,0.1534098238,-0.1177733615,0.100070186,0.0374316685,0.0464155935,0.1006969139,0.0734966248,-0.1858698428,0.6246291399,0.147647351,-0.1944740117,0.2767927051,0.2709063888,0.5297806263,-0.3042984307,0.2565316558,-0.5095701218,0.0652529001,-0.0230914187,-0.0145437596,0.2186551988,-0.020721972,-0.2121624202,0.0158288926,0.5923122168,-0.0836780667,0.2734875381,-0.57916224,0.0949639827,-0.1913348734,0.034152355,0.0636695474,-0.2047151774,-0.0523584187,0.3863441348,0.0919497311,0.3154585958,0.1602817774,0.0510833338,0.0798699334,-0.2253530473,-0.2350505739,0.3363234699,-0.3768565059,0.0920383558,-0.0176931173,0.0938790813,-0.0638038367,0.1120190248,0.3361779451,0.0326004587,-0.128001526,0.0294485986,0.1963148117,-0.1661822349,0.1402871758,0.0773598477,0.1913028061,0.1933745444,0.0350063629,-0.0156836379,0.2076539844,-0.2216719985,0.1775767207,-0.0542802997,-0.0747133866,0.0711769462,0.0403783172,-0.3698907197,-0.0121967075,-0.0474696793,0.1864667684,-0.23310031,-0.048721496,0.0236351732,0.0822172239,0.0582793653,0.047925096,0.0049781557,-0.1919564456,-0.1822061539,-0.1390272975,-0.0032419825,0.0922108144,-0.2094767392,-0.2617242932,-0.0523435548,-0.1232490763,0.1783037037,0.0588379279,-0.3590371013,0.0510254167,0.019273432,-0.3653303087,-0.2013600469,0.1733924001,0.2676690519,-0.0996206775,-0.0422345139,0.1916035116,0.0632886291,0.1146331578,-0.5482739806,0.0834660977,0.1778419167,-0.2469699979,0.2043911219,0.2162791938,0.1485340595,-0.2755032778,-0.1960345507,0.4387866855,0.1604105234,0.315912962,-0.3300254345,0.116101861,-0.0726469457,-0.0394018255,-0.1906339079,-0.0408082679,-0.277281642,0.0730238482,-0.1318593472,0.0129822334,-0.4569184184,-0.0449360423,0.2962621152,0.0887641758,0.1758723557,-0.0012164807,-0.399846226,0.2133122981,0.1771268845,-0.6013535261,0.2978383601,-0.0043616337,0.1892076284,-0.1050189212,0.0955833048,-0.1625430584,0.2484440655,0.1140429005,0.090748094,-0.1994652301,0.1418524235,-0.1079259515,0.0268462822,0.0263701156,-0.1406289488,-0.2336931378,-0.3589504957,0.2713825405,-0.1661832929,-0.1345185637,-0.4018543959,-0.2621856332,0.0360565595,-0.0740050003,-0.0823338255,0.1507581174,0.1855677366,0.190434888,0.26341784,-0.1818942726,0.3743162453,-0.4130297601,-0.0353545919,-0.0750095248,-0.2867960632,-0.0991648287,0.0896557719,-0.5234113932,0.0914847627,-0.2289899141,-0.0931907445,-0.2501759529,0.0135480622,0.1418226808,-0.2678500712,-0.2850030065,0.3085908592,0.1939281374,-0.142482236,0.040662054,0.2663511038,0.0103072627,0.0469011813,0.1537833363,-0.0896200389,0.1165602952,-0.063298367,-0.1767405272,-0.0966183841,-0.0750613436,-0.0704168677,-0.0217595734,0.0633770004,0.3462581038,0.3721836209,-0.0898904651,-0.052042447,0.0356094316,0.06296473,-0.3976880014,0.5225032568,-0.2261542529,-0.3608259261,0.1192016751,0.0238555167,0.0651473776,0.4862691164,0.0605229028,0.0497316308,0.0117824115,-0.0789448172,0.2525928319,0.2211969346,0.3510203063,-0.091124773,0.0191658437,-0.1647072583,0.0154163484,-0.1018712595,-0.0632551014,0.608886838,0.185083881,0.2399424464,0.0992583558,0.6996648312,-0.0611373,-0.7380955219,0.2168067545,-0.2425099015,0.1921664476,-0.1181217954,-0.3539586067,0.3988561332,-0.2094196975,0.064374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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2005","title":"Setting to torch format not working with torchvision and MNIST","comments":"Hi @lhoestq\r\n\r\n# Using Array3D\r\nI tried this:\r\n```python\r\nfeatures = datasets.Features({\r\n            \"image\": datasets.Array3D(shape=(1,28,28),dtype=\"float32\"),\r\n            \"label\": datasets.features.ClassLabel(names=[\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]),\r\n        })\r\ntrain_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n```\r\nand it didn't fix the issue.\r\n\r\nDuring the `prepare_train_features:\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter the `map`:\r\n\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'float'>\r\nLabel:\r\n<class 'list'> 60000\r\n<class 'int'>\r\n```\r\nFrom the DataLoader batch:\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\nIt is the same as before.\r\n\r\n---\r\n\r\nUsing `datasets.Sequence(datasets.Array2D(shape=(28,28),dtype=\"float32\"))` gave an error during `map`:\r\n\r\n```python\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-95-d28e69289084> in <module>()\r\n      3             \"label\": datasets.features.ClassLabel(names=[\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]),\r\n      4         })\r\n----> 5 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n\r\n15 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n    446                     num_proc=num_proc,\r\n    447                 )\r\n--> 448                 for k, dataset in self.items()\r\n    449             }\r\n    450         )\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    446                     num_proc=num_proc,\r\n    447                 )\r\n--> 448                 for k, dataset in self.items()\r\n    449             }\r\n    450         )\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1307                 fn_kwargs=fn_kwargs,\r\n   1308                 new_fingerprint=new_fingerprint,\r\n-> 1309                 update_data=update_data,\r\n   1310             )\r\n   1311         else:\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    202         }\r\n    203         # apply actual function\r\n--> 204         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    205         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    206         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    335             # Call actual function\r\n    336 \r\n--> 337             out = func(self, *args, **kwargs)\r\n    338 \r\n    339             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n   1580                     if update_data:\r\n   1581                         batch = cast_to_python_objects(batch)\r\n-> 1582                         writer.write_batch(batch)\r\n   1583             if update_data:\r\n   1584                 writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    274             typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n    275             typed_sequence_examples[col] = typed_sequence\r\n--> 276         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    277         self.write_table(pa_table, writer_batch_size)\r\n    278 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n     95                 out = pa.ExtensionArray.from_storage(type, pa.array(self.data, type.storage_dtype))\r\n     96             else:\r\n---> 97                 out = pa.array(self.data, type=type)\r\n     98             if trying_type and out[0].as_py() != self.data[0]:\r\n     99                 raise TypeError(\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: extension\r\n```","body":"Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.","comment_length":447,"text":"Setting to torch format not working with torchvision and MNIST \n Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. \n Hi @lhoestq\r\n\r\n# Using Array3D\r\nI tried this:\r\n```python\r\nfeatures = datasets.Features({\r\n            \"image\": datasets.Array3D(shape=(1,28,28),dtype=\"float32\"),\r\n            \"label\": datasets.features.ClassLabel(names=[\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]),\r\n        })\r\ntrain_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n```\r\nand it didn't fix the issue.\r\n\r\nDuring the `prepare_train_features:\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter the `map`:\r\n\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'float'>\r\nLabel:\r\n<class 'list'> 60000\r\n<class 'int'>\r\n```\r\nFrom the DataLoader batch:\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\nIt is the same as before.\r\n\r\n---\r\n\r\nUsing `datasets.Sequence(datasets.Array2D(shape=(28,28),dtype=\"float32\"))` gave an error during `map`:\r\n\r\n```python\r\nArrowNotImplementedError                  Traceback (most recent call last)\r\n<ipython-input-95-d28e69289084> in <module>()\r\n      3             \"label\": datasets.features.ClassLabel(names=[\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]),\r\n      4         })\r\n----> 5 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n\r\n15 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n    446                     num_proc=num_proc,\r\n    447                 )\r\n--> 448                 for k, dataset in self.items()\r\n    449             }\r\n    450         )\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    446                     num_proc=num_proc,\r\n    447                 )\r\n--> 448                 for k, dataset in self.items()\r\n    449             }\r\n    450         )\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1307                 fn_kwargs=fn_kwargs,\r\n   1308                 new_fingerprint=new_fingerprint,\r\n-> 1309                 update_data=update_data,\r\n   1310             )\r\n   1311         else:\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    202         }\r\n    203         # apply actual function\r\n--> 204         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    205         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    206         # re-apply format to the output\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    335             # Call actual function\r\n    336 \r\n--> 337             out = func(self, *args, **kwargs)\r\n    338 \r\n    339             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n   1580                     if update_data:\r\n   1581                         batch = cast_to_python_objects(batch)\r\n-> 1582                         writer.write_batch(batch)\r\n   1583             if update_data:\r\n   1584                 writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    274             typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n    275             typed_sequence_examples[col] = typed_sequence\r\n--> 276         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    277         self.write_table(pa_table, writer_batch_size)\r\n    278 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/arrow_writer.py in __arrow_array__(self, type)\r\n     95                 out = pa.ExtensionArray.from_storage(type, pa.array(self.data, type.storage_dtype))\r\n     96             else:\r\n---> 97                 out = pa.array(self.data, type=type)\r\n     98             if trying_type and out[0].as_py() != self.data[0]:\r\n     99                 raise TypeError(\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: extension\r\n```","embeddings":[-0.12737526,-0.3511648178,-0.017932741,0.3536089063,0.4760023057,0.0887141973,0.7330293059,0.3804347813,0.0655255616,-0.0380423479,-0.1111534312,0.3819508553,-0.2329687476,-0.3339346349,0.0351093672,-0.56149441,0.2225798965,-0.0998275802,-0.2807984054,-0.0613472909,-0.243133992,0.0354893506,-0.2184970975,-0.1084961668,-0.547935307,0.0440959707,-0.0195557699,-0.0765685439,-0.06229949,-0.1391803324,0.1223469973,-0.1744965166,0.4444530904,0.7012936473,-0.0001277011,0.0703018233,0.3642728925,-0.1737781912,0.0499992296,-0.1610135287,0.0400467664,-0.2573829293,0.0727764145,-0.0946834609,-0.2234939188,-0.1708452553,0.0959103853,-0.17902942,-0.0281423349,0.4191688895,0.077129975,0.2790103853,0.2466612458,0.1386305541,0.2278117537,0.5182864666,-0.2734378576,-0.0561165325,0.2261500657,0.37547791,0.1240043715,0.5647351146,-0.2913345993,-0.0362333432,0.266944766,0.1290054172,-0.1014554724,-0.3140054941,0.0491575636,0.089462921,0.4219441712,-0.1494393945,-0.0807789043,-0.1436640322,-0.143978551,-0.1273805052,0.0405908599,0.2803191841,-0.0718406066,-0.1872386485,-0.5392424464,0.3052915633,-0.1486232579,0.2131595016,-0.2805242538,-0.0034206891,-0.1381631345,0.2928087115,-0.0471190289,-0.112712875,0.1930810809,-0.1234148592,0.1300757527,-0.0098258834,-0.1436316669,-0.0534907505,-0.3895993233,-0.3150125444,-0.2001479119,-0.1602650434,0.1664855033,0.1392757297,-0.1163957268,0.1280712485,0.1826325953,0.2680338025,0.105782643,0.3207025826,-0.0858922377,-0.2895810604,-0.0469189361,0.0877334401,-0.2962270379,-0.435752362,0.1545339972,0.3252966106,0.2452212572,0.0687427595,0.1298554242,-0.3239412606,-0.1089225262,-0.0095866034,0.075225018,0.285038203,-0.1186793,0.24390468,0.4804830253,0.3972030282,-0.1538181752,0.0695624724,0.0250724964,0.0315209553,-0.440495193,-0.2856986523,0.1534098238,-0.1177733615,0.100070186,0.0374316685,0.0464155935,0.1006969139,0.0734966248,-0.1858698428,0.6246291399,0.147647351,-0.1944740117,0.2767927051,0.2709063888,0.5297806263,-0.3042984307,0.2565316558,-0.5095701218,0.0652529001,-0.0230914187,-0.0145437596,0.2186551988,-0.020721972,-0.2121624202,0.0158288926,0.5923122168,-0.0836780667,0.2734875381,-0.57916224,0.0949639827,-0.1913348734,0.034152355,0.0636695474,-0.2047151774,-0.0523584187,0.3863441348,0.0919497311,0.3154585958,0.1602817774,0.0510833338,0.0798699334,-0.2253530473,-0.2350505739,0.3363234699,-0.3768565059,0.0920383558,-0.0176931173,0.0938790813,-0.0638038367,0.1120190248,0.3361779451,0.0326004587,-0.128001526,0.0294485986,0.1963148117,-0.1661822349,0.1402871758,0.0773598477,0.1913028061,0.1933745444,0.0350063629,-0.0156836379,0.2076539844,-0.2216719985,0.1775767207,-0.0542802997,-0.0747133866,0.0711769462,0.0403783172,-0.3698907197,-0.0121967075,-0.0474696793,0.1864667684,-0.23310031,-0.048721496,0.0236351732,0.0822172239,0.0582793653,0.047925096,0.0049781557,-0.1919564456,-0.1822061539,-0.1390272975,-0.0032419825,0.0922108144,-0.2094767392,-0.2617242932,-0.0523435548,-0.1232490763,0.1783037037,0.0588379279,-0.3590371013,0.0510254167,0.019273432,-0.3653303087,-0.2013600469,0.1733924001,0.2676690519,-0.0996206775,-0.0422345139,0.1916035116,0.0632886291,0.1146331578,-0.5482739806,0.0834660977,0.1778419167,-0.2469699979,0.2043911219,0.2162791938,0.1485340595,-0.2755032778,-0.1960345507,0.4387866855,0.1604105234,0.315912962,-0.3300254345,0.116101861,-0.0726469457,-0.0394018255,-0.1906339079,-0.0408082679,-0.277281642,0.0730238482,-0.1318593472,0.0129822334,-0.4569184184,-0.0449360423,0.2962621152,0.0887641758,0.1758723557,-0.0012164807,-0.399846226,0.2133122981,0.1771268845,-0.6013535261,0.2978383601,-0.0043616337,0.1892076284,-0.1050189212,0.0955833048,-0.1625430584,0.2484440655,0.1140429005,0.090748094,-0.1994652301,0.1418524235,-0.1079259515,0.0268462822,0.0263701156,-0.1406289488,-0.2336931378,-0.3589504957,0.2713825405,-0.1661832929,-0.1345185637,-0.4018543959,-0.2621856332,0.0360565595,-0.0740050003,-0.0823338255,0.1507581174,0.1855677366,0.190434888,0.26341784,-0.1818942726,0.3743162453,-0.4130297601,-0.0353545919,-0.0750095248,-0.2867960632,-0.0991648287,0.0896557719,-0.5234113932,0.0914847627,-0.2289899141,-0.0931907445,-0.2501759529,0.0135480622,0.1418226808,-0.2678500712,-0.2850030065,0.3085908592,0.1939281374,-0.142482236,0.040662054,0.2663511038,0.0103072627,0.0469011813,0.1537833363,-0.0896200389,0.1165602952,-0.063298367,-0.1767405272,-0.0966183841,-0.0750613436,-0.0704168677,-0.0217595734,0.0633770004,0.3462581038,0.3721836209,-0.0898904651,-0.052042447,0.0356094316,0.06296473,-0.3976880014,0.5225032568,-0.2261542529,-0.3608259261,0.1192016751,0.0238555167,0.0651473776,0.4862691164,0.0605229028,0.0497316308,0.0117824115,-0.0789448172,0.2525928319,0.2211969346,0.3510203063,-0.091124773,0.0191658437,-0.1647072583,0.0154163484,-0.1018712595,-0.0632551014,0.608886838,0.185083881,0.2399424464,0.0992583558,0.6996648312,-0.0611373,-0.7380955219,0.2168067545,-0.2425099015,0.1921664476,-0.1181217954,-0.3539586067,0.3988561332,-0.2094196975,0.0643741786,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2005","title":"Setting to torch format not working with torchvision and MNIST","comments":"# Convert raw tensors to torch format\r\nStrangely, converting to torch tensors works perfectly on `raw_dataset`:\r\n```python\r\nraw_dataset.set_format('torch',columns=['image','label'])\r\n```\r\nTypes:\r\n```\r\nImage:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nUsing this for transforms:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                examples[\"image\"][example_idx].numpy()\r\n            ))\r\n        else:\r\n            images.append(examples[\"image\"][example_idx].numpy())\r\n        labels.append(examples[\"label\"][example_idx])\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n```\r\n\r\nInside `prepare_train_features`:\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter `map`:\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\nDataLoader batch:\r\n\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\n---\r\n\r\n## Using `torch` format:\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\nDataLoader batches:\r\n\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\n---\r\n## Using the features - `Array3D`:\r\n\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter `map`:\r\n```\r\nImage:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter DataLoader `batch`:\r\n```\r\nImage:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nThe last one works perfectly.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29076344\/110491452-4cf09c00-8117-11eb-8a47-73bf3fc0c3dc.png)\r\n\r\nI wonder why this worked, and others didn't.\r\n\r\n\r\n\r\n\r\n\r\n\r\n","body":"Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.","comment_length":299,"text":"Setting to torch format not working with torchvision and MNIST \n Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. \n # Convert raw tensors to torch format\r\nStrangely, converting to torch tensors works perfectly on `raw_dataset`:\r\n```python\r\nraw_dataset.set_format('torch',columns=['image','label'])\r\n```\r\nTypes:\r\n```\r\nImage:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nUsing this for transforms:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                examples[\"image\"][example_idx].numpy()\r\n            ))\r\n        else:\r\n            images.append(examples[\"image\"][example_idx].numpy())\r\n        labels.append(examples[\"label\"][example_idx])\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n```\r\n\r\nInside `prepare_train_features`:\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter `map`:\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\nDataLoader batch:\r\n\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\n---\r\n\r\n## Using `torch` format:\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\nDataLoader batches:\r\n\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\n---\r\n## Using the features - `Array3D`:\r\n\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter `map`:\r\n```\r\nImage:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter DataLoader `batch`:\r\n```\r\nImage:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nThe last one works perfectly.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29076344\/110491452-4cf09c00-8117-11eb-8a47-73bf3fc0c3dc.png)\r\n\r\nI wonder why this worked, and others didn't.\r\n\r\n\r\n\r\n\r\n\r\n\r\n","embeddings":[-0.12737526,-0.3511648178,-0.017932741,0.3536089063,0.4760023057,0.0887141973,0.7330293059,0.3804347813,0.0655255616,-0.0380423479,-0.1111534312,0.3819508553,-0.2329687476,-0.3339346349,0.0351093672,-0.56149441,0.2225798965,-0.0998275802,-0.2807984054,-0.0613472909,-0.243133992,0.0354893506,-0.2184970975,-0.1084961668,-0.547935307,0.0440959707,-0.0195557699,-0.0765685439,-0.06229949,-0.1391803324,0.1223469973,-0.1744965166,0.4444530904,0.7012936473,-0.0001277011,0.0703018233,0.3642728925,-0.1737781912,0.0499992296,-0.1610135287,0.0400467664,-0.2573829293,0.0727764145,-0.0946834609,-0.2234939188,-0.1708452553,0.0959103853,-0.17902942,-0.0281423349,0.4191688895,0.077129975,0.2790103853,0.2466612458,0.1386305541,0.2278117537,0.5182864666,-0.2734378576,-0.0561165325,0.2261500657,0.37547791,0.1240043715,0.5647351146,-0.2913345993,-0.0362333432,0.266944766,0.1290054172,-0.1014554724,-0.3140054941,0.0491575636,0.089462921,0.4219441712,-0.1494393945,-0.0807789043,-0.1436640322,-0.143978551,-0.1273805052,0.0405908599,0.2803191841,-0.0718406066,-0.1872386485,-0.5392424464,0.3052915633,-0.1486232579,0.2131595016,-0.2805242538,-0.0034206891,-0.1381631345,0.2928087115,-0.0471190289,-0.112712875,0.1930810809,-0.1234148592,0.1300757527,-0.0098258834,-0.1436316669,-0.0534907505,-0.3895993233,-0.3150125444,-0.2001479119,-0.1602650434,0.1664855033,0.1392757297,-0.1163957268,0.1280712485,0.1826325953,0.2680338025,0.105782643,0.3207025826,-0.0858922377,-0.2895810604,-0.0469189361,0.0877334401,-0.2962270379,-0.435752362,0.1545339972,0.3252966106,0.2452212572,0.0687427595,0.1298554242,-0.3239412606,-0.1089225262,-0.0095866034,0.075225018,0.285038203,-0.1186793,0.24390468,0.4804830253,0.3972030282,-0.1538181752,0.0695624724,0.0250724964,0.0315209553,-0.440495193,-0.2856986523,0.1534098238,-0.1177733615,0.100070186,0.0374316685,0.0464155935,0.1006969139,0.0734966248,-0.1858698428,0.6246291399,0.147647351,-0.1944740117,0.2767927051,0.2709063888,0.5297806263,-0.3042984307,0.2565316558,-0.5095701218,0.0652529001,-0.0230914187,-0.0145437596,0.2186551988,-0.020721972,-0.2121624202,0.0158288926,0.5923122168,-0.0836780667,0.2734875381,-0.57916224,0.0949639827,-0.1913348734,0.034152355,0.0636695474,-0.2047151774,-0.0523584187,0.3863441348,0.0919497311,0.3154585958,0.1602817774,0.0510833338,0.0798699334,-0.2253530473,-0.2350505739,0.3363234699,-0.3768565059,0.0920383558,-0.0176931173,0.0938790813,-0.0638038367,0.1120190248,0.3361779451,0.0326004587,-0.128001526,0.0294485986,0.1963148117,-0.1661822349,0.1402871758,0.0773598477,0.1913028061,0.1933745444,0.0350063629,-0.0156836379,0.2076539844,-0.2216719985,0.1775767207,-0.0542802997,-0.0747133866,0.0711769462,0.0403783172,-0.3698907197,-0.0121967075,-0.0474696793,0.1864667684,-0.23310031,-0.048721496,0.0236351732,0.0822172239,0.0582793653,0.047925096,0.0049781557,-0.1919564456,-0.1822061539,-0.1390272975,-0.0032419825,0.0922108144,-0.2094767392,-0.2617242932,-0.0523435548,-0.1232490763,0.1783037037,0.0588379279,-0.3590371013,0.0510254167,0.019273432,-0.3653303087,-0.2013600469,0.1733924001,0.2676690519,-0.0996206775,-0.0422345139,0.1916035116,0.0632886291,0.1146331578,-0.5482739806,0.0834660977,0.1778419167,-0.2469699979,0.2043911219,0.2162791938,0.1485340595,-0.2755032778,-0.1960345507,0.4387866855,0.1604105234,0.315912962,-0.3300254345,0.116101861,-0.0726469457,-0.0394018255,-0.1906339079,-0.0408082679,-0.277281642,0.0730238482,-0.1318593472,0.0129822334,-0.4569184184,-0.0449360423,0.2962621152,0.0887641758,0.1758723557,-0.0012164807,-0.399846226,0.2133122981,0.1771268845,-0.6013535261,0.2978383601,-0.0043616337,0.1892076284,-0.1050189212,0.0955833048,-0.1625430584,0.2484440655,0.1140429005,0.090748094,-0.1994652301,0.1418524235,-0.1079259515,0.0268462822,0.0263701156,-0.1406289488,-0.2336931378,-0.3589504957,0.2713825405,-0.1661832929,-0.1345185637,-0.4018543959,-0.2621856332,0.0360565595,-0.0740050003,-0.0823338255,0.1507581174,0.1855677366,0.190434888,0.26341784,-0.1818942726,0.3743162453,-0.4130297601,-0.0353545919,-0.0750095248,-0.2867960632,-0.0991648287,0.0896557719,-0.5234113932,0.0914847627,-0.2289899141,-0.0931907445,-0.2501759529,0.0135480622,0.1418226808,-0.2678500712,-0.2850030065,0.3085908592,0.1939281374,-0.142482236,0.040662054,0.2663511038,0.0103072627,0.0469011813,0.1537833363,-0.0896200389,0.1165602952,-0.063298367,-0.1767405272,-0.0966183841,-0.0750613436,-0.0704168677,-0.0217595734,0.0633770004,0.3462581038,0.3721836209,-0.0898904651,-0.052042447,0.0356094316,0.06296473,-0.3976880014,0.5225032568,-0.2261542529,-0.3608259261,0.1192016751,0.0238555167,0.0651473776,0.4862691164,0.0605229028,0.0497316308,0.0117824115,-0.0789448172,0.2525928319,0.2211969346,0.3510203063,-0.091124773,0.0191658437,-0.1647072583,0.0154163484,-0.1018712595,-0.0632551014,0.608886838,0.185083881,0.2399424464,0.0992583558,0.6996648312,-0.0611373,-0.7380955219,0.2168067545,-0.2425099015,0.1921664476,-0.1181217954,-0.3539586067,0.3988561332,-0.20941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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2005","title":"Setting to torch format not working with torchvision and MNIST","comments":"Concluding, the way it works right now is:\r\n\r\n1. Converting raw dataset to `torch` format.\r\n2. Use the transform and apply using `map`, ensure the returned values are tensors. \r\n3. When mapping, use `features` with `image` being `Array3D` type.","body":"Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.","comment_length":39,"text":"Setting to torch format not working with torchvision and MNIST \n Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. \n Concluding, the way it works right now is:\r\n\r\n1. Converting raw dataset to `torch` format.\r\n2. Use the transform and apply using `map`, ensure the returned values are tensors. \r\n3. When mapping, use `features` with `image` being `Array3D` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2005","title":"Setting to torch format not working with torchvision and MNIST","comments":"What the dataset returns depends on the feature type.\r\nFor a feature type that is Sequence(Sequence(Sequence(Value(\"uint8\")))), a dataset formatted as \"torch\" return lists of lists of tensors. This is because the lists lengths may vary.\r\nFor a feature type that is Array3D on the other hand it returns one tensor. This is because the size of the tensor is fixed and defined bu the Array3D type.","body":"Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.","comment_length":66,"text":"Setting to torch format not working with torchvision and MNIST \n Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. \n What the dataset returns depends on the feature type.\r\nFor a feature type that is Sequence(Sequence(Sequence(Value(\"uint8\")))), a dataset formatted as \"torch\" return lists of lists of tensors. This is because the lists lengths may vary.\r\nFor a feature type that is Array3D on the other hand it returns one tensor. This is because the size of the tensor is fixed and defined bu the Array3D type.","embeddings":[-0.12737526,-0.3511648178,-0.017932741,0.3536089063,0.4760023057,0.0887141973,0.7330293059,0.3804347813,0.0655255616,-0.0380423479,-0.1111534312,0.3819508553,-0.2329687476,-0.3339346349,0.0351093672,-0.56149441,0.2225798965,-0.0998275802,-0.2807984054,-0.0613472909,-0.243133992,0.0354893506,-0.2184970975,-0.1084961668,-0.547935307,0.0440959707,-0.0195557699,-0.0765685439,-0.06229949,-0.1391803324,0.1223469973,-0.1744965166,0.4444530904,0.7012936473,-0.0001277011,0.0703018233,0.3642728925,-0.1737781912,0.0499992296,-0.1610135287,0.0400467664,-0.2573829293,0.0727764145,-0.0946834609,-0.2234939188,-0.1708452553,0.0959103853,-0.17902942,-0.0281423349,0.4191688895,0.077129975,0.2790103853,0.2466612458,0.1386305541,0.2278117537,0.5182864666,-0.2734378576,-0.0561165325,0.2261500657,0.37547791,0.1240043715,0.5647351146,-0.2913345993,-0.0362333432,0.266944766,0.1290054172,-0.1014554724,-0.3140054941,0.0491575636,0.089462921,0.4219441712,-0.1494393945,-0.0807789043,-0.1436640322,-0.143978551,-0.1273805052,0.0405908599,0.2803191841,-0.0718406066,-0.1872386485,-0.5392424464,0.3052915633,-0.1486232579,0.2131595016,-0.2805242538,-0.0034206891,-0.1381631345,0.2928087115,-0.0471190289,-0.112712875,0.1930810809,-0.1234148592,0.1300757527,-0.0098258834,-0.1436316669,-0.0534907505,-0.3895993233,-0.3150125444,-0.2001479119,-0.1602650434,0.1664855033,0.1392757297,-0.1163957268,0.1280712485,0.1826325953,0.2680338025,0.105782643,0.3207025826,-0.0858922377,-0.2895810604,-0.0469189361,0.0877334401,-0.2962270379,-0.435752362,0.1545339972,0.3252966106,0.2452212572,0.0687427595,0.1298554242,-0.3239412606,-0.1089225262,-0.0095866034,0.075225018,0.285038203,-0.1186793,0.24390468,0.4804830253,0.3972030282,-0.1538181752,0.0695624724,0.0250724964,0.0315209553,-0.440495193,-0.2856986523,0.1534098238,-0.1177733615,0.100070186,0.0374316685,0.0464155935,0.1006969139,0.0734966248,-0.1858698428,0.6246291399,0.147647351,-0.1944740117,0.2767927051,0.2709063888,0.5297806263,-0.3042984307,0.2565316558,-0.5095701218,0.0652529001,-0.0230914187,-0.0145437596,0.2186551988,-0.020721972,-0.2121624202,0.0158288926,0.5923122168,-0.0836780667,0.2734875381,-0.57916224,0.0949639827,-0.1913348734,0.034152355,0.0636695474,-0.2047151774,-0.0523584187,0.3863441348,0.0919497311,0.3154585958,0.1602817774,0.0510833338,0.0798699334,-0.2253530473,-0.2350505739,0.3363234699,-0.3768565059,0.0920383558,-0.0176931173,0.0938790813,-0.0638038367,0.1120190248,0.3361779451,0.0326004587,-0.128001526,0.0294485986,0.1963148117,-0.1661822349,0.1402871758,0.0773598477,0.1913028061,0.1933745444,0.0350063629,-0.0156836379,0.2076539844,-0.2216719985,0.1775767207,-0.0542802997,-0.0747133866,0.0711769462,0.0403783172,-0.3698907197,-0.0121967075,-0.0474696793,0.1864667684,-0.23310031,-0.048721496,0.0236351732,0.0822172239,0.0582793653,0.047925096,0.0049781557,-0.1919564456,-0.1822061539,-0.1390272975,-0.0032419825,0.0922108144,-0.2094767392,-0.2617242932,-0.0523435548,-0.1232490763,0.1783037037,0.0588379279,-0.3590371013,0.0510254167,0.019273432,-0.3653303087,-0.2013600469,0.1733924001,0.2676690519,-0.0996206775,-0.0422345139,0.1916035116,0.0632886291,0.1146331578,-0.5482739806,0.0834660977,0.1778419167,-0.2469699979,0.2043911219,0.2162791938,0.1485340595,-0.2755032778,-0.1960345507,0.4387866855,0.1604105234,0.315912962,-0.3300254345,0.116101861,-0.0726469457,-0.0394018255,-0.1906339079,-0.0408082679,-0.277281642,0.0730238482,-0.1318593472,0.0129822334,-0.4569184184,-0.0449360423,0.2962621152,0.0887641758,0.1758723557,-0.0012164807,-0.399846226,0.2133122981,0.1771268845,-0.6013535261,0.2978383601,-0.0043616337,0.1892076284,-0.1050189212,0.0955833048,-0.1625430584,0.2484440655,0.1140429005,0.090748094,-0.1994652301,0.1418524235,-0.1079259515,0.0268462822,0.0263701156,-0.1406289488,-0.2336931378,-0.3589504957,0.2713825405,-0.1661832929,-0.1345185637,-0.4018543959,-0.2621856332,0.0360565595,-0.0740050003,-0.0823338255,0.1507581174,0.1855677366,0.190434888,0.26341784,-0.1818942726,0.3743162453,-0.4130297601,-0.0353545919,-0.0750095248,-0.2867960632,-0.0991648287,0.0896557719,-0.5234113932,0.0914847627,-0.2289899141,-0.0931907445,-0.2501759529,0.0135480622,0.1418226808,-0.2678500712,-0.2850030065,0.3085908592,0.1939281374,-0.142482236,0.040662054,0.2663511038,0.0103072627,0.0469011813,0.1537833363,-0.0896200389,0.1165602952,-0.063298367,-0.1767405272,-0.0966183841,-0.0750613436,-0.0704168677,-0.0217595734,0.0633770004,0.3462581038,0.3721836209,-0.0898904651,-0.052042447,0.0356094316,0.06296473,-0.3976880014,0.5225032568,-0.2261542529,-0.3608259261,0.1192016751,0.0238555167,0.0651473776,0.4862691164,0.0605229028,0.0497316308,0.0117824115,-0.0789448172,0.2525928319,0.2211969346,0.3510203063,-0.091124773,0.0191658437,-0.1647072583,0.0154163484,-0.1018712595,-0.0632551014,0.608886838,0.185083881,0.2399424464,0.0992583558,0.6996648312,-0.0611373,-0.7380955219,0.2168067545,-0.2425099015,0.1921664476,-0.1181217954,-0.3539586067,0.3988561332,-0.2094196975,0.0643741786,-0.08596712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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2005","title":"Setting to torch format not working with torchvision and MNIST","comments":"Okay, that makes sense.\r\nRaw images are list of Array2D, hence we get a single tensor when `set_format` is used. But, why should I need to convert the raw images to `torch` format when `map` does this internally?\r\n\r\nUsing `Array3D` did not work with `map` when raw images weren't `set_format`ted to torch type.","body":"Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.","comment_length":53,"text":"Setting to torch format not working with torchvision and MNIST \n Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. \n Okay, that makes sense.\r\nRaw images are list of Array2D, hence we get a single tensor when `set_format` is used. But, why should I need to convert the raw images to `torch` format when `map` does this internally?\r\n\r\nUsing `Array3D` did not work with `map` when raw images weren't `set_format`ted to torch 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2005","title":"Setting to torch format not working with torchvision and MNIST","comments":"I understand that `map` needs to know what kind of output tensors are expected, and thus converting the raw dataset to `torch` format is necessary. Closing the issue since it is resolved.","body":"Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.","comment_length":32,"text":"Setting to torch format not working with torchvision and MNIST \n Hi\r\n\r\nI am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object.\r\n\r\nA snippet of what I am trying to do:\r\n```python\r\ndef prepare_features(examples):\r\n    images = []\r\n    labels = []\r\n    for example_idx, example in enumerate(examples[\"image\"]):\r\n        if transform is not None:\r\n            images.append(transform(\r\n                np.array(examples[\"image\"][example_idx], dtype=np.uint8)\r\n            ))\r\n        else:\r\n            images.append(torch.tensor(np.array(examples[\"image\"][example_idx], dtype=np.uint8)))\r\n        labels.append(torch.tensor(examples[\"label\"][example_idx]))\r\n    output = {\"label\":labels, \"image\":images}\r\n    return output\r\n\r\nraw_dataset = load_dataset('mnist')\r\ntrain_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000)\r\ntrain_dataset.set_format(\"torch\",columns=[\"image\",\"label\"])\r\n```\r\n\r\nAfter this, I check the type of the following:\r\n```python\r\nprint(type(train_dataset[\"train\"][\"label\"]))\r\nprint(type(train_dataset[\"train\"][\"image\"][0]))\r\n```\r\nThis leads to the following output:\r\n\r\n```python\r\n<class 'torch.Tensor'>\r\n<class 'list'>\r\n```\r\nI use `torch.utils.DataLoader` for batches, the type of `batch[\"train\"][\"image\"]` is also `<class 'list'>`.\r\n\r\nI don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue?\r\n\r\nThanks,\r\nGunjan\r\n\r\nEDIT:\r\nI just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28).\r\n\r\nEDIT 2:\r\nInside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list. \n I understand that `map` needs to know what kind of output tensors are expected, and thus converting the raw dataset to `torch` format is necessary. Closing the issue since it is resolved.","embeddings":[-0.12737526,-0.3511648178,-0.017932741,0.3536089063,0.4760023057,0.0887141973,0.7330293059,0.3804347813,0.0655255616,-0.0380423479,-0.1111534312,0.3819508553,-0.2329687476,-0.3339346349,0.0351093672,-0.56149441,0.2225798965,-0.0998275802,-0.2807984054,-0.0613472909,-0.243133992,0.0354893506,-0.2184970975,-0.1084961668,-0.547935307,0.0440959707,-0.0195557699,-0.0765685439,-0.06229949,-0.1391803324,0.1223469973,-0.1744965166,0.4444530904,0.7012936473,-0.0001277011,0.0703018233,0.3642728925,-0.1737781912,0.0499992296,-0.1610135287,0.0400467664,-0.2573829293,0.0727764145,-0.0946834609,-0.2234939188,-0.1708452553,0.0959103853,-0.17902942,-0.0281423349,0.4191688895,0.077129975,0.2790103853,0.2466612458,0.1386305541,0.2278117537,0.5182864666,-0.2734378576,-0.0561165325,0.2261500657,0.37547791,0.1240043715,0.5647351146,-0.2913345993,-0.0362333432,0.266944766,0.1290054172,-0.1014554724,-0.3140054941,0.0491575636,0.089462921,0.4219441712,-0.1494393945,-0.0807789043,-0.1436640322,-0.143978551,-0.1273805052,0.0405908599,0.2803191841,-0.0718406066,-0.1872386485,-0.5392424464,0.3052915633,-0.1486232579,0.2131595016,-0.2805242538,-0.0034206891,-0.1381631345,0.2928087115,-0.0471190289,-0.112712875,0.1930810809,-0.1234148592,0.1300757527,-0.0098258834,-0.1436316669,-0.0534907505,-0.3895993233,-0.3150125444,-0.2001479119,-0.1602650434,0.1664855033,0.1392757297,-0.1163957268,0.1280712485,0.1826325953,0.2680338025,0.105782643,0.3207025826,-0.0858922377,-0.2895810604,-0.0469189361,0.0877334401,-0.2962270379,-0.435752362,0.1545339972,0.3252966106,0.2452212572,0.0687427595,0.1298554242,-0.3239412606,-0.1089225262,-0.0095866034,0.075225018,0.285038203,-0.1186793,0.24390468,0.4804830253,0.3972030282,-0.1538181752,0.0695624724,0.0250724964,0.0315209553,-0.440495193,-0.2856986523,0.1534098238,-0.1177733615,0.100070186,0.0374316685,0.0464155935,0.1006969139,0.0734966248,-0.1858698428,0.6246291399,0.147647351,-0.1944740117,0.2767927051,0.2709063888,0.5297806263,-0.3042984307,0.2565316558,-0.5095701218,0.0652529001,-0.0230914187,-0.0145437596,0.2186551988,-0.020721972,-0.2121624202,0.0158288926,0.5923122168,-0.0836780667,0.2734875381,-0.57916224,0.0949639827,-0.1913348734,0.034152355,0.0636695474,-0.2047151774,-0.0523584187,0.3863441348,0.0919497311,0.3154585958,0.1602817774,0.0510833338,0.0798699334,-0.2253530473,-0.2350505739,0.3363234699,-0.3768565059,0.0920383558,-0.0176931173,0.0938790813,-0.0638038367,0.1120190248,0.3361779451,0.0326004587,-0.128001526,0.0294485986,0.1963148117,-0.1661822349,0.1402871758,0.0773598477,0.1913028061,0.1933745444,0.0350063629,-0.0156836379,0.2076539844,-0.2216719985,0.1775767207,-0.0542802997,-0.0747133866,0.0711769462,0.0403783172,-0.3698907197,-0.0121967075,-0.0474696793,0.1864667684,-0.23310031,-0.048721496,0.0236351732,0.0822172239,0.0582793653,0.047925096,0.0049781557,-0.1919564456,-0.1822061539,-0.1390272975,-0.0032419825,0.0922108144,-0.2094767392,-0.2617242932,-0.0523435548,-0.1232490763,0.1783037037,0.0588379279,-0.3590371013,0.0510254167,0.019273432,-0.3653303087,-0.2013600469,0.1733924001,0.2676690519,-0.0996206775,-0.0422345139,0.1916035116,0.0632886291,0.1146331578,-0.5482739806,0.0834660977,0.1778419167,-0.2469699979,0.2043911219,0.2162791938,0.1485340595,-0.2755032778,-0.1960345507,0.4387866855,0.1604105234,0.315912962,-0.3300254345,0.116101861,-0.0726469457,-0.0394018255,-0.1906339079,-0.0408082679,-0.277281642,0.0730238482,-0.1318593472,0.0129822334,-0.4569184184,-0.0449360423,0.2962621152,0.0887641758,0.1758723557,-0.0012164807,-0.399846226,0.2133122981,0.1771268845,-0.6013535261,0.2978383601,-0.0043616337,0.1892076284,-0.1050189212,0.0955833048,-0.1625430584,0.2484440655,0.1140429005,0.090748094,-0.1994652301,0.1418524235,-0.1079259515,0.0268462822,0.0263701156,-0.1406289488,-0.2336931378,-0.3589504957,0.2713825405,-0.1661832929,-0.1345185637,-0.4018543959,-0.2621856332,0.0360565595,-0.0740050003,-0.0823338255,0.1507581174,0.1855677366,0.190434888,0.26341784,-0.1818942726,0.3743162453,-0.4130297601,-0.0353545919,-0.0750095248,-0.2867960632,-0.0991648287,0.0896557719,-0.5234113932,0.0914847627,-0.2289899141,-0.0931907445,-0.2501759529,0.0135480622,0.1418226808,-0.2678500712,-0.2850030065,0.3085908592,0.1939281374,-0.142482236,0.040662054,0.2663511038,0.0103072627,0.0469011813,0.1537833363,-0.0896200389,0.1165602952,-0.063298367,-0.1767405272,-0.0966183841,-0.0750613436,-0.0704168677,-0.0217595734,0.0633770004,0.3462581038,0.3721836209,-0.0898904651,-0.052042447,0.0356094316,0.06296473,-0.3976880014,0.5225032568,-0.2261542529,-0.3608259261,0.1192016751,0.0238555167,0.0651473776,0.4862691164,0.0605229028,0.0497316308,0.0117824115,-0.0789448172,0.2525928319,0.2211969346,0.3510203063,-0.091124773,0.0191658437,-0.1647072583,0.0154163484,-0.1018712595,-0.0632551014,0.608886838,0.185083881,0.2399424464,0.0992583558,0.6996648312,-0.0611373,-0.7380955219,0.2168067545,-0.2425099015,0.1921664476,-0.1181217954,-0.3539586067,0.3988561332,-0.2094196975,0.0643741786,-0.0859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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2003","title":"Messages are being printed to the `stdout`","comments":"This is expected to show this message to the user via stdout.\r\nThis way the users see it directly and can cancel the downloading if they want to.\r\nCould you elaborate why it would be better to have it in stderr instead of stdout ?","body":"In this code segment, we can see some messages are being printed to the `stdout`.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/7e60bb509b595e8edc60a87f32b2bacfc065d607\/src\/datasets\/builder.py#L545-L554\r\nAccording to the comment, it is done intentionally, but I don't really understand why don't we log it with a higher level or print it directly to the `stderr`.\r\nIn my opinion, this kind of messages should never printed to the stdout. At least some configuration\/flag should make it possible to provide in order to explicitly prevent the package to contaminate the stdout.\r\n","comment_length":45,"text":"Messages are being printed to the `stdout` \n In this code segment, we can see some messages are being printed to the `stdout`.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/7e60bb509b595e8edc60a87f32b2bacfc065d607\/src\/datasets\/builder.py#L545-L554\r\nAccording to the comment, it is done intentionally, but I don't really understand why don't we log it with a higher level or print it directly to the `stderr`.\r\nIn my opinion, this kind of messages should never printed to the stdout. At least some configuration\/flag should make it possible to provide in order to explicitly prevent the package to contaminate the stdout.\r\n \n This is expected to show this message to the user via stdout.\r\nThis way the users see it directly and can cancel the downloading if they want to.\r\nCould you elaborate why it would be better to have it in stderr instead of stdout 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2003","title":"Messages are being printed to the `stdout`","comments":"@lhoestq, sorry for the late reply\r\n\r\nI completely understand why you decided to output a message that is always shown. The only problem is that the message is printed to the `stdout`. For example, if the user runs `python run_glue.py > log_file`, it will redirect `stdout` to the file named  `log_file`, and the message will not be shown to the user.\r\n\r\nInstead, we should print this message to `stderr`.  Even in the case of `python run_glue.py > log_file` only `stdout` is being redirected and so the message is always shown.","body":"In this code segment, we can see some messages are being printed to the `stdout`.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/7e60bb509b595e8edc60a87f32b2bacfc065d607\/src\/datasets\/builder.py#L545-L554\r\nAccording to the comment, it is done intentionally, but I don't really understand why don't we log it with a higher level or print it directly to the `stderr`.\r\nIn my opinion, this kind of messages should never printed to the stdout. At least some configuration\/flag should make it possible to provide in order to explicitly prevent the package to contaminate the stdout.\r\n","comment_length":90,"text":"Messages are being printed to the `stdout` \n In this code segment, we can see some messages are being printed to the `stdout`.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/7e60bb509b595e8edc60a87f32b2bacfc065d607\/src\/datasets\/builder.py#L545-L554\r\nAccording to the comment, it is done intentionally, but I don't really understand why don't we log it with a higher level or print it directly to the `stderr`.\r\nIn my opinion, this kind of messages should never printed to the stdout. At least some configuration\/flag should make it possible to provide in order to explicitly prevent the package to contaminate the stdout.\r\n \n @lhoestq, sorry for the late reply\r\n\r\nI completely understand why you decided to output a message that is always shown. The only problem is that the message is printed to the `stdout`. For example, if the user runs `python run_glue.py > log_file`, it will redirect `stdout` to the file named  `log_file`, and the message will not be shown to the user.\r\n\r\nInstead, we should print this message to `stderr`.  Even in the case of `python run_glue.py > log_file` only `stdout` is being redirected and so the message is always shown.","embeddings":[0.054125268,-0.4283034801,-0.0178409368,0.1863200217,0.1776289493,-0.1547809094,0.3838965297,0.1615999639,0.0672421008,0.2444254011,0.190782994,0.3053060472,-0.1425594836,0.2849757373,0.2610240579,0.1417885572,-0.0724066272,0.0228824783,-0.4255489409,0.107640557,-0.0089996178,0.1110258028,0.1996388882,0.4757939875,-0.6451058388,-0.0969342664,0.2199249417,0.0682550073,-0.1504664123,-0.5180291533,-0.0038762686,0.1959688216,-0.0711483508,0.2187255025,-0.0001107846,0.1114448011,0.558391273,0.1802080423,-0.5341908932,-0.1494897157,-0.0687197596,-0.2411948144,0.3232271075,-0.1385637671,-0.1318151653,-0.263232559,0.2714948058,-0.3419891894,0.3885952532,0.2979872823,0.2185657918,0.2383882999,0.0107336165,0.2430755496,0.1254059374,0.2416490316,0.0376767814,-0.0428685956,-0.0429108776,0.3080660999,-0.2036425173,0.4286541939,-0.0826231539,-0.2804137468,0.0262908377,0.1417250037,0.1627256423,-0.3487530351,0.0097641358,0.0993574187,0.2654751539,-0.4483452737,-0.1097153425,-0.3162628114,-0.0963749588,-0.1171589941,0.1848396957,0.1446038634,-0.1712843478,0.3809222281,-0.190245837,-0.2524473071,-0.2295149714,-0.073409155,-0.124781318,0.019961467,-0.320853591,0.2482610345,-0.2218214869,0.2160465717,-0.1857299507,-0.2165313214,-0.1245649159,-0.0516973436,-0.0614772849,0.0057034413,0.115155071,-0.0911643878,-0.0116026597,-0.1248358861,-0.2013799846,0.0620362461,0.2232733965,0.1918914467,0.4884245694,0.0879197717,0.5125946403,0.2814555168,0.1907999218,-0.04213164,0.0905307159,-0.2021928281,0.3088915944,0.1708641648,0.2888863087,-0.2328936458,0.2866511941,-0.0900234655,-0.0649691075,0.1431056261,-0.061183352,0.0710895211,-0.0234851856,0.2419447452,0.0153705021,-0.1141114309,0.3781521916,-0.0502083115,0.0626935288,-0.0070137731,-0.1976067871,0.0261096414,-0.3317315578,0.0355130285,-0.1024647728,-0.049990233,0.067420274,0.0976911858,0.153589204,-0.1075139344,-0.1262116134,-0.0576669611,0.4185282886,0.4810254872,-0.2644611895,0.29549402,0.1943539679,-0.2578784525,-0.1323119849,0.1952917576,0.0771409944,-0.2959942222,0.1112999618,0.1830358356,-0.3067573309,0.3234333694,-0.2131379992,0.0265495963,-0.0012505222,-0.1195104942,0.235718742,-0.1698851883,-0.0174311064,-0.217944026,0.1805113405,0.3643777966,-0.2229035795,-0.1052753404,-0.0771993324,-0.4211502373,0.3274549544,-0.2325004041,0.1644014567,0.3366909623,-0.3936917186,-0.3250600696,0.4573607147,-0.3337568641,-0.1708879024,0.2365000099,-0.4048209786,0.4652937353,0.2076699138,0.0043006013,-0.0059952196,0.0376351476,0.1742016226,-0.1800449938,0.3051739931,0.1302233487,-0.2291006446,-0.0451015867,-0.1515160203,-0.00341606,-0.0958907083,-0.0259335395,-0.0169936493,-0.1171620861,0.4299755394,0.2623735666,0.0772481635,-0.2361664027,0.2408175766,0.2613838911,-0.0685643181,-0.0481198914,-0.0634819716,0.0062270737,-0.1658295989,0.0544401035,-0.3115944862,-0.2219983935,0.1507137269,0.1317087561,-0.1692024469,-0.3026206493,0.1607024223,0.2380982488,0.1132995039,0.302678138,-0.1198463142,0.5206785798,-0.3534439504,0.0884945691,-0.1319672763,-0.0621947758,0.257437855,-0.1281383336,-0.075649552,0.2081562132,-0.093410708,0.167134732,0.0381667428,0.4232925177,-0.0043456359,0.1183209568,-0.3124951422,0.0500708185,0.0097992299,-0.0047034323,0.0895714983,0.1420443952,-0.1465670317,0.1471806169,0.0384487025,-0.0887246281,0.172126621,0.150696978,-0.0438549928,0.0488436855,-0.1227513924,0.1988383234,-0.4711333811,-0.3036490977,-0.2785580456,-0.1713651419,0.303311497,0.1184073612,-0.3035778403,0.2379687428,0.7682510614,-0.0301368106,0.0205507204,0.0657910481,-0.2529127598,0.0181573071,0.1102729589,0.2093629688,0.1760621667,0.2615461648,0.2001331747,0.0508067124,0.006275434,0.0126902675,0.2241543382,0.0651092231,-0.2292538136,0.0869500786,-0.0851280615,-0.226238057,-0.2331650257,0.2796397209,-0.5140915513,-0.1945192218,-0.3919097185,0.0182173494,-0.2353746146,-0.4033135772,-0.5682982802,-0.0971945673,-0.0938994214,-0.3950068057,0.3544939458,-0.0493450053,-0.2513554096,0.0566962585,0.3190596402,0.276720196,0.0284163244,0.1718168855,-0.4514873326,0.2630797327,-0.4042059481,0.0542790033,-0.2848892212,0.1247040033,0.3621223271,-0.2116229683,-0.0718007088,-0.2785495222,-0.0587097704,0.2219152004,-0.2349594533,0.5601465702,0.3041003048,0.0496970862,0.2447494566,-0.1081943884,0.0988120511,-0.3349266946,-0.0422325283,-0.2306946963,0.1663376391,0.1885206699,-0.3057380319,-0.2492800206,-0.3592651486,-0.339918375,0.2599515617,-0.2132177651,0.2335208356,-0.1511961669,-0.112375237,0.1376269609,-0.3935935199,0.4972854555,-0.0881851092,-0.6302135587,-0.1551142335,-0.1244047806,-0.0908231884,-0.1654584408,0.1136178747,-0.2327751964,-0.2250757068,-0.5604439974,-0.2937108278,-0.2211570442,-0.1356341988,0.2647372186,0.3401813209,0.1737589836,0.115520142,-0.1217627823,-0.0775534511,-0.0946765617,0.0891970992,-0.3017315567,0.2018404305,0.2314448059,0.1525161266,0.0308498573,0.1316176653,0.3769789934,0.123182714,-0.2599914372,-0.1977273077,0.9344843626,-0.307679683,-0.1384760588,-0.0149093661,0.12179672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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2000","title":"Windows Permission Error (most recent version of datasets)","comments":"Hi @itsLuisa !\r\n\r\nCould you give us more information about the error you're getting, please?\r\nA copy-paste of the Traceback would be nice to get a better understanding of what is wrong :) ","body":"Hi everyone,\r\nCan anyone help me with why the dataset loading script below raises a Windows Permission Error? I stuck quite closely to https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py , only I want to load the data from three local three-column tsv-files (id\\ttokens\\tpos_tags\\n). I am using the most recent version of datasets. Thank you in advance!\r\nLuisa\r\n\r\nMy script:\r\n```\r\nimport datasets\r\nimport csv\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\nclass SampleConfig(datasets.BuilderConfig):\r\n\r\n    def __init__(self, **kwargs):\r\n        super(SampleConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n    BUILDER_CONFIGS = [\r\n        SampleConfig(name=\"conll2003\", version=datasets.Version(\"1.0.0\"), description=\"Conll2003 dataset\"),\r\n    ]\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=\"Dataset with words and their POS-Tags\",\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"pos_tags\": datasets.Sequence(\r\n                        datasets.features.ClassLabel(\r\n                            names=[\r\n                                \"''\",\r\n                                \",\",\r\n                                \"-LRB-\",\r\n                                \"-RRB-\",\r\n                                \".\",\r\n                                \":\",\r\n                                \"CC\",\r\n                                \"CD\",\r\n                                \"DT\",\r\n                                \"EX\",\r\n                                \"FW\",\r\n                                \"HYPH\",\r\n                                \"IN\",\r\n                                \"JJ\",\r\n                                \"JJR\",\r\n                                \"JJS\",\r\n                                \"MD\",\r\n                                \"NN\",\r\n                                \"NNP\",\r\n                                \"NNPS\",\r\n                                \"NNS\",\r\n                                \"PDT\",\r\n                                \"POS\",\r\n                                \"PRP\",\r\n                                \"PRP$\",\r\n                                \"RB\",\r\n                                \"RBR\",\r\n                                \"RBS\",\r\n                                \"RP\",\r\n                                \"TO\",\r\n                                \"UH\",\r\n                                \"VB\",\r\n                                \"VBD\",\r\n                                \"VBG\",\r\n                                \"VBN\",\r\n                                \"VBP\",\r\n                                \"VBZ\",\r\n                                \"WDT\",\r\n                                \"WP\",\r\n                                \"WRB\",\r\n                                \"``\"\r\n                            ]\r\n                        )\r\n                    ),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/catalog.ldc.upenn.edu\/LDC2011T03\",\r\n            citation=\"Weischedel, Ralph, et al. OntoNotes Release 4.0 LDC2011T03. Web Download. Philadelphia: Linguistic Data Consortium, 2011.\",\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        loaded_files = dl_manager.download_and_extract(self.config.data_files)\r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": loaded_files[\"train\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={\"filepath\": loaded_files[\"test\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": loaded_files[\"val\"]})\r\n        ]\r\n\r\n    def _generate_examples(self, filepath):\r\n        logger.info(\"generating examples from = %s\", filepath)\r\n        with open(filepath, encoding=\"cp1252\") as f:\r\n            data = csv.reader(f, delimiter=\"\\t\")\r\n            ids = list()\r\n            tokens = list()\r\n            pos_tags = list()\r\n            for id_, line in enumerate(data):\r\n                #print(line)\r\n                if len(line) == 1:\r\n                    if tokens:\r\n                        yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n                        ids = list()\r\n                        tokens = list()\r\n                        pos_tags = list()\r\n                else:\r\n                    ids.append(line[0])\r\n                    tokens.append(line[1])\r\n                    pos_tags.append(line[2])\r\n            # last example\r\n            yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n\r\n\r\ndef main():\r\n    dataset = datasets.load_dataset(\r\n        \"data_loading.py\", data_files={\r\n            \"train\": \"train.tsv\",\r\n            \"test\": \"test.tsv\",\r\n            \"val\": \"val.tsv\"\r\n        }\r\n    )\r\n\r\n    #print(dataset)\r\n\r\nif __name__==\"__main__\":\r\n    main()\r\n```\r\n","comment_length":33,"text":"Windows Permission Error (most recent version of datasets) \n Hi everyone,\r\nCan anyone help me with why the dataset loading script below raises a Windows Permission Error? I stuck quite closely to https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py , only I want to load the data from three local three-column tsv-files (id\\ttokens\\tpos_tags\\n). I am using the most recent version of datasets. Thank you in advance!\r\nLuisa\r\n\r\nMy script:\r\n```\r\nimport datasets\r\nimport csv\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\nclass SampleConfig(datasets.BuilderConfig):\r\n\r\n    def __init__(self, **kwargs):\r\n        super(SampleConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n    BUILDER_CONFIGS = [\r\n        SampleConfig(name=\"conll2003\", version=datasets.Version(\"1.0.0\"), description=\"Conll2003 dataset\"),\r\n    ]\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=\"Dataset with words and their POS-Tags\",\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"pos_tags\": datasets.Sequence(\r\n                        datasets.features.ClassLabel(\r\n                            names=[\r\n                                \"''\",\r\n                                \",\",\r\n                                \"-LRB-\",\r\n                                \"-RRB-\",\r\n                                \".\",\r\n                                \":\",\r\n                                \"CC\",\r\n                                \"CD\",\r\n                                \"DT\",\r\n                                \"EX\",\r\n                                \"FW\",\r\n                                \"HYPH\",\r\n                                \"IN\",\r\n                                \"JJ\",\r\n                                \"JJR\",\r\n                                \"JJS\",\r\n                                \"MD\",\r\n                                \"NN\",\r\n                                \"NNP\",\r\n                                \"NNPS\",\r\n                                \"NNS\",\r\n                                \"PDT\",\r\n                                \"POS\",\r\n                                \"PRP\",\r\n                                \"PRP$\",\r\n                                \"RB\",\r\n                                \"RBR\",\r\n                                \"RBS\",\r\n                                \"RP\",\r\n                                \"TO\",\r\n                                \"UH\",\r\n                                \"VB\",\r\n                                \"VBD\",\r\n                                \"VBG\",\r\n                                \"VBN\",\r\n                                \"VBP\",\r\n                                \"VBZ\",\r\n                                \"WDT\",\r\n                                \"WP\",\r\n                                \"WRB\",\r\n                                \"``\"\r\n                            ]\r\n                        )\r\n                    ),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/catalog.ldc.upenn.edu\/LDC2011T03\",\r\n            citation=\"Weischedel, Ralph, et al. OntoNotes Release 4.0 LDC2011T03. Web Download. Philadelphia: Linguistic Data Consortium, 2011.\",\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        loaded_files = dl_manager.download_and_extract(self.config.data_files)\r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": loaded_files[\"train\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={\"filepath\": loaded_files[\"test\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": loaded_files[\"val\"]})\r\n        ]\r\n\r\n    def _generate_examples(self, filepath):\r\n        logger.info(\"generating examples from = %s\", filepath)\r\n        with open(filepath, encoding=\"cp1252\") as f:\r\n            data = csv.reader(f, delimiter=\"\\t\")\r\n            ids = list()\r\n            tokens = list()\r\n            pos_tags = list()\r\n            for id_, line in enumerate(data):\r\n                #print(line)\r\n                if len(line) == 1:\r\n                    if tokens:\r\n                        yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n                        ids = list()\r\n                        tokens = list()\r\n                        pos_tags = list()\r\n                else:\r\n                    ids.append(line[0])\r\n                    tokens.append(line[1])\r\n                    pos_tags.append(line[2])\r\n            # last example\r\n            yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n\r\n\r\ndef main():\r\n    dataset = datasets.load_dataset(\r\n        \"data_loading.py\", data_files={\r\n            \"train\": \"train.tsv\",\r\n            \"test\": \"test.tsv\",\r\n            \"val\": \"val.tsv\"\r\n        }\r\n    )\r\n\r\n    #print(dataset)\r\n\r\nif __name__==\"__main__\":\r\n    main()\r\n```\r\n \n Hi @itsLuisa !\r\n\r\nCould you give us more information about the error you're getting, please?\r\nA copy-paste of the Traceback would be nice to get a better understanding of what is wrong :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2000","title":"Windows Permission Error (most recent version of datasets)","comments":"Hello @SBrandeis , this is it:\r\n```\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 537, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 656, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 982, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 297, in finalize\r\n    self.write_on_file()\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 230, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\\array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\\array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 97, in __arrow_array__\r\n    out = pa.array(self.data, type=type)\r\n  File \"pyarrow\\array.pxi\", line 305, in pyarrow.lib.array\r\n  File \"pyarrow\\array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n  File \"pyarrow\\error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\\error.pxi\", line 107, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Expected bytes, got a 'list' object\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/Users\/Luisa\/Documents\/Uni\/WS 2020,21\/Neural Networks\/Final_Project\/NN_Project\/data_loading.py\", line 122, in <module>\r\n    main()\r\n  File \"C:\/Users\/Luisa\/Documents\/Uni\/WS 2020,21\/Neural Networks\/Final_Project\/NN_Project\/data_loading.py\", line 111, in main\r\n    dataset = datasets.load_dataset(\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 586, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\contextlib.py\", line 131, in __exit__\r\n    self.gen.throw(type, value, traceback)\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 543, in incomplete_dir\r\n    shutil.rmtree(tmp_dir)\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 740, in rmtree\r\n    return _rmtree_unsafe(path, onerror)\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 618, in _rmtree_unsafe\r\n    onerror(os.unlink, fullname, sys.exc_info())\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 616, in _rmtree_unsafe\r\n    os.unlink(fullname)\r\nPermissionError: [WinError 32] Der Prozess kann nicht auf die Datei zugreifen, da sie von einem anderen Prozess verwendet wird: 'C:\\\\Users\\\\Luisa\\\\.cache\\\\huggingface\\\\datasets\\\\sample\\\\default-20ee7d51a6a9454f\\\\0.0.0\\\\5fc4c3a355ea77ab446bd31fca5082437600b8364d29b2b95264048bd1f398b1.incomplete\\\\sample-train.arrow'\r\n\r\nProcess finished with exit code 1\r\n```","body":"Hi everyone,\r\nCan anyone help me with why the dataset loading script below raises a Windows Permission Error? I stuck quite closely to https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py , only I want to load the data from three local three-column tsv-files (id\\ttokens\\tpos_tags\\n). I am using the most recent version of datasets. Thank you in advance!\r\nLuisa\r\n\r\nMy script:\r\n```\r\nimport datasets\r\nimport csv\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\nclass SampleConfig(datasets.BuilderConfig):\r\n\r\n    def __init__(self, **kwargs):\r\n        super(SampleConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n    BUILDER_CONFIGS = [\r\n        SampleConfig(name=\"conll2003\", version=datasets.Version(\"1.0.0\"), description=\"Conll2003 dataset\"),\r\n    ]\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=\"Dataset with words and their POS-Tags\",\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"pos_tags\": datasets.Sequence(\r\n                        datasets.features.ClassLabel(\r\n                            names=[\r\n                                \"''\",\r\n                                \",\",\r\n                                \"-LRB-\",\r\n                                \"-RRB-\",\r\n                                \".\",\r\n                                \":\",\r\n                                \"CC\",\r\n                                \"CD\",\r\n                                \"DT\",\r\n                                \"EX\",\r\n                                \"FW\",\r\n                                \"HYPH\",\r\n                                \"IN\",\r\n                                \"JJ\",\r\n                                \"JJR\",\r\n                                \"JJS\",\r\n                                \"MD\",\r\n                                \"NN\",\r\n                                \"NNP\",\r\n                                \"NNPS\",\r\n                                \"NNS\",\r\n                                \"PDT\",\r\n                                \"POS\",\r\n                                \"PRP\",\r\n                                \"PRP$\",\r\n                                \"RB\",\r\n                                \"RBR\",\r\n                                \"RBS\",\r\n                                \"RP\",\r\n                                \"TO\",\r\n                                \"UH\",\r\n                                \"VB\",\r\n                                \"VBD\",\r\n                                \"VBG\",\r\n                                \"VBN\",\r\n                                \"VBP\",\r\n                                \"VBZ\",\r\n                                \"WDT\",\r\n                                \"WP\",\r\n                                \"WRB\",\r\n                                \"``\"\r\n                            ]\r\n                        )\r\n                    ),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/catalog.ldc.upenn.edu\/LDC2011T03\",\r\n            citation=\"Weischedel, Ralph, et al. OntoNotes Release 4.0 LDC2011T03. Web Download. Philadelphia: Linguistic Data Consortium, 2011.\",\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        loaded_files = dl_manager.download_and_extract(self.config.data_files)\r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": loaded_files[\"train\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={\"filepath\": loaded_files[\"test\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": loaded_files[\"val\"]})\r\n        ]\r\n\r\n    def _generate_examples(self, filepath):\r\n        logger.info(\"generating examples from = %s\", filepath)\r\n        with open(filepath, encoding=\"cp1252\") as f:\r\n            data = csv.reader(f, delimiter=\"\\t\")\r\n            ids = list()\r\n            tokens = list()\r\n            pos_tags = list()\r\n            for id_, line in enumerate(data):\r\n                #print(line)\r\n                if len(line) == 1:\r\n                    if tokens:\r\n                        yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n                        ids = list()\r\n                        tokens = list()\r\n                        pos_tags = list()\r\n                else:\r\n                    ids.append(line[0])\r\n                    tokens.append(line[1])\r\n                    pos_tags.append(line[2])\r\n            # last example\r\n            yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n\r\n\r\ndef main():\r\n    dataset = datasets.load_dataset(\r\n        \"data_loading.py\", data_files={\r\n            \"train\": \"train.tsv\",\r\n            \"test\": \"test.tsv\",\r\n            \"val\": \"val.tsv\"\r\n        }\r\n    )\r\n\r\n    #print(dataset)\r\n\r\nif __name__==\"__main__\":\r\n    main()\r\n```\r\n","comment_length":230,"text":"Windows Permission Error (most recent version of datasets) \n Hi everyone,\r\nCan anyone help me with why the dataset loading script below raises a Windows Permission Error? I stuck quite closely to https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py , only I want to load the data from three local three-column tsv-files (id\\ttokens\\tpos_tags\\n). I am using the most recent version of datasets. Thank you in advance!\r\nLuisa\r\n\r\nMy script:\r\n```\r\nimport datasets\r\nimport csv\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\nclass SampleConfig(datasets.BuilderConfig):\r\n\r\n    def __init__(self, **kwargs):\r\n        super(SampleConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n    BUILDER_CONFIGS = [\r\n        SampleConfig(name=\"conll2003\", version=datasets.Version(\"1.0.0\"), description=\"Conll2003 dataset\"),\r\n    ]\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=\"Dataset with words and their POS-Tags\",\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"pos_tags\": datasets.Sequence(\r\n                        datasets.features.ClassLabel(\r\n                            names=[\r\n                                \"''\",\r\n                                \",\",\r\n                                \"-LRB-\",\r\n                                \"-RRB-\",\r\n                                \".\",\r\n                                \":\",\r\n                                \"CC\",\r\n                                \"CD\",\r\n                                \"DT\",\r\n                                \"EX\",\r\n                                \"FW\",\r\n                                \"HYPH\",\r\n                                \"IN\",\r\n                                \"JJ\",\r\n                                \"JJR\",\r\n                                \"JJS\",\r\n                                \"MD\",\r\n                                \"NN\",\r\n                                \"NNP\",\r\n                                \"NNPS\",\r\n                                \"NNS\",\r\n                                \"PDT\",\r\n                                \"POS\",\r\n                                \"PRP\",\r\n                                \"PRP$\",\r\n                                \"RB\",\r\n                                \"RBR\",\r\n                                \"RBS\",\r\n                                \"RP\",\r\n                                \"TO\",\r\n                                \"UH\",\r\n                                \"VB\",\r\n                                \"VBD\",\r\n                                \"VBG\",\r\n                                \"VBN\",\r\n                                \"VBP\",\r\n                                \"VBZ\",\r\n                                \"WDT\",\r\n                                \"WP\",\r\n                                \"WRB\",\r\n                                \"``\"\r\n                            ]\r\n                        )\r\n                    ),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/catalog.ldc.upenn.edu\/LDC2011T03\",\r\n            citation=\"Weischedel, Ralph, et al. OntoNotes Release 4.0 LDC2011T03. Web Download. Philadelphia: Linguistic Data Consortium, 2011.\",\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        loaded_files = dl_manager.download_and_extract(self.config.data_files)\r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": loaded_files[\"train\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={\"filepath\": loaded_files[\"test\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": loaded_files[\"val\"]})\r\n        ]\r\n\r\n    def _generate_examples(self, filepath):\r\n        logger.info(\"generating examples from = %s\", filepath)\r\n        with open(filepath, encoding=\"cp1252\") as f:\r\n            data = csv.reader(f, delimiter=\"\\t\")\r\n            ids = list()\r\n            tokens = list()\r\n            pos_tags = list()\r\n            for id_, line in enumerate(data):\r\n                #print(line)\r\n                if len(line) == 1:\r\n                    if tokens:\r\n                        yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n                        ids = list()\r\n                        tokens = list()\r\n                        pos_tags = list()\r\n                else:\r\n                    ids.append(line[0])\r\n                    tokens.append(line[1])\r\n                    pos_tags.append(line[2])\r\n            # last example\r\n            yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n\r\n\r\ndef main():\r\n    dataset = datasets.load_dataset(\r\n        \"data_loading.py\", data_files={\r\n            \"train\": \"train.tsv\",\r\n            \"test\": \"test.tsv\",\r\n            \"val\": \"val.tsv\"\r\n        }\r\n    )\r\n\r\n    #print(dataset)\r\n\r\nif __name__==\"__main__\":\r\n    main()\r\n```\r\n \n Hello @SBrandeis , this is it:\r\n```\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 537, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 656, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 982, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 297, in finalize\r\n    self.write_on_file()\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 230, in write_on_file\r\n    pa_array = pa.array(typed_sequence)\r\n  File \"pyarrow\\array.pxi\", line 222, in pyarrow.lib.array\r\n  File \"pyarrow\\array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 97, in __arrow_array__\r\n    out = pa.array(self.data, type=type)\r\n  File \"pyarrow\\array.pxi\", line 305, in pyarrow.lib.array\r\n  File \"pyarrow\\array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n  File \"pyarrow\\error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\\error.pxi\", line 107, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Expected bytes, got a 'list' object\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\/Users\/Luisa\/Documents\/Uni\/WS 2020,21\/Neural Networks\/Final_Project\/NN_Project\/data_loading.py\", line 122, in <module>\r\n    main()\r\n  File \"C:\/Users\/Luisa\/Documents\/Uni\/WS 2020,21\/Neural Networks\/Final_Project\/NN_Project\/data_loading.py\", line 111, in main\r\n    dataset = datasets.load_dataset(\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 586, in download_and_prepare\r\n    self._save_info()\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\contextlib.py\", line 131, in __exit__\r\n    self.gen.throw(type, value, traceback)\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 543, in incomplete_dir\r\n    shutil.rmtree(tmp_dir)\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 740, in rmtree\r\n    return _rmtree_unsafe(path, onerror)\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 618, in _rmtree_unsafe\r\n    onerror(os.unlink, fullname, sys.exc_info())\r\n  File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 616, in _rmtree_unsafe\r\n    os.unlink(fullname)\r\nPermissionError: [WinError 32] Der Prozess kann nicht auf die Datei zugreifen, da sie von einem anderen Prozess verwendet wird: 'C:\\\\Users\\\\Luisa\\\\.cache\\\\huggingface\\\\datasets\\\\sample\\\\default-20ee7d51a6a9454f\\\\0.0.0\\\\5fc4c3a355ea77ab446bd31fca5082437600b8364d29b2b95264048bd1f398b1.incomplete\\\\sample-train.arrow'\r\n\r\nProcess finished with exit code 1\r\n```","embeddings":[-0.1825569272,0.1823850721,-0.0400314778,0.2590482235,0.0813782141,0.1336378604,0.4580342472,0.0122542866,0.1864634454,0.0741284713,-0.0634557605,-0.0112269307,-0.1087958515,0.1572928578,-0.0323617235,-0.0282511655,0.1453506798,0.0480685383,0.0319974497,0.1396457404,-0.4052302539,-0.0071208877,-0.2379359007,0.1073663011,-0.2231619507,0.278344065,-0.0157263745,0.3908289373,-0.0338031724,-0.2350183874,0.2101650983,0.3624857664,0.662093997,0.245353058,-0.0001126285,-0.048450049,0.0420433767,-0.0201318935,0.2068192661,0.2423872203,0.1341389418,-0.0451870672,0.0041253492,-0.1066693291,-0.1698371172,0.2000023574,-0.0654302686,-0.3538345098,0.1139979437,0.4263345599,0.1871957481,0.2474624962,-0.1806875318,-0.0106535321,0.5910394788,0.047598172,-0.1261592358,0.1927935481,0.2900241613,-0.4428050816,0.214141503,0.1609825641,-0.2268584669,0.0855402425,0.3818270862,-0.163264066,-0.1299571693,-0.2830388844,0.1584062725,0.00005305,0.727063477,-0.1692433953,-0.3009923995,-0.0341043584,0.1393089741,-0.2671689689,0.2275048792,0.3365985155,-0.0505269095,0.0753582716,-0.2347000837,-0.167150721,-0.4434008598,0.1358958036,0.1775047481,-0.2292983681,-0.084932968,0.2297086567,0.0906637236,-0.1301721931,0.3544327915,0.1294433624,0.0419623218,0.2517595291,-0.4671482146,0.3360512853,0.2092746347,0.2980217934,-0.0320270881,0.2779500782,-0.1361709833,-0.2106272429,0.2018981278,0.1250198483,0.1772745848,-0.0542738736,0.1519606858,0.2411280125,0.1375094652,-0.0225111879,0.0402428471,-0.117440857,-0.4900486767,-0.6061118841,0.1152087823,0.2454818487,0.3073368371,-0.0892671719,-0.3613361418,0.1164757684,0.1181355715,0.0680992082,0.2068185359,0.4097331166,0.2663407922,0.0119451405,0.3739327192,0.2714998126,-0.0628641695,-0.0258001015,-0.0862123594,0.0042463713,-0.2458985001,-0.0498456098,0.4257872105,-0.344258368,-0.1134994924,0.1173631623,0.0353439488,-0.1213498861,0.0494542569,-0.0187720023,0.1164598688,0.4388581216,0.2584182322,0.034997154,0.1450432539,-0.1851189882,-0.0282773599,0.2364417911,-0.132414341,-0.3404768407,-0.1883570999,0.1011299416,0.0170740597,0.0499140508,-0.218305707,-0.133036688,0.1228498816,-0.0184608568,0.1442888379,-0.1007024646,-0.2660652995,-0.3397949338,-0.1015146449,0.8314390182,-0.5272637606,0.1402395666,-0.1147450805,-0.3047668636,0.1072497219,0.3311969638,-0.0764998347,0.1113562062,-0.214482978,0.1516456157,-0.1357124448,-0.3680853248,-0.1788965762,0.3780059218,-0.017753955,-0.103679657,0.2437345237,-0.0390078723,0.0444860794,-0.2033561915,-0.0898012519,0.134128198,0.1384616196,0.2605628967,0.2249749005,0.0075680758,0.2322096527,0.3124230802,0.0553449914,-0.0627514571,0.2918676436,-0.2186992466,0.189552173,-0.3010883629,0.2245462686,0.4166680276,0.1158194914,0.3091973662,0.0332210027,-0.2322232127,-0.4455744326,0.2652016878,0.3054819703,-0.1239581481,0.0469031185,-0.2565841079,-0.1502389461,0.2109269798,-0.3883987963,0.1167708114,0.0899116099,0.297970891,-0.1774025708,-0.320898056,0.034541335,0.0563448928,-0.2444298863,-0.1111344919,-0.153433159,0.0384588875,-0.3066704273,0.0180423688,0.2049439102,-0.0148219531,0.415769726,-0.074380219,0.0026424972,0.3591325581,0.2803249657,-0.0367785916,0.036796052,0.0983095542,-0.2261272371,0.0771442056,0.1272577792,-0.0095818341,0.0916867405,-0.1025229618,-0.0255540945,0.1265098751,-0.2259499133,0.0041122376,-0.2879075408,0.1314649135,-0.0096176816,0.0248836838,-0.024606308,-0.2199265212,0.3593811691,0.5096359849,-0.3285354376,-0.0453583524,-0.102629602,-0.0515072905,0.2793931961,-0.1505599469,-0.1328611523,0.068707265,0.0991909578,0.2852436006,0.1979034096,0.2263821661,0.4385739267,0.0644598529,-0.1653025299,0.0529506505,0.2032765597,-0.0528064743,0.1483865529,-0.3353100717,0.0793047324,0.2603348792,-0.2992096245,0.1275261194,-0.3775518239,-0.1403336674,0.1440867037,0.3369528651,-0.5138478875,-0.1326299012,-0.0149593158,-0.1334604025,0.1105089188,-0.3155808747,-0.2831487656,0.0130276047,-0.201353848,0.1626410633,-0.2732628286,-0.1256408542,-0.4015872478,0.0573596619,0.2376947105,-0.0031728048,-0.0217249822,0.0865617096,-0.023845451,0.0146014299,0.49329561,-0.2829744816,0.4370732903,-0.1266709417,0.3814791143,-0.2898307145,0.106090866,-0.2475935519,-0.3344739974,0.3203744292,0.246234566,0.0983890146,-0.0262450539,-0.2058806717,0.1195861474,-0.1224583089,-0.0811637938,0.1755108088,0.3042165041,-0.5918186307,-0.25063923,-0.1534152478,-0.2042932659,-0.3533948064,0.2209898382,0.135534808,0.3110541701,0.2072859555,-0.1218600199,0.0530254319,0.0434561335,0.3111526668,-0.3197958469,-0.0964614972,0.013731421,-0.1502657682,-0.5285490155,0.220506236,0.3835156858,-0.1217221916,0.1091838852,-0.1158387959,0.1251094043,-0.2140979767,0.2546712458,-0.2282879651,0.1662585884,0.1928665638,0.0734918788,0.0805564895,-0.1694658101,-0.0990942419,-0.3450900614,-0.2438754141,-0.264610678,0.154290542,0.3330400884,-0.131513983,0.2149935812,0.3426207602,0.2264254987,0.4461936355,-0.1276842952,0.4316768348,0.2818519771,-0.6437909603,0.1521578729,-0.1861309707,-0.053072609,0.36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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2000","title":"Windows Permission Error (most recent version of datasets)","comments":"Hi @itsLuisa, thanks for sharing the Traceback.\r\n\r\nYou are defining the \"id\" field as a `string` feature:\r\n```python\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n    ...\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    # ^^ here\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"pos_tags\": datasets.Sequence(datasets.features.ClassLabel(names=[...])),\r\n[...]\r\n```\r\n\r\nBut in the `_generate_examples`, the \"id\" field is a list:\r\n```python\r\nids = list()\r\n```\r\n\r\nChanging:\r\n```python\r\n\"id\": datasets.Value(\"string\"),\r\n```\r\nInto:\r\n```python\r\n\"id\": datasets.Sequence(datasets.Value(\"string\")),\r\n```\r\n\r\nShould fix your issue.\r\n\r\nLet me know if this helps!","body":"Hi everyone,\r\nCan anyone help me with why the dataset loading script below raises a Windows Permission Error? I stuck quite closely to https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py , only I want to load the data from three local three-column tsv-files (id\\ttokens\\tpos_tags\\n). I am using the most recent version of datasets. Thank you in advance!\r\nLuisa\r\n\r\nMy script:\r\n```\r\nimport datasets\r\nimport csv\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\nclass SampleConfig(datasets.BuilderConfig):\r\n\r\n    def __init__(self, **kwargs):\r\n        super(SampleConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n    BUILDER_CONFIGS = [\r\n        SampleConfig(name=\"conll2003\", version=datasets.Version(\"1.0.0\"), description=\"Conll2003 dataset\"),\r\n    ]\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=\"Dataset with words and their POS-Tags\",\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"pos_tags\": datasets.Sequence(\r\n                        datasets.features.ClassLabel(\r\n                            names=[\r\n                                \"''\",\r\n                                \",\",\r\n                                \"-LRB-\",\r\n                                \"-RRB-\",\r\n                                \".\",\r\n                                \":\",\r\n                                \"CC\",\r\n                                \"CD\",\r\n                                \"DT\",\r\n                                \"EX\",\r\n                                \"FW\",\r\n                                \"HYPH\",\r\n                                \"IN\",\r\n                                \"JJ\",\r\n                                \"JJR\",\r\n                                \"JJS\",\r\n                                \"MD\",\r\n                                \"NN\",\r\n                                \"NNP\",\r\n                                \"NNPS\",\r\n                                \"NNS\",\r\n                                \"PDT\",\r\n                                \"POS\",\r\n                                \"PRP\",\r\n                                \"PRP$\",\r\n                                \"RB\",\r\n                                \"RBR\",\r\n                                \"RBS\",\r\n                                \"RP\",\r\n                                \"TO\",\r\n                                \"UH\",\r\n                                \"VB\",\r\n                                \"VBD\",\r\n                                \"VBG\",\r\n                                \"VBN\",\r\n                                \"VBP\",\r\n                                \"VBZ\",\r\n                                \"WDT\",\r\n                                \"WP\",\r\n                                \"WRB\",\r\n                                \"``\"\r\n                            ]\r\n                        )\r\n                    ),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/catalog.ldc.upenn.edu\/LDC2011T03\",\r\n            citation=\"Weischedel, Ralph, et al. OntoNotes Release 4.0 LDC2011T03. Web Download. Philadelphia: Linguistic Data Consortium, 2011.\",\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        loaded_files = dl_manager.download_and_extract(self.config.data_files)\r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": loaded_files[\"train\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={\"filepath\": loaded_files[\"test\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": loaded_files[\"val\"]})\r\n        ]\r\n\r\n    def _generate_examples(self, filepath):\r\n        logger.info(\"generating examples from = %s\", filepath)\r\n        with open(filepath, encoding=\"cp1252\") as f:\r\n            data = csv.reader(f, delimiter=\"\\t\")\r\n            ids = list()\r\n            tokens = list()\r\n            pos_tags = list()\r\n            for id_, line in enumerate(data):\r\n                #print(line)\r\n                if len(line) == 1:\r\n                    if tokens:\r\n                        yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n                        ids = list()\r\n                        tokens = list()\r\n                        pos_tags = list()\r\n                else:\r\n                    ids.append(line[0])\r\n                    tokens.append(line[1])\r\n                    pos_tags.append(line[2])\r\n            # last example\r\n            yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n\r\n\r\ndef main():\r\n    dataset = datasets.load_dataset(\r\n        \"data_loading.py\", data_files={\r\n            \"train\": \"train.tsv\",\r\n            \"test\": \"test.tsv\",\r\n            \"val\": \"val.tsv\"\r\n        }\r\n    )\r\n\r\n    #print(dataset)\r\n\r\nif __name__==\"__main__\":\r\n    main()\r\n```\r\n","comment_length":73,"text":"Windows Permission Error (most recent version of datasets) \n Hi everyone,\r\nCan anyone help me with why the dataset loading script below raises a Windows Permission Error? I stuck quite closely to https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py , only I want to load the data from three local three-column tsv-files (id\\ttokens\\tpos_tags\\n). I am using the most recent version of datasets. Thank you in advance!\r\nLuisa\r\n\r\nMy script:\r\n```\r\nimport datasets\r\nimport csv\r\n\r\nlogger = datasets.logging.get_logger(__name__)\r\n\r\n\r\nclass SampleConfig(datasets.BuilderConfig):\r\n\r\n    def __init__(self, **kwargs):\r\n        super(SampleConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n    BUILDER_CONFIGS = [\r\n        SampleConfig(name=\"conll2003\", version=datasets.Version(\"1.0.0\"), description=\"Conll2003 dataset\"),\r\n    ]\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=\"Dataset with words and their POS-Tags\",\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"pos_tags\": datasets.Sequence(\r\n                        datasets.features.ClassLabel(\r\n                            names=[\r\n                                \"''\",\r\n                                \",\",\r\n                                \"-LRB-\",\r\n                                \"-RRB-\",\r\n                                \".\",\r\n                                \":\",\r\n                                \"CC\",\r\n                                \"CD\",\r\n                                \"DT\",\r\n                                \"EX\",\r\n                                \"FW\",\r\n                                \"HYPH\",\r\n                                \"IN\",\r\n                                \"JJ\",\r\n                                \"JJR\",\r\n                                \"JJS\",\r\n                                \"MD\",\r\n                                \"NN\",\r\n                                \"NNP\",\r\n                                \"NNPS\",\r\n                                \"NNS\",\r\n                                \"PDT\",\r\n                                \"POS\",\r\n                                \"PRP\",\r\n                                \"PRP$\",\r\n                                \"RB\",\r\n                                \"RBR\",\r\n                                \"RBS\",\r\n                                \"RP\",\r\n                                \"TO\",\r\n                                \"UH\",\r\n                                \"VB\",\r\n                                \"VBD\",\r\n                                \"VBG\",\r\n                                \"VBN\",\r\n                                \"VBP\",\r\n                                \"VBZ\",\r\n                                \"WDT\",\r\n                                \"WP\",\r\n                                \"WRB\",\r\n                                \"``\"\r\n                            ]\r\n                        )\r\n                    ),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/catalog.ldc.upenn.edu\/LDC2011T03\",\r\n            citation=\"Weischedel, Ralph, et al. OntoNotes Release 4.0 LDC2011T03. Web Download. Philadelphia: Linguistic Data Consortium, 2011.\",\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        loaded_files = dl_manager.download_and_extract(self.config.data_files)\r\n        return [\r\n            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={\"filepath\": loaded_files[\"train\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={\"filepath\": loaded_files[\"test\"]}),\r\n            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={\"filepath\": loaded_files[\"val\"]})\r\n        ]\r\n\r\n    def _generate_examples(self, filepath):\r\n        logger.info(\"generating examples from = %s\", filepath)\r\n        with open(filepath, encoding=\"cp1252\") as f:\r\n            data = csv.reader(f, delimiter=\"\\t\")\r\n            ids = list()\r\n            tokens = list()\r\n            pos_tags = list()\r\n            for id_, line in enumerate(data):\r\n                #print(line)\r\n                if len(line) == 1:\r\n                    if tokens:\r\n                        yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n                        ids = list()\r\n                        tokens = list()\r\n                        pos_tags = list()\r\n                else:\r\n                    ids.append(line[0])\r\n                    tokens.append(line[1])\r\n                    pos_tags.append(line[2])\r\n            # last example\r\n            yield id_, {\"id\": ids, \"tokens\": tokens, \"pos_tags\": pos_tags}\r\n\r\n\r\ndef main():\r\n    dataset = datasets.load_dataset(\r\n        \"data_loading.py\", data_files={\r\n            \"train\": \"train.tsv\",\r\n            \"test\": \"test.tsv\",\r\n            \"val\": \"val.tsv\"\r\n        }\r\n    )\r\n\r\n    #print(dataset)\r\n\r\nif __name__==\"__main__\":\r\n    main()\r\n```\r\n \n Hi @itsLuisa, thanks for sharing the Traceback.\r\n\r\nYou are defining the \"id\" field as a `string` feature:\r\n```python\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n    ...\r\n\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            features=datasets.Features(\r\n                {\r\n                    \"id\": datasets.Value(\"string\"),\r\n                    # ^^ here\r\n                    \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n                    \"pos_tags\": datasets.Sequence(datasets.features.ClassLabel(names=[...])),\r\n[...]\r\n```\r\n\r\nBut in the `_generate_examples`, the \"id\" field is a list:\r\n```python\r\nids = list()\r\n```\r\n\r\nChanging:\r\n```python\r\n\"id\": datasets.Value(\"string\"),\r\n```\r\nInto:\r\n```python\r\n\"id\": datasets.Sequence(datasets.Value(\"string\")),\r\n```\r\n\r\nShould fix your issue.\r\n\r\nLet me know if this helps!","embeddings":[-0.1825569272,0.1823850721,-0.0400314778,0.2590482235,0.0813782141,0.1336378604,0.4580342472,0.0122542866,0.1864634454,0.0741284713,-0.0634557605,-0.0112269307,-0.1087958515,0.1572928578,-0.0323617235,-0.0282511655,0.1453506798,0.0480685383,0.0319974497,0.1396457404,-0.4052302539,-0.0071208877,-0.2379359007,0.1073663011,-0.2231619507,0.278344065,-0.0157263745,0.3908289373,-0.0338031724,-0.2350183874,0.2101650983,0.3624857664,0.662093997,0.245353058,-0.0001126285,-0.048450049,0.0420433767,-0.0201318935,0.2068192661,0.2423872203,0.1341389418,-0.0451870672,0.0041253492,-0.1066693291,-0.1698371172,0.2000023574,-0.0654302686,-0.3538345098,0.1139979437,0.4263345599,0.1871957481,0.2474624962,-0.1806875318,-0.0106535321,0.5910394788,0.047598172,-0.1261592358,0.1927935481,0.2900241613,-0.4428050816,0.214141503,0.1609825641,-0.2268584669,0.0855402425,0.3818270862,-0.163264066,-0.1299571693,-0.2830388844,0.1584062725,0.00005305,0.727063477,-0.1692433953,-0.3009923995,-0.0341043584,0.1393089741,-0.2671689689,0.2275048792,0.3365985155,-0.0505269095,0.0753582716,-0.2347000837,-0.167150721,-0.4434008598,0.1358958036,0.1775047481,-0.2292983681,-0.084932968,0.2297086567,0.0906637236,-0.1301721931,0.3544327915,0.1294433624,0.0419623218,0.2517595291,-0.4671482146,0.3360512853,0.2092746347,0.2980217934,-0.0320270881,0.2779500782,-0.1361709833,-0.2106272429,0.2018981278,0.1250198483,0.1772745848,-0.0542738736,0.1519606858,0.2411280125,0.1375094652,-0.0225111879,0.0402428471,-0.117440857,-0.4900486767,-0.6061118841,0.1152087823,0.2454818487,0.3073368371,-0.0892671719,-0.3613361418,0.1164757684,0.1181355715,0.0680992082,0.2068185359,0.4097331166,0.2663407922,0.0119451405,0.3739327192,0.2714998126,-0.0628641695,-0.0258001015,-0.0862123594,0.0042463713,-0.2458985001,-0.0498456098,0.4257872105,-0.344258368,-0.1134994924,0.1173631623,0.0353439488,-0.1213498861,0.0494542569,-0.0187720023,0.1164598688,0.4388581216,0.2584182322,0.034997154,0.1450432539,-0.1851189882,-0.0282773599,0.2364417911,-0.132414341,-0.3404768407,-0.1883570999,0.1011299416,0.0170740597,0.0499140508,-0.218305707,-0.133036688,0.1228498816,-0.0184608568,0.1442888379,-0.1007024646,-0.2660652995,-0.3397949338,-0.1015146449,0.8314390182,-0.5272637606,0.1402395666,-0.1147450805,-0.3047668636,0.1072497219,0.3311969638,-0.0764998347,0.1113562062,-0.214482978,0.1516456157,-0.1357124448,-0.3680853248,-0.1788965762,0.3780059218,-0.017753955,-0.103679657,0.2437345237,-0.0390078723,0.0444860794,-0.2033561915,-0.0898012519,0.134128198,0.1384616196,0.2605628967,0.2249749005,0.0075680758,0.2322096527,0.3124230802,0.0553449914,-0.0627514571,0.2918676436,-0.2186992466,0.189552173,-0.3010883629,0.2245462686,0.4166680276,0.1158194914,0.3091973662,0.0332210027,-0.2322232127,-0.4455744326,0.2652016878,0.3054819703,-0.1239581481,0.0469031185,-0.2565841079,-0.1502389461,0.2109269798,-0.3883987963,0.1167708114,0.0899116099,0.297970891,-0.1774025708,-0.320898056,0.034541335,0.0563448928,-0.2444298863,-0.1111344919,-0.153433159,0.0384588875,-0.3066704273,0.0180423688,0.2049439102,-0.0148219531,0.415769726,-0.074380219,0.0026424972,0.3591325581,0.2803249657,-0.0367785916,0.036796052,0.0983095542,-0.2261272371,0.0771442056,0.1272577792,-0.0095818341,0.0916867405,-0.1025229618,-0.0255540945,0.1265098751,-0.2259499133,0.0041122376,-0.2879075408,0.1314649135,-0.0096176816,0.0248836838,-0.024606308,-0.2199265212,0.3593811691,0.5096359849,-0.3285354376,-0.0453583524,-0.102629602,-0.0515072905,0.2793931961,-0.1505599469,-0.1328611523,0.068707265,0.0991909578,0.2852436006,0.1979034096,0.2263821661,0.4385739267,0.0644598529,-0.1653025299,0.0529506505,0.2032765597,-0.0528064743,0.1483865529,-0.3353100717,0.0793047324,0.2603348792,-0.2992096245,0.1275261194,-0.3775518239,-0.1403336674,0.1440867037,0.3369528651,-0.5138478875,-0.1326299012,-0.0149593158,-0.1334604025,0.1105089188,-0.3155808747,-0.2831487656,0.0130276047,-0.201353848,0.1626410633,-0.2732628286,-0.1256408542,-0.4015872478,0.0573596619,0.2376947105,-0.0031728048,-0.0217249822,0.0865617096,-0.023845451,0.0146014299,0.49329561,-0.2829744816,0.4370732903,-0.1266709417,0.3814791143,-0.2898307145,0.106090866,-0.2475935519,-0.3344739974,0.3203744292,0.246234566,0.0983890146,-0.0262450539,-0.2058806717,0.1195861474,-0.1224583089,-0.0811637938,0.1755108088,0.3042165041,-0.5918186307,-0.25063923,-0.1534152478,-0.2042932659,-0.3533948064,0.2209898382,0.135534808,0.3110541701,0.2072859555,-0.1218600199,0.0530254319,0.0434561335,0.3111526668,-0.3197958469,-0.0964614972,0.013731421,-0.1502657682,-0.5285490155,0.220506236,0.3835156858,-0.1217221916,0.1091838852,-0.1158387959,0.1251094043,-0.2140979767,0.2546712458,-0.2282879651,0.1662585884,0.1928665638,0.0734918788,0.0805564895,-0.1694658101,-0.0990942419,-0.3450900614,-0.2438754141,-0.264610678,0.154290542,0.3330400884,-0.131513983,0.2149935812,0.3426207602,0.2264254987,0.4461936355,-0.1276842952,0.4316768348,0.2818519771,-0.6437909603,0.1521578729,-0.1861309707,-0.053072609,0.3601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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1996","title":"Error when exploring `arabic_speech_corpus`","comments":"Actually soundfile is not a dependency of this dataset.\r\nThe error comes from a bug that was fixed in this commit: https:\/\/github.com\/huggingface\/datasets\/pull\/1767\/commits\/c304e63629f4453367de2fd42883a78768055532\r\nBasically the library used to consider the `import soundfile` in the docstring as a dependency, while it's just here as a code example.\r\n\r\nUpdating the viewer to the latest version of `datasets` should fix this issue\r\n","body":"Navigate to https:\/\/huggingface.co\/datasets\/viewer\/?dataset=arabic_speech_corpus\r\n\r\nError:\r\n```\r\nImportError: To be able to use this dataset, you need to install the following dependencies['soundfile'] using 'pip install soundfile' for instance'\r\nTraceback:\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/script_runner.py\", line 332, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 233, in <module>\r\n    configs = get_confs(option)\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 604, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 588, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 145, in get_confs\r\n    module_path = nlp.load.prepare_module(path, dataset=True\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 342, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\n```","comment_length":58,"text":"Error when exploring `arabic_speech_corpus` \n Navigate to https:\/\/huggingface.co\/datasets\/viewer\/?dataset=arabic_speech_corpus\r\n\r\nError:\r\n```\r\nImportError: To be able to use this dataset, you need to install the following dependencies['soundfile'] using 'pip install soundfile' for instance'\r\nTraceback:\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/script_runner.py\", line 332, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 233, in <module>\r\n    configs = get_confs(option)\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 604, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 588, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 145, in get_confs\r\n    module_path = nlp.load.prepare_module(path, dataset=True\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 342, in prepare_module\r\n    f\"To be able to use this {module_type}, you need to install the following dependencies\"\r\n``` \n Actually soundfile is not a dependency of this dataset.\r\nThe error comes from a bug that was fixed in this commit: https:\/\/github.com\/huggingface\/datasets\/pull\/1767\/commits\/c304e63629f4453367de2fd42883a78768055532\r\nBasically the library used to consider the `import soundfile` in the docstring as a dependency, while it's just here as a code example.\r\n\r\nUpdating the viewer to the latest version of `datasets` should fix this issue\r\n","embeddings":[-0.2591242194,-0.1135890707,-0.0363894068,0.2228231728,0.0417897142,0.0414845236,0.0535821207,0.3140556812,-0.1731435061,0.0504592061,-0.2981741428,0.096908465,-0.1112824306,-0.04064418,0.106541194,-0.3713718355,0.0633603632,0.2032412887,0.005492107,-0.013290599,-0.069745712,0.4542385638,-0.3286394179,-0.0139067518,-0.0968818739,-0.2608732879,-0.0601527542,-0.1154938638,-0.2918647826,-0.4660024047,0.1471929848,-0.076424405,0.0929136053,0.3895767331,-0.0001143709,-0.0865352377,0.3681040704,-0.1396298409,-0.2088981122,-0.4318641722,0.1541046649,-0.1369885504,-0.0995633006,-0.1122164428,-0.0662241131,-0.1979067475,0.0398826748,-0.3098419011,0.291513294,0.3186619878,0.2341214418,0.1870319992,0.2347778231,-0.0418161787,0.2049755454,-0.001658614,-0.1434957534,0.2094648927,0.1850297153,0.0741269141,-0.1772945672,0.5136584044,-0.3165772259,-0.0839223936,0.0919756144,-0.1553822309,-0.0313259773,-0.402174741,0.1798986644,-0.0613103509,0.4357353151,-0.361638546,-0.2422319204,-0.2717438042,0.1527153701,-0.23318322,0.2647298574,0.3108150065,-0.2944638133,0.1316215843,0.0926432088,-0.2117515504,-0.0141713871,0.1655197144,0.0573513359,0.2916793227,-0.2994027436,-0.1460861266,0.3502129912,-0.1967080832,-0.248562932,0.0474212132,-0.0852020159,0.2425550222,-0.0651767328,-0.0221833549,0.1632066518,0.1580073833,-0.038769275,0.0165190455,-0.1195856184,0.2261201441,-0.179515183,0.2216259688,-0.014147697,0.1789913625,0.2417352796,-0.0200956222,0.2245754004,0.4123170674,0.0120387496,0.0000338793,-0.0760709643,-0.1948828399,-0.4148888886,-0.0548379421,0.5135284066,-0.2739714384,-0.1555497646,0.0554313846,-0.0786811337,-0.2209295183,0.0962209627,0.4137216508,-0.1246656626,-0.0105801523,0.1273990124,0.3244036734,-0.2293329239,-0.2025601268,0.0115854368,0.2179549485,-0.0469130166,0.1443846077,0.2509663999,-0.463083297,0.5074711442,0.0049599991,0.170227915,0.1916871667,-0.0764003545,-0.0905369222,-0.0938392431,0.3047252297,-0.0437420234,0.1594018787,0.2130078077,-0.0378161259,-0.2543689013,0.0117811859,0.0311254319,-0.2173605859,-0.0682519376,0.1823175699,-0.3190977275,-0.0554397292,-0.1916362494,0.2330007702,0.1791097969,-0.496638447,-0.0456907302,0.0339263417,-0.531935215,-0.0135487672,0.0597186871,0.4067554772,-0.0727342069,-0.2927673757,-0.0841919407,0.0253014583,0.1555551142,0.1164548472,-0.0388877057,0.0291495975,-0.2782105803,0.3276979029,0.3454294503,-0.5265828967,-0.4309079945,0.1337174028,0.1185409948,0.2625019252,0.331253171,-0.2331041694,0.2469700873,-0.2004732341,0.1713262349,0.3000825942,0.1903238297,-0.1005732864,-0.1891236007,-0.1464096457,-0.0314402953,0.0946110785,-0.1408831626,0.1675128043,-0.0015275398,-0.0004478886,0.1974836141,0.0762266964,0.0679527372,0.2516246438,0.2334304005,0.0558910705,0.0565039031,-0.4098466039,0.0838468522,0.0377535149,-0.3253620863,0.425362289,-0.4172182977,-0.1359889805,-0.2352312803,-0.0995791852,-0.2411767989,-0.1433660686,0.1581530273,0.2674131095,0.1796350032,0.3149423897,-0.2450620979,-0.0018464492,0.1778332293,-0.0270125307,-0.6278404593,0.08613047,-0.213807106,-0.111375235,0.3707222939,0.2790821195,0.1563702971,-0.1181210577,-0.0805430114,0.2213092595,-0.0635786131,0.3369646966,-0.4050099254,0.1184294224,0.1225035116,-0.3153544664,0.38484326,0.1509910375,0.124534443,-0.055124674,0.3454687595,0.190918833,0.2136061043,0.1452511549,0.1701558977,0.0841465667,0.4480590522,0.1295617819,-0.2246260196,-0.3109557629,0.4079634249,-0.2685557008,0.5072845221,0.0207666755,-0.3180295825,0.0073704054,0.5209943652,-0.0545143411,0.1844329238,0.1243077293,-0.3222737908,-0.1683200747,0.2562192082,0.1419637054,0.4471825361,0.1286046356,0.0779226944,0.196065858,0.0627725869,-0.3435527384,0.2704863548,0.0305357389,0.0706947371,0.2770344317,0.0956616327,0.0486453585,-0.5301122665,-0.0327140242,-0.1379942298,0.1653428078,-0.1218276396,0.016171189,-0.3099750876,-0.5472095013,-0.1764027178,-0.2263454944,-0.3613773584,-0.1843630821,0.0760281458,0.2584050596,0.0806570798,0.3466959596,0.1766165793,0.0651235208,-0.1550276279,-0.183698222,-0.2633356154,-0.0257067997,-0.1293578595,0.0739425123,0.3326383531,0.2319948673,0.219444856,-0.124803029,-0.0389512628,-0.2586819828,-0.178327024,0.2145289332,-0.2075853795,0.1726353765,0.1471442282,0.1403462887,-0.0251215193,-0.3683167696,0.2150206566,0.0055194492,-0.0820420235,0.1785067469,-0.1032170579,0.0165980794,-0.1582727134,-0.2487445176,-0.2342053056,-0.5632388592,0.0237041507,0.0359938331,0.1542959362,0.6405217648,-0.0233093947,0.2233794779,-0.1866188198,0.4464220703,-0.1898086518,-0.2654583752,0.3701419234,-0.3133110106,-0.3851189911,0.0832386091,0.0741175562,0.5423164368,-0.1641801596,-0.4779160917,-0.0479016714,-0.2519604862,-0.0908003151,-0.02739962,0.1235230565,0.3727428913,0.1244206727,-0.0558640882,-0.1544632614,-0.112755768,-0.2120387852,0.0303627662,0.1103438288,0.1173396856,0.3938210011,0.0364331193,0.5581880212,0.1893629134,0.078609027,0.5083714724,0.2056812793,0.4620286822,-0.1761783212,-0.500648737,-0.064554818,-0.0106145479,0.07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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1994","title":"not being able to get wikipedia es language","comments":"@lhoestq  I really appreciate if you could help me providiing processed datasets, I do not really have access to enough resources to run the apache-beam and need to run the codes on these datasets. Only en\/de\/fr currently works, but I need all the languages more or less. thanks ","body":"Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help ","comment_length":48,"text":"not being able to get wikipedia es language \n Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help  \n @lhoestq  I really appreciate if you could help me providiing processed datasets, I do not really have access to enough resources to run the apache-beam and need to run the codes on these datasets. Only en\/de\/fr currently works, but I need all the languages more or less. thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1994","title":"not being able to get wikipedia es language","comments":"Hi @dorost1234, I think I can help you a little. I\u2019ve processed some Wikipedia datasets (Spanish inclusive) using the HF\/datasets library during recent research.\r\n\r\n@lhoestq Could you help me to upload these preprocessed datasets to Huggingface's repositories? To be more precise, I've built datasets from the following languages using the 20201201 dumps: Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish. Process these datasets have high costs that most of the community can't afford. I think these preprocessed datasets I have could be helpful for someone without access to high-resource machines to process Wikipedia's dumps like @dorost1234\r\n\r\n","body":"Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help ","comment_length":96,"text":"not being able to get wikipedia es language \n Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help  \n Hi @dorost1234, I think I can help you a little. I\u2019ve processed some Wikipedia datasets (Spanish inclusive) using the HF\/datasets library during recent research.\r\n\r\n@lhoestq Could you help me to upload these preprocessed datasets to Huggingface's repositories? To be more precise, I've built datasets from the following languages using the 20201201 dumps: Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish. Process these datasets have high costs that most of the community can't afford. I think these preprocessed datasets I have could be helpful for someone without access to high-resource machines to process Wikipedia's dumps like @dorost1234\r\n\r\n","embeddings":[-0.342106998,0.0471930392,-0.1062649339,0.0620172322,0.200454846,0.1777444929,0.1768746823,0.3401965797,0.1373334974,0.0921178386,0.4105240405,0.3346937597,0.0160005912,0.3076457381,0.1113204286,-0.3370872438,0.0556506142,0.0752215236,-0.1947185397,-0.2001760751,-0.1320842654,0.1037431955,-0.2725183666,-0.0701157525,-0.1488354355,0.1070382744,0.1227778196,-0.0196700636,-0.192601651,-0.1862009615,0.0680222437,-0.036213655,0.2280335724,0.1079433188,-0.0001084146,0.0806313604,0.5717446804,-0.1583800614,-0.4590220749,-0.130322203,-0.081363827,-0.3462582827,0.2564504743,-0.3715744317,-0.3347848058,-0.0256394912,0.3187549412,-0.5857167244,0.2412877679,0.1479617506,0.2182685584,-0.074210979,0.3515349925,-0.0048845191,0.4105975032,0.218606472,-0.0360494033,-0.1243073717,0.0106633129,0.0249649677,0.0606173314,0.4618479013,0.0472859293,-0.1142473742,0.3039729893,-0.3094903827,0.2656840682,-0.3731164932,0.4229684472,0.2541317344,1.0161632299,-0.1197014451,0.0995212495,0.1050666645,0.0040745316,0.1287817061,0.2428365648,0.2005350441,-0.3642728925,-0.0796793103,0.1134936661,-0.2830438912,-0.335678637,0.3455581665,-0.1017287895,0.5368958116,0.1090375558,0.1416433603,-0.1509752721,-0.1852888316,0.0773017183,-0.0707799047,0.1030773669,0.3541413844,-0.0930447504,0.1351349652,0.2457793206,0.1751329154,0.0443397351,-0.0947965086,-0.2797648609,0.1917012036,0.368519485,0.0461870804,0.0220659487,0.0323531926,0.3556627333,-0.1849304587,0.2050388753,-0.030573098,0.0079394793,0.06664785,-0.0586212762,-0.3365755379,-0.6453832984,0.2325167805,-0.0009588756,-0.1367516071,0.192342177,0.1587269008,-0.3540838063,-0.2998664379,-0.0057489178,0.2017250508,0.1522044241,0.2051189095,0.2833256125,-0.0867259279,-0.3417105973,-0.3844312429,-0.0466403812,0.2829635143,-0.4593487978,0.0780525506,0.2093625367,0.1067860499,0.3750687838,-0.0355147943,-0.0439997055,0.0425268859,0.1307713985,0.0788350478,-0.1385989934,0.1594749987,0.1490190625,0.2470012158,0.199904263,-0.222467348,-0.052217897,0.0016842578,-0.0903736725,-0.1905186325,-0.18565543,0.1717608422,0.1227803901,0.1018858626,0.0458261073,0.3413800597,0.1713995934,-0.2258878946,0.1436647028,-0.0075095682,-0.1345748454,-0.0918872654,0.3624586463,0.372572124,-0.6563616395,0.1040466204,-0.0495637171,0.1877678782,0.143699944,-0.2795007229,-0.281387955,0.4237986207,-0.093520321,-0.1007723063,0.3089175224,-0.2708930373,0.0815908834,0.1968420744,0.067334421,-0.2822613418,0.2371014357,-0.1123532802,0.0422963984,0.1624549776,0.1141090319,0.1663445979,0.116460517,-0.0702252239,-0.2196758091,-0.1222946942,-0.0431540161,0.1172707975,0.2665446103,-0.1195803061,0.1249019429,0.5266057849,0.3261623085,-0.2632548511,0.0764246359,0.5432159305,-0.3122918904,0.0806591958,0.1918928772,-0.0554747656,-0.0498997234,0.1809134334,-0.239194259,0.4039117098,0.0790574178,-0.0053859027,-0.2668075264,-0.0530283935,-0.2360287756,-0.4283643067,0.2114921659,0.0466564186,-0.0355188176,0.2530859709,0.0529352501,-0.0900353342,-0.278223455,-0.1389373243,-0.8122121692,0.2934658229,-0.2860761285,-0.0800810456,-0.0875601247,0.0202554129,0.0639516488,-0.1236343607,-0.2867787182,0.1009152383,0.1873278469,0.0372007079,-0.011949122,0.16449368,0.1220298633,-0.2648282647,0.3166707158,0.3054229021,0.1199404374,0.0179688167,-0.2019145489,0.1108080298,0.0477327667,0.3355243504,0.0727865323,0.2685893774,0.185183093,0.1680476964,-0.1562812775,-0.1591639668,0.302156806,0.4226678014,0.1818974614,-0.145703271,0.0370389149,0.0772163868,0.451574564,0.1048435643,-0.0922244564,-0.1263718456,-0.3054101467,-0.0594792664,0.4833448827,-0.1791246086,-0.0160230976,0.2791057527,-0.0161974188,0.0771187916,-0.1511853486,-0.1175531745,0.4446229041,0.0723261684,0.5178593993,-0.0257208422,-0.1244166493,-0.1868189722,-0.1908410639,-0.2879425287,-0.1234138981,0.2847407758,-0.312468946,0.2768905461,-0.4178833961,-0.5642531514,-0.2084293067,0.4299700558,-0.3480241597,-0.3430698216,-0.0680083781,0.0778862834,-0.0229108091,0.2500188053,0.107210204,-0.035542842,0.2320447713,0.0074737356,-0.2216777354,-0.5956392884,-0.4356403053,0.0326633267,0.2823260725,0.0373752452,0.1272295564,-0.1569059193,-0.0672418326,-0.1452952772,-0.4021958709,0.3414442241,-0.0869849101,-0.0084638475,0.1166531071,0.4272188842,-0.19598113,-0.0950585008,0.3098010421,0.1152016073,-0.1476498395,-0.0692656934,-0.084672384,-0.0623619594,-0.0308230799,-0.4383021891,-0.1505855471,-0.3273377717,-0.1820004731,0.1689832062,0.1332353055,0.3523068726,0.2645772398,0.2091489136,0.3283595741,0.0954612941,-0.0965576619,-0.15933685,0.2170184106,-0.3185723126,-0.3167462349,0.1040425673,-0.1413134485,0.2305069566,0.1135405153,-0.146329999,0.0680313334,0.1159731671,0.0200549383,-0.1013204157,0.3257164955,0.7469013929,-0.0126242423,-0.0016661583,-0.0988114774,0.0478255115,0.0307381041,-0.4368053079,0.347318083,0.1777924001,0.5277687311,-0.0701474324,0.7515996695,0.1148355156,0.1166792214,0.1321653128,-0.0752609968,0.066186063,-0.272472769,-0.1884595752,0.292780757,0.0403270945,-0.30309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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1994","title":"not being able to get wikipedia es language","comments":"Thank you so much @jonatasgrosman , I greatly appreciate your help with them. \r\nYes, I unfortunately does not have access to a good resource and need it for my\r\nresearch. I greatly appreciate @lhoestq  your help with uploading the processed datasets in huggingface datasets. This would be really helpful for some users like me with not access to high-memory GPU resources.\r\n\r\nthank you both so much again.\r\n\r\nOn Sat, Mar 6, 2021 at 12:55 AM Jonatas Grosman <notifications@github.com>\r\nwrote:\r\n\r\n> Hi @dorost1234 <https:\/\/github.com\/dorost1234>, I think I can help you a\r\n> little. I\u2019ve processed some Wikipedia datasets (Spanish inclusive) using\r\n> the HF\/datasets library during recent research.\r\n>\r\n> @lhoestq <https:\/\/github.com\/lhoestq> Could you help me to upload these\r\n> preprocessed datasets to Huggingface's repositories? To be more precise,\r\n> I've built datasets from the following languages using the 20201201 dumps:\r\n> Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish.\r\n> Process these datasets have high costs that most of the community can't\r\n> afford. I think these preprocessed datasets I have could be helpful for\r\n> someone without access to high-resource machines to process Wikipedia's\r\n> dumps like @dorost1234 <https:\/\/github.com\/dorost1234>\r\n>\r\n> \u2014\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/1994#issuecomment-791798195>,\r\n> or unsubscribe\r\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMWMK5GFJFU3ACCJFUDTCFVNZANCNFSM4YUZIF4A>\r\n> .\r\n>\r\n","body":"Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help ","comment_length":222,"text":"not being able to get wikipedia es language \n Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help  \n Thank you so much @jonatasgrosman , I greatly appreciate your help with them. \r\nYes, I unfortunately does not have access to a good resource and need it for my\r\nresearch. I greatly appreciate @lhoestq  your help with uploading the processed datasets in huggingface datasets. This would be really helpful for some users like me with not access to high-memory GPU resources.\r\n\r\nthank you both so much again.\r\n\r\nOn Sat, Mar 6, 2021 at 12:55 AM Jonatas Grosman <notifications@github.com>\r\nwrote:\r\n\r\n> Hi @dorost1234 <https:\/\/github.com\/dorost1234>, I think I can help you a\r\n> little. I\u2019ve processed some Wikipedia datasets (Spanish inclusive) using\r\n> the HF\/datasets library during recent research.\r\n>\r\n> @lhoestq <https:\/\/github.com\/lhoestq> Could you help me to upload these\r\n> preprocessed datasets to Huggingface's repositories? To be more precise,\r\n> I've built datasets from the following languages using the 20201201 dumps:\r\n> Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish.\r\n> Process these datasets have high costs that most of the community can't\r\n> afford. I think these preprocessed datasets I have could be helpful for\r\n> someone without access to high-resource machines to process Wikipedia's\r\n> dumps like @dorost1234 <https:\/\/github.com\/dorost1234>\r\n>\r\n> \u2014\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/1994#issuecomment-791798195>,\r\n> or unsubscribe\r\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMWMK5GFJFU3ACCJFUDTCFVNZANCNFSM4YUZIF4A>\r\n> .\r\n>\r\n","embeddings":[-0.342106998,0.0471930392,-0.1062649339,0.0620172322,0.200454846,0.1777444929,0.1768746823,0.3401965797,0.1373334974,0.0921178386,0.4105240405,0.3346937597,0.0160005912,0.3076457381,0.1113204286,-0.3370872438,0.0556506142,0.0752215236,-0.1947185397,-0.2001760751,-0.1320842654,0.1037431955,-0.2725183666,-0.0701157525,-0.1488354355,0.1070382744,0.1227778196,-0.0196700636,-0.192601651,-0.1862009615,0.0680222437,-0.036213655,0.2280335724,0.1079433188,-0.0001084146,0.0806313604,0.5717446804,-0.1583800614,-0.4590220749,-0.130322203,-0.081363827,-0.3462582827,0.2564504743,-0.3715744317,-0.3347848058,-0.0256394912,0.3187549412,-0.5857167244,0.2412877679,0.1479617506,0.2182685584,-0.074210979,0.3515349925,-0.0048845191,0.4105975032,0.218606472,-0.0360494033,-0.1243073717,0.0106633129,0.0249649677,0.0606173314,0.4618479013,0.0472859293,-0.1142473742,0.3039729893,-0.3094903827,0.2656840682,-0.3731164932,0.4229684472,0.2541317344,1.0161632299,-0.1197014451,0.0995212495,0.1050666645,0.0040745316,0.1287817061,0.2428365648,0.2005350441,-0.3642728925,-0.0796793103,0.1134936661,-0.2830438912,-0.335678637,0.3455581665,-0.1017287895,0.5368958116,0.1090375558,0.1416433603,-0.1509752721,-0.1852888316,0.0773017183,-0.0707799047,0.1030773669,0.3541413844,-0.0930447504,0.1351349652,0.2457793206,0.1751329154,0.0443397351,-0.0947965086,-0.2797648609,0.1917012036,0.368519485,0.0461870804,0.0220659487,0.0323531926,0.3556627333,-0.1849304587,0.2050388753,-0.030573098,0.0079394793,0.06664785,-0.0586212762,-0.3365755379,-0.6453832984,0.2325167805,-0.0009588756,-0.1367516071,0.192342177,0.1587269008,-0.3540838063,-0.2998664379,-0.0057489178,0.2017250508,0.1522044241,0.2051189095,0.2833256125,-0.0867259279,-0.3417105973,-0.3844312429,-0.0466403812,0.2829635143,-0.4593487978,0.0780525506,0.2093625367,0.1067860499,0.3750687838,-0.0355147943,-0.0439997055,0.0425268859,0.1307713985,0.0788350478,-0.1385989934,0.1594749987,0.1490190625,0.2470012158,0.199904263,-0.222467348,-0.052217897,0.0016842578,-0.0903736725,-0.1905186325,-0.18565543,0.1717608422,0.1227803901,0.1018858626,0.0458261073,0.3413800597,0.1713995934,-0.2258878946,0.1436647028,-0.0075095682,-0.1345748454,-0.0918872654,0.3624586463,0.372572124,-0.6563616395,0.1040466204,-0.0495637171,0.1877678782,0.143699944,-0.2795007229,-0.281387955,0.4237986207,-0.093520321,-0.1007723063,0.3089175224,-0.2708930373,0.0815908834,0.1968420744,0.067334421,-0.2822613418,0.2371014357,-0.1123532802,0.0422963984,0.1624549776,0.1141090319,0.1663445979,0.116460517,-0.0702252239,-0.2196758091,-0.1222946942,-0.0431540161,0.1172707975,0.2665446103,-0.1195803061,0.1249019429,0.5266057849,0.3261623085,-0.2632548511,0.0764246359,0.5432159305,-0.3122918904,0.0806591958,0.1918928772,-0.0554747656,-0.0498997234,0.1809134334,-0.239194259,0.4039117098,0.0790574178,-0.0053859027,-0.2668075264,-0.0530283935,-0.2360287756,-0.4283643067,0.2114921659,0.0466564186,-0.0355188176,0.2530859709,0.0529352501,-0.0900353342,-0.278223455,-0.1389373243,-0.8122121692,0.2934658229,-0.2860761285,-0.0800810456,-0.0875601247,0.0202554129,0.0639516488,-0.1236343607,-0.2867787182,0.1009152383,0.1873278469,0.0372007079,-0.011949122,0.16449368,0.1220298633,-0.2648282647,0.3166707158,0.3054229021,0.1199404374,0.0179688167,-0.2019145489,0.1108080298,0.0477327667,0.3355243504,0.0727865323,0.2685893774,0.185183093,0.1680476964,-0.1562812775,-0.1591639668,0.302156806,0.4226678014,0.1818974614,-0.145703271,0.0370389149,0.0772163868,0.451574564,0.1048435643,-0.0922244564,-0.1263718456,-0.3054101467,-0.0594792664,0.4833448827,-0.1791246086,-0.0160230976,0.2791057527,-0.0161974188,0.0771187916,-0.1511853486,-0.1175531745,0.4446229041,0.0723261684,0.5178593993,-0.0257208422,-0.1244166493,-0.1868189722,-0.1908410639,-0.2879425287,-0.1234138981,0.2847407758,-0.312468946,0.2768905461,-0.4178833961,-0.5642531514,-0.2084293067,0.4299700558,-0.3480241597,-0.3430698216,-0.0680083781,0.0778862834,-0.0229108091,0.2500188053,0.107210204,-0.035542842,0.2320447713,0.0074737356,-0.2216777354,-0.5956392884,-0.4356403053,0.0326633267,0.2823260725,0.0373752452,0.1272295564,-0.1569059193,-0.0672418326,-0.1452952772,-0.4021958709,0.3414442241,-0.0869849101,-0.0084638475,0.1166531071,0.4272188842,-0.19598113,-0.0950585008,0.3098010421,0.1152016073,-0.1476498395,-0.0692656934,-0.084672384,-0.0623619594,-0.0308230799,-0.4383021891,-0.1505855471,-0.3273377717,-0.1820004731,0.1689832062,0.1332353055,0.3523068726,0.2645772398,0.2091489136,0.3283595741,0.0954612941,-0.0965576619,-0.15933685,0.2170184106,-0.3185723126,-0.3167462349,0.1040425673,-0.1413134485,0.2305069566,0.1135405153,-0.146329999,0.0680313334,0.1159731671,0.0200549383,-0.1013204157,0.3257164955,0.7469013929,-0.0126242423,-0.0016661583,-0.0988114774,0.0478255115,0.0307381041,-0.4368053079,0.347318083,0.1777924001,0.5277687311,-0.0701474324,0.7515996695,0.1148355156,0.1166792214,0.1321653128,-0.0752609968,0.066186063,-0.272472769,-0.1884595752,0.292780757,0.0403270945,-0.3030905426,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1994","title":"not being able to get wikipedia es language","comments":"Hi @dorost1234, so sorry, but looking at my files here, I figure out that I've preprocessed files using the HF\/datasets for all the languages previously listed by me (Portuguese, Russian, French, Japanese, Chinese, and Turkish) except the Spanish (on my tests I've used the [wikicorpus](https:\/\/www.cs.upc.edu\/~nlp\/wikicorpus\/) instead).\r\n\r\nOnly with the Spanish Wikipedia's dump, I had the same `KeyError: '000nbsp'` problem already reported here https:\/\/github.com\/huggingface\/datasets\/issues\/577\r\n\r\nSo nowadays, even with access to a high resource machine, you couldn't be able to get Wikipedia's Spanish data using the HF\/datasets :(\r\n\r\n\r\n\r\n\r\n","body":"Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help ","comment_length":86,"text":"not being able to get wikipedia es language \n Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help  \n Hi @dorost1234, so sorry, but looking at my files here, I figure out that I've preprocessed files using the HF\/datasets for all the languages previously listed by me (Portuguese, Russian, French, Japanese, Chinese, and Turkish) except the Spanish (on my tests I've used the [wikicorpus](https:\/\/www.cs.upc.edu\/~nlp\/wikicorpus\/) instead).\r\n\r\nOnly with the Spanish Wikipedia's dump, I had the same `KeyError: '000nbsp'` problem already reported here https:\/\/github.com\/huggingface\/datasets\/issues\/577\r\n\r\nSo nowadays, even with access to a high resource machine, you couldn't be able to get Wikipedia's Spanish data using the HF\/datasets :(\r\n\r\n\r\n\r\n\r\n","embeddings":[-0.342106998,0.0471930392,-0.1062649339,0.0620172322,0.200454846,0.1777444929,0.1768746823,0.3401965797,0.1373334974,0.0921178386,0.4105240405,0.3346937597,0.0160005912,0.3076457381,0.1113204286,-0.3370872438,0.0556506142,0.0752215236,-0.1947185397,-0.2001760751,-0.1320842654,0.1037431955,-0.2725183666,-0.0701157525,-0.1488354355,0.1070382744,0.1227778196,-0.0196700636,-0.192601651,-0.1862009615,0.0680222437,-0.036213655,0.2280335724,0.1079433188,-0.0001084146,0.0806313604,0.5717446804,-0.1583800614,-0.4590220749,-0.130322203,-0.081363827,-0.3462582827,0.2564504743,-0.3715744317,-0.3347848058,-0.0256394912,0.3187549412,-0.5857167244,0.2412877679,0.1479617506,0.2182685584,-0.074210979,0.3515349925,-0.0048845191,0.4105975032,0.218606472,-0.0360494033,-0.1243073717,0.0106633129,0.0249649677,0.0606173314,0.4618479013,0.0472859293,-0.1142473742,0.3039729893,-0.3094903827,0.2656840682,-0.3731164932,0.4229684472,0.2541317344,1.0161632299,-0.1197014451,0.0995212495,0.1050666645,0.0040745316,0.1287817061,0.2428365648,0.2005350441,-0.3642728925,-0.0796793103,0.1134936661,-0.2830438912,-0.335678637,0.3455581665,-0.1017287895,0.5368958116,0.1090375558,0.1416433603,-0.1509752721,-0.1852888316,0.0773017183,-0.0707799047,0.1030773669,0.3541413844,-0.0930447504,0.1351349652,0.2457793206,0.1751329154,0.0443397351,-0.0947965086,-0.2797648609,0.1917012036,0.368519485,0.0461870804,0.0220659487,0.0323531926,0.3556627333,-0.1849304587,0.2050388753,-0.030573098,0.0079394793,0.06664785,-0.0586212762,-0.3365755379,-0.6453832984,0.2325167805,-0.0009588756,-0.1367516071,0.192342177,0.1587269008,-0.3540838063,-0.2998664379,-0.0057489178,0.2017250508,0.1522044241,0.2051189095,0.2833256125,-0.0867259279,-0.3417105973,-0.3844312429,-0.0466403812,0.2829635143,-0.4593487978,0.0780525506,0.2093625367,0.1067860499,0.3750687838,-0.0355147943,-0.0439997055,0.0425268859,0.1307713985,0.0788350478,-0.1385989934,0.1594749987,0.1490190625,0.2470012158,0.199904263,-0.222467348,-0.052217897,0.0016842578,-0.0903736725,-0.1905186325,-0.18565543,0.1717608422,0.1227803901,0.1018858626,0.0458261073,0.3413800597,0.1713995934,-0.2258878946,0.1436647028,-0.0075095682,-0.1345748454,-0.0918872654,0.3624586463,0.372572124,-0.6563616395,0.1040466204,-0.0495637171,0.1877678782,0.143699944,-0.2795007229,-0.281387955,0.4237986207,-0.093520321,-0.1007723063,0.3089175224,-0.2708930373,0.0815908834,0.1968420744,0.067334421,-0.2822613418,0.2371014357,-0.1123532802,0.0422963984,0.1624549776,0.1141090319,0.1663445979,0.116460517,-0.0702252239,-0.2196758091,-0.1222946942,-0.0431540161,0.1172707975,0.2665446103,-0.1195803061,0.1249019429,0.5266057849,0.3261623085,-0.2632548511,0.0764246359,0.5432159305,-0.3122918904,0.0806591958,0.1918928772,-0.0554747656,-0.0498997234,0.1809134334,-0.239194259,0.4039117098,0.0790574178,-0.0053859027,-0.2668075264,-0.0530283935,-0.2360287756,-0.4283643067,0.2114921659,0.0466564186,-0.0355188176,0.2530859709,0.0529352501,-0.0900353342,-0.278223455,-0.1389373243,-0.8122121692,0.2934658229,-0.2860761285,-0.0800810456,-0.0875601247,0.0202554129,0.0639516488,-0.1236343607,-0.2867787182,0.1009152383,0.1873278469,0.0372007079,-0.011949122,0.16449368,0.1220298633,-0.2648282647,0.3166707158,0.3054229021,0.1199404374,0.0179688167,-0.2019145489,0.1108080298,0.0477327667,0.3355243504,0.0727865323,0.2685893774,0.185183093,0.1680476964,-0.1562812775,-0.1591639668,0.302156806,0.4226678014,0.1818974614,-0.145703271,0.0370389149,0.0772163868,0.451574564,0.1048435643,-0.0922244564,-0.1263718456,-0.3054101467,-0.0594792664,0.4833448827,-0.1791246086,-0.0160230976,0.2791057527,-0.0161974188,0.0771187916,-0.1511853486,-0.1175531745,0.4446229041,0.0723261684,0.5178593993,-0.0257208422,-0.1244166493,-0.1868189722,-0.1908410639,-0.2879425287,-0.1234138981,0.2847407758,-0.312468946,0.2768905461,-0.4178833961,-0.5642531514,-0.2084293067,0.4299700558,-0.3480241597,-0.3430698216,-0.0680083781,0.0778862834,-0.0229108091,0.2500188053,0.107210204,-0.035542842,0.2320447713,0.0074737356,-0.2216777354,-0.5956392884,-0.4356403053,0.0326633267,0.2823260725,0.0373752452,0.1272295564,-0.1569059193,-0.0672418326,-0.1452952772,-0.4021958709,0.3414442241,-0.0869849101,-0.0084638475,0.1166531071,0.4272188842,-0.19598113,-0.0950585008,0.3098010421,0.1152016073,-0.1476498395,-0.0692656934,-0.084672384,-0.0623619594,-0.0308230799,-0.4383021891,-0.1505855471,-0.3273377717,-0.1820004731,0.1689832062,0.1332353055,0.3523068726,0.2645772398,0.2091489136,0.3283595741,0.0954612941,-0.0965576619,-0.15933685,0.2170184106,-0.3185723126,-0.3167462349,0.1040425673,-0.1413134485,0.2305069566,0.1135405153,-0.146329999,0.0680313334,0.1159731671,0.0200549383,-0.1013204157,0.3257164955,0.7469013929,-0.0126242423,-0.0016661583,-0.0988114774,0.0478255115,0.0307381041,-0.4368053079,0.347318083,0.1777924001,0.5277687311,-0.0701474324,0.7515996695,0.1148355156,0.1166792214,0.1321653128,-0.0752609968,0.066186063,-0.272472769,-0.1884595752,0.292780757,0.0403270945,-0.30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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1994","title":"not being able to get wikipedia es language","comments":"Thanks a lot for the information and help. This would be great to have\nthese datasets.\n@lhoestq <https:\/\/github.com\/lhoestq>  Do you know a way I could get\nsmaller amount of these data like 1 GBtype of each language to deal with\ncomputatioanl requirements? thanks\n\nOn Sat, Mar 6, 2021 at 5:36 PM Jonatas Grosman <notifications@github.com>\nwrote:\n\n> Hi @dorost1234 <https:\/\/github.com\/dorost1234>, so sorry, but looking at\n> my files here, I figure out that I've preprocessed files using the\n> HF\/datasets for all the languages previously listed by me (Portuguese,\n> Russian, French, Japanese, Chinese, and Turkish) except the Spanish (on my\n> tests I've used the wikicorpus <https:\/\/www.cs.upc.edu\/~nlp\/wikicorpus\/>\n> instead).\n>\n> Only with the Spanish Wikipedia's dump, I had the same KeyError: '000nbsp'\n> problem already reported here #577\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/577>\n>\n> So nowadays, even with access to a high resource machine, you couldn't be\n> able to get Wikipedia's Spanish data using the HF\/datasets :(\n>\n> \u2014\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/1994#issuecomment-791985546>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMWMO7WOHWLOROPD6Q3TCJKXPANCNFSM4YUZIF4A>\n> .\n>\n","body":"Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help ","comment_length":189,"text":"not being able to get wikipedia es language \n Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help  \n Thanks a lot for the information and help. This would be great to have\nthese datasets.\n@lhoestq <https:\/\/github.com\/lhoestq>  Do you know a way I could get\nsmaller amount of these data like 1 GBtype of each language to deal with\ncomputatioanl requirements? thanks\n\nOn Sat, Mar 6, 2021 at 5:36 PM Jonatas Grosman <notifications@github.com>\nwrote:\n\n> Hi @dorost1234 <https:\/\/github.com\/dorost1234>, so sorry, but looking at\n> my files here, I figure out that I've preprocessed files using the\n> HF\/datasets for all the languages previously listed by me (Portuguese,\n> Russian, French, Japanese, Chinese, and Turkish) except the Spanish (on my\n> tests I've used the wikicorpus <https:\/\/www.cs.upc.edu\/~nlp\/wikicorpus\/>\n> instead).\n>\n> Only with the Spanish Wikipedia's dump, I had the same KeyError: '000nbsp'\n> problem already reported here #577\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/577>\n>\n> So nowadays, even with access to a high resource machine, you couldn't be\n> able to get Wikipedia's Spanish data using the HF\/datasets :(\n>\n> \u2014\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/1994#issuecomment-791985546>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMWMO7WOHWLOROPD6Q3TCJKXPANCNFSM4YUZIF4A>\n> .\n>\n","embeddings":[-0.342106998,0.0471930392,-0.1062649339,0.0620172322,0.200454846,0.1777444929,0.1768746823,0.3401965797,0.1373334974,0.0921178386,0.4105240405,0.3346937597,0.0160005912,0.3076457381,0.1113204286,-0.3370872438,0.0556506142,0.0752215236,-0.1947185397,-0.2001760751,-0.1320842654,0.1037431955,-0.2725183666,-0.0701157525,-0.1488354355,0.1070382744,0.1227778196,-0.0196700636,-0.192601651,-0.1862009615,0.0680222437,-0.036213655,0.2280335724,0.1079433188,-0.0001084146,0.0806313604,0.5717446804,-0.1583800614,-0.4590220749,-0.130322203,-0.081363827,-0.3462582827,0.2564504743,-0.3715744317,-0.3347848058,-0.0256394912,0.3187549412,-0.5857167244,0.2412877679,0.1479617506,0.2182685584,-0.074210979,0.3515349925,-0.0048845191,0.4105975032,0.218606472,-0.0360494033,-0.1243073717,0.0106633129,0.0249649677,0.0606173314,0.4618479013,0.0472859293,-0.1142473742,0.3039729893,-0.3094903827,0.2656840682,-0.3731164932,0.4229684472,0.2541317344,1.0161632299,-0.1197014451,0.0995212495,0.1050666645,0.0040745316,0.1287817061,0.2428365648,0.2005350441,-0.3642728925,-0.0796793103,0.1134936661,-0.2830438912,-0.335678637,0.3455581665,-0.1017287895,0.5368958116,0.1090375558,0.1416433603,-0.1509752721,-0.1852888316,0.0773017183,-0.0707799047,0.1030773669,0.3541413844,-0.0930447504,0.1351349652,0.2457793206,0.1751329154,0.0443397351,-0.0947965086,-0.2797648609,0.1917012036,0.368519485,0.0461870804,0.0220659487,0.0323531926,0.3556627333,-0.1849304587,0.2050388753,-0.030573098,0.0079394793,0.06664785,-0.0586212762,-0.3365755379,-0.6453832984,0.2325167805,-0.0009588756,-0.1367516071,0.192342177,0.1587269008,-0.3540838063,-0.2998664379,-0.0057489178,0.2017250508,0.1522044241,0.2051189095,0.2833256125,-0.0867259279,-0.3417105973,-0.3844312429,-0.0466403812,0.2829635143,-0.4593487978,0.0780525506,0.2093625367,0.1067860499,0.3750687838,-0.0355147943,-0.0439997055,0.0425268859,0.1307713985,0.0788350478,-0.1385989934,0.1594749987,0.1490190625,0.2470012158,0.199904263,-0.222467348,-0.052217897,0.0016842578,-0.0903736725,-0.1905186325,-0.18565543,0.1717608422,0.1227803901,0.1018858626,0.0458261073,0.3413800597,0.1713995934,-0.2258878946,0.1436647028,-0.0075095682,-0.1345748454,-0.0918872654,0.3624586463,0.372572124,-0.6563616395,0.1040466204,-0.0495637171,0.1877678782,0.143699944,-0.2795007229,-0.281387955,0.4237986207,-0.093520321,-0.1007723063,0.3089175224,-0.2708930373,0.0815908834,0.1968420744,0.067334421,-0.2822613418,0.2371014357,-0.1123532802,0.0422963984,0.1624549776,0.1141090319,0.1663445979,0.116460517,-0.0702252239,-0.2196758091,-0.1222946942,-0.0431540161,0.1172707975,0.2665446103,-0.1195803061,0.1249019429,0.5266057849,0.3261623085,-0.2632548511,0.0764246359,0.5432159305,-0.3122918904,0.0806591958,0.1918928772,-0.0554747656,-0.0498997234,0.1809134334,-0.239194259,0.4039117098,0.0790574178,-0.0053859027,-0.2668075264,-0.0530283935,-0.2360287756,-0.4283643067,0.2114921659,0.0466564186,-0.0355188176,0.2530859709,0.0529352501,-0.0900353342,-0.278223455,-0.1389373243,-0.8122121692,0.2934658229,-0.2860761285,-0.0800810456,-0.0875601247,0.0202554129,0.0639516488,-0.1236343607,-0.2867787182,0.1009152383,0.1873278469,0.0372007079,-0.011949122,0.16449368,0.1220298633,-0.2648282647,0.3166707158,0.3054229021,0.1199404374,0.0179688167,-0.2019145489,0.1108080298,0.0477327667,0.3355243504,0.0727865323,0.2685893774,0.185183093,0.1680476964,-0.1562812775,-0.1591639668,0.302156806,0.4226678014,0.1818974614,-0.145703271,0.0370389149,0.0772163868,0.451574564,0.1048435643,-0.0922244564,-0.1263718456,-0.3054101467,-0.0594792664,0.4833448827,-0.1791246086,-0.0160230976,0.2791057527,-0.0161974188,0.0771187916,-0.1511853486,-0.1175531745,0.4446229041,0.0723261684,0.5178593993,-0.0257208422,-0.1244166493,-0.1868189722,-0.1908410639,-0.2879425287,-0.1234138981,0.2847407758,-0.312468946,0.2768905461,-0.4178833961,-0.5642531514,-0.2084293067,0.4299700558,-0.3480241597,-0.3430698216,-0.0680083781,0.0778862834,-0.0229108091,0.2500188053,0.107210204,-0.035542842,0.2320447713,0.0074737356,-0.2216777354,-0.5956392884,-0.4356403053,0.0326633267,0.2823260725,0.0373752452,0.1272295564,-0.1569059193,-0.0672418326,-0.1452952772,-0.4021958709,0.3414442241,-0.0869849101,-0.0084638475,0.1166531071,0.4272188842,-0.19598113,-0.0950585008,0.3098010421,0.1152016073,-0.1476498395,-0.0692656934,-0.084672384,-0.0623619594,-0.0308230799,-0.4383021891,-0.1505855471,-0.3273377717,-0.1820004731,0.1689832062,0.1332353055,0.3523068726,0.2645772398,0.2091489136,0.3283595741,0.0954612941,-0.0965576619,-0.15933685,0.2170184106,-0.3185723126,-0.3167462349,0.1040425673,-0.1413134485,0.2305069566,0.1135405153,-0.146329999,0.0680313334,0.1159731671,0.0200549383,-0.1013204157,0.3257164955,0.7469013929,-0.0126242423,-0.0016661583,-0.0988114774,0.0478255115,0.0307381041,-0.4368053079,0.347318083,0.1777924001,0.5277687311,-0.0701474324,0.7515996695,0.1148355156,0.1166792214,0.1321653128,-0.0752609968,0.066186063,-0.272472769,-0.1884595752,0.292780757,0.0403270945,-0.3030905426,0.38984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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1994","title":"not being able to get wikipedia es language","comments":"Hi ! As mentioned above the Spanish configuration have parsing issues from `mwparserfromhell`. I haven't tested with the latest `mwparserfromhell` >=0.6 though. Which version of `mwparserfromhell` are you using ?\r\n\r\n> @lhoestq Could you help me to upload these preprocessed datasets to Huggingface's repositories? To be more precise, I've built datasets from the following languages using the 20201201 dumps: Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish. Process these datasets have high costs that most of the community can't afford. I think these preprocessed datasets I have could be helpful for someone without access to high-resource machines to process Wikipedia's dumps like @dorost1234\r\n\r\nThat would be awesome ! Feel free to ping me on slack so we can put the processed wikipedia files on google storage with the other ones we've already preprocessed.\r\n\r\n> Do you know a way I could get smaller amount of these data like 1 GBtype of each language to deal with computatioanl requirements? thanks\r\n\r\nI'd suggest to copy the [wikipedia.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wikipedia\/wikipedia.py) to a new script `custom_wikipedia.py` and modify it to only download and process only a subset of the raw data files.\r\nYou can for example replace [this line](https:\/\/github.com\/huggingface\/datasets\/blob\/64e59fc45ca2134218b3e42e83fddddbe840ff74\/datasets\/wikipedia\/wikipedia.py#L446) by:\r\n```python\r\n            if total_bytes >= (1 << 30):  # stop if the total amount of data is >= 1GB\r\n                break\r\n            else:\r\n                xml_urls.append(_base_url(lang) + fname)\r\n```\r\n\r\nThen you can load your custom wikipedia dataset with\r\n```python\r\nload_dataset(\"path\/to\/my\/custom_wikipedia.py\", f\"{date}.{language}\")\r\n```","body":"Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help ","comment_length":231,"text":"not being able to get wikipedia es language \n Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help  \n Hi ! As mentioned above the Spanish configuration have parsing issues from `mwparserfromhell`. I haven't tested with the latest `mwparserfromhell` >=0.6 though. Which version of `mwparserfromhell` are you using ?\r\n\r\n> @lhoestq Could you help me to upload these preprocessed datasets to Huggingface's repositories? To be more precise, I've built datasets from the following languages using the 20201201 dumps: Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish. Process these datasets have high costs that most of the community can't afford. I think these preprocessed datasets I have could be helpful for someone without access to high-resource machines to process Wikipedia's dumps like @dorost1234\r\n\r\nThat would be awesome ! Feel free to ping me on slack so we can put the processed wikipedia files on google storage with the other ones we've already preprocessed.\r\n\r\n> Do you know a way I could get smaller amount of these data like 1 GBtype of each language to deal with computatioanl requirements? thanks\r\n\r\nI'd suggest to copy the [wikipedia.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wikipedia\/wikipedia.py) to a new script `custom_wikipedia.py` and modify it to only download and process only a subset of the raw data files.\r\nYou can for example replace [this line](https:\/\/github.com\/huggingface\/datasets\/blob\/64e59fc45ca2134218b3e42e83fddddbe840ff74\/datasets\/wikipedia\/wikipedia.py#L446) by:\r\n```python\r\n            if total_bytes >= (1 << 30):  # stop if the total amount of data is >= 1GB\r\n                break\r\n            else:\r\n                xml_urls.append(_base_url(lang) + fname)\r\n```\r\n\r\nThen you can load your custom wikipedia dataset with\r\n```python\r\nload_dataset(\"path\/to\/my\/custom_wikipedia.py\", f\"{date}.{language}\")\r\n```","embeddings":[-0.342106998,0.0471930392,-0.1062649339,0.0620172322,0.200454846,0.1777444929,0.1768746823,0.3401965797,0.1373334974,0.0921178386,0.4105240405,0.3346937597,0.0160005912,0.3076457381,0.1113204286,-0.3370872438,0.0556506142,0.0752215236,-0.1947185397,-0.2001760751,-0.1320842654,0.1037431955,-0.2725183666,-0.0701157525,-0.1488354355,0.1070382744,0.1227778196,-0.0196700636,-0.192601651,-0.1862009615,0.0680222437,-0.036213655,0.2280335724,0.1079433188,-0.0001084146,0.0806313604,0.5717446804,-0.1583800614,-0.4590220749,-0.130322203,-0.081363827,-0.3462582827,0.2564504743,-0.3715744317,-0.3347848058,-0.0256394912,0.3187549412,-0.5857167244,0.2412877679,0.1479617506,0.2182685584,-0.074210979,0.3515349925,-0.0048845191,0.4105975032,0.218606472,-0.0360494033,-0.1243073717,0.0106633129,0.0249649677,0.0606173314,0.4618479013,0.0472859293,-0.1142473742,0.3039729893,-0.3094903827,0.2656840682,-0.3731164932,0.4229684472,0.2541317344,1.0161632299,-0.1197014451,0.0995212495,0.1050666645,0.0040745316,0.1287817061,0.2428365648,0.2005350441,-0.3642728925,-0.0796793103,0.1134936661,-0.2830438912,-0.335678637,0.3455581665,-0.1017287895,0.5368958116,0.1090375558,0.1416433603,-0.1509752721,-0.1852888316,0.0773017183,-0.0707799047,0.1030773669,0.3541413844,-0.0930447504,0.1351349652,0.2457793206,0.1751329154,0.0443397351,-0.0947965086,-0.2797648609,0.1917012036,0.368519485,0.0461870804,0.0220659487,0.0323531926,0.3556627333,-0.1849304587,0.2050388753,-0.030573098,0.0079394793,0.06664785,-0.0586212762,-0.3365755379,-0.6453832984,0.2325167805,-0.0009588756,-0.1367516071,0.192342177,0.1587269008,-0.3540838063,-0.2998664379,-0.0057489178,0.2017250508,0.1522044241,0.2051189095,0.2833256125,-0.0867259279,-0.3417105973,-0.3844312429,-0.0466403812,0.2829635143,-0.4593487978,0.0780525506,0.2093625367,0.1067860499,0.3750687838,-0.0355147943,-0.0439997055,0.0425268859,0.1307713985,0.0788350478,-0.1385989934,0.1594749987,0.1490190625,0.2470012158,0.199904263,-0.222467348,-0.052217897,0.0016842578,-0.0903736725,-0.1905186325,-0.18565543,0.1717608422,0.1227803901,0.1018858626,0.0458261073,0.3413800597,0.1713995934,-0.2258878946,0.1436647028,-0.0075095682,-0.1345748454,-0.0918872654,0.3624586463,0.372572124,-0.6563616395,0.1040466204,-0.0495637171,0.1877678782,0.143699944,-0.2795007229,-0.281387955,0.4237986207,-0.093520321,-0.1007723063,0.3089175224,-0.2708930373,0.0815908834,0.1968420744,0.067334421,-0.2822613418,0.2371014357,-0.1123532802,0.0422963984,0.1624549776,0.1141090319,0.1663445979,0.116460517,-0.0702252239,-0.2196758091,-0.1222946942,-0.0431540161,0.1172707975,0.2665446103,-0.1195803061,0.1249019429,0.5266057849,0.3261623085,-0.2632548511,0.0764246359,0.5432159305,-0.3122918904,0.0806591958,0.1918928772,-0.0554747656,-0.0498997234,0.1809134334,-0.239194259,0.4039117098,0.0790574178,-0.0053859027,-0.2668075264,-0.0530283935,-0.2360287756,-0.4283643067,0.2114921659,0.0466564186,-0.0355188176,0.2530859709,0.0529352501,-0.0900353342,-0.278223455,-0.1389373243,-0.8122121692,0.2934658229,-0.2860761285,-0.0800810456,-0.0875601247,0.0202554129,0.0639516488,-0.1236343607,-0.2867787182,0.1009152383,0.1873278469,0.0372007079,-0.011949122,0.16449368,0.1220298633,-0.2648282647,0.3166707158,0.3054229021,0.1199404374,0.0179688167,-0.2019145489,0.1108080298,0.0477327667,0.3355243504,0.0727865323,0.2685893774,0.185183093,0.1680476964,-0.1562812775,-0.1591639668,0.302156806,0.4226678014,0.1818974614,-0.145703271,0.0370389149,0.0772163868,0.451574564,0.1048435643,-0.0922244564,-0.1263718456,-0.3054101467,-0.0594792664,0.4833448827,-0.1791246086,-0.0160230976,0.2791057527,-0.0161974188,0.0771187916,-0.1511853486,-0.1175531745,0.4446229041,0.0723261684,0.5178593993,-0.0257208422,-0.1244166493,-0.1868189722,-0.1908410639,-0.2879425287,-0.1234138981,0.2847407758,-0.312468946,0.2768905461,-0.4178833961,-0.5642531514,-0.2084293067,0.4299700558,-0.3480241597,-0.3430698216,-0.0680083781,0.0778862834,-0.0229108091,0.2500188053,0.107210204,-0.035542842,0.2320447713,0.0074737356,-0.2216777354,-0.5956392884,-0.4356403053,0.0326633267,0.2823260725,0.0373752452,0.1272295564,-0.1569059193,-0.0672418326,-0.1452952772,-0.4021958709,0.3414442241,-0.0869849101,-0.0084638475,0.1166531071,0.4272188842,-0.19598113,-0.0950585008,0.3098010421,0.1152016073,-0.1476498395,-0.0692656934,-0.084672384,-0.0623619594,-0.0308230799,-0.4383021891,-0.1505855471,-0.3273377717,-0.1820004731,0.1689832062,0.1332353055,0.3523068726,0.2645772398,0.2091489136,0.3283595741,0.0954612941,-0.0965576619,-0.15933685,0.2170184106,-0.3185723126,-0.3167462349,0.1040425673,-0.1413134485,0.2305069566,0.1135405153,-0.146329999,0.0680313334,0.1159731671,0.0200549383,-0.1013204157,0.3257164955,0.7469013929,-0.0126242423,-0.0016661583,-0.0988114774,0.0478255115,0.0307381041,-0.4368053079,0.347318083,0.1777924001,0.5277687311,-0.0701474324,0.7515996695,0.1148355156,0.1166792214,0.1321653128,-0.0752609968,0.066186063,-0.272472769,-0.1884595752,0.292780757,0.04032709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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1994","title":"not being able to get wikipedia es language","comments":"Hi @lhoestq!\r\n\r\n> Hi ! As mentioned above the Spanish configuration have parsing issues from mwparserfromhell. I haven't tested with the latest mwparserfromhell >=0.6 though. Which version of mwparserfromhell are you using ?\r\n\r\nI'm using the latest mwparserfromhell version (0.6)\r\n\r\n> That would be awesome ! Feel free to ping me on slack so we can put the processed wikipedia files on google storage with the other ones we've already preprocessed.\r\n\r\nI'll ping you there \ud83d\udc4d ","body":"Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help ","comment_length":76,"text":"not being able to get wikipedia es language \n Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help  \n Hi @lhoestq!\r\n\r\n> Hi ! As mentioned above the Spanish configuration have parsing issues from mwparserfromhell. I haven't tested with the latest mwparserfromhell >=0.6 though. Which version of mwparserfromhell are you using ?\r\n\r\nI'm using the latest mwparserfromhell version (0.6)\r\n\r\n> That would be awesome ! Feel free to ping me on slack so we can put the processed wikipedia files on google storage with the other ones we've already preprocessed.\r\n\r\nI'll ping you there \ud83d\udc4d 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1994","title":"not being able to get wikipedia es language","comments":"Thank you so much @jonatasgrosman  and @lhoestq  this would be a great help. I am really thankful to you both and to wonderful Huggingface dataset library allowing us to train models at scale.","body":"Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help ","comment_length":33,"text":"not being able to get wikipedia es language \n Hi\r\nI am trying to run a code with wikipedia of config 20200501.es, getting:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_mlm_t5.py\", line 608, in <module>\r\n    main()\r\n  File \"run_mlm_t5.py\", line 359, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/libs\/anaconda3\/envs\/success432\/lib\/python3.7\/site-packages\/datasets-1.2.1-py3.7.egg\/datasets\/builder.py\", line 1050, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\ndatasets.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\nthanks @lhoestq  for any suggestion\/help  \n Thank you so much @jonatasgrosman  and @lhoestq  this would be a great help. I am really thankful to you both and to wonderful Huggingface dataset library allowing us to train models at scale.","embeddings":[-0.342106998,0.0471930392,-0.1062649339,0.0620172322,0.200454846,0.1777444929,0.1768746823,0.3401965797,0.1373334974,0.0921178386,0.4105240405,0.3346937597,0.0160005912,0.3076457381,0.1113204286,-0.3370872438,0.0556506142,0.0752215236,-0.1947185397,-0.2001760751,-0.1320842654,0.1037431955,-0.2725183666,-0.0701157525,-0.1488354355,0.1070382744,0.1227778196,-0.0196700636,-0.192601651,-0.1862009615,0.0680222437,-0.036213655,0.2280335724,0.1079433188,-0.0001084146,0.0806313604,0.5717446804,-0.1583800614,-0.4590220749,-0.130322203,-0.081363827,-0.3462582827,0.2564504743,-0.3715744317,-0.3347848058,-0.0256394912,0.3187549412,-0.5857167244,0.2412877679,0.1479617506,0.2182685584,-0.074210979,0.3515349925,-0.0048845191,0.4105975032,0.218606472,-0.0360494033,-0.1243073717,0.0106633129,0.0249649677,0.0606173314,0.4618479013,0.0472859293,-0.1142473742,0.3039729893,-0.3094903827,0.2656840682,-0.3731164932,0.4229684472,0.2541317344,1.0161632299,-0.1197014451,0.0995212495,0.1050666645,0.0040745316,0.1287817061,0.2428365648,0.2005350441,-0.3642728925,-0.0796793103,0.1134936661,-0.2830438912,-0.335678637,0.3455581665,-0.1017287895,0.5368958116,0.1090375558,0.1416433603,-0.1509752721,-0.1852888316,0.0773017183,-0.0707799047,0.1030773669,0.3541413844,-0.0930447504,0.1351349652,0.2457793206,0.1751329154,0.0443397351,-0.0947965086,-0.2797648609,0.1917012036,0.368519485,0.0461870804,0.0220659487,0.0323531926,0.3556627333,-0.1849304587,0.2050388753,-0.030573098,0.0079394793,0.06664785,-0.0586212762,-0.3365755379,-0.6453832984,0.2325167805,-0.0009588756,-0.1367516071,0.192342177,0.1587269008,-0.3540838063,-0.2998664379,-0.0057489178,0.2017250508,0.1522044241,0.2051189095,0.2833256125,-0.0867259279,-0.3417105973,-0.3844312429,-0.0466403812,0.2829635143,-0.4593487978,0.0780525506,0.2093625367,0.1067860499,0.3750687838,-0.0355147943,-0.0439997055,0.0425268859,0.1307713985,0.0788350478,-0.1385989934,0.1594749987,0.1490190625,0.2470012158,0.199904263,-0.222467348,-0.052217897,0.0016842578,-0.0903736725,-0.1905186325,-0.18565543,0.1717608422,0.1227803901,0.1018858626,0.0458261073,0.3413800597,0.1713995934,-0.2258878946,0.1436647028,-0.0075095682,-0.1345748454,-0.0918872654,0.3624586463,0.372572124,-0.6563616395,0.1040466204,-0.0495637171,0.1877678782,0.143699944,-0.2795007229,-0.281387955,0.4237986207,-0.093520321,-0.1007723063,0.3089175224,-0.2708930373,0.0815908834,0.1968420744,0.067334421,-0.2822613418,0.2371014357,-0.1123532802,0.0422963984,0.1624549776,0.1141090319,0.1663445979,0.116460517,-0.0702252239,-0.2196758091,-0.1222946942,-0.0431540161,0.1172707975,0.2665446103,-0.1195803061,0.1249019429,0.5266057849,0.3261623085,-0.2632548511,0.0764246359,0.5432159305,-0.3122918904,0.0806591958,0.1918928772,-0.0554747656,-0.0498997234,0.1809134334,-0.239194259,0.4039117098,0.0790574178,-0.0053859027,-0.2668075264,-0.0530283935,-0.2360287756,-0.4283643067,0.2114921659,0.0466564186,-0.0355188176,0.2530859709,0.0529352501,-0.0900353342,-0.278223455,-0.1389373243,-0.8122121692,0.2934658229,-0.2860761285,-0.0800810456,-0.0875601247,0.0202554129,0.0639516488,-0.1236343607,-0.2867787182,0.1009152383,0.1873278469,0.0372007079,-0.011949122,0.16449368,0.1220298633,-0.2648282647,0.3166707158,0.3054229021,0.1199404374,0.0179688167,-0.2019145489,0.1108080298,0.0477327667,0.3355243504,0.0727865323,0.2685893774,0.185183093,0.1680476964,-0.1562812775,-0.1591639668,0.302156806,0.4226678014,0.1818974614,-0.145703271,0.0370389149,0.0772163868,0.451574564,0.1048435643,-0.0922244564,-0.1263718456,-0.3054101467,-0.0594792664,0.4833448827,-0.1791246086,-0.0160230976,0.2791057527,-0.0161974188,0.0771187916,-0.1511853486,-0.1175531745,0.4446229041,0.0723261684,0.5178593993,-0.0257208422,-0.1244166493,-0.1868189722,-0.1908410639,-0.2879425287,-0.1234138981,0.2847407758,-0.312468946,0.2768905461,-0.4178833961,-0.5642531514,-0.2084293067,0.4299700558,-0.3480241597,-0.3430698216,-0.0680083781,0.0778862834,-0.0229108091,0.2500188053,0.107210204,-0.035542842,0.2320447713,0.0074737356,-0.2216777354,-0.5956392884,-0.4356403053,0.0326633267,0.2823260725,0.0373752452,0.1272295564,-0.1569059193,-0.0672418326,-0.1452952772,-0.4021958709,0.3414442241,-0.0869849101,-0.0084638475,0.1166531071,0.4272188842,-0.19598113,-0.0950585008,0.3098010421,0.1152016073,-0.1476498395,-0.0692656934,-0.084672384,-0.0623619594,-0.0308230799,-0.4383021891,-0.1505855471,-0.3273377717,-0.1820004731,0.1689832062,0.1332353055,0.3523068726,0.2645772398,0.2091489136,0.3283595741,0.0954612941,-0.0965576619,-0.15933685,0.2170184106,-0.3185723126,-0.3167462349,0.1040425673,-0.1413134485,0.2305069566,0.1135405153,-0.146329999,0.0680313334,0.1159731671,0.0200549383,-0.1013204157,0.3257164955,0.7469013929,-0.0126242423,-0.0016661583,-0.0988114774,0.0478255115,0.0307381041,-0.4368053079,0.347318083,0.1777924001,0.5277687311,-0.0701474324,0.7515996695,0.1148355156,0.1166792214,0.1321653128,-0.0752609968,0.066186063,-0.272472769,-0.1884595752,0.292780757,0.0403270945,-0.3030905426,0.38984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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1993","title":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original? ","comments":"Hi ! That looks like a bug, can you provide some code so that we can reproduce ?\r\nIt's not supposed to update the original dataset","body":"I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n","comment_length":26,"text":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original?  \n I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n \n Hi ! That looks like a bug, can you provide some code so that we can reproduce ?\r\nIt's not supposed to update the original 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1993","title":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original? ","comments":"Hi, I experimented with RAG. \r\n\r\nActually, you can run the [use_own_knowldge_dataset.py](https:\/\/github.com\/shamanez\/transformers\/blob\/rag-end-to-end-retrieval\/examples\/research_projects\/rag\/use_own_knowledge_dataset.py#L80). In the 80 you can save the dataset object to the disk with save_to_disk. Then in order to compute the embeddings in this use **load_from_disk**. \r\n\r\nThen finally save it. You can see the original dataset object (CSV after splitting also will be changed)\r\n\r\nOne more thing- when I save the dataset object with **save_to_disk** it name the arrow file with cache.... rather than using dataset. arrow.  Can you add a variable that we can feed a name to save_to_disk function?","body":"I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n","comment_length":91,"text":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original?  \n I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n \n Hi, I experimented with RAG. \r\n\r\nActually, you can run the [use_own_knowldge_dataset.py](https:\/\/github.com\/shamanez\/transformers\/blob\/rag-end-to-end-retrieval\/examples\/research_projects\/rag\/use_own_knowledge_dataset.py#L80). In the 80 you can save the dataset object to the disk with save_to_disk. Then in order to compute the embeddings in this use **load_from_disk**. \r\n\r\nThen finally save it. You can see the original dataset object (CSV after splitting also will be changed)\r\n\r\nOne more thing- when I save the dataset object with **save_to_disk** it name the arrow file with cache.... rather than using dataset. arrow.  Can you add a variable that we can feed a name to save_to_disk function?","embeddings":[-0.2981736064,-0.0828652084,0.0267273188,0.1493998915,0.3067896664,0.1228007376,-0.0562063009,-0.0614770278,0.0445269383,0.0541939586,-0.0652618036,0.373824507,0.0215632059,0.1011979878,0.1474010199,0.2532070875,0.1987998039,0.2516977489,-0.2688197494,-0.0971940234,-0.2197251469,-0.2309300452,0.0529332533,-0.3456068635,-0.2162120938,-0.2678647637,-0.1700974256,0.0145703144,0.1032246277,-0.0398215204,0.1639603972,0.1300251782,0.3564484715,0.2765147686,-0.0001222565,0.0449472219,-0.3267861307,-0.1661964506,-0.3974144757,-0.1439903378,0.1779921055,-0.2638602853,0.0647072569,-0.3373181522,-0.0446428657,-0.1765089631,-0.107605584,-0.2636471987,0.6995682716,-0.3083574772,0.0656189248,-0.054051958,-0.2422589958,0.0614366159,-0.0760419965,0.2449961752,0.1191361323,0.1519682109,-0.0484969094,0.2226030082,-0.1404744536,0.2548884451,-0.2061859518,-0.1893691421,0.3967930377,-0.0019805473,0.0755400509,-0.3313621581,0.0782873556,-0.0002817264,0.7876380682,-0.4693832099,-0.3323039114,-0.2304031104,0.2201095819,-0.0707611889,0.043340873,0.206916675,0.0135489209,0.2221802473,-0.4472397566,-0.8105933666,-0.1399762779,0.1488463879,0.0819213465,-0.4945161641,0.0238880273,0.1483843029,0.2077623159,0.289745003,0.372831434,-0.254982233,-0.174267292,0.1039844602,-0.3044407666,-0.2441856712,-0.2922518253,0.0569520146,-0.0547048226,0.3812344372,0.1349292845,-0.0329647623,-0.2412497103,0.1737683564,0.1737943292,0.2897120714,0.1109258682,0.1706047058,0.1379611641,0.0049738353,-0.2106245607,-0.1055162549,0.1187181026,0.1315823942,0.5387070775,-0.2161186934,0.2105357051,0.04565515,0.1234001964,0.0126175126,0.0549343489,-0.0481300615,-0.1775926948,0.3871252239,0.0820179284,0.1812818646,0.1506492347,0.2366101146,0.0228765868,-0.1150496453,-0.1261828989,-0.034810666,-0.2900650203,0.3913136125,0.20096232,-0.0995993018,0.1868816465,0.4064928889,-0.434612304,-0.1599060297,0.0141043393,-0.2374427021,0.5390252471,0.1750427932,0.0522038862,0.2025613487,0.0051055625,-0.0455207899,-0.1667996198,0.519553721,-0.2719989121,-0.1811145395,0.1025095806,0.0902323723,-0.1660820544,0.2113942653,-0.4340537488,0.1076642126,0.0573433675,-0.1293478906,0.1663846523,0.0311017428,-0.3366870284,-0.278573066,0.0628452227,0.3072368205,-0.4218387306,-0.1641575843,0.0618918389,-0.0940397754,-0.2057061642,0.2003053725,-0.2843594849,0.2975263,-0.2760423422,-0.2422124594,0.5749604106,-0.0287337955,-0.481277436,0.1532562077,0.0253595226,0.0056969882,0.0016571193,0.6175714731,0.3191406429,-0.1399003863,-0.4540526867,0.3999417424,0.1578346789,0.1049262434,0.0218707919,-0.2957147658,0.2183943689,-0.2868964076,-0.1871246696,0.3673587143,0.2825940847,0.2646553814,0.3615919352,0.0234334562,0.1554546952,0.3602969646,0.0220470112,0.0340864584,-0.1074186936,0.052772861,-0.6752665043,-0.0008339359,0.1554773301,-0.5374922752,0.1736091971,-0.1934855133,-0.0924271867,-0.165345639,-0.0885926932,-0.0135686425,-0.0897683352,-0.0308930166,-0.0441634394,-0.2119160444,-0.2746407092,0.4201807082,-0.0376425385,0.1762324125,-0.6207967401,0.4170429111,0.1973149329,-0.2141876221,-0.1617540121,-0.0837895796,0.2295793295,0.0185931753,-0.0512569696,0.3639732897,0.1689340323,0.372076273,0.0582000352,0.1233947948,0.0281126741,-0.1681797951,0.2201291025,-0.0435152948,0.1642446816,-0.1232943609,-0.2530221641,0.0886408612,-0.1890542954,0.1372634321,0.086750105,-0.1663254499,-0.0118455375,-0.3778166175,-0.0842535794,-0.2564434409,-0.2976899743,0.1848404706,0.445897907,0.0632535964,0.0249878038,-0.1737427562,0.316331625,-0.1756477356,0.0119170323,0.0152700162,-0.3919644356,-0.0695018098,-0.1718242615,0.4888994694,0.1010778844,0.0891606286,0.0631394237,0.1061022803,0.1201624349,0.0404632613,0.0341845043,0.0189376473,0.5688123703,0.3238120377,0.0262624957,0.0392558388,-0.1586344689,0.4841207862,0.1311215311,-0.1580862105,-0.2029995918,0.0602509975,-0.1202484593,0.2065158486,-0.4876646399,-0.1865930557,-0.3327080011,0.1092467234,-0.2565141618,0.6586427093,0.2108451575,0.2717781067,0.1587678641,0.0456164107,-0.1248292401,-0.4218960404,-0.0536406897,-0.031254977,0.0374605246,-0.0909010321,-0.018166393,0.0350252353,0.4715485275,0.1138012186,-0.0306689758,-0.5500047803,0.0920290872,-0.0775489062,0.2022336721,0.0827576369,-0.1859748811,0.1372915953,-0.2366597801,-0.0745643452,-0.1108954623,-0.4580129087,-0.1761930138,-0.0046438426,0.0675838739,0.3397305608,0.0321200006,-0.3095202744,-0.0657437518,-0.0772382021,-0.1141663566,-0.1819245815,-0.0122263776,-0.0531609021,0.165982753,-0.2359533757,0.2322050631,0.0053282385,-0.2006025761,-0.4661620557,0.4412536323,0.1245172247,-0.1237185821,0.264113903,-0.2815726995,-0.1656348705,0.478548497,-0.5246481895,-0.1548048854,0.0558007322,0.1381165832,-0.3403528631,0.4552120864,0.302120924,0.0476573855,0.0140215773,-0.0724772513,-0.2228309512,0.0957019553,0.0429991521,0.403321296,-0.1240115091,0.1896506846,-0.0526103452,0.4766650498,0.0482534617,-0.1304227561,0.2802925706,0.1950444132,0.4728262424,-0.1371495724,-0.120395042,-0.4867364764,-0.2577689886,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1993","title":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original? ","comments":"@lhoestq   I also found that cache in tmp directory gets updated after transformations. This is really problematic when using datasets interactively. Let's say we use the shards function to a dataset loaded with csv, atm when we do transformations to shards and combine them it updates the original csv cache. ","body":"I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n","comment_length":50,"text":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original?  \n I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n \n @lhoestq   I also found that cache in tmp directory gets updated after transformations. This is really problematic when using datasets interactively. Let's say we use the shards function to a dataset loaded with csv, atm when we do transformations to shards and combine them it updates the original csv cache. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1993","title":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original? ","comments":"I plan to update the save_to_disk method in #2025 so I can make sure the new save_to_disk doesn't corrupt your cache files.\r\nBut from your last message it looks like save_to_disk isn't the root cause right ?","body":"I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n","comment_length":37,"text":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original?  \n I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n \n I plan to update the save_to_disk method in #2025 so I can make sure the new save_to_disk doesn't corrupt your cache files.\r\nBut from your last message it looks like save_to_disk isn't the root cause right ?","embeddings":[-0.3105080128,-0.0680618212,0.0046882723,0.213742435,0.3093694746,0.0905763432,-0.0354265086,0.0527892485,-0.0732766688,0.006163144,0.0222795159,0.3242502511,0.0898376629,0.1039876118,0.1767604351,0.2547088563,0.2500333786,0.1937623173,-0.3340500295,-0.0268145371,-0.2293639928,-0.1129300967,0.1087111235,-0.2835570276,-0.330468595,-0.3502230942,-0.061735969,0.0674631819,0.1010746285,0.002596098,0.16506356,0.1604154557,0.2431449294,0.2890101075,-0.0001189022,0.003664962,-0.2793877125,-0.0914112702,-0.3182113469,-0.062790595,0.0237718113,-0.1687760204,0.0643625483,-0.2784352303,-0.0268728323,0.0422446094,-0.1302841306,-0.2041808516,0.6294941902,-0.2092859745,0.1225881949,-0.1459359527,-0.1816229522,0.0268533751,-0.2328369319,0.2694652677,0.0554410666,0.0393736362,0.0392527133,0.3304750621,-0.0896674395,0.2254993767,-0.3003205359,-0.2334360778,0.41138345,-0.061823599,0.1770342141,-0.352799058,0.1796052903,-0.0407533422,0.8042439222,-0.3945490718,-0.2202159911,-0.2704587579,0.0771651119,-0.126911059,0.1884325445,0.0927256495,0.0741950646,0.1356611997,-0.5429519415,-0.8058210015,-0.079282321,0.0439732037,0.0849198475,-0.5327285528,-0.028630957,0.1143597662,0.2698810399,0.280354321,0.51173383,-0.1818695813,-0.2572776973,0.1177860498,-0.2956849933,-0.2979613543,-0.2101858705,0.2007384449,-0.0370490476,0.2396800071,0.1317304671,-0.0441242456,-0.2862701714,0.073063381,0.1336907148,0.334923327,0.0764580145,0.111513488,0.0928423256,0.0828307644,-0.1914321631,-0.08878804,0.3011989594,-0.0253592879,0.6220217347,-0.2496522218,0.3028310835,-0.0809742883,0.1319755465,0.0198019072,0.1669227332,-0.030759193,-0.1410048902,0.2388069779,0.1260763407,0.2384852767,0.1625214666,0.2277155071,-0.0890761018,-0.0952495337,-0.1974941194,-0.0420266464,-0.2991293371,0.4488770962,0.1797114462,-0.0550356284,0.0623022504,0.444839865,-0.4268714786,-0.1361715198,-0.0558103733,-0.2085800767,0.33602795,0.2015105486,0.1050624326,0.20187217,-0.0205758177,-0.0619851537,-0.080488652,0.6188934445,-0.4360120595,-0.2547796369,0.0720278099,0.1330969334,-0.1292629987,0.1620911658,-0.6500713229,0.1481628269,0.1823387146,-0.1603377759,0.2286168933,0.0785053745,-0.3790595233,-0.2501143813,-0.0768620595,0.2681693137,-0.3776040375,-0.0919734538,0.1421821713,-0.1908731163,-0.078642644,0.2338188887,-0.3230122328,0.2565499544,-0.2728489339,-0.2951149344,0.530872941,0.1095688939,-0.4193350673,0.1607092619,-0.0415808894,-0.0676332787,0.0187847894,0.5070464015,0.1284545362,-0.2125234902,-0.3108474612,0.2478173226,0.1098065823,0.0558847487,-0.0957123861,-0.1768736988,0.2356046587,-0.2986460924,-0.0614739247,0.4517145455,0.2861812413,0.4201847315,0.2112652659,0.0092199091,0.0990410745,0.3704631031,-0.074640356,-0.0521767922,-0.2253950983,0.2060769796,-0.6755854487,0.0409301892,0.1792390049,-0.6058937907,0.3495005071,-0.1251087338,-0.0100670494,-0.2055464089,-0.068482697,0.028655747,-0.0467946827,0.0033021199,-0.0353210457,-0.1637226939,-0.2710678577,0.533338666,-0.0845813602,0.0758440867,-0.620757401,0.3459838331,0.1316459775,-0.1627105623,-0.3209216893,-0.0708475113,0.2944808006,0.0222208984,-0.1368241757,0.388717562,0.1961941868,0.4301020503,0.0012601739,0.2552749217,0.0939521492,-0.0422644056,0.0979755148,-0.199407652,0.111207284,-0.1304408908,-0.1259728223,-0.056881424,-0.2236297131,0.0619360767,0.0439170226,-0.2145471871,0.0435210504,-0.3417886794,0.0263666082,-0.2048581392,-0.2158522308,0.1839472353,0.3429169357,0.1599927396,-0.1657072604,-0.1038518772,0.2246282846,-0.2585544288,-0.0839284286,0.0609280728,-0.1497137994,-0.096774675,-0.1595600098,0.5698390603,0.1799433529,0.0722898543,0.0952895284,0.1322449148,0.0780074671,-0.075699985,0.0063114837,-0.0098382747,0.5663112402,0.2622880936,-0.0351031497,0.1173703372,-0.1512570232,0.436498642,0.158765927,-0.0320275798,-0.333922267,0.0973033905,-0.12079864,0.0436149649,-0.3793645799,-0.115243651,-0.2534185648,0.0617879443,-0.1707229167,0.7471997142,0.2428154051,0.1477638632,0.0102008535,0.107787177,-0.129888624,-0.3767109811,-0.1586440355,0.1013624296,-0.0191389024,-0.0515207089,0.1256586313,-0.0810722709,0.3847855031,-0.0449679457,-0.0184505098,-0.5178281665,0.070510067,-0.067127496,0.0722048208,0.0966846272,-0.1959410906,0.1818971038,-0.3614756763,-0.0663395822,-0.126332745,-0.4590773284,-0.1805945337,0.0563868172,0.0017029888,0.2645662427,-0.0602228716,-0.2389340103,0.0197035652,-0.0604273193,-0.1862342358,-0.2104735672,0.0663478002,0.0624297,0.1308450997,-0.2201138586,0.2857636213,0.0032416596,-0.3236318529,-0.6576063633,0.4279029369,0.1779450178,-0.2001407146,0.3122074902,-0.110426791,-0.0269734878,0.4068779349,-0.5890811682,-0.1938370913,0.0083895,0.104708612,-0.4142176509,0.2215412706,0.3652362227,-0.0205243528,-0.0421430618,-0.0373719111,-0.2552675009,0.0953872278,0.1543393731,0.4772271216,-0.2085597962,0.2322051674,0.0358263813,0.3859612942,-0.0511958785,-0.0470176712,0.3821455538,0.2632104158,0.5747004151,-0.2582440078,-0.1334565133,-0.5062974095,-0.2519566119,-0.3096196651,-0.0341153145,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1993","title":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original? ","comments":"ok, one more thing. When we use save_to_disk there are two files other than .arrow. dataset_info.json and state.json. Sometimes most of the fields in the dataset_infor.json are null, especially when saving dataset objects. Anyways I think load_from_disk uses the arrow files mentioned in state.json right? ","body":"I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n","comment_length":45,"text":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original?  \n I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n \n ok, one more thing. When we use save_to_disk there are two files other than .arrow. dataset_info.json and state.json. Sometimes most of the fields in the dataset_infor.json are null, especially when saving dataset objects. Anyways I think load_from_disk uses the arrow files mentioned in state.json right? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1993","title":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original? ","comments":"Perfect.  For now, I am loading the dataset from CSV in my interactive process and will wait until you make the PR!","body":"I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n","comment_length":22,"text":"How to load a dataset with load_from disk and save it again after doing transformations without changing the original?  \n I am using the latest datasets library.  In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place.  \r\n\r\nWhen I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it.  How to prevent from this?\r\n \n Perfect.  For now, I am loading the dataset from CSV in my interactive process and will wait until you make the PR!","embeddings":[-0.3435497284,-0.1861001253,-0.0237765033,0.1979099065,0.235196054,0.0903734267,-0.0758549795,-0.0891268402,-0.0024480005,0.0613528565,0.0818682164,0.288820982,0.0089173801,0.2742179036,0.211016655,0.2311643064,0.2571016252,0.3034494519,-0.3161807954,-0.0310900286,-0.1955247968,-0.1905546933,0.0157797616,-0.2773298025,-0.1335471421,-0.3582568467,-0.0840454772,-0.0099302558,0.0723503977,0.0670775324,0.1237914488,0.1421202421,0.3197945058,0.3073055744,-0.0001188234,0.0112640066,-0.3935620785,-0.1174851358,-0.2952136695,-0.1752285957,0.0198726803,-0.1928234398,0.0426131189,-0.282327652,-0.0355925113,-0.0789245367,-0.1382195801,-0.1842427701,0.6355670094,-0.2757766247,0.0882279724,-0.1013195962,-0.2604755163,0.1003407985,-0.3609260023,0.3109507263,0.1697656363,0.0327253751,-0.0230117068,0.3523843884,-0.0059232889,0.1834039837,-0.2202827185,-0.2364939749,0.368504107,-0.0557336733,0.1962572187,-0.3266142011,0.1366037875,-0.050418485,0.7573289275,-0.3973721862,-0.2224439383,-0.2200314552,0.2223112136,-0.1780084819,0.1028199121,0.2710522115,-0.0004193895,0.1309111714,-0.5915709138,-0.8006381392,-0.1265453547,0.0599240996,0.0174780302,-0.6005370617,-0.0098792957,0.1061759517,0.2177744657,0.2815048099,0.4598244429,-0.2353613228,-0.2161782384,0.1422934383,-0.294994086,-0.2745671868,-0.2943738103,0.1907633543,-0.0581794083,0.1961970776,0.0701111555,-0.0923652202,-0.2840215266,0.1482525617,0.1528834552,0.3160923719,0.1148205847,0.180868119,0.0832407027,0.0590583868,-0.2331293374,-0.0536452942,0.1533101946,0.0603940263,0.5767165422,-0.2273498625,0.1721357107,0.0239444021,0.0714495629,0.0731745958,0.1662715375,-0.0902282894,-0.117442593,0.3433509171,0.1202501431,0.2047808617,0.1899908036,0.2010244429,-0.0489036813,-0.1141749471,-0.1551879346,-0.0595462397,-0.3120179772,0.41968894,0.1253365278,0.0080648456,0.1199120879,0.5349015594,-0.4715520144,-0.1450412124,0.0144295339,-0.1974763125,0.3453066945,0.1837601215,0.1647830755,0.1672118604,-0.0709511787,-0.0298968963,-0.1003757268,0.6497283578,-0.3161403537,-0.1092399731,0.1016733795,0.1262910515,-0.1379485428,0.1107459962,-0.7127209306,0.1979913712,0.0198915936,-0.1377772242,0.160496667,0.0238329396,-0.3551610112,-0.1828225255,-0.0532662161,0.1804096848,-0.38580212,-0.1600945145,0.1927725524,-0.2199116349,-0.1124547571,0.2244847864,-0.3141235113,0.2418796122,-0.2732774913,-0.357734561,0.5291256309,0.1001327857,-0.3341226578,0.1677851826,-0.0348184444,-0.0790609345,0.0231738556,0.5614807606,0.1681259871,-0.1092801839,-0.4543691874,0.3388874233,0.0994205475,0.1335136443,0.0695958138,-0.1695295423,0.2710565031,-0.311650902,-0.1427363902,0.480778873,0.2460567653,0.3417785764,0.3239541948,-0.024512453,0.0995803922,0.407705605,-0.1512080431,0.0790723637,-0.2095583975,0.1262258887,-0.6138045788,-0.0311102159,0.1936899126,-0.5951254368,0.3615722954,-0.1226511449,-0.0642847121,-0.2044179887,-0.0127755674,0.0749249533,-0.0688732266,-0.002293925,-0.1109882668,-0.1776316017,-0.2606809735,0.4837686718,-0.0590841062,0.0931875706,-0.6138254404,0.3396213949,0.1712860465,-0.2045485526,-0.2430604994,-0.0967049971,0.2103912383,0.0319461636,-0.0962717235,0.3377920091,0.1939775348,0.4088332057,0.0129824532,0.1993591636,0.0663033053,-0.0132702179,0.0733956248,-0.2320130467,0.136041373,-0.0811839551,-0.1756194234,0.0617034361,-0.2363677323,0.0892639831,0.1581325233,-0.204508841,0.0544074439,-0.3416891694,0.0190343335,-0.2046752274,-0.1858683228,0.2192384452,0.3088561893,0.1363543272,-0.226831913,-0.2074437737,0.2330199033,-0.2737059891,-0.0912187025,0.0353360139,-0.2595542967,0.0168843996,-0.1204081327,0.5437600017,0.118866384,0.1275318563,0.0053325994,0.119435586,0.1251199394,0.0129794646,0.0339159742,-0.0343918912,0.5523133278,0.2647176087,-0.0509434678,0.0724444613,-0.1624070555,0.4198661447,0.1504427195,-0.1196854413,-0.3011409938,0.0994732305,-0.1615885943,0.0660834089,-0.4680760503,-0.0841383561,-0.2472438812,0.2366381884,-0.2964737117,0.7885241508,0.259465605,0.1625138223,0.1108180657,0.0459692441,-0.1312014014,-0.4180258214,-0.0573595986,0.0374375694,0.0125371478,-0.0483345091,0.075237073,-0.003702749,0.4305587113,-0.0291063748,-0.0665599629,-0.4269747138,0.1278461218,-0.0823732764,0.1029738337,0.1104518101,-0.2120501697,0.2974178791,-0.4168384969,-0.0541332215,-0.1244075149,-0.4201669693,-0.2594532669,0.0294520706,0.0546952635,0.261502713,0.0107821692,-0.2051879019,-0.0094756298,-0.0083108591,-0.1173740625,-0.1713139862,0.0468005538,0.0203509293,0.1569827646,-0.2946605682,0.2539893985,0.0822965875,-0.2376686633,-0.5970135927,0.3417876363,0.1599008292,-0.1892437637,0.2897326052,-0.1801794171,-0.0554160178,0.3803261817,-0.5858959556,-0.0945967212,0.0443135947,0.141354993,-0.4069656134,0.3347259164,0.308191359,-0.0225071684,-0.0081292214,-0.0677946508,-0.2454162687,0.0576772951,0.1356204152,0.3997930884,-0.2072359622,0.2203688174,-0.05733677,0.3448049128,-0.0246945042,-0.1513893008,0.2897460163,0.1356435865,0.424543947,-0.1676989496,-0.1270466447,-0.5524557233,-0.17350021,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1992","title":"`datasets.map` multi processing much slower than single processing ","comments":"Hi @hwijeen, you might want to look at issues #1796 and #1949. I think it could be something related to the I\/O operations being performed.","body":"Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n","comment_length":25,"text":"`datasets.map` multi processing much slower than single processing  \n Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n \n Hi @hwijeen, you might want to look at issues #1796 and #1949. I think it could be something related to the I\/O operations being performed.","embeddings":[-0.4131574631,-0.3192059696,-0.0879166499,0.3574213982,-0.1027497426,0.0203935038,0.3424057961,0.1157955751,0.0579245947,-0.0040248358,0.065698877,0.414229691,0.186818257,0.2118217349,-0.1675523669,0.0080591897,0.1905092746,-0.0455829203,0.1740608811,-0.0060444372,-0.1642414182,0.1392722279,-0.4981355667,-0.0023563658,-0.416852802,-0.1493239552,0.1035198569,0.1356756389,-0.1574589014,-0.2285336107,-0.2626928687,0.1599624604,-0.0759368762,0.5732403994,-0.0001228117,-0.1976781934,0.0128008407,0.2621522248,0.0754679441,0.0612592809,-0.1325607896,-0.2765828371,-0.049440071,-0.1092961952,0.1161134019,0.2015769333,-0.0607613437,-0.4477421343,0.0259235501,0.0857624561,0.0549793281,0.3612498343,-0.3099785149,0.0393089876,-0.3776819408,0.1687246561,-0.1538441032,0.050548397,0.2789547443,-0.1809555143,-0.1093195379,0.3070887327,-0.0712712407,0.2966097295,0.0082043353,-0.082981579,0.0533868819,-0.450948894,0.18975465,0.3490732908,0.0353319421,-0.0632039234,-0.1647021174,-0.2374163568,-0.3268585801,-0.1349690557,0.202265054,0.1401260793,0.1821072549,-0.0051691858,-0.5538257957,0.1473838985,0.3451812565,-0.0721948594,-0.1189694703,0.0112252105,0.1275280416,0.3288134634,0.3404774368,0.1203624234,-0.050015267,-0.2203397751,0.319372952,0.2178269476,-0.7854886651,-0.0352138765,0.2125976831,-0.1020861268,-0.0854895785,-0.1859987378,-0.1023483351,0.3468084037,-0.3305373192,0.0954948738,0.3070702255,-0.0764189139,0.0073991055,0.1116778031,-0.0347225517,-0.1712293178,-0.3314741254,0.0631494969,0.0929078758,-0.3245918453,-0.0648111552,0.1292852163,-0.3052825928,-0.0903897285,-0.1549672335,0.0295762606,-0.2378287911,-0.1023422629,0.1326784045,0.0770512819,-0.0129047334,0.7939462066,-0.2598311007,0.1157042608,-0.472381264,-0.5671402812,-0.0263562519,-0.1367470026,-0.372112751,0.2238743752,0.1279514879,-0.0222434774,0.140732348,0.3258404434,0.050365489,-0.2243557423,0.3272614479,-0.5305342674,0.1972386986,0.0816774815,0.1585823596,0.5495990515,-0.1144087911,0.3264811635,-0.1295257509,0.2438604385,-0.5274049044,-0.2451353967,0.1805239618,-0.0436880216,0.1031669304,0.1064126492,-0.4637281895,0.4980573356,0.3198492229,-0.218412444,-0.1693590134,-0.199179545,-0.6358721852,-0.1516774148,-0.0613874942,0.167331323,-0.330935359,0.2380714715,-0.3223769963,0.1468189508,0.4350244403,0.6122487783,-0.1885802299,0.3844541609,0.0008836694,0.1984124482,-0.0064408001,-0.076507166,-0.3745238185,0.5169079304,-0.1592252403,-0.0011449816,-0.1906943768,0.1531788558,0.2403192073,-0.0547442548,0.3251417875,0.293664664,0.0304956846,0.3874751031,-0.2230064124,-0.1107543111,0.1460555792,0.0392807163,-0.0841827542,-0.0912581235,-0.013679794,-0.29399997,0.3163222075,-0.0127959643,0.0252214484,0.4302806556,-0.3920022845,-0.151980117,-0.147237286,-0.2302908748,-0.0732991621,0.3663209975,-0.0278735459,0.0501863472,0.4308092594,0.0351639725,0.1925522983,0.1138258949,-0.0405154489,-0.0642077401,-0.1118852496,-0.080577679,-0.1543217748,-0.1789610237,-0.0090868864,0.4500130415,0.1817546785,-0.1041791365,-0.0312027484,-0.1325511336,0.0812653601,0.0562892333,-0.1986998469,-0.0696268603,0.0854894966,0.0502698235,-0.0791463181,0.2618642449,0.4633000791,-0.0539056547,0.1709706336,0.1155771986,0.3728671074,-0.0540255979,0.0484472886,-0.1458726823,0.1021566167,-0.2390238941,-0.0239049681,0.312073946,0.0839073509,0.4422298372,0.0316376686,-0.1361154616,-0.0101459734,0.2965461612,0.1542539299,0.0568739846,0.3458305299,0.3308038712,0.2301621139,0.3350547254,-0.1322713941,0.3230473697,0.6291481256,0.0487079881,-0.3480742872,0.0437146612,-0.1839314401,-0.3695401549,0.0226528216,-0.1412669867,0.5598792434,0.0200398825,0.2318464816,-0.0362140015,-0.1006811336,-0.0587041229,-0.0230610687,0.1029917896,0.2264130712,-0.1050920933,0.295919925,-0.0159352459,-0.0601109825,-0.2421924025,0.3389692605,0.2527018189,-0.2600570619,0.1031630635,-0.2926941216,0.1555970013,0.1763422191,-0.100340791,-0.2901907265,-0.2128409296,-0.1558169425,-0.0844460279,0.2200267166,-0.0549233854,0.2770826519,-0.1361938566,-0.105542168,-0.0475386642,0.0010066793,-0.1382571906,-0.1498213112,-0.0109747723,0.2863363922,0.226347819,0.0418869965,-0.0677929968,-0.2715706229,-0.0647876933,-0.2076481581,-0.1236517504,0.1410822719,0.0555924624,-0.1938761622,-0.0573871136,-0.2245586514,0.153676793,0.1814560443,-0.2490199953,-0.189970836,0.0516333245,-0.1066285148,-0.2332419008,0.0450814515,-0.1557418853,-0.0870592445,-0.1077109352,0.2935895324,-0.1721993983,0.341969043,-0.2260829508,0.0634779185,-0.0797818005,-0.1079568714,0.015877638,-0.3331030905,-0.3667624295,-0.0119899632,-0.018404644,-0.192117095,-0.1344933957,0.0897425786,0.1568120271,0.1717814654,-0.1599652022,0.1699120104,-0.3907409012,0.1194331646,0.0705737844,0.0674815774,0.4676005542,0.0276590642,0.0626560152,0.0156416148,-0.4355051219,-0.0635614619,0.3192830384,0.0368367285,0.0172181092,0.3445580304,0.0313459262,0.6403149962,0.4947387576,-0.1645864695,0.2020886242,0.0096630668,-0.1716504693,-0.4027168453,-0.218011409,0.1240068749,-0.26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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1992","title":"`datasets.map` multi processing much slower than single processing ","comments":"I see that many people are experiencing the same issue. Is this problem considered an \"official\" bug that is worth a closer look? @lhoestq","body":"Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n","comment_length":24,"text":"`datasets.map` multi processing much slower than single processing  \n Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n \n I see that many people are experiencing the same issue. Is this problem considered an \"official\" bug that is worth a closer look? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1992","title":"`datasets.map` multi processing much slower than single processing ","comments":"Yes this is an official bug. On my side I haven't managed to reproduce it but @theo-m has. We'll investigate this !","body":"Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n","comment_length":22,"text":"`datasets.map` multi processing much slower than single processing  \n Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n \n Yes this is an official bug. On my side I haven't managed to reproduce it but @theo-m has. We'll investigate this !","embeddings":[-0.4096490145,-0.304094851,-0.0847013071,0.3398919106,-0.089700602,0.0127338329,0.3527610004,0.1181460395,0.0582124367,0.0094833281,0.0727031678,0.4454630613,0.1630734205,0.1893298775,-0.1400846541,0.0302210581,0.2145635486,-0.0554908104,0.1930122077,-0.0090904068,-0.162576586,0.1556021571,-0.502361834,0.0061127511,-0.4096408486,-0.1224155501,0.1134260371,0.1184026748,-0.1367546767,-0.2299796939,-0.2356911898,0.1276423633,-0.0973548442,0.5718792081,-0.0001234951,-0.1887065619,0.039246738,0.275313586,0.0935694575,0.0743591338,-0.1399452388,-0.267549932,-0.0483155064,-0.0967941359,0.1221139655,0.2130559534,-0.0675207898,-0.4581288397,-0.0038366562,0.0869868025,0.0545999445,0.3697355986,-0.3081156015,0.0285564456,-0.3477623165,0.1482301354,-0.165335685,0.0483662859,0.2727541924,-0.1806854904,-0.1124003753,0.3173137307,-0.0748661608,0.3036655784,-0.0166217722,-0.0566674583,0.0528347939,-0.4617325664,0.1841140687,0.3488671184,0.004646197,-0.0733980536,-0.1476436257,-0.2400643229,-0.3316350877,-0.1445308179,0.2060097307,0.1393589079,0.1707959175,-0.0030378841,-0.5398437977,0.1457110643,0.356788367,-0.0727699474,-0.1326304674,0.0157895591,0.121410206,0.3247838914,0.3295474052,0.1282672286,-0.0393188782,-0.1861974001,0.3062698543,0.2130707502,-0.7936059237,-0.0484880321,0.2307216674,-0.1161449552,-0.0666975006,-0.169345215,-0.1044374928,0.3401468992,-0.3380639255,0.10364829,0.2965787351,-0.0940639079,0.0115040448,0.1055324078,-0.0219706185,-0.1720061302,-0.3138705194,0.0885760486,0.1108507439,-0.3309875727,-0.0520168133,0.126475811,-0.292132467,-0.0832153484,-0.1314231455,0.0433552377,-0.2141340226,-0.065792352,0.1219370291,0.0876592174,-0.0205289386,0.8091928959,-0.2801376283,0.1047492623,-0.4791386724,-0.5906727314,-0.018312715,-0.1310725808,-0.3497509658,0.215964213,0.1177537814,-0.0320881195,0.1305220425,0.3339615166,0.0465606414,-0.2266206741,0.3357838392,-0.5207974315,0.2082923353,0.1060209498,0.1533152759,0.5560808182,-0.1143068969,0.3106354773,-0.1215611845,0.264598012,-0.5323991179,-0.2379590422,0.1448973715,-0.0522088818,0.1052640155,0.1218633652,-0.4693053365,0.4962759018,0.3239688873,-0.241521284,-0.1572944224,-0.1935511678,-0.6807100773,-0.1566354334,-0.0482701249,0.1644489467,-0.3335207105,0.2270424664,-0.3317820728,0.1459442377,0.446377635,0.5977518559,-0.1945764571,0.3576439321,-0.0237722658,0.1998110414,-0.0121902153,-0.0591245107,-0.3943870068,0.521302402,-0.1812318712,-0.006930348,-0.2187439203,0.1681263298,0.2308537513,-0.0585272126,0.3284294307,0.274037838,0.0182395671,0.4006508589,-0.2312323153,-0.1404776722,0.1745206714,0.0220593773,-0.0664573088,-0.0973705351,-0.0106211863,-0.2823180556,0.3429299593,0.0160740335,0.0190716367,0.4239673316,-0.3839474022,-0.1766140908,-0.1408476382,-0.2507996261,-0.0771214515,0.3548492789,-0.0162000526,0.0330526866,0.436034292,0.0295186676,0.1947910041,0.1215821877,-0.0341638736,-0.0828239173,-0.1245478168,-0.0914572999,-0.1777034402,-0.1682785898,-0.0154164759,0.4407097995,0.2054557502,-0.1136585698,-0.044865191,-0.1442036033,0.0908382908,0.0958688334,-0.2234875858,-0.096726127,0.0980231315,0.0393655561,-0.0858789384,0.2614976168,0.4735931158,-0.0647708029,0.1389831007,0.088604033,0.3819343448,-0.051806163,0.0125038223,-0.1227142587,0.1179005653,-0.2445401996,-0.0177454352,0.3030709624,0.1027351022,0.4237642288,0.0093257474,-0.1245474666,0.0180587508,0.2912706733,0.1467743367,0.0439320877,0.3255986273,0.3314091265,0.2144910395,0.3231862187,-0.1254148334,0.343501389,0.636177063,0.0632761568,-0.3252539635,0.0318580419,-0.178333059,-0.3582537174,0.0271824468,-0.1357810199,0.5621811152,0.0008782812,0.2314520627,-0.0275592264,-0.1016284302,-0.0770715773,-0.0270144995,0.1100118309,0.2270747125,-0.1055658236,0.3235618174,-0.0128858238,-0.045421686,-0.2321382016,0.3384540975,0.225801006,-0.2655066848,0.1165241599,-0.3123525381,0.192536667,0.1699938029,-0.1106924117,-0.2881991267,-0.2154274285,-0.1364985555,-0.0801257566,0.1979137212,-0.0297544003,0.2590248585,-0.1393475384,-0.1104804501,-0.0400148444,0.0211554971,-0.1237185448,-0.1452609748,-0.0168919303,0.2741608322,0.2089086026,0.0379456952,-0.0567800179,-0.2843343019,-0.0929552242,-0.2236779183,-0.1036633924,0.1599895358,0.0655342788,-0.1882638186,-0.0869254544,-0.2204577029,0.1812093854,0.1581970602,-0.265048027,-0.1830227226,0.0601653159,-0.119067207,-0.2122448236,0.0102412989,-0.1542371958,-0.0883423015,-0.0925781727,0.2850442529,-0.1693919003,0.3432775438,-0.214786306,0.0628611073,-0.0864122286,-0.1067367718,-0.0121780084,-0.3401830792,-0.3741228878,-0.0052905926,-0.0206397008,-0.211735338,-0.143867299,0.0744261071,0.153165862,0.147621721,-0.1737326086,0.1924836785,-0.3921591043,0.1361328363,0.0613372996,0.0266093183,0.4794920385,0.0184000414,0.0740958676,0.0087161502,-0.4381608069,-0.0841046795,0.2906069756,0.0134121599,0.0183809716,0.3479084074,0.0413982533,0.6347267628,0.4861660004,-0.1431156844,0.192154035,-0.0242669154,-0.1544582248,-0.3937441111,-0.2161056697,0.1225521564,-0.262909323,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1992","title":"`datasets.map` multi processing much slower than single processing ","comments":"Thank you for the reply! I would be happy to follow the discussions related to the issue.\r\nIf you do not mind, could you also give a little more explanation on my p.s.2? I am having a hard time figuring out why the single processing `map` uses all of my cores.\r\n@lhoestq @theo-m ","body":"Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n","comment_length":53,"text":"`datasets.map` multi processing much slower than single processing  \n Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n \n Thank you for the reply! I would be happy to follow the discussions related to the issue.\r\nIf you do not mind, could you also give a little more explanation on my p.s.2? I am having a hard time figuring out why the single processing `map` uses all of my cores.\r\n@lhoestq @theo-m 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1992","title":"`datasets.map` multi processing much slower than single processing ","comments":"Regarding your ps2: It depends what function you pass to `map`.\r\nFor example, fast tokenizers from `transformers` in Rust tokenize texts and parallelize the tokenization over all the cores.","body":"Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n","comment_length":29,"text":"`datasets.map` multi processing much slower than single processing  \n Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n \n Regarding your ps2: It depends what function you pass to `map`.\r\nFor example, fast tokenizers from `transformers` in Rust tokenize texts and parallelize the tokenization over all the cores.","embeddings":[-0.4439504147,-0.2767181396,-0.0788298249,0.3751109242,-0.0908027664,-0.0050794897,0.3211759031,0.0643401295,-0.0458027311,-0.0092515377,0.0477654375,0.4086365104,0.2179492712,0.1524443328,-0.1457520574,-0.0019726125,0.2254880369,-0.0213480853,0.1467742175,0.017389413,-0.2218794972,0.1027975529,-0.5076216459,0.0070183063,-0.43289572,-0.1630928069,0.0852133259,0.0920830145,-0.1628730446,-0.2309202701,-0.2925642431,0.2094140053,-0.0740564167,0.5870193243,-0.0001180967,-0.1688386947,-0.0100619318,0.263231039,0.1063561291,0.0013800593,-0.0580675416,-0.2970840931,-0.0959245935,-0.0974916518,0.1089042947,0.1052417606,-0.0769347697,-0.4238997102,0.1259805709,0.0137929441,0.1001286283,0.3818843067,-0.2749175429,-0.034933567,-0.3510386348,0.1526193619,-0.1374157369,-0.0240369998,0.241744712,-0.1240661666,-0.1272667646,0.3543689549,-0.1285182983,0.2587579787,0.0789577588,-0.0478510372,0.0509219579,-0.4571819603,0.1748204231,0.4000486732,0.0125549957,-0.0977234021,-0.1687846333,-0.2309564352,-0.2791661918,-0.1573265046,0.2020871639,0.104083024,0.1339534968,-0.0070664226,-0.6016136408,0.2399581224,0.3542020917,-0.1202202514,-0.1160987765,0.0890455022,0.119870998,0.3441294134,0.3367708027,0.077030234,-0.08753106,-0.2567175329,0.2177376896,0.2279644012,-0.8232127428,-0.0756709501,0.2212411165,-0.1676312685,-0.0521672927,-0.1305063516,-0.1465456486,0.3569334149,-0.3285430372,0.1192994565,0.2981947958,-0.0345787741,0.0140539873,0.1581130326,-0.010236118,-0.2339845896,-0.312699914,0.0593383312,0.0346820764,-0.262565434,-0.0355956405,0.0848609731,-0.3532977104,-0.0575250313,-0.2106844038,0.0534194596,-0.2790293396,-0.0660476759,0.1619876027,0.1405691653,0.0106093036,0.7866417766,-0.3018142581,0.0486737937,-0.3648938239,-0.5528380275,-0.0429502241,-0.1930681467,-0.3997564018,0.2632676959,0.1055118889,0.0493484512,0.1494627297,0.2821126282,0.0765934512,-0.1413563937,0.3494647145,-0.5429766774,0.1496422738,0.0726197287,0.154560104,0.5177941918,-0.138474986,0.314902097,-0.1971454918,0.2203018218,-0.475266695,-0.2318782359,0.2151331753,0.013539426,0.0885788277,0.0796750262,-0.5455653667,0.5509293675,0.2919282317,-0.172665447,-0.1275849491,-0.1853656173,-0.6103130579,-0.1781904399,-0.0018492461,0.1226013973,-0.2417798936,0.1732539535,-0.2710019946,0.1222158447,0.4497342408,0.6742501855,-0.2196296602,0.4246218801,0.0158408955,0.3035623729,0.0449148156,-0.1139074489,-0.3109883964,0.4910986125,-0.1610904932,-0.0346942134,-0.1811563224,0.1404015571,0.3190973103,-0.0290230568,0.2742301822,0.3147366345,0.0265183486,0.3793387413,-0.1575172096,-0.1305783689,0.1962330639,0.0451965034,-0.1150431037,-0.1356102675,-0.0503521562,-0.2750127614,0.224800542,-0.0303576495,0.0487311482,0.3911471069,-0.3012832105,-0.1758214831,-0.1415572315,-0.2277328521,-0.0599743053,0.3407979012,0.0430903807,0.0639524162,0.460814625,0.0015955908,0.2276877761,0.1355304271,-0.0380949266,-0.1376335174,-0.0313482508,-0.0491703339,-0.1346637458,-0.2262031883,-0.0678611919,0.3672987521,0.2238070518,-0.0643264651,0.0883311555,-0.1257754117,0.0670795441,0.0064629288,-0.2424031049,-0.0341825634,0.0654881671,0.1089310721,-0.0454825759,0.2792190015,0.4492981434,-0.0380155668,0.1182730347,0.1675735861,0.3643091619,-0.0421776362,0.0124646351,-0.1279924363,0.0809552521,-0.229295373,-0.0607795864,0.2941325307,0.1038633659,0.4727625847,0.0048596333,-0.176206395,0.0205464344,0.2855567634,0.1121584997,0.1042972803,0.2605012655,0.3831946254,0.2929072082,0.3807497919,-0.1670197695,0.2910594642,0.5920889974,-0.0072922744,-0.3589833379,0.0816680118,-0.2233637869,-0.4092293978,0.0779984668,-0.1123960838,0.5411706567,0.0466136485,0.2133837789,-0.0555458404,-0.1274143606,-0.0524572879,-0.006476284,0.0726008639,0.1973280609,-0.0815938562,0.2709230781,0.0563166067,-0.0644898787,-0.2889431417,0.3361002803,0.2415702641,-0.2043419331,0.0760013685,-0.2641548812,0.112465322,0.1709750891,-0.0607179031,-0.21242553,-0.1904428899,-0.121468313,-0.1239397004,0.1627361178,-0.0067014256,0.2737019658,-0.0764560699,-0.0927326679,-0.0275587533,-0.0323879272,-0.1445223689,-0.1797593385,0.0104280403,0.2918093204,0.2076402009,0.1169967428,-0.0755054876,-0.232487008,-0.0665045828,-0.2315778434,-0.0556138493,0.079541795,0.0135089662,-0.1537777334,-0.0750604197,-0.2716275752,0.0461053438,0.1981592625,-0.3055951595,-0.2410659492,0.0426123813,-0.1434981525,-0.2572675049,0.0451567695,-0.1229170039,-0.0881868526,-0.1083155796,0.3268550038,-0.1563324779,0.2969707847,-0.2059313208,0.0423742197,-0.0350836627,-0.1536312848,0.0389460288,-0.3043987155,-0.3341442049,0.0511069298,-0.0429351628,-0.1612553298,-0.131100744,0.1183316559,0.1708219945,0.1495732218,-0.1346123219,0.1372227222,-0.3764241636,0.1445037723,0.0494961403,0.1159208119,0.4347307682,0.0688747242,0.0020963789,0.0667256936,-0.419267118,-0.0635158271,0.2809041142,0.0510030612,0.0156693384,0.3901453316,0.0600493215,0.6509938836,0.506364882,-0.1862688661,0.1578833163,0.0148957791,-0.1145068929,-0.4058781862,-0.1702889204,0.1095638052,-0.220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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1992","title":"`datasets.map` multi processing much slower than single processing ","comments":"I am still experiencing this issue with datasets 1.9.0..\r\nHas there been a further investigation? \r\n<img width=\"442\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/29157715\/126143387-8b5ddca2-a896-4e18-abf7-4fbf62a48b41.png\">\r\n","body":"Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n","comment_length":19,"text":"`datasets.map` multi processing much slower than single processing  \n Hi, thank you for the great library.\r\n\r\nI've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.\r\nMy data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.\r\n\r\nI noticed that `map` function with `num_proc=mp.cpu_count() \/\/2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.\r\n\r\nWhat could be the reason? I would be happy to provide information necessary to spot the reason.\r\n\r\np.s. I was experiencing the imbalance issue mentioned in [here](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-705177036) when I was using multi processing.\r\np.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. \r\n![Screen Shot 2021-03-05 at 11 04 59](https:\/\/user-images.githubusercontent.com\/29157715\/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)\r\n \n I am still experiencing this issue with datasets 1.9.0..\r\nHas there been a further investigation? \r\n<img width=\"442\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/29157715\/126143387-8b5ddca2-a896-4e18-abf7-4fbf62a48b41.png\">\r\n","embeddings":[-0.4408694506,-0.2572331727,-0.1001466885,0.3424948156,-0.1136195362,0.0254862215,0.335966289,0.1372743845,0.0238077715,-0.0266041029,0.0728430375,0.426761657,0.1649507433,0.1844829768,-0.1783974469,0.0735184923,0.1910468787,-0.0237313621,0.1997010559,-0.0175593514,-0.172121197,0.1267192066,-0.4863221943,-0.0251146331,-0.418707788,-0.1551897079,0.1128484309,0.1078351289,-0.1613447219,-0.2424036711,-0.2254241258,0.1812607646,-0.0989795253,0.5712867379,-0.0001213278,-0.1757612377,0.0262497552,0.26557675,0.0566576533,0.0632180348,-0.1749771684,-0.26562953,-0.0603124537,-0.1064622477,0.1302662641,0.1959712207,-0.0412265956,-0.4592748284,0.0279739648,0.0956204832,0.0683356822,0.3693211377,-0.3358564377,0.0190168042,-0.3772294223,0.1757356524,-0.1743134111,0.0439391397,0.2703153491,-0.189501822,-0.1327500939,0.2942250967,-0.0594553053,0.2955110371,-0.0293099452,-0.0800075904,0.0435416587,-0.4581356645,0.2109427005,0.3413070142,0.0336227156,-0.0598060898,-0.2014713734,-0.2505912185,-0.3135108352,-0.0960752293,0.1881505698,0.1586238891,0.143742457,0.000274898,-0.5647354126,0.1250050813,0.3329138756,-0.0845777914,-0.1606686711,0.0009775285,0.1214764491,0.3006643951,0.3285801113,0.1026582047,-0.0240339898,-0.2341752946,0.3186653852,0.2144552469,-0.7959993482,-0.0313890278,0.2321540564,-0.1166578382,-0.0702337921,-0.2011479288,-0.0824341848,0.3807694614,-0.3303593695,0.0957021937,0.3113458455,-0.0816923156,0.0368814319,0.1326556355,-0.0200128127,-0.1649896055,-0.3204186261,0.0855832472,0.0375055708,-0.3145132959,-0.0588686801,0.1090919524,-0.2920226157,-0.0776981115,-0.1529199481,0.0578000583,-0.2044586688,-0.0747551695,0.1519744843,0.0931527093,-0.0251007862,0.7979210615,-0.2888891995,0.0677869245,-0.4647265077,-0.5510899425,-0.0266884044,-0.1435279697,-0.3716950715,0.2059828192,0.1467278749,-0.0441316739,0.1381739825,0.2995373011,0.0675618723,-0.1942556351,0.3298442066,-0.5215236545,0.1908664405,0.089770101,0.1518644542,0.5952267051,-0.1308276206,0.3086303473,-0.114612408,0.2425247729,-0.5119680762,-0.2261331826,0.1801623553,-0.0164731424,0.0661159083,0.108579129,-0.4507105947,0.4928016961,0.310757786,-0.2005254179,-0.1917562038,-0.2191664875,-0.6590129733,-0.1549583077,-0.0378507301,0.1781466007,-0.3581593335,0.2357657999,-0.3264361322,0.1465032846,0.4354635179,0.5881886482,-0.1908946335,0.3742271364,-0.0167533867,0.2168684006,-0.005193877,-0.0612444021,-0.409089148,0.5504702926,-0.1573844999,-0.0077369595,-0.1996926665,0.132253468,0.2520763874,-0.0667857826,0.2841029167,0.2935760915,0.0093998676,0.3959366977,-0.2198728025,-0.1206632107,0.1546422243,0.07583303,-0.052067332,-0.1174090207,-0.0040198578,-0.2566078305,0.3107918203,0.0013408916,0.0485638939,0.4202738404,-0.376527369,-0.181443274,-0.1402570158,-0.2091579288,-0.089415051,0.3687587082,-0.0112051889,0.036914859,0.3978402317,0.025036376,0.1763718128,0.1276012063,-0.0591956191,-0.083436206,-0.0933097526,-0.070499301,-0.1872852594,-0.1743258983,-0.022125937,0.4432820082,0.2146649063,-0.1109524071,-0.0165356025,-0.1274927109,0.0751287788,0.0695862398,-0.186287567,-0.0800083727,0.0683454126,0.044272162,-0.0770368278,0.2573568821,0.457980603,-0.0575390272,0.1573877633,0.0783579201,0.3972081244,-0.0391156785,0.0224031191,-0.143343389,0.1175412908,-0.2493546307,-0.0373166203,0.3127611876,0.1098824888,0.4193719327,0.0704618618,-0.1443873942,0.0071856701,0.3014125526,0.1643483043,0.0150564266,0.3293915689,0.3466155529,0.2566779554,0.3388873041,-0.115452908,0.3463789821,0.6238071918,0.0678065345,-0.3361598849,0.0286232531,-0.1701198816,-0.3801422417,0.0395852625,-0.1131452098,0.5269431472,0.0170898419,0.2577416003,-0.0255265627,-0.1162775308,-0.0751271471,-0.0387657471,0.1148215756,0.2368961126,-0.0769186318,0.3169465065,-0.035439834,-0.0635434389,-0.2647989988,0.3754664958,0.2200479507,-0.2382181734,0.0989622325,-0.3365543187,0.1643039137,0.1856621504,-0.123356171,-0.2650382519,-0.2415278107,-0.1381919086,-0.0913198218,0.2174679488,-0.0295518301,0.2439494133,-0.1340822577,-0.0943095237,-0.0267706104,0.0108898859,-0.1664037853,-0.1559698433,0.0049409857,0.2704434991,0.2264301628,0.0572019555,-0.0552712008,-0.2538318932,-0.0788089633,-0.2205043435,-0.099053055,0.1591480523,0.0593895093,-0.1872386932,-0.0787216723,-0.2335619628,0.1749194413,0.1698775887,-0.2436436862,-0.1829372495,0.0668812394,-0.1159514412,-0.2466948181,0.0244951714,-0.149846226,-0.0929289088,-0.1155197471,0.2580092251,-0.1713645607,0.3255140781,-0.1932542473,0.0785440803,-0.0488123782,-0.1118045449,-0.0171387941,-0.3181794882,-0.3501659036,0.026869826,-0.0320225693,-0.2036227137,-0.1449833363,0.0673169643,0.180342719,0.1953355521,-0.1574309468,0.1874525249,-0.4001614153,0.1639227569,0.0731303617,0.0248603169,0.4335301518,0.0243834369,0.0598816089,-0.0029196024,-0.4167127311,-0.072429046,0.2753514349,0.0008795081,-0.0096099852,0.3409216404,0.0701248199,0.6287553906,0.503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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1990","title":"OSError: Memory mapping file failed: Cannot allocate memory","comments":"Do you think this is trying to bring the dataset into memory and if I can avoid it to save on memory so it only brings a batch into memory? @lhoestq  thank you","body":"Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n","comment_length":33,"text":"OSError: Memory mapping file failed: Cannot allocate memory \n Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n \n Do you think this is trying to bring the dataset into memory and if I can avoid it to save on memory so it only brings a batch into memory? @lhoestq  thank 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1990","title":"OSError: Memory mapping file failed: Cannot allocate memory","comments":"It's not trying to bring the dataset into memory.\r\n\r\nActually, it's trying to memory map the dataset file, which is different. It allows to load large dataset files without filling up memory.\r\n\r\nWhat dataset did you use to get this error ?\r\nOn what OS are you running ? What's your python and pyarrow version ?","body":"Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n","comment_length":56,"text":"OSError: Memory mapping file failed: Cannot allocate memory \n Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n \n It's not trying to bring the dataset into memory.\r\n\r\nActually, it's trying to memory map the dataset file, which is different. It allows to load large dataset files without filling up memory.\r\n\r\nWhat dataset did you use to get this error ?\r\nOn what OS are you running ? What's your python and pyarrow version ?","embeddings":[-0.2615419328,-0.0372641645,0.0520401485,0.6045719385,0.4538374543,0.2834013402,0.1456435472,0.2724665403,0.1785508692,0.1056612283,-0.0581841916,0.2273861468,-0.1771185547,-0.1459168196,-0.0370903499,-0.1933317184,0.084020853,0.0134716695,-0.5429398417,0.1604688615,-0.35815534,0.1157062352,-0.185632959,-0.1171597093,-0.2980377078,-0.1035162807,-0.0006043313,-0.0976312384,0.0188779235,-0.3274089992,0.2548563778,-0.1293895692,0.0864005461,0.5501937866,-0.0001231607,-0.0137761598,0.2835690081,-0.1672083884,-0.2533998191,-0.1284161955,-0.0416564196,-0.0297981855,0.0984970108,-0.2252508551,-0.0119242286,-0.052592624,0.2317306399,-0.34451437,0.4429748058,0.2243882269,0.1404946446,0.0699657425,0.4524520934,-0.0441127867,0.173906967,0.3341218829,0.0713807344,0.3320225477,-0.2908716202,-0.4356866479,-0.0304129403,0.3930506408,-0.039941147,0.0052876757,0.5653295517,-0.1445832551,0.0253530722,-0.3368177116,0.1123084202,-0.0072496962,0.5341086388,-0.490260005,-0.0283727888,-0.0147870975,-0.0533835031,-0.0952400044,0.2970977426,0.2689523995,-0.2722630501,-0.1447603852,-0.1304171085,-0.3249985576,-0.3197005689,0.4428686202,-0.0909217522,0.1910010129,-0.0247160532,0.3037818074,0.5653706789,-0.2050897777,-0.2340620607,-0.0758121908,0.1365436316,0.3287970722,-0.2677206099,-0.1660289764,-0.3029488623,-0.2047091722,0.3511367142,-0.4987058938,-0.3861933053,-0.1260007769,0.151669845,0.0212391727,0.4036456943,0.2817353308,-0.2963656187,0.3228372633,0.3122969866,0.1742716432,-0.2495381683,-0.1589899361,0.0767950714,-0.0373786837,-0.1110499874,-0.1914832443,0.0837740824,-0.0016539318,-0.0137460371,-0.0218034852,-0.2128316164,-0.1232094243,-0.0538497157,0.5055233836,-0.1040841639,-0.0846191123,0.373827666,0.1444597691,-0.0126846684,-0.0080771344,-0.0563751087,0.4104228318,-0.3436276913,0.284306854,0.0502635874,-0.0011586546,0.3263411224,-0.0727059469,-0.0331281535,-0.0566817522,0.1620447189,-0.3580230474,0.0087110661,0.1846879423,0.1788559556,0.2493289113,0.2503515184,-0.1479860991,-0.1526842713,0.1969967932,-0.163468048,-0.2433037609,0.038736742,0.0109290453,0.0980136842,0.2277469039,-0.277513355,0.1828102767,0.6528620124,-0.0775153115,-0.0572649427,-0.0801966265,-0.2296683937,-0.1687685549,0.2708779573,0.519313395,-0.0646530613,-0.1054837331,-0.1169700846,0.1677043438,0.2510127127,0.4413834214,-0.1638228297,0.2519159317,-0.097221449,0.0167985056,0.4141839147,-0.3105092943,-0.341904074,-0.005623993,-0.0252293199,-0.134322226,0.0232777502,0.1321309656,0.0774000436,0.1156655923,0.1119258851,0.3104183376,0.0561037064,0.2800839543,-0.3041983247,-0.2975762486,0.2528690994,0.0747464374,0.0653967336,-0.204082936,-0.0608494729,0.810767591,0.2972948551,-0.2623122931,0.1555345356,0.3147752583,0.1061372161,0.0064363666,0.0871187598,-0.2199127823,-0.2514474392,-0.0734247714,-0.0566988736,0.3305937052,-0.1451888531,-0.0414808579,0.1537224948,-0.1186705902,-0.1722497493,-0.3908631504,0.0622953624,0.0130732879,0.1235822812,0.0900844857,0.081060715,0.0560085624,-0.0939699262,0.2478925139,-0.5998023152,0.171503067,-0.2896074653,-0.107694298,-0.0214403253,-0.0624916069,0.0274150521,-0.0474654585,-0.068555817,0.1519924104,0.0771347657,-0.1671152562,-0.0906850323,0.0034200561,0.3101095259,-0.3221141994,0.1657055467,0.2421721667,0.1955043375,-0.0940227509,-0.1976960003,-0.1558783054,0.1051341444,0.3647213876,0.0791877508,0.1687752157,0.0262405463,0.0895476341,0.1797861308,-0.2070017755,0.218163535,0.01235501,0.2408843338,0.095006451,-0.0293943677,-0.3097331524,0.4583239555,0.2681971192,0.2772995234,0.2424818277,-0.4937297702,-0.0740422904,-0.065651603,-0.0718549192,0.3221566379,0.0500783995,-0.1195640638,0.1201550812,0.196337834,0.0097032078,0.3097595274,0.1743885428,0.4940783083,-0.0187082645,0.1057226509,-0.0806349367,-0.099587217,-0.1468482018,0.0500457101,0.5077639222,-0.175380379,-0.0157852471,-0.1864265651,-0.4006144702,-0.2753976882,0.164314881,-0.4642424881,-0.1369274259,-0.3849302828,0.3247582912,0.0695629567,0.2584396601,0.4160758257,-0.0507141761,0.3513425589,-0.1406714618,0.0687720701,-0.230412662,-0.1190755963,-0.0627371892,0.4409618676,-0.2204726189,0.0868323967,0.1567287594,-0.3195576966,-0.2135092765,-0.1457150131,0.1469898671,-0.0808212906,0.1255745143,0.0596423596,0.5084351897,-0.1233984753,-0.2323765159,0.1721098423,0.089907065,-0.0588723905,-0.0373000093,0.0232937448,0.1553850919,0.0540889502,-0.2931139171,-0.179757297,-0.508184433,0.4014800787,-0.033890903,0.1513629258,0.3279185891,0.1617626399,0.1156149805,-0.1213662103,0.0894483551,-0.1621506959,0.0160200186,0.3207914829,-0.1488257647,-0.2114350796,-0.0290636532,0.0627378225,0.306303829,0.2301850766,-0.6157578826,-0.0209107678,-0.1136818826,-0.0108944643,-0.0588507131,0.286311388,0.3913796842,0.078921251,0.107800208,0.1375889033,-0.1677574217,0.094906956,-0.0409829542,0.3248968422,0.2200790793,0.5234260559,0.0637268201,0.8384488225,0.341964066,0.1871405095,0.2288259119,-0.0220659021,0.0835645646,0.0049702087,-0.3481871486,-0.0082089063,-0.0368972011,0.061999395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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1990","title":"OSError: Memory mapping file failed: Cannot allocate memory","comments":"Dear @lhoestq \r\nthank you so much for coming back to me. Please find info below:\r\n1) Dataset name: I used  wikipedia with config 20200501.en\r\n2) I got these pyarrow in my environment:\r\npyarrow                   2.0.0                     <pip>\r\npyarrow                   3.0.0                     <pip>\r\n\r\n3) python version 3.7.10\r\n4) OS version \r\n\r\nlsb_release -a\r\nNo LSB modules are available.\r\nDistributor ID:\tDebian\r\nDescription:\tDebian GNU\/Linux 10 (buster)\r\nRelease:\t10\r\nCodename:\tbuster\r\n\r\n\r\nIs there a way I could solve the memory issue and if I could run this model, I am using  GeForce GTX 108, \r\nthanks \r\n","body":"Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n","comment_length":88,"text":"OSError: Memory mapping file failed: Cannot allocate memory \n Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n \n Dear @lhoestq \r\nthank you so much for coming back to me. Please find info below:\r\n1) Dataset name: I used  wikipedia with config 20200501.en\r\n2) I got these pyarrow in my environment:\r\npyarrow                   2.0.0                     <pip>\r\npyarrow                   3.0.0                     <pip>\r\n\r\n3) python version 3.7.10\r\n4) OS version \r\n\r\nlsb_release -a\r\nNo LSB modules are available.\r\nDistributor ID:\tDebian\r\nDescription:\tDebian GNU\/Linux 10 (buster)\r\nRelease:\t10\r\nCodename:\tbuster\r\n\r\n\r\nIs there a way I could solve the memory issue and if I could run this model, I am using  GeForce GTX 108, \r\nthanks \r\n","embeddings":[-0.2615419328,-0.0372641645,0.0520401485,0.6045719385,0.4538374543,0.2834013402,0.1456435472,0.2724665403,0.1785508692,0.1056612283,-0.0581841916,0.2273861468,-0.1771185547,-0.1459168196,-0.0370903499,-0.1933317184,0.084020853,0.0134716695,-0.5429398417,0.1604688615,-0.35815534,0.1157062352,-0.185632959,-0.1171597093,-0.2980377078,-0.1035162807,-0.0006043313,-0.0976312384,0.0188779235,-0.3274089992,0.2548563778,-0.1293895692,0.0864005461,0.5501937866,-0.0001231607,-0.0137761598,0.2835690081,-0.1672083884,-0.2533998191,-0.1284161955,-0.0416564196,-0.0297981855,0.0984970108,-0.2252508551,-0.0119242286,-0.052592624,0.2317306399,-0.34451437,0.4429748058,0.2243882269,0.1404946446,0.0699657425,0.4524520934,-0.0441127867,0.173906967,0.3341218829,0.0713807344,0.3320225477,-0.2908716202,-0.4356866479,-0.0304129403,0.3930506408,-0.039941147,0.0052876757,0.5653295517,-0.1445832551,0.0253530722,-0.3368177116,0.1123084202,-0.0072496962,0.5341086388,-0.490260005,-0.0283727888,-0.0147870975,-0.0533835031,-0.0952400044,0.2970977426,0.2689523995,-0.2722630501,-0.1447603852,-0.1304171085,-0.3249985576,-0.3197005689,0.4428686202,-0.0909217522,0.1910010129,-0.0247160532,0.3037818074,0.5653706789,-0.2050897777,-0.2340620607,-0.0758121908,0.1365436316,0.3287970722,-0.2677206099,-0.1660289764,-0.3029488623,-0.2047091722,0.3511367142,-0.4987058938,-0.3861933053,-0.1260007769,0.151669845,0.0212391727,0.4036456943,0.2817353308,-0.2963656187,0.3228372633,0.3122969866,0.1742716432,-0.2495381683,-0.1589899361,0.0767950714,-0.0373786837,-0.1110499874,-0.1914832443,0.0837740824,-0.0016539318,-0.0137460371,-0.0218034852,-0.2128316164,-0.1232094243,-0.0538497157,0.5055233836,-0.1040841639,-0.0846191123,0.373827666,0.1444597691,-0.0126846684,-0.0080771344,-0.0563751087,0.4104228318,-0.3436276913,0.284306854,0.0502635874,-0.0011586546,0.3263411224,-0.0727059469,-0.0331281535,-0.0566817522,0.1620447189,-0.3580230474,0.0087110661,0.1846879423,0.1788559556,0.2493289113,0.2503515184,-0.1479860991,-0.1526842713,0.1969967932,-0.163468048,-0.2433037609,0.038736742,0.0109290453,0.0980136842,0.2277469039,-0.277513355,0.1828102767,0.6528620124,-0.0775153115,-0.0572649427,-0.0801966265,-0.2296683937,-0.1687685549,0.2708779573,0.519313395,-0.0646530613,-0.1054837331,-0.1169700846,0.1677043438,0.2510127127,0.4413834214,-0.1638228297,0.2519159317,-0.097221449,0.0167985056,0.4141839147,-0.3105092943,-0.341904074,-0.005623993,-0.0252293199,-0.134322226,0.0232777502,0.1321309656,0.0774000436,0.1156655923,0.1119258851,0.3104183376,0.0561037064,0.2800839543,-0.3041983247,-0.2975762486,0.2528690994,0.0747464374,0.0653967336,-0.204082936,-0.0608494729,0.810767591,0.2972948551,-0.2623122931,0.1555345356,0.3147752583,0.1061372161,0.0064363666,0.0871187598,-0.2199127823,-0.2514474392,-0.0734247714,-0.0566988736,0.3305937052,-0.1451888531,-0.0414808579,0.1537224948,-0.1186705902,-0.1722497493,-0.3908631504,0.0622953624,0.0130732879,0.1235822812,0.0900844857,0.081060715,0.0560085624,-0.0939699262,0.2478925139,-0.5998023152,0.171503067,-0.2896074653,-0.107694298,-0.0214403253,-0.0624916069,0.0274150521,-0.0474654585,-0.068555817,0.1519924104,0.0771347657,-0.1671152562,-0.0906850323,0.0034200561,0.3101095259,-0.3221141994,0.1657055467,0.2421721667,0.1955043375,-0.0940227509,-0.1976960003,-0.1558783054,0.1051341444,0.3647213876,0.0791877508,0.1687752157,0.0262405463,0.0895476341,0.1797861308,-0.2070017755,0.218163535,0.01235501,0.2408843338,0.095006451,-0.0293943677,-0.3097331524,0.4583239555,0.2681971192,0.2772995234,0.2424818277,-0.4937297702,-0.0740422904,-0.065651603,-0.0718549192,0.3221566379,0.0500783995,-0.1195640638,0.1201550812,0.196337834,0.0097032078,0.3097595274,0.1743885428,0.4940783083,-0.0187082645,0.1057226509,-0.0806349367,-0.099587217,-0.1468482018,0.0500457101,0.5077639222,-0.175380379,-0.0157852471,-0.1864265651,-0.4006144702,-0.2753976882,0.164314881,-0.4642424881,-0.1369274259,-0.3849302828,0.3247582912,0.0695629567,0.2584396601,0.4160758257,-0.0507141761,0.3513425589,-0.1406714618,0.0687720701,-0.230412662,-0.1190755963,-0.0627371892,0.4409618676,-0.2204726189,0.0868323967,0.1567287594,-0.3195576966,-0.2135092765,-0.1457150131,0.1469898671,-0.0808212906,0.1255745143,0.0596423596,0.5084351897,-0.1233984753,-0.2323765159,0.1721098423,0.089907065,-0.0588723905,-0.0373000093,0.0232937448,0.1553850919,0.0540889502,-0.2931139171,-0.179757297,-0.508184433,0.4014800787,-0.033890903,0.1513629258,0.3279185891,0.1617626399,0.1156149805,-0.1213662103,0.0894483551,-0.1621506959,0.0160200186,0.3207914829,-0.1488257647,-0.2114350796,-0.0290636532,0.0627378225,0.306303829,0.2301850766,-0.6157578826,-0.0209107678,-0.1136818826,-0.0108944643,-0.0588507131,0.286311388,0.3913796842,0.078921251,0.107800208,0.1375889033,-0.1677574217,0.094906956,-0.0409829542,0.3248968422,0.2200790793,0.5234260559,0.0637268201,0.8384488225,0.341964066,0.1871405095,0.2288259119,-0.0220659021,0.0835645646,0.0049702087,-0.3481871486,-0.0082089063,-0.0368972011,0.061999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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1990","title":"OSError: Memory mapping file failed: Cannot allocate memory","comments":"I noticed that the error happens when loading the validation dataset.\r\nWhat value of `data_args.validation_split_percentage` did you use ?","body":"Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n","comment_length":19,"text":"OSError: Memory mapping file failed: Cannot allocate memory \n Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n \n I noticed that the error happens when loading the validation dataset.\r\nWhat value of `data_args.validation_split_percentage` did you use 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1990","title":"OSError: Memory mapping file failed: Cannot allocate memory","comments":"Dear @lhoestq \r\n\r\nthank you very much for the very sharp observation, indeed, this happens there, I use the default value of 5, I basically plan to subsample a part of the large  dataset and choose it as validation set. Do you think this is bringing the data into memory during subsampling? Is there a way I could avoid this?\r\n\r\nThank you very much for the great help.\r\n\r\n\r\nOn Mon, Mar 8, 2021 at 11:28 AM Quentin Lhoest ***@***.***>\r\nwrote:\r\n\r\n> I noticed that the error happens when loading the validation dataset.\r\n> What value of data_args.validation_split_percentage did you use ?\r\n>\r\n> \u2014\r\n> You are receiving this because you authored the thread.\r\n> Reply to this email directly, view it on GitHub\r\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/1990#issuecomment-792655644>,\r\n> or unsubscribe\r\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMS337ZUJ7HGGVVCCR3TCSREFANCNFSM4YTYAQ2A>\r\n> .\r\n>\r\n","body":"Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n","comment_length":133,"text":"OSError: Memory mapping file failed: Cannot allocate memory \n Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n \n Dear @lhoestq \r\n\r\nthank you very much for the very sharp observation, indeed, this happens there, I use the default value of 5, I basically plan to subsample a part of the large  dataset and choose it as validation set. Do you think this is bringing the data into memory during subsampling? Is there a way I could avoid this?\r\n\r\nThank you very much for the great help.\r\n\r\n\r\nOn Mon, Mar 8, 2021 at 11:28 AM Quentin Lhoest ***@***.***>\r\nwrote:\r\n\r\n> I noticed that the error happens when loading the validation dataset.\r\n> What value of data_args.validation_split_percentage did you use ?\r\n>\r\n> \u2014\r\n> You are receiving this because you authored the thread.\r\n> Reply to this email directly, view it on GitHub\r\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/1990#issuecomment-792655644>,\r\n> or unsubscribe\r\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AS37NMS337ZUJ7HGGVVCCR3TCSREFANCNFSM4YTYAQ2A>\r\n> .\r\n>\r\n","embeddings":[-0.2615419328,-0.0372641645,0.0520401485,0.6045719385,0.4538374543,0.2834013402,0.1456435472,0.2724665403,0.1785508692,0.1056612283,-0.0581841916,0.2273861468,-0.1771185547,-0.1459168196,-0.0370903499,-0.1933317184,0.084020853,0.0134716695,-0.5429398417,0.1604688615,-0.35815534,0.1157062352,-0.185632959,-0.1171597093,-0.2980377078,-0.1035162807,-0.0006043313,-0.0976312384,0.0188779235,-0.3274089992,0.2548563778,-0.1293895692,0.0864005461,0.5501937866,-0.0001231607,-0.0137761598,0.2835690081,-0.1672083884,-0.2533998191,-0.1284161955,-0.0416564196,-0.0297981855,0.0984970108,-0.2252508551,-0.0119242286,-0.052592624,0.2317306399,-0.34451437,0.4429748058,0.2243882269,0.1404946446,0.0699657425,0.4524520934,-0.0441127867,0.173906967,0.3341218829,0.0713807344,0.3320225477,-0.2908716202,-0.4356866479,-0.0304129403,0.3930506408,-0.039941147,0.0052876757,0.5653295517,-0.1445832551,0.0253530722,-0.3368177116,0.1123084202,-0.0072496962,0.5341086388,-0.490260005,-0.0283727888,-0.0147870975,-0.0533835031,-0.0952400044,0.2970977426,0.2689523995,-0.2722630501,-0.1447603852,-0.1304171085,-0.3249985576,-0.3197005689,0.4428686202,-0.0909217522,0.1910010129,-0.0247160532,0.3037818074,0.5653706789,-0.2050897777,-0.2340620607,-0.0758121908,0.1365436316,0.3287970722,-0.2677206099,-0.1660289764,-0.3029488623,-0.2047091722,0.3511367142,-0.4987058938,-0.3861933053,-0.1260007769,0.151669845,0.0212391727,0.4036456943,0.2817353308,-0.2963656187,0.3228372633,0.3122969866,0.1742716432,-0.2495381683,-0.1589899361,0.0767950714,-0.0373786837,-0.1110499874,-0.1914832443,0.0837740824,-0.0016539318,-0.0137460371,-0.0218034852,-0.2128316164,-0.1232094243,-0.0538497157,0.5055233836,-0.1040841639,-0.0846191123,0.373827666,0.1444597691,-0.0126846684,-0.0080771344,-0.0563751087,0.4104228318,-0.3436276913,0.284306854,0.0502635874,-0.0011586546,0.3263411224,-0.0727059469,-0.0331281535,-0.0566817522,0.1620447189,-0.3580230474,0.0087110661,0.1846879423,0.1788559556,0.2493289113,0.2503515184,-0.1479860991,-0.1526842713,0.1969967932,-0.163468048,-0.2433037609,0.038736742,0.0109290453,0.0980136842,0.2277469039,-0.277513355,0.1828102767,0.6528620124,-0.0775153115,-0.0572649427,-0.0801966265,-0.2296683937,-0.1687685549,0.2708779573,0.519313395,-0.0646530613,-0.1054837331,-0.1169700846,0.1677043438,0.2510127127,0.4413834214,-0.1638228297,0.2519159317,-0.097221449,0.0167985056,0.4141839147,-0.3105092943,-0.341904074,-0.005623993,-0.0252293199,-0.134322226,0.0232777502,0.1321309656,0.0774000436,0.1156655923,0.1119258851,0.3104183376,0.0561037064,0.2800839543,-0.3041983247,-0.2975762486,0.2528690994,0.0747464374,0.0653967336,-0.204082936,-0.0608494729,0.810767591,0.2972948551,-0.2623122931,0.1555345356,0.3147752583,0.1061372161,0.0064363666,0.0871187598,-0.2199127823,-0.2514474392,-0.0734247714,-0.0566988736,0.3305937052,-0.1451888531,-0.0414808579,0.1537224948,-0.1186705902,-0.1722497493,-0.3908631504,0.0622953624,0.0130732879,0.1235822812,0.0900844857,0.081060715,0.0560085624,-0.0939699262,0.2478925139,-0.5998023152,0.171503067,-0.2896074653,-0.107694298,-0.0214403253,-0.0624916069,0.0274150521,-0.0474654585,-0.068555817,0.1519924104,0.0771347657,-0.1671152562,-0.0906850323,0.0034200561,0.3101095259,-0.3221141994,0.1657055467,0.2421721667,0.1955043375,-0.0940227509,-0.1976960003,-0.1558783054,0.1051341444,0.3647213876,0.0791877508,0.1687752157,0.0262405463,0.0895476341,0.1797861308,-0.2070017755,0.218163535,0.01235501,0.2408843338,0.095006451,-0.0293943677,-0.3097331524,0.4583239555,0.2681971192,0.2772995234,0.2424818277,-0.4937297702,-0.0740422904,-0.065651603,-0.0718549192,0.3221566379,0.0500783995,-0.1195640638,0.1201550812,0.196337834,0.0097032078,0.3097595274,0.1743885428,0.4940783083,-0.0187082645,0.1057226509,-0.0806349367,-0.099587217,-0.1468482018,0.0500457101,0.5077639222,-0.175380379,-0.0157852471,-0.1864265651,-0.4006144702,-0.2753976882,0.164314881,-0.4642424881,-0.1369274259,-0.3849302828,0.3247582912,0.0695629567,0.2584396601,0.4160758257,-0.0507141761,0.3513425589,-0.1406714618,0.0687720701,-0.230412662,-0.1190755963,-0.0627371892,0.4409618676,-0.2204726189,0.0868323967,0.1567287594,-0.3195576966,-0.2135092765,-0.1457150131,0.1469898671,-0.0808212906,0.1255745143,0.0596423596,0.5084351897,-0.1233984753,-0.2323765159,0.1721098423,0.089907065,-0.0588723905,-0.0373000093,0.0232937448,0.1553850919,0.0540889502,-0.2931139171,-0.179757297,-0.508184433,0.4014800787,-0.033890903,0.1513629258,0.3279185891,0.1617626399,0.1156149805,-0.1213662103,0.0894483551,-0.1621506959,0.0160200186,0.3207914829,-0.1488257647,-0.2114350796,-0.0290636532,0.0627378225,0.306303829,0.2301850766,-0.6157578826,-0.0209107678,-0.1136818826,-0.0108944643,-0.0588507131,0.286311388,0.3913796842,0.078921251,0.107800208,0.1375889033,-0.1677574217,0.094906956,-0.0409829542,0.3248968422,0.2200790793,0.5234260559,0.0637268201,0.8384488225,0.341964066,0.1871405095,0.2288259119,-0.0220659021,0.0835645646,0.0049702087,-0.3481871486,-0.0082089063,-0.0368972011,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1990","title":"OSError: Memory mapping file failed: Cannot allocate memory","comments":"Methods like `dataset.shard`, `dataset.train_test_split`, `dataset.select` etc. don't bring the dataset in memory. \r\nThe only time when samples are brought to memory is when you access elements via `dataset[0]`, `dataset[:10]`, `dataset[\"my_column_names\"]`.\r\n\r\nBut it's possible that trying to use those methods to build your validation set doesn't fix the issue since, if I understand correctly, the error happens when when the dataset arrow file is opened (just before the 5% percentage is applied).\r\n\r\nDid you try to reproduce this issue in a google colab ? This would be super helpful to investigate why this happened.\r\n\r\nAlso maybe you can try clearing your cache at `~\/.cache\/huggingface\/datasets` and try again. If the arrow file was corrupted somehow, removing it and rebuilding may fix the issue.","body":"Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n","comment_length":121,"text":"OSError: Memory mapping file failed: Cannot allocate memory \n Hi,\r\nI am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https:\/\/github.com\/huggingface\/transformers\/blob\/v4.3.2\/examples\/language-modeling\/run_mlm.py \r\n```\r\npython run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir \/dara\/test  --max_seq_length 128\r\n```\r\n\r\nI am using transformer version: 4.3.2 \r\n\r\nBut I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset?\r\nSpecially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq  for your help and suggestions:\r\n\r\n```\r\n  File \"run_mlm.py\", line 441, in <module>\r\n    main()\r\n  File \"run_mlm.py\", line 233, in main\r\n    split=f\"train[{data_args.validation_split_percentage}%:]\",\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 740, in as_dataset\r\n    map_tuple=True,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 757, in _build_single_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 829, in _as_dataset\r\n    in_memory=in_memory,\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 215, in read\r\n    return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\n\r\n\r\n \n Methods like `dataset.shard`, `dataset.train_test_split`, `dataset.select` etc. don't bring the dataset in memory. \r\nThe only time when samples are brought to memory is when you access elements via `dataset[0]`, `dataset[:10]`, `dataset[\"my_column_names\"]`.\r\n\r\nBut it's possible that trying to use those methods to build your validation set doesn't fix the issue since, if I understand correctly, the error happens when when the dataset arrow file is opened (just before the 5% percentage is applied).\r\n\r\nDid you try to reproduce this issue in a google colab ? This would be super helpful to investigate why this happened.\r\n\r\nAlso maybe you can try clearing your cache at `~\/.cache\/huggingface\/datasets` and try again. If the arrow file was corrupted somehow, removing it and rebuilding may fix the issue.","embeddings":[-0.2615419328,-0.0372641645,0.0520401485,0.6045719385,0.4538374543,0.2834013402,0.1456435472,0.2724665403,0.1785508692,0.1056612283,-0.0581841916,0.2273861468,-0.1771185547,-0.1459168196,-0.0370903499,-0.1933317184,0.084020853,0.0134716695,-0.5429398417,0.1604688615,-0.35815534,0.1157062352,-0.185632959,-0.1171597093,-0.2980377078,-0.1035162807,-0.0006043313,-0.0976312384,0.0188779235,-0.3274089992,0.2548563778,-0.1293895692,0.0864005461,0.5501937866,-0.0001231607,-0.0137761598,0.2835690081,-0.1672083884,-0.2533998191,-0.1284161955,-0.0416564196,-0.0297981855,0.0984970108,-0.2252508551,-0.0119242286,-0.052592624,0.2317306399,-0.34451437,0.4429748058,0.2243882269,0.1404946446,0.0699657425,0.4524520934,-0.0441127867,0.173906967,0.3341218829,0.0713807344,0.3320225477,-0.2908716202,-0.4356866479,-0.0304129403,0.3930506408,-0.039941147,0.0052876757,0.5653295517,-0.1445832551,0.0253530722,-0.3368177116,0.1123084202,-0.0072496962,0.5341086388,-0.490260005,-0.0283727888,-0.0147870975,-0.0533835031,-0.0952400044,0.2970977426,0.2689523995,-0.2722630501,-0.1447603852,-0.1304171085,-0.3249985576,-0.3197005689,0.4428686202,-0.0909217522,0.1910010129,-0.0247160532,0.3037818074,0.5653706789,-0.2050897777,-0.2340620607,-0.0758121908,0.1365436316,0.3287970722,-0.2677206099,-0.1660289764,-0.3029488623,-0.2047091722,0.3511367142,-0.4987058938,-0.3861933053,-0.1260007769,0.151669845,0.0212391727,0.4036456943,0.2817353308,-0.2963656187,0.3228372633,0.3122969866,0.1742716432,-0.2495381683,-0.1589899361,0.0767950714,-0.0373786837,-0.1110499874,-0.1914832443,0.0837740824,-0.0016539318,-0.0137460371,-0.0218034852,-0.2128316164,-0.1232094243,-0.0538497157,0.5055233836,-0.1040841639,-0.0846191123,0.373827666,0.1444597691,-0.0126846684,-0.0080771344,-0.0563751087,0.4104228318,-0.3436276913,0.284306854,0.0502635874,-0.0011586546,0.3263411224,-0.0727059469,-0.0331281535,-0.0566817522,0.1620447189,-0.3580230474,0.0087110661,0.1846879423,0.1788559556,0.2493289113,0.2503515184,-0.1479860991,-0.1526842713,0.1969967932,-0.163468048,-0.2433037609,0.038736742,0.0109290453,0.0980136842,0.2277469039,-0.277513355,0.1828102767,0.6528620124,-0.0775153115,-0.0572649427,-0.0801966265,-0.2296683937,-0.1687685549,0.2708779573,0.519313395,-0.0646530613,-0.1054837331,-0.1169700846,0.1677043438,0.2510127127,0.4413834214,-0.1638228297,0.2519159317,-0.097221449,0.0167985056,0.4141839147,-0.3105092943,-0.341904074,-0.005623993,-0.0252293199,-0.134322226,0.0232777502,0.1321309656,0.0774000436,0.1156655923,0.1119258851,0.3104183376,0.0561037064,0.2800839543,-0.3041983247,-0.2975762486,0.2528690994,0.0747464374,0.0653967336,-0.204082936,-0.0608494729,0.810767591,0.2972948551,-0.2623122931,0.1555345356,0.3147752583,0.1061372161,0.0064363666,0.0871187598,-0.2199127823,-0.2514474392,-0.0734247714,-0.0566988736,0.3305937052,-0.1451888531,-0.0414808579,0.1537224948,-0.1186705902,-0.1722497493,-0.3908631504,0.0622953624,0.0130732879,0.1235822812,0.0900844857,0.081060715,0.0560085624,-0.0939699262,0.2478925139,-0.5998023152,0.171503067,-0.2896074653,-0.107694298,-0.0214403253,-0.0624916069,0.0274150521,-0.0474654585,-0.068555817,0.1519924104,0.0771347657,-0.1671152562,-0.0906850323,0.0034200561,0.3101095259,-0.3221141994,0.1657055467,0.2421721667,0.1955043375,-0.0940227509,-0.1976960003,-0.1558783054,0.1051341444,0.3647213876,0.0791877508,0.1687752157,0.0262405463,0.0895476341,0.1797861308,-0.2070017755,0.218163535,0.01235501,0.2408843338,0.095006451,-0.0293943677,-0.3097331524,0.4583239555,0.2681971192,0.2772995234,0.2424818277,-0.4937297702,-0.0740422904,-0.065651603,-0.0718549192,0.3221566379,0.0500783995,-0.1195640638,0.1201550812,0.196337834,0.0097032078,0.3097595274,0.1743885428,0.4940783083,-0.0187082645,0.1057226509,-0.0806349367,-0.099587217,-0.1468482018,0.0500457101,0.5077639222,-0.175380379,-0.0157852471,-0.1864265651,-0.4006144702,-0.2753976882,0.164314881,-0.4642424881,-0.1369274259,-0.3849302828,0.3247582912,0.0695629567,0.2584396601,0.4160758257,-0.0507141761,0.3513425589,-0.1406714618,0.0687720701,-0.230412662,-0.1190755963,-0.0627371892,0.4409618676,-0.2204726189,0.0868323967,0.1567287594,-0.3195576966,-0.2135092765,-0.1457150131,0.1469898671,-0.0808212906,0.1255745143,0.0596423596,0.5084351897,-0.1233984753,-0.2323765159,0.1721098423,0.089907065,-0.0588723905,-0.0373000093,0.0232937448,0.1553850919,0.0540889502,-0.2931139171,-0.179757297,-0.508184433,0.4014800787,-0.033890903,0.1513629258,0.3279185891,0.1617626399,0.1156149805,-0.1213662103,0.0894483551,-0.1621506959,0.0160200186,0.3207914829,-0.1488257647,-0.2114350796,-0.0290636532,0.0627378225,0.306303829,0.2301850766,-0.6157578826,-0.0209107678,-0.1136818826,-0.0108944643,-0.0588507131,0.286311388,0.3913796842,0.078921251,0.107800208,0.1375889033,-0.1677574217,0.094906956,-0.0409829542,0.3248968422,0.2200790793,0.5234260559,0.0637268201,0.8384488225,0.341964066,0.1871405095,0.2288259119,-0.0220659021,0.0835645646,0.0049702087,-0.3481871486,-0.0082089063,-0.0368972011,0.0619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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"It seems that I get parsing errors for various fields in my data. For example now I get this:\r\n```\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: https:\/\/www.netgalley.com\/catalog\/book\/121872\r\n```","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":128,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n It seems that I get parsing errors for various fields in my data. For example now I get this:\r\n```\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: https:\/\/www.netgalley.com\/catalog\/book\/121872\r\n```","embeddings":[0.1789145917,-0.057323277,0.0051793605,0.1343670487,0.4113538861,0.3502125442,0.6419943571,0.1637074947,-0.1613174826,0.072349824,0.1788543761,0.063875109,-0.0607732385,0.0305257887,-0.1302312016,-0.1193707809,0.0890839919,0.1734792441,0.1816422939,-0.0444667041,-0.2867427766,-0.0398255885,-0.1566803157,0.3662883341,-0.2972057462,-0.3766840994,0.210543111,-0.1205281168,-0.1131336242,-0.4633953273,0.2718096077,0.0861199424,0.0026804437,0.403427273,-0.0001030963,0.0587566718,0.2967477739,0.1559889019,-0.3984076679,-0.4321283996,-0.1893598735,-0.0234080609,0.0345639475,-0.304477036,0.097983405,-0.2846682072,-0.0905411765,-0.2756657004,0.1046864018,0.4386056066,0.3266401291,0.2373397052,-0.2064999342,-0.2293033898,0.2311526239,0.1250885427,-0.030509105,0.0494754016,-0.1344388425,-0.0064582322,0.451570183,0.4609141946,-0.2723953128,0.2647711635,0.1353649944,0.1463634074,0.1377247274,-0.3910478354,0.27630952,0.1961430758,0.7452605367,-0.2964998186,-0.2943909466,-0.1697050184,0.1373761445,-0.4167192578,0.1813386828,0.0700835735,0.0063757841,-0.0470148064,-0.0331626348,0.0610272661,0.0807420164,0.0881801546,-0.0580747053,0.0383287892,-0.0986413881,0.3288169205,0.1125301197,-0.0747505948,0.2975304723,-0.0059944061,-0.0885701403,0.0415447913,-0.4325243533,-0.0296081789,-0.03688097,0.0516761839,-0.1181376427,-0.0387405939,0.0183867961,-0.3749561906,-0.147435531,0.3403448462,0.2151345611,0.2662760913,0.2706991434,0.5616859198,0.0911723003,-0.0761628821,0.0308042038,-0.0558076203,-0.0015736214,-0.4626432359,0.2433211356,0.0989312381,0.2934700251,-0.2398587912,-0.5455211401,0.1924418211,-0.0281014573,0.0940958261,0.1541178674,0.3049504757,0.0221223086,0.2065178007,0.0793474615,0.1936084628,-0.0091703711,-0.1172052622,-0.2069239616,0.0976077914,-0.2002792507,-0.1501065046,0.0502693951,-0.0720527992,0.1124494746,0.0640596598,-0.0010049804,-0.0760566592,-0.0163035393,-0.3864637911,-0.0127481688,0.3588932157,-0.1032631844,0.4203503132,0.2520543039,-0.4185499847,0.0596994981,0.1802112162,-0.3688518703,-0.0338136367,-0.2121571898,0.2862130404,-0.1030440852,-0.1879812926,-0.1200915053,0.0468235761,0.4193198979,-0.2145541459,0.1575899273,-0.3543839455,-0.309322238,-0.2777701914,-0.0663041547,0.2214326262,-0.6722466946,0.0043895538,-0.1475469172,-0.0291129798,0.0528067499,0.2960192561,-0.0810045302,0.2062918693,-0.0705920011,0.0303499643,0.1395510882,-0.3906908929,-0.1104637533,0.1035439596,-0.142003268,-0.2442642599,0.070480749,-0.0403397307,0.0917311311,-0.0916265324,0.058231581,-0.1318777055,-0.1996322423,-0.0951331779,-0.1437040716,0.053811159,0.6539097428,0.0927327424,-0.0168001503,0.0756469369,-0.1002281159,-0.3448366225,-0.0425324664,-0.1009552553,0.2062191814,0.2147901803,0.0556399524,0.2983595431,0.145952329,-0.0644701123,-0.4424965978,0.1017011255,-0.040051531,0.1395646781,-0.1451429576,-0.1923436373,-0.3297650516,-0.0537857413,-0.2079764754,0.0183234606,0.1633132845,0.0683606789,-0.2214220017,-0.0204836689,0.0140847312,-0.0758842081,-0.0172138363,0.1093306914,-0.1040249839,0.2656518817,-0.095116362,0.116894044,-0.0267394595,0.2531200051,0.43260625,0.2400517166,-0.0466214903,0.3670240343,0.084860824,-0.3270230889,0.0078406269,-0.1726583391,0.0181292314,-0.0838744566,-0.0163266789,0.1145248264,0.2768397033,-0.108597219,-0.084719412,0.1411858499,-0.1772009283,0.2762840688,-0.1619747579,0.2004608065,0.2599627972,-0.1093925983,0.1251222789,-0.2127108425,0.2158810496,-0.1297983676,0.2343145311,0.007142364,-0.2617174685,0.0677190274,0.4220534861,0.0015308688,-0.0035241439,0.00977387,-0.1586679667,0.1657464951,-0.0130174728,0.5957184434,0.3528428078,0.2059558332,-0.1638755053,-0.0582680255,-0.2622591853,-0.1369839311,0.2124618143,0.0951014534,0.0298523735,0.0572964214,0.0261099748,-0.0664389655,-0.1765730977,-0.2433386892,0.2082313001,0.3464300931,-0.5103119016,-0.0029065402,-0.3452730775,-0.2318636626,-0.3185971379,-0.0987911522,-0.2351592779,-0.4590440691,0.0904380977,-0.0774912238,-0.1903926283,0.1450765133,-0.0375639684,0.0079175811,-0.0910728574,0.1520520449,-0.0975843072,-0.4702496231,-0.2045716196,0.1811123788,0.0074801398,0.0469536185,0.4813604951,-0.1431984752,-0.1245553046,-0.0785719454,-0.2953169048,0.0040265005,-0.2730655372,0.1366042942,0.1816227883,0.1038595065,0.0084614437,-0.1486846209,0.2930162847,-0.0715195164,0.0246399287,0.1069572195,0.0452331603,-0.2920734584,-0.2664699554,-0.4750190973,-0.4745302498,-0.1774818748,0.0669479668,-0.0759082735,-0.0335288644,0.5198027492,0.0684422851,0.0656808615,0.0931449682,0.0232527219,-0.3467432261,-0.0935406461,0.2490476817,-0.1790918559,-0.2102454752,0.1960575432,0.1093097106,0.1175282523,-0.2039331645,-0.3139223754,0.0728625879,-0.0576014481,0.0589448512,0.0813107938,0.1948960274,0.2138828188,0.2671016455,-0.14800331,-0.3270515203,-0.3317119777,0.138173148,-0.0719680116,0.130635187,-0.106256038,0.2940857708,-0.1520837694,0.3265088797,0.0217723008,-0.2126559913,0.1918890625,-0.0478479452,0.520732224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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"Not sure if this helps, this is how I load my files (as in the sample scripts on transformers):\r\n\r\n```\r\n    if data_args.train_file.endswith(\".csv\"):\r\n        # Loading a dataset from local csv files\r\n        datasets = load_dataset(\"csv\", data_files=data_files)\r\n```","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":35,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n Not sure if this helps, this is how I load my files (as in the sample scripts on transformers):\r\n\r\n```\r\n    if data_args.train_file.endswith(\".csv\"):\r\n        # Loading a dataset from local csv files\r\n        datasets = load_dataset(\"csv\", data_files=data_files)\r\n```","embeddings":[0.1789145917,-0.057323277,0.0051793605,0.1343670487,0.4113538861,0.3502125442,0.6419943571,0.1637074947,-0.1613174826,0.072349824,0.1788543761,0.063875109,-0.0607732385,0.0305257887,-0.1302312016,-0.1193707809,0.0890839919,0.1734792441,0.1816422939,-0.0444667041,-0.2867427766,-0.0398255885,-0.1566803157,0.3662883341,-0.2972057462,-0.3766840994,0.210543111,-0.1205281168,-0.1131336242,-0.4633953273,0.2718096077,0.0861199424,0.0026804437,0.403427273,-0.0001030963,0.0587566718,0.2967477739,0.1559889019,-0.3984076679,-0.4321283996,-0.1893598735,-0.0234080609,0.0345639475,-0.304477036,0.097983405,-0.2846682072,-0.0905411765,-0.2756657004,0.1046864018,0.4386056066,0.3266401291,0.2373397052,-0.2064999342,-0.2293033898,0.2311526239,0.1250885427,-0.030509105,0.0494754016,-0.1344388425,-0.0064582322,0.451570183,0.4609141946,-0.2723953128,0.2647711635,0.1353649944,0.1463634074,0.1377247274,-0.3910478354,0.27630952,0.1961430758,0.7452605367,-0.2964998186,-0.2943909466,-0.1697050184,0.1373761445,-0.4167192578,0.1813386828,0.0700835735,0.0063757841,-0.0470148064,-0.0331626348,0.0610272661,0.0807420164,0.0881801546,-0.0580747053,0.0383287892,-0.0986413881,0.3288169205,0.1125301197,-0.0747505948,0.2975304723,-0.0059944061,-0.0885701403,0.0415447913,-0.4325243533,-0.0296081789,-0.03688097,0.0516761839,-0.1181376427,-0.0387405939,0.0183867961,-0.3749561906,-0.147435531,0.3403448462,0.2151345611,0.2662760913,0.2706991434,0.5616859198,0.0911723003,-0.0761628821,0.0308042038,-0.0558076203,-0.0015736214,-0.4626432359,0.2433211356,0.0989312381,0.2934700251,-0.2398587912,-0.5455211401,0.1924418211,-0.0281014573,0.0940958261,0.1541178674,0.3049504757,0.0221223086,0.2065178007,0.0793474615,0.1936084628,-0.0091703711,-0.1172052622,-0.2069239616,0.0976077914,-0.2002792507,-0.1501065046,0.0502693951,-0.0720527992,0.1124494746,0.0640596598,-0.0010049804,-0.0760566592,-0.0163035393,-0.3864637911,-0.0127481688,0.3588932157,-0.1032631844,0.4203503132,0.2520543039,-0.4185499847,0.0596994981,0.1802112162,-0.3688518703,-0.0338136367,-0.2121571898,0.2862130404,-0.1030440852,-0.1879812926,-0.1200915053,0.0468235761,0.4193198979,-0.2145541459,0.1575899273,-0.3543839455,-0.309322238,-0.2777701914,-0.0663041547,0.2214326262,-0.6722466946,0.0043895538,-0.1475469172,-0.0291129798,0.0528067499,0.2960192561,-0.0810045302,0.2062918693,-0.0705920011,0.0303499643,0.1395510882,-0.3906908929,-0.1104637533,0.1035439596,-0.142003268,-0.2442642599,0.070480749,-0.0403397307,0.0917311311,-0.0916265324,0.058231581,-0.1318777055,-0.1996322423,-0.0951331779,-0.1437040716,0.053811159,0.6539097428,0.0927327424,-0.0168001503,0.0756469369,-0.1002281159,-0.3448366225,-0.0425324664,-0.1009552553,0.2062191814,0.2147901803,0.0556399524,0.2983595431,0.145952329,-0.0644701123,-0.4424965978,0.1017011255,-0.040051531,0.1395646781,-0.1451429576,-0.1923436373,-0.3297650516,-0.0537857413,-0.2079764754,0.0183234606,0.1633132845,0.0683606789,-0.2214220017,-0.0204836689,0.0140847312,-0.0758842081,-0.0172138363,0.1093306914,-0.1040249839,0.2656518817,-0.095116362,0.116894044,-0.0267394595,0.2531200051,0.43260625,0.2400517166,-0.0466214903,0.3670240343,0.084860824,-0.3270230889,0.0078406269,-0.1726583391,0.0181292314,-0.0838744566,-0.0163266789,0.1145248264,0.2768397033,-0.108597219,-0.084719412,0.1411858499,-0.1772009283,0.2762840688,-0.1619747579,0.2004608065,0.2599627972,-0.1093925983,0.1251222789,-0.2127108425,0.2158810496,-0.1297983676,0.2343145311,0.007142364,-0.2617174685,0.0677190274,0.4220534861,0.0015308688,-0.0035241439,0.00977387,-0.1586679667,0.1657464951,-0.0130174728,0.5957184434,0.3528428078,0.2059558332,-0.1638755053,-0.0582680255,-0.2622591853,-0.1369839311,0.2124618143,0.0951014534,0.0298523735,0.0572964214,0.0261099748,-0.0664389655,-0.1765730977,-0.2433386892,0.2082313001,0.3464300931,-0.5103119016,-0.0029065402,-0.3452730775,-0.2318636626,-0.3185971379,-0.0987911522,-0.2351592779,-0.4590440691,0.0904380977,-0.0774912238,-0.1903926283,0.1450765133,-0.0375639684,0.0079175811,-0.0910728574,0.1520520449,-0.0975843072,-0.4702496231,-0.2045716196,0.1811123788,0.0074801398,0.0469536185,0.4813604951,-0.1431984752,-0.1245553046,-0.0785719454,-0.2953169048,0.0040265005,-0.2730655372,0.1366042942,0.1816227883,0.1038595065,0.0084614437,-0.1486846209,0.2930162847,-0.0715195164,0.0246399287,0.1069572195,0.0452331603,-0.2920734584,-0.2664699554,-0.4750190973,-0.4745302498,-0.1774818748,0.0669479668,-0.0759082735,-0.0335288644,0.5198027492,0.0684422851,0.0656808615,0.0931449682,0.0232527219,-0.3467432261,-0.0935406461,0.2490476817,-0.1790918559,-0.2102454752,0.1960575432,0.1093097106,0.1175282523,-0.2039331645,-0.3139223754,0.0728625879,-0.0576014481,0.0589448512,0.0813107938,0.1948960274,0.2138828188,0.2671016455,-0.14800331,-0.3270515203,-0.3317119777,0.138173148,-0.0719680116,0.130635187,-0.106256038,0.2940857708,-0.1520837694,0.3265088797,0.0217723008,-0.2126559913,0.1918890625,-0.0478479452,0.520732224,0.0683805421,-0.4193749726,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"Since this worked out of the box in a few examples before, I wonder if it's some quoting issue or something else. ","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":22,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n Since this worked out of the box in a few examples before, I wonder if it's some quoting issue or something else. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"Hi @ioana-blue,\r\nCan you share a sample from your .csv? A dummy where you get this error will also help.\r\n\r\nI tried this csv:\r\n```csv\r\nfeature,label\r\n1.2,not nurse\r\n1.3,nurse\r\n1.5,surgeon\r\n```\r\nand the following snippet:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"csv\",data_files=['test.csv'])\r\n\r\nprint(d)\r\nprint(d['train']['label'])\r\n```\r\nand this works perfectly fine for me:\r\n```sh\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['feature', 'label'],\r\n        num_rows: 3\r\n    })\r\n})\r\n['not nurse', 'nurse', 'surgeon']\r\n```\r\nI'm sure your csv is more complicated than this one. But it is hard to tell where the issue might be without looking at a sample.","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":95,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n Hi @ioana-blue,\r\nCan you share a sample from your .csv? A dummy where you get this error will also help.\r\n\r\nI tried this csv:\r\n```csv\r\nfeature,label\r\n1.2,not nurse\r\n1.3,nurse\r\n1.5,surgeon\r\n```\r\nand the following snippet:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"csv\",data_files=['test.csv'])\r\n\r\nprint(d)\r\nprint(d['train']['label'])\r\n```\r\nand this works perfectly fine for me:\r\n```sh\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['feature', 'label'],\r\n        num_rows: 3\r\n    })\r\n})\r\n['not nurse', 'nurse', 'surgeon']\r\n```\r\nI'm sure your csv is more complicated than this one. But it is hard to tell where the issue might be without looking at a sample.","embeddings":[0.1789145917,-0.057323277,0.0051793605,0.1343670487,0.4113538861,0.3502125442,0.6419943571,0.1637074947,-0.1613174826,0.072349824,0.1788543761,0.063875109,-0.0607732385,0.0305257887,-0.1302312016,-0.1193707809,0.0890839919,0.1734792441,0.1816422939,-0.0444667041,-0.2867427766,-0.0398255885,-0.1566803157,0.3662883341,-0.2972057462,-0.3766840994,0.210543111,-0.1205281168,-0.1131336242,-0.4633953273,0.2718096077,0.0861199424,0.0026804437,0.403427273,-0.0001030963,0.0587566718,0.2967477739,0.1559889019,-0.3984076679,-0.4321283996,-0.1893598735,-0.0234080609,0.0345639475,-0.304477036,0.097983405,-0.2846682072,-0.0905411765,-0.2756657004,0.1046864018,0.4386056066,0.3266401291,0.2373397052,-0.2064999342,-0.2293033898,0.2311526239,0.1250885427,-0.030509105,0.0494754016,-0.1344388425,-0.0064582322,0.451570183,0.4609141946,-0.2723953128,0.2647711635,0.1353649944,0.1463634074,0.1377247274,-0.3910478354,0.27630952,0.1961430758,0.7452605367,-0.2964998186,-0.2943909466,-0.1697050184,0.1373761445,-0.4167192578,0.1813386828,0.0700835735,0.0063757841,-0.0470148064,-0.0331626348,0.0610272661,0.0807420164,0.0881801546,-0.0580747053,0.0383287892,-0.0986413881,0.3288169205,0.1125301197,-0.0747505948,0.2975304723,-0.0059944061,-0.0885701403,0.0415447913,-0.4325243533,-0.0296081789,-0.03688097,0.0516761839,-0.1181376427,-0.0387405939,0.0183867961,-0.3749561906,-0.147435531,0.3403448462,0.2151345611,0.2662760913,0.2706991434,0.5616859198,0.0911723003,-0.0761628821,0.0308042038,-0.0558076203,-0.0015736214,-0.4626432359,0.2433211356,0.0989312381,0.2934700251,-0.2398587912,-0.5455211401,0.1924418211,-0.0281014573,0.0940958261,0.1541178674,0.3049504757,0.0221223086,0.2065178007,0.0793474615,0.1936084628,-0.0091703711,-0.1172052622,-0.2069239616,0.0976077914,-0.2002792507,-0.1501065046,0.0502693951,-0.0720527992,0.1124494746,0.0640596598,-0.0010049804,-0.0760566592,-0.0163035393,-0.3864637911,-0.0127481688,0.3588932157,-0.1032631844,0.4203503132,0.2520543039,-0.4185499847,0.0596994981,0.1802112162,-0.3688518703,-0.0338136367,-0.2121571898,0.2862130404,-0.1030440852,-0.1879812926,-0.1200915053,0.0468235761,0.4193198979,-0.2145541459,0.1575899273,-0.3543839455,-0.309322238,-0.2777701914,-0.0663041547,0.2214326262,-0.6722466946,0.0043895538,-0.1475469172,-0.0291129798,0.0528067499,0.2960192561,-0.0810045302,0.2062918693,-0.0705920011,0.0303499643,0.1395510882,-0.3906908929,-0.1104637533,0.1035439596,-0.142003268,-0.2442642599,0.070480749,-0.0403397307,0.0917311311,-0.0916265324,0.058231581,-0.1318777055,-0.1996322423,-0.0951331779,-0.1437040716,0.053811159,0.6539097428,0.0927327424,-0.0168001503,0.0756469369,-0.1002281159,-0.3448366225,-0.0425324664,-0.1009552553,0.2062191814,0.2147901803,0.0556399524,0.2983595431,0.145952329,-0.0644701123,-0.4424965978,0.1017011255,-0.040051531,0.1395646781,-0.1451429576,-0.1923436373,-0.3297650516,-0.0537857413,-0.2079764754,0.0183234606,0.1633132845,0.0683606789,-0.2214220017,-0.0204836689,0.0140847312,-0.0758842081,-0.0172138363,0.1093306914,-0.1040249839,0.2656518817,-0.095116362,0.116894044,-0.0267394595,0.2531200051,0.43260625,0.2400517166,-0.0466214903,0.3670240343,0.084860824,-0.3270230889,0.0078406269,-0.1726583391,0.0181292314,-0.0838744566,-0.0163266789,0.1145248264,0.2768397033,-0.108597219,-0.084719412,0.1411858499,-0.1772009283,0.2762840688,-0.1619747579,0.2004608065,0.2599627972,-0.1093925983,0.1251222789,-0.2127108425,0.2158810496,-0.1297983676,0.2343145311,0.007142364,-0.2617174685,0.0677190274,0.4220534861,0.0015308688,-0.0035241439,0.00977387,-0.1586679667,0.1657464951,-0.0130174728,0.5957184434,0.3528428078,0.2059558332,-0.1638755053,-0.0582680255,-0.2622591853,-0.1369839311,0.2124618143,0.0951014534,0.0298523735,0.0572964214,0.0261099748,-0.0664389655,-0.1765730977,-0.2433386892,0.2082313001,0.3464300931,-0.5103119016,-0.0029065402,-0.3452730775,-0.2318636626,-0.3185971379,-0.0987911522,-0.2351592779,-0.4590440691,0.0904380977,-0.0774912238,-0.1903926283,0.1450765133,-0.0375639684,0.0079175811,-0.0910728574,0.1520520449,-0.0975843072,-0.4702496231,-0.2045716196,0.1811123788,0.0074801398,0.0469536185,0.4813604951,-0.1431984752,-0.1245553046,-0.0785719454,-0.2953169048,0.0040265005,-0.2730655372,0.1366042942,0.1816227883,0.1038595065,0.0084614437,-0.1486846209,0.2930162847,-0.0715195164,0.0246399287,0.1069572195,0.0452331603,-0.2920734584,-0.2664699554,-0.4750190973,-0.4745302498,-0.1774818748,0.0669479668,-0.0759082735,-0.0335288644,0.5198027492,0.0684422851,0.0656808615,0.0931449682,0.0232527219,-0.3467432261,-0.0935406461,0.2490476817,-0.1790918559,-0.2102454752,0.1960575432,0.1093097106,0.1175282523,-0.2039331645,-0.3139223754,0.0728625879,-0.0576014481,0.0589448512,0.0813107938,0.1948960274,0.2138828188,0.2671016455,-0.14800331,-0.3270515203,-0.3317119777,0.138173148,-0.0719680116,0.130635187,-0.106256038,0.2940857708,-0.1520837694,0.3265088797,0.0217723008,-0.2126559913,0.1918890625,-0.0478479452,0.520732224,0.0683805421,-0.4193749726,0.0658850446,-0.119107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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"I've had versions where it worked fain. For this dataset, I had all kind of parsing issues that I couldn't understand. What I ended up doing is strip all the columns that I didn't need and also make the label 0\/1. \r\n\r\nI think one line that may have caused a problem was the csv version of this:\r\n\r\n```crawl-data\/CC-MAIN-2017-47\/segments\/1510934806225.78\/wet\/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz        Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job. ^M ('Rose', '', 'Blakey')  journalist      F       38      journalist      https:\/\/www.netgalley.com\/catalog\/book\/121872   _ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job.    She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.```\r\n\r\nThe error I got in this case is this one: https:\/\/github.com\/huggingface\/datasets\/issues\/1989#issuecomment-790842771\r\n\r\nNote, this line was part of a much larger file and until this line I guess it was working fine. ","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":197,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n I've had versions where it worked fain. For this dataset, I had all kind of parsing issues that I couldn't understand. What I ended up doing is strip all the columns that I didn't need and also make the label 0\/1. \r\n\r\nI think one line that may have caused a problem was the csv version of this:\r\n\r\n```crawl-data\/CC-MAIN-2017-47\/segments\/1510934806225.78\/wet\/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz        Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job. ^M ('Rose', '', 'Blakey')  journalist      F       38      journalist      https:\/\/www.netgalley.com\/catalog\/book\/121872   _ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job.    She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.```\r\n\r\nThe error I got in this case is this one: https:\/\/github.com\/huggingface\/datasets\/issues\/1989#issuecomment-790842771\r\n\r\nNote, this line was part of a much larger file and until this line I guess it was working fine. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"Hi @ioana-blue,\r\n\r\nWhat is the separator you're using for the csv? I see there are only two commas in the given line, but they don't seem like appropriate points. Also, is this a string part of one line, or an entire line? There should also be a label, right?","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":49,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n Hi @ioana-blue,\r\n\r\nWhat is the separator you're using for the csv? I see there are only two commas in the given line, but they don't seem like appropriate points. Also, is this a string part of one line, or an entire line? There should also be a label, right?","embeddings":[0.1789145917,-0.057323277,0.0051793605,0.1343670487,0.4113538861,0.3502125442,0.6419943571,0.1637074947,-0.1613174826,0.072349824,0.1788543761,0.063875109,-0.0607732385,0.0305257887,-0.1302312016,-0.1193707809,0.0890839919,0.1734792441,0.1816422939,-0.0444667041,-0.2867427766,-0.0398255885,-0.1566803157,0.3662883341,-0.2972057462,-0.3766840994,0.210543111,-0.1205281168,-0.1131336242,-0.4633953273,0.2718096077,0.0861199424,0.0026804437,0.403427273,-0.0001030963,0.0587566718,0.2967477739,0.1559889019,-0.3984076679,-0.4321283996,-0.1893598735,-0.0234080609,0.0345639475,-0.304477036,0.097983405,-0.2846682072,-0.0905411765,-0.2756657004,0.1046864018,0.4386056066,0.3266401291,0.2373397052,-0.2064999342,-0.2293033898,0.2311526239,0.1250885427,-0.030509105,0.0494754016,-0.1344388425,-0.0064582322,0.451570183,0.4609141946,-0.2723953128,0.2647711635,0.1353649944,0.1463634074,0.1377247274,-0.3910478354,0.27630952,0.1961430758,0.7452605367,-0.2964998186,-0.2943909466,-0.1697050184,0.1373761445,-0.4167192578,0.1813386828,0.0700835735,0.0063757841,-0.0470148064,-0.0331626348,0.0610272661,0.0807420164,0.0881801546,-0.0580747053,0.0383287892,-0.0986413881,0.3288169205,0.1125301197,-0.0747505948,0.2975304723,-0.0059944061,-0.0885701403,0.0415447913,-0.4325243533,-0.0296081789,-0.03688097,0.0516761839,-0.1181376427,-0.0387405939,0.0183867961,-0.3749561906,-0.147435531,0.3403448462,0.2151345611,0.2662760913,0.2706991434,0.5616859198,0.0911723003,-0.0761628821,0.0308042038,-0.0558076203,-0.0015736214,-0.4626432359,0.2433211356,0.0989312381,0.2934700251,-0.2398587912,-0.5455211401,0.1924418211,-0.0281014573,0.0940958261,0.1541178674,0.3049504757,0.0221223086,0.2065178007,0.0793474615,0.1936084628,-0.0091703711,-0.1172052622,-0.2069239616,0.0976077914,-0.2002792507,-0.1501065046,0.0502693951,-0.0720527992,0.1124494746,0.0640596598,-0.0010049804,-0.0760566592,-0.0163035393,-0.3864637911,-0.0127481688,0.3588932157,-0.1032631844,0.4203503132,0.2520543039,-0.4185499847,0.0596994981,0.1802112162,-0.3688518703,-0.0338136367,-0.2121571898,0.2862130404,-0.1030440852,-0.1879812926,-0.1200915053,0.0468235761,0.4193198979,-0.2145541459,0.1575899273,-0.3543839455,-0.309322238,-0.2777701914,-0.0663041547,0.2214326262,-0.6722466946,0.0043895538,-0.1475469172,-0.0291129798,0.0528067499,0.2960192561,-0.0810045302,0.2062918693,-0.0705920011,0.0303499643,0.1395510882,-0.3906908929,-0.1104637533,0.1035439596,-0.142003268,-0.2442642599,0.070480749,-0.0403397307,0.0917311311,-0.0916265324,0.058231581,-0.1318777055,-0.1996322423,-0.0951331779,-0.1437040716,0.053811159,0.6539097428,0.0927327424,-0.0168001503,0.0756469369,-0.1002281159,-0.3448366225,-0.0425324664,-0.1009552553,0.2062191814,0.2147901803,0.0556399524,0.2983595431,0.145952329,-0.0644701123,-0.4424965978,0.1017011255,-0.040051531,0.1395646781,-0.1451429576,-0.1923436373,-0.3297650516,-0.0537857413,-0.2079764754,0.0183234606,0.1633132845,0.0683606789,-0.2214220017,-0.0204836689,0.0140847312,-0.0758842081,-0.0172138363,0.1093306914,-0.1040249839,0.2656518817,-0.095116362,0.116894044,-0.0267394595,0.2531200051,0.43260625,0.2400517166,-0.0466214903,0.3670240343,0.084860824,-0.3270230889,0.0078406269,-0.1726583391,0.0181292314,-0.0838744566,-0.0163266789,0.1145248264,0.2768397033,-0.108597219,-0.084719412,0.1411858499,-0.1772009283,0.2762840688,-0.1619747579,0.2004608065,0.2599627972,-0.1093925983,0.1251222789,-0.2127108425,0.2158810496,-0.1297983676,0.2343145311,0.007142364,-0.2617174685,0.0677190274,0.4220534861,0.0015308688,-0.0035241439,0.00977387,-0.1586679667,0.1657464951,-0.0130174728,0.5957184434,0.3528428078,0.2059558332,-0.1638755053,-0.0582680255,-0.2622591853,-0.1369839311,0.2124618143,0.0951014534,0.0298523735,0.0572964214,0.0261099748,-0.0664389655,-0.1765730977,-0.2433386892,0.2082313001,0.3464300931,-0.5103119016,-0.0029065402,-0.3452730775,-0.2318636626,-0.3185971379,-0.0987911522,-0.2351592779,-0.4590440691,0.0904380977,-0.0774912238,-0.1903926283,0.1450765133,-0.0375639684,0.0079175811,-0.0910728574,0.1520520449,-0.0975843072,-0.4702496231,-0.2045716196,0.1811123788,0.0074801398,0.0469536185,0.4813604951,-0.1431984752,-0.1245553046,-0.0785719454,-0.2953169048,0.0040265005,-0.2730655372,0.1366042942,0.1816227883,0.1038595065,0.0084614437,-0.1486846209,0.2930162847,-0.0715195164,0.0246399287,0.1069572195,0.0452331603,-0.2920734584,-0.2664699554,-0.4750190973,-0.4745302498,-0.1774818748,0.0669479668,-0.0759082735,-0.0335288644,0.5198027492,0.0684422851,0.0656808615,0.0931449682,0.0232527219,-0.3467432261,-0.0935406461,0.2490476817,-0.1790918559,-0.2102454752,0.1960575432,0.1093097106,0.1175282523,-0.2039331645,-0.3139223754,0.0728625879,-0.0576014481,0.0589448512,0.0813107938,0.1948960274,0.2138828188,0.2671016455,-0.14800331,-0.3270515203,-0.3317119777,0.138173148,-0.0719680116,0.130635187,-0.106256038,0.2940857708,-0.1520837694,0.3265088797,0.0217723008,-0.2126559913,0.1918890625,-0.0478479452,0.520732224,0.0683805421,-0.4193749726,0.0658850446,-0.1191072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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"Sorry for the confusion, the sample above was from a tsv that was used to derive the csv. Let me construct the csv again (I had remove it). \r\n\r\nThis is the line in the csv - this is the whole line:\r\n```crawl-data\/CC-MAIN-2017-47\/segments\/1510934806225.78\/wet\/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz,Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead,\"('Rose', '', 'Blakey')\",journalist,F,38,journalist,https:\/\/www.netgalley.com\/catalog\/book\/121872,_ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job., She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.```","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":139,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n Sorry for the confusion, the sample above was from a tsv that was used to derive the csv. Let me construct the csv again (I had remove it). \r\n\r\nThis is the line in the csv - this is the whole line:\r\n```crawl-data\/CC-MAIN-2017-47\/segments\/1510934806225.78\/wet\/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz,Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead,\"('Rose', '', 'Blakey')\",journalist,F,38,journalist,https:\/\/www.netgalley.com\/catalog\/book\/121872,_ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job., She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.```","embeddings":[0.1789145917,-0.057323277,0.0051793605,0.1343670487,0.4113538861,0.3502125442,0.6419943571,0.1637074947,-0.1613174826,0.072349824,0.1788543761,0.063875109,-0.0607732385,0.0305257887,-0.1302312016,-0.1193707809,0.0890839919,0.1734792441,0.1816422939,-0.0444667041,-0.2867427766,-0.0398255885,-0.1566803157,0.3662883341,-0.2972057462,-0.3766840994,0.210543111,-0.1205281168,-0.1131336242,-0.4633953273,0.2718096077,0.0861199424,0.0026804437,0.403427273,-0.0001030963,0.0587566718,0.2967477739,0.1559889019,-0.3984076679,-0.4321283996,-0.1893598735,-0.0234080609,0.0345639475,-0.304477036,0.097983405,-0.2846682072,-0.0905411765,-0.2756657004,0.1046864018,0.4386056066,0.3266401291,0.2373397052,-0.2064999342,-0.2293033898,0.2311526239,0.1250885427,-0.030509105,0.0494754016,-0.1344388425,-0.0064582322,0.451570183,0.4609141946,-0.2723953128,0.2647711635,0.1353649944,0.1463634074,0.1377247274,-0.3910478354,0.27630952,0.1961430758,0.7452605367,-0.2964998186,-0.2943909466,-0.1697050184,0.1373761445,-0.4167192578,0.1813386828,0.0700835735,0.0063757841,-0.0470148064,-0.0331626348,0.0610272661,0.0807420164,0.0881801546,-0.0580747053,0.0383287892,-0.0986413881,0.3288169205,0.1125301197,-0.0747505948,0.2975304723,-0.0059944061,-0.0885701403,0.0415447913,-0.4325243533,-0.0296081789,-0.03688097,0.0516761839,-0.1181376427,-0.0387405939,0.0183867961,-0.3749561906,-0.147435531,0.3403448462,0.2151345611,0.2662760913,0.2706991434,0.5616859198,0.0911723003,-0.0761628821,0.0308042038,-0.0558076203,-0.0015736214,-0.4626432359,0.2433211356,0.0989312381,0.2934700251,-0.2398587912,-0.5455211401,0.1924418211,-0.0281014573,0.0940958261,0.1541178674,0.3049504757,0.0221223086,0.2065178007,0.0793474615,0.1936084628,-0.0091703711,-0.1172052622,-0.2069239616,0.0976077914,-0.2002792507,-0.1501065046,0.0502693951,-0.0720527992,0.1124494746,0.0640596598,-0.0010049804,-0.0760566592,-0.0163035393,-0.3864637911,-0.0127481688,0.3588932157,-0.1032631844,0.4203503132,0.2520543039,-0.4185499847,0.0596994981,0.1802112162,-0.3688518703,-0.0338136367,-0.2121571898,0.2862130404,-0.1030440852,-0.1879812926,-0.1200915053,0.0468235761,0.4193198979,-0.2145541459,0.1575899273,-0.3543839455,-0.309322238,-0.2777701914,-0.0663041547,0.2214326262,-0.6722466946,0.0043895538,-0.1475469172,-0.0291129798,0.0528067499,0.2960192561,-0.0810045302,0.2062918693,-0.0705920011,0.0303499643,0.1395510882,-0.3906908929,-0.1104637533,0.1035439596,-0.142003268,-0.2442642599,0.070480749,-0.0403397307,0.0917311311,-0.0916265324,0.058231581,-0.1318777055,-0.1996322423,-0.0951331779,-0.1437040716,0.053811159,0.6539097428,0.0927327424,-0.0168001503,0.0756469369,-0.1002281159,-0.3448366225,-0.0425324664,-0.1009552553,0.2062191814,0.2147901803,0.0556399524,0.2983595431,0.145952329,-0.0644701123,-0.4424965978,0.1017011255,-0.040051531,0.1395646781,-0.1451429576,-0.1923436373,-0.3297650516,-0.0537857413,-0.2079764754,0.0183234606,0.1633132845,0.0683606789,-0.2214220017,-0.0204836689,0.0140847312,-0.0758842081,-0.0172138363,0.1093306914,-0.1040249839,0.2656518817,-0.095116362,0.116894044,-0.0267394595,0.2531200051,0.43260625,0.2400517166,-0.0466214903,0.3670240343,0.084860824,-0.3270230889,0.0078406269,-0.1726583391,0.0181292314,-0.0838744566,-0.0163266789,0.1145248264,0.2768397033,-0.108597219,-0.084719412,0.1411858499,-0.1772009283,0.2762840688,-0.1619747579,0.2004608065,0.2599627972,-0.1093925983,0.1251222789,-0.2127108425,0.2158810496,-0.1297983676,0.2343145311,0.007142364,-0.2617174685,0.0677190274,0.4220534861,0.0015308688,-0.0035241439,0.00977387,-0.1586679667,0.1657464951,-0.0130174728,0.5957184434,0.3528428078,0.2059558332,-0.1638755053,-0.0582680255,-0.2622591853,-0.1369839311,0.2124618143,0.0951014534,0.0298523735,0.0572964214,0.0261099748,-0.0664389655,-0.1765730977,-0.2433386892,0.2082313001,0.3464300931,-0.5103119016,-0.0029065402,-0.3452730775,-0.2318636626,-0.3185971379,-0.0987911522,-0.2351592779,-0.4590440691,0.0904380977,-0.0774912238,-0.1903926283,0.1450765133,-0.0375639684,0.0079175811,-0.0910728574,0.1520520449,-0.0975843072,-0.4702496231,-0.2045716196,0.1811123788,0.0074801398,0.0469536185,0.4813604951,-0.1431984752,-0.1245553046,-0.0785719454,-0.2953169048,0.0040265005,-0.2730655372,0.1366042942,0.1816227883,0.1038595065,0.0084614437,-0.1486846209,0.2930162847,-0.0715195164,0.0246399287,0.1069572195,0.0452331603,-0.2920734584,-0.2664699554,-0.4750190973,-0.4745302498,-0.1774818748,0.0669479668,-0.0759082735,-0.0335288644,0.5198027492,0.0684422851,0.0656808615,0.0931449682,0.0232527219,-0.3467432261,-0.0935406461,0.2490476817,-0.1790918559,-0.2102454752,0.1960575432,0.1093097106,0.1175282523,-0.2039331645,-0.3139223754,0.0728625879,-0.0576014481,0.0589448512,0.0813107938,0.1948960274,0.2138828188,0.2671016455,-0.14800331,-0.3270515203,-0.3317119777,0.138173148,-0.0719680116,0.130635187,-0.106256038,0.2940857708,-0.1520837694,0.3265088797,0.0217723008,-0.2126559913,0.1918890625,-0.0478479452,0.520732224,0.0683805421,-0.4193749726,0.0658850446,-0.119107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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"Hi,\r\nJust in case you want to use tsv directly, you can use the separator argument while loading the dataset.\r\n```python\r\nd = load_dataset(\"csv\",data_files=['test.csv'],sep=\"\\t\")\r\n```\r\n\r\nAdditionally, I don't face the issues with the following csv (same as the one you provided):\r\n\r\n```sh\r\nlink1,text1,info1,info2,info3,info4,info5,link2,text2,text3\r\ncrawl-data\/CC-MAIN-2017-47\/segments\/1510934806225.78\/wet\/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz,Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead,\"('Rose', '', 'Blakey')\",journalist,F,38,journalist,https:\/\/www.netgalley.com\/catalog\/book\/121872,_ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job., She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.\r\n```\r\nOutput after loading:\r\n```sh\r\n{'link1': 'crawl-data\/CC-MAIN-2017-47\/segments\/1510934806225.78\/wet\/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz', 'text1': 'Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead', 'info1': \"('Rose', '', 'Blakey')\", 'info2': 'journalist', 'info3': 'F', 'info4': 38, 'info5': 'journalist', 'link2': 'https:\/\/www.netgalley.com\/catalog\/book\/121872', 'text2': '_ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job.', 'text3': ' She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.'}\r\n```\r\nCan you check once if the tsv works for you directly using the separator argument? The conversion from tsv to csv could create issues, I'm only guessing though.","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":292,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n Hi,\r\nJust in case you want to use tsv directly, you can use the separator argument while loading the dataset.\r\n```python\r\nd = load_dataset(\"csv\",data_files=['test.csv'],sep=\"\\t\")\r\n```\r\n\r\nAdditionally, I don't face the issues with the following csv (same as the one you provided):\r\n\r\n```sh\r\nlink1,text1,info1,info2,info3,info4,info5,link2,text2,text3\r\ncrawl-data\/CC-MAIN-2017-47\/segments\/1510934806225.78\/wet\/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz,Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead,\"('Rose', '', 'Blakey')\",journalist,F,38,journalist,https:\/\/www.netgalley.com\/catalog\/book\/121872,_ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job., She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.\r\n```\r\nOutput after loading:\r\n```sh\r\n{'link1': 'crawl-data\/CC-MAIN-2017-47\/segments\/1510934806225.78\/wet\/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz', 'text1': 'Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead', 'info1': \"('Rose', '', 'Blakey')\", 'info2': 'journalist', 'info3': 'F', 'info4': 38, 'info5': 'journalist', 'link2': 'https:\/\/www.netgalley.com\/catalog\/book\/121872', 'text2': '_ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job.', 'text3': ' She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.'}\r\n```\r\nCan you check once if the tsv works for you directly using the separator argument? The conversion from tsv to csv could create issues, I'm only guessing though.","embeddings":[0.1789145917,-0.057323277,0.0051793605,0.1343670487,0.4113538861,0.3502125442,0.6419943571,0.1637074947,-0.1613174826,0.072349824,0.1788543761,0.063875109,-0.0607732385,0.0305257887,-0.1302312016,-0.1193707809,0.0890839919,0.1734792441,0.1816422939,-0.0444667041,-0.2867427766,-0.0398255885,-0.1566803157,0.3662883341,-0.2972057462,-0.3766840994,0.210543111,-0.1205281168,-0.1131336242,-0.4633953273,0.2718096077,0.0861199424,0.0026804437,0.403427273,-0.0001030963,0.0587566718,0.2967477739,0.1559889019,-0.3984076679,-0.4321283996,-0.1893598735,-0.0234080609,0.0345639475,-0.304477036,0.097983405,-0.2846682072,-0.0905411765,-0.2756657004,0.1046864018,0.4386056066,0.3266401291,0.2373397052,-0.2064999342,-0.2293033898,0.2311526239,0.1250885427,-0.030509105,0.0494754016,-0.1344388425,-0.0064582322,0.451570183,0.4609141946,-0.2723953128,0.2647711635,0.1353649944,0.1463634074,0.1377247274,-0.3910478354,0.27630952,0.1961430758,0.7452605367,-0.2964998186,-0.2943909466,-0.1697050184,0.1373761445,-0.4167192578,0.1813386828,0.0700835735,0.0063757841,-0.0470148064,-0.0331626348,0.0610272661,0.0807420164,0.0881801546,-0.0580747053,0.0383287892,-0.0986413881,0.3288169205,0.1125301197,-0.0747505948,0.2975304723,-0.0059944061,-0.0885701403,0.0415447913,-0.4325243533,-0.0296081789,-0.03688097,0.0516761839,-0.1181376427,-0.0387405939,0.0183867961,-0.3749561906,-0.147435531,0.3403448462,0.2151345611,0.2662760913,0.2706991434,0.5616859198,0.0911723003,-0.0761628821,0.0308042038,-0.0558076203,-0.0015736214,-0.4626432359,0.2433211356,0.0989312381,0.2934700251,-0.2398587912,-0.5455211401,0.1924418211,-0.0281014573,0.0940958261,0.1541178674,0.3049504757,0.0221223086,0.2065178007,0.0793474615,0.1936084628,-0.0091703711,-0.1172052622,-0.2069239616,0.0976077914,-0.2002792507,-0.1501065046,0.0502693951,-0.0720527992,0.1124494746,0.0640596598,-0.0010049804,-0.0760566592,-0.0163035393,-0.3864637911,-0.0127481688,0.3588932157,-0.1032631844,0.4203503132,0.2520543039,-0.4185499847,0.0596994981,0.1802112162,-0.3688518703,-0.0338136367,-0.2121571898,0.2862130404,-0.1030440852,-0.1879812926,-0.1200915053,0.0468235761,0.4193198979,-0.2145541459,0.1575899273,-0.3543839455,-0.309322238,-0.2777701914,-0.0663041547,0.2214326262,-0.6722466946,0.0043895538,-0.1475469172,-0.0291129798,0.0528067499,0.2960192561,-0.0810045302,0.2062918693,-0.0705920011,0.0303499643,0.1395510882,-0.3906908929,-0.1104637533,0.1035439596,-0.142003268,-0.2442642599,0.070480749,-0.0403397307,0.0917311311,-0.0916265324,0.058231581,-0.1318777055,-0.1996322423,-0.0951331779,-0.1437040716,0.053811159,0.6539097428,0.0927327424,-0.0168001503,0.0756469369,-0.1002281159,-0.3448366225,-0.0425324664,-0.1009552553,0.2062191814,0.2147901803,0.0556399524,0.2983595431,0.145952329,-0.0644701123,-0.4424965978,0.1017011255,-0.040051531,0.1395646781,-0.1451429576,-0.1923436373,-0.3297650516,-0.0537857413,-0.2079764754,0.0183234606,0.1633132845,0.0683606789,-0.2214220017,-0.0204836689,0.0140847312,-0.0758842081,-0.0172138363,0.1093306914,-0.1040249839,0.2656518817,-0.095116362,0.116894044,-0.0267394595,0.2531200051,0.43260625,0.2400517166,-0.0466214903,0.3670240343,0.084860824,-0.3270230889,0.0078406269,-0.1726583391,0.0181292314,-0.0838744566,-0.0163266789,0.1145248264,0.2768397033,-0.108597219,-0.084719412,0.1411858499,-0.1772009283,0.2762840688,-0.1619747579,0.2004608065,0.2599627972,-0.1093925983,0.1251222789,-0.2127108425,0.2158810496,-0.1297983676,0.2343145311,0.007142364,-0.2617174685,0.0677190274,0.4220534861,0.0015308688,-0.0035241439,0.00977387,-0.1586679667,0.1657464951,-0.0130174728,0.5957184434,0.3528428078,0.2059558332,-0.1638755053,-0.0582680255,-0.2622591853,-0.1369839311,0.2124618143,0.0951014534,0.0298523735,0.0572964214,0.0261099748,-0.0664389655,-0.1765730977,-0.2433386892,0.2082313001,0.3464300931,-0.5103119016,-0.0029065402,-0.3452730775,-0.2318636626,-0.3185971379,-0.0987911522,-0.2351592779,-0.4590440691,0.0904380977,-0.0774912238,-0.1903926283,0.1450765133,-0.0375639684,0.0079175811,-0.0910728574,0.1520520449,-0.0975843072,-0.4702496231,-0.2045716196,0.1811123788,0.0074801398,0.0469536185,0.4813604951,-0.1431984752,-0.1245553046,-0.0785719454,-0.2953169048,0.0040265005,-0.2730655372,0.1366042942,0.1816227883,0.1038595065,0.0084614437,-0.1486846209,0.2930162847,-0.0715195164,0.0246399287,0.1069572195,0.0452331603,-0.2920734584,-0.2664699554,-0.4750190973,-0.4745302498,-0.1774818748,0.0669479668,-0.0759082735,-0.0335288644,0.5198027492,0.0684422851,0.0656808615,0.0931449682,0.0232527219,-0.3467432261,-0.0935406461,0.2490476817,-0.1790918559,-0.2102454752,0.1960575432,0.1093097106,0.1175282523,-0.2039331645,-0.3139223754,0.0728625879,-0.0576014481,0.0589448512,0.0813107938,0.1948960274,0.2138828188,0.2671016455,-0.14800331,-0.3270515203,-0.3317119777,0.138173148,-0.0719680116,0.130635187,-0.106256038,0.2940857708,-0.1520837694,0.3265088797,0.0217723008,-0.2126559913,0.1918890625,-0.0478479452,0.520732224,0.0683805421,-0.4193749726,0.0658850446,-0.119107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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"thanks for the tip. very strange :\/ I'll check my datasets version as well. \r\n\r\nI will have more similar experiments soon so I'll let you know if I manage to get rid of this. ","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":34,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n thanks for the tip. very strange :\/ I'll check my datasets version as well. \r\n\r\nI will have more similar experiments soon so I'll let you know if I manage to get rid of this. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1989","title":"Question\/problem with dataset labels","comments":"No problem at all. I thought I'd be able to solve this but I'm unable to replicate the issue :\/","body":"Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric. ","comment_length":20,"text":"Question\/problem with dataset labels \n Hi, I'm using a dataset with two labels \"nurse\" and \"not nurse\". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are \"nurse\" and \"surgeon\". \r\n\r\nThis is the trace I get:\r\n\r\n```\r\nFile \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 523, in <module>\r\n    main()\r\n  File \"..\/..\/..\/models\/tr-4.3.2\/run_puppets.py\", line 249, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 650, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1028, in _prepare_split\r\n    writer.write_table(table)\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/datasets\/arrow_writer.py\", line 292, in write_table\r\n    pa_table = pa_table.cast(self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n  File \"pyarrow\/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n  File \"\/dccstor\/redrug_ier\/envs\/last-tr\/lib\/python3.8\/site-packages\/pyarrow\/compute.py\", line 87, in cast\r\n    return call_function(\"cast\", [arr], options)\r\n  File \"pyarrow\/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n  File \"pyarrow\/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: not nurse\r\n```\r\n\r\nAny ideas how to fix this? For now, I'll probably make them numeric.  \n No problem at all. I thought I'd be able to solve this but I'm unable to replicate the issue :\/","embeddings":[0.1789145917,-0.057323277,0.0051793605,0.1343670487,0.4113538861,0.3502125442,0.6419943571,0.1637074947,-0.1613174826,0.072349824,0.1788543761,0.063875109,-0.0607732385,0.0305257887,-0.1302312016,-0.1193707809,0.0890839919,0.1734792441,0.1816422939,-0.0444667041,-0.2867427766,-0.0398255885,-0.1566803157,0.3662883341,-0.2972057462,-0.3766840994,0.210543111,-0.1205281168,-0.1131336242,-0.4633953273,0.2718096077,0.0861199424,0.0026804437,0.403427273,-0.0001030963,0.0587566718,0.2967477739,0.1559889019,-0.3984076679,-0.4321283996,-0.1893598735,-0.0234080609,0.0345639475,-0.304477036,0.097983405,-0.2846682072,-0.0905411765,-0.2756657004,0.1046864018,0.4386056066,0.3266401291,0.2373397052,-0.2064999342,-0.2293033898,0.2311526239,0.1250885427,-0.030509105,0.0494754016,-0.1344388425,-0.0064582322,0.451570183,0.4609141946,-0.2723953128,0.2647711635,0.1353649944,0.1463634074,0.1377247274,-0.3910478354,0.27630952,0.1961430758,0.7452605367,-0.2964998186,-0.2943909466,-0.1697050184,0.1373761445,-0.4167192578,0.1813386828,0.0700835735,0.0063757841,-0.0470148064,-0.0331626348,0.0610272661,0.0807420164,0.0881801546,-0.0580747053,0.0383287892,-0.0986413881,0.3288169205,0.1125301197,-0.0747505948,0.2975304723,-0.0059944061,-0.0885701403,0.0415447913,-0.4325243533,-0.0296081789,-0.03688097,0.0516761839,-0.1181376427,-0.0387405939,0.0183867961,-0.3749561906,-0.147435531,0.3403448462,0.2151345611,0.2662760913,0.2706991434,0.5616859198,0.0911723003,-0.0761628821,0.0308042038,-0.0558076203,-0.0015736214,-0.4626432359,0.2433211356,0.0989312381,0.2934700251,-0.2398587912,-0.5455211401,0.1924418211,-0.0281014573,0.0940958261,0.1541178674,0.3049504757,0.0221223086,0.2065178007,0.0793474615,0.1936084628,-0.0091703711,-0.1172052622,-0.2069239616,0.0976077914,-0.2002792507,-0.1501065046,0.0502693951,-0.0720527992,0.1124494746,0.0640596598,-0.0010049804,-0.0760566592,-0.0163035393,-0.3864637911,-0.0127481688,0.3588932157,-0.1032631844,0.4203503132,0.2520543039,-0.4185499847,0.0596994981,0.1802112162,-0.3688518703,-0.0338136367,-0.2121571898,0.2862130404,-0.1030440852,-0.1879812926,-0.1200915053,0.0468235761,0.4193198979,-0.2145541459,0.1575899273,-0.3543839455,-0.309322238,-0.2777701914,-0.0663041547,0.2214326262,-0.6722466946,0.0043895538,-0.1475469172,-0.0291129798,0.0528067499,0.2960192561,-0.0810045302,0.2062918693,-0.0705920011,0.0303499643,0.1395510882,-0.3906908929,-0.1104637533,0.1035439596,-0.142003268,-0.2442642599,0.070480749,-0.0403397307,0.0917311311,-0.0916265324,0.058231581,-0.1318777055,-0.1996322423,-0.0951331779,-0.1437040716,0.053811159,0.6539097428,0.0927327424,-0.0168001503,0.0756469369,-0.1002281159,-0.3448366225,-0.0425324664,-0.1009552553,0.2062191814,0.2147901803,0.0556399524,0.2983595431,0.145952329,-0.0644701123,-0.4424965978,0.1017011255,-0.040051531,0.1395646781,-0.1451429576,-0.1923436373,-0.3297650516,-0.0537857413,-0.2079764754,0.0183234606,0.1633132845,0.0683606789,-0.2214220017,-0.0204836689,0.0140847312,-0.0758842081,-0.0172138363,0.1093306914,-0.1040249839,0.2656518817,-0.095116362,0.116894044,-0.0267394595,0.2531200051,0.43260625,0.2400517166,-0.0466214903,0.3670240343,0.084860824,-0.3270230889,0.0078406269,-0.1726583391,0.0181292314,-0.0838744566,-0.0163266789,0.1145248264,0.2768397033,-0.108597219,-0.084719412,0.1411858499,-0.1772009283,0.2762840688,-0.1619747579,0.2004608065,0.2599627972,-0.1093925983,0.1251222789,-0.2127108425,0.2158810496,-0.1297983676,0.2343145311,0.007142364,-0.2617174685,0.0677190274,0.4220534861,0.0015308688,-0.0035241439,0.00977387,-0.1586679667,0.1657464951,-0.0130174728,0.5957184434,0.3528428078,0.2059558332,-0.1638755053,-0.0582680255,-0.2622591853,-0.1369839311,0.2124618143,0.0951014534,0.0298523735,0.0572964214,0.0261099748,-0.0664389655,-0.1765730977,-0.2433386892,0.2082313001,0.3464300931,-0.5103119016,-0.0029065402,-0.3452730775,-0.2318636626,-0.3185971379,-0.0987911522,-0.2351592779,-0.4590440691,0.0904380977,-0.0774912238,-0.1903926283,0.1450765133,-0.0375639684,0.0079175811,-0.0910728574,0.1520520449,-0.0975843072,-0.4702496231,-0.2045716196,0.1811123788,0.0074801398,0.0469536185,0.4813604951,-0.1431984752,-0.1245553046,-0.0785719454,-0.2953169048,0.0040265005,-0.2730655372,0.1366042942,0.1816227883,0.1038595065,0.0084614437,-0.1486846209,0.2930162847,-0.0715195164,0.0246399287,0.1069572195,0.0452331603,-0.2920734584,-0.2664699554,-0.4750190973,-0.4745302498,-0.1774818748,0.0669479668,-0.0759082735,-0.0335288644,0.5198027492,0.0684422851,0.0656808615,0.0931449682,0.0232527219,-0.3467432261,-0.0935406461,0.2490476817,-0.1790918559,-0.2102454752,0.1960575432,0.1093097106,0.1175282523,-0.2039331645,-0.3139223754,0.0728625879,-0.0576014481,0.0589448512,0.0813107938,0.1948960274,0.2138828188,0.2671016455,-0.14800331,-0.3270515203,-0.3317119777,0.138173148,-0.0719680116,0.130635187,-0.106256038,0.2940857708,-0.1520837694,0.3265088797,0.0217723008,-0.2126559913,0.1918890625,-0.0478479452,0.520732224,0.0683805421,-0.4193749726,0.0658850446,-0.1191072389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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1988","title":"Readme.md is misleading about kinds of datasets?","comments":"Hi ! Yes it's possible to use image data. There are already a few of them available (MNIST, CIFAR..)","body":"Hi!\r\n\r\nAt the README.MD, you say: \"efficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV\/JSON\/text. \"\r\n\r\nBut here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/templates\/new_dataset_script.py#L82-L117\r\n\r\nYou mention other kinds of datasets, with images and so on. I'm confused. \r\n\r\nIs it possible to use it to store, say, imagenet locally? ","comment_length":19,"text":"Readme.md is misleading about kinds of datasets? \n Hi!\r\n\r\nAt the README.MD, you say: \"efficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV\/JSON\/text. \"\r\n\r\nBut here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/templates\/new_dataset_script.py#L82-L117\r\n\r\nYou mention other kinds of datasets, with images and so on. I'm confused. \r\n\r\nIs it possible to use it to store, say, imagenet locally?  \n Hi ! Yes it's possible to use image data. There are already a few of them available (MNIST, CIFAR..)","embeddings":[-0.1048939675,-0.40778476,-0.1252106577,0.3528693914,0.2064331174,0.0773042664,0.282169342,-0.013219526,0.1014454737,-0.0921515226,-0.2971546054,-0.0767041966,-0.0591904446,0.5414652228,0.5551636815,-0.0550173968,0.1947559863,0.0077369232,-0.1606640965,0.0243537035,-0.1311549246,-0.0184780583,-0.1919618845,-0.0560768805,-0.2441978604,0.0734436214,-0.2431037128,0.2023607492,-0.3524551094,-0.3153180778,0.1265478283,0.142026186,0.2038796246,0.151556462,-0.000114474,0.0261500701,0.2794184685,-0.2648744583,-0.0799028724,-0.2754318118,-0.210036397,-0.1958542466,0.1365066469,-0.3097448051,-0.0344094075,-0.3639695942,0.3208791912,-0.2029702365,0.4163274169,0.3321006596,0.1734585315,0.1770457923,-0.0120291691,0.071558252,0.1994965523,0.7043319941,-0.235324055,0.0765626132,0.3966988623,0.2751343846,0.1317591816,0.1904014796,-0.1471002996,0.2446244359,0.6670558453,0.2847822905,-0.063408114,-0.5953469872,0.1768101603,0.3605561852,0.7247242928,-0.2514562011,-0.3772625625,-0.2556195259,-0.3492871225,-0.1461131126,-0.0531076752,0.2958315313,-0.0094538117,0.234074384,-0.5905610323,-0.2758911848,-0.1851313412,-0.0329567306,0.0618031733,0.2776287496,-0.3768876493,0.2667973042,0.1519197822,0.1478042752,0.0193916522,-0.241119206,0.0904686004,0.2136386037,0.1061239913,-0.2127500772,-0.3010907173,0.173503831,0.4054274857,0.0513445102,-0.0248880852,-0.0001729852,-0.2388933897,0.263392657,0.333024621,-0.0660579428,-0.064734444,-0.0764091685,0.2803301215,-0.0680018663,0.2875709236,-0.0922331959,-0.2132638395,-0.0572031885,-0.1317405552,-0.3184929788,0.13540034,-0.2608077824,-0.0464894362,0.0581916906,0.1385325789,-0.1853195131,-0.1996646821,0.2353790253,-0.0096433489,-0.1611839235,0.1309799552,0.2234884351,0.1141237915,-0.1701417267,0.0089839054,0.1949697286,-0.2847525775,0.1963899434,0.0610174015,-0.2041507661,0.3392297029,-0.104999952,0.2044096738,0.0342721306,0.1778145283,-0.2119627893,0.3175321519,0.4655555487,-0.0916534141,0.0578254648,0.0791012719,0.0507043339,-0.4007256329,0.1867390871,-0.4734035432,-0.2855846286,-0.1491551101,0.0366014242,-0.1271826476,0.0141973309,-0.4138632715,0.2678916454,0.1148289219,0.1325330138,0.237514466,0.0565747954,-0.3937700391,-0.2800178826,0.2161932737,0.2000338137,-0.4700542986,0.1166008115,-0.0538984425,-0.2484962493,-0.0362886488,0.2947596908,-0.193214044,0.1562079638,-0.2128867656,0.1740450412,0.1366384178,-0.4921633005,0.2029118538,0.3513148129,0.3671303689,-0.1108635813,0.0441715084,0.1537500173,0.0429131538,0.0106786927,-0.2065613121,0.2118646204,0.0240099058,-0.1647772193,-0.0510587171,-0.4474026859,-0.0594857372,0.2002526224,0.0996519998,0.1039628312,0.1326233,-0.0051940652,0.0929168984,-0.0749805421,0.3068466187,0.0156941004,0.1508465409,0.1359285116,0.1380657703,-0.0318658687,-0.4235838056,0.1685129404,0.0633087978,0.076366052,0.0761730149,-0.3972407877,-0.0179494843,-0.0966624618,-0.0226482507,-0.3566534817,0.0199158248,-0.069963634,0.1150769293,-0.1959699243,-0.5987902284,0.3285928369,-0.1511595547,0.0230142307,-0.4239824712,0.141873166,-0.1346036345,0.1803492904,-0.0286204834,-0.0403109193,-0.2251121998,-0.3223880827,0.2558919489,0.3308746219,0.0934398025,0.072260499,0.1576122344,0.565074861,0.1312624514,-0.2301090509,0.3880794346,-0.1910669655,0.2906061709,-0.0601735599,-0.4578298032,0.4475467801,-0.0550349243,0.1308474094,0.0638445094,-0.1445367187,0.2165029198,-0.0154157709,-0.2617865503,-0.151565209,-0.0750195459,0.0945293903,0.3611973524,-0.0716756284,-0.3708128929,0.0365550667,0.2601163089,-0.1820849925,-0.0158427525,0.2417083979,-0.1150560528,-0.3044469655,0.1658321023,0.0687477961,0.4986746013,0.0392027944,0.0669747517,0.287188977,0.0804619491,-0.167108357,0.133818537,0.0213156771,0.2222685367,-0.0037227371,-0.2135404199,-0.0318362936,-0.1227990538,0.0990001559,0.0228221398,0.0743144974,-0.2289332896,0.2501445115,-0.1536984742,-0.2292582691,0.0220878907,0.0896803215,-0.3034615815,0.0047843838,-0.1613828838,-0.0531568415,0.0799912587,-0.1376591921,-0.0357058197,0.4570162892,-0.1464422047,0.1405964345,-0.328478694,0.0716090351,0.0750908256,0.0750454441,0.3216892779,0.1382000893,0.400268048,-0.3740539551,-0.1360500604,-0.3646126688,-0.1546107084,0.4024239779,-0.2423143238,0.3959169388,0.342402935,0.2233851999,-0.1198301986,0.2312307209,0.1523539573,-0.2863830328,-0.1678372473,-0.3098679483,-0.0997557715,-0.2488845736,-0.3115008771,-0.0051123207,-0.3916942477,-0.1449797899,0.2766243219,0.3025512993,0.3917849064,0.2861984074,0.1617548019,0.3849445879,-0.2790232897,0.0002967174,-0.369348079,-0.660445869,0.280684948,-0.2496239096,-0.3559561968,0.2686167061,0.1082687527,0.1110761687,0.0524176806,-0.5647812486,-0.3803999126,0.073514536,-0.0298809744,0.3173148036,0.2644761801,0.1411495209,-0.3616519868,0.021610884,-0.2539398074,-0.2714328468,-0.0720840171,-0.0069525447,0.1494468302,0.384890765,0.2619087994,-0.0657447129,0.4962334633,0.1023640931,-0.0069294879,0.4028249085,0.0429874808,0.4390588999,-0.5036187768,0.1889145374,0.3229768276,-0.2280289084,-0.08935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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1983","title":"The size of CoNLL-2003 is not consistant with the official release.","comments":"Hi,\r\n\r\nif you inspect the raw data, you can find there are 946 occurrences of `-DOCSTART- -X- -X- O` in the train split and `14041 + 946 = 14987`, which is exactly the number of sentences the authors report. `-DOCSTART-` is a special line that acts as a boundary between two different documents and is filtered out in our implementation.\r\n\r\n@lhoestq What do you think about including these lines? ([Link](https:\/\/github.com\/flairNLP\/flair\/issues\/1097) to a similar issue in the flairNLP repo)","body":"Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.\r\nThe statistics of conll-2003 in this repo is  : \r\n\\#train 14041  \\#dev 3250 \\#test 3453\r\nWhile the official statistics is:\r\n\\#train 14987 \\#dev 3466 \\#test 3684\r\nWish for your reply~","comment_length":78,"text":"The size of CoNLL-2003 is not consistant with the official release. \n Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.\r\nThe statistics of conll-2003 in this repo is  : \r\n\\#train 14041  \\#dev 3250 \\#test 3453\r\nWhile the official statistics is:\r\n\\#train 14987 \\#dev 3466 \\#test 3684\r\nWish for your reply~ \n Hi,\r\n\r\nif you inspect the raw data, you can find there are 946 occurrences of `-DOCSTART- -X- -X- O` in the train split and `14041 + 946 = 14987`, which is exactly the number of sentences the authors report. `-DOCSTART-` is a special line that acts as a boundary between two different documents and is filtered out in our implementation.\r\n\r\n@lhoestq What do you think about including these lines? ([Link](https:\/\/github.com\/flairNLP\/flair\/issues\/1097) to a similar issue in the flairNLP repo)","embeddings":[0.1660706699,-0.3441519439,-0.026519794,0.3718201518,-0.3747011423,-0.0020881079,0.0613716431,-0.070897989,-0.9394959211,-0.0062836837,0.1252351701,0.1568083316,0.081358403,0.0008895223,-0.0184590258,0.0292958673,0.1648515314,-0.0400795117,0.2795078754,-0.1671278775,-0.0582850873,0.4750356674,-0.5303708911,0.0231517497,-0.8211599588,0.0690073296,-0.1038991064,0.1645847559,-0.3679313362,-0.3551759124,0.2766619027,0.0723338574,0.1248623282,0.1931931823,-0.0001210675,-0.435992837,0.2224059701,-0.0981824324,-0.1973603368,0.2145414054,-0.193009004,-0.4295057356,-0.038219817,-0.1875087768,0.2144868076,0.4310618043,-0.5452502966,0.2614451051,0.376606524,0.3219401538,0.1062864661,-0.0599874817,0.1251414269,-0.0609836057,0.5427105427,0.044348523,-0.0532875881,0.2690054774,0.1820478737,0.2210755646,-0.0648549423,0.3887670636,0.0820056722,-0.2496798337,-0.0430985987,-0.0280937646,-0.2199691534,-0.2218949944,0.0012414544,0.4098291993,0.4609613121,-0.2167000175,-0.3023409545,-0.6893437505,0.2730959952,-0.2258210778,0.1093596816,-0.0073540686,-0.0039547747,-0.0349640995,-0.1026145592,-0.3313957453,-0.2891083062,-0.1153049543,-0.2888514102,0.3619834781,-0.0982110873,0.0420258865,0.1299825907,0.2171084732,0.6810007095,-0.035297446,-0.037436787,-0.059974473,0.0213083457,-0.2826972008,0.0769811496,-0.0876311585,0.4273091257,-0.3164520264,-0.3112627864,-0.4441926479,0.0422772653,-0.0517050996,0.3466984034,0.0796580166,0.1834950298,0.4299295247,0.0845104232,-0.044189319,0.1412351578,0.114253521,-0.3085027933,-0.104844898,-0.4565196335,0.0863843933,-0.4163788557,-0.4130695164,-0.1330944002,0.1724139899,-0.1113018841,-0.2724635601,0.0154795712,0.0970013514,0.2453032285,0.568240881,-0.0069987499,-0.1539082527,-0.0962361097,-0.2754597962,-0.0487606972,-0.125920862,-0.0837792233,-0.0407452844,-0.0431362875,-0.4689645767,0.1422903091,-0.0367282778,0.3424780965,0.1044097841,0.0004998242,-0.190817818,0.1123936474,-0.0760022998,-0.2280941159,-0.0431931168,0.0182659924,0.4265727103,-0.2598370016,0.1983314008,-0.0825977921,-0.3248425126,-0.263859719,0.0226160027,-0.2561658621,-0.1643792093,0.6193922758,0.0523145199,0.2874726951,-0.1028845236,0.2342781276,-0.2183289379,-0.1448468417,-0.075727649,0.2444783598,0.1550737917,-0.243281275,-0.0339366682,0.0535974391,-0.0178699493,0.0158104803,0.2335743606,-0.1454826593,0.4814392328,-0.0997478813,-0.158286795,0.3144389093,-0.5198950171,-0.1304037869,0.1959632933,0.1512755156,-0.0103271725,-0.1246015429,-0.287071228,0.5772898197,-0.0048112338,0.0324353911,0.0367222652,0.0223802384,0.0059987605,-0.3243201673,-0.1682890207,-0.4615073204,-0.311951071,-0.0392338447,-0.4152747393,-0.3570280075,-0.1325614899,0.4206111729,0.1739861518,-0.0713801682,0.1050239056,0.2062046975,0.2779878378,0.1916780323,0.3133241832,-0.1001001298,-0.0194032844,0.3702386022,0.1440892518,0.5833430886,-0.2024097294,-0.3424496949,0.0180991739,0.3618111312,-0.2660242915,0.0464325994,0.0784569159,0.2490747869,0.1045739874,-0.2230343521,0.229434073,-0.0686060861,0.0829589963,-0.022883229,-0.1679258645,0.0076258061,-0.0514643341,0.0765403956,0.3759826124,0.0501769334,0.2213463932,-0.043064557,0.1560602933,0.3733198345,0.0259110723,0.1593764722,-0.1503411084,0.1712959856,0.0713375136,0.003965931,-0.3762101233,-0.0671595261,0.1555728763,-0.1190159768,0.2018063068,0.3937220573,-0.0849646702,0.1251069754,-0.1524040103,-0.103281498,-0.0504986458,-0.043562945,-0.4440820217,-0.0775448456,0.2097178549,0.0434644893,0.1147812456,-0.1278449744,0.0794446915,0.2715919912,-0.0406669043,-0.3521394432,0.3497368991,-0.3610708117,-0.0431854911,0.1686316282,0.1574980021,0.1628735363,0.2707741559,0.4084943831,0.0136154192,-0.1261952072,-0.2134845555,0.2715884745,0.2755286396,0.2698619664,0.313046068,0.1287536621,0.2043976486,-0.0576743782,-0.0522325337,0.0049607134,-0.1004546061,-0.2025470436,-0.1476150304,0.0883112922,-0.3175346255,-0.019139152,-0.2643443644,0.1351879239,-0.2632873356,0.2045490295,-0.2654726505,-0.1398562044,-0.0968471915,-0.4157758951,0.3907619417,-0.2395300418,0.2784121335,0.0863355771,-0.2383735478,-0.4046698511,0.0646499246,-0.1899420172,-0.1168667153,0.3373421431,-0.3670263588,0.1029953063,0.1408559382,-0.3336954713,0.4816080928,-0.1656112522,-0.1645951122,0.2019170523,-0.3982416093,-0.0883902982,0.1549080759,-0.188340053,-0.0865180567,-0.010428749,-0.2035202384,-0.1064208746,0.1076328978,-0.1134156287,-0.2887963951,0.0965061337,-0.2495456636,0.0297890548,-0.1858461648,0.0634325072,-0.3321940899,-0.0946239978,0.1867458671,-0.3426503539,0.287134707,-0.1742215604,-0.3361068368,0.0613681041,-0.1169488579,-0.1123993099,0.0519327298,-0.1871530414,-0.1176806837,-0.2642972469,-0.5564438701,0.3457522392,-0.0687346905,0.1330453455,-0.2591458559,0.1160944849,0.309104681,0.025470769,0.0253795292,-0.3204879463,0.032914076,-0.0702275261,-0.0610334761,0.5930926204,-0.2280661911,0.0729788989,0.1565073133,0.1225825325,0.4253528416,0.05936886,-0.0481373966,0.0182672143,0.1644159555,-0.1071258932,0.1683371216,0.2077216208,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1983","title":"The size of CoNLL-2003 is not consistant with the official release.","comments":"We should mention in the Conll2003 dataset card that these lines have been removed indeed.\r\n\r\nIf some users are interested in using these lines (maybe to recombine documents ?) then we can add a parameter to the conll2003 dataset to include them.\r\n\r\nBut IMO the default config should stay the current one (without the `-DOCSTART-` stuff), so that you can directly train NER models without additional preprocessing. Let me know what you think","body":"Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.\r\nThe statistics of conll-2003 in this repo is  : \r\n\\#train 14041  \\#dev 3250 \\#test 3453\r\nWhile the official statistics is:\r\n\\#train 14987 \\#dev 3466 \\#test 3684\r\nWish for your reply~","comment_length":73,"text":"The size of CoNLL-2003 is not consistant with the official release. \n Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.\r\nThe statistics of conll-2003 in this repo is  : \r\n\\#train 14041  \\#dev 3250 \\#test 3453\r\nWhile the official statistics is:\r\n\\#train 14987 \\#dev 3466 \\#test 3684\r\nWish for your reply~ \n We should mention in the Conll2003 dataset card that these lines have been removed indeed.\r\n\r\nIf some users are interested in using these lines (maybe to recombine documents ?) then we can add a parameter to the conll2003 dataset to include them.\r\n\r\nBut IMO the default config should stay the current one (without the `-DOCSTART-` stuff), so that you can directly train NER models without additional preprocessing. Let me know what you think","embeddings":[-0.0790517628,0.0053060674,0.0227088984,0.1767311543,-0.1750467122,0.0091935722,0.1926335841,0.1599837542,-1.0088423491,0.0919716656,0.1161217391,0.0397600345,-0.0169360023,0.1187621579,-0.0615689158,0.3097825348,0.0787528902,0.0600117221,0.1479182392,-0.1191559359,-0.3082509637,0.358076632,-0.3310672045,0.0405783467,-0.5341429114,0.1651314795,-0.0984805524,0.1878358722,-0.434206605,-0.4354428649,0.3668316007,0.1122712269,0.4130791724,0.0164174419,-0.0001118863,-0.1192543954,0.2520217597,-0.1412217766,-0.076693818,0.1694634557,-0.050209824,-0.2738068402,0.0147658866,-0.0273164678,-0.0503348671,0.2545052767,-0.1091153324,0.2557539642,0.0737720877,0.3681807518,0.2139769644,0.0384481065,0.1702184826,-0.0514829643,0.3541508615,0.2172906995,-0.0525220372,0.3811748326,-0.19514893,0.1040109694,-0.2099635452,0.2688074112,0.1873317361,-0.2268101722,0.3166727722,0.1540002376,0.2569654584,-0.2214953005,-0.2121857852,0.3500757813,0.2903466523,-0.2142493129,-0.1941875219,-0.2296766937,0.2725404799,-0.457477659,0.060404595,0.0433200635,-0.0058561708,0.0137385046,-0.2138532996,-0.5553431511,-0.292645514,0.0585920289,-0.3667196333,0.3881666064,-0.115728572,-0.1022905111,0.1817389131,0.0599916689,0.6260008216,0.010335274,-0.1692814678,-0.1431291848,-0.1197364405,-0.2859581709,-0.0559346043,0.0654557869,0.2638859153,-0.0636547729,-0.1156925261,-0.4510706365,0.2121991366,-0.248263225,-0.0093579367,0.113195397,0.2837645411,0.239855811,0.1120828465,0.2224410921,0.2796357572,0.1129842103,-0.2029008418,-0.0597663336,-0.2434735,-0.0676073134,-0.0179156046,-0.2810794711,0.1291228384,0.1988326162,0.0639458224,-0.0796382278,-0.1012124643,0.018493589,0.0820036754,0.4512069225,0.0034321337,-0.1984367222,-0.084528923,-0.3352283835,-0.1166150644,-0.2821266055,-0.0829380751,-0.0459283814,0.0355485119,-0.2012436688,0.1888842732,-0.1283838898,0.2089136243,0.3528966904,0.1774379164,-0.1730100662,0.1446496844,-0.0073264125,-0.2975330353,-0.1950110346,-0.058565557,0.1618616581,-0.2799871266,0.0485449918,-0.0993474349,-0.3043549359,-0.4162815511,0.1212233454,-0.1019023657,0.0118265729,0.6224346757,0.1664651483,0.0826453716,-0.1743585914,0.0671644732,-0.4080204666,-0.3539324403,-0.1758713424,0.3193433881,0.2330732197,-0.2300450057,-0.1173345223,-0.0911655352,-0.1505451947,0.1310708076,0.0084172813,-0.1777452826,0.1105761901,-0.0720570385,-0.2215397954,0.2518273294,-0.3360654116,-0.3417873979,0.1112436801,0.2198238075,-0.3266861439,-0.0330128595,-0.0516684912,0.3488057852,-0.0335538462,0.0222429186,0.0991626233,0.0220309403,-0.2132713199,-0.3179375231,-0.2815461457,-0.3954845965,-0.1032078117,0.1473606974,-0.1974997967,-0.352946043,0.0315568335,0.3055689931,0.1924332082,0.0112376949,0.3021039963,0.3747840822,-0.1904214919,0.0783938915,0.2963239551,-0.6002673507,0.0333616734,-0.0880930722,0.2312198132,0.729577601,-0.0865769759,-0.0486960374,-0.1075349674,0.1938242018,-0.210212782,0.1165840477,0.0602159649,0.2370423377,0.0884938166,-0.1364407688,0.0785467029,-0.2363155037,0.1135212928,-0.2278850824,-0.1792073548,-0.0897696763,-0.0024871451,0.0092414422,0.0679097846,-0.1765299141,0.2218803465,0.0440063402,0.3089011312,0.3044915795,-0.3136169314,0.0809995681,-0.0011140064,0.007004465,0.0568197891,0.0082929349,-0.271427691,-0.3744516373,0.2555375993,-0.0846404284,0.0808002502,0.465287447,-0.069869563,0.2097191215,-0.0009587118,-0.1454469562,-0.0171781667,-0.0766737312,-0.5671272874,-0.2756134868,0.3597340882,-0.0090087485,0.3806385398,-0.3556628525,-0.0670061111,0.2865484357,0.0019143085,-0.4107649624,0.4418158531,-0.3745262027,-0.0814533606,0.1061326787,0.4837848544,0.0947721824,0.2629711628,0.2847138047,0.1773799807,-0.0606921576,-0.0977045298,0.3825392127,0.0797182918,0.6006683707,0.2228209972,0.0153301898,0.0987114608,0.0872502849,-0.163764134,0.0181887634,0.1527938843,-0.2198047489,-0.2572900653,0.0601755977,-0.067753002,-0.0747493133,-0.0177049655,-0.3243751228,-0.1396196932,0.0603800006,-0.2385694534,0.071347326,-0.0294600278,-0.5579313636,0.3389033079,-0.2254012674,0.2155260295,0.1509417444,0.08237914,-0.5002079606,0.1482927799,-0.1069184691,-0.3531935811,0.0481997505,-0.418269217,0.0668951496,0.2385117561,-0.3264962733,0.3696917892,0.1347669512,0.1904295087,0.2230056673,-0.3871462345,-0.1640752703,0.2841857672,-0.1811352521,0.0183174349,-0.2151919305,-0.0226862896,-0.3398996294,0.257486701,-0.1655430198,-0.4132044315,-0.0645678937,-0.1689619273,-0.0013992144,0.0684766918,0.0359239392,0.0072923438,-0.0268487707,-0.0757276416,-0.400631249,0.2760815918,-0.0114549706,-0.2453077883,0.255941987,0.0025324323,-0.1818640828,-0.0200049113,-0.1705810428,0.1318047941,-0.1081926003,-0.6059451103,0.2363824993,-0.0703084022,-0.0106604658,-0.1274934262,-0.0597981513,0.3178860545,0.2325284183,-0.0260879043,0.094920896,0.0383140296,0.2087802291,-0.1287229657,0.4611245692,-0.0976861566,0.0773097128,0.0459611267,0.3247405887,0.2846692502,-0.2928981781,-0.1263490021,0.0874618664,0.3358834684,0.0002202532,0.2209355682,0.0259134062,0.0353934281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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1983","title":"The size of CoNLL-2003 is not consistant with the official release.","comments":"@lhoestq Yes, I agree adding a small note should be sufficient.\r\n\r\nCurrently, NLTK's `ConllCorpusReader` ignores the `-DOCSTART-` lines so I think it's ok if we do the same. If there is an interest in the future to use these lines, then we can include them.","body":"Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.\r\nThe statistics of conll-2003 in this repo is  : \r\n\\#train 14041  \\#dev 3250 \\#test 3453\r\nWhile the official statistics is:\r\n\\#train 14987 \\#dev 3466 \\#test 3684\r\nWish for your reply~","comment_length":45,"text":"The size of CoNLL-2003 is not consistant with the official release. \n Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.\r\nThe statistics of conll-2003 in this repo is  : \r\n\\#train 14041  \\#dev 3250 \\#test 3453\r\nWhile the official statistics is:\r\n\\#train 14987 \\#dev 3466 \\#test 3684\r\nWish for your reply~ \n @lhoestq Yes, I agree adding a small note should be sufficient.\r\n\r\nCurrently, NLTK's `ConllCorpusReader` ignores the `-DOCSTART-` lines so I think it's ok if we do the same. If there is an interest in the future to use these lines, then we can include them.","embeddings":[0.1301080137,0.0962899402,0.0394509584,0.0533670038,-0.2169309855,0.0075207311,0.1542738676,-0.0001621469,-0.9731163979,0.0672688559,0.2056128085,0.0701422319,-0.0513292663,-0.0842716172,-0.0803269595,0.3419833183,-0.0675743446,0.327860564,0.2285470366,-0.1227636263,-0.3668461442,0.2775174081,-0.274977982,-0.0082388809,-0.393466413,0.0806119815,-0.1419335157,0.2710108459,-0.2415265888,-0.532017827,0.3974419236,0.2227784991,0.0772376209,0.0288814083,-0.000115512,-0.2126500458,0.3872515857,-0.215965271,-0.2099744678,0.4730767608,-0.1129044816,-0.7660289407,-0.044372905,-0.4447828233,0.0319344439,0.5121642351,-0.0817523897,0.1743455529,0.2542611659,0.161358431,0.1676934958,0.2393181622,0.2375080734,0.1333176941,0.3475002348,-0.02496868,-0.1696096063,0.5508455634,-0.2081135809,0.1203334332,-0.2780005038,0.3548775017,0.1467530131,-0.2336138338,0.1369725913,-0.0380336381,0.2645008266,-0.0674744695,-0.1419992447,0.2909509242,0.29439044,-0.2650747299,-0.1194988489,-0.5421310663,0.267749697,-0.4985669255,0.0995380431,-0.0365589336,-0.0151115526,0.017058922,-0.2545989752,-0.4198880196,-0.3282954991,0.1200169846,-0.1598795503,0.418084234,-0.0272401609,0.0301428102,0.1538592726,0.1586638987,0.5796195269,0.0767197609,-0.2444941103,0.0215668604,-0.0377822928,-0.4418598413,0.1902304292,-0.1309911013,0.1642335951,-0.2104474902,-0.1204381213,-0.4199705422,-0.0901070833,-0.0593098961,0.0193503629,0.0543407723,0.4128799736,0.3364706039,0.2408224642,-0.100061059,0.4114401639,0.1436223984,-0.252967,-0.1811205596,-0.1239445433,0.1675828397,-0.2642978132,-0.2406897545,0.1624811888,0.1565834135,-0.0349364914,-0.073528789,-0.0618176274,-0.0178566203,0.2747400701,0.3580447435,0.0686169714,-0.0945587531,0.0771590322,-0.2723608911,-0.0594635494,-0.3487521708,0.0059081572,-0.017153291,-0.1843848675,-0.0562592819,0.3026262522,-0.3127999902,0.2598237395,0.3169444501,0.1503162235,0.012402623,0.0943745896,0.0488491356,-0.0882604346,-0.1484766304,0.0212390274,0.1710014939,-0.3730175495,0.2206998616,0.107878916,-0.3871753216,-0.4981610477,0.0574468337,-0.3294167519,-0.0661147758,0.5692340732,0.0615522526,0.1533857584,-0.2984735668,0.2479482889,-0.1679144204,-0.1764439642,-0.1377005279,0.2795294523,0.0724272355,0.0772195309,-0.2327023596,-0.2192714065,-0.1420802176,0.2104848772,0.0464700945,-0.1911343485,0.1837269217,-0.0846440643,-0.0169071853,0.4908646345,-0.3189494014,-0.2493038177,0.4301478565,-0.0368908755,-0.219440043,-0.0988788158,0.1389304399,0.1706574112,-0.1958432049,-0.0332034044,0.1517599076,0.0310926605,0.1289447546,-0.3339414895,-0.2219436765,-0.2317954898,-0.2167307884,0.163366586,-0.2227857858,-0.2178170532,0.2956934869,0.4338988662,0.0595176332,-0.069379814,0.2499163151,0.3685540557,-0.2128676474,-0.1087667048,0.4909599721,-0.3863342702,0.0884430408,0.0807879269,0.2146434635,0.747834444,-0.265327245,-0.2305809855,-0.1599819362,0.2598367035,-0.0037722851,0.1104198247,0.1521407664,0.1595448852,0.2314088047,0.1487793028,0.0730554014,-0.1615217477,0.0371756814,0.0772234946,-0.1853340864,-0.0302614309,-0.1165647879,0.1630454957,0.3133796751,0.040984273,0.2251074761,-0.1252541244,0.369248271,0.0459581986,-0.0481309481,0.0709815845,-0.166158542,0.0461778529,0.267226845,-0.0127223209,-0.3932291269,-0.4152544737,0.0613912866,0.0116798775,0.0987932384,0.5511188507,-0.243895039,0.0746922344,-0.0841133595,-0.1902338266,-0.1499232352,-0.2079464793,-0.2950230241,-0.2136440873,0.3231470585,-0.2128845304,0.153433755,-0.3101692796,-0.0633996874,0.4416494071,-0.185323745,-0.1847084761,0.3180700541,-0.2418947369,-0.0890740305,0.248134926,0.2256144583,0.0159505494,0.2709316313,0.283038944,0.1357545555,-0.2408515513,-0.1283157915,0.1752324104,0.2388232797,0.4394446611,0.239195779,0.0922869816,0.114813894,0.0340718515,-0.0045291381,-0.0054153185,-0.0277312137,-0.2279874086,-0.3621948659,0.1602542102,-0.1504976451,-0.065082632,-0.3093796074,-0.2254503071,-0.3511163294,0.1018531621,-0.1388074458,-0.1369154751,0.1145197153,-0.5290244818,0.4189198911,-0.0392219238,0.2031296194,0.2047443986,-0.2497678101,-0.5104724765,0.0853452086,-0.0410059057,-0.306143254,0.3523700833,-0.4794403911,0.0280888788,0.2486198843,-0.4806945622,0.3634321988,-0.0830034241,-0.1581347138,0.2018323392,-0.205129683,-0.3833319247,0.1208054572,-0.2449854612,0.2246058285,-0.153999567,0.000564967,-0.0720465705,0.2123025805,-0.4482307732,-0.1638085991,0.1669665575,-0.3378913999,0.1977341026,-0.0912566856,0.0160743296,0.0817922354,-0.1622832417,0.2011844367,-0.181869328,0.1154790744,-0.1136728451,0.0167072359,0.2259347588,-0.081131734,-0.1662234217,0.0699585751,-0.2387206852,0.0106036831,-0.3078627884,-0.7261509299,0.0743746087,0.0595251843,-0.0601580255,-0.1150825992,0.0376050062,0.2131718397,0.1127520353,0.052319292,-0.0855741352,-0.1226233169,0.1621295065,0.0945110768,0.5319262743,-0.222887814,0.3096297979,-0.0022927178,0.159055993,0.2853764296,-0.0007306936,-0.2252567559,-0.0015945244,0.2930024862,0.258847326,0.3286583722,0.3251229525,0.1030992642,-0.137955427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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1983","title":"The size of CoNLL-2003 is not consistant with the official release.","comments":"I added a mention of this in conll2003's dataset card:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc9796920da88486c3b97690969aabf03d6b4088\/datasets\/conll2003\/README.md#conll2003\r\n\r\nEdit: just saw your PR @mariosasko (noticed it too late ^^)\r\nLet me take a look at it :)","body":"Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.\r\nThe statistics of conll-2003 in this repo is  : \r\n\\#train 14041  \\#dev 3250 \\#test 3453\r\nWhile the official statistics is:\r\n\\#train 14987 \\#dev 3466 \\#test 3684\r\nWish for your reply~","comment_length":30,"text":"The size of CoNLL-2003 is not consistant with the official release. \n Thanks for the dataset sharing! But when I use conll-2003, I meet some questions.\r\nThe statistics of conll-2003 in this repo is  : \r\n\\#train 14041  \\#dev 3250 \\#test 3453\r\nWhile the official statistics is:\r\n\\#train 14987 \\#dev 3466 \\#test 3684\r\nWish for your reply~ \n I added a mention of this in conll2003's dataset card:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/fc9796920da88486c3b97690969aabf03d6b4088\/datasets\/conll2003\/README.md#conll2003\r\n\r\nEdit: just saw your PR @mariosasko (noticed it too late ^^)\r\nLet me take a look at it :)","embeddings":[-0.11778,-0.1490903944,-0.1239807233,0.4063589871,-0.1421683133,-0.1860256195,0.2695673406,0.0108112395,-0.976584971,0.129761517,0.0317089558,-0.0465303175,0.0295431539,0.1740378886,-0.073604621,0.1277457923,0.062856324,-0.1194887534,-0.0711038485,-0.161968857,-0.1469121724,0.4227974713,-0.3452403247,-0.0569811948,-0.4710490704,0.2424959093,-0.1226277947,0.0701174885,-0.5567559004,-0.323337853,0.4087007642,-0.0866105109,0.1769321561,0.3554422557,-0.0001040467,-0.1748574525,0.2246979922,-0.115742363,-0.0655561239,0.2404021025,-0.2004329711,-0.1920388937,-0.1547529548,-0.0302857477,0.0281113908,0.2732361555,-0.3089978099,0.2993923724,0.182216689,0.2430881262,0.2945328057,0.0809215605,0.2179147005,-0.1760448515,0.2989802659,0.1917587072,-0.1165719777,0.2260445803,-0.1307196319,0.2275835723,-0.0872837901,0.2860423326,0.334974438,-0.172430411,0.0849063024,-0.026191758,-0.0870816037,-0.0864435136,0.0954536274,0.3211065233,0.394913137,-0.3121786416,-0.4026528597,-0.191369161,0.0310513992,-0.3462634087,0.0563204102,0.0096388916,0.0461317413,-0.0584371202,-0.4982578754,-0.1443539709,-0.20755665,-0.0425582565,-0.3624979854,0.4512245059,-0.2420071065,0.0177786704,0.1008713171,-0.0527409203,0.4142463505,0.0274848901,-0.0922444984,-0.0860951245,-0.2407745719,-0.3211268485,0.1106194928,-0.126775369,0.4134696722,-0.1761906147,-0.3213562965,-0.4195230305,0.0159686469,-0.3341854215,0.1879396141,0.2340653241,0.0104155121,0.1668908894,0.1190471947,0.0761188492,0.2723383307,0.0930758119,-0.244910121,-0.1001408249,-0.2591290176,-0.0199954938,-0.1423551887,-0.2868362069,-0.1039165035,0.0991119668,-0.0918708965,-0.1583966315,-0.0469875634,0.1526241302,-0.0079525858,0.4125003219,-0.1267403662,-0.0555189848,-0.0259355456,-0.2938326895,-0.2362737656,-0.306984812,-0.1196088865,-0.206977427,-0.1334817261,-0.1495285779,0.2941322625,-0.1133824214,0.2720679939,0.2048839331,0.0209798776,-0.131098941,0.0350976177,-0.0036285412,-0.2124439478,-0.2451636791,-0.0987363979,0.1680230498,-0.2560645342,-0.1284075975,0.057140518,-0.3249761164,-0.3823211789,0.1705315709,-0.1979279965,-0.0290262792,0.5316371322,0.0959364995,0.0505769923,0.1557330638,0.1403920799,-0.3408176601,-0.0699074641,-0.2292314768,0.3176356256,0.0836290792,-0.1907851696,-0.0787202716,-0.0876957774,-0.2094607353,0.1775773764,0.2158054709,-0.0621547401,-0.0934578031,-0.1083809659,-0.2343145609,0.1496353745,-0.3616794348,-0.4399799109,0.2209210694,0.1757685542,-0.2946114838,0.077080667,-0.0218824986,0.3208502531,0.0463332199,0.091138944,0.0918140337,0.0417531803,-0.0348821804,-0.1790225655,-0.3428456187,-0.3542283177,-0.1385772228,0.2041584253,-0.3445739448,-0.14810507,0.0730544552,0.3761892319,0.2262465805,-0.0707721785,0.2620154619,0.4964691699,-0.1420655251,0.0202060826,0.2618133724,-0.347964406,0.0389451049,0.1203958243,0.1105892137,0.7259976268,-0.1705368161,-0.2706593871,0.0465808474,0.2493647486,-0.0999445319,0.2031153738,0.0404588208,0.3650392592,-0.0040919506,-0.1104710177,0.1139745936,-0.0178981572,-0.02927568,0.1010706723,-0.1370583624,-0.001614105,-0.0653423741,0.1549784839,0.2403028458,-0.0411923267,0.1002099738,-0.0479528755,0.2280957848,0.3012150824,-0.2031024694,0.2046176642,0.042294316,0.0624803081,0.2254555225,0.0878704935,-0.4247793853,-0.2324251086,0.277583003,-0.1241131946,0.2256472111,0.4845538139,-0.2323309481,0.1195513457,-0.029378511,0.0251130182,-0.0848059431,0.0052104597,-0.3428599834,-0.2204592228,0.181563139,0.0575422943,0.2655125856,-0.3689698279,0.0382841416,0.3136286139,-0.0431068912,-0.3063026071,0.2688687742,-0.4913647175,-0.0822650492,0.2280891091,0.4648602009,0.1076152325,0.2788805068,0.4253003001,0.0682944357,0.0518131293,-0.0445777103,0.1996300668,0.0479048975,0.4186436236,0.1436415315,0.0831096172,0.0965783969,-0.0389881842,-0.0646341145,-0.0050440542,0.0042763432,-0.082438536,-0.2426883727,-0.090787217,-0.0100195725,-0.0121672619,-0.2176555991,-0.251437664,-0.1626991481,0.1214177981,-0.3009015024,0.0510753095,-0.0633108839,-0.3588641286,0.1681368649,0.0136897592,0.3135440052,0.2385151833,0.1388934851,-0.4799479544,0.2259171307,-0.1357045323,-0.3781635761,0.2379622906,-0.4166471362,0.0695397556,0.0819515511,-0.2997785807,0.3862543702,-0.1278282702,0.0064578187,0.1638875455,-0.3423283696,-0.2572751641,0.3563469648,-0.0730230883,-0.1017332077,-0.2386434525,0.0057295705,-0.303976506,0.0857309029,-0.2400321513,-0.2603770196,-0.0834300369,-0.0932432711,0.1943808496,0.0187601596,-0.027861258,0.0240296163,0.1147530302,0.15723674,-0.2134676427,0.1495198756,-0.2803869545,-0.380674988,0.1139078438,-0.0351745225,-0.2225524038,0.0481997281,0.0305072814,0.0931165442,-0.1576381326,-0.6883491874,-0.0888285488,-0.157608673,0.1290762424,-0.1252102405,0.1556968093,0.2230455875,0.0539965928,-0.1211584955,-0.1104235202,-0.1563749313,0.041126851,0.094594799,0.6660491228,-0.1945737302,0.2059250325,0.2436729968,0.4627051353,0.4733084738,-0.2580979168,-0.0867398977,-0.0067991968,0.2599477172,0.0124997627,0.3047555983,0.348764509,-0.0693453774,-0.04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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1981","title":"wmt datasets fail to load","comments":"yes, of course, I reverted to the version before that and it works ;)\r\n\r\nbut since a new release was just made you will probably need to make a hotfix.\r\n\r\nand add the wmt to the tests?","body":"on master:\r\n```\r\npython -c 'from datasets import load_dataset; load_dataset(\"wmt14\", \"de-en\")'\r\nDownloading and preparing dataset wmt14\/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/stas\/.cache\/huggingface\/datasets\/wmt14\/de-en\/1.0.0\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e...\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 634, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt14\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\/wmt_utils.py\", line 760, in _split_generators\r\n    extraction_map = dict(downloaded_files, **manual_files)\r\n```\r\n\r\nit worked fine recently. same problem if I try wmt16.\r\n\r\ngit bisect points to this commit from Feb 25 as the culprit https:\/\/github.com\/huggingface\/datasets\/commit\/792f1d9bb1c5361908f73e2ef7f0181b2be409fa\r\n\r\n@albertvillanova ","comment_length":37,"text":"wmt datasets fail to load \n on master:\r\n```\r\npython -c 'from datasets import load_dataset; load_dataset(\"wmt14\", \"de-en\")'\r\nDownloading and preparing dataset wmt14\/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/stas\/.cache\/huggingface\/datasets\/wmt14\/de-en\/1.0.0\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e...\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 634, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt14\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\/wmt_utils.py\", line 760, in _split_generators\r\n    extraction_map = dict(downloaded_files, **manual_files)\r\n```\r\n\r\nit worked fine recently. same problem if I try wmt16.\r\n\r\ngit bisect points to this commit from Feb 25 as the culprit https:\/\/github.com\/huggingface\/datasets\/commit\/792f1d9bb1c5361908f73e2ef7f0181b2be409fa\r\n\r\n@albertvillanova  \n yes, of course, I reverted to the version before that and it works ;)\r\n\r\nbut since a new release was just made you will probably need to make a hotfix.\r\n\r\nand add the wmt to the tests?","embeddings":[-0.3290719688,-0.0584938265,-0.0104082478,0.5395736694,0.3104315698,0.0041043591,0.2097796947,0.0872702822,0.3129351139,0.1078865156,-0.0224996712,-0.1256612837,-0.296931684,0.1887943596,0.1114164367,0.1848195195,-0.1364242435,0.0058543072,-0.821605444,0.015868606,-0.1838497221,0.1984840035,-0.167342186,-0.0969723165,-0.5068030357,0.2375522107,-0.0300084837,0.2536428869,-0.2113710791,-0.4816319048,0.5141910315,0.0309756044,0.0518511981,0.5576848388,-0.0001141044,0.1515254825,0.2935239077,-0.01024595,-0.2466028333,-0.2974118888,-0.5779017806,-0.3827317059,0.0219598692,0.2245493531,-0.1884617954,0.0820968226,-0.2400668561,-0.3640685081,0.2820504308,0.2943232954,0.2214949429,0.4151587784,0.2789464891,-0.3091937304,0.0510975905,0.2907721698,-0.1379881054,-0.0280019324,0.0135747483,-0.1352173835,0.0232767537,0.0316457935,0.0733231753,0.3297724426,0.3457936347,-0.1587231904,0.2857407629,-0.0081027998,0.2792252004,0.0577791668,0.1964938641,-0.075779289,-0.2360399216,-0.0594310798,-0.130695492,-0.4276536703,0.2592030466,0.2658741474,0.0303441808,0.2660063207,-0.3589408398,0.0739574134,0.2212079316,0.0317872539,-0.3881673515,0.3771407902,-0.3114940822,0.0018198559,0.2749304175,-0.1902687997,0.1660659462,-0.1114594862,-0.0187486093,0.0837211162,-0.5072568655,0.2616916895,0.0255597904,0.2756205797,-0.0800576732,0.1582372934,-0.2520163655,0.014928068,0.2089373171,-0.0155652929,0.1787930727,0.1386247426,-0.109944649,-0.0626590401,0.2749227881,0.0550982095,-0.1853418797,0.1351207048,-0.1703789979,-0.4433655143,-0.089175649,-0.1894311756,0.1525510997,-0.0720274746,-0.2599219978,0.0635320917,0.135518983,-0.3142132461,0.0204186551,0.2855190933,0.0484095998,0.170236975,0.1179608032,0.00365311,-0.0042028385,-0.0357706062,-0.3564473689,-0.3906199932,-0.2991639674,0.0611203648,0.4078392386,-0.2185730487,0.2283025235,0.0004109528,-0.1310051382,-0.1273209304,-0.2269295007,-0.0077418489,0.2188936472,0.4824034274,0.071092926,0.2990789115,0.2443664521,0.1595918238,-0.0081311166,-0.0074383789,-0.0995391086,-0.2956005335,0.1295208484,0.2581728995,-0.0815234408,0.1570645422,-0.2294500917,-0.1136633456,0.1050200537,0.3215996325,-0.19287166,-0.2417548001,-0.1637251675,-0.0666386709,0.5321374536,0.5977163315,0.0095659709,-0.194243744,-0.3947505057,-0.0471861549,0.2344344705,0.332223624,-0.0298235733,0.1007020026,-0.26099509,-0.2470602542,-0.0820458457,-0.059449859,-0.1832846254,0.1371451616,-0.1313237101,0.2751179039,0.0324510708,-0.1263800412,0.0145307528,-0.2265354246,0.34289065,0.0891364664,-0.1580777168,0.0836280808,-0.079416573,-0.3164910972,0.2474278659,0.1886273175,0.1936812997,-0.1896170974,0.2157250047,0.0242878571,0.0684091523,0.0570893697,0.1292500794,0.0385188013,0.0848584697,-0.0127780912,-0.0889184177,0.0534026809,-0.4473019838,0.2964880764,0.2691924274,0.0437409878,0.1173462942,0.1208167449,-0.1622187644,0.1335769892,-0.3434743881,-0.1887686104,0.0876794457,0.1349028647,-0.0035151422,-0.1450345963,-0.0388088152,-0.023978943,-0.2259172648,0.1487141699,0.2445334345,0.0667199939,-0.1433457583,-0.1252084821,0.2802841961,0.2743451595,0.1635603011,-0.1697696,-0.1630631983,0.090857625,0.1399752051,0.395195812,-0.0871860459,0.1039211378,0.1791915148,-0.4736975133,0.1699429303,0.1361609697,-0.1402692944,-0.2992374897,0.1204287559,0.0424622707,-0.1103156656,0.2675850391,0.1175593957,0.0357345492,0.1648183763,0.0709900931,0.1453140378,-0.3879213929,0.280146718,-0.0923160762,0.3428555131,0.2670232058,0.0239535514,-0.0425498374,0.3805395365,0.1634019315,0.2022353113,0.0150986891,-0.3856002092,-0.0856616274,-0.0661873668,0.0501848124,0.3967731297,0.0735268518,0.0935864076,0.1786359698,0.1149715856,-0.302526772,0.4467021525,-0.0749787018,-0.0767071843,0.30438748,-0.0235597752,-0.0722774938,-0.2620562017,0.5669642687,0.0582100004,0.0062176031,-0.1975153834,-0.2630082965,-0.4342718422,0.2357833087,-0.3500567377,-0.5691426992,-0.2052633166,-0.2057430893,-0.2758672237,0.1181358621,-0.0988537818,0.2478714138,0.0687464401,0.2188895941,-0.0490783527,0.3465979993,-0.0977031887,-0.2259616405,-0.1064973697,0.0391054899,0.5312060118,-0.2995474339,0.327535212,-0.1957136095,0.063988708,-0.2002592385,-0.2282969952,0.0872321725,-0.0478701182,0.3465410471,0.2117685378,-0.0232825149,0.364433527,-0.1089356244,0.3259528279,-0.1572975963,-0.0153347533,0.0034697694,-0.044544246,-0.1426709145,-0.2228537053,-0.3446230888,-0.4408054054,-0.3883455396,-0.0106526911,-0.0261881314,-0.0418932587,0.091757603,-0.1342888921,-0.0532308593,-0.0178111736,-0.025891291,-0.3486146033,-0.2984477878,0.2000667304,-0.3020641506,-0.2819243073,0.2392926961,0.13543652,0.4544383287,0.040127337,-0.3937389553,0.5808199644,-0.1391113997,-0.1142625138,0.1804570556,0.3090633154,-0.0905662403,0.1654050052,-0.0495853089,-0.1657474041,-0.2646009028,-0.1806016117,-0.2320661098,0.1591044813,-0.1456702203,0.062785618,0.010418159,0.8047193289,0.0654786751,-0.2943529487,0.1827455461,0.1267124414,0.2218532413,-0.0006962097,-0.5895354152,0.2010116726,-0.0986168161,0.2215839028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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1981","title":"wmt datasets fail to load","comments":"@stas00 it is fixed. @lhoestq are you releasing the hot fix or would you prefer me to do it?","body":"on master:\r\n```\r\npython -c 'from datasets import load_dataset; load_dataset(\"wmt14\", \"de-en\")'\r\nDownloading and preparing dataset wmt14\/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/stas\/.cache\/huggingface\/datasets\/wmt14\/de-en\/1.0.0\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e...\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 634, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt14\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\/wmt_utils.py\", line 760, in _split_generators\r\n    extraction_map = dict(downloaded_files, **manual_files)\r\n```\r\n\r\nit worked fine recently. same problem if I try wmt16.\r\n\r\ngit bisect points to this commit from Feb 25 as the culprit https:\/\/github.com\/huggingface\/datasets\/commit\/792f1d9bb1c5361908f73e2ef7f0181b2be409fa\r\n\r\n@albertvillanova ","comment_length":19,"text":"wmt datasets fail to load \n on master:\r\n```\r\npython -c 'from datasets import load_dataset; load_dataset(\"wmt14\", \"de-en\")'\r\nDownloading and preparing dataset wmt14\/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/stas\/.cache\/huggingface\/datasets\/wmt14\/de-en\/1.0.0\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e...\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 634, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt14\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\/wmt_utils.py\", line 760, in _split_generators\r\n    extraction_map = dict(downloaded_files, **manual_files)\r\n```\r\n\r\nit worked fine recently. same problem if I try wmt16.\r\n\r\ngit bisect points to this commit from Feb 25 as the culprit https:\/\/github.com\/huggingface\/datasets\/commit\/792f1d9bb1c5361908f73e2ef7f0181b2be409fa\r\n\r\n@albertvillanova  \n @stas00 it is fixed. @lhoestq are you releasing the hot fix or would you prefer me to do it?","embeddings":[-0.3290719688,-0.0584938265,-0.0104082478,0.5395736694,0.3104315698,0.0041043591,0.2097796947,0.0872702822,0.3129351139,0.1078865156,-0.0224996712,-0.1256612837,-0.296931684,0.1887943596,0.1114164367,0.1848195195,-0.1364242435,0.0058543072,-0.821605444,0.015868606,-0.1838497221,0.1984840035,-0.167342186,-0.0969723165,-0.5068030357,0.2375522107,-0.0300084837,0.2536428869,-0.2113710791,-0.4816319048,0.5141910315,0.0309756044,0.0518511981,0.5576848388,-0.0001141044,0.1515254825,0.2935239077,-0.01024595,-0.2466028333,-0.2974118888,-0.5779017806,-0.3827317059,0.0219598692,0.2245493531,-0.1884617954,0.0820968226,-0.2400668561,-0.3640685081,0.2820504308,0.2943232954,0.2214949429,0.4151587784,0.2789464891,-0.3091937304,0.0510975905,0.2907721698,-0.1379881054,-0.0280019324,0.0135747483,-0.1352173835,0.0232767537,0.0316457935,0.0733231753,0.3297724426,0.3457936347,-0.1587231904,0.2857407629,-0.0081027998,0.2792252004,0.0577791668,0.1964938641,-0.075779289,-0.2360399216,-0.0594310798,-0.130695492,-0.4276536703,0.2592030466,0.2658741474,0.0303441808,0.2660063207,-0.3589408398,0.0739574134,0.2212079316,0.0317872539,-0.3881673515,0.3771407902,-0.3114940822,0.0018198559,0.2749304175,-0.1902687997,0.1660659462,-0.1114594862,-0.0187486093,0.0837211162,-0.5072568655,0.2616916895,0.0255597904,0.2756205797,-0.0800576732,0.1582372934,-0.2520163655,0.014928068,0.2089373171,-0.0155652929,0.1787930727,0.1386247426,-0.109944649,-0.0626590401,0.2749227881,0.0550982095,-0.1853418797,0.1351207048,-0.1703789979,-0.4433655143,-0.089175649,-0.1894311756,0.1525510997,-0.0720274746,-0.2599219978,0.0635320917,0.135518983,-0.3142132461,0.0204186551,0.2855190933,0.0484095998,0.170236975,0.1179608032,0.00365311,-0.0042028385,-0.0357706062,-0.3564473689,-0.3906199932,-0.2991639674,0.0611203648,0.4078392386,-0.2185730487,0.2283025235,0.0004109528,-0.1310051382,-0.1273209304,-0.2269295007,-0.0077418489,0.2188936472,0.4824034274,0.071092926,0.2990789115,0.2443664521,0.1595918238,-0.0081311166,-0.0074383789,-0.0995391086,-0.2956005335,0.1295208484,0.2581728995,-0.0815234408,0.1570645422,-0.2294500917,-0.1136633456,0.1050200537,0.3215996325,-0.19287166,-0.2417548001,-0.1637251675,-0.0666386709,0.5321374536,0.5977163315,0.0095659709,-0.194243744,-0.3947505057,-0.0471861549,0.2344344705,0.332223624,-0.0298235733,0.1007020026,-0.26099509,-0.2470602542,-0.0820458457,-0.059449859,-0.1832846254,0.1371451616,-0.1313237101,0.2751179039,0.0324510708,-0.1263800412,0.0145307528,-0.2265354246,0.34289065,0.0891364664,-0.1580777168,0.0836280808,-0.079416573,-0.3164910972,0.2474278659,0.1886273175,0.1936812997,-0.1896170974,0.2157250047,0.0242878571,0.0684091523,0.0570893697,0.1292500794,0.0385188013,0.0848584697,-0.0127780912,-0.0889184177,0.0534026809,-0.4473019838,0.2964880764,0.2691924274,0.0437409878,0.1173462942,0.1208167449,-0.1622187644,0.1335769892,-0.3434743881,-0.1887686104,0.0876794457,0.1349028647,-0.0035151422,-0.1450345963,-0.0388088152,-0.023978943,-0.2259172648,0.1487141699,0.2445334345,0.0667199939,-0.1433457583,-0.1252084821,0.2802841961,0.2743451595,0.1635603011,-0.1697696,-0.1630631983,0.090857625,0.1399752051,0.395195812,-0.0871860459,0.1039211378,0.1791915148,-0.4736975133,0.1699429303,0.1361609697,-0.1402692944,-0.2992374897,0.1204287559,0.0424622707,-0.1103156656,0.2675850391,0.1175593957,0.0357345492,0.1648183763,0.0709900931,0.1453140378,-0.3879213929,0.280146718,-0.0923160762,0.3428555131,0.2670232058,0.0239535514,-0.0425498374,0.3805395365,0.1634019315,0.2022353113,0.0150986891,-0.3856002092,-0.0856616274,-0.0661873668,0.0501848124,0.3967731297,0.0735268518,0.0935864076,0.1786359698,0.1149715856,-0.302526772,0.4467021525,-0.0749787018,-0.0767071843,0.30438748,-0.0235597752,-0.0722774938,-0.2620562017,0.5669642687,0.0582100004,0.0062176031,-0.1975153834,-0.2630082965,-0.4342718422,0.2357833087,-0.3500567377,-0.5691426992,-0.2052633166,-0.2057430893,-0.2758672237,0.1181358621,-0.0988537818,0.2478714138,0.0687464401,0.2188895941,-0.0490783527,0.3465979993,-0.0977031887,-0.2259616405,-0.1064973697,0.0391054899,0.5312060118,-0.2995474339,0.327535212,-0.1957136095,0.063988708,-0.2002592385,-0.2282969952,0.0872321725,-0.0478701182,0.3465410471,0.2117685378,-0.0232825149,0.364433527,-0.1089356244,0.3259528279,-0.1572975963,-0.0153347533,0.0034697694,-0.044544246,-0.1426709145,-0.2228537053,-0.3446230888,-0.4408054054,-0.3883455396,-0.0106526911,-0.0261881314,-0.0418932587,0.091757603,-0.1342888921,-0.0532308593,-0.0178111736,-0.025891291,-0.3486146033,-0.2984477878,0.2000667304,-0.3020641506,-0.2819243073,0.2392926961,0.13543652,0.4544383287,0.040127337,-0.3937389553,0.5808199644,-0.1391113997,-0.1142625138,0.1804570556,0.3090633154,-0.0905662403,0.1654050052,-0.0495853089,-0.1657474041,-0.2646009028,-0.1806016117,-0.2320661098,0.1591044813,-0.1456702203,0.062785618,0.010418159,0.8047193289,0.0654786751,-0.2943529487,0.1827455461,0.1267124414,0.2218532413,-0.0006962097,-0.5895354152,0.2010116726,-0.0986168161,0.2215839028,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1981","title":"wmt datasets fail to load","comments":"I'll do a patch release for this issue early tomorrow.\r\n\r\nAnd yes we absolutly need tests for the wmt datasets: The missing tests for wmt are an artifact from the early development of the lib but now we have tools to generate automatically the dummy data used for tests :)","body":"on master:\r\n```\r\npython -c 'from datasets import load_dataset; load_dataset(\"wmt14\", \"de-en\")'\r\nDownloading and preparing dataset wmt14\/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/stas\/.cache\/huggingface\/datasets\/wmt14\/de-en\/1.0.0\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e...\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 634, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt14\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\/wmt_utils.py\", line 760, in _split_generators\r\n    extraction_map = dict(downloaded_files, **manual_files)\r\n```\r\n\r\nit worked fine recently. same problem if I try wmt16.\r\n\r\ngit bisect points to this commit from Feb 25 as the culprit https:\/\/github.com\/huggingface\/datasets\/commit\/792f1d9bb1c5361908f73e2ef7f0181b2be409fa\r\n\r\n@albertvillanova ","comment_length":50,"text":"wmt datasets fail to load \n on master:\r\n```\r\npython -c 'from datasets import load_dataset; load_dataset(\"wmt14\", \"de-en\")'\r\nDownloading and preparing dataset wmt14\/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/stas\/.cache\/huggingface\/datasets\/wmt14\/de-en\/1.0.0\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e...\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 634, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt14\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\/wmt_utils.py\", line 760, in _split_generators\r\n    extraction_map = dict(downloaded_files, **manual_files)\r\n```\r\n\r\nit worked fine recently. same problem if I try wmt16.\r\n\r\ngit bisect points to this commit from Feb 25 as the culprit https:\/\/github.com\/huggingface\/datasets\/commit\/792f1d9bb1c5361908f73e2ef7f0181b2be409fa\r\n\r\n@albertvillanova  \n I'll do a patch release for this issue early tomorrow.\r\n\r\nAnd yes we absolutly need tests for the wmt datasets: The missing tests for wmt are an artifact from the early development of the lib but now we have tools to generate automatically the dummy data used for tests :)","embeddings":[-0.3290719688,-0.0584938265,-0.0104082478,0.5395736694,0.3104315698,0.0041043591,0.2097796947,0.0872702822,0.3129351139,0.1078865156,-0.0224996712,-0.1256612837,-0.296931684,0.1887943596,0.1114164367,0.1848195195,-0.1364242435,0.0058543072,-0.821605444,0.015868606,-0.1838497221,0.1984840035,-0.167342186,-0.0969723165,-0.5068030357,0.2375522107,-0.0300084837,0.2536428869,-0.2113710791,-0.4816319048,0.5141910315,0.0309756044,0.0518511981,0.5576848388,-0.0001141044,0.1515254825,0.2935239077,-0.01024595,-0.2466028333,-0.2974118888,-0.5779017806,-0.3827317059,0.0219598692,0.2245493531,-0.1884617954,0.0820968226,-0.2400668561,-0.3640685081,0.2820504308,0.2943232954,0.2214949429,0.4151587784,0.2789464891,-0.3091937304,0.0510975905,0.2907721698,-0.1379881054,-0.0280019324,0.0135747483,-0.1352173835,0.0232767537,0.0316457935,0.0733231753,0.3297724426,0.3457936347,-0.1587231904,0.2857407629,-0.0081027998,0.2792252004,0.0577791668,0.1964938641,-0.075779289,-0.2360399216,-0.0594310798,-0.130695492,-0.4276536703,0.2592030466,0.2658741474,0.0303441808,0.2660063207,-0.3589408398,0.0739574134,0.2212079316,0.0317872539,-0.3881673515,0.3771407902,-0.3114940822,0.0018198559,0.2749304175,-0.1902687997,0.1660659462,-0.1114594862,-0.0187486093,0.0837211162,-0.5072568655,0.2616916895,0.0255597904,0.2756205797,-0.0800576732,0.1582372934,-0.2520163655,0.014928068,0.2089373171,-0.0155652929,0.1787930727,0.1386247426,-0.109944649,-0.0626590401,0.2749227881,0.0550982095,-0.1853418797,0.1351207048,-0.1703789979,-0.4433655143,-0.089175649,-0.1894311756,0.1525510997,-0.0720274746,-0.2599219978,0.0635320917,0.135518983,-0.3142132461,0.0204186551,0.2855190933,0.0484095998,0.170236975,0.1179608032,0.00365311,-0.0042028385,-0.0357706062,-0.3564473689,-0.3906199932,-0.2991639674,0.0611203648,0.4078392386,-0.2185730487,0.2283025235,0.0004109528,-0.1310051382,-0.1273209304,-0.2269295007,-0.0077418489,0.2188936472,0.4824034274,0.071092926,0.2990789115,0.2443664521,0.1595918238,-0.0081311166,-0.0074383789,-0.0995391086,-0.2956005335,0.1295208484,0.2581728995,-0.0815234408,0.1570645422,-0.2294500917,-0.1136633456,0.1050200537,0.3215996325,-0.19287166,-0.2417548001,-0.1637251675,-0.0666386709,0.5321374536,0.5977163315,0.0095659709,-0.194243744,-0.3947505057,-0.0471861549,0.2344344705,0.332223624,-0.0298235733,0.1007020026,-0.26099509,-0.2470602542,-0.0820458457,-0.059449859,-0.1832846254,0.1371451616,-0.1313237101,0.2751179039,0.0324510708,-0.1263800412,0.0145307528,-0.2265354246,0.34289065,0.0891364664,-0.1580777168,0.0836280808,-0.079416573,-0.3164910972,0.2474278659,0.1886273175,0.1936812997,-0.1896170974,0.2157250047,0.0242878571,0.0684091523,0.0570893697,0.1292500794,0.0385188013,0.0848584697,-0.0127780912,-0.0889184177,0.0534026809,-0.4473019838,0.2964880764,0.2691924274,0.0437409878,0.1173462942,0.1208167449,-0.1622187644,0.1335769892,-0.3434743881,-0.1887686104,0.0876794457,0.1349028647,-0.0035151422,-0.1450345963,-0.0388088152,-0.023978943,-0.2259172648,0.1487141699,0.2445334345,0.0667199939,-0.1433457583,-0.1252084821,0.2802841961,0.2743451595,0.1635603011,-0.1697696,-0.1630631983,0.090857625,0.1399752051,0.395195812,-0.0871860459,0.1039211378,0.1791915148,-0.4736975133,0.1699429303,0.1361609697,-0.1402692944,-0.2992374897,0.1204287559,0.0424622707,-0.1103156656,0.2675850391,0.1175593957,0.0357345492,0.1648183763,0.0709900931,0.1453140378,-0.3879213929,0.280146718,-0.0923160762,0.3428555131,0.2670232058,0.0239535514,-0.0425498374,0.3805395365,0.1634019315,0.2022353113,0.0150986891,-0.3856002092,-0.0856616274,-0.0661873668,0.0501848124,0.3967731297,0.0735268518,0.0935864076,0.1786359698,0.1149715856,-0.302526772,0.4467021525,-0.0749787018,-0.0767071843,0.30438748,-0.0235597752,-0.0722774938,-0.2620562017,0.5669642687,0.0582100004,0.0062176031,-0.1975153834,-0.2630082965,-0.4342718422,0.2357833087,-0.3500567377,-0.5691426992,-0.2052633166,-0.2057430893,-0.2758672237,0.1181358621,-0.0988537818,0.2478714138,0.0687464401,0.2188895941,-0.0490783527,0.3465979993,-0.0977031887,-0.2259616405,-0.1064973697,0.0391054899,0.5312060118,-0.2995474339,0.327535212,-0.1957136095,0.063988708,-0.2002592385,-0.2282969952,0.0872321725,-0.0478701182,0.3465410471,0.2117685378,-0.0232825149,0.364433527,-0.1089356244,0.3259528279,-0.1572975963,-0.0153347533,0.0034697694,-0.044544246,-0.1426709145,-0.2228537053,-0.3446230888,-0.4408054054,-0.3883455396,-0.0106526911,-0.0261881314,-0.0418932587,0.091757603,-0.1342888921,-0.0532308593,-0.0178111736,-0.025891291,-0.3486146033,-0.2984477878,0.2000667304,-0.3020641506,-0.2819243073,0.2392926961,0.13543652,0.4544383287,0.040127337,-0.3937389553,0.5808199644,-0.1391113997,-0.1142625138,0.1804570556,0.3090633154,-0.0905662403,0.1654050052,-0.0495853089,-0.1657474041,-0.2646009028,-0.1806016117,-0.2320661098,0.1591044813,-0.1456702203,0.062785618,0.010418159,0.8047193289,0.0654786751,-0.2943529487,0.1827455461,0.1267124414,0.2218532413,-0.0006962097,-0.5895354152,0.2010116726,-0.0986168161,0.2215839028,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1981","title":"wmt datasets fail to load","comments":"still facing the same issue or similar:\r\nfrom datasets import load_dataset\r\nwtm14_test = load_dataset('wmt14',\"de-en\",cache_dir='.\/datasets')\r\n\r\n~.cache\\huggingface\\modules\\datasets_modules\\datasets\\wmt14\\43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\\wmt_utils.py in _split_generators(self, dl_manager)\r\n758 # Extract manually downloaded files.\r\n759 manual_files = dl_manager.extract(manual_paths_dict)\r\n--> 760 extraction_map = dict(downloaded_files, **manual_files)\r\n761\r\n762 for language in self.config.language_pair:\r\n\r\nTypeError: type object argument after ** must be a mapping, not list","body":"on master:\r\n```\r\npython -c 'from datasets import load_dataset; load_dataset(\"wmt14\", \"de-en\")'\r\nDownloading and preparing dataset wmt14\/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/stas\/.cache\/huggingface\/datasets\/wmt14\/de-en\/1.0.0\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e...\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 634, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt14\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\/wmt_utils.py\", line 760, in _split_generators\r\n    extraction_map = dict(downloaded_files, **manual_files)\r\n```\r\n\r\nit worked fine recently. same problem if I try wmt16.\r\n\r\ngit bisect points to this commit from Feb 25 as the culprit https:\/\/github.com\/huggingface\/datasets\/commit\/792f1d9bb1c5361908f73e2ef7f0181b2be409fa\r\n\r\n@albertvillanova ","comment_length":52,"text":"wmt datasets fail to load \n on master:\r\n```\r\npython -c 'from datasets import load_dataset; load_dataset(\"wmt14\", \"de-en\")'\r\nDownloading and preparing dataset wmt14\/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/stas\/.cache\/huggingface\/datasets\/wmt14\/de-en\/1.0.0\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e...\r\nTraceback (most recent call last):\r\n  File \"<string>\", line 1, in <module>\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 578, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 634, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt14\/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\/wmt_utils.py\", line 760, in _split_generators\r\n    extraction_map = dict(downloaded_files, **manual_files)\r\n```\r\n\r\nit worked fine recently. same problem if I try wmt16.\r\n\r\ngit bisect points to this commit from Feb 25 as the culprit https:\/\/github.com\/huggingface\/datasets\/commit\/792f1d9bb1c5361908f73e2ef7f0181b2be409fa\r\n\r\n@albertvillanova  \n still facing the same issue or similar:\r\nfrom datasets import load_dataset\r\nwtm14_test = load_dataset('wmt14',\"de-en\",cache_dir='.\/datasets')\r\n\r\n~.cache\\huggingface\\modules\\datasets_modules\\datasets\\wmt14\\43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\\wmt_utils.py in _split_generators(self, dl_manager)\r\n758 # Extract manually downloaded files.\r\n759 manual_files = dl_manager.extract(manual_paths_dict)\r\n--> 760 extraction_map = dict(downloaded_files, **manual_files)\r\n761\r\n762 for language in self.config.language_pair:\r\n\r\nTypeError: type object argument after ** must be a mapping, not list","embeddings":[-0.3290719688,-0.0584938265,-0.0104082478,0.5395736694,0.3104315698,0.0041043591,0.2097796947,0.0872702822,0.3129351139,0.1078865156,-0.0224996712,-0.1256612837,-0.296931684,0.1887943596,0.1114164367,0.1848195195,-0.1364242435,0.0058543072,-0.821605444,0.015868606,-0.1838497221,0.1984840035,-0.167342186,-0.0969723165,-0.5068030357,0.2375522107,-0.0300084837,0.2536428869,-0.2113710791,-0.4816319048,0.5141910315,0.0309756044,0.0518511981,0.5576848388,-0.0001141044,0.1515254825,0.2935239077,-0.01024595,-0.2466028333,-0.2974118888,-0.5779017806,-0.3827317059,0.0219598692,0.2245493531,-0.1884617954,0.0820968226,-0.2400668561,-0.3640685081,0.2820504308,0.2943232954,0.2214949429,0.4151587784,0.2789464891,-0.3091937304,0.0510975905,0.2907721698,-0.1379881054,-0.0280019324,0.0135747483,-0.1352173835,0.0232767537,0.0316457935,0.0733231753,0.3297724426,0.3457936347,-0.1587231904,0.2857407629,-0.0081027998,0.2792252004,0.0577791668,0.1964938641,-0.075779289,-0.2360399216,-0.0594310798,-0.130695492,-0.4276536703,0.2592030466,0.2658741474,0.0303441808,0.2660063207,-0.3589408398,0.0739574134,0.2212079316,0.0317872539,-0.3881673515,0.3771407902,-0.3114940822,0.0018198559,0.2749304175,-0.1902687997,0.1660659462,-0.1114594862,-0.0187486093,0.0837211162,-0.5072568655,0.2616916895,0.0255597904,0.2756205797,-0.0800576732,0.1582372934,-0.2520163655,0.014928068,0.2089373171,-0.0155652929,0.1787930727,0.1386247426,-0.109944649,-0.0626590401,0.2749227881,0.0550982095,-0.1853418797,0.1351207048,-0.1703789979,-0.4433655143,-0.089175649,-0.1894311756,0.1525510997,-0.0720274746,-0.2599219978,0.0635320917,0.135518983,-0.3142132461,0.0204186551,0.2855190933,0.0484095998,0.170236975,0.1179608032,0.00365311,-0.0042028385,-0.0357706062,-0.3564473689,-0.3906199932,-0.2991639674,0.0611203648,0.4078392386,-0.2185730487,0.2283025235,0.0004109528,-0.1310051382,-0.1273209304,-0.2269295007,-0.0077418489,0.2188936472,0.4824034274,0.071092926,0.2990789115,0.2443664521,0.1595918238,-0.0081311166,-0.0074383789,-0.0995391086,-0.2956005335,0.1295208484,0.2581728995,-0.0815234408,0.1570645422,-0.2294500917,-0.1136633456,0.1050200537,0.3215996325,-0.19287166,-0.2417548001,-0.1637251675,-0.0666386709,0.5321374536,0.5977163315,0.0095659709,-0.194243744,-0.3947505057,-0.0471861549,0.2344344705,0.332223624,-0.0298235733,0.1007020026,-0.26099509,-0.2470602542,-0.0820458457,-0.059449859,-0.1832846254,0.1371451616,-0.1313237101,0.2751179039,0.0324510708,-0.1263800412,0.0145307528,-0.2265354246,0.34289065,0.0891364664,-0.1580777168,0.0836280808,-0.079416573,-0.3164910972,0.2474278659,0.1886273175,0.1936812997,-0.1896170974,0.2157250047,0.0242878571,0.0684091523,0.0570893697,0.1292500794,0.0385188013,0.0848584697,-0.0127780912,-0.0889184177,0.0534026809,-0.4473019838,0.2964880764,0.2691924274,0.0437409878,0.1173462942,0.1208167449,-0.1622187644,0.1335769892,-0.3434743881,-0.1887686104,0.0876794457,0.1349028647,-0.0035151422,-0.1450345963,-0.0388088152,-0.023978943,-0.2259172648,0.1487141699,0.2445334345,0.0667199939,-0.1433457583,-0.1252084821,0.2802841961,0.2743451595,0.1635603011,-0.1697696,-0.1630631983,0.090857625,0.1399752051,0.395195812,-0.0871860459,0.1039211378,0.1791915148,-0.4736975133,0.1699429303,0.1361609697,-0.1402692944,-0.2992374897,0.1204287559,0.0424622707,-0.1103156656,0.2675850391,0.1175593957,0.0357345492,0.1648183763,0.0709900931,0.1453140378,-0.3879213929,0.280146718,-0.0923160762,0.3428555131,0.2670232058,0.0239535514,-0.0425498374,0.3805395365,0.1634019315,0.2022353113,0.0150986891,-0.3856002092,-0.0856616274,-0.0661873668,0.0501848124,0.3967731297,0.0735268518,0.0935864076,0.1786359698,0.1149715856,-0.302526772,0.4467021525,-0.0749787018,-0.0767071843,0.30438748,-0.0235597752,-0.0722774938,-0.2620562017,0.5669642687,0.0582100004,0.0062176031,-0.1975153834,-0.2630082965,-0.4342718422,0.2357833087,-0.3500567377,-0.5691426992,-0.2052633166,-0.2057430893,-0.2758672237,0.1181358621,-0.0988537818,0.2478714138,0.0687464401,0.2188895941,-0.0490783527,0.3465979993,-0.0977031887,-0.2259616405,-0.1064973697,0.0391054899,0.5312060118,-0.2995474339,0.327535212,-0.1957136095,0.063988708,-0.2002592385,-0.2282969952,0.0872321725,-0.0478701182,0.3465410471,0.2117685378,-0.0232825149,0.364433527,-0.1089356244,0.3259528279,-0.1572975963,-0.0153347533,0.0034697694,-0.044544246,-0.1426709145,-0.2228537053,-0.3446230888,-0.4408054054,-0.3883455396,-0.0106526911,-0.0261881314,-0.0418932587,0.091757603,-0.1342888921,-0.0532308593,-0.0178111736,-0.025891291,-0.3486146033,-0.2984477878,0.2000667304,-0.3020641506,-0.2819243073,0.2392926961,0.13543652,0.4544383287,0.040127337,-0.3937389553,0.5808199644,-0.1391113997,-0.1142625138,0.1804570556,0.3090633154,-0.0905662403,0.1654050052,-0.0495853089,-0.1657474041,-0.2646009028,-0.1806016117,-0.2320661098,0.1591044813,-0.1456702203,0.062785618,0.010418159,0.8047193289,0.0654786751,-0.2943529487,0.1827455461,0.1267124414,0.2218532413,-0.0006962097,-0.5895354152,0.2010116726,-0.0986168161,0.2215839028,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1977","title":"ModuleNotFoundError: No module named 'apache_beam' for wikipedia datasets ","comments":"I sometimes also get this error with other languages of the same dataset:\r\n\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n\r\n@lhoestq \r\n","body":"Hi\r\nI am trying to run run_mlm.py code [1] of huggingface with following \"wikipedia\"\/ \"20200501.aa\"  dataset:\r\n\r\n`python run_mlm.py     --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia     --dataset_config_name 20200501.aa     --do_train     --do_eval     --output_dir \/tmp\/test-mlm --max_seq_length 256\r\n`\r\n\r\nI am getting this error, but as per documentation, huggingface dataset provide processed version of this dataset and users can load it without requiring setup extra settings for apache-beam. could you help me please to load this dataset? \r\nDo you think I can run run_ml.py with this dataset? or anyway I could subsample and train the model? I greatly appreciate providing the processed version of all languages for this dataset, which allow the user to use them without setting up apache-beam,. thanks \r\n\r\nI really appreciate your help.\r\n@lhoestq \r\n\r\nthanks.\r\n\r\n[1] https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py\r\n\r\nerror I get: \r\n\r\n```\r\n>>> import datasets \r\n>>> datasets.load_dataset(\"wikipedia\", \"20200501.aa\")\r\nDownloading and preparing dataset wikipedia\/20200501.aa (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/dara\/temp\/cache_home_2\/datasets\/wikipedia\/20200501.aa\/1.0.0\/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 1099, in _download_and_prepare\r\n    import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n\r\n```","comment_length":55,"text":"ModuleNotFoundError: No module named 'apache_beam' for wikipedia datasets  \n Hi\r\nI am trying to run run_mlm.py code [1] of huggingface with following \"wikipedia\"\/ \"20200501.aa\"  dataset:\r\n\r\n`python run_mlm.py     --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia     --dataset_config_name 20200501.aa     --do_train     --do_eval     --output_dir \/tmp\/test-mlm --max_seq_length 256\r\n`\r\n\r\nI am getting this error, but as per documentation, huggingface dataset provide processed version of this dataset and users can load it without requiring setup extra settings for apache-beam. could you help me please to load this dataset? \r\nDo you think I can run run_ml.py with this dataset? or anyway I could subsample and train the model? I greatly appreciate providing the processed version of all languages for this dataset, which allow the user to use them without setting up apache-beam,. thanks \r\n\r\nI really appreciate your help.\r\n@lhoestq \r\n\r\nthanks.\r\n\r\n[1] https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py\r\n\r\nerror I get: \r\n\r\n```\r\n>>> import datasets \r\n>>> datasets.load_dataset(\"wikipedia\", \"20200501.aa\")\r\nDownloading and preparing dataset wikipedia\/20200501.aa (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/dara\/temp\/cache_home_2\/datasets\/wikipedia\/20200501.aa\/1.0.0\/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 1099, in _download_and_prepare\r\n    import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n\r\n``` \n I sometimes also get this error with other languages of the same dataset:\r\n\r\n  File \"\/dara\/libs\/anaconda3\/envs\/code\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n\r\n@lhoestq \r\n","embeddings":[-0.2131412625,-0.3382999003,0.001538666,0.3398338854,0.2197447717,0.235442251,0.2507144213,0.2680625618,0.1814851314,-0.0144523093,-0.0359319523,0.0364899971,-0.171056658,0.1019865423,0.1627780348,-0.4244243205,0.1850165129,-0.1324697435,-0.28719607,-0.0623331778,-0.2229189277,0.3467077613,-0.2787266672,0.0804548115,-0.0931125656,-0.1117043048,-0.0233219042,0.177757442,-0.054072462,-0.1476043314,0.1096976846,-0.2670561969,0.2810361981,0.3396104574,-0.0001204881,0.0703728497,0.3882136047,-0.210515365,-0.3313601017,-0.350505054,0.1729956865,-0.1294179261,0.138146013,-0.2263075113,-0.2553460896,-0.1438835114,0.1525821835,-0.2568327487,0.6282421947,-0.0624543279,0.1526159346,0.3100936413,0.3425408602,-0.0296261031,0.1350966841,0.2360670418,-0.0238424726,0.3365868032,-0.1699708253,-0.1384140104,0.1368692964,0.5407437086,-0.2211783677,-0.0125280395,0.4623476565,-0.2002913803,0.0987885296,-0.5092285275,0.116753906,0.2758406997,0.5031315088,-0.26792714,-0.1555137187,-0.0499551333,0.0338076949,-0.03536411,0.0232853089,0.22989133,-0.1179501638,0.0734570548,0.2295575142,-0.3673401475,-0.1967659593,0.2305391133,0.2532696426,0.2152464986,-0.0669510439,0.2225007564,0.1748564541,-0.1025937721,-0.4621145427,-0.0402746014,0.156126067,0.4422611892,-0.231231764,0.002823259,-0.1473004669,0.2895689607,0.2478105426,-0.1703071743,-0.4538461268,0.115305379,0.271007508,0.0321764424,0.0142410379,0.0148335658,0.1804619879,-0.1404565275,0.2022216916,0.1521080583,0.0284997057,0.0471678078,-0.0834322572,-0.0926120132,-0.6663607955,-0.0144305415,0.2571786344,-0.1104114279,-0.1701249182,0.0090121748,-0.548682034,-0.2558385134,-0.1178343967,0.4914840758,-0.0876239315,-0.0432364196,0.3066846132,0.2725155652,-0.3196043372,-0.332947433,-0.0259876121,0.3970681727,-0.4042425454,0.2580560446,0.1624427736,-0.185209617,0.3300159872,-0.0969913378,0.0612081997,-0.0126665076,0.093832843,0.0160338152,-0.2645788193,0.1376620084,0.2601518929,0.2935320139,0.3347854614,-0.1072975174,-0.1303373873,-0.0130061731,-0.2017825842,-0.0785866901,0.0849881843,0.0104481429,-0.1748300493,0.1516970396,-0.1632316411,0.2782359719,-0.0621964559,0.0589513369,-0.0485334769,0.251856178,-0.0731160566,-0.1545423865,0.4467759132,0.7152784467,-0.0924548283,-0.1832813919,-0.1248655617,0.0888795927,-0.1330818832,0.0378335416,-0.0201482009,0.2038291842,-0.1944642514,0.0074124318,0.2688691616,-0.4080123603,0.0281282626,0.0515699685,0.0758030936,0.0689504147,0.1730379313,-0.1063590124,-0.2876467109,-0.0430139713,-0.0504935347,0.2006358802,0.1387092173,-0.0972392559,-0.1221533567,-0.2608413398,0.0938873217,0.233961612,0.104605414,-0.0214352626,0.0598472171,0.5199069977,0.225365594,-0.1468617469,0.1068723127,0.3651059866,-0.103207089,0.2124211788,0.0801012442,-0.2059350163,-0.1896687299,0.0508289747,-0.2155381143,0.5097803473,-0.1102500632,-0.0496578589,-0.2062759697,-0.0623012409,-0.1766985357,-0.5256921053,0.0174633265,-0.1082244441,0.13924779,0.3834108114,-0.1024011075,0.2250570953,0.005566176,0.199066326,-0.8539184928,0.1584817022,-0.1625045389,0.0418303609,0.1188349947,0.2998978794,0.1418554336,-0.2450328022,0.1450186968,0.2261148095,0.0557804815,0.0124180801,0.0420554914,-0.1097348183,0.1478796601,-0.3845858276,0.2552294135,0.0223892294,0.0874389112,0.0594493076,0.0067111249,0.2668873668,0.154275924,0.3922149241,0.1603031754,0.1677394658,-0.0652832985,0.0154548204,-0.2106283009,-0.3001226485,0.3233882189,0.1925318241,0.331528157,-0.2222321182,-0.1197232157,-0.3948613703,0.2577231824,-0.025115706,0.227075994,-0.056015756,-0.6535443664,0.290453583,0.1373188943,-0.2333566248,0.1929884106,0.2338313311,-0.1762011796,0.2047182173,0.175831452,0.0648411438,0.2654977441,0.088253893,0.4785864651,-0.0819546282,-0.2294490039,-0.1114422157,-0.2178023607,-0.1793961972,-0.2691538036,0.1448301375,-0.3526336253,0.1320126653,-0.2323406637,-0.5582601428,-0.3051196039,-0.021738369,-0.5030117035,-0.0974111259,-0.3454745114,0.1530036777,0.1994351745,0.4281540513,0.2881691754,-0.0364836417,0.0833428353,-0.1567291617,-0.1880505979,-0.1502944678,-0.2787014246,-0.0067598391,0.3752514124,0.1393429637,0.1550534964,-0.2120730877,-0.0861392841,-0.0383539386,-0.4270922244,0.2244886011,-0.2542711496,0.2628424466,0.188561663,0.4130579233,-0.1757036746,-0.0502504744,0.3604340255,0.0225338917,-0.0979226083,-0.0710498542,0.0758107305,0.0790889561,0.0070314542,-0.287669003,-0.274710238,-0.3001061082,0.1849595755,0.113288939,-0.0709090233,0.3787707984,0.2008128613,0.1534817517,-0.0516646802,0.0577601455,-0.1449172944,0.0399068967,0.3708283007,-0.2393716276,-0.3559527993,0.1864742041,-0.1147243679,0.2194794416,-0.0391898118,-0.2681618631,-0.1775782257,0.3354676366,-0.1826422513,-0.0108241942,0.3228735626,0.2339251786,-0.1341770738,0.1562602818,-0.1174859405,-0.0410772935,-0.0801702961,-0.4929061532,0.3673178256,0.248804003,0.6314407587,-0.0803147256,0.8895260096,0.2763777077,0.2054400295,0.1781024039,-0.026158208,0.3253340125,-0.0767209008,-0.291257441,-0.1696092784,0.016621178,0.1385060251,0.306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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1977","title":"ModuleNotFoundError: No module named 'apache_beam' for wikipedia datasets ","comments":"Hi ! Thanks for reporting\r\nSome wikipedia configurations do require the user to have `apache_beam` in order to parse the wikimedia data.\r\n\r\nOn the other hand regarding your second issue\r\n```\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\nI've never experienced this, can you open a new issue for this specific error and provide more details please ?\r\nFor example what script did you use to get this, what language did you use, what's your environment details (os, python version, pyarrow version)..","body":"Hi\r\nI am trying to run run_mlm.py code [1] of huggingface with following \"wikipedia\"\/ \"20200501.aa\"  dataset:\r\n\r\n`python run_mlm.py     --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia     --dataset_config_name 20200501.aa     --do_train     --do_eval     --output_dir \/tmp\/test-mlm --max_seq_length 256\r\n`\r\n\r\nI am getting this error, but as per documentation, huggingface dataset provide processed version of this dataset and users can load it without requiring setup extra settings for apache-beam. could you help me please to load this dataset? \r\nDo you think I can run run_ml.py with this dataset? or anyway I could subsample and train the model? I greatly appreciate providing the processed version of all languages for this dataset, which allow the user to use them without setting up apache-beam,. thanks \r\n\r\nI really appreciate your help.\r\n@lhoestq \r\n\r\nthanks.\r\n\r\n[1] https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py\r\n\r\nerror I get: \r\n\r\n```\r\n>>> import datasets \r\n>>> datasets.load_dataset(\"wikipedia\", \"20200501.aa\")\r\nDownloading and preparing dataset wikipedia\/20200501.aa (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/dara\/temp\/cache_home_2\/datasets\/wikipedia\/20200501.aa\/1.0.0\/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 1099, in _download_and_prepare\r\n    import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n\r\n```","comment_length":84,"text":"ModuleNotFoundError: No module named 'apache_beam' for wikipedia datasets  \n Hi\r\nI am trying to run run_mlm.py code [1] of huggingface with following \"wikipedia\"\/ \"20200501.aa\"  dataset:\r\n\r\n`python run_mlm.py     --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia     --dataset_config_name 20200501.aa     --do_train     --do_eval     --output_dir \/tmp\/test-mlm --max_seq_length 256\r\n`\r\n\r\nI am getting this error, but as per documentation, huggingface dataset provide processed version of this dataset and users can load it without requiring setup extra settings for apache-beam. could you help me please to load this dataset? \r\nDo you think I can run run_ml.py with this dataset? or anyway I could subsample and train the model? I greatly appreciate providing the processed version of all languages for this dataset, which allow the user to use them without setting up apache-beam,. thanks \r\n\r\nI really appreciate your help.\r\n@lhoestq \r\n\r\nthanks.\r\n\r\n[1] https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py\r\n\r\nerror I get: \r\n\r\n```\r\n>>> import datasets \r\n>>> datasets.load_dataset(\"wikipedia\", \"20200501.aa\")\r\nDownloading and preparing dataset wikipedia\/20200501.aa (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/dara\/temp\/cache_home_2\/datasets\/wikipedia\/20200501.aa\/1.0.0\/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/dara\/temp\/libs\/anaconda3\/envs\/codes\/lib\/python3.7\/site-packages\/datasets-1.3.0-py3.7.egg\/datasets\/builder.py\", line 1099, in _download_and_prepare\r\n    import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n\r\n``` \n Hi ! Thanks for reporting\r\nSome wikipedia configurations do require the user to have `apache_beam` in order to parse the wikimedia data.\r\n\r\nOn the other hand regarding your second issue\r\n```\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```\r\nI've never experienced this, can you open a new issue for this specific error and provide more details please ?\r\nFor example what script did you use to get this, what language did you use, what's your environment details (os, python version, pyarrow version)..","embeddings":[-0.2131412625,-0.3382999003,0.001538666,0.3398338854,0.2197447717,0.235442251,0.2507144213,0.2680625618,0.1814851314,-0.0144523093,-0.0359319523,0.0364899971,-0.171056658,0.1019865423,0.1627780348,-0.4244243205,0.1850165129,-0.1324697435,-0.28719607,-0.0623331778,-0.2229189277,0.3467077613,-0.2787266672,0.0804548115,-0.0931125656,-0.1117043048,-0.0233219042,0.177757442,-0.054072462,-0.1476043314,0.1096976846,-0.2670561969,0.2810361981,0.3396104574,-0.0001204881,0.0703728497,0.3882136047,-0.210515365,-0.3313601017,-0.350505054,0.1729956865,-0.1294179261,0.138146013,-0.2263075113,-0.2553460896,-0.1438835114,0.1525821835,-0.2568327487,0.6282421947,-0.0624543279,0.1526159346,0.3100936413,0.3425408602,-0.0296261031,0.1350966841,0.2360670418,-0.0238424726,0.3365868032,-0.1699708253,-0.1384140104,0.1368692964,0.5407437086,-0.2211783677,-0.0125280395,0.4623476565,-0.2002913803,0.0987885296,-0.5092285275,0.116753906,0.2758406997,0.5031315088,-0.26792714,-0.1555137187,-0.0499551333,0.0338076949,-0.03536411,0.0232853089,0.22989133,-0.1179501638,0.0734570548,0.2295575142,-0.3673401475,-0.1967659593,0.2305391133,0.2532696426,0.2152464986,-0.0669510439,0.2225007564,0.1748564541,-0.1025937721,-0.4621145427,-0.0402746014,0.156126067,0.4422611892,-0.231231764,0.002823259,-0.1473004669,0.2895689607,0.2478105426,-0.1703071743,-0.4538461268,0.115305379,0.271007508,0.0321764424,0.0142410379,0.0148335658,0.1804619879,-0.1404565275,0.2022216916,0.1521080583,0.0284997057,0.0471678078,-0.0834322572,-0.0926120132,-0.6663607955,-0.0144305415,0.2571786344,-0.1104114279,-0.1701249182,0.0090121748,-0.548682034,-0.2558385134,-0.1178343967,0.4914840758,-0.0876239315,-0.0432364196,0.3066846132,0.2725155652,-0.3196043372,-0.332947433,-0.0259876121,0.3970681727,-0.4042425454,0.2580560446,0.1624427736,-0.185209617,0.3300159872,-0.0969913378,0.0612081997,-0.0126665076,0.093832843,0.0160338152,-0.2645788193,0.1376620084,0.2601518929,0.2935320139,0.3347854614,-0.1072975174,-0.1303373873,-0.0130061731,-0.2017825842,-0.0785866901,0.0849881843,0.0104481429,-0.1748300493,0.1516970396,-0.1632316411,0.2782359719,-0.0621964559,0.0589513369,-0.0485334769,0.251856178,-0.0731160566,-0.1545423865,0.4467759132,0.7152784467,-0.0924548283,-0.1832813919,-0.1248655617,0.0888795927,-0.1330818832,0.0378335416,-0.0201482009,0.2038291842,-0.1944642514,0.0074124318,0.2688691616,-0.4080123603,0.0281282626,0.0515699685,0.0758030936,0.0689504147,0.1730379313,-0.1063590124,-0.2876467109,-0.0430139713,-0.0504935347,0.2006358802,0.1387092173,-0.0972392559,-0.1221533567,-0.2608413398,0.0938873217,0.233961612,0.104605414,-0.0214352626,0.0598472171,0.5199069977,0.225365594,-0.1468617469,0.1068723127,0.3651059866,-0.103207089,0.2124211788,0.0801012442,-0.2059350163,-0.1896687299,0.0508289747,-0.2155381143,0.5097803473,-0.1102500632,-0.0496578589,-0.2062759697,-0.0623012409,-0.1766985357,-0.5256921053,0.0174633265,-0.1082244441,0.13924779,0.3834108114,-0.1024011075,0.2250570953,0.005566176,0.199066326,-0.8539184928,0.1584817022,-0.1625045389,0.0418303609,0.1188349947,0.2998978794,0.1418554336,-0.2450328022,0.1450186968,0.2261148095,0.0557804815,0.0124180801,0.0420554914,-0.1097348183,0.1478796601,-0.3845858276,0.2552294135,0.0223892294,0.0874389112,0.0594493076,0.0067111249,0.2668873668,0.154275924,0.3922149241,0.1603031754,0.1677394658,-0.0652832985,0.0154548204,-0.2106283009,-0.3001226485,0.3233882189,0.1925318241,0.331528157,-0.2222321182,-0.1197232157,-0.3948613703,0.2577231824,-0.025115706,0.227075994,-0.056015756,-0.6535443664,0.290453583,0.1373188943,-0.2333566248,0.1929884106,0.2338313311,-0.1762011796,0.2047182173,0.175831452,0.0648411438,0.2654977441,0.088253893,0.4785864651,-0.0819546282,-0.2294490039,-0.1114422157,-0.2178023607,-0.1793961972,-0.2691538036,0.1448301375,-0.3526336253,0.1320126653,-0.2323406637,-0.5582601428,-0.3051196039,-0.021738369,-0.5030117035,-0.0974111259,-0.3454745114,0.1530036777,0.1994351745,0.4281540513,0.2881691754,-0.0364836417,0.0833428353,-0.1567291617,-0.1880505979,-0.1502944678,-0.2787014246,-0.0067598391,0.3752514124,0.1393429637,0.1550534964,-0.2120730877,-0.0861392841,-0.0383539386,-0.4270922244,0.2244886011,-0.2542711496,0.2628424466,0.188561663,0.4130579233,-0.1757036746,-0.0502504744,0.3604340255,0.0225338917,-0.0979226083,-0.0710498542,0.0758107305,0.0790889561,0.0070314542,-0.287669003,-0.274710238,-0.3001061082,0.1849595755,0.113288939,-0.0709090233,0.3787707984,0.2008128613,0.1534817517,-0.0516646802,0.0577601455,-0.1449172944,0.0399068967,0.3708283007,-0.2393716276,-0.3559527993,0.1864742041,-0.1147243679,0.2194794416,-0.0391898118,-0.2681618631,-0.1775782257,0.3354676366,-0.1826422513,-0.0108241942,0.3228735626,0.2339251786,-0.1341770738,0.1562602818,-0.1174859405,-0.0410772935,-0.0801702961,-0.4929061532,0.3673178256,0.248804003,0.6314407587,-0.0803147256,0.8895260096,0.2763777077,0.2054400295,0.1781024039,-0.026158208,0.3253340125,-0.0767209008,-0.291257441,-0.1696092784,0.016621178,0.1385060251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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1973","title":"Question: what gets stored in the datasets cache and why is it so huge?","comments":"Echo'ing this observation: I have a few datasets in the neighborhood of 2GB CSVs uncompressed, and when I use something like `Dataset.save_to_disk()` it's ~18GB on disk.\r\n\r\nIf this is unexpected behavior, would be happy to help run debugging as needed.","body":"I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you!","comment_length":40,"text":"Question: what gets stored in the datasets cache and why is it so huge? \n I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you! \n Echo'ing this observation: I have a few datasets in the neighborhood of 2GB CSVs uncompressed, and when I use something like `Dataset.save_to_disk()` it's ~18GB on disk.\r\n\r\nIf this is unexpected behavior, would be happy to help run debugging as needed.","embeddings":[-0.0360606462,-0.0662569851,-0.1086274385,0.5382812619,0.1768519878,0.3046650887,-0.0687095299,0.256344825,-0.1498931944,-0.1115516722,-0.0044143545,-0.2384364158,-0.1599156111,-0.2295420617,0.1550879031,0.2438128591,0.1417650431,-0.0338777266,-0.0882983729,-0.0986522809,-0.1149732172,0.0371307395,0.0000266192,-0.1759963185,-0.5011152625,-0.1951541156,-0.1269186437,-0.0226079263,-0.1835439503,-0.1439898908,0.2084015608,-0.0900361612,0.3271475136,0.4473099709,-0.0001115048,-0.362196058,0.3227512538,-0.008409529,-0.3896471858,0.3312594295,-0.3773738742,-0.3923428059,-0.104653351,-0.1323722154,0.3790541589,-0.0305659622,-0.0775615722,-0.4155649245,-0.1001973376,0.236139521,0.2581386864,-0.2053128183,-0.3442173898,0.1373020858,0.1090577245,0.0737133399,-0.1573267132,-0.2792854011,0.2627800107,0.2356608063,-0.0979836285,0.1034083143,0.0833162814,-0.0201201383,0.2829732597,-0.0790503696,-0.3153593838,-0.2074168772,0.6619532704,0.2052862197,0.868317306,-0.3482807279,-0.0808232054,-0.2358752936,-0.1497370452,-0.230252862,0.1697393507,0.4599973857,0.0505639389,0.0040057399,-0.5909302831,-0.4143614471,-0.126802966,-0.133024171,-0.2453342229,-0.2898691297,-0.3402654529,0.0371112004,0.1638762504,-0.0343983844,0.3052972555,-0.3209097087,-0.0971701443,0.2020126134,-0.3501282036,-0.2808207273,-0.1091755852,0.4334006906,0.2432497591,0.0263104197,-0.2546927035,-0.1597532928,-0.1192888916,-0.049323611,0.2371750176,0.415301621,-0.1042509079,0.0482306182,-0.049397178,-0.348623395,-0.1449474841,-0.1352476478,0.1227397695,0.0719255283,0.4723917246,-0.4745317698,-0.1642237604,-0.3412801325,0.1787273735,0.2651431859,0.0549180731,0.0278724711,0.087542668,0.1612014174,-0.113969028,0.2380318344,-0.2866079211,-0.1712253392,0.0806446001,-0.1550655216,-0.2483567297,-0.1152959988,-0.0712154582,0.248961091,0.3250068128,-0.2040179968,0.2558794916,0.0902936906,-0.0581718944,-0.1221661717,0.1817219257,-0.3663792312,0.4594609737,0.3355002701,-0.1150589138,0.4209492803,-0.1799025387,0.1078725606,-0.2113251984,0.2094638646,-0.4173648953,-0.4349770248,-0.0622864813,0.0899464861,-0.2316663861,0.0077563892,-0.3615067303,0.0903249383,0.4243746996,0.1884943247,0.2217032462,-0.1052607223,-0.3592414558,-0.2606439292,-0.1278331131,0.1828977466,-0.5495997071,0.2316555083,-0.0171850193,0.2038089335,0.0265679266,0.3862605393,-0.0676840916,0.0514887571,-0.0738906711,-0.1387885064,-0.0273051374,0.0410684757,-0.6250160336,0.1765685976,0.3490022719,-0.0522845425,-0.0821781605,0.3963996768,0.0915794894,-0.0462785922,-0.1137974858,0.2983604968,-0.2702443004,-0.142247498,-0.3710263968,-0.1000405848,0.0486651435,-0.0800512284,0.0584715568,-0.2000578046,-0.0648278594,0.2171606123,-0.0068974877,0.1431343555,0.2213953137,0.380055666,-0.0400363021,0.2462723255,0.2545773387,0.0045563751,-0.3890997171,0.295788914,0.3068705499,-0.6919739842,0.1563736349,-0.1010126173,0.116968289,0.1049530879,-0.0330854319,-0.1894969791,-0.0202213936,0.08104489,0.0512779169,-0.1019686386,-0.0221285317,0.4722431898,0.003787111,-0.1720621735,-0.3683245778,-0.205806911,0.0847271383,0.0366322547,-0.1773812622,0.0202002451,0.0359575339,-0.1158996299,-0.1611272395,0.1403155178,0.1411769539,-0.1652727574,0.2010636926,0.5026519895,-0.0098972637,0.1133935004,0.3529167473,-0.0542698205,0.1295189708,-0.182449311,-0.2455490977,0.0227384232,-0.1056368575,0.0891257897,-0.0018707897,-0.1094690338,0.0670585856,0.0806366354,0.2521679997,0.0496883616,0.1556522697,-0.110239245,0.3004677892,0.3928951621,-0.0488347411,0.2108535618,0.4110551775,-0.2141384929,0.0022680063,0.163585484,-0.2870114148,-0.4692252874,0.0958473831,0.3619622886,0.4408290386,0.0798801929,0.3315306902,-0.1054326594,0.2634226084,-0.1867278963,0.0832477659,0.0455255657,0.0130668087,-0.1717012078,0.1634775698,-0.002815672,-0.0295009632,0.280049324,-0.0815472305,0.236097008,-0.0313673951,0.4049853981,-0.2644424736,-0.0564952083,-0.0029137924,0.3724592924,-0.2667140961,-0.1263842583,-0.1069569215,-0.0781468824,0.2320266664,-0.2643210888,-0.2530310452,0.6069620252,0.0467410535,0.1223386526,0.1749621034,-0.0454662256,-0.3047426343,0.1147944331,0.1704497039,-0.3243663311,0.2530548275,0.2005155385,0.1197219044,-0.2608419061,-0.1869264245,-0.1394843906,0.1243320853,0.2161901593,-0.3505869508,0.030197816,-0.2612145245,0.1456113309,-0.3643140197,-0.1169227809,-0.0494125262,0.1684373319,-0.0416090861,-0.0869178921,0.1603957862,-0.0047819167,-0.2482867539,0.0057886313,0.2127772719,-0.1623176187,-0.0298233572,0.1968926191,-0.1227709427,0.0286222864,-0.1135850474,-0.0954577699,-0.3736051023,-0.796487093,0.3856806755,0.1086682752,-0.2260926962,0.1569166183,0.1852319539,-0.0288626626,0.1390071064,-0.7766131163,0.2781107426,-0.2700860202,0.1859996915,0.0507349037,-0.0482592918,0.2186818868,-0.245657891,0.0444292389,-0.1558119804,-0.3363452852,-0.2957929373,0.1520014703,0.5615339875,-0.2364708334,-0.034599416,0.3850360215,0.5469372272,0.4004915059,-0.0670359656,-0.067981489,0.4184623659,0.5475918055,-0.3115632832,-0.0791823193,0.2339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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1973","title":"Question: what gets stored in the datasets cache and why is it so huge?","comments":"Thanks @ioana-blue for pointing out this problem (and thanks also @justin-yan). You are right that current implementation of the datasets caching files take too much memory. We are definitely changing this and optimizing the defaults, so that the file sizes are considerably reduced. I will come back to you as soon as this is fixed.","body":"I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you!","comment_length":55,"text":"Question: what gets stored in the datasets cache and why is it so huge? \n I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you! \n Thanks @ioana-blue for pointing out this problem (and thanks also @justin-yan). You are right that current implementation of the datasets caching files take too much memory. We are definitely changing this and optimizing the defaults, so that the file sizes are considerably reduced. I will come back to you as soon as this is fixed.","embeddings":[-0.0833511651,0.0121250013,-0.1291754693,0.5186682343,0.1493166834,0.2426836044,-0.0757901371,0.3019647598,-0.0877328739,-0.0359353572,-0.0295592695,-0.2534342706,-0.0936839208,-0.2008035481,0.0723080933,0.1365639567,0.0901818648,-0.0653728023,-0.0765932277,-0.0797738656,-0.1062290147,-0.0433256477,-0.0135121122,-0.1718011051,-0.5766088963,-0.1823077798,-0.125397265,-0.0051542921,-0.2007423937,-0.1783480495,0.1808399558,-0.0291627496,0.3237573504,0.3823104501,-0.0001080761,-0.3183884621,0.3569486737,0.0258181766,-0.35998106,0.3442702293,-0.5000380874,-0.297462225,-0.1312585324,-0.1360443085,0.3404588699,-0.0457876362,-0.0374122113,-0.3942119479,-0.0077165314,0.2449931949,0.2759654224,-0.2257376611,-0.305583477,0.0927578658,0.2078104913,0.0236138646,-0.1338836402,-0.2668149173,0.2703861892,0.1654694229,-0.1730728447,0.1816094667,0.0901528075,-0.0411953926,0.2849489152,-0.0867872685,-0.2421435118,-0.203481257,0.6005621552,0.2197853029,0.9331120849,-0.3052316308,-0.100264661,-0.1678259373,-0.1438965201,-0.151291132,0.2134750932,0.4073275328,0.0708834007,-0.0055508926,-0.5161411166,-0.4197382331,-0.1441660076,-0.1212616116,-0.1735284775,-0.251204282,-0.2740025818,0.0589072704,0.161728397,-0.0618541129,0.4042038023,-0.2412802577,-0.1609716415,0.1731912047,-0.3817909956,-0.2755789757,-0.2229240835,0.4864886403,0.2402953506,0.0038443923,-0.2600577772,-0.109291926,-0.1039826646,-0.0202954579,0.2225336134,0.3948453367,-0.1059476063,0.1032147035,-0.0339701734,-0.3564936519,-0.2345282584,-0.1782754809,0.1071284637,0.0232658144,0.4347737134,-0.4517464638,-0.1963359118,-0.3539862931,0.2049482316,0.1451401711,-0.0209944863,-0.0245751273,0.1459482014,0.1945821941,-0.1634193063,0.2311792821,-0.2916731834,-0.2418981194,0.0145778842,-0.21426332,-0.2492374033,-0.062359143,-0.1481209248,0.3357031047,0.3038076162,-0.1790136844,0.280624181,0.0677447021,-0.0248508584,-0.1428686231,0.2385396063,-0.3573527038,0.4010714591,0.328265816,-0.1311090738,0.2729564607,-0.2001665831,0.1455440074,-0.190713495,0.1966866404,-0.5129279494,-0.4545753598,-0.0051716887,0.1176294163,-0.10547591,0.037894208,-0.3192513287,0.0472883806,0.4130821824,0.1691479236,0.1751920283,-0.1207905561,-0.4018245041,-0.232427299,-0.1263591647,0.1870438606,-0.389220655,0.2134615779,0.009946526,0.2114602923,0.05776041,0.3836231828,-0.1483823806,0.0776749998,-0.0870076343,-0.1209373623,0.0001559153,-0.079579778,-0.636710763,0.1218225881,0.381228596,-0.1184332147,-0.0189932287,0.4304911792,0.0283242892,-0.0962783918,-0.1710998863,0.2358875573,-0.2705536783,-0.1395442039,-0.2917768061,-0.1208130792,-0.0083782794,-0.0380461402,0.0865780488,-0.1643362194,-0.0146144964,0.330711633,-0.0058595021,0.1374899894,0.0867403299,0.3432820439,-0.0301416107,0.2613257468,0.3053285778,0.0460471138,-0.4341362715,0.27020818,0.1865427196,-0.6862898469,0.1906158924,-0.1346430629,0.1436361521,0.0420822687,-0.0126282424,-0.1946732551,0.0207596421,0.0416758806,0.0679130852,-0.2097644061,-0.0188928451,0.573993206,-0.0988553315,-0.1863345951,-0.3965398967,-0.216348961,0.0431044549,0.0391309336,-0.073134616,-0.0137702739,0.0558140986,-0.0892109498,-0.1688681096,0.1361524612,0.2321187705,-0.1308439374,0.217418775,0.5695483088,-0.0355291814,0.0977270901,0.371270448,-0.1007415727,0.0756179765,-0.1984051317,-0.2419497371,-0.0179809146,-0.1506020278,0.1063494533,0.0278175846,-0.1154013798,0.0016547996,0.0606407076,0.2964203358,0.0728689805,0.1380551755,-0.105885841,0.3245917559,0.4382680655,-0.0780995041,0.176500082,0.4605905414,-0.1509393007,-0.0410198718,0.1564587951,-0.2423934191,-0.4789295197,0.1506143659,0.331137985,0.3907627761,0.10922084,0.2850667536,-0.1033120528,0.2868413031,-0.1608241349,0.0812226087,0.0667128861,0.0219489355,-0.2884733677,0.0932127684,-0.0410421491,-0.0126916254,0.2164717466,-0.055618044,0.2983462811,-0.0109923044,0.3491840065,-0.2550541162,-0.1621956378,-0.0070126061,0.3860312402,-0.2329478413,-0.1407172233,-0.1002913192,-0.0584415197,0.2660431862,-0.1486243904,-0.2300077677,0.5778571963,0.0820519626,0.1423575133,0.088034831,-0.0799468681,-0.3060518205,0.111074701,0.24933514,-0.3045134842,0.1999036223,0.1163125038,0.0619489402,-0.2768292129,-0.1332397014,-0.1086862162,0.1835201979,0.198715046,-0.3027626276,0.0109501537,-0.3178112209,0.138032198,-0.3626973927,-0.1848442107,-0.1165110394,0.1484915912,-0.0446062349,-0.0062331846,0.2158530802,-0.0736985654,-0.162884444,-0.0196522772,0.1723953187,-0.1615498811,0.0438599549,0.3077871799,-0.1309025288,0.0314119086,-0.2782609761,-0.0471064523,-0.3229497373,-0.8187430501,0.2975212932,0.1009911448,-0.2260670811,0.1321332455,0.2460592389,0.0601855591,0.1901030242,-0.7528636456,0.1599376947,-0.2401581258,0.1551108658,-0.0018200498,-0.0307548046,0.2011798173,-0.1798158139,0.0073360302,-0.1245791614,-0.1967882961,-0.2765602469,0.1456563324,0.4915038943,-0.192502737,-0.0633852035,0.3370993137,0.5671008825,0.5190479755,0.0446704403,-0.0705253929,0.3782269955,0.4487402737,-0.3152042031,-0.0880659968,0.2686746716,-0.14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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1973","title":"Question: what gets stored in the datasets cache and why is it so huge?","comments":"Thank you! Also I noticed that the files don't seem to be cleaned after the jobs finish. Last night I had only 3 jobs running, but the cache was still at 180GB. ","body":"I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you!","comment_length":32,"text":"Question: what gets stored in the datasets cache and why is it so huge? \n I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you! \n Thank you! Also I noticed that the files don't seem to be cleaned after the jobs finish. Last night I had only 3 jobs running, but the cache was still at 180GB. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1973","title":"Question: what gets stored in the datasets cache and why is it so huge?","comments":"Hi ! As Albert said they can sometimes take more space that expected but we'll fix that soon.\r\n\r\nAlso, to give more details about caching: computations on a dataset are cached by default so that you don't have to recompute them the next time you run them.\r\n\r\nSo by default the cache files stay on your disk when you job is finished (so that if you re-execute it, it will be reloaded from the cache).\r\nFeel free to clear your cache after your job has finished, or disable caching using\r\n```python\r\nimport datasets\r\n\r\ndatasets.set_caching_enabled(False)\r\n```","body":"I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you!","comment_length":95,"text":"Question: what gets stored in the datasets cache and why is it so huge? \n I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you! \n Hi ! As Albert said they can sometimes take more space that expected but we'll fix that soon.\r\n\r\nAlso, to give more details about caching: computations on a dataset are cached by default so that you don't have to recompute them the next time you run them.\r\n\r\nSo by default the cache files stay on your disk when you job is finished (so that if you re-execute it, it will be reloaded from the cache).\r\nFeel free to clear your cache after your job has finished, or disable caching using\r\n```python\r\nimport datasets\r\n\r\ndatasets.set_caching_enabled(False)\r\n```","embeddings":[-0.0611622036,-0.0334960222,-0.1297230124,0.5127940774,0.1173948124,0.2473039627,-0.0104232728,0.2427644581,-0.0756754801,-0.0384477898,-0.0576454513,-0.2856124043,-0.062841095,-0.1385336965,0.1360729337,0.0736555532,0.0571287647,-0.0924019665,-0.0630483553,-0.160524711,-0.1804127246,0.0023389754,-0.043512404,-0.1090048254,-0.6069411039,-0.1671299934,-0.1760504842,0.013318684,-0.1266205758,-0.2256072462,0.1812643111,-0.0810930282,0.3298203349,0.5128854513,-0.0001129724,-0.3193747401,0.3587917089,0.0624000914,-0.4379311204,0.2950620949,-0.3354638219,-0.4271190464,-0.0203636494,-0.1686972231,0.3214415014,-0.0767427906,-0.132932812,-0.4167048633,-0.0587260686,0.2930725813,0.2494700253,-0.1366453171,-0.3039160669,0.1178890467,0.1235289797,-0.0146650802,-0.0452343635,-0.2453737408,0.2158931941,0.1646102369,0.0025354703,0.147770077,0.0169417318,0.000306403,0.2684416175,-0.0396770462,-0.2254711986,-0.311825633,0.6033416986,0.2232895494,0.8363260031,-0.2880611718,-0.202964142,-0.2979716957,-0.1118043363,-0.2330391854,0.1009243131,0.4950154722,0.0988687202,-0.0455195792,-0.4867727458,-0.3448884785,-0.0777859464,-0.0465623252,-0.2821796536,-0.1423802972,-0.2524193227,0.1125405356,0.1055423319,-0.0284945481,0.3575477898,-0.3363456726,-0.0157869067,0.2345681787,-0.3509426117,-0.2011492401,-0.1322093159,0.5577651262,0.1247597337,0.0052215718,-0.3312217295,-0.1804830283,-0.1822399199,0.0167691,0.1956836283,0.383474052,-0.175694719,0.1844043881,0.0359155759,-0.3828605711,-0.2349632233,-0.1346769482,0.1312394291,-0.0012338117,0.4762334526,-0.4796627164,-0.098683998,-0.3029206395,0.170734942,0.2444289476,-0.0966221392,0.0273224618,0.1169028357,0.2148654312,-0.2545852959,0.2108535916,-0.2304310501,-0.1243429109,-0.0487615392,-0.1454728693,-0.2492411733,-0.0925647914,-0.067214489,0.2685973346,0.285683006,-0.2234884351,0.3373494446,0.0277760718,0.1063642949,-0.1411570907,0.2592744827,-0.3753346801,0.3710655272,0.4655985832,-0.0162447132,0.3749172986,-0.1396909803,0.1204946488,-0.270875901,0.2553671598,-0.5015741587,-0.4222280383,0.0191155411,0.104588069,-0.2029598951,-0.0228899047,-0.3181248009,-0.0490736701,0.4451746643,0.2298892289,0.2339739054,-0.2258072942,-0.3481408358,-0.2585402131,-0.1718460917,0.1132037342,-0.3837774694,0.2007804215,0.0188006144,0.2824506462,0.0434069298,0.4158514738,-0.1315063983,0.1567609906,-0.053813342,-0.1209238023,0.0304627083,-0.1566626281,-0.6271005273,0.1202648431,0.4168612063,-0.0274888612,-0.1223238111,0.4630418122,0.0704054013,-0.0158333648,-0.1096921042,0.3284002542,-0.1558927745,-0.0901832432,-0.292834729,-0.0386738218,0.1061438322,-0.0216424521,0.1188717186,-0.2305828631,-0.1309048682,0.1836871356,0.0527519099,0.1259609312,0.0694234669,0.3437756598,-0.0101463469,0.3748395443,0.3232589364,-0.0078653451,-0.3653471172,0.2573634684,0.2419636548,-0.6245020032,0.1455274075,-0.1339732111,0.1272249967,0.145066157,-0.0969081521,-0.2118736058,-0.0223334916,0.0297688544,0.1151991263,-0.2108961046,-0.0449691117,0.5452654958,-0.0635576248,-0.1103802994,-0.418202579,-0.170804143,0.0938118473,0.0492023975,-0.1359282136,0.0650078729,0.1010837629,-0.0895123556,-0.174693495,0.1081861854,0.2973416746,-0.1439442188,0.1077988073,0.5784066916,-0.0145122204,0.1164007708,0.423370868,-0.0106687611,0.0905218795,-0.1879730672,-0.1697003543,0.0567103922,-0.1616250128,0.1676454693,-0.0029339595,-0.0588439256,0.0638449341,0.1050605476,0.1870781779,0.0860394165,0.1927024722,-0.0662449822,0.3792734444,0.4196597338,-0.0819109827,0.1654995084,0.4912531972,-0.1889422238,0.0204286166,0.1143535301,-0.3488580883,-0.4260418713,0.0824612379,0.3058714271,0.4276180863,0.0918692946,0.2786064148,-0.0187046304,0.1948915273,-0.196464777,0.1110937744,0.1457482576,-0.0842692032,-0.2639130056,0.1610333174,-0.0088864975,0.0190624893,0.1868859828,-0.1532470137,0.3150743842,-0.0810955986,0.4258531332,-0.2198471129,-0.0666332394,-0.0421657376,0.1854019612,-0.2399906963,-0.2696067095,-0.164760679,-0.091042459,0.2942963541,-0.1827508509,-0.2300923616,0.4699304104,0.0185604449,0.0107689556,0.1471359432,-0.0653238669,-0.3312746882,0.0642062724,0.1377596259,-0.2550904453,0.2090715319,0.0785148069,0.0315914005,-0.2303849459,-0.1585240811,-0.1949876696,0.107504569,0.1837016493,-0.2883615196,0.0521753766,-0.2645152211,0.1547861248,-0.2806056142,-0.1142954752,-0.0577976145,0.1149192452,-0.0509900562,0.0269431025,0.2000137419,-0.0313189924,-0.2764130831,0.0005234366,0.2373341471,-0.0265397169,0.0129480707,0.2392877042,-0.1557642072,-0.0176599529,-0.1436425298,-0.0100040752,-0.3633743823,-0.7905566096,0.3151326776,0.0385604016,-0.1788747907,0.2086083293,0.1866608411,0.0924993902,0.1387996078,-0.708563447,0.0724817663,-0.3142879307,0.2180024832,0.0143601261,0.0765080154,0.1385978162,-0.1808548272,0.0806726962,-0.1142867804,-0.2508400679,-0.4035722017,0.202921316,0.3824837506,-0.2886651754,-0.0345722921,0.2057446092,0.4963887036,0.5578774214,-0.0190250184,-0.1098964736,0.37469267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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1973","title":"Question: what gets stored in the datasets cache and why is it so huge?","comments":"Hi @ioana-blue, we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs.","body":"I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you!","comment_length":34,"text":"Question: what gets stored in the datasets cache and why is it so huge? \n I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you! \n Hi @ioana-blue, we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs.","embeddings":[-0.1255369484,-0.0336780734,-0.148232758,0.5212608576,0.1614960432,0.2426841706,-0.0925324187,0.2584299445,-0.0802801028,-0.0226318613,-0.0793154463,-0.1993971169,-0.0890249535,-0.1937061399,0.0600060374,0.1441113949,0.1028742194,-0.0664708018,-0.0705818161,-0.0968135297,-0.1211709902,0.0194596611,0.0352946147,-0.1679355055,-0.5302938819,-0.1817645133,-0.1013382226,-0.0436650887,-0.1651499122,-0.17851533,0.177683115,-0.0290396884,0.3176939487,0.4055050015,-0.0001109538,-0.3099729419,0.3714547157,0.1150019318,-0.4696372747,0.3074622452,-0.3643327653,-0.3740325272,-0.026445087,-0.1428351551,0.3908945322,0.0068974965,-0.0497719459,-0.4210889935,-0.0684004277,0.1861254722,0.2560692728,-0.1310575604,-0.3215969205,0.1046227515,0.1198371351,0.01389091,-0.146869272,-0.2766178846,0.2572325766,0.3019111156,-0.106885761,0.2367847115,0.0618183538,0.047615312,0.2798642218,-0.0198587086,-0.1303207129,-0.1588064134,0.6128367186,0.2203997523,0.9452199936,-0.3251812756,-0.1090440676,-0.2270799428,-0.1699434817,-0.1856394112,0.2053114325,0.3774011433,0.0623725355,-0.0036444445,-0.4708918333,-0.4204160869,-0.1368875504,-0.1069780588,-0.1935634911,-0.2392660975,-0.2930272818,0.0738702193,0.2006108016,-0.0358287878,0.3408772647,-0.2122146636,-0.1762360334,0.1255204082,-0.3456109464,-0.2916094065,-0.1444860846,0.4545297325,0.166012302,0.0392155647,-0.3247304559,-0.1198164672,-0.1323130429,0.0058710747,0.2860298157,0.3430542946,-0.0669386536,0.1038033813,-0.0429320224,-0.3748679757,-0.1334179938,-0.1865023524,0.1570681483,0.0631488264,0.4441578686,-0.5209648609,-0.0817443058,-0.3450357318,0.16527614,0.1584222019,0.0572482571,-0.0358495004,0.1435464323,0.1911145449,-0.110761255,0.2182204127,-0.2996869683,-0.214574486,0.017782798,-0.1605705768,-0.2281496823,-0.131633997,-0.0949164629,0.2396894097,0.2744666934,-0.164727509,0.2371573448,0.063488923,-0.0907145888,-0.1422198415,0.234905526,-0.4032544196,0.3749283552,0.4167662859,-0.0743716359,0.3681034148,-0.1970903575,0.1527627856,-0.227425918,0.244900763,-0.4916736484,-0.4740643501,-0.1170701385,0.108580485,-0.161923483,-0.0288123973,-0.3449948728,0.0922386199,0.3634020388,0.153567642,0.1603097767,-0.0919574946,-0.4179095328,-0.228961587,-0.1571575254,0.1740286499,-0.429195255,0.1551629603,-0.1355956793,0.1866011471,0.0713289827,0.365894109,-0.1201933548,0.0764698386,-0.0518555455,-0.1532371938,0.040617872,-0.105731748,-0.6648839712,0.1029138267,0.3574771881,-0.1740622371,-0.0367924348,0.4561774433,-0.0024712256,-0.0370603167,-0.2303043306,0.290750742,-0.2054569125,-0.1454718411,-0.3278050721,-0.077662088,0.0378576815,-0.0154083706,0.0659245476,-0.2320688069,-0.037144199,0.2797748744,-0.0108113438,0.193445757,0.1148358583,0.3457117677,0.031427227,0.3540056944,0.3015316725,-0.0218943059,-0.3611468673,0.2509370744,0.2351715714,-0.7048804164,0.1734369397,-0.1321182251,0.1448073238,0.1432245374,-0.0207026955,-0.1719018817,-0.0045520132,0.0870919824,0.0700759515,-0.178489238,-0.0182370413,0.4595890641,-0.0254144967,-0.1279367208,-0.3539280295,-0.1449450701,0.0487825051,0.0091999453,-0.1537999213,0.0313860886,0.0623050369,-0.0768781304,-0.1596943587,0.1524797529,0.2265719473,-0.1928770393,0.2952666283,0.5493305326,-0.0663553551,0.1707333922,0.3852481246,-0.0702986121,0.1017337367,-0.1299525499,-0.2927914858,-0.0375097282,-0.1519739479,0.1403201371,-0.0249337684,-0.029297566,0.0416705012,0.0892701671,0.3436175287,0.0104550952,0.1606137902,-0.1470387429,0.2927565575,0.4069283009,-0.0240144711,0.2519698441,0.47829023,-0.1528939605,0.0289916713,0.1744274199,-0.2387561053,-0.5282307267,0.1187324747,0.3788320124,0.4037412703,0.1002401188,0.3153688908,-0.1507233381,0.29839921,-0.1419318765,0.0769500509,0.080037564,-0.0390971676,-0.2749990821,0.1334964931,-0.0680702552,-0.0156767294,0.1987346411,-0.0829682723,0.3320787549,-0.0365241729,0.3400944471,-0.2261348516,-0.1101819202,0.0874783173,0.3231965899,-0.2059070915,-0.2550525665,-0.1116032153,-0.0590515845,0.2781714797,-0.1644494236,-0.1198915988,0.4901140034,0.0762803853,0.101694271,0.0700767115,-0.0708680451,-0.316455096,0.1030517593,0.1625664532,-0.2699268758,0.2397953272,0.1029200628,0.0852553025,-0.3361773193,-0.151069209,-0.1490524411,0.1190942228,0.2485793829,-0.2534008324,0.0126693146,-0.3917896152,0.1810095906,-0.2936210036,-0.1614059508,-0.0395210125,0.162891984,-0.1019608229,-0.0407595113,0.1762523502,0.0103803249,-0.2491561472,-0.0023531609,0.2122996747,-0.1259872913,0.0549067594,0.2655703127,-0.1126466468,0.0555636249,-0.1928707212,-0.0497126058,-0.366995573,-0.7784826756,0.2513565123,0.1395721436,-0.1240602359,0.1695470661,0.2248826474,-0.0192549191,0.1598345339,-0.7960984707,0.0848229229,-0.2673851848,0.2629162669,0.0260320809,0.0380782112,0.2360247225,-0.1774861664,0.028453432,-0.1528524905,-0.2566244602,-0.3062720299,0.1367166787,0.4193833172,-0.231095165,-0.0271235034,0.3263312578,0.5199459791,0.4282681048,0.0144179054,-0.0571797565,0.3847506344,0.5536596775,-0.388767004,-0.0796085298,0.2006558776,-0.0727028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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1965","title":"Can we parallelized the add_faiss_index process over dataset shards ?","comments":"Hi !\r\nAs far as I know not all faiss indexes can be computed in parallel and then merged. \r\nFor example [here](https:\/\/github.com\/facebookresearch\/faiss\/wiki\/Special-operations-on-indexes#splitting-and-merging-indexes) is is mentioned that only IndexIVF indexes can be merged.\r\nMoreover faiss already works using multithreading to parallelize the workload over your different CPU cores. You can find more info [here](https:\/\/github.com\/facebookresearch\/faiss\/wiki\/Threads-and-asynchronous-calls#internal-threading)\r\nSo I feel like the gains we would get by implementing a parallel `add_faiss_index` would not be that important, but let me know what you think.\r\n","body":"I am thinking of making the  **add_faiss_index** process faster. What if we run the add_faiss_index process on separate dataset shards and then combine them before (dataset.concatenate) saving the faiss.index file ?\r\n\r\nI feel theoretically this will reduce the accuracy of retrieval since it affects the indexing process.\r\n\r\n@lhoestq\r\n","comment_length":79,"text":"Can we parallelized the add_faiss_index process over dataset shards ? \n I am thinking of making the  **add_faiss_index** process faster. What if we run the add_faiss_index process on separate dataset shards and then combine them before (dataset.concatenate) saving the faiss.index file ?\r\n\r\nI feel theoretically this will reduce the accuracy of retrieval since it affects the indexing process.\r\n\r\n@lhoestq\r\n \n Hi !\r\nAs far as I know not all faiss indexes can be computed in parallel and then merged. \r\nFor example [here](https:\/\/github.com\/facebookresearch\/faiss\/wiki\/Special-operations-on-indexes#splitting-and-merging-indexes) is is mentioned that only IndexIVF indexes can be merged.\r\nMoreover faiss already works using multithreading to parallelize the workload over your different CPU cores. You can find more info [here](https:\/\/github.com\/facebookresearch\/faiss\/wiki\/Threads-and-asynchronous-calls#internal-threading)\r\nSo I feel like the gains we would get by implementing a parallel `add_faiss_index` would not be that important, but let me know what you think.\r\n","embeddings":[-0.2789407969,-0.0627319291,-0.1573459059,0.1106119156,-0.3771443665,0.2478514761,0.2097888738,0.1062705293,0.1371025592,0.1719250977,-0.1766027361,-0.1353845596,0.3414870501,0.1139966771,-0.3853704035,0.24953866,0.3513414562,-0.1033916771,0.2524386942,0.1125721633,-0.317397058,0.0103294859,0.029140437,-0.2674212158,-0.178014636,0.3508556485,-0.2582988143,0.0532396846,-0.0132865068,-0.2483889163,-0.2480458915,0.3926287889,0.067936942,0.3004924357,-0.0001269445,-0.0502257869,0.1069203466,0.0691175684,0.1533449739,0.5967572331,-0.2711214423,0.0120974537,-0.0762948021,-0.1729471833,-0.0013871079,-0.3030721545,-0.0339291841,-0.2620636523,-0.2039729506,-0.1719792485,-0.010249096,-0.1237887368,0.0848588869,0.0481446125,-0.0428055003,-0.2038855404,-0.1553710401,-0.1455569118,0.0888659507,0.223718822,0.2321635634,0.1709749401,0.0189927835,-0.1704597175,0.087762326,-0.0448629223,0.4525159895,-0.0526509918,0.0105196806,-0.1225785241,0.1086316556,0.0454834178,-0.4651236236,-0.2125894278,0.0684300289,0.0824425891,-0.2274452597,-0.1753317863,0.0390132815,0.0092715155,0.0714065358,-0.4730629027,0.049428571,0.1115148365,0.3911833167,0.0608306788,0.2611269355,0.1117506102,0.4002321959,-0.1047056168,0.0262903646,-0.1128797978,-0.0963885337,0.1314448863,-0.6142691374,0.0232872348,-0.0899243951,-0.2996670008,0.0209422838,-0.1793354005,-0.3022100329,0.2885174751,0.0202772673,0.1198161766,0.0016666742,-0.1536525786,0.2066206783,-0.250123322,-0.0125038568,-0.4881715775,-0.1144803241,0.1374482512,0.1478853822,-0.120116204,-0.6300248504,-0.1439967901,-0.3549608886,-0.1606156528,-0.0620705113,-0.1819740832,0.0238425918,-0.0995018035,0.1697277874,0.1614187956,0.3377495706,-0.0016715091,-0.1338706017,0.0112249758,-0.0672990829,-0.0540435165,-0.0022989584,-0.2795442045,0.2434519082,0.5302842259,-0.0042607989,-0.2697486579,-0.4165178835,-0.0278765894,-0.0764702484,0.1563667655,0.0336068086,-0.1561209559,0.0336155035,0.0777927712,-0.0235536657,0.0020426498,-0.0585453063,0.1687937975,-0.2429046035,0.1656707376,-0.2159646302,-0.3029153943,0.0556750037,0.0054091727,-0.0320964903,-0.1371088624,-0.1657654494,0.5699167848,0.1761478484,-0.0432697684,-0.0384708233,0.1838904023,-0.3282163739,-0.1131456792,0.1609256566,0.1564359665,-0.2961857617,-0.2146445662,-0.1436220556,-0.18578583,0.006723281,0.5707305074,-0.0211948249,0.0817070678,-0.287550211,0.5438025594,0.2934736013,-0.2815659344,0.0505191758,0.0885654539,-0.0611449778,-0.1653109938,0.0520843193,0.1922506392,0.1179279983,-0.0568645895,0.2276799828,0.2944645584,-0.0839017108,-0.1194113791,-0.2615699768,-0.3065003753,0.493680656,0.2430091202,-0.1071245745,0.0761088356,0.043990504,-0.7627868652,0.2860036194,-0.1323405355,0.1944724917,0.0383248217,0.2891156077,0.442709893,0.4458394647,-0.1140970737,0.0472650602,0.2294330001,-0.15346843,-0.001935605,0.3032384217,-0.3450814784,0.4740671217,-0.0507173128,0.0305639897,0.3955182433,-0.0676674023,-0.2117543072,0.1425748765,-0.3888503909,-0.331433624,0.4658032656,-0.3955696523,-0.1659502089,-0.0724599287,0.226380825,-0.0312096868,-0.033708334,-0.3186895251,0.0959291533,0.1591546685,-0.2405869514,-0.1059324145,0.3029584587,0.0247303136,0.0376866609,0.9103011489,0.2225018591,-0.0706852973,0.2421095669,0.0581994504,-0.25952214,-0.1585788429,-0.1893134713,0.3330977261,0.0545157604,0.088710919,0.5726435184,0.002658088,-0.1720752865,0.1380843222,0.161930114,0.0660996661,-0.0243275408,-0.0152292252,-0.1266034693,-0.1130965874,0.0886519253,-0.0277289692,0.5910162926,-0.2684080601,-0.0822856203,-0.1726530641,-0.0034399868,-0.0326986425,0.0887022391,-0.042020902,-0.1948680133,0.3121991754,0.1168773323,0.0520308949,-0.1584303528,0.380494982,-0.1136476398,-0.0397205241,0.3633241057,0.2026499957,0.0750135109,0.1684561819,-0.05808606,-0.0571013205,-0.1315051168,0.0688443705,0.0319430418,-0.011060901,-0.0850690529,0.1229411289,0.080090642,-0.1491916329,-0.2043133527,-0.396911025,-0.1968986541,0.2237753868,0.012626024,-0.2535367608,-0.0580514595,-0.0775191262,0.5329561234,-0.0946544781,-0.2684037983,-0.3124780953,-0.017047016,0.1478615552,-0.1259402335,0.2970302105,0.1887925416,-0.053231474,0.1188512146,-0.0868266001,-0.3709445,-0.0934977531,-0.1448415518,-0.0173402298,-0.1443885565,-0.2989169657,-0.2838769853,-0.1429505646,-0.1644759923,0.1537098587,-0.0450639203,-0.0964550599,-0.2686010003,0.0291599222,0.0496852957,0.1025891304,0.074838832,-0.3937217891,-0.2396276742,0.3449154496,-0.0618611723,0.0971541405,-0.3533826768,-0.2882671356,-0.1973514408,-0.2623565495,-0.2343729287,-0.3656171858,0.0202944782,0.1619838923,-0.1117467508,-0.1317776442,0.0018892486,0.0815019831,-0.2325874418,0.3353889287,0.0072120754,0.0734021515,-0.006610916,0.1284045875,-0.2605769634,0.1623670757,0.1447145343,-0.1115813255,-0.0068714102,0.0925001502,0.1554604173,0.2978833616,0.1614468992,0.1604870558,-0.1416181326,-0.0257230587,-0.1567706168,0.7340026498,0.2577253282,-0.0254654419,0.152501151,0.1177449226,0.0364684947,0.0644167885,-0.1403583139,0.2962310612,0.088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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1965","title":"Can we parallelized the add_faiss_index process over dataset shards ?","comments":"Actually, you are right. I also had the same idea. I am trying this in the context of end-ton-end retrieval training in RAG.  So far I have parallelized the embedding re-computation within the training loop by using datasets shards. \r\n\r\nThen I was thinking of can I calculate the indexes for each shard and combined them with **concatenate**  before I save.","body":"I am thinking of making the  **add_faiss_index** process faster. What if we run the add_faiss_index process on separate dataset shards and then combine them before (dataset.concatenate) saving the faiss.index file ?\r\n\r\nI feel theoretically this will reduce the accuracy of retrieval since it affects the indexing process.\r\n\r\n@lhoestq\r\n","comment_length":60,"text":"Can we parallelized the add_faiss_index process over dataset shards ? \n I am thinking of making the  **add_faiss_index** process faster. What if we run the add_faiss_index process on separate dataset shards and then combine them before (dataset.concatenate) saving the faiss.index file ?\r\n\r\nI feel theoretically this will reduce the accuracy of retrieval since it affects the indexing process.\r\n\r\n@lhoestq\r\n \n Actually, you are right. I also had the same idea. I am trying this in the context of end-ton-end retrieval training in RAG.  So far I have parallelized the embedding re-computation within the training loop by using datasets shards. \r\n\r\nThen I was thinking of can I calculate the indexes for each shard and combined them with **concatenate**  before I save.","embeddings":[-0.2849661112,-0.1696963161,-0.091261059,0.1680023968,-0.3235114217,0.3758886158,0.3347997963,0.0697201118,-0.0358868092,0.1546950489,-0.0876063108,0.0618726648,0.3824176192,-0.0200762153,-0.3032027185,0.1434956789,0.2809820771,-0.0584644936,0.2126163095,0.0394795276,-0.3338530064,-0.1382644624,-0.0715480521,-0.2807034552,-0.2238429785,0.1862551868,-0.3562583029,0.1115740836,-0.0008239155,-0.3895473778,-0.1900932938,0.3138128817,0.2559099495,0.0299698468,-0.0001277621,-0.0997196585,0.1779816151,0.0183813926,0.1161235645,0.6891212463,-0.2972025275,-0.026710866,-0.0404046066,-0.2445941269,0.0822643191,-0.2505580485,0.0011064857,-0.25172925,-0.0702404454,-0.2690679133,-0.0466796048,-0.0607918501,0.0575929023,0.0574581996,0.0406849682,-0.3079018891,-0.1213591099,0.1398369372,0.0839523226,0.2245052606,0.1343079507,0.0507928059,-0.0477382094,-0.1600026488,0.0726076812,0.1129783392,0.2452798784,0.1347260922,-0.0512386933,-0.1212150753,0.2552976012,-0.099278748,-0.5349696279,-0.1582330018,0.2663683295,-0.1595634073,-0.1438806504,-0.1659998596,0.104799062,0.0622216016,0.0662736371,-0.5065560341,-0.0104388818,0.0325589664,0.4056906998,0.0920149088,0.2955592871,0.1298534572,0.3458766937,0.0445572883,0.0997549519,-0.2029110193,-0.1997184157,0.1131251976,-0.6591157317,-0.0331773572,-0.2063943744,-0.2228663415,-0.1241202652,-0.0351017751,-0.1060766876,0.1871549338,0.2389025986,0.1609723717,-0.005884049,-0.0895845294,0.03438434,-0.1298580468,-0.1368224323,-0.4281221032,-0.1075878367,0.0488347597,0.0350251943,-0.1958869249,-0.5263495445,-0.0633085445,-0.4865528941,-0.0719926059,-0.102642633,-0.173439756,-0.1677077711,-0.1291918904,0.1297932118,0.0206174087,0.282286346,-0.1592240632,-0.170183748,0.0673740134,-0.0559870228,0.0045553483,-0.0279973727,-0.2161835879,0.0174257401,0.512678206,-0.0436444283,-0.2863789201,-0.3827866912,0.0505875498,-0.0481587462,0.1748678684,0.066413708,-0.2154343724,0.120924972,0.0772427544,-0.1290807575,-0.167744875,-0.0365751684,0.2454080284,-0.1966933906,0.2098246813,-0.2311400771,-0.2099299133,0.1646127403,-0.0386470743,-0.034817595,-0.1441643238,-0.0446498059,0.3352725506,0.3445595801,-0.0064366502,0.0062948894,0.1591218412,-0.28740412,-0.1396418661,0.2494934797,0.2338727713,-0.4014918208,-0.1169342399,-0.0487821698,0.0194945522,0.1672157645,0.5547193885,-0.1314323097,0.1645195782,-0.3813923597,0.5477818251,0.3399351835,-0.2826054096,-0.0182866473,0.0328421071,-0.1953346729,-0.096134454,0.263723284,0.1584376693,0.4460000992,0.0293704383,0.2704944611,0.2283853441,-0.066930443,-0.3063270748,-0.3454902768,-0.2478902787,0.2212375849,0.1413300633,-0.0717564598,0.1985466033,0.0511377342,-0.8309345841,0.3635245264,-0.0840666741,0.215919137,-0.0107421456,0.2092398107,0.3811731339,0.3770609498,-0.078118287,0.0771491155,0.1599858254,-0.0280741211,-0.0409755819,0.2488015443,-0.3538302481,0.4831629992,-0.0756302774,0.0241911225,0.4689189196,-0.1199094728,-0.2062516809,0.0621985607,-0.3314244151,-0.294170469,0.5498024225,-0.477976352,-0.0968439877,-0.1887799203,0.2802512944,0.0779903233,-0.1488750726,-0.2816527486,0.0475954078,0.0733578354,-0.2442056537,-0.0596472137,0.2386415154,-0.0809267387,0.1019926742,0.8778350949,0.1095690578,-0.1541573256,0.0711275265,0.1545335948,-0.3248102963,-0.0852531046,-0.2058341205,0.3224862218,0.2268131673,0.0249040499,0.3609706461,0.0308591612,-0.0908334851,0.0195496716,0.0234930795,0.0144608794,0.0800584033,-0.1024847478,0.0013242186,-0.0705540776,0.0450991653,-0.1120695174,0.3265722394,-0.1385033727,-0.1785280257,-0.2923904955,-0.0533653907,-0.0340082347,0.1097715273,-0.0718640909,-0.1530020833,0.3807485402,0.1627696007,0.1704768687,-0.12954624,0.2641075552,-0.194244653,0.024381429,0.2502625883,0.0645522252,0.2118131965,0.0696620122,-0.0877100155,-0.0326046273,-0.0260461178,0.0928725675,0.0639977083,-0.0158800166,-0.0416265838,0.0864127874,0.3875234723,-0.3104661107,-0.1104385033,-0.2621289492,-0.1685064137,0.1309842467,0.0351331495,-0.1759462953,-0.0140682012,-0.0472524725,0.5214400291,-0.1426884234,-0.3105710149,-0.1479194611,-0.1127294376,0.1196701005,-0.1296887845,0.2577828169,0.1593421251,0.1317600906,0.1190094948,-0.0478240699,-0.2820173502,0.0005472153,-0.170217514,0.0225036182,-0.2009871006,-0.3025679886,-0.2304276675,-0.3104281127,-0.1213891357,0.069540143,0.1559575647,-0.0151106054,-0.2252917439,0.1130053252,0.0651460513,0.1385291666,-0.1371595562,-0.3187462986,-0.2540183365,0.3161889613,-0.1976729631,-0.0086671589,-0.4010066092,-0.1311909854,-0.1447111815,-0.1944415271,-0.2484068274,-0.3096928,-0.1726858318,0.1525397152,-0.0263112243,-0.0767534673,-0.0058424822,-0.0332636461,-0.1281506121,0.6561332345,-0.0107417135,0.2173006535,0.0476656184,0.0933474228,-0.2506474555,0.2637120485,0.2129894942,-0.0281591471,0.0079064956,0.1556269079,0.2947208881,0.3695460558,0.0974454731,0.0661053285,0.0408578329,0.0288635343,-0.1932774633,0.9015139937,0.2490787357,-0.1850935221,0.1608608216,0.2594344914,0.1495266557,0.007931333,-0.1160737947,0.2399917841,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1965","title":"Can we parallelized the add_faiss_index process over dataset shards ?","comments":"@lhoestq  As you mentioned faiss is already using multiprocessing. I tried to do the add_index with faiss for a dataset object inside a RAY actor and the process became very slow... if fact it takes so much time. It is because a ray actor comes with a single CPU core unless we assign it more. I also tried assigning more cores but still running add_index in the main process is very fast. ","body":"I am thinking of making the  **add_faiss_index** process faster. What if we run the add_faiss_index process on separate dataset shards and then combine them before (dataset.concatenate) saving the faiss.index file ?\r\n\r\nI feel theoretically this will reduce the accuracy of retrieval since it affects the indexing process.\r\n\r\n@lhoestq\r\n","comment_length":72,"text":"Can we parallelized the add_faiss_index process over dataset shards ? \n I am thinking of making the  **add_faiss_index** process faster. What if we run the add_faiss_index process on separate dataset shards and then combine them before (dataset.concatenate) saving the faiss.index file ?\r\n\r\nI feel theoretically this will reduce the accuracy of retrieval since it affects the indexing process.\r\n\r\n@lhoestq\r\n \n @lhoestq  As you mentioned faiss is already using multiprocessing. I tried to do the add_index with faiss for a dataset object inside a RAY actor and the process became very slow... if fact it takes so much time. It is because a ray actor comes with a single CPU core unless we assign it more. I also tried assigning more cores but still running add_index in the main process is very fast. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1964","title":"Datasets.py function load_dataset does not match squad dataset","comments":"Hi !\r\n\r\nTo fix 1, an you try to run this code ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"squad\", download_mode=\"force_redownload\")\r\n```\r\nMaybe the file your downloaded was corrupted, in this case redownloading this way should fix your issue 1.\r\n\r\nRegarding your 2nd point, you're right that loading the raw json this way doesn't give you a dataset with the column \"context\", \"question\" and \"answers\". Indeed the squad format is a very nested format so you have to preprocess the data. You can do it this way:\r\n```python\r\ndef process_squad(examples):\r\n    \"\"\"\r\n    Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n    to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n    \"\"\"\r\n    out = {\"context\": [], \"question\": [], \"answers\":[]} \r\n    for paragraphs in examples[\"paragraphs\"]: \r\n        for paragraph in paragraphs: \r\n            for qa in paragraph[\"qas\"]: \r\n                answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n                out[\"context\"].append(paragraph[\"context\"].strip()) \r\n                out[\"question\"].append(qa[\"question\"].strip()) \r\n                out[\"answers\"].append(answers) \r\n    return out\r\n\r\ndatasets = load_dataset(extension, data_files=data_files, field=\"data\")\r\ncolumn_names = datasets[\"train\"].column_names\r\n\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n    datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n```\r\n\r\nHope that helps :)","body":"### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot!","comment_length":170,"text":"Datasets.py function load_dataset does not match squad dataset \n ### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot! \n Hi !\r\n\r\nTo fix 1, an you try to run this code ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"squad\", download_mode=\"force_redownload\")\r\n```\r\nMaybe the file your downloaded was corrupted, in this case redownloading this way should fix your issue 1.\r\n\r\nRegarding your 2nd point, you're right that loading the raw json this way doesn't give you a dataset with the column \"context\", \"question\" and \"answers\". Indeed the squad format is a very nested format so you have to preprocess the data. You can do it this way:\r\n```python\r\ndef process_squad(examples):\r\n    \"\"\"\r\n    Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n    to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n    \"\"\"\r\n    out = {\"context\": [], \"question\": [], \"answers\":[]} \r\n    for paragraphs in examples[\"paragraphs\"]: \r\n        for paragraph in paragraphs: \r\n            for qa in paragraph[\"qas\"]: \r\n                answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n                out[\"context\"].append(paragraph[\"context\"].strip()) \r\n                out[\"question\"].append(qa[\"question\"].strip()) \r\n                out[\"answers\"].append(answers) \r\n    return out\r\n\r\ndatasets = load_dataset(extension, data_files=data_files, field=\"data\")\r\ncolumn_names = datasets[\"train\"].column_names\r\n\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n    datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n```\r\n\r\nHope that helps 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1964","title":"Datasets.py function load_dataset does not match squad dataset","comments":"Thks for quickly answering\uff01\r\n### 1 I try the first way,but seems not work \r\n```\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 503, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 218, in main\r\n    datasets = load_dataset(data_args.dataset_name, download_mode=\"force_redownload\")\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\n### 2 I try the second way,and run the examples\/question-answering\/run_qa.py,it lead to another bug orz..\r\n```\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 523, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 379, in main\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1120, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1091, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"examples\/question-answering\/run_qa.py\", line 339, in prepare_train_features\r\n    if len(answers[\"answer_start\"]) == 0:\r\nTypeError: list indices must be integers or slices, not str\r\n```\r\n## may be the function prepare_train_features in run_qa.py need to fix,I think is that the prep\r\n```python\r\nfor i, offsets in enumerate(offset_mapping):\r\n        # We will label impossible answers with the index of the CLS token.\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        answers = examples[answer_column_name][sample_index]\r\n        print(examples,answers)\r\n        # If no answers are given, set the cls_index as answer.\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n``` ","body":"### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot!","comment_length":434,"text":"Datasets.py function load_dataset does not match squad dataset \n ### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot! \n Thks for quickly answering\uff01\r\n### 1 I try the first way,but seems not work \r\n```\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 503, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 218, in main\r\n    datasets = load_dataset(data_args.dataset_name, download_mode=\"force_redownload\")\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\n### 2 I try the second way,and run the examples\/question-answering\/run_qa.py,it lead to another bug orz..\r\n```\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 523, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 379, in main\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1120, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1091, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"examples\/question-answering\/run_qa.py\", line 339, in prepare_train_features\r\n    if len(answers[\"answer_start\"]) == 0:\r\nTypeError: list indices must be integers or slices, not str\r\n```\r\n## may be the function prepare_train_features in run_qa.py need to fix,I think is that the prep\r\n```python\r\nfor i, offsets in enumerate(offset_mapping):\r\n        # We will label impossible answers with the index of the CLS token.\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        answers = examples[answer_column_name][sample_index]\r\n        print(examples,answers)\r\n        # If no answers are given, set the cls_index as answer.\r\n        if len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[\"answer_start\"][0]\r\n            end_char = start_char + len(answers[\"text\"][0])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n\r\n    return tokenized_examples\r\n``` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1964","title":"Datasets.py function load_dataset does not match squad dataset","comments":"## I have fixed it, @lhoestq \r\n### the first section change as you said and add [\"id\"]\r\n```python\r\ndef process_squad(examples):\r\n    \"\"\"\r\n    Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n    to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n    \"\"\"\r\n    # print(examples)\r\n    out = {\"context\": [], \"question\": [], \"answers\":[],\"id\":[]} \r\n    for paragraphs in examples[\"paragraphs\"]: \r\n        for paragraph in paragraphs: \r\n            for qa in paragraph[\"qas\"]: \r\n                answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n                out[\"context\"].append(paragraph[\"context\"].strip()) \r\n                out[\"question\"].append(qa[\"question\"].strip()) \r\n                out[\"answers\"].append(answers) \r\n                out[\"id\"].append(qa[\"id\"]) \r\n    return out\r\ncolumn_names = datasets[\"train\"].column_names if training_args.do_train else datasets[\"validation\"].column_names\r\n# print(datasets[\"train\"].column_names)\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n    datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n# Preprocessing the datasets.\r\n# Preprocessing is slighlty different for training and evaluation.\r\nif training_args.do_train:\r\n    column_names = datasets[\"train\"].column_names\r\nelse:\r\n    column_names = datasets[\"validation\"].column_names\r\n# print(column_names)\r\nquestion_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\ncontext_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\nanswer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n```\r\n### the second section\r\n```python\r\ndef prepare_train_features(examples):\r\n    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n    # in one example possible giving several features when a context is long, each of those features having a\r\n    # context that overlaps a bit the context of the previous feature.\r\n    tokenized_examples = tokenizer(\r\n        examples[question_column_name if pad_on_right else context_column_name],\r\n        examples[context_column_name if pad_on_right else question_column_name],\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=data_args.max_seq_length,\r\n        stride=data_args.doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\" if data_args.pad_to_max_length else False,\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n    # its corresponding example. This key gives us just that.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position in the original context. This will\r\n    # help us compute the start_positions and end_positions.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    # Let's label those examples!\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        # We will label impossible answers with the index of the CLS token.\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        answers = examples[answer_column_name][sample_index]\r\n        # print(examples,answers,offset_mapping,tokenized_examples)\r\n        # If no answers are given, set the cls_index as answer.\r\n        if len(answers) == 0:#len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[0][\"answer_start\"]\r\n            end_char = start_char + len(answers[0][\"text\"])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n    return tokenized_examples\r\n```","body":"### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot!","comment_length":569,"text":"Datasets.py function load_dataset does not match squad dataset \n ### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot! \n ## I have fixed it, @lhoestq \r\n### the first section change as you said and add [\"id\"]\r\n```python\r\ndef process_squad(examples):\r\n    \"\"\"\r\n    Process a dataset in the squad format with columns \"title\" and \"paragraphs\"\r\n    to return the dataset with columns \"context\", \"question\" and \"answers\".\r\n    \"\"\"\r\n    # print(examples)\r\n    out = {\"context\": [], \"question\": [], \"answers\":[],\"id\":[]} \r\n    for paragraphs in examples[\"paragraphs\"]: \r\n        for paragraph in paragraphs: \r\n            for qa in paragraph[\"qas\"]: \r\n                answers = [{\"answer_start\": answer[\"answer_start\"], \"text\": answer[\"text\"].strip()} for answer in qa[\"answers\"]] \r\n                out[\"context\"].append(paragraph[\"context\"].strip()) \r\n                out[\"question\"].append(qa[\"question\"].strip()) \r\n                out[\"answers\"].append(answers) \r\n                out[\"id\"].append(qa[\"id\"]) \r\n    return out\r\ncolumn_names = datasets[\"train\"].column_names if training_args.do_train else datasets[\"validation\"].column_names\r\n# print(datasets[\"train\"].column_names)\r\nif set(column_names) == {\"title\", \"paragraphs\"}:\r\n    datasets = datasets.map(process_squad, batched=True, remove_columns=column_names)\r\n# Preprocessing the datasets.\r\n# Preprocessing is slighlty different for training and evaluation.\r\nif training_args.do_train:\r\n    column_names = datasets[\"train\"].column_names\r\nelse:\r\n    column_names = datasets[\"validation\"].column_names\r\n# print(column_names)\r\nquestion_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\ncontext_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\nanswer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n```\r\n### the second section\r\n```python\r\ndef prepare_train_features(examples):\r\n    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n    # in one example possible giving several features when a context is long, each of those features having a\r\n    # context that overlaps a bit the context of the previous feature.\r\n    tokenized_examples = tokenizer(\r\n        examples[question_column_name if pad_on_right else context_column_name],\r\n        examples[context_column_name if pad_on_right else question_column_name],\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=data_args.max_seq_length,\r\n        stride=data_args.doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\" if data_args.pad_to_max_length else False,\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n    # its corresponding example. This key gives us just that.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n    # The offset mappings will give us a map from token to character position in the original context. This will\r\n    # help us compute the start_positions and end_positions.\r\n    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\r\n\r\n    # Let's label those examples!\r\n    tokenized_examples[\"start_positions\"] = []\r\n    tokenized_examples[\"end_positions\"] = []\r\n\r\n    for i, offsets in enumerate(offset_mapping):\r\n        # We will label impossible answers with the index of the CLS token.\r\n        input_ids = tokenized_examples[\"input_ids\"][i]\r\n        cls_index = input_ids.index(tokenizer.cls_token_id)\r\n\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        answers = examples[answer_column_name][sample_index]\r\n        # print(examples,answers,offset_mapping,tokenized_examples)\r\n        # If no answers are given, set the cls_index as answer.\r\n        if len(answers) == 0:#len(answers[\"answer_start\"]) == 0:\r\n            tokenized_examples[\"start_positions\"].append(cls_index)\r\n            tokenized_examples[\"end_positions\"].append(cls_index)\r\n        else:\r\n            # Start\/end character index of the answer in the text.\r\n            start_char = answers[0][\"answer_start\"]\r\n            end_char = start_char + len(answers[0][\"text\"])\r\n\r\n            # Start token index of the current span in the text.\r\n            token_start_index = 0\r\n            while sequence_ids[token_start_index] != (1 if pad_on_right else 0):\r\n                token_start_index += 1\r\n\r\n            # End token index of the current span in the text.\r\n            token_end_index = len(input_ids) - 1\r\n            while sequence_ids[token_end_index] != (1 if pad_on_right else 0):\r\n                token_end_index -= 1\r\n\r\n            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\r\n            if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):\r\n                tokenized_examples[\"start_positions\"].append(cls_index)\r\n                tokenized_examples[\"end_positions\"].append(cls_index)\r\n            else:\r\n                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\r\n                # Note: we could go after the last offset if the answer is the last word (edge case).\r\n                while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:\r\n                    token_start_index += 1\r\n                tokenized_examples[\"start_positions\"].append(token_start_index - 1)\r\n                while offsets[token_end_index][1] >= end_char:\r\n                    token_end_index -= 1\r\n                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\r\n    return tokenized_examples\r\n```","embeddings":[-0.3537200987,0.0625078902,0.0333654545,0.3963988423,0.5554530621,0.0057210135,0.5457583666,0.3404688239,-0.0983101875,-0.1216232106,-0.1650660336,0.4508374333,0.1084147692,-0.0774192885,0.1859294176,0.2271618843,-0.1242239624,-0.0123368138,-0.1461081356,-0.2893434763,-0.1109781116,0.1282911897,-0.0823139772,0.2603987455,-0.1788990498,0.0166908093,0.2006343901,0.2723646462,-0.0037961667,-0.2199688405,0.3428422213,-0.2606053054,0.2361012548,0.5758861899,-0.0001148581,0.1132558137,-0.0115434257,-0.3397289217,-0.5327045321,-0.2002218962,-0.1021837071,-0.3757722974,0.1486449242,-0.2605553567,-0.1595326066,0.093737483,0.0203164779,-0.4645369053,0.3544548452,0.4883137345,0.213982597,0.2522563636,-0.1001010016,0.0775510594,0.1142548099,-0.0009760508,-0.06950894,0.1360990405,0.3347509801,-0.0582686849,0.0025541722,0.1724455059,0.0828086361,0.0568236373,0.2703095675,0.0064037591,0.199115023,-0.316155225,0.0564422533,0.3177836239,0.4627765119,-0.4630158544,-0.334705174,-0.2631344795,0.133104369,-0.2565729618,0.1320165247,0.0672778934,0.0511586368,-0.0424272642,0.0477764271,0.0376646332,0.1114287302,0.0368287303,0.0068916786,0.1999943703,0.0288672037,0.2383961529,-0.201539591,-0.1135483086,0.1148922518,-0.083149083,0.0818328112,0.392493546,-0.828517139,-0.0814113691,-0.2042951286,-0.1421720982,0.2562874556,0.0819008574,0.0386924408,-0.0269711483,0.148668915,0.1044574156,0.1687794775,0.3692035675,0.3052028716,0.2674016058,-0.0663638264,0.0668205246,-0.3717198968,0.2254103124,-0.3225816488,-0.1618057936,0.0960089266,0.0835877731,0.0062298947,-0.1927381605,-0.5517169237,0.1761802584,0.1544087082,0.0026421051,0.0974367931,0.324791342,0.0759165287,0.3802291751,0.0263226684,0.1048553437,-0.294623524,-0.3704161942,-0.2408784777,0.0330561139,-0.0880101547,0.0268361289,0.0183388889,-0.0356007777,0.3227834404,-0.1957616955,0.2114947587,-0.1790799946,0.1150045693,-0.2556711733,-0.1285898685,-0.0065955352,0.0298101194,0.0322048925,0.1742971241,-0.3730719388,-0.0482525341,0.1222925037,-0.3712684512,-0.0336521827,-0.1820591837,0.1871163845,-0.1391085088,0.0208789185,-0.2373687923,0.1698867232,0.3073554337,-0.1878401935,0.1063959599,-0.3961229026,-0.1664000899,-0.2122270316,0.3151646554,0.3882876039,-0.4074572027,-0.2117103338,0.1249395758,-0.0952139571,-0.0381632037,0.1216655001,-0.1516055316,0.1555615067,-0.20622392,-0.0070253811,0.7145847082,-0.8443276286,-0.4243383706,0.1099728644,-0.3059237599,-0.0194989499,-0.0710134581,0.066407755,0.1930091679,0.0147336535,0.0827305093,0.5066136718,-0.0362928361,0.0914031267,-0.1724580824,-0.2712134719,0.2526653707,0.1708515584,0.0318142921,-0.0380649008,0.0388377123,0.4989839792,0.332213819,0.1426163316,0.1112044156,-0.0299585946,-0.0359400138,-0.0821635574,0.114605628,-0.1481092274,-0.6798553467,0.1638657302,-0.1644516885,0.0439888574,0.2410787791,-0.1508302242,-0.2135134637,-0.0917369127,-0.4252310395,-0.1023878902,0.0743926764,0.1495984495,-0.0585200973,-0.1226450801,-0.159884721,0.3514788449,-0.2154513896,0.2284579277,-0.4849060774,-0.0965390429,-0.0637453571,-0.0362155363,0.0537013672,0.3318411708,0.087192513,-0.0709578693,-0.105753161,0.497995466,0.0180258732,0.1023961678,0.2883635163,-0.2016443759,0.0996336117,-0.0990453884,0.0020643868,0.2259092182,0.1946656704,-0.0812613964,0.0224434528,0.1418915838,0.1574391872,0.0746554807,-0.0048610461,-0.1536109,0.139052406,0.0118647469,0.0111010959,0.0971629173,0.0520384163,0.2940087616,0.5483121872,0.1111362055,-0.1868935674,0.1310922652,0.3315558434,-0.0650450215,0.2757264376,-0.0043974128,-0.171322912,-0.0783392414,0.1010062397,0.178945601,0.5017948747,-0.00362671,-0.1863486022,-0.1349211335,-0.063677758,-0.0468234979,0.0711337104,-0.1824291945,0.255372107,0.2577778697,0.1839800775,0.0547975115,-0.1531583816,-0.0497436859,0.0207577217,0.2905252874,-0.3741133809,0.0461585224,-0.1272047311,0.0979508162,-0.3777190447,0.133810699,-0.3502556384,-0.1833577752,-0.135731101,-0.1108105704,0.309304595,0.2101667076,-0.3485357165,0.1555947661,0.2402642965,-0.5443775654,0.1576044559,0.1009899378,-0.3830037117,0.0529526137,0.1205421314,-0.0696215704,-0.0969110429,-0.1528277695,0.0173257664,-0.1201020107,-0.168004334,0.1868712455,-0.0529078133,0.576833725,-0.0123318527,0.1703878939,-0.0075948159,-0.0680116788,0.166756928,-0.1843080521,-0.1167619601,-0.1657404155,-0.1121703759,0.0364880748,-0.0776862428,-0.622492671,-0.4673993886,-0.2988173664,-0.1805043668,0.0007811155,0.2649473548,0.0936486349,0.0102780862,0.3407399952,-0.0355759114,0.0361259319,-0.4035873711,-0.2818406522,0.2556055784,0.0628466681,-0.2947934866,-0.0994649678,0.0628204569,0.2072016597,-0.0956361443,-0.3958399296,-0.2739957571,0.2307893485,-0.0112170214,-0.0910095647,0.0472379848,0.1151574999,-0.1613552123,0.1061526462,-0.2067129463,-0.5061612129,0.1450342834,0.1470152438,0.6205662489,-0.1660099775,0.2980054021,-0.0526382029,0.6775237918,0.1890775263,-0.2280175686,0.2107886672,-0.2105231285,0.3032869399,0.0035384649,-0.2769274116,0.3039433956,0.0719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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1964","title":"Datasets.py function load_dataset does not match squad dataset","comments":"I'm glad you managed to fix run_qa.py for your case :)\r\n\r\nRegarding the checksum error, I'm not able to reproduce on my side.\r\nThis errors says that the downloaded file doesn't match the expected file.\r\n\r\nCould you try running this and let me know if you get the same output as me ?\r\n```python\r\nfrom datasets.utils.info_utils import get_size_checksum_dict\r\nfrom datasets import cached_path\r\n\r\nget_size_checksum_dict(cached_path(\"https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json\"))\r\n# {'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```","body":"### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot!","comment_length":69,"text":"Datasets.py function load_dataset does not match squad dataset \n ### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot! \n I'm glad you managed to fix run_qa.py for your case :)\r\n\r\nRegarding the checksum error, I'm not able to reproduce on my side.\r\nThis errors says that the downloaded file doesn't match the expected file.\r\n\r\nCould you try running this and let me know if you get the same output as me ?\r\n```python\r\nfrom datasets.utils.info_utils import get_size_checksum_dict\r\nfrom datasets import cached_path\r\n\r\nget_size_checksum_dict(cached_path(\"https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json\"))\r\n# {'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```","embeddings":[-0.3537200987,0.0625078902,0.0333654545,0.3963988423,0.5554530621,0.0057210135,0.5457583666,0.3404688239,-0.0983101875,-0.1216232106,-0.1650660336,0.4508374333,0.1084147692,-0.0774192885,0.1859294176,0.2271618843,-0.1242239624,-0.0123368138,-0.1461081356,-0.2893434763,-0.1109781116,0.1282911897,-0.0823139772,0.2603987455,-0.1788990498,0.0166908093,0.2006343901,0.2723646462,-0.0037961667,-0.2199688405,0.3428422213,-0.2606053054,0.2361012548,0.5758861899,-0.0001148581,0.1132558137,-0.0115434257,-0.3397289217,-0.5327045321,-0.2002218962,-0.1021837071,-0.3757722974,0.1486449242,-0.2605553567,-0.1595326066,0.093737483,0.0203164779,-0.4645369053,0.3544548452,0.4883137345,0.213982597,0.2522563636,-0.1001010016,0.0775510594,0.1142548099,-0.0009760508,-0.06950894,0.1360990405,0.3347509801,-0.0582686849,0.0025541722,0.1724455059,0.0828086361,0.0568236373,0.2703095675,0.0064037591,0.199115023,-0.316155225,0.0564422533,0.3177836239,0.4627765119,-0.4630158544,-0.334705174,-0.2631344795,0.133104369,-0.2565729618,0.1320165247,0.0672778934,0.0511586368,-0.0424272642,0.0477764271,0.0376646332,0.1114287302,0.0368287303,0.0068916786,0.1999943703,0.0288672037,0.2383961529,-0.201539591,-0.1135483086,0.1148922518,-0.083149083,0.0818328112,0.392493546,-0.828517139,-0.0814113691,-0.2042951286,-0.1421720982,0.2562874556,0.0819008574,0.0386924408,-0.0269711483,0.148668915,0.1044574156,0.1687794775,0.3692035675,0.3052028716,0.2674016058,-0.0663638264,0.0668205246,-0.3717198968,0.2254103124,-0.3225816488,-0.1618057936,0.0960089266,0.0835877731,0.0062298947,-0.1927381605,-0.5517169237,0.1761802584,0.1544087082,0.0026421051,0.0974367931,0.324791342,0.0759165287,0.3802291751,0.0263226684,0.1048553437,-0.294623524,-0.3704161942,-0.2408784777,0.0330561139,-0.0880101547,0.0268361289,0.0183388889,-0.0356007777,0.3227834404,-0.1957616955,0.2114947587,-0.1790799946,0.1150045693,-0.2556711733,-0.1285898685,-0.0065955352,0.0298101194,0.0322048925,0.1742971241,-0.3730719388,-0.0482525341,0.1222925037,-0.3712684512,-0.0336521827,-0.1820591837,0.1871163845,-0.1391085088,0.0208789185,-0.2373687923,0.1698867232,0.3073554337,-0.1878401935,0.1063959599,-0.3961229026,-0.1664000899,-0.2122270316,0.3151646554,0.3882876039,-0.4074572027,-0.2117103338,0.1249395758,-0.0952139571,-0.0381632037,0.1216655001,-0.1516055316,0.1555615067,-0.20622392,-0.0070253811,0.7145847082,-0.8443276286,-0.4243383706,0.1099728644,-0.3059237599,-0.0194989499,-0.0710134581,0.066407755,0.1930091679,0.0147336535,0.0827305093,0.5066136718,-0.0362928361,0.0914031267,-0.1724580824,-0.2712134719,0.2526653707,0.1708515584,0.0318142921,-0.0380649008,0.0388377123,0.4989839792,0.332213819,0.1426163316,0.1112044156,-0.0299585946,-0.0359400138,-0.0821635574,0.114605628,-0.1481092274,-0.6798553467,0.1638657302,-0.1644516885,0.0439888574,0.2410787791,-0.1508302242,-0.2135134637,-0.0917369127,-0.4252310395,-0.1023878902,0.0743926764,0.1495984495,-0.0585200973,-0.1226450801,-0.159884721,0.3514788449,-0.2154513896,0.2284579277,-0.4849060774,-0.0965390429,-0.0637453571,-0.0362155363,0.0537013672,0.3318411708,0.087192513,-0.0709578693,-0.105753161,0.497995466,0.0180258732,0.1023961678,0.2883635163,-0.2016443759,0.0996336117,-0.0990453884,0.0020643868,0.2259092182,0.1946656704,-0.0812613964,0.0224434528,0.1418915838,0.1574391872,0.0746554807,-0.0048610461,-0.1536109,0.139052406,0.0118647469,0.0111010959,0.0971629173,0.0520384163,0.2940087616,0.5483121872,0.1111362055,-0.1868935674,0.1310922652,0.3315558434,-0.0650450215,0.2757264376,-0.0043974128,-0.171322912,-0.0783392414,0.1010062397,0.178945601,0.5017948747,-0.00362671,-0.1863486022,-0.1349211335,-0.063677758,-0.0468234979,0.0711337104,-0.1824291945,0.255372107,0.2577778697,0.1839800775,0.0547975115,-0.1531583816,-0.0497436859,0.0207577217,0.2905252874,-0.3741133809,0.0461585224,-0.1272047311,0.0979508162,-0.3777190447,0.133810699,-0.3502556384,-0.1833577752,-0.135731101,-0.1108105704,0.309304595,0.2101667076,-0.3485357165,0.1555947661,0.2402642965,-0.5443775654,0.1576044559,0.1009899378,-0.3830037117,0.0529526137,0.1205421314,-0.0696215704,-0.0969110429,-0.1528277695,0.0173257664,-0.1201020107,-0.168004334,0.1868712455,-0.0529078133,0.576833725,-0.0123318527,0.1703878939,-0.0075948159,-0.0680116788,0.166756928,-0.1843080521,-0.1167619601,-0.1657404155,-0.1121703759,0.0364880748,-0.0776862428,-0.622492671,-0.4673993886,-0.2988173664,-0.1805043668,0.0007811155,0.2649473548,0.0936486349,0.0102780862,0.3407399952,-0.0355759114,0.0361259319,-0.4035873711,-0.2818406522,0.2556055784,0.0628466681,-0.2947934866,-0.0994649678,0.0628204569,0.2072016597,-0.0956361443,-0.3958399296,-0.2739957571,0.2307893485,-0.0112170214,-0.0910095647,0.0472379848,0.1151574999,-0.1613552123,0.1061526462,-0.2067129463,-0.5061612129,0.1450342834,0.1470152438,0.6205662489,-0.1660099775,0.2980054021,-0.0526382029,0.6775237918,0.1890775263,-0.2280175686,0.2107886672,-0.2105231285,0.30328693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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1964","title":"Datasets.py function load_dataset does not match squad dataset","comments":"I run the code,and it show below:\r\n```\r\n>>> from datasets.utils.info_utils import get_size_checksum_dict\r\n>>> from datasets import cached_path\r\n>>> get_size_checksum_dict(cached_path(\"https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json\"))\r\nDownloading: 30.3MB [04:13, 120kB\/s]\r\n{'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```","body":"### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot!","comment_length":29,"text":"Datasets.py function load_dataset does not match squad dataset \n ### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot! \n I run the code,and it show below:\r\n```\r\n>>> from datasets.utils.info_utils import get_size_checksum_dict\r\n>>> from datasets import cached_path\r\n>>> get_size_checksum_dict(cached_path(\"https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json\"))\r\nDownloading: 30.3MB [04:13, 120kB\/s]\r\n{'num_bytes': 30288272, 'checksum': '3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955'}\r\n```","embeddings":[-0.3537200987,0.0625078902,0.0333654545,0.3963988423,0.5554530621,0.0057210135,0.5457583666,0.3404688239,-0.0983101875,-0.1216232106,-0.1650660336,0.4508374333,0.1084147692,-0.0774192885,0.1859294176,0.2271618843,-0.1242239624,-0.0123368138,-0.1461081356,-0.2893434763,-0.1109781116,0.1282911897,-0.0823139772,0.2603987455,-0.1788990498,0.0166908093,0.2006343901,0.2723646462,-0.0037961667,-0.2199688405,0.3428422213,-0.2606053054,0.2361012548,0.5758861899,-0.0001148581,0.1132558137,-0.0115434257,-0.3397289217,-0.5327045321,-0.2002218962,-0.1021837071,-0.3757722974,0.1486449242,-0.2605553567,-0.1595326066,0.093737483,0.0203164779,-0.4645369053,0.3544548452,0.4883137345,0.213982597,0.2522563636,-0.1001010016,0.0775510594,0.1142548099,-0.0009760508,-0.06950894,0.1360990405,0.3347509801,-0.0582686849,0.0025541722,0.1724455059,0.0828086361,0.0568236373,0.2703095675,0.0064037591,0.199115023,-0.316155225,0.0564422533,0.3177836239,0.4627765119,-0.4630158544,-0.334705174,-0.2631344795,0.133104369,-0.2565729618,0.1320165247,0.0672778934,0.0511586368,-0.0424272642,0.0477764271,0.0376646332,0.1114287302,0.0368287303,0.0068916786,0.1999943703,0.0288672037,0.2383961529,-0.201539591,-0.1135483086,0.1148922518,-0.083149083,0.0818328112,0.392493546,-0.828517139,-0.0814113691,-0.2042951286,-0.1421720982,0.2562874556,0.0819008574,0.0386924408,-0.0269711483,0.148668915,0.1044574156,0.1687794775,0.3692035675,0.3052028716,0.2674016058,-0.0663638264,0.0668205246,-0.3717198968,0.2254103124,-0.3225816488,-0.1618057936,0.0960089266,0.0835877731,0.0062298947,-0.1927381605,-0.5517169237,0.1761802584,0.1544087082,0.0026421051,0.0974367931,0.324791342,0.0759165287,0.3802291751,0.0263226684,0.1048553437,-0.294623524,-0.3704161942,-0.2408784777,0.0330561139,-0.0880101547,0.0268361289,0.0183388889,-0.0356007777,0.3227834404,-0.1957616955,0.2114947587,-0.1790799946,0.1150045693,-0.2556711733,-0.1285898685,-0.0065955352,0.0298101194,0.0322048925,0.1742971241,-0.3730719388,-0.0482525341,0.1222925037,-0.3712684512,-0.0336521827,-0.1820591837,0.1871163845,-0.1391085088,0.0208789185,-0.2373687923,0.1698867232,0.3073554337,-0.1878401935,0.1063959599,-0.3961229026,-0.1664000899,-0.2122270316,0.3151646554,0.3882876039,-0.4074572027,-0.2117103338,0.1249395758,-0.0952139571,-0.0381632037,0.1216655001,-0.1516055316,0.1555615067,-0.20622392,-0.0070253811,0.7145847082,-0.8443276286,-0.4243383706,0.1099728644,-0.3059237599,-0.0194989499,-0.0710134581,0.066407755,0.1930091679,0.0147336535,0.0827305093,0.5066136718,-0.0362928361,0.0914031267,-0.1724580824,-0.2712134719,0.2526653707,0.1708515584,0.0318142921,-0.0380649008,0.0388377123,0.4989839792,0.332213819,0.1426163316,0.1112044156,-0.0299585946,-0.0359400138,-0.0821635574,0.114605628,-0.1481092274,-0.6798553467,0.1638657302,-0.1644516885,0.0439888574,0.2410787791,-0.1508302242,-0.2135134637,-0.0917369127,-0.4252310395,-0.1023878902,0.0743926764,0.1495984495,-0.0585200973,-0.1226450801,-0.159884721,0.3514788449,-0.2154513896,0.2284579277,-0.4849060774,-0.0965390429,-0.0637453571,-0.0362155363,0.0537013672,0.3318411708,0.087192513,-0.0709578693,-0.105753161,0.497995466,0.0180258732,0.1023961678,0.2883635163,-0.2016443759,0.0996336117,-0.0990453884,0.0020643868,0.2259092182,0.1946656704,-0.0812613964,0.0224434528,0.1418915838,0.1574391872,0.0746554807,-0.0048610461,-0.1536109,0.139052406,0.0118647469,0.0111010959,0.0971629173,0.0520384163,0.2940087616,0.5483121872,0.1111362055,-0.1868935674,0.1310922652,0.3315558434,-0.0650450215,0.2757264376,-0.0043974128,-0.171322912,-0.0783392414,0.1010062397,0.178945601,0.5017948747,-0.00362671,-0.1863486022,-0.1349211335,-0.063677758,-0.0468234979,0.0711337104,-0.1824291945,0.255372107,0.2577778697,0.1839800775,0.0547975115,-0.1531583816,-0.0497436859,0.0207577217,0.2905252874,-0.3741133809,0.0461585224,-0.1272047311,0.0979508162,-0.3777190447,0.133810699,-0.3502556384,-0.1833577752,-0.135731101,-0.1108105704,0.309304595,0.2101667076,-0.3485357165,0.1555947661,0.2402642965,-0.5443775654,0.1576044559,0.1009899378,-0.3830037117,0.0529526137,0.1205421314,-0.0696215704,-0.0969110429,-0.1528277695,0.0173257664,-0.1201020107,-0.168004334,0.1868712455,-0.0529078133,0.576833725,-0.0123318527,0.1703878939,-0.0075948159,-0.0680116788,0.166756928,-0.1843080521,-0.1167619601,-0.1657404155,-0.1121703759,0.0364880748,-0.0776862428,-0.622492671,-0.4673993886,-0.2988173664,-0.1805043668,0.0007811155,0.2649473548,0.0936486349,0.0102780862,0.3407399952,-0.0355759114,0.0361259319,-0.4035873711,-0.2818406522,0.2556055784,0.0628466681,-0.2947934866,-0.0994649678,0.0628204569,0.2072016597,-0.0956361443,-0.3958399296,-0.2739957571,0.2307893485,-0.0112170214,-0.0910095647,0.0472379848,0.1151574999,-0.1613552123,0.1061526462,-0.2067129463,-0.5061612129,0.1450342834,0.1470152438,0.6205662489,-0.1660099775,0.2980054021,-0.0526382029,0.6775237918,0.1890775263,-0.2280175686,0.2107886672,-0.2105231285,0.30328693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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1964","title":"Datasets.py function load_dataset does not match squad dataset","comments":"Alright ! So in this case redownloading the file with `download_mode=\"force_redownload\"` should fix it. Can you try using `download_mode=\"force_redownload\"` again ?\r\n\r\nNot sure why it didn't work for you the first time though :\/","body":"### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot!","comment_length":34,"text":"Datasets.py function load_dataset does not match squad dataset \n ### 1 When I try to train lxmert,and follow the code in README that --dataset name:\r\n```shell \r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --dataset_name squad --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nthe bug is that:\r\n```\r\nDownloading and preparing dataset squad\/plain_text (download: 33.51 MiB, generated: 85.75 MiB, post-processed: Unknown size, total: 119.27 MiB) to \/home2\/zhenggo1\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/4c81550d83a2ac7c7ce23783bd8ff36642800e6633c1f18417fb58c3ff50cdd7...\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 217, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 746, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 573, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 633, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home2\/zhenggo1\/anaconda3\/envs\/lpot\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/rajpurkar.github.io\/SQuAD-explorer\/dataset\/train-v1.1.json']\r\n```\r\nAnd I try to find the [checksum link](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/squad\/dataset_infos.json)\r\n,is the problem plain_text do not have a checksum?\r\n\r\n### 2 When I try to train lxmert,and use local dataset:\r\n```\r\npython examples\/question-answering\/run_qa.py --model_name_or_path unc-nlp\/lxmert-base-uncased --train_file $SQUAD_DIR\/train-v1.1.json --validation_file $SQUAD_DIR\/dev-v1.1.json --do_train --do_eval --per_device_train_batch_size 12 --learning_rate 3e-5 --num_train_epochs 2 --max_seq_length 384 --doc_stride 128 --output_dir \/home2\/zhenggo1\/checkpoint\/lxmert_squad\r\n```\r\nThe bug is that \r\n```\r\n['title', 'paragraphs']\r\nTraceback (most recent call last):\r\n  File \"examples\/question-answering\/run_qa.py\", line 501, in <module>\r\n    main()\r\n  File \"examples\/question-answering\/run_qa.py\", line 273, in main\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\nIndexError: list index out of range\r\n```\r\nI print the answer_column_name and find that local squad dataset need the package datasets to preprocessing so that the code below can work:\r\n```\r\nif training_args.do_train:\r\n        column_names = datasets[\"train\"].column_names\r\n    else:\r\n        column_names = datasets[\"validation\"].column_names\r\n    print(datasets[\"train\"].column_names)\r\n    question_column_name = \"question\" if \"question\" in column_names else column_names[0]\r\n    context_column_name = \"context\" if \"context\" in column_names else column_names[1]\r\n    answer_column_name = \"answers\" if \"answers\" in column_names else column_names[2]\r\n``` \r\n## Please tell me how to fix the bug,thks a lot! \n Alright ! So in this case redownloading the file with `download_mode=\"force_redownload\"` should fix it. Can you try using `download_mode=\"force_redownload\"` again ?\r\n\r\nNot sure why it didn't work for you the first time though :\/","embeddings":[-0.3537200987,0.0625078902,0.0333654545,0.3963988423,0.5554530621,0.0057210135,0.5457583666,0.3404688239,-0.0983101875,-0.1216232106,-0.1650660336,0.4508374333,0.1084147692,-0.0774192885,0.1859294176,0.2271618843,-0.1242239624,-0.0123368138,-0.1461081356,-0.2893434763,-0.1109781116,0.1282911897,-0.0823139772,0.2603987455,-0.1788990498,0.0166908093,0.2006343901,0.2723646462,-0.0037961667,-0.2199688405,0.3428422213,-0.2606053054,0.2361012548,0.5758861899,-0.0001148581,0.1132558137,-0.0115434257,-0.3397289217,-0.5327045321,-0.2002218962,-0.1021837071,-0.3757722974,0.1486449242,-0.2605553567,-0.1595326066,0.093737483,0.0203164779,-0.4645369053,0.3544548452,0.4883137345,0.213982597,0.2522563636,-0.1001010016,0.0775510594,0.1142548099,-0.0009760508,-0.06950894,0.1360990405,0.3347509801,-0.0582686849,0.0025541722,0.1724455059,0.0828086361,0.0568236373,0.2703095675,0.0064037591,0.199115023,-0.316155225,0.0564422533,0.3177836239,0.4627765119,-0.4630158544,-0.334705174,-0.2631344795,0.133104369,-0.2565729618,0.1320165247,0.0672778934,0.0511586368,-0.0424272642,0.0477764271,0.0376646332,0.1114287302,0.0368287303,0.0068916786,0.1999943703,0.0288672037,0.2383961529,-0.201539591,-0.1135483086,0.1148922518,-0.083149083,0.0818328112,0.392493546,-0.828517139,-0.0814113691,-0.2042951286,-0.1421720982,0.2562874556,0.0819008574,0.0386924408,-0.0269711483,0.148668915,0.1044574156,0.1687794775,0.3692035675,0.3052028716,0.2674016058,-0.0663638264,0.0668205246,-0.3717198968,0.2254103124,-0.3225816488,-0.1618057936,0.0960089266,0.0835877731,0.0062298947,-0.1927381605,-0.5517169237,0.1761802584,0.1544087082,0.0026421051,0.0974367931,0.324791342,0.0759165287,0.3802291751,0.0263226684,0.1048553437,-0.294623524,-0.3704161942,-0.2408784777,0.0330561139,-0.0880101547,0.0268361289,0.0183388889,-0.0356007777,0.3227834404,-0.1957616955,0.2114947587,-0.1790799946,0.1150045693,-0.2556711733,-0.1285898685,-0.0065955352,0.0298101194,0.0322048925,0.1742971241,-0.3730719388,-0.0482525341,0.1222925037,-0.3712684512,-0.0336521827,-0.1820591837,0.1871163845,-0.1391085088,0.0208789185,-0.2373687923,0.1698867232,0.3073554337,-0.1878401935,0.1063959599,-0.3961229026,-0.1664000899,-0.2122270316,0.3151646554,0.3882876039,-0.4074572027,-0.2117103338,0.1249395758,-0.0952139571,-0.0381632037,0.1216655001,-0.1516055316,0.1555615067,-0.20622392,-0.0070253811,0.7145847082,-0.8443276286,-0.4243383706,0.1099728644,-0.3059237599,-0.0194989499,-0.0710134581,0.066407755,0.1930091679,0.0147336535,0.0827305093,0.5066136718,-0.0362928361,0.0914031267,-0.1724580824,-0.2712134719,0.2526653707,0.1708515584,0.0318142921,-0.0380649008,0.0388377123,0.4989839792,0.332213819,0.1426163316,0.1112044156,-0.0299585946,-0.0359400138,-0.0821635574,0.114605628,-0.1481092274,-0.6798553467,0.1638657302,-0.1644516885,0.0439888574,0.2410787791,-0.1508302242,-0.2135134637,-0.0917369127,-0.4252310395,-0.1023878902,0.0743926764,0.1495984495,-0.0585200973,-0.1226450801,-0.159884721,0.3514788449,-0.2154513896,0.2284579277,-0.4849060774,-0.0965390429,-0.0637453571,-0.0362155363,0.0537013672,0.3318411708,0.087192513,-0.0709578693,-0.105753161,0.497995466,0.0180258732,0.1023961678,0.2883635163,-0.2016443759,0.0996336117,-0.0990453884,0.0020643868,0.2259092182,0.1946656704,-0.0812613964,0.0224434528,0.1418915838,0.1574391872,0.0746554807,-0.0048610461,-0.1536109,0.139052406,0.0118647469,0.0111010959,0.0971629173,0.0520384163,0.2940087616,0.5483121872,0.1111362055,-0.1868935674,0.1310922652,0.3315558434,-0.0650450215,0.2757264376,-0.0043974128,-0.171322912,-0.0783392414,0.1010062397,0.178945601,0.5017948747,-0.00362671,-0.1863486022,-0.1349211335,-0.063677758,-0.0468234979,0.0711337104,-0.1824291945,0.255372107,0.2577778697,0.1839800775,0.0547975115,-0.1531583816,-0.0497436859,0.0207577217,0.2905252874,-0.3741133809,0.0461585224,-0.1272047311,0.0979508162,-0.3777190447,0.133810699,-0.3502556384,-0.1833577752,-0.135731101,-0.1108105704,0.309304595,0.2101667076,-0.3485357165,0.1555947661,0.2402642965,-0.5443775654,0.1576044559,0.1009899378,-0.3830037117,0.0529526137,0.1205421314,-0.0696215704,-0.0969110429,-0.1528277695,0.0173257664,-0.1201020107,-0.168004334,0.1868712455,-0.0529078133,0.576833725,-0.0123318527,0.1703878939,-0.0075948159,-0.0680116788,0.166756928,-0.1843080521,-0.1167619601,-0.1657404155,-0.1121703759,0.0364880748,-0.0776862428,-0.622492671,-0.4673993886,-0.2988173664,-0.1805043668,0.0007811155,0.2649473548,0.0936486349,0.0102780862,0.3407399952,-0.0355759114,0.0361259319,-0.4035873711,-0.2818406522,0.2556055784,0.0628466681,-0.2947934866,-0.0994649678,0.0628204569,0.2072016597,-0.0956361443,-0.3958399296,-0.2739957571,0.2307893485,-0.0112170214,-0.0910095647,0.0472379848,0.1151574999,-0.1613552123,0.1061526462,-0.2067129463,-0.5061612129,0.1450342834,0.1470152438,0.6205662489,-0.1660099775,0.2980054021,-0.0526382029,0.6775237918,0.1890775263,-0.2280175686,0.2107886672,-0.2105231285,0.3032869399,0.0035384649,-0.2769274116,0.3039433956,0.0719058588,0.0459164828,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1963","title":"bug in SNLI dataset ","comments":"Hi ! The labels -1 correspond to the examples without gold labels in the original snli dataset.\r\nFeel free to remove these examples if you don't need them by using\r\n```python\r\ndata = data.filter(lambda x: x[\"label\"] != -1)\r\n```","body":"Hi\r\nThere is label of -1 in train set of SNLI dataset, please find the code below:\r\n\r\n```\r\nimport numpy as np \r\nimport datasets \r\ndata = datasets.load_dataset(\"snli\")[\"train\"]\r\nlabels = []\r\nfor d in data:\r\n   labels.append(d[\"label\"])\r\nprint(np.unique(labels))\r\n```\r\n\r\nand results:\r\n\r\n`[-1  0  1  2]`\r\n\r\nversion of datasets used:\r\n`datasets                  1.2.1                     <pip>\r\n`\r\n\r\nthanks for your help. @lhoestq ","comment_length":39,"text":"bug in SNLI dataset  \n Hi\r\nThere is label of -1 in train set of SNLI dataset, please find the code below:\r\n\r\n```\r\nimport numpy as np \r\nimport datasets \r\ndata = datasets.load_dataset(\"snli\")[\"train\"]\r\nlabels = []\r\nfor d in data:\r\n   labels.append(d[\"label\"])\r\nprint(np.unique(labels))\r\n```\r\n\r\nand results:\r\n\r\n`[-1  0  1  2]`\r\n\r\nversion of datasets used:\r\n`datasets                  1.2.1                     <pip>\r\n`\r\n\r\nthanks for your help. @lhoestq  \n Hi ! The labels -1 correspond to the examples without gold labels in the original snli dataset.\r\nFeel free to remove these examples if you don't need them by using\r\n```python\r\ndata = data.filter(lambda x: x[\"label\"] != -1)\r\n```","embeddings":[0.1758222431,-0.2825255096,-0.1035732776,0.3496335149,0.2219778597,0.0471981168,0.3874210715,0.1183437407,0.0808190703,0.2969244123,-0.2027283907,0.6121100187,-0.1427199841,0.0958901942,0.0018212209,-0.0009762677,0.2330546677,0.3379025459,0.2097955346,-0.4966840744,-0.2359668463,-0.0235047974,-0.3817799091,0.2652831078,-0.2869722247,0.1784489006,-0.0155612463,-0.1333378255,0.1334049553,-0.4898706973,0.1604635864,-0.0986117944,0.1226140335,0.2325982898,-0.0001058666,-0.2743193209,0.1129734814,0.0678980052,-0.3696534932,-0.2605839968,-0.1101465896,-0.0220090337,-0.146892488,-0.1628938764,-0.2096339464,-0.1724426448,0.0142517593,0.0935407504,0.1124736294,0.2662241459,0.2374289483,0.0673883259,-0.1264111251,0.1347338408,0.2391382456,-0.3994213939,0.1405956298,0.1139144152,0.0496283025,0.049375765,0.4056082666,0.6512392759,-0.0620471761,-0.043695204,0.1137125939,0.090373829,0.127294898,-0.3254041672,-0.0787117556,0.3404750824,0.1091918424,-0.3263964653,-0.4188061655,0.0663319081,0.1710607111,-0.4623311758,-0.0283233002,0.2538726032,0.140697822,0.2034579664,0.0178657025,0.0447488539,-0.0742185861,0.3183240891,0.0500646271,0.659071207,-0.0445929728,0.1819166839,0.0840544328,0.0472337678,-0.1453344226,0.1597234756,0.068557404,0.3448960483,-0.5432954431,-0.0704533681,-0.0477732383,-0.0830213055,-0.1924338788,0.1565322131,0.135423407,0.0860434622,-0.1599017829,0.2858231068,-0.0184930209,0.0122394478,0.1436226815,0.3150083721,0.1404030174,-0.2455681562,0.097809203,-0.0502138287,-0.1937011033,-0.2610455155,0.4902793467,0.0590683483,0.0314709209,-0.0605653413,-0.3164850771,0.2388178259,-0.3513689339,0.0303709246,-0.0477262735,0.0027172163,-0.0162866618,-0.0127467709,-0.0026860826,0.051894065,0.0969416201,-0.2098995298,-0.2651704848,0.0638557971,-0.0640470386,-0.1516255438,-0.0483408347,-0.1381340027,0.1749141216,0.0200463757,-0.3853110075,-0.1688892096,0.1255019605,0.0578714497,0.2358458042,0.1766265929,-0.2485652566,0.4699509144,-0.1297376156,-0.1469859779,0.0430618562,-0.0063615078,-0.2545925975,-0.1627761275,-0.3255548477,0.25451985,0.0457574874,-0.03966932,0.0741639063,-0.1403101981,0.146074757,0.0239902772,0.3011030257,-0.2547019422,-0.1741402149,0.0351589397,0.1350886822,-0.0714575574,-0.1860977113,-0.1540900171,-0.1498029679,-0.220589757,0.2482090592,0.3248289227,0.0830720887,-0.0982796997,-0.1811051667,0.1455697566,0.6714295745,-0.4713222086,-0.5759915113,-0.0373700559,-0.3127920032,-0.3483288586,-0.0722914711,0.5257780552,-0.2541721463,0.1841239035,0.0559160449,-0.0284524187,0.0702485591,-0.0407907329,-0.4820291698,-0.2443044633,0.4282407165,0.2879720926,0.1184493378,0.0484846048,-0.0653521344,-0.1245332807,0.3911313117,0.000452248,0.1557037681,0.2652800381,0.5162929893,0.1337868422,0.1086844057,-0.2567136586,-0.0195428692,0.1996148825,-0.1025182009,0.3829938769,0.1140017509,-0.0327498019,-0.2263214588,-0.2294123173,-0.3714199662,-0.1633773148,0.172794342,0.1106389314,0.1249597743,-0.0002137626,-0.0690630972,0.2349174768,-0.1951018721,0.0574191883,-0.2534842491,0.1734796315,-0.0722109526,0.0002446634,-0.2396927923,0.199630022,0.1941972524,0.1150439009,-0.1352175027,0.239359051,-0.2861438096,-0.2572818995,0.1280991435,0.0865210295,0.0745254755,-0.616065979,-0.2318005562,0.5943585634,0.1214367598,0.0328817405,-0.1761128753,0.2059393972,-0.1515797824,-0.1561628282,-0.3645664155,0.2804860175,0.1399029195,-0.0466484278,-0.08440274,-0.3972603083,-0.0248351693,-0.492898494,0.3556153774,0.128847599,-0.3839584291,-0.0716296509,0.321644336,-0.0507559404,0.1607387066,-0.018025944,-0.1524935514,0.0303000286,0.2578040361,0.1180636063,0.1480195522,0.1922579706,-0.0845910609,0.1927807182,-0.152892977,-0.1079919189,-0.0311143938,0.1146148965,-0.0190106314,0.0687712729,0.2076686472,0.1067494899,-0.17784141,-0.2461322248,-0.1395559311,0.268166095,-0.232404992,-0.0760472789,-0.1674968004,-0.2094908506,-0.5057194829,-0.1521824896,-0.0507111475,-0.099207297,0.1847671568,-0.0889074653,-0.0105202412,0.3142932653,0.1960565299,0.1012288854,0.1451552063,0.1357943267,-0.1855854541,-0.3651711345,-0.2351708263,0.0717458799,-0.3185686469,0.2699109912,0.362963438,-0.1586459428,-0.0961316675,-0.0891949683,-0.2441946864,-0.0196911767,0.0663825274,0.0541803502,0.0247739851,-0.0128802359,-0.2053201646,0.2964856625,0.2392027676,0.010781995,-0.0804010257,0.165114373,-0.0892625302,-0.0484052077,-0.2911716402,-0.4643014669,-0.5519579053,0.0023524363,0.0160962772,-0.1708481461,0.1917137355,0.1217435896,0.0539649054,-0.0223809853,0.1511522979,0.1787069142,-0.2152539194,-0.1564620882,0.1699820012,0.04657932,-0.2690292001,-0.1365805119,0.0395993926,-0.0324099883,-0.1843263805,-0.2561134398,-0.1648022085,0.1473015845,0.1070359349,0.0568785146,0.0316865295,-0.0339900665,0.1185097322,-0.0793804601,-0.2076565921,0.1215779185,0.1340607852,-0.3273540437,0.4176501632,-0.0254115332,0.2975836098,-0.0089342277,0.057175722,0.6455105543,-0.1563932449,0.0258548092,-0.1597404182,0.0220553521,0.1431916803,-0.2573871613,0.1051399559,0.40435779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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1959","title":"Bug in skip_rows argument of load_dataset function ?","comments":"Hi,\r\n\r\ntry `skiprows` instead. This part is not properly documented in the docs it seems.\r\n\r\n@lhoestq I'll fix this as part of a bigger PR that fixes typos in the docs.","body":"Hello everyone,\r\n\r\nI'm quite new to Git so sorry in advance if I'm breaking some ground rules of issues posting... :\/\r\nI tried to use the load_dataset function, from Huggingface datasets library, on a csv file using the skip_rows argument described on Huggingface page to skip the first row containing column names\r\n\r\n`test_dataset = load_dataset('csv', data_files=['test_wLabel.tsv'], delimiter='\\t', column_names=[\"id\", \"sentence\", \"label\"], skip_rows=1)`\r\n\r\nBut I got the following error message\r\n\r\n`__init__() got an unexpected keyword argument 'skip_rows'`\r\n\r\nHave I used the wrong argument ? Am I missing something or is this a bug ?\r\n\r\nThank you very much for your time,\r\nBest regards,\r\nArthur","comment_length":31,"text":"Bug in skip_rows argument of load_dataset function ? \n Hello everyone,\r\n\r\nI'm quite new to Git so sorry in advance if I'm breaking some ground rules of issues posting... :\/\r\nI tried to use the load_dataset function, from Huggingface datasets library, on a csv file using the skip_rows argument described on Huggingface page to skip the first row containing column names\r\n\r\n`test_dataset = load_dataset('csv', data_files=['test_wLabel.tsv'], delimiter='\\t', column_names=[\"id\", \"sentence\", \"label\"], skip_rows=1)`\r\n\r\nBut I got the following error message\r\n\r\n`__init__() got an unexpected keyword argument 'skip_rows'`\r\n\r\nHave I used the wrong argument ? Am I missing something or is this a bug ?\r\n\r\nThank you very much for your time,\r\nBest regards,\r\nArthur \n Hi,\r\n\r\ntry `skiprows` instead. This part is not properly documented in the docs it seems.\r\n\r\n@lhoestq I'll fix this as part of a bigger PR that fixes typos in the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1957","title":"[request] make load_metric api intutive","comments":"I agree with this proposal. IMO, `num_process` can also be misleading without reading the docs because this option may seem to leverage `multiprocessing` to compute the final result, which is not the case.\r\n\r\n@lhoestq @albertvillanova Are you OK with breaking the API for v2.0 and renaming the params as follows:\r\n* `num_process` -> `world_size`\r\n* `process_id` -> `rank`","body":"```\r\nmetric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)\r\n```\r\n\r\nMay I suggest that `num_process` is confusing as it's singular yet expects a plural value and either \r\n* be deprecated in favor of `num_processes` which is more intuitive since it's plural as its expected value\r\n* or even better why not mimic the established dist environment convention for that purpose, which uses `world_size`. \r\n\r\nSame for `process_id` - why reinvent the naming and needing to explain that this is **NOT** `PID`, when we have `rank` already. That is:\r\n\r\n```\r\nmetric = load_metric('glue', 'mrpc', world_size=world_size, rank=rank)\r\n```\r\n\r\nThis then fits like a glove into the pytorch DDP and alike envs. and we just need to call:\r\n\r\n* `dist.get_world_size()`\r\n* `dist.get_rank()`\r\n\r\nSo it'd be as simple as:\r\n\r\n```\r\nmetric = load_metric('glue', 'mrpc', world_size=dist.get_world_size(), rank=dist.get_rank())\r\n```\r\n\r\nFrom: https:\/\/pytorch.org\/docs\/stable\/distributed.html#torch.distributed.init_process_group\r\n\r\n* `world_size (int, optional)` \u2013 Number of processes participating in the job. Required if store is specified.\r\n* `rank (int, optional)` \u2013 Rank of the current process. Required if store is specified.\r\n\r\nAnd may be an example would be useful, so that the user doesn't even need to think about where to get `dist`:\r\n```\r\nimport torch.distributed as dist\r\nif dist.is_initialized():\r\n    metric = load_metric(metric_name, world_size=dist.get_world_size(), rank=dist.get_rank())\r\nelse:\r\n    metric = load_metric(metric_name)\r\n```\r\n\r\nI'm aware this is pytorch-centric, but it's better than no examples, IMHO.\r\n\r\nThank you.","comment_length":58,"text":"[request] make load_metric api intutive \n ```\r\nmetric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)\r\n```\r\n\r\nMay I suggest that `num_process` is confusing as it's singular yet expects a plural value and either \r\n* be deprecated in favor of `num_processes` which is more intuitive since it's plural as its expected value\r\n* or even better why not mimic the established dist environment convention for that purpose, which uses `world_size`. \r\n\r\nSame for `process_id` - why reinvent the naming and needing to explain that this is **NOT** `PID`, when we have `rank` already. That is:\r\n\r\n```\r\nmetric = load_metric('glue', 'mrpc', world_size=world_size, rank=rank)\r\n```\r\n\r\nThis then fits like a glove into the pytorch DDP and alike envs. and we just need to call:\r\n\r\n* `dist.get_world_size()`\r\n* `dist.get_rank()`\r\n\r\nSo it'd be as simple as:\r\n\r\n```\r\nmetric = load_metric('glue', 'mrpc', world_size=dist.get_world_size(), rank=dist.get_rank())\r\n```\r\n\r\nFrom: https:\/\/pytorch.org\/docs\/stable\/distributed.html#torch.distributed.init_process_group\r\n\r\n* `world_size (int, optional)` \u2013 Number of processes participating in the job. Required if store is specified.\r\n* `rank (int, optional)` \u2013 Rank of the current process. Required if store is specified.\r\n\r\nAnd may be an example would be useful, so that the user doesn't even need to think about where to get `dist`:\r\n```\r\nimport torch.distributed as dist\r\nif dist.is_initialized():\r\n    metric = load_metric(metric_name, world_size=dist.get_world_size(), rank=dist.get_rank())\r\nelse:\r\n    metric = load_metric(metric_name)\r\n```\r\n\r\nI'm aware this is pytorch-centric, but it's better than no examples, IMHO.\r\n\r\nThank you. \n I agree with this proposal. IMO, `num_process` can also be misleading without reading the docs because this option may seem to leverage `multiprocessing` to compute the final result, which is not the case.\r\n\r\n@lhoestq @albertvillanova Are you OK with breaking the API for v2.0 and renaming the params as follows:\r\n* `num_process` -> `world_size`\r\n* `process_id` -> 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1957","title":"[request] make load_metric api intutive","comments":"I don't think that's a good idea for 2.0, we may have a new library for metrics anyway.\r\n\r\nNote that we will need an API that also makes sense for TF users","body":"```\r\nmetric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)\r\n```\r\n\r\nMay I suggest that `num_process` is confusing as it's singular yet expects a plural value and either \r\n* be deprecated in favor of `num_processes` which is more intuitive since it's plural as its expected value\r\n* or even better why not mimic the established dist environment convention for that purpose, which uses `world_size`. \r\n\r\nSame for `process_id` - why reinvent the naming and needing to explain that this is **NOT** `PID`, when we have `rank` already. That is:\r\n\r\n```\r\nmetric = load_metric('glue', 'mrpc', world_size=world_size, rank=rank)\r\n```\r\n\r\nThis then fits like a glove into the pytorch DDP and alike envs. and we just need to call:\r\n\r\n* `dist.get_world_size()`\r\n* `dist.get_rank()`\r\n\r\nSo it'd be as simple as:\r\n\r\n```\r\nmetric = load_metric('glue', 'mrpc', world_size=dist.get_world_size(), rank=dist.get_rank())\r\n```\r\n\r\nFrom: https:\/\/pytorch.org\/docs\/stable\/distributed.html#torch.distributed.init_process_group\r\n\r\n* `world_size (int, optional)` \u2013 Number of processes participating in the job. Required if store is specified.\r\n* `rank (int, optional)` \u2013 Rank of the current process. Required if store is specified.\r\n\r\nAnd may be an example would be useful, so that the user doesn't even need to think about where to get `dist`:\r\n```\r\nimport torch.distributed as dist\r\nif dist.is_initialized():\r\n    metric = load_metric(metric_name, world_size=dist.get_world_size(), rank=dist.get_rank())\r\nelse:\r\n    metric = load_metric(metric_name)\r\n```\r\n\r\nI'm aware this is pytorch-centric, but it's better than no examples, IMHO.\r\n\r\nThank you.","comment_length":32,"text":"[request] make load_metric api intutive \n ```\r\nmetric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)\r\n```\r\n\r\nMay I suggest that `num_process` is confusing as it's singular yet expects a plural value and either \r\n* be deprecated in favor of `num_processes` which is more intuitive since it's plural as its expected value\r\n* or even better why not mimic the established dist environment convention for that purpose, which uses `world_size`. \r\n\r\nSame for `process_id` - why reinvent the naming and needing to explain that this is **NOT** `PID`, when we have `rank` already. That is:\r\n\r\n```\r\nmetric = load_metric('glue', 'mrpc', world_size=world_size, rank=rank)\r\n```\r\n\r\nThis then fits like a glove into the pytorch DDP and alike envs. and we just need to call:\r\n\r\n* `dist.get_world_size()`\r\n* `dist.get_rank()`\r\n\r\nSo it'd be as simple as:\r\n\r\n```\r\nmetric = load_metric('glue', 'mrpc', world_size=dist.get_world_size(), rank=dist.get_rank())\r\n```\r\n\r\nFrom: https:\/\/pytorch.org\/docs\/stable\/distributed.html#torch.distributed.init_process_group\r\n\r\n* `world_size (int, optional)` \u2013 Number of processes participating in the job. Required if store is specified.\r\n* `rank (int, optional)` \u2013 Rank of the current process. Required if store is specified.\r\n\r\nAnd may be an example would be useful, so that the user doesn't even need to think about where to get `dist`:\r\n```\r\nimport torch.distributed as dist\r\nif dist.is_initialized():\r\n    metric = load_metric(metric_name, world_size=dist.get_world_size(), rank=dist.get_rank())\r\nelse:\r\n    metric = load_metric(metric_name)\r\n```\r\n\r\nI'm aware this is pytorch-centric, but it's better than no examples, IMHO.\r\n\r\nThank you. \n I don't think that's a good idea for 2.0, we may have a new library for metrics anyway.\r\n\r\nNote that we will need an API that also makes sense for TF 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1956","title":"[distributed env] potentially unsafe parallel execution","comments":"You can pass the same `experiment_id` for all the metrics of the same group, and use another `experiment_id` for the other groups.\r\nMaybe we can add an environment variable that sets the default value for `experiment_id` ? What do you think ?","body":"```\r\nmetric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)\r\n```\r\n\r\npresumes that there is only one set of parallel processes running - and will intermittently fail if you have multiple sets running as they will surely overwrite each other. Similar to https:\/\/github.com\/huggingface\/datasets\/issues\/1942 (but for a different reason).\r\nThat's why dist environments use some unique to a group identifier so that each group is dealt with separately. \r\n\r\ne.g. the env-way of pytorch dist syncing is done with a unique per set `MASTER_ADDRESS+MASTER_PORT`\r\n\r\nSo ideally this interface should ask for a shared secret to do the right thing.\r\n\r\nI'm not reporting an immediate need, but am only flagging that this will hit someone down the road.\r\n\r\nThis problem can be remedied by adding a new optional `shared_secret` option, which can then be used to differentiate different groups of processes. and this secret should be part of the file lock name and the experiment.\r\n\r\nThank you","comment_length":42,"text":"[distributed env] potentially unsafe parallel execution \n ```\r\nmetric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)\r\n```\r\n\r\npresumes that there is only one set of parallel processes running - and will intermittently fail if you have multiple sets running as they will surely overwrite each other. Similar to https:\/\/github.com\/huggingface\/datasets\/issues\/1942 (but for a different reason).\r\nThat's why dist environments use some unique to a group identifier so that each group is dealt with separately. \r\n\r\ne.g. the env-way of pytorch dist syncing is done with a unique per set `MASTER_ADDRESS+MASTER_PORT`\r\n\r\nSo ideally this interface should ask for a shared secret to do the right thing.\r\n\r\nI'm not reporting an immediate need, but am only flagging that this will hit someone down the road.\r\n\r\nThis problem can be remedied by adding a new optional `shared_secret` option, which can then be used to differentiate different groups of processes. and this secret should be part of the file lock name and the experiment.\r\n\r\nThank you \n You can pass the same `experiment_id` for all the metrics of the same group, and use another `experiment_id` for the other groups.\r\nMaybe we can add an environment variable that sets the default value for `experiment_id` ? What do you think 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1956","title":"[distributed env] potentially unsafe parallel execution","comments":"Ah, you're absolutely correct, @lhoestq - it's exactly the equivalent of the shared secret. Thank you!","body":"```\r\nmetric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)\r\n```\r\n\r\npresumes that there is only one set of parallel processes running - and will intermittently fail if you have multiple sets running as they will surely overwrite each other. Similar to https:\/\/github.com\/huggingface\/datasets\/issues\/1942 (but for a different reason).\r\nThat's why dist environments use some unique to a group identifier so that each group is dealt with separately. \r\n\r\ne.g. the env-way of pytorch dist syncing is done with a unique per set `MASTER_ADDRESS+MASTER_PORT`\r\n\r\nSo ideally this interface should ask for a shared secret to do the right thing.\r\n\r\nI'm not reporting an immediate need, but am only flagging that this will hit someone down the road.\r\n\r\nThis problem can be remedied by adding a new optional `shared_secret` option, which can then be used to differentiate different groups of processes. and this secret should be part of the file lock name and the experiment.\r\n\r\nThank you","comment_length":16,"text":"[distributed env] potentially unsafe parallel execution \n ```\r\nmetric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)\r\n```\r\n\r\npresumes that there is only one set of parallel processes running - and will intermittently fail if you have multiple sets running as they will surely overwrite each other. Similar to https:\/\/github.com\/huggingface\/datasets\/issues\/1942 (but for a different reason).\r\nThat's why dist environments use some unique to a group identifier so that each group is dealt with separately. \r\n\r\ne.g. the env-way of pytorch dist syncing is done with a unique per set `MASTER_ADDRESS+MASTER_PORT`\r\n\r\nSo ideally this interface should ask for a shared secret to do the right thing.\r\n\r\nI'm not reporting an immediate need, but am only flagging that this will hit someone down the road.\r\n\r\nThis problem can be remedied by adding a new optional `shared_secret` option, which can then be used to differentiate different groups of processes. and this secret should be part of the file lock name and the experiment.\r\n\r\nThank you \n Ah, you're absolutely correct, @lhoestq - it's exactly the equivalent of the shared secret. Thank you!","embeddings":[-0.2438624203,-0.5634172559,-0.0252403282,-0.0789237022,-0.0883313641,-0.0465874597,0.4310415983,-0.094735682,0.6884128451,0.3382450044,-0.0324464552,0.2416061014,0.0294990465,0.0651078969,-0.0662357882,0.0515502393,-0.0137192803,-0.0775892362,-0.2323585749,-0.1388348192,-0.258167088,0.1642918587,-0.1347393095,0.1182623878,-0.1310550421,-0.0675265938,-0.293854177,0.3518728316,-0.2069532126,-0.3734535575,-0.0511100814,0.5885394216,-0.1115160733,0.3470314145,-0.0001025435,-0.0290896483,0.2390752733,-0.1840211004,0.0937753469,-0.0225164928,-0.0063960506,-0.2147799581,0.2390904129,-0.6090909839,0.2548007965,-0.3178233206,0.3465066254,-0.5903226137,0.4716377556,-0.1917704493,0.2114199549,0.4353037775,-0.2383697033,-0.2103887349,0.0767698884,-0.1481731981,0.0649377778,0.3659193814,0.4074763954,-0.1650011092,-0.313985467,-0.0395437144,0.043598678,0.3771082759,0.5423665047,0.216079846,-0.0078244032,-0.3159538805,-0.3685427904,0.544993639,0.0689924657,-0.0175680891,-0.1552130133,-0.0432232507,-0.1245836392,-0.0624338761,0.0405220389,0.0801767707,-0.2795662284,0.095741272,-0.063394323,0.2075612098,-0.1429089159,-0.0452653691,0.0962147862,0.4674387872,0.1451297998,0.3589620292,0.3896103203,0.2626070678,-0.595140934,0.2887125909,-0.0776974261,-0.1011503413,-0.1418187171,-0.0618655644,-0.0439788438,-0.1890520006,0.2088943273,0.1767123789,-0.2218020707,-0.0964426249,0.4521384835,0.2948691845,0.1431004405,0.1408369839,-0.0696253553,0.2683866322,0.300517112,0.0206614006,0.0306791496,0.1363249123,0.2108891904,0.0027403666,0.0445302986,-0.0351561271,0.0238796473,0.0576753095,-0.442153126,0.1751860827,0.0797957256,-0.1001359746,0.0160644874,0.1829918027,0.3118669987,-0.2796678245,0.1751922965,-0.1004711762,-0.2744460702,0.2910319865,-0.1642244458,0.0376682244,-0.3985897303,-0.0958595946,-0.0917951539,0.0960873514,0.3988892734,0.0868708789,0.4171565175,0.1293655336,0.2241773605,0.1347905993,0.2759168446,0.178447932,-0.0017330297,-0.2029621452,0.2175432146,-0.081027478,-0.2209538668,-0.2160374969,-0.2931857705,-0.0774588361,-0.1473874003,0.2421827614,-0.3560352027,0.2010423988,0.3800360858,-0.1475385576,-0.1068175137,-0.098329626,0.5739683509,-0.1218078882,0.0830891579,-0.0840706825,0.020189194,0.4858983457,0.0717014894,-0.2658756375,0.2152179331,-0.276860863,-0.0522133298,0.2739189565,-0.0850304663,-0.2639813721,-0.2457928061,0.1607364565,0.1307996362,-0.481888175,-0.4057360888,0.2260941267,-0.4426649809,-0.1356583685,0.5243067741,0.3977626264,0.3573784232,-0.1086715981,0.182147935,0.0534409247,0.0158823803,-0.0299053807,-0.1290168166,-0.1947342157,0.0986651331,0.053240262,-0.1217258796,-0.0514579751,0.2830915451,0.0329827741,0.3958420157,0.0993602872,0.0005168345,-0.0211902335,0.097968325,0.1156114787,0.212317735,0.1543367356,-0.09075284,0.2483416796,-0.3218515217,0.1370418966,0.1254315227,-0.2333319485,0.0656790137,-0.1063255072,-0.2659734488,-0.3408946097,0.1597786844,0.1032047719,-0.4180200696,-0.275488019,-0.2062266171,0.4432168901,-0.0138518624,-0.1089850813,-0.2169679552,-0.1268142313,-0.334615767,0.0727749094,-0.1406827867,0.0365250446,0.074713625,-0.1410052478,-0.164787665,0.3544574082,0.3195937574,0.1061796099,0.3022877872,0.1927321255,0.0328771695,-0.1241667792,0.1496131718,0.0756309703,0.1013348401,0.0801173076,-0.0873717889,0.256319344,-0.1455116272,-0.1093319952,-0.1375808716,0.13144508,-0.0765736476,-0.1365867108,-0.2957275808,-0.1035109982,0.610616982,0.317109406,0.231996119,0.0240163058,-0.0837848485,-0.0262041688,0.1193377599,0.1129376218,0.0377228856,-0.1472759694,0.2499714494,-0.0389145724,-0.0289272256,0.3769492507,0.5130673647,0.2503073215,0.071699664,0.3051802218,0.0016121762,-0.2875819504,0.0358735695,0.0561415479,-0.1126169339,0.0684218258,-0.0435020328,0.0493567586,-0.1518482566,-0.3390766382,0.1330272555,-0.0304418411,-0.1809448004,0.2108364254,-0.0941149294,0.2258033901,-0.0496726111,-0.1246389225,-0.2088010162,-0.4025353193,0.2573352158,0.0917731747,-0.4607612491,0.0951692164,-0.0385735333,0.2664901316,-0.1746661365,0.0735596344,-0.1235503629,-0.3214639127,-0.0437071472,0.0795470849,-0.0880632102,0.2526984811,0.7304578424,-0.1311330795,-0.2009440213,-0.3724404573,-0.1643543392,0.0462005772,-0.3329814374,0.4429244697,0.4895553589,0.0533521473,-0.1120601445,-0.1844489574,0.0840663761,-0.3830341697,-0.2660089135,-0.1845015883,0.1068547517,-0.3374786973,-0.3387934864,-0.368558228,-0.4663522243,-0.3014016151,0.2419752926,-0.1762255877,0.1574790031,0.0984287485,-0.404407382,0.0433088169,0.2788309157,-0.1425389946,-0.499094069,-0.5869216323,0.0771412998,-0.2953599095,-0.1439429671,-0.0453416817,0.1576921195,-0.2012320012,0.1635486037,-0.2414560467,-0.7453762293,0.0937659591,0.2354438454,-0.0996864215,0.0434934199,0.3776494563,-0.0950522795,-0.2161876708,-0.2172877789,0.0611052252,0.4402588904,-0.0263326485,0.1234301403,-0.0219239611,0.0885738879,-0.0025034384,0.5244187117,0.231462732,0.0415040255,0.3437654376,-0.191254586,0.4083034396,-0.0738660693,-0.2043543309,0.2984809875,0.18654249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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1954","title":"add a new column ","comments":"Hi\r\nnot sure how change the lable after creation, but this is an issue not dataset request. thanks ","body":"Hi\r\nI'd need to add a new column to the dataset, I was wondering how this can be done? thanks \r\n@lhoestq ","comment_length":18,"text":"add a new column  \n Hi\r\nI'd need to add a new column to the dataset, I was wondering how this can be done? thanks \r\n@lhoestq  \n Hi\r\nnot sure how change the lable after creation, but this is an issue not dataset request. thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1954","title":"add a new column ","comments":"Hi ! Currently you have to use `map` . You can see an example of how to do it in this comment: https:\/\/github.com\/huggingface\/datasets\/issues\/853#issuecomment-727872188\r\n\r\nIn the future we'll add support for a more native way of adding a new column ;)","body":"Hi\r\nI'd need to add a new column to the dataset, I was wondering how this can be done? thanks \r\n@lhoestq ","comment_length":40,"text":"add a new column  \n Hi\r\nI'd need to add a new column to the dataset, I was wondering how this can be done? thanks \r\n@lhoestq  \n Hi ! Currently you have to use `map` . You can see an example of how to do it in this comment: https:\/\/github.com\/huggingface\/datasets\/issues\/853#issuecomment-727872188\r\n\r\nIn the future we'll add support for a more native way of adding a new column ;)","embeddings":[-0.2675053477,-0.3267511427,-0.2293746471,-0.0306972917,0.0712796822,0.1932588071,0.2296767533,0.098611936,0.2282399833,0.1450353712,-0.155760482,0.1634853631,0.0027359948,0.51192379,0.2227695286,-0.1781963706,-0.0625528023,0.1988129318,-0.0534236208,0.0027416525,-0.3495630622,0.0484526381,0.2379430085,-0.1702793986,-0.2531002462,-0.2114895731,-0.0195244942,0.0866771787,-0.2067594081,-0.3567590415,0.0423742458,0.2098613083,-0.0749002248,0.1859726012,-0.0000973422,-0.0641719848,-0.0359068289,-0.134630084,-0.1033529937,-0.0347682685,-0.2414609045,-0.1699303687,-0.069243513,-0.0861014277,-0.418582648,-0.1042871177,-0.0217464622,-0.153125748,0.1256458312,0.1134686843,0.4063779116,0.2553823292,-0.0764760301,-0.3638369143,0.1776876897,0.1461547911,-0.2223314196,0.0608458295,0.0292487275,-0.0947456285,-0.08670111,0.2065436393,0.3743553162,0.1480084509,0.1906539947,0.2405007929,-0.1616064459,0.0865648165,0.1893065721,0.327333957,0.4445179701,-0.1060111448,-0.3236677051,-0.2550575733,0.0308138691,-0.3803779185,-0.0968920738,-0.0229365844,0.095169425,0.1734922826,0.0142113063,-0.1790034175,-0.1423597932,0.1836372018,-0.2275916338,0.2113582641,-0.2669085264,-0.0052920156,0.1706732512,-0.0678176358,-0.2897088826,0.3743713796,-0.0731748566,0.1141525805,-0.1223932579,-0.1932972223,0.1971586049,0.1913174838,0.2126955539,0.0485491827,0.0913344994,-0.054155089,-0.110760808,0.1634424031,0.0828428566,-0.1297962815,0.20901829,-0.0888698623,0.2225174159,0.0488693193,0.0825487822,-0.3772425354,0.2139924467,-0.0154408403,-0.0938181058,-0.194901064,0.2368553579,-0.1049163118,-0.1222472042,-0.1332412362,0.2561585605,-0.0918196142,0.0656536818,0.4747041166,0.0947093293,-0.158417061,-0.045620691,0.1981340647,0.080589734,-0.1449050009,-0.3359633982,-0.0149343796,0.0481394231,0.1808351576,0.034531232,-0.0946614072,0.4725374877,-0.0024814336,-0.0302333087,0.1297015548,0.0783943534,-0.0043350221,0.1934849769,0.1643794328,0.004470835,-0.0576049238,0.040354792,-0.1187347621,-0.1352625489,-0.0327263959,-0.0106876418,-0.1250885576,-0.3687202632,0.3041077256,0.104108341,-0.2859869897,-0.0987835079,0.4664135277,-0.0598139763,-0.2486292273,0.0581969544,0.3222052455,-0.3405737877,-0.0693770349,0.0408708975,-0.0870443881,-0.252587229,-0.2556756139,-0.0349228345,-0.2431697249,-0.1837906539,0.0473110788,-0.0754375681,0.079724595,-0.0735879391,0.2277758718,0.2582001686,0.1087667197,-0.2271649092,-0.1417523324,-0.2780780792,-0.09710031,0.14764902,0.3498815298,0.1436208785,0.0415607616,-0.0008696713,0.0429348759,-0.184850052,0.0126023944,0.0297190491,-0.0185100157,0.022534281,0.1027058288,-0.0757717863,0.1558429152,0.31769225,0.0230158847,0.1711735725,-0.1810259819,-0.070137389,0.2003710419,0.4800208211,0.0673372,-0.1219241694,-0.2546574771,-0.5289201736,-0.0148887308,0.0389770493,-0.0887867808,0.1192283481,-0.3768486679,-0.3831667304,-0.0615021959,-0.1084753796,-0.0569789447,0.2907108366,0.1430888921,0.067939356,-0.1913001686,-0.1594552696,0.1520783603,-0.0369494706,0.0091191288,-0.2650406361,0.1284197569,0.0173635744,-0.1359552443,0.0963114575,0.1147398278,0.1042970493,0.0309456773,0.1269075274,0.2909142971,-0.1034535021,0.3359621763,0.2832551897,0.143500492,0.1118826717,0.0508651622,0.0449773669,-0.1548962891,0.0173943769,0.0894271657,-0.4237238467,0.2668480277,0.1428694278,-0.0632410347,-0.0419426002,-0.0038181678,0.2277862132,-0.1741270572,0.0426250398,-0.4620880783,0.018292645,-0.0694311634,0.0898766369,-0.0262677725,-0.406491667,0.2828010917,0.3379993439,0.0339879729,0.0409408547,0.1030980051,-0.2593681216,0.0518031158,0.1649906337,0.2050795555,0.2228690088,0.4660227597,0.2596595883,-0.0010431162,-0.1270840913,-0.1808649898,-0.0776134878,-0.2090056092,-0.0364621021,0.1165696159,0.2806752622,-0.3271846175,-0.5827208757,0.0823420584,-0.0810477436,0.1986769587,-0.1886476874,-0.2168995738,-0.090699397,0.0099926675,-0.0009752501,-0.2157727331,-0.1718835235,-0.2708534598,0.2800835073,0.3527169228,-0.2082553655,0.0128023848,-0.0188154746,0.2867797017,0.0408826396,0.1994555146,-0.074971959,-0.2418559343,0.145731926,0.3013759255,-0.2270241231,0.0597375222,0.6364293694,-0.1969311386,0.1622603238,-0.4014016688,-0.4579341412,0.0456495844,-0.0933285877,0.2280401587,0.1930567622,0.4515183568,-0.113910526,-0.1788264662,0.2495382875,-0.0848386213,-0.3495542109,-0.2163494527,-0.1054063886,0.1298486441,-0.0546000153,-0.3900428116,0.0308279917,-0.3032822013,0.5029949546,-0.0068603801,0.0459585637,0.0246298183,0.0617438667,0.0616711304,-0.3266998827,-0.0044154814,-0.5509761572,-0.3817510009,0.3346668482,-0.4371896088,-0.2686751187,-0.0591444373,-0.0356205069,0.136780858,-0.1566551626,-0.095357053,-0.3855728507,-0.1848876774,0.2939253747,-0.0176991746,0.1278983057,0.4841794372,-0.1433973014,-0.2456701696,-0.2272544652,-0.312632829,0.1525451243,0.3414388597,0.1171690524,-0.2383913994,0.2780508697,-0.0030252272,0.294416815,-0.0667823926,-0.1000752151,0.3630546033,-0.4525365531,0.4308469892,-0.0333026275,-0.2724324465,0.1023563296,0.170372352,0.16822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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1949","title":"Enable Fast Filtering using Arrow Dataset","comments":"Hi @gchhablani :)\r\nThanks for proposing your help !\r\n\r\nI'll be doing a refactor of some parts related to filtering in the scope of https:\/\/github.com\/huggingface\/datasets\/issues\/1877\r\nSo I would first wait for this refactor to be done before working on the filtering. In particular because I plan to make things simpler to manipulate.\r\n\r\nYour feedback on this refactor would also be appreciated since it also aims at making the core code more accessible (basically my goal is that no one's ever \"having troubles getting started\" ^^)\r\n\r\nThis will be available in a few days, I will be able to give you more details at that time if you don't mind waiting a bit !","body":"Hi @lhoestq,\r\n\r\nAs mentioned in Issue #1796, I would love to work on enabling fast filtering\/mapping. Can you please share the expectations? It would be great if you could point me to the relevant methods\/files involved. Or the docs or maybe an overview of `arrow_dataset.py`. I only ask this because I am having trouble getting started ;-;\r\n\r\nAny help would be appreciated.\r\n\r\nThanks,\r\nGunjan","comment_length":113,"text":"Enable Fast Filtering using Arrow Dataset \n Hi @lhoestq,\r\n\r\nAs mentioned in Issue #1796, I would love to work on enabling fast filtering\/mapping. Can you please share the expectations? It would be great if you could point me to the relevant methods\/files involved. Or the docs or maybe an overview of `arrow_dataset.py`. I only ask this because I am having trouble getting started ;-;\r\n\r\nAny help would be appreciated.\r\n\r\nThanks,\r\nGunjan \n Hi @gchhablani :)\r\nThanks for proposing your help !\r\n\r\nI'll be doing a refactor of some parts related to filtering in the scope of https:\/\/github.com\/huggingface\/datasets\/issues\/1877\r\nSo I would first wait for this refactor to be done before working on the filtering. In particular because I plan to make things simpler to manipulate.\r\n\r\nYour feedback on this refactor would also be appreciated since it also aims at making the core code more accessible (basically my goal is that no one's ever \"having troubles getting started\" ^^)\r\n\r\nThis will be available in a few days, I will be able to give you more details at that time if you don't mind waiting a bit 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1949","title":"Enable Fast Filtering using Arrow Dataset","comments":"Sure! I don't mind waiting. I'll check the refactor and try to understand what you're trying to do :)","body":"Hi @lhoestq,\r\n\r\nAs mentioned in Issue #1796, I would love to work on enabling fast filtering\/mapping. Can you please share the expectations? It would be great if you could point me to the relevant methods\/files involved. Or the docs or maybe an overview of `arrow_dataset.py`. I only ask this because I am having trouble getting started ;-;\r\n\r\nAny help would be appreciated.\r\n\r\nThanks,\r\nGunjan","comment_length":19,"text":"Enable Fast Filtering using Arrow Dataset \n Hi @lhoestq,\r\n\r\nAs mentioned in Issue #1796, I would love to work on enabling fast filtering\/mapping. Can you please share the expectations? It would be great if you could point me to the relevant methods\/files involved. Or the docs or maybe an overview of `arrow_dataset.py`. I only ask this because I am having trouble getting started ;-;\r\n\r\nAny help would be appreciated.\r\n\r\nThanks,\r\nGunjan \n Sure! I don't mind waiting. I'll check the refactor and try to understand what you're trying to do :)","embeddings":[-0.1475265026,-0.0889887959,-0.2098283023,-0.0918968394,0.0624384396,-0.2331448346,-0.0470115282,0.2564979196,0.1409168392,-0.165285185,-0.2117004097,0.5572015047,-0.1610823572,0.2340772152,-0.0721702799,-0.1115348861,-0.163025409,-0.0334027894,-0.1529833227,-0.0570407026,0.160586223,-0.0926778093,-0.1693582982,-0.0569531918,0.0641346052,-0.0823348612,0.4899257421,-0.1223512962,-0.3783577681,-0.5257975459,0.180488348,0.4125720263,-0.2424077839,0.2819820344,-0.0001122101,-0.0977285206,0.3756682873,0.0460015684,-0.1633183658,0.2018047422,-0.3833836019,-0.5163570642,0.3023416102,-0.0402294919,-0.2140232474,-0.3167768419,-0.4475963414,-0.6233633757,0.3333078921,0.2781315446,0.1977690309,0.0779646039,0.1296117902,0.0343558192,0.2299921364,0.1702783704,-0.2757145464,-0.1611308753,0.690895319,-0.1348021477,-0.1131289452,0.4136619866,-0.0534187928,-0.1600668281,0.2612384558,-0.3271079063,0.0970113128,-0.5140107274,0.2721892297,0.244067207,0.5241881013,-0.0686452687,-0.2503685355,-0.1154986694,-0.295946151,-0.0541290008,-0.0665444285,-0.059307754,-0.1523031741,0.195379585,-0.1410022974,-0.3106043935,-0.3834908009,0.0470867008,0.1975131333,0.3864691257,-0.1185574308,-0.0790665895,0.1377027333,-0.3078043759,0.2436964065,-0.1139409989,0.0077825147,0.3805395961,-0.2030074298,-0.0627390966,0.1559075713,0.4094288051,0.2585114539,0.1290134341,-0.0537548661,0.3599945307,0.1209415272,0.0324548408,-0.0365011655,0.0077360962,-0.0455789194,0.4321999252,0.150579989,-0.2071461827,-0.2090796083,0.0376978219,0.0157882031,-0.2357439697,-0.0105468379,-0.2417128086,-0.0886400864,-0.4509317875,-0.0292536505,-0.1328237951,-0.148855716,-0.2649073601,0.3727755249,0.2461594939,-0.0908480585,-0.042247802,-0.2776985765,-0.1313280314,-0.2057754397,0.0612553358,0.0159739405,-0.1020879522,-0.1534935832,-0.290271312,0.1802892983,-0.0157666206,-0.10327252,-0.0190820396,-0.0743510872,0.2151860148,0.3460691571,0.0992514789,0.411332339,0.3538830876,-0.4210656881,-0.0184229221,0.115955621,0.0654646084,-0.2028469443,0.1139784157,-0.2340939492,-0.5071300268,-0.0122117652,0.1530790627,-0.1667325348,-0.1694045961,0.0689224526,0.5219022632,-0.1484542191,-0.0539499,0.130165413,-0.0499783121,-0.0169830658,-0.3549636602,0.1976926029,0.1616173834,-0.5772004724,0.0571639501,-0.3905580044,-0.0376618318,0.2938017547,0.1009637266,-0.4094146192,-0.2548648417,0.1447502971,-0.0853995532,0.6621947289,-0.1478880197,-0.5032480955,0.0212403536,-0.1166824177,0.2362808585,0.1395219266,0.0708171725,0.2376329005,-0.1459852606,0.0106067592,0.5886858702,-0.1571557671,-0.1358809769,-0.3851483464,-0.0330214538,0.0929226056,0.0147239659,-0.0122914733,-0.0489020683,0.2204797417,-0.3853618801,0.1980082989,0.0757929757,0.1681527793,-0.046123866,0.5657210946,0.1406765878,0.3982255459,0.2781224549,-0.0813048854,0.2465830594,-0.0564617142,-0.1005464718,-0.3765888512,-0.0060677938,-0.1080690846,0.1723631918,-0.1178417206,-0.0534229502,0.14563407,-0.2586216033,0.1876114011,-0.1119330898,-0.2767269313,0.1864242703,-0.1401674151,-0.0221047364,0.0777286291,0.0473793671,0.1509610713,0.1477293372,-0.03628847,-0.2032631934,-0.0335153602,-0.0979410037,-0.1415888965,-0.0482927598,0.1992948055,0.2076708525,0.3779627681,0.5796352029,0.0671273619,-0.4598431289,0.4000549018,0.1309711635,0.1228171587,0.0600435212,-0.2230873108,0.6486538649,-0.4913371205,0.1301896721,0.0743035227,0.0571306497,0.1209030896,0.091638729,-0.3350029886,0.1061155424,0.2322738767,-0.2754030228,0.2704346478,-0.0466508307,-0.1053303406,0.4582201242,-0.1308725029,0.2124437094,-0.2018691301,0.2182039022,0.0384851173,-0.137300089,0.0497142747,0.4776943922,0.0552189313,0.3242245615,0.1072229967,-0.2351413369,0.077988036,0.0317963623,0.4003838301,0.0332389995,0.3895933926,-0.0226081554,0.2586921453,-0.0754904523,-0.1984192878,-0.3335967362,-0.0943608657,0.1433903277,-0.0976475477,-0.1084431633,0.1243613362,-0.2786762416,0.0896425322,-0.4824868739,0.0609073527,-0.1182475463,0.1991486549,-0.2700444162,-0.0258981995,0.2583579123,-0.476493448,0.2215439528,0.0698770285,-0.2258888185,-0.2692493796,-0.2329246998,0.1807035804,0.118802242,0.0096916314,0.2544655204,0.3076722622,0.4234210551,0.185579434,-0.0607866235,-0.5851409435,0.0178731307,0.0574484468,0.2471532375,-0.1005003303,0.0984463096,0.0665504485,0.0384896025,0.1948616058,-0.1776289344,0.003502131,-0.2151720375,-0.1799066067,0.1618772596,0.1814917326,-0.007470869,-0.402543813,-0.3848935664,0.0839819536,0.0953602344,0.1507592052,0.0864581913,0.1575749964,0.0498173162,-0.21996966,0.1554563046,0.1019880101,0.0315358602,0.3705923855,-0.1950341463,-0.1830601543,-0.1638777405,-0.2358260602,-0.0301395897,0.3896688521,-0.1173681915,0.1325734407,-0.3845604062,0.2650758922,0.0392279178,-0.0429855809,0.2871629,0.1398596913,-0.0358021371,-0.2890634239,-0.0778519958,0.0468321629,-0.1745110005,0.1589364707,0.2671407461,0.2515628338,-0.0135229062,0.6781641841,0.2385448664,0.3053124845,-0.0278060716,0.2382785678,-0.0804032236,0.0050881519,-0.363689661,-0.2951539457,-0.150416731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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1948","title":"dataset loading logger level","comments":"These warnings are showed when there's a call to `.map` to say to the user that a dataset is reloaded from the cache instead of being recomputed.\r\nThey are warnings since we want to make sure the users know that it's not recomputed.","body":"on master I get this with `--dataset_name wmt16 --dataset_config ro-en`:\r\n\r\n```\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-2e01bead8cf42e26.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-ac3bebaf4f91f776.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-810c3e61259d73a9.arrow\r\n```\r\n\r\nwhy are those WARNINGs? Should be INFO, no?\r\n\r\nwarnings should only be used when a user needs to pay attention to something, this is just informative - I'd even say it should be DEBUG, but definitely not WARNING.\r\n\r\nThank you.\r\n","comment_length":43,"text":"dataset loading logger level \n on master I get this with `--dataset_name wmt16 --dataset_config ro-en`:\r\n\r\n```\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-2e01bead8cf42e26.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-ac3bebaf4f91f776.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-810c3e61259d73a9.arrow\r\n```\r\n\r\nwhy are those WARNINGs? Should be INFO, no?\r\n\r\nwarnings should only be used when a user needs to pay attention to something, this is just informative - I'd even say it should be DEBUG, but definitely not WARNING.\r\n\r\nThank you.\r\n \n These warnings are showed when there's a call to `.map` to say to the user that a dataset is reloaded from the cache instead of being recomputed.\r\nThey are warnings since we want to make sure the users know that it's not recomputed.","embeddings":[-0.151090309,-0.3636071384,-0.0170671083,0.3475191295,0.4003514051,0.4622991979,0.4706430733,0.143284902,0.2523232996,-0.0047669052,0.0243790075,-0.0168783832,-0.2481703609,-0.1975330561,-0.3672408462,0.1944217086,-0.117108494,-0.0153791243,-0.3518670797,-0.0757232457,-0.1171182394,0.0906820446,-0.0266264919,0.5081018806,-0.7216521502,-0.0836909041,0.1400509477,0.1188435033,0.0099192094,-0.7821527719,0.2707004249,-0.128009975,0.1496111751,0.0640889332,-0.000122115,0.2060291022,0.6381807923,0.0515546985,-0.4871644378,0.02924826,-0.4754450619,-0.478941083,0.2521521151,-0.1042924523,-0.0297280084,-0.3696423173,0.1922228932,-0.4077891707,0.3286340237,0.1021776944,0.1139923781,0.1136904508,-0.1395543218,0.0326823704,0.3232573569,0.2458929718,0.037366733,0.0479511358,-0.0644855797,0.070087947,-0.2977598906,0.4409292042,-0.2956631184,0.052874893,0.240166679,-0.0134788146,0.2989350855,-0.2302694768,0.1710722744,0.3026792407,0.5181794167,-0.0112023316,0.1131071821,-0.4032346308,-0.2267826349,-0.0801946446,0.1673657596,0.1750006676,0.071756959,0.1927925944,-0.377902478,0.1259964406,0.1591122448,-0.0451121181,0.2188045681,0.3621397316,-0.3730171025,0.2935863733,0.1711701751,-0.0791650489,0.1143631712,-0.2527516484,-0.3305520117,0.1376243085,-0.2099725008,0.013834741,-0.1173723415,0.392984122,-0.1036160216,0.0236903653,0.1074151769,-0.0235768147,0.1798293591,0.1378366351,0.4080687165,0.0065904092,0.4469654262,0.0536668263,-0.1491612047,-0.1865981668,0.1863411218,-0.0538994484,0.0364211015,-0.140154317,0.5328091383,0.1328554749,0.1173180267,-0.151405856,-0.0866409466,0.0324665718,-0.1064751893,-0.2918034792,0.012836,0.1817286015,-0.0469556972,-0.0455386788,-0.1222740337,-0.0642848387,0.0570406467,-0.173807919,-0.0831567347,-0.3341276944,-0.3643180132,0.1104174107,-0.0247186851,-0.207774058,0.3294540644,0.1876461804,-0.1365977526,0.0210160818,0.109282136,-0.1964774877,0.1147880629,0.627643466,-0.228299126,0.2196660042,0.3440072834,0.1959386468,-0.1997303069,0.4353930354,-0.410954088,-0.4699918926,0.0184684638,0.0884320661,-0.1389927715,0.0448096395,-0.308349669,0.1366654783,0.3585318327,0.1251950711,0.2424760759,-0.1067552045,-0.3246652782,-0.1552183926,-0.1979091167,0.5811362267,-0.1848368049,-0.3025492728,-0.3280834854,-0.4322116673,0.2125727087,0.1218341365,-0.2136898041,0.2800593078,-0.3107714653,-0.0972535238,0.3082108498,-0.1050789505,0.0822921246,0.4469822347,-0.2502985001,0.1680027246,0.2125217766,0.0836042091,-0.2156756669,-0.1224220842,0.0135816662,-0.4536339045,0.0406977534,-0.1546110809,-0.1473661363,-0.1895036399,0.2960463166,0.0144892475,-0.2710362673,-0.0140961269,0.0367450602,-0.1554628164,0.2397705764,0.1271273047,0.1040881723,0.0431242064,-0.1647823155,0.0198250562,-0.0450833403,0.3280397356,-0.6091821194,0.0961715579,0.1165381074,-0.0305937845,0.0045087794,-0.0909322351,-0.0490077138,-0.0273274016,-0.3792020679,-0.1640519053,-0.0221960694,0.0016261688,-0.0596196055,-0.0850864947,-0.0751335546,0.2245910466,-0.5344884396,0.0863711834,0.0056907381,-0.1661028564,0.0117708649,0.3507805169,-0.2235099226,-0.1054344699,0.1355479658,0.0792681947,-0.1547690928,0.2173436284,0.1086671054,0.2987656891,-0.0428354889,0.3947451711,0.0307760108,-0.1995355487,0.4566539824,-0.1920306832,-0.1919047236,-0.1184583381,0.1470098346,0.1488821208,-0.0293637179,0.2458742708,-0.3589741588,0.0014749457,0.0724731684,0.1763548106,-0.1827459037,-0.5318390727,-0.0535599738,0.1382286251,0.2236326486,0.3524547815,0.0328621119,0.0614067689,0.5036969781,-0.0225223694,-0.0350022763,0.1725946218,-0.1172601059,-0.1267620027,0.2107435018,0.0995993614,0.400642097,0.1433577538,0.0964034051,0.2135555297,0.0398832411,-0.0287604891,0.2845728695,0.0011580769,0.2919470072,0.0412642993,-0.4688796699,-0.053495463,-0.2464312762,0.0426365919,-0.1107656434,-0.1338513196,-0.548420012,-0.1352434307,-0.1794122308,-0.2473459095,-0.3247794509,-0.2180150151,-0.2057373375,-0.4174848199,0.0159907546,-0.196761623,-0.3559078276,0.1864648908,-0.1796554178,0.2185807824,0.1087459549,0.2702391744,-0.3594065309,-0.2073978037,-0.186862722,0.0056629335,-0.1753740758,-0.1987687945,0.0720702484,-0.300937891,0.2761229873,-0.2969670594,-0.1735821217,0.1628869176,-0.3161101639,-0.0158542413,0.1541595608,0.0007796765,0.0341844596,-0.1384242177,0.0944360048,0.1207671911,-0.0642790124,-0.3195793331,0.1604953706,0.1289811879,-0.155172497,-0.2121983469,-0.3349220753,-0.1513204426,-0.1797146797,-0.3641599417,0.189716205,0.3864744008,0.002457493,0.1253540367,-0.2772358358,0.2045513988,-0.351603359,-0.6407558918,0.041935984,-0.0941983685,0.0447054617,0.0233052131,0.1199380681,0.1616583914,-0.0717071369,-0.8328558803,0.0837572068,-0.1131227836,-0.2497929931,0.2192757875,-0.0311910827,-0.0892969817,0.2955489457,-0.0040108827,-0.0155311041,-0.2937012911,0.0342544802,-0.4827652276,0.2337228656,0.0650281832,0.4955634475,-0.127476573,0.667124927,0.1786766946,-0.0275564641,0.197088927,0.0470813066,0.6038406491,-0.1499833167,-0.1548581421,-0.1112248451,0.0743583962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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1948","title":"dataset loading logger level","comments":"Thank you for explaining the intention, @lhoestq \r\n\r\n1. Could it be then made more human-friendly? Currently the hex gibberish tells me nothing of what's really going on. e.g. the following is instructive, IMHO:\r\n\r\n```\r\nWARNING: wmt16\/ro-en\/train dataset was loaded from cache instead of being recomputed\r\nWARNING: wmt16\/ro-en\/validation dataset was loaded from cache instead of being recomputed\r\nWARNING: wmt16\/ro-en\/test dataset was loaded from cache instead of being recomputed\r\n```\r\nnote that it removes the not so useful hex info and tells the user instead which split it's referring to - but probably no harm in keeping the path if it helps the debug. But the key is that now the warning is telling me what it is it's warning me about.\r\n```\r\nWarning:Loading cache path\r\n```\r\non the other hand isn't telling what it is warning about.\r\n\r\nAnd I still suggest this is INFO level, otherwise you need to turn all 'using cache' statements to WARNING to be consistent. The user is most likely well aware the cache is used for models, etc. So this feels very similar.\r\n\r\n2. Should there be a way for a user to void warranty by having a flag - `I know I'm expecting the cached version to load if it's available - please do not warn me about it=True`\r\n\r\nTo explain the need: Warnings are a problem, they constantly take attention away because they could be the harbinger of a problem. Therefore I prefer not to have any warnings in the log, and if I get any I usually try to deal with those so that my log is clean. \r\n\r\nIt's less of an issue for somebody doing long runs. It's a huge issue for someone who does a new run every few minutes and on the lookout for any potential problems which is what I have been doing a lot of integrating DeepSpeed and other things. And since there are already problems to deal with during the integration it's nice to have a clean log to start with. \r\n\r\nI hope my need is not unreasonable and I was able to explain it adequately. \r\n\r\nThank you.","body":"on master I get this with `--dataset_name wmt16 --dataset_config ro-en`:\r\n\r\n```\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-2e01bead8cf42e26.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-ac3bebaf4f91f776.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-810c3e61259d73a9.arrow\r\n```\r\n\r\nwhy are those WARNINGs? Should be INFO, no?\r\n\r\nwarnings should only be used when a user needs to pay attention to something, this is just informative - I'd even say it should be DEBUG, but definitely not WARNING.\r\n\r\nThank you.\r\n","comment_length":351,"text":"dataset loading logger level \n on master I get this with `--dataset_name wmt16 --dataset_config ro-en`:\r\n\r\n```\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-2e01bead8cf42e26.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-ac3bebaf4f91f776.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-810c3e61259d73a9.arrow\r\n```\r\n\r\nwhy are those WARNINGs? Should be INFO, no?\r\n\r\nwarnings should only be used when a user needs to pay attention to something, this is just informative - I'd even say it should be DEBUG, but definitely not WARNING.\r\n\r\nThank you.\r\n \n Thank you for explaining the intention, @lhoestq \r\n\r\n1. Could it be then made more human-friendly? Currently the hex gibberish tells me nothing of what's really going on. e.g. the following is instructive, IMHO:\r\n\r\n```\r\nWARNING: wmt16\/ro-en\/train dataset was loaded from cache instead of being recomputed\r\nWARNING: wmt16\/ro-en\/validation dataset was loaded from cache instead of being recomputed\r\nWARNING: wmt16\/ro-en\/test dataset was loaded from cache instead of being recomputed\r\n```\r\nnote that it removes the not so useful hex info and tells the user instead which split it's referring to - but probably no harm in keeping the path if it helps the debug. But the key is that now the warning is telling me what it is it's warning me about.\r\n```\r\nWarning:Loading cache path\r\n```\r\non the other hand isn't telling what it is warning about.\r\n\r\nAnd I still suggest this is INFO level, otherwise you need to turn all 'using cache' statements to WARNING to be consistent. The user is most likely well aware the cache is used for models, etc. So this feels very similar.\r\n\r\n2. Should there be a way for a user to void warranty by having a flag - `I know I'm expecting the cached version to load if it's available - please do not warn me about it=True`\r\n\r\nTo explain the need: Warnings are a problem, they constantly take attention away because they could be the harbinger of a problem. Therefore I prefer not to have any warnings in the log, and if I get any I usually try to deal with those so that my log is clean. \r\n\r\nIt's less of an issue for somebody doing long runs. It's a huge issue for someone who does a new run every few minutes and on the lookout for any potential problems which is what I have been doing a lot of integrating DeepSpeed and other things. And since there are already problems to deal with during the integration it's nice to have a clean log to start with. \r\n\r\nI hope my need is not unreasonable and I was able to explain it adequately. \r\n\r\nThank you.","embeddings":[-0.1465872675,-0.2024423927,0.0129192071,0.3102096915,0.4003248215,0.3999482989,0.4905202687,0.2311711758,0.1243601069,0.0282325111,0.0093349731,-0.147262916,-0.2483211011,-0.188297689,-0.2508576214,0.0609391592,-0.1264946759,-0.031552773,-0.385997504,-0.1280371398,0.011019526,-0.0093774199,0.0819806829,0.4615404904,-0.69535321,-0.0577312857,0.0907774121,0.269282788,0.04841673,-0.7044392824,0.2687951028,-0.0610610768,0.1792588234,0.1538811177,-0.0001220934,0.130161792,0.626830101,0.006786231,-0.6243699193,0.0161634833,-0.3979128003,-0.3823045492,0.1909533143,-0.0893724859,-0.0878836066,-0.3329226673,0.3174687624,-0.398244381,0.3191033006,0.1539319158,0.0972733125,0.130963847,-0.1569447517,0.1304767281,0.3219229877,0.2671613693,-0.066899851,0.062543571,-0.027156949,0.1869474202,-0.3544816077,0.3654177785,-0.1897873282,-0.0355945677,0.168256402,-0.025154613,0.2707450092,-0.0829854682,0.0980230868,0.457021296,0.4693352878,-0.1381201595,0.0960084051,-0.3951651752,-0.114280127,-0.3058123589,0.1722683012,0.1817942113,-0.1236345321,0.1642877609,-0.278645575,0.0962313861,0.0633188337,-0.0412001908,0.2753942609,0.5356911421,-0.2549032271,0.199491173,0.232157737,-0.1086486205,0.318099618,-0.0647453815,-0.292390883,0.0557426736,-0.3284548223,0.0961241871,-0.156981498,0.2279085368,-0.127032131,0.0550274663,0.0303926487,0.0645745769,0.1766626239,0.0465370044,0.3021320403,0.0782378837,0.3390364349,-0.0437264033,-0.2039043158,-0.0232509989,0.190676406,-0.0522891022,-0.1306730509,-0.0884445235,0.4426140189,0.2055938244,0.0061232545,-0.0329376422,-0.1966416687,0.0259849578,-0.1524892002,-0.3309821784,0.024868533,0.1910765469,-0.0790168047,-0.0314244404,-0.1334362924,-0.0265853889,-0.0438799746,-0.2736995518,-0.0795496926,-0.2813799977,-0.3722065687,0.1771147996,-0.1128016785,-0.1633096784,0.1749125272,0.1784884781,-0.0563448071,0.0769142658,-0.0438766368,-0.1474408209,0.1251858175,0.4888392091,-0.1188975126,0.2109150141,0.315344125,0.114410758,-0.2508600056,0.3219142556,-0.3088692129,-0.4429717362,-0.1388504952,0.056897305,-0.1872850657,-0.042705588,-0.2263440192,0.2049746364,0.2080996037,0.2616475523,0.3043168485,-0.0870516226,-0.2893235683,-0.1400413662,-0.1068019271,0.6045224071,-0.2300405055,-0.3448925614,-0.2233905047,-0.390848875,0.3220081627,0.1940955967,-0.1252973825,0.3429298103,-0.2669639289,-0.1275547594,0.2065176219,-0.199307546,0.1538253278,0.5722748041,-0.2666302323,0.3573934436,0.2487148046,0.0757367015,-0.3795433939,-0.1587415636,0.0931627974,-0.4061235487,0.0346498452,-0.219547376,-0.1422888488,-0.3101754487,0.3162405193,0.0956732556,-0.1243333444,-0.0407623909,-0.0270057153,-0.059658967,0.5715073943,0.0659455955,0.0881292075,0.0159739293,-0.0474747047,0.0137909576,-0.1247672737,0.3047455549,-0.4733557701,0.0033311769,0.2965785563,0.1541423351,0.0607184656,-0.0349128097,-0.1101765782,-0.041664362,-0.4651914239,-0.1592132896,-0.014823894,0.0448270179,-0.2315159291,-0.133993879,-0.1228299066,0.3283220232,-0.5877948999,0.0591800176,-0.1173660383,-0.3510161936,0.0371697359,0.3243008852,-0.2366799265,0.0116760097,0.1059909984,0.0001265008,-0.1354235411,0.2674871981,-0.0427938104,0.311644882,-0.0823953971,0.5417579412,0.038802281,-0.1560539007,0.3681598306,-0.2089685947,-0.2602341473,-0.0510693863,0.113723956,0.0264503155,0.0294059273,0.2619304955,-0.2610865831,-0.1066248864,0.0779918954,0.0519058481,-0.1736053228,-0.3629187942,0.1012513787,0.1045231819,0.2224511802,0.3565239608,-0.0605902523,0.1401377916,0.373893708,-0.0226562619,-0.0009537109,0.2139959484,-0.0660070106,-0.1223928779,0.1966656595,0.0896252021,0.1997384131,0.1502249688,0.178512007,0.2778914869,-0.0631349906,-0.057255663,0.2577065229,0.0119202621,0.4047441483,0.0357543752,-0.456192404,0.0109884143,-0.2117695659,-0.0841504335,-0.1447158456,-0.0529185422,-0.6462906003,0.003194595,-0.268383503,-0.3433909416,-0.3749732077,-0.252673924,-0.1563916355,-0.3785588741,0.0086878147,-0.3024218976,-0.3954238892,0.1360277683,-0.3061369061,0.230926007,0.1001675949,0.2513026893,-0.2852820158,-0.0661872253,-0.2680495977,-0.0152586149,-0.0835170969,-0.2519970238,0.0985292122,-0.490531534,0.1629213393,-0.1752011329,-0.2026423514,0.3088508248,-0.1975004226,0.0879951939,0.1897615939,-0.0893781185,-0.0432891361,-0.2671490312,0.0250475947,0.1185781434,0.008313857,-0.354283154,0.0778483227,0.1457306594,-0.1193108782,-0.091356039,-0.3239561319,-0.1959176064,-0.1272505373,-0.367288202,0.2959487438,0.4755595326,0.0311138406,0.0013714295,-0.1817460358,0.2356292009,-0.2804739475,-0.7693060637,-0.0325261652,-0.1196923032,0.1096624807,-0.0435089134,0.1002776027,0.1628791541,0.0031549782,-0.8037417531,0.1855158359,-0.1419694424,-0.3532150686,0.160369873,-0.0036665837,-0.1876031607,0.2181845605,-0.0146573279,0.0057902932,-0.2862210572,0.0531042032,-0.4676643908,0.4226564169,0.1411723197,0.4266456068,0.1103252023,0.4797783792,0.1754454821,-0.0096641118,0.2206352055,-0.0405065082,0.6518876553,-0.0414569713,-0.0822485909,0.0396552198,0.0212174859,-0.2239888012,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1948","title":"dataset loading logger level","comments":"Hey, any news about the issue? So many warnings when I'm really ok with the dataset not being recomputed :)","body":"on master I get this with `--dataset_name wmt16 --dataset_config ro-en`:\r\n\r\n```\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-2e01bead8cf42e26.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-ac3bebaf4f91f776.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-810c3e61259d73a9.arrow\r\n```\r\n\r\nwhy are those WARNINGs? Should be INFO, no?\r\n\r\nwarnings should only be used when a user needs to pay attention to something, this is just informative - I'd even say it should be DEBUG, but definitely not WARNING.\r\n\r\nThank you.\r\n","comment_length":20,"text":"dataset loading logger level \n on master I get this with `--dataset_name wmt16 --dataset_config ro-en`:\r\n\r\n```\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-2e01bead8cf42e26.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-ac3bebaf4f91f776.arrow\r\nWARNING:datasets.arrow_dataset:Loading cached processed dataset at \/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/9dc00622c30446e99c4c63d12a484ea4fb653f2f37c867d6edcec839d7eae50f\/cache-810c3e61259d73a9.arrow\r\n```\r\n\r\nwhy are those WARNINGs? Should be INFO, no?\r\n\r\nwarnings should only be used when a user needs to pay attention to something, this is just informative - I'd even say it should be DEBUG, but definitely not WARNING.\r\n\r\nThank you.\r\n \n Hey, any news about the issue? So many warnings when I'm really ok with the dataset not being recomputed :)","embeddings":[-0.2018208951,-0.2147746682,-0.0191504266,0.412352562,0.4625152051,0.4148588777,0.4687033892,0.1274305284,0.1240049824,-0.0095585957,0.0188383404,-0.0816432014,-0.2626186013,-0.1146499664,-0.3601998389,0.256483227,-0.069249101,-0.035224501,-0.4280809462,-0.0789844841,-0.1100225374,0.0888151154,-0.0381991528,0.4029662609,-0.7883700728,-0.084462896,0.1141557097,0.0337202698,-0.014049775,-0.7113651633,0.2712530196,-0.0159591157,0.1750287116,0.1797815412,-0.0001194563,0.0807437599,0.5874010921,0.0346957445,-0.5454556942,0.0873998031,-0.3945814371,-0.4607205093,0.2950294912,0.0680098012,-0.0818453357,-0.4350318015,0.1053904891,-0.4060834944,0.343233496,0.0942688584,0.174123317,0.1127503067,-0.0637698323,0.0258959979,0.280120641,0.244505465,-0.044320859,0.0054731988,0.0054390025,0.054671485,-0.3033310473,0.436964035,-0.1826979071,0.0547141135,0.1919063181,-0.0574981198,0.2136777043,-0.187232241,0.1827512532,0.3480859697,0.5612227917,0.0119367177,-0.0213172529,-0.4384615123,-0.1327173412,-0.2757665813,0.1628347188,0.2771089971,0.0638028905,0.2064712644,-0.3305918574,0.0531376973,0.1248337477,-0.1494838148,0.1364181489,0.2955522835,-0.4321038723,0.1811940968,0.1696339548,-0.083792977,0.1944062263,-0.1643511504,-0.349773556,0.0897092223,-0.3535853326,0.0463145413,-0.1576972455,0.4689552188,-0.0739591196,0.2159979194,0.0167468507,0.0333992802,0.1682042331,-0.0259478595,0.4356084466,0.015156297,0.3082087636,-0.0288095865,-0.1062850431,-0.1515377909,0.1799237728,-0.0751504675,0.0315021463,-0.1234558001,0.4663625062,0.036062479,0.1819918305,-0.2072843015,-0.2261759639,0.0814376399,-0.0244262964,-0.2710516155,0.0001726879,0.1685473174,-0.0864167064,0.0307932198,-0.0959156156,-0.0316218026,-0.0068023303,-0.209455207,-0.1628880799,-0.3538591862,-0.319665432,0.1527389288,0.0468784198,-0.2135726213,0.2720476985,0.213695243,-0.0537762567,0.0015835599,0.000041171,-0.1857968569,0.02915035,0.592905879,-0.2160650492,0.2531104088,0.2836388052,0.2028167546,-0.1459082216,0.3028621078,-0.30979985,-0.5017568469,-0.0863301381,0.1424664557,-0.2130523026,-0.0198232234,-0.2632061839,0.076511316,0.3247368336,0.2773109078,0.1310303509,-0.1277332008,-0.2539335787,-0.1192452386,-0.1299889684,0.5831536651,-0.1704386175,-0.2282027304,-0.3527733684,-0.369110018,0.250470221,0.14596048,-0.1416144073,0.2105039656,-0.3315038979,-0.2182206064,0.189138934,-0.0765953064,0.0075308611,0.4234822392,-0.1885275394,0.1410767287,0.2532602251,0.0574838221,-0.2100540251,-0.1748186499,-0.0686213821,-0.3766585886,0.0073149013,-0.1962324083,-0.1279810965,-0.2532563508,0.3050473928,0.0753614455,-0.2619214058,-0.0613042824,-0.0347082838,-0.1696528643,0.2877303362,0.1290989369,0.1004646569,0.0401727334,-0.0632459372,0.0750579163,-0.0070595229,0.2962646186,-0.6046158671,0.0570388176,0.2527226508,0.0283459295,-0.0281593017,-0.0778326541,-0.0409895964,0.0045937882,-0.4720389545,-0.2500114143,0.0077041071,0.0479584038,-0.123851195,-0.1356071383,-0.1704794616,0.3103652298,-0.56722188,0.1139791682,0.0017968486,-0.130100742,0.031194143,0.278026104,-0.0537165776,-0.0033408329,0.0597091876,0.0926168486,-0.1194309443,0.1714720428,0.079550527,0.3461105824,-0.0664780363,0.3482441306,0.0421643108,-0.22758843,0.3439954221,-0.1905627102,-0.187661767,-0.0942144394,0.1217285916,0.1971225441,-0.0955973566,0.2936930358,-0.2681666315,-0.0041472698,0.1174794212,0.11582876,-0.1516924798,-0.5100905299,0.0351169892,0.1173784584,0.25505054,0.3448004723,-0.0433742888,0.1233636066,0.4002448916,0.033824306,-0.0325983986,0.1823849678,-0.1059868038,-0.0716442764,0.1637209803,0.1360666901,0.3655403256,0.1999433488,0.0699330568,0.1784570962,0.0452745184,-0.0914447233,0.3495111465,-0.0858696029,0.3064243793,0.1464744061,-0.3910761178,-0.0546413139,-0.3296447694,0.1122017577,-0.0881776288,-0.0445918851,-0.5437096357,-0.1114684567,-0.3159329891,-0.2503342927,-0.4220110178,-0.234174341,-0.22402367,-0.391638875,0.0054609785,-0.140047431,-0.300239414,0.1975164115,-0.1929402053,0.2459293157,0.0707642063,0.36000067,-0.3217198849,-0.1065909192,-0.2888944149,0.0322102718,-0.0777425468,-0.2631666958,0.2102131546,-0.2736949325,0.2459831834,-0.3104972541,-0.1889104247,0.1447823942,-0.2983018458,0.0963018909,0.1681459397,-0.012931684,0.0876922086,-0.2143983841,0.0786359981,0.1471222788,-0.0858602077,-0.356882751,0.10549362,0.184101969,-0.0835096985,-0.2449282259,-0.2904905677,-0.1300165653,-0.1924382746,-0.3609960377,0.1438363045,0.3930369616,-0.0703908876,0.0316412114,-0.2543497086,0.1763506234,-0.393045485,-0.5929751396,0.0932759792,-0.0530354902,-0.0479280464,0.0473506637,0.1402879655,0.2511121929,-0.0766196996,-0.8887055516,0.1407379359,-0.1085184366,-0.2693580985,0.1427567154,0.0030161543,-0.0376037844,0.2874903679,-0.0281252172,-0.058440797,-0.3251954615,-0.0211485717,-0.5583203435,0.4083808661,-0.0712125748,0.4945524931,-0.1182284132,0.617167294,0.2078094333,-0.061916694,0.2547045946,0.0239904001,0.60737288,-0.1072017923,-0.2783243656,-0.0848598853,-0.0239933319,-0.0853272602,-0.00903245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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"Hi !\r\n\r\nThe cache at `~\/.cache\/huggingface\/metrics` stores the users data for metrics computations (hence the arrow files).\r\n\r\nHowever python modules (i.e. dataset scripts, metric scripts) are stored in `~\/.cache\/huggingface\/modules\/datasets_modules`.\r\n\r\nIn particular the metrics are cached in `~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/`\r\n\r\nFeel free to take a look at your cache and let me know if you find any issue that would help explaining why you had an issue with `rouge` with no connection. I'm doing some tests on my side to try to reproduce the issue you have\r\n","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":84,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n Hi !\r\n\r\nThe cache at `~\/.cache\/huggingface\/metrics` stores the users data for metrics computations (hence the arrow files).\r\n\r\nHowever python modules (i.e. dataset scripts, metric scripts) are stored in `~\/.cache\/huggingface\/modules\/datasets_modules`.\r\n\r\nIn particular the metrics are cached in `~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/`\r\n\r\nFeel free to take a look at your cache and let me know if you find any issue that would help explaining why you had an issue with `rouge` with no connection. I'm doing some tests on my side to try to reproduce the issue you have\r\n","embeddings":[-0.0358740725,-0.0009466419,0.0741713271,0.1184607968,0.0837402493,-0.1097532511,0.1720347852,0.2317183316,0.263871789,0.1391707808,0.0678788871,0.1733187735,-0.2854747474,-0.0121780438,0.1369281113,0.0551351197,-0.0628577545,-0.0301498175,-0.325932622,-0.1252780259,-0.2200645804,0.5307022333,-0.0628679842,0.0645004809,-0.4562329352,0.1752285659,-0.1284715831,0.3620730042,-0.0833548233,-0.584066689,0.3293269575,0.1146462336,0.0539533086,0.4142387807,-0.000119533,-0.1037964821,0.2400082946,-0.1888287961,-0.2738633454,-0.2778948545,-0.075361982,-0.2522022128,0.5798787475,-0.0966041684,-0.1344479173,0.0916116387,0.0100134797,-0.4268659651,0.4767811894,0.121173963,0.1166027337,0.4053646624,0.0331894234,-0.3333912194,0.2213087976,-0.104605563,0.0415684804,0.4594830275,0.0687569603,0.0936744362,-0.1464713663,0.2114187777,0.3470683396,-0.184419781,0.309864521,0.0112119317,0.00131537,-0.1081184596,-0.0284116585,0.3066345155,0.2365594357,-0.1305871606,-0.2608057261,0.1283788681,-0.0879927501,-0.4039272666,0.1538643092,-0.0939178318,0.0763653293,0.0891124755,0.0873675048,-0.1980054379,-0.0160592515,0.1474253088,-0.1284569502,-0.1398061663,-0.1876245439,0.0414875671,-0.0495051667,0.0219498575,-0.5056313872,0.4271095395,-0.183152318,0.1320415884,-0.4020826221,0.1370484233,0.2792122364,0.1743569076,0.0485688336,0.5940818787,0.0544064604,0.0976389945,0.3240977824,0.1152189597,-0.1439858377,0.4637179077,0.1471063793,-0.0318703651,0.229068175,0.2400322706,-0.0997861177,-0.2906394005,0.2207398117,-0.2854764462,0.1802102029,0.2259304225,0.2352595031,-0.3656279445,0.0109472349,0.2513976395,-0.1731025726,-0.0880765393,0.0778518245,0.3774317801,0.0209159143,-0.0662802085,0.3336496055,0.3538492024,-0.2321899384,-0.0483000167,-0.4542716742,-0.1579865664,-0.1675678939,0.3084725738,0.1213161945,-0.0429608561,0.5225880146,-0.137724787,0.3675760031,-0.0634157658,0.2979551554,0.1495726556,-0.0578732304,0.2460677922,0.0045490917,0.1730860323,0.3100202382,-0.0152883707,-0.1046919227,-0.313659668,-0.214396596,-0.4901871383,0.086323522,0.0288708042,-0.3260264695,0.1841583699,0.3849876821,0.0545696914,-0.2149495631,-0.2581464648,0.1204283237,0.0444046967,-0.0732085928,-0.1320746392,0.5114512444,0.7114232183,-0.2573743463,-0.4081455171,0.2153412402,0.0144806914,-0.1083219424,0.1975735277,-0.0630953982,0.1563919634,-0.3403097093,-0.1043980196,0.4113843739,-0.5171518922,-0.4667468071,-0.0168590825,-0.3854107857,-0.1010334939,0.1008030176,-0.0357385799,0.0745937452,0.2175187916,0.1196304932,-0.1724984199,0.2414471209,-0.3219715357,-0.2602002919,-0.2055730373,0.1193033457,-0.0603425615,0.2362509519,0.1523997933,-0.0052174143,-0.073207669,0.1255847514,0.1020373926,0.1981779188,0.378146112,0.2921807468,0.1836390048,0.1978509873,0.1566252261,-0.1678197384,0.2583167851,-0.4689180255,0.1527780443,-0.0503249019,-0.0147543289,-0.2192664593,-0.2279687673,-0.0850848034,-0.4767327011,0.0177706815,0.0121323569,0.2931835949,0.3102684021,-0.1623553485,0.2714135349,0.0924602225,0.2920165062,-0.6656681895,0.0322583541,-0.1113724336,-0.33567366,-0.0986841321,0.1647350043,0.0611554831,-0.008172608,-0.1790619493,0.3883354664,-0.0169802532,0.1749452502,0.3794831336,0.0891622975,-0.0574563257,-0.4079510868,-0.0328812003,0.1797966808,0.0542467199,-0.0016229018,-0.2007852644,0.323792547,-0.0825717226,0.1331495494,-0.2450239509,0.3666039705,-0.0399597995,-0.0688230768,-0.5726243258,-0.0933298841,0.3485019505,-0.1906701922,0.4904285371,-0.1709937453,-0.0320257135,0.0218921844,0.0991401896,0.1401264817,0.2501839697,-0.0037819657,-0.2171487063,-0.0424428843,0.0269454364,-0.1459605545,0.4352152348,0.136262551,0.1439883709,0.1243445575,0.2096870393,-0.1694747359,0.0424716659,-0.0767036676,-0.1214765087,0.0951082706,0.1849317551,0.168072179,-0.2335238755,-0.1956410855,-0.3971342742,0.0322263464,-0.3484332263,0.0946538001,-0.2037186772,0.3464001119,0.3030623794,-0.0559965037,-0.3411486447,-0.2638901174,0.1385770738,-0.1877672374,0.0301673356,0.2229873985,-0.2555914521,0.4870513678,0.1352511793,-0.1863757521,-0.3071427047,-0.2004612088,-0.1395705789,-0.150298059,-0.2870164514,0.0871473402,0.0249529704,-0.2135687172,0.1292314827,-0.0343739353,-0.3483106792,0.0483233929,0.051832296,0.5172300935,0.3936597109,0.0021313401,-0.2833370268,0.2200516164,0.5371545553,-0.3732428253,-0.309099704,0.0990039334,0.0583365485,0.2021277249,-0.2552041411,-0.3111086488,-0.0462787375,-0.3856249452,0.6103236079,0.1012190729,-0.1091394797,-0.0645339042,0.012322762,0.1742912531,-0.2101198584,0.3627804518,-0.3324130177,-0.6493901014,0.3374326825,-0.1905549616,-0.3684265316,0.0284811556,0.0337616466,0.3815257549,-0.0200926159,-0.5352692008,-0.3893999755,0.0445665605,0.2492183745,-0.1060669869,0.0623193979,0.580855906,-0.0551422276,-0.0207334682,-0.0587935299,-0.1301974803,0.434307009,-0.1079049259,0.1379557103,-0.1012509242,0.4365177751,0.0528162457,0.7605785131,0.482316494,0.2873231769,0.2661584616,0.035839349,0.4865826666,-0.2143168747,-0.1602762789,0.1947723031,0.1949837506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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"Thank you for clarifying that the metrics files are to be found elsewhere, @lhoestq \r\n\r\n> The cache at ~\/.cache\/huggingface\/metrics stores the users data for metrics computations (hence the arrow files).\r\n\r\ncould it be renamed to reflect that? otherwise it misleadingly suggests that it's the metrics. Perhaps `~\/.cache\/huggingface\/metrics-user-data`?\r\n\r\nAnd there are so many `.lock` files w\/o corresponding files under `~\/.cache\/huggingface\/metrics\/`. Why are they there? \r\n\r\nfor example after I wipe out the dir completely and do one training I end up with:\r\n```\r\n~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock\r\n```\r\nwhat is that lock file locking when nothing is running?","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":93,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n Thank you for clarifying that the metrics files are to be found elsewhere, @lhoestq \r\n\r\n> The cache at ~\/.cache\/huggingface\/metrics stores the users data for metrics computations (hence the arrow files).\r\n\r\ncould it be renamed to reflect that? otherwise it misleadingly suggests that it's the metrics. Perhaps `~\/.cache\/huggingface\/metrics-user-data`?\r\n\r\nAnd there are so many `.lock` files w\/o corresponding files under `~\/.cache\/huggingface\/metrics\/`. Why are they there? \r\n\r\nfor example after I wipe out the dir completely and do one training I end up with:\r\n```\r\n~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock\r\n```\r\nwhat is that lock file locking when nothing is running?","embeddings":[0.2474486679,0.0509269387,0.0548085459,0.2140230685,0.1152904257,0.1876896173,0.2276507616,0.3381584883,0.2461999655,0.1004048139,0.1633262634,0.120030582,-0.3513062,-0.1723562777,0.0993687585,-0.0273564663,0.108128421,-0.0378123932,-0.2317160219,-0.164974153,-0.2525990903,0.5168857574,-0.0808430165,0.0938802287,-0.564119637,0.2315038443,-0.1601383835,0.2439415455,-0.0434489399,-0.5871254206,0.3680899441,0.1198711395,-0.0636295155,0.5067222118,-0.0001260311,-0.1185866445,0.2515877485,-0.1634837091,-0.2404909581,-0.1307276189,-0.0129830427,-0.2789452374,0.4915622771,-0.2036509514,0.1352380216,-0.0842312649,0.1214475334,-0.4259888232,0.3512769043,0.0431348793,0.0366241485,0.2477326244,-0.1989923418,-0.288210541,0.1527168602,-0.0924047977,0.0252721328,0.3664670289,-0.0836111903,-0.0094820866,-0.2560975552,0.3051272333,0.201936543,-0.1374050081,0.3521669805,0.0150463143,-0.0426552668,-0.2301341146,0.0013805062,0.2264509797,0.2374706417,-0.1862585992,-0.2228959501,-0.0533670969,-0.0899376571,-0.2590976655,0.2228358388,-0.0521903224,0.0696589276,0.1424809545,0.0115555236,-0.1976010054,0.0148122869,-0.0037729971,-0.1053485796,-0.0057458789,-0.1575381905,-0.0152483471,-0.0514961481,0.1989749968,-0.3450107872,0.3006658554,-0.0482684039,0.2071722299,-0.2239120901,0.0468739755,0.1978999823,0.1199527979,0.0460014939,0.4275059104,0.0228465553,0.0263509676,0.4165912867,0.0909777731,-0.1048306525,0.4695401788,0.3464441597,-0.0673513412,0.0851758644,0.1602268964,-0.1593470424,-0.2561867535,0.1269487292,-0.2231046706,0.2775918245,0.0905745476,0.0148455482,-0.409976095,0.0961333141,0.3147747219,-0.2490134984,-0.23452878,0.0893958881,0.3153007329,0.2518271208,-0.0041998359,0.1808401793,0.209176138,-0.2426620275,-0.0500778817,-0.3978200257,-0.1054066643,-0.2283210009,0.3502309918,0.0507635511,-0.1394725144,0.7004565001,-0.260506779,0.3893989325,-0.1397276372,0.2826374471,0.2533380091,0.0007684092,0.4032491148,-0.0881246626,0.2115938067,0.3516604602,0.0514885075,-0.1892664135,-0.2525210977,-0.2373767644,-0.5181609392,0.0860111713,-0.0284101684,-0.5039556623,0.2390986979,0.4408912063,0.1475693733,-0.0292732287,-0.1374207586,0.2848838866,0.0987269208,0.0174057744,-0.1247298494,0.3923794925,0.7586100101,-0.2284236103,-0.4756804407,0.1044251546,-0.0117908381,-0.0543393828,0.147732392,-0.0291076768,0.2321294397,-0.28400141,0.1421148628,0.3668580651,-0.3609597385,-0.3620804846,-0.0106194112,-0.3919947743,-0.1379084438,0.1821041554,0.0160973482,0.0728418529,0.143812865,0.1258191019,-0.3850254416,0.1877111942,-0.4215300977,-0.3467738926,-0.1896634847,-0.0304805059,-0.1046682894,0.2787402868,0.037039917,0.0111542493,0.1009099483,0.103630051,0.174416706,0.1748632342,0.2626431882,0.2613887191,0.1620635241,0.2222009897,0.1930808276,-0.311049521,0.2377901077,-0.3628157973,-0.0176313967,0.1102191657,-0.073212944,-0.1398175955,-0.2352026701,-0.1244361848,-0.527023077,-0.0462953858,-0.0360660926,0.1183855608,0.1227987111,-0.2726368308,0.1473123133,-0.0587887019,0.3274554312,-0.5532151461,0.0320394821,0.0278662313,-0.1970100999,-0.2257992178,0.1093442887,-0.0025995036,0.0232681595,-0.0975060761,0.380703032,-0.0499365255,0.1832061112,0.5586856604,0.3541171253,-0.1420735121,-0.3481737375,0.0158954021,0.0016878804,-0.1003440693,-0.0012896619,-0.2387945056,0.3765332997,0.134847939,0.1348783821,-0.3614094555,0.2761767507,-0.2093584388,-0.1233218312,-0.5444701314,-0.1353482604,0.4068960547,-0.2476063371,0.3068441749,-0.0123205194,0.0024220273,0.0304550156,0.1419316679,0.0483441949,0.0429760255,0.0081629893,-0.2377903163,-0.053385146,-0.0278111082,0.003766177,0.4933545589,0.1662803441,0.2230534703,0.014451649,0.1677996218,-0.2444672883,0.0901625752,0.1739695817,-0.2210897356,0.1015084758,0.1378706992,0.2083474994,-0.2946519852,-0.050339099,-0.2348016798,0.0318766832,-0.3016398251,0.1512337625,-0.1539792418,0.3830914795,0.0986963734,-0.0210696273,-0.2364776731,-0.333298862,0.2775138319,-0.3183354437,-0.0270291232,0.2005967796,-0.3842666745,0.6387848854,0.018266527,0.016717406,-0.2889690101,-0.0855253935,0.0128287217,-0.2142503858,-0.1412100494,0.0268610641,0.0817524493,-0.2668958008,0.0615631044,-0.1273287684,-0.4955730736,0.0512227751,-0.0161663014,0.6132103205,0.2828388214,0.0391496904,-0.3447293341,0.0620119385,0.2875090539,-0.2682441473,-0.3798858821,0.0450980328,0.0512684956,0.2367150038,-0.3826585412,-0.333468914,0.1506506801,-0.3048092723,0.5795782208,-0.0784145147,-0.0105534084,-0.1236330047,-0.134855777,0.2229509801,-0.34937343,0.2714141905,-0.3231592476,-0.7829341292,0.4090441465,-0.0861361027,-0.0492391251,0.0389753282,0.0890199468,0.3141541183,-0.1025536954,-0.7185192108,-0.4109581411,0.0587699935,0.1837124825,-0.042348247,0.0645665973,0.4620451033,-0.0869352445,-0.0113906544,-0.1259307265,-0.1665929556,0.4881491363,-0.1238272935,0.186942786,-0.1846831143,0.3211714923,0.1527525187,1.0194034576,0.5859872103,0.3531093001,0.1362784952,0.2488948405,0.4330224097,-0.1152088344,-0.0847556815,0.2251204997,0.2476467788,-0.134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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"The lock files come from an issue with filelock (see comment in the code [here](https:\/\/github.com\/benediktschmitt\/py-filelock\/blob\/master\/filelock.py#L394-L398)). Basically on unix there're always .lock files left behind. I haven't dove into this issue","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":30,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n The lock files come from an issue with filelock (see comment in the code [here](https:\/\/github.com\/benediktschmitt\/py-filelock\/blob\/master\/filelock.py#L394-L398)). Basically on unix there're always .lock files left behind. I haven't dove into this issue","embeddings":[0.0978651121,0.0017499296,0.1070125327,0.1418968439,-0.0015061367,0.0359290205,0.2448608726,0.2713269293,0.2091092318,0.1213858202,0.1688691676,0.1361201853,-0.3981262147,-0.0413279571,0.0657574683,0.077555269,0.0212283693,-0.0395922288,-0.3166533113,-0.1807789952,-0.2565910518,0.5491791368,-0.1192011982,0.0093765138,-0.5109118223,0.2307155579,-0.2306171954,0.277212292,-0.0274775904,-0.581561625,0.389434129,0.121536456,0.0478550903,0.4788641036,-0.0001274045,-0.1663458943,0.1572808176,-0.1869959533,-0.1530368477,-0.1362828463,0.0305324215,-0.296793133,0.5233107805,-0.2339410633,0.079851374,-0.0288459454,0.102354154,-0.2997952998,0.4383219481,0.0624674,0.0467604399,0.3075246513,-0.0342115685,-0.2570320666,0.0702645779,-0.0924271196,0.0015265134,0.3444680572,-0.2050896734,0.0921004117,-0.2054682672,0.2364491075,0.2473953664,-0.1546228379,0.2657466829,0.0201909356,-0.0640991554,-0.1387817711,-0.0949208513,0.3408105969,0.1928483993,-0.1521597654,-0.23427926,0.0592422895,-0.1122282296,-0.3646720052,0.2042401731,-0.0701380074,0.1202769727,0.1426341534,0.0680519566,-0.1823931187,-0.0471961349,0.0707622021,-0.1521102935,0.0407557935,-0.1404026747,0.0206547938,-0.0408887789,0.1827445179,-0.4113306701,0.3544477224,0.0115481168,0.1267995685,-0.3444469571,0.1168110892,0.2434752136,0.028262224,0.0463358462,0.4056189358,-0.058036983,0.0199387167,0.3959121406,0.1504532993,-0.0784413069,0.4660251439,0.2361447364,0.0168702435,0.1764601469,0.1849360615,-0.2115632892,-0.2638486624,0.1365805715,-0.236214444,0.4281723499,0.1122872084,0.110580571,-0.4319956005,0.1628943384,0.2799922526,-0.1437161267,-0.1873987168,0.0960946009,0.3751057386,0.1744754314,0.0033347695,0.2302564681,0.3915508389,-0.2153754234,0.0076531689,-0.3904958367,-0.2105622739,-0.0540188849,0.3026128113,0.135630101,-0.1125969067,0.6726797223,-0.2044869661,0.4261627495,-0.0901329294,0.3285324275,0.2119804323,0.0645420924,0.3416905999,-0.0264643971,0.2123863548,0.2896437049,0.1480878145,-0.2393878549,-0.2758550048,-0.1812229455,-0.5380879641,0.0798784643,-0.0424434766,-0.4659706652,0.1792515814,0.4528223276,0.1464153528,-0.1015214771,-0.3035165668,0.2009056956,-0.0098103043,-0.008477659,-0.1502698809,0.5102133155,0.6958425641,-0.3465329707,-0.3804927468,0.0367823429,0.0076954146,-0.0054186331,0.2611059546,0.0363228954,0.1693779826,-0.3158248365,0.1305027753,0.3877377808,-0.3373550475,-0.4568701684,-0.0679737255,-0.3683251441,-0.1110464409,0.1929325461,0.0133991111,0.1307076961,0.1547204852,0.0300893057,-0.219019711,0.2606108487,-0.3739937544,-0.3569114506,-0.2306001633,0.0067292596,-0.0920921713,0.3358702362,0.0176148601,0.0747954398,0.0229984261,0.0826772675,0.0752030835,0.162635982,0.278436482,0.3372353315,0.1000661328,0.2661374509,0.1485668123,-0.2092305124,0.2349851131,-0.4146922231,0.0708448216,0.1174756885,0.0061449613,-0.1154760197,-0.2034732699,-0.0905117095,-0.4177539051,-0.0547221527,-0.049519483,0.2109538019,0.2493541539,-0.1767959446,0.0389834866,0.086997278,0.2594942451,-0.4751551449,0.0103413193,-0.0947172791,-0.335790813,-0.0653162375,0.1485408694,0.0108669186,0.0354320034,-0.1438509971,0.3710696399,-0.1724669188,0.2730033696,0.450623095,0.2415375561,-0.1196843088,-0.3655080199,0.0325872302,0.1424403638,-0.1088574454,0.0363418609,-0.2451748252,0.3829999268,0.21202676,0.0713483319,-0.3359493613,0.3146027327,-0.0740355998,-0.0987766758,-0.4815725982,-0.1280817837,0.3917382658,-0.2381811887,0.2410218567,-0.1055556089,0.0105510326,0.0025617529,0.2497153431,0.07995525,0.233252883,0.0780286416,-0.2227705419,-0.0949146673,0.0184847414,-0.1300748289,0.4449999034,0.190678373,0.227855593,0.0538468696,0.1052548885,-0.188812986,0.1054110155,-0.0117074698,-0.16059497,0.1285531968,0.2171354592,0.2108034641,-0.2892757952,-0.1394120455,-0.4038005769,-0.0391597971,-0.3721092343,0.0799339265,-0.2586631775,0.4104159176,0.183658272,0.173447758,-0.2760920227,-0.3279844224,0.2431277186,-0.2901171744,-0.0558715053,0.2372846007,-0.3406104147,0.5479469299,0.1450073868,-0.2062622458,-0.295977056,-0.1340124011,-0.0942150056,-0.2200794071,-0.2425167114,0.0989216641,0.1037221998,-0.1725022644,0.0372332409,-0.0137783829,-0.4823435843,0.0696525127,0.0511408858,0.6196216345,0.3521490395,0.0815300345,-0.3057146072,0.083496958,0.4136028588,-0.4095363319,-0.2688114941,0.1824415028,0.0434782021,0.2113901079,-0.2620833814,-0.3617442846,0.0265498981,-0.2976148427,0.6853885651,0.096109271,-0.0340798348,-0.1620347947,-0.0633253679,0.1737497002,-0.323364228,0.3805911243,-0.2930787206,-0.6595548987,0.5047425032,-0.1025177017,-0.2188171595,-0.0036964265,0.0586478226,0.3344171047,-0.089447394,-0.6739205122,-0.4296785295,0.0067402273,0.2661030293,-0.0254283547,0.1197582409,0.5252697468,-0.0839203671,0.0137430709,-0.0674872324,-0.2023647428,0.5829992294,-0.0621240363,0.1375797838,-0.082530953,0.3729041815,0.12414217,0.911796689,0.4719629884,0.3536292315,0.2457000464,0.0920005068,0.3813191652,-0.1479361802,-0.1129252985,0.3361164033,0.2327181101,-0.1589669287,0.2437696308,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"are you sure you need an external lock file? if it's a single purpose locking in the same scope you can lock the caller `__file__` instead, e.g. here is how one can `flock` the script file itself to ensure atomic printing:\r\n\r\n```\r\nimport fcntl\r\ndef printflock(*msgs):\r\n    \"\"\" print in multiprocess env so that the outputs from different processes don't get interleaved \"\"\"\r\n    with open(__file__, \"r\") as fh:\r\n        fcntl.flock(fh, fcntl.LOCK_EX)\r\n        try:\r\n            print(*msgs)\r\n        finally:\r\n            fcntl.flock(fh, fcntl.LOCK_UN)\r\n```\r\n","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":75,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n are you sure you need an external lock file? if it's a single purpose locking in the same scope you can lock the caller `__file__` instead, e.g. here is how one can `flock` the script file itself to ensure atomic printing:\r\n\r\n```\r\nimport fcntl\r\ndef printflock(*msgs):\r\n    \"\"\" print in multiprocess env so that the outputs from different processes don't get interleaved \"\"\"\r\n    with open(__file__, \"r\") as fh:\r\n        fcntl.flock(fh, fcntl.LOCK_EX)\r\n        try:\r\n            print(*msgs)\r\n        finally:\r\n            fcntl.flock(fh, fcntl.LOCK_UN)\r\n```\r\n","embeddings":[-0.0958084464,-0.0942348838,0.1255570352,0.0927403942,-0.0868547782,-0.0296325516,0.2317219824,0.2375220805,0.2348071039,0.1782363057,0.0034624427,0.14741005,-0.2378918976,-0.0178872645,-0.0204138942,0.1772465557,0.007517321,-0.0802016482,-0.3421198428,-0.1448375136,-0.2741203606,0.53156811,-0.1308241487,-0.0719346926,-0.4290018976,0.2123154998,-0.2263918221,0.1211149171,-0.0081585767,-0.3395671546,0.1049853936,0.3507529199,0.1030155346,0.4625885189,-0.0001223227,-0.2479463816,0.1706754416,-0.1718239933,-0.1011753306,-0.0645022318,0.1405719966,-0.3108265102,0.4765364528,-0.2978203893,0.1028483659,0.0841091052,0.1408373266,-0.3395661116,0.43585217,-0.0378726833,0.0893844143,0.2628902197,0.1200164706,-0.1775288135,-0.0331903175,-0.1780336201,0.0121771665,0.2705992162,-0.1119368449,0.0844490603,-0.2585883141,0.2509045303,0.2237693965,-0.2024630159,0.1423094124,0.0610956363,-0.1400081366,-0.2547037601,-0.1634257287,0.2697402239,0.1364804059,-0.3204964697,-0.3008389771,0.0492605083,-0.117218554,-0.3431062102,0.0262462962,-0.0340269618,-0.0005137632,0.0618584864,0.1232936308,-0.1993052363,-0.0104406094,0.0003190125,-0.013987557,0.0958361998,-0.1606515646,0.0895802081,-0.1322297305,0.2888585627,-0.3445695341,0.3877014816,0.0099186832,0.1042271033,-0.2629313469,0.1574040651,0.2871651947,-0.1045001149,0.1025784984,0.3618665636,-0.0343261063,0.0118681435,0.5354156494,0.1580042988,-0.0143520003,0.3216001093,0.2761745751,0.0193977691,0.2049499601,0.4004882574,-0.2367353141,-0.2947693467,0.2996625304,-0.209830761,0.3451402485,0.1116673872,0.1468068808,-0.474060744,0.3149008751,0.3492853642,-0.0679861754,-0.0676057488,-0.0274083279,0.3686693311,0.2322140932,0.0972456336,0.359259367,0.2219015509,-0.2242739499,-0.0752867386,-0.3204058111,-0.1706400663,-0.0248745047,0.4072385132,-0.0777525827,-0.0594260283,0.5295740962,-0.0586507991,0.5135621428,-0.0885761976,0.0644367635,0.1758888364,0.0725545138,0.2391681373,0.0254030153,0.2357241213,0.3454734087,0.2439838797,-0.2318499535,-0.3414825499,0.0639522895,-0.4619214237,0.1058227047,-0.0182386171,-0.3980556726,0.195152536,0.6058903337,0.1725607663,-0.1805835366,-0.2601388395,0.3114195168,0.0852632076,-0.0760378465,-0.0874109194,0.4565057456,0.7431334853,-0.3583593667,-0.3069776893,0.1075284332,-0.1093930081,0.0635075793,0.1491250396,0.0973032042,0.096509248,-0.2766647339,-0.117821686,0.3571416438,-0.4726914167,-0.472574532,0.0852729604,-0.4719013572,-0.0274127889,0.2066820413,0.007604328,0.2238250226,0.2170172036,0.1575410813,-0.1981640011,0.2113953978,-0.2767271996,-0.3929208815,-0.1785394549,-0.1324971318,-0.0649424791,0.2047929466,0.0115920985,0.0840649679,0.0313538127,0.2286920995,0.0897837356,0.188824147,0.2832199037,0.3775959313,0.1742916405,0.2464331836,0.1135068908,-0.2764574587,0.2135853469,-0.4569099247,0.1178642586,0.0813397989,-0.0489264615,-0.021800328,-0.1058617309,-0.0418673828,-0.4147400856,0.0085285567,-0.0434777662,0.3030488491,0.249597609,-0.1796232611,0.2385921478,-0.0130311493,0.291190058,-0.4203335643,-0.1497296542,-0.0477628708,-0.3882257342,0.0575981699,0.1718961,0.0015680999,0.0748586655,-0.1377814859,0.4197938442,-0.057103619,0.2876296043,0.2521417141,0.1678893566,-0.1971282214,-0.2420713753,-0.0125151826,0.0657090023,-0.0356098711,0.0254984871,-0.1123302802,0.490195483,0.0970405266,0.0857904926,-0.3372980058,0.2576314807,-0.1290608197,-0.06232173,-0.5252739787,-0.1358704865,0.3787258565,-0.2251196802,0.2366429567,-0.131506294,0.0301113445,0.07980223,0.1785757542,0.2459708154,0.1715176404,-0.0005889279,-0.1394901127,-0.0125992363,0.0347564183,-0.0848471075,0.2582921386,0.2357061058,0.1050706059,0.0881278664,0.1087510213,-0.1732836664,0.1077506989,-0.0638256297,-0.1016960293,0.1692502648,0.1857384294,0.1188376173,-0.2927369773,-0.177698791,-0.3255646229,-0.0320762694,-0.2366850227,-0.0204165373,0.0345232971,0.3458484411,0.009169586,0.0865000635,-0.2900829911,-0.404525429,0.217493996,-0.1798904985,-0.0688815787,0.1823814362,-0.4417120814,0.4122616053,0.1074886024,-0.2418729216,-0.3294233084,-0.1597177833,-0.078658022,-0.2074675411,-0.2837306261,0.1251960248,0.222012341,-0.0486109182,0.0800969303,-0.1392987221,-0.3460685909,0.0014418731,-0.0510540158,0.6285656095,0.2911643088,-0.0305407196,-0.1663736403,0.0547243059,0.449611634,-0.5593898296,-0.1918304265,0.1030515358,-0.0518236309,0.1852306873,-0.255188942,-0.2687283754,0.0448057204,-0.4148482084,0.6636121869,0.0234176144,-0.0101746758,-0.2235938907,-0.1076810881,0.1781018823,-0.2553531229,0.4349594414,-0.1497935802,-0.5847468972,0.4244233072,-0.1281866878,-0.2230434418,-0.1408714801,0.0822282284,0.17599231,-0.1449057758,-0.5932175517,-0.5006266832,-0.1014444679,0.2448946387,-0.0263824984,0.1439140439,0.4210920036,-0.1415075511,-0.0179313254,-0.0129031828,-0.1239335686,0.532720685,-0.1963026226,0.0532477461,-0.0795462579,0.4616315663,0.0842699707,0.8287326097,0.5620367527,0.3317268491,0.1832953542,0.0219699498,0.4228357673,-0.136825338,-0.1904710233,0.3991360962,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"OK, this issue is not about caching but some internal conflict\/race condition it seems, I have just run into it on my normal env:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 356, in _finalize\r\n    self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 655, in <module>\r\n    main()\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 619, in main\r\n    test_results = trainer.predict(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-master\/src\/transformers\/trainer_seq2seq.py\", line 121, in predict\r\n    return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-master\/src\/transformers\/trainer.py\", line 1706, in predict\r\n    output = self.prediction_loop(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-master\/src\/transformers\/trainer.py\", line 1813, in prediction_loop\r\n    metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 556, in compute_metrics\r\n    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 388, in compute\r\n    self._finalize()\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 358, in _finalize\r\n    raise ValueError(\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```\r\n\r\nI'm just running `run_seq2seq.py` under DeepSpeed:\r\n\r\n```\r\nexport BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 deepspeed --num_gpus=2 examples\/seq2seq\/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps  --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000  --sortish_sampler --task translation_en_to_ro  --val_max_target_length 128 --warmup_steps 500 --max_train_samples 100 --max_val_samples 100 --max_test_samples 100 --dataset_name wmt16 --dataset_config ro-en  --source_prefix \"translate English to Romanian: \" --deepspeed examples\/tests\/deepspeed\/ds_config.json\r\n```\r\n\r\nIt finished the evaluation OK and crashed on the prediction part of the Trainer. But the eval \/ predict parts no longer run under Deepspeed, it's just plain ddp.\r\n\r\nIs this some kind of race condition? It happens intermittently - there is nothing else running at the same time.\r\n\r\nBut if 2 independent instances  of the same script were to run at the same time it's clear to see that this problem would happen. Perhaps it'd help to create a unique hash which is shared between all processes in the group and use that as the default experiment id?\r\n","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":409,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n OK, this issue is not about caching but some internal conflict\/race condition it seems, I have just run into it on my normal env:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 356, in _finalize\r\n    self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 655, in <module>\r\n    main()\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 619, in main\r\n    test_results = trainer.predict(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-master\/src\/transformers\/trainer_seq2seq.py\", line 121, in predict\r\n    return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-master\/src\/transformers\/trainer.py\", line 1706, in predict\r\n    output = self.prediction_loop(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-master\/src\/transformers\/trainer.py\", line 1813, in prediction_loop\r\n    metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 556, in compute_metrics\r\n    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 388, in compute\r\n    self._finalize()\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 358, in _finalize\r\n    raise ValueError(\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```\r\n\r\nI'm just running `run_seq2seq.py` under DeepSpeed:\r\n\r\n```\r\nexport BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 deepspeed --num_gpus=2 examples\/seq2seq\/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps  --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000  --sortish_sampler --task translation_en_to_ro  --val_max_target_length 128 --warmup_steps 500 --max_train_samples 100 --max_val_samples 100 --max_test_samples 100 --dataset_name wmt16 --dataset_config ro-en  --source_prefix \"translate English to Romanian: \" --deepspeed examples\/tests\/deepspeed\/ds_config.json\r\n```\r\n\r\nIt finished the evaluation OK and crashed on the prediction part of the Trainer. But the eval \/ predict parts no longer run under Deepspeed, it's just plain ddp.\r\n\r\nIs this some kind of race condition? It happens intermittently - there is nothing else running at the same time.\r\n\r\nBut if 2 independent instances  of the same script were to run at the same time it's clear to see that this problem would happen. Perhaps it'd help to create a unique hash which is shared between all processes in the group and use that as the default experiment 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"When you're using metrics in a distributed setup, there are two cases:\r\n1. you're doing two completely different experiments (two evaluations) and the 2 metrics jobs have nothing to do with each other\r\n2. you're doing one experiment (one evaluation) but use multiple processes to feed the data to the metric.\r\n\r\nIn case 1. you just need to provide two different `experiment_id` so that the metrics don't collide.\r\nIn case 2. they must have the same experiment_id (or use the default one), but in this case you also need to provide the `num_processes` and `process_id`\r\n\r\nIf understand correctly you're in situation 2.\r\n\r\nIf so, you make sure that you instantiate the metrics with both the right `num_processes` and `process_id` parameters ?\r\n\r\nIf they're not set, then the cache files of the two metrics collide it can cause issues. For example if one metric finishes before the other, then the cache file is deleted and the other metric gets a FileNotFoundError\r\nThere's more information in the [documentation](https:\/\/huggingface.co\/docs\/datasets\/loading_metrics.html#distributed-setups) if you want\r\n\r\nHope that helps !","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":173,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n When you're using metrics in a distributed setup, there are two cases:\r\n1. you're doing two completely different experiments (two evaluations) and the 2 metrics jobs have nothing to do with each other\r\n2. you're doing one experiment (one evaluation) but use multiple processes to feed the data to the metric.\r\n\r\nIn case 1. you just need to provide two different `experiment_id` so that the metrics don't collide.\r\nIn case 2. they must have the same experiment_id (or use the default one), but in this case you also need to provide the `num_processes` and `process_id`\r\n\r\nIf understand correctly you're in situation 2.\r\n\r\nIf so, you make sure that you instantiate the metrics with both the right `num_processes` and `process_id` parameters ?\r\n\r\nIf they're not set, then the cache files of the two metrics collide it can cause issues. For example if one metric finishes before the other, then the cache file is deleted and the other metric gets a FileNotFoundError\r\nThere's more information in the [documentation](https:\/\/huggingface.co\/docs\/datasets\/loading_metrics.html#distributed-setups) if you want\r\n\r\nHope that helps !","embeddings":[-0.0598983429,0.0068152775,0.084484525,0.1580613703,0.0199416559,-0.0320760161,0.2774336934,0.2207652032,0.3088784218,0.1602413505,0.0177518763,0.1031419486,-0.2257237434,-0.0206120964,-0.0592386834,-0.0310602225,-0.0435301624,-0.0667761266,-0.3691173196,-0.1325116158,-0.3067215085,0.501986146,-0.0801183656,0.0445248485,-0.4807972014,0.1278136522,-0.1756111234,0.2225741297,-0.0789252147,-0.558731854,0.3777546883,0.1831186414,0.0421271063,0.6000635624,-0.0001176384,-0.1433923841,0.1927302778,-0.124524422,-0.083281219,-0.1930171698,0.0741741657,-0.3329316378,0.4511277974,-0.2442206591,0.0344935618,-0.051198341,0.0247724187,-0.3721569777,0.4274136722,-0.0064346162,0.1307448,0.3117668629,-0.068226777,-0.3215267956,-0.0999848619,-0.1143518686,0.0776162222,0.4171811044,-0.1312239319,0.0894623995,-0.2742326856,0.1245770603,0.212353453,0.0292280708,0.2388039976,0.024607148,-0.0142720928,-0.1256713569,-0.0685603768,0.2887807488,0.1676787883,-0.1594269574,-0.2014690191,0.0085315043,-0.2289424837,-0.4411575794,0.1714061648,-0.124580346,0.0746701732,0.103214927,-0.0762275234,-0.154982388,-0.0232046694,0.1175038964,-0.1456239074,0.0324369818,-0.1697130799,0.1212585792,-0.027921401,0.1497502178,-0.3855251968,0.3516908884,-0.0194257125,0.0714746565,-0.3793978989,0.136480391,0.2671203017,0.0217041448,0.1065052971,0.4761018157,-0.0047422666,-0.0132049061,0.3891662359,0.1737164408,-0.0644987375,0.5233271122,0.2027726769,0.0692863762,0.2185038477,0.2179413289,-0.1748858243,-0.3413075507,0.2836264968,-0.3860961497,0.3315577805,0.1842790097,0.0930345803,-0.4868882298,0.0585246794,0.1844706088,-0.0363664255,-0.1577919573,0.0875825584,0.3153523207,0.1501623392,0.0135483872,0.2400891483,0.3981119096,-0.2120680511,0.0780819207,-0.3880885541,-0.1634971648,-0.1441083103,0.4293542206,0.0517114885,-0.1105698496,0.6856907606,-0.1622716486,0.4642171562,-0.061395552,0.1907998472,0.1755948961,0.0188385695,0.2674921453,0.0719169155,0.175892666,0.2669182122,0.10486193,-0.2120070457,-0.3701926768,-0.188737154,-0.5549445748,0.1263828725,0.0321527384,-0.4145857096,0.2830307484,0.3431289494,0.2120162845,-0.0794245973,-0.2337050438,0.1338794082,0.0511640869,-0.0858735442,-0.1279808432,0.4915291369,0.7377231717,-0.2220460027,-0.3446711898,0.0168370027,-0.0487126447,-0.0864723176,0.3050107956,-0.0926363841,0.2284962535,-0.2772685885,0.1222094968,0.3515832722,-0.4234384298,-0.4113765061,0.0080032414,-0.4272915125,-0.1334397495,0.1083738208,-0.0730173066,0.2581312656,0.1308469474,0.0548113696,-0.1267488599,0.1568037421,-0.283675164,-0.3865198493,-0.1858029962,-0.0045480025,-0.1014779657,0.300691992,0.0563218743,0.0986357033,0.0875678733,0.0597034022,0.1062118262,0.26060763,0.2477435917,0.2262085676,0.0003543365,0.289907515,0.1102003902,-0.1867316067,0.2562479675,-0.3439131379,0.1210736707,0.1683987677,-0.0155254724,-0.200071454,-0.2549752593,-0.1508955508,-0.4215324521,0.0472918451,-0.0709691867,0.3166153729,0.1520330608,-0.1438020319,0.0916495696,0.1583401114,0.1830408126,-0.4161327779,0.0770388246,-0.0725939348,-0.3368439674,-0.0147532811,0.0729850233,0.0808983073,0.0420653485,-0.1817387789,0.4776448011,-0.2574470937,0.2789967656,0.383679986,0.1822809577,-0.0755051225,-0.3026510477,0.0588997863,0.0483248942,-0.0166241489,0.001170759,-0.1728834212,0.3591372073,0.0592466183,0.095292896,-0.3097137511,0.3031127155,-0.046105627,-0.1465944946,-0.4951242507,-0.1190972924,0.3968292475,-0.3081524968,0.2047468275,-0.1520290971,0.0571071133,0.0319924727,0.1416758448,0.1343099326,0.126549229,-0.0061651319,-0.161784932,-0.0100206947,-0.0072231968,-0.1396538019,0.5526068211,0.1933413148,0.2127412409,0.1210232303,0.0394823477,-0.203192085,0.0009544448,0.0007292124,-0.1463176459,0.1554473341,0.2229654342,0.133715868,-0.2336518914,-0.0772265121,-0.3163039982,0.0065659764,-0.3755759597,0.0749985501,-0.1550010145,0.4108365178,0.0599399842,0.0543511435,-0.2262229621,-0.2606708407,0.2092506886,-0.1175970435,-0.0695596486,0.3137815297,-0.183540374,0.5583069324,0.0173088554,-0.0202201214,-0.2246251702,-0.2112009376,-0.0854687914,-0.1483573616,-0.1897707731,0.2323055267,0.13009803,-0.166483596,0.0585788451,-0.0695105493,-0.3264140785,0.0377147757,0.0303655788,0.6761788726,0.2347936928,0.0001523599,-0.251847893,0.028367741,0.4868867397,-0.4140199721,-0.2675405741,0.0591720492,0.0195234343,0.0564181395,-0.2959034443,-0.3545251191,0.070414193,-0.3689333797,0.564774394,0.0072399732,-0.0687930733,-0.2838418186,-0.0883293897,0.1034515724,-0.2374043167,0.2703514099,-0.3315576911,-0.7032841444,0.3768460155,-0.3030439019,-0.2822323442,-0.0451202691,0.0785690397,0.4289895594,-0.0625871941,-0.5393745899,-0.4895910025,0.0535351411,0.1895357817,-0.0105361529,0.1493699104,0.5207619667,-0.1010604426,-0.0189073421,-0.0783482045,-0.2318599075,0.6520729065,-0.0872166455,0.1458659172,-0.1006416231,0.416723609,0.0971859023,0.8765502572,0.5474234819,0.2963705659,0.2949184179,0.121151261,0.3381679952,-0.0262518153,-0.1907776147,0.4884671271,0.2340406775,-0.144677192,0.21662434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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"Thank you for explaining that in a great way, @lhoestq \r\n\r\nSo the bottom line is that the `transformers` examples are broken since they don't do any of that. At least `run_seq2seq.py` just does `metric = load_metric(metric_name)`\r\n\r\nWhat test would you recommend to reliably reproduce this bug in `examples\/seq2seq\/run_seq2seq.py`?","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":48,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n Thank you for explaining that in a great way, @lhoestq \r\n\r\nSo the bottom line is that the `transformers` examples are broken since they don't do any of that. At least `run_seq2seq.py` just does `metric = load_metric(metric_name)`\r\n\r\nWhat test would you recommend to reliably reproduce this bug in `examples\/seq2seq\/run_seq2seq.py`?","embeddings":[0.0750928149,-0.1511457562,0.1568090171,0.1809438467,-0.0645122975,-0.0121280113,0.3743320405,0.2074542642,0.2033585608,0.0637144893,0.232380569,0.1092878655,-0.3682080805,-0.2463029474,0.1191040576,-0.1598070711,0.0618006103,0.0787240714,-0.3983020484,-0.1459854245,-0.2288619876,0.5977004766,-0.2141059488,0.0435038507,-0.5726758242,0.278558135,-0.2246072292,0.1813751459,0.1039820015,-0.510097146,0.4914008677,0.1075301394,-0.0393089764,0.6184602976,-0.0001310607,-0.1159780473,0.2905943692,-0.2518160641,-0.145431608,-0.2269473076,-0.1584118903,-0.2461212128,0.4491089284,-0.2053947747,0.0618131571,0.0542072617,0.1260381341,-0.1677697599,0.4823168218,0.0367282964,0.0278775282,0.4897834957,-0.1625120193,-0.3433550298,0.0686691403,0.145645678,0.0055331509,0.2566850483,-0.0682889149,0.011801051,-0.2210907787,0.2391419262,0.2916741371,-0.0231562499,0.2404492795,0.0032988689,-0.0292709004,-0.0683813989,-0.1375892758,0.3957450092,0.2470199168,-0.1506510675,-0.3314959705,-0.0105179334,-0.3707772791,-0.1938928515,0.3266067505,-0.1777870804,0.0734513476,0.1111949384,-0.0882413089,-0.1193780005,-0.0133474991,0.0675627515,-0.2427281737,0.0775505379,-0.088917084,0.0202900227,-0.0826743543,0.0575080924,-0.2245983928,0.2535657585,-0.0392755792,0.1483736187,-0.3013089895,-0.007587533,0.1568772644,-0.0926359668,0.0377053246,0.3995665312,-0.0197044369,0.1337978989,0.3218588531,0.145534873,-0.0883822143,0.5639249086,0.2473172098,0.1956648976,0.1423614025,0.2767653465,-0.0769011751,-0.2230588645,0.1054571271,-0.1692461222,0.3477960229,0.0648900047,0.0919375047,-0.4264973402,0.1201483607,0.2431017458,-0.0429426841,-0.1472582519,0.1478135586,0.4604143202,0.1981285214,0.0761523247,0.1781300604,0.3603515625,-0.1025502235,-0.0604590587,-0.3435696661,-0.1752094775,-0.2113113105,0.2515896857,0.2330026925,0.1246434376,0.6372714043,-0.2465672493,0.361093998,-0.1116089821,0.306776613,0.285213083,0.1577807218,0.4128853679,-0.1351288855,0.2474038452,0.3334746063,0.0782879218,-0.2614819705,-0.1910322607,-0.108800821,-0.4993456602,0.209586665,-0.0593761206,-0.6320038438,0.2099979818,0.3444989324,0.1252350211,-0.088011682,-0.29063043,0.1899120957,0.0113037731,0.0194324162,0.0363644734,0.5021131635,0.8084053397,-0.1071282178,-0.5354682207,-0.0404285192,0.0006152861,-0.0503945276,0.1723441631,-0.0216381773,0.1558131725,-0.2820476592,0.0270247981,0.3047235012,-0.3449210227,-0.3754405975,-0.0424837582,-0.2123759389,-0.0130924257,0.1726590544,-0.099288635,0.159601137,0.0654157996,0.0390030555,-0.1574855894,0.178449437,-0.1477150023,-0.2838848233,-0.3624432683,0.0209673345,-0.0074382541,0.3962644041,0.1487722546,-0.0420768484,0.2471259087,-0.0140904114,0.1546952873,0.1208314672,0.2546764612,0.1531657875,-0.2394436002,0.3301900029,-0.0040534297,-0.3316984475,0.3137979209,-0.4128647149,-0.0016717316,0.171761021,0.0299317986,-0.3266585171,-0.1333886981,-0.0559235513,-0.4057338536,-0.0640614405,-0.0681678653,0.1189898998,0.0956791639,-0.1376004517,0.0342143402,0.293838948,0.2461948246,-0.4998989403,0.0841354206,0.0144144064,-0.2128111571,-0.2256056517,0.01389293,-0.0110580968,-0.1588858217,-0.2033104748,0.3690865934,-0.0667352453,0.3717402816,0.2778237462,0.3218532503,0.0117869796,-0.4765178561,-0.0597838424,0.2749159336,-0.053749986,-0.0168479159,-0.4565024376,0.3429724574,0.1099388376,0.0574722849,-0.2757979333,0.3847721517,-0.036700666,-0.2347462922,-0.5471624732,-0.1155283675,0.4180024564,-0.2595297992,0.2554793954,-0.106464237,0.1143487394,-0.2071104646,0.1366577446,-0.0392934233,0.2872008383,0.1524625272,-0.3554177284,-0.0619288348,0.033507321,-0.080422543,0.3691939116,0.0703881159,0.2867376208,-0.0314374417,0.0858655795,-0.1533981711,0.168610692,0.1308805943,-0.118894428,0.1236365065,0.2123601288,0.1744197607,-0.283901304,-0.166707918,-0.2365742922,0.064098157,-0.3988458514,0.1490962207,-0.011422853,0.5295215249,0.0435043611,-0.0604522265,-0.2244169116,-0.2906612754,0.1571871042,-0.2209062129,-0.0536589622,0.3262691796,-0.222744599,0.6643281579,0.0525766462,-0.1865573078,-0.4059668183,-0.0892629623,-0.1126825809,-0.1945126504,-0.2627268136,0.0734461546,-0.1226369664,-0.1925716251,0.0805480257,0.1065049395,-0.483492583,0.1850721389,0.2666202784,0.6751850843,0.3146781921,-0.0235575959,-0.137926966,-0.0140281953,0.3056758046,-0.4094474614,-0.2437088788,0.308364898,-0.0299442541,0.1579077989,-0.2975551784,-0.253415823,0.1630838513,-0.2594065666,0.5369822979,0.0357925594,-0.039002385,0.0486771017,-0.0482484996,0.1972010434,-0.2278416455,0.2416341901,-0.0970920995,-0.4955857396,0.4939000905,-0.0899777114,-0.232549876,-0.233001709,-0.259604156,0.2908662558,-0.0499920622,-0.4904942513,-0.2846312523,-0.1554356068,0.1077007949,0.0514273718,0.0468786247,0.5173733234,-0.0740085691,0.0814283565,-0.1966250241,-0.1054342091,0.4829234183,-0.0403454676,0.2072467208,-0.1482871175,0.252377063,0.1457008719,0.9683125019,0.4021627605,0.2108834833,0.1378668994,0.1212735698,0.2691400051,-0.03276572,-0.1612855941,0.1290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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"To give more context, we are just using the metrics for the `comput_metric` function and nothing else. Is there something else we can use that just applies the function to the full arrays of predictions and labels? Because that's all we need, all the gathering has already been done because the datasets Metric multiprocessing relies on file storage and thus does not work in a multi-node distributed setup (whereas the Trainer does).\r\n\r\nOtherwise, we'll have to switch to something else to compute the metrics :-(","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":85,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n To give more context, we are just using the metrics for the `comput_metric` function and nothing else. Is there something else we can use that just applies the function to the full arrays of predictions and labels? Because that's all we need, all the gathering has already been done because the datasets Metric multiprocessing relies on file storage and thus does not work in a multi-node distributed setup (whereas the Trainer does).\r\n\r\nOtherwise, we'll have to switch to something else to compute the metrics :-(","embeddings":[0.0360684097,0.0379045084,0.0943586305,0.1253585219,0.176023975,0.0507231429,0.1819939315,0.2530153394,0.2025461346,0.251921773,0.0820863023,0.1800011545,-0.3751119971,0.0665903017,0.1416402161,-0.0772908852,0.0167336687,0.0033793277,-0.3133099973,-0.1381399632,-0.2079617828,0.4481794238,-0.1228016689,0.0743648484,-0.5695955753,0.1498474181,-0.1701162308,0.0805468708,-0.1255107522,-0.5812905431,0.3919239938,0.1458094865,0.0017238059,0.4191245139,-0.0001262029,-0.1343241781,0.1936454922,-0.2333316952,-0.1047410443,-0.2225060165,0.1268722415,-0.3101156354,0.5224650502,-0.146082744,-0.0495458841,-0.0838488117,0.0543664955,-0.4214364886,0.4781624377,0.1183948815,0.0403270535,0.3758564293,0.034930747,-0.2492558956,-0.0176795013,-0.0774545372,0.0102454899,0.3299077451,-0.0517302752,-0.0297793802,-0.2794109285,0.2436843216,0.2794153988,-0.0088509116,0.3128017187,-0.0297786128,-0.0145224724,-0.1737946719,-0.1312471628,0.2608571649,0.1949307024,-0.2089758515,-0.3556877673,-0.0132943876,-0.0411426052,-0.5314995646,0.1682944447,-0.1075419858,-0.0336157791,0.0474928319,-0.1316332966,-0.1672554612,-0.0719782338,0.0874651,-0.1760182083,0.0398989767,-0.1421070397,0.0187715013,0.0959144533,0.2716259956,-0.2097330242,0.4107071459,0.1014254019,0.1463738382,-0.3606158197,0.0825564787,0.1721960157,-0.0282386262,0.0502630398,0.3950385451,0.0560898148,0.1017731205,0.375590086,0.0602638312,-0.045978725,0.4311771989,0.2344252318,0.1357567757,0.1160663068,0.2224400491,-0.1367132515,-0.2810336053,0.1833153069,-0.1449779719,0.3832835853,0.2245602608,-0.1369666755,-0.3769397736,0.063728869,0.2464854419,-0.0436970703,-0.2041589022,0.1965016872,0.2750817239,0.1341587901,0.1387406886,0.1868602186,0.3029897213,-0.17224738,-0.1656983942,-0.3989240825,-0.0746448487,-0.2094953656,0.3997785449,0.1894150972,-0.0411997586,0.6616476774,-0.3590566218,0.5086171031,-0.2140405327,0.1602294296,0.145807758,0.097427167,0.2898179591,-0.0214464571,0.0991971567,0.3203160167,0.0583865009,-0.1933315545,-0.3334262073,-0.2335591316,-0.5762785673,0.1160737276,-0.0552396998,-0.5121427774,0.2240834087,0.1648786515,0.3089993,-0.1530394852,-0.1348861158,0.1365174651,-0.0361995623,-0.1778376698,-0.0181775913,0.6248615384,0.5545912981,-0.3065955639,-0.4664386511,0.109280616,-0.0107354699,-0.1018487811,0.1589731723,-0.0603356697,0.3344116509,-0.1820726693,0.0870406851,0.4544463754,-0.408008486,-0.4083961844,-0.2533523142,-0.2293063104,-0.1433107108,0.0884948522,-0.0339344069,0.1266498864,0.2276443839,0.0750585496,-0.2100956887,0.1428106725,-0.3406258523,-0.2292552292,-0.256660223,-0.0860029906,0.0165199991,0.4695231915,-0.0004947771,-0.0502117053,0.066992186,0.0552925058,0.0273389686,0.2316122353,0.2274737656,0.1849201769,-0.0041497685,0.1867880225,-0.0504863523,-0.2215746939,0.3026397526,-0.293017894,0.0360219739,0.1714074165,-0.0174638852,-0.1159898937,-0.0948145688,-0.0791351125,-0.3731910884,-0.0357883871,-0.0878942832,0.232910797,0.1419439316,-0.2297814637,0.1230704486,-0.0462224931,0.2593328953,-0.3241704702,0.1597561389,-0.0609496757,-0.3210313618,-0.0040195277,0.2121324092,-0.1006768197,0.042405732,-0.1941397637,0.3698192537,-0.1435660422,0.1763956845,0.3263867497,0.1948139369,-0.0946418121,-0.4680911601,-0.1868380457,0.0226582251,-0.0780186951,-0.0425728224,-0.191508159,0.4211587608,0.1166608259,0.1690180749,-0.2548603117,0.3526454568,-0.1358468682,-0.0465467684,-0.513710916,-0.1119693369,0.3400008082,-0.4222242832,0.3011046052,-0.183504805,-0.025069749,-0.0542991757,0.2423658371,0.1083917469,0.1255672574,0.1039023697,-0.2200978696,-0.070863083,-0.0215853434,-0.1932946146,0.4366632402,0.1965451688,0.2774853706,0.0860513449,0.0942830667,-0.1230531856,0.0770650208,0.1215845793,-0.0986283645,0.0737261772,0.1942550987,0.148744002,-0.3226958513,-0.0074757859,-0.3011007309,0.0195339154,-0.3337210119,0.0596701726,-0.2368711233,0.3630669713,0.1683836579,-0.0021910882,-0.2333547324,-0.2706065476,0.2153623998,-0.1962648332,-0.0221892428,0.3150249422,-0.2984651625,0.6049127579,0.061546158,-0.022624407,-0.2279758751,-0.1495145708,-0.0368123651,-0.1780066639,-0.1940803528,0.096849516,0.0374841876,-0.1499424875,0.1426575482,-0.1025383621,-0.3601033986,0.0933407098,0.0195365828,0.6119935513,0.3463463485,-0.0454063676,-0.3527431786,0.0991575643,0.4500066638,-0.407998234,-0.2767021954,0.1133303493,0.0314443186,0.1227029637,-0.2230903208,-0.2890613377,0.01126655,-0.2815251946,0.5991318226,0.0410658494,-0.0020701666,-0.2288421988,-0.0237935055,0.1773279607,-0.3290063441,0.2469102144,-0.2935864925,-0.7149354815,0.4781392515,-0.0908914655,-0.2723424137,-0.1058706567,0.067531243,0.434768647,-0.0653754249,-0.6561143994,-0.4437874556,0.0903977379,0.0918385908,-0.0425770208,0.1582767665,0.5028115511,-0.0640258715,0.0077045537,-0.1060191914,-0.2926110923,0.5380766988,-0.1749769151,0.1555449963,-0.2270885706,0.2927685976,0.2596622407,0.9461427331,0.5229724646,0.1494284719,0.2371035963,0.1094772294,0.4445635676,-0.0699854121,-0.1495136172,0.3707888126,0.1129481867,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"OK, it definitely leads to a race condition in how it's used right now. Here is how you can reproduce it - by injecting a random  sleep time different for each process before the locks are acquired. \r\n```\r\n--- a\/src\/datasets\/metric.py\r\n+++ b\/src\/datasets\/metric.py\r\n@@ -348,6 +348,16 @@ class Metric(MetricInfoMixin):\r\n\r\n         elif self.process_id == 0:\r\n             # Let's acquire a lock on each node files to be sure they are finished writing\r\n+\r\n+            import time\r\n+            import random\r\n+            import os\r\n+            pid = os.getpid()\r\n+            random.seed(pid)\r\n+            secs = random.randint(1, 15)\r\n+            time.sleep(secs)\r\n+            print(f\"sleeping {secs}\")\r\n+\r\n             file_paths, filelocks = self._get_all_cache_files()\r\n\r\n             # Read the predictions and references\r\n@@ -385,7 +395,10 @@ class Metric(MetricInfoMixin):\r\n\r\n         if predictions is not None:\r\n             self.add_batch(predictions=predictions, references=references)\r\n+        print(\"FINALIZE START\")\r\n+\r\n         self._finalize()\r\n+        print(\"FINALIZE END\")\r\n\r\n         self.cache_file_name = None\r\n         self.filelock = None\r\n```\r\n\r\nthen run with 2 procs: `python -m torch.distributed.launch --nproc_per_node=2`\r\n```\r\nexport BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 examples\/seq2seq\/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps  --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000  --sortish_sampler --task translation_en_to_ro  --val_max_target_length 128 --warmup_steps 500 --max_train_samples 10 --max_val_samples 10 --max_test_samples 10  --dataset_name wmt16 --dataset_config ro-en --source_prefix \"translate English to Romanian: \"\r\n```\r\n\r\n```\r\n***** Running Evaluation *****\r\n  Num examples = 10\r\n  Batch size = 16\r\n  0%|                                                                                                                                      | 0\/1 [00:00<?, ?it\/s]FINALIZE START\r\nFINALIZE START\r\nsleeping 11\r\nFINALIZE END\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:11<00:00, 11.06s\/it]\r\nsleeping 11\r\nTraceback (most recent call last):\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 368, in _finalize\r\n    self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 645, in <module>\r\n    main()\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 601, in main\r\n    metrics = trainer.evaluate(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer_seq2seq.py\", line 74, in evaluate\r\n    return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer.py\", line 1703, in evaluate\r\n    output = self.prediction_loop(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer.py\", line 1876, in prediction_loop\r\n    metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 556, in compute_metrics\r\n    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 402, in compute\r\n    self._finalize()\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 370, in _finalize\r\n    raise ValueError(\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":452,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n OK, it definitely leads to a race condition in how it's used right now. Here is how you can reproduce it - by injecting a random  sleep time different for each process before the locks are acquired. \r\n```\r\n--- a\/src\/datasets\/metric.py\r\n+++ b\/src\/datasets\/metric.py\r\n@@ -348,6 +348,16 @@ class Metric(MetricInfoMixin):\r\n\r\n         elif self.process_id == 0:\r\n             # Let's acquire a lock on each node files to be sure they are finished writing\r\n+\r\n+            import time\r\n+            import random\r\n+            import os\r\n+            pid = os.getpid()\r\n+            random.seed(pid)\r\n+            secs = random.randint(1, 15)\r\n+            time.sleep(secs)\r\n+            print(f\"sleeping {secs}\")\r\n+\r\n             file_paths, filelocks = self._get_all_cache_files()\r\n\r\n             # Read the predictions and references\r\n@@ -385,7 +395,10 @@ class Metric(MetricInfoMixin):\r\n\r\n         if predictions is not None:\r\n             self.add_batch(predictions=predictions, references=references)\r\n+        print(\"FINALIZE START\")\r\n+\r\n         self._finalize()\r\n+        print(\"FINALIZE END\")\r\n\r\n         self.cache_file_name = None\r\n         self.filelock = None\r\n```\r\n\r\nthen run with 2 procs: `python -m torch.distributed.launch --nproc_per_node=2`\r\n```\r\nexport BS=16; rm -r output_dir; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 examples\/seq2seq\/run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --do_train --do_predict --evaluation_strategy=steps  --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --per_device_train_batch_size $BS --predict_with_generate --eval_steps 25000  --sortish_sampler --task translation_en_to_ro  --val_max_target_length 128 --warmup_steps 500 --max_train_samples 10 --max_val_samples 10 --max_test_samples 10  --dataset_name wmt16 --dataset_config ro-en --source_prefix \"translate English to Romanian: \"\r\n```\r\n\r\n```\r\n***** Running Evaluation *****\r\n  Num examples = 10\r\n  Batch size = 16\r\n  0%|                                                                                                                                      | 0\/1 [00:00<?, ?it\/s]FINALIZE START\r\nFINALIZE START\r\nsleeping 11\r\nFINALIZE END\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:11<00:00, 11.06s\/it]\r\nsleeping 11\r\nTraceback (most recent call last):\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 368, in _finalize\r\n    self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 236, in read_files\r\n    pa_table = self._read_files(files, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 171, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 302, in _get_dataset_from_filename\r\n    pa_table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/arrow_reader.py\", line 322, in read_table\r\n    stream = stream_from(filename)\r\n  File \"pyarrow\/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n  File \"pyarrow\/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '\/home\/stas\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow'. Detail: [errno 2] No such file or directory\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 645, in <module>\r\n    main()\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 601, in main\r\n    metrics = trainer.evaluate(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer_seq2seq.py\", line 74, in evaluate\r\n    return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer.py\", line 1703, in evaluate\r\n    output = self.prediction_loop(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer.py\", line 1876, in prediction_loop\r\n    metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 556, in compute_metrics\r\n    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 402, in compute\r\n    self._finalize()\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 370, in _finalize\r\n    raise ValueError(\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```","embeddings":[-0.0844155699,-0.0657195449,0.0652180836,0.2722371519,-0.0870862305,-0.032664571,0.1643525809,0.2697967291,0.3311934769,0.0832053274,0.098261185,0.1941852719,-0.3580646217,-0.0889754966,-0.0156052159,0.0498044565,0.0008351138,-0.1240264401,-0.4149836302,-0.1004132852,-0.3499764502,0.5602373481,-0.1571679413,0.0035177716,-0.4620630443,0.1669693291,-0.1928113848,0.2385937572,-0.1055250093,-0.5698463917,0.2600385845,0.3488971293,-0.0173991658,0.5673115849,-0.000121866,-0.156548962,0.2898033559,-0.1259326637,-0.0928893313,-0.1496436298,0.0516416617,-0.3085162044,0.4341697991,-0.1971871704,-0.0820829719,-0.0098867286,0.075831078,-0.495130688,0.4286834002,0.0644462407,0.0891190469,0.4689293504,-0.0374805965,-0.1865046918,-0.0093006808,-0.1247958094,0.0236409735,0.3594599664,-0.0119030736,0.0050282986,-0.2551394999,0.2341878414,0.2350952029,-0.0686758906,0.3630651534,0.0002415854,-0.0232570134,-0.1683153063,-0.1944670528,0.3529827297,0.1351855695,-0.1981303096,-0.2590878606,0.0288155973,-0.1893539131,-0.4624345005,0.1429929435,-0.1314374804,0.0301056616,0.088087298,0.0458046198,0.0006789111,0.006420983,0.0480430722,-0.0750627741,0.0268831421,-0.1102879792,0.0966649428,-0.0770767033,0.267449975,-0.3081120551,0.21201244,-0.0161630362,0.1204218641,-0.43568331,0.0894821882,0.2162855715,0.0534023084,0.0726227984,0.4313857853,-0.0319301262,0.1022296101,0.397911638,0.1139199957,-0.0671368837,0.4132384956,0.2391605079,-0.0104756225,0.1874959916,0.2739362419,-0.0854610801,-0.2305916995,0.2011027634,-0.2459424585,0.3607820272,0.0959146693,0.1355988532,-0.3899984956,0.0805313736,0.3011382222,-0.0750805512,-0.1568284333,0.1580274254,0.3855433464,0.085239619,-0.0463728569,0.3243296742,0.3055721819,-0.2333544642,0.0019363248,-0.4296098948,-0.2353733927,-0.119487904,0.3071151078,0.0591079742,-0.0130565641,0.6048250794,-0.1873202324,0.3196305037,-0.0907878578,0.3079951704,0.1744770408,0.0746124089,0.2866277099,-0.0819513202,0.1851965189,0.3427527249,0.1244142652,-0.2457848489,-0.2860185802,-0.0978619531,-0.4152011573,0.1885588765,0.000531118,-0.5068399906,0.239495337,0.4041298926,0.2187768668,-0.0533623956,-0.1476347297,0.2692391574,-0.0410024524,-0.158067435,-0.017097719,0.437728554,0.8433099985,-0.2321900427,-0.4020535648,0.1269695163,-0.0551433563,0.0210924614,0.2261457443,-0.0151569322,0.131389752,-0.2769354284,0.1363208145,0.3145765662,-0.3304304481,-0.5092270374,-0.0667772964,-0.437472254,-0.0340688936,0.1925942451,0.109986119,0.0847023278,0.1234846264,0.1166357398,-0.2643081546,0.2656232119,-0.3458692431,-0.3824667335,-0.2897080481,-0.0370660089,0.0175562743,0.3021219373,0.0015976339,-0.0653040186,0.0374565944,0.1378031969,0.1916084439,0.1767644733,0.2403720915,0.2350229025,0.0061784941,0.2478129268,0.1133008152,-0.2364605367,0.3414187133,-0.3823010921,0.0915210992,0.1842734665,0.0344474316,-0.1589950174,-0.1162987277,-0.1790624857,-0.4125052691,0.0269138478,-0.1299059093,0.1790639162,0.1748148352,-0.1957106292,0.1845989525,-0.0311742108,0.2445206791,-0.4866950214,0.0809158459,-0.0441642366,-0.3607316613,-0.0655888617,0.0390097611,0.0376205966,0.0411496051,-0.138989225,0.3688038886,-0.1038145944,0.2514378428,0.2193906009,0.2252401263,-0.0583164766,-0.3309867978,-0.1056120545,0.1239272431,0.0039939252,-0.045171421,-0.1759320796,0.3884069026,0.090015173,0.14617531,-0.3499466777,0.2182744592,-0.0972633138,-0.1663038135,-0.555668056,-0.0380490161,0.4728535712,-0.2647351027,0.2770218849,-0.1631315947,0.0137206158,0.0223108213,0.233317107,0.154551968,0.1547574848,0.0433734059,-0.233309418,-0.0644800887,0.0418287627,-0.1615593135,0.4600605667,0.1955893338,0.2431957573,0.1327683926,0.087455593,-0.2459524572,0.0757683069,0.0206983369,-0.1691020578,0.159304902,0.1514809728,0.1435250938,-0.3288494051,-0.1380015016,-0.2743545771,0.0329185165,-0.3341398835,0.1020790935,-0.1349844486,0.4001249671,0.154652074,0.0645313635,-0.1272897869,-0.2716541886,0.1869915426,-0.1014484242,0.0305638071,0.2809856236,-0.2944428027,0.5300469995,0.110001944,-0.181198895,-0.3561827242,-0.2111852467,-0.0076867859,-0.1622644514,-0.1794365942,0.19308348,0.0939458907,-0.1835871041,-0.0286585875,-0.0898886323,-0.354308933,0.0646219328,0.0579995736,0.680811584,0.2862638831,0.0012738783,-0.2250635922,0.0685060695,0.4061901569,-0.4061918259,-0.3184202313,0.0798092559,-0.0271717571,0.1367471367,-0.3171879351,-0.2778091431,0.1093105599,-0.3724223077,0.6279050708,0.0351591259,-0.0284729768,-0.1151442304,-0.006565087,0.2452505231,-0.2081536949,0.2181218863,-0.3708445728,-0.6790494919,0.3761582971,-0.0701826364,-0.2920279801,-0.1314426363,0.0402270742,0.3392747641,-0.0791691542,-0.5926974416,-0.522321403,-0.0324288048,0.3241592348,0.0222350433,-0.0101590138,0.5464061499,-0.0936023146,-0.0244744457,-0.0935700014,-0.1157884747,0.6498821378,-0.1600061059,0.125362426,-0.041815564,0.5212675333,0.1402976364,0.9083513021,0.5984097719,0.1078086942,0.2427328527,0.0859564245,0.3766086996,-0.1323872656,-0.1446452439,0.3193695247,0.1811810881,-0.12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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"I tried to adjust `run_seq2seq.py` and trainer to use the suggested dist env:\r\n```\r\n    import torch.distributed as dist\r\n    metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())\r\n```\r\nand in `trainer.py` added to call just for rank 0:\r\n```\r\n        if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:\r\n            metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n```\r\nand then the process hangs in a deadlock. \r\n\r\nHere is the tb:\r\n```\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/filelock.py\", line 275 in acquire\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 306 in _check_all_processes_locks\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 501 in _init_writer\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 440 in add_batch\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 397 in compute\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 558 in compute_metrics\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer.py\", line 1876 in prediction_loop\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer.py\", line 1703 in evaluate\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer_seq2seq.py\", line 74 in evaluate\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 603 in main\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 651 in <module>\r\n```\r\n\r\nBut this sounds right, since in the above diff I set up a distributed metric and only called one process - so it's blocking on waiting for other processes to do the same.\r\n\r\nSo one working solution is to leave:\r\n\r\n```\r\n    metric = load_metric(metric_name)\r\n```\r\nalone, and only call `compute_metrics` from rank 0\r\n```\r\n        if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:\r\n            metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n```\r\n\r\nso we now no longer use the distributed env as far as `datasets` is concerned, it's just a single process.\r\n\r\nAre there any repercussions\/side-effects to this proposed change in Trainer?  If it always gathers all inputs on rank 0 then this is how it should have been done in first place - i.e. only run for rank 0. It appears that currently it was re-calculating the metrics on all processes on the same data just to throw the results away other than for rank 0. Unless I missed something.\r\n","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":302,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n I tried to adjust `run_seq2seq.py` and trainer to use the suggested dist env:\r\n```\r\n    import torch.distributed as dist\r\n    metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())\r\n```\r\nand in `trainer.py` added to call just for rank 0:\r\n```\r\n        if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:\r\n            metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n```\r\nand then the process hangs in a deadlock. \r\n\r\nHere is the tb:\r\n```\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/filelock.py\", line 275 in acquire\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 306 in _check_all_processes_locks\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 501 in _init_writer\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 440 in add_batch\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/metric.py\", line 397 in compute\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 558 in compute_metrics\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer.py\", line 1876 in prediction_loop\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer.py\", line 1703 in evaluate\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/transformers-mp-pp\/src\/transformers\/trainer_seq2seq.py\", line 74 in evaluate\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 603 in main\r\n  File \"examples\/seq2seq\/run_seq2seq.py\", line 651 in <module>\r\n```\r\n\r\nBut this sounds right, since in the above diff I set up a distributed metric and only called one process - so it's blocking on waiting for other processes to do the same.\r\n\r\nSo one working solution is to leave:\r\n\r\n```\r\n    metric = load_metric(metric_name)\r\n```\r\nalone, and only call `compute_metrics` from rank 0\r\n```\r\n        if self.is_world_process_zero() and self.compute_metrics is not None and preds is not None and label_ids is not None:\r\n            metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n```\r\n\r\nso we now no longer use the distributed env as far as `datasets` is concerned, it's just a single process.\r\n\r\nAre there any repercussions\/side-effects to this proposed change in Trainer?  If it always gathers all inputs on rank 0 then this is how it should have been done in first place - i.e. only run for rank 0. It appears that currently it was re-calculating the metrics on all processes on the same data just to throw the results away other than for rank 0. Unless I missed something.\r\n","embeddings":[0.0881400257,-0.1092867628,0.1158301681,0.2856828272,0.0845733956,0.0806555972,0.2349973619,0.2377158999,0.2347908914,0.2356441766,0.0788630545,0.166712448,-0.373960644,-0.2242649049,0.0668825433,0.0503915213,-0.0005046903,-0.1439883858,-0.221030131,-0.1951075941,-0.2040551752,0.5319712758,-0.088830635,-0.0502556562,-0.5913400054,0.2881834209,-0.1651010513,0.1823531091,-0.0684032291,-0.617151022,0.3700884283,0.1490116119,0.0829517618,0.5546138883,-0.0001318881,-0.1994537562,0.2629815638,-0.2211295068,-0.1534065604,-0.2687932551,0.1527822465,-0.2575644553,0.571503222,-0.1630466133,-0.1416184306,-0.0879457891,0.0900792032,-0.4483617544,0.4259662926,0.1269228458,-0.0067988681,0.3528015912,-0.0033912866,-0.2833948433,0.097712405,-0.1208251268,0.0339141265,0.3804101944,-0.0241650287,0.0620026514,-0.327988416,0.2068395466,0.2323785722,-0.0643238276,0.3330114484,-0.0780323222,-0.1014687717,-0.1370963901,-0.0787223428,0.3096213937,0.2080832869,-0.1693377495,-0.2303849608,0.0603585951,0.0139125641,-0.343267709,0.1953567713,-0.0388792939,-0.0130721033,0.04169387,0.0024041771,-0.1951526105,0.0043453164,0.0744460374,-0.1773838401,0.1295827478,-0.0661413819,0.0334934853,0.0162886679,0.2591890097,-0.1607431322,0.3018882573,0.0713185444,0.1371362656,-0.3619210124,0.170331955,0.2666286528,0.057864666,-0.0056573586,0.319526583,-0.0361856818,0.0114500206,0.4184844494,0.0381960087,-0.1389734447,0.362706542,0.2786945701,0.1029436886,0.1162616462,0.1878528744,-0.0855451375,-0.2297569364,0.0198366959,-0.1975078732,0.3149439096,0.2442042977,-0.0334214009,-0.4205731153,0.1195659563,0.295242995,-0.1290903389,-0.0933247954,0.1693427861,0.3671609759,0.0882651433,0.0787011161,0.3353294134,0.2471677959,-0.199189499,-0.0173158702,-0.4135643542,-0.0986426994,-0.1242088079,0.2724477649,0.2196628004,-0.1628999412,0.6351852417,-0.1787698269,0.4696894288,-0.0300205424,0.1313095689,0.1857780814,0.1316873729,0.2409881353,-0.0306473374,0.2101943344,0.3361890912,0.3006996512,-0.1925242394,-0.1458505243,-0.1428042948,-0.5472254157,0.1147262827,-0.096416086,-0.4572981298,0.2726358473,0.3576215804,0.1117123365,-0.1076127216,-0.1904318482,0.1932600141,-0.0918901116,-0.0997676253,-0.0660918951,0.5843692422,0.7293358445,-0.2147573531,-0.4012365341,0.098839514,0.0325715914,-0.1022926718,0.1529448926,-0.0788165107,0.1933204234,-0.3543674052,-0.0127081899,0.3378298879,-0.4275298417,-0.5466860533,-0.1503923386,-0.3531804681,-0.1286658049,0.2151797116,0.0550851822,0.129709959,0.1625282913,0.1186125427,-0.1818707138,0.1395101547,-0.3199318945,-0.2878010869,-0.1782021075,0.054649163,0.0048474343,0.328759104,-0.0217356887,-0.0645741001,0.1194041222,0.1203885376,0.1731573492,0.1427398622,0.3467543721,0.1570518166,0.0381508358,0.2471780181,0.1363053173,-0.3056511879,0.305378437,-0.3536006808,0.0743503794,0.0327291712,0.0219307262,-0.1138970107,-0.2049700618,-0.1658814549,-0.4732397497,-0.1058141738,-0.0443967432,0.1530443281,0.101691477,-0.1681249887,0.1105814353,-0.0655051917,0.2719031572,-0.4879302084,0.0612640902,-0.0638747662,-0.3398908675,-0.0635269657,0.16161789,0.0212047491,0.0049574156,-0.1978687644,0.3824717104,-0.1466091573,0.2167610377,0.2523966432,0.1297360212,-0.0266656876,-0.5362629294,-0.024970945,0.1701551974,-0.0606264137,0.0106334873,-0.2244264483,0.3717922568,0.1169154719,0.1528419852,-0.3292899132,0.2574849725,-0.1934431642,-0.0311923902,-0.4751604199,-0.0524111949,0.461251229,-0.1249243543,0.3616275787,-0.0769227818,0.0478943437,-0.0536687672,0.1575220674,0.1602757126,0.0998461619,0.0825526267,-0.1949329972,-0.0521236435,0.0845899135,-0.2958513796,0.418176353,0.1859461218,0.2945014834,0.0671907365,0.138365075,-0.1661348194,0.0406900011,-0.0327739678,-0.1795000285,0.2156122923,0.2768219709,0.191608727,-0.2365643233,-0.0302204862,-0.2584660649,0.0539821386,-0.339664638,0.0545165725,-0.2114023566,0.5269723535,0.1609437466,-0.001784038,-0.3060412407,-0.2587700784,0.1985844225,-0.1509752721,0.1073668972,0.2856147289,-0.1824020594,0.366370976,0.1217021942,-0.1664306521,-0.2195236683,-0.2145324796,-0.0526186749,-0.2615052164,-0.2276444286,-0.0176186599,0.1051164493,-0.181295082,-0.0146132056,-0.0838483274,-0.3236866295,0.0789873451,0.0525829904,0.5157334208,0.3757797182,0.0781337321,-0.3018793762,0.1728724241,0.4036900997,-0.4335684776,-0.2911327779,0.0549028963,0.0282635931,0.2603513598,-0.2404197305,-0.3637054861,0.0195961576,-0.3122047782,0.4976966977,0.0147305662,-0.0670966133,-0.2401181161,0.0159149691,0.2207764983,-0.1401596814,0.3164168,-0.2179742008,-0.7374212146,0.4974017739,-0.0844749361,-0.2427448332,-0.0719342381,0.0304230992,0.3310313225,0.0592322648,-0.674808383,-0.3775847554,0.0442798994,0.2911629379,-0.1235283613,0.138741374,0.5976380706,-0.0984463319,0.0906325802,-0.084837988,-0.164619714,0.514677465,-0.2370685041,0.2622246742,-0.0529930331,0.3953697681,0.205136925,1.0660384893,0.4365729094,0.1375798136,0.2404038608,0.0790980682,0.3862739801,-0.1463313997,-0.1985452324,0.2925935388,0.2944891751,-0.012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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"But no, since \r\n`\r\n metric = load_metric(metric_name)\r\n`\r\nis called for each process, the race condition is still there. So still getting:\r\n\r\n```\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```\r\n\r\ni.e. the only way to fix this is to `load_metric` only for rank 0, but this requires huge changes in the code and all end users' code.\r\n","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":76,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n But no, since \r\n`\r\n metric = load_metric(metric_name)\r\n`\r\nis called for each process, the race condition is still there. So still getting:\r\n\r\n```\r\nValueError: Error in finalize: another metric instance is already using the local cache file. Please specify an experiment_id to avoid colision between distributed metric instances.\r\n```\r\n\r\ni.e. the only way to fix this is to `load_metric` only for rank 0, but this requires huge changes in the code and all end users' code.\r\n","embeddings":[-0.0743820369,0.0189331546,0.0986257046,0.2385591269,0.0015655096,0.0210894197,0.1744567901,0.2997217178,0.3662584722,0.1215545535,0.0272279736,0.1309887916,-0.3144579828,-0.0137835871,-0.0090082521,-0.0354207791,-0.0174290687,-0.0118403193,-0.2157492489,-0.1407078803,-0.3549723625,0.4909443557,-0.094041802,-0.0196759515,-0.4025662541,0.1862027049,-0.1208144575,0.1642288417,-0.0712104589,-0.6548614502,0.3760336339,0.2119780928,0.0550227165,0.5668932199,-0.0001228389,-0.1647618562,0.2224575281,-0.1654272527,-0.0895339325,-0.2089778632,0.0419953763,-0.3018239141,0.4746413529,-0.1672160625,-0.0354671106,-0.066387251,0.0834047124,-0.4613867998,0.4682587683,-0.0254345387,0.0765321851,0.3800902069,-0.1043446735,-0.2316598892,-0.0495066196,-0.1613818258,0.0241747182,0.4295062721,-0.0905898958,0.0036928728,-0.3325567842,0.2633509636,0.1676269174,0.0209394712,0.2453759462,-0.0559759326,0.064929001,-0.1188937649,-0.1050406396,0.3209834695,0.1520304829,-0.1883409619,-0.2298358232,0.0978398696,-0.1813545078,-0.5551991463,0.1535269469,-0.0972424373,0.0039800592,0.0392485633,-0.0380287506,-0.0952162445,-0.0568385944,0.056629993,-0.1502937675,0.0717534721,-0.1363277435,0.0569492243,-0.0620997027,0.2655829191,-0.3586181104,0.3487266302,-0.0165708736,0.1322408617,-0.4776332974,0.1030918807,0.233364895,0.0192754772,0.063808322,0.3710057735,0.0047831195,0.1104969233,0.4419555068,0.0989984944,-0.0648535118,0.4883598387,0.2653134167,-0.097262986,0.172475934,0.1843720973,-0.1093547717,-0.3044114709,0.1303544492,-0.2305703312,0.3590710759,0.1428924501,0.0501234122,-0.4300651252,0.0268946644,0.3125571609,-0.0175362881,-0.1046422049,0.1151105165,0.3204920292,0.0802922249,0.0376737975,0.2430389971,0.3135303259,-0.2338243127,0.0523923822,-0.4096278548,-0.209206,-0.0599776506,0.3604540527,0.0806869939,-0.0867773667,0.6622604728,-0.2452789098,0.4363716841,-0.0098047946,0.2046338618,0.1801846921,-0.0522345975,0.335319519,0.0205427725,0.2025182992,0.4229381979,0.1180685014,-0.2378182411,-0.222247839,-0.1965861768,-0.4890339077,0.1327867061,-0.0115742479,-0.4982536137,0.2183241993,0.2810570896,0.236883074,-0.0750576779,-0.1865112633,0.1740934998,0.0632326603,-0.1740942299,-0.0775207281,0.5013920665,0.8000811934,-0.3106375635,-0.4090100527,0.0621440969,0.0198931396,0.059744738,0.1834789366,-0.0264085419,0.1122234389,-0.2398672551,0.043061886,0.4500413835,-0.3818278015,-0.44047153,-0.0102439271,-0.4845240712,-0.0927990898,0.1529062241,0.0130148763,0.1167048067,0.1743211001,0.0596258231,-0.2304722071,0.1583907753,-0.2232081443,-0.4099032879,-0.3159253597,0.0827811882,0.0175182074,0.351108253,0.0312786065,-0.0216585081,0.0502975211,0.0938851088,0.2171548158,0.1900270581,0.2495134324,0.2989781201,0.0074488814,0.2794372141,0.0626640096,-0.1863881797,0.3330546618,-0.3847153783,0.1210732087,0.1558227986,0.0101862922,-0.2554594278,-0.233665213,-0.2074742615,-0.3817099631,0.0072999345,-0.1816835999,0.2119551748,0.2074861526,-0.2354964465,0.1523828954,0.0227620788,0.2163387686,-0.4231482446,0.0148601681,-0.0297127701,-0.331325978,-0.0202226397,0.0606493354,0.0434454679,0.0521167554,-0.2072704732,0.4505540133,-0.2041242868,0.271848917,0.3489960134,0.1949971169,-0.073145166,-0.3825898468,-0.0086809983,0.1035968363,-0.0373466983,0.003466916,-0.2115324885,0.3656711578,0.0740349218,0.1468586773,-0.351149112,0.2701358497,-0.0933459401,-0.1494020373,-0.5120645761,-0.0564063489,0.3746648431,-0.2972075343,0.3142363429,-0.1348352283,0.0955447927,-0.0313753709,0.2216869891,0.2303462178,0.1324336529,0.0526792556,-0.2068916112,-0.0816363618,0.0431526639,-0.2377364486,0.377917558,0.1831710339,0.2262164205,0.080045715,0.1042996049,-0.1794141382,0.0618315712,0.0174040738,-0.0939754769,0.1558281928,0.2129647136,0.1486931294,-0.3308373392,-0.0466651432,-0.2257911265,0.0777691454,-0.3252052367,0.0463929921,-0.1676025093,0.3622933328,0.1042036414,0.0760788769,-0.320507735,-0.200661242,0.2137132585,-0.1206795275,0.0558301024,0.3729785383,-0.3125834167,0.5460637808,0.1275182664,-0.1835206598,-0.2804273367,-0.2134806514,-0.0769835263,-0.1774238348,-0.1685278118,0.1996259987,0.1353262067,-0.1900614798,0.0364903957,0.0679248273,-0.2923831046,0.0072688437,0.0544490032,0.6009891033,0.2198510915,0.1127616242,-0.3618911207,0.0661276281,0.4660863578,-0.4653955102,-0.2988180816,0.1108197719,-0.005610731,0.2263551503,-0.2566636801,-0.3030112386,0.1503475606,-0.3555740416,0.6306397915,0.0361626931,0.0040374417,-0.1828644872,-0.0212518219,0.1606975347,-0.145521313,0.1835410893,-0.3220936954,-0.7370195389,0.3779354692,-0.0818951651,-0.2262859195,-0.0287562869,0.1227793247,0.3893444538,-0.0262093954,-0.5944405198,-0.4810172617,0.0726495683,0.2060054392,0.0201442447,0.0549132936,0.5922463536,-0.0786325336,-0.0039966004,-0.0683099702,-0.114582628,0.5944651961,-0.1417892873,0.1444726586,-0.0960227996,0.4891734719,0.1768221408,0.9024285078,0.5595239401,0.3208563328,0.285921365,0.1434484273,0.3294373453,-0.0935019553,-0.1837992519,0.3400885463,0.2125222236,-0.10732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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"OK, here is a workaround that works. The onus here is absolutely on the user:\r\n\r\n```\r\ndiff --git a\/examples\/seq2seq\/run_seq2seq.py b\/examples\/seq2seq\/run_seq2seq.py\r\nindex 2a060dac5..c82fd83ea 100755\r\n--- a\/examples\/seq2seq\/run_seq2seq.py\r\n+++ b\/examples\/seq2seq\/run_seq2seq.py\r\n@@ -520,7 +520,11 @@ def main():\r\n\r\n     # Metric\r\n     metric_name = \"rouge\" if data_args.task.startswith(\"summarization\") else \"sacrebleu\"\r\n-    metric = load_metric(metric_name)\r\n+    import torch.distributed as dist\r\n+    if dist.is_initialized():\r\n+        metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())\r\n+    else:\r\n+        metric = load_metric(metric_name)\r\n\r\n     def postprocess_text(preds, labels):\r\n         preds = [pred.strip() for pred in preds]\r\n@@ -548,12 +552,17 @@ def main():\r\n         # Some simple post-processing\r\n         decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\r\n\r\n+        kwargs = dict(predictions=decoded_preds, references=decoded_labels)\r\n+        if metric_name == \"rouge\":\r\n+            kwargs.update(use_stemmer=True)\r\n+        result = metric.compute(**kwargs) # must call for all processes\r\n+        if result is None: # only process with rank-0 will return metrics, others None\r\n+            return {}\r\n+\r\n         if metric_name == \"rouge\":\r\n-            result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\r\n             # Extract a few results from ROUGE\r\n             result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\r\n         else:\r\n-            result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n             result = {\"bleu\": result[\"score\"]}\r\n\r\n         prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\r\n```\r\n\r\nThis is not user-friendly to say the least. And it's still wasteful as we don't need other processes to do anything.\r\n\r\nBut it solves the current race condition.\r\n\r\nClearly this calls for a design discussion as it's the responsibility of the Trainer to handle this and not user's. Perhaps in the `transformers` land?","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":233,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n OK, here is a workaround that works. The onus here is absolutely on the user:\r\n\r\n```\r\ndiff --git a\/examples\/seq2seq\/run_seq2seq.py b\/examples\/seq2seq\/run_seq2seq.py\r\nindex 2a060dac5..c82fd83ea 100755\r\n--- a\/examples\/seq2seq\/run_seq2seq.py\r\n+++ b\/examples\/seq2seq\/run_seq2seq.py\r\n@@ -520,7 +520,11 @@ def main():\r\n\r\n     # Metric\r\n     metric_name = \"rouge\" if data_args.task.startswith(\"summarization\") else \"sacrebleu\"\r\n-    metric = load_metric(metric_name)\r\n+    import torch.distributed as dist\r\n+    if dist.is_initialized():\r\n+        metric = load_metric(metric_name, num_process=dist.get_world_size(), process_id=dist.get_rank())\r\n+    else:\r\n+        metric = load_metric(metric_name)\r\n\r\n     def postprocess_text(preds, labels):\r\n         preds = [pred.strip() for pred in preds]\r\n@@ -548,12 +552,17 @@ def main():\r\n         # Some simple post-processing\r\n         decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\r\n\r\n+        kwargs = dict(predictions=decoded_preds, references=decoded_labels)\r\n+        if metric_name == \"rouge\":\r\n+            kwargs.update(use_stemmer=True)\r\n+        result = metric.compute(**kwargs) # must call for all processes\r\n+        if result is None: # only process with rank-0 will return metrics, others None\r\n+            return {}\r\n+\r\n         if metric_name == \"rouge\":\r\n-            result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\r\n             # Extract a few results from ROUGE\r\n             result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\r\n         else:\r\n-            result = metric.compute(predictions=decoded_preds, references=decoded_labels)\r\n             result = {\"bleu\": result[\"score\"]}\r\n\r\n         prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\r\n```\r\n\r\nThis is not user-friendly to say the least. And it's still wasteful as we don't need other processes to do anything.\r\n\r\nBut it solves the current race condition.\r\n\r\nClearly this calls for a design discussion as it's the responsibility of the Trainer to handle this and not user's. Perhaps in the `transformers` land?","embeddings":[-0.0640703589,-0.0787641034,0.0965643525,0.2534787655,-0.0056881383,-0.0212866962,0.3123223782,0.2638015747,0.2110244334,0.2581807971,0.0451734066,0.3624677956,-0.3526070714,-0.1786394417,0.0343574695,-0.034716554,-0.0441434048,-0.1339790523,-0.3583231866,-0.1239942759,-0.190798983,0.4987677038,-0.0344169177,-0.0297840927,-0.3931267858,0.224177435,-0.164009124,0.1788709909,0.0643501431,-0.5618361831,0.3226022422,0.1360853016,0.0452935137,0.5764806867,-0.0001236324,-0.1678349376,0.1872864068,-0.2450377643,-0.1874425858,-0.271746397,0.2271195054,-0.3114353418,0.4968193769,-0.2364772558,-0.1577860862,0.0378682949,-0.0054120263,-0.3434016407,0.4236991405,0.0571904667,0.085931316,0.3330073953,-0.032325916,-0.1901235878,0.1212329939,-0.0653862208,-0.0368981287,0.391304642,-0.0438003577,0.0700681061,-0.2391371131,0.2996984422,0.1755198389,-0.0402260795,0.314748466,-0.0494450517,-0.2011662573,-0.0775264129,-0.105209291,0.308021009,0.2359141856,-0.223406136,-0.2375836819,0.0531364717,-0.1129199192,-0.3445173502,0.1445093304,-0.0051630242,-0.0187254827,0.0235215779,0.0591396205,-0.1390450746,-0.0627024695,0.0998460129,-0.0452240445,0.0139156533,-0.1335917711,0.1054865494,-0.0017543795,0.1352349818,-0.3275977373,0.306905359,0.0391775332,0.1289857626,-0.3677838743,0.1243339702,0.2337081581,0.0210840479,0.0918095335,0.4560725987,0.0586389378,0.0463430211,0.4054613411,0.1031668037,-0.1804311275,0.4910764992,0.2882263064,0.1206449419,0.1309707463,0.2508909106,-0.0607221946,-0.2949403524,0.1342707276,-0.2586985826,0.2855698466,0.2531366348,0.0631746277,-0.4151259363,0.0464722887,0.1691827774,-0.0770844817,-0.1774351299,0.1314891577,0.4097194076,0.1488403976,0.0084704608,0.3483400047,0.3792473972,-0.1620044112,0.005470606,-0.3775833845,-0.1619015038,-0.0832369179,0.3270745277,0.1530539989,-0.1119453609,0.6152184606,-0.2334931195,0.4344537258,-0.0676151067,0.2086048126,0.2173037529,0.0877950042,0.1757381558,-0.0002522572,0.1726161093,0.3499711752,0.3042697906,-0.2953206599,-0.1659086645,-0.1075638756,-0.5697390437,0.1044747531,-0.0256729499,-0.3686076999,0.252402693,0.3646577597,0.1765206307,-0.0878772363,-0.1766688824,0.2183123082,0.0424640067,-0.1899272203,-0.1322890371,0.5401469469,0.795589745,-0.223470822,-0.3928968012,0.0861313418,-0.0302689224,-0.0688859746,0.1711060703,-0.1304824352,0.1734703034,-0.3124321699,0.0132106543,0.4526499212,-0.4541595578,-0.5230979919,-0.0551201962,-0.3473708034,-0.1022441834,0.1810635328,0.0192643367,0.1447815597,0.1427844316,0.1297154278,-0.1455388516,0.2052158266,-0.2678942382,-0.3874981999,-0.2713224292,0.0696757361,-0.0526352227,0.2598974407,0.019360641,0.0312664509,-0.0005676097,0.1223189384,0.112574324,0.1530454457,0.3094593287,0.1673349887,0.1094968691,0.2271713763,0.0797586888,-0.2535483241,0.280608058,-0.2641236186,0.096486561,0.0547574796,-0.0591282099,-0.2017363757,-0.191734314,-0.1189767569,-0.4788504839,-0.0093121016,0.0025924593,0.1778894514,0.098006621,-0.0833217427,0.0272526108,0.0022785261,0.2093143463,-0.4971334338,0.0486030243,-0.1358307302,-0.3562149107,-0.0538285002,0.162473321,0.082434766,0.0275184531,-0.2172619998,0.4142546058,-0.2254364938,0.2963485718,0.2505712509,0.0866480023,-0.1558147371,-0.4436963201,0.0706837699,0.1534367651,-0.0243405979,0.0595748425,-0.2230667919,0.4005185962,0.057605084,0.088164255,-0.3915153742,0.320607543,-0.0622925423,-0.1256732643,-0.5161961913,-0.065331772,0.4644509256,-0.2338357419,0.306891948,-0.1485267282,-0.1147532538,0.0304505322,0.0552103035,0.1526011825,0.2161269635,0.0492043793,-0.2106391788,0.0539288856,0.0803134516,-0.2700403631,0.4026062787,0.2301347256,0.2446660399,0.0396017358,0.2069373727,-0.1649561971,0.0762510151,-0.0633525997,-0.1690059602,0.1789561957,0.2994227707,0.16940552,-0.2687003314,-0.0116007859,-0.3858012557,-0.0177335758,-0.316115737,0.1085886434,-0.1061817184,0.3600185215,0.0628945455,0.027881762,-0.2497003078,-0.2115068734,0.2172032595,-0.128764689,-0.0082145529,0.3403942883,-0.1438351423,0.4247652292,0.1005562171,-0.2894504964,-0.2572137713,-0.1998008192,-0.0760467201,-0.1793352067,-0.2656765878,0.0456999801,0.218057245,-0.1610600501,0.0553274378,-0.1083599702,-0.2996193469,0.0607741922,0.0864420161,0.5465068817,0.294896543,0.0005504977,-0.1870801449,0.0301565975,0.4708809257,-0.463945508,-0.2469661385,0.0697957352,0.0687668622,0.3005344272,-0.3028265834,-0.4108238518,0.0369482189,-0.4303351343,0.6223106384,0.0505601317,0.0192916226,-0.2766919434,0.0470691025,0.1442534924,-0.1902227104,0.3671061695,-0.3098250329,-0.6642648578,0.4911518693,-0.153203994,-0.2969996631,-0.0493560918,0.0273516737,0.4338546991,0.0113272937,-0.6472849846,-0.427665025,0.0734205619,0.3311350048,-0.0820795298,0.1234300286,0.5292959213,-0.1036516801,0.0432882756,-0.0859871879,-0.1932816356,0.4761613905,-0.2479908019,0.2904284596,-0.0502362959,0.4710008502,0.2042387277,1.0362510681,0.4318812191,0.1980848312,0.2408500165,0.0621297508,0.3678739667,-0.1329009831,-0.2530627847,0.3594072163,0.215783909,-0.065166086,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"I don't see how this could be the responsibility of `Trainer`, who hasn't the faintest idea of what a `datasets.Metric` is. The trainer takes a function `compute_metrics` that goes from predictions + labels to metric results, there is nothing there. That computation is done on all processes  \r\n\r\nThe fact a `datasets.Metric` object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in `datasets`. Especially since, as I mentioned before, the multiprocessing part of `datasets.Metric` has a deep flaw since it can't work in a multinode environment. So you actually need to do the job of gather predictions and labels yourself.\r\n\r\nThe changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels `number_of_processes` times I believe, which is not going to make the metric computation any faster.\r\n\r\n","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":144,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n I don't see how this could be the responsibility of `Trainer`, who hasn't the faintest idea of what a `datasets.Metric` is. The trainer takes a function `compute_metrics` that goes from predictions + labels to metric results, there is nothing there. That computation is done on all processes  \r\n\r\nThe fact a `datasets.Metric` object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in `datasets`. Especially since, as I mentioned before, the multiprocessing part of `datasets.Metric` has a deep flaw since it can't work in a multinode environment. So you actually need to do the job of gather predictions and labels yourself.\r\n\r\nThe changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels `number_of_processes` times I believe, which is not going to make the metric computation any faster.\r\n\r\n","embeddings":[-0.0986766294,0.0874648094,0.0754722878,0.1241439432,0.1474277377,-0.0701845735,0.3525932133,0.2356343269,0.1611245424,0.2098139673,0.000421444,0.214056015,-0.3455352783,0.1935886294,0.0740699172,-0.0832463354,-0.0075628199,-0.1163947061,-0.2869692147,0.0001233007,-0.261204958,0.4958016276,-0.1959237009,0.1165345833,-0.4939318895,0.0160643514,-0.1405585408,0.0436572582,-0.1571676433,-0.6827939153,0.1779743582,0.2850542068,0.0590012819,0.6136043668,-0.0001197262,-0.1275459379,0.1432348788,-0.0766494647,-0.1142276749,-0.101868622,0.0062036146,-0.3750232458,0.5041076541,-0.2027844936,-0.0246382952,-0.1871017665,-0.000907287,-0.3866353333,0.4851273,0.1960166842,0.1148971319,0.3724759221,-0.0633555204,-0.2893129587,-0.0304380469,-0.179746449,-0.0004408027,0.3063096106,-0.1180348918,0.1041778475,-0.2638342679,0.2987607419,0.1747556776,-0.1579250693,0.3985736668,0.0108563146,0.0237378236,-0.0750850588,-0.026344303,0.228096053,0.3640272319,-0.2081997395,-0.4027554691,0.0213231351,-0.1136104017,-0.4449650645,0.0867719799,-0.1544950157,-0.1124913022,0.017352581,-0.0512008294,-0.0918699801,-0.1205045357,-0.1065767035,-0.1400079429,0.0415050797,-0.1515364647,0.1079932898,0.0986205488,0.2940391898,-0.2250009924,0.4625895619,0.107808806,0.0874904841,-0.5861869454,0.051559452,0.1689260602,-0.137893185,0.026183527,0.3476356268,0.1393156648,0.1647456735,0.4749913812,0.0469469056,-0.0859803259,0.495952338,0.1158409417,-0.0413894244,0.2166435719,0.2328991294,-0.1324020177,-0.3073429167,0.1509372443,-0.2894469798,0.4075081646,0.1650965512,-0.0240274984,-0.3971653283,0.0111141028,0.2321667373,-0.1615175307,-0.3076352179,0.1635745168,0.3015667498,-0.0188990217,0.1546543688,0.2649487555,0.2677026987,-0.2153810561,0.0336757265,-0.3627006114,-0.1388621628,-0.2221658081,0.5311321616,-0.033613015,-0.1031351015,0.4908144474,-0.2240339071,0.3613184988,-0.114314951,0.1868821234,0.1512250751,0.0729259402,0.1448817104,-0.0949600264,0.0191628709,0.1993149817,-0.0007020317,-0.2345672101,-0.3136923611,-0.1904584765,-0.5587620735,0.0460788868,0.0113418596,-0.4121508598,0.168701455,0.1926000267,0.4390551746,-0.0838328302,-0.0827364922,0.1543191671,-0.0658370554,-0.2503941059,-0.0533928685,0.538708806,0.6572615504,-0.2708118856,-0.3530547917,0.1573342681,-0.0764822289,0.0305980127,0.1866123974,-0.0675412193,0.3315973282,-0.1351325661,0.0338992961,0.318702817,-0.3188119233,-0.2820299566,-0.1126638576,-0.2808205187,-0.0668343529,0.1820889562,-0.0332553238,0.2036238611,0.1777180731,0.0793947801,-0.2812536359,0.199252516,-0.2796541154,-0.3488397598,-0.2571011186,-0.0160810836,0.0350257978,0.4142493606,0.1447869688,-0.0155540835,0.2191104591,0.0989591479,0.0124070393,0.1865232289,0.1369633824,0.2173769474,0.0301675629,0.2122123092,-0.0668710098,-0.259488225,0.1811954081,-0.2249533683,0.2644392848,0.181269899,-0.1109844595,-0.1313766092,-0.0319106393,-0.0450462811,-0.3800312877,0.038146954,0.0193699189,0.2461988032,0.063606061,-0.1863696277,0.2601462603,-0.0229211096,0.1970857531,-0.3077025115,0.0054115532,-0.0493264832,-0.2693814635,-0.0472342819,0.2843676805,0.0135070439,0.0620762482,-0.1563108563,0.4471603036,-0.0971780792,0.1208168939,0.2460498959,0.2522501945,-0.1001493707,-0.2472563386,0.016244486,-0.01773897,0.0309684929,-0.0384248942,-0.1867370754,0.4048735499,-0.0063228183,0.1802697033,-0.1444068253,0.2286265343,-0.1040408611,-0.0146845682,-0.6104436517,-0.0441228263,0.1817787588,-0.358833164,0.2854744196,-0.0732167214,-0.1879296005,0.0873126239,0.2204131186,0.0784531012,-0.0388826355,0.1840817332,-0.1221867502,-0.1293780357,-0.0313919559,-0.0752816051,0.4372566938,0.198172912,0.265868485,0.136862576,0.1789865494,-0.2482047379,0.1564263254,0.1467076093,-0.19835338,0.0213169325,0.1952255666,0.1843651831,-0.2286498398,-0.1335852593,-0.2828978896,0.0912203491,-0.3479496241,0.1114817262,-0.1617618948,0.3325738907,-0.0256056022,0.0918035433,-0.1845480949,-0.2695588171,0.2477204055,-0.1749944687,-0.0806614682,0.3019294441,-0.1997397393,0.5645967126,0.0458001383,-0.0543940477,-0.1539081335,-0.1289447099,0.024809733,-0.1263793409,-0.1107395217,0.2380958647,0.144310236,-0.1409785599,0.0804135874,-0.1126384661,-0.3777036667,0.0318094678,-0.0719139948,0.6136100292,0.3686548471,-0.0671826899,-0.3057754934,0.0558075421,0.4334175289,-0.4616743624,-0.1821401119,-0.0158735793,0.0210346337,0.1119773015,-0.2284412086,-0.4090143442,-0.0333864987,-0.3924374878,0.6301872134,-0.0302866921,0.0650706887,-0.1423585862,0.0048807203,0.3127186894,-0.2132500559,0.2855618596,-0.3027739823,-0.6883821487,0.3615089357,-0.1526074857,-0.2784527242,-0.0922783613,0.1246677935,0.4610554278,-0.1010721028,-0.639030993,-0.48743999,-0.0583331324,0.1837807447,-0.0854850635,0.1019595116,0.5934659839,0.0402632505,-0.1249963343,-0.0401615165,-0.3275840878,0.6002625227,-0.2267073095,-0.0799337775,-0.0480876155,0.3807918429,0.1421984136,0.9527528882,0.5312842727,0.0835819617,0.2418905199,0.0609008297,0.4276578426,-0.1242132187,-0.2087624371,0.3594114184,0.05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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"Right, to clarify, I meant it'd be good to have it sorted on the library side and not requiring the user to figure it out. This is too complex and error-prone and if not coded correctly the bug will be intermittent which is even worse.\r\n\r\nOh I guess I wasn't clear in my message - in no way I'm proposing that we use this workaround code - I was just showing what I had to do to make it work.\r\n\r\nWe are on the same page.\r\n\r\n> The changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels number_of_processes times I believe, which is not going to make the metric computation any faster.\r\n\r\nAnd yes, this is another problem that my workaround introduces. Thank you for pointing it out, @sgugger \r\n","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":139,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n Right, to clarify, I meant it'd be good to have it sorted on the library side and not requiring the user to figure it out. This is too complex and error-prone and if not coded correctly the bug will be intermittent which is even worse.\r\n\r\nOh I guess I wasn't clear in my message - in no way I'm proposing that we use this workaround code - I was just showing what I had to do to make it work.\r\n\r\nWe are on the same page.\r\n\r\n> The changes you are proposing Stas are making the code less readable and also concatenate all the predictions and labels number_of_processes times I believe, which is not going to make the metric computation any faster.\r\n\r\nAnd yes, this is another problem that my workaround introduces. Thank you for pointing it out, @sgugger \r\n","embeddings":[0.0017203506,0.0483337678,0.0741655752,0.0518631414,0.0032177684,-0.0890675113,0.2733488679,0.3028243482,0.2559508085,0.157837823,0.0953509733,0.1828419864,-0.3078093231,0.1192955226,0.0508190766,-0.0088097211,-0.0289928019,-0.1110823229,-0.2872489989,-0.0546385124,-0.2833441198,0.574783206,-0.1937366277,0.0275069624,-0.4175572991,0.180085063,-0.0967399552,0.0959717706,-0.0746753663,-0.6333745122,0.3431346118,0.1919608712,0.0437812433,0.4339032471,-0.00011918,-0.153466478,0.2158040702,-0.1456783116,-0.0593994819,-0.1108042374,0.0721555278,-0.3260098696,0.472986728,-0.146902591,-0.013560731,-0.0872117355,0.0065102926,-0.4241975546,0.3669990599,0.0343662463,0.1324311495,0.2945926785,-0.0216743201,-0.292167455,0.071168609,-0.148108542,0.0353575274,0.3607213795,-0.1339002997,0.0896664858,-0.2697998285,0.266195178,0.2188452482,-0.1134704798,0.3259405792,0.0134763289,-0.0287550781,-0.0886272714,-0.1024975181,0.2506771386,0.2891428173,-0.1953328699,-0.2615005672,0.0619682595,-0.2091754377,-0.379871279,0.1182642654,-0.1241420805,-0.0386012234,0.0183159318,0.0663574338,-0.1046716347,-0.0890833363,-0.0081161847,-0.0986792445,0.0158464666,-0.1301837862,0.0467522182,0.018719878,0.1890254319,-0.3581730425,0.4645082653,0.0015239388,0.1163826883,-0.4526612163,0.0794150531,0.2340442836,0.0034457755,0.1275369823,0.4340135157,0.0486906394,0.0966068804,0.4923918843,0.0736068636,-0.1517188698,0.5450241566,0.1368220001,-0.0942575857,0.2153577954,0.2788958251,-0.0592239425,-0.3349413276,0.2765874863,-0.2964963317,0.3546170294,0.1382145137,0.0487446971,-0.5286619663,0.0650700331,0.1770535409,-0.1106246486,-0.2184835076,0.1539464444,0.3556601703,0.0642174557,0.0728146583,0.3231688142,0.2419796586,-0.2418756336,0.0730488822,-0.3921151161,-0.0962854847,-0.156184569,0.3900668323,0.042701792,-0.067355752,0.5413695574,-0.2072089463,0.3952532709,-0.0542634018,0.2074732333,0.2733632624,0.0856185108,0.2450954765,-0.097115308,0.1106680036,0.2532865107,0.0348394178,-0.2827269435,-0.3384743333,-0.163116917,-0.5935837626,0.0358846262,0.0273312069,-0.4786864817,0.1723166704,0.3216293454,0.3057265282,-0.1627590805,-0.1374487877,0.127164498,0.0738493204,-0.1078673005,-0.0605453402,0.5470593572,0.6318542957,-0.3070908487,-0.3632516861,0.0832097679,-0.0528882816,0.0849456191,0.1514370143,0.0182663072,0.1231782883,-0.2710851729,0.066081062,0.3751960099,-0.2643640339,-0.4211470485,-0.095526062,-0.3790399432,-0.1714232266,0.1483892649,0.0196404122,0.2099822611,0.1557330191,0.0462164879,-0.2413901687,0.2183355242,-0.2098482847,-0.4395233095,-0.348697722,0.018681718,-0.0433584191,0.2190101296,0.0419359468,0.0363063253,0.1706880331,0.1256624311,0.0660397112,0.1798494458,0.1231299415,0.3237704933,-0.0355295874,0.2978153229,0.0444417521,-0.2541936636,0.1762868762,-0.3174492717,0.213624835,0.1256953478,-0.1005328298,-0.2092880607,-0.1439248621,-0.0070462106,-0.4147659838,0.0263381079,-0.0470577665,0.270124197,0.1364537627,-0.1191124693,0.1284606904,0.107264854,0.2114131749,-0.4049317241,0.0130384667,-0.0703825802,-0.31916219,0.0298931058,0.1800133735,0.032949958,0.1019104421,-0.1698641926,0.3908302188,-0.1540256292,0.2272182852,0.3048533797,0.2851654887,-0.1629730016,-0.3259815872,0.0220399052,0.1105295494,-0.0162725355,0.0648799911,-0.202249378,0.4049359858,0.0456023328,0.1391701996,-0.2539230287,0.2988235056,-0.0855141878,-0.0431870483,-0.596321106,-0.0990096331,0.3305991292,-0.3289653659,0.2105221152,-0.1463254839,-0.0314475074,0.0823649466,0.2473679781,0.123350367,0.0772458389,0.0978807807,-0.1947132051,-0.085629411,0.0829138905,-0.1318768561,0.304479152,0.2182020247,0.1607309878,0.1080423743,0.125892967,-0.1936116517,0.1297408491,0.0885624886,-0.1993184537,-0.0519842841,0.2548471093,0.153946653,-0.2797272503,-0.0694240481,-0.4146265388,0.0284549408,-0.3391500115,0.0933108181,-0.1681764126,0.2760876417,0.0427147858,0.0185734294,-0.2692074776,-0.2986362875,0.2447029054,-0.2117600143,-0.1059977636,0.3040297925,-0.3279455006,0.6078708768,0.0554264411,-0.1463516653,-0.2999764979,-0.0674372017,-0.0466689654,-0.1146391928,-0.2106843442,0.2572805583,0.2162186056,-0.113994725,0.1512454599,-0.0240464527,-0.4987678826,0.0363867991,0.0080175931,0.6357993484,0.3888975978,0.0157717094,-0.2617827058,0.0316111185,0.3991930783,-0.4438582659,-0.2224477828,0.0916183367,-0.014788514,0.2465290576,-0.3239530027,-0.4238109887,0.1243079305,-0.4012077749,0.7106443644,-0.0169693604,0.0168244522,-0.2196318805,-0.0416192003,0.215982601,-0.2111341655,0.3168085217,-0.3373783827,-0.6845728159,0.4224869013,-0.1047696993,-0.2446500212,-0.0255974308,0.0576751344,0.3486339748,-0.1622273475,-0.5894123316,-0.4763513803,-0.0748244822,0.199990958,-0.0194934551,0.061285425,0.5578100085,-0.0331993625,-0.051837597,-0.0575106777,-0.3019850254,0.6153208613,-0.205465734,0.0410426259,-0.0076326211,0.4433331192,0.1814221442,0.8166956306,0.5588751435,0.2393333018,0.2656949461,0.1434507966,0.3056056499,-0.1414476931,-0.0946223736,0.3185797036,0.1920531392,-0.0673523694,0.19471465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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"> The fact a datasets.Metric object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in datasets\r\n\r\nYes totally, this use case is supposed to be supported by `datasets`. And in this case there shouldn't be any collision between the metrics. I'm looking into it :)\r\nMy guess is that at one point the metric isn't using the right file name. It's supposed to use one with a unique uuid in order to avoid the collisions.","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":85,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n > The fact a datasets.Metric object cannot be used as a simple compute function in a multi-process environment is, in my opinion, a bug in datasets\r\n\r\nYes totally, this use case is supposed to be supported by `datasets`. And in this case there shouldn't be any collision between the metrics. I'm looking into it :)\r\nMy guess is that at one point the metric isn't using the right file name. It's supposed to use one with a unique uuid in order to avoid the collisions.","embeddings":[-0.1335230619,0.0371194929,0.0925977081,0.216525197,0.1155614927,0.0259055011,0.2892284095,0.2465747297,0.2430669516,0.1485799849,0.0223329589,0.1771802157,-0.2399053425,0.1134241223,-0.0412850939,-0.009081373,-0.0321525261,-0.0648063049,-0.2784070075,-0.0532979928,-0.3712781966,0.4944949448,-0.213693738,0.0575829558,-0.4752726257,0.0689635426,-0.198795557,0.1876269132,-0.120708622,-0.48742342,0.3815600574,0.1813322008,0.0492469296,0.648494184,-0.0001204536,-0.1653455496,0.2496981621,-0.0945594534,-0.1752892435,-0.2627056539,-0.0832363367,-0.3161787391,0.4207959473,-0.2127367109,0.1441112161,-0.0956146568,0.032539878,-0.4734930098,0.3829439878,0.0713166073,0.1092243865,0.4045968354,-0.0290133543,-0.3105271459,-0.0231044702,-0.0660713911,0.0089851022,0.3649280965,-0.1367458403,0.0707322732,-0.2014990449,0.1797533333,0.1654919833,0.0047445553,0.2488140315,0.0069307359,-0.0829743892,-0.1220625713,0.0122205429,0.3364715576,0.2785382569,-0.254947722,-0.3165333271,0.0869153515,-0.21462892,-0.4481872022,0.1836500317,-0.1002554223,0.0678673834,0.0524204895,-0.0563672669,-0.1756006628,-0.0516794063,0.0565954186,-0.1596506387,-0.1350136995,-0.2554564476,0.1798077077,0.0026671889,0.1714439839,-0.3266620338,0.2590613663,0.0486182012,0.1027816683,-0.5279275775,0.2013274133,0.173094213,-0.0289445519,0.1072831377,0.4302815199,-0.0310643651,0.1589917988,0.4029908478,0.1944817603,-0.0551076867,0.4064210057,0.1782255322,0.0014902785,0.2801542282,0.1749690473,-0.2232084125,-0.3422784209,0.1502284855,-0.3470233083,0.3246978819,0.0795400366,0.1799817979,-0.3473767638,-0.0161977392,0.1810603887,-0.1118371859,-0.1612889022,0.0996878743,0.2822615504,0.186663121,-0.0516057201,0.2157453597,0.4055103362,-0.1794903576,0.0225586556,-0.420535773,-0.1213239208,-0.1182068884,0.4025931954,0.0458073281,-0.1762454361,0.5597066879,-0.176439777,0.3955861926,-0.149099499,0.3421090245,0.13213934,0.0209955033,0.3017513156,0.0507131368,0.2459193766,0.2705455124,-0.0035898776,-0.1020763442,-0.3254132569,-0.1897502989,-0.5088412762,0.0911111012,-0.0049069198,-0.451523304,0.2914340496,0.2894193232,0.2298967391,-0.072845839,-0.2428895235,0.0437052138,0.0391706675,-0.2443353534,-0.1741629839,0.5107157826,0.671084702,-0.3306004107,-0.3469324112,-0.077282019,-0.0398799777,-0.0461158194,0.2499621809,-0.0406612679,0.2462102622,-0.2826662362,0.1334010065,0.2764788866,-0.3176396489,-0.4375066161,-0.0121191032,-0.36036852,-0.1362518817,0.0509688668,-0.0041754171,0.1422252357,0.1615346223,0.0578495823,-0.1849369705,0.1697222888,-0.3091491461,-0.3021737039,-0.2162054777,0.0122868987,-0.0623264462,0.3494416773,0.0934797451,0.0866662934,0.053355027,0.0463506356,0.0644136518,0.2311803699,0.2167264968,0.2976898849,-0.0360914208,0.30195117,-0.0816925466,-0.2597101331,0.3623234034,-0.163040027,0.0962197557,0.102365531,0.026928924,-0.175643295,-0.0892352238,-0.1704858094,-0.4365580082,0.0410097279,-0.0181949623,0.2263961136,0.1631458849,-0.1785077155,0.2397717983,0.1039646044,0.1861555874,-0.4449467361,0.0735413954,-0.0304206479,-0.2937315404,-0.0068508256,0.1123702824,0.0569214076,0.0660690591,-0.197527796,0.4633436799,-0.0460408553,0.261434406,0.4305955172,0.1458352953,-0.1093317121,-0.3408472538,-0.0262473654,-0.00136146,0.0535816252,-0.0572734661,-0.2175344676,0.3621328175,0.0230479632,0.1479338855,-0.2466674149,0.3745265603,-0.0810135901,-0.0561903939,-0.5400524139,-0.0346522927,0.3048755229,-0.293292582,0.3406456709,-0.1490434855,-0.0125545645,-0.0973657221,0.2400292605,0.1414795667,0.1339054704,0.0312433224,-0.3177606463,-0.0283261798,0.0000272399,-0.0193968154,0.5301395059,0.1484722495,0.2678799033,0.1348848641,0.0870616436,-0.162880674,0.0027407247,0.0954414681,0.005738534,0.0730264336,0.2060080916,0.1222143024,-0.2972860932,-0.0545394905,-0.2019452006,0.0336649604,-0.3863704801,0.1017447934,-0.247481674,0.4286676049,0.1342421174,0.109720774,-0.220001623,-0.2977715731,0.1170777902,-0.1271331459,-0.0012805379,0.3124436736,-0.1825164855,0.5101054311,0.1200421602,-0.1082284674,-0.2282790542,-0.1558557898,-0.0329200365,-0.161180079,-0.1028815433,0.2642015815,0.1315704435,-0.1125555485,0.1509696394,-0.2350515574,-0.4278851748,0.0632872656,0.0025523594,0.7433798313,0.4111458063,-0.0036345341,-0.2799821198,0.1605406702,0.4311717749,-0.4666101635,-0.2351148427,0.1392445266,-0.0111166295,0.0810798034,-0.2477896959,-0.3338740468,0.067698814,-0.3375909925,0.6766374111,-0.0679348186,-0.0718750283,-0.2298683375,-0.0328819603,0.2590990067,-0.2438746393,0.2232509404,-0.3715209961,-0.6017815471,0.3536577523,-0.1585828215,-0.3658097386,-0.0293399096,0.08941558,0.4120741487,0.0269325301,-0.5248664021,-0.4424886703,-0.0557454824,0.2325976044,0.0255852006,0.0923559219,0.4470262825,-0.1033083424,0.0136129595,-0.2129089534,-0.3032337427,0.5885711908,-0.1253105402,0.0313164629,-0.0246884041,0.4544475973,0.1785299927,0.8193620443,0.5739848018,0.2213240117,0.2435995489,0.0272405241,0.41929546,0.010075666,-0.2407452911,0.3913172185,0.0835299939,-0.1487617642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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1942","title":"[experiment] missing default_experiment-1-0.arrow","comments":"I just opened #1966 to fix this :)\r\n@stas00 if have a chance feel free to try it !","body":"the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you.","comment_length":19,"text":"[experiment] missing default_experiment-1-0.arrow \n the original report was pretty bad and incomplete - my apologies!\r\n\r\nPlease see the complete version here: https:\/\/github.com\/huggingface\/datasets\/issues\/1942#issuecomment-786336481\r\n\r\n------------\r\n\r\nAs mentioned here https:\/\/github.com\/huggingface\/datasets\/issues\/1939 metrics don't get cached, looking at my local `~\/.cache\/huggingface\/metrics` - there are many `*.arrow.lock` files but zero metrics files.\r\n\r\nw\/o the network I get:\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow\r\n```\r\nthere is just `~\/.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow.lock`\r\n\r\nI did run the same `run_seq2seq.py` script on the instance with network and it worked just fine, but only the lock file was left behind.\r\n\r\nthis is with master.\r\n\r\nThank you. \n I just opened #1966 to fix this :)\r\n@stas00 if have a chance feel free to try it 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1941","title":"Loading of FAISS index fails for index_name = 'exact'","comments":"Works great \ud83d\udc4d I just put a minor comment on the commit, I think you meant to pass the `train_size` from the one obtained from the config.\r\n\r\nThanks for a quick response!","body":"Hi,\r\n\r\nIt looks like loading of FAISS index now fails when using index_name = 'exact'.\r\n\r\nFor example, from the RAG [model card](https:\/\/huggingface.co\/facebook\/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage).\r\n\r\nRunning `transformers==4.3.2` and datasets installed from source on latest `master` branch.\r\n\r\n```bash\r\n(venv) sergey_mkrtchyan datasets (master) $ python\r\nPython 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)\r\n[Clang 6.0 (clang-600.0.57)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n>>> tokenizer = RagTokenizer.from_pretrained(\"facebook\/rag-token-nq\")\r\n>>> retriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=True)\r\nUsing custom data configuration dummy.psgs_w100.nq.no_index-dummy=True,with_index=False\r\nReusing dataset wiki_dpr (\/Users\/sergey_mkrtchyan\/.cache\/huggingface\/datasets\/wiki_dpr\/dummy.psgs_w100.nq.no_index-dummy=True,with_index=False\/0.0.0\/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\nUsing custom data configuration dummy.psgs_w100.nq.exact-50b6cda57ff32ab4\r\nReusing dataset wiki_dpr (\/Users\/sergey_mkrtchyan\/.cache\/huggingface\/datasets\/wiki_dpr\/dummy.psgs_w100.nq.exact-50b6cda57ff32ab4\/0.0.0\/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\n  0%|                                                                                                                                                                                                                   | 0\/10 [00:00<?, ?it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/transformers\/models\/rag\/retrieval_rag.py\", line 425, in from_pretrained\r\n    return cls(\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/transformers\/models\/rag\/retrieval_rag.py\", line 387, in __init__\r\n    self.init_retrieval()\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/transformers\/models\/rag\/retrieval_rag.py\", line 458, in init_retrieval\r\n    self.index.init_index()\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/transformers\/models\/rag\/retrieval_rag.py\", line 284, in init_index\r\n    self.dataset = load_dataset(\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/builder.py\", line 734, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/utils\/py_utils.py\", line 195, in map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/builder.py\", line 769, in _build_single_dataset\r\n    post_processed = self._post_process(ds, resources_paths)\r\n  File \"\/Users\/sergey_mkrtchyan\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_dpr\/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb\/wiki_dpr.py\", line 205, in _post_process\r\n    dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/arrow_dataset.py\", line 2516, in add_faiss_index\r\n    super().add_faiss_index(\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/search.py\", line 416, in add_faiss_index\r\n    faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=faiss_verbose)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/search.py\", line 281, in add_vectors\r\n    self.faiss_index.add(vecs)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/faiss\/__init__.py\", line 104, in replacement_add\r\n    self.add_c(n, swig_ptr(x))\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/faiss\/swigfaiss.py\", line 3263, in add\r\n    return _swigfaiss.IndexHNSW_add(self, n, x)\r\nRuntimeError: Error in virtual void faiss::IndexHNSW::add(faiss::Index::idx_t, const float *) at \/Users\/runner\/work\/faiss-wheels\/faiss-wheels\/faiss\/faiss\/IndexHNSW.cpp:356: Error: 'is_trained' failed\r\n>>>\r\n```\r\n\r\nThe issue seems to be related to the scalar quantization in faiss added in this commit: 8c5220307c33f00e01c3bf7b8. Reverting it fixes the issue.\r\n\r\n\r\n","comment_length":32,"text":"Loading of FAISS index fails for index_name = 'exact' \n Hi,\r\n\r\nIt looks like loading of FAISS index now fails when using index_name = 'exact'.\r\n\r\nFor example, from the RAG [model card](https:\/\/huggingface.co\/facebook\/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage).\r\n\r\nRunning `transformers==4.3.2` and datasets installed from source on latest `master` branch.\r\n\r\n```bash\r\n(venv) sergey_mkrtchyan datasets (master) $ python\r\nPython 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)\r\n[Clang 6.0 (clang-600.0.57)] on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n>>> tokenizer = RagTokenizer.from_pretrained(\"facebook\/rag-token-nq\")\r\n>>> retriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=True)\r\nUsing custom data configuration dummy.psgs_w100.nq.no_index-dummy=True,with_index=False\r\nReusing dataset wiki_dpr (\/Users\/sergey_mkrtchyan\/.cache\/huggingface\/datasets\/wiki_dpr\/dummy.psgs_w100.nq.no_index-dummy=True,with_index=False\/0.0.0\/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\nUsing custom data configuration dummy.psgs_w100.nq.exact-50b6cda57ff32ab4\r\nReusing dataset wiki_dpr (\/Users\/sergey_mkrtchyan\/.cache\/huggingface\/datasets\/wiki_dpr\/dummy.psgs_w100.nq.exact-50b6cda57ff32ab4\/0.0.0\/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)\r\n  0%|                                                                                                                                                                                                                   | 0\/10 [00:00<?, ?it\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/transformers\/models\/rag\/retrieval_rag.py\", line 425, in from_pretrained\r\n    return cls(\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/transformers\/models\/rag\/retrieval_rag.py\", line 387, in __init__\r\n    self.init_retrieval()\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/transformers\/models\/rag\/retrieval_rag.py\", line 458, in init_retrieval\r\n    self.index.init_index()\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/transformers\/models\/rag\/retrieval_rag.py\", line 284, in init_index\r\n    self.dataset = load_dataset(\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/load.py\", line 750, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/builder.py\", line 734, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/utils\/py_utils.py\", line 195, in map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/builder.py\", line 769, in _build_single_dataset\r\n    post_processed = self._post_process(ds, resources_paths)\r\n  File \"\/Users\/sergey_mkrtchyan\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_dpr\/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb\/wiki_dpr.py\", line 205, in _post_process\r\n    dataset.add_faiss_index(\"embeddings\", custom_index=index)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/arrow_dataset.py\", line 2516, in add_faiss_index\r\n    super().add_faiss_index(\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/search.py\", line 416, in add_faiss_index\r\n    faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=faiss_verbose)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/huggingface\/datasets\/src\/datasets\/search.py\", line 281, in add_vectors\r\n    self.faiss_index.add(vecs)\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/faiss\/__init__.py\", line 104, in replacement_add\r\n    self.add_c(n, swig_ptr(x))\r\n  File \"\/Users\/sergey_mkrtchyan\/workspace\/cformers\/venv\/lib\/python3.8\/site-packages\/faiss\/swigfaiss.py\", line 3263, in add\r\n    return _swigfaiss.IndexHNSW_add(self, n, x)\r\nRuntimeError: Error in virtual void faiss::IndexHNSW::add(faiss::Index::idx_t, const float *) at \/Users\/runner\/work\/faiss-wheels\/faiss-wheels\/faiss\/faiss\/IndexHNSW.cpp:356: Error: 'is_trained' failed\r\n>>>\r\n```\r\n\r\nThe issue seems to be related to the scalar quantization in faiss added in this commit: 8c5220307c33f00e01c3bf7b8. Reverting it fixes the issue.\r\n\r\n\r\n \n Works great \ud83d\udc4d I just put a minor comment on the commit, I think you meant to pass the `train_size` from the one obtained from the config.\r\n\r\nThanks for a quick response!","embeddings":[-0.0889257565,-0.1103842705,0.0109983413,0.0540016554,0.4101668894,-0.0569174886,0.2968126535,0.2508972585,0.2696110904,0.1997553408,-0.2217194587,0.139844045,0.136316672,-0.1376480311,-0.1762496382,0.2843903303,0.2129584849,0.123024039,-0.0870195404,-0.1029342785,-0.2702940702,0.2029289007,-0.2149687111,0.2133810818,-0.3408923149,0.4708951414,-0.1025647521,0.1511468738,-0.1406457871,-0.5798454881,0.323237896,-0.0542232022,0.2287583798,0.279283613,-0.0001177264,0.1951242834,0.5912719369,-0.0638967305,-0.3142662942,0.0912119225,0.1551293135,0.2524677217,0.188416183,-0.1187293231,-0.2891574204,-0.2747856379,0.169850722,-0.0614712201,0.1880251914,0.209688589,0.1545539945,0.1129425019,0.3915553093,-0.2391127646,0.3550436497,-0.4309919775,-0.2338058501,0.4001970589,0.0305760968,-0.0565604307,-0.0222198851,0.0928998664,0.1818179339,-0.0270642173,-0.0330323465,-0.1413348466,0.3804668784,0.0840317309,0.2681783438,0.1212422028,0.2935319543,-0.0311049707,-0.4443510175,0.0353066139,0.1401630938,0.0986513719,0.2189644426,-0.4058190286,0.0952564403,0.2862180769,0.0806031749,0.0194833223,0.0560727865,0.2368140668,-0.0019370971,0.390316844,-0.1297442168,0.0979245082,0.0764907449,-0.2108365446,-0.2144456953,0.0950188935,-0.2728638649,-0.0225361474,-0.6444880366,0.1160787269,0.0512801819,-0.2159349918,0.0352995507,0.0424009375,-0.2709305882,0.2906615436,0.1008517146,0.0380018242,-0.0305454731,0.0233890675,-0.0013722524,0.0068335258,0.1125111952,0.0044880873,-0.1529048979,-0.0514266565,-0.2755156159,-0.0540647916,-0.6907650828,-0.3722564876,-0.0145713603,-0.2811401784,-0.4073481262,-0.0867943242,-0.2471618354,0.1289564669,0.2523566484,0.7027882934,0.0485681109,-0.0792193636,0.0272349175,0.1643967777,-0.135249719,0.1639370322,-0.2256950885,-0.3065262437,-0.2085856944,-0.0223727431,0.3425043821,-0.2723258734,0.2192728817,0.0867487192,-0.0316480026,0.0625711307,-0.2385466397,-0.0671575963,0.1852024198,0.319519192,-0.2793678939,0.1558532715,0.2347674817,0.0704580098,0.0292036925,-0.0564666688,-0.1806442291,-0.3224920928,0.11471311,0.172033444,-0.0485996492,-0.1230743751,0.3416301906,0.0278548207,-0.1601530164,0.1821374446,-0.0173215792,-0.2101818025,-0.2364784926,0.0118759815,0.4402866662,0.3116513193,-0.1029743105,-0.2342440337,-0.0569787994,0.0164476484,-0.0192490257,0.1451807171,-0.1825048625,0.1064566746,-0.3530344367,0.0480398051,0.2476074845,-0.2162941098,-0.0942614898,0.0434443876,0.0055873683,0.048692733,0.0026495929,-0.2383570224,-0.0799198523,0.1082673445,0.1997901797,0.1565131098,0.2076743692,-0.1675224453,-0.2941005528,-0.454654783,0.0983941481,0.3206001818,0.0272102952,0.0472556986,0.0218186025,-0.1154953465,0.3780491352,-0.159472838,-0.0510468259,0.1109238639,0.3147558868,0.2252455205,0.5496624708,-0.1197111905,-0.0283096433,0.4137910604,-0.1743458658,0.049115818,0.0225034487,0.0811982453,-0.3368809223,0.062507309,-0.0826765969,-0.0540839732,0.050944034,0.1031437144,0.1682233065,-0.2200391442,-0.2104469389,0.2421289533,-0.3746232688,0.2127416283,-0.2988705635,0.1358976513,-0.1310333312,-0.3043181598,-0.0259588826,0.1280353069,0.3351846933,-0.2089738995,-0.1438956708,0.3537579477,0.1275344789,-0.5363496542,0.3885994852,-0.0711898431,-0.2205817252,-0.2585739493,-0.0819249153,0.4026681781,0.0992996469,0.0287675895,-0.1217490137,0.4608269036,-0.0165670477,0.2404149622,-0.0125429183,-0.13944453,0.3590926826,-0.1018888429,0.0247064345,-0.2612526119,0.1761949062,0.1161095127,0.1975640804,-0.2353008091,0.0291063692,-0.1079016179,-0.1206863746,0.1251834333,-0.0890994966,0.1797404587,-0.1933805048,-0.018068783,-0.1605866998,-0.4791119993,0.2939761579,0.2113705724,-0.2033737153,-0.2445664257,-0.0433400683,-0.2511078119,0.4474205375,0.0702858269,-0.1768762469,0.1726860702,0.175490424,-0.2408617139,-0.4516462386,-0.1435257047,0.1325661689,-0.0127125662,-0.3138774931,0.168381229,-0.0572857261,0.0419997945,-0.0414767638,-0.2650158107,-0.3554300368,-0.1907569021,0.1494047791,0.0260543432,-0.0674646646,0.4098317027,-0.1587007791,-0.0234234408,0.1372434646,-0.2612144947,-0.3281145096,-0.1754921675,-0.3357377648,0.0243652146,-0.1582147181,-0.1349006593,-0.0027408276,0.0413393416,-0.1111522913,-0.4048219919,-0.2892029285,0.2777305543,-0.0530993342,0.1911062896,0.0515837409,-0.1222086698,-0.0527465828,-0.0224399511,0.3487251997,-0.2121926695,0.0221102089,0.107708171,-0.0921973512,-0.0403692089,-0.1590484232,-0.0769054443,0.0546558984,-0.2552226484,0.2541982234,-0.098238118,0.0092389965,0.1670096666,0.095018357,0.1037694886,-0.2616454363,0.0031433697,-0.3037983179,-0.0857035294,0.3209959269,0.0239907205,-0.272444427,-0.0610823706,0.0665887445,-0.2728331387,0.2826738656,-0.0803059042,-0.2875355184,-0.0009769005,0.2117819935,0.1499058455,0.1871914864,-0.0716412887,-0.2176822573,-0.1821729988,-0.0779760256,-0.0572831295,0.3550330698,-0.1895646453,0.2287379354,0.0150354514,-0.0614278838,-0.3030913472,0.6028997898,0.0793468505,-0.2462953031,0.4174587429,-0.0564926416,0.2219815701,-0.1502774358,-0.170541361,0.23742261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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1940","title":"Side effect when filtering data due to `does_function_return_dict` call in `Dataset.map()`","comments":"Thanks for the report !\r\n\r\nCurrently we don't have a way to let the user easily disable this behavior.\r\nHowever I agree that we should support stateful processing functions, ideally by removing `does_function_return_dict`.\r\n\r\nWe needed this function in order to know whether the `map` functions needs to write data or not. if `does_function_return_dict` returns False then we don't write anything.\r\n\r\nInstead of checking the output of the processing function outside of the for loop that iterates through the dataset to process it, we can check the output of the first processed example and at that point decide if we need to write data or not.\r\n\r\nTherefore it's definitely possible to fix this unwanted behavior, any contribution going into this direction is welcome :)","body":"Hi there!\r\n\r\nIn my codebase I have a function to filter rows in a dataset, selecting only a certain number of examples per class. The function passes a extra argument to maintain a counter of the number of dataset rows\/examples already selected per each class, which are the ones I want to keep in the end:\r\n\r\n```python\r\n      def fill_train_examples_per_class(example, per_class_limit: int, counter: collections.Counter):\r\n          label = int(example['label'])\r\n          current_counter = counter.get(label, 0)\r\n          if current_counter < per_class_limit:\r\n              counter[label] = current_counter + 1\r\n              return True\r\n          return False\r\n```\r\n\r\nAt some point I invoke it through the `Dataset.filter()` method in the `arrow_dataset.py` module like this:\r\n\r\n```python\r\n...\r\nkwargs = {\"per_class_limit\": train_examples_per_class_limit, \"counter\": Counter()}\r\ndatasets['train'] = datasets['train'].filter(fill_train_examples_per_class,  num_proc=1, fn_kwargs=kwargs)\r\n...\r\n```\r\n\r\nThe problem is that, passing a stateful container (the counter,) provokes a side effect in the new filtered dataset obtained. This is due to the fact that at some point in `filter()`, the `map()`'s function `does_function_return_dict` is invoked in  line [1290](https:\/\/github.com\/huggingface\/datasets\/blob\/96578adface7e4bc1f3e8bafbac920d72ca1ca60\/src\/datasets\/arrow_dataset.py#L1290). \r\n\r\nWhen this occurs, the state of the counter is initially modified by the effects of the function call on the 1 or 2 rows selected in lines 1288 and 1289 of the same file (which are marked as `test_inputs` & `test_indices` respectively in lines 1288 and 1289. This happens out of the control of the user (which for example can't reset the state of the counter before continuing the execution,) provoking in the end an undesired side effect in the results obtained. \r\n\r\nIn my case, the resulting dataset -despite of the counter results are ok- lacks an instance of the classes 0 and 1 (which happen to be the classes of the first two examples of my dataset.) The rest of the classes I have in my dataset, contain the right number of examples as they were not affected by the effects of `does_function_return_dict` call.\r\n\r\nI've debugged my code extensively and made a workaround myself hardcoding the necessary stuff (basically putting `update_data=True` in line 1290,) and then I obtain the results I expected without the side effect.\r\n\r\nIs there a way to avoid that call to `does_function_return_dict` in map()'s line 1290 ? (e.g. extracting the required information that `does_function_return_dict` returns without making the testing calls to the user function on dataset rows 0 & 1) \r\n\r\nThanks in advance,\r\n\r\nFrancisco Perez-Sorrosal\r\n\r\n","comment_length":123,"text":"Side effect when filtering data due to `does_function_return_dict` call in `Dataset.map()` \n Hi there!\r\n\r\nIn my codebase I have a function to filter rows in a dataset, selecting only a certain number of examples per class. The function passes a extra argument to maintain a counter of the number of dataset rows\/examples already selected per each class, which are the ones I want to keep in the end:\r\n\r\n```python\r\n      def fill_train_examples_per_class(example, per_class_limit: int, counter: collections.Counter):\r\n          label = int(example['label'])\r\n          current_counter = counter.get(label, 0)\r\n          if current_counter < per_class_limit:\r\n              counter[label] = current_counter + 1\r\n              return True\r\n          return False\r\n```\r\n\r\nAt some point I invoke it through the `Dataset.filter()` method in the `arrow_dataset.py` module like this:\r\n\r\n```python\r\n...\r\nkwargs = {\"per_class_limit\": train_examples_per_class_limit, \"counter\": Counter()}\r\ndatasets['train'] = datasets['train'].filter(fill_train_examples_per_class,  num_proc=1, fn_kwargs=kwargs)\r\n...\r\n```\r\n\r\nThe problem is that, passing a stateful container (the counter,) provokes a side effect in the new filtered dataset obtained. This is due to the fact that at some point in `filter()`, the `map()`'s function `does_function_return_dict` is invoked in  line [1290](https:\/\/github.com\/huggingface\/datasets\/blob\/96578adface7e4bc1f3e8bafbac920d72ca1ca60\/src\/datasets\/arrow_dataset.py#L1290). \r\n\r\nWhen this occurs, the state of the counter is initially modified by the effects of the function call on the 1 or 2 rows selected in lines 1288 and 1289 of the same file (which are marked as `test_inputs` & `test_indices` respectively in lines 1288 and 1289. This happens out of the control of the user (which for example can't reset the state of the counter before continuing the execution,) provoking in the end an undesired side effect in the results obtained. \r\n\r\nIn my case, the resulting dataset -despite of the counter results are ok- lacks an instance of the classes 0 and 1 (which happen to be the classes of the first two examples of my dataset.) The rest of the classes I have in my dataset, contain the right number of examples as they were not affected by the effects of `does_function_return_dict` call.\r\n\r\nI've debugged my code extensively and made a workaround myself hardcoding the necessary stuff (basically putting `update_data=True` in line 1290,) and then I obtain the results I expected without the side effect.\r\n\r\nIs there a way to avoid that call to `does_function_return_dict` in map()'s line 1290 ? (e.g. extracting the required information that `does_function_return_dict` returns without making the testing calls to the user function on dataset rows 0 & 1) \r\n\r\nThanks in advance,\r\n\r\nFrancisco Perez-Sorrosal\r\n\r\n \n Thanks for the report !\r\n\r\nCurrently we don't have a way to let the user easily disable this behavior.\r\nHowever I agree that we should support stateful processing functions, ideally by removing `does_function_return_dict`.\r\n\r\nWe needed this function in order to know whether the `map` functions needs to write data or not. if `does_function_return_dict` returns False then we don't write anything.\r\n\r\nInstead of checking the output of the processing function outside of the for loop that iterates through the dataset to process it, we can check the output of the first processed example and at that point decide if we need to write data or not.\r\n\r\nTherefore it's definitely possible to fix this unwanted behavior, any contribution going into this direction is welcome :)","embeddings":[-0.4214114249,0.0289576147,-0.1575038433,0.0184577908,-0.084425889,-0.3201851845,0.2051187158,0.1946579814,0.2660327554,0.1206628382,0.2073837817,0.5199099183,-0.0788978338,0.0967270359,0.0301455557,0.2262496203,0.1668142974,-0.032501027,-0.1896086782,-0.1237709373,-0.2070304006,-0.0553835072,-0.2351946831,0.0294677727,0.1121999249,-0.2066931427,0.2833378315,0.0864987075,0.1280570924,-0.233986333,0.379776001,0.2778170407,-0.3832846582,0.4839723408,-0.0001190907,0.0343095772,0.1299140453,-0.0802711844,-0.2356485426,-0.4382512271,-0.2481366247,0.0078954305,0.1348444819,-0.1925271153,0.2354865968,-0.1303691268,-0.4192903936,-0.3500099182,0.5882077217,0.2834120989,0.1079882756,0.105306901,-0.343233794,0.1123894006,0.4360818267,0.3156699836,0.1410355121,0.3769839108,0.2226104736,-0.4172928631,-0.089796707,0.6554317474,-0.419348985,0.1199867725,-0.1365879774,0.1172127947,0.1939310879,0.0126645528,0.3233865798,0.1725642383,0.1055694222,-0.3248958588,-0.1459566951,-0.420864135,-0.1007794887,0.0005761604,0.0750974044,-0.2886882424,-0.2021785378,-0.1206648573,-0.1208331883,-0.012387082,-0.0200270172,0.1681484133,-0.2169067562,0.1855434477,0.0811168924,0.2631781995,0.0817807019,-0.0514533445,0.2090521157,-0.3612852395,0.2060837448,0.2147341669,0.1196141541,0.0732956082,0.3768185079,-0.4789855778,0.1241235957,-0.13665995,0.1146184802,0.1747404039,-0.2579136491,0.2301839143,0.4253011346,0.085000217,-0.1979258806,0.6640278101,0.1729907244,-0.1068491042,-0.0037359183,0.0037666485,0.2818769813,0.0269299001,0.2999613285,-0.1395051032,0.1103360206,-0.1578751206,-0.2358426154,0.4175386727,-0.3590551019,0.2780917585,0.0907847881,0.0966719389,0.3004828691,0.4036149085,0.1682981998,-0.1376380175,0.003388444,0.5379956365,-0.1066150218,-0.3244780898,-0.2710621357,-0.0770937875,-0.011990536,0.0786589086,0.0169853605,0.1104542688,-0.0109124994,-0.0541719161,0.0757818967,-0.2363334149,0.7913022637,0.4118511677,-0.3480505049,0.1737432331,0.2374269217,-0.2243672758,-0.1260404438,0.2483870536,-0.0546916015,-0.1973662376,0.1852192581,0.1147135645,0.0059861485,0.3745835125,-0.0160887148,0.1991495788,0.4185599089,-0.471998781,0.3922097385,-0.5384453535,-0.3335294724,-0.2172567099,0.1258204728,0.525978744,-0.819714427,-0.1214056835,-0.2428824008,-0.1178346276,0.0761058852,0.1740315706,-0.2996686697,-0.0011380088,-0.2255411148,0.1341030598,0.3071460724,-0.4831515849,-0.705484271,0.0982058793,-0.2767548561,0.499204278,-0.1460386366,0.0213056188,0.3568475246,-0.008165176,0.3267332315,0.1796566099,-0.0669032261,0.1762675196,0.0203271583,0.1293668896,0.3243874311,-0.0954314321,-0.0437130406,-0.077088058,-0.074971199,-0.4875876904,0.281059891,0.0821083933,0.1924644411,-0.3172644973,0.4052571058,0.1898687333,0.0079568848,-0.2883609235,-0.2915593386,0.0308367405,0.0936341137,-0.24900195,-0.2050819397,-0.1570568383,-0.1389681697,0.3914354146,-0.0558801778,0.1572161317,0.1144860759,-0.0418155044,0.00553917,0.0628047958,0.0852909014,0.080448173,-0.4057528675,-0.0257785544,0.0938627049,0.2385653853,0.3390497267,-0.2808124125,-0.2351316363,0.1347765326,0.0226554684,-0.0478546508,-0.2866383493,0.219219178,0.5091303587,-0.0345370322,-0.2714932263,0.0800034404,-0.1027276367,0.4487369657,-0.0084447358,0.3058345914,0.1903862655,-0.2705367208,-0.0997638702,0.4954223633,0.0765313879,0.4916887283,-0.1645125449,0.136452347,0.2814358473,0.2763494253,-0.3366055191,-0.5444409847,-0.2632301152,-0.0805498734,0.1870295554,-0.1061945185,-0.155970186,-0.0380207151,0.0843561813,-0.0368897617,0.1383838505,0.103854686,-0.0823239088,-0.000345651,0.4746937752,0.4552512467,0.5759060979,0.0555185191,-0.1408450902,0.0276647117,-0.0354824476,-0.0613747053,0.1117442772,0.2467335612,0.1628268063,0.3262067139,0.3910287619,-0.1081223041,-0.2600623667,-0.1121191531,-0.1188485101,-0.1379218549,-0.2318265587,0.3826011419,-0.1168807521,-0.2817385197,-0.2118333727,-0.1495019346,0.051916115,-0.1294428259,-0.0069106244,0.4019418955,-0.3745889664,0.0674525797,-0.0789706782,0.3998449147,0.1533491313,-0.4363389611,0.1452000886,-0.3576052785,-0.1495454907,-0.0052298307,0.0020299521,0.0059776395,0.4475282729,0.282324791,-0.210486576,0.0630706325,-0.1092976108,0.1017176583,-0.007605616,-0.0080981012,0.3318776488,0.0187597405,-0.2166742086,0.0701481327,0.0484458171,0.0242532827,-0.1498593092,-0.1106217429,0.0191021357,0.2351116985,-0.2889119983,0.1924333423,-0.1723965108,-0.1195878163,0.1122066975,-0.1309978515,0.2291354388,0.1243820488,0.2222427875,0.0680092126,-0.0073051597,-0.2129320949,-0.4279069602,-0.0134776644,-0.1785435081,-0.144994691,0.0711149648,-0.2463677824,-0.2123691887,0.14317213,0.597771585,-0.5670164824,-0.198012501,0.1690313816,0.280459553,-0.1854302883,0.0043560239,0.3448859155,0.1838807613,-0.0802197531,-0.3217480481,-0.0644872263,0.1970244944,0.3125908077,0.0231229048,0.0797691122,0.1033611,0.0153850578,0.6843212247,0.2735551894,-0.1441142112,-0.0708047748,0.2465561479,0.2138903141,-0.3259014189,-0.0774517357,-0.34776932,-0.233711198,-0.1915657371,-0.1238080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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1939","title":"[firewalled env] OFFLINE mode","comments":"Thanks for reporting and for all the details and suggestions.\r\n\r\nI'm totally in favor of having a HF_DATASETS_OFFLINE env variable to disable manually all the connection checks, remove retries etc.\r\n\r\nMoreover you may know that the use case that you are mentioning is already supported from `datasets` 1.3.0, i.e. you already can:\r\n- first load datasets and metrics from an instance with internet connection\r\n- then be able to reload datasets and metrics from another instance without connection (as long as the filesystem is shared)\r\n\r\nThis is already implemented, but currently it only works if the requests return a `ConnectionError` (or any error actually). Not sure why it would hang instead of returning an error.\r\n\r\nMaybe this is just a issue with the timeout value being not set or too high ?\r\nIs there a way I can have access to one of the instances on which there's this issue (we can discuss this offline) ?\r\n","body":"This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks.","comment_length":156,"text":"[firewalled env] OFFLINE mode \n This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks. \n Thanks for reporting and for all the details and suggestions.\r\n\r\nI'm totally in favor of having a HF_DATASETS_OFFLINE env variable to disable manually all the connection checks, remove retries etc.\r\n\r\nMoreover you may know that the use case that you are mentioning is already supported from `datasets` 1.3.0, i.e. you already can:\r\n- first load datasets and metrics from an instance with internet connection\r\n- then be able to reload datasets and metrics from another instance without connection (as long as the filesystem is shared)\r\n\r\nThis is already implemented, but currently it only works if the requests return a `ConnectionError` (or any error actually). Not sure why it would hang instead of returning an error.\r\n\r\nMaybe this is just a issue with the timeout value being not set or too high ?\r\nIs there a way I can have access to one of the instances on which there's this issue (we can discuss this offline) ?\r\n","embeddings":[-0.4748800695,0.1685334891,-0.0625536442,0.1050562486,0.0443113707,-0.2284921706,0.4647973776,0.0716796964,0.2545911074,0.0485309139,0.0184395313,0.0636148751,0.0845065862,0.1993038058,0.0180914346,0.1079676971,0.0199452993,-0.1096389145,-0.2528404295,0.0637163967,-0.2635913491,-0.0536728576,-0.2544517219,-0.2575809062,0.078367807,-0.5080999136,-0.2168778479,-0.0105694095,0.0096480669,-0.2275560498,0.3089389205,0.2612626255,0.2518863976,0.1530115753,-0.0001084152,-0.0537566915,-0.1393844485,-0.0361674428,-0.272887677,0.0223880373,-0.3179339468,-0.3802416921,0.1650776267,-0.4613099992,-0.171355173,-0.174725309,0.1464560628,-0.6594020128,0.5154665709,-0.0763156116,0.0945165008,0.0003517794,-0.3278796375,-0.0994149074,-0.3226190805,-0.1980506182,-0.1814403385,0.1710348725,0.0883782059,-0.2174991071,-0.0455865599,0.3952884078,-0.3122881055,0.1565717608,0.3260605037,0.0182255022,-0.338113457,-0.1188222617,-0.1281385869,0.3376224041,0.1812187731,-0.2216326147,-0.4262379706,-0.4555667341,-0.0790792406,-0.370331347,0.3025760651,0.0889306292,-0.1169174388,0.2809565067,-0.3825724125,-0.2138668299,-0.1247217432,-0.0012391666,-0.0308274534,0.3738206923,0.0736688673,0.1736761779,-0.0692528114,0.1060303599,0.0134036681,-0.3340767622,0.2569811642,0.0270416345,-0.5665541291,0.0265186541,0.1224480569,-0.0067842589,0.0168159846,0.4544952512,-0.1779251099,-0.0429254211,0.4983155131,0.195036903,0.0398950391,0.2975893617,0.1519620121,-0.1790611893,0.3776472807,0.1602634937,-0.0568716712,-0.1345084161,-0.1136473343,-0.0706924796,0.1811163574,0.0640624613,0.1197685003,-0.1377031356,-0.1166463196,0.046965342,-0.0323215127,0.2401375026,0.3313703537,0.3093738258,-0.0555984639,-0.2358305752,0.2333134562,0.1667176485,-0.1382888556,-0.06039121,0.072590515,-0.0984998196,-0.1313179582,0.082668893,0.3286071122,0.2563716769,0.2747864425,-0.1755116731,0.1528570056,0.0238279738,0.5519109368,-0.2458668649,0.1138633564,0.3021980822,0.1545313895,-0.2306014448,0.0980398506,0.2813174427,-0.0375363044,0.0506099239,0.0799426436,-0.3858196735,0.4044837654,0.1439911574,-0.1975332648,-0.0476032272,0.1782198101,0.0742536411,-0.0721478388,-0.4519341588,0.1831779182,0.0667779595,-0.1193479002,-0.0543197319,0.4079632163,0.4936104715,-0.4202961922,0.0228122212,-0.221729517,0.019100355,-0.1197147667,0.1713059396,-0.2794617414,0.1356073618,-0.1801660359,-0.2079320103,0.2443795949,-0.3084554672,-0.3225480914,0.4538064003,-0.1916149855,0.2314610779,0.3152425885,0.1994496435,0.2296581119,-0.0338119008,0.1222665012,0.3941685557,-0.2495333552,-0.0643453002,-0.1496422589,-0.1908909678,0.2760188878,0.1059188098,0.2374101728,-0.1076447815,-0.086140126,-0.2940383255,-0.0350805186,-0.083207719,0.2003037333,-0.1944311261,0.0856611282,0.0736428425,0.0493785702,0.1411918253,-0.4604979455,0.3037669957,-0.2521933019,0.0747477561,-0.3391274512,-0.3241582215,0.2313053608,-0.0713331848,-0.1031431407,-0.3441320658,0.0740442052,0.0512403548,-0.1017183214,-0.0374327078,-0.0173988789,0.0242225807,-0.0870222002,0.1135079041,0.1581827104,-0.0672886223,0.0744352862,0.0992200971,-0.2595793903,-0.1118372455,-0.0186913777,-0.1419194341,-0.0033473987,0.507107079,0.0670183226,0.434106648,0.1801059842,0.43449682,0.100204356,0.3320184648,0.2241401672,0.0292811133,0.0667587593,-0.0281432383,-0.5865287185,0.8847305179,-0.0772907659,0.4949632585,-0.0253681988,-0.0896625817,0.2581321895,-0.070058696,-0.5748965144,-0.0896323398,-0.0292923599,0.2472509444,0.1246782467,-0.0695684403,-0.3645498455,0.1309467256,0.0615998544,0.2113251835,0.4444034696,-0.2755915225,0.2542671561,-0.0863385573,0.4632284939,0.2097619325,0.2965559363,0.2528910935,0.0414115787,0.1776760668,-0.0744988695,-0.2386737466,0.3642019629,0.1546070129,0.3154804409,0.1600142121,0.0825260282,-0.1450218856,-0.1656669974,-0.2701164484,0.1441674232,-0.2285314947,-0.052432321,0.3188200295,-0.3138054311,-0.5460381508,-0.0330595821,0.2567465603,-0.1731722355,-0.1265536398,0.232597664,0.1215340123,-0.3687728345,0.3979503214,-0.2202257961,0.4702404141,-0.0649726614,-0.4042862654,0.2372114658,-0.3736272752,0.0975892395,0.0727022961,0.2432580888,-0.090477258,0.6828110218,0.2652270794,-0.0664931238,-0.172499001,-0.2048208117,0.1804469377,0.0453945622,0.5188974738,0.1410698295,0.097885415,-0.1078779474,0.0392978527,0.266864717,-0.131492421,0.2002531737,-0.1014679745,0.0052114641,0.1161197722,-0.2464787811,-0.5637351274,-0.2847306728,-0.2684567571,0.1167265549,-0.2222549319,0.2011254579,-0.0231130943,0.1001847163,-0.050306119,0.1435732543,0.0995229259,-0.0322472528,-0.568808198,0.1139889956,-0.1637168378,0.0997769907,0.0580960028,-0.0110152299,0.3906186521,0.5869692564,-0.4705568254,-0.2902962863,0.3895433247,0.286469698,0.0516619869,-0.3373523057,0.6150392294,0.0365746021,-0.0880156383,-0.0884277374,0.3548750579,0.4860231876,-0.1864490658,-0.0539083518,0.1791349649,0.7724372149,0.202256009,0.2877366543,-0.1666050255,0.4881629646,0.2260150015,0.1102786735,0.304939419,-0.1625722647,0.0179588497,0.2034069151,-0.0815214068,-0.5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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1939","title":"[firewalled env] OFFLINE mode","comments":"I'm on master, so using all the available bells and whistles already.\r\n\r\nIf you look at the common issues - it for example tries to look up files if they appear in `_PACKAGED_DATASETS_MODULES` which it shouldn't do.\r\n\r\n--------------\r\n\r\nYes, there is a nuance to it. As I mentioned it's firewalled - that is it has a network but making any calls outside - it just hangs in:\r\n\r\n```\r\nsin_addr=inet_addr(\"xx.xx.xx.xx\")}, [28->16]) = 0\r\nclose(5)                                = 0\r\nsocket(AF_INET, SOCK_STREAM|SOCK_CLOEXEC, IPPROTO_TCP) = 5\r\nconnect(5, {sa_family=AF_INET, sin_port=htons(3128), sin_addr=inet_addr(\"yy.yy.yy.yy\")}, 16^C) = ? ERESTARTSYS (To be restarted if SA_RESTART is set)\r\n```\r\nuntil it times out.\r\n\r\nThat's why we need to be able to tell the software that there is no network to rely on even if there is one (good for testing too).\r\n\r\nSo what I'm thinking is that this is a simple matter of pre-ambling any network call wrappers with:\r\n\r\n```\r\nif HF_DATASETS_OFFLINE:\r\n    assert \"Attempting to make a network call under Offline mode\"\r\n```\r\n\r\nand then fixing up if there is anything else to fix to make it work.\r\n\r\n--------------\r\n\r\nOtherwise I think the only other problem I encountered is that we need to find a way to pre-cache metrics, for some reason it's not caching it and wanting to fetch it from online.\r\n\r\nWhich is extra strange since it already has those files in the `datasets` repo itself that is on the filesystem.\r\n\r\nThe workaround I had to do is to copy `rouge\/rouge.py` (with the parent folder) from the datasets repo to the current dir - and then it proceeded.","body":"This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks.","comment_length":257,"text":"[firewalled env] OFFLINE mode \n This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks. \n I'm on master, so using all the available bells and whistles already.\r\n\r\nIf you look at the common issues - it for example tries to look up files if they appear in `_PACKAGED_DATASETS_MODULES` which it shouldn't do.\r\n\r\n--------------\r\n\r\nYes, there is a nuance to it. As I mentioned it's firewalled - that is it has a network but making any calls outside - it just hangs in:\r\n\r\n```\r\nsin_addr=inet_addr(\"xx.xx.xx.xx\")}, [28->16]) = 0\r\nclose(5)                                = 0\r\nsocket(AF_INET, SOCK_STREAM|SOCK_CLOEXEC, IPPROTO_TCP) = 5\r\nconnect(5, {sa_family=AF_INET, sin_port=htons(3128), sin_addr=inet_addr(\"yy.yy.yy.yy\")}, 16^C) = ? ERESTARTSYS (To be restarted if SA_RESTART is set)\r\n```\r\nuntil it times out.\r\n\r\nThat's why we need to be able to tell the software that there is no network to rely on even if there is one (good for testing too).\r\n\r\nSo what I'm thinking is that this is a simple matter of pre-ambling any network call wrappers with:\r\n\r\n```\r\nif HF_DATASETS_OFFLINE:\r\n    assert \"Attempting to make a network call under Offline mode\"\r\n```\r\n\r\nand then fixing up if there is anything else to fix to make it work.\r\n\r\n--------------\r\n\r\nOtherwise I think the only other problem I encountered is that we need to find a way to pre-cache metrics, for some reason it's not caching it and wanting to fetch it from online.\r\n\r\nWhich is extra strange since it already has those files in the `datasets` repo itself that is on the filesystem.\r\n\r\nThe workaround I had to do is to copy `rouge\/rouge.py` (with the parent folder) from the datasets repo to the current dir - and then it proceeded.","embeddings":[-0.4748800695,0.1685334891,-0.0625536442,0.1050562486,0.0443113707,-0.2284921706,0.4647973776,0.0716796964,0.2545911074,0.0485309139,0.0184395313,0.0636148751,0.0845065862,0.1993038058,0.0180914346,0.1079676971,0.0199452993,-0.1096389145,-0.2528404295,0.0637163967,-0.2635913491,-0.0536728576,-0.2544517219,-0.2575809062,0.078367807,-0.5080999136,-0.2168778479,-0.0105694095,0.0096480669,-0.2275560498,0.3089389205,0.2612626255,0.2518863976,0.1530115753,-0.0001084152,-0.0537566915,-0.1393844485,-0.0361674428,-0.272887677,0.0223880373,-0.3179339468,-0.3802416921,0.1650776267,-0.4613099992,-0.171355173,-0.174725309,0.1464560628,-0.6594020128,0.5154665709,-0.0763156116,0.0945165008,0.0003517794,-0.3278796375,-0.0994149074,-0.3226190805,-0.1980506182,-0.1814403385,0.1710348725,0.0883782059,-0.2174991071,-0.0455865599,0.3952884078,-0.3122881055,0.1565717608,0.3260605037,0.0182255022,-0.338113457,-0.1188222617,-0.1281385869,0.3376224041,0.1812187731,-0.2216326147,-0.4262379706,-0.4555667341,-0.0790792406,-0.370331347,0.3025760651,0.0889306292,-0.1169174388,0.2809565067,-0.3825724125,-0.2138668299,-0.1247217432,-0.0012391666,-0.0308274534,0.3738206923,0.0736688673,0.1736761779,-0.0692528114,0.1060303599,0.0134036681,-0.3340767622,0.2569811642,0.0270416345,-0.5665541291,0.0265186541,0.1224480569,-0.0067842589,0.0168159846,0.4544952512,-0.1779251099,-0.0429254211,0.4983155131,0.195036903,0.0398950391,0.2975893617,0.1519620121,-0.1790611893,0.3776472807,0.1602634937,-0.0568716712,-0.1345084161,-0.1136473343,-0.0706924796,0.1811163574,0.0640624613,0.1197685003,-0.1377031356,-0.1166463196,0.046965342,-0.0323215127,0.2401375026,0.3313703537,0.3093738258,-0.0555984639,-0.2358305752,0.2333134562,0.1667176485,-0.1382888556,-0.06039121,0.072590515,-0.0984998196,-0.1313179582,0.082668893,0.3286071122,0.2563716769,0.2747864425,-0.1755116731,0.1528570056,0.0238279738,0.5519109368,-0.2458668649,0.1138633564,0.3021980822,0.1545313895,-0.2306014448,0.0980398506,0.2813174427,-0.0375363044,0.0506099239,0.0799426436,-0.3858196735,0.4044837654,0.1439911574,-0.1975332648,-0.0476032272,0.1782198101,0.0742536411,-0.0721478388,-0.4519341588,0.1831779182,0.0667779595,-0.1193479002,-0.0543197319,0.4079632163,0.4936104715,-0.4202961922,0.0228122212,-0.221729517,0.019100355,-0.1197147667,0.1713059396,-0.2794617414,0.1356073618,-0.1801660359,-0.2079320103,0.2443795949,-0.3084554672,-0.3225480914,0.4538064003,-0.1916149855,0.2314610779,0.3152425885,0.1994496435,0.2296581119,-0.0338119008,0.1222665012,0.3941685557,-0.2495333552,-0.0643453002,-0.1496422589,-0.1908909678,0.2760188878,0.1059188098,0.2374101728,-0.1076447815,-0.086140126,-0.2940383255,-0.0350805186,-0.083207719,0.2003037333,-0.1944311261,0.0856611282,0.0736428425,0.0493785702,0.1411918253,-0.4604979455,0.3037669957,-0.2521933019,0.0747477561,-0.3391274512,-0.3241582215,0.2313053608,-0.0713331848,-0.1031431407,-0.3441320658,0.0740442052,0.0512403548,-0.1017183214,-0.0374327078,-0.0173988789,0.0242225807,-0.0870222002,0.1135079041,0.1581827104,-0.0672886223,0.0744352862,0.0992200971,-0.2595793903,-0.1118372455,-0.0186913777,-0.1419194341,-0.0033473987,0.507107079,0.0670183226,0.434106648,0.1801059842,0.43449682,0.100204356,0.3320184648,0.2241401672,0.0292811133,0.0667587593,-0.0281432383,-0.5865287185,0.8847305179,-0.0772907659,0.4949632585,-0.0253681988,-0.0896625817,0.2581321895,-0.070058696,-0.5748965144,-0.0896323398,-0.0292923599,0.2472509444,0.1246782467,-0.0695684403,-0.3645498455,0.1309467256,0.0615998544,0.2113251835,0.4444034696,-0.2755915225,0.2542671561,-0.0863385573,0.4632284939,0.2097619325,0.2965559363,0.2528910935,0.0414115787,0.1776760668,-0.0744988695,-0.2386737466,0.3642019629,0.1546070129,0.3154804409,0.1600142121,0.0825260282,-0.1450218856,-0.1656669974,-0.2701164484,0.1441674232,-0.2285314947,-0.052432321,0.3188200295,-0.3138054311,-0.5460381508,-0.0330595821,0.2567465603,-0.1731722355,-0.1265536398,0.232597664,0.1215340123,-0.3687728345,0.3979503214,-0.2202257961,0.4702404141,-0.0649726614,-0.4042862654,0.2372114658,-0.3736272752,0.0975892395,0.0727022961,0.2432580888,-0.090477258,0.6828110218,0.2652270794,-0.0664931238,-0.172499001,-0.2048208117,0.1804469377,0.0453945622,0.5188974738,0.1410698295,0.097885415,-0.1078779474,0.0392978527,0.266864717,-0.131492421,0.2002531737,-0.1014679745,0.0052114641,0.1161197722,-0.2464787811,-0.5637351274,-0.2847306728,-0.2684567571,0.1167265549,-0.2222549319,0.2011254579,-0.0231130943,0.1001847163,-0.050306119,0.1435732543,0.0995229259,-0.0322472528,-0.568808198,0.1139889956,-0.1637168378,0.0997769907,0.0580960028,-0.0110152299,0.3906186521,0.5869692564,-0.4705568254,-0.2902962863,0.3895433247,0.286469698,0.0516619869,-0.3373523057,0.6150392294,0.0365746021,-0.0880156383,-0.0884277374,0.3548750579,0.4860231876,-0.1864490658,-0.0539083518,0.1791349649,0.7724372149,0.202256009,0.2877366543,-0.1666050255,0.4881629646,0.2260150015,0.1102786735,0.304939419,-0.1625722647,0.0179588497,0.2034069151,-0.0815214068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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1939","title":"[firewalled env] OFFLINE mode","comments":"Ok understand better the hanging issue.\r\nI guess catching connection errors is not enough, we should also avoid all the hangings.\r\nCurrently the offline mode tests are only done by simulating an instant connection fail that returns an error, let's have another connection mock that hangs instead.\r\n\r\nI'll also take a look at why you had to do this for `rouge`.\r\n","body":"This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks.","comment_length":61,"text":"[firewalled env] OFFLINE mode \n This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks. \n Ok understand better the hanging issue.\r\nI guess catching connection errors is not enough, we should also avoid all the hangings.\r\nCurrently the offline mode tests are only done by simulating an instant connection fail that returns an error, let's have another connection mock that hangs instead.\r\n\r\nI'll also take a look at why you had to do this for `rouge`.\r\n","embeddings":[-0.4748800695,0.1685334891,-0.0625536442,0.1050562486,0.0443113707,-0.2284921706,0.4647973776,0.0716796964,0.2545911074,0.0485309139,0.0184395313,0.0636148751,0.0845065862,0.1993038058,0.0180914346,0.1079676971,0.0199452993,-0.1096389145,-0.2528404295,0.0637163967,-0.2635913491,-0.0536728576,-0.2544517219,-0.2575809062,0.078367807,-0.5080999136,-0.2168778479,-0.0105694095,0.0096480669,-0.2275560498,0.3089389205,0.2612626255,0.2518863976,0.1530115753,-0.0001084152,-0.0537566915,-0.1393844485,-0.0361674428,-0.272887677,0.0223880373,-0.3179339468,-0.3802416921,0.1650776267,-0.4613099992,-0.171355173,-0.174725309,0.1464560628,-0.6594020128,0.5154665709,-0.0763156116,0.0945165008,0.0003517794,-0.3278796375,-0.0994149074,-0.3226190805,-0.1980506182,-0.1814403385,0.1710348725,0.0883782059,-0.2174991071,-0.0455865599,0.3952884078,-0.3122881055,0.1565717608,0.3260605037,0.0182255022,-0.338113457,-0.1188222617,-0.1281385869,0.3376224041,0.1812187731,-0.2216326147,-0.4262379706,-0.4555667341,-0.0790792406,-0.370331347,0.3025760651,0.0889306292,-0.1169174388,0.2809565067,-0.3825724125,-0.2138668299,-0.1247217432,-0.0012391666,-0.0308274534,0.3738206923,0.0736688673,0.1736761779,-0.0692528114,0.1060303599,0.0134036681,-0.3340767622,0.2569811642,0.0270416345,-0.5665541291,0.0265186541,0.1224480569,-0.0067842589,0.0168159846,0.4544952512,-0.1779251099,-0.0429254211,0.4983155131,0.195036903,0.0398950391,0.2975893617,0.1519620121,-0.1790611893,0.3776472807,0.1602634937,-0.0568716712,-0.1345084161,-0.1136473343,-0.0706924796,0.1811163574,0.0640624613,0.1197685003,-0.1377031356,-0.1166463196,0.046965342,-0.0323215127,0.2401375026,0.3313703537,0.3093738258,-0.0555984639,-0.2358305752,0.2333134562,0.1667176485,-0.1382888556,-0.06039121,0.072590515,-0.0984998196,-0.1313179582,0.082668893,0.3286071122,0.2563716769,0.2747864425,-0.1755116731,0.1528570056,0.0238279738,0.5519109368,-0.2458668649,0.1138633564,0.3021980822,0.1545313895,-0.2306014448,0.0980398506,0.2813174427,-0.0375363044,0.0506099239,0.0799426436,-0.3858196735,0.4044837654,0.1439911574,-0.1975332648,-0.0476032272,0.1782198101,0.0742536411,-0.0721478388,-0.4519341588,0.1831779182,0.0667779595,-0.1193479002,-0.0543197319,0.4079632163,0.4936104715,-0.4202961922,0.0228122212,-0.221729517,0.019100355,-0.1197147667,0.1713059396,-0.2794617414,0.1356073618,-0.1801660359,-0.2079320103,0.2443795949,-0.3084554672,-0.3225480914,0.4538064003,-0.1916149855,0.2314610779,0.3152425885,0.1994496435,0.2296581119,-0.0338119008,0.1222665012,0.3941685557,-0.2495333552,-0.0643453002,-0.1496422589,-0.1908909678,0.2760188878,0.1059188098,0.2374101728,-0.1076447815,-0.086140126,-0.2940383255,-0.0350805186,-0.083207719,0.2003037333,-0.1944311261,0.0856611282,0.0736428425,0.0493785702,0.1411918253,-0.4604979455,0.3037669957,-0.2521933019,0.0747477561,-0.3391274512,-0.3241582215,0.2313053608,-0.0713331848,-0.1031431407,-0.3441320658,0.0740442052,0.0512403548,-0.1017183214,-0.0374327078,-0.0173988789,0.0242225807,-0.0870222002,0.1135079041,0.1581827104,-0.0672886223,0.0744352862,0.0992200971,-0.2595793903,-0.1118372455,-0.0186913777,-0.1419194341,-0.0033473987,0.507107079,0.0670183226,0.434106648,0.1801059842,0.43449682,0.100204356,0.3320184648,0.2241401672,0.0292811133,0.0667587593,-0.0281432383,-0.5865287185,0.8847305179,-0.0772907659,0.4949632585,-0.0253681988,-0.0896625817,0.2581321895,-0.070058696,-0.5748965144,-0.0896323398,-0.0292923599,0.2472509444,0.1246782467,-0.0695684403,-0.3645498455,0.1309467256,0.0615998544,0.2113251835,0.4444034696,-0.2755915225,0.2542671561,-0.0863385573,0.4632284939,0.2097619325,0.2965559363,0.2528910935,0.0414115787,0.1776760668,-0.0744988695,-0.2386737466,0.3642019629,0.1546070129,0.3154804409,0.1600142121,0.0825260282,-0.1450218856,-0.1656669974,-0.2701164484,0.1441674232,-0.2285314947,-0.052432321,0.3188200295,-0.3138054311,-0.5460381508,-0.0330595821,0.2567465603,-0.1731722355,-0.1265536398,0.232597664,0.1215340123,-0.3687728345,0.3979503214,-0.2202257961,0.4702404141,-0.0649726614,-0.4042862654,0.2372114658,-0.3736272752,0.0975892395,0.0727022961,0.2432580888,-0.090477258,0.6828110218,0.2652270794,-0.0664931238,-0.172499001,-0.2048208117,0.1804469377,0.0453945622,0.5188974738,0.1410698295,0.097885415,-0.1078779474,0.0392978527,0.266864717,-0.131492421,0.2002531737,-0.1014679745,0.0052114641,0.1161197722,-0.2464787811,-0.5637351274,-0.2847306728,-0.2684567571,0.1167265549,-0.2222549319,0.2011254579,-0.0231130943,0.1001847163,-0.050306119,0.1435732543,0.0995229259,-0.0322472528,-0.568808198,0.1139889956,-0.1637168378,0.0997769907,0.0580960028,-0.0110152299,0.3906186521,0.5869692564,-0.4705568254,-0.2902962863,0.3895433247,0.286469698,0.0516619869,-0.3373523057,0.6150392294,0.0365746021,-0.0880156383,-0.0884277374,0.3548750579,0.4860231876,-0.1864490658,-0.0539083518,0.1791349649,0.7724372149,0.202256009,0.2877366543,-0.1666050255,0.4881629646,0.2260150015,0.1102786735,0.304939419,-0.1625722647,0.0179588497,0.2034069151,-0.08152140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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1939","title":"[firewalled env] OFFLINE mode","comments":"FWIW, I think instant failure on the behalf of a network call is the simplest solution to correctly represent the environment and having the caller to sort it out is the next thing to do, since here it is the case of having no functional network, it's just that the software doesn't know this is the case, because there is some network. So we just need to help it to bail out instantly rather than hang waiting for it to time out. And afterwards everything else you said.","body":"This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks.","comment_length":88,"text":"[firewalled env] OFFLINE mode \n This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks. \n FWIW, I think instant failure on the behalf of a network call is the simplest solution to correctly represent the environment and having the caller to sort it out is the next thing to do, since here it is the case of having no functional network, it's just that the software doesn't know this is the case, because there is some network. So we just need to help it to bail out instantly rather than hang waiting for it to time out. And afterwards everything else you said.","embeddings":[-0.4748800695,0.1685334891,-0.0625536442,0.1050562486,0.0443113707,-0.2284921706,0.4647973776,0.0716796964,0.2545911074,0.0485309139,0.0184395313,0.0636148751,0.0845065862,0.1993038058,0.0180914346,0.1079676971,0.0199452993,-0.1096389145,-0.2528404295,0.0637163967,-0.2635913491,-0.0536728576,-0.2544517219,-0.2575809062,0.078367807,-0.5080999136,-0.2168778479,-0.0105694095,0.0096480669,-0.2275560498,0.3089389205,0.2612626255,0.2518863976,0.1530115753,-0.0001084152,-0.0537566915,-0.1393844485,-0.0361674428,-0.272887677,0.0223880373,-0.3179339468,-0.3802416921,0.1650776267,-0.4613099992,-0.171355173,-0.174725309,0.1464560628,-0.6594020128,0.5154665709,-0.0763156116,0.0945165008,0.0003517794,-0.3278796375,-0.0994149074,-0.3226190805,-0.1980506182,-0.1814403385,0.1710348725,0.0883782059,-0.2174991071,-0.0455865599,0.3952884078,-0.3122881055,0.1565717608,0.3260605037,0.0182255022,-0.338113457,-0.1188222617,-0.1281385869,0.3376224041,0.1812187731,-0.2216326147,-0.4262379706,-0.4555667341,-0.0790792406,-0.370331347,0.3025760651,0.0889306292,-0.1169174388,0.2809565067,-0.3825724125,-0.2138668299,-0.1247217432,-0.0012391666,-0.0308274534,0.3738206923,0.0736688673,0.1736761779,-0.0692528114,0.1060303599,0.0134036681,-0.3340767622,0.2569811642,0.0270416345,-0.5665541291,0.0265186541,0.1224480569,-0.0067842589,0.0168159846,0.4544952512,-0.1779251099,-0.0429254211,0.4983155131,0.195036903,0.0398950391,0.2975893617,0.1519620121,-0.1790611893,0.3776472807,0.1602634937,-0.0568716712,-0.1345084161,-0.1136473343,-0.0706924796,0.1811163574,0.0640624613,0.1197685003,-0.1377031356,-0.1166463196,0.046965342,-0.0323215127,0.2401375026,0.3313703537,0.3093738258,-0.0555984639,-0.2358305752,0.2333134562,0.1667176485,-0.1382888556,-0.06039121,0.072590515,-0.0984998196,-0.1313179582,0.082668893,0.3286071122,0.2563716769,0.2747864425,-0.1755116731,0.1528570056,0.0238279738,0.5519109368,-0.2458668649,0.1138633564,0.3021980822,0.1545313895,-0.2306014448,0.0980398506,0.2813174427,-0.0375363044,0.0506099239,0.0799426436,-0.3858196735,0.4044837654,0.1439911574,-0.1975332648,-0.0476032272,0.1782198101,0.0742536411,-0.0721478388,-0.4519341588,0.1831779182,0.0667779595,-0.1193479002,-0.0543197319,0.4079632163,0.4936104715,-0.4202961922,0.0228122212,-0.221729517,0.019100355,-0.1197147667,0.1713059396,-0.2794617414,0.1356073618,-0.1801660359,-0.2079320103,0.2443795949,-0.3084554672,-0.3225480914,0.4538064003,-0.1916149855,0.2314610779,0.3152425885,0.1994496435,0.2296581119,-0.0338119008,0.1222665012,0.3941685557,-0.2495333552,-0.0643453002,-0.1496422589,-0.1908909678,0.2760188878,0.1059188098,0.2374101728,-0.1076447815,-0.086140126,-0.2940383255,-0.0350805186,-0.083207719,0.2003037333,-0.1944311261,0.0856611282,0.0736428425,0.0493785702,0.1411918253,-0.4604979455,0.3037669957,-0.2521933019,0.0747477561,-0.3391274512,-0.3241582215,0.2313053608,-0.0713331848,-0.1031431407,-0.3441320658,0.0740442052,0.0512403548,-0.1017183214,-0.0374327078,-0.0173988789,0.0242225807,-0.0870222002,0.1135079041,0.1581827104,-0.0672886223,0.0744352862,0.0992200971,-0.2595793903,-0.1118372455,-0.0186913777,-0.1419194341,-0.0033473987,0.507107079,0.0670183226,0.434106648,0.1801059842,0.43449682,0.100204356,0.3320184648,0.2241401672,0.0292811133,0.0667587593,-0.0281432383,-0.5865287185,0.8847305179,-0.0772907659,0.4949632585,-0.0253681988,-0.0896625817,0.2581321895,-0.070058696,-0.5748965144,-0.0896323398,-0.0292923599,0.2472509444,0.1246782467,-0.0695684403,-0.3645498455,0.1309467256,0.0615998544,0.2113251835,0.4444034696,-0.2755915225,0.2542671561,-0.0863385573,0.4632284939,0.2097619325,0.2965559363,0.2528910935,0.0414115787,0.1776760668,-0.0744988695,-0.2386737466,0.3642019629,0.1546070129,0.3154804409,0.1600142121,0.0825260282,-0.1450218856,-0.1656669974,-0.2701164484,0.1441674232,-0.2285314947,-0.052432321,0.3188200295,-0.3138054311,-0.5460381508,-0.0330595821,0.2567465603,-0.1731722355,-0.1265536398,0.232597664,0.1215340123,-0.3687728345,0.3979503214,-0.2202257961,0.4702404141,-0.0649726614,-0.4042862654,0.2372114658,-0.3736272752,0.0975892395,0.0727022961,0.2432580888,-0.090477258,0.6828110218,0.2652270794,-0.0664931238,-0.172499001,-0.2048208117,0.1804469377,0.0453945622,0.5188974738,0.1410698295,0.097885415,-0.1078779474,0.0392978527,0.266864717,-0.131492421,0.2002531737,-0.1014679745,0.0052114641,0.1161197722,-0.2464787811,-0.5637351274,-0.2847306728,-0.2684567571,0.1167265549,-0.2222549319,0.2011254579,-0.0231130943,0.1001847163,-0.050306119,0.1435732543,0.0995229259,-0.0322472528,-0.568808198,0.1139889956,-0.1637168378,0.0997769907,0.0580960028,-0.0110152299,0.3906186521,0.5869692564,-0.4705568254,-0.2902962863,0.3895433247,0.286469698,0.0516619869,-0.3373523057,0.6150392294,0.0365746021,-0.0880156383,-0.0884277374,0.3548750579,0.4860231876,-0.1864490658,-0.0539083518,0.1791349649,0.7724372149,0.202256009,0.2877366543,-0.1666050255,0.4881629646,0.2260150015,0.1102786735,0.304939419,-0.1625722647,0.0179588497,0.2034069151,-0.0815214068,-0.5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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1939","title":"[firewalled env] OFFLINE mode","comments":"Update on this: \r\n\r\nI managed to create a mock environment for tests that makes the connections hang until timeout.\r\nI managed to reproduce the issue you're having in this environment.\r\n\r\nI'll update the offline test cases to also test the robustness to connection hangings, and make sure we set proper timeouts where it's needed in the code. This should cover the _automatic_ section you mentioned.","body":"This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks.","comment_length":65,"text":"[firewalled env] OFFLINE mode \n This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks. \n Update on this: \r\n\r\nI managed to create a mock environment for tests that makes the connections hang until timeout.\r\nI managed to reproduce the issue you're having in this environment.\r\n\r\nI'll update the offline test cases to also test the robustness to connection hangings, and make sure we set proper timeouts where it's needed in the code. This should cover the _automatic_ section you mentioned.","embeddings":[-0.4748800695,0.1685334891,-0.0625536442,0.1050562486,0.0443113707,-0.2284921706,0.4647973776,0.0716796964,0.2545911074,0.0485309139,0.0184395313,0.0636148751,0.0845065862,0.1993038058,0.0180914346,0.1079676971,0.0199452993,-0.1096389145,-0.2528404295,0.0637163967,-0.2635913491,-0.0536728576,-0.2544517219,-0.2575809062,0.078367807,-0.5080999136,-0.2168778479,-0.0105694095,0.0096480669,-0.2275560498,0.3089389205,0.2612626255,0.2518863976,0.1530115753,-0.0001084152,-0.0537566915,-0.1393844485,-0.0361674428,-0.272887677,0.0223880373,-0.3179339468,-0.3802416921,0.1650776267,-0.4613099992,-0.171355173,-0.174725309,0.1464560628,-0.6594020128,0.5154665709,-0.0763156116,0.0945165008,0.0003517794,-0.3278796375,-0.0994149074,-0.3226190805,-0.1980506182,-0.1814403385,0.1710348725,0.0883782059,-0.2174991071,-0.0455865599,0.3952884078,-0.3122881055,0.1565717608,0.3260605037,0.0182255022,-0.338113457,-0.1188222617,-0.1281385869,0.3376224041,0.1812187731,-0.2216326147,-0.4262379706,-0.4555667341,-0.0790792406,-0.370331347,0.3025760651,0.0889306292,-0.1169174388,0.2809565067,-0.3825724125,-0.2138668299,-0.1247217432,-0.0012391666,-0.0308274534,0.3738206923,0.0736688673,0.1736761779,-0.0692528114,0.1060303599,0.0134036681,-0.3340767622,0.2569811642,0.0270416345,-0.5665541291,0.0265186541,0.1224480569,-0.0067842589,0.0168159846,0.4544952512,-0.1779251099,-0.0429254211,0.4983155131,0.195036903,0.0398950391,0.2975893617,0.1519620121,-0.1790611893,0.3776472807,0.1602634937,-0.0568716712,-0.1345084161,-0.1136473343,-0.0706924796,0.1811163574,0.0640624613,0.1197685003,-0.1377031356,-0.1166463196,0.046965342,-0.0323215127,0.2401375026,0.3313703537,0.3093738258,-0.0555984639,-0.2358305752,0.2333134562,0.1667176485,-0.1382888556,-0.06039121,0.072590515,-0.0984998196,-0.1313179582,0.082668893,0.3286071122,0.2563716769,0.2747864425,-0.1755116731,0.1528570056,0.0238279738,0.5519109368,-0.2458668649,0.1138633564,0.3021980822,0.1545313895,-0.2306014448,0.0980398506,0.2813174427,-0.0375363044,0.0506099239,0.0799426436,-0.3858196735,0.4044837654,0.1439911574,-0.1975332648,-0.0476032272,0.1782198101,0.0742536411,-0.0721478388,-0.4519341588,0.1831779182,0.0667779595,-0.1193479002,-0.0543197319,0.4079632163,0.4936104715,-0.4202961922,0.0228122212,-0.221729517,0.019100355,-0.1197147667,0.1713059396,-0.2794617414,0.1356073618,-0.1801660359,-0.2079320103,0.2443795949,-0.3084554672,-0.3225480914,0.4538064003,-0.1916149855,0.2314610779,0.3152425885,0.1994496435,0.2296581119,-0.0338119008,0.1222665012,0.3941685557,-0.2495333552,-0.0643453002,-0.1496422589,-0.1908909678,0.2760188878,0.1059188098,0.2374101728,-0.1076447815,-0.086140126,-0.2940383255,-0.0350805186,-0.083207719,0.2003037333,-0.1944311261,0.0856611282,0.0736428425,0.0493785702,0.1411918253,-0.4604979455,0.3037669957,-0.2521933019,0.0747477561,-0.3391274512,-0.3241582215,0.2313053608,-0.0713331848,-0.1031431407,-0.3441320658,0.0740442052,0.0512403548,-0.1017183214,-0.0374327078,-0.0173988789,0.0242225807,-0.0870222002,0.1135079041,0.1581827104,-0.0672886223,0.0744352862,0.0992200971,-0.2595793903,-0.1118372455,-0.0186913777,-0.1419194341,-0.0033473987,0.507107079,0.0670183226,0.434106648,0.1801059842,0.43449682,0.100204356,0.3320184648,0.2241401672,0.0292811133,0.0667587593,-0.0281432383,-0.5865287185,0.8847305179,-0.0772907659,0.4949632585,-0.0253681988,-0.0896625817,0.2581321895,-0.070058696,-0.5748965144,-0.0896323398,-0.0292923599,0.2472509444,0.1246782467,-0.0695684403,-0.3645498455,0.1309467256,0.0615998544,0.2113251835,0.4444034696,-0.2755915225,0.2542671561,-0.0863385573,0.4632284939,0.2097619325,0.2965559363,0.2528910935,0.0414115787,0.1776760668,-0.0744988695,-0.2386737466,0.3642019629,0.1546070129,0.3154804409,0.1600142121,0.0825260282,-0.1450218856,-0.1656669974,-0.2701164484,0.1441674232,-0.2285314947,-0.052432321,0.3188200295,-0.3138054311,-0.5460381508,-0.0330595821,0.2567465603,-0.1731722355,-0.1265536398,0.232597664,0.1215340123,-0.3687728345,0.3979503214,-0.2202257961,0.4702404141,-0.0649726614,-0.4042862654,0.2372114658,-0.3736272752,0.0975892395,0.0727022961,0.2432580888,-0.090477258,0.6828110218,0.2652270794,-0.0664931238,-0.172499001,-0.2048208117,0.1804469377,0.0453945622,0.5188974738,0.1410698295,0.097885415,-0.1078779474,0.0392978527,0.266864717,-0.131492421,0.2002531737,-0.1014679745,0.0052114641,0.1161197722,-0.2464787811,-0.5637351274,-0.2847306728,-0.2684567571,0.1167265549,-0.2222549319,0.2011254579,-0.0231130943,0.1001847163,-0.050306119,0.1435732543,0.0995229259,-0.0322472528,-0.568808198,0.1139889956,-0.1637168378,0.0997769907,0.0580960028,-0.0110152299,0.3906186521,0.5869692564,-0.4705568254,-0.2902962863,0.3895433247,0.286469698,0.0516619869,-0.3373523057,0.6150392294,0.0365746021,-0.0880156383,-0.0884277374,0.3548750579,0.4860231876,-0.1864490658,-0.0539083518,0.1791349649,0.7724372149,0.202256009,0.2877366543,-0.1666050255,0.4881629646,0.2260150015,0.1102786735,0.304939419,-0.1625722647,0.0179588497,0.2034069151,-0.0815214068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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1939","title":"[firewalled env] OFFLINE mode","comments":"I lost access to the firewalled setup, but I emulated it with:\r\n\r\n```\r\nsudo ufw enable\r\nsudo ufw default deny outgoing\r\n```\r\n(thanks @mfuntowicz)\r\n\r\nI was able to test `HF_DATASETS_OFFLINE=1` and it worked great - i.e. didn't try to reach out with it and used the cached files instead.\r\n\r\nThank you!","body":"This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks.","comment_length":51,"text":"[firewalled env] OFFLINE mode \n This issue comes from a need to be able to run `datasets` in a firewalled env, which currently makes the software hang until it times out, as it's unable to complete the network calls.\r\n\r\nI propose the following approach to solving this problem, using the example of `run_seq2seq.py` as a sample program. There are 2 possible ways to going about it.\r\n\r\n## 1. Manual\r\n\r\nmanually prepare data and metrics files, that is transfer to the firewalled instance the dataset and the metrics and run:\r\n\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --train_file xyz.csv --validation_file xyz.csv ...\r\n```\r\n\r\n`datasets` must not make any network calls and if there is a logic to do that and something is missing it should assert that this or that action requires network and therefore it can't proceed.\r\n\r\n## 2. Automatic\r\n\r\nIn some clouds one can prepare a datastorage ahead of time with a normal networked environment but which doesn't have gpus and then one switches to the gpu instance which is firewalled, but it can access all the cached data. This is the ideal situation, since in this scenario we don't have to do anything manually, but simply run the same application twice:\r\n\r\n1. on the non-firewalled instance:\r\n```\r\nrun_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nwhich should download and cached everything.\r\n\r\n2. and then immediately after on the firewalled instance, which shares the same filesystem\r\n```\r\nDATASETS_OFFLINE=1 run_seq2seq.py  --dataset_name wmt16 --dataset_config ro-en ...\r\n```\r\n\r\nand the metrics and datasets should be cached by the invocation number 1 and any network calls be skipped and if the logic is missing data it should assert and not try to fetch any data from online.\r\n\r\n## Common Issues\r\n\r\n1. for example currently `datasets` tries to look up online datasets if the files contain json or csv, despite the paths already provided\r\n\r\n```\r\n     if dataset and path in _PACKAGED_DATASETS_MODULES:\r\n```\r\n\r\n2. it has an issue with metrics. e.g. I had to manually copy `rouge\/rouge.py` from the `datasets` repo to the current dir - or it was hanging.\r\n\r\nI had to comment out `head_hf_s3(...)` calls to make things work. So all those `try: head_hf_s3(...)` shouldn't be tried with `DATASETS_OFFLINE=1`\r\n\r\nHere is the corresponding issue for `transformers`: https:\/\/github.com\/huggingface\/transformers\/issues\/10379\r\n\r\nThanks. \n I lost access to the firewalled setup, but I emulated it with:\r\n\r\n```\r\nsudo ufw enable\r\nsudo ufw default deny outgoing\r\n```\r\n(thanks @mfuntowicz)\r\n\r\nI was able to test `HF_DATASETS_OFFLINE=1` and it worked great - i.e. didn't try to reach out with it and used the cached files instead.\r\n\r\nThank you!","embeddings":[-0.4748800695,0.1685334891,-0.0625536442,0.1050562486,0.0443113707,-0.2284921706,0.4647973776,0.0716796964,0.2545911074,0.0485309139,0.0184395313,0.0636148751,0.0845065862,0.1993038058,0.0180914346,0.1079676971,0.0199452993,-0.1096389145,-0.2528404295,0.0637163967,-0.2635913491,-0.0536728576,-0.2544517219,-0.2575809062,0.078367807,-0.5080999136,-0.2168778479,-0.0105694095,0.0096480669,-0.2275560498,0.3089389205,0.2612626255,0.2518863976,0.1530115753,-0.0001084152,-0.0537566915,-0.1393844485,-0.0361674428,-0.272887677,0.0223880373,-0.3179339468,-0.3802416921,0.1650776267,-0.4613099992,-0.171355173,-0.174725309,0.1464560628,-0.6594020128,0.5154665709,-0.0763156116,0.0945165008,0.0003517794,-0.3278796375,-0.0994149074,-0.3226190805,-0.1980506182,-0.1814403385,0.1710348725,0.0883782059,-0.2174991071,-0.0455865599,0.3952884078,-0.3122881055,0.1565717608,0.3260605037,0.0182255022,-0.338113457,-0.1188222617,-0.1281385869,0.3376224041,0.1812187731,-0.2216326147,-0.4262379706,-0.4555667341,-0.0790792406,-0.370331347,0.3025760651,0.0889306292,-0.1169174388,0.2809565067,-0.3825724125,-0.2138668299,-0.1247217432,-0.0012391666,-0.0308274534,0.3738206923,0.0736688673,0.1736761779,-0.0692528114,0.1060303599,0.0134036681,-0.3340767622,0.2569811642,0.0270416345,-0.5665541291,0.0265186541,0.1224480569,-0.0067842589,0.0168159846,0.4544952512,-0.1779251099,-0.0429254211,0.4983155131,0.195036903,0.0398950391,0.2975893617,0.1519620121,-0.1790611893,0.3776472807,0.1602634937,-0.0568716712,-0.1345084161,-0.1136473343,-0.0706924796,0.1811163574,0.0640624613,0.1197685003,-0.1377031356,-0.1166463196,0.046965342,-0.0323215127,0.2401375026,0.3313703537,0.3093738258,-0.0555984639,-0.2358305752,0.2333134562,0.1667176485,-0.1382888556,-0.06039121,0.072590515,-0.0984998196,-0.1313179582,0.082668893,0.3286071122,0.2563716769,0.2747864425,-0.1755116731,0.1528570056,0.0238279738,0.5519109368,-0.2458668649,0.1138633564,0.3021980822,0.1545313895,-0.2306014448,0.0980398506,0.2813174427,-0.0375363044,0.0506099239,0.0799426436,-0.3858196735,0.4044837654,0.1439911574,-0.1975332648,-0.0476032272,0.1782198101,0.0742536411,-0.0721478388,-0.4519341588,0.1831779182,0.0667779595,-0.1193479002,-0.0543197319,0.4079632163,0.4936104715,-0.4202961922,0.0228122212,-0.221729517,0.019100355,-0.1197147667,0.1713059396,-0.2794617414,0.1356073618,-0.1801660359,-0.2079320103,0.2443795949,-0.3084554672,-0.3225480914,0.4538064003,-0.1916149855,0.2314610779,0.3152425885,0.1994496435,0.2296581119,-0.0338119008,0.1222665012,0.3941685557,-0.2495333552,-0.0643453002,-0.1496422589,-0.1908909678,0.2760188878,0.1059188098,0.2374101728,-0.1076447815,-0.086140126,-0.2940383255,-0.0350805186,-0.083207719,0.2003037333,-0.1944311261,0.0856611282,0.0736428425,0.0493785702,0.1411918253,-0.4604979455,0.3037669957,-0.2521933019,0.0747477561,-0.3391274512,-0.3241582215,0.2313053608,-0.0713331848,-0.1031431407,-0.3441320658,0.0740442052,0.0512403548,-0.1017183214,-0.0374327078,-0.0173988789,0.0242225807,-0.0870222002,0.1135079041,0.1581827104,-0.0672886223,0.0744352862,0.0992200971,-0.2595793903,-0.1118372455,-0.0186913777,-0.1419194341,-0.0033473987,0.507107079,0.0670183226,0.434106648,0.1801059842,0.43449682,0.100204356,0.3320184648,0.2241401672,0.0292811133,0.0667587593,-0.0281432383,-0.5865287185,0.8847305179,-0.0772907659,0.4949632585,-0.0253681988,-0.0896625817,0.2581321895,-0.070058696,-0.5748965144,-0.0896323398,-0.0292923599,0.2472509444,0.1246782467,-0.0695684403,-0.3645498455,0.1309467256,0.0615998544,0.2113251835,0.4444034696,-0.2755915225,0.2542671561,-0.0863385573,0.4632284939,0.2097619325,0.2965559363,0.2528910935,0.0414115787,0.1776760668,-0.0744988695,-0.2386737466,0.3642019629,0.1546070129,0.3154804409,0.1600142121,0.0825260282,-0.1450218856,-0.1656669974,-0.2701164484,0.1441674232,-0.2285314947,-0.052432321,0.3188200295,-0.3138054311,-0.5460381508,-0.0330595821,0.2567465603,-0.1731722355,-0.1265536398,0.232597664,0.1215340123,-0.3687728345,0.3979503214,-0.2202257961,0.4702404141,-0.0649726614,-0.4042862654,0.2372114658,-0.3736272752,0.0975892395,0.0727022961,0.2432580888,-0.090477258,0.6828110218,0.2652270794,-0.0664931238,-0.172499001,-0.2048208117,0.1804469377,0.0453945622,0.5188974738,0.1410698295,0.097885415,-0.1078779474,0.0392978527,0.266864717,-0.131492421,0.2002531737,-0.1014679745,0.0052114641,0.1161197722,-0.2464787811,-0.5637351274,-0.2847306728,-0.2684567571,0.1167265549,-0.2222549319,0.2011254579,-0.0231130943,0.1001847163,-0.050306119,0.1435732543,0.0995229259,-0.0322472528,-0.568808198,0.1139889956,-0.1637168378,0.0997769907,0.0580960028,-0.0110152299,0.3906186521,0.5869692564,-0.4705568254,-0.2902962863,0.3895433247,0.286469698,0.0516619869,-0.3373523057,0.6150392294,0.0365746021,-0.0880156383,-0.0884277374,0.3548750579,0.4860231876,-0.1864490658,-0.0539083518,0.1791349649,0.7724372149,0.202256009,0.2877366543,-0.1666050255,0.4881629646,0.2260150015,0.1102786735,0.304939419,-0.1625722647,0.0179588497,0.2034069151,-0.0815214068,-0.53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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1924","title":"Anonymous Dataset Addition (i.e Anonymous PR?)","comments":"Hi !\r\nI guess you can add a dataset without the fields that must be kept anonymous, and then update those when the anonymity period is over.\r\nYou can also make the PR from an anonymous org.\r\nPinging @yjernite just to make sure it's ok","body":"Hello,\r\nThanks a lot for your librairy.\r\nWe plan to submit a paper on OpenReview using the Anonymous setting. Is it possible to add a new dataset without breaking the anonimity, with a link to the paper ? \r\nCheers \r\n@eusip","comment_length":45,"text":"Anonymous Dataset Addition (i.e Anonymous PR?) \n Hello,\r\nThanks a lot for your librairy.\r\nWe plan to submit a paper on OpenReview using the Anonymous setting. Is it possible to add a new dataset without breaking the anonimity, with a link to the paper ? \r\nCheers \r\n@eusip \n Hi !\r\nI guess you can add a dataset without the fields that must be kept anonymous, and then update those when the anonymity period is over.\r\nYou can also make the PR from an anonymous org.\r\nPinging @yjernite just to make sure it's ok","embeddings":[-0.2978561223,0.5536879897,-0.0238176081,-0.1903211623,-0.0758951455,-0.1079585776,0.5019997954,0.0080968365,0.0642958507,0.0534695312,0.0712104365,0.1038329452,0.0041556037,0.1634862423,-0.038204141,0.0795786381,0.0454185903,-0.0966099054,0.0962188765,-0.2013753802,-0.4753124416,-0.2437654734,0.0791259259,-0.5910321474,0.1504401416,-0.2484027892,-0.2008707821,-0.2064752281,-0.0254556611,-0.4402409494,-0.2508310676,0.2532382011,0.0878261477,0.1687669903,-0.0001136982,0.0365013182,0.2275120318,0.0229101274,-0.2287050039,-0.1465033889,-0.4085339904,-0.1241108775,-0.0218316149,-0.1218808517,0.0511337407,-0.3378623426,0.1586951762,-0.4428370297,-0.2920340896,0.0187059697,0.187534377,0.2154204845,0.1018245146,-0.1916258484,0.1721318513,0.3090620339,-0.0694505945,-0.0765855312,-0.2525062859,0.2380914986,0.1200364232,0.1911775321,0.2108571678,-0.3427754939,0.2185998857,0.255967319,0.135845229,-0.0866364315,0.0983041152,-0.0139201935,0.5642251968,0.1111585796,-0.2762329876,0.0066860383,0.1424970478,-0.2253250182,0.1219130531,0.360276401,-0.1878941357,0.0065119327,-0.3873705268,-0.4332360923,-0.1487916559,0.2721284628,0.3960341513,-0.0827069655,0.2204151154,0.219899103,-0.1080729589,-0.0076145832,-0.0657849163,-0.131126374,-0.1911907196,0.0271343086,0.2725528479,-0.0197917856,0.0595823154,0.0854259059,-0.0407377742,0.1603176296,0.0219724867,-0.2648992836,-0.266192764,-0.101888001,0.1051796004,-0.2567987442,-0.0329098739,0.2582083642,0.1456508487,-0.1770040691,0.5126866102,-0.1028508022,0.2065980434,0.2185723186,-0.1635549068,-0.4257535934,0.0924938023,-0.2446938902,-0.0193248596,0.1227926016,-0.1386152506,0.1385225058,-0.3871082067,0.1158605218,0.1389353573,-0.3016420007,0.2873123884,-0.058875829,0.0300295111,-0.107908763,0.0076900152,-0.146346271,-0.1829976141,0.0220316928,0.1334694922,0.1433653831,0.0655953661,0.0237891339,0.327932179,0.2830860019,0.3779454827,0.0594570599,0.1582080573,0.0805631131,0.0714396834,-0.2646244764,-0.0486896634,-0.5071710944,0.0606984794,0.2869585752,0.4154135287,0.3485382497,-0.2451441735,0.1902703643,-0.1741662472,-0.2699770629,0.2741080821,0.2919701338,-0.0598984323,-0.1097665876,0.3346720636,0.2572618723,0.0371020697,-0.0198328253,-0.0654725432,0.3630783856,-0.1588656306,-0.2072477937,0.1318625063,-0.1387540698,-0.2335201204,-0.0035389329,-0.2095681131,-0.1997006834,0.0260429531,-0.2894255519,-0.0541625917,-0.2797907591,-0.3725432754,-0.3235584497,-0.255299896,-0.2175352722,0.4006320238,0.5792213678,0.2217822671,-0.1478639841,-0.2846814692,-0.0699517876,-0.1696240604,-0.3042348027,-0.4500306249,-0.1257377714,0.132505551,0.0718635246,-0.2021477968,0.3229564726,0.3146029711,-0.1585004628,0.2660982311,-0.2799395323,0.1645666063,0.1382402778,0.5464157462,-0.0631281361,0.1361641288,0.2277239114,-0.1811691076,-0.2020771503,-0.328779161,0.4175469577,0.1670813411,-0.5736303329,0.2955128849,-0.2195345163,0.0891285613,-0.1777299941,0.0052080504,0.2768193781,-0.284775883,-0.2003248781,-0.07579308,0.1646350026,0.023851119,0.1015504226,-0.4024479687,-0.2272196859,0.127779007,0.0510368347,-0.2499553263,-0.0462137721,-0.1039530337,-0.1574058086,0.1313714683,0.1555237472,-0.1213012636,-0.0245550033,0.7049100995,0.4160323739,-0.0812954009,-0.2752478421,0.1455888301,0.133646518,-0.1570249349,0.2209073603,-0.6624383926,0.5039723516,-0.0585588031,-0.0115834353,-0.1644259244,0.2266272008,0.4233987331,-0.2268246859,-0.1009383202,-0.2740346789,0.2443836033,0.5010877252,0.2135253847,-0.1423729211,-0.4439190924,0.2676128149,-0.4807438254,-0.0149002178,-0.0389987268,-0.0193484724,-0.1518962383,0.2312796414,-0.0101371743,0.1605871618,0.1751214862,0.3742703497,-0.2108206898,0.0669731125,0.2131949812,-0.1076783016,0.276350379,-0.0201313589,-0.3504347205,-0.1547375917,0.4559481442,0.2296287417,0.0018173329,0.0319887139,0.2925581336,0.171434626,-0.0211071018,-0.0420381501,0.2286534905,-0.1715062857,0.1336688846,0.2258008569,-0.2456106395,-0.007508724,0.3010831773,0.0826040581,-0.1294431984,0.2552319169,-0.2911655903,0.812263608,-0.3899851143,0.1093774661,0.0818701759,-0.242402941,0.0765795931,0.1667841077,0.158855617,-0.3950011134,0.5738779902,0.1488127112,0.0059595197,-0.4898711443,-0.4616575539,-0.033103928,0.185569495,0.0171722677,-0.1285817325,-0.2001145035,-0.2259712666,-0.0200328324,-0.0314349271,-0.2088885158,-0.2280631363,-0.132307902,-0.1854385287,-0.0510286056,-0.1308766752,-0.206087634,-0.0663331598,0.2258476913,0.4678015113,0.4428364933,-0.0063269543,0.2578885853,-0.1487094909,-0.2320683002,-0.1050001904,-0.320115149,-0.2184946388,-0.4413897097,0.28193295,-0.3696287274,-0.2477011979,0.2761833072,-0.1013437733,-0.0418128707,0.3652010858,-0.529271841,-0.0816141069,0.1441634595,0.3721830547,0.1676898897,-0.1993681788,0.5032022595,0.1920815557,-0.2018285692,-0.1152354255,-0.1001846567,0.21195364,-0.072530508,0.5440939665,-0.3213922381,-0.050977923,-0.070249334,0.6683695912,0.4097921848,-0.0773719773,-0.031078646,-0.1300684661,0.5164962411,0.0133107482,-0.0727206469,-0.0798474401,0.151953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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1924","title":"Anonymous Dataset Addition (i.e Anonymous PR?)","comments":"Hello,\r\nI would prefer to do the reverse: adding a link to an anonymous paper without the people names\/institution in the PR. Would it be conceivable ?\r\nCheers\r\n","body":"Hello,\r\nThanks a lot for your librairy.\r\nWe plan to submit a paper on OpenReview using the Anonymous setting. Is it possible to add a new dataset without breaking the anonimity, with a link to the paper ? \r\nCheers \r\n@eusip","comment_length":28,"text":"Anonymous Dataset Addition (i.e Anonymous PR?) \n Hello,\r\nThanks a lot for your librairy.\r\nWe plan to submit a paper on OpenReview using the Anonymous setting. Is it possible to add a new dataset without breaking the anonimity, with a link to the paper ? \r\nCheers \r\n@eusip \n Hello,\r\nI would prefer to do the reverse: adding a link to an anonymous paper without the people names\/institution in the PR. Would it be conceivable ?\r\nCheers\r\n","embeddings":[-0.2011668831,0.4423335195,0.0201971103,-0.0765717253,-0.0947154462,-0.0944121331,0.4387371838,-0.0027232436,0.0362781845,0.0934396684,-0.0507460982,0.0669827163,0.0037659982,0.0874343961,0.1370116174,0.1177665591,0.1308901608,-0.0895970315,0.2099281102,-0.2495173812,-0.5651833415,-0.1786230206,0.0916349292,-0.5738092065,0.23158741,-0.207276538,-0.2125302702,-0.1660725027,0.0858120248,-0.3669403493,-0.2833172381,0.1583455801,-0.0273778979,0.2683500051,-0.0001262668,0.1422165483,0.2734455168,0.0246185511,-0.2487211525,-0.1675764769,-0.5025137067,-0.0314126499,-0.0990062207,-0.1659391075,0.0814038664,-0.3552331924,0.1146846637,-0.3773088753,-0.3385237753,0.0273261201,0.0901736543,0.1165459156,0.0729364231,-0.0961636305,0.1395261139,0.4059197903,-0.018656116,0.0075163785,-0.2865670323,0.2626431882,0.2199782282,0.0724457428,0.1970395595,-0.3708330393,0.2289111912,0.1912171692,0.0853175521,-0.0382951051,0.0687063485,0.1095335111,0.6216089725,0.0948709175,-0.2760731578,0.0327650793,0.147463575,-0.0852832496,0.0675747171,0.3558513522,-0.1859707385,0.0162687916,-0.4244497716,-0.506978333,-0.1157958657,0.2613269687,0.3833504021,-0.1147986129,0.3029622138,0.2377171516,-0.1090036109,0.0225542542,-0.1252333224,-0.1423774809,-0.2535927296,0.1280827224,0.3269246221,-0.0503585227,0.0798263326,0.0344047956,-0.0289014559,0.0962052792,0.0563223921,-0.2872754633,-0.2855663896,-0.0859597772,0.0643912703,-0.3160105646,-0.0880991966,0.2874583602,0.0498653986,-0.158363983,0.5381066799,-0.0784931779,0.1278113425,0.2147779316,-0.230950281,-0.4649635255,0.0294684209,-0.2643438578,-0.0542346984,0.0519972518,-0.2531796098,0.1072175652,-0.4038323164,0.1170241907,0.0738791749,-0.3461569548,0.3234764934,-0.0996323004,-0.0508287996,0.0475881547,0.0394910723,-0.0566453002,-0.2179220915,-0.0798602328,0.127335012,0.2161314934,0.0571418367,0.044197984,0.2492774278,0.2355629802,0.2972955406,-0.0089328056,0.2083230168,0.0413716249,0.1397211403,-0.2625546455,-0.0491480008,-0.6527947187,0.0888416544,0.3641912341,0.2272872776,0.2947358787,-0.2152953744,0.0771596283,-0.1591785252,-0.2361368537,0.1870181262,0.1990443319,-0.1162700206,-0.1539996713,0.2643208504,0.3141643107,-0.0525802039,-0.0466035381,0.0177290533,0.4519748688,-0.2121914476,-0.2798964679,0.2454179972,-0.1773500592,-0.1309855729,0.1275194585,-0.1967158169,-0.1245191246,-0.0778549537,-0.3614664078,0.0002923247,-0.2300691456,-0.3864842951,-0.221759975,-0.3067471087,-0.148767069,0.3727555573,0.6480480433,0.1632297337,-0.0901934057,-0.1348420382,-0.0588171333,-0.0446889363,-0.2744155526,-0.4228567481,-0.1009271741,0.1049097553,-0.0332545787,-0.3364382386,0.4607168734,0.3744395673,-0.1109757423,0.3731579781,-0.269818902,0.1481581628,0.2017128915,0.5984317064,-0.0929670185,0.1491685212,0.3131767809,-0.0584281608,-0.231814146,-0.3104812801,0.3493329585,0.1211912334,-0.5198779702,0.2872042954,-0.130935654,0.1329517066,-0.0196609981,-0.1120867133,0.1630742401,-0.3360396922,-0.1026534066,-0.1313270479,0.2828582823,0.0319539905,0.1305522025,-0.3676549494,-0.1331425756,0.223424688,0.1517508924,-0.228390485,0.0358835943,-0.0845753103,-0.1171071529,0.1633034945,0.1684955657,-0.1520997286,-0.0657869726,0.710524261,0.4017373919,-0.0259381682,-0.3726828694,0.1247756258,0.1605218947,-0.0716081113,0.0807434842,-0.6590612531,0.4140938818,-0.0702203438,0.0705798864,-0.108390823,0.1661629975,0.2895028293,-0.2371481508,0.0395896994,-0.1682233214,0.3143056035,0.5559506416,0.3530573845,-0.1535458863,-0.4435145855,0.1051558256,-0.6972011328,-0.0495921783,-0.026042847,-0.0027955971,-0.2676233053,0.2278954983,-0.0403534733,0.2359807938,0.2688292563,0.2979460061,-0.1101177037,0.1627380103,0.2760838568,-0.0626733378,0.2161822915,-0.1061522886,-0.3277377188,-0.1313817054,0.4049280584,0.2382092774,0.0116231069,0.0085196709,0.3042285144,0.1753628254,-0.1000528708,-0.0469267294,0.2736749351,-0.1836227924,0.1325827837,0.2519230843,-0.205432117,-0.0217876714,0.2373282909,0.1182722375,-0.0469168164,0.1121253148,-0.3568478525,0.778847754,-0.4367331564,-0.0640919358,0.0964713767,-0.1648805439,0.1710062176,0.1025388539,0.0354846083,-0.1941321641,0.4717495739,0.1212736145,-0.009404582,-0.4361022711,-0.3261881769,-0.021046428,0.2483045608,-0.0025580148,-0.0462470613,-0.151955381,-0.1663593352,-0.0428271741,-0.0476744473,-0.0424825996,-0.2063824087,-0.1596759707,-0.2316061109,-0.1420589238,-0.0228530951,-0.0611937232,-0.1075326949,0.3066417575,0.4745171964,0.3733968139,0.0308912918,0.1602347046,-0.1567804515,-0.2718474567,-0.0966227427,-0.3991087675,-0.0479865856,-0.4468705952,0.2575535476,-0.311026752,-0.2784337699,0.3430234492,-0.1163650304,-0.0179087929,0.4083646536,-0.5690831542,-0.0278029591,0.127594009,0.3076961339,0.1703378856,-0.1437247097,0.3931418657,0.2275318652,-0.0822774693,-0.0902018547,-0.1061115414,0.2046216428,-0.1738822907,0.6187602282,-0.1944050044,-0.0548236817,-0.0545141436,0.6473221183,0.3969494998,-0.1100327671,-0.0457413495,-0.2120825797,0.555854857,0.0750215128,-0.0713288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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1922","title":"How to update the \"wino_bias\" dataset","comments":"Hi @JieyuZhao !\r\n\r\nYou can edit the dataset card of wino_bias to update the URL via a Pull Request. This would be really appreciated :)\r\n\r\nThe dataset card is the README.md file you can find at https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/wino_bias\r\nAlso the homepage url is also mentioned in the wino_bias.py so feel free to update it there as well.\r\n\r\nYou can create a Pull Request directly from the github interface by editing the files you want and submit a PR, or from a local clone of the repository.\r\n\r\nThanks for noticing !","body":"Hi all,\r\n\r\nThanks for the efforts to collect all the datasets! But I think there is a problem with the wino_bias dataset. The current link is not correct. How can I update that?\r\n\r\nThanks!","comment_length":89,"text":"How to update the \"wino_bias\" dataset \n Hi all,\r\n\r\nThanks for the efforts to collect all the datasets! But I think there is a problem with the wino_bias dataset. The current link is not correct. How can I update that?\r\n\r\nThanks! \n Hi @JieyuZhao !\r\n\r\nYou can edit the dataset card of wino_bias to update the URL via a Pull Request. This would be really appreciated :)\r\n\r\nThe dataset card is the README.md file you can find at https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/wino_bias\r\nAlso the homepage url is also mentioned in the wino_bias.py so feel free to update it there as well.\r\n\r\nYou can create a Pull Request directly from the github interface by editing the files you want and submit a PR, or from a local clone of the repository.\r\n\r\nThanks for noticing !","embeddings":[-0.3595491946,0.1663745791,-0.0910964832,0.1053340286,0.0010983016,0.1791955829,-0.0393261202,0.0637117624,0.0622652695,-0.1287824512,-0.2621755898,-0.0001704918,0.2578838766,0.1593094617,0.2776818871,-0.3877265155,0.1113262102,0.1100791767,0.1167294681,-0.2110313922,-0.309186995,-0.0822069719,-0.2789872587,0.0970853269,-0.0021148934,-0.0778445676,-0.5115855932,0.2775803506,-0.270675987,-0.4728704989,0.3751768172,0.4130046964,-0.0921292379,0.3960892856,-0.0001064684,-0.2544566095,0.4334570467,0.117040202,-0.151261121,-0.0557776429,-0.1133984029,-0.185517177,-0.3816221058,-0.1367891133,0.0211595744,-0.1777675748,0.1367512643,0.1966270208,0.1025712192,0.4926286042,0.2142109126,0.2089529485,0.3577846587,-0.1554494798,0.3749152422,0.2227902114,0.2564052641,0.3468582332,0.0206908993,-0.1691053063,0.1622935236,0.2506525218,-0.1467977911,-0.1894206107,-0.0663957,-0.0445823632,0.0470025316,-0.1570632309,0.1275359094,0.1428323239,0.154863894,-0.0296167023,-0.3587404191,-0.0778496116,0.1910637617,-0.7335625291,0.165397957,0.0577254631,0.1552003771,-0.0117981331,0.1111088023,-0.518104434,-0.1214332953,0.3081212044,0.1125403568,0.1010735631,-0.0518354401,0.1025832146,0.1344674975,0.021949796,0.3273358345,0.0410104468,-0.2244393677,-0.0568372905,-0.2383382022,-0.1983612031,-0.1284375042,0.4627984762,0.1581492424,-0.0870212168,-0.2016758025,-0.0480877534,-0.2221380323,-0.160610646,0.3178705871,-0.0264144875,-0.139250353,-0.1350999027,0.1666113138,0.1985975355,-0.0380894132,0.086371921,-0.0215295386,-0.349781245,-0.1778565645,-0.0127986157,0.2721118331,-0.0768700242,-0.3750704527,-0.0947862789,0.0259466898,-0.1254574507,0.0305153821,0.0824875981,-0.1555774212,0.2190558761,-0.0637154728,0.1419501454,-0.2471201718,-0.4623448253,-0.2801394761,0.0670003891,-0.2803134024,-0.0033133586,0.1079281121,-0.1923789382,0.4917120934,-0.0540024936,-0.1442541778,-0.0563039929,-0.2679831088,0.0572684705,-0.0964815021,0.0256993696,-0.0513382815,0.0214462988,0.0096340887,0.2765556872,-0.0625869259,0.1380599141,-0.382538259,-0.2012334913,-0.0192093402,0.0240553021,-0.0979103968,-0.3637704551,-0.3967584372,0.2190155387,-0.1084562987,-0.0248063877,0.0903782323,-0.083959572,-0.325486511,-0.1047406942,0.3892816901,0.4097493589,0.0005988537,0.2222694606,-0.0285612568,0.0435792096,0.0198257845,0.1556759924,-0.1559701562,0.0572348237,-0.0255526211,-0.2854391038,0.2380369902,-0.4642197192,-0.4162271023,-0.2647117674,-0.2668766677,-0.3149490654,-0.2621735334,0.1892431974,-0.2789892256,0.0009048752,-0.1267001629,0.4232994914,-0.1128286645,-0.0350897089,-0.2599846125,-0.13261199,-0.1460773647,0.1583602279,0.0443662256,0.1021287441,-0.0254891552,-0.0300826151,0.4650369287,0.1059122086,-0.0444547124,0.0974038318,0.4143883884,0.1672912985,0.0513468683,-0.0249313638,-0.1223677546,-0.1004391462,0.0400929749,-0.0212420598,0.325835228,-0.097712025,-0.4292385578,0.0725061521,-0.0063626524,-0.1980825663,0.1160103604,0.032533098,0.1374223381,0.1323659271,-0.1745865196,-0.051063668,-0.1280091703,0.226971373,0.201635465,0.3748253882,-0.144493565,-0.0283053089,0.4443071485,0.2948619425,0.1618736684,-0.0843273774,-0.1209082156,0.5419946909,-0.1632207036,0.1217008978,0.482005626,0.25588274,0.2863232791,-0.5517087579,0.1105966493,0.1785252541,-0.1446702033,0.3147725761,-0.1308719516,0.1476735622,-0.2084826827,0.0985418633,-0.3138096035,0.4175610244,0.2844434977,-0.0370110124,0.1749881357,0.2364445925,-0.0953710377,-0.0353623927,0.0069961031,-0.2239562571,-0.3241182566,0.3490578532,0.10750328,-0.0751613975,-0.1951504648,0.0845110044,-0.4713924229,-0.18563582,-0.1845969707,0.12506558,0.2852442861,0.0596687049,0.0369316712,0.1783207804,0.0880993828,-0.1388009042,0.2750367522,0.253480643,-0.1154989824,-0.1864898056,0.1920571923,0.1174200997,-0.270031482,0.250079602,0.10697864,0.3870291114,-0.1194834039,-0.0423878431,0.10714975,-0.2513252795,-0.1118524969,-0.2089509666,-0.3875476122,-0.250233978,0.126520291,0.2735853493,-0.0377792493,0.323630482,-0.097894758,0.4073313475,0.2222720981,0.2817659676,-0.0579492003,-0.1924304664,-0.0709382668,-0.0261849798,0.1622069478,0.0077293878,0.1705101728,-0.3521750569,0.3545436263,-0.501827538,-0.2015229911,-0.0719844997,0.0605509207,0.4237366915,0.1837680042,0.0672684982,-0.0619829632,-0.0454567485,0.2018213719,-0.5429760814,0.1377966851,-0.1011143848,0.0560781769,0.1222836599,0.2462984025,-0.2002692819,0.0757783279,-0.1129235327,-0.0110854274,0.0062888195,0.2626053989,0.083349444,0.0803580731,-0.1830531061,-0.1386593133,-0.1123519912,-0.2118853331,-0.5318339467,0.294545114,-0.3595643342,-0.4036683738,0.0698623732,-0.0449920334,0.129984349,-0.2164371461,-0.4553367794,-0.181292206,-0.1102129892,-0.0260291696,-0.0176295452,0.2905609906,0.2023494542,-0.2502673268,-0.2868277729,-0.0613818429,-0.2773211002,-0.0220623054,0.1599816978,0.4155290425,0.2630163729,-0.1384329945,-0.0856813565,0.7499744296,0.1584288925,-0.3041239679,0.2625769973,-0.1049516127,0.3394186497,0.2625924051,-0.4821255803,0.0951007381,0.0758165643,0.361826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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1919","title":"Failure to save with save_to_disk","comments":"Hi thanks for reporting and for proposing a fix :)\r\n\r\nI just merged a fix, feel free to try it from the master branch !","body":"When I try to save a dataset locally using the `save_to_disk` method I get the error:\r\n\r\n```bash\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/content\/squad\/train\/squad-train.arrow'\r\n```\r\n\r\nTo replicate:\r\n\r\n1. Install `datasets` from master\r\n2. Run this code:\r\n\r\n    ```python\r\n    from datasets import load_dataset\r\n    squad = load_dataset(\"squad\")   # or any other dataset\r\n    squad.save_to_disk(\"squad\")     # error here\r\n    ```\r\n\r\nThe problem is that the method is not creating a directory with the name `dataset_path` for saving the dataset in (i.e. it's not creating the *train* and *validation* directories in this case). After creating the directory the problem resolves.\r\nI'll open a PR soon doing that and linking this issue.\r\n","comment_length":25,"text":"Failure to save with save_to_disk \n When I try to save a dataset locally using the `save_to_disk` method I get the error:\r\n\r\n```bash\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/content\/squad\/train\/squad-train.arrow'\r\n```\r\n\r\nTo replicate:\r\n\r\n1. Install `datasets` from master\r\n2. Run this code:\r\n\r\n    ```python\r\n    from datasets import load_dataset\r\n    squad = load_dataset(\"squad\")   # or any other dataset\r\n    squad.save_to_disk(\"squad\")     # error here\r\n    ```\r\n\r\nThe problem is that the method is not creating a directory with the name `dataset_path` for saving the dataset in (i.e. it's not creating the *train* and *validation* directories in this case). After creating the directory the problem resolves.\r\nI'll open a PR soon doing that and linking this issue.\r\n \n Hi thanks for reporting and for proposing a fix :)\r\n\r\nI just merged a fix, feel free to try it from the master branch !","embeddings":[-0.0436869934,0.142949298,0.0570114776,0.1895621121,0.5194172263,0.2885248661,0.192212835,0.2388131469,-0.1313640922,0.1859047413,0.141578272,0.3680593073,-0.3196108043,-0.2508209646,0.0698751137,0.0474057831,0.2884759605,0.0203925744,-0.0373371616,-0.0828118548,-0.1238706633,0.264870733,0.0996688977,0.0421990082,-0.5081453919,-0.0061072712,-0.1618735492,0.31453076,0.001071627,-0.2673496306,0.1534147561,-0.3283669651,0.2090518028,0.5840054154,-0.0001187165,0.0369130969,-0.0382387005,-0.1583705544,-0.4442630112,-0.4077847898,-0.221984148,-0.1730088443,0.1148869023,-0.4092869163,0.0701678395,-0.0506983846,0.2495624125,-0.1575463563,0.3871827722,0.2822141647,0.1549114436,-0.0382053778,0.1740321517,-0.2288421541,-0.1663120538,0.6161901355,-0.3467484713,0.2955402732,-0.0347836353,-0.1686111987,0.3699814379,0.0192853771,-0.0258871969,-0.1304126829,0.28187567,0.1215031296,0.0884760767,-0.330727458,-0.013628141,0.0122261383,0.614715457,-0.6311994791,-0.5074194074,0.224433586,0.0965238065,0.019315023,0.1897397637,0.2495189756,-0.2263125926,0.1998587698,-0.1867233664,-0.3792485595,-0.3171930611,0.2024367154,0.2337694913,-0.1623820364,-0.0914282575,-0.0537081771,0.1051189676,-0.0304780751,-0.0507375374,-0.2843352258,0.0843666345,0.1800297052,-0.1861409843,-0.1386528015,-0.1079159379,-0.0201213434,-0.137375772,0.120416373,0.1969716698,-0.2985674143,0.0002792501,0.3721286058,0.1506853849,0.0598145351,0.2832764387,0.3988432884,0.3854604661,0.0902727023,0.0267784055,-0.0459410772,-0.041089762,-0.1973222941,0.5454720259,-0.0097808056,0.3190205693,-0.1826929003,-0.2289980054,0.4254073203,-0.1825689673,0.1510066986,0.0385164618,0.2409756631,0.111803785,-0.0846430436,-0.0101934876,0.4569025636,0.0640194342,0.3838762045,-0.1783630103,0.1913471967,-0.0088196388,0.2253187895,0.2253583223,-0.1830714494,0.1848752797,0.0567193106,-0.0188269503,-0.0164779108,-0.0026833476,-0.2802461088,0.0282900464,0.3156400323,0.072548911,0.1089627668,0.1198396832,-0.4458800554,-0.1219691038,0.2643061876,-0.0774846897,-0.4749075174,-0.2428726852,0.0788345858,0.0020117979,-0.0123701124,-0.2620709538,-0.1679551005,0.205144152,-0.115492247,-0.064013809,0.0571926869,-0.0576198176,-0.416885674,0.4127009511,0.3620338142,-0.506675899,0.2732725441,0.0345090143,-0.123007603,0.159774214,0.5587685108,-0.0151765971,0.3559604883,-0.2971852124,0.311873883,0.5128965974,-0.40454036,-0.2994139194,0.1477932185,-0.1420666873,-0.3448434174,0.0593093149,0.1312277913,-0.1309546828,-0.1517180353,0.0372945443,0.0817090347,-0.1437611431,0.0410480499,-0.4923837483,0.1228282973,-0.0063296775,0.0801651329,-0.1582182348,-0.0098179309,0.2346519828,0.0741199479,0.3167810738,-0.2465216964,0.1298096031,0.4385485947,0.7910145521,-0.1569506973,-0.1734226346,-0.0515779667,-0.3099271953,0.1068462878,0.2395672798,-0.0529066622,-0.1526204497,-0.0953178927,-0.1524760127,0.0408612192,-0.4206057489,0.0266656391,0.0151800141,-0.0559189469,0.1668254435,0.1911595017,-0.3386030197,0.1531254202,-0.3734537661,0.0766417757,0.054072801,0.4180463254,-0.3401333988,-0.3832112253,-0.2234160751,0.0831986815,0.0262242835,-0.0200842619,-0.1568193138,0.4635842144,0.0581358857,0.1698818505,0.1769722253,0.059483245,0.1188471317,-0.1710497886,-0.0211702865,0.3229103386,0.3793407083,0.0707868263,-0.6439212561,-0.0796288475,-0.0559942573,0.0869714543,0.1420308352,0.0986385494,0.1159187704,0.0188671127,0.008736846,-0.0919285342,0.1190077364,0.0041110129,0.1784569323,-0.2022560239,-0.3054654002,-0.0537458211,0.0045893178,0.0100243511,0.4323045611,0.1164100617,0.1207392812,-0.0221861303,-0.0730500519,0.4694502652,0.539755702,-0.0449205339,-0.0467782505,0.0906326398,-0.2236265689,-0.132350415,0.1095442101,-0.1445541531,0.2041365057,0.578312695,0.0744415969,0.1398697793,-0.237594232,-0.0113389026,-0.0262815934,0.2607398629,-0.293199718,-0.1521639973,-0.2273589224,0.1628912985,-0.2301882356,0.0590158775,-0.0771367252,-0.2169804573,0.0288824532,0.3544380963,-0.0217200462,-0.0699991062,0.007992289,0.0586082898,-0.1687670797,-0.359392643,-0.0122328019,-0.0431214832,-0.0647554994,-0.0405164324,0.2432489395,-0.093178913,0.407433331,-0.1362606883,0.0064516044,-0.6529046893,0.0417982414,-0.0146363229,0.006026695,0.3284969628,0.0468836725,0.3231469393,-0.2639580965,-0.2300036401,0.3766773045,0.0304342322,-0.1758868098,0.1453618705,0.1346272379,-0.3108990192,-0.1181407645,-0.3630990684,-0.3233220577,-0.2766486704,0.1131914258,-0.2195637077,0.1284826845,-0.0175772347,0.2915680707,0.2227851897,0.1160493568,-0.0855979994,-0.0912680626,-0.3929232657,0.6367709041,-0.310070008,-0.4007944167,0.1726480722,0.1183610633,0.029365208,-0.0310871731,-0.3465647399,-0.3220780492,0.0218669232,0.1416007876,-0.1745146364,0.2809893787,0.3479137719,-0.0128053017,0.0259512234,0.019551171,-0.3645197451,0.1986777633,0.5634405613,0.5095309615,-0.2741743624,0.4664591253,0.0063155796,0.5923214555,0.1623983383,0.2559560239,0.4696496427,0.0460422821,0.3521324098,-0.2076819688,-0.3169225156,0.1021856144,-0.1217054576,-0.5222668052,-0.171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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1915","title":"Unable to download `wiki_dpr`","comments":"Thanks for reporting ! This is a bug. For now feel free to set `ignore_verifications=False` in `load_dataset`.\r\nI'm working on a fix","body":"I am trying to download the `wiki_dpr` dataset. Specifically, I want to download `psgs_w100.multiset.no_index` with no embeddings\/no index. In order to do so, I ran:\r\n\r\n`curr_dataset = load_dataset(\"wiki_dpr\", embeddings_name=\"multiset\", index_name=\"no_index\")` \r\n\r\nHowever, I got the following error:\r\n`datasets.utils.info_utils.UnexpectedDownloadedFile: {'embeddings_index'}`\r\n\r\nI tried adding in flags `with_embeddings=False` and `with_index=False`:\r\n\r\n`curr_dataset = load_dataset(\"wiki_dpr\", with_embeddings=False, with_index=False, embeddings_name=\"multiset\", index_name=\"no_index\")`\r\n\r\nBut I got the following error:\r\n`raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {\u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_5\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_15\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_30\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_36\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_18\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_41\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_13\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_48\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_10\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_23\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_14\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_34\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_43\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_40\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_47\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_3\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_24\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_7\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_33\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_46\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_42\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_27\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_29\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_26\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_22\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_4\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_20\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_39\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_6\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_16\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_8\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_35\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_49\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_17\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_25\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_0\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_38\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_12\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_44\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_1\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_32\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_19\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_31\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_37\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_9\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_11\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_21\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_28\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_45\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_2\u2019}`\r\n\r\nIs there anything else I need to set to download the dataset?\r\n\r\n**UPDATE**: just running `curr_dataset = load_dataset(\"wiki_dpr\", with_embeddings=False, with_index=False)` gives me the same error.\r\n","comment_length":22,"text":"Unable to download `wiki_dpr` \n I am trying to download the `wiki_dpr` dataset. Specifically, I want to download `psgs_w100.multiset.no_index` with no embeddings\/no index. In order to do so, I ran:\r\n\r\n`curr_dataset = load_dataset(\"wiki_dpr\", embeddings_name=\"multiset\", index_name=\"no_index\")` \r\n\r\nHowever, I got the following error:\r\n`datasets.utils.info_utils.UnexpectedDownloadedFile: {'embeddings_index'}`\r\n\r\nI tried adding in flags `with_embeddings=False` and `with_index=False`:\r\n\r\n`curr_dataset = load_dataset(\"wiki_dpr\", with_embeddings=False, with_index=False, embeddings_name=\"multiset\", index_name=\"no_index\")`\r\n\r\nBut I got the following error:\r\n`raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {\u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_5\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_15\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_30\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_36\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_18\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_41\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_13\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_48\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_10\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_23\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_14\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_34\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_43\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_40\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_47\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_3\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_24\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_7\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_33\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_46\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_42\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_27\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_29\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_26\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_22\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_4\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_20\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_39\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_6\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_16\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_8\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_35\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_49\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_17\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_25\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_0\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_38\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_12\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_44\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_1\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_32\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_19\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_31\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_37\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_9\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_11\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_21\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_28\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_45\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_2\u2019}`\r\n\r\nIs there anything else I need to set to download the dataset?\r\n\r\n**UPDATE**: just running `curr_dataset = load_dataset(\"wiki_dpr\", with_embeddings=False, with_index=False)` gives me the same error.\r\n \n Thanks for reporting ! This is a bug. For now feel free to set `ignore_verifications=False` in `load_dataset`.\r\nI'm working on a fix","embeddings":[-0.3591079712,-0.3630983829,-0.0504424088,0.3148263693,0.3717034757,0.4270488024,0.3198949397,0.0625339374,0.2385216206,0.1804990321,0.1647016555,-0.0206091162,0.0954112709,-0.0210907198,-0.1673092544,-0.0334004611,0.1001311168,0.1070557833,0.3710588217,-0.0622109734,-0.329595685,0.2548207641,-0.3576647937,-0.231895268,-0.1536287069,-0.143520534,-0.0590852164,-0.0766759217,-0.4447588623,-0.4724355638,0.5257073641,0.2139309347,0.456645906,0.2094175071,-0.000123334,-0.0081851818,0.2570953667,-0.276065439,-0.2267439216,-0.1994098872,-0.5099616051,-0.2134321183,0.1228160188,-0.2729622424,0.2952129245,-0.2128572315,-0.0749510676,-0.0928959846,0.074530825,0.1510064006,0.1449033767,-0.0625398681,0.1508243978,-0.2355563939,0.0945451707,-0.1769310385,-0.1318905056,0.1710799336,-0.0272569992,0.1341178864,0.2691080272,0.0560868569,-0.2518814504,0.2277733535,0.1170533374,0.0164909512,0.2614754736,-0.304164052,0.4016195536,0.5526133776,0.923730731,0.1302185357,-0.2386934608,0.0382870622,0.211455673,-0.3757750988,0.1635375172,0.2258236408,-0.2530870736,0.200611487,-0.2965897322,-0.0683538169,-0.0212516002,0.3237015009,-0.0989618227,0.3529938459,0.2022208571,0.1463739276,0.1856656522,-0.2036290169,0.0743476376,-0.1115095615,-0.2810247838,0.0874456316,-0.2282613516,0.0073461859,-0.1403028518,0.0571529195,0.2315741628,0.3335160315,0.0137926796,0.1705392301,0.4314410686,0.1014831588,0.2653803229,0.2508428395,-0.1195526347,0.2461987734,0.3145948052,0.547288239,0.2549657226,-0.0376866497,-0.0095993467,-0.0076847337,-0.0618706718,-0.1101422459,-0.0867578164,-0.3650256991,-0.299870044,0.1207373217,-0.2721976042,-0.1857746094,-0.2455056757,0.3989276588,0.0512363166,0.4941689968,0.1345018893,0.1692253351,-0.0389331952,0.045549307,-0.096598126,-0.0991690308,-0.1295962483,0.0827541575,0.3832916021,-0.3321835995,0.3954805434,0.0359180942,0.0670721903,0.0898475721,-0.0672724321,-0.0200424772,0.2051387131,0.3828620315,0.0285898894,0.2937918305,0.107425645,-0.2261042297,-0.0246159267,0.2774973512,-0.1818651706,-0.4335758388,-0.0587910637,0.124901168,-0.2760751247,-0.0717422366,-0.3263556063,0.1071712226,0.0178034566,-0.1447193474,0.158366397,0.0138511891,-0.2004513741,-0.3192541003,0.3162532151,0.5299692154,-0.53788656,-0.0565230511,-0.0876784995,-0.2763260901,0.1355169863,0.0153356371,-0.2887880504,-0.0896552205,-0.3782499731,0.1199998185,0.1308039427,-0.4048190117,-0.5335974097,0.4260773361,0.0165747441,0.1425583661,0.3088404834,0.0687623024,0.2286393195,0.0907653645,-0.109613128,0.360917598,0.1335101128,-0.2713665366,-0.0036884013,-0.3318436444,0.3804715872,0.0646600276,-0.0475530401,-0.0033561576,0.118311286,0.1810515225,0.4451654851,0.0042786784,0.031223679,0.2706511021,0.1180464923,0.0901454762,0.1339274943,0.1229389459,-0.51377666,0.2290322632,0.1504512876,0.0457544737,-0.3320191801,-0.094936803,-0.3201955557,-0.0873651728,-0.3744758666,-0.2137416899,-0.0082012471,0.1704927832,-0.0325004943,-0.1602595001,-0.0984629914,0.0415371247,-0.05279148,0.0277336389,-0.1782583445,0.1469732225,-0.1374594718,0.014372942,0.1123541743,-0.0683239251,0.318995446,-0.0505682491,0.0679063499,0.3047980666,0.2043274045,-0.0166690685,-0.0066899275,-0.005671245,0.258731097,-0.5035472512,-0.0205891393,0.3803394437,0.1576838642,-0.0605140664,-0.2632324398,0.224481225,-0.0290870704,0.3685749769,-0.2061018944,0.0416202657,0.3276460767,-0.1760827154,0.2287088186,-0.3677020967,0.2900660038,0.5791848898,-0.2493056208,-0.1534954756,-0.0482501648,0.0391812027,0.1322937459,-0.2087436318,-0.0871027559,0.0236714669,0.0552035943,-0.0481585823,0.1448453516,0.0600990131,0.3681735992,0.247254774,0.0936580449,-0.1286984682,0.1636072844,-0.3218720853,0.2134936303,-0.0119457636,-0.1579790264,0.5060125589,-0.0821752176,-0.0530164614,-0.2347956598,-0.0330483504,0.2623149455,0.2831532955,-0.3046387136,-0.1779405475,-0.6171994209,0.0053771483,0.0949762836,-0.1705055088,-0.2037487328,-0.2974213064,0.1051949412,-0.074800849,0.124833256,0.1406850666,-0.2971715331,-0.1109372154,-0.1286422014,-0.202541694,-0.0407080688,-0.0536280163,-0.331849426,-0.0249450263,0.2382584959,0.3014809191,0.2516848743,0.058719039,-0.3458539844,-0.3976098895,-0.0931589454,0.0518293828,-0.1394761503,0.0614196137,0.336607933,0.481333077,-0.0528129339,-0.3433493674,0.0972987488,-0.0614315197,-0.0334287025,-0.0621375032,-0.0878678113,-0.0453506224,-0.1144122407,-0.6167302728,-0.1330221742,-0.3135217726,-0.0966890827,0.0850574449,-0.0683745295,-0.0727720186,0.1985067874,-0.1263793856,0.2616435885,-0.0850995928,-0.1912420541,-0.1607696265,0.5849156976,-0.1287938356,-0.3757933974,0.5222776532,0.0111422595,-0.0262258314,0.3314102292,-0.5823156238,0.1069680825,0.1025796235,0.2082475722,0.0025481533,0.2310561985,0.305845499,0.0286073294,-0.0586488731,-0.0875662118,-0.093996942,-0.0564806722,-0.2831987143,0.509218514,-0.0105568729,0.2368230969,-0.1851426214,0.6094356179,0.3000097275,0.1178231388,0.3601537943,-0.0355622433,0.1675811857,0.1629151553,-0.1403379142,0.0368618518,-0.1783125699,0.3249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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1915","title":"Unable to download `wiki_dpr`","comments":"I just merged a fix :)\r\n\r\nWe'll do a patch release soon. In the meantime feel free to try it from the master branch\r\nThanks again for reporting !","body":"I am trying to download the `wiki_dpr` dataset. Specifically, I want to download `psgs_w100.multiset.no_index` with no embeddings\/no index. In order to do so, I ran:\r\n\r\n`curr_dataset = load_dataset(\"wiki_dpr\", embeddings_name=\"multiset\", index_name=\"no_index\")` \r\n\r\nHowever, I got the following error:\r\n`datasets.utils.info_utils.UnexpectedDownloadedFile: {'embeddings_index'}`\r\n\r\nI tried adding in flags `with_embeddings=False` and `with_index=False`:\r\n\r\n`curr_dataset = load_dataset(\"wiki_dpr\", with_embeddings=False, with_index=False, embeddings_name=\"multiset\", index_name=\"no_index\")`\r\n\r\nBut I got the following error:\r\n`raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {\u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_5\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_15\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_30\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_36\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_18\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_41\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_13\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_48\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_10\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_23\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_14\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_34\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_43\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_40\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_47\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_3\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_24\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_7\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_33\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_46\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_42\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_27\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_29\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_26\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_22\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_4\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_20\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_39\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_6\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_16\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_8\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_35\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_49\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_17\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_25\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_0\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_38\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_12\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_44\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_1\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_32\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_19\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_31\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_37\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_9\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_11\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_21\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_28\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_45\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_2\u2019}`\r\n\r\nIs there anything else I need to set to download the dataset?\r\n\r\n**UPDATE**: just running `curr_dataset = load_dataset(\"wiki_dpr\", with_embeddings=False, with_index=False)` gives me the same error.\r\n","comment_length":29,"text":"Unable to download `wiki_dpr` \n I am trying to download the `wiki_dpr` dataset. Specifically, I want to download `psgs_w100.multiset.no_index` with no embeddings\/no index. In order to do so, I ran:\r\n\r\n`curr_dataset = load_dataset(\"wiki_dpr\", embeddings_name=\"multiset\", index_name=\"no_index\")` \r\n\r\nHowever, I got the following error:\r\n`datasets.utils.info_utils.UnexpectedDownloadedFile: {'embeddings_index'}`\r\n\r\nI tried adding in flags `with_embeddings=False` and `with_index=False`:\r\n\r\n`curr_dataset = load_dataset(\"wiki_dpr\", with_embeddings=False, with_index=False, embeddings_name=\"multiset\", index_name=\"no_index\")`\r\n\r\nBut I got the following error:\r\n`raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {\u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_5\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_15\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_30\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_36\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_18\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_41\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_13\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_48\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_10\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_23\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_14\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_34\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_43\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_40\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_47\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_3\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_24\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_7\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_33\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_46\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_42\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_27\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_29\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_26\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_22\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_4\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_20\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_39\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_6\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_16\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_8\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_35\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_49\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_17\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_25\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_0\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_38\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_12\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_44\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_1\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_32\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_19\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_31\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_37\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_9\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_11\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_21\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_28\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_45\u2019, \u2018https:\/\/dl.fbaipublicfiles.com\/rag\/rag_multiset_embeddings\/wiki_passages_2\u2019}`\r\n\r\nIs there anything else I need to set to download the dataset?\r\n\r\n**UPDATE**: just running `curr_dataset = load_dataset(\"wiki_dpr\", with_embeddings=False, with_index=False)` gives me the same error.\r\n \n I just merged a fix :)\r\n\r\nWe'll do a patch release soon. In the meantime feel free to try it from the master branch\r\nThanks again for reporting !","embeddings":[-0.3591079712,-0.3630983829,-0.0504424088,0.3148263693,0.3717034757,0.4270488024,0.3198949397,0.0625339374,0.2385216206,0.1804990321,0.1647016555,-0.0206091162,0.0954112709,-0.0210907198,-0.1673092544,-0.0334004611,0.1001311168,0.1070557833,0.3710588217,-0.0622109734,-0.329595685,0.2548207641,-0.3576647937,-0.231895268,-0.1536287069,-0.143520534,-0.0590852164,-0.0766759217,-0.4447588623,-0.4724355638,0.5257073641,0.2139309347,0.456645906,0.2094175071,-0.000123334,-0.0081851818,0.2570953667,-0.276065439,-0.2267439216,-0.1994098872,-0.5099616051,-0.2134321183,0.1228160188,-0.2729622424,0.2952129245,-0.2128572315,-0.0749510676,-0.0928959846,0.074530825,0.1510064006,0.1449033767,-0.0625398681,0.1508243978,-0.2355563939,0.0945451707,-0.1769310385,-0.1318905056,0.1710799336,-0.0272569992,0.1341178864,0.2691080272,0.0560868569,-0.2518814504,0.2277733535,0.1170533374,0.0164909512,0.2614754736,-0.304164052,0.4016195536,0.5526133776,0.923730731,0.1302185357,-0.2386934608,0.0382870622,0.211455673,-0.3757750988,0.1635375172,0.2258236408,-0.2530870736,0.200611487,-0.2965897322,-0.0683538169,-0.0212516002,0.3237015009,-0.0989618227,0.3529938459,0.2022208571,0.1463739276,0.1856656522,-0.2036290169,0.0743476376,-0.1115095615,-0.2810247838,0.0874456316,-0.2282613516,0.0073461859,-0.1403028518,0.0571529195,0.2315741628,0.3335160315,0.0137926796,0.1705392301,0.4314410686,0.1014831588,0.2653803229,0.2508428395,-0.1195526347,0.2461987734,0.3145948052,0.547288239,0.2549657226,-0.0376866497,-0.0095993467,-0.0076847337,-0.0618706718,-0.1101422459,-0.0867578164,-0.3650256991,-0.299870044,0.1207373217,-0.2721976042,-0.1857746094,-0.2455056757,0.3989276588,0.0512363166,0.4941689968,0.1345018893,0.1692253351,-0.0389331952,0.045549307,-0.096598126,-0.0991690308,-0.1295962483,0.0827541575,0.3832916021,-0.3321835995,0.3954805434,0.0359180942,0.0670721903,0.0898475721,-0.0672724321,-0.0200424772,0.2051387131,0.3828620315,0.0285898894,0.2937918305,0.107425645,-0.2261042297,-0.0246159267,0.2774973512,-0.1818651706,-0.4335758388,-0.0587910637,0.124901168,-0.2760751247,-0.0717422366,-0.3263556063,0.1071712226,0.0178034566,-0.1447193474,0.158366397,0.0138511891,-0.2004513741,-0.3192541003,0.3162532151,0.5299692154,-0.53788656,-0.0565230511,-0.0876784995,-0.2763260901,0.1355169863,0.0153356371,-0.2887880504,-0.0896552205,-0.3782499731,0.1199998185,0.1308039427,-0.4048190117,-0.5335974097,0.4260773361,0.0165747441,0.1425583661,0.3088404834,0.0687623024,0.2286393195,0.0907653645,-0.109613128,0.360917598,0.1335101128,-0.2713665366,-0.0036884013,-0.3318436444,0.3804715872,0.0646600276,-0.0475530401,-0.0033561576,0.118311286,0.1810515225,0.4451654851,0.0042786784,0.031223679,0.2706511021,0.1180464923,0.0901454762,0.1339274943,0.1229389459,-0.51377666,0.2290322632,0.1504512876,0.0457544737,-0.3320191801,-0.094936803,-0.3201955557,-0.0873651728,-0.3744758666,-0.2137416899,-0.0082012471,0.1704927832,-0.0325004943,-0.1602595001,-0.0984629914,0.0415371247,-0.05279148,0.0277336389,-0.1782583445,0.1469732225,-0.1374594718,0.014372942,0.1123541743,-0.0683239251,0.318995446,-0.0505682491,0.0679063499,0.3047980666,0.2043274045,-0.0166690685,-0.0066899275,-0.005671245,0.258731097,-0.5035472512,-0.0205891393,0.3803394437,0.1576838642,-0.0605140664,-0.2632324398,0.224481225,-0.0290870704,0.3685749769,-0.2061018944,0.0416202657,0.3276460767,-0.1760827154,0.2287088186,-0.3677020967,0.2900660038,0.5791848898,-0.2493056208,-0.1534954756,-0.0482501648,0.0391812027,0.1322937459,-0.2087436318,-0.0871027559,0.0236714669,0.0552035943,-0.0481585823,0.1448453516,0.0600990131,0.3681735992,0.247254774,0.0936580449,-0.1286984682,0.1636072844,-0.3218720853,0.2134936303,-0.0119457636,-0.1579790264,0.5060125589,-0.0821752176,-0.0530164614,-0.2347956598,-0.0330483504,0.2623149455,0.2831532955,-0.3046387136,-0.1779405475,-0.6171994209,0.0053771483,0.0949762836,-0.1705055088,-0.2037487328,-0.2974213064,0.1051949412,-0.074800849,0.124833256,0.1406850666,-0.2971715331,-0.1109372154,-0.1286422014,-0.202541694,-0.0407080688,-0.0536280163,-0.331849426,-0.0249450263,0.2382584959,0.3014809191,0.2516848743,0.058719039,-0.3458539844,-0.3976098895,-0.0931589454,0.0518293828,-0.1394761503,0.0614196137,0.336607933,0.481333077,-0.0528129339,-0.3433493674,0.0972987488,-0.0614315197,-0.0334287025,-0.0621375032,-0.0878678113,-0.0453506224,-0.1144122407,-0.6167302728,-0.1330221742,-0.3135217726,-0.0966890827,0.0850574449,-0.0683745295,-0.0727720186,0.1985067874,-0.1263793856,0.2616435885,-0.0850995928,-0.1912420541,-0.1607696265,0.5849156976,-0.1287938356,-0.3757933974,0.5222776532,0.0111422595,-0.0262258314,0.3314102292,-0.5823156238,0.1069680825,0.1025796235,0.2082475722,0.0025481533,0.2310561985,0.305845499,0.0286073294,-0.0586488731,-0.0875662118,-0.093996942,-0.0564806722,-0.2831987143,0.509218514,-0.0105568729,0.2368230969,-0.1851426214,0.6094356179,0.3000097275,0.1178231388,0.3601537943,-0.0355622433,0.1675811857,0.1629151553,-0.1403379142,0.0368618518,-0.1783125699,0.324907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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1911","title":"Saving processed dataset running infinitely","comments":"am suspicious of this thing? what's the purpose of this? pickling and unplickling\r\n`self = pickle.loads(pickle.dumps(self))`\r\n\r\n```\r\n    def save_to_disk(self, dataset_path: str, fs=None):\r\n        \"\"\"\r\n        Saves a dataset to a dataset directory, or in a filesystem using either :class:`datasets.filesystem.S3FileSystem` or any implementation of ``fsspec.spec.AbstractFileSystem``.\r\n\r\n        Args:\r\n            dataset_path (``str``): path (e.g. ``dataset\/train``) or remote uri (e.g. ``s3:\/\/my-bucket\/dataset\/train``) of the dataset directory where the dataset will be saved to\r\n            fs (Optional[:class:`datasets.filesystem.S3FileSystem`,``fsspec.spec.AbstractFileSystem``],  `optional`, defaults ``None``): instance of :class:`datasets.filesystem.S3FileSystem` or ``fsspec.spec.AbstractFileSystem`` used to download the files from remote filesystem.\r\n        \"\"\"\r\n        assert (\r\n            not self.list_indexes()\r\n        ), \"please remove all the indexes using `dataset.drop_index` before saving a dataset\"\r\n        self = pickle.loads(pickle.dumps(self))\r\n  ```","body":"I have a text dataset of size 220M.\r\n\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes.\r\n\r\nfilter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https:\/\/github.com\/huggingface\/datasets\/issues\/1796)\r\n\r\n```dataset._data = dataset._data.filter(...)```\r\nIt took 1 hr for the filter.\r\n\r\nThen i use `save_to_disk()` on processed dataset and it is running forever.\r\n\r\nI have been waiting since 8 hrs, it has not written a single byte. \r\n\r\nInfact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. \r\nSecond process is the one.\r\n<img width=\"1672\" alt=\"Screenshot 2021-02-19 at 6 36 53 PM\" src=\"https:\/\/user-images.githubusercontent.com\/20911334\/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png\">\r\n\r\n\r\nI am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function.","comment_length":103,"text":"Saving processed dataset running infinitely \n I have a text dataset of size 220M.\r\n\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes.\r\n\r\nfilter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https:\/\/github.com\/huggingface\/datasets\/issues\/1796)\r\n\r\n```dataset._data = dataset._data.filter(...)```\r\nIt took 1 hr for the filter.\r\n\r\nThen i use `save_to_disk()` on processed dataset and it is running forever.\r\n\r\nI have been waiting since 8 hrs, it has not written a single byte. \r\n\r\nInfact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. \r\nSecond process is the one.\r\n<img width=\"1672\" alt=\"Screenshot 2021-02-19 at 6 36 53 PM\" src=\"https:\/\/user-images.githubusercontent.com\/20911334\/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png\">\r\n\r\n\r\nI am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function. \n am suspicious of this thing? what's the purpose of this? pickling and unplickling\r\n`self = pickle.loads(pickle.dumps(self))`\r\n\r\n```\r\n    def save_to_disk(self, dataset_path: str, fs=None):\r\n        \"\"\"\r\n        Saves a dataset to a dataset directory, or in a filesystem using either :class:`datasets.filesystem.S3FileSystem` or any implementation of ``fsspec.spec.AbstractFileSystem``.\r\n\r\n        Args:\r\n            dataset_path (``str``): path (e.g. ``dataset\/train``) or remote uri (e.g. ``s3:\/\/my-bucket\/dataset\/train``) of the dataset directory where the dataset will be saved to\r\n            fs (Optional[:class:`datasets.filesystem.S3FileSystem`,``fsspec.spec.AbstractFileSystem``],  `optional`, defaults ``None``): instance of :class:`datasets.filesystem.S3FileSystem` or ``fsspec.spec.AbstractFileSystem`` used to download the files from remote filesystem.\r\n        \"\"\"\r\n        assert (\r\n            not self.list_indexes()\r\n        ), \"please remove all the indexes using `dataset.drop_index` before saving a dataset\"\r\n        self = pickle.loads(pickle.dumps(self))\r\n  ```","embeddings":[-0.2176190168,0.1967649609,-0.1150818914,0.263695389,0.1485698819,-0.0731196851,0.1913734376,0.2333744466,-0.1932332069,-0.0838291422,0.0687725544,0.3565655351,-0.1087135077,0.2635340989,0.0180146117,0.2948513329,0.2866332531,0.1167820469,0.2218978852,0.0443398654,-0.1144503951,-0.039229136,-0.1053009704,0.0729433745,-0.2676576376,-0.2402478009,0.0895973071,-0.0787024647,-0.3543228209,-0.2827144265,-0.0402493291,0.2020813376,-0.020740699,0.5996127725,-0.0001320559,-0.3027279377,0.2690651119,-0.0898026377,-0.500980556,0.0918379053,0.1252096891,-0.0274518523,-0.0236187503,-0.4111998379,0.3176500797,0.0550618842,0.1007526144,-0.2602964044,0.2392185777,0.0291388854,0.0483903848,0.1627709866,-0.1344563365,0.4667539597,0.3288221657,0.5879324675,-0.0920103565,0.068196021,0.2598213553,-0.1367048174,-0.0111972671,0.213149175,-0.4668712914,-0.1813862175,0.3238168061,-0.0626365021,-0.1642286032,-0.485502094,0.1900697201,0.1010173261,0.3124753237,-0.5746484995,-0.3016631305,-0.2907087207,-0.3085360825,-0.4616621733,0.303160429,0.2828127742,-0.1148828343,0.0892646834,-0.3573212624,-0.0885956511,-0.0714923143,0.013924689,-0.1595258266,-0.0218514595,-0.2625794709,0.2816871703,0.1571230441,0.1349899769,0.2138314247,-0.5275251269,0.2432615906,0.1830554456,-0.2409775853,-0.0892678052,-0.128101632,0.2305015922,0.068454504,0.2216311097,-0.2834169865,0.0942212194,0.1737063825,0.0587529503,0.3496392965,-0.1559289396,0.1240393147,0.1688316613,0.1533984393,-0.3493698835,-0.1055040732,0.0989832655,0.1423719376,0.1292568445,0.2541599572,-0.4392640591,0.0998979211,-0.1352922022,-0.1503771544,0.293505311,-0.1735006124,-0.0440574475,-0.0193763841,0.2404387593,-0.189639315,-0.1738820374,-0.213664934,-0.1226866096,-0.282875061,0.1256302446,0.0212364867,-0.0136175109,-0.0764163733,0.189699471,-0.0462357253,-0.0983829647,-0.0973695442,-0.0382032655,-0.2951149642,0.0666370541,0.1504892856,-0.6224698424,0.2766810954,0.5634760857,0.0076077431,0.3507173359,-0.1286654323,-0.1917122602,-0.1637352556,0.3982414305,-0.0726503059,-0.1950869113,-0.0195657127,-0.0449417122,-0.2271591276,0.0960474312,-0.4880655706,0.0129513796,0.4187315106,-0.3198018074,0.113004379,-0.2247399688,-0.4087262154,-0.2898054123,0.0422482193,0.3129444122,-0.6525022388,0.1624652743,-0.2219654173,0.0037870104,0.3684933782,0.2907514572,-0.0184265543,0.3519964516,-0.2924845219,0.4722724259,0.4642398655,0.1513800025,-0.5824525952,0.2184977829,-0.0032660235,0.0871213526,-0.1679913849,0.2806184292,0.3480526209,-0.1936855912,0.0448203869,0.1299171746,0.0902841464,-0.0739290118,-0.2451011091,-0.1558860838,0.1333705038,0.1133300513,-0.1586099267,-0.0949898362,0.1699920446,-0.2811574638,0.3692251742,0.1862333864,0.2380425632,0.2262436748,0.4387608171,0.376616925,-0.0299835764,-0.2525387704,0.0410481878,-0.0314515792,0.2027600706,-0.2562841177,-0.3878393769,-0.3444662988,-0.0672249421,0.3747113049,-0.1951195449,0.0566943064,-0.1595136523,0.1894023567,0.0622430369,0.2978353202,-0.1763309389,0.1621255726,-0.428573966,-0.0729774162,-0.2045764029,0.1255356222,0.0948560983,-0.0802917704,-0.2075641155,-0.0644673705,0.111855194,-0.0987707898,0.0527718514,0.1788642108,0.3130970597,0.0153764123,-0.0880960226,0.2765067816,0.0521606021,-0.0698793381,-0.0566345677,-0.1350646168,0.4371303916,0.0080777425,-0.1692182124,0.1955073178,-0.0692287609,0.269998163,0.0169115625,-0.2877047956,0.1833025068,0.1248816699,-0.0894945711,0.1032610908,0.1317162216,0.2835094631,0.3211947978,0.2160081118,-0.1547553986,0.4590097368,0.6726176143,-0.3963784575,0.0546120107,0.3500018418,0.1655372381,-0.3123154938,-0.1193112507,0.1344985813,0.4746424258,0.0471061245,0.1163870096,-0.0722296908,0.0566727556,-0.1296166629,0.3597228825,0.1670878828,0.2787831724,0.266392678,0.2064008117,-0.0926338956,-0.3180205226,-0.2046259493,0.2067177743,0.3337043822,-0.2496601641,0.4450703561,-0.2661706805,-0.1963251382,-0.0248068664,-0.0397417173,-0.1011844352,-0.1752339751,-0.2606779933,0.2353005111,-0.0188816637,-0.001637742,0.2302643359,0.29997316,0.1067367792,-0.3236914575,-0.2955575287,-0.0433314107,-0.2829555273,-0.0933734104,0.3848826885,0.1502899081,0.4872848094,-0.0357298031,-0.2362879962,-0.6306734085,-0.3318595886,0.1534067988,-0.002999156,0.5537433028,0.0621285886,0.2313983291,-0.2958818972,0.1094467267,-0.0570730716,-0.0468581729,-0.2712249756,-0.1202104017,0.0726316348,-0.1153623015,0.0249984972,0.0948592946,-0.4184019864,0.0370903127,0.3668830693,-0.3680104911,0.3206292391,-0.2364815921,-0.1106603146,0.1673944741,-0.0467545763,-0.1276377589,-0.2547926009,-0.3890378773,0.2348028421,0.1060612202,-0.2417595685,-0.0012645205,0.0302166902,-0.2923387587,0.2514457405,-0.6670500636,0.1034713611,-0.2575093806,0.1251004934,0.0880426168,0.0954703465,0.3562363684,-0.1353866607,0.1612423211,-0.0044977837,-0.4499634802,-0.090728581,-0.0605865605,0.1561434269,0.2193526477,0.6643550992,0.0217189845,0.6045765877,0.0560661219,0.1380069256,0.2675991356,-0.0902759358,0.3034040928,-0.2566714883,-0.3080062866,0.1277370155,-0.17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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1911","title":"Saving processed dataset running infinitely","comments":"Tried finding the root cause but was unsuccessful.\r\nI am using lazy tokenization with `dataset.set_transform()`, it works like a charm with almost same performance as pre-compute.","body":"I have a text dataset of size 220M.\r\n\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes.\r\n\r\nfilter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https:\/\/github.com\/huggingface\/datasets\/issues\/1796)\r\n\r\n```dataset._data = dataset._data.filter(...)```\r\nIt took 1 hr for the filter.\r\n\r\nThen i use `save_to_disk()` on processed dataset and it is running forever.\r\n\r\nI have been waiting since 8 hrs, it has not written a single byte. \r\n\r\nInfact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. \r\nSecond process is the one.\r\n<img width=\"1672\" alt=\"Screenshot 2021-02-19 at 6 36 53 PM\" src=\"https:\/\/user-images.githubusercontent.com\/20911334\/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png\">\r\n\r\n\r\nI am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function.","comment_length":26,"text":"Saving processed dataset running infinitely \n I have a text dataset of size 220M.\r\n\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes.\r\n\r\nfilter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https:\/\/github.com\/huggingface\/datasets\/issues\/1796)\r\n\r\n```dataset._data = dataset._data.filter(...)```\r\nIt took 1 hr for the filter.\r\n\r\nThen i use `save_to_disk()` on processed dataset and it is running forever.\r\n\r\nI have been waiting since 8 hrs, it has not written a single byte. \r\n\r\nInfact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. \r\nSecond process is the one.\r\n<img width=\"1672\" alt=\"Screenshot 2021-02-19 at 6 36 53 PM\" src=\"https:\/\/user-images.githubusercontent.com\/20911334\/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png\">\r\n\r\n\r\nI am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function. \n Tried finding the root cause but was unsuccessful.\r\nI am using lazy tokenization with `dataset.set_transform()`, it works like a charm with almost same performance as pre-compute.","embeddings":[-0.3561964035,0.1683889478,-0.0956779867,0.2705768049,0.2809066176,-0.055566512,0.2274563462,0.2229010761,-0.3108579814,-0.1395974904,0.1082137525,0.175986141,-0.1438532025,0.2344981879,0.0023731389,0.2562197745,0.3514589071,0.1079189405,0.370406419,0.1087035164,-0.0698908716,0.0259365421,-0.1417382807,0.0397690423,-0.3694447577,-0.3420152068,0.1376417428,-0.2580528855,-0.3062804937,-0.2669488788,-0.0369216688,0.3850825429,0.0711701214,0.5333968997,-0.0001309843,-0.3164223433,0.2313465625,-0.0396906398,-0.2950721979,0.147365585,0.1879237145,-0.0768317059,-0.0761477426,-0.3226693869,0.228089273,-0.0560969152,0.0666025877,-0.1789620221,0.3069119453,-0.099914059,0.024462603,0.0963327587,-0.2024442405,0.3944402933,0.0781609491,0.5052488446,-0.0299684722,-0.0143226553,0.2417495102,-0.1317525357,-0.0599417277,0.2605648637,-0.4225959182,-0.1757376939,0.2483236045,-0.0603317432,-0.1893056929,-0.4330962598,0.3536810875,0.1250943244,0.2908191085,-0.5002196431,-0.2704515457,-0.2737931609,-0.2251416445,-0.496601522,0.3358698487,0.2875787914,-0.0780523568,0.0703711137,-0.450178355,-0.0643272996,-0.1219843104,-0.0093971854,-0.2441366762,0.0015158034,-0.2214839607,0.1797708869,-0.0281713419,0.0159979779,0.2974232733,-0.4438245296,0.1197837591,0.0765056983,-0.2945407033,-0.0409833752,-0.3404235244,0.2206659764,0.1488776505,0.1337817609,-0.247090444,0.1759064794,0.2328191847,-0.0440793559,0.1266121417,0.0395753346,0.0933191627,0.137498796,0.2706351876,-0.4037394226,-0.1725402325,0.0876222551,0.0085124671,0.1798761189,0.4154669046,-0.5015193224,-0.0385920517,-0.1568151265,-0.1673543453,0.2999088466,-0.1609703898,-0.0266306419,-0.0335096046,0.3120589554,-0.255743742,-0.1600605845,-0.1566638499,-0.0816945583,-0.1733277142,-0.00999643,0.0484994277,-0.0495843478,-0.0274326168,0.2023169845,0.0920676515,-0.0578010231,-0.0601685122,0.0159271527,-0.2328036875,-0.0403911062,0.1218574494,-0.6531044841,0.3523284495,0.3579154611,0.0929572433,0.4303992093,-0.2356288135,-0.1576525122,-0.0987181365,0.3413205743,0.0289170183,-0.1152943,0.0900052935,-0.0103598442,-0.22735551,0.045630239,-0.3860265315,0.1007698476,0.4957956374,-0.1020928547,-0.0312961563,-0.2901702523,-0.4178996384,-0.1631322056,0.1399657577,0.2322128564,-0.5348827243,0.1970506907,-0.1168977544,0.1143586263,0.3382962346,0.4622505605,0.0029967006,0.391759485,-0.253549099,0.3449238539,0.3348969221,0.217957437,-0.5527255535,0.1113338172,0.0105000501,-0.0713317022,-0.1493799686,0.2181277275,0.6505599022,-0.2163791656,0.1967260391,0.144339487,0.0347693004,0.0030682553,-0.3002889454,-0.2102754861,0.0704109669,0.1161040068,-0.0734507367,-0.1079778746,-0.0262140706,-0.0873260722,0.2607068717,0.087332502,0.3065142632,0.3071137667,0.2978533506,0.3108500838,0.0385424942,-0.2755227685,0.0926343054,-0.0664934963,0.2263993472,-0.2249599546,-0.3080353141,-0.3852899075,-0.0595657602,0.331705451,-0.1536318213,0.0673741996,-0.079326421,0.1915822327,-0.0316917934,0.2812978625,-0.0916687921,0.3534356058,-0.2316103876,0.0081601106,-0.0361627936,0.0677646548,0.1614734083,-0.2569529712,0.0035996104,0.0137703959,0.1429991126,-0.0656035691,0.0499034375,0.170520857,0.0855399221,0.0511174388,-0.1799753755,0.2203267515,0.0789977387,-0.1262023151,0.0091753472,-0.129791826,0.2911994159,-0.0562337823,-0.137433663,0.209573105,-0.0264990907,0.2317998707,0.0407148078,-0.2505154312,0.2457159907,0.0342696197,-0.0115949623,0.0257075839,0.2258248925,0.186943233,0.2858631909,0.1908427179,-0.1367448419,0.4230031073,0.638071835,-0.2912369967,-0.0326803103,0.2630530298,0.0070483703,-0.2236629277,-0.0833157003,0.0391303375,0.2409757823,0.1381784528,0.0359574258,-0.0949811414,0.0806814805,-0.1128686145,0.3657848835,0.1423152834,0.3048703969,0.2377583236,0.2720299661,-0.0556193516,-0.3268786073,-0.1154103503,0.312830776,0.3043539524,-0.1911308318,0.249332279,-0.3187403083,-0.2655820251,-0.0087982062,0.0150732314,-0.2098757923,-0.1644063592,-0.2142040133,0.2902170122,0.0964989066,0.1142695695,0.2372078151,0.1645357758,0.0896114781,-0.3337947428,-0.2886410058,-0.0336719826,-0.2719524801,-0.1009411514,0.3449321985,0.1193480939,0.5602348447,-0.0377671458,-0.2010662854,-0.5184054375,-0.3823202848,0.1836897135,0.095488131,0.4375211,-0.0720780939,0.2666137516,-0.2688071728,0.0775148794,-0.0349436626,-0.1127791032,-0.1897428185,-0.0965391099,0.0818462297,-0.1162786931,0.0815770254,0.1189096645,-0.414113611,0.0816564634,0.2215959877,-0.4189993441,0.2398495376,-0.4049991965,-0.1667506844,-0.0364277437,-0.0301092751,-0.1684183627,-0.1815478951,-0.3694897294,0.404592365,0.1032464877,-0.2782887518,0.0854258388,0.0241050757,-0.1648419201,0.3543712199,-0.5036894679,0.2920398116,-0.2637021542,0.1855168194,0.1028890386,-0.0470127612,0.4037770331,-0.054596547,0.1448313147,0.034324158,-0.5332224965,-0.0501898043,-0.1899865866,0.1441877633,0.2329204381,0.7339385152,0.0965076461,0.5828547478,0.0113101359,0.0956182927,0.188200593,-0.0941795409,0.1587119699,-0.1883860677,-0.3327953815,0.0815476552,-0.0654175654,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1911","title":"Saving processed dataset running infinitely","comments":"Hi ! This very probably comes from the hack you used.\r\n\r\nThe pickling line was added an a sanity check because save_to_disk uses the same assumptions as pickling for a dataset object. The main assumption is that memory mapped pyarrow tables must be reloadable from the disk. In your case it's not possible since you altered the pyarrow table.\r\nI would suggest you to rebuild a valid Dataset object from your new pyarrow table. To do so you must first save your new table to a file, and then make a new Dataset object from that arrow file.\r\n\r\nYou can save the raw arrow table (without all the `datasets.Datasets` metadata) by calling `map` with `cache_file_name=\"path\/to\/outut.arrow\"` and `function=None`. Having `function=None` makes the `map` write your dataset on disk with no data transformation.\r\n\r\nOnce you have your new arrow file, load it with `datasets.Dataset.from_file` to have a brand new Dataset object :)\r\n\r\nIn the future we'll have a better support for the fast filtering method from pyarrow so you don't have to do this very unpractical workaround. Since it breaks somes assumptions regarding the core behavior of Dataset objects, this is very discouraged.","body":"I have a text dataset of size 220M.\r\n\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes.\r\n\r\nfilter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https:\/\/github.com\/huggingface\/datasets\/issues\/1796)\r\n\r\n```dataset._data = dataset._data.filter(...)```\r\nIt took 1 hr for the filter.\r\n\r\nThen i use `save_to_disk()` on processed dataset and it is running forever.\r\n\r\nI have been waiting since 8 hrs, it has not written a single byte. \r\n\r\nInfact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. \r\nSecond process is the one.\r\n<img width=\"1672\" alt=\"Screenshot 2021-02-19 at 6 36 53 PM\" src=\"https:\/\/user-images.githubusercontent.com\/20911334\/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png\">\r\n\r\n\r\nI am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function.","comment_length":191,"text":"Saving processed dataset running infinitely \n I have a text dataset of size 220M.\r\n\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes.\r\n\r\nfilter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https:\/\/github.com\/huggingface\/datasets\/issues\/1796)\r\n\r\n```dataset._data = dataset._data.filter(...)```\r\nIt took 1 hr for the filter.\r\n\r\nThen i use `save_to_disk()` on processed dataset and it is running forever.\r\n\r\nI have been waiting since 8 hrs, it has not written a single byte. \r\n\r\nInfact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. \r\nSecond process is the one.\r\n<img width=\"1672\" alt=\"Screenshot 2021-02-19 at 6 36 53 PM\" src=\"https:\/\/user-images.githubusercontent.com\/20911334\/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png\">\r\n\r\n\r\nI am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function. \n Hi ! This very probably comes from the hack you used.\r\n\r\nThe pickling line was added an a sanity check because save_to_disk uses the same assumptions as pickling for a dataset object. The main assumption is that memory mapped pyarrow tables must be reloadable from the disk. In your case it's not possible since you altered the pyarrow table.\r\nI would suggest you to rebuild a valid Dataset object from your new pyarrow table. To do so you must first save your new table to a file, and then make a new Dataset object from that arrow file.\r\n\r\nYou can save the raw arrow table (without all the `datasets.Datasets` metadata) by calling `map` with `cache_file_name=\"path\/to\/outut.arrow\"` and `function=None`. Having `function=None` makes the `map` write your dataset on disk with no data transformation.\r\n\r\nOnce you have your new arrow file, load it with `datasets.Dataset.from_file` to have a brand new Dataset object :)\r\n\r\nIn the future we'll have a better support for the fast filtering method from pyarrow so you don't have to do this very unpractical workaround. Since it breaks somes assumptions regarding the core behavior of Dataset objects, this is very discouraged.","embeddings":[-0.3835667074,0.2207914442,-0.0753484517,0.2524212301,0.1676334143,-0.0859722495,0.0995910391,0.2425324768,-0.1649823487,-0.0584910735,0.0330239199,0.4020829201,-0.0848712102,0.2319572419,0.0172859058,0.2511723638,0.190382123,0.0988868326,0.1988591403,0.1922226101,-0.0973070562,-0.0212861095,-0.1073065996,0.0238847807,-0.3752510846,-0.3421777189,0.1020547897,-0.176468417,-0.2869735658,-0.3915145099,0.025844872,0.2738236785,0.0088607669,0.4969805777,-0.0001254971,-0.2490330487,0.2621245682,-0.0777269378,-0.3850048184,0.1543710083,0.1895141304,-0.0919579417,-0.0632527322,-0.3167696893,0.2532064319,-0.0840291232,-0.0716398135,-0.148009181,0.3763958514,-0.0836270675,0.093504861,0.1139939278,0.000027077,0.3896321356,0.1677815616,0.5001136661,-0.0288456958,0.0879574567,0.3362618983,-0.0670923293,-0.1371211708,0.27834481,-0.4867144525,-0.1411877722,0.2645096481,-0.09666688,-0.1675312221,-0.3948975801,0.3739179671,0.2074767649,0.261888206,-0.5467400551,-0.2214086056,-0.3172277808,-0.170903787,-0.5744575858,0.3465484381,0.3022803962,-0.1129226759,0.0919190571,-0.3701660931,-0.175999552,-0.1269787252,0.025989458,-0.1026383266,-0.043541003,-0.2155543417,0.253354758,0.1353450119,-0.0528708026,0.2719113231,-0.3670327961,0.0432741791,0.1267949194,-0.348074615,-0.0000376811,-0.4093147516,0.2114434987,0.1972282976,0.1091903672,-0.17486009,0.1767202616,0.2158230692,-0.0245413166,0.1190609559,-0.0190227367,0.0418545082,0.1248172522,0.1343432516,-0.341491878,-0.1594240218,0.0596138239,0.0896649361,0.0389786735,0.342050761,-0.4655125737,0.0720364526,-0.1747670472,-0.0930221528,0.273494482,-0.151959613,-0.0404583961,-0.0540088713,0.304574728,-0.2286708951,-0.1745485961,-0.1305063218,-0.0846120268,-0.1934614629,-0.0743912011,0.0262205694,-0.0696877688,-0.1318622977,0.2878908515,0.1656465828,-0.096797511,-0.0424892455,-0.0020873728,-0.2887094617,-0.0223020725,0.024013916,-0.5841047764,0.408166796,0.4192660153,0.0451572165,0.3685524166,-0.2013343126,-0.1447459161,-0.095655553,0.4664761424,-0.0685854778,-0.1822187304,0.0261735991,0.0107502295,-0.1690286845,0.0607698634,-0.557675302,0.0929521918,0.4866020977,-0.0743257329,-0.0206744783,-0.2441198379,-0.3163408339,-0.2649134398,0.1202724874,0.255748421,-0.6958663464,0.2083716094,-0.2188268602,0.1379354745,0.3864471018,0.4180851877,-0.0557118058,0.2867590487,-0.209656924,0.4172917306,0.233522743,0.2801867723,-0.6572457552,0.0674076602,-0.0122792264,-0.0770693049,-0.2024056166,0.2173897028,0.5708351731,-0.1448692977,0.0347846188,0.1399073899,0.0250293668,0.0227942038,-0.3262345493,-0.2562339008,0.0538614988,0.114188686,-0.1550353765,-0.2059192061,0.0014636974,-0.266328305,0.3237319589,0.0752424151,0.3228730261,0.3021536767,0.2688813806,0.2784599364,0.0033835014,-0.3233175278,0.0001042504,0.0160897318,0.1061026752,-0.2561635077,-0.4138259888,-0.3335952759,-0.0771510154,0.3796461225,-0.1549665183,0.1034822166,-0.0588509478,0.0857046396,0.0420032218,0.1270328313,-0.0741478056,0.2337340117,-0.2826394737,-0.0001098073,-0.115852721,0.0829696134,0.0813676342,-0.3049838245,0.0064164116,-0.0618377775,0.1211540177,-0.0209339373,0.0009330697,0.2589646578,0.1091075316,0.0138087384,-0.1826982647,0.1420538872,0.0493600667,-0.2159519643,-0.0053009465,-0.2205975205,0.3107066154,-0.0643308535,-0.1287136227,0.2295851111,-0.0448109694,0.2971573472,-0.0409098938,-0.2173621655,0.1645674556,-0.0057499204,0.0359961577,0.0714560226,0.0766403005,0.1678945571,0.2786027491,0.2042236626,-0.1401461512,0.4174443185,0.6351094246,-0.2277691811,-0.0020448521,0.2752580047,-0.0467976145,-0.2161701173,-0.0883960351,0.0545103662,0.2103078514,0.1702259332,-0.0626562163,-0.125707835,0.0470386706,-0.1102022976,0.3756606877,0.1766787022,0.3735621274,0.1716313064,0.2496705353,-0.1166419983,-0.3490358591,-0.0377165973,0.2515732646,0.3713640869,-0.2111776173,0.2102584392,-0.3709145188,-0.2967136502,-0.024760358,-0.0927341506,-0.1991704106,-0.1360981762,-0.1633742005,0.2816187143,0.0414899699,0.213025853,0.2939043939,0.2184886485,0.0265323035,-0.2598508894,-0.3517384529,-0.1167461351,-0.3186908364,-0.0677342489,0.4512522519,0.1509422958,0.562354207,0.1044084132,-0.1226967275,-0.5189983845,-0.2806610167,0.1452239603,0.0051963651,0.407317102,-0.0914313048,0.3319068551,-0.3093451262,0.0313942134,-0.0224245191,-0.1245275512,-0.2509892881,-0.0310691986,0.0188751332,-0.0936461911,0.1211017221,-0.0205935799,-0.3942092955,0.0286190175,0.2887539864,-0.3527413011,0.3154911101,-0.2839599848,-0.0240322258,0.027449118,-0.0858804733,-0.193931967,-0.2213399559,-0.3033426106,0.3021124303,0.1005964056,-0.271892637,0.1194721982,0.0966610909,-0.022728093,0.2998028696,-0.4964134097,0.3107545674,-0.3027847111,0.1872274876,0.0311478153,-0.0686372668,0.4508425295,-0.0096569639,0.0962054878,0.0648788735,-0.4937604368,-0.069287926,-0.191551432,0.1666082889,0.1861975342,0.6179507971,0.0621536411,0.6712687612,0.0298438482,0.0587151684,0.1744910181,-0.0372415073,0.1861652434,-0.2480670959,-0.3244780004,0.0117498096,-0.1864834428,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1907","title":"DBPedia14 Dataset Checksum bug?","comments":"Hi ! :)\r\n\r\nThis looks like the same issue as https:\/\/github.com\/huggingface\/datasets\/issues\/1856 \r\nBasically google drive has quota issues that makes it inconvenient for downloading files.\r\n\r\nIf the quota of a file is exceeded, you have to wait 24h for the quota to reset (which is painful).\r\n\r\nThe error says that the checksum of the downloaded file doesn't match because google drive returns a text file with the \"Quota Exceeded\" error instead of the actual data file.","body":"Hi there!!!\r\n\r\nI've been using successfully the DBPedia dataset (https:\/\/huggingface.co\/datasets\/dbpedia_14) with my codebase in the last couple of weeks, but in the last couple of days now I get this error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/conditional_classification\/basic_pipeline.py\", line 178, in <module>\r\n    main()\r\n  File \".\/conditional_classification\/basic_pipeline.py\", line 128, in main\r\n    corpus.load_data(limit_train_examples_per_class=args.data_args.train_examples_per_class,\r\n  File \"\/home\/fp\/dev\/conditional_classification\/conditional_classification\/datasets_base.py\", line 83, in load_data\r\n    datasets = load_dataset(self.name, split=dataset_split)\r\n  File \"\/home\/fp\/anaconda3\/envs\/conditional\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 609, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/fp\/anaconda3\/envs\/conditional\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 526, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/fp\/anaconda3\/envs\/conditional\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 586, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/home\/fp\/anaconda3\/envs\/conditional\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']\r\n```\r\n\r\nI've seen this has happened before in other datasets as reported in #537.\r\n\r\nI've tried clearing my cache and call again `load_dataset` but still is not working. My same codebase is successfully downloading and using other datasets (e.g. AGNews) without any problem, so I guess something has happened specifically to the DBPedia dataset in the last few days. \r\n\r\nCan you please check if there's a problem with the checksums? \r\n\r\nOr this is related to any other stuff? I've seen that the path in the cache for the dataset is `\/home\/fp\/.cache\/huggingface\/datasets\/d_bpedia14\/dbpedia_14\/2.0.0\/a70413e39e7a716afd0e90c9e53cb053691f56f9ef5fe317bd07f2c368e8e897...` and includes `d_bpedia14` instead maybe of `dbpedia_14`. Was this maybe a bug introduced recently?\r\n\r\nThanks!","comment_length":75,"text":"DBPedia14 Dataset Checksum bug? \n Hi there!!!\r\n\r\nI've been using successfully the DBPedia dataset (https:\/\/huggingface.co\/datasets\/dbpedia_14) with my codebase in the last couple of weeks, but in the last couple of days now I get this error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/conditional_classification\/basic_pipeline.py\", line 178, in <module>\r\n    main()\r\n  File \".\/conditional_classification\/basic_pipeline.py\", line 128, in main\r\n    corpus.load_data(limit_train_examples_per_class=args.data_args.train_examples_per_class,\r\n  File \"\/home\/fp\/dev\/conditional_classification\/conditional_classification\/datasets_base.py\", line 83, in load_data\r\n    datasets = load_dataset(self.name, split=dataset_split)\r\n  File \"\/home\/fp\/anaconda3\/envs\/conditional\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 609, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/fp\/anaconda3\/envs\/conditional\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 526, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/fp\/anaconda3\/envs\/conditional\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 586, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/home\/fp\/anaconda3\/envs\/conditional\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']\r\n```\r\n\r\nI've seen this has happened before in other datasets as reported in #537.\r\n\r\nI've tried clearing my cache and call again `load_dataset` but still is not working. My same codebase is successfully downloading and using other datasets (e.g. AGNews) without any problem, so I guess something has happened specifically to the DBPedia dataset in the last few days. \r\n\r\nCan you please check if there's a problem with the checksums? \r\n\r\nOr this is related to any other stuff? I've seen that the path in the cache for the dataset is `\/home\/fp\/.cache\/huggingface\/datasets\/d_bpedia14\/dbpedia_14\/2.0.0\/a70413e39e7a716afd0e90c9e53cb053691f56f9ef5fe317bd07f2c368e8e897...` and includes `d_bpedia14` instead maybe of `dbpedia_14`. Was this maybe a bug introduced recently?\r\n\r\nThanks! \n Hi ! :)\r\n\r\nThis looks like the same issue as https:\/\/github.com\/huggingface\/datasets\/issues\/1856 \r\nBasically google drive has quota issues that makes it inconvenient for downloading files.\r\n\r\nIf the quota of a file is exceeded, you have to wait 24h for the quota to reset (which is painful).\r\n\r\nThe error says that the checksum of the downloaded file doesn't match because google drive returns a text file with the \"Quota Exceeded\" error instead of the actual data file.","embeddings":[-0.2483563125,0.35080567,-0.1227644235,0.2624529004,0.1046657935,-0.0048445282,0.3205699623,0.5194298029,-0.087741904,-0.0413587727,0.0776256993,-0.1982264817,0.0031295526,0.3474114835,-0.2444945425,-0.0099745383,0.098670274,-0.1543822438,-0.0066188341,0.0506980233,-0.176279366,0.0808001161,-0.277286768,-0.3061199486,-0.0021763062,0.2340680808,0.014913098,-0.1259736717,-0.001067427,-0.2701264322,0.5489282608,0.1795443147,0.0238150284,0.4143281579,-0.0001126272,-0.0021212655,0.3832170963,-0.0588617101,-0.328229636,0.105318971,-0.3156419694,-0.1499086767,-0.0790167302,-0.0825071856,-0.1530301273,-0.5050341487,-0.2010857463,-0.1114249155,-0.104029946,0.261859864,0.2175973356,0.2089748532,0.0917270258,-0.0789880306,-0.1643640399,0.0381903574,-0.0346372761,0.0000728814,0.1079570428,-0.1750721484,0.0917512923,0.1509220302,-0.1619757265,0.0288006309,0.1156822592,-0.2075259537,0.0970106125,-0.2640064955,0.2317044139,0.240515843,0.2926249504,-0.128769815,-0.3720597327,-0.2977039814,-0.0750023425,-0.245641768,0.2302350849,0.1716050953,-0.067303285,-0.0363245681,-0.3061080277,0.252392441,0.1045450494,0.0709883124,-0.1234941557,0.1498817354,0.0577276759,-0.0749311298,0.1063356474,-0.0413521416,0.6009082198,-0.1663401127,-0.2048625946,0.0807095915,-0.4355798364,-0.1634497792,0.0560006686,0.6840990782,0.1832090616,0.4219745696,-0.1995084137,0.1657820642,-0.1209183633,-0.0922681913,0.1504204273,-0.0343498252,-0.0519279428,0.4296023548,0.2829835415,0.3001469374,0.1284109205,0.1946967095,0.1358951181,-0.2661388516,0.2309339345,0.0963650122,0.0433475263,-0.2907250226,-0.2783105969,0.276026845,-0.1270309091,-0.1380530298,0.2978390455,0.2877974212,-0.4330213964,0.1959685087,0.0082532791,0.0210149046,0.0442288406,-0.2511606216,-0.1368148774,-0.1471276432,-0.0469176024,0.137075603,0.1180858687,0.031462837,0.2896967828,-0.0035000346,0.1563295871,-0.1104827821,0.2000196576,-0.3637543023,0.2052349597,0.5173292756,-0.0956809148,0.2081980556,0.089403756,-0.3298082948,-0.0150805023,-0.1024182662,-0.2392274141,-0.1406963617,-0.3736905754,0.2286854237,-0.3321997821,0.0105260788,-0.5877588987,-0.2102768719,0.3507718146,-0.2136421949,0.0752197951,-0.3401685059,-0.2169393599,-0.1177302599,0.3215620518,0.3882475495,-0.2872908115,0.1926524937,-0.5058418512,-0.0116746584,0.1311635673,0.0566376485,-0.3111607134,0.3517674804,-0.3844766021,-0.2458629757,0.0601757877,-0.5608974099,-0.3146588802,0.1328949034,0.1920131296,0.3282431662,-0.1377325356,-0.2058589309,0.0681966692,-0.2593610585,0.1219714582,0.0948211327,0.0682110935,0.1584233344,-0.2493803948,-0.0130494172,0.2194643766,0.1278644204,0.1831167638,0.00329765,0.0568596125,0.1512579173,0.2725539207,-0.1036928073,-0.0148238773,0.1306653172,0.2006486058,0.0827575177,0.0509694889,-0.0756189004,-0.3890631199,0.1855844557,-0.2650212049,0.0332782269,-0.1553544253,-0.0455868579,-0.19924815,-0.1255375147,-0.4297518432,-0.3456073105,0.2111902684,0.2249656916,-0.1547851264,0.1536783427,0.046917703,0.1888696998,-0.3862114549,-0.0471783876,-0.0983198136,0.3950824738,-0.0024916609,0.0562320389,0.0013942948,0.0921594203,0.1276094764,-0.0379030854,-0.0082374858,0.4010099769,0.3606117666,0.038932316,0.190829441,0.3519996405,0.256611973,-0.2802025974,-0.2643060088,0.3049584329,0.1222953796,-0.0713119507,0.1305744052,0.3840272129,-0.2733516693,0.0554502793,0.126211822,0.0377751514,0.4955723882,-0.1079363376,-0.2369183451,-0.2576626241,0.176587835,0.1049608141,0.0675177202,0.1393899769,-0.0053886087,0.0798845366,0.2055527419,-0.080689393,-0.1177334562,-0.042633567,0.121971339,-0.035609182,0.0879070237,0.7510873079,0.4312459826,-0.0813290998,0.0834221691,0.1749548316,-0.0722506121,-0.0217371378,0.0707214326,0.078366138,0.1058773994,0.3351549804,0.058534801,0.0462715179,-0.1494187713,-0.2088983655,0.173096478,0.3105882406,-0.3180760741,0.0239008889,-0.3514176905,-0.2420360297,-0.3531622291,-0.1688136905,-0.2286220491,-0.438159287,-0.0157749671,0.3220815957,0.0368187949,0.2074562609,-0.4492830634,0.0925884247,0.1287255585,-0.2775695026,0.0639834106,0.1320360452,-0.0243535209,0.0451560877,0.57372576,0.2313758731,0.2702275813,-0.3618479073,-0.1297322214,-0.523191154,-0.3683932126,0.0594919212,0.0910136774,0.3205645382,0.1584272683,0.1651458293,0.1059408858,-0.3236399591,0.0281092543,0.317777127,-0.0200341064,-0.0149845202,-0.0636154339,0.0657196939,0.0275884718,0.0795964599,-0.2562216222,-0.0792468712,-0.2330019623,0.1119934544,0.0559557192,0.0892030075,-0.0778987408,0.1029612124,-0.0143481353,0.0297972932,-0.1901176423,-0.376108855,0.1693154871,-0.09028887,-0.441747874,-0.0958381593,-0.0300432798,0.371925056,-0.0784174949,-0.3715616465,0.006258986,-0.1335083842,0.1186246425,0.2444375604,-0.3846087158,0.2823690474,0.0073235747,0.016855523,-0.1849660724,0.0201206245,-0.0567025393,-0.2161255777,0.5088825226,-0.1245597601,-0.1940550208,0.0573147088,0.3122811317,0.4735923111,-0.1680437326,0.2759514749,0.0246083308,0.4678413868,-0.1390938908,-0.4603390992,-0.1930557489,-0.095010139,-0.0335226022,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1906","title":"Feature Request: Support for Pandas `Categorical`","comments":"We already have a ClassLabel type that does this kind of mapping between the label ids (integers) and actual label values (strings).\r\n\r\nI wonder if actually we should use the DictionaryType from Arrow and the Categorical type from pandas for the `datasets` ClassLabel feature type.\r\nCurrently ClassLabel corresponds to `pa.int64()` in pyarrow and `dtype('int64')` in pandas (so the label names are lost during conversions).\r\n\r\nWhat do you think ?","body":"```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.Series([\"a\", \"b\", \"c\", \"a\"], dtype=\"category\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws NotImplementedError\r\n# TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table\r\n```\r\n\r\nI'm curious if https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L796 could be built out in a way similar to `Sequence`?\r\n\r\ne.g. a `Map` class (or whatever name the maintainers might prefer) that can accept:\r\n\r\n```\r\nindex_type = generate_from_arrow_type(pa_type.index_type)\r\nvalue_type = generate_from_arrow_type(pa_type.value_type)\r\n```\r\n\r\nand then additional code points to modify:\r\n\r\n- FeatureType: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L694\r\n- A branch to handle Map in get_nested_type: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L719\r\n- I don't quite understand what `encode_nested_example` does but perhaps a branch there? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L755\r\n- Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L775\r\n\r\nI couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints.","comment_length":69,"text":"Feature Request: Support for Pandas `Categorical` \n ```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.Series([\"a\", \"b\", \"c\", \"a\"], dtype=\"category\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws NotImplementedError\r\n# TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table\r\n```\r\n\r\nI'm curious if https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L796 could be built out in a way similar to `Sequence`?\r\n\r\ne.g. a `Map` class (or whatever name the maintainers might prefer) that can accept:\r\n\r\n```\r\nindex_type = generate_from_arrow_type(pa_type.index_type)\r\nvalue_type = generate_from_arrow_type(pa_type.value_type)\r\n```\r\n\r\nand then additional code points to modify:\r\n\r\n- FeatureType: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L694\r\n- A branch to handle Map in get_nested_type: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L719\r\n- I don't quite understand what `encode_nested_example` does but perhaps a branch there? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L755\r\n- Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L775\r\n\r\nI couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints. \n We already have a ClassLabel type that does this kind of mapping between the label ids (integers) and actual label values (strings).\r\n\r\nI wonder if actually we should use the DictionaryType from Arrow and the Categorical type from pandas for the `datasets` ClassLabel feature type.\r\nCurrently ClassLabel corresponds to `pa.int64()` in pyarrow and `dtype('int64')` in pandas (so the label names are lost during conversions).\r\n\r\nWhat do you think 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1906","title":"Feature Request: Support for Pandas `Categorical`","comments":"Now that I've heard you explain ClassLabel, that makes a lot of sense!  While DictionaryType for Arrow (I think) can have arbitrarily typed keys, so it won't cover all potential cases, pandas' Category is *probably* the most common use for that pyarrow type, and ClassLabel should match that perfectly?\r\n\r\nOther thoughts:\r\n\r\n- changing the resulting patype on ClassLabel might be backward-incompatible?  I'm not totally sure if users of the `datasets` library tend to directly access the `patype` attribute (I don't think we really do, but we haven't been using it for very long yet).\r\n- would ClassLabel's dtype change to `dict[int64, string]`?  It seems like in practice a ClassLabel (when not explicitly specified) would be constructed from the DictionaryType branch of `generate_from_arrow_type`, so it's not totally clear to me that anyone ever actually accesses\/uses that dtype?\r\n- I don't quite know how `.int2str` and `.str2int` are used in practice - would those be kept?  Perhaps the implementation might actually be substantially smaller if we can just delegate to pyarrow's dict methods?\r\n\r\nAnother idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L932 , and then don't touch anything else.\r\n\r\nIn practice, I don't think this would be backward-incompatible in a way anyone would care about since the current behavior just throws an exception, and this way, we could support *reading* a pandas Categorical into a `Dataset` as a ClassLabel.  I *think* from there, while it would require some custom glue it wouldn't be too hard to convert the ClassLabel into a pandas Category if we want to go back - I think this would improve on the current behavior without risking changing the behavior of ClassLabel in a backward-incompat way.\r\n\r\nThoughts?  I'm not sure if this is overly cautious.  Whichever approach you think is better, I'd be happy to take it on!\r\n","body":"```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.Series([\"a\", \"b\", \"c\", \"a\"], dtype=\"category\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws NotImplementedError\r\n# TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table\r\n```\r\n\r\nI'm curious if https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L796 could be built out in a way similar to `Sequence`?\r\n\r\ne.g. a `Map` class (or whatever name the maintainers might prefer) that can accept:\r\n\r\n```\r\nindex_type = generate_from_arrow_type(pa_type.index_type)\r\nvalue_type = generate_from_arrow_type(pa_type.value_type)\r\n```\r\n\r\nand then additional code points to modify:\r\n\r\n- FeatureType: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L694\r\n- A branch to handle Map in get_nested_type: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L719\r\n- I don't quite understand what `encode_nested_example` does but perhaps a branch there? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L755\r\n- Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L775\r\n\r\nI couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints.","comment_length":319,"text":"Feature Request: Support for Pandas `Categorical` \n ```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.Series([\"a\", \"b\", \"c\", \"a\"], dtype=\"category\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws NotImplementedError\r\n# TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table\r\n```\r\n\r\nI'm curious if https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L796 could be built out in a way similar to `Sequence`?\r\n\r\ne.g. a `Map` class (or whatever name the maintainers might prefer) that can accept:\r\n\r\n```\r\nindex_type = generate_from_arrow_type(pa_type.index_type)\r\nvalue_type = generate_from_arrow_type(pa_type.value_type)\r\n```\r\n\r\nand then additional code points to modify:\r\n\r\n- FeatureType: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L694\r\n- A branch to handle Map in get_nested_type: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L719\r\n- I don't quite understand what `encode_nested_example` does but perhaps a branch there? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L755\r\n- Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L775\r\n\r\nI couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints. \n Now that I've heard you explain ClassLabel, that makes a lot of sense!  While DictionaryType for Arrow (I think) can have arbitrarily typed keys, so it won't cover all potential cases, pandas' Category is *probably* the most common use for that pyarrow type, and ClassLabel should match that perfectly?\r\n\r\nOther thoughts:\r\n\r\n- changing the resulting patype on ClassLabel might be backward-incompatible?  I'm not totally sure if users of the `datasets` library tend to directly access the `patype` attribute (I don't think we really do, but we haven't been using it for very long yet).\r\n- would ClassLabel's dtype change to `dict[int64, string]`?  It seems like in practice a ClassLabel (when not explicitly specified) would be constructed from the DictionaryType branch of `generate_from_arrow_type`, so it's not totally clear to me that anyone ever actually accesses\/uses that dtype?\r\n- I don't quite know how `.int2str` and `.str2int` are used in practice - would those be kept?  Perhaps the implementation might actually be substantially smaller if we can just delegate to pyarrow's dict methods?\r\n\r\nAnother idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L932 , and then don't touch anything else.\r\n\r\nIn practice, I don't think this would be backward-incompatible in a way anyone would care about since the current behavior just throws an exception, and this way, we could support *reading* a pandas Categorical into a `Dataset` as a ClassLabel.  I *think* from there, while it would require some custom glue it wouldn't be too hard to convert the ClassLabel into a pandas Category if we want to go back - I think this would improve on the current behavior without risking changing the behavior of ClassLabel in a backward-incompat way.\r\n\r\nThoughts?  I'm not sure if this is overly cautious.  Whichever approach you think is better, I'd be happy to take it on!\r\n","embeddings":[0.0230096243,-0.0102735767,-0.1426058412,0.2076647431,0.2370132208,0.1672788262,0.1267411113,0.2487185448,-0.0738426745,-0.2155069113,0.1673993021,0.2824072838,-0.2035112977,0.4252623618,0.1327462345,-0.2236522436,0.1246828139,0.0284454841,0.077850081,0.3117170632,-0.3771744668,-0.0838787854,-0.0995519832,0.2389727235,-0.1983468533,-0.1293813884,-0.45648247,-0.3532522023,-0.2593736649,-0.8477243185,0.3983210921,0.3807965219,0.1931584775,0.1735817343,-0.000115819,-0.3079189658,0.2058762759,-0.0425236411,-0.0503565595,-0.4431983232,-0.2918069661,-0.2464248687,0.3244947791,-0.2901013196,-0.2068761438,-0.292574048,0.0067172511,-0.2727668285,0.2317002714,0.0666810945,0.1667516083,-0.0497691333,-0.0274424106,-0.1615715176,0.1318348497,0.4021803737,-0.237100631,0.284689635,0.5595126152,-0.322232008,0.2314834446,0.0884723142,0.0117629515,-0.3249572814,0.6005215049,0.0490307808,0.2645990551,-0.5582161546,-0.3079175055,0.1526240557,0.3408336937,-0.3584251702,-0.5394771099,-0.4483566582,-0.0119229034,-0.2346872836,-0.0061525847,-0.1371355653,-0.1556818336,0.1616322845,0.112262167,-0.0731579587,-0.2399319559,0.1873058379,-0.0844331756,0.1476809233,-0.0443651713,-0.1675475389,0.2131358683,-0.099540472,0.003308367,-0.1552573591,0.0259761289,0.6048634052,-0.2247818261,-0.1363115907,0.0729947016,0.2108622342,0.0177890956,-0.0083071152,0.1002103984,0.01343753,-0.231400162,-0.0256741904,0.3791758418,0.292435199,0.1800874174,-0.086328879,-0.1288450658,0.3581373394,-0.0041660462,0.0536369346,-0.1282648146,-0.1226120666,-0.1723236442,0.1386386752,-0.0707128495,0.2454463691,-0.0304598641,-0.0372074991,-0.0177116375,-0.0263489336,0.0152251087,0.2628429532,-0.065954715,0.0928890333,-0.0641002133,0.0835099369,0.2764406502,0.120650351,-0.0680939406,0.0020986979,-0.2102169693,0.1605477333,-0.0775292516,-0.185875833,-0.1492251903,-0.0344319195,0.1192694381,0.1085190102,-0.2276078761,-0.0936202332,0.1229106933,0.0074040387,-0.4257945716,0.1622291803,0.2187957615,-0.0850061551,-0.3504912853,0.1319193393,0.1542150974,-0.2472275048,-0.2392049581,0.1113661602,-0.2031580359,0.1457660347,-0.3309437335,0.4760418832,-0.008096843,0.0497261994,0.2327813506,0.0547782816,-0.263841778,-0.3284746408,-0.1710059941,0.452237606,-0.3215060234,-0.2309907079,0.0531622022,-0.0306914076,0.0361553393,0.0772850141,-0.1583134085,0.1192366183,-0.0716489926,0.1776524633,0.5333985686,-0.1753790528,-0.1190234646,0.3577374816,0.0055855042,0.1976584643,0.2812623382,0.2622909248,0.1901309639,-0.2151019275,0.0931514278,0.128313452,-0.1040712446,-0.2355068028,0.149151817,-0.3304395676,0.1761069745,0.1930086315,-0.0684674233,0.2835514843,0.0743933991,-0.3360271156,0.3749298751,-0.2894718051,0.1078301296,0.063677147,0.306604445,0.4409077764,0.0205318797,-0.479870975,-0.1257184148,0.3306059539,-0.0952279717,0.1742290556,-0.3567067087,-0.4952701032,-0.0397885852,0.2739695907,-0.2659189999,0.1416194439,-0.0091220392,-0.2109060735,0.2343253344,0.2042154521,-0.3539479673,0.5252873302,-0.0650035888,0.2001345307,-0.2408441156,0.1756791472,-0.0458536372,0.0768375322,-0.1055376828,0.3452531993,-0.0846971571,-0.119718574,0.2849961519,0.0615107976,0.2428294271,-0.0946210027,-0.3645075262,0.229264617,0.526425004,0.04913757,-0.0908039883,-0.1106586307,0.0843873024,-0.0667051449,0.016678663,0.582357347,-0.4817159772,0.2271652371,0.1415827572,0.1412526667,0.0998748615,0.2575952411,-0.2499683946,-0.1974934191,0.1438122392,0.2578533888,-0.2217444628,0.0667169839,-0.1658438444,0.191597715,0.3040448725,-0.1511575133,0.1042441651,0.103822872,0.1065929979,-0.1703601927,0.2263924032,0.1434833258,0.2757690549,0.0754674077,0.0635722578,0.1523475945,-0.1569283754,-0.0685348958,0.364499718,0.0272788499,-0.0856467262,-0.2118576765,0.0292044468,0.0771163031,0.0599482432,-0.3495509624,-0.0224032812,-0.0369299576,-0.1203360185,0.1702971458,-0.1160883382,-0.5219683051,0.1412714422,-0.5717269778,0.007809924,-0.1792367697,-0.0621801689,0.0817525163,0.0744452327,0.1140420958,-0.3413842618,-0.0354355201,0.1047067568,-0.2842971385,-0.2089479566,-0.4510963261,0.2566649616,0.0999105349,-0.0136359641,-0.1809960306,0.2080218941,0.0345660634,-0.0310896784,-0.2123503685,-0.2790402174,0.0493886098,-0.1699927449,0.0264709871,0.2983824909,-0.253008157,-0.1749667674,-0.2625124156,0.1982944012,0.1198830456,-0.1365111768,-0.2098926306,-0.1893094927,0.0895449668,-0.1031980589,0.1020331457,-0.4095129073,-0.378429234,0.676147759,0.2192848772,0.1426874399,0.1680996716,-0.0058762957,0.0730166435,-0.0896329507,-0.1047137678,-0.1668846905,0.2988793254,0.2073049098,-0.1608503014,-0.3180474043,-0.0798370764,-0.364660114,0.1057950407,0.2121289074,-0.1410655975,-0.3041145802,0.0048825415,0.5083081722,0.1135894135,0.0284402966,0.2844410539,0.2777577043,0.0575979911,0.1198542416,0.0421048477,0.0113995001,0.177919805,-0.2589248419,0.2157589197,0.4879332483,0.1209347025,0.2910847962,0.0937247127,-0.013683538,0.4954909682,-0.1015301943,0.2374844849,-0.1354480833,-0.2925463319,-0.3849201202,-0.3812013865,0.08736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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1906","title":"Feature Request: Support for Pandas `Categorical`","comments":"I think we can first keep the int64 precision but with an arrow Dictionary for ClassLabel, and focus on the connection with arrow and pandas.\r\n\r\nIn this scope, I really like the idea of checking for the dictionary type:\r\n\r\n> Another idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L932 , and then don't touch anything else.\r\n\r\nThis looks like a great start.\r\n\r\nThen as you said we'd have to add the conversion from classlabel to the correct arrow dictionary type. Arrow is already able to convert from arrow Dictionary to pandas Categorical so it should be enough.\r\n\r\nI can see two things that we must take case of to make this change backward compatible:\r\n- first we must still be able to load an arrow file with arrow int64 dtype and `datasets` ClassLabel type without crashing. This can be fixed by casting the arrow int64 array to an arrow Dictionary array on-the-fly when loading the table in the ArrowReader.\r\n- then we still have to return integers when accessing examples from a ClassLabel column. Currently it would return the strings values since it's based on the pandas behavior for converting from pandas to python\/numpy. To do so we just have to adapt the python\/numpy extractors in formatting.py (it takes care of converting an arrow table to a dictionary of python objects by doing arrow table -> pandas dataframe -> python dictionary)\r\n\r\nAny help on this matter is very much welcome :)","body":"```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.Series([\"a\", \"b\", \"c\", \"a\"], dtype=\"category\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws NotImplementedError\r\n# TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table\r\n```\r\n\r\nI'm curious if https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L796 could be built out in a way similar to `Sequence`?\r\n\r\ne.g. a `Map` class (or whatever name the maintainers might prefer) that can accept:\r\n\r\n```\r\nindex_type = generate_from_arrow_type(pa_type.index_type)\r\nvalue_type = generate_from_arrow_type(pa_type.value_type)\r\n```\r\n\r\nand then additional code points to modify:\r\n\r\n- FeatureType: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L694\r\n- A branch to handle Map in get_nested_type: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L719\r\n- I don't quite understand what `encode_nested_example` does but perhaps a branch there? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L755\r\n- Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L775\r\n\r\nI couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints.","comment_length":260,"text":"Feature Request: Support for Pandas `Categorical` \n ```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.Series([\"a\", \"b\", \"c\", \"a\"], dtype=\"category\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws NotImplementedError\r\n# TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table\r\n```\r\n\r\nI'm curious if https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L796 could be built out in a way similar to `Sequence`?\r\n\r\ne.g. a `Map` class (or whatever name the maintainers might prefer) that can accept:\r\n\r\n```\r\nindex_type = generate_from_arrow_type(pa_type.index_type)\r\nvalue_type = generate_from_arrow_type(pa_type.value_type)\r\n```\r\n\r\nand then additional code points to modify:\r\n\r\n- FeatureType: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L694\r\n- A branch to handle Map in get_nested_type: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L719\r\n- I don't quite understand what `encode_nested_example` does but perhaps a branch there? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L755\r\n- Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L775\r\n\r\nI couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints. \n I think we can first keep the int64 precision but with an arrow Dictionary for ClassLabel, and focus on the connection with arrow and pandas.\r\n\r\nIn this scope, I really like the idea of checking for the dictionary type:\r\n\r\n> Another idea that just occurred to me: add a branch in here to generate a ClassLabel if the dict key is int64 and the values are string: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L932 , and then don't touch anything else.\r\n\r\nThis looks like a great start.\r\n\r\nThen as you said we'd have to add the conversion from classlabel to the correct arrow dictionary type. Arrow is already able to convert from arrow Dictionary to pandas Categorical so it should be enough.\r\n\r\nI can see two things that we must take case of to make this change backward compatible:\r\n- first we must still be able to load an arrow file with arrow int64 dtype and `datasets` ClassLabel type without crashing. This can be fixed by casting the arrow int64 array to an arrow Dictionary array on-the-fly when loading the table in the ArrowReader.\r\n- then we still have to return integers when accessing examples from a ClassLabel column. Currently it would return the strings values since it's based on the pandas behavior for converting from pandas to python\/numpy. To do so we just have to adapt the python\/numpy extractors in formatting.py (it takes care of converting an arrow table to a dictionary of python objects by doing arrow table -> pandas dataframe -> python dictionary)\r\n\r\nAny help on this matter is very much welcome :)","embeddings":[0.0230096243,-0.0102735767,-0.1426058412,0.2076647431,0.2370132208,0.1672788262,0.1267411113,0.2487185448,-0.0738426745,-0.2155069113,0.1673993021,0.2824072838,-0.2035112977,0.4252623618,0.1327462345,-0.2236522436,0.1246828139,0.0284454841,0.077850081,0.3117170632,-0.3771744668,-0.0838787854,-0.0995519832,0.2389727235,-0.1983468533,-0.1293813884,-0.45648247,-0.3532522023,-0.2593736649,-0.8477243185,0.3983210921,0.3807965219,0.1931584775,0.1735817343,-0.000115819,-0.3079189658,0.2058762759,-0.0425236411,-0.0503565595,-0.4431983232,-0.2918069661,-0.2464248687,0.3244947791,-0.2901013196,-0.2068761438,-0.292574048,0.0067172511,-0.2727668285,0.2317002714,0.0666810945,0.1667516083,-0.0497691333,-0.0274424106,-0.1615715176,0.1318348497,0.4021803737,-0.237100631,0.284689635,0.5595126152,-0.322232008,0.2314834446,0.0884723142,0.0117629515,-0.3249572814,0.6005215049,0.0490307808,0.2645990551,-0.5582161546,-0.3079175055,0.1526240557,0.3408336937,-0.3584251702,-0.5394771099,-0.4483566582,-0.0119229034,-0.2346872836,-0.0061525847,-0.1371355653,-0.1556818336,0.1616322845,0.112262167,-0.0731579587,-0.2399319559,0.1873058379,-0.0844331756,0.1476809233,-0.0443651713,-0.1675475389,0.2131358683,-0.099540472,0.003308367,-0.1552573591,0.0259761289,0.6048634052,-0.2247818261,-0.1363115907,0.0729947016,0.2108622342,0.0177890956,-0.0083071152,0.1002103984,0.01343753,-0.231400162,-0.0256741904,0.3791758418,0.292435199,0.1800874174,-0.086328879,-0.1288450658,0.3581373394,-0.0041660462,0.0536369346,-0.1282648146,-0.1226120666,-0.1723236442,0.1386386752,-0.0707128495,0.2454463691,-0.0304598641,-0.0372074991,-0.0177116375,-0.0263489336,0.0152251087,0.2628429532,-0.065954715,0.0928890333,-0.0641002133,0.0835099369,0.2764406502,0.120650351,-0.0680939406,0.0020986979,-0.2102169693,0.1605477333,-0.0775292516,-0.185875833,-0.1492251903,-0.0344319195,0.1192694381,0.1085190102,-0.2276078761,-0.0936202332,0.1229106933,0.0074040387,-0.4257945716,0.1622291803,0.2187957615,-0.0850061551,-0.3504912853,0.1319193393,0.1542150974,-0.2472275048,-0.2392049581,0.1113661602,-0.2031580359,0.1457660347,-0.3309437335,0.4760418832,-0.008096843,0.0497261994,0.2327813506,0.0547782816,-0.263841778,-0.3284746408,-0.1710059941,0.452237606,-0.3215060234,-0.2309907079,0.0531622022,-0.0306914076,0.0361553393,0.0772850141,-0.1583134085,0.1192366183,-0.0716489926,0.1776524633,0.5333985686,-0.1753790528,-0.1190234646,0.3577374816,0.0055855042,0.1976584643,0.2812623382,0.2622909248,0.1901309639,-0.2151019275,0.0931514278,0.128313452,-0.1040712446,-0.2355068028,0.149151817,-0.3304395676,0.1761069745,0.1930086315,-0.0684674233,0.2835514843,0.0743933991,-0.3360271156,0.3749298751,-0.2894718051,0.1078301296,0.063677147,0.306604445,0.4409077764,0.0205318797,-0.479870975,-0.1257184148,0.3306059539,-0.0952279717,0.1742290556,-0.3567067087,-0.4952701032,-0.0397885852,0.2739695907,-0.2659189999,0.1416194439,-0.0091220392,-0.2109060735,0.2343253344,0.2042154521,-0.3539479673,0.5252873302,-0.0650035888,0.2001345307,-0.2408441156,0.1756791472,-0.0458536372,0.0768375322,-0.1055376828,0.3452531993,-0.0846971571,-0.119718574,0.2849961519,0.0615107976,0.2428294271,-0.0946210027,-0.3645075262,0.229264617,0.526425004,0.04913757,-0.0908039883,-0.1106586307,0.0843873024,-0.0667051449,0.016678663,0.582357347,-0.4817159772,0.2271652371,0.1415827572,0.1412526667,0.0998748615,0.2575952411,-0.2499683946,-0.1974934191,0.1438122392,0.2578533888,-0.2217444628,0.0667169839,-0.1658438444,0.191597715,0.3040448725,-0.1511575133,0.1042441651,0.103822872,0.1065929979,-0.1703601927,0.2263924032,0.1434833258,0.2757690549,0.0754674077,0.0635722578,0.1523475945,-0.1569283754,-0.0685348958,0.364499718,0.0272788499,-0.0856467262,-0.2118576765,0.0292044468,0.0771163031,0.0599482432,-0.3495509624,-0.0224032812,-0.0369299576,-0.1203360185,0.1702971458,-0.1160883382,-0.5219683051,0.1412714422,-0.5717269778,0.007809924,-0.1792367697,-0.0621801689,0.0817525163,0.0744452327,0.1140420958,-0.3413842618,-0.0354355201,0.1047067568,-0.2842971385,-0.2089479566,-0.4510963261,0.2566649616,0.0999105349,-0.0136359641,-0.1809960306,0.2080218941,0.0345660634,-0.0310896784,-0.2123503685,-0.2790402174,0.0493886098,-0.1699927449,0.0264709871,0.2983824909,-0.253008157,-0.1749667674,-0.2625124156,0.1982944012,0.1198830456,-0.1365111768,-0.2098926306,-0.1893094927,0.0895449668,-0.1031980589,0.1020331457,-0.4095129073,-0.378429234,0.676147759,0.2192848772,0.1426874399,0.1680996716,-0.0058762957,0.0730166435,-0.0896329507,-0.1047137678,-0.1668846905,0.2988793254,0.2073049098,-0.1608503014,-0.3180474043,-0.0798370764,-0.364660114,0.1057950407,0.2121289074,-0.1410655975,-0.3041145802,0.0048825415,0.5083081722,0.1135894135,0.0284402966,0.2844410539,0.2777577043,0.0575979911,0.1198542416,0.0421048477,0.0113995001,0.177919805,-0.2589248419,0.2157589197,0.4879332483,0.1209347025,0.2910847962,0.0937247127,-0.013683538,0.4954909682,-0.1015301943,0.2374844849,-0.1354480833,-0.2925463319,-0.3849201202,-0.3812013865,0.0873693228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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1898","title":"ALT dataset has repeating instances in all splits","comments":"I just merged a fix, we'll do a patch release soon. Thanks again for reporting, and sorry for the inconvenience.\r\nIn the meantime you can load `ALT` using `datasets` from the master branch","body":"The [ALT](https:\/\/huggingface.co\/datasets\/alt) dataset has all the same instances within each split :\/\r\nSeemed like a great dataset for some experiments I wanted to carry out, especially since its medium-sized, and has all splits.\r\n\r\nWould be great if this could be fixed :)\r\n\r\nAdded a snapshot of the contents from `explore-datset` feature, for quick reference.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/33179372\/108206321-442a2d00-714c-11eb-882f-b4b6e708ef9c.png)\r\n","comment_length":33,"text":"ALT dataset has repeating instances in all splits \n The [ALT](https:\/\/huggingface.co\/datasets\/alt) dataset has all the same instances within each split :\/\r\nSeemed like a great dataset for some experiments I wanted to carry out, especially since its medium-sized, and has all splits.\r\n\r\nWould be great if this could be fixed :)\r\n\r\nAdded a snapshot of the contents from `explore-datset` feature, for quick reference.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/33179372\/108206321-442a2d00-714c-11eb-882f-b4b6e708ef9c.png)\r\n \n I just merged a fix, we'll do a patch release soon. Thanks again for reporting, and sorry for the inconvenience.\r\nIn the meantime you can load `ALT` using `datasets` from the master branch","embeddings":[-0.2530450225,-0.7312532663,-0.0555956438,0.4320774376,0.3174535334,-0.1736770868,0.3799054027,0.1424697042,0.2794243693,0.331989646,-0.1614301652,0.0980525613,0.0777056143,-0.0084827486,-0.0531923398,-0.0700326785,-0.0420137346,0.2138660252,-0.001591517,-0.2287296951,-0.136103645,0.1931893826,-0.3067333102,0.0349796563,-0.1967672557,-0.2158623934,-0.0085895853,0.0501792505,0.0790567994,-0.278033793,0.0863051564,0.1110689417,0.0143353166,0.4517409205,-0.0001077245,0.1526554823,0.4967345595,0.0332457721,-0.2704990506,-0.2112117112,-0.3161489069,0.2112973183,0.2924031019,-0.1687503904,-0.0563588105,-0.2315426916,-0.1243325248,-0.4026524723,0.3349613249,-0.0742322877,0.2620726824,0.4766892195,-0.3479264975,-0.4059582055,0.438123703,0.5822082758,-0.1347050071,-0.1824561656,0.3820548654,0.4519361854,0.0182801615,0.3951468766,0.1519043744,0.2581334412,0.1004771739,0.1850468218,0.1533161849,-0.1135968566,0.0046806978,0.267268002,0.3437399864,-0.0407965966,-0.0840745196,-0.208226651,0.0375719853,0.0121965185,0.242644608,0.0158100352,0.020386735,0.1143068373,0.2770204544,0.2895072699,0.0548395067,-0.0553695373,0.0077201026,-0.0340844393,-0.1891286373,0.0874743387,0.1575710177,-0.1861014962,0.3643367887,-0.0746586546,-0.1419699788,-0.202799961,-0.3039722443,-0.0242452417,-0.1317781806,-0.1549829096,0.1851761192,0.3924604952,0.3807055652,0.2239830196,-0.3758049607,0.0164204333,0.2636727095,0.0733937696,0.303794086,-0.0638524815,0.1854224503,0.078543663,-0.1949545741,0.0184880849,-0.2299306393,-0.0099541424,-0.2303521335,-0.0250952318,0.248890236,-0.119489044,-0.4183261395,-0.116393365,0.1127997339,-0.1395734698,0.0736995637,0.3457525373,0.3099732399,0.1333232224,-0.0907328874,0.0623501018,-0.1016265303,-0.4191182554,-0.2110074311,-0.2572085261,-0.3241529167,-0.0920962244,-0.0299138203,-0.114004679,0.268192023,0.2965404391,0.0488082208,-0.1254488081,0.2154405564,0.0645246506,0.1204699874,-0.0578569062,0.1036735997,0.3334433138,0.153557539,-0.0137160979,-0.0288760252,0.0631649271,-0.111020796,-0.0017046058,0.1181939021,0.2778229713,-0.0948350877,0.1378462166,0.0151464585,0.2947776318,-0.1778623164,0.0043671816,-0.1145090386,0.0638617799,0.0688562542,-0.0965424031,0.254160434,0.4378035069,-0.228737399,-0.3377067447,-0.5115407705,-0.0721741095,0.1281719357,0.5561010242,-0.1574354917,-0.1233198792,-0.399990648,0.2835876048,0.1919445693,0.2184081227,-0.2081331313,0.1348673403,-0.1094422191,-0.0671303421,0.0755431131,0.1349571198,0.3912534416,0.008007139,0.0901445225,0.2794891,0.287633121,-0.0885440782,-0.2506302297,-0.0650307685,0.0467608906,-0.041387476,-0.2071636319,-0.32281515,0.1217947751,0.0372686833,0.1147334799,-0.0210297983,-0.0985308513,0.0281672217,0.2101825625,0.2042817771,0.3248484135,-0.0377073213,-0.2207812667,0.0477528796,-0.1736790687,-0.147826463,0.2951383889,-0.418310672,-0.2752805054,0.1193790808,-0.3506122231,-0.0463987626,0.1903411895,0.2829102576,-0.1916191727,0.0162029378,-0.0466967709,-0.2341548949,0.1645257622,0.0080304416,-0.1809080094,0.2991646528,-0.1965728998,0.0187053531,0.0256757382,0.1080784872,0.1730462611,-0.2686104476,-0.0383830518,0.4057914615,0.3260442615,0.1635503173,0.0293142907,-0.4810571373,0.4122965932,-0.2348932475,0.1248606667,0.2347392589,0.1278838664,-0.2357152849,-0.1550873518,0.1593185216,0.1920385361,0.2863152921,-0.0333648995,0.208559826,0.3136180639,-0.1635531336,0.2406594902,-0.5274125338,0.1388350278,-0.0694969669,0.0954828709,0.1965302825,-0.2810149491,0.3699359298,0.1038458869,0.10755077,0.2117594332,0.0147755118,-0.5166527629,-0.0949606746,-0.0444386676,0.0235481486,0.1444617659,0.3443719447,0.0418831855,-0.1660429984,0.3011958897,-0.1309051663,0.3186788857,0.1758503616,-0.1268957257,0.092217356,0.1232438982,-0.1161591187,-0.5793968439,-0.1676859856,0.3131718636,0.063004531,-0.2004716843,-0.22012043,-0.5879811049,-0.013966009,-0.1368472576,0.010807205,-0.1271443069,-0.1715045869,0.2827455997,0.0558801629,0.0002116532,0.366748035,0.3949826956,0.4391366541,-0.0003229588,0.442353636,-0.3167379797,-0.1895712167,-0.3355871141,0.1189157069,0.4297174513,0.3283501565,0.4452002645,-0.0780442134,-0.021039784,-0.2582357824,-0.3862937689,0.1428810507,0.0395937078,-0.0219456907,0.1286536753,-0.1326865405,0.0025743707,-0.1702553481,0.1888428479,-0.4004678428,-0.4218910933,0.1067008898,-0.1009612754,-0.2328895628,-0.1279150695,-0.296223253,0.0629430562,-0.3463539779,0.1321719587,0.0558892526,0.0171966497,0.0587159172,-0.1139376312,-0.0360047854,-0.0893377736,-0.1253001839,-0.5492932796,0.0673271716,0.0928752869,-0.2612448931,-0.133163482,-0.0414444283,-0.2199798375,-0.1416226327,0.032470651,-0.4009286761,0.187947154,-0.4680107534,-0.0788509622,0.1960986406,0.0292475149,0.2848639786,-0.0054312022,-0.2143379301,-0.0154975187,-0.296048075,0.1560645252,0.0158260241,0.1873741597,-0.0449537896,0.0046860641,0.0418864228,0.3934039474,0.318161577,-0.1329952329,0.0471848287,-0.1528198421,0.1869278997,-0.4131629765,-0.1799942404,0.0803353935,0.0075400821,-0.1402645111,0.38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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1895","title":"Bug Report: timestamp[ns] not recognized","comments":"Thanks for reporting !\r\n\r\nYou're right, `string_to_arrow` should be able to take `\"timestamp[ns]\"` as input and return the right pyarrow timestamp type.\r\nFeel free to suggest a fix for `string_to_arrow` and open a PR if you want to contribute ! This would be very appreciated :)\r\n\r\nTo give you more context:\r\n\r\nAs you may know we define the features types of a dataset using the `Features` object in combination with feature types like `Value`. For example\r\n```python\r\nfeatures = Features({\r\n    \"age\": Value(\"int32\")\r\n})\r\n```\r\nHowever under the hood we are actually using pyarrow to store the data, and so we have a mapping between the feature types of `datasets` and the types of pyarrow.\r\n\r\nFor example, the `Value` feature types are created from a pyarrow type with `Value(str(pa_type))`.\r\nHowever it looks like the conversion back to a pyarrow type doesn't work with `\"timestamp[ns]\"`.\r\nThis is the `string_to_arrow` function you highlighted that does this conversion, so we should fix that.\r\n\r\n","body":"Repro:\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.date_range(\"2018-01-01\", periods=3, freq=\"H\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.\r\n```\r\n\r\nThe factory function seems to be just \"timestamp\": https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\r\n\r\nIt seems like https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L36-L43 could have a little bit of additional structure for handling these cases?  I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.\r\n\r\nAlternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!\r\n\r\n```\r\n$ pip list  # only the relevant libraries\/versions\r\ndatasets                      1.2.1\r\npandas                        1.0.3\r\npyarrow                       3.0.0\r\n```","comment_length":159,"text":"Bug Report: timestamp[ns] not recognized \n Repro:\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.date_range(\"2018-01-01\", periods=3, freq=\"H\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.\r\n```\r\n\r\nThe factory function seems to be just \"timestamp\": https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\r\n\r\nIt seems like https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L36-L43 could have a little bit of additional structure for handling these cases?  I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.\r\n\r\nAlternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!\r\n\r\n```\r\n$ pip list  # only the relevant libraries\/versions\r\ndatasets                      1.2.1\r\npandas                        1.0.3\r\npyarrow                       3.0.0\r\n``` \n Thanks for reporting !\r\n\r\nYou're right, `string_to_arrow` should be able to take `\"timestamp[ns]\"` as input and return the right pyarrow timestamp type.\r\nFeel free to suggest a fix for `string_to_arrow` and open a PR if you want to contribute ! This would be very appreciated :)\r\n\r\nTo give you more context:\r\n\r\nAs you may know we define the features types of a dataset using the `Features` object in combination with feature types like `Value`. For example\r\n```python\r\nfeatures = Features({\r\n    \"age\": Value(\"int32\")\r\n})\r\n```\r\nHowever under the hood we are actually using pyarrow to store the data, and so we have a mapping between the feature types of `datasets` and the types of pyarrow.\r\n\r\nFor example, the `Value` feature types are created from a pyarrow type with `Value(str(pa_type))`.\r\nHowever it looks like the conversion back to a pyarrow type doesn't work with `\"timestamp[ns]\"`.\r\nThis is the `string_to_arrow` function you highlighted that does this conversion, so we should fix that.\r\n\r\n","embeddings":[-0.1527938545,0.3287227452,0.004078465,0.0871678293,0.094773151,-0.0773305222,0.4513304532,0.3623484373,-0.491699934,-0.2842443287,0.2675407529,0.5988483429,-0.1803753823,0.0181875285,0.0292804502,-0.1218024567,0.1887533069,0.2048997879,0.0169893932,0.2929658294,-0.411757797,0.0337461047,-0.0582276806,0.4490803778,-0.2121623605,-0.098631613,-0.0603366084,0.0371372662,-0.1723987162,-0.6164935827,-0.0294905044,-0.2923269272,0.2255375087,0.458345443,-0.0001204796,-0.0532897338,0.3580789566,-0.003380205,-0.1952470243,0.2531554103,0.2438883036,-0.2023748159,0.2693036795,-0.2410607934,0.0408186913,-0.2710432708,-0.0724558979,-0.0755451173,0.0478876121,0.5628821254,0.1174847186,0.3736271858,0.4515483677,0.2590070069,0.8312047124,0.269094348,-0.2306951582,0.056339018,0.477792263,0.0477021076,0.2724705338,0.0612961426,0.01842664,-0.2222912312,0.3558754027,0.1927733719,0.2080835104,-0.2612546384,-0.2831192613,0.0733712092,0.5608472228,-0.4021904171,-0.3237973154,-0.2525907159,-0.3008702695,-0.3398887813,0.1143042594,-0.0021664116,0.0236200877,0.1635642946,0.2240136117,0.1570541412,-0.2538725734,0.3059591949,0.0345591567,0.1951960921,0.036505729,0.2330444902,-0.1538335681,-0.163309142,0.4218271077,-0.0682979152,0.2391823679,0.2117653489,0.0548948757,-0.0026040752,0.0709574968,0.0192168001,-0.0629317686,0.2086346149,-0.3227862716,-0.055237975,-0.0514091104,0.0474792868,0.1782118976,0.1103378609,-0.0465708449,0.1961290538,0.1691955924,0.0495364591,0.2670128345,-0.075864315,0.0448122546,-0.3755866885,0.2224445045,0.1253210306,0.5929380655,-0.1710079014,-0.1433101594,0.0585863478,-0.3615436554,-0.1077894643,0.1066295952,0.1574509144,-0.1743690819,0.404202342,0.2209697515,0.106683895,-0.0398546904,-0.1873346418,-0.1595071107,0.0632436201,-0.312923789,-0.0837123692,-0.0525706373,0.08560922,-0.1687335223,0.1228703037,-0.2593176663,-0.0080446023,-0.2724782825,-0.0224871561,-0.0080480557,0.259834379,-0.3691702485,0.1590273976,0.0179573316,-0.2260663509,-0.3272784352,0.2760610878,-0.3891295493,-0.3840898871,-0.2853721082,0.0803797394,-0.2141950727,-0.0022991954,-0.1201459393,0.0653199926,0.3260180354,-0.2303648144,0.1859744936,0.0410010777,-0.0157311149,-0.4460178018,-0.0160409063,-0.0757956058,-0.5357329845,-0.0214967299,-0.0532195196,-0.1864697337,0.2656601369,0.1523074657,0.0214728136,0.1077250987,-0.0525948666,0.1655796766,0.1830980778,-0.0440341681,-0.1000685766,-0.0628414825,0.1198739707,-0.1564222574,-0.0947118253,0.1092776731,-0.0736897215,-0.3927891552,0.0568929277,0.1541986614,-0.1479741037,-0.1493551284,-0.2447480261,-0.2944935262,0.3030258119,0.1998855472,0.2393239588,-0.0407982394,0.0045123682,-0.5796675086,0.1131130308,-0.0502080023,0.1263220906,-0.0548149981,0.5200362206,0.2141897976,-0.070763059,-0.3474243283,-0.4297995865,0.1461998075,-0.0691066831,0.1610938907,-0.4859769046,-0.3641879857,-0.2271838784,0.2800627053,0.1993660182,0.1383026838,-0.0436110385,-0.0297358986,0.1434597224,0.3332371116,-0.1515100002,0.0788408443,-0.1270583719,0.2203107327,-0.2441525012,0.2964921892,-0.2026634216,-0.2622415125,-0.12086045,0.3751481175,0.1991807073,0.0832859799,0.0865875706,0.2168290019,0.15634799,0.0290441755,-0.4961464107,0.2458914518,0.1520669758,-0.2512366176,-0.2621866167,0.3554765582,0.2291227132,-0.0469216704,0.0833044574,0.4930122793,-0.2353567034,0.031903673,-0.1030333191,0.1757520139,-0.0606435575,0.0314014256,-0.213278532,0.0498013049,-0.2454682589,0.1282763481,0.1705575883,0.0274255667,-0.4170612395,0.2885534763,0.4863225818,-0.1517165005,0.1151833758,0.1635690182,-0.2545307577,-0.1554028541,0.0685561299,0.0153659713,0.1739568114,0.1825347543,-0.0270138849,-0.0015853908,-0.3035330772,0.0931105688,0.2514898479,0.131937921,0.1806957722,-0.0920817405,0.1278416663,0.1615659148,0.0097596887,-0.1166407019,-0.0649031997,0.3374016285,-0.6451544166,0.1693527997,-0.2670386434,-0.2664468884,-0.0653513521,-0.5234569907,-0.2097799778,-0.4976215065,0.2060505748,0.0130747203,-0.129035458,0.1445627809,-0.2396755666,0.3295800686,0.0367025547,-0.0488287732,-0.1746848822,-0.3294133246,-0.3490490615,0.0426957756,0.1227638125,0.1496353447,0.238105759,-0.2039757818,0.2300887406,-0.1850446165,-0.5263569951,0.0271414816,-0.2452932596,0.5031446815,0.109993659,0.128013432,0.1552547216,-0.1925951242,0.1817753166,-0.2458168268,-0.3332672715,0.037178956,-0.1570665687,0.1477027982,-0.1943172812,-0.0874928012,-0.5084860325,-0.3264511824,0.2179849893,-0.1214900985,0.2835149765,0.4560049772,-0.1136700958,0.0235686321,-0.0690424815,0.0206422918,-0.2456250638,-0.0137555143,0.4213277698,-0.1864165813,-0.3269939423,-0.0897910222,-0.1323457062,-0.4214044809,0.043569304,-0.1114263237,-0.5367970467,-0.0529440045,0.4217863679,0.1056533083,0.0251024012,0.3719080985,0.178548485,0.0630282909,-0.1003422514,-0.2586354613,0.0566188991,0.151097104,0.1743383557,0.2761527002,0.2530393004,-0.0747738034,0.965936482,0.1138239056,-0.4457321763,0.389862448,-0.3384912312,0.1157409027,-0.0298454762,-0.0037478837,-0.2835678458,-0.23766188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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1895","title":"Bug Report: timestamp[ns] not recognized","comments":"Thanks for the clarification @lhoestq !\r\n\r\nThis may be a little bit of a stupid question, but I wanted to clarify one more thing before I took a stab at this:\r\n\r\nWhen the features get inferred, I believe they already have a pyarrow schema (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L234).\r\n\r\nWe then convert it to a string (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L778) only to convert it back into the arrow type (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L143, and https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L35).  Is there a reason for this round-trip?\r\n\r\nI'll open a PR later to add `timestamp` support to `string_to_arrow`, but I'd be curious to understand since it feels like there may be some opportunities to simplify!","body":"Repro:\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.date_range(\"2018-01-01\", periods=3, freq=\"H\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.\r\n```\r\n\r\nThe factory function seems to be just \"timestamp\": https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\r\n\r\nIt seems like https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L36-L43 could have a little bit of additional structure for handling these cases?  I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.\r\n\r\nAlternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!\r\n\r\n```\r\n$ pip list  # only the relevant libraries\/versions\r\ndatasets                      1.2.1\r\npandas                        1.0.3\r\npyarrow                       3.0.0\r\n```","comment_length":100,"text":"Bug Report: timestamp[ns] not recognized \n Repro:\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.date_range(\"2018-01-01\", periods=3, freq=\"H\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.\r\n```\r\n\r\nThe factory function seems to be just \"timestamp\": https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\r\n\r\nIt seems like https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L36-L43 could have a little bit of additional structure for handling these cases?  I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.\r\n\r\nAlternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!\r\n\r\n```\r\n$ pip list  # only the relevant libraries\/versions\r\ndatasets                      1.2.1\r\npandas                        1.0.3\r\npyarrow                       3.0.0\r\n``` \n Thanks for the clarification @lhoestq !\r\n\r\nThis may be a little bit of a stupid question, but I wanted to clarify one more thing before I took a stab at this:\r\n\r\nWhen the features get inferred, I believe they already have a pyarrow schema (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_dataset.py#L234).\r\n\r\nWe then convert it to a string (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L778) only to convert it back into the arrow type (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L143, and https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L35).  Is there a reason for this round-trip?\r\n\r\nI'll open a PR later to add `timestamp` support to `string_to_arrow`, but I'd be curious to understand since it feels like there may be some opportunities to simplify!","embeddings":[-0.0739534423,0.2633909583,0.0234291479,0.1172331348,0.0697290748,-0.102800779,0.3482510448,0.2648758888,-0.53681463,-0.2700381577,0.2399619818,0.4913219213,-0.1497713774,-0.0816278756,0.0596875735,-0.1228537112,0.1500014812,0.1810293347,0.0497963615,0.2760607302,-0.4647652805,0.0268085431,-0.1178036183,0.4554687142,-0.1349889189,-0.0517363809,-0.1678003371,0.1312940568,-0.222699672,-0.6291897893,-0.006809386,-0.2124488503,0.1671675146,0.5190418959,-0.0001214839,-0.0243573692,0.3058465421,-0.081429705,-0.0674782395,0.2833605409,0.3476228714,-0.0902878717,0.2400758564,-0.2225877345,0.0005373039,-0.2519564331,-0.0341052264,0.0672973394,0.144416064,0.490727663,0.1079129949,0.3850309551,0.4924409389,0.2931482792,0.8187010288,0.2305018455,-0.2327064723,0.2020156682,0.4911263287,-0.0119446041,0.3040035367,0.1110465974,-0.0202678852,-0.3036396503,0.4174173176,0.2343477756,0.2308800668,-0.2514504492,-0.3207892478,0.012445624,0.4684594572,-0.4220660925,-0.273563236,-0.2206660956,-0.2268998325,-0.3210521936,0.0590606742,0.0244471561,-0.09441825,0.1667205542,0.1804581732,0.1114420369,-0.2595479786,0.1966615617,0.0458390526,0.2566615045,0.0918182582,0.2258583903,-0.1358292997,-0.0887856483,0.3104703724,0.0069876313,0.197939083,0.260856241,0.1146385446,-0.0238555409,0.1147630811,0.1258624345,-0.054354351,0.218019411,-0.2752971053,-0.1187834144,0.0749938563,0.0247224476,0.1478242427,0.1771616638,-0.0778387636,0.1958505064,0.1620578617,0.1409114301,0.2483191192,-0.0972781628,0.0117753046,-0.3109066486,0.0915321112,0.0369888917,0.4998414516,-0.1672780067,-0.0820687935,0.0809872672,-0.3534942567,-0.0999075323,0.1619462818,0.2031747252,-0.1754030287,0.2231131196,0.2975910306,0.064635396,-0.0271315984,-0.1359474659,-0.1771470457,0.0740092099,-0.3025508523,0.0419389606,-0.1343196034,0.1324589252,-0.1791499108,0.1216739267,-0.2732574046,0.0485023148,-0.294860363,-0.0083999876,-0.0298526324,0.289180398,-0.3410614431,0.0869282633,0.0180822741,-0.2282739878,-0.2970764339,0.2654173672,-0.261515975,-0.4053736925,-0.3469693363,0.0767558888,-0.2768322527,0.0730395988,-0.0575086288,0.042672962,0.318020165,-0.2170475274,0.1977300346,0.1241649389,0.0056941085,-0.4275840223,0.0414000712,0.0141745731,-0.468375653,-0.007270257,0.0773429349,-0.2030068785,0.2537609339,0.1049124599,-0.001687185,0.0774715096,-0.0125497961,0.2026353031,-0.0127511136,-0.0462616347,-0.0595328547,-0.0833548754,0.0376259685,-0.1311553568,-0.0338489637,0.0006076856,-0.1404957622,-0.3639112413,0.029856557,-0.0254544672,-0.0889196321,-0.2139908075,-0.3913457692,-0.3040256202,0.1618695408,0.2460020185,0.2437745333,-0.0502096824,-0.0592442937,-0.5101994872,0.2055175751,-0.0247060806,0.0671349466,0.0306335464,0.4889732301,0.263794899,-0.0420458168,-0.2111530751,-0.3733316958,0.1900897622,-0.1035777479,0.2703511715,-0.5419317484,-0.4424498081,-0.2253599316,0.3193966448,0.1861850768,0.0407054089,-0.0255179685,-0.0455303937,0.2115215063,0.3166394532,-0.0906739682,0.0358716398,-0.1910474747,0.218768537,-0.321942389,0.388936758,-0.2348883301,-0.264787972,-0.1389497519,0.3878708482,0.2070742697,0.0228760764,0.1657654643,0.2556868494,0.1249759197,0.062481232,-0.4905864,0.2075892389,0.1242336109,-0.1908746958,-0.3281776011,0.2835147083,0.1718753725,-0.0430423878,0.0834822729,0.4734211266,-0.2386840582,-0.0334388949,-0.0330949835,0.1673658341,-0.0946146101,-0.0246200506,-0.1773063391,0.0732662678,-0.2078598142,0.1559969336,0.1349333823,0.0551781915,-0.3709949255,0.2192381918,0.4112526476,-0.1022621021,-0.0292528383,0.0717290714,-0.230445683,-0.0914861858,0.1116986871,-0.0674773976,0.1516866386,0.1919134855,-0.0590074658,0.1059635431,-0.2441176176,0.0980612412,0.266556412,0.1425211281,0.2044153363,-0.1282246262,0.0713001937,0.1824331731,-0.0108701093,-0.1943412274,-0.0333507173,0.3838225007,-0.6351848245,0.1070168242,-0.1585384458,-0.3386041224,-0.0780429393,-0.5197437406,-0.2630250454,-0.4474776089,0.2224151045,-0.0186964963,-0.1537584811,0.0871340036,-0.2732034028,0.2935717404,-0.0245447215,-0.0188590027,-0.2927134037,-0.3856007159,-0.2980498374,0.0385946408,0.1031715795,0.1414165944,0.1863087565,-0.2234013975,0.1416748613,-0.0752855316,-0.5809757113,0.0517930016,-0.2343017459,0.4214703143,0.0943552181,0.1354868263,-0.0083564147,-0.2265824825,0.2086318433,-0.1833892167,-0.2840283811,0.0269487742,-0.1109141409,0.0774557143,-0.256203115,0.0519671366,-0.4750356674,-0.3110540509,0.3278072774,-0.0538620502,0.244488582,0.4900130033,-0.2033354938,0.0783633962,-0.1862731129,-0.0581388883,-0.1955919415,-0.0609742589,0.4027327597,-0.1493617743,-0.3285061717,-0.1424711496,-0.0957720131,-0.3075481653,0.0162439365,-0.2464179248,-0.5779743195,0.0321428441,0.3910689056,0.0669242963,0.0162401516,0.3755374551,0.0941712707,0.0749978945,-0.0748711303,-0.1395760924,0.0567207858,0.1027210876,0.1628661454,0.1843212396,0.2286666632,-0.1184219047,1.0252785683,0.1548281908,-0.3985675573,0.2973780036,-0.4068078101,0.1511539221,0.100807488,0.0101968441,-0.2597646415,-0.1983235627,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1895","title":"Bug Report: timestamp[ns] not recognized","comments":"The objective in terms of design is to make it easy to create Features in a pythonic way. So for example we use a string to define a Value type.\r\nThat's why when inferring the Features from an arrow schema we have to find the right string definitions for Value types. I guess we could also have a constructor `Value.from_arrow_type` to avoid recreating the arrow type, but this could create silent errors if the pyarrow type doesn't have a valid mapping with the string definition. The \"round-trip\" is used to enforce that the ground truth is the string definition, not the pyarrow type, and also as a sanity check.\r\n\r\nLet me know if that makes sense ","body":"Repro:\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.date_range(\"2018-01-01\", periods=3, freq=\"H\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.\r\n```\r\n\r\nThe factory function seems to be just \"timestamp\": https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\r\n\r\nIt seems like https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L36-L43 could have a little bit of additional structure for handling these cases?  I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.\r\n\r\nAlternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!\r\n\r\n```\r\n$ pip list  # only the relevant libraries\/versions\r\ndatasets                      1.2.1\r\npandas                        1.0.3\r\npyarrow                       3.0.0\r\n```","comment_length":116,"text":"Bug Report: timestamp[ns] not recognized \n Repro:\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.date_range(\"2018-01-01\", periods=3, freq=\"H\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.\r\n```\r\n\r\nThe factory function seems to be just \"timestamp\": https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\r\n\r\nIt seems like https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L36-L43 could have a little bit of additional structure for handling these cases?  I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.\r\n\r\nAlternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!\r\n\r\n```\r\n$ pip list  # only the relevant libraries\/versions\r\ndatasets                      1.2.1\r\npandas                        1.0.3\r\npyarrow                       3.0.0\r\n``` \n The objective in terms of design is to make it easy to create Features in a pythonic way. So for example we use a string to define a Value type.\r\nThat's why when inferring the Features from an arrow schema we have to find the right string definitions for Value types. I guess we could also have a constructor `Value.from_arrow_type` to avoid recreating the arrow type, but this could create silent errors if the pyarrow type doesn't have a valid mapping with the string definition. The \"round-trip\" is used to enforce that the ground truth is the string definition, not the pyarrow type, and also as a sanity check.\r\n\r\nLet me know if that makes sense 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1895","title":"Bug Report: timestamp[ns] not recognized","comments":"OK I think I understand now:\r\n\r\nFeatures are datasets' internal representation of a schema type, distinct from pyarrow's schema.\r\nValue() corresponds to pyarrow's \"primitive\" types (e.g. `int` or `string`, but not things like `list` or `dict`).\r\n`get_nested_type()` (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L698) and `generate_from_arrow_type()` (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L778) *should* be inverses of each other, and similarly, for the primitive values, `string_to_arrow()` and `Value.__call__` (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L146) should be inverses of each other?\r\n\r\nThanks for taking the time to answer - I just wanted to make sure I understood before opening a PR so I'm not disrupting anything about how the codebase is expected to work!","body":"Repro:\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.date_range(\"2018-01-01\", periods=3, freq=\"H\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.\r\n```\r\n\r\nThe factory function seems to be just \"timestamp\": https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\r\n\r\nIt seems like https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L36-L43 could have a little bit of additional structure for handling these cases?  I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.\r\n\r\nAlternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!\r\n\r\n```\r\n$ pip list  # only the relevant libraries\/versions\r\ndatasets                      1.2.1\r\npandas                        1.0.3\r\npyarrow                       3.0.0\r\n```","comment_length":96,"text":"Bug Report: timestamp[ns] not recognized \n Repro:\r\n\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\nimport pyarrow\r\n\r\ndf = pd.DataFrame(pd.date_range(\"2018-01-01\", periods=3, freq=\"H\"))\r\npyarrow.Table.from_pandas(df)\r\nDataset.from_pandas(df)\r\n# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.\r\n```\r\n\r\nThe factory function seems to be just \"timestamp\": https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\r\n\r\nIt seems like https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L36-L43 could have a little bit of additional structure for handling these cases?  I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.\r\n\r\nAlternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!\r\n\r\n```\r\n$ pip list  # only the relevant libraries\/versions\r\ndatasets                      1.2.1\r\npandas                        1.0.3\r\npyarrow                       3.0.0\r\n``` \n OK I think I understand now:\r\n\r\nFeatures are datasets' internal representation of a schema type, distinct from pyarrow's schema.\r\nValue() corresponds to pyarrow's \"primitive\" types (e.g. `int` or `string`, but not things like `list` or `dict`).\r\n`get_nested_type()` (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L698) and `generate_from_arrow_type()` (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L778) *should* be inverses of each other, and similarly, for the primitive values, `string_to_arrow()` and `Value.__call__` (https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L146) should be inverses of each other?\r\n\r\nThanks for taking the time to answer - I just wanted to make sure I understood before opening a PR so I'm not disrupting anything about how the codebase is expected to work!","embeddings":[-0.1425731182,0.3246956468,-0.0001188292,0.1199892834,0.0674353838,-0.0765276998,0.3947218657,0.2770192325,-0.4754169285,-0.2832741439,0.2034531385,0.5407942533,-0.1618396789,0.0006553546,0.0657191873,-0.1031982005,0.16099599,0.2012021244,0.0746155903,0.2967378497,-0.5015542507,0.0138936304,-0.0977718383,0.4974121749,-0.11246714,-0.1238003075,-0.1548793018,0.1586416662,-0.2299353778,-0.6235607266,0.0846156627,-0.2241215706,0.2440818846,0.5928259492,-0.0001198721,0.0047001434,0.3186536133,-0.0295721721,-0.0745582134,0.2892119884,0.3251001835,-0.1471627504,0.1617639065,-0.2490819097,0.0260687321,-0.3570151627,-0.0386652276,0.0843394399,0.0631872639,0.5538393259,0.1039331779,0.380754441,0.5164234638,0.2587676942,0.791349709,0.2428612709,-0.2411892265,0.163986057,0.4420024157,-0.0212106947,0.3544448018,0.0041861106,-0.0741578713,-0.3006661832,0.4204317927,0.1880339831,0.1718084663,-0.2704502344,-0.3142211437,0.0297426507,0.5606445074,-0.4373676181,-0.2953945994,-0.177010268,-0.2630363107,-0.3189742267,0.1110462248,0.0432382002,-0.0600381382,0.1891306937,0.1954855174,0.1987176389,-0.2890897095,0.1917297244,0.0323539339,0.1801727265,0.0781724304,0.2091457844,-0.1302066743,-0.1141496673,0.3688918948,-0.0431594849,0.1968961507,0.2251400501,0.0955176577,-0.0600453764,0.1935303956,0.0480217934,-0.0821159407,0.1949988306,-0.3199300766,-0.1341323704,0.0023784265,0.0736258551,0.1694238186,0.0672128573,-0.049991414,0.2378932834,0.2057169825,0.1243259311,0.2681448758,-0.088194117,-0.0147982286,-0.2870153487,0.135250017,0.067397058,0.4890877008,-0.1417488456,-0.1287476569,0.0631199107,-0.3517041206,-0.1332259476,0.1493448168,0.2231163234,-0.1738203317,0.3179601133,0.2768835425,0.0402333811,-0.0383706167,-0.1485457122,-0.1716760993,0.1480696052,-0.2860532999,-0.009941984,-0.1201558486,0.1745955199,-0.2415946722,0.2068478614,-0.3203718662,0.0915210694,-0.2103444487,-0.0939712003,-0.0475952365,0.3335162699,-0.3074407876,0.0906602815,-0.0102368863,-0.2213965356,-0.3277479708,0.2611623704,-0.2281775177,-0.4078338146,-0.3511123359,0.0891685635,-0.1868232042,0.099014625,-0.1131187081,0.0563398115,0.3458622396,-0.262634933,0.1782426089,0.0927590951,-0.0486754738,-0.4944669604,0.0261735301,-0.0464595631,-0.5584202409,0.0517700948,0.06821955,-0.240746215,0.2964725196,0.1136727706,0.0055103558,0.1590037346,-0.0400644392,0.0783476382,0.0067199171,-0.0000014846,-0.0172602944,-0.0826756433,0.1535087079,-0.1110581085,-0.0318773873,-0.0040984913,-0.1063617468,-0.413466841,0.0506184064,0.0435642786,-0.1433010101,-0.2232991904,-0.3076542318,-0.2335506976,0.219019413,0.2242191434,0.2780328989,-0.0184108522,-0.0233402047,-0.4954235256,0.1061686203,-0.0543606095,0.0125319781,0.0036810304,0.4620353281,0.244200632,-0.052324675,-0.3410769999,-0.3519198596,0.2165426314,-0.1543737352,0.266556114,-0.4333074391,-0.4322145879,-0.2469987124,0.2843517959,0.2359921336,0.0394693278,-0.0127509618,-0.0075019482,0.1634652317,0.313750416,-0.1070332974,0.0478356034,-0.2006307989,0.1544554085,-0.2843419909,0.3436804116,-0.2499451786,-0.2929549217,-0.165871799,0.370357126,0.1957437694,0.0318404697,0.1319417506,0.2155221552,0.1618633717,0.0285041574,-0.4760746062,0.2356525213,0.1555476487,-0.1008736789,-0.3313786089,0.246456936,0.1859114766,-0.0580180734,0.0594786443,0.4856963158,-0.1908375472,-0.0566003099,-0.0343563482,0.1692690998,-0.0513303131,-0.0013236309,-0.101103507,0.1046526656,-0.2391401827,0.230587095,0.0001366253,0.056585785,-0.3509805799,0.2909885943,0.4456031024,-0.1674019396,0.0147179514,0.083494164,-0.1784943491,-0.1631649584,0.0915302783,-0.0366803855,0.1846927851,0.1808855832,-0.0249650907,0.0427777357,-0.3133356273,0.1275976747,0.2737653852,0.1087989062,0.1261407733,-0.1336376816,0.0752126053,0.2009641379,0.00074131,-0.213247776,-0.0554308631,0.4063256681,-0.6351138353,0.1275184751,-0.1732435524,-0.2968247235,0.0163698308,-0.4004116356,-0.1705930978,-0.4447685778,0.1812200397,0.0509731323,-0.1545256078,0.0874485895,-0.2516877651,0.307404995,0.0014126778,0.0575638525,-0.2588644624,-0.3532381058,-0.308522135,0.0650167465,0.1218183935,0.1679161787,0.2874653339,-0.2560241222,0.1563140452,-0.1229113117,-0.5098377466,0.0441641547,-0.2387231588,0.4635874629,0.1750925034,0.140240863,0.0216932073,-0.2076889724,0.200883761,-0.1902018338,-0.2915955484,0.0148491627,-0.1831306368,0.1171564236,-0.2185306996,0.0000787072,-0.5082910657,-0.3376797736,0.2521597445,-0.0579733998,0.3331135511,0.4523911178,-0.2341374159,0.0739863068,-0.1119891182,-0.0105879586,-0.2284050286,-0.0708668455,0.3865037262,-0.1658471972,-0.3249434233,-0.1007075086,-0.1360381544,-0.3380487859,0.0097035924,-0.1703654379,-0.6069763303,-0.0062818001,0.4324361384,0.0875600278,0.0493195206,0.3641599417,0.0953375474,0.0434650518,-0.0619656146,-0.201996401,-0.0165941697,0.1031663939,0.1257831603,0.2682313919,0.244355008,-0.1529213339,0.9732429385,0.1925334632,-0.4252699912,0.2978717983,-0.3933077753,0.1986407191,0.1053328216,-0.0577903911,-0.2490445673,-0.1892093718,-0.04953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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1894","title":"benchmarking against MMapIndexedDataset","comments":"Hi sam !\r\nIndeed we can expect the performances to be very close since both MMapIndexedDataset and the `datasets` implem use memory mapping. With memory mapping what determines the I\/O performance is the speed of your hard drive\/SSD.\r\n\r\nIn terms of performance we're pretty close to the optimal speed for reading text, even though I found recently that we could still slightly improve speed for big datasets (see [here](https:\/\/github.com\/huggingface\/datasets\/issues\/1803)).\r\n\r\nIn terms of number of examples and example sizes, the only limit is the available disk space you have.\r\n\r\nI haven't used `psrecord` yet but it seems to be a very interesting tool for benchmarking. Currently for benchmarks we only have github actions to avoid regressions in terms of speed. But it would be cool to have benchmarks with comparisons with other dataset tools ! This would be useful to many people","body":"I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens).\r\n\r\nQuestions:\r\n1) Is this (basically identical) performance expected? \r\n2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples\/larger examples?)\r\n3) Should I be using different benchmarking tools than `psrecord`\/how do you guys do benchmarks?\r\n\r\nThanks in advance! Sam","comment_length":141,"text":"benchmarking against MMapIndexedDataset \n I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens).\r\n\r\nQuestions:\r\n1) Is this (basically identical) performance expected? \r\n2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples\/larger examples?)\r\n3) Should I be using different benchmarking tools than `psrecord`\/how do you guys do benchmarks?\r\n\r\nThanks in advance! Sam \n Hi sam !\r\nIndeed we can expect the performances to be very close since both MMapIndexedDataset and the `datasets` implem use memory mapping. With memory mapping what determines the I\/O performance is the speed of your hard drive\/SSD.\r\n\r\nIn terms of performance we're pretty close to the optimal speed for reading text, even though I found recently that we could still slightly improve speed for big datasets (see [here](https:\/\/github.com\/huggingface\/datasets\/issues\/1803)).\r\n\r\nIn terms of number of examples and example sizes, the only limit is the available disk space you have.\r\n\r\nI haven't used `psrecord` yet but it seems to be a very interesting tool for benchmarking. Currently for benchmarks we only have github actions to avoid regressions in terms of speed. But it would be cool to have benchmarks with comparisons with other dataset tools ! This would be useful to many 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1894","title":"benchmarking against MMapIndexedDataset","comments":"Also I would be interested to know what data types `MMapIndexedDataset` supports. Is there some documentation somewhere ?","body":"I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens).\r\n\r\nQuestions:\r\n1) Is this (basically identical) performance expected? \r\n2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples\/larger examples?)\r\n3) Should I be using different benchmarking tools than `psrecord`\/how do you guys do benchmarks?\r\n\r\nThanks in advance! Sam","comment_length":18,"text":"benchmarking against MMapIndexedDataset \n I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens).\r\n\r\nQuestions:\r\n1) Is this (basically identical) performance expected? \r\n2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples\/larger examples?)\r\n3) Should I be using different benchmarking tools than `psrecord`\/how do you guys do benchmarks?\r\n\r\nThanks in advance! Sam \n Also I would be interested to know what data types `MMapIndexedDataset` supports. Is there some documentation somewhere ?","embeddings":[-0.2623206377,-0.0587809421,-0.1896093339,0.2777880728,-0.227726087,-0.0737620816,0.0111633465,0.1357609332,-0.2933735251,-0.1568026692,-0.1532005072,0.3774213493,0.0027406048,-0.3708997667,0.2234075814,-0.1318493038,0.3597462773,0.1027279869,-0.2230797857,0.0388148762,0.004757049,0.0907562524,-0.3878263533,-0.0007551571,-0.308994323,0.1608976424,0.0237744935,0.1252493113,-0.0787538067,-0.327116996,0.2054045796,0.2334433347,0.0119257383,0.6243491173,-0.0001231747,-0.2159653604,-0.0060990448,0.1145242155,0.1292808205,0.4733529687,-0.355368048,-0.1873503625,0.1760196388,-0.0643589869,0.1672016978,-0.1405496299,-0.1546870023,-0.7546443939,0.0663225204,0.166835174,0.0619084761,0.3860285878,-0.4022429585,0.0922332853,0.0853737816,-0.0240259543,-0.2072671652,0.189033702,0.5677084923,-0.0037737319,-0.0012585642,-0.0574584752,-0.1934132576,0.0176083986,0.1063681766,-0.0935725346,-0.1897952706,-0.0806949511,0.1602991521,0.5714413524,0.3033574522,-0.3853615224,-0.1944885999,-0.3469295502,-0.2907654643,0.1559844762,0.1440193355,-0.304181397,-0.0016855652,0.0526980609,-0.5018880367,0.1885734648,0.1384049654,-0.166680187,0.0034871246,0.4123747349,0.2059561908,0.0534100756,0.463853389,-0.2791369557,0.2155865878,-0.2781588435,-0.0903226882,0.1010188982,-0.5098439455,-0.2299691886,-0.0520547517,-0.0863875672,0.135603115,0.3474746048,-0.2032877505,0.5046434402,0.429818213,0.0884598643,0.1067866907,0.2343621403,0.1511718035,0.0173080042,0.0804942399,-0.1772749275,-0.372879535,0.096013248,0.1723684818,0.0604582168,-0.210566476,-0.2381728142,-0.3924916089,-0.4892415702,-0.2800405324,0.0489859879,0.3160374463,-0.3449766636,0.045167394,0.2040766776,0.0596403033,0.1809966713,-0.4886460304,0.055304911,-0.2804618478,-0.2519147098,0.0053385948,0.0612695068,-0.0932258442,0.3304063082,0.117045477,-0.0538370311,0.2595546842,-0.0688250065,0.2348473221,0.0566518083,0.51203233,-0.3035805225,-0.0164029244,0.0229024831,-0.1128176227,0.1444650739,0.0295833126,0.3873952627,-0.3637986481,0.0013563817,-0.1206983998,-0.1722417772,0.3445302844,-0.0133164339,-0.3269232213,-0.3736244738,-0.575011909,0.3550662398,-0.1666037738,-0.2867623866,0.2065920681,-0.0011886382,-0.1739472747,-0.2382938266,0.0253138449,-0.1241655499,-0.235348165,0.0866830572,-0.3662692606,-0.0079210345,-0.1065398455,0.357750237,-0.3426887989,0.2114575654,0.0139793782,0.1618296057,0.3885022402,-0.5090394616,-0.4399956763,-0.0519217774,0.2221059501,0.2691320479,0.0002896641,0.1311459839,0.2970203459,-0.2753646374,-0.0625803918,0.0255434271,0.1016711816,0.2055302113,-0.1206864789,-0.2674798071,0.2851322293,0.1665535122,-0.2390076518,-0.3818077445,-0.1458150148,-0.259470731,-0.0230305456,-0.0676745251,-0.2381089032,0.0109963994,0.0580346175,-0.1880514324,-0.0189225599,-0.2777186036,-0.0237315167,0.2902144194,-0.256510675,-0.0430307873,0.3490846157,-0.0300245993,0.0151800364,-0.1060819998,0.0208048839,-0.2491456419,-0.0057928399,-0.1321569979,0.2593689263,0.123262465,-0.3601054251,-0.2201318592,0.269644469,-0.0733439028,0.2368864119,-0.1186155677,0.3427739739,-0.1157050282,-0.0664257482,0.2260990441,-0.1223699376,-0.0454593264,-0.1864117533,0.1043651998,0.3241366446,0.2530105114,0.5111225247,0.4740772545,-0.1185918376,-0.1677387506,0.1293274164,0.1104747951,0.1646763533,-0.2225419283,-0.0931732133,0.6572032571,0.3265316486,0.3121195138,0.0560516044,-0.3682840168,-0.1889240742,0.2153066844,0.1976000369,0.1536179483,0.1227065697,0.4277952909,0.3569436371,0.0901561007,0.2018396854,0.3611363173,0.436733067,0.0122092217,-0.0282971486,0.1319124252,-0.4084214866,-0.2810285985,0.3035345972,-0.0571420006,0.6194142103,-0.0001335044,0.3581352234,0.0060083275,0.1161910594,0.1531037837,0.3110364974,0.1907286346,-0.3497518897,-0.1504902393,0.1073554382,-0.0877887234,0.1404762864,-0.2416676432,-0.1674517244,0.1516312212,-0.0311502051,0.2706885934,-0.2101578265,0.2350755781,0.1818676889,-0.1810306013,-0.240647614,-0.2521364987,0.0457765795,-0.0088928426,0.170753926,0.0854838118,-0.0188961383,0.3148322403,-0.3668530285,-0.3901950717,0.1375721544,-0.0929022059,-0.4670560062,0.0459489413,0.4081196785,0.1970107406,0.3088477254,0.0678155348,0.2102432698,-0.0006256719,-0.1477908194,0.0600795187,-0.112185061,0.1130770296,-0.1586785614,-0.3379225731,-0.3725426495,-0.1332513541,-0.0509335175,-0.2197098434,-0.0060843122,-0.3206317127,0.0545925386,-0.0176921636,0.1768091023,0.0380966999,-0.1530308127,0.0944231898,0.1511808634,-0.1855606139,0.1327320933,-0.62751472,-0.0694225803,0.2902387679,-0.4144516289,0.2188192904,-0.1059561744,-0.6133030057,0.1753400266,-0.0784829706,-0.2060005367,0.001224825,0.1281179935,0.2923304439,0.1140855104,-0.1864194721,-0.007726239,-0.0248297248,0.2191719711,0.173627153,0.0818920508,0.3506791294,-0.1272312999,0.0651778728,-0.0971101522,-0.1086509973,-0.021585824,-0.2666110098,0.1335418075,-0.0696092322,0.2099888325,-0.1552820355,0.7983125448,0.2943360209,-0.1711910665,-0.0251084603,-0.1154659018,-0.0341901518,-0.2214478999,0.0346540734,-0.0982867926,0.1160926074,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1894","title":"benchmarking against MMapIndexedDataset","comments":"no docs haha, it's written to support integer numpy arrays.\r\n\r\nYou can build one in fairseq with, roughly:\r\n```bash\r\n\r\nwget https:\/\/s3.amazonaws.com\/research.metamind.io\/wikitext\/wikitext-103-raw-v1.zip\r\nunzip wikitext-103-raw-v1.zip\r\nexport dd=$HOME\/fairseq-py\/wikitext-103-raw\r\n\r\nexport mm_dir=$HOME\/mmap_wikitext2\r\nmkdir -p gpt2_bpe\r\nwget -O gpt2_bpe\/encoder.json https:\/\/dl.fbaipublicfiles.com\/fairseq\/gpt2_bpe\/encoder.json\r\nwget -O gpt2_bpe\/vocab.bpe https:\/\/dl.fbaipublicfiles.com\/fairseq\/gpt2_bpe\/vocab.bpe\r\nwget -O gpt2_bpe\/dict.txt https:\/\/dl.fbaipublicfiles.com\/fairseq\/gpt2_bpe\/dict.txt\r\nfor SPLIT in train valid; do \\\r\n    python -m examples.roberta.multiprocessing_bpe_encoder \\\r\n        --encoder-json gpt2_bpe\/encoder.json \\\r\n        --vocab-bpe gpt2_bpe\/vocab.bpe \\\r\n        --inputs \/scratch\/stories_small\/${SPLIT}.txt \\\r\n        --outputs \/scratch\/stories_small\/${SPLIT}.bpe \\\r\n        --keep-empty \\\r\n        --workers 60; \\\r\ndone\r\n\r\nmkdir -p $mm_dir\r\nfairseq-preprocess \\\r\n    --only-source \\\r\n    --srcdict gpt2_bpe\/dict.txt \\\r\n    --trainpref $dd\/wiki.train.bpe \\\r\n    --validpref $dd\/wiki.valid.bpe \\\r\n    --destdir $mm_dir \\\r\n    --workers 60 \\\r\n    --dataset-impl mmap\r\n```\r\n\r\nI'm noticing in my benchmarking that it's much smaller on disk than arrow (200mb vs 900mb), and that both incur significant cost by increasing the number of data loader workers. \r\nThis somewhat old [post](https:\/\/ray-project.github.io\/2017\/10\/15\/fast-python-serialization-with-ray-and-arrow.html) suggests there are some gains to be had from using `pyarrow.serialize(array).tobuffer()`. I haven't yet figured out how much of this stuff `pa.Table` does under the hood.\r\n\r\nThe `MMapIndexedDataset` bottlenecks we are working on improving (by using arrow) are:\r\n1) `MMapIndexedDataset`'s index, which stores offsets, basically gets read in its entirety by each dataloading process.\r\n2) we have separate, identical, `MMapIndexedDatasets` on each dataloading worker, so there's redundancy there; we wonder if there is a way that arrow can somehow dedupe these in shared memory.\r\n\r\nIt will take me a few hours to get `MMapIndexedDataset` benchmarks out of `fairseq`\/onto a branch in this repo, but I'm happy to invest the time if you're interested in collaborating on some performance hacking.","body":"I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens).\r\n\r\nQuestions:\r\n1) Is this (basically identical) performance expected? \r\n2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples\/larger examples?)\r\n3) Should I be using different benchmarking tools than `psrecord`\/how do you guys do benchmarks?\r\n\r\nThanks in advance! Sam","comment_length":249,"text":"benchmarking against MMapIndexedDataset \n I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens).\r\n\r\nQuestions:\r\n1) Is this (basically identical) performance expected? \r\n2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples\/larger examples?)\r\n3) Should I be using different benchmarking tools than `psrecord`\/how do you guys do benchmarks?\r\n\r\nThanks in advance! Sam \n no docs haha, it's written to support integer numpy arrays.\r\n\r\nYou can build one in fairseq with, roughly:\r\n```bash\r\n\r\nwget https:\/\/s3.amazonaws.com\/research.metamind.io\/wikitext\/wikitext-103-raw-v1.zip\r\nunzip wikitext-103-raw-v1.zip\r\nexport dd=$HOME\/fairseq-py\/wikitext-103-raw\r\n\r\nexport mm_dir=$HOME\/mmap_wikitext2\r\nmkdir -p gpt2_bpe\r\nwget -O gpt2_bpe\/encoder.json https:\/\/dl.fbaipublicfiles.com\/fairseq\/gpt2_bpe\/encoder.json\r\nwget -O gpt2_bpe\/vocab.bpe https:\/\/dl.fbaipublicfiles.com\/fairseq\/gpt2_bpe\/vocab.bpe\r\nwget -O gpt2_bpe\/dict.txt https:\/\/dl.fbaipublicfiles.com\/fairseq\/gpt2_bpe\/dict.txt\r\nfor SPLIT in train valid; do \\\r\n    python -m examples.roberta.multiprocessing_bpe_encoder \\\r\n        --encoder-json gpt2_bpe\/encoder.json \\\r\n        --vocab-bpe gpt2_bpe\/vocab.bpe \\\r\n        --inputs \/scratch\/stories_small\/${SPLIT}.txt \\\r\n        --outputs \/scratch\/stories_small\/${SPLIT}.bpe \\\r\n        --keep-empty \\\r\n        --workers 60; \\\r\ndone\r\n\r\nmkdir -p $mm_dir\r\nfairseq-preprocess \\\r\n    --only-source \\\r\n    --srcdict gpt2_bpe\/dict.txt \\\r\n    --trainpref $dd\/wiki.train.bpe \\\r\n    --validpref $dd\/wiki.valid.bpe \\\r\n    --destdir $mm_dir \\\r\n    --workers 60 \\\r\n    --dataset-impl mmap\r\n```\r\n\r\nI'm noticing in my benchmarking that it's much smaller on disk than arrow (200mb vs 900mb), and that both incur significant cost by increasing the number of data loader workers. \r\nThis somewhat old [post](https:\/\/ray-project.github.io\/2017\/10\/15\/fast-python-serialization-with-ray-and-arrow.html) suggests there are some gains to be had from using `pyarrow.serialize(array).tobuffer()`. I haven't yet figured out how much of this stuff `pa.Table` does under the hood.\r\n\r\nThe `MMapIndexedDataset` bottlenecks we are working on improving (by using arrow) are:\r\n1) `MMapIndexedDataset`'s index, which stores offsets, basically gets read in its entirety by each dataloading process.\r\n2) we have separate, identical, `MMapIndexedDatasets` on each dataloading worker, so there's redundancy there; we wonder if there is a way that arrow can somehow dedupe these in shared memory.\r\n\r\nIt will take me a few hours to get `MMapIndexedDataset` benchmarks out of `fairseq`\/onto a branch in this repo, but I'm happy to invest the time if you're interested in collaborating on some performance hacking.","embeddings":[-0.2432021052,0.023352541,-0.1691768616,0.1820509285,-0.1022186801,-0.0226155818,0.1294504404,0.2604651451,-0.2088269889,-0.0699171424,-0.155699119,0.5033259988,-0.0702501908,-0.4188563228,0.2817451656,-0.0300162956,0.3354423642,0.1515544802,-0.1571365446,0.0486279726,-0.1405944675,0.1506153196,-0.3253050447,0.0204201993,-0.3509258628,0.122545585,-0.0095216176,0.2243740261,-0.0806345567,-0.3892317712,0.2625464201,0.1312130243,-0.0679286122,0.5093360543,-0.0001197014,-0.1623321921,-0.0542118922,0.1078669131,0.0268320143,0.540053606,-0.1131937727,-0.3049707711,0.0386117063,-0.1792382598,0.0806340948,-0.1571841538,-0.1240352243,-0.6968595386,0.0376429297,0.112498045,0.0433407277,0.3202779293,-0.287357986,0.1792372763,0.0780043751,-0.0878257155,-0.2255390435,0.2030549645,0.6038826704,-0.0669367611,-0.0622575395,0.0722511038,-0.1325347275,0.0774309486,0.0730090886,0.0020603244,-0.1156363711,-0.0859563053,0.0608329996,0.4867009521,0.1345411241,-0.3546428084,-0.2642897069,-0.4171714485,-0.3238388896,0.0939705223,0.2001515329,-0.2242229581,-0.045118209,0.1037718952,-0.5512924194,0.2541922927,0.1437454224,-0.0920725763,-0.0109138638,0.4918263257,0.3039819896,0.0423975363,0.4399318397,-0.2640122771,0.1324423403,-0.2057048827,0.0011939199,0.1028615758,-0.4656966925,-0.2803414762,-0.0441006422,-0.250459969,0.1140584946,0.2019072175,-0.1421616822,0.515226841,0.4101502001,0.0859821811,0.075697735,0.2415622324,0.0921152011,0.0314107649,0.0018228889,-0.09552598,-0.2015071213,0.1789547205,0.1996313035,0.0112764891,-0.2609249651,-0.1755254269,-0.3801255226,-0.3332433403,-0.3777097166,0.0147047155,0.3565771878,-0.3525733352,0.0801032111,0.2087020427,0.0533464961,0.2673592567,-0.4570310116,0.0394971967,-0.3195516467,-0.2048750669,-0.0434496328,0.1353322864,-0.0892045274,0.2397130728,0.1953056157,0.1026565209,0.2620453835,-0.0591993704,0.1804069132,0.1563943475,0.5779836774,-0.2525075972,-0.0220910367,-0.0524834134,-0.1771284789,0.1634490192,0.0495934784,0.4309071898,-0.3159751594,0.0107475482,-0.1292083561,-0.1222500205,0.374181211,-0.0066924528,-0.2167480588,-0.301915437,-0.3807257414,0.3237059414,-0.0806941465,-0.2796670496,0.2008896619,-0.0380026847,-0.2599069178,-0.2586502433,0.1129405051,-0.104804866,-0.1829633862,0.0902451426,-0.2681652009,0.1017137095,-0.0574376918,0.4228224456,-0.3233219385,0.2451714128,-0.0476449318,0.2953229547,0.3699166477,-0.4035232663,-0.4890653193,-0.0134465992,0.18437998,0.2378026843,-0.0289858598,0.2881448269,0.2071392089,-0.3476954997,0.0604116172,0.086655207,0.1298025548,0.3404920101,-0.191495046,-0.4372926652,0.4101485014,0.1844226867,-0.2632358372,-0.5926275849,-0.2096512914,-0.220100373,0.0141147422,-0.1993325502,-0.2652760148,-0.0197391007,0.0547744371,-0.3296548724,-0.0481884144,-0.2119642049,-0.0164226443,0.4075005352,-0.3809975088,-0.0671109483,0.410477072,-0.0325264037,0.0160775185,-0.126000762,-0.0352367759,-0.3404727876,0.0188441556,-0.124628529,0.2839566469,0.1574340165,-0.372543931,-0.2051105797,0.2095497102,-0.0340516791,0.0869028717,-0.1760457903,0.2704811096,-0.1397456527,-0.1292769313,0.2784688175,-0.1737473607,-0.0782441348,-0.1303406656,0.1673095971,0.262606591,0.0895880014,0.4840814173,0.36742571,-0.0200807415,-0.1213861853,0.1851388961,0.1798506826,0.1355849355,-0.2332924604,-0.0173051562,0.6673050523,0.158408612,0.3197748661,0.0334665813,-0.3281886876,-0.2300444841,0.2341326475,0.2983640432,0.1582586169,0.1329226196,0.3186577857,0.3469450474,0.0745346472,0.2600978017,0.43803671,0.5110450983,0.0919650421,0.0119462172,0.0950751305,-0.3309466243,-0.3392916918,0.2181062102,-0.1530645341,0.6769615412,0.0056273658,0.3541246653,-0.0272403862,0.0668689162,0.1295981407,0.3824511468,0.2104080319,-0.4022667706,-0.1451212466,0.0917438716,-0.1520504057,0.1312493086,-0.1228138506,-0.138861984,0.1244563162,-0.0374373756,0.227590546,-0.0976811647,0.3413497806,0.1647017747,-0.3163818717,-0.1967754215,-0.2952255309,0.1427739859,-0.0410444699,0.1379371881,0.1017521098,0.0208522659,0.2121637464,-0.2811616361,-0.2880559564,0.0261986479,-0.0989259854,-0.4190021753,0.041986037,0.4446631372,0.1643384844,0.3288487196,0.0893413201,0.0642520711,0.0240686946,-0.1532045007,0.0006648721,-0.1560738534,0.1611467451,-0.0476363078,-0.3093944788,-0.4757262766,-0.2025446296,0.0364159048,-0.2672000527,-0.0642173439,-0.2295372635,0.2396284491,-0.0640902743,0.1067321599,-0.107920602,-0.1804136485,0.064130567,0.0804081485,-0.140421167,0.1453793645,-0.5900020003,0.0552156344,0.3555607498,-0.3177753389,0.1301364899,-0.1445241868,-0.4670259058,0.2165990025,-0.0551492088,-0.1931108981,-0.049044691,0.0232459009,0.2404927164,0.1181667224,-0.1977451593,-0.0716168731,-0.0266945828,0.1901101768,0.1161490977,0.1605773717,0.3275685012,-0.1320878863,0.0220072344,-0.1042530164,0.0397158265,0.0453550145,-0.2836326659,0.2459726781,-0.0552967489,0.1432182044,-0.1069723666,0.8057386279,0.2422968745,-0.2431987375,-0.0703333765,-0.0903486609,-0.0702882782,-0.1221860126,-0.0307255723,0.0003347387,0.17546792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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1893","title":"wmt19 is broken","comments":"This was also mentioned in https:\/\/github.com\/huggingface\/datasets\/issues\/488 \r\n\r\nThe bucket where is data was stored seems to be unavailable now. Maybe we can change the URL to the ones in https:\/\/conferences.unite.un.org\/uncorpus\/en\/downloadoverview ?","body":"1. Check which lang pairs we have: `--dataset_name wmt19`:\r\n\r\nPlease pick one among the available configs: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']\r\n\r\n \r\n2. OK, let's pick `ru-en`:\r\n\r\n`--dataset_name wmt19 --dataset_config \"ru-en\"`\r\n\r\nno cookies:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/run_seq2seq.py\", line 661, in <module>\r\n    main()\r\n  File \".\/run_seq2seq.py\", line 317, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 628, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt19\/436092de5f3faaf0fc28bc84875475b384e90a5470fa6afaee11039ceddc5052\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/download_manager.py\", line 276, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/download_manager.py\", line 191, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 233, in map_nested\r\n    mapped = [\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 234, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 190, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 190, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 172, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/download_manager.py\", line 211, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/file_utils.py\", line 274, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/file_utils.py\", line 584, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-ru.tar.gz\r\n```","comment_length":30,"text":"wmt19 is broken \n 1. Check which lang pairs we have: `--dataset_name wmt19`:\r\n\r\nPlease pick one among the available configs: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']\r\n\r\n \r\n2. OK, let's pick `ru-en`:\r\n\r\n`--dataset_name wmt19 --dataset_config \"ru-en\"`\r\n\r\nno cookies:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/run_seq2seq.py\", line 661, in <module>\r\n    main()\r\n  File \".\/run_seq2seq.py\", line 317, in main\r\n    datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/load.py\", line 740, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 572, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/builder.py\", line 628, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/stas\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt19\/436092de5f3faaf0fc28bc84875475b384e90a5470fa6afaee11039ceddc5052\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/download_manager.py\", line 276, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/download_manager.py\", line 191, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 233, in map_nested\r\n    mapped = [\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 234, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 190, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 190, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/py_utils.py\", line 172, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/download_manager.py\", line 211, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/file_utils.py\", line 274, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/datasets-master\/src\/datasets\/utils\/file_utils.py\", line 584, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-ru.tar.gz\r\n``` \n This was also mentioned in https:\/\/github.com\/huggingface\/datasets\/issues\/488 \r\n\r\nThe bucket where is data was stored seems to be unavailable now. Maybe we can change the URL to the ones in https:\/\/conferences.unite.un.org\/uncorpus\/en\/downloadoverview 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1892","title":"request to mirror wmt datasets, as they are really slow to download","comments":"Yes that would be awesome. Not only the download speeds are awful, but also some files are missing.\r\nWe list all the URLs in the datasets\/wmt19\/wmt_utils.py so we can make a script to download them all and host on S3.\r\nAlso I think most of the materials are under the CC BY-NC-SA 3.0 license (must double check) so it should be possible to redistribute the data with no issues.\r\n\r\ncc @patrickvonplaten who knows more about the wmt scripts","body":"Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you!","comment_length":78,"text":"request to mirror wmt datasets, as they are really slow to download \n Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you! \n Yes that would be awesome. Not only the download speeds are awful, but also some files are missing.\r\nWe list all the URLs in the datasets\/wmt19\/wmt_utils.py so we can make a script to download them all and host on S3.\r\nAlso I think most of the materials are under the CC BY-NC-SA 3.0 license (must double check) so it should be possible to redistribute the data with no issues.\r\n\r\ncc @patrickvonplaten who knows more about the wmt scripts","embeddings":[-0.194238469,-0.3241634369,0.0704936907,0.2124052793,0.0100606102,0.0652933568,0.1663089246,0.4544945955,0.056202095,-0.1142761186,-0.2518411875,-0.0738516971,-0.0602755286,0.310782969,-0.0818374977,-0.0826895609,-0.0564791039,0.1170890555,-0.6240917444,-0.0960814133,0.0491623133,-0.1243582368,-0.0437549204,-0.0403349809,-0.0081339916,0.2965478599,-0.220490545,0.0053677587,-0.2378391623,0.0302223265,0.0825243667,0.5002441406,0.2514034212,0.3773466349,-0.0001185067,-0.1264594942,0.1999659538,0.1051914617,-0.2532367408,0.0950692743,-0.4915260077,-0.3680053353,-0.235564217,-0.016106084,-0.1161408946,-0.1211741045,-0.1031256467,-0.0419021584,0.2931009531,-0.1038033813,0.0933474675,0.4699967206,-0.4896414876,-0.0356685296,0.5301848054,0.3358390927,-0.2962783575,0.3221007884,0.5346037745,-0.138974309,0.3522563875,0.423499465,-0.1568779647,0.3637658954,0.6082298756,-0.188751027,0.0055280928,-0.0134862866,-0.248511374,0.652790606,0.720821023,-0.1008875892,-0.5376054645,-0.0442308113,-0.0979527459,0.1767011881,-0.3057165146,0.1743835956,0.0661836788,0.2955296636,-0.4052235484,-0.3033839762,-0.057906244,0.0288335346,-0.0891483799,0.4804267585,0.0012672525,-0.0247035343,-0.1784487516,-0.1355646998,0.447550416,-0.4463375807,0.2125969678,0.0525240749,0.0689759851,-0.3961450458,-0.4048391283,-0.1257929951,0.1217897981,0.0788985491,-0.0996380746,0.0322684087,0.060873691,-0.0059303278,0.053396605,-0.0918024257,-0.2332044095,-0.2772554755,0.2760227323,0.1725937873,0.0821032822,-0.0665007085,-0.1882131249,-0.1074698642,-0.4842549264,-0.0299393963,0.0422553979,-0.3843735158,-0.1701832265,-0.2568981647,0.1291088313,-0.1630939841,-0.2392384708,0.0911244228,-0.2050314546,0.2176314592,0.3381380737,0.1368322372,-0.1975701302,-0.1293524206,-0.0453770421,-0.1888690144,0.1018535569,0.4096263945,0.1961645782,0.1012696177,0.0809571892,0.01252288,0.2291604728,0.3779648542,0.2429531813,0.2257295847,0.0011985829,0.2455646694,-0.2264270484,0.5534642935,-0.0571046621,0.494664669,-0.2405672669,0.0189298727,-0.395958662,-0.1411177516,0.1163505614,0.1434739679,-0.2485123426,-0.2411218286,-0.3235337436,-0.1215084046,-0.358140111,0.1436905265,-0.0027514824,-0.160331279,-0.1871885955,0.0441947542,0.1369813532,0.5623070002,-0.3566907942,-0.0392406173,0.1318229735,0.0986153781,0.424931556,0.4538913071,-0.1545871943,0.0098788086,-0.2259876579,-0.0569346696,-0.0896128193,-0.2199738175,0.0895927176,0.4570536613,-0.2056543082,-0.0597877093,0.2275102884,0.1248131469,0.1572809815,-0.3333368301,-0.0803302228,0.4827212393,-0.0005445417,-0.0532289445,-0.240019381,-0.5139433146,0.321305871,0.3085314929,0.1036258489,0.135584861,-0.1118637845,0.0921425149,0.4986327887,-0.0057322043,0.030869225,0.0384275615,0.2461281717,0.4436832666,0.021693971,-0.0548787564,0.1014436707,0.0964070857,0.1379815936,0.0048364475,0.3148323596,-0.2072585076,0.0021639727,-0.1151764542,0.0240758881,0.2152531892,0.0360904336,-0.2255754471,-0.1383960992,-0.3562449813,-0.0284167528,0.0065095904,-0.0618620403,0.035450656,0.3565728962,0.3909591436,0.0855044574,0.3153090477,0.4274924099,-0.1052074805,0.2611545622,0.0331897177,-0.0113981282,-0.2118983418,-0.0324237496,0.380189538,0.48837322,0.4331637621,0.2952303588,-0.540738225,0.5896484852,0.1838252544,0.1421567798,-0.2557565272,-0.181696713,0.2997314334,-0.1090607792,0.1404949427,0.0827980563,-0.077605173,-0.0244501363,-0.1666247845,-0.169337675,0.0878170207,0.2612858415,-0.061919976,-0.2795540392,0.0631749704,0.0156494044,0.2879464626,0.1335858405,-0.1323908418,0.0646268725,-0.0883881748,-0.3523101211,0.1846619248,0.1971083879,0.1618145406,0.1227264032,0.2624022365,0.081486091,0.1348206401,0.3998582363,-0.2348161787,0.0997165963,-0.0268699564,0.0908876956,0.1765918881,-0.157308042,-0.0949692652,-0.2644998431,-0.1340385973,-0.0339329764,-0.1693633795,-0.205760777,-0.2036472559,-0.3950927556,-0.1671643704,-0.5224645138,-0.0712078735,-0.1530241221,-0.2038117051,0.0044244444,0.0359447189,-0.0924441144,-0.0787629038,-0.15335913,0.1934635043,-0.3043530881,0.1676543653,-0.2028636634,0.037273936,0.0826232433,-0.0348152369,0.1339907497,0.1073842794,0.3420969546,-0.1645022482,0.024506405,-0.6451830268,-0.1008385792,-0.089119181,0.2413382679,-0.00529119,-0.0128091332,0.4223403037,0.3099903464,0.1228703186,0.1148653924,-0.1887265444,-0.057935629,0.026614951,-0.1791533381,-0.1181851849,-0.0117393313,-0.1499120891,-0.4867734015,-0.1689320356,-0.0910814926,-0.2901754081,-0.0047472958,-0.0703091174,-0.0402645543,-0.1997772604,-0.2292350531,0.0202789493,-0.1326332986,-0.4535301626,0.4614348114,0.0416385792,-0.237315923,0.1984031945,0.1946207881,-0.2962462008,0.0509123318,-0.4404743314,-0.0259731617,-0.0599781126,-0.1563954204,0.1378271133,0.3616072536,0.0748511702,-0.0016155969,-0.0182547197,-0.1750897914,-0.170382753,-0.3151818514,0.0791268945,0.3384957612,0.0421886668,0.1538708359,0.1167562827,0.9036080837,0.21061261,0.086070329,0.1693006009,0.3139078617,0.0126044797,0.330495894,-0.1501098573,0.0602103658,-0.1646620482,0.15268789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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1892","title":"request to mirror wmt datasets, as they are really slow to download","comments":"Yeah, the scripts are pretty ugly! A big refactor would make sense here...and I also remember that the datasets were veeery slow to download","body":"Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you!","comment_length":24,"text":"request to mirror wmt datasets, as they are really slow to download \n Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you! \n Yeah, the scripts are pretty ugly! A big refactor would make sense here...and I also remember that the datasets were veeery slow to download","embeddings":[-0.380508244,-0.1753539741,0.0075874529,0.1037696823,-0.0104693696,0.0599777699,0.1670318693,0.5077069998,0.2716593742,-0.0307254717,-0.2506194115,-0.221631512,0.033997003,0.24326846,-0.1237284541,-0.2390306145,-0.1749914736,0.1651735455,-0.7791355252,-0.1799131185,0.106156148,-0.1956078261,-0.1282520443,0.0072468398,-0.0354572758,0.1832383424,-0.128082782,0.0092026293,-0.281499505,0.1942979246,0.0807311609,0.4943597913,0.1801086366,0.2579829693,-0.0001139865,-0.1233249083,0.3037301004,0.1550533772,-0.1294935197,0.2597908378,-0.3924541473,-0.3952484727,-0.1610328406,-0.0658745393,-0.0436388627,-0.1273960024,0.0423421934,-0.1791125238,0.2340647131,-0.1549867839,0.1716459394,0.3462652862,-0.4791024029,-0.0595961213,0.3936366737,0.286072433,-0.3739835024,0.3643722832,0.5096567273,-0.1086651608,0.0914191604,0.3541181982,0.0158592202,0.2641918957,0.4950572848,-0.2407833487,0.0755990446,0.0502780229,-0.2511317134,0.5559908152,0.7872530222,-0.0566438399,-0.3280628622,0.1233147085,-0.1975928098,0.2542445362,-0.2248382717,0.056848038,0.0246378183,0.3577070534,-0.4675727785,-0.305555284,0.0396127999,-0.07427378,-0.1059214771,0.2843854427,0.0716390535,-0.012037307,-0.0921839774,-0.1142882854,0.2289706022,-0.4278957248,0.084317714,0.0660024062,0.0435412303,-0.407456249,-0.3956961632,0.0255869534,0.2399980873,0.1858924627,0.0770375058,0.1213208511,-0.0796723068,-0.06023518,0.1222130656,0.2509377301,-0.2132793516,-0.3227464557,0.348944962,0.0415046737,-0.0507071316,0.0060407449,-0.0399916917,-0.1021285579,-0.3949147761,0.123759985,0.0564692654,-0.3286577463,-0.2215178907,-0.2023866326,0.0951999277,-0.2182261646,-0.1477472037,0.1160875112,-0.2152334452,0.324593991,0.3515886962,-0.0812580511,-0.2032058984,-0.106238246,-0.0876734629,-0.0906698555,-0.1020311862,0.3286113441,0.2291557342,0.1653606147,-0.0103698168,-0.0122763645,0.2687145174,0.3333777785,0.1240342855,0.1441444457,-0.0162177868,0.1667734385,-0.3101998568,0.4530290365,-0.0016281067,0.5766244531,-0.2522424459,0.1281220168,-0.4634134471,-0.1512120515,0.093342565,0.2227725685,-0.0691977069,-0.1474102587,-0.3374592364,-0.1135531589,-0.3930585086,0.083896108,-0.0484141186,-0.0942333713,-0.1931762695,0.1001078635,-0.0327635333,0.507394135,-0.4661897719,0.1300623417,0.0478365198,-0.0847914591,0.5431494117,0.5093151927,-0.2762378752,0.1490685791,-0.3273758292,-0.1876093745,0.0762817785,-0.2453272492,0.0465539619,0.5941820145,-0.2964412868,-0.0330521204,0.2292263508,0.1116807237,0.2743132412,-0.2730723917,-0.1289038211,0.3666533828,-0.0583093874,-0.0247745924,-0.2994121909,-0.5489884019,0.222985968,0.2651913762,0.1280792356,0.3034206331,-0.0386638604,0.0988603085,0.6538231969,-0.008927796,-0.0234397613,-0.0003190556,0.0472183228,0.3054360747,0.1835785806,-0.0747923404,0.0838956013,0.0323624983,0.0159604382,-0.0498866178,0.5514832735,-0.2607074678,-0.1371174753,-0.1333980262,0.1238410249,0.2173400074,0.0750877187,-0.1869909018,-0.333196789,-0.4164792895,-0.0838466212,0.3183391094,-0.1707099974,-0.023818193,0.3621007204,0.3282922208,0.0042818831,0.4077354968,0.2721312046,-0.2106322348,0.2551041245,-0.0920156762,-0.0419658013,-0.1011896655,-0.08891581,0.3310208917,0.5127508044,0.2911525071,0.3215765953,-0.4997344613,0.5782851577,0.2840396166,0.0891665742,-0.3063915074,-0.1912351251,0.2503745258,-0.1309273541,0.074878104,0.1060547233,-0.1318771243,-0.0196299646,-0.2404821962,-0.1276592314,0.0532319993,0.4599595666,0.123571977,-0.1596833915,0.1282088459,-0.042849768,0.2617393732,0.0907394588,-0.1311024576,-0.0340095125,-0.0717116669,-0.1973294616,0.2812795937,0.0401278697,0.010960673,0.153315112,0.3378612101,0.0244841855,0.2259532362,0.3778088391,-0.2540162504,0.0651439354,-0.091539897,0.0669928715,0.1442798227,-0.054981485,-0.104156442,-0.4047582448,-0.181286037,0.0463717021,-0.0976759791,-0.1688509881,-0.3446543515,-0.4082675874,-0.0992145315,-0.5792316198,0.0557994395,-0.0046382234,-0.2675746083,0.1047198325,0.0755682513,-0.1571830809,0.1201933175,-0.1703465283,0.2722745538,-0.2807755172,0.3172829449,-0.288212359,0.0609800443,0.0805845857,0.0599450469,0.1933198422,0.059323702,0.3335085809,-0.1804134101,0.0672978908,-0.5876153111,-0.1100950018,-0.0758057684,0.3115236163,-0.0588632636,-0.0066559305,0.4242179692,0.2609001398,-0.0444026515,-0.074484773,-0.1252694577,-0.1272205561,-0.1251815408,-0.1961204261,-0.1452279985,-0.0458365791,-0.2629895508,-0.4468740821,-0.1661346406,-0.0215503182,-0.2584233284,0.098836638,0.0352506004,-0.1489526033,-0.3318729699,-0.1897393167,-0.0466475934,-0.1492340118,-0.3901198804,0.4578678906,0.0261714738,-0.2646767795,0.1498559713,0.2164316326,-0.5094377995,0.0981018767,-0.2976005971,-0.0140467947,-0.1650477201,-0.2264684439,0.1445492655,0.2360325009,0.0886883587,-0.0517388694,-0.0951496214,-0.0671269596,-0.096291624,-0.0946700722,-0.015709905,0.3045810163,0.0664640591,0.1675754488,-0.0576209649,0.9596548676,0.0216907207,0.1754843593,0.0679390207,0.271738112,0.0398697183,0.2014282495,-0.2668823004,0.0835354403,-0.12491878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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1892","title":"request to mirror wmt datasets, as they are really slow to download","comments":"I'm downloading them.\r\nI'm starting with the ones hosted on http:\/\/data.statmt.org which are the slowest ones","body":"Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you!","comment_length":16,"text":"request to mirror wmt datasets, as they are really slow to download \n Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you! \n I'm downloading them.\r\nI'm starting with the ones hosted on http:\/\/data.statmt.org which are the slowest ones","embeddings":[-0.4048575759,-0.3140096664,0.0369965173,0.1695623994,-0.0139842182,0.1483641565,0.0976714566,0.4406719506,0.201249823,-0.0260525476,-0.3467169404,-0.2557108998,0.0894272178,0.1072438285,-0.1605696976,-0.2548781633,-0.0824236795,0.1284085363,-0.6802170873,-0.1375779659,0.0916178077,-0.2926720381,-0.1066290364,0.1005208194,-0.1244999692,0.1187914237,-0.1336611956,-0.0403247587,-0.2713430822,0.2231260687,0.0854254514,0.3694525957,0.1696945727,0.2424310148,-0.0001144641,-0.1100508869,0.1767383218,0.2066615075,-0.2290118337,0.0147697991,-0.4497104585,-0.4104295671,-0.2166112363,0.010225093,-0.048070807,-0.2220502496,-0.0168272033,-0.1880827248,0.203047052,-0.2183157802,0.1400485188,0.29976511,-0.4613548517,-0.1171535552,0.3193843365,0.2256761342,-0.2811764777,0.3676026165,0.3717106283,0.0632952526,0.1310904473,0.284540832,-0.0112572461,0.5359501243,0.3685594499,-0.166326046,0.1632967442,0.1413364708,-0.2258806676,0.6267239451,0.8606649637,-0.0362282246,-0.3107337058,0.1644583493,-0.104365848,0.3088447154,-0.1453811675,0.2164489031,0.0218988359,0.3345198631,-0.3599195778,-0.2622787058,0.0219189562,-0.0385216847,-0.1772525609,0.2454934269,0.0386833213,-0.0167623181,-0.1030218899,-0.1719418317,0.2029070258,-0.5088528395,0.1482295394,0.0559971072,0.047647588,-0.3436841965,-0.602165997,-0.0119132502,0.2365089208,0.1315677762,-0.0645153001,0.0113891168,-0.1155203953,-0.0040341085,0.1955769211,0.021670023,-0.3318411708,-0.3245331943,0.2537920475,-0.1227808073,-0.1207693815,-0.0051750974,-0.0560189411,-0.0316803418,-0.3978105783,-0.1201777682,-0.0525082275,-0.4321282804,-0.2400128841,-0.2409909964,0.0043355208,-0.2385877371,-0.1764444262,0.0435199924,-0.2159591615,0.1803094298,0.2785016894,-0.055914633,-0.1992187947,-0.1912965626,-0.1133068204,-0.2688353062,0.0762467459,0.399210304,0.1202946007,0.0832927898,-0.0349252783,0.0040843477,0.255731225,0.3186903596,0.2280763239,0.0805885345,-0.0507082418,0.163294211,-0.1923894435,0.5492449403,0.006165639,0.5649519563,-0.2161950916,0.0496356189,-0.4902123213,-0.0830659866,0.166442126,0.1738640219,0.014853837,-0.1995098591,-0.3133862317,-0.0792067796,-0.5097865462,0.1552453041,-0.1281242371,-0.0444266796,-0.1475669146,0.1225653365,0.0442835763,0.4127905965,-0.2928535938,0.015791852,0.0534084216,-0.0462496355,0.4894139171,0.505053699,-0.2413398921,0.125004977,-0.2376469225,-0.1175844669,0.0515243858,-0.1229912266,-0.0325720869,0.5273026824,-0.3150410652,-0.2354463786,0.1770163327,0.2023500055,0.2160562724,-0.2299128622,-0.0736211091,0.3995553255,-0.0265602395,-0.0433853231,-0.2944585383,-0.4869575202,0.2586955726,0.2374853492,0.0530503057,0.195983842,-0.0057679331,0.1948939264,0.4590452313,0.0916238353,-0.0071796691,-0.04296951,0.0914264917,0.2943187654,0.0716167986,-0.0905445144,0.1710486561,-0.0536939576,0.1256409287,-0.0315473638,0.590339601,-0.1503253132,0.0074809697,-0.0631246865,0.1456245482,0.3890587986,0.0475445203,-0.1497329921,-0.25374493,-0.2909080684,0.048538968,0.3243517876,-0.2179533392,-0.0125326514,0.5686083436,0.4224882126,0.0976043567,0.4273555577,0.3591609895,-0.1608591229,0.212768212,-0.0247060694,-0.1188272759,-0.1153676957,-0.0895709693,0.3114093542,0.5767362714,0.185341537,0.3630093932,-0.5933862329,0.5415121317,0.1586470902,0.035349384,-0.2379420698,-0.1708571762,0.2270596772,-0.0726936087,0.0169404428,0.0890948474,-0.0511591621,0.0289126188,-0.1342879236,-0.0268503148,0.0258786548,0.4235852361,0.0438396372,-0.1089968458,0.1734325141,-0.0720521435,0.1587706506,0.0192032922,-0.087114118,0.0364314839,-0.0771665052,-0.399472177,0.2457520962,0.1586457938,0.0353569873,0.1561485231,0.309667021,0.1416003257,0.1711187363,0.2627328634,-0.1946193278,0.0222405493,-0.0450107977,-0.0230077766,0.1137780026,-0.1154776141,-0.1428893805,-0.2855250835,-0.0421554036,0.0820326731,-0.0846410245,-0.0614049211,-0.3466530144,-0.5324671268,-0.1312478483,-0.617423296,-0.0382481627,-0.1032452136,-0.2420078218,0.0315125249,0.0466377065,0.0030832421,0.055459667,-0.1959473044,0.263687849,-0.2539597154,0.2536245584,-0.2014015466,0.117071569,0.0113308877,0.0463702902,0.1688543409,0.2607702315,0.3691004515,-0.1120979339,0.1326038986,-0.54004848,0.0408738106,-0.0130174793,0.3866237402,-0.0338755883,-0.2162379622,0.4594663978,0.1832925081,0.0527516827,0.0272366572,-0.1683708727,-0.0866200924,-0.0670042783,-0.1649515331,-0.0319061428,0.0660424307,-0.2619406879,-0.5064035058,-0.1169011891,-0.0841074064,-0.257537365,-0.0112296492,-0.0393469259,-0.1664268821,-0.1902399808,-0.2238335162,-0.0161364991,-0.2360783815,-0.4750446677,0.4797661006,0.0775588006,-0.2248677462,0.1456604749,0.22342287,-0.4729546607,0.1071243435,-0.3786622286,0.0849280134,-0.1833435297,-0.1696145236,0.1855350733,0.279622376,-0.056123849,-0.1013823748,-0.0752929002,-0.0664281473,-0.1520847082,-0.1004991606,0.0640997887,0.390765965,0.0871325582,0.0543973558,-0.0144240502,0.9082939625,0.2233340591,0.0690710992,0.0383333601,0.2785713375,-0.0878232867,0.2137203664,-0.2204850763,0.0769744962,-0.0739088431,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1892","title":"request to mirror wmt datasets, as they are really slow to download","comments":"@lhoestq better to use our new git-based system than just raw S3, no? (that way we have built-in CDN etc.)","body":"Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you!","comment_length":20,"text":"request to mirror wmt datasets, as they are really slow to download \n Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you! \n @lhoestq better to use our new git-based system than just raw S3, no? (that way we have built-in CDN etc.)","embeddings":[-0.2889099419,-0.330678761,0.0120119527,0.1093232706,0.0525675081,-0.0814258605,0.161522463,0.4271324575,0.0938708335,-0.0190335866,-0.3289594054,-0.0868706703,0.0092277732,0.3145236373,-0.2300491333,-0.13434048,0.0110245394,0.1879598051,-0.5668653846,-0.1316286623,0.1182784885,-0.1686478406,-0.0159086976,-0.0560586043,0.033592239,0.2922874689,-0.1292638481,-0.0466057733,-0.4010473788,0.2060649842,0.0686515346,0.4430897832,0.2044611275,0.423340559,-0.0001147051,-0.1628362983,0.2551142871,0.1594582349,-0.2941845357,0.1020636782,-0.5625475645,-0.2940482795,-0.1721081883,0.0206452962,-0.2282908559,0.0565156303,-0.0642556846,-0.1407652646,0.2692017853,-0.2676649988,0.1218051687,0.3570899963,-0.569530189,-0.0760531351,0.4883638024,0.3561578095,-0.3749561608,0.3744753301,0.4599542916,0.0103055267,0.2937456071,0.3776448667,0.0498915091,0.4903302789,0.5588130951,-0.1442912519,0.1084049791,0.1010665521,-0.2936745882,0.6387222409,0.8014050722,-0.0934529752,-0.4955377579,0.0299208537,-0.1658012271,0.1371797621,-0.1666875035,0.0896818563,-0.0822557807,0.2906906009,-0.5258622766,-0.4199448228,-0.0245539527,0.0971614346,-0.1622619927,0.3614453375,-0.0499788225,-0.0575058535,-0.02159914,-0.0868925452,0.2871056199,-0.5339570045,0.0277758427,0.0092751235,0.0158333238,-0.5079792142,-0.5281723738,-0.1124781296,0.1427506804,0.0715579167,-0.2179968804,-0.0615719035,0.0635953322,0.0075886487,0.1665517688,0.0226126723,-0.3363720179,-0.1470309347,0.2428098321,-0.0826789066,-0.0626366735,-0.1126049981,-0.1358261853,-0.058529038,-0.4983781874,-0.059105143,-0.0641234145,-0.3968077898,-0.0560140759,-0.3704043329,0.0905914307,-0.2271958143,-0.2425270975,-0.0228242557,-0.1544200182,0.1946366131,0.230892241,0.0609242953,-0.1811841577,-0.0949192941,-0.0341663994,-0.3538129628,0.0227362309,0.3785385191,0.1024874747,0.0440883413,0.0467234477,0.0125861587,0.2723509967,0.3997442126,0.1198748946,0.1355108321,-0.0892270878,0.1303317547,-0.3001799285,0.5513956547,-0.082259573,0.5401500463,-0.2474909574,0.0239216164,-0.3828434348,-0.1484455466,-0.0855847448,0.1434902996,-0.1461866945,-0.2256681025,-0.4768670499,-0.1261459887,-0.3702212572,0.0962527469,-0.0732416958,-0.0139557952,-0.1799243689,0.1466061622,0.009652365,0.2471744865,-0.2330057025,-0.0535838269,0.0196600463,-0.0174877122,0.5455886722,0.604721725,-0.2435891926,0.0085157417,-0.1989867091,-0.1130551696,0.0107872877,-0.2240668386,-0.0656671226,0.5325779915,-0.2225827873,-0.0993728042,0.1051367372,0.1408649385,0.1468102038,-0.2809913456,-0.2059138715,0.5454112291,-0.0261678938,0.0190587342,-0.2581395805,-0.5230137706,0.1062318683,0.2587064505,0.0255726036,0.268631041,0.0157456771,0.16113621,0.4051992297,-0.0458651483,-0.0070546712,-0.0064185131,0.3837785423,0.3038677573,-0.0046905219,-0.0034467319,0.1089395434,0.0643636212,0.0773331672,0.0847619474,0.4987878501,-0.1055862233,0.0438254699,-0.1553094983,0.1709591746,0.3255119324,0.0487534925,-0.1046043038,-0.1730397791,-0.3651247025,-0.0260843728,0.0542201363,-0.0070962016,-0.0475674719,0.5461928248,0.3452306688,0.0251750313,0.3276378214,0.2912020385,-0.2134888321,0.1773380786,0.0235403571,-0.0474236421,-0.2182141691,-0.1485566348,0.4687215984,0.5035502315,0.4017015696,0.3517154753,-0.437682271,0.4014915526,0.0752760693,0.0569213666,-0.20259206,-0.2804099619,0.1560456455,-0.0736740455,0.0032426224,-0.0218051374,-0.1877586246,0.0269898977,-0.173040092,-0.0699176639,0.1036519706,0.3284134865,0.0244786292,-0.2184794843,0.2030522674,0.0279873461,0.1575505286,0.210099861,-0.130979538,0.0774587244,-0.0152056394,-0.2841055393,0.1486790776,0.1963616461,0.1138747856,0.1733242422,0.3031970561,0.1898444742,0.1547533423,0.4193894863,-0.2129912972,0.0615872256,-0.0796338916,0.2978202701,0.2055939734,-0.0357468911,-0.1473859251,-0.1455067992,0.0025367574,0.0777263343,-0.1255670935,-0.0882344469,-0.242634654,-0.3631213307,-0.1498048156,-0.4077998996,-0.0824510381,-0.0949652493,-0.1468745023,0.0854016468,0.0987483859,-0.0776861385,-0.0030245779,-0.0181440376,0.3205004632,-0.2589112818,0.0728623345,-0.2940064073,0.034422975,0.074730821,0.0384311713,0.1803443283,-0.1175260246,0.3160448968,-0.1246628985,0.0540366024,-0.5954364538,-0.0566791892,0.023898758,0.3337820172,0.1331888288,-0.1336387694,0.4270215034,0.1770924181,0.0450295471,0.037895523,-0.1569055021,-0.2003164887,-0.1181856394,-0.1908142865,-0.0698712468,-0.0480867401,-0.1044219434,-0.4379579127,-0.1385015845,-0.0337491333,-0.3620450795,-0.0011296557,0.0098884907,-0.0695373937,-0.2653432786,-0.2764092386,-0.0574303046,-0.216629833,-0.5555568337,0.3413765728,0.080528833,-0.1662790179,0.0229264461,0.3018043935,-0.3650659621,0.0230336301,-0.4307643771,-0.0529677644,-0.0700539649,-0.0424139202,0.2023964077,0.2894767225,0.0451646335,-0.1432104856,-0.0733305216,-0.1906097531,-0.0912860483,-0.21619533,0.176628232,0.3960534036,0.0840989128,0.2189153433,0.1232558563,1.0079665184,0.2507517934,0.0240488034,0.0811173096,0.3469982743,-0.0633275062,0.1654553115,-0.1328163147,0.0492813066,-0.12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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1892","title":"request to mirror wmt datasets, as they are really slow to download","comments":"Hi there! What about mirroring other datasets like [CCAligned](http:\/\/www.statmt.org\/cc-aligned\/) as well? All of them are really slow to download...","body":"Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you!","comment_length":19,"text":"request to mirror wmt datasets, as they are really slow to download \n Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.\r\n\r\nThank you! \n Hi there! What about mirroring other datasets like [CCAligned](http:\/\/www.statmt.org\/cc-aligned\/) as well? All of them are really slow to download...","embeddings":[-0.4533652067,-0.2987294793,0.025840329,0.0953520834,0.0633116737,0.1306117624,0.1700920761,0.3957928419,0.1734652519,-0.0313709117,-0.3652692139,-0.2737765312,0.088961713,0.1364364326,-0.2166112512,-0.2842341363,-0.0774134025,0.1845018417,-0.6560146213,-0.0945099965,0.0889509171,-0.279163748,-0.1122347787,0.0673550442,-0.1825857013,0.1138007641,-0.1940655857,-0.078008309,-0.2568913102,0.2480968833,0.1000407562,0.4948063195,0.1452533603,0.1917610019,-0.0001115643,-0.1205842495,0.1339687705,0.1791660786,-0.1833459288,0.0755409002,-0.4271095693,-0.4210446775,-0.206117928,-0.0069485088,-0.0308518503,-0.0645366833,0.0036409094,-0.1748835444,0.1349056065,-0.2770914733,0.1740433276,0.3209134936,-0.4346610904,-0.1656979769,0.2092712373,0.1681494862,-0.3191287816,0.4005478024,0.372367084,0.0798139423,0.1421227455,0.3162364364,0.0168634597,0.4983966053,0.3506410718,-0.1838132292,0.1472118944,0.0500291511,-0.2190327793,0.6171071529,0.8538615108,-0.0323402472,-0.3614169061,0.081701301,-0.0730727836,0.2521522343,-0.1240054891,0.2352237701,0.065579325,0.3448860049,-0.3703088462,-0.1901499182,0.0174524505,-0.0345721319,-0.1391809732,0.1898563951,0.0677536055,-0.014643481,0.0002018088,-0.1298258752,0.1122831851,-0.4537322223,0.1158561781,0.0629514307,0.0163100678,-0.3893347681,-0.635459125,0.0101259835,0.2764846683,0.1575452834,-0.0388114303,0.0490174405,-0.0579124652,0.0390404463,0.1954578012,0.0526031144,-0.4259386063,-0.2127525061,0.2363422513,-0.1321239322,-0.1464287937,-0.0241445079,-0.03088044,-0.0777754337,-0.3911233842,-0.1104234457,-0.1206786186,-0.4086256623,-0.2459783852,-0.2724027336,0.0756813735,-0.1354753822,-0.2428304702,0.0765254423,-0.148246929,0.255220741,0.2507253289,-0.0396424495,-0.1306824088,-0.1669987887,-0.0732308254,-0.2595624626,0.0398897566,0.3467866778,0.1148952991,0.0578033999,-0.008806997,-0.0239840914,0.2864491642,0.3671682477,0.1391729712,0.0806249753,-0.0923131108,0.1349512637,-0.2060675621,0.5131795406,-0.049688492,0.5609839559,-0.2632514536,0.0608974248,-0.4727104306,-0.0784134269,0.1329189241,0.1850572228,-0.0187475327,-0.1934295297,-0.3408353627,-0.0866525173,-0.508890748,0.1438502073,-0.1031327173,-0.0403425023,-0.0972526968,0.1143690571,0.0187666267,0.3315489292,-0.247204572,-0.0003451428,-0.0170195121,-0.0288817305,0.5151874423,0.5833716393,-0.2031151503,0.0625599548,-0.2692393661,-0.1607312858,0.07301002,-0.1312357038,-0.1041017026,0.4834480584,-0.2764461935,-0.1444901675,0.1274021566,0.2426800877,0.2038331628,-0.1879128367,-0.2015033364,0.4507701695,-0.0695234835,0.0060679861,-0.3058156967,-0.4991873205,0.2828388214,0.2676871717,0.0950532183,0.1852358878,0.0225590486,0.1796814203,0.4464389384,0.0197755601,-0.0142345298,-0.060972698,0.0963782519,0.2129719406,0.109075591,-0.1011640504,0.1067563817,0.006201013,0.0905489102,-0.0009378008,0.603282094,-0.1688725054,0.0364057496,-0.0957001299,0.1448493898,0.3055301309,0.056768287,-0.1229590699,-0.2521158755,-0.3168972731,0.0281309448,0.3709683716,-0.1691688895,0.0176037066,0.6709168553,0.3714136481,0.008181802,0.4439528883,0.3283910453,-0.0830568001,0.1510577798,-0.0613625422,-0.102488488,-0.0870329514,-0.1109926477,0.3682469726,0.5460481048,0.211865142,0.4045197368,-0.6099270582,0.4348448515,0.147054255,0.0658671632,-0.2525050938,-0.1484913081,0.2789262235,-0.0311724916,0.01728094,0.0853483751,-0.0653776899,0.0736509934,-0.1408689022,-0.0159690492,0.0526177362,0.4756917953,0.0757464692,-0.037897829,0.1649621427,-0.0631501153,0.1534461975,-0.0315430388,-0.0445493907,0.0133088073,-0.0814798325,-0.3321457207,0.2723237872,0.1694981307,0.0537483022,0.101567328,0.3268964589,0.1421146393,0.1775099486,0.2146548331,-0.2004043758,0.0469595306,-0.1030609086,0.0099084964,0.1526409984,-0.0576948971,-0.1003470272,-0.2901275754,-0.1204491109,0.053908594,-0.0899086595,-0.0290524252,-0.3824288547,-0.4512633979,-0.143550247,-0.5411095023,-0.0016523902,-0.0690981522,-0.2199509889,0.0562035926,0.0572453588,-0.0788402185,0.1437694877,-0.2530442178,0.3106376827,-0.2327993065,0.2745689154,-0.205199644,0.1259423643,0.0675288215,0.0898927301,0.1804967374,0.1047966108,0.3822270036,-0.1670306921,0.1141697839,-0.5119097233,-0.0177616756,-0.0242309403,0.3699085712,-0.0403418057,-0.252263099,0.3800510466,0.1723470986,-0.0044594011,0.0415007696,-0.1325880736,-0.0710140094,-0.0739686638,-0.1925674081,-0.1041545793,0.0052565499,-0.2395409495,-0.5335741043,-0.15722619,-0.0306915417,-0.2878680825,-0.0191134494,0.1079019159,-0.1367851198,-0.2487726063,-0.2053250819,-0.0397003591,-0.1796513796,-0.4369858801,0.5049145222,0.1020168886,-0.2334940732,0.1415332854,0.1679624319,-0.4875907004,0.112102136,-0.3013065755,0.1335948855,-0.1507068723,-0.1151943579,0.2397340983,0.2208531201,-0.070752494,-0.1018785909,-0.0752782896,-0.0089765107,-0.1336323768,-0.0897337273,0.1049977392,0.4027851522,0.0105418079,-0.0181641355,0.0198422521,0.8850696087,0.2129698843,0.0863801017,0.0515487231,0.295338124,-0.0427562594,0.1333878636,-0.2499415278,0.1628310978,-0.1173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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1877","title":"Allow concatenation of both in-memory and on-disk datasets","comments":"I started working on this. My idea is to first add the pyarrow Table wrappers InMemoryTable and MemoryMappedTable that both implement what's necessary regarding copy\/pickle. Then have another wrapper that takes the concatenation of InMemoryTable\/MemoryMappedTable objects.\r\n\r\nWhat's important here is that concatenating two tables into one doesn't double the memory used (`total_allocated_bytes()` stays the same).","body":"This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova ","comment_length":55,"text":"Allow concatenation of both in-memory and on-disk datasets \n This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova  \n I started working on this. My idea is to first add the pyarrow Table wrappers InMemoryTable and MemoryMappedTable that both implement what's necessary regarding copy\/pickle. Then have another wrapper that takes the concatenation of InMemoryTable\/MemoryMappedTable objects.\r\n\r\nWhat's important here is that concatenating two tables into one doesn't double the memory used (`total_allocated_bytes()` stays the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1877","title":"Allow concatenation of both in-memory and on-disk datasets","comments":"Hi @lhoestq @albertvillanova,\r\n\r\nI checked the linked issues and PR, this seems like a great idea. Would you mind elaborating on the in-memory and memory-mapped datasets? \r\nBased on my understanding, it is something like this, please correct me if I am wrong:\r\n1. For in-memory datasets, we don't have any dataset files so the entire dataset is pickled to the cache during loading, and then whenever required it is unpickled .\r\n2. For on-disk\/memory-mapped datasets, we have the data files provided, so they can be re-loaded from the paths, and only the file-paths are stored while pickling.\r\n\r\nIf this is correct, will the feature also handle pickling\/unpickling of a concatenated dataset? Will this be cached?\r\n\r\nThis also leads me to ask whether datasets are chunked during pickling? \r\n\r\nThanks,\r\nGunjan","body":"This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova ","comment_length":129,"text":"Allow concatenation of both in-memory and on-disk datasets \n This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova  \n Hi @lhoestq @albertvillanova,\r\n\r\nI checked the linked issues and PR, this seems like a great idea. Would you mind elaborating on the in-memory and memory-mapped datasets? \r\nBased on my understanding, it is something like this, please correct me if I am wrong:\r\n1. For in-memory datasets, we don't have any dataset files so the entire dataset is pickled to the cache during loading, and then whenever required it is unpickled .\r\n2. For on-disk\/memory-mapped datasets, we have the data files provided, so they can be re-loaded from the paths, and only the file-paths are stored while pickling.\r\n\r\nIf this is correct, will the feature also handle pickling\/unpickling of a concatenated dataset? Will this be cached?\r\n\r\nThis also leads me to ask whether datasets are chunked during pickling? \r\n\r\nThanks,\r\nGunjan","embeddings":[-0.3151938915,0.0239811186,-0.0091471439,0.3324131966,-0.0929977,-0.0160011314,-0.1415656209,0.0513885133,-0.1234135851,-0.0343258008,0.012674707,0.508906126,0.015509028,0.4306793809,0.1224206537,0.0571519397,0.1958594322,0.1693573892,-0.4769207835,0.1094922423,-0.3694703281,-0.2283259928,-0.2241987735,-0.4162859917,-0.2465887219,0.0351609178,-0.458506614,0.1797388792,-0.5705538988,-0.2730188668,0.0891333893,0.3404117227,0.0331604257,0.0554767735,-0.0001229169,-0.1215621307,-0.1313509792,-0.1438875347,-0.4560647607,-0.1900413632,-0.4508099258,-0.4511183202,0.0536833927,0.0278961174,0.4002720118,-0.1607562602,0.1130949184,-0.1156750619,-0.0251444653,-0.04614041,0.0328180306,0.136922732,0.2183957547,-0.06498041,0.1313886642,0.4564218819,-0.2105983645,-0.0154868495,0.4654994309,-0.1170650795,0.1297414303,-0.2641114891,-0.0361640975,-0.2052618563,0.4992964268,0.0941745117,-0.419972986,-0.2418172657,-0.1013240814,0.2512696087,0.6391003728,-0.3239811063,-0.4200568199,-0.3742827773,0.1631272137,-0.3263413906,0.1764194816,0.2312669903,-0.0021583738,0.102301985,-0.0673531815,-0.4811716974,-0.2130007297,0.2608930171,0.2544135451,0.2590686679,0.2200955153,0.1209522262,0.0647674575,0.0149428332,0.2294029444,-0.3686328828,-0.1657604724,0.2453332692,-0.3499107659,-0.0451852307,0.1129574478,-0.0423486419,0.5332869291,0.3057990372,0.3247354627,0.0516036898,-0.5262040496,0.2660189271,0.2431846261,0.1561827958,-0.0963965878,0.1706820726,-0.0694975629,-0.1371653527,0.237611115,0.0166346114,-0.1367421299,-0.0226723664,0.0251740646,-0.1259246022,-0.0738968551,0.1945527792,0.1652775854,-0.1814727932,-0.0189234577,-0.2162984908,0.0778371245,-0.0743919238,0.0621999353,0.0568561107,-0.0136168804,0.351400882,0.3560393751,-0.1627322435,0.0549262799,-0.081479162,-0.2937661111,0.3898349404,0.3329309821,0.0588356704,-0.1926192939,0.2399522662,0.0409079604,0.2022750527,-0.1028457433,-0.2446871698,0.4061219394,0.0637177378,0.0030735596,-0.5130050778,-0.0187864676,-0.0329550914,-0.3379784524,0.5166099668,-0.1849387288,-0.3788852096,-0.3063006699,-0.0302096177,-0.1421592981,-0.0467224903,-0.217796579,0.5460851789,0.3333918154,-0.215039894,0.1148192137,0.1583981216,-0.1153829321,-0.484054774,0.1431287825,0.4034638405,-0.4882220924,-0.0285890736,0.0979509056,0.0205815285,0.062108051,0.131243214,-0.3371044993,-0.0256573148,-0.3812057078,0.2984582484,0.330996722,0.059259899,-0.1576823592,-0.007598164,0.0237680133,0.1462474763,0.4264763594,0.3102689385,0.0304750167,0.0982170478,0.3803618252,0.3373641968,-0.172657162,-0.383688271,-0.0291046184,-0.4970319271,0.443430692,-0.138687551,-0.1300171912,0.2080399692,0.2209337801,-0.6818778515,0.1491347998,-0.1081970036,0.2218994796,-0.1341847777,0.3370450437,0.0576104783,-0.0608253144,-0.1623553038,-0.6230329871,0.1108442172,-0.3560378253,0.0800078213,-0.1722413898,-0.2401232719,0.2953734696,0.0905296355,-0.0372997113,0.1557226926,-0.036793761,0.0580456965,0.0346238762,-0.4091594517,-0.4521028399,0.2373283058,0.0837838277,0.0530395843,-0.2692818642,0.334718734,0.1345411092,-0.0918150395,-0.1463078856,0.217555359,0.0298361201,0.1256483644,0.0788572729,0.4706090391,-0.157760486,0.0567779727,0.1230105907,0.3960999846,0.1758621186,0.2842260301,0.069804579,-0.7420755029,0.200221926,-0.2985211611,-0.0355480425,0.3061635196,-0.1743585914,0.2201001048,0.1943643987,-0.1398901492,0.0687406063,-0.0497543104,-0.2310173362,-0.2290154696,-0.0816831142,0.215775758,0.0627810135,0.3361774087,-0.525601685,0.1454780698,0.1831400841,0.0464512818,0.133184284,0.1387510598,-0.0849193856,-0.2781789601,-0.0172249656,0.0637630895,0.7007017732,0.2160703391,0.0262388047,-0.0180966146,0.0740410909,-0.125470683,0.2335386723,-0.0067183003,0.1687889248,0.4369444549,0.3996045291,0.11076653,-0.0637710169,0.0234743711,0.2618205845,-0.2111879587,-0.1233824268,-0.2116711885,-0.436606288,-0.0754052773,-0.2309647202,-0.3501520753,-0.1998724639,-0.0431916304,0.0764245018,0.2593502998,0.0600147992,0.1693040878,0.1015223116,0.7371382713,-0.4011029899,-0.3640144169,-0.0570496209,0.0777585357,0.1655644327,-0.0318345577,0.3715845942,-0.0787614882,0.3381142914,0.2861672938,-0.0335315466,-0.2892336547,-0.2818521559,0.1511435062,0.0167326778,0.0454625934,0.173060447,-0.0999790356,0.0313671529,-0.4025486708,0.0488804393,0.4723199904,-0.2581701875,0.1338032633,0.0513441041,-0.1683002412,-0.2078544796,-0.1897789985,-0.2680081129,-0.3550593853,0.4333010614,0.0087036677,0.084102869,0.1416249424,0.191974014,-0.3095085323,0.223487705,0.1859374791,-0.2188668698,-0.2029943466,0.3382450938,-0.1542316526,-0.0722792447,-0.1777091026,-0.1409837008,0.1397572607,0.4569010437,-0.1309050024,-0.0475999899,0.0050769402,0.4843650758,-0.093959555,0.1180795208,0.7127976418,0.4727385044,0.0355076343,-0.1927271485,-0.1481276155,0.2134426981,0.3343856633,0.0561791174,0.3356004655,-0.2804927528,-0.0089960704,0.34156546,0.1196296737,0.0275281612,0.4800341129,0.1667901576,0.3584747016,-0.1939470172,-0.0502044261,-0.2021125555,-0.452493965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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1877","title":"Allow concatenation of both in-memory and on-disk datasets","comments":"Hi ! Yes you're totally right about your two points :)\r\n\r\nAnd in the case of a concatenated dataset, then we should reload each sub-table depending on whether it's in-memory or memory mapped. That means the dataset will be made of several blocks in order to keep track of what's from memory and what's memory mapped. This allows to pickle\/unpickle concatenated datasets","body":"This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova ","comment_length":62,"text":"Allow concatenation of both in-memory and on-disk datasets \n This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova  \n Hi ! Yes you're totally right about your two points :)\r\n\r\nAnd in the case of a concatenated dataset, then we should reload each sub-table depending on whether it's in-memory or memory mapped. That means the dataset will be made of several blocks in order to keep track of what's from memory and what's memory mapped. This allows to pickle\/unpickle concatenated 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1877","title":"Allow concatenation of both in-memory and on-disk datasets","comments":"Hi @lhoestq\r\n\r\nThanks, that sounds nice. Can you explain where the issue of the double memory may arise? Also, why is the existing `concatenate_datasets` not sufficient for this purpose?","body":"This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova ","comment_length":29,"text":"Allow concatenation of both in-memory and on-disk datasets \n This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova  \n Hi @lhoestq\r\n\r\nThanks, that sounds nice. Can you explain where the issue of the double memory may arise? Also, why is the existing `concatenate_datasets` not sufficient for this purpose?","embeddings":[-0.3921350837,0.1095163152,-0.0102479458,0.3699243367,0.0255508721,0.1648331285,-0.2444256991,0.199427858,-0.1821034551,0.1366394758,0.0495681241,0.4666678607,0.0200924296,0.3617293537,0.1092143208,-0.0306011587,0.160487473,0.2564076483,-0.401722312,0.1629248708,-0.3001021743,-0.1728203893,-0.1916837394,-0.4789800048,-0.1616440713,0.1648575813,-0.4099677205,0.1326929331,-0.505335331,-0.3240083456,-0.0337188505,0.2305967659,0.1441221833,0.1116464809,-0.0001183449,-0.139306128,-0.0883800983,-0.1393118948,-0.4981412292,-0.13762559,-0.3153511882,-0.4476878643,0.0293858964,0.0132387895,0.3109204471,-0.307611227,0.0527275875,-0.1188952774,-0.0023293418,-0.0532878786,0.1089298204,0.060827665,0.2846040428,-0.1830016375,0.1651664227,0.4658094347,-0.2200627178,-0.0311097912,0.4206276834,-0.1952694058,0.2418729365,-0.2360848486,-0.1178748235,-0.1633916348,0.3987491727,0.1727331281,-0.3037188649,-0.1314041764,-0.0412971489,0.2176211774,0.5865792036,-0.4311450422,-0.3342105448,-0.2628730536,0.2460080832,-0.2954332232,0.1332033873,0.2357889414,-0.1375348568,0.1732293665,0.0094016939,-0.4855881631,-0.1796577573,0.2952824235,0.27376315,0.2759509087,0.3529571593,0.1340430677,0.214188993,0.0620447025,0.2141938359,-0.2129987031,-0.2546743751,0.1283379346,-0.3424053192,-0.0140579948,0.0599339753,-0.1500598043,0.5483281016,0.1297712177,0.2871190608,-0.0448517241,-0.4067798257,0.3076332211,0.0919863284,0.2553535104,-0.2252778858,0.1636935472,-0.0637872741,-0.2130728662,0.3011719882,-0.0163453389,-0.1694171429,-0.0730684251,0.2098757029,-0.2031953931,-0.0720810965,0.1567242891,0.0766474977,-0.2511428297,-0.1469202191,-0.1524340361,0.093894586,-0.0379308611,0.139798969,0.133142367,0.1158497259,0.4211546779,0.302969873,-0.1989288926,0.0078565152,0.0276407059,-0.2613214254,0.3779640794,0.353638798,0.1148882285,-0.1973733306,0.3130430281,-0.0070334114,0.1946331263,-0.0352153406,-0.1502510458,0.312420547,0.1093138233,0.0563113615,-0.4541546404,-0.0355091467,-0.0233203173,-0.2534182668,0.6008579731,-0.1377720535,-0.3231899738,-0.503111124,0.0469376184,-0.0669114292,-0.023708418,-0.2471259087,0.5314822793,0.3372127414,-0.2383345664,0.061134547,0.1914597005,-0.1751529723,-0.4846293628,0.221705094,0.3638979793,-0.4608438015,0.0812958777,0.0106251603,0.028483903,0.0968002081,0.1960922331,-0.3160741627,-0.0223341174,-0.4253902137,0.2173309475,0.2514075935,-0.0291145034,-0.0864312351,-0.0521700643,-0.158220917,0.0194218103,0.5092132092,0.4111901522,0.053894639,0.1372525841,0.4314586222,0.2517196834,-0.1865962893,-0.2832247615,-0.0806560069,-0.5113966465,0.4175637066,-0.1091271415,-0.1119985878,0.1417613477,0.1748517305,-0.5082270503,0.2905801535,-0.1933499873,0.2741513252,-0.144450143,0.3921687007,-0.0364353508,-0.1589792222,-0.1589272618,-0.5136506557,0.161558643,-0.1798149347,0.1487389952,-0.1148836538,-0.2364256084,0.2844363153,0.0667648837,0.0482148789,0.1300471872,0.0389023907,0.0784216598,0.0149993943,-0.352771759,-0.2117245048,0.2260375321,0.1711194813,-0.0062981127,-0.1863906682,0.3505262733,0.0912903994,-0.0637104735,-0.0793347806,0.201258868,0.0118027693,0.236667648,0.0625833422,0.4659431875,-0.1970269829,0.0314246342,0.1769622713,0.3687469065,0.142022863,0.2543242276,0.0953699052,-0.5583907366,0.16449821,-0.2625587583,-0.0583055876,0.2322974205,0.0301949233,0.1346611083,0.054290507,-0.1179548576,0.0882850662,-0.0218894891,-0.0929088742,-0.2552785277,0.0091869179,0.2155604511,-0.0128443493,0.3394584358,-0.5138894916,0.1045169681,0.1417919844,0.0801378936,0.1494958401,0.1846862286,-0.0975309014,-0.2499236465,0.0110410387,0.1050735936,0.7278345823,0.3327358961,-0.0270749852,0.024019016,-0.0232421663,-0.1357819289,0.246051237,-0.0523551889,0.2259603143,0.3271225095,0.5175660849,0.0978712067,-0.0231261924,0.0357372314,0.280864507,-0.0925224349,-0.1162450835,-0.2990508974,-0.4070843458,-0.0361857377,-0.2963101268,-0.2642375529,-0.1862796843,-0.031216355,0.1388061345,0.3211254179,-0.0032379187,0.074707143,0.1011650413,0.5987221599,-0.3522721231,-0.4385094345,-0.0326087326,0.0550653003,0.0571304299,0.0250616223,0.4078399241,-0.1361310035,0.4487454295,0.4095326066,-0.1099669784,-0.363945663,-0.3495539129,0.0442864634,0.0677511171,0.0573991463,0.0872165337,-0.1416820586,0.1894736737,-0.4799762666,0.0716041028,0.4400590956,-0.2252619267,0.262676388,0.0469375364,-0.1753203124,-0.3075061142,-0.1543862969,-0.1919998527,-0.4514460266,0.4154670537,-0.1236037612,0.0698882118,0.0775341094,0.195018664,-0.3288708627,0.1795968115,0.1767162085,-0.2035758644,-0.1530013978,0.3337669969,-0.1142297164,0.0010321037,-0.1347980052,-0.1396705955,0.1908705533,0.4543942511,-0.1955454499,-0.1592120081,-0.0649244711,0.3299049735,-0.2098087221,0.1074899286,0.6054486036,0.4714114666,-0.0591280423,-0.0853744671,-0.2051151842,0.254046917,0.2081807405,0.1479589194,0.1304111481,-0.1937831938,0.0110789984,0.2495637983,0.0844728053,0.1532485932,0.4600158334,0.0520453788,0.3349918425,-0.2104030102,-0.1420198083,-0.2575081289,-0.3768157661,-0.0843170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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1877","title":"Allow concatenation of both in-memory and on-disk datasets","comments":"Hi @lhoestq,\r\n\r\nWill the `add_item` feature also help with lazy writing (or no caching) during `map`\/`filter`?","body":"This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova ","comment_length":16,"text":"Allow concatenation of both in-memory and on-disk datasets \n This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova  \n Hi @lhoestq,\r\n\r\nWill the `add_item` feature also help with lazy writing (or no caching) during `map`\/`filter`?","embeddings":[-0.4765581787,0.0608365983,-0.0707229972,0.0521826521,0.0756110996,0.0150534501,-0.2226113826,0.343570739,0.0733319372,0.0877744108,0.0297729317,0.613764286,-0.0133163817,0.4729108214,0.1156108305,0.0217176229,0.1311561614,0.2873371243,-0.4156746864,0.158914268,-0.242046088,-0.2265802175,-0.0592831299,-0.4308310449,-0.0040398268,0.0221989062,-0.2348327935,0.0460517295,-0.4015104473,-0.3713645041,0.0051823263,0.3903060555,0.0715252161,0.004581973,-0.0001091117,-0.0961322635,-0.0213993974,-0.1660830975,-0.4541787803,-0.1732280552,-0.3239759505,-0.3942295909,0.0978121907,-0.05641184,0.2198666036,-0.1939130872,0.1246484667,-0.0921968073,0.0510853529,-0.0005946931,0.1770361215,0.1682467163,0.1792136729,-0.0644020513,0.2513578534,0.4772345722,-0.2069153339,-0.016978953,0.5437409878,-0.2866382599,0.0655305386,-0.0498586558,-0.1694198102,-0.1345374435,0.5407468677,0.1566751301,-0.0944058001,-0.3209829032,-0.0573862791,0.2175630182,0.4746558964,-0.4267606437,-0.4392644465,-0.2462813109,0.1049217731,-0.1887442172,0.0964329839,0.2361290753,-0.1394093335,0.2127306908,-0.0762295052,-0.3423056901,-0.1845762432,0.2520519197,0.3293735683,0.3210098445,0.3376605511,0.0152412867,0.1598050743,0.0632552952,0.2139872462,-0.3032201827,-0.2217297703,0.1727042794,-0.2810246944,-0.0871396512,0.0919322819,0.0622821264,0.4882809222,0.0839881524,0.3856853843,0.114587307,-0.2556549609,0.3995407224,0.1065206602,0.049226664,-0.1860399395,0.1075042263,0.0387987047,-0.2714679539,0.113678135,-0.0038706104,0.0327558108,0.0663645566,0.1317946315,-0.2102599889,-0.1473457217,0.1261440814,0.1737753451,-0.2988483906,-0.1008138955,-0.145976156,0.044921305,0.0515245646,0.1264670789,0.0187107194,0.0435861163,0.2391199768,0.3044169247,-0.0500586219,0.0550269522,-0.1532796025,-0.1885490119,0.3325956762,0.3076521456,-0.0385489762,-0.1532024145,0.2341603935,-0.0660620555,0.2304021418,0.1649900824,-0.168360576,0.4443362653,0.2302446365,0.0097289821,-0.4723738432,-0.0464850999,-0.0752672181,-0.399423033,0.4876351058,-0.1724241227,-0.3116793931,-0.2956554592,0.1612209082,-0.0140455505,-0.0826084539,-0.1136311889,0.549939692,0.1494811028,-0.4128620923,0.0774163008,0.1391511112,-0.2023865432,-0.4482164085,0.163980633,0.2338346839,-0.5422617197,-0.0117075182,-0.0156527236,-0.011032261,0.0200100616,0.1657969952,-0.3622570932,0.0428361334,-0.3063444793,0.260096401,0.3495652974,-0.0053011328,-0.2179085463,0.0879691169,-0.1191324964,0.0412280634,0.4386925697,0.4519631565,0.2561971843,0.0754784569,0.4115336239,0.3871443272,-0.2556595802,-0.1236731485,-0.0714348406,-0.395652622,0.3593601584,-0.0549358614,-0.2022868544,0.1958160698,0.2372691184,-0.6348543167,0.1601854116,-0.2363996208,0.2605082691,-0.1372983158,0.5229123831,-0.0187939592,-0.1351219267,-0.1498906463,-0.476847142,0.0531755909,-0.2135912329,0.0618376061,-0.2344727963,-0.3581313491,0.2459336817,0.1108099297,0.0520032384,0.1000967547,0.1329471767,-0.0339875035,0.0112846186,-0.3725003302,-0.2047203183,0.1839380264,0.1429634541,-0.0163461883,-0.0828609243,0.3200978637,-0.0363274552,-0.1325560659,-0.043002598,0.1616273969,-0.0701386184,0.1692525446,0.0912618712,0.3741360307,-0.2031278759,0.1750642359,0.1850980967,0.462964505,0.2235046774,0.2856938541,0.0593158044,-0.4190938175,0.198394984,-0.2558090687,-0.2348174304,0.3836270273,0.0461267196,0.1875648052,0.1859799474,-0.0598320961,0.2270471454,-0.024106767,-0.1483356804,-0.2353376895,-0.0343403146,0.1370093822,0.0364806391,0.3631222248,-0.318318218,0.237388432,0.2140039951,0.023440402,0.1837716103,0.2534226179,-0.0868161023,-0.3094965518,0.0938750431,0.1604813486,0.599206686,0.4242342412,-0.0878359452,-0.0488775447,0.0394226462,-0.0914463773,0.2606401145,0.0424238332,0.1299591511,0.3358275294,0.5084390044,0.1565901637,-0.1096974462,-0.1188924462,0.1821382493,-0.1932054609,-0.0549293421,-0.1851835698,-0.2611820102,-0.0383874923,-0.1816255152,-0.1657211334,-0.2075845599,-0.0977215841,0.2218884379,0.3331531882,-0.1846591234,0.1372748315,0.1484766454,0.6496680975,-0.2763092816,-0.4283191264,-0.1805220842,-0.1743451208,0.1853334308,0.0614532158,0.3481795192,-0.0459122695,0.4973226488,0.4200582504,-0.0873132199,-0.3659249246,-0.4064377844,0.0658799931,0.0051850188,0.1079395935,0.0827780366,-0.0497721769,0.1289793551,-0.5469331741,0.0883527696,0.2471522987,-0.3253917396,0.1705880612,0.0624139532,-0.1377744526,-0.3601431847,-0.1490687877,-0.2621841133,-0.5986356139,0.4414026141,-0.1484771073,0.1800319105,0.0249648765,0.1966509968,-0.4081294537,0.0214182399,0.2013935149,-0.1242209822,-0.1794524491,0.2523640394,-0.15564394,-0.0787100419,-0.1698462367,-0.2005185932,0.0577689856,0.5631287098,-0.1712462604,-0.207875967,-0.0578831322,0.4450137913,-0.0747942254,0.0141645949,0.5879991651,0.5294589996,-0.1370869577,-0.1355694234,-0.1235170662,0.1481075883,0.2665321231,-0.0376133546,0.1700484306,-0.069788307,-0.109271057,0.3424431682,-0.0055072121,0.0933607072,0.4374438226,0.152698189,0.3345212638,-0.2667293549,-0.0711629689,-0.2311915904,-0.40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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1877","title":"Allow concatenation of both in-memory and on-disk datasets","comments":"> Can you explain where the issue of the double memory may arise?\r\n\r\nWe have to keep each block (in-memory vs memory mapped) separated in order to be able to reload them with pickle.\r\nOn the other hand we also need to have the full table from mixed in-memory and memory mapped data in order to iterate or extract data conveniently. That means that each block is accessible twice: once in the full table, and once in the separated blocks. But since pyarrow tables concatenation doesn't double the memory, then building the full table doesn't cost memory which is what we want :)\r\n\r\n> Also, why is the existing concatenate_datasets not sufficient for this purpose?\r\n\r\nThe existing `concatenate_datasets` doesn't support having both in-memory and memory mapped data together (there's no fancy block separation logic). It works for datasets fully in-memory or fully memory mapped but not a mix of the two.\r\n\r\n> Will the add_item feature also help with lazy writing (or no caching) during map\/filter?\r\n\r\nIt will enable the implementation of the fast, masked filter from this discussion: https:\/\/github.com\/huggingface\/datasets\/issues\/1949\r\nHowever I don't think this will affect map.","body":"This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova ","comment_length":188,"text":"Allow concatenation of both in-memory and on-disk datasets \n This is a prerequisite for the addition of the `add_item` feature (see #1870).\r\nCurrently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).\r\nThis assumption is used for pickling for example:\r\n- in-memory dataset can just be pickled\/unpickled in-memory\r\n- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling\r\n\r\nMaybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future\r\n\r\nOne idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.\r\nThen the dataset would be the concatenation of all these tables.\r\n\r\nDepending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.\r\n\r\nIf you have some ideas you would like to share about the design\/API feel free to do so :)\r\n\r\ncc @albertvillanova  \n > Can you explain where the issue of the double memory may arise?\r\n\r\nWe have to keep each block (in-memory vs memory mapped) separated in order to be able to reload them with pickle.\r\nOn the other hand we also need to have the full table from mixed in-memory and memory mapped data in order to iterate or extract data conveniently. That means that each block is accessible twice: once in the full table, and once in the separated blocks. But since pyarrow tables concatenation doesn't double the memory, then building the full table doesn't cost memory which is what we want :)\r\n\r\n> Also, why is the existing concatenate_datasets not sufficient for this purpose?\r\n\r\nThe existing `concatenate_datasets` doesn't support having both in-memory and memory mapped data together (there's no fancy block separation logic). It works for datasets fully in-memory or fully memory mapped but not a mix of the two.\r\n\r\n> Will the add_item feature also help with lazy writing (or no caching) during map\/filter?\r\n\r\nIt will enable the implementation of the fast, masked filter from this discussion: https:\/\/github.com\/huggingface\/datasets\/issues\/1949\r\nHowever I don't think this will affect map.","embeddings":[-0.4205480516,0.1177791432,-0.0135113075,0.2829381526,0.0721307024,0.0939797312,-0.1494367421,0.2320156842,-0.1620398611,0.1522656828,0.0206330512,0.510204792,-0.0248010047,0.3577524722,0.153740406,-0.0333436169,0.1533055007,0.2407618612,-0.432516396,0.2023283541,-0.2718376219,-0.2322957516,-0.1211226955,-0.4407121241,-0.1328163743,0.1058929712,-0.3701851666,0.085054554,-0.4612610042,-0.417299509,0.0272538252,0.2392499447,0.1475351006,0.0204422213,-0.0001143608,-0.0304890182,-0.0908041373,-0.1432089806,-0.4931333959,-0.144099012,-0.302980274,-0.4612712562,-0.001470246,-0.0359222107,0.2839404941,-0.3497850001,0.0704570338,0.0037824237,-0.0004687344,-0.0690577403,0.1282015294,0.0832635909,0.2490193993,-0.1535838544,0.2830585241,0.4180071056,-0.2433202416,0.0545799695,0.4837106466,-0.2297801673,0.1604688168,-0.185549736,-0.1114508659,-0.1264527738,0.3167591691,0.2640161514,-0.2564167678,-0.1504638195,-0.062841706,0.2610198557,0.4842351377,-0.460285604,-0.3712317944,-0.3262190223,0.1695735902,-0.2965211868,0.1417034566,0.2897109389,-0.1246990263,0.181557402,-0.0053669689,-0.3174619675,-0.1614489257,0.2725508809,0.295029074,0.268396467,0.3663906455,0.1452648193,0.2541914582,0.0573609583,0.2632886469,-0.1695788652,-0.2025769353,0.1410523355,-0.2625716031,-0.0054378361,0.0481507108,-0.1600821912,0.5918854475,0.0995221511,0.2872602642,0.022782702,-0.2712804079,0.3238212466,0.1234028339,0.1842763871,-0.2756664753,0.1439854503,-0.058196716,-0.1266408861,0.2939161956,0.0151917348,-0.1816811413,-0.0323906243,0.1392959952,-0.1682103276,-0.1076435372,0.202600196,0.0467827283,-0.2848707438,-0.1391761601,-0.1701781899,0.0513958298,0.0725707412,0.1154181659,0.0110624637,0.0228989609,0.3052885532,0.3008713424,-0.1711031049,0.0266159531,0.0167589281,-0.3064671159,0.3657286167,0.3710740209,0.0442401133,-0.1348829567,0.271768719,-0.0841675028,0.3043233156,-0.0053500663,-0.1916876435,0.3680019379,0.1365817338,0.0408077426,-0.4994348586,-0.0858418569,-0.0360954702,-0.2986930907,0.6121868491,-0.0858688578,-0.3561389148,-0.4303496182,0.0681181476,-0.0598043427,-0.0137253208,-0.263925761,0.5533694029,0.3309740424,-0.2825146019,0.040916305,0.1475729644,-0.1118465886,-0.5068644881,0.2198455483,0.2586522698,-0.5528047085,0.0585637093,0.12899445,-0.0345776305,0.1111791581,0.2120650113,-0.397354871,0.0458505228,-0.3841030002,0.2939403951,0.1699150652,0.0413726941,-0.2576788664,-0.1045229286,-0.0269715153,0.0448917523,0.4616477787,0.4100912511,0.0950642824,0.16308707,0.3194943368,0.3140036464,-0.2028822452,-0.1930931658,-0.091433093,-0.5326014161,0.5056709647,-0.0021548409,-0.1473309547,0.0495729074,0.2052704245,-0.5217081308,0.3012337089,-0.2306774706,0.2859789133,-0.1803401411,0.3845082223,-0.088817656,-0.1506255567,-0.1423925906,-0.5311309695,0.2147483081,-0.291357547,0.1576973051,-0.2815657854,-0.2635592818,0.2890248895,0.0733938068,-0.000241285,0.0676864684,0.0810715258,0.0158447046,0.0758395642,-0.3457562029,-0.2225687504,0.1293768734,0.1590712517,-0.0178132951,-0.151096493,0.3943977654,0.0196057074,-0.1607129574,-0.0763068572,0.195953995,-0.0492699035,0.1961109638,0.1199967042,0.427000314,-0.1109359264,0.0082428129,0.0712700784,0.272744596,0.1453538686,0.293161422,0.0296722818,-0.5536985397,0.1499249488,-0.1731536537,-0.0905447677,0.2890941799,0.1202573702,0.1383823007,0.1417947263,-0.1665379405,0.1052098721,-0.0745431334,0.0005404817,-0.2177435458,-0.0516589284,0.246276319,0.1018992364,0.3133161068,-0.4512616396,0.2090350091,0.2587777078,0.0302924812,0.1629493982,0.1270131767,-0.1180138215,-0.270550102,0.0862549841,0.148076877,0.6196273565,0.3672582805,-0.0011291382,-0.0006010781,-0.0432569049,-0.0995766819,0.2032078654,-0.049649857,0.2204270959,0.3111623228,0.4858122766,0.0462777354,-0.0847223774,0.0187398307,0.2424550354,-0.0236544032,-0.0614503361,-0.2882328629,-0.4230681658,-0.0768891722,-0.2263135463,-0.207323119,-0.1776858419,-0.0387442037,0.2044095099,0.3084001839,-0.1329572052,0.1332468241,0.1385305077,0.4870517552,-0.3491360545,-0.3730885386,-0.107596986,-0.0817747265,0.0371586047,0.0833978504,0.4131780565,-0.1326893866,0.4439466,0.4218161404,-0.1286580265,-0.3170163631,-0.3468725979,0.1252694428,-0.039119333,0.0798845813,0.0871986449,-0.0926862359,0.1265868396,-0.4788066447,0.1119153574,0.3859839737,-0.2844462097,0.2566552758,0.0273776781,-0.1551009566,-0.3382604718,-0.2619175613,-0.3027262986,-0.4580030143,0.4380853772,-0.1251059324,0.1152242571,0.0953672007,0.2327349186,-0.2977924347,0.0700946674,0.1275105923,-0.1762500554,-0.1304361075,0.3235555589,-0.135790199,-0.0962289423,-0.1681300104,-0.1917956173,0.1267280579,0.4574133754,-0.2145803869,-0.0556686707,-0.1127313972,0.4080162644,-0.1579324305,0.0697523355,0.6263289452,0.5570223927,-0.0811090991,-0.0401119292,-0.1109493598,0.2213413715,0.2556411624,0.1116929799,0.118866533,-0.1989126951,0.0869155452,0.3723876476,-0.0004664562,0.0835760385,0.4383431077,-0.0265469048,0.297645092,-0.2404049039,-0.0903695971,-0.2160644233,-0.4245319366,-0.031698361,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1876","title":" load_dataset(\"multi_woz_v22\") NonMatchingChecksumError","comments":"Thanks for reporting !\r\nThis is due to the changes made in the data files in the multiwoz repo: https:\/\/github.com\/budzianowski\/multiwoz\/pull\/59\r\nI'm opening a PR to update the checksums of the data files.","body":"Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n\r\n`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`\r\n\r\n\r\nThis will give the following error:\r\n\r\n```\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_003.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_004.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_005.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_006.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_007.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_008.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_009.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_010.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_012.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_013.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_014.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_015.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_016.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_017.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_002.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_002.json']\r\n```\r\n","comment_length":32,"text":" load_dataset(\"multi_woz_v22\") NonMatchingChecksumError \n Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n\r\n`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`\r\n\r\n\r\nThis will give the following error:\r\n\r\n```\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_003.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_004.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_005.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_006.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_007.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_008.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_009.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_010.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_012.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_013.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_014.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_015.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_016.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_017.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_002.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_002.json']\r\n```\r\n \n Thanks for reporting !\r\nThis is due to the changes made in the data files in the multiwoz repo: https:\/\/github.com\/budzianowski\/multiwoz\/pull\/59\r\nI'm opening a PR to update the checksums of the data files.","embeddings":[-0.2244368494,0.1764702946,-0.0287194345,0.1495722383,0.1895723343,0.0018984437,0.3650578558,0.4820295274,0.2514897287,0.1666063964,-0.0970349461,0.1781593114,-0.0822444186,0.0992015228,-0.1845492125,0.2732406855,0.0765193701,-0.0256412663,-0.1682780683,-0.0625795946,-0.2430096716,0.3292944729,-0.1593268216,-0.0338407457,-0.3486862779,0.130385235,0.173908785,0.336238414,-0.0838693827,-0.1523166895,0.4145028293,0.2369774878,0.0439615399,0.3686157465,-0.0001172875,0.163338542,0.2778438032,-0.1433111131,-0.1339644343,-0.1817747802,-0.5618035793,-0.3552265167,-0.1035682037,-0.1154623255,0.0706329644,0.3033826053,0.0572008304,-0.0867421255,0.2431910932,0.1712264121,0.1535670757,0.3319171369,0.2157697976,0.096905008,0.0455294512,-0.0774404854,-0.1376802474,0.0922583491,0.2744395733,-0.1569582969,-0.2395652235,0.2651384175,-0.1804820746,0.2693834305,-0.08042676,-0.151098907,0.1479792297,-0.1058241054,0.2105871737,0.5727722049,0.2205697149,-0.0554821864,-0.1762661785,-0.031950675,-0.2201738805,0.0833650157,0.3634192646,0.1834743917,-0.2078651488,0.0045875246,-0.4487929642,0.1443090439,-0.0263798088,0.2792589366,0.2121898234,0.3145956695,0.2003566772,0.0726620853,0.2279323786,-0.2064414769,0.1992104501,-0.3010312915,-0.1609574109,0.1293215007,-0.6801476479,-0.0664964691,0.0034521031,0.3319556117,0.4404266775,0.2289481759,0.4523443282,0.3736301064,-0.1520804018,0.1170620769,0.3968931139,0.1744454056,0.0657053292,-0.0545444787,0.1692391336,0.2095874697,-0.0953853428,0.23737818,-0.0305045638,-0.3612305224,0.349316746,0.1929925084,0.2069615573,-0.3434550762,-0.1813292205,0.0824042559,-0.2454786599,-0.1945316195,0.2718029022,0.2862129211,-0.0944880024,0.5524677038,-0.1653458029,0.2156011462,-0.1538057327,-0.184701696,-0.2573401332,-0.1516558081,0.0730739161,0.1531955004,0.2943665981,0.0656549037,0.3595133722,-0.0321129337,0.3835370243,-0.2031213194,0.333525002,0.0902094916,-0.069919236,0.2554130256,0.0134760654,0.2010277361,0.2036009133,-0.0260661189,-0.0622884482,0.2220196426,-0.332572788,-0.6167247295,0.1093568653,0.1607629508,-0.3595766425,-0.0804914013,0.0163278151,-0.2801302671,0.3050760031,-0.4042750597,-0.1545268744,-0.3094389141,-0.0430206098,-0.1179552451,0.0098633282,0.0942928717,-0.0934405178,0.0649290308,-0.0455776192,-0.1832666099,0.3468402326,0.2667627037,-0.2780414522,-0.1645423472,-0.1394770145,0.0187303592,0.2207069397,-0.0911147445,-0.3638109565,0.2024427652,-0.071666114,0.6232899427,0.1856193095,0.1097929999,-0.347364068,-0.1108146757,0.2366856933,0.0823844671,0.1350486577,0.1764032245,-0.2450098842,-0.3122074902,0.3476401865,0.1528952271,0.0161080807,-0.2277660966,0.0021591219,0.0893484354,0.5888454914,-0.229704082,-0.0971514285,0.173809126,0.0959679857,-0.2113368064,0.0982792377,0.0511620082,-0.4866259396,0.3225809634,-0.1565800011,-0.0987997502,0.3440404832,-0.0598260574,-0.0504282936,-0.1761434972,-0.3390328586,0.0107523073,0.0809487924,0.2748901248,0.083496578,-0.2535201013,-0.0927836373,0.3571139276,-0.3864992857,-0.0857931376,-0.4520960152,0.1969179064,-0.0879131109,0.0851735622,0.0764899477,0.1571474075,0.1050598249,-0.1094213575,-0.1870695502,0.5129078031,0.321377933,0.238428399,-0.0831710547,0.3510250747,-0.1519880891,-0.0498669744,-0.1007987484,0.5829588175,-0.043825414,-0.2062136233,-0.1941188127,0.4879204333,-0.1729985923,0.4535035193,-0.0467061065,0.1363264918,0.3366037309,-0.1314040124,-0.267632544,-0.094632037,0.4545117021,-0.1980919838,-0.166961506,0.2530014515,-0.2133246213,0.0163294896,0.3733881712,-0.1440781504,0.1259907186,-0.0151448352,0.2458733171,-0.2552571595,-0.0198079348,0.492271781,0.3632516265,0.1011830643,-0.017946247,-0.0273172073,-0.2294692695,-0.1744433939,0.1194066629,0.0973706469,0.3296886981,0.5370066166,0.0413935557,-0.3653722107,-0.4267033637,-0.0148492791,0.0290301628,0.1656199098,-0.441488415,-0.2537639439,-0.4441935718,0.2288448811,-0.3637366891,-0.3436980546,-0.4129472375,-0.4648984671,0.1210602671,0.2281651497,-0.019953087,0.3193200827,-0.3111640215,0.0683446303,-0.13192828,0.0605431758,0.0400538072,-0.2777514756,-0.0817120597,-0.0046348502,0.395745039,0.0018831765,-0.0578058697,-0.3077501059,-0.0692631528,-0.1458399743,-0.1667571664,-0.0308500472,-0.1108122692,-0.0727645904,0.258897841,0.3025155365,0.1404629946,-0.3886933029,0.2458182424,-0.0367405601,-0.2834911942,0.1877612323,-0.1721364707,-0.0189697389,0.1081124917,-0.3866266012,-0.0947575793,-0.3375903666,-0.0101160659,0.0664086565,0.2717181146,0.2532503307,0.0835985392,-0.067855671,0.135645777,0.2740627229,-0.3200554848,-0.8128382564,0.4206303358,-0.0225661304,-0.2601571679,-0.0509394854,0.0214568581,0.2018916756,0.0964379087,-0.4472939074,-0.2804222107,-0.0651813745,-0.3137300909,0.1682505161,-0.3037391305,0.3359626532,0.118827641,-0.0578323677,-0.244994387,-0.294922471,0.3670241833,0.0858874843,0.5151624084,-0.2128699422,0.2413334697,0.0072190906,0.59493047,0.2273386568,0.2582996488,0.0617827326,0.1863201261,0.1531594396,-0.0817795843,-0.3245143592,0.1173866391,-0.2651423812,0.004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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1876","title":" load_dataset(\"multi_woz_v22\") NonMatchingChecksumError","comments":"I just merged the fix. It will be available in the new release of `datasets` later today.\r\nYou'll be able to get the new version with\r\n```\r\npip install --upgrade datasets\r\n```","body":"Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n\r\n`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`\r\n\r\n\r\nThis will give the following error:\r\n\r\n```\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_003.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_004.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_005.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_006.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_007.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_008.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_009.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_010.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_012.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_013.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_014.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_015.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_016.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_017.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_002.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_002.json']\r\n```\r\n","comment_length":32,"text":" load_dataset(\"multi_woz_v22\") NonMatchingChecksumError \n Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n\r\n`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`\r\n\r\n\r\nThis will give the following error:\r\n\r\n```\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_003.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_004.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_005.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_006.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_007.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_008.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_009.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_010.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_012.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_013.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_014.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_015.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_016.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_017.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_002.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_002.json']\r\n```\r\n \n I just merged the fix. It will be available in the new release of `datasets` later today.\r\nYou'll be able to get the new version with\r\n```\r\npip install --upgrade datasets\r\n```","embeddings":[-0.2244368494,0.1764702946,-0.0287194345,0.1495722383,0.1895723343,0.0018984437,0.3650578558,0.4820295274,0.2514897287,0.1666063964,-0.0970349461,0.1781593114,-0.0822444186,0.0992015228,-0.1845492125,0.2732406855,0.0765193701,-0.0256412663,-0.1682780683,-0.0625795946,-0.2430096716,0.3292944729,-0.1593268216,-0.0338407457,-0.3486862779,0.130385235,0.173908785,0.336238414,-0.0838693827,-0.1523166895,0.4145028293,0.2369774878,0.0439615399,0.3686157465,-0.0001172875,0.163338542,0.2778438032,-0.1433111131,-0.1339644343,-0.1817747802,-0.5618035793,-0.3552265167,-0.1035682037,-0.1154623255,0.0706329644,0.3033826053,0.0572008304,-0.0867421255,0.2431910932,0.1712264121,0.1535670757,0.3319171369,0.2157697976,0.096905008,0.0455294512,-0.0774404854,-0.1376802474,0.0922583491,0.2744395733,-0.1569582969,-0.2395652235,0.2651384175,-0.1804820746,0.2693834305,-0.08042676,-0.151098907,0.1479792297,-0.1058241054,0.2105871737,0.5727722049,0.2205697149,-0.0554821864,-0.1762661785,-0.031950675,-0.2201738805,0.0833650157,0.3634192646,0.1834743917,-0.2078651488,0.0045875246,-0.4487929642,0.1443090439,-0.0263798088,0.2792589366,0.2121898234,0.3145956695,0.2003566772,0.0726620853,0.2279323786,-0.2064414769,0.1992104501,-0.3010312915,-0.1609574109,0.1293215007,-0.6801476479,-0.0664964691,0.0034521031,0.3319556117,0.4404266775,0.2289481759,0.4523443282,0.3736301064,-0.1520804018,0.1170620769,0.3968931139,0.1744454056,0.0657053292,-0.0545444787,0.1692391336,0.2095874697,-0.0953853428,0.23737818,-0.0305045638,-0.3612305224,0.349316746,0.1929925084,0.2069615573,-0.3434550762,-0.1813292205,0.0824042559,-0.2454786599,-0.1945316195,0.2718029022,0.2862129211,-0.0944880024,0.5524677038,-0.1653458029,0.2156011462,-0.1538057327,-0.184701696,-0.2573401332,-0.1516558081,0.0730739161,0.1531955004,0.2943665981,0.0656549037,0.3595133722,-0.0321129337,0.3835370243,-0.2031213194,0.333525002,0.0902094916,-0.069919236,0.2554130256,0.0134760654,0.2010277361,0.2036009133,-0.0260661189,-0.0622884482,0.2220196426,-0.332572788,-0.6167247295,0.1093568653,0.1607629508,-0.3595766425,-0.0804914013,0.0163278151,-0.2801302671,0.3050760031,-0.4042750597,-0.1545268744,-0.3094389141,-0.0430206098,-0.1179552451,0.0098633282,0.0942928717,-0.0934405178,0.0649290308,-0.0455776192,-0.1832666099,0.3468402326,0.2667627037,-0.2780414522,-0.1645423472,-0.1394770145,0.0187303592,0.2207069397,-0.0911147445,-0.3638109565,0.2024427652,-0.071666114,0.6232899427,0.1856193095,0.1097929999,-0.347364068,-0.1108146757,0.2366856933,0.0823844671,0.1350486577,0.1764032245,-0.2450098842,-0.3122074902,0.3476401865,0.1528952271,0.0161080807,-0.2277660966,0.0021591219,0.0893484354,0.5888454914,-0.229704082,-0.0971514285,0.173809126,0.0959679857,-0.2113368064,0.0982792377,0.0511620082,-0.4866259396,0.3225809634,-0.1565800011,-0.0987997502,0.3440404832,-0.0598260574,-0.0504282936,-0.1761434972,-0.3390328586,0.0107523073,0.0809487924,0.2748901248,0.083496578,-0.2535201013,-0.0927836373,0.3571139276,-0.3864992857,-0.0857931376,-0.4520960152,0.1969179064,-0.0879131109,0.0851735622,0.0764899477,0.1571474075,0.1050598249,-0.1094213575,-0.1870695502,0.5129078031,0.321377933,0.238428399,-0.0831710547,0.3510250747,-0.1519880891,-0.0498669744,-0.1007987484,0.5829588175,-0.043825414,-0.2062136233,-0.1941188127,0.4879204333,-0.1729985923,0.4535035193,-0.0467061065,0.1363264918,0.3366037309,-0.1314040124,-0.267632544,-0.094632037,0.4545117021,-0.1980919838,-0.166961506,0.2530014515,-0.2133246213,0.0163294896,0.3733881712,-0.1440781504,0.1259907186,-0.0151448352,0.2458733171,-0.2552571595,-0.0198079348,0.492271781,0.3632516265,0.1011830643,-0.017946247,-0.0273172073,-0.2294692695,-0.1744433939,0.1194066629,0.0973706469,0.3296886981,0.5370066166,0.0413935557,-0.3653722107,-0.4267033637,-0.0148492791,0.0290301628,0.1656199098,-0.441488415,-0.2537639439,-0.4441935718,0.2288448811,-0.3637366891,-0.3436980546,-0.4129472375,-0.4648984671,0.1210602671,0.2281651497,-0.019953087,0.3193200827,-0.3111640215,0.0683446303,-0.13192828,0.0605431758,0.0400538072,-0.2777514756,-0.0817120597,-0.0046348502,0.395745039,0.0018831765,-0.0578058697,-0.3077501059,-0.0692631528,-0.1458399743,-0.1667571664,-0.0308500472,-0.1108122692,-0.0727645904,0.258897841,0.3025155365,0.1404629946,-0.3886933029,0.2458182424,-0.0367405601,-0.2834911942,0.1877612323,-0.1721364707,-0.0189697389,0.1081124917,-0.3866266012,-0.0947575793,-0.3375903666,-0.0101160659,0.0664086565,0.2717181146,0.2532503307,0.0835985392,-0.067855671,0.135645777,0.2740627229,-0.3200554848,-0.8128382564,0.4206303358,-0.0225661304,-0.2601571679,-0.0509394854,0.0214568581,0.2018916756,0.0964379087,-0.4472939074,-0.2804222107,-0.0651813745,-0.3137300909,0.1682505161,-0.3037391305,0.3359626532,0.118827641,-0.0578323677,-0.244994387,-0.294922471,0.3670241833,0.0858874843,0.5151624084,-0.2128699422,0.2413334697,0.0072190906,0.59493047,0.2273386568,0.2582996488,0.0617827326,0.1863201261,0.1531594396,-0.0817795843,-0.3245143592,0.1173866391,-0.2651423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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1876","title":" load_dataset(\"multi_woz_v22\") NonMatchingChecksumError","comments":"Hi, I still meet the error when loading the datasets after upgradeing datasets.\r\n\r\nraise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json']","body":"Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n\r\n`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`\r\n\r\n\r\nThis will give the following error:\r\n\r\n```\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_003.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_004.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_005.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_006.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_007.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_008.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_009.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_010.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_012.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_013.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_014.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_015.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_016.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_017.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_002.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_002.json']\r\n```\r\n","comment_length":27,"text":" load_dataset(\"multi_woz_v22\") NonMatchingChecksumError \n Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n\r\n`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`\r\n\r\n\r\nThis will give the following error:\r\n\r\n```\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_003.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_004.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_005.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_006.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_007.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_008.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_009.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_010.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_012.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_013.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_014.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_015.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_016.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_017.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_002.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_002.json']\r\n```\r\n \n Hi, I still meet the error when loading the datasets after upgradeing datasets.\r\n\r\nraise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json']","embeddings":[-0.2244368494,0.1764702946,-0.0287194345,0.1495722383,0.1895723343,0.0018984437,0.3650578558,0.4820295274,0.2514897287,0.1666063964,-0.0970349461,0.1781593114,-0.0822444186,0.0992015228,-0.1845492125,0.2732406855,0.0765193701,-0.0256412663,-0.1682780683,-0.0625795946,-0.2430096716,0.3292944729,-0.1593268216,-0.0338407457,-0.3486862779,0.130385235,0.173908785,0.336238414,-0.0838693827,-0.1523166895,0.4145028293,0.2369774878,0.0439615399,0.3686157465,-0.0001172875,0.163338542,0.2778438032,-0.1433111131,-0.1339644343,-0.1817747802,-0.5618035793,-0.3552265167,-0.1035682037,-0.1154623255,0.0706329644,0.3033826053,0.0572008304,-0.0867421255,0.2431910932,0.1712264121,0.1535670757,0.3319171369,0.2157697976,0.096905008,0.0455294512,-0.0774404854,-0.1376802474,0.0922583491,0.2744395733,-0.1569582969,-0.2395652235,0.2651384175,-0.1804820746,0.2693834305,-0.08042676,-0.151098907,0.1479792297,-0.1058241054,0.2105871737,0.5727722049,0.2205697149,-0.0554821864,-0.1762661785,-0.031950675,-0.2201738805,0.0833650157,0.3634192646,0.1834743917,-0.2078651488,0.0045875246,-0.4487929642,0.1443090439,-0.0263798088,0.2792589366,0.2121898234,0.3145956695,0.2003566772,0.0726620853,0.2279323786,-0.2064414769,0.1992104501,-0.3010312915,-0.1609574109,0.1293215007,-0.6801476479,-0.0664964691,0.0034521031,0.3319556117,0.4404266775,0.2289481759,0.4523443282,0.3736301064,-0.1520804018,0.1170620769,0.3968931139,0.1744454056,0.0657053292,-0.0545444787,0.1692391336,0.2095874697,-0.0953853428,0.23737818,-0.0305045638,-0.3612305224,0.349316746,0.1929925084,0.2069615573,-0.3434550762,-0.1813292205,0.0824042559,-0.2454786599,-0.1945316195,0.2718029022,0.2862129211,-0.0944880024,0.5524677038,-0.1653458029,0.2156011462,-0.1538057327,-0.184701696,-0.2573401332,-0.1516558081,0.0730739161,0.1531955004,0.2943665981,0.0656549037,0.3595133722,-0.0321129337,0.3835370243,-0.2031213194,0.333525002,0.0902094916,-0.069919236,0.2554130256,0.0134760654,0.2010277361,0.2036009133,-0.0260661189,-0.0622884482,0.2220196426,-0.332572788,-0.6167247295,0.1093568653,0.1607629508,-0.3595766425,-0.0804914013,0.0163278151,-0.2801302671,0.3050760031,-0.4042750597,-0.1545268744,-0.3094389141,-0.0430206098,-0.1179552451,0.0098633282,0.0942928717,-0.0934405178,0.0649290308,-0.0455776192,-0.1832666099,0.3468402326,0.2667627037,-0.2780414522,-0.1645423472,-0.1394770145,0.0187303592,0.2207069397,-0.0911147445,-0.3638109565,0.2024427652,-0.071666114,0.6232899427,0.1856193095,0.1097929999,-0.347364068,-0.1108146757,0.2366856933,0.0823844671,0.1350486577,0.1764032245,-0.2450098842,-0.3122074902,0.3476401865,0.1528952271,0.0161080807,-0.2277660966,0.0021591219,0.0893484354,0.5888454914,-0.229704082,-0.0971514285,0.173809126,0.0959679857,-0.2113368064,0.0982792377,0.0511620082,-0.4866259396,0.3225809634,-0.1565800011,-0.0987997502,0.3440404832,-0.0598260574,-0.0504282936,-0.1761434972,-0.3390328586,0.0107523073,0.0809487924,0.2748901248,0.083496578,-0.2535201013,-0.0927836373,0.3571139276,-0.3864992857,-0.0857931376,-0.4520960152,0.1969179064,-0.0879131109,0.0851735622,0.0764899477,0.1571474075,0.1050598249,-0.1094213575,-0.1870695502,0.5129078031,0.321377933,0.238428399,-0.0831710547,0.3510250747,-0.1519880891,-0.0498669744,-0.1007987484,0.5829588175,-0.043825414,-0.2062136233,-0.1941188127,0.4879204333,-0.1729985923,0.4535035193,-0.0467061065,0.1363264918,0.3366037309,-0.1314040124,-0.267632544,-0.094632037,0.4545117021,-0.1980919838,-0.166961506,0.2530014515,-0.2133246213,0.0163294896,0.3733881712,-0.1440781504,0.1259907186,-0.0151448352,0.2458733171,-0.2552571595,-0.0198079348,0.492271781,0.3632516265,0.1011830643,-0.017946247,-0.0273172073,-0.2294692695,-0.1744433939,0.1194066629,0.0973706469,0.3296886981,0.5370066166,0.0413935557,-0.3653722107,-0.4267033637,-0.0148492791,0.0290301628,0.1656199098,-0.441488415,-0.2537639439,-0.4441935718,0.2288448811,-0.3637366891,-0.3436980546,-0.4129472375,-0.4648984671,0.1210602671,0.2281651497,-0.019953087,0.3193200827,-0.3111640215,0.0683446303,-0.13192828,0.0605431758,0.0400538072,-0.2777514756,-0.0817120597,-0.0046348502,0.395745039,0.0018831765,-0.0578058697,-0.3077501059,-0.0692631528,-0.1458399743,-0.1667571664,-0.0308500472,-0.1108122692,-0.0727645904,0.258897841,0.3025155365,0.1404629946,-0.3886933029,0.2458182424,-0.0367405601,-0.2834911942,0.1877612323,-0.1721364707,-0.0189697389,0.1081124917,-0.3866266012,-0.0947575793,-0.3375903666,-0.0101160659,0.0664086565,0.2717181146,0.2532503307,0.0835985392,-0.067855671,0.135645777,0.2740627229,-0.3200554848,-0.8128382564,0.4206303358,-0.0225661304,-0.2601571679,-0.0509394854,0.0214568581,0.2018916756,0.0964379087,-0.4472939074,-0.2804222107,-0.0651813745,-0.3137300909,0.1682505161,-0.3037391305,0.3359626532,0.118827641,-0.0578323677,-0.244994387,-0.294922471,0.3670241833,0.0858874843,0.5151624084,-0.2128699422,0.2413334697,0.0072190906,0.59493047,0.2273386568,0.2582996488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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1876","title":" load_dataset(\"multi_woz_v22\") NonMatchingChecksumError","comments":"This must be related to https:\/\/github.com\/budzianowski\/multiwoz\/pull\/72\r\nThose files have changed, let me update the checksums for this dataset.\r\n\r\nFor now you can use `ignore_verifications=True` in `load_dataset` to skip the checksum verification.","body":"Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n\r\n`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`\r\n\r\n\r\nThis will give the following error:\r\n\r\n```\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_003.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_004.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_005.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_006.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_007.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_008.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_009.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_010.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_012.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_013.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_014.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_015.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_016.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_017.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_002.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_002.json']\r\n```\r\n","comment_length":31,"text":" load_dataset(\"multi_woz_v22\") NonMatchingChecksumError \n Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n\r\n`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`\r\n\r\n\r\nThis will give the following error:\r\n\r\n```\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dialog_acts.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_003.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_004.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_005.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_006.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_007.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_008.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_009.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_010.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_012.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_013.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_014.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_015.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_016.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/train\/dialogues_017.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/dev\/dialogues_002.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_001.json', 'https:\/\/github.com\/budzianowski\/multiwoz\/raw\/master\/data\/MultiWOZ_2.2\/test\/dialogues_002.json']\r\n```\r\n \n This must be related to https:\/\/github.com\/budzianowski\/multiwoz\/pull\/72\r\nThose files have changed, let me update the checksums for this dataset.\r\n\r\nFor now you can use `ignore_verifications=True` in `load_dataset` to skip the checksum verification.","embeddings":[-0.2244368494,0.1764702946,-0.0287194345,0.1495722383,0.1895723343,0.0018984437,0.3650578558,0.4820295274,0.2514897287,0.1666063964,-0.0970349461,0.1781593114,-0.0822444186,0.0992015228,-0.1845492125,0.2732406855,0.0765193701,-0.0256412663,-0.1682780683,-0.0625795946,-0.2430096716,0.3292944729,-0.1593268216,-0.0338407457,-0.3486862779,0.130385235,0.173908785,0.336238414,-0.0838693827,-0.1523166895,0.4145028293,0.2369774878,0.0439615399,0.3686157465,-0.0001172875,0.163338542,0.2778438032,-0.1433111131,-0.1339644343,-0.1817747802,-0.5618035793,-0.3552265167,-0.1035682037,-0.1154623255,0.0706329644,0.3033826053,0.0572008304,-0.0867421255,0.2431910932,0.1712264121,0.1535670757,0.3319171369,0.2157697976,0.096905008,0.0455294512,-0.0774404854,-0.1376802474,0.0922583491,0.2744395733,-0.1569582969,-0.2395652235,0.2651384175,-0.1804820746,0.2693834305,-0.08042676,-0.151098907,0.1479792297,-0.1058241054,0.2105871737,0.5727722049,0.2205697149,-0.0554821864,-0.1762661785,-0.031950675,-0.2201738805,0.0833650157,0.3634192646,0.1834743917,-0.2078651488,0.0045875246,-0.4487929642,0.1443090439,-0.0263798088,0.2792589366,0.2121898234,0.3145956695,0.2003566772,0.0726620853,0.2279323786,-0.2064414769,0.1992104501,-0.3010312915,-0.1609574109,0.1293215007,-0.6801476479,-0.0664964691,0.0034521031,0.3319556117,0.4404266775,0.2289481759,0.4523443282,0.3736301064,-0.1520804018,0.1170620769,0.3968931139,0.1744454056,0.0657053292,-0.0545444787,0.1692391336,0.2095874697,-0.0953853428,0.23737818,-0.0305045638,-0.3612305224,0.349316746,0.1929925084,0.2069615573,-0.3434550762,-0.1813292205,0.0824042559,-0.2454786599,-0.1945316195,0.2718029022,0.2862129211,-0.0944880024,0.5524677038,-0.1653458029,0.2156011462,-0.1538057327,-0.184701696,-0.2573401332,-0.1516558081,0.0730739161,0.1531955004,0.2943665981,0.0656549037,0.3595133722,-0.0321129337,0.3835370243,-0.2031213194,0.333525002,0.0902094916,-0.069919236,0.2554130256,0.0134760654,0.2010277361,0.2036009133,-0.0260661189,-0.0622884482,0.2220196426,-0.332572788,-0.6167247295,0.1093568653,0.1607629508,-0.3595766425,-0.0804914013,0.0163278151,-0.2801302671,0.3050760031,-0.4042750597,-0.1545268744,-0.3094389141,-0.0430206098,-0.1179552451,0.0098633282,0.0942928717,-0.0934405178,0.0649290308,-0.0455776192,-0.1832666099,0.3468402326,0.2667627037,-0.2780414522,-0.1645423472,-0.1394770145,0.0187303592,0.2207069397,-0.0911147445,-0.3638109565,0.2024427652,-0.071666114,0.6232899427,0.1856193095,0.1097929999,-0.347364068,-0.1108146757,0.2366856933,0.0823844671,0.1350486577,0.1764032245,-0.2450098842,-0.3122074902,0.3476401865,0.1528952271,0.0161080807,-0.2277660966,0.0021591219,0.0893484354,0.5888454914,-0.229704082,-0.0971514285,0.173809126,0.0959679857,-0.2113368064,0.0982792377,0.0511620082,-0.4866259396,0.3225809634,-0.1565800011,-0.0987997502,0.3440404832,-0.0598260574,-0.0504282936,-0.1761434972,-0.3390328586,0.0107523073,0.0809487924,0.2748901248,0.083496578,-0.2535201013,-0.0927836373,0.3571139276,-0.3864992857,-0.0857931376,-0.4520960152,0.1969179064,-0.0879131109,0.0851735622,0.0764899477,0.1571474075,0.1050598249,-0.1094213575,-0.1870695502,0.5129078031,0.321377933,0.238428399,-0.0831710547,0.3510250747,-0.1519880891,-0.0498669744,-0.1007987484,0.5829588175,-0.043825414,-0.2062136233,-0.1941188127,0.4879204333,-0.1729985923,0.4535035193,-0.0467061065,0.1363264918,0.3366037309,-0.1314040124,-0.267632544,-0.094632037,0.4545117021,-0.1980919838,-0.166961506,0.2530014515,-0.2133246213,0.0163294896,0.3733881712,-0.1440781504,0.1259907186,-0.0151448352,0.2458733171,-0.2552571595,-0.0198079348,0.492271781,0.3632516265,0.1011830643,-0.017946247,-0.0273172073,-0.2294692695,-0.1744433939,0.1194066629,0.0973706469,0.3296886981,0.5370066166,0.0413935557,-0.3653722107,-0.4267033637,-0.0148492791,0.0290301628,0.1656199098,-0.441488415,-0.2537639439,-0.4441935718,0.2288448811,-0.3637366891,-0.3436980546,-0.4129472375,-0.4648984671,0.1210602671,0.2281651497,-0.019953087,0.3193200827,-0.3111640215,0.0683446303,-0.13192828,0.0605431758,0.0400538072,-0.2777514756,-0.0817120597,-0.0046348502,0.395745039,0.0018831765,-0.0578058697,-0.3077501059,-0.0692631528,-0.1458399743,-0.1667571664,-0.0308500472,-0.1108122692,-0.0727645904,0.258897841,0.3025155365,0.1404629946,-0.3886933029,0.2458182424,-0.0367405601,-0.2834911942,0.1877612323,-0.1721364707,-0.0189697389,0.1081124917,-0.3866266012,-0.0947575793,-0.3375903666,-0.0101160659,0.0664086565,0.2717181146,0.2532503307,0.0835985392,-0.067855671,0.135645777,0.2740627229,-0.3200554848,-0.8128382564,0.4206303358,-0.0225661304,-0.2601571679,-0.0509394854,0.0214568581,0.2018916756,0.0964379087,-0.4472939074,-0.2804222107,-0.0651813745,-0.3137300909,0.1682505161,-0.3037391305,0.3359626532,0.118827641,-0.0578323677,-0.244994387,-0.294922471,0.3670241833,0.0858874843,0.5151624084,-0.2128699422,0.2413334697,0.0072190906,0.59493047,0.2273386568,0.2582996488,0.0617827326,0.1863201261,0.1531594396,-0.0817795843,-0.3245143592,0.1173866391,-0.265142381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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1872","title":"Adding a new column to the dataset after set_format was called","comments":"Hi ! Indeed if you add a column to a formatted dataset, then the new dataset gets a new formatting in which:\r\n```\r\nnew formatted columns = (all columns - previously unformatted columns)\r\n```\r\nTherefore the new column is going to be formatted using the `torch` formatting.\r\n\r\nIf you want your new column to be unformatted you can re-run this line:\r\n```python\r\ndata.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)\r\n```","body":"Hi, \r\n\r\nthanks for the nice library. I'm in the process of creating a custom dataset, which has a mix of tensors and lists of strings. I stumbled upon an error and want to know if its a problem on my side. \r\n\r\nI load some lists of strings and integers, then call `data.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)`. This converts the integer columns into tensors, but keeps the lists of strings as they are. I then call `map` to add a new column to my dataset, which is a **list of strings**. Once I iterate through my dataset, I get an error that the new column can't be converted into a tensor (which is probably caused by `set_format`). \r\n\r\nBelow some pseudo code:\r\n```python\r\n    def augment_func(sample: Dict) -> Dict:\r\n        # do something\r\n        return {\r\n         \"some_integer_column1\" : augmented_data[\"some_integer_column1\"],  # <-- tensor\r\n         \"some_integer_column2\" : augmented_data[\"some_integer_column2\"],  # <-- tensor\r\n         \"NEW_COLUMN\": targets,  # <-- list of strings\r\n        }\r\n\r\n\r\n    data = datasets.load_dataset(__file__, data_dir=\"...\", split=\"train\")\r\n    data.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)\r\n\r\n    augmented_dataset = data.map(augment_func, batched=False)\r\n    \r\n    for sample in augmented_dataset:\r\n        print(sample)  # fails\r\n\r\n```\r\n\r\nand the exception:\r\n```python\r\nTraceback (most recent call last):\r\n  File \"dataset.py\", line 487, in <module>\r\n    main()\r\n  File \"dataset.py\", line 471, in main\r\n    for sample in augmented_dataset:\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 697, in __iter__\r\n    yield self._getitem(\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1069, in _getitem\r\n    outputs = self._convert_outputs(\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 890, in _convert_outputs\r\n    v = map_nested(command, v, **map_nested_kwargs)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in command\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in <listcomp>\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in command\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in <listcomp>\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 851, in command\r\n    return torch.tensor(x, **format_kwargs)\r\nTypeError: new(): invalid data type 'str'\r\n```\r\n\r\nThanks!\r\n","comment_length":67,"text":"Adding a new column to the dataset after set_format was called \n Hi, \r\n\r\nthanks for the nice library. I'm in the process of creating a custom dataset, which has a mix of tensors and lists of strings. I stumbled upon an error and want to know if its a problem on my side. \r\n\r\nI load some lists of strings and integers, then call `data.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)`. This converts the integer columns into tensors, but keeps the lists of strings as they are. I then call `map` to add a new column to my dataset, which is a **list of strings**. Once I iterate through my dataset, I get an error that the new column can't be converted into a tensor (which is probably caused by `set_format`). \r\n\r\nBelow some pseudo code:\r\n```python\r\n    def augment_func(sample: Dict) -> Dict:\r\n        # do something\r\n        return {\r\n         \"some_integer_column1\" : augmented_data[\"some_integer_column1\"],  # <-- tensor\r\n         \"some_integer_column2\" : augmented_data[\"some_integer_column2\"],  # <-- tensor\r\n         \"NEW_COLUMN\": targets,  # <-- list of strings\r\n        }\r\n\r\n\r\n    data = datasets.load_dataset(__file__, data_dir=\"...\", split=\"train\")\r\n    data.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)\r\n\r\n    augmented_dataset = data.map(augment_func, batched=False)\r\n    \r\n    for sample in augmented_dataset:\r\n        print(sample)  # fails\r\n\r\n```\r\n\r\nand the exception:\r\n```python\r\nTraceback (most recent call last):\r\n  File \"dataset.py\", line 487, in <module>\r\n    main()\r\n  File \"dataset.py\", line 471, in main\r\n    for sample in augmented_dataset:\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 697, in __iter__\r\n    yield self._getitem(\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1069, in _getitem\r\n    outputs = self._convert_outputs(\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 890, in _convert_outputs\r\n    v = map_nested(command, v, **map_nested_kwargs)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in command\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in <listcomp>\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in command\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in <listcomp>\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 851, in command\r\n    return torch.tensor(x, **format_kwargs)\r\nTypeError: new(): invalid data type 'str'\r\n```\r\n\r\nThanks!\r\n \n Hi ! Indeed if you add a column to a formatted dataset, then the new dataset gets a new formatting in which:\r\n```\r\nnew formatted columns = (all columns - previously unformatted columns)\r\n```\r\nTherefore the new column is going to be formatted using the `torch` formatting.\r\n\r\nIf you want your new column to be unformatted you can re-run this line:\r\n```python\r\ndata.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)\r\n```","embeddings":[-0.163837567,-0.1748755127,-0.0401732326,-0.0492338166,0.4729719162,0.2625342906,0.6921063662,0.4177582264,0.16875045,-0.2805859745,0.1421070397,0.3377909064,-0.234039858,-0.0259004012,0.1978132278,-0.1026414856,0.1268425137,0.0638930127,0.2461459339,0.0586879924,-0.3777551651,0.1032360643,-0.1540207863,-0.2058055401,-0.1765669137,-0.3008010983,-0.0024790808,-0.1966911554,-0.1602552682,-0.2089734375,0.1871719062,-0.239169538,-0.0005563378,0.6491594315,-0.0001178686,0.1240975857,0.1696617603,0.031614352,-0.0399997793,-0.1928997934,-0.2241786718,-0.3875743151,-0.1664160639,-0.3292916119,0.1402370483,-0.090086028,-0.4265715182,-0.7763667107,0.2612175345,0.5016534925,0.1658125967,0.1779137254,-0.0243937224,-0.0470924303,0.2402329147,0.2883871496,-0.2155929655,-0.0182730332,-0.0107632391,0.1259519309,0.5092816353,0.1936923265,-0.2842826843,0.1020674407,0.106398195,0.2241102755,-0.2291458696,-0.0425992012,-0.0292203575,0.1993377954,0.6996715069,-0.254180491,-0.3642950058,-0.2796714902,0.0794099048,-0.4402051568,0.119070828,-0.0155838113,-0.0778750554,-0.1121089906,0.0913000554,-0.1839411706,-0.0351652093,0.3412312865,-0.1856348664,0.0189567804,-0.0303626508,0.1920932084,-0.1479987055,-0.0397582315,-0.1474709958,0.1262124032,0.0943790153,0.2419148088,-0.4515655339,-0.1876330823,0.033643987,-0.5349569917,-0.2338584661,-0.1157705784,0.1772542596,0.0031550087,-0.187983036,0.1016870588,0.1837739646,-0.0243844315,-0.2377842367,0.202795282,-0.058971107,-0.2029019147,0.0786278844,-0.1089506671,0.2219047099,-0.2733579278,0.1720267087,0.0052718245,0.415762037,0.1181959957,-0.0770012587,0.2277686745,-0.2703928947,-0.2729817927,0.0384290256,0.0916894153,-0.027375767,0.1280095875,0.140044421,0.2697374821,-0.0306830835,0.1996581256,-0.0518040881,0.0073224418,-0.1836284101,0.0456944257,0.1025165766,0.1123385131,-0.1570651531,0.2218846977,-0.13670744,-0.0665040687,0.0907948315,-0.2595322132,0.3443582356,0.1951227039,-0.1425132006,0.3772317767,0.1710849255,-0.0373196006,-0.1732723415,0.2277605534,-0.2527373135,-0.3213145733,-0.4081810117,0.0944307446,-0.0362261981,-0.1098731384,0.0837554261,0.1692531705,0.5626441836,-0.1914578974,0.1816345602,-0.2311049104,-0.3539713025,-0.2420078516,-0.0327599496,0.1081718057,-0.6691618562,0.0941578373,0.0791116133,0.3833465278,0.0772274733,0.1891558468,-0.0684040338,0.3359542787,-0.2292346805,0.0447732247,0.6186287999,0.0500695035,-0.1436017752,0.065374814,-0.1200989783,0.1486835629,-0.0731995106,0.1894952059,0.0845489725,-0.0850279555,0.3703552485,0.0103158476,-0.1337237805,-0.1136995554,-0.0130396793,0.1656283587,0.5390791893,-0.2064686865,0.0425463095,0.1790866256,-0.1250628978,0.2282993644,-0.017285509,-0.0337756947,-0.0073143095,0.1034121662,0.2751893401,0.0653935894,0.0355755165,-0.2091534138,-0.3796861172,-0.0080606909,0.4330004454,0.021443544,-0.00806686,-0.1484539062,-0.3776355088,0.0075415815,-0.0111386655,0.2967942953,0.0175653826,0.166564554,-0.1642278135,-0.1772773117,-0.338902384,0.0949272737,-0.1961665452,-0.0513706133,-0.2434315234,0.1282278299,0.1284624189,-0.2729171216,-0.1948995143,0.3867044747,0.1357645839,-0.0230477694,-0.1044372097,0.2309470475,0.0756757632,-0.1251296699,-0.4924203455,0.2001788765,0.1332603395,0.1238413602,-0.1664903164,-0.1851825267,-0.0113888839,-0.2167836577,-0.0754593462,0.050736133,0.0049540126,0.2880735099,-0.3090748787,0.1987280101,0.1029938012,0.0727731362,-0.2182666212,-0.3290410638,-0.0560583398,0.0218479354,0.0440452658,-0.2663611472,-0.3668257594,0.0163654499,0.6044391394,0.0032727038,0.1298659891,0.1421494484,0.1310873926,0.0832142532,0.0108531453,0.0890294835,0.4790549278,0.0814551562,-0.0663492233,0.023984842,-0.2592504323,-0.098174952,0.0403772667,0.2914122641,0.2057984024,0.0817342773,0.2835640609,0.0091479477,0.0822448358,0.0117116934,0.2326989472,0.1617716104,-0.5018972754,-0.0692816153,-0.127254352,0.2658206522,-0.2941197157,-0.6862946153,0.0144574651,-0.3176656961,-0.2221376598,0.0013733351,0.0248833746,0.0532939658,-0.0381292999,0.1013478041,0.2265940011,-0.4410072267,-0.0932101756,-0.2546205223,-0.0083848387,0.0140890237,0.3546907604,-0.3220357895,0.4234782755,0.1450334787,-0.1792608052,-0.1153635308,-0.3000035286,-0.0179306921,-0.3884204924,-0.0696965829,0.257756561,0.1516362876,-0.1477588415,-0.1888682097,0.3712129593,0.1980449259,0.2000721395,0.1980653405,0.1245261729,0.0569173545,-0.0044003814,-0.5688668489,-0.1075387448,-0.0420352966,0.0825474933,-0.0878118873,0.0984746888,0.0212098062,0.2226237953,0.1303482354,0.2398899049,-0.0894926563,-0.3793279827,-0.0635955632,0.3975252807,-0.3226929605,-0.2073272765,0.1397510022,-0.0274451841,0.181092605,0.2993002832,-0.3082209527,-0.1508528888,-0.1311350614,0.173268497,-0.2809301317,-0.0994715616,0.5064876676,0.0120290006,-0.0551418066,-0.1404468119,-0.2117807865,0.1956448406,0.2700185776,0.0633992553,0.2628600001,0.547018826,0.088467814,0.3008495867,0.0786023214,-0.4974924028,0.2550722659,-0.30017066,0.1966451108,-0.2971628308,-0.3937258124,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1872","title":"Adding a new column to the dataset after set_format was called","comments":"Ok cool :) \r\nAlso I just did a PR to mention this behavior in the documentation","body":"Hi, \r\n\r\nthanks for the nice library. I'm in the process of creating a custom dataset, which has a mix of tensors and lists of strings. I stumbled upon an error and want to know if its a problem on my side. \r\n\r\nI load some lists of strings and integers, then call `data.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)`. This converts the integer columns into tensors, but keeps the lists of strings as they are. I then call `map` to add a new column to my dataset, which is a **list of strings**. Once I iterate through my dataset, I get an error that the new column can't be converted into a tensor (which is probably caused by `set_format`). \r\n\r\nBelow some pseudo code:\r\n```python\r\n    def augment_func(sample: Dict) -> Dict:\r\n        # do something\r\n        return {\r\n         \"some_integer_column1\" : augmented_data[\"some_integer_column1\"],  # <-- tensor\r\n         \"some_integer_column2\" : augmented_data[\"some_integer_column2\"],  # <-- tensor\r\n         \"NEW_COLUMN\": targets,  # <-- list of strings\r\n        }\r\n\r\n\r\n    data = datasets.load_dataset(__file__, data_dir=\"...\", split=\"train\")\r\n    data.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)\r\n\r\n    augmented_dataset = data.map(augment_func, batched=False)\r\n    \r\n    for sample in augmented_dataset:\r\n        print(sample)  # fails\r\n\r\n```\r\n\r\nand the exception:\r\n```python\r\nTraceback (most recent call last):\r\n  File \"dataset.py\", line 487, in <module>\r\n    main()\r\n  File \"dataset.py\", line 471, in main\r\n    for sample in augmented_dataset:\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 697, in __iter__\r\n    yield self._getitem(\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1069, in _getitem\r\n    outputs = self._convert_outputs(\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 890, in _convert_outputs\r\n    v = map_nested(command, v, **map_nested_kwargs)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in command\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in <listcomp>\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in command\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in <listcomp>\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 851, in command\r\n    return torch.tensor(x, **format_kwargs)\r\nTypeError: new(): invalid data type 'str'\r\n```\r\n\r\nThanks!\r\n","comment_length":16,"text":"Adding a new column to the dataset after set_format was called \n Hi, \r\n\r\nthanks for the nice library. I'm in the process of creating a custom dataset, which has a mix of tensors and lists of strings. I stumbled upon an error and want to know if its a problem on my side. \r\n\r\nI load some lists of strings and integers, then call `data.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)`. This converts the integer columns into tensors, but keeps the lists of strings as they are. I then call `map` to add a new column to my dataset, which is a **list of strings**. Once I iterate through my dataset, I get an error that the new column can't be converted into a tensor (which is probably caused by `set_format`). \r\n\r\nBelow some pseudo code:\r\n```python\r\n    def augment_func(sample: Dict) -> Dict:\r\n        # do something\r\n        return {\r\n         \"some_integer_column1\" : augmented_data[\"some_integer_column1\"],  # <-- tensor\r\n         \"some_integer_column2\" : augmented_data[\"some_integer_column2\"],  # <-- tensor\r\n         \"NEW_COLUMN\": targets,  # <-- list of strings\r\n        }\r\n\r\n\r\n    data = datasets.load_dataset(__file__, data_dir=\"...\", split=\"train\")\r\n    data.set_format(\"torch\", columns=[\"some_integer_column1\", \"some_integer_column2\"], output_all_columns=True)\r\n\r\n    augmented_dataset = data.map(augment_func, batched=False)\r\n    \r\n    for sample in augmented_dataset:\r\n        print(sample)  # fails\r\n\r\n```\r\n\r\nand the exception:\r\n```python\r\nTraceback (most recent call last):\r\n  File \"dataset.py\", line 487, in <module>\r\n    main()\r\n  File \"dataset.py\", line 471, in main\r\n    for sample in augmented_dataset:\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 697, in __iter__\r\n    yield self._getitem(\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1069, in _getitem\r\n    outputs = self._convert_outputs(\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 890, in _convert_outputs\r\n    v = map_nested(command, v, **map_nested_kwargs)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in command\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in <listcomp>\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in command\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 850, in <listcomp>\r\n    return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n  File \"lib\/python3.8\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 851, in command\r\n    return torch.tensor(x, **format_kwargs)\r\nTypeError: new(): invalid data type 'str'\r\n```\r\n\r\nThanks!\r\n \n Ok cool :) \r\nAlso I just did a PR to mention this behavior in the documentation","embeddings":[-0.163837567,-0.1748755127,-0.0401732326,-0.0492338166,0.4729719162,0.2625342906,0.6921063662,0.4177582264,0.16875045,-0.2805859745,0.1421070397,0.3377909064,-0.234039858,-0.0259004012,0.1978132278,-0.1026414856,0.1268425137,0.0638930127,0.2461459339,0.0586879924,-0.3777551651,0.1032360643,-0.1540207863,-0.2058055401,-0.1765669137,-0.3008010983,-0.0024790808,-0.1966911554,-0.1602552682,-0.2089734375,0.1871719062,-0.239169538,-0.0005563378,0.6491594315,-0.0001178686,0.1240975857,0.1696617603,0.031614352,-0.0399997793,-0.1928997934,-0.2241786718,-0.3875743151,-0.1664160639,-0.3292916119,0.1402370483,-0.090086028,-0.4265715182,-0.7763667107,0.2612175345,0.5016534925,0.1658125967,0.1779137254,-0.0243937224,-0.0470924303,0.2402329147,0.2883871496,-0.2155929655,-0.0182730332,-0.0107632391,0.1259519309,0.5092816353,0.1936923265,-0.2842826843,0.1020674407,0.106398195,0.2241102755,-0.2291458696,-0.0425992012,-0.0292203575,0.1993377954,0.6996715069,-0.254180491,-0.3642950058,-0.2796714902,0.0794099048,-0.4402051568,0.119070828,-0.0155838113,-0.0778750554,-0.1121089906,0.0913000554,-0.1839411706,-0.0351652093,0.3412312865,-0.1856348664,0.0189567804,-0.0303626508,0.1920932084,-0.1479987055,-0.0397582315,-0.1474709958,0.1262124032,0.0943790153,0.2419148088,-0.4515655339,-0.1876330823,0.033643987,-0.5349569917,-0.2338584661,-0.1157705784,0.1772542596,0.0031550087,-0.187983036,0.1016870588,0.1837739646,-0.0243844315,-0.2377842367,0.202795282,-0.058971107,-0.2029019147,0.0786278844,-0.1089506671,0.2219047099,-0.2733579278,0.1720267087,0.0052718245,0.415762037,0.1181959957,-0.0770012587,0.2277686745,-0.2703928947,-0.2729817927,0.0384290256,0.0916894153,-0.027375767,0.1280095875,0.140044421,0.2697374821,-0.0306830835,0.1996581256,-0.0518040881,0.0073224418,-0.1836284101,0.0456944257,0.1025165766,0.1123385131,-0.1570651531,0.2218846977,-0.13670744,-0.0665040687,0.0907948315,-0.2595322132,0.3443582356,0.1951227039,-0.1425132006,0.3772317767,0.1710849255,-0.0373196006,-0.1732723415,0.2277605534,-0.2527373135,-0.3213145733,-0.4081810117,0.0944307446,-0.0362261981,-0.1098731384,0.0837554261,0.1692531705,0.5626441836,-0.1914578974,0.1816345602,-0.2311049104,-0.3539713025,-0.2420078516,-0.0327599496,0.1081718057,-0.6691618562,0.0941578373,0.0791116133,0.3833465278,0.0772274733,0.1891558468,-0.0684040338,0.3359542787,-0.2292346805,0.0447732247,0.6186287999,0.0500695035,-0.1436017752,0.065374814,-0.1200989783,0.1486835629,-0.0731995106,0.1894952059,0.0845489725,-0.0850279555,0.3703552485,0.0103158476,-0.1337237805,-0.1136995554,-0.0130396793,0.1656283587,0.5390791893,-0.2064686865,0.0425463095,0.1790866256,-0.1250628978,0.2282993644,-0.017285509,-0.0337756947,-0.0073143095,0.1034121662,0.2751893401,0.0653935894,0.0355755165,-0.2091534138,-0.3796861172,-0.0080606909,0.4330004454,0.021443544,-0.00806686,-0.1484539062,-0.3776355088,0.0075415815,-0.0111386655,0.2967942953,0.0175653826,0.166564554,-0.1642278135,-0.1772773117,-0.338902384,0.0949272737,-0.1961665452,-0.0513706133,-0.2434315234,0.1282278299,0.1284624189,-0.2729171216,-0.1948995143,0.3867044747,0.1357645839,-0.0230477694,-0.1044372097,0.2309470475,0.0756757632,-0.1251296699,-0.4924203455,0.2001788765,0.1332603395,0.1238413602,-0.1664903164,-0.1851825267,-0.0113888839,-0.2167836577,-0.0754593462,0.050736133,0.0049540126,0.2880735099,-0.3090748787,0.1987280101,0.1029938012,0.0727731362,-0.2182666212,-0.3290410638,-0.0560583398,0.0218479354,0.0440452658,-0.2663611472,-0.3668257594,0.0163654499,0.6044391394,0.0032727038,0.1298659891,0.1421494484,0.1310873926,0.0832142532,0.0108531453,0.0890294835,0.4790549278,0.0814551562,-0.0663492233,0.023984842,-0.2592504323,-0.098174952,0.0403772667,0.2914122641,0.2057984024,0.0817342773,0.2835640609,0.0091479477,0.0822448358,0.0117116934,0.2326989472,0.1617716104,-0.5018972754,-0.0692816153,-0.127254352,0.2658206522,-0.2941197157,-0.6862946153,0.0144574651,-0.3176656961,-0.2221376598,0.0013733351,0.0248833746,0.0532939658,-0.0381292999,0.1013478041,0.2265940011,-0.4410072267,-0.0932101756,-0.2546205223,-0.0083848387,0.0140890237,0.3546907604,-0.3220357895,0.4234782755,0.1450334787,-0.1792608052,-0.1153635308,-0.3000035286,-0.0179306921,-0.3884204924,-0.0696965829,0.257756561,0.1516362876,-0.1477588415,-0.1888682097,0.3712129593,0.1980449259,0.2000721395,0.1980653405,0.1245261729,0.0569173545,-0.0044003814,-0.5688668489,-0.1075387448,-0.0420352966,0.0825474933,-0.0878118873,0.0984746888,0.0212098062,0.2226237953,0.1303482354,0.2398899049,-0.0894926563,-0.3793279827,-0.0635955632,0.3975252807,-0.3226929605,-0.2073272765,0.1397510022,-0.0274451841,0.181092605,0.2993002832,-0.3082209527,-0.1508528888,-0.1311350614,0.173268497,-0.2809301317,-0.0994715616,0.5064876676,0.0120290006,-0.0551418066,-0.1404468119,-0.2117807865,0.1956448406,0.2700185776,0.0633992553,0.2628600001,0.547018826,0.088467814,0.3008495867,0.0786023214,-0.4974924028,0.2550722659,-0.30017066,0.1966451108,-0.2971628308,-0.3937258124,-0.0502180122,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1867","title":"ERROR WHEN USING SET_TRANSFORM() ","comments":"Hi @alejandrocros it looks like an incompatibility with the current Trainer @sgugger \r\nIndeed currently the Trainer of `transformers` doesn't support a dataset with a transform\r\n\r\nIt looks like it comes from this line: https:\/\/github.com\/huggingface\/transformers\/blob\/f51188cbe74195c14c5b3e2e8f10c2f435f9751a\/src\/transformers\/trainer.py#L442\r\n\r\nThis line sets the format to not return certain unused columns. But this has two issues:\r\n1. it forgets to also set the format_kwargs (this causes the error you got):\r\n```python\r\ndataset.set_format(type=dataset.format[\"type\"], columns=columns, format_kwargs=dataset.format[\"format_kwargs\"])\r\n```\r\n2. the Trainer wants to keep only the fields that are used as input for a model. However for a dataset with a transform, the output fields are often different from the columns fields. For example from a column \"text\" in the dataset, the strings can be transformed on-the-fly into \"input_ids\". If you want your dataset to only output certain fields and not other you must change your transform function.\r\n","body":"Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n","comment_length":139,"text":"ERROR WHEN USING SET_TRANSFORM()  \n Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n \n Hi @alejandrocros it looks like an incompatibility with the current Trainer @sgugger \r\nIndeed currently the Trainer of `transformers` doesn't support a dataset with a transform\r\n\r\nIt looks like it comes from this line: https:\/\/github.com\/huggingface\/transformers\/blob\/f51188cbe74195c14c5b3e2e8f10c2f435f9751a\/src\/transformers\/trainer.py#L442\r\n\r\nThis line sets the format to not return certain unused columns. But this has two issues:\r\n1. it forgets to also set the format_kwargs (this causes the error you got):\r\n```python\r\ndataset.set_format(type=dataset.format[\"type\"], columns=columns, format_kwargs=dataset.format[\"format_kwargs\"])\r\n```\r\n2. the Trainer wants to keep only the fields that are used as input for a model. However for a dataset with a transform, the output fields are often different from the columns fields. For example from a column \"text\" in the dataset, the strings can be transformed on-the-fly into \"input_ids\". If you want your dataset to only output certain fields and not other you must change your transform function.\r\n","embeddings":[-0.1826189756,-0.2732517719,0.1393030435,0.1847398877,0.7292689681,0.1197050363,0.6009586453,0.2287764698,-0.3227846622,0.0544771031,0.2355112433,0.0333268568,-0.0883406326,-0.0234047007,0.1892216057,-0.5358201861,-0.0138219129,0.1288610101,-0.4634343982,-0.1831161827,-0.27669698,0.3134228885,-0.3415367901,0.1193406507,-0.3186003268,-0.0096082669,-0.047695376,0.016471222,0.0844151899,-0.1718301773,0.2334779501,-0.2800752223,0.1231957451,0.3974503875,-0.0001255372,0.1195578203,0.0606352575,-0.2890718281,-0.2367867827,-0.3003558218,-0.1437315047,-0.1552786827,-0.0154345548,-0.2408748716,-0.4479370713,-0.1890559047,0.0066991188,-0.2687551975,0.5653699636,0.7264817953,0.1215873957,0.2663597167,0.0996964872,-0.2382416725,-0.1261355132,0.1903086156,-0.1291115582,-0.0224978216,0.0317756385,0.2165266722,0.1336240321,0.1615502834,0.2006310076,-0.1752620935,0.4064543247,-0.0619789995,-0.1365289241,0.0435062647,0.0006193888,0.0104680918,0.2298866063,-0.3454308212,-0.420854032,-0.2735254467,0.1654442102,-0.2141008228,0.2700577974,0.1503871977,-0.066397734,0.22794348,-0.2600681484,-0.1098380312,-0.0588664711,0.0313304402,-0.1728429347,0.1623857617,-0.0065073655,0.1303315759,0.0931790695,0.1813862473,0.2739946544,0.1153549775,0.1876621246,0.3649872243,-0.265356034,-0.05478606,-0.1434788406,0.0108277686,-0.2889722586,-0.2492735982,-0.1723645478,-0.2085095048,0.0230882131,-0.0149517776,0.1071462706,0.6613349319,-0.2454049438,0.4977247119,0.0151178716,-0.1120734289,0.2516205609,-0.0635674,-0.0569484457,-0.3780822754,-0.0096246395,0.4175055921,0.0937968418,-0.006041795,-0.2874678373,-0.1932771057,-0.2089736909,-0.0578821562,0.1327706873,0.217985943,0.0588070415,0.2713345587,0.5032314658,0.3485563695,-0.1059520543,-0.2050584853,-0.1715610176,0.135013476,-0.3857185841,-0.1006987318,0.1464199424,-0.1412298381,0.3286191225,0.0532120019,0.0755415633,0.3815326989,0.1520244032,-0.191182673,0.2819835246,0.1436703354,-0.0143271564,0.2014978528,0.4693849087,0.10144265,-0.1861768216,0.2664014995,-0.2484502494,-0.4411483705,0.0609503128,0.0914024562,-0.0530007631,-0.0377959199,-0.1594837457,0.1583796293,0.4205289483,-0.0675125122,0.0763092414,-0.4352326989,0.0418038815,0.1099921837,0.090025045,0.0778380185,-0.2328578383,-0.3426035345,0.3258585334,0.1530645639,-0.143343091,0.2205605805,-0.4285855889,0.2562271655,-0.0625739917,-0.1584211737,0.2098882198,-0.3840966821,-0.0710864812,-0.0994279832,-0.1054709256,-0.219163999,0.0696848631,-0.1317368597,-0.0530189537,-0.1544256955,-0.2207259983,0.5086032748,-0.0608968958,0.2324942946,0.0340902545,-0.0222444125,0.1885959953,0.1376521885,0.0248874463,0.1576400846,-0.320507586,0.5120714307,0.0987583771,-0.2441777289,0.2444199026,0.4102393985,-0.244097501,-0.1919647157,0.0468020588,0.2764771283,-0.0697960332,-0.148902759,0.1784002781,0.1963512599,-0.0604719073,0.0484519824,-0.2369389385,-0.0641728118,-0.2133747637,-0.1585211456,-0.0149899647,0.1101620644,0.0207734834,-0.0464728251,-0.2484817207,0.2723915279,-0.095426932,0.1349620223,-0.5259901881,0.2280209661,-0.2145735472,-0.0914447233,-0.0678843781,0.2231209576,0.2404938191,-0.2305194438,-0.3247486055,0.3036762774,-0.2122558355,0.0371924043,-0.3943189681,0.2457401305,0.3090952635,-0.5342994928,0.0032722938,0.2634411156,0.1639743596,-0.127455622,-0.383022517,0.1136109233,0.0757724941,0.3314516544,0.1057743058,0.3340388238,0.0536119379,-0.0884053633,-0.2020374686,-0.0776441023,-0.0154921887,-0.0838234276,0.1649821252,0.0597501025,-0.2288423777,-0.1983881891,0.4673170149,0.148841694,0.0216302704,0.1293901354,-0.1397807598,0.0306708906,0.1618632525,-0.0950354412,0.562422514,0.0324369743,-0.2305446267,0.1875147969,0.0697727501,-0.1226138026,0.4002860487,-0.0731957108,0.0252318047,-0.106548816,0.0615078025,0.0952067301,-0.1649064571,-0.289491564,0.25625211,0.2521972656,-0.4736778736,0.2136499882,0.0259740539,0.1696037799,-0.5800776482,-0.3827403784,0.0345899239,-0.2872623503,-0.1098969206,-0.0208649188,0.1248787344,0.4439495802,0.1025984958,0.072272338,0.2448716611,-0.158022657,-0.0618830435,0.0459858961,-0.1911002547,0.0143575687,-0.1871763021,-0.2879667282,-0.0504698642,-0.3237556815,-0.0552909076,-0.1035404056,-0.1793443263,0.1855938435,-0.2955768704,0.0113008786,0.3231825829,0.1871942282,-0.1992335618,-0.3188306391,0.4316528141,0.0009160679,0.0959324092,-0.0127255181,-0.0703296512,-0.2425712794,-0.0997440368,-0.4264292717,-0.0446449108,-0.1689539254,0.0303072091,-0.2177828997,-0.1313049346,0.4203010499,0.2371348441,0.0843030065,0.2997512817,0.1740505844,-0.0198321659,-0.2111925334,0.4776291847,-0.2067952901,-0.3576395214,-0.1271426529,-0.0129219079,0.2218758017,0.2340473831,-0.1764791906,-0.178256005,-0.2040158212,-0.1126637682,-0.3014701605,0.435983181,0.4326145947,0.0603171624,0.1686085016,-0.050865747,-0.5156285167,0.3171764314,0.1424028128,0.4169045985,-0.0929954201,0.4798959196,0.0866109133,0.7751232386,0.0111009004,-0.3219003677,0.260661304,-0.1630743444,0.2260283828,-0.1403172314,-0.4625391364,0.2768841088,0.2438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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1867","title":"ERROR WHEN USING SET_TRANSFORM() ","comments":"FYI that option can be removed with `remove_unused_columns = False` in your `TrainingArguments`, so there is a workaround @alexvaca0 while the fix in `Trainer` is underway.\r\n\r\n@lhoestq I think I will just use the line you suggested and if someone is using the columns that are removed in their transform they will need to change `remove_unused_columns` to `False`. We might switch the default of that argument in the next version if that proves too bug-proof.","body":"Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n","comment_length":75,"text":"ERROR WHEN USING SET_TRANSFORM()  \n Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n \n FYI that option can be removed with `remove_unused_columns = False` in your `TrainingArguments`, so there is a workaround @alexvaca0 while the fix in `Trainer` is underway.\r\n\r\n@lhoestq I think I will just use the line you suggested and if someone is using the columns that are removed in their transform they will need to change `remove_unused_columns` to `False`. We might switch the default of that argument in the next version if that proves too bug-proof.","embeddings":[-0.1826189756,-0.2732517719,0.1393030435,0.1847398877,0.7292689681,0.1197050363,0.6009586453,0.2287764698,-0.3227846622,0.0544771031,0.2355112433,0.0333268568,-0.0883406326,-0.0234047007,0.1892216057,-0.5358201861,-0.0138219129,0.1288610101,-0.4634343982,-0.1831161827,-0.27669698,0.3134228885,-0.3415367901,0.1193406507,-0.3186003268,-0.0096082669,-0.047695376,0.016471222,0.0844151899,-0.1718301773,0.2334779501,-0.2800752223,0.1231957451,0.3974503875,-0.0001255372,0.1195578203,0.0606352575,-0.2890718281,-0.2367867827,-0.3003558218,-0.1437315047,-0.1552786827,-0.0154345548,-0.2408748716,-0.4479370713,-0.1890559047,0.0066991188,-0.2687551975,0.5653699636,0.7264817953,0.1215873957,0.2663597167,0.0996964872,-0.2382416725,-0.1261355132,0.1903086156,-0.1291115582,-0.0224978216,0.0317756385,0.2165266722,0.1336240321,0.1615502834,0.2006310076,-0.1752620935,0.4064543247,-0.0619789995,-0.1365289241,0.0435062647,0.0006193888,0.0104680918,0.2298866063,-0.3454308212,-0.420854032,-0.2735254467,0.1654442102,-0.2141008228,0.2700577974,0.1503871977,-0.066397734,0.22794348,-0.2600681484,-0.1098380312,-0.0588664711,0.0313304402,-0.1728429347,0.1623857617,-0.0065073655,0.1303315759,0.0931790695,0.1813862473,0.2739946544,0.1153549775,0.1876621246,0.3649872243,-0.265356034,-0.05478606,-0.1434788406,0.0108277686,-0.2889722586,-0.2492735982,-0.1723645478,-0.2085095048,0.0230882131,-0.0149517776,0.1071462706,0.6613349319,-0.2454049438,0.4977247119,0.0151178716,-0.1120734289,0.2516205609,-0.0635674,-0.0569484457,-0.3780822754,-0.0096246395,0.4175055921,0.0937968418,-0.006041795,-0.2874678373,-0.1932771057,-0.2089736909,-0.0578821562,0.1327706873,0.217985943,0.0588070415,0.2713345587,0.5032314658,0.3485563695,-0.1059520543,-0.2050584853,-0.1715610176,0.135013476,-0.3857185841,-0.1006987318,0.1464199424,-0.1412298381,0.3286191225,0.0532120019,0.0755415633,0.3815326989,0.1520244032,-0.191182673,0.2819835246,0.1436703354,-0.0143271564,0.2014978528,0.4693849087,0.10144265,-0.1861768216,0.2664014995,-0.2484502494,-0.4411483705,0.0609503128,0.0914024562,-0.0530007631,-0.0377959199,-0.1594837457,0.1583796293,0.4205289483,-0.0675125122,0.0763092414,-0.4352326989,0.0418038815,0.1099921837,0.090025045,0.0778380185,-0.2328578383,-0.3426035345,0.3258585334,0.1530645639,-0.143343091,0.2205605805,-0.4285855889,0.2562271655,-0.0625739917,-0.1584211737,0.2098882198,-0.3840966821,-0.0710864812,-0.0994279832,-0.1054709256,-0.219163999,0.0696848631,-0.1317368597,-0.0530189537,-0.1544256955,-0.2207259983,0.5086032748,-0.0608968958,0.2324942946,0.0340902545,-0.0222444125,0.1885959953,0.1376521885,0.0248874463,0.1576400846,-0.320507586,0.5120714307,0.0987583771,-0.2441777289,0.2444199026,0.4102393985,-0.244097501,-0.1919647157,0.0468020588,0.2764771283,-0.0697960332,-0.148902759,0.1784002781,0.1963512599,-0.0604719073,0.0484519824,-0.2369389385,-0.0641728118,-0.2133747637,-0.1585211456,-0.0149899647,0.1101620644,0.0207734834,-0.0464728251,-0.2484817207,0.2723915279,-0.095426932,0.1349620223,-0.5259901881,0.2280209661,-0.2145735472,-0.0914447233,-0.0678843781,0.2231209576,0.2404938191,-0.2305194438,-0.3247486055,0.3036762774,-0.2122558355,0.0371924043,-0.3943189681,0.2457401305,0.3090952635,-0.5342994928,0.0032722938,0.2634411156,0.1639743596,-0.127455622,-0.383022517,0.1136109233,0.0757724941,0.3314516544,0.1057743058,0.3340388238,0.0536119379,-0.0884053633,-0.2020374686,-0.0776441023,-0.0154921887,-0.0838234276,0.1649821252,0.0597501025,-0.2288423777,-0.1983881891,0.4673170149,0.148841694,0.0216302704,0.1293901354,-0.1397807598,0.0306708906,0.1618632525,-0.0950354412,0.562422514,0.0324369743,-0.2305446267,0.1875147969,0.0697727501,-0.1226138026,0.4002860487,-0.0731957108,0.0252318047,-0.106548816,0.0615078025,0.0952067301,-0.1649064571,-0.289491564,0.25625211,0.2521972656,-0.4736778736,0.2136499882,0.0259740539,0.1696037799,-0.5800776482,-0.3827403784,0.0345899239,-0.2872623503,-0.1098969206,-0.0208649188,0.1248787344,0.4439495802,0.1025984958,0.072272338,0.2448716611,-0.158022657,-0.0618830435,0.0459858961,-0.1911002547,0.0143575687,-0.1871763021,-0.2879667282,-0.0504698642,-0.3237556815,-0.0552909076,-0.1035404056,-0.1793443263,0.1855938435,-0.2955768704,0.0113008786,0.3231825829,0.1871942282,-0.1992335618,-0.3188306391,0.4316528141,0.0009160679,0.0959324092,-0.0127255181,-0.0703296512,-0.2425712794,-0.0997440368,-0.4264292717,-0.0446449108,-0.1689539254,0.0303072091,-0.2177828997,-0.1313049346,0.4203010499,0.2371348441,0.0843030065,0.2997512817,0.1740505844,-0.0198321659,-0.2111925334,0.4776291847,-0.2067952901,-0.3576395214,-0.1271426529,-0.0129219079,0.2218758017,0.2340473831,-0.1764791906,-0.178256005,-0.2040158212,-0.1126637682,-0.3014701605,0.435983181,0.4326145947,0.0603171624,0.1686085016,-0.050865747,-0.5156285167,0.3171764314,0.1424028128,0.4169045985,-0.0929954201,0.4798959196,0.0866109133,0.7751232386,0.0111009004,-0.3219003677,0.260661304,-0.1630743444,0.2260283828,-0.1403172314,-0.4625391364,0.2768841088,0.2438232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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1867","title":"ERROR WHEN USING SET_TRANSFORM() ","comments":"I've tried your solutions @sgugger @lhoestq and the good news is that it throws no error. However, TPU training is taking forever, in 1 hour it has only trained 1 batch of 8192 elements, which doesn't make much sense... Is it possible that \"on the fly\" tokenization of batches is slowing down TPU training to that extent?","body":"Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n","comment_length":57,"text":"ERROR WHEN USING SET_TRANSFORM()  \n Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n \n I've tried your solutions @sgugger @lhoestq and the good news is that it throws no error. However, TPU training is taking forever, in 1 hour it has only trained 1 batch of 8192 elements, which doesn't make much sense... Is it possible that \"on the fly\" tokenization of batches is slowing down TPU training to that extent?","embeddings":[-0.1826189756,-0.2732517719,0.1393030435,0.1847398877,0.7292689681,0.1197050363,0.6009586453,0.2287764698,-0.3227846622,0.0544771031,0.2355112433,0.0333268568,-0.0883406326,-0.0234047007,0.1892216057,-0.5358201861,-0.0138219129,0.1288610101,-0.4634343982,-0.1831161827,-0.27669698,0.3134228885,-0.3415367901,0.1193406507,-0.3186003268,-0.0096082669,-0.047695376,0.016471222,0.0844151899,-0.1718301773,0.2334779501,-0.2800752223,0.1231957451,0.3974503875,-0.0001255372,0.1195578203,0.0606352575,-0.2890718281,-0.2367867827,-0.3003558218,-0.1437315047,-0.1552786827,-0.0154345548,-0.2408748716,-0.4479370713,-0.1890559047,0.0066991188,-0.2687551975,0.5653699636,0.7264817953,0.1215873957,0.2663597167,0.0996964872,-0.2382416725,-0.1261355132,0.1903086156,-0.1291115582,-0.0224978216,0.0317756385,0.2165266722,0.1336240321,0.1615502834,0.2006310076,-0.1752620935,0.4064543247,-0.0619789995,-0.1365289241,0.0435062647,0.0006193888,0.0104680918,0.2298866063,-0.3454308212,-0.420854032,-0.2735254467,0.1654442102,-0.2141008228,0.2700577974,0.1503871977,-0.066397734,0.22794348,-0.2600681484,-0.1098380312,-0.0588664711,0.0313304402,-0.1728429347,0.1623857617,-0.0065073655,0.1303315759,0.0931790695,0.1813862473,0.2739946544,0.1153549775,0.1876621246,0.3649872243,-0.265356034,-0.05478606,-0.1434788406,0.0108277686,-0.2889722586,-0.2492735982,-0.1723645478,-0.2085095048,0.0230882131,-0.0149517776,0.1071462706,0.6613349319,-0.2454049438,0.4977247119,0.0151178716,-0.1120734289,0.2516205609,-0.0635674,-0.0569484457,-0.3780822754,-0.0096246395,0.4175055921,0.0937968418,-0.006041795,-0.2874678373,-0.1932771057,-0.2089736909,-0.0578821562,0.1327706873,0.217985943,0.0588070415,0.2713345587,0.5032314658,0.3485563695,-0.1059520543,-0.2050584853,-0.1715610176,0.135013476,-0.3857185841,-0.1006987318,0.1464199424,-0.1412298381,0.3286191225,0.0532120019,0.0755415633,0.3815326989,0.1520244032,-0.191182673,0.2819835246,0.1436703354,-0.0143271564,0.2014978528,0.4693849087,0.10144265,-0.1861768216,0.2664014995,-0.2484502494,-0.4411483705,0.0609503128,0.0914024562,-0.0530007631,-0.0377959199,-0.1594837457,0.1583796293,0.4205289483,-0.0675125122,0.0763092414,-0.4352326989,0.0418038815,0.1099921837,0.090025045,0.0778380185,-0.2328578383,-0.3426035345,0.3258585334,0.1530645639,-0.143343091,0.2205605805,-0.4285855889,0.2562271655,-0.0625739917,-0.1584211737,0.2098882198,-0.3840966821,-0.0710864812,-0.0994279832,-0.1054709256,-0.219163999,0.0696848631,-0.1317368597,-0.0530189537,-0.1544256955,-0.2207259983,0.5086032748,-0.0608968958,0.2324942946,0.0340902545,-0.0222444125,0.1885959953,0.1376521885,0.0248874463,0.1576400846,-0.320507586,0.5120714307,0.0987583771,-0.2441777289,0.2444199026,0.4102393985,-0.244097501,-0.1919647157,0.0468020588,0.2764771283,-0.0697960332,-0.148902759,0.1784002781,0.1963512599,-0.0604719073,0.0484519824,-0.2369389385,-0.0641728118,-0.2133747637,-0.1585211456,-0.0149899647,0.1101620644,0.0207734834,-0.0464728251,-0.2484817207,0.2723915279,-0.095426932,0.1349620223,-0.5259901881,0.2280209661,-0.2145735472,-0.0914447233,-0.0678843781,0.2231209576,0.2404938191,-0.2305194438,-0.3247486055,0.3036762774,-0.2122558355,0.0371924043,-0.3943189681,0.2457401305,0.3090952635,-0.5342994928,0.0032722938,0.2634411156,0.1639743596,-0.127455622,-0.383022517,0.1136109233,0.0757724941,0.3314516544,0.1057743058,0.3340388238,0.0536119379,-0.0884053633,-0.2020374686,-0.0776441023,-0.0154921887,-0.0838234276,0.1649821252,0.0597501025,-0.2288423777,-0.1983881891,0.4673170149,0.148841694,0.0216302704,0.1293901354,-0.1397807598,0.0306708906,0.1618632525,-0.0950354412,0.562422514,0.0324369743,-0.2305446267,0.1875147969,0.0697727501,-0.1226138026,0.4002860487,-0.0731957108,0.0252318047,-0.106548816,0.0615078025,0.0952067301,-0.1649064571,-0.289491564,0.25625211,0.2521972656,-0.4736778736,0.2136499882,0.0259740539,0.1696037799,-0.5800776482,-0.3827403784,0.0345899239,-0.2872623503,-0.1098969206,-0.0208649188,0.1248787344,0.4439495802,0.1025984958,0.072272338,0.2448716611,-0.158022657,-0.0618830435,0.0459858961,-0.1911002547,0.0143575687,-0.1871763021,-0.2879667282,-0.0504698642,-0.3237556815,-0.0552909076,-0.1035404056,-0.1793443263,0.1855938435,-0.2955768704,0.0113008786,0.3231825829,0.1871942282,-0.1992335618,-0.3188306391,0.4316528141,0.0009160679,0.0959324092,-0.0127255181,-0.0703296512,-0.2425712794,-0.0997440368,-0.4264292717,-0.0446449108,-0.1689539254,0.0303072091,-0.2177828997,-0.1313049346,0.4203010499,0.2371348441,0.0843030065,0.2997512817,0.1740505844,-0.0198321659,-0.2111925334,0.4776291847,-0.2067952901,-0.3576395214,-0.1271426529,-0.0129219079,0.2218758017,0.2340473831,-0.1764791906,-0.178256005,-0.2040158212,-0.1126637682,-0.3014701605,0.435983181,0.4326145947,0.0603171624,0.1686085016,-0.050865747,-0.5156285167,0.3171764314,0.1424028128,0.4169045985,-0.0929954201,0.4798959196,0.0866109133,0.7751232386,0.0111009004,-0.3219003677,0.260661304,-0.1630743444,0.2260283828,-0.1403172314,-0.4625391364,0.2768841088,0.2438232154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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1867","title":"ERROR WHEN USING SET_TRANSFORM() ","comments":"I don't know what the value of `padding` is in your lines of code pasted above so I can't say for sure. The first batch will be very slow on TPU since it compiles everything, so that's normal (1 hour is long but 8192 elements is also large). Then if your batches are not of the same lengths, it will recompile everything at each step instead of using the same graph, which will be very slow, so you should double check you are using padding to make everything the exact same shape. ","body":"Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n","comment_length":92,"text":"ERROR WHEN USING SET_TRANSFORM()  \n Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n \n I don't know what the value of `padding` is in your lines of code pasted above so I can't say for sure. The first batch will be very slow on TPU since it compiles everything, so that's normal (1 hour is long but 8192 elements is also large). Then if your batches are not of the same lengths, it will recompile everything at each step instead of using the same graph, which will be very slow, so you should double check you are using padding to make everything the exact same shape. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1867","title":"ERROR WHEN USING SET_TRANSFORM() ","comments":"I have tried now on a GPU and it goes smooth! Amazing feature .set_transform() instead of .map()! Now I can pre-train my model without the hard disk limitation. Thanks for your work all HuggingFace team!! :clap: ","body":"Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n","comment_length":36,"text":"ERROR WHEN USING SET_TRANSFORM()  \n Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n \n I have tried now on a GPU and it goes smooth! Amazing feature .set_transform() instead of .map()! Now I can pre-train my model without the hard disk limitation. Thanks for your work all HuggingFace team!! :clap: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1867","title":"ERROR WHEN USING SET_TRANSFORM() ","comments":"In the end, to make it work I turned to A-100 gpus instead of TPUS, among other changes. Set_transform doesn't work as expected and slows down training very much even in GPUs, and applying map destroys the disk, as it multiplies by 100 the size of the data passed to it (due to inefficient implementation converting strings to int64 floats I guess). For that reason, I chose to use datasets to load the data as text, and then edit the Collator from Transformers to tokenize every batch it receives before processing it. That way, I'm being able to train fast, without memory breaks, without the disk being unnecessarily filled, while making use of GPUs almost all the time I'm paying for them (the map function over the whole dataset took ~15hrs, in which you're not training at all). I hope this info helps others that are looking for training a language model from scratch cheaply, I'm going to close the issue as the optimal solution I found after many experiments to the problem posted in it is explained above. ","body":"Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n","comment_length":179,"text":"ERROR WHEN USING SET_TRANSFORM()  \n Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n \n In the end, to make it work I turned to A-100 gpus instead of TPUS, among other changes. Set_transform doesn't work as expected and slows down training very much even in GPUs, and applying map destroys the disk, as it multiplies by 100 the size of the data passed to it (due to inefficient implementation converting strings to int64 floats I guess). For that reason, I chose to use datasets to load the data as text, and then edit the Collator from Transformers to tokenize every batch it receives before processing it. That way, I'm being able to train fast, without memory breaks, without the disk being unnecessarily filled, while making use of GPUs almost all the time I'm paying for them (the map function over the whole dataset took ~15hrs, in which you're not training at all). I hope this info helps others that are looking for training a language model from scratch cheaply, I'm going to close the issue as the optimal solution I found after many experiments to the problem posted in it is explained above. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1867","title":"ERROR WHEN USING SET_TRANSFORM() ","comments":"Great comment @alexvaca0 . I think that we could re-open the issue as a reformulation of why it takes so much space to save the arrow. Saving a 1% of oscar corpus takes more thank 600 GB (it breaks when it pass 600GB because it is the free memory that I have at this moment) when the full dataset is 1,3 TB. I have a 1TB M.2 NVMe disk that I can not train on because the saved .arrow files goes crazily big. If you can share your Collator I will be grateful. ","body":"Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n","comment_length":93,"text":"ERROR WHEN USING SET_TRANSFORM()  \n Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1825#issuecomment-774202797\r\n\r\nHowever, when I try to use Trainer from transformers with such dataset, it throws an error:\r\n\r\n```\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.\r\nException in device=TPU:0: __init__() missing 1 required positional argument: 'transform'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 330, in _mp_start_fn\r\n    _start_fn(index, pf_cfg, fn, args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 324, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 368, in _mp_fn\r\n    main()\r\n  File \"\/home\/alejandro_vaca\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 332, in main\r\n    data_collator=data_collator,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 286, in __init__\r\n    self._remove_unused_columns(self.train_dataset, description=\"training\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.7\/lib\/python3.6\/site-packages\/transformers\/trainer.py\", line 359, in _remove_unused_columns\r\n    dataset.set_format(type=dataset.format[\"type\"], columns=columns)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/fingerprint.py\", line 312, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/arrow_dataset.py\", line 818, in set_format\r\n    _ = get_formatter(type, **format_kwargs)\r\n  File \"\/home\/alejandro_vaca\/datasets\/src\/datasets\/formatting\/__init__.py\", line 112, in get_formatter\r\n    return _FORMAT_TYPES[format_type](**format_kwargs)\r\nTypeError: __init__() missing 1 required positional argument: 'transform'\r\n```\r\n\r\nThe code I'm using:\r\n\r\n```{python}\r\n\r\n    def tokenize_function(examples):\r\n        # Remove empty lines\r\n        examples[\"text\"] = [line for line in examples[\"text\"] if len(line) > 0 and not line.isspace()]\r\n        return tokenizer(examples[\"text\"], padding=padding, truncation=True, max_length=data_args.max_seq_length)\r\n\r\n    datasets.set_transform(tokenize_function)\r\n\r\n    data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)\r\n\r\n    # Initialize our Trainer\r\n    trainer = Trainer(\r\n        model=model,\r\n        args=training_args,\r\n        train_dataset=datasets[\"train\"] if training_args.do_train else None,\r\n        eval_dataset=datasets[\"val\"] if training_args.do_eval else None,\r\n        tokenizer=tokenizer,\r\n        data_collator=data_collator,\r\n    )\r\n```\r\n\r\nI've installed from source, master branch.\r\n \n Great comment @alexvaca0 . I think that we could re-open the issue as a reformulation of why it takes so much space to save the arrow. Saving a 1% of oscar corpus takes more thank 600 GB (it breaks when it pass 600GB because it is the free memory that I have at this moment) when the full dataset is 1,3 TB. I have a 1TB M.2 NVMe disk that I can not train on because the saved .arrow files goes crazily big. If you can share your Collator I will be grateful. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1859","title":"Error \"in void don't know how to serialize this type of index\" when saving index to disk when device=0 (GPU)","comments":"Hi @corticalstack ! Thanks for reporting. Indeed in the recent versions of Faiss we must use `getDevice` to check if the index in on GPU.\r\n\r\nI'm opening a PR","body":"Error serializing faiss index.  Error as follows:\r\n\r\n`Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at \/home\/conda\/feedstock_root\/build_artifacts\/faiss-split_1612472484670\/work\/faiss\/impl\/index_write.cpp:453: don't know how to serialize this type of index`\r\n\r\n\r\nNote:\r\n\r\n`torch.cuda.is_available()` reports:\r\n\r\n```\r\nCuda is available\r\ncuda:0\r\n\r\n```\r\n\r\nAdding index, device=0 for GPU.\r\n\r\n`dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)`\r\n\r\nHowever, during a quick debug, self.faiss_index has no attr \"device\" when checked in` search.py, method save`, so fails to transform gpu index to cpu index.  If I add index without device, index is saved OK.\r\n\r\n\r\n```\r\ndef save(self, file: str):\r\n        \"\"\"Serialize the FaissIndex on disk\"\"\"\r\n        import faiss  # noqa: F811\r\n\r\n        if (\r\n            hasattr(self.faiss_index, \"device\")\r\n            and self.faiss_index.device is not None\r\n            and self.faiss_index.device > -1\r\n        ):\r\n            index = faiss.index_gpu_to_cpu(self.faiss_index)\r\n        else:\r\n            index = self.faiss_index\r\n        faiss.write_index(index, file)\r\n```\r\n","comment_length":29,"text":"Error \"in void don't know how to serialize this type of index\" when saving index to disk when device=0 (GPU) \n Error serializing faiss index.  Error as follows:\r\n\r\n`Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at \/home\/conda\/feedstock_root\/build_artifacts\/faiss-split_1612472484670\/work\/faiss\/impl\/index_write.cpp:453: don't know how to serialize this type of index`\r\n\r\n\r\nNote:\r\n\r\n`torch.cuda.is_available()` reports:\r\n\r\n```\r\nCuda is available\r\ncuda:0\r\n\r\n```\r\n\r\nAdding index, device=0 for GPU.\r\n\r\n`dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)`\r\n\r\nHowever, during a quick debug, self.faiss_index has no attr \"device\" when checked in` search.py, method save`, so fails to transform gpu index to cpu index.  If I add index without device, index is saved OK.\r\n\r\n\r\n```\r\ndef save(self, file: str):\r\n        \"\"\"Serialize the FaissIndex on disk\"\"\"\r\n        import faiss  # noqa: F811\r\n\r\n        if (\r\n            hasattr(self.faiss_index, \"device\")\r\n            and self.faiss_index.device is not None\r\n            and self.faiss_index.device > -1\r\n        ):\r\n            index = faiss.index_gpu_to_cpu(self.faiss_index)\r\n        else:\r\n            index = self.faiss_index\r\n        faiss.write_index(index, file)\r\n```\r\n \n Hi @corticalstack ! Thanks for reporting. Indeed in the recent versions of Faiss we must use `getDevice` to check if the index in on GPU.\r\n\r\nI'm opening a PR","embeddings":[-0.0712384582,-0.3793422878,-0.0126395551,0.1242901012,0.3250301182,0.1440671384,0.3014210463,0.5358146429,0.4775857925,0.3618176281,0.0682528615,-0.0320642032,0.1739509106,-0.0848504752,-0.1260111183,0.1587210149,0.4708423615,0.2794695199,0.4284847975,-0.0465701222,-0.4897720814,0.0811465085,0.0083706705,0.0710052326,0.0153226191,-0.0360257663,-0.0760059804,-0.0318541415,-0.1387515366,-0.4813572764,-0.1281306595,-0.2600694299,0.4309383333,0.1854503453,-0.0001175451,0.0909124911,0.2596594691,-0.1979711354,-0.0848168135,-0.066420801,0.3481684029,0.0831488296,0.1509593874,-0.3121950924,-0.2275109142,-0.3267764449,-0.0085452357,-0.1260353476,-0.179897964,0.2844127417,0.165733844,-0.1552814245,0.4602343142,-0.0657822639,-0.0353392623,0.0855674967,-0.2641057074,-0.1356429309,0.0376558676,0.5125663877,0.2819295824,0.3212586641,0.0330720544,-0.280249238,-0.1809240431,-0.000955855,0.6283458471,-0.3459066153,0.1626062989,-0.155659467,0.0611039288,-0.1890729964,-0.3456079066,0.2895677388,0.2570062876,-0.4825872779,-0.0632231086,0.0443118513,-0.2356054336,0.1721524298,0.6439068317,-0.2015724033,0.0719451681,-0.1651588976,0.2031634748,-0.0524165481,-0.0817488804,0.0932567418,0.0007190898,-0.1320931762,-0.1938658804,0.0544876792,0.159401387,0.1064695045,-0.3842536211,-0.1285613775,0.0794626027,-0.4333073199,-0.1163278893,0.1132654771,-0.4117616713,0.0138393734,0.1884579062,0.0297547486,-0.2788296342,-0.0231560301,-0.0122408941,-0.202277571,-0.0987560973,0.0644984469,0.0437913686,-0.3351603448,-0.1625306606,0.0169744045,-0.3138410151,0.1340081394,0.2640893757,-0.3600541353,-0.5404109359,0.1515235156,-0.2051830441,0.2541145682,0.1186715141,0.3394500017,0.3243599832,0.1193249375,0.2600001693,0.2940564752,-0.2567103505,0.0723281801,-0.1546052098,-0.1204363033,0.3157219589,0.1273838133,-0.0999876782,-0.4395748675,-0.055944223,0.0610690489,-0.2190777659,0.0148686077,0.0390028544,-0.3672314882,0.137136668,0.4929429293,0.0687847063,0.1333899051,0.3695411086,-0.1329853237,-0.1448982358,0.3369978368,0.0195288118,-0.2087257951,-0.423784852,0.1341565996,0.2424482405,-0.0107827382,0.3436016738,0.0485057347,0.0278701372,-0.0147276456,0.0952939317,0.133586064,0.0630662665,-0.2642409503,0.4410449862,0.0570823178,-0.0643290058,-0.0238724928,0.1438179016,-0.1553903669,0.243175894,0.4800496995,0.4059357047,0.2361027747,-0.3073669672,0.2976962626,0.4374848008,-0.2929921448,-0.0848619044,0.0937048346,-0.1128476784,-0.4517088532,0.3368116319,0.0265279058,0.1492991447,-0.0534780622,0.221041128,-0.1418749839,-0.0379879512,-0.1587764174,-0.3545044661,-0.3207323253,0.3053153157,-0.0515019819,-0.0426942334,0.1113008261,-0.1175483987,-0.5868125558,0.1098725423,-0.0735581592,-0.0875956491,-0.0569470823,0.9322971702,0.0278815273,0.2512435615,-0.0496051162,0.2162654847,-0.0534441769,0.0089386702,-0.1493970305,-0.399633497,-0.0829541832,-0.0047222939,-0.0199991483,0.3082149625,0.1228873208,0.0533143319,-0.1235305741,-0.1038722843,0.2014550269,-0.2821288407,-0.1687683761,-0.1973066479,0.0413984358,0.0628545806,0.1378224194,-0.337028265,-0.4472599626,-0.1559965909,0.2468762398,0.3647454381,-0.239956677,-0.0033568651,0.3587985933,0.1815855205,-0.0744804665,0.2085341811,0.0854679346,-0.0874268785,-0.0320986323,-0.0945054591,0.4734265208,0.4213306904,-0.03208239,0.1105897427,0.3010748327,0.2076686323,0.0950360745,-0.1479107738,0.1323513389,0.4306560457,0.0921608582,0.0992562771,-0.131262362,-0.0976164341,0.3995724618,0.1208027825,-0.2026572078,-0.6101469994,0.1275203973,-0.0361715369,-0.0758984387,0.2764007151,-0.0513894372,-0.1054836139,0.0300882608,0.0499136001,-0.8405379057,0.1827556491,0.043939665,-0.2287994474,-0.1709542572,-0.0537621602,-0.1136793569,0.3696154654,-0.1543500423,-0.1558543742,0.1671735495,-0.1639797837,-0.0550496988,-0.0069373245,0.0262612067,-0.1074864268,0.0510736667,-0.3698427379,0.0085143363,0.0789709836,0.2077314854,-0.2933821976,-0.1897430867,-0.0669160187,0.0192803759,0.1494904459,0.2111279964,-0.026634505,0.2719708085,-0.2457418591,0.2465221733,0.2640500367,0.0451867878,-0.1872443706,-0.068279393,-0.050330665,-0.0573591441,0.0380743295,0.0472577699,0.1168290004,-0.1141843423,0.082290642,-0.2946701944,0.0565170422,0.0235375203,-0.2850291729,-0.1330879778,-0.1121563911,0.2326910049,0.0897386521,0.115924485,0.179619357,-0.0158458371,-0.2538018227,0.1694006622,-0.2895036042,0.0810748264,-0.2157639414,-0.093047902,-0.0153563842,-0.2302937508,0.0575717315,-0.0329140499,0.1174361855,-0.1404099315,0.2576200366,0.2002503276,0.3634287417,-0.2162118554,-0.1813200414,-0.1422646195,0.1777137816,0.0998021141,-0.4149801731,0.3305240273,0.1879826933,-0.0797430426,0.0312867276,0.0676988661,0.1965959668,0.1595302075,0.1310822964,-0.3357588351,0.1694398671,0.4078317285,0.0772916228,-0.1620270014,-0.0135013033,-0.0939559937,0.0710933357,0.0425379202,0.2906353772,-0.1924156696,0.3714401722,-0.1209215075,0.527189374,-0.0674376711,-0.4186014235,0.2030136883,-0.0951289833,0.1291924268,-0.082832776,-0.1675729603,0.4965711534,0.3667693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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1859","title":"Error \"in void don't know how to serialize this type of index\" when saving index to disk when device=0 (GPU)","comments":"I fixed this issue. It should work fine now.\r\nFeel free to try it out by installing `datasets` from source.\r\nOtherwise you can wait for the next release of `datasets` (in a few days)","body":"Error serializing faiss index.  Error as follows:\r\n\r\n`Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at \/home\/conda\/feedstock_root\/build_artifacts\/faiss-split_1612472484670\/work\/faiss\/impl\/index_write.cpp:453: don't know how to serialize this type of index`\r\n\r\n\r\nNote:\r\n\r\n`torch.cuda.is_available()` reports:\r\n\r\n```\r\nCuda is available\r\ncuda:0\r\n\r\n```\r\n\r\nAdding index, device=0 for GPU.\r\n\r\n`dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)`\r\n\r\nHowever, during a quick debug, self.faiss_index has no attr \"device\" when checked in` search.py, method save`, so fails to transform gpu index to cpu index.  If I add index without device, index is saved OK.\r\n\r\n\r\n```\r\ndef save(self, file: str):\r\n        \"\"\"Serialize the FaissIndex on disk\"\"\"\r\n        import faiss  # noqa: F811\r\n\r\n        if (\r\n            hasattr(self.faiss_index, \"device\")\r\n            and self.faiss_index.device is not None\r\n            and self.faiss_index.device > -1\r\n        ):\r\n            index = faiss.index_gpu_to_cpu(self.faiss_index)\r\n        else:\r\n            index = self.faiss_index\r\n        faiss.write_index(index, file)\r\n```\r\n","comment_length":34,"text":"Error \"in void don't know how to serialize this type of index\" when saving index to disk when device=0 (GPU) \n Error serializing faiss index.  Error as follows:\r\n\r\n`Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at \/home\/conda\/feedstock_root\/build_artifacts\/faiss-split_1612472484670\/work\/faiss\/impl\/index_write.cpp:453: don't know how to serialize this type of index`\r\n\r\n\r\nNote:\r\n\r\n`torch.cuda.is_available()` reports:\r\n\r\n```\r\nCuda is available\r\ncuda:0\r\n\r\n```\r\n\r\nAdding index, device=0 for GPU.\r\n\r\n`dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)`\r\n\r\nHowever, during a quick debug, self.faiss_index has no attr \"device\" when checked in` search.py, method save`, so fails to transform gpu index to cpu index.  If I add index without device, index is saved OK.\r\n\r\n\r\n```\r\ndef save(self, file: str):\r\n        \"\"\"Serialize the FaissIndex on disk\"\"\"\r\n        import faiss  # noqa: F811\r\n\r\n        if (\r\n            hasattr(self.faiss_index, \"device\")\r\n            and self.faiss_index.device is not None\r\n            and self.faiss_index.device > -1\r\n        ):\r\n            index = faiss.index_gpu_to_cpu(self.faiss_index)\r\n        else:\r\n            index = self.faiss_index\r\n        faiss.write_index(index, file)\r\n```\r\n \n I fixed this issue. It should work fine now.\r\nFeel free to try it out by installing `datasets` from source.\r\nOtherwise you can wait for the next release of `datasets` (in a few days)","embeddings":[-0.2384788841,-0.2405906171,-0.0386406891,0.1195868924,0.3427581191,0.152492255,0.3444892168,0.5221388936,0.4608196318,0.2513044775,-0.079141818,0.1412855983,0.1216126382,-0.0570589788,-0.1395932287,0.1362419277,0.4380633831,0.1934212744,0.3012018204,-0.0583262667,-0.400860697,0.0912531316,0.0359445699,0.0129263112,-0.18736431,-0.0472335294,-0.0613629334,-0.0423774272,-0.1474945396,-0.4240972698,-0.0577419847,-0.154525429,0.4029407203,0.3433766663,-0.0001137439,0.0171603374,0.2634907365,-0.198621437,-0.1538046747,-0.1396831423,0.2965035141,0.0760403275,0.1519305706,-0.3508684635,-0.2409550399,-0.2891043127,-0.0485756733,-0.2439465076,-0.1022812054,0.2978606224,0.2040632516,-0.0875000507,0.4052405655,-0.1325298846,0.0500694141,0.110026218,-0.2817083001,-0.1140927747,0.0078178281,0.4504535794,0.2506672144,0.298507601,-0.0069536157,-0.2596974075,-0.0916546956,-0.0183353834,0.3743934631,-0.3835607767,0.2296165824,-0.1212559193,0.1396841705,-0.3347601891,-0.361761421,0.2229541838,0.1872790754,-0.4804652035,-0.0755150393,0.0607192926,-0.2271499634,0.1341762394,0.5808408856,-0.1446946859,0.0683102831,-0.1654362082,0.1633944362,0.0585158244,-0.1198725328,0.1065055951,0.0583300814,-0.1843249947,-0.0953072831,0.064227432,0.1779869199,0.1287582815,-0.5028725266,-0.0867344141,0.0247371625,-0.4174596369,-0.0460305735,0.072453253,-0.3467902243,0.1453314573,0.1766988635,0.0592344068,-0.2870437801,0.0337667428,-0.0274505001,-0.1457376629,0.0066589694,0.0383950844,0.0344570316,-0.330335319,-0.1660042107,-0.0315668434,-0.3789484203,0.0761541799,0.1963506192,-0.3188083172,-0.5184018612,0.1290775836,-0.0136288051,0.2290712446,0.141697824,0.4266785085,0.3175201416,0.0353451818,0.2645550966,0.2988888323,-0.2037705034,0.0669840127,-0.1860590875,-0.1827611625,0.2399476618,0.1644988358,0.0138555197,-0.4622967541,0.064864412,0.0778760836,-0.1108913347,0.1539482325,0.0939454958,-0.2833533287,0.094420217,0.4206935763,0.0270249993,0.2014466673,0.3102342188,-0.0711252019,-0.1758990884,0.3338373601,-0.00733579,-0.2411532253,-0.5131024122,0.1491003633,0.213733837,-0.0529846251,0.2680781186,0.0859570205,0.1033834144,-0.0308677033,0.0688061267,0.0914394855,0.0358478613,-0.3160327077,0.495166719,0.0128610181,-0.1686315089,-0.0478117578,0.1192981452,-0.1202775836,0.1759350151,0.5109654665,0.3312971294,0.1718973964,-0.2703888118,0.3007789254,0.4135612845,-0.3300421238,-0.1987008601,0.064755708,-0.0192649029,-0.3254494071,0.1825144887,0.0729461759,0.2850210071,-0.0070310356,0.2279463261,-0.0312298518,0.0020127299,-0.1611647159,-0.3785850108,-0.3233622015,0.4342168868,0.0403834768,-0.0712841079,0.0773933083,-0.1179749966,-0.6022039056,0.1653825641,-0.1568247229,-0.0420191102,-0.0337346904,0.9180984497,0.0664086193,0.248209998,-0.0480834469,0.0745991617,0.021119928,0.1085083857,-0.1187084839,-0.4229264855,-0.1303247958,0.0156875271,-0.0758009329,0.2126974463,0.0030957297,0.0767877474,-0.1611407697,-0.1121302396,0.1742811054,-0.3099654913,-0.0134581411,-0.1755194664,0.0850166082,-0.0379409082,0.1815442592,-0.3314419687,-0.4518476129,-0.1660560519,0.2707016468,0.3499483168,-0.2712790072,-0.0366942734,0.3937572837,0.0932658836,-0.0825445727,0.1525270343,0.0257431716,0.0043484732,-0.0181564875,0.0768706128,0.4259207845,0.3218758106,-0.0369861759,0.0271981694,0.3717030287,0.2110736221,0.0548550002,-0.1873320192,0.1108266041,0.4963282049,0.1157274246,0.0822515935,-0.1317607909,-0.0508645326,0.2979769707,0.1687548608,-0.2600298524,-0.6055720448,0.2507573962,0.0219843611,-0.007665365,0.2330774665,-0.0285571907,-0.1289513558,0.0609241389,0.1440113038,-0.7042905092,0.1790257096,0.0768935233,-0.170310691,-0.0954483673,-0.1197600514,-0.1216633692,0.3036922812,-0.2471762896,-0.1520445347,0.1871303916,-0.1039698794,-0.0682562292,-0.0682849586,-0.0093313809,-0.1152828783,0.0440150984,-0.3456337154,0.0185122639,0.0293475483,0.2506072521,-0.2507233918,-0.267932415,-0.0913451836,-0.0071544861,0.1249451339,0.2417454869,-0.007397505,0.3755143285,-0.1992133558,0.2434767932,0.282722801,0.0155903231,-0.1905139834,-0.1179165095,0.0238807704,-0.0235805847,0.043543987,0.0319900215,0.1436030865,-0.0923393294,0.1365600228,-0.320015043,-0.0451029986,-0.0297245719,-0.3586837649,-0.036515478,-0.0903935507,0.1915640086,0.0737377703,0.010656327,0.1889198869,-0.0424713157,-0.1706620604,0.1441256702,-0.235605821,0.0102608604,-0.2453027666,-0.1029474437,-0.0885342658,-0.3024118543,0.0760996938,0.0420689322,0.1523383707,-0.2104491144,0.2786490917,0.1540673226,0.3944187462,-0.2255447656,-0.2214528769,-0.2535198629,0.3299390674,-0.1207605526,-0.4436139166,0.3380658627,0.2211083919,0.0755026266,0.0768547654,-0.0015967459,0.32285586,0.1641940624,0.2143816799,-0.2751164138,0.2191844583,0.3376360834,0.0541074239,-0.1252539158,-0.0836514011,-0.0990648493,0.0410788544,-0.00585037,0.3779468238,-0.2007446736,0.4935574532,-0.0686324537,0.5453831553,0.0227312129,-0.4466867447,0.2302495539,-0.0553989448,0.1944939196,-0.1707660854,-0.2438559234,0.5023388863,0.2270676196,-0.18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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1859","title":"Error \"in void don't know how to serialize this type of index\" when saving index to disk when device=0 (GPU)","comments":"Thanks for such a quick fix and merge to master, pip installed git master, tested all OK","body":"Error serializing faiss index.  Error as follows:\r\n\r\n`Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at \/home\/conda\/feedstock_root\/build_artifacts\/faiss-split_1612472484670\/work\/faiss\/impl\/index_write.cpp:453: don't know how to serialize this type of index`\r\n\r\n\r\nNote:\r\n\r\n`torch.cuda.is_available()` reports:\r\n\r\n```\r\nCuda is available\r\ncuda:0\r\n\r\n```\r\n\r\nAdding index, device=0 for GPU.\r\n\r\n`dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)`\r\n\r\nHowever, during a quick debug, self.faiss_index has no attr \"device\" when checked in` search.py, method save`, so fails to transform gpu index to cpu index.  If I add index without device, index is saved OK.\r\n\r\n\r\n```\r\ndef save(self, file: str):\r\n        \"\"\"Serialize the FaissIndex on disk\"\"\"\r\n        import faiss  # noqa: F811\r\n\r\n        if (\r\n            hasattr(self.faiss_index, \"device\")\r\n            and self.faiss_index.device is not None\r\n            and self.faiss_index.device > -1\r\n        ):\r\n            index = faiss.index_gpu_to_cpu(self.faiss_index)\r\n        else:\r\n            index = self.faiss_index\r\n        faiss.write_index(index, file)\r\n```\r\n","comment_length":17,"text":"Error \"in void don't know how to serialize this type of index\" when saving index to disk when device=0 (GPU) \n Error serializing faiss index.  Error as follows:\r\n\r\n`Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at \/home\/conda\/feedstock_root\/build_artifacts\/faiss-split_1612472484670\/work\/faiss\/impl\/index_write.cpp:453: don't know how to serialize this type of index`\r\n\r\n\r\nNote:\r\n\r\n`torch.cuda.is_available()` reports:\r\n\r\n```\r\nCuda is available\r\ncuda:0\r\n\r\n```\r\n\r\nAdding index, device=0 for GPU.\r\n\r\n`dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)`\r\n\r\nHowever, during a quick debug, self.faiss_index has no attr \"device\" when checked in` search.py, method save`, so fails to transform gpu index to cpu index.  If I add index without device, index is saved OK.\r\n\r\n\r\n```\r\ndef save(self, file: str):\r\n        \"\"\"Serialize the FaissIndex on disk\"\"\"\r\n        import faiss  # noqa: F811\r\n\r\n        if (\r\n            hasattr(self.faiss_index, \"device\")\r\n            and self.faiss_index.device is not None\r\n            and self.faiss_index.device > -1\r\n        ):\r\n            index = faiss.index_gpu_to_cpu(self.faiss_index)\r\n        else:\r\n            index = self.faiss_index\r\n        faiss.write_index(index, file)\r\n```\r\n \n Thanks for such a quick fix and merge to master, pip installed git master, tested all OK","embeddings":[-0.1146762744,-0.3390221596,-0.0211910177,0.1105611771,0.3647998273,0.1579398662,0.2933622599,0.5287505984,0.504611671,0.3331744075,0.0193728264,0.0713495985,0.1117654741,-0.0630019233,-0.1396043301,0.1634267718,0.4838933647,0.2276034504,0.33570081,-0.0381595157,-0.401009798,0.1175061092,0.0855751932,0.0203524493,-0.0746072978,0.027452521,-0.0272676796,-0.0571344234,-0.1761827022,-0.430722177,-0.0974790081,-0.2497538477,0.3530046046,0.2987298071,-0.0001167819,0.0765003487,0.2537879348,-0.2185033411,-0.1212787703,-0.1918153167,0.3125521541,0.0238258801,0.2112206519,-0.3889563382,-0.2219991833,-0.2291416824,-0.0148390858,-0.0824066401,-0.0860950053,0.2563896179,0.1728011221,-0.1268796772,0.426910609,-0.0993105397,0.048668839,0.1324171722,-0.2699905038,-0.081328027,0.0310128517,0.4924758375,0.3509413004,0.3124850094,-0.0470326245,-0.3270618916,-0.1348573714,-0.0353984609,0.6270079613,-0.4113226831,0.1714873165,-0.1755583286,0.0252775438,-0.2577891648,-0.3364470005,0.2810265422,0.1917248368,-0.4463648498,-0.1149210483,0.0594530776,-0.2262235135,0.1179195642,0.6050459146,-0.2198892385,0.0826855674,-0.1929790527,0.2028477788,0.0529569313,-0.1074484065,0.0764327869,0.0183136519,-0.1368501484,-0.2525518239,0.0414270312,0.1735977978,0.1504006833,-0.4110235572,-0.1585874707,0.0316705741,-0.4751079082,-0.1077526957,0.1082183123,-0.3547216654,0.0536165573,0.2421877533,0.0392326675,-0.302978158,0.0532989688,-0.0838188231,-0.1678367406,-0.0528734811,0.0116879614,0.0138864294,-0.3690967262,-0.1901766658,0.0614837259,-0.3394028842,0.1539418995,0.2605766356,-0.3318852782,-0.5334686637,0.1121128052,-0.0934316814,0.224335596,0.1077442244,0.3150539994,0.3123532832,0.0913958251,0.2175917774,0.3172948658,-0.2520931661,0.1195910946,-0.1601220071,-0.1714918613,0.2880524397,0.0833575502,-0.0394231007,-0.4399599731,0.0343991257,0.1377116144,-0.1891623735,0.0371744297,0.0833467767,-0.3589977026,0.0460222997,0.4789451957,0.0850206465,0.1684921682,0.3972036839,-0.1274073869,-0.1334162652,0.3661162853,-0.0772539601,-0.192571193,-0.5117437243,0.1412725896,0.2128684968,-0.0281150304,0.1784328818,0.031009173,0.1252801865,-0.0399937928,0.0840364173,0.1249946132,0.1171110272,-0.2871429026,0.4271494448,-0.0141213136,-0.035585437,-0.0640763864,0.1413884312,-0.1511024386,0.2421639115,0.5180215836,0.4163944721,0.2536924183,-0.2635124922,0.2852970064,0.4269224405,-0.3314129412,-0.1441108733,0.0950621888,-0.0796561539,-0.3813628256,0.2808741033,0.0293669421,0.2075395882,-0.1095142886,0.1899430454,-0.1288816929,-0.0122308601,-0.1432453096,-0.3757482767,-0.3430812955,0.3114051223,-0.0660417452,-0.0519273803,0.1272798926,-0.1354821026,-0.5923423767,0.1196643636,-0.1409867257,-0.0515021943,-0.0383368507,0.9742364883,0.0753188357,0.2244439572,-0.0890067667,0.1671759337,-0.082271859,0.0197584089,-0.1304292828,-0.4603835642,-0.0925274566,0.0079037333,-0.1039909199,0.2397685349,0.0991542861,0.0758389011,-0.1709728241,-0.1432718188,0.1668157279,-0.296772778,-0.1551455408,-0.1298324615,0.1190659776,0.069975771,0.1621151268,-0.3236829937,-0.4899028242,-0.2084936202,0.2800624073,0.363855809,-0.2614922225,-0.0434338637,0.3414683044,0.0304946825,-0.0277129523,0.1776825786,0.097763218,-0.044090502,-0.0421458036,-0.0168216266,0.5092594028,0.4029051363,-0.0575097315,0.0993845984,0.2719661593,0.1814074367,0.0969095379,-0.1989655048,0.0933970511,0.4091677666,0.0790104792,0.092893891,-0.1172558963,0.0038422062,0.3509766757,0.1438655257,-0.2183193564,-0.5096514821,0.1285246015,-0.0430186875,-0.0327102318,0.2649704218,0.0011569771,-0.1233770475,0.0679899082,0.0005137897,-0.7569183707,0.1782088876,0.035366565,-0.2462668121,-0.1474381536,-0.0495026298,-0.1168398783,0.3600614965,-0.1883478165,-0.1926207244,0.2656208873,-0.0701297,-0.0270441975,0.018068919,0.0745554492,-0.1314165741,0.0339823961,-0.3221395612,0.0470151119,0.1045816466,0.2126232237,-0.2320215255,-0.2859646082,-0.0888023004,-0.0023957214,0.1029872373,0.2418350726,-0.0353085399,0.2771553099,-0.1721949875,0.1879617125,0.2446773946,0.0301045831,-0.2019225508,-0.1163958833,0.0249054618,-0.0574817173,0.056234289,-0.0197934061,0.1009035558,-0.1188319772,0.0695459992,-0.2965686321,-0.0026126639,-0.0111070527,-0.3667127788,-0.0308046229,-0.1012416705,0.2145470828,0.0998611748,0.0570703968,0.2294715494,0.0136418613,-0.2189856768,0.1472661495,-0.2851096988,0.0995737687,-0.2638249099,-0.031423822,-0.0933679268,-0.277186662,0.046300251,-0.0066175321,0.1611276418,-0.1750767231,0.2500221431,0.1096313223,0.3826157749,-0.1994668096,-0.1604940444,-0.2403205186,0.2455213368,0.0141066639,-0.4077498913,0.3803664148,0.2327859402,0.0825452954,-0.0168326776,0.0531429425,0.2793097496,0.1768025309,0.1367385536,-0.3660625219,0.2124079615,0.3130120337,0.0957227051,-0.1149212345,-0.0534125566,-0.026912706,0.0467293113,0.0268329103,0.3343476951,-0.1983141303,0.4334018528,-0.1179565266,0.6137663722,-0.0294546019,-0.3339177072,0.2581992447,-0.0253974665,0.1039876044,-0.1415958405,-0.2537579536,0.4257482886,0.305231154,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1857","title":"Unable to upload \"community provided\" dataset - 400 Client Error","comments":"Hi ! We're in the process of switching the community datasets to git repos, exactly like what we're doing for models.\r\nYou can find an example here:\r\nhttps:\/\/huggingface.co\/datasets\/lhoestq\/custom_squad\/tree\/main\r\n\r\nWe'll update the CLI in the coming days and do a new release :)\r\n\r\nAlso cc @julien-c maybe we can make improve the error message ?","body":"Hi,\r\ni'm trying to a upload a dataset as described [here](https:\/\/huggingface.co\/docs\/datasets\/v1.2.0\/share_dataset.html#sharing-a-community-provided-dataset). This is what happens:\r\n\r\n``` \r\n$ datasets-cli login\r\n$ datasets-cli upload_dataset my_dataset\r\nAbout to upload file \/path\/to\/my_dataset\/dataset_infos.json to S3 under filename my_dataset\/dataset_infos.json and namespace username\r\nAbout to upload file \/path\/to\/my_dataset\/my_dataset.py to S3 under filename my_dataset\/my_dataset.py and namespace username\r\nProceed? [Y\/n] Y\r\nUploading... This might take a while if files are large\r\n400 Client Error: Bad Request for url: https:\/\/huggingface.co\/api\/datasets\/presign\r\nhuggingface.co migrated to a new model hosting system.\r\nYou need to upgrade to transformers v3.5+ to upload new models.\r\nMore info at https:\/\/discuss.hugginface.co or https:\/\/twitter.com\/julien_c. Thank you! \r\n```\r\nI'm using the latest releases of datasets and transformers.","comment_length":54,"text":"Unable to upload \"community provided\" dataset - 400 Client Error \n Hi,\r\ni'm trying to a upload a dataset as described [here](https:\/\/huggingface.co\/docs\/datasets\/v1.2.0\/share_dataset.html#sharing-a-community-provided-dataset). This is what happens:\r\n\r\n``` \r\n$ datasets-cli login\r\n$ datasets-cli upload_dataset my_dataset\r\nAbout to upload file \/path\/to\/my_dataset\/dataset_infos.json to S3 under filename my_dataset\/dataset_infos.json and namespace username\r\nAbout to upload file \/path\/to\/my_dataset\/my_dataset.py to S3 under filename my_dataset\/my_dataset.py and namespace username\r\nProceed? [Y\/n] Y\r\nUploading... This might take a while if files are large\r\n400 Client Error: Bad Request for url: https:\/\/huggingface.co\/api\/datasets\/presign\r\nhuggingface.co migrated to a new model hosting system.\r\nYou need to upgrade to transformers v3.5+ to upload new models.\r\nMore info at https:\/\/discuss.hugginface.co or https:\/\/twitter.com\/julien_c. Thank you! \r\n```\r\nI'm using the latest releases of datasets and transformers. \n Hi ! We're in the process of switching the community datasets to git repos, exactly like what we're doing for models.\r\nYou can find an example here:\r\nhttps:\/\/huggingface.co\/datasets\/lhoestq\/custom_squad\/tree\/main\r\n\r\nWe'll update the CLI in the coming days and do a new release :)\r\n\r\nAlso cc @julien-c maybe we can make improve the error message 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1856","title":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError","comments":"Hi ! This issue may be related to #996 \r\nThis comes probably from the Quota Exceeded error from Google Drive.\r\nCan you try again tomorrow and see if you still have the error ?\r\n\r\nOn my side I didn't get any error today with `load_dataset(\"amazon_polarity\")`","body":"Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n```","comment_length":45,"text":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError \n Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n``` \n Hi ! This issue may be related to #996 \r\nThis comes probably from the Quota Exceeded error from Google Drive.\r\nCan you try again tomorrow and see if you still have the error ?\r\n\r\nOn my side I didn't get any error today with `load_dataset(\"amazon_polarity\")`","embeddings":[-0.1446352303,0.1181077734,-0.121307157,0.2410930544,0.128062129,-0.0035925508,0.344353348,0.0653002113,0.2865491509,0.23939161,0.0281222612,-0.0647892952,0.0097159557,0.1506800503,-0.1385413408,0.2059793174,0.0107034752,-0.0445432663,-0.1888069808,0.1773511469,-0.368370235,0.3735696971,-0.0902100056,-0.2902166247,-0.0568014644,0.0482145026,0.2492216974,0.1227689907,-0.0999508873,-0.3141925335,0.2892129421,0.0641637966,0.1367656142,0.3216083646,-0.0001227321,-0.0760453939,0.4037206769,0.0215304326,-0.3489623964,-0.3050134182,-0.3421134949,-0.2206279337,0.0789371431,-0.1413576603,0.1984624118,0.226269424,-0.0134389466,-0.0203781053,0.2310121506,0.1465004981,0.164618969,0.3472891152,0.4247147143,0.2376425117,0.336298883,0.0346841216,0.0294039361,0.4239823818,0.1685522795,-0.1180216447,-0.151582852,0.1364068836,-0.0350834653,0.4699849188,0.0731047764,-0.0709814727,0.1299203187,-0.3009367287,0.2216953486,0.2402327508,0.5259546638,-0.1580471992,-0.3384304941,0.0157191157,-0.2012751997,-0.3522212207,0.3682412207,0.3873554468,-0.0813419223,-0.2479474545,-0.4934211373,0.127337411,-0.1564018875,0.2325276136,-0.1409677267,0.1816923171,-0.0141904699,0.1430938542,0.2745828927,-0.256382674,0.4774478078,-0.1528468728,-0.0680743158,0.1682090759,-0.5600778461,-0.1012885347,-0.0274246838,0.4298371375,0.2686576247,0.5032892823,0.1269349754,0.2249221951,0.0311237425,0.2137777954,0.3584070206,0.2740326822,-0.2249949425,0.3298181891,0.1838583648,0.3255170286,0.0375998206,0.0343653485,-0.129116416,-0.3544429243,0.670812428,0.0256587323,0.3402949572,-0.4990726411,-0.4904987812,0.3258250058,-0.2727227211,-0.1630820781,-0.0561086535,0.1552018225,-0.1133430749,0.0365046225,0.0853408575,-0.0136228995,-0.1336150765,0.1910001934,-0.1091789976,0.1557029933,-0.1040167212,0.2002133131,0.3021281362,-0.1143079102,0.3013885915,-0.1079606935,0.3744319677,-0.2520242631,0.5605089664,-0.2755386233,-0.1011085734,0.5101605654,-0.0266280323,-0.0405234657,-0.0029191871,0.2849735022,-0.1475282609,0.2997645438,-0.2269744575,-0.2836966217,0.0125993993,0.1088242903,-0.512270093,-0.1845045388,-0.2347881645,-0.3862113953,0.2250039428,-0.3119521439,-0.077526167,-0.3307490051,0.030206101,-0.1781544983,0.1285468638,0.4043677747,-0.3409209549,0.0198289491,-0.3198651373,-0.0909231827,0.3040816784,0.3598715365,-0.1228100136,0.0390357226,-0.257070601,-0.3024713099,-0.1342588812,-0.1597056836,-0.4444053173,0.0475131422,-0.1507032216,0.2654606402,-0.0001137547,-0.000171975,0.0030774677,0.0456521623,0.2740553021,0.1631098688,-0.0405791737,0.1599233598,-0.162685439,-0.3306627274,0.2296402901,0.230322063,0.2603027821,-0.0007047705,0.2450987697,-0.1309292316,0.0556210726,-0.0284383371,-0.0195899066,0.3621577919,0.3402877748,-0.094012484,-0.0205230787,-0.0889068022,-0.5740942359,0.1598230749,-0.309572041,0.1014345512,-0.1388898939,0.1446614712,-0.1564300656,-0.2206208706,-0.0964881182,0.0483622141,0.0302265398,0.2930377126,0.1999130845,-0.1771686673,0.0637950823,-0.0643372759,-0.2649933398,0.0469801612,-0.382153064,0.4421381056,-0.0644131154,-0.0300862696,0.1348583996,-0.0121622793,0.1962991208,-0.1785702854,-0.0394668728,0.2556773126,0.099367097,0.3484787643,-0.0179110821,0.6057217717,0.0546131022,-0.0496350229,-0.053094238,0.4482397735,0.0615543276,-0.1253736615,-0.2357844561,0.541423142,-0.190831691,0.2926019132,-0.0797188357,0.0411804132,0.2328560054,-0.0834397301,-0.1304754317,-0.145867005,0.3699472845,0.1853739768,-0.1306202114,-0.0103301555,-0.028405631,-0.0796814859,0.2859376073,-0.1639742255,0.0210786089,0.0837235078,-0.0233296901,-0.1471817791,0.1689704061,0.723451972,0.4756741226,0.1242609769,-0.1934708059,-0.2028280646,-0.0221904963,-0.0442498028,0.1061170921,0.1723461747,0.0280860495,0.4393385947,0.2144734263,-0.1984520257,-0.3193229735,-0.2452728152,0.2342373133,0.3231551349,-0.4198349118,-0.0584383309,0.0039443872,0.1874771267,-0.2392983437,0.0287349336,-0.2095084935,-0.3734353483,-0.264025718,0.5433931351,-0.0794598758,0.0803993419,-0.2748629153,0.1581510007,0.1703451723,-0.4426254332,0.003161696,-0.0818632841,-0.0020267947,-0.0054456177,0.3528393209,-0.1443244964,0.3782065809,-0.1511015892,0.0522559211,-0.599588275,-0.310205847,0.0240140911,-0.0560188256,0.2117903382,0.1166588143,0.2397703528,-0.054990977,-0.0856558233,-0.014656513,0.1234324649,-0.1965638101,0.1444491893,-0.1703918427,0.0828320831,0.0760672092,-0.1404228806,-0.0595666803,-0.3863202929,0.1085538566,0.413972944,0.2004535645,-0.0529636778,0.0042032301,0.066558741,0.0606756583,0.1433402598,-0.2855795324,-0.5523830652,0.4413995743,-0.0899475738,-0.2269182652,0.057241369,0.009802782,0.2371595502,0.4438067675,-0.462461412,-0.2290543914,-0.3841306865,-0.1036187857,-0.0784264207,0.111617744,0.2703568339,0.1271469742,-0.0600731634,0.0835442543,-0.4889653027,-0.0108485837,0.2261229157,0.3510299027,-0.0546096601,0.2301254123,-0.3080968261,0.5490029454,-0.0987386033,0.0003188615,0.1891236454,0.2644511759,0.3137302697,0.0006335465,-0.1188187823,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1856","title":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError","comments":"@lhoestq Hi! I encounter the same error when loading `yelp_review_full`.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset_yp = load_dataset(\"yelp_review_full\")\r\n```\r\n\r\nWhen you say the \"Quota Exceeded from Google drive\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?","body":"Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n```","comment_length":45,"text":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError \n Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n``` \n @lhoestq Hi! I encounter the same error when loading `yelp_review_full`.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset_yp = load_dataset(\"yelp_review_full\")\r\n```\r\n\r\nWhen you say the \"Quota Exceeded from Google drive\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?","embeddings":[-0.0611734577,0.1423272789,-0.1086319983,0.221383661,0.1263362318,0.1217507571,0.2452765852,0.0103803724,0.3931340277,0.1096663773,-0.1395590156,-0.0201449152,0.0363817997,0.0252378229,-0.1712938547,0.364323169,0.0185451135,-0.0113467071,-0.0788380429,-0.0102700591,-0.30445382,0.2593735754,-0.0600827113,-0.0840556398,-0.0598397739,0.1535959691,0.3166562915,0.0442227907,-0.0833672881,-0.3201696277,0.4210704565,0.1378976405,-0.0156709719,0.3289815187,-0.0001259096,-0.0500239134,0.4167665839,0.0320632271,-0.3987612724,-0.3099810779,-0.4581512809,-0.3251411617,0.1509957314,-0.100701496,0.1499893963,0.1633685529,0.0053270529,-0.2619084418,0.0953765139,0.1001130268,0.117274262,0.3501996994,0.105993107,0.2015099078,0.3452163935,0.1226299033,-0.0187581033,0.2913204134,0.1881213188,-0.1821518689,-0.2705043852,0.0405861363,-0.0015808828,0.524055779,0.2074133009,-0.065812543,0.2831406891,-0.2702280879,0.2464372814,0.4334842265,0.5518841147,-0.1611353755,-0.3142518401,-0.1084907204,-0.1443194747,-0.2082730383,0.2683827281,0.4797148108,-0.1679522544,-0.1982161701,-0.5444876552,0.1608868092,-0.0506577082,0.1889034659,-0.132513836,0.246868059,0.034653727,0.1829144806,0.1495759487,-0.17243433,0.6086639166,-0.3743198216,0.0349230245,0.2907087505,-0.5965977907,-0.00776981,0.0340772346,0.5633322001,0.2247852087,0.5783308148,0.1639446467,0.2058051378,0.0257192608,0.2746677697,0.506039083,0.2200191021,-0.1086446121,0.3596937954,0.1931715757,0.2153612673,0.1479297131,0.1244066581,-0.0030034045,-0.2710251212,0.618827939,-0.0734223798,0.2509362698,-0.5128179193,-0.5945706367,0.3519128561,-0.2508152127,-0.1227589026,0.0529643372,0.2121903747,-0.1759034097,0.1633972973,-0.0488658249,-0.0933008939,-0.1332674772,0.0890614912,-0.2012096941,0.1213541999,-0.0276430454,0.2276746333,0.2279391736,-0.0355741829,0.3027086854,-0.1383718103,0.3369037807,-0.203156963,0.4234705269,-0.1530131102,-0.1576988995,0.4656793773,0.0188060924,0.1361016035,0.077129744,0.1752080619,-0.2254126668,0.317854166,-0.1811051071,-0.191312477,0.1732186079,0.0843120068,-0.6098444462,-0.1870330423,-0.2142927945,-0.4069159031,0.3283523619,-0.3127764165,0.0074014249,-0.3569791019,0.0626097918,-0.14311409,-0.0513427816,0.4348807931,-0.2782552838,-0.1656878293,-0.3917514384,-0.1966852993,0.3818414509,0.4646115899,-0.1238603368,0.0903289914,-0.2280271351,-0.16861929,0.0388513617,-0.1546451896,-0.4479780197,0.0987027809,-0.1993888617,0.4591367841,0.0024011473,0.1677615941,-0.097883068,-0.0189726278,0.311175108,0.1539816707,-0.0105006089,0.0756677464,-0.1189428195,-0.1285549104,0.3238051236,0.263197124,0.1939359158,0.0001208673,0.1493890733,-0.0769864544,0.0875307024,-0.0165592395,-0.0044122222,0.2684107125,0.3694301248,0.12750341,-0.0572713837,-0.036968559,-0.4602802694,0.1719496548,-0.419696331,0.0224219933,-0.0614067167,0.0927605703,-0.1885295808,-0.1335383952,-0.2406832576,0.0501293167,-0.0554182567,0.3447307944,0.1498196125,-0.1016497537,0.075372301,0.0877255276,-0.4383293092,0.030736668,-0.4001354277,0.2532045841,0.0287610237,0.1389985085,0.0798331574,0.0776893198,0.1592817754,-0.0172329172,-0.020363912,0.2922261655,0.1412290931,0.4036790729,-0.0789693147,0.4830584824,-0.0287256408,-0.1002454236,-0.0511055626,0.4246054292,0.1246158928,-0.2297820151,-0.2653988898,0.6342540979,-0.1246944517,0.3354456127,-0.0368466116,0.1040458903,0.2729403675,0.0248604082,-0.2758755684,-0.1860741675,0.4675994813,0.1056900993,0.0031508841,0.1219839305,0.0486664921,-0.1620047539,0.3148164451,-0.3001989126,0.0459326021,0.0504410788,-0.0494226478,-0.2442610413,0.1841947436,0.6842710376,0.5695011616,0.0790135413,-0.0502621643,-0.1501575261,-0.170257628,-0.1112228259,0.028954763,0.1859032512,-0.0549864322,0.4992842674,0.1912119985,-0.2243994772,-0.4243579805,-0.1603443772,0.1699371189,0.3191200793,-0.4955602586,-0.0845628306,-0.1221753657,0.2251867056,-0.284299165,-0.0633714572,-0.1669766754,-0.5736444592,-0.1112596244,0.4169124067,-0.0569943935,0.181970194,-0.4180186987,0.1734418124,-0.0535651036,-0.4603134096,0.0476913266,-0.2303589582,-0.0511448421,-0.0123480652,0.3434800506,-0.0785725713,0.3597759306,-0.2547629476,0.1363714337,-0.6831150055,-0.2296449393,0.0053691883,-0.1194750294,0.1411395818,0.041313339,0.1324565709,-0.0009103297,-0.0025763779,0.0279338472,0.0969524235,-0.2848416269,0.1665333658,-0.1978720427,0.0331816413,0.1929992884,0.0042981002,-0.1131908,-0.3015261889,0.1296286583,0.4311295152,0.0800789297,-0.0818815231,-0.1446488351,0.1002122015,0.1218437776,0.307833463,-0.3872552812,-0.4542714953,0.5385531783,-0.0599849783,-0.1861253232,-0.0017487928,0.0950822532,0.2571623027,0.4127817452,-0.4884949028,-0.1079789326,-0.3561542928,-0.1380737573,0.0689078793,-0.0369922258,0.2624653876,0.0366223045,-0.0105526224,0.0430209152,-0.4865606129,0.0323370732,0.0484947935,0.3351130784,-0.0962446406,0.1513513476,-0.2685446739,0.5760503411,0.071352534,0.0197706576,0.093815133,0.271107465,0.3968070745,0.0199433714,-0.0723116323,-0.0836176351,-0.0744836256,0.037522953,0.1200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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1856","title":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError","comments":"> When you say the \"Quota Exceeded from Google drive\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?\r\n\r\nEach file on Google Drive can be downloaded only a certain amount of times per day because of a quota. The quota is reset every day. So if too many people download the dataset the same day, then the quota is likely to exceed.\r\nThat's a really bad limitations of Google Drive and we should definitely find another host for these dataset than Google Drive.\r\nFor now I would suggest to wait and try again later..\r\n\r\nSo far the issue happened with CNN DailyMail, Amazon Polarity and Yelp Reviews. \r\nAre you experiencing the issue with other datasets ? @calebchiam @dtch1997 ","body":"Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n```","comment_length":127,"text":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError \n Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n``` \n > When you say the \"Quota Exceeded from Google drive\". Is this a quota from the dataset owner? or the quota from our (the runner) Google Drive?\r\n\r\nEach file on Google Drive can be downloaded only a certain amount of times per day because of a quota. The quota is reset every day. So if too many people download the dataset the same day, then the quota is likely to exceed.\r\nThat's a really bad limitations of Google Drive and we should definitely find another host for these dataset than Google Drive.\r\nFor now I would suggest to wait and try again later..\r\n\r\nSo far the issue happened with CNN DailyMail, Amazon Polarity and Yelp Reviews. \r\nAre you experiencing the issue with other datasets ? @calebchiam @dtch1997 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1856","title":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError","comments":"@lhoestq Gotcha, that is quite problematic...for what it's worth, I've had no issues with the other datasets I tried, such as `yelp_reviews_full` and `amazon_reviews_multi`.","body":"Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n```","comment_length":24,"text":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError \n Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n``` \n @lhoestq Gotcha, that is quite problematic...for what it's worth, I've had no issues with the other datasets I tried, such as `yelp_reviews_full` and `amazon_reviews_multi`.","embeddings":[-0.194786638,0.0554474331,-0.0836697891,0.1578223705,0.1719484329,0.0804431736,0.2375732958,0.099151589,0.2416200787,-0.0136137661,-0.0839820951,0.0607005432,-0.0039978959,-0.1410388201,-0.3183495402,0.3028692007,0.0808663368,0.0319708996,-0.1793727428,0.036818359,-0.3233240247,0.2446756661,-0.0489678606,-0.2615421116,-0.1571846455,0.1928241253,0.1952106357,0.0186479744,-0.1012964174,-0.3044863641,0.5374702811,0.199249506,-0.0120766601,0.318572551,-0.0001195672,0.0853057429,0.3367518187,0.0248466954,-0.3156446218,-0.3974169791,-0.3859312832,-0.2944520414,0.0969609991,-0.0750944391,0.1068804562,0.1773181409,0.1115883663,-0.1239965186,0.1156422347,0.1848811209,0.2053506076,0.3852194548,0.1166317239,0.1972644776,0.3125816286,0.3064412773,-0.1069201529,0.2679606676,0.1251927316,-0.1011527255,-0.1667243242,0.0929330066,-0.1170698479,0.3990475833,0.1447162926,-0.0405137092,0.3200861216,-0.1548224092,0.2880885601,0.4979780316,0.38207376,-0.1294228435,-0.3618169129,0.0308115426,-0.2295023799,-0.2095724046,0.2629090548,0.3812124133,-0.0578768328,-0.2178773135,-0.4667551517,0.2266641259,-0.0254905,0.2627213001,0.0525064915,0.3400252759,0.1289118528,0.1233245432,0.1598106176,-0.2095304579,0.4995460212,-0.244139865,-0.0085651474,0.1588152498,-0.5495276451,0.0090561146,-0.0314955376,0.4228042066,0.2175273746,0.526178062,0.1308601648,0.2861514688,0.0186714996,0.2018932998,0.2349801213,0.1130001545,-0.0022747032,0.3624728918,0.1853784174,0.2451706976,0.0670079961,0.1450264454,0.0917718858,-0.1510998011,0.6740022302,-0.1330978721,0.2948665917,-0.4654795825,-0.4914773405,0.3247948289,-0.1839044094,-0.0913163647,0.0314727761,0.2384021729,-0.145765394,0.2540999353,0.0444367416,0.0352183171,-0.1544977129,0.2191961408,-0.1957147866,-0.0111426571,-0.0717064664,0.2342016697,0.2611872256,0.0155249527,0.3998512626,-0.0320103317,0.2115763128,-0.1109667793,0.3717316389,-0.1075839102,-0.0641368777,0.3863778412,-0.0871023536,0.0800858811,0.1076514125,0.120585382,-0.1914372593,0.1178879291,-0.0960544124,-0.1347409189,-0.0384671241,0.1501204371,-0.564566493,-0.1816153079,0.0755826533,-0.3771919906,0.3030200303,-0.3675339818,-0.0443699211,-0.4381237924,-0.0404126644,-0.1279566884,0.0071101333,0.3058854938,-0.113079384,-0.0507401749,-0.3328308761,-0.1052065939,0.4337944388,0.2970632315,-0.0183742121,-0.0539242849,-0.1458172351,-0.2737306952,-0.0362811908,-0.0873362944,-0.2959800065,0.1222153977,-0.1102306172,0.48588413,-0.0137295835,0.3003837466,-0.2076560706,0.0164326988,0.2473590672,0.0874254778,0.0156229073,0.127114743,-0.2721618414,-0.2168179154,0.4197224975,0.3042122722,0.1447952539,-0.0549958386,0.0315452851,-0.0376000069,0.1919880211,0.0012869322,-0.0891600624,0.1462565213,0.3222911358,0.0391501933,0.028895136,-0.0679283366,-0.4993548393,0.2035862803,-0.2444885522,0.0501396395,-0.0717329159,0.0586968586,-0.144768253,-0.2020163834,-0.2211794704,0.0414226055,0.0379387103,0.3838430047,0.1825157702,-0.1179737598,0.0241606478,0.1218319535,-0.3390520215,0.0490049012,-0.4581525922,0.2769997418,0.041090291,0.1103897989,0.111227043,0.0895713717,0.0747309923,-0.0100377146,-0.0309221353,0.3922051489,0.0827847123,0.20969522,-0.0304660294,0.4936658442,-0.1033023968,-0.1565171629,-0.1688938886,0.5129125714,0.1365161389,-0.1889180839,-0.2576062679,0.5926942229,-0.1301007122,0.3979545534,-0.0644440204,0.1393132955,0.2574175596,-0.076796256,-0.3569566309,-0.2310631573,0.5517593622,-0.1549686044,-0.1069904193,0.0832182616,-0.157275185,-0.1169730276,0.0496916696,-0.1312956363,0.1258497685,0.0575028695,0.1097996756,-0.1443409771,0.1292147636,0.5851309896,0.5111247897,0.1082654819,-0.1515368074,-0.0196426623,-0.3857214451,-0.0863465518,0.0441454612,0.1460323781,0.040510416,0.3825343847,0.1978774816,-0.1548850238,-0.4980463684,-0.1963295192,0.0285993442,0.2656152248,-0.4287972748,0.0746725351,-0.3176436424,0.2282875776,-0.3036859334,-0.083618477,-0.1786009371,-0.570351541,-0.0427637249,0.3845753372,-0.0711474419,0.2192089409,-0.5256514549,0.1033714265,-0.043353498,-0.4788769484,0.0370989516,-0.247161299,-0.0079333717,-0.0047993744,0.3693635762,0.0695809573,0.440527916,-0.2635098994,0.0232031848,-0.5357100964,-0.260789901,-0.0754989013,-0.096307613,0.0138617987,0.145665437,0.1418268979,0.0332270749,-0.2047023028,0.136900261,0.0476083942,-0.1899101436,0.2861983478,-0.2202901244,-0.1222561076,0.1410218179,-0.1501275003,-0.0176324341,-0.22995013,0.0179057028,0.3132324517,0.112975575,-0.0485048927,-0.0028420032,0.0647495463,-0.1155693829,0.3681407571,-0.2924708128,-0.5026839375,0.5586988926,0.001054663,-0.1893103272,0.0034450891,0.0447999425,0.2240406722,0.3211057484,-0.5155474544,-0.0771052018,-0.3473516107,-0.1309498996,0.1703898162,-0.0433537029,0.3131451011,0.1213181391,-0.0129036149,-0.0031683033,-0.4504831433,0.0553054027,-0.0679176524,0.5599042177,-0.2180964351,0.0785483643,-0.2356140614,0.4960045218,0.124429442,-0.1527095437,-0.0077510071,0.1388252676,0.3288232982,-0.1095913649,-0.2257803679,-0.1408218443,-0.1820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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1856","title":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError","comments":"Same issue today with \"big_patent\", though the symptoms are slightly different.\r\n\r\nWhen running\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nload_dataset(\"big_patent\", split=\"validation\")\r\n```\r\n\r\nI get the following\r\n`FileNotFoundError: Local file \\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist`\r\n\r\nI had to look into `6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5`  (which is a file instead of a folder) and got the following:\r\n\r\n`<!DOCTYPE html><html><head><title>Google Drive - Quota exceeded<\/title><meta http-equiv=\"content-type\" content=\"text\/html; charset=utf-8\"\/><link href=&#47;static&#47;doclist&#47;client&#47;css&#47;4033072956&#45;untrustedcontent.css rel=\"stylesheet\" nonce=\"JV0t61Smks2TEKdFCGAUFA\"><link rel=\"icon\" href=\"\/\/ssl.gstatic.com\/images\/branding\/product\/1x\/drive_2020q4_32dp.png\"\/><style nonce=\"JV0t61Smks2TEKdFCGAUFA\">#gbar,#guser{font-size:13px;padding-top:0px !important;}#gbar{height:22px}#guser{padding-bottom:7px !important;text-align:right}.gbh,.gbd{border-top:1px solid #c9d7f1;font-size:1px}.gbh{height:0;position:absolute;top:24px;width:100%}@media all{.gb1{height:22px;margin-right:.5em;vertical-align:top}#gbar{float:left}}a.gb1,a.gb4{text-decoration:underline !important}a.gb1,a.gb4{color:#00c !important}.gbi .gb4{color:#dd8e27 !important}.gbf .gb4{color:#900 !important}\r\n<\/style><script nonce=\"iNUHigT+ENVQ3UZrLkFtRw\"><\/script><\/head><body><div id=gbar><nobr><a target=_blank class=gb1 href=\"https:\/\/www.google.fr\/webhp?tab=ow\">Search<\/a> <a target=_blank class=gb1 href=\"http:\/\/www.google.fr\/imghp?hl=en&tab=oi\">Images<\/a> <a target=_blank class=gb1 href=\"https:\/\/maps.google.fr\/maps?hl=en&tab=ol\">Maps<\/a> <a target=_blank class=gb1 href=\"https:\/\/play.google.com\/?hl=en&tab=o8\">Play<\/a> <a target=_blank class=gb1 href=\"https:\/\/www.youtube.com\/?gl=FR&tab=o1\">YouTube<\/a> <a target=_blank class=gb1 href=\"https:\/\/news.google.com\/?tab=on\">News<\/a> <a target=_blank class=gb1 href=\"https:\/\/mail.google.com\/mail\/?tab=om\">Gmail<\/a> <b class=gb1>Drive<\/b> <a target=_blank class=gb1 style=\"text-decoration:none\" href=\"https:\/\/www.google.fr\/intl\/en\/about\/products?tab=oh\"><u>More<\/u> &raquo;<\/a><\/nobr><\/div><div id=guser width=100%><nobr><span id=gbn class=gbi><\/span><span id=gbf class=gbf><\/span><span id=gbe><\/span><a target=\"_self\" href=\"\/settings?hl=en_US\" class=gb4>Settings<\/a> | <a target=_blank  href=\"\/\/support.google.com\/drive\/?p=web_home&hl=en_US\" class=gb4>Help<\/a> | <a target=_top id=gb_70 href=\"https:\/\/accounts.google.com\/ServiceLogin?hl=en&passive=true&continue=https:\/\/drive.google.com\/uc%3Fexport%3Ddownload%26id%3D1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa&service=writely&ec=GAZAMQ\" class=gb4>Sign in<\/a><\/nobr><\/div><div class=gbh style=left:0><\/div><div class=gbh style=right:0><\/div><div class=\"uc-main\"><div id=\"uc-text\"><p class=\"uc-error-caption\">Sorry, you can&#39;t view or download this file at this time.<\/p><p class=\"uc-error-subcaption\">Too many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.<\/p><\/div><\/div><div class=\"uc-footer\"><hr class=\"uc-footer-divider\">&copy; 2021 Google - <a class=\"goog-link\" href=\"\/\/support.google.com\/drive\/?p=web_home\">Help<\/a> - <a class=\"goog-link\" href=\"\/\/support.google.com\/drive\/bin\/answer.py?hl=en_US&amp;answer=2450387\">Privacy & Terms<\/a><\/div><\/body><\/html>`","body":"Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n```","comment_length":230,"text":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError \n Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n``` \n Same issue today with \"big_patent\", though the symptoms are slightly different.\r\n\r\nWhen running\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nload_dataset(\"big_patent\", split=\"validation\")\r\n```\r\n\r\nI get the following\r\n`FileNotFoundError: Local file \\huggingface\\datasets\\downloads\\6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5\\bigPatentData\\train.tar.gz doesn't exist`\r\n\r\nI had to look into `6159313604f4f2c01e7d1cac52139343b6c07f73f6de348d09be6213478455c5`  (which is a file instead of a folder) and got the following:\r\n\r\n`<!DOCTYPE html><html><head><title>Google Drive - Quota exceeded<\/title><meta http-equiv=\"content-type\" content=\"text\/html; charset=utf-8\"\/><link href=&#47;static&#47;doclist&#47;client&#47;css&#47;4033072956&#45;untrustedcontent.css rel=\"stylesheet\" nonce=\"JV0t61Smks2TEKdFCGAUFA\"><link rel=\"icon\" href=\"\/\/ssl.gstatic.com\/images\/branding\/product\/1x\/drive_2020q4_32dp.png\"\/><style nonce=\"JV0t61Smks2TEKdFCGAUFA\">#gbar,#guser{font-size:13px;padding-top:0px !important;}#gbar{height:22px}#guser{padding-bottom:7px !important;text-align:right}.gbh,.gbd{border-top:1px solid #c9d7f1;font-size:1px}.gbh{height:0;position:absolute;top:24px;width:100%}@media all{.gb1{height:22px;margin-right:.5em;vertical-align:top}#gbar{float:left}}a.gb1,a.gb4{text-decoration:underline !important}a.gb1,a.gb4{color:#00c !important}.gbi .gb4{color:#dd8e27 !important}.gbf .gb4{color:#900 !important}\r\n<\/style><script nonce=\"iNUHigT+ENVQ3UZrLkFtRw\"><\/script><\/head><body><div id=gbar><nobr><a target=_blank class=gb1 href=\"https:\/\/www.google.fr\/webhp?tab=ow\">Search<\/a> <a target=_blank class=gb1 href=\"http:\/\/www.google.fr\/imghp?hl=en&tab=oi\">Images<\/a> <a target=_blank class=gb1 href=\"https:\/\/maps.google.fr\/maps?hl=en&tab=ol\">Maps<\/a> <a target=_blank class=gb1 href=\"https:\/\/play.google.com\/?hl=en&tab=o8\">Play<\/a> <a target=_blank class=gb1 href=\"https:\/\/www.youtube.com\/?gl=FR&tab=o1\">YouTube<\/a> <a target=_blank class=gb1 href=\"https:\/\/news.google.com\/?tab=on\">News<\/a> <a target=_blank class=gb1 href=\"https:\/\/mail.google.com\/mail\/?tab=om\">Gmail<\/a> <b class=gb1>Drive<\/b> <a target=_blank class=gb1 style=\"text-decoration:none\" href=\"https:\/\/www.google.fr\/intl\/en\/about\/products?tab=oh\"><u>More<\/u> &raquo;<\/a><\/nobr><\/div><div id=guser width=100%><nobr><span id=gbn class=gbi><\/span><span id=gbf class=gbf><\/span><span id=gbe><\/span><a target=\"_self\" href=\"\/settings?hl=en_US\" class=gb4>Settings<\/a> | <a target=_blank  href=\"\/\/support.google.com\/drive\/?p=web_home&hl=en_US\" class=gb4>Help<\/a> | <a target=_top id=gb_70 href=\"https:\/\/accounts.google.com\/ServiceLogin?hl=en&passive=true&continue=https:\/\/drive.google.com\/uc%3Fexport%3Ddownload%26id%3D1J3mucMFTWrgAYa3LuBZoLRR3CzzYD3fa&service=writely&ec=GAZAMQ\" class=gb4>Sign in<\/a><\/nobr><\/div><div class=gbh style=left:0><\/div><div class=gbh style=right:0><\/div><div class=\"uc-main\"><div id=\"uc-text\"><p class=\"uc-error-caption\">Sorry, you can&#39;t view or download this file at this time.<\/p><p class=\"uc-error-subcaption\">Too many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.<\/p><\/div><\/div><div class=\"uc-footer\"><hr class=\"uc-footer-divider\">&copy; 2021 Google - <a class=\"goog-link\" href=\"\/\/support.google.com\/drive\/?p=web_home\">Help<\/a> - <a class=\"goog-link\" href=\"\/\/support.google.com\/drive\/bin\/answer.py?hl=en_US&amp;answer=2450387\">Privacy & Terms<\/a><\/div><\/body><\/html>`","embeddings":[-0.1977470964,0.1699575484,-0.0510927998,0.2398218662,0.1494399905,-0.0500105023,0.2648400664,0.2264509648,0.3186415434,0.0602953546,-0.0621266589,0.0072547733,-0.0048490805,-0.1367188245,-0.2075065523,0.3518310189,0.1385649145,-0.0169535168,-0.2259685993,0.0192921013,-0.2470269948,0.2507219613,-0.0814839229,-0.1623295397,-0.0814451873,0.2453400195,0.185918808,0.1196510941,-0.0924422145,-0.4332829714,0.3541623354,0.1207953468,0.0554162674,0.3615809679,-0.000124711,0.1088824868,0.3887200952,0.0579437539,-0.2862367332,-0.1727553755,-0.4470102191,-0.3646294475,-0.0332945846,0.0008812062,0.1849218458,0.0702594444,0.0694613382,-0.063148044,0.0055411682,0.2387625873,0.1421351284,0.4269422889,0.2395886481,0.1177865267,0.2658692002,0.2129864693,-0.0901237801,0.2691429257,0.2103940696,-0.2142736167,-0.2508116066,0.1191412657,-0.1859516799,0.414689064,0.103681609,0.0116963284,0.2535794973,-0.1878900826,0.2364885658,0.5302149653,0.3473127186,-0.0848420411,-0.4574839473,-0.1686614156,-0.1927171648,-0.3431209922,0.3405172825,0.2637237608,-0.0864364654,-0.1382098645,-0.3554598391,0.1351724267,-0.0891586468,0.2401632071,-0.0999407172,0.1585044861,0.1089744642,0.0511102416,0.0896147639,-0.283146441,0.4739673436,-0.4253416359,-0.0152056171,0.1840294003,-0.5253770947,0.0538717397,0.0191307981,0.5155975819,0.3433676362,0.4453732371,0.1638211012,0.1542468518,-0.113208957,0.1749989688,0.1005771756,0.164857313,-0.1246671528,0.2837972641,0.334449023,0.2328185886,0.0663328096,0.1549246907,-0.0565032437,-0.2741813958,0.5267841816,-0.1108523384,0.313787967,-0.5724887252,-0.4306190014,0.3364908397,-0.1928468943,-0.0282177832,0.0675675794,0.2280802429,-0.2387456298,0.3099794388,-0.0643480495,0.0004804438,-0.1664965302,0.0502480827,-0.2033563405,-0.0187495016,-0.0810727403,0.2882333994,0.3058470488,-0.0299485289,0.3222577274,-0.0148619581,0.2578918934,-0.0937811062,0.2736911178,-0.1426098198,0.0097336601,0.4259118736,-0.2763498724,0.0317265801,0.0826817825,0.0599154346,-0.14994964,0.1888548881,-0.1219960228,-0.1037561074,0.0341112874,0.0855986401,-0.7176007032,-0.1909341216,0.0236068051,-0.4370221794,0.2319756001,-0.4660434127,-0.0455491692,-0.579919517,0.0250526201,-0.1446424425,0.1525873095,0.3106122613,-0.1575115323,-0.1112049595,-0.1945011467,-0.0421186164,0.3214511275,0.3376633227,-0.0974220037,0.0249704383,-0.2604079545,-0.1538295597,0.0182919856,-0.2152289599,-0.4789546132,0.0974535346,-0.1639339775,0.5817306042,0.1618592888,0.164980486,-0.2130648941,0.0506925024,0.2002895921,0.0676911175,-0.0125752799,0.163019225,-0.2348095179,-0.1929581016,0.3607847989,0.2327778339,0.2313600928,-0.024613047,0.0942839235,-0.0469427705,0.2663872242,-0.0295545198,-0.1264053881,0.1404914707,0.2977230251,-0.0565797649,-0.0152171683,-0.0363365822,-0.451076448,0.28875646,-0.1545195878,0.0236894451,-0.1039606258,0.1078216061,-0.129286468,-0.2308788747,-0.2409704179,-0.0934643894,-0.0329290666,0.4628794491,0.1729547232,0.0034063782,0.028061077,0.2453964949,-0.2640742064,0.1012686566,-0.58715415,0.2537441254,0.109455049,0.0982457623,0.0388326831,0.0806021243,0.0688670129,-0.0402693488,-0.0483747236,0.3387963176,0.2107178122,0.1517692059,-0.0045256522,0.5949782729,-0.0514891185,-0.0401122868,-0.0681998283,0.4075601697,0.1674464047,-0.1222714782,-0.2599912584,0.6478692889,-0.1487545967,0.4013353586,-0.0244220961,-0.0253933091,0.2595122159,-0.1413267404,-0.4156427681,-0.0606277175,0.4904745519,-0.0587507896,-0.1178299859,0.0945851952,-0.2039511204,-0.1534876078,0.2384014726,-0.1865176409,-0.0035637394,0.0259025954,-0.1144873649,-0.0729258582,0.1115812436,0.6173869371,0.6931012869,0.0906759426,-0.1532764137,-0.0719935223,-0.2084608376,-0.1319147497,0.0589391328,0.1659445763,-0.0344939902,0.4840489924,0.2609243989,-0.174152717,-0.454282701,-0.2900517881,0.0969517305,0.1641105264,-0.4628485143,0.0809603855,-0.3064672649,0.1440186054,-0.447399646,0.0089243902,-0.3047971427,-0.4628069103,-0.1676448882,0.5472712517,-0.0157070011,0.2009985298,-0.6645430326,0.1647136211,-0.0594837628,-0.4675349593,0.0693726167,-0.1088770702,-0.0143261012,-0.0495197065,0.3760409653,-0.0842460617,0.3908686936,-0.191077143,-0.0181511585,-0.5628102422,-0.4042610824,-0.0315472782,-0.0961567909,-0.0866458192,0.1487677097,0.0661067739,0.2045079023,-0.1082295701,0.1219889745,0.0034571928,-0.2515302896,0.1921658814,-0.13921386,-0.0867666751,0.1303486377,-0.1573179215,-0.0555603839,-0.2416144013,0.1264010817,0.2230749875,0.0426238962,0.0844557211,-0.0340708643,0.1342974901,-0.184631452,0.2868455052,-0.2110422105,-0.4735352993,0.4939221442,0.0137702432,-0.208632797,-0.1078974828,0.0114796618,0.203142941,0.3830661178,-0.512294054,-0.0500408672,-0.3821271956,-0.0122855278,0.1495184898,-0.0381680317,0.2434116453,0.0776330978,0.000671882,-0.0634186715,-0.3752563596,0.0119352052,-0.0515981019,0.5554400682,-0.1658086777,0.1307981014,-0.2195640504,0.520567596,0.0204533804,-0.1773886681,-0.0150971878,0.1209319681,0.4579964578,-0.0820356086,-0.2375092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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1856","title":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError","comments":"A similar issue arises when trying to stream the dataset\r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> iter_dset = load_dataset(\"amazon_polarity\", split=\"test\", streaming=True)\r\n>>> iter(iter_dset).__next__()\r\n\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n~\\lib\\tarfile.py in nti(s)\r\n    186             s = nts(s, \"ascii\", \"strict\")\r\n--> 187             n = int(s.strip() or \"0\", 8)\r\n    188         except ValueError:\r\n\r\nValueError: invalid literal for int() with base 8: 'e nonce='\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nInvalidHeaderError                        Traceback (most recent call last)\r\n~\\lib\\tarfile.py in next(self)\r\n   2288             try:\r\n-> 2289                 tarinfo = self.tarinfo.fromtarfile(self)\r\n   2290             except EOFHeaderError as e:\r\n\r\n~\\lib\\tarfile.py in fromtarfile(cls, tarfile)\r\n   1094         buf = tarfile.fileobj.read(BLOCKSIZE)\r\n-> 1095         obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)\r\n   1096         obj.offset = tarfile.fileobj.tell() - BLOCKSIZE\r\n\r\n~\\lib\\tarfile.py in frombuf(cls, buf, encoding, errors)\r\n   1036\r\n-> 1037         chksum = nti(buf[148:156])\r\n   1038         if chksum not in calc_chksums(buf):\r\n\r\n~\\lib\\tarfile.py in nti(s)\r\n    188         except ValueError:\r\n--> 189             raise InvalidHeaderError(\"invalid header\")\r\n    190     return n\r\n\r\nInvalidHeaderError: invalid header\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nReadError                                 Traceback (most recent call last)\r\n<ipython-input-5-6b9058341b2b> in <module>\r\n----> 1 iter(iter_dset).__next__()\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n    363\r\n    364     def __iter__(self):\r\n--> 365         for key, example in self._iter():\r\n    366             if self.features:\r\n    367                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n    360         else:\r\n    361             ex_iterable = self._ex_iterable\r\n--> 362         yield from ex_iterable\r\n    363\r\n    364     def __iter__(self):\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n     77\r\n     78     def __iter__(self):\r\n---> 79         yield from self.generate_examples_fn(**self.kwargs)\r\n     80\r\n     81     def shuffle_data_sources(self, seed: Optional[int]) -> \"ExamplesIterable\":\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\amazon_polarity\\56923eeb72030cb6c4ea30c8a4e1162c26b25973475ac1f44340f0ec0f2936f4\\amazon_polarity.py in _generate_examples(self, filepath, files)\r\n    114     def _generate_examples(self, filepath, files):\r\n    115         \"\"\"Yields examples.\"\"\"\r\n--> 116         for path, f in files:\r\n    117             if path == filepath:\r\n    118                 lines = (line.decode(\"utf-8\") for line in f)\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in __iter__(self)\r\n    616\r\n    617     def __iter__(self):\r\n--> 618         yield from self.generator(*self.args, **self.kwargs)\r\n    619\r\n    620\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_urlpath(cls, urlpath, use_auth_token)\r\n    644     ) -> Generator[Tuple, None, None]:\r\n    645         with xopen(urlpath, \"rb\", use_auth_token=use_auth_token) as f:\r\n--> 646             yield from cls._iter_from_fileobj(f)\r\n    647\r\n    648     @classmethod\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_fileobj(cls, f)\r\n    624     @classmethod\r\n    625     def _iter_from_fileobj(cls, f) -> Generator[Tuple, None, None]:\r\n--> 626         stream = tarfile.open(fileobj=f, mode=\"r|*\")\r\n    627         for tarinfo in stream:\r\n    628             file_path = tarinfo.name\r\n\r\n~\\lib\\tarfile.py in open(cls, name, mode, fileobj, bufsize, **kwargs)\r\n   1603             stream = _Stream(name, filemode, comptype, fileobj, bufsize)\r\n   1604             try:\r\n-> 1605                 t = cls(name, filemode, stream, **kwargs)\r\n   1606             except:\r\n   1607                 stream.close()\r\n\r\n~\\lib\\tarfile.py in __init__(self, name, mode, fileobj, format, tarinfo, dereference, ignore_zeros, encoding, errors, pax_headers, debug, errorlevel, copybufsize)\r\n   1484             if self.mode == \"r\":\r\n   1485                 self.firstmember = None\r\n-> 1486                 self.firstmember = self.next()\r\n   1487\r\n   1488             if self.mode == \"a\":\r\n\r\n~\\lib\\tarfile.py in next(self)\r\n   2299                     continue\r\n   2300                 elif self.offset == 0:\r\n-> 2301                     raise ReadError(str(e))\r\n   2302             except EmptyHeaderError:\r\n   2303                 if self.offset == 0:\r\n\r\nReadError: invalid header\r\n\r\n```","body":"Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n```","comment_length":430,"text":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError \n Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n``` \n A similar issue arises when trying to stream the dataset\r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> iter_dset = load_dataset(\"amazon_polarity\", split=\"test\", streaming=True)\r\n>>> iter(iter_dset).__next__()\r\n\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n~\\lib\\tarfile.py in nti(s)\r\n    186             s = nts(s, \"ascii\", \"strict\")\r\n--> 187             n = int(s.strip() or \"0\", 8)\r\n    188         except ValueError:\r\n\r\nValueError: invalid literal for int() with base 8: 'e nonce='\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nInvalidHeaderError                        Traceback (most recent call last)\r\n~\\lib\\tarfile.py in next(self)\r\n   2288             try:\r\n-> 2289                 tarinfo = self.tarinfo.fromtarfile(self)\r\n   2290             except EOFHeaderError as e:\r\n\r\n~\\lib\\tarfile.py in fromtarfile(cls, tarfile)\r\n   1094         buf = tarfile.fileobj.read(BLOCKSIZE)\r\n-> 1095         obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)\r\n   1096         obj.offset = tarfile.fileobj.tell() - BLOCKSIZE\r\n\r\n~\\lib\\tarfile.py in frombuf(cls, buf, encoding, errors)\r\n   1036\r\n-> 1037         chksum = nti(buf[148:156])\r\n   1038         if chksum not in calc_chksums(buf):\r\n\r\n~\\lib\\tarfile.py in nti(s)\r\n    188         except ValueError:\r\n--> 189             raise InvalidHeaderError(\"invalid header\")\r\n    190     return n\r\n\r\nInvalidHeaderError: invalid header\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nReadError                                 Traceback (most recent call last)\r\n<ipython-input-5-6b9058341b2b> in <module>\r\n----> 1 iter(iter_dset).__next__()\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n    363\r\n    364     def __iter__(self):\r\n--> 365         for key, example in self._iter():\r\n    366             if self.features:\r\n    367                 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n    360         else:\r\n    361             ex_iterable = self._ex_iterable\r\n--> 362         yield from ex_iterable\r\n    363\r\n    364     def __iter__(self):\r\n\r\n~\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n     77\r\n     78     def __iter__(self):\r\n---> 79         yield from self.generate_examples_fn(**self.kwargs)\r\n     80\r\n     81     def shuffle_data_sources(self, seed: Optional[int]) -> \"ExamplesIterable\":\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\amazon_polarity\\56923eeb72030cb6c4ea30c8a4e1162c26b25973475ac1f44340f0ec0f2936f4\\amazon_polarity.py in _generate_examples(self, filepath, files)\r\n    114     def _generate_examples(self, filepath, files):\r\n    115         \"\"\"Yields examples.\"\"\"\r\n--> 116         for path, f in files:\r\n    117             if path == filepath:\r\n    118                 lines = (line.decode(\"utf-8\") for line in f)\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in __iter__(self)\r\n    616\r\n    617     def __iter__(self):\r\n--> 618         yield from self.generator(*self.args, **self.kwargs)\r\n    619\r\n    620\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_urlpath(cls, urlpath, use_auth_token)\r\n    644     ) -> Generator[Tuple, None, None]:\r\n    645         with xopen(urlpath, \"rb\", use_auth_token=use_auth_token) as f:\r\n--> 646             yield from cls._iter_from_fileobj(f)\r\n    647\r\n    648     @classmethod\r\n\r\n~\\lib\\site-packages\\datasets\\utils\\streaming_download_manager.py in _iter_from_fileobj(cls, f)\r\n    624     @classmethod\r\n    625     def _iter_from_fileobj(cls, f) -> Generator[Tuple, None, None]:\r\n--> 626         stream = tarfile.open(fileobj=f, mode=\"r|*\")\r\n    627         for tarinfo in stream:\r\n    628             file_path = tarinfo.name\r\n\r\n~\\lib\\tarfile.py in open(cls, name, mode, fileobj, bufsize, **kwargs)\r\n   1603             stream = _Stream(name, filemode, comptype, fileobj, bufsize)\r\n   1604             try:\r\n-> 1605                 t = cls(name, filemode, stream, **kwargs)\r\n   1606             except:\r\n   1607                 stream.close()\r\n\r\n~\\lib\\tarfile.py in __init__(self, name, mode, fileobj, format, tarinfo, dereference, ignore_zeros, encoding, errors, pax_headers, debug, errorlevel, copybufsize)\r\n   1484             if self.mode == \"r\":\r\n   1485                 self.firstmember = None\r\n-> 1486                 self.firstmember = self.next()\r\n   1487\r\n   1488             if self.mode == \"a\":\r\n\r\n~\\lib\\tarfile.py in next(self)\r\n   2299                     continue\r\n   2300                 elif self.offset == 0:\r\n-> 2301                     raise ReadError(str(e))\r\n   2302             except EmptyHeaderError:\r\n   2303                 if self.offset == 0:\r\n\r\nReadError: invalid header\r\n\r\n```","embeddings":[-0.3396336138,0.0200672802,-0.0462275408,0.1762579381,0.1373031586,0.0859517381,0.1996393353,0.1355160475,0.2324955463,0.0852480903,-0.0440216064,0.0785093009,-0.0269584656,-0.0893350765,-0.241651684,0.1742836535,0.0135002285,0.0783391595,-0.1813730001,0.0802159905,-0.2044000328,0.2318350822,-0.0237761401,-0.3260401785,0.0269846059,0.2008012384,0.2500697374,0.0716428831,-0.1279125214,-0.331507802,0.432639569,0.1706275344,0.1264738292,0.2313700318,-0.000121988,0.0715929568,0.3433465362,0.0438676625,-0.4028812349,-0.3314690292,-0.5118097067,-0.2734090388,0.0981745794,-0.0344884396,0.0742517337,0.2143782079,0.1406931579,-0.2628238797,0.1020971984,0.1484522671,0.1718685925,0.4790240526,0.066658169,0.2748759091,0.2406763434,0.230782479,-0.0905768946,0.2313465178,0.0997234806,0.0193205066,-0.2282278091,0.1479409337,-0.1595358551,0.4649395347,0.1188286915,-0.0806528777,0.1982028782,-0.1641778946,0.2228663415,0.4493606389,0.4076819718,-0.1614754498,-0.3458153903,0.0039179632,-0.2119717449,-0.3545734584,0.264346689,0.325620383,-0.167423293,-0.2234293669,-0.3788588345,0.2002657205,-0.1209719256,0.1817811728,0.0096021099,0.3345081806,0.0884696245,0.1090186909,0.0860065967,-0.192297399,0.6059759259,-0.4239427447,-0.0331788212,0.2171289474,-0.5905471444,0.0941464305,-0.10895513,0.4130987525,0.129306674,0.4920393527,0.2682411075,0.2719372809,0.0759435669,0.1920031011,0.199972406,0.1044061929,-0.021661168,0.2966597974,0.1272659749,0.2680594325,0.0198740568,0.0256532542,0.0591737926,-0.1552529335,0.7049292326,-0.0350279547,0.3174941242,-0.4742216766,-0.4424120784,0.2072789371,-0.2103000432,-0.1511010826,0.1717415601,0.1510764956,-0.1908127069,0.3247170746,-0.0195601862,-0.0719651282,-0.1899942011,0.0691601411,-0.2097629607,-0.1184892431,-0.0130805522,0.2303152233,0.261105597,-0.172308296,0.2835671306,-0.0401160382,0.1978954375,-0.0505983084,0.4349854887,-0.1042983979,-0.0069570271,0.4169873595,-0.1348938793,0.1164352074,0.1445657462,0.1696691364,-0.1715204418,0.1391023099,-0.0139422175,-0.1864034832,0.0993028134,0.1260034293,-0.5512733459,-0.1957074702,0.0660774708,-0.382258296,0.1953532994,-0.4674838781,-0.1038698032,-0.4537023306,-0.0622938201,-0.1100453883,0.1329802871,0.2866211534,-0.1503734142,-0.0936488509,-0.2468950003,-0.1209957376,0.620651722,0.261572063,-0.0460528061,-0.0956213996,-0.1598232239,-0.2123491317,0.0610620752,-0.1104859561,-0.3963792622,0.2134480178,-0.0982595161,0.6506130695,0.1028319523,0.3170910776,-0.0433874652,0.0410703868,0.2137349099,0.1910857409,-0.0679419935,0.1569680125,-0.2200043052,-0.1681281775,0.374604404,0.2574886382,0.0381480716,0.038806621,0.08618249,-0.0348947123,0.2547188699,-0.0219782442,-0.0287520438,0.059429083,0.2873673737,-0.0255311783,-0.039671354,-0.0977116525,-0.5329663157,0.2139113992,-0.040840961,-0.0291517284,-0.0855145976,0.1365224272,-0.0650838092,-0.1719651073,-0.2723731697,0.0603406504,0.0187557079,0.5706145763,0.1598653793,-0.1549819708,0.0005252273,0.2817040682,-0.3974834681,0.0167293921,-0.5769188404,0.3520233631,0.1505058855,0.0608170219,0.1069431007,0.0319899879,0.0785437971,0.012369886,-0.1454937905,0.3607150316,0.1243355423,0.2475082427,-0.0487473495,0.5428755879,-0.049120117,-0.1858500093,-0.1670026034,0.5169445276,0.1374016702,-0.1340281367,-0.1367209554,0.4937773049,-0.1745805144,0.4801003039,0.0012226123,0.1034566462,0.3119765222,0.0508231856,-0.4369245768,-0.1397515535,0.5391433835,-0.222505942,-0.1112374961,0.0408918522,-0.2503997087,-0.1103743389,0.0893643647,-0.1246842295,-0.0489791036,0.0926908925,-0.0289266799,-0.1548250765,0.2153679729,0.6824833751,0.5565808415,0.0539537668,0.0183781795,-0.0441818759,-0.3000760972,-0.0840936005,0.1238500625,0.1724035889,0.0052346592,0.4140297771,0.1253278404,-0.1790626347,-0.4833469391,-0.2680533826,-0.0007617731,0.2470505834,-0.3836142421,0.159287706,-0.3329057395,0.13046287,-0.3312423825,-0.1855242401,-0.1911399812,-0.5112914443,-0.2278959304,0.5458831787,-0.0623065084,0.1683181673,-0.5661447644,0.0279054362,0.0268037897,-0.4693711996,-0.046508681,-0.2232596725,0.0325048827,-0.0373716392,0.3232023716,0.0084928293,0.4533035457,-0.285110265,0.074076727,-0.6053283215,-0.2280043662,-0.0451453887,-0.0089588054,-0.012292942,0.1886445284,0.2977719009,0.0560750291,-0.2649860084,0.1661819369,0.0008709875,-0.1674936861,0.3275071681,-0.2753470838,-0.008415821,0.1983834058,-0.1120812669,-0.1162018478,-0.3021372855,0.0101340385,0.2615034282,0.104896374,0.0999210849,-0.052273158,-0.0186162814,-0.0896061063,0.3046801388,-0.2119317353,-0.6586390734,0.4983043075,-0.013727434,-0.1273737848,-0.004054714,0.1055630893,0.297164917,0.503552556,-0.4784957767,0.0594993979,-0.367715776,-0.1108704954,0.1143191531,-0.0332088657,0.2091499716,0.1441220045,0.0102597801,-0.0136540318,-0.3916193843,0.0012658327,-0.0076320544,0.5102401972,-0.1134527624,0.2106687874,-0.2357725203,0.5270025134,0.0822432265,-0.1733269691,0.0447748601,0.1432361156,0.310123682,-0.12177255,-0.2576994002,-0.0960216224,-0.1461247206,-0.0112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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1856","title":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError","comments":"This error still happens, but for a different reason now: Google Drive returns a warning instead of the dataset.","body":"Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n```","comment_length":19,"text":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError \n Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n``` \n This error still happens, but for a different reason now: Google Drive returns a warning instead of the dataset.","embeddings":[-0.2818935513,0.157643497,-0.0683752745,0.0996030271,0.1080743447,0.1604388803,0.4640992582,0.0867969245,0.2902541161,0.1448860765,0.0573568568,-0.029875854,-0.1013725549,0.0111130401,-0.2888024449,0.2514442503,0.1300299615,0.0149667012,-0.0264267083,0.0253289938,-0.3752836585,0.3360217214,0.0080567924,-0.1954816729,-0.0802754238,0.1096615642,0.2515250146,0.0523748547,-0.0549783893,-0.3080164492,0.3911385238,0.109067373,0.0863800794,0.2491898984,-0.0001244017,0.1367356032,0.4725239575,0.0462978669,-0.3219804466,-0.3518791497,-0.3527870774,-0.1789288223,0.0767271966,-0.1095434055,0.0943822637,0.1538326442,0.1843471229,-0.1008012816,0.005624651,0.1687117517,0.1377440095,0.4085964561,0.1559181958,0.2669349313,0.3612015247,0.136753872,-0.0892411917,0.3420318365,0.0529460274,-0.0744578466,-0.1879213303,0.1577874422,-0.1144133657,0.4283054471,0.103371039,0.1259227842,0.1193635687,-0.1513029933,0.1711051017,0.3020322323,0.4482569098,-0.1588790864,-0.2892172039,-0.0821085796,-0.1882714182,-0.2163630873,0.4192531109,0.3321742713,-0.0782689303,-0.1632986516,-0.5042745471,0.1227358133,-0.0828812942,0.1494855434,-0.1100330278,0.308974117,0.0850527361,0.1518603861,0.2012846768,-0.0969835222,0.5320153832,-0.2935945988,-0.0401084125,0.0761827901,-0.4099257588,-0.1115001068,0.053089872,0.4475029409,0.2069280297,0.4909378886,0.1260728687,0.1858650744,-0.0557570904,0.1976652592,0.3302619457,0.1149149016,-0.0637690946,0.2425672561,0.2020403743,0.2838535905,0.194979921,0.073264271,0.1349798739,-0.2560988367,0.7037336826,0.0940790102,0.4534050822,-0.4141043425,-0.5380063057,0.2098855376,-0.2590503991,-0.2192291021,-0.0550614782,0.1752760559,-0.0867399722,0.0158992242,0.0497909226,0.0568719655,-0.1054668501,0.1876798123,-0.1393575519,0.011649983,-0.0414178744,0.2205449641,0.2411549389,-0.0478801616,0.2614209652,-0.0447395928,0.1560101956,-0.0601862147,0.2995097935,-0.1770502627,-0.052896671,0.3793474138,-0.0195882712,0.0099855335,0.0983517841,0.0751971006,-0.2024612129,0.2074354887,-0.148243323,-0.1460564584,-0.0178961176,0.0684326291,-0.5958090425,-0.1628493071,-0.1112009436,-0.3697939217,0.0925987884,-0.3799521029,-0.0587036312,-0.4458446205,-0.0907687694,-0.1810726523,0.0383388177,0.3248111904,-0.1116021872,-0.0512622893,-0.3044002354,-0.1428101808,0.3169144392,0.3511483669,-0.0226268079,0.0665222332,-0.355814755,-0.2695032358,-0.1014653593,-0.0832127929,-0.3409039676,0.1460952312,-0.2365045398,0.4539052248,-0.0365562104,0.2166079283,-0.260966599,0.0323913246,0.2840608954,0.1771358848,-0.0244021192,0.1422452629,-0.1424107701,-0.3236004114,0.3133017123,0.1736726612,0.1739373654,0.1411558837,0.2960702777,-0.2286575288,0.1685990095,-0.0430443697,-0.1364441067,0.2350418419,0.383408159,-0.1586282849,-0.0454803519,0.0154866334,-0.7338739634,0.1876769662,-0.1413418204,0.064768821,-0.276466161,0.0789955109,-0.1097573265,-0.2757861316,-0.1822751164,0.075117439,-0.0170089696,0.4422847629,0.1450638771,0.1200782284,0.1799540222,-0.0424418338,-0.3468942046,0.057557486,-0.4906476438,0.4986576438,-0.0394539386,0.0768303573,0.1349903643,-0.0396660231,0.0432046279,-0.101392135,-0.0230619647,0.3145608604,-0.0046183174,0.4022879004,-0.0506324433,0.6662473083,-0.0067205001,-0.2054367214,-0.0903728902,0.4893098176,0.0871132985,-0.0825019404,-0.3901653588,0.6032892466,-0.2195237875,0.3223555088,-0.1396546215,0.0944915712,0.2128821015,-0.1007196382,-0.2553088367,-0.2009882778,0.2932603955,0.1216921732,-0.0275091045,-0.0064580524,-0.198383376,-0.1467863768,0.3726359606,-0.157708019,0.0379658341,0.1513922364,0.0436377898,-0.1762381345,0.1766264588,0.8572224975,0.4925215244,0.0664718747,-0.0863617882,0.0027842498,-0.3731661439,-0.0505019315,0.0576867536,0.1327785403,0.0073375874,0.4572012126,0.1445972323,-0.183729127,-0.4953969419,-0.1696343124,0.0815501139,0.2710424662,-0.5151886344,0.1458177865,-0.1595975459,0.2877820432,-0.3408718705,0.0461599492,-0.243197158,-0.514929533,-0.1293784976,0.5444689393,-0.1110474765,0.1279799789,-0.3368759155,0.1666512191,0.0830555558,-0.5815590024,-0.0325228907,-0.1204138175,0.0590391494,-0.0343585089,0.3373580277,-0.0241350643,0.4372457564,-0.3424980938,0.0673441812,-0.8136794567,-0.2465453297,0.014013567,-0.249101162,0.1384048015,0.2002249956,0.1789909154,0.0585865229,-0.2799109817,0.0807246193,0.0037174092,-0.2423229069,0.1416928917,-0.2077366114,0.0415963605,0.1092547625,-0.1879568696,0.053320609,-0.1798085272,0.0543711372,0.3068779409,0.0889706165,0.0182179231,0.0933937579,0.0578586012,0.0789091587,0.1313986182,-0.2548109591,-0.5290400386,0.4313287139,0.0999893919,-0.1335071027,0.0172490943,0.0369656943,0.1472673118,0.4163756669,-0.5289620161,-0.147408396,-0.3743583858,-0.0407263748,0.1663170308,-0.0284456536,0.3360123634,0.2364349663,0.0084694531,0.0739890188,-0.5291323066,-0.0030942173,0.0624897666,0.5206924081,0.017277455,0.253973037,-0.2557185888,0.5220206976,-0.1869588643,-0.2215260714,0.0310926028,0.063385509,0.4639904797,-0.0526616573,-0.1812781245,-0.1120377332,-0.1693264395,-0.122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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1856","title":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError","comments":"Hi ! Thanks for reporting. Google Drive changed the way to bypass the warning message recently.\r\n\r\nThe latest release `1.18.4` fixes this for datasets loaded in a regular way.\r\n\r\nWe opened a PR to fix this recently for streaming mode at #3843 - we'll do a new release once the fix is merged :)","body":"Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n```","comment_length":54,"text":"load_dataset(\"amazon_polarity\") NonMatchingChecksumError \n Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError.\r\n\r\nTo reproduce:\r\n```\r\nload_dataset(\"amazon_polarity\")\r\n```\r\nThis will give the following error:\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-3-8559a03fe0f8> in <module>()\r\n----> 1 dataset = load_dataset(\"amazon_polarity\")\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download']\r\n``` \n Hi ! Thanks for reporting. Google Drive changed the way to bypass the warning message recently.\r\n\r\nThe latest release `1.18.4` fixes this for datasets loaded in a regular way.\r\n\r\nWe opened a PR to fix this recently for streaming mode at #3843 - we'll do a new release once the fix is merged :)","embeddings":[-0.3437190354,0.0966543481,-0.0553667508,0.0980788767,0.1595422775,0.1303637177,0.4060622752,0.1062448844,0.2796751857,0.1851271987,0.0166727956,-0.0457291156,-0.0545699485,0.0795310065,-0.3115004897,0.1836311817,0.093634285,0.0024182529,-0.033171311,0.0503356755,-0.3750116229,0.3412531912,-0.0266329963,-0.2965781093,-0.059958145,0.1087334976,0.259834379,0.0387638323,-0.0591045991,-0.2781040072,0.2894013226,0.1775958836,0.0605969876,0.267273128,-0.0001210742,0.1270347089,0.480676502,0.0464458205,-0.3447754979,-0.346660465,-0.2960899472,-0.1657345593,0.0632346496,-0.097384952,0.0632874742,0.1760632843,0.1763350219,-0.1667176485,0.0476676375,0.0863106698,0.1507999897,0.4383392334,0.1362611204,0.3280369043,0.3653405309,0.1059550941,-0.0536529794,0.320271045,0.0322195292,-0.0164396446,-0.1721757799,0.2014843524,-0.0879839435,0.3938726187,0.0837550983,0.0840173662,0.1581204236,-0.2272022516,0.2346659303,0.3827571869,0.4093052149,-0.1783312559,-0.3232583702,-0.0660705343,-0.1668351144,-0.2191598713,0.5109215975,0.385063976,-0.0968676731,-0.1910343468,-0.5273732543,0.1616867483,-0.0737693459,0.1343280524,-0.0071523669,0.3346124887,0.1289458871,0.1375039667,0.181722641,-0.1301643401,0.5540943146,-0.3970541358,-0.1002428606,0.1011974737,-0.475274682,-0.0878621116,-0.0108202817,0.4421529174,0.1674641371,0.4825418293,0.2137374729,0.2440245748,-0.0563523322,0.2347067297,0.3133950233,0.0801914036,-0.04488419,0.2166512161,0.2387593091,0.2919817567,0.1821403652,0.1133241355,0.1322492808,-0.2957067788,0.716014564,0.0752938762,0.3360696137,-0.3598341346,-0.465077132,0.190993011,-0.2696163952,-0.1599083394,0.0353951827,0.1882270575,-0.0926532298,0.0192113332,0.0215031561,0.0143797677,-0.1030496284,0.1953545809,-0.1607060134,0.0002713708,-0.0145915356,0.2040700614,0.261760354,-0.2098516077,0.2886704206,-0.0185684841,0.1071045846,-0.0472658426,0.319562465,-0.1647793502,-0.0842153952,0.4354430437,0.0402423404,-0.0251324214,0.0261423755,0.0519357435,-0.1841760278,0.1972327083,-0.070198752,-0.1349185407,0.0448981747,0.1315767169,-0.5635558963,-0.1565745324,-0.1134006158,-0.3174130917,0.1316187978,-0.4293006361,-0.0743637756,-0.3585139513,-0.1085529923,-0.1802556068,0.0567106456,0.3284081519,-0.146276325,-0.0697274879,-0.2830065489,-0.1560033113,0.3516500294,0.3689597547,-0.0318278968,-0.0546371676,-0.3250086308,-0.2465788275,-0.0392634794,-0.0676871985,-0.2981138825,0.2138304859,-0.2134948671,0.3856245279,-0.0419421718,0.2436478585,-0.1208401695,0.0607504509,0.2761464417,0.2066648602,-0.0617968179,0.1932893842,-0.1972587854,-0.2605552375,0.2622168362,0.2096924633,0.0728528574,0.0349000357,0.3055433929,-0.2898939252,0.1232303903,-0.0740723088,-0.0887224153,0.2596243024,0.3531556129,-0.1601412594,0.0054060677,0.0219354406,-0.8069176674,0.2076688707,-0.1250990778,0.0438221805,-0.2080069035,0.0909941867,-0.063458316,-0.2499109954,-0.1973543763,0.1065252945,0.0235328525,0.4256384075,0.1528571397,0.0639320239,0.1585089713,-0.0270110499,-0.2861685455,0.005739775,-0.4672679901,0.5076681376,-0.0282692816,0.0528613292,0.2067257315,-0.1315978914,0.0975864306,-0.127996996,-0.1205122471,0.3930998743,0.0208601933,0.4677700102,-0.0458772853,0.6695402265,0.0266333763,-0.1984112561,-0.088630341,0.5136282444,0.1034500152,-0.112022765,-0.4597767889,0.6217970252,-0.2478137165,0.3044341803,-0.0774139911,0.0824286565,0.2450808436,0.0102119334,-0.2788457572,-0.2074122578,0.3316258788,0.0841060281,-0.0032650433,0.0434622243,-0.2294031829,-0.1264122128,0.3236085773,-0.1479403377,0.0144747673,0.1599473059,0.0149361873,-0.2462240607,0.1631525457,0.8612816334,0.4531297386,0.1087522283,-0.0397240035,-0.0220866296,-0.289424628,-0.1360545456,0.1524955034,0.1083447039,-0.0076908953,0.4529647231,0.1704592854,-0.1970335841,-0.552380085,-0.1582715511,0.178463608,0.2709361911,-0.4319086969,0.123382397,-0.1641664058,0.2488365173,-0.2989057004,0.0503403097,-0.2531737685,-0.4916104078,-0.0802284032,0.5802767277,-0.1813134551,0.2203902751,-0.2964736223,0.1043273509,0.1429526806,-0.6436562538,-0.0710990652,-0.1555427462,0.0790001303,-0.0280625317,0.3886137605,-0.0031124731,0.4329706728,-0.2728953958,0.0523366109,-0.8050489426,-0.2903859019,-0.0138204787,-0.2236666679,0.103193,0.1598290056,0.1895769686,0.0721710697,-0.2586636245,0.0477522872,-0.0632999316,-0.2463998646,0.2343103886,-0.206464678,0.0155873103,0.0839190707,-0.1991234869,0.0733004287,-0.2302728444,0.0301667973,0.3000383079,0.0802564099,0.006124848,0.036447417,-0.0605327636,0.1478476077,0.1328968406,-0.2649776936,-0.5636628866,0.4835601449,0.0687231198,-0.1624477357,0.0728613436,-0.0215876698,0.1557217538,0.4163084924,-0.4797168076,-0.1270244867,-0.4049365819,-0.0301018525,0.1600026786,-0.0579416454,0.3158017695,0.2516758144,-0.0009761989,0.0770346746,-0.4939174354,0.0413347073,0.0286512729,0.394384414,-0.0338165425,0.3414817154,-0.2903015018,0.4758897126,-0.140789181,-0.1457151324,0.0493923873,0.1098996028,0.411280483,-0.1020603627,-0.0777266771,-0.0594740137,-0.13453345,-0.0823289827,0.086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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1854","title":"Feature Request: Dataset.add_item","comments":"Hi @sshleifer.\r\n\r\nI am not sure of understanding the need of the `add_item` approach...\r\n\r\nBy just reading your \"Desired API\" section, I would say you could (nearly) get it with a 1-column Dataset:\r\n```python\r\ndata = {\"input_ids\": [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])]}\r\nds = Dataset.from_dict(data)\r\nassert (ds[\"input_ids\"][0] == np.array([4,4,2])).all()\r\n```","body":"I'm trying to integrate `huggingface\/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.\r\nIs this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.\r\n\r\n### Desired API\r\n\r\n```python\r\nimport numpy as np\r\ntokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\ndef build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:\r\n   \"\"\"FIXME\"\"\"\r\n   dataset = EmptyDataset()\r\n   for t in tokenized: dataset.append(t)\r\n   return dataset\r\nds = build_dataset_from_tokenized(tokenized)\r\nassert (ds[0] == np.array([4,4,2])).all()\r\n```\r\n\r\n### What I tried\r\ngrep, google for \"add one entry at a time\", \"datasets.append\"\r\n\r\n### Current Code\r\nThis code achieves the same result but doesn't fit into the `add_item` abstraction.\r\n\r\n```python\r\n    dataset = load_dataset('text', data_files={'train': 'train.txt'})\r\n    tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)\r\n    def tokenize_function(examples):\r\n        ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']\r\n        return {'input_ids': [x[1:] for x in ids]}\r\n    ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)\r\n\tprint(ds['train'][0]) => np array\r\n```\r\n\r\nThanks in advance!","comment_length":48,"text":"Feature Request: Dataset.add_item \n I'm trying to integrate `huggingface\/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.\r\nIs this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.\r\n\r\n### Desired API\r\n\r\n```python\r\nimport numpy as np\r\ntokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\ndef build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:\r\n   \"\"\"FIXME\"\"\"\r\n   dataset = EmptyDataset()\r\n   for t in tokenized: dataset.append(t)\r\n   return dataset\r\nds = build_dataset_from_tokenized(tokenized)\r\nassert (ds[0] == np.array([4,4,2])).all()\r\n```\r\n\r\n### What I tried\r\ngrep, google for \"add one entry at a time\", \"datasets.append\"\r\n\r\n### Current Code\r\nThis code achieves the same result but doesn't fit into the `add_item` abstraction.\r\n\r\n```python\r\n    dataset = load_dataset('text', data_files={'train': 'train.txt'})\r\n    tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)\r\n    def tokenize_function(examples):\r\n        ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']\r\n        return {'input_ids': [x[1:] for x in ids]}\r\n    ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)\r\n\tprint(ds['train'][0]) => np array\r\n```\r\n\r\nThanks in advance! \n Hi @sshleifer.\r\n\r\nI am not sure of understanding the need of the `add_item` approach...\r\n\r\nBy just reading your \"Desired API\" section, I would say you could (nearly) get it with a 1-column Dataset:\r\n```python\r\ndata = {\"input_ids\": [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])]}\r\nds = Dataset.from_dict(data)\r\nassert (ds[\"input_ids\"][0] == np.array([4,4,2])).all()\r\n```","embeddings":[-0.2646298409,0.1603154093,-0.0458905734,0.1386155337,-0.0252806228,0.1201700866,0.1569381356,0.1071401313,0.058670368,0.0440863669,0.1095488444,0.5823287368,-0.1573915929,0.138007164,0.4451274276,-0.1356466115,0.1021329612,0.2048072368,-0.1752964556,0.0704091564,-0.2645285428,-0.0905520767,-0.1519214809,-0.1790196747,-0.0579325259,-0.061322201,-0.4128628969,-0.3023573458,-0.2655974329,-0.7929652929,-0.0123855649,-0.0009123574,-0.0839308575,0.2624169588,-0.0001283345,0.0117334425,0.1670986712,-0.0539960302,-0.5640701652,-0.0456967317,-0.1553967893,-0.2387477756,0.1454959214,-0.3138717115,0.2260569036,-0.7180809379,0.3229591548,-0.1545696706,0.089084737,0.125013724,-0.0003758298,0.494810909,0.1991044879,-0.3639475703,0.2621561289,-0.0453420877,-0.1395283341,-0.1721628904,0.3763104081,-0.2068813294,0.0824214742,-0.1125964671,0.2658927441,-0.0915565714,0.4066393673,0.1262135953,-0.0127451709,-0.1426652074,-0.1565271169,0.2903876007,0.2264159918,-0.419044286,-0.6362015009,-0.4910083711,0.3017780781,-0.0399683453,-0.1348128319,0.060155388,-0.0711733475,-0.0760381594,-0.069809854,-0.3770206571,-0.111912109,0.2381709218,0.0717944801,0.4428234696,0.1425431371,0.1643607467,0.1441977918,-0.1271944046,-0.2345662117,0.0151866274,-0.1232940331,0.3333287835,-0.1031110138,-0.1166460291,0.1092310101,-0.0470127016,0.4755124152,-0.061299704,0.173726812,0.1157122627,-0.3675787151,0.0866340697,0.171476528,-0.1008807644,0.2137441039,-0.2119158655,-0.3128740191,0.0772754475,0.4092112184,0.0620396063,-0.1824602634,0.1073736846,-0.2618193328,-0.3582352996,-0.0681386217,0.4037789404,-0.1207616106,-0.0273630079,-0.0106998915,0.0841955394,-0.0472879857,0.173677966,0.1717312634,-0.3679118156,-0.2393834144,0.2014809549,0.1263076216,-0.0102789896,0.0816346034,0.217958957,-0.1080401167,0.1765950322,0.1008231193,-0.001051515,-0.014450158,-0.0649894327,0.0691936165,0.0783881024,-0.0114106443,-0.1804757416,0.2521993816,0.0106602665,0.0532554165,-0.0456522033,-0.1014854684,-0.4404917955,-0.4781494141,0.2198809236,-0.1078879684,-0.1812904775,-0.2244119793,-0.0641979277,0.0042391834,-0.1642895639,-0.1952681541,0.5241122246,0.3831294477,-0.2358407676,0.1302087307,-0.1283583939,-0.5635914207,-0.1791516542,0.1944267601,0.3399145603,-0.4468756914,-0.3274410367,-0.1016851589,-0.0158187244,-0.4132902026,0.1295030862,-0.0944460928,0.2800901532,-0.4338248074,0.4084133506,0.3091626167,-0.1658173501,-0.2207370549,0.1308985353,-0.1782537401,0.0376557037,0.0842344984,0.3958645165,0.2953098118,0.060019996,0.4510013461,0.2585290372,-0.0795216337,-0.0899351388,0.0736893639,-0.4146664441,0.3007451296,-0.0111934626,-0.1263205856,-0.0905314982,0.0279579051,-0.5407937765,0.1644920558,-0.1066171676,0.3368983269,-0.0383560583,0.4019224942,-0.0076305624,-0.1058239862,-0.5203376412,-0.3627278507,0.1384117454,-0.0773245841,0.1222194284,-0.1124037206,-0.4358099401,-0.1482965052,0.4267143309,-0.1347390115,-0.0214686058,-0.0691511258,0.0572068244,-0.0093465028,-0.1200825647,-0.4359641373,0.1428783089,0.2283416539,0.3076541424,-0.4393087029,-0.049837932,0.2686482966,-0.1849064827,-0.1217021868,0.1733721644,-0.141075328,-0.1443530619,0.208371371,0.2823854685,-0.0867410675,-0.27010414,0.1203915998,0.2604833245,0.2900448143,0.3060085773,-0.1001439542,-0.2057063431,0.3140789568,-0.2532829642,-0.2291707546,0.5499863029,0.0419301353,0.4261950552,0.1370570213,0.0392848514,0.0201952625,0.2511620224,-0.2537923455,-0.2578029633,-0.0499058366,0.1488565952,0.2123851925,-0.1196054667,-0.1045179814,0.1626659185,0.1422627568,0.1596186012,0.1770738661,0.1443223208,-0.2144739032,0.0623016506,0.0679695085,0.0149866864,0.4575430453,0.0544013269,0.1145165935,0.0659159645,0.111045748,0.0568909645,0.0589827783,0.031461373,-0.1170743406,0.0384728573,0.5389946699,0.0615468398,-0.0218392145,-0.1388209462,0.1136970893,-0.0432560146,-0.1218187734,0.0397563912,0.0448243991,0.0722149983,-0.2530289888,-0.3716776073,-0.0849188417,-0.0843767971,0.2689409852,0.0314313322,0.0156413484,0.301081568,0.137987569,0.7158766985,0.1047442034,-0.4731730223,-0.0859230235,0.0065621044,0.2182613462,-0.101071611,-0.1031731069,0.3030089736,0.402112186,0.0869909003,-0.0316570625,-0.3475198448,-0.5039143562,0.1095275655,-0.051787585,0.4337313473,0.3948709071,-0.335601896,-0.1123425141,-0.2517050803,0.1836480349,0.0373475961,-0.1394076496,-0.1860643178,-0.0976857767,-0.2373919785,-0.0496559888,0.1030326337,-0.206016019,-0.1766934991,0.4733741581,0.1536125243,0.2519126236,-0.2228932679,0.0982955918,0.0336947665,-0.1396936774,-0.1208668947,0.0718317702,-0.0042209281,0.4730607271,-0.3619567752,-0.1762785763,-0.1606230736,-0.5109411478,0.1114847213,0.377494216,-0.2879175246,-0.3993910253,-0.2613127232,0.3807624578,-0.0936847404,0.2705568075,0.4955573678,0.0041033556,0.1533956528,0.0573997162,-0.287943095,0.374073267,0.1856687218,0.0599000379,0.0809817091,0.1704524159,0.0867763981,0.663498342,0.1845435947,-0.1084073484,0.1433631629,-0.5918194056,0.3627535999,-0.2168656737,-0.2220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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1854","title":"Feature Request: Dataset.add_item","comments":"Hi @sshleifer :) \r\n\r\nWe don't have methods like `Dataset.add_batch` or `Dataset.add_entry\/add_item` yet.\r\nBut that's something we'll add pretty soon. Would an API that looks roughly like this help ? Do you have suggestions ?\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset\r\n\r\ntokenized = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\n# API suggestion (not available yet)\r\nd = Dataset()\r\nfor input_ids in tokenized:\r\n    d.add_item({\"input_ids\": input_ids})\r\n\r\nprint(d[0][\"input_ids\"])\r\n# [4, 4, 2]\r\n```\r\n\r\nCurrently you can define a dataset with what @albertvillanova suggest, or via a generator using dataset builders. It's also possible to [concatenate datasets](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=concatenate#datasets.concatenate_datasets).","body":"I'm trying to integrate `huggingface\/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.\r\nIs this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.\r\n\r\n### Desired API\r\n\r\n```python\r\nimport numpy as np\r\ntokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\ndef build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:\r\n   \"\"\"FIXME\"\"\"\r\n   dataset = EmptyDataset()\r\n   for t in tokenized: dataset.append(t)\r\n   return dataset\r\nds = build_dataset_from_tokenized(tokenized)\r\nassert (ds[0] == np.array([4,4,2])).all()\r\n```\r\n\r\n### What I tried\r\ngrep, google for \"add one entry at a time\", \"datasets.append\"\r\n\r\n### Current Code\r\nThis code achieves the same result but doesn't fit into the `add_item` abstraction.\r\n\r\n```python\r\n    dataset = load_dataset('text', data_files={'train': 'train.txt'})\r\n    tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)\r\n    def tokenize_function(examples):\r\n        ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']\r\n        return {'input_ids': [x[1:] for x in ids]}\r\n    ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)\r\n\tprint(ds['train'][0]) => np array\r\n```\r\n\r\nThanks in advance!","comment_length":92,"text":"Feature Request: Dataset.add_item \n I'm trying to integrate `huggingface\/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https:\/\/github.com\/pytorch\/fairseq\/blob\/master\/fairseq\/data\/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.\r\nIs this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.\r\n\r\n### Desired API\r\n\r\n```python\r\nimport numpy as np\r\ntokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\ndef build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:\r\n   \"\"\"FIXME\"\"\"\r\n   dataset = EmptyDataset()\r\n   for t in tokenized: dataset.append(t)\r\n   return dataset\r\nds = build_dataset_from_tokenized(tokenized)\r\nassert (ds[0] == np.array([4,4,2])).all()\r\n```\r\n\r\n### What I tried\r\ngrep, google for \"add one entry at a time\", \"datasets.append\"\r\n\r\n### Current Code\r\nThis code achieves the same result but doesn't fit into the `add_item` abstraction.\r\n\r\n```python\r\n    dataset = load_dataset('text', data_files={'train': 'train.txt'})\r\n    tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)\r\n    def tokenize_function(examples):\r\n        ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']\r\n        return {'input_ids': [x[1:] for x in ids]}\r\n    ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)\r\n\tprint(ds['train'][0]) => np array\r\n```\r\n\r\nThanks in advance! \n Hi @sshleifer :) \r\n\r\nWe don't have methods like `Dataset.add_batch` or `Dataset.add_entry\/add_item` yet.\r\nBut that's something we'll add pretty soon. Would an API that looks roughly like this help ? Do you have suggestions ?\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset\r\n\r\ntokenized = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])\r\n\r\n# API suggestion (not available yet)\r\nd = Dataset()\r\nfor input_ids in tokenized:\r\n    d.add_item({\"input_ids\": input_ids})\r\n\r\nprint(d[0][\"input_ids\"])\r\n# [4, 4, 2]\r\n```\r\n\r\nCurrently you can define a dataset with what @albertvillanova suggest, or via a generator using dataset builders. It's also possible to [concatenate datasets](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=concatenate#datasets.concatenate_datasets).","embeddings":[-0.2646298409,0.1603154093,-0.0458905734,0.1386155337,-0.0252806228,0.1201700866,0.1569381356,0.1071401313,0.058670368,0.0440863669,0.1095488444,0.5823287368,-0.1573915929,0.138007164,0.4451274276,-0.1356466115,0.1021329612,0.2048072368,-0.1752964556,0.0704091564,-0.2645285428,-0.0905520767,-0.1519214809,-0.1790196747,-0.0579325259,-0.061322201,-0.4128628969,-0.3023573458,-0.2655974329,-0.7929652929,-0.0123855649,-0.0009123574,-0.0839308575,0.2624169588,-0.0001283345,0.0117334425,0.1670986712,-0.0539960302,-0.5640701652,-0.0456967317,-0.1553967893,-0.2387477756,0.1454959214,-0.3138717115,0.2260569036,-0.7180809379,0.3229591548,-0.1545696706,0.089084737,0.125013724,-0.0003758298,0.494810909,0.1991044879,-0.3639475703,0.2621561289,-0.0453420877,-0.1395283341,-0.1721628904,0.3763104081,-0.2068813294,0.0824214742,-0.1125964671,0.2658927441,-0.0915565714,0.4066393673,0.1262135953,-0.0127451709,-0.1426652074,-0.1565271169,0.2903876007,0.2264159918,-0.419044286,-0.6362015009,-0.4910083711,0.3017780781,-0.0399683453,-0.1348128319,0.060155388,-0.0711733475,-0.0760381594,-0.069809854,-0.3770206571,-0.111912109,0.2381709218,0.0717944801,0.4428234696,0.1425431371,0.1643607467,0.1441977918,-0.1271944046,-0.2345662117,0.0151866274,-0.1232940331,0.3333287835,-0.1031110138,-0.1166460291,0.1092310101,-0.0470127016,0.4755124152,-0.061299704,0.173726812,0.1157122627,-0.3675787151,0.0866340697,0.171476528,-0.1008807644,0.2137441039,-0.2119158655,-0.3128740191,0.0772754475,0.4092112184,0.0620396063,-0.1824602634,0.1073736846,-0.2618193328,-0.3582352996,-0.0681386217,0.4037789404,-0.1207616106,-0.0273630079,-0.0106998915,0.0841955394,-0.0472879857,0.173677966,0.1717312634,-0.3679118156,-0.2393834144,0.2014809549,0.1263076216,-0.0102789896,0.0816346034,0.217958957,-0.1080401167,0.1765950322,0.1008231193,-0.001051515,-0.014450158,-0.0649894327,0.0691936165,0.0783881024,-0.0114106443,-0.1804757416,0.2521993816,0.0106602665,0.0532554165,-0.0456522033,-0.1014854684,-0.4404917955,-0.4781494141,0.2198809236,-0.1078879684,-0.1812904775,-0.2244119793,-0.0641979277,0.0042391834,-0.1642895639,-0.1952681541,0.5241122246,0.3831294477,-0.2358407676,0.1302087307,-0.1283583939,-0.5635914207,-0.1791516542,0.1944267601,0.3399145603,-0.4468756914,-0.3274410367,-0.1016851589,-0.0158187244,-0.4132902026,0.1295030862,-0.0944460928,0.2800901532,-0.4338248074,0.4084133506,0.3091626167,-0.1658173501,-0.2207370549,0.1308985353,-0.1782537401,0.0376557037,0.0842344984,0.3958645165,0.2953098118,0.060019996,0.4510013461,0.2585290372,-0.0795216337,-0.0899351388,0.0736893639,-0.4146664441,0.3007451296,-0.0111934626,-0.1263205856,-0.0905314982,0.0279579051,-0.5407937765,0.1644920558,-0.1066171676,0.3368983269,-0.0383560583,0.4019224942,-0.0076305624,-0.1058239862,-0.5203376412,-0.3627278507,0.1384117454,-0.0773245841,0.1222194284,-0.1124037206,-0.4358099401,-0.1482965052,0.4267143309,-0.1347390115,-0.0214686058,-0.0691511258,0.0572068244,-0.0093465028,-0.1200825647,-0.4359641373,0.1428783089,0.2283416539,0.3076541424,-0.4393087029,-0.049837932,0.2686482966,-0.1849064827,-0.1217021868,0.1733721644,-0.141075328,-0.1443530619,0.208371371,0.2823854685,-0.0867410675,-0.27010414,0.1203915998,0.2604833245,0.2900448143,0.3060085773,-0.1001439542,-0.2057063431,0.3140789568,-0.2532829642,-0.2291707546,0.5499863029,0.0419301353,0.4261950552,0.1370570213,0.0392848514,0.0201952625,0.2511620224,-0.2537923455,-0.2578029633,-0.0499058366,0.1488565952,0.2123851925,-0.1196054667,-0.1045179814,0.1626659185,0.1422627568,0.1596186012,0.1770738661,0.1443223208,-0.2144739032,0.0623016506,0.0679695085,0.0149866864,0.4575430453,0.0544013269,0.1145165935,0.0659159645,0.111045748,0.0568909645,0.0589827783,0.031461373,-0.1170743406,0.0384728573,0.5389946699,0.0615468398,-0.0218392145,-0.1388209462,0.1136970893,-0.0432560146,-0.1218187734,0.0397563912,0.0448243991,0.0722149983,-0.2530289888,-0.3716776073,-0.0849188417,-0.0843767971,0.2689409852,0.0314313322,0.0156413484,0.301081568,0.137987569,0.7158766985,0.1047442034,-0.4731730223,-0.0859230235,0.0065621044,0.2182613462,-0.101071611,-0.1031731069,0.3030089736,0.402112186,0.0869909003,-0.0316570625,-0.3475198448,-0.5039143562,0.1095275655,-0.051787585,0.4337313473,0.3948709071,-0.335601896,-0.1123425141,-0.2517050803,0.1836480349,0.0373475961,-0.1394076496,-0.1860643178,-0.0976857767,-0.2373919785,-0.0496559888,0.1030326337,-0.206016019,-0.1766934991,0.4733741581,0.1536125243,0.2519126236,-0.2228932679,0.0982955918,0.0336947665,-0.1396936774,-0.1208668947,0.0718317702,-0.0042209281,0.4730607271,-0.3619567752,-0.1762785763,-0.1606230736,-0.5109411478,0.1114847213,0.377494216,-0.2879175246,-0.3993910253,-0.2613127232,0.3807624578,-0.0936847404,0.2705568075,0.4955573678,0.0041033556,0.1533956528,0.0573997162,-0.287943095,0.374073267,0.1856687218,0.0599000379,0.0809817091,0.1704524159,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1849","title":"Add TIMIT","comments":"@patrickvonplaten Could you please help me with how the output text has to be represented in the data? TIMIT has Words, Phonemes and texts. Also has lot on info on the speaker and the dialect. Could you please help me? An example of how to arrange it would be super helpful!\r\n\r\n","body":"## Adding a Dataset\r\n- **Name:** *TIMIT*\r\n- **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems*\r\n\r\n- **Paper:** *Homepage*: http:\/\/groups.inf.ed.ac.uk\/ami\/corpus\/ \/ *Wikipedia*: https:\/\/en.wikipedia.org\/wiki\/TIMIT\r\n- **Data:** *https:\/\/deepai.org\/dataset\/timit*\r\n- **Motivation:** Important speech dataset\r\n\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":51,"text":"Add TIMIT \n ## Adding a Dataset\r\n- **Name:** *TIMIT*\r\n- **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems*\r\n\r\n- **Paper:** *Homepage*: http:\/\/groups.inf.ed.ac.uk\/ami\/corpus\/ \/ *Wikipedia*: https:\/\/en.wikipedia.org\/wiki\/TIMIT\r\n- **Data:** *https:\/\/deepai.org\/dataset\/timit*\r\n- **Motivation:** Important speech dataset\r\n\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n @patrickvonplaten Could you please help me with how the output text has to be represented in the data? TIMIT has Words, Phonemes and texts. Also has lot on info on the speaker and the dialect. Could you please help me? An example of how to arrange it would be super helpful!\r\n\r\n","embeddings":[0.0733412355,-0.3110238612,-0.1762830466,0.1887156516,0.0858767852,-0.1072419658,0.1176897883,0.0632870495,-0.4261901975,-0.1358919293,-0.2996531427,0.1368214786,-0.0700436831,0.3138222992,0.2315153927,-0.3812035322,-0.0924161598,0.1239683703,-0.2485858947,-0.1681149155,-0.0206748378,0.2014280111,0.0602086633,-0.017016774,-0.0788321272,-0.1857829541,-0.1064575762,-0.19129695,-0.2009379417,-0.4111648798,-0.1293073744,0.0393785276,0.0996622592,0.521894455,-0.0001090629,-0.1116432026,-0.0847964659,-0.2752177417,-0.048206795,-0.1977025419,0.0026013448,0.0172410663,-0.1876087934,0.0332623161,-0.1161857545,0.0043508285,0.1767341495,-0.2566524446,0.152622968,0.2560269833,0.1796585619,-0.2397240102,0.0037524428,0.120319806,0.1837881505,0.3532900214,-0.1659121662,-0.1861263961,0.442364037,0.3691049516,0.154524371,0.2054741234,-0.2338324636,-0.1602454036,0.1172219515,0.1588024646,-0.1280131787,-0.4249416292,-0.2001318932,0.1614860892,1.0803807974,-0.2730861008,-0.2102272958,-0.1682152897,-0.0067150947,-0.0388446786,0.0545272641,0.0947851166,-0.1075404435,0.2822042704,-0.1284112781,-0.0873562098,-0.4036233127,0.2998380661,0.1751087606,-0.0903957784,-0.1651895642,-0.0817210227,-0.2652519941,-0.0224783812,-0.1265884787,-0.0691512898,0.1829085946,0.1443703324,0.1264306903,-0.2070368081,-0.0722235516,-0.2396785468,0.1297903657,0.0799331218,0.1578906029,0.1282496452,-0.3613839447,-0.035532821,0.3212117255,-0.1483234763,0.1604641378,-0.3020412922,0.0304557309,-0.3613665104,0.1621885449,-0.2847991586,-0.2428912669,0.2361746877,0.1377900392,-0.0467887186,0.1481570601,-0.2279863954,0.0026647793,0.0527142324,-0.0180348344,0.0589291118,-0.0303587299,0.0837378055,-0.1445214301,0.1464492083,0.1182098612,0.2381437719,-0.0150136463,-0.1115037352,0.0840686038,-0.0457720794,-0.2555977702,0.1817157567,0.0520271473,-0.1000453383,0.1685810685,0.1960580498,-0.011818029,-0.0784360245,-0.1366565526,-0.1784195602,0.2777072787,-0.1461721361,0.0527808405,0.1390263438,-0.1903186888,-0.0470301062,-0.3633146882,0.1211081743,0.1679523736,-0.0422931872,-0.0453132279,0.1867241114,-0.0124824429,-0.156264171,-0.1769187301,0.711476624,0.2749207914,-0.1027399525,-0.0075083468,0.1939911097,-0.5860921741,-0.105024077,0.1776429713,0.0585591421,-0.6696823835,0.0212520659,-0.0378686897,0.1611167639,0.2975309491,0.2125638127,0.3395764232,0.586378336,0.0210056063,0.2667877078,0.171755597,-0.2706251442,-0.1431101263,0.1369380355,-0.1598664969,-0.1170849726,-0.0473485291,0.1449374706,0.4792860448,-0.101877138,0.0294850636,0.1940071881,-0.2014424205,-0.0235529151,-0.0290610548,-0.0371894427,-0.1204591691,0.0778586268,-0.1989880204,-0.106796056,-0.1251796186,0.217751056,0.2246510237,0.0652947649,0.3470469713,0.069211565,0.3847998381,0.1909002364,0.129735589,-0.4917135239,0.4033924043,-0.1964217871,0.1526651829,0.4782302082,-0.1473187506,-0.167142421,-0.3596304655,-0.2206088901,-0.0516645722,0.2606676221,0.2205816954,0.1991176605,-0.009456072,0.0857204795,0.0355820432,0.4501103759,-0.0075565879,-0.2419346571,-0.3201273382,0.2504458129,0.0951381922,-0.3446833789,-0.014947948,0.4989426136,-0.0831925943,0.1493805498,0.1380867064,0.2466288507,0.0038797308,0.0135975964,-0.2022036761,-0.1101445854,0.2628404498,-0.4228825867,0.3313645124,0.06269072,0.0637856349,-0.2258459479,0.0393554047,-0.0205577482,0.1559752524,0.0706196427,-0.1424725205,-0.1016159207,-0.1329055876,0.0496608876,-0.0569060482,-0.1106606722,0.0648706481,0.1193826869,-0.0562707148,-0.2600478232,-0.262647301,0.2423307896,0.2686450779,0.006195752,0.092106007,0.0466340072,-0.2050528377,0.2062917203,-0.0478995517,-0.4742149115,-0.0054341042,0.2985425293,0.193499878,-0.0386918783,0.1178182811,-0.0000569245,0.0734971911,0.144157365,-0.1406797022,0.2691238225,-0.168408528,0.0144770807,-0.2006818354,-0.0646961257,-0.2875880897,0.0392232202,-0.1764378697,-0.1992070526,0.0765008181,-0.1841205955,-0.2453136891,-0.3167244196,0.099512428,0.0610859059,0.2686392367,0.1480069607,0.0491264388,-0.0101225134,0.2124205083,0.1507262439,0.0232352819,-0.0001346469,-0.0215184875,-0.0320803262,-0.1561594307,0.1696841866,0.0760947093,0.3061435819,0.4528852105,-0.3062444031,0.0146126365,0.0802450404,-0.0178312529,0.2304171473,-0.3757922351,0.2834007144,0.0289359558,-0.0000253201,-0.3605325818,-0.4115389287,0.0161460415,-0.0315226577,-0.291928798,0.0743878484,-0.0956783667,-0.1340010762,0.1157216802,-0.4445347786,-0.2971717417,-0.3025036752,0.3352980316,0.0821444616,0.2249576449,0.1699913144,-0.1208080798,0.1753780097,0.1636454761,0.4341614246,-0.3202284276,-0.3624622822,0.3046229482,-0.2548819482,-0.297619313,-0.0871013105,0.0060070879,0.0260227639,0.0635265186,-0.2088310272,0.1419960558,-0.0100438697,0.0497111231,0.0847021714,0.1780128628,0.1583238989,0.0765187666,-0.0798770636,-0.2320663631,0.0328554511,0.1476105601,0.1669758856,0.1966291964,0.2978936434,0.0147548905,0.0068026502,0.3493381441,0.4952243865,0.0325859189,0.062058378,0.2822734118,0.0033729149,-0.0174730867,-0.3049688637,0.1944867671,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1849","title":"Add TIMIT","comments":"Hey @vrindaprabhu - sure I'll help you :-) Could you open a first PR for TIMIT where you copy-paste more or less the `librispeech_asr` script: https:\/\/github.com\/huggingface\/datasets\/blob\/28be129db862ec89a87ac9349c64df6b6118aff4\/datasets\/librispeech_asr\/librispeech_asr.py#L93 (obviously replacing all the naming and links correctly...) and then you can list all possible outputs in the features dict: https:\/\/github.com\/huggingface\/datasets\/blob\/28be129db862ec89a87ac9349c64df6b6118aff4\/datasets\/librispeech_asr\/librispeech_asr.py#L104 (words, phonemes should probably be of kind `datasets.Sequence(datasets.Value(\"string\"))` and texts I think should be of type `\"text\": datasets.Value(\"string\")`.\r\n\r\nWhen you've opened a first PR, I think it'll be much easier for us to take a look together :-) ","body":"## Adding a Dataset\r\n- **Name:** *TIMIT*\r\n- **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems*\r\n\r\n- **Paper:** *Homepage*: http:\/\/groups.inf.ed.ac.uk\/ami\/corpus\/ \/ *Wikipedia*: https:\/\/en.wikipedia.org\/wiki\/TIMIT\r\n- **Data:** *https:\/\/deepai.org\/dataset\/timit*\r\n- **Motivation:** Important speech dataset\r\n\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":85,"text":"Add TIMIT \n ## Adding a Dataset\r\n- **Name:** *TIMIT*\r\n- **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems*\r\n\r\n- **Paper:** *Homepage*: http:\/\/groups.inf.ed.ac.uk\/ami\/corpus\/ \/ *Wikipedia*: https:\/\/en.wikipedia.org\/wiki\/TIMIT\r\n- **Data:** *https:\/\/deepai.org\/dataset\/timit*\r\n- **Motivation:** Important speech dataset\r\n\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hey @vrindaprabhu - sure I'll help you :-) Could you open a first PR for TIMIT where you copy-paste more or less the `librispeech_asr` script: https:\/\/github.com\/huggingface\/datasets\/blob\/28be129db862ec89a87ac9349c64df6b6118aff4\/datasets\/librispeech_asr\/librispeech_asr.py#L93 (obviously replacing all the naming and links correctly...) and then you can list all possible outputs in the features dict: https:\/\/github.com\/huggingface\/datasets\/blob\/28be129db862ec89a87ac9349c64df6b6118aff4\/datasets\/librispeech_asr\/librispeech_asr.py#L104 (words, phonemes should probably be of kind `datasets.Sequence(datasets.Value(\"string\"))` and texts I think should be of type `\"text\": datasets.Value(\"string\")`.\r\n\r\nWhen you've opened a first PR, I think it'll be much easier for us to take a look together :-) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1849","title":"Add TIMIT","comments":"I am sorry! I created the PR [#1903](https:\/\/github.com\/huggingface\/datasets\/pull\/1903#). Requesting your comments! CircleCI tests are failing, will address them along with your comments!","body":"## Adding a Dataset\r\n- **Name:** *TIMIT*\r\n- **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems*\r\n\r\n- **Paper:** *Homepage*: http:\/\/groups.inf.ed.ac.uk\/ami\/corpus\/ \/ *Wikipedia*: https:\/\/en.wikipedia.org\/wiki\/TIMIT\r\n- **Data:** *https:\/\/deepai.org\/dataset\/timit*\r\n- **Motivation:** Important speech dataset\r\n\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":22,"text":"Add TIMIT \n ## Adding a Dataset\r\n- **Name:** *TIMIT*\r\n- **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems*\r\n\r\n- **Paper:** *Homepage*: http:\/\/groups.inf.ed.ac.uk\/ami\/corpus\/ \/ *Wikipedia*: https:\/\/en.wikipedia.org\/wiki\/TIMIT\r\n- **Data:** *https:\/\/deepai.org\/dataset\/timit*\r\n- **Motivation:** Important speech dataset\r\n\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I am sorry! I created the PR [#1903](https:\/\/github.com\/huggingface\/datasets\/pull\/1903#). Requesting your comments! CircleCI tests are failing, will address them along with your comments!","embeddings":[-0.2418492585,-0.3422915041,-0.1415262073,0.071031943,-0.0340815671,-0.0950556621,0.1323646009,0.1322909743,-0.3182575405,0.2184933424,-0.3416487277,0.185334608,-0.2274647206,0.2172827274,0.1103418171,-0.274307251,-0.0984166339,0.0761334673,0.0409776382,-0.0154816629,0.0028428342,0.1751541942,-0.1123562455,-0.2232402563,-0.030483596,-0.1709174365,-0.0104662273,-0.1952399015,-0.2521417737,-0.4360906482,-0.2043971121,0.1632164568,-0.0707419217,0.478176862,-0.000098801,-0.0432505235,0.3008354902,-0.2335778475,-0.2098337859,-0.0855702981,0.1470597982,0.0775862336,-0.2077464014,0.0137366457,-0.3632184267,0.081985265,0.0991953835,-0.1189360246,0.1455515176,0.3298049867,0.302805841,0.2105756998,0.1843785495,-0.14840509,0.291999191,0.1259447336,-0.2449664474,-0.0080540609,0.3158089221,0.2650029063,-0.0971473306,0.3574872017,-0.0624654032,-0.0115335323,0.088795051,-0.0562791713,-0.1725324392,-0.287977159,-0.0953781977,0.2019649148,0.6051986814,-0.3522334695,-0.3222532272,-0.1541470587,0.1114054844,-0.2598361075,0.0426243395,0.0666830689,-0.3443680406,0.2692187726,-0.1005103216,-0.1166076884,-0.1049024016,0.1386529356,0.1667456627,-0.0524291359,-0.1955411732,-0.2295540124,0.049006924,-0.1806072146,-0.3074808121,0.1258800626,0.0600780919,0.0210451595,0.0164981037,-0.2695724368,0.0644150525,-0.0103564216,0.1720127612,0.272112608,0.2065294534,0.2726242542,-0.1945527643,0.0149006238,0.0328458287,-0.0967329964,-0.013960856,-0.270267874,0.2339609116,-0.029196674,0.0716520175,-0.0332951508,0.0932587683,0.2528772056,-0.030330563,-0.1892641783,-0.0086388923,-0.1349222511,-0.1671446264,-0.0076821512,-0.1076601967,-0.031766437,0.0806055963,0.3081170917,-0.0160189029,0.1887349188,0.0280749369,0.2908256948,-0.1090196073,-0.1577567458,-0.0441772155,0.0036540849,0.0693574324,0.2889721096,0.3754378259,0.0159705095,0.3058305085,-0.010897629,0.0807985663,0.0981955677,-0.0059638731,-0.0427165478,0.2109492421,0.0302290097,0.1373911202,-0.0289445873,-0.1801170111,-0.0946732759,-0.2171858251,-0.1117169261,0.1574805975,-0.006221666,-0.1678809971,0.310387224,-0.036877688,-0.2231855094,0.062778011,0.5657606125,0.1443596929,-0.2489610463,0.0116325552,0.2420761734,-0.5268597603,0.0170172211,0.1285327524,0.2361277491,-0.1103185117,-0.1806617528,0.028469773,0.1733750254,0.2071208805,0.3799465299,0.0689258352,0.1884655207,-0.0609649643,0.2828649879,0.0272428263,-0.2947927415,-0.3006776571,-0.0205902513,-0.2514826655,-0.0225665607,0.1542674303,0.1046828181,0.1874707043,-0.0937609375,0.2120353878,-0.0455918014,0.0191751495,-0.0072499989,-0.2553142309,-0.1871530861,-0.1492057443,0.1607584357,-0.0763448551,-0.093991518,0.0210798513,0.1162800565,0.2667903602,-0.0516692996,0.2920298278,0.2032511681,0.3211770654,-0.0704505816,0.1548584849,-0.4971804023,0.3612621129,-0.1764655858,-0.0748251304,0.5619053841,-0.1617320925,-0.3172180355,-0.2374285758,-0.1571286321,0.0772495642,0.1063983142,0.3205331862,0.2693744302,0.0062883026,0.247822836,-0.0563230887,0.3235151172,-0.0477462113,-0.0809992924,-0.1075146124,-0.0702726319,-0.1081745699,-0.198239997,0.2134743631,0.3048537076,-0.2491975129,0.100148946,0.1128685176,0.2063148022,0.0032749476,0.1444820464,-0.0971877947,-0.0155652482,0.3050767183,-0.5114291906,-0.0984993353,0.1117218584,0.1653170139,-0.1006155685,-0.0515682511,0.0679313317,0.4120126367,-0.2124266922,0.0480910204,0.010254438,0.1641913056,-0.000184682,-0.1825164557,-0.1345059276,0.2180036604,0.0489832386,0.0848791227,-0.1027614549,-0.2898696363,0.0887442753,0.1933972687,-0.0700599179,0.2708614469,0.1330530494,-0.2582097054,-0.0150856543,-0.0243688151,-0.2392414808,0.069826968,0.3799447417,0.1702325642,-0.099079825,0.063788116,-0.1242570803,0.1739594191,0.1781301647,-0.3330646455,0.2277119756,0.1620798707,0.0017155075,-0.5846610069,-0.0186314862,-0.1945096254,0.0477673672,-0.220249474,-0.2804267704,0.0822401643,-0.1402014494,0.0397124551,-0.0347185098,-0.0160561763,0.0751506463,0.3684802353,0.2897178829,-0.1119676158,0.0800262764,-0.0768050104,0.3573745489,0.0550491661,-0.0802040324,-0.1860490292,-0.1291750222,-0.0031819162,0.2529430389,0.1803863198,0.0536898151,0.4089682996,-0.1718646884,0.2351417989,-0.0699668303,-0.2305305153,0.2565394938,-0.3240991235,0.1748987734,0.0680630133,-0.0792354718,-0.3135059476,-0.5873499513,-0.0457584821,-0.2532635927,-0.3723116219,0.0103995232,-0.2013486475,-0.1547277421,-0.2350603044,-0.4306767285,-0.1491475105,-0.458687216,0.4364933968,0.2045632303,-0.0967970192,0.3382937014,-0.2340962738,0.1816582531,-0.0209201481,0.5309724212,-0.3852647841,-0.3563811183,0.2105848044,-0.3785663247,-0.3260809481,-0.277775377,-0.0822068602,0.1823603362,0.0284910779,-0.2786485851,-0.045653794,-0.1603798419,0.2161771357,0.0745411366,0.0627595335,0.190913707,0.2142303288,-0.2686978579,-0.1128789186,-0.0405480973,0.1360976398,0.0716317147,0.1438774168,0.1299643368,-0.135065034,0.0226934608,0.5563359261,0.2371796072,0.011820606,0.4369502366,-0.0162489023,0.3077051938,-0.1899001747,-0.1996940672,0.2642294765,0.0733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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1844","title":"Update Open Subtitles corpus with original sentence IDs","comments":"Hi ! You're right this can can useful.\r\nThis should be easy to add, so feel free to give it a try if you want to contribute :)\r\nI think we just need to add it to the _generate_examples method of the OpenSubtitles dataset builder [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/open_subtitles\/open_subtitles.py#L103)","body":"Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https:\/\/huggingface.co\/datasets\/open_subtitles).\r\n\r\nI can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.\r\n\r\nI think I should tag @abhishekkrthakur as he's the one who added it in the first place.\r\n\r\nThanks!","comment_length":46,"text":"Update Open Subtitles corpus with original sentence IDs \n Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https:\/\/huggingface.co\/datasets\/open_subtitles).\r\n\r\nI can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.\r\n\r\nI think I should tag @abhishekkrthakur as he's the one who added it in the first place.\r\n\r\nThanks! \n Hi ! You're right this can can useful.\r\nThis should be easy to add, so feel free to give it a try if you want to contribute :)\r\nI think we just need to add it to the _generate_examples method of the OpenSubtitles dataset builder [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/open_subtitles\/open_subtitles.py#L103)","embeddings":[0.2011127472,0.1604523808,-0.048341617,-0.2089405507,-0.1301655918,0.2428105474,0.2711240649,0.2571239769,-0.2969917953,-0.0386743955,-0.3145644665,0.2453640103,0.215855971,-0.0763090253,0.0854991674,-0.3325570524,0.117993623,0.0025806152,0.5615056753,-0.2076058537,-0.3436259627,0.1963971108,0.0123375561,-0.1273038834,-0.1921984106,0.3085808158,-0.4058002532,0.0476240404,0.0575446412,-0.5313264728,0.0231936891,0.53368783,0.2939296663,0.0675309375,-0.0001140425,-0.1719845384,0.2067976594,-0.2393595129,-0.2624618709,-0.0709322691,0.087852411,-0.3210977018,-0.233664602,-0.3475590646,0.024387246,0.0029209892,0.0848526284,-0.3639572561,-0.1932421178,0.1132182404,0.1002750471,-0.0030477983,0.0137683395,-0.1201956421,0.576749146,-0.0308856275,0.1840917468,0.310872823,0.5937849283,0.2088736743,0.1791096926,0.3953019381,-0.426784724,-0.4061588049,-0.2225939482,-0.059054561,-0.0444161557,-0.2765921354,-0.0753083751,0.1490181535,0.4300260246,-0.1163203642,-0.7273470163,-0.4193949997,0.0371321552,-0.2022924572,-0.2925474346,0.4444063604,0.3025398254,0.3318837881,0.1208859906,-0.130856052,0.087955229,-0.0798967779,0.3085599244,0.1870670319,0.2493821979,-0.1455203444,0.1123643667,-0.1316785812,-0.2491819113,0.1970633268,-0.4026860297,0.0355916992,-0.0091518778,-0.1193122715,-0.1048696861,-0.4867628813,-0.0857825354,-0.0360756852,0.0594599918,0.0893293247,-0.2421433032,-0.1071767956,0.1065069735,0.298519969,0.4347006679,0.2094463259,-0.0822046176,0.043052882,-0.1378408521,-0.0407979973,-0.2322012633,0.2316170037,-0.318601191,0.2469429672,0.139196381,0.0557126105,-0.0039227339,0.1390759498,-0.4542080164,-0.0586221181,-0.1460408717,0.0145326732,0.0881806687,0.2371002883,0.2280703038,0.1176126897,-0.0117670847,-0.3454347253,-0.0980366468,-0.0226032846,0.0525417402,0.0726901889,-0.1043014079,-0.3754671514,0.3222724497,-0.2243033648,-0.1549776644,-0.1410826445,-0.2971782684,0.1499846727,0.0397361554,-0.1925652623,0.041253943,0.0150707783,-0.1407560706,-0.2130510658,0.0375022851,0.2365282476,-0.1183135211,-0.3088016808,-0.382745564,0.1078483015,-0.3966288269,-0.3141866028,0.3781790137,0.372253418,0.192082867,-0.2534308434,0.2706927359,0.1460497379,-0.263715148,0.1551269293,-0.1551273167,0.6188797355,-0.0313073024,-0.1125205979,0.4339675903,0.161625877,0.1579474211,0.1029895842,0.4129118025,0.0553356409,-0.010922513,0.0593733303,-0.4170093834,-0.6448662877,-0.013277702,0.0296999384,0.0956353769,0.0773247108,0.3346988559,-0.0057369992,0.6432266235,-0.3904713392,-0.1378060728,-0.0248264931,0.0137326214,0.1115522534,-0.1739796996,-0.1675906926,-0.2826334834,0.1758721322,-0.0657504275,0.1716849804,0.3092030883,0.0702335387,0.4772826433,-0.1497455686,0.1135960072,0.0308297016,-0.085541591,0.4020220339,0.1651075482,0.1393790692,0.1653700471,0.1226354837,0.10773094,0.3007267714,0.1516304612,-0.297226578,-0.0077399779,-0.0184939653,0.1842532307,-0.2402290702,0.0268614292,0.0802457035,-0.1575202346,0.2302193791,-0.3226493597,0.1063893214,-0.1910547167,0.0320156887,-0.2304773033,-0.1093338057,0.1902425736,0.0240315404,-0.0246546753,0.347586602,-0.1366307586,0.1259577721,-0.136586979,0.483628124,-0.070655711,0.4251635969,0.1638557762,-0.1920099556,0.349667877,-0.2763001919,0.1353270859,-0.0511862487,-0.1747100949,-0.0120673822,-0.0625551268,0.0382844992,-0.0326817855,-0.0537497289,-0.0300624482,0.0676899701,-0.1735156178,0.0570390671,-0.5073088408,-0.3154097497,-0.0423308052,0.065723151,-0.4276809096,0.0606065989,-0.1491335034,0.191170752,0.1527017057,-0.3350786567,-0.016220564,0.3151860535,-0.030840382,0.0488446653,0.0017458451,0.1214143336,0.0330554061,0.2184406966,0.1731770933,0.1966905296,0.5540255904,-0.3212427497,0.2545955181,0.3876331151,-0.2462096065,-0.0123349885,0.2942351103,0.2663767636,-0.2216776013,0.5088400841,0.0155414334,0.0300173871,-0.0983900577,0.0024214564,-0.2300807536,-0.4314886928,0.0451223589,-0.4368382394,-0.0910854712,-0.028428724,0.2096208334,-0.2737998366,-0.3143653274,0.3792945743,0.2540464997,0.2135637403,-0.1455390751,0.0765380114,0.0547238924,-0.563849926,0.2563215494,0.0055905236,-0.1694342494,-0.1402970254,0.1002022177,-0.3154429495,0.094026193,-0.352427721,-0.2280275971,-0.2467195839,-0.0456149764,-0.0267667286,0.4491200447,-0.297927469,-0.1281398982,-0.2595208585,-0.0963046625,-0.0421307608,-0.1636430472,-0.2893582284,-0.0660986826,0.0033795612,-0.2084629685,-0.1378657818,-0.2410983741,-0.4264033139,0.4629023969,-0.0151848234,0.1453615576,0.3218548894,-0.1029266864,-0.1351843178,-0.0863540471,0.1237719581,-0.0471829474,-0.5483480096,0.1013475955,-0.2635506988,-0.0949600115,-0.220122844,-0.1992489845,-0.1949390173,-0.3742797673,-0.1243757531,0.1396112144,0.1885885447,-0.1656323075,0.513258934,0.343097806,0.0108562298,0.0152145019,-0.0075512682,-0.1736867726,-0.2486013919,0.1173659265,0.376344651,0.097822383,0.0116750374,0.1348611265,0.1020317748,0.0141495904,0.0617834479,0.038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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1844","title":"Update Open Subtitles corpus with original sentence IDs","comments":"Hey @lhoestq , absolutely yes! Just one question before I start implementing. The ids found in the zip file have this format: \r\n(the following is line `22497315` of the `ids` file of the `de-en` dump)\r\n\r\n\r\n`de\/2017\/7006210\/7063319.xml.gz  en\/2017\/7006210\/7050201.xml.gz  335     339 340` (every space is actually a tab, aside from the space between `339` and `340`)\r\n\r\n\r\nWhere filenames encode the information like this: `lang\/year\/imdb_id\/opensubtitles_id.xml.gz` whereas the numbers correspond to the sentence ids which are linked together (i.e. sentence `335` of the German subtitle corresponds to lines `339` and `340` of the English file)\r\n\r\nThat being said, do you think I should stick to the raw sentence id (and replace the current sequential id) or should I include more detailed metadata (or both things maybe)?\r\n\r\nGoing with raw ID is surely simpler, but including `year`, `imdbId` and `subtitleId` should save space as they're just integers; besides, any operation (like filtering or grouping) will be much easier if users don't have to manually parse the ids every time.\r\nAs for the language-specific sentenceIds, what could be the best option? A list of integers or a comma-separated string?\r\n\r\n**Note:** I did not find any official information about this encoding, but it appears to check out:\r\nhttps:\/\/www.imdb.com\/title\/tt7006210\/, https:\/\/www.opensubtitles.org\/en\/subtitles\/7063319 and https:\/\/www.opensubtitles.org\/en\/subtitles\/7050201 all link to the same episode, so I guess (I hope!) it's correct.\r\n\r\n","body":"Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https:\/\/huggingface.co\/datasets\/open_subtitles).\r\n\r\nI can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.\r\n\r\nI think I should tag @abhishekkrthakur as he's the one who added it in the first place.\r\n\r\nThanks!","comment_length":217,"text":"Update Open Subtitles corpus with original sentence IDs \n Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https:\/\/huggingface.co\/datasets\/open_subtitles).\r\n\r\nI can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.\r\n\r\nI think I should tag @abhishekkrthakur as he's the one who added it in the first place.\r\n\r\nThanks! \n Hey @lhoestq , absolutely yes! Just one question before I start implementing. The ids found in the zip file have this format: \r\n(the following is line `22497315` of the `ids` file of the `de-en` dump)\r\n\r\n\r\n`de\/2017\/7006210\/7063319.xml.gz  en\/2017\/7006210\/7050201.xml.gz  335     339 340` (every space is actually a tab, aside from the space between `339` and `340`)\r\n\r\n\r\nWhere filenames encode the information like this: `lang\/year\/imdb_id\/opensubtitles_id.xml.gz` whereas the numbers correspond to the sentence ids which are linked together (i.e. sentence `335` of the German subtitle corresponds to lines `339` and `340` of the English file)\r\n\r\nThat being said, do you think I should stick to the raw sentence id (and replace the current sequential id) or should I include more detailed metadata (or both things maybe)?\r\n\r\nGoing with raw ID is surely simpler, but including `year`, `imdbId` and `subtitleId` should save space as they're just integers; besides, any operation (like filtering or grouping) will be much easier if users don't have to manually parse the ids every time.\r\nAs for the language-specific sentenceIds, what could be the best option? A list of integers or a comma-separated string?\r\n\r\n**Note:** I did not find any official information about this encoding, but it appears to check out:\r\nhttps:\/\/www.imdb.com\/title\/tt7006210\/, https:\/\/www.opensubtitles.org\/en\/subtitles\/7063319 and https:\/\/www.opensubtitles.org\/en\/subtitles\/7050201 all link to the same episode, so I guess (I hope!) it's correct.\r\n\r\n","embeddings":[0.2530398667,0.2482580245,-0.0133868707,-0.1263538003,-0.2649042308,0.1654839814,0.2578907013,0.2178697288,-0.3385523558,-0.1133102104,-0.3191735744,0.321539402,0.1591855288,-0.148518905,0.0773393437,-0.2948979139,0.1316313446,-0.1250012219,0.4648747444,-0.230000779,-0.3619815707,0.3236779869,-0.0370355844,-0.0936009884,-0.0615350418,0.3529788256,-0.4526572227,0.263805449,-0.0446696505,-0.4046934247,-0.0694766417,0.5757681727,0.3239983916,0.1294881254,-0.00011813,-0.2874706686,0.1034887284,-0.3369553983,-0.1963880211,0.0130040152,-0.0088700457,-0.4548185766,-0.3890919685,-0.4192702472,0.1541495472,-0.0261845551,0.1112106964,-0.3906293809,0.1209641024,0.0818594918,0.0413355418,-0.2076831311,0.1595655531,-0.0756243318,0.5919961333,0.0116517739,0.2608609796,0.3210159838,0.6891136169,0.1703936756,0.0037902112,0.3019774258,-0.2869798839,-0.3634003401,-0.1178448573,-0.052475512,0.1650882661,-0.3370415568,-0.0982493311,0.0535925254,0.3832520545,-0.1271126866,-0.6749776602,-0.4048579931,-0.1287252605,-0.2527377903,-0.2395202219,0.2474868596,0.2107637078,0.3021943271,0.2062903345,-0.083272174,0.0339344703,-0.0094237775,0.1293780506,0.5042916536,0.3008296788,-0.136718899,0.1875466853,-0.1222259253,-0.1583456546,0.1058051065,-0.3015627563,0.0585046262,0.0942883044,-0.2353825122,-0.1615969092,-0.6308349371,0.0744334906,-0.0400858186,0.0709191784,0.1509939283,-0.1224713326,-0.1361684352,0.1698249876,0.4294911325,0.4613443017,0.1024640873,-0.1521368176,0.0124330735,0.0659148395,0.0766409338,-0.2118076831,0.1515748054,-0.3587512374,0.4623745978,-0.0251668692,0.0540593192,0.0812720433,0.1748130471,-0.3938657343,-0.1647633761,-0.1746284962,-0.1631082594,0.089661397,0.3201333284,0.1945272982,0.0853317603,-0.0248157345,-0.3484392464,-0.0304052774,-0.0231606606,-0.0796527043,-0.0301865581,-0.0908204019,-0.2682775259,0.2477035969,-0.2299614251,-0.1800294667,-0.1032726467,-0.2297187299,0.1282507926,0.0665439293,-0.235731259,-0.0581265166,0.005192549,-0.1624842137,-0.2596414983,-0.0665081665,0.2597441673,-0.1997034699,-0.2225962579,-0.376270622,0.0550482906,-0.3740643859,-0.2558541,0.4713926017,0.410369277,0.1603391767,-0.3092692494,0.2056641579,0.2361136079,-0.2818244994,0.1293491572,-0.2269668281,0.678419888,-0.2738695145,0.0262140688,0.3277464211,0.2242723852,0.2916445434,0.0869118124,0.348831445,-0.0180480406,0.1029496267,0.1082908139,-0.3351129889,-0.5341982841,0.0047101206,0.1675186008,0.0081042051,0.1024184525,0.329133153,-0.0977428705,0.535001874,-0.4903379679,-0.005577025,-0.0045308685,-0.0041214139,0.1031665057,-0.2395101935,-0.2100030184,-0.1895283908,0.1265672892,-0.0182916187,0.0177376457,0.2712003291,0.0873961747,0.4465692341,-0.1188609228,0.1369723529,0.0870912522,-0.1791390479,0.4175780714,0.181468606,0.3354213238,-0.0550518185,0.0875709504,-0.0712575391,0.2913719416,0.0699675158,-0.2244154811,0.0047409148,-0.0830375627,0.1683905721,-0.2202780694,0.0211334862,0.2027320117,-0.0942851231,0.0535805114,-0.3407467008,0.0740640387,0.0300336331,0.0245651677,-0.3404153883,-0.0929094553,0.0669452921,0.0342075676,-0.0637577176,0.2528347373,-0.0690941364,-0.041800756,-0.0741921291,0.4374390543,0.081607312,0.1990164816,0.0798434019,-0.1302948743,0.4036843777,-0.1952096969,0.3103134632,0.0083686532,-0.1633295864,-0.1201769859,-0.1196015403,0.0521892793,-0.1344232559,-0.1201385334,-0.1318626255,-0.0429544412,-0.1800505221,0.0462143347,-0.5524294376,-0.30593431,-0.1259777546,0.0782290027,-0.3559444249,0.2043480575,-0.0337421484,0.0700286329,0.1798924059,-0.3022330105,-0.183556661,0.2993932664,0.1039041579,-0.0285239536,0.0837333649,0.1877774298,0.1098017022,0.2179263532,0.2721732259,0.1856481731,0.5387432575,-0.3212424517,0.1539241523,0.3775050044,-0.2700375021,0.0931314901,0.166388616,0.337448746,-0.0746063367,0.4613173008,0.1328694373,-0.0213731285,-0.0950568318,0.1000728086,-0.1170583591,-0.4870334268,0.0527894236,-0.4062760472,-0.0933156684,-0.0032705336,0.2948794663,-0.4022113681,-0.4208998978,0.3982215524,0.4475120306,0.2819494009,-0.1660983562,0.0499904528,-0.055149138,-0.535300374,0.1435553581,-0.0170987472,0.026980307,-0.0771987438,-0.0382113047,-0.3761484921,0.0811533481,-0.275706023,-0.3715783656,-0.2372110486,-0.102462925,0.0563879237,0.325153023,-0.1816831231,-0.1408331394,-0.3250474632,-0.0982267335,0.0337392986,-0.1249266788,-0.2722986937,0.0482243709,-0.0597654581,-0.2288581133,-0.1042749733,-0.3540494442,-0.3807478249,0.3775185943,0.0417255238,0.2504079342,0.2632560134,-0.1098387688,-0.2220441252,0.0035443078,0.0477158874,-0.0502441227,-0.5488939881,0.1463500708,-0.2529658377,-0.0806219429,-0.239562124,-0.2055959404,-0.0889166594,-0.2992272377,-0.0037737396,0.1729840636,0.2357706577,-0.1624863744,0.3506118357,0.353094548,0.1605845392,0.012619881,0.0374525525,-0.1594702005,-0.1314894259,0.233860001,0.4142292142,0.1699800342,0.1112870499,0.0068856925,0.2042997479,-0.0031313354,0.0956231579,0.0647799298,0.2745959461,0.2683442235,0.1946626157,0.095864661,-0.3804878294,-0.0181422625,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1844","title":"Update Open Subtitles corpus with original sentence IDs","comments":"I like the idea of having `year`, `imdbId` and `subtitleId` as columns for filtering for example.\r\nAnd for the `sentenceIds` a list of integers is fine.","body":"Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https:\/\/huggingface.co\/datasets\/open_subtitles).\r\n\r\nI can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.\r\n\r\nI think I should tag @abhishekkrthakur as he's the one who added it in the first place.\r\n\r\nThanks!","comment_length":26,"text":"Update Open Subtitles corpus with original sentence IDs \n Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https:\/\/huggingface.co\/datasets\/open_subtitles).\r\n\r\nI can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.\r\n\r\nI think I should tag @abhishekkrthakur as he's the one who added it in the first place.\r\n\r\nThanks! \n I like the idea of having `year`, `imdbId` and `subtitleId` as columns for filtering for example.\r\nAnd for the `sentenceIds` a list of integers is 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1844","title":"Update Open Subtitles corpus with original sentence IDs","comments":"Something like this? (adapted from [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/open_subtitles\/open_subtitles.py#L114))\r\n\r\n```python\r\nresult = (\r\n  sentence_counter,\r\n    {\r\n      \"id\": str(sentence_counter),\r\n      \"meta\": {\r\n        \"year\": year,\r\n        \"imdbId\": imdb_id,\r\n        \"subtitleId\": {l1: l1_sub_id, l2: l2_sub_id},\r\n        \"sentenceIds\": {l1: [... source_sids ...], l2: [... target_sids ...]},\r\n        # or maybe src\/tgt? I'd go with the first one for consistency with 'translation'\r\n        \"subtitleId\": {\"src\": l1_sub_id, \"tgt\": l2_sub_id},\r\n        \"sentenceIds\": {\"src\": [... source_sids ...], \"tgt\": [... target_sids ...]},\r\n      },\r\n      \"translation\": {l1: x, l2: y},\r\n    },\r\n  )\r\n```\r\nOr at top level, avoiding nesting into 'meta'?","body":"Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https:\/\/huggingface.co\/datasets\/open_subtitles).\r\n\r\nI can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.\r\n\r\nI think I should tag @abhishekkrthakur as he's the one who added it in the first place.\r\n\r\nThanks!","comment_length":79,"text":"Update Open Subtitles corpus with original sentence IDs \n Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https:\/\/huggingface.co\/datasets\/open_subtitles).\r\n\r\nI can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.\r\n\r\nI think I should tag @abhishekkrthakur as he's the one who added it in the first place.\r\n\r\nThanks! \n Something like this? (adapted from [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/open_subtitles\/open_subtitles.py#L114))\r\n\r\n```python\r\nresult = (\r\n  sentence_counter,\r\n    {\r\n      \"id\": str(sentence_counter),\r\n      \"meta\": {\r\n        \"year\": year,\r\n        \"imdbId\": imdb_id,\r\n        \"subtitleId\": {l1: l1_sub_id, l2: l2_sub_id},\r\n        \"sentenceIds\": {l1: [... source_sids ...], l2: [... target_sids ...]},\r\n        # or maybe src\/tgt? I'd go with the first one for consistency with 'translation'\r\n        \"subtitleId\": {\"src\": l1_sub_id, \"tgt\": l2_sub_id},\r\n        \"sentenceIds\": {\"src\": [... source_sids ...], \"tgt\": [... target_sids ...]},\r\n      },\r\n      \"translation\": {l1: x, l2: y},\r\n    },\r\n  )\r\n```\r\nOr at top level, avoiding nesting into 'meta'?","embeddings":[0.2209786177,0.0347748846,-0.0174859799,-0.1517959833,-0.2049596608,0.3104095161,0.3499246836,0.2389916182,-0.4182053208,-0.1190219447,-0.399220258,0.2717107236,0.2253196388,-0.1207069606,0.1057459116,-0.2710324526,0.1577949226,-0.0647950619,0.5990246534,-0.1972804368,-0.3916414976,0.2315596342,0.0232855491,-0.1052898169,-0.191948697,0.3326811492,-0.5179935098,0.1893836856,0.0558610819,-0.4463605881,-0.0519319028,0.5385537744,0.2594017088,0.0686634034,-0.000120175,-0.1887046397,0.1532710344,-0.2464017272,-0.247608915,-0.0682022721,0.1630222648,-0.2673374414,-0.2945556939,-0.3069764972,0.0979103446,-0.0597781427,0.0081208786,-0.4214204252,-0.1565286368,0.0395493954,0.0464882366,-0.041784782,0.0353239141,-0.1026243046,0.5562952161,-0.0020258531,0.2494301498,0.321056664,0.6099584103,0.2494790703,0.2338652015,0.3001668155,-0.4769385457,-0.4708833694,-0.1287600398,-0.0272079315,0.0282106437,-0.1922350079,-0.0698113441,0.041339118,0.3280802369,-0.1330782175,-0.7111257911,-0.4584240913,-0.0461190492,-0.2136909068,-0.4144753218,0.3474140763,0.3577758074,0.3390949667,0.0551942997,-0.1647026688,0.1730967015,0.0233025476,0.2741304934,0.2407243252,0.234087944,-0.0719094723,0.1593380421,-0.1263435483,-0.3018881083,0.096115306,-0.3530394435,-0.0231116414,0.0552212633,-0.180324018,-0.0399188921,-0.5612187982,-0.0688753128,-0.0634699985,0.1200460643,0.036731869,-0.1203038245,-0.0787198544,0.1772689968,0.3284093738,0.3404316306,0.0905413851,-0.1424031109,0.026658006,-0.1131897494,-0.0110521941,-0.1618149579,0.2903782725,-0.3519211113,0.2706139684,0.1015037969,0.1630832106,-0.0103811556,0.0979341194,-0.4913239181,-0.0882689059,-0.1628619134,0.0120426901,0.1813017279,0.2778910995,0.2103706896,0.2165264189,0.019492548,-0.3489281535,-0.1100478694,0.0282664336,0.0772974715,0.0654936135,-0.1279768646,-0.3086250126,0.3220805228,-0.2250380516,-0.0650683418,-0.2030833811,-0.3065249026,0.1305768937,0.073270835,-0.1596947312,0.091713272,0.0978728831,-0.1367485672,-0.1229436547,-0.0138521027,0.2216383666,-0.1644517183,-0.3561444581,-0.2158158869,0.0465602167,-0.3473252058,-0.3419598341,0.2420505136,0.4023239613,0.2266892791,-0.2590724826,0.2422270477,0.1845205873,-0.3498832881,0.0592204221,-0.131371513,0.6238114834,-0.0077665779,-0.0832960904,0.3877950013,0.2720652521,0.0859149992,0.1677537709,0.4032240808,0.0536360554,0.0470075458,0.0432600379,-0.4101873636,-0.6244882941,0.0383599438,0.0482292734,-0.0345022529,0.1305969507,0.3557191789,0.0006950055,0.522342205,-0.4122362733,-0.0007474608,-0.0331968367,-0.0157671981,0.0964461267,-0.1680337191,-0.2356581688,-0.2997710705,0.1134860888,-0.0649364069,0.1316911429,0.2948752642,0.0013579959,0.4646084011,-0.1661217362,0.0793835521,-0.0345390551,-0.0843929499,0.3948904276,0.1212474778,0.1921689659,-0.0429225489,0.1107505858,0.165411666,0.2414632291,0.1915427893,-0.2982216775,0.0493588373,-0.0343862846,0.2178050429,-0.2670888901,-0.0152094038,0.1529762447,-0.1249362901,0.1704556495,-0.3960731924,-0.0161923468,-0.0776912048,0.0775526389,-0.1972882748,-0.1050522923,0.1627872884,0.0376423076,-0.0499208607,0.3225724995,-0.0850336775,0.0583695322,0.0013717192,0.4663871527,-0.0264587458,0.4859485626,0.0477542058,-0.1175577193,0.3558054566,-0.2490113676,0.1458485723,-0.0890548155,-0.166750282,-0.1384648681,-0.0018220597,0.0463398993,-0.0706598535,-0.0386064425,-0.037169911,-0.0073784562,-0.3098450601,0.0987724587,-0.4832093418,-0.3038065434,-0.0789630115,0.0930195823,-0.4492996931,0.1416916251,-0.1375737786,0.23616229,0.1031445265,-0.3040553331,-0.0771716908,0.2178610712,-0.0448907763,-0.0042250142,-0.0443806984,0.0655253753,0.0941631272,0.2101776302,0.1771959811,0.2408743948,0.6037320495,-0.2918301821,0.2677742541,0.421508193,-0.2798588872,0.0090122642,0.3630819321,0.3062956035,-0.1610841006,0.500187993,0.0151149537,-0.029345803,-0.155715242,0.0255987309,-0.1557438672,-0.3463326991,0.022558514,-0.509444356,-0.0530931763,-0.0738402233,0.1577112228,-0.2463462055,-0.3293325305,0.2478004098,0.2494488508,0.242607519,-0.1832521111,0.0030818116,0.0344854742,-0.528622508,0.3113759756,-0.0645060688,-0.1091002375,-0.1835632175,-0.0054513593,-0.3063260317,0.0851275697,-0.3004768491,-0.2967128158,-0.2904579937,-0.1231262237,-0.030331267,0.4129615426,-0.2559935153,-0.1472994089,-0.2908369005,0.0264723469,0.0178984199,-0.1615315825,-0.3175318241,0.0234167539,-0.1008801013,-0.2108381987,-0.0929469317,-0.2280549705,-0.3786453009,0.4878309965,-0.0483484156,0.1076342687,0.269292444,-0.0781845748,-0.2370766252,-0.1841226816,0.147393629,-0.0502740331,-0.5810415149,0.0722607672,-0.1957204938,-0.0286291912,-0.2978210747,-0.1449042261,-0.2162144184,-0.3066577315,-0.0989226922,0.1704230309,0.2413598299,-0.234220475,0.5072835684,0.3699317276,-0.0104725845,-0.0442712493,0.0359912775,-0.1981700063,-0.2408281565,0.1244890839,0.4949030578,0.0304683782,0.0134623917,0.1655344963,0.1943969876,0.1037394106,0.0954170525,-0.0017627431,0.3459439874,0.1287500709,0.233825773,0.0904993191,-0.6101649404,0.0591402911,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"That's awesome! Actually, I just noticed that this dataset might become a bit too big!\r\n\r\nMuST-C is the main dataset used for IWSLT19 and should probably be added as a standalone dataset. Would you be interested also in adding `datasets\/MuST-C` instead?\r\n\r\nDescription: \r\n_MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations._\r\n\r\nPaper: https:\/\/www.aclweb.org\/anthology\/N19-1202.pdf\r\n\r\nDataset: https:\/\/ict.fbk.eu\/must-c\/ (One needs to fill out a short from to download the data, but it's very easy).\r\n\r\nIt would be awesome if you're interested in adding this datates. I'm very happy to guide you through the PR! I think the easiest way to start would probably be to read [this README on how to add a dataset](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md) and open a PR. Think you can copy & paste some code from:\r\n\r\n- Librispeech_asr: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/librispeech_asr\/librispeech_asr.py\r\n- Flores Translation: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/flores\/flores.py\r\n\r\nThink all the rest can be handled on the PR :-) ","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":188,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n That's awesome! Actually, I just noticed that this dataset might become a bit too big!\r\n\r\nMuST-C is the main dataset used for IWSLT19 and should probably be added as a standalone dataset. Would you be interested also in adding `datasets\/MuST-C` instead?\r\n\r\nDescription: \r\n_MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations._\r\n\r\nPaper: https:\/\/www.aclweb.org\/anthology\/N19-1202.pdf\r\n\r\nDataset: https:\/\/ict.fbk.eu\/must-c\/ (One needs to fill out a short from to download the data, but it's very easy).\r\n\r\nIt would be awesome if you're interested in adding this datates. I'm very happy to guide you through the PR! I think the easiest way to start would probably be to read [this README on how to add a dataset](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md) and open a PR. Think you can copy & paste some code from:\r\n\r\n- Librispeech_asr: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/librispeech_asr\/librispeech_asr.py\r\n- Flores Translation: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/flores\/flores.py\r\n\r\nThink all the rest can be handled on the PR :-) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"Hi @patrickvonplaten \r\nI have tried downloading this dataset, but the connection seems to reset all the time. I have tried it via the browser, wget, and using gdown . But it gives me an error message. _\"The server is busy or down, pls try again\"_ (rephrasing the message here)\r\n\r\nI have completed adding 4 datasets in the previous data sprint (including the IWSLT dataset #1676 ) ...so just checking if you are able to download it at your end. Otherwise will write to the dataset authors to update the links. \r\n\r\n\r\n\r\n\r\n","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":90,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hi @patrickvonplaten \r\nI have tried downloading this dataset, but the connection seems to reset all the time. I have tried it via the browser, wget, and using gdown . But it gives me an error message. _\"The server is busy or down, pls try again\"_ (rephrasing the message here)\r\n\r\nI have completed adding 4 datasets in the previous data sprint (including the IWSLT dataset #1676 ) ...so just checking if you are able to download it at your end. Otherwise will write to the dataset authors to update the links. \r\n\r\n\r\n\r\n\r\n","embeddings":[-0.4057501853,0.1195164844,-0.0039104824,0.1509025097,0.0220463183,-0.0853217021,-0.1335308552,-0.0062558954,-0.2400020957,0.1257295012,-0.2750507295,-0.3189477026,0.1023326293,0.1398473829,0.1449174583,-0.0939304903,0.120543018,0.025905041,-0.3361466527,-0.2657552063,-0.0817216411,0.0491207801,-0.2169910222,-0.288932234,-0.2452686727,-0.0799722299,-0.3513832688,-0.1246387288,-0.4012856185,-0.1996175051,0.1150277704,0.2857916951,0.0613894835,0.3938317895,-0.0001092176,-0.1187215894,0.2797668576,-0.2949183583,-0.4690204859,-0.2130618989,-0.4673829377,-0.2018324584,-0.3875800669,0.0961183831,-0.0633576289,-0.1115117967,0.2050208896,0.1171565801,0.3408107162,0.5179665685,0.1969754994,0.0698204041,0.4441018701,-0.2930302918,0.0693575963,-0.0417867489,-0.0196989011,0.2912706137,0.253423363,0.02117474,0.1961011142,0.0892119184,-0.0523033552,-0.1398488432,-0.0679250136,-0.2415895164,-0.0877483785,-0.4039773047,0.4737977386,0.3026017249,0.885436058,-0.1235516518,-0.0572129674,0.0557519533,0.3473540843,-0.1545779705,0.0594960861,-0.0361596756,-0.2303109765,0.2189463526,0.0370013006,-0.3095414639,-0.254293859,0.4501921535,-0.0263029877,0.32997334,-0.0426365212,0.0961557552,0.1398076266,-0.2091662586,-0.1120555326,-0.0645513311,-0.1094444394,0.132955417,-0.3483422399,-0.0717624128,0.1117440313,-0.0141074546,0.0450829305,-0.2468639165,-0.2309931815,0.0057506594,-0.0893442258,-0.0871781036,0.1881148964,0.3799712956,-0.0295595024,-0.115954712,0.1522289217,-0.0173908081,0.0141991302,0.0099379616,-0.1114291325,0.0547410883,-0.3408961594,0.1652857363,0.0806239992,-0.2036334872,-0.2136302739,0.0881192088,-0.2003660947,-0.046621304,-0.1008245349,0.4376762509,-0.0591083951,0.3093274236,-0.0603768192,0.1930448562,-0.1479225904,-0.3695376515,-0.087126255,-0.0167545434,-0.2718517482,0.0807316676,0.2328395993,0.2596525252,0.0590137839,0.0937125012,0.0492124557,-0.0665861368,-0.2398619503,-0.0384640992,0.2115903497,0.1285411716,0.0698484033,0.2205139101,-0.0920844674,-0.2250446826,-0.0426823683,0.100471355,0.0837401226,-0.0745024607,-0.1287995279,0.1920661777,-0.1797726303,-0.3141009808,-0.3180534244,0.5080114007,-0.2414616644,-0.0652867705,0.0323659144,0.1024618074,-0.4688642323,-0.0210200418,0.0407257043,0.5596446395,-0.4436152279,-0.0307838526,0.0880186334,-0.0309036765,0.1453659236,0.1575247496,-0.154580906,0.3193620145,-0.0214490909,0.301163584,-0.028509561,-0.223050043,-0.6605327129,0.0781061873,-0.0966477618,-0.0666845366,0.0618451983,-0.0090193376,0.6679314971,0.0456297025,-0.1564008594,0.4466237724,0.0494121611,-0.0715120807,-0.1420307904,-0.3319181204,0.289217025,-0.0197947584,0.1687025875,-0.0537235737,0.1330806613,0.3083949387,0.3169301152,-0.0140785202,0.2964548469,0.3474662602,0.0816651136,-0.0188949984,0.0484906547,0.0245989747,-0.3104139864,0.1486809105,0.2196630239,0.4836666882,-0.0978778079,0.0032812564,-0.2344828248,-0.1308935732,-0.3603484333,-0.1023183763,0.1876636147,0.3728756607,0.1479760408,0.0966864675,-0.0775567219,0.3941006958,-0.2766844034,-0.1055152342,-0.0879714563,0.424913317,-0.0061780321,-0.2714403868,0.1861996055,0.2040861696,0.3342067599,-0.0953066051,-0.0261943303,0.2505721748,-0.2482030541,0.3651418388,0.1121957004,0.0992307588,0.2652243078,-0.5659905076,0.2199379355,0.4432961941,0.2206995487,-0.3255328834,0.0056900908,-0.1550021023,0.2652459145,0.1565917134,0.0905761272,0.029094642,0.455296427,-0.0814787969,-0.1654933542,0.0345614478,0.2561272681,0.225124985,-0.1797230989,-0.1153329685,-0.2786634862,0.2571583986,0.3868488967,-0.3051303029,0.2623223364,0.157049492,-0.0361990221,-0.2613961101,-0.0196962506,-0.1009156406,0.2762663066,0.1733915657,0.3040486276,0.0963478163,0.0565766357,-0.0622084476,0.2041077316,0.0213307906,-0.1722148806,0.2142886817,0.1372830868,-0.2012933046,-0.399173677,0.0878325775,0.1849177033,0.3150724471,-0.0980789214,-0.2744605839,-0.2113194466,-0.2693474293,-0.3065424263,-0.0919738412,-0.3515456617,-0.080779314,0.1875156909,0.0719188303,0.1317643225,0.2187929153,0.2243707925,0.2338600457,-0.3013218939,-0.0422889367,0.0280266888,-0.3736102283,-0.4183723629,0.0749583468,0.3446518183,0.2606092095,0.2495827228,-0.1936534345,0.0172339696,-0.3335565627,-0.205454722,0.269944489,-0.1151761264,0.049855832,-0.2566846907,0.3304830194,-0.0554927289,-0.253680855,-0.1510607302,-0.0729965419,0.0780905187,-0.0291395858,-0.3341872096,-0.1840234846,0.0849074498,-0.5110989809,-0.4248243272,-0.2990658879,0.3247789443,-0.3723615706,-0.0713724643,0.1569874883,0.0470274054,0.1290336102,-0.079925105,0.0045363926,-0.4204454422,-0.0920868441,0.162025407,-0.0662740991,-0.4101423323,0.1232520416,0.1440681964,0.1636272073,-0.0456789844,-0.3998613656,0.2495915741,-0.1116474122,0.288593322,0.2177091539,0.1170541868,0.3275977373,-0.1510184258,-0.0764590949,-0.1397576332,-0.1971049458,0.0362469554,0.3786502779,0.4517078102,0.1034541726,0.0949351266,0.0015626032,0.5036545992,0.2424418181,-0.0031210566,0.1995590478,-0.0323325731,0.345185101,-0.1202696934,-0.4784105122,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"@skyprince999, I think I'm getting the same error you're getting :-\/\r\n\r\n```\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nIt would be great if you could write the authors to see whether they can fix it.\r\nAlso cc @lhoestq - do you think we could mirror the dataset? ","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":117,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n @skyprince999, I think I'm getting the same error you're getting :-\/\r\n\r\n```\r\nSorry, you can't view or download this file at this time.\r\n\r\nToo many users have viewed or downloaded this file recently. Please try accessing the file again later. If the file you are trying to access is particularly large or is shared with many people, it may take up to 24 hours to be able to view or download the file. If you still can't access a file after 24 hours, contact your domain administrator.\r\n```\r\n\r\nIt would be great if you could write the authors to see whether they can fix it.\r\nAlso cc @lhoestq - do you think we could mirror the dataset? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"Also there are huge those datasets. Think downloading MuST-C v1.2 amounts to ~ 1000GB... because there are 14 possible configs each around 60-70GB. I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in `datasets` no? cc @lhoestq ","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":64,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Also there are huge those datasets. Think downloading MuST-C v1.2 amounts to ~ 1000GB... because there are 14 possible configs each around 60-70GB. I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in `datasets` no? cc @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"> Also cc @lhoestq - do you think we could mirror the dataset?\r\n\r\nYes we can mirror it if the authors are fine with it. You can create a dataset repo on huggingface.co (possibly under the relevant org) and add the mirrored data files.\r\n\r\n> I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in datasets no? cc @lhoestq\r\n\r\nIf there are different download links for each configuration we can make the dataset builder download only the files related to the requested configuration.","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":110,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n > Also cc @lhoestq - do you think we could mirror the dataset?\r\n\r\nYes we can mirror it if the authors are fine with it. You can create a dataset repo on huggingface.co (possibly under the relevant org) and add the mirrored data files.\r\n\r\n> I think users mostly will only use one of the 14 configs so that they would only need, in theory, will have to download ~60GB which is ok. But I think this functionality doesn't exist yet in datasets no? cc @lhoestq\r\n\r\nIf there are different download links for each configuration we can make the dataset builder download only the files related to the requested configuration.","embeddings":[-0.3701684475,0.0756932944,-0.0500222258,0.1428010464,-0.1143720597,-0.0225355364,0.0245045293,0.2528821826,-0.2171293497,0.2163129747,-0.3634768128,-0.2713863552,-0.0952886492,0.2548368871,0.1178233773,-0.2256662697,0.0715246871,0.0628933534,-0.4934399128,-0.1806575656,-0.0167998634,0.0800376758,-0.077132009,-0.160868004,-0.2286911756,0.0514315665,-0.3497377634,-0.2192535102,-0.3488781154,-0.2112865299,0.0303797573,0.2346605062,0.2090707272,0.353375107,-0.0001047189,-0.0938933566,0.2101813108,-0.2275529355,-0.4023526311,-0.1474176943,-0.1694556326,-0.216504693,-0.4638263285,0.0432070866,-0.2301793396,-0.193292141,0.1055440456,0.0908902064,0.3507657051,0.3985465169,0.2088294625,0.1681186706,0.1384950131,-0.1770462096,0.1294429749,-0.027113013,-0.0644722506,0.1953025311,0.2781899273,0.2423403263,0.0250680596,0.4822728038,0.0510125756,-0.1121770144,0.0645824596,-0.3023509383,-0.1228587478,-0.4970511198,0.0585182793,0.4549770355,0.7421863079,-0.2327513993,-0.2601963878,-0.3296455145,0.2417101711,-0.0608051941,-0.0412625857,0.1268950254,-0.2630253732,0.3054477572,-0.0407549068,-0.3075760901,-0.1850809753,0.3004446924,-0.0246442109,0.2349756211,-0.1525672972,-0.0494456552,0.0938344225,-0.3000545502,-0.3330549598,-0.1387972087,-0.0195224248,0.1839544177,-0.1306201965,-0.3575485945,0.1271692067,-0.0699242428,0.2695179582,-0.2468396872,-0.1844830364,-0.1602424681,-0.108653605,-0.0174748898,0.1974957436,0.4110043943,0.050860066,-0.0448150188,0.0490414798,-0.2103945464,0.083898291,0.0806303397,-0.0542864203,0.2022230923,-0.4636357725,0.0314629823,-0.0657023564,-0.1599666476,-0.1069284752,0.0018301914,0.0456215851,0.0221909508,0.0139318323,0.2629641593,-0.0254197288,0.2773423493,-0.0889125466,0.3318893909,0.0076956139,-0.2727166414,-0.0556416027,0.0387540944,-0.2634615898,0.2528849244,0.2420615256,0.24993819,0.2341479063,-0.0182297472,0.2714515328,0.0734630302,-0.1332444698,-0.0212271083,0.1294781268,0.0178689677,0.0261993352,0.1165113747,-0.2889254987,-0.1574309021,-0.3001947701,-0.0070913052,0.0725396276,-0.1672895104,-0.1503563076,0.2312588841,-0.2765887678,-0.2198792547,-0.3784280121,0.7613043785,-0.157165423,-0.0671317205,-0.0258608311,0.1604849994,-0.4984615147,0.0312190149,-0.0184656791,0.572507143,-0.2136995643,-0.2364332378,0.1852799058,-0.0407377779,-0.0329288356,0.3206631839,-0.1548604965,0.2370501459,-0.064322941,0.3969767392,0.0862114877,-0.3365670145,-0.3337926269,0.200361982,-0.163074404,0.0126820942,0.2968087792,-0.1083985344,0.6236365438,-0.3085472584,-0.0656214729,0.4407989085,0.0436888039,0.0763146728,-0.2191424817,-0.441157192,0.1737977117,0.2197506577,0.0945778117,0.0077126264,-0.0541920774,0.330504328,0.2851980925,-0.277430892,0.3457450271,0.1845484078,-0.0375552997,-0.0314101912,-0.0289490111,-0.249455139,-0.2396082282,0.0701973289,0.1038628593,0.4097924531,0.2049586326,-0.2575562298,-0.143522203,-0.2272382975,-0.2329358906,-0.1436645091,0.2024192512,0.1730025411,0.2226167321,-0.0140872439,-0.1622647941,0.3276186883,-0.3089619577,-0.1129497215,-0.1202503964,0.2134663165,0.0581184216,-0.1602449566,0.2480307221,0.326151222,0.1055110171,0.0229459014,0.081945397,0.1655614227,-0.2360561639,0.2517371774,0.1058901474,0.232981652,0.3496467471,-0.4438665807,0.3718279004,0.3226096332,0.1047069579,-0.1649798155,-0.2093898207,0.111028187,0.3269998133,0.1179435924,0.1451898664,-0.0675860122,0.2856110334,0.0367305614,-0.2883065939,-0.1736773551,0.111066319,0.1211387739,0.0097599858,0.1580423117,-0.3687121868,0.1698787063,0.4562642574,-0.1424197406,0.1689661145,0.3246617019,-0.2069304883,-0.1832818389,0.0871323794,-0.1043376699,0.2261552662,0.3713853061,0.3932768404,0.1242209226,0.2300581932,-0.0834785402,0.1232806593,-0.1006213874,-0.2778021693,0.1717960685,-0.006828893,-0.0828555673,-0.3244183958,0.0117567005,-0.0339590013,0.1024274155,-0.1919405013,-0.4238792062,-0.0389805734,-0.4275493026,-0.3429191113,0.0687207654,-0.2192068547,0.0812050998,0.3772016764,0.0171472691,-0.2011986673,0.19204171,0.2446411103,0.2857397199,-0.3791689873,-0.0679683313,-0.0292636752,-0.2193164825,-0.1448599696,0.159105584,0.2893210351,0.1332319826,0.2628844678,-0.2597988844,0.0545173064,-0.2878354788,-0.3296543062,0.259139508,-0.1816742271,0.0342454389,-0.2146514505,0.1287184358,-0.0098619349,-0.3360490799,-0.1346302032,0.1360162795,-0.0662714913,-0.1435022801,-0.3694386482,-0.1905807704,0.0158170145,-0.4421801865,-0.5470240712,-0.4363861084,0.4418897927,-0.2328890115,-0.0117504233,0.0389080197,0.1485845745,0.0861272812,-0.261469692,0.199953258,-0.2780721784,-0.2521601319,0.1967475414,-0.2384424657,-0.3239270747,-0.2167912126,0.1482231617,0.0329869427,-0.0588274375,-0.3675884604,0.0656409115,-0.2171866298,0.3849049211,0.1657076925,0.3021327853,0.2084798664,-0.0548925586,-0.0997457579,-0.0484945402,-0.1371175051,0.0944610164,0.2940378487,0.120740898,-0.0373637341,-0.013512589,0.0706404895,0.7331622243,0.4433308244,0.0359318964,0.1378315985,0.0668264329,0.4425567687,-0.2520739734,-0.2575766146,0.4864844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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"I have written to the dataset authors, highlighting this issue. Waiting for their response. \r\n\r\nUpdate on 25th Feb: \r\nThe authors have replied back, they are updating the download link and will revert back shortly! \r\n\r\n```\r\nfirst of all thanks a lot for being interested in MuST-C and for building the data-loader.\r\n\r\nBefore answering your request, I'd like to clarify that the creation, maintenance, and expansion of MuST-c are not supported by any funded project, so this means that we need to find economic support for all these activities. This also includes permanently moving all the data to AWS or GCP.  We are working at this with the goal of facilitating the use of MuST-C, but this is not something that can happen today. We hope to have some news ASAP and you will be among the first to be informed.\r\n\r\nI hope you understand our situation.\r\n```\r\n\r\n","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":147,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I have written to the dataset authors, highlighting this issue. Waiting for their response. \r\n\r\nUpdate on 25th Feb: \r\nThe authors have replied back, they are updating the download link and will revert back shortly! \r\n\r\n```\r\nfirst of all thanks a lot for being interested in MuST-C and for building the data-loader.\r\n\r\nBefore answering your request, I'd like to clarify that the creation, maintenance, and expansion of MuST-c are not supported by any funded project, so this means that we need to find economic support for all these activities. This also includes permanently moving all the data to AWS or GCP.  We are working at this with the goal of facilitating the use of MuST-C, but this is not something that can happen today. We hope to have some news ASAP and you will be among the first to be informed.\r\n\r\nI hope you understand our situation.\r\n```\r\n\r\n","embeddings":[-0.1994687915,0.2436533868,-0.0974727422,-0.0784687102,-0.0106336242,-0.1451659054,0.0524760596,0.0750111043,-0.1210070625,0.3005029559,-0.1688125432,-0.1214635521,-0.0929058343,0.1014922336,0.049240537,0.0520469919,0.0159369204,0.1417456716,-0.4235424995,-0.2137577981,0.0676920414,0.1652442366,-0.1971707344,-0.1185603291,-0.2417682558,-0.0051750476,-0.1987970322,-0.1627392024,-0.2773719728,-0.1755132079,0.3096407056,0.1861113161,-0.0493674017,0.4138047099,-0.0001041711,-0.0886685774,0.268043071,-0.2699055076,-0.5759402514,-0.1869329661,-0.269900769,-0.301694572,-0.3962218165,0.0005752879,-0.1565587819,-0.2501755059,0.1901202202,0.084521383,0.1982961744,0.5663022399,0.2318571657,0.1294274628,0.1595150083,-0.2813528776,0.2131444067,0.1161144152,-0.1201322004,0.3304124773,0.3230338991,-0.0537693277,0.0512337647,0.335177809,0.1430394351,-0.1815527827,-0.0345126614,-0.4015649855,-0.1171030998,-0.2742467523,0.0207959507,0.202059269,0.6257651448,-0.2948607802,-0.1283823252,-0.0489043817,0.2616833746,-0.0491095744,0.073444508,-0.0219563041,-0.1568531096,0.2644263804,0.2554604709,-0.1469140202,-0.3002218008,0.3491802216,-0.1034195945,0.2759414613,-0.1623297334,-0.0958297029,0.0670067444,-0.1347406358,-0.19822523,0.0434117056,0.0794893727,0.0268600471,-0.2630914748,-0.2374757975,0.19051148,-0.129990682,-0.0011346381,-0.2518422604,-0.033065442,-0.1378911436,-0.2423718125,0.1690661758,0.0275314618,0.3915992975,0.0478057414,0.025896851,0.0454382002,-0.1120334715,0.016895419,0.0804202929,-0.0751743764,0.1211415753,-0.4399229586,0.2164539099,-0.11259491,-0.0830751881,-0.3828395903,0.2213929892,0.0121347317,0.2058012635,-0.0827772468,0.2824524045,0.112904951,0.3342103958,0.0786044076,0.3183695972,-0.0523516759,-0.2399520874,-0.0853364095,0.1575847566,-0.3082746267,0.1993889362,0.314044863,0.1970307082,0.2421513349,0.0385026112,0.3347852528,0.1033802405,-0.2505058944,0.0173218437,0.162882641,0.1811018288,0.0541346706,0.1432269216,-0.126215741,-0.107607834,-0.1103474945,-0.007238606,0.1177454144,-0.1387742013,-0.1597446948,0.2793841064,-0.0914549604,-0.1464096904,-0.2564215958,0.4864542186,-0.1666961461,-0.2322057635,-0.0334202461,-0.0739752576,-0.2427843958,0.0647967979,-0.0570576787,0.3744244277,-0.4999311864,-0.0448739864,-0.0608987287,0.0390914753,0.215345636,0.2850775421,-0.1969279945,0.2209675163,-0.1583905965,0.1526072174,-0.0754710436,-0.3909397125,-0.4706937075,0.2447117716,-0.1340881139,0.0226223562,0.2044219971,-0.1413873583,0.4970313311,-0.3135374486,-0.0905554816,0.2267127484,0.1310690939,0.02090111,-0.3485865891,-0.263844192,0.2214559317,0.0960479453,-0.0052674101,0.0703194365,0.148497045,0.2947321534,0.2475513816,-0.1966798306,0.1935806721,0.2508864701,0.0429260284,-0.1177087203,0.0268251635,-0.0362832285,-0.2171421945,0.0160815232,0.0533794314,0.5594960451,0.0495687462,-0.1825216413,-0.2243374437,-0.0805802122,-0.0910207182,-0.0941779092,0.2362570465,0.2504659295,-0.0004784898,-0.0016528046,0.0261907969,0.3673695922,-0.5647367239,-0.3607930243,-0.0539700687,0.3357362747,-0.0739974231,-0.1965979487,0.1826985776,0.1574588567,0.2013040483,0.0631332994,-0.0389977545,0.0641008988,-0.1801032275,0.3866425455,-0.0082447873,0.0721091703,0.2638548911,-0.611143589,0.2364506274,0.3763166368,0.2113754749,-0.2172627896,-0.1177735776,0.1943618804,0.2179944962,0.1046343371,0.1812203974,-0.1003988683,0.4180644453,0.0332072824,-0.1543388665,-0.218464002,0.1855140924,0.251880765,-0.2344928682,0.0502601229,-0.2499331385,0.1158877611,0.5303115845,-0.0432329327,0.2990735471,0.2960605025,-0.0204640534,-0.2250282317,0.143171221,-0.1725830883,0.3313243985,0.3123635352,0.1839494556,0.0182741731,0.1111947298,-0.231856972,0.2586854994,0.0642221645,-0.1848547012,0.266246587,0.1669276655,-0.0963255689,-0.4054495394,-0.0904437304,0.1006516665,0.1656437814,-0.2015103102,-0.2808836401,-0.0934414566,-0.0040671653,-0.034043774,0.0120926248,-0.1151717305,-0.1763385832,0.369687438,0.1451015621,-0.2763325274,0.2118778378,0.1701673269,0.2720695436,-0.3504620492,-0.1271258742,-0.2708378732,-0.3859514296,-0.3327582479,0.1698759198,0.1569398791,0.2146903276,0.1392999589,-0.1036741734,0.1219480932,-0.2698876262,-0.3286662102,0.1964510828,-0.2829557955,0.192641288,-0.1522850692,0.1086609364,-0.0662890077,-0.3872899115,-0.1975033581,-0.039969027,-0.1109140515,0.0299118198,-0.4460125864,-0.0419856459,-0.1716645807,-0.4508725703,-0.5141522884,-0.487308234,0.1961442381,-0.2305824757,0.0891241953,0.2859636545,0.1802773476,-0.0689475536,0.0484408662,0.1605442464,-0.3384575546,-0.3026748002,0.2091612816,-0.2544575334,-0.2924198508,-0.1641889513,0.0963803083,0.2745873034,-0.1203303561,-0.3925468028,0.2880357206,-0.164059937,0.3043846488,0.047377564,0.0017230014,0.3503230214,-0.1856275499,-0.1792620718,-0.2170024067,-0.1752566546,0.0425246954,0.3976612389,0.2070634514,-0.0541818067,0.0527150743,0.1086416692,0.5772771239,0.1353759617,-0.0217989199,0.2615281641,0.0909397826,0.4808558524,-0.2286502868,-0.3318850994,0.30997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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"Awesome, actually @lhoestq let's just ask the authors if we should host the dataset no? They could just use our links then as well for their website - what do you think? Is it fine to use our AWS dataset storage also as external links? ","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":45,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Awesome, actually @lhoestq let's just ask the authors if we should host the dataset no? They could just use our links then as well for their website - what do you think? Is it fine to use our AWS dataset storage also as external links? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"Yes definitely. Shall we suggest them to create a dataset repository under their org on huggingface.co ? @julien-c \r\nThe dataset is around 1TB","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":23,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Yes definitely. Shall we suggest them to create a dataset repository under their org on huggingface.co ? @julien-c \r\nThe dataset is around 1TB","embeddings":[-0.2819742262,0.1316514462,-0.114289701,0.1891249716,-0.0761730298,0.0238953866,-0.015186389,0.1323354542,-0.292786479,0.1568823755,-0.2882780433,-0.227353245,-0.1356900185,0.177145943,0.1977107376,-0.1647572368,0.1265198737,-0.0231165942,-0.3271674216,-0.2998180985,-0.0985968709,0.076150097,-0.0886971802,-0.1920019239,-0.2697936594,-0.0368123688,-0.311208725,-0.2177725881,-0.3672375083,-0.2110482156,0.0392684266,0.0594678856,0.1656423807,0.5584837794,-0.0001052459,-0.1206114292,0.1992835402,-0.2385989279,-0.2963815629,-0.1344755143,-0.2026971877,-0.2349897027,-0.6540787816,0.0331206843,-0.2515168786,-0.1497751623,0.0242077503,-0.0051272535,0.1849745363,0.5032078028,0.2287637293,0.1169008538,0.2910440862,-0.1472678483,0.2438445836,0.0753873661,-0.1609886885,0.155164212,0.2891928554,0.2260188609,0.1128324866,0.508826077,0.049854219,-0.2827511132,-0.0035755008,-0.3058139384,-0.1964981854,-0.4243428707,0.1494263262,0.2748365104,0.547150135,-0.2278184444,-0.2290821224,-0.1697379351,0.2611936331,-0.1568030864,-0.0499189682,0.1122894138,-0.168975696,0.3771129847,0.101904802,-0.3955154419,-0.211272195,0.2854039371,0.0232806113,0.2817807198,-0.157686606,-0.0990013704,0.1004225016,-0.2646625936,-0.3853080273,-0.0833880454,-0.007706542,0.0923128948,-0.1722644866,-0.3142679334,0.2135915905,-0.0067905239,0.2582921088,-0.3227830529,-0.2262986153,-0.151602596,-0.193173781,-0.0800545737,0.0986224115,0.3844141662,-0.0672855973,-0.0160431433,0.1091509312,-0.2292179614,0.0716155097,0.1443646401,-0.0746077672,0.1786736399,-0.4144827127,0.0734653249,0.0291879494,-0.2194577008,-0.1896154135,0.120626308,-0.0307190884,0.0599309839,-0.0112485364,0.3262389004,0.0525543429,0.2642126083,-0.1074369103,0.3535129428,-0.0494072251,-0.2231225371,-0.090708904,0.1249392033,-0.1816266775,0.1585262865,0.2224279791,0.2521287501,0.0808241218,-0.0307100322,0.2543049455,0.1120356917,-0.2170091569,-0.0438588075,0.2266108245,0.0343673863,-0.0065230625,0.0574704446,-0.2188868374,-0.1199041978,-0.2017616034,0.0561885647,0.0737984404,-0.1303486526,-0.2450791001,0.2372362018,-0.1433439106,-0.2346129566,-0.3658261001,0.6624663472,-0.0989387482,-0.0618829094,0.0915619135,0.2291860431,-0.4113318026,0.0413746201,-0.0740232915,0.3984498382,-0.1373505741,-0.1309414059,0.1131423563,0.0383187346,0.0138813425,0.2907513976,-0.0383745469,0.1933073103,-0.0324502029,0.3480066657,0.0341861285,-0.404607296,-0.4445278049,0.1492796689,-0.2617088556,-0.0849773213,0.230523169,-0.0753984526,0.5631130934,-0.2706470191,-0.0062919785,0.2914619446,-0.0247240309,0.0928732306,-0.2349769324,-0.4155744612,0.2283686846,0.169958204,0.0837148726,-0.0453879014,-0.0390071794,0.3225706816,0.1939514577,-0.264046967,0.2739546001,0.263848424,0.1122649089,-0.1178820357,-0.0105329249,-0.226553008,-0.1622185558,-0.0458204113,0.1651752293,0.5588303208,0.0393424667,-0.248207882,-0.1093644872,-0.1796623915,-0.1966807395,-0.1557609141,0.237955749,0.2530170381,0.1944989264,0.1405171752,-0.1418555081,0.2613063455,-0.3185066581,-0.1850554645,-0.1366381794,0.2391347736,-0.0062543959,-0.2149600089,0.2408408672,0.4956155121,0.0868682861,-0.0789923891,0.0718966648,0.055457864,-0.3503284752,0.173853904,0.1244394928,0.2339427918,0.3274999261,-0.5012203455,0.2246926427,0.2709726095,0.1081655324,-0.1598505229,-0.1166042313,0.1506719291,0.2914888561,0.0830967277,0.0569923185,-0.0883582607,0.3350683451,0.0963170305,-0.1294327676,-0.082415767,0.1184362248,0.1973696053,0.0082634343,0.1231061146,-0.3071097434,0.1754607409,0.5225203037,-0.18223387,0.3080971241,0.3316549659,-0.0987378433,-0.135523051,0.0856194496,-0.1874738187,0.2695055306,0.3026119769,0.2179222256,-0.0037574889,0.3238419592,-0.0748646334,0.1274349242,0.0160299223,-0.2442697436,0.1969398707,0.1947351992,-0.1007377133,-0.3468711078,0.0454010665,-0.0385369882,0.2338499874,-0.0725072771,-0.3474930525,0.1309443563,-0.2900649011,-0.263168782,0.0869787112,-0.2649092078,0.0776728168,0.3261661232,-0.0220975652,-0.079115063,0.2016181946,0.244778499,0.3428672254,-0.4011153877,-0.0804300681,-0.0175808799,-0.3277688622,-0.1439275891,0.1810654402,0.2856897116,0.1181080714,0.2969580293,-0.1818919182,0.147623986,-0.2822354138,-0.3307644427,0.2242473364,-0.2791380882,0.0409083478,-0.1901490241,0.0344603434,-0.0575047024,-0.3769896924,-0.1464236826,0.0630080551,-0.1137537658,-0.1005490869,-0.4217520356,-0.1302698255,0.0265244674,-0.4483323991,-0.5188705921,-0.4268541932,0.4857845902,-0.2541282773,-0.0696323812,0.0043212459,0.022377247,0.1944224089,-0.0926828682,0.1866732687,-0.2760030031,-0.2769815922,0.1621173024,-0.2088313252,-0.3052783608,-0.2177145183,0.0425678976,0.0888220519,-0.1137360334,-0.4125672281,0.1012409553,-0.1500974,0.3791904747,0.1974512488,0.1598887146,0.2622482181,-0.1320288628,-0.1125875562,-0.1074818224,-0.2057563215,0.0209976658,0.3534580767,0.2022938281,-0.0050759776,0.1006621644,0.0742811114,0.6177754998,0.3007921278,-0.014930604,0.2079491913,0.1091752201,0.4607527256,-0.2662662864,-0.4412497282,0.5007209182,0.1288642138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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"Sounds good! \r\n\r\nOrder of magnitude is storage costs ~$20 per TB per month (not including bandwidth). \r\n\r\nHappy to provide this to the community as I feel this is an important dataset. Let us know what the authors want to do!\r\n\r\n","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":40,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Sounds good! \r\n\r\nOrder of magnitude is storage costs ~$20 per TB per month (not including bandwidth). \r\n\r\nHappy to provide this to the community as I feel this is an important dataset. Let us know what the authors want to do!\r\n\r\n","embeddings":[-0.2674563229,0.3468384743,-0.1150629446,0.0743555352,-0.1094922796,0.0280111264,0.0628126711,0.2007724792,-0.3066087365,0.2189829797,-0.2691016197,-0.206533283,-0.1899873912,0.1393337846,0.1780733615,-0.1505705714,0.1918693185,0.0074416245,-0.2874937057,-0.2704440951,-0.0860427767,-0.0386056602,-0.1379612833,-0.2685125172,-0.229930982,-0.0946117565,-0.3455687165,-0.2869116366,-0.3232319057,-0.1962370276,-0.0556502976,0.1350371689,0.1240529045,0.4940181375,-0.0001045567,-0.2592286766,0.1250719279,-0.2645716369,-0.3532478213,-0.0380953699,-0.1996164322,-0.1757065058,-0.6599071622,0.0304601323,-0.0956022218,-0.2508485913,0.0672225356,-0.0263301507,0.1266006827,0.5544310212,0.2140147686,-0.0130658662,0.1716801971,-0.1371425092,0.2871406376,0.0758833066,-0.2084584683,0.0772271976,0.3049242795,0.1136055216,0.0316927955,0.4821645319,-0.0075356467,-0.2882493138,-0.0318442546,-0.3790358901,-0.1772185713,-0.4207233191,0.1050826088,0.3116881847,0.6529287696,-0.2026355416,-0.1981211603,-0.0961150676,0.266823262,-0.159811765,-0.049897138,0.1737100035,-0.1241329759,0.3310909867,0.1392672956,-0.2483858317,-0.2472933233,0.3075198233,0.054465156,0.30402565,-0.086230889,-0.0989881605,0.0883807614,-0.274225086,-0.2766878605,-0.0626630485,0.0072822026,0.1095095798,-0.1418611854,-0.3701093495,0.2169772238,-0.1320178509,0.0793473497,-0.3674499094,-0.1592274159,-0.1363395154,-0.0830795243,0.0713027418,-0.0183546804,0.3584363759,-0.080280818,-0.07807675,0.1004720926,-0.2737582326,0.0465741605,0.1279942989,-0.0759718493,0.3356284499,-0.3972549438,0.0821795613,-0.0861617476,-0.1665338278,-0.2463089526,0.1953467131,-0.0466941856,0.0915033594,-0.089351885,0.3172927499,0.0441723429,0.3904410899,-0.0830957815,0.3368884027,0.0517918617,-0.192899242,-0.0603897274,0.2241698503,-0.2017291188,0.2546380758,0.2324511409,0.4521965683,0.049443651,0.0183007848,0.2040760517,0.0686422884,-0.0888890028,-0.0491717458,0.2716386318,0.0259583276,-0.0311120562,-0.0047629378,-0.2496473342,-0.1610568315,-0.203546837,-0.0107688364,0.1420149356,-0.1101741716,-0.2979933619,0.2533713281,-0.0716987997,-0.2677494287,-0.2793786526,0.7518096566,-0.1782979518,-0.1115564406,0.0697045997,0.1738110781,-0.46041888,0.0459398553,-0.1368118078,0.3723906577,-0.2363218665,-0.0399011075,0.0980855674,0.20547387,0.0443731844,0.2060778439,-0.0810586736,0.2622190416,0.0549096651,0.3178981543,0.0442697592,-0.4065377712,-0.4463209212,0.1585043073,-0.1760579348,-0.1846080869,0.2989750803,-0.0001517988,0.6347365975,-0.3393875659,-0.0137222102,0.3644084036,-0.0631121844,0.0718456954,-0.2582840025,-0.4786252379,0.1988205761,0.1503828168,0.0412006006,-0.0575377829,0.06441921,0.369481653,0.2183164656,-0.327760756,0.240490675,0.2785543501,0.0622734018,-0.1536896229,-0.0451561287,-0.1693333983,-0.158728525,-0.083061032,0.1781626195,0.4612787664,0.1114623472,-0.2092922181,-0.0072706006,-0.2170674354,-0.1183109656,-0.1257411689,0.2582389414,0.2570565343,0.1128951386,0.118896462,-0.1245715842,0.1912956089,-0.2764888108,-0.247979179,0.0048344885,0.234055236,0.0556535311,-0.2928238213,0.2693490982,0.4156476557,0.1235693842,0.0075204344,-0.035468515,0.1215612739,-0.2071395963,0.2657057047,0.1701867282,0.3067458868,0.2318335623,-0.4714109302,0.3065223694,0.3407137692,0.1727475524,-0.2098979354,-0.218340531,0.1196065396,0.3287129104,0.0522700585,0.0505121872,-0.091818139,0.2911438644,0.0929449946,-0.1188122034,-0.1293872148,0.1732148677,0.2709854841,-0.0985399559,0.1335847527,-0.2680142522,0.2048004866,0.5615863204,-0.1304108948,0.3069731891,0.1990514398,-0.0527817309,-0.2780837715,0.1204248443,-0.1962444186,0.2916373312,0.2810159922,0.2256419808,0.016477989,0.2985712886,-0.1521057636,0.1900838912,-0.0507359542,-0.2516885698,0.1603920162,0.203517586,-0.1073486209,-0.3217076957,0.0468225628,0.0054197176,0.1432108879,0.0397218801,-0.264711231,0.1132286265,-0.2363813072,-0.1619854718,0.0370658934,-0.159675926,-0.0032517808,0.3915063143,0.0485030971,-0.2069420218,0.1535012573,0.2165166736,0.3693400621,-0.394174397,-0.0828145817,0.0096045109,-0.3672121167,-0.1786138117,0.1865738779,0.1984503269,0.1628403366,0.2797743082,-0.1282354444,0.1851775497,-0.2126670927,-0.3121667504,0.1550998539,-0.2662827075,0.0524388626,-0.2734869123,0.018325977,-0.0566889942,-0.3820500076,-0.265337199,0.0315834396,0.0263812412,0.0323769487,-0.3927341104,-0.1335774809,0.0208352692,-0.3891116679,-0.5565213561,-0.4471624494,0.3865203559,-0.2517313063,-0.0092638088,-0.0017902148,0.0099090226,0.1746537685,-0.0799356401,0.2039350122,-0.2852186859,-0.2310419977,0.1599523574,-0.2169172615,-0.3349026442,-0.1540761292,0.039112553,0.0620787479,-0.1150843576,-0.4363949597,0.1783678383,-0.098703444,0.3334511817,0.1652608812,0.010133327,0.2976228893,-0.1061075404,-0.1565746218,-0.1825390607,-0.1687907726,-0.0481080674,0.4235719144,0.1204992831,-0.0134769389,-0.0380831659,0.1223323792,0.5573822856,0.2054240108,0.0274663251,0.2063635737,0.1498259306,0.4568346143,-0.2330971062,-0.3974888325,0.5278069973,0.1368787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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"Great! @skyprince999, do you think you could ping the authors here or link to this thread? I think it could be a cool idea to host the dataset on our side then","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":32,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Great! @skyprince999, do you think you could ping the authors here or link to this thread? I think it could be a cool idea to host the dataset on our side then","embeddings":[-0.2638154328,0.255651027,-0.1020891964,0.067426078,-0.1163012162,0.0049871961,0.2088760734,0.1324665993,-0.2380465269,0.1958873272,-0.2925175428,-0.1947898269,-0.1235653386,0.1762092859,0.2179549187,-0.0965976119,0.1338677108,-0.0480607823,-0.2836484611,-0.3060131967,-0.0538708344,-0.0600476265,-0.1137716994,-0.1840386093,-0.2231121659,-0.031440556,-0.3102774918,-0.2115971297,-0.2723539472,-0.2186130881,0.0249478724,0.143304795,0.1645143926,0.4807238579,-0.000103108,-0.1160424873,0.1976706982,-0.1972872615,-0.2979801595,-0.113288179,-0.2479342371,-0.1520431191,-0.6573248506,0.0125980163,-0.3099523187,-0.2509469688,0.0265708677,0.0222529732,0.1163543761,0.5167062879,0.2408426702,0.055473119,0.1673075557,-0.134918943,0.2340431362,0.1127671525,-0.2015993148,0.1049611196,0.3035980761,0.1237042695,0.0485425889,0.4394952953,-0.0261507332,-0.2713295817,-0.0585949644,-0.3038038909,-0.2033460438,-0.3597015738,0.0331385471,0.347229898,0.666474998,-0.1610847712,-0.1860350221,-0.1502617002,0.2188349813,-0.1186533347,-0.0508356765,0.0833063647,-0.1209420189,0.3679973483,0.161706239,-0.3044969141,-0.2177587897,0.2728983462,0.0427058563,0.4257723689,-0.0621481575,-0.0788865462,-0.0586894676,-0.2083937079,-0.213214919,-0.0897469819,0.0146057568,0.0752056241,-0.1660140008,-0.3493968248,0.3036048412,-0.1503922194,0.1601200104,-0.3179901242,-0.1644588858,-0.1152175739,-0.1738077402,0.028868787,0.0701214522,0.3264145851,-0.023237342,-0.0438519493,0.1637263447,-0.1892008632,0.0626120418,0.1544866562,-0.0323628299,0.2342712879,-0.4513018131,0.2286697626,0.0270993803,-0.1980846822,-0.238577798,0.1215214133,-0.0300242435,0.0816451088,0.0075868592,0.2975362539,0.0489691496,0.3509753346,-0.1010926291,0.32719329,-0.0031223649,-0.2137394696,-0.0711701661,0.1336993277,-0.2383320481,0.17205818,0.2494342178,0.4215917289,0.0535096489,0.0821038634,0.3226335347,0.0747349858,-0.1555283219,0.0260720532,0.2722114623,-0.0337342247,0.0475034229,0.0337505341,-0.2039002925,-0.1663172394,-0.2246453762,0.0592587739,0.1313941479,-0.1161021218,-0.2872762382,0.2791669369,-0.1298475713,-0.2352979928,-0.2771536708,0.7251247168,-0.1624794304,-0.0774156302,0.1132643595,0.2274977416,-0.4088668823,0.0521916486,-0.0945313126,0.3656329215,-0.2517017126,-0.0169540811,0.1709535122,0.1352590024,0.0225041509,0.1482346654,-0.0828428566,0.3158314228,-0.0581727885,0.349792093,0.0392581969,-0.3666366339,-0.3150377274,0.1317054778,-0.2357329726,-0.0350298472,0.2565309107,-0.1328445971,0.6240297556,-0.3125279844,-0.0122896107,0.4339906275,0.0065104268,0.1316176504,-0.1982861757,-0.3214910328,0.23603715,0.1815402508,-0.005643649,-0.0182655342,0.0441294461,0.2750940025,0.2775231898,-0.3302541077,0.2896015048,0.2214820534,0.0354990028,-0.1381884366,0.0164784417,-0.1379673481,-0.1545863748,-0.0750247315,0.1970406026,0.5770363212,0.1224235892,-0.2467807382,-0.1696626693,-0.1954882592,-0.1923277974,-0.1729772389,0.2730987966,0.2822175324,0.0562135093,0.1398088634,-0.1364933401,0.2650596499,-0.3373715878,-0.21615161,-0.0441902131,0.2488891929,0.0546995029,-0.2353997529,0.1652757376,0.4716205001,0.1654156893,-0.031769935,0.0046441965,0.0743105859,-0.273206532,0.2347431779,0.1541242898,0.2190746218,0.3090022802,-0.5264615417,0.2203454375,0.2899042964,0.0991034657,-0.2010914087,-0.0736402646,0.1721486598,0.2992254794,0.1554922909,0.0093087321,-0.011654702,0.3077354133,0.0305271167,-0.1802012324,-0.0600098111,0.132446155,0.2860775292,-0.1437016129,0.0011757519,-0.2945253849,0.2530354261,0.4543097913,-0.1788542271,0.2692893147,0.1650078148,-0.0455254577,-0.1397265494,0.1442493945,-0.2406744957,0.2386839539,0.3034240007,0.3386998773,0.023740489,0.2088297755,-0.0828210264,0.1922583878,-0.0472447239,-0.3074480891,0.1286107302,0.1818548292,-0.1868811846,-0.3317626119,-0.0036381716,-0.0241872352,0.1092714295,-0.1317532957,-0.2908289135,0.1634647846,-0.2110120803,-0.2189671844,-0.0618507229,-0.2294008285,0.0262314454,0.4298076928,0.0383161567,-0.2114010006,0.2007107139,0.2322894037,0.3449686766,-0.4511022568,-0.0329894498,-0.0573889762,-0.3996440768,-0.1822762191,0.2001166493,0.214673683,0.1815421879,0.2603405416,-0.1186480001,0.1492659301,-0.2710829377,-0.3136157691,0.1868878007,-0.2719041109,0.120898962,-0.1901511848,0.0092401784,-0.0203032251,-0.4290495813,-0.2016885132,0.088761963,-0.030420633,-0.08557491,-0.4141586423,-0.0888712928,-0.0415390395,-0.4858826995,-0.4796178043,-0.4333893359,0.2988663316,-0.271073699,-0.0247242376,-0.0173538141,0.0402075648,0.0702034682,-0.0206962842,0.1687847525,-0.3169855177,-0.2643295825,0.111153923,-0.3181254864,-0.2912344337,-0.2173937112,0.0507119857,0.1230551153,-0.1351741254,-0.3304983079,0.1405244023,-0.0506784394,0.359983623,0.1792303622,0.0934568122,0.2319622487,-0.0519551486,-0.1444593072,-0.1684286743,-0.1450237185,0.0257617887,0.377843231,0.1647441387,0.0651211143,-0.0047894511,0.05896255,0.6301655173,0.1760899723,0.047284212,0.2631708384,0.0810749903,0.3796306849,-0.2192723751,-0.4116697311,0.5353423357,0.1132875308,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"Done. They replied back, and they want to have a call over a meet\/ skype. Is that possible ? \r\nBtw @patrickvonplaten you are looped in that email (_pls check you gmail account_)  ","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":32,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Done. They replied back, and they want to have a call over a meet\/ skype. Is that possible ? \r\nBtw @patrickvonplaten you are looped in that email (_pls check you gmail account_)  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"@gegallego  there were some concerns regarding dataset usage & attribution by a for-profit company, so couldn't take it forward. Also the download links were unstable. \r\nBut I guess if you want to test the fairseq benchmarks, you can connect with them directly for downloading the dataset.  ","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":46,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n @gegallego  there were some concerns regarding dataset usage & attribution by a for-profit company, so couldn't take it forward. Also the download links were unstable. \r\nBut I guess if you want to test the fairseq benchmarks, you can connect with them directly for downloading the dataset.  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"Yes, that dataset is not easy to download... I had to copy it to my Google Drive and use `rsync` to be able to download it.\r\nHowever, we could add the dataset with a manual download, right?","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":37,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Yes, that dataset is not easy to download... I had to copy it to my Google Drive and use `rsync` to be able to download it.\r\nHowever, we could add the dataset with a manual download, right?","embeddings":[-0.3261700571,0.258577466,-0.0921744332,-0.0333687477,-0.0554307587,0.0201777853,0.0042871945,0.1221487597,-0.3325349391,0.2870291173,-0.2463117093,-0.1663521677,-0.0883385614,0.0430673994,0.1519542783,-0.046092838,0.100588128,0.0084114205,-0.281600982,-0.3170711398,-0.0999767557,0.0702602789,-0.1038936526,-0.1798479259,-0.1023112983,-0.1136202365,-0.2879876792,-0.1292617768,-0.3628219962,-0.1610624194,0.0828778818,0.2118594646,0.105418317,0.3385773599,-0.0001049832,-0.1688212454,0.2188206315,-0.2101087868,-0.4650355875,-0.1227784529,-0.2633396387,-0.1900159568,-0.5593047142,-0.0589392446,-0.1318376213,-0.1538991183,0.190631181,0.1063847914,0.1001442298,0.5843331814,0.2289637476,0.0718084648,0.1717671901,-0.2216983438,0.3009495735,0.1132302135,-0.1445614547,0.2121609002,0.2020394355,0.1368398666,0.187990889,0.2498551309,0.0623006821,-0.1541409194,-0.0312925652,-0.261718303,-0.1831347942,-0.4605362415,0.1067013592,0.4221014082,0.6699703336,-0.1604393721,-0.2226737291,-0.1112763435,0.3161411583,-0.1318680793,-0.041566968,0.0762252808,-0.1802818477,0.2422288358,0.0878616571,-0.3472367525,-0.1816349775,0.2786091268,0.0030743231,0.3151161075,-0.098924309,-0.0851302147,0.0590563603,-0.1815015078,-0.3160818815,-0.0776870623,0.0200668816,0.1641677916,-0.048442252,-0.2551894188,0.260302633,-0.1156946421,0.1274390221,-0.3338730931,-0.190826565,-0.112936534,-0.1809577346,0.0327018015,-0.0087539991,0.3533169627,-0.0389137715,0.0067527103,0.1603179574,-0.2577740848,0.1317540556,0.0949309617,-0.1060991213,0.1061939597,-0.4424651563,0.2819646299,0.0175171215,-0.1521286815,-0.3608736992,0.0009045305,-0.0983712077,0.0952640176,-0.1840298623,0.2711251378,0.0594117008,0.2206312567,-0.029945977,0.3511986434,-0.0816177949,-0.1676343679,-0.0635019541,0.1821158528,-0.1835427135,0.1410641223,0.2064755857,0.4040096402,0.1354864538,-0.0382832438,0.2191948742,0.1065601483,-0.2609094679,-0.0230158325,0.1315094233,0.0475583784,0.1305355281,0.2011219114,-0.2909487486,-0.2401168495,-0.3164714575,0.0372383147,0.1165111288,-0.0324811749,-0.1799694747,0.2699035406,-0.1149367914,-0.2823370993,-0.3300674558,0.6548683047,-0.1671614498,-0.1585751772,0.0269490536,0.1318160892,-0.407247752,0.0298855491,-0.0919469222,0.3723148108,-0.3409881592,-0.0958041251,0.040777918,0.0129324039,0.1060925797,0.1649013758,-0.0454858318,0.3797605336,-0.1272123456,0.3340143859,0.17381154,-0.2973581553,-0.4280662537,0.1087315232,-0.1839875877,0.0562566221,0.2124160379,-0.0888002664,0.6740357876,-0.2896543741,-0.0530236699,0.5153852701,-0.0072862851,0.0726594105,-0.186473161,-0.387589246,0.2624821663,0.116539456,0.1313602924,0.108828038,0.1810255498,0.3644085824,0.3500255942,-0.270799011,0.1428896338,0.3335073292,0.1223838404,-0.1555511355,0.0128864944,-0.037968386,-0.2598908544,0.0124909235,0.0837915912,0.5217408538,0.0192428827,-0.2437839061,-0.1016326249,-0.2844207883,-0.1674671471,-0.0977725983,0.2394647449,0.3028955758,0.1553970575,0.0371332243,0.0348586328,0.2901745439,-0.3526088297,-0.2636925578,-0.0762509704,0.1294235885,-0.0167735424,-0.1908477396,0.1762052029,0.2271558791,0.1990326196,-0.0735970438,-0.017036872,0.1629077792,-0.232838124,0.3128562272,0.046399992,0.1513501555,0.3106359839,-0.5115110874,0.1572942436,0.416193217,0.1467899382,-0.2736437023,-0.2458366305,0.1721504927,0.3308235109,0.1557471156,0.2433720678,-0.0184064247,0.4958963096,0.1071761325,-0.1974094659,-0.0540437028,0.1498032212,0.3694210351,-0.0956173837,-0.0542924516,-0.3067092001,0.2138965428,0.6212291121,-0.1969293207,0.2406687289,0.252047807,-0.078820996,-0.1463530511,0.0290928129,-0.1906289458,0.2503053248,0.352750659,0.3592058718,0.1053783,0.153202638,-0.1018713862,0.1213005856,0.0863634795,-0.2057517022,0.1337576658,0.1239680275,-0.0941411182,-0.4168773293,0.0197334085,0.0448354296,0.1779794991,-0.1667906344,-0.3204413056,0.1141039059,-0.1600840837,-0.176881507,0.0601245798,-0.1996935457,-0.1120592356,0.3303552568,0.0985862464,-0.1896595657,0.2049132138,0.2834319472,0.2777814567,-0.4166877866,-0.152523607,-0.0159285106,-0.3203995228,-0.2732067704,0.1860576868,0.2324534059,0.2177059203,0.2699485719,-0.2892925739,0.1873935014,-0.3356782496,-0.3198469579,0.2317831963,-0.2485967875,0.0674072877,-0.2411665469,0.0807213262,0.0027066029,-0.3551658988,-0.299734503,-0.0125589371,-0.1087993756,-0.0983541757,-0.4978320003,-0.2145543098,-0.0878387541,-0.5328082442,-0.5634722114,-0.3825628757,0.3526053429,-0.1424562335,-0.0276196953,0.0433124378,0.004082893,0.1098218262,0.029335482,0.2357150018,-0.2832208872,-0.0561409518,0.2433838248,-0.2432825565,-0.4001317918,-0.1083380654,0.0537583083,0.0501201563,-0.0360996947,-0.3312400877,0.2080515027,-0.1621937305,0.3929625154,0.1319549531,0.1293594539,0.3479273319,-0.1876190156,-0.1273127049,-0.1407837123,-0.1103456616,0.0769040734,0.4300549328,0.2432563901,-0.0991315767,0.1157812253,0.0324397944,0.6805483699,0.1629751176,0.0503169149,0.1335610896,-0.0671359673,0.4736748636,-0.2270119637,-0.2605061829,0.457993865,0.04270925,-0.114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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1843","title":"MustC Speech Translation","comments":"yes that is possible. I couldn't unfortunately complete this PR, If you would like to add it, please feel free to do it. ","body":"## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":23,"text":"MustC Speech Translation \n ## Adding a Dataset\r\n- **Name:** *IWSLT19*\r\n- **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.*\r\n- **Hompage:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation*\r\n- **Data:** *https:\/\/sites.google.com\/view\/iwslt-evaluation-2019\/speech-translation* - all data under \"Allowed Training Data\" and \"Development and Evalutaion Data for TED\/How2\"\r\n- **Motivation:** Important speech dataset\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n yes that is possible. I couldn't unfortunately complete this PR, If you would like to add it, please feel free to do it. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1840","title":"Add common voice","comments":"Hey @BirgerMoell - awesome that you started working on Common Voice. Common Voice is a bit special since, there is no direct download link to download the data. In these cases we usually consider two options:\r\n\r\n1) Find a hacky solution to extract the download link somehow from the XLM tree of the website \r\n2) If this doesn't work we force the user to download the data himself and add a `\"data_dir\"` as an input parameter. E.g. you can take a look at how it is done for [this](https:\/\/github.com\/huggingface\/datasets\/blob\/66f2a7eece98d2778bd22bb5034cb7c2376032d4\/datasets\/arxiv_dataset\/arxiv_dataset.py#L66) \r\n\r\nAlso the documentation here: https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html?highlight=data_dir#downloading-data-files-and-organizing-splits (especially the \"note\") might be helpful.","body":"## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":100,"text":"Add common voice \n ## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hey @BirgerMoell - awesome that you started working on Common Voice. Common Voice is a bit special since, there is no direct download link to download the data. In these cases we usually consider two options:\r\n\r\n1) Find a hacky solution to extract the download link somehow from the XLM tree of the website \r\n2) If this doesn't work we force the user to download the data himself and add a `\"data_dir\"` as an input parameter. E.g. you can take a look at how it is done for [this](https:\/\/github.com\/huggingface\/datasets\/blob\/66f2a7eece98d2778bd22bb5034cb7c2376032d4\/datasets\/arxiv_dataset\/arxiv_dataset.py#L66) \r\n\r\nAlso the documentation here: https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html?highlight=data_dir#downloading-data-files-and-organizing-splits (especially the \"note\") might be helpful.","embeddings":[-0.1914141327,-0.2330227643,-0.0579186045,-0.1640419066,0.1464050263,-0.077341415,0.2238348275,0.2807310224,-0.2090623528,0.3071741164,-0.4487543404,0.0414794385,-0.0504973307,-0.061810866,0.1337499022,-0.1799681336,-0.2989138067,0.0928419083,0.0731951296,-0.089301683,0.1916294396,0.0703539327,0.006779355,0.010302417,-0.0648635328,-0.0836325213,0.0658920258,-0.0585368574,0.0435861982,-0.1326310337,-0.1019375101,0.2207966596,-0.1319609284,0.4909032583,-0.0000958251,-0.0298786294,0.1239590496,-0.171576485,-0.1875044107,-0.1483193487,-0.2629153728,0.1999739707,-0.3399197459,0.1386548281,-0.2324215323,-0.0841777921,0.0835847184,-0.173622787,0.367010653,0.4226413667,0.321483463,0.1754924357,-0.068599917,0.0331244133,0.1989008486,0.1055693999,-0.186442107,0.1450786144,0.1981421858,-0.0047167116,-0.0269427747,0.3730793893,0.0401432291,-0.110422723,-0.1406654716,0.0541818626,-0.0711617246,-0.4051722288,0.0466371551,0.4960680008,0.5813153386,-0.2943419814,-0.1319930851,-0.2549909055,0.0939375609,-0.0744588748,-0.0119608771,0.1040292308,-0.3731024861,0.2526135147,0.1400448531,-0.1729530841,-0.0926651284,0.1806473434,0.0982044414,0.2873306274,-0.0902398825,-0.3477494717,0.035027843,-0.129786849,-0.2782455981,0.1741712093,0.3745585084,0.0719093382,-0.2025437951,-0.2045180351,0.1722052991,-0.4943517745,0.3373634219,-0.2338496894,0.3088485599,0.1937015057,-0.2733047903,0.1480517685,-0.0929492861,0.1415398717,0.0336594768,-0.2610833943,0.47900635,0.0796311498,0.1017048433,0.0869898796,-0.0355172679,0.0955113247,-0.1683771014,0.008145717,0.1157443076,0.1286963373,-0.2746973634,-0.0248591751,-0.0774223804,0.0341903716,0.1007621586,0.3143828213,-0.1674354374,0.2423110604,0.012937298,0.1006355435,-0.0871869996,-0.4383678138,-0.1132012904,-0.0901504755,-0.0422652811,0.2203017473,0.2512874901,0.0081313821,0.1794687361,-0.0383709297,0.3635953367,-0.0449105389,-0.0580287762,0.1659001857,-0.1160877869,-0.0063175331,0.1776334792,0.1012066081,-0.0947201923,-0.0506712049,-0.1521701962,-0.0814013034,0.0066563096,-0.2400241345,-0.234915942,0.3818749487,0.2067500353,-0.091620408,0.1750999093,0.4059943259,0.0205771737,-0.1133105829,0.0576032996,0.2704983652,-0.3937545717,0.0019520298,0.2353151143,0.1386979967,-0.1030028164,-0.2127184272,-0.0887004435,0.0259471405,-0.0899717435,0.2812158465,-0.0547237471,0.1473571807,-0.0783701465,0.5168426037,0.3876639605,-0.3474629521,-0.3929833472,0.1568109095,-0.3701220155,-0.0025552269,0.1129033267,-0.0325602703,0.2321348339,0.0054643154,0.3086158633,0.2736943364,-0.0658044145,0.2518997788,-0.2528394759,-0.2755353451,-0.1130043194,0.2201479971,-0.2039204538,-0.0475509055,-0.021995414,0.0114409169,0.3305029869,-0.2774728835,0.2274192274,0.1372638792,0.3657888472,0.0801173374,0.0349297076,-0.1751105189,-0.0472088046,-0.0706851706,-0.0469404459,0.2627592683,-0.1196191162,-0.5422856212,-0.3234484494,-0.2906109691,-0.0741249472,0.0678827167,0.3533306718,0.2043393552,0.1240557283,0.090327315,-0.1320174336,0.1620310396,-0.0152115356,0.0198962148,0.0743757933,0.0913552046,-0.2356636822,0.0062168064,0.2639211118,0.1740455031,-0.1094267294,-0.0470192805,0.0121951774,0.4601745903,-0.3491534591,0.4136956036,-0.0707971677,0.2818537951,0.2362557054,-0.2704651058,0.0932089537,0.237088114,0.030820692,-0.0098860031,0.194771111,0.020402411,0.4368931055,-0.1829052567,0.2546635568,-0.0160999708,0.3013763726,-0.0786268562,-0.1048425436,-0.0826984495,-0.027450094,-0.0776250064,0.1535747349,-0.1632675081,-0.5607640147,0.2125445753,0.5365672708,-0.1590388864,0.3975815475,0.2310604602,-0.3788747787,-0.0340253115,0.0417590626,-0.1818191707,0.0579486452,0.3344874978,0.2248823792,0.0508948825,-0.0263416916,-0.2014659196,0.1271747053,-0.0536994971,-0.2016319036,0.3989920616,0.1725071222,-0.0104918908,-0.5637577176,-0.1607815325,-0.1286462247,-0.098063007,-0.2647343278,-0.1571461707,-0.1429148316,-0.4420362115,-0.1284156144,0.0389535166,-0.130406186,-0.1147743762,0.4792996347,0.3162687719,-0.0680870786,0.2069611549,0.0379830599,0.3899508417,0.0894132927,-0.0279653538,0.0022464218,-0.0561508201,-0.168734163,0.2636246085,0.0540253855,-0.1238390505,0.2293983698,-0.0452980548,0.1493048817,-0.0334438048,-0.3091451824,0.3005314171,-0.1655344516,-0.3047591448,-0.120697327,0.0727315396,0.1589967012,-0.3374803662,-0.0105486307,-0.1580181122,-0.2190054506,-0.0341279544,0.022927044,-0.0131090013,-0.3320544958,-0.4506056309,-0.2056814879,-0.5207069516,0.1198896468,-0.2591120899,0.1161835939,0.1122034118,-0.2233251482,0.1942960471,-0.1867551208,0.4712706208,-0.3216316998,-0.186349526,0.3625208139,-0.368829459,-0.3724723458,-0.1673838049,-0.0606763586,0.3532105982,-0.0775346234,-0.2152739614,-0.1116482764,-0.0815466046,0.1689911634,-0.0473101176,0.0326159559,0.1993018538,0.1591008902,-0.2522543669,-0.1729519963,0.0519597717,0.0556859188,0.2103472948,0.0103447344,0.0118938144,0.0778008848,0.2278718203,0.7560041547,0.0810778067,-0.0760716572,0.1922338158,-0.2419106513,0.0715439692,0.0979513451,-0.0580223612,0.3411958516,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1840","title":"Add common voice","comments":"I added a Work in Progress pull request (hope that is ok). I've made a card for the dataset and filled out the common_voice.py file with information about the datset (not completely).\r\n\r\nI didn't manage to get the tagging tool working locally on my machine but will look into that later.\r\n\r\nLeft to do.\r\n\r\n- Tag the dataset\r\n- Add missing information and update common_voice.py\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/1886","body":"## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":66,"text":"Add common voice \n ## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I added a Work in Progress pull request (hope that is ok). I've made a card for the dataset and filled out the common_voice.py file with information about the datset (not completely).\r\n\r\nI didn't manage to get the tagging tool working locally on my machine but will look into that later.\r\n\r\nLeft to do.\r\n\r\n- Tag the dataset\r\n- Add missing information and update common_voice.py\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/1886","embeddings":[-0.1344472319,-0.2228563577,0.0001928245,-0.1578856409,0.1937064826,0.0049486011,0.2681259215,0.3020458221,-0.2495004237,0.1795224696,-0.3955019414,0.2300344706,-0.0425767265,-0.0264860932,0.0067098695,-0.100829035,-0.1025349498,0.1595255136,0.104188852,-0.2480209321,0.1949149221,0.066846244,0.113306269,-0.079002507,-0.3241567016,-0.1644456834,0.0231045038,-0.1435564458,-0.0003262647,-0.1549275666,-0.0453543141,0.271556288,-0.2102986276,0.6402723193,-0.0001004912,-0.0444369279,0.034191668,-0.2457211912,-0.0831898525,-0.1936043054,-0.1155194193,0.1076655388,-0.2947714627,0.1147531345,-0.2959162891,-0.2513290942,-0.0181471184,-0.2323185205,0.3075140715,0.3086764514,0.2740311027,0.0791771412,0.0037059225,0.1020501032,0.1946553439,0.150016889,-0.2205498368,0.1714189798,0.3242516816,-0.1366104037,-0.0471704826,0.466268301,0.0955390558,-0.2063768059,0.072122775,0.0336857252,-0.1408482492,-0.4390898943,0.074878417,0.4949438274,0.5199844241,-0.4264089763,-0.3617741764,-0.3173406422,0.1082020923,-0.2514179945,0.101029627,-0.117975235,-0.2557395697,0.2239129543,0.1083872914,-0.1283470839,-0.0489875153,0.0811170936,0.1294689775,0.4218024313,0.031774193,-0.2574902177,-0.0835851654,-0.1344355196,-0.4091210961,0.2767671645,0.2451603264,0.2131911367,-0.2955951393,-0.1637175083,0.2193738818,-0.5752841234,0.2983843386,-0.1418928951,0.2043370605,0.1789840609,-0.3365133703,0.118501775,-0.0730809122,0.1383013278,0.1229962558,-0.1810256541,0.2307826877,-0.0775236487,-0.0508183204,0.0256609339,-0.0354845338,0.1108120829,-0.0737333149,0.0099485992,0.1576343775,0.0028997003,-0.2117083967,0.1503167301,-0.1971748471,-0.0134349549,0.0216207597,0.2945629358,-0.1580664068,0.3248334229,-0.0321693234,0.0962950736,0.0461165011,-0.1675426513,-0.1163627058,-0.1051338688,-0.0598575883,0.1783371121,0.2899332941,-0.2306269258,0.2474514097,0.0906155556,0.2151056379,0.0239891596,-0.1130202934,0.1488958597,0.0352118611,0.0130945509,-0.0530398674,0.1488322169,-0.0161975995,-0.0461042896,-0.1122690737,-0.1488526613,0.1025853455,-0.099144429,-0.4022328258,0.3375139534,0.1065637469,-0.0816783831,0.3401318192,0.3973253667,0.1810902953,-0.2607650459,0.0863159448,0.2044627815,-0.4028556049,-0.1337232888,0.298826009,0.0428128727,-0.250143826,-0.1295451522,-0.003745341,0.0662233084,-0.0445564017,0.220628798,0.0588412844,0.0657696277,-0.0801879242,0.4226998389,0.2906529307,-0.2918366492,-0.3610621095,0.1677829325,-0.2368706614,0.1416334808,0.1983561218,-0.1059633046,0.2048781216,-0.0048461971,0.5065509677,0.2191433907,-0.2216200233,0.0596206151,-0.2887995541,-0.1531593353,0.0818340927,0.0917185843,-0.1394603699,-0.0696659982,-0.1532015353,-0.110891059,0.2634885609,-0.2050185502,0.179952383,0.1328167766,0.4978140891,0.1184108183,0.1031948924,-0.203864947,-0.1884199977,0.0100561492,0.0054535698,0.3142545819,-0.0859167278,-0.523368597,-0.2934357226,-0.3056114614,-0.1241282672,0.0104292948,0.3078225255,0.2625297308,0.0256660394,0.0203962699,-0.2492913753,0.121369414,0.0112154894,0.079527095,0.0230166875,0.0897838324,-0.2401885241,-0.1604061872,0.228862375,0.3455804884,-0.1591655463,-0.0602729991,-0.0589507483,0.3045244515,-0.3686720431,0.3352653086,-0.0782301873,0.3569921255,0.184901759,-0.1701756865,0.0201045815,0.1942465454,0.074120447,-0.1760630906,0.1142837107,0.1050393581,0.337246269,-0.1475910842,0.0875649154,-0.160045132,0.3091047108,0.0102095837,-0.0980091617,-0.0631413087,-0.0762401596,-0.1810005456,0.2161413133,-0.0155315371,-0.6039136648,0.2193379253,0.6066966057,-0.0502643771,0.2664574981,0.189058423,-0.305285275,0.0091059478,0.0964586809,-0.2371928096,0.077658385,0.2682398558,0.1686557978,0.0385631993,-0.0325515419,-0.1328755319,0.0287766457,-0.061764881,-0.0482396446,0.5199928284,0.2396480143,0.1290621608,-0.4704148173,-0.2076030225,-0.103740707,0.0820092782,-0.2697705328,-0.1634817868,-0.0728347301,-0.3710719645,-0.0508547053,-0.0506706573,-0.3408683538,-0.1255007684,0.3960850239,0.3298542202,0.0087162899,0.4096944034,0.0511541069,0.1571533084,0.0600988045,-0.1292621493,-0.0713003501,-0.0899616331,-0.1671464294,0.2367101908,0.1432028264,-0.1959049702,0.233914569,-0.0050524822,0.1264352053,0.035935659,-0.4879772663,0.1857514232,-0.4026751518,-0.2273771614,0.0057523223,0.0000285043,0.0893843547,-0.4461905658,0.0706716999,-0.2756741345,-0.2109452337,-0.0913038775,-0.1745148599,0.0008462273,-0.300188303,-0.5085436106,-0.1244488209,-0.5271833539,0.1792955995,-0.2819218934,0.1161316559,0.2779878676,-0.1688766479,0.1067907289,-0.2514390945,0.4454709291,-0.3080193102,-0.225850746,0.2266342491,-0.1953063458,-0.3069180846,0.0314145386,-0.1508218646,0.5303356647,-0.0159355123,-0.0882406831,-0.1784488112,-0.0283808243,0.2868050933,-0.031425938,0.0683951601,0.2362618744,0.2672896385,-0.2033075839,-0.1630727947,-0.1307861358,0.0234084204,0.1973066032,0.0784574971,-0.0149753941,0.1801187843,0.1350790262,0.8288182616,0.0600885041,-0.2452115417,0.2792580128,-0.296586871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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1840","title":"Add common voice","comments":"I saw that this current datasets package holds common voice version 6.1, how to add the new version 7.0 that is already available?","body":"## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":23,"text":"Add common voice \n ## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n I saw that this current datasets package holds common voice version 6.1, how to add the new version 7.0 that is already available?","embeddings":[-0.3583087325,-0.3505200446,-0.0120857507,-0.2336006016,-0.4511430562,0.0678055957,-0.0332450792,0.5403199792,-0.1008659378,0.0819955841,-0.2226710767,0.2464447469,-0.2389797419,0.062518917,-0.0957738534,-0.2583045363,0.1143467054,0.1522571445,0.1945269257,-0.1707910746,-0.123772487,0.1081565619,-0.071734637,-0.0980904251,-0.0489303209,-0.3299816549,-0.069553338,-0.234756425,-0.2684201896,-0.2428330183,0.0787592828,0.3951269388,0.221631676,0.6137544513,-0.00011296,0.0369841307,0.2248368561,-0.0053241202,-0.0318537503,-0.4036145508,-0.3523946404,-0.0387222096,-0.4229106009,0.2763931453,0.0303716697,-0.2925685346,0.0765862167,-0.2329180241,0.2964344919,0.4324236214,0.1952519119,-0.0060098465,-0.0025335534,-0.0392263606,-0.0591592714,-0.048849117,-0.2672991157,0.105133228,0.290602684,0.1202008128,0.0884373635,0.3285418749,0.059757337,-0.2225568891,-0.0912673026,-0.0335992873,0.1242105439,-0.3518328965,0.1870647669,0.525444746,0.8187986016,-0.1758557409,-0.51790905,0.052990362,0.2256488055,-0.5650975108,0.0629062206,-0.2294737399,-0.0726037696,0.1246920824,-0.0325592011,-0.4394741654,-0.3120790124,0.2894971669,0.0527337044,0.4834754169,-0.053100124,-0.0381600447,0.173190698,-0.1924634576,-0.0355533808,0.2462065816,0.0150486846,0.1640366316,-0.1574243009,-0.3648066819,0.0056743333,-0.6697840095,0.3093846142,-0.3363507092,0.0419617109,0.0821385756,-0.5721991658,-0.0074512018,-0.1185385212,0.1249562651,0.3058880568,-0.0965118036,0.2650654614,0.0735945925,0.3321962953,0.2107670605,-0.12379691,-0.0260279216,-0.1223926619,0.1329934597,0.2573221624,-0.1187134236,0.1172549501,0.0278942548,-0.1467115879,-0.0580621138,-0.0731732547,0.1269407868,-0.0080199325,0.4083308578,-0.1058555543,0.2186564505,-0.0672868118,-0.3519267738,-0.0113657499,0.0162776746,0.1375810653,0.2443421781,0.1602339298,-0.1981728226,0.1990042329,0.0639214069,-0.0607338436,0.1218746081,-0.1249557808,0.0599375516,-0.1711161733,0.0588583574,-0.1557285041,0.2951803505,-0.243722409,0.1194914207,-0.2472893596,0.1394109577,-0.1457367092,-0.2465073913,-0.6074976921,0.1597610861,0.1824714839,-0.286849916,-0.1688610911,0.5957245827,-0.02395791,-0.3270303607,-0.1152329743,-0.0151460012,-0.6260635257,-0.1805375665,0.0503846072,0.0609329194,-0.5975379348,-0.0544128716,-0.2836185694,0.1483686566,-0.1469398737,0.0973580033,0.0205849744,-0.1988085061,-0.0188898947,0.1360522658,0.6023395658,-0.2568680048,-0.472247988,-0.0051235473,-0.1269848049,0.1606515795,0.2540985644,0.0555866994,0.0355231166,0.0624702722,0.3193097413,0.0569342077,-0.2086107731,-0.3324227631,-0.037032377,-0.2771084607,0.0098928297,-0.0205792692,0.183425054,-0.0148753766,0.1188873574,-0.023406107,0.2341675013,-0.1518340558,0.0420250818,0.1412571818,0.5577682257,-0.0538387299,0.1346652061,-0.1314640045,-0.4031545222,0.3028141856,0.167447418,-0.0168377757,0.0043080891,-0.3037020862,-0.2519779801,-0.1957811117,-0.0206959657,0.1076036096,0.107005842,0.29244712,0.0871509165,-0.1415990591,-0.2610583901,0.2843593359,-0.0394396298,0.0135582509,0.0271975566,0.1737163812,-0.2213439792,-0.1612254679,0.008954755,0.2356240302,-0.0091513349,-0.1330046356,0.0682942495,0.2488459051,-0.37084499,0.2565930486,0.0896470323,0.2607083917,0.2067421079,0.0292018875,0.1968203336,-0.0769713745,-0.2600275278,0.1898593307,0.2240023762,0.1012992486,0.5602582693,-0.2129795998,0.027173711,-0.1476974934,0.2779568732,0.2172684371,0.0569284447,-0.1163059324,-0.1520901769,-0.0620781295,-0.1280087829,-0.1812620759,-0.4334093332,0.2044012249,0.6716204286,-0.2312890887,0.124227725,0.0205118489,-0.1680588722,-0.0677416176,0.1473888159,-0.1010412648,0.0184535962,0.2179794461,0.1691266447,-0.0114138145,-0.2184937,0.0649029911,-0.0729962289,0.0423560031,0.0611832142,0.2779956758,0.0952612087,0.1804366857,-0.2621836066,-0.3175487816,0.1097433791,0.2680022418,0.0483687706,-0.1998076439,-0.1968043447,-0.1461077929,-0.3705078959,0.1207581311,-0.4275482893,0.0045883209,0.2391370684,0.5503568053,0.1884569675,0.2761739492,-0.0432571843,0.1865424961,0.0296728201,0.0411835536,0.2411410511,0.2766310275,-0.2519948781,0.114255026,-0.0284984205,-0.3570275307,0.1756064892,-0.1956447065,0.0254307184,-0.2157187313,-0.6510056853,0.0992536694,-0.3668784499,-0.1459919065,-0.1394307315,-0.0352446474,0.002031459,-0.2772301435,-0.1499962509,-0.3285685182,-0.2084062994,-0.1698665917,-0.3971535265,0.1299882978,-0.0255087204,-0.7505218983,-0.2900678813,-0.4802567065,0.1090316847,-0.263093859,0.2323487997,0.3844939768,-0.1095775813,0.0757077783,-0.142426461,0.4647646844,-0.1809783578,-0.1663756222,0.2461502999,-0.1605455577,-0.1785614789,0.2599546015,-0.317912966,0.3131913543,0.1304612309,-0.1283942461,-0.266074121,-0.0200658478,0.5114756823,0.0006043583,0.0398446321,0.5504133105,0.1172466949,-0.1072288305,-0.1324882656,-0.1672574133,-0.1765636653,0.6956040263,0.2377966791,0.3587529361,0.3140869439,0.118737638,0.9821810722,0.219408229,-0.235082075,0.1927705109,-0.2220004201,0.254365623,0.0762626231,-0.02925127,0.0316703692,0.2397876531,0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1840","title":"Add common voice","comments":"Common voice still appears to be a 6.1. Is the plan still to upgrade to 7.0?","body":"## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":16,"text":"Add common voice \n ## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Common voice still appears to be a 6.1. Is the plan still to upgrade to 7.0?","embeddings":[-0.212288782,-0.3657417893,-0.0668141693,-0.2508972883,-0.1655756384,-0.1441819966,0.2083911449,0.5740476251,-0.1634232551,0.186655432,-0.126723811,0.1299788505,-0.2213793397,-0.0540742986,-0.1652474403,-0.1725153625,0.1874606013,0.0923385546,0.2241400927,-0.29895854,-0.0933467969,0.1990834922,-0.1787375659,-0.1158704832,-0.1860627085,-0.1055603027,-0.1526005417,-0.2419622242,-0.2203149945,-0.1130823046,0.0443881601,0.4690734744,0.0795972347,0.5579512119,-0.0001046535,-0.1845187843,0.2418551147,0.0168310385,-0.0724780634,-0.2648538053,-0.2515432835,0.1319344044,-0.3341265619,0.3444904685,-0.048146233,-0.0579086281,0.1070131212,-0.1967975944,0.2341525406,0.3688364029,0.2583069205,-0.0136432284,0.014282288,0.1099391282,-0.007754406,0.1064426079,-0.4248710573,0.0865987092,0.4412421584,-0.1369137168,-0.0564036146,0.4784085155,0.1274657398,-0.2771306634,-0.06901104,-0.0170733519,0.1057698354,-0.5599305034,0.1629081517,0.4651477039,0.6900516748,0.0175512042,-0.3366566598,-0.036740493,0.1048594713,-0.3643995523,0.0682281628,-0.1492109001,-0.216660887,0.1462437809,-0.0547460318,-0.3675630689,-0.1766159534,0.2877969742,-0.0618911348,0.5196655989,-0.0716795251,-0.1053368077,0.1286565065,-0.0499939173,-0.2074703872,0.3025312722,0.0712443143,0.2348330319,-0.2899763882,-0.3146694899,0.1962273866,-0.568025589,0.2116849422,-0.3232828081,0.022609422,0.1194935963,-0.4698700905,-0.011624028,0.1091328934,0.1773583889,0.2233207226,-0.264595449,0.3360676467,0.0100876223,0.2774430215,0.2530713379,-0.1718142629,0.0888563916,-0.154180482,-0.047302369,0.2638063729,0.009329821,0.0333143137,0.1802935898,-0.0991161466,-0.0583956242,-0.1035145,0.0210808907,0.0198668838,0.5997171998,-0.0690639913,0.1571788341,0.052763667,-0.2874729037,-0.1216201857,-0.1065517813,-0.153203845,0.126297757,0.1578852385,-0.1246758327,-0.0262158848,0.0052191732,0.1414031982,0.0467407815,-0.1792702526,0.1119175032,-0.0801710635,0.135052979,-0.4101982117,0.2198100686,-0.0529015251,0.2463169098,-0.1418680102,0.0512709953,-0.0546144098,-0.1561244875,-0.4318855405,0.2339617163,0.1758865267,-0.2263720334,0.0441806279,0.6501262784,0.1130123064,-0.2118238062,0.0096850833,-0.0361012667,-0.529024601,-0.0632703751,0.0059484201,-0.1261337101,-0.3892020881,0.0639018863,-0.1535572857,0.1559354663,-0.082112357,0.2057420611,0.0985909924,-0.2858921289,0.0044295196,0.3121830523,0.5102220774,-0.2425796688,-0.3882066309,0.1096516028,-0.1480751783,0.0206191894,0.1453526765,-0.0719215423,0.0699651986,-0.0438024551,0.3958625495,-0.0408571176,-0.2668868303,-0.0666539371,-0.246374175,-0.3827997446,-0.0311329626,0.0005526681,0.1112619042,-0.0445417352,-0.056850262,-0.1794106513,0.2555509806,-0.1259678155,0.1345784217,-0.0367951132,0.6218765974,-0.0916803703,0.1145967916,-0.0754359365,-0.202585727,0.1128243729,0.2571750283,0.2060905397,0.2456638217,-0.3547163904,-0.0856863558,-0.21274212,-0.0360358842,0.0545154959,0.1968338639,0.1136262193,-0.0278522223,-0.110583432,-0.5458111763,0.2714337707,-0.0095059136,-0.0964279622,0.1014898792,0.2550775409,-0.2199948281,-0.2209795266,0.0949908867,0.2314733863,-0.1702898443,-0.1373859048,0.0577247888,0.1549637467,-0.31154567,0.2170803845,-0.0071203778,0.3339135051,0.1734928489,0.0640392154,0.1653980762,-0.0315970555,-0.1488147229,0.0373058505,0.2335179299,0.1370458305,0.6064636111,-0.1916490495,0.0816155896,-0.0297903009,0.1449408531,0.2400354743,-0.041971311,-0.0504565686,-0.1256776601,-0.2160374224,-0.0840091333,-0.0793718174,-0.5027114749,0.2676641643,0.8282558918,-0.1747683436,0.2108537555,0.1966155618,-0.0386811793,-0.2392300814,0.0908162594,-0.2361426502,0.0696978346,0.1967337579,0.1901500076,0.0564595386,0.0041269679,-0.0012249004,-0.0912853926,0.0520459898,0.0529155135,0.2620077729,0.1411346942,0.2089927495,-0.4558651745,-0.2906168103,0.0131131811,0.0921792239,-0.0010771136,-0.1442277879,-0.0270307492,-0.0678548887,-0.0512762107,0.026212804,-0.5136848092,-0.2845892608,0.3097816408,0.5721812844,0.2574832737,0.2921495438,-0.1729960144,0.3641633391,0.0575199649,0.0992044583,-0.0613313578,0.1348889172,-0.2179659456,0.1907962561,0.0504498668,-0.5909531713,0.1263215393,-0.078265503,-0.0013551597,-0.0687788352,-0.5928501487,0.1951497644,-0.4950177372,-0.3941292763,-0.1676770896,-0.0791253224,-0.0607001707,-0.1611372828,-0.1343837529,-0.2869052589,-0.3333822191,-0.0602044165,-0.1703652591,0.0217674635,-0.113040626,-0.637762785,-0.1040431708,-0.4915504456,0.1989617199,-0.3888785839,0.1001653671,0.4282773435,-0.1760313362,0.0407500789,-0.3048014343,0.3650483489,-0.2575838864,-0.1872794032,0.060937196,-0.0101753939,-0.2747886479,0.1028204262,-0.1514662355,0.375621289,0.0350395329,-0.198867321,-0.2573615313,0.0183868166,0.4275102019,0.0733528435,-0.1540133357,0.5069414377,0.1603864729,-0.2183658779,0.0618038587,-0.041346319,-0.1062904149,0.7294496894,0.1537773609,0.1810548156,0.1310876757,0.2127350122,1.0733977556,0.2027256042,-0.179096207,0.1467721313,-0.042910628,0.2066021711,-0.0555126481,-0.1231501848,0.3671613038,0.1533619016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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1840","title":"Add common voice","comments":"We actually already have the code and everything ready to add Common Voice 7.0 to `datasets` but are still waiting for the common voice authors to give us the green light :-) \r\n\r\nAlso gently pinging @phirework and @milupo here","body":"## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":39,"text":"Add common voice \n ## Adding a Dataset\r\n- **Name:** *common voice*\r\n- **Description:** *Mozilla Common Voice Dataset*\r\n- **Paper:** Homepage: https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Data:** https:\/\/voice.mozilla.org\/en\/datasets\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/common_voice\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n We actually already have the code and everything ready to add Common Voice 7.0 to `datasets` but are still waiting for the common voice authors to give us the green light :-) \r\n\r\nAlso gently pinging @phirework and @milupo here","embeddings":[-0.2997157276,-0.3046942651,-0.0558256581,-0.2554713786,-0.0452647619,-0.1200118661,0.3751795888,0.4150422513,-0.1786256731,0.2442780733,-0.2419281751,0.2421718389,-0.1680075675,0.0401819013,-0.1121172607,-0.2051169276,-0.1020664722,0.1860421747,0.130093202,-0.1297650188,0.1236407235,0.0410158299,-0.0580401644,-0.055674348,-0.108508952,-0.158836931,0.1077604368,-0.1456524432,-0.1025010198,-0.0975888744,-0.1130715683,0.3124789596,-0.0929817855,0.5205681324,-0.0000979289,0.0031481234,0.1807405502,-0.1111007705,-0.121040374,-0.0984868258,-0.164940998,0.1644488275,-0.3461675644,0.1795709282,-0.2246636301,-0.1976143569,0.0268054437,-0.1786659658,0.3509579003,0.3816343248,0.3205156326,0.151622951,-0.1492058635,0.0768591687,0.0651150793,0.0958067849,-0.3383458555,0.0203739218,0.3209688365,-0.1146345586,-0.1539549828,0.5010684133,0.0760050789,-0.2019957006,-0.1328154057,-0.0536538921,-0.0366767198,-0.365932852,0.0654970258,0.4969236553,0.5530768037,-0.3023500144,-0.2377183586,-0.1883892417,0.0488121733,-0.3551272452,0.0650405139,-0.1347194314,-0.2425762117,0.2061186135,-0.0093255946,-0.1270161718,-0.1186333895,0.1061887443,0.0347380489,0.3404384553,-0.0759058967,-0.2928094268,0.0524219386,-0.2072509676,-0.1913802624,0.2425209731,0.1695743352,0.0488224067,-0.3529066145,-0.2758617699,0.1832634062,-0.5263786912,0.2768947184,-0.2328686863,0.1848496497,0.2292558104,-0.3539293408,0.1382435858,-0.0276690423,0.1728387773,0.1678762436,-0.2109764367,0.3850753009,-0.0074310405,0.1075921133,0.125879854,-0.0201573223,0.0655276477,-0.1522655189,0.0393162109,0.0910002589,0.0064696129,-0.1800404638,0.068994835,-0.0967471898,0.0896330699,0.087120913,0.1807114929,-0.0152545497,0.413964659,-0.0560009219,0.1999533176,-0.0940194502,-0.420884639,-0.1162773594,-0.0923728198,-0.0842955261,0.2084309012,0.2386600673,0.0856587887,0.1522180587,-0.0014705547,0.1679679751,0.1334123164,-0.0063277232,0.171965763,-0.0947775394,-0.0129334619,-0.032032799,0.0756084472,-0.0480532125,0.0427488983,-0.1080125719,-0.07612589,0.0597693436,-0.1688537896,-0.4016616046,0.3549240232,0.173176825,-0.1724216491,0.0869968683,0.5263699293,0.1076769754,-0.2415852845,0.0923307091,0.0550015271,-0.4321806133,-0.0077831228,0.124739863,0.0589628443,-0.3426752388,-0.0457688756,-0.1860962063,0.108968854,-0.0427124798,0.2602841258,-0.0131405508,-0.0633401126,-0.0410635546,0.4589951932,0.4043879211,-0.2546795011,-0.3047835231,0.104848519,-0.2691967785,0.1286435425,0.1881868839,-0.0691028759,0.1670770049,0.0116526112,0.4161009192,0.221503675,-0.1878843457,0.1107496396,-0.2233035564,-0.2984908819,-0.0401437506,0.214110449,-0.0261508618,-0.0684123039,-0.0766996816,-0.1077928469,0.2201779038,-0.3481806517,0.1668971628,0.068515338,0.4814202487,-0.016604064,0.0529351197,-0.2091061026,-0.0497382171,-0.0102234054,0.172469601,0.2277234048,0.0075381538,-0.4798466265,-0.1500712037,-0.2800252736,-0.028648423,0.1312409937,0.3575532436,0.2416049838,-0.0828519464,-0.0125534423,-0.2370411158,0.2343580872,-0.1382074207,-0.0884753019,0.0671743527,0.0797985569,-0.2392370403,-0.2055065781,0.1406307071,0.1957275718,-0.2576422393,-0.0480752327,0.0303939413,0.2433694601,-0.3623760641,0.2844461501,-0.0221640244,0.4125812948,0.1862628758,-0.1595634073,0.0844379365,0.1095276847,-0.084064059,0.0161770489,0.252365768,0.1908131242,0.5376534462,-0.2533795834,0.1319310814,-0.1034213826,0.262791872,-0.0426128246,-0.1228751838,-0.0158881228,-0.0311141051,-0.1692356467,-0.0582403876,-0.1665895879,-0.5551195145,0.2394699752,0.7250708938,-0.1100955829,0.389018476,0.1089486405,-0.2504043877,-0.0797852501,0.086509876,-0.2661176026,0.0759419501,0.3093786538,0.1534548849,-0.0417130105,-0.0653712973,-0.0787651539,0.0411868356,0.0013618201,-0.1318703145,0.4382002354,0.249136135,-0.0153119238,-0.5578546524,-0.2528485656,-0.0287106633,0.0409277566,-0.1772175431,-0.0816054344,-0.1543692648,-0.2630941272,0.0255185012,0.1956021041,-0.1673733592,-0.0786170736,0.484654963,0.5129979849,-0.0350923054,0.3236542344,-0.0282575693,0.378495276,0.1235508472,0.0276985895,0.012827374,-0.0780289993,-0.1555377245,0.2779359818,0.1266658306,-0.3119519353,0.2351816297,-0.002088845,0.0621385947,-0.0443330407,-0.445027411,0.2288291305,-0.3427721262,-0.2699197829,-0.139547497,-0.0290598869,0.013344774,-0.380752027,-0.0617954098,-0.2316725552,-0.2783703506,-0.0046560857,-0.1626798958,0.0159253851,-0.292206943,-0.4726035297,-0.1195879281,-0.6256839037,0.1306799054,-0.3599594831,0.0559182577,0.3191873729,-0.1926985234,0.0843610689,-0.1834622175,0.4459072948,-0.2844459116,-0.1573083699,0.2122927904,-0.2217804492,-0.3173958659,-0.1377673745,-0.1752355397,0.2940689623,0.0024016043,-0.1533203721,-0.2817248106,-0.047955554,0.3374905884,0.0557391979,-0.0758603439,0.2566205263,0.2396881133,-0.230399996,-0.116281122,-0.0656578466,0.0186417531,0.4348553121,0.0341203474,0.0900647491,0.0945612565,0.0900407732,0.8501754999,0.0512960479,-0.2068036497,0.2388617992,-0.1580538452,0.1691264063,-0.020701617,-0.0797610879,0.3066936433,0.1692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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1838","title":"Add tedlium","comments":"Hi @patrickvonplaten \r\nI can have a look to this dataset later since I am trying to add the OpenSLR dataset https:\/\/github.com\/huggingface\/datasets\/pull\/2173\r\nHopefully I have enough space since the compressed file is 21GB. The release 3 is even bigger: 54GB :-0","body":"## Adding a Dataset\r\n- **Name:** *tedlium*\r\n- **Description:** *The TED-LIUM 1-3 corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech.*\r\n- **Paper:** Homepage: http:\/\/www.openslr.org\/7\/, https:\/\/lium.univ-lemans.fr\/en\/ted-lium2\/ &, https:\/\/www.openslr.org\/51\/\r\n- **Data:** http:\/\/www.openslr.org\/7\/\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/tedlium\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":40,"text":"Add tedlium \n ## Adding a Dataset\r\n- **Name:** *tedlium*\r\n- **Description:** *The TED-LIUM 1-3 corpus is English-language TED talks, with transcriptions, sampled at 16kHz. It contains about 118 hours of speech.*\r\n- **Paper:** Homepage: http:\/\/www.openslr.org\/7\/, https:\/\/lium.univ-lemans.fr\/en\/ted-lium2\/ &, https:\/\/www.openslr.org\/51\/\r\n- **Data:** http:\/\/www.openslr.org\/7\/\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/tedlium\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n Hi @patrickvonplaten \r\nI can have a look to this dataset later since I am trying to add the OpenSLR dataset https:\/\/github.com\/huggingface\/datasets\/pull\/2173\r\nHopefully I have enough space since the compressed file is 21GB. The release 3 is even bigger: 54GB 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1837","title":"Add VCTK","comments":"@patrickvonplaten I'd like to take this, if nobody has already done it. I have added datasets before through the datasets sprint, but I feel rusty on the details, so I'll look at the guide as well as similar audio PRs (#1878 in particular comes to mind). If there is any detail I should be aware of please, let me know! Otherwise, I'll try to write up a PR in the coming days.","body":"## Adding a Dataset\r\n- **Name:** *VCTK*\r\n- **Description:** *This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.*\r\n- **Paper:** Homepage: https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443\r\n- **Data:** https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/vctk\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":72,"text":"Add VCTK \n ## Adding a Dataset\r\n- **Name:** *VCTK*\r\n- **Description:** *This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.*\r\n- **Paper:** Homepage: https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443\r\n- **Data:** https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/vctk\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n @patrickvonplaten I'd like to take this, if nobody has already done it. I have added datasets before through the datasets sprint, but I feel rusty on the details, so I'll look at the guide as well as similar audio PRs (#1878 in particular comes to mind). If there is any detail I should be aware of please, let me know! Otherwise, I'll try to write up a PR in the coming 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1837","title":"Add VCTK","comments":"That sounds great @jaketae - let me know if you need any help i.e. feel free to ping me on a first PR :-)","body":"## Adding a Dataset\r\n- **Name:** *VCTK*\r\n- **Description:** *This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.*\r\n- **Paper:** Homepage: https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443\r\n- **Data:** https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/vctk\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":24,"text":"Add VCTK \n ## Adding a Dataset\r\n- **Name:** *VCTK*\r\n- **Description:** *This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.*\r\n- **Paper:** Homepage: https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443\r\n- **Data:** https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443\r\n- **Motivation:** Important speech dataset\r\n- **TFDatasets Implementation**: https:\/\/www.tensorflow.org\/datasets\/catalog\/vctk\r\n\r\nIf interested in tackling this issue, feel free to tag @patrickvonplaten\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n That sounds great @jaketae - let me know if you need any help i.e. feel free to ping me on a first PR :-)","embeddings":[-0.3135915995,-0.2029016912,-0.1356343329,-0.0900804922,-0.0944113359,-0.1734260917,0.3951478004,0.2453567982,-0.3127599359,0.2707167864,-0.1096358076,0.1168669686,-0.1142662168,0.1300080568,0.1152627394,-0.3554120958,-0.1647339761,0.1606167853,-0.1079572141,-0.1504991353,0.2609814405,0.0187867209,-0.0407276042,-0.1190951392,-0.0860337093,-0.1744621396,-0.0132663865,-0.1820103526,0.1001793221,0.0498463288,-0.1814104915,0.1622000337,-0.0637603849,0.2416307777,-0.0001002713,0.0358700305,0.0198775288,-0.2418049425,-0.0944009423,0.1208236814,0.1992049217,0.0740528777,-0.2453387976,-0.1761368364,-0.3554413915,0.0968959332,-0.0397950076,-0.0500366092,0.3716908693,0.5023970008,0.3055783212,0.2836968899,-0.0911158845,0.0573594421,0.2309709936,0.1583594084,-0.199403882,-0.1983229369,0.1893822849,0.2177787572,-0.2660373449,0.4228188992,-0.0862518474,-0.0737783462,-0.1338100582,0.0306466352,-0.1196888462,-0.1718829125,-0.009096317,0.4764467776,0.4801934958,-0.0493532345,-0.1768513024,-0.1964567602,0.0143499207,-0.3132344782,-0.1130322218,-0.0983893573,-0.3130527139,0.2490035295,0.0306208786,-0.0427292511,-0.213936612,0.1305220723,0.0762944445,0.0119983228,-0.1009445786,-0.1705646813,-0.022421727,-0.1672097296,-0.1254976094,0.1463167965,0.1824587286,0.0758065879,-0.4277946055,-0.2661474645,0.1896698028,-0.3929664493,0.2041816711,0.0977043509,0.1833260804,0.2759028077,-0.3698582351,0.1319358945,-0.0196087174,0.152890116,-0.0500700623,-0.2043293566,0.1581792682,-0.3778085709,-0.1865838915,0.0882663652,-0.0769573897,-0.0569353215,-0.0336537771,0.0507434011,-0.277415216,-0.018969262,-0.1828331947,0.1228020117,0.0919173285,0.146272853,-0.0671251044,0.2502866983,0.1214558482,0.3750350773,0.0316149034,0.1339315623,-0.069790408,-0.3958027065,-0.2131822258,0.1856038123,0.0026724718,0.0385288782,0.1713633239,-0.1063368544,0.1846541613,0.0350489765,0.1882571131,0.0571008883,-0.1136672646,-0.0071116192,0.2116017491,-0.0650096387,0.0342014171,-0.0594338886,-0.0551893003,0.0471476465,-0.1513167024,-0.0265908036,0.0312357433,-0.1004490927,-0.4605145454,0.3012481928,0.0177253503,-0.2599003017,-0.2623464465,0.7171254158,0.0673444718,-0.1463068724,0.0323352553,0.1512540579,-0.2100664675,-0.0508713722,0.1740781665,0.1584102362,-0.5402798057,0.0217827335,-0.1255013049,0.0986488089,0.0981223062,0.4815719724,-0.0788438916,0.1746781915,-0.1146763116,0.3127865791,0.0287970174,-0.3075059056,-0.0431631617,0.2002870291,-0.2427612543,0.0068724705,0.1243043616,-0.0709023923,0.4216413498,-0.0226074178,-0.0871586353,0.3132594228,-0.1738310754,0.1811171472,-0.1573590189,-0.3355261683,0.0180219933,0.124338232,0.0042297649,-0.0968971997,-0.1512528956,0.0655184314,0.1447872818,-0.3025968671,0.2267369479,0.1233469173,0.1722745746,0.0951579958,0.0036674314,-0.2729312778,0.0329725444,-0.2434871495,0.4155171514,0.3565231264,0.0891464651,-0.4021550417,-0.0786708891,-0.2524615526,0.243132472,-0.0273006409,0.3375536203,0.0801841021,0.0782235786,0.0992867723,-0.1672991514,0.2128519416,-0.2080102414,-0.170057565,0.0265372209,0.1786541045,-0.0007964062,-0.1991288513,0.1189926937,0.302610904,-0.2216375172,-0.0814379752,0.0412651524,0.1282048225,-0.3350170553,0.308025986,-0.1254750788,0.2034543753,0.0677631572,-0.3715631664,-0.0825705901,-0.0011239301,-0.0122457305,-0.0729687959,0.086181879,0.1786013544,0.4598097801,-0.0477283746,0.0240418222,-0.0810171515,0.2450692654,-0.0724680424,0.0409986787,-0.056967847,0.1367741972,0.0354314335,0.1298565716,-0.2070425302,-0.4838925898,0.3711637557,0.3321948647,-0.0215502232,0.2217503786,0.214538753,-0.2433197349,0.0041675484,0.0259630773,-0.344096154,0.0492359549,0.2859391868,0.1424257159,-0.1985260248,0.0495184176,-0.1819752902,0.1892172992,-0.1114097908,-0.1485902369,0.3832983673,0.1667473465,0.0301071629,-0.6629039049,-0.1687893569,-0.0054330276,0.0387005135,-0.1421827078,0.049045451,-0.1651218683,-0.2114197761,0.0328861289,0.0047184126,0.2011745572,0.0653202459,0.3143887222,0.2804605067,-0.3485196531,0.1220624819,0.2489376515,0.3700993359,0.1855760366,-0.065296419,-0.0748273209,-0.3271684647,-0.0151438499,0.3038365841,0.0531487092,0.1188125536,0.1946083456,0.0336279385,0.1682503223,-0.1218979061,-0.3707176149,0.2434929758,-0.3447680473,-0.0324604064,-0.0345983841,0.1605591476,-0.253239125,-0.6958924532,-0.2130845785,-0.1539247781,-0.2912614048,0.0779067203,-0.0910542235,-0.1112193987,-0.2269643843,-0.5343697667,-0.1877423227,-0.474578619,0.1227673665,-0.1189156473,0.1604571342,0.1402742416,-0.0135311764,0.244236052,0.0641833991,0.237492308,-0.3577746749,-0.1021197662,0.2189091891,-0.4256825149,-0.2975470722,-0.2630823851,-0.2718887627,0.1280818135,-0.1941146106,-0.3128372431,-0.0668875426,-0.104191035,0.2720160484,0.0289672874,0.0143445414,0.2745602429,-0.0683153346,-0.2390916348,-0.1120450497,0.0625244156,0.2346590459,0.227971077,-0.0734159052,0.0480143577,0.125296846,0.070223771,0.431991756,0.089892067,-0.2271106541,0.291023761,-0.1184677705,0.0951234922,-0.152216807,-0.0585865751,0.4433011115,0.09202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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1831","title":"Some question about raw dataset download info in the project .","comments":"Hi ! The `dl_manager` is a `DownloadManager` object and is responsible for downloading the raw data files.\r\nIt is used by dataset builders in their `_split_generators` method to download the raw data files that are necessary to build the datasets splits.\r\n\r\nThe `Conll2003` class is a dataset builder, and so you can download all the raw data files by calling `_split_generators` with a download manager:\r\n```python\r\nfrom datasets import DownloadManager\r\nfrom datasets.load import import_main_class\r\n\r\nconll2003_builder = import_main_class(...)\r\n\r\ndl_manager = DownloadManager()\r\nsplis_generators = conll2003_builder._split_generators(dl_manager)\r\n```\r\n\r\nThen you can see what files have been downloaded with\r\n```python\r\ndl_manager.get_recorded_sizes_checksums()\r\n```\r\nIt returns a dictionary with the format {url: {num_bytes: int, checksum: str}}\r\n\r\nThen you can get the actual location of the downloaded files with\r\n```python\r\nfrom datasets import cached_path\r\n\r\nlocal_path_to_downloaded_file = cached_path(url)\r\n```\r\n\r\n------------------\r\n\r\nNote that you can also get the urls from the Dataset object:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nconll2003 = load_dataset(\"conll2003\")\r\nprint(conll2003[\"train\"].download_checksums)\r\n```\r\nIt returns the same dictionary with the format {url: {num_bytes: int, checksum: str}}","body":"Hi , i review the code in \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py\r\nin the _split_generators function is the truly logic of download raw datasets with dl_manager\r\nand use Conll2003 cls by use import_main_class in load_dataset function\r\nMy question is that , with this logic it seems that i can not have the raw dataset download location\r\nin variable in downloaded_files in _split_generators.\r\nIf someone also want use huggingface datasets as raw dataset downloader,\r\nhow can he retrieve the raw dataset download path from attributes in \r\ndatasets.dataset_dict.DatasetDict ?","comment_length":166,"text":"Some question about raw dataset download info in the project . \n Hi , i review the code in \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py\r\nin the _split_generators function is the truly logic of download raw datasets with dl_manager\r\nand use Conll2003 cls by use import_main_class in load_dataset function\r\nMy question is that , with this logic it seems that i can not have the raw dataset download location\r\nin variable in downloaded_files in _split_generators.\r\nIf someone also want use huggingface datasets as raw dataset downloader,\r\nhow can he retrieve the raw dataset download path from attributes in \r\ndatasets.dataset_dict.DatasetDict ? \n Hi ! The `dl_manager` is a `DownloadManager` object and is responsible for downloading the raw data files.\r\nIt is used by dataset builders in their `_split_generators` method to download the raw data files that are necessary to build the datasets splits.\r\n\r\nThe `Conll2003` class is a dataset builder, and so you can download all the raw data files by calling `_split_generators` with a download manager:\r\n```python\r\nfrom datasets import DownloadManager\r\nfrom datasets.load import import_main_class\r\n\r\nconll2003_builder = import_main_class(...)\r\n\r\ndl_manager = DownloadManager()\r\nsplis_generators = conll2003_builder._split_generators(dl_manager)\r\n```\r\n\r\nThen you can see what files have been downloaded with\r\n```python\r\ndl_manager.get_recorded_sizes_checksums()\r\n```\r\nIt returns a dictionary with the format {url: {num_bytes: int, checksum: str}}\r\n\r\nThen you can get the actual location of the downloaded files with\r\n```python\r\nfrom datasets import cached_path\r\n\r\nlocal_path_to_downloaded_file = cached_path(url)\r\n```\r\n\r\n------------------\r\n\r\nNote that you can also get the urls from the Dataset object:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nconll2003 = load_dataset(\"conll2003\")\r\nprint(conll2003[\"train\"].download_checksums)\r\n```\r\nIt returns the same dictionary with the format {url: {num_bytes: int, checksum: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1831","title":"Some question about raw dataset download info in the project .","comments":"I am afraid that there is not a very straightforward way to get that location.\r\n\r\nAnother option, from _split_generators would be to use:\r\n- `dl_manager._download_config.cache_dir` to get the directory where all the raw downloaded files are:\r\n  ```python\r\n  download_dir = dl_manager._download_config.cache_dir\r\n  ```\r\n- the function `datasets.utils.file_utils.hash_url_to_filename` to get the filenames of the raw downloaded files:\r\n  ```python\r\n  filenames = [hash_url_to_filename(url) for url in urls_to_download.values()]\r\n  ```\r\nTherefore the complete path to the raw downloaded files would be the join of both:\r\n```python\r\ndownloaded_paths = [os.path.join(download_dir, filename) for filename in filenames]\r\n```\r\n\r\nMaybe it would be interesting to make these paths accessible more easily. I could work on this. What do you think, @lhoestq ?","body":"Hi , i review the code in \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py\r\nin the _split_generators function is the truly logic of download raw datasets with dl_manager\r\nand use Conll2003 cls by use import_main_class in load_dataset function\r\nMy question is that , with this logic it seems that i can not have the raw dataset download location\r\nin variable in downloaded_files in _split_generators.\r\nIf someone also want use huggingface datasets as raw dataset downloader,\r\nhow can he retrieve the raw dataset download path from attributes in \r\ndatasets.dataset_dict.DatasetDict ?","comment_length":111,"text":"Some question about raw dataset download info in the project . \n Hi , i review the code in \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py\r\nin the _split_generators function is the truly logic of download raw datasets with dl_manager\r\nand use Conll2003 cls by use import_main_class in load_dataset function\r\nMy question is that , with this logic it seems that i can not have the raw dataset download location\r\nin variable in downloaded_files in _split_generators.\r\nIf someone also want use huggingface datasets as raw dataset downloader,\r\nhow can he retrieve the raw dataset download path from attributes in \r\ndatasets.dataset_dict.DatasetDict ? \n I am afraid that there is not a very straightforward way to get that location.\r\n\r\nAnother option, from _split_generators would be to use:\r\n- `dl_manager._download_config.cache_dir` to get the directory where all the raw downloaded files are:\r\n  ```python\r\n  download_dir = dl_manager._download_config.cache_dir\r\n  ```\r\n- the function `datasets.utils.file_utils.hash_url_to_filename` to get the filenames of the raw downloaded files:\r\n  ```python\r\n  filenames = [hash_url_to_filename(url) for url in urls_to_download.values()]\r\n  ```\r\nTherefore the complete path to the raw downloaded files would be the join of both:\r\n```python\r\ndownloaded_paths = [os.path.join(download_dir, filename) for filename in filenames]\r\n```\r\n\r\nMaybe it would be interesting to make these paths accessible more easily. I could work on this. What do you think, @lhoestq ?","embeddings":[-0.1346984506,-0.0291421283,-0.0565953217,0.5307526588,0.118836984,-0.1144693121,0.1020086557,-0.0082435263,0.0415084288,0.0833945125,-0.4197905958,0.207283318,-0.1245639473,0.5449789166,0.1708198935,-0.0960756987,-0.0993765071,0.1221581697,-0.1015595347,-0.0675221384,-0.3862855434,0.2964140475,-0.2933220863,0.1700219661,-0.1204365417,-0.0437129624,-0.0389077775,0.3107964396,-0.4215294421,-0.2855050862,0.2851445973,0.2104239166,0.38972646,0.2601925135,-0.0001252634,0.0339560695,0.2576283216,-0.3376943469,-0.5291388035,-0.3045315742,-0.6149904728,-0.0184274614,-0.0735696927,-0.2560712695,0.1428723633,-0.0621147715,-0.0816688538,-0.1980143338,0.0248030238,0.0257190745,0.0569270216,0.3073020279,-0.0705493763,0.0733200833,0.2743446529,0.2889507413,0.0069416389,0.1065994874,-0.0094362842,0.3097071648,0.1909423769,0.3141844571,0.1917632371,0.1161840111,0.4544143081,0.3511873484,-0.3605449498,-0.3333880901,0.0019012884,0.2414935082,0.3311742246,-0.3499562144,-0.6002609134,-0.4451579154,-0.1447591633,-0.5862122178,0.0866652057,0.1451350749,-0.4294036329,0.2783696651,-0.5737096667,-0.470205754,-0.107822679,-0.0630755946,-0.1245901063,0.3833278418,-0.3021124601,0.0366093814,0.1222617403,0.366537869,0.1831099838,-0.2040058076,0.0064977114,0.2628435791,-0.1273599416,-0.0968480706,-0.0692084581,0.2800370157,0.1793465018,0.0004258784,-0.0932023153,-0.2625410855,-0.0224481337,0.0367786027,0.3256850243,0.0887944326,0.0588160753,0.2508298755,0.2419353873,0.1852357239,0.2261740714,-0.2565537691,-0.0756491125,-0.0928039774,-0.3982129097,0.1689798385,0.023065459,-0.0881559849,-0.2564201951,-0.2584711909,-0.117320776,-0.3153946102,0.3284130096,0.3631953597,-0.1064461619,0.0919000506,-0.0218641907,0.0300095454,0.1342038512,-0.2513070703,-0.0602543615,-0.1366773695,-0.1498392075,0.1586862206,-0.2355437279,-0.0740527585,0.3717945516,0.0417128019,0.0303050894,0.0919628292,-0.0051716962,-0.1027109027,0.1987164915,0.1128025353,0.0319495089,0.2906243801,-0.2086739689,-0.2854421735,-0.2164021283,-0.1238752306,-0.2959266603,-0.4238964915,0.010931341,-0.026596522,-0.1496953666,0.1380343139,-0.2426622361,0.0063026431,0.0641364083,0.3371734619,0.0211319569,0.006673615,-0.4359383285,-0.2875217795,0.4370478988,0.4985270202,-0.2790235579,-0.1094861701,0.091829516,-0.3653411865,0.0410041586,0.0893710405,-0.3769408464,0.526019156,-0.4255958796,-0.1558313668,0.4934948087,-0.4139986038,-0.2834487259,0.548224926,0.0819413885,-0.0119388681,0.3946042657,-0.0027310946,0.2809688449,-0.0996628776,-0.06136683,0.5419443846,0.1237987727,-0.1109535769,-0.0387092419,-0.2857447565,-0.1188867688,-0.0995088667,-0.1401036531,0.2085418105,0.0630515516,-0.1487575024,0.5694390535,-0.0648537576,0.2024183124,0.3641732931,0.016141776,0.2217395306,-0.039749153,0.032757625,-0.6237203479,0.1450575739,-0.291588068,-0.2566764951,-0.3464303017,-0.3692541122,-0.2835113108,0.1359041929,-0.2205851376,-0.3565536439,-0.0813381076,0.1932128072,0.4799261391,-0.0377768092,-0.3269172013,0.5276400447,-0.0264157411,0.1004448459,-0.1584222466,0.0395960882,0.1531455368,0.1568753421,-0.0285879895,0.1156432182,0.1160011515,-0.2031235099,0.0879380926,0.4732969105,0.1667213589,0.2244895995,0.2488667816,0.0866522789,0.2675855458,0.1815995276,-0.050037697,-0.2840552032,0.1166388243,0.2693530917,0.0217542127,0.405077666,-0.0402296744,0.2749062181,0.2071458101,-0.0586074106,0.1375477016,-0.0461245924,0.1909854561,-0.1717652828,-0.1450582147,0.1957189739,0.3370574415,0.1461298615,-0.2831913829,0.2967513204,0.4335455,-0.0422255136,-0.2803872824,0.0072161951,-0.2794591486,-0.0143303955,0.0462974794,0.7960909009,0.4438539743,0.0230253264,0.191422984,0.0300603881,0.2835537791,-0.0816387832,0.3543806672,0.1328087896,-0.080934912,-0.019625986,-0.4542382061,-0.1663334519,0.193083778,-0.0439952202,0.1845147312,0.1720055491,-0.3527051508,0.0789799988,-0.243078649,-0.3965581954,-0.247439757,-0.1886555552,0.0342917405,-0.1788604558,-0.2145890892,-0.046575807,-0.1373553574,-0.1525721103,-0.0752802789,-0.0230884254,-0.284653604,0.0519280583,-0.1761706173,-0.1079143286,-0.0871832892,-0.0735015348,0.2006447762,0.008561397,0.0978720486,-0.4697065651,0.0869958177,-0.2451253086,0.1868368238,0.3407445252,0.0611883104,0.162994504,0.3850090206,0.1543998122,-0.1284118295,-0.0355447195,-0.1043887436,0.1108019426,-0.1283786148,-0.4418491423,0.126455456,0.0732035413,-0.0949317366,-0.207849592,-0.7182449102,-0.1001876965,0.2222401351,-0.1430765241,0.2521550357,-0.1681589484,-0.223976627,0.0424416177,-0.3285970688,0.0512887016,-0.1883378178,-0.7643768787,0.2837579846,-0.361350745,-0.241732046,-0.0683541372,-0.1362402737,0.1521255076,0.0578375719,-0.1992673874,-0.0171373226,-0.1693499684,0.2199527472,0.1156684533,0.3086595237,0.0356929079,-0.0309625939,0.1584858745,-0.150248006,-0.0465518758,0.2151976377,-0.342245698,0.3589676917,0.3947508335,-0.0425758064,0.2158914804,0.7951549292,0.2289339453,-0.1130506098,0.1516202539,-0.1019391418,0.390619874,0.3349591196,-0.2302670628,-0.0480876714,0.0123575199,-0.03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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1831","title":"Some question about raw dataset download info in the project .","comments":"Sure it would be nice to have an easier access to these paths !\r\nThe dataset builder could have a method to return those, what do you think ?\r\nFeel free to work on this @albertvillanova , it would be a nice addition :) \r\n\r\nYour suggestion does work as well @albertvillanova if you complete it by specifying `etag=` to `hash_url_to_filename`.\r\n\r\nThe ETag is obtained by a HEAD request and is used to know if the file on the remote host has changed. Therefore if a file is updated on the remote host, then the hash returned by `hash_url_to_filename` is different.","body":"Hi , i review the code in \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py\r\nin the _split_generators function is the truly logic of download raw datasets with dl_manager\r\nand use Conll2003 cls by use import_main_class in load_dataset function\r\nMy question is that , with this logic it seems that i can not have the raw dataset download location\r\nin variable in downloaded_files in _split_generators.\r\nIf someone also want use huggingface datasets as raw dataset downloader,\r\nhow can he retrieve the raw dataset download path from attributes in \r\ndatasets.dataset_dict.DatasetDict ?","comment_length":100,"text":"Some question about raw dataset download info in the project . \n Hi , i review the code in \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py\r\nin the _split_generators function is the truly logic of download raw datasets with dl_manager\r\nand use Conll2003 cls by use import_main_class in load_dataset function\r\nMy question is that , with this logic it seems that i can not have the raw dataset download location\r\nin variable in downloaded_files in _split_generators.\r\nIf someone also want use huggingface datasets as raw dataset downloader,\r\nhow can he retrieve the raw dataset download path from attributes in \r\ndatasets.dataset_dict.DatasetDict ? \n Sure it would be nice to have an easier access to these paths !\r\nThe dataset builder could have a method to return those, what do you think ?\r\nFeel free to work on this @albertvillanova , it would be a nice addition :) \r\n\r\nYour suggestion does work as well @albertvillanova if you complete it by specifying `etag=` to `hash_url_to_filename`.\r\n\r\nThe ETag is obtained by a HEAD request and is used to know if the file on the remote host has changed. Therefore if a file is updated on the remote host, then the hash returned by `hash_url_to_filename` is different.","embeddings":[-0.1199871525,-0.1007275581,-0.0441223904,0.5240066648,0.0438982211,-0.14139992,0.1183401495,0.0170494411,0.0113154026,0.1344257444,-0.3461932838,0.1511439532,-0.1354893446,0.5920690298,0.1894122958,-0.0379635394,-0.0841844305,0.1217005774,-0.1077714339,-0.0879005864,-0.3565678596,0.2822732031,-0.2687031925,0.165711835,-0.1946666539,-0.0248464216,-0.0181015562,0.3191277683,-0.4727252126,-0.2737624943,0.3005141318,0.2128322572,0.4005346894,0.1742072105,-0.0001249202,0.0375019796,0.2728613615,-0.3402907848,-0.5269052982,-0.2696060538,-0.6916824579,-0.0082495483,-0.0579732992,-0.2070541084,0.1547249407,0.0112637961,-0.053100694,-0.1325006634,0.0858320147,0.1186293438,0.0518584922,0.3697153628,-0.0970060304,0.0601943322,0.3183321357,0.2908330262,0.007793223,0.1266438365,-0.0761737525,0.3405940533,0.1056883559,0.302442044,0.2225824594,0.1028109491,0.4406277239,0.3373490274,-0.3781242967,-0.2769117951,0.0373095162,0.2423309535,0.3537122309,-0.2812837064,-0.58412534,-0.4083456099,-0.1191254482,-0.4685427547,0.1080773845,0.121445097,-0.4281236529,0.3086461127,-0.5675059557,-0.4780251682,-0.1658395231,-0.0852148011,-0.137549445,0.3164402246,-0.277533561,0.0663057417,0.0792562366,0.3843625188,0.2701301575,-0.2281227857,-0.021869285,0.2174347788,-0.1073611826,-0.1102793217,-0.0512238331,0.2954008281,0.1776958704,0.0195236113,-0.1246257648,-0.2577974796,-0.0622397587,0.0344964676,0.3408131301,0.0334263556,0.130103305,0.2412094921,0.2612249851,0.1766114682,0.1660885662,-0.2614012361,-0.115149036,-0.0674973726,-0.3462032378,0.1671791971,0.0185234584,-0.1103438139,-0.1785894781,-0.2391816229,-0.0279649924,-0.3496949673,0.314170599,0.3573299646,-0.1220672652,0.0427074023,-0.1295641065,-0.0033858558,0.1085045636,-0.3434873223,-0.0575917736,-0.2041842341,-0.1811356246,0.1146542653,-0.2467762232,-0.1190599427,0.3445102274,0.0368776731,0.0044905422,0.0511671118,-0.0212293789,-0.0554253049,0.2633837759,0.1153134927,0.0225037448,0.2498401701,-0.172318995,-0.2595724463,-0.194195807,-0.1401342303,-0.3265097737,-0.4692923427,-0.0550042801,-0.0365222543,-0.1397644877,0.0882554948,-0.2045928538,-0.0359483957,-0.0043262164,0.2903865874,0.0153338183,0.0368744805,-0.437574327,-0.261407584,0.4150717854,0.5498954654,-0.2640757263,-0.124882713,0.1626147479,-0.4113045931,0.0055077816,0.0851147249,-0.376221627,0.4481256902,-0.3954285383,-0.1491193324,0.4284542203,-0.3486127555,-0.2835324407,0.5134182572,0.0685734376,-0.0092668291,0.4227092564,0.0049932599,0.3071328998,-0.0831540376,-0.0749478117,0.5160323977,0.1115894988,-0.112585254,-0.0146319792,-0.2521874011,-0.1562729627,-0.0869760215,-0.0983398035,0.2540663779,0.1340302378,-0.1878966391,0.5806202292,-0.0906960666,0.2048040032,0.2849582434,0.0666126609,0.1446066797,-0.0434155166,0.0592394546,-0.6398258209,0.2061683983,-0.2713476121,-0.2402375489,-0.3789283931,-0.3321031332,-0.2896721363,0.1395770609,-0.2251721472,-0.3142985404,-0.0874109492,0.1475146711,0.4725340903,0.0423079804,-0.3148836493,0.5093697309,-0.0809046626,0.1222967356,-0.1639381498,0.0552342087,0.1615948528,0.1928499341,-0.0075578629,0.0963905826,0.117884092,-0.1511209607,0.0378694758,0.4245799184,0.1222041473,0.2514210641,0.3347149789,0.0576162823,0.2651583254,0.1458984911,-0.0056864223,-0.2740897834,0.0745327771,0.2104532719,0.0384140052,0.4083472192,-0.0198515505,0.2785645425,0.1696805805,-0.0084310053,0.103900522,-0.0308023803,0.1452540606,-0.1656621993,-0.1733821779,0.1735544205,0.3252809644,0.0908264816,-0.2493944466,0.2692500353,0.4680553973,-0.0680283308,-0.3477786183,0.1148413718,-0.2691214681,-0.055716712,0.124627687,0.8814340234,0.401753217,0.0123439897,0.2361507863,0.0718542114,0.2702481449,-0.0683172345,0.364923209,0.0819866583,-0.0913176388,-0.0149565851,-0.4808924198,-0.191566214,0.1524692923,-0.0226064268,0.1827385724,0.1612289995,-0.3683389723,0.033082407,-0.2861868441,-0.3713711798,-0.2221476436,-0.1441488564,-0.0842059329,-0.1802142113,-0.2356018871,-0.0917684063,-0.1567493081,-0.2011985332,-0.1180167273,0.0290215742,-0.222662434,0.0688054413,-0.1916803718,-0.155370906,-0.0985752493,-0.0907019079,0.1838143915,-0.0397568643,0.0875819921,-0.4963960648,0.1469944268,-0.3051645756,0.2047581673,0.3494753838,0.0336925536,0.176849544,0.4060946107,0.2000522166,-0.0665533319,0.0030451966,-0.1094028354,0.0911620408,-0.1093048006,-0.389409095,0.1404078156,0.0943241268,-0.0475681536,-0.2293685675,-0.6929691434,-0.0416314527,0.2408768535,-0.1981221735,0.2762157321,-0.1055136174,-0.1378174424,0.0386818759,-0.4083726108,-0.0131057324,-0.186048612,-0.8774716854,0.2753805518,-0.3544985354,-0.2303793877,-0.0539300665,-0.0597018078,0.178771615,0.0648877248,-0.2197252661,-0.0151435025,-0.1151275188,0.2784604132,0.1276265383,0.2927650511,0.0518944338,-0.0566531457,0.164882943,-0.1588708907,-0.0507449247,0.2370913327,-0.3477135003,0.3656221628,0.4061171114,-0.0131742787,0.2261146009,0.7650014758,0.185265854,-0.0783657506,0.1470417082,-0.1283288151,0.3387116194,0.3169541061,-0.1909824908,-0.0454266481,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1831","title":"Some question about raw dataset download info in the project .","comments":"Once #1846 will be merged, the paths to the raw downloaded files will be accessible as:\r\n```python\r\nbuilder_instance.dl_manager.downloaded_paths\r\n``` ","body":"Hi , i review the code in \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py\r\nin the _split_generators function is the truly logic of download raw datasets with dl_manager\r\nand use Conll2003 cls by use import_main_class in load_dataset function\r\nMy question is that , with this logic it seems that i can not have the raw dataset download location\r\nin variable in downloaded_files in _split_generators.\r\nIf someone also want use huggingface datasets as raw dataset downloader,\r\nhow can he retrieve the raw dataset download path from attributes in \r\ndatasets.dataset_dict.DatasetDict ?","comment_length":19,"text":"Some question about raw dataset download info in the project . \n Hi , i review the code in \r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/conll2003\/conll2003.py\r\nin the _split_generators function is the truly logic of download raw datasets with dl_manager\r\nand use Conll2003 cls by use import_main_class in load_dataset function\r\nMy question is that , with this logic it seems that i can not have the raw dataset download location\r\nin variable in downloaded_files in _split_generators.\r\nIf someone also want use huggingface datasets as raw dataset downloader,\r\nhow can he retrieve the raw dataset download path from attributes in \r\ndatasets.dataset_dict.DatasetDict ? \n Once #1846 will be merged, the paths to the raw downloaded files will be accessible as:\r\n```python\r\nbuilder_instance.dl_manager.downloaded_paths\r\n``` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1830","title":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?","comments":"Hi @wumpusman \r\n`datasets` has a caching mechanism that allows to cache the results of `.map` so that when you want to re-run it later it doesn't recompute it again.\r\nSo when you do `.map`, what actually happens is:\r\n1. compute the hash used to identify your `map` for the cache\r\n2. apply your function on every batch\r\n\r\nThis can explain the time difference between your different experiments.\r\n\r\nThe hash computation time depends of how complex your function is. For a tokenizer, the hash computation scans the lists of the words in the tokenizer to identify this tokenizer. Usually it takes 2-3 seconds.\r\n\r\nAlso note that you can disable caching though using\r\n```python\r\nimport datasets\r\n\r\ndatasets.set_caching_enabled(False)\r\n```","body":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n","comment_length":116,"text":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? \n This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n \n Hi @wumpusman \r\n`datasets` has a caching mechanism that allows to cache the results of `.map` so that when you want to re-run it later it doesn't recompute it again.\r\nSo when you do `.map`, what actually happens is:\r\n1. compute the hash used to identify your `map` for the cache\r\n2. apply your function on every batch\r\n\r\nThis can explain the time difference between your different experiments.\r\n\r\nThe hash computation time depends of how complex your function is. For a tokenizer, the hash computation scans the lists of the words in the tokenizer to identify this tokenizer. Usually it takes 2-3 seconds.\r\n\r\nAlso note that you can disable caching though using\r\n```python\r\nimport datasets\r\n\r\ndatasets.set_caching_enabled(False)\r\n```","embeddings":[-0.4040468037,-0.0130073428,-0.1024706438,0.1034291983,0.1453053504,-0.1225539744,0.2550541461,0.189088881,0.1306775063,-0.0065023075,-0.0542231686,0.4988860488,-0.1724810004,-0.2727595866,-0.1320426911,0.0650426894,0.2197590619,0.0224171188,0.2416453809,-0.03428654,-0.1218727604,0.1023019925,-0.1767257303,0.2413996309,-0.3152630031,-0.0779930353,0.0812313482,-0.2096335888,-0.0027567474,-0.5689615011,-0.2084299624,0.1581248492,0.0209429283,0.5331252813,-0.0001148737,0.0403426066,-0.0291637182,0.2731475234,0.1347175092,0.0443611667,0.3529542685,-0.1942853481,-0.1685831547,-0.2257665098,-0.0541709252,-0.2808631957,-0.0221225843,-0.2241773009,0.0683081374,0.03004192,0.1608587801,0.1151950732,-0.3964318633,-0.0595159791,-0.0024588848,-0.1042250693,-0.0991546437,0.0140264928,0.071329318,-0.255187422,-0.2116566598,0.1949048191,-0.2130429149,-0.1478140801,0.2687232494,0.2163694054,0.0346116014,-0.2425245792,0.1680867523,0.2316348851,0.0670110211,-0.0272130258,-0.017125072,-0.3350791633,-0.370691061,-0.0284823496,0.146122545,-0.4334974587,-0.1206515282,-0.0896027014,-0.4140653312,0.2037731558,0.2642425895,-0.1223938689,-0.2039308697,0.435243994,0.0954279751,0.1497347653,0.1041521281,-0.0997518599,-0.2807916701,-0.2643776536,-0.1595768332,0.3733043671,-0.3415869772,-0.1739550233,0.2692955434,-0.3091349304,-0.14078857,0.1142799705,-0.1424314529,0.194665283,0.2083414793,-0.0471201427,0.0663070083,0.2460043877,0.0384478755,0.173846364,0.0296903811,-0.4802925289,-0.3160505593,-0.0903609022,-0.188874945,0.0202136673,0.3458716273,-0.2424587309,-0.2370802015,-0.072583504,-0.0205964632,-0.171307236,-0.3799443841,-0.0983371884,0.0658612549,0.185454756,0.0587473363,0.0610382333,-0.3647983074,-0.2021667808,-0.0928319693,-0.0767263323,-0.128627494,-0.1720007658,-0.1044918671,0.2542574704,-0.1882735491,0.0913059115,0.2601749003,0.0789951906,-0.1153759062,0.2103088349,0.2196046263,-0.3643229902,0.1047284082,-0.1130543575,0.074800998,0.436026901,-0.0055075004,0.0017841377,-0.2595285773,0.2082757056,-0.2649086416,-0.2264890373,0.2413854897,0.1191601008,-0.0831423551,-0.122161001,-0.4942295253,0.4385454953,0.2539224625,-0.1163537055,-0.0749134794,-0.1809133887,-0.5298196077,-0.2167420387,0.2679248154,-0.0187716689,-0.0722125247,-0.1937800497,0.3418038189,0.1921720505,0.3103512228,0.6430558562,-0.386482656,0.7953374386,-0.1250498444,0.7483612299,0.2579936981,-0.2477056235,-0.377818048,0.116638504,-0.1784688383,0.2722213864,-0.2818516493,-0.1200631186,0.7026370764,-0.2252786309,0.3419845998,0.1061444208,0.2469955236,0.3040959239,-0.2483731955,-0.0491901487,0.5504582524,-0.0802227855,-0.2242528498,-0.150981456,-0.1534067094,0.1111330166,-0.0315542296,-0.1203731075,-0.1554094404,0.0617320798,0.0151116839,0.0270558968,-0.0637358427,0.2192265391,0.1471125484,0.2171940356,0.0741173998,0.0683925077,0.4290306866,-0.006116644,0.1055165529,-0.0645505115,-0.1504328996,-0.1419277936,0.1726531982,0.1282207072,0.0678987801,0.0891928524,0.2311721146,-0.1069797724,0.0154438466,0.1270231307,0.4437769949,-0.3041421175,0.1406869292,-0.160289079,-0.1061222926,-0.1936032474,0.1408739388,0.1087328643,-0.1834660321,0.1624329686,0.1247908548,0.3094665408,-0.1730695963,0.2074124515,0.1145593002,0.2654830515,0.0448613353,0.1117050797,0.0164180808,-0.1618009508,-0.101143539,0.2487526983,0.7665551305,0.293423444,-0.0981309041,-0.1918504685,-0.0074495161,0.2052231431,0.1254599541,0.0902863443,-0.0172139611,0.3012900651,0.4247050881,0.1712665409,0.350858897,0.4743336141,0.5267609954,-0.1261985302,-0.2109235376,0.1169244349,-0.3413845897,-0.4390027523,-0.0324556194,-0.4132475555,0.1679466665,0.1043307185,0.223832801,-0.168983534,-0.0138754928,0.0025527822,0.2727616429,0.2319285125,-0.0062529156,-0.0347080603,0.1677317023,0.2354774922,-0.3278361559,-0.1683433354,-0.017744882,0.2466843575,-0.0202035345,-0.0880928412,0.0660286173,0.1790383607,0.4836559892,-0.0730335638,0.0438799784,-0.2336488962,-0.086826764,0.0112860957,0.0730059966,0.1429340839,0.2016051859,0.0416048281,0.0553793311,-0.1324757189,-0.0326357819,-0.2036225647,-0.3690441251,0.0673273802,0.0478155538,-0.1053127348,0.3012902737,0.1895495206,-0.0706468001,-0.0284742992,-0.4572891295,0.0877392069,-0.1803108454,0.0098784408,-0.1076653674,-0.1534436494,-0.1983680278,0.0400028937,0.1168603525,-0.2781751454,-0.2616514862,-0.3224228621,-0.1180729493,-0.1744088978,0.1405443698,-0.0200541224,0.0511423238,-0.0312802456,0.1222996935,0.1008714736,0.129345417,-0.2491520643,-0.1672082841,0.1939766407,-0.0520039871,-0.0121717555,-0.3335229158,-0.2497054636,0.3890587389,-0.0699702948,-0.0141936084,-0.2141370177,0.0942139179,0.1669799536,0.1398722082,-0.2460222244,0.2176393867,-0.3044712245,0.2614798546,0.0384436436,-0.2167224288,0.394102484,0.2282124758,-0.1222438663,0.1697704941,-0.2478057742,0.1691340357,-0.0590032451,-0.235063985,0.0723133609,0.484038353,0.2467962503,0.6211673617,0.0489818566,-0.0133375674,-0.0001047923,-0.3113279939,-0.131582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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1830","title":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?","comments":"Hi @lhoestq ,\r\n\r\nThanks for the reply. It's entirely possible that is the issue. Since it's a side project I won't be looking at it till later this week, but, I'll verify it by disabling caching and hopefully I'll see the same runtime. \r\n\r\nAppreciate the reference,\r\n\r\nMichael","body":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n","comment_length":47,"text":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? \n This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n \n Hi @lhoestq ,\r\n\r\nThanks for the reply. It's entirely possible that is the issue. Since it's a side project I won't be looking at it till later this week, but, I'll verify it by disabling caching and hopefully I'll see the same runtime. \r\n\r\nAppreciate the reference,\r\n\r\nMichael","embeddings":[-0.4040468037,-0.0130073428,-0.1024706438,0.1034291983,0.1453053504,-0.1225539744,0.2550541461,0.189088881,0.1306775063,-0.0065023075,-0.0542231686,0.4988860488,-0.1724810004,-0.2727595866,-0.1320426911,0.0650426894,0.2197590619,0.0224171188,0.2416453809,-0.03428654,-0.1218727604,0.1023019925,-0.1767257303,0.2413996309,-0.3152630031,-0.0779930353,0.0812313482,-0.2096335888,-0.0027567474,-0.5689615011,-0.2084299624,0.1581248492,0.0209429283,0.5331252813,-0.0001148737,0.0403426066,-0.0291637182,0.2731475234,0.1347175092,0.0443611667,0.3529542685,-0.1942853481,-0.1685831547,-0.2257665098,-0.0541709252,-0.2808631957,-0.0221225843,-0.2241773009,0.0683081374,0.03004192,0.1608587801,0.1151950732,-0.3964318633,-0.0595159791,-0.0024588848,-0.1042250693,-0.0991546437,0.0140264928,0.071329318,-0.255187422,-0.2116566598,0.1949048191,-0.2130429149,-0.1478140801,0.2687232494,0.2163694054,0.0346116014,-0.2425245792,0.1680867523,0.2316348851,0.0670110211,-0.0272130258,-0.017125072,-0.3350791633,-0.370691061,-0.0284823496,0.146122545,-0.4334974587,-0.1206515282,-0.0896027014,-0.4140653312,0.2037731558,0.2642425895,-0.1223938689,-0.2039308697,0.435243994,0.0954279751,0.1497347653,0.1041521281,-0.0997518599,-0.2807916701,-0.2643776536,-0.1595768332,0.3733043671,-0.3415869772,-0.1739550233,0.2692955434,-0.3091349304,-0.14078857,0.1142799705,-0.1424314529,0.194665283,0.2083414793,-0.0471201427,0.0663070083,0.2460043877,0.0384478755,0.173846364,0.0296903811,-0.4802925289,-0.3160505593,-0.0903609022,-0.188874945,0.0202136673,0.3458716273,-0.2424587309,-0.2370802015,-0.072583504,-0.0205964632,-0.171307236,-0.3799443841,-0.0983371884,0.0658612549,0.185454756,0.0587473363,0.0610382333,-0.3647983074,-0.2021667808,-0.0928319693,-0.0767263323,-0.128627494,-0.1720007658,-0.1044918671,0.2542574704,-0.1882735491,0.0913059115,0.2601749003,0.0789951906,-0.1153759062,0.2103088349,0.2196046263,-0.3643229902,0.1047284082,-0.1130543575,0.074800998,0.436026901,-0.0055075004,0.0017841377,-0.2595285773,0.2082757056,-0.2649086416,-0.2264890373,0.2413854897,0.1191601008,-0.0831423551,-0.122161001,-0.4942295253,0.4385454953,0.2539224625,-0.1163537055,-0.0749134794,-0.1809133887,-0.5298196077,-0.2167420387,0.2679248154,-0.0187716689,-0.0722125247,-0.1937800497,0.3418038189,0.1921720505,0.3103512228,0.6430558562,-0.386482656,0.7953374386,-0.1250498444,0.7483612299,0.2579936981,-0.2477056235,-0.377818048,0.116638504,-0.1784688383,0.2722213864,-0.2818516493,-0.1200631186,0.7026370764,-0.2252786309,0.3419845998,0.1061444208,0.2469955236,0.3040959239,-0.2483731955,-0.0491901487,0.5504582524,-0.0802227855,-0.2242528498,-0.150981456,-0.1534067094,0.1111330166,-0.0315542296,-0.1203731075,-0.1554094404,0.0617320798,0.0151116839,0.0270558968,-0.0637358427,0.2192265391,0.1471125484,0.2171940356,0.0741173998,0.0683925077,0.4290306866,-0.006116644,0.1055165529,-0.0645505115,-0.1504328996,-0.1419277936,0.1726531982,0.1282207072,0.0678987801,0.0891928524,0.2311721146,-0.1069797724,0.0154438466,0.1270231307,0.4437769949,-0.3041421175,0.1406869292,-0.160289079,-0.1061222926,-0.1936032474,0.1408739388,0.1087328643,-0.1834660321,0.1624329686,0.1247908548,0.3094665408,-0.1730695963,0.2074124515,0.1145593002,0.2654830515,0.0448613353,0.1117050797,0.0164180808,-0.1618009508,-0.101143539,0.2487526983,0.7665551305,0.293423444,-0.0981309041,-0.1918504685,-0.0074495161,0.2052231431,0.1254599541,0.0902863443,-0.0172139611,0.3012900651,0.4247050881,0.1712665409,0.350858897,0.4743336141,0.5267609954,-0.1261985302,-0.2109235376,0.1169244349,-0.3413845897,-0.4390027523,-0.0324556194,-0.4132475555,0.1679466665,0.1043307185,0.223832801,-0.168983534,-0.0138754928,0.0025527822,0.2727616429,0.2319285125,-0.0062529156,-0.0347080603,0.1677317023,0.2354774922,-0.3278361559,-0.1683433354,-0.017744882,0.2466843575,-0.0202035345,-0.0880928412,0.0660286173,0.1790383607,0.4836559892,-0.0730335638,0.0438799784,-0.2336488962,-0.086826764,0.0112860957,0.0730059966,0.1429340839,0.2016051859,0.0416048281,0.0553793311,-0.1324757189,-0.0326357819,-0.2036225647,-0.3690441251,0.0673273802,0.0478155538,-0.1053127348,0.3012902737,0.1895495206,-0.0706468001,-0.0284742992,-0.4572891295,0.0877392069,-0.1803108454,0.0098784408,-0.1076653674,-0.1534436494,-0.1983680278,0.0400028937,0.1168603525,-0.2781751454,-0.2616514862,-0.3224228621,-0.1180729493,-0.1744088978,0.1405443698,-0.0200541224,0.0511423238,-0.0312802456,0.1222996935,0.1008714736,0.129345417,-0.2491520643,-0.1672082841,0.1939766407,-0.0520039871,-0.0121717555,-0.3335229158,-0.2497054636,0.3890587389,-0.0699702948,-0.0141936084,-0.2141370177,0.0942139179,0.1669799536,0.1398722082,-0.2460222244,0.2176393867,-0.3044712245,0.2614798546,0.0384436436,-0.2167224288,0.394102484,0.2282124758,-0.1222438663,0.1697704941,-0.2478057742,0.1691340357,-0.0590032451,-0.235063985,0.0723133609,0.484038353,0.2467962503,0.6211673617,0.0489818566,-0.0133375674,-0.0001047923,-0.3113279939,-0.1315821707,-0.431189537,-0.0755139217,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1830","title":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?","comments":"I believe this is an actual issue, tokenizing a ~4GB txt file went from an hour and a half to ~10 minutes when I switched from my pre-trained tokenizer(on the same dataset) to the default gpt2 tokenizer.\r\nBoth were loaded using:\r\n```\r\nAutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\r\n```\r\nI trained the tokenizer using ByteLevelBPETokenizer from the Tokenizers library and save it to a tokenizer.json file.\r\n\r\nI have tested the caching ideas above, changing the number of process, the TOKENIZERS_PARALLELISM env variable, keep_in_memory=True and batching with different sizes.\r\n\r\nApologies I can't really upload much code, but wanted to back up the finding and hopefully a fix\/the problem can be found.\r\nI will comment back if I find a fix as well.","body":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n","comment_length":117,"text":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? \n This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n \n I believe this is an actual issue, tokenizing a ~4GB txt file went from an hour and a half to ~10 minutes when I switched from my pre-trained tokenizer(on the same dataset) to the default gpt2 tokenizer.\r\nBoth were loaded using:\r\n```\r\nAutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)\r\n```\r\nI trained the tokenizer using ByteLevelBPETokenizer from the Tokenizers library and save it to a tokenizer.json file.\r\n\r\nI have tested the caching ideas above, changing the number of process, the TOKENIZERS_PARALLELISM env variable, keep_in_memory=True and batching with different sizes.\r\n\r\nApologies I can't really upload much code, but wanted to back up the finding and hopefully a fix\/the problem can be found.\r\nI will comment back if I find a fix as well.","embeddings":[-0.4040468037,-0.0130073428,-0.1024706438,0.1034291983,0.1453053504,-0.1225539744,0.2550541461,0.189088881,0.1306775063,-0.0065023075,-0.0542231686,0.4988860488,-0.1724810004,-0.2727595866,-0.1320426911,0.0650426894,0.2197590619,0.0224171188,0.2416453809,-0.03428654,-0.1218727604,0.1023019925,-0.1767257303,0.2413996309,-0.3152630031,-0.0779930353,0.0812313482,-0.2096335888,-0.0027567474,-0.5689615011,-0.2084299624,0.1581248492,0.0209429283,0.5331252813,-0.0001148737,0.0403426066,-0.0291637182,0.2731475234,0.1347175092,0.0443611667,0.3529542685,-0.1942853481,-0.1685831547,-0.2257665098,-0.0541709252,-0.2808631957,-0.0221225843,-0.2241773009,0.0683081374,0.03004192,0.1608587801,0.1151950732,-0.3964318633,-0.0595159791,-0.0024588848,-0.1042250693,-0.0991546437,0.0140264928,0.071329318,-0.255187422,-0.2116566598,0.1949048191,-0.2130429149,-0.1478140801,0.2687232494,0.2163694054,0.0346116014,-0.2425245792,0.1680867523,0.2316348851,0.0670110211,-0.0272130258,-0.017125072,-0.3350791633,-0.370691061,-0.0284823496,0.146122545,-0.4334974587,-0.1206515282,-0.0896027014,-0.4140653312,0.2037731558,0.2642425895,-0.1223938689,-0.2039308697,0.435243994,0.0954279751,0.1497347653,0.1041521281,-0.0997518599,-0.2807916701,-0.2643776536,-0.1595768332,0.3733043671,-0.3415869772,-0.1739550233,0.2692955434,-0.3091349304,-0.14078857,0.1142799705,-0.1424314529,0.194665283,0.2083414793,-0.0471201427,0.0663070083,0.2460043877,0.0384478755,0.173846364,0.0296903811,-0.4802925289,-0.3160505593,-0.0903609022,-0.188874945,0.0202136673,0.3458716273,-0.2424587309,-0.2370802015,-0.072583504,-0.0205964632,-0.171307236,-0.3799443841,-0.0983371884,0.0658612549,0.185454756,0.0587473363,0.0610382333,-0.3647983074,-0.2021667808,-0.0928319693,-0.0767263323,-0.128627494,-0.1720007658,-0.1044918671,0.2542574704,-0.1882735491,0.0913059115,0.2601749003,0.0789951906,-0.1153759062,0.2103088349,0.2196046263,-0.3643229902,0.1047284082,-0.1130543575,0.074800998,0.436026901,-0.0055075004,0.0017841377,-0.2595285773,0.2082757056,-0.2649086416,-0.2264890373,0.2413854897,0.1191601008,-0.0831423551,-0.122161001,-0.4942295253,0.4385454953,0.2539224625,-0.1163537055,-0.0749134794,-0.1809133887,-0.5298196077,-0.2167420387,0.2679248154,-0.0187716689,-0.0722125247,-0.1937800497,0.3418038189,0.1921720505,0.3103512228,0.6430558562,-0.386482656,0.7953374386,-0.1250498444,0.7483612299,0.2579936981,-0.2477056235,-0.377818048,0.116638504,-0.1784688383,0.2722213864,-0.2818516493,-0.1200631186,0.7026370764,-0.2252786309,0.3419845998,0.1061444208,0.2469955236,0.3040959239,-0.2483731955,-0.0491901487,0.5504582524,-0.0802227855,-0.2242528498,-0.150981456,-0.1534067094,0.1111330166,-0.0315542296,-0.1203731075,-0.1554094404,0.0617320798,0.0151116839,0.0270558968,-0.0637358427,0.2192265391,0.1471125484,0.2171940356,0.0741173998,0.0683925077,0.4290306866,-0.006116644,0.1055165529,-0.0645505115,-0.1504328996,-0.1419277936,0.1726531982,0.1282207072,0.0678987801,0.0891928524,0.2311721146,-0.1069797724,0.0154438466,0.1270231307,0.4437769949,-0.3041421175,0.1406869292,-0.160289079,-0.1061222926,-0.1936032474,0.1408739388,0.1087328643,-0.1834660321,0.1624329686,0.1247908548,0.3094665408,-0.1730695963,0.2074124515,0.1145593002,0.2654830515,0.0448613353,0.1117050797,0.0164180808,-0.1618009508,-0.101143539,0.2487526983,0.7665551305,0.293423444,-0.0981309041,-0.1918504685,-0.0074495161,0.2052231431,0.1254599541,0.0902863443,-0.0172139611,0.3012900651,0.4247050881,0.1712665409,0.350858897,0.4743336141,0.5267609954,-0.1261985302,-0.2109235376,0.1169244349,-0.3413845897,-0.4390027523,-0.0324556194,-0.4132475555,0.1679466665,0.1043307185,0.223832801,-0.168983534,-0.0138754928,0.0025527822,0.2727616429,0.2319285125,-0.0062529156,-0.0347080603,0.1677317023,0.2354774922,-0.3278361559,-0.1683433354,-0.017744882,0.2466843575,-0.0202035345,-0.0880928412,0.0660286173,0.1790383607,0.4836559892,-0.0730335638,0.0438799784,-0.2336488962,-0.086826764,0.0112860957,0.0730059966,0.1429340839,0.2016051859,0.0416048281,0.0553793311,-0.1324757189,-0.0326357819,-0.2036225647,-0.3690441251,0.0673273802,0.0478155538,-0.1053127348,0.3012902737,0.1895495206,-0.0706468001,-0.0284742992,-0.4572891295,0.0877392069,-0.1803108454,0.0098784408,-0.1076653674,-0.1534436494,-0.1983680278,0.0400028937,0.1168603525,-0.2781751454,-0.2616514862,-0.3224228621,-0.1180729493,-0.1744088978,0.1405443698,-0.0200541224,0.0511423238,-0.0312802456,0.1222996935,0.1008714736,0.129345417,-0.2491520643,-0.1672082841,0.1939766407,-0.0520039871,-0.0121717555,-0.3335229158,-0.2497054636,0.3890587389,-0.0699702948,-0.0141936084,-0.2141370177,0.0942139179,0.1669799536,0.1398722082,-0.2460222244,0.2176393867,-0.3044712245,0.2614798546,0.0384436436,-0.2167224288,0.394102484,0.2282124758,-0.1222438663,0.1697704941,-0.2478057742,0.1691340357,-0.0590032451,-0.235063985,0.0723133609,0.484038353,0.2467962503,0.6211673617,0.0489818566,-0.0133375674,-0.0001047923,-0.3113279939,-0.1315821707,-0.431189537,-0.0755139217,0.2071558088,-0.09913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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1830","title":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?","comments":"Hi @johncookds do you think this can come from one tokenizer being faster than the other one ? Can you try to compare their speed without using `datasets` just to make sure ?","body":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n","comment_length":33,"text":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? \n This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n \n Hi @johncookds do you think this can come from one tokenizer being faster than the other one ? Can you try to compare their speed without using `datasets` just to make sure ?","embeddings":[-0.4040468037,-0.0130073428,-0.1024706438,0.1034291983,0.1453053504,-0.1225539744,0.2550541461,0.189088881,0.1306775063,-0.0065023075,-0.0542231686,0.4988860488,-0.1724810004,-0.2727595866,-0.1320426911,0.0650426894,0.2197590619,0.0224171188,0.2416453809,-0.03428654,-0.1218727604,0.1023019925,-0.1767257303,0.2413996309,-0.3152630031,-0.0779930353,0.0812313482,-0.2096335888,-0.0027567474,-0.5689615011,-0.2084299624,0.1581248492,0.0209429283,0.5331252813,-0.0001148737,0.0403426066,-0.0291637182,0.2731475234,0.1347175092,0.0443611667,0.3529542685,-0.1942853481,-0.1685831547,-0.2257665098,-0.0541709252,-0.2808631957,-0.0221225843,-0.2241773009,0.0683081374,0.03004192,0.1608587801,0.1151950732,-0.3964318633,-0.0595159791,-0.0024588848,-0.1042250693,-0.0991546437,0.0140264928,0.071329318,-0.255187422,-0.2116566598,0.1949048191,-0.2130429149,-0.1478140801,0.2687232494,0.2163694054,0.0346116014,-0.2425245792,0.1680867523,0.2316348851,0.0670110211,-0.0272130258,-0.017125072,-0.3350791633,-0.370691061,-0.0284823496,0.146122545,-0.4334974587,-0.1206515282,-0.0896027014,-0.4140653312,0.2037731558,0.2642425895,-0.1223938689,-0.2039308697,0.435243994,0.0954279751,0.1497347653,0.1041521281,-0.0997518599,-0.2807916701,-0.2643776536,-0.1595768332,0.3733043671,-0.3415869772,-0.1739550233,0.2692955434,-0.3091349304,-0.14078857,0.1142799705,-0.1424314529,0.194665283,0.2083414793,-0.0471201427,0.0663070083,0.2460043877,0.0384478755,0.173846364,0.0296903811,-0.4802925289,-0.3160505593,-0.0903609022,-0.188874945,0.0202136673,0.3458716273,-0.2424587309,-0.2370802015,-0.072583504,-0.0205964632,-0.171307236,-0.3799443841,-0.0983371884,0.0658612549,0.185454756,0.0587473363,0.0610382333,-0.3647983074,-0.2021667808,-0.0928319693,-0.0767263323,-0.128627494,-0.1720007658,-0.1044918671,0.2542574704,-0.1882735491,0.0913059115,0.2601749003,0.0789951906,-0.1153759062,0.2103088349,0.2196046263,-0.3643229902,0.1047284082,-0.1130543575,0.074800998,0.436026901,-0.0055075004,0.0017841377,-0.2595285773,0.2082757056,-0.2649086416,-0.2264890373,0.2413854897,0.1191601008,-0.0831423551,-0.122161001,-0.4942295253,0.4385454953,0.2539224625,-0.1163537055,-0.0749134794,-0.1809133887,-0.5298196077,-0.2167420387,0.2679248154,-0.0187716689,-0.0722125247,-0.1937800497,0.3418038189,0.1921720505,0.3103512228,0.6430558562,-0.386482656,0.7953374386,-0.1250498444,0.7483612299,0.2579936981,-0.2477056235,-0.377818048,0.116638504,-0.1784688383,0.2722213864,-0.2818516493,-0.1200631186,0.7026370764,-0.2252786309,0.3419845998,0.1061444208,0.2469955236,0.3040959239,-0.2483731955,-0.0491901487,0.5504582524,-0.0802227855,-0.2242528498,-0.150981456,-0.1534067094,0.1111330166,-0.0315542296,-0.1203731075,-0.1554094404,0.0617320798,0.0151116839,0.0270558968,-0.0637358427,0.2192265391,0.1471125484,0.2171940356,0.0741173998,0.0683925077,0.4290306866,-0.006116644,0.1055165529,-0.0645505115,-0.1504328996,-0.1419277936,0.1726531982,0.1282207072,0.0678987801,0.0891928524,0.2311721146,-0.1069797724,0.0154438466,0.1270231307,0.4437769949,-0.3041421175,0.1406869292,-0.160289079,-0.1061222926,-0.1936032474,0.1408739388,0.1087328643,-0.1834660321,0.1624329686,0.1247908548,0.3094665408,-0.1730695963,0.2074124515,0.1145593002,0.2654830515,0.0448613353,0.1117050797,0.0164180808,-0.1618009508,-0.101143539,0.2487526983,0.7665551305,0.293423444,-0.0981309041,-0.1918504685,-0.0074495161,0.2052231431,0.1254599541,0.0902863443,-0.0172139611,0.3012900651,0.4247050881,0.1712665409,0.350858897,0.4743336141,0.5267609954,-0.1261985302,-0.2109235376,0.1169244349,-0.3413845897,-0.4390027523,-0.0324556194,-0.4132475555,0.1679466665,0.1043307185,0.223832801,-0.168983534,-0.0138754928,0.0025527822,0.2727616429,0.2319285125,-0.0062529156,-0.0347080603,0.1677317023,0.2354774922,-0.3278361559,-0.1683433354,-0.017744882,0.2466843575,-0.0202035345,-0.0880928412,0.0660286173,0.1790383607,0.4836559892,-0.0730335638,0.0438799784,-0.2336488962,-0.086826764,0.0112860957,0.0730059966,0.1429340839,0.2016051859,0.0416048281,0.0553793311,-0.1324757189,-0.0326357819,-0.2036225647,-0.3690441251,0.0673273802,0.0478155538,-0.1053127348,0.3012902737,0.1895495206,-0.0706468001,-0.0284742992,-0.4572891295,0.0877392069,-0.1803108454,0.0098784408,-0.1076653674,-0.1534436494,-0.1983680278,0.0400028937,0.1168603525,-0.2781751454,-0.2616514862,-0.3224228621,-0.1180729493,-0.1744088978,0.1405443698,-0.0200541224,0.0511423238,-0.0312802456,0.1222996935,0.1008714736,0.129345417,-0.2491520643,-0.1672082841,0.1939766407,-0.0520039871,-0.0121717555,-0.3335229158,-0.2497054636,0.3890587389,-0.0699702948,-0.0141936084,-0.2141370177,0.0942139179,0.1669799536,0.1398722082,-0.2460222244,0.2176393867,-0.3044712245,0.2614798546,0.0384436436,-0.2167224288,0.394102484,0.2282124758,-0.1222438663,0.1697704941,-0.2478057742,0.1691340357,-0.0590032451,-0.235063985,0.0723133609,0.484038353,0.2467962503,0.6211673617,0.0489818566,-0.0133375674,-0.0001047923,-0.3113279939,-0.1315821707,-0.431189537,-0.0755139217,0.2071558088,-0.099136449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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1830","title":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?","comments":"Hi yes, I'm closing the loop here with some timings below. The issue seems to be at least somewhat\/mainly with the tokenizer's themselves. Moreover legacy saves of the trainer tokenizer perform faster but differently than the new tokenizer.json saves(note nothing about the training process\/adding of special tokens changed between the top two trained tokenizer tests, only the way it was saved). This is only a 3x slowdown vs like a 10x but I think the slowdown is most likely due to this.\r\n\r\n```\r\ntrained tokenizer - tokenizer.json save (same results for AutoTokenizer legacy_format=False):\r\nTokenizer time(seconds): 0.32767510414123535\r\nTokenized avg. length: 323.01\r\n\r\ntrained tokenizer - AutoTokenizer legacy_format=True:\r\nTokenizer time(seconds): 0.09258866310119629\r\nTokenized avg. length: 301.01\r\n\r\nGPT2 Tokenizer from huggingface\r\nTokenizer time(seconds): 0.1010282039642334\r\nTokenized avg. length: 461.21\r\n```","body":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n","comment_length":124,"text":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? \n This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n \n Hi yes, I'm closing the loop here with some timings below. The issue seems to be at least somewhat\/mainly with the tokenizer's themselves. Moreover legacy saves of the trainer tokenizer perform faster but differently than the new tokenizer.json saves(note nothing about the training process\/adding of special tokens changed between the top two trained tokenizer tests, only the way it was saved). This is only a 3x slowdown vs like a 10x but I think the slowdown is most likely due to this.\r\n\r\n```\r\ntrained tokenizer - tokenizer.json save (same results for AutoTokenizer legacy_format=False):\r\nTokenizer time(seconds): 0.32767510414123535\r\nTokenized avg. length: 323.01\r\n\r\ntrained tokenizer - AutoTokenizer legacy_format=True:\r\nTokenizer time(seconds): 0.09258866310119629\r\nTokenized avg. length: 301.01\r\n\r\nGPT2 Tokenizer from huggingface\r\nTokenizer time(seconds): 0.1010282039642334\r\nTokenized avg. length: 461.21\r\n```","embeddings":[-0.4040468037,-0.0130073428,-0.1024706438,0.1034291983,0.1453053504,-0.1225539744,0.2550541461,0.189088881,0.1306775063,-0.0065023075,-0.0542231686,0.4988860488,-0.1724810004,-0.2727595866,-0.1320426911,0.0650426894,0.2197590619,0.0224171188,0.2416453809,-0.03428654,-0.1218727604,0.1023019925,-0.1767257303,0.2413996309,-0.3152630031,-0.0779930353,0.0812313482,-0.2096335888,-0.0027567474,-0.5689615011,-0.2084299624,0.1581248492,0.0209429283,0.5331252813,-0.0001148737,0.0403426066,-0.0291637182,0.2731475234,0.1347175092,0.0443611667,0.3529542685,-0.1942853481,-0.1685831547,-0.2257665098,-0.0541709252,-0.2808631957,-0.0221225843,-0.2241773009,0.0683081374,0.03004192,0.1608587801,0.1151950732,-0.3964318633,-0.0595159791,-0.0024588848,-0.1042250693,-0.0991546437,0.0140264928,0.071329318,-0.255187422,-0.2116566598,0.1949048191,-0.2130429149,-0.1478140801,0.2687232494,0.2163694054,0.0346116014,-0.2425245792,0.1680867523,0.2316348851,0.0670110211,-0.0272130258,-0.017125072,-0.3350791633,-0.370691061,-0.0284823496,0.146122545,-0.4334974587,-0.1206515282,-0.0896027014,-0.4140653312,0.2037731558,0.2642425895,-0.1223938689,-0.2039308697,0.435243994,0.0954279751,0.1497347653,0.1041521281,-0.0997518599,-0.2807916701,-0.2643776536,-0.1595768332,0.3733043671,-0.3415869772,-0.1739550233,0.2692955434,-0.3091349304,-0.14078857,0.1142799705,-0.1424314529,0.194665283,0.2083414793,-0.0471201427,0.0663070083,0.2460043877,0.0384478755,0.173846364,0.0296903811,-0.4802925289,-0.3160505593,-0.0903609022,-0.188874945,0.0202136673,0.3458716273,-0.2424587309,-0.2370802015,-0.072583504,-0.0205964632,-0.171307236,-0.3799443841,-0.0983371884,0.0658612549,0.185454756,0.0587473363,0.0610382333,-0.3647983074,-0.2021667808,-0.0928319693,-0.0767263323,-0.128627494,-0.1720007658,-0.1044918671,0.2542574704,-0.1882735491,0.0913059115,0.2601749003,0.0789951906,-0.1153759062,0.2103088349,0.2196046263,-0.3643229902,0.1047284082,-0.1130543575,0.074800998,0.436026901,-0.0055075004,0.0017841377,-0.2595285773,0.2082757056,-0.2649086416,-0.2264890373,0.2413854897,0.1191601008,-0.0831423551,-0.122161001,-0.4942295253,0.4385454953,0.2539224625,-0.1163537055,-0.0749134794,-0.1809133887,-0.5298196077,-0.2167420387,0.2679248154,-0.0187716689,-0.0722125247,-0.1937800497,0.3418038189,0.1921720505,0.3103512228,0.6430558562,-0.386482656,0.7953374386,-0.1250498444,0.7483612299,0.2579936981,-0.2477056235,-0.377818048,0.116638504,-0.1784688383,0.2722213864,-0.2818516493,-0.1200631186,0.7026370764,-0.2252786309,0.3419845998,0.1061444208,0.2469955236,0.3040959239,-0.2483731955,-0.0491901487,0.5504582524,-0.0802227855,-0.2242528498,-0.150981456,-0.1534067094,0.1111330166,-0.0315542296,-0.1203731075,-0.1554094404,0.0617320798,0.0151116839,0.0270558968,-0.0637358427,0.2192265391,0.1471125484,0.2171940356,0.0741173998,0.0683925077,0.4290306866,-0.006116644,0.1055165529,-0.0645505115,-0.1504328996,-0.1419277936,0.1726531982,0.1282207072,0.0678987801,0.0891928524,0.2311721146,-0.1069797724,0.0154438466,0.1270231307,0.4437769949,-0.3041421175,0.1406869292,-0.160289079,-0.1061222926,-0.1936032474,0.1408739388,0.1087328643,-0.1834660321,0.1624329686,0.1247908548,0.3094665408,-0.1730695963,0.2074124515,0.1145593002,0.2654830515,0.0448613353,0.1117050797,0.0164180808,-0.1618009508,-0.101143539,0.2487526983,0.7665551305,0.293423444,-0.0981309041,-0.1918504685,-0.0074495161,0.2052231431,0.1254599541,0.0902863443,-0.0172139611,0.3012900651,0.4247050881,0.1712665409,0.350858897,0.4743336141,0.5267609954,-0.1261985302,-0.2109235376,0.1169244349,-0.3413845897,-0.4390027523,-0.0324556194,-0.4132475555,0.1679466665,0.1043307185,0.223832801,-0.168983534,-0.0138754928,0.0025527822,0.2727616429,0.2319285125,-0.0062529156,-0.0347080603,0.1677317023,0.2354774922,-0.3278361559,-0.1683433354,-0.017744882,0.2466843575,-0.0202035345,-0.0880928412,0.0660286173,0.1790383607,0.4836559892,-0.0730335638,0.0438799784,-0.2336488962,-0.086826764,0.0112860957,0.0730059966,0.1429340839,0.2016051859,0.0416048281,0.0553793311,-0.1324757189,-0.0326357819,-0.2036225647,-0.3690441251,0.0673273802,0.0478155538,-0.1053127348,0.3012902737,0.1895495206,-0.0706468001,-0.0284742992,-0.4572891295,0.0877392069,-0.1803108454,0.0098784408,-0.1076653674,-0.1534436494,-0.1983680278,0.0400028937,0.1168603525,-0.2781751454,-0.2616514862,-0.3224228621,-0.1180729493,-0.1744088978,0.1405443698,-0.0200541224,0.0511423238,-0.0312802456,0.1222996935,0.1008714736,0.129345417,-0.2491520643,-0.1672082841,0.1939766407,-0.0520039871,-0.0121717555,-0.3335229158,-0.2497054636,0.3890587389,-0.0699702948,-0.0141936084,-0.2141370177,0.0942139179,0.1669799536,0.1398722082,-0.2460222244,0.2176393867,-0.3044712245,0.2614798546,0.0384436436,-0.2167224288,0.394102484,0.2282124758,-0.1222438663,0.1697704941,-0.2478057742,0.1691340357,-0.0590032451,-0.235063985,0.0723133609,0.484038353,0.2467962503,0.6211673617,0.0489818566,-0.0133375674,-0.0001047923,-0.3113279939,-0.1315821707,-0.431189537,-0.0755139217,0.2071558088,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1830","title":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?","comments":"@lhoestq ,\r\n\r\nHi, which version of datasets has datasets.set_caching_enabled(False)? I get \r\nmodule 'datasets' has no attribute 'set_caching_enabled'. To hopefully get around this, I reran my code on a new set of data, and did so only once.\r\n\r\n@johncookds , thanks for chiming in, it looks this might be an issue of Tokenizer.\r\n\r\n**Tokenizer**: The runtime of GPT2TokenizerFast.from_pretrained(\"gpt2\") on 1000 chars is: **143 ms**\r\n**SlowTokenizer**: The runtime of a locally saved and loaded Tokenizer using the same vocab  on 1000 chars is:  **4.43 s**\r\n\r\nThat being said, I compared performance on the map function:\r\n\r\nRunning Tokenizer versus using it in the map function for 1000 chars goes from **141 ms** to **356 ms** \r\nRunning SlowTokenizer versus using it in the map function for 1000 chars with a single element goes from **4.43 s** to **9.76 s**\r\n\r\nI'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\r\n\r\n@lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\r\n\r\nRegards,\r\n\r\nMichael","body":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n","comment_length":182,"text":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? \n This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n \n @lhoestq ,\r\n\r\nHi, which version of datasets has datasets.set_caching_enabled(False)? I get \r\nmodule 'datasets' has no attribute 'set_caching_enabled'. To hopefully get around this, I reran my code on a new set of data, and did so only once.\r\n\r\n@johncookds , thanks for chiming in, it looks this might be an issue of Tokenizer.\r\n\r\n**Tokenizer**: The runtime of GPT2TokenizerFast.from_pretrained(\"gpt2\") on 1000 chars is: **143 ms**\r\n**SlowTokenizer**: The runtime of a locally saved and loaded Tokenizer using the same vocab  on 1000 chars is:  **4.43 s**\r\n\r\nThat being said, I compared performance on the map function:\r\n\r\nRunning Tokenizer versus using it in the map function for 1000 chars goes from **141 ms** to **356 ms** \r\nRunning SlowTokenizer versus using it in the map function for 1000 chars with a single element goes from **4.43 s** to **9.76 s**\r\n\r\nI'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\r\n\r\n@lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\r\n\r\nRegards,\r\n\r\nMichael","embeddings":[-0.4040468037,-0.0130073428,-0.1024706438,0.1034291983,0.1453053504,-0.1225539744,0.2550541461,0.189088881,0.1306775063,-0.0065023075,-0.0542231686,0.4988860488,-0.1724810004,-0.2727595866,-0.1320426911,0.0650426894,0.2197590619,0.0224171188,0.2416453809,-0.03428654,-0.1218727604,0.1023019925,-0.1767257303,0.2413996309,-0.3152630031,-0.0779930353,0.0812313482,-0.2096335888,-0.0027567474,-0.5689615011,-0.2084299624,0.1581248492,0.0209429283,0.5331252813,-0.0001148737,0.0403426066,-0.0291637182,0.2731475234,0.1347175092,0.0443611667,0.3529542685,-0.1942853481,-0.1685831547,-0.2257665098,-0.0541709252,-0.2808631957,-0.0221225843,-0.2241773009,0.0683081374,0.03004192,0.1608587801,0.1151950732,-0.3964318633,-0.0595159791,-0.0024588848,-0.1042250693,-0.0991546437,0.0140264928,0.071329318,-0.255187422,-0.2116566598,0.1949048191,-0.2130429149,-0.1478140801,0.2687232494,0.2163694054,0.0346116014,-0.2425245792,0.1680867523,0.2316348851,0.0670110211,-0.0272130258,-0.017125072,-0.3350791633,-0.370691061,-0.0284823496,0.146122545,-0.4334974587,-0.1206515282,-0.0896027014,-0.4140653312,0.2037731558,0.2642425895,-0.1223938689,-0.2039308697,0.435243994,0.0954279751,0.1497347653,0.1041521281,-0.0997518599,-0.2807916701,-0.2643776536,-0.1595768332,0.3733043671,-0.3415869772,-0.1739550233,0.2692955434,-0.3091349304,-0.14078857,0.1142799705,-0.1424314529,0.194665283,0.2083414793,-0.0471201427,0.0663070083,0.2460043877,0.0384478755,0.173846364,0.0296903811,-0.4802925289,-0.3160505593,-0.0903609022,-0.188874945,0.0202136673,0.3458716273,-0.2424587309,-0.2370802015,-0.072583504,-0.0205964632,-0.171307236,-0.3799443841,-0.0983371884,0.0658612549,0.185454756,0.0587473363,0.0610382333,-0.3647983074,-0.2021667808,-0.0928319693,-0.0767263323,-0.128627494,-0.1720007658,-0.1044918671,0.2542574704,-0.1882735491,0.0913059115,0.2601749003,0.0789951906,-0.1153759062,0.2103088349,0.2196046263,-0.3643229902,0.1047284082,-0.1130543575,0.074800998,0.436026901,-0.0055075004,0.0017841377,-0.2595285773,0.2082757056,-0.2649086416,-0.2264890373,0.2413854897,0.1191601008,-0.0831423551,-0.122161001,-0.4942295253,0.4385454953,0.2539224625,-0.1163537055,-0.0749134794,-0.1809133887,-0.5298196077,-0.2167420387,0.2679248154,-0.0187716689,-0.0722125247,-0.1937800497,0.3418038189,0.1921720505,0.3103512228,0.6430558562,-0.386482656,0.7953374386,-0.1250498444,0.7483612299,0.2579936981,-0.2477056235,-0.377818048,0.116638504,-0.1784688383,0.2722213864,-0.2818516493,-0.1200631186,0.7026370764,-0.2252786309,0.3419845998,0.1061444208,0.2469955236,0.3040959239,-0.2483731955,-0.0491901487,0.5504582524,-0.0802227855,-0.2242528498,-0.150981456,-0.1534067094,0.1111330166,-0.0315542296,-0.1203731075,-0.1554094404,0.0617320798,0.0151116839,0.0270558968,-0.0637358427,0.2192265391,0.1471125484,0.2171940356,0.0741173998,0.0683925077,0.4290306866,-0.006116644,0.1055165529,-0.0645505115,-0.1504328996,-0.1419277936,0.1726531982,0.1282207072,0.0678987801,0.0891928524,0.2311721146,-0.1069797724,0.0154438466,0.1270231307,0.4437769949,-0.3041421175,0.1406869292,-0.160289079,-0.1061222926,-0.1936032474,0.1408739388,0.1087328643,-0.1834660321,0.1624329686,0.1247908548,0.3094665408,-0.1730695963,0.2074124515,0.1145593002,0.2654830515,0.0448613353,0.1117050797,0.0164180808,-0.1618009508,-0.101143539,0.2487526983,0.7665551305,0.293423444,-0.0981309041,-0.1918504685,-0.0074495161,0.2052231431,0.1254599541,0.0902863443,-0.0172139611,0.3012900651,0.4247050881,0.1712665409,0.350858897,0.4743336141,0.5267609954,-0.1261985302,-0.2109235376,0.1169244349,-0.3413845897,-0.4390027523,-0.0324556194,-0.4132475555,0.1679466665,0.1043307185,0.223832801,-0.168983534,-0.0138754928,0.0025527822,0.2727616429,0.2319285125,-0.0062529156,-0.0347080603,0.1677317023,0.2354774922,-0.3278361559,-0.1683433354,-0.017744882,0.2466843575,-0.0202035345,-0.0880928412,0.0660286173,0.1790383607,0.4836559892,-0.0730335638,0.0438799784,-0.2336488962,-0.086826764,0.0112860957,0.0730059966,0.1429340839,0.2016051859,0.0416048281,0.0553793311,-0.1324757189,-0.0326357819,-0.2036225647,-0.3690441251,0.0673273802,0.0478155538,-0.1053127348,0.3012902737,0.1895495206,-0.0706468001,-0.0284742992,-0.4572891295,0.0877392069,-0.1803108454,0.0098784408,-0.1076653674,-0.1534436494,-0.1983680278,0.0400028937,0.1168603525,-0.2781751454,-0.2616514862,-0.3224228621,-0.1180729493,-0.1744088978,0.1405443698,-0.0200541224,0.0511423238,-0.0312802456,0.1222996935,0.1008714736,0.129345417,-0.2491520643,-0.1672082841,0.1939766407,-0.0520039871,-0.0121717555,-0.3335229158,-0.2497054636,0.3890587389,-0.0699702948,-0.0141936084,-0.2141370177,0.0942139179,0.1669799536,0.1398722082,-0.2460222244,0.2176393867,-0.3044712245,0.2614798546,0.0384436436,-0.2167224288,0.394102484,0.2282124758,-0.1222438663,0.1697704941,-0.2478057742,0.1691340357,-0.0590032451,-0.235063985,0.0723133609,0.484038353,0.2467962503,0.6211673617,0.0489818566,-0.0133375674,-0.0001047923,-0.3113279939,-0.1315821707,-0.431189537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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1830","title":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?","comments":"Thanks for the experiments @johncookds and @wumpusman ! \r\n\r\n> Hi, which version of datasets has datasets.set_caching_enabled(False)?\r\n\r\nCurrently you have to install `datasets` from source to have this feature, but this will be available in the next release in a few days.\r\n\r\n> I'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\r\n\r\nCould you also try with double the number of characters ? This should let us have an idea of the fixed cost (hashing) and the dynamic cost (actual tokenization, grows with the size of the input)\r\n\r\n> @lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\r\n\r\nFeel free to post an issue on the `transformers` repo. Also I'm sure there should be related issues so you can also look for someone with the same concerns on the `transformers` repo.","body":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n","comment_length":157,"text":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? \n This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n \n Thanks for the experiments @johncookds and @wumpusman ! \r\n\r\n> Hi, which version of datasets has datasets.set_caching_enabled(False)?\r\n\r\nCurrently you have to install `datasets` from source to have this feature, but this will be available in the next release in a few days.\r\n\r\n> I'm trying to figure out why the overhead of map would increase the time by double (figured it would be a fixed increase in time)? Though maybe this is expected behavior.\r\n\r\nCould you also try with double the number of characters ? This should let us have an idea of the fixed cost (hashing) and the dynamic cost (actual tokenization, grows with the size of the input)\r\n\r\n> @lhoestq, do you by chance know how I can redirect this issue to Tokenizer?\r\n\r\nFeel free to post an issue on the `transformers` repo. Also I'm sure there should be related issues so you can also look for someone with the same concerns on the `transformers` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1830","title":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?","comments":"@lhoestq,\r\n\r\nI just checked that previous run time was actually  3000 chars. I increased it to 6k chars, again, roughly double.\r\n\r\nSlowTokenizer **7.4 s** to **15.7 s**\r\nTokenizer: **276 ms** to **616 ms**\r\n\r\nI'll post this issue on Tokenizer, seems it hasn't quite been raised (albeit I noticed a similar issue that might relate).\r\n\r\nRegards,\r\n\r\nMichael","body":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n","comment_length":56,"text":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? \n This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n \n @lhoestq,\r\n\r\nI just checked that previous run time was actually  3000 chars. I increased it to 6k chars, again, roughly double.\r\n\r\nSlowTokenizer **7.4 s** to **15.7 s**\r\nTokenizer: **276 ms** to **616 ms**\r\n\r\nI'll post this issue on Tokenizer, seems it hasn't quite been raised (albeit I noticed a similar issue that might relate).\r\n\r\nRegards,\r\n\r\nMichael","embeddings":[-0.4040468037,-0.0130073428,-0.1024706438,0.1034291983,0.1453053504,-0.1225539744,0.2550541461,0.189088881,0.1306775063,-0.0065023075,-0.0542231686,0.4988860488,-0.1724810004,-0.2727595866,-0.1320426911,0.0650426894,0.2197590619,0.0224171188,0.2416453809,-0.03428654,-0.1218727604,0.1023019925,-0.1767257303,0.2413996309,-0.3152630031,-0.0779930353,0.0812313482,-0.2096335888,-0.0027567474,-0.5689615011,-0.2084299624,0.1581248492,0.0209429283,0.5331252813,-0.0001148737,0.0403426066,-0.0291637182,0.2731475234,0.1347175092,0.0443611667,0.3529542685,-0.1942853481,-0.1685831547,-0.2257665098,-0.0541709252,-0.2808631957,-0.0221225843,-0.2241773009,0.0683081374,0.03004192,0.1608587801,0.1151950732,-0.3964318633,-0.0595159791,-0.0024588848,-0.1042250693,-0.0991546437,0.0140264928,0.071329318,-0.255187422,-0.2116566598,0.1949048191,-0.2130429149,-0.1478140801,0.2687232494,0.2163694054,0.0346116014,-0.2425245792,0.1680867523,0.2316348851,0.0670110211,-0.0272130258,-0.017125072,-0.3350791633,-0.370691061,-0.0284823496,0.146122545,-0.4334974587,-0.1206515282,-0.0896027014,-0.4140653312,0.2037731558,0.2642425895,-0.1223938689,-0.2039308697,0.435243994,0.0954279751,0.1497347653,0.1041521281,-0.0997518599,-0.2807916701,-0.2643776536,-0.1595768332,0.3733043671,-0.3415869772,-0.1739550233,0.2692955434,-0.3091349304,-0.14078857,0.1142799705,-0.1424314529,0.194665283,0.2083414793,-0.0471201427,0.0663070083,0.2460043877,0.0384478755,0.173846364,0.0296903811,-0.4802925289,-0.3160505593,-0.0903609022,-0.188874945,0.0202136673,0.3458716273,-0.2424587309,-0.2370802015,-0.072583504,-0.0205964632,-0.171307236,-0.3799443841,-0.0983371884,0.0658612549,0.185454756,0.0587473363,0.0610382333,-0.3647983074,-0.2021667808,-0.0928319693,-0.0767263323,-0.128627494,-0.1720007658,-0.1044918671,0.2542574704,-0.1882735491,0.0913059115,0.2601749003,0.0789951906,-0.1153759062,0.2103088349,0.2196046263,-0.3643229902,0.1047284082,-0.1130543575,0.074800998,0.436026901,-0.0055075004,0.0017841377,-0.2595285773,0.2082757056,-0.2649086416,-0.2264890373,0.2413854897,0.1191601008,-0.0831423551,-0.122161001,-0.4942295253,0.4385454953,0.2539224625,-0.1163537055,-0.0749134794,-0.1809133887,-0.5298196077,-0.2167420387,0.2679248154,-0.0187716689,-0.0722125247,-0.1937800497,0.3418038189,0.1921720505,0.3103512228,0.6430558562,-0.386482656,0.7953374386,-0.1250498444,0.7483612299,0.2579936981,-0.2477056235,-0.377818048,0.116638504,-0.1784688383,0.2722213864,-0.2818516493,-0.1200631186,0.7026370764,-0.2252786309,0.3419845998,0.1061444208,0.2469955236,0.3040959239,-0.2483731955,-0.0491901487,0.5504582524,-0.0802227855,-0.2242528498,-0.150981456,-0.1534067094,0.1111330166,-0.0315542296,-0.1203731075,-0.1554094404,0.0617320798,0.0151116839,0.0270558968,-0.0637358427,0.2192265391,0.1471125484,0.2171940356,0.0741173998,0.0683925077,0.4290306866,-0.006116644,0.1055165529,-0.0645505115,-0.1504328996,-0.1419277936,0.1726531982,0.1282207072,0.0678987801,0.0891928524,0.2311721146,-0.1069797724,0.0154438466,0.1270231307,0.4437769949,-0.3041421175,0.1406869292,-0.160289079,-0.1061222926,-0.1936032474,0.1408739388,0.1087328643,-0.1834660321,0.1624329686,0.1247908548,0.3094665408,-0.1730695963,0.2074124515,0.1145593002,0.2654830515,0.0448613353,0.1117050797,0.0164180808,-0.1618009508,-0.101143539,0.2487526983,0.7665551305,0.293423444,-0.0981309041,-0.1918504685,-0.0074495161,0.2052231431,0.1254599541,0.0902863443,-0.0172139611,0.3012900651,0.4247050881,0.1712665409,0.350858897,0.4743336141,0.5267609954,-0.1261985302,-0.2109235376,0.1169244349,-0.3413845897,-0.4390027523,-0.0324556194,-0.4132475555,0.1679466665,0.1043307185,0.223832801,-0.168983534,-0.0138754928,0.0025527822,0.2727616429,0.2319285125,-0.0062529156,-0.0347080603,0.1677317023,0.2354774922,-0.3278361559,-0.1683433354,-0.017744882,0.2466843575,-0.0202035345,-0.0880928412,0.0660286173,0.1790383607,0.4836559892,-0.0730335638,0.0438799784,-0.2336488962,-0.086826764,0.0112860957,0.0730059966,0.1429340839,0.2016051859,0.0416048281,0.0553793311,-0.1324757189,-0.0326357819,-0.2036225647,-0.3690441251,0.0673273802,0.0478155538,-0.1053127348,0.3012902737,0.1895495206,-0.0706468001,-0.0284742992,-0.4572891295,0.0877392069,-0.1803108454,0.0098784408,-0.1076653674,-0.1534436494,-0.1983680278,0.0400028937,0.1168603525,-0.2781751454,-0.2616514862,-0.3224228621,-0.1180729493,-0.1744088978,0.1405443698,-0.0200541224,0.0511423238,-0.0312802456,0.1222996935,0.1008714736,0.129345417,-0.2491520643,-0.1672082841,0.1939766407,-0.0520039871,-0.0121717555,-0.3335229158,-0.2497054636,0.3890587389,-0.0699702948,-0.0141936084,-0.2141370177,0.0942139179,0.1669799536,0.1398722082,-0.2460222244,0.2176393867,-0.3044712245,0.2614798546,0.0384436436,-0.2167224288,0.394102484,0.2282124758,-0.1222438663,0.1697704941,-0.2478057742,0.1691340357,-0.0590032451,-0.235063985,0.0723133609,0.484038353,0.2467962503,0.6211673617,0.0489818566,-0.0133375674,-0.0001047923,-0.3113279939,-0.1315821707,-0.431189537,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1830","title":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer?","comments":"Hi, \r\nI'm following up here as I found my exact issue. It was with saving and re-loading the tokenizer. When I trained then processed the data without saving and reloading it, it was 10x-100x faster than when I saved and re-loaded it.\r\nBoth resulted in the exact same tokenized datasets as well. \r\nThere is additionally a bug where the older legacy tokenizer save does not preserve a learned tokenizing behavior if trained from scratch.\r\nUnderstand its not exactly Datasets related but hope it can help someone if they have the same issue.\r\nThanks!","body":"This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n","comment_length":93,"text":"using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? \n This could total relate to me misunderstanding particular call functions, but I added words to a GPT2Tokenizer, and saved it to disk (note I'm only showing snippets but I can share more) and the map function ran much slower: \r\n\r\n````\r\ndef save_tokenizer(original_tokenizer,text,path=\"simpledata\/tokenizer\"):\r\n    words_unique = set(text.split(\" \"))\r\n    for i in words_unique:\r\n        original_tokenizer.add_tokens(i)\r\n    original_tokenizer.save_pretrained(path)\r\n\r\ntokenizer2 = GPT2Tokenizer.from_pretrained(os.path.join(experiment_path,experiment_name,\"tokenizer_squad\"))\r\n\r\ntrain_set_baby=Dataset.from_dict({\"text\":[train_set[\"text\"][0][0:50]]})\r\n````\r\n\r\nI then applied the dataset map function on a fairly small set of text:\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\n\r\n\r\nThe run time for train_set_baby.map was 6 seconds, and the batch itself was 2.6 seconds\r\n\r\n**100% 1\/1 [00:02<00:00, 2.60s\/ba] CPU times: user 5.96 s, sys: 36 ms, total: 5.99 s Wall time: 5.99 s**\r\n\r\nIn comparison using (even after adding additional tokens): \r\n`\r\ntokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")`\r\n\r\n```\r\n%%time\r\ntrain_set_baby = train_set_baby.map(lambda d:tokenizer2(d[\"text\"]),batched=True)\r\n\r\n```\r\nThe time is \r\n**100% 1\/1 [00:00<00:00, 34.09ba\/s] CPU times: user 68.1 ms, sys: 16 \u00b5s, total: 68.1 ms Wall time: 62.9 ms**\r\n\r\nIt seems this might relate to the tokenizer save or load function, however, the issue appears to come up when I apply the loaded tokenizer  to the map function. \r\n\r\nI should also add that playing around with the amount of words I add to the tokenizer before I save it to disk and load it into memory  appears to impact the time it takes to run the map function. \r\n\r\n\r\n \n Hi, \r\nI'm following up here as I found my exact issue. It was with saving and re-loading the tokenizer. When I trained then processed the data without saving and reloading it, it was 10x-100x faster than when I saved and re-loaded it.\r\nBoth resulted in the exact same tokenized datasets as well. \r\nThere is additionally a bug where the older legacy tokenizer save does not preserve a learned tokenizing behavior if trained from scratch.\r\nUnderstand its not exactly Datasets related but hope it can help someone if they have the same issue.\r\nThanks!","embeddings":[-0.4040468037,-0.0130073428,-0.1024706438,0.1034291983,0.1453053504,-0.1225539744,0.2550541461,0.189088881,0.1306775063,-0.0065023075,-0.0542231686,0.4988860488,-0.1724810004,-0.2727595866,-0.1320426911,0.0650426894,0.2197590619,0.0224171188,0.2416453809,-0.03428654,-0.1218727604,0.1023019925,-0.1767257303,0.2413996309,-0.3152630031,-0.0779930353,0.0812313482,-0.2096335888,-0.0027567474,-0.5689615011,-0.2084299624,0.1581248492,0.0209429283,0.5331252813,-0.0001148737,0.0403426066,-0.0291637182,0.2731475234,0.1347175092,0.0443611667,0.3529542685,-0.1942853481,-0.1685831547,-0.2257665098,-0.0541709252,-0.2808631957,-0.0221225843,-0.2241773009,0.0683081374,0.03004192,0.1608587801,0.1151950732,-0.3964318633,-0.0595159791,-0.0024588848,-0.1042250693,-0.0991546437,0.0140264928,0.071329318,-0.255187422,-0.2116566598,0.1949048191,-0.2130429149,-0.1478140801,0.2687232494,0.2163694054,0.0346116014,-0.2425245792,0.1680867523,0.2316348851,0.0670110211,-0.0272130258,-0.017125072,-0.3350791633,-0.370691061,-0.0284823496,0.146122545,-0.4334974587,-0.1206515282,-0.0896027014,-0.4140653312,0.2037731558,0.2642425895,-0.1223938689,-0.2039308697,0.435243994,0.0954279751,0.1497347653,0.1041521281,-0.0997518599,-0.2807916701,-0.2643776536,-0.1595768332,0.3733043671,-0.3415869772,-0.1739550233,0.2692955434,-0.3091349304,-0.14078857,0.1142799705,-0.1424314529,0.194665283,0.2083414793,-0.0471201427,0.0663070083,0.2460043877,0.0384478755,0.173846364,0.0296903811,-0.4802925289,-0.3160505593,-0.0903609022,-0.188874945,0.0202136673,0.3458716273,-0.2424587309,-0.2370802015,-0.072583504,-0.0205964632,-0.171307236,-0.3799443841,-0.0983371884,0.0658612549,0.185454756,0.0587473363,0.0610382333,-0.3647983074,-0.2021667808,-0.0928319693,-0.0767263323,-0.128627494,-0.1720007658,-0.1044918671,0.2542574704,-0.1882735491,0.0913059115,0.2601749003,0.0789951906,-0.1153759062,0.2103088349,0.2196046263,-0.3643229902,0.1047284082,-0.1130543575,0.074800998,0.436026901,-0.0055075004,0.0017841377,-0.2595285773,0.2082757056,-0.2649086416,-0.2264890373,0.2413854897,0.1191601008,-0.0831423551,-0.122161001,-0.4942295253,0.4385454953,0.2539224625,-0.1163537055,-0.0749134794,-0.1809133887,-0.5298196077,-0.2167420387,0.2679248154,-0.0187716689,-0.0722125247,-0.1937800497,0.3418038189,0.1921720505,0.3103512228,0.6430558562,-0.386482656,0.7953374386,-0.1250498444,0.7483612299,0.2579936981,-0.2477056235,-0.377818048,0.116638504,-0.1784688383,0.2722213864,-0.2818516493,-0.1200631186,0.7026370764,-0.2252786309,0.3419845998,0.1061444208,0.2469955236,0.3040959239,-0.2483731955,-0.0491901487,0.5504582524,-0.0802227855,-0.2242528498,-0.150981456,-0.1534067094,0.1111330166,-0.0315542296,-0.1203731075,-0.1554094404,0.0617320798,0.0151116839,0.0270558968,-0.0637358427,0.2192265391,0.1471125484,0.2171940356,0.0741173998,0.0683925077,0.4290306866,-0.006116644,0.1055165529,-0.0645505115,-0.1504328996,-0.1419277936,0.1726531982,0.1282207072,0.0678987801,0.0891928524,0.2311721146,-0.1069797724,0.0154438466,0.1270231307,0.4437769949,-0.3041421175,0.1406869292,-0.160289079,-0.1061222926,-0.1936032474,0.1408739388,0.1087328643,-0.1834660321,0.1624329686,0.1247908548,0.3094665408,-0.1730695963,0.2074124515,0.1145593002,0.2654830515,0.0448613353,0.1117050797,0.0164180808,-0.1618009508,-0.101143539,0.2487526983,0.7665551305,0.293423444,-0.0981309041,-0.1918504685,-0.0074495161,0.2052231431,0.1254599541,0.0902863443,-0.0172139611,0.3012900651,0.4247050881,0.1712665409,0.350858897,0.4743336141,0.5267609954,-0.1261985302,-0.2109235376,0.1169244349,-0.3413845897,-0.4390027523,-0.0324556194,-0.4132475555,0.1679466665,0.1043307185,0.223832801,-0.168983534,-0.0138754928,0.0025527822,0.2727616429,0.2319285125,-0.0062529156,-0.0347080603,0.1677317023,0.2354774922,-0.3278361559,-0.1683433354,-0.017744882,0.2466843575,-0.0202035345,-0.0880928412,0.0660286173,0.1790383607,0.4836559892,-0.0730335638,0.0438799784,-0.2336488962,-0.086826764,0.0112860957,0.0730059966,0.1429340839,0.2016051859,0.0416048281,0.0553793311,-0.1324757189,-0.0326357819,-0.2036225647,-0.3690441251,0.0673273802,0.0478155538,-0.1053127348,0.3012902737,0.1895495206,-0.0706468001,-0.0284742992,-0.4572891295,0.0877392069,-0.1803108454,0.0098784408,-0.1076653674,-0.1534436494,-0.1983680278,0.0400028937,0.1168603525,-0.2781751454,-0.2616514862,-0.3224228621,-0.1180729493,-0.1744088978,0.1405443698,-0.0200541224,0.0511423238,-0.0312802456,0.1222996935,0.1008714736,0.129345417,-0.2491520643,-0.1672082841,0.1939766407,-0.0520039871,-0.0121717555,-0.3335229158,-0.2497054636,0.3890587389,-0.0699702948,-0.0141936084,-0.2141370177,0.0942139179,0.1669799536,0.1398722082,-0.2460222244,0.2176393867,-0.3044712245,0.2614798546,0.0384436436,-0.2167224288,0.394102484,0.2282124758,-0.1222438663,0.1697704941,-0.2478057742,0.1691340357,-0.0590032451,-0.235063985,0.0723133609,0.484038353,0.2467962503,0.6211673617,0.0489818566,-0.0133375674,-0.0001047923,-0.3113279939,-0.1315821707,-0.431189537,-0.0755139217,0.2071558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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1827","title":"Regarding On-the-fly Data Loading","comments":"Hi @acul3 \r\n\r\nIssue #1776 talks about  doing on-the-fly data pre-processing, which I think is solved in the next release as mentioned in the issue #1825. I also look forward to using this feature, though :)\r\n\r\nI wanted to ask about on-the-fly data loading from the cache (before pre-processing).","body":"Hi,\r\n\r\nI was wondering if it is possible to load images\/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.\r\n\r\nThanks,\r\nGunjan","comment_length":48,"text":"Regarding On-the-fly Data Loading \n Hi,\r\n\r\nI was wondering if it is possible to load images\/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.\r\n\r\nThanks,\r\nGunjan \n Hi @acul3 \r\n\r\nIssue #1776 talks about  doing on-the-fly data pre-processing, which I think is solved in the next release as mentioned in the issue #1825. I also look forward to using this feature, though :)\r\n\r\nI wanted to ask about on-the-fly data loading from the cache (before pre-processing).","embeddings":[-0.1726847142,-0.2835986018,-0.1183969602,0.2190789431,0.3321972191,0.0917301774,0.5707204938,0.1317177862,0.1204758659,0.0670047775,0.0713878274,-0.0857139006,-0.0965600237,0.2116191685,0.2725099623,0.2309678495,0.0025834758,0.0690006167,0.0591212176,-0.0284568463,-0.0402434245,-0.0728218779,-0.0123700965,-0.362198323,-0.4274348319,-0.0935060233,-0.1793473214,0.0571449585,-0.153655991,-0.2890402675,0.1886149794,0.2540030479,0.2708257139,0.1172411516,-0.0001203638,-0.0124503793,0.3828080595,-0.1435648203,-0.3206498921,0.1220704764,-0.0993443206,0.1157685742,0.106421046,-0.1725357622,-0.0760356486,0.4242566228,0.1247473955,-0.3189669847,0.4757882953,-0.081970863,0.0456120484,-0.069350563,-0.3194617033,0.030737374,0.2106879205,0.1520231217,-0.0428887308,-0.0413794033,0.3662909865,-0.1179742813,-0.1022316962,0.3062210381,-0.0025014977,0.0041443375,0.2204100341,-0.0154390763,0.0600902811,-0.4758675396,-0.0239071082,0.1586290151,0.5854740143,0.0645013526,-0.3178856969,-0.3903407753,0.0198414288,-0.3572824895,0.0862496868,0.382142365,-0.3288044333,0.0932317525,-0.5378326178,-0.4087218344,-0.2208578736,-0.114024736,-0.090202786,-0.2912571132,0.0688989162,-0.0724284202,0.0858769715,0.2078725994,0.6285194159,-0.1119770184,-0.1109058037,0.4937479198,-0.272139281,-0.2085574567,-0.4864164889,-0.1967238635,-0.0057810238,-0.1826279014,0.2753618062,0.1797651201,-0.1919866353,0.0621586256,0.3562072217,0.0273071248,0.1653118432,-0.031149257,0.0064679109,-0.2958640456,-0.2373790443,-0.0634201765,-0.4045855105,0.136860624,-0.1152536124,-0.2681531608,-0.4467125535,-0.250644356,0.2545191944,-0.2527233064,0.1836283952,-0.0403485224,-0.2313980907,0.312717706,-0.028800603,0.0083063543,0.2975400686,-0.1674126387,-0.077529043,-0.2540169954,-0.0478608571,-0.0181819089,-0.2493307739,0.1330200434,0.2042916268,0.0589198172,0.1179031208,-0.1313331276,0.0041415063,0.0270401686,-0.0003011711,-0.3179264069,0.3636932969,0.2754960954,-0.0150987152,-0.2916888893,0.0312473699,0.333538115,-0.0201603994,0.5988048315,-0.2974314392,-0.0827195048,0.3635725379,0.0118181836,-0.0296235587,-0.2398855984,-0.2626531422,0.4184547961,0.2489563823,0.0859438553,0.2036076039,0.0772561058,-0.2851469517,-0.0212870687,0.1105753183,0.350160569,-0.4026871324,-0.3087047637,0.2794791758,0.0222667381,-0.1220834181,-0.0091872336,-0.248816818,0.3373199701,0.0116719166,0.037483491,0.1123319566,-0.2921566367,-0.0236151498,0.4355233014,0.0490387045,-0.0319646671,-0.009075881,0.4269596636,0.1020324528,0.1715197563,0.295953244,0.7635238171,0.0538489595,0.0748611689,-0.1958502978,-0.3414077461,-0.2395244092,0.3621118069,0.2415037155,0.2589144409,0.2517335117,0.0273116045,0.0837511122,-0.2263398767,-0.0170133896,0.1305214167,-0.0317249894,0.1669828594,-0.0416582264,0.2819933891,-0.4686065018,0.2828407884,-0.1840272546,-0.0459719226,0.1098968163,-0.0095281713,0.3888610899,-0.2076938301,0.0993757248,0.1737114936,-0.0955861062,-0.2128090113,-0.0997159183,-0.0099680647,-0.4909341931,0.8521465063,-0.4758826494,0.2114950716,-0.1044537723,0.107416369,0.4491933584,-0.1081207469,-0.19637914,-0.1719347388,-0.2991917133,0.0181719642,0.0061659301,0.4675185382,0.1323865056,0.2539481521,-0.0984479487,0.1048576757,0.2965150774,0.0731375143,0.2004057616,-0.3838969767,0.0407693163,-0.1957342029,-0.4062644839,0.6067577004,0.0608738475,0.1954904348,0.2967914045,-0.2305458933,-0.0120987436,-0.1654074788,-0.0860128924,0.1190435216,0.0079784598,0.106078364,0.5033367872,-0.0313715898,-0.1819934845,-0.2241875976,-0.039342802,-0.0225382932,-0.1010949016,0.1562383324,-0.0333746783,-0.0968439132,0.0403099582,-0.0376495793,0.3064706028,0.086568445,-0.0088048233,0.1440639645,0.2152051926,-0.0710984617,0.1003785059,-0.0256861597,0.4002476633,0.0196015462,0.066852279,0.054219991,-0.4141884744,0.3761070073,0.2402523011,-0.2068132311,-0.1194646209,0.0249521323,0.1802029461,-0.2877338529,0.0511291102,0.0785601512,-0.2679390609,0.2447863221,0.0626737773,-0.0354731083,0.2312708497,-0.1007864252,-0.0505560227,1.058915019,-0.0778698623,0.0396899469,-0.1933262646,-0.0724977404,0.2199304551,0.0177281313,0.1843344718,-0.11261338,0.2349985838,0.1298400313,-0.1127245799,0.2563572228,-0.1056977063,0.1894396096,0.219392702,0.1237749234,-0.3309439719,-0.0084988605,-0.2049747854,0.0421055704,-0.1971979886,-0.4081793427,-0.2879915237,-0.4751538336,0.3778953552,0.112087518,-0.1671583802,0.0250952411,-0.4167984128,-0.0401691794,0.2077399194,0.3384683132,0.1406713873,0.3773111403,0.0208095033,-0.1806992143,-0.1592862159,-0.1036747843,-0.2029184848,-0.6760934591,-0.093938835,-0.1416723728,-0.1950516105,-0.0356874391,0.0936200768,0.0362055525,0.6651348472,-0.8735558391,-0.0531983189,0.0262281261,-0.1240393519,-0.2042838335,-0.0786603913,0.1373702139,-0.2599746883,-0.0103772916,-0.0497465432,0.2246382236,0.1929009855,0.0336729549,0.0733998492,0.2787472606,0.0319000855,0.1225322708,0.8831708431,-0.371219635,-0.065941833,0.2678942978,0.1573865861,0.3801428676,-0.2386514395,0.2636007071,-0.1346945912,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1827","title":"Regarding On-the-fly Data Loading","comments":"Hi ! Currently when you load a dataset via `load_dataset` for example, then the dataset is memory-mapped from an Arrow file on disk. Therefore there's almost no RAM usage even if your dataset contains TB of data.\r\nUsually at training time only one batch of data at a time is loaded in memory.\r\n\r\nDoes that answer your question or were you thinking about something else ?","body":"Hi,\r\n\r\nI was wondering if it is possible to load images\/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.\r\n\r\nThanks,\r\nGunjan","comment_length":66,"text":"Regarding On-the-fly Data Loading \n Hi,\r\n\r\nI was wondering if it is possible to load images\/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.\r\n\r\nThanks,\r\nGunjan \n Hi ! Currently when you load a dataset via `load_dataset` for example, then the dataset is memory-mapped from an Arrow file on disk. Therefore there's almost no RAM usage even if your dataset contains TB of data.\r\nUsually at training time only one batch of data at a time is loaded in memory.\r\n\r\nDoes that answer your question or were you thinking about something else ?","embeddings":[-0.286943078,-0.3630418181,-0.1495332122,0.2731824219,0.414362222,0.1709423512,0.4223019183,0.1292583793,0.1851403415,0.0802383274,0.121642299,-0.0679904073,-0.2427961379,0.1964000762,0.1893507391,0.2370749265,-0.0065636951,0.1383550018,-0.1860133559,0.0667945966,-0.0094696237,-0.2281021625,-0.1345320791,-0.3600880802,-0.3519837558,-0.1788701564,-0.2061012089,-0.081674464,-0.0664418563,-0.1807874441,0.0383012444,0.1454581469,0.2661631107,0.2168910652,-0.0001134483,0.0263619404,0.1878576428,-0.1615202576,-0.4358328581,0.1928040832,-0.0849268138,0.0534444526,0.1489462852,-0.2591665685,-0.0432943068,0.2636418343,0.1821040213,-0.3938759565,0.3936271966,-0.0597929992,0.1287946999,-0.0312944241,-0.3372347951,0.0115782944,0.0496058911,0.1317645311,0.0707556158,0.0703822523,0.2710404396,-0.0599783845,-0.1753790826,0.3019686937,0.004044468,0.04812067,0.3422016799,0.0536040962,0.0754288882,-0.3361243308,-0.0324160419,0.0897561833,0.5630343556,-0.0347455889,-0.155330807,-0.3663960993,0.1146519333,-0.2698690593,-0.0625268295,0.489333421,-0.4659104049,0.0407533422,-0.5683214068,-0.4256089032,-0.2526451349,-0.0436525494,-0.0367207378,-0.4736000896,0.132329464,0.0079848934,0.1394614279,0.1308273971,0.343752265,-0.231676504,-0.0460172929,0.4015500844,-0.2577864826,-0.0960031673,-0.6005839109,-0.2998289764,0.0383353271,-0.2760585845,0.1747638434,0.1581802815,-0.095134899,0.1061405912,0.3347076774,-0.019386448,0.0244126618,0.0254782867,-0.0001031627,-0.275926739,-0.2644038498,-0.0578746609,-0.4595869184,0.0949928835,0.0331917182,-0.3943001032,-0.4661898017,0.0087624053,0.1717686653,-0.2818394005,0.0412119217,-0.013791346,-0.2435890436,0.2964889705,0.0139826648,-0.0481421165,0.4604890943,-0.2862051129,-0.1249799207,-0.2746886313,-0.0637196377,0.0656898022,-0.3060081303,0.2646063566,0.1331712753,0.1531426758,0.0667293072,-0.1371395141,-0.0669994801,0.036404524,0.0199883021,-0.3356121778,0.2059192061,0.1551752388,-0.1178060398,-0.2792707682,-0.0381688736,0.3118372858,0.006095598,0.5027794838,-0.2631541789,-0.069812201,0.4293095469,0.0817824006,0.0833737627,-0.1546327919,-0.6008709669,0.3655919731,0.1369956434,0.1333419383,0.070576556,-0.0077770185,-0.3763471842,0.0051170397,0.2648199797,0.3271280229,-0.3991860747,-0.3566908538,0.4185029566,-0.0158250816,-0.1391628832,0.1062798724,-0.3086325228,0.4188513458,0.0300433338,-0.1001220644,0.0982156247,-0.2143739313,-0.0543934517,0.3533630073,0.0158604402,-0.1013925895,0.0247092582,0.572217226,0.0938572213,0.2492914796,0.329151988,0.7063392997,0.1441548765,0.1200050861,-0.1268433928,-0.2603088915,-0.3210431337,0.3683959842,0.164371863,0.1685014516,0.0841636136,0.0194546748,0.234720543,-0.2200764269,0.0599162243,0.2151897699,-0.0518270917,0.1326141506,-0.1009771004,0.2299596518,-0.4384998381,0.2532017827,0.0444661118,0.0821051747,0.1139745936,-0.0054874746,0.3631191552,-0.2487814277,0.1507816315,0.2011341751,-0.0444285497,-0.3101141155,-0.225204125,-0.0229294375,-0.4112915099,0.6960084438,-0.4796499908,0.2173445076,-0.1416359097,0.0989393145,0.5003316998,-0.0849593729,-0.0852534771,-0.2335910052,-0.4635132253,0.0353132933,0.0689441934,0.3818085194,0.1407142431,0.1782442778,0.0316990651,0.0492442623,0.1813828796,0.0268362239,0.1826771796,-0.3104831576,0.1619306803,-0.1521638334,-0.3586111367,0.4180773497,0.0945809186,0.2759921551,0.3086817265,-0.1523555964,0.0141109461,-0.1444022208,-0.0534743853,0.1024145037,0.0438667648,0.226858452,0.4621651173,0.1707957387,-0.2648866773,-0.345797509,0.0504017062,-0.1120194867,-0.0432916172,0.2249034345,-0.1269934475,0.0364626758,0.0329791345,-0.0982062221,0.2339931279,0.2031129748,-0.0120156836,0.1703813374,0.2188822925,0.0095251286,0.0882775038,0.062237855,0.4783914387,-0.1956154704,-0.0780261382,-0.0296746604,-0.2640933096,0.2508116364,0.1417489797,-0.1726943702,-0.1391683221,-0.0242390316,0.0047018984,-0.3065868616,-0.0998248756,0.3313088715,-0.1850053966,0.3601140678,-0.0126785412,0.0224999301,0.1696310043,0.0071446649,-0.0975894257,0.9094578028,-0.046964597,-0.2100371271,-0.021173507,-0.0736701265,-0.0035659403,0.0343072638,0.3430948257,-0.0483931974,0.3704320192,0.140648514,-0.1775661558,0.1492753774,0.2240161002,0.1529071629,0.2234522253,-0.0167874638,-0.3906622827,0.1024204642,-0.2542730272,0.0477848612,-0.0878183767,-0.338757962,-0.224340111,-0.3469252288,0.3597928286,0.0980084389,-0.0353977121,-0.007406882,-0.4434201419,-0.0958467573,0.1404809058,0.1994834989,0.1051898003,0.3841191828,0.1694537401,-0.1420047879,-0.1363412291,-0.0258121118,0.0546567142,-0.4763892889,-0.0898559168,0.0492235459,-0.1751328707,-0.0948596522,0.040258389,0.0185468849,0.8389362693,-0.8290775418,0.1393468529,-0.0379304364,-0.2141063213,-0.2819705307,0.0518494621,0.0399550274,-0.2387923598,-0.0956739113,0.0473068841,0.3101844788,0.1588173509,-0.102908656,0.0373466238,0.298878938,0.1454367489,0.1473434269,0.7117272019,-0.212352708,-0.1662727445,0.1837857813,-0.0557092316,0.2883814871,-0.1327329874,0.2319425642,-0.1628266573,-0.2090507299,-0.2608184516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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1825","title":"Datasets library not suitable for huge text datasets.","comments":"Hi ! Looks related to #861 \r\n\r\nYou are right: tokenizing a dataset using map takes a lot of space since it can store `input_ids` but also `token_type_ids`, `attention_mask` and `special_tokens_mask`. Moreover if your tokenization function returns python integers then by default they'll be stored as int64 which can take a lot of space. Padding can also increase the size of the tokenized dataset.\r\n\r\nTo make things more convenient, we recently added a \"lazy map\" feature that allows to tokenize each batch at training time as you mentioned. For example you'll be able to do\r\n```python\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\ndef encode(batch):\r\n    return tokenizer(batch[\"text\"], padding=\"longest\", truncation=True, max_length=512, return_tensors=\"pt\")\r\n\r\ndataset.set_transform(encode)\r\nprint(dataset.format)\r\n# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}\r\nprint(dataset[:2])\r\n# {'input_ids': tensor([[  101,  2572,  3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}\r\n\r\n```\r\nIn this example the `encode` transform is applied on-the-fly on the \"text\" column.\r\n\r\nThis feature will be available in the next release 2.0 which will happen in a few days.\r\nYou can already play with it by installing `datasets` from source if you want :)\r\n\r\nHope that helps !","body":"Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions??","comment_length":197,"text":"Datasets library not suitable for huge text datasets. \n Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions?? \n Hi ! Looks related to #861 \r\n\r\nYou are right: tokenizing a dataset using map takes a lot of space since it can store `input_ids` but also `token_type_ids`, `attention_mask` and `special_tokens_mask`. Moreover if your tokenization function returns python integers then by default they'll be stored as int64 which can take a lot of space. Padding can also increase the size of the tokenized dataset.\r\n\r\nTo make things more convenient, we recently added a \"lazy map\" feature that allows to tokenize each batch at training time as you mentioned. For example you'll be able to do\r\n```python\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\ndef encode(batch):\r\n    return tokenizer(batch[\"text\"], padding=\"longest\", truncation=True, max_length=512, return_tensors=\"pt\")\r\n\r\ndataset.set_transform(encode)\r\nprint(dataset.format)\r\n# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}\r\nprint(dataset[:2])\r\n# {'input_ids': tensor([[  101,  2572,  3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}\r\n\r\n```\r\nIn this example the `encode` transform is applied on-the-fly on the \"text\" column.\r\n\r\nThis feature will be available in the next release 2.0 which will happen in a few days.\r\nYou can already play with it by installing `datasets` from source if you want :)\r\n\r\nHope that helps !","embeddings":[-0.2644477785,0.0704934821,0.0091305831,0.1295920014,0.3082516789,0.0080511766,0.2901795208,0.3717779517,-0.2807389498,-0.0181723814,-0.048823636,0.1473779678,-0.2360719144,0.0108855832,0.0396098942,0.0288687628,0.2195769548,0.2025509775,0.1469030827,0.0644162074,-0.0421971455,-0.2423410267,-0.2289886475,-0.0132892057,-0.4302169383,-0.2289030105,-0.130100742,-0.2940586507,-0.3577356339,-0.4037176669,-0.0329179354,0.1460277736,0.4799691737,0.4330447912,-0.0001187578,-0.1787714958,0.1574260443,0.0388377905,-0.091521956,0.0657222122,0.2080893964,-0.3404701948,-0.1510676444,-0.2540647089,0.1679103673,-0.1874020994,0.0126407435,-0.1223173738,0.2558726966,-0.022112092,0.1069587767,0.1478157789,-0.1175837219,0.1175930575,0.2055493891,-0.0257249586,-0.067175068,-0.0461689681,0.3811223805,-0.0764790028,-0.187481612,0.3004252911,-0.1841914803,-0.0576366261,0.3920024931,-0.0049807709,-0.1302286685,-0.3428998888,0.2336874157,0.2656778097,0.3912026584,-0.3416002393,-0.2912170589,-0.4751839638,-0.1442976296,-0.1671044528,-0.1160393953,0.1333686113,0.1011420563,0.1327646375,-0.5343756676,-0.3300271034,-0.0942187086,0.2805031836,-0.1244559139,0.2533674836,-0.0309925396,0.0617218614,0.0674949214,-0.3011275232,0.1197618097,-0.3782680929,0.1251965016,0.4119563103,-0.2103306204,-0.1967040151,-0.162057966,-0.131968081,0.4441077709,-0.2146405429,-0.3352544904,0.0751733482,-0.2477160096,-0.0298868325,0.2738423347,0.2010719776,-0.1895847917,0.3804612458,0.1412707418,-0.2511979938,-0.0760981366,-0.1168308482,-0.5041647553,0.0023232726,0.242370829,-0.3414865732,-0.2537666857,-0.0109220063,0.0321966819,0.0937678665,-0.3200736344,0.2049039751,0.1570596844,0.2677529752,-0.1823857278,0.1182553396,-0.2504811287,0.1530012339,-0.1295621693,0.0136844954,-0.0348925665,-0.0809213817,-0.3118556142,0.0885422602,0.1323872507,0.0369310565,0.1483543813,-0.0311594773,0.0830833986,0.1199528426,0.2286500633,-0.3431074619,0.0696898922,0.0881681591,-0.0551424436,0.3614559472,-0.2426368594,0.0543855242,-0.2421161383,0.3458733559,-0.3575131595,-0.3633337617,0.1150057763,0.0956483856,-0.2124391645,-0.0192326438,-0.4771781266,0.3124535382,0.4131705165,0.0353512764,0.0308051649,-0.2233496308,-0.2891534865,-0.1119893193,0.2520446479,0.2172581702,-0.3746162057,-0.0462599099,-0.0060771499,0.2774538696,0.3547437489,0.7049410939,-0.2344234437,0.4682484269,-0.0217173658,0.5970696807,0.1308767945,-0.0120305633,-0.5504743457,0.306410104,0.1361112893,-0.0626283363,-0.0474245176,0.0487032607,0.5649160147,-0.1264642328,0.2421895266,0.3477282822,-0.1075709909,0.2397238165,-0.2524233758,-0.3894592524,0.2543738484,0.2126838565,0.2313421071,-0.4073577225,-0.1076531634,0.5631249547,0.1074653864,-0.2344982773,0.3512014151,0.0694597587,0.1932828426,0.1367788911,-0.0244933888,-0.1917746514,-0.1133494154,0.138760373,0.1284853667,-0.0340240113,-0.0177696273,-0.3649345636,0.1988357306,-0.0558937266,-0.0451731496,0.0023124169,-0.0087510115,-0.2987191975,-0.0523148142,0.0450563654,-0.1235791892,-0.2520162165,-0.1439156234,0.1190637127,-0.0510434993,0.0653499141,0.0642399043,-0.0879655033,-0.0818806812,0.2881655991,-0.1019601226,-0.1098917425,-0.0430198386,0.1155033335,0.1792636067,-0.1729746014,-0.1989774257,0.6291497946,0.3313725889,-0.0527769439,0.2620900869,0.0579496026,0.0874846652,-0.1010522097,-0.0608754344,0.1980132908,0.1038440242,0.1809868366,0.037133649,-0.2367585897,0.184265852,-0.0231454857,0.0577142648,-0.0625004098,0.0631765053,0.1399936825,0.4071207047,0.3443516493,-0.0991740823,-0.1226790249,0.3456887901,-0.2547423244,0.0944562778,0.3692593873,-0.4270763099,-0.370239526,0.0560605228,-0.2841128409,0.073677212,0.1215593964,0.0545074455,-0.0743528157,0.3321838975,0.004992832,0.2567667663,0.1768510193,0.0905410051,0.0599088483,0.1524800658,0.2476588041,-0.0831809565,-0.1484982371,0.2264687419,0.2812068462,-0.0641535372,0.0165543146,-0.1782043725,-0.147877723,0.0071278196,-0.0921132267,-0.1096905544,0.0529508367,0.1498169303,-0.0503600165,-0.0050369781,0.2096641213,0.0560787916,0.3908675611,0.0888429284,-0.2763009369,0.0869960859,-0.0899240077,-0.3656361103,-0.0193913653,0.2563130558,-0.3312307596,0.2955761552,0.1239056662,0.1111488491,-0.3529420793,-0.5111264586,0.055365812,-0.0807594806,0.0430213548,0.0051294304,0.0119368415,-0.3470669687,0.0450911373,-0.0829578936,-0.2204847634,-0.048679471,0.0377844833,-0.1488906741,-0.1909849197,0.099119328,-0.1178259403,-0.2849353254,-0.1407158971,0.1821797192,-0.1708399057,0.144347325,0.0215969048,0.058514379,0.0398076661,-0.0908531174,-0.216559723,0.0396113619,-0.3456558883,0.4540537,0.053776063,-0.1599427313,-0.0739158913,-0.0598627143,-0.0437619947,0.3011980355,-0.4520861208,0.2026535124,-0.2943244874,0.2322741151,-0.0625725538,0.1154094338,0.062830694,0.0229553003,0.0532675311,0.0854735672,-0.1402453333,-0.1986550242,-0.0442462415,0.1966251731,0.0458277091,0.1926076561,0.3319135308,0.8323061466,0.064997755,-0.1224136129,0.2346021682,0.1743752807,0.1243966073,-0.3345831931,-0.1610758156,0.1468512416,-0.0445961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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1825","title":"Datasets library not suitable for huge text datasets.","comments":"How recently was `set_transform` added? I am actually trying to implement it and getting an error:\r\n\r\n`AttributeError: 'Dataset' object has no attribute 'set_transform'\r\n`\r\n\r\nI'm on v.1.2.1.\r\n\r\nEDIT: Oh, wait I see now it's in the v.2.0. Whoops! This should be really useful.","body":"Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions??","comment_length":43,"text":"Datasets library not suitable for huge text datasets. \n Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions?? \n How recently was `set_transform` added? I am actually trying to implement it and getting an error:\r\n\r\n`AttributeError: 'Dataset' object has no attribute 'set_transform'\r\n`\r\n\r\nI'm on v.1.2.1.\r\n\r\nEDIT: Oh, wait I see now it's in the v.2.0. Whoops! This should be really useful.","embeddings":[-0.4678060412,0.1656083465,0.0304235257,0.1393032819,0.3698442876,-0.0799572915,0.1343442947,0.4200882018,-0.4338594377,-0.1087068021,0.1051200852,0.0978749618,-0.2596930265,0.1617353857,0.1377583444,0.0071735461,0.2880678475,0.1773830205,-0.0256084055,0.1219855323,-0.068343088,-0.1277218163,-0.2867692113,-0.0003999639,-0.5252425671,-0.2634716034,-0.0174378678,-0.2435948402,-0.4448638558,-0.3248973191,-0.1924744993,0.1364666373,0.4324246347,0.5125051141,-0.0001255967,-0.2210252136,0.2472047806,-0.0384248644,-0.2875906825,-0.0394756272,0.293053627,-0.3577700257,-0.1968452185,-0.2132425606,0.1938291043,-0.3745193481,-0.0414699279,-0.1099502668,0.1161912084,0.2161804438,0.0858882964,0.0541299358,0.0701664165,0.0395160988,0.1645486206,-0.0014555308,-0.1042268798,-0.0228799507,0.4538724422,0.0654921979,-0.1268360168,0.1399395615,-0.12165346,-0.0212641973,0.4577085376,-0.0739728734,-0.2455075085,-0.2150257528,0.1715577692,0.2889243364,0.5197036862,-0.4864006937,-0.3977509141,-0.5967864394,-0.1693623364,-0.2333569825,-0.0051260851,0.1050855666,0.064477928,0.1954610646,-0.5726397634,-0.4743477106,-0.1159548759,0.3932580948,-0.2551800013,0.1143131405,-0.1285423338,0.0937680304,0.1853441149,-0.1451212466,0.1337652802,-0.2565786839,0.0210567191,0.1875143945,-0.3344244659,-0.1271075159,-0.1622018218,-0.021164,0.4064775705,0.0946194306,-0.4326750338,0.0650007576,-0.1359995008,-0.0083531858,0.2543174624,0.4710545838,-0.2227854133,0.2742271125,0.2237857282,-0.2196394056,0.0120657673,-0.080084011,-0.4790991247,-0.1735170484,0.0792802125,-0.358417809,-0.120091565,-0.1453280896,-0.0396949798,-0.0014614257,-0.1404424459,0.1979501396,0.1161104217,0.1320023835,-0.0579987131,0.1802783459,-0.1072323769,0.2354936749,-0.1325275749,-0.2000354528,0.002107711,-0.020393068,-0.2910279334,0.1360286772,0.1617564857,-0.0009595223,0.0848516077,-0.1097623631,0.0947208032,0.1195635498,0.1449590027,-0.323037684,0.0003708185,0.1692297161,-0.1279814839,0.4077442586,-0.1729893684,-0.0469149798,-0.2752201855,0.3672422767,-0.2981247008,-0.3904721141,-0.0615494438,0.0727412924,-0.4090559185,-0.048892457,-0.5169223547,0.4131361842,0.4666951895,0.0010689226,0.0228556469,-0.2046528012,-0.1611898541,-0.0093320664,0.170317173,0.1939947903,-0.6206810474,-0.0736391172,-0.0242317617,0.1578421146,0.2399742454,0.6512376666,-0.1410071254,0.340269655,-0.0592872202,0.3595706522,0.2331751436,0.1929224283,-0.5321199298,0.2934281528,-0.0334726572,-0.2285967767,-0.0180567205,0.0428134017,0.3913392425,-0.1193694323,0.0282697044,0.4206727147,-0.0616831332,0.1134212613,-0.2282675803,-0.3263324499,0.1932424307,0.2130988836,0.2006414533,-0.1420846581,0.0438174717,0.4546659291,0.0379386954,-0.1514840275,0.4403600395,0.091993764,0.217300117,0.1510068029,0.0594997033,-0.2520761192,0.176645726,0.0390712395,0.1651211083,-0.0873571485,-0.0359869972,-0.2174773961,0.0089027276,-0.0221343096,0.0318895541,0.0726806074,-0.1267323494,-0.15514189,-0.0079077687,0.1740851104,-0.1608186811,-0.1197563335,-0.23613213,0.072443381,-0.1739453971,0.0709985644,0.0937701315,-0.1703380048,0.0002909967,0.1770595163,-0.1185424551,-0.1547638178,-0.0633910447,0.2232189924,-0.0343861319,-0.1681163013,-0.3214416504,0.6541765928,0.2707799375,-0.062552765,0.2410966605,-0.0750871524,0.0416625328,-0.1014060453,-0.0262598339,0.1974630952,0.0318035707,0.1712822914,0.0407972075,-0.21162799,0.0912056342,-0.0658040643,0.1283020824,-0.0092189023,0.1961651146,0.0282079093,0.3526709378,0.2581016421,-0.0891507044,-0.1441114247,0.5428144932,-0.2562134862,0.257979244,0.4638553858,-0.4305692017,-0.3219676912,0.0750782192,-0.1099267378,0.2708714306,0.1814125776,0.0384941138,-0.005050791,0.2958002687,-0.0585589409,0.226982072,0.129266277,0.2312542796,0.1655011028,0.1857072562,0.1758005172,-0.1652547121,-0.1809598655,0.2347026318,0.3232022822,-0.1956056803,0.0806342736,-0.2088699192,-0.1941362321,-0.0987124592,0.1454793811,-0.1387936622,0.0052878088,0.0636099949,-0.0917775184,0.1439724714,0.1453877687,0.1162511706,0.3910382688,0.0875247419,-0.3043805361,0.1152973697,-0.0589727871,-0.4323800802,-0.0351220965,0.2794806659,-0.4264618456,0.2161747068,0.1520200521,0.0763974115,-0.28428635,-0.5105540156,0.0153107001,0.0186661482,0.2145485431,-0.0979565829,0.020444002,-0.1042166203,0.1304889321,-0.0322462022,-0.1589455456,-0.1028219536,0.0985742286,-0.1815346777,-0.2435962707,0.1276798546,-0.1638975441,-0.2868719399,-0.2101882696,0.2809813917,-0.1480194032,0.0006387518,0.1361135989,0.1532779485,-0.0221642666,0.0062422557,-0.0760251433,0.1199329719,-0.2126324326,0.5063880682,0.155168891,-0.2401786298,-0.1168237105,0.0823830068,-0.1706950217,0.4046607614,-0.4827156067,0.2901175618,-0.3174150288,0.1822003871,-0.0747014508,0.1196025982,0.1363299936,-0.0662222058,0.091195114,0.0743866339,-0.2566071153,-0.2644274831,-0.0348302983,0.319036305,-0.0538880974,0.3580652475,0.5046021342,0.5707176924,-0.128400594,-0.2025967389,0.302220434,0.0581376217,0.1859385669,-0.3812960088,-0.2078484744,0.3076785803,-0.113078370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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1825","title":"Datasets library not suitable for huge text datasets.","comments":"Yes indeed it was added a few days ago. The code is available on master\r\nWe'll do a release next week :)\r\n\r\nFeel free to install `datasets` from source to try it out though, I would love to have some feedbacks","body":"Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions??","comment_length":41,"text":"Datasets library not suitable for huge text datasets. \n Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions?? \n Yes indeed it was added a few days ago. The code is available on master\r\nWe'll do a release next week :)\r\n\r\nFeel free to install `datasets` from source to try it out though, I would love to have some 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1825","title":"Datasets library not suitable for huge text datasets.","comments":"For information: it's now available in `datasets` 1.3.0.\r\nThe 2.0 is reserved for even cooler features ;)","body":"Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions??","comment_length":17,"text":"Datasets library not suitable for huge text datasets. \n Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions?? \n For information: it's now available in `datasets` 1.3.0.\r\nThe 2.0 is reserved for even cooler features ;)","embeddings":[-0.4345221519,0.1332366019,0.0117715299,0.2648242116,0.2677350342,0.020276349,0.0753503516,0.3607457876,-0.3179919422,-0.0622065589,0.035968475,0.0512572899,-0.2549733818,0.1889113784,0.052126471,0.0839582682,0.1855184138,0.1725188196,0.1144125983,0.1579608023,0.0227796771,-0.1485536993,-0.2536755502,0.0014495688,-0.500429213,-0.2469414175,0.001542429,-0.2171645463,-0.4491963685,-0.3404015005,-0.1768149287,0.1993858516,0.5116481781,0.4627266228,-0.0001238177,-0.2813290656,0.2467759699,0.0012895344,-0.2723431289,0.0447508805,0.3876777589,-0.3245376348,-0.2008230388,-0.1749605089,0.1608986109,-0.2847053111,0.0148903253,-0.0699783564,0.0965193138,0.0494214557,0.0779271871,0.0929794982,0.0187179893,0.1376075447,0.1242062077,0.0346610248,-0.0822470486,0.0537681244,0.450540334,0.0497524925,-0.1552138776,0.1750814766,-0.1166119501,0.0095225954,0.3391453922,-0.1126097068,-0.1405123919,-0.1884115785,0.2254876196,0.2983859181,0.5142073631,-0.4272146523,-0.2893555462,-0.5464208126,-0.2044093609,-0.3196335435,-0.0669308677,0.1782543808,-0.0131109236,0.2042060941,-0.4558200836,-0.4421231747,-0.1168552041,0.3653897047,-0.2350143194,0.1488129497,-0.1368505657,0.1141696051,0.165915221,-0.2179446071,0.1126268208,-0.3288344741,0.0219105557,0.1951112449,-0.3774071336,-0.1529268175,-0.2038584799,-0.0359365866,0.4822560847,-0.0165515207,-0.4332740009,0.1530094594,-0.1389319003,0.0294946805,0.3459942341,0.2049816549,-0.1033098772,0.264049083,0.1982870251,-0.1752960384,-0.0434123948,-0.0784640163,-0.5217927098,-0.0583364256,0.1153693572,-0.467672497,-0.2794636488,-0.1709811538,-0.07688234,0.0893994644,-0.1661054492,0.3055479527,0.1728393286,0.1523613483,-0.1039775908,0.0859919488,-0.2191548795,0.1837836951,-0.0974596068,-0.1802397817,0.0022475657,-0.1083154082,-0.2347271442,0.1755829602,0.1531202048,-0.043590188,0.1302113384,-0.0576467887,0.0576003231,0.0121801309,0.1697938591,-0.3426538706,-0.0727010965,0.1154427007,-0.0238889977,0.3663884103,-0.2478738576,0.0542965382,-0.2058062255,0.3545786738,-0.2399349213,-0.3611765504,-0.0457572043,0.0719051436,-0.340780288,0.020672312,-0.5490440726,0.3761832118,0.4263476431,0.0905840993,0.011853788,-0.1186689585,-0.24796094,-0.016100876,0.172204569,0.1334883869,-0.5778154731,0.0366079248,-0.0681395009,0.1124098897,0.3034361899,0.754522562,-0.1512485296,0.3304150999,-0.0916665569,0.4423208535,0.145202443,0.159895286,-0.5134213567,0.4223712683,0.0371215418,-0.164030388,-0.0128785549,0.1075425148,0.3807855546,-0.1396206617,0.1872460544,0.3340221345,-0.0645646378,0.0848616734,-0.2775012851,-0.3438349664,0.1302629858,0.288086772,0.1969875246,-0.2810756862,-0.0097941812,0.3730165064,0.1329231858,-0.1553367674,0.3648222387,0.1345099509,0.1909362078,0.1730598807,-0.0348844379,-0.2371498346,0.1483793855,0.0740290731,0.1063018963,-0.1766885221,-0.0091572646,-0.3077905178,0.1440496892,-0.0243835747,0.0311875548,0.1103275791,-0.1044520289,-0.219715625,-0.0854507387,0.1351994723,-0.1043639556,-0.1191572174,-0.3198543191,0.1355397701,-0.0892160609,-0.0005721598,0.062965624,-0.1361074448,0.041191835,0.224611789,-0.1476466656,-0.1313588023,0.021907825,0.1872600168,0.0183313452,-0.2357047498,-0.2702253163,0.5758532882,0.2275518328,0.0480047278,0.2099347413,-0.0309427567,0.0352990106,-0.0920437574,0.0470425598,0.2821452618,0.1014203727,0.1170904115,0.0349614583,-0.2804763317,0.1036198661,-0.0223459899,0.1937521845,0.006490774,0.2591932118,0.0792194158,0.3156825602,0.3179970682,-0.1806907803,-0.1745746583,0.3581426442,-0.3210413158,0.2150854319,0.4250735343,-0.4366922379,-0.3382962644,0.0725767687,-0.2350004613,0.1056227908,0.142742306,0.0731131583,-0.0428087004,0.3631473482,-0.0525317565,0.1752878577,0.1317133904,0.1352782249,0.2091443986,0.1257627159,0.1298955083,-0.1594125479,-0.0640987232,0.1919549555,0.3366985321,-0.1090408564,0.0508374013,-0.2087420374,-0.3060903251,0.0004508442,0.2116795033,-0.1640042812,0.042693004,0.1251249313,0.0437285565,0.1306495816,0.0544447936,0.1263915896,0.3693228662,0.0503856726,-0.3098164201,0.1500760764,-0.0545791127,-0.4672508836,-0.0370833911,0.3112077713,-0.3574154675,0.2815753818,0.2022108436,0.0586500317,-0.3188081682,-0.4476560354,0.0214609336,0.1317866743,0.2077723891,-0.1410446167,0.0467339642,-0.1914004982,0.1635475755,-0.1014137268,-0.1061106697,-0.0787284598,0.0649727359,-0.1645175815,-0.1977962554,0.1205721647,-0.0024966493,-0.2704381347,-0.19324781,0.224264279,-0.1564694494,0.0722561777,0.0276173782,0.064383015,-0.0258462578,-0.0570879094,-0.1401272118,0.0512430221,-0.313062191,0.4259621501,0.2022450566,-0.1976532489,-0.0776709914,0.1161190644,-0.0912989229,0.3588635623,-0.5269432068,0.3114678562,-0.1990622729,0.1910693496,-0.0856881663,0.017711157,0.0093990229,-0.1782578677,0.1044141427,0.125222981,-0.2186260968,-0.255084604,-0.1566656381,0.3327920735,-0.034482915,0.3154123425,0.4613989294,0.5344964862,-0.0702422932,-0.1545268148,0.3131116629,0.1734436452,0.2241769284,-0.3325264156,-0.0860123485,0.2662095726,-0.0626249537,-0.1146854684,0.475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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1825","title":"Datasets library not suitable for huge text datasets.","comments":"Hi @alexvaca0 , we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs.","body":"Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions??","comment_length":35,"text":"Datasets library not suitable for huge text datasets. \n Hi,\r\n\r\nI'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training. \r\nMoreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk  usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).\r\n\r\nAny suggestions?? \n Hi @alexvaca0 , we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs.","embeddings":[-0.4028155208,0.0771392435,0.0086002536,0.3193157315,0.3035772443,0.0233495664,0.0301555488,0.3559309542,-0.2499681413,-0.0241902824,0.0388909541,0.1070225537,-0.2629811168,0.1188514456,0.0347161479,0.1091509685,0.2107038796,0.1721984446,0.1067035645,0.146104753,0.0033639085,-0.0768989548,-0.2359556705,-0.0442245565,-0.4917674363,-0.2467965335,-0.0052548172,-0.2256905138,-0.416131705,-0.3108104765,-0.1463014185,0.2057922333,0.4900791645,0.4980648756,-0.0001231333,-0.269946456,0.2363723516,0.0186441652,-0.3187198043,0.0508903265,0.4693198502,-0.3577676415,-0.2028603107,-0.2081337422,0.1659426242,-0.2171029598,-0.0014259464,-0.1183160767,0.1075647473,0.0541204512,0.0970020145,0.130324468,0.0105504636,0.1590501219,0.101554513,0.0934823006,-0.0940500125,0.0596261993,0.4631817937,0.0898426846,-0.1676841974,0.2201166749,-0.1535094678,0.0275999047,0.3630459607,-0.0872998089,-0.0916152596,-0.1663148254,0.2746692002,0.2592946291,0.5688686371,-0.4413167238,-0.261215359,-0.545519948,-0.221926555,-0.2722447515,0.004418415,0.1677701473,-0.037591964,0.1915647388,-0.4412501752,-0.4712109268,-0.0885306075,0.3492920399,-0.2183879167,0.0799290761,-0.1663344651,0.102665931,0.2382712811,-0.2418371588,0.102812469,-0.3338505328,0.0022664245,0.1644580215,-0.3736112416,-0.1478253454,-0.2142490298,-0.057693895,0.4252462387,0.0029207745,-0.415071547,0.1444519013,-0.1237988397,0.0363089368,0.3516076505,0.2578930855,-0.099096708,0.2503686249,0.1979775131,-0.1737039536,-0.0160005689,-0.1105490923,-0.5208440423,-0.066299513,0.1376723647,-0.5249603987,-0.2645240426,-0.1763340384,-0.0862714052,0.0780038387,-0.0980500206,0.2459835708,0.1834304184,0.181846872,-0.0734423846,0.1200844571,-0.1878760457,0.1694710404,-0.1226170287,-0.1676702648,0.0043567512,-0.1066701487,-0.2287060469,0.1115951836,0.1700580269,-0.0129800774,0.1193298697,-0.0604935177,0.03899711,0.0096373605,0.1425361633,-0.3770751059,-0.0747798011,0.1250582784,-0.0162472688,0.3819413483,-0.2452010512,0.0438579172,-0.231059745,0.3684508801,-0.2714810669,-0.3721601069,-0.1170408577,0.0728527829,-0.3259705901,-0.0053129266,-0.609857142,0.3593026996,0.4154552519,0.0878379345,-0.0540280528,-0.102754645,-0.2495370954,-0.0512957126,0.1633385271,0.1536833495,-0.5558549166,0.0424050689,-0.105006054,0.1381257772,0.3448683023,0.7818447948,-0.1540688574,0.3443174958,-0.0837314427,0.3829895556,0.1397251934,0.0865029097,-0.5390255451,0.4260479808,0.0805559084,-0.146929875,0.0165783502,0.108947143,0.3508238196,-0.1401628107,0.1549426317,0.3197361827,-0.0168230794,0.0955384597,-0.3492801487,-0.3166877329,0.1213875636,0.3060807288,0.1666428149,-0.3364423513,-0.0267380495,0.3738551438,0.1500833482,-0.1257288009,0.3451298475,0.1695325971,0.2498047352,0.1797233522,-0.003174836,-0.2126871198,0.1286491156,0.0907980949,0.1697634459,-0.1913997829,-0.0275453646,-0.2772671282,0.1492590904,-0.0167024639,0.0196446124,0.1086308062,-0.0833172873,-0.2104953378,-0.0578190275,0.1369724125,-0.0665684193,-0.1515645534,-0.3148004711,0.1400073916,-0.0645669997,0.0235904809,0.0524303429,-0.116723448,0.0183351226,0.2091658115,-0.1672680229,-0.1363949031,0.0121231256,0.1948985308,0.0151766548,-0.2138117105,-0.1999004036,0.5545613766,0.2331965268,0.0591159351,0.2807889879,-0.0021756282,0.053805925,-0.0984087959,0.0032171234,0.2129262537,0.0822622254,0.150980249,-0.012264736,-0.2621985674,0.1246827915,0.018197218,0.2300263643,0.017934218,0.2678823471,0.0690589547,0.3252793849,0.3191105127,-0.1439495832,-0.1528828442,0.3700921535,-0.3132962286,0.2127896398,0.4723603427,-0.4098826945,-0.3531783223,0.0724826008,-0.1715939343,0.1418943852,0.1543644071,0.0579908714,-0.092488952,0.3867768645,-0.0452662185,0.2055207789,0.1396352947,0.1116908416,0.1776803732,0.1302106678,0.115786843,-0.1611314118,-0.0654386654,0.1719735414,0.3680223525,-0.1127545685,0.0396668427,-0.2015713602,-0.2762778997,0.0119576966,0.2238869667,-0.1521978378,0.0127018597,0.0925220028,0.0416020341,0.1437939256,0.0740731284,0.2089531124,0.3292385042,0.0085600037,-0.307706356,0.1153598651,-0.0328585468,-0.4963890314,-0.0283088312,0.3630423546,-0.341493994,0.2948131859,0.1539149731,0.076633364,-0.3204392493,-0.4033299088,-0.0133970762,0.1026637033,0.22246176,-0.1516816914,0.0797144473,-0.2505561113,0.1806444526,-0.0657986626,-0.1377865076,-0.0574445799,0.0679614693,-0.1512971371,-0.2519557178,0.0981158093,-0.0180098768,-0.3014727533,-0.1782745421,0.2398389727,-0.1504434198,0.1131775454,0.0142042711,0.0704071447,-0.0160824694,-0.0572495908,-0.1329592466,0.0318354033,-0.3583616316,0.4102056623,0.2523451746,-0.1754553914,-0.0017284822,0.1510429233,-0.086694181,0.2985329926,-0.536164403,0.2482324541,-0.2271320075,0.1707008481,-0.0989369527,0.0939537883,0.0085230693,-0.1408994347,0.1126374751,0.1357520074,-0.2377644032,-0.2494041175,-0.1299275756,0.3253559768,-0.0392635763,0.3188129663,0.5074319243,0.4777010679,-0.0744746029,-0.1131634191,0.3097053766,0.1841125339,0.2432103008,-0.3235909045,-0.1137646511,0.2541649938,-0.0705374926,-0.1344346404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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1821","title":"Provide better exception message when one of many files results in an exception","comments":"Hi!\r\n\r\nThank you for reporting this issue. I agree that the information about the exception should be more clear and explicit.\r\n\r\nI could take on this issue.\r\n\r\nOn the meantime, as you can see from the exception stack trace, HF Datasets uses pandas to read the CSV files. You can pass arguments to `pandas.read_csv` by passing additional keyword arguments to `load_dataset`. For example, you may find useful this argument:\r\n- `error_bad_lines` : bool, default True\r\n  Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these \u201cbad lines\u201d will be dropped from the DataFrame that is returned.\r\n\r\nYou could try:\r\n```python\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files), error_bad_lines=False)\r\n```\r\n","body":"I find when I process many files, i.e.\r\n\r\n```\r\ntrain_files = glob.glob('rain*.csv')\r\nvalidation_files = glob.glob(validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n```\r\n\r\nI sometimes encounter an error due to one of the files being misformed (i.e. no data, or a comma in a field that isn't quoted, etc).\r\n\r\nFor example, this is the tail of an exception which I suspect is due to a stray comma.\r\n\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 756, in pandas._libs.parsers.TextReader.read\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 783, in pandas._libs.parsers.TextReader._read_low_memory\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 827, in pandas._libs.parsers.TextReader._read_rows\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 814, in pandas._libs.parsers.TextReader._tokenize_rows\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 1951, in pandas._libs.parsers.raise_parser_error\r\n> pandas.errors.ParserError: Error tokenizing data. C error: Expected 2 fields in line 559, saw 3\r\n\r\nIt would be nice if the exception trace contained the name of the file being processed (I have 250 separate files!)","comment_length":129,"text":"Provide better exception message when one of many files results in an exception \n I find when I process many files, i.e.\r\n\r\n```\r\ntrain_files = glob.glob('rain*.csv')\r\nvalidation_files = glob.glob(validation*.csv')\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files))\r\n```\r\n\r\nI sometimes encounter an error due to one of the files being misformed (i.e. no data, or a comma in a field that isn't quoted, etc).\r\n\r\nFor example, this is the tail of an exception which I suspect is due to a stray comma.\r\n\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 756, in pandas._libs.parsers.TextReader.read\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 783, in pandas._libs.parsers.TextReader._read_low_memory\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 827, in pandas._libs.parsers.TextReader._read_rows\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 814, in pandas._libs.parsers.TextReader._tokenize_rows\r\n>   File \"pandas\/_libs\/parsers.pyx\", line 1951, in pandas._libs.parsers.raise_parser_error\r\n> pandas.errors.ParserError: Error tokenizing data. C error: Expected 2 fields in line 559, saw 3\r\n\r\nIt would be nice if the exception trace contained the name of the file being processed (I have 250 separate files!) \n Hi!\r\n\r\nThank you for reporting this issue. I agree that the information about the exception should be more clear and explicit.\r\n\r\nI could take on this issue.\r\n\r\nOn the meantime, as you can see from the exception stack trace, HF Datasets uses pandas to read the CSV files. You can pass arguments to `pandas.read_csv` by passing additional keyword arguments to `load_dataset`. For example, you may find useful this argument:\r\n- `error_bad_lines` : bool, default True\r\n  Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these \u201cbad lines\u201d will be dropped from the DataFrame that is returned.\r\n\r\nYou could try:\r\n```python\r\ndatasets = load_dataset(\"csv\", data_files=dict(train=train_files, validation=validation_files), error_bad_lines=False)\r\n```\r\n","embeddings":[-0.1087328643,-0.4190312624,0.0075155268,0.2815199196,0.184535712,0.1635629386,0.1409228891,0.3934292495,0.2262881994,0.2882854939,0.2727559209,-0.2174746692,-0.0589207113,-0.2714683115,-0.2407846451,-0.1619681716,0.0860742107,0.1235782728,0.2567390203,-0.0407727547,-0.04355729,0.1729320735,0.1145248041,0.2511814535,-0.4897430241,-0.2769164145,-0.3172519505,0.3817558289,0.2166707367,-0.5677108765,0.000150643,0.0310577564,0.1003034189,0.5251937509,-0.0001157862,-0.1504169106,0.150186792,0.0271878988,-0.3778850734,-0.1116750389,-0.1856664419,-0.6877264977,-0.0241563059,-0.2813296616,0.358089596,-0.0366705395,0.2445521355,-0.1590948254,0.3761559427,0.3548002541,0.0947954953,-0.1653120667,0.2582828104,-0.0023046003,0.3424384892,0.1431551725,0.0669529811,0.2645777464,0.3179498017,-0.4284532666,-0.2257969826,0.0672468171,-0.2100593746,0.060488984,0.072225824,0.0547770523,0.3765808046,-0.1714562327,-0.2216404676,0.4012447298,0.2046240717,-0.1446513832,-0.5563559532,-0.6639549732,0.1868021935,-0.299066335,-0.0956107154,0.3052101433,-0.224656716,0.1164680719,0.3411990404,-0.1564714909,-0.3696013987,-0.0656503066,-0.0943443105,-0.1774515957,0.1163334325,-0.0665725842,0.0795393437,-0.0301015154,-0.2295916528,-0.0244011693,-0.1405241638,-0.0659567118,-0.5154403448,-0.0611237176,0.1398732513,-0.594663322,0.2344194651,-0.3872507811,0.3423087001,0.1268074214,-0.0876092389,0.3240608573,0.3673042655,-0.180264622,0.0446514003,-0.073589474,0.4588177502,0.097556524,-0.1286380738,-0.1786259413,0.0580481887,-0.2865338326,0.0131826662,0.491589874,0.0972695798,-0.2422264218,-0.56257689,0.1740452945,-0.2998229861,-0.129499957,-0.1019075364,0.3863109648,-0.1491400152,0.294147253,-0.071840927,0.0743074864,0.1058579758,-0.3490169644,0.0190026928,0.0955065712,-0.1309894323,0.1971978396,-0.1066791713,0.042652946,-0.1053275689,0.212528646,0.1612885594,-0.1958126575,-0.0266665928,-0.4223652482,-0.0123045053,0.1347548068,0.0360869244,0.2787174284,0.1180074811,-0.1997915804,-0.015529871,0.0390922278,-0.5052164793,-0.4055265486,-0.1516358107,-0.0190636199,0.2258602232,0.2026077211,-0.118459411,0.2543493807,-0.065301612,-0.1149035096,-0.0961175859,-0.1168498471,0.2591307163,-0.3146848679,0.0744724944,0.5779359937,-0.3668700159,0.2835256159,-0.1532492936,0.0071681733,0.3014096618,0.501670301,-0.2213502079,0.39205724,-0.3043756783,0.2214913666,0.0214416888,-0.2410392314,0.0696125552,0.5922524333,-0.0995081216,0.3384661973,0.4496501684,-0.0548262373,0.0299315285,-0.1319982409,0.2314151824,0.1058333963,-0.2101560682,0.1193098426,-0.1383868307,-0.0164880119,0.0586396866,-0.0880045891,-0.0457933284,-0.1698718518,0.3214453757,-0.1717794687,0.6836057305,-0.2008433342,-0.198603496,0.3270806074,0.3706170321,0.1801689714,-0.3365702331,0.1081396043,-0.1433928013,-0.0562939867,-0.0956453979,-0.0406308472,-0.3002382517,-0.3844315112,0.0482085273,-0.1880551279,0.1139161587,0.0421368554,0.0909382999,0.1164957806,-0.2879985869,-0.1191728339,0.1267445534,0.4215732217,-0.3311342895,0.2209107429,0.207485348,0.0408954434,-0.1284812987,-0.0950806364,0.1966287941,0.0105177909,0.389231056,-0.238566041,-0.0453906916,0.4332703054,0.0857673287,-0.0695130229,-0.0058437306,0.00813655,0.1619238406,0.1554265022,-0.2069426924,-0.1082888991,-0.2072972506,0.015207666,-0.160050258,0.1527825445,0.1803387702,0.1812221408,0.1355612725,0.0601340458,0.199920997,0.0841965824,0.2385478467,-0.018100366,0.0409563221,-0.1881004125,-0.1225963458,-0.0814209506,-0.5105944276,-0.0667410195,0.3919047415,-0.0359766707,0.1042282656,0.3644504845,0.2609383762,0.0450894274,0.424387455,0.200959444,0.302917093,0.2737497687,-0.166833967,-0.1221467257,-0.2403468937,-0.0530329049,0.1558333784,0.2600558996,-0.0753523186,0.2811388969,-0.0913240835,0.1730599105,-0.0010684173,-0.5670700073,-0.0049581947,0.1933502704,-0.413593024,0.1167614385,-0.3286731243,-0.4525234699,-0.3685514927,0.0141993221,-0.1077616587,-0.2883921564,-0.0081439633,0.1077218056,-0.1718173772,-0.0819019526,0.0299162623,0.1688558608,0.0349316746,0.2562255263,-0.0761338174,0.1002630889,-0.1177725792,-0.1330836266,0.3697405756,-0.139603287,0.0051866383,-0.0098292409,-0.2386259735,-0.3681987524,0.0867851526,0.0194472205,-0.0267849416,-0.1194075644,0.244999662,0.3923146427,-0.1222421974,-0.5848962665,0.1295443475,0.011956431,0.0188376699,0.0164076444,0.1567737907,0.0439011529,-0.0345854722,-0.1233541071,-0.1896684021,-0.4294611812,0.1302319914,-0.4879535735,0.3233160377,0.25190413,0.2206076831,0.1107888818,-0.0591552444,0.3506516814,0.2963022888,-0.2689560354,0.1074777544,-0.1403618306,-0.1288823932,0.0123325856,0.0599699281,-0.1671210527,0.1541715711,-0.4859209955,-0.2744222283,-0.0720594376,-0.0810689181,-0.1790816188,0.0421768464,-0.0532373823,0.0425296649,-0.1384543031,0.1540128589,-0.0546145439,-0.0860621706,0.0819201097,0.5511020422,-0.1832885444,0.380451262,0.2542597353,0.4371030927,0.0799585655,0.2348530889,0.348305285,0.1514683068,0.2243076265,0.4164159298,-0.0817876309,0.0672468469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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1818","title":"Loading local dataset raise requests.exceptions.ConnectTimeout","comments":"Hi ! Thanks for reporting. This was indeed a bug introduced when we moved the `json` dataset loader inside the `datasets` package (before that, the `json` loader was fetched online, as all the other dataset scripts).\r\n\r\nThis should be fixed on master now. Feel free to install `datasets` from source to try it out.\r\nThe fix will be available in the next release of `datasets` in a few days","body":"Load local dataset:\r\n```\r\ndataset = load_dataset('json', data_files=[\"..\/..\/data\/json.json\"])\r\ntrain = dataset[\"train\"]\r\nprint(train.features)\r\ntrain1 = train.map(lambda x: {\"labels\": 1})\r\nprint(train1[:2])\r\n```\r\n\r\nbut it raised requests.exceptions.ConnectTimeout:\r\n\r\n```\r\n\/Users\/littlely\/myvirtual\/tf2\/bin\/python3.7 \/Users\/littlely\/projects\/python_projects\/pytorch_learning\/nlp\/dataset\/transformers_datasets.py\r\nTraceback (most recent call last):\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connection.py\", line 160, in _new_conn\r\n    (self._dns_host, self.port), self.timeout, **extra_kw\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/util\/connection.py\", line 84, in create_connection\r\n    raise err\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/util\/connection.py\", line 74, in create_connection\r\n    sock.connect(sa)\r\nsocket.timeout: timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connectionpool.py\", line 677, in urlopen\r\n    chunked=chunked,\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connectionpool.py\", line 381, in _make_request\r\n    self._validate_conn(conn)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connectionpool.py\", line 978, in _validate_conn\r\n    conn.connect()\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connection.py\", line 309, in connect\r\n    conn = self._new_conn()\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connection.py\", line 167, in _new_conn\r\n    % (self.host, self.timeout),\r\nurllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/adapters.py\", line 449, in send\r\n    timeout=timeout\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connectionpool.py\", line 727, in urlopen\r\n    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/util\/retry.py\", line 439, in increment\r\n    raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/json\/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/littlely\/projects\/python_projects\/pytorch_learning\/nlp\/dataset\/transformers_datasets.py\", line 12, in <module>\r\n    dataset = load_dataset('json', data_files=[\"..\/..\/data\/json.json\"])\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 591, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset, max_retries=download_config.max_retries)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 232, in head_hf_s3\r\n    max_retries=max_retries,\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 523, in http_head\r\n    max_retries=max_retries,\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 458, in _request_with_retry\r\n    raise err\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 454, in _request_with_retry\r\n    response = requests.request(verb.upper(), url, **params)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/sessions.py\", line 530, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/sessions.py\", line 643, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/json\/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\nWhy it want to connect a remote url when I load local datasets, and how can I fix it?","comment_length":69,"text":"Loading local dataset raise requests.exceptions.ConnectTimeout \n Load local dataset:\r\n```\r\ndataset = load_dataset('json', data_files=[\"..\/..\/data\/json.json\"])\r\ntrain = dataset[\"train\"]\r\nprint(train.features)\r\ntrain1 = train.map(lambda x: {\"labels\": 1})\r\nprint(train1[:2])\r\n```\r\n\r\nbut it raised requests.exceptions.ConnectTimeout:\r\n\r\n```\r\n\/Users\/littlely\/myvirtual\/tf2\/bin\/python3.7 \/Users\/littlely\/projects\/python_projects\/pytorch_learning\/nlp\/dataset\/transformers_datasets.py\r\nTraceback (most recent call last):\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connection.py\", line 160, in _new_conn\r\n    (self._dns_host, self.port), self.timeout, **extra_kw\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/util\/connection.py\", line 84, in create_connection\r\n    raise err\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/util\/connection.py\", line 74, in create_connection\r\n    sock.connect(sa)\r\nsocket.timeout: timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connectionpool.py\", line 677, in urlopen\r\n    chunked=chunked,\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connectionpool.py\", line 381, in _make_request\r\n    self._validate_conn(conn)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connectionpool.py\", line 978, in _validate_conn\r\n    conn.connect()\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connection.py\", line 309, in connect\r\n    conn = self._new_conn()\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connection.py\", line 167, in _new_conn\r\n    % (self.host, self.timeout),\r\nurllib3.exceptions.ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)')\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/adapters.py\", line 449, in send\r\n    timeout=timeout\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/connectionpool.py\", line 727, in urlopen\r\n    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/urllib3\/util\/retry.py\", line 439, in increment\r\n    raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/json\/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/littlely\/projects\/python_projects\/pytorch_learning\/nlp\/dataset\/transformers_datasets.py\", line 12, in <module>\r\n    dataset = load_dataset('json', data_files=[\"..\/..\/data\/json.json\"])\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 591, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset, max_retries=download_config.max_retries)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 232, in head_hf_s3\r\n    max_retries=max_retries,\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 523, in http_head\r\n    max_retries=max_retries,\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 458, in _request_with_retry\r\n    raise err\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 454, in _request_with_retry\r\n    response = requests.request(verb.upper(), url, **params)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/sessions.py\", line 530, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/sessions.py\", line 643, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/Users\/littlely\/myvirtual\/tf2\/lib\/python3.7\/site-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/json\/json.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x1181e9940>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\nWhy it want to connect a remote url when I load local datasets, and how can I fix it? \n Hi ! Thanks for reporting. This was indeed a bug introduced when we moved the `json` dataset loader inside the `datasets` package (before that, the `json` loader was fetched online, as all the other dataset scripts).\r\n\r\nThis should be fixed on master now. Feel free to install `datasets` from source to try it out.\r\nThe fix will be available in the next release of `datasets` in a few 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1817","title":"pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 599 but got length 1500","comments":"Hi !\r\nThe error you have is due to the `input_ids` column not having the same number of examples as the other columns.\r\nIndeed you're concatenating the `input_ids` at this line:\r\n\r\nhttps:\/\/github.com\/LuCeHe\/GenericTools\/blob\/431835d8e13ec24dceb5ee4dc4ae58f0e873b091\/KerasTools\/lm_preprocessing.py#L134\r\n\r\nHowever the other columns are kept unchanged, and therefore you end up with an `input_ids` column with 599 elements while the others columns like `attention_mask` have 1500.\r\n\r\nTo fix that you can instead concatenate them all using\r\n```python\r\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n```\r\n\r\nAlso you may need to drop the \"text\" column before applying `group_texts` since strings can't be concatenated with lists. You can drop it at the tokenization step:\r\n```python\r\ndset = dset.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    remove_columns=[\"text\"]\r\n)\r\n```","body":"I am trying to preprocess any dataset in this package with GPT-2 tokenizer, so I need to structure the datasets as long sequences of text without padding. I've been following a couple of your tutorials and here you can find the script that is failing right at the end\r\n\r\nhttps:\/\/github.com\/LuCeHe\/GenericTools\/blob\/master\/KerasTools\/lm_preprocessing.py\r\n\r\nIn the last iteration of the last dset.map, it gives the error that I copied in the title. Another issue that I have, if I leave the batch_size set as 1000 in the last .map, I'm afraid it's going to lose most text, so I'm considering setting both writer_batch_size and batch_size to 300 K, but I'm not sure it's the best way to go.\r\n\r\nCan you help me?\r\nThanks!","comment_length":116,"text":"pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 599 but got length 1500 \n I am trying to preprocess any dataset in this package with GPT-2 tokenizer, so I need to structure the datasets as long sequences of text without padding. I've been following a couple of your tutorials and here you can find the script that is failing right at the end\r\n\r\nhttps:\/\/github.com\/LuCeHe\/GenericTools\/blob\/master\/KerasTools\/lm_preprocessing.py\r\n\r\nIn the last iteration of the last dset.map, it gives the error that I copied in the title. Another issue that I have, if I leave the batch_size set as 1000 in the last .map, I'm afraid it's going to lose most text, so I'm considering setting both writer_batch_size and batch_size to 300 K, but I'm not sure it's the best way to go.\r\n\r\nCan you help me?\r\nThanks! \n Hi !\r\nThe error you have is due to the `input_ids` column not having the same number of examples as the other columns.\r\nIndeed you're concatenating the `input_ids` at this line:\r\n\r\nhttps:\/\/github.com\/LuCeHe\/GenericTools\/blob\/431835d8e13ec24dceb5ee4dc4ae58f0e873b091\/KerasTools\/lm_preprocessing.py#L134\r\n\r\nHowever the other columns are kept unchanged, and therefore you end up with an `input_ids` column with 599 elements while the others columns like `attention_mask` have 1500.\r\n\r\nTo fix that you can instead concatenate them all using\r\n```python\r\nconcatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n```\r\n\r\nAlso you may need to drop the \"text\" column before applying `group_texts` since strings can't be concatenated with lists. You can drop it at the tokenization step:\r\n```python\r\ndset = dset.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    remove_columns=[\"text\"]\r\n)\r\n```","embeddings":[-0.155002296,-0.0703644529,-0.0086344574,0.1708289087,0.2552530169,0.0364475176,0.5036029816,0.5221663713,-0.524661541,0.0104543045,0.3807778358,0.2319069505,0.0748812631,-0.1745078415,0.1261400282,-0.2886483371,0.0141824838,0.2477580458,-0.0138815129,0.1944544166,-0.0676677376,0.3307289779,-0.3295415342,0.2716697752,-0.3865055442,-0.0922516733,-0.1422722787,-0.1698894501,-0.1691986173,-0.5931814313,0.0828088745,-0.228557989,0.0478052273,0.2794103026,-0.0001236288,-0.1168290973,0.4077006876,-0.1066744849,0.0701526999,-0.0044740783,0.0422801189,-0.2361371666,0.0165137015,-0.2340285033,0.0912679061,-0.5475600958,-0.2920790911,0.2607315779,0.2835215032,0.3589550257,0.1257847846,-0.0448946021,0.1790397018,-0.0412862003,0.5702495575,0.0536095574,0.0055336687,0.1160502806,0.1458778381,-0.2013936937,0.0534413718,0.2144300789,-0.2680252492,-0.1032517701,0.0580859445,-0.0039267489,0.0565320142,-0.2023723572,0.2105338126,0.1588908434,0.3037465513,-0.3780476153,-0.0483502857,-0.2628532946,0.1766878068,-0.3986361921,-0.0885518119,-0.060647849,-0.1943920553,0.1883023083,-0.1021537259,0.1745141596,-0.2574053705,0.0938389152,0.0956305861,0.2773377597,0.1587263346,0.2896916568,-0.0706556216,-0.170455128,0.0365101434,0.1347864866,-0.2240155935,0.2108975053,-0.2329891622,-0.03535638,-0.129837513,-0.2215498388,0.4244503975,-0.1432873756,0.2827808559,-0.1606893837,0.7112699151,-0.118645817,-0.0855468288,0.4598975778,0.2372592837,0.2067829072,-0.1270625293,-0.3863641322,0.2913980186,0.1470927745,-0.2602185905,-0.6117146611,0.2757360935,0.1679496765,0.0617795885,0.1802980602,-0.1971882433,0.4800275862,-0.49648121,-0.0251220632,-0.1530764401,0.0553499162,0.0796749368,0.0637729615,-0.0022886391,0.0737530217,0.0643044338,-0.2553144097,-0.102769427,0.2139763832,-0.3910429776,0.0601491854,0.108048901,0.0560679771,0.3017202616,0.0623576455,0.0746015981,0.0635029078,-0.0101668006,-0.0717614666,0.2443722337,0.2771908343,-0.1186136901,0.1828497052,0.2038530856,-0.1445943713,-0.301787138,0.2960030437,-0.327039808,-0.3279041946,-0.1085567176,0.0631789789,0.1062358767,0.1053576842,-0.2185850292,0.150951609,0.6920489073,-0.0988643765,0.1823328137,-0.4258134067,0.1980241686,-0.3123607934,0.1485884637,0.1613928229,-0.567311883,0.1217048019,0.3949619532,0.3733777106,0.3328751624,0.3813624978,-0.0831799135,0.5039740801,-0.090698503,0.7552221417,0.3342810869,-0.088839069,-0.2165766656,0.3936943412,-0.064330481,-0.2323006392,0.0295620374,-0.2319584787,0.2128834426,-0.0096776718,0.1547478586,-0.1016394943,-0.1074724048,-0.0029568034,-0.2887978852,-0.1346039921,0.1084654257,-0.0687080324,-0.1076743975,-0.2563621104,-0.2608839571,-0.410367161,0.1153546721,-0.1635286063,0.3597878218,0.3475106359,0.0196291078,0.026715342,0.3291780949,0.09509442,-0.1781709939,-0.0235901047,0.0442914218,-0.034959048,-0.2201670855,-0.3523425758,-0.2672821581,0.1990024149,0.0381783172,0.1776503325,0.0093461052,-0.2265123725,0.1835489571,-0.1633616388,-0.0336497128,-0.2718361914,0.0508424453,0.0964222252,-0.1822098196,-0.1217020303,-0.173158586,-0.4061906636,0.0997496471,-0.0075107748,0.2952218652,-0.0537830442,0.0143547142,0.3127261996,0.1311428398,-0.3347700834,-0.3954313695,0.2027165443,-0.0828412622,-0.1941531301,-0.1910981089,-0.1059259474,-0.0302319415,-0.0867645591,0.1134169549,0.3114247918,0.0878982991,0.3004992604,-0.2077561468,-0.031966202,0.0630754307,0.0686767474,-0.2348009795,0.010069998,0.0778633356,-0.0475963801,0.1326374859,0.0023546249,-0.3153679371,0.1728616804,0.1770055741,0.015508987,-0.0637862533,0.1986921877,-0.0801163837,-0.0147262001,0.1154872105,-0.1970091164,0.1011901796,0.1659036577,-0.0247780811,-0.0547355078,-0.206338048,-0.1961253583,0.2262758017,0.073609978,0.2035349458,0.5516645312,0.0526557639,0.1273688078,0.0501684658,-0.0934747234,0.2298325896,0.355068922,-0.3621885777,0.1933803111,-0.2526600957,0.0196315181,-0.0059756753,-0.6113991141,0.1719790548,-0.0306187682,0.0457914285,-0.1291826069,-0.1622223854,0.3853451014,0.1819876581,0.1849831939,0.5495913625,-0.167556107,-0.1204284579,-0.2368342578,-0.1843619943,-0.0954642966,0.2132757753,-0.1506299973,-0.0212398209,0.1077955365,-0.1174562797,-0.2242490947,-0.5022531152,0.3119056523,-0.3466986716,-0.1121642813,0.4024557173,0.1223672107,-0.2633654773,-0.2280546427,0.055442825,0.229954347,-0.279808104,0.3484917283,-0.0896362588,-0.2746524811,-0.1506217271,-0.1697364002,0.1745996326,-0.1914544106,0.1772340089,-0.1534639895,0.1468400061,0.0637494847,0.4653074741,0.0369056687,-0.0069357813,0.0424283706,0.0574550591,0.2105319202,0.4343683422,-0.2766295373,-0.2573385835,0.0865589529,-0.1728406549,0.3282194138,0.2733305097,-0.3643850088,0.1617097706,-0.0685935691,0.0936416462,-0.3578870595,0.0590331927,0.4605173469,0.1466504782,0.0051058815,-0.114196457,0.0346330069,0.3736735582,-0.2701492012,0.0551125109,0.0552536957,0.6357032061,-0.0250455793,0.7274159789,0.2814561427,-0.1197825223,0.1013612822,-0.2490906417,-0.1202093959,-0.2398970127,0.1625431031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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1811","title":"Unable to add Multi-label Datasets","comments":"Thanks for adding this dataset! As far as I know `supervised_keys` is mostly a holdover from TFDS, but isn't really used, so feel free to drop it (@lhoestq  or @thomwolf correct me if I'm wrong). It definitely shouldn't be blocking :) ","body":"I am trying to add [CIFAR-100](https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as \r\n`supervised_keys=(\"img\", \"fine_label\")` raises no issue. But trying `supervised_keys=(\"img\", \"fine_label\",\"coarse_label\")` leads to this error : \r\n\r\n```python\r\nTraceback (most recent call last):\r\n  File \"test_script.py\", line 2, in <module>\r\n    d = load_dataset('.\/datasets\/cifar100')\r\n  File \"~\/datasets\/src\/datasets\/load.py\", line 668, in load_dataset\r\n    **config_kwargs,\r\n  File \"~\/datasets\/src\/datasets\/builder.py\", line 896, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"~\/datasets\/src\/datasets\/builder.py\", line 247, in __init__\r\n    info.update(self._info())\r\n  File \"~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cifar100\/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f\/cifar100.py\", line 79, in _info\r\n    citation=_CITATION,\r\n  File \"<string>\", line 19, in __init__\r\n  File \"~\/datasets\/src\/datasets\/info.py\", line 136, in __post_init__\r\n    self.supervised_keys = SupervisedKeysData(*self.supervised_keys)\r\nTypeError: __init__() takes from 1 to 3 positional arguments but 4 were given\r\n```\r\nIs there a way I can fix this?\r\n\r\nAlso, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset?\r\n\r\nThanks,\r\nGunjan","comment_length":41,"text":"Unable to add Multi-label Datasets \n I am trying to add [CIFAR-100](https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as \r\n`supervised_keys=(\"img\", \"fine_label\")` raises no issue. But trying `supervised_keys=(\"img\", \"fine_label\",\"coarse_label\")` leads to this error : \r\n\r\n```python\r\nTraceback (most recent call last):\r\n  File \"test_script.py\", line 2, in <module>\r\n    d = load_dataset('.\/datasets\/cifar100')\r\n  File \"~\/datasets\/src\/datasets\/load.py\", line 668, in load_dataset\r\n    **config_kwargs,\r\n  File \"~\/datasets\/src\/datasets\/builder.py\", line 896, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"~\/datasets\/src\/datasets\/builder.py\", line 247, in __init__\r\n    info.update(self._info())\r\n  File \"~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cifar100\/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f\/cifar100.py\", line 79, in _info\r\n    citation=_CITATION,\r\n  File \"<string>\", line 19, in __init__\r\n  File \"~\/datasets\/src\/datasets\/info.py\", line 136, in __post_init__\r\n    self.supervised_keys = SupervisedKeysData(*self.supervised_keys)\r\nTypeError: __init__() takes from 1 to 3 positional arguments but 4 were given\r\n```\r\nIs there a way I can fix this?\r\n\r\nAlso, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset?\r\n\r\nThanks,\r\nGunjan \n Thanks for adding this dataset! As far as I know `supervised_keys` is mostly a holdover from TFDS, but isn't really used, so feel free to drop it (@lhoestq  or @thomwolf correct me if I'm wrong). It definitely shouldn't be blocking :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1811","title":"Unable to add Multi-label Datasets","comments":"Thanks @yjernite @lhoestq \r\n\r\nThe template for new dataset makes it slightly confusing. I suppose the comment suggesting its update can be removed.","body":"I am trying to add [CIFAR-100](https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as \r\n`supervised_keys=(\"img\", \"fine_label\")` raises no issue. But trying `supervised_keys=(\"img\", \"fine_label\",\"coarse_label\")` leads to this error : \r\n\r\n```python\r\nTraceback (most recent call last):\r\n  File \"test_script.py\", line 2, in <module>\r\n    d = load_dataset('.\/datasets\/cifar100')\r\n  File \"~\/datasets\/src\/datasets\/load.py\", line 668, in load_dataset\r\n    **config_kwargs,\r\n  File \"~\/datasets\/src\/datasets\/builder.py\", line 896, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"~\/datasets\/src\/datasets\/builder.py\", line 247, in __init__\r\n    info.update(self._info())\r\n  File \"~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cifar100\/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f\/cifar100.py\", line 79, in _info\r\n    citation=_CITATION,\r\n  File \"<string>\", line 19, in __init__\r\n  File \"~\/datasets\/src\/datasets\/info.py\", line 136, in __post_init__\r\n    self.supervised_keys = SupervisedKeysData(*self.supervised_keys)\r\nTypeError: __init__() takes from 1 to 3 positional arguments but 4 were given\r\n```\r\nIs there a way I can fix this?\r\n\r\nAlso, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset?\r\n\r\nThanks,\r\nGunjan","comment_length":22,"text":"Unable to add Multi-label Datasets \n I am trying to add [CIFAR-100](https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as \r\n`supervised_keys=(\"img\", \"fine_label\")` raises no issue. But trying `supervised_keys=(\"img\", \"fine_label\",\"coarse_label\")` leads to this error : \r\n\r\n```python\r\nTraceback (most recent call last):\r\n  File \"test_script.py\", line 2, in <module>\r\n    d = load_dataset('.\/datasets\/cifar100')\r\n  File \"~\/datasets\/src\/datasets\/load.py\", line 668, in load_dataset\r\n    **config_kwargs,\r\n  File \"~\/datasets\/src\/datasets\/builder.py\", line 896, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"~\/datasets\/src\/datasets\/builder.py\", line 247, in __init__\r\n    info.update(self._info())\r\n  File \"~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cifar100\/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f\/cifar100.py\", line 79, in _info\r\n    citation=_CITATION,\r\n  File \"<string>\", line 19, in __init__\r\n  File \"~\/datasets\/src\/datasets\/info.py\", line 136, in __post_init__\r\n    self.supervised_keys = SupervisedKeysData(*self.supervised_keys)\r\nTypeError: __init__() takes from 1 to 3 positional arguments but 4 were given\r\n```\r\nIs there a way I can fix this?\r\n\r\nAlso, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset?\r\n\r\nThanks,\r\nGunjan \n Thanks @yjernite @lhoestq \r\n\r\nThe template for new dataset makes it slightly confusing. I suppose the comment suggesting its update can be removed.","embeddings":[0.0216598418,-0.2712222338,0.0181464944,0.1329571754,0.3712174594,0.3166499734,0.6164801717,-0.4762451947,0.1994253397,0.2660482228,-0.2177258879,0.1535530984,-0.2759885192,0.3479327261,0.1080327854,-0.1204760596,0.1780963987,-0.217401877,0.0256008487,-0.033268027,-0.2215413302,0.0074294899,-0.2346253097,-0.2840359509,-0.6864250302,-0.2522574961,-0.348762989,-0.1600370556,-0.2611855567,-0.6842919588,0.1742349118,0.3276168406,-0.1799799353,0.4868170321,-0.0001189698,0.053939525,0.0060154423,-0.0270139854,-0.1315755099,-0.0884083286,0.0338955149,-0.0997653976,-0.0230811313,-0.1876841038,-0.0205910355,-0.3216594756,-0.0478544869,-0.2678296268,0.2622791529,0.1708982736,0.1447003931,-0.1211824119,0.3119575679,-0.1777848601,-0.0340049379,0.2013507783,-0.1293978244,0.1572745889,0.1325554699,-0.1618613303,0.4810110629,0.2527094781,-0.0045298049,-0.1883502454,0.4128499031,-0.0407814495,-0.1479687542,-0.5755074024,0.1524925977,0.3097066581,0.6528835297,-0.0376973525,-0.6485276222,-0.144102335,0.3986090422,-0.1604739726,-0.1403426677,0.010431719,-0.0038597537,-0.2375007272,-0.3692445457,-0.0498816818,-0.2118247896,0.2764362693,0.333170861,0.3576138914,0.0522686765,0.35098809,0.2702969611,-0.0573835894,-0.0252628159,-0.1056556776,0.0451553315,0.2221504897,-0.2898245156,0.1726470292,-0.0475980975,0.1391517818,0.1451030672,-0.1027215347,0.0525578931,-0.3232679665,-0.211719349,0.1196939275,0.1027332097,-0.0196695887,-0.0022725062,0.4025920331,-0.0366642326,-0.0274585523,0.1102099642,-0.2352029085,-0.0683441162,-0.1296106279,0.3619584143,0.1870019734,-0.1424213946,0.1166465431,0.0462243594,-0.15703246,-0.061152637,-0.0322760865,0.3567192256,0.309689492,0.2346779108,0.0138949612,0.1058847159,0.4291902483,0.1078813076,0.0548968427,-0.1495378613,0.35435009,-0.1401570141,0.0218004473,0.2909840345,0.0014130031,0.1271663308,0.3206980526,0.2556933761,-0.1487238407,0.0474536568,-0.2202002108,0.1937618554,0.3569915891,-0.0255860426,0.2145913243,-0.0456783921,-0.3797814846,-0.1100404412,0.04526655,-0.3311825991,-0.0257803425,-0.052225735,0.0699773654,0.1953081638,-0.0917193443,-0.4771164656,0.2014684528,0.1235159412,0.0339203253,-0.0702257752,-0.2045563459,-0.2669656575,-0.2525112927,0.2751828134,0.401172787,-0.2575544119,-0.209166497,-0.1150707379,0.16939044,-0.2034942359,0.2128368467,-0.2620731592,0.4487377703,-0.0940307975,0.0628931224,0.042674195,-0.8922803998,-0.0666826069,-0.0147576723,0.0866596475,-0.128503859,0.1614068151,0.3256177604,0.1087990627,0.0289789066,0.1426028311,0.1137600467,-0.1924496591,-0.0482096337,0.1788373441,0.1565864831,-0.1048276648,0.3070019782,0.2102843821,-0.2103247494,0.1564217657,-0.1566660702,-0.0160315279,-0.1635690033,0.12073908,0.3795161843,0.2092370689,-0.0499869846,0.0313238017,0.0380041413,-0.4350197017,0.4339157343,0.2955799401,0.3049954176,-0.1049287096,-0.1757050157,-0.0116091687,0.0613495074,-0.4852899611,-0.1146177873,0.0656736344,0.2741549015,-0.0914811268,-0.0599610843,0.0205955021,0.1393007636,0.4397166073,0.1631942391,0.0898363367,0.0351097733,-0.1254134476,0.031797003,-0.0982277617,0.0179512184,0.4089220166,-0.0632736459,0.0348855965,0.4561132491,0.1855800599,-0.3028663397,-0.1581857651,0.0631943867,0.4092926979,-0.2760539651,-0.2940944433,-0.2007402331,0.2320427746,-0.3411729634,-0.155014053,0.0775262788,-0.3345649242,0.2867106795,-0.0063107894,0.1957471073,0.1347314268,0.1028221473,-0.1135910451,-0.2067493051,0.0379192606,-0.1822332293,-0.0228359047,-0.2354649007,0.0413877219,0.0655713156,0.0258155391,0.0400971286,-0.0610706173,-0.0535848029,0.0286562536,0.2431066483,0.1560307741,0.5032753944,0.2892154753,0.2665799856,-0.1650920212,-0.2013620734,-0.2896959782,0.0578938462,0.1002955586,-0.0812715143,0.1857181787,0.1985814273,0.1882681102,0.0334806368,-0.2237827629,-0.4800541997,0.3684946597,0.1311239153,-0.2911791503,0.0136999218,-0.3054610789,-0.209730491,-0.0478190146,-0.2719694078,-0.0329390801,-0.273193121,0.100284718,0.2303827852,0.1110883728,0.2187402695,0.0505732298,0.2245625407,-0.2533396184,0.0736529753,-0.0290039685,-0.1045127735,-0.002999285,-0.0265577007,0.3638055325,-0.3310750723,0.5024328232,0.0892854407,-0.1429113895,-0.204916358,-0.4361688793,0.0277465582,-0.1707712859,0.2560727,0.0792692378,-0.0453793332,-0.120553337,-0.2528287768,0.3231553733,0.1433298141,-0.067182377,-0.02225996,-0.2092654854,-0.3181765974,-0.3291820884,-0.0019011601,-0.2415915877,-0.0313346125,0.1783394068,0.0039089699,-0.0620984249,0.6229845881,0.1421978921,-0.3269357383,0.1114385575,0.1087919623,-0.0204633344,-0.3791366518,0.230792135,-0.3280832469,-0.245816052,0.1112751365,-0.2078543007,0.4561773539,-0.0236298926,-0.074241221,-0.0516700149,0.06004408,0.0250432417,-0.0242213272,0.2214048356,0.1233270913,0.1223821193,0.0012233047,-0.2397067994,-0.4332716763,0.3612817526,-0.230379194,0.2302520275,-0.3453046679,0.2405825853,0.0744778737,0.2453038096,0.0080904132,0.0415512472,0.4380138814,-0.2188450545,0.3824874759,-0.1967352331,-0.5095447898,0.2663565278,0.0068039051,0.0746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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1810","title":"Add Hateful Memes Dataset","comments":"Hi @gchhablani since Array2D doesn't support images of different sizes, I would suggest to store in the dataset the paths to the image file instead of the image data. This has the advantage of not decompressing the data (images are often compressed using jpeg, png etc.). Users can still apply `.map` to load the images if they want to. Though it would en up being Sequences features.\r\n\r\nIn the future we'll add support for ragged tensors for this case and update the relevant dataset with this feature.","body":"## Add Hateful Memes Dataset\r\n- **Name:** Hateful Memes\r\n- **Description:** [https:\/\/ai.facebook.com\/blog\/hateful-memes-challenge-and-data-set]( https:\/\/ai.facebook.com\/blog\/hateful-memes-challenge-and-data-set)\r\n- **Paper:** [https:\/\/arxiv.org\/pdf\/2005.04790.pdf](https:\/\/arxiv.org\/pdf\/2005.04790.pdf)\r\n- **Data:** [This link](https:\/\/drivendata-competition-fb-hateful-memes-data.s3.amazonaws.com\/XjiOc5ycDBRRNwbhRlgH.zip?AWSAccessKeyId=AKIARVBOBDCY4MWEDJKS&Signature=DaUuGgZWUgDHzEPPbyJ2PhSJ56Q%3D&Expires=1612816874)\r\n- **Motivation:** Including multi-modal datasets to \ud83e\udd17 datasets.\r\n\r\nI will be adding this dataset. It requires the user to sign an agreement on DrivenData. So, it will be used with a manual download.\r\n\r\nThe issue with this dataset is that the images are of different sizes. The image datasets added so far (CIFAR-10 and MNIST) have a uniform shape throughout.\r\nSo something like \r\n```python\r\n datasets.Array2D(shape=(28, 28), dtype=\"uint8\")\r\n```\r\nwon't work for the images. How would I add image features then? I checked `datasets\/features.py` but couldn't figure out the appropriate class for this. I'm assuming I would want to avoid re-sizing at all since we want the user to be able to access the original images.\r\n\r\nAlso, in case I want to load only a subset of the data, since the actual data is around 8.8GB, how would that be possible?\r\n\r\nThanks,\r\nGunjan","comment_length":87,"text":"Add Hateful Memes Dataset \n ## Add Hateful Memes Dataset\r\n- **Name:** Hateful Memes\r\n- **Description:** [https:\/\/ai.facebook.com\/blog\/hateful-memes-challenge-and-data-set]( https:\/\/ai.facebook.com\/blog\/hateful-memes-challenge-and-data-set)\r\n- **Paper:** [https:\/\/arxiv.org\/pdf\/2005.04790.pdf](https:\/\/arxiv.org\/pdf\/2005.04790.pdf)\r\n- **Data:** [This link](https:\/\/drivendata-competition-fb-hateful-memes-data.s3.amazonaws.com\/XjiOc5ycDBRRNwbhRlgH.zip?AWSAccessKeyId=AKIARVBOBDCY4MWEDJKS&Signature=DaUuGgZWUgDHzEPPbyJ2PhSJ56Q%3D&Expires=1612816874)\r\n- **Motivation:** Including multi-modal datasets to \ud83e\udd17 datasets.\r\n\r\nI will be adding this dataset. It requires the user to sign an agreement on DrivenData. So, it will be used with a manual download.\r\n\r\nThe issue with this dataset is that the images are of different sizes. The image datasets added so far (CIFAR-10 and MNIST) have a uniform shape throughout.\r\nSo something like \r\n```python\r\n datasets.Array2D(shape=(28, 28), dtype=\"uint8\")\r\n```\r\nwon't work for the images. How would I add image features then? I checked `datasets\/features.py` but couldn't figure out the appropriate class for this. I'm assuming I would want to avoid re-sizing at all since we want the user to be able to access the original images.\r\n\r\nAlso, in case I want to load only a subset of the data, since the actual data is around 8.8GB, how would that be possible?\r\n\r\nThanks,\r\nGunjan \n Hi @gchhablani since Array2D doesn't support images of different sizes, I would suggest to store in the dataset the paths to the image file instead of the image data. This has the advantage of not decompressing the data (images are often compressed using jpeg, png etc.). Users can still apply `.map` to load the images if they want to. Though it would en up being Sequences features.\r\n\r\nIn the future we'll add support for ragged tensors for this case and update the relevant dataset with this feature.","embeddings":[-0.2504210472,0.15920645,-0.1446140856,0.0486409999,0.1048059687,0.2664602399,0.444988817,-0.0732126981,-0.0832349584,-0.2253455967,-0.0230498239,-0.2821851373,-0.5021380186,0.2475005686,0.0156487133,-0.1245207638,0.0323905088,0.0315233096,-0.1846613884,-0.0556139722,-0.0390983522,-0.1718231291,0.1833574176,-0.2621174455,-0.5729023218,-0.0794128329,-0.2922713757,0.2743028998,-0.1746696681,-0.4757303596,0.0342119224,0.3668724895,0.1425356716,0.5114071369,-0.0001180374,0.0613285005,-0.0814565793,-0.1974319369,0.0460136309,0.0979811102,0.0788964629,-0.5317852497,-0.1205243692,-0.2593847811,0.1425022781,-0.4997737706,0.2076667249,-0.0505467318,0.6721628904,-0.0005184649,0.1403406113,-0.1678145826,0.2236776948,-0.029542353,0.0302443169,0.7310441732,-0.0535114408,-0.2448932976,0.0850695521,0.1053892821,0.2414755225,0.2661069632,-0.1824454665,0.0981596634,0.4686677754,-0.1495052427,-0.0850304961,-0.3651400805,0.0692548603,0.3533005714,0.3158091307,-0.1157739684,-0.2830895483,-0.4789533019,-0.0519871153,0.0470750518,-0.0973815098,0.2933705449,-0.0024280832,-0.0083623491,-0.4078708887,-0.1113797426,-0.3908492029,0.4031023383,-0.0128045585,-0.1113748103,0.0441607237,0.3313264251,-0.0470419489,-0.3176805675,-0.0503538102,-0.0321193412,0.3344113231,0.2942711115,0.2471242994,0.0357241593,-0.233834669,-0.2644758224,0.4943545759,-0.2746843398,-0.0365184322,-0.0611696504,-0.5127977729,-0.1091958955,0.1728673279,-0.2186565399,-0.4133867621,0.2884222865,0.142345041,0.0239077061,-0.0680184364,-0.0000656558,-0.1721003503,0.0852599442,-0.0206120834,-0.0413677394,0.2201980501,0.0065697813,-0.2225710154,-0.2236707956,0.1090388522,0.0613116771,-0.079758428,0.1821876615,0.0928377137,-0.0467927866,-0.069164969,0.2312929332,0.0642930046,-0.1113332435,-0.1213239431,-0.0465073399,-0.0835090652,0.2189407796,0.3173370957,0.4066416323,0.3729762435,-0.050235115,0.0361435004,0.1877490431,0.1703289747,-0.153628394,0.239740625,0.102562815,0.1239588559,-0.0443040691,-0.0259082131,0.1373515576,-0.1783611476,0.1234838665,-0.296526581,-0.0349515602,-0.0787504464,0.0607702471,-0.3073102534,0.1393744498,-0.0952757224,0.3325945437,0.04459906,-0.0998574942,0.0798218846,-0.0691168234,-0.2916133106,-0.4136890173,0.0731455311,0.5717698932,-0.4198009372,-0.0388678946,-0.2598000467,0.0048770937,-0.3514771163,0.1411675215,-0.0738463476,0.0804831609,-0.142295748,-0.0395541564,0.1971670538,-0.1646540314,-0.2917400301,0.1448032856,0.0503726937,0.2691359818,0.199737072,0.4102457762,0.1679878533,-0.0923593566,-0.0039954693,0.6235691309,-0.2270171046,-0.0232366286,0.216776222,-0.4549306631,0.090089567,0.3774319291,0.3031720817,-0.2699362338,0.366201371,-0.0529149659,0.1597317755,-0.2786393166,0.1843125671,0.0729182139,0.3280514777,0.0044640056,0.0812786371,-0.1909705251,-0.5832839608,0.2914872468,-0.1301626563,-0.2425613403,-0.1890538931,-0.0745365024,-0.2226565182,-0.1695448756,-0.5431597829,-0.1439875215,0.080965206,0.0673680156,0.1173334271,-0.0855121985,-0.1075575128,0.3108456731,0.037763834,0.0376631394,0.0759399161,0.3562743664,0.2526936233,-0.2590902746,-0.1550349891,0.193331033,-0.0770662054,-0.2776979208,0.0310585853,0.4657758176,0.2533091009,0.1999029815,-0.0602821521,0.5921089053,0.3864941299,-0.4306572974,0.4246513844,0.2981195748,0.1676470041,-0.1962485909,-0.1448151022,0.416193068,-0.1086042821,0.0944967568,-0.0901602134,-0.1568721086,0.1208517626,0.1988417357,-0.0697348937,-0.4860266745,-0.1375582665,0.3124346435,-0.0352833606,-0.0333198868,0.0816607848,-0.0311006028,0.1444080919,0.1584203094,0.1950880587,0.1174780205,-0.2045092583,0.2176683545,0.1160252914,-0.0820590928,0.5058944821,0.2438028753,-0.2884396315,0.1893194914,0.0441551432,0.070334971,-0.0591774695,0.0718808621,0.1592857093,0.1814645529,0.2923966646,-0.097211875,-0.2935050726,-0.3649154603,-0.0464099757,-0.1391712874,0.0285090171,-0.2159312814,-0.1760035157,-0.0752429143,-0.1771730036,0.0619004332,-0.273111999,0.3103006184,0.2432677001,-0.0844474509,-0.0076462994,0.0986702889,-0.1790386438,0.4522792995,0.1903005987,-0.1124393195,0.2018894255,0.2738096416,-0.1722243875,0.023474969,0.3414861858,0.0180947911,0.3103942275,-0.2085749805,0.2875601947,-0.3016797006,-0.5269958973,-0.0177559536,-0.127862975,0.4184361398,0.1583189368,0.056980744,0.1466142535,-0.0490887575,0.2545942366,0.0839701518,-0.1261508763,-0.3579851985,-0.3124555051,-0.1176280752,0.087633431,0.1875389665,-0.293767482,-0.2434359193,0.4645284414,0.5125316381,0.1423606277,0.2152908146,0.5227329731,-0.156318441,0.1688376963,0.2114794105,-0.0623731613,-0.0816042498,0.4824644625,-0.2617923617,-0.2787492871,-0.0011846425,-0.200517118,-0.2126512676,0.363460511,-0.4871141911,-0.1780950576,-0.1202250049,0.1878046393,0.0320536643,-0.0260774158,0.0824709162,-0.0281694625,0.1132401004,-0.0663222373,-0.0998316407,0.0764128268,0.3912824988,0.2211259156,0.0891547129,-0.0360578895,0.2834275961,0.4973535836,0.0504425019,-0.0778694227,0.4475955069,0.059290342,0.2628367841,-0.002623196,-0.3571556509,0.3998303413,0.197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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1808","title":"writing Datasets in a human readable format","comments":"AFAIK, there is currently no built-in method on the `Dataset` object to do this.\r\nHowever, a workaround is to directly use the Arrow table backing the dataset, **but it implies loading the whole dataset in memory** (correct me if I'm mistaken @lhoestq).\r\n\r\nYou can convert the Arrow table to a pandas dataframe to save the data as csv as follows:\r\n```python\r\narrow_table = dataset.data\r\ndataframe = arrow_table.to_pandas()\r\ndataframe.to_csv(\"\/path\/to\/file.csv\")\r\n```\r\n\r\nSimilarly, you can convert the dataset to a Python dict and save it as JSON:\r\n```python\r\nimport json\r\narrow_table = dataset.data\r\npy_dict = arrow_table.to_pydict()\r\nwith open(\"\/path\/to\/file.json\", \"w+\") as f:\r\n    json.dump(py_dict, f)\r\n```","body":"Hi\r\nI see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable  format to a file like json? thanks @lhoestq ","comment_length":101,"text":"writing Datasets in a human readable format \n Hi\r\nI see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable  format to a file like json? thanks @lhoestq  \n AFAIK, there is currently no built-in method on the `Dataset` object to do this.\r\nHowever, a workaround is to directly use the Arrow table backing the dataset, **but it implies loading the whole dataset in memory** (correct me if I'm mistaken @lhoestq).\r\n\r\nYou can convert the Arrow table to a pandas dataframe to save the data as csv as follows:\r\n```python\r\narrow_table = dataset.data\r\ndataframe = arrow_table.to_pandas()\r\ndataframe.to_csv(\"\/path\/to\/file.csv\")\r\n```\r\n\r\nSimilarly, you can convert the dataset to a Python dict and save it as JSON:\r\n```python\r\nimport json\r\narrow_table = dataset.data\r\npy_dict = arrow_table.to_pydict()\r\nwith open(\"\/path\/to\/file.json\", \"w+\") as f:\r\n    json.dump(py_dict, f)\r\n```","embeddings":[-0.1064517573,0.2575984001,-0.0731424093,0.124281235,0.4906324446,0.2974198163,-0.1625235528,0.3496721387,0.0443107188,0.0258972384,-0.1533560306,0.3309133351,-0.4147045612,0.2707338035,0.2647757828,-0.1661130488,0.0718676373,-0.0046492941,0.0115184709,0.1498278081,-0.1541504264,-0.05089375,0.2294982523,-0.0256731845,-0.0389475413,-0.3820806444,-0.2350030839,-0.1319584846,-0.2951259911,-0.2115113586,-0.087023221,0.0582865924,0.1421895921,0.0619914234,-0.000117436,-0.090346083,0.0592741333,-0.1883002818,-0.4185785353,-0.1192436665,-0.257265389,-0.4672527611,0.2826541662,-0.388561368,0.0572322868,-0.5000993609,0.1672141254,-0.3590875864,0.4838615954,0.2706120014,0.2117938548,-0.1439215094,0.3098011613,0.2972798944,0.0524505749,0.6159294248,-0.3162021935,0.1544303447,-0.0757002309,0.0732607916,0.3559266031,-0.1744703054,-0.1545521617,-0.3159635663,0.3756185472,0.0782597363,-0.2154064029,-0.0293780938,-0.0379322022,0.2223854065,0.756688714,-0.7057831287,-0.5702959299,-0.0148927746,0.0362194851,-0.0766560137,-0.1443181634,0.5130928755,0.0262532253,0.2297064811,-0.1699939519,-0.5853190422,-0.3661932349,0.0704637915,0.3859952092,-0.1131257564,-0.1840336323,-0.0402981117,0.0515578464,0.0164258908,0.31370911,-0.176554203,-0.0960149541,0.1227475703,-0.150960952,-0.2741802037,-0.5675682425,-0.2052541971,0.0101159187,0.0844311565,0.4611463845,-0.0475819334,-0.0811262503,0.0940760672,0.3766885102,0.0881229118,0.188190043,0.0747268647,0.1809167713,-0.2470789105,0.0940365717,-0.2720762789,0.0188911464,0.1625400782,0.4624987841,-0.0868978351,0.0454677418,-0.2265895158,-0.0494800098,0.0183054637,-0.1780694127,0.1294333339,-0.3326537907,0.1597582102,0.0246283002,0.1252074242,0.1071007475,0.2388100177,0.1295621246,-0.0150892157,0.1851650476,0.1889404058,0.0421168618,0.1680559516,-0.0994377881,0.0223751292,0.0488014072,-0.0798074752,0.0742932633,-0.4312080145,0.3465843201,-0.1667003334,0.2300370783,0.2840815485,0.1565832645,0.0880093202,-0.0418890566,-0.5203971863,-0.2893023491,0.2648203373,0.0096722208,-0.1103153452,-0.3392949998,0.0516568497,-0.1047599241,-0.1274159104,-0.7118952274,0.0507395752,0.0460144728,0.0264461786,0.1481796503,-0.0401071124,-0.1091865525,-0.5510830879,0.1095230356,0.0285975505,-0.576321125,0.1701294929,0.0707032606,-0.0269818287,0.0793612823,0.2944976091,0.1584192812,0.3940740824,0.0567225516,0.1613829583,0.3168116212,-0.1142696813,-0.0339379646,0.3281467259,0.1536334455,0.0527911782,-0.2635242939,0.4900129139,0.4048589766,0.0110457633,-0.1125622839,0.0533574186,0.1434189975,0.0167620722,-0.1362846196,-0.3738783896,0.1573848575,0.0359619595,-0.3810765445,-0.0902455226,0.3390375078,-0.0026245373,0.302003473,-0.2979596555,0.261669904,0.5488736629,0.3298592567,0.0892501846,-0.1869285256,-0.2176185399,-0.2303956449,-0.1483852565,0.473742485,-0.1687740237,-0.1578415185,-0.3411399424,0.1417446733,0.0717261359,-0.1318061352,0.2105413675,0.0646364987,-0.2472129762,-0.1737648696,0.1570231616,-0.2253973186,0.3347906768,-0.0315161124,0.1102832705,-0.2123126388,0.1414784044,-0.004306261,-0.2985953689,0.1239468232,0.0093764998,0.0077000391,0.0071598659,-0.0397443697,0.3718758523,0.171789065,0.0548245013,0.0678471997,0.5374658704,0.0692658946,-0.217672348,0.1381903291,0.2048177421,0.218248263,-0.0328975357,-0.686059773,0.5157960057,0.0122280223,0.0096767591,-0.0234441217,0.2769929171,0.1521539092,0.1961560845,-0.1398269832,0.0576458164,0.0091584511,0.0627285913,-0.1090610996,-0.228632018,-0.4677338898,0.0491144024,0.5178409815,-0.3079947531,0.5596216321,0.0245202035,-0.0674514472,-0.119786121,0.139173612,0.1694332957,0.0988084748,0.0131214242,0.0084054992,-0.0152110709,-0.1322947294,0.0793197006,0.1636240035,-0.175905332,0.2189535797,-0.1926093251,-0.0880049616,-0.1375405788,-0.2838563323,0.2554503083,-0.1744093746,0.0286313854,-0.2344365269,0.0963485539,-0.1480449885,0.0690904409,-0.0803010464,0.0835794732,-0.1047159508,-0.266687721,-0.3607384562,0.1366749853,0.101683639,-0.1860567033,-0.068729274,0.2181332707,-0.1224072799,-0.0292222481,-0.2400660366,-0.0789850354,0.0528297499,0.0032243826,0.1677333415,0.2358342856,0.4595133662,-0.1864616275,0.3139979541,-0.3245364726,0.0425703526,-0.2352767736,-0.0733075216,0.2968516648,-0.0385017358,0.3675038218,-0.4057822526,0.0554117076,-0.0578178652,0.068049483,-0.1202371269,0.0670581311,0.1310899109,-0.1453340948,0.0033633357,0.1358358115,-0.331548214,-0.0918807089,0.5880922675,-0.0212842803,0.0851841718,-0.2015008628,0.1822723746,0.0390201882,-0.0594496615,-0.1134716719,0.1022591069,-0.4799207747,0.3875850141,-0.2450955063,-0.3918482363,0.2173522115,0.3804702163,-0.1191383526,-0.0605500191,-0.1390841752,0.0108353263,-0.1433646083,-0.1700054705,-0.053838037,0.1813925952,0.0168658532,0.2060139924,0.1637835056,0.064199768,-0.2167957276,0.1224522814,0.3177887499,0.0929094329,-0.0520332344,0.3663929701,0.0803732425,-0.0182100609,0.3591733277,0.2471932918,0.2278045565,-0.0486975871,0.4381061792,-0.1248669848,0.0292693451,0.0408791304,-0.3164154887,-0.189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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1808","title":"writing Datasets in a human readable format","comments":"Indeed this works as long as you have enough memory.\r\nIt would be amazing to have export options like csv, json etc. !\r\n\r\nIt should be doable to implement something that iterates through the dataset batch by batch to write to csv for example.\r\nThere is already an `export` method but currently the only export type that is supported is `tfrecords`.","body":"Hi\r\nI see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable  format to a file like json? thanks @lhoestq ","comment_length":61,"text":"writing Datasets in a human readable format \n Hi\r\nI see there is a save_to_disk function to save data, but this is not human readable format, is there a way I could save a Dataset object in a human readable  format to a file like json? thanks @lhoestq  \n Indeed this works as long as you have enough memory.\r\nIt would be amazing to have export options like csv, json etc. !\r\n\r\nIt should be doable to implement something that iterates through the dataset batch by batch to write to csv for example.\r\nThere is already an `export` method but currently the only export type that is supported is `tfrecords`.","embeddings":[-0.2098388523,0.0492693856,-0.1076665297,0.0664265603,0.5001397729,0.2327695489,-0.1172598526,0.3574723303,0.0007521105,0.149274841,-0.1676979065,0.0527242981,-0.3711419106,0.4290185571,0.3048388958,-0.1157784685,0.0749913231,-0.1399861872,0.0092282267,0.1033091769,-0.1731934994,0.0031017275,0.185680151,-0.104470022,-0.0076001934,-0.4232765138,-0.2167166322,-0.2166220844,-0.1934353411,-0.1114805341,-0.1856017858,0.0026591034,0.3307574987,0.0372143053,-0.0001181302,-0.1858900487,-0.0230949037,-0.1615507007,-0.3787545562,-0.088904269,-0.2690251172,-0.2977554798,0.0627992153,-0.402382642,0.0958815962,-0.2471900433,0.4078348875,-0.3925501704,0.2636525631,0.2429079562,0.1875607669,-0.1289436668,0.2089093029,0.2194775045,0.0918987915,0.6853557825,-0.3673555851,0.0276128408,0.0916349739,0.187260136,0.4378953874,-0.2874055207,-0.1316048354,-0.3443317115,0.343981564,0.0561668612,-0.0894858018,-0.0855567008,-0.011304005,0.2284470648,0.8819463253,-0.5850656629,-0.4965889454,0.0562340692,-0.0367571339,-0.0845804811,-0.1327416897,0.4962075353,0.0031539435,0.2550381124,-0.3079487681,-0.6020662785,-0.4352656603,0.0158189349,0.4432986379,-0.0723043829,-0.1896705925,-0.04866229,0.0651546344,0.1475459933,0.3959632516,-0.1175903231,-0.1520476192,0.1304979324,-0.1174586117,-0.3553515971,-0.6162095666,-0.0790424421,0.0551156811,0.0091142301,0.3531559408,-0.081389986,0.0763224065,0.0985915363,0.4005744457,0.0226747524,0.3162567914,-0.0479763411,0.1818596721,-0.4053877592,0.1173192486,-0.2435553223,0.0092502451,0.1748861074,0.4574065506,-0.0047270153,-0.0768976063,-0.2648996413,0.0425273515,-0.0964032635,-0.0780492052,-0.0268151686,-0.4872848094,0.0815216228,0.0194435436,-0.1135362983,0.0742608681,0.0766617134,-0.0136129288,-0.1499937922,0.2075235695,0.1393625587,0.0309044737,0.1772511303,-0.1824786216,-0.0466998853,-0.0887516439,-0.0720064715,0.1316601634,-0.3586089313,0.3845713437,-0.2493529618,0.2336791158,0.0582666993,0.1374220252,0.1378741711,-0.1352057457,-0.5077313185,-0.3294370472,0.201381892,-0.1224831715,-0.1518190801,-0.3533756435,0.0441593826,-0.0617387444,-0.2341740727,-0.6611484289,0.1238480136,0.1232445017,-0.0381360948,0.0982439071,-0.0732536614,-0.1500760615,-0.4955060482,0.0272695906,-0.0362048224,-0.6494598389,0.188305527,0.1602357626,-0.0922067091,0.1351405829,0.3854272962,0.2524668276,0.5349962711,-0.0050936947,0.19084993,0.279283464,0.0055641709,0.1569122523,0.3973152041,0.1233955696,-0.1115937009,-0.2646249235,0.4624730647,0.5034453273,-0.1858539432,-0.1742169112,0.0487023927,0.1154660359,-0.0147593711,-0.1799330264,-0.349065721,0.0157871991,-0.0664079338,-0.2319363058,-0.0439234041,0.5080922246,0.0998144001,0.2476150542,-0.3435503244,0.3596270382,0.5124644637,0.3237612247,0.1429360509,-0.2747886479,-0.2920857668,-0.2649782598,-0.2304356694,0.3505921364,-0.2540549636,0.2577134073,-0.3342514634,0.2862891853,0.0189476907,0.0523889288,0.2952235043,0.0199203696,-0.0535012223,-0.1708802879,0.1500766724,-0.3343756497,0.2304334193,-0.0469821878,-0.0708581209,-0.1806437671,0.1290433705,0.006797886,-0.2635652423,0.0730489269,-0.1115209684,-0.0074959802,-0.1651372313,0.0253623724,0.305508852,0.0756190792,0.0746358484,0.1550768763,0.6029985547,0.0291009638,-0.0924293026,0.061812751,0.0457080118,0.0842041448,-0.0397253223,-0.732578516,0.4708710015,-0.0299039949,-0.0231565628,-0.0380999707,0.1662543118,0.0391301513,0.0894585475,0.0318249017,0.0888793096,0.1403706819,0.0402228944,-0.2641543448,-0.2821545303,-0.4200903773,0.0944668204,0.6116841435,-0.3029912412,0.4891398549,-0.0324115194,0.1629302353,-0.0735493004,0.1590601355,0.0599536747,0.1638482362,0.0063388962,0.1523171514,-0.0200324561,-0.100285612,0.0414518863,0.1809062213,-0.3330414295,0.1086975262,-0.1366849989,-0.273260951,-0.026308069,-0.2412550002,0.2050079405,-0.1510562897,0.0028075317,-0.253469497,-0.0020915945,-0.1439948231,0.0682080537,-0.0892927051,0.2924490869,-0.0737616271,-0.2302346081,-0.2821521163,0.2217075378,0.0110720415,-0.2715150714,0.0190669838,0.39490363,-0.2087052763,0.1366722435,-0.2805602252,-0.0104772337,0.0772219449,0.0013887953,0.1501663029,0.2121299207,0.5001707673,-0.2471012175,0.2344092876,-0.3675446808,-0.0073752617,-0.1600474566,-0.0063728634,0.3519592583,-0.101243414,0.3395303786,-0.4360646307,0.0627961382,-0.1589544564,0.0349303111,-0.0650752261,-0.074305594,0.1325968355,-0.2086403817,-0.0840452462,0.1942927539,-0.3629709482,-0.1273270398,0.5436404347,-0.0702461004,0.1053248942,-0.2141474187,0.0746374577,-0.0660374984,-0.1418339759,-0.1646412462,0.074482359,-0.6360775232,0.2971100807,-0.3602563441,-0.4670175016,0.2232372016,0.5383545756,-0.1780404299,0.0747439638,-0.2141808271,0.1051238477,0.0104011782,-0.2355577946,-0.0313845314,0.2207895517,0.0310314111,0.084916316,0.172157526,0.0622602627,-0.1802071482,0.0317772888,0.3285797238,0.2549753785,-0.0333706029,0.2822730243,0.2257814258,0.0145830298,0.2588456273,0.2226624489,0.1427477747,0.0221684892,0.4139010906,-0.0327833705,0.0022067367,0.347582072,-0.303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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1805","title":"can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index","comments":"Hi ! Indeed we used to require mapping functions to be picklable with `pickle` or `dill` in order to cache the resulting datasets. And FAISS indexes are not picklable unfortunately.\r\n\r\nBut since #1703 this is no longer required (the caching will simply be disabled). This change will be available in the next release of `datasets`, or you can also install `datasets` from source.","body":"So, I have the following instances in my dataset\r\n\r\n```\r\n{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of \r\nthis increase in rotation?', \r\n'answer': 'C', \r\n'example_id': 'ARCCH_Mercury_7175875', \r\n'options':[{'option_context': 'One effect of increased amperage in the planetary world (..)', 'option_id': 'A', 'option_text': 'Planetary density will decrease.'},\r\n (...)]}\r\n```\r\n\r\nThe `options` value is always an list with 4 options, each one is a dict with `option_context`; `option_id` and `option_text`.\r\n\r\nI would like to overwrite the `option_context` of each instance of my dataset for a dpr result that I am developing. Then, I trained a model already and save it in a FAISS index\r\n```\r\ndpr_dataset = load_dataset(\r\n            \"text\",\r\n            data_files=ARC_CORPUS_TEXT,\r\n            cache_dir=CACHE_DIR,\r\n            split=\"train[:100%]\",\r\n        )\r\ndpr_dataset.load_faiss_index(\"embeddings\", f\"{ARC_CORPUS_FAISS}\")\r\ntorch.set_grad_enabled(False)\r\n```\r\n\r\nThen, as a processor of my dataset, I created a map function that calls the `dpr_dataset` for each _option_\r\n\r\n```\r\ndef generate_context(example):\r\n    question_text = example['question']\r\n    for option in example['options']:\r\n        question_with_option = question_text + \" \" + option['option_text']\r\n        tokenize_text =  question_tokenizer(question_with_option, return_tensors=\"pt\").to(device)\r\n        question_embed = (\r\n            question_encoder(**tokenize_text)\r\n        )[0][0].cpu().numpy()\r\n        _, retrieved_examples = dpr_dataset.get_nearest_examples(\r\n            \"embeddings\", question_embed, k=10\r\n        )\r\n    #    option[\"option_context\"] = retrieved_examples[\"text\"]\r\n    #    option[\"option_context\"] = \" \".join(option[\"option_context\"]).strip()\r\n    #result_dict = {\r\n    #    'example_id': example['example_id'],\r\n    #    'answer': example['answer'],\r\n    #    'question': question_text,\r\n        #options': example['options']\r\n    # }\r\n    return example\r\n```\r\n\r\nI intentionally commented on this portion of the code.\r\n\r\nBut when I call the `map` method, `ds_with_context = dataset.map(generate_context,load_from_cache_file=False)`\r\n\r\nIt calls the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-55-75a458ce205c> in <module>\r\n----> 1 ds_with_context = dataset.map(generate_context,load_from_cache_file=False)\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1257                 fn_kwargs=fn_kwargs,\r\n   1258                 new_fingerprint=new_fingerprint,\r\n-> 1259                 update_data=update_data,\r\n   1260             )\r\n   1261         else:\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    155         }\r\n    156         # apply actual function\r\n--> 157         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    158         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    159         # re-apply format to the output\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    387     file = StringIO()\r\n    388     with _no_cache_fields(obj):\r\n--> 389         dump(obj, file)\r\n    390     return file.getvalue()\r\n    391 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    359 def dump(obj, file):\r\n    360     \"\"\"pickle an object to a file\"\"\"\r\n--> 361     Pickler(file, recurse=True).dump(obj)\r\n    362     return\r\n    363 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    452             raise PicklingError(msg)\r\n    453         else:\r\n--> 454             StockPickler.dump(self, obj)\r\n    455         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    456         return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in save_function(pickler, obj)\r\n    554                 dill._dill._create_function,\r\n    555                 (obj.__code__, globs, obj.__name__, obj.__defaults__, obj.__closure__, obj.__dict__, fkwdefaults),\r\n--> 556                 obj=obj,\r\n    557             )\r\n    558         else:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    784         write(MARK)\r\n    785         for element in obj:\r\n--> 786             save(element)\r\n    787 \r\n    788         if id(obj) in memo:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    885                 k, v = tmp[0]\r\n    886                 save(k)\r\n--> 887                 save(v)\r\n    888                 write(SETITEM)\r\n    889             # else tmp is empty, and we're done\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    885                 k, v = tmp[0]\r\n    886                 save(k)\r\n--> 887                 save(v)\r\n    888                 write(SETITEM)\r\n    889             # else tmp is empty, and we're done\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle SwigPyObject objects\r\n```\r\n\r\nWhich I have no idea how to solve\/deal with it\r\n\r\n","comment_length":63,"text":"can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index \n So, I have the following instances in my dataset\r\n\r\n```\r\n{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of \r\nthis increase in rotation?', \r\n'answer': 'C', \r\n'example_id': 'ARCCH_Mercury_7175875', \r\n'options':[{'option_context': 'One effect of increased amperage in the planetary world (..)', 'option_id': 'A', 'option_text': 'Planetary density will decrease.'},\r\n (...)]}\r\n```\r\n\r\nThe `options` value is always an list with 4 options, each one is a dict with `option_context`; `option_id` and `option_text`.\r\n\r\nI would like to overwrite the `option_context` of each instance of my dataset for a dpr result that I am developing. Then, I trained a model already and save it in a FAISS index\r\n```\r\ndpr_dataset = load_dataset(\r\n            \"text\",\r\n            data_files=ARC_CORPUS_TEXT,\r\n            cache_dir=CACHE_DIR,\r\n            split=\"train[:100%]\",\r\n        )\r\ndpr_dataset.load_faiss_index(\"embeddings\", f\"{ARC_CORPUS_FAISS}\")\r\ntorch.set_grad_enabled(False)\r\n```\r\n\r\nThen, as a processor of my dataset, I created a map function that calls the `dpr_dataset` for each _option_\r\n\r\n```\r\ndef generate_context(example):\r\n    question_text = example['question']\r\n    for option in example['options']:\r\n        question_with_option = question_text + \" \" + option['option_text']\r\n        tokenize_text =  question_tokenizer(question_with_option, return_tensors=\"pt\").to(device)\r\n        question_embed = (\r\n            question_encoder(**tokenize_text)\r\n        )[0][0].cpu().numpy()\r\n        _, retrieved_examples = dpr_dataset.get_nearest_examples(\r\n            \"embeddings\", question_embed, k=10\r\n        )\r\n    #    option[\"option_context\"] = retrieved_examples[\"text\"]\r\n    #    option[\"option_context\"] = \" \".join(option[\"option_context\"]).strip()\r\n    #result_dict = {\r\n    #    'example_id': example['example_id'],\r\n    #    'answer': example['answer'],\r\n    #    'question': question_text,\r\n        #options': example['options']\r\n    # }\r\n    return example\r\n```\r\n\r\nI intentionally commented on this portion of the code.\r\n\r\nBut when I call the `map` method, `ds_with_context = dataset.map(generate_context,load_from_cache_file=False)`\r\n\r\nIt calls the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-55-75a458ce205c> in <module>\r\n----> 1 ds_with_context = dataset.map(generate_context,load_from_cache_file=False)\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1257                 fn_kwargs=fn_kwargs,\r\n   1258                 new_fingerprint=new_fingerprint,\r\n-> 1259                 update_data=update_data,\r\n   1260             )\r\n   1261         else:\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    155         }\r\n    156         # apply actual function\r\n--> 157         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    158         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    159         # re-apply format to the output\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    387     file = StringIO()\r\n    388     with _no_cache_fields(obj):\r\n--> 389         dump(obj, file)\r\n    390     return file.getvalue()\r\n    391 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    359 def dump(obj, file):\r\n    360     \"\"\"pickle an object to a file\"\"\"\r\n--> 361     Pickler(file, recurse=True).dump(obj)\r\n    362     return\r\n    363 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    452             raise PicklingError(msg)\r\n    453         else:\r\n--> 454             StockPickler.dump(self, obj)\r\n    455         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    456         return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in save_function(pickler, obj)\r\n    554                 dill._dill._create_function,\r\n    555                 (obj.__code__, globs, obj.__name__, obj.__defaults__, obj.__closure__, obj.__dict__, fkwdefaults),\r\n--> 556                 obj=obj,\r\n    557             )\r\n    558         else:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    784         write(MARK)\r\n    785         for element in obj:\r\n--> 786             save(element)\r\n    787 \r\n    788         if id(obj) in memo:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    885                 k, v = tmp[0]\r\n    886                 save(k)\r\n--> 887                 save(v)\r\n    888                 write(SETITEM)\r\n    889             # else tmp is empty, and we're done\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    885                 k, v = tmp[0]\r\n    886                 save(k)\r\n--> 887                 save(v)\r\n    888                 write(SETITEM)\r\n    889             # else tmp is empty, and we're done\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle SwigPyObject objects\r\n```\r\n\r\nWhich I have no idea how to solve\/deal with it\r\n\r\n \n Hi ! Indeed we used to require mapping functions to be picklable with `pickle` or `dill` in order to cache the resulting datasets. And FAISS indexes are not picklable unfortunately.\r\n\r\nBut since #1703 this is no longer required (the caching will simply be disabled). This change will be available in the next release of `datasets`, or you can also install `datasets` from source.","embeddings":[0.06904158,-0.3887840509,-0.0696357936,0.2468255013,0.1642265022,0.0277648028,0.1784276962,0.0541120283,0.4089650214,0.5476052165,-0.0500990711,0.4959974289,0.115793407,-0.1273343563,-0.267626375,0.0515106916,0.0849947408,0.0134227211,0.0216204002,-0.2425854504,-0.5357055068,-0.0484026559,-0.1984261274,0.0493396372,-0.0398413986,0.0180335399,-0.0332831852,0.0954476371,-0.0584461205,-0.4539258182,0.4045750499,0.257684648,0.2116607726,-0.0134229129,-0.0001266745,0.0679022521,0.2053468674,-0.021252377,-0.1646225452,0.086750403,-0.001575422,0.3058409989,0.2944130301,-0.354249239,-0.2179245949,-0.2550703585,-0.0504558645,-0.134555921,0.0680654272,0.1915952861,0.0670926794,0.1456792355,-0.1130726263,-0.1586890072,0.2215849906,-0.0109133879,-0.0620617904,0.1621502042,0.2719885111,0.3066604137,0.2544573843,0.2748863101,-0.2460707575,0.0368728079,-0.0725996867,0.0926401168,0.3378049135,0.0920075402,0.155671373,0.0969465524,0.2498126328,-0.2737818956,-0.4702080786,-0.4137730896,-0.061396569,-0.1567487866,-0.2627948523,-0.5509217381,-0.0172370858,0.2482920587,0.1254633218,0.1021858975,-0.0639564916,0.5212545395,0.4118613303,0.0359554738,0.0144240391,0.210933432,0.1066410914,-0.0576482825,0.1366034746,-0.1332527101,-0.0283684339,-0.1321200579,-0.2131488323,-0.1242142096,0.4846388996,-0.2354142964,0.245058015,0.0846258178,-0.1290584356,0.401452601,0.0691412911,0.107549645,0.1615174562,-0.118626982,-0.0983864889,0.0395724252,-0.117117323,0.0870361477,-0.1678486615,0.1142573133,0.0268994607,-0.0553063974,-0.631529808,-0.0129407663,0.3205948472,-0.1449027658,-0.1014321595,0.2506060302,-0.6014733911,0.34290874,0.1318702847,0.5309945941,0.0145976041,0.1166362464,0.1949887425,0.2470773607,-0.2960154414,0.4217503667,-0.2266984284,-0.0420274772,0.0346693248,-0.0588204525,-0.1218073964,0.4785673618,0.2849897146,-0.2606017888,0.097831212,-0.0784839243,0.0227258429,-0.1106626913,0.3640687764,0.1970125288,-0.0533642881,0.0772830248,0.1710085273,-0.0954404548,-0.1744895428,0.1174802035,0.1384007186,-0.1021388397,0.1903932989,0.0709636062,-0.1389862746,-0.1317988187,-0.2130447179,0.1211962253,-0.0878358111,0.2369573712,0.187057212,-0.1878823042,-0.0876030996,-0.1486045569,0.2090401649,0.5161315799,-0.2350449562,-0.0758556351,0.0244935937,0.1951778829,0.3501953185,0.2010139525,-0.326797992,0.256172508,0.0371250957,0.232217297,0.3163653016,-0.4490192533,-0.6639236212,-0.0901995748,-0.0756988227,0.0397732891,0.3317385614,0.0975898504,0.3703524768,0.2930629551,-0.1459473521,0.517279923,0.1696157157,0.0434674174,-0.2609554231,-0.344840616,0.0231414568,0.1270146668,-0.0733682066,0.1632385254,-0.0440482982,-0.2676261961,0.3614017367,-0.1503375173,-0.1021282971,0.2690655887,0.5216957331,0.2159873843,0.2130895555,-0.2032638788,-0.0844261721,0.1282851845,-0.1522228718,0.0228898693,-0.0782172158,-0.2090477645,-0.0135675445,0.1977491081,-0.3135472238,-0.1740483791,-0.0746521577,0.1529769897,0.2881989479,-0.1318628043,-0.0989452079,0.049343016,-0.339966625,0.2049800456,-0.5214464068,0.4270998836,-0.0935477465,-0.3137369156,-0.187201947,0.2583495975,0.0134848785,-0.1509953737,-0.158976227,0.1067028642,0.4748960435,-0.4063611031,0.1756881326,-0.3079205453,-0.0195666682,-0.1098886207,0.1134148315,0.0090821665,0.1443973482,0.2156585157,-0.009515278,0.6227203012,0.3724325597,0.1507984847,-0.0765272379,0.1891476214,0.1473806798,-0.1591730714,-0.052828595,-0.5078774691,-0.1828075796,0.3740829527,0.1135972589,0.0283298828,-0.4408773184,0.4054227173,0.2401168942,0.0603241175,-0.1184272468,0.1541838497,-0.0568144694,-0.0367468372,0.0292891059,-0.2139268368,0.105295524,0.3675740957,-0.2779199481,-0.3208730817,0.0087620532,-0.0421885885,0.0650617704,0.3736092448,-0.042365253,-0.2475410551,0.0935092494,-0.0054279529,-0.3840718567,-0.3060503006,-0.03811929,0.0701655,-0.1114427149,0.1743900478,-0.3749520183,-0.149213627,0.0872083902,-0.4578383565,0.191630736,-0.0981231406,0.1739199758,0.225857988,-0.2370437384,0.4466663599,0.1191981435,0.1598459333,0.0529770367,-0.1998583525,-0.1200764999,-0.1755337268,-0.4576171041,-0.0922895148,-0.2286255509,0.4021472335,0.1773949713,0.2158044875,-0.0975214317,-0.2927103043,0.0211023055,0.0287384707,0.1406073123,0.2020925283,0.1879706383,0.0467985831,-0.2393500805,-0.1135700494,0.0707941055,0.166129753,-0.2033124864,-0.1762379706,-0.043042507,-0.0814640969,0.1018627435,-0.2113613635,-0.2890201807,-0.2259377986,0.1515955329,0.0919152722,0.1095537916,0.0218211338,0.3725827038,0.0551367439,-0.2080986202,-0.108014144,-0.272910744,0.0094556408,-0.0735264048,-0.0414995626,-0.2861770391,-0.5065011382,-0.3398023546,-0.2370948046,0.1956369877,-0.2353993952,-0.3361153305,-0.3440832198,0.159289673,0.1556718647,0.140961498,0.376039356,0.2247824073,-0.0875745639,-0.1666593105,-0.3055527508,0.4445741475,0.1400023252,0.0882999972,0.1077333391,0.4067178071,-0.1840823442,0.5277171135,0.1247858405,-0.5209571123,0.3717174232,-0.0140295494,0.0158724152,-0.0675409809,-0.2851006985,0.1656641513,0.1925272942,-0.1816551536,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1805","title":"can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index","comments":"I totally forgot to answer this issue, I'm so sorry. \r\n\r\nI was able to get it working by installing `datasets` from source. Huge thanks!","body":"So, I have the following instances in my dataset\r\n\r\n```\r\n{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of \r\nthis increase in rotation?', \r\n'answer': 'C', \r\n'example_id': 'ARCCH_Mercury_7175875', \r\n'options':[{'option_context': 'One effect of increased amperage in the planetary world (..)', 'option_id': 'A', 'option_text': 'Planetary density will decrease.'},\r\n (...)]}\r\n```\r\n\r\nThe `options` value is always an list with 4 options, each one is a dict with `option_context`; `option_id` and `option_text`.\r\n\r\nI would like to overwrite the `option_context` of each instance of my dataset for a dpr result that I am developing. Then, I trained a model already and save it in a FAISS index\r\n```\r\ndpr_dataset = load_dataset(\r\n            \"text\",\r\n            data_files=ARC_CORPUS_TEXT,\r\n            cache_dir=CACHE_DIR,\r\n            split=\"train[:100%]\",\r\n        )\r\ndpr_dataset.load_faiss_index(\"embeddings\", f\"{ARC_CORPUS_FAISS}\")\r\ntorch.set_grad_enabled(False)\r\n```\r\n\r\nThen, as a processor of my dataset, I created a map function that calls the `dpr_dataset` for each _option_\r\n\r\n```\r\ndef generate_context(example):\r\n    question_text = example['question']\r\n    for option in example['options']:\r\n        question_with_option = question_text + \" \" + option['option_text']\r\n        tokenize_text =  question_tokenizer(question_with_option, return_tensors=\"pt\").to(device)\r\n        question_embed = (\r\n            question_encoder(**tokenize_text)\r\n        )[0][0].cpu().numpy()\r\n        _, retrieved_examples = dpr_dataset.get_nearest_examples(\r\n            \"embeddings\", question_embed, k=10\r\n        )\r\n    #    option[\"option_context\"] = retrieved_examples[\"text\"]\r\n    #    option[\"option_context\"] = \" \".join(option[\"option_context\"]).strip()\r\n    #result_dict = {\r\n    #    'example_id': example['example_id'],\r\n    #    'answer': example['answer'],\r\n    #    'question': question_text,\r\n        #options': example['options']\r\n    # }\r\n    return example\r\n```\r\n\r\nI intentionally commented on this portion of the code.\r\n\r\nBut when I call the `map` method, `ds_with_context = dataset.map(generate_context,load_from_cache_file=False)`\r\n\r\nIt calls the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-55-75a458ce205c> in <module>\r\n----> 1 ds_with_context = dataset.map(generate_context,load_from_cache_file=False)\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1257                 fn_kwargs=fn_kwargs,\r\n   1258                 new_fingerprint=new_fingerprint,\r\n-> 1259                 update_data=update_data,\r\n   1260             )\r\n   1261         else:\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    155         }\r\n    156         # apply actual function\r\n--> 157         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    158         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    159         # re-apply format to the output\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    387     file = StringIO()\r\n    388     with _no_cache_fields(obj):\r\n--> 389         dump(obj, file)\r\n    390     return file.getvalue()\r\n    391 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    359 def dump(obj, file):\r\n    360     \"\"\"pickle an object to a file\"\"\"\r\n--> 361     Pickler(file, recurse=True).dump(obj)\r\n    362     return\r\n    363 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    452             raise PicklingError(msg)\r\n    453         else:\r\n--> 454             StockPickler.dump(self, obj)\r\n    455         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    456         return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in save_function(pickler, obj)\r\n    554                 dill._dill._create_function,\r\n    555                 (obj.__code__, globs, obj.__name__, obj.__defaults__, obj.__closure__, obj.__dict__, fkwdefaults),\r\n--> 556                 obj=obj,\r\n    557             )\r\n    558         else:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    784         write(MARK)\r\n    785         for element in obj:\r\n--> 786             save(element)\r\n    787 \r\n    788         if id(obj) in memo:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    885                 k, v = tmp[0]\r\n    886                 save(k)\r\n--> 887                 save(v)\r\n    888                 write(SETITEM)\r\n    889             # else tmp is empty, and we're done\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    885                 k, v = tmp[0]\r\n    886                 save(k)\r\n--> 887                 save(v)\r\n    888                 write(SETITEM)\r\n    889             # else tmp is empty, and we're done\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle SwigPyObject objects\r\n```\r\n\r\nWhich I have no idea how to solve\/deal with it\r\n\r\n","comment_length":24,"text":"can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index \n So, I have the following instances in my dataset\r\n\r\n```\r\n{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of \r\nthis increase in rotation?', \r\n'answer': 'C', \r\n'example_id': 'ARCCH_Mercury_7175875', \r\n'options':[{'option_context': 'One effect of increased amperage in the planetary world (..)', 'option_id': 'A', 'option_text': 'Planetary density will decrease.'},\r\n (...)]}\r\n```\r\n\r\nThe `options` value is always an list with 4 options, each one is a dict with `option_context`; `option_id` and `option_text`.\r\n\r\nI would like to overwrite the `option_context` of each instance of my dataset for a dpr result that I am developing. Then, I trained a model already and save it in a FAISS index\r\n```\r\ndpr_dataset = load_dataset(\r\n            \"text\",\r\n            data_files=ARC_CORPUS_TEXT,\r\n            cache_dir=CACHE_DIR,\r\n            split=\"train[:100%]\",\r\n        )\r\ndpr_dataset.load_faiss_index(\"embeddings\", f\"{ARC_CORPUS_FAISS}\")\r\ntorch.set_grad_enabled(False)\r\n```\r\n\r\nThen, as a processor of my dataset, I created a map function that calls the `dpr_dataset` for each _option_\r\n\r\n```\r\ndef generate_context(example):\r\n    question_text = example['question']\r\n    for option in example['options']:\r\n        question_with_option = question_text + \" \" + option['option_text']\r\n        tokenize_text =  question_tokenizer(question_with_option, return_tensors=\"pt\").to(device)\r\n        question_embed = (\r\n            question_encoder(**tokenize_text)\r\n        )[0][0].cpu().numpy()\r\n        _, retrieved_examples = dpr_dataset.get_nearest_examples(\r\n            \"embeddings\", question_embed, k=10\r\n        )\r\n    #    option[\"option_context\"] = retrieved_examples[\"text\"]\r\n    #    option[\"option_context\"] = \" \".join(option[\"option_context\"]).strip()\r\n    #result_dict = {\r\n    #    'example_id': example['example_id'],\r\n    #    'answer': example['answer'],\r\n    #    'question': question_text,\r\n        #options': example['options']\r\n    # }\r\n    return example\r\n```\r\n\r\nI intentionally commented on this portion of the code.\r\n\r\nBut when I call the `map` method, `ds_with_context = dataset.map(generate_context,load_from_cache_file=False)`\r\n\r\nIt calls the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-55-75a458ce205c> in <module>\r\n----> 1 ds_with_context = dataset.map(generate_context,load_from_cache_file=False)\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1257                 fn_kwargs=fn_kwargs,\r\n   1258                 new_fingerprint=new_fingerprint,\r\n-> 1259                 update_data=update_data,\r\n   1260             )\r\n   1261         else:\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    155         }\r\n    156         # apply actual function\r\n--> 157         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    158         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    159         # re-apply format to the output\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    387     file = StringIO()\r\n    388     with _no_cache_fields(obj):\r\n--> 389         dump(obj, file)\r\n    390     return file.getvalue()\r\n    391 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    359 def dump(obj, file):\r\n    360     \"\"\"pickle an object to a file\"\"\"\r\n--> 361     Pickler(file, recurse=True).dump(obj)\r\n    362     return\r\n    363 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    452             raise PicklingError(msg)\r\n    453         else:\r\n--> 454             StockPickler.dump(self, obj)\r\n    455         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    456         return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in save_function(pickler, obj)\r\n    554                 dill._dill._create_function,\r\n    555                 (obj.__code__, globs, obj.__name__, obj.__defaults__, obj.__closure__, obj.__dict__, fkwdefaults),\r\n--> 556                 obj=obj,\r\n    557             )\r\n    558         else:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    784         write(MARK)\r\n    785         for element in obj:\r\n--> 786             save(element)\r\n    787 \r\n    788         if id(obj) in memo:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    885                 k, v = tmp[0]\r\n    886                 save(k)\r\n--> 887                 save(v)\r\n    888                 write(SETITEM)\r\n    889             # else tmp is empty, and we're done\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    880                 for k, v in tmp:\r\n    881                     save(k)\r\n--> 882                     save(v)\r\n    883                 write(SETITEMS)\r\n    884             elif n:\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    939             # we only care about session the first pass thru\r\n    940             pickler._session = False\r\n--> 941         StockPickler.save_dict(pickler, obj)\r\n    942         log.info(\"# D2\")\r\n    943     return\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    854 \r\n    855         self.memoize(obj)\r\n--> 856         self._batch_setitems(obj.items())\r\n    857 \r\n    858     dispatch[dict] = save_dict\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    885                 k, v = tmp[0]\r\n    886                 save(k)\r\n--> 887                 save(v)\r\n    888                 write(SETITEM)\r\n    889             # else tmp is empty, and we're done\r\n\r\n\/usr\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle SwigPyObject objects\r\n```\r\n\r\nWhich I have no idea how to solve\/deal with it\r\n\r\n \n I totally forgot to answer this issue, I'm so sorry. \r\n\r\nI was able to get it working by installing `datasets` from source. Huge thanks!","embeddings":[0.06904158,-0.3887840509,-0.0696357936,0.2468255013,0.1642265022,0.0277648028,0.1784276962,0.0541120283,0.4089650214,0.5476052165,-0.0500990711,0.4959974289,0.115793407,-0.1273343563,-0.267626375,0.0515106916,0.0849947408,0.0134227211,0.0216204002,-0.2425854504,-0.5357055068,-0.0484026559,-0.1984261274,0.0493396372,-0.0398413986,0.0180335399,-0.0332831852,0.0954476371,-0.0584461205,-0.4539258182,0.4045750499,0.257684648,0.2116607726,-0.0134229129,-0.0001266745,0.0679022521,0.2053468674,-0.021252377,-0.1646225452,0.086750403,-0.001575422,0.3058409989,0.2944130301,-0.354249239,-0.2179245949,-0.2550703585,-0.0504558645,-0.134555921,0.0680654272,0.1915952861,0.0670926794,0.1456792355,-0.1130726263,-0.1586890072,0.2215849906,-0.0109133879,-0.0620617904,0.1621502042,0.2719885111,0.3066604137,0.2544573843,0.2748863101,-0.2460707575,0.0368728079,-0.0725996867,0.0926401168,0.3378049135,0.0920075402,0.155671373,0.0969465524,0.2498126328,-0.2737818956,-0.4702080786,-0.4137730896,-0.061396569,-0.1567487866,-0.2627948523,-0.5509217381,-0.0172370858,0.2482920587,0.1254633218,0.1021858975,-0.0639564916,0.5212545395,0.4118613303,0.0359554738,0.0144240391,0.210933432,0.1066410914,-0.0576482825,0.1366034746,-0.1332527101,-0.0283684339,-0.1321200579,-0.2131488323,-0.1242142096,0.4846388996,-0.2354142964,0.245058015,0.0846258178,-0.1290584356,0.401452601,0.0691412911,0.107549645,0.1615174562,-0.118626982,-0.0983864889,0.0395724252,-0.117117323,0.0870361477,-0.1678486615,0.1142573133,0.0268994607,-0.0553063974,-0.631529808,-0.0129407663,0.3205948472,-0.1449027658,-0.1014321595,0.2506060302,-0.6014733911,0.34290874,0.1318702847,0.5309945941,0.0145976041,0.1166362464,0.1949887425,0.2470773607,-0.2960154414,0.4217503667,-0.2266984284,-0.0420274772,0.0346693248,-0.0588204525,-0.1218073964,0.4785673618,0.2849897146,-0.2606017888,0.097831212,-0.0784839243,0.0227258429,-0.1106626913,0.3640687764,0.1970125288,-0.0533642881,0.0772830248,0.1710085273,-0.0954404548,-0.1744895428,0.1174802035,0.1384007186,-0.1021388397,0.1903932989,0.0709636062,-0.1389862746,-0.1317988187,-0.2130447179,0.1211962253,-0.0878358111,0.2369573712,0.187057212,-0.1878823042,-0.0876030996,-0.1486045569,0.2090401649,0.5161315799,-0.2350449562,-0.0758556351,0.0244935937,0.1951778829,0.3501953185,0.2010139525,-0.326797992,0.256172508,0.0371250957,0.232217297,0.3163653016,-0.4490192533,-0.6639236212,-0.0901995748,-0.0756988227,0.0397732891,0.3317385614,0.0975898504,0.3703524768,0.2930629551,-0.1459473521,0.517279923,0.1696157157,0.0434674174,-0.2609554231,-0.344840616,0.0231414568,0.1270146668,-0.0733682066,0.1632385254,-0.0440482982,-0.2676261961,0.3614017367,-0.1503375173,-0.1021282971,0.2690655887,0.5216957331,0.2159873843,0.2130895555,-0.2032638788,-0.0844261721,0.1282851845,-0.1522228718,0.0228898693,-0.0782172158,-0.2090477645,-0.0135675445,0.1977491081,-0.3135472238,-0.1740483791,-0.0746521577,0.1529769897,0.2881989479,-0.1318628043,-0.0989452079,0.049343016,-0.339966625,0.2049800456,-0.5214464068,0.4270998836,-0.0935477465,-0.3137369156,-0.187201947,0.2583495975,0.0134848785,-0.1509953737,-0.158976227,0.1067028642,0.4748960435,-0.4063611031,0.1756881326,-0.3079205453,-0.0195666682,-0.1098886207,0.1134148315,0.0090821665,0.1443973482,0.2156585157,-0.009515278,0.6227203012,0.3724325597,0.1507984847,-0.0765272379,0.1891476214,0.1473806798,-0.1591730714,-0.052828595,-0.5078774691,-0.1828075796,0.3740829527,0.1135972589,0.0283298828,-0.4408773184,0.4054227173,0.2401168942,0.0603241175,-0.1184272468,0.1541838497,-0.0568144694,-0.0367468372,0.0292891059,-0.2139268368,0.105295524,0.3675740957,-0.2779199481,-0.3208730817,0.0087620532,-0.0421885885,0.0650617704,0.3736092448,-0.042365253,-0.2475410551,0.0935092494,-0.0054279529,-0.3840718567,-0.3060503006,-0.03811929,0.0701655,-0.1114427149,0.1743900478,-0.3749520183,-0.149213627,0.0872083902,-0.4578383565,0.191630736,-0.0981231406,0.1739199758,0.225857988,-0.2370437384,0.4466663599,0.1191981435,0.1598459333,0.0529770367,-0.1998583525,-0.1200764999,-0.1755337268,-0.4576171041,-0.0922895148,-0.2286255509,0.4021472335,0.1773949713,0.2158044875,-0.0975214317,-0.2927103043,0.0211023055,0.0287384707,0.1406073123,0.2020925283,0.1879706383,0.0467985831,-0.2393500805,-0.1135700494,0.0707941055,0.166129753,-0.2033124864,-0.1762379706,-0.043042507,-0.0814640969,0.1018627435,-0.2113613635,-0.2890201807,-0.2259377986,0.1515955329,0.0919152722,0.1095537916,0.0218211338,0.3725827038,0.0551367439,-0.2080986202,-0.108014144,-0.272910744,0.0094556408,-0.0735264048,-0.0414995626,-0.2861770391,-0.5065011382,-0.3398023546,-0.2370948046,0.1956369877,-0.2353993952,-0.3361153305,-0.3440832198,0.159289673,0.1556718647,0.140961498,0.376039356,0.2247824073,-0.0875745639,-0.1666593105,-0.3055527508,0.4445741475,0.1400023252,0.0882999972,0.1077333391,0.4067178071,-0.1840823442,0.5277171135,0.1247858405,-0.5209571123,0.3717174232,-0.0140295494,0.0158724152,-0.0675409809,-0.2851006985,0.1656641513,0.1925272942,-0.1816551536,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1803","title":"Querying examples from big datasets is slower than small datasets","comments":"Hello, @lhoestq \/ @gaceladri  : We have been seeing similar behavior with bigger datasets, where querying time increases. Are you folks aware of any solution that fixes this problem yet?  ","body":"After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed.","comment_length":30,"text":"Querying examples from big datasets is slower than small datasets \n After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed. \n Hello, @lhoestq \/ @gaceladri  : We have been seeing similar behavior with bigger datasets, where querying time increases. Are you folks aware of any solution that fixes this problem yet?  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1803","title":"Querying examples from big datasets is slower than small datasets","comments":"Hi ! I'm pretty sure that it can be fixed by using the Arrow IPC file format instead of the raw streaming format but I haven't tested yet.\r\nI'll take a look at it soon and let you know","body":"After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed.","comment_length":39,"text":"Querying examples from big datasets is slower than small datasets \n After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed. \n Hi ! I'm pretty sure that it can be fixed by using the Arrow IPC file format instead of the raw streaming format but I haven't tested yet.\r\nI'll take a look at it soon and let you know","embeddings":[-0.4054936171,0.0586443171,-0.0799714625,0.1315324455,-0.0796937943,-0.1776915044,0.2441749275,0.4918096364,-0.231398806,0.1801942289,0.0814933404,0.055089511,0.1250858605,-0.1007566378,0.1307256222,-0.273406297,0.0807416663,0.1248459145,-0.0751322359,-0.0345290378,0.0240299944,-0.1902869195,-0.3486269414,0.0184702277,-0.0663269982,-0.2240722179,-0.2307065576,0.1072061211,-0.1658504605,-0.417394191,-0.0595365614,-0.1528194398,0.2299299836,0.4270310104,-0.0001130733,0.0164052267,0.2279306203,0.0894177258,-0.1997470111,0.1829340011,0.1748079509,-0.2838544846,0.0868743137,-0.4097666144,-0.047154028,-0.2971978486,0.0934281796,0.0189011656,-0.0412533842,0.3089759946,0.1403642744,0.5131544471,-0.1917047799,-0.1336632371,0.623067379,0.2051521689,-0.3506302536,0.5299629569,0.5806369781,-0.0487995744,-0.3053155243,0.1361406893,-0.0814444497,0.2701411545,0.0370220914,0.1979730874,-0.1128048003,0.1795920134,0.1238104329,0.4088888764,0.3083902597,-0.0893917903,-0.314886421,-0.2920183241,-0.0569284223,-0.2332935333,0.0203074124,0.3264197111,-0.258379221,0.2713423073,-0.3137764633,0.1547778845,-0.105650574,0.1795520484,-0.1511591077,-0.0686156824,0.1150508747,0.1985104829,0.0937960669,0.0133130373,0.151192978,-0.1696210504,0.0284059383,-0.0521108173,-0.2883044779,-0.0881545097,0.1556635648,0.1755786091,0.2685056031,0.1593101472,0.4223206639,0.1182835624,0.2651936114,0.0943621695,0.0451356918,-0.0090215756,-0.2695650458,-0.000978097,0.3478802443,0.2753004134,-0.2945160568,0.0208778251,-0.2383211255,-0.1844542176,0.1815629154,-0.1973295957,-0.0274535324,-0.1337165982,0.0043231687,-0.0873047113,-0.3421146274,-0.1278225183,-0.0265576821,0.4784684777,-0.1834655702,-0.0265724137,-0.2126669139,0.122223787,-0.3353427052,-0.2540251613,-0.1243124604,-0.1883357316,-0.1600804329,0.2303074747,0.1097953022,0.0560976602,0.3020737767,0.058540564,-0.1985150278,0.2419624776,0.3923716843,-0.180865705,0.1044564322,0.0197549015,0.0112557681,0.0635319874,-0.1637397707,0.2447643578,-0.3910897076,0.1972706318,-0.1953568757,-0.3384783268,0.0306392331,0.0919930637,-0.1056957245,-0.0997598916,-0.2270807326,0.2462454289,-0.0694222674,-0.1997463107,-0.0774374455,-0.0941937268,0.0402900465,-0.1531330943,0.1413004994,0.0776268393,-0.2506591082,0.2018505782,-0.0451865606,0.3970346451,0.2543899119,0.5308368206,-0.5031036139,0.1623610705,-0.132778585,0.2966162264,0.2320235521,-0.0281323362,-0.6667606235,0.4973528087,-0.004258499,0.0241891816,0.2724842727,0.1617120504,0.3516778648,0.1078481972,-0.0490624495,0.3837120235,0.1839479655,0.2279311866,-0.3400998116,-0.2303100824,-0.0487276353,0.347974956,-0.3244735301,-0.1571216136,0.3112509251,-0.3645975888,0.4906598926,-0.2065642923,-0.1836265028,0.2509784997,0.0870239511,-0.2777459025,0.1183484569,0.0964874178,0.0859434977,0.242176488,-0.1894870698,-0.0184647031,0.0450902246,-0.1888088584,-0.0722152367,0.0619630329,-0.0552603528,-0.0473853871,-0.0448970497,-0.0918301344,0.3306871057,-0.0035315091,0.164733693,-0.1632062942,-0.2693369985,-0.0094458498,-0.2626185715,0.0792928189,0.0950683355,-0.1213107258,0.0093064588,-0.2492527664,-0.0884235278,-0.0017328389,-0.1519371569,0.3275276423,0.1502290517,0.1502473801,0.223530978,0.1522480249,0.1419565082,-0.0849310458,0.3879742324,0.2968659997,0.075315088,0.0365262404,-0.4053477347,0.5263332129,0.3933131695,0.3160547614,0.2904030383,-0.3733905554,0.1644445658,0.0483795069,0.1105019897,0.4106391072,0.2937985361,0.2421562374,0.5489552617,0.1663255692,-0.3161974847,0.1155823469,0.4506140351,-0.2496014982,0.0180420037,0.1873041689,-0.4217130542,-0.1856174171,0.3702158034,-0.150969401,0.3265801966,0.3312532008,0.1606444567,-0.1239553019,0.0274431203,-0.2904406786,0.2625498772,0.0967946574,0.1414081901,-0.2201083004,0.4368430972,-0.0920058116,-0.2460109293,-0.3815978765,-0.0392074697,0.192131713,-0.1264010072,0.3072988987,-0.0982985049,0.053618893,-0.0324512795,0.1308004558,0.0790725499,-0.0865352377,0.0450386032,0.1935409456,-0.181210503,-0.1583380401,-0.0551914535,0.2278954685,0.2617880702,-0.2087610364,-0.0017746408,-0.0716872513,-0.3983874917,0.0803131238,0.2775991857,0.1493917406,0.1450458467,-0.0469177924,-0.1854478121,-0.2801742256,-0.3152382076,-0.0065734186,0.0799065456,0.2866188288,-0.0709002763,0.0763942152,-0.324452132,-0.1447660178,0.0637101158,-0.232910037,-0.1513257623,0.2760073543,-0.1807471663,0.0008984554,0.088783294,-0.3163755536,-0.1673076302,-0.3001769483,0.3784309924,0.1727609485,0.2083455771,0.1075554192,0.3943434358,0.1223714128,0.0096602468,0.1228192747,0.0379166566,-0.4149514139,0.5190879107,-0.180263564,-0.4500640631,-0.4888216555,-0.2429303229,-0.1641484052,0.4524995983,-0.5341605544,-0.0311471894,-0.5311310291,0.4127760828,0.1235397533,0.1021677181,0.049603384,-0.2131572515,-0.2089558542,0.1292297095,0.0826174915,-0.0064191199,-0.087850906,0.2021380067,-0.0615213774,0.0337112471,-0.0098683694,0.7692419887,0.0689440593,-0.012904753,0.2635357678,-0.0949033275,0.051906649,-0.1647451371,0.1458878964,0.0905036107,-0.2608803511,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1803","title":"Querying examples from big datasets is slower than small datasets","comments":"My workaround is to shard the dataset into splits in my ssd disk and feed the data in different training sessions. But it is a bit of a pain when we need to reload the last training session with the rest of the split with the Trainer in transformers.\r\n\r\nI mean, when I split the training and then reloads the model and optimizer, it not gets the correct global_status of the optimizer, so I need to hardcode some things. I'm planning to open an issue in transformers and think about it.\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset(\"bookcorpus\", split=\"train[:25%]\")\r\nwikicorpus = load_dataset(\"wikicorpus\", split=\"train[:25%]\")\r\nopenwebtext = load_dataset(\"openwebtext\", split=\"train[:25%]\")\r\n\r\nbig_dataset = datasets.concatenate_datasets([wikicorpus, openwebtext, book_corpus])\r\nbig_dataset.shuffle(seed=42)\r\nbig_dataset = big_dataset.map(encode, batched=True, num_proc=20, load_from_cache_file=True, writer_batch_size=5000)\r\nbig_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/linear_bert\",\r\n    overwrite_output_dir=True,\r\n    per_device_train_batch_size=71,\r\n    save_steps=500,\r\n    save_total_limit=10,\r\n    logging_first_step=True,\r\n    logging_steps=100,\r\n    gradient_accumulation_steps=9,\r\n    fp16=True,\r\n    dataloader_num_workers=20,\r\n    warmup_steps=24000,\r\n    learning_rate=0.000545205002870214,\r\n    adam_epsilon=1e-6,\r\n    adam_beta2=0.98,\r\n    weight_decay=0.01,\r\n    max_steps=138974,  # the total number of steps after concatenating 100% datasets\r\n    max_grad_norm=1.0,\r\n)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=big_dataset,\r\n    tokenizer=tokenizer))\r\n```\r\n\r\nI do one training pass with the total steps of this shard and I use len(bbig)\/batchsize to stop the training (hardcoded in the trainer.py) when I pass over all the examples in this split.\r\n\r\nNow Im working, I will edit the comment with a more elaborated answer when I left the work.","body":"After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed.","comment_length":218,"text":"Querying examples from big datasets is slower than small datasets \n After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed. \n My workaround is to shard the dataset into splits in my ssd disk and feed the data in different training sessions. But it is a bit of a pain when we need to reload the last training session with the rest of the split with the Trainer in transformers.\r\n\r\nI mean, when I split the training and then reloads the model and optimizer, it not gets the correct global_status of the optimizer, so I need to hardcode some things. I'm planning to open an issue in transformers and think about it.\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset(\"bookcorpus\", split=\"train[:25%]\")\r\nwikicorpus = load_dataset(\"wikicorpus\", split=\"train[:25%]\")\r\nopenwebtext = load_dataset(\"openwebtext\", split=\"train[:25%]\")\r\n\r\nbig_dataset = datasets.concatenate_datasets([wikicorpus, openwebtext, book_corpus])\r\nbig_dataset.shuffle(seed=42)\r\nbig_dataset = big_dataset.map(encode, batched=True, num_proc=20, load_from_cache_file=True, writer_batch_size=5000)\r\nbig_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/linear_bert\",\r\n    overwrite_output_dir=True,\r\n    per_device_train_batch_size=71,\r\n    save_steps=500,\r\n    save_total_limit=10,\r\n    logging_first_step=True,\r\n    logging_steps=100,\r\n    gradient_accumulation_steps=9,\r\n    fp16=True,\r\n    dataloader_num_workers=20,\r\n    warmup_steps=24000,\r\n    learning_rate=0.000545205002870214,\r\n    adam_epsilon=1e-6,\r\n    adam_beta2=0.98,\r\n    weight_decay=0.01,\r\n    max_steps=138974,  # the total number of steps after concatenating 100% datasets\r\n    max_grad_norm=1.0,\r\n)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=big_dataset,\r\n    tokenizer=tokenizer))\r\n```\r\n\r\nI do one training pass with the total steps of this shard and I use len(bbig)\/batchsize to stop the training (hardcoded in the trainer.py) when I pass over all the examples in this split.\r\n\r\nNow Im working, I will edit the comment with a more elaborated answer when I left the work.","embeddings":[-0.4054936171,0.0586443171,-0.0799714625,0.1315324455,-0.0796937943,-0.1776915044,0.2441749275,0.4918096364,-0.231398806,0.1801942289,0.0814933404,0.055089511,0.1250858605,-0.1007566378,0.1307256222,-0.273406297,0.0807416663,0.1248459145,-0.0751322359,-0.0345290378,0.0240299944,-0.1902869195,-0.3486269414,0.0184702277,-0.0663269982,-0.2240722179,-0.2307065576,0.1072061211,-0.1658504605,-0.417394191,-0.0595365614,-0.1528194398,0.2299299836,0.4270310104,-0.0001130733,0.0164052267,0.2279306203,0.0894177258,-0.1997470111,0.1829340011,0.1748079509,-0.2838544846,0.0868743137,-0.4097666144,-0.047154028,-0.2971978486,0.0934281796,0.0189011656,-0.0412533842,0.3089759946,0.1403642744,0.5131544471,-0.1917047799,-0.1336632371,0.623067379,0.2051521689,-0.3506302536,0.5299629569,0.5806369781,-0.0487995744,-0.3053155243,0.1361406893,-0.0814444497,0.2701411545,0.0370220914,0.1979730874,-0.1128048003,0.1795920134,0.1238104329,0.4088888764,0.3083902597,-0.0893917903,-0.314886421,-0.2920183241,-0.0569284223,-0.2332935333,0.0203074124,0.3264197111,-0.258379221,0.2713423073,-0.3137764633,0.1547778845,-0.105650574,0.1795520484,-0.1511591077,-0.0686156824,0.1150508747,0.1985104829,0.0937960669,0.0133130373,0.151192978,-0.1696210504,0.0284059383,-0.0521108173,-0.2883044779,-0.0881545097,0.1556635648,0.1755786091,0.2685056031,0.1593101472,0.4223206639,0.1182835624,0.2651936114,0.0943621695,0.0451356918,-0.0090215756,-0.2695650458,-0.000978097,0.3478802443,0.2753004134,-0.2945160568,0.0208778251,-0.2383211255,-0.1844542176,0.1815629154,-0.1973295957,-0.0274535324,-0.1337165982,0.0043231687,-0.0873047113,-0.3421146274,-0.1278225183,-0.0265576821,0.4784684777,-0.1834655702,-0.0265724137,-0.2126669139,0.122223787,-0.3353427052,-0.2540251613,-0.1243124604,-0.1883357316,-0.1600804329,0.2303074747,0.1097953022,0.0560976602,0.3020737767,0.058540564,-0.1985150278,0.2419624776,0.3923716843,-0.180865705,0.1044564322,0.0197549015,0.0112557681,0.0635319874,-0.1637397707,0.2447643578,-0.3910897076,0.1972706318,-0.1953568757,-0.3384783268,0.0306392331,0.0919930637,-0.1056957245,-0.0997598916,-0.2270807326,0.2462454289,-0.0694222674,-0.1997463107,-0.0774374455,-0.0941937268,0.0402900465,-0.1531330943,0.1413004994,0.0776268393,-0.2506591082,0.2018505782,-0.0451865606,0.3970346451,0.2543899119,0.5308368206,-0.5031036139,0.1623610705,-0.132778585,0.2966162264,0.2320235521,-0.0281323362,-0.6667606235,0.4973528087,-0.004258499,0.0241891816,0.2724842727,0.1617120504,0.3516778648,0.1078481972,-0.0490624495,0.3837120235,0.1839479655,0.2279311866,-0.3400998116,-0.2303100824,-0.0487276353,0.347974956,-0.3244735301,-0.1571216136,0.3112509251,-0.3645975888,0.4906598926,-0.2065642923,-0.1836265028,0.2509784997,0.0870239511,-0.2777459025,0.1183484569,0.0964874178,0.0859434977,0.242176488,-0.1894870698,-0.0184647031,0.0450902246,-0.1888088584,-0.0722152367,0.0619630329,-0.0552603528,-0.0473853871,-0.0448970497,-0.0918301344,0.3306871057,-0.0035315091,0.164733693,-0.1632062942,-0.2693369985,-0.0094458498,-0.2626185715,0.0792928189,0.0950683355,-0.1213107258,0.0093064588,-0.2492527664,-0.0884235278,-0.0017328389,-0.1519371569,0.3275276423,0.1502290517,0.1502473801,0.223530978,0.1522480249,0.1419565082,-0.0849310458,0.3879742324,0.2968659997,0.075315088,0.0365262404,-0.4053477347,0.5263332129,0.3933131695,0.3160547614,0.2904030383,-0.3733905554,0.1644445658,0.0483795069,0.1105019897,0.4106391072,0.2937985361,0.2421562374,0.5489552617,0.1663255692,-0.3161974847,0.1155823469,0.4506140351,-0.2496014982,0.0180420037,0.1873041689,-0.4217130542,-0.1856174171,0.3702158034,-0.150969401,0.3265801966,0.3312532008,0.1606444567,-0.1239553019,0.0274431203,-0.2904406786,0.2625498772,0.0967946574,0.1414081901,-0.2201083004,0.4368430972,-0.0920058116,-0.2460109293,-0.3815978765,-0.0392074697,0.192131713,-0.1264010072,0.3072988987,-0.0982985049,0.053618893,-0.0324512795,0.1308004558,0.0790725499,-0.0865352377,0.0450386032,0.1935409456,-0.181210503,-0.1583380401,-0.0551914535,0.2278954685,0.2617880702,-0.2087610364,-0.0017746408,-0.0716872513,-0.3983874917,0.0803131238,0.2775991857,0.1493917406,0.1450458467,-0.0469177924,-0.1854478121,-0.2801742256,-0.3152382076,-0.0065734186,0.0799065456,0.2866188288,-0.0709002763,0.0763942152,-0.324452132,-0.1447660178,0.0637101158,-0.232910037,-0.1513257623,0.2760073543,-0.1807471663,0.0008984554,0.088783294,-0.3163755536,-0.1673076302,-0.3001769483,0.3784309924,0.1727609485,0.2083455771,0.1075554192,0.3943434358,0.1223714128,0.0096602468,0.1228192747,0.0379166566,-0.4149514139,0.5190879107,-0.180263564,-0.4500640631,-0.4888216555,-0.2429303229,-0.1641484052,0.4524995983,-0.5341605544,-0.0311471894,-0.5311310291,0.4127760828,0.1235397533,0.1021677181,0.049603384,-0.2131572515,-0.2089558542,0.1292297095,0.0826174915,-0.0064191199,-0.087850906,0.2021380067,-0.0615213774,0.0337112471,-0.0098683694,0.7692419887,0.0689440593,-0.012904753,0.2635357678,-0.0949033275,0.051906649,-0.1647451371,0.1458878964,0.0905036107,-0.2608803511,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1803","title":"Querying examples from big datasets is slower than small datasets","comments":"I just tested and using the Arrow File format doesn't improve the speed... This will need further investigation.\r\n\r\nMy guess is that it has to iterate over the record batches or chunks of a ChunkedArray in order to retrieve elements.\r\n\r\nHowever if we know in advance in which chunk the element is, and at what index it is, then we can access it instantaneously. But this requires dealing with the chunked arrays instead of the pyarrow Table directly which is not practical.","body":"After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed.","comment_length":82,"text":"Querying examples from big datasets is slower than small datasets \n After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed. \n I just tested and using the Arrow File format doesn't improve the speed... This will need further investigation.\r\n\r\nMy guess is that it has to iterate over the record batches or chunks of a ChunkedArray in order to retrieve elements.\r\n\r\nHowever if we know in advance in which chunk the element is, and at what index it is, then we can access it instantaneously. But this requires dealing with the chunked arrays instead of the pyarrow Table directly which is not practical.","embeddings":[-0.4054936171,0.0586443171,-0.0799714625,0.1315324455,-0.0796937943,-0.1776915044,0.2441749275,0.4918096364,-0.231398806,0.1801942289,0.0814933404,0.055089511,0.1250858605,-0.1007566378,0.1307256222,-0.273406297,0.0807416663,0.1248459145,-0.0751322359,-0.0345290378,0.0240299944,-0.1902869195,-0.3486269414,0.0184702277,-0.0663269982,-0.2240722179,-0.2307065576,0.1072061211,-0.1658504605,-0.417394191,-0.0595365614,-0.1528194398,0.2299299836,0.4270310104,-0.0001130733,0.0164052267,0.2279306203,0.0894177258,-0.1997470111,0.1829340011,0.1748079509,-0.2838544846,0.0868743137,-0.4097666144,-0.047154028,-0.2971978486,0.0934281796,0.0189011656,-0.0412533842,0.3089759946,0.1403642744,0.5131544471,-0.1917047799,-0.1336632371,0.623067379,0.2051521689,-0.3506302536,0.5299629569,0.5806369781,-0.0487995744,-0.3053155243,0.1361406893,-0.0814444497,0.2701411545,0.0370220914,0.1979730874,-0.1128048003,0.1795920134,0.1238104329,0.4088888764,0.3083902597,-0.0893917903,-0.314886421,-0.2920183241,-0.0569284223,-0.2332935333,0.0203074124,0.3264197111,-0.258379221,0.2713423073,-0.3137764633,0.1547778845,-0.105650574,0.1795520484,-0.1511591077,-0.0686156824,0.1150508747,0.1985104829,0.0937960669,0.0133130373,0.151192978,-0.1696210504,0.0284059383,-0.0521108173,-0.2883044779,-0.0881545097,0.1556635648,0.1755786091,0.2685056031,0.1593101472,0.4223206639,0.1182835624,0.2651936114,0.0943621695,0.0451356918,-0.0090215756,-0.2695650458,-0.000978097,0.3478802443,0.2753004134,-0.2945160568,0.0208778251,-0.2383211255,-0.1844542176,0.1815629154,-0.1973295957,-0.0274535324,-0.1337165982,0.0043231687,-0.0873047113,-0.3421146274,-0.1278225183,-0.0265576821,0.4784684777,-0.1834655702,-0.0265724137,-0.2126669139,0.122223787,-0.3353427052,-0.2540251613,-0.1243124604,-0.1883357316,-0.1600804329,0.2303074747,0.1097953022,0.0560976602,0.3020737767,0.058540564,-0.1985150278,0.2419624776,0.3923716843,-0.180865705,0.1044564322,0.0197549015,0.0112557681,0.0635319874,-0.1637397707,0.2447643578,-0.3910897076,0.1972706318,-0.1953568757,-0.3384783268,0.0306392331,0.0919930637,-0.1056957245,-0.0997598916,-0.2270807326,0.2462454289,-0.0694222674,-0.1997463107,-0.0774374455,-0.0941937268,0.0402900465,-0.1531330943,0.1413004994,0.0776268393,-0.2506591082,0.2018505782,-0.0451865606,0.3970346451,0.2543899119,0.5308368206,-0.5031036139,0.1623610705,-0.132778585,0.2966162264,0.2320235521,-0.0281323362,-0.6667606235,0.4973528087,-0.004258499,0.0241891816,0.2724842727,0.1617120504,0.3516778648,0.1078481972,-0.0490624495,0.3837120235,0.1839479655,0.2279311866,-0.3400998116,-0.2303100824,-0.0487276353,0.347974956,-0.3244735301,-0.1571216136,0.3112509251,-0.3645975888,0.4906598926,-0.2065642923,-0.1836265028,0.2509784997,0.0870239511,-0.2777459025,0.1183484569,0.0964874178,0.0859434977,0.242176488,-0.1894870698,-0.0184647031,0.0450902246,-0.1888088584,-0.0722152367,0.0619630329,-0.0552603528,-0.0473853871,-0.0448970497,-0.0918301344,0.3306871057,-0.0035315091,0.164733693,-0.1632062942,-0.2693369985,-0.0094458498,-0.2626185715,0.0792928189,0.0950683355,-0.1213107258,0.0093064588,-0.2492527664,-0.0884235278,-0.0017328389,-0.1519371569,0.3275276423,0.1502290517,0.1502473801,0.223530978,0.1522480249,0.1419565082,-0.0849310458,0.3879742324,0.2968659997,0.075315088,0.0365262404,-0.4053477347,0.5263332129,0.3933131695,0.3160547614,0.2904030383,-0.3733905554,0.1644445658,0.0483795069,0.1105019897,0.4106391072,0.2937985361,0.2421562374,0.5489552617,0.1663255692,-0.3161974847,0.1155823469,0.4506140351,-0.2496014982,0.0180420037,0.1873041689,-0.4217130542,-0.1856174171,0.3702158034,-0.150969401,0.3265801966,0.3312532008,0.1606444567,-0.1239553019,0.0274431203,-0.2904406786,0.2625498772,0.0967946574,0.1414081901,-0.2201083004,0.4368430972,-0.0920058116,-0.2460109293,-0.3815978765,-0.0392074697,0.192131713,-0.1264010072,0.3072988987,-0.0982985049,0.053618893,-0.0324512795,0.1308004558,0.0790725499,-0.0865352377,0.0450386032,0.1935409456,-0.181210503,-0.1583380401,-0.0551914535,0.2278954685,0.2617880702,-0.2087610364,-0.0017746408,-0.0716872513,-0.3983874917,0.0803131238,0.2775991857,0.1493917406,0.1450458467,-0.0469177924,-0.1854478121,-0.2801742256,-0.3152382076,-0.0065734186,0.0799065456,0.2866188288,-0.0709002763,0.0763942152,-0.324452132,-0.1447660178,0.0637101158,-0.232910037,-0.1513257623,0.2760073543,-0.1807471663,0.0008984554,0.088783294,-0.3163755536,-0.1673076302,-0.3001769483,0.3784309924,0.1727609485,0.2083455771,0.1075554192,0.3943434358,0.1223714128,0.0096602468,0.1228192747,0.0379166566,-0.4149514139,0.5190879107,-0.180263564,-0.4500640631,-0.4888216555,-0.2429303229,-0.1641484052,0.4524995983,-0.5341605544,-0.0311471894,-0.5311310291,0.4127760828,0.1235397533,0.1021677181,0.049603384,-0.2131572515,-0.2089558542,0.1292297095,0.0826174915,-0.0064191199,-0.087850906,0.2021380067,-0.0615213774,0.0337112471,-0.0098683694,0.7692419887,0.0689440593,-0.012904753,0.2635357678,-0.0949033275,0.051906649,-0.1647451371,0.1458878964,0.0905036107,-0.2608803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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1803","title":"Querying examples from big datasets is slower than small datasets","comments":"I have a dataset with about 2.7 million rows (which I'm loading via `load_from_disk`), and I need to fetch around 300k (particular) rows of it, by index. Currently this is taking a really long time (~8 hours). I tried sharding the large dataset but overall it doesn't change how long it takes to fetch the desired rows.\r\n\r\nI actually have enough RAM that I could fit the large dataset in memory. Would having the large dataset in memory speed up querying? To find out, I tried to load (a column of) the large dataset into memory like this:\r\n```\r\ncolumn_data = large_ds['column_name']\r\n```\r\nbut in itself this takes a really long time.\r\n\r\nI'm pretty stuck - do you have any ideas what I should do? ","body":"After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed.","comment_length":125,"text":"Querying examples from big datasets is slower than small datasets \n After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed. \n I have a dataset with about 2.7 million rows (which I'm loading via `load_from_disk`), and I need to fetch around 300k (particular) rows of it, by index. Currently this is taking a really long time (~8 hours). I tried sharding the large dataset but overall it doesn't change how long it takes to fetch the desired rows.\r\n\r\nI actually have enough RAM that I could fit the large dataset in memory. Would having the large dataset in memory speed up querying? To find out, I tried to load (a column of) the large dataset into memory like this:\r\n```\r\ncolumn_data = large_ds['column_name']\r\n```\r\nbut in itself this takes a really long time.\r\n\r\nI'm pretty stuck - do you have any ideas what I should do? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1803","title":"Querying examples from big datasets is slower than small datasets","comments":"Hi ! Feel free to post a message on the [forum](https:\/\/discuss.huggingface.co\/c\/datasets\/10). I'd be happy to help you with this.\r\n\r\nIn your post on the forum, feel free to add more details about your setup:\r\nWhat are column names and types of your dataset ?\r\nHow was the dataset constructed ?\r\nIs the dataset shuffled ?\r\nIs the dataset tokenized ?\r\nAre you on a SSD or an HDD ?\r\n\r\nI'm sure we can figure something out.\r\nFor example on my laptop I can access the 6 millions articles from wikipedia in less than a minute.","body":"After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed.","comment_length":95,"text":"Querying examples from big datasets is slower than small datasets \n After some experiments with bookcorpus I noticed that querying examples from big datasets is slower than small datasets.\r\nFor example\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nb1 = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nb50 = load_dataset(\"bookcorpus\", split=\"train[:50%]\")\r\nb100 = load_dataset(\"bookcorpus\", split=\"train[:100%]\")\r\n\r\n%timeit _ = b1[-1]                                                                     \r\n# 12.2 \u00b5s \u00b1 70.4 ns per loop (mean \u00b1 std. dev. of 7 runs, 100000 loops each)\r\n\r\n%timeit _ = b50[-1]                                                                    \r\n# 92.5 \u00b5s \u00b1 1.24 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n%timeit _ = b100[-1]                                                                      \r\n# 177 \u00b5s \u00b1 3.13 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 10000 loops each)\r\n\r\n```\r\n\r\nIt looks like the time to fetch the example increases with the size of the dataset.\r\n\r\nThis is maybe due to the use of the Arrow streaming format to store the data on disk. I guess pyarrow needs to iterate through the file as a stream to find the queried sample.\r\n\r\nMaybe switching to the Arrow IPC file format could help fixing this issue.\r\n\r\nIndeed according to the [documentation](https:\/\/arrow.apache.org\/docs\/format\/Columnar.html?highlight=arrow1#ipc-file-format), it's identical to the streaming format except that it contains the memory offsets of each sample, which could fix the issue:\r\n> We define a \u201cfile format\u201d supporting random access that is build with the stream format. The file starts and ends with a magic string ARROW1 (plus padding). What follows in the file is identical to the stream format. At the end of the file, we write a footer containing a redundant copy of the schema (which is a part of the streaming format) plus memory offsets and sizes for each of the data blocks in the file. This enables random access any record batch in the file. See File.fbs for the precise details of the file footer.\r\n\r\ncc @gaceladri since it can help speed up your training when this one is fixed. \n Hi ! Feel free to post a message on the [forum](https:\/\/discuss.huggingface.co\/c\/datasets\/10). I'd be happy to help you with this.\r\n\r\nIn your post on the forum, feel free to add more details about your setup:\r\nWhat are column names and types of your dataset ?\r\nHow was the dataset constructed ?\r\nIs the dataset shuffled ?\r\nIs the dataset tokenized ?\r\nAre you on a SSD or an HDD ?\r\n\r\nI'm sure we can figure something out.\r\nFor example on my laptop I can access the 6 millions articles from wikipedia in less than a minute.","embeddings":[-0.4054936171,0.0586443171,-0.0799714625,0.1315324455,-0.0796937943,-0.1776915044,0.2441749275,0.4918096364,-0.231398806,0.1801942289,0.0814933404,0.055089511,0.1250858605,-0.1007566378,0.1307256222,-0.273406297,0.0807416663,0.1248459145,-0.0751322359,-0.0345290378,0.0240299944,-0.1902869195,-0.3486269414,0.0184702277,-0.0663269982,-0.2240722179,-0.2307065576,0.1072061211,-0.1658504605,-0.417394191,-0.0595365614,-0.1528194398,0.2299299836,0.4270310104,-0.0001130733,0.0164052267,0.2279306203,0.0894177258,-0.1997470111,0.1829340011,0.1748079509,-0.2838544846,0.0868743137,-0.4097666144,-0.047154028,-0.2971978486,0.0934281796,0.0189011656,-0.0412533842,0.3089759946,0.1403642744,0.5131544471,-0.1917047799,-0.1336632371,0.623067379,0.2051521689,-0.3506302536,0.5299629569,0.5806369781,-0.0487995744,-0.3053155243,0.1361406893,-0.0814444497,0.2701411545,0.0370220914,0.1979730874,-0.1128048003,0.1795920134,0.1238104329,0.4088888764,0.3083902597,-0.0893917903,-0.314886421,-0.2920183241,-0.0569284223,-0.2332935333,0.0203074124,0.3264197111,-0.258379221,0.2713423073,-0.3137764633,0.1547778845,-0.105650574,0.1795520484,-0.1511591077,-0.0686156824,0.1150508747,0.1985104829,0.0937960669,0.0133130373,0.151192978,-0.1696210504,0.0284059383,-0.0521108173,-0.2883044779,-0.0881545097,0.1556635648,0.1755786091,0.2685056031,0.1593101472,0.4223206639,0.1182835624,0.2651936114,0.0943621695,0.0451356918,-0.0090215756,-0.2695650458,-0.000978097,0.3478802443,0.2753004134,-0.2945160568,0.0208778251,-0.2383211255,-0.1844542176,0.1815629154,-0.1973295957,-0.0274535324,-0.1337165982,0.0043231687,-0.0873047113,-0.3421146274,-0.1278225183,-0.0265576821,0.4784684777,-0.1834655702,-0.0265724137,-0.2126669139,0.122223787,-0.3353427052,-0.2540251613,-0.1243124604,-0.1883357316,-0.1600804329,0.2303074747,0.1097953022,0.0560976602,0.3020737767,0.058540564,-0.1985150278,0.2419624776,0.3923716843,-0.180865705,0.1044564322,0.0197549015,0.0112557681,0.0635319874,-0.1637397707,0.2447643578,-0.3910897076,0.1972706318,-0.1953568757,-0.3384783268,0.0306392331,0.0919930637,-0.1056957245,-0.0997598916,-0.2270807326,0.2462454289,-0.0694222674,-0.1997463107,-0.0774374455,-0.0941937268,0.0402900465,-0.1531330943,0.1413004994,0.0776268393,-0.2506591082,0.2018505782,-0.0451865606,0.3970346451,0.2543899119,0.5308368206,-0.5031036139,0.1623610705,-0.132778585,0.2966162264,0.2320235521,-0.0281323362,-0.6667606235,0.4973528087,-0.004258499,0.0241891816,0.2724842727,0.1617120504,0.3516778648,0.1078481972,-0.0490624495,0.3837120235,0.1839479655,0.2279311866,-0.3400998116,-0.2303100824,-0.0487276353,0.347974956,-0.3244735301,-0.1571216136,0.3112509251,-0.3645975888,0.4906598926,-0.2065642923,-0.1836265028,0.2509784997,0.0870239511,-0.2777459025,0.1183484569,0.0964874178,0.0859434977,0.242176488,-0.1894870698,-0.0184647031,0.0450902246,-0.1888088584,-0.0722152367,0.0619630329,-0.0552603528,-0.0473853871,-0.0448970497,-0.0918301344,0.3306871057,-0.0035315091,0.164733693,-0.1632062942,-0.2693369985,-0.0094458498,-0.2626185715,0.0792928189,0.0950683355,-0.1213107258,0.0093064588,-0.2492527664,-0.0884235278,-0.0017328389,-0.1519371569,0.3275276423,0.1502290517,0.1502473801,0.223530978,0.1522480249,0.1419565082,-0.0849310458,0.3879742324,0.2968659997,0.075315088,0.0365262404,-0.4053477347,0.5263332129,0.3933131695,0.3160547614,0.2904030383,-0.3733905554,0.1644445658,0.0483795069,0.1105019897,0.4106391072,0.2937985361,0.2421562374,0.5489552617,0.1663255692,-0.3161974847,0.1155823469,0.4506140351,-0.2496014982,0.0180420037,0.1873041689,-0.4217130542,-0.1856174171,0.3702158034,-0.150969401,0.3265801966,0.3312532008,0.1606444567,-0.1239553019,0.0274431203,-0.2904406786,0.2625498772,0.0967946574,0.1414081901,-0.2201083004,0.4368430972,-0.0920058116,-0.2460109293,-0.3815978765,-0.0392074697,0.192131713,-0.1264010072,0.3072988987,-0.0982985049,0.053618893,-0.0324512795,0.1308004558,0.0790725499,-0.0865352377,0.0450386032,0.1935409456,-0.181210503,-0.1583380401,-0.0551914535,0.2278954685,0.2617880702,-0.2087610364,-0.0017746408,-0.0716872513,-0.3983874917,0.0803131238,0.2775991857,0.1493917406,0.1450458467,-0.0469177924,-0.1854478121,-0.2801742256,-0.3152382076,-0.0065734186,0.0799065456,0.2866188288,-0.0709002763,0.0763942152,-0.324452132,-0.1447660178,0.0637101158,-0.232910037,-0.1513257623,0.2760073543,-0.1807471663,0.0008984554,0.088783294,-0.3163755536,-0.1673076302,-0.3001769483,0.3784309924,0.1727609485,0.2083455771,0.1075554192,0.3943434358,0.1223714128,0.0096602468,0.1228192747,0.0379166566,-0.4149514139,0.5190879107,-0.180263564,-0.4500640631,-0.4888216555,-0.2429303229,-0.1641484052,0.4524995983,-0.5341605544,-0.0311471894,-0.5311310291,0.4127760828,0.1235397533,0.1021677181,0.049603384,-0.2131572515,-0.2089558542,0.1292297095,0.0826174915,-0.0064191199,-0.087850906,0.2021380067,-0.0615213774,0.0337112471,-0.0098683694,0.7692419887,0.0689440593,-0.012904753,0.2635357678,-0.0949033275,0.051906649,-0.1647451371,0.1458878964,0.0905036107,-0.2608803511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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1797","title":"Connection error","comments":"Hi ! For future references let me add a link to our discussion here : https:\/\/github.com\/huggingface\/datasets\/issues\/759#issuecomment-770684693\r\n\r\nLet me know if you manage to fix your proxy issue or if we can do something on our end to help you :)","body":"Hi\r\nI am hitting to the error, help me and thanks.\r\n\r\n`train_data = datasets.load_dataset(\"xsum\", split=\"train\")`\r\n`ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/xsum\/xsum.py`","comment_length":40,"text":"Connection error \n Hi\r\nI am hitting to the error, help me and thanks.\r\n\r\n`train_data = datasets.load_dataset(\"xsum\", split=\"train\")`\r\n`ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/xsum\/xsum.py` \n Hi ! For future references let me add a link to our discussion here : https:\/\/github.com\/huggingface\/datasets\/issues\/759#issuecomment-770684693\r\n\r\nLet me know if you manage to fix your proxy issue or if we can do something on our end to help you :)","embeddings":[-0.2624680996,-0.4733125567,-0.1055454686,0.1787157357,0.562163651,-0.1030881405,-0.0170716885,0.2268313915,0.1645879149,0.2273807228,-0.3096103966,-0.0045985654,0.2044959962,0.443228662,0.2047396898,-0.2917329371,-0.0440437049,-0.0842047855,-0.4558957517,0.0675014555,0.0823032334,0.0975515321,-0.1073436663,0.0312042311,-0.3415580392,-0.0755456164,-0.1273664385,0.2207470983,-0.2686606348,-0.1070599109,0.4869900942,0.1393877864,0.0232104808,0.5769346356,-0.0001075649,0.1149471328,0.3164846003,0.0242445804,-0.223999083,-0.33114025,-0.1180704832,-0.0296365749,0.2868143916,-0.0743958578,-0.3245800436,0.0551903173,0.1663982123,-0.2252040654,0.597260654,0.1780353189,0.2932913601,0.4454437494,0.3417463601,-0.1741364151,-0.0087845353,-0.2359611988,-0.0033456399,0.4921544492,0.1815267652,0.0471874774,0.213283807,0.233723402,0.0923611745,-0.1159159169,0.2341250628,-0.1357224286,0.0822611749,-0.3459514081,0.034813907,0.3595282733,0.557164669,-0.1973253042,-0.2897788584,0.0463784263,-0.0473954342,-0.1539386511,0.2277335525,0.0708233267,-0.0782245174,0.1751246154,-0.2249851078,-0.214958176,-0.2798596323,0.2543148696,-0.0623600334,0.0234312005,-0.2333235741,0.1285450757,0.066239208,-0.132538259,-0.12612997,0.157585308,0.0271808878,0.1737780869,-0.4677267075,-0.0263176393,-0.1510633975,0.230041042,0.2147616893,0.1480178237,-0.1089281887,0.176799491,-0.1645568162,0.2396333069,0.2342701405,0.0991906896,-0.0924919024,-0.080631882,0.2583271563,0.2595568299,-0.1094023585,-0.1085266024,-0.0784777477,-0.2059405595,-0.3137497902,-0.062031053,0.2355893999,-0.2644431889,-0.2093426883,0.0777385458,0.1508231759,-0.0532449372,0.1676166505,0.4941645861,-0.2439726591,0.1843062937,0.0398412868,0.1510654837,-0.2680298984,-0.0373108126,-0.3129376173,0.1806302667,-0.1964211762,-0.0615591407,-0.0442417897,-0.1772384048,0.2160775959,-0.0785509795,0.3434203267,-0.1640195251,0.0547124855,-0.1044938639,-0.3085640371,0.2629107237,0.1635641903,-0.0282108132,0.2825821638,-0.0607512631,-0.0348773003,-0.2365302444,-0.3340730667,-0.3741346598,-0.0552138425,0.2754744887,-0.0566122793,-0.0085337404,-0.3372317255,0.0182861052,-0.0396474004,0.1264602989,-0.1866016686,-0.0632073134,-0.1109022647,-0.1256732792,0.4100495577,0.2269438207,-0.0682605281,-0.2711590827,0.3355163634,-0.3717980385,0.1890808195,0.1869220287,-0.1268949658,-0.0194468331,-0.1061921418,0.1100192741,0.1655587703,-0.3452140987,-0.3684436679,0.1967104226,-0.2838747203,-0.0734609514,0.0429037847,0.0068499376,0.0907800794,0.1455781758,0.2033013552,0.2076736391,-0.0318414196,-0.043676544,-0.1337381154,-0.2656517625,0.0427703001,0.2702589035,0.0227740202,0.0227792356,0.1320153177,-0.0515481979,0.2843168676,0.0502961464,0.0927804336,0.2592829466,0.2045941204,0.2270036787,0.0053051566,-0.3561691344,0.0282296278,0.0911857635,0.0471842065,0.2919330895,-0.0791223496,-0.1105626151,-0.3629341125,-0.1520826221,-0.1835043579,-0.0659433082,0.1536897123,-0.2198696434,0.1973173767,0.2385952175,-0.1191703454,0.2829247415,-0.1795577705,0.2405000925,-0.4697937071,0.4009589255,-0.1937504411,0.0068577528,0.0877900422,0.093488425,0.1160451248,-0.2712267935,-0.1282043308,0.4408646822,-0.2160945684,0.3192709088,0.389751941,0.0405472703,0.256257534,-0.458319962,-0.0476714186,-0.0960997418,0.0659594163,0.0526324585,0.2186976671,0.2048560381,0.0553214327,0.1382392198,0.135387525,0.1918755323,0.3227006793,0.0606966577,-0.0891448855,0.0702666044,0.2163235098,-0.1981064826,0.0355753079,-0.1962534189,-0.2209892124,-0.0054439199,0.0077681267,-0.0190759338,-0.0522501655,0.0720913336,-0.1022571996,-0.0005349888,0.072940357,0.0690679401,0.2663550675,0.2399249077,-0.0378363021,0.2792978883,0.0580011383,-0.1021743342,0.185502544,0.0684306175,0.0449506678,-0.0382117555,-0.1211706698,-0.1182554737,-0.2871050835,-0.0011871299,-0.2210567147,0.179521665,-0.247075066,-0.0214799866,-0.0927760676,-0.3123773038,0.0658893958,-0.1617256105,-0.2731583118,-0.3480960429,-0.0534439236,0.1365808845,0.0331345461,0.1988117844,-0.3494465649,0.1007373407,0.155825004,0.0168126933,-0.1158866808,-0.1174212024,-0.1286856532,0.1565780342,0.0093747275,0.0626327097,0.3976941109,-0.2893912494,-0.1418686509,-0.105853945,-0.2319445759,0.0629834086,-0.1083935797,0.3044180572,0.277816087,0.4564883411,-0.086418204,-0.0520351678,0.605652988,-0.1802677661,-0.0275544822,-0.0341372415,0.0227979887,-0.1331533045,0.1109787449,-0.299169749,-0.5104176998,-0.2131534219,0.1405189931,-0.0693684369,0.1047188267,0.1160707101,0.0068443767,0.1172981635,-0.1809821874,0.1372247487,-0.3094083667,-0.8145682812,0.1136590019,-0.0933670551,-0.4647526443,0.1229601279,0.1953829229,0.2569904029,0.1191847622,-0.3289041221,-0.3935401738,-0.033409223,0.09856911,0.1863355637,0.140938893,0.1660903245,-0.2663914859,-0.1748818904,-0.0119509129,0.026689576,-0.0368766636,-0.2150575817,0.4406511188,-0.0084777055,0.4517733157,-0.0168994535,0.4030779302,0.2012053132,0.0466530882,0.4095164835,-0.3087844551,0.2212135047,-0.0356283337,-0.603844583,0.2042991966,-0.1897949129,0.061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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1796","title":"Filter on dataset too much slowww","comments":"When I use the filter on the arrow table directly, it works like butter. But I can't find a way to update the table in `Dataset` object.\r\n\r\n```\r\nds_table = dataset.data.filter(mask=dataset['flag'])\r\n```","body":"I have a dataset with 50M rows.\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.\r\n\r\nWhen I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.\r\nBelow are the variants I tried.\r\n1. filter() with batch size 1024, single process (takes roughly 3 hr)\r\n2. filter() with batch size 1024, 96 processes (takes 5-6 hrs \u00af\\\\\\_(\u30c4)\\_\/\u00af)\r\n3. filter() with loading all data in memory, only a single boolean column (never ends).\r\n\r\nCan someone please help?\r\n\r\nBelow is a sample code for small dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\ndataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})\r\n\r\ndef _amplify(data):\r\n        return data\r\n\r\ndataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])\r\n```\r\n","comment_length":32,"text":"Filter on dataset too much slowww \n I have a dataset with 50M rows.\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.\r\n\r\nWhen I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.\r\nBelow are the variants I tried.\r\n1. filter() with batch size 1024, single process (takes roughly 3 hr)\r\n2. filter() with batch size 1024, 96 processes (takes 5-6 hrs \u00af\\\\\\_(\u30c4)\\_\/\u00af)\r\n3. filter() with loading all data in memory, only a single boolean column (never ends).\r\n\r\nCan someone please help?\r\n\r\nBelow is a sample code for small dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\ndataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})\r\n\r\ndef _amplify(data):\r\n        return data\r\n\r\ndataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])\r\n```\r\n \n When I use the filter on the arrow table directly, it works like butter. But I can't find a way to update the table in `Dataset` object.\r\n\r\n```\r\nds_table = dataset.data.filter(mask=dataset['flag'])\r\n```","embeddings":[-0.2712090611,-0.059731666,-0.114763163,-0.2779481411,-0.170650363,-0.2504285872,0.1707617491,0.2090248764,0.2598769367,-0.1163968369,-0.0861833245,0.2958269715,-0.0557140596,0.4060816467,-0.180425927,0.2453580648,0.1307907403,0.297056675,0.1960131675,0.0333571322,-0.0224025566,-0.1882677227,-0.2929413021,-0.0703883544,-0.0096254768,-0.2621649802,0.1859868318,-0.3086514473,-0.2785188854,-0.2126028389,-0.0173911992,0.5965904593,-0.0182769466,0.5038164854,-0.0001307184,-0.2338380963,0.2439537793,0.0724606216,0.0831183866,0.1498079598,-0.2113010138,-0.1286040395,-0.014037692,-0.0004979747,-0.1250068545,0.002729736,-0.3317998648,-0.2216515541,0.3499701023,0.0944688246,-0.0277750231,-0.1412250102,-0.2549342811,0.3521134555,0.2052817196,0.3541748226,0.1411342919,-0.1513690203,0.5533320904,-0.3358145058,0.0539450236,0.6059803367,-0.4179320335,-0.1313287318,0.2100652605,-0.2898597717,0.0583834425,-0.1515574008,0.2810197771,0.084834516,0.1335704476,-0.1620167792,-0.2986915112,-0.2322742343,-0.0849645361,-0.4459281862,-0.0143870069,-0.093405813,-0.2579454482,0.0345795825,-0.5128167868,-0.3484371901,0.0861519277,0.1266560107,-0.3261454105,0.3600158095,-0.006306435,0.2096572816,0.1311976016,-0.2452675253,0.06995029,-0.2122394294,0.1913420558,0.3051820099,-0.6571980119,-0.1016245335,-0.096286118,0.1605431437,-0.0209174547,0.0603329986,-0.184784472,0.5039317608,0.2157584131,-0.0072758566,0.0003438363,0.2503774464,0.1227045953,0.4484069049,0.280266434,-0.145333305,-0.2991339564,0.1851134747,-0.0733834952,0.0487983041,0.0876706019,-0.057178542,-0.3887727559,-0.1275837868,-0.3137276471,-0.0533896498,-0.2973858416,-0.059795659,0.6444664001,0.5041763783,-0.1133061051,-0.0650423318,-0.0732961521,-0.2993195355,-0.1293625981,0.017078558,0.032341212,-0.1580843776,-0.1048117131,0.2392276824,0.0378134847,-0.2345514894,-0.1911473572,-0.0324770808,-0.1686941683,0.1806276292,0.0712083355,-0.3621056974,0.2372625619,0.413911283,-0.2685701549,0.3007687032,0.0842118859,0.055668354,-0.1265523136,0.1775274575,-0.3156157434,-0.3334103525,0.2214105278,-0.0167185534,-0.0847421587,0.1368683875,-0.1805811822,0.4090796113,0.2862333059,-0.2051102072,-0.1643706262,-0.1768735349,-0.595266521,-0.1606847793,0.2633840442,0.2223779261,-0.5520042181,0.0523661226,-0.3266305327,0.2614666224,0.4948300123,0.3584709466,-0.0671725869,0.2171266228,0.1002386138,-0.070361577,0.2984155416,-0.0785397142,-0.4758496284,0.5441429615,-0.0614160672,0.012077041,0.1328438818,0.155942142,0.5201342106,-0.2442356944,0.3975709081,0.337546438,-0.1997921616,0.0735185072,0.0002533132,-0.0091579854,0.2259215713,0.1488590688,-0.3431073427,-0.022170566,-0.0321321823,-0.426648289,0.1524595916,0.1380381584,-0.0138798021,0.2082129866,0.2469478995,0.1468655616,0.0114980666,-0.1173818856,-0.002903319,-0.0856793895,0.0895480141,-0.2041616291,-0.2678201497,-0.2276494056,-0.2681233287,0.1229263097,0.0763175488,0.2027755231,-0.1417338103,-0.1732809395,0.1918347627,-0.2997716069,-0.0245427042,0.4423805177,-0.1199391857,-0.0766720176,0.1876756102,0.0041585388,0.3773688972,-0.1478652209,0.1558626592,-0.0638166666,0.2566291094,0.0597918741,-0.0512209646,0.2228671908,0.4853754342,-0.2909352779,-0.3249587119,0.1911716908,0.0907264799,0.3223301768,0.1594703197,-0.0299946852,0.2172129899,-0.0669633523,-0.19752644,0.5967999101,-0.0842720866,0.4515283704,0.0961709395,-0.0534643419,0.3551806509,0.2579828501,-0.1379966438,-0.224004209,0.2483920008,-0.0168593042,0.1414318383,0.191679582,-0.1041448414,0.3914271593,-0.0069532744,-0.1684072316,-0.0578750633,0.2389459014,0.1703025848,0.0307574421,0.1890267134,0.0051107556,0.1146775112,0.1844976097,-0.1427407414,-0.0982926339,0.081593819,-0.0547583327,0.2547175586,0.1561012119,0.3075129986,-0.2542977333,0.3530647159,-0.1137326285,-0.1732011735,-0.1776498109,0.2034720778,0.333828032,-0.2167353928,0.0699750334,-0.3757014275,-0.535559535,0.2271120101,-0.1121494323,0.22655797,0.0880119279,0.2230781019,0.0783291161,0.1746549904,0.285586983,0.19949992,0.1593718231,0.166126743,-0.4469213784,-0.1344804317,-0.1596938968,-0.0713537633,-0.1074967459,0.2684340775,0.3947562873,0.3735952377,0.2810976207,0.048986651,-0.1861354113,-0.5580239296,-0.2156649977,-0.0151027096,0.0744738132,0.2174893618,0.1304199845,-0.3905109465,-0.050193429,0.0361119956,-0.1326581687,-0.1809353977,0.0968123153,-0.264318347,0.0712426528,0.1325804293,0.1171383932,-0.23426494,-0.0533800982,-0.1480298936,-0.0901711583,0.289006263,-0.2378833294,0.0384709165,-0.3095988929,-0.2900487483,-0.0745963454,0.062701799,-0.1542517245,0.2361339927,0.2845228016,-0.0103315674,-0.1767285317,-0.0545509867,-0.2922154367,0.5495020747,-0.2610016763,0.1806714833,-0.4031961858,-0.286221981,0.1927287281,0.0184681565,0.3718920946,-0.1154165938,0.1365425885,-0.0016428275,-0.4650854468,-0.1635533124,-0.1761951447,0.0963357612,0.2124688625,0.8827466965,0.0940158293,0.3070503473,0.0628353581,-0.0016844188,0.0701517612,0.2428095192,-0.1153798103,-0.2179842293,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1796","title":"Filter on dataset too much slowww","comments":"Hi ! Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.\r\nUsing a mask directly on the arrow table doesn't do any read or write operation therefore it's way quicker.\r\n\r\nReplacing the old table by the new one should do the job:\r\n```python\r\ndataset._data = dataset._data.filter(...)\r\n```\r\n\r\nNote: this is a **workaround** and in general users shouldn't have to do that. In particular if you did some `shuffle` or `select` before that then it would not work correctly since the indices mapping (index from `__getitem__` -> index in the table) would not be valid anymore. But if you haven't done any `shuffle`, `select`, `shard`, `train_test_split` etc. then it should work.\r\n\r\nIdeally it would be awesome to update the filter function to allow masking this way !\r\nIf you would like to give it a shot I will be happy to help :) ","body":"I have a dataset with 50M rows.\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.\r\n\r\nWhen I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.\r\nBelow are the variants I tried.\r\n1. filter() with batch size 1024, single process (takes roughly 3 hr)\r\n2. filter() with batch size 1024, 96 processes (takes 5-6 hrs \u00af\\\\\\_(\u30c4)\\_\/\u00af)\r\n3. filter() with loading all data in memory, only a single boolean column (never ends).\r\n\r\nCan someone please help?\r\n\r\nBelow is a sample code for small dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\ndataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})\r\n\r\ndef _amplify(data):\r\n        return data\r\n\r\ndataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])\r\n```\r\n","comment_length":162,"text":"Filter on dataset too much slowww \n I have a dataset with 50M rows.\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.\r\n\r\nWhen I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.\r\nBelow are the variants I tried.\r\n1. filter() with batch size 1024, single process (takes roughly 3 hr)\r\n2. filter() with batch size 1024, 96 processes (takes 5-6 hrs \u00af\\\\\\_(\u30c4)\\_\/\u00af)\r\n3. filter() with loading all data in memory, only a single boolean column (never ends).\r\n\r\nCan someone please help?\r\n\r\nBelow is a sample code for small dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\ndataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})\r\n\r\ndef _amplify(data):\r\n        return data\r\n\r\ndataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])\r\n```\r\n \n Hi ! Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.\r\nUsing a mask directly on the arrow table doesn't do any read or write operation therefore it's way quicker.\r\n\r\nReplacing the old table by the new one should do the job:\r\n```python\r\ndataset._data = dataset._data.filter(...)\r\n```\r\n\r\nNote: this is a **workaround** and in general users shouldn't have to do that. In particular if you did some `shuffle` or `select` before that then it would not work correctly since the indices mapping (index from `__getitem__` -> index in the table) would not be valid anymore. But if you haven't done any `shuffle`, `select`, `shard`, `train_test_split` etc. then it should work.\r\n\r\nIdeally it would be awesome to update the filter function to allow masking this way !\r\nIf you would like to give it a shot I will be happy to help :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1796","title":"Filter on dataset too much slowww","comments":"Hi @lhoestq @ayubSubhaniya,\r\n\r\nIf there's no progress on this one, can I try working on it?\r\n\r\nThanks,\r\nGunjan","body":"I have a dataset with 50M rows.\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.\r\n\r\nWhen I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.\r\nBelow are the variants I tried.\r\n1. filter() with batch size 1024, single process (takes roughly 3 hr)\r\n2. filter() with batch size 1024, 96 processes (takes 5-6 hrs \u00af\\\\\\_(\u30c4)\\_\/\u00af)\r\n3. filter() with loading all data in memory, only a single boolean column (never ends).\r\n\r\nCan someone please help?\r\n\r\nBelow is a sample code for small dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\ndataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})\r\n\r\ndef _amplify(data):\r\n        return data\r\n\r\ndataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])\r\n```\r\n","comment_length":18,"text":"Filter on dataset too much slowww \n I have a dataset with 50M rows.\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.\r\n\r\nWhen I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.\r\nBelow are the variants I tried.\r\n1. filter() with batch size 1024, single process (takes roughly 3 hr)\r\n2. filter() with batch size 1024, 96 processes (takes 5-6 hrs \u00af\\\\\\_(\u30c4)\\_\/\u00af)\r\n3. filter() with loading all data in memory, only a single boolean column (never ends).\r\n\r\nCan someone please help?\r\n\r\nBelow is a sample code for small dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\ndataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})\r\n\r\ndef _amplify(data):\r\n        return data\r\n\r\ndataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])\r\n```\r\n \n Hi @lhoestq @ayubSubhaniya,\r\n\r\nIf there's no progress on this one, can I try working on it?\r\n\r\nThanks,\r\nGunjan","embeddings":[-0.2252546996,-0.2595492601,-0.1091805696,-0.2148160636,-0.1230366156,-0.269241631,0.2215615809,0.2114835232,0.2634298503,-0.123314254,0.0279618911,0.090880774,-0.116068624,0.4082371593,-0.2278894037,0.1516521722,0.1011282429,0.213109985,0.4031715691,0.0390016884,-0.0577567853,-0.0807915404,-0.3609131277,-0.0298266672,-0.084706299,-0.2762124538,0.2782402933,-0.2476797551,-0.2799648941,-0.1902088523,-0.117655322,0.4328626096,0.0416844748,0.5227159262,-0.000128576,-0.384228617,0.2686328292,0.1016895473,0.2251769751,0.1034961194,-0.1696645468,-0.0080827391,-0.0367199033,-0.0586646497,-0.2172694355,0.0669343695,-0.2896471322,-0.2367753685,0.4300411642,0.1496510208,-0.0269949771,-0.2186436504,-0.3358837664,0.3563361466,0.1525760591,0.3359158635,0.1869012415,-0.2261263877,0.7080863714,-0.3562733829,0.0696439669,0.472147882,-0.4560546279,-0.1615430415,0.1487727165,-0.3453471661,0.1096845344,-0.3132957518,0.2409458607,0.176920563,0.0656614229,-0.0571096055,-0.2008105218,-0.2538344562,-0.045438882,-0.4103205204,0.0202633683,0.0456011929,-0.3242428005,-0.0042452426,-0.5788224936,-0.2189129293,0.0876276642,0.158389762,-0.3853456378,0.3735460639,0.0321886279,0.1135237738,0.1993607432,-0.215529263,0.0075017055,-0.2545441091,0.2832427919,0.3554543555,-0.674097538,-0.1508339942,-0.1581910998,0.2203153521,0.0110632014,0.0257151257,-0.3736515939,0.46273458,0.2508269846,-0.040211305,-0.0583137199,0.2461997569,0.0641968772,0.4818011224,0.2978991568,-0.219860673,-0.2987447381,0.2547263801,-0.0507870615,0.0679056197,0.0823750198,-0.0540153347,-0.5015363097,-0.170279786,-0.2727939785,-0.10285642,-0.4168030322,-0.120347321,0.668340981,0.4683458507,-0.1007532477,-0.1219638139,-0.1264315546,-0.2415731996,-0.1646133959,0.0298383143,0.0279094558,-0.0445403717,-0.0681126043,0.1991347671,0.0899436623,-0.2929748595,-0.1792654544,0.0473016202,-0.1829548031,0.1261359155,0.2369165123,-0.3831785619,0.13238132,0.292304039,-0.2854670584,0.3071512878,0.0823027566,0.1024769023,-0.1182383671,0.1186317205,-0.3777612448,-0.3367712498,0.2411236316,0.0114388652,-0.1120516658,0.1816656739,-0.0739685744,0.3442537487,0.306984067,-0.0880860984,-0.2214004248,-0.1915690303,-0.5816186666,-0.158752054,0.2008324116,0.0921893939,-0.4657712579,0.0186849385,-0.2798204422,0.210533604,0.5880839229,0.4387430549,-0.03694015,0.224987179,-0.0430899151,0.0483519398,0.2181489319,-0.112259388,-0.3453876376,0.623346746,-0.039500691,0.0077329222,0.1885050386,0.0785486177,0.5663010478,-0.2632850707,0.5140783787,0.3756265938,-0.2153287977,0.1178362891,-0.0218422543,-0.10364829,0.1589710116,0.2371780723,-0.2816350162,-0.0733494461,-0.0703740865,-0.3758479059,0.1747685671,0.0260584168,0.030902924,0.1709758937,0.2361922264,0.062734805,0.0111601958,-0.1820666343,0.1184111238,-0.0608216114,-0.050430499,-0.1652128398,-0.1754475087,-0.1815262139,-0.2594470084,-0.009087028,0.0641421527,0.2332099229,-0.1163491011,-0.1710831076,0.1199571043,-0.2128815055,0.0389979929,0.487057656,-0.0661606491,-0.0465142801,0.1828293353,-0.0481044054,0.3280166984,-0.1143477932,0.1694704741,0.0258435123,0.3714915812,0.0142312702,0.0571064577,0.1386633962,0.5027495027,-0.2991530001,-0.3137467802,0.1767044514,0.1542363316,0.3389374912,0.1094481423,0.016436182,0.1302595288,-0.1143233329,-0.1493640542,0.5312942863,-0.0404825136,0.4238991737,0.0877424106,-0.0993584171,0.3138713539,0.1738130003,-0.0924373791,-0.2166422606,0.5034923553,0.0138215674,0.1286027282,0.2449540347,-0.1035164595,0.3022047579,-0.04750029,-0.3135300577,-0.059206225,0.2680288255,0.1906663477,0.0849729031,0.1419332027,-0.0174565259,0.0390058644,0.1978618503,-0.1883351803,-0.1547813118,0.0823922828,-0.0621349774,0.258713901,0.1034958884,0.2353141904,-0.2182342857,0.2533356845,-0.1286801845,-0.0980202109,-0.2866779268,0.2284122109,0.3222565651,-0.2280865908,0.0149425017,-0.3553006351,-0.5684863329,0.2344612628,-0.1010885537,0.2024694681,0.0458728783,0.1592308432,0.0498124883,0.28446123,0.1175139099,0.1196427196,0.2055429518,0.2214313596,-0.4647490382,-0.1081777811,-0.1323721856,-0.041707769,-0.0571200885,0.2584053576,0.3142126203,0.3845154941,0.306491226,-0.0974844322,-0.1131294072,-0.5374432802,-0.1481490433,0.0105012637,0.1324184984,0.1226705164,0.1452452987,-0.314394474,-0.1035704985,-0.0251875464,0.0196898859,-0.1629319787,0.0662417337,-0.1998528242,0.0767853707,0.1098999381,0.1567242444,-0.2886810005,-0.0323659219,-0.2201781124,-0.0711628944,0.2759138644,-0.3124832213,-0.117224656,-0.2404733896,-0.2843366861,-0.063058123,0.0203151796,-0.1998870969,0.2910321951,0.2740531266,-0.01750651,-0.1282386631,-0.0829764009,-0.3217385113,0.5405508876,-0.2413505465,0.1424674392,-0.4174325466,-0.2339668721,0.1934642047,-0.0433743484,0.2607778609,-0.1583853513,0.1518045962,0.0461019576,-0.3172047436,-0.186086297,-0.1872590035,0.2764711976,0.2739782035,0.7350676656,0.0479093269,0.3134377599,0.1614057422,0.0978119075,0.0234633312,0.2742680311,-0.2951162159,-0.1754238904,-0.3233992755,-0.0374852084,0.24710068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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1796","title":"Filter on dataset too much slowww","comments":"Sure @gchhablani feel free to start working on it, this would be very appreciated :)\r\nThis feature is would be really awesome, especially since arrow allows to mask really quickly and without having to rewrite the dataset on disk","body":"I have a dataset with 50M rows.\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.\r\n\r\nWhen I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.\r\nBelow are the variants I tried.\r\n1. filter() with batch size 1024, single process (takes roughly 3 hr)\r\n2. filter() with batch size 1024, 96 processes (takes 5-6 hrs \u00af\\\\\\_(\u30c4)\\_\/\u00af)\r\n3. filter() with loading all data in memory, only a single boolean column (never ends).\r\n\r\nCan someone please help?\r\n\r\nBelow is a sample code for small dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\ndataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})\r\n\r\ndef _amplify(data):\r\n        return data\r\n\r\ndataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])\r\n```\r\n","comment_length":39,"text":"Filter on dataset too much slowww \n I have a dataset with 50M rows.\r\nFor pre-processing, I need to tokenize this and filter rows with the large sequence.\r\n\r\nMy tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.\r\n\r\nWhen I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.\r\nBelow are the variants I tried.\r\n1. filter() with batch size 1024, single process (takes roughly 3 hr)\r\n2. filter() with batch size 1024, 96 processes (takes 5-6 hrs \u00af\\\\\\_(\u30c4)\\_\/\u00af)\r\n3. filter() with loading all data in memory, only a single boolean column (never ends).\r\n\r\nCan someone please help?\r\n\r\nBelow is a sample code for small dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('glue', 'mrpc', split='train')\r\ndataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})\r\n\r\ndef _amplify(data):\r\n        return data\r\n\r\ndataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])\r\n```\r\n \n Sure @gchhablani feel free to start working on it, this would be very appreciated :)\r\nThis feature is would be really awesome, especially since arrow allows to mask really quickly and without having to rewrite the dataset on 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1790","title":"ModuleNotFoundError: No module named 'apache_beam', when specific languages.","comments":"Hi !\r\n\r\nApache Beam is a framework used to define data transformation pipelines. These pipeline can then be run in many runtimes: DataFlow, Spark, Flink, etc. There also exist a local runner called the DirectRunner.\r\nWikipedia is a dataset that requires some parsing, so to allow the processing to be run on this kind of runtime we're using Apache Beam.\r\n\r\nAt Hugging Face we've already processed certain versions of wikipedia (the `20200501.en` one for example) so that users can directly download the processed version instead of using Apache Beam to process it.\r\nHowever for the japanese language we haven't processed it so you'll have to run the processing on your side.\r\nSo you do need Apache Beam to process `20200501.ja`.\r\n\r\nYou can install Apache Beam with\r\n```\r\npip install apache-beam\r\n```\r\n\r\nI think we can probably improve the error message to let users know of this subtlety.\r\nWhat #498 implied is that Apache Beam is not needed when you process a dataset that doesn't use Apache Beam.","body":"```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?","comment_length":167,"text":"ModuleNotFoundError: No module named 'apache_beam', when specific languages. \n ```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct? \n Hi !\r\n\r\nApache Beam is a framework used to define data transformation pipelines. These pipeline can then be run in many runtimes: DataFlow, Spark, Flink, etc. There also exist a local runner called the DirectRunner.\r\nWikipedia is a dataset that requires some parsing, so to allow the processing to be run on this kind of runtime we're using Apache Beam.\r\n\r\nAt Hugging Face we've already processed certain versions of wikipedia (the `20200501.en` one for example) so that users can directly download the processed version instead of using Apache Beam to process it.\r\nHowever for the japanese language we haven't processed it so you'll have to run the processing on your side.\r\nSo you do need Apache Beam to process `20200501.ja`.\r\n\r\nYou can install Apache Beam with\r\n```\r\npip install apache-beam\r\n```\r\n\r\nI think we can probably improve the error message to let users know of this subtlety.\r\nWhat #498 implied is that Apache Beam is not needed when you process a dataset that doesn't use Apache 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1790","title":"ModuleNotFoundError: No module named 'apache_beam', when specific languages.","comments":"Thanks for your reply! \r\nI understood.\r\n\r\nI tried again with installing apache-beam, add ` beam_runner=\"DirectRunner\"` and an anther `mwparserfromhell` is also required so I installed it.\r\nbut, it also failed. It exited 1 without error message.\r\n\r\n```py\r\nimport datasets\r\n# BTW, 20200501.ja doesn't exist at wikipedia, so I specified date argument\r\nwiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\".\/datasets\", beam_runner=\"DirectRunner\")\r\nprint(wiki)\r\n```\r\nand its log is below\r\n```\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia\/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to .\/datasets\/wikipedia\/20210120.ja-date=20210120,language=ja\/0.0.0\/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nKilled\r\n```\r\n\r\nI also tried on another machine because it may caused by insufficient resources.\r\n```\r\n$ python main.py\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia\/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to .\/datasets\/wikipedia\/20210120.ja-date=20210120,language=ja\/0.0.0\/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\n\r\nTraceback (most recent call last):\r\n  File \"main.py\", line 3, in <module>\r\n    wiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\".\/datasets\", beam_runner=\"DirectRunner\")\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 609, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 526, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1069, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/pipeline.py\", line 561, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/direct\/direct_runner.py\", line 126, in run_pipeline\r\n    return runner.run_pipeline(pipeline, options)\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 182, in run_pipeline\r\n    self._latest_run_result = self.run_via_runner_api(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 193, in run_via_runner_api\r\n    return self.run_stages(stage_context, stages)\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 358, in run_stages\r\n    stage_results = self._run_stage(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 549, in _run_stage\r\n    last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 595, in _run_bundle\r\n    result, splits = bundle_manager.process_bundle(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 888, in process_bundle\r\n    self._send_input_to_worker(process_bundle_id, transform_id, elements)\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 765, in _send_input_to_worker\r\n    data_out.write(byte_stream)\r\n  File \"apache_beam\/coders\/stream.pyx\", line 42, in apache_beam.coders.stream.OutputStream.write\r\n  File \"apache_beam\/coders\/stream.pyx\", line 47, in apache_beam.coders.stream.OutputStream.write\r\n  File \"apache_beam\/coders\/stream.pyx\", line 109, in apache_beam.coders.stream.OutputStream.extend\r\nAssertionError: OutputStream realloc failed.\r\n```\r\n\r\n","body":"```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?","comment_length":279,"text":"ModuleNotFoundError: No module named 'apache_beam', when specific languages. \n ```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct? \n Thanks for your reply! \r\nI understood.\r\n\r\nI tried again with installing apache-beam, add ` beam_runner=\"DirectRunner\"` and an anther `mwparserfromhell` is also required so I installed it.\r\nbut, it also failed. It exited 1 without error message.\r\n\r\n```py\r\nimport datasets\r\n# BTW, 20200501.ja doesn't exist at wikipedia, so I specified date argument\r\nwiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\".\/datasets\", beam_runner=\"DirectRunner\")\r\nprint(wiki)\r\n```\r\nand its log is below\r\n```\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia\/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to .\/datasets\/wikipedia\/20210120.ja-date=20210120,language=ja\/0.0.0\/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\nKilled\r\n```\r\n\r\nI also tried on another machine because it may caused by insufficient resources.\r\n```\r\n$ python main.py\r\nUsing custom data configuration 20210120.ja\r\nDownloading and preparing dataset wikipedia\/20210120.ja-date=20210120,language=ja (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to .\/datasets\/wikipedia\/20210120.ja-date=20210120,language=ja\/0.0.0\/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63...\r\n\r\nTraceback (most recent call last):\r\n  File \"main.py\", line 3, in <module>\r\n    wiki = datasets.load_dataset(\"wikipedia\", language=\"ja\", date=\"20210120\", cache_dir=\".\/datasets\", beam_runner=\"DirectRunner\")\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 609, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 526, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1069, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/pipeline.py\", line 561, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/direct\/direct_runner.py\", line 126, in run_pipeline\r\n    return runner.run_pipeline(pipeline, options)\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 182, in run_pipeline\r\n    self._latest_run_result = self.run_via_runner_api(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 193, in run_via_runner_api\r\n    return self.run_stages(stage_context, stages)\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 358, in run_stages\r\n    stage_results = self._run_stage(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 549, in _run_stage\r\n    last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 595, in _run_bundle\r\n    result, splits = bundle_manager.process_bundle(\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 888, in process_bundle\r\n    self._send_input_to_worker(process_bundle_id, transform_id, elements)\r\n  File \"\/home\/miyamonz\/.cache\/pypoetry\/virtualenvs\/try-datasets-4t4JWXxu-py3.8\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 765, in _send_input_to_worker\r\n    data_out.write(byte_stream)\r\n  File \"apache_beam\/coders\/stream.pyx\", line 42, in apache_beam.coders.stream.OutputStream.write\r\n  File \"apache_beam\/coders\/stream.pyx\", line 47, in apache_beam.coders.stream.OutputStream.write\r\n  File \"apache_beam\/coders\/stream.pyx\", line 109, in apache_beam.coders.stream.OutputStream.extend\r\nAssertionError: OutputStream realloc failed.\r\n```\r\n\r\n","embeddings":[0.0917942896,0.0745991319,0.0195861496,0.251845777,0.2570836842,0.0409060419,0.3200161457,0.3480713069,0.1667267084,0.1959524751,0.2174150348,0.0488914922,-0.0974617451,-0.2695732117,0.0972716436,-0.3023030162,0.2464139014,0.0408723839,-0.1644416153,-0.2330404669,-0.3931181431,0.1340927631,-0.3705989122,0.0350908488,-0.1296226531,0.3397006094,-0.0780813843,0.0638152286,0.0173252411,-0.2700100541,0.0453570858,-0.324616313,0.1215118393,0.3739317358,-0.0001173204,0.1343809068,0.4923765659,-0.2628591955,-0.3836726844,-0.2569671571,-0.1403349489,-0.4439759254,0.0448291525,-0.4475933611,-0.1867608875,-0.2697493434,0.0532009304,-0.3586476445,0.395362854,0.2671947777,0.1607774794,0.2647910118,0.1899022758,-0.041800715,0.418223381,0.0558920279,-0.2807168067,0.001108119,-0.0778786838,0.1042615697,0.0328844711,0.3811944425,-0.2652494013,0.0351823829,0.219607994,-0.2989069521,0.0275765453,-0.5322272182,0.1891094148,0.1666529626,0.7942336202,-0.3169869483,-0.1359947622,0.0134905996,-0.0399667807,0.0834234431,0.2887854874,0.1149210781,-0.1483090073,-0.0395828001,0.4454575777,-0.2758440971,-0.1213793457,0.3104602993,0.0970906764,0.4800923765,0.0280743092,0.285240382,-0.2220673412,-0.1609825641,-0.0193839762,-0.3305304348,0.2255716175,0.3868534863,-0.2711615562,0.0091108838,0.0303918943,0.1701530665,0.0020982851,-0.2076063156,-0.2619356215,0.1003814191,0.1692396998,-0.0123883486,0.0890526325,0.1528998315,0.3734167516,0.1121669933,0.2206748128,-0.1710113436,0.0056283185,0.0340125859,-0.0521680787,-0.3492521644,-0.3236457109,0.1878946424,0.3179504871,-0.1621291339,-0.0892107636,0.0642646402,-0.6158861518,-0.2876563966,-0.2265936136,0.3501834869,0.0944344401,0.3287703395,0.1527711451,0.2902515829,-0.2045259774,-0.3957129121,-0.0542582273,0.4753558338,-0.3645497561,0.2409627885,0.087393932,0.1367838532,0.1506344676,-0.1451434493,-0.0452821516,0.0375527404,-0.0371271111,0.0036610053,-0.1230324283,0.3735723197,0.1352759451,0.5192132592,0.2212901413,-0.0427361988,-0.0936333016,0.1430326551,-0.182173118,-0.2547640502,0.0409881771,0.0198266562,-0.3785459697,0.1690728813,0.0418639407,-0.1529215425,-0.0193206761,-0.01373877,0.1795716882,0.0659218878,-0.0775272995,-0.24829337,0.1928215921,0.7632588148,-0.1837839335,-0.0071699829,-0.0386874974,-0.0945175886,-0.1337481886,-0.1851740181,-0.0107638203,0.3875401318,-0.2644916773,-0.06522277,0.6910915971,-0.5287438035,-0.0048345681,0.1956304312,-0.0111865513,0.0338991694,0.2492822856,-0.1067635268,-0.2923358977,-0.1760918498,-0.259635061,0.0050270432,0.130420655,-0.0345540307,-0.3318171799,-0.0714585781,0.0876164213,0.2745759189,0.3723239303,0.0291089024,0.2889192104,0.6823456287,0.1276972145,-0.0668428093,0.2065407038,0.5193422437,-0.0871524811,-0.02277302,0.1958383918,-0.0088050636,-0.235602349,0.2555713058,-0.1881491244,0.2531144321,0.0075532491,0.0816442966,-0.1720115095,-0.1806830317,-0.3440953791,-0.2144478112,0.0655369386,0.0168310143,0.0882710665,0.3130510449,-0.3127648532,0.439604193,-0.197692737,0.013464028,-0.4467652142,0.0812010467,-0.1344239563,-0.0401019268,-0.0064320094,0.0986566991,0.3700562716,-0.225478217,-0.1251113862,0.1368416697,0.0944711342,-0.0113428039,-0.0501034856,-0.110739477,0.1207010895,-0.3321649134,0.4237888455,0.1321918815,0.1378662437,-0.1063949019,0.1518630087,0.087618798,0.3021607697,0.4313708246,0.0128362458,-0.0354976282,-0.0844971165,-0.0023270249,-0.2098860145,-0.2393537313,0.2860515416,0.362285763,0.2126557231,-0.0158595368,0.1649516076,-0.3541213274,0.4365091324,0.0414417125,0.2351138145,-0.0608063415,-0.450204879,0.2783480287,0.0984192416,-0.1064570919,0.3670893908,0.1384744048,-0.2372380793,0.1293950081,0.2279010564,-0.0575799905,0.092207022,0.1565444469,0.4523301721,0.1082719192,-0.2713344693,0.0434037186,-0.0382356793,-0.1230822653,-0.0780402496,0.115962863,-0.2822549045,-0.05891186,-0.3727226555,-0.4914531708,-0.3270525634,0.0718242377,-0.4088910222,-0.2334057838,-0.1688344926,-0.1222827435,-0.1346062422,0.3930952847,0.2123800069,-0.2270422429,-0.0681200176,-0.3959479034,-0.2092330903,-0.2032681257,-0.2683122754,-0.0390979871,0.2976367176,0.1895361543,0.192569524,-0.2675949335,-0.0959035605,-0.20994398,-0.1003018096,0.1910176873,0.0083410237,-0.099483192,-0.0512177721,0.456911087,-0.0361206084,0.0497274473,0.3699610233,-0.0084841112,-0.1452653557,0.1777404249,0.0052207164,0.1532685161,-0.0275911447,-0.5211430788,-0.2453733236,-0.392180413,-0.2898659408,0.2246519476,0.0522166938,0.0134093035,0.1566919237,0.1527421176,-0.0422593057,0.0668442696,0.0891847908,0.0916851461,0.4245998859,-0.1491640508,-0.2796567082,0.3423870802,-0.1902858913,0.0289856307,0.0970074683,-0.1730143279,-0.0193443969,0.3139023185,-0.1163840815,-0.0652336404,0.2874525487,0.5190108418,-0.1943499744,0.0928247049,-0.0690189078,0.0083449809,-0.1444002986,-0.2441487312,0.4245213866,0.297408253,0.3056080043,-0.043531213,0.7822398543,0.0108604189,0.2556969523,0.4260956943,0.0762988031,0.2692808211,-0.348682791,-0.0499942712,0.0104912361,0.254405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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1790","title":"ModuleNotFoundError: No module named 'apache_beam', when specific languages.","comments":"Hi @miyamonz,\r\n\r\nI tried replicating this issue using the same snippet used by you. I am able to download the dataset without any issues, although I stopped it in the middle because the dataset is huge.\r\n\r\nBased on a similar issue [here](https:\/\/github.com\/google-research\/fixmatch\/issues\/23), it could be related to your environment setup, although I am just guessing here. Can you share these details?","body":"```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?","comment_length":61,"text":"ModuleNotFoundError: No module named 'apache_beam', when specific languages. \n ```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct? \n Hi @miyamonz,\r\n\r\nI tried replicating this issue using the same snippet used by you. I am able to download the dataset without any issues, although I stopped it in the middle because the dataset is huge.\r\n\r\nBased on a similar issue [here](https:\/\/github.com\/google-research\/fixmatch\/issues\/23), it could be related to your environment setup, although I am just guessing here. Can you share these details?","embeddings":[-0.0868910477,0.1544118226,0.0349213034,0.2511788607,0.3533329964,0.1083717346,0.3212845027,0.4499050975,0.3734617233,0.0088102743,0.102325432,0.0063319677,-0.0099073453,-0.0225594807,-0.0207931511,-0.2547829747,0.1721131206,0.0012150535,-0.2130487263,-0.2989025116,-0.3872275054,0.0866747573,-0.3275980651,0.0159582961,-0.0521656424,0.155601874,-0.0279715098,0.1574117094,0.0401064716,-0.1749257743,0.2103829086,-0.1249948144,-0.0505453423,0.3043377995,-0.000107928,0.0701222792,0.4934644401,-0.2194035649,-0.3645669818,-0.4068615735,-0.0887664631,-0.347299546,0.0214760397,-0.410813123,-0.1609840095,-0.1692377776,0.027584387,-0.424421072,0.2876330912,0.2971323729,0.2558148801,0.3765882254,0.1853017509,-0.0902392045,0.4627014101,-0.2508678436,-0.1734852195,0.152273953,0.1026163325,-0.0133816032,0.0926948115,0.4647519588,-0.3321602941,0.1229287907,0.3978904486,-0.2104093581,-0.1099664122,-0.5223609805,0.1911682338,0.2853013873,0.8053057194,-0.3187530339,-0.2136244327,-0.0639347881,-0.0466213264,0.0935501829,0.1669068485,0.1960045397,-0.1385709345,0.0132206343,0.2564665973,-0.27760759,-0.1129507571,0.2547970414,0.0206995755,0.434542954,0.1130512282,0.290637821,-0.1213328838,-0.1309855431,0.0601092987,-0.3038204908,0.1790289581,0.4227370322,-0.393653661,0.0983674303,0.0753656924,0.0314873047,0.2047479749,-0.0097002788,-0.1731485426,0.2701100409,0.1781290025,0.0558500886,0.1880670786,0.2425366193,0.2048566341,0.2180304527,0.3791832328,-0.0918194652,-0.0715203583,0.02139025,0.0087919412,-0.303060472,-0.2551554441,0.2065252811,0.3416895568,-0.2393628508,-0.2113465816,0.1680399925,-0.3933001757,-0.1952902377,-0.1890354902,0.4464722872,0.0903828144,0.3386282921,0.1270755529,0.3620221615,-0.2277982086,-0.4340928197,-0.1698250324,0.2832837701,-0.2518642247,0.1006729528,0.1977664679,0.1170082912,0.1428786665,-0.1512924433,0.0498563163,0.0672362298,0.1198379323,0.0282127038,-0.0978283435,0.3394678831,0.1440699846,0.5026990175,0.1971620619,-0.0773207396,-0.1979626715,0.0690227002,-0.3356408179,-0.0876067355,0.0300218817,0.1454830319,-0.4106610715,0.1392475069,0.0276989378,-0.1609510332,-0.0260570478,-0.1307491064,0.0702846199,-0.0336305313,-0.0718866885,-0.3641683161,0.1907553971,0.7568348646,-0.1171793342,-0.0507880189,0.1492203921,-0.0326972418,-0.0346767493,0.0621211827,-0.0179945864,0.0897547454,-0.2658486664,0.0493914075,0.5301237106,-0.4971586764,-0.3745914698,0.1492233872,-0.1303744167,0.0674136579,0.193470031,0.0981658325,-0.223392114,-0.0491434783,-0.0737069398,0.32162112,0.1220701709,0.1127692088,-0.3805288672,-0.0859911293,0.206578061,0.1555681378,0.2180370688,0.0378918611,0.2462634146,0.1764459312,0.1007474288,-0.2408952415,0.2300170809,0.4026339352,0.0822453201,-0.0768727884,0.2185535282,-0.0472873859,-0.3427559733,0.3855517209,-0.2262893766,0.2532649636,-0.1266787201,0.0323280282,-0.2458299547,-0.0815848038,-0.3950968087,-0.3153595924,0.1326069832,-0.0861049891,0.1729106605,0.4195102155,-0.0904738083,0.5479971766,-0.0806826502,0.0869766548,-0.3802219331,0.3531749547,-0.1568661332,-0.0223212987,0.0899974629,0.1627476811,0.2166815847,-0.2049010694,-0.147184819,0.2453877628,0.0316126756,0.175381422,0.0656094626,-0.1363481283,0.1981263161,-0.417429477,0.2811176479,0.3059633076,0.2019191384,0.0005689365,-0.0248592496,0.31941396,0.2762654126,0.3443269432,0.1001222581,0.1419816464,0.1740662158,-0.115653336,-0.1184245646,-0.2050980926,0.3828374743,0.2041463405,0.2647698522,-0.0008264494,0.0527264811,-0.192902118,0.4928575754,-0.0328532271,0.3465650678,-0.0434932075,-0.4553969204,0.1410555542,0.0728564709,0.0346844718,0.3330875039,0.1398335248,-0.1654711217,0.1523960531,0.1527922004,-0.0811330453,0.070357874,0.0401980095,0.2829311192,0.1309210509,-0.0361876376,0.0502580702,-0.1252750605,-0.0067715566,-0.2078783512,0.1457998157,-0.2919570506,0.0663333833,-0.3126637042,-0.3919141293,-0.1884225458,0.2504111826,-0.47329247,-0.3105266392,-0.1453045309,-0.0280135628,-0.1986221969,0.2472381592,-0.0033547888,-0.192531988,0.1633858979,-0.4583185315,-0.1651168615,-0.3762441278,-0.3328979611,0.0173073206,0.3358540535,0.133705914,0.2784568369,-0.2997746468,0.0515952557,-0.4177368283,-0.2721670866,0.1581750214,-0.0346399918,0.0360914879,0.0005482019,0.4427484274,-0.0048957947,0.0365115628,0.3516401052,-0.2354400307,-0.1929251105,0.3119670451,0.0282668155,-0.0133800702,0.0411232896,-0.6374550462,-0.3070060909,-0.4252651632,-0.1391487718,0.0489384271,-0.0108339433,0.0516505577,0.1416082233,-0.0006669456,0.0320225321,-0.0082557173,-0.0140781803,0.1327501237,0.5062490106,-0.1456731111,-0.5656459928,0.3194148839,-0.1603342742,0.0624311306,0.1956934184,-0.1955649406,-0.0620265044,0.1375658214,-0.0365846641,0.0259969458,0.2926805317,0.3939027488,-0.257181108,0.0382182263,-0.1365457326,0.0062151481,-0.0437446237,-0.0114895953,0.2950164974,0.2645323575,0.1776743829,-0.0364728346,0.7936730981,0.0073540937,0.2100244761,0.4813813567,0.0773186609,0.2936622798,-0.3421624899,-0.1942974031,0.008329886,-0.0027579318,0.01497089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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1790","title":"ModuleNotFoundError: No module named 'apache_beam', when specific languages.","comments":"thanks for your reply and sorry for my late response.\r\n\r\n## environment\r\nmy local machine environment info\r\n- Ubuntu on WSL2\r\n\r\n`lsb_release -a`\r\n```\r\nNo LSB modules are available.\r\nDistributor ID: Ubuntu\r\nDescription:    Ubuntu 20.04.2 LTS\r\nRelease:        20.04\r\nCodename:       focal\r\n```\r\n\r\nRTX 2070 super\r\nInside WSL, there is no nvidia-msi command. I don't know why.\r\nBut, `torch.cuda.is_available()` is true and when I start something ML training code GPU usage is growing up, so I think it works.\r\n\r\nFrom PowerShell, there is nvidia-smi.exe and result is below.\r\n```\r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 470.05       Driver Version: 470.05       CUDA Version: 11.3     |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU  Name            TCC\/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n| Fan  Temp  Perf  Pwr:Usage\/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n|                               |                      |               MIG M. |\r\n|===============================+======================+======================|\r\n|   0  NVIDIA GeForce ... WDDM  | 00000000:09:00.0  On |                  N\/A |\r\n|  0%   30C    P8    19W \/ 175W |    523MiB \/  8192MiB |      3%      Default |\r\n|                               |                      |                  N\/A |\r\n+-------------------------------+----------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------+\r\n| Processes:                                                                  |\r\n|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n|        ID   ID                                                   Usage      |\r\n|=============================================================================|\r\n|    0   N\/A  N\/A      1728    C+G   Insufficient Permissions        N\/A      |\r\n|    0   N\/A  N\/A      3672    C+G   ...ekyb3d8bbwe\\YourPhone.exe    N\/A      |\r\n|    0   N\/A  N\/A      6304    C+G   ...2txyewy\\TextInputHost.exe    N\/A      |\r\n|    0   N\/A  N\/A      8648    C+G   C:\\Windows\\explorer.exe         N\/A      |\r\n|    0   N\/A  N\/A      9536    C+G   ...y\\ShellExperienceHost.exe    N\/A      |\r\n|    0   N\/A  N\/A     10668    C+G   ...5n1h2txyewy\\SearchApp.exe    N\/A      |\r\n|    0   N\/A  N\/A     10948    C+G   ...artMenuExperienceHost.exe    N\/A      |\r\n|    0   N\/A  N\/A     11988    C+G   ...8wekyb3d8bbwe\\Cortana.exe    N\/A      |\r\n|    0   N\/A  N\/A     12464    C+G   ...cw5n1h2txyewy\\LockApp.exe    N\/A      |\r\n|    0   N\/A  N\/A     13280    C+G   ...upport\\CEF\\Max Helper.exe    N\/A      |\r\n|    0   N\/A  N\/A     15948    C+G   ...t\\GoogleIMEJaRenderer.exe    N\/A      |\r\n|    0   N\/A  N\/A     16128    C+G   ...ram Files\\Slack\\Slack.exe    N\/A      |\r\n|    0   N\/A  N\/A     19096    C+G   ...8bbwe\\WindowsTerminal.exe    N\/A      |\r\n+-----------------------------------------------------------------------------+\r\n```\r\n\r\nI don't know what should I show in such a case. If it's not enough, please tell me some commands.\r\n\r\n---\r\n## what I did\r\nI surveyed more and I found 2 issues.\r\n\r\nAbout the first one, I wrote it as a new issue.\r\nhttps:\/\/github.com\/huggingface\/datasets\/issues\/2031\r\n\r\nThe error I mentioned in the previous comment above, which occurred on my local machine, is no longer occurring.\r\n\r\nBut, it still failed. In the previous comment, I wrote `AssertionError: OutputStream realloc failed.` happen on another machine. It also happens on my local machine.\r\n\r\nHere's what I've tried.\r\n\r\nthe wikipedia.py downloads these xml.bz2 files based on dumpstatus.json\r\nIn Japanese Wikipedia dataset that I specified, it will download these 6 files.\r\n\r\n\r\n`https:\/\/dumps.wikimedia.org\/jawiki\/20210120\/dumpstatus.json`\r\nand filtered json based on wikipedia.py is below.\r\n```json\r\n {\r\n   \"jobs\": {\r\n     \"articlesmultistreamdump\": {\r\n       \"files\": {\r\n         \"jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\"\r\n         }\r\n       }\r\n     }\r\n   }\r\n }\r\n```\r\n\r\nSo, I tried running with fewer resources by modifying this line.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/13a5b7db992ad5cf77895e4c0f76595314390418\/datasets\/wikipedia\/wikipedia.py#L524\r\nI changed it like this. just change filepaths list.\r\n`            | \"Initialize\" >> beam.Create(filepaths[:1])`\r\n\r\nand I added a print line inside for the loop of _extract_content.\r\nlike this `if(i % 100000 == 0): print(i)`\r\n\r\nfirst, without modification, it always stops after all _extract_content is done.\r\n\r\n- `filepaths[:1]` then it succeeded.\r\n- `filepaths[:2]` then it failed.\r\nI don't try all patterns because each pattern takes a long time.\r\n\r\n### my opinion\r\nIt seems it's successful when the entire file size is small.\r\n  \r\nso, at least it doesn't file-specific issue.\r\n\r\n\r\nI don't know it's true but I think when beam_writter writes into a file, it consumes memory depends on its entire file.\r\nbut It's correct Apache Beam's behavior? I'm not familiar with this library.\r\n","body":"```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?","comment_length":606,"text":"ModuleNotFoundError: No module named 'apache_beam', when specific languages. \n ```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct? \n thanks for your reply and sorry for my late response.\r\n\r\n## environment\r\nmy local machine environment info\r\n- Ubuntu on WSL2\r\n\r\n`lsb_release -a`\r\n```\r\nNo LSB modules are available.\r\nDistributor ID: Ubuntu\r\nDescription:    Ubuntu 20.04.2 LTS\r\nRelease:        20.04\r\nCodename:       focal\r\n```\r\n\r\nRTX 2070 super\r\nInside WSL, there is no nvidia-msi command. I don't know why.\r\nBut, `torch.cuda.is_available()` is true and when I start something ML training code GPU usage is growing up, so I think it works.\r\n\r\nFrom PowerShell, there is nvidia-smi.exe and result is below.\r\n```\r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 470.05       Driver Version: 470.05       CUDA Version: 11.3     |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU  Name            TCC\/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n| Fan  Temp  Perf  Pwr:Usage\/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n|                               |                      |               MIG M. |\r\n|===============================+======================+======================|\r\n|   0  NVIDIA GeForce ... WDDM  | 00000000:09:00.0  On |                  N\/A |\r\n|  0%   30C    P8    19W \/ 175W |    523MiB \/  8192MiB |      3%      Default |\r\n|                               |                      |                  N\/A |\r\n+-------------------------------+----------------------+----------------------+\r\n\r\n+-----------------------------------------------------------------------------+\r\n| Processes:                                                                  |\r\n|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n|        ID   ID                                                   Usage      |\r\n|=============================================================================|\r\n|    0   N\/A  N\/A      1728    C+G   Insufficient Permissions        N\/A      |\r\n|    0   N\/A  N\/A      3672    C+G   ...ekyb3d8bbwe\\YourPhone.exe    N\/A      |\r\n|    0   N\/A  N\/A      6304    C+G   ...2txyewy\\TextInputHost.exe    N\/A      |\r\n|    0   N\/A  N\/A      8648    C+G   C:\\Windows\\explorer.exe         N\/A      |\r\n|    0   N\/A  N\/A      9536    C+G   ...y\\ShellExperienceHost.exe    N\/A      |\r\n|    0   N\/A  N\/A     10668    C+G   ...5n1h2txyewy\\SearchApp.exe    N\/A      |\r\n|    0   N\/A  N\/A     10948    C+G   ...artMenuExperienceHost.exe    N\/A      |\r\n|    0   N\/A  N\/A     11988    C+G   ...8wekyb3d8bbwe\\Cortana.exe    N\/A      |\r\n|    0   N\/A  N\/A     12464    C+G   ...cw5n1h2txyewy\\LockApp.exe    N\/A      |\r\n|    0   N\/A  N\/A     13280    C+G   ...upport\\CEF\\Max Helper.exe    N\/A      |\r\n|    0   N\/A  N\/A     15948    C+G   ...t\\GoogleIMEJaRenderer.exe    N\/A      |\r\n|    0   N\/A  N\/A     16128    C+G   ...ram Files\\Slack\\Slack.exe    N\/A      |\r\n|    0   N\/A  N\/A     19096    C+G   ...8bbwe\\WindowsTerminal.exe    N\/A      |\r\n+-----------------------------------------------------------------------------+\r\n```\r\n\r\nI don't know what should I show in such a case. If it's not enough, please tell me some commands.\r\n\r\n---\r\n## what I did\r\nI surveyed more and I found 2 issues.\r\n\r\nAbout the first one, I wrote it as a new issue.\r\nhttps:\/\/github.com\/huggingface\/datasets\/issues\/2031\r\n\r\nThe error I mentioned in the previous comment above, which occurred on my local machine, is no longer occurring.\r\n\r\nBut, it still failed. In the previous comment, I wrote `AssertionError: OutputStream realloc failed.` happen on another machine. It also happens on my local machine.\r\n\r\nHere's what I've tried.\r\n\r\nthe wikipedia.py downloads these xml.bz2 files based on dumpstatus.json\r\nIn Japanese Wikipedia dataset that I specified, it will download these 6 files.\r\n\r\n\r\n`https:\/\/dumps.wikimedia.org\/jawiki\/20210120\/dumpstatus.json`\r\nand filtered json based on wikipedia.py is below.\r\n```json\r\n {\r\n   \"jobs\": {\r\n     \"articlesmultistreamdump\": {\r\n       \"files\": {\r\n         \"jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream1.xml-p1p114794.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream2.xml-p114795p390428.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream3.xml-p390429p902407.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream4.xml-p902408p1721646.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream5.xml-p1721647p2807947.bz2\"\r\n         },\r\n         \"jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\": {\r\n           \"url\": \"\/jawiki\/20210120\/jawiki-20210120-pages-articles-multistream6.xml-p2807948p4290013.bz2\"\r\n         }\r\n       }\r\n     }\r\n   }\r\n }\r\n```\r\n\r\nSo, I tried running with fewer resources by modifying this line.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/13a5b7db992ad5cf77895e4c0f76595314390418\/datasets\/wikipedia\/wikipedia.py#L524\r\nI changed it like this. just change filepaths list.\r\n`            | \"Initialize\" >> beam.Create(filepaths[:1])`\r\n\r\nand I added a print line inside for the loop of _extract_content.\r\nlike this `if(i % 100000 == 0): print(i)`\r\n\r\nfirst, without modification, it always stops after all _extract_content is done.\r\n\r\n- `filepaths[:1]` then it succeeded.\r\n- `filepaths[:2]` then it failed.\r\nI don't try all patterns because each pattern takes a long time.\r\n\r\n### my opinion\r\nIt seems it's successful when the entire file size is small.\r\n  \r\nso, at least it doesn't file-specific issue.\r\n\r\n\r\nI don't know it's true but I think when beam_writter writes into a file, it consumes memory depends on its entire file.\r\nbut It's correct Apache Beam's behavior? I'm not familiar with this library.\r\n","embeddings":[-0.0164622311,-0.1208429337,-0.0449261479,0.1289527118,0.3602375388,0.0117902383,0.4635161161,0.3990748227,0.2657029033,0.2212447822,0.3692156672,-0.0926491097,0.0788542926,-0.0503802449,0.3171323836,-0.3475853801,0.3893397152,-0.1023074314,-0.0446148254,-0.2853878438,-0.3970679343,0.0972740725,-0.327434361,0.204333216,-0.2763909101,0.3834485114,0.0855949596,0.0011822655,0.2684019208,-0.0742643848,0.0483055077,-0.3112154603,0.2795861363,0.1183508188,-0.0001173796,0.1539295018,0.278711319,-0.3796845078,-0.2545412481,-0.3259486854,0.1180826053,-0.5497899652,0.0536575466,-0.4102146924,-0.3081610799,-0.3167407513,-0.0167241152,-0.3641595542,0.2351645082,0.2283264399,0.1696380079,0.2152368277,-0.0553050004,0.0463573895,0.4933214486,0.0602145866,-0.3058097661,-0.0181186721,0.069760412,0.0744795501,0.1465839148,0.6420863867,-0.2314606458,-0.0152312061,0.0673134327,-0.0167856645,0.1663643569,-0.3263391256,0.19459562,0.0899543688,0.4828100801,-0.2662925422,-0.0162716396,0.0410083421,-0.1090239137,-0.1496426016,0.2806896865,0.2335036099,-0.2153159529,0.0255010594,0.446775943,-0.075121589,0.0193944462,0.1508821994,-0.0414782725,0.6642962694,0.0698077157,0.3139586449,-0.1830180436,-0.0387369879,-0.2058107555,-0.1763622612,0.2024045289,0.1869180501,-0.4122222066,0.0578316189,0.1730617136,-0.1539803296,-0.1882955134,0.0296372641,-0.2198068798,0.022954246,0.5886098742,0.0437284037,-0.1492434144,0.1641064882,0.2641566396,-0.1119217053,0.2892181575,-0.2116505355,-0.0529462434,-0.0375723504,-0.1176789403,-0.3208047152,-0.6114045382,0.2161325961,0.3799573481,-0.1107203215,-0.3758541346,0.0015660009,-0.7844842076,-0.1767554581,-0.0009919176,0.5293400884,0.2928486764,0.418664515,0.2891373038,0.2036619633,-0.2172861695,-0.3478887975,-0.0793721676,0.2945079505,-0.3448713422,0.2523286641,0.1170190796,0.1859697104,0.2121743113,-0.2013958693,-0.0995041579,0.0343454182,0.0177220199,-0.0544664115,0.1690147072,0.4799576998,0.0484015569,0.4080634713,0.2257784307,0.0116720451,-0.1172764748,0.2429509014,-0.154858768,-0.3944120705,-0.1468821168,0.0511061251,-0.016755797,0.1968870014,0.3628740013,0.0426825397,0.1228851378,0.012357777,0.253030926,0.0178576149,-0.1054161936,-0.212425977,0.2507418394,0.5459201932,-0.303904146,-0.1106563732,0.3961590827,-0.105383873,-0.078605324,0.1152247265,-0.097856611,0.163681522,-0.2283571512,-0.0972187445,0.4880371392,-0.5070934892,-0.0110670151,0.0804193318,-0.1673003882,-0.1454685032,0.4160169363,-0.0158101302,-0.215586096,-0.1384606659,-0.2177503705,0.0179399848,0.0955032632,-0.0426162966,-0.2979273498,-0.0848434418,0.2965357006,0.21431683,0.236806035,-0.1862458289,0.1066011116,0.7018893957,0.180423528,-0.1468919516,0.1232184842,0.1206345037,0.2017994672,-0.0314943567,0.2057330459,0.081673488,-0.319511503,0.1778331995,-0.1611637324,0.2450395823,-0.0391570479,0.0096766958,0.1222385839,-0.0296207815,-0.1899358928,-0.4091172814,0.0764830336,0.1182895973,0.0033131961,0.221707359,-0.3516422212,0.2491336018,-0.6650169492,0.0230833925,-0.1228884459,0.0653185621,-0.2248590589,-0.1393245459,-0.2083543092,0.2041692883,0.1631704569,-0.2657233477,-0.0550111122,-0.0124198413,0.0863434672,-0.1019486636,-0.1688940376,0.1001053229,0.1083895713,-0.1080817431,0.3500638306,0.0759367421,0.2190008312,0.0002390871,-0.0466164351,0.1902189553,0.3913536072,0.4031084478,0.0441157036,-0.0826886818,0.1671350598,-0.0675747916,-0.2320238948,-0.2165590972,0.1896586716,0.316590935,0.0288632903,-0.0392221026,0.0378770046,-0.1052472144,0.592294991,0.1246693283,0.297246784,-0.0710373074,-0.3637948334,0.1717136204,0.2669872046,-0.136199981,0.3765210509,0.1118713319,-0.2514681816,0.0740888342,0.1832255572,-0.1975178421,0.1446842402,0.0935384706,0.1617263407,0.1095689833,-0.1530915946,-0.00496019,-0.1756981611,0.0117729921,-0.0014159677,-0.1109336689,-0.2594310939,0.1029188037,-0.3478597403,-0.4859148264,-0.2813389003,0.0404133014,-0.2785394788,-0.0732485875,-0.1160379052,-0.0294321608,-0.2187058777,0.4304548502,0.2571213245,0.021932533,-0.0411654711,-0.3306412995,-0.1026054248,-0.2048814148,-0.2449599057,-0.003624555,0.3105489612,0.1761790067,0.2550486326,-0.3488143682,0.0870179385,-0.108633928,-0.3360551894,0.1465450972,0.0418365188,-0.2310839593,-0.1000549942,0.1832405925,0.0651180074,-0.0239215121,0.4265317917,-0.1253953725,-0.1427242905,-0.0013663948,-0.049899675,-0.0958694741,-0.1020317972,-0.4964431524,-0.2645772099,-0.4758715034,-0.4259921908,0.2412468791,0.0810968503,-0.1281322837,0.16065301,0.292352736,-0.1703210622,0.0371363312,0.0311211087,0.2079810202,0.2317314893,-0.0090702567,-0.3604699969,0.4372163713,-0.0842040181,0.0385988802,0.1158174276,-0.2603441477,0.013745781,0.2989229858,-0.0371980071,-0.0009432279,0.2632432282,0.7203304768,-0.225091055,0.0127088632,-0.0141631505,-0.3290919363,0.0510527492,0.0073770634,0.5198993087,0.1697766036,0.4387988746,0.0848533288,0.8618529439,-0.0845359787,-0.0700841323,0.3741458356,0.0939900205,0.207183376,-0.316272378,-0.0314853527,0.2195483893,0.0864551291,-0.010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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1790","title":"ModuleNotFoundError: No module named 'apache_beam', when specific languages.","comments":"I don't know if this is related, but there is this issue on the wikipedia processing that you reported at #2031 (open PR is at #2037 ) .\r\nDoes the fix your proposed at #2037 helps in your case ?\r\n\r\nAnd for information, the DirectRunner of Apache Beam is not optimized for memory intensive tasks, so you must be right when you say that it uses the memory for the entire file.","body":"```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?","comment_length":72,"text":"ModuleNotFoundError: No module named 'apache_beam', when specific languages. \n ```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct? \n I don't know if this is related, but there is this issue on the wikipedia processing that you reported at #2031 (open PR is at #2037 ) .\r\nDoes the fix your proposed at #2037 helps in your case ?\r\n\r\nAnd for information, the DirectRunner of Apache Beam is not optimized for memory intensive tasks, so you must be right when you say that it uses the memory for the entire file.","embeddings":[0.1117349043,-0.0064410353,0.008770884,0.3851280212,0.3766983449,0.0904633775,0.1866939515,0.4343985915,0.2380293459,0.1273683906,0.1705141813,0.0224920474,-0.0121388175,-0.2175724953,0.0293770581,-0.3352380395,0.2749054134,-0.0193757229,-0.170393005,-0.2335792184,-0.3629105985,0.1133486554,-0.4148781896,-0.1094507873,-0.1601733416,0.1928723603,-0.0480315238,0.0954622552,0.1394944191,-0.3413199782,-0.1171517968,-0.1067331508,-0.0180490203,0.3060228229,-0.0001087955,0.0912562832,0.5745626688,-0.1888058186,-0.2846512198,0.0406400599,-0.0371731855,-0.495447129,-0.0139747849,-0.4732246399,-0.0351132341,-0.2119355202,0.1526018977,-0.3777803779,0.3918661177,0.1425276399,0.2509561777,0.2651480734,0.0663075224,-0.0078427643,0.5856633186,-0.0968204886,-0.0510738976,-0.1122035012,-0.2888480127,-0.0498995297,-0.2235361189,0.599576354,-0.2624768615,0.0702701062,0.3599029779,-0.2300790399,-0.149204731,-0.4521154761,0.3267112672,0.1715229154,0.8122545481,-0.2932122052,-0.1780870855,-0.1161256284,-0.1816053092,0.0567492656,0.2139713913,0.3344722688,-0.1897802502,-0.0612507463,0.4706632793,-0.227505818,-0.0409827977,0.0693851858,0.020294413,0.3031225502,0.0945488587,0.1988317519,0.1080079526,-0.0590918399,-0.0815237164,-0.2535690367,0.0567316376,0.2874174416,-0.3248102665,0.1121491566,0.1160264686,0.062517181,0.1234364212,-0.2639259398,-0.2927086353,0.2417604625,0.2671032548,0.0355527736,0.2241144478,0.1502585113,0.0286995266,0.1377189308,0.5333328247,-0.1961988807,-0.1896426678,-0.0155713782,0.0257251393,-0.2397148013,-0.083557196,-0.070114553,0.1421355158,-0.1068671793,-0.0019677756,0.1800702661,-0.2986363471,-0.1659310758,-0.2292383164,0.5316998959,0.0575341061,0.5937864184,0.2013931125,0.126219213,-0.343328476,-0.2992616296,-0.1430035681,0.3405376673,-0.3400125802,0.2080896795,0.0598624051,0.2209750414,0.2671977878,-0.1440650076,0.0279361475,-0.0987526625,0.1566368192,-0.1712189764,-0.0322682187,0.4470678568,0.0875977725,0.4486974478,0.1538452506,-0.0098935515,-0.0720622018,0.2632384896,-0.2432984412,-0.3493455946,0.2551338673,0.1173766479,-0.287961781,0.2902911603,0.0025140997,-0.0284827761,0.1678847522,-0.0079855034,0.0465840176,-0.0180220101,-0.2205018252,-0.3830631375,0.2032209784,0.796346128,-0.069289431,0.0477906503,0.0506921969,-0.0662884638,0.1584432423,0.1949176788,-0.0704009309,0.2086960524,-0.1676570475,-0.0316931754,0.5124144554,-0.5858023763,-0.1874885708,0.2188697606,0.0223700199,0.033020895,0.266482085,0.107522808,-0.1900941581,-0.1260578781,-0.0653749332,0.1762634218,0.1519229859,0.3302550614,-0.5582460761,0.0229572281,0.0813492984,0.1652372479,0.3261908293,-0.0923689976,-0.013221873,0.5644573569,0.2360059023,-0.2773537338,0.2775585949,0.4496910274,-0.1457103342,-0.021472482,0.3341239393,-0.0170702338,0.0314774066,0.2832883596,-0.2546029091,0.2395661175,0.017152505,-0.1440320611,-0.0935746357,-0.009036338,-0.217204392,-0.3565832376,0.1877317429,-0.0370384529,-0.0251046866,0.2928380966,-0.090611212,0.3070314527,-0.198390156,-0.0440984257,-0.3090109825,0.0575743206,-0.2160296738,-0.009151265,0.0731722489,0.0985509753,0.1550627649,-0.039197538,-0.1397496313,0.2085858583,0.2723045051,0.0015527311,0.064366959,-0.0344755128,0.1558855325,-0.2128017694,0.3646449447,0.2092391551,0.2507326901,-0.1380883306,-0.0111312671,0.1207710877,0.2412226498,0.3808264136,0.1354600191,-0.0667670816,0.0642810762,0.0613384545,-0.0879729241,-0.2283333838,0.1964172274,0.3356743753,0.0988572016,0.0624628328,0.0435915925,-0.2446837723,0.5019389987,0.0910699517,0.1994129121,0.0351188853,-0.5036807656,-0.0295866113,0.1220312491,-0.123221539,0.2044374496,0.1894410253,-0.0085466793,-0.0047064982,0.3041694462,-0.0965253934,0.2194781303,0.1881991476,0.3777441382,0.021214461,0.0054696547,-0.0665690377,-0.1015988737,0.0596394688,-0.1179074049,0.1842221469,-0.1934273839,-0.0259490423,-0.286480695,-0.4760195315,-0.1996368617,0.3563892543,-0.3619391918,-0.2971886098,-0.0733863041,-0.0943983123,-0.251352638,0.2048183233,0.1730594784,-0.0069653168,0.0783338472,-0.2935427427,-0.1500487328,-0.5236815214,-0.2300798893,0.0003816478,0.3928463161,0.05052628,0.2879631221,-0.0820050389,-0.0886784419,-0.1884259135,-0.0557332896,0.097169362,0.0015285301,-0.0784612224,-0.1432334036,0.4003280103,0.0184107292,0.0845485553,0.346652329,-0.1144120619,-0.0997573361,0.1798037142,0.1147885472,0.1361715198,0.0201299712,-0.407571137,-0.1568488032,-0.5240032673,-0.036450047,0.1353272945,0.14698717,-0.0409758911,0.1697188765,0.0780954957,0.1428804249,0.141445592,-0.0451132134,0.101059787,0.3315563202,-0.1958372146,-0.4916899204,0.2615267634,-0.1101638898,0.0772579312,0.2027608901,-0.3696198761,-0.1323046833,0.0450658016,-0.0470005199,-0.0215638541,0.3600570261,0.4021160603,-0.1137850657,-0.0945901349,-0.0554751232,-0.0545101985,-0.1878399998,-0.3114802241,0.126268521,0.1768587083,0.2660343051,0.124463588,0.8918671608,0.061335057,0.528016448,0.4090193808,0.1065370813,0.2370456606,-0.3116820157,-0.0100682499,0.0871226564,-0.021293534,-0.1122227311,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1790","title":"ModuleNotFoundError: No module named 'apache_beam', when specific languages.","comments":"the #2037 doesn't solve my problem directly, but I found the point!\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/349ac4398a3bcae6356f14c5754483383a60e8a4\/datasets\/wikipedia\/wikipedia.py#L523\r\nthis `beam.transforms.Reshuffle()` cause the memory error.\r\n\r\nit makes sense if I consider the shuffle means. Beam's reshuffle seems need put all data in memory.\r\nPreviously I doubt that this line causes error, but at that time another bug showed in #2037 made error, so I can't found it.\r\n\r\nAnyway, I comment out this line, and run load_dataset, then it works!\r\n\r\n```python\r\nwiki = datasets.load_dataset(\r\n    \".\/wikipedia.py\",\r\n    cache_dir=\".\/datasets\",\r\n    beam_runner=\"DirectRunner\",\r\n    language=\"ja\",\r\n    date=\"20210120\",\r\n)[\"train\"]\r\n```\r\n![image](https:\/\/user-images.githubusercontent.com\/6331508\/112283369-6a9f3300-8ccb-11eb-82e5-827bf7fddfb9.png)\r\n\r\nDataset has already shuffle function. https:\/\/github.com\/huggingface\/datasets\/blob\/349ac4398a3bcae6356f14c5754483383a60e8a4\/src\/datasets\/arrow_dataset.py#L2069\r\nSo, though I don't know it's difference correctly, but I think Beam's reshuffle isn't be needed. How do you think?","body":"```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?","comment_length":111,"text":"ModuleNotFoundError: No module named 'apache_beam', when specific languages. \n ```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct? \n the #2037 doesn't solve my problem directly, but I found the point!\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/349ac4398a3bcae6356f14c5754483383a60e8a4\/datasets\/wikipedia\/wikipedia.py#L523\r\nthis `beam.transforms.Reshuffle()` cause the memory error.\r\n\r\nit makes sense if I consider the shuffle means. Beam's reshuffle seems need put all data in memory.\r\nPreviously I doubt that this line causes error, but at that time another bug showed in #2037 made error, so I can't found it.\r\n\r\nAnyway, I comment out this line, and run load_dataset, then it works!\r\n\r\n```python\r\nwiki = datasets.load_dataset(\r\n    \".\/wikipedia.py\",\r\n    cache_dir=\".\/datasets\",\r\n    beam_runner=\"DirectRunner\",\r\n    language=\"ja\",\r\n    date=\"20210120\",\r\n)[\"train\"]\r\n```\r\n![image](https:\/\/user-images.githubusercontent.com\/6331508\/112283369-6a9f3300-8ccb-11eb-82e5-827bf7fddfb9.png)\r\n\r\nDataset has already shuffle function. https:\/\/github.com\/huggingface\/datasets\/blob\/349ac4398a3bcae6356f14c5754483383a60e8a4\/src\/datasets\/arrow_dataset.py#L2069\r\nSo, though I don't know it's difference correctly, but I think Beam's reshuffle isn't be needed. How do you think?","embeddings":[0.119434841,0.110886395,0.0094729532,0.4295069575,0.5010867715,0.0540743396,0.1819471717,0.4008020759,-0.0547771603,0.316262126,0.1112686098,0.0270881485,-0.1956253052,-0.3097158074,0.0873773694,-0.3430968225,0.269775033,-0.0960953832,-0.2489880174,-0.2321131676,-0.3576426208,0.0421388857,-0.4852919579,-0.1931643486,-0.2590049207,0.3534104824,-0.1904881597,0.2451300025,-0.0117135011,-0.17570813,-0.0820924789,-0.1067039967,-0.1121078283,0.3131394386,-0.0001133347,0.1047418267,0.3758174777,-0.2779262066,-0.3259073198,-0.1989419013,-0.0553082637,-0.3000113964,-0.1625952423,-0.3917806149,-0.0717182159,-0.2675777078,0.1196648106,-0.3548972011,0.422327131,0.201214999,0.2053963393,0.225887537,0.0281292796,-0.0164729133,0.6700220704,0.0871259719,-0.1116377413,0.0683365986,-0.1488498002,0.0098396195,-0.1615006328,0.549500823,-0.2070959359,0.074329488,0.2397671491,-0.2371826023,-0.2322136909,-0.3541304469,0.2216895223,0.1782290488,0.6323610544,-0.3489941955,-0.124347426,-0.1607485861,-0.0539470986,0.1443855762,0.2540586889,0.1864249408,-0.1200918108,-0.1221161336,0.1319267005,-0.1353091449,0.0127926497,0.2098914981,0.230622232,0.424031496,0.0276736021,0.2996921539,0.1630720496,-0.1836214811,0.1571726948,-0.2461177707,0.0766194835,0.357293427,-0.3937429488,0.0347965136,0.0281328484,0.0014923022,0.108503826,-0.1232893765,-0.1755147129,0.1744697839,0.3144144118,-0.0094594676,0.1199689955,0.4047933221,0.0989428982,0.26471892,0.1185763478,-0.1872927994,-0.063955538,-0.0435254909,-0.0904999748,-0.4336491525,-0.262034744,0.2131782025,0.1339182705,-0.0305909198,-0.1540098786,0.056545902,-0.4272989929,-0.1620934904,-0.0612861179,0.4848382175,0.1439730823,0.500371933,0.2122715861,0.1486690044,-0.2858898342,-0.2623577416,-0.2568903267,0.4522441626,-0.481220603,0.1976672858,0.0383407511,0.2481758595,0.2800936699,-0.1724491417,0.1591964513,0.0334207118,0.0613640845,-0.1664860398,0.048756659,0.4103954434,0.0119138006,0.3502828777,0.1928022653,0.1393462718,-0.0928634256,0.2679367065,-0.3535985053,-0.3601426184,0.2299660593,0.0713830814,-0.3113717437,0.1920525134,-0.1264992952,-0.0647272021,0.1432967186,0.0446434766,0.2136843801,-0.0210933238,-0.1541174054,-0.3666602075,0.1856174916,0.777557373,-0.1078999266,-0.0855644569,0.033053983,-0.2180709094,0.1770857871,0.06840837,-0.07541731,0.0966386795,-0.271671325,0.0151542276,0.569583416,-0.4580412805,-0.1668783426,-0.0221467726,-0.0789949968,0.0365227982,0.189515695,0.1257303208,-0.2894612551,-0.104720287,0.117308259,0.1043076292,0.0383791775,0.2566961646,-0.4343810081,-0.1676717252,0.0616063438,0.2503466308,0.2828432024,-0.0925019979,0.0311437473,0.5866729021,0.1889612526,-0.1102685556,0.2881116867,0.4524781406,-0.0726473853,0.0778378174,0.16384238,-0.0598281808,-0.2577006221,0.29026106,-0.1085456833,0.2222910076,0.050758332,-0.0135211656,-0.0683132559,-0.2290741056,-0.297770381,-0.282966882,0.1336334944,0.0285771936,0.0880181268,0.1543253958,-0.2626301944,0.3814667165,-0.3416258693,0.0295177028,-0.5024129152,0.2508628964,-0.1954639256,-0.1462162435,0.0308585577,0.08732526,0.1917511374,-0.1371607929,-0.1381656677,0.391384244,0.2522928417,-0.0563739017,-0.0376479924,-0.0895878524,0.1325113177,-0.2032930702,0.3407712281,0.0584762096,0.1820220649,-0.1298588067,0.1848866343,0.1325737238,0.0625198483,0.3970299959,0.1156466007,-0.0693381056,-0.0677482262,0.1494114399,-0.1136081815,-0.3245390654,0.3509789705,0.332865864,0.1574705243,0.08859317,0.0377293639,-0.2971400619,0.6881403923,0.1426716894,0.1136762351,-0.0574944168,-0.4095008373,0.0693988726,0.1513948888,0.0344957672,0.4079182148,0.1365347356,-0.1149398386,0.1409748793,0.2153791785,-0.0987564772,0.1317031085,0.2377418131,0.4221460521,0.0527265966,-0.0896576345,-0.0112266522,-0.1930359602,-0.1728314012,0.0667530224,0.1590051949,-0.2549693286,-0.1209206656,-0.4197220504,-0.517444849,-0.275336206,0.1492746472,-0.3977880478,-0.245301798,-0.1070484668,-0.0726701394,-0.3224294484,0.3194853365,0.2790144384,-0.0490602814,0.0168626588,-0.3235235512,-0.1718972772,-0.2900437415,-0.2571320832,0.0216138382,0.3672423363,0.0608446002,0.2379075438,-0.2665831745,-0.1685305834,-0.1087961793,-0.1508057266,0.1511537284,0.0263009146,-0.1442512125,-0.1114368215,0.351028502,-0.1820258945,-0.1018859893,0.4775199294,-0.0571261868,-0.2166934162,0.2488492876,0.183147043,0.0790500864,-0.1002003998,-0.4466011226,-0.1296705604,-0.5109665394,-0.0740841255,0.0898636878,0.0974504128,0.1583665609,0.2631686926,0.1819608361,-0.1276183277,-0.0301727708,-0.1160964295,-0.0521410853,0.2722156942,-0.1465057135,-0.3368284702,0.2014806122,-0.1044794023,-0.0021723937,0.3633410931,-0.1813987643,-0.097296685,0.0007995882,-0.2872744203,-0.0532515272,0.3520984352,0.5604915023,-0.1533249021,-0.0316332802,-0.0331121609,-0.0858613476,-0.1202542633,0.0027140847,0.3534732461,0.0590062961,0.2731181085,0.1735912561,0.932516098,-0.0268989336,0.4621212482,0.4056243598,0.049927447,0.3116858602,-0.3958239853,-0.0405504853,0.2475835532,0.1420385987,-0.0782605857,0.19396355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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1790","title":"ModuleNotFoundError: No module named 'apache_beam', when specific languages.","comments":"The reshuffle is needed when you use parallelism.\r\nThe objective is to redistribute the articles evenly on the workers, since the `_extract_content` step generated many articles per file. By using reshuffle, we can split the processing of the articles of one file into several workers. Without reshuffle, all the articles of one file would be processed on the same worker that read the file, making the whole process take a very long time.","body":"```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?","comment_length":73,"text":"ModuleNotFoundError: No module named 'apache_beam', when specific languages. \n ```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct? \n The reshuffle is needed when you use parallelism.\r\nThe objective is to redistribute the articles evenly on the workers, since the `_extract_content` step generated many articles per file. By using reshuffle, we can split the processing of the articles of one file into several workers. Without reshuffle, all the articles of one file would be processed on the same worker that read the file, making the whole process take a very long 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1790","title":"ModuleNotFoundError: No module named 'apache_beam', when specific languages.","comments":"Maybe the reshuffle step can be added only if the runner is not a DirectRunner ?","body":"```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct?","comment_length":16,"text":"ModuleNotFoundError: No module named 'apache_beam', when specific languages. \n ```py\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.ja', cache_dir='.\/datasets')\r\n```\r\nthen `ModuleNotFoundError: No module named 'apache_beam'` happend.\r\n\r\nThe error doesn't appear when it's '20200501.en'.\r\nI don't know Apache Beam, but according to #498 it isn't necessary when it's saved to local. is it correct? \n Maybe the reshuffle step can be added only if the runner is not a DirectRunner ?","embeddings":[-0.0378297865,0.0517897308,0.045922339,0.1618950516,0.2217684835,0.0495494045,0.2877710462,0.3032413721,0.0259332228,0.179933846,0.2910352945,-0.0192492269,0.1045147106,-0.0232278313,0.1100320294,-0.3740752339,0.3071031868,-0.0370492227,-0.0959916338,-0.281830281,-0.442635417,0.1054399982,-0.379992038,-0.1579352915,-0.0031889877,0.2331445068,-0.05683605,0.2654174268,0.2003732473,-0.1437357068,-0.0345408544,-0.2057222873,-0.0999974385,0.3504476547,-0.000123889,0.2205034047,0.3199234605,-0.293764621,-0.2419773638,-0.1562591195,0.0231217556,-0.3215471506,-0.1388468146,-0.3631034791,-0.101666145,-0.0253451038,0.0665042177,-0.3819905818,0.6048766375,0.1928147525,0.1098594815,0.4210021794,-0.020772513,-0.0147780627,0.3787573278,-0.0865190327,-0.1000084653,-0.0779461712,-0.2010744512,0.2293732613,-0.171431005,0.3713362515,-0.2388273776,0.0087697785,0.0032124519,-0.2786224782,-0.0851293057,-0.4114531279,0.1897355914,0.2687947154,0.4893785715,-0.2717169523,-0.2248738855,-0.2171139568,0.0023349314,-0.0665183142,0.2809661031,0.0015106902,0.0660388917,0.0086567244,0.5118035674,-0.3065735996,0.1025713012,-0.0175952259,-0.1090355292,0.6667248011,0.2183995992,0.357163012,-0.0904260501,0.1953649521,0.332996577,-0.2005532831,0.0652890056,0.1246236116,-0.3468623161,-0.0545837805,0.0373448953,0.1232199669,-0.1677461118,-0.009198511,-0.2472332567,0.1628329456,0.3050271571,0.063653037,0.0405441672,0.2389967591,0.1641890854,0.3014983833,0.2608810365,-0.3187677264,0.1340390891,0.0162616652,0.1202592701,-0.5691347718,-0.5156914592,0.3740118742,0.0190277621,-0.0261211637,-0.1588702649,-0.0768229663,-0.3505404294,-0.2740905285,-0.0533224158,0.3207163513,0.1593187302,0.3862256706,0.137473613,0.304372102,-0.2998504937,-0.1806768328,-0.0933372676,0.1023257375,-0.2912924588,0.0453901216,-0.0807791501,0.0810583979,0.1793289036,-0.1545542479,0.1975855678,0.2324463874,-0.0161839221,0.0808045417,-0.0513106994,0.4848632216,0.2038983852,0.3424087167,0.4302805364,0.1734014153,-0.2173183113,0.3705198169,-0.4540519714,-0.3109500706,0.1928400844,-0.0105659207,-0.1869196445,0.0678293332,-0.0408775695,-0.0447849706,0.1960520595,-0.1627370268,0.2673732936,-0.1260741204,-0.1356477141,-0.2988694012,0.0402593277,0.6308399439,-0.1694732308,-0.0710991621,0.0578628331,-0.0961322412,0.1474748254,-0.0768611059,-0.0319539048,0.2058146298,-0.2901695669,-0.0351816416,0.9126730561,-0.5441179276,0.046596624,-0.0909166038,-0.3012412786,0.0395175368,0.5204709768,-0.0860053599,-0.2685622573,-0.2502494156,-0.0669794902,0.280220747,0.0710942894,0.1019854546,-0.2831763625,0.1158957928,0.1996939331,0.2040532976,0.347771883,0.1337680519,-0.0263225343,0.3089297712,0.1329003274,-0.043742165,0.210990116,0.3810606003,-0.3102937639,0.0953511894,0.3288868964,0.1389881968,-0.0363512002,0.273640424,-0.2340336293,0.1261845827,0.2200858444,-0.0863326341,-0.0491343476,-0.3538447917,-0.447189182,-0.1766669452,-0.017715564,0.0479140356,0.100673534,0.2592098713,-0.4386266768,0.3504111767,-0.2382588834,0.1331081688,-0.3313281834,0.3779862523,-0.1291907132,-0.0094744954,-0.0357464924,0.1551730484,0.1023946106,-0.2608760893,-0.0117665604,0.3821970522,0.1847783774,0.0009458666,0.0661202073,-0.2647804916,0.1108395979,-0.0088176252,0.3106170297,0.2222774923,-0.0423321202,-0.1995381862,0.1582317352,0.38154006,-0.0263559707,0.5533995628,0.0767531693,0.0388492607,0.0933386534,0.0327161737,-0.1753463149,-0.0997213349,0.3674673438,0.2749312222,0.1782632768,-0.1733285636,-0.0387798697,-0.1163836643,0.6952420473,0.1830939502,0.1338067204,0.0048368541,-0.2690501511,0.1514980644,-0.0059789564,0.0746214315,0.3360258937,0.202152282,0.0381283127,0.1766224205,0.2083055079,-0.1654423028,0.0427262001,0.1187444925,0.3286023736,0.3047626317,-0.2040672302,-0.0914360657,-0.3562949598,-0.0728420764,-0.0272835717,0.0737777576,-0.199899435,-0.0119142653,-0.2538814247,-0.4885871112,-0.4647909403,-0.0220778082,-0.5278829932,-0.3266161382,0.1899416745,0.0178199653,-0.1534126103,0.5467302203,0.3333817422,-0.1433529854,-0.0060545877,-0.419012785,-0.2095358819,-0.26168859,-0.2897976339,0.0098996479,0.3442018926,-0.0150851095,0.064774394,-0.3819509745,-0.0901692286,-0.1610023528,-0.2000038922,0.0520419963,-0.0357182696,-0.3044560552,-0.0490274988,0.3831196725,-0.1667331606,0.0269654505,0.3886791468,0.1256408095,-0.2855384648,0.2123602331,0.1414213926,0.0906042233,0.0217540469,-0.5343981385,-0.2619627714,-0.4425385892,-0.1127432287,0.3377592862,-0.0179360975,-0.2148833126,0.2542978227,-0.177093938,0.0141038094,-0.2195478529,-0.0476564765,-0.0188096277,0.2171597034,-0.0823240355,-0.2377469391,0.1454093605,-0.1483948231,-0.0491365977,0.1627832651,-0.1970965117,-0.2041375041,0.2359644622,-0.2119003385,-0.155411616,0.6291600466,0.791995585,-0.058462251,0.0516695939,-0.0522565953,-0.0450738333,-0.1296370327,-0.0457174741,0.4874377847,-0.0442974418,0.3777843416,-0.0016312762,0.8330232501,-0.0795279145,0.5151055455,0.2486043721,0.0314508714,0.357662946,-0.5895619988,0.0750974342,0.1400813758,0.1810805351,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1786","title":"How to use split dataset ","comments":"By default, all 3 splits will be loaded if you run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"lambada\")\r\nprint(dataset[\"train\"])\r\nprint(dataset[\"valid\"])\r\n\r\n```\r\n\r\nIf you wanted to do load this manually, you could do this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndata_files = {\r\n   \"train\": \"data\/lambada\/train.txt\",\r\n   \"valid\": \"data\/lambada\/valid.txt\",\r\n   \"test\": \"data\/lambada\/test.txt\",\r\n}\r\nds = load_dataset(\"text\", data_files=data_files)\r\n```","body":"![Capture1](https:\/\/user-images.githubusercontent.com\/78090287\/106057436-cb6a1f00-6111-11eb-8c9c-3658065b1fdf.PNG)\r\n\r\nHey,\r\nI want to split the lambada dataset into corpus, test, train and valid txt files (like penn treebank) but I am not able to achieve this. What I am doing is, executing the lambada.py file in my project but its not giving desired results. Any help will be appreciated!","comment_length":56,"text":"How to use split dataset  \n ![Capture1](https:\/\/user-images.githubusercontent.com\/78090287\/106057436-cb6a1f00-6111-11eb-8c9c-3658065b1fdf.PNG)\r\n\r\nHey,\r\nI want to split the lambada dataset into corpus, test, train and valid txt files (like penn treebank) but I am not able to achieve this. What I am doing is, executing the lambada.py file in my project but its not giving desired results. Any help will be appreciated! \n By default, all 3 splits will be loaded if you run the following:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"lambada\")\r\nprint(dataset[\"train\"])\r\nprint(dataset[\"valid\"])\r\n\r\n```\r\n\r\nIf you wanted to do load this manually, you could do this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndata_files = {\r\n   \"train\": \"data\/lambada\/train.txt\",\r\n   \"valid\": \"data\/lambada\/valid.txt\",\r\n   \"test\": \"data\/lambada\/test.txt\",\r\n}\r\nds = load_dataset(\"text\", data_files=data_files)\r\n```","embeddings":[-0.3074781597,-0.046431113,-0.0625262633,0.4651071429,0.1305702031,0.2037909329,0.2765963376,0.5807616711,-0.4295654595,0.0150459204,-0.3925747275,0.254095912,0.1838493347,0.3340367377,-0.1944946945,-0.4850153923,0.0906330496,0.2447201163,0.0513657033,-0.1060134172,-0.07859727,0.1636883616,-0.4311268926,-0.1009087116,-0.5181128979,-0.1812312454,-0.2913366556,0.1126133949,-0.3164643347,0.0334359258,0.0643469393,0.1915279776,0.2770765722,0.4318369627,-0.00012366,0.0267235059,-0.0548695512,-0.251575917,-0.145966813,-0.0903863758,0.0458019525,0.0903777853,0.1970305294,-0.3328788877,-0.1814205348,-0.4163512588,-0.0681711361,0.0015483622,0.6935947537,0.4342878461,0.0081108157,-0.4164196253,-0.005543787,0.1647204608,0.367272526,0.3402585387,-0.0229089577,-0.0657090917,0.1916342378,0.0812231302,0.0823046118,0.0832441226,-0.0158376154,0.3440508246,0.0033670156,0.185282737,-0.2075952888,-0.4350948334,0.0201418605,0.1973951459,0.6681491733,-0.3729459643,-0.1618387252,-0.2038089186,-0.1345122904,-0.4180262387,-0.0779000297,0.4423206747,-0.1205714867,0.1378150731,-0.2383987606,0.1256262809,-0.2445560098,0.3950924575,-0.0653971806,0.2446008474,-0.0851913691,0.1580072045,0.1469615698,0.2207309157,0.179539606,-0.238950327,-0.1969656497,0.0609343536,-0.33901757,-0.1809932292,-0.4194630682,-0.2334724963,0.2693914771,0.4270803034,-0.1869285405,-0.1305606067,-0.1368005723,-0.1355264187,0.511703968,0.0301423278,0.2042330801,0.3433418572,0.0389579013,-0.0347348452,-0.2185376137,-0.1680016369,-0.1378880292,-0.2395493239,-0.0726651475,0.3465918005,0.2915106714,-0.2763409317,-0.1029529944,-0.5062012672,-0.5687624812,-0.2831332684,0.0132070417,0.226178661,0.0955337211,-0.087401852,-0.0726813674,0.3877472281,-0.3162638843,-0.1819904596,0.0231045298,0.0718567744,-0.2617369294,-0.0702702254,0.3142620325,-0.4424532652,0.1238225624,-0.0749288872,0.0452285521,-0.3328416646,0.1651387364,-0.3937927485,0.0545788966,0.2088656276,0.2188968211,-0.0664284006,0.1271425337,0.1447395831,-0.2334046513,0.104900308,-0.1808667034,-0.0801759586,0.1558273733,-0.0196957737,0.0611532554,0.1351347417,-0.4866174161,0.2976647019,0.0896814466,0.1010288969,0.0367785692,0.1594928354,-0.1407074034,-0.0847328827,0.2250472009,0.134390071,-0.2925608456,0.0847757012,0.0228861179,-0.2819042206,0.2453172356,0.110860236,-0.1238232777,0.5956167579,-0.03305142,0.0815179124,0.4777341783,-0.7511567473,-0.1438726634,0.375092417,0.2684719861,-0.267881453,0.0732861385,-0.1370616108,0.5824891329,-0.0600705929,-0.1912472248,0.5537322164,-0.2710953951,0.0165227335,0.0668279082,-0.2530251443,-0.0888290927,0.2614716589,0.0190685932,-0.0778126791,0.2262897342,0.1465780139,0.366556555,-0.1042811647,0.308786273,-0.0434692241,0.242729038,0.0071516386,0.1786873341,-0.3716312647,-0.2358853668,0.0932078063,0.0062300847,-0.0141476244,0.0756585151,-0.2274846584,-0.2097719461,-0.3526072502,-0.378256321,-0.1538071483,0.0048068264,-0.196291253,0.2621349692,0.0109238541,-0.2893612683,0.0339712389,0.0975752473,0.4323088825,0.0469637886,0.4479081035,0.1255497336,-0.0886189491,0.0299216919,0.0520881601,0.0634901151,-0.1969065517,-0.1304466724,0.4200092852,0.387339741,-0.0858752206,-0.0276352838,-0.2635817528,0.2716654539,-0.2346802205,0.0363780484,-0.0131422896,0.0668626577,-0.2364047766,-0.3931208253,0.151324302,-0.2954005301,0.2507550716,-0.0045375316,0.1367302239,0.1403153241,-0.2662443519,0.1603447199,0.0253539756,-0.1090277135,-0.2627272606,0.2647825778,-0.3021570444,0.1317908764,-0.0627249852,0.4427015781,-0.1344834119,-0.0271012913,0.0019468812,-0.014633242,0.3422632217,0.1277846545,0.4383707643,0.360081315,0.1405371428,0.116945684,0.1188058928,0.2640932798,-0.0330177024,0.1776481122,0.1477965117,0.213997826,0.1553716511,-0.2412400693,-0.1930330247,-0.0302048922,-0.0356371067,0.2965058386,0.3059887588,-0.088938944,0.1264159381,-0.2307716161,-0.4963020682,-0.3396852314,-0.3279790878,0.1289585531,0.0233278181,0.0331730172,0.0473307297,0.2906074822,0.1235113218,0.1898518354,0.0364795402,-0.0740121007,0.0600012466,-0.0685784966,-0.1514945626,-0.1349993348,0.0668269694,0.3735645115,0.7918115854,0.2371259183,-0.318859309,0.0106522609,-0.2567154169,0.0796766654,0.2232891917,-0.3098639846,0.049828466,0.1674468517,-0.0184569322,0.0096676741,-0.099256672,0.1296456605,0.0814134553,-0.1913520694,0.007976234,-0.0498731807,0.0759786069,-0.0945540741,-0.5739889741,-0.1470645517,-0.2587658465,0.4122140408,0.238113448,0.1185838729,-0.0971847847,0.0823044553,0.0910289809,0.0020560918,0.0315576866,0.1899357438,-0.1119549945,0.1132454425,-0.2350589633,-0.2499632984,0.3675474226,0.1742352545,-0.057356324,-0.0786823556,-0.2121851593,0.2335443199,-0.0393019952,-0.0227115043,0.0712002218,0.1150887311,0.2429207265,-0.2319124639,0.1688636541,-0.090231359,0.0654569939,0.1491007954,-0.2139852494,0.2626654208,-0.1342073679,0.3909491599,0.0201898441,0.6240466833,0.4335097671,-0.0120473206,0.0362733565,-0.1909749806,-0.3649744689,-0.078871794,-0.2182035893,0.0700497329,-0.1186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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1785","title":"Not enough disk space (Needed: Unknown size) when caching on a cluster","comments":"Hi ! \r\n\r\nWhat do you mean by \"disk_usage(\".\").free` can't compute on the cluster's shared disk\" exactly ?\r\nDoes it return 0 ?","body":"I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.\r\n\r\nThe exact error thrown:\r\n\r\n```bash\r\n>>> load_dataset(dataset, cache_dir=\"\/path\/to\/cluster\/shared\/path\")\r\nOSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)\r\n```\r\n\r\n\r\n[`utils.has_sufficient_disk_space`](https:\/\/github.com\/huggingface\/datasets\/blob\/8a03ab7d123a76ee744304f21ce868c75f411214\/src\/datasets\/utils\/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(\".\").free` can't compute on the cluster's shared disk).\r\n\r\n\r\nThis is exactly where the error gets thrown:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L502\r\n\r\n```python\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n    raise IOError(\r\n          \"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})\".format(\r\n          utils.size_str(self.info.size_in_bytes or 0),\r\n          utils.size_str(self.info.download_size or 0),\r\n          utils.size_str(self.info.dataset_size or 0),\r\n          utils.size_str(self.info.post_processing_size or 0),\r\n       )\r\n    )\r\n\r\n```\r\n\r\nWhat would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. \r\nWould it be possible to pass a flag to skip this check on disk space?","comment_length":22,"text":"Not enough disk space (Needed: Unknown size) when caching on a cluster \n I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.\r\n\r\nThe exact error thrown:\r\n\r\n```bash\r\n>>> load_dataset(dataset, cache_dir=\"\/path\/to\/cluster\/shared\/path\")\r\nOSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)\r\n```\r\n\r\n\r\n[`utils.has_sufficient_disk_space`](https:\/\/github.com\/huggingface\/datasets\/blob\/8a03ab7d123a76ee744304f21ce868c75f411214\/src\/datasets\/utils\/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(\".\").free` can't compute on the cluster's shared disk).\r\n\r\n\r\nThis is exactly where the error gets thrown:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L502\r\n\r\n```python\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n    raise IOError(\r\n          \"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})\".format(\r\n          utils.size_str(self.info.size_in_bytes or 0),\r\n          utils.size_str(self.info.download_size or 0),\r\n          utils.size_str(self.info.dataset_size or 0),\r\n          utils.size_str(self.info.post_processing_size or 0),\r\n       )\r\n    )\r\n\r\n```\r\n\r\nWhat would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. \r\nWould it be possible to pass a flag to skip this check on disk space? \n Hi ! \r\n\r\nWhat do you mean by \"disk_usage(\".\").free` can't compute on the cluster's shared disk\" exactly ?\r\nDoes it return 0 ?","embeddings":[-0.0777432546,-0.4187171161,-0.0846218243,0.2431606501,-0.0970096961,0.1506603807,0.0705348626,0.3401208818,0.3962355852,0.3933159113,0.4183927476,-0.1389313489,-0.0912498608,0.0038917698,-0.1739582866,0.2417320162,-0.0082299486,-0.0606454238,0.3455670178,0.1274867654,-0.405728817,0.0130443303,0.0944502652,-0.3736138344,-0.542624414,-0.1880920529,-0.1662807167,0.1566853374,0.2269620299,-0.2247435004,0.3221060932,-0.0054948069,-0.1011393741,0.5720781088,-0.000115944,0.0363123342,0.1715248972,-0.1809052825,-0.4399374723,-0.0386959203,0.2173043787,-0.4069226086,-0.2233585268,-0.3619658649,-0.0103093972,0.16035676,-0.0511811189,-0.4436121881,0.1765249521,-0.0691841096,0.1679693311,0.3530050814,-0.2203017622,-0.0926141366,-0.265940696,0.1767231971,-0.046913106,0.5180592537,0.2992629409,0.0753154457,-0.278730154,0.220809415,-0.1333248466,0.1601159424,0.4138147831,-0.1359979659,-0.0745279565,-0.1845136285,0.196237877,-0.0268672947,0.4277347624,-0.1950327456,-0.5273439288,-0.5711030364,0.0500526614,-0.1597716212,0.439658612,0.0364257619,-0.4096871614,0.1226714104,-0.3018596768,-0.3372609317,-0.2010516077,-0.2099774629,0.0532090776,0.1641300172,0.0016833327,0.083085537,0.3270326853,0.0175013132,-0.0256257802,0.0264363643,0.1729609668,0.2536420822,-0.2865471244,0.0151761649,-0.0948409587,0.2101237476,0.101119183,0.2106985748,0.0610056035,-0.1125408858,0.0735730827,0.2540668547,0.0136580197,0.2466122806,-0.2650673985,-0.0978883803,0.2030567378,-0.0030463671,0.1735390574,-0.1133197173,0.0481721535,-0.3508072197,0.1162030324,-0.0180879608,0.2040126473,-0.0714850947,-0.3821848929,0.1272478998,0.2050054371,0.3049914837,0.2122824043,0.2848548591,0.0810501873,0.2399788499,0.2188417315,0.1994525045,-0.0902340636,-0.0968263894,-0.1159810424,-0.0147075355,0.0885305181,0.1693156362,0.3448050916,0.0367706008,0.2115975916,-0.0179041158,0.4590659142,-0.1054459512,-0.0721758902,-0.2352618277,-0.1191628948,0.4233517647,0.1197756976,0.0117573794,0.2519826591,0.1500506103,-0.1510386318,0.2677905858,-0.3836438954,-0.4553592801,0.0440193452,0.0724626184,-0.3528414071,0.2011191398,-0.4066096246,-0.0384400897,0.3097243011,-0.1824393868,0.1011645198,0.0928381905,-0.3887099922,-0.2462706715,0.1381942779,0.3473314941,-0.1142510027,0.0037163277,-0.3107164502,0.1621557027,0.0525881574,0.4145958126,0.131162703,0.1386437565,-0.4277253449,0.138010487,0.1127065942,-0.3308421373,-0.5418027639,0.3061300516,-0.1718518436,0.2516016066,0.537789166,0.2614465952,-0.0163331181,-0.2336914986,0.2327867597,0.271453768,-0.2196908742,0.0563449599,-0.3533278108,-0.2634562254,0.0278719384,0.0614294,0.3454007506,0.1085645556,0.0109477919,-0.0060027656,0.1818708628,0.0549793318,0.2010059208,0.3193238974,0.3190516531,-0.3444590569,-0.0738658607,0.0217245314,-0.5194780231,0.3491626382,0.1930718422,-0.2100150585,-0.0646497682,-0.443295449,-0.2809630632,0.0622078627,0.2482933402,0.3274293542,0.0486466065,-0.0373882763,0.249466747,-0.306935966,-0.0596500039,0.1668305844,-0.2572431266,-0.0578986779,0.1028762013,-0.0371632501,-0.0423917137,-0.140039593,0.0494481213,0.0646104813,0.0650270507,0.1349681914,-0.0143349981,0.4134909809,-0.1063747108,0.2247718275,-0.0903512463,0.1346000135,0.1523135304,0.3512614369,0.0020521295,0.1752402633,0.0069469591,-0.1697661281,-0.1110437587,0.2095975876,-0.0455697291,0.2853596509,-0.082658127,0.0640584752,0.0624382496,0.0560384765,0.1237755492,-0.0592896119,0.2901299,-0.1966567934,0.4879351854,0.0813175663,0.0228467751,-0.0144203082,0.069439292,0.1592282355,0.2913535535,0.1565068215,0.2970966697,-0.0345214605,-0.0058793798,0.2817410529,0.2664145827,0.1560027152,0.015309358,-0.1753182411,0.0451875925,-0.2055585682,0.0528198555,-0.0503015332,0.1580183506,0.2922110558,0.0067300084,-0.2964170873,0.0960466042,-0.309045732,0.2155113816,0.0772978142,-0.2874605954,-0.0579072982,0.0557797439,0.1828185171,0.0615936369,0.1889596432,-0.45891729,-0.314848125,0.1746363044,0.6444194317,-0.2373899072,-0.0952426791,-0.3729638457,0.47324121,0.0780528784,-0.4257940054,-0.0265632216,-0.2685465217,-0.1055517048,0.0170707349,0.3254656196,-0.2578275502,0.3435428143,0.0094133504,0.0713445768,-0.4250703454,0.1581226587,-0.1697877198,0.0267442688,0.1604891568,-0.1307636648,0.3355614543,-0.1221082732,-0.1611018181,0.1600524932,-0.0386517271,-0.0417478047,0.0091869533,0.1570615619,0.1753278226,-0.3232641518,0.0315406322,-0.1793826818,-0.5495167375,0.1743905246,-0.1816838086,0.2716465294,0.103640452,0.1733868867,-0.1049473435,0.7099817991,-0.1731761396,-0.2496192306,-0.6116562486,0.2382910401,-0.0803573653,-0.029552035,-0.0471714363,0.0296280216,0.2122024596,0.4242192209,-0.4782416821,-0.4002625942,-0.1671660841,0.2294674516,-0.2420354337,-0.0321569964,0.179103747,-0.1486543715,-0.0734726936,-0.1409638077,-0.0281644203,0.052829124,0.0094645806,0.2762105465,0.2927938402,0.2192623615,0.3207839429,0.7062381506,-0.1594575793,0.2500558496,0.2755180597,0.1817433685,0.6162175536,0.0905259475,-0.0536038391,0.0400996394,0.0080741094,-0.6150441766,0.018974980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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1785","title":"Not enough disk space (Needed: Unknown size) when caching on a cluster","comments":"Yes, that's right. It shows 0 free space even though there is. I suspect it might have to do with permissions on the shared disk.\r\n\r\n```python\r\n>>> disk_usage(\".\")\r\nusage(total=999999, used=999999, free=0)\r\n```","body":"I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.\r\n\r\nThe exact error thrown:\r\n\r\n```bash\r\n>>> load_dataset(dataset, cache_dir=\"\/path\/to\/cluster\/shared\/path\")\r\nOSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)\r\n```\r\n\r\n\r\n[`utils.has_sufficient_disk_space`](https:\/\/github.com\/huggingface\/datasets\/blob\/8a03ab7d123a76ee744304f21ce868c75f411214\/src\/datasets\/utils\/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(\".\").free` can't compute on the cluster's shared disk).\r\n\r\n\r\nThis is exactly where the error gets thrown:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L502\r\n\r\n```python\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n    raise IOError(\r\n          \"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})\".format(\r\n          utils.size_str(self.info.size_in_bytes or 0),\r\n          utils.size_str(self.info.download_size or 0),\r\n          utils.size_str(self.info.dataset_size or 0),\r\n          utils.size_str(self.info.post_processing_size or 0),\r\n       )\r\n    )\r\n\r\n```\r\n\r\nWhat would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. \r\nWould it be possible to pass a flag to skip this check on disk space?","comment_length":32,"text":"Not enough disk space (Needed: Unknown size) when caching on a cluster \n I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.\r\n\r\nThe exact error thrown:\r\n\r\n```bash\r\n>>> load_dataset(dataset, cache_dir=\"\/path\/to\/cluster\/shared\/path\")\r\nOSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)\r\n```\r\n\r\n\r\n[`utils.has_sufficient_disk_space`](https:\/\/github.com\/huggingface\/datasets\/blob\/8a03ab7d123a76ee744304f21ce868c75f411214\/src\/datasets\/utils\/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(\".\").free` can't compute on the cluster's shared disk).\r\n\r\n\r\nThis is exactly where the error gets thrown:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L502\r\n\r\n```python\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n    raise IOError(\r\n          \"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})\".format(\r\n          utils.size_str(self.info.size_in_bytes or 0),\r\n          utils.size_str(self.info.download_size or 0),\r\n          utils.size_str(self.info.dataset_size or 0),\r\n          utils.size_str(self.info.post_processing_size or 0),\r\n       )\r\n    )\r\n\r\n```\r\n\r\nWhat would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. \r\nWould it be possible to pass a flag to skip this check on disk space? \n Yes, that's right. It shows 0 free space even though there is. I suspect it might have to do with permissions on the shared disk.\r\n\r\n```python\r\n>>> disk_usage(\".\")\r\nusage(total=999999, used=999999, free=0)\r\n```","embeddings":[-0.0777432546,-0.4187171161,-0.0846218243,0.2431606501,-0.0970096961,0.1506603807,0.0705348626,0.3401208818,0.3962355852,0.3933159113,0.4183927476,-0.1389313489,-0.0912498608,0.0038917698,-0.1739582866,0.2417320162,-0.0082299486,-0.0606454238,0.3455670178,0.1274867654,-0.405728817,0.0130443303,0.0944502652,-0.3736138344,-0.542624414,-0.1880920529,-0.1662807167,0.1566853374,0.2269620299,-0.2247435004,0.3221060932,-0.0054948069,-0.1011393741,0.5720781088,-0.000115944,0.0363123342,0.1715248972,-0.1809052825,-0.4399374723,-0.0386959203,0.2173043787,-0.4069226086,-0.2233585268,-0.3619658649,-0.0103093972,0.16035676,-0.0511811189,-0.4436121881,0.1765249521,-0.0691841096,0.1679693311,0.3530050814,-0.2203017622,-0.0926141366,-0.265940696,0.1767231971,-0.046913106,0.5180592537,0.2992629409,0.0753154457,-0.278730154,0.220809415,-0.1333248466,0.1601159424,0.4138147831,-0.1359979659,-0.0745279565,-0.1845136285,0.196237877,-0.0268672947,0.4277347624,-0.1950327456,-0.5273439288,-0.5711030364,0.0500526614,-0.1597716212,0.439658612,0.0364257619,-0.4096871614,0.1226714104,-0.3018596768,-0.3372609317,-0.2010516077,-0.2099774629,0.0532090776,0.1641300172,0.0016833327,0.083085537,0.3270326853,0.0175013132,-0.0256257802,0.0264363643,0.1729609668,0.2536420822,-0.2865471244,0.0151761649,-0.0948409587,0.2101237476,0.101119183,0.2106985748,0.0610056035,-0.1125408858,0.0735730827,0.2540668547,0.0136580197,0.2466122806,-0.2650673985,-0.0978883803,0.2030567378,-0.0030463671,0.1735390574,-0.1133197173,0.0481721535,-0.3508072197,0.1162030324,-0.0180879608,0.2040126473,-0.0714850947,-0.3821848929,0.1272478998,0.2050054371,0.3049914837,0.2122824043,0.2848548591,0.0810501873,0.2399788499,0.2188417315,0.1994525045,-0.0902340636,-0.0968263894,-0.1159810424,-0.0147075355,0.0885305181,0.1693156362,0.3448050916,0.0367706008,0.2115975916,-0.0179041158,0.4590659142,-0.1054459512,-0.0721758902,-0.2352618277,-0.1191628948,0.4233517647,0.1197756976,0.0117573794,0.2519826591,0.1500506103,-0.1510386318,0.2677905858,-0.3836438954,-0.4553592801,0.0440193452,0.0724626184,-0.3528414071,0.2011191398,-0.4066096246,-0.0384400897,0.3097243011,-0.1824393868,0.1011645198,0.0928381905,-0.3887099922,-0.2462706715,0.1381942779,0.3473314941,-0.1142510027,0.0037163277,-0.3107164502,0.1621557027,0.0525881574,0.4145958126,0.131162703,0.1386437565,-0.4277253449,0.138010487,0.1127065942,-0.3308421373,-0.5418027639,0.3061300516,-0.1718518436,0.2516016066,0.537789166,0.2614465952,-0.0163331181,-0.2336914986,0.2327867597,0.271453768,-0.2196908742,0.0563449599,-0.3533278108,-0.2634562254,0.0278719384,0.0614294,0.3454007506,0.1085645556,0.0109477919,-0.0060027656,0.1818708628,0.0549793318,0.2010059208,0.3193238974,0.3190516531,-0.3444590569,-0.0738658607,0.0217245314,-0.5194780231,0.3491626382,0.1930718422,-0.2100150585,-0.0646497682,-0.443295449,-0.2809630632,0.0622078627,0.2482933402,0.3274293542,0.0486466065,-0.0373882763,0.249466747,-0.306935966,-0.0596500039,0.1668305844,-0.2572431266,-0.0578986779,0.1028762013,-0.0371632501,-0.0423917137,-0.140039593,0.0494481213,0.0646104813,0.0650270507,0.1349681914,-0.0143349981,0.4134909809,-0.1063747108,0.2247718275,-0.0903512463,0.1346000135,0.1523135304,0.3512614369,0.0020521295,0.1752402633,0.0069469591,-0.1697661281,-0.1110437587,0.2095975876,-0.0455697291,0.2853596509,-0.082658127,0.0640584752,0.0624382496,0.0560384765,0.1237755492,-0.0592896119,0.2901299,-0.1966567934,0.4879351854,0.0813175663,0.0228467751,-0.0144203082,0.069439292,0.1592282355,0.2913535535,0.1565068215,0.2970966697,-0.0345214605,-0.0058793798,0.2817410529,0.2664145827,0.1560027152,0.015309358,-0.1753182411,0.0451875925,-0.2055585682,0.0528198555,-0.0503015332,0.1580183506,0.2922110558,0.0067300084,-0.2964170873,0.0960466042,-0.309045732,0.2155113816,0.0772978142,-0.2874605954,-0.0579072982,0.0557797439,0.1828185171,0.0615936369,0.1889596432,-0.45891729,-0.314848125,0.1746363044,0.6444194317,-0.2373899072,-0.0952426791,-0.3729638457,0.47324121,0.0780528784,-0.4257940054,-0.0265632216,-0.2685465217,-0.1055517048,0.0170707349,0.3254656196,-0.2578275502,0.3435428143,0.0094133504,0.0713445768,-0.4250703454,0.1581226587,-0.1697877198,0.0267442688,0.1604891568,-0.1307636648,0.3355614543,-0.1221082732,-0.1611018181,0.1600524932,-0.0386517271,-0.0417478047,0.0091869533,0.1570615619,0.1753278226,-0.3232641518,0.0315406322,-0.1793826818,-0.5495167375,0.1743905246,-0.1816838086,0.2716465294,0.103640452,0.1733868867,-0.1049473435,0.7099817991,-0.1731761396,-0.2496192306,-0.6116562486,0.2382910401,-0.0803573653,-0.029552035,-0.0471714363,0.0296280216,0.2122024596,0.4242192209,-0.4782416821,-0.4002625942,-0.1671660841,0.2294674516,-0.2420354337,-0.0321569964,0.179103747,-0.1486543715,-0.0734726936,-0.1409638077,-0.0281644203,0.052829124,0.0094645806,0.2762105465,0.2927938402,0.2192623615,0.3207839429,0.7062381506,-0.1594575793,0.2500558496,0.2755180597,0.1817433685,0.6162175536,0.0905259475,-0.0536038391,0.0400996394,0.0080741094,-0.615044176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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1785","title":"Not enough disk space (Needed: Unknown size) when caching on a cluster","comments":"That's an interesting behavior...\r\nDo you know any other way to get the free space that works in your case ?\r\nAlso if it's a permission issue could you try fix the permissions and let mus know if that helped ?","body":"I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.\r\n\r\nThe exact error thrown:\r\n\r\n```bash\r\n>>> load_dataset(dataset, cache_dir=\"\/path\/to\/cluster\/shared\/path\")\r\nOSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)\r\n```\r\n\r\n\r\n[`utils.has_sufficient_disk_space`](https:\/\/github.com\/huggingface\/datasets\/blob\/8a03ab7d123a76ee744304f21ce868c75f411214\/src\/datasets\/utils\/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(\".\").free` can't compute on the cluster's shared disk).\r\n\r\n\r\nThis is exactly where the error gets thrown:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L502\r\n\r\n```python\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n    raise IOError(\r\n          \"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})\".format(\r\n          utils.size_str(self.info.size_in_bytes or 0),\r\n          utils.size_str(self.info.download_size or 0),\r\n          utils.size_str(self.info.dataset_size or 0),\r\n          utils.size_str(self.info.post_processing_size or 0),\r\n       )\r\n    )\r\n\r\n```\r\n\r\nWhat would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. \r\nWould it be possible to pass a flag to skip this check on disk space?","comment_length":41,"text":"Not enough disk space (Needed: Unknown size) when caching on a cluster \n I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.\r\n\r\nThe exact error thrown:\r\n\r\n```bash\r\n>>> load_dataset(dataset, cache_dir=\"\/path\/to\/cluster\/shared\/path\")\r\nOSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)\r\n```\r\n\r\n\r\n[`utils.has_sufficient_disk_space`](https:\/\/github.com\/huggingface\/datasets\/blob\/8a03ab7d123a76ee744304f21ce868c75f411214\/src\/datasets\/utils\/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(\".\").free` can't compute on the cluster's shared disk).\r\n\r\n\r\nThis is exactly where the error gets thrown:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L502\r\n\r\n```python\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n    raise IOError(\r\n          \"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})\".format(\r\n          utils.size_str(self.info.size_in_bytes or 0),\r\n          utils.size_str(self.info.download_size or 0),\r\n          utils.size_str(self.info.dataset_size or 0),\r\n          utils.size_str(self.info.post_processing_size or 0),\r\n       )\r\n    )\r\n\r\n```\r\n\r\nWhat would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. \r\nWould it be possible to pass a flag to skip this check on disk space? \n That's an interesting behavior...\r\nDo you know any other way to get the free space that works in your case ?\r\nAlso if it's a permission issue could you try fix the permissions and let mus know if that helped ?","embeddings":[-0.0777432546,-0.4187171161,-0.0846218243,0.2431606501,-0.0970096961,0.1506603807,0.0705348626,0.3401208818,0.3962355852,0.3933159113,0.4183927476,-0.1389313489,-0.0912498608,0.0038917698,-0.1739582866,0.2417320162,-0.0082299486,-0.0606454238,0.3455670178,0.1274867654,-0.405728817,0.0130443303,0.0944502652,-0.3736138344,-0.542624414,-0.1880920529,-0.1662807167,0.1566853374,0.2269620299,-0.2247435004,0.3221060932,-0.0054948069,-0.1011393741,0.5720781088,-0.000115944,0.0363123342,0.1715248972,-0.1809052825,-0.4399374723,-0.0386959203,0.2173043787,-0.4069226086,-0.2233585268,-0.3619658649,-0.0103093972,0.16035676,-0.0511811189,-0.4436121881,0.1765249521,-0.0691841096,0.1679693311,0.3530050814,-0.2203017622,-0.0926141366,-0.265940696,0.1767231971,-0.046913106,0.5180592537,0.2992629409,0.0753154457,-0.278730154,0.220809415,-0.1333248466,0.1601159424,0.4138147831,-0.1359979659,-0.0745279565,-0.1845136285,0.196237877,-0.0268672947,0.4277347624,-0.1950327456,-0.5273439288,-0.5711030364,0.0500526614,-0.1597716212,0.439658612,0.0364257619,-0.4096871614,0.1226714104,-0.3018596768,-0.3372609317,-0.2010516077,-0.2099774629,0.0532090776,0.1641300172,0.0016833327,0.083085537,0.3270326853,0.0175013132,-0.0256257802,0.0264363643,0.1729609668,0.2536420822,-0.2865471244,0.0151761649,-0.0948409587,0.2101237476,0.101119183,0.2106985748,0.0610056035,-0.1125408858,0.0735730827,0.2540668547,0.0136580197,0.2466122806,-0.2650673985,-0.0978883803,0.2030567378,-0.0030463671,0.1735390574,-0.1133197173,0.0481721535,-0.3508072197,0.1162030324,-0.0180879608,0.2040126473,-0.0714850947,-0.3821848929,0.1272478998,0.2050054371,0.3049914837,0.2122824043,0.2848548591,0.0810501873,0.2399788499,0.2188417315,0.1994525045,-0.0902340636,-0.0968263894,-0.1159810424,-0.0147075355,0.0885305181,0.1693156362,0.3448050916,0.0367706008,0.2115975916,-0.0179041158,0.4590659142,-0.1054459512,-0.0721758902,-0.2352618277,-0.1191628948,0.4233517647,0.1197756976,0.0117573794,0.2519826591,0.1500506103,-0.1510386318,0.2677905858,-0.3836438954,-0.4553592801,0.0440193452,0.0724626184,-0.3528414071,0.2011191398,-0.4066096246,-0.0384400897,0.3097243011,-0.1824393868,0.1011645198,0.0928381905,-0.3887099922,-0.2462706715,0.1381942779,0.3473314941,-0.1142510027,0.0037163277,-0.3107164502,0.1621557027,0.0525881574,0.4145958126,0.131162703,0.1386437565,-0.4277253449,0.138010487,0.1127065942,-0.3308421373,-0.5418027639,0.3061300516,-0.1718518436,0.2516016066,0.537789166,0.2614465952,-0.0163331181,-0.2336914986,0.2327867597,0.271453768,-0.2196908742,0.0563449599,-0.3533278108,-0.2634562254,0.0278719384,0.0614294,0.3454007506,0.1085645556,0.0109477919,-0.0060027656,0.1818708628,0.0549793318,0.2010059208,0.3193238974,0.3190516531,-0.3444590569,-0.0738658607,0.0217245314,-0.5194780231,0.3491626382,0.1930718422,-0.2100150585,-0.0646497682,-0.443295449,-0.2809630632,0.0622078627,0.2482933402,0.3274293542,0.0486466065,-0.0373882763,0.249466747,-0.306935966,-0.0596500039,0.1668305844,-0.2572431266,-0.0578986779,0.1028762013,-0.0371632501,-0.0423917137,-0.140039593,0.0494481213,0.0646104813,0.0650270507,0.1349681914,-0.0143349981,0.4134909809,-0.1063747108,0.2247718275,-0.0903512463,0.1346000135,0.1523135304,0.3512614369,0.0020521295,0.1752402633,0.0069469591,-0.1697661281,-0.1110437587,0.2095975876,-0.0455697291,0.2853596509,-0.082658127,0.0640584752,0.0624382496,0.0560384765,0.1237755492,-0.0592896119,0.2901299,-0.1966567934,0.4879351854,0.0813175663,0.0228467751,-0.0144203082,0.069439292,0.1592282355,0.2913535535,0.1565068215,0.2970966697,-0.0345214605,-0.0058793798,0.2817410529,0.2664145827,0.1560027152,0.015309358,-0.1753182411,0.0451875925,-0.2055585682,0.0528198555,-0.0503015332,0.1580183506,0.2922110558,0.0067300084,-0.2964170873,0.0960466042,-0.309045732,0.2155113816,0.0772978142,-0.2874605954,-0.0579072982,0.0557797439,0.1828185171,0.0615936369,0.1889596432,-0.45891729,-0.314848125,0.1746363044,0.6444194317,-0.2373899072,-0.0952426791,-0.3729638457,0.47324121,0.0780528784,-0.4257940054,-0.0265632216,-0.2685465217,-0.1055517048,0.0170707349,0.3254656196,-0.2578275502,0.3435428143,0.0094133504,0.0713445768,-0.4250703454,0.1581226587,-0.1697877198,0.0267442688,0.1604891568,-0.1307636648,0.3355614543,-0.1221082732,-0.1611018181,0.1600524932,-0.0386517271,-0.0417478047,0.0091869533,0.1570615619,0.1753278226,-0.3232641518,0.0315406322,-0.1793826818,-0.5495167375,0.1743905246,-0.1816838086,0.2716465294,0.103640452,0.1733868867,-0.1049473435,0.7099817991,-0.1731761396,-0.2496192306,-0.6116562486,0.2382910401,-0.0803573653,-0.029552035,-0.0471714363,0.0296280216,0.2122024596,0.4242192209,-0.4782416821,-0.4002625942,-0.1671660841,0.2294674516,-0.2420354337,-0.0321569964,0.179103747,-0.1486543715,-0.0734726936,-0.1409638077,-0.0281644203,0.052829124,0.0094645806,0.2762105465,0.2927938402,0.2192623615,0.3207839429,0.7062381506,-0.1594575793,0.2500558496,0.2755180597,0.1817433685,0.6162175536,0.0905259475,-0.0536038391,0.0400996394,0.0080741094,-0.6150441766,0.018974980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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1785","title":"Not enough disk space (Needed: Unknown size) when caching on a cluster","comments":"I think its an issue on the clusters end (unclear exactly why -- maybe something with docker containers?), will close the issue","body":"I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.\r\n\r\nThe exact error thrown:\r\n\r\n```bash\r\n>>> load_dataset(dataset, cache_dir=\"\/path\/to\/cluster\/shared\/path\")\r\nOSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)\r\n```\r\n\r\n\r\n[`utils.has_sufficient_disk_space`](https:\/\/github.com\/huggingface\/datasets\/blob\/8a03ab7d123a76ee744304f21ce868c75f411214\/src\/datasets\/utils\/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(\".\").free` can't compute on the cluster's shared disk).\r\n\r\n\r\nThis is exactly where the error gets thrown:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L502\r\n\r\n```python\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n    raise IOError(\r\n          \"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})\".format(\r\n          utils.size_str(self.info.size_in_bytes or 0),\r\n          utils.size_str(self.info.download_size or 0),\r\n          utils.size_str(self.info.dataset_size or 0),\r\n          utils.size_str(self.info.post_processing_size or 0),\r\n       )\r\n    )\r\n\r\n```\r\n\r\nWhat would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. \r\nWould it be possible to pass a flag to skip this check on disk space?","comment_length":22,"text":"Not enough disk space (Needed: Unknown size) when caching on a cluster \n I'm running some experiments where I'm caching datasets on a cluster and accessing it through multiple compute nodes. However, I get an error when loading the cached dataset from the shared disk.\r\n\r\nThe exact error thrown:\r\n\r\n```bash\r\n>>> load_dataset(dataset, cache_dir=\"\/path\/to\/cluster\/shared\/path\")\r\nOSError: Not enough disk space. Needed: Unknown size (download: Unknown size, generated: Unknown size, post-processed: Unknown size)\r\n```\r\n\r\n\r\n[`utils.has_sufficient_disk_space`](https:\/\/github.com\/huggingface\/datasets\/blob\/8a03ab7d123a76ee744304f21ce868c75f411214\/src\/datasets\/utils\/py_utils.py#L332) fails on each job because of how the cluster system is designed (`disk_usage(\".\").free` can't compute on the cluster's shared disk).\r\n\r\n\r\nThis is exactly where the error gets thrown:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L502\r\n\r\n```python\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n    raise IOError(\r\n          \"Not enough disk space. Needed: {} (download: {}, generated: {}, post-processed: {})\".format(\r\n          utils.size_str(self.info.size_in_bytes or 0),\r\n          utils.size_str(self.info.download_size or 0),\r\n          utils.size_str(self.info.dataset_size or 0),\r\n          utils.size_str(self.info.post_processing_size or 0),\r\n       )\r\n    )\r\n\r\n```\r\n\r\nWhat would be a good way to circumvent this? my current fix is to manually comment out that part, but that is not ideal. \r\nWould it be possible to pass a flag to skip this check on disk space? \n I think its an issue on the clusters end (unclear exactly why -- maybe something with docker containers?), will close the issue","embeddings":[-0.0777432546,-0.4187171161,-0.0846218243,0.2431606501,-0.0970096961,0.1506603807,0.0705348626,0.3401208818,0.3962355852,0.3933159113,0.4183927476,-0.1389313489,-0.0912498608,0.0038917698,-0.1739582866,0.2417320162,-0.0082299486,-0.0606454238,0.3455670178,0.1274867654,-0.405728817,0.0130443303,0.0944502652,-0.3736138344,-0.542624414,-0.1880920529,-0.1662807167,0.1566853374,0.2269620299,-0.2247435004,0.3221060932,-0.0054948069,-0.1011393741,0.5720781088,-0.000115944,0.0363123342,0.1715248972,-0.1809052825,-0.4399374723,-0.0386959203,0.2173043787,-0.4069226086,-0.2233585268,-0.3619658649,-0.0103093972,0.16035676,-0.0511811189,-0.4436121881,0.1765249521,-0.0691841096,0.1679693311,0.3530050814,-0.2203017622,-0.0926141366,-0.265940696,0.1767231971,-0.046913106,0.5180592537,0.2992629409,0.0753154457,-0.278730154,0.220809415,-0.1333248466,0.1601159424,0.4138147831,-0.1359979659,-0.0745279565,-0.1845136285,0.196237877,-0.0268672947,0.4277347624,-0.1950327456,-0.5273439288,-0.5711030364,0.0500526614,-0.1597716212,0.439658612,0.0364257619,-0.4096871614,0.1226714104,-0.3018596768,-0.3372609317,-0.2010516077,-0.2099774629,0.0532090776,0.1641300172,0.0016833327,0.083085537,0.3270326853,0.0175013132,-0.0256257802,0.0264363643,0.1729609668,0.2536420822,-0.2865471244,0.0151761649,-0.0948409587,0.2101237476,0.101119183,0.2106985748,0.0610056035,-0.1125408858,0.0735730827,0.2540668547,0.0136580197,0.2466122806,-0.2650673985,-0.0978883803,0.2030567378,-0.0030463671,0.1735390574,-0.1133197173,0.0481721535,-0.3508072197,0.1162030324,-0.0180879608,0.2040126473,-0.0714850947,-0.3821848929,0.1272478998,0.2050054371,0.3049914837,0.2122824043,0.2848548591,0.0810501873,0.2399788499,0.2188417315,0.1994525045,-0.0902340636,-0.0968263894,-0.1159810424,-0.0147075355,0.0885305181,0.1693156362,0.3448050916,0.0367706008,0.2115975916,-0.0179041158,0.4590659142,-0.1054459512,-0.0721758902,-0.2352618277,-0.1191628948,0.4233517647,0.1197756976,0.0117573794,0.2519826591,0.1500506103,-0.1510386318,0.2677905858,-0.3836438954,-0.4553592801,0.0440193452,0.0724626184,-0.3528414071,0.2011191398,-0.4066096246,-0.0384400897,0.3097243011,-0.1824393868,0.1011645198,0.0928381905,-0.3887099922,-0.2462706715,0.1381942779,0.3473314941,-0.1142510027,0.0037163277,-0.3107164502,0.1621557027,0.0525881574,0.4145958126,0.131162703,0.1386437565,-0.4277253449,0.138010487,0.1127065942,-0.3308421373,-0.5418027639,0.3061300516,-0.1718518436,0.2516016066,0.537789166,0.2614465952,-0.0163331181,-0.2336914986,0.2327867597,0.271453768,-0.2196908742,0.0563449599,-0.3533278108,-0.2634562254,0.0278719384,0.0614294,0.3454007506,0.1085645556,0.0109477919,-0.0060027656,0.1818708628,0.0549793318,0.2010059208,0.3193238974,0.3190516531,-0.3444590569,-0.0738658607,0.0217245314,-0.5194780231,0.3491626382,0.1930718422,-0.2100150585,-0.0646497682,-0.443295449,-0.2809630632,0.0622078627,0.2482933402,0.3274293542,0.0486466065,-0.0373882763,0.249466747,-0.306935966,-0.0596500039,0.1668305844,-0.2572431266,-0.0578986779,0.1028762013,-0.0371632501,-0.0423917137,-0.140039593,0.0494481213,0.0646104813,0.0650270507,0.1349681914,-0.0143349981,0.4134909809,-0.1063747108,0.2247718275,-0.0903512463,0.1346000135,0.1523135304,0.3512614369,0.0020521295,0.1752402633,0.0069469591,-0.1697661281,-0.1110437587,0.2095975876,-0.0455697291,0.2853596509,-0.082658127,0.0640584752,0.0624382496,0.0560384765,0.1237755492,-0.0592896119,0.2901299,-0.1966567934,0.4879351854,0.0813175663,0.0228467751,-0.0144203082,0.069439292,0.1592282355,0.2913535535,0.1565068215,0.2970966697,-0.0345214605,-0.0058793798,0.2817410529,0.2664145827,0.1560027152,0.015309358,-0.1753182411,0.0451875925,-0.2055585682,0.0528198555,-0.0503015332,0.1580183506,0.2922110558,0.0067300084,-0.2964170873,0.0960466042,-0.309045732,0.2155113816,0.0772978142,-0.2874605954,-0.0579072982,0.0557797439,0.1828185171,0.0615936369,0.1889596432,-0.45891729,-0.314848125,0.1746363044,0.6444194317,-0.2373899072,-0.0952426791,-0.3729638457,0.47324121,0.0780528784,-0.4257940054,-0.0265632216,-0.2685465217,-0.1055517048,0.0170707349,0.3254656196,-0.2578275502,0.3435428143,0.0094133504,0.0713445768,-0.4250703454,0.1581226587,-0.1697877198,0.0267442688,0.1604891568,-0.1307636648,0.3355614543,-0.1221082732,-0.1611018181,0.1600524932,-0.0386517271,-0.0417478047,0.0091869533,0.1570615619,0.1753278226,-0.3232641518,0.0315406322,-0.1793826818,-0.5495167375,0.1743905246,-0.1816838086,0.2716465294,0.103640452,0.1733868867,-0.1049473435,0.7099817991,-0.1731761396,-0.2496192306,-0.6116562486,0.2382910401,-0.0803573653,-0.029552035,-0.0471714363,0.0296280216,0.2122024596,0.4242192209,-0.4782416821,-0.4002625942,-0.1671660841,0.2294674516,-0.2420354337,-0.0321569964,0.179103747,-0.1486543715,-0.0734726936,-0.1409638077,-0.0281644203,0.052829124,0.0094645806,0.2762105465,0.2927938402,0.2192623615,0.3207839429,0.7062381506,-0.1594575793,0.2500558496,0.2755180597,0.1817433685,0.6162175536,0.0905259475,-0.0536038391,0.0400996394,0.0080741094,-0.6150441766,0.01897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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1784","title":"JSONDecodeError on JSON with multiple lines","comments":"Hi !\r\n\r\nThe `json` dataset script does support this format. For example loading a dataset with this format works on my side:\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n\r\nCan you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?\r\n\r\n","body":"Hello :),\r\n\r\nI have been trying to load data using a JSON file. Based on the [docs](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files), the following format is supported:\r\n\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported.\r\n\r\nWhen I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either.\r\n\r\nPlease let me know :)\r\n\r\nThanks,\r\nGunjan","comment_length":49,"text":"JSONDecodeError on JSON with multiple lines \n Hello :),\r\n\r\nI have been trying to load data using a JSON file. Based on the [docs](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files), the following format is supported:\r\n\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported.\r\n\r\nWhen I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either.\r\n\r\nPlease let me know :)\r\n\r\nThanks,\r\nGunjan \n Hi !\r\n\r\nThe `json` dataset script does support this format. For example loading a dataset with this format works on my side:\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n\r\nCan you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?\r\n\r\n","embeddings":[0.0756989047,-0.004834638,-0.0292827431,0.4874006808,0.3547834456,0.1495195478,0.4615655243,0.0634569228,0.1587526202,0.0187912937,0.1641427726,0.2806328535,-0.0464724079,0.2131021619,0.077341415,-0.2579198778,0.0545295775,0.2539742589,0.2304694355,0.093923308,-0.1753567457,0.2821831107,-0.1558834761,-0.0859780163,-0.1164932847,0.1821443141,-0.1478194445,0.2230660915,0.1152197272,-0.6116786003,0.199168548,0.1072017103,0.1027609408,0.4384293556,-0.0001222827,0.1784515083,0.2798017859,-0.2406275123,-0.437752068,-0.2415917069,-0.320045948,-0.2146712393,0.3941478133,-0.216466099,-0.0171037875,-0.5522251725,-0.1071552709,-0.2231709659,0.8473547697,0.3154249489,0.0736973509,-0.0370574035,0.1545831263,0.0937757269,0.1382793635,0.3976793885,0.1040199548,0.3903293014,0.1572732627,0.3830772638,0.0803718418,-0.1940742433,-0.0791405886,0.1125906631,0.2857994139,0.1097656786,-0.0717052966,-0.0807544291,0.0151098603,0.4550840259,0.6541770697,-0.1046296731,-0.4009039104,-0.4307039976,-0.1288366765,-0.1373524815,0.2610408366,0.1544523239,-0.1068272069,0.1755381823,-0.2287271768,-0.1890083104,-0.253189832,0.258387804,-0.0928343758,-0.3173473179,-0.2160383761,0.2146330774,0.1909279376,-0.1639520824,-0.1476037353,-0.2164838314,-0.3134977221,0.1610168219,-0.0158154685,0.0244269148,-0.1692642719,-0.2996096313,0.3776483238,0.0606462993,0.2912250161,0.0090573318,-0.3051211834,0.1320208758,0.4233737886,-0.0412974209,0.4679245651,-0.2059043348,0.0228297338,-0.0509501658,0.2377184778,-0.2316978425,-0.1745301634,-0.0246609468,0.0667581111,-0.2337434739,0.1367664486,-0.1931565702,0.0409572013,-0.1141456068,-0.3299007714,-0.2379553616,-0.0778570026,0.2825756967,0.3413724899,0.1305530369,0.0085191252,0.4388341904,0.151241526,-0.1779143363,0.0612650961,0.03361414,0.1091153249,0.1203234196,0.2002600133,-0.1847383827,0.1546734571,0.1673329771,-0.031202672,-0.1802987605,-0.22481516,0.0954530612,0.0053398209,0.2034656852,0.1514878124,0.0381725281,0.0465457104,-0.3754882812,-0.1229787245,0.2614931464,-0.3664222658,-0.1086113676,-0.2158961743,0.0045943297,-0.2165379226,-0.042754326,-0.7504179478,0.2429645956,-0.0581944734,-0.3593727946,0.0278873239,-0.1414280534,0.1401708722,-0.289802134,0.0539408103,0.484359175,-0.5632103682,-0.3126644492,0.290502429,-0.1905960143,-0.1055358797,0.3626500368,-0.410725832,0.2188340575,-0.2073307037,0.3010171056,0.3687204421,-0.3092291951,-0.2301794887,0.5485827327,0.1091068685,0.3371363878,0.2294214517,-0.166246891,0.2583563626,-0.0088671762,0.2358898371,0.1111461073,0.139396593,0.0451238118,0.1017302871,-0.0208238997,0.3151668608,0.2690217197,-0.3838232458,0.0342161693,0.0319788903,0.0770256668,0.3040084541,-0.2865880132,-0.1244976372,0.300216943,-0.2268988639,0.0445505753,0.0317665003,0.1740537286,-0.7827896476,0.0318350345,0.1511203051,-0.1864999384,-0.3102796376,-0.0598722361,-0.0096396944,0.018044956,-0.459142983,-0.0520047881,-0.043289952,0.0529062413,0.1861691922,0.0698304251,-0.1512107551,0.26028198,0.0843929574,0.1272289455,-0.2944525778,0.3275438249,0.113003619,-0.1274397075,0.0821474716,-0.0147800939,0.1743901968,-0.3622354865,-0.0237970762,0.3017584383,0.4365182817,-0.2380350083,-0.1748333722,0.0855652019,0.2663771808,0.2200504243,-0.0744529739,0.2505381703,0.223716557,-0.2906742692,-0.3921757638,0.2784978747,-0.088645637,0.4363918304,0.0388432629,-0.1501077861,0.423946172,0.1045752615,-0.2950928807,-0.1024177745,0.1572768241,0.1999422461,0.0250990391,0.0371618345,-0.3032545745,-0.193817094,0.5734389424,-0.1982163042,0.1366867721,0.3101391792,-0.1643854976,-0.122413367,0.0132789621,-0.023327589,0.3320555389,0.1498828232,-0.3159901798,0.0376023762,0.2749466002,-0.2184944153,0.1245727986,-0.1084819809,0.2380923778,0.0401965491,0.1783394068,-0.0797343478,-0.330512464,-0.4385953248,0.1077532545,0.0704088658,-0.5264180303,0.113750726,-0.4291846454,-0.0921923444,-0.0660933629,-0.2417521179,-0.1806002408,-0.2090834528,-0.1459433138,0.068922624,0.0243189838,-0.0922085494,0.0943390876,0.1706511229,0.1414880604,-0.1998651624,-0.1468555778,-0.1468743831,-0.2379731387,-0.0691627041,0.2437769026,-0.0099238567,0.007075578,-0.3262991309,-0.1477711946,-0.1586072445,-0.0661571175,0.109212473,-0.1067405641,0.1586824656,0.146321699,0.482786715,-0.0861451924,-0.0333815441,0.3210743368,0.2586284578,-0.2111027539,0.2427027076,0.1239626259,-0.0303509329,-0.0638520271,-0.0726980343,0.0486168303,-0.1663968116,0.6842475533,-0.0452208295,0.1938864738,-0.0541440547,0.1299834996,0.0972091407,-0.2045085877,0.0939908549,-0.1179483831,-0.2161597461,0.2748553157,-0.1578396261,-0.4564421773,0.0866717771,0.1376078129,-0.1101882309,-0.0661053807,-0.4292573929,-0.1951761991,-0.0504839197,0.1136903092,-0.1206954196,-0.0474421643,0.2318428457,-0.0019965582,0.0809704736,-0.0558322445,-0.3087209761,0.1214718446,0.0186988898,0.0840990543,0.2554667294,0.7511204481,-0.1643750668,0.1048239917,0.4095941186,-0.0908322558,0.3358144164,-0.2318022549,0.2796764076,-0.2613416314,-0.0558581464,-0.1485669166,-0.1008694842,0.055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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1784","title":"JSONDecodeError on JSON with multiple lines","comments":"Hi Quentin!\r\n\r\nI apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it.\r\n\r\nI repeated it with `datasets 1.2.1` and `pyarrow  2.0.0` and it worked.\r\n\r\nClosing this issue. Again, sorry for the bother.\r\n\r\nThanks,\r\nGunjan","body":"Hello :),\r\n\r\nI have been trying to load data using a JSON file. Based on the [docs](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files), the following format is supported:\r\n\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported.\r\n\r\nWhen I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either.\r\n\r\nPlease let me know :)\r\n\r\nThanks,\r\nGunjan","comment_length":56,"text":"JSONDecodeError on JSON with multiple lines \n Hello :),\r\n\r\nI have been trying to load data using a JSON file. Based on the [docs](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files), the following format is supported:\r\n\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported.\r\n\r\nWhen I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either.\r\n\r\nPlease let me know :)\r\n\r\nThanks,\r\nGunjan \n Hi Quentin!\r\n\r\nI apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it.\r\n\r\nI repeated it with `datasets 1.2.1` and `pyarrow  2.0.0` and it worked.\r\n\r\nClosing this issue. Again, sorry for the bother.\r\n\r\nThanks,\r\nGunjan","embeddings":[0.1118792072,-0.0649543628,-0.0127836056,0.509495914,0.3668510318,0.1452076733,0.4116669297,0.0697856471,0.116488494,0.0775828362,0.1227522716,0.3139997125,-0.0133123985,0.1743163913,0.1052531078,-0.3321564198,0.056305293,0.242220372,0.2812244892,0.0448313579,-0.1883261949,0.3222274482,-0.1211521253,-0.021525478,-0.1003587916,0.2009462863,-0.1490634829,0.2098397315,0.0983382314,-0.5983390808,0.1811593324,0.0787486359,0.0896519274,0.4094790816,-0.0001244392,0.1713657826,0.2335566431,-0.2385098338,-0.4193005562,-0.2048569024,-0.2344136983,-0.1837661862,0.4074413478,-0.2153499424,-0.0434500948,-0.6132315397,-0.1171405688,-0.057215374,0.8382027149,0.2899007499,0.0557004213,0.0221735276,0.2437990606,0.0981308147,0.1929415911,0.3184738457,0.1145073026,0.4430990219,0.2087321728,0.338168025,0.0679094046,-0.1586386412,-0.0711870641,0.0835569054,0.280957669,0.1195681542,-0.0219780076,-0.1146162972,-0.0106241079,0.4550395012,0.6182894111,-0.1244438142,-0.3540066779,-0.4397033155,-0.1298505664,-0.2184529155,0.2876081169,0.1772159338,-0.1519283801,0.1779494435,-0.1595244706,-0.1754688919,-0.3075227141,0.3047269881,-0.0470938049,-0.2925513089,-0.1445957422,0.2423642874,0.2514731884,-0.1574359983,-0.1731507629,-0.1707074642,-0.3005772233,0.1942362934,-0.0374578722,0.0332614407,-0.1622178853,-0.2602837682,0.3593869805,0.0219991133,0.2777704895,-0.0156081272,-0.2210023552,0.1750971228,0.3867213726,-0.0310707763,0.3983153701,-0.2118357122,0.0357383192,-0.0887702927,0.1991060227,-0.1957683861,-0.1575291157,-0.0030412157,0.0626828149,-0.2527766824,0.1424385309,-0.2047821879,0.0941172689,-0.128095746,-0.3872186244,-0.2571391463,-0.1042992398,0.3170131147,0.3308335245,0.0327450186,0.0213842969,0.4348160028,0.1162052304,-0.2527853251,0.0686020181,0.0260868575,0.1068151295,0.1247438118,0.1768325269,-0.2131353468,0.1577491909,0.1446985006,-0.020176921,-0.1401219964,-0.2365128398,0.1040509418,-0.0314321332,0.2174638808,0.0933527872,0.0051284656,0.065684773,-0.3857875168,-0.1371872276,0.2650327086,-0.357152909,-0.1853625178,-0.2478067875,-0.0130293518,-0.2101794779,-0.0484983437,-0.7054870129,0.2720554173,-0.0497150049,-0.3916545808,0.0422807261,-0.1151323467,0.2001676857,-0.2836830914,0.0393819921,0.4265245795,-0.5727180839,-0.3258422911,0.3022897542,-0.201630488,-0.0620368347,0.3925494552,-0.4634450674,0.2692922056,-0.1763111651,0.3346166909,0.3625286222,-0.3054618537,-0.2864094377,0.476051569,0.0608116463,0.3308361769,0.2555824816,-0.1510689557,0.2409437746,0.0393104143,0.2559550405,0.0936086625,0.1579339653,0.0332766436,0.0208977256,-0.0401273556,0.3830362558,0.2622578144,-0.3467324078,0.0336253196,0.0298387222,0.0210787412,0.3198065162,-0.3416233063,-0.1486139148,0.3134537637,-0.1775080264,0.0725343972,0.0418675579,0.1931006163,-0.7275482416,0.0468464419,0.0616339333,-0.0923423618,-0.328135699,-0.0458588116,-0.0145272473,0.0376030318,-0.4042335451,-0.0969732851,-0.0637844577,0.0427362137,0.2181390673,0.1092857569,-0.1538801491,0.2307792157,0.0312642269,0.1312134564,-0.3026619554,0.372982949,0.0696923211,-0.1885002553,0.0212982036,-0.0272981878,0.206064865,-0.3279631436,-0.0248574186,0.2660458088,0.3668588102,-0.2670189738,-0.1989662945,0.0652980506,0.2587857246,0.0962710753,-0.0828846321,0.2987167537,0.2130886018,-0.2320663184,-0.3045404255,0.3062797487,0.0069992328,0.4205735922,0.0265317839,-0.1826451123,0.3832916319,0.1058386415,-0.2332844585,-0.0060083168,0.2103117108,0.2088834792,0.0312987082,0.0615092516,-0.3478973806,-0.1770059019,0.5678311586,-0.2046071887,0.112140663,0.3307668567,-0.1569916606,-0.0826685578,0.0503760092,-0.1266833395,0.3211077452,0.1670219898,-0.3652741611,0.0371902436,0.2347430587,-0.2011923045,0.1160394996,-0.0974501669,0.2182054222,0.0531891771,0.2291597575,-0.0788060874,-0.4170311987,-0.4203803241,0.1339421272,0.110830456,-0.5512763858,0.1252056658,-0.3722783625,-0.0822068155,-0.0405491106,-0.3005830944,-0.2753337622,-0.1956277639,-0.0834283903,0.0990590006,0.0312615968,-0.0518527552,0.1712452471,0.1700271368,0.1119886413,-0.245759815,-0.2071046233,-0.2123643309,-0.2687690854,-0.0905148014,0.2157162875,-0.0490054823,-0.0251578577,-0.3031786978,-0.2045316249,-0.1507426798,-0.1372845322,0.0808488876,-0.170614928,0.2122220993,0.0848781765,0.4887607396,-0.0839299336,-0.1283809543,0.3590871394,0.2363953292,-0.2614621818,0.2470536977,0.0831591785,0.018430192,-0.0464081392,-0.0690745115,0.005278151,-0.1544251442,0.7430199981,-0.0833438262,0.2016305178,-0.0415605605,0.1645389646,0.1299038827,-0.2662785053,0.100053072,-0.1210284382,-0.2139341831,0.2681325674,-0.1243895292,-0.4402165413,0.072198689,0.1469231844,-0.0704936609,-0.0599183403,-0.4526904821,-0.2472373396,-0.038853351,0.2286962718,-0.1373573691,-0.0575727709,0.2596032619,-0.0101235891,0.0912241712,-0.0265845638,-0.334239006,0.117329061,0.0551325083,0.1163617969,0.1643093973,0.6633324623,-0.2180379182,0.2359458506,0.3052803576,-0.0470109694,0.3674233854,-0.2426069677,0.259614557,-0.2432775795,-0.0913032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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1783","title":"Dataset Examples Explorer","comments":"Hi @ChewKokWah,\r\n\r\nWe're working on it! In the meantime, you can still find the dataset explorer at the following URL: https:\/\/huggingface.co\/datasets\/viewer\/","body":"In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.\r\n\r\nHope HuggingFace can re-enable the feature that at least allow viewing of  the first 20  examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation.","comment_length":21,"text":"Dataset Examples Explorer \n In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.\r\n\r\nHope HuggingFace can re-enable the feature that at least allow viewing of  the first 20  examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation. \n Hi @ChewKokWah,\r\n\r\nWe're working on it! In the meantime, you can still find the dataset explorer at the following URL: https:\/\/huggingface.co\/datasets\/viewer\/","embeddings":[-0.3861365914,-0.3873980641,-0.0618844815,0.2974122167,0.1567154527,0.2989473045,0.0844833627,0.3552836776,-0.0643111393,0.3763421774,0.0037855837,0.2994420528,-0.2900573909,0.5288341045,0.3926576674,-0.2897698879,0.0115508502,0.2083534747,-0.1008172706,0.1148869991,0.0792255774,0.0855689645,-0.1743282527,0.1514346898,-0.4029159546,-0.1880390346,-0.3037720025,-0.0746549815,-0.2611444592,-0.5259741545,0.0975277424,-0.096980527,0.3257598579,0.0875246227,-0.0001094514,-0.1372068524,0.2247065306,0.0796680152,-0.4892878234,-0.059252806,-0.3846616447,-0.0151574807,0.3065688312,-0.160370186,-0.2138927281,-0.4064326286,0.2569763362,0.060766831,0.3277089596,-0.0574039184,0.1862078011,0.4664745033,-0.101199545,-0.309589535,-0.0577799752,0.5932620168,-0.3776036501,0.1418806612,0.4056650996,0.1719203442,-0.055873353,0.3942483366,0.4434017837,0.1074619591,0.0666836277,0.1224726513,-0.0140311671,-0.1412602812,0.035226807,0.6123383641,0.4841229916,0.1082843915,-0.3699077666,-0.4363870919,-0.0279569943,-0.1658657789,-0.1121710092,0.382439822,0.070182316,0.2538605034,-0.3750244081,0.1134655699,-0.1830792427,-0.0366972871,-0.0914128274,-0.0198506881,-0.3480017781,-0.071208328,0.4680240452,-0.0432211272,0.0942932814,0.2341705263,-0.0011738002,0.1652157605,-0.0263169371,-0.2085904032,0.1860803217,0.1761122644,0.1291079372,0.2292695642,0.1167370975,0.3246678412,-0.0312280841,0.1755796075,0.2116531134,-0.1055743247,0.4409830868,0.2119702101,0.1845704615,0.3149191439,0.0476228483,-0.042671524,-0.381049186,0.157037273,-0.3530599475,-0.2361382991,-0.0104282359,-0.169717446,-0.0877128765,-0.1154186949,-0.2391984165,-0.0636856928,0.1094625071,0.4093828499,-0.0947325677,-0.3719478548,-0.3213818669,-0.1324314177,0.0660622194,-0.3266577125,-0.1745932847,0.0009161785,-0.282733351,0.030079376,0.144810617,-0.5260006785,0.4474388063,-0.0701306015,-0.0689212009,0.0031333715,0.334326148,0.1553655118,0.3408134878,0.1037811339,-0.0048323544,-0.2238629311,0.155713439,0.0739415511,-0.2017610073,-0.0292448308,-0.1053102538,-0.3409497738,-0.3423970044,0.1137071028,-0.2802437544,0.0596916303,-0.3804872632,0.4780719876,-0.4661561549,-0.2010666579,-0.0501739606,0.1169522181,-0.255343765,0.0600068644,0.3718675971,0.2557033598,-0.3894895315,-0.3772592247,0.0182705354,-0.2124765813,-0.3901623189,-0.037462756,-0.344284147,0.3350663483,-0.5363284945,0.235228464,0.3367296159,-0.2228100151,-0.0518802553,0.0936631188,0.1573923677,-0.0549474545,0.0014534025,-0.1481589973,0.4459006786,-0.0305543151,-0.3026451766,-0.0790591314,0.0599405281,-0.0368748344,0.0009243836,-0.1719297171,-0.0129102254,0.0563326664,-0.1486153454,0.1010532603,0.1191323847,0.0383442864,0.4723883271,-0.2317597419,-0.1125413254,0.1484722197,0.2192695141,0.2432035655,0.0111545511,-0.4020672739,-0.1583313048,0.0949400812,0.0038213802,-0.180739522,0.30763188,-0.3185966313,-0.113110669,-0.0341547132,-0.2596442401,-0.2621262372,0.1584979594,-0.1365663111,0.1611819267,0.1251431406,-0.1892434061,0.08858978,-0.3987357616,0.187005043,-0.3901411891,0.2090323716,0.1105593592,0.0536062457,0.0102647813,0.0267262701,-0.0408343114,-0.2687215507,0.0686031654,0.221715048,0.1641524285,-0.1090132892,0.1630971432,0.4362353384,0.208985284,-0.3917674422,-0.0104676588,-0.1331777424,-0.1332426518,0.2064635158,-0.7598805428,0.3216645122,0.2521365583,0.1578047723,0.317338407,0.1152514145,0.023177091,0.111087881,-0.2549541593,-0.2837450504,0.1308498681,0.0769312978,0.1748405248,-0.0528696813,-0.6406166553,0.1478694379,0.2648247778,-0.0942745283,-0.0380185768,0.0963488072,-0.3271735609,-0.0201585311,0.4219538271,0.0524048917,0.0221044496,0.2686966658,0.2439734191,0.0857682675,-0.1344521195,0.1146751642,0.1895128489,0.0684709623,0.0004230356,-0.3579305708,-0.173208192,0.0783771053,-0.2876156867,-0.0603781566,0.1106496826,0.1175418198,-0.2738903463,0.0905591995,-0.3630374968,-0.4837638736,-0.069794178,0.0704646781,0.1552468687,-0.3996530175,0.0803671479,0.2161303461,-0.1143753901,0.0583186224,-0.162574172,0.3223586679,-0.297540158,0.7413788438,-0.1173184812,0.1161013916,-0.1537316442,0.1364964247,0.2291521132,0.3248565793,0.3808384836,-0.223133713,0.1128322482,-0.5781584382,-0.5961934328,0.4128218889,0.0946190879,0.2726972699,0.3713106215,0.1063176394,-0.4431185722,-0.0808980688,0.0142680882,-0.3306340277,-0.2247574329,-0.1559310108,-0.0645111352,0.1442426592,-0.0447174907,-0.1804035902,-0.1693991423,-0.1982709318,0.375235945,-0.0849383101,0.1998657435,0.1477505267,0.0412277952,0.375274986,-0.5501070023,-0.0154579962,-0.1189945638,-0.4080892801,0.0377525948,-0.6036459804,-0.3449813128,0.1304653734,-0.1481451243,-0.0367349349,-0.118149586,-0.2437891811,-0.2489361763,-0.2817431092,-0.046446465,-0.0438788831,0.0582123101,0.336622715,-0.0722353309,-0.0918443277,0.0653492138,-0.1439099461,-0.0627382174,-0.0116690723,0.1149907112,-0.1781760901,-0.0343371555,0.1868714988,0.3779286742,0.2188417315,-0.1248089671,0.0270542298,-0.4515987635,0.2974494398,-0.0454939976,-0.07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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1783","title":"Dataset Examples Explorer","comments":"Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. \r\nHope similar feature can be retain in the future version.","body":"In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.\r\n\r\nHope HuggingFace can re-enable the feature that at least allow viewing of  the first 20  examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation.","comment_length":36,"text":"Dataset Examples Explorer \n In the Older version of the Dataset, there are a useful Dataset Explorer that allow user to visualize the examples (training, test and validation) of a particular dataset, it is no longer there in current version.\r\n\r\nHope HuggingFace can re-enable the feature that at least allow viewing of  the first 20  examples of a particular dataset, or alternatively can extract 20 examples for each datasets and make those part of the Dataset Card Documentation. \n Glad to see that it still exist, this existing one is more than good enough for me, it is feature rich, simple to use and concise. \r\nHope similar feature can be retain in the future version.","embeddings":[-0.3628720045,-0.4238744974,-0.0469127297,0.3083789945,0.1272349954,0.2346735299,0.1710146368,0.3707437217,-0.0460052155,0.3754509687,0.0373594649,0.2407734096,-0.3106932044,0.5867730379,0.425044924,-0.371111691,0.0176204182,0.1814071983,-0.0853752345,0.0868427306,0.0558809489,0.0799584836,-0.1806430072,0.1946162581,-0.4375636578,-0.2032096237,-0.2906790078,-0.0181118343,-0.2234526873,-0.5350555182,0.0933511034,-0.1131595075,0.318122983,0.0995311812,-0.0001116613,-0.1911393255,0.2427989393,0.1175147444,-0.5374827981,-0.017024342,-0.3509198427,-0.0632480681,0.3206753433,-0.1844154298,-0.1859279424,-0.3335364759,0.2009563446,0.0871430859,0.3625756204,-0.1000136361,0.1540151387,0.4514330626,-0.1147692949,-0.2746319771,0.021525532,0.6503049135,-0.4158145189,0.1027740315,0.4872650206,0.1220444217,-0.0628314987,0.436434269,0.3849204779,0.1084414795,0.082786575,0.1359821707,0.0367397927,-0.1285455823,-0.0026759566,0.6877962947,0.4381465912,0.0931990221,-0.3632138669,-0.5371533632,-0.094430171,-0.2077556103,-0.123566322,0.3460617065,0.045661848,0.2922644615,-0.3274420202,0.1238086373,-0.2014064342,-0.0435199961,-0.031609647,-0.0419529602,-0.3505464196,-0.0388570651,0.4434104264,-0.0454552062,0.0321435891,0.1650788635,0.0274939202,0.1676371545,-0.0362644419,-0.1789749712,0.2143942416,0.1790531129,0.2006743848,0.1832567006,0.0436740443,0.2886774242,-0.0051313275,0.1875259876,0.2100379318,-0.1485780478,0.4455875456,0.1579916477,0.198757574,0.290583849,0.1111951321,-0.0247339904,-0.3412183225,0.1907035261,-0.3514389098,-0.2364153862,0.0480780192,-0.1570365429,-0.0745462552,-0.1104142219,-0.1966985762,-0.1001117453,0.0987718701,0.3787342906,-0.0820647627,-0.3699345589,-0.3043927848,-0.132314533,0.0953299776,-0.3404927254,-0.1633529812,0.0134238387,-0.2573073506,0.07651674,0.1408937424,-0.4891138673,0.4031631052,-0.0637202039,-0.0823209435,0.0182059221,0.3176814616,0.1376454979,0.3231967986,0.0846775249,-0.0698207393,-0.2001847029,0.1453597397,0.0466810837,-0.2402201742,-0.0332044587,-0.1150539815,-0.4029281437,-0.3197678924,0.0904974714,-0.1786216497,0.1010476425,-0.5091859698,0.5675261617,-0.4719489217,-0.1423956603,-0.052287437,0.1688278466,-0.2888948023,0.0808207989,0.3657828867,0.3202440441,-0.3815145195,-0.402302593,0.0040496085,-0.2024496198,-0.354172647,-0.0102657899,-0.3748866022,0.3842636049,-0.5060606003,0.1932112724,0.3482882082,-0.2705029547,-0.0268902443,0.0732418075,0.1342188269,-0.0153008252,0.0547206663,-0.1656402797,0.5171090961,-0.0580826513,-0.3266589046,-0.0058833905,0.0500524789,0.0045681633,-0.0057807006,-0.1436333656,-0.0126427999,0.0987416208,-0.1880908757,0.082975395,0.1046871617,-0.0291380771,0.5324881077,-0.2547031343,-0.062380895,0.1240162924,0.2447225451,0.1867270172,0.0332897976,-0.3907249272,-0.2663160264,0.0874274001,0.0173915755,-0.1704370826,0.3375398517,-0.3268709481,-0.1081181169,-0.0301380176,-0.264152348,-0.2458115965,0.1156627536,-0.1632660478,0.1129676774,0.1194049194,-0.2301286161,0.1028624848,-0.4876904786,0.1936997324,-0.3489359319,0.1925223768,0.1154591665,0.08967641,-0.0020306529,0.0805486366,-0.0232968833,-0.2826875448,0.052323807,0.1806110144,0.1651832312,-0.1275327951,0.2021798342,0.4183845222,0.2501938939,-0.3330259621,-0.0042097811,-0.2055593133,-0.071884647,0.1867924929,-0.7351933122,0.3116651475,0.2892594337,0.1737939566,0.276227951,0.0559668019,0.0272191186,0.0849289671,-0.2509565651,-0.2338690907,0.1624337435,0.0834244862,0.1351511478,0.0118411081,-0.6773616076,0.179995209,0.3371834755,-0.1255377829,-0.0636920333,0.1597159505,-0.283005178,-0.0718507692,0.422870338,0.0580269322,0.0078619411,0.2213018984,0.1757678092,0.0611357428,-0.1028247252,0.1389880627,0.2311141044,0.059535902,-0.0213477742,-0.362592876,-0.1599088758,0.1234794483,-0.2691359818,-0.0851850137,0.1261221319,0.067501232,-0.2492976934,0.0882234201,-0.312413156,-0.4699494541,-0.0558281988,0.0536702834,0.1064356714,-0.3449403346,0.0961487666,0.1833516359,-0.1305019855,0.0808016434,-0.0739755332,0.4409167767,-0.2626431882,0.6817628145,-0.1589347124,0.1216822714,-0.1466044635,0.0929053277,0.1964097917,0.2997838557,0.395537138,-0.1922944933,0.1253749579,-0.6362916231,-0.5710225701,0.4147366881,0.114051111,0.2819750309,0.3237912357,0.1212586164,-0.480971992,-0.0920611545,0.0238983538,-0.3108506799,-0.1952959597,-0.1606523693,-0.0696496442,0.0866157934,-0.0036351357,-0.1760458946,-0.2337187082,-0.1928891242,0.3730760217,-0.1350295991,0.2156967968,0.1801916361,0.022220606,0.3586769998,-0.5773912072,0.0656881705,-0.1230051145,-0.4196787179,-0.034191139,-0.539200604,-0.3599954844,0.0488108844,-0.1813386828,-0.1222140566,-0.0861305147,-0.2249406278,-0.2832895219,-0.2888917923,-0.0127118332,-0.0550171845,0.1428121626,0.3644660413,-0.0129617769,-0.0480523892,0.0823384747,-0.2271732837,-0.0411864668,-0.0195229668,0.1034803912,-0.1157057807,0.0113717662,0.1424403787,0.4282975197,0.1344205439,-0.0786102414,0.0477127843,-0.4147192836,0.2543253899,-0.0843585953,-0.1063033864,0.1534173042,-0.0989839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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1781","title":"AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import ","comments":"Hi ! I'm not able to reproduce the issue. Can you try restarting your runtime ?\r\n\r\nThe PyExtensionType is available in pyarrow starting 0.17.1 iirc. If restarting your runtime doesn't fix this, can you try updating pyarrow ?\r\n```\r\npip install pyarrow --upgrade\r\n```","body":"I'm using Colab. And suddenly this morning, there is this error. Have a look below!\r\n\r\n![screenshot-colab research google com-2021 01 26-08-15-36](https:\/\/user-images.githubusercontent.com\/45964869\/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)\r\n","comment_length":44,"text":"AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import  \n I'm using Colab. And suddenly this morning, there is this error. Have a look below!\r\n\r\n![screenshot-colab research google com-2021 01 26-08-15-36](https:\/\/user-images.githubusercontent.com\/45964869\/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)\r\n \n Hi ! I'm not able to reproduce the issue. Can you try restarting your runtime ?\r\n\r\nThe PyExtensionType is available in pyarrow starting 0.17.1 iirc. If restarting your runtime doesn't fix this, can you try updating pyarrow ?\r\n```\r\npip install pyarrow --upgrade\r\n```","embeddings":[-0.1790662259,-0.1585770994,-0.0443940833,0.2205853313,0.1801975965,-0.0639269799,0.1799789518,0.2682418525,-0.311421901,0.1932341158,-0.0361046977,0.4329881668,-0.1667326093,-0.1118558422,0.08164116,-0.067264691,0.2507495582,0.3972359598,0.2283162028,0.0152990064,-0.1987970024,0.2214333564,-0.3317900002,0.2289108038,-0.3630690873,0.2572512329,0.0558958426,-0.1920558065,-0.2301744372,-0.5764158368,0.4622827768,-0.457690388,0.1837862581,0.0198195726,-0.0001144319,-0.0085836183,0.3469851613,0.0369327553,-0.0782001764,-0.179927215,0.0146695673,-0.0796009824,0.3153556585,-0.0688825548,0.1376845986,-0.049098216,-0.0455625169,0.3671183288,-0.0516867824,0.2796535194,0.2375448793,-0.0251752865,0.5402342081,0.0167230759,0.5227000713,-0.1609021574,-0.2256377041,0.6437642574,0.3970014751,-0.3397271037,0.3303521574,-0.0623879507,-0.3161487877,0.1467274725,0.3413374126,0.0093860058,0.4785271287,-0.2968401313,-0.0349214338,0.0639048517,0.1993095577,-0.106132485,-0.0536232851,0.1321716458,0.2737747133,-0.4418088794,0.1780537218,0.3744233847,-0.0651033148,0.1674182564,0.3446937799,-0.0282741636,-0.1547164768,0.1121773794,-0.3299468458,0.4400127232,-0.0608913675,-0.0126784639,0.1606597602,-0.014353618,0.4384037852,0.1720194966,-0.146457091,0.1746094525,-0.1346684247,0.1695789099,0.0346121825,0.3614486456,-0.016898077,-0.3039343059,-0.1646404862,-0.2202556729,0.185238108,0.4585883021,-0.1547237784,0.296910435,0.0425983295,-0.1898492277,0.1939644963,0.0431142598,-0.1044290736,0.1421603113,-0.1413403302,-0.3777513504,0.2381351292,-0.0861328915,0.4219927192,-0.1524940878,-0.4551401138,-0.3460401297,-0.5161126256,0.0715425164,-0.0819104239,0.2317310572,-0.1786213517,0.1398227513,0.0135104461,0.2279352993,-0.1297752708,-0.2341376245,-0.0415705703,0.3183738291,-0.1867560446,-0.2240166664,-0.0605837144,-0.1977543384,-0.03448724,0.0647213534,0.0029570737,-0.1728652567,-0.3638984561,-0.231403172,-0.2383738458,0.3250190616,-0.1241195798,-0.0144523475,0.1709778607,-0.5575419664,-0.1424941123,0.3194749057,-0.5617517233,-0.4043883681,-0.2904806435,0.1659997851,-0.1723260581,-0.1548972428,-0.3065091968,-0.1803507656,-0.0339657068,-0.2712192237,-0.134447813,-0.4794248044,-0.0160602592,-0.4012240171,-0.0779944062,0.0237126052,-0.4536841214,-0.1713841707,-0.0673347339,-0.0593353547,0.3455157578,0.0348362587,-0.1741034687,0.0599535629,-0.08434508,0.1987013072,0.0848146528,-0.1136741415,-0.2233837992,-0.2362246662,-0.0785076916,-0.0386033542,-0.0020760684,-0.2877239287,-0.0197688248,-0.0431312881,0.4431330562,0.2906011641,-0.1093343198,0.2179859579,-0.3110338748,-0.1981665194,0.3580446541,0.1255996376,0.0955358669,-0.1678241789,0.0892780572,-0.2828473449,0.0909027904,-0.1441042423,-0.0333426073,-0.0247795042,0.5660248995,0.0915235952,-0.1175733432,-0.0555668958,0.0603022575,0.1770724803,-0.1671856344,0.1522559077,-0.3447423875,-0.0380014926,-0.3247757852,0.3926260173,-0.2453923523,-0.0746597871,0.0887517035,-0.1059178561,-0.1791073531,0.4000236094,-0.0704847723,0.0581471808,0.1778243631,0.1856284589,0.0744912401,0.4301854074,-0.270832032,-0.4979071617,-0.1084052026,0.0034129983,0.2324229479,0.0094567342,-0.1076803729,-0.0670001134,-0.0296544079,0.1820956469,-0.2738328874,0.1543227136,0.0993480384,-0.4678512514,-0.0098041911,-0.2234818041,0.0692760423,-0.0187455416,0.8860567808,0.1675926,0.254861325,0.1322639734,-0.2607864141,0.1686573178,0.0046349959,0.092463553,0.2096389085,0.1399048716,0.0681173578,0.2411949486,-0.181121707,-0.1170749813,-0.025532376,0.0542542972,0.2834241688,0.2026453614,0.1976955384,0.0019476041,-0.4756842554,0.0932431668,0.236591965,-0.173293978,0.3250072002,0.0668008924,-0.3597570062,0.0757200792,0.0793354735,-0.0889667869,0.2429632545,0.1491228789,0.5926089883,-0.0030886792,0.265087992,0.0802495033,-0.1685391068,-0.227558732,-0.0866515636,0.2750094235,-0.2485767454,0.2147495747,-0.3447979093,-0.0234028008,-0.1641614139,-0.5490457416,-0.1036438271,-0.126457274,-0.1215843335,0.2856437564,0.1915252656,0.0521730147,0.2701273263,-0.0963661373,0.0707283691,-0.4580160081,-0.0769520402,-0.2203986943,-0.3743089139,0.0262891296,0.125177443,-0.0590720661,-0.0304245558,-0.3601930737,0.1301649362,0.0701581091,-0.3697500825,0.1027367115,-0.2174154371,0.3510778546,0.1288847178,0.138935551,-0.2283433229,-0.2265936732,0.3410273492,-0.2279568166,-0.2316577584,0.1729710698,-0.2530034184,-0.1466672271,-0.0388243198,-0.0024364509,-0.4087469876,-0.135675177,-0.0016151909,0.0617810786,0.0647763386,0.2473357022,0.3024420142,0.2300611138,0.0246636327,-0.0055180066,0.1005854756,0.1662604958,0.4291490912,-0.1283849478,-0.1799859107,0.3432487845,-0.2596538365,-0.0454661138,-0.1025901437,-0.2102573067,-0.4968859553,-0.0208501443,0.4942528009,-0.1182472929,-0.0494846925,0.195320338,0.1326886714,0.0062993877,0.0963878632,-0.0298702251,-0.4402624667,0.3675146997,0.2336600572,0.0597360283,0.0384743735,-0.1328354329,0.5266131163,-0.3150179982,-0.0349395014,0.3293399513,-0.2715450227,-0.0616858229,0.0273055118,-0.375697135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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1781","title":"AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import ","comments":"Yes indeed.\r\n\r\nAlso it looks like Pyarrow 3.0.0 got released on pypi 10 hours ago. This might be related to the bug, I'll investigate\r\nEDIT: looks like the 3.0.0 release doesn't have unexpected breaking changes for us, so I don't think the issue comes from that","body":"I'm using Colab. And suddenly this morning, there is this error. Have a look below!\r\n\r\n![screenshot-colab research google com-2021 01 26-08-15-36](https:\/\/user-images.githubusercontent.com\/45964869\/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)\r\n","comment_length":46,"text":"AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import  \n I'm using Colab. And suddenly this morning, there is this error. Have a look below!\r\n\r\n![screenshot-colab research google com-2021 01 26-08-15-36](https:\/\/user-images.githubusercontent.com\/45964869\/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)\r\n \n Yes indeed.\r\n\r\nAlso it looks like Pyarrow 3.0.0 got released on pypi 10 hours ago. This might be related to the bug, I'll investigate\r\nEDIT: looks like the 3.0.0 release doesn't have unexpected breaking changes for us, so I don't think the issue comes from 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1781","title":"AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import ","comments":"Installing datasets installs pyarrow>=0.17.1 so in theory it doesn't matter which version of pyarrow colab has by default (which is currently pyarrow 0.14.1).\r\n\r\nAlso now the colab runtime refresh the pyarrow version automatically after the update from pip (previously you needed to restart your runtime).\r\n\r\nI guess what happened is that Colab didn't refresh pyarrow for some reason, and the AttributeError was raised *before* the pyarrow version check from `datasets` at https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/__init__.py#L60","body":"I'm using Colab. And suddenly this morning, there is this error. Have a look below!\r\n\r\n![screenshot-colab research google com-2021 01 26-08-15-36](https:\/\/user-images.githubusercontent.com\/45964869\/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)\r\n","comment_length":72,"text":"AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import  \n I'm using Colab. And suddenly this morning, there is this error. Have a look below!\r\n\r\n![screenshot-colab research google com-2021 01 26-08-15-36](https:\/\/user-images.githubusercontent.com\/45964869\/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)\r\n \n Installing datasets installs pyarrow>=0.17.1 so in theory it doesn't matter which version of pyarrow colab has by default (which is currently pyarrow 0.14.1).\r\n\r\nAlso now the colab runtime refresh the pyarrow version automatically after the update from pip (previously you needed to restart your runtime).\r\n\r\nI guess what happened is that Colab didn't refresh pyarrow for some reason, and the AttributeError was raised *before* the pyarrow version check from `datasets` at https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/__init__.py#L60","embeddings":[-0.4181626439,0.150439024,-0.019076623,0.1533482373,0.1342275739,-0.0236932579,0.2542464137,0.2454890162,-0.3241707385,0.1116399616,0.0381206125,0.3774304986,-0.0813133493,-0.0478629395,-0.0118858498,0.0418873914,0.3027802706,0.3409284055,0.2071904242,0.0259101894,-0.2342766374,0.0909717605,-0.3210463226,0.2035171092,-0.4118345976,0.2203688473,-0.0174811408,-0.1700021774,-0.3172970712,-0.6792119145,0.5830789208,-0.2323222309,0.2768806219,0.1425857395,-0.0001209328,-0.0209162738,0.3580555916,0.1024649292,-0.2893368304,-0.1401554644,-0.1511045843,-0.1866816431,0.3294048905,0.0138392476,0.2098614872,-0.2561298907,-0.0134524936,0.2181422859,-0.080727227,0.2746795416,0.1697658896,0.0567860603,0.4970565736,0.0424643382,0.3831528127,-0.071018897,-0.1261998862,0.5384056568,0.2573763132,-0.3175978959,0.3273626864,0.0955689847,-0.3002936244,0.2453082353,0.3384176791,0.0261107702,0.3156374991,-0.1950557381,-0.0080030542,0.2326883525,0.4724449515,-0.2055785507,-0.3742718995,-0.1450867057,0.2462435961,-0.3504833579,0.2955636084,0.1823577136,0.0134681668,0.1489953399,0.1497543305,-0.0184530206,-0.066981405,0.0797984451,-0.3698368967,0.4304915071,-0.1151573136,0.0948726386,0.1171312258,-0.066792652,0.5531333089,-0.0353317596,-0.2178254575,0.2489418089,-0.2337918431,0.1079049408,0.0849388614,0.383694768,0.0191436578,-0.1634806246,-0.2799125612,-0.3150515556,0.0434298851,0.3021691144,-0.0314206555,0.2702073753,0.0722960085,-0.0455675982,0.2369808257,0.0130285798,-0.0343974493,0.1162366569,-0.1001426578,-0.3603296578,0.2520412803,-0.0574507527,0.6233623028,-0.2348875254,-0.420814544,-0.2791058421,-0.4959393144,-0.1293876171,-0.1401707828,0.1667962372,-0.2977566421,0.2598457634,-0.1432746053,0.1083350405,-0.25399068,-0.1861823797,-0.0646512508,0.1568896621,-0.1410632133,-0.1038216278,-0.0783214942,-0.1958213747,-0.0038999112,0.0601154342,-0.1246229187,-0.0684761256,-0.2699031234,-0.3182118237,-0.1636870354,0.4307076037,-0.2343376428,0.1612659991,0.1176150292,-0.5139939785,-0.1687964052,0.2821596265,-0.5118224025,-0.4434715807,-0.2082923055,0.0998681337,-0.3005480468,-0.1936513484,-0.4257184565,-0.0990353525,0.0882480592,-0.481126368,-0.0242845472,-0.5677361488,0.0094444519,-0.3748410344,-0.0311756358,0.2954781353,-0.6035419106,-0.1913348883,-0.1196309403,-0.143869862,0.1422236115,-0.0320601985,-0.1355499476,0.0877708271,-0.0691845268,0.0052972008,0.0978054181,-0.201942578,-0.4289782643,-0.12813209,0.0391551964,0.0890583992,0.0629899874,-0.1787740737,0.0733823925,-0.0738649815,0.3456241786,0.2120342851,-0.1600933373,0.0766813233,-0.094631426,-0.1960214227,0.3323121667,0.1353757977,0.0791467503,-0.0963715017,0.1051179245,-0.1528168619,0.1042477936,-0.0824322104,-0.0228677914,0.0151800336,0.5127726793,0.0129785007,-0.1164855957,-0.1611536592,-0.1859289855,0.2242620438,0.0850028098,-0.0598823577,-0.4205748737,-0.1836570352,-0.2416515052,0.2941110432,-0.1884559542,-0.2594415545,-0.0057253679,0.1202157661,-0.1577471048,0.3912128806,-0.1406080425,0.3141974211,0.0699943304,0.2449356467,-0.0793026537,0.3649939895,-0.1673131436,-0.3892548084,-0.1534980088,0.0461947657,0.1388100386,-0.0449939333,-0.1133975834,0.1098341197,-0.0093778027,0.2966071069,-0.2214619964,0.1578570604,0.1264435202,-0.1821916997,0.0310812052,-0.3086495996,-0.0047076247,0.0026354652,0.6934741139,0.1561753899,0.2425137311,0.1873312294,-0.1939786822,0.0710175335,0.0452383682,0.1769446135,0.0338250846,0.0847995728,-0.0266789943,0.2097433507,0.024180308,0.0152949709,-0.124628,0.1560415477,0.3652108312,0.1491890252,0.1532502174,-0.0186699107,-0.4601625204,0.0689493641,0.383128494,0.1304515749,0.4296892881,0.0333592482,-0.3057473898,0.0427863896,-0.0728984252,0.0013502085,0.0672047064,0.1850849092,0.5060129166,-0.0201589353,0.2183589935,0.2089821696,-0.1588149071,-0.3102003336,0.0411301889,0.3739798367,-0.3386669755,0.2943220735,-0.3655646145,-0.111781247,-0.1900116056,-0.6143972874,-0.2298163772,-0.139765203,-0.1837114692,0.3203368485,0.2036804259,0.1484705657,0.0623821504,-0.135220319,0.0419023074,-0.4303118587,0.069035545,-0.1785099655,-0.2635135353,-0.0004695377,0.0800385773,-0.1433688253,0.0108111631,-0.4225106835,0.0828151554,-0.0152948219,-0.3495781124,0.0856365636,-0.3275427222,0.4431426823,0.2561699748,0.1518147588,-0.2441195846,-0.1336629242,0.3365137875,-0.217584908,-0.2805583775,-0.0804267302,-0.1813363135,-0.0858778656,0.0960069001,-0.0889935195,-0.4324345589,-0.1147843525,0.0239354949,0.1166306436,0.0384806171,0.3249641657,0.2670057714,0.1228089854,-0.0381520726,0.0272235703,0.0604037233,0.0145734949,0.3432318568,-0.043967396,-0.0917213112,0.2418819517,-0.1861955971,-0.1578913778,0.0988411903,-0.3921024799,-0.606420517,-0.1058822721,0.4493834376,-0.080487445,0.0285757277,0.3183552325,0.210398972,0.05888227,-0.0014327982,-0.1303637624,-0.5016965866,0.2687036991,0.1951235384,0.0392837301,0.1592850536,-0.11933209,0.5885620713,-0.1180830076,-0.1307075024,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1781","title":"AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import ","comments":"Yes colab doesn\u2019t reload preloaded library unless you restart the instance. Maybe we should move the check on top of the init ","body":"I'm using Colab. And suddenly this morning, there is this error. Have a look below!\r\n\r\n![screenshot-colab research google com-2021 01 26-08-15-36](https:\/\/user-images.githubusercontent.com\/45964869\/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)\r\n","comment_length":22,"text":"AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' during import  \n I'm using Colab. And suddenly this morning, there is this error. Have a look below!\r\n\r\n![screenshot-colab research google com-2021 01 26-08-15-36](https:\/\/user-images.githubusercontent.com\/45964869\/105799890-fdaf3b80-5fae-11eb-8f06-11b65cdccc30.png)\r\n \n Yes colab doesn\u2019t reload preloaded library unless you restart the instance. Maybe we should move the check on top of the init 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1776","title":"[Question & Bug Report] Can we preprocess a dataset on the fly?","comments":"We are very actively working on this. How does your dataset look like in practice (number\/size\/type of files)?","body":"I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it.  Can we preprocess a dataset on the fly without generating cache?\r\n\r\nBTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/0281f9d881f3a55c89aeaa642f1ba23444b64083\/src\/datasets\/arrow_dataset.py#L1532","comment_length":18,"text":"[Question & Bug Report] Can we preprocess a dataset on the fly? \n I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it.  Can we preprocess a dataset on the fly without generating cache?\r\n\r\nBTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/0281f9d881f3a55c89aeaa642f1ba23444b64083\/src\/datasets\/arrow_dataset.py#L1532 \n We are very actively working on this. How does your dataset look like in practice (number\/size\/type of files)?","embeddings":[-0.3903958201,-0.1971480101,-0.07739079,-0.0038245281,0.3977741301,0.3356423378,0.2610038817,0.2968967557,-0.084641166,0.1327671707,0.0693483576,0.1286533326,-0.1356009245,0.2288652807,0.2126400322,0.1509196609,0.2128424346,0.0664261952,0.0356727354,0.2065104991,-0.2539820969,-0.1531421393,-0.1100583076,-0.212891683,-0.4570014477,-0.2720251679,-0.1605053544,0.0205966514,-0.2341964692,-0.1767743677,0.3146640956,0.3214798272,0.0866012499,0.5502457023,-0.0001117338,0.008893053,0.4019140005,-0.0934246853,-0.1076299772,-0.1060749441,0.1285001636,-0.2643048763,-0.1480513215,-0.2470785379,-0.1567154676,-0.0504478477,0.1196824685,-0.6373549104,0.3856018484,0.1254341304,0.2115146071,0.128656894,-0.0810716078,0.1382953972,0.3179634213,0.3585823476,-0.3582457304,-0.0789366886,0.2596543431,-0.0795592964,0.0480676144,0.1202432886,-0.3685803413,-0.0111847473,0.3391450942,0.1131046638,-0.2082394958,-0.6446156502,0.1036311015,0.276486367,0.49214077,-0.5251832008,-0.6757846475,-0.2348736078,0.1164427474,-0.3433661759,0.0445728451,0.4104189277,-0.1506752223,0.0462534279,-0.4445935488,-0.3685857356,0.007384201,-0.3361414373,0.2708496153,-0.1538696289,-0.2351511568,0.0023274606,0.0621331371,0.1466435045,0.3794543743,-0.2408935875,-0.3575361371,0.5096227527,-0.2625169456,-0.2431843877,-0.2934468687,0.1031612903,0.2890485227,0.1290298402,0.0322178863,-0.1347647905,-0.0453485623,-0.0331239849,0.1808281094,0.0479214564,-0.0593688935,-0.1961773932,0.3188611269,-0.2196499556,0.0754331648,-0.0793797448,0.0598629154,-0.3965128958,0.3271101117,0.0377091952,0.3040319681,-0.32267344,0.4087907672,-0.0180785656,0.0922285989,-0.0135145253,-0.4056245685,0.1021770164,-0.1372481585,-0.0178536512,0.1294896901,0.0807888061,0.0287715979,-0.173416838,-0.1040252373,0.0153777571,0.0140405511,0.2762532234,0.1291560978,-0.225557074,0.0598062761,0.1925711781,-0.0866798386,-0.1647258699,0.1988245845,-0.4564754963,0.1030178517,0.3438337743,0.0327928253,0.0540712178,-0.06752152,0.3300350904,-0.1948367059,0.4849701524,-0.2016842663,-0.2776722312,0.0362526886,0.1042887717,-0.0199730638,0.0556141399,-0.6019009948,0.1712470353,0.5740996003,0.2262290269,0.2237465233,0.2339284569,-0.2646639049,-0.1679983735,0.1193476245,0.4449639022,-0.189895004,0.2122202665,0.2736875415,0.0231916141,0.1523585618,0.1042374074,0.0275389515,0.2540634573,-0.063007541,-0.1503755003,0.3098082244,-0.1066606641,0.1157015264,0.4008814991,-0.0550089292,-0.2682392001,0.0742101893,0.1855351776,-0.0364895724,-0.1308399588,0.0000576921,0.3976077735,0.0050129583,0.1950966269,-0.1847909838,-0.4482399523,-0.3217773736,0.0058107013,-0.0378195792,0.1062782556,-0.1856068522,-0.3014105856,0.0877304524,-0.1978133023,0.1949757338,0.3726005554,-0.0443876237,0.0690668374,0.0505192615,-0.1592517346,-0.366135776,0.2145513147,-0.1230579466,0.0163033884,0.18168284,-0.4576365054,0.1819765866,0.0651791766,0.0800102875,0.1132603362,0.0453959852,-0.3407961726,0.3032753468,-0.0114895562,-0.4556630552,0.2294508666,-0.191767633,0.1052213982,-0.3946021497,-0.0814101025,0.1480355561,-0.06047507,-0.0355989411,-0.1678518206,-0.2173678726,-0.00864325,-0.0828095376,0.3009221852,-0.0961251631,0.1768975705,-0.0134273153,0.168675676,-0.1474278718,-0.1476063132,-0.0205906946,-0.1321052313,0.1468913108,-0.1920770556,-0.2768490016,0.2378067821,-0.1526382416,0.4071863294,-0.0565594919,-0.1704910994,0.2015391737,-0.0626928955,-0.046720691,0.1874064505,0.011347251,-0.0184700694,0.7148051858,-0.1235886514,-0.0870060027,-0.1725437194,-0.0114093842,-0.0265265126,-0.0448284857,0.2636304498,-0.0986315608,-0.1139736176,-0.228943184,0.0929387957,0.2143616378,0.1423552632,0.0484319404,0.1799986213,0.0954068303,-0.0961784422,0.2947970629,0.010229283,0.4094163775,-0.0306621846,0.217375055,0.1128181815,-0.1889411211,0.171087265,0.0641639605,0.0664077103,-0.1352333426,0.1167892814,0.1735371798,-0.1462999731,0.1443755031,0.0346317515,-0.2236531228,0.1055327132,-0.1247566193,0.0065264278,0.0684499368,-0.0014727175,-0.4323998988,0.4077481329,0.0714962184,-0.1607204676,-0.1685139388,-0.1798398942,0.2189041227,0.0133905103,0.2648711503,-0.1633476466,0.4653586149,-0.051857233,0.0478228517,-0.3106508553,0.188993901,0.0980869606,0.0368409231,0.3631496131,-0.0228803158,0.0573131777,0.2028739601,0.1895760149,-0.0003710148,-0.1338364035,-0.1611996889,-0.2465218157,-0.0330792442,0.0012594801,-0.2042115182,0.0467956923,-0.1116845608,-0.2437698245,0.1459947973,-0.000191208,0.0341539495,0.24361597,0.0226549488,-0.1487399489,-0.1455842257,0.079873994,-0.0226015337,-0.7236916423,0.2888316512,-0.0164141487,-0.3574145734,0.0216522235,0.0053142523,-0.0362320058,0.3411466777,-0.605407238,0.0128710484,-0.3743886352,0.0355547108,-0.1713722199,0.1643203348,0.2667718232,0.0743670687,-0.071507223,0.0220159795,-0.1048546284,0.0018189943,-0.1901122034,0.0152298519,0.2821198702,0.4061176777,0.0109551968,0.6870184541,0.1741272062,0.0132037643,0.3461167216,0.0442665368,0.5539386272,-0.277326405,-0.0958695188,-0.1771088541,-0.2428266555,-0.1766515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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1776","title":"[Question & Bug Report] Can we preprocess a dataset on the fly?","comments":"It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm_wwm.py","body":"I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it.  Can we preprocess a dataset on the fly without generating cache?\r\n\r\nBTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/0281f9d881f3a55c89aeaa642f1ba23444b64083\/src\/datasets\/arrow_dataset.py#L1532","comment_length":21,"text":"[Question & Bug Report] Can we preprocess a dataset on the fly? \n I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it.  Can we preprocess a dataset on the fly without generating cache?\r\n\r\nBTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/0281f9d881f3a55c89aeaa642f1ba23444b64083\/src\/datasets\/arrow_dataset.py#L1532 \n It's a text file with many lines (about 1B) of Chinese sentences. I use it to train language model using https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm_wwm.py","embeddings":[-0.3749497831,-0.3105449677,-0.025356181,0.0781464875,0.4190768003,0.3276045918,0.2523874938,0.2493899167,-0.1120243371,0.0602596849,-0.0421746373,0.0807497799,-0.1185980663,0.1677815318,0.3189421296,0.0842123479,0.2063732594,0.0334929265,-0.0025367262,0.1758535057,-0.2277178764,-0.165063709,-0.0601746254,-0.1133634001,-0.5667706132,-0.2768295705,-0.2453327775,-0.0490853488,-0.1583475769,-0.2284193635,0.275698185,0.234082222,0.0893836394,0.5178625584,-0.0001183977,0.0513982028,0.3754434586,-0.0806588978,-0.1265788823,-0.1006739661,0.1375207007,-0.2211702019,-0.1770978719,-0.1919109672,-0.1763421595,-0.0012004221,0.1539304107,-0.4922321737,0.5047305226,0.1946835071,0.1448628455,0.2098596096,-0.0517701283,0.2219605446,0.2185227722,0.3186280429,-0.2461316735,0.144497931,0.2155944854,-0.016662417,-0.0376082994,0.1634667963,-0.3166071773,-0.0151862083,0.2694856524,0.1486750096,-0.1537855864,-0.5454908013,0.12963368,0.3487482965,0.3943709731,-0.56211555,-0.674554944,-0.2740337849,0.1808799356,-0.3332337737,0.0932866931,0.4548129737,-0.1409767717,0.2020023465,-0.4392782152,-0.4125903845,0.0781616643,-0.3117173016,0.2973233759,-0.0474813022,-0.26030761,0.0327828899,0.0513391271,0.1321082711,0.3066859841,-0.2835433185,-0.3123467267,0.5733699799,-0.248072654,-0.2415750623,-0.3126248419,0.1712335199,0.2013997138,-0.0665585697,0.0682614893,-0.1289230883,0.0041906396,-0.0502716117,0.1260250211,0.039293427,-0.0833768249,-0.2117564976,0.2936162949,-0.1786146164,0.0200481806,-0.0229564458,0.0223846883,-0.325699985,0.2305509746,0.0885231346,0.2718850076,-0.1417565346,0.3139848411,-0.0136686498,-0.0189775229,-0.0311282482,-0.3354018331,0.1593687981,-0.1914641559,-0.0439447723,0.1445102543,0.1041965783,-0.030840667,-0.2627187371,-0.0954937488,0.0899274126,-0.0553904288,0.3115380704,0.1160631478,-0.1680899262,0.0952999592,0.18655321,-0.1242667958,-0.2649587095,0.0811405852,-0.4365721643,0.0148296906,0.2506143749,-0.0197152831,0.0720883459,-0.0367933847,0.3380571902,-0.1779045165,0.4492940307,-0.2043383867,-0.2573890686,0.1642541438,0.0534856468,-0.0083526773,0.0555140488,-0.4702357352,0.3273878396,0.6150686145,0.2876583338,0.2803550065,0.3247433007,-0.3547723889,-0.0543346629,0.111244522,0.5088639259,-0.2824386358,0.0445280075,0.3910319507,-0.0378996059,0.1227306202,0.2004511058,-0.0386202559,0.3778627515,-0.0348490328,-0.1377107948,0.254900068,-0.0279567167,-0.004930364,0.4297841489,-0.1329644769,-0.2408827692,0.0994630307,0.2273842692,-0.1922977716,-0.0534686111,0.0641369522,0.3943487704,0.0507263318,0.1347920001,-0.1889541596,-0.5016103983,-0.2405148745,-0.0254901741,-0.051484853,0.1133788601,-0.1985218972,-0.0784508139,0.1419783831,-0.1865409166,0.176210016,0.3741005957,-0.1776565462,0.104966253,-0.0246022623,-0.239148438,-0.3564998209,0.2378043532,-0.1851833761,0.0727882981,0.1977110654,-0.4412870407,0.2564163506,0.0928590894,0.0211978368,0.0967225656,-0.0041481513,-0.3817107081,0.327762574,0.0220820736,-0.4443692565,0.2501779795,-0.3590976596,0.0738288611,-0.4026620686,-0.1143499687,0.1062477306,0.0095006414,-0.1003314927,-0.094527714,-0.2604745924,0.0071637603,-0.1008772627,0.2901645899,-0.1616960764,0.227222383,-0.1203622669,0.0991880819,-0.0737567991,-0.2234307379,-0.0107732685,-0.1301418245,0.1044430733,-0.2295478284,-0.2490978539,0.2669839561,-0.0294239223,0.4033820629,0.0256114807,-0.2489114255,0.2217794508,-0.0411257967,-0.0857890472,0.0846273154,0.1030280665,-0.0495610014,0.7756674886,-0.0289043859,-0.1323899776,-0.2946957648,0.1223669425,-0.032211598,0.050943207,0.2729354799,-0.1622239351,-0.1328100115,-0.2435219139,0.0138938036,0.2328962684,0.0966448411,0.0882694572,0.3098804355,0.0511683114,-0.1263473928,0.2850105166,0.0187537316,0.4396485686,-0.0250515621,0.1615845263,0.1324831098,-0.2792648971,0.2479462773,0.0971257538,0.0697617605,-0.2154580653,0.1413437873,0.0990858078,-0.1793445945,0.0310041215,0.0360496268,-0.2645478845,0.1805561334,-0.2056177706,-0.1350847632,0.1120666489,0.0225045737,-0.3421093524,0.3792150319,0.103795521,-0.0551779829,-0.0645874441,-0.1764418185,0.1415843517,-0.0590537488,0.3099807799,-0.2132001966,0.3635677993,-0.0960223675,-0.0447247028,-0.2309784889,0.1858299822,0.1836382151,0.0575980917,0.3153477907,0.0150038954,0.0612768978,0.1458824575,0.1730890423,0.03150969,-0.1268611252,-0.2293548435,-0.2512013316,0.0078260573,-0.002918558,-0.1471859664,0.0929360017,-0.1853148192,-0.1976004988,0.2195747793,-0.0661494881,0.0594113842,0.3334246874,-0.0181256551,0.0040230104,-0.1771635711,0.0646656826,-0.0010531198,-0.7291204333,0.3107971847,0.0556921028,-0.3598349392,-0.0095338486,-0.0833324417,0.0246738512,0.3075171709,-0.6023690104,0.0590275042,-0.3641526997,0.022292247,-0.1750625819,0.1446501315,0.1694180518,-0.0068356548,0.0020276711,0.0335193649,-0.0329024866,0.0712472722,-0.1892556846,0.0448544733,0.2905829847,0.6020951271,0.001918658,0.677452445,0.2382281572,-0.010103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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1776","title":"[Question & Bug Report] Can we preprocess a dataset on the fly?","comments":"Indeed I will submit a PR in a fez days to enable processing on-the-fly :)\r\nThis can be useful in language modeling for tokenization, padding etc.\r\n","body":"I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it.  Can we preprocess a dataset on the fly without generating cache?\r\n\r\nBTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/0281f9d881f3a55c89aeaa642f1ba23444b64083\/src\/datasets\/arrow_dataset.py#L1532","comment_length":26,"text":"[Question & Bug Report] Can we preprocess a dataset on the fly? \n I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it.  Can we preprocess a dataset on the fly without generating cache?\r\n\r\nBTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/0281f9d881f3a55c89aeaa642f1ba23444b64083\/src\/datasets\/arrow_dataset.py#L1532 \n Indeed I will submit a PR in a fez days to enable processing on-the-fly :)\r\nThis can be useful in language modeling for tokenization, padding etc.\r\n","embeddings":[-0.3806301355,-0.1969681829,-0.0746243596,-0.0068640667,0.4007776082,0.3399396241,0.2778782547,0.263427645,-0.0995364562,0.1461998075,0.1342773885,0.1849375814,-0.1421174705,0.2875471711,0.2689658701,0.1431531012,0.1967978925,0.0332212746,0.0725330934,0.23776564,-0.2820772529,-0.1595617831,-0.1298619509,-0.1796594113,-0.4086602628,-0.3066292107,-0.1547997147,-0.0939180106,-0.2736937702,-0.2001533508,0.2956175804,0.2963145673,0.0653250068,0.465472281,-0.0001154027,0.0105220759,0.4278814197,-0.0547123067,-0.0995460376,-0.1435340941,0.1376840323,-0.2483471483,-0.1386116147,-0.2409489602,-0.1121577248,-0.0338170566,0.0784299448,-0.6800120473,0.3494402766,0.1158155948,0.1876232773,0.1196270287,-0.0841414332,0.1558762938,0.2843365967,0.3790517151,-0.3493456244,-0.1307842582,0.1972600967,-0.0772146508,0.0815445036,0.0771728605,-0.359899044,-0.0484260358,0.2894827425,0.0892697796,-0.2145828605,-0.6604301333,0.1388300955,0.2746991515,0.4689072967,-0.5630953312,-0.7029758692,-0.2193475068,0.1402742267,-0.3208349347,0.0382909775,0.4482021928,-0.1715643257,0.0731241927,-0.4151315689,-0.4202463031,0.0413668342,-0.3086306751,0.2753442824,-0.0720978752,-0.2119609118,0.0252016485,0.034201704,0.1810992807,0.4665647149,-0.2718546689,-0.3090370893,0.4835742712,-0.2577773631,-0.1970091015,-0.2645561993,0.0677481517,0.2692093849,0.0593970232,0.0312879607,-0.1208760664,-0.0593945198,-0.0603432357,0.1857628673,0.0467237532,-0.0205967501,-0.188860476,0.2885476351,-0.2618692517,0.1094831303,-0.0513226241,0.0204735938,-0.3383103311,0.3059313595,0.0955198556,0.2437417209,-0.2711470127,0.3885354102,-0.0272215083,0.0765126199,-0.0235701893,-0.4008276463,0.0876809806,-0.1159519106,-0.0047159297,0.1546853483,0.096196264,0.0672433153,-0.2288593948,-0.0770790279,0.0140275694,0.0041487277,0.2325498909,0.072771199,-0.1973730028,0.0311659817,0.2105323821,-0.0917334408,-0.2058473527,0.1751339138,-0.4416748583,0.0907105356,0.2982267439,0.0351358987,0.0468139425,-0.0299171954,0.3525289893,-0.236672923,0.5580145121,-0.1571931541,-0.2958838046,0.046776969,0.0742287561,-0.0204167459,0.0069830599,-0.5607430935,0.234284237,0.6066606045,0.2160427123,0.2605373263,0.2320741117,-0.3053673208,-0.1547010988,0.1365791261,0.4368867874,-0.2761692107,0.1823856533,0.2781616449,0.0397016071,0.2247021049,0.028512964,-0.0242422968,0.2706718147,-0.0475754179,-0.1463966668,0.3211702108,-0.0999133587,0.1367200911,0.3996351957,-0.0590499789,-0.2813992202,0.081068255,0.1655155271,-0.0632078126,-0.0565833561,-0.0004899409,0.4461781085,0.0238893647,0.1589461416,-0.1804377586,-0.4752100408,-0.2243528366,-0.0135994283,0.0118641201,0.1263299286,-0.1286602467,-0.2577133477,0.0810363814,-0.2028314918,0.199029237,0.3393393457,-0.0400335081,0.0700502843,0.0501671359,-0.2194067091,-0.3255533576,0.2392324805,-0.1290400326,0.0725405589,0.1684771925,-0.4414997995,0.2057801932,0.1176253408,0.0929606333,0.1650901884,0.0184887648,-0.3471727371,0.2571828365,0.0080345338,-0.4888250828,0.2847564518,-0.2818238735,0.0605553575,-0.3192244768,-0.0891122371,0.1544453949,-0.026418956,-0.0729771405,-0.117057994,-0.2385203838,0.0320946388,-0.1021132097,0.2757893503,-0.0663902313,0.1682732105,-0.0866347328,0.1256416887,-0.1447451264,-0.1290698946,-0.0076086516,-0.1717793643,0.1499705762,-0.1908589005,-0.1879913211,0.2441369593,-0.0906544477,0.4551089704,-0.0186686572,-0.1764632612,0.1805130243,0.0065298025,-0.0552366637,0.1250545233,0.0155817894,-0.0882805586,0.7388350368,-0.1510331631,-0.0979687944,-0.1166276708,0.0265188515,0.0043680449,-0.0117035331,0.2582092881,-0.1133449152,-0.1405486763,-0.2205936909,0.0856269002,0.2107994109,0.1212838516,0.0395467691,0.2407395691,0.070957236,-0.0643182918,0.3207211494,0.0227348041,0.4563131034,-0.046853222,0.2302242517,0.157525897,-0.2121620625,0.147954464,0.0611933805,0.0646841079,-0.13868846,0.118262507,0.1323515773,-0.1580020636,0.1392466724,-0.028587671,-0.2757461071,0.0956975296,-0.1378237307,-0.1189022735,0.1435193419,-0.0278891902,-0.4474339783,0.468906939,0.05117134,-0.1783297509,-0.1720498204,-0.2506058514,0.2144922763,-0.0087660635,0.233284533,-0.1438086778,0.4303097427,-0.0180496797,0.0589523837,-0.2585681081,0.1374772489,0.1043521389,0.0309754312,0.3830716908,-0.0061089993,0.0010167625,0.208538115,0.2050906122,-0.001941492,-0.1447562575,-0.2002999038,-0.2609392405,-0.0236170497,-0.0058719674,-0.2088480294,0.0928851366,-0.1521229595,-0.2390484661,0.1361910552,-0.0025109316,0.0520357974,0.1913918257,-0.0341625996,-0.0971090719,-0.1601897925,0.0680320486,-0.0249221716,-0.6827620268,0.2437421829,0.017617479,-0.3386533856,-0.023872152,0.0088692727,0.0437363163,0.3255078793,-0.5940142274,0.0640683994,-0.4057153761,0.0709718466,-0.2209239602,0.1615331024,0.3194848299,0.1000271887,-0.0216584522,0.0339365676,-0.1603554636,-0.0201173481,-0.168243289,-0.0235502236,0.3433021307,0.4535853863,0.0314815566,0.6971470714,0.1354644448,-0.0072680274,0.3471180201,0.037520349,0.4989186227,-0.3026138246,-0.1154119,-0.1929935366,-0.2688210905,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1776","title":"[Question & Bug Report] Can we preprocess a dataset on the fly?","comments":"Hi @acul3,\r\n\r\nPlease look at the discussion on a related Issue #1825. I think using `set_transform` after building from source should do.","body":"I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it.  Can we preprocess a dataset on the fly without generating cache?\r\n\r\nBTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/0281f9d881f3a55c89aeaa642f1ba23444b64083\/src\/datasets\/arrow_dataset.py#L1532","comment_length":22,"text":"[Question & Bug Report] Can we preprocess a dataset on the fly? \n I know we can use `Datasets.map` to preprocess a dataset, but I'm using it with very large corpus which generates huge cache file (several TB cache from a 400 GB text file). I have no disk large enough to save it.  Can we preprocess a dataset on the fly without generating cache?\r\n\r\nBTW, I tried raising `writer_batch_size`. Seems that argument doesn't have any effect when it's larger than `batch_size`, because you are saving all the batch instantly after it's processed. Please check the following code:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/0281f9d881f3a55c89aeaa642f1ba23444b64083\/src\/datasets\/arrow_dataset.py#L1532 \n Hi @acul3,\r\n\r\nPlease look at the discussion on a related Issue #1825. I think using `set_transform` after building from source should do.","embeddings":[-0.4329630136,-0.149762705,-0.0210898854,0.0117295887,0.4850175381,0.2730880976,0.3065044582,0.2762750387,-0.1747130752,0.1026615873,0.0982053503,0.1963543445,-0.0868418068,0.2460448146,0.2638710439,0.1422843635,0.2081043124,0.08988031,-0.096630387,0.1875966489,-0.295986861,-0.1425745487,-0.0633271337,-0.1839763373,-0.46941787,-0.2427725941,-0.1292227507,0.001916178,-0.181621775,-0.203106001,0.2927230895,0.2324055135,-0.0148586556,0.4824579358,-0.0001141936,0.0488191023,0.412786454,-0.1013961732,-0.1420305073,-0.1252627522,0.1022013649,-0.3030607104,-0.1348371804,-0.2586301267,-0.1555446684,-0.1020514742,0.0397011638,-0.6355341673,0.4321356118,0.1466097385,0.2200481743,0.1474144459,-0.0451228656,0.1245692372,0.2611001432,0.3140592873,-0.3821599185,-0.1072558537,0.180836916,0.0386675075,0.0378094502,0.1519301087,-0.326919049,0.0053006238,0.3591570258,0.1101513281,-0.2363422066,-0.5635491014,0.1477532685,0.2872587442,0.4784781337,-0.560829699,-0.7194266319,-0.2661271691,0.1451830268,-0.3385506868,0.1005583555,0.4154743552,-0.170515433,0.0658453032,-0.4523686171,-0.4309619963,0.0252115261,-0.3683648109,0.2405146509,-0.144568041,-0.2335285097,0.0204998776,0.0057092081,0.1899290532,0.4107083678,-0.2188436091,-0.3381609023,0.4355342686,-0.2185551375,-0.2187634856,-0.2661304176,0.0814255625,0.2457327992,0.1398300827,0.0155080305,-0.1161653027,-0.0325907953,-0.0893719718,0.1360760033,0.1203241125,-0.0844900087,-0.1408303976,0.2896365523,-0.2251360416,0.0950003862,-0.098081395,0.0825609788,-0.4091348052,0.3306641877,0.1045778021,0.3203131855,-0.3002013862,0.3989455402,0.0140089439,0.1325963587,0.0150183141,-0.3814519644,0.145734176,-0.1104523465,0.0512313768,0.1466380209,0.142272979,0.0613337867,-0.1713287979,-0.1215861589,-0.0579744615,-0.0176576432,0.3037647605,0.1017586887,-0.2035060525,0.0960330591,0.1968317479,-0.069305785,-0.1474777311,0.1593564004,-0.4074446559,0.1773698032,0.3492155075,-0.0258463807,0.0895796493,-0.0435275286,0.3530143797,-0.1998040676,0.5373976827,-0.1354406178,-0.3122022748,0.0356287472,0.0945883542,-0.0531800017,0.0373736508,-0.5619783998,0.1426612884,0.6161594391,0.2149135768,0.2612337172,0.1844769716,-0.2494729757,-0.1610206962,0.1391241848,0.4578093588,-0.2424228489,0.1542511582,0.3046616018,0.0511818081,0.1385352463,0.0961020291,-0.0289497059,0.2064516246,-0.0387377702,-0.1997522414,0.3259778321,-0.0721958131,0.0467531718,0.3394372165,-0.0908213481,-0.2645249963,0.0823537037,0.1384712756,-0.0276599843,-0.0962675363,-0.0548363104,0.4266054332,-0.0195711553,0.2360433042,-0.1810713112,-0.4425275922,-0.2827946544,-0.0746699944,0.0176095404,0.137852028,-0.196992442,-0.2738490999,0.0570900589,-0.1821342707,0.2325855792,0.3486989141,-0.0456053466,0.0465126596,0.0585953332,-0.1447848231,-0.3847301304,0.2210926712,-0.10427057,0.0422115959,0.1136271581,-0.4316208363,0.1256343722,0.1094216779,0.0725463331,0.1065265983,0.0396175422,-0.3019027114,0.294749856,-0.0410910696,-0.4444380403,0.2486053556,-0.1694128513,0.112823531,-0.4200748801,-0.0761258751,0.1506418884,-0.0647382587,-0.0618649907,-0.1236242577,-0.1879749298,-0.0074727675,-0.1552285254,0.3102422953,-0.1509397626,0.2376552671,-0.0833659768,0.1479143947,-0.1031506434,-0.2216746509,0.0134715196,-0.1461161673,0.1204976365,-0.1898351312,-0.2374291122,0.2405589223,-0.125441432,0.4528132379,-0.0244471598,-0.1067399904,0.19655931,-0.0574267358,-0.0650350153,0.098939389,-0.0469948873,-0.0435777158,0.7283758521,-0.0893419087,-0.0646382645,-0.0855534598,0.0716886148,0.0146110766,-0.0450502597,0.2503600121,-0.1430051625,-0.1239451095,-0.2058190554,0.1123894826,0.2271235883,0.1460506618,0.0281832051,0.2094620317,0.0540075414,-0.1065891832,0.331243515,0.0195442215,0.4602996409,-0.0285264142,0.2461500764,0.1056514382,-0.2152439803,0.2044366449,0.0851661637,0.0561343469,-0.1728527695,0.1126303226,0.1550612152,-0.069464922,0.0361564942,0.0005299171,-0.224599123,0.0833590478,-0.1207841113,-0.0309183504,0.0240624063,0.0583936274,-0.4411794543,0.3388668597,0.0531037003,-0.1855516434,-0.1853674948,-0.2100474685,0.2090856135,0.0030894969,0.191182062,-0.1702626795,0.4499090612,-0.0654062256,0.0290908013,-0.2961729169,0.1470841169,0.0948989466,-0.0052774232,0.3585245013,0.0148649886,0.0273944866,0.2091139704,0.1998988837,0.0595944412,-0.1688087285,-0.1806282252,-0.2080806196,-0.0564350933,0.0179906879,-0.1513291448,-0.006899714,-0.0297326576,-0.2902205884,0.1317759156,-0.027170239,0.022543136,0.2314047217,0.0408415608,-0.1508438587,-0.0609039739,0.0651347861,-0.0411670655,-0.6186643839,0.301677078,-0.0587813146,-0.3465429544,-0.0441343002,-0.027905494,0.0178735331,0.3690830171,-0.603469789,0.0759540871,-0.4336880147,0.0787584335,-0.1546194106,0.1942055672,0.3209499419,0.1343019754,-0.095620364,-0.0347259119,-0.1735876054,0.0148214698,-0.1583309025,-0.0087433467,0.2583470941,0.4254817069,-0.0141152879,0.7667742968,0.1563379019,-0.0408220962,0.3145268261,0.017723931,0.587883234,-0.3564191163,-0.1258499771,-0.181350708,-0.2166433185,-0.14913733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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1775","title":"Efficient ways to iterate the dataset","comments":"It seems that selecting a subset of colums directly from the dataset, i.e., dataset[\"column\"], is slow.","body":"For a large dataset that does not fits the memory, how can I select only a subset of features from each example?\r\n\r\nIf I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this?\r\n\r\nThanks","comment_length":16,"text":"Efficient ways to iterate the dataset \n For a large dataset that does not fits the memory, how can I select only a subset of features from each example?\r\n\r\nIf I iterate over the dataset and then select the subset of features one by one, the resulted memory usage will be huge. Any ways to solve this?\r\n\r\nThanks \n It seems that selecting a subset of colums directly from the dataset, i.e., dataset[\"column\"], is slow.","embeddings":[-0.272621274,-0.3842424452,-0.0938193724,0.3328414559,-0.064883776,0.0687170699,-0.0869265497,0.2765877545,0.1568154693,0.1853520125,0.1041573808,0.0968727618,0.075772956,0.1713013947,0.0594490469,0.02440935,-0.1179833263,0.2368059754,-0.1938435286,-0.0943645537,-0.3235397935,-0.3371296525,-0.3414105773,-0.413921237,-0.0448212847,-0.2763502598,-0.0030553236,-0.0701165721,-0.1004956514,-0.2022732198,0.0649098828,-0.1059204489,0.1723704934,0.1313776672,-0.0001110769,-0.3096601963,-0.0061720056,-0.0167973097,-0.2181179821,0.2127902359,-0.4324012399,-0.1536879987,-0.209673211,-0.4574201703,0.0789363161,-0.193544507,0.0418520048,-0.3434140682,-0.3945272565,0.0019652992,0.1722190231,0.3632744551,-0.4281520247,-0.2193821967,0.1636476517,0.2020667642,-0.1860643774,-0.1688093543,0.3972516656,-0.0317177549,0.1105101332,0.1986813247,0.0027408882,0.0643330738,0.4581536949,-0.0933370367,-0.2695295811,-0.0912525281,0.2907192707,0.3570749164,0.7055131197,0.0142396716,-0.0204161704,-0.2548285127,-0.1146911904,-0.4821657538,-0.0554719344,0.2097758949,-0.1812402755,0.1794072241,-0.3754857481,-0.282171011,-0.1214113608,0.1009940654,0.2251813561,-0.1510582417,0.0702474043,0.0095484257,0.5192238688,-0.222860828,0.2806361318,-0.1883521229,0.042395819,0.1281944066,-0.5794634223,-0.3097940385,-0.1118838117,-0.0624917336,0.3452713192,0.107684426,0.08151225,0.0904842541,0.2307037711,0.0643644556,0.3840838373,-0.1338301599,-0.1883253604,0.1345576942,0.1456446201,-0.160663262,-0.0020948441,-0.0907806307,-0.2182660252,0.0937924832,0.2085401565,-0.4199842215,-0.1569328755,-0.1290027648,-0.149421066,-0.1261437833,-0.1378621161,-0.0162505209,-0.0246443041,0.4202540517,0.0619461909,-0.1889308989,-0.4265065789,-0.0111111114,-0.2233894914,-0.2640571594,-0.2986492515,0.2844229043,-0.0498715825,0.0189673565,0.0652078763,0.1994225979,0.1540557295,-0.1386214793,-0.1547173858,-0.1392389834,0.0547332019,-0.3360626101,0.2683524489,-0.0082828514,-0.1358440518,-0.0077023222,-0.0597725362,0.336125195,-0.3245050013,0.2191703767,0.1463289112,-0.5682111382,0.183412537,0.0710723624,0.0017380278,-0.0667319819,-0.3420160115,0.4201233983,-0.0989892408,0.4257798791,-0.0995337963,0.0706477538,-0.3074536622,-0.1526209116,0.372374475,0.4698694348,-0.3293994665,0.051087141,0.1202649251,0.13932015,0.3856441379,0.4193019271,-0.220395565,0.1525849849,-0.1224395558,0.0959463716,0.1902549267,0.1820035726,-0.5025990009,0.3416081369,0.108068198,0.0722895935,0.2504362464,0.5023416877,0.0730348751,0.0638115257,-0.1298099905,0.3935356736,-0.2775791883,0.1868896633,-0.0036721453,-0.3479519784,0.2195587009,0.0396700948,0.2890775204,0.2412097603,-0.008328557,0.044327341,0.2857303619,-0.3043045998,-0.1506115645,0.1334234476,0.5146899819,-0.0039331494,-0.0409264006,-0.3039368987,-0.3744559288,0.2273036391,0.4574954808,-0.5265105367,0.3239097595,-0.1340290457,0.1073588058,0.018804796,0.1776627302,0.1412481368,0.0175361764,-0.0444856323,0.2947908342,-0.4913444817,-0.2310359031,0.0906002671,-0.3048676252,-0.1007343754,-0.2995914519,-0.0163098555,0.5265932083,0.0019104267,0.2137762606,-0.0286798533,-0.0328834429,-0.2907178104,0.14983356,0.1483504176,0.3771882653,-0.0658315644,0.1169122458,0.3529287875,0.0880182907,-0.1726694256,0.0474601462,-0.3250952065,0.2133404762,0.031034952,-0.210539028,0.1571555734,0.0987472236,0.5199263692,0.2963037193,-0.0036353387,0.027996894,-0.0309289023,0.2764967978,0.0714425743,0.1936671436,0.1736863703,0.1350107789,0.35304901,-0.6447686553,0.0454972126,0.199426353,0.0886545852,-0.0918916166,0.0251979101,-0.1220782474,0.0939576253,0.1217482761,-0.3537807465,-0.0296607222,0.0877936333,0.0354814269,-0.1350253373,0.0879065171,0.10482876,0.1275731772,0.2802185714,0.3064313829,-0.3647532761,-0.0424480997,-0.2706400454,-0.1003278941,0.0671235397,0.0494892485,0.1145843267,0.0018403609,0.0724698827,-0.355209887,-0.1168477088,0.2057601959,0.3313178122,0.2393414825,-0.1104752049,-0.1124653295,0.5158164501,-0.0581075549,-0.1062864065,-0.1323304027,0.5061990619,-0.0120506072,0.0168139953,0.1103373021,-0.0603423305,-0.069999136,0.0045810724,0.3546584249,0.1378186643,0.3736892641,0.12142355,-0.1678830236,-0.4728028476,0.1906893551,0.1277216971,0.2162524611,0.0835691318,-0.1990766376,0.1286506951,-0.3519138992,0.1047185734,-0.1672060639,0.1980487108,-0.0278880168,0.0853997767,-0.0343408547,-0.0196806658,0.3902205229,-0.3208624721,-0.2228164524,-0.292335242,0.3292638063,0.0246352442,0.2442338169,-0.0116648702,0.0537039451,-0.2610080242,-0.0664389506,0.0236434694,0.0586672686,-0.1673668623,0.2899850905,-0.0720577836,-0.1938501447,-0.0757572055,0.0666714013,-0.028663991,0.5141797066,-0.4075911641,0.0645164996,-0.5274323821,-0.0467714816,-0.1802237332,0.0347189903,-0.0563657843,-0.2087927014,-0.0364183746,0.0583287887,-0.0273865499,0.2015375346,0.1771997958,0.054727219,0.3681704998,0.1776552051,0.2260527313,0.2913785875,0.3049386442,0.2829522789,0.2341083735,-0.0918738768,-0.132628575,0.0755493417,-0.0428376831,0.2239385396,-0.1186179668,-0.244764491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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1774","title":"is it possible to make slice to be more compatible like python list and numpy?","comments":"Hi ! Thanks for reporting.\r\nI am working on changes in the way data are sliced from arrow. I can probably fix your issue with the changes I'm doing.\r\nIf you have some code to reproduce the issue it would be nice so I can make sure that this case will be supported :)\r\nI'll make a PR in a few days ","body":"Hi,\r\nsee below error:\r\n```\r\nAssertionError: Requested slice [:10000000000000000] incompatible with 20 examples.\r\n```","comment_length":62,"text":"is it possible to make slice to be more compatible like python list and numpy? \n Hi,\r\nsee below error:\r\n```\r\nAssertionError: Requested slice [:10000000000000000] incompatible with 20 examples.\r\n``` \n Hi ! Thanks for reporting.\r\nI am working on changes in the way data are sliced from arrow. I can probably fix your issue with the changes I'm doing.\r\nIf you have some code to reproduce the issue it would be nice so I can make sure that this case will be supported :)\r\nI'll make a PR in a few days ","embeddings":[0.0045192172,-0.06874533,-0.24153319,0.0569718927,0.4289458394,-0.3012439907,0.1267030835,0.3713265359,-0.1317171156,0.4324041903,-0.0149546824,0.7657305598,-0.0799334198,0.2857099771,-0.3763241768,-0.4880154133,-0.0210640207,0.1064821929,0.2514834702,0.1202106699,0.0552423708,0.0996666849,-0.3396672308,0.1170674637,-0.1888773739,-0.179093495,0.2490351796,-0.0215795748,-0.0056372839,-0.2691525221,-0.1261418462,-0.4162088037,0.0354158655,0.1907055676,-0.0001093078,0.1138291806,0.5290846229,0.042696517,-0.2076610029,-0.0507463217,0.2870479524,-0.487957716,0.2421301901,-0.2416446954,0.0444297679,0.0554977059,-0.3735236526,0.010754359,0.4677677155,-0.0264655594,0.2545258105,0.0062989909,0.3490653336,0.0459645353,0.0709797516,0.3102607727,-0.2072952092,-0.1372469664,0.5058743954,0.4029471874,0.2683990598,0.1032428667,-0.0779396668,0.040543437,-0.4270553887,0.0044288989,-0.0684800744,-0.3739693165,-0.1177275181,0.573800385,0.1728356332,0.1928459704,-0.2151761502,-0.0106723728,-0.3015369475,-0.2650514543,-0.1554951519,0.1012288854,-0.0972411335,0.1049901024,0.0690572336,0.0057898541,-0.1316060871,0.3030082881,0.2846271992,0.4896726608,-0.035527613,0.1082353294,0.1635646969,-0.3503347039,0.2268579453,0.1243717968,-0.0745611191,-0.0966618955,-0.1592775136,-0.0053485418,-0.5801075101,0.1688659042,0.1484343112,-0.0063061547,0.323872745,0.4233803749,-0.1502631009,0.1502199322,0.234686926,-0.0638783276,-0.0861096159,0.0048741279,0.1310970485,-0.1369228065,0.1043624878,-0.0289418213,0.2619714141,-0.3201720119,-0.2399541736,0.0034458842,-0.0258917715,-0.0790593252,-0.0733677894,-0.2654659152,-0.3296071589,-0.0969409198,0.3133118153,0.1284629852,-0.0212011002,0.096530579,-0.1779138446,0.2430861592,-0.1994260103,-0.1251432896,0.0082124947,0.0385231972,0.1689667255,-0.483379364,-0.0331377424,-0.2485744357,-0.0058779297,-0.0048196171,0.3314142227,0.0867033154,-0.1492839754,-0.0148875462,0.062819384,0.0198610872,0.0747855604,-0.2142450064,0.1739360392,0.067209661,-0.0024390426,0.4090491831,-0.5157783031,-0.1685249209,-0.147864148,0.1307652444,0.4457748234,0.2731143236,0.2207438052,-0.1539462507,0.0057326038,-0.3226754069,-0.0811057612,-0.1813516766,0.2451282144,-0.309543252,0.2209000736,-0.3931464255,-0.3490618169,0.0853376165,-0.4180666208,0.2275340259,0.2932496369,0.7705193758,-0.1812043488,-0.2930043042,-0.0307123773,0.3370466828,0.4182480574,0.1050323322,-0.2453847677,0.2037253678,0.0509392545,-0.1719106883,-0.3283394575,0.2673069537,0.15334481,-0.2094565779,-0.0209724996,0.1696024537,0.0461627729,-0.0351623185,-0.2500964701,-0.4086686373,0.2898615003,-0.0888149589,-0.0698139966,-0.1406375021,0.205048278,-0.3768809438,0.2609752119,-0.0919979513,0.0247562341,0.0110767772,0.4300157726,-0.4044119716,0.1026900262,-0.0949773118,0.1602244973,-0.1923354417,-0.1811702996,-0.0422615074,-0.1741902679,0.0141710266,0.1139547676,0.2723791003,0.2118366957,0.1726776212,0.14381814,-0.4210804403,0.3586548269,-0.1922202557,0.0335088223,-0.033112105,-0.1949820071,0.0527647324,-0.1643980145,0.3395628035,-0.3022041619,-0.3105169535,-0.1181154996,0.1678342372,0.23984465,-0.189041242,-0.2328326702,0.2748931646,0.101476267,-0.1820404828,-0.268628031,0.0532165542,0.161516875,-0.4648517668,0.233528465,0.2280954868,0.0137516083,-0.0216453671,0.0080416631,0.2652312815,-0.542475462,0.3630341291,0.0904720724,0.2836489975,0.1975425482,0.1266445369,0.0652361587,-0.0316096395,0.3338298798,0.1231466234,-0.1631327569,-0.1990456134,-0.1483731121,0.012621426,-0.003789986,-0.0754330158,0.2589483261,0.282145679,0.0397437662,0.1176440716,-0.0154709909,-0.4251084328,0.1360585392,0.2354196757,-0.0837447271,-0.1584738642,0.2301815897,-0.33596313,0.2332283109,0.452904433,0.028897604,-0.1256339699,0.0725977942,-0.0046532042,0.0529952198,0.2206350118,-0.296528697,0.0165035054,-0.0617351197,0.1354552358,-0.3268111944,0.2384214401,0.0465466157,-0.8412544727,-0.1915154159,-0.3362053633,0.0675206706,0.0370615907,-0.210921824,0.307638973,0.1330839097,0.1019628569,-0.0271361265,0.324450016,-0.1016389579,-0.1711580306,-0.3002125025,0.1642317623,0.1031269506,0.3488458097,-0.0832882375,0.1877930909,0.0227539055,0.0295580793,-0.4092308581,0.0001540455,-0.0346478075,0.556363225,0.1088159159,-0.0025661278,-0.1718282551,-0.3161525428,0.1827595979,-0.4125513434,-0.0562067293,0.3178083599,0.0368712023,-0.2372612953,0.010753314,0.0160018001,-0.6098569632,-0.3351960778,0.3220384717,0.2363469899,0.1585870832,-0.1259809881,0.1917729527,-0.1530326903,0.273591727,-0.074640967,-0.0318100564,0.0687904805,0.1247193143,-0.1566830128,-0.1934564412,-0.1348921657,-0.1770818084,-0.1167836115,-0.0765605867,0.0415161811,0.1929940581,-0.3029778302,-0.0505099446,-0.1226354912,0.1321017146,0.3967476487,0.1560400128,-0.1027520299,-0.0566878691,-0.1804210395,0.1074578986,0.0232549608,-0.1332282424,0.4811725616,0.3465818465,0.1279286891,0.368840009,0.2477864623,-0.1196170077,0.2847773731,0.2797137797,0.0390914492,-0.1042084172,-0.2776113749,0.2276642919,-0.0037570784,-0.2275750935,0.01224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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1773","title":"bug in loading datasets ","comments":"Looks like an issue with your csv file. Did you use the right delimiter ?\r\nApparently at line 37 the CSV reader from pandas reads 2 fields instead of 1.","body":"Hi,\r\nI need to load a dataset, I use these commands:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={'train': 'sick\/train.csv',\r\n                                          'test':  'sick\/test.csv',\r\n                                          'validation': 'sick\/validation.csv'})\r\nprint(dataset['validation'])\r\n```\r\nthe dataset in sick\/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq \r\n\r\n                            \r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv\/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/julia\/cache_home_2\/datasets\/csv\/default-61468fc71a743ec1\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nTraceback (most recent call last):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 485, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 604, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 959, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/tqdm-4.49.0-py3.7.egg\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/julia\/cache_home_2\/modules\/datasets_modules\/datasets\/csv\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2\/csv.py\", line 129, in _generate_tables\r\n    for batch_idx, df in enumerate(csv_file_reader):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1029, in __next__\r\n    return self.get_chunk()\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1079, in get_chunk\r\n    return self.read(nrows=size)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1052, in read\r\n    index, columns, col_dict = self._engine.read(nrows)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 2056, in read\r\n    data = self._reader.read(nrows)\r\n  File \"pandas\/_libs\/parsers.pyx\", line 756, in pandas._libs.parsers.TextReader.read\r\n  File \"pandas\/_libs\/parsers.pyx\", line 783, in pandas._libs.parsers.TextReader._read_low_memory\r\n  File \"pandas\/_libs\/parsers.pyx\", line 827, in pandas._libs.parsers.TextReader._read_rows\r\n  File \"pandas\/_libs\/parsers.pyx\", line 814, in pandas._libs.parsers.TextReader._tokenize_rows\r\n  File \"pandas\/_libs\/parsers.pyx\", line 1951, in pandas._libs.parsers.raise_parser_error\r\npandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2\r\n\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"write_sick.py\", line 19, in <module>\r\n    'validation': 'sick\/validation.csv'})\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 534, in download_and_prepare\r\n    self._save_info()\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/contextlib.py\", line 130, in __exit__\r\n    self.gen.throw(type, value, traceback)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 491, in incomplete_dir\r\n    shutil.rmtree(tmp_dir)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/shutil.py\", line 498, in rmtree\r\n    onerror(os.rmdir, path, sys.exc_info())\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/shutil.py\", line 496, in rmtree\r\n    os.rmdir(path)\r\nOSError: [Errno 39] Directory not empty: '\/julia\/cache_home_2\/datasets\/csv\/default-61468fc71a743ec1\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'\r\n```\r\n\r\n","comment_length":30,"text":"bug in loading datasets  \n Hi,\r\nI need to load a dataset, I use these commands:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={'train': 'sick\/train.csv',\r\n                                          'test':  'sick\/test.csv',\r\n                                          'validation': 'sick\/validation.csv'})\r\nprint(dataset['validation'])\r\n```\r\nthe dataset in sick\/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq \r\n\r\n                            \r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv\/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/julia\/cache_home_2\/datasets\/csv\/default-61468fc71a743ec1\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nTraceback (most recent call last):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 485, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 604, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 959, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/tqdm-4.49.0-py3.7.egg\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/julia\/cache_home_2\/modules\/datasets_modules\/datasets\/csv\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2\/csv.py\", line 129, in _generate_tables\r\n    for batch_idx, df in enumerate(csv_file_reader):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1029, in __next__\r\n    return self.get_chunk()\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1079, in get_chunk\r\n    return self.read(nrows=size)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1052, in read\r\n    index, columns, col_dict = self._engine.read(nrows)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 2056, in read\r\n    data = self._reader.read(nrows)\r\n  File \"pandas\/_libs\/parsers.pyx\", line 756, in pandas._libs.parsers.TextReader.read\r\n  File \"pandas\/_libs\/parsers.pyx\", line 783, in pandas._libs.parsers.TextReader._read_low_memory\r\n  File \"pandas\/_libs\/parsers.pyx\", line 827, in pandas._libs.parsers.TextReader._read_rows\r\n  File \"pandas\/_libs\/parsers.pyx\", line 814, in pandas._libs.parsers.TextReader._tokenize_rows\r\n  File \"pandas\/_libs\/parsers.pyx\", line 1951, in pandas._libs.parsers.raise_parser_error\r\npandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2\r\n\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"write_sick.py\", line 19, in <module>\r\n    'validation': 'sick\/validation.csv'})\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 534, in download_and_prepare\r\n    self._save_info()\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/contextlib.py\", line 130, in __exit__\r\n    self.gen.throw(type, value, traceback)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 491, in incomplete_dir\r\n    shutil.rmtree(tmp_dir)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/shutil.py\", line 498, in rmtree\r\n    onerror(os.rmdir, path, sys.exc_info())\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/shutil.py\", line 496, in rmtree\r\n    os.rmdir(path)\r\nOSError: [Errno 39] Directory not empty: '\/julia\/cache_home_2\/datasets\/csv\/default-61468fc71a743ec1\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'\r\n```\r\n\r\n \n Looks like an issue with your csv file. Did you use the right delimiter ?\r\nApparently at line 37 the CSV reader from pandas reads 2 fields instead of 1.","embeddings":[-0.2842670083,-0.2714768052,-0.1463154405,0.4608483613,0.3019686937,0.2408371568,0.0834566206,0.5121120214,0.0764491484,0.0585810058,-0.0004540544,-0.2665965557,-0.10810256,0.2536355257,-0.0337027758,0.0352051184,-0.0331644155,0.1680267006,-0.0946208239,0.019386515,-0.2684040964,0.2590358853,-0.0957386568,0.0894601047,-0.0035116654,-0.1219344884,0.0992638245,0.2603849769,-0.2975808978,-0.3687266707,0.3527499437,-0.1765222102,0.3671296537,0.2056512088,-0.0001100329,0.109722428,0.4414513707,-0.222237736,-0.2273045331,-0.2782835364,-0.3186444938,-0.5255689621,-0.0424169973,-0.2519200146,-0.0720899403,-0.1986101866,-0.1186174303,-0.358505249,0.1968753785,0.4839046001,0.3277643323,-0.2024365067,-0.0772970542,-0.2182596326,0.1736675203,0.0825364217,0.0177823696,0.0985001624,0.0383216366,0.1259890795,0.5457251668,0.22481516,-0.0871027187,0.062276613,0.1328184605,-0.0650291443,0.2201796621,-0.3268524706,0.1852118075,0.1893909127,0.8423666954,-0.1272012442,-0.1620606333,-0.14171803,0.1230335012,-0.309578687,0.0035668784,0.3891044259,-0.1592620909,0.1557286382,-0.1961616576,0.022634821,-0.056327071,0.1439458579,0.1991666853,-0.1276440918,-0.256634742,0.2265536189,-0.066450499,-0.0968799889,0.0019838086,-0.0634838715,-0.1318117976,0.220077008,-0.3061932921,-0.0059892815,-0.0865267888,-0.0474983081,0.1305490732,0.0191281587,0.2057310492,-0.3622546494,0.1416555792,0.1029283404,0.3085215986,0.3533758223,-0.0966351852,0.0186193027,0.0468764491,0.1750154495,0.0544107519,-0.2243265808,-0.1458094567,-0.4689761698,0.2325953245,0.2488867044,0.3317735791,-0.1506784409,-0.2495945245,0.1341956556,-0.0551047288,0.2364847958,0.1205831841,0.5153110623,-0.307118088,0.1684069484,-0.062956959,0.02560132,-0.0268063359,-0.2216439694,-0.295609206,0.0240087099,-0.1846616417,-0.1395942122,0.2067635804,-0.2110509127,0.2445815951,0.079535611,0.0408378132,-0.3071087897,0.0538251624,-0.3537349403,-0.023229761,0.4643990993,0.0900522098,0.1324354857,0.2597099245,-0.0839310288,-0.218993932,0.1771682501,-0.3590294123,-0.5951446295,-0.0110709053,0.26149863,-0.1140651032,0.1193498001,-0.4246470928,0.0700019151,0.160954386,-0.0954999104,-0.0518162772,-0.1787497252,-0.2614167333,-0.3476524353,0.1499703825,0.3107527196,-0.5323678851,0.1687188745,-0.3266535997,-0.2732284069,0.297688961,0.2120352238,-0.1209672764,0.3310961425,-0.2531271875,0.0592158996,0.3152398765,-0.3005893826,-0.3999977708,0.3908524215,0.016317809,0.1309871674,0.0034007411,0.0098110065,0.3307875693,0.0078690965,0.1102341935,0.212696299,-0.1486558169,-0.0314865559,-0.262057066,-0.1641774923,-0.0509140231,0.156482935,0.1396570951,0.1109460518,0.1691527665,-0.3006534874,-0.0518827923,-0.065568164,0.2696269155,0.3614275157,0.2116627544,0.1624421924,0.1707906276,0.0812941045,-0.535584569,0.3256810606,0.0115941083,-0.1998816878,-0.0919436142,-0.1241044924,-0.5685514212,-0.0164452605,-0.1286688447,-0.00269557,0.1094136685,-0.105446212,-0.0243037492,-0.0921541229,-0.0302179251,0.5924558043,-0.1262022704,-0.1524438113,-0.1371084154,0.2782709002,0.0124592735,0.055331625,0.0212132409,0.0994580835,0.1813473701,-0.1014740244,-0.3069878519,0.3099374771,0.0669195652,-0.0598004498,-0.0391001031,-0.0881186426,-0.1291055232,-0.1401408911,-0.0137780439,0.157358557,0.2477229089,-0.0469300412,0.1126644611,0.1488340795,-0.2980319262,0.0036978342,-0.1184609905,0.0627791062,0.5067969561,0.0400967784,0.0703584403,-0.3693780303,0.2232140005,-0.1662302166,0.3108948171,-0.0223938841,-0.2173395753,-0.3514190018,0.1450280249,0.0782161579,-0.0974682942,0.4313172102,-0.0909135789,-0.1065372974,-0.0840541273,0.3009824455,0.5254251361,0.0834561214,-0.1675480157,0.2054767311,0.2845359147,-0.0061824503,0.2890945375,-0.1024241671,0.3282971978,0.2726708055,-0.0958034098,-0.1561962217,-0.1069378108,-0.1512015164,0.1809896976,0.480276674,-0.296161443,0.0076901154,-0.0392973498,-0.1293672323,-0.0263259336,-0.125998646,0.0485745929,-0.0580259934,-0.1010541618,0.1505691707,0.0410420112,-0.1340404749,-0.1771068275,0.2878244519,0.1359101534,-0.2367094755,-0.1092019901,-0.1559366435,-0.2683488727,0.1195478737,0.0350314416,0.01967692,0.2585455179,-0.1997407377,0.0914035216,-0.1030231938,-0.049859073,-0.0447158515,-0.001954356,0.3377043307,0.1962086111,0.2400903255,0.0899587795,0.0516655073,0.2236879468,-0.0502688475,-0.0530633293,0.3498876691,-0.1486122906,-0.0987843126,-0.0612148754,-0.4378128946,-0.5983160138,-0.3591504395,0.0012708708,0.0437375307,0.0452357754,0.2158264667,0.2280218601,-0.1275926828,0.3982062936,-0.0774030164,-0.3031054139,-0.5205017328,0.2376762778,-0.3503072262,-0.1679317504,0.0081502553,-0.0291733481,0.1761376858,0.0675562024,-0.5610889792,0.3567280471,-0.23838,0.0649895445,-0.2308612466,-0.3174830675,0.220419839,-0.1381314099,-0.0619107075,-0.248017773,-0.1541718543,-0.0304518882,0.0611269623,0.1230327561,-0.0677224621,0.5385031104,0.0502661429,0.5127761364,0.3299145997,-0.0016039216,0.4850444198,0.0683038458,0.3586151302,-0.2322539389,-0.3839135766,-0.0448417515,-0.0905613229,-0.2724584341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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1773","title":"bug in loading datasets ","comments":"Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files=data_files, sep=\"\\t\")\r\n```\r\n\r\nfor example to use a tab separator.\r\n\r\nYou can see the full list of arguments here: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/packaged_modules\/csv\/csv.py\r\n\r\n(I've not found the list in the documentation though, we definitely must add them !)","body":"Hi,\r\nI need to load a dataset, I use these commands:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={'train': 'sick\/train.csv',\r\n                                          'test':  'sick\/test.csv',\r\n                                          'validation': 'sick\/validation.csv'})\r\nprint(dataset['validation'])\r\n```\r\nthe dataset in sick\/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq \r\n\r\n                            \r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv\/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/julia\/cache_home_2\/datasets\/csv\/default-61468fc71a743ec1\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nTraceback (most recent call last):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 485, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 604, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 959, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/tqdm-4.49.0-py3.7.egg\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/julia\/cache_home_2\/modules\/datasets_modules\/datasets\/csv\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2\/csv.py\", line 129, in _generate_tables\r\n    for batch_idx, df in enumerate(csv_file_reader):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1029, in __next__\r\n    return self.get_chunk()\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1079, in get_chunk\r\n    return self.read(nrows=size)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1052, in read\r\n    index, columns, col_dict = self._engine.read(nrows)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 2056, in read\r\n    data = self._reader.read(nrows)\r\n  File \"pandas\/_libs\/parsers.pyx\", line 756, in pandas._libs.parsers.TextReader.read\r\n  File \"pandas\/_libs\/parsers.pyx\", line 783, in pandas._libs.parsers.TextReader._read_low_memory\r\n  File \"pandas\/_libs\/parsers.pyx\", line 827, in pandas._libs.parsers.TextReader._read_rows\r\n  File \"pandas\/_libs\/parsers.pyx\", line 814, in pandas._libs.parsers.TextReader._tokenize_rows\r\n  File \"pandas\/_libs\/parsers.pyx\", line 1951, in pandas._libs.parsers.raise_parser_error\r\npandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2\r\n\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"write_sick.py\", line 19, in <module>\r\n    'validation': 'sick\/validation.csv'})\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 534, in download_and_prepare\r\n    self._save_info()\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/contextlib.py\", line 130, in __exit__\r\n    self.gen.throw(type, value, traceback)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 491, in incomplete_dir\r\n    shutil.rmtree(tmp_dir)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/shutil.py\", line 498, in rmtree\r\n    onerror(os.rmdir, path, sys.exc_info())\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/shutil.py\", line 496, in rmtree\r\n    os.rmdir(path)\r\nOSError: [Errno 39] Directory not empty: '\/julia\/cache_home_2\/datasets\/csv\/default-61468fc71a743ec1\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'\r\n```\r\n\r\n","comment_length":64,"text":"bug in loading datasets  \n Hi,\r\nI need to load a dataset, I use these commands:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={'train': 'sick\/train.csv',\r\n                                          'test':  'sick\/test.csv',\r\n                                          'validation': 'sick\/validation.csv'})\r\nprint(dataset['validation'])\r\n```\r\nthe dataset in sick\/train.csv are simple csv files representing the data. I am getting this error, do you have an idea how I can solve this? thank you @lhoestq \r\n\r\n                            \r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv\/default-61468fc71a743ec1 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/julia\/cache_home_2\/datasets\/csv\/default-61468fc71a743ec1\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nTraceback (most recent call last):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 485, in incomplete_dir\r\n    yield tmp_dir\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 604, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 959, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/tqdm-4.49.0-py3.7.egg\/tqdm\/std.py\", line 1133, in __iter__\r\n    for obj in iterable:\r\n  File \"\/julia\/cache_home_2\/modules\/datasets_modules\/datasets\/csv\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2\/csv.py\", line 129, in _generate_tables\r\n    for batch_idx, df in enumerate(csv_file_reader):\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1029, in __next__\r\n    return self.get_chunk()\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1079, in get_chunk\r\n    return self.read(nrows=size)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 1052, in read\r\n    index, columns, col_dict = self._engine.read(nrows)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/pandas-1.2.0-py3.7-linux-x86_64.egg\/pandas\/io\/parsers.py\", line 2056, in read\r\n    data = self._reader.read(nrows)\r\n  File \"pandas\/_libs\/parsers.pyx\", line 756, in pandas._libs.parsers.TextReader.read\r\n  File \"pandas\/_libs\/parsers.pyx\", line 783, in pandas._libs.parsers.TextReader._read_low_memory\r\n  File \"pandas\/_libs\/parsers.pyx\", line 827, in pandas._libs.parsers.TextReader._read_rows\r\n  File \"pandas\/_libs\/parsers.pyx\", line 814, in pandas._libs.parsers.TextReader._tokenize_rows\r\n  File \"pandas\/_libs\/parsers.pyx\", line 1951, in pandas._libs.parsers.raise_parser_error\r\npandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 37, saw 2\r\n\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"write_sick.py\", line 19, in <module>\r\n    'validation': 'sick\/validation.csv'})\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 534, in download_and_prepare\r\n    self._save_info()\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/contextlib.py\", line 130, in __exit__\r\n    self.gen.throw(type, value, traceback)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/site-packages\/datasets-1.2.0-py3.7.egg\/datasets\/builder.py\", line 491, in incomplete_dir\r\n    shutil.rmtree(tmp_dir)\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/shutil.py\", line 498, in rmtree\r\n    onerror(os.rmdir, path, sys.exc_info())\r\n  File \"\/julia\/libs\/anaconda3\/envs\/success\/lib\/python3.7\/shutil.py\", line 496, in rmtree\r\n    os.rmdir(path)\r\nOSError: [Errno 39] Directory not empty: '\/julia\/cache_home_2\/datasets\/csv\/default-61468fc71a743ec1\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2.incomplete'\r\n```\r\n\r\n \n Note that you can pass any argument you would pass to `pandas.read_csv` as kwargs to `load_dataset`. For example you can do\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files=data_files, sep=\"\\t\")\r\n```\r\n\r\nfor example to use a tab separator.\r\n\r\nYou can see the full list of arguments here: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/packaged_modules\/csv\/csv.py\r\n\r\n(I've not found the list in the documentation though, we definitely must add them !)","embeddings":[-0.2842670083,-0.2714768052,-0.1463154405,0.4608483613,0.3019686937,0.2408371568,0.0834566206,0.5121120214,0.0764491484,0.0585810058,-0.0004540544,-0.2665965557,-0.10810256,0.2536355257,-0.0337027758,0.0352051184,-0.0331644155,0.1680267006,-0.0946208239,0.019386515,-0.2684040964,0.2590358853,-0.0957386568,0.0894601047,-0.0035116654,-0.1219344884,0.0992638245,0.2603849769,-0.2975808978,-0.3687266707,0.3527499437,-0.1765222102,0.3671296537,0.2056512088,-0.0001100329,0.109722428,0.4414513707,-0.222237736,-0.2273045331,-0.2782835364,-0.3186444938,-0.5255689621,-0.0424169973,-0.2519200146,-0.0720899403,-0.1986101866,-0.1186174303,-0.358505249,0.1968753785,0.4839046001,0.3277643323,-0.2024365067,-0.0772970542,-0.2182596326,0.1736675203,0.0825364217,0.0177823696,0.0985001624,0.0383216366,0.1259890795,0.5457251668,0.22481516,-0.0871027187,0.062276613,0.1328184605,-0.0650291443,0.2201796621,-0.3268524706,0.1852118075,0.1893909127,0.8423666954,-0.1272012442,-0.1620606333,-0.14171803,0.1230335012,-0.309578687,0.0035668784,0.3891044259,-0.1592620909,0.1557286382,-0.1961616576,0.022634821,-0.056327071,0.1439458579,0.1991666853,-0.1276440918,-0.256634742,0.2265536189,-0.066450499,-0.0968799889,0.0019838086,-0.0634838715,-0.1318117976,0.220077008,-0.3061932921,-0.0059892815,-0.0865267888,-0.0474983081,0.1305490732,0.0191281587,0.2057310492,-0.3622546494,0.1416555792,0.1029283404,0.3085215986,0.3533758223,-0.0966351852,0.0186193027,0.0468764491,0.1750154495,0.0544107519,-0.2243265808,-0.1458094567,-0.4689761698,0.2325953245,0.2488867044,0.3317735791,-0.1506784409,-0.2495945245,0.1341956556,-0.0551047288,0.2364847958,0.1205831841,0.5153110623,-0.307118088,0.1684069484,-0.062956959,0.02560132,-0.0268063359,-0.2216439694,-0.295609206,0.0240087099,-0.1846616417,-0.1395942122,0.2067635804,-0.2110509127,0.2445815951,0.079535611,0.0408378132,-0.3071087897,0.0538251624,-0.3537349403,-0.023229761,0.4643990993,0.0900522098,0.1324354857,0.2597099245,-0.0839310288,-0.218993932,0.1771682501,-0.3590294123,-0.5951446295,-0.0110709053,0.26149863,-0.1140651032,0.1193498001,-0.4246470928,0.0700019151,0.160954386,-0.0954999104,-0.0518162772,-0.1787497252,-0.2614167333,-0.3476524353,0.1499703825,0.3107527196,-0.5323678851,0.1687188745,-0.3266535997,-0.2732284069,0.297688961,0.2120352238,-0.1209672764,0.3310961425,-0.2531271875,0.0592158996,0.3152398765,-0.3005893826,-0.3999977708,0.3908524215,0.016317809,0.1309871674,0.0034007411,0.0098110065,0.3307875693,0.0078690965,0.1102341935,0.212696299,-0.1486558169,-0.0314865559,-0.262057066,-0.1641774923,-0.0509140231,0.156482935,0.1396570951,0.1109460518,0.1691527665,-0.3006534874,-0.0518827923,-0.065568164,0.2696269155,0.3614275157,0.2116627544,0.1624421924,0.1707906276,0.0812941045,-0.535584569,0.3256810606,0.0115941083,-0.1998816878,-0.0919436142,-0.1241044924,-0.5685514212,-0.0164452605,-0.1286688447,-0.00269557,0.1094136685,-0.105446212,-0.0243037492,-0.0921541229,-0.0302179251,0.5924558043,-0.1262022704,-0.1524438113,-0.1371084154,0.2782709002,0.0124592735,0.055331625,0.0212132409,0.0994580835,0.1813473701,-0.1014740244,-0.3069878519,0.3099374771,0.0669195652,-0.0598004498,-0.0391001031,-0.0881186426,-0.1291055232,-0.1401408911,-0.0137780439,0.157358557,0.2477229089,-0.0469300412,0.1126644611,0.1488340795,-0.2980319262,0.0036978342,-0.1184609905,0.0627791062,0.5067969561,0.0400967784,0.0703584403,-0.3693780303,0.2232140005,-0.1662302166,0.3108948171,-0.0223938841,-0.2173395753,-0.3514190018,0.1450280249,0.0782161579,-0.0974682942,0.4313172102,-0.0909135789,-0.1065372974,-0.0840541273,0.3009824455,0.5254251361,0.0834561214,-0.1675480157,0.2054767311,0.2845359147,-0.0061824503,0.2890945375,-0.1024241671,0.3282971978,0.2726708055,-0.0958034098,-0.1561962217,-0.1069378108,-0.1512015164,0.1809896976,0.480276674,-0.296161443,0.0076901154,-0.0392973498,-0.1293672323,-0.0263259336,-0.125998646,0.0485745929,-0.0580259934,-0.1010541618,0.1505691707,0.0410420112,-0.1340404749,-0.1771068275,0.2878244519,0.1359101534,-0.2367094755,-0.1092019901,-0.1559366435,-0.2683488727,0.1195478737,0.0350314416,0.01967692,0.2585455179,-0.1997407377,0.0914035216,-0.1030231938,-0.049859073,-0.0447158515,-0.001954356,0.3377043307,0.1962086111,0.2400903255,0.0899587795,0.0516655073,0.2236879468,-0.0502688475,-0.0530633293,0.3498876691,-0.1486122906,-0.0987843126,-0.0612148754,-0.4378128946,-0.5983160138,-0.3591504395,0.0012708708,0.0437375307,0.0452357754,0.2158264667,0.2280218601,-0.1275926828,0.3982062936,-0.0774030164,-0.3031054139,-0.5205017328,0.2376762778,-0.3503072262,-0.1679317504,0.0081502553,-0.0291733481,0.1761376858,0.0675562024,-0.5610889792,0.3567280471,-0.23838,0.0649895445,-0.2308612466,-0.3174830675,0.220419839,-0.1381314099,-0.0619107075,-0.248017773,-0.1541718543,-0.0304518882,0.0611269623,0.1230327561,-0.0677224621,0.5385031104,0.0502661429,0.5127761364,0.3299145997,-0.0016039216,0.4850444198,0.0683038458,0.3586151302,-0.2322539389,-0.3839135766,-0.0448417515,-0.0905613229,-0.2724584341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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1771","title":"Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.2.1\/datasets\/csv\/csv.py","comments":"Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library.\r\nSee PR #1726 \r\nWe'll do a new release soon :)","body":"Hi,\r\nWhen I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset?\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/tom\/pyenv\/pystory\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/tom\/pyenv\/pystory\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 343, in cached_path\r\n    max_retries=download_config.max_retries,\r\n  File \"\/home\/tom\/pyenv\/pystory\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 617, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.2.1\/datasets\/csv\/csv.py\r\n\r\n```","comment_length":36,"text":"Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.2.1\/datasets\/csv\/csv.py \n Hi,\r\nWhen I load_dataset from local csv files, below error happened, looks raw.githubusercontent.com was blocked by the chinese government. But why it need to download csv.py? should it include when pip install the dataset?\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/tom\/pyenv\/pystory\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/tom\/pyenv\/pystory\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 343, in cached_path\r\n    max_retries=download_config.max_retries,\r\n  File \"\/home\/tom\/pyenv\/pystory\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 617, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.2.1\/datasets\/csv\/csv.py\r\n\r\n``` \n Indeed in 1.2.1 the script to process csv file is downloaded. Starting from the next release though we include the csv processing directly in the library.\r\nSee PR #1726 \r\nWe'll do a new release soon 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1770","title":"how can I combine 2 dataset with different\/same features?","comments":"Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)\r\nFor now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https:\/\/github.com\/huggingface\/datasets\/issues\/853#issuecomment-727872188","body":"to combine 2 dataset by one-one map like ds = zip(ds1, ds2):\r\nds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} \r\nor different feature:\r\nds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}","comment_length":45,"text":"how can I combine 2 dataset with different\/same features? \n to combine 2 dataset by one-one map like ds = zip(ds1, ds2):\r\nds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} \r\nor different feature:\r\nds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} \n Hi ! Currently we don't have a way to `zip` datasets but we plan to add this soon :)\r\nFor now you'll need to use `map` to add the fields from one dataset to the other. See the comment here for more info : https:\/\/github.com\/huggingface\/datasets\/issues\/853#issuecomment-727872188","embeddings":[-0.3618090153,-0.4964470565,-0.0687218681,0.1217603683,0.0697285309,0.3581112623,-0.092245914,0.2210621834,-0.0352847092,0.0138700567,-0.3246749341,0.4096489549,0.0693914667,0.7398912311,0.1613286585,-0.2478816509,0.078949064,0.129455775,-0.4716953337,0.1984139681,0.0128847482,0.0042593097,-0.0246831551,-0.0849265978,-0.0984388143,0.2673225105,-0.2330110967,-0.1992989331,-0.1573341936,0.0235670432,0.3827166855,0.2693840265,-0.0531156473,0.2027809918,-0.0001115768,0.0722315609,-0.0460229665,-0.1208759993,0.1077825353,-0.4201931953,-0.0392919108,-0.5601700544,-0.1074662507,-0.0181255024,-0.1781563014,-0.2882148921,-0.2893857956,-0.2174081355,0.3418446779,-0.1690038443,0.1079963818,0.1024667546,0.1639198214,-0.0635811985,0.2932220101,0.4440595806,-0.0876196325,-0.069648549,0.4562326968,0.0492846109,0.5520344973,0.1969706416,-0.019789217,0.0005702212,0.2822974622,0.1590328515,-0.2205993831,-0.20029594,-0.0121477181,0.3940691054,0.4385496378,-0.2857182324,-0.3185835481,-0.189653784,0.1420851201,-0.1715218872,-0.1700829864,0.3175992072,0.0211353395,0.02320919,-0.4666415751,-0.3988069892,0.0761492923,0.1548091769,-0.0889578015,0.1069054008,-0.0845471174,0.1540905237,-0.0578114167,-0.2493236959,0.1112819239,-0.4102554917,-0.089173317,0.2716374397,-0.2897037268,-0.3540632129,-0.2580055594,-0.2053414881,0.7211526036,0.1921920478,-0.1244395897,-0.131403178,-0.4426646233,0.2014518231,0.2647921741,0.2856196463,0.2340371311,0.0185884591,0.0427907445,-0.2273828536,-0.131199345,-0.0583620593,0.4423027337,-0.1362012029,-0.1892872453,-0.2544163465,0.4779630005,-0.2223193645,0.1482369304,-0.4064995944,0.0526720248,-0.2132237703,-0.1734143347,-0.0509308726,0.0698622018,-0.0080112331,-0.2046570927,0.2878879905,0.2749117911,-0.2954862416,-0.1059869528,-0.0737898126,0.1206281409,0.1911255121,-0.012508153,-0.3862589896,0.1450714916,0.6239692569,-0.1406674236,0.2539289892,0.3124404848,-0.1426234841,-0.1376311481,-0.1937665194,0.3240775466,0.1543080658,0.0961580873,0.1441503167,-0.2133418322,-0.0007746207,-0.3544187844,-0.1190918982,0.0511283167,0.1720260233,-0.1338262707,-0.1185326949,-0.3662887216,0.669705689,-0.0450399779,-0.1963850111,-0.1601148397,0.4825178385,-0.3460585773,-0.1729307175,0.1248904914,0.0466939658,-0.2356795967,-0.4060325921,0.1128408611,-0.0408670418,-0.2736009657,0.4258361459,-0.2547849417,0.0148463221,-0.2920529544,0.0254394338,0.4448811114,-0.0706946105,-0.2393836975,0.4166375697,-0.0338701159,-0.0262855366,-0.0796054527,0.1589786559,0.1578690559,0.1973277479,0.0528209619,0.5103905201,-0.2451841384,-0.2101137489,0.0171056483,-0.4156886935,0.4063560963,-0.0769312903,-0.0820079893,0.0215606131,0.0277162995,-0.4218937457,0.1445216238,-0.15991126,0.2335322052,0.1152569503,0.0342923552,0.0504973084,-0.0890547559,-0.4413529634,-0.4822226763,-0.0164514408,-0.1436119974,-0.1910775304,0.1615774632,-0.4283260703,-0.0080508031,-0.3666362762,-0.0625188798,-0.1186320409,0.0343080238,0.0187574625,0.1252595782,-0.1757806689,-0.2722326815,0.2241860926,0.5275174975,0.0491098091,-0.3530474305,0.5951489806,0.240128696,0.0126929348,-0.0470200293,0.1135402992,0.0824418664,-0.0154712535,0.1004678532,0.0125805149,-0.5362690091,0.3304072917,0.4451425374,-0.1900831908,0.2659787536,-0.2425027937,0.1363273859,-0.0252018459,0.0164538473,-0.2492333651,-0.3427091539,0.3071554005,0.0362624452,0.2554853857,0.1160895601,0.0826603174,0.1574560553,-0.1364686638,0.047816705,0.0063440022,-0.011840228,0.0121240411,0.3719067574,0.2906228304,-0.1891371459,0.1635454446,0.254083246,0.0006374083,0.0234006979,0.0033061143,-0.3710803688,0.0487012006,0.0733456686,-0.0457924716,0.5803026557,0.3118932247,-0.0138465557,0.1654967964,0.1007250622,0.1608840525,-0.0108852163,-0.0204664636,-0.0809135586,0.1754918993,0.307376802,0.1203590706,-0.0418767892,0.2153899223,0.1384509355,-0.1983055174,-0.0339096487,-0.2077614367,-0.2305031568,-0.2206584811,-0.3354868293,-0.1023770049,-0.4055423439,0.0058899112,-0.0967234746,0.1889790297,0.067526117,0.0023474752,0.2830423713,0.2402458936,-0.1767149866,-0.1294568777,-0.0370173678,0.2874001563,0.2032704949,0.1446807086,0.154216826,0.101480864,0.38425529,-0.0276632365,-0.0555931553,-0.6561787724,-0.3905450106,0.1056405306,-0.1866333336,0.16265136,-0.0132696228,0.0612567216,0.0317173265,-0.2150374055,0.2902244925,0.3680696785,-0.1453747004,-0.1525584161,-0.0711419731,0.0424222611,-0.153492406,-0.2626842856,-0.2550150454,-0.130769223,0.4938812852,-0.2122130543,0.0253909901,-0.3388878107,0.0590585954,-0.145503521,-0.0381393991,-0.0373349972,-0.2105336636,-0.4417386055,0.3633479774,-0.1019067094,-0.1232514456,-0.0622192658,-0.1185194403,0.0129733961,0.0582186133,-0.0850687996,-0.1113943607,-0.3798117936,0.2991844714,-0.0581444874,0.3092561066,0.2840961516,0.0622070655,0.1154543906,0.028177442,-0.4375109076,0.0236828197,0.0776255801,0.2953591049,0.0861718655,0.0952155665,-0.1847838014,0.5392614007,0.4855138361,0.2069980949,0.314810276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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1770","title":"how can I combine 2 dataset with different\/same features?","comments":"Good to hear.\r\nCurrently I did not use map , just fetch src and tgt from the 2 dataset and merge them.\r\nIt will be a release if you can deal with it at the backend.\r\nThanks.","body":"to combine 2 dataset by one-one map like ds = zip(ds1, ds2):\r\nds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} \r\nor different feature:\r\nds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'}","comment_length":37,"text":"how can I combine 2 dataset with different\/same features? \n to combine 2 dataset by one-one map like ds = zip(ds1, ds2):\r\nds1: {'text'}, ds2: {'text'}, combine ds:{'src', 'tgt'} \r\nor different feature:\r\nds1: {'src'}, ds2: {'tgt'}, combine ds:{'src', 'tgt'} \n Good to hear.\r\nCurrently I did not use map , just fetch src and tgt from the 2 dataset and merge them.\r\nIt will be a release if you can deal with it at the backend.\r\nThanks.","embeddings":[-0.3885659575,-0.4940942526,-0.1406932473,0.0185404997,-0.0224656239,0.2798508108,-0.1746791303,0.3226885796,-0.0510883927,0.0088932943,-0.1925193518,0.6096795201,0.0999665633,0.5465123653,0.0698018521,-0.11272192,0.0745652765,0.1688348651,-0.4552721381,0.1778494567,0.0620454885,-0.0594893433,-0.0334581845,-0.0982747376,-0.0798539296,0.2818122506,-0.1274135411,-0.2967349291,-0.2358781695,0.0145080378,0.2774324417,0.2345999032,-0.1273669004,0.2523550987,-0.0001082497,0.1177333593,0.0513774976,-0.1531057805,0.2523333728,-0.43814677,-0.1970332414,-0.4797204137,-0.1601139903,-0.1272794157,-0.1080754325,-0.1871521175,-0.2989039123,-0.3243273795,0.2503189445,-0.1271040291,0.1325851083,-0.0880205035,0.1223926917,-0.0137145016,0.2999750674,0.363771081,-0.0602158383,-0.1029037163,0.5676066875,0.0092757046,0.4451414049,0.2175048441,-0.1230507195,-0.0073250057,0.3124162853,0.048622299,-0.0380412228,-0.2631063163,0.0710004494,0.2478632182,0.5259116888,-0.1586421877,-0.0991821215,0.0621924251,0.0316509567,-0.0059683854,-0.1653365195,0.2786548734,0.0410577916,0.0446243025,-0.5148202777,-0.44294855,0.0948954821,0.1656849831,-0.0767568573,0.2319708616,0.0322263762,0.1849222928,-0.2162769139,-0.2405795306,0.2670075893,-0.4301855862,-0.0571950935,0.3326957822,-0.212325722,-0.3501248658,-0.2511488795,-0.4439242482,0.6490111947,-0.0200989395,0.0990228429,-0.0340400115,-0.380594492,0.280482769,0.2375020683,0.2917183042,0.3173268139,0.189862594,-0.0087112775,-0.4177415371,-0.3030484021,0.0329587683,0.4209108949,-0.2382072657,-0.1569514126,-0.1717440933,0.4447456598,-0.3677548468,0.2103084624,-0.5202794075,-0.0881638229,-0.1420803666,-0.2262052298,-0.0958222449,0.175053224,0.0489226729,-0.2405053079,0.1676711142,0.3340087831,-0.2938676476,-0.0370179676,-0.0422511697,0.1603026092,0.04718237,-0.0015432743,-0.1771240234,0.0949376151,0.5621643066,-0.3058844805,0.1829916388,0.3147560656,-0.1040003076,-0.0195233803,-0.2901396155,0.3635196686,0.0882699341,0.0345524587,0.2711079717,-0.2068246007,0.1299701035,-0.4372511208,-0.1448739618,0.1334382892,0.2379768342,0.0191794056,-0.0671926066,-0.2039432526,0.5709993839,0.0468189046,-0.2714456916,-0.1705272645,0.5460534692,-0.2374549955,-0.2488662302,-0.0948712006,-0.0343842693,-0.3212696016,-0.3335896134,0.0642851144,-0.0274077561,-0.2287088484,0.3375684023,-0.3624535203,0.0571283549,-0.1392395794,0.0410392694,0.552892983,-0.0943096653,-0.3914399147,0.511829257,-0.007198059,-0.146579057,-0.115699023,0.2643761337,0.2004470825,0.1718614548,0.1076660454,0.5094723701,-0.2747680545,-0.0966094509,-0.0189080052,-0.418153435,0.4239629507,-0.1348892599,-0.0575222224,-0.0474160314,0.1543736905,-0.4866879582,0.1541965455,-0.1080196425,0.2645322084,0.1089467481,0.017614454,-0.1733415723,-0.0762553662,-0.3590360582,-0.3984617591,-0.1078947857,-0.3227751553,-0.1823046356,0.0847267658,-0.3686960638,-0.0110871708,-0.4826446474,-0.0400576256,0.0088729654,0.0902565941,-0.0263376534,0.0050436859,-0.2365912497,-0.156092003,0.1332933009,0.5647348762,-0.0498310141,-0.2055795044,0.5626952052,0.2106457651,0.0336607695,0.0225735214,-0.0472389124,0.1355435103,-0.0224668141,-0.0492446311,0.0592307821,-0.4545077682,0.3014397323,0.3840034008,-0.2035782039,0.2649064362,-0.3029321134,0.142938152,0.0698782802,0.0661109239,-0.2787417173,-0.2938680649,0.2300539315,0.1171721816,0.2025708258,0.0860554427,0.0840895623,0.1174589396,-0.176944375,0.0689538345,-0.047432363,-0.1171294376,-0.030280469,0.2370960265,0.4099989533,0.0255227983,0.2372204363,0.247135371,0.0381263606,0.1396588087,-0.0745834336,-0.3314553499,-0.0346246697,-0.0187520646,-0.0686756447,0.5103157759,0.254093796,-0.1636122167,0.1103900447,0.2492556572,0.2375882268,-0.0140336351,-0.109353289,0.0952235311,0.1363039315,0.3759461641,0.16348809,0.102835685,0.1159216315,0.2855808139,-0.1664953828,0.113533631,-0.3573138118,-0.2230840921,-0.1297074258,-0.2990342677,-0.0198958907,-0.2856923044,-0.0178767089,-0.0423254296,0.0760572925,0.0820795819,0.0532877035,0.3012829721,0.0908265039,-0.1270110607,-0.1823029965,0.045443207,0.1271011978,0.1881293952,0.1879554391,0.1435558945,0.158248812,0.3156916201,0.0445809476,-0.0184111632,-0.48882249,-0.3923448324,0.1243815124,-0.0000142496,-0.0214338861,-0.0514348447,-0.0697956756,0.0403116681,-0.1614388824,0.1152889431,0.2696072161,-0.1647182107,-0.2373158485,-0.1565071195,-0.0114669735,-0.1103405803,-0.3104776144,-0.2459207475,-0.2052778006,0.182214126,-0.2187639028,0.0829838812,-0.5726172328,0.0895796716,-0.2523791194,0.0419618227,-0.0752221644,-0.1315330416,-0.368395716,0.3846784532,-0.1310067475,-0.1836591363,-0.063963972,-0.0795925856,0.1381900012,0.1339152902,-0.0233511105,0.0152277648,-0.2839080989,0.2396766245,-0.1025734767,0.1269342154,0.2458895147,0.2217749208,0.0532566346,0.0195346735,-0.3257350028,-0.0101708891,0.1243051738,0.2586684823,0.1649142951,-0.0023148581,-0.1266412586,0.4759716094,0.41021052,0.0864422619,0.2461429238,0.0690271258,-0.0310459081,-0.0720844269,-0.265080452,-0.19327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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1769","title":"_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2","comments":"Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?","body":"It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.\r\n\r\nThe script I use is https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm_wwm.py\r\n\r\nScript args:\r\n\r\n```\r\n--model_name_or_path\r\n..\/..\/..\/model\/chinese-roberta-wwm-ext\r\n--train_file\r\n\/nfs\/volume-377-2\/bert\/data\/test\/train.txt\r\n--output_dir\r\ntest\r\n--do_train\r\n--per_device_train_batch_size\r\n2\r\n--gradient_accumulation_steps\r\n2\r\n--learning_rate\r\n1e-4\r\n--max_steps\r\n1000\r\n--warmup_steps\r\n10\r\n--save_steps\r\n1000\r\n--save_total_limit\r\n1\r\n--seed\r\n23333\r\n--max_seq_length\r\n512\r\n--preprocessing_num_workers\r\n2\r\n--cache_dir\r\n\/nfs\/volume-377-2\/bert\/data\/test\/cache\r\n```\r\n\r\nWhere the `\/nfs\/volume-377-2\/bert\/data\/test\/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.\r\n\r\nFull Traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 398, in <module>\r\n    main()\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 325, in main\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in map\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 644, in get\r\n    raise self._value\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 424, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n```\r\n","comment_length":21,"text":"_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 \n It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.\r\n\r\nThe script I use is https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm_wwm.py\r\n\r\nScript args:\r\n\r\n```\r\n--model_name_or_path\r\n..\/..\/..\/model\/chinese-roberta-wwm-ext\r\n--train_file\r\n\/nfs\/volume-377-2\/bert\/data\/test\/train.txt\r\n--output_dir\r\ntest\r\n--do_train\r\n--per_device_train_batch_size\r\n2\r\n--gradient_accumulation_steps\r\n2\r\n--learning_rate\r\n1e-4\r\n--max_steps\r\n1000\r\n--warmup_steps\r\n10\r\n--save_steps\r\n1000\r\n--save_total_limit\r\n1\r\n--seed\r\n23333\r\n--max_seq_length\r\n512\r\n--preprocessing_num_workers\r\n2\r\n--cache_dir\r\n\/nfs\/volume-377-2\/bert\/data\/test\/cache\r\n```\r\n\r\nWhere the `\/nfs\/volume-377-2\/bert\/data\/test\/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.\r\n\r\nFull Traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 398, in <module>\r\n    main()\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 325, in main\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in map\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 644, in get\r\n    raise self._value\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 424, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n```\r\n \n Hi ! What version of python and datasets do you have ? And also what version of dill and pickle 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1769","title":"_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2","comments":"> Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?\r\n\r\npython==3.6.10\r\ndatasets==1.2.1\r\ndill==0.3.2\r\npickle.format_version==4.0","body":"It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.\r\n\r\nThe script I use is https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm_wwm.py\r\n\r\nScript args:\r\n\r\n```\r\n--model_name_or_path\r\n..\/..\/..\/model\/chinese-roberta-wwm-ext\r\n--train_file\r\n\/nfs\/volume-377-2\/bert\/data\/test\/train.txt\r\n--output_dir\r\ntest\r\n--do_train\r\n--per_device_train_batch_size\r\n2\r\n--gradient_accumulation_steps\r\n2\r\n--learning_rate\r\n1e-4\r\n--max_steps\r\n1000\r\n--warmup_steps\r\n10\r\n--save_steps\r\n1000\r\n--save_total_limit\r\n1\r\n--seed\r\n23333\r\n--max_seq_length\r\n512\r\n--preprocessing_num_workers\r\n2\r\n--cache_dir\r\n\/nfs\/volume-377-2\/bert\/data\/test\/cache\r\n```\r\n\r\nWhere the `\/nfs\/volume-377-2\/bert\/data\/test\/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.\r\n\r\nFull Traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 398, in <module>\r\n    main()\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 325, in main\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in map\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 644, in get\r\n    raise self._value\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 424, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n```\r\n","comment_length":26,"text":"_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 \n It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.\r\n\r\nThe script I use is https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm_wwm.py\r\n\r\nScript args:\r\n\r\n```\r\n--model_name_or_path\r\n..\/..\/..\/model\/chinese-roberta-wwm-ext\r\n--train_file\r\n\/nfs\/volume-377-2\/bert\/data\/test\/train.txt\r\n--output_dir\r\ntest\r\n--do_train\r\n--per_device_train_batch_size\r\n2\r\n--gradient_accumulation_steps\r\n2\r\n--learning_rate\r\n1e-4\r\n--max_steps\r\n1000\r\n--warmup_steps\r\n10\r\n--save_steps\r\n1000\r\n--save_total_limit\r\n1\r\n--seed\r\n23333\r\n--max_seq_length\r\n512\r\n--preprocessing_num_workers\r\n2\r\n--cache_dir\r\n\/nfs\/volume-377-2\/bert\/data\/test\/cache\r\n```\r\n\r\nWhere the `\/nfs\/volume-377-2\/bert\/data\/test\/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.\r\n\r\nFull Traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 398, in <module>\r\n    main()\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 325, in main\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in map\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 644, in get\r\n    raise self._value\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 424, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n```\r\n \n > Hi ! What version of python and datasets do you have ? And also what version of dill and pickle ?\r\n\r\npython==3.6.10\r\ndatasets==1.2.1\r\ndill==0.3.2\r\npickle.format_version==4.0","embeddings":[-0.2903597653,-0.2974283993,0.1132445857,0.1903711855,0.166158393,-0.0522105806,0.3321136534,0.2303635925,0.29939574,0.2801507711,0.0167170409,0.3376862705,-0.1110442802,0.2174146324,0.0041834014,-0.1060649753,-0.1158019528,-0.1348113567,-0.3830081224,0.0106051518,-0.2922798097,0.3295297623,-0.3364185989,0.178581655,-0.6447761059,-0.0980493426,-0.0480723754,0.2914254069,0.0867962465,-0.0838413835,0.091296576,-0.0355660208,0.0124873333,0.495975703,-0.0001100203,0.0433959849,0.2030376494,-0.0829199255,-0.0667256936,-0.4173735082,0.0713781044,-0.0390120484,0.070758529,-0.0548069626,0.1504491121,0.1799487174,0.1406906098,-0.0804709494,0.2613464594,0.1155149564,0.1944084316,0.5662001371,0.1144360006,-0.076135695,-0.1252603531,0.2157489657,-0.0322175771,0.2660860121,0.1158297211,-0.1664748639,-0.2567045093,0.2081440091,-0.0091969082,0.2851698399,-0.1553526968,0.3262346983,0.0900292844,-0.2589209974,0.1284050196,0.3979728222,0.0784635693,-0.232056573,-0.3231726289,-0.0680227727,-0.0854364112,-0.1713582575,0.0877538249,0.1660903543,-0.1176943481,0.0851684958,-0.2029177397,0.1319692582,0.2207043022,0.1039848626,0.2320752591,0.3622331023,-0.031342186,0.3686209619,0.2795686424,-0.1373411715,-0.2073924094,-0.1868893355,0.0720700994,0.0632183775,-0.0458192751,-0.1408384144,0.045508083,-0.0947569758,0.073656261,-0.1785828322,-0.1766469479,0.1712978035,-0.2389756292,0.3193747997,0.3000181019,-0.1272128671,-0.0990271121,0.2027037889,0.4027116001,-0.0739725083,-0.2251318693,0.1159959584,0.3312569559,-0.3447957933,-0.1041944399,0.1564332843,0.329223603,-0.1115081087,-0.149697721,-0.1945511699,-0.1339909732,-0.1725845933,-0.010302064,0.2903743684,0.0592746921,0.3461214006,0.0566079207,0.1878037006,-0.5467142463,-0.0829292685,-0.3398105502,0.218926996,-0.1916940212,0.0626075044,0.2220834941,0.2673146129,0.3550154865,0.1994703859,-0.0371265337,-0.1523097605,0.2351196259,-0.2494036108,-0.1287895143,0.1925622076,0.1652551144,0.2886993587,0.2680864036,-0.3080254495,-0.0635940209,0.0783043131,-0.2370546311,-0.2890685201,-0.0729562566,0.139649719,-0.0142909233,0.1257887632,-0.1699093133,0.0447562262,0.4315924942,-0.0497591682,-0.1882438213,-0.2977280915,-0.3939255178,-0.2207495868,-0.0378487483,0.3762208223,-0.2341665477,-0.1520403773,0.1977583915,-0.1235371456,0.5048832893,0.5401949883,-0.2475932837,-0.0822768435,-0.1492327303,0.6606734991,0.0360711515,0.1096854657,-0.2572190762,0.202608645,-0.0988949314,-0.1114991456,-0.0841935053,0.0370499715,-0.3572835028,-0.0326678194,0.4061434865,0.1351237893,0.0573740341,0.192356661,-0.271171689,-0.3141673207,0.1697528809,-0.0544769354,0.0685291588,-0.2757891119,-0.1262007654,-0.0896317139,0.4380550981,-0.2241077721,0.0792603567,-0.0038885754,0.1627585292,-0.0515407771,-0.0201107692,-0.2338376492,-0.5796083808,0.2878185511,-0.2233290672,0.2118987888,-0.064044036,-0.1344355494,0.2768068016,-0.0169793814,-0.3259687424,-0.0686179921,0.0756605044,0.0722182989,0.0509032123,-0.1822725981,-0.0842780322,-0.1165589243,-0.1049529836,-0.0149970902,-0.4686059952,0.0425728932,-0.4037323892,-0.4181522727,-0.189516753,0.2076245993,0.1067516208,-0.0068623866,-0.1171695143,0.1369666457,0.1502617449,-0.0461106859,-0.0495142005,0.0562658869,-0.0190868974,-0.0961284563,0.021799013,0.219856739,0.1103201881,-0.1780940443,-0.2147070765,0.4751463234,0.0284074377,0.2698736787,0.0416779704,0.0957993492,0.2242285013,0.0413596444,0.1322552115,-0.1709280461,0.1677090824,0.1714064628,0.1670323014,0.0913200006,-0.239271,0.039523486,0.5964310765,0.2025493234,0.4119851291,0.0081310831,-0.2059065104,-0.1373883784,-0.1942453682,0.0325800702,0.6064072251,0.1613005549,0.0643860176,0.001883429,-0.1250701845,-0.1186368167,0.0121865347,-0.0157128964,0.0383681133,0.1391063333,0.110177435,-0.0765566528,-0.303314656,-0.1031182781,0.01030437,0.3184674084,-0.3177983165,0.0145384418,-0.3326050341,0.4729814231,-0.2347498834,0.0097457618,-0.1918038428,-0.2922730744,-0.1737028062,0.3688523471,-0.2211097777,0.1546901911,0.1278085411,-0.2074619383,-0.1040067896,0.2358911335,0.0728950351,-0.0771441162,-0.2809194922,-0.0019136857,0.4048869908,-0.0430667438,0.0471233055,0.1536123306,-0.2586254776,-0.0553867705,-0.13959831,0.0791322961,-0.0761685297,0.2284352332,0.2975741327,0.2531296015,-0.0203261487,-0.3027900457,0.278198719,-0.2991173267,-0.1905634552,-0.0421640575,0.0779534876,-0.1181903258,-0.3645498157,-0.3952235579,-0.4591894448,-0.3345877528,0.4066636562,-0.0599519573,0.1627806872,0.2841716111,0.174965024,0.2511926591,0.1270663142,-0.1767324805,-0.2883633971,0.1619478464,0.22296682,0.0186432451,-0.2608917952,-0.0488841124,-0.1683621109,-0.0411670841,-0.2099181116,-0.1619539857,0.0679083541,-0.4185375571,0.250623107,0.1012951508,0.2987335026,0.6218903065,0.2733477652,-0.1571446955,-0.0458586663,-0.2676429152,0.1766682565,0.2072681785,0.2516293228,-0.0044127959,0.1994465888,-0.0090489136,0.7935594916,0.2588758171,-0.2274359912,0.2747074664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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1769","title":"_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2","comments":"Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`.\r\n\r\nHowever objects like `typing.Union[str, NoneType]` are not picklable in python <3.7.\r\nCan you try to update your python version to python>=3.7 ?\r\n","body":"It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.\r\n\r\nThe script I use is https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm_wwm.py\r\n\r\nScript args:\r\n\r\n```\r\n--model_name_or_path\r\n..\/..\/..\/model\/chinese-roberta-wwm-ext\r\n--train_file\r\n\/nfs\/volume-377-2\/bert\/data\/test\/train.txt\r\n--output_dir\r\ntest\r\n--do_train\r\n--per_device_train_batch_size\r\n2\r\n--gradient_accumulation_steps\r\n2\r\n--learning_rate\r\n1e-4\r\n--max_steps\r\n1000\r\n--warmup_steps\r\n10\r\n--save_steps\r\n1000\r\n--save_total_limit\r\n1\r\n--seed\r\n23333\r\n--max_seq_length\r\n512\r\n--preprocessing_num_workers\r\n2\r\n--cache_dir\r\n\/nfs\/volume-377-2\/bert\/data\/test\/cache\r\n```\r\n\r\nWhere the `\/nfs\/volume-377-2\/bert\/data\/test\/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.\r\n\r\nFull Traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 398, in <module>\r\n    main()\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 325, in main\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in map\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 644, in get\r\n    raise self._value\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 424, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n```\r\n","comment_length":41,"text":"_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union when calling datasets.map with num_proc=2 \n It may be a bug of multiprocessing with Datasets, when I disable the multiprocessing by set num_proc to None, everything works fine.\r\n\r\nThe script I use is https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm_wwm.py\r\n\r\nScript args:\r\n\r\n```\r\n--model_name_or_path\r\n..\/..\/..\/model\/chinese-roberta-wwm-ext\r\n--train_file\r\n\/nfs\/volume-377-2\/bert\/data\/test\/train.txt\r\n--output_dir\r\ntest\r\n--do_train\r\n--per_device_train_batch_size\r\n2\r\n--gradient_accumulation_steps\r\n2\r\n--learning_rate\r\n1e-4\r\n--max_steps\r\n1000\r\n--warmup_steps\r\n10\r\n--save_steps\r\n1000\r\n--save_total_limit\r\n1\r\n--seed\r\n23333\r\n--max_seq_length\r\n512\r\n--preprocessing_num_workers\r\n2\r\n--cache_dir\r\n\/nfs\/volume-377-2\/bert\/data\/test\/cache\r\n```\r\n\r\nWhere the `\/nfs\/volume-377-2\/bert\/data\/test\/train.txt` is just a toy example with 10000 lines of random string, you should be able to reproduce this error esaily.\r\n\r\nFull Traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 398, in <module>\r\n    main()\r\n  File \"\/nfs\/volume-377-2\/bert\/transformers\/examples\/language-modeling\/run_mlm_wwm.py\", line 325, in main\r\n    load_from_cache_file=not data_args.overwrite_cache,\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in map\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1318, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 644, in get\r\n    raise self._value\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 424, in _handle_tasks\r\n    put(task)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/connection.py\", line 209, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/multiprocess\/reduction.py\", line 54, in dumps\r\n    cls(buf, protocol, *args, **kwds).dump(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/home\/luban\/miniconda3\/envs\/py36\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n```\r\n \n Multiprocessing in python require all the functions to be picklable. More specifically, functions need to be picklable with `dill`.\r\n\r\nHowever objects like `typing.Union[str, NoneType]` are not picklable in python <3.7.\r\nCan you try to update your python version to python>=3.7 ?\r\n","embeddings":[-0.2903597653,-0.2974283993,0.1132445857,0.1903711855,0.166158393,-0.0522105806,0.3321136534,0.2303635925,0.29939574,0.2801507711,0.0167170409,0.3376862705,-0.1110442802,0.2174146324,0.0041834014,-0.1060649753,-0.1158019528,-0.1348113567,-0.3830081224,0.0106051518,-0.2922798097,0.3295297623,-0.3364185989,0.178581655,-0.6447761059,-0.0980493426,-0.0480723754,0.2914254069,0.0867962465,-0.0838413835,0.091296576,-0.0355660208,0.0124873333,0.495975703,-0.0001100203,0.0433959849,0.2030376494,-0.0829199255,-0.0667256936,-0.4173735082,0.0713781044,-0.0390120484,0.070758529,-0.0548069626,0.1504491121,0.1799487174,0.1406906098,-0.0804709494,0.2613464594,0.1155149564,0.1944084316,0.5662001371,0.1144360006,-0.076135695,-0.1252603531,0.2157489657,-0.0322175771,0.2660860121,0.1158297211,-0.1664748639,-0.2567045093,0.2081440091,-0.0091969082,0.2851698399,-0.1553526968,0.3262346983,0.0900292844,-0.2589209974,0.1284050196,0.3979728222,0.0784635693,-0.232056573,-0.3231726289,-0.0680227727,-0.0854364112,-0.1713582575,0.0877538249,0.1660903543,-0.1176943481,0.0851684958,-0.2029177397,0.1319692582,0.2207043022,0.1039848626,0.2320752591,0.3622331023,-0.031342186,0.3686209619,0.2795686424,-0.1373411715,-0.2073924094,-0.1868893355,0.0720700994,0.0632183775,-0.0458192751,-0.1408384144,0.045508083,-0.0947569758,0.073656261,-0.1785828322,-0.1766469479,0.1712978035,-0.2389756292,0.3193747997,0.3000181019,-0.1272128671,-0.0990271121,0.2027037889,0.4027116001,-0.0739725083,-0.2251318693,0.1159959584,0.3312569559,-0.3447957933,-0.1041944399,0.1564332843,0.329223603,-0.1115081087,-0.149697721,-0.1945511699,-0.1339909732,-0.1725845933,-0.010302064,0.2903743684,0.0592746921,0.3461214006,0.0566079207,0.1878037006,-0.5467142463,-0.0829292685,-0.3398105502,0.218926996,-0.1916940212,0.0626075044,0.2220834941,0.2673146129,0.3550154865,0.1994703859,-0.0371265337,-0.1523097605,0.2351196259,-0.2494036108,-0.1287895143,0.1925622076,0.1652551144,0.2886993587,0.2680864036,-0.3080254495,-0.0635940209,0.0783043131,-0.2370546311,-0.2890685201,-0.0729562566,0.139649719,-0.0142909233,0.1257887632,-0.1699093133,0.0447562262,0.4315924942,-0.0497591682,-0.1882438213,-0.2977280915,-0.3939255178,-0.2207495868,-0.0378487483,0.3762208223,-0.2341665477,-0.1520403773,0.1977583915,-0.1235371456,0.5048832893,0.5401949883,-0.2475932837,-0.0822768435,-0.1492327303,0.6606734991,0.0360711515,0.1096854657,-0.2572190762,0.202608645,-0.0988949314,-0.1114991456,-0.0841935053,0.0370499715,-0.3572835028,-0.0326678194,0.4061434865,0.1351237893,0.0573740341,0.192356661,-0.271171689,-0.3141673207,0.1697528809,-0.0544769354,0.0685291588,-0.2757891119,-0.1262007654,-0.0896317139,0.4380550981,-0.2241077721,0.0792603567,-0.0038885754,0.1627585292,-0.0515407771,-0.0201107692,-0.2338376492,-0.5796083808,0.2878185511,-0.2233290672,0.2118987888,-0.064044036,-0.1344355494,0.2768068016,-0.0169793814,-0.3259687424,-0.0686179921,0.0756605044,0.0722182989,0.0509032123,-0.1822725981,-0.0842780322,-0.1165589243,-0.1049529836,-0.0149970902,-0.4686059952,0.0425728932,-0.4037323892,-0.4181522727,-0.189516753,0.2076245993,0.1067516208,-0.0068623866,-0.1171695143,0.1369666457,0.1502617449,-0.0461106859,-0.0495142005,0.0562658869,-0.0190868974,-0.0961284563,0.021799013,0.219856739,0.1103201881,-0.1780940443,-0.2147070765,0.4751463234,0.0284074377,0.2698736787,0.0416779704,0.0957993492,0.2242285013,0.0413596444,0.1322552115,-0.1709280461,0.1677090824,0.1714064628,0.1670323014,0.0913200006,-0.239271,0.039523486,0.5964310765,0.2025493234,0.4119851291,0.0081310831,-0.2059065104,-0.1373883784,-0.1942453682,0.0325800702,0.6064072251,0.1613005549,0.0643860176,0.001883429,-0.1250701845,-0.1186368167,0.0121865347,-0.0157128964,0.0383681133,0.1391063333,0.110177435,-0.0765566528,-0.303314656,-0.1031182781,0.01030437,0.3184674084,-0.3177983165,0.0145384418,-0.3326050341,0.4729814231,-0.2347498834,0.0097457618,-0.1918038428,-0.2922730744,-0.1737028062,0.3688523471,-0.2211097777,0.1546901911,0.1278085411,-0.2074619383,-0.1040067896,0.2358911335,0.0728950351,-0.0771441162,-0.2809194922,-0.0019136857,0.4048869908,-0.0430667438,0.0471233055,0.1536123306,-0.2586254776,-0.0553867705,-0.13959831,0.0791322961,-0.0761685297,0.2284352332,0.2975741327,0.2531296015,-0.0203261487,-0.3027900457,0.278198719,-0.2991173267,-0.1905634552,-0.0421640575,0.0779534876,-0.1181903258,-0.3645498157,-0.3952235579,-0.4591894448,-0.3345877528,0.4066636562,-0.0599519573,0.1627806872,0.2841716111,0.174965024,0.2511926591,0.1270663142,-0.1767324805,-0.2883633971,0.1619478464,0.22296682,0.0186432451,-0.2608917952,-0.0488841124,-0.1683621109,-0.0411670841,-0.2099181116,-0.1619539857,0.0679083541,-0.4185375571,0.250623107,0.1012951508,0.2987335026,0.6218903065,0.2733477652,-0.1571446955,-0.0458586663,-0.2676429152,0.1766682565,0.2072681785,0.2516293228,-0.0044127959,0.1994465888,-0.0090489136,0.7935594916,0.2588758171,-0.2274359912,0.2747074664,0.0024317801,-0.1775773615,-0.0838076547,-0.157329604,-0.063519977,-0.323271215,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1766","title":"Issues when run two programs compute the same metrics","comments":"Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace \"default_experiment\" with the experiment id that you provide in the arrow filename. \r\n\r\nAlso when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate.","body":"I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read\/and\/write to the same location (.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow) where it caches the batches:\r\n\r\n```\r\nFile \"train_matching_min.py\", line 160, in <module>ch_9_label\r\n    avg_loss = valid(epoch, args.batch, args.validation, args.with_label)\r\n  File \"train_matching_min.py\", line 93, in valid\r\n    bleu += eval.compute()\r\n  File \"\/u\/tlhoang\/projects\/seal\/match\/models\/eval.py\", line 23, in compute\r\n    return self.metric.compute()['score']\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/metric.py\", line 387, in compute\r\n    self._finalize()\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/metric.py\", line 355, in _finalize\r\n    self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 231, in read_files\r\n    pa_table = self._read_files(files)\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 170, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict)\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 299, in _get_dataset_from_filename\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 481, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396\r\n``` ","comment_length":69,"text":"Issues when run two programs compute the same metrics \n I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read\/and\/write to the same location (.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow) where it caches the batches:\r\n\r\n```\r\nFile \"train_matching_min.py\", line 160, in <module>ch_9_label\r\n    avg_loss = valid(epoch, args.batch, args.validation, args.with_label)\r\n  File \"train_matching_min.py\", line 93, in valid\r\n    bleu += eval.compute()\r\n  File \"\/u\/tlhoang\/projects\/seal\/match\/models\/eval.py\", line 23, in compute\r\n    return self.metric.compute()['score']\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/metric.py\", line 387, in compute\r\n    self._finalize()\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/metric.py\", line 355, in _finalize\r\n    self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 231, in read_files\r\n    pa_table = self._read_files(files)\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 170, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict)\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 299, in _get_dataset_from_filename\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 481, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396\r\n```  \n Hi ! To avoid collisions you can specify a `experiment_id` when instantiating your metric using `load_metric`. It will replace \"default_experiment\" with the experiment id that you provide in the arrow filename. \r\n\r\nAlso when two `experiment_id` collide we're supposed to detect it using our locking mechanism. Not sure why it didn't work in your case. Could you share some code that reproduces the issue ? This would help us investigate.","embeddings":[-0.3611746132,-0.1505349725,-0.0431874171,0.3872220218,0.267059505,-0.1068402603,0.0815510601,0.3045549393,-0.1409017295,0.2166149169,-0.3760763705,-0.0134595446,0.0852669552,0.0051951916,-0.1859996617,-0.0460625142,0.0034708965,-0.0094137192,0.0772821903,-0.0010643345,-0.2850643992,0.227998957,0.0163463801,0.2554321587,-0.1016236469,-0.1028563008,-0.013551767,-0.1215876862,-0.0857034922,-0.4742900133,0.435684979,0.0281861611,-0.1604654789,0.7840995193,-0.0001188638,0.099064149,0.2483720183,0.0258432329,-0.0703957081,-0.3716357052,0.0305294432,-0.0560124815,0.1552931517,-0.2752086818,0.0879628584,-0.1430897117,0.0955486447,-0.5427463651,0.3801245987,0.164743185,0.1546675563,0.5314354897,-0.4868223071,-0.1824205071,0.1283859909,-0.0579498075,-0.0503404476,0.5445207357,0.0008056638,0.2044068873,0.0235877763,0.4144544601,-0.1186394989,0.2631291151,0.0570788719,0.0971547663,0.4060327411,-0.015863508,0.1656940132,-0.1240436584,0.257058531,-0.3838199377,-0.2255284339,0.1382777393,-0.198722437,-0.5372884274,0.1851033419,0.1369577646,-0.1783016771,-0.1763565391,-0.2779467702,0.0720578209,-0.2121041268,-0.1963711381,0.0815001726,-0.2687269449,-0.1574116647,0.164332062,0.1694015861,0.3121425509,0.1240120903,0.0018443464,-0.1553128809,-0.008267967,-0.4027771652,0.054256916,-0.3037120998,-0.3437744081,-0.0953906998,0.2882916033,0.378970623,-0.0451941267,0.2840242088,0.2224460244,0.0309231561,0.7004981041,0.1498498172,0.2902023792,0.2009002417,0.1810625941,-0.3100050688,-0.1913619936,0.1990121007,-0.5310234427,0.2414128631,0.2200143933,-0.0132561866,-0.3235704899,-0.4480689466,0.0615485795,0.1938808113,-0.0467135496,0.1330566704,0.3195029795,0.0339151844,0.0123235732,0.1278748959,0.4262725711,-0.2209419608,0.2100048512,-0.2910306752,0.1002703831,-0.4435735643,0.3646081686,0.0474664383,-0.0839140639,0.2243039757,-0.0320464298,0.403193891,-0.3089308441,0.3652119339,-0.4421754479,-0.0029980617,0.0954085141,0.0782008916,0.225120455,0.2995713949,-0.098140642,-0.081104748,-0.0738897845,-0.187210083,-0.1491118819,0.5365419388,0.1563584208,-0.013178803,0.4289540946,-0.3462406993,-0.0732095689,0.3937607408,-0.2173101753,0.0699690506,0.0065712999,-0.4017104805,-0.3470825553,0.1470744908,0.3551437259,-0.2117386758,-0.0738259852,0.2929493189,0.129828155,-0.0102830911,0.4685421288,-0.2201554477,0.1626340151,-0.2866150141,-0.024593858,0.5620930791,-0.8298754692,-0.3216198087,0.1176252291,-0.163472876,-0.3332692385,0.0825219378,0.0429988578,0.3266257942,-0.0876047909,0.0890730992,-0.2243258208,0.1395228654,-0.1509531438,-0.3216227889,-0.3151447773,-0.0638329834,-0.2678596377,0.2974461913,-0.047140453,0.2434967458,-0.2063741088,-0.0600684881,-0.1215572208,0.0200320091,0.2438928783,0.1884081215,-0.028605096,0.099135004,-0.2850906551,0.2925028503,0.1448740959,-0.5383432508,-0.2961361706,0.1691065133,-0.2005111128,-0.1927509755,-0.0857767761,-0.0706390068,-0.1958038062,0.0659025609,0.136058867,0.2209204584,0.278021425,-0.0576278158,0.3118051291,-0.0584468171,0.0707678422,-0.0973505303,0.0738313496,-0.1232286021,-0.2663716674,-0.0980604962,0.0787088349,0.3763604462,-0.0226036236,-0.1681235582,0.3127592504,-0.1168536097,0.2160870582,0.1792309731,0.1188419238,0.1721316278,-0.2322574258,0.0048484607,-0.0386074148,0.1945935041,-0.0987797379,0.4015487432,0.2953327596,-0.0228908621,0.1296619624,0.2755026519,0.0170342363,-0.1007524133,0.0285557341,0.1727861464,-0.190131247,0.3023916483,-0.0971518382,0.0521629862,-0.1124897897,0.0968029946,-0.1043636352,0.1355577111,0.1627224982,-0.0012208633,-0.1695545763,-0.0369496532,0.073288016,-0.2711120844,0.2888995409,0.7128472328,0.0229041651,0.1526313871,0.1330121607,0.0188372079,-0.1760327816,0.1453155577,0.209442988,-0.1075597852,0.0993787572,0.1662219763,-0.0816874504,-0.0364570096,0.0903407335,0.0381227024,-0.0213953033,-0.1647393852,0.3923781514,-0.0219715536,0.0753559023,-0.5359921455,0.0141770039,-0.0288164504,-0.1689181924,-0.1003929675,0.3601456285,0.0499683656,0.0707454607,-0.2301941812,0.5003135204,-0.275437355,0.2575498521,0.0213136356,-0.1551043391,-0.3020187914,-0.0712804124,0.1685049534,0.0605503395,0.3412746191,-0.1754945517,-0.0699492022,-0.2088632137,-0.1869240254,-0.0449571498,0.2885992825,0.4888947904,-0.0525448881,-0.1393486857,-0.3070409596,-0.3469005823,0.3138327301,-0.4048361182,-0.2079191208,-0.1324315965,0.0150443558,-0.2289836705,-0.1872957051,-0.1068752706,-0.0245066341,-0.2213677019,0.208386302,-0.2001251131,0.0360720046,0.0712476075,-0.1740555614,0.1142761707,0.0748567805,0.1112008393,-0.2778829634,-0.1941749454,0.0222590528,-0.4008953869,-0.1950280666,-0.0420212522,0.1363772899,0.4599738121,0.2368850708,-0.4259492457,-0.4368488193,-0.1682187617,-0.1173759624,0.102015242,-0.0418617651,0.0551192351,-0.0682302192,-0.1335440278,-0.1503509581,-0.1933322251,0.502068758,-0.2520491481,0.06857346,-0.1603963971,-0.1222267151,0.0443004556,0.8054549098,0.4146428406,0.0838827565,0.3142866492,0.2035724372,0.3654498756,0.0039692703,-0.3886853456,0.1884925067,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1766","title":"Issues when run two programs compute the same metrics","comments":"Thank you for your response. I fixed the issue by set \"keep_in_memory=True\" when load_metric. \r\nI cannot share the entire source code but below is the wrapper I wrote:\r\n\r\n```python\r\nclass Evaluation:\r\n    def __init__(self, metric='sacrebleu'):\r\n        # self.metric = load_metric(metric, keep_in_memory=True)\r\n        self.metric = load_metric(metric)\r\n\r\n    def add(self, predictions, references):\r\n        self.metric.add_batch(predictions=predictions, references=references)\r\n\r\n    def compute(self):\r\n        return self.metric.compute()['score']\r\n```\r\n\r\nThen call the given wrapper as follows:\r\n\r\n```python\r\neval = Evaluation(metric='sacrebleu')\r\nfor query, candidates,  labels in tqdm(dataset):\r\n    predictions = net.generate(query)\r\n    references = [[s] for s in labels]\r\n    eval.add(predictions, references)\r\n    if n % 100 == 0:\r\n        bleu += eval.compute()\r\n        eval = Evaluation(metric='sacrebleu')","body":"I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read\/and\/write to the same location (.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow) where it caches the batches:\r\n\r\n```\r\nFile \"train_matching_min.py\", line 160, in <module>ch_9_label\r\n    avg_loss = valid(epoch, args.batch, args.validation, args.with_label)\r\n  File \"train_matching_min.py\", line 93, in valid\r\n    bleu += eval.compute()\r\n  File \"\/u\/tlhoang\/projects\/seal\/match\/models\/eval.py\", line 23, in compute\r\n    return self.metric.compute()['score']\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/metric.py\", line 387, in compute\r\n    self._finalize()\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/metric.py\", line 355, in _finalize\r\n    self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 231, in read_files\r\n    pa_table = self._read_files(files)\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 170, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict)\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 299, in _get_dataset_from_filename\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 481, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396\r\n``` ","comment_length":94,"text":"Issues when run two programs compute the same metrics \n I got the following error when running two different programs that both compute sacreblue metrics. It seems that both read\/and\/write to the same location (.cache\/huggingface\/metrics\/sacrebleu\/default\/default_experiment-1-0.arrow) where it caches the batches:\r\n\r\n```\r\nFile \"train_matching_min.py\", line 160, in <module>ch_9_label\r\n    avg_loss = valid(epoch, args.batch, args.validation, args.with_label)\r\n  File \"train_matching_min.py\", line 93, in valid\r\n    bleu += eval.compute()\r\n  File \"\/u\/tlhoang\/projects\/seal\/match\/models\/eval.py\", line 23, in compute\r\n    return self.metric.compute()['score']\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/metric.py\", line 387, in compute\r\n    self._finalize()\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/metric.py\", line 355, in _finalize\r\n    self.data = Dataset(**reader.read_files([{\"filename\": f} for f in file_paths]))\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 231, in read_files\r\n    pa_table = self._read_files(files)\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 170, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict)\r\n  File \"\/dccstor\/know\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_reader.py\", line 299, in _get_dataset_from_filename\r\n    pa_table = f.read_all()\r\n  File \"pyarrow\/ipc.pxi\", line 481, in pyarrow.lib.RecordBatchReader.read_all\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Expected to read 1819307375 metadata bytes, but only read 454396\r\n```  \n Thank you for your response. I fixed the issue by set \"keep_in_memory=True\" when load_metric. \r\nI cannot share the entire source code but below is the wrapper I wrote:\r\n\r\n```python\r\nclass Evaluation:\r\n    def __init__(self, metric='sacrebleu'):\r\n        # self.metric = load_metric(metric, keep_in_memory=True)\r\n        self.metric = load_metric(metric)\r\n\r\n    def add(self, predictions, references):\r\n        self.metric.add_batch(predictions=predictions, references=references)\r\n\r\n    def compute(self):\r\n        return self.metric.compute()['score']\r\n```\r\n\r\nThen call the given wrapper as follows:\r\n\r\n```python\r\neval = Evaluation(metric='sacrebleu')\r\nfor query, candidates,  labels in tqdm(dataset):\r\n    predictions = net.generate(query)\r\n    references = [[s] for s in labels]\r\n    eval.add(predictions, references)\r\n    if n % 100 == 0:\r\n        bleu += eval.compute()\r\n        eval = Evaluation(metric='sacrebleu')","embeddings":[-0.3611746132,-0.1505349725,-0.0431874171,0.3872220218,0.267059505,-0.1068402603,0.0815510601,0.3045549393,-0.1409017295,0.2166149169,-0.3760763705,-0.0134595446,0.0852669552,0.0051951916,-0.1859996617,-0.0460625142,0.0034708965,-0.0094137192,0.0772821903,-0.0010643345,-0.2850643992,0.227998957,0.0163463801,0.2554321587,-0.1016236469,-0.1028563008,-0.013551767,-0.1215876862,-0.0857034922,-0.4742900133,0.435684979,0.0281861611,-0.1604654789,0.7840995193,-0.0001188638,0.099064149,0.2483720183,0.0258432329,-0.0703957081,-0.3716357052,0.0305294432,-0.0560124815,0.1552931517,-0.2752086818,0.0879628584,-0.1430897117,0.0955486447,-0.5427463651,0.3801245987,0.164743185,0.1546675563,0.5314354897,-0.4868223071,-0.1824205071,0.1283859909,-0.0579498075,-0.0503404476,0.5445207357,0.0008056638,0.2044068873,0.0235877763,0.4144544601,-0.1186394989,0.2631291151,0.0570788719,0.0971547663,0.4060327411,-0.015863508,0.1656940132,-0.1240436584,0.257058531,-0.3838199377,-0.2255284339,0.1382777393,-0.198722437,-0.5372884274,0.1851033419,0.1369577646,-0.1783016771,-0.1763565391,-0.2779467702,0.0720578209,-0.2121041268,-0.1963711381,0.0815001726,-0.2687269449,-0.1574116647,0.164332062,0.1694015861,0.3121425509,0.1240120903,0.0018443464,-0.1553128809,-0.008267967,-0.4027771652,0.054256916,-0.3037120998,-0.3437744081,-0.0953906998,0.2882916033,0.378970623,-0.0451941267,0.2840242088,0.2224460244,0.0309231561,0.7004981041,0.1498498172,0.2902023792,0.2009002417,0.1810625941,-0.3100050688,-0.1913619936,0.1990121007,-0.5310234427,0.2414128631,0.2200143933,-0.0132561866,-0.3235704899,-0.4480689466,0.0615485795,0.1938808113,-0.0467135496,0.1330566704,0.3195029795,0.0339151844,0.0123235732,0.1278748959,0.4262725711,-0.2209419608,0.2100048512,-0.2910306752,0.1002703831,-0.4435735643,0.3646081686,0.0474664383,-0.0839140639,0.2243039757,-0.0320464298,0.403193891,-0.3089308441,0.3652119339,-0.4421754479,-0.0029980617,0.0954085141,0.0782008916,0.225120455,0.2995713949,-0.098140642,-0.081104748,-0.0738897845,-0.187210083,-0.1491118819,0.5365419388,0.1563584208,-0.013178803,0.4289540946,-0.3462406993,-0.0732095689,0.3937607408,-0.2173101753,0.0699690506,0.0065712999,-0.4017104805,-0.3470825553,0.1470744908,0.3551437259,-0.2117386758,-0.0738259852,0.2929493189,0.129828155,-0.0102830911,0.4685421288,-0.2201554477,0.1626340151,-0.2866150141,-0.024593858,0.5620930791,-0.8298754692,-0.3216198087,0.1176252291,-0.163472876,-0.3332692385,0.0825219378,0.0429988578,0.3266257942,-0.0876047909,0.0890730992,-0.2243258208,0.1395228654,-0.1509531438,-0.3216227889,-0.3151447773,-0.0638329834,-0.2678596377,0.2974461913,-0.047140453,0.2434967458,-0.2063741088,-0.0600684881,-0.1215572208,0.0200320091,0.2438928783,0.1884081215,-0.028605096,0.099135004,-0.2850906551,0.2925028503,0.1448740959,-0.5383432508,-0.2961361706,0.1691065133,-0.2005111128,-0.1927509755,-0.0857767761,-0.0706390068,-0.1958038062,0.0659025609,0.136058867,0.2209204584,0.278021425,-0.0576278158,0.3118051291,-0.0584468171,0.0707678422,-0.0973505303,0.0738313496,-0.1232286021,-0.2663716674,-0.0980604962,0.0787088349,0.3763604462,-0.0226036236,-0.1681235582,0.3127592504,-0.1168536097,0.2160870582,0.1792309731,0.1188419238,0.1721316278,-0.2322574258,0.0048484607,-0.0386074148,0.1945935041,-0.0987797379,0.4015487432,0.2953327596,-0.0228908621,0.1296619624,0.2755026519,0.0170342363,-0.1007524133,0.0285557341,0.1727861464,-0.190131247,0.3023916483,-0.0971518382,0.0521629862,-0.1124897897,0.0968029946,-0.1043636352,0.1355577111,0.1627224982,-0.0012208633,-0.1695545763,-0.0369496532,0.073288016,-0.2711120844,0.2888995409,0.7128472328,0.0229041651,0.1526313871,0.1330121607,0.0188372079,-0.1760327816,0.1453155577,0.209442988,-0.1075597852,0.0993787572,0.1662219763,-0.0816874504,-0.0364570096,0.0903407335,0.0381227024,-0.0213953033,-0.1647393852,0.3923781514,-0.0219715536,0.0753559023,-0.5359921455,0.0141770039,-0.0288164504,-0.1689181924,-0.1003929675,0.3601456285,0.0499683656,0.0707454607,-0.2301941812,0.5003135204,-0.275437355,0.2575498521,0.0213136356,-0.1551043391,-0.3020187914,-0.0712804124,0.1685049534,0.0605503395,0.3412746191,-0.1754945517,-0.0699492022,-0.2088632137,-0.1869240254,-0.0449571498,0.2885992825,0.4888947904,-0.0525448881,-0.1393486857,-0.3070409596,-0.3469005823,0.3138327301,-0.4048361182,-0.2079191208,-0.1324315965,0.0150443558,-0.2289836705,-0.1872957051,-0.1068752706,-0.0245066341,-0.2213677019,0.208386302,-0.2001251131,0.0360720046,0.0712476075,-0.1740555614,0.1142761707,0.0748567805,0.1112008393,-0.2778829634,-0.1941749454,0.0222590528,-0.4008953869,-0.1950280666,-0.0420212522,0.1363772899,0.4599738121,0.2368850708,-0.4259492457,-0.4368488193,-0.1682187617,-0.1173759624,0.102015242,-0.0418617651,0.0551192351,-0.0682302192,-0.1335440278,-0.1503509581,-0.1933322251,0.502068758,-0.2520491481,0.06857346,-0.1603963971,-0.1222267151,0.0443004556,0.8054549098,0.4146428406,0.0838827565,0.3142866492,0.2035724372,0.3654498756,0.0039692703,-0.388685345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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1765","title":"Error iterating over Dataset with DataLoader","comments":"Instead of:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n```\r\nIt should be:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n```\r\n\r\n`batch_sampler` accepts a Sampler object or an Iterable, so you get an error.","body":"I have a Dataset that I've mapped a tokenizer over:\r\n\r\n```\r\nencoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])\r\nencoded_dataset[:1]\r\n```\r\n```\r\n{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\r\n 'input_ids': tensor([[  101,   178,  1198,  1400,  1714, 22233, 21365,  4515,  8618,  1113,\r\n            102]]),\r\n 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\r\n```\r\n\r\nWhen I try to iterate as in the docs, I get errors:\r\n\r\n```\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\nnext(iter(dataloader))\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-45-05180ba8aa35> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataloader.py in __init__(self, loader)\r\n    411         self._timeout = loader.timeout\r\n    412         self._collate_fn = loader.collate_fn\r\n--> 413         self._sampler_iter = iter(self._index_sampler)\r\n    414         self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()\r\n    415         self._persistent_workers = loader.persistent_workers\r\n\r\nTypeError: 'int' object is not iterable\r\n\r\n\r\n```","comment_length":30,"text":"Error iterating over Dataset with DataLoader \n I have a Dataset that I've mapped a tokenizer over:\r\n\r\n```\r\nencoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])\r\nencoded_dataset[:1]\r\n```\r\n```\r\n{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\r\n 'input_ids': tensor([[  101,   178,  1198,  1400,  1714, 22233, 21365,  4515,  8618,  1113,\r\n            102]]),\r\n 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\r\n```\r\n\r\nWhen I try to iterate as in the docs, I get errors:\r\n\r\n```\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\nnext(iter(dataloader))\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-45-05180ba8aa35> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataloader.py in __init__(self, loader)\r\n    411         self._timeout = loader.timeout\r\n    412         self._collate_fn = loader.collate_fn\r\n--> 413         self._sampler_iter = iter(self._index_sampler)\r\n    414         self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()\r\n    415         self._persistent_workers = loader.persistent_workers\r\n\r\nTypeError: 'int' object is not iterable\r\n\r\n\r\n``` \n Instead of:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n```\r\nIt should be:\r\n```python\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n```\r\n\r\n`batch_sampler` accepts a Sampler object or an Iterable, so you get an error.","embeddings":[-0.2379294932,0.0554564521,-0.0456708856,0.2057333887,0.1531800032,0.00014107,0.7921316028,0.3068522215,0.0211306885,0.1527129412,0.0467958041,0.2304260731,-0.2809546292,-0.2416442186,-0.0902263075,-0.0604503341,-0.0764540955,0.0790703148,-0.1224215627,0.0328939222,-0.1435404718,-0.086729899,-0.1925974041,0.0319360159,-0.2509384155,-0.1322708279,-0.1035913602,-0.0947072953,-0.3341249228,-0.5228338242,0.2897110581,0.1179489493,0.4238479137,0.6812455058,-0.0001170856,0.0532652996,0.1204966977,-0.1216139197,-0.2641813755,-0.232062906,0.1392339319,-0.0516221598,0.0336749442,-0.3547690809,0.2605599761,-0.331272155,-0.017206395,-0.2235834897,0.1987376958,0.2407110631,0.1273105145,0.4878684282,-0.0130664473,0.0686062798,0.04524187,0.2505432963,-0.019511655,0.1971431524,0.1916248351,0.0102274111,-0.3597660363,0.1168679073,-0.1690709442,0.0475237295,0.135968551,-0.1127389744,-0.217760846,-0.1320601255,-0.0326831453,-0.0796288997,0.5732844472,-0.2473982126,-0.3338905275,-0.1655160934,-0.2215303928,-0.2673027515,0.319212079,-0.1650728881,-0.1586042792,-0.2139902264,0.1157573313,0.2405707538,-0.2040264159,0.1367142946,-0.25916031,0.2086893469,-0.0498498268,0.304499954,0.2167702019,-0.2556965649,0.3407104909,0.1005515233,0.0440492071,0.2752284706,-0.5098053217,-0.2515716553,0.0933656469,-0.4813280404,-0.0889511406,0.1817471981,0.2512769699,0.0659232885,-0.0267228652,0.2420649678,0.4608971775,0.1867473274,0.1686100066,0.3430075943,0.0560028069,-0.1008399948,0.1115823984,-0.0628378764,-0.3309622705,-0.304035604,0.2297867239,0.1007610634,0.2355149388,0.0638043508,-0.24075225,-0.0010412553,-0.2854396999,-0.1408834308,0.1115429774,0.1466972083,0.2154821754,0.0784652904,-0.0211962853,0.0494476669,-0.0294592027,-0.1010558978,-0.0631030872,0.1225908026,-0.2360044122,0.0062334132,0.2692131698,-0.0449522883,-0.0005978197,0.1379322708,-0.0163492449,0.1064936593,0.1838307977,-0.1677454114,0.2751210034,-0.066830501,0.0526376925,0.3259077668,0.2427886128,0.0527041815,-0.1517042071,0.0064420728,-0.4252358377,-0.0854079202,-0.0711294115,0.1343848705,-0.0657273754,-0.0596553646,-0.4029016495,-0.1930606961,0.3476107717,-0.1592839658,0.0990208313,-0.2618944049,0.0679650232,-0.0949438438,0.0716987848,0.3785112798,-0.4588417411,-0.1389319897,0.1558684111,0.100578852,0.268353045,0.2800276279,-0.2775180936,0.5750991702,-0.4041410387,0.3146153092,0.3382745087,-0.4575732052,-0.3010725677,0.30232355,-0.1728587449,0.1828440428,0.1311056763,0.0220962148,0.4072544873,-0.2117989957,0.1262145638,0.1312219352,-0.1754143536,-0.084498167,-0.0188895371,0.40735659,0.5204417706,0.0579210706,0.1899082512,0.0911744684,-0.2563124001,0.1866555065,0.0407245047,-0.1624023467,0.0752803087,0.1461818963,0.08729247,0.1154016927,0.2047132254,-0.1541337818,-0.2414141595,0.0174530409,0.08473593,0.1103395596,-0.1569616348,0.1011083499,0.0156297926,0.1833895743,-0.4093043208,-0.1476449072,0.1057991534,0.3391777277,-0.1085056514,-0.2262801528,-0.278257817,0.0101970183,-0.1502237916,0.1229054332,-0.3177072704,0.0756461397,0.0731091648,-0.1621202379,-0.1109917238,0.0012578749,0.2185538709,-0.2305950075,-0.0198753402,0.152743876,0.0733504519,0.0837663114,-0.1409202218,0.0840372369,0.2027758658,-0.0805915147,-0.0350414291,0.0263240449,0.0633541942,-0.2463466823,0.2860279679,0.3557419777,-0.0415121466,0.5438959002,-0.1846143901,0.1865570545,0.0854834467,0.1429632455,-0.2297751456,0.1214375868,0.2915955782,0.165278703,0.2727060616,-0.0742381364,-0.1061879173,-0.0162541084,0.135785684,0.034656506,-0.1672892421,0.0645060986,-0.2023294568,0.078733027,0.1431891769,-0.3583348989,0.2936249375,-0.0419645049,0.1355626434,-0.2297315598,0.1093874052,0.0548776314,-0.0068245805,0.1373674572,0.0501099378,0.1365749091,-0.0179888159,0.032990437,-0.1993452013,-0.2619042993,-0.0321639255,0.4030472338,-0.2017506361,0.420080632,-0.2251237631,-0.0806137398,-0.1357363909,-0.4637432098,-0.1244161502,-0.488583684,-0.2934887409,0.4377196431,-0.0523557365,0.2884217203,0.1593024582,0.0659951046,0.6007106304,-0.1201817542,-0.1299342215,-0.1651609242,-0.1434867382,0.002023285,0.3916653693,-0.3905928731,0.3192386329,-0.14857319,-0.0364399292,-0.4931823909,-0.1713028252,0.23434636,-0.2515105605,-0.1061097458,0.250123322,0.160312131,-0.1276741028,-0.3462354839,0.2360218316,-0.0971406028,-0.0598608814,0.3538429141,-0.0178579725,0.164885059,0.1942387223,-0.1618052274,-0.1143744439,-0.2210423201,0.1488950402,-0.024225384,0.3098029792,0.1279512346,0.0508767813,0.0688194558,0.0538865477,-0.0885351226,-0.0529175512,-0.4608557522,0.4211562574,-0.3188976645,-0.2935510576,-0.0610978752,-0.005195491,0.3583402038,0.374879539,-0.2954478264,-0.0019789448,0.1968028694,0.0960602537,-0.1780664027,0.0133007132,0.1617016345,0.1165711582,-0.0526863299,-0.0528675951,0.1458159238,0.1710808575,-0.1876334697,0.2580990195,0.159302935,0.4765362144,0.2614045143,0.6606869102,0.0389066897,-0.3858367205,0.3321016431,-0.1400644928,-0.0007825629,0.086658068,-0.6534711719,0.1830661446,-0.2897427082,-0.02632441,-0.1086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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1765","title":"Error iterating over Dataset with DataLoader","comments":"@mariosasko I thought that would fix it, but now I'm getting a different error:\r\n\r\n```\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  \/pytorch\/torch\/csrc\/utils\/tensor_numpy.cpp:141.)\r\n  return torch.tensor(x, **format_kwargs)\r\n---------------------------------------------------------------------------\r\nRuntimeError                              Traceback (most recent call last)\r\n<ipython-input-20-3af1d82bf93a> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n5 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/_utils\/collate.py in default_collate(batch)\r\n     53             storage = elem.storage()._new_shared(numel)\r\n     54             out = elem.new(storage)\r\n---> 55         return torch.stack(batch, 0, out=out)\r\n     56     elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\r\n     57             and elem_type.__name__ != 'string_':\r\n\r\nRuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1\r\n```\r\n\r\nAny thoughts what this means?I Do I need padding?","body":"I have a Dataset that I've mapped a tokenizer over:\r\n\r\n```\r\nencoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])\r\nencoded_dataset[:1]\r\n```\r\n```\r\n{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\r\n 'input_ids': tensor([[  101,   178,  1198,  1400,  1714, 22233, 21365,  4515,  8618,  1113,\r\n            102]]),\r\n 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\r\n```\r\n\r\nWhen I try to iterate as in the docs, I get errors:\r\n\r\n```\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\nnext(iter(dataloader))\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-45-05180ba8aa35> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataloader.py in __init__(self, loader)\r\n    411         self._timeout = loader.timeout\r\n    412         self._collate_fn = loader.collate_fn\r\n--> 413         self._sampler_iter = iter(self._index_sampler)\r\n    414         self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()\r\n    415         self._persistent_workers = loader.persistent_workers\r\n\r\nTypeError: 'int' object is not iterable\r\n\r\n\r\n```","comment_length":169,"text":"Error iterating over Dataset with DataLoader \n I have a Dataset that I've mapped a tokenizer over:\r\n\r\n```\r\nencoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])\r\nencoded_dataset[:1]\r\n```\r\n```\r\n{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\r\n 'input_ids': tensor([[  101,   178,  1198,  1400,  1714, 22233, 21365,  4515,  8618,  1113,\r\n            102]]),\r\n 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\r\n```\r\n\r\nWhen I try to iterate as in the docs, I get errors:\r\n\r\n```\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\nnext(iter(dataloader))\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-45-05180ba8aa35> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataloader.py in __init__(self, loader)\r\n    411         self._timeout = loader.timeout\r\n    412         self._collate_fn = loader.collate_fn\r\n--> 413         self._sampler_iter = iter(self._index_sampler)\r\n    414         self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()\r\n    415         self._persistent_workers = loader.persistent_workers\r\n\r\nTypeError: 'int' object is not iterable\r\n\r\n\r\n``` \n @mariosasko I thought that would fix it, but now I'm getting a different error:\r\n\r\n```\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  \/pytorch\/torch\/csrc\/utils\/tensor_numpy.cpp:141.)\r\n  return torch.tensor(x, **format_kwargs)\r\n---------------------------------------------------------------------------\r\nRuntimeError                              Traceback (most recent call last)\r\n<ipython-input-20-3af1d82bf93a> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n5 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/_utils\/collate.py in default_collate(batch)\r\n     53             storage = elem.storage()._new_shared(numel)\r\n     54             out = elem.new(storage)\r\n---> 55         return torch.stack(batch, 0, out=out)\r\n     56     elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \\\r\n     57             and elem_type.__name__ != 'string_':\r\n\r\nRuntimeError: stack expects each tensor to be equal size, but got [7] at entry 0 and [10] at entry 1\r\n```\r\n\r\nAny thoughts what this means?I Do I need padding?","embeddings":[-0.2379294932,0.0554564521,-0.0456708856,0.2057333887,0.1531800032,0.00014107,0.7921316028,0.3068522215,0.0211306885,0.1527129412,0.0467958041,0.2304260731,-0.2809546292,-0.2416442186,-0.0902263075,-0.0604503341,-0.0764540955,0.0790703148,-0.1224215627,0.0328939222,-0.1435404718,-0.086729899,-0.1925974041,0.0319360159,-0.2509384155,-0.1322708279,-0.1035913602,-0.0947072953,-0.3341249228,-0.5228338242,0.2897110581,0.1179489493,0.4238479137,0.6812455058,-0.0001170856,0.0532652996,0.1204966977,-0.1216139197,-0.2641813755,-0.232062906,0.1392339319,-0.0516221598,0.0336749442,-0.3547690809,0.2605599761,-0.331272155,-0.017206395,-0.2235834897,0.1987376958,0.2407110631,0.1273105145,0.4878684282,-0.0130664473,0.0686062798,0.04524187,0.2505432963,-0.019511655,0.1971431524,0.1916248351,0.0102274111,-0.3597660363,0.1168679073,-0.1690709442,0.0475237295,0.135968551,-0.1127389744,-0.217760846,-0.1320601255,-0.0326831453,-0.0796288997,0.5732844472,-0.2473982126,-0.3338905275,-0.1655160934,-0.2215303928,-0.2673027515,0.319212079,-0.1650728881,-0.1586042792,-0.2139902264,0.1157573313,0.2405707538,-0.2040264159,0.1367142946,-0.25916031,0.2086893469,-0.0498498268,0.304499954,0.2167702019,-0.2556965649,0.3407104909,0.1005515233,0.0440492071,0.2752284706,-0.5098053217,-0.2515716553,0.0933656469,-0.4813280404,-0.0889511406,0.1817471981,0.2512769699,0.0659232885,-0.0267228652,0.2420649678,0.4608971775,0.1867473274,0.1686100066,0.3430075943,0.0560028069,-0.1008399948,0.1115823984,-0.0628378764,-0.3309622705,-0.304035604,0.2297867239,0.1007610634,0.2355149388,0.0638043508,-0.24075225,-0.0010412553,-0.2854396999,-0.1408834308,0.1115429774,0.1466972083,0.2154821754,0.0784652904,-0.0211962853,0.0494476669,-0.0294592027,-0.1010558978,-0.0631030872,0.1225908026,-0.2360044122,0.0062334132,0.2692131698,-0.0449522883,-0.0005978197,0.1379322708,-0.0163492449,0.1064936593,0.1838307977,-0.1677454114,0.2751210034,-0.066830501,0.0526376925,0.3259077668,0.2427886128,0.0527041815,-0.1517042071,0.0064420728,-0.4252358377,-0.0854079202,-0.0711294115,0.1343848705,-0.0657273754,-0.0596553646,-0.4029016495,-0.1930606961,0.3476107717,-0.1592839658,0.0990208313,-0.2618944049,0.0679650232,-0.0949438438,0.0716987848,0.3785112798,-0.4588417411,-0.1389319897,0.1558684111,0.100578852,0.268353045,0.2800276279,-0.2775180936,0.5750991702,-0.4041410387,0.3146153092,0.3382745087,-0.4575732052,-0.3010725677,0.30232355,-0.1728587449,0.1828440428,0.1311056763,0.0220962148,0.4072544873,-0.2117989957,0.1262145638,0.1312219352,-0.1754143536,-0.084498167,-0.0188895371,0.40735659,0.5204417706,0.0579210706,0.1899082512,0.0911744684,-0.2563124001,0.1866555065,0.0407245047,-0.1624023467,0.0752803087,0.1461818963,0.08729247,0.1154016927,0.2047132254,-0.1541337818,-0.2414141595,0.0174530409,0.08473593,0.1103395596,-0.1569616348,0.1011083499,0.0156297926,0.1833895743,-0.4093043208,-0.1476449072,0.1057991534,0.3391777277,-0.1085056514,-0.2262801528,-0.278257817,0.0101970183,-0.1502237916,0.1229054332,-0.3177072704,0.0756461397,0.0731091648,-0.1621202379,-0.1109917238,0.0012578749,0.2185538709,-0.2305950075,-0.0198753402,0.152743876,0.0733504519,0.0837663114,-0.1409202218,0.0840372369,0.2027758658,-0.0805915147,-0.0350414291,0.0263240449,0.0633541942,-0.2463466823,0.2860279679,0.3557419777,-0.0415121466,0.5438959002,-0.1846143901,0.1865570545,0.0854834467,0.1429632455,-0.2297751456,0.1214375868,0.2915955782,0.165278703,0.2727060616,-0.0742381364,-0.1061879173,-0.0162541084,0.135785684,0.034656506,-0.1672892421,0.0645060986,-0.2023294568,0.078733027,0.1431891769,-0.3583348989,0.2936249375,-0.0419645049,0.1355626434,-0.2297315598,0.1093874052,0.0548776314,-0.0068245805,0.1373674572,0.0501099378,0.1365749091,-0.0179888159,0.032990437,-0.1993452013,-0.2619042993,-0.0321639255,0.4030472338,-0.2017506361,0.420080632,-0.2251237631,-0.0806137398,-0.1357363909,-0.4637432098,-0.1244161502,-0.488583684,-0.2934887409,0.4377196431,-0.0523557365,0.2884217203,0.1593024582,0.0659951046,0.6007106304,-0.1201817542,-0.1299342215,-0.1651609242,-0.1434867382,0.002023285,0.3916653693,-0.3905928731,0.3192386329,-0.14857319,-0.0364399292,-0.4931823909,-0.1713028252,0.23434636,-0.2515105605,-0.1061097458,0.250123322,0.160312131,-0.1276741028,-0.3462354839,0.2360218316,-0.0971406028,-0.0598608814,0.3538429141,-0.0178579725,0.164885059,0.1942387223,-0.1618052274,-0.1143744439,-0.2210423201,0.1488950402,-0.024225384,0.3098029792,0.1279512346,0.0508767813,0.0688194558,0.0538865477,-0.0885351226,-0.0529175512,-0.4608557522,0.4211562574,-0.3188976645,-0.2935510576,-0.0610978752,-0.005195491,0.3583402038,0.374879539,-0.2954478264,-0.0019789448,0.1968028694,0.0960602537,-0.1780664027,0.0133007132,0.1617016345,0.1165711582,-0.0526863299,-0.0528675951,0.1458159238,0.1710808575,-0.1876334697,0.2580990195,0.159302935,0.4765362144,0.2614045143,0.6606869102,0.0389066897,-0.3858367205,0.3321016431,-0.1400644928,-0.0007825629,0.086658068,-0.6534711719,0.1830661446,-0.2897427082,-0.02632441,-0.10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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1765","title":"Error iterating over Dataset with DataLoader","comments":"Yes, padding is an answer. \r\n\r\nThis can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding. ","body":"I have a Dataset that I've mapped a tokenizer over:\r\n\r\n```\r\nencoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])\r\nencoded_dataset[:1]\r\n```\r\n```\r\n{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\r\n 'input_ids': tensor([[  101,   178,  1198,  1400,  1714, 22233, 21365,  4515,  8618,  1113,\r\n            102]]),\r\n 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\r\n```\r\n\r\nWhen I try to iterate as in the docs, I get errors:\r\n\r\n```\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\nnext(iter(dataloader))\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-45-05180ba8aa35> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataloader.py in __init__(self, loader)\r\n    411         self._timeout = loader.timeout\r\n    412         self._collate_fn = loader.collate_fn\r\n--> 413         self._sampler_iter = iter(self._index_sampler)\r\n    414         self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()\r\n    415         self._persistent_workers = loader.persistent_workers\r\n\r\nTypeError: 'int' object is not iterable\r\n\r\n\r\n```","comment_length":23,"text":"Error iterating over Dataset with DataLoader \n I have a Dataset that I've mapped a tokenizer over:\r\n\r\n```\r\nencoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])\r\nencoded_dataset[:1]\r\n```\r\n```\r\n{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\r\n 'input_ids': tensor([[  101,   178,  1198,  1400,  1714, 22233, 21365,  4515,  8618,  1113,\r\n            102]]),\r\n 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\r\n```\r\n\r\nWhen I try to iterate as in the docs, I get errors:\r\n\r\n```\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\nnext(iter(dataloader))\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-45-05180ba8aa35> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataloader.py in __init__(self, loader)\r\n    411         self._timeout = loader.timeout\r\n    412         self._collate_fn = loader.collate_fn\r\n--> 413         self._sampler_iter = iter(self._index_sampler)\r\n    414         self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()\r\n    415         self._persistent_workers = loader.persistent_workers\r\n\r\nTypeError: 'int' object is not iterable\r\n\r\n\r\n``` \n Yes, padding is an answer. \r\n\r\nThis can be solved easily by passing a callable to the collate_fn arg of DataLoader that adds padding. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1765","title":"Error iterating over Dataset with DataLoader","comments":"dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4)\r\nbatch = next(iter(dataloader))\r\n\r\ngetting \r\nValueError: cannot reshape array of size 8192 into shape (1,512,4)\r\n\r\nI had put padding as 2048 for encoded_dataset\r\nkindly help","body":"I have a Dataset that I've mapped a tokenizer over:\r\n\r\n```\r\nencoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])\r\nencoded_dataset[:1]\r\n```\r\n```\r\n{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\r\n 'input_ids': tensor([[  101,   178,  1198,  1400,  1714, 22233, 21365,  4515,  8618,  1113,\r\n            102]]),\r\n 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\r\n```\r\n\r\nWhen I try to iterate as in the docs, I get errors:\r\n\r\n```\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\nnext(iter(dataloader))\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-45-05180ba8aa35> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataloader.py in __init__(self, loader)\r\n    411         self._timeout = loader.timeout\r\n    412         self._collate_fn = loader.collate_fn\r\n--> 413         self._sampler_iter = iter(self._index_sampler)\r\n    414         self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()\r\n    415         self._persistent_workers = loader.persistent_workers\r\n\r\nTypeError: 'int' object is not iterable\r\n\r\n\r\n```","comment_length":28,"text":"Error iterating over Dataset with DataLoader \n I have a Dataset that I've mapped a tokenizer over:\r\n\r\n```\r\nencoded_dataset.set_format(type='torch',columns=['attention_mask','input_ids','token_type_ids'])\r\nencoded_dataset[:1]\r\n```\r\n```\r\n{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\r\n 'input_ids': tensor([[  101,   178,  1198,  1400,  1714, 22233, 21365,  4515,  8618,  1113,\r\n            102]]),\r\n 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\r\n```\r\n\r\nWhen I try to iterate as in the docs, I get errors:\r\n\r\n```\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\nnext(iter(dataloader))\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-45-05180ba8aa35> in <module>()\r\n      1 dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_sampler=32)\r\n----> 2 next(iter(dataloader))\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataloader.py in __init__(self, loader)\r\n    411         self._timeout = loader.timeout\r\n    412         self._collate_fn = loader.collate_fn\r\n--> 413         self._sampler_iter = iter(self._index_sampler)\r\n    414         self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()\r\n    415         self._persistent_workers = loader.persistent_workers\r\n\r\nTypeError: 'int' object is not iterable\r\n\r\n\r\n``` \n dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4)\r\nbatch = next(iter(dataloader))\r\n\r\ngetting \r\nValueError: cannot reshape array of size 8192 into shape (1,512,4)\r\n\r\nI had put padding as 2048 for encoded_dataset\r\nkindly help","embeddings":[-0.2379294932,0.0554564521,-0.0456708856,0.2057333887,0.1531800032,0.00014107,0.7921316028,0.3068522215,0.0211306885,0.1527129412,0.0467958041,0.2304260731,-0.2809546292,-0.2416442186,-0.0902263075,-0.0604503341,-0.0764540955,0.0790703148,-0.1224215627,0.0328939222,-0.1435404718,-0.086729899,-0.1925974041,0.0319360159,-0.2509384155,-0.1322708279,-0.1035913602,-0.0947072953,-0.3341249228,-0.5228338242,0.2897110581,0.1179489493,0.4238479137,0.6812455058,-0.0001170856,0.0532652996,0.1204966977,-0.1216139197,-0.2641813755,-0.232062906,0.1392339319,-0.0516221598,0.0336749442,-0.3547690809,0.2605599761,-0.331272155,-0.017206395,-0.2235834897,0.1987376958,0.2407110631,0.1273105145,0.4878684282,-0.0130664473,0.0686062798,0.04524187,0.2505432963,-0.019511655,0.1971431524,0.1916248351,0.0102274111,-0.3597660363,0.1168679073,-0.1690709442,0.0475237295,0.135968551,-0.1127389744,-0.217760846,-0.1320601255,-0.0326831453,-0.0796288997,0.5732844472,-0.2473982126,-0.3338905275,-0.1655160934,-0.2215303928,-0.2673027515,0.319212079,-0.1650728881,-0.1586042792,-0.2139902264,0.1157573313,0.2405707538,-0.2040264159,0.1367142946,-0.25916031,0.2086893469,-0.0498498268,0.304499954,0.2167702019,-0.2556965649,0.3407104909,0.1005515233,0.0440492071,0.2752284706,-0.5098053217,-0.2515716553,0.0933656469,-0.4813280404,-0.0889511406,0.1817471981,0.2512769699,0.0659232885,-0.0267228652,0.2420649678,0.4608971775,0.1867473274,0.1686100066,0.3430075943,0.0560028069,-0.1008399948,0.1115823984,-0.0628378764,-0.3309622705,-0.304035604,0.2297867239,0.1007610634,0.2355149388,0.0638043508,-0.24075225,-0.0010412553,-0.2854396999,-0.1408834308,0.1115429774,0.1466972083,0.2154821754,0.0784652904,-0.0211962853,0.0494476669,-0.0294592027,-0.1010558978,-0.0631030872,0.1225908026,-0.2360044122,0.0062334132,0.2692131698,-0.0449522883,-0.0005978197,0.1379322708,-0.0163492449,0.1064936593,0.1838307977,-0.1677454114,0.2751210034,-0.066830501,0.0526376925,0.3259077668,0.2427886128,0.0527041815,-0.1517042071,0.0064420728,-0.4252358377,-0.0854079202,-0.0711294115,0.1343848705,-0.0657273754,-0.0596553646,-0.4029016495,-0.1930606961,0.3476107717,-0.1592839658,0.0990208313,-0.2618944049,0.0679650232,-0.0949438438,0.0716987848,0.3785112798,-0.4588417411,-0.1389319897,0.1558684111,0.100578852,0.268353045,0.2800276279,-0.2775180936,0.5750991702,-0.4041410387,0.3146153092,0.3382745087,-0.4575732052,-0.3010725677,0.30232355,-0.1728587449,0.1828440428,0.1311056763,0.0220962148,0.4072544873,-0.2117989957,0.1262145638,0.1312219352,-0.1754143536,-0.084498167,-0.0188895371,0.40735659,0.5204417706,0.0579210706,0.1899082512,0.0911744684,-0.2563124001,0.1866555065,0.0407245047,-0.1624023467,0.0752803087,0.1461818963,0.08729247,0.1154016927,0.2047132254,-0.1541337818,-0.2414141595,0.0174530409,0.08473593,0.1103395596,-0.1569616348,0.1011083499,0.0156297926,0.1833895743,-0.4093043208,-0.1476449072,0.1057991534,0.3391777277,-0.1085056514,-0.2262801528,-0.278257817,0.0101970183,-0.1502237916,0.1229054332,-0.3177072704,0.0756461397,0.0731091648,-0.1621202379,-0.1109917238,0.0012578749,0.2185538709,-0.2305950075,-0.0198753402,0.152743876,0.0733504519,0.0837663114,-0.1409202218,0.0840372369,0.2027758658,-0.0805915147,-0.0350414291,0.0263240449,0.0633541942,-0.2463466823,0.2860279679,0.3557419777,-0.0415121466,0.5438959002,-0.1846143901,0.1865570545,0.0854834467,0.1429632455,-0.2297751456,0.1214375868,0.2915955782,0.165278703,0.2727060616,-0.0742381364,-0.1061879173,-0.0162541084,0.135785684,0.034656506,-0.1672892421,0.0645060986,-0.2023294568,0.078733027,0.1431891769,-0.3583348989,0.2936249375,-0.0419645049,0.1355626434,-0.2297315598,0.1093874052,0.0548776314,-0.0068245805,0.1373674572,0.0501099378,0.1365749091,-0.0179888159,0.032990437,-0.1993452013,-0.2619042993,-0.0321639255,0.4030472338,-0.2017506361,0.420080632,-0.2251237631,-0.0806137398,-0.1357363909,-0.4637432098,-0.1244161502,-0.488583684,-0.2934887409,0.4377196431,-0.0523557365,0.2884217203,0.1593024582,0.0659951046,0.6007106304,-0.1201817542,-0.1299342215,-0.1651609242,-0.1434867382,0.002023285,0.3916653693,-0.3905928731,0.3192386329,-0.14857319,-0.0364399292,-0.4931823909,-0.1713028252,0.23434636,-0.2515105605,-0.1061097458,0.250123322,0.160312131,-0.1276741028,-0.3462354839,0.2360218316,-0.0971406028,-0.0598608814,0.3538429141,-0.0178579725,0.164885059,0.1942387223,-0.1618052274,-0.1143744439,-0.2210423201,0.1488950402,-0.024225384,0.3098029792,0.1279512346,0.0508767813,0.0688194558,0.0538865477,-0.0885351226,-0.0529175512,-0.4608557522,0.4211562574,-0.3188976645,-0.2935510576,-0.0610978752,-0.005195491,0.3583402038,0.374879539,-0.2954478264,-0.0019789448,0.1968028694,0.0960602537,-0.1780664027,0.0133007132,0.1617016345,0.1165711582,-0.0526863299,-0.0528675951,0.1458159238,0.1710808575,-0.1876334697,0.2580990195,0.159302935,0.4765362144,0.2614045143,0.6606869102,0.0389066897,-0.3858367205,0.3321016431,-0.1400644928,-0.0007825629,0.086658068,-0.6534711719,0.1830661446,-0.2897427082,-0.02632441,-0.108607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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1762","title":"Unable to format dataset to CUDA Tensors","comments":"Hi ! You can get CUDA tensors with\r\n\r\n```python\r\ndataset.set_format(\"torch\", columns=columns, device=\"cuda\")\r\n```\r\n\r\nIndeed `set_format` passes the `**kwargs` to `torch.tensor`","body":"Hi,\r\n\r\nI came across this [link](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.\r\n\r\nI tried this, but Dataset doesn't support assignment:\r\n```\r\n  columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']\r\n\r\n        samples.set_format(type='torch', columns = columns)\r\n        for column in columns:\r\n            samples[column].to(torch.device(self.config.device))\r\n```\r\nThere should be an option to do so, or if there is already a way to do this, please let me know.\r\n\r\nThanks,\r\nGunjan","comment_length":20,"text":"Unable to format dataset to CUDA Tensors \n Hi,\r\n\r\nI came across this [link](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.\r\n\r\nI tried this, but Dataset doesn't support assignment:\r\n```\r\n  columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']\r\n\r\n        samples.set_format(type='torch', columns = columns)\r\n        for column in columns:\r\n            samples[column].to(torch.device(self.config.device))\r\n```\r\nThere should be an option to do so, or if there is already a way to do this, please let me know.\r\n\r\nThanks,\r\nGunjan \n Hi ! You can get CUDA tensors with\r\n\r\n```python\r\ndataset.set_format(\"torch\", columns=columns, device=\"cuda\")\r\n```\r\n\r\nIndeed `set_format` passes the `**kwargs` to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1762","title":"Unable to format dataset to CUDA Tensors","comments":"Hi @lhoestq,\r\n\r\nThanks a lot. Is this true for all format types?\r\n\r\nAs in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'?","body":"Hi,\r\n\r\nI came across this [link](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.\r\n\r\nI tried this, but Dataset doesn't support assignment:\r\n```\r\n  columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']\r\n\r\n        samples.set_format(type='torch', columns = columns)\r\n        for column in columns:\r\n            samples[column].to(torch.device(self.config.device))\r\n```\r\nThere should be an option to do so, or if there is already a way to do this, please let me know.\r\n\r\nThanks,\r\nGunjan","comment_length":38,"text":"Unable to format dataset to CUDA Tensors \n Hi,\r\n\r\nI came across this [link](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.\r\n\r\nI tried this, but Dataset doesn't support assignment:\r\n```\r\n  columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']\r\n\r\n        samples.set_format(type='torch', columns = columns)\r\n        for column in columns:\r\n            samples[column].to(torch.device(self.config.device))\r\n```\r\nThere should be an option to do so, or if there is already a way to do this, please let me know.\r\n\r\nThanks,\r\nGunjan \n Hi @lhoestq,\r\n\r\nThanks a lot. Is this true for all format types?\r\n\r\nAs in, for 'torch', I can have `**kwargs` to `torch.tensor` and for 'tf' those args are passed to `tf.Tensor`, and the same for 'numpy' and 'pandas'?","embeddings":[-0.1914244592,-0.4953543544,-0.0635152757,0.1430512965,0.5737578273,0.3578240275,0.6109963059,0.3751015067,-0.0064690905,0.093313165,-0.1843090802,0.238564074,-0.1888935864,0.2082742453,0.1855475008,-0.3550991416,0.2348218411,-0.1543388367,-0.0376627818,0.0704863742,-0.2085145414,-0.0241091959,-0.1562636644,-0.2900007665,-0.1949911267,-0.1617193669,0.0201715548,-0.4400323927,-0.1024810895,-0.0641930923,0.2913292944,0.104888238,0.3066851795,0.4878616333,-0.0001256564,-0.0178643558,-0.0497946441,-0.1510568112,-0.1228252649,-0.2663969994,0.0131856529,-0.2795284688,0.3273176849,-0.2697499394,-0.3616902232,0.1351331323,0.0503888875,-0.3767498434,0.200864166,0.5606645942,0.0873320624,0.1567419022,0.2239812762,0.0313126706,-0.1401109546,0.4742643237,-0.3869950175,0.0321357176,0.1239138469,0.2313414663,0.2390954942,0.121947214,-0.1170619056,0.0366932191,0.2436490506,0.0068774805,-0.3590634465,-0.2845258713,0.0415751301,0.2316781729,0.7356418967,-0.305837065,-0.4198352993,0.0801290721,0.0384078212,-0.3957471251,-0.1968512535,0.6596462727,-0.3205678761,0.0670806766,-0.3185796142,0.1147153229,-0.2370989323,0.1259754151,-0.2630985379,0.0228494518,-0.2386255264,-0.00528268,0.0134357577,0.1373595893,0.2915566266,0.1084817946,0.1864098608,0.1729704142,-0.4968365729,-0.1123953313,-0.5637843609,-0.1750736088,0.145513311,0.0750934631,0.1889846027,0.0470552519,-0.1527156979,0.0456818715,-0.1689350009,0.224168852,0.0323081762,0.1593714356,-0.052119717,-0.2683012784,0.339374572,-0.0639518201,-0.2907772958,-0.5000951886,-0.294485569,0.1828467697,0.0936030969,-0.0746787786,-0.2550733685,-0.1188581735,-0.0248166993,0.1076056436,-0.2666125596,0.249533087,0.0253214706,0.3020795584,0.3945814669,0.1859082431,0.0624374934,0.0885022879,0.0459994301,0.0976752639,-0.2249604762,-0.2046596855,0.0503197499,-0.3494428098,-0.0520413965,0.1319437474,-0.02984442,-0.0050222706,-0.1101642549,0.0849643946,0.4755417109,0.1198236719,-0.1078406125,0.4093901813,0.2108049691,0.2150567323,-0.2697773278,0.1633630097,-0.4828787744,-0.050340388,-0.4977714121,-0.0458525456,0.2782033086,-0.0052368548,-0.2089643925,0.2063405514,0.2674427032,0.1263454705,0.1163300574,-0.1863862425,-0.1187278032,-0.2959930599,0.4839713573,0.0943365097,-0.346241802,-0.0135652162,0.5384857059,-0.0493879654,0.2286550105,0.3445949554,0.1999177635,-0.1558273286,-0.1161526293,-0.3076292574,0.514144659,-0.3327646852,0.0620910712,-0.2046283185,0.238594085,-0.2397407144,-0.0306929629,0.0901034251,0.2652629912,0.0417132713,0.1719368994,0.2446942329,-0.0256077517,0.0062519168,-0.020202009,0.0564026684,0.459436506,-0.0907293856,-0.1993737668,0.3107618392,-0.2633801699,-0.1151411682,0.162151441,-0.0987520665,-0.0622772612,0.012384885,0.2049012482,0.0305001959,-0.1359109133,0.0358943231,-0.4950877428,-0.1287979037,0.3969790339,0.1687712073,0.1217771024,-0.1747484952,-0.0030755748,-0.0099151405,0.0079434421,0.0448172875,-0.0464094318,-0.2493266314,-0.0874044448,-0.0615764409,-0.3723646998,0.1812834293,-0.2245939821,0.1582474411,-0.2071206272,-0.1247370765,0.0592541248,-0.0716325566,0.0630290061,0.2352627367,0.0131812859,-0.2124841213,0.0284208134,0.0819359645,0.2183371037,-0.0558925457,-0.5166804194,0.2893258929,0.2828788459,0.104551442,-0.1155059114,0.0311221592,0.0055087954,-0.1629833579,-0.3709156811,0.4067362845,0.0599326901,0.230954513,-0.3554264009,0.3759162426,0.1866152138,0.1095321998,-0.0815121084,0.0628495738,-0.0306959264,0.154332459,0.1242306978,-0.0269195717,-0.8723095059,-0.0826110169,0.0893359408,-0.0524719991,0.1247086152,0.0851079971,-0.1595858186,0.1669843048,0.2630163133,-0.3978264332,-0.1376062632,0.0414259285,0.1940864325,-0.048570931,0.0565142669,-0.233563751,0.0186636318,-0.0982431695,-0.0915792584,-0.0727772489,0.107151702,-0.0184024405,0.0337337218,0.3695034683,-0.0305227917,0.0190546066,-0.5362110138,-0.0158574786,-0.3519634902,0.0977476537,-0.3490992188,-0.3521323204,0.1291348189,-0.0189528149,-0.1611277312,0.0534757115,0.3019583523,0.2789478302,0.1680697799,0.2721075416,0.1798411161,-0.1224969178,-0.2118988186,-0.2402275205,0.1241373718,0.000944814,0.4400053918,-0.2165757865,0.2178514898,-0.2075906545,0.1460314691,-0.2733972967,0.0451167375,-0.1020593345,-0.1112595499,-0.1189441085,0.08324451,0.3903753459,0.0218295809,-0.0288239792,0.135019049,0.1823802292,-0.0211636052,0.0235007703,-0.3299600482,-0.0373772122,-0.2545598149,-0.2003005892,-0.232990548,-0.2443696707,0.2478479892,-0.0416196585,0.1478679627,-0.3123798072,0.3225725591,-0.1367617995,-0.0515976213,0.0416276306,0.0545709059,-0.458408922,0.6442713141,-0.2775747478,-0.4975637794,0.1916207522,0.1685626656,0.3659411967,0.448874265,0.0472360961,0.1143314838,0.1473894864,-0.1106012315,0.0562096797,0.0760409087,0.2353334278,-0.0226625111,0.157155171,0.1175529584,-0.0740970597,-0.0103896093,0.0545890033,0.2211627066,0.2090297937,0.4589605927,0.0763145536,0.6128472686,0.0670986921,-0.6483486295,0.0579884239,-0.2046810538,0.3649874926,-0.1180188134,-0.2335178256,0.3266437054,-0.0169422813,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1762","title":"Unable to format dataset to CUDA Tensors","comments":"Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.\r\nWe don't support the kwargs for pandas on the other hand.","body":"Hi,\r\n\r\nI came across this [link](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.\r\n\r\nI tried this, but Dataset doesn't support assignment:\r\n```\r\n  columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']\r\n\r\n        samples.set_format(type='torch', columns = columns)\r\n        for column in columns:\r\n            samples[column].to(torch.device(self.config.device))\r\n```\r\nThere should be an option to do so, or if there is already a way to do this, please let me know.\r\n\r\nThanks,\r\nGunjan","comment_length":26,"text":"Unable to format dataset to CUDA Tensors \n Hi,\r\n\r\nI came across this [link](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.\r\n\r\nI tried this, but Dataset doesn't support assignment:\r\n```\r\n  columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']\r\n\r\n        samples.set_format(type='torch', columns = columns)\r\n        for column in columns:\r\n            samples[column].to(torch.device(self.config.device))\r\n```\r\nThere should be an option to do so, or if there is already a way to do this, please let me know.\r\n\r\nThanks,\r\nGunjan \n Yes the keywords arguments are passed to the convert function like `np.array`, `torch.tensor` or `tensorflow.ragged.constant`.\r\nWe don't support the kwargs for pandas on the other hand.","embeddings":[-0.2339969426,-0.4713830352,-0.0587259531,0.1454277188,0.5945415497,0.3813997209,0.5536247492,0.3486228287,0.0204021242,0.0847379118,-0.1387395561,0.3407218456,-0.1757797003,0.1476910412,0.1723191291,-0.3778468668,0.3084686399,-0.0880436674,-0.0487243161,0.0729149655,-0.2402442247,-0.0089956438,-0.1322969198,-0.2712605894,-0.173953414,-0.2240069062,-0.0373401642,-0.3639823198,-0.1242673844,-0.0815783963,0.280126363,0.0425559804,0.2684721947,0.479721576,-0.0001250429,-0.0380646735,-0.0376146324,-0.1130806655,-0.0815804303,-0.3308831453,0.143385753,-0.3101426661,0.3145223856,-0.2309604138,-0.4044010341,0.1008605212,0.0041844072,-0.3171213269,0.2870211303,0.5416358709,0.103670679,0.1582948267,0.2337694168,0.0570037179,-0.2884039283,0.3508221805,-0.3622895777,0.029085191,0.1062355489,0.0989784002,0.2423032969,0.1262860745,-0.1447199285,-0.009933468,0.3419010639,-0.0117045538,-0.3300116956,-0.343046397,0.0289544445,0.1749110073,0.5755484104,-0.3697309792,-0.3937684298,0.0476243757,0.1024229005,-0.4620788097,-0.2309355736,0.6167895794,-0.3567797244,0.1083152667,-0.3492470682,0.0367215872,-0.2219844311,0.1147983447,-0.2369441837,-0.0129355257,-0.1936635822,-0.0190675873,0.111353457,0.1237546578,0.3501030505,0.1134016663,0.2030448914,0.1549072415,-0.4486976564,-0.0980172381,-0.5485565066,-0.1681699306,0.1375159323,0.0402063243,0.1304198951,0.0079594404,-0.1278871298,0.0056559616,-0.2242074311,0.3471833169,0.0669080615,0.1643438339,-0.0432013609,-0.2695736289,0.3038643897,-0.0564141087,-0.2316732109,-0.5269571543,-0.316483289,0.1881118268,0.0535000786,-0.0537037998,-0.2678466141,-0.1250574142,-0.0247980822,0.2073383927,-0.2367475629,0.3064680099,0.0266959406,0.175603658,0.4172639251,0.2402886152,0.0884256884,0.1147113591,0.0082564084,0.1830558926,-0.2545364499,-0.1619744003,0.0182341728,-0.408423394,-0.0000098696,0.114477843,-0.0146229621,0.0369337648,-0.1712637842,0.1097175553,0.4806713462,0.1500868648,-0.0555732399,0.3168988526,0.2587451339,0.171579197,-0.1793723553,0.1278153509,-0.4333970845,-0.0524579324,-0.4958718717,-0.0392540209,0.1906520277,0.0205783788,-0.2681034207,0.2509735525,0.1977762133,0.1804834902,0.1070223823,-0.1601639986,-0.1691135317,-0.3387620151,0.4698261321,0.0842390805,-0.3453785181,0.0297357813,0.434987694,0.0004083192,0.2666268945,0.3180944026,0.174704507,-0.1362379342,-0.0582676083,-0.3067197502,0.5030120611,-0.3811711371,-0.002110458,-0.1177432239,0.1807863861,-0.2046895921,-0.1161084548,0.0791502222,0.2161780149,0.0598128401,0.1382626891,0.2432869822,-0.0262607913,-0.0294425562,-0.0116635039,-0.0437627174,0.555408299,-0.1015026867,-0.1470306069,0.3318081796,-0.2507635951,-0.113105461,0.1760647446,-0.0809093043,-0.0702735409,0.063646093,0.2080050111,0.0232507326,-0.1183819696,0.0695905983,-0.4207933247,-0.0812511146,0.3631971478,0.1965861619,-0.0006355069,-0.1281881928,-0.0671002492,0.0156985279,0.0038450852,-0.0556031615,-0.0155436303,-0.388495326,-0.0255307052,-0.0298726223,-0.2764238715,0.2545250058,-0.2664058805,0.172347039,-0.2724768817,0.0402159765,0.0574771687,-0.1314865947,0.1593995988,0.2320960909,0.0638310835,-0.2490208894,0.048407007,0.1410157382,0.1752935052,-0.0210816301,-0.4079723358,0.2039381415,0.2236028016,0.0174957719,-0.0682967976,0.141394645,-0.0304877721,-0.1648557484,-0.4090501666,0.3824997842,-0.0257203504,0.2120332271,-0.3623810112,0.4157853723,0.20953843,0.0852198824,-0.0370515585,0.0753452778,-0.0228061192,0.0823445246,0.1773440689,-0.0755217895,-0.8429781199,-0.0377741903,0.0881043673,0.0032723898,0.09828493,0.1012567803,-0.1349601299,0.0992460772,0.293107599,-0.3925173283,-0.1315825284,0.0586476363,0.1630243063,0.0702608079,0.0551951453,-0.1624016315,0.0284021366,-0.0688166469,-0.0366205983,-0.172326535,0.131505087,-0.0544202663,0.0410273485,0.3336190283,-0.0218048412,0.1382029355,-0.4633518457,0.0450826026,-0.4018615484,-0.0179238003,-0.3308508396,-0.3282232881,0.0491726622,-0.0365940444,-0.2419452816,0.1314435303,0.3399285078,0.3103050292,0.2322828323,0.1405813694,0.2255209386,-0.0986832827,-0.2828424573,-0.2550128996,0.1266607791,0.0233456176,0.3692157269,-0.1241345257,0.1733714044,-0.195393905,0.2128217965,-0.3078772426,0.0772547796,-0.1389772892,-0.1688556969,-0.0307458434,0.1255291551,0.3994468749,-0.1166265905,-0.0213176664,0.1272454411,0.1648911834,0.0273488835,0.0256968681,-0.2978483438,-0.0583029315,-0.1860079616,-0.2147425562,-0.2217249423,-0.2360036522,0.253924638,-0.0015282225,0.1012187228,-0.2703938782,0.428529501,-0.1214859039,0.0936646014,-0.0723860636,0.094597362,-0.4932852089,0.6376007199,-0.2505540252,-0.5051738024,0.1750522554,0.1669685543,0.4039465785,0.3599305749,-0.0157587193,0.1210851297,0.1616235971,-0.0264710654,0.0623376891,0.0371967033,0.2368351221,-0.0230726916,0.131292358,0.1076188013,-0.0747549087,-0.0488992855,0.099597685,0.3001369536,0.1771588475,0.559242785,0.0930162221,0.6866155267,0.1286579221,-0.5674318075,0.010748093,-0.2740852237,0.3683100939,-0.1180503145,-0.3410993516,0.2362449467,-0.0375306904,0.179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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1762","title":"Unable to format dataset to CUDA Tensors","comments":"Thanks @lhoestq,\r\nWould it be okay if I added this to the docs and made a PR?","body":"Hi,\r\n\r\nI came across this [link](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.\r\n\r\nI tried this, but Dataset doesn't support assignment:\r\n```\r\n  columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']\r\n\r\n        samples.set_format(type='torch', columns = columns)\r\n        for column in columns:\r\n            samples[column].to(torch.device(self.config.device))\r\n```\r\nThere should be an option to do so, or if there is already a way to do this, please let me know.\r\n\r\nThanks,\r\nGunjan","comment_length":17,"text":"Unable to format dataset to CUDA Tensors \n Hi,\r\n\r\nI came across this [link](https:\/\/huggingface.co\/docs\/datasets\/torch_tensorflow.html) where the docs show show to convert a dataset to a particular format. I see that there is an option to convert it to tensors, but I don't see any option to convert it to CUDA tensors.\r\n\r\nI tried this, but Dataset doesn't support assignment:\r\n```\r\n  columns=['input_ids', 'token_type_ids', 'attention_mask', 'start_positions','end_positions']\r\n\r\n        samples.set_format(type='torch', columns = columns)\r\n        for column in columns:\r\n            samples[column].to(torch.device(self.config.device))\r\n```\r\nThere should be an option to do so, or if there is already a way to do this, please let me know.\r\n\r\nThanks,\r\nGunjan \n Thanks @lhoestq,\r\nWould it be okay if I added this to the docs and made a PR?","embeddings":[-0.2076083124,-0.4493276179,-0.0808781087,0.142959103,0.5672462583,0.3796422184,0.5310692787,0.3309663832,-0.0181646831,0.1409579068,-0.0788681507,0.2365308106,-0.2230986804,0.1325985044,0.1703114212,-0.3306939304,0.2573104203,-0.0979152545,-0.0379618742,0.0790335238,-0.1359184682,-0.0128564648,-0.1050756574,-0.3105601072,-0.2359376848,-0.1902676523,0.0561589748,-0.3857475519,-0.1967529207,-0.0576163493,0.1955188215,0.1002451479,0.29746297,0.4419077337,-0.0001257112,-0.0405382775,-0.0763713568,-0.1063870415,-0.080232136,-0.2797963619,0.1008523479,-0.2619596422,0.3520948589,-0.1931514144,-0.4167786539,0.1588604003,0.0376700573,-0.3366959691,0.2211165577,0.5201233625,0.1055461466,0.201981917,0.2504799366,0.024018513,-0.1411984116,0.4127913415,-0.4059521258,0.0593819618,0.1868831068,0.1419476122,0.2361409068,0.1956163645,-0.1018036827,0.0367225483,0.2884670198,-0.0079526799,-0.3925335705,-0.3323218226,0.0225080829,0.1464354545,0.7426348925,-0.3099430799,-0.3527896404,0.1035459638,0.0786293224,-0.3819108605,-0.2360118926,0.6073791981,-0.3357323706,0.1058309004,-0.3175977767,-0.0252773352,-0.2114556581,0.1135248095,-0.2010389268,-0.0210831333,-0.2168882936,-0.0134135429,0.0800354704,0.1509759128,0.3454316556,0.212830469,0.2072887421,0.146277532,-0.457313329,-0.1075192839,-0.5549705625,-0.1373179555,0.1715201437,0.0919843167,0.197463721,-0.002513587,-0.1347372383,0.0147332316,-0.2574051321,0.2582004666,0.0727009997,0.2164323777,-0.0381977893,-0.2039446384,0.3264054954,-0.0522569455,-0.3161253333,-0.5621752739,-0.3139206767,0.2240673155,0.0709721297,-0.1325156987,-0.2899289429,-0.0630590469,-0.0203232691,0.1519998461,-0.195232898,0.3259002268,0.0424672514,0.1394878179,0.4517869651,0.1938624978,0.0105005354,0.0338832811,0.0050432449,0.1696887314,-0.3044994771,-0.0738299713,0.0310795661,-0.3389312625,-0.0471709073,0.0743234605,-0.0217698961,-0.007042604,-0.1374233067,0.1592934132,0.528816402,0.1485944837,-0.0566933826,0.3177837431,0.2165926993,0.2340798825,-0.2023090124,0.1190707684,-0.4427163005,-0.1329214722,-0.5485044718,-0.0207916033,0.226234585,0.0080709346,-0.1792021841,0.2097907364,0.2465562373,0.1304863691,0.1013705879,-0.0826847628,-0.1323800981,-0.3094055057,0.4982712269,0.1535867304,-0.3233247101,0.0316362903,0.4733214974,-0.0435247943,0.2070112228,0.3417697847,0.1974048018,-0.050364919,-0.1204217672,-0.3095732033,0.4586009383,-0.4009733498,-0.024447076,-0.1857907623,0.1177423745,-0.2924159765,-0.0787742212,0.0899528787,0.2781101465,0.0961890295,0.1539414227,0.1623061746,-0.0119309342,0.0019589656,-0.0272789262,-0.0119462311,0.4723087251,-0.0996463746,-0.1939267218,0.3507114649,-0.2216272205,-0.0664068758,0.1824704558,-0.0920220986,-0.0442125946,0.0883365497,0.2601041198,0.0278739631,-0.1492639631,0.0622367114,-0.4646399319,-0.1267797053,0.434522599,0.2147192508,0.0734812245,-0.1725203544,-0.0275027994,0.0007657374,-0.0201715771,-0.0845566466,-0.028565878,-0.3560156226,-0.0588525571,-0.1167134345,-0.2934119999,0.2282381803,-0.3179704547,0.2113440931,-0.3106459379,0.0084493449,0.05475666,-0.0987833515,0.0593863875,0.2388004661,0.0139859179,-0.255692184,0.0610446706,0.0880655795,0.1665399373,-0.0616914034,-0.3724156618,0.2719942629,0.2509323359,-0.0638868287,-0.1255123764,0.0395871699,-0.0599653982,-0.1379622072,-0.4441963732,0.3333820403,0.009532907,0.1833406538,-0.3780225515,0.3914254606,0.1607538313,0.1127121523,-0.0941014662,0.019278327,0.0129382582,0.1788825095,0.1063875332,-0.0939074531,-0.8347510695,-0.0346029177,0.0099747889,-0.009352969,0.1038298309,0.0902130902,-0.0889422148,0.1563893557,0.2987086177,-0.3835813105,-0.1463311315,0.0697986335,0.1932675242,0.0536920652,0.0900284275,-0.1724131256,-0.0065649915,-0.1495097131,-0.0629123747,-0.1108783409,0.1053052619,-0.0268622749,0.0198155977,0.3037309647,-0.0329217874,0.1207721233,-0.5268215537,-0.0821063891,-0.3464764059,0.0970350206,-0.3644326031,-0.3428700268,0.0165922493,0.0103524057,-0.1444885582,0.1534781605,0.3193197548,0.2531527877,0.2624109089,0.1782203615,0.1932383478,-0.0999086872,-0.1598995328,-0.2299977094,0.1815827489,0.0119407261,0.3474968672,-0.1560917199,0.204447493,-0.3022927344,0.1655821502,-0.3841092288,0.0206680726,-0.0932870582,-0.104485698,-0.1126242355,0.1318238974,0.4242872596,-0.0067447647,0.0133429272,0.1077385843,0.1408372372,-0.0286890734,-0.0282626059,-0.339951098,-0.0449544862,-0.2176354229,-0.281904906,-0.1910872608,-0.2567911148,0.256862998,-0.0152868703,0.1554702967,-0.3396693468,0.3959318995,-0.1299618036,0.0046285097,-0.0234765373,0.0288503002,-0.5347702503,0.6057141423,-0.2924804091,-0.5323602557,0.1957694292,0.1842218935,0.3844261765,0.392991513,0.0005280214,0.1903716028,0.1710219234,-0.0711646229,0.0433815904,0.0737030581,0.3059945703,-0.0140764154,0.1499236375,0.1225319579,-0.1269413978,-0.0466792248,0.0888376534,0.2946559489,0.1153137833,0.4837462604,0.0614362136,0.6757726073,0.0886685252,-0.5966697335,0.074806653,-0.2757519186,0.3746644557,-0.1389728934,-0.2873623073,0.295643419,-0.031816341,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1759","title":"wikipedia dataset incomplete","comments":"Hi !\r\nFrom what pickle file fo you get this ?\r\nI guess you mean the dataset loaded using `load_dataset` ?","body":"Hey guys,\r\n\r\nI am using the https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/wikipedia dataset.\r\nUnfortunately, I found out that there is an incompleteness for the German dataset.\r\n For reasons unknown to me, the number of inhabitants has been removed from many pages:\r\nThorey-sur-Ouche has 128 inhabitants according to the webpage (https:\/\/de.wikipedia.org\/wiki\/Thorey-sur-Ouche).\r\nThe pickle file however shows: franz\u00f6sische Gemeinde mit  Einwohnern (Stand).\r\n Is it possible to fix this?\r\n\r\nBest regards \r\nChris\r\n","comment_length":21,"text":"wikipedia dataset incomplete \n Hey guys,\r\n\r\nI am using the https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/wikipedia dataset.\r\nUnfortunately, I found out that there is an incompleteness for the German dataset.\r\n For reasons unknown to me, the number of inhabitants has been removed from many pages:\r\nThorey-sur-Ouche has 128 inhabitants according to the webpage (https:\/\/de.wikipedia.org\/wiki\/Thorey-sur-Ouche).\r\nThe pickle file however shows: franz\u00f6sische Gemeinde mit  Einwohnern (Stand).\r\n Is it possible to fix this?\r\n\r\nBest regards \r\nChris\r\n \n Hi !\r\nFrom what pickle file fo you get this ?\r\nI guess you mean the dataset loaded using `load_dataset` ?","embeddings":[0.1241123378,0.0906564444,-0.0648524389,0.4390091002,-0.1451850832,0.1351810396,0.1400446743,-0.0993287042,0.3315919638,0.1742715687,0.1878574491,-0.0555863045,0.3833341599,-0.3714283407,0.0213763621,-0.2188196033,0.1357136071,0.1867661327,-0.353669852,-0.2992884517,-0.1205291748,0.4303146303,-0.2972927988,-0.3738275468,0.0041588228,0.19129242,0.0423987173,-0.0265655871,-0.0398979373,-0.2545049489,0.0718956441,-0.1289307326,0.0232157223,0.2944527268,-0.0001212063,-0.2076835334,0.1829906702,0.0327180289,-0.5496045947,-0.0704218149,-0.318674922,-0.3378354311,-0.0553493388,-0.3048529327,0.3356400728,0.1256951839,0.2353796661,0.0285403319,-0.0430291593,-0.1988056302,0.109937042,-0.3088945448,0.1718199402,0.0193357505,0.3870480955,0.5492720008,0.1299690902,0.418032676,-0.0086954348,-0.1311780214,0.0025488234,0.5580912828,0.1151680574,-0.0844878107,0.1515882313,-0.0853332505,0.0486637726,-0.3207388222,0.4474427402,0.4326772094,0.7783439755,0.0387785062,-0.2873822451,-0.2301689982,-0.025564,0.0607123598,0.4423192143,0.0954175889,-0.0011091381,0.0861819386,0.0174800642,-0.3801380992,0.0836794153,0.2865539491,-0.2406740189,0.6932728887,-0.0522080064,0.1574092954,-0.1112748161,0.0383665152,-0.5061631799,-0.1751054674,-0.1195133105,0.6439186335,-0.012000083,0.1531442702,0.1128866076,0.0546699315,0.3744640648,-0.1240142137,-0.3182374239,-0.1055599824,-0.4076496661,0.0868473873,0.4266243279,-0.0020243784,0.3739506006,-0.2473478317,0.2485174686,0.0315778702,0.0509780347,0.253721118,0.1115050092,-0.2443264574,-0.0742328838,-0.0230276305,0.0975892469,-0.2007698417,0.0104226209,0.4637053013,-0.2206739187,-0.2525127232,-0.185787797,0.0229531098,-0.1924729943,0.314414233,0.0461675338,-0.0150527935,-0.3274431229,-0.3958059847,0.0411142036,0.0460367687,-0.0717362761,-0.1020293087,-0.1323872358,-0.227997154,0.3639759719,0.0687114224,-0.0924394056,-0.1216051579,0.0250204597,-0.3007547855,0.0401272103,0.1244297475,0.2084525526,0.2604510486,-0.1159794927,-0.6150658131,0.0189969148,0.0594440587,-0.3399091065,-0.1113068163,-0.714305222,0.1314143091,-0.1911712885,-0.0315862261,-0.2961347997,0.1198506355,0.2013316005,-0.4084000289,0.1167671159,0.0557347946,-0.1401756108,-0.0329447761,0.3443155587,0.5783535838,-0.1809437871,-0.2524649501,-0.2087493092,0.0897938833,0.353200525,0.4228056967,-0.0118461531,0.3736517727,-0.1751294583,0.1606291533,0.0374035127,0.0762989819,-0.4493754506,0.0295907371,0.127416417,0.012836772,-0.3067496419,0.0343672782,0.0258893091,0.3042875528,0.0671055391,0.1469595134,0.3602668941,-0.0720262006,-0.3002051413,-0.1694299728,0.5182990432,-0.0393483154,0.1673227102,0.0278055016,0.2274997383,0.1395635605,0.6033773422,0.1070422903,0.3978745043,0.789696157,0.0262635704,0.2337703109,0.2118063271,-0.0106214238,-0.2573165596,-0.0086753704,-0.0164199304,0.3641389012,0.1543468535,-0.0990670845,-0.1662692875,-0.0136340894,0.0195926055,-0.1372879893,0.0141813159,0.1456440389,-0.0317268781,0.0626532882,0.1838206798,-0.3371160626,-0.254968375,-0.0783704221,-0.6629975438,0.3918353915,-0.1417583674,0.0992441401,-0.3523250222,0.1621439308,-0.0117894178,-0.142051518,-0.1318246126,0.0079831313,0.3144644499,-0.078461282,0.4909600019,0.0006882547,0.1212997511,-0.3434089422,-0.0658256337,0.1046491191,0.0298574697,0.0752246752,-0.6009052396,-0.1981950849,0.2485599816,0.226744771,-0.2269640267,0.2283167094,0.5967853069,0.1458377838,0.0836337805,-0.4401924014,0.38652426,0.2097622454,0.1051055044,0.069303669,-0.261615634,0.0292556901,-0.0195674468,0.1131461412,0.3585663438,0.3546279967,-0.29358688,-0.3039520681,0.2648385465,-0.2347448021,-0.166177392,0.1754751652,0.3176705539,-0.1919918358,0.3871274889,0.0223829336,0.1180270687,0.2003120035,0.1394442618,0.1772623956,0.1525942832,0.1820432395,-0.330262363,-0.1329103708,-0.071037814,0.3756258488,-0.0305539984,-0.0649510026,-0.0540692508,-0.0113120107,0.0340101533,0.0396412872,-0.8664521575,-0.1790496707,0.030365875,-0.0061965608,-0.1516415775,0.2164192051,-0.0783867091,-0.1252055615,-0.2134806961,0.1640907377,-0.1619288772,-0.1873651743,-0.2594039142,-0.0083929719,0.0020934211,-0.0685687736,0.1145582572,-0.467007041,-0.4988545179,-0.6564518809,-0.38599962,0.2722924054,0.000618317,0.0336333402,0.1947737634,0.4568445086,-0.42540887,0.1016558111,0.131040588,-0.0450766347,0.0273406692,0.0357402973,-0.2880314589,0.0095882779,-0.071097441,-0.1275347173,-0.23204723,0.0671186075,0.1465851963,0.3558940887,0.2403901964,0.3179811239,0.042869024,-0.2407635301,-0.0785083696,-0.1428986192,-0.373886168,-0.4261963964,0.0893008336,-0.2573982179,-0.2928767502,0.0356932953,-0.0971708,-0.0374456979,-0.115373455,-0.5714575648,0.0845571607,-0.1595594436,0.1899251789,-0.3796055019,0.1761974394,0.4080471694,-0.0137708457,0.1159530953,-0.0897347257,-0.1603077054,-0.0024278515,0.3095690608,0.6256231666,-0.1449142396,0.1137359738,0.0443239957,0.631234169,0.4565292895,0.4432752132,0.1186548099,0.0140662948,0.2230178714,-0.1847384274,-0.0118874479,0.1425248533,-0.0815662965,-0.0956149399,0.4853443503,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1759","title":"wikipedia dataset incomplete","comments":"yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. ","body":"Hey guys,\r\n\r\nI am using the https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/wikipedia dataset.\r\nUnfortunately, I found out that there is an incompleteness for the German dataset.\r\n For reasons unknown to me, the number of inhabitants has been removed from many pages:\r\nThorey-sur-Ouche has 128 inhabitants according to the webpage (https:\/\/de.wikipedia.org\/wiki\/Thorey-sur-Ouche).\r\nThe pickle file however shows: franz\u00f6sische Gemeinde mit  Einwohnern (Stand).\r\n Is it possible to fix this?\r\n\r\nBest regards \r\nChris\r\n","comment_length":28,"text":"wikipedia dataset incomplete \n Hey guys,\r\n\r\nI am using the https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/wikipedia dataset.\r\nUnfortunately, I found out that there is an incompleteness for the German dataset.\r\n For reasons unknown to me, the number of inhabitants has been removed from many pages:\r\nThorey-sur-Ouche has 128 inhabitants according to the webpage (https:\/\/de.wikipedia.org\/wiki\/Thorey-sur-Ouche).\r\nThe pickle file however shows: franz\u00f6sische Gemeinde mit  Einwohnern (Stand).\r\n Is it possible to fix this?\r\n\r\nBest regards \r\nChris\r\n \n yes sorry, I used the `load_dataset`function and saved the data to a pickle file so I don't always have to reload it and are able to work offline. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1759","title":"wikipedia dataset incomplete","comments":"The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https:\/\/github.com\/earwig\/mwparserfromhell\r\n\r\nThere also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.\r\n\r\n","body":"Hey guys,\r\n\r\nI am using the https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/wikipedia dataset.\r\nUnfortunately, I found out that there is an incompleteness for the German dataset.\r\n For reasons unknown to me, the number of inhabitants has been removed from many pages:\r\nThorey-sur-Ouche has 128 inhabitants according to the webpage (https:\/\/de.wikipedia.org\/wiki\/Thorey-sur-Ouche).\r\nThe pickle file however shows: franz\u00f6sische Gemeinde mit  Einwohnern (Stand).\r\n Is it possible to fix this?\r\n\r\nBest regards \r\nChris\r\n","comment_length":48,"text":"wikipedia dataset incomplete \n Hey guys,\r\n\r\nI am using the https:\/\/github.com\/huggingface\/datasets\/tree\/master\/datasets\/wikipedia dataset.\r\nUnfortunately, I found out that there is an incompleteness for the German dataset.\r\n For reasons unknown to me, the number of inhabitants has been removed from many pages:\r\nThorey-sur-Ouche has 128 inhabitants according to the webpage (https:\/\/de.wikipedia.org\/wiki\/Thorey-sur-Ouche).\r\nThe pickle file however shows: franz\u00f6sische Gemeinde mit  Einwohnern (Stand).\r\n Is it possible to fix this?\r\n\r\nBest regards \r\nChris\r\n \n The wikipedia articles are processed using the `mwparserfromhell` library. Even if it works well in most cases, such issues can happen unfortunately. You can find the repo here: https:\/\/github.com\/earwig\/mwparserfromhell\r\n\r\nThere also exist other datasets based on wikipedia that were processed differently (and are often cleaner) such as `wiki40b`.\r\n\r\n","embeddings":[0.145783633,0.1651307046,-0.0604214706,0.4512960911,-0.1779640317,0.1422897428,0.1325682402,-0.0599784143,0.2211329937,0.1373941153,0.1483723372,-0.0644864812,0.4038364589,-0.4707553387,-0.0247412473,-0.1964903921,0.1687633395,0.1441214383,-0.3371717632,-0.2833011746,-0.150470674,0.4101845622,-0.3018541932,-0.3771216571,0.0009892298,0.1730692834,0.0294008255,-0.1099493206,0.0388543122,-0.255102247,0.0370923541,-0.1234474033,0.0171350595,0.2694829702,-0.0001211163,-0.1979540586,0.1709197611,0.0784731358,-0.4775889814,-0.0129319895,-0.2857728004,-0.3218949437,-0.064013049,-0.3470281959,0.3659273982,0.1441548318,0.2030157298,0.0585192963,-0.0170048568,-0.1380969882,0.1241210103,-0.3913951814,0.2005907446,0.0655082017,0.4258055389,0.5858446956,0.1269980669,0.2719853222,-0.0048444187,-0.1762056053,0.0176583491,0.5391578078,0.0057500992,-0.0616088994,0.1903233975,-0.0977577493,0.0417255089,-0.375428915,0.5308150649,0.4282439351,0.6989920139,0.0609150454,-0.2956066132,-0.2108039111,-0.0446788296,0.1271712184,0.4841116369,0.1465952694,0.0158949103,0.0581623539,-0.0173052158,-0.3101015389,0.0548868589,0.2982166708,-0.2402050644,0.7044153214,-0.0301209353,0.1586293578,-0.1816008389,0.0438450165,-0.3793136477,-0.2826716602,-0.0948735401,0.6344087124,0.0443101265,0.1643259674,0.0751382336,0.0772433355,0.3611153364,-0.2755673528,-0.3678851128,-0.1502043605,-0.3200321496,0.0574990436,0.4369731843,-0.0295739174,0.3606520295,-0.200948894,0.2603800297,0.0274022203,0.0076454156,0.280877471,0.1464324296,-0.1832085103,0.0047896113,-0.0615609214,0.0689780787,-0.2187531292,0.0432224944,0.5090514421,-0.2550533116,-0.2354683429,-0.2305583358,-0.0571923517,-0.1718482971,0.358758539,0.0155743603,0.0492518358,-0.3456348777,-0.3288669586,0.0548176505,0.0684341863,-0.0149544394,-0.076012373,-0.1218015626,-0.1956166625,0.3033376038,0.0875600502,-0.1614001989,-0.143944785,0.005945567,-0.3173173964,0.0513227582,0.1171248853,0.2250036299,0.2758552134,-0.1570949256,-0.6372517347,0.0443666168,0.1000973582,-0.3013814688,-0.0364339538,-0.6779907942,0.1429633498,-0.2141105086,-0.0380949564,-0.2095004022,0.1293669045,0.2918624282,-0.3391788304,0.0900603682,0.0538967252,-0.1480180025,-0.0453009531,0.3423948586,0.5277428627,-0.0726070404,-0.2131535262,-0.240062505,0.185655728,0.3504770994,0.4081870615,0.0336732157,0.3773176968,-0.0445005782,0.0923473015,-0.0536393002,0.1296361834,-0.4094777107,0.0275954809,0.1863747835,0.0908563286,-0.3522014916,0.0342830569,0.0083405105,0.2894448042,0.028009614,0.1449129134,0.3542480171,-0.0304772258,-0.3212764263,-0.1869341582,0.486083746,-0.0571016073,0.2496021241,0.0195017811,0.1994181126,0.1431911886,0.5697168112,0.0964566693,0.4354119599,0.7665311694,0.0510880984,0.2489742935,0.2217193395,-0.0409097932,-0.140591085,-0.0486743562,0.0215399005,0.3681215644,0.1580456942,-0.1656516045,-0.1193706095,0.0043172794,0.0545834191,-0.1634843349,0.0601624586,0.1263922453,0.0036240914,0.1342677474,0.2368512005,-0.4029847682,-0.2450559586,-0.1003006026,-0.5714448094,0.4301270843,-0.1574401259,0.0866298378,-0.3112455606,0.1544338614,-0.046983514,-0.0862774476,-0.159728229,-0.0192471482,0.2688753307,-0.090005435,0.5272328258,0.0586883686,0.1302475035,-0.3522474766,-0.0622084849,0.1057255641,0.0245693717,0.1013292223,-0.6246506572,-0.2231550813,0.1938699633,0.2869718969,-0.2390442342,0.2374240458,0.5918765068,0.1011636853,0.1432625353,-0.4077341259,0.3371963203,0.1860398948,-0.0232663974,0.0664843842,-0.1998917907,0.0710164085,-0.0652072728,0.1524333358,0.3409911096,0.3338623643,-0.294464767,-0.3108476102,0.2318411916,-0.229502514,-0.2038976252,0.1474936157,0.2784415781,-0.2145889252,0.3905881345,0.0154753011,0.1806613505,0.2805579901,0.1175009906,0.1110365018,0.1559869647,0.2408441752,-0.3074939251,-0.0712040141,-0.0985700563,0.3704074919,-0.0236846171,-0.0726825222,-0.0377204977,-0.0324173644,0.03895678,0.0751195252,-0.8128618002,-0.2056323886,0.0372341052,-0.0130473068,-0.1882798523,0.1903685778,-0.0759144574,-0.1974064708,-0.1964819431,0.2040979415,-0.1688502282,-0.2201683372,-0.2426234633,0.014934645,0.0277103335,-0.0396465696,0.0803911239,-0.4445254803,-0.5066559911,-0.6288619041,-0.4411588609,0.2037249506,0.012816513,0.0592472665,0.0856286436,0.3786281347,-0.4475949109,0.0401363708,0.1387779415,-0.0454167165,0.1066057235,0.0998874083,-0.2520989776,-0.1221778393,-0.0513879843,-0.1038913876,-0.1540354043,0.1118792892,0.0949263647,0.3399053216,0.2324995548,0.2452100962,0.0753850043,-0.2840758264,-0.0840278044,-0.1073355228,-0.3288804889,-0.2740887702,0.1363540739,-0.2322098166,-0.3054614365,0.0712974221,-0.0898418278,-0.0707059354,-0.0583204627,-0.5821250677,0.0886529684,-0.1367853135,0.1188964024,-0.3265385032,0.1683312356,0.3140134215,0.0313094854,0.1092464998,-0.0234486405,-0.097319819,-0.0231414456,0.2916723788,0.5615307689,-0.15699628,0.1271216422,0.0614072233,0.5506908894,0.5221112967,0.4804535806,0.0566064045,0.0543397851,0.1798391342,-0.1528558284,0.0092240963,0.1997855902,-0.137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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1758","title":"dataset.search() (elastic) cannot reliably retrieve search results","comments":"Hi !\r\nI tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.\r\nMaybe this is because the index is not updated yet on the ElasticSearch side ?","body":"I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.\r\n\r\nThe problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.\r\n\r\nI am indexing data that looks like the following from the HF SQuAD 2.0 data set:\r\n\r\n```\r\n['57318658e6313a140071d02b',\r\n '56f7165e3d8e2e1400e3733a',\r\n '570e2f6e0b85d914000d7d21',\r\n '5727e58aff5b5019007d97d0',\r\n '5a3b5a503ff257001ab8441f',\r\n '57262fab271a42140099d725']\r\n```\r\n\r\n\r\n\r\nTo reproduce the issue, try:\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import BertTokenizerFast, BertForQuestionAnswering\r\nfrom elasticsearch import Elasticsearch\r\nimport numpy as np\r\nimport collections\r\nfrom tqdm.auto import tqdm\r\nimport torch\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\r\nmax_length = 384 # The maximum length of a feature (question and context)\r\ndoc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\r\npad_on_right = tokenizer.padding_side == \"right\"\r\nsquad_v2 = True\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef prepare_validation_features(examples):\r\n    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n    # in one example possible giving several features when a context is long, each of those features having a\r\n    # context that overlaps a bit the context of the previous feature.\r\n    tokenized_examples = tokenizer(\r\n        examples[\"question\" if pad_on_right else \"context\"],\r\n        examples[\"context\" if pad_on_right else \"question\"],\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=max_length,\r\n        stride=doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n    # its corresponding example. This key gives us just that.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n    # We keep the example_id that gave us this feature and we will store the offset mappings.\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i in range(len(tokenized_examples[\"input_ids\"])):\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        context_index = 1 if pad_on_right else 0\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\r\n        # position is part of the context or not.\r\n        tokenized_examples[\"offset_mapping\"][i] = [\r\n            (list(o) if sequence_ids[k] == context_index else None)\r\n            for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n        ]\r\n\r\n    return tokenized_examples\r\n\r\n\r\n\r\n\r\n\r\n# build base examples, features set of training data\r\nshuffled_idx = pd.read_csv('https:\/\/raw.githubusercontent.com\/afogarty85\/temp\/main\/idx.csv')['idx'].to_list()\r\nexamples = load_dataset(\"squad_v2\").shuffle(seed=1)['train']\r\nfeatures = load_dataset(\"squad_v2\").shuffle(seed=1)['train'].map(\r\n    prepare_validation_features,\r\n    batched=True,\r\n    remove_columns=['answers', 'context', 'id', 'question', 'title'])\r\n# reorder features by the training process\r\nfeatures = features.select(indices=shuffled_idx)\r\n# get the example ids to match with the \"example\" data; get unique entries\r\nid_list = list(dict.fromkeys(features['example_id']))\r\n# now search for their index positions in the examples data set; load elastic search\r\nes = Elasticsearch([{'host': 'localhost'}]).ping()\r\n# add an index to the id column for the examples\r\nexamples.add_elasticsearch_index(column='id')\r\n# retrieve the example index\r\nexample_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]\r\nexample_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]\r\n\r\nexample_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]\r\nexample_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]\r\n\r\nlen(example_idx_k1)  # should be 130319\r\nlen(example_idx_k2)  # should be 130319\r\n\r\n#trial 1 lengths:\r\n# k=1: 130314\r\n# k=3: 130319\r\n\r\n# trial 2:\r\n# just run k=3 first: 130310\r\n# try k=1 after k=3: 130319\r\n```\r\n\r\n","comment_length":41,"text":"dataset.search() (elastic) cannot reliably retrieve search results \n I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.\r\n\r\nThe problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.\r\n\r\nI am indexing data that looks like the following from the HF SQuAD 2.0 data set:\r\n\r\n```\r\n['57318658e6313a140071d02b',\r\n '56f7165e3d8e2e1400e3733a',\r\n '570e2f6e0b85d914000d7d21',\r\n '5727e58aff5b5019007d97d0',\r\n '5a3b5a503ff257001ab8441f',\r\n '57262fab271a42140099d725']\r\n```\r\n\r\n\r\n\r\nTo reproduce the issue, try:\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import BertTokenizerFast, BertForQuestionAnswering\r\nfrom elasticsearch import Elasticsearch\r\nimport numpy as np\r\nimport collections\r\nfrom tqdm.auto import tqdm\r\nimport torch\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\r\nmax_length = 384 # The maximum length of a feature (question and context)\r\ndoc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\r\npad_on_right = tokenizer.padding_side == \"right\"\r\nsquad_v2 = True\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef prepare_validation_features(examples):\r\n    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n    # in one example possible giving several features when a context is long, each of those features having a\r\n    # context that overlaps a bit the context of the previous feature.\r\n    tokenized_examples = tokenizer(\r\n        examples[\"question\" if pad_on_right else \"context\"],\r\n        examples[\"context\" if pad_on_right else \"question\"],\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=max_length,\r\n        stride=doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n    # its corresponding example. This key gives us just that.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n    # We keep the example_id that gave us this feature and we will store the offset mappings.\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i in range(len(tokenized_examples[\"input_ids\"])):\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        context_index = 1 if pad_on_right else 0\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\r\n        # position is part of the context or not.\r\n        tokenized_examples[\"offset_mapping\"][i] = [\r\n            (list(o) if sequence_ids[k] == context_index else None)\r\n            for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n        ]\r\n\r\n    return tokenized_examples\r\n\r\n\r\n\r\n\r\n\r\n# build base examples, features set of training data\r\nshuffled_idx = pd.read_csv('https:\/\/raw.githubusercontent.com\/afogarty85\/temp\/main\/idx.csv')['idx'].to_list()\r\nexamples = load_dataset(\"squad_v2\").shuffle(seed=1)['train']\r\nfeatures = load_dataset(\"squad_v2\").shuffle(seed=1)['train'].map(\r\n    prepare_validation_features,\r\n    batched=True,\r\n    remove_columns=['answers', 'context', 'id', 'question', 'title'])\r\n# reorder features by the training process\r\nfeatures = features.select(indices=shuffled_idx)\r\n# get the example ids to match with the \"example\" data; get unique entries\r\nid_list = list(dict.fromkeys(features['example_id']))\r\n# now search for their index positions in the examples data set; load elastic search\r\nes = Elasticsearch([{'host': 'localhost'}]).ping()\r\n# add an index to the id column for the examples\r\nexamples.add_elasticsearch_index(column='id')\r\n# retrieve the example index\r\nexample_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]\r\nexample_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]\r\n\r\nexample_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]\r\nexample_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]\r\n\r\nlen(example_idx_k1)  # should be 130319\r\nlen(example_idx_k2)  # should be 130319\r\n\r\n#trial 1 lengths:\r\n# k=1: 130314\r\n# k=3: 130319\r\n\r\n# trial 2:\r\n# just run k=3 first: 130310\r\n# try k=1 after k=3: 130319\r\n```\r\n\r\n \n Hi !\r\nI tried your code on my side and I was able to workaround this issue by waiting a few seconds before querying the index.\r\nMaybe this is because the index is not updated yet on the ElasticSearch side 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1758","title":"dataset.search() (elastic) cannot reliably retrieve search results","comments":"Thanks for the feedback! I added a 30 second \"sleep\" and that seemed to work well!","body":"I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.\r\n\r\nThe problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.\r\n\r\nI am indexing data that looks like the following from the HF SQuAD 2.0 data set:\r\n\r\n```\r\n['57318658e6313a140071d02b',\r\n '56f7165e3d8e2e1400e3733a',\r\n '570e2f6e0b85d914000d7d21',\r\n '5727e58aff5b5019007d97d0',\r\n '5a3b5a503ff257001ab8441f',\r\n '57262fab271a42140099d725']\r\n```\r\n\r\n\r\n\r\nTo reproduce the issue, try:\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import BertTokenizerFast, BertForQuestionAnswering\r\nfrom elasticsearch import Elasticsearch\r\nimport numpy as np\r\nimport collections\r\nfrom tqdm.auto import tqdm\r\nimport torch\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\r\nmax_length = 384 # The maximum length of a feature (question and context)\r\ndoc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\r\npad_on_right = tokenizer.padding_side == \"right\"\r\nsquad_v2 = True\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef prepare_validation_features(examples):\r\n    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n    # in one example possible giving several features when a context is long, each of those features having a\r\n    # context that overlaps a bit the context of the previous feature.\r\n    tokenized_examples = tokenizer(\r\n        examples[\"question\" if pad_on_right else \"context\"],\r\n        examples[\"context\" if pad_on_right else \"question\"],\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=max_length,\r\n        stride=doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n    # its corresponding example. This key gives us just that.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n    # We keep the example_id that gave us this feature and we will store the offset mappings.\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i in range(len(tokenized_examples[\"input_ids\"])):\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        context_index = 1 if pad_on_right else 0\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\r\n        # position is part of the context or not.\r\n        tokenized_examples[\"offset_mapping\"][i] = [\r\n            (list(o) if sequence_ids[k] == context_index else None)\r\n            for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n        ]\r\n\r\n    return tokenized_examples\r\n\r\n\r\n\r\n\r\n\r\n# build base examples, features set of training data\r\nshuffled_idx = pd.read_csv('https:\/\/raw.githubusercontent.com\/afogarty85\/temp\/main\/idx.csv')['idx'].to_list()\r\nexamples = load_dataset(\"squad_v2\").shuffle(seed=1)['train']\r\nfeatures = load_dataset(\"squad_v2\").shuffle(seed=1)['train'].map(\r\n    prepare_validation_features,\r\n    batched=True,\r\n    remove_columns=['answers', 'context', 'id', 'question', 'title'])\r\n# reorder features by the training process\r\nfeatures = features.select(indices=shuffled_idx)\r\n# get the example ids to match with the \"example\" data; get unique entries\r\nid_list = list(dict.fromkeys(features['example_id']))\r\n# now search for their index positions in the examples data set; load elastic search\r\nes = Elasticsearch([{'host': 'localhost'}]).ping()\r\n# add an index to the id column for the examples\r\nexamples.add_elasticsearch_index(column='id')\r\n# retrieve the example index\r\nexample_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]\r\nexample_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]\r\n\r\nexample_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]\r\nexample_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]\r\n\r\nlen(example_idx_k1)  # should be 130319\r\nlen(example_idx_k2)  # should be 130319\r\n\r\n#trial 1 lengths:\r\n# k=1: 130314\r\n# k=3: 130319\r\n\r\n# trial 2:\r\n# just run k=3 first: 130310\r\n# try k=1 after k=3: 130319\r\n```\r\n\r\n","comment_length":16,"text":"dataset.search() (elastic) cannot reliably retrieve search results \n I am trying to use elastic search to retrieve the indices of items in the dataset in their precise order, given shuffled training indices.\r\n\r\nThe problem I have is that I cannot retrieve reliable results with my data on my first search. I have to run the search **twice** to get the right answer.\r\n\r\nI am indexing data that looks like the following from the HF SQuAD 2.0 data set:\r\n\r\n```\r\n['57318658e6313a140071d02b',\r\n '56f7165e3d8e2e1400e3733a',\r\n '570e2f6e0b85d914000d7d21',\r\n '5727e58aff5b5019007d97d0',\r\n '5a3b5a503ff257001ab8441f',\r\n '57262fab271a42140099d725']\r\n```\r\n\r\n\r\n\r\nTo reproduce the issue, try:\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import BertTokenizerFast, BertForQuestionAnswering\r\nfrom elasticsearch import Elasticsearch\r\nimport numpy as np\r\nimport collections\r\nfrom tqdm.auto import tqdm\r\nimport torch\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\r\nmax_length = 384 # The maximum length of a feature (question and context)\r\ndoc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\r\npad_on_right = tokenizer.padding_side == \"right\"\r\nsquad_v2 = True\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef prepare_validation_features(examples):\r\n    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n    # in one example possible giving several features when a context is long, each of those features having a\r\n    # context that overlaps a bit the context of the previous feature.\r\n    tokenized_examples = tokenizer(\r\n        examples[\"question\" if pad_on_right else \"context\"],\r\n        examples[\"context\" if pad_on_right else \"question\"],\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=max_length,\r\n        stride=doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n    # its corresponding example. This key gives us just that.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n    # We keep the example_id that gave us this feature and we will store the offset mappings.\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i in range(len(tokenized_examples[\"input_ids\"])):\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        context_index = 1 if pad_on_right else 0\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\r\n        # position is part of the context or not.\r\n        tokenized_examples[\"offset_mapping\"][i] = [\r\n            (list(o) if sequence_ids[k] == context_index else None)\r\n            for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n        ]\r\n\r\n    return tokenized_examples\r\n\r\n\r\n\r\n\r\n\r\n# build base examples, features set of training data\r\nshuffled_idx = pd.read_csv('https:\/\/raw.githubusercontent.com\/afogarty85\/temp\/main\/idx.csv')['idx'].to_list()\r\nexamples = load_dataset(\"squad_v2\").shuffle(seed=1)['train']\r\nfeatures = load_dataset(\"squad_v2\").shuffle(seed=1)['train'].map(\r\n    prepare_validation_features,\r\n    batched=True,\r\n    remove_columns=['answers', 'context', 'id', 'question', 'title'])\r\n# reorder features by the training process\r\nfeatures = features.select(indices=shuffled_idx)\r\n# get the example ids to match with the \"example\" data; get unique entries\r\nid_list = list(dict.fromkeys(features['example_id']))\r\n# now search for their index positions in the examples data set; load elastic search\r\nes = Elasticsearch([{'host': 'localhost'}]).ping()\r\n# add an index to the id column for the examples\r\nexamples.add_elasticsearch_index(column='id')\r\n# retrieve the example index\r\nexample_idx_k1 = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]\r\nexample_idx_k1 = [item for sublist in example_idx_k1 for item in sublist]\r\n\r\nexample_idx_k2 = [examples.search(index_name='id', query=i, k=3).indices for i in id_list]\r\nexample_idx_k2 = [item for sublist in example_idx_k2 for item in sublist]\r\n\r\nlen(example_idx_k1)  # should be 130319\r\nlen(example_idx_k2)  # should be 130319\r\n\r\n#trial 1 lengths:\r\n# k=1: 130314\r\n# k=3: 130319\r\n\r\n# trial 2:\r\n# just run k=3 first: 130310\r\n# try k=1 after k=3: 130319\r\n```\r\n\r\n \n Thanks for the feedback! I added a 30 second \"sleep\" and that seemed to work 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1757","title":"FewRel","comments":"@dspoka Please check the following link : https:\/\/github.com\/thunlp\/FewRel\r\nThis link mentions two versions of the datasets. Also, this one seems to be the official link.\r\n\r\nI am assuming this is the correct link and implementing based on the same.","body":"## Adding a Dataset\r\n- **Name:** FewRel\r\n- **Description:**  Large-Scale Supervised Few-Shot Relation Classification Dataset\r\n- **Paper:** @inproceedings{han2018fewrel,\r\n               title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},\r\n               author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong},\r\n               booktitle={EMNLP},\r\n               year={2018}}\r\n- **Data:** https:\/\/github.com\/ProKil\/FewRel\r\n- **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":39,"text":"FewRel \n ## Adding a Dataset\r\n- **Name:** FewRel\r\n- **Description:**  Large-Scale Supervised Few-Shot Relation Classification Dataset\r\n- **Paper:** @inproceedings{han2018fewrel,\r\n               title={FewRel:A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation},\r\n               author={Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong},\r\n               booktitle={EMNLP},\r\n               year={2018}}\r\n- **Data:** https:\/\/github.com\/ProKil\/FewRel\r\n- **Motivation:** relationship extraction dataset that's been used by some state of the art systems that should be incorporated.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n \n @dspoka Please check the following link : https:\/\/github.com\/thunlp\/FewRel\r\nThis link mentions two versions of the datasets. Also, this one seems to be the official link.\r\n\r\nI am assuming this is the correct link and implementing based on the same.","embeddings":[-0.1948349923,-0.0344234481,-0.1016143709,0.1268530041,-0.0582779087,-0.1250493228,0.4315415025,0.1708205491,0.1060709804,0.1873810887,-0.4312252402,0.0766163617,-0.0305741187,-0.3551288247,0.2079174072,-0.1194455251,-0.0137296366,0.2213974893,0.0144300517,-0.2777475715,-0.208515957,0.2417953908,0.0616172217,-0.1108966023,-0.2568879128,-0.3504311442,-0.2791405618,0.2126297504,-0.2346280068,-0.3258255422,0.3127015233,0.3423486054,0.236085698,0.4270271361,-0.000102641,-0.1168433502,0.1504649669,-0.0227653738,0.2300339341,0.065228872,0.2461844981,-0.581530571,0.0220020264,-0.270445466,-0.0693214983,-0.3054866791,-0.0593082197,-0.0659846961,0.3989920914,0.2640653849,0.3045372069,-0.0256966613,0.2329767197,-0.415032059,0.0251856335,0.0579345711,-0.1375132352,0.3970343471,0.406915307,-0.048483558,-0.0412055813,-0.0503174476,0.1264362484,-0.1019397005,0.3068552613,-0.0098813819,-0.073044695,-0.5875650048,0.0355400294,0.4702458978,0.3035934567,0.1698890477,-0.2683259547,0.104188703,-0.0309116635,-0.1546928436,-0.1621081382,-0.0901775435,-0.0814457089,0.0900968611,-0.0095678195,-0.1053602919,-0.3569051623,0.2582896054,-0.1199415326,0.1873029619,-0.1128008142,-0.0755117759,0.1000802442,-0.0299591348,-0.2914909124,0.0547142662,-0.0248714257,0.226285696,-0.1493637562,-0.2985912263,0.04510976,0.2989329398,0.5806908607,0.2920951247,-0.1349260658,-0.0020116165,-0.0244322624,0.1738010645,-0.0050677597,0.0620811656,0.1071795821,-0.0976178795,0.2153973579,0.0976598486,0.1153569296,0.1360460967,-0.3167721629,0.0317346342,-0.0946736336,-0.2656799853,-0.2686154544,-0.1873129308,0.0141022345,0.3091271818,-0.1353947818,0.1039839685,-0.104213044,0.3705091476,-0.1415278912,0.3739591241,-0.1348130405,0.0459486619,0.1294860989,0.0374454074,-0.2365318686,0.2692069113,-0.0996681228,-0.1687610447,0.1625194252,0.0585832931,0.2478317171,-0.2568924725,0.0748605505,0.0678149462,-0.0504781604,-0.1628346443,0.3042094409,0.1541785151,-0.1740553379,-0.0858008787,0.0715481341,-0.2308282107,-0.3051958382,-0.1649234742,-0.0587764792,0.0251882523,-0.2654542327,0.2546764314,-0.0919789299,-0.1681697071,0.1078646034,0.4329473972,-0.1162305996,0.0310744662,-0.0215807576,0.1758451015,-0.2366054505,-0.0912673846,0.2016696483,0.2709169686,-0.407050699,-0.0816617608,-0.2495727241,-0.0352467522,0.1647792161,0.1570712179,-0.1957983375,0.1144887954,0.020055797,0.075720638,0.4357150793,-0.135609597,-0.3292346895,0.5616938472,-0.1428675652,0.043967139,0.1381468922,0.3059666753,0.3618828356,-0.0729374439,0.0767347664,0.2616869211,-0.2014493793,-0.1164130569,-0.1320562363,-0.3784996271,-0.3847476542,-0.0944835693,0.5402752757,0.1610286087,0.3405368626,-0.1514064372,0.3001446724,0.1266346425,0.2024376541,0.1396225393,0.5302109122,0.1900881976,-0.0358964689,-0.2318319231,-0.1146017015,0.0589498766,-0.0395759419,0.1955925524,0.1803219616,-0.1537195146,-0.2410766631,-0.135657832,-0.0562960096,-0.3382447362,0.1935910583,0.2385710925,0.1584198624,-0.2077828795,-0.1073490232,0.299267143,0.1788813472,-0.1213110536,-0.0368953981,0.2745821774,-0.4515448213,0.177214548,0.0068607121,0.253030926,0.0016299685,-0.230342865,0.3806056082,0.3563605845,-0.1053834483,-0.2045086622,0.428384304,-0.0621716045,0.1458102465,-0.2260694653,0.1204099432,-0.1315041184,0.0909258649,0.244899556,-0.0000739391,0.5810728073,0.2931474447,0.0285525266,0.1140239462,-0.0070644561,0.1190366223,0.0095453598,0.1411252618,-0.3736830056,0.087313883,0.3077257872,-0.0203570556,-0.1098162085,-0.467192024,0.1483647823,-0.1070786417,-0.0307745114,0.0113055492,0.1671342403,0.1091932207,-0.0787724257,-0.1423222721,0.3321632445,0.2356979251,0.4182392061,-0.0649807304,-0.0916898847,-0.0043933867,-0.0287387259,0.0683248788,0.2646042705,0.302741468,0.0663110316,0.2482176572,0.2664308846,-0.2229568809,-0.2070318311,-0.0658157542,0.155260846,0.0077507147,0.1695458591,0.1440908909,0.2144911736,-0.261707902,0.0228633825,-0.0659873858,-0.2564628124,0.2115870416,-0.1428839266,0.2090875357,0.3292405903,-0.4815833867,0.227349177,-0.4066334963,0.3680802286,0.0983323455,-0.0784763694,-0.3023244739,0.2722123861,0.1302017719,-0.2210624665,0.5940274596,0.0488381162,-0.1143660098,-0.4036473036,-0.734056592,0.3076915443,-0.2309062332,0.4652511179,0.1293065846,-0.0113285072,-0.0347547941,-0.1014852226,0.0613072664,-0.5267003775,-0.2977417111,-0.2505534589,-0.3947771788,0.0163212717,0.0256120227,-0.434248358,-0.3470573127,-0.242942065,0.2471621633,0.1174121872,0.1606267691,0.2522360981,-0.076719664,-0.0131175816,0.0112109128,0.0919826925,-0.188360095,-0.1630577445,0.2885794342,-0.4790608883,-0.5077258348,0.1003755257,-0.1816789955,-0.048965916,-0.0948415548,-0.1655977517,-0.480766207,-0.075268954,0.3006833494,0.2141378671,-0.089953132,0.2338048667,-0.3037539721,-0.3084408641,0.0085154437,0.2025715411,0.4028569758,-0.4210573733,0.1648031622,-0.0959573165,0.5690150857,-0.1091206297,0.3569287062,0.0350179411,-0.3291341066,-0.0378538817,-0.1166755855,0.4221532643,0.0258365534,-0.1519756317,0.2515668273,-0.1604850441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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1755","title":"Using select\/reordering datasets slows operations down immensely","comments":"Thanks for the input! I gave that a try by adding this after my selection \/ reordering operations, but before the big computation task of `score_squad`\r\n\r\n```\r\nexamples = examples.flatten_indices()\r\nfeatures = features.flatten_indices()\r\n```\r\n\r\nThat helped quite a bit!","body":"I am using portions of HF's helpful work in preparing \/ scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour.\r\n\r\nThe below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score.\r\n\r\n\r\nThe problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes.\r\n\r\n\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import BertTokenizerFast, BertForQuestionAnswering\r\nfrom elasticsearch import Elasticsearch\r\nimport numpy as np\r\nimport collections\r\nfrom tqdm.auto import tqdm\r\nimport torch\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\r\nmax_length = 384 # The maximum length of a feature (question and context)\r\ndoc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\r\npad_on_right = tokenizer.padding_side == \"right\"\r\nsquad_v2 = True\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef prepare_validation_features(examples):\r\n    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n    # in one example possible giving several features when a context is long, each of those features having a\r\n    # context that overlaps a bit the context of the previous feature.\r\n    tokenized_examples = tokenizer(\r\n        examples[\"question\" if pad_on_right else \"context\"],\r\n        examples[\"context\" if pad_on_right else \"question\"],\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=max_length,\r\n        stride=doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n    # its corresponding example. This key gives us just that.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n    # We keep the example_id that gave us this feature and we will store the offset mappings.\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i in range(len(tokenized_examples[\"input_ids\"])):\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        context_index = 1 if pad_on_right else 0\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\r\n        # position is part of the context or not.\r\n        tokenized_examples[\"offset_mapping\"][i] = [\r\n            (list(o) if sequence_ids[k] == context_index else None)\r\n            for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n        ]\r\n\r\n    return tokenized_examples\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30):\r\n    all_start_logits, all_end_logits = starting_logits, ending_logits\r\n    # Build a map example to its corresponding features.\r\n    example_id_to_index = {k: i for i, k in enumerate(examples[\"id\"])}\r\n    features_per_example = collections.defaultdict(list)\r\n\r\n    for i, feature in enumerate(features):\r\n        features_per_example[example_id_to_index[feature[\"example_id\"]]].append(i)\r\n\r\n    # The dictionaries we have to fill.\r\n    predictions = collections.OrderedDict()\r\n\r\n    # Logging.\r\n    print(f\"Post-processing {len(examples)} example predictions split into {len(features)} features.\")\r\n\r\n    # Let's loop over all the examples!\r\n    for example_index, example in enumerate(tqdm(examples)):\r\n        # Those are the indices of the features associated to the current example.\r\n        feature_indices = features_per_example[example_index]\r\n\r\n        min_null_score = None # Only used if squad_v2 is True.\r\n        valid_answers = []\r\n\r\n        context = example[\"context\"]\r\n        # Looping through all the features associated to the current example.\r\n        for feature_index in feature_indices:\r\n\r\n            # We grab the predictions of the model for this feature.\r\n            start_logits = all_start_logits[feature_index]\r\n            end_logits = all_end_logits[feature_index]\r\n            # This is what will allow us to map some the positions in our logits to span of texts in the original\r\n            # context.\r\n            offset_mapping = features[feature_index][\"offset_mapping\"]\r\n\r\n            # Update minimum null prediction.\r\n            cls_index = features[feature_index][\"input_ids\"].index(tokenizer.cls_token_id)\r\n            feature_null_score = start_logits[cls_index] + end_logits[cls_index]\r\n            if min_null_score is None or min_null_score < feature_null_score:\r\n                min_null_score = feature_null_score\r\n\r\n            # Go through all possibilities for the `n_best_size` greater start and end logits.\r\n            start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()\r\n            end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()\r\n            for start_index in start_indexes:\r\n                for end_index in end_indexes:\r\n                    # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond\r\n                    # to part of the input_ids that are not in the context.\r\n                    if (\r\n                        start_index >= len(offset_mapping)\r\n                        or end_index >= len(offset_mapping)\r\n                        or offset_mapping[start_index] is None\r\n                        or offset_mapping[end_index] is None\r\n                    ):\r\n                        continue\r\n                    # Don't consider answers with a length that is either < 0 or > max_answer_length.\r\n                    if end_index < start_index or end_index - start_index + 1 > max_answer_length:\r\n                        continue\r\n\r\n                    start_char = offset_mapping[start_index][0]\r\n                    end_char = offset_mapping[end_index][1]\r\n                    valid_answers.append(\r\n                        {\r\n                            \"score\": start_logits[start_index] + end_logits[end_index],\r\n                            \"text\": context[start_char: end_char]\r\n                        }\r\n                    )\r\n\r\n\r\n        if len(valid_answers) > 0:\r\n            best_answer = sorted(valid_answers, key=lambda x: x[\"score\"], reverse=True)[0]\r\n        else:\r\n            # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid\r\n            # failure.\r\n            best_answer = {\"text\": \"\", \"score\": 0.0}\r\n\r\n        # Let's pick our final answer: the best one or the null answer (only for squad_v2)\r\n        if not squad_v2:\r\n            predictions[example[\"id\"]] = best_answer[\"text\"]\r\n        else:\r\n            answer = best_answer[\"text\"] if best_answer[\"score\"] > min_null_score else \"\"\r\n            predictions[example[\"id\"]] = answer\r\n\r\n    return predictions\r\n\r\n\r\n\r\n# build base examples, features from training data\r\nexamples = load_dataset(\"squad_v2\").shuffle(seed=5)['train']\r\nfeatures = load_dataset(\"squad_v2\").shuffle(seed=5)['train'].map(\r\n    prepare_validation_features,\r\n    batched=True,\r\n    remove_columns=['answers', 'context', 'id', 'question', 'title'])\r\n\r\n# sim some shuffled training indices that we want to use to re-order the data to compare how we did\r\nshuffle_idx = np.arange(0, 131754)\r\nnp.random.shuffle(shuffle_idx)\r\n# create a new dataset with rows selected following the training shuffle\r\nfeatures = features.select(indices=shuffle_idx)\r\n# get unique example ids to match with the \"example\" data\r\nid_list = list(dict.fromkeys(features['example_id']))\r\n# now search for their index positions; load elastic search\r\nes = Elasticsearch([{'host': 'localhost'}]).ping()\r\n# add an index to the id column for the examples\r\nexamples.add_elasticsearch_index(column='id')\r\n# search the examples for their index position\r\nexample_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]\r\n# drop the elastic search\r\nexamples.drop_index(index_name='id')\r\n# put examples in the right order\r\nexamples = examples.select(indices=example_idx)\r\n\r\n# generate some fake data\r\nlogits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)}\r\n\r\n\r\ndef score_squad(logits, n_best_size, max_answer):\r\n    # proceed with QA calculation\r\n    final_predictions = postprocess_qa_predictions(examples=examples,\r\n                                                   features=features,\r\n                                                   starting_logits=logits['starting_logits'],\r\n                                                   ending_logits=logits['ending_logits'],\r\n                                                   n_best_size=20,\r\n                                                   max_answer_length=30)\r\n    metric = load_metric(\"squad_v2\")\r\n    formatted_predictions = [{\"id\": k, \"prediction_text\": v, \"no_answer_probability\": 0.0} for k, v in final_predictions.items()]\r\n    references = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\r\n    metrics = metric.compute(predictions=formatted_predictions, references=references)\r\n    return metrics\r\n\r\nmetrics = score_squad(logits, n_best_size=20, max_answer=30)\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","comment_length":39,"text":"Using select\/reordering datasets slows operations down immensely \n I am using portions of HF's helpful work in preparing \/ scoring the SQuAD 2.0 data. The problem I have is that after using `select` to re-ordering the dataset, computations slow down immensely where the total scoring process on 131k training examples would take maybe 3 minutes, now take over an hour.\r\n\r\nThe below example should be reproducible and I have ran myself down this path because I want to use HF's scoring functions and helpful data preparation, but use my own trainer. The training process uses shuffle and therefore the order I trained on no longer matches the original data set order. So, to score my results correctly, the original data set needs to match the order of the training. This requires that I: (1) collect the index for each row of data emitted during training, and (2) use this index information to re-order the datasets correctly so the orders match when I go to score.\r\n\r\n\r\nThe problem is, the dataset class starts performing very poorly as soon as you start manipulating its order by immense magnitudes.\r\n\r\n\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\nfrom transformers import BertTokenizerFast, BertForQuestionAnswering\r\nfrom elasticsearch import Elasticsearch\r\nimport numpy as np\r\nimport collections\r\nfrom tqdm.auto import tqdm\r\nimport torch\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\r\nmax_length = 384 # The maximum length of a feature (question and context)\r\ndoc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed.\r\npad_on_right = tokenizer.padding_side == \"right\"\r\nsquad_v2 = True\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef prepare_validation_features(examples):\r\n    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results\r\n    # in one example possible giving several features when a context is long, each of those features having a\r\n    # context that overlaps a bit the context of the previous feature.\r\n    tokenized_examples = tokenizer(\r\n        examples[\"question\" if pad_on_right else \"context\"],\r\n        examples[\"context\" if pad_on_right else \"question\"],\r\n        truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n        max_length=max_length,\r\n        stride=doc_stride,\r\n        return_overflowing_tokens=True,\r\n        return_offsets_mapping=True,\r\n        padding=\"max_length\",\r\n    )\r\n\r\n    # Since one example might give us several features if it has a long context, we need a map from a feature to\r\n    # its corresponding example. This key gives us just that.\r\n    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\r\n\r\n    # We keep the example_id that gave us this feature and we will store the offset mappings.\r\n    tokenized_examples[\"example_id\"] = []\r\n\r\n    for i in range(len(tokenized_examples[\"input_ids\"])):\r\n        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\r\n        sequence_ids = tokenized_examples.sequence_ids(i)\r\n        context_index = 1 if pad_on_right else 0\r\n\r\n        # One example can give several spans, this is the index of the example containing this span of text.\r\n        sample_index = sample_mapping[i]\r\n        tokenized_examples[\"example_id\"].append(examples[\"id\"][sample_index])\r\n\r\n        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token\r\n        # position is part of the context or not.\r\n        tokenized_examples[\"offset_mapping\"][i] = [\r\n            (list(o) if sequence_ids[k] == context_index else None)\r\n            for k, o in enumerate(tokenized_examples[\"offset_mapping\"][i])\r\n        ]\r\n\r\n    return tokenized_examples\r\n\r\n# from https:\/\/colab.research.google.com\/github\/huggingface\/notebooks\/blob\/master\/examples\/question_answering.ipynb#scrollTo=941LPhDWeYv-\r\ndef postprocess_qa_predictions(examples, features, starting_logits, ending_logits, n_best_size = 20, max_answer_length = 30):\r\n    all_start_logits, all_end_logits = starting_logits, ending_logits\r\n    # Build a map example to its corresponding features.\r\n    example_id_to_index = {k: i for i, k in enumerate(examples[\"id\"])}\r\n    features_per_example = collections.defaultdict(list)\r\n\r\n    for i, feature in enumerate(features):\r\n        features_per_example[example_id_to_index[feature[\"example_id\"]]].append(i)\r\n\r\n    # The dictionaries we have to fill.\r\n    predictions = collections.OrderedDict()\r\n\r\n    # Logging.\r\n    print(f\"Post-processing {len(examples)} example predictions split into {len(features)} features.\")\r\n\r\n    # Let's loop over all the examples!\r\n    for example_index, example in enumerate(tqdm(examples)):\r\n        # Those are the indices of the features associated to the current example.\r\n        feature_indices = features_per_example[example_index]\r\n\r\n        min_null_score = None # Only used if squad_v2 is True.\r\n        valid_answers = []\r\n\r\n        context = example[\"context\"]\r\n        # Looping through all the features associated to the current example.\r\n        for feature_index in feature_indices:\r\n\r\n            # We grab the predictions of the model for this feature.\r\n            start_logits = all_start_logits[feature_index]\r\n            end_logits = all_end_logits[feature_index]\r\n            # This is what will allow us to map some the positions in our logits to span of texts in the original\r\n            # context.\r\n            offset_mapping = features[feature_index][\"offset_mapping\"]\r\n\r\n            # Update minimum null prediction.\r\n            cls_index = features[feature_index][\"input_ids\"].index(tokenizer.cls_token_id)\r\n            feature_null_score = start_logits[cls_index] + end_logits[cls_index]\r\n            if min_null_score is None or min_null_score < feature_null_score:\r\n                min_null_score = feature_null_score\r\n\r\n            # Go through all possibilities for the `n_best_size` greater start and end logits.\r\n            start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()\r\n            end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()\r\n            for start_index in start_indexes:\r\n                for end_index in end_indexes:\r\n                    # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond\r\n                    # to part of the input_ids that are not in the context.\r\n                    if (\r\n                        start_index >= len(offset_mapping)\r\n                        or end_index >= len(offset_mapping)\r\n                        or offset_mapping[start_index] is None\r\n                        or offset_mapping[end_index] is None\r\n                    ):\r\n                        continue\r\n                    # Don't consider answers with a length that is either < 0 or > max_answer_length.\r\n                    if end_index < start_index or end_index - start_index + 1 > max_answer_length:\r\n                        continue\r\n\r\n                    start_char = offset_mapping[start_index][0]\r\n                    end_char = offset_mapping[end_index][1]\r\n                    valid_answers.append(\r\n                        {\r\n                            \"score\": start_logits[start_index] + end_logits[end_index],\r\n                            \"text\": context[start_char: end_char]\r\n                        }\r\n                    )\r\n\r\n\r\n        if len(valid_answers) > 0:\r\n            best_answer = sorted(valid_answers, key=lambda x: x[\"score\"], reverse=True)[0]\r\n        else:\r\n            # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid\r\n            # failure.\r\n            best_answer = {\"text\": \"\", \"score\": 0.0}\r\n\r\n        # Let's pick our final answer: the best one or the null answer (only for squad_v2)\r\n        if not squad_v2:\r\n            predictions[example[\"id\"]] = best_answer[\"text\"]\r\n        else:\r\n            answer = best_answer[\"text\"] if best_answer[\"score\"] > min_null_score else \"\"\r\n            predictions[example[\"id\"]] = answer\r\n\r\n    return predictions\r\n\r\n\r\n\r\n# build base examples, features from training data\r\nexamples = load_dataset(\"squad_v2\").shuffle(seed=5)['train']\r\nfeatures = load_dataset(\"squad_v2\").shuffle(seed=5)['train'].map(\r\n    prepare_validation_features,\r\n    batched=True,\r\n    remove_columns=['answers', 'context', 'id', 'question', 'title'])\r\n\r\n# sim some shuffled training indices that we want to use to re-order the data to compare how we did\r\nshuffle_idx = np.arange(0, 131754)\r\nnp.random.shuffle(shuffle_idx)\r\n# create a new dataset with rows selected following the training shuffle\r\nfeatures = features.select(indices=shuffle_idx)\r\n# get unique example ids to match with the \"example\" data\r\nid_list = list(dict.fromkeys(features['example_id']))\r\n# now search for their index positions; load elastic search\r\nes = Elasticsearch([{'host': 'localhost'}]).ping()\r\n# add an index to the id column for the examples\r\nexamples.add_elasticsearch_index(column='id')\r\n# search the examples for their index position\r\nexample_idx = [examples.search(index_name='id', query=i, k=1).indices for i in id_list]\r\n# drop the elastic search\r\nexamples.drop_index(index_name='id')\r\n# put examples in the right order\r\nexamples = examples.select(indices=example_idx)\r\n\r\n# generate some fake data\r\nlogits = {'starting_logits': torch.randn(131754, 384), 'ending_logits': torch.randn(131754, 384)}\r\n\r\n\r\ndef score_squad(logits, n_best_size, max_answer):\r\n    # proceed with QA calculation\r\n    final_predictions = postprocess_qa_predictions(examples=examples,\r\n                                                   features=features,\r\n                                                   starting_logits=logits['starting_logits'],\r\n                                                   ending_logits=logits['ending_logits'],\r\n                                                   n_best_size=20,\r\n                                                   max_answer_length=30)\r\n    metric = load_metric(\"squad_v2\")\r\n    formatted_predictions = [{\"id\": k, \"prediction_text\": v, \"no_answer_probability\": 0.0} for k, v in final_predictions.items()]\r\n    references = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\r\n    metrics = metric.compute(predictions=formatted_predictions, references=references)\r\n    return metrics\r\n\r\nmetrics = score_squad(logits, n_best_size=20, max_answer=30)\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \n Thanks for the input! I gave that a try by adding this after my selection \/ reordering operations, but before the big computation task of `score_squad`\r\n\r\n```\r\nexamples = examples.flatten_indices()\r\nfeatures = features.flatten_indices()\r\n```\r\n\r\nThat helped quite a bit!","embeddings":[-0.2229693085,0.2477027923,-0.0138683552,-0.1000716835,-0.003328807,-0.1465102434,-0.0916016847,0.1460768282,-0.3497589529,0.1094219536,-0.2728040516,0.3873890042,0.2126539499,-0.1992182434,-0.0613091849,-0.0526460782,-0.1135229096,0.0000033445,-0.0314249322,-0.0937863365,-0.1005084962,-0.3484925032,-0.3477401137,-0.145543471,-0.2218254358,-0.1603693813,0.0549748018,0.3512459695,-0.1260461956,-0.3404070437,0.0977792069,0.5284638405,-0.0601430126,0.3460890353,-0.0001203739,-0.1140628681,-0.301356703,-0.0590985864,-0.4936899543,0.0832120925,-0.0697157905,-0.0438395105,-0.1616155952,0.0779754221,-0.1216855645,-0.1129818335,-0.0549107939,-0.3639614284,0.4560742378,-0.0008517363,0.1208517849,0.220481649,-0.5714429021,-0.2556709349,0.1670394242,0.0162417125,-0.0953832343,-0.0365809016,0.4183206856,0.1212784946,-0.1140871421,0.3431174755,-0.0950435996,-0.1506789178,0.2451233715,0.1294504702,-0.2699114084,0.1256278604,0.005174641,0.0222797319,0.2026703358,-0.1132044941,-0.5826923847,-0.3719269037,0.341755867,-0.0127385156,0.2049164623,-0.2810357511,0.4607771039,-0.0416236669,-0.306301266,0.2016853541,0.0358903036,0.0214370992,0.1613190323,0.2261903286,0.0236478597,-0.0106987497,-0.0192314014,-0.1237981394,0.1931634396,-0.187747255,0.0458304808,0.3385604024,-0.485106051,-0.2102170885,-0.1109896228,-0.2480468452,0.030924825,0.1953477114,0.0991410986,0.4193668067,0.0793582574,-0.3105548918,0.1926971674,0.2943916023,-0.0932467952,0.3354053497,0.0661656633,-0.0558295101,-0.0670939237,-0.027262589,0.05751976,-0.3548845947,-0.1153057069,0.0621315576,-0.416890353,-0.1396674961,-0.4675750732,0.0101275723,-0.2367890626,0.026424244,0.3597140312,0.3569377363,-0.1339115798,-0.1230955496,0.0204742644,0.1510910392,-0.3827776909,0.1194274426,-0.1549317241,-0.1807512939,-0.1282603741,0.4981126487,0.0682006031,0.0105687119,-0.0445278138,0.1479117125,0.6504570246,0.1346221715,0.1789738238,-0.0625602156,0.3049418628,0.1330021918,-0.1644210666,0.0284394585,-0.0693844259,-0.0828607082,-0.2640684247,0.2404960692,-0.4224526882,-0.1730779409,0.2738597393,0.1426498741,0.053241685,-0.1324371696,-0.0261703134,0.1152576506,0.0700723827,-0.3271812797,0.0334452242,-0.2640089989,0.0580386445,0.0417545848,0.1055177599,0.264410466,-0.4662193656,-0.0805213153,0.0947195888,0.5643754601,0.2235662043,0.3344970942,-0.2951166928,0.0774924085,0.0623126552,0.2190618217,0.4485758841,-0.1682099849,-0.4064477086,0.0793168619,-0.3333152533,0.1301809698,0.1100042984,0.2987503409,0.4087423086,-0.1437912285,-0.0917459428,0.2847900689,0.0783123896,-0.0005592417,-0.2430839092,-0.1494386792,-0.1460276693,0.2493930757,-0.0854728371,0.0004539371,0.0658750907,0.2544437647,0.0936773643,-0.212437734,-0.1569136828,0.1440961361,-0.0487086475,0.2361472845,0.009082377,-0.2017503828,-0.2624258995,0.2542325854,-0.1482537538,-0.0874456838,0.1352851391,-0.2490602881,-0.2674162686,-0.2243238986,-0.3613672256,-0.0841277689,-0.0326935686,-0.0000497185,0.2648528516,-0.374301374,-0.1358233392,0.2545201778,-0.29800722,0.1125548556,0.1867353618,0.3353077769,0.0942640156,-0.0836718678,-0.1753809452,0.223179847,0.0413799025,-0.0102596274,-0.0425318852,0.4816074669,-0.0305584166,-0.2010594904,0.300344646,0.1919036806,0.1189256087,-0.0048329839,0.1848534644,-0.0567650683,-0.1038584858,-0.0857224688,-0.4112776518,0.7307934761,-0.6390340924,0.3906633854,-0.0081895739,-0.3914916515,-0.1364453137,0.2339695692,-0.0671845451,0.028089473,0.1946619898,0.0692243576,0.3068402112,0.0219235756,-0.1708946526,0.1148176268,0.3807358742,0.1116098538,-0.116190806,0.0593309514,0.1933869421,0.0581158064,-0.1526626647,-0.4381038547,0.2674148977,0.2144753784,-0.0098131234,-0.0738034323,-0.0876859352,-0.0899261758,-0.0617198683,-0.0450996533,-0.0257454459,0.3969434798,0.3169572949,0.2558146119,-0.2715595961,-0.0461911708,0.0497649349,-0.0269536227,-0.3187946081,0.3653787971,-0.2655484378,0.3262257278,-0.0995716602,0.0995328948,-0.2339483351,-0.1894762069,0.2998906076,-0.0532458164,0.0420344025,0.2280463427,0.0082486607,0.2510022223,0.193622604,-0.4015026987,0.0486457944,0.1283143908,-0.0853393897,-0.0386849381,0.1792995185,0.2340388447,0.299641192,0.1631267071,-0.2064593881,-0.0706808493,-0.3488852978,-0.0585122183,0.0901514664,0.1498829275,-0.0947868451,-0.1986880302,-0.505982399,-0.336107105,-0.0967887044,0.1074076146,-0.189230904,0.0047539468,-0.2101000994,0.0690367818,0.1734790504,-0.4781151414,0.0168381706,0.0162553135,0.0344494693,0.0098374942,0.066954568,0.1476120055,-0.1610152125,-0.1116630211,-0.1796435267,0.0880680829,-0.3538632095,-0.2686766982,0.2319982201,-0.1055770144,-0.008286356,-0.4196908772,-0.0765763521,-0.5765689611,0.2538782358,0.0160375256,-0.1266462505,-0.0915807411,-0.0984586105,-0.3653621674,0.2959348261,0.3322464824,-0.2109398097,0.032138925,0.110281691,-0.0774022862,0.0623431467,0.5486826301,0.0462288484,-0.4003959298,0.1239522099,0.1109858006,1.1333417892,-0.0141810263,0.1114459336,-0.0653473362,0.1698570549,0.0690061226,-0.349622041,-0.1671998799,0.2396910191,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1747","title":"datasets slicing with seed ","comments":"Hi :) \r\nThe slicing API from https:\/\/huggingface.co\/docs\/datasets\/splits.html doesn't shuffle the data.\r\nYou can shuffle and then take a subset of your dataset with\r\n```python\r\n# shuffle and take the first 100 examples\r\ndataset = dataset.shuffle(seed=42).select(range(100))\r\n```\r\n\r\nYou can find more information about shuffling and selecting rows in the documentation: https:\/\/huggingface.co\/docs\/datasets\/processing.html#selecting-sorting-shuffling-splitting-rows","body":"Hi\r\nI need to slice a dataset with random seed, I looked into documentation here https:\/\/huggingface.co\/docs\/datasets\/splits.html \r\nI could not find a seed option, could you assist me please how I can get a slice for different seeds?\r\nthank you.\r\n@lhoestq  ","comment_length":50,"text":"datasets slicing with seed  \n Hi\r\nI need to slice a dataset with random seed, I looked into documentation here https:\/\/huggingface.co\/docs\/datasets\/splits.html \r\nI could not find a seed option, could you assist me please how I can get a slice for different seeds?\r\nthank you.\r\n@lhoestq   \n Hi :) \r\nThe slicing API from https:\/\/huggingface.co\/docs\/datasets\/splits.html doesn't shuffle the data.\r\nYou can shuffle and then take a subset of your dataset with\r\n```python\r\n# shuffle and take the first 100 examples\r\ndataset = dataset.shuffle(seed=42).select(range(100))\r\n```\r\n\r\nYou can find more information about shuffling and selecting rows in the documentation: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1747","title":"datasets slicing with seed ","comments":"thank you so much\n\nOn Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hi :)\n> The slicing API doesn't shuffle the data.\n> You can shuffle and then take a subset of your dataset with\n>\n> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))\n>\n> You can find more information about shuffling and selecting rows in the\n> documentation:\n> https:\/\/huggingface.co\/docs\/datasets\/processing.html#selecting-sorting-shuffling-splitting-rows\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/1747#issuecomment-762278134>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ>\n> .\n>\n","body":"Hi\r\nI need to slice a dataset with random seed, I looked into documentation here https:\/\/huggingface.co\/docs\/datasets\/splits.html \r\nI could not find a seed option, could you assist me please how I can get a slice for different seeds?\r\nthank you.\r\n@lhoestq  ","comment_length":103,"text":"datasets slicing with seed  \n Hi\r\nI need to slice a dataset with random seed, I looked into documentation here https:\/\/huggingface.co\/docs\/datasets\/splits.html \r\nI could not find a seed option, could you assist me please how I can get a slice for different seeds?\r\nthank you.\r\n@lhoestq   \n thank you so much\n\nOn Mon, Jan 18, 2021 at 3:17 PM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hi :)\n> The slicing API doesn't shuffle the data.\n> You can shuffle and then take a subset of your dataset with\n>\n> # shuffle and take the first 100 examplesdataset = dataset.shuffle(seed=42).select(range(100))\n>\n> You can find more information about shuffling and selecting rows in the\n> documentation:\n> https:\/\/huggingface.co\/docs\/datasets\/processing.html#selecting-sorting-shuffling-splitting-rows\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/1747#issuecomment-762278134>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/AM3GZM5D5MDPLJGI4IG3UADS2Q7GPANCNFSM4WHLOZJQ>\n> .\n>\n","embeddings":[-0.0242704786,-0.5800485015,-0.1025025472,0.0901766643,0.3337330222,-0.0313543528,0.0862427354,0.0778434575,-0.1555906981,0.6615405679,-0.0357807092,0.1159344763,-0.1422756314,0.6808801293,0.1332575381,-0.3228478432,0.0889167264,-0.1975939274,0.2540716231,-0.149235338,-0.0809885114,-0.0049858391,-0.1872225553,-0.1387491673,-0.2912373245,-0.2559998631,-0.171070084,0.2226489484,-0.1419475228,-0.009559893,0.0738407299,0.0576182567,0.2052414566,0.1588170379,-0.000118139,-0.139104709,0.0257986262,0.0376942456,0.0179532841,-0.0529340282,-0.2801009417,0.3696419299,-0.1711483747,-0.1028147414,-0.4033963084,0.0392841026,0.1133556515,-0.2076660842,0.0146942846,-0.1915639639,0.098517783,0.0506312661,0.0284842215,-0.1905965805,0.2813759744,0.6558921337,-0.0894705504,-0.1163896844,0.063131474,0.5073189139,0.2269356847,0.0002389428,0.1760697961,0.1714318693,0.0334674008,0.0956163257,-0.3726251721,-0.4360311031,0.0508839004,0.4526301324,0.4963739514,0.1073247269,-0.3182222843,-0.1937035471,-0.0015614852,-0.2984308898,-0.2344824076,0.5080899,-0.1129811406,-0.0207737647,-0.0342491008,0.0239949524,-0.108331494,0.0960868746,0.329854846,0.455275774,-0.1264501214,0.1400949061,0.4577620924,-0.0861060843,-0.0909518972,0.0599522032,-0.1416305751,0.2137380093,-0.0388232544,-0.2371636629,-0.3499301076,0.7325920463,0.5226100087,0.1343268007,-0.1152156293,0.1385985911,-0.0751083791,-0.1247148514,0.3147113919,-0.3212530613,0.1867842674,0.2914988101,-0.0277669262,-0.0767461881,0.1510375887,0.0350168422,0.1655572802,0.1850872934,-0.3834688365,-0.1994805187,-0.085770756,-0.014803418,-0.0018438157,-0.5842655301,-0.1403471678,-0.3088261187,-0.0198216457,0.1910836399,0.0416997895,-0.4040729702,-0.2972889841,-0.0018221317,-0.1055932119,-0.4199671745,-0.3290487826,0.1051816568,0.0158047527,0.111004144,-0.0712986141,-0.3014401495,0.1724160761,0.1104671434,0.1535440087,0.0028018493,-0.4736948013,0.2348037064,-0.1354741454,0.1330885291,0.1170670539,-0.1257555932,0.1066314131,0.1307789534,-0.1033917591,-0.0394009054,-0.1314308643,-0.0652163774,0.1241779104,0.0127374995,0.0695370585,0.0033158404,-0.4873548746,0.311891675,-0.2089270502,0.047704719,-0.0773766264,0.3150740862,-0.3417730033,-0.2146972865,0.3613391519,0.013794262,-0.2427788675,-0.3567782044,0.0290716179,0.0296073928,0.3583237231,0.353829205,-0.1359320581,-0.2312394232,-0.1669854671,0.3993638158,0.00805155,0.3812057674,-0.1486324817,0.1341962516,-0.1041427925,-0.1667921692,0.1973541826,0.1176743805,0.1574713439,-0.0099299653,-0.0779606253,0.050833337,-0.3968096673,0.0489632078,-0.1658023894,-0.2540096045,0.055411458,0.2026689202,0.0343664251,-0.0920500979,0.2964076698,-0.2191883326,0.480355233,0.0400295779,0.1040017679,0.0423704088,0.1516966969,0.2454891801,0.0378708206,-0.2893220782,-0.1509354711,0.073101446,0.0028931748,-0.0156618543,0.2078377008,-0.2937504351,-0.061011225,-0.0298586376,0.0571585149,-0.0456082039,-0.0513974652,0.0000218835,0.4871122539,0.1023581848,-0.1233881861,-0.0375184454,-0.1614747941,0.0974078253,-0.7664036751,0.3914408386,0.0388752148,-0.2232470065,0.0756954402,-0.0344887823,0.0620724857,-0.1102513894,0.0592324026,0.3625947833,0.2247779071,-0.0178070217,0.1429630071,0.1382005662,0.2544497848,-0.1579792053,0.007788376,-0.2979333401,0.1091153696,-0.0585157201,-0.2237074971,0.3307854235,-0.4219641089,0.0152340652,-0.1238243431,-0.1087066308,-0.0145641025,0.0868039727,-0.1273791194,-0.065780662,0.1646885425,-0.2167832404,0.0568321496,-0.0496253073,-0.5082008243,0.2045126259,0.1152578741,-0.3140591383,-0.0271635558,0.0599233545,0.0868315101,0.2323121727,0.0578234829,0.136695683,0.1389480382,0.3809364736,0.1306705177,-0.2710238397,0.395421505,-0.1604272723,0.0631251782,0.1573937833,0.0463623814,-0.1512766331,-0.2365282029,-0.1928135753,-0.1225320101,0.2646406293,-0.0871590152,0.127707094,-0.0990575776,-0.2676284015,-0.4211467803,-0.0128322178,-0.1435071826,-0.4676619768,-0.2551902235,-0.1288243532,0.1196715757,0.3570855558,0.0584838837,0.1540054828,0.1172048226,0.1035313904,-0.192457214,0.4856258035,-0.3060836196,0.095652096,0.0159236994,0.1082333624,0.0052157082,0.5828412771,0.2883780897,-0.1631916016,-0.2167949378,-0.1514893323,-0.0317186564,0.0742449462,-0.2648353279,0.6869398952,0.2674885988,0.174036786,-0.3678111136,-0.1260756701,0.2882689238,-0.1811948866,-0.0253332872,-0.1145413443,0.1908085048,-0.1281627119,0.200338617,-0.0837968886,-0.3759827912,-0.1047151312,0.2560347319,-0.0970777571,-0.1402120143,-0.0218529887,0.0314281546,-0.3185782731,-0.044652123,-0.3788219094,-0.1389826685,-0.5120393634,0.0317750424,-0.2471351922,-0.2191503346,0.0938065648,-0.0099170664,-0.2453134358,0.0794136897,0.0780087784,0.5433710217,-0.0363137834,0.1234697104,-0.1115003079,0.0138579523,0.6331720352,-0.3010635376,0.1640507132,-0.0399133042,-0.1221870705,-0.0517044738,-0.2836710811,0.3046175241,0.3274873197,0.312335223,-0.0451563373,0.768820107,0.032335408,0.2276027501,-0.0585773066,-0.0526451245,0.0181535911,0.0496937372,-0.2660487592,0.3946563601,0.359762430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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1745","title":"difference between wsc and wsc.fixed for superglue","comments":"From the description given in the dataset script for `wsc.fixed`:\r\n```\r\nThis version fixes issues where the spans are not actually substrings of the text.\r\n```","body":"Hi\r\nI see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq ","comment_length":26,"text":"difference between wsc and wsc.fixed for superglue \n Hi\r\nI see two versions of wsc in superglue, and I am not sure what is the differences and which one is the original one. could you help to discuss the differences? thanks @lhoestq  \n From the description given in the dataset script for `wsc.fixed`:\r\n```\r\nThis version fixes issues where the spans are not actually substrings of the text.\r\n```","embeddings":[-0.1165046319,-0.4438869059,0.0426136553,-0.3368446231,-0.0602852553,-0.3824757338,0.4176901281,-0.2519408464,-0.1031499952,0.054486338,0.1969245225,-0.146696955,0.1097188964,0.1931587756,0.098360531,-0.2450374216,0.3370181918,-0.0297049787,0.2183911204,-0.0973014235,-0.2882769108,-0.0660792813,-0.4690131247,-0.0129365521,-0.6201684475,-0.0405249335,-0.0461752936,-0.0209756084,-0.202447027,-0.2518570423,0.5749289989,0.339104265,0.3738506138,-0.0397943296,-0.0001158088,-0.199822247,0.5318863392,0.0798769891,0.3641519845,0.4048228562,-0.4408574104,-0.4927159548,-0.0975277573,-0.0858862773,0.1681796461,0.249312371,-0.1676397026,-0.3478982449,0.1804486066,-0.1944323033,0.1312809587,-0.0228099246,0.0137679586,-0.1547960639,-0.0083533507,-0.2238358408,-0.1393196881,0.2698023319,0.3193906546,0.0427220277,0.1924658567,0.4580579102,-0.2261031866,-0.0896212086,0.1591403782,0.0118364505,-0.5829805136,-0.0270515736,0.0613174327,0.4081568122,0.4068083763,0.0251670796,-0.4480766952,-0.0524281301,-0.0332935452,0.1166869998,0.1952300221,0.3218772411,0.3227186799,0.1619338691,-0.4081544876,-0.0240215193,0.1407888979,-0.4149757624,-0.3853112757,0.3058308959,-0.1230528504,0.2891016304,-0.1223356426,0.0770807117,0.2488511652,-0.0006772426,-0.1661952585,-0.0300209261,-0.4951646328,-0.2110798806,-0.217123419,0.2030080557,-0.1595705152,0.2685799599,0.1453615278,0.0540380478,0.1727085859,-0.074678205,-0.0876556784,0.1890463382,0.0804159418,-0.0715229735,0.1665004641,-0.1243544444,-0.2451315224,0.0617137402,0.4321227968,-0.3441905081,-0.2333057225,-0.0550082698,0.3404221833,-0.1166873649,-0.2876285017,0.1818105727,-0.0015121206,-0.4694730639,-0.0372020528,0.2117013484,0.0783709735,0.2736050189,-0.0158583168,0.2528206408,-0.0467464849,-0.2208691835,-0.1682070345,0.0275320709,0.0647709519,0.2110201567,-0.1216972619,-0.4539420903,-0.033943221,0.4554688334,-0.5253096223,-0.139845401,-0.4471267462,-0.1749224514,0.0238407571,-0.1458441168,-0.2342798114,0.164782539,-0.0649746954,0.0967292264,0.082372129,0.0846462697,-0.0599702783,-0.082592912,-0.1808074117,0.0868717879,-0.0978944078,-0.2788570821,0.4834412634,0.2546778023,-0.1537862718,0.0886731967,0.2153271735,-0.0972260833,-0.1082553267,-0.2127085626,0.0479025878,0.1230947673,-0.6595558524,-0.0180035178,0.1797147393,-0.0761959553,0.3189204335,0.0976558775,0.1346985549,-0.2406017184,-0.0522274561,0.2427656651,-0.0070213135,0.0770991668,-0.5214035511,0.2309281379,0.4672337174,0.0627568886,0.0758894533,0.0268515404,0.2087683827,-0.0016146232,-0.3322051764,-0.2706946433,-0.0721659437,-0.2226541042,-0.2867246866,0.0091016293,0.2818875313,-0.1519309282,0.0136288907,0.0160741434,0.0633374453,-0.0027107908,0.2329680026,0.1287259907,0.01502789,-0.1269881874,-0.0684637502,0.1541464031,-0.046953436,-0.3897000253,-0.3581689298,-0.107981436,-0.2921330929,-0.0466267727,0.2770764828,-0.0608710386,-0.0595487058,0.1147930995,0.2504432797,-0.1975950599,-0.014917572,0.1125035286,-0.0262016356,-0.4090225101,-0.4172938466,0.0371238329,-0.2061022967,0.1091822609,0.230171442,0.271671474,-0.0824755356,-0.330368042,0.1588196456,0.3281594813,0.1995610446,0.1326562017,-0.271525979,0.6245141029,-0.3346571624,0.2080749422,-0.198858887,-0.1412716061,0.1792337149,-0.5478566885,0.3255382776,0.1101532727,0.1726595014,-0.0353356414,-0.2805935144,0.2657681108,0.1534332484,-0.0480337329,-0.0009915096,-0.0549673736,-0.0492547303,-0.2937425673,-0.0547298752,-0.0695325956,-0.0577465519,-0.0149122719,0.2772341371,0.0630943179,-0.5504789948,0.1491101682,0.7143086195,-0.2740681171,0.0435123034,0.1654292047,-0.3776516914,-0.2486041486,-0.1394638866,0.280959934,0.3068526089,0.0137192775,0.0569086932,0.0276713036,-0.0188716482,-0.092522718,0.4488794804,-0.1777626872,-0.4682577252,-0.0699948221,-0.1501349658,0.0450520515,0.0252174456,0.3341430426,-0.545355916,-0.2040148079,0.0825195983,-0.448271215,-0.3298107088,-0.1052339152,-0.4454060793,-0.0635501072,-0.2789914906,-0.0894788355,-0.131238386,-0.4173159301,-0.1389562041,0.2662662268,-0.0660323128,0.5702053308,-0.2510118783,0.7024792433,-0.2471657544,0.0324246362,-0.2356300354,0.0417775102,-0.0437815748,-0.158128202,-0.0723070428,0.1260567456,0.3512503207,-0.1640392393,-0.3056465089,0.0262424797,-0.0177334249,0.1230627596,0.3480786681,0.1450388432,0.1119326949,-0.1225036532,0.1085024998,-0.5787023306,0.3448266387,-0.2222723812,0.2646610737,0.0796502307,-0.019484248,-0.2624861896,-0.2568075359,-0.1245551482,-0.117643185,-0.3266426027,-0.1079664975,-0.1618291885,-0.16266267,0.0219603814,0.4068734944,0.1268577129,-0.118953228,-0.3631409109,0.2663448155,0.2002221197,-0.3214403391,0.0324532986,-0.2388673872,-0.2139357924,-0.2120586634,-0.050197307,0.3093805015,-0.3111266792,-0.4103981555,0.3222432137,0.2700643837,0.2628370821,-0.1162113175,-0.0704913288,-0.3537479937,-0.3785714507,-0.2534999549,-0.0969455615,0.214597255,0.3455377221,-0.0914842859,-0.0452797115,0.1181723922,0.2031222731,0.0522397496,0.269436121,0.3256165981,-0.1411495954,0.0911589116,-0.102659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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1743","title":"Issue while Creating Custom Metric","comments":"Currently it's only possible to define the features for the two columns `references` and `predictions`.\r\nThe data for these columns can then be passed to `metric.add_batch` and `metric.compute`.\r\nInstead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.\r\n\r\nFor example \r\n```python\r\nfeatures = datasets.Features({\r\n    'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n    \"references\": datasets.Sequence({\r\n        \"references_ids\": datasets.Value(\"int32\"),\r\n        \"offset_mapping\": datasets.Value(\"int32\"),\r\n        'text': datasets.Value('string'),\r\n        \"ground\": datasets.Value(\"int32\")\r\n    }),\r\n})\r\n```\r\n\r\nAnother option would be to simply have the two features like \r\n```python\r\nfeatures = datasets.Features({\r\n    'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n    \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n})\r\n```\r\nand keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).\r\n\r\n\r\nWhat is the metric you would like to implement ?\r\n\r\nI'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https:\/\/github.com\/huggingface\/datasets\/pull\/1577)) and I'd like to know if it's something that can be interesting for users.\r\n\r\nWhat do you think ?","body":"Hi Team,\r\n\r\nI am trying to create a custom metric for my training as follows, where f1 is my own metric:\r\n\r\n```python\r\n def _info(self):\r\n        # TODO: Specifies the datasets.MetricInfo object\r\n        return datasets.MetricInfo(\r\n            # This is the description that will appear on the metrics page.\r\n            description=_DESCRIPTION,\r\n            citation=_CITATION,\r\n            inputs_description=_KWARGS_DESCRIPTION,\r\n            # This defines the format of each prediction and reference\r\n            features = datasets.Features({'predictions':datasets.Sequence(datasets.Value(\"int32\")), \"references\": datasets.Sequence(datasets.Value(\"int32\")),\"offset_mapping\":datasets.Sequence(datasets.Value(\"int32\")),'text':datasets.Sequence(datasets.Value('string')),\"ground\":datasets.Sequence(datasets.Value(\"int32\")),}),\r\n            # Homepage of the metric for documentation\r\n            homepage=\"http:\/\/metric.homepage\",\r\n            # Additional links to the codebase or references\r\n            codebase_urls=[\"http:\/\/github.com\/path\/to\/codebase\/of\/new_metric\"],\r\n            reference_urls=[\"http:\/\/path.to.reference.url\/new_metric\"]\r\n        )\r\n\r\n    def _compute(self,predictions,references,text,offset_mapping,spans):\r\n\r\n        pred_spans = []\r\n\r\n        for i,preds in enumerate(predictions):\r\n            current_preds = []\r\n            for j,token_preds in enumerate(preds):\r\n                if (preds>0.5):\r\n                    current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))\r\n            pred_spans.append(current_spans)\r\n        \r\n        return {\r\n            \"Token Wise F1\": f1_score(references,predictions,labels=[0,1]),\r\n            \"Offset Wise F1\": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])\r\n        }\r\n\r\n```\r\n\r\nI believe this is not correct. But that's not the issue I am facing right now. I get this error :\r\n```python\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-144-ed7349b50821> in <module>()\r\n----> 1 new_metric.compute(predictions=inputs[\"labels\"],references=inputs[\"labels\"], text=inputs[\"text\"], offset_mapping=inputs[\"offset_mapping\"],ground=inputs[\"ground\"] )\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/features.py in encode_batch(self, batch)\r\n    802         encoded_batch = {}\r\n    803         if set(batch) != set(self):\r\n--> 804             print(batch)\r\n    805             print(self)\r\n    806             raise ValueError(\"Column mismatch between batch {} and features {}\".format(set(batch), set(self)))\r\n\r\nValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}\r\n```\r\nOn checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.\r\n\r\nHow do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?\r\n\r\nThanks,\r\nGunjan","comment_length":151,"text":"Issue while Creating Custom Metric \n Hi Team,\r\n\r\nI am trying to create a custom metric for my training as follows, where f1 is my own metric:\r\n\r\n```python\r\n def _info(self):\r\n        # TODO: Specifies the datasets.MetricInfo object\r\n        return datasets.MetricInfo(\r\n            # This is the description that will appear on the metrics page.\r\n            description=_DESCRIPTION,\r\n            citation=_CITATION,\r\n            inputs_description=_KWARGS_DESCRIPTION,\r\n            # This defines the format of each prediction and reference\r\n            features = datasets.Features({'predictions':datasets.Sequence(datasets.Value(\"int32\")), \"references\": datasets.Sequence(datasets.Value(\"int32\")),\"offset_mapping\":datasets.Sequence(datasets.Value(\"int32\")),'text':datasets.Sequence(datasets.Value('string')),\"ground\":datasets.Sequence(datasets.Value(\"int32\")),}),\r\n            # Homepage of the metric for documentation\r\n            homepage=\"http:\/\/metric.homepage\",\r\n            # Additional links to the codebase or references\r\n            codebase_urls=[\"http:\/\/github.com\/path\/to\/codebase\/of\/new_metric\"],\r\n            reference_urls=[\"http:\/\/path.to.reference.url\/new_metric\"]\r\n        )\r\n\r\n    def _compute(self,predictions,references,text,offset_mapping,spans):\r\n\r\n        pred_spans = []\r\n\r\n        for i,preds in enumerate(predictions):\r\n            current_preds = []\r\n            for j,token_preds in enumerate(preds):\r\n                if (preds>0.5):\r\n                    current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))\r\n            pred_spans.append(current_spans)\r\n        \r\n        return {\r\n            \"Token Wise F1\": f1_score(references,predictions,labels=[0,1]),\r\n            \"Offset Wise F1\": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])\r\n        }\r\n\r\n```\r\n\r\nI believe this is not correct. But that's not the issue I am facing right now. I get this error :\r\n```python\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-144-ed7349b50821> in <module>()\r\n----> 1 new_metric.compute(predictions=inputs[\"labels\"],references=inputs[\"labels\"], text=inputs[\"text\"], offset_mapping=inputs[\"offset_mapping\"],ground=inputs[\"ground\"] )\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/features.py in encode_batch(self, batch)\r\n    802         encoded_batch = {}\r\n    803         if set(batch) != set(self):\r\n--> 804             print(batch)\r\n    805             print(self)\r\n    806             raise ValueError(\"Column mismatch between batch {} and features {}\".format(set(batch), set(self)))\r\n\r\nValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}\r\n```\r\nOn checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.\r\n\r\nHow do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?\r\n\r\nThanks,\r\nGunjan \n Currently it's only possible to define the features for the two columns `references` and `predictions`.\r\nThe data for these columns can then be passed to `metric.add_batch` and `metric.compute`.\r\nInstead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references and predictions.\r\n\r\nFor example \r\n```python\r\nfeatures = datasets.Features({\r\n    'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n    \"references\": datasets.Sequence({\r\n        \"references_ids\": datasets.Value(\"int32\"),\r\n        \"offset_mapping\": datasets.Value(\"int32\"),\r\n        'text': datasets.Value('string'),\r\n        \"ground\": datasets.Value(\"int32\")\r\n    }),\r\n})\r\n```\r\n\r\nAnother option would be to simply have the two features like \r\n```python\r\nfeatures = datasets.Features({\r\n    'predictions':datasets.Sequence(datasets.Value(\"int32\")),\r\n    \"references\": datasets.Sequence(datasets.Value(\"int32\")),\r\n})\r\n```\r\nand keep `offset_mapping`, `text` and `ground` as as parameters for the computation (i.e. kwargs when calling `metric.compute`).\r\n\r\n\r\nWhat is the metric you would like to implement ?\r\n\r\nI'm asking since we consider allowing additional fields as requested in the `Comet` metric (see PR and discussion [here](https:\/\/github.com\/huggingface\/datasets\/pull\/1577)) and I'd like to know if it's something that can be interesting for users.\r\n\r\nWhat do you think ?","embeddings":[-0.2388352007,-0.3020202518,-0.1457836926,0.1864611059,0.4193514287,-0.0384185873,0.106930159,0.19828628,0.0518294647,0.2997829318,-0.0557398088,0.2383317798,-0.1781434864,0.1406398267,-0.0143401977,-0.1415147632,-0.3350710273,0.1471369863,0.3345439434,-0.0345635414,-0.3107739687,0.1606325507,-0.069587566,0.1216962636,-0.2355847806,-0.0933226347,0.2366884053,-0.0159126148,-0.4124858677,-0.3041528165,0.0990642533,0.0513419546,-0.149600476,0.5381695032,-0.0000985569,0.0695141777,0.2994537354,-0.0466109328,-0.0341314264,-0.1648347974,0.0899349824,-0.0930585712,-0.0194854271,-0.3661278486,-0.3656154275,-0.2527798414,-0.3794954121,-0.2056539357,0.1416984946,0.5694257021,0.3465053737,0.2177312523,-0.0514513589,-0.4145726264,-0.0320219919,-0.2802481055,-0.2366340607,0.1862058491,-0.3302085102,-0.1422023922,-0.0290144905,0.2373677641,0.0977393314,0.2280803323,0.4603567123,0.0747326687,0.4640995562,-0.0288445782,-0.0639058128,0.0993279815,-0.0348311998,-0.2291294634,-0.138928622,-0.086506106,-0.0435681529,-0.6063225269,0.000866775,0.041297853,-0.1401290447,0.0708582774,-0.213723883,0.2448111624,-0.242516309,0.1692857444,-0.2068609595,0.2759689689,-0.1333458424,-0.0129320482,0.2290190905,0.0137924077,0.088157095,-0.1253055483,0.1343980581,-0.030086752,-0.0864596441,-0.102865167,0.0322972387,-0.2499298751,0.071289286,-0.0608593412,0.1571666747,-0.0135507304,0.0931812674,-0.0254779793,-0.2397663593,0.410728544,-0.1583964229,0.1640750319,0.0718855336,0.2834461629,-0.1001683697,-0.076656796,0.2461405247,-0.2452431917,0.0941842049,0.1683179736,0.0826832131,0.0549368151,-0.4134446383,0.1224458292,0.017110968,0.0622441471,0.363204807,0.1792094707,-0.0475458242,-0.0578017496,-0.0745770186,0.1806987673,-0.3036069572,0.1323247403,-0.3576906025,0.186811924,-0.2822223604,0.2094503194,0.102309145,0.3251958489,0.4162543118,-0.0829675421,0.286404103,-0.4359333515,0.0148107437,-0.1164849699,0.0146783115,-0.037698701,-0.116946809,-0.0906927958,0.2322407961,-0.32481879,-0.0325562023,-0.0127761299,-0.0772035941,-0.2612805963,-0.0464587547,0.3655208945,-0.1033221483,0.0883795992,0.1066239253,0.2783166766,0.1253259778,0.1891436875,0.0639014095,0.0877345353,-0.6182097197,-0.1497565061,0.2072361559,-0.0406126045,-0.0707487762,0.0159722082,0.0374544375,0.0529674031,0.0853644535,0.1257972121,0.0015913615,0.5011470914,-0.1231235564,0.0715176761,0.4473967552,-0.3901565671,-0.2748812735,-0.0695148408,-0.2604046464,-0.182681933,-0.0402026139,0.1134250984,0.2639745474,-0.0010217377,0.3351184428,-0.0332489051,0.0672770068,-0.0819639713,-0.336643368,-0.0550780781,-0.110137932,0.0128997751,0.2926904559,0.1166745052,0.115680851,0.0590457879,-0.0790484622,-0.069792673,-0.0462030694,0.2923014164,0.1704942733,-0.1382119209,-0.1373235732,-0.3439494669,0.0773457363,0.1063652709,-0.148277089,0.1029536799,0.1586437076,-0.1641625613,-0.7732864022,-0.0339807272,-0.1352519244,-0.1356810331,0.3323923349,-0.0662694052,0.0512315929,-0.0233430769,-0.1197001338,-0.0865130946,-0.4374274611,-0.0387742259,0.0409117527,0.1444449127,-0.077667743,-0.1523495615,0.2320803255,0.6334596276,0.3142646849,0.2431090474,-0.1536217779,0.4181961417,0.1928747147,-0.0966607258,0.0709495097,0.2647994161,0.1443881691,-0.4176365137,0.0823048502,0.0835839361,0.0380928293,-0.1041174009,-0.1718855947,0.4496607482,-0.0595270917,-0.0188563298,-0.0873197019,-0.0377625525,0.0356232151,-0.0133336354,-0.2262201905,-0.360584408,0.1040816531,-0.2649138272,-0.074418202,-0.1512782425,0.0140222004,-0.0469461717,0.5437553525,0.0859436989,0.020997908,-0.0018737854,0.0927261636,-0.0672308877,-0.40878883,-0.1390052438,0.3537845314,0.2374161929,0.0789087862,0.3056024313,-0.181652993,-0.2899184227,0.1207427308,0.1682116985,-0.2001344264,0.1640179753,0.1174229234,-0.1201038957,-0.0836910084,0.0853296295,-0.136768803,0.1860298961,-0.3435234129,-0.063168399,-0.1156024113,0.1328385919,-0.0778240561,-0.3552176952,0.0478062369,-0.3057578206,0.0757077262,0.1828735173,0.1641632915,0.2744426429,0.1596274674,0.149750337,0.2517192066,-0.0102645345,0.0825760588,-0.2220394164,0.0131280273,0.2060865313,-0.1667333543,0.0321329646,0.3787149489,0.0630213395,0.2129551619,-0.1641378999,-0.3228057027,0.0167389028,-0.0855586529,0.3682919741,0.2106132954,-0.0868878365,-0.1201217026,0.0202160794,0.4172611237,-0.0720684901,-0.059700042,0.0572300069,-0.1489229798,-0.0393495448,-0.151238665,-0.0986143425,0.1086128727,-0.3211775124,-0.1853629351,0.211649254,-0.0162883531,0.240393132,0.1772322059,0.2700444758,0.3833012283,-0.119669266,-0.4149218202,-0.27283144,0.3139650822,-0.49654001,-0.1606483907,0.0105524594,-0.2040606439,0.3531615138,-0.065951407,-0.4769181907,-0.4896347821,0.0200474542,0.2011553943,-0.067957513,-0.1374583393,0.1257937998,-0.1389677227,-0.2248542309,-0.2828626931,-0.3725372851,0.0969550386,-0.1559907794,0.0767360777,-0.1679982394,0.2963856757,0.0796193704,0.5379875898,0.3091501296,-0.0959010199,0.1537613869,-0.1665041894,0.1702274978,-0.2196134925,-0.2937709689,0.0400277041,0.2761957645,-0.009206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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1743","title":"Issue while Creating Custom Metric","comments":"Hi @lhoestq,\r\n\r\nI am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.\r\n\r\nIncluding them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know.","body":"Hi Team,\r\n\r\nI am trying to create a custom metric for my training as follows, where f1 is my own metric:\r\n\r\n```python\r\n def _info(self):\r\n        # TODO: Specifies the datasets.MetricInfo object\r\n        return datasets.MetricInfo(\r\n            # This is the description that will appear on the metrics page.\r\n            description=_DESCRIPTION,\r\n            citation=_CITATION,\r\n            inputs_description=_KWARGS_DESCRIPTION,\r\n            # This defines the format of each prediction and reference\r\n            features = datasets.Features({'predictions':datasets.Sequence(datasets.Value(\"int32\")), \"references\": datasets.Sequence(datasets.Value(\"int32\")),\"offset_mapping\":datasets.Sequence(datasets.Value(\"int32\")),'text':datasets.Sequence(datasets.Value('string')),\"ground\":datasets.Sequence(datasets.Value(\"int32\")),}),\r\n            # Homepage of the metric for documentation\r\n            homepage=\"http:\/\/metric.homepage\",\r\n            # Additional links to the codebase or references\r\n            codebase_urls=[\"http:\/\/github.com\/path\/to\/codebase\/of\/new_metric\"],\r\n            reference_urls=[\"http:\/\/path.to.reference.url\/new_metric\"]\r\n        )\r\n\r\n    def _compute(self,predictions,references,text,offset_mapping,spans):\r\n\r\n        pred_spans = []\r\n\r\n        for i,preds in enumerate(predictions):\r\n            current_preds = []\r\n            for j,token_preds in enumerate(preds):\r\n                if (preds>0.5):\r\n                    current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))\r\n            pred_spans.append(current_spans)\r\n        \r\n        return {\r\n            \"Token Wise F1\": f1_score(references,predictions,labels=[0,1]),\r\n            \"Offset Wise F1\": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])\r\n        }\r\n\r\n```\r\n\r\nI believe this is not correct. But that's not the issue I am facing right now. I get this error :\r\n```python\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-144-ed7349b50821> in <module>()\r\n----> 1 new_metric.compute(predictions=inputs[\"labels\"],references=inputs[\"labels\"], text=inputs[\"text\"], offset_mapping=inputs[\"offset_mapping\"],ground=inputs[\"ground\"] )\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/features.py in encode_batch(self, batch)\r\n    802         encoded_batch = {}\r\n    803         if set(batch) != set(self):\r\n--> 804             print(batch)\r\n    805             print(self)\r\n    806             raise ValueError(\"Column mismatch between batch {} and features {}\".format(set(batch), set(self)))\r\n\r\nValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}\r\n```\r\nOn checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.\r\n\r\nHow do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?\r\n\r\nThanks,\r\nGunjan","comment_length":75,"text":"Issue while Creating Custom Metric \n Hi Team,\r\n\r\nI am trying to create a custom metric for my training as follows, where f1 is my own metric:\r\n\r\n```python\r\n def _info(self):\r\n        # TODO: Specifies the datasets.MetricInfo object\r\n        return datasets.MetricInfo(\r\n            # This is the description that will appear on the metrics page.\r\n            description=_DESCRIPTION,\r\n            citation=_CITATION,\r\n            inputs_description=_KWARGS_DESCRIPTION,\r\n            # This defines the format of each prediction and reference\r\n            features = datasets.Features({'predictions':datasets.Sequence(datasets.Value(\"int32\")), \"references\": datasets.Sequence(datasets.Value(\"int32\")),\"offset_mapping\":datasets.Sequence(datasets.Value(\"int32\")),'text':datasets.Sequence(datasets.Value('string')),\"ground\":datasets.Sequence(datasets.Value(\"int32\")),}),\r\n            # Homepage of the metric for documentation\r\n            homepage=\"http:\/\/metric.homepage\",\r\n            # Additional links to the codebase or references\r\n            codebase_urls=[\"http:\/\/github.com\/path\/to\/codebase\/of\/new_metric\"],\r\n            reference_urls=[\"http:\/\/path.to.reference.url\/new_metric\"]\r\n        )\r\n\r\n    def _compute(self,predictions,references,text,offset_mapping,spans):\r\n\r\n        pred_spans = []\r\n\r\n        for i,preds in enumerate(predictions):\r\n            current_preds = []\r\n            for j,token_preds in enumerate(preds):\r\n                if (preds>0.5):\r\n                    current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))\r\n            pred_spans.append(current_spans)\r\n        \r\n        return {\r\n            \"Token Wise F1\": f1_score(references,predictions,labels=[0,1]),\r\n            \"Offset Wise F1\": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])\r\n        }\r\n\r\n```\r\n\r\nI believe this is not correct. But that's not the issue I am facing right now. I get this error :\r\n```python\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-144-ed7349b50821> in <module>()\r\n----> 1 new_metric.compute(predictions=inputs[\"labels\"],references=inputs[\"labels\"], text=inputs[\"text\"], offset_mapping=inputs[\"offset_mapping\"],ground=inputs[\"ground\"] )\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/features.py in encode_batch(self, batch)\r\n    802         encoded_batch = {}\r\n    803         if set(batch) != set(self):\r\n--> 804             print(batch)\r\n    805             print(self)\r\n    806             raise ValueError(\"Column mismatch between batch {} and features {}\".format(set(batch), set(self)))\r\n\r\nValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}\r\n```\r\nOn checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.\r\n\r\nHow do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?\r\n\r\nThanks,\r\nGunjan \n Hi @lhoestq,\r\n\r\nI am doing text segmentation and the metric is effectively dice score on character offsets. So I need to pass the actual spans and I want to be able to get the spans based on predictions using offset_mapping.\r\n\r\nIncluding them in references seems like a good idea. I'll try it out and get back to you. If there's a better way to write a metric function for the same, please let me know.","embeddings":[-0.2388352007,-0.3020202518,-0.1457836926,0.1864611059,0.4193514287,-0.0384185873,0.106930159,0.19828628,0.0518294647,0.2997829318,-0.0557398088,0.2383317798,-0.1781434864,0.1406398267,-0.0143401977,-0.1415147632,-0.3350710273,0.1471369863,0.3345439434,-0.0345635414,-0.3107739687,0.1606325507,-0.069587566,0.1216962636,-0.2355847806,-0.0933226347,0.2366884053,-0.0159126148,-0.4124858677,-0.3041528165,0.0990642533,0.0513419546,-0.149600476,0.5381695032,-0.0000985569,0.0695141777,0.2994537354,-0.0466109328,-0.0341314264,-0.1648347974,0.0899349824,-0.0930585712,-0.0194854271,-0.3661278486,-0.3656154275,-0.2527798414,-0.3794954121,-0.2056539357,0.1416984946,0.5694257021,0.3465053737,0.2177312523,-0.0514513589,-0.4145726264,-0.0320219919,-0.2802481055,-0.2366340607,0.1862058491,-0.3302085102,-0.1422023922,-0.0290144905,0.2373677641,0.0977393314,0.2280803323,0.4603567123,0.0747326687,0.4640995562,-0.0288445782,-0.0639058128,0.0993279815,-0.0348311998,-0.2291294634,-0.138928622,-0.086506106,-0.0435681529,-0.6063225269,0.000866775,0.041297853,-0.1401290447,0.0708582774,-0.213723883,0.2448111624,-0.242516309,0.1692857444,-0.2068609595,0.2759689689,-0.1333458424,-0.0129320482,0.2290190905,0.0137924077,0.088157095,-0.1253055483,0.1343980581,-0.030086752,-0.0864596441,-0.102865167,0.0322972387,-0.2499298751,0.071289286,-0.0608593412,0.1571666747,-0.0135507304,0.0931812674,-0.0254779793,-0.2397663593,0.410728544,-0.1583964229,0.1640750319,0.0718855336,0.2834461629,-0.1001683697,-0.076656796,0.2461405247,-0.2452431917,0.0941842049,0.1683179736,0.0826832131,0.0549368151,-0.4134446383,0.1224458292,0.017110968,0.0622441471,0.363204807,0.1792094707,-0.0475458242,-0.0578017496,-0.0745770186,0.1806987673,-0.3036069572,0.1323247403,-0.3576906025,0.186811924,-0.2822223604,0.2094503194,0.102309145,0.3251958489,0.4162543118,-0.0829675421,0.286404103,-0.4359333515,0.0148107437,-0.1164849699,0.0146783115,-0.037698701,-0.116946809,-0.0906927958,0.2322407961,-0.32481879,-0.0325562023,-0.0127761299,-0.0772035941,-0.2612805963,-0.0464587547,0.3655208945,-0.1033221483,0.0883795992,0.1066239253,0.2783166766,0.1253259778,0.1891436875,0.0639014095,0.0877345353,-0.6182097197,-0.1497565061,0.2072361559,-0.0406126045,-0.0707487762,0.0159722082,0.0374544375,0.0529674031,0.0853644535,0.1257972121,0.0015913615,0.5011470914,-0.1231235564,0.0715176761,0.4473967552,-0.3901565671,-0.2748812735,-0.0695148408,-0.2604046464,-0.182681933,-0.0402026139,0.1134250984,0.2639745474,-0.0010217377,0.3351184428,-0.0332489051,0.0672770068,-0.0819639713,-0.336643368,-0.0550780781,-0.110137932,0.0128997751,0.2926904559,0.1166745052,0.115680851,0.0590457879,-0.0790484622,-0.069792673,-0.0462030694,0.2923014164,0.1704942733,-0.1382119209,-0.1373235732,-0.3439494669,0.0773457363,0.1063652709,-0.148277089,0.1029536799,0.1586437076,-0.1641625613,-0.7732864022,-0.0339807272,-0.1352519244,-0.1356810331,0.3323923349,-0.0662694052,0.0512315929,-0.0233430769,-0.1197001338,-0.0865130946,-0.4374274611,-0.0387742259,0.0409117527,0.1444449127,-0.077667743,-0.1523495615,0.2320803255,0.6334596276,0.3142646849,0.2431090474,-0.1536217779,0.4181961417,0.1928747147,-0.0966607258,0.0709495097,0.2647994161,0.1443881691,-0.4176365137,0.0823048502,0.0835839361,0.0380928293,-0.1041174009,-0.1718855947,0.4496607482,-0.0595270917,-0.0188563298,-0.0873197019,-0.0377625525,0.0356232151,-0.0133336354,-0.2262201905,-0.360584408,0.1040816531,-0.2649138272,-0.074418202,-0.1512782425,0.0140222004,-0.0469461717,0.5437553525,0.0859436989,0.020997908,-0.0018737854,0.0927261636,-0.0672308877,-0.40878883,-0.1390052438,0.3537845314,0.2374161929,0.0789087862,0.3056024313,-0.181652993,-0.2899184227,0.1207427308,0.1682116985,-0.2001344264,0.1640179753,0.1174229234,-0.1201038957,-0.0836910084,0.0853296295,-0.136768803,0.1860298961,-0.3435234129,-0.063168399,-0.1156024113,0.1328385919,-0.0778240561,-0.3552176952,0.0478062369,-0.3057578206,0.0757077262,0.1828735173,0.1641632915,0.2744426429,0.1596274674,0.149750337,0.2517192066,-0.0102645345,0.0825760588,-0.2220394164,0.0131280273,0.2060865313,-0.1667333543,0.0321329646,0.3787149489,0.0630213395,0.2129551619,-0.1641378999,-0.3228057027,0.0167389028,-0.0855586529,0.3682919741,0.2106132954,-0.0868878365,-0.1201217026,0.0202160794,0.4172611237,-0.0720684901,-0.059700042,0.0572300069,-0.1489229798,-0.0393495448,-0.151238665,-0.0986143425,0.1086128727,-0.3211775124,-0.1853629351,0.211649254,-0.0162883531,0.240393132,0.1772322059,0.2700444758,0.3833012283,-0.119669266,-0.4149218202,-0.27283144,0.3139650822,-0.49654001,-0.1606483907,0.0105524594,-0.2040606439,0.3531615138,-0.065951407,-0.4769181907,-0.4896347821,0.0200474542,0.2011553943,-0.067957513,-0.1374583393,0.1257937998,-0.1389677227,-0.2248542309,-0.2828626931,-0.3725372851,0.0969550386,-0.1559907794,0.0767360777,-0.1679982394,0.2963856757,0.0796193704,0.5379875898,0.3091501296,-0.0959010199,0.1537613869,-0.1665041894,0.1702274978,-0.2196134925,-0.2937709689,0.0400277041,0.2761957645,-0.00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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1733","title":"connection issue with glue, what is the data url for glue? ","comments":"Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/glue\/glue.py","body":"Hi\r\nmy codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not\r\nthanks ","comment_length":20,"text":"connection issue with glue, what is the data url for glue?  \n Hi\r\nmy codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not\r\nthanks  \n Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/glue\/glue.py","embeddings":[-0.0552913994,0.0118692834,-0.0427740142,0.2993855476,0.3010600507,-0.2684803307,0.2035407424,0.0815812498,0.1122899055,0.126336351,0.0642895028,-0.0648157224,0.2632441223,0.1945556253,0.2005842328,-0.0129128126,-0.1107104123,-0.0857953802,-0.3790926933,0.0695378631,-0.2274179757,0.1339347214,-0.2569567859,0.1540927887,-0.2331764847,-0.0182273686,-0.2408938557,0.1416757405,-0.1169200763,-0.3114697337,0.2458859384,0.2722536027,0.0157695338,0.2451800853,-0.0001094538,-0.085432075,0.4476234019,0.0518655442,-0.0857244059,-0.0963327363,-0.4144301116,-0.1583484858,0.1298841983,-0.2039457262,-0.172820285,0.4333641827,0.1408487856,-0.3225314021,0.1861434132,0.1474582404,0.2255823165,0.3084511757,0.2373654991,0.037909925,0.3510044217,-0.2436030507,0.1521846205,0.4593857527,0.3264584243,-0.1734827757,0.113360852,0.1072214246,0.1378021985,-0.0038308683,-0.0707272291,0.0960997865,-0.2217198759,-0.3329151571,0.0700250566,0.3006030917,0.3522767723,-0.145694539,-0.2870568335,0.0623582155,-0.1634307504,0.3614005446,0.3465101719,0.2372853011,0.0861449018,0.2820421755,-0.1781607717,-0.2360922396,-0.2536133528,0.2970753312,0.0505382232,-0.0472006425,-0.2656107247,0.2072946429,-0.0061237258,-0.2143399715,-0.0764592513,-0.1481080204,-0.0374755003,0.1016432494,-0.1890322417,-0.1231699586,-0.1298278272,0.539232254,0.3787512183,0.0442047007,0.0274433456,0.0363213941,-0.1351932734,0.2267657816,0.4534760714,-0.1265631914,0.1274647862,-0.0749848261,0.558650732,0.1327300072,-0.1617051959,-0.2315634042,0.0721815377,-0.2383840233,-0.1969603598,-0.1852637827,0.4247693121,-0.3179559112,-0.0180465151,-0.0207218789,-0.2300951034,0.0216086395,0.0870369077,0.3594588339,-0.3293738961,0.0328834988,-0.0079367915,-0.1125487909,-0.1614854783,-0.1475437433,-0.1700424999,0.0387516581,-0.2326640636,0.0795596838,0.0292003993,-0.2172808349,0.0885176808,0.0719203278,-0.1632151157,-0.085661903,0.0172570609,-0.1295012534,0.1328124851,0.1723618656,0.1386316717,0.2162532657,-0.1134451553,-0.3098357618,-0.039017044,-0.1024172157,-0.3364962339,-0.0733487457,-0.014145284,0.169818759,-0.4461430907,0.0614763573,-0.0504904576,-0.1832020879,-0.3620137572,0.0507001504,0.0676263869,0.0361977257,0.0771713182,-0.4720363319,0.328597039,0.4581777453,-0.2045367658,0.0299887862,0.2047373801,-0.2899807096,-0.1610530615,0.2446179986,-0.0275420807,-0.2317216247,-0.3104969561,0.3071649373,0.2097474933,-0.4537995756,-0.5420528054,0.5876682401,-0.1791019887,-0.2067996562,-0.0125934463,0.1852538139,0.149897024,0.0511213839,0.0089088762,0.5321074724,-0.0603240207,-0.1297146678,-0.2670769393,-0.2808828354,0.0112549728,0.0750852898,-0.1924652606,-0.1969628036,0.3012685776,-0.3112698793,0.2584023774,0.2315500081,-0.0003224949,0.2314703912,0.4709127843,0.1136943847,0.191098243,0.0146156047,-0.3011435568,0.0670153126,0.0895378441,0.0161551274,-0.1345145851,-0.1280486584,-0.040525768,0.0316529498,-0.0519643165,-0.0366878211,0.1640718132,-0.0981393754,0.1427839249,0.2040522397,-0.095654048,0.5069954395,0.0449108481,0.2054917663,-0.3476516604,0.7957617044,-0.0628449991,-0.0831364542,0.1679748446,0.1581304371,0.3577303886,-0.2406215221,-0.0793361738,0.5479306579,0.0030467724,0.2087198049,0.4648137987,0.3619560897,0.2616003752,-0.2679555714,0.176691547,0.1234835014,0.1318592578,0.0528849438,0.2697487473,0.0565306731,-0.0929097086,0.3933546543,0.2558864653,0.2535201013,-0.0885492265,-0.2306993604,-0.3375173211,0.0257895011,0.1821230799,-0.0102817575,0.3197614849,0.0665562302,-0.1968088746,0.0343665332,0.2283052355,-0.2139407694,0.1477039605,-0.1218866035,-0.242770493,-0.0533956476,0.0949436128,0.4247909486,0.397308141,0.1556915045,-0.1571696997,0.1278558224,-0.1030102223,-0.1715970635,0.1571452916,0.0120760379,-0.1970631778,-0.2106176764,-0.1756360382,-0.1759063154,-0.062820144,-0.0401063003,-0.2609094083,0.0049221944,-0.4890078306,-0.1162075996,-0.42560938,-0.4683541358,-0.2294389606,-0.2413150668,-0.4593434036,-0.4235491157,-0.2111191154,-0.1315097809,0.0404940136,0.0419472121,-0.0451764576,0.2296126485,-0.1644717008,0.0167804677,-0.0442774966,-0.1598864049,-0.3016820252,0.0908692554,0.0384635665,0.3077384531,0.3564952016,-0.131058529,0.2043779343,-0.3110781014,-0.1825407743,0.0033888323,0.1127296984,0.5722730756,0.4303124547,0.3833498955,0.3812004626,0.0102159157,0.386446327,-0.0884319842,0.1346813887,-0.1440852284,0.1837702245,-0.1304980963,0.1290677786,-0.1468420625,-0.3778319657,-0.0945281982,0.2201990485,-0.3047661185,0.088039957,-0.1557004452,-0.0484125949,0.2364871055,-0.383141309,-0.1040025577,-0.2310832292,-0.5189390182,0.1645469666,-0.0906724259,-0.4740213156,-0.0076211207,0.0513649695,0.1766807586,0.0559087172,-0.3993262053,-0.5216785073,-0.0929370522,0.0221441817,0.3070882559,0.3270330429,0.1475023031,-0.2089836299,0.0304434504,-0.1695893109,-0.2473968714,0.1664075851,0.0020562306,0.2740554512,0.5102769732,0.2605576813,0.0484128483,0.1950868219,0.1677782387,-0.1330775619,0.2533980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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1731","title":"Couldn't reach swda.py","comments":"Hi @yangp725,\r\nThe SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of \ud83e\udd17`datasets`.\r\nYou can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https:\/\/github.com\/huggingface\/datasets\/issues\/1641#issuecomment-751571471).\r\nLet me know if this helps !","body":"ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.2.0\/datasets\/swda\/swda.py\r\n","comment_length":54,"text":"Couldn't reach swda.py \n ConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.2.0\/datasets\/swda\/swda.py\r\n \n Hi @yangp725,\r\nThe SWDA has been added very recently and has not been released yet, thus it is not available in the `1.2.0` version of \ud83e\udd17`datasets`.\r\nYou can still access it by installing the latest version of the library (master branch), by following instructions in [this issue](https:\/\/github.com\/huggingface\/datasets\/issues\/1641#issuecomment-751571471).\r\nLet me know if this helps !","embeddings":[-0.2362142503,-0.3874821961,-0.1583183408,-0.0046996926,0.3615036309,-0.0226995479,-0.0685163513,0.2251031548,0.0263329204,0.218404904,-0.1205207929,-0.007295799,0.0098906029,0.3339485228,-0.0140376193,-0.2254329771,0.0509144589,0.0313128009,-0.256431818,0.0775595978,0.0202031508,0.325912118,-0.1554065049,0.2297449112,-0.1134689227,-0.0685833097,-0.0201578867,-0.0775326192,-0.175789386,-0.4362722039,0.5638242364,0.1927216649,0.1256830394,0.3472210169,-0.0001007638,-0.0771251395,0.4186194837,-0.0503711626,-0.1755157858,-0.309874028,0.0497583449,-0.1840837002,0.212349236,0.1146646366,-0.2763558328,-0.3036899567,0.1272522956,-0.0963162184,0.4207452238,0.0446711183,0.3689472973,0.2689450681,0.483820796,-0.4246512055,-0.0924544185,0.1234186217,-0.082154952,0.2442761213,0.0605740286,-0.0762190372,0.139697358,0.352983892,0.1305269897,-0.0450913236,0.301171571,-0.045279853,-0.1961440891,-0.2342961282,0.239060387,0.1528021693,0.5129566789,-0.2608686388,-0.4449080527,-0.0117749497,0.06313245,0.0231450815,0.1606127024,-0.0162839703,-0.1544131637,0.1218490675,-0.0956054926,-0.0866331384,-0.1289826334,0.1462022811,0.115401946,0.0305994917,-0.2691115737,0.0719906092,0.2066189647,-0.3192819357,0.0528106168,0.1959657371,-0.1088816002,0.2442480624,-0.178972438,0.0687169731,0.0610098392,-0.0150783351,0.3950170875,0.0805762783,-0.1344826221,0.0119044837,0.0585580021,0.0515343063,0.1297624707,0.0434685461,0.0165331922,-0.3237161636,0.2677385807,0.2697731853,0.1876418144,-0.2395910472,-0.0104302671,-0.2571239173,-0.3182106912,-0.3037604988,0.2617524564,-0.2514619529,-0.1170674264,0.0519806109,-0.0632575229,-0.0098854918,0.0848204046,0.4707405865,0.0258671548,0.0295214802,0.1808131039,0.2489558458,0.0418968089,0.253698796,-0.3355825245,-0.0228681862,-0.0963060781,-0.0400750414,0.1465428323,-0.277179122,0.3220079243,-0.0852720737,0.0394149013,-0.0062831119,-0.2069952339,0.1721775383,-0.063818261,0.36161533,0.1384340525,0.1034318581,0.1152971238,0.0605930574,-0.0969779938,-0.2377071381,-0.1938384175,-0.2642819285,-0.2458227128,0.2662176192,-0.0662668422,0.0833410248,-0.101832442,0.0710770041,-0.1863034964,-0.1065629497,-0.0815844238,0.0334060416,0.0264020786,-0.1412682682,0.3396386802,0.3164157271,-0.2243431211,-0.1330294162,0.2190386653,-0.3956375718,-0.0767425522,0.202038303,-0.2344354987,-0.0362960026,-0.2745136619,0.0762544349,0.2386868,-0.2935951948,-0.4345347583,0.0180809591,-0.0682203546,-0.145633921,0.0590169169,-0.0120171206,-0.1774288416,0.1605425775,0.0308523513,0.0477609262,-0.0305401757,-0.0809975117,0.0124896606,-0.2905766666,-0.1029108539,0.0454880446,-0.008517215,0.1289531887,0.1767852157,-0.0447493047,0.2707518935,0.0093451925,0.0446858406,0.21540685,0.2782873809,0.3302980363,-0.0016547708,-0.2491439879,-0.2228259295,0.0563065112,0.1059809774,0.1817086935,-0.2402734309,-0.1508601457,-0.2314825356,0.0776703805,0.0272419248,-0.2084968984,0.2749223411,0.0467883609,0.4856116474,0.2913158834,-0.2161174268,0.2894093096,-0.0706809461,0.2030579001,-0.3141874373,0.3132790923,-0.1704610288,-0.0810309798,0.2489076704,0.3151202798,-0.006789349,-0.1510169357,-0.0285708997,0.2379435152,-0.3230615854,0.067503646,0.2759997547,0.0608956926,0.0068592695,-0.3545849323,0.0661717653,-0.0347228311,0.1070296019,0.075018242,-0.006090587,0.2346146703,-0.0405365936,0.1180555373,0.0864085704,0.2939911485,0.5806789994,0.1721523851,-0.1451497078,-0.0839139149,0.0711118951,-0.3425208628,0.2790347636,-0.2193441242,-0.1918277144,0.0295599606,0.2969753146,-0.0106134815,-0.0616576895,0.2379881293,-0.2565304041,0.0416484326,0.0365801416,0.2131392211,0.280184716,0.3061872125,-0.014422928,0.1521448642,-0.0207854547,-0.1786594838,0.1268230081,0.0331972949,0.0013121923,-0.0468086451,0.0002218561,-0.0261392631,-0.4086170495,-0.1547626555,-0.4498315752,0.1796073914,-0.0121430214,-0.0576352663,-0.2008185834,-0.42852965,-0.0639552996,-0.3473838568,-0.4321454465,-0.3055025935,-0.0869297832,0.123040095,0.0512889028,0.2503718138,-0.0344964713,0.2276600301,0.0568357036,0.1265101135,-0.2285927087,-0.0626287162,-0.0975805745,0.1578879505,0.0749975666,0.1486072987,0.4697656333,-0.1726647019,0.2427733392,-0.3078085482,-0.3838958144,-0.0681613684,-0.1170026734,0.135069266,0.4359375536,0.2606773376,0.1073088795,-0.3065447509,0.3773047626,-0.3965027034,-0.2221936435,-0.1747205704,-0.1398114562,-0.1904285103,-0.1356692612,-0.0715870336,-0.3290016651,-0.3216324747,0.4432864785,0.0032814273,0.0138701145,-0.1150308773,0.1491563767,0.354808718,-0.3191218972,0.0954770744,-0.2415756434,-0.5166891217,0.1373102367,-0.2456361055,-0.5283465385,0.293569535,0.0682730377,0.3508424461,-0.3473393917,-0.3184232116,-0.2784529626,-0.2883039713,0.0639864281,0.0537601076,0.1304268092,0.367338419,-0.055444818,-0.158501029,-0.0401856452,-0.367146343,0.0235889722,-0.2736007273,0.2128954083,-0.0115489615,0.5309565663,0.0039977925,0.3552599847,0.2560673058,-0.0179176331,0.2243462652,-0.085212253,0.3694252968,-0.1641628295,-0.4451016188,0.145262748,-0.0163218156,0.3346743584,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1729","title":"Is there support for Deep learning datasets?","comments":"Hi @ZurMaD!\r\nThanks for your interest in \ud83e\udd17 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617 \r\nMNIST is also on the way: #1730 \r\n\r\nIf you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated \ud83d\ude80\r\n","body":"I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https:\/\/github.com\/DZPeru\/fish-datasets","comment_length":55,"text":"Is there support for Deep learning datasets? \n I looked around this repository and looking the datasets I think that there's no support for images-datasets. Or am I missing something? For example to add a repo like this https:\/\/github.com\/DZPeru\/fish-datasets \n Hi @ZurMaD!\r\nThanks for your interest in \ud83e\udd17 `datasets`. Support for image datasets is at an early stage, with CIFAR-10 added in #1617 \r\nMNIST is also on the way: #1730 \r\n\r\nIf you feel like adding another image dataset, I would advise starting by reading the [ADD_NEW_DATASET.md](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md) guide. New datasets are always very much appreciated \ud83d\ude80\r\n","embeddings":[-0.2464449555,-0.0732379109,-0.2349893898,-0.0977336168,0.2804594338,0.0302638169,0.2250186205,0.0864046291,0.0842465088,0.2471515536,-0.0170774795,0.0281189773,-0.3046905696,0.3419502676,0.2932601869,-0.1668531895,0.3030381501,0.029771775,0.1124463677,-0.1883488297,-0.1011667475,-0.0620983243,-0.1791171879,-0.3200937212,-0.1909748465,-0.0778028518,-0.2675436139,-0.2924896181,-0.2347606122,-0.2387118638,0.3272100389,0.0372786708,0.2575370669,0.6088250875,-0.0001094966,0.0936006978,0.1888516992,0.0344999172,0.1902192682,-0.2489059865,-0.4143947959,-0.3501292765,-0.0992010087,-0.1630510539,-0.1417375803,-0.1659959406,0.3405531943,-0.0920914561,0.2136177272,0.1095001474,0.1909378469,0.0286527984,0.1526862234,-0.0124512631,0.1259145886,0.4637610614,-0.4192467928,0.0787267014,0.3635125458,-0.0840877965,0.3061642051,0.3910604715,-0.0415558629,-0.0781772956,0.0574290119,0.1204648539,0.0095714182,-0.6005347967,-0.0693713874,0.3455680311,0.4273528755,0.16948919,-0.4737809896,-0.2742058039,0.1135304719,-0.3056729138,-0.363910675,0.3375839591,0.0854369625,-0.0274839327,-0.5430440903,-0.550498426,-0.1716226637,0.3525954187,0.0071944292,0.1653153002,0.0343288891,0.0596226752,0.0275784601,-0.0464016572,-0.0350121148,-0.1218505725,-0.0354858451,0.1890714467,-0.3749040365,-0.3790487349,-0.3840422332,-0.0489446558,0.378639549,0.1953843385,0.0268187691,0.0727604032,-0.5005358458,0.198321715,0.2776097357,-0.0487883911,-0.526247859,0.23842673,0.4992908835,-0.10259673,0.2686316669,0.0182736013,-0.2126104385,0.0345907696,-0.0959782973,-0.3556536436,0.082033582,0.0555136763,0.2450485229,-0.3079968393,-0.2041275054,-0.1634489,-0.0028695005,-0.002901478,-0.0299655441,-0.0549219325,-0.0771602616,0.3101503849,0.0049461052,-0.2031582743,-0.0077231703,0.1446855962,-0.3078582287,0.1587817967,0.1561163962,-0.0348398201,0.1697469801,-0.357203275,0.2749171555,0.1219056994,0.1673989743,-0.1888694167,0.0184282605,0.4009966254,-0.1780541092,-0.3972993791,-0.0999811292,0.1693808138,-0.108338654,0.0679147467,-0.4045160413,0.072082065,-0.2208457291,0.1123012602,-0.0265457053,-0.310079217,-0.5300796628,0.4608233571,-0.1473255903,-0.1077617556,0.148238942,-0.051006414,-0.3330506384,-0.1678077281,0.3007892966,-0.0945542753,-0.1657098085,-0.1141156182,-0.3624179661,0.2976293266,-0.0095219007,0.1803171337,-0.1027760282,-0.1470511556,-0.0156495795,0.1419146657,0.4793444276,-0.1160755679,-0.1822009832,-0.097726211,0.3983662724,-0.3402421772,-0.2129779458,0.4864846766,0.2005528659,0.0731717274,-0.27010113,0.3275510967,-0.1403850019,-0.18258591,0.1647537947,-0.2831168771,-0.2380251735,0.4602395594,0.4331450462,-0.012694926,0.3475661874,-0.2191439718,-0.1625747234,-0.006864273,0.0831413418,-0.0199107304,0.662574172,0.1291887015,0.1037046164,-0.3360137641,-0.0976158008,0.2703981996,0.2004428953,0.4144092798,0.091187194,0.0119064162,0.27502653,0.202672556,-0.0872692689,-0.1572868228,0.0078640534,-0.1088456959,0.1064412743,0.0308883358,-0.353969723,0.0372102968,-0.0923408195,0.2923875153,-0.2196456492,0.6330333352,-0.1056521088,-0.0154705551,0.0786022842,0.196305722,-0.1879234016,-0.1049830243,0.2716382444,0.0510785058,-0.0152541846,-0.0681521595,0.3380039632,0.4709867537,0.5233405232,-0.609627068,0.1475699246,0.012997875,0.0229643155,0.0183824226,-0.223199755,0.2636472285,-0.0607782267,0.2634519637,-0.0805983469,0.0552256256,0.2424298376,-0.0176500455,-0.1863025874,0.0340176076,-0.1354718506,-0.2317546308,0.0777997449,-0.3133499324,-0.3291055858,-0.4214858711,-0.0325010531,-0.0755662173,0.1321131587,0.2652238905,-0.1466513723,0.1360957026,0.2810122669,0.4580815434,0.2789267302,0.2799292207,-0.1267132908,-0.2005002499,-0.0899405181,0.0111509459,-0.0068551637,0.2319177091,0.4414569736,-0.09220732,0.2556333244,0.0863187909,-0.2216324508,-0.1761960536,-0.0946418494,-0.0566557385,0.2319621593,-0.0603158511,-0.0935873911,-0.4871141613,0.1330734491,0.1424444616,-0.3022167385,-0.0720267668,0.0389214419,-0.2811459601,0.3109104633,0.2504755557,-0.1876751184,0.5262498856,-0.1141057462,0.1686607003,-0.3002625406,0.0219657943,0.0994812846,0.1673241258,0.1026539505,-0.3192421794,0.3941636682,0.4315930009,-0.0025457474,-0.2596161664,-0.6218697429,0.3442952335,-0.1961560547,0.1873702854,-0.0038358874,0.038285736,0.1127832979,0.31848225,0.1826595515,-0.2116437852,-0.1564266086,-0.3393561542,0.0348302945,-0.1265832186,-0.1262586266,0.2830400467,-0.4299687743,-0.019820502,0.1755001247,0.2003930062,-0.0757167786,0.3603032231,0.4419185817,-0.1968563944,0.0153541872,-0.1385998279,-0.014558116,-0.2842707038,0.4448160231,-0.1399222314,-0.2531705797,0.3671397865,-0.0729259104,0.082773231,0.2562768161,-0.6753509641,-0.1112541035,-0.207938835,0.0338670239,0.2140515745,0.0628792346,-0.0092199938,-0.2110792994,-0.0579751506,-0.0709017888,-0.2626710832,-0.1191503331,0.2377869338,0.3308423758,0.3337474167,0.3474175334,-0.0508259721,0.2955156863,0.0418461822,-0.3043087125,0.5080927014,0.170845598,0.3578542769,-0.0822675452,-0.0730764344,0.1549730152,-0.0139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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1728","title":"Add an entry to an arrow dataset","comments":"Hi @ameet-1997,\r\nI think what you are looking for is the `concatenate_datasets` function: https:\/\/huggingface.co\/docs\/datasets\/processing.html?highlight=concatenate#concatenate-several-datasets\r\n\r\nFor your use case, I would use the [`map` method](https:\/\/huggingface.co\/docs\/datasets\/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.\r\n\r\nLet me know If this helps!","body":"Is it possible to add an entry to a dataset object?\r\n\r\n**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**\r\n\r\nFor example, say we have the following code:\r\n\r\n``` python\r\nfrom datasets import load_dataset\r\n\r\n# Load a dataset and print the first examples in the training set\r\nsquad_dataset = load_dataset('squad')\r\nprint(squad_dataset['train'][0])\r\n```\r\n\r\nIs it possible to add an entry to `squad_dataset`? Something like the following?\r\n\r\n``` python\r\nsquad_dataset.append({'text': \"This is a new sentence\"})\r\n```\r\n\r\nThe motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.\r\n\r\nIf the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?\r\n","comment_length":43,"text":"Add an entry to an arrow dataset \n Is it possible to add an entry to a dataset object?\r\n\r\n**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**\r\n\r\nFor example, say we have the following code:\r\n\r\n``` python\r\nfrom datasets import load_dataset\r\n\r\n# Load a dataset and print the first examples in the training set\r\nsquad_dataset = load_dataset('squad')\r\nprint(squad_dataset['train'][0])\r\n```\r\n\r\nIs it possible to add an entry to `squad_dataset`? Something like the following?\r\n\r\n``` python\r\nsquad_dataset.append({'text': \"This is a new sentence\"})\r\n```\r\n\r\nThe motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.\r\n\r\nIf the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?\r\n \n Hi @ameet-1997,\r\nI think what you are looking for is the `concatenate_datasets` function: https:\/\/huggingface.co\/docs\/datasets\/processing.html?highlight=concatenate#concatenate-several-datasets\r\n\r\nFor your use case, I would use the [`map` method](https:\/\/huggingface.co\/docs\/datasets\/processing.html?highlight=concatenate#processing-data-with-map) to transform the SQuAD sentences and the `concatenate` the original and mapped dataset.\r\n\r\nLet me know If this helps!","embeddings":[0.1183504239,0.1227682754,-0.0548609123,-0.0218543373,0.1973935813,0.3098824024,0.170818463,-0.1640379131,-0.0837013274,-0.0505276099,0.2646122575,0.5609719157,-0.2010443658,0.0916972011,0.3351857066,-0.2962880433,0.1142137274,0.0437108278,-0.2922752798,-0.0279929936,0.0021185945,-0.1372906566,0.2007560581,-0.0317320488,-0.2767151892,-0.1182214543,-0.1379704624,-0.2123791128,-0.138488844,-0.5027133822,0.008782669,-0.0272243004,0.0590722486,0.3376502395,-0.000117042,0.0920339152,0.2727537155,0.1627476662,-0.5811193585,-0.3001923859,-0.3158358037,-0.5750210881,0.1763750762,-0.1296510398,0.1810124815,-0.340570271,0.1682196707,-0.516061008,0.3616964817,0.1440825015,0.1551565081,0.0048572132,0.3422509134,-0.1587884426,0.1316577643,0.2934283912,-0.1651616395,0.0660046414,0.0775927529,0.1870577186,0.210771367,0.1433064193,0.2321269512,-0.1839473099,0.5127686858,0.2322122306,-0.0962173641,-0.1791201383,-0.1925228685,0.1984659582,0.64759022,-0.415719837,-0.7800760269,-0.3988769352,0.5506299734,-0.16825293,-0.2445785254,0.2328118235,0.0239017699,0.1169849485,0.0696600899,-0.4716575444,-0.1921496391,0.2013756186,0.0445766747,0.2364700288,0.0233941972,0.0998460054,0.0355715565,-0.1198580414,0.2599288821,0.0791534111,-0.1234588847,0.2387579829,-0.309689939,0.0351709463,-0.2527564764,-0.1268173605,0.0663558617,-0.0297921486,0.4854209423,-0.1098813489,-0.3336729109,0.0790681094,0.2000619918,-0.0342736021,0.1430096328,0.0687184557,-0.3912691176,-0.3791761696,-0.1096804142,-0.2170026153,-0.1550947279,-0.1713347435,0.2424238622,-0.1673100144,0.1473714411,0.313683629,0.0629964322,-0.0935476422,-0.302016288,0.059125375,-0.2725579143,0.0612748526,0.4182109535,0.109153524,0.1732089669,0.4408244491,0.3726042807,-0.1566441506,0.0253166705,0.0513319708,-0.0543777086,0.3669013083,-0.2423496246,-0.0477180257,0.0371122248,-0.1206810251,0.1456056237,-0.1919995546,0.0828311518,-0.0404513627,0.2058393657,0.0076227454,-0.0446058661,0.1492536664,0.1150151417,-0.1696118563,-0.0430958346,0.2046017498,-0.0826668441,-0.0649525002,-0.4545913041,0.088045232,-0.0275905952,-0.3335460424,-0.0333478004,0.4336046576,-0.0151358461,0.0004981061,0.1414936632,0.009133257,-0.2703191638,-0.1619907767,-0.0206436161,0.1551439911,-0.512889266,-0.4108776152,-0.2430013418,0.0905044153,-0.5492098331,-0.0854313895,-0.1658569276,0.4677471817,0.005220708,0.047719121,0.5233315825,-0.1115105674,-0.167239517,-0.1299366355,-0.3428252637,0.0833971202,-0.0064481604,0.4439302087,0.1647202224,0.2340108603,0.0283424053,0.2201442719,0.0826974437,-0.3359245062,0.278637141,0.0015661336,0.2065098733,-0.1058211625,-0.3012523949,-0.0254255347,0.1261385232,-0.1821459681,0.1216382831,-0.2194676548,0.2375243455,0.306014955,0.16865848,0.2052364349,-0.1291643679,-0.1226723939,-0.2524817586,-0.0185622871,0.1810260266,0.0529476367,-0.0552381873,-0.2419681251,-0.0322534144,0.3225970864,-0.0610444359,0.0148529364,0.0318089277,0.0758635402,-0.0819452256,-0.2782067358,-0.3902855515,0.4074019194,-0.0087648733,0.2047669888,-0.2296218425,0.1660542935,0.2363984734,0.0036824329,-0.3240821064,0.2122142911,-0.1640319079,0.0895017013,0.0789454207,0.1134709269,-0.1195055768,0.2341029197,0.1487384588,0.0327494219,0.1080177873,-0.3662585318,0.0763284415,-0.0375731699,0.0954278409,-0.0913993344,-0.3540546596,0.2417161614,0.1802269071,0.358039856,-0.1495783925,0.2765904665,0.1072116494,-0.0300822426,-0.4279772937,-0.2050343454,-0.1618532091,0.0159433894,0.279984206,-0.1748908162,-0.1389584094,0.0067604636,-0.0259438623,-0.1193043813,0.1013053954,0.1819251925,-0.5684722662,0.289631784,-0.0735917762,0.3036424816,0.3615439832,0.2742063403,-0.0376004875,0.1356413215,0.1420550942,0.0517570712,0.0522803739,0.1145410091,0.4417301118,0.0077748694,0.4800372124,0.1265679449,-0.0580536574,-0.0096648177,-0.0327755064,-0.0302462541,-0.3340159059,-0.0168455876,-0.0518505089,-0.1416765749,-0.5122766495,-0.1967370212,-0.0311987214,-0.3337020576,-0.053238906,-0.2285900712,-0.0717634708,0.1076778322,0.1049499661,0.2497825176,-0.076599963,-0.0839089006,0.1579049975,-0.1696772426,0.258208096,-0.0121931871,-0.0431305915,0.0414119065,0.3619517684,0.0993797183,0.1087101474,-0.3433416784,-0.1797894388,0.0348750688,-0.0459054634,0.1033415049,0.0131804319,0.0726373568,-0.1497794539,-0.2909355164,0.081686914,0.023718087,-0.1779968739,-0.1926491857,-0.0532523021,-0.0540067218,0.045900017,-0.0440836027,-0.5674846768,-0.1944701672,0.4599407911,0.1678510457,-0.0582460165,0.1613591909,0.2073466778,0.2443645895,-0.0269283876,-0.0540948398,0.0441003218,-0.1064452827,0.4240250885,-0.1946118176,-0.1915540844,-0.030255856,-0.3298680484,0.0344480462,0.0987454131,-0.2150246352,0.0016386468,-0.1454131454,0.2108080983,-0.1521884203,0.1087907255,0.3900870383,0.049431283,-0.0747972503,-0.1380809098,-0.4104962945,0.1876413077,0.2697172761,0.1381595582,-0.0397371389,0.263351649,0.0126135089,0.3805563748,0.1919939965,-0.304358542,0.0460212901,-0.3049993813,0.2573345602,-0.0679783672,-0.3093288839,-0.2927863598,-0.3105886281,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1728","title":"Add an entry to an arrow dataset","comments":"That's a great idea! Thank you so much!\r\n\r\nWhen I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?\r\n\r\nCode:\r\n``` python\r\ncombined_dataset = concatenate_datasets([datasets, modified_dataset])\r\n```\r\n\r\nError:\r\n```\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nOutput:\r\n```\r\n(Pdb) datasets\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n        num_rows: 493\r\n    })\r\n})\r\n(Pdb) modified_dataset\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n        num_rows: 493\r\n    })\r\n})\r\n```\r\n\r\nThe error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate solution?","body":"Is it possible to add an entry to a dataset object?\r\n\r\n**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**\r\n\r\nFor example, say we have the following code:\r\n\r\n``` python\r\nfrom datasets import load_dataset\r\n\r\n# Load a dataset and print the first examples in the training set\r\nsquad_dataset = load_dataset('squad')\r\nprint(squad_dataset['train'][0])\r\n```\r\n\r\nIs it possible to add an entry to `squad_dataset`? Something like the following?\r\n\r\n``` python\r\nsquad_dataset.append({'text': \"This is a new sentence\"})\r\n```\r\n\r\nThe motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.\r\n\r\nIf the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?\r\n","comment_length":123,"text":"Add an entry to an arrow dataset \n Is it possible to add an entry to a dataset object?\r\n\r\n**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**\r\n\r\nFor example, say we have the following code:\r\n\r\n``` python\r\nfrom datasets import load_dataset\r\n\r\n# Load a dataset and print the first examples in the training set\r\nsquad_dataset = load_dataset('squad')\r\nprint(squad_dataset['train'][0])\r\n```\r\n\r\nIs it possible to add an entry to `squad_dataset`? Something like the following?\r\n\r\n``` python\r\nsquad_dataset.append({'text': \"This is a new sentence\"})\r\n```\r\n\r\nThe motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.\r\n\r\nIf the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?\r\n \n That's a great idea! Thank you so much!\r\n\r\nWhen I try that solution, I get the following error when I try to concatenate `datasets` and `modified_dataset`. I have also attached the output I get when I print out those two variables. Am I missing something?\r\n\r\nCode:\r\n``` python\r\ncombined_dataset = concatenate_datasets([datasets, modified_dataset])\r\n```\r\n\r\nError:\r\n```\r\nAttributeError: 'DatasetDict' object has no attribute 'features'\r\n```\r\n\r\nOutput:\r\n```\r\n(Pdb) datasets\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n        num_rows: 493\r\n    })\r\n})\r\n(Pdb) modified_dataset\r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['attention_mask', 'input_ids', 'special_tokens_mask'],\r\n        num_rows: 493\r\n    })\r\n})\r\n```\r\n\r\nThe error is stemming from the fact that the attribute `datasets.features` does not exist. Would it not be possible to use `concatenate_datasets` in such a case? Is there an alternate 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1728","title":"Add an entry to an arrow dataset","comments":"You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`\r\n\r\nDidn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.","body":"Is it possible to add an entry to a dataset object?\r\n\r\n**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**\r\n\r\nFor example, say we have the following code:\r\n\r\n``` python\r\nfrom datasets import load_dataset\r\n\r\n# Load a dataset and print the first examples in the training set\r\nsquad_dataset = load_dataset('squad')\r\nprint(squad_dataset['train'][0])\r\n```\r\n\r\nIs it possible to add an entry to `squad_dataset`? Something like the following?\r\n\r\n``` python\r\nsquad_dataset.append({'text': \"This is a new sentence\"})\r\n```\r\n\r\nThe motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.\r\n\r\nIf the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?\r\n","comment_length":42,"text":"Add an entry to an arrow dataset \n Is it possible to add an entry to a dataset object?\r\n\r\n**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**\r\n\r\nFor example, say we have the following code:\r\n\r\n``` python\r\nfrom datasets import load_dataset\r\n\r\n# Load a dataset and print the first examples in the training set\r\nsquad_dataset = load_dataset('squad')\r\nprint(squad_dataset['train'][0])\r\n```\r\n\r\nIs it possible to add an entry to `squad_dataset`? Something like the following?\r\n\r\n``` python\r\nsquad_dataset.append({'text': \"This is a new sentence\"})\r\n```\r\n\r\nThe motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.\r\n\r\nIf the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?\r\n \n You should do `combined_dataset = concatenate_datasets([datasets['train'], modified_dataset['train']])`\r\n\r\nDidn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.","embeddings":[0.1145721227,0.2276223749,-0.0684058368,-0.0251238011,0.1537322551,0.2710112035,0.226799041,-0.1038071588,0.0163868833,-0.0527591147,0.3032632768,0.5584480166,-0.2891287804,0.047076717,0.3657113016,-0.280523628,0.0873283595,0.0396179557,-0.2587966621,-0.0790688768,-0.0180866886,-0.2019383013,0.2285862714,-0.0326016434,-0.228646785,-0.1232241467,-0.0993934721,-0.262957871,-0.1477606595,-0.4609306753,0.0618369095,-0.0937316194,0.0361965336,0.251676023,-0.0001185569,0.1118827239,0.2736075521,0.1426105648,-0.5661526322,-0.2433639467,-0.259247005,-0.5845739841,0.2074297965,-0.1865363866,0.2207546234,-0.3495488167,0.1405095756,-0.5783141255,0.2560048699,0.2211306393,0.1515809,-0.0092613129,0.2489777058,-0.1223886013,0.2697330415,0.2970865071,-0.1688393652,0.0161133036,0.0952636451,0.2036064416,0.1866354793,0.0433867425,0.1943320781,-0.1995443255,0.4199509025,0.2216569334,-0.11024376,-0.1197424829,-0.120614931,0.1585541219,0.7166870832,-0.3376507759,-0.7637710571,-0.3833065331,0.4716689289,-0.2002213597,-0.2719372511,0.2291770875,0.0322279632,0.100159511,0.0578414537,-0.5695364475,-0.1689431518,0.1344406754,-0.0713209584,0.2662101686,0.1282932311,0.1429059654,-0.0294441059,0.0496059395,0.4449470341,0.0718639791,-0.0850041285,0.2023862749,-0.275421679,-0.0111991679,-0.2009694129,-0.1555013657,0.022890646,-0.0264489166,0.4322582483,-0.0977271944,-0.349150151,0.0062519102,0.2315907329,-0.0902674645,0.0625158623,0.1639588773,-0.3666475415,-0.5172889829,-0.0959439203,-0.2136398405,-0.1333695799,-0.108360745,0.274302274,-0.1353407055,0.1114093438,0.2889271677,0.0096789235,-0.1018484384,-0.34245345,0.010887946,-0.2589613795,-0.0176445786,0.4288860261,0.1240437105,0.1030766889,0.4695477188,0.3152875304,-0.2198577672,0.0048457375,0.0921725333,-0.0695167407,0.2748762071,-0.1877287477,0.0636667535,-0.053088326,-0.2365311384,0.0488272458,-0.1673048288,0.1473578811,-0.0812282339,0.1897466034,0.0458911844,-0.0276269838,0.1726948172,0.1745205075,-0.1622058004,-0.1053395048,0.2671445906,-0.0765427202,-0.2106023729,-0.3816097677,0.0947101936,-0.0319601744,-0.3137810528,-0.0676094815,0.3853579462,0.0281780735,0.0744016841,0.141911298,-0.1503307819,-0.2972721457,-0.1496002823,-0.0254141912,0.0646295771,-0.6223896146,-0.330209285,-0.3623220026,0.0471313372,-0.4713605046,-0.0428867377,-0.1817537397,0.6620752811,-0.0138257304,-0.0914267153,0.5936508775,-0.0985190496,-0.2818264663,-0.192326799,-0.29875049,0.0530211516,0.0030412513,0.5245484114,0.2708130181,0.2221010625,0.0133398613,0.1885728538,0.0027442609,-0.2894113958,0.2583531141,-0.0393458456,0.2655002177,-0.0190553162,-0.1200523749,0.0276346356,0.2107279897,-0.0663183555,0.1436348855,-0.2056606561,0.2251610458,0.0847937167,0.2002291232,0.1562264711,-0.1784159839,-0.1116406843,-0.2688981593,0.0021332121,0.2222335339,0.0003280949,0.0635620952,-0.2276721299,-0.0282906704,0.3634390831,0.0011588238,0.0436765365,0.0339295343,-0.01935184,-0.0979097486,-0.2692063749,-0.3994077742,0.4055222869,-0.0663043708,0.1545792073,-0.2101407498,0.2363847941,0.2581991553,0.0021447032,-0.3161668181,0.2485579401,-0.2150732726,0.072397843,0.091633305,0.003637125,-0.042057164,0.2055780143,0.1146879494,0.0066276598,0.065740779,-0.3981017172,0.2017666698,0.0086334189,0.0744551942,-0.1684645116,-0.3035768569,0.2037469894,0.0876132548,0.3461208045,-0.1305732578,0.2628031671,0.0295077022,0.0066060149,-0.4481200874,-0.169487536,-0.1674040854,-0.0128956717,0.3151218295,-0.2133638263,-0.0394492522,-0.0212720353,0.0690554455,-0.1229185611,0.164433077,0.1388319582,-0.4902573526,0.208188042,-0.0712753013,0.3739586771,0.3999239802,0.2587515712,0.0306262709,0.0953699797,0.1411890537,0.017081609,0.1153032929,0.1724141389,0.3782645166,0.0006368248,0.3976308405,0.1009947807,-0.0365315303,-0.0323385783,-0.0555147193,0.0155075481,-0.3034598231,-0.077748172,-0.0351036154,-0.0424127616,-0.4622249007,-0.2190282345,0.0756054074,-0.4277582169,-0.0240824334,-0.2205747962,-0.0749537423,0.0449578203,-0.040751107,0.2646930516,-0.1134312823,-0.0750159249,0.1873975843,-0.2155476958,0.2505766451,0.0112599647,-0.072178781,0.0076076663,0.4727571011,0.1681151837,0.1057905555,-0.3163026273,-0.1212039888,0.0094633382,-0.0259412955,0.1020844951,0.0664756149,0.0212311354,-0.0882798508,-0.2201028615,0.0009320721,-0.0085922442,-0.1276937723,-0.2165890336,-0.0300458875,0.0397780314,0.0676835105,-0.1183193401,-0.6376333833,-0.1809846014,0.3180422783,0.1766311377,-0.0548113473,0.0939994603,0.1549415439,0.3043794334,0.0776009187,-0.1957989633,0.0346550234,-0.0302676409,0.424775809,-0.1688498259,-0.1342032403,-0.0297146272,-0.3406842351,0.0908480659,0.1222209781,-0.2838962674,0.1603948474,-0.1505679488,0.2198680341,-0.127745986,0.0273664091,0.3573589027,0.0602986179,-0.0297792889,-0.1033775434,-0.3401471674,0.1137283072,0.2512102723,0.0733656585,-0.0979011804,0.2904837728,0.0094959065,0.4994259775,0.0853195935,-0.2391103804,0.014282614,-0.2247809619,0.2057155222,-0.1449179798,-0.3179531395,-0.3026532233,-0.3183403909,-0.33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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1728","title":"Add an entry to an arrow dataset","comments":"> Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.\r\n\r\nMy opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.\r\nFor instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split=\"train\"` is given for example it's always returning a Dataset.","body":"Is it possible to add an entry to a dataset object?\r\n\r\n**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**\r\n\r\nFor example, say we have the following code:\r\n\r\n``` python\r\nfrom datasets import load_dataset\r\n\r\n# Load a dataset and print the first examples in the training set\r\nsquad_dataset = load_dataset('squad')\r\nprint(squad_dataset['train'][0])\r\n```\r\n\r\nIs it possible to add an entry to `squad_dataset`? Something like the following?\r\n\r\n``` python\r\nsquad_dataset.append({'text': \"This is a new sentence\"})\r\n```\r\n\r\nThe motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.\r\n\r\nIf the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?\r\n","comment_length":96,"text":"Add an entry to an arrow dataset \n Is it possible to add an entry to a dataset object?\r\n\r\n**Motivation: I want to transform the sentences in the dataset and add them to the original dataset**\r\n\r\nFor example, say we have the following code:\r\n\r\n``` python\r\nfrom datasets import load_dataset\r\n\r\n# Load a dataset and print the first examples in the training set\r\nsquad_dataset = load_dataset('squad')\r\nprint(squad_dataset['train'][0])\r\n```\r\n\r\nIs it possible to add an entry to `squad_dataset`? Something like the following?\r\n\r\n``` python\r\nsquad_dataset.append({'text': \"This is a new sentence\"})\r\n```\r\n\r\nThe motivation for doing this is that I want to transform the sentences in the squad dataset and add them to the original dataset.\r\n\r\nIf the above doesn't work, is there any other way of achieving the motivation mentioned above? Perhaps by creating a new arrow dataset by using the older one and the transformer sentences?\r\n \n > Didn't we talk about returning a Dataset instead of a DatasetDict with load_dataset and no split provided @lhoestq? Not sure it's the way to go but I'm wondering if it's not simpler for some use-cases.\r\n\r\nMy opinion is that users should always know in advance what type of objects they're going to get. Otherwise the development workflow on their side is going to be pretty chaotic with sometimes unexpected behaviors.\r\nFor instance is `split=` is not specified it's currently always returning a DatasetDict. And if `split=\"train\"` is given for example it's always returning a Dataset.","embeddings":[0.148923099,0.2032505423,-0.053670004,0.0051242253,0.1111596525,0.0731266066,0.273730725,-0.0879939646,0.0876799151,0.0217209589,0.3423003554,0.4724981785,-0.3235454559,0.074565649,0.3323621154,-0.2733576596,0.0625657812,0.0564744137,-0.2627886534,-0.1407849193,-0.162413016,-0.1628737152,0.2530605793,0.0563627779,-0.2190205157,-0.1572354585,-0.0339656062,-0.2609067261,-0.1684876382,-0.4851483405,0.1064505875,-0.07251513,0.1209618077,0.1816625446,-0.0001208031,0.1351278275,0.2979466915,0.1087860689,-0.649518013,-0.3481771946,-0.2126668096,-0.5955251455,0.3207849562,-0.2333696336,0.1756075919,-0.3336721063,0.2961094379,-0.5603634119,0.1842574328,0.1638450176,0.1235807464,0.0644707233,0.1811811775,-0.0514905676,0.3474474251,0.2932673395,-0.2006346732,-0.0563739426,0.1106201485,0.2912874222,0.2085744292,0.0371348225,0.2039234936,-0.1755607873,0.4447499216,0.2522757351,-0.1014347598,-0.2049790025,-0.2078369558,0.2939831913,0.6473250389,-0.2176996022,-0.748470366,-0.4845074415,0.4366750419,-0.1822227538,-0.1971818507,0.2327401936,-0.0499619879,0.1590688676,-0.0214077383,-0.5370777845,-0.1212952062,0.1997885555,-0.0781413838,0.2104478776,0.0671586096,0.1875111312,-0.0743544549,0.0980985984,0.4603864253,-0.1218010858,0.0015314725,0.09527383,-0.2415177673,-0.0359287411,-0.167870149,-0.0899103358,-0.0488909744,-0.011518646,0.4208772779,-0.0647685379,-0.3213697076,-0.0447110087,0.31207937,-0.1362739801,0.0155989397,0.1233666912,-0.3104012311,-0.56049788,-0.03761952,-0.1776010096,-0.07526806,-0.0327271484,0.2470703423,-0.0488372482,0.2522363067,0.2627443969,0.0140607636,-0.1079403013,-0.3875339627,-0.0179843139,-0.2659661174,-0.0414333045,0.3194258809,0.2228212059,0.1218316555,0.5000299215,0.2188774645,-0.2792986333,0.0607988089,0.0001286661,-0.031761229,0.2036891878,-0.1720125675,0.1425707936,-0.123877883,-0.2613933682,-0.0565125048,-0.1344753504,0.1910654306,-0.1047654226,0.2506734431,0.0635320246,-0.1267582029,0.2028165162,0.0935413092,-0.1517158747,-0.1709172428,0.2163983136,-0.1591831744,-0.3000670075,-0.265322715,0.0598790832,-0.0181106552,-0.1914280057,-0.0541082658,0.3932879865,-0.052761402,0.1217951626,0.0635990202,-0.2271443307,-0.3002089262,-0.2059619725,-0.1810673028,0.0652472973,-0.5810016394,-0.3230880797,-0.3965204656,-0.0007034457,-0.4486553371,-0.030775208,-0.2260406911,0.7877774239,0.038224753,-0.2032297701,0.6214304566,-0.0511002392,-0.282556653,-0.1758330166,-0.271505028,0.1707303524,0.1053495109,0.5825029016,0.3470585942,0.179490909,-0.0493706241,0.1778184026,-0.1028186306,-0.2526222169,0.2119360715,-0.2013582438,0.2867557108,-0.044158116,-0.0076798014,0.1050690487,0.1689320952,-0.0399998538,0.1117276773,-0.1499639452,0.1267689615,-0.0233246386,0.2841738462,0.1198530048,-0.1577977091,-0.111959964,-0.431922704,-0.0166914091,0.1539265066,-0.0038995047,0.0790131316,-0.2508654594,0.0055507189,0.3583600521,-0.0269216802,0.1621829569,-0.0060971617,0.0200405102,-0.0497466438,-0.2303821594,-0.3420437276,0.3552744687,-0.1019709408,0.2033836246,-0.2506042123,0.2522015274,0.299546808,0.1008187234,-0.287771523,0.1638204604,-0.2433373034,-0.0385141447,0.1070636883,0.0011131002,0.0557104275,0.1099162698,0.0411931388,0.1264181137,0.0943575725,-0.2818855047,0.1626383066,0.0383653939,0.1223354638,-0.0892992765,-0.2213629186,0.3601261079,0.1634077877,0.3806601763,-0.1537361145,0.2221234143,-0.0061401171,0.1120590568,-0.4190380275,-0.1833490431,-0.2857301235,0.0351908281,0.3241585493,-0.1198494509,-0.0863564461,-0.0843106508,0.3114069998,-0.0787733942,0.2204767168,0.0828586593,-0.3383288682,0.1602659971,-0.0215830114,0.3791629076,0.4297638535,0.2466750592,-0.0312965326,-0.0034333984,0.0568315089,-0.0030326303,0.1057171896,0.2794118226,0.4154753387,-0.08432208,0.2271009684,0.0517538227,-0.0090965657,-0.0256557651,-0.0598952584,-0.0686763376,-0.2929493785,-0.0567642637,-0.0812907591,0.0581328794,-0.4557873905,-0.2034327239,0.0852361917,-0.5229114294,0.0822524056,-0.2457781732,-0.1431063712,0.045576591,0.0284416284,0.3708215356,-0.2270681709,-0.0604219846,0.2260522991,-0.1569586396,0.1129825786,-0.0194963254,-0.0490853898,-0.0899859145,0.4903205633,0.0772578642,0.0964477882,-0.3023446798,-0.2243509591,0.104368031,0.0484220237,0.1497203708,0.1132983267,0.0604018979,0.026830852,-0.2328117788,-0.0854227766,-0.027918892,-0.1692318171,-0.2233995944,-0.0223854464,0.0732144117,0.0249797795,-0.2803646326,-0.5676974058,-0.1390215158,0.3047088981,0.1716894954,-0.0562261678,-0.0078882314,0.1825787872,0.1792978495,0.0547783487,-0.1193318143,0.046669282,0.013216503,0.3442359567,-0.1703812033,-0.1163793281,-0.1266447455,-0.4445589483,0.0552888215,0.2123786509,-0.3078700006,0.1286229789,-0.1602936536,0.2679947913,-0.0304954033,0.0031080828,0.3798833787,0.1025995612,0.0253196005,-0.0556906946,-0.2710007429,0.1926504821,0.1774702519,0.085074231,0.0125161121,0.3000861704,-0.0044603921,0.6486833692,-0.0794266164,-0.3330708146,-0.0649883971,-0.1147667617,0.326797843,-0.2337396145,-0.2221952677,-0.3267285228,-0.31865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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1727","title":"BLEURT score calculation raises UnrecognizedFlagError","comments":"And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!","body":"Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n```","comment_length":18,"text":"BLEURT score calculation raises UnrecognizedFlagError \n Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n``` \n And I have the same error with TF 2.4.1. I believe this issue should be reopened. Any ideas?!","embeddings":[-0.3299125135,-0.3983678818,0.0414073691,0.4000958502,0.3001840711,-0.2275916785,0.2741967142,0.2562865913,0.0054997536,0.2942811847,-0.0122730052,0.0224692263,-0.0850862861,0.0461418107,-0.2817866504,0.1284505874,-0.209513098,-0.0770389214,0.4217458963,0.0855357051,-0.3209675848,0.1901822984,-0.1129608974,-0.1827558875,-0.3120544553,0.0189661421,0.3288890123,-0.0016792981,-0.1310292333,-0.2663407624,0.1180293337,-0.1967550069,0.1121654436,0.5339958668,-0.0001142144,-0.1661914289,0.1390840411,-0.1814889759,-0.1773090065,-0.1401195824,-0.0342725813,-0.2571440339,-0.1122626364,-0.2426050603,0.0441503674,0.1876826435,-0.1606330574,-0.0122861192,0.1843592823,0.3979115784,0.1449992061,0.1509114951,0.0781051069,0.0778455064,-0.1381148547,-0.3425809443,-0.1521806866,0.3253555,-0.0669247732,0.0316266343,-0.209743306,0.221054256,0.1617935449,0.214526251,0.2830664814,-0.1113334671,0.3766462803,-0.0479228683,0.2515899241,0.1977617294,-0.0995697305,-0.2013901174,-0.2185860127,0.1886230856,0.1277872771,-0.6358430386,-0.0538012832,-0.0609726347,-0.1685434878,-0.3811208308,-0.3757064044,-0.1014486775,-0.1010174826,-0.1352425069,-0.1513607055,0.1787613928,-0.3350163996,0.1010795608,0.0664991736,0.037248496,0.2274393886,0.0955426246,0.1029124409,0.2206829786,-0.4990333319,-0.1053616852,0.0160875209,-0.1888549775,-0.0162346847,-0.0325364172,0.2466262728,-0.0158617459,-0.0560192503,0.1580464989,-0.1570668668,0.5400379896,0.1700801402,0.0842136517,0.3933697343,0.5855426192,0.0929541662,-0.0040577799,-0.0056776549,-0.5385399461,0.2322417945,0.0733196586,-0.1784122437,-0.1894301772,-0.4930152595,0.0014649347,-0.0609924719,0.0312800631,0.2731887996,0.4516296387,-0.3899906874,-0.2442983538,0.2423392832,-0.0518201552,-0.0440993942,-0.0485079139,-0.278729707,0.0635427013,-0.0296433624,0.1907825172,0.0879457742,-0.6552608609,0.3337691426,-0.1572265476,0.256819278,-0.2171640247,0.0846604779,-0.2342718989,-0.0140116513,0.0292475577,-0.055336684,-0.3133440316,0.3256939352,-0.2782121897,0.0963667035,0.0749984458,-0.0507273413,-0.4088027775,0.0596363954,0.1178674996,-0.1385251433,0.0586275198,-0.1454676092,0.205530867,0.3106637597,-0.0517417118,0.0607756674,-0.1468340755,-0.1884095222,-0.1321550906,0.5624065995,-0.02965164,-0.2255628407,-0.0499477759,-0.0330220945,0.0214515924,0.360920608,0.3322623372,0.0474140309,0.1374679953,-0.1043959707,-0.1068220213,0.2952225506,-0.3557236493,-0.1823655665,-0.1604952812,0.0411742367,-0.1827295721,0.0532729402,-0.14260526,-0.0261133779,-0.1544633806,0.4189432859,0.0851458907,-0.1766564548,-0.1190728992,-0.4814083576,-0.3502425551,-0.091270633,0.0589542314,0.5071213245,-0.0762533695,0.1716108322,0.2642099559,0.0554546081,-0.0777342767,0.0007423353,0.1357578784,0.2604104877,-0.2453193814,0.1736738533,-0.1839156747,0.19437778,0.2270919085,0.1480082572,0.1326930821,0.0645504296,-0.0957873538,-0.3693134785,0.0117412331,0.0408739001,-0.0906421617,0.1942002624,-0.0236327965,0.020072462,-0.0462615639,-0.1779586524,0.0190616455,-0.6478570104,0.0577800311,0.2421558648,-0.0119237667,0.0095317839,-0.1117237285,0.0777575672,0.3475860655,0.1864814013,0.0795118362,-0.1525591016,0.3241609931,0.1469948739,-0.2106697559,0.0685734451,-0.1213834658,-0.0259960033,0.3158133626,-0.0452680923,0.0832680464,0.0991952717,-0.1209597886,0.2441323251,0.0936465636,-0.0794048607,0.1794829667,0.2946690023,-0.0805851817,-0.0727350041,0.1661313027,0.1495787203,-0.1780600399,0.1724616438,-0.1364564002,0.0664393827,-0.2640105784,0.0271362178,-0.2498497814,0.4172476232,0.0708090067,0.0645807236,0.1937785298,-0.025959827,-0.0589471012,-0.0350064449,0.0832065344,0.2039912492,-0.0499570891,0.2260341942,0.0716900676,-0.166817978,-0.1371464133,0.0104705878,0.0662441626,0.2401139736,0.3439278305,0.1682401597,-0.1599610299,-0.2710843086,-0.161084801,0.2464816123,0.2385196686,-0.1506380886,-0.0568524748,0.0756938085,0.1517330408,-0.2876555622,-0.2880500853,-0.253351301,-0.1597195268,0.0883836225,0.2925890684,0.257884413,-0.0014518611,0.3952098191,0.4617116153,0.0593588874,-0.0513374992,0.0502368845,-0.2153857648,-0.3148036599,0.088668257,0.0649697036,-0.101613991,0.2333034426,-0.1021954417,-0.194899261,-0.3437841535,-0.4103054404,0.0100563504,-0.2103194147,0.4719873071,0.1816637367,-0.004250186,-0.3588891327,-0.1189134493,0.1462681592,0.1211081371,-0.039833039,-0.019685775,-0.1493832171,0.0391473398,-0.0708142668,-0.2220977545,0.1640029252,-0.3041469455,0.240951851,0.0763138384,0.0306515042,-0.0612248778,0.0523096174,0.5045964718,0.5048177242,-0.0379235446,-0.2633743584,0.0904082954,0.1380197257,-0.336689651,-0.3102604747,0.0216101333,0.0162846167,0.5390122533,0.1647621095,-0.4579927325,-0.31728971,-0.0815850124,-0.2054210007,-0.2434681058,0.1044477895,0.1045603976,0.0453711972,-0.0166840106,-0.1219532117,-0.1212315559,0.0335266553,-0.2295121402,0.6499086022,-0.2761822343,0.2803352475,0.3270616829,0.863853395,0.1714121252,-0.0271327849,0.0819885209,0.1208694801,0.0416551083,-0.0073013082,-0.0545647815,0.627938509,-0.2941339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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1727","title":"BLEURT score calculation raises UnrecognizedFlagError","comments":"I'm seeing the same issue with TF 2.4.1 when running the following in https:\/\/colab.research.google.com\/github\/huggingface\/datasets\/blob\/master\/notebooks\/Overview.ipynb:\r\n```\r\n!pip install git+https:\/\/github.com\/google-research\/bleurt.git\r\nreferences = [\"foo bar baz\", \"one two three\"]\r\nbleurt_metric = load_metric('bleurt')\r\npredictions =  [\"foo bar\", \"four five six\"]\r\nbleurt_metric.compute(predictions=predictions, references=references)\r\n```","body":"Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n```","comment_length":39,"text":"BLEURT score calculation raises UnrecognizedFlagError \n Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n``` \n I'm seeing the same issue with TF 2.4.1 when running the following in https:\/\/colab.research.google.com\/github\/huggingface\/datasets\/blob\/master\/notebooks\/Overview.ipynb:\r\n```\r\n!pip install git+https:\/\/github.com\/google-research\/bleurt.git\r\nreferences = [\"foo bar baz\", \"one two three\"]\r\nbleurt_metric = load_metric('bleurt')\r\npredictions =  [\"foo bar\", \"four five six\"]\r\nbleurt_metric.compute(predictions=predictions, references=references)\r\n```","embeddings":[-0.3299125135,-0.3983678818,0.0414073691,0.4000958502,0.3001840711,-0.2275916785,0.2741967142,0.2562865913,0.0054997536,0.2942811847,-0.0122730052,0.0224692263,-0.0850862861,0.0461418107,-0.2817866504,0.1284505874,-0.209513098,-0.0770389214,0.4217458963,0.0855357051,-0.3209675848,0.1901822984,-0.1129608974,-0.1827558875,-0.3120544553,0.0189661421,0.3288890123,-0.0016792981,-0.1310292333,-0.2663407624,0.1180293337,-0.1967550069,0.1121654436,0.5339958668,-0.0001142144,-0.1661914289,0.1390840411,-0.1814889759,-0.1773090065,-0.1401195824,-0.0342725813,-0.2571440339,-0.1122626364,-0.2426050603,0.0441503674,0.1876826435,-0.1606330574,-0.0122861192,0.1843592823,0.3979115784,0.1449992061,0.1509114951,0.0781051069,0.0778455064,-0.1381148547,-0.3425809443,-0.1521806866,0.3253555,-0.0669247732,0.0316266343,-0.209743306,0.221054256,0.1617935449,0.214526251,0.2830664814,-0.1113334671,0.3766462803,-0.0479228683,0.2515899241,0.1977617294,-0.0995697305,-0.2013901174,-0.2185860127,0.1886230856,0.1277872771,-0.6358430386,-0.0538012832,-0.0609726347,-0.1685434878,-0.3811208308,-0.3757064044,-0.1014486775,-0.1010174826,-0.1352425069,-0.1513607055,0.1787613928,-0.3350163996,0.1010795608,0.0664991736,0.037248496,0.2274393886,0.0955426246,0.1029124409,0.2206829786,-0.4990333319,-0.1053616852,0.0160875209,-0.1888549775,-0.0162346847,-0.0325364172,0.2466262728,-0.0158617459,-0.0560192503,0.1580464989,-0.1570668668,0.5400379896,0.1700801402,0.0842136517,0.3933697343,0.5855426192,0.0929541662,-0.0040577799,-0.0056776549,-0.5385399461,0.2322417945,0.0733196586,-0.1784122437,-0.1894301772,-0.4930152595,0.0014649347,-0.0609924719,0.0312800631,0.2731887996,0.4516296387,-0.3899906874,-0.2442983538,0.2423392832,-0.0518201552,-0.0440993942,-0.0485079139,-0.278729707,0.0635427013,-0.0296433624,0.1907825172,0.0879457742,-0.6552608609,0.3337691426,-0.1572265476,0.256819278,-0.2171640247,0.0846604779,-0.2342718989,-0.0140116513,0.0292475577,-0.055336684,-0.3133440316,0.3256939352,-0.2782121897,0.0963667035,0.0749984458,-0.0507273413,-0.4088027775,0.0596363954,0.1178674996,-0.1385251433,0.0586275198,-0.1454676092,0.205530867,0.3106637597,-0.0517417118,0.0607756674,-0.1468340755,-0.1884095222,-0.1321550906,0.5624065995,-0.02965164,-0.2255628407,-0.0499477759,-0.0330220945,0.0214515924,0.360920608,0.3322623372,0.0474140309,0.1374679953,-0.1043959707,-0.1068220213,0.2952225506,-0.3557236493,-0.1823655665,-0.1604952812,0.0411742367,-0.1827295721,0.0532729402,-0.14260526,-0.0261133779,-0.1544633806,0.4189432859,0.0851458907,-0.1766564548,-0.1190728992,-0.4814083576,-0.3502425551,-0.091270633,0.0589542314,0.5071213245,-0.0762533695,0.1716108322,0.2642099559,0.0554546081,-0.0777342767,0.0007423353,0.1357578784,0.2604104877,-0.2453193814,0.1736738533,-0.1839156747,0.19437778,0.2270919085,0.1480082572,0.1326930821,0.0645504296,-0.0957873538,-0.3693134785,0.0117412331,0.0408739001,-0.0906421617,0.1942002624,-0.0236327965,0.020072462,-0.0462615639,-0.1779586524,0.0190616455,-0.6478570104,0.0577800311,0.2421558648,-0.0119237667,0.0095317839,-0.1117237285,0.0777575672,0.3475860655,0.1864814013,0.0795118362,-0.1525591016,0.3241609931,0.1469948739,-0.2106697559,0.0685734451,-0.1213834658,-0.0259960033,0.3158133626,-0.0452680923,0.0832680464,0.0991952717,-0.1209597886,0.2441323251,0.0936465636,-0.0794048607,0.1794829667,0.2946690023,-0.0805851817,-0.0727350041,0.1661313027,0.1495787203,-0.1780600399,0.1724616438,-0.1364564002,0.0664393827,-0.2640105784,0.0271362178,-0.2498497814,0.4172476232,0.0708090067,0.0645807236,0.1937785298,-0.025959827,-0.0589471012,-0.0350064449,0.0832065344,0.2039912492,-0.0499570891,0.2260341942,0.0716900676,-0.166817978,-0.1371464133,0.0104705878,0.0662441626,0.2401139736,0.3439278305,0.1682401597,-0.1599610299,-0.2710843086,-0.161084801,0.2464816123,0.2385196686,-0.1506380886,-0.0568524748,0.0756938085,0.1517330408,-0.2876555622,-0.2880500853,-0.253351301,-0.1597195268,0.0883836225,0.2925890684,0.257884413,-0.0014518611,0.3952098191,0.4617116153,0.0593588874,-0.0513374992,0.0502368845,-0.2153857648,-0.3148036599,0.088668257,0.0649697036,-0.101613991,0.2333034426,-0.1021954417,-0.194899261,-0.3437841535,-0.4103054404,0.0100563504,-0.2103194147,0.4719873071,0.1816637367,-0.004250186,-0.3588891327,-0.1189134493,0.1462681592,0.1211081371,-0.039833039,-0.019685775,-0.1493832171,0.0391473398,-0.0708142668,-0.2220977545,0.1640029252,-0.3041469455,0.240951851,0.0763138384,0.0306515042,-0.0612248778,0.0523096174,0.5045964718,0.5048177242,-0.0379235446,-0.2633743584,0.0904082954,0.1380197257,-0.336689651,-0.3102604747,0.0216101333,0.0162846167,0.5390122533,0.1647621095,-0.4579927325,-0.31728971,-0.0815850124,-0.2054210007,-0.2434681058,0.1044477895,0.1045603976,0.0453711972,-0.0166840106,-0.1219532117,-0.1212315559,0.0335266553,-0.2295121402,0.6499086022,-0.2761822343,0.2803352475,0.3270616829,0.863853395,0.1714121252,-0.0271327849,0.0819885209,0.1208694801,0.0416551083,-0.0073013082,-0.0545647815,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1727","title":"BLEURT score calculation raises UnrecognizedFlagError","comments":"@aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?\r\n\r\nI tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error. \r\nHowever, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.\r\nThis made me suspect that the problem is somehow related to the Jupyter notebook.\r\n\r\nMore environment details:\r\n```\r\nOS: Ubuntu Linux 18.04\r\nconda==4.8.3\r\npython==3.8.5\r\ndatasets==1.3.0\r\ntensorflow==2.4.0\r\nBLEURT==0.0.1\r\nnotebook==6.2.0\r\n```","body":"Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n```","comment_length":112,"text":"BLEURT score calculation raises UnrecognizedFlagError \n Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n``` \n @aleSuglia @oscartackstrom - Are you getting the error when running your code in a Jupyter notebook ?\r\n\r\nI tried reproducing this error again, and was unable to do so from the python command line console in a virtual environment similar to the one I originally used (and unfortunately no longer have access to) when I first got the error. \r\nHowever, I've managed to reproduce the error by running the same code in a Jupyter notebook running a kernel from the same virtual environment.\r\nThis made me suspect that the problem is somehow related to the Jupyter notebook.\r\n\r\nMore environment details:\r\n```\r\nOS: Ubuntu Linux 18.04\r\nconda==4.8.3\r\npython==3.8.5\r\ndatasets==1.3.0\r\ntensorflow==2.4.0\r\nBLEURT==0.0.1\r\nnotebook==6.2.0\r\n```","embeddings":[-0.3299125135,-0.3983678818,0.0414073691,0.4000958502,0.3001840711,-0.2275916785,0.2741967142,0.2562865913,0.0054997536,0.2942811847,-0.0122730052,0.0224692263,-0.0850862861,0.0461418107,-0.2817866504,0.1284505874,-0.209513098,-0.0770389214,0.4217458963,0.0855357051,-0.3209675848,0.1901822984,-0.1129608974,-0.1827558875,-0.3120544553,0.0189661421,0.3288890123,-0.0016792981,-0.1310292333,-0.2663407624,0.1180293337,-0.1967550069,0.1121654436,0.5339958668,-0.0001142144,-0.1661914289,0.1390840411,-0.1814889759,-0.1773090065,-0.1401195824,-0.0342725813,-0.2571440339,-0.1122626364,-0.2426050603,0.0441503674,0.1876826435,-0.1606330574,-0.0122861192,0.1843592823,0.3979115784,0.1449992061,0.1509114951,0.0781051069,0.0778455064,-0.1381148547,-0.3425809443,-0.1521806866,0.3253555,-0.0669247732,0.0316266343,-0.209743306,0.221054256,0.1617935449,0.214526251,0.2830664814,-0.1113334671,0.3766462803,-0.0479228683,0.2515899241,0.1977617294,-0.0995697305,-0.2013901174,-0.2185860127,0.1886230856,0.1277872771,-0.6358430386,-0.0538012832,-0.0609726347,-0.1685434878,-0.3811208308,-0.3757064044,-0.1014486775,-0.1010174826,-0.1352425069,-0.1513607055,0.1787613928,-0.3350163996,0.1010795608,0.0664991736,0.037248496,0.2274393886,0.0955426246,0.1029124409,0.2206829786,-0.4990333319,-0.1053616852,0.0160875209,-0.1888549775,-0.0162346847,-0.0325364172,0.2466262728,-0.0158617459,-0.0560192503,0.1580464989,-0.1570668668,0.5400379896,0.1700801402,0.0842136517,0.3933697343,0.5855426192,0.0929541662,-0.0040577799,-0.0056776549,-0.5385399461,0.2322417945,0.0733196586,-0.1784122437,-0.1894301772,-0.4930152595,0.0014649347,-0.0609924719,0.0312800631,0.2731887996,0.4516296387,-0.3899906874,-0.2442983538,0.2423392832,-0.0518201552,-0.0440993942,-0.0485079139,-0.278729707,0.0635427013,-0.0296433624,0.1907825172,0.0879457742,-0.6552608609,0.3337691426,-0.1572265476,0.256819278,-0.2171640247,0.0846604779,-0.2342718989,-0.0140116513,0.0292475577,-0.055336684,-0.3133440316,0.3256939352,-0.2782121897,0.0963667035,0.0749984458,-0.0507273413,-0.4088027775,0.0596363954,0.1178674996,-0.1385251433,0.0586275198,-0.1454676092,0.205530867,0.3106637597,-0.0517417118,0.0607756674,-0.1468340755,-0.1884095222,-0.1321550906,0.5624065995,-0.02965164,-0.2255628407,-0.0499477759,-0.0330220945,0.0214515924,0.360920608,0.3322623372,0.0474140309,0.1374679953,-0.1043959707,-0.1068220213,0.2952225506,-0.3557236493,-0.1823655665,-0.1604952812,0.0411742367,-0.1827295721,0.0532729402,-0.14260526,-0.0261133779,-0.1544633806,0.4189432859,0.0851458907,-0.1766564548,-0.1190728992,-0.4814083576,-0.3502425551,-0.091270633,0.0589542314,0.5071213245,-0.0762533695,0.1716108322,0.2642099559,0.0554546081,-0.0777342767,0.0007423353,0.1357578784,0.2604104877,-0.2453193814,0.1736738533,-0.1839156747,0.19437778,0.2270919085,0.1480082572,0.1326930821,0.0645504296,-0.0957873538,-0.3693134785,0.0117412331,0.0408739001,-0.0906421617,0.1942002624,-0.0236327965,0.020072462,-0.0462615639,-0.1779586524,0.0190616455,-0.6478570104,0.0577800311,0.2421558648,-0.0119237667,0.0095317839,-0.1117237285,0.0777575672,0.3475860655,0.1864814013,0.0795118362,-0.1525591016,0.3241609931,0.1469948739,-0.2106697559,0.0685734451,-0.1213834658,-0.0259960033,0.3158133626,-0.0452680923,0.0832680464,0.0991952717,-0.1209597886,0.2441323251,0.0936465636,-0.0794048607,0.1794829667,0.2946690023,-0.0805851817,-0.0727350041,0.1661313027,0.1495787203,-0.1780600399,0.1724616438,-0.1364564002,0.0664393827,-0.2640105784,0.0271362178,-0.2498497814,0.4172476232,0.0708090067,0.0645807236,0.1937785298,-0.025959827,-0.0589471012,-0.0350064449,0.0832065344,0.2039912492,-0.0499570891,0.2260341942,0.0716900676,-0.166817978,-0.1371464133,0.0104705878,0.0662441626,0.2401139736,0.3439278305,0.1682401597,-0.1599610299,-0.2710843086,-0.161084801,0.2464816123,0.2385196686,-0.1506380886,-0.0568524748,0.0756938085,0.1517330408,-0.2876555622,-0.2880500853,-0.253351301,-0.1597195268,0.0883836225,0.2925890684,0.257884413,-0.0014518611,0.3952098191,0.4617116153,0.0593588874,-0.0513374992,0.0502368845,-0.2153857648,-0.3148036599,0.088668257,0.0649697036,-0.101613991,0.2333034426,-0.1021954417,-0.194899261,-0.3437841535,-0.4103054404,0.0100563504,-0.2103194147,0.4719873071,0.1816637367,-0.004250186,-0.3588891327,-0.1189134493,0.1462681592,0.1211081371,-0.039833039,-0.019685775,-0.1493832171,0.0391473398,-0.0708142668,-0.2220977545,0.1640029252,-0.3041469455,0.240951851,0.0763138384,0.0306515042,-0.0612248778,0.0523096174,0.5045964718,0.5048177242,-0.0379235446,-0.2633743584,0.0904082954,0.1380197257,-0.336689651,-0.3102604747,0.0216101333,0.0162846167,0.5390122533,0.1647621095,-0.4579927325,-0.31728971,-0.0815850124,-0.2054210007,-0.2434681058,0.1044477895,0.1045603976,0.0453711972,-0.0166840106,-0.1219532117,-0.1212315559,0.0335266553,-0.2295121402,0.6499086022,-0.2761822343,0.2803352475,0.3270616829,0.863853395,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1727","title":"BLEURT score calculation raises UnrecognizedFlagError","comments":"This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.\r\n\r\nRunning this before calling bleurt fixes it:\r\n```\r\nimport sys\r\nsys.argv = sys.argv[:1]\r\n```\r\n\r\nNot the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?\r\n\r\nThis is the output of `print(sys.argv)` when running on colab:\r\n```\r\n['\/usr\/local\/lib\/python3.7\/dist-packages\/ipykernel_launcher.py', '-f', '\/root\/.local\/share\/jupyter\/runtime\/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']\r\n```","body":"Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n```","comment_length":71,"text":"BLEURT score calculation raises UnrecognizedFlagError \n Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n``` \n This happens when running the notebook on colab. The issue seems to be that colab populates sys.argv with arguments not handled by bleurt.\r\n\r\nRunning this before calling bleurt fixes it:\r\n```\r\nimport sys\r\nsys.argv = sys.argv[:1]\r\n```\r\n\r\nNot the most elegant solution. Perhaps it needs to be fixed in the bleurt code itself rather than huggingface?\r\n\r\nThis is the output of `print(sys.argv)` when running on colab:\r\n```\r\n['\/usr\/local\/lib\/python3.7\/dist-packages\/ipykernel_launcher.py', '-f', '\/root\/.local\/share\/jupyter\/runtime\/kernel-a857a78c-44d6-4b9d-b18a-030b858ee327.json']\r\n```","embeddings":[-0.3299125135,-0.3983678818,0.0414073691,0.4000958502,0.3001840711,-0.2275916785,0.2741967142,0.2562865913,0.0054997536,0.2942811847,-0.0122730052,0.0224692263,-0.0850862861,0.0461418107,-0.2817866504,0.1284505874,-0.209513098,-0.0770389214,0.4217458963,0.0855357051,-0.3209675848,0.1901822984,-0.1129608974,-0.1827558875,-0.3120544553,0.0189661421,0.3288890123,-0.0016792981,-0.1310292333,-0.2663407624,0.1180293337,-0.1967550069,0.1121654436,0.5339958668,-0.0001142144,-0.1661914289,0.1390840411,-0.1814889759,-0.1773090065,-0.1401195824,-0.0342725813,-0.2571440339,-0.1122626364,-0.2426050603,0.0441503674,0.1876826435,-0.1606330574,-0.0122861192,0.1843592823,0.3979115784,0.1449992061,0.1509114951,0.0781051069,0.0778455064,-0.1381148547,-0.3425809443,-0.1521806866,0.3253555,-0.0669247732,0.0316266343,-0.209743306,0.221054256,0.1617935449,0.214526251,0.2830664814,-0.1113334671,0.3766462803,-0.0479228683,0.2515899241,0.1977617294,-0.0995697305,-0.2013901174,-0.2185860127,0.1886230856,0.1277872771,-0.6358430386,-0.0538012832,-0.0609726347,-0.1685434878,-0.3811208308,-0.3757064044,-0.1014486775,-0.1010174826,-0.1352425069,-0.1513607055,0.1787613928,-0.3350163996,0.1010795608,0.0664991736,0.037248496,0.2274393886,0.0955426246,0.1029124409,0.2206829786,-0.4990333319,-0.1053616852,0.0160875209,-0.1888549775,-0.0162346847,-0.0325364172,0.2466262728,-0.0158617459,-0.0560192503,0.1580464989,-0.1570668668,0.5400379896,0.1700801402,0.0842136517,0.3933697343,0.5855426192,0.0929541662,-0.0040577799,-0.0056776549,-0.5385399461,0.2322417945,0.0733196586,-0.1784122437,-0.1894301772,-0.4930152595,0.0014649347,-0.0609924719,0.0312800631,0.2731887996,0.4516296387,-0.3899906874,-0.2442983538,0.2423392832,-0.0518201552,-0.0440993942,-0.0485079139,-0.278729707,0.0635427013,-0.0296433624,0.1907825172,0.0879457742,-0.6552608609,0.3337691426,-0.1572265476,0.256819278,-0.2171640247,0.0846604779,-0.2342718989,-0.0140116513,0.0292475577,-0.055336684,-0.3133440316,0.3256939352,-0.2782121897,0.0963667035,0.0749984458,-0.0507273413,-0.4088027775,0.0596363954,0.1178674996,-0.1385251433,0.0586275198,-0.1454676092,0.205530867,0.3106637597,-0.0517417118,0.0607756674,-0.1468340755,-0.1884095222,-0.1321550906,0.5624065995,-0.02965164,-0.2255628407,-0.0499477759,-0.0330220945,0.0214515924,0.360920608,0.3322623372,0.0474140309,0.1374679953,-0.1043959707,-0.1068220213,0.2952225506,-0.3557236493,-0.1823655665,-0.1604952812,0.0411742367,-0.1827295721,0.0532729402,-0.14260526,-0.0261133779,-0.1544633806,0.4189432859,0.0851458907,-0.1766564548,-0.1190728992,-0.4814083576,-0.3502425551,-0.091270633,0.0589542314,0.5071213245,-0.0762533695,0.1716108322,0.2642099559,0.0554546081,-0.0777342767,0.0007423353,0.1357578784,0.2604104877,-0.2453193814,0.1736738533,-0.1839156747,0.19437778,0.2270919085,0.1480082572,0.1326930821,0.0645504296,-0.0957873538,-0.3693134785,0.0117412331,0.0408739001,-0.0906421617,0.1942002624,-0.0236327965,0.020072462,-0.0462615639,-0.1779586524,0.0190616455,-0.6478570104,0.0577800311,0.2421558648,-0.0119237667,0.0095317839,-0.1117237285,0.0777575672,0.3475860655,0.1864814013,0.0795118362,-0.1525591016,0.3241609931,0.1469948739,-0.2106697559,0.0685734451,-0.1213834658,-0.0259960033,0.3158133626,-0.0452680923,0.0832680464,0.0991952717,-0.1209597886,0.2441323251,0.0936465636,-0.0794048607,0.1794829667,0.2946690023,-0.0805851817,-0.0727350041,0.1661313027,0.1495787203,-0.1780600399,0.1724616438,-0.1364564002,0.0664393827,-0.2640105784,0.0271362178,-0.2498497814,0.4172476232,0.0708090067,0.0645807236,0.1937785298,-0.025959827,-0.0589471012,-0.0350064449,0.0832065344,0.2039912492,-0.0499570891,0.2260341942,0.0716900676,-0.166817978,-0.1371464133,0.0104705878,0.0662441626,0.2401139736,0.3439278305,0.1682401597,-0.1599610299,-0.2710843086,-0.161084801,0.2464816123,0.2385196686,-0.1506380886,-0.0568524748,0.0756938085,0.1517330408,-0.2876555622,-0.2880500853,-0.253351301,-0.1597195268,0.0883836225,0.2925890684,0.257884413,-0.0014518611,0.3952098191,0.4617116153,0.0593588874,-0.0513374992,0.0502368845,-0.2153857648,-0.3148036599,0.088668257,0.0649697036,-0.101613991,0.2333034426,-0.1021954417,-0.194899261,-0.3437841535,-0.4103054404,0.0100563504,-0.2103194147,0.4719873071,0.1816637367,-0.004250186,-0.3588891327,-0.1189134493,0.1462681592,0.1211081371,-0.039833039,-0.019685775,-0.1493832171,0.0391473398,-0.0708142668,-0.2220977545,0.1640029252,-0.3041469455,0.240951851,0.0763138384,0.0306515042,-0.0612248778,0.0523096174,0.5045964718,0.5048177242,-0.0379235446,-0.2633743584,0.0904082954,0.1380197257,-0.336689651,-0.3102604747,0.0216101333,0.0162846167,0.5390122533,0.1647621095,-0.4579927325,-0.31728971,-0.0815850124,-0.2054210007,-0.2434681058,0.1044477895,0.1045603976,0.0453711972,-0.0166840106,-0.1219532117,-0.1212315559,0.0335266553,-0.2295121402,0.6499086022,-0.2761822343,0.2803352475,0.3270616829,0.863853395,0.1714121252,-0.02713278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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1727","title":"BLEURT score calculation raises UnrecognizedFlagError","comments":"I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT codebase.","body":"Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n```","comment_length":25,"text":"BLEURT score calculation raises UnrecognizedFlagError \n Calling the `compute` method for **bleurt** metric fails with an `UnrecognizedFlagError` for `FLAGS.bleurt_batch_size`. \r\n\r\nMy environment:\r\n```\r\npython==3.8.5\r\ndatasets==1.2.0\r\ntensorflow==2.3.1\r\ncudatoolkit==11.0.221\r\n```\r\n\r\nTest code for reproducing the error:\r\n```\r\nfrom datasets import load_metric\r\nbleurt = load_metric('bleurt')\r\ngen_text = \"I am walking on the promenade today\"\r\nref_text = \"I am walking along the promenade on this sunny day\"\r\nbleurt.compute(predictions=[test_text], references=[test_text])\r\n```\r\n\r\nError Output:\r\n```\r\nUsing default BLEURT-Base checkpoint for sequence maximum length 128. You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').\r\nINFO:tensorflow:Reading checkpoint \/home\/ubuntu\/.cache\/huggingface\/metrics\/bleurt\/default\/downloads\/extracted\/9aee35580225730ac5422599f35c4986e4c49cafd08082123342b1019720dac4\/bleurt-base-128.\r\nINFO:tensorflow:Config file found, reading.\r\nINFO:tensorflow:Will load checkpoint bert_custom\r\nINFO:tensorflow:Performs basic checks...\r\nINFO:tensorflow:... name:bert_custom\r\nINFO:tensorflow:... vocab_file:vocab.txt\r\nINFO:tensorflow:... bert_config_file:bert_config.json\r\nINFO:tensorflow:... do_lower_case:True\r\nINFO:tensorflow:... max_seq_length:128\r\nINFO:tensorflow:Creating BLEURT scorer.\r\nINFO:tensorflow:Loading model...\r\nINFO:tensorflow:BLEURT initialized.\r\n---------------------------------------------------------------------------\r\nUnrecognizedFlagError                     Traceback (most recent call last)\r\n<ipython-input-12-8b3f4322318a> in <module>\r\n      2 gen_text = \"I am walking on the promenade today\"\r\n      3 ref_text = \"I am walking along the promenade on this sunny day\"\r\n----> 4 bleurt.compute(predictions=[gen_text], references=[ref_text])\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/datasets\/metric.py in compute(self, *args, **kwargs)\r\n    396             references = self.data[\"references\"]\r\n    397             with temp_seed(self.seed):\r\n--> 398                 output = self._compute(predictions=predictions, references=references, **kwargs)\r\n    399 \r\n    400             if self.buf_writer is not None:\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bleurt\/b1de33e1cbbcb1dbe276c887efa1fad68c6aff913885108078fa1ad408908778\/bleurt.py in _compute(self, predictions, references)\r\n    103 \r\n    104     def _compute(self, predictions, references):\r\n--> 105         scores = self.scorer.score(references=references, candidates=predictions)\r\n    106         return {\"scores\": scores}\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/bleurt\/score.py in score(self, references, candidates, batch_size)\r\n    164     \"\"\"\r\n    165     if not batch_size:\r\n--> 166       batch_size = FLAGS.bleurt_batch_size\r\n    167 \r\n    168     candidates, references = list(candidates), list(references)\r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/tensorflow\/python\/platform\/flags.py in __getattr__(self, name)\r\n     83     # a flag.\r\n     84     if not wrapped.is_parsed():\r\n---> 85       wrapped(_sys.argv)\r\n     86     return wrapped.__getattr__(name)\r\n     87 \r\n\r\n~\/anaconda3\/envs\/noved\/lib\/python3.8\/site-packages\/absl\/flags\/_flagvalues.py in __call__(self, argv, known_only)\r\n    643     for name, value in unknown_flags:\r\n    644       suggestions = _helpers.get_flag_suggestions(name, list(self))\r\n--> 645       raise _exceptions.UnrecognizedFlagError(\r\n    646           name, value, suggestions=suggestions)\r\n    647 \r\n\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n\r\nPossible Fix:\r\nModify `_compute` method https:\/\/github.com\/huggingface\/datasets\/blob\/7e64851a12263dc74d41c668167918484c8000ab\/metrics\/bleurt\/bleurt.py#L104\r\nto receive a `batch_size` argument, for example:\r\n```\r\ndef _compute(self, predictions, references, batch_size=1):\r\n    scores = self.scorer.score(references=references, candidates=predictions, batch_size=batch_size)\r\n    return {\"scores\": scores}\r\n``` \n I got the error when running it from the command line. It looks more like an error that should be fixed in the BLEURT 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1725","title":"load the local dataset","comments":"You should rephrase your question or give more examples and details on what you want to do.\r\n\r\nit\u2019s not possible to understand it and help you with only this information.","body":"your guidebook's example is like\r\n>>>from datasets import load_dataset\r\n>>> dataset = load_dataset('json', data_files='my_file.json')\r\nbut the first arg is path...\r\nso how should i do if i want to load the local dataset for model training?\r\ni will be grateful if you can help me handle this problem!\r\nthanks a lot!","comment_length":30,"text":"load the local dataset \n your guidebook's example is like\r\n>>>from datasets import load_dataset\r\n>>> dataset = load_dataset('json', data_files='my_file.json')\r\nbut the first arg is path...\r\nso how should i do if i want to load the local dataset for model training?\r\ni will be grateful if you can help me handle this problem!\r\nthanks a lot! \n You should rephrase your question or give more examples and details on what you want to do.\r\n\r\nit\u2019s not possible to understand it and help you with only this information.","embeddings":[-0.1804081053,0.013499002,-0.1037141457,-0.0130341612,0.2134609818,0.1470807493,0.2700249851,0.2880049646,0.4363234639,0.0544830486,0.1162025779,0.4947062135,-0.0745658726,0.2974509597,0.2197879851,-0.1235246137,0.0534672737,0.2126086503,-0.2095779181,-0.2524624467,-0.1248621941,0.0600582771,-0.0063393991,0.115239121,0.0288788117,-0.1423294246,0.0913052633,0.5297982097,-0.1131520942,-0.3507753909,0.1977621168,-0.1289591193,0.4495811462,0.2374496162,-0.0000996339,0.2585187554,0.2192779928,-0.2029905617,-0.1397664696,-0.4289161265,-0.3890913725,-0.0182180032,0.4359698594,-0.3825985193,-0.2413374037,-0.193669349,0.1122729182,-0.1140697002,0.4548487067,0.5852871537,0.2613310814,-0.0851335749,0.0730208233,-0.016471386,-0.1200057715,0.2349961251,0.1261286438,0.5693177581,-0.0351350754,0.0236935075,0.1668702662,0.1679306328,-0.0048863706,0.1744319946,0.2951236069,0.1507116258,0.2086285353,-0.1883335412,0.0194293633,-0.0166477263,0.543004334,-0.1054640636,-0.1357060075,0.1112841442,-0.1266473085,-0.0491798073,-0.0290388782,0.2329448611,-0.1778219789,0.4197865427,0.042490419,-0.1539365053,-0.2715227902,0.3597122133,0.0952370167,0.1745562851,-0.2555059195,0.1659582555,0.1680125743,0.0616418421,-0.1496705115,-0.064267613,0.1697380245,0.283551991,-0.0429452881,0.0360108837,0.1783190072,-0.0266477074,0.0429214053,-0.0336224735,0.0597616546,0.1215231717,0.0151272807,0.4068186581,0.14132303,-0.0603359379,0.1202225015,0.1193656474,0.0246319901,0.1288490593,-0.2984278798,-0.1590562016,-0.3900526166,-0.237193644,-0.1974733919,0.0538755544,0.2985183895,-0.3917785287,0.0706147999,-0.1277719289,-0.0662080869,-0.2095964402,0.1045247093,0.459592551,-0.2042670101,0.0449056178,0.0241914894,0.183881402,-0.1363729239,-0.0699679703,-0.2046466321,0.201271683,-0.1179721281,-0.2125794739,0.0378543437,0.1141893715,0.6578570008,-0.1471209079,-0.1344776899,-0.1377892792,0.4352629185,0.025790425,0.0769679695,0.3557407558,0.177524507,-0.0939830616,0.2335483432,-0.2354285419,-0.3238989711,0.253567338,-0.4369358718,-0.3963064551,0.0372236446,0.2864255309,-0.0015867417,-0.0293876156,-0.3729645312,0.1193521321,-0.2197752297,0.0926272422,-0.2116462588,-0.0017827078,0.0263524521,-0.2408631146,0.4416387379,0.2379226983,-0.5031754375,-0.0197728407,0.0899934694,-0.2318455428,0.1753769815,0.033525534,-0.6356109381,0.5764732957,-0.0785183683,0.1443059295,0.6426224709,-0.278113395,0.0568954162,0.4205006361,-0.2087589502,-0.1774463952,0.0441889018,-0.0274734329,-0.4110879302,0.1960454583,0.2484508157,0.5545178056,0.0291259028,0.0272961203,-0.1241591722,-0.0672422424,0.008020726,0.2939467132,-0.1867227405,0.213648811,0.130880639,0.005861748,0.3009951413,-0.2669842839,-0.1808281392,0.3929591179,0.1529488564,-0.127654016,0.0471435897,0.2223428935,-0.2559236288,0.0447063372,0.0038740032,-0.227761969,0.0014641505,-0.0632902384,-0.2188643068,-0.0520210117,-0.3524464965,-0.0175709967,0.1918036044,0.0984412953,0.2418265939,-0.0412261561,-0.3753814101,0.0178711563,-0.4167240858,0.1365870684,-0.3241452575,0.0080959955,-0.1709156185,-0.0025115889,0.0867375135,-0.1733758152,-0.0086334897,-0.2545939088,-0.1253509372,0.3699713945,0.2719967663,0.1170046329,0.1235614866,0.1868063658,-0.1348803937,-0.0490514226,0.0882867724,0.1166790873,0.2536686659,0.0933782309,-0.3188493848,0.3371166587,0.0640420765,0.0670153797,0.1694770306,-0.1314997822,0.431586951,-0.1802596897,-0.2370909601,0.0997731388,0.0120466938,0.0793500617,0.1840080321,0.0215452556,-0.4251138568,-0.1393190771,0.2735140622,-0.1181033999,0.00432431,0.040310692,-0.1925407499,-0.0169023257,0.1137784496,0.2351687253,0.3781880438,0.2797128558,0.0750256702,0.0664956123,-0.0970142484,-0.1405507475,-0.0004030906,-0.0310391337,0.1065654233,-0.054551322,-0.1856284589,-0.3148079515,-0.1685457677,-0.0794031024,-0.0456368439,0.2858555913,-0.1693475246,0.1779532135,-0.1955839247,-0.0627032518,-0.338786602,-0.0970545188,0.1272835732,0.1369191408,-0.1404073983,-0.0968635455,-0.1612037122,0.0268023051,0.0185635537,0.0378132313,-0.1304678619,-0.1197356433,0.1198437437,-0.2314549387,-0.3636472523,0.1404199749,0.3530862927,0.2672512829,0.083815001,-0.4258140326,-0.137434274,0.3108015656,0.027889112,0.1481626481,0.1081307456,0.4255303741,0.0291996431,0.4161866009,-0.090715766,-0.156022191,0.3424634933,0.0791388154,-0.052951511,-0.1151214242,-0.0137034375,-0.0771543831,-0.169735685,-0.8360049129,-0.4267987311,-0.2437295765,0.2515364289,0.3813188672,0.203326866,0.1878329366,0.4112026989,0.3307701647,-0.0392426513,0.2694143057,-0.0949388668,-0.5751318932,0.2998142242,-0.3364127874,-0.3522518277,0.2509348392,-0.0576538593,0.3363949656,-0.3029782474,-0.3647485673,-0.2921612859,0.0185072403,-0.0856117755,-0.1314375699,0.1504500508,0.2429223061,-0.1419465095,-0.1863694042,-0.1153180823,-0.0638701022,0.3799186647,0.1802685857,0.0839897841,0.2681705952,0.4132495522,-0.2054721415,0.4035097361,-0.1192240492,-0.173917383,0.1824161857,-0.1665041745,0.0020543232,-0.1057960913,-0.0438924916,-0.0701341406,0.0149789806,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1725","title":"load the local dataset","comments":"sorry for that.\r\ni want to know how could i load the train set and the test set from the local ,which api or function should i use .\r\n","body":"your guidebook's example is like\r\n>>>from datasets import load_dataset\r\n>>> dataset = load_dataset('json', data_files='my_file.json')\r\nbut the first arg is path...\r\nso how should i do if i want to load the local dataset for model training?\r\ni will be grateful if you can help me handle this problem!\r\nthanks a lot!","comment_length":29,"text":"load the local dataset \n your guidebook's example is like\r\n>>>from datasets import load_dataset\r\n>>> dataset = load_dataset('json', data_files='my_file.json')\r\nbut the first arg is path...\r\nso how should i do if i want to load the local dataset for model training?\r\ni will be grateful if you can help me handle this problem!\r\nthanks a lot! \n sorry for that.\r\ni want to know how could i load the train set and the test set from the local ,which api or function should i use .\r\n","embeddings":[-0.3174705207,0.0638200939,-0.0952593461,0.0737695917,0.119674623,0.1557237059,0.1555574834,0.3187198937,0.5008644462,0.0614467226,0.1642027497,0.4374155104,-0.1208815649,0.4828317761,0.3047558069,-0.1635474712,0.0707763433,0.2294304967,-0.2788066268,-0.2441236973,-0.1344443262,0.0736518875,-0.0855388269,0.1153904349,0.1242739707,-0.0878494158,0.068395339,0.4703999162,-0.1047726199,-0.3025941849,0.2307722569,-0.1746587753,0.4277434945,0.2090977281,-0.0001064755,0.2021758258,0.1668529958,-0.2235588431,-0.0543571301,-0.6375690103,-0.339815259,0.0122430706,0.5199456215,-0.239844352,-0.2735789418,-0.3229026794,0.0742032379,-0.2420977503,0.5297538042,0.6811374426,0.1637198776,-0.1159812957,-0.0274794437,0.0788089857,-0.1082483754,0.1993105263,0.0425917953,0.4848556221,0.2161073983,-0.1329426765,0.1207038686,0.0659026802,0.0594503693,0.1458467692,0.3084445596,0.1042212099,0.1001513749,-0.3671630621,-0.03228781,-0.0068275719,0.6595649719,-0.1519090384,-0.0672272295,0.1932344437,-0.1363980472,0.0188089032,0.0514709093,0.20400998,-0.1985253841,0.3418989778,-0.139582634,-0.303314209,-0.2920081019,0.3929930031,0.1436104327,0.0630981103,-0.1361532956,0.191981867,0.1232660413,0.0773561895,-0.0476346426,-0.1123594195,0.1810999364,0.4176240861,-0.147015661,0.0318899527,0.1204608008,-0.1583307683,-0.0212166645,0.1425637603,-0.0351912454,0.2076310515,-0.1413223445,0.2797667384,0.1994033903,0.0905409157,0.2510216236,0.2301160544,-0.0413734615,0.0451927371,-0.2475229204,-0.0928240344,-0.384180069,-0.146848157,-0.2581519186,0.1096848547,0.1985315084,-0.489693284,0.2011567205,-0.1036792174,-0.1590282768,-0.3006119728,0.1027131304,0.3576223552,-0.2637363672,0.1756728739,0.026358461,0.1759695411,-0.1818370372,-0.0355486237,-0.1697660983,0.2390910387,-0.087726891,-0.0803422928,0.0881424919,0.0737368092,0.6081708074,-0.1651269495,-0.0182265453,-0.071615696,0.5714467168,-0.0449614152,0.104512237,0.3682502806,0.1844315976,-0.1087694168,0.1584704071,-0.1697958857,-0.3091650605,0.266433239,-0.4438110292,-0.4999341667,0.0858391151,0.2154586464,0.0015287043,-0.0579149798,-0.4173059165,0.1919698566,-0.2347786576,0.0992010087,-0.2122064978,-0.0167671833,-0.0949358791,-0.1602852494,0.4525190592,0.1626813114,-0.5647460818,-0.0626615286,0.232469365,-0.1813011169,0.1460630894,0.077684857,-0.6236317754,0.5268677473,-0.0807110667,0.1067872569,0.7347121239,-0.3785433471,0.156506896,0.4080643952,-0.1055254489,-0.289902091,-0.1411564201,-0.0831240788,-0.4013299942,0.1834557205,0.2094933242,0.7176532745,-0.0399099477,-0.0396732092,-0.0849481151,-0.1355524361,0.0854758024,0.3924347162,0.0022945537,0.1123969182,0.0790324137,-0.056993667,0.2535614967,-0.2241216749,-0.2327947915,0.3431114852,0.2093584985,-0.1896333694,0.0995650142,0.1646358073,-0.2271852791,0.0807110891,-0.0943072587,-0.0907400176,0.0667656437,0.0093658278,-0.2246161699,-0.1069226936,-0.3677223027,0.0222594719,0.0560126193,-0.0122846644,0.2122127116,0.0821349919,-0.4414839447,-0.0597798154,-0.3856554329,0.1207780838,-0.3741035461,0.0258612614,-0.0800241232,0.0126402108,0.0898481682,-0.156071499,-0.0130083757,-0.3751966953,-0.1884447336,0.370516032,0.3448432982,0.1940324008,0.1215705201,0.2672254443,0.0299345255,-0.047066398,-0.0173276402,0.1069503278,0.2393458188,0.0013541033,-0.349444896,0.3390460908,0.0386656746,0.0980397537,0.2118876725,-0.1019683778,0.3982222378,-0.102011919,-0.1447942406,0.1466412693,0.0330731422,-0.0230385568,0.2643129826,-0.0116129369,-0.3277983963,-0.1617969871,0.1831050217,-0.1951005757,-0.0211493224,-0.0869825035,-0.1502008587,-0.1252111942,0.2040324658,0.1477635354,0.3505549431,0.1726505905,0.0883328915,0.1220305637,-0.0217351578,-0.0865062401,-0.0081358254,-0.0589315258,0.1279908419,-0.0403368361,-0.2419560105,-0.2958584428,-0.1434846222,-0.0562508292,0.0435062498,0.2657514215,-0.1682803035,0.1302164346,-0.1576895416,-0.0896330699,-0.339509517,-0.1717470586,0.1737675518,0.1312695295,-0.0780021846,-0.2171972394,-0.1090754047,0.017932415,0.0351296104,0.1275705546,-0.121084936,-0.1230857223,0.1192730814,-0.2470790148,-0.4121358395,0.1210517138,0.4731254578,0.3146317899,0.2221260965,-0.3679429889,-0.164971143,0.3988380432,0.0432445966,0.2520950139,0.1601738334,0.4491714537,-0.0479058065,0.2613448501,-0.074271664,-0.1335169673,0.2895405889,-0.0687779784,-0.0948221982,-0.1374575496,-0.0300958734,-0.0145067871,-0.1382264942,-0.8957135677,-0.4849814177,-0.097554639,0.2685419023,0.3610462844,0.2054995298,0.0800819024,0.3286765218,0.3222788572,0.0620690249,0.1969749331,-0.0913054943,-0.6318541169,0.1565038562,-0.3055170178,-0.3713766932,0.1807253212,0.0271573234,0.2557729483,-0.338681221,-0.4009631574,-0.4390980601,0.0541658811,0.0394946374,-0.1106428504,0.0456797965,0.1686145812,-0.2539508045,-0.0385424905,-0.0096106809,-0.0296596251,0.3398182988,0.2781490982,0.016129164,0.2597625554,0.3580315113,-0.2566970289,0.532569468,-0.0517986044,-0.1453032941,0.1072095037,-0.1643764824,-0.1630872488,-0.1720696837,-0.0392161906,-0.032288935,0.0151129691,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1725","title":"load the local dataset","comments":"thanks a lot \r\ni find that the problem is i dont use vpn...\r\nso i have to keep my net work even if i want to load the local data ?","body":"your guidebook's example is like\r\n>>>from datasets import load_dataset\r\n>>> dataset = load_dataset('json', data_files='my_file.json')\r\nbut the first arg is path...\r\nso how should i do if i want to load the local dataset for model training?\r\ni will be grateful if you can help me handle this problem!\r\nthanks a lot!","comment_length":31,"text":"load the local dataset \n your guidebook's example is like\r\n>>>from datasets import load_dataset\r\n>>> dataset = load_dataset('json', data_files='my_file.json')\r\nbut the first arg is path...\r\nso how should i do if i want to load the local dataset for model training?\r\ni will be grateful if you can help me handle this problem!\r\nthanks a lot! \n thanks a lot \r\ni find that the problem is i dont use vpn...\r\nso i have to keep my net work even if i want to load the local data ?","embeddings":[-0.2262580544,0.0726394951,-0.0507601015,0.0298312698,0.1185123622,0.063802816,0.2788845003,0.2106349766,0.4118477702,0.1336882561,0.1261633039,0.4746668637,0.0714066476,0.4720021486,0.3170851171,-0.0984342843,0.1031512767,0.2740089297,-0.1861927062,-0.2366859019,-0.1793768257,0.0070895357,-0.1479733437,0.0982294828,0.0665092617,-0.1281439811,0.1553800553,0.4630153775,-0.0324669965,-0.3530551195,0.3163314164,-0.1135789379,0.5149089098,0.2220309228,-0.0001110482,0.3156653047,0.2686825693,-0.1798223108,-0.176581502,-0.4398923516,-0.3317746818,-0.0825264752,0.5880652666,-0.3125265539,-0.3187110126,-0.1652936339,0.1679867506,-0.1342593282,0.4851852059,0.5206210613,0.1277266443,-0.0649732426,-0.0465647876,0.0319498666,-0.2079118788,0.0409767069,0.0914772376,0.5332658291,-0.0358720645,-0.0306603406,0.2445849627,0.2169679403,0.0101462267,0.1108647063,0.4078392684,0.2312994301,0.1855111569,-0.2728904784,-0.0108615942,0.029412169,0.6502358913,-0.0345303304,-0.0615688451,0.1497109979,-0.1731130332,-0.0593592636,0.0818984658,0.1594633311,-0.1583940238,0.4750199914,-0.0652493611,-0.267205447,-0.3562138081,0.5089408159,0.1050527841,0.1424237788,-0.2099211216,0.1786520481,0.2246071398,0.0834832639,-0.0239068996,-0.1044606417,0.2395540923,0.3202842772,-0.0881080478,0.0494631305,0.1082440168,-0.0218074657,-0.0792380944,-0.1370923668,0.0832048506,0.1110032201,-0.0430995561,0.3450957537,0.2440096289,-0.0805026665,0.0778989792,0.114690356,0.0827255473,0.0343719237,-0.1902605742,-0.1788253337,-0.4520168006,-0.2575865984,-0.2210620344,0.0694191158,0.388713181,-0.4943304062,0.2136765569,-0.099729538,-0.0802950338,-0.1920658797,0.1916211247,0.3994500339,-0.3089072704,0.1041301861,-0.0222692955,0.1574257612,-0.2376615405,-0.0765827894,-0.1493881643,0.0304349363,-0.0418887474,-0.1859297305,0.0700737759,0.1622484475,0.4741714597,-0.1978305131,-0.0110634416,-0.0682446361,0.398191303,-0.0363114998,0.0524886884,0.4357510805,0.1996600628,-0.1175832897,0.1381771415,-0.2458844185,-0.3373399973,0.3195278347,-0.4019109905,-0.4656979144,0.1041049361,0.1509398669,0.041095823,-0.115384005,-0.3019272387,0.1050076112,-0.2668136954,0.031799905,-0.2557733655,0.0546940342,-0.1697489619,-0.1358542591,0.5634297729,0.3151148856,-0.5412475467,0.0201918539,0.0491371863,-0.2760152817,0.2094730139,0.1329900771,-0.6482594013,0.5789049268,-0.0216294937,0.0538205467,0.6857836843,-0.3355177939,-0.0480752476,0.5132650733,-0.2959933281,-0.3279221356,0.0262847431,0.0608985461,-0.3787731826,0.1456359327,0.2567851543,0.7998130918,-0.0743965954,-0.0323773958,-0.1378634274,-0.0969179943,0.146775648,0.3137151003,-0.12660034,0.2838522494,0.1752076298,-0.0933167636,0.1890191734,-0.1494246274,-0.2203900069,0.3022560179,0.1621784121,-0.0572287962,0.0590971932,0.350907445,-0.3783249855,0.0031324769,-0.0467180498,-0.3301528394,-0.0039017866,-0.110073477,-0.1614359766,-0.1613136828,-0.3629112542,0.1327986866,0.0395569615,0.0078608813,0.2077229023,-0.0180349424,-0.3729109764,0.0122258915,-0.3724631071,0.1111291572,-0.3929052353,0.0575090796,-0.1297760606,0.0845783204,0.0042424733,-0.115063116,0.0015265071,-0.3098142147,-0.1387270093,0.3397436142,0.1583430916,0.2285282612,0.1951051652,0.1558132321,-0.0385021828,-0.2769066691,0.0877685845,0.0464889258,0.2177213877,-0.0373213068,-0.3309574127,0.3192175925,-0.1494605243,0.1390111595,0.0939441696,-0.1213916913,0.4528521299,-0.1952369064,-0.2817572057,0.1229007468,0.0119883381,0.126000613,0.2017009407,0.0835389122,-0.4605388343,-0.172970295,0.1462798417,-0.1816798747,-0.0034767515,0.0519896038,-0.0585378073,-0.0531862155,0.1458414048,0.1503778994,0.4096986651,0.2233097106,0.1376297027,0.2141470611,-0.0171461683,-0.1415855438,0.055437699,-0.0227767769,0.1109442338,-0.0638661087,-0.2919035256,-0.2469857484,-0.118993789,-0.0623462833,0.0299576428,0.2335624248,-0.187639758,0.2312341779,-0.2249925286,-0.2441207021,-0.2344409078,-0.1185711324,0.111547336,0.2049376816,-0.1599368602,-0.1388336271,-0.1216986924,0.089702487,0.0070075262,0.1787019074,-0.1082115397,-0.2284968793,0.1359652579,-0.3116207421,-0.4153101146,0.0083473269,0.3807973564,0.2456946671,0.1344306916,-0.4152359366,-0.1526008099,0.3851387501,0.0807947367,0.170309484,0.2366387248,0.4177155495,-0.0022947465,0.4468155801,-0.1556139141,-0.0613436252,0.3115589321,0.0588511601,-0.1194642633,-0.0135620506,-0.0880732611,-0.0143259335,-0.1511420012,-0.8512554765,-0.63345927,-0.2101437151,0.3240525723,0.3335136175,0.2899512649,0.2690646052,0.348797977,0.3145422935,-0.1079902053,0.1839396507,-0.0700643212,-0.6561444402,0.261235863,-0.2274521887,-0.3461762965,0.2240776718,-0.048554372,0.3382542431,-0.3104378283,-0.4571043551,-0.2013507783,-0.0710650608,0.0937449485,-0.1810588092,0.2116857171,0.2588582635,-0.2723754942,-0.0588330254,0.0396798402,0.0404402278,0.3224676549,0.2134728134,-0.0062858616,0.3955478966,0.4657087028,-0.2823897898,0.4909546971,-0.1352150589,-0.0588798895,0.2291333973,-0.1228797361,0.0602015406,-0.1580145359,-0.0853770375,-0.0694195405,0.0933696479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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1724","title":"could not run models on a offline server successfully","comments":"Hi @lkcao !\r\nYour issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets\/datasets\/text`: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/text\/text.py.\r\nThen you can change the line 221 of `run_mlm_new.py` into:\r\n```python\r\n  datasets = load_dataset('\/path\/to\/text.py', data_files=data_files)\r\n```\r\nWhere `\/path\/to\/text.py` is the path on the server where you saved the `text.py` script.","body":"Hi, I really need your help about this.\r\nI am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:\r\n![image](https:\/\/user-images.githubusercontent.com\/49967236\/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png)\r\n\r\nis there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...","comment_length":64,"text":"could not run models on a offline server successfully \n Hi, I really need your help about this.\r\nI am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:\r\n![image](https:\/\/user-images.githubusercontent.com\/49967236\/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png)\r\n\r\nis there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... \n Hi @lkcao !\r\nYour issue is indeed related to `datasets`. In addition to installing the package manually, you will need to download the `text.py` script on your server. You'll find it (under `datasets\/datasets\/text`: https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/text\/text.py.\r\nThen you can change the line 221 of `run_mlm_new.py` into:\r\n```python\r\n  datasets = load_dataset('\/path\/to\/text.py', data_files=data_files)\r\n```\r\nWhere `\/path\/to\/text.py` is the path on the server where you saved the `text.py` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1724","title":"could not run models on a offline server successfully","comments":"We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline","body":"Hi, I really need your help about this.\r\nI am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:\r\n![image](https:\/\/user-images.githubusercontent.com\/49967236\/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png)\r\n\r\nis there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...","comment_length":24,"text":"could not run models on a offline server successfully \n Hi, I really need your help about this.\r\nI am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:\r\n![image](https:\/\/user-images.githubusercontent.com\/49967236\/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png)\r\n\r\nis there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... \n We're working on including the local dataset builders (csv, text, json etc.) directly in the `datasets` package so that they can be used offline","embeddings":[-0.1060371175,0.0922159106,-0.046883624,0.3895358443,0.3865782022,0.0059097745,0.4341858625,0.3095848262,-0.1331249028,0.0817197114,0.2425644696,0.1654364914,-0.2283213139,0.1497392505,-0.0170877595,-0.2990975976,-0.0592587888,0.1828980446,-0.1974499673,-0.2429979891,0.1269834936,-0.0581292957,-0.0441667959,0.2301857471,-0.21554856,-0.4256712496,0.0423487984,0.1900561005,-0.5258668661,-0.1492341012,0.1743561178,-0.2595254183,0.4095986187,0.2606842816,-0.0001232795,0.1577787846,-0.0261516292,-0.1618656665,-0.1889204234,-0.168033421,0.2139279693,-0.1500169039,0.2031965107,-0.343084842,0.1663936973,0.2027682215,0.0882515311,-0.2262526304,0.1819858998,0.2225345969,0.0811528936,0.3456409574,-0.0080614006,-0.0516863428,-0.0894696116,-0.0270259585,-0.2824190557,0.3596595228,-0.1925052106,-0.1691604704,0.3959687054,0.2600797713,-0.3926531672,0.2729837298,0.0724465623,-0.0229853652,0.2645563185,-0.2598523796,0.3517810106,0.4191648364,0.1268899292,0.1497762948,-0.0386599675,-0.0704283938,0.1231043637,-0.3096102774,0.1817725003,0.5108624697,-0.2465870827,0.3363565207,-0.4833446145,-0.2510819137,-0.3149570823,0.1009634361,0.0626166612,0.3504547477,-0.0984806642,0.1237138882,0.0024207146,0.0537846945,-0.1307026446,-0.1126635671,0.0507372916,-0.0992250144,0.0065360749,0.1016821787,-0.0649581552,0.4636526704,-0.0791180581,0.1569879055,0.0828815699,-0.1005582884,0.1395375133,0.1461672485,0.0042678369,0.2160400152,-0.0977964327,-0.0820842907,0.326407969,0.3431952,-0.2357829809,-0.127774775,-0.2011373192,-0.0050393478,-0.3550642133,-0.1234038919,0.0834385082,-0.2681816816,0.1247945651,-0.0364502594,-0.0826351121,0.0757178068,-0.0164595135,0.2262731045,-0.2433173805,-0.1361799836,0.0051484997,0.1221495718,-0.262883991,-0.5288695097,0.1647808999,-0.1130513996,-0.2026740164,0.0002788581,0.1247887239,-0.1336740702,0.3475115001,-0.2760000825,0.2243671715,-0.0759025514,0.2380285263,-0.4224008024,0.2049113214,0.4813667536,0.0758747384,-0.081188418,0.3487046957,-0.0194177367,0.1453605145,0.1722142249,0.0393870845,-0.4563670456,0.2807501256,0.0020650353,-0.2078278512,-0.1681116074,-0.2502406538,0.0999007374,0.0361761861,-0.1831386685,0.0544170551,0.1322178394,-0.1089423746,-0.0522018746,0.5060938001,0.4959051311,-0.6791664958,0.3387345374,-0.3162170053,0.1019221842,0.214804396,0.2621174455,-0.2264838517,0.6079880595,-0.2353295088,-0.3891132474,0.2655009329,0.0433162041,-0.0551420934,0.305403173,-0.1246066391,-0.3638512194,0.0518736392,-0.1435494274,0.336173296,0.3802532256,-0.1806554645,0.1694301963,-0.1315045059,-0.1806259453,-0.2857104838,-0.4504421651,0.1794899851,0.2346063703,0.3742853403,0.2641890645,0.012556795,-0.5038218498,0.0280284155,0.0363710709,-0.0293478053,-0.0439176522,0.1664084047,0.3082428873,-0.0919730291,-0.0644746497,-0.112760365,0.1963846534,-0.1144931987,0.5098642111,-0.3852114975,0.2672775686,0.0571122691,-0.0079921652,-0.1296545863,0.076863572,-0.0258728843,-0.2126410007,0.016152421,0.1104285046,-0.2051447779,0.0521740802,0.0527118742,0.1214864329,-0.0695323795,0.2951959074,-0.2672995031,-0.1024870872,-0.2109735608,-0.2992351651,0.0634127408,-0.0716551915,-0.1867232323,0.2357448488,-0.2127280831,-0.080583334,0.4685858488,0.1320838034,0.1254627407,-0.0921301693,0.4863225222,0.2074817717,0.0990616009,0.1751621068,-0.1444108486,0.5445819497,0.1018009633,0.4248337746,0.0000196064,0.2099057138,0.3192688525,0.0931873694,-0.052761782,-0.1990206391,0.2491931766,0.5976501107,0.0451320335,-0.2945423126,-0.1576206088,-0.1352266371,0.1207931861,0.1714498401,0.2262714505,-0.2169025987,-0.080658704,0.1490546018,0.1672179699,0.1669870466,0.2008853853,0.1179870218,-0.2305525541,0.3015746772,-0.1642576158,-0.2037895769,0.2730614841,0.2957656085,0.3667021692,0.0228051282,0.0161165912,-0.2569947243,-0.3741302192,-0.2441598177,0.0841519907,0.2269873023,-0.0501484536,0.3766278028,-0.5777348876,0.0765169188,-0.1849752218,0.1236771196,-0.7295866013,0.2035398781,0.1181023344,-0.1463213414,-0.1182480976,0.2264245749,-0.0453697629,0.4255085588,-0.1549142152,-0.1408883929,0.1204851642,-0.3105560541,-0.3584547341,-0.0728731602,0.2951801717,0.1145786867,0.4369487762,-0.071857594,-0.26792413,0.1672137678,-0.2344250977,0.0772113055,0.2278923094,0.1281882823,0.1719551533,0.3319923282,-0.1141428053,0.2252657264,0.4916746616,-0.2578901947,0.2110649496,-0.1713347137,-0.1653002799,0.0724964589,-0.220841065,-0.3486708403,-0.393298775,-0.1253815293,0.2265319824,0.3537360132,0.1589218378,-0.0445102453,0.4573685229,-0.0929059312,-0.2593960166,0.1491314024,0.1031404436,-0.7283962965,0.2829964459,0.0349384099,-0.2215238065,0.3270794153,0.1820893884,-0.0318351723,-0.0948087871,-0.5988149047,0.0097073819,0.2486181408,0.1019084901,0.1571266204,0.1113996878,0.5097570419,-0.1424682736,0.0674903095,-0.1166140363,-0.1063201278,0.1910106838,-0.1679154336,0.1807020009,-0.0488632061,0.6888198853,0.0007349898,0.3086834848,-0.390596211,0.0166347846,0.17128326,-0.0299936868,0.40847224,-0.1971964985,-0.0439686812,0.014222241,0.0347858369,-0.7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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1724","title":"could not run models on a offline server successfully","comments":"The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon","body":"Hi, I really need your help about this.\r\nI am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:\r\n![image](https:\/\/user-images.githubusercontent.com\/49967236\/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png)\r\n\r\nis there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...","comment_length":38,"text":"could not run models on a offline server successfully \n Hi, I really need your help about this.\r\nI am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:\r\n![image](https:\/\/user-images.githubusercontent.com\/49967236\/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png)\r\n\r\nis there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... \n The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon","embeddings":[-0.0688206926,0.1015685052,-0.0669888109,0.3511100113,0.3767763674,-0.0086315172,0.4361560941,0.2894013524,-0.1399609298,0.0760931149,0.2346403003,0.1608860195,-0.2274725735,0.1778730303,-0.0325524807,-0.3421509564,-0.0454738364,0.1792569011,-0.1910052598,-0.2585516274,0.1118428409,-0.0674841031,-0.0758616179,0.2248041332,-0.172747612,-0.4493913054,0.028594479,0.1861250699,-0.537869215,-0.1589070708,0.1751311123,-0.2850415707,0.4374857247,0.256544441,-0.0001215812,0.1360872984,-0.0246050414,-0.1551845372,-0.2021266073,-0.2065086514,0.2451994121,-0.2061143517,0.2791469991,-0.3414956331,0.1687799245,0.1580099016,0.0705215707,-0.1988772452,0.1477961838,0.2522169054,0.1006830335,0.3377572,-0.0068500517,-0.0385962427,-0.159553498,-0.0426585227,-0.2651070654,0.3764236867,-0.1992182434,-0.216839686,0.3203285038,0.1805513203,-0.4037184715,0.2859338522,0.0476996899,-0.0072088926,0.3059214056,-0.2814565301,0.328284651,0.3897051513,0.0928827003,0.1497496516,-0.0353211053,-0.0996459574,0.0985234976,-0.3189719617,0.1626932472,0.4870989025,-0.2380461097,0.3007386625,-0.4002816379,-0.2382664233,-0.2836399674,0.1429473609,0.0697753504,0.4067317247,-0.1066973731,0.0930780992,-0.0250147469,0.0395161957,-0.1332188845,-0.1188887358,0.0877983421,-0.0954038501,-0.0036215484,0.1037962288,-0.0244902447,0.4370110035,-0.117531769,0.1676568538,0.1017598063,-0.1033442393,0.1169741079,0.0955589935,0.0173146091,0.2371708453,-0.1497069895,-0.0764175728,0.3308113515,0.3549759686,-0.2280217707,-0.1040030345,-0.1588992029,-0.0280609783,-0.3715130389,-0.1064284965,0.1462471485,-0.223219052,0.1276468933,-0.0170747302,-0.1228610501,0.0449146479,0.0043858583,0.201846227,-0.2599536777,-0.0614691675,0.0148380194,0.1398490369,-0.2758599818,-0.5037819743,0.1586385071,-0.1060098559,-0.1690450311,-0.0141743161,0.1528880447,-0.1425388455,0.3483355939,-0.2321776599,0.2416369766,-0.0999860615,0.2083473653,-0.4021793902,0.1685494781,0.4523255825,0.1266523749,-0.0599456392,0.3684250414,-0.046919357,0.1518625915,0.1702974886,0.0549549162,-0.4641089439,0.286265403,0.0226029456,-0.1837539524,-0.2005116493,-0.2791651785,0.1201579869,0.0192346498,-0.1471369714,0.0545253977,0.1439160705,-0.101535067,-0.0637280494,0.5197229981,0.4969885349,-0.6513744593,0.343490839,-0.3135584295,0.1347789317,0.2270342559,0.2421960384,-0.2208391577,0.5864211917,-0.2081828713,-0.4126344323,0.2668750286,0.0500167273,-0.0629508495,0.2916077375,-0.1212403476,-0.3211032152,0.0613924712,-0.2034472823,0.3311758339,0.3757843673,-0.1575743258,0.1672545075,-0.1323935539,-0.1724302769,-0.3133817315,-0.4422492683,0.1823100299,0.233255595,0.395018816,0.2591549158,-0.0004789559,-0.4904565215,0.0822837427,0.0750812963,-0.030376263,-0.0373420939,0.1735895276,0.322699964,-0.0871328339,-0.050399676,-0.1006901041,0.1844821274,-0.0768969432,0.5632579923,-0.3706647158,0.2933447659,0.0315240994,-0.0384810418,-0.1228797659,0.0736027732,0.0064319707,-0.2515653074,0.0297923647,0.1520210207,-0.2159970999,0.0400627218,0.0401393212,0.1142153144,-0.0814347714,0.32415694,-0.2643056214,-0.1023516133,-0.1939538717,-0.3051472306,0.0810594186,-0.0574015453,-0.2148802876,0.2094394714,-0.2124065161,-0.0432941429,0.4440833032,0.1399232447,0.1407678127,-0.0312370732,0.478983283,0.2490859181,0.1185441688,0.1844966561,-0.0986160487,0.5424450636,0.0634376928,0.4350113571,0.0048439461,0.2102136314,0.326266259,0.1305159032,-0.0781854615,-0.2040623426,0.282686621,0.5560109019,0.0814245418,-0.2923345864,-0.1697802097,-0.1641284972,0.096555829,0.1423974931,0.2526114881,-0.2213065624,-0.0806182474,0.1542587131,0.1293653995,0.1689746082,0.2208225578,0.0985001624,-0.2407869399,0.326325655,-0.1443661749,-0.2146850675,0.2925775349,0.3399430215,0.393356353,0.0169404782,0.0489334017,-0.2314456105,-0.3556240201,-0.2272410244,0.0943486243,0.2546875477,-0.0677916482,0.3628233075,-0.5687308908,0.1182087138,-0.1478933543,0.0290822964,-0.7140902877,0.193494603,0.0739201009,-0.1115890965,-0.1312538981,0.2420375943,-0.0791335031,0.3908633292,-0.1166283637,-0.1547572762,0.119524844,-0.2907871306,-0.3274575174,-0.0611502193,0.2906631529,0.1177891865,0.3798543811,-0.0413909331,-0.2935419381,0.1742339581,-0.2367729396,0.0490828827,0.1882865876,0.1058585718,0.1678858548,0.3347955346,-0.1121794954,0.23501499,0.4916298389,-0.2610308826,0.2256456465,-0.1642932147,-0.1681237668,0.1103269085,-0.1871712953,-0.3800619245,-0.4347546101,-0.1456546932,0.2535462379,0.376981616,0.1690071374,-0.0515177213,0.4320072532,-0.1171561554,-0.2062694579,0.1546186507,0.1036776751,-0.7016678452,0.2701812387,0.0347405747,-0.2287964374,0.29579404,0.1756510884,-0.0413084105,-0.1743876487,-0.5842974186,-0.0135889994,0.25187096,0.1386651695,0.1520708799,0.0982301682,0.4951812625,-0.1266094446,0.0680923313,-0.0909656584,-0.0951695889,0.1854781955,-0.1201190427,0.1145274788,-0.0571163371,0.742993176,-0.0356152765,0.2881908119,-0.3934966922,0.0458831191,0.1955237687,-0.0219292026,0.3647749722,-0.2171272635,-0.0775315166,-0.0422316827,-0.0208114944,-0.7154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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1724","title":"could not run models on a offline server successfully","comments":"> The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\n> You can now use them offline\r\n> \r\n> ```python\r\n> datasets = load_dataset('text', data_files=data_files)\r\n> ```\r\n> \r\n> We'll do a new release soon\r\n\r\nso the new version release now?","body":"Hi, I really need your help about this.\r\nI am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:\r\n![image](https:\/\/user-images.githubusercontent.com\/49967236\/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png)\r\n\r\nis there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out...","comment_length":52,"text":"could not run models on a offline server successfully \n Hi, I really need your help about this.\r\nI am trying to fine-tuning a RoBERTa on a remote server, which is strictly banning internet. I try to install all the packages by hand and try to run run_mlm.py on the server. It works well on colab, but when I try to run it on this offline server, it shows:\r\n![image](https:\/\/user-images.githubusercontent.com\/49967236\/104276256-25a88600-546a-11eb-9776-8ec695dfa24e.png)\r\n\r\nis there anything I can do? Is it possible to download all the things in cache and upload it to the server? Please help me out... \n > The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\n> You can now use them offline\r\n> \r\n> ```python\r\n> datasets = load_dataset('text', data_files=data_files)\r\n> ```\r\n> \r\n> We'll do a new release soon\r\n\r\nso the new version release now?","embeddings":[-0.0705997646,0.0818422586,-0.067387253,0.3484895825,0.3655336201,-0.0059052366,0.4193034768,0.2942718565,-0.1489263922,0.0716177747,0.2294794321,0.1583710313,-0.2269311994,0.1734987497,-0.0371109247,-0.3468999863,-0.0394554771,0.1774479449,-0.1939214319,-0.2578321099,0.0990721583,-0.061965622,-0.0807371214,0.2230645716,-0.1615162343,-0.4670543373,0.0276674759,0.1842142045,-0.5382243991,-0.1700838804,0.1658654809,-0.283714354,0.4375509918,0.2521188557,-0.0001213945,0.1277884841,-0.0248076674,-0.1500540227,-0.2117671371,-0.2167317718,0.2522087097,-0.2124804705,0.2840510011,-0.3407836258,0.1697348654,0.1742061079,0.0756895021,-0.1966519952,0.1607891172,0.2545123696,0.1027426422,0.3456666768,-0.0017496607,-0.0426849723,-0.1672260165,-0.0622239225,-0.2551248968,0.3821850121,-0.2034241557,-0.2088387758,0.3286895454,0.1911961883,-0.3957904279,0.28680107,0.0446274206,0.0000419508,0.3129147887,-0.2795209289,0.323861897,0.3837933242,0.0983648077,0.1457062662,-0.050395526,-0.0933907926,0.0946790725,-0.3246632218,0.1679434627,0.4823390245,-0.2290003151,0.3012810349,-0.3965063095,-0.2433443069,-0.2756930888,0.1433299333,0.0731505752,0.4021547139,-0.1145825982,0.0903980434,-0.0285767373,0.0393344574,-0.1420435309,-0.1136839837,0.0838163346,-0.0864124745,-0.00361577,0.1033836454,-0.0319475718,0.4332543015,-0.1196015477,0.1626177728,0.1086184308,-0.1116893217,0.1194215491,0.0874299109,0.0294075161,0.2210605145,-0.1583593786,-0.0785427988,0.3308566213,0.3641965091,-0.2270265073,-0.1102049425,-0.149982363,-0.0261561871,-0.349898994,-0.1013975069,0.1574403048,-0.2232752442,0.1326773018,-0.0291154329,-0.1286501884,0.0402707495,-0.0050060549,0.2086393684,-0.2704685628,-0.0478030406,0.0200174898,0.1302569807,-0.2841922641,-0.5011151433,0.1586946696,-0.1097302362,-0.1684495211,-0.0036691439,0.1457222402,-0.1474144161,0.341573745,-0.2266682535,0.2293043286,-0.1161577702,0.2034096867,-0.3967208862,0.1668656617,0.4541451335,0.1340209246,-0.0559192151,0.3632282615,-0.0435790904,0.1427352577,0.1752673388,0.0568508878,-0.4620818496,0.2975709438,0.0243984107,-0.1721846163,-0.2028435469,-0.2606344223,0.1229296178,0.0196395274,-0.1636729091,0.054811351,0.1466071308,-0.1068672538,-0.0756872892,0.518643558,0.5003830194,-0.6551429629,0.3357151449,-0.3109006882,0.1295129806,0.2178948075,0.2354239374,-0.2067971826,0.5655161142,-0.2079125047,-0.4268912673,0.2711883783,0.0439608134,-0.0732552111,0.2840721905,-0.1325544715,-0.3194464147,0.0735415891,-0.198613748,0.327812463,0.3712581098,-0.1727527082,0.1563497484,-0.1345729679,-0.1739404052,-0.318761766,-0.4410797954,0.1834992021,0.2193382531,0.3940840065,0.2665600181,0.0074794665,-0.4645365477,0.0909483805,0.0913659409,-0.0380537249,-0.0364861004,0.1778343767,0.3347792625,-0.0872904882,-0.0518357754,-0.1034801453,0.1783381253,-0.0663980618,0.5493116379,-0.3502076864,0.2871763706,0.0241941735,-0.0407498144,-0.1121701077,0.0731939226,0.0052383589,-0.2501824796,0.0480812117,0.1526526511,-0.2161738873,0.0541430302,0.042806752,0.1116733849,-0.0890432224,0.3269085288,-0.2754415572,-0.1051766723,-0.1878639907,-0.3095560968,0.0890194774,-0.0565584823,-0.212663427,0.2083296329,-0.2054125369,-0.0481308885,0.4253845215,0.1450350136,0.1370151192,-0.017994618,0.4820729792,0.2605344057,0.1178944111,0.1872793287,-0.0823040977,0.5436932445,0.0677114725,0.4257641137,0.0182650015,0.2127682716,0.3230121434,0.1354770511,-0.0674882308,-0.2129700482,0.2741913795,0.5513848066,0.091666162,-0.3043964803,-0.1712460369,-0.1530237496,0.1117714942,0.1362790465,0.254383862,-0.2206159383,-0.0868165791,0.1520422846,0.1127185449,0.1753354073,0.2232252359,0.0989453644,-0.2311029881,0.3421707749,-0.1296846271,-0.2052721232,0.296341002,0.3406378329,0.3999171257,0.0022243706,0.0476659685,-0.2282091081,-0.3398965895,-0.2150229812,0.0960685983,0.2548360229,-0.071879141,0.3595644534,-0.5689226389,0.1225346774,-0.1531618088,0.0271621924,-0.7147392631,0.1970946491,0.0718186721,-0.090321742,-0.117038615,0.24285689,-0.0787803605,0.3732526004,-0.117195487,-0.1490920186,0.1266219169,-0.2835884392,-0.3315928578,-0.0591550991,0.2754873037,0.1071223989,0.3786298633,-0.0468066819,-0.2891301811,0.17163077,-0.2428634912,0.0421195552,0.1895648539,0.1023742184,0.177362144,0.3391303718,-0.1183856875,0.2422783822,0.4870125949,-0.2876998186,0.2076154053,-0.1738831252,-0.1704434901,0.117872335,-0.1792330891,-0.3962234259,-0.4508716464,-0.151104331,0.2607577443,0.3716067374,0.1726300865,-0.0400325842,0.4205482006,-0.1183398589,-0.2045203298,0.1578614414,0.0993773267,-0.696569562,0.2649095654,0.040320579,-0.2331422865,0.3088751137,0.1726256162,-0.0531161949,-0.1818626076,-0.5868797302,-0.0214212835,0.256139636,0.1477552205,0.1491210312,0.0979867652,0.5046492219,-0.1231771484,0.0652968809,-0.0895049423,-0.0938149244,0.1784211695,-0.1206737459,0.1079897583,-0.0565727279,0.76003474,-0.0328369215,0.2919978499,-0.3816135228,0.0486582704,0.1910071373,-0.0224883631,0.3683617413,-0.2188501209,-0.0778906867,-0.0568119809,-0.0303967763,-0.7145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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"Thanks for reporting !\r\nI was able to reproduce thanks to your code and find the origin of the bug.\r\nThe cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.\r\nI'm opening a PR to fix this.\r\n\r\nAlso we plan to do a new release in the coming days so you can expect the fix to be available soon.\r\nNote that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to.","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":129,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n Thanks for reporting !\r\nI was able to reproduce thanks to your code and find the origin of the bug.\r\nThe cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting list would not always be in the same order and therefore the function that computes the hash used by the cache would not always return the same result.\r\nI'm opening a PR to fix this.\r\n\r\nAlso we plan to do a new release in the coming days so you can expect the fix to be available soon.\r\nNote that you can still specify `cache_file_name=` in the second `map()` call to name the cache file yourself if you want to.","embeddings":[-0.1930393726,0.1210535541,0.0500952005,0.1900934726,0.0076969219,0.0369213931,-0.0168548934,0.2303498536,0.0167332795,-0.1084433347,0.1029985696,0.4139710963,0.2249117643,-0.1117237061,-0.0011100402,0.2446131408,0.3074613512,0.3216940165,-0.1533999145,-0.2088759243,-0.2891754806,0.1171123162,-0.3328206539,-0.092429027,-0.4170501232,0.2123280317,-0.0168774482,-0.4208398163,0.1122606322,-0.2555866539,0.3177160025,0.0858283564,0.1992189288,0.5570235252,-0.0001258203,-0.2676019073,0.3540959358,-0.0694738552,-0.2052473277,0.0768211856,-0.2844775021,0.0380443037,-0.1126935929,0.0233671591,0.0584392846,0.0587090738,0.0379663296,-0.6514899731,0.3060197532,0.1629111767,0.102592662,0.033276476,-0.0953430757,0.2345797122,0.0502028316,-0.0120253302,-0.0387438312,-0.3330449462,0.1630381346,-0.3523213267,0.0621471033,0.5163400769,-0.1757435054,-0.1419030875,0.3870675266,-0.0117806941,0.0179351512,-0.2646991313,0.3020609319,-0.0644705594,0.5929020047,-0.3732129037,-0.4453589916,-0.2218152285,-0.2183965743,-0.1797114164,0.3478957415,-0.2814225554,0.115124248,0.1367774606,-0.5952916145,-0.3717092276,0.2665030062,0.1268505901,-0.0126040196,0.118829295,-0.0442643128,0.1275966167,-0.2583603263,0.0305633396,0.278693229,-0.5107082129,-0.020411972,0.4761810005,-0.4573765695,-0.114198938,-0.0813957304,0.0444577448,0.031320218,0.2521778643,-0.1078691259,0.0848287344,-0.1045756191,-0.0248278677,0.1718008816,0.3819052577,0.1599488705,0.3959278464,-0.0466323309,-0.2572212815,-0.4361515045,-0.1423511654,0.1015104204,0.0623166673,0.537632525,0.0291995946,-0.2670723796,-0.1527193785,0.1350922734,0.1104742214,-0.1131508872,-0.154272154,0.020687161,0.1861227155,-0.2187203914,0.5657741427,-0.0489024818,0.0113220746,-0.2269070297,-0.0754960403,-0.2520525157,-0.1949410737,-0.2797417641,0.4240118265,0.107980758,-0.0087579098,0.1931643337,0.1137457117,0.1412912309,-0.2485303879,0.2450801283,-0.1952346861,0.1446888447,0.0313405395,-0.122426711,0.5588548183,0.0238276497,0.0820711404,-0.0578298308,0.2793508768,-0.3909225166,-0.0032497863,0.2678503692,0.0766011178,-0.1049885824,-0.051326897,-0.1365459859,-0.0477274917,0.6230661273,-0.3534024954,0.0028586881,-0.1369546503,-0.2181598991,-0.3552233577,0.1964023113,0.4151437283,-0.2203010619,-0.1271440536,-0.2892490625,0.4262260199,0.3456280828,0.1904119849,-0.0997599214,0.2238963842,-0.2436235696,0.2533464134,0.3463941514,-0.3264125288,-0.7522772551,-0.0623180978,0.2395538688,0.2138240188,-0.2641494572,0.0091178091,0.0585763827,0.0220128987,-0.0473067649,0.1247596368,0.0710947663,0.0479407869,-0.1934205443,-0.3237839639,0.3307866454,-0.1534485519,0.373434782,0.2039108723,-0.2786331475,-0.0654283091,0.1897790283,-0.0733121186,0.204567641,0.2111120969,0.0859391093,0.0963291302,0.3425368965,-0.144525975,-0.2208454311,0.2361599505,-0.0074976902,-0.4080512822,0.05238121,-0.0693818182,0.0342106409,-0.1751439422,-0.2368721217,-0.250767231,0.0469596423,0.2982925773,0.2158326656,0.0493254699,-0.0628557652,0.4346975684,0.2247962803,-0.0474488139,-0.1369978189,0.2314382493,0.1378540844,-0.0723532364,-0.2622561157,-0.007789142,0.3865086436,0.2050782442,-0.1728083491,0.1999227405,0.2821433246,0.1305397302,-0.1580862552,0.0377752185,-0.0964976549,0.076044932,-0.1803530306,0.0189615674,0.0912419036,-0.2024737746,0.3389697969,-0.0982242227,0.0435390286,0.1728527993,-0.1738719493,-0.1164959967,-0.0194160827,-0.0575414672,0.1215716228,-0.2605112791,-0.1267734617,-0.1007431298,0.4018777013,0.1480001211,0.3170870245,0.1431048512,0.5319442153,0.1638038456,0.0702496767,-0.0754961893,-0.595046699,-0.153305307,0.0420683324,0.1067333892,0.4031660557,0.0140680997,0.1372346729,-0.1065353453,0.1262332499,0.0536733977,0.0839905441,0.0515747443,0.0034557909,0.1623288542,0.0819173902,0.1239183173,-0.1544849575,0.3739548624,0.2223696411,0.0224084184,-0.1201334149,-0.0047830353,-0.4614660442,0.2616464496,-0.0094154151,-0.1877422035,-0.1089543253,-0.3240405321,-0.0293718316,0.1750336438,0.2323505729,0.1702650785,-0.1067733094,0.1637891084,-0.0965859368,-0.0634079278,-0.2861728072,-0.0442264751,-0.3516021371,-0.1043687165,0.0276290718,-0.3332431018,0.1092685685,-0.1978000402,0.0448775329,-0.3304952085,-0.2496137768,0.0687255412,0.1424214393,-0.191361472,-0.1454960704,-0.1331452131,-0.3406224549,-0.0777615905,0.1052851975,-0.3755034506,-0.0589957796,-0.1942424774,0.226897493,-0.0732832849,0.0613905266,-0.0987810716,0.0450629443,0.0523568131,-0.0922801569,-0.1205202043,-0.0991960391,-0.1073513255,-0.1778948605,-0.1473686546,-0.2940423191,-0.1225034297,-0.5240326524,-0.2998071015,0.536532402,-0.1247007027,-0.0793028027,-0.1053363308,-0.0294937789,0.3053979874,0.4665106535,-0.5257647634,-0.0645799488,-0.2027752846,-0.18987225,-0.1475555003,0.3041585684,0.3952634335,0.1911331415,0.1284653395,-0.4285719097,-0.2033012211,0.0207050201,0.001100692,0.4422153831,-0.0327308625,0.2021819651,-0.0818895251,0.8030892611,0.6181532145,-0.2177960426,0.2308448106,0.1147788912,0.3255800009,-0.1409276873,-0.197888568,-0.1367323548,-0.342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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"Thanks for the fast reply, waiting for the fix :)\r\n\r\nI tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:\r\n```\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n    cache_file_names={k: f'.cache\/{str(k)}' for k in tokenized_datasets}\r\n)\r\n```\r\n\r\nand got an error:\r\n```\r\nmultiprocess.pool.RemoteTraceback:\r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 157, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1491, in _map_single\r\n    tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)\r\n  File \"\/usr\/lib\/python3.6\/tempfile.py\", line 690, in NamedTemporaryFile\r\n    (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\r\n  File \"\/usr\/lib\/python3.6\/tempfile.py\", line 401, in _mkstemp_inner\r\n    fd = _os.open(file, flags, 0o600)\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache\/tmpsvszxtop'\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 48, in <module>\r\n    cache_file_names={k: f'.cache\/{str(k)}' for k in tokenized_datasets}\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in map\r\n    for k, dataset in self.items()\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1317, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1317, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 644, in get\r\n    raise self._value\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache\/tmpsvszxtop'\r\n```\r\n","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":229,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n Thanks for the fast reply, waiting for the fix :)\r\n\r\nI tried to use `cache_file_names` and wasn't sure how, I tried to give it the following:\r\n```\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n    cache_file_names={k: f'.cache\/{str(k)}' for k in tokenized_datasets}\r\n)\r\n```\r\n\r\nand got an error:\r\n```\r\nmultiprocess.pool.RemoteTraceback:\r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 157, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1491, in _map_single\r\n    tmp_file = tempfile.NamedTemporaryFile(\"wb\", dir=os.path.dirname(cache_file_name), delete=False)\r\n  File \"\/usr\/lib\/python3.6\/tempfile.py\", line 690, in NamedTemporaryFile\r\n    (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\r\n  File \"\/usr\/lib\/python3.6\/tempfile.py\", line 401, in _mkstemp_inner\r\n    fd = _os.open(file, flags, 0o600)\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache\/tmpsvszxtop'\r\n\"\"\"\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 48, in <module>\r\n    cache_file_names={k: f'.cache\/{str(k)}' for k in tokenized_datasets}\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in map\r\n    for k, dataset in self.items()\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 303, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1317, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1317, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/venv\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 644, in get\r\n    raise self._value\r\nFileNotFoundError: [Errno 2] No such file or directory: '_00000_of_00060.cache\/tmpsvszxtop'\r\n```\r\n","embeddings":[-0.1930393726,0.1210535541,0.0500952005,0.1900934726,0.0076969219,0.0369213931,-0.0168548934,0.2303498536,0.0167332795,-0.1084433347,0.1029985696,0.4139710963,0.2249117643,-0.1117237061,-0.0011100402,0.2446131408,0.3074613512,0.3216940165,-0.1533999145,-0.2088759243,-0.2891754806,0.1171123162,-0.3328206539,-0.092429027,-0.4170501232,0.2123280317,-0.0168774482,-0.4208398163,0.1122606322,-0.2555866539,0.3177160025,0.0858283564,0.1992189288,0.5570235252,-0.0001258203,-0.2676019073,0.3540959358,-0.0694738552,-0.2052473277,0.0768211856,-0.2844775021,0.0380443037,-0.1126935929,0.0233671591,0.0584392846,0.0587090738,0.0379663296,-0.6514899731,0.3060197532,0.1629111767,0.102592662,0.033276476,-0.0953430757,0.2345797122,0.0502028316,-0.0120253302,-0.0387438312,-0.3330449462,0.1630381346,-0.3523213267,0.0621471033,0.5163400769,-0.1757435054,-0.1419030875,0.3870675266,-0.0117806941,0.0179351512,-0.2646991313,0.3020609319,-0.0644705594,0.5929020047,-0.3732129037,-0.4453589916,-0.2218152285,-0.2183965743,-0.1797114164,0.3478957415,-0.2814225554,0.115124248,0.1367774606,-0.5952916145,-0.3717092276,0.2665030062,0.1268505901,-0.0126040196,0.118829295,-0.0442643128,0.1275966167,-0.2583603263,0.0305633396,0.278693229,-0.5107082129,-0.020411972,0.4761810005,-0.4573765695,-0.114198938,-0.0813957304,0.0444577448,0.031320218,0.2521778643,-0.1078691259,0.0848287344,-0.1045756191,-0.0248278677,0.1718008816,0.3819052577,0.1599488705,0.3959278464,-0.0466323309,-0.2572212815,-0.4361515045,-0.1423511654,0.1015104204,0.0623166673,0.537632525,0.0291995946,-0.2670723796,-0.1527193785,0.1350922734,0.1104742214,-0.1131508872,-0.154272154,0.020687161,0.1861227155,-0.2187203914,0.5657741427,-0.0489024818,0.0113220746,-0.2269070297,-0.0754960403,-0.2520525157,-0.1949410737,-0.2797417641,0.4240118265,0.107980758,-0.0087579098,0.1931643337,0.1137457117,0.1412912309,-0.2485303879,0.2450801283,-0.1952346861,0.1446888447,0.0313405395,-0.122426711,0.5588548183,0.0238276497,0.0820711404,-0.0578298308,0.2793508768,-0.3909225166,-0.0032497863,0.2678503692,0.0766011178,-0.1049885824,-0.051326897,-0.1365459859,-0.0477274917,0.6230661273,-0.3534024954,0.0028586881,-0.1369546503,-0.2181598991,-0.3552233577,0.1964023113,0.4151437283,-0.2203010619,-0.1271440536,-0.2892490625,0.4262260199,0.3456280828,0.1904119849,-0.0997599214,0.2238963842,-0.2436235696,0.2533464134,0.3463941514,-0.3264125288,-0.7522772551,-0.0623180978,0.2395538688,0.2138240188,-0.2641494572,0.0091178091,0.0585763827,0.0220128987,-0.0473067649,0.1247596368,0.0710947663,0.0479407869,-0.1934205443,-0.3237839639,0.3307866454,-0.1534485519,0.373434782,0.2039108723,-0.2786331475,-0.0654283091,0.1897790283,-0.0733121186,0.204567641,0.2111120969,0.0859391093,0.0963291302,0.3425368965,-0.144525975,-0.2208454311,0.2361599505,-0.0074976902,-0.4080512822,0.05238121,-0.0693818182,0.0342106409,-0.1751439422,-0.2368721217,-0.250767231,0.0469596423,0.2982925773,0.2158326656,0.0493254699,-0.0628557652,0.4346975684,0.2247962803,-0.0474488139,-0.1369978189,0.2314382493,0.1378540844,-0.0723532364,-0.2622561157,-0.007789142,0.3865086436,0.2050782442,-0.1728083491,0.1999227405,0.2821433246,0.1305397302,-0.1580862552,0.0377752185,-0.0964976549,0.076044932,-0.1803530306,0.0189615674,0.0912419036,-0.2024737746,0.3389697969,-0.0982242227,0.0435390286,0.1728527993,-0.1738719493,-0.1164959967,-0.0194160827,-0.0575414672,0.1215716228,-0.2605112791,-0.1267734617,-0.1007431298,0.4018777013,0.1480001211,0.3170870245,0.1431048512,0.5319442153,0.1638038456,0.0702496767,-0.0754961893,-0.595046699,-0.153305307,0.0420683324,0.1067333892,0.4031660557,0.0140680997,0.1372346729,-0.1065353453,0.1262332499,0.0536733977,0.0839905441,0.0515747443,0.0034557909,0.1623288542,0.0819173902,0.1239183173,-0.1544849575,0.3739548624,0.2223696411,0.0224084184,-0.1201334149,-0.0047830353,-0.4614660442,0.2616464496,-0.0094154151,-0.1877422035,-0.1089543253,-0.3240405321,-0.0293718316,0.1750336438,0.2323505729,0.1702650785,-0.1067733094,0.1637891084,-0.0965859368,-0.0634079278,-0.2861728072,-0.0442264751,-0.3516021371,-0.1043687165,0.0276290718,-0.3332431018,0.1092685685,-0.1978000402,0.0448775329,-0.3304952085,-0.2496137768,0.0687255412,0.1424214393,-0.191361472,-0.1454960704,-0.1331452131,-0.3406224549,-0.0777615905,0.1052851975,-0.3755034506,-0.0589957796,-0.1942424774,0.226897493,-0.0732832849,0.0613905266,-0.0987810716,0.0450629443,0.0523568131,-0.0922801569,-0.1205202043,-0.0991960391,-0.1073513255,-0.1778948605,-0.1473686546,-0.2940423191,-0.1225034297,-0.5240326524,-0.2998071015,0.536532402,-0.1247007027,-0.0793028027,-0.1053363308,-0.0294937789,0.3053979874,0.4665106535,-0.5257647634,-0.0645799488,-0.2027752846,-0.18987225,-0.1475555003,0.3041585684,0.3952634335,0.1911331415,0.1284653395,-0.4285719097,-0.2033012211,0.0207050201,0.001100692,0.4422153831,-0.0327308625,0.2021819651,-0.0818895251,0.8030892611,0.6181532145,-0.2177960426,0.2308448106,0.1147788912,0.3255800009,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"The documentation says\r\n```\r\ncache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the\r\n    results of the computation instead of the automatically generated cache file name.\r\n    You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.\r\n```\r\nWhat is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like\r\n```python\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}\r\n```","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":90,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n The documentation says\r\n```\r\ncache_file_names (`Optional[Dict[str, str]]`, defaults to `None`): Provide the name of a cache file to use to store the\r\n    results of the computation instead of the automatically generated cache file name.\r\n    You have to provide one :obj:`cache_file_name` per dataset in the dataset dictionary.\r\n```\r\nWhat is expected is simply the name of a file, not a path. The file will be located in the cache directory of the `wikitext` dataset. You can try again with something like\r\n```python\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}' for k in tokenized_datasets}\r\n```","embeddings":[-0.1930393726,0.1210535541,0.0500952005,0.1900934726,0.0076969219,0.0369213931,-0.0168548934,0.2303498536,0.0167332795,-0.1084433347,0.1029985696,0.4139710963,0.2249117643,-0.1117237061,-0.0011100402,0.2446131408,0.3074613512,0.3216940165,-0.1533999145,-0.2088759243,-0.2891754806,0.1171123162,-0.3328206539,-0.092429027,-0.4170501232,0.2123280317,-0.0168774482,-0.4208398163,0.1122606322,-0.2555866539,0.3177160025,0.0858283564,0.1992189288,0.5570235252,-0.0001258203,-0.2676019073,0.3540959358,-0.0694738552,-0.2052473277,0.0768211856,-0.2844775021,0.0380443037,-0.1126935929,0.0233671591,0.0584392846,0.0587090738,0.0379663296,-0.6514899731,0.3060197532,0.1629111767,0.102592662,0.033276476,-0.0953430757,0.2345797122,0.0502028316,-0.0120253302,-0.0387438312,-0.3330449462,0.1630381346,-0.3523213267,0.0621471033,0.5163400769,-0.1757435054,-0.1419030875,0.3870675266,-0.0117806941,0.0179351512,-0.2646991313,0.3020609319,-0.0644705594,0.5929020047,-0.3732129037,-0.4453589916,-0.2218152285,-0.2183965743,-0.1797114164,0.3478957415,-0.2814225554,0.115124248,0.1367774606,-0.5952916145,-0.3717092276,0.2665030062,0.1268505901,-0.0126040196,0.118829295,-0.0442643128,0.1275966167,-0.2583603263,0.0305633396,0.278693229,-0.5107082129,-0.020411972,0.4761810005,-0.4573765695,-0.114198938,-0.0813957304,0.0444577448,0.031320218,0.2521778643,-0.1078691259,0.0848287344,-0.1045756191,-0.0248278677,0.1718008816,0.3819052577,0.1599488705,0.3959278464,-0.0466323309,-0.2572212815,-0.4361515045,-0.1423511654,0.1015104204,0.0623166673,0.537632525,0.0291995946,-0.2670723796,-0.1527193785,0.1350922734,0.1104742214,-0.1131508872,-0.154272154,0.020687161,0.1861227155,-0.2187203914,0.5657741427,-0.0489024818,0.0113220746,-0.2269070297,-0.0754960403,-0.2520525157,-0.1949410737,-0.2797417641,0.4240118265,0.107980758,-0.0087579098,0.1931643337,0.1137457117,0.1412912309,-0.2485303879,0.2450801283,-0.1952346861,0.1446888447,0.0313405395,-0.122426711,0.5588548183,0.0238276497,0.0820711404,-0.0578298308,0.2793508768,-0.3909225166,-0.0032497863,0.2678503692,0.0766011178,-0.1049885824,-0.051326897,-0.1365459859,-0.0477274917,0.6230661273,-0.3534024954,0.0028586881,-0.1369546503,-0.2181598991,-0.3552233577,0.1964023113,0.4151437283,-0.2203010619,-0.1271440536,-0.2892490625,0.4262260199,0.3456280828,0.1904119849,-0.0997599214,0.2238963842,-0.2436235696,0.2533464134,0.3463941514,-0.3264125288,-0.7522772551,-0.0623180978,0.2395538688,0.2138240188,-0.2641494572,0.0091178091,0.0585763827,0.0220128987,-0.0473067649,0.1247596368,0.0710947663,0.0479407869,-0.1934205443,-0.3237839639,0.3307866454,-0.1534485519,0.373434782,0.2039108723,-0.2786331475,-0.0654283091,0.1897790283,-0.0733121186,0.204567641,0.2111120969,0.0859391093,0.0963291302,0.3425368965,-0.144525975,-0.2208454311,0.2361599505,-0.0074976902,-0.4080512822,0.05238121,-0.0693818182,0.0342106409,-0.1751439422,-0.2368721217,-0.250767231,0.0469596423,0.2982925773,0.2158326656,0.0493254699,-0.0628557652,0.4346975684,0.2247962803,-0.0474488139,-0.1369978189,0.2314382493,0.1378540844,-0.0723532364,-0.2622561157,-0.007789142,0.3865086436,0.2050782442,-0.1728083491,0.1999227405,0.2821433246,0.1305397302,-0.1580862552,0.0377752185,-0.0964976549,0.076044932,-0.1803530306,0.0189615674,0.0912419036,-0.2024737746,0.3389697969,-0.0982242227,0.0435390286,0.1728527993,-0.1738719493,-0.1164959967,-0.0194160827,-0.0575414672,0.1215716228,-0.2605112791,-0.1267734617,-0.1007431298,0.4018777013,0.1480001211,0.3170870245,0.1431048512,0.5319442153,0.1638038456,0.0702496767,-0.0754961893,-0.595046699,-0.153305307,0.0420683324,0.1067333892,0.4031660557,0.0140680997,0.1372346729,-0.1065353453,0.1262332499,0.0536733977,0.0839905441,0.0515747443,0.0034557909,0.1623288542,0.0819173902,0.1239183173,-0.1544849575,0.3739548624,0.2223696411,0.0224084184,-0.1201334149,-0.0047830353,-0.4614660442,0.2616464496,-0.0094154151,-0.1877422035,-0.1089543253,-0.3240405321,-0.0293718316,0.1750336438,0.2323505729,0.1702650785,-0.1067733094,0.1637891084,-0.0965859368,-0.0634079278,-0.2861728072,-0.0442264751,-0.3516021371,-0.1043687165,0.0276290718,-0.3332431018,0.1092685685,-0.1978000402,0.0448775329,-0.3304952085,-0.2496137768,0.0687255412,0.1424214393,-0.191361472,-0.1454960704,-0.1331452131,-0.3406224549,-0.0777615905,0.1052851975,-0.3755034506,-0.0589957796,-0.1942424774,0.226897493,-0.0732832849,0.0613905266,-0.0987810716,0.0450629443,0.0523568131,-0.0922801569,-0.1205202043,-0.0991960391,-0.1073513255,-0.1778948605,-0.1473686546,-0.2940423191,-0.1225034297,-0.5240326524,-0.2998071015,0.536532402,-0.1247007027,-0.0793028027,-0.1053363308,-0.0294937789,0.3053979874,0.4665106535,-0.5257647634,-0.0645799488,-0.2027752846,-0.18987225,-0.1475555003,0.3041585684,0.3952634335,0.1911331415,0.1284653395,-0.4285719097,-0.2033012211,0.0207050201,0.001100692,0.4422153831,-0.0327308625,0.2021819651,-0.0818895251,0.8030892611,0.6181532145,-0.2177960426,0.2308448106,0.1147788912,0.3255800009,-0.1409276873,-0.1978885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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"Managed to get `cache_file_names` working and caching works well with it\r\nHad to make a small modification for it to work:\r\n```\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}\r\n```","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":31,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n Managed to get `cache_file_names` working and caching works well with it\r\nHad to make a small modification for it to work:\r\n```\r\ncache_file_names = {k: f'tokenized_and_grouped_{str(k)}.arrow' for k in tokenized_datasets}\r\n```","embeddings":[-0.1930393726,0.1210535541,0.0500952005,0.1900934726,0.0076969219,0.0369213931,-0.0168548934,0.2303498536,0.0167332795,-0.1084433347,0.1029985696,0.4139710963,0.2249117643,-0.1117237061,-0.0011100402,0.2446131408,0.3074613512,0.3216940165,-0.1533999145,-0.2088759243,-0.2891754806,0.1171123162,-0.3328206539,-0.092429027,-0.4170501232,0.2123280317,-0.0168774482,-0.4208398163,0.1122606322,-0.2555866539,0.3177160025,0.0858283564,0.1992189288,0.5570235252,-0.0001258203,-0.2676019073,0.3540959358,-0.0694738552,-0.2052473277,0.0768211856,-0.2844775021,0.0380443037,-0.1126935929,0.0233671591,0.0584392846,0.0587090738,0.0379663296,-0.6514899731,0.3060197532,0.1629111767,0.102592662,0.033276476,-0.0953430757,0.2345797122,0.0502028316,-0.0120253302,-0.0387438312,-0.3330449462,0.1630381346,-0.3523213267,0.0621471033,0.5163400769,-0.1757435054,-0.1419030875,0.3870675266,-0.0117806941,0.0179351512,-0.2646991313,0.3020609319,-0.0644705594,0.5929020047,-0.3732129037,-0.4453589916,-0.2218152285,-0.2183965743,-0.1797114164,0.3478957415,-0.2814225554,0.115124248,0.1367774606,-0.5952916145,-0.3717092276,0.2665030062,0.1268505901,-0.0126040196,0.118829295,-0.0442643128,0.1275966167,-0.2583603263,0.0305633396,0.278693229,-0.5107082129,-0.020411972,0.4761810005,-0.4573765695,-0.114198938,-0.0813957304,0.0444577448,0.031320218,0.2521778643,-0.1078691259,0.0848287344,-0.1045756191,-0.0248278677,0.1718008816,0.3819052577,0.1599488705,0.3959278464,-0.0466323309,-0.2572212815,-0.4361515045,-0.1423511654,0.1015104204,0.0623166673,0.537632525,0.0291995946,-0.2670723796,-0.1527193785,0.1350922734,0.1104742214,-0.1131508872,-0.154272154,0.020687161,0.1861227155,-0.2187203914,0.5657741427,-0.0489024818,0.0113220746,-0.2269070297,-0.0754960403,-0.2520525157,-0.1949410737,-0.2797417641,0.4240118265,0.107980758,-0.0087579098,0.1931643337,0.1137457117,0.1412912309,-0.2485303879,0.2450801283,-0.1952346861,0.1446888447,0.0313405395,-0.122426711,0.5588548183,0.0238276497,0.0820711404,-0.0578298308,0.2793508768,-0.3909225166,-0.0032497863,0.2678503692,0.0766011178,-0.1049885824,-0.051326897,-0.1365459859,-0.0477274917,0.6230661273,-0.3534024954,0.0028586881,-0.1369546503,-0.2181598991,-0.3552233577,0.1964023113,0.4151437283,-0.2203010619,-0.1271440536,-0.2892490625,0.4262260199,0.3456280828,0.1904119849,-0.0997599214,0.2238963842,-0.2436235696,0.2533464134,0.3463941514,-0.3264125288,-0.7522772551,-0.0623180978,0.2395538688,0.2138240188,-0.2641494572,0.0091178091,0.0585763827,0.0220128987,-0.0473067649,0.1247596368,0.0710947663,0.0479407869,-0.1934205443,-0.3237839639,0.3307866454,-0.1534485519,0.373434782,0.2039108723,-0.2786331475,-0.0654283091,0.1897790283,-0.0733121186,0.204567641,0.2111120969,0.0859391093,0.0963291302,0.3425368965,-0.144525975,-0.2208454311,0.2361599505,-0.0074976902,-0.4080512822,0.05238121,-0.0693818182,0.0342106409,-0.1751439422,-0.2368721217,-0.250767231,0.0469596423,0.2982925773,0.2158326656,0.0493254699,-0.0628557652,0.4346975684,0.2247962803,-0.0474488139,-0.1369978189,0.2314382493,0.1378540844,-0.0723532364,-0.2622561157,-0.007789142,0.3865086436,0.2050782442,-0.1728083491,0.1999227405,0.2821433246,0.1305397302,-0.1580862552,0.0377752185,-0.0964976549,0.076044932,-0.1803530306,0.0189615674,0.0912419036,-0.2024737746,0.3389697969,-0.0982242227,0.0435390286,0.1728527993,-0.1738719493,-0.1164959967,-0.0194160827,-0.0575414672,0.1215716228,-0.2605112791,-0.1267734617,-0.1007431298,0.4018777013,0.1480001211,0.3170870245,0.1431048512,0.5319442153,0.1638038456,0.0702496767,-0.0754961893,-0.595046699,-0.153305307,0.0420683324,0.1067333892,0.4031660557,0.0140680997,0.1372346729,-0.1065353453,0.1262332499,0.0536733977,0.0839905441,0.0515747443,0.0034557909,0.1623288542,0.0819173902,0.1239183173,-0.1544849575,0.3739548624,0.2223696411,0.0224084184,-0.1201334149,-0.0047830353,-0.4614660442,0.2616464496,-0.0094154151,-0.1877422035,-0.1089543253,-0.3240405321,-0.0293718316,0.1750336438,0.2323505729,0.1702650785,-0.1067733094,0.1637891084,-0.0965859368,-0.0634079278,-0.2861728072,-0.0442264751,-0.3516021371,-0.1043687165,0.0276290718,-0.3332431018,0.1092685685,-0.1978000402,0.0448775329,-0.3304952085,-0.2496137768,0.0687255412,0.1424214393,-0.191361472,-0.1454960704,-0.1331452131,-0.3406224549,-0.0777615905,0.1052851975,-0.3755034506,-0.0589957796,-0.1942424774,0.226897493,-0.0732832849,0.0613905266,-0.0987810716,0.0450629443,0.0523568131,-0.0922801569,-0.1205202043,-0.0991960391,-0.1073513255,-0.1778948605,-0.1473686546,-0.2940423191,-0.1225034297,-0.5240326524,-0.2998071015,0.536532402,-0.1247007027,-0.0793028027,-0.1053363308,-0.0294937789,0.3053979874,0.4665106535,-0.5257647634,-0.0645799488,-0.2027752846,-0.18987225,-0.1475555003,0.3041585684,0.3952634335,0.1911331415,0.1284653395,-0.4285719097,-0.2033012211,0.0207050201,0.001100692,0.4422153831,-0.0327308625,0.2021819651,-0.0818895251,0.8030892611,0.6181532145,-0.2177960426,0.2308448106,0.1147788912,0.3255800009,-0.1409276873,-0.1978885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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.\r\nI can confirm that this is how it works in `datasets==1.1.3`","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":41,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n Another comment on `cache_file_names`, it doesn't save the produced cached files in the dataset's cache folder, it requires to give a path to an existing directory for it to work.\r\nI can confirm that this is how it works in 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"Oh yes indeed ! Maybe we need to update the docstring to mention that it is a path","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":18,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n Oh yes indeed ! Maybe we need to update the docstring to mention that it is a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n# CHANGE\r\nprint('hello')\r\n# CHANGE\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n...\r\n```\r\nI am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird\r\n\r\nEdit: accidently clicked the close issue button ","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":136,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n I upgraded to the latest version and I encountered some strange behaviour, the script I posted in the OP doesn't trigger recalculation, however, if I add the following change it does trigger partial recalculation, I am not sure if its something wrong on my machine or a bug:\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n# CHANGE\r\nprint('hello')\r\n# CHANGE\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n...\r\n```\r\nI am using datasets in the `run_mlm.py` script in the transformers examples and I found that if I change the script without touching any of the preprocessing. it still triggers recalculation which is very weird\r\n\r\nEdit: accidently clicked the close issue button 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.\r\n\r\nNot sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.\r\nMoreover we're already ignoring the line definition for lambda functions.","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":86,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n This is because the `group_texts` line definition changes (it is defined 3 lines later than in the previous call). Currently if a function is moved elsewhere in a script we consider it to be different.\r\n\r\nNot sure this is actually a good idea to keep this behavior though. We had this as a security in the early development of the lib but now the recursive hashing of objects is robust so we can probably remove that.\r\nMoreover we're already ignoring the line definition for lambda 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"Sounds great, thank you for your quick responses and help! Looking forward for the next release.","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":16,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n Sounds great, thank you for your quick responses and help! Looking forward for the next release.","embeddings":[-0.1930393726,0.1210535541,0.0500952005,0.1900934726,0.0076969219,0.0369213931,-0.0168548934,0.2303498536,0.0167332795,-0.1084433347,0.1029985696,0.4139710963,0.2249117643,-0.1117237061,-0.0011100402,0.2446131408,0.3074613512,0.3216940165,-0.1533999145,-0.2088759243,-0.2891754806,0.1171123162,-0.3328206539,-0.092429027,-0.4170501232,0.2123280317,-0.0168774482,-0.4208398163,0.1122606322,-0.2555866539,0.3177160025,0.0858283564,0.1992189288,0.5570235252,-0.0001258203,-0.2676019073,0.3540959358,-0.0694738552,-0.2052473277,0.0768211856,-0.2844775021,0.0380443037,-0.1126935929,0.0233671591,0.0584392846,0.0587090738,0.0379663296,-0.6514899731,0.3060197532,0.1629111767,0.102592662,0.033276476,-0.0953430757,0.2345797122,0.0502028316,-0.0120253302,-0.0387438312,-0.3330449462,0.1630381346,-0.3523213267,0.0621471033,0.5163400769,-0.1757435054,-0.1419030875,0.3870675266,-0.0117806941,0.0179351512,-0.2646991313,0.3020609319,-0.0644705594,0.5929020047,-0.3732129037,-0.4453589916,-0.2218152285,-0.2183965743,-0.1797114164,0.3478957415,-0.2814225554,0.115124248,0.1367774606,-0.5952916145,-0.3717092276,0.2665030062,0.1268505901,-0.0126040196,0.118829295,-0.0442643128,0.1275966167,-0.2583603263,0.0305633396,0.278693229,-0.5107082129,-0.020411972,0.4761810005,-0.4573765695,-0.114198938,-0.0813957304,0.0444577448,0.031320218,0.2521778643,-0.1078691259,0.0848287344,-0.1045756191,-0.0248278677,0.1718008816,0.3819052577,0.1599488705,0.3959278464,-0.0466323309,-0.2572212815,-0.4361515045,-0.1423511654,0.1015104204,0.0623166673,0.537632525,0.0291995946,-0.2670723796,-0.1527193785,0.1350922734,0.1104742214,-0.1131508872,-0.154272154,0.020687161,0.1861227155,-0.2187203914,0.5657741427,-0.0489024818,0.0113220746,-0.2269070297,-0.0754960403,-0.2520525157,-0.1949410737,-0.2797417641,0.4240118265,0.107980758,-0.0087579098,0.1931643337,0.1137457117,0.1412912309,-0.2485303879,0.2450801283,-0.1952346861,0.1446888447,0.0313405395,-0.122426711,0.5588548183,0.0238276497,0.0820711404,-0.0578298308,0.2793508768,-0.3909225166,-0.0032497863,0.2678503692,0.0766011178,-0.1049885824,-0.051326897,-0.1365459859,-0.0477274917,0.6230661273,-0.3534024954,0.0028586881,-0.1369546503,-0.2181598991,-0.3552233577,0.1964023113,0.4151437283,-0.2203010619,-0.1271440536,-0.2892490625,0.4262260199,0.3456280828,0.1904119849,-0.0997599214,0.2238963842,-0.2436235696,0.2533464134,0.3463941514,-0.3264125288,-0.7522772551,-0.0623180978,0.2395538688,0.2138240188,-0.2641494572,0.0091178091,0.0585763827,0.0220128987,-0.0473067649,0.1247596368,0.0710947663,0.0479407869,-0.1934205443,-0.3237839639,0.3307866454,-0.1534485519,0.373434782,0.2039108723,-0.2786331475,-0.0654283091,0.1897790283,-0.0733121186,0.204567641,0.2111120969,0.0859391093,0.0963291302,0.3425368965,-0.144525975,-0.2208454311,0.2361599505,-0.0074976902,-0.4080512822,0.05238121,-0.0693818182,0.0342106409,-0.1751439422,-0.2368721217,-0.250767231,0.0469596423,0.2982925773,0.2158326656,0.0493254699,-0.0628557652,0.4346975684,0.2247962803,-0.0474488139,-0.1369978189,0.2314382493,0.1378540844,-0.0723532364,-0.2622561157,-0.007789142,0.3865086436,0.2050782442,-0.1728083491,0.1999227405,0.2821433246,0.1305397302,-0.1580862552,0.0377752185,-0.0964976549,0.076044932,-0.1803530306,0.0189615674,0.0912419036,-0.2024737746,0.3389697969,-0.0982242227,0.0435390286,0.1728527993,-0.1738719493,-0.1164959967,-0.0194160827,-0.0575414672,0.1215716228,-0.2605112791,-0.1267734617,-0.1007431298,0.4018777013,0.1480001211,0.3170870245,0.1431048512,0.5319442153,0.1638038456,0.0702496767,-0.0754961893,-0.595046699,-0.153305307,0.0420683324,0.1067333892,0.4031660557,0.0140680997,0.1372346729,-0.1065353453,0.1262332499,0.0536733977,0.0839905441,0.0515747443,0.0034557909,0.1623288542,0.0819173902,0.1239183173,-0.1544849575,0.3739548624,0.2223696411,0.0224084184,-0.1201334149,-0.0047830353,-0.4614660442,0.2616464496,-0.0094154151,-0.1877422035,-0.1089543253,-0.3240405321,-0.0293718316,0.1750336438,0.2323505729,0.1702650785,-0.1067733094,0.1637891084,-0.0965859368,-0.0634079278,-0.2861728072,-0.0442264751,-0.3516021371,-0.1043687165,0.0276290718,-0.3332431018,0.1092685685,-0.1978000402,0.0448775329,-0.3304952085,-0.2496137768,0.0687255412,0.1424214393,-0.191361472,-0.1454960704,-0.1331452131,-0.3406224549,-0.0777615905,0.1052851975,-0.3755034506,-0.0589957796,-0.1942424774,0.226897493,-0.0732832849,0.0613905266,-0.0987810716,0.0450629443,0.0523568131,-0.0922801569,-0.1205202043,-0.0991960391,-0.1073513255,-0.1778948605,-0.1473686546,-0.2940423191,-0.1225034297,-0.5240326524,-0.2998071015,0.536532402,-0.1247007027,-0.0793028027,-0.1053363308,-0.0294937789,0.3053979874,0.4665106535,-0.5257647634,-0.0645799488,-0.2027752846,-0.18987225,-0.1475555003,0.3041585684,0.3952634335,0.1911331415,0.1284653395,-0.4285719097,-0.2033012211,0.0207050201,0.001100692,0.4422153831,-0.0327308625,0.2021819651,-0.0818895251,0.8030892611,0.6181532145,-0.2177960426,0.2308448106,0.1147788912,0.3255800009,-0.1409276873,-0.197888568,-0.1367323548,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1718","title":"Possible cache miss in datasets","comments":"I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!\r\n\r\n\r\n```python\r\n    # Datasets\r\n    train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))\r\n    if args.dev_split >= len(train):\r\n        raise ValueError(\"Not enough dev files\")\r\n    dev = []\r\n    state = random.Random(1001)\r\n    for _ in range(args.dev_split):\r\n        dev.append(train.pop(state.randint(0, len(train) - 1)))\r\n\r\n    max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\r\n\r\n    def tokenize_function(examples):\r\n        return tokenizer(examples['text'], return_special_tokens_mask=True)\r\n\r\n    def group_texts(examples):\r\n        # Concatenate all texts from our dataset and generate chunks of max_seq_length\r\n        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n        total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n        # Truncate (not implementing padding)\r\n        total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n        # Split by chunks of max_seq_length\r\n        result = {\r\n            k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\r\n            for k, t in concatenated_examples.items()\r\n        }\r\n        return result\r\n\r\n    datasets = load_dataset(\r\n        'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]}, \r\n        cache_dir=args.data_cache_dir)\r\n    datasets = datasets.map(tokenize_function, \r\n        batched=True, remove_columns=['text'], \r\n        cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},\r\n        load_from_cache_file=not args.overwrite_cache)\r\n    datasets = datasets.map(group_texts, \r\n        batched=True,\r\n        cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},\r\n        load_from_cache_file=not args.overwrite_cache)\r\n```\r\n\r\nAnd this is the log\r\n\r\n```\r\n04\/26\/2021 10:26:59 - WARNING - datasets.builder -   Using custom data configuration DBNL-f8d988ad33ccf2c1\r\n04\/26\/2021 10:26:59 - WARNING - datasets.builder -   Reusing dataset text (\/home\/manjavacasema\/data\/.cache\/text\/DBNL-f8d988ad33ccf2c1\/0.0.0\/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13\/13 [00:00<00:00, 21.07ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 40\/40 [00:01<00:00, 24.28ba\/s]\r\n04\/26\/2021 10:27:01 - WARNING - datasets.arrow_dataset -   Loading cached processed dataset at \/home\/manjavacasema\/data\/.cache\/train-grouped\r\n04\/26\/2021 10:27:01 - WARNING - datasets.arrow_dataset -   Loading cached processed dataset at \/home\/manjavacasema\/data\/.cache\/dev-grouped\r\n```\r\n","body":"Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n```","comment_length":274,"text":"Possible cache miss in datasets \n Hi,\r\n\r\nI am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.\r\nI have attached an example script that for me reproduces the problem.\r\nIn the attached example the second map function always recomputes instead of loading from cache.\r\nIs this a bug or am I doing something wrong?\r\nIs there a way for fix this and avoid all the recomputation?\r\n\r\nThanks\r\n\r\nEdit:\r\ntransformers==3.5.1\r\ndatasets==1.2.0\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\n\r\ndatasets = load_dataset('wikitext', 'wikitext-103-raw-v1')\r\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)\r\n\r\n\r\ncolumn_names = datasets[\"train\"].column_names\r\ntext_column_name = \"text\" if \"text\" in column_names else column_names[0]\r\ndef tokenize_function(examples):\r\n    return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n\r\ntokenized_datasets = datasets.map(\r\n    tokenize_function,\r\n    batched=True,\r\n    num_proc=60,\r\n    remove_columns=[text_column_name],\r\n    load_from_cache_file=True,\r\n)\r\nmax_seq_length = tokenizer.model_max_length\r\ndef group_texts(examples):\r\n    # Concatenate all texts.\r\n    concatenated_examples = {\r\n        k: sum(examples[k], []) for k in examples.keys()}\r\n    total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\r\n    # customize this part to your needs.\r\n    total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n    # Split by chunks of max_len.\r\n    result = {\r\n        k: [t[i: i + max_seq_length]\r\n            for i in range(0, total_length, max_seq_length)]\r\n        for k, t in concatenated_examples.items()\r\n    }\r\n    return result\r\n\r\ntokenized_datasets = tokenized_datasets.map(\r\n    group_texts,\r\n    batched=True,\r\n    num_proc=60,\r\n    load_from_cache_file=True,\r\n)\r\nprint(tokenized_datasets)\r\n\r\nprint('finished')\r\n``` \n I am having a similar issue where only the grouped files are loaded from cache while the tokenized ones aren't. I can confirm both datasets are being stored to file, but only the grouped version is loaded from cache. Not sure what might be going on. But I've tried to remove all kinds of non deterministic behaviour, but still no luck. Thanks for the help!\r\n\r\n\r\n```python\r\n    # Datasets\r\n    train = sorted(glob(args.data_dir + '*.{}'.format(args.ext)))\r\n    if args.dev_split >= len(train):\r\n        raise ValueError(\"Not enough dev files\")\r\n    dev = []\r\n    state = random.Random(1001)\r\n    for _ in range(args.dev_split):\r\n        dev.append(train.pop(state.randint(0, len(train) - 1)))\r\n\r\n    max_seq_length = min(args.max_seq_length, tokenizer.model_max_length)\r\n\r\n    def tokenize_function(examples):\r\n        return tokenizer(examples['text'], return_special_tokens_mask=True)\r\n\r\n    def group_texts(examples):\r\n        # Concatenate all texts from our dataset and generate chunks of max_seq_length\r\n        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\r\n        total_length = len(concatenated_examples[list(examples.keys())[0]])\r\n        # Truncate (not implementing padding)\r\n        total_length = (total_length \/\/ max_seq_length) * max_seq_length\r\n        # Split by chunks of max_seq_length\r\n        result = {\r\n            k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]\r\n            for k, t in concatenated_examples.items()\r\n        }\r\n        return result\r\n\r\n    datasets = load_dataset(\r\n        'text', name='DBNL', data_files={'train': train[:10], 'dev': dev[:5]}, \r\n        cache_dir=args.data_cache_dir)\r\n    datasets = datasets.map(tokenize_function, \r\n        batched=True, remove_columns=['text'], \r\n        cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-tokenized') for k in datasets},\r\n        load_from_cache_file=not args.overwrite_cache)\r\n    datasets = datasets.map(group_texts, \r\n        batched=True,\r\n        cache_file_names={k: os.path.join(args.data_cache_dir, f'{k}-grouped') for k in datasets},\r\n        load_from_cache_file=not args.overwrite_cache)\r\n```\r\n\r\nAnd this is the log\r\n\r\n```\r\n04\/26\/2021 10:26:59 - WARNING - datasets.builder -   Using custom data configuration DBNL-f8d988ad33ccf2c1\r\n04\/26\/2021 10:26:59 - WARNING - datasets.builder -   Reusing dataset text (\/home\/manjavacasema\/data\/.cache\/text\/DBNL-f8d988ad33ccf2c1\/0.0.0\/e16f44aa1b321ece1f87b07977cc5d70be93d69b20486d6dacd62e12cf25c9a5)\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13\/13 [00:00<00:00, 21.07ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 40\/40 [00:01<00:00, 24.28ba\/s]\r\n04\/26\/2021 10:27:01 - WARNING - datasets.arrow_dataset -   Loading cached processed dataset at \/home\/manjavacasema\/data\/.cache\/train-grouped\r\n04\/26\/2021 10:27:01 - WARNING - datasets.arrow_dataset -   Loading cached processed dataset at \/home\/manjavacasema\/data\/.cache\/dev-grouped\r\n```\r\n","embeddings":[-0.1930393726,0.1210535541,0.0500952005,0.1900934726,0.0076969219,0.0369213931,-0.0168548934,0.2303498536,0.0167332795,-0.1084433347,0.1029985696,0.4139710963,0.2249117643,-0.1117237061,-0.0011100402,0.2446131408,0.3074613512,0.3216940165,-0.1533999145,-0.2088759243,-0.2891754806,0.1171123162,-0.3328206539,-0.092429027,-0.4170501232,0.2123280317,-0.0168774482,-0.4208398163,0.1122606322,-0.2555866539,0.3177160025,0.0858283564,0.1992189288,0.5570235252,-0.0001258203,-0.2676019073,0.3540959358,-0.0694738552,-0.2052473277,0.0768211856,-0.2844775021,0.0380443037,-0.1126935929,0.0233671591,0.0584392846,0.0587090738,0.0379663296,-0.6514899731,0.3060197532,0.1629111767,0.102592662,0.033276476,-0.0953430757,0.2345797122,0.0502028316,-0.0120253302,-0.0387438312,-0.3330449462,0.1630381346,-0.3523213267,0.0621471033,0.5163400769,-0.1757435054,-0.1419030875,0.3870675266,-0.0117806941,0.0179351512,-0.2646991313,0.3020609319,-0.0644705594,0.5929020047,-0.3732129037,-0.4453589916,-0.2218152285,-0.2183965743,-0.1797114164,0.3478957415,-0.2814225554,0.115124248,0.1367774606,-0.5952916145,-0.3717092276,0.2665030062,0.1268505901,-0.0126040196,0.118829295,-0.0442643128,0.1275966167,-0.2583603263,0.0305633396,0.278693229,-0.5107082129,-0.020411972,0.4761810005,-0.4573765695,-0.114198938,-0.0813957304,0.0444577448,0.031320218,0.2521778643,-0.1078691259,0.0848287344,-0.1045756191,-0.0248278677,0.1718008816,0.3819052577,0.1599488705,0.3959278464,-0.0466323309,-0.2572212815,-0.4361515045,-0.1423511654,0.1015104204,0.0623166673,0.537632525,0.0291995946,-0.2670723796,-0.1527193785,0.1350922734,0.1104742214,-0.1131508872,-0.154272154,0.020687161,0.1861227155,-0.2187203914,0.5657741427,-0.0489024818,0.0113220746,-0.2269070297,-0.0754960403,-0.2520525157,-0.1949410737,-0.2797417641,0.4240118265,0.107980758,-0.0087579098,0.1931643337,0.1137457117,0.1412912309,-0.2485303879,0.2450801283,-0.1952346861,0.1446888447,0.0313405395,-0.122426711,0.5588548183,0.0238276497,0.0820711404,-0.0578298308,0.2793508768,-0.3909225166,-0.0032497863,0.2678503692,0.0766011178,-0.1049885824,-0.051326897,-0.1365459859,-0.0477274917,0.6230661273,-0.3534024954,0.0028586881,-0.1369546503,-0.2181598991,-0.3552233577,0.1964023113,0.4151437283,-0.2203010619,-0.1271440536,-0.2892490625,0.4262260199,0.3456280828,0.1904119849,-0.0997599214,0.2238963842,-0.2436235696,0.2533464134,0.3463941514,-0.3264125288,-0.7522772551,-0.0623180978,0.2395538688,0.2138240188,-0.2641494572,0.0091178091,0.0585763827,0.0220128987,-0.0473067649,0.1247596368,0.0710947663,0.0479407869,-0.1934205443,-0.3237839639,0.3307866454,-0.1534485519,0.373434782,0.2039108723,-0.2786331475,-0.0654283091,0.1897790283,-0.0733121186,0.204567641,0.2111120969,0.0859391093,0.0963291302,0.3425368965,-0.144525975,-0.2208454311,0.2361599505,-0.0074976902,-0.4080512822,0.05238121,-0.0693818182,0.0342106409,-0.1751439422,-0.2368721217,-0.250767231,0.0469596423,0.2982925773,0.2158326656,0.0493254699,-0.0628557652,0.4346975684,0.2247962803,-0.0474488139,-0.1369978189,0.2314382493,0.1378540844,-0.0723532364,-0.2622561157,-0.007789142,0.3865086436,0.2050782442,-0.1728083491,0.1999227405,0.2821433246,0.1305397302,-0.1580862552,0.0377752185,-0.0964976549,0.076044932,-0.1803530306,0.0189615674,0.0912419036,-0.2024737746,0.3389697969,-0.0982242227,0.0435390286,0.1728527993,-0.1738719493,-0.1164959967,-0.0194160827,-0.0575414672,0.1215716228,-0.2605112791,-0.1267734617,-0.1007431298,0.4018777013,0.1480001211,0.3170870245,0.1431048512,0.5319442153,0.1638038456,0.0702496767,-0.0754961893,-0.595046699,-0.153305307,0.0420683324,0.1067333892,0.4031660557,0.0140680997,0.1372346729,-0.1065353453,0.1262332499,0.0536733977,0.0839905441,0.0515747443,0.0034557909,0.1623288542,0.0819173902,0.1239183173,-0.1544849575,0.3739548624,0.2223696411,0.0224084184,-0.1201334149,-0.0047830353,-0.4614660442,0.2616464496,-0.0094154151,-0.1877422035,-0.1089543253,-0.3240405321,-0.0293718316,0.1750336438,0.2323505729,0.1702650785,-0.1067733094,0.1637891084,-0.0965859368,-0.0634079278,-0.2861728072,-0.0442264751,-0.3516021371,-0.1043687165,0.0276290718,-0.3332431018,0.1092685685,-0.1978000402,0.0448775329,-0.3304952085,-0.2496137768,0.0687255412,0.1424214393,-0.191361472,-0.1454960704,-0.1331452131,-0.3406224549,-0.0777615905,0.1052851975,-0.3755034506,-0.0589957796,-0.1942424774,0.226897493,-0.0732832849,0.0613905266,-0.0987810716,0.0450629443,0.0523568131,-0.0922801569,-0.1205202043,-0.0991960391,-0.1073513255,-0.1778948605,-0.1473686546,-0.2940423191,-0.1225034297,-0.5240326524,-0.2998071015,0.536532402,-0.1247007027,-0.0793028027,-0.1053363308,-0.0294937789,0.3053979874,0.4665106535,-0.5257647634,-0.0645799488,-0.2027752846,-0.18987225,-0.1475555003,0.3041585684,0.3952634335,0.1911331415,0.1284653395,-0.4285719097,-0.2033012211,0.0207050201,0.001100692,0.4422153831,-0.0327308625,0.2021819651,-0.0818895251,0.8030892611,0.6181532145,-0.2177960426,0.2308448106,0.1147788912,0.32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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1717","title":"SciFact dataset - minor changes","comments":"Hi Dave,\r\nYou are more than welcome to open a PR to make these changes! \ud83e\udd17\r\nYou will find the relevant information about opening a PR in the [contributing guide](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/CONTRIBUTING.md) and in the [dataset addition guide](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nPinging also @lhoestq for the Google cloud matter.","body":"Hi,\r\n\r\nSciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!\r\n\r\nI'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nIt also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n\r\nThanks,\r\n\r\nDave","comment_length":44,"text":"SciFact dataset - minor changes \n Hi,\r\n\r\nSciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!\r\n\r\nI'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nIt also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n\r\nThanks,\r\n\r\nDave \n Hi Dave,\r\nYou are more than welcome to open a PR to make these changes! \ud83e\udd17\r\nYou will find the relevant information about opening a PR in the [contributing guide](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/CONTRIBUTING.md) and in the [dataset addition guide](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nPinging also @lhoestq for the Google cloud matter.","embeddings":[0.1588933617,-0.1973490417,-0.0676539242,0.1470401138,0.1484657675,-0.1071610376,-0.2045498639,-0.0401365869,0.0873415917,-0.0903791636,-0.0821836367,0.0158945657,-0.0085386354,0.4948686659,0.1395404339,-0.2466709316,0.2298517078,-0.0996816456,-0.026662264,-0.2833099961,-0.0561362617,0.2272369713,0.1043683589,-0.1867742091,-0.0700056925,-0.2488317639,-0.3025515676,0.3253338635,-0.4019877315,-0.3336291313,-0.0090463301,0.4374462068,0.0153007675,0.3517856002,-0.0001106163,-0.1575024277,0.3256404996,-0.0009351738,-0.2085137516,-0.1126065403,-0.162084192,-0.096408911,0.0218258165,0.071520403,-0.0710576251,-0.0761889145,-0.1483071595,-0.124201335,0.3208521307,0.0993056372,0.2183046639,0.2332518697,0.179612264,-0.2070899904,-0.0511534885,0.2912590504,-0.0473824404,0.3453986347,0.1674585044,0.3302257061,0.025699567,0.4653010368,-0.0388631821,-0.1789093316,0.6320386529,0.0213729311,-0.3761971891,-0.4856018424,0.1486368775,0.2742673159,0.5723049641,-0.3517041504,-0.6798822284,-0.2079389691,0.2144465744,-0.073569268,-0.0122222416,0.316181004,0.0381379463,0.0511892289,0.0710301921,-0.4668088555,-0.3095673621,-0.1419863254,0.0620473512,0.0905595496,-0.1406484842,0.0051066061,0.1124816686,-0.0500589721,-0.0696471035,-0.2305473238,-0.1012077853,0.1105057597,-0.0996387154,-0.2565585971,-0.2176599503,0.297652036,0.0186595768,0.3842715025,0.0801841542,-0.0068234601,-0.2027323991,-0.0759955645,0.326421082,-0.0812568069,0.0278393105,0.1425428092,0.2957104146,0.1814731359,0.4126073122,-0.1415103227,0.1716012657,-0.1521790922,-0.4780347049,-0.1535412073,0.1730055064,-0.1835856438,0.1002775803,-0.0033972061,0.1850010604,-0.1852900684,-0.1225436181,0.2218345851,-0.1419371516,-0.0247634184,-0.0591177754,0.1648083031,0.0859883577,-0.2317878753,-0.2415515184,0.1570417434,0.2464354932,0.1684089154,0.0972013846,0.043922592,0.330231756,-0.0416658968,0.1220899448,0.186925441,0.0519484989,0.0592010245,-0.1077931076,0.3586030602,-0.0418989845,0.1905272752,0.0802967474,0.1038988978,-0.3220552206,0.0591778941,0.0409943685,-0.1317871213,-0.4527026713,0.1883636117,-0.31850788,-0.3556963503,-0.1726116985,0.1786646396,-0.2416288853,0.0080965115,0.2086093724,0.0757346749,-0.086124897,-0.2084441036,0.329996109,0.5944766998,-0.1610799581,-0.0999741703,-0.2164887786,-0.1343189031,-0.0417121276,0.2109253705,-0.0506248809,0.0558876321,-0.0204849038,0.1024541408,0.0101120761,-0.3500709236,-0.2602942586,-0.046818655,-0.1536080092,-0.0353873558,0.0790935531,0.1806231141,-0.107394807,-0.0908078924,-0.0359525941,0.0308668986,0.0005205862,-0.0852445215,-0.3125625253,-0.3891200125,0.0260638129,0.0143070379,-0.0739083663,0.3470021784,0.1622290313,-0.2902589142,0.2583916783,-0.1702040136,-0.0315410011,0.4124395549,0.5787875056,0.0700063407,0.0150519069,0.0433175191,-0.4493978024,0.0813691691,-0.2138365656,0.3823771477,-0.2618270516,-0.3103593588,-0.4002998769,-0.0186415166,0.1500386447,-0.4743153453,0.0841954798,-0.1355000138,0.2801226974,0.1153332219,-0.1389507949,0.299204886,0.0006324258,0.2137969732,-0.42919752,0.1492035091,-0.0950798541,-0.0213581398,0.3690761924,0.2145181447,0.118515335,-0.1379200667,0.0254460499,0.5660966039,-0.0693006068,0.4441081882,0.563893795,0.4257847667,0.347258687,-0.0107713165,0.2729820907,-0.1193465143,0.0613526553,0.0304935239,-0.5344094038,0.371244818,-0.2114973217,0.1566208303,0.1573420316,0.2463967204,0.0629866719,0.2265214771,-0.2491347343,-0.003994999,-0.1361677498,0.1006820276,0.2312166393,0.0416550674,-0.5127981305,0.2248272449,0.0237503927,-0.2264443189,-0.2222786099,0.3270247281,-0.4630303979,-0.1636605263,0.4058668911,0.3480612934,0.1047010869,0.2272708565,-0.0109121948,0.1717864126,0.2524057031,-0.1232980117,0.3218128979,0.2035265863,-0.1904883534,-0.2553881705,0.1692811251,0.2428085357,-0.2287008762,0.0455408357,-0.1582133174,0.1512369812,-0.2233811766,-0.0805480331,0.0692929626,-0.4180279374,-0.256213516,0.1239786372,-0.4829692245,-0.3185680211,-0.1371294707,0.0616204441,-0.1055617705,0.0678856298,-0.024173988,0.3342973292,-0.1543316543,-0.0404443443,-0.2764347792,-0.0694546849,-0.0403007083,0.0962014496,0.1196943521,0.1254648715,0.4261551201,-0.0977291167,0.126293689,-0.486353606,-0.5364232063,0.0431924872,-0.1406095922,0.35860762,0.1961200982,0.3100593686,-0.0980533808,0.0648044944,0.0551871285,-0.1514209062,0.0159155279,-0.3629391789,-0.1526401937,-0.0399961472,-0.0153281828,-0.2927731574,-0.160278365,-0.0898766443,0.5506942868,0.0937382802,0.0564837791,0.2059240788,0.1169883013,-0.090377219,-0.1289117485,-0.0748206973,-0.1009100527,-0.3535017967,0.318916142,-0.1554074287,-0.4775128365,0.0284705739,0.1739633381,0.1253253073,-0.2889413238,-0.3194074929,-0.3141020238,-0.4386745095,0.1385427564,0.1424367577,0.376802057,0.3760669231,-0.0236699786,-0.1428286135,-0.096005477,-0.4281664789,-0.2592428327,0.2307000607,0.1189916432,-0.1817500144,-0.2812539935,-0.0279712435,0.929833591,0.0400967859,-0.0338755585,-0.095644705,-0.1299847811,0.2708821297,0.0201997235,-0.3125040829,0.337089479,-0.174241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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1717","title":"SciFact dataset - minor changes","comments":"> I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nSure ! Also feel free to ping us for reviews or if we can help :)\r\n\r\n> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n\r\nWhat makes you think that ?\r\nAfaik there's no scifact on our google storage\r\n","body":"Hi,\r\n\r\nSciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!\r\n\r\nI'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nIt also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n\r\nThanks,\r\n\r\nDave","comment_length":91,"text":"SciFact dataset - minor changes \n Hi,\r\n\r\nSciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!\r\n\r\nI'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nIt also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n\r\nThanks,\r\n\r\nDave \n > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nSure ! Also feel free to ping us for reviews or if we can help :)\r\n\r\n> It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n\r\nWhat makes you think that ?\r\nAfaik there's no scifact on our google storage\r\n","embeddings":[0.1750965118,-0.1627209932,-0.1382708549,0.1695231795,0.171601519,-0.1054838747,-0.0107192928,0.0339706019,0.2187184989,0.0415754393,-0.1746508926,0.0221550968,-0.0037148951,0.5154721737,0.166182369,-0.2211896926,0.2049407661,-0.1498279423,-0.1490776688,-0.3085084558,-0.0934292749,0.2177199125,0.1105605587,-0.1985752881,-0.053899508,-0.1465506405,-0.2876536846,0.3299089968,-0.3410831094,-0.2185066938,0.0059862831,0.2872893214,-0.0213820469,0.315020144,-0.000102045,-0.108007133,0.3075104058,-0.070631668,-0.2162955105,-0.0264658704,-0.1062400192,-0.0436003692,0.0415757969,0.0017974518,-0.1029068604,0.0601079315,-0.0751407295,-0.2309908122,0.3554613888,-0.0319922231,0.3132331073,0.1185379252,0.0779164582,-0.1776697338,0.0726440698,0.2887015343,-0.133516103,0.2008469105,0.1898788363,0.3068340719,-0.1447353363,0.6204882264,-0.0216451697,-0.1450505853,0.5957466364,-0.0318081863,-0.3159663975,-0.4047898054,0.1408154964,0.3081873655,0.4457279444,-0.2545996606,-0.6352859139,-0.227434054,0.1143576726,-0.1083268151,-0.0184525438,0.2890632153,0.0686166883,0.0535121337,0.0243590213,-0.4525232911,-0.3507419825,-0.1081755832,-0.047113698,0.108327046,-0.1657474786,-0.0440968312,0.091824986,-0.0021018211,-0.0841014311,-0.182271868,-0.0529444069,0.094959788,-0.1866751313,-0.2585341632,-0.1515841186,0.2619608939,0.0508794934,0.362362653,0.1547906846,0.0821071193,-0.1197000667,-0.1157878116,0.2962263525,-0.0366734713,0.0230399873,0.0158712436,0.2667606771,0.060267631,0.3479256332,-0.0917803794,0.1576174349,-0.130873695,-0.5323855877,-0.1689876467,0.0656358749,-0.170612976,-0.0131803751,-0.0349683277,0.1527695209,-0.1324877739,-0.1000986397,0.2938219309,-0.0978160575,-0.029558083,-0.0576104075,0.2187986672,0.0609581918,-0.2394988388,-0.3341899514,0.202619046,0.1900565177,0.0827171877,0.1475896984,0.0390844308,0.2758976519,-0.0857573748,0.0215234868,0.2337734401,0.1375716627,-0.0556045473,-0.021000633,0.3844462335,-0.0098000346,0.1058368757,0.0518009514,0.1202451736,-0.2956322134,-0.0409873463,0.0416215993,-0.110791035,-0.4523085058,0.2638582289,-0.3114067316,-0.3310674727,-0.1912941188,0.1648497432,-0.1809655577,0.0624513067,0.1114881709,0.1220203713,-0.1212252602,-0.2155525088,0.2229269296,0.418141067,-0.1954847425,-0.1882674992,-0.315848738,-0.151160419,0.0142155308,0.2107599825,0.0462556072,0.0258883312,-0.1377400458,0.1400977671,-0.0074031297,-0.3021460176,-0.2463244349,0.0408311337,-0.1063932553,0.0342386626,0.110600546,0.1690256596,0.0097783562,-0.0350154676,-0.0310879257,0.1615830511,0.0088427402,-0.0714623332,-0.2766363025,-0.490786314,0.0404329002,0.0260183979,0.063353844,0.1510100067,0.182021156,-0.2856173217,0.3023248017,-0.1926104575,-0.018312104,0.4099454582,0.5346345305,0.0429560654,0.0295976475,0.0317672789,-0.5193303823,0.147583589,-0.218706429,0.4071362615,-0.2894701958,-0.2436176836,-0.2690857053,-0.0541456528,0.0588006489,-0.4067020416,0.2099795192,-0.1151897609,0.2097765058,0.0788095817,-0.0493905619,0.2961065471,0.0036831291,0.2046887577,-0.4347661138,0.1959660649,-0.135314554,0.0174518488,0.2929261923,0.1585515887,0.0795653015,-0.1501151025,0.0340366326,0.501691401,-0.0295417011,0.4781923294,0.502761662,0.3911731243,0.4086720645,-0.0107059507,0.2504283488,-0.0324828364,0.0246457253,-0.0572975911,-0.4910120368,0.2947308719,-0.1037092805,0.1660445929,0.1834676117,0.2979111373,0.0981002077,0.2008259445,-0.2598776221,-0.156166926,-0.0991061777,0.0865385458,0.208753854,0.0503789261,-0.4136379361,0.2915058732,0.077395916,-0.2357574701,-0.0906447694,0.2380052805,-0.4635380507,-0.2474510372,0.4250988364,0.3064067662,0.0493452474,0.3404064775,0.0635357201,0.0494972691,0.1891489476,-0.1588693857,0.4017777145,0.1743432432,-0.0399962589,-0.1824591309,0.1818552315,0.0983664542,-0.3777604997,-0.045032084,-0.2103866786,0.0755845532,-0.2193056345,-0.1137075126,0.0073696109,-0.3583326936,-0.1370738447,0.0920670331,-0.4101584256,-0.4434770644,-0.0507215597,0.0759968832,-0.248207137,0.0382062197,-0.1408405602,0.3447827101,-0.0321776532,0.0043004225,-0.3078268468,-0.0855194852,0.0033639602,0.1974467486,0.1433514804,0.1119345725,0.4356259704,-0.0864042863,0.1355852783,-0.4129348695,-0.6301935911,0.1747861952,-0.2031736374,0.3550866246,0.2258835435,0.2690949738,-0.0462635644,0.0270503275,-0.0165732019,-0.1791456193,-0.0409112498,-0.3050620556,-0.1499483287,0.0350809097,-0.0846811458,-0.2805574238,-0.1690338403,-0.0637585372,0.4855988324,0.095969528,0.148746863,0.2344144583,0.1089552194,-0.0266425125,-0.1086443737,0.011763663,-0.2309374064,-0.3808627725,0.2564818263,-0.1707243174,-0.4585993886,-0.0391090438,0.0907628611,0.1902610511,-0.2478321195,-0.329780221,-0.2076347321,-0.4169572294,0.0906143486,0.1164740473,0.273201406,0.3255670369,0.0061794859,-0.1889618039,-0.135603711,-0.2109445333,-0.142507568,0.1865995675,0.0911460444,-0.2945891321,-0.3575014472,0.0876167491,0.822694838,-0.160870105,0.0603462793,-0.1181040779,-0.0738643333,0.2442931831,-0.0199389625,-0.2384810448,0.4008345306,-0.218167901,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1717","title":"SciFact dataset - minor changes","comments":"\r\n\r\n> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n> \r\n> Sure ! Also feel free to ping us for reviews or if we can help :)\r\n> \r\nOK! We're organizing a [shared task](https:\/\/sdproc.org\/2021\/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.\r\n\r\n> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n> \r\n> What makes you think that ?\r\n> Afaik there's no scifact on our google storage\r\n\r\nYou're right, I had the data cached on my machine somewhere. \r\n\r\n","body":"Hi,\r\n\r\nSciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!\r\n\r\nI'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nIt also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n\r\nThanks,\r\n\r\nDave","comment_length":152,"text":"SciFact dataset - minor changes \n Hi,\r\n\r\nSciFact dataset creator here. First of all, thanks for adding the dataset to Huggingface, much appreciated!\r\n\r\nI'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n\r\nIt also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n\r\nThanks,\r\n\r\nDave \n \r\n\r\n> > I'd like to make a few minor changes, including the citation information and the `_URL` from which to download the dataset. Can I submit a PR for this?\r\n> \r\n> Sure ! Also feel free to ping us for reviews or if we can help :)\r\n> \r\nOK! We're organizing a [shared task](https:\/\/sdproc.org\/2021\/sharedtasks.html#sciver) based on the dataset, and I made some updates and changed the download URL - so the current code points to a dead URL. I'll update appropriately once the task is finalized and make a PR.\r\n\r\n> > It also looks like the dataset is being downloaded directly from Huggingface's Google cloud account rather than via the `_URL` in [scifact.py](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/scifact\/scifact.py). Can you help me update the version on gcloud?\r\n> \r\n> What makes you think that ?\r\n> Afaik there's no scifact on our google storage\r\n\r\nYou're right, I had the data cached on my machine somewhere. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1713","title":"Installation using conda","comments":"Great! Did you guys have a timeframe in mind for the next release?\r\n\r\nThank you for all the great work in developing this library.","body":"Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers\/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.","comment_length":24,"text":"Installation using conda \n Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers\/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. \n Great! Did you guys have a timeframe in mind for the next release?\r\n\r\nThank you for all the great work in developing this library.","embeddings":[-0.0539567955,-0.0376583301,-0.1801617593,0.1761816591,0.191117093,-0.1741129607,0.1625533998,-0.0670141056,-0.3270974159,-0.221177429,-0.2294666469,0.0597516261,-0.188989535,0.5104839802,0.3297406733,-0.277556479,0.2336804867,0.186041683,-0.6403389573,-0.0971227363,0.116015628,0.3619967103,-0.2395424843,-0.1564272493,-0.3490127921,-0.0934232697,-0.13800475,-0.1415150762,-0.1676210165,-0.2464525402,0.5433064103,0.1990732551,0.213730216,0.6924842,-0.0001173334,-0.2475258708,0.2480924875,-0.0030501266,-0.2502144873,-0.039988324,-0.2281609029,-0.3681676686,0.0377836451,0.0528491288,-0.019863531,0.0676723197,-0.057624232,-0.0261221956,0.4012193978,-0.0857416764,0.1677254438,0.3654803336,0.2736262083,-0.0947252661,-0.1673788875,0.433835417,-0.3844279945,-0.170255363,0.2569201887,0.0808812082,0.4432737231,0.0667961538,0.0478788279,-0.2124155611,0.3321673274,0.1987416595,-0.2067472786,-0.3278177381,-0.2350192666,0.2532398403,0.8008554578,-0.2084442675,-0.6760145426,-0.2062558681,-0.1349032521,-0.0220964476,-0.0845387205,-0.1256479323,-0.1271618903,0.1594615728,-0.3282053173,-0.6029955745,-0.3283930123,0.0234305058,-0.0905868635,0.5560168028,-0.140727669,-0.0073349727,0.2014150769,-0.0811844841,0.3116420507,0.0117755476,-0.0864330903,0.2956623435,-0.0735695139,-0.3632636368,-0.1712514162,0.3323934078,0.2996618748,0.2440695465,-0.374333024,-0.0830928087,-0.1668815166,-0.1910725981,0.3662338853,0.1367796212,0.0134646604,0.391607374,0.2731012702,-0.0827826038,0.5417615175,-0.0868362039,0.03892814,0.0535690747,-0.3300046027,-0.3209067285,-0.0340300053,-0.3715228438,0.1857255846,-0.3058177829,0.6140985489,-0.3056350946,0.0181265455,0.2475005239,-0.1649832129,0.2572250962,0.0021929587,0.1915689558,0.2798495591,0.2018961459,-0.0706252903,-0.0543984249,-0.1438066661,0.1062562168,0.243094191,-0.1890078038,0.1164429337,-0.1818544716,0.408199966,0.3598951101,-0.0997912437,0.1833704263,-0.1032745168,0.4986630678,-0.4198851883,0.1544222683,-0.3225454092,0.1515449882,-0.2846422195,0.061768163,0.1592708081,-0.0116853099,-0.2567635477,0.0046030814,-0.2204418778,-0.2602276802,-0.2112771273,-0.0464954861,-0.2655153573,-0.0555664301,-0.0740792006,0.2384800613,0.2092833966,-0.0630939603,0.1236935332,0.2759695053,-0.350055486,-0.2852534354,0.1542486548,-0.4082544446,-0.1795652658,0.1058377326,-0.1141242832,-0.086944364,-0.0579333343,-0.3439196646,0.4250664413,-0.507025063,-0.1991416514,0.013025946,0.0835282132,0.123656258,-0.0307742599,0.2835633159,0.299123466,-0.0579781793,-0.1886734515,0.3549758792,0.073148571,-0.3144322336,0.0118684927,-0.2986495495,0.1209744439,0.1903316081,0.292054981,0.293209523,-0.0972980186,-0.0304683801,0.1906959713,0.1425542086,0.0983592644,0.2162647396,0.442346096,0.3299351037,0.1988466084,-0.1986918747,-0.4077663124,0.0472801737,0.1282979548,0.0997155905,-0.0727272481,-0.188121438,0.0218185782,0.1400581002,0.0449401364,-0.1936963052,-0.047278136,-0.1908100098,0.4457260668,-0.1792605668,-0.4518477917,0.8676214218,0.4596750736,0.3349504173,-0.1556449234,0.2536652386,-0.1142603531,0.0273682885,-0.1045836806,0.2058419287,-0.0777752623,-0.3490960002,0.1965986937,0.1790527552,-0.1173755378,-0.0738114268,0.3485862017,0.2091695219,0.4061680138,-0.0318085738,0.2890359461,-0.2030059993,-0.0077361902,0.1084442064,-0.1507164687,-0.1262257099,-0.2082740813,-0.0573269241,0.1094209552,0.133650139,0.3417794704,0.1257269382,-0.2899388969,0.0026717156,-0.0403357483,-0.0039170687,0.1421415359,-0.0493624061,-0.1159399748,-0.1446531266,0.0648135394,-0.2677936852,-0.1272663176,0.2468546778,-0.1077841297,0.379997313,0.3027243614,0.2017099708,0.3015313148,0.155334264,0.0641661361,-0.1555909514,-0.0644165501,-0.0047028014,0.0192373674,0.1764005274,0.0269128364,-0.1974040121,-0.2620178461,0.1294233948,0.0004806252,-0.2759349048,-0.3955468833,-0.1232315898,0.0388639607,-0.0889190361,-0.0142150717,-0.025792053,-0.1741257757,-0.4335142672,-0.2850687206,0.0618094578,-0.0128492685,-0.2115723789,0.3294836879,0.1963211745,-0.0935901105,0.400668174,-0.2807404697,-0.1898596734,-0.2390385568,0.2319550216,-0.0324195772,0.1155240387,0.0665080845,-0.1564191431,0.1868631393,-0.0507902727,0.3589324653,-0.1714442968,-0.5654632449,0.1169636771,-0.1695115119,0.3379515707,-0.0229033399,0.153260693,0.115314506,-0.1332169771,-0.0962347761,-0.1714169979,0.0874570385,-0.3237069249,-0.0788585097,-0.2840440869,-0.174318552,-0.1860374808,-0.4251682162,-0.1508772224,0.6054794192,0.193875134,-0.1933217794,0.0701252818,0.0246518441,0.0297382157,-0.2609931827,0.2217420042,0.0211420208,-0.20427683,0.4779252708,-0.1709124744,-0.2292917818,0.0040923553,-0.0835775211,0.3345167637,-0.0463145748,-0.3969550431,0.1388846189,0.0042131525,0.0415259153,0.1889078319,0.249149248,0.547753334,0.0593655482,0.1654090732,-0.0135443173,-0.3746863902,-0.238566041,0.0132685238,0.2313839197,-0.0185508989,0.0202506669,-0.2800436616,0.3675199449,0.2129456699,-0.0010246282,0.0391097777,0.3218259513,0.5508820415,0.0800627172,-0.2101369053,-0.0043514953,0.099270783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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1713","title":"Installation using conda","comments":"I think we can have `datasets` on conda by next week. Will see what I can do!","body":"Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers\/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.","comment_length":17,"text":"Installation using conda \n Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers\/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. \n I think we can have `datasets` on conda by next week. Will see what I can do!","embeddings":[-0.0149538312,-0.0426130258,-0.1236476302,0.1562701762,0.2540784776,-0.009232902,0.1276855618,-0.1303180307,-0.2962277532,-0.2111263871,-0.2347903401,0.0809221417,-0.1403378844,0.6276564002,0.4649843276,-0.1856292635,0.1998481005,0.2295516729,-0.5902295113,-0.0625070333,0.1372841001,0.2634812891,-0.1885326356,-0.1960845441,-0.359808445,-0.0598923415,-0.113546215,-0.1968571693,-0.032453455,-0.2604272366,0.5524133444,0.159466356,0.2478712946,0.5726000667,-0.000118327,-0.0796176419,0.1754110008,-0.0648775473,-0.3543756604,0.0248022713,-0.3400885463,-0.3828353286,-0.0146474903,0.0758211985,-0.0000120852,0.0436181724,0.0059324247,-0.0815959498,0.3228554428,-0.0134683615,0.1626114994,0.3992315233,0.2094788998,-0.1016364098,-0.1831054389,0.4471345246,-0.3012720346,-0.1141189635,0.1077052578,0.0791413113,0.5704078674,0.023409361,0.0221342593,-0.1581074744,0.1740729958,0.2197768241,-0.2278297395,-0.2767653167,-0.1618104279,0.2351419032,0.7575784326,-0.3252034187,-0.6390735507,-0.2428076267,0.0055800704,-0.0127070127,-0.1900894791,-0.0598195679,-0.1736702919,0.2446870804,-0.2471012622,-0.6649011374,-0.2245127559,0.1040985435,-0.0616721809,0.4285608232,-0.1106449515,0.0321358144,0.2054019421,0.0325856656,0.2649052143,-0.0849942341,-0.0678414628,0.140592739,-0.0855201185,-0.2367231995,-0.2231902629,0.3559328318,0.3252642155,0.1699939519,-0.3703601956,-0.1642525643,-0.202173695,-0.1812936813,0.2401769608,0.0896565914,0.0804530829,0.4821060002,0.1383990645,-0.1284577549,0.4942330122,-0.062205296,-0.0256393421,0.0839998052,-0.2074997127,-0.3596906662,-0.1000820696,-0.2758504748,0.050233081,-0.3096505404,0.555665195,-0.149875164,0.1206117347,0.2145168632,-0.0139045715,0.1138209775,-0.0119964546,0.3075073063,0.3615592122,0.0330429077,-0.0756502971,-0.1224210635,-0.0381389149,0.1636590958,0.2613064647,-0.2416059524,0.143258661,-0.2239934653,0.3553830385,0.3323233426,-0.0616959222,0.2217128873,-0.1798949689,0.4900628626,-0.3739978671,0.2030829042,-0.2409490049,0.2028486878,-0.2840315104,0.0101918885,0.0449461192,-0.0374019295,-0.1617485583,-0.0304193981,-0.1271844953,-0.344838053,-0.3084508181,-0.055344943,-0.2809699476,-0.1488776058,-0.0635116473,0.2492283732,0.1657638401,-0.0337959081,0.1868308038,0.3834117651,-0.4151777923,-0.371511966,0.1071176976,-0.4512655139,-0.1253461689,0.0196368117,-0.0484802127,-0.0370420888,-0.1207711175,-0.3446337283,0.4303440452,-0.4813029468,-0.2709692717,-0.0350110382,0.0457312837,0.1971788406,-0.1197522283,0.3125359416,0.2328032553,0.0341631547,-0.0303027425,0.3453214765,0.0070707104,-0.3574683368,0.1885863394,-0.2112373561,0.1206962764,0.2460269928,0.2226640284,0.3194247484,-0.1441022456,-0.1359203756,0.1034693643,0.1362401992,0.134831354,0.2435184717,0.3739115,0.3233315051,0.162702471,-0.295645535,-0.4294299781,0.0684101731,0.1442991346,0.0211063884,-0.1536229104,-0.2209580541,-0.0352288149,0.1679532677,0.0712398142,-0.1438247263,-0.0619867183,-0.1933827847,0.4818265736,-0.1869964451,-0.383025825,0.9310396314,0.4141563773,0.3214601278,0.0335685685,0.2666040957,-0.0673376247,0.0461876914,-0.0373503491,0.2509399652,-0.0717819929,-0.2659291327,0.2164591104,0.1663508117,-0.170663029,-0.1484289467,0.3806863427,0.1441078484,0.2186952382,0.0165106896,0.1651927531,-0.1783978641,-0.0853505284,0.0703583658,-0.0803250521,-0.2433673143,-0.1189964935,-0.0555518232,0.0645686015,0.2935171723,0.3672555387,0.2279906422,-0.1893626451,-0.1606578827,-0.0933784917,-0.01783875,0.2225294858,-0.0340419337,-0.1180749536,-0.179772079,-0.0553299226,-0.2879695892,0.011367199,0.3001757264,-0.2451165468,0.5096589327,0.2987688184,0.2240209579,0.3043924868,0.2122796476,0.1009572968,-0.2046419382,-0.0767455772,-0.0063806823,-0.0253976416,0.1729675084,0.0567822009,-0.2454538196,-0.2457572669,0.0504124723,0.0275614504,-0.2938050032,-0.3847328424,-0.0910779312,-0.1032777503,-0.1019808576,-0.1778522134,-0.0333461575,-0.211353451,-0.2985105813,-0.1774642169,0.0904571563,-0.027546538,-0.2283671796,0.3074950874,0.1781060547,-0.1851152033,0.4231058061,-0.2911998034,-0.3212713599,-0.1236142144,0.2300423235,-0.0725589693,0.0876067504,0.0645356327,-0.1275738031,0.1365977824,-0.1487146616,0.429533869,-0.219564572,-0.4638290703,0.1157692447,-0.1211456135,0.3696663976,-0.0518055744,0.1717076451,0.0728386343,-0.0547171868,-0.0372060575,-0.0015676746,0.1516359895,-0.2317011207,0.0019128555,-0.30086115,-0.2050743699,-0.1011213213,-0.5442736745,-0.1078187376,0.5183225274,0.130866304,-0.2203021199,-0.0608644225,0.0415272638,0.0609666854,-0.2225181013,0.2633149922,0.0544292666,-0.1433713883,0.5334441662,-0.1355813891,-0.1888212711,-0.0515728369,-0.1765880883,0.3594616652,0.0756084546,-0.420894444,0.2093579918,0.0704013109,0.1192298308,0.1845211834,0.1898522824,0.5759529471,0.0368786715,0.2318338305,-0.0334946625,-0.3571576476,-0.1830795854,-0.0663716644,0.2051074952,-0.0745304078,0.0747422427,-0.3267486691,0.2876844704,0.1867843121,-0.0531546175,0.1046712101,0.2102616727,0.6634022593,0.1246430129,-0.1961597055,-0.0709323436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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1713","title":"Installation using conda","comments":"`datasets` has been added to the huggingface channel thanks to @LysandreJik :)\r\nIt depends on conda-forge though\r\n\r\n```\r\nconda install -c huggingface -c conda-forge datasets\r\n```","body":"Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers\/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience.","comment_length":26,"text":"Installation using conda \n Will a conda package for installing datasets be added to the huggingface conda channel? I have installed transformers using conda and would like to use the datasets library to use some of the scripts in the transformers\/examples folder but am unable to do so at the moment as datasets can only be installed using pip and using pip in a conda environment is generally a bad idea in my experience. \n `datasets` has been added to the huggingface channel thanks to @LysandreJik :)\r\nIt depends on conda-forge though\r\n\r\n```\r\nconda install -c huggingface -c conda-forge datasets\r\n```","embeddings":[-0.0296039023,-0.0399743132,-0.1089975238,0.1860422194,0.2394067347,-0.0445190966,0.1131713986,-0.1059008911,-0.3314240277,-0.302870661,-0.2650924623,0.1040904149,-0.1357483417,0.5306632519,0.3688568771,-0.0758271888,0.2095017582,0.2115162164,-0.6172249317,-0.0298800245,0.1432552785,0.2838039994,-0.1970897764,-0.1503042281,-0.3642676175,0.056296628,-0.0738267377,-0.1842348874,0.0266973823,-0.2115781009,0.5577281117,0.1635947675,0.2185151726,0.520349741,-0.0001208874,-0.0354604088,0.213015303,-0.0774974898,-0.3361149728,-0.0101666823,-0.389367491,-0.2792269886,-0.0301751755,0.0580167435,0.0097176535,0.0661545321,0.0211060625,-0.1383633763,0.3403495848,0.058080785,0.1515555829,0.3557485044,0.2534594536,-0.0241507553,-0.1675552875,0.4902023077,-0.2422238588,-0.0713035986,0.0869594291,0.0984996483,0.5578453541,0.0065273545,-0.0347136557,-0.100368768,0.2996935248,0.1745139956,-0.3399006724,-0.2819991112,-0.0511117317,0.2685140967,0.7587833405,-0.3713587523,-0.6890044808,-0.3122138679,-0.0279159416,0.1089591235,-0.1525970846,-0.1076399982,-0.1356344223,0.2149150968,-0.290166676,-0.6198328733,-0.1621965766,0.1124493405,-0.0005172484,0.3889229596,-0.1620222777,0.0777498335,0.2113397717,-0.0557194799,0.171326831,-0.0997946039,-0.0325682126,0.1761730015,-0.1066201851,-0.170584619,-0.2545810044,0.4082921445,0.2948686481,0.2181316167,-0.4194543958,-0.135757193,-0.2369313389,-0.183127597,0.1929246485,0.0634049773,0.1054866239,0.4957329333,0.0893854424,-0.1445681304,0.43280828,-0.0399534181,-0.0460183807,0.0161799416,-0.2737437487,-0.3993260562,-0.0891322643,-0.2870233953,0.0064034625,-0.2886557579,0.4500457346,-0.1929966509,0.1821888983,0.2403699905,0.0320765451,0.1322605014,-0.0038805385,0.2970759571,0.3454447389,0.0610121265,-0.0838139877,-0.0881427154,-0.0199419558,0.1140609384,0.2309093475,-0.3153795004,0.1855082214,-0.2256006896,0.4308143556,0.3417765498,-0.0638408288,0.2185708135,-0.0955144539,0.4455742538,-0.3512631357,0.2501911819,-0.1407224238,0.1761865169,-0.2922187448,-0.0109037859,0.0705821589,-0.004938202,-0.0855336487,-0.0491808765,-0.1708822995,-0.2739294171,-0.2653611898,-0.1015576869,-0.2199379206,-0.1350846589,0.0281362738,0.2281555235,0.1965715736,-0.0241776705,0.2415091395,0.4308135211,-0.3204354048,-0.4266450703,0.0783582628,-0.4344707727,-0.1541642249,-0.032543283,-0.0673796013,-0.1104503348,-0.1799430251,-0.366201818,0.3999581635,-0.5274589062,-0.3011988699,0.0217517093,0.1033757403,0.2396851778,-0.113210164,0.2434560806,0.1589307636,0.0802019313,0.0272397157,0.2447111458,0.0145050157,-0.3280427456,0.2043245286,-0.2349990308,0.106342718,0.2239687443,0.1952615529,0.3022698164,-0.1420414597,-0.0918333381,0.15574871,0.138288483,0.1555514336,0.267722249,0.3016324341,0.2884352803,0.0832171291,-0.3093565702,-0.3711173534,0.1130677611,0.1507466882,0.0016270534,-0.1291172206,-0.2351766676,-0.0462832563,0.1461788416,0.0964464843,-0.1716506332,-0.0775404125,-0.0991508588,0.4647377729,-0.2203253806,-0.3421970606,0.9912434816,0.4688918889,0.3277896345,-0.0909310356,0.2403007448,-0.047502812,0.0740584955,-0.0532221198,0.2777820528,-0.0386894457,-0.2761130631,0.2325638831,0.2086801678,-0.1758487821,-0.0812153965,0.277384609,0.1935042143,0.1843614578,-0.0035291989,0.1218149364,-0.2264946103,-0.0739820972,0.0571250021,0.0318089128,-0.2204486132,-0.1256756932,0.0195601881,0.0984865129,0.311791122,0.3137337565,0.2109018266,-0.2605006397,-0.2747134566,-0.0778359175,-0.0633036643,0.2634271085,-0.0131231667,-0.0589004792,-0.2266942859,-0.1191508621,-0.1861940324,-0.0210871138,0.3124918044,-0.2640961111,0.4855315685,0.2817257345,0.2035993785,0.3454295695,0.1963165849,0.0788591057,-0.1851339936,-0.0295420066,0.0008035467,0.0157449134,0.2013032436,0.0155260162,-0.3022401035,-0.3382844627,0.0539068803,0.0779838264,-0.2943549752,-0.4199885726,-0.0840438083,-0.1885014921,-0.0586015619,-0.2047686279,-0.0325729325,-0.2585756481,-0.3729180396,-0.2393331975,0.0493080653,-0.1605867594,-0.2397894412,0.2936691642,0.1699875742,-0.2903734148,0.3241269588,-0.304095149,-0.4081711769,-0.1580365151,0.2665275037,-0.0613298677,0.0668960959,0.0522693023,-0.0840248019,0.1296420544,-0.1698678583,0.4550246,-0.169860363,-0.4443704486,0.102739431,-0.1414861381,0.3694041967,0.0276789796,0.1013228446,0.0970082283,-0.0839463845,-0.0104794595,-0.0044471305,0.1619687676,-0.2443733662,0.0076658605,-0.290904969,-0.1808828712,-0.0214433428,-0.5090830922,-0.1175971702,0.5027697682,0.1387522668,-0.2172674537,-0.0292484071,0.0144085074,0.1492461264,-0.1999724209,0.2633713484,0.0334010907,-0.0873203203,0.5326375365,-0.105519928,-0.1174501628,-0.0264560059,-0.1532169133,0.4213302135,0.0937683359,-0.3301803768,0.1915220618,-0.0114307301,0.0560151376,0.1847115308,0.1781056523,0.5187995434,0.0392140485,0.2465070337,-0.0237926338,-0.3398368657,-0.1718513966,-0.160054341,0.2083532512,-0.0710422099,0.0630536154,-0.271345973,0.2838387489,0.2142466307,-0.1129646823,0.0421449058,0.1831898391,0.6972621083,0.1781626195,-0.281950295,0.013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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1710","title":"IsADirectoryError when trying to download C4","comments":"I haven't tested C4 on my side so there so there may be a few bugs in the code\/adjustments to make.\r\nHere it looks like in c4.py, line 190 one of the `files_to_download` is `'\/'` which is invalid.\r\nValid files are paths to local files or URLs to remote files.","body":"**TLDR**:\r\n\r\nI fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.\r\n\r\nHow can the problem be fixed? \r\n\r\n**VERBOSE**:\r\n\r\nI use Python version 3.7 and have the following dependencies listed in my project:\r\n\r\n```\r\ndatasets==1.2.0\r\napache-beam==2.26.0\r\n```\r\n\r\nWhen running the following code, where `\/data\/huggingface\/unpacked\/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"c4\", \"en\", data_dir=\"\/data\/huggingface\/unpacked\", beam_runner='DirectRunner')\r\n```\r\n\r\nI get the following stacktrace:\r\n\r\n```\r\n\/Users\/fredriko\/venv\/misc\/bin\/python \/Users\/fredriko\/source\/misc\/main.py\r\nDownloading and preparing dataset c4\/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/fredriko\/.cache\/huggingface\/datasets\/c4\/en\/2.3.0\/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/fredriko\/source\/misc\/main.py\", line 3, in <module>\r\n    load_dataset(\"c4\", \"en\", data_dir=\"\/data\/huggingface\/unpacked\", beam_runner='DirectRunner')\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1066, in _download_and_prepare\r\n    pipeline=pipeline,\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 582, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/fredriko\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/c4\/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283\/c4.py\", line 190, in _split_generators\r\n    file_paths = dl_manager.download_and_extract(files_to_download)\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 258, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 189, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 117, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 80, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/'\r\n\r\nProcess finished with exit code 1\r\n```","comment_length":50,"text":"IsADirectoryError when trying to download C4 \n **TLDR**:\r\n\r\nI fail to download C4 and see a stacktrace originating in `IsADirectoryError` as an explanation for failure.\r\n\r\nHow can the problem be fixed? \r\n\r\n**VERBOSE**:\r\n\r\nI use Python version 3.7 and have the following dependencies listed in my project:\r\n\r\n```\r\ndatasets==1.2.0\r\napache-beam==2.26.0\r\n```\r\n\r\nWhen running the following code, where `\/data\/huggingface\/unpacked\/` contains a single unzipped `wet.paths` file manually downloaded as per the instructions for C4:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"c4\", \"en\", data_dir=\"\/data\/huggingface\/unpacked\", beam_runner='DirectRunner')\r\n```\r\n\r\nI get the following stacktrace:\r\n\r\n```\r\n\/Users\/fredriko\/venv\/misc\/bin\/python \/Users\/fredriko\/source\/misc\/main.py\r\nDownloading and preparing dataset c4\/en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/fredriko\/.cache\/huggingface\/datasets\/c4\/en\/2.3.0\/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283...\r\nTraceback (most recent call last):\r\n  File \"\/Users\/fredriko\/source\/misc\/main.py\", line 3, in <module>\r\n    load_dataset(\"c4\", \"en\", data_dir=\"\/data\/huggingface\/unpacked\", beam_runner='DirectRunner')\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 612, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 527, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1066, in _download_and_prepare\r\n    pipeline=pipeline,\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 582, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/fredriko\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/c4\/8304cf264cc42bdebcb13fca4b9cb36368a96f557d36f9dc969bebbe2568b283\/c4.py\", line 190, in _split_generators\r\n    file_paths = dl_manager.download_and_extract(files_to_download)\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 258, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 189, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 117, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict(path)\r\n  File \"\/Users\/fredriko\/venv\/misc\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 80, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/'\r\n\r\nProcess finished with exit code 1\r\n``` \n I haven't tested C4 on my side so there so there may be a few bugs in the code\/adjustments to make.\r\nHere it looks like in c4.py, line 190 one of the `files_to_download` is `'\/'` which is invalid.\r\nValid files are paths to local files or URLs to remote files.","embeddings":[-0.2110013068,-0.0166831054,0.002013599,0.2388121039,0.3003631532,0.0115945125,0.0879322886,0.2422638535,-0.1057231128,0.0968610942,-0.1910670251,-0.0968224183,-0.3083791435,-0.0378795564,0.0463685207,-0.0706733167,-0.1771388352,0.241326645,-0.4923979044,0.1420897543,-0.0148674138,0.4154529572,-0.1527776271,0.0248496011,-0.094913356,-0.105762437,-0.0448619463,0.2398068756,-0.0043044961,-0.431681782,0.6017952561,0.2295489162,0.1245357692,0.4874401093,-0.0001212964,0.2981967628,0.317119956,-0.1303761899,-0.3978928626,-0.2918590605,-0.0663455352,-0.0388181917,0.3356330693,-0.1701166928,0.1311804503,0.035082899,0.0841413587,-0.3720006645,0.2927715778,0.2490392476,0.1561473906,0.407823503,0.2956752777,0.0461126007,0.1961436719,-0.0739455968,0.0258721635,0.3925033808,-0.3268428147,0.2190594375,-0.1065383852,0.1551535875,0.061704576,0.0032203929,0.5413600206,-0.2076385319,0.1880460829,-0.0309579633,0.1410353035,0.0395150706,0.6169292331,-0.3673050702,-0.2273660153,0.0226472281,0.0485328585,-0.0480972268,0.5799092054,0.0286890045,-0.1914091855,0.1599879265,-0.3129753172,0.0555075221,-0.2554164231,0.180970639,-0.2790956795,-0.2163836211,-0.121321924,0.1668666899,0.3539424539,0.1593830884,0.0721893609,-0.1522883922,0.0950876772,0.1023896933,-0.3253191411,0.1324953139,-0.079841733,0.4449476302,-0.0588747934,-0.135213688,-0.0323127359,-0.0894959718,-0.1571263075,0.0771519467,-0.0998350158,0.5191348791,-0.1745387763,-0.0579019561,0.1916341782,0.1299021542,0.0251296032,0.0934778899,-0.0617643595,-0.2957741618,-0.3996480107,0.1983498484,-0.0308842752,-0.1465162635,-0.5061504245,-0.051147934,-0.6028206944,0.0617649481,-0.0486882254,0.4410617352,-0.2156828791,0.2982066274,0.427164048,0.1172027513,-0.0777522549,-0.1600181907,-0.0464469083,0.1821557879,-0.3387562037,-0.2175408304,0.146545738,-0.3067020178,0.0565164387,-0.057250414,0.1242894232,0.1198472083,-0.1313167661,0.083299838,-0.3962429762,0.3249585629,-0.0569178984,0.3533375263,0.141952157,-0.2476044744,-0.0419186801,-0.2503644526,-0.0288922265,-0.1689238399,-0.0059046121,0.0625003278,-0.2621732056,-0.0186396968,-0.0239851754,-0.3702903986,-0.0880559757,-0.0250878558,-0.3069533706,-0.3325378299,0.0403667279,-0.387948066,0.2867855728,0.7887271047,-0.37308231,-0.062373139,-0.3939866424,-0.3207274377,-0.088637799,0.1597679108,0.0083917333,0.4683425426,-0.3715884387,-0.0817715451,0.0349983349,-0.430870533,-0.3826598227,0.4031050205,-0.2026176751,0.181424588,0.2387690842,-0.1593855619,-0.0379303358,-0.0491600074,0.0392383374,0.073790893,0.1009896919,-0.2146940827,-0.3035178781,-0.0859721005,0.0347923078,0.1836927235,0.1889918745,0.1237996444,0.2890424132,0.0238364823,0.4252669811,-0.1544864923,0.1230125576,0.435883224,0.3003919125,0.0804158077,-0.2277423143,0.2824690938,-0.7021694779,0.1369891167,-0.0688533261,-0.0525572635,-0.188316077,0.0565886833,-0.3794920743,0.1098476052,-0.2681518793,-0.0508044325,0.0427405573,0.2153688669,0.0251013692,0.2253341377,0.0545547009,0.9237930775,-0.0291264597,0.2412460148,-0.1177422404,0.2889007926,-0.1239601597,-0.0006952902,0.2360188663,0.059098497,0.2810580134,-0.2603555918,-0.0164311752,0.4496192038,-0.3032152057,0.2257034332,-0.2356710434,0.2374099791,0.1409540772,-0.4850714803,0.2437880486,0.1826347411,0.0155507112,0.0794943646,0.3024570942,0.0373970866,-0.0111021306,0.294472754,0.261115849,0.2077235281,0.1051314324,-0.1697059274,0.186281085,0.0056127477,0.4510717988,-0.085326232,0.6482319832,-0.2358091921,-0.0486219153,0.0710991621,-0.0294509474,-0.0853036344,-0.2401970625,0.1699893326,-0.1940018684,0.1319160014,0.3667606413,0.2274281085,0.3210941851,0.1386129111,-0.0640742704,0.177634269,-0.0354291461,-0.0930108204,0.1974637359,0.0143876793,-0.1937520951,0.1127503589,-0.0337735526,0.071177125,-0.2204743177,-0.3273619413,-0.1083015278,0.0916903019,-0.5798996091,0.0877991691,-0.3344514966,-0.1501529217,-0.4275587499,0.1584721953,-0.1326646209,-0.3841993511,-0.2535524666,0.3956749141,-0.1553426683,0.0745387822,-0.0524655245,-0.3114846051,-0.0429090299,-0.3456931412,-0.2296126336,0.1166532636,-0.3646844923,-0.0188131277,0.2823852599,-0.163425982,0.1477771252,-0.3827365935,0.1993676722,-0.389424324,-0.193093136,0.3145964742,-0.1097067967,-0.1174798235,0.2981440127,0.2071418166,-0.1809550226,-0.3646033704,0.0009787113,0.0255349614,-0.1015324593,0.1881582737,0.0071540694,-0.0530275106,-0.2206927985,-0.323942095,-0.3468903303,-0.4373090863,0.2280209959,-0.2074750364,-0.0289415903,0.3682849109,0.1105989814,0.2621778846,-0.1250937283,0.0355542637,0.2126612961,0.0951536819,0.1580765694,0.0309461709,-0.29599002,0.1105617136,-0.1883838326,0.1125982702,0.4232776761,-0.6604568958,0.3621883392,-0.1609885246,0.2337641418,-0.1173595935,0.2308192253,0.3478416502,0.0552455708,0.0476462953,-0.1626119465,0.0329933874,0.0554185025,-0.2411595434,0.352789551,0.0200572927,0.1407480538,0.1069205329,0.7421910763,0.1976411641,-0.3773144782,0.5262420177,0.2205745727,0.5429071188,-0.0232981835,-0.1862657517,-0.0334116183,0.0498204976,0.0856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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1706","title":"Error when downloading a large dataset on slow connection.","comments":"Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ?\r\n\r\nIt looks like the downloaded file is corrupted and can't be extracted using `tarfile`.\r\nCould you try loading it again with \r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```","body":"I receive the following error after about an hour trying to download the `openwebtext` dataset.\r\n\r\nThe code used is:\r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"openwebtext\")\r\n```\r\n\r\n> Traceback (most recent call last):                                                                                                                                                                                                                             [4\/28]\r\n>   File \"<stdin>\", line 1, in <module>\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_dataset\r\n>     ignore_verifications=ignore_verifications,\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 515, in download_and_prepare\r\n>     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 570, in _download_and_prepare\r\n>     split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n>   File \"\/home\/lucadiliello\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/openwebtext\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02\/openwebtext.py\", line 62, in _split_generators\r\n>     dl_dir = dl_manager.download_and_extract(_URL)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n>     return self.extract(self.download(url_or_urls))\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 235, in extract\r\n>     num_proc=num_proc,\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n>     return function(data_struct)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 343, in cached_path\r\n>     tar_file.extractall(output_path_extracted)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 2000, in extractall\r\n>     numeric_owner=numeric_owner)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 2042, in extract\r\n>     numeric_owner=numeric_owner)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 2112, in _extract_member\r\n>     self.makefile(tarinfo, targetpath)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 2161, in makefile\r\n>     copyfileobj(source, target, tarinfo.size, ReadError, bufsize)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 253, in copyfileobj\r\n>     buf = src.read(remainder)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/lzma.py\", line 200, in read\r\n>     return self._buffer.read(size)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/_compression.py\", line 68, in readinto\r\n>     data = self.read(len(byte_view))\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/_compression.py\", line 99, in read\r\n>     raise EOFError(\"Compressed file ended before the \"\r\n> EOFError: Compressed file ended before the end-of-stream marker was reached\r\n","comment_length":44,"text":"Error when downloading a large dataset on slow connection. \n I receive the following error after about an hour trying to download the `openwebtext` dataset.\r\n\r\nThe code used is:\r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"openwebtext\")\r\n```\r\n\r\n> Traceback (most recent call last):                                                                                                                                                                                                                             [4\/28]\r\n>   File \"<stdin>\", line 1, in <module>\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_dataset\r\n>     ignore_verifications=ignore_verifications,\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 515, in download_and_prepare\r\n>     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 570, in _download_and_prepare\r\n>     split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n>   File \"\/home\/lucadiliello\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/openwebtext\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02\/openwebtext.py\", line 62, in _split_generators\r\n>     dl_dir = dl_manager.download_and_extract(_URL)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n>     return self.extract(self.download(url_or_urls))\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 235, in extract\r\n>     num_proc=num_proc,\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n>     return function(data_struct)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 343, in cached_path\r\n>     tar_file.extractall(output_path_extracted)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 2000, in extractall\r\n>     numeric_owner=numeric_owner)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 2042, in extract\r\n>     numeric_owner=numeric_owner)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 2112, in _extract_member\r\n>     self.makefile(tarinfo, targetpath)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 2161, in makefile\r\n>     copyfileobj(source, target, tarinfo.size, ReadError, bufsize)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/tarfile.py\", line 253, in copyfileobj\r\n>     buf = src.read(remainder)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/lzma.py\", line 200, in read\r\n>     return self._buffer.read(size)\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/_compression.py\", line 68, in readinto\r\n>     data = self.read(len(byte_view))\r\n>   File \"\/home\/lucadiliello\/anaconda3\/envs\/nlp\/lib\/python3.7\/_compression.py\", line 99, in read\r\n>     raise EOFError(\"Compressed file ended before the \"\r\n> EOFError: Compressed file ended before the end-of-stream marker was reached\r\n \n Hi ! Is this an issue you have with `openwebtext` specifically or also with other datasets ?\r\n\r\nIt looks like the downloaded file is corrupted and can't be extracted using `tarfile`.\r\nCould you try loading it again with \r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```","embeddings":[-0.4910078049,0.0278396662,-0.1026933491,0.2193090171,0.2197147459,0.110001415,0.118274942,0.4798275232,0.0537419096,0.0058270204,-0.1662466526,-0.1080515906,-0.0206885375,0.09193407,-0.2218508869,-0.1143092886,-0.3376919031,0.2518778145,-0.1198240146,-0.0132287266,-0.2608829439,0.1014634371,-0.145431608,0.090435937,0.0286519937,-0.1973609328,0.2813675404,0.0957638249,-0.3298174739,-0.3242034912,0.1182493195,-0.0770616531,-0.1571705341,0.1978920549,-0.0001039065,0.1097812057,0.3197879195,-0.0127757546,-0.2622531056,-0.0126416013,-0.5175206661,-0.4060268998,-0.1065353081,-0.2328351736,0.1429639906,-0.3205480576,-0.0369036645,-0.4010324776,0.202750966,0.3491779864,0.3192969263,0.4104012847,0.2654625177,-0.1568136513,0.2949921787,-0.3527448177,-0.1403333992,0.0242081843,0.0966816396,0.1845524609,0.0158708096,-0.0254063047,-0.0977822691,0.3270840347,0.0858378485,-0.1159041077,0.1312582642,-0.1467746347,0.233209461,0.3168919384,0.6235703826,0.1186791435,-0.20031479,-0.0985546932,-0.1308957934,-0.0556169972,0.151860401,0.349847585,-0.263335675,0.1681705117,-0.0905272663,-0.1982218623,-0.1184892729,0.1373558342,-0.2389908135,0.209534958,-0.1198301837,0.1606208235,0.0837471113,-0.0340166725,0.1818950921,-0.2236408591,0.0057414072,-0.0193282627,-0.3181525171,0.0871534273,0.1587081403,0.0071564391,0.1576059014,0.1561466604,0.1806851625,-0.0814055875,-0.1455614567,0.0343292467,0.2485306412,0.1881300658,-0.2394278347,-0.1837043315,0.3369806409,0.0097603854,0.086356461,-0.0245990139,-0.0561116189,-0.4480530322,0.0691169053,-0.0665593967,0.4185087979,-0.0500821881,-0.6267598867,0.0847598687,0.0171588864,0.0917018354,0.0974617749,0.134782359,-0.3348250687,0.1765510738,0.2755548954,-0.0854988396,-0.2178014815,-0.2556179464,-0.287570864,-0.0248287618,-0.000398937,-0.1589295864,0.0877899602,-0.053948693,0.1952300072,-0.1422249824,-0.0910601318,-0.0516946539,-0.0116248503,-0.2797981799,-0.0472413972,0.4205085039,-0.117083542,0.217013225,0.0714032352,0.0680958405,-0.1618653089,0.0930211097,-0.148138687,-0.5448227525,-0.204013586,0.2917512655,-0.1607732773,-0.1163366884,0.0674725622,-0.1778175682,0.06017204,-0.0622055978,-0.2368813455,-0.1359258592,-0.3001925945,-0.0643514469,0.1700168997,0.4760253727,-0.1476195902,0.1128111631,-0.1583244354,-0.0900966674,0.1099371091,0.3871322274,-0.2834582925,0.3439683616,-0.1608299464,-0.0327681415,0.4121798277,-0.2027587891,-0.4505096972,0.4190320075,-0.2133525163,-0.0175491925,-0.0150948204,-0.0155986957,0.247559458,-0.2489022017,0.0600092858,0.4046271145,-0.2269219607,0.1832318604,-0.2877351344,-0.3469765484,0.0445903689,0.314044416,0.1543741524,-0.0975275189,0.073860243,0.3041230142,0.1083477139,0.2421858162,0.1819605082,0.2064588368,-0.0133030163,0.0129645886,-0.2803225815,0.0975571424,-0.2467361987,0.2262407392,0.2003411502,0.1243642643,-0.1202566773,0.0499298647,-0.5512596369,-0.0064605637,-0.1038976386,0.2097591162,0.1960365325,-0.0458704643,0.1451184601,0.2851141691,0.1329975277,0.2275888622,0.0520906821,-0.0502490923,-0.0580034144,0.0837767497,-0.0531922542,0.065541923,0.3550397754,-0.2205490768,-0.0292083267,-0.0798258036,-0.2772213221,0.2578870058,-0.1733716875,0.0356222764,-0.0232283715,0.1471143663,0.1160377115,-0.1642075181,0.0679106638,0.1062736958,0.3960361779,-0.0606126487,0.1969256699,-0.0338592194,0.1103016064,-0.0591172874,0.0435753763,0.0712376535,0.3167003989,-0.1312634945,0.2426616699,0.0766423568,0.2193253189,0.179530412,0.1072681695,-0.054201562,0.0830672681,0.0046871221,0.4841132164,-0.0464976095,0.0474111699,0.2676337063,0.0586583279,-0.1077326462,-0.2526250184,0.4030234814,0.4827991724,0.0838006437,0.2591084838,0.0453576967,0.1262770891,-0.2797545493,0.3492495418,0.3568131924,0.2874477804,0.2896438539,0.0301173292,-0.1230559945,-0.2845939696,-0.3535315096,0.3476915658,0.608286202,-0.1231075972,-0.0709432438,-0.1247352287,-0.0788890198,0.1208879426,0.0440233424,-0.0377581157,-0.4296837747,-0.0154182985,0.0485089384,-0.143442899,0.1439752132,-0.1638388634,0.2240302116,0.4131111503,0.0995168015,-0.1292496473,-0.1925107539,-0.1532576531,0.1184123158,0.3695813417,-0.0189591553,0.2307117283,-0.2334080637,-0.071258001,-0.3312543631,0.123114273,-0.018692553,0.0426615924,0.0100765182,0.2998990119,0.4455482364,0.1532676369,-0.2030158043,0.2708804011,-0.2101938725,0.0325599946,0.1356427372,0.0261241738,0.0646527857,0.0309957042,-0.2389444709,-0.3499540687,-0.6167093515,0.1416400969,-0.1712803692,0.1445546895,0.0156337209,-0.1197377443,0.1246792153,0.0642575175,0.0857084394,-0.193493858,-0.5720279813,0.3628085554,-0.154495433,-0.347192198,-0.0274207089,0.1902089417,0.1565325856,-0.022583887,-0.5953269601,0.0783892646,-0.446057111,0.0887931585,-0.2679098248,0.0684628487,0.1131035984,-0.0909366831,-0.2421576828,-0.0365531445,0.0605466366,0.103685841,-0.0470477194,0.1688963324,-0.1197036132,0.3804698586,0.1618496031,0.0838429481,0.2322383076,-0.0005098601,0.3763803244,0.1972851753,0.1473808438,-0.062704578,-0.1118450463,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1701","title":"Some datasets miss dataset_infos.json or dummy_data.zip","comments":"Thanks for reporting.\r\nWe should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.\r\n\r\nFor c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.\r\n","body":"While working on dataset REAME generation script at https:\/\/github.com\/madlag\/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json : \r\n\r\n```\r\nc4\r\nlm1b\r\nreclor\r\nwikihow\r\n```\r\n\r\nAnd some does not have a dummy_data.zip : \r\n\r\n```\r\nkor_nli\r\nmath_dataset\r\nmlqa\r\nms_marco\r\nnewsgroup\r\nqa4mre\r\nqangaroo\r\nreddit_tifu\r\nsuper_glue\r\ntrivia_qa\r\nweb_of_science\r\nwmt14\r\nwmt15\r\nwmt16\r\nwmt17\r\nwmt18\r\nwmt19\r\nxtreme\r\n```\r\n\r\nBut it seems that some of those last do have a \"dummy\" directory .\r\n\r\n","comment_length":42,"text":"Some datasets miss dataset_infos.json or dummy_data.zip \n While working on dataset REAME generation script at https:\/\/github.com\/madlag\/datasets_readme_generator , I noticed that some datasets miss a dataset_infos.json : \r\n\r\n```\r\nc4\r\nlm1b\r\nreclor\r\nwikihow\r\n```\r\n\r\nAnd some does not have a dummy_data.zip : \r\n\r\n```\r\nkor_nli\r\nmath_dataset\r\nmlqa\r\nms_marco\r\nnewsgroup\r\nqa4mre\r\nqangaroo\r\nreddit_tifu\r\nsuper_glue\r\ntrivia_qa\r\nweb_of_science\r\nwmt14\r\nwmt15\r\nwmt16\r\nwmt17\r\nwmt18\r\nwmt19\r\nxtreme\r\n```\r\n\r\nBut it seems that some of those last do have a \"dummy\" directory .\r\n\r\n \n Thanks for reporting.\r\nWe should indeed add all the missing dummy_data.zip and also the dataset_infos.json at least for lm1b, reclor and wikihow.\r\n\r\nFor c4 I haven't tested the script and I think we'll require some optimizations regarding beam datasets before processing it.\r\n","embeddings":[0.1359894276,0.2731491327,-0.046184022,0.2401537597,0.2858270407,0.3439723551,0.2180934101,0.0033975567,-0.046791587,0.0312941,0.1941788048,0.0641290918,-0.0075305644,0.125732705,0.1229167581,-0.0037490961,0.3180769682,0.1551839411,0.0810289979,-0.3036448956,-0.6437272429,0.289752394,-0.2662017941,-0.2682595551,-0.3690704703,0.2852796316,-0.1664090157,0.1071849167,-0.076620549,-0.258440882,0.1300564408,0.0621439666,0.1241826564,0.2200273722,-0.0001218869,-0.1835252196,0.3358968496,-0.318447113,-0.6375688314,0.1391728073,-0.2461787611,-0.5139753819,-0.2116565853,-0.0337942131,0.0850708559,-0.3283745348,0.2760594189,-0.4367847443,0.0252547059,0.2878524363,0.1385961175,0.0108711906,0.0054563372,0.019353617,0.4706355333,0.5881893635,-0.0361154415,0.0234698635,-0.0308680721,0.1876572967,0.16875723,0.3128627837,-0.000031814,-0.0429410897,0.2564952374,0.0667167678,-0.0803639218,-0.313919574,0.0929040536,0.0367850214,0.5615602732,-0.3702829778,-0.57642138,-0.4374137819,0.2442951351,-0.0141523341,0.2991722524,0.1897802353,0.2458381951,-0.0496902652,-0.113386102,-0.2367790937,0.230628103,0.1129053608,0.2745579779,0.3560731709,-0.013902422,0.1380821466,-0.3792663813,0.0509369262,0.5013102889,-0.6761803031,-0.0721880421,-0.0376387462,-0.0408893935,0.0017078262,-0.2982947528,-0.0323235504,0.0126889264,-0.0287912749,-0.0522561073,-0.0006028205,-0.0576110333,0.0159178562,-0.0656326339,0.0639261454,0.2777886391,0.4664542973,0.0937818959,-0.2195849121,0.2740783989,-0.0936787277,-0.1424447447,-0.0849497914,-0.1278651655,0.3348123133,-0.2248756737,0.0716191381,-0.3074820936,0.0207017157,-0.1901276708,-0.1387444437,-0.1389830709,0.0208207294,-0.0931715593,0.5326836705,0.0541503876,0.1507941484,0.0578553416,-0.4711914361,-0.1495305598,0.046051167,-0.2453964204,0.2048980445,0.2601968944,0.268576175,0.5244526863,-0.0994325578,-0.2592103779,0.0434183478,0.3255158961,0.1851344109,-0.0268367305,0.2875718772,0.0094899181,0.3180415332,-0.1978770792,0.0012944319,0.0012153551,0.2754144669,-0.2969889641,-0.189390108,-0.1847658455,0.1388822645,-0.2081683278,0.0584152006,-0.4075929523,0.0272928681,0.0436878465,-0.2390128523,0.0437335782,-0.1227899194,0.019875573,-0.2703769505,0.1394422948,0.4934485555,-0.3240852356,-0.0644108057,-0.3538264334,0.1564725339,0.3093661368,-0.1211022809,0.0391098224,0.1585644186,-0.5670543313,-0.1100330055,0.3649954796,-0.4209957123,0.0515694283,-0.0461734235,0.169695273,0.0378894769,0.2327091843,0.2033316046,-0.212942481,-0.0323861837,-0.1897785962,-0.0280067399,-0.0973539576,-0.0145749338,-0.2613099515,-0.1372375637,0.201224342,0.3041262329,0.0936595052,0.0770851448,-0.0792812929,-0.1432590336,0.0890172422,-0.035811048,0.1986543834,0.3075972795,-0.3370240629,0.119671613,0.3427751362,0.2289583981,-0.914237082,0.1389713287,-0.0475766622,-0.1395034045,0.0272238664,-0.3427241743,-0.0693246722,-0.2794781625,-0.0491127633,-0.2503945231,0.0087548112,0.4585579336,0.2593406141,-0.1326010227,-0.2570742667,0.0305096209,-0.1728773117,0.2510372996,-0.5224624872,0.3718422353,0.004179833,0.1032609865,0.1918188781,0.101075165,0.1200629473,-0.1771908551,0.0113642123,0.3651761711,0.1379337758,0.0009049419,0.2633714676,0.2109721452,0.0876649991,-0.070728153,0.1987474412,0.117308192,0.0558156595,-0.1434938908,-0.0086810878,0.2717199326,-0.1113152727,0.0177729391,-0.1500590295,-0.0267463587,0.2357837856,0.0685027167,0.1884488165,-0.2720340192,0.057404846,0.3031097651,0.3498010933,0.0565030687,-0.1295443624,0.0957012847,0.4859601259,-0.3183689415,-0.0276597552,-0.0212369431,-0.3904747367,-0.044519186,0.2228826284,0.2744318545,0.4791937768,0.2456193566,-0.2215274125,-0.180290997,0.1141359061,-0.364847064,0.1915976554,0.0979761034,-0.1553996801,0.3336868286,0.0521303564,0.0374680012,-0.407613039,-0.0300660804,0.3921745718,0.1541529596,-0.3328437209,-0.2906156182,-0.0937220603,-0.2301835567,-0.0803006068,0.2770461738,-0.1640733331,-0.5771505237,0.2277849019,0.0148212621,-0.3227028847,0.3482872844,0.2542717159,0.0687833577,-0.3534528017,0.0768535957,-0.6138683558,-0.003677343,-0.2684159875,0.0301392935,0.1393002123,0.104258962,0.1292711943,-0.7210150361,0.1649117023,-0.5698032975,-0.2802166939,0.0580845736,-0.0072912183,0.1889294237,0.2599308491,0.0986851677,-0.0037966648,-0.1208011806,0.0712174177,0.1265302747,-0.1768732667,0.0677923188,-0.0114057483,-0.0121208653,-0.1999921799,-0.3856360316,-0.2595992088,-0.22407718,-0.1673861146,-0.136222899,0.0212258995,0.0338353962,-0.1363120079,-0.033357203,0.0263157953,-0.1303356439,-0.1572937518,-0.4554775953,0.5669626594,-0.1628836989,-0.0462445617,0.209676072,-0.3875752985,0.0092359427,-0.0341530442,-0.6006202102,-0.2145354152,0.0365673415,0.0606159568,-0.0902967826,0.0497137159,0.3813123703,0.3126040399,0.0970507562,-0.2882058322,-0.1956235915,-0.067770645,-0.1374189556,0.4222305417,-0.279386729,-0.0224152543,-0.3571079373,0.398136586,0.2477406859,-0.1056586355,0.1083179116,0.1153872833,0.4375533164,-0.0171891749,0.0605185069,0.0567205064,-0.0126334252,0.2005238533,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1687","title":"Question: Shouldn't .info be a part of DatasetDict?","comments":"We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to work.","body":"Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. \r\n\r\nFor instance:\r\n```\r\n>>> ds = datasets.load_dataset(\"conll2002\", \"es\")\r\n>>> ds.info\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nAttributeError: 'DatasetDict' object has no attribute 'info'\r\n```\r\n\r\nI could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. \r\n\r\nIntuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.\r\n\r\nIt is entirely possible that I am missing another perspective","comment_length":26,"text":"Question: Shouldn't .info be a part of DatasetDict? \n Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. \r\n\r\nFor instance:\r\n```\r\n>>> ds = datasets.load_dataset(\"conll2002\", \"es\")\r\n>>> ds.info\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nAttributeError: 'DatasetDict' object has no attribute 'info'\r\n```\r\n\r\nI could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. \r\n\r\nIntuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.\r\n\r\nIt is entirely possible that I am missing another perspective \n We could do something. There is a part of `.info` which is split specific (cache files, split instructions) but maybe if could be made to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1687","title":"Question: Shouldn't .info be a part of DatasetDict?","comments":"Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). ","body":"Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. \r\n\r\nFor instance:\r\n```\r\n>>> ds = datasets.load_dataset(\"conll2002\", \"es\")\r\n>>> ds.info\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nAttributeError: 'DatasetDict' object has no attribute 'info'\r\n```\r\n\r\nI could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. \r\n\r\nIntuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.\r\n\r\nIt is entirely possible that I am missing another perspective","comment_length":27,"text":"Question: Shouldn't .info be a part of DatasetDict? \n Currently, only `Dataset` contains the .info or .features, but as many datasets contains standard splits (train, test) and thus the underlying information is the same (or at least should be) across the datasets. \r\n\r\nFor instance:\r\n```\r\n>>> ds = datasets.load_dataset(\"conll2002\", \"es\")\r\n>>> ds.info\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nAttributeError: 'DatasetDict' object has no attribute 'info'\r\n```\r\n\r\nI could imagine that this wouldn't work for datasets dicts which hold entirely different datasets (multimodal datasets), but it seems odd that splits of the same dataset is treated the same as what is essentially different datasets. \r\n\r\nIntuitively it would also make sense that if a dataset is supplied via. the load_dataset that is have a common .info which covers the entire dataset.\r\n\r\nIt is entirely possible that I am missing another perspective \n Yes this was kinda the idea I was going for. DatasetDict.info would be the shared info amongs the datasets (maybe even some info on how they differ). 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1686","title":"Dataset Error: DaNE contains empty samples at the end","comments":"One the PR is merged the fix will be available in the next release of `datasets`.\r\n\r\nIf you don't want to wait the next release you can still load the script from the master branch with\r\n\r\n```python\r\nload_dataset(\"dane\", script_version=\"master\")\r\n```","body":"The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors.\r\n\r\n```python\r\n>>> import datasets\r\n[...]\r\n>>> dataset = datasets.load_dataset(\"dane\")\r\n[...]\r\n>>> dataset[\"test\"][-1]\r\n{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}\r\n>>> dataset[\"train\"][-1]\r\n{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}\r\n```\r\n\r\nBest,\r\nKenneth","comment_length":40,"text":"Dataset Error: DaNE contains empty samples at the end \n The dataset DaNE, contains empty samples at the end. It is naturally easy to remove using a filter but should probably not be there, to begin with as it can cause errors.\r\n\r\n```python\r\n>>> import datasets\r\n[...]\r\n>>> dataset = datasets.load_dataset(\"dane\")\r\n[...]\r\n>>> dataset[\"test\"][-1]\r\n{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}\r\n>>> dataset[\"train\"][-1]\r\n{'dep_ids': [], 'dep_labels': [], 'lemmas': [], 'morph_tags': [], 'ner_tags': [], 'pos_tags': [], 'sent_id': '', 'text': '', 'tok_ids': [], 'tokens': []}\r\n```\r\n\r\nBest,\r\nKenneth \n One the PR is merged the fix will be available in the next release of `datasets`.\r\n\r\nIf you don't want to wait the next release you can still load the script from the master branch with\r\n\r\n```python\r\nload_dataset(\"dane\", script_version=\"master\")\r\n```","embeddings":[-0.1232687905,-0.1505773515,-0.2077827305,-0.0634229407,0.2767205834,0.1347071677,0.3861126602,0.3464493752,0.2240535468,0.2550488412,0.1427296549,0.2462115139,-0.1021410227,0.1205538809,-0.0594546609,-0.189043507,0.0712748617,0.2300376892,-0.2116483599,-0.2163271606,-0.1598127633,0.0545276366,-0.2814107239,-0.0294996072,-0.2529697418,-0.0086915977,-0.0732685253,0.000903021,-0.181566745,-0.339361459,0.1875905842,0.0553387143,-0.0221887324,0.472243011,-0.0000967098,0.0357762761,0.2746002972,0.0090655759,-0.2073474526,-0.1868923903,-0.2046446502,-0.1032754928,0.016476728,-0.2946446836,-0.0948273465,-0.119078815,-0.1217465252,-0.2512132227,0.2761532068,0.3788674176,0.3510321975,-0.0203607064,-0.1133409366,-0.233607173,0.0938120931,0.0186805166,0.0067269937,0.0080389073,0.1170281321,-0.158007279,0.1715448797,0.3733156025,-0.1356579363,-0.0398959704,0.1303146333,0.0744022503,0.3552424908,-0.396034658,0.1225465909,0.1647951901,0.476218909,-0.2359470278,-0.2052012682,-0.095294483,0.0928829834,-0.3406413198,0.0278559774,0.0826479718,-0.1892317683,0.1356574893,-0.1832286417,0.1252121031,-0.0296160169,0.1406727582,-0.22200495,0.2924418449,0.1040622592,0.0105551416,0.0352573544,-0.1616911292,-0.0072539877,-0.1755838096,-0.0992462188,0.1262255907,-0.1759640276,-0.178135559,0.1104308814,-0.3320736587,0.0922228247,0.2341314256,0.0549229272,-0.0728612691,0.0724771842,0.2050118595,0.3890073001,0.1582598388,0.1183600649,0.1860062629,0.0431001447,0.0392940342,-0.0792645365,0.0688089505,-0.0300588105,-0.3939440846,0.2165605873,0.0729623586,0.1235312074,-0.0328079984,-0.3163638711,0.1423445195,0.0572096668,0.105277501,-0.0099749016,0.3338144422,-0.073283188,0.1919693947,-0.0296997838,0.1066347957,-0.1325368583,0.0875127912,-0.3342106938,0.1476406306,-0.1924995184,-0.4847509265,0.1393510848,0.1399340928,0.259888798,0.1122575328,-0.0875671804,0.0437997393,0.2365788519,-0.2299486399,0.1596099138,0.2711679637,0.2072999328,0.1032985076,0.1049730405,-0.3105363846,-0.0968494788,0.1971830428,-0.2414344996,-0.1270575225,-0.2391119301,0.4493574202,-0.0192511845,-0.1367019564,-0.3514400125,0.1592812836,0.0225653257,-0.1922840178,-0.0744035766,-0.1213260442,-0.0971533135,-0.1482298672,0.3134791553,0.2801155448,-0.3103978038,-0.1445904672,0.0392432958,-0.1823048592,0.1620915234,0.2586766481,-0.1053339988,0.1341364384,-0.2297564,0.1065836996,0.3605087101,-0.3388747275,-0.1952200383,0.1234817058,-0.0189936142,-0.0715915188,-0.1197774783,-0.0189193822,0.0368326977,0.1500175595,0.1854106933,0.2362961918,-0.0505880937,-0.0243352931,-0.1854705513,-0.0553223044,0.2880092561,0.1449862272,0.09852916,0.035712488,-0.142558381,0.1447718889,0.2507312894,-0.0788625032,0.0050781532,0.4619210958,0.4397904277,0.0230416376,-0.0082344152,-0.0932403952,-0.1799519658,0.0302312542,0.1275368333,0.2458103448,-0.0125913695,-0.1820110828,-0.2893087268,-0.1439445317,-0.2131991386,-0.1518132538,0.3282752931,0.0149561418,-0.0991956368,0.0585254207,-0.2611064017,0.1758178025,-0.1464342922,0.1212603897,-0.2127426416,0.3394411504,-0.2513133585,0.0080370512,-0.0149493618,0.129990235,0.117881909,0.0251401346,-0.0241430234,0.259624362,0.1666731536,-0.0331909545,0.1301199645,-0.1600823551,0.0945522934,-0.1683371812,-0.1287045479,0.3237847686,0.1293809712,-0.0326088183,-0.2479771376,0.1031958833,0.106739305,0.1904259771,0.1538407505,0.0503829904,0.1857273877,-0.2024238408,0.1165678501,-0.2406700701,0.1367219537,-0.1152270287,0.1116154268,-0.0330059007,-0.1394121051,-0.0811002851,0.3397686183,0.0697710812,0.2045668215,0.1477720141,-0.1715923399,0.0466526486,0.0244666263,0.3108148575,0.2799334824,0.310554564,0.0669835135,0.0863323882,0.0967035815,-0.0259545837,0.1524069607,0.0576315708,-0.0488425605,0.267711997,0.0449269712,0.0368419029,-0.2552139163,-0.2440847903,0.0002030683,0.3363626897,-0.1759390831,-0.0267413799,-0.3138723075,-0.1280578077,0.0148754241,0.1995943338,-0.0780418813,-0.3177387714,0.2236334085,0.0047461521,-0.3061591089,0.2955164909,-0.0162079223,0.0966826603,0.1927609295,-0.0293254815,-0.1610547006,-0.2629687488,-0.234137401,0.2838199437,0.0835726261,0.2928678393,0.2347479761,-0.2635474801,-0.1672602892,-0.043928232,-0.4346902072,0.049269069,-0.1462500989,0.2544342577,0.2226761281,0.3057717383,-0.2177507579,-0.2630028725,0.2211736292,-0.2390861958,-0.2774768174,0.0902240425,0.1239492893,-0.0732543916,-0.2961527407,-0.5132988691,-0.2596700191,-0.3618588448,-0.1057882681,0.1025441512,-0.0004766732,0.0969894305,0.3217784166,0.0193617698,-0.0249998663,0.0289331451,-0.1692986488,-0.2669264674,0.3266661763,-0.2702935934,-0.3725543916,0.1384802908,-0.1809381992,0.2575161457,-0.2692220509,-0.4536553323,-0.1316346526,-0.0194885172,-0.0114393728,-0.0574130863,0.0379805006,0.1411522925,0.134551093,-0.2727219164,-0.1700938195,-0.0769099072,0.0196130797,-0.1201296896,0.2499282509,-0.2742264867,0.3223972023,0.0168702379,0.2349817008,0.2456393689,-0.175684765,0.073930636,-0.1861131936,0.2673369348,-0.1300089657,-0.3965242803,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1683","title":"`ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext","comments":"Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.\r\n\r\nTo fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two.","body":"It seems to fail the final batch ):\r\n\r\nsteps to reproduce:\r\n```\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nimport torch\r\nfrom transformers import file_utils, set_seed\r\nfrom transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast\r\nMAX_SEQ_LENGTH = 256\r\nctx_encoder = DPRContextEncoder.from_pretrained(\"facebook\/dpr-ctx_encoder-single-nq-base\", cache_dir=\"..\/datasets\/\")\r\nctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(\r\n    \"facebook\/dpr-ctx_encoder-single-nq-base\", \r\n    cache_dir=\"..datasets\/\"\r\n)\r\n\r\ndataset = load_dataset('text', \r\n                data_files='data\/raw\/ARC_Corpus.txt',\r\n                cache_dir='..\/datasets')\r\n\r\ntorch.set_grad_enabled(False)\r\nds_with_embeddings = dataset.map(\r\n    lambda example: {\r\n        'embeddings': ctx_encoder(\r\n            **ctx_tokenizer(\r\n                example[\"text\"], \r\n                padding='max_length', \r\n                truncation=True, \r\n                max_length=MAX_SEQ_LENGTH,\r\n                return_tensors=\"pt\"\r\n            )\r\n        )[0][0].numpy(),\r\n    },\r\n    batched=True,\r\n    load_from_cache_file=False,\r\n    batch_size=1000\r\n)\r\n```\r\nARC Corpus can be obtained from [here](https:\/\/ai2-datasets.s3-us-west-2.amazonaws.com\/arc\/ARC-V1-Feb2018.zip)\r\n\r\nAnd then the error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-13-67d139bb2ed3> in <module>\r\n     14     batched=True,\r\n     15     load_from_cache_file=False,\r\n---> 16     batch_size=1000\r\n     17 )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1257                 fn_kwargs=fn_kwargs,\r\n   1258                 new_fingerprint=new_fingerprint,\r\n-> 1259                 update_data=update_data,\r\n   1260             )\r\n   1261         else:\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    155         }\r\n    156         # apply actual function\r\n--> 157         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    158         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    159         # re-apply format to the output\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    161             # Call actual function\r\n    162 \r\n--> 163             out = func(self, *args, **kwargs)\r\n    164 \r\n    165             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n   1526                     if update_data:\r\n   1527                         batch = cast_to_python_objects(batch)\r\n-> 1528                         writer.write_batch(batch)\r\n   1529             if update_data:\r\n   1530                 writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    276             typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n    277             typed_sequence_examples[col] = typed_sequence\r\n--> 278         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    279         self.write_table(pa_table)\r\n    280 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_arrays()\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.validate()\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Column 1 named text expected length 768 but got length 1000\r\n```","comment_length":68,"text":"`ArrowInvalid` occurs while running `Dataset.map()` function for DPRContext \n It seems to fail the final batch ):\r\n\r\nsteps to reproduce:\r\n```\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nimport torch\r\nfrom transformers import file_utils, set_seed\r\nfrom transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast\r\nMAX_SEQ_LENGTH = 256\r\nctx_encoder = DPRContextEncoder.from_pretrained(\"facebook\/dpr-ctx_encoder-single-nq-base\", cache_dir=\"..\/datasets\/\")\r\nctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(\r\n    \"facebook\/dpr-ctx_encoder-single-nq-base\", \r\n    cache_dir=\"..datasets\/\"\r\n)\r\n\r\ndataset = load_dataset('text', \r\n                data_files='data\/raw\/ARC_Corpus.txt',\r\n                cache_dir='..\/datasets')\r\n\r\ntorch.set_grad_enabled(False)\r\nds_with_embeddings = dataset.map(\r\n    lambda example: {\r\n        'embeddings': ctx_encoder(\r\n            **ctx_tokenizer(\r\n                example[\"text\"], \r\n                padding='max_length', \r\n                truncation=True, \r\n                max_length=MAX_SEQ_LENGTH,\r\n                return_tensors=\"pt\"\r\n            )\r\n        )[0][0].numpy(),\r\n    },\r\n    batched=True,\r\n    load_from_cache_file=False,\r\n    batch_size=1000\r\n)\r\n```\r\nARC Corpus can be obtained from [here](https:\/\/ai2-datasets.s3-us-west-2.amazonaws.com\/arc\/ARC-V1-Feb2018.zip)\r\n\r\nAnd then the error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-13-67d139bb2ed3> in <module>\r\n     14     batched=True,\r\n     15     load_from_cache_file=False,\r\n---> 16     batch_size=1000\r\n     17 )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/dataset_dict.py in <dictcomp>(.0)\r\n    301                     num_proc=num_proc,\r\n    302                 )\r\n--> 303                 for k, dataset in self.items()\r\n    304             }\r\n    305         )\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1257                 fn_kwargs=fn_kwargs,\r\n   1258                 new_fingerprint=new_fingerprint,\r\n-> 1259                 update_data=update_data,\r\n   1260             )\r\n   1261         else:\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    155         }\r\n    156         # apply actual function\r\n--> 157         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    158         datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n    159         # re-apply format to the output\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    161             # Call actual function\r\n    162 \r\n--> 163             out = func(self, *args, **kwargs)\r\n    164 \r\n    165             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n   1526                     if update_data:\r\n   1527                         batch = cast_to_python_objects(batch)\r\n-> 1528                         writer.write_batch(batch)\r\n   1529             if update_data:\r\n   1530                 writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    276             typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n    277             typed_sequence_examples[col] = typed_sequence\r\n--> 278         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    279         self.write_table(pa_table)\r\n    280 \r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_arrays()\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.validate()\r\n\r\n~\/.cache\/pypoetry\/virtualenvs\/masters-utTTC0p8-py3.7\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Column 1 named text expected length 768 but got length 1000\r\n``` \n Looks like the mapping function returns a dictionary with a 768-dim array in the `embeddings` field. Since the map is batched, we actually expect the `embeddings` field to be an array of shape (batch_size, 768) to have one embedding per example in the batch.\r\n\r\nTo fix that can you try to remove one of the `[0]` ? In my opinion you only need one of them, not two.","embeddings":[-0.5113657713,-0.340737462,-0.1262720525,0.0608496368,0.0776377022,0.1417664886,-0.018712217,0.2698065937,-0.2622149587,0.0491960905,0.1108063161,0.5883788466,-0.0307827368,-0.1317675114,-0.4038944542,-0.0905743986,-0.0616570264,0.1219060645,0.2424558401,-0.089935638,-0.3668372035,0.0883089751,-0.2699972093,-0.1003229246,-0.1718577743,-0.2321972847,0.2313919067,-0.0377138183,0.003301251,-0.6815324426,0.6883983016,-0.2978188097,0.2429684848,0.1627676785,-0.0001183986,0.2131496072,0.4500014782,0.0111980177,-0.0065675713,0.080258362,-0.2403251082,0.194777444,0.0855849758,-0.1638190746,0.0858565792,-0.0353686213,-0.0441782735,-0.427708596,0.3808458447,0.195334971,0.1014310941,0.2174219787,0.0514748059,-0.0151883606,-0.0687452629,0.1960485876,-0.1333302259,-0.2255924344,0.2797706425,0.1810365021,0.1458378285,0.5812528729,-0.0482616462,0.2692404985,0.1576283872,-0.1972575933,0.0579736903,-0.3331651092,0.1915374994,-0.1121736318,0.2094191313,-0.2229378819,-0.2514075637,0.1366557777,-0.1958025694,-0.3983700573,0.1623699516,-0.1368175,0.051956825,0.036535617,-0.1455416232,0.139839381,-0.2271478921,0.24815014,-0.1595502347,0.158727631,-0.0363634005,0.3123621643,-0.0161653459,-0.1319190264,0.1796032041,0.1299351603,-0.1233132333,0.0910461843,-0.2346746773,-0.1503433883,-0.2761262655,-0.3692839444,0.0213410761,-0.1542137265,-0.1089864597,0.1875861883,0.540751636,0.1412303448,0.0859375447,0.1132425293,0.242615208,0.8317361474,-0.0535715595,-0.6531259418,0.0678203776,0.0701442957,-0.0338044018,-0.3524084985,0.256862551,0.1501419544,0.1474722475,0.0349667855,-0.279946506,0.1407098919,-0.438165009,0.0077407854,-0.2134210616,0.3998953104,0.2957156003,-0.1497726738,0.2224410474,0.1331149936,0.1421479881,-0.1057698429,-0.0059415619,-0.0893860981,-0.1892879009,-0.1479352415,0.1425799429,-0.1967147738,0.2172679454,-0.2264876217,-0.0113379322,-0.0850389674,0.3367851079,0.0655955449,0.4326565266,0.2180086821,0.0231516995,0.2297055721,-0.0104063805,0.1910956949,-0.1135064363,-0.0793193206,-0.1578666717,-0.3309911489,-0.3654819429,0.1561720073,0.3952356279,-0.028295733,-0.0704108402,0.0058605992,0.5582671165,-0.0122786565,0.3374227583,-0.2378217578,0.1193173453,0.0060363831,-0.0863270462,0.184869349,-0.3499477208,-0.0525056981,0.0317243487,0.280678153,0.0482468344,0.0491322652,-0.3638934791,0.0915571451,-0.1223412901,0.5995523334,0.4406459033,-0.7461100221,-0.3960719705,0.5548933744,0.0000231852,-0.2747846246,-0.0202935971,-0.0612332262,0.4194323719,-0.1286513954,0.1339125484,0.0815142617,-0.0028018788,0.0602680556,-0.0769046396,0.0175310653,0.2058888823,0.130353868,0.0866857395,-0.0168716852,0.2668988109,-0.0338122919,-0.0874593481,0.0137310801,0.3313800395,0.4629959762,-0.1180306226,-0.1043538675,0.2504345775,0.0164172519,-0.3472789526,0.1601376832,-0.1680821031,-0.0168550368,-0.343441695,-0.2692348957,-0.1579743922,0.1876064539,-0.3379364312,-0.2994133234,0.0545108058,0.0237101484,-0.023135744,-0.1506970078,-0.2348758578,-0.3109050393,-0.0031263814,0.0129285213,-0.371036768,-0.081058912,-0.1028760746,-0.1666499376,-0.2229582369,-0.0823807046,0.1944281906,-0.1868701428,-0.2059344798,0.3810172379,-0.3576883078,0.1575224847,-0.2036373615,0.0353445709,0.0965872109,-0.7572340965,-0.1935379505,-0.0853928477,0.0226561949,-0.1436367929,0.2973734736,0.0332045108,0.4531671405,0.32563591,-0.2301132083,0.0423419885,0.1064608321,-0.0633365661,-0.1695415229,-0.2364452034,-0.1860896647,-0.1981634051,0.1607283205,-0.0286631752,-0.1010631099,0.033602152,0.1005503759,0.032848306,0.051831156,0.0886938199,-0.3016294837,0.1139409393,0.2114739269,0.1100961193,0.1420832723,0.0793418661,0.0846390128,0.0585502237,-0.0404893346,0.0700683594,0.3686750531,0.2179716974,-0.0281671304,0.257704705,0.0106333932,0.315379262,-0.256785363,-0.225231126,0.2095829397,0.0708614513,-0.2471293062,0.1702718586,-0.1032790393,-0.1220331639,0.0348452218,-0.1799590886,0.2601111531,-0.0945540741,-0.0504049994,0.0102419816,-0.061194364,0.4547729194,-0.0581415668,-0.0782623664,0.3865163326,-0.1864851266,-0.2859807014,-0.1884989291,-0.2462268472,-0.1040349305,0.0507002547,0.0092101879,0.2503574491,0.1913103312,0.0721683577,-0.3318425417,-0.2322167158,0.3374351859,-0.0510996468,-0.3957129419,0.1137748212,-0.2494402528,-0.382923454,-0.4515089691,0.1395195872,0.1534736603,-0.2197473049,0.1152105033,-0.1087608859,-0.22306256,0.1489222348,-0.5410323143,-0.2287052721,-0.2145576924,-0.0828818455,-0.3405638635,-0.0386743769,0.182748735,0.1774793565,0.0308759715,-0.0209289491,-0.0506052971,-0.1819638908,0.199599281,0.2329193056,-0.3783580363,0.0198900681,-0.0408562608,-0.1620057225,0.3158453703,0.5299435854,-0.1742092669,0.4087524414,-0.0935854092,0.0415871069,-0.3694301844,0.1419584006,0.5250942707,0.2435423434,-0.0890006721,-0.0797964483,0.1214501634,0.0565610826,-0.1646126956,0.2331374139,-0.1432997137,0.5282084942,0.0230465196,0.7464090586,0.1865098327,-0.6531713605,0.3472135961,-0.0085074985,-0.0273385886,0.0829499215,-0.1887917072,-0.094296217,-0.1831742972,0.08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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1681","title":"Dataset \"dane\" missing","comments":"Hi @KennethEnevoldsen ,\r\nI think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git@master","body":"the `dane` dataset appear to be missing in the latest version (1.1.3).\r\n\r\n```python\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.3'\r\n>>> \"dane\" in datasets.list_datasets()\r\nTrue\r\n```\r\n\r\nAs we can see it should be present, but doesn't seem to be findable when using `load_dataset`.\r\n\r\n```python\r\n>>> datasets.load_dataset(\"dane\")\r\nTraceback (most recent call last):\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dane\/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dane\/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 588, in load_dataset\r\n    module_path, hash = prepare_module(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 280, in prepare_module\r\n    raise FileNotFoundError(\r\nFileNotFoundError: Couldn't find file locally at dane\/dane.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dane\/dane.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dane\/dane.py\r\n```\r\n\r\nThis issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.","comment_length":56,"text":"Dataset \"dane\" missing \n the `dane` dataset appear to be missing in the latest version (1.1.3).\r\n\r\n```python\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.3'\r\n>>> \"dane\" in datasets.list_datasets()\r\nTrue\r\n```\r\n\r\nAs we can see it should be present, but doesn't seem to be findable when using `load_dataset`.\r\n\r\n```python\r\n>>> datasets.load_dataset(\"dane\")\r\nTraceback (most recent call last):\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dane\/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dane\/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 588, in load_dataset\r\n    module_path, hash = prepare_module(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 280, in prepare_module\r\n    raise FileNotFoundError(\r\nFileNotFoundError: Couldn't find file locally at dane\/dane.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dane\/dane.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dane\/dane.py\r\n```\r\n\r\nThis issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data. \n Hi @KennethEnevoldsen ,\r\nI think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of datasets.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of datasets using pip:\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git@master","embeddings":[-0.0066535207,-0.0612563305,-0.0992998853,0.1284519881,0.2658341527,0.2934038043,0.4646624029,0.1487023532,0.2840229869,0.0827282742,0.1303288788,0.0408590771,-0.0920234174,-0.1245878786,0.2971785367,-0.2793469429,0.2202173769,0.0512977093,0.1837167591,-0.1475976259,-0.2369974256,0.254214108,-0.2888587415,-0.090038985,-0.0924229249,0.0823839828,-0.101888001,0.139782533,-0.0649531037,-0.6014867425,0.642777741,0.0417708345,0.3791090846,0.4024262726,-0.0001110658,0.0540831015,0.2432838678,0.0235112887,-0.4869872332,-0.3152087033,-0.6912311316,-0.4789561927,0.1658877283,-0.1466356218,-0.0642058998,0.1301175803,0.0734554678,-0.4600993395,0.0440699533,0.2021569163,0.2513066232,0.0974107385,0.1838599741,-0.2588645518,0.0307783745,0.2624579072,-0.1520386636,0.1597358584,0.1850463301,0.198756665,0.3487948477,0.307885021,0.148994565,0.0193946417,0.2877072096,0.1553142667,0.2871349752,-0.1641148031,-0.0070116892,0.0631371737,0.6540952325,-0.140773505,-0.7040755749,-0.3533023298,0.1485740989,-0.3803811371,0.1837790608,-0.1397375762,-0.1242031157,0.1525912434,-0.2222140729,-0.2455667406,0.0295432061,0.2610390186,-0.2916905284,0.3245614767,-0.1398500353,-0.021707261,-0.2066091746,-0.0592098124,-0.0247959569,-0.1159208044,0.1139637828,0.2741007805,-0.2491634041,-0.0674469098,0.1185003966,-0.2061502486,-0.0327990986,0.1498194188,0.0173948165,-0.171906203,-0.0063734083,0.1006967723,0.353479445,0.2497101575,0.1984507889,-0.0159756802,0.257517904,0.3605662584,0.1864643693,-0.1657250971,-0.2060384154,-0.2400937676,0.2269962579,0.0962073132,0.2703241408,-0.0575917736,-0.2305234224,-0.0208626539,0.3652468622,-0.1499254704,-0.0439337641,0.1124223247,-0.0977388695,0.3643301725,-0.0803220347,0.1735870242,-0.1319596618,0.031080192,-0.248398155,-0.0731434748,-0.0761916637,-0.3178832531,0.2205925286,-0.1794002652,0.4029653072,-0.0104438094,-0.2701882422,0.1550481021,-0.0678124875,0.0290778093,-0.1138985455,0.2672907114,-0.0218402129,0.1548041701,0.0522527695,-0.4291962683,-0.1739230156,-0.0561629161,-0.2590444982,-0.1839904487,-0.5109384656,0.2261655629,-0.1876500249,-0.3296757936,-0.4136569798,0.1709020734,-0.0866976231,-0.2888192534,-0.0393894129,-0.1422567815,-0.3087976575,-0.1636295021,0.3612360954,0.4254740477,-0.3754051328,-0.2220775485,-0.2986675799,-0.1379340887,0.0524830744,0.0378002822,-0.1871097982,0.1209383085,-0.2908596396,-0.1904940009,0.5775368214,-0.5136830807,-0.2738463879,0.0612903386,0.1417578012,0.1254551262,0.0390238091,-0.1962939799,0.0934103876,-0.0833633393,0.0603376888,0.4086598456,0.0167406127,-0.1915555745,0.0878219157,-0.0913955644,-0.0085834293,0.2126212865,0.1517920941,0.0569025017,0.1812868565,0.076675944,0.1600443721,0.0915104002,0.0494675525,0.4845329821,0.1955732405,0.3065835238,0.038520325,-0.0549055301,-0.5542677641,0.4240736961,0.3048123419,-0.1309112906,-0.1590324044,0.033567857,-0.2935138941,-0.0039210613,-0.2610232234,-0.1701722294,0.0945450142,0.2176745832,0.1462025344,0.2157948613,-0.3023059666,0.1623969078,0.1202042028,0.2067893296,-0.2911315262,0.1884755641,-0.0311723724,0.044327125,0.022034185,0.2709725797,0.1022873223,-0.0330045931,-0.0559417345,0.1777423471,0.1901222318,0.2124840021,0.2953181863,-0.1923425347,0.2499668002,-0.0279774927,0.129624784,0.0328726359,0.0824973509,0.0115447761,-0.2562762201,0.0155946324,0.1473733485,0.1286751181,0.0872903168,0.253621012,0.2938048542,-0.0222274382,0.3696301281,-0.3660769165,0.0822673589,-0.1461671442,0.4610933959,-0.1435316205,-0.0517950356,0.1415269822,0.343142122,-0.0648482218,0.0488349125,0.2475116551,-0.2129967809,0.0466700569,0.1423934847,0.5272797942,0.6025570035,0.2355483919,0.3136324883,-0.1858260036,0.0877967328,-0.0984764025,0.1036738679,0.1213465407,0.0915519297,-0.0236821659,0.1333335042,0.0904912278,-0.1985484511,-0.1832408756,0.0555040538,0.1326709241,-0.0741935521,0.063425824,-0.368658036,-0.0351405106,-0.125416398,0.1148936823,-0.2466208041,-0.4466947913,-0.0464512855,-0.0794595033,0.0844679847,0.0710717216,-0.1870524734,0.0809297487,-0.101423122,-0.084213011,-0.2959759533,-0.1488332897,-0.2996692359,0.0720545575,0.0946173742,-0.1181530878,0.1933088899,-0.3682515919,0.1760058254,-0.4141762555,-0.4692050815,-0.0031071687,-0.0848735571,0.3349460959,0.4064882696,0.026953673,-0.2145118117,-0.1444413215,0.2156667858,-0.2421434224,-0.1893730909,0.0909964368,-0.0489478149,-0.0040461635,0.0551481545,-0.2715997994,-0.5440988541,-0.2131244242,0.0858798698,0.2569324374,-0.0612959377,0.1413228214,0.0455826446,0.1604282856,-0.0533168055,0.3135327399,-0.205416441,-0.4513518512,0.3560549021,-0.2946029305,-0.2575352192,0.2806711495,-0.1606235802,0.4164973199,-0.2432595491,-0.5850550532,0.3026738763,-0.1646489948,0.2575046718,-0.0604185946,0.18009606,0.3521588147,-0.0163948424,-0.0678970367,-0.2659996748,-0.3340036273,-0.2347367257,-0.0102192378,0.4603375196,-0.096155718,0.3872702122,0.0390981138,0.5496211052,0.1955940872,-0.0551208816,0.1557836682,-0.0753046945,0.3423407078,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1681","title":"Dataset \"dane\" missing","comments":"The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dane` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dane\")\r\n```","body":"the `dane` dataset appear to be missing in the latest version (1.1.3).\r\n\r\n```python\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.3'\r\n>>> \"dane\" in datasets.list_datasets()\r\nTrue\r\n```\r\n\r\nAs we can see it should be present, but doesn't seem to be findable when using `load_dataset`.\r\n\r\n```python\r\n>>> datasets.load_dataset(\"dane\")\r\nTraceback (most recent call last):\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dane\/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dane\/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 588, in load_dataset\r\n    module_path, hash = prepare_module(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 280, in prepare_module\r\n    raise FileNotFoundError(\r\nFileNotFoundError: Couldn't find file locally at dane\/dane.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dane\/dane.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dane\/dane.py\r\n```\r\n\r\nThis issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data.","comment_length":52,"text":"Dataset \"dane\" missing \n the `dane` dataset appear to be missing in the latest version (1.1.3).\r\n\r\n```python\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.3'\r\n>>> \"dane\" in datasets.list_datasets()\r\nTrue\r\n```\r\n\r\nAs we can see it should be present, but doesn't seem to be findable when using `load_dataset`.\r\n\r\n```python\r\n>>> datasets.load_dataset(\"dane\")\r\nTraceback (most recent call last):\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dane\/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dane\/dane.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 588, in load_dataset\r\n    module_path, hash = prepare_module(\r\n  File \"\/home\/kenneth\/.Envs\/EDP\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 280, in prepare_module\r\n    raise FileNotFoundError(\r\nFileNotFoundError: Couldn't find file locally at dane\/dane.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dane\/dane.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dane\/dane.py\r\n```\r\n\r\nThis issue might be relevant to @ophelielacroix from the Alexandra Institut whom created the data. \n The `dane` dataset was added recently, that's why it wasn't available yet. We did an intermediate release today just before the v2.0.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dane` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dane\")\r\n```","embeddings":[-0.0066535207,-0.0612563305,-0.0992998853,0.1284519881,0.2658341527,0.2934038043,0.4646624029,0.1487023532,0.2840229869,0.0827282742,0.1303288788,0.0408590771,-0.0920234174,-0.1245878786,0.2971785367,-0.2793469429,0.2202173769,0.0512977093,0.1837167591,-0.1475976259,-0.2369974256,0.254214108,-0.2888587415,-0.090038985,-0.0924229249,0.0823839828,-0.101888001,0.139782533,-0.0649531037,-0.6014867425,0.642777741,0.0417708345,0.3791090846,0.4024262726,-0.0001110658,0.0540831015,0.2432838678,0.0235112887,-0.4869872332,-0.3152087033,-0.6912311316,-0.4789561927,0.1658877283,-0.1466356218,-0.0642058998,0.1301175803,0.0734554678,-0.4600993395,0.0440699533,0.2021569163,0.2513066232,0.0974107385,0.1838599741,-0.2588645518,0.0307783745,0.2624579072,-0.1520386636,0.1597358584,0.1850463301,0.198756665,0.3487948477,0.307885021,0.148994565,0.0193946417,0.2877072096,0.1553142667,0.2871349752,-0.1641148031,-0.0070116892,0.0631371737,0.6540952325,-0.140773505,-0.7040755749,-0.3533023298,0.1485740989,-0.3803811371,0.1837790608,-0.1397375762,-0.1242031157,0.1525912434,-0.2222140729,-0.2455667406,0.0295432061,0.2610390186,-0.2916905284,0.3245614767,-0.1398500353,-0.021707261,-0.2066091746,-0.0592098124,-0.0247959569,-0.1159208044,0.1139637828,0.2741007805,-0.2491634041,-0.0674469098,0.1185003966,-0.2061502486,-0.0327990986,0.1498194188,0.0173948165,-0.171906203,-0.0063734083,0.1006967723,0.353479445,0.2497101575,0.1984507889,-0.0159756802,0.257517904,0.3605662584,0.1864643693,-0.1657250971,-0.2060384154,-0.2400937676,0.2269962579,0.0962073132,0.2703241408,-0.0575917736,-0.2305234224,-0.0208626539,0.3652468622,-0.1499254704,-0.0439337641,0.1124223247,-0.0977388695,0.3643301725,-0.0803220347,0.1735870242,-0.1319596618,0.031080192,-0.248398155,-0.0731434748,-0.0761916637,-0.3178832531,0.2205925286,-0.1794002652,0.4029653072,-0.0104438094,-0.2701882422,0.1550481021,-0.0678124875,0.0290778093,-0.1138985455,0.2672907114,-0.0218402129,0.1548041701,0.0522527695,-0.4291962683,-0.1739230156,-0.0561629161,-0.2590444982,-0.1839904487,-0.5109384656,0.2261655629,-0.1876500249,-0.3296757936,-0.4136569798,0.1709020734,-0.0866976231,-0.2888192534,-0.0393894129,-0.1422567815,-0.3087976575,-0.1636295021,0.3612360954,0.4254740477,-0.3754051328,-0.2220775485,-0.2986675799,-0.1379340887,0.0524830744,0.0378002822,-0.1871097982,0.1209383085,-0.2908596396,-0.1904940009,0.5775368214,-0.5136830807,-0.2738463879,0.0612903386,0.1417578012,0.1254551262,0.0390238091,-0.1962939799,0.0934103876,-0.0833633393,0.0603376888,0.4086598456,0.0167406127,-0.1915555745,0.0878219157,-0.0913955644,-0.0085834293,0.2126212865,0.1517920941,0.0569025017,0.1812868565,0.076675944,0.1600443721,0.0915104002,0.0494675525,0.4845329821,0.1955732405,0.3065835238,0.038520325,-0.0549055301,-0.5542677641,0.4240736961,0.3048123419,-0.1309112906,-0.1590324044,0.033567857,-0.2935138941,-0.0039210613,-0.2610232234,-0.1701722294,0.0945450142,0.2176745832,0.1462025344,0.2157948613,-0.3023059666,0.1623969078,0.1202042028,0.2067893296,-0.2911315262,0.1884755641,-0.0311723724,0.044327125,0.022034185,0.2709725797,0.1022873223,-0.0330045931,-0.0559417345,0.1777423471,0.1901222318,0.2124840021,0.2953181863,-0.1923425347,0.2499668002,-0.0279774927,0.129624784,0.0328726359,0.0824973509,0.0115447761,-0.2562762201,0.0155946324,0.1473733485,0.1286751181,0.0872903168,0.253621012,0.2938048542,-0.0222274382,0.3696301281,-0.3660769165,0.0822673589,-0.1461671442,0.4610933959,-0.1435316205,-0.0517950356,0.1415269822,0.343142122,-0.0648482218,0.0488349125,0.2475116551,-0.2129967809,0.0466700569,0.1423934847,0.5272797942,0.6025570035,0.2355483919,0.3136324883,-0.1858260036,0.0877967328,-0.0984764025,0.1036738679,0.1213465407,0.0915519297,-0.0236821659,0.1333335042,0.0904912278,-0.1985484511,-0.1832408756,0.0555040538,0.1326709241,-0.0741935521,0.063425824,-0.368658036,-0.0351405106,-0.125416398,0.1148936823,-0.2466208041,-0.4466947913,-0.0464512855,-0.0794595033,0.0844679847,0.0710717216,-0.1870524734,0.0809297487,-0.101423122,-0.084213011,-0.2959759533,-0.1488332897,-0.2996692359,0.0720545575,0.0946173742,-0.1181530878,0.1933088899,-0.3682515919,0.1760058254,-0.4141762555,-0.4692050815,-0.0031071687,-0.0848735571,0.3349460959,0.4064882696,0.026953673,-0.2145118117,-0.1444413215,0.2156667858,-0.2421434224,-0.1893730909,0.0909964368,-0.0489478149,-0.0040461635,0.0551481545,-0.2715997994,-0.5440988541,-0.2131244242,0.0858798698,0.2569324374,-0.0612959377,0.1413228214,0.0455826446,0.1604282856,-0.0533168055,0.3135327399,-0.205416441,-0.4513518512,0.3560549021,-0.2946029305,-0.2575352192,0.2806711495,-0.1606235802,0.4164973199,-0.2432595491,-0.5850550532,0.3026738763,-0.1646489948,0.2575046718,-0.0604185946,0.18009606,0.3521588147,-0.0163948424,-0.0678970367,-0.2659996748,-0.3340036273,-0.2347367257,-0.0102192378,0.4603375196,-0.096155718,0.3872702122,0.0390981138,0.5496211052,0.1955940872,-0.0551208816,0.1557836682,-0.0753046945,0.3423407078,0.0128896516,-0.3261141777,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1679","title":"Can't import cc100 dataset","comments":"cc100 was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `cc100` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nlang = \"en\"\r\ndataset = load_dataset(\"cc100\", lang=lang, split=\"train\")\r\n```","body":"There is some issue to import cc100 dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"cc100\")\r\n```\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/cc100\/cc100.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/cc100\/cc100.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    280                 raise FileNotFoundError(\r\n    281                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 282                         combined_path, github_file_path, file_path\r\n    283                     )\r\n    284                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at cc100\/cc100.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/cc100\/cc100.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/cc100\/cc100.py","comment_length":45,"text":"Can't import cc100 dataset \n There is some issue to import cc100 dataset.\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"cc100\")\r\n```\r\n\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/cc100\/cc100.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/cc100\/cc100.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    280                 raise FileNotFoundError(\r\n    281                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 282                         combined_path, github_file_path, file_path\r\n    283                     )\r\n    284                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at cc100\/cc100.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/cc100\/cc100.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/cc100\/cc100.py \n cc100 was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `cc100` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nlang = \"en\"\r\ndataset = load_dataset(\"cc100\", lang=lang, split=\"train\")\r\n```","embeddings":[-0.2749530673,-0.3488157392,-0.1628864706,0.2345296443,0.376396805,0.1457457542,0.0886662602,0.0246397574,0.0158823952,0.2067761719,-0.1255749017,0.058414761,0.0227050297,0.3194364309,0.0983807594,-0.005402416,0.1296453923,0.1373708844,-0.3452748954,0.0146202436,-0.0807719454,0.2495070249,-0.085307695,-0.1574202925,-0.2753781974,-0.1641575098,-0.0514012128,0.1796361804,-0.3012573421,-0.4615064263,0.6025828719,0.1790027618,0.2557296753,0.4899493456,-0.0001045807,0.0461228415,0.3770821393,0.0239945892,-0.4418845177,-0.3079560697,-0.2751539052,-0.2639507353,0.1835988909,-0.0436674058,-0.1347506642,0.201985091,-0.1778894812,-0.2138003409,0.3010198772,0.0500014164,0.3266026676,0.3059599996,0.3633733094,-0.3747950196,0.0341681354,0.0937169716,0.0094008027,0.3512433767,0.2063264847,0.1514515132,0.5028682351,0.1674754173,-0.0955764651,-0.0340652615,0.2659811676,-0.1589617431,-0.1439435929,-0.366864562,0.1385574937,0.1350733191,0.5673771501,-0.2247601897,-0.3478521109,0.0083858827,0.1170568168,-0.5926403999,0.1050112918,0.110301204,0.1089515463,0.2087957114,-0.3087430894,-0.1390147954,-0.1013323516,0.2098259628,-0.1649814993,-0.0933538303,-0.320030123,-0.0166480467,0.1543783695,-0.1073016152,-0.1980895847,0.0044630994,-0.031594675,0.181926474,-0.2933140397,0.0186268166,0.0426845625,0.4454786181,0.1327247769,0.1679537594,0.0847056061,-0.0797265843,0.044417832,0.244242847,0.0643805116,0.1364514083,-0.0355199352,0.0517161451,0.2195052952,0.2635697722,-0.0347134359,-0.2701790929,-0.1346898377,-0.2716605067,-0.0493012518,0.0229159426,0.350949496,-0.2061715126,-0.2172701508,0.0527681075,0.2327941358,0.0468728989,0.136940673,0.5143303275,-0.003946661,0.1669722646,-0.0405057818,0.1405848414,-0.01133803,-0.0045871357,-0.2370131612,0.0864198953,-0.1366158724,-0.1119011864,0.2177563012,-0.5572939515,0.2780295312,0.1383111924,0.0646387264,0.0165715162,-0.1541118622,0.0250986814,-0.0274689011,0.2688960433,0.0131191416,0.1907918453,0.141219601,-0.0905668959,-0.1207038984,-0.1980187744,-0.4211934209,-0.1977618337,-0.2723000348,0.2675927579,-0.1505774111,-0.1318505704,-0.2584421933,-0.1919153333,-0.0768684968,-0.1178111583,-0.0770635381,-0.0027255749,-0.0963254198,-0.1600158215,0.2762827277,0.52097404,-0.3317390084,-0.0715729371,-0.181727618,-0.1196101457,0.0628939942,0.0969097689,-0.2397205383,-0.2497815043,-0.14684771,-0.3261528313,-0.1567421705,-0.5559664965,-0.4181702435,0.1911296546,-0.0677199587,0.0445034578,0.0309950747,-0.1653467119,-0.0856773779,-0.0136372391,-0.204861328,0.0122111384,-0.049923528,-0.0878971294,-0.0892958939,-0.2032905519,0.2163340598,-0.0259719323,-0.1203064099,-0.0841754079,0.2792485952,-0.178420037,0.0360985212,-0.1472934633,-0.0455059558,0.2831318378,0.28106758,0.1500968039,-0.0131318895,-0.096602425,-0.5498054028,0.1840482354,0.0591754019,0.2070122808,-0.2800960839,-0.1901307702,-0.4212655723,0.0353376679,-0.1500855237,-0.1384312958,0.1622018814,-0.0023704628,0.2669632435,0.0558734536,-0.1457965225,0.3959629834,-0.0193073023,0.2013513297,-0.2098923773,0.1347904652,-0.1404971182,0.1456405371,0.1808162928,0.209728241,0.1623943299,-0.1691637337,-0.0460646078,0.3943288028,-0.1744179726,0.530454576,0.0294824895,-0.0860908329,0.3313724697,-0.4779743254,-0.0278088786,-0.1411225796,0.277826339,0.0638917759,-0.0410025679,0.1373913735,-0.1340174973,0.2316181362,0.1290092617,0.0426906608,0.2338794917,0.1133559048,-0.0380368419,-0.1478625238,0.2096565962,0.0880584717,0.612246573,0.0224679932,-0.125888899,-0.047208406,0.1204791516,0.0573649704,-0.0698103905,0.0109027401,-0.3110825121,0.2613328695,0.2233102471,0.2125795633,0.1484139413,0.2743114829,-0.1790673435,-0.057594832,0.0863918066,-0.112077035,0.2854093909,0.0789457187,0.1874593347,-0.0491946973,0.130128026,0.0304458383,-0.2594585121,-0.1271762699,-0.032301113,0.146824643,-0.2639596164,0.090302065,-0.4293751419,-0.1814528555,-0.0166764092,-0.1330062002,-0.0983510911,-0.1650461107,-0.3125822842,0.122342065,0.0383008756,0.0193284359,-0.1376667917,0.0725121945,-0.1019553989,-0.1644335389,-0.1568349302,-0.3101901412,0.025382679,0.1606173515,0.3345532417,-0.0981037766,0.4525795281,-0.1812592149,0.1574148089,0.0079964912,-0.4218347967,0.1091921479,-0.2060811967,0.2028568983,0.2215285897,0.1929978579,-0.2877087295,-0.156739518,0.3035696149,-0.0503761843,-0.0871437639,0.0492058061,-0.1616818309,-0.3335810006,-0.0298849978,-0.2988216281,-0.5281748176,-0.3252052963,0.1262627095,0.2590164244,-0.0417268686,0.4280340672,0.1962852925,0.0906430706,0.0179838836,0.1230181158,-0.0773329362,-0.6638807654,0.4207583368,-0.3592381477,-0.4082060754,0.1353978068,0.0066997116,0.4727200568,-0.0671639889,-0.2308905274,0.0575049445,-0.2374916375,0.0838246197,0.2534953654,0.273425281,0.1750728786,-0.0735047385,-0.1103817448,-0.0898962766,-0.1655896604,-0.2545473576,-0.1683807671,0.1834418029,-0.1109104827,0.2447137386,-0.1323466897,0.6540396214,0.2099393904,-0.1649854034,0.3992759585,-0.1709168255,0.5142349005,-0.0351865031,-0.4970191121,-0.0146232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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1675","title":"Add the 800GB Pile dataset?","comments":"The pile dataset would be very nice.\r\nBenchmarks show that pile trained models achieve better results than most of actually trained models","body":"## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n","comment_length":22,"text":"Add the 800GB Pile dataset? \n ## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n \n The pile dataset would be very nice.\r\nBenchmarks show that pile trained models achieve better results than most of actually trained 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1675","title":"Add the 800GB Pile dataset?","comments":"The pile can very easily be added and adapted using this [tfds implementation](https:\/\/github.com\/EleutherAI\/The-Pile\/blob\/master\/the_pile\/tfds_pile.py) from the repo. \r\n\r\nHowever, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud Storage.","body":"## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n","comment_length":45,"text":"Add the 800GB Pile dataset? \n ## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n \n The pile can very easily be added and adapted using this [tfds implementation](https:\/\/github.com\/EleutherAI\/The-Pile\/blob\/master\/the_pile\/tfds_pile.py) from the repo. \r\n\r\nHowever, the question is whether you'd be ok with 800GB+ cached in your local disk, since the tfds implementation was designed to offload the storage to Google Cloud 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1675","title":"Add the 800GB Pile dataset?","comments":"With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)\r\nI'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards","body":"## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n","comment_length":35,"text":"Add the 800GB Pile dataset? \n ## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n \n With the dataset streaming feature (see #2375) it will be more convenient to play with such big datasets :)\r\nI'm currently adding C4 (see #2511 ) but I can probably start working on this afterwards","embeddings":[-0.4098331034,0.1263111085,-0.1624413729,0.1556787789,0.0171111673,0.1715144813,0.0879780725,0.2739042044,0.011103591,0.019485062,-0.1574598253,0.1408568323,-0.5226863027,0.192629233,0.1664162129,-0.0006090707,-0.0227244943,-0.0548107065,-0.1018029079,-0.0283942707,-0.042558752,-0.0505990051,0.0230189022,-0.4305403531,-0.2151899636,-0.1103021279,-0.1426184922,-0.0110516138,-0.3041887879,-0.2418607175,-0.2696644962,0.1471849829,0.4014956653,0.5093926191,-0.0000992309,-0.1877329946,0.1837464273,-0.1823872179,-0.3778296709,-0.1405142546,-0.0579623953,-0.3062309921,-0.2828385234,-0.0362581983,-0.2138492912,-0.1490485072,0.1165734082,-0.4075603187,0.0837146118,0.3522391617,0.2853194773,0.1292305589,0.028258143,-0.1187565252,0.3448964953,-0.0372173861,-0.1411593109,0.2165897042,0.3609104753,0.1932851821,-0.1349958479,0.42317608,0.3995490372,-0.1363987327,0.2003374249,-0.0137786493,-0.0137635078,-0.1184780449,-0.0939949825,0.3289185464,0.3396664262,-0.1999338567,-0.416947186,-0.3320512474,0.0004093489,-0.4861880541,-0.1864102781,0.2802343667,0.0130024422,0.2654020786,0.0118499016,-0.2891337276,-0.1023034602,0.2159856856,0.0264109615,0.2852253318,-0.0916785821,-0.1814775318,0.1847205013,-0.0452431478,-0.1685019284,0.0620114021,-0.0701507702,0.0169383176,-0.3413780332,-0.2053023726,0.3815687597,0.3479739726,0.4029490054,-0.2013194859,-0.0007641301,0.0900324583,0.0076760538,-0.0363318399,0.4183678627,0.0228529926,-0.2576278746,-0.2429987937,0.3162668347,0.0615197308,0.0845505148,0.0780184045,-0.0812592208,-0.109158121,-0.1546163857,-0.1037386954,-0.131176576,0.1341690719,0.024996819,-0.0110356547,0.2473470122,-0.0406346321,-0.0132628605,0.1060569137,-0.0132587207,0.2237422913,0.0462209955,0.179082036,0.1645918041,-0.5682691932,-0.2182086557,0.1096915454,-0.4494864643,-0.0582360178,0.1746323854,-0.0966311768,0.0979664624,-0.093815811,0.2153852135,0.2556799948,0.1541841477,-0.032111045,0.0867116377,-0.0207196753,-0.0739556402,-0.219107002,0.0245267358,-0.0203439575,-0.2771773338,0.0620876178,0.008937764,-0.2364451438,-0.3791476786,0.2482879758,-0.1052084714,0.0647910833,-0.3975297511,0.6843155026,-0.0518117882,0.2303264588,0.0298150163,-0.0720965415,0.1268395782,0.0094611244,0.0543078631,0.1860060841,-0.5823349953,-0.0688423142,-0.6220449209,0.1332652271,-0.048302792,0.23295708,-0.1152959913,0.0331350043,-0.1026658043,-0.0032313957,0.5506247878,0.0903272852,-0.1896681786,0.1784516424,-0.0943489075,-0.0666787103,0.0360305645,0.166351527,0.1789021492,-0.0276673492,0.2125173658,0.3831298649,-0.1318815202,-0.0458412506,-0.2773420811,-0.3492940664,0.0065038488,0.4455781579,0.0955940038,-0.3235457242,0.2116486728,0.0790048391,0.2011428326,-0.0387859792,0.1662488878,-0.0941343755,0.3228883147,0.0941027626,-0.0326916911,-0.2544544935,-0.4213958085,0.0129303262,0.0664446577,0.3924232721,0.0327239223,-0.2007573843,0.1176968887,-0.0324536301,0.0745065138,-0.0610411279,0.24777475,0.0449669287,0.0116757331,-0.1699575335,-0.2375018448,-0.0224223435,-0.0895086527,-0.1390724927,-0.2041968554,-0.0277161859,-0.211376071,-0.1865868419,0.0991932601,0.2034651339,-0.3931650221,-0.1438767463,0.1542894244,0.1564688385,-0.0995218977,-0.1483729929,0.1739114374,0.589936018,0.2877263427,-0.1312032491,0.2223951221,-0.0966124907,-0.0549186617,0.0523668528,-0.1657312959,0.3457504213,0.2843394279,-0.0630708039,0.1501615494,-0.1299120188,0.2165811807,0.0404009894,0.0111626014,-0.3215346336,0.2626162469,0.017928483,0.2361111343,-0.0284166448,-0.378952831,0.1693694144,0.380448252,0.1784754843,0.2226568162,0.2837972939,-0.2051712424,-0.285794735,0.041163601,-0.2445848286,0.1751973927,0.4254031181,0.2662835717,0.0939711854,0.1909112632,-0.1473923177,0.0472895131,-0.0415800102,-0.0142360535,0.2097346634,0.3988668919,-0.0117707476,-0.3059681058,-0.2796691954,-0.0838693455,0.1702593267,0.125722155,-0.1005367264,-0.0214955118,-0.1116395518,0.1045796573,0.0312195905,-0.2022458166,-0.1765328199,0.1887108237,-0.0844262689,-0.2668888867,0.0207093898,-0.1601111591,0.4609529376,0.0269854292,0.1990112662,0.0462487005,-0.2685699463,-0.1023698896,0.2859627306,0.2906585038,-0.256511718,0.3059352338,0.0507842563,0.1975107938,-0.1086905003,-0.5585720539,0.2651889622,-0.0712607875,0.3164297044,0.2004011571,0.0077922037,0.1900359094,-0.0581640974,-0.1472811252,-0.2070458978,-0.0193798356,-0.153292194,-0.0415223315,0.030775249,-0.2517190576,-0.3398703933,-0.4018904567,-0.5428557396,0.2682358325,0.0710560605,0.0720275193,-0.004570663,0.1488371491,0.3448063731,0.0270824581,0.0566587001,-0.1289211065,-0.3212639987,0.3212068081,-0.149311319,-0.240281716,-0.0765934363,-0.2067047805,-0.0238753948,0.0814590231,-0.501999557,0.1597308815,-0.1152676418,0.2250425965,0.0298296846,0.0394681692,0.1675286591,-0.0220976397,-0.2421857864,-0.0792160332,0.0473500453,0.1114284396,0.3575184047,0.330948025,-0.244711563,0.2910519838,0.2060319632,0.3839906454,-0.0426986925,-0.2878244817,0.406321615,0.3795241117,0.3640135825,-0.2093789876,-0.0007664657,0.2635859549,0.08078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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1675","title":"Add the 800GB Pile dataset?","comments":"Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!","body":"## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n","comment_length":47,"text":"Add the 800GB Pile dataset? \n ## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n \n Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!","embeddings":[-0.2462544739,0.1785694659,-0.1075271368,0.2075333744,-0.0628189445,0.2498984635,0.1501436085,0.1807032526,0.1075051203,-0.0014590707,-0.3454721272,0.0975166634,-0.4746151567,0.2544946969,0.1928560734,-0.0652398095,-0.032202214,-0.2071850151,-0.1370310485,-0.1494104564,-0.06065806,-0.0598101765,0.0282126125,-0.3862361908,-0.1723033935,0.004146033,-0.3139385283,0.0742300451,-0.3200513422,-0.1177404895,-0.1273551136,0.1806448847,0.432210654,0.5957205296,-0.0001031671,-0.1962912828,0.024265008,-0.1736913919,-0.3333541155,-0.1857631207,-0.0035195744,-0.3440428674,-0.4653924406,0.0684908777,-0.1177530363,-0.2050124109,0.12677145,-0.3534972966,-0.0913880542,0.1521177739,0.2241453081,0.149783507,0.0541178472,-0.1607087702,0.4485835135,0.1492411792,-0.130315721,0.273645699,0.3025313914,0.041326385,-0.0410155579,0.3837419152,0.4280636907,-0.1955244541,0.2748353779,-0.0034523201,-0.0380527973,-0.1331808567,-0.1561854482,0.3036518991,0.3889554739,-0.1051894799,-0.487639159,-0.3463985026,-0.107987918,-0.315996021,-0.2576558292,0.412088871,0.0678864419,0.2484545857,0.1746350676,-0.3019080758,-0.1186246648,0.2390800864,-0.0445908681,0.3197979033,-0.0408892706,-0.1356092095,0.1942106783,-0.0039509782,-0.0605598278,-0.0825138167,-0.1060341001,0.1061292589,-0.261744678,-0.1970933378,0.33981511,0.360209614,0.4732288718,-0.343878895,-0.1671775728,0.0080113756,-0.1477238685,-0.0648703426,0.5987275839,-0.0423334874,-0.3046991527,-0.2190734744,0.2182767987,0.0477403998,0.1607235819,0.0447870195,-0.0993976444,-0.0348435156,-0.2174886316,-0.2602546215,-0.1236837283,-0.0555649623,0.0519261882,-0.117215611,0.1994657964,-0.1382020265,-0.0506327599,0.1108685061,0.1273375005,0.2053894997,-0.0192008335,0.2568579912,0.121724695,-0.4795180261,-0.1827813089,0.1190001294,-0.4479356706,-0.1070014089,0.101488933,-0.1534747928,-0.0588073991,-0.0950969383,0.2930878401,0.370467931,0.1002270803,0.0000388057,0.1805009544,0.0745559484,-0.0098785674,-0.1554697156,0.0954120681,0.0298353042,-0.3180525601,0.1465946734,-0.05322586,-0.2526592016,-0.4137487411,0.1962387115,-0.1080147251,-0.001286297,-0.5398012996,0.6560422778,-0.0744825155,0.3185945153,0.0379105546,-0.0938696861,0.1458636522,-0.0064918697,0.0280066803,0.1340823323,-0.6778559089,-0.0360390209,-0.666544497,0.0231845882,-0.0199850611,0.2567601204,-0.1694158167,0.1129339933,-0.2431821823,-0.0524932332,0.5646255612,0.2066770047,-0.1768901497,0.2358557731,-0.0061378046,0.0615539253,-0.0677525848,0.1928419024,0.0997923687,-0.0896512419,0.2306838632,0.3897214234,-0.1044965535,-0.153836742,-0.2931567729,-0.2979383171,0.0302868839,0.477201134,0.1861846894,-0.2131325454,0.3239218593,0.0539867505,0.2610428631,-0.0270515569,0.1740340292,-0.1687909812,0.4095945954,0.2165106237,0.0469269343,-0.1935575008,-0.3710539043,0.0883245543,-0.0564857982,0.4205211699,0.1146350726,-0.1526171118,0.1054009423,-0.0260515492,0.1093332246,-0.0851702541,0.1698955446,0.0053043626,-0.0936794803,-0.3116691113,-0.2342717797,0.0772787854,-0.0754422694,-0.0829077065,-0.1737835258,0.1861634254,-0.2260220349,-0.1404219568,-0.0134574817,0.2356663048,-0.4520278573,-0.1736225188,0.1470247209,0.0358707085,-0.1718818694,-0.1805039495,0.3125281334,0.593639791,0.2700743377,-0.1895742565,0.3841978312,-0.2826440334,-0.0948998332,-0.037112467,-0.0977637842,0.3466219306,0.141826272,-0.0143075706,0.0712347478,-0.103793636,0.1114261001,0.1357508451,0.0565716289,-0.2372091562,0.2847626507,0.0584240519,0.1625307053,-0.0734737441,-0.3168832362,0.2308573872,0.2888997793,0.188281104,0.1037332714,0.2862325013,-0.2110560387,-0.2141566426,0.0827909186,-0.3201047778,0.29331249,0.4313607812,0.3334782422,0.1507135481,0.1923593432,-0.0678071603,-0.0338193439,0.0887047648,-0.0418704785,0.1549538523,0.4150850773,0.0493681133,-0.2622881234,-0.2563100457,-0.0996499062,0.1201703921,0.0827069953,-0.0965997875,0.0342443101,-0.1537794769,0.0391568877,-0.0015208692,-0.2632212043,-0.2255914509,0.1800459027,-0.1216264814,-0.2089032829,0.0168169048,-0.1368888617,0.4582442343,-0.148108691,0.2264863104,0.0739084706,-0.2982954085,-0.0681068748,0.2394589633,0.3514464796,-0.1933685243,0.383833766,0.0832781866,0.0436142683,-0.1888152659,-0.5572310686,0.2148355544,-0.131484881,0.3213920295,0.1718160957,-0.0137503073,0.1581781954,-0.0338955112,-0.1430461556,-0.0654320568,-0.0057403627,-0.2261835486,-0.0729996413,0.0239962153,-0.2119414508,-0.2607219219,-0.4658943713,-0.4638248384,0.2482122332,0.0824124664,0.0538888127,-0.0242492966,0.0566325188,0.2967192531,0.0132699283,-0.1570475996,-0.1987161487,-0.3436394632,0.340357244,-0.1229745075,-0.2107488066,0.0278549567,-0.239579916,-0.1833103448,0.1086484417,-0.594632566,0.1073942259,-0.0048695211,0.2758696973,0.0069912579,0.1516615152,0.1241982505,-0.1518832296,-0.1421293616,-0.081577599,-0.0377443545,0.1081052721,0.5077723861,0.3683866858,-0.1968954504,0.2154929489,0.2673070133,0.5058031678,-0.0379618742,-0.1533984244,0.4270523787,0.5014266372,0.392534107,-0.238448143,-0.0938844606,0.3584389985,0.073021486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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1675","title":"Add the 800GB Pile dataset?","comments":"Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:\r\n\r\n```python\r\n# Install master branch of `datasets`\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git#egg=datasets[streaming]\r\npip install zstandard\r\n\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"json\", data_files=\"https:\/\/the-eye.eu\/public\/AI\/pile\/train\/00.jsonl.zst\", streaming=True, split=\"train\")\r\nnext(iter(dset))\r\n# {'meta': {'pile_set_name': 'Pile-CC'},\r\n# 'text': 'It is done, and submitted. You can play \u201cSurvival of the Tastiest\u201d on Android, and on the web ... '}\r\n```\r\n\r\nNext step is to add the Pile as a \"canonical\" dataset that can be streamed without specifying the file names explicitly :)","body":"## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n","comment_length":92,"text":"Add the 800GB Pile dataset? \n ## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n \n Hi folks, thanks to some awesome work by @lhoestq and @albertvillanova you can now stream the Pile as follows:\r\n\r\n```python\r\n# Install master branch of `datasets`\r\npip install git+https:\/\/github.com\/huggingface\/datasets.git#egg=datasets[streaming]\r\npip install zstandard\r\n\r\nfrom datasets import load_dataset\r\n\r\ndset = load_dataset(\"json\", data_files=\"https:\/\/the-eye.eu\/public\/AI\/pile\/train\/00.jsonl.zst\", streaming=True, split=\"train\")\r\nnext(iter(dset))\r\n# {'meta': {'pile_set_name': 'Pile-CC'},\r\n# 'text': 'It is done, and submitted. You can play \u201cSurvival of the Tastiest\u201d on Android, and on the web ... '}\r\n```\r\n\r\nNext step is to add the Pile as a \"canonical\" dataset that can be streamed without specifying the file names explicitly :)","embeddings":[-0.3223569691,-0.0436929688,-0.1469397694,0.0411979742,0.2053308487,0.0567985885,0.1199193895,0.2633144259,-0.0495686866,0.1010038331,-0.2534806132,0.1415169835,-0.453261137,0.1955078095,0.157208994,0.0147248944,-0.0914569497,-0.0514797457,0.0360941589,-0.0275311302,0.1478369832,-0.0608970076,0.132908076,-0.3931410313,0.1203901395,0.0574042201,-0.1081212461,0.0364227742,-0.3161754906,-0.2588157058,-0.2680271268,0.0863038376,0.3348543942,0.5441961884,-0.0001011025,-0.1211931184,0.2058726847,-0.3016029894,-0.3517262042,-0.291303277,0.0903971344,-0.2420322001,-0.3066551089,-0.130350709,-0.1966381073,-0.2078080326,0.1834151745,-0.3442660272,0.28452003,0.341537267,0.2460384667,0.1315052509,-0.0304514002,0.0425570235,0.3156402409,-0.0613367409,-0.1409148872,0.2879666388,0.4384595454,0.131225422,-0.0695037246,0.390666008,0.2101586908,-0.0690988302,0.0879188105,-0.102880843,0.0603302978,-0.2037926316,-0.090234071,0.4779412746,0.2258951813,-0.3107229173,-0.4418420494,-0.2665180266,-0.0485248864,-0.5264925957,-0.2674874663,0.4536276162,-0.1578510851,0.2263097614,0.0106866965,-0.1838753074,-0.1374783814,0.2025263608,0.0351605341,0.2958590686,-0.050167013,-0.1749762297,0.0153176319,-0.0711371228,-0.202226758,0.0046313517,-0.0453153811,0.0850836933,-0.4025754035,-0.248159647,0.3415242136,0.1628439724,0.331109643,-0.1066936255,0.2155127078,0.1476169676,0.0707817525,-0.0287207086,0.450353533,0.0320552066,-0.2049900591,-0.3580890894,0.2392516732,0.2649254799,0.0162714999,0.0166314021,-0.0751740858,0.0401686393,-0.1365391314,-0.0610720403,-0.0502994284,-0.0035038099,-0.0086774798,-0.0772495791,0.0323535725,0.0265965629,-0.0499776192,0.1562220752,-0.0496002212,0.3794095814,-0.0218507051,0.2079280466,0.0882696956,-0.436363548,-0.1796023548,0.0789486542,-0.3663024902,-0.1088271961,0.1638758481,-0.1643036902,0.1702489853,-0.2545806766,0.3023360968,0.2581272721,0.1764388829,-0.0198114831,0.0513743199,0.0377178267,0.1977613866,-0.2589660585,0.0989611,-0.1191890612,-0.3118190169,0.0759711415,0.030774245,-0.2165573686,-0.2595750988,0.2630078197,-0.2195437998,0.1194420084,-0.339209348,0.5862973332,0.0106500732,0.1276053786,-0.0754742846,-0.0523760803,0.0848475993,-0.0357882008,0.0291658957,0.1398172826,-0.5509948134,-0.131236732,-0.5367796421,0.1494332552,0.0903180093,0.1384186596,-0.0874186307,-0.0488986634,-0.0450920947,0.045368772,0.5971069932,-0.0393128358,-0.3197206855,0.1944663227,-0.2178276479,0.082122609,0.1501761079,0.1490412802,0.3288455009,-0.0766837522,0.1707689464,0.4196424782,-0.082190901,0.0360239856,-0.2637160718,-0.4479620457,-0.0619452186,0.4485101998,-0.0011915879,-0.2839028239,0.2976228297,0.1281043142,0.3932348788,-0.0307257567,0.1780168116,-0.0209076554,0.3714098632,0.2155564427,-0.1040035263,-0.2432037741,-0.3883219659,-0.0004523165,0.1256648004,0.3175265491,-0.0576177426,-0.176865831,0.0995964855,-0.0897155628,-0.0054206564,-0.0083347308,0.2842760682,0.215275079,-0.0720103234,-0.205806613,-0.2596538067,-0.0198443774,-0.2136233747,-0.1910217255,-0.2605654895,0.0733034611,-0.2180713862,-0.2537564933,0.15475218,0.0511855371,-0.3584303856,-0.0731421113,0.0983453393,0.1717747003,-0.1573737264,0.0545240566,0.2587210834,0.6038361788,0.2455324531,-0.2508397102,0.1503962874,0.1796720177,0.0597122386,0.0767359883,-0.328433156,0.2855479419,0.2483708709,0.0522318855,0.2417669296,-0.123791337,0.3073280752,0.0731959119,-0.2831407785,-0.1669170409,0.3048636317,0.0451128893,0.0771817043,-0.1612460613,-0.4005177617,0.265421629,0.4768961966,0.1106094122,0.1505624652,0.3148940504,-0.1522031128,-0.283857882,0.0053650695,-0.2078521997,0.1680807769,0.3904982805,0.3401862085,0.0501147099,0.1904364824,-0.1668256521,0.0935665965,-0.0266901311,-0.1783637851,0.3081232309,0.4929266572,-0.0784886405,-0.3999209702,-0.244451493,-0.1103419811,0.0061995997,0.1426928788,-0.0133943185,0.0114843175,-0.1119273305,-0.0182371214,-0.1914657056,-0.1208035871,-0.2223506719,0.1170553789,0.1016682386,-0.262424171,0.0269336216,-0.1841677874,0.2350763977,0.0711468831,0.069557935,0.0398696773,-0.3471658528,-0.0365973935,0.2696183026,0.1716452986,-0.1558896452,0.2812961638,-0.0741847456,0.0166828353,-0.0781815127,-0.4989754558,0.266802907,-0.0905107483,0.2021902055,0.1259572804,0.2397974133,0.1978953332,-0.0552802645,-0.0750290602,-0.3651872277,-0.0118545061,-0.0598126352,0.034238115,0.0973975956,-0.2828164399,-0.3978762031,-0.4410095215,-0.5356467366,0.2150579691,0.1740148664,0.1467034072,0.0685802847,0.1819939911,0.2245611697,0.1612925828,0.1206091717,-0.1552394032,-0.4950091541,0.3499086499,-0.1595913619,-0.3062624931,-0.0049661868,-0.014390111,-0.0345366821,0.1636470258,-0.4432415664,-0.0470626205,-0.0167907476,0.0818646252,0.0182889774,0.104462944,0.118752338,0.0270555075,-0.2058415711,-0.097542949,0.1790212393,0.0209461302,0.4673111439,0.3781637847,-0.09892492,0.4266079664,0.2625722587,0.4059220254,-0.0381824225,-0.1138415709,0.4830045402,0.346957922,0.3760917485,-0.1459474415,0.0600003265,0.2370053083,0.002286854,-0.0762184486,0.55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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1675","title":"Add the 800GB Pile dataset?","comments":"> Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!\r\n\r\nHi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.\r\n\r\nMy current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train \/ dev \/ test splits that EleutherAI released [here](https:\/\/the-eye.eu\/public\/AI\/pile\/), e.g.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# Load a single component\r\nyoutube_subtitles = load_dataset(\"the_pile\", \"youtube_subtitles\")\r\n# Load the train \/ dev \/ test splits of the whole corpus\r\ndset = load_dataset(\"the_pile\", \"all\")\r\n```\r\n\r\nIdeally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.\r\n\r\ncc @lhoestq ","body":"## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n","comment_length":199,"text":"Add the 800GB Pile dataset? \n ## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n \n > Hi folks! Just wanted to follow up on this -- would be really nice to get the Pile on HF Datasets... unclear if it would be easy to also add partitions of the Pile subject to the original 22 datasets used, but that would be nice too!\r\n\r\nHi @siddk thanks to a tip from @richarddwang it seems we can access some of the partitions that EleutherAI created for the Pile [here](https:\/\/the-eye.eu\/public\/AI\/pile_preliminary_components\/). What's missing are links to the preprocessed versions of pre-existing datasets like DeepMind Mathematics and OpenSubtitles, but worst case we do the processing ourselves and host these components on the Hub.\r\n\r\nMy current idea is that we could provide 23 configs: one for each of the 22 datasets and an `all` config that links to the train \/ dev \/ test splits that EleutherAI released [here](https:\/\/the-eye.eu\/public\/AI\/pile\/), e.g.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# Load a single component\r\nyoutube_subtitles = load_dataset(\"the_pile\", \"youtube_subtitles\")\r\n# Load the train \/ dev \/ test splits of the whole corpus\r\ndset = load_dataset(\"the_pile\", \"all\")\r\n```\r\n\r\nIdeally we'd like everything to be compatible with the streaming API and there's ongoing work by @albertvillanova to make this happen for the various compression algorithms.\r\n\r\ncc @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1675","title":"Add the 800GB Pile dataset?","comments":"Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https:\/\/github.com\/huggingface\/datasets\/pull\/2817#issuecomment-901874049) :)","body":"## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n","comment_length":21,"text":"Add the 800GB Pile dataset? \n ## Adding a Dataset\r\n- **Name:** The Pile\r\n- **Description:** The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality datasets combined together. See [here](https:\/\/twitter.com\/nabla_theta\/status\/1345130408170541056?s=20) for the Twitter announcement\r\n- **Paper:** https:\/\/pile.eleuther.ai\/paper.pdf\r\n- **Data:** https:\/\/pile.eleuther.ai\/\r\n- **Motivation:** Enables hardcore  (GPT-3 scale!) language modelling\r\n\r\n## Remarks\r\nGiven the extreme size of this dataset, I'm not sure how feasible this will be to include in `datasets` \ud83e\udd2f  . I'm also unsure how many `datasets` users are pretraining LMs, so the usage of this dataset may not warrant the effort to integrate it.\r\n \n Ah I just saw that @lhoestq is already thinking about the specifying of one or more subsets in [this PR](https:\/\/github.com\/huggingface\/datasets\/pull\/2817#issuecomment-901874049) :)","embeddings":[-0.3140250444,0.1560877711,-0.157755807,0.1820491105,-0.027711926,0.2084934264,0.1576407254,0.1906449646,0.0126804197,-0.0463636816,-0.1654876918,0.1902200729,-0.4964599013,0.2447127551,0.2264724523,-0.036290586,-0.0239854418,-0.117916815,-0.0887088031,-0.0244249031,-0.0195851661,-0.0417656042,0.0144501971,-0.4073126018,-0.167057544,-0.0601609461,-0.1501535177,0.0104317749,-0.3633566797,-0.200304538,-0.237642318,0.1232232302,0.3964087367,0.502411902,-0.0001000399,-0.1629320234,0.1292916089,-0.1930175722,-0.362067163,-0.2543430924,-0.1096574739,-0.3248431087,-0.3422295749,-0.0353268646,-0.1831485331,-0.1879443377,0.1316821724,-0.3572055995,0.0566397607,0.2555432618,0.2685053051,0.2546108663,0.0170974322,-0.1162618324,0.3399556577,0.0631387904,-0.169928968,0.257361263,0.3639290929,0.1733178347,-0.0872363523,0.4222867191,0.4526141882,-0.2000396252,0.258446604,0.0176956858,-0.0658392385,-0.1251063049,-0.1947441995,0.3474349678,0.3824141026,-0.1905806363,-0.5288470387,-0.4103041887,-0.0564834289,-0.4034163356,-0.2425067425,0.344522655,0.0575302169,0.332824558,0.0233515445,-0.2500684559,-0.0692026913,0.1912065297,0.0271331575,0.3145010471,-0.1006790847,-0.1568935961,0.120230563,0.0075310203,-0.1577138305,0.0121188499,-0.0387093984,-0.0001409127,-0.3294295967,-0.2154621929,0.3981156051,0.3535758257,0.4854921699,-0.1341152042,0.0018772121,0.0193458535,-0.0771902874,-0.0032304337,0.5196982622,0.024914287,-0.2072120011,-0.2758996189,0.2686921358,0.0638092756,0.1761342883,0.086870119,-0.047157757,-0.0809495747,-0.1676562726,-0.1773598045,-0.0507859848,0.127234295,-0.0632344708,-0.0446244888,0.2358297855,-0.1035479829,-0.0050903591,0.055820968,0.0438573286,0.1919285655,0.0104029188,0.2573389709,0.1459525377,-0.4894524515,-0.2063297629,0.183729887,-0.4527623951,-0.0691873208,0.1031783223,-0.1375151873,0.0783142745,-0.1020732895,0.2345260382,0.2292526215,0.1346583813,0.0306340363,0.1371716857,-0.0269555617,-0.0739035681,-0.2033777982,0.0568886176,0.0850887001,-0.3138953447,0.1317081004,0.0163719989,-0.2196156681,-0.3382868171,0.2284154743,-0.2176370472,0.0264339987,-0.4154157341,0.7231199145,-0.0329928249,0.2563389242,0.0751016885,-0.0533589609,0.0356976353,0.0082695978,0.0736957863,0.1863314807,-0.5746176243,-0.1319470406,-0.5834824443,0.0483263321,-0.1022877768,0.2690888643,-0.1963113695,0.1377868205,-0.1480196118,0.0586056672,0.5879366994,0.1688159108,-0.1505534798,0.1420860738,-0.1099500656,0.009615723,-0.0103814499,0.1500778496,0.1274719089,-0.0512013063,0.2615861595,0.3900918663,-0.1389925778,-0.1138648242,-0.2623089552,-0.377990514,0.0675957352,0.3910391033,0.1070266664,-0.2736819386,0.2186495215,0.0501531251,0.1668393761,-0.0123430695,0.1976042986,-0.1248912737,0.2841337025,0.1168326139,-0.0177331921,-0.3275248408,-0.4190087616,0.0483474024,0.0419962704,0.3821239471,0.0728235766,-0.2030946314,0.0589445308,0.005783753,0.1332860589,-0.1204906926,0.2177647799,0.0485019125,-0.0301405974,-0.2270911634,-0.3296844959,-0.0630915835,-0.0892224908,-0.0877662897,-0.2958340943,0.0060742958,-0.1867309809,-0.1411352009,0.0270735174,0.2707060277,-0.4790112972,-0.1836998314,0.2493359745,0.0859609172,-0.130210802,-0.1862048656,0.2459146976,0.6053516865,0.2642050683,-0.1209844127,0.2040129453,-0.2006687373,-0.0422985032,0.0218253527,-0.1840438098,0.4000040591,0.2446723282,-0.0618102476,0.1006869897,-0.1020419598,0.1329485625,0.0554240122,-0.0784556791,-0.2822150588,0.2581147552,0.1021332741,0.2248966396,0.0236439798,-0.3749103248,0.1169881374,0.2765392661,0.1346707046,0.2371223718,0.2638746798,-0.1729671657,-0.2526938915,-0.0043546734,-0.2683978379,0.2763849199,0.4303976893,0.271143496,0.0645138994,0.184170261,-0.1474291235,0.0317661613,0.0813246891,-0.0447636172,0.1689247936,0.402156055,0.0353724696,-0.3077982068,-0.2223669291,-0.0800784528,0.0917734131,0.0741328597,-0.0449300483,0.0448769554,-0.0952308401,0.0547971576,0.0300011206,-0.2347225398,-0.1994048208,0.1858643442,-0.1503640413,-0.3074035347,0.0467671491,-0.1291742474,0.5056379437,-0.0863483921,0.1858696043,0.0908082128,-0.2864463329,-0.0711079612,0.2793172598,0.2715525329,-0.2613441646,0.4053997695,0.0030316042,0.0979189053,-0.2049828768,-0.5722048879,0.2662444711,-0.1563777179,0.3840333819,0.2316687405,0.0047286176,0.1451892257,-0.0371908583,-0.1277272105,-0.1264147758,-0.0322177187,-0.2840772569,-0.0060539264,0.0704042837,-0.2615520358,-0.3562017679,-0.3958789706,-0.5152435303,0.3340537548,0.0680235848,0.0318070501,-0.000996761,0.0940476283,0.3164206147,-0.0580389835,-0.012133399,-0.1471477747,-0.2930302024,0.3443108499,-0.1152916849,-0.2608080208,-0.0380178615,-0.2216433883,-0.0507546738,0.104109779,-0.4849395454,0.0580915697,-0.0900533721,0.2685629725,0.0396258198,0.1388692707,0.1441310793,-0.1144477725,-0.221587196,-0.1144572422,-0.0163236819,0.1449478269,0.3994907737,0.3285677731,-0.1540107131,0.2489542365,0.2208058834,0.4202838242,-0.0282783546,-0.2335667461,0.4258720875,0.3500135839,0.4011774063,-0.1807069331,-0.0191543009,0.2930106819,0.0757228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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1674","title":"dutch_social can't be loaded","comments":"Hi @koenvandenberge and @alighofrani95!\r\nThe datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.\r\nMeanwhile, you can still load the datasets using one of the techniques described in this issue: #1641 \r\nLet me know if this helps!","body":"Hi all,\r\n\r\nI'm trying to import the `dutch_social` dataset described [here](https:\/\/huggingface.co\/datasets\/dutch_social).\r\n\r\nHowever, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.\r\n\r\n```\r\n(base) Koens-MacBook-Pro:~ koenvandenberge$ python\r\nPython 3.7.4 (default, Aug 13 2019, 15:17:50) \r\n[Clang 4.0.1 (tags\/RELEASE_401\/final)] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\ndataset = load_dataset(\r\n   'dutch_social')\r\n>>> dataset = load_dataset(\r\n...    'dutch_social')\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 2, in <module>\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at dutch_social\/dutch_social.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n```","comment_length":59,"text":"dutch_social can't be loaded \n Hi all,\r\n\r\nI'm trying to import the `dutch_social` dataset described [here](https:\/\/huggingface.co\/datasets\/dutch_social).\r\n\r\nHowever, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.\r\n\r\n```\r\n(base) Koens-MacBook-Pro:~ koenvandenberge$ python\r\nPython 3.7.4 (default, Aug 13 2019, 15:17:50) \r\n[Clang 4.0.1 (tags\/RELEASE_401\/final)] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\ndataset = load_dataset(\r\n   'dutch_social')\r\n>>> dataset = load_dataset(\r\n...    'dutch_social')\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 2, in <module>\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at dutch_social\/dutch_social.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n``` \n Hi @koenvandenberge and @alighofrani95!\r\nThe datasets you're experiencing issues with were most likely added recently to the `datasets` library, meaning they have not been released yet. They will be released with the v2 of the library.\r\nMeanwhile, you can still load the datasets using one of the techniques described in this issue: #1641 \r\nLet me know if this helps!","embeddings":[-0.1440130919,-0.1277865618,-0.1560761034,0.2933968306,0.2167993635,-0.1348885447,-0.0120579246,0.0937409475,0.3958035707,-0.0862966329,-0.2941347361,-0.0078357765,0.0196760818,-0.0421471745,0.1373223066,0.1136013195,0.2246757895,0.0936486647,0.144613862,-0.1379558891,-0.2554816604,0.4084303081,0.0364790969,0.1195254549,0.0535758808,0.2432906479,-0.0837842226,0.3191888928,-0.0633874536,-0.2948622704,0.3469977081,0.1354592592,0.1217035055,0.5947429538,-0.0001220059,0.0153691275,0.4793719053,-0.0934169069,-0.6241431832,-0.4102129638,0.0205617975,-0.2827773392,0.0646422952,-0.0720438063,-0.0622491986,-0.2690680027,-0.0105327405,-0.474494487,0.2986169159,0.4165929258,0.1114755422,-0.0108238775,0.0761229396,-0.0746558085,0.0295876656,0.3834807873,0.0695490241,0.5175902247,0.2559808195,0.0191765875,0.4757993519,0.139318645,-0.1636501998,-0.1841302067,0.1682499945,-0.1772353053,-0.0807946846,-0.2221838981,0.1476258039,-0.074964039,0.4582820535,-0.0886856616,-0.264942199,-0.2424675226,0.0461236127,0.0547623821,0.276529938,0.1222155318,-0.3778547943,0.2942798138,-0.1758255213,-0.2210489362,-0.1348641068,0.542472899,0.0668623,0.0545753092,-0.1998028606,0.0972559825,0.2403409779,-0.1409109533,-0.0025449127,-0.1723716855,0.1879131049,0.2741535306,0.0476628169,0.275170058,0.109135069,0.1229047924,0.0124698281,0.2088605762,-0.0083227707,0.0611036904,-0.2934058607,0.2661069036,0.1451622844,-0.063431032,0.1614208817,-0.0578602739,0.1387722641,0.2737375498,-0.2378363311,-0.0220349152,-0.5131173134,-0.2261775881,-0.4047652185,-0.1627119333,0.0526988059,-0.1887488216,-0.2392709255,-0.0537533425,0.2383565456,0.1124858856,0.1500710696,0.4265648723,-0.0568392836,0.1309242398,0.0157592501,0.3348211646,-0.0972028598,0.1625242531,0.0043877871,0.0115900803,-0.2395053059,-0.1033063531,0.3564893603,-0.1404215097,0.3913652897,-0.0133134797,0.0193966776,-0.2262601852,-0.0651753247,-0.1723169386,-0.0208002795,0.4110073447,-0.0504028127,0.1848825663,0.2301233411,-0.3826444447,-0.2856127322,0.0220117606,-0.4266529381,-0.2579602599,-0.1370801181,0.0865042135,-0.3576443791,-0.0483067632,-0.2518779337,0.0720809549,-0.2459039837,-0.3608632684,-0.1423634887,-0.0626245961,0.0569238551,-0.1635662615,0.4047258198,0.6451685429,-0.259563148,-0.1338703334,-0.2519385815,-0.0979309827,0.1166802645,0.1549301893,-0.2774693072,0.1003124788,-0.4217948914,-0.1295697242,0.5071360469,-0.3881822526,-0.1034957767,0.043269366,0.0442071557,0.4348086715,0.0677174553,-0.0021925054,-0.2236290574,0.0936073661,0.1177948564,0.365460515,0.2958675623,-0.3151164651,-0.0436034799,-0.08808887,0.2152402699,0.4358336627,-0.0789023265,-0.065279454,0.3209886551,-0.0179740302,0.0676686615,-0.0443313085,-0.1122181565,0.3995007277,-0.1002356708,0.6040776372,-0.1201559752,0.0579323918,-0.1615790576,0.1620278955,0.3629038632,-0.0949904695,-0.2131394148,0.0145916874,-0.5914059281,0.1086386964,-0.2440940589,-0.0796029046,0.0442409627,0.3385514021,0.2097835839,0.4188049734,-0.1303366423,0.5874919891,-0.1820048988,-0.0004448143,-0.136841476,0.3376156092,-0.077340886,0.0069129346,-0.025883032,0.4640442133,0.19163315,-0.0753638968,0.1306604892,0.1775824875,0.2757100463,0.1431743801,0.3152182102,-0.1459710449,0.013373767,-0.1851287335,0.0264447127,0.2751175463,0.1668300778,-0.0880569145,-0.2502496839,0.1557591856,-0.0062821158,0.2842067778,0.1452289671,0.0032311126,0.2915836871,-0.0235694479,-0.0459581092,-0.1600633413,0.3323235512,-0.1702431589,0.5120025873,-0.006221151,-0.2748087049,0.1538019031,0.0134518212,-0.1130990013,0.0663182139,0.2667791247,-0.1694904119,0.1820977181,0.1649412066,0.2306359261,0.5306184292,0.1501768082,0.0584150627,-0.1042482406,0.1984350532,-0.226388678,0.196112588,0.0779637173,0.3595768809,0.1046478078,0.1316241622,0.1227424741,-0.2572664917,-0.5361442566,-0.149571985,0.0522079468,-0.2917066514,0.3217304945,-0.185494706,-0.4013975859,-0.3152742684,-0.1355844587,-0.2492753416,-0.3631968498,-0.1829154044,0.1399168819,0.0379369855,-0.0491964184,-0.1167751774,0.1715294868,-0.2130713314,-0.3847946823,-0.3287187815,-0.0870077685,-0.049715437,-0.0937781259,0.4112957716,0.100119248,0.1564811319,-0.4932972789,0.1215285957,0.094456993,-0.4239718616,-0.1459390521,-0.0658269599,0.1950836778,-0.0813921466,0.3485629559,-0.2351174653,-0.3012833893,0.3619758785,-0.019668784,-0.0797758698,0.1490667313,0.2029022425,-0.042098213,-0.1645146012,-0.0364278592,-0.8044582009,-0.3288797438,0.1570771933,0.2175286114,0.0154114179,0.4426355064,0.3873107731,0.0452336743,-0.0348101072,0.1303268522,-0.1764192879,-0.3531501889,0.6498783231,-0.3994504213,-0.2847266793,0.1182230935,0.2317685336,0.2142133862,-0.3526259363,-0.4864182472,0.0804351568,0.0773071796,0.2877722383,-0.0448323004,0.1416716278,-0.0867542922,-0.0689283088,-0.0460393466,-0.1349615753,-0.0135841006,-0.116391331,-0.3259768188,0.2562913597,0.0006416994,0.1128346696,-0.2048086971,0.5850800276,0.1846069694,0.1297905594,0.3108214438,-0.0963969231,0.686024785,-0.1007470489,-0.4953562319,-0.0185463745,-0.0426108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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1674","title":"dutch_social can't be loaded","comments":"I just did the release :)\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dutch_social` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dutch_social\")\r\n```","body":"Hi all,\r\n\r\nI'm trying to import the `dutch_social` dataset described [here](https:\/\/huggingface.co\/datasets\/dutch_social).\r\n\r\nHowever, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.\r\n\r\n```\r\n(base) Koens-MacBook-Pro:~ koenvandenberge$ python\r\nPython 3.7.4 (default, Aug 13 2019, 15:17:50) \r\n[Clang 4.0.1 (tags\/RELEASE_401\/final)] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\ndataset = load_dataset(\r\n   'dutch_social')\r\n>>> dataset = load_dataset(\r\n...    'dutch_social')\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 2, in <module>\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at dutch_social\/dutch_social.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n```","comment_length":36,"text":"dutch_social can't be loaded \n Hi all,\r\n\r\nI'm trying to import the `dutch_social` dataset described [here](https:\/\/huggingface.co\/datasets\/dutch_social).\r\n\r\nHowever, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.\r\n\r\n```\r\n(base) Koens-MacBook-Pro:~ koenvandenberge$ python\r\nPython 3.7.4 (default, Aug 13 2019, 15:17:50) \r\n[Clang 4.0.1 (tags\/RELEASE_401\/final)] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\ndataset = load_dataset(\r\n   'dutch_social')\r\n>>> dataset = load_dataset(\r\n...    'dutch_social')\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 2, in <module>\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at dutch_social\/dutch_social.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n``` \n I just did the release :)\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `dutch_social` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"dutch_social\")\r\n```","embeddings":[-0.1440130919,-0.1277865618,-0.1560761034,0.2933968306,0.2167993635,-0.1348885447,-0.0120579246,0.0937409475,0.3958035707,-0.0862966329,-0.2941347361,-0.0078357765,0.0196760818,-0.0421471745,0.1373223066,0.1136013195,0.2246757895,0.0936486647,0.144613862,-0.1379558891,-0.2554816604,0.4084303081,0.0364790969,0.1195254549,0.0535758808,0.2432906479,-0.0837842226,0.3191888928,-0.0633874536,-0.2948622704,0.3469977081,0.1354592592,0.1217035055,0.5947429538,-0.0001220059,0.0153691275,0.4793719053,-0.0934169069,-0.6241431832,-0.4102129638,0.0205617975,-0.2827773392,0.0646422952,-0.0720438063,-0.0622491986,-0.2690680027,-0.0105327405,-0.474494487,0.2986169159,0.4165929258,0.1114755422,-0.0108238775,0.0761229396,-0.0746558085,0.0295876656,0.3834807873,0.0695490241,0.5175902247,0.2559808195,0.0191765875,0.4757993519,0.139318645,-0.1636501998,-0.1841302067,0.1682499945,-0.1772353053,-0.0807946846,-0.2221838981,0.1476258039,-0.074964039,0.4582820535,-0.0886856616,-0.264942199,-0.2424675226,0.0461236127,0.0547623821,0.276529938,0.1222155318,-0.3778547943,0.2942798138,-0.1758255213,-0.2210489362,-0.1348641068,0.542472899,0.0668623,0.0545753092,-0.1998028606,0.0972559825,0.2403409779,-0.1409109533,-0.0025449127,-0.1723716855,0.1879131049,0.2741535306,0.0476628169,0.275170058,0.109135069,0.1229047924,0.0124698281,0.2088605762,-0.0083227707,0.0611036904,-0.2934058607,0.2661069036,0.1451622844,-0.063431032,0.1614208817,-0.0578602739,0.1387722641,0.2737375498,-0.2378363311,-0.0220349152,-0.5131173134,-0.2261775881,-0.4047652185,-0.1627119333,0.0526988059,-0.1887488216,-0.2392709255,-0.0537533425,0.2383565456,0.1124858856,0.1500710696,0.4265648723,-0.0568392836,0.1309242398,0.0157592501,0.3348211646,-0.0972028598,0.1625242531,0.0043877871,0.0115900803,-0.2395053059,-0.1033063531,0.3564893603,-0.1404215097,0.3913652897,-0.0133134797,0.0193966776,-0.2262601852,-0.0651753247,-0.1723169386,-0.0208002795,0.4110073447,-0.0504028127,0.1848825663,0.2301233411,-0.3826444447,-0.2856127322,0.0220117606,-0.4266529381,-0.2579602599,-0.1370801181,0.0865042135,-0.3576443791,-0.0483067632,-0.2518779337,0.0720809549,-0.2459039837,-0.3608632684,-0.1423634887,-0.0626245961,0.0569238551,-0.1635662615,0.4047258198,0.6451685429,-0.259563148,-0.1338703334,-0.2519385815,-0.0979309827,0.1166802645,0.1549301893,-0.2774693072,0.1003124788,-0.4217948914,-0.1295697242,0.5071360469,-0.3881822526,-0.1034957767,0.043269366,0.0442071557,0.4348086715,0.0677174553,-0.0021925054,-0.2236290574,0.0936073661,0.1177948564,0.365460515,0.2958675623,-0.3151164651,-0.0436034799,-0.08808887,0.2152402699,0.4358336627,-0.0789023265,-0.065279454,0.3209886551,-0.0179740302,0.0676686615,-0.0443313085,-0.1122181565,0.3995007277,-0.1002356708,0.6040776372,-0.1201559752,0.0579323918,-0.1615790576,0.1620278955,0.3629038632,-0.0949904695,-0.2131394148,0.0145916874,-0.5914059281,0.1086386964,-0.2440940589,-0.0796029046,0.0442409627,0.3385514021,0.2097835839,0.4188049734,-0.1303366423,0.5874919891,-0.1820048988,-0.0004448143,-0.136841476,0.3376156092,-0.077340886,0.0069129346,-0.025883032,0.4640442133,0.19163315,-0.0753638968,0.1306604892,0.1775824875,0.2757100463,0.1431743801,0.3152182102,-0.1459710449,0.013373767,-0.1851287335,0.0264447127,0.2751175463,0.1668300778,-0.0880569145,-0.2502496839,0.1557591856,-0.0062821158,0.2842067778,0.1452289671,0.0032311126,0.2915836871,-0.0235694479,-0.0459581092,-0.1600633413,0.3323235512,-0.1702431589,0.5120025873,-0.006221151,-0.2748087049,0.1538019031,0.0134518212,-0.1130990013,0.0663182139,0.2667791247,-0.1694904119,0.1820977181,0.1649412066,0.2306359261,0.5306184292,0.1501768082,0.0584150627,-0.1042482406,0.1984350532,-0.226388678,0.196112588,0.0779637173,0.3595768809,0.1046478078,0.1316241622,0.1227424741,-0.2572664917,-0.5361442566,-0.149571985,0.0522079468,-0.2917066514,0.3217304945,-0.185494706,-0.4013975859,-0.3152742684,-0.1355844587,-0.2492753416,-0.3631968498,-0.1829154044,0.1399168819,0.0379369855,-0.0491964184,-0.1167751774,0.1715294868,-0.2130713314,-0.3847946823,-0.3287187815,-0.0870077685,-0.049715437,-0.0937781259,0.4112957716,0.100119248,0.1564811319,-0.4932972789,0.1215285957,0.094456993,-0.4239718616,-0.1459390521,-0.0658269599,0.1950836778,-0.0813921466,0.3485629559,-0.2351174653,-0.3012833893,0.3619758785,-0.019668784,-0.0797758698,0.1490667313,0.2029022425,-0.042098213,-0.1645146012,-0.0364278592,-0.8044582009,-0.3288797438,0.1570771933,0.2175286114,0.0154114179,0.4426355064,0.3873107731,0.0452336743,-0.0348101072,0.1303268522,-0.1764192879,-0.3531501889,0.6498783231,-0.3994504213,-0.2847266793,0.1182230935,0.2317685336,0.2142133862,-0.3526259363,-0.4864182472,0.0804351568,0.0773071796,0.2877722383,-0.0448323004,0.1416716278,-0.0867542922,-0.0689283088,-0.0460393466,-0.1349615753,-0.0135841006,-0.116391331,-0.3259768188,0.2562913597,0.0006416994,0.1128346696,-0.2048086971,0.5850800276,0.1846069694,0.1297905594,0.3108214438,-0.0963969231,0.686024785,-0.1007470489,-0.4953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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1674","title":"dutch_social can't be loaded","comments":"@lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently?","body":"Hi all,\r\n\r\nI'm trying to import the `dutch_social` dataset described [here](https:\/\/huggingface.co\/datasets\/dutch_social).\r\n\r\nHowever, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.\r\n\r\n```\r\n(base) Koens-MacBook-Pro:~ koenvandenberge$ python\r\nPython 3.7.4 (default, Aug 13 2019, 15:17:50) \r\n[Clang 4.0.1 (tags\/RELEASE_401\/final)] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\ndataset = load_dataset(\r\n   'dutch_social')\r\n>>> dataset = load_dataset(\r\n...    'dutch_social')\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 2, in <module>\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at dutch_social\/dutch_social.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n```","comment_length":28,"text":"dutch_social can't be loaded \n Hi all,\r\n\r\nI'm trying to import the `dutch_social` dataset described [here](https:\/\/huggingface.co\/datasets\/dutch_social).\r\n\r\nHowever, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.\r\n\r\n```\r\n(base) Koens-MacBook-Pro:~ koenvandenberge$ python\r\nPython 3.7.4 (default, Aug 13 2019, 15:17:50) \r\n[Clang 4.0.1 (tags\/RELEASE_401\/final)] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\ndataset = load_dataset(\r\n   'dutch_social')\r\n>>> dataset = load_dataset(\r\n...    'dutch_social')\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 2, in <module>\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at dutch_social\/dutch_social.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n``` \n @lhoestq could you also shed light on the Hindi Wikipedia Dataset for issue number #1673. Will this also be available in the new release that you committed recently?","embeddings":[-0.1440130919,-0.1277865618,-0.1560761034,0.2933968306,0.2167993635,-0.1348885447,-0.0120579246,0.0937409475,0.3958035707,-0.0862966329,-0.2941347361,-0.0078357765,0.0196760818,-0.0421471745,0.1373223066,0.1136013195,0.2246757895,0.0936486647,0.144613862,-0.1379558891,-0.2554816604,0.4084303081,0.0364790969,0.1195254549,0.0535758808,0.2432906479,-0.0837842226,0.3191888928,-0.0633874536,-0.2948622704,0.3469977081,0.1354592592,0.1217035055,0.5947429538,-0.0001220059,0.0153691275,0.4793719053,-0.0934169069,-0.6241431832,-0.4102129638,0.0205617975,-0.2827773392,0.0646422952,-0.0720438063,-0.0622491986,-0.2690680027,-0.0105327405,-0.474494487,0.2986169159,0.4165929258,0.1114755422,-0.0108238775,0.0761229396,-0.0746558085,0.0295876656,0.3834807873,0.0695490241,0.5175902247,0.2559808195,0.0191765875,0.4757993519,0.139318645,-0.1636501998,-0.1841302067,0.1682499945,-0.1772353053,-0.0807946846,-0.2221838981,0.1476258039,-0.074964039,0.4582820535,-0.0886856616,-0.264942199,-0.2424675226,0.0461236127,0.0547623821,0.276529938,0.1222155318,-0.3778547943,0.2942798138,-0.1758255213,-0.2210489362,-0.1348641068,0.542472899,0.0668623,0.0545753092,-0.1998028606,0.0972559825,0.2403409779,-0.1409109533,-0.0025449127,-0.1723716855,0.1879131049,0.2741535306,0.0476628169,0.275170058,0.109135069,0.1229047924,0.0124698281,0.2088605762,-0.0083227707,0.0611036904,-0.2934058607,0.2661069036,0.1451622844,-0.063431032,0.1614208817,-0.0578602739,0.1387722641,0.2737375498,-0.2378363311,-0.0220349152,-0.5131173134,-0.2261775881,-0.4047652185,-0.1627119333,0.0526988059,-0.1887488216,-0.2392709255,-0.0537533425,0.2383565456,0.1124858856,0.1500710696,0.4265648723,-0.0568392836,0.1309242398,0.0157592501,0.3348211646,-0.0972028598,0.1625242531,0.0043877871,0.0115900803,-0.2395053059,-0.1033063531,0.3564893603,-0.1404215097,0.3913652897,-0.0133134797,0.0193966776,-0.2262601852,-0.0651753247,-0.1723169386,-0.0208002795,0.4110073447,-0.0504028127,0.1848825663,0.2301233411,-0.3826444447,-0.2856127322,0.0220117606,-0.4266529381,-0.2579602599,-0.1370801181,0.0865042135,-0.3576443791,-0.0483067632,-0.2518779337,0.0720809549,-0.2459039837,-0.3608632684,-0.1423634887,-0.0626245961,0.0569238551,-0.1635662615,0.4047258198,0.6451685429,-0.259563148,-0.1338703334,-0.2519385815,-0.0979309827,0.1166802645,0.1549301893,-0.2774693072,0.1003124788,-0.4217948914,-0.1295697242,0.5071360469,-0.3881822526,-0.1034957767,0.043269366,0.0442071557,0.4348086715,0.0677174553,-0.0021925054,-0.2236290574,0.0936073661,0.1177948564,0.365460515,0.2958675623,-0.3151164651,-0.0436034799,-0.08808887,0.2152402699,0.4358336627,-0.0789023265,-0.065279454,0.3209886551,-0.0179740302,0.0676686615,-0.0443313085,-0.1122181565,0.3995007277,-0.1002356708,0.6040776372,-0.1201559752,0.0579323918,-0.1615790576,0.1620278955,0.3629038632,-0.0949904695,-0.2131394148,0.0145916874,-0.5914059281,0.1086386964,-0.2440940589,-0.0796029046,0.0442409627,0.3385514021,0.2097835839,0.4188049734,-0.1303366423,0.5874919891,-0.1820048988,-0.0004448143,-0.136841476,0.3376156092,-0.077340886,0.0069129346,-0.025883032,0.4640442133,0.19163315,-0.0753638968,0.1306604892,0.1775824875,0.2757100463,0.1431743801,0.3152182102,-0.1459710449,0.013373767,-0.1851287335,0.0264447127,0.2751175463,0.1668300778,-0.0880569145,-0.2502496839,0.1557591856,-0.0062821158,0.2842067778,0.1452289671,0.0032311126,0.2915836871,-0.0235694479,-0.0459581092,-0.1600633413,0.3323235512,-0.1702431589,0.5120025873,-0.006221151,-0.2748087049,0.1538019031,0.0134518212,-0.1130990013,0.0663182139,0.2667791247,-0.1694904119,0.1820977181,0.1649412066,0.2306359261,0.5306184292,0.1501768082,0.0584150627,-0.1042482406,0.1984350532,-0.226388678,0.196112588,0.0779637173,0.3595768809,0.1046478078,0.1316241622,0.1227424741,-0.2572664917,-0.5361442566,-0.149571985,0.0522079468,-0.2917066514,0.3217304945,-0.185494706,-0.4013975859,-0.3152742684,-0.1355844587,-0.2492753416,-0.3631968498,-0.1829154044,0.1399168819,0.0379369855,-0.0491964184,-0.1167751774,0.1715294868,-0.2130713314,-0.3847946823,-0.3287187815,-0.0870077685,-0.049715437,-0.0937781259,0.4112957716,0.100119248,0.1564811319,-0.4932972789,0.1215285957,0.094456993,-0.4239718616,-0.1459390521,-0.0658269599,0.1950836778,-0.0813921466,0.3485629559,-0.2351174653,-0.3012833893,0.3619758785,-0.019668784,-0.0797758698,0.1490667313,0.2029022425,-0.042098213,-0.1645146012,-0.0364278592,-0.8044582009,-0.3288797438,0.1570771933,0.2175286114,0.0154114179,0.4426355064,0.3873107731,0.0452336743,-0.0348101072,0.1303268522,-0.1764192879,-0.3531501889,0.6498783231,-0.3994504213,-0.2847266793,0.1182230935,0.2317685336,0.2142133862,-0.3526259363,-0.4864182472,0.0804351568,0.0773071796,0.2877722383,-0.0448323004,0.1416716278,-0.0867542922,-0.0689283088,-0.0460393466,-0.1349615753,-0.0135841006,-0.116391331,-0.3259768188,0.2562913597,0.0006416994,0.1128346696,-0.2048086971,0.5850800276,0.1846069694,0.1297905594,0.3108214438,-0.0963969231,0.686024785,-0.1007470489,-0.4953562319,-0.0185463745,-0.0426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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1674","title":"dutch_social can't be loaded","comments":"Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 thread.","body":"Hi all,\r\n\r\nI'm trying to import the `dutch_social` dataset described [here](https:\/\/huggingface.co\/datasets\/dutch_social).\r\n\r\nHowever, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.\r\n\r\n```\r\n(base) Koens-MacBook-Pro:~ koenvandenberge$ python\r\nPython 3.7.4 (default, Aug 13 2019, 15:17:50) \r\n[Clang 4.0.1 (tags\/RELEASE_401\/final)] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\ndataset = load_dataset(\r\n   'dutch_social')\r\n>>> dataset = load_dataset(\r\n...    'dutch_social')\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 2, in <module>\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at dutch_social\/dutch_social.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n```","comment_length":49,"text":"dutch_social can't be loaded \n Hi all,\r\n\r\nI'm trying to import the `dutch_social` dataset described [here](https:\/\/huggingface.co\/datasets\/dutch_social).\r\n\r\nHowever, the code that should load the data doesn't seem to be working, in particular because the corresponding files can't be found at the provided links.\r\n\r\n```\r\n(base) Koens-MacBook-Pro:~ koenvandenberge$ python\r\nPython 3.7.4 (default, Aug 13 2019, 15:17:50) \r\n[Clang 4.0.1 (tags\/RELEASE_401\/final)] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset\r\ndataset = load_dataset(\r\n   'dutch_social')\r\n>>> dataset = load_dataset(\r\n...    'dutch_social')\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 2, in <module>\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/koenvandenberge\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at dutch_social\/dutch_social.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/dutch_social\/dutch_social.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/dutch_social\/dutch_social.py\r\n``` \n Okay. Could you comment on the #1673 thread? Actually @thomwolf had commented that if i use datasets library from source, it would allow me to download the Hindi Wikipedia Dataset but even the version 1.1.3 gave me the same issue. The details are there in the issue #1673 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1673","title":"Unable to Download Hindi Wikipedia Dataset","comments":"Currently this dataset is only available when the library is installed from source since it was added after the last release.\r\n\r\nWe pin the dataset version with the library version so that people can have a reproducible dataset and processing when pinning the library.\r\n\r\nWe'll see if we can provide access to newer datasets with a warning that they are newer than your library version, that would help in cases like yours.","body":"I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.\r\n\r\n![Code](https:\/\/user-images.githubusercontent.com\/30871963\/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png)\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)\r\n","comment_length":72,"text":"Unable to Download Hindi Wikipedia Dataset \n I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.\r\n\r\n![Code](https:\/\/user-images.githubusercontent.com\/30871963\/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png)\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)\r\n \n Currently this dataset is only available when the library is installed from source since it was added after the last release.\r\n\r\nWe pin the dataset version with the library version so that people can have a reproducible dataset and processing when pinning the library.\r\n\r\nWe'll see if we can provide access to newer datasets with a warning that they are newer than your library version, that would help in cases like yours.","embeddings":[-0.1846334487,0.0373013057,-0.0611194484,0.2292183638,0.053209424,0.1153836846,-0.0216608755,0.3467042446,0.2222079933,0.0246456787,0.398733139,0.074465245,0.0466891788,0.2172787488,0.1481214762,-0.3831970096,0.0711239949,-0.0032809081,0.0407369472,0.1277780086,0.0758774877,0.2772662044,-0.2721773088,-0.0085311243,-0.5351586938,-0.0147672929,0.2069775462,-0.1029816121,-0.3329057097,-0.3878406584,0.453076005,0.0746859983,0.3272311687,0.2894625962,-0.000117929,0.0840014741,0.5217901468,-0.1599767059,-0.3209836483,-0.4747384191,-0.1340344846,-0.3125717938,0.1033498496,-0.2150286734,-0.1281321496,0.2258340567,0.3669331372,-0.2974428236,0.1387929171,0.1242169738,0.1640716642,-0.1203647479,0.386944592,-0.1464688033,0.203081727,0.2014706582,-0.0552636422,0.2983807623,0.0545501336,0.1054257676,-0.0732383355,0.1809621155,0.0445848443,-0.1273255348,0.3103481233,-0.3043245375,-0.2426697612,-0.4269345701,0.4759139121,0.3865130842,1.1295317411,-0.3715818822,-0.1677476466,0.1515140682,-0.0448209159,0.2206408381,0.4101853073,0.4490496218,-0.2433068007,-0.0120517062,0.1708031297,-0.3701305687,-0.157833159,0.3645862639,0.0316441916,0.2004282922,0.0091386847,0.1854778826,-0.0307361204,-0.2145123035,-0.02501937,-0.0500654131,0.120045878,0.3106352389,-0.2569937706,0.1385988295,-0.127401948,0.1306022853,0.0987364575,-0.1533349901,-0.0414458886,-0.0029117034,-0.0950385034,-0.0041448404,0.3619229496,-0.0806551874,0.0078141429,0.2096252292,0.256455183,0.2672003806,-0.0139932446,-0.0607570074,0.0611205436,-0.4209552705,-0.5915756226,-0.0259812288,-0.0162632968,-0.2765037715,-0.1287475973,0.0044562402,-0.3457309008,-0.2075077295,0.0737193525,0.0334329158,0.0313108116,0.1312571019,0.0619303472,0.0491787419,-0.1501957923,-0.3245725036,-0.050696224,0.2413212657,-0.300229311,0.0361862145,0.2658056617,-0.2571329176,0.3741752803,0.0476975664,-0.1977427155,0.0010906622,-0.0669957474,-0.2411717176,-0.3081691265,0.197338298,0.1855812818,0.4436905682,-0.2013188899,-0.1000121832,-0.038498003,0.1480777115,-0.2725658119,-0.012922259,0.0705824345,0.074526526,-0.2666237652,-0.1187896803,0.0480042249,0.2205324024,-0.0582599565,-0.4137549102,0.009203448,-0.0192557257,-0.1150910109,-0.3171354234,0.3101770878,0.4562660754,-0.7055321336,0.0829726681,-0.1259951144,-0.0040928465,0.0826830119,-0.0513191372,-0.2339766324,0.5790066719,-0.2916668653,0.0442174561,0.5752339363,-0.2936263978,-0.5257562399,-0.0133434022,-0.02399645,0.057880789,0.0556753725,0.2580733299,0.1837553829,0.006373134,-0.1392722875,0.3432026803,0.097191155,-0.1311730295,-0.2822996378,-0.0902168378,0.3322113454,0.153289929,0.2401341796,-0.1168657541,0.1273595244,0.4729164839,0.3813784719,0.0354138464,0.1606386602,0.3658227324,-0.2541016042,0.09451738,0.2299745679,-0.088264592,-0.0512497053,0.2550355494,-0.0206331778,-0.071366027,-0.1211463735,0.0199509189,-0.403144598,0.0117723644,-0.2332554311,0.1814034879,0.0689975396,0.0132382959,0.1103869081,0.3523840308,-0.0559859313,0.1727205366,-0.2753531933,0.030645363,-0.3404828608,0.4449898899,-0.1094449088,-0.0813350677,0.0301134735,0.1406916529,0.1717761159,-0.0056818076,-0.151619792,0.0053042262,0.1093395278,0.0264145229,0.2505449057,-0.0275604129,0.1490451247,-0.5346199274,0.2565496862,0.5513415933,0.0377882309,-0.0155817978,0.0395439006,0.0551243499,-0.2308329344,0.0603769757,-0.069590807,0.1959153116,0.3569629788,0.0926788896,0.0055166283,0.0128296521,0.2818467319,0.4182784557,0.2217084467,-0.0167972948,-0.0961955115,0.0408422239,0.57889992,0.0564463139,0.0157234799,0.1267824769,-0.2564577162,-0.0707025006,0.1073689535,0.1339578629,0.038056232,0.1063374206,0.0162664559,-0.207527101,0.0751836598,-0.1791910976,0.2018443793,0.1663945168,0.2951563597,0.1557773203,-0.1634331346,0.0725022703,-0.1826339811,-0.0771216527,0.0450495668,0.3206934333,-0.1707876623,-0.050545305,-0.3483173251,-0.518684566,-0.3468508124,-0.306591779,-0.486923337,-0.2183832675,-0.0752952248,-0.1249911115,0.0173766036,0.0453165025,-0.0387022905,-0.1222059354,0.0449226983,-0.3332616687,-0.0671157539,-0.3668973446,-0.3348782063,-0.0264077224,0.5341210365,0.0615052544,0.2562438548,-0.2601419389,-0.0517231934,-0.6239216328,-0.229999125,0.0948204845,-0.046230305,-0.1074339077,0.073105216,0.6945303679,-0.1953697503,0.0020850198,0.0359014384,0.2028860152,-0.0650187805,-0.1063526422,-0.0644028634,0.0037223068,0.1378206462,-0.5049888492,-0.3756238818,-0.2445554584,-0.0648974106,0.1017139927,0.056053523,-0.0199140925,0.0931154191,0.0310917161,0.267934233,0.151895389,-0.0283386596,-0.1419942528,0.347107619,-0.1432011425,-0.5207159519,0.5949020982,0.0072855256,0.1874605864,0.0847608,-0.3583533168,0.3445145488,0.0796519369,0.2515495121,0.1710092872,-0.0006898724,0.2284472585,-0.2805490494,0.2448115349,0.0850279555,-0.1836838275,0.1268242598,-0.3386278152,0.4829137921,0.3686639667,0.2770755887,-0.0187711753,0.6054495573,0.3167237937,0.0584708042,0.3383820057,0.1432934701,0.0349115692,-0.2171928585,-0.2373215407,0.1322418898,-0.0000805427,0.0751705319,0.1479733139,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1673","title":"Unable to Download Hindi Wikipedia Dataset","comments":"So for now, should i try and install the library from source and then try out the same piece of code? Will it work then, considering both the versions will match then?","body":"I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.\r\n\r\n![Code](https:\/\/user-images.githubusercontent.com\/30871963\/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png)\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)\r\n","comment_length":32,"text":"Unable to Download Hindi Wikipedia Dataset \n I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.\r\n\r\n![Code](https:\/\/user-images.githubusercontent.com\/30871963\/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png)\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)\r\n \n So for now, should i try and install the library from source and then try out the same piece of code? Will it work then, considering both the versions will match then?","embeddings":[-0.1116185784,0.1364629418,-0.0870575085,0.2553951144,0.0008571927,0.0589530841,-0.0200850423,0.3475781381,0.1966427863,-0.031186102,0.4182320535,0.0535380915,0.0738414675,0.2070575953,0.1762293726,-0.3135467768,0.1087693498,0.1200553849,0.0042044101,0.0592677221,0.0683595464,0.2611467242,-0.2298688442,-0.0048639877,-0.4776678979,0.0012445815,0.2221390009,-0.0502518117,-0.2584412098,-0.3565151095,0.4109671116,0.0497172885,0.2961830199,0.3686352372,-0.0001199416,0.1368225962,0.4905959666,-0.1850735098,-0.2386745512,-0.4604792297,-0.0924596488,-0.3030233681,0.0296326261,-0.2857756913,-0.1497669965,0.1927249879,0.3124535382,-0.2782360315,0.1295937598,0.1415741444,0.170101583,-0.0960211977,0.3933209181,-0.0845570192,0.2663611174,0.1897556335,0.0195259675,0.3279036582,-0.0306914207,0.1860106587,-0.0879555792,0.2282851338,0.1306127608,-0.1248108596,0.2921603918,-0.2631483376,-0.2344123572,-0.3487629294,0.4009373188,0.2763460875,1.0225684643,-0.3250437081,-0.2268597484,0.1726696491,-0.0803990588,0.1519275308,0.4632243216,0.3294928074,-0.200940758,-0.0213521793,0.1633627117,-0.4233598709,-0.1424345672,0.3965321183,-0.0582766086,0.1761892885,-0.0224964004,0.1639204472,-0.0848843008,-0.2016354501,0.0008347647,-0.1162046045,0.1215322092,0.2226236612,-0.1990119219,0.1172073185,-0.1028320417,0.154689759,0.0547818169,-0.1945848018,0.0123956772,0.0211747456,-0.114752695,-0.0663920864,0.3393965662,-0.0875509679,-0.0246436354,0.2418071628,0.2941322327,0.2672395706,-0.0053095212,-0.0251796693,0.135896638,-0.4400852919,-0.5205779672,-0.0086791916,-0.010131767,-0.3164439797,-0.1114299819,0.0814468339,-0.2926316261,-0.2897930741,0.0461605452,0.0422914028,-0.0381762385,0.1850257218,0.0670786351,0.0251767281,-0.183930099,-0.2609624267,-0.0484742597,0.3083530366,-0.3088774681,0.0374192894,0.2097858936,-0.2037178576,0.4332460761,0.0159764159,-0.1486077309,0.0410353504,-0.0718801171,-0.2721830606,-0.2053116113,0.1476372182,0.1482718438,0.4279994667,-0.2541537285,-0.0450664088,-0.0453885384,0.16307652,-0.1861615479,0.0238913614,0.1941388696,0.0877622217,-0.3134622872,-0.0832369104,0.0934656262,0.1890525818,0.0000830017,-0.5406222939,-0.0220427122,-0.0361358151,-0.0432399698,-0.2813766003,0.3444929719,0.4325047433,-0.7107768059,0.131054014,-0.1130259186,0.0302892216,0.0470137857,-0.0309073608,-0.2615732849,0.6532417536,-0.3513746262,0.0233924296,0.6675368547,-0.3990435004,-0.4707461596,-0.0478582866,-0.0367799699,0.0960422829,0.0560680963,0.2257205248,0.166121304,-0.0386955701,-0.0958093107,0.2801710963,0.0846248269,-0.1513461024,-0.3334873617,-0.089302972,0.3929633796,0.1288661063,0.2982840538,-0.1237699762,0.0904327407,0.444034487,0.3604435027,-0.0058935569,0.0943313688,0.3124651909,-0.2774246037,0.0735908374,0.1572695673,-0.1025939211,-0.0308641419,0.1844977736,-0.0134440791,-0.1441207379,-0.0776043087,-0.0127051789,-0.4661594331,-0.0546104833,-0.2460792959,0.1904679686,0.0802894384,0.0531753562,0.1517300755,0.2807612121,-0.0819265917,0.1642741114,-0.2459920198,0.0311402213,-0.4025273025,0.5011726618,-0.1499479413,-0.0836176574,0.07487005,0.1212408692,0.184238866,-0.0470681638,-0.1886332482,0.0152834347,0.0330634303,0.0542501546,0.1806891263,-0.133483544,0.2101243138,-0.4950474501,0.2544282079,0.581487596,0.0968516245,-0.0262989048,0.0826093629,-0.0026080934,-0.2105977833,0.0477733761,0.0073690796,0.0598729588,0.3191693425,0.1081823781,-0.0205736812,-0.0164077785,0.2179352939,0.4814141393,0.2567302585,-0.0057961638,-0.1379146874,0.0355149694,0.6405736804,0.0470729433,-0.0323285684,0.0782957971,-0.1548196524,-0.0652377531,0.1192677319,0.1161776558,0.120758757,0.1098808125,-0.0378070809,-0.2099820524,0.0756020471,-0.1881776303,0.1930868626,0.2020437121,0.3380408883,0.2567362189,-0.1854952574,0.0773996189,-0.1787091643,-0.0802071989,0.0539321303,0.2617740631,-0.1984218061,-0.0188062768,-0.3552405238,-0.5050302148,-0.4395568073,-0.33516711,-0.4656643867,-0.1712058336,-0.0870599672,-0.1600824296,-0.0653273538,0.1061923206,-0.0200817306,-0.1783478409,-0.0367398299,-0.3817269802,-0.0138923321,-0.3240439296,-0.3724229336,-0.0291352049,0.5464525819,0.0917799473,0.2754091024,-0.3189601004,-0.0864539817,-0.6308422089,-0.2829709649,0.1234155968,-0.0257624649,-0.1141891479,0.1440612227,0.6267017722,-0.1173535436,-0.0635198429,0.0890231133,0.1732285023,-0.0416595303,-0.2011984438,-0.033210665,0.0631767586,0.1438006759,-0.5816696882,-0.3861139119,-0.2711100578,-0.1043009833,0.1433054358,0.0773236081,-0.1253488362,0.0453780778,0.0498803593,0.3385449648,0.1959766448,-0.0305155218,-0.0556683466,0.3645651937,-0.1890694946,-0.5188432336,0.5080653429,-0.0794422776,0.2510534823,0.1673784256,-0.3368943632,0.2751417458,0.0962032452,0.2145345211,0.179372564,0.1414342374,0.2075333595,-0.2547532618,0.2343722582,0.068531923,-0.1730420589,0.2150754035,-0.35712713,0.4659358859,0.4194562733,0.2468277365,-0.1778564304,0.6441901326,0.2383691072,-0.0121935122,0.3310514092,0.1444189847,0.1175789535,-0.191721186,-0.1829325855,0.0724566877,0.00495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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1673","title":"Unable to Download Hindi Wikipedia Dataset","comments":"Hey, so i tried installing the library from source using the commands : **git clone https:\/\/github.com\/huggingface\/datasets**,  **cd datasets** and then **pip3 install -e .**. But i still am facing the same error that file is not found. Please advise.\r\n\r\nThe Datasets library version now is 1.1.3 by installing from source as compared to the earlier 1.0.3 that i had loaded using pip command but I am still getting same error\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103479005-69f3b080-4df0-11eb-83ae-58d7bb56a90e.png)\r\n","body":"I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.\r\n\r\n![Code](https:\/\/user-images.githubusercontent.com\/30871963\/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png)\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)\r\n","comment_length":71,"text":"Unable to Download Hindi Wikipedia Dataset \n I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.\r\n\r\n![Code](https:\/\/user-images.githubusercontent.com\/30871963\/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png)\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)\r\n \n Hey, so i tried installing the library from source using the commands : **git clone https:\/\/github.com\/huggingface\/datasets**,  **cd datasets** and then **pip3 install -e .**. But i still am facing the same error that file is not found. Please advise.\r\n\r\nThe Datasets library version now is 1.1.3 by installing from source as compared to the earlier 1.0.3 that i had loaded using pip command but I am still getting same error\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103479005-69f3b080-4df0-11eb-83ae-58d7bb56a90e.png)\r\n","embeddings":[-0.1465503126,0.0689324141,-0.0685979575,0.2491659224,0.0940222442,0.0981624722,-0.0108038336,0.300265044,0.2056314349,0.0105530787,0.3993399441,0.1249028668,0.094412744,0.1712741852,0.2068688273,-0.3449133039,0.0655948073,0.1091009825,-0.0037668047,0.1099171191,0.1411299109,0.3182806969,-0.2545852959,-0.0223163571,-0.4677815437,0.0209264811,0.1805852801,-0.1294429004,-0.3263757825,-0.3986920714,0.4391380847,0.0544427261,0.2593984604,0.3542151749,-0.0001193817,0.098571986,0.5011398792,-0.1628396213,-0.3172684014,-0.4880221188,-0.1099457964,-0.3709641099,0.0777489766,-0.2636030614,-0.1019325778,0.1779790819,0.3313590288,-0.1715381294,0.0750310197,0.166800946,0.1584782153,-0.0895957723,0.4187116921,-0.1109170541,0.2155592144,0.2446872592,-0.0607995801,0.2880851626,0.0487542935,0.0981846377,-0.0256014336,0.1852132529,0.050882712,-0.0827116445,0.3174597919,-0.2934673429,-0.2573735714,-0.3573926985,0.4213846028,0.3379175663,1.02250278,-0.398583889,-0.273886472,0.1128012687,-0.005135268,0.1822863072,0.4232428968,0.4133462906,-0.2505421638,-0.015567231,0.0776664764,-0.3905896842,-0.1346762478,0.376213938,0.0835084394,0.107283175,-0.0483075678,0.1569276154,-0.0117750047,-0.193001911,-0.0286203381,-0.0906697512,0.121988818,0.3296678364,-0.2907230556,0.1777351499,-0.1184544936,0.156657815,0.0986154079,-0.180471912,-0.019992372,0.0240814723,-0.1072402447,0.0255629774,0.3462226093,-0.0297972932,-0.0316005982,0.2042375654,0.2528335452,0.3454883099,0.0378434807,-0.057752721,0.066608347,-0.3518583179,-0.5248504281,-0.0550721474,-0.0665054545,-0.2746732533,-0.1836228073,0.0162873939,-0.2484945506,-0.1921250671,0.1000580043,0.0523053966,0.0006563097,0.1718006432,0.0245504193,0.0292796567,-0.1951865256,-0.2448223531,-0.0415181071,0.3407041728,-0.3416930437,0.0294138417,0.2804209888,-0.268083632,0.451384604,0.0620260201,-0.1783179641,0.0249203332,-0.0775749758,-0.2494254261,-0.2391320914,0.2149781138,0.1716963947,0.4294681251,-0.1778288484,-0.0899565071,-0.0230659153,0.0799571648,-0.2160615623,0.0227570962,0.0705771744,0.0649512336,-0.3262450099,-0.1010823548,-0.0728589147,0.1652237177,-0.0362290777,-0.4435551763,0.0267364979,-0.0415836424,-0.0482419953,-0.2789181173,0.3309089839,0.4413673878,-0.670966804,0.052452635,-0.1534172446,0.000172739,0.1014323533,0.0084355427,-0.2476768047,0.5937273502,-0.3557330072,0.0363896377,0.5849054456,-0.3862292469,-0.5270808339,0.0094767921,-0.0043359026,0.0828576759,0.0765917078,0.2425976694,0.1698553264,0.0387084521,-0.190120399,0.313441664,0.0714813843,-0.0468932912,-0.2660342157,-0.1286066324,0.3043842912,0.1595204026,0.2136811316,-0.1706256419,0.165395841,0.4260933399,0.3458833396,-0.0076898569,0.1791165918,0.4467867315,-0.1761472821,0.140531674,0.2131312788,-0.1383676827,-0.0526880063,0.1961433589,0.0010356202,-0.0422132239,-0.2385879159,-0.0071198586,-0.4226621687,-0.0625063404,-0.2837450802,0.1142023727,0.0646001622,0.0346823633,0.132375136,0.2899646759,-0.0708186701,0.2151221633,-0.233903721,0.0451227576,-0.420712471,0.4064812064,-0.1321009248,-0.1083956137,0.054941576,0.1968874335,0.1279750168,-0.0218865201,-0.1831482351,0.0394128859,0.0695022941,0.0251194462,0.2400258034,-0.120562315,0.1684936434,-0.5526188016,0.2112795562,0.4790015519,0.0803966075,0.0479688346,-0.0337376334,-0.0516845994,-0.2463632226,0.0547255278,-0.0189790037,0.1343645751,0.3730354905,0.1178276092,-0.0066963318,-0.0023127843,0.2520800233,0.4505518377,0.2667425871,0.0331759229,-0.052323088,0.0564053319,0.6210753322,0.0723135322,0.0047869459,0.1327172667,-0.2396251708,-0.1066733897,0.0785985589,0.1280668676,0.1346791685,0.1067078412,0.0131321168,-0.2335936278,0.1373036653,-0.1548273414,0.1748354882,0.1962943375,0.2368424684,0.1871284395,-0.1233921275,0.0761108994,-0.1863161474,-0.0816784725,0.0659447089,0.3190715015,-0.1899593771,-0.0299229771,-0.3684742749,-0.5022326708,-0.3432816863,-0.3228256702,-0.4344170988,-0.2218503952,-0.0949574262,-0.0881347433,0.0537266769,0.0500501208,0.0405996032,-0.2014796585,0.0236134008,-0.340752244,-0.0830789432,-0.325429827,-0.3207295835,-0.050599739,0.5199856758,0.1368112415,0.3006576598,-0.3158456385,-0.0924753398,-0.6595547199,-0.2461890876,0.1009241417,-0.0530718677,-0.0160337575,0.1856158823,0.6253229976,-0.2144840062,0.0281605292,0.0658021718,0.1511944532,-0.081744507,-0.0750561059,-0.0750973895,-0.0248522907,0.0920032933,-0.5493107438,-0.369910121,-0.272647649,-0.0164931472,0.0344290137,0.058830712,-0.0658104271,0.0304345563,0.1151822954,0.1985549629,0.2137798816,-0.012564918,-0.1578126401,0.4093869328,-0.1883184463,-0.5284603834,0.6092122793,-0.0119419405,0.2732456028,0.0532611683,-0.313065052,0.2904496789,0.0179675743,0.2536846399,0.1646978408,0.1323915124,0.1832594424,-0.2988812625,0.2472976446,0.0479275919,-0.1605923772,0.1197418198,-0.3451824784,0.4536977112,0.3488155603,0.2859294415,-0.0269231107,0.6235115528,0.3517344296,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1673","title":"Unable to Download Hindi Wikipedia Dataset","comments":"Looks like the wikipedia dump for hindi at the date of 05\/05\/2020 is not available anymore.\r\nYou can try to load a more recent version of wikipedia\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"wikipedia\", language=\"hi\", date=\"20210101\", split=\"train\", beam_runner=\"DirectRunner\")\r\n```","body":"I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.\r\n\r\n![Code](https:\/\/user-images.githubusercontent.com\/30871963\/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png)\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)\r\n","comment_length":40,"text":"Unable to Download Hindi Wikipedia Dataset \n I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.\r\n\r\n![Code](https:\/\/user-images.githubusercontent.com\/30871963\/103437466-1f3a3300-4c4e-11eb-9d54-fc9601abfeec.png)\r\n\r\n![Error](https:\/\/user-images.githubusercontent.com\/30871963\/103437407-7ee40e80-4c4d-11eb-8151-a86eb664e6be.png)\r\n \n Looks like the wikipedia dump for hindi at the date of 05\/05\/2020 is not available anymore.\r\nYou can try to load a more recent version of wikipedia\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"wikipedia\", language=\"hi\", date=\"20210101\", split=\"train\", beam_runner=\"DirectRunner\")\r\n```","embeddings":[-0.1142456904,0.0112914881,-0.0827291012,0.188191995,0.0503296554,0.203110978,-0.0217098705,0.4027991295,0.2084719241,-0.0058662095,0.2971007526,-0.01063844,0.1007415429,0.1128164455,0.1189810112,-0.3750500679,0.0604316592,-0.0714059174,0.1233816445,0.0742849037,0.0124701401,0.2530111372,-0.2966147363,-0.000915119,-0.5076264143,0.0811902434,0.0929644704,-0.1746855974,-0.3081796169,-0.3290758431,0.3946946263,0.032021571,0.3380321264,0.267832309,-0.0001185627,0.0496360399,0.5291934013,-0.1734580994,-0.3872661889,-0.3783107996,-0.1402136832,-0.3079809248,0.0476080514,-0.2449628711,-0.0817109272,0.0995807946,0.3573267162,-0.2165108323,0.119702585,0.2080284059,0.1690301895,-0.0981415585,0.3833535612,-0.2002480477,0.1789742857,0.2349620014,-0.0561930016,0.3583011329,-0.0010121538,-0.0040538209,-0.0369790271,0.1388836801,0.046540305,-0.1358784586,0.2164182961,-0.2033793777,-0.1063366607,-0.380720377,0.4467658699,0.2975744009,1.0478886366,-0.3198606968,-0.2823239565,0.214803502,0.0255117435,0.1601150185,0.3726471961,0.3713812828,-0.2544006407,0.0224460494,0.2328926027,-0.4139357805,-0.0935643092,0.4551313818,0.0004587615,0.1820247024,0.0393327549,0.1147852093,-0.0187585372,-0.265148133,-0.0293263476,-0.1063046306,0.1576577574,0.3844853044,-0.2153396159,0.0957845375,-0.1552342474,0.150580287,0.0482715629,-0.2602938116,-0.0801319703,-0.0422489978,-0.0851458535,-0.0182959754,0.4143214822,-0.1023116261,0.0436332449,0.2053697705,0.2462697923,0.3137840331,0.0165848266,-0.0224873368,0.1246273592,-0.4093409777,-0.4507754147,-0.0420527533,0.0805299729,-0.2413734794,-0.1452439725,0.0494721644,-0.3812088668,-0.2045060843,-0.0651382655,0.0024246085,-0.0156679265,0.1061700061,0.1126058847,0.0522613674,-0.1497132927,-0.3151382804,-0.0803760365,0.1926774681,-0.3331976235,0.1103744507,0.2177271396,-0.2642215788,0.2979155779,0.1510023922,-0.2088230401,-0.0396617167,-0.1556182653,-0.2326006144,-0.3740469515,0.1542014033,0.1772901863,0.4825568795,-0.1454354376,-0.1282797903,-0.0095931599,0.1143854484,-0.3579782844,0.0362945534,0.0355905481,0.084700264,-0.2616971731,-0.0836329684,0.0690908581,0.177265659,-0.0340588652,-0.4436794519,0.0225847475,-0.0526538417,-0.1188783944,-0.2595073581,0.3008177876,0.4370456338,-0.5658004284,0.0131420568,-0.208628282,0.0295671951,0.0542645529,0.0161206499,-0.2735312283,0.66962111,-0.2844646275,-0.0045776512,0.5242993832,-0.266395241,-0.4838491976,-0.014880049,-0.0151251247,0.09028925,-0.0813735351,0.2021463066,0.2086894065,-0.0237698592,0.0005578518,0.3112415969,0.0934147984,-0.194594577,-0.2605804801,-0.0950673744,0.4047927856,0.1130468547,0.2699636519,-0.0457578339,0.1129858568,0.6010671258,0.4021862149,0.1348844469,0.1459055841,0.3607157469,-0.240617767,0.1704009771,0.2450812459,-0.1539526582,-0.0987970456,0.2499047071,0.0002758898,0.0224659145,-0.1439981014,-0.0125864912,-0.44327721,0.0313378796,-0.2176449746,0.1527222246,0.0891195312,-0.0159838405,0.0716809407,0.430372715,0.0207281336,0.0172469784,-0.1806950718,0.016178282,-0.3506973088,0.4398330748,-0.1203364357,-0.0601956099,-0.0120089427,0.1021561399,0.2002846897,0.0723434389,-0.1172060594,-0.0264040567,0.2186687887,0.055788178,0.2651782334,-0.1387015134,0.1483439654,-0.5472911,0.2296742052,0.5981794596,0.0541749336,-0.0634030178,0.0449294746,0.0571675524,-0.1294469535,0.0145988837,-0.1710965186,0.2312998176,0.3597036898,0.0177478809,0.033297196,-0.0226083715,0.2005066723,0.388009429,0.160476163,0.0257180482,-0.0517596751,-0.0501822196,0.5676726699,0.013026678,0.0404762663,0.1214449555,-0.2276733816,-0.1060064733,0.07224489,-0.0035087985,0.0372728705,0.0625212267,0.0145789078,-0.2079574913,0.13756603,-0.1434830576,0.2105375975,0.1933050752,0.2895304263,0.1703340262,-0.1188584939,0.0961954817,-0.1260169297,-0.001003818,0.032303568,0.3559486866,-0.142358005,-0.0748434812,-0.314160496,-0.3821249306,-0.346678853,-0.3569432497,-0.4646942019,-0.320218116,-0.0665283948,-0.1526101232,0.0118520819,0.0595371015,-0.0201086067,-0.1933003515,0.0828795731,-0.3502337337,-0.0672850534,-0.3931134939,-0.2638324201,-0.0219011996,0.4575640559,0.1035848036,0.1910099834,-0.2944647372,-0.1393680423,-0.6602706909,-0.2131776959,0.0333165675,-0.0553252883,-0.0693884715,0.0611698329,0.7040323019,-0.1448130459,-0.0219321381,0.0930676758,0.125358507,-0.0922289193,-0.0308833253,-0.0188181549,0.0705837309,0.1434679627,-0.4446650743,-0.3900807798,-0.1972253025,-0.0793293864,0.046562694,0.0004414085,-0.0096682468,0.10412018,-0.0156586692,0.2523651421,0.0786527023,-0.0663231984,-0.0295413435,0.4097995758,-0.1150566563,-0.4930728376,0.5355672836,-0.0323044658,0.0980366543,0.0833553746,-0.3445494771,0.3909450173,0.1141969487,0.3440346718,0.178769514,-0.0629390851,0.1142304763,-0.3059741557,0.2277783304,0.1264754087,-0.0927611738,0.0243690051,-0.2515067458,0.5262636542,0.3555581272,0.2398677319,-0.0959963873,0.6890134811,0.309278667,0.09135212,0.3108575046,0.1147760823,0.0427565351,-0.1477428824,-0.3294082582,0.0390819907,-0.001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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1672","title":"load_dataset hang on file_lock","comments":"Having the same issue with `datasets 1.1.3` of `1.5.0` (both tracebacks look the same) and `kilt_wikipedia`, Ubuntu 20.04\r\n\r\n```py\r\nIn [1]: from datasets import load_dataset                                                                                                                                                                          \r\n\r\nIn [2]: wikipedia = load_dataset('kilt_wikipedia')['full']                                                                                                                   \r\nDownloading: 7.37kB [00:00, 2.74MB\/s]                                                                                                                                                                              \r\nDownloading: 3.33kB [00:00, 1.44MB\/s]                                                                                                                                                                              \r\n^C---------------------------------------------------------------------------\r\nOSError                                   Traceback (most recent call last)\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/utils\/filelock.py in _acquire(self)\r\n    380         try:\r\n--> 381             fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\n    382         except (IOError, OSError):\r\n\r\nOSError: [Errno 37] No locks available\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nKeyboardInterrupt                         Traceback (most recent call last)\r\n<ipython-input-2-f412d3d46ec9> in <module>\r\n----> 1 wikipedia = load_dataset('kilt_wikipedia')['full']\r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, sav\r\ne_infos, script_version, **config_kwargs)\r\n    601         hash=hash,\r\n    602         features=features,\r\n--> 603         **config_kwargs,\r\n    604     )\r\n    605 \r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/builder.py in __init__(self, *args, **kwargs)\r\n    841     def __init__(self, *args, **kwargs):\r\n    842         self._writer_batch_size = kwargs.pop(\"writer_batch_size\", self._writer_batch_size)\r\n--> 843         super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n    844 \r\n    845     @abc.abstractmethod\r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)\r\n    174             os.makedirs(self._cache_dir_root, exist_ok=True)\r\n    175         lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, \"_\") + \".lock\")\r\n--> 176         with FileLock(lock_path):\r\n    177             if os.path.exists(self._cache_dir):  # check if data exist\r\n    178                 if len(os.listdir(self._cache_dir)) > 0:\r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/utils\/filelock.py in __enter__(self)\r\n    312 \r\n    313     def __enter__(self):\r\n--> 314         self.acquire()\r\n    315         return self\r\n    316 \r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/utils\/filelock.py in acquire(self, timeout, poll_intervall)\r\n    261                     if not self.is_locked:\r\n    262                         logger().debug(\"Attempting to acquire lock %s on %s\", lock_id, lock_filename)\r\n--> 263                         self._acquire()\r\n    264 \r\n    265                 if self.is_locked:\r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/utils\/filelock.py in _acquire(self)\r\n    379 \r\n    380         try:\r\n--> 381             fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\n    382         except (IOError, OSError):\r\n    383             os.close(fd)\r\n\r\nKeyboardInterrupt: \r\n\r\n```","body":"I am trying to load the squad dataset. Fails on Windows 10 but succeeds in Colab.\r\nTransformers:  3.3.1\r\nDatasets:  1.0.2\r\nWindows 10 (also tested in WSL)\r\n\r\n```\r\ndatasets.logging.set_verbosity_debug()\r\ndatasets.\r\ntrain_dataset = load_dataset('squad', split='train')\r\nvalid_dataset = load_dataset('squad', split='validation')\r\n\r\ntrain_dataset.features\r\n```\r\n\r\n```\r\nhttps:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py not found in cache or force_download set to True, downloading to C:\\Users\\simpl\\.cache\\huggingface\\datasets\\tmpzj_o_6u7\r\nDownloading:\r\n5.24k\/? [00:00<00:00, 134kB\/s]\r\nstoring https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py in cache at C:\\Users\\simpl\\.cache\\huggingface\\datasets\\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py\r\ncreating metadata file for C:\\Users\\simpl\\.cache\\huggingface\\datasets\\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py\r\n\r\nChecking C:\\Users\\simpl\\.cache\\huggingface\\datasets\\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py at C:\\Users\\simpl\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\squad\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py at C:\\Users\\simpl\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\squad\\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py to C:\\Users\\simpl\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\squad\\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\\squad.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py at C:\\Users\\simpl\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\squad\\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\\squad.json\r\nNo config specified, defaulting to first: squad\/plain_text\r\n```\r\n\r\nInterrupting the jupyter kernel we are in a file lock.\r\n\r\nIn Google Colab the download is ok. In contrast to a local run in colab dataset_infos.json is downloaded\r\n```\r\nhttps:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/dataset_infos.json not found in cache or force_download set to True, downloading to \/root\/.cache\/huggingface\/datasets\/tmptl9ha_ad\r\n\r\nDownloading:\r\n2.19k\/? [00:00<00:00, 26.2kB\/s]\r\n```","comment_length":234,"text":"load_dataset hang on file_lock \n I am trying to load the squad dataset. Fails on Windows 10 but succeeds in Colab.\r\nTransformers:  3.3.1\r\nDatasets:  1.0.2\r\nWindows 10 (also tested in WSL)\r\n\r\n```\r\ndatasets.logging.set_verbosity_debug()\r\ndatasets.\r\ntrain_dataset = load_dataset('squad', split='train')\r\nvalid_dataset = load_dataset('squad', split='validation')\r\n\r\ntrain_dataset.features\r\n```\r\n\r\n```\r\nhttps:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py not found in cache or force_download set to True, downloading to C:\\Users\\simpl\\.cache\\huggingface\\datasets\\tmpzj_o_6u7\r\nDownloading:\r\n5.24k\/? [00:00<00:00, 134kB\/s]\r\nstoring https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py in cache at C:\\Users\\simpl\\.cache\\huggingface\\datasets\\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py\r\ncreating metadata file for C:\\Users\\simpl\\.cache\\huggingface\\datasets\\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py\r\n\r\nChecking C:\\Users\\simpl\\.cache\\huggingface\\datasets\\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py at C:\\Users\\simpl\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\squad\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py at C:\\Users\\simpl\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\squad\\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py to C:\\Users\\simpl\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\squad\\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\\squad.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/squad.py at C:\\Users\\simpl\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\squad\\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\\squad.json\r\nNo config specified, defaulting to first: squad\/plain_text\r\n```\r\n\r\nInterrupting the jupyter kernel we are in a file lock.\r\n\r\nIn Google Colab the download is ok. In contrast to a local run in colab dataset_infos.json is downloaded\r\n```\r\nhttps:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/squad\/dataset_infos.json not found in cache or force_download set to True, downloading to \/root\/.cache\/huggingface\/datasets\/tmptl9ha_ad\r\n\r\nDownloading:\r\n2.19k\/? [00:00<00:00, 26.2kB\/s]\r\n``` \n Having the same issue with `datasets 1.1.3` of `1.5.0` (both tracebacks look the same) and `kilt_wikipedia`, Ubuntu 20.04\r\n\r\n```py\r\nIn [1]: from datasets import load_dataset                                                                                                                                                                          \r\n\r\nIn [2]: wikipedia = load_dataset('kilt_wikipedia')['full']                                                                                                                   \r\nDownloading: 7.37kB [00:00, 2.74MB\/s]                                                                                                                                                                              \r\nDownloading: 3.33kB [00:00, 1.44MB\/s]                                                                                                                                                                              \r\n^C---------------------------------------------------------------------------\r\nOSError                                   Traceback (most recent call last)\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/utils\/filelock.py in _acquire(self)\r\n    380         try:\r\n--> 381             fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\n    382         except (IOError, OSError):\r\n\r\nOSError: [Errno 37] No locks available\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nKeyboardInterrupt                         Traceback (most recent call last)\r\n<ipython-input-2-f412d3d46ec9> in <module>\r\n----> 1 wikipedia = load_dataset('kilt_wikipedia')['full']\r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, sav\r\ne_infos, script_version, **config_kwargs)\r\n    601         hash=hash,\r\n    602         features=features,\r\n--> 603         **config_kwargs,\r\n    604     )\r\n    605 \r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/builder.py in __init__(self, *args, **kwargs)\r\n    841     def __init__(self, *args, **kwargs):\r\n    842         self._writer_batch_size = kwargs.pop(\"writer_batch_size\", self._writer_batch_size)\r\n--> 843         super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n    844 \r\n    845     @abc.abstractmethod\r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)\r\n    174             os.makedirs(self._cache_dir_root, exist_ok=True)\r\n    175         lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, \"_\") + \".lock\")\r\n--> 176         with FileLock(lock_path):\r\n    177             if os.path.exists(self._cache_dir):  # check if data exist\r\n    178                 if len(os.listdir(self._cache_dir)) > 0:\r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/utils\/filelock.py in __enter__(self)\r\n    312 \r\n    313     def __enter__(self):\r\n--> 314         self.acquire()\r\n    315         return self\r\n    316 \r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/utils\/filelock.py in acquire(self, timeout, poll_intervall)\r\n    261                     if not self.is_locked:\r\n    262                         logger().debug(\"Attempting to acquire lock %s on %s\", lock_id, lock_filename)\r\n--> 263                         self._acquire()\r\n    264 \r\n    265                 if self.is_locked:\r\n\r\n~\/anaconda3\/envs\/transformers2\/lib\/python3.7\/site-packages\/datasets\/utils\/filelock.py in _acquire(self)\r\n    379 \r\n    380         try:\r\n--> 381             fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\n    382         except (IOError, OSError):\r\n    383             os.close(fd)\r\n\r\nKeyboardInterrupt: \r\n\r\n```","embeddings":[-0.3021154404,-0.0215215832,-0.0879950076,0.178936407,0.5179888606,0.1499255449,0.5325869918,-0.0388461575,0.0199962519,0.0043181255,-0.2535487115,0.25555709,-0.0103855347,-0.1183214337,-0.0975556076,0.0359974504,0.0950897262,0.1051173806,0.0844943151,0.1125974804,-0.0238301829,0.4240731001,-0.3112899065,-0.2331582755,-0.7260748744,-0.0725926012,0.0726541728,0.2130880803,-0.268955946,-0.2011618763,0.5048645139,0.1790501773,0.2133804262,0.5419107676,-0.0001225752,0.0556077734,0.2359667122,0.0357728191,-0.2530398667,-0.2616369724,-0.2748567462,-0.2957787216,0.1831113249,0.012881794,-0.1750219017,0.7024013996,0.1763481945,-0.4491305947,0.4564883709,0.2201069146,0.1482032835,0.3765569031,-0.0643603578,-0.2676066756,0.1338297576,-0.1435051858,-0.2925020754,0.4845799506,0.4809325337,-0.1317549795,0.1103980094,0.1550246179,-0.0124723623,-0.168261677,0.0610582121,0.0491241217,-0.2083521485,-0.4002087712,0.4759041369,0.1590743959,0.462074101,-0.1445714831,-0.2766668797,-0.0671958849,0.3508067429,0.1159304976,0.5585261583,0.2401416451,-0.0911733434,0.154171586,-0.0766754821,0.1578611284,0.0243432038,-0.0620982833,0.0891702473,0.2551713884,-0.0876449049,0.0554294996,0.1462422758,0.0595081635,0.0929599479,-0.0263972823,-0.0479124971,0.1122642159,-0.5934708714,0.1357288659,-0.330829829,0.3397850692,-0.1689827591,0.0296289809,0.0081195422,0.1290463209,0.3702765703,0.1749778092,-0.0086369375,0.0606242754,0.1405549794,0.0206459891,0.3935605884,0.128599301,-0.3604629338,0.1481845677,-0.3828315437,-0.3367047906,0.1523135751,0.0648473948,0.1792359948,-0.2723613679,-0.2743980885,-0.0824539289,0.0945712551,0.0038924548,0.3614347279,0.5836243629,-0.0673661828,-0.0278212335,-0.1538965106,-0.0384557545,-0.3419782817,0.1740929484,-0.1228892058,0.0187594723,-0.1587666869,0.1141898558,0.4133546352,-0.4438305497,0.3186836243,0.0627911836,0.1318190396,-0.0032936137,0.0621202029,-0.2714750171,-0.2003184855,0.3339361548,0.0880344287,0.2697606981,-0.0520738587,-0.4359939694,-0.1121630594,-0.0120501053,-0.1778403372,-0.0692958608,0.0639475808,0.1069511026,-0.287111789,0.012819564,-0.1811254025,-0.0161171686,0.1178004816,-0.061824739,0.1045139655,-0.2027835846,-0.2577009499,-0.1545438617,0.1867063791,0.6341595054,-0.4503531754,0.0353727788,0.3156071305,-0.3282568157,-0.1789106429,0.2429857254,-0.1054776087,-0.193110764,-0.4120923877,0.0113430647,0.2249480039,-0.557004571,-0.7323595285,0.2544721067,-0.2879525423,-0.108532533,0.3105348647,0.24202995,0.3244645894,-0.1146094203,0.1471154541,0.0610773563,-0.0618501641,0.0426971018,-0.1446962357,-0.1810022891,0.0861845315,0.2156251222,0.0729595497,-0.0619786195,0.1285819709,0.6232088804,0.1470398307,0.1933713555,-0.0859030411,0.3554547429,0.3095434904,-0.1419392377,-0.0124201002,-0.2539836466,-0.7716570497,0.4025574923,-0.064985469,0.1990300715,-0.0898044258,-0.2064938247,-0.1742604226,0.0108299283,-0.3812264502,-0.1427406967,0.0050982153,-0.0223159324,0.1340504885,-0.0619087219,0.0002701475,0.6825737357,-0.0598089062,0.1077914089,-0.2840501368,0.1238967851,-0.2325538248,-0.2640245557,0.0058739875,-0.0486497916,0.1146543249,-0.2998555303,-0.1581552476,0.1780064106,-0.017896438,0.1784740686,0.0875405744,-0.0402759425,0.2334705144,-0.1706898659,-0.0394272134,-0.0271221623,0.10674119,-0.2455772758,-0.1253591627,0.2161942422,-0.3088027835,-0.0180273224,-0.0053029177,-0.1447438002,0.1779081523,0.1409148723,-0.0248716529,0.0228372086,0.2542496026,0.3635209501,0.5597595572,0.2122932971,0.2021749467,0.1637285352,0.6630067229,0.0458530672,-0.1473339051,-0.0978469625,-0.0810114443,0.0671536699,0.1066267192,0.0425916016,0.5582988858,0.1253881305,0.0176518466,0.0071026068,-0.0917788669,-0.3237576485,0.1106104702,0.0651510507,0.0299530141,0.2512910366,0.1034510806,0.0331908837,-0.1568048894,-0.284660995,0.2054218203,0.1000614911,-0.1784664094,0.0483886711,-0.1830164194,0.1056167856,-0.1569919437,0.1695332527,-0.3918553889,-0.1113442555,-0.2496089041,0.4572722614,0.4709657729,0.1210713387,-0.2346424907,0.0072332034,0.146064505,-0.3017837107,-0.1085824668,-0.0973616913,-0.0893343315,-0.0566942245,0.6666872501,-0.149059698,0.2231175005,-0.0336790383,-0.0768713951,-0.003031556,-0.2530536354,0.1237075552,-0.1360322833,0.4206443429,0.0739684999,0.1953096539,-0.2648068368,-0.0657883957,0.2203661799,-0.1555208713,-0.1244605482,-0.184746936,-0.0504730865,-0.1968497336,-0.0472335778,-0.1776357591,-0.4348340333,-0.3862979114,0.020922523,-0.2410447896,-0.1923210323,0.340686053,0.0218808055,0.3659332991,0.1288529783,0.0627439097,-0.1347733289,-0.621917069,0.2681832612,-0.0112997582,-0.3586463332,0.1750245243,-0.0209160596,-0.1368217021,-0.0861629397,-0.3688417971,-0.200804159,-0.2929191589,0.3318698704,-0.1156508848,-0.0950155482,0.3846439719,-0.1934524626,0.0707099363,-0.0797456577,-0.2999759614,0.0513800345,-0.037532907,0.1813334972,-0.0543623082,0.07557524,-0.0318417996,0.6378086805,0.346427381,-0.0828953087,0.2503506839,0.0616514124,0.3179033399,-0.105567269,-0.3772533536,0.1237836331,0.0217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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1671","title":"connection issue ","comments":"Also, mayjor issue for me is the format issue, even if I go through changing the whole code to use load_from_disk, then if I do \r\n\r\nd = datasets.load_from_disk(\"imdb\")\r\nd = d[\"train\"][:10] => the format of this is no more in datasets format\r\nthis is different from you call load_datasets(\"train[10]\")\r\n\r\ncould you tell me how I can make the two datastes the same format @lhoestq \r\n\r\n","body":"Hi\r\nI am getting this connection issue, resulting in large failure on cloud, @lhoestq  I appreciate your help on this.\r\n\r\nIf I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks\r\n\r\nI tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks\r\n\r\n```\r\nimdb = datasets.load_dataset(\"imdb\")\r\nimdb.save_to_disk(\"\/idiap\/temp\/rkarimi\/hf_datasets\/imdb\")\r\n>>> os.environ[\"HF_HOME\"]=\"\/idiap\/temp\/rkarimi\/hf_datasets\/\"\r\n>>> imdb = datasets.load_dataset(\"imdb\")\r\nReusing dataset imdb (\/idiap\/temp\/rkarimi\/cache_home_2\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)\r\n```\r\n\r\nI tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still  downloading data. could you tell me how to fix this issue @lhoestq  thanks \r\n\r\nAlso this is on cloud, so I save them in a path, copy it to \"another machine\" to load the data\r\n\r\n### Error stack\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/finetune_t5_trainer.py\", line 344, in <module>\r\n    main()\r\n  File \".\/finetune_t5_trainer.py\", line 232, in main\r\n    for task in data_args.eval_tasks} if training_args.do_test else None\r\n  File \".\/finetune_t5_trainer.py\", line 232, in <dictcomp>\r\n    for task in data_args.eval_tasks} if training_args.do_test else None\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 136, in get_dataset\r\n    split = self.get_sampled_split(split, n_obs)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 64, in get_sampled_split\r\n    dataset = self.load_dataset(split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 454, in load_dataset\r\n    split=split, script_version=\"master\")\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 403, in http_head\r\n    url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 542, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 655, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n```\r\n","comment_length":64,"text":"connection issue  \n Hi\r\nI am getting this connection issue, resulting in large failure on cloud, @lhoestq  I appreciate your help on this.\r\n\r\nIf I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks\r\n\r\nI tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks\r\n\r\n```\r\nimdb = datasets.load_dataset(\"imdb\")\r\nimdb.save_to_disk(\"\/idiap\/temp\/rkarimi\/hf_datasets\/imdb\")\r\n>>> os.environ[\"HF_HOME\"]=\"\/idiap\/temp\/rkarimi\/hf_datasets\/\"\r\n>>> imdb = datasets.load_dataset(\"imdb\")\r\nReusing dataset imdb (\/idiap\/temp\/rkarimi\/cache_home_2\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)\r\n```\r\n\r\nI tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still  downloading data. could you tell me how to fix this issue @lhoestq  thanks \r\n\r\nAlso this is on cloud, so I save them in a path, copy it to \"another machine\" to load the data\r\n\r\n### Error stack\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/finetune_t5_trainer.py\", line 344, in <module>\r\n    main()\r\n  File \".\/finetune_t5_trainer.py\", line 232, in main\r\n    for task in data_args.eval_tasks} if training_args.do_test else None\r\n  File \".\/finetune_t5_trainer.py\", line 232, in <dictcomp>\r\n    for task in data_args.eval_tasks} if training_args.do_test else None\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 136, in get_dataset\r\n    split = self.get_sampled_split(split, n_obs)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 64, in get_sampled_split\r\n    dataset = self.load_dataset(split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 454, in load_dataset\r\n    split=split, script_version=\"master\")\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 403, in http_head\r\n    url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 542, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 655, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n```\r\n \n Also, mayjor issue for me is the format issue, even if I go through changing the whole code to use load_from_disk, then if I do \r\n\r\nd = datasets.load_from_disk(\"imdb\")\r\nd = d[\"train\"][:10] => the format of this is no more in datasets format\r\nthis is different from you call load_datasets(\"train[10]\")\r\n\r\ncould you tell me how I can make the two datastes the same format @lhoestq \r\n\r\n","embeddings":[-0.4165683091,0.2338398546,0.0117213819,0.3550438881,0.4361407161,-0.1840167344,0.1718588769,0.203017801,-0.1961055249,0.0112288008,-0.2930115461,0.1609504372,0.1448516548,0.2955971062,0.0645913035,-0.0235872623,-0.1781959832,0.0235761348,-0.4069025815,-0.1952103525,0.0163711477,0.1511496902,0.1948605627,0.1142529994,-0.1687250137,-0.2025160342,0.0332008079,0.3280472457,0.0736503303,-0.1991920322,0.2894149423,0.2229556888,0.1790002882,0.4932547808,-0.0001225407,-0.0549697652,0.1337938607,-0.1734607965,-0.4072666168,-0.4631280303,-0.0064323978,0.1484323442,0.2245416194,-0.3105448782,-0.0386022925,0.1304718256,-0.020976603,-0.4665897191,0.4220196903,0.3426231742,0.1340467781,-0.0510152169,0.1519358307,0.1134421676,0.2371073812,0.1746752858,0.0939037874,0.2888202071,0.2243531346,0.0739746988,0.1331334859,-0.2380449176,-0.2881765068,-0.2595244944,0.3810807765,0.0185023304,0.0558203273,-0.1920615882,0.1628101617,0.3489706218,0.6868793964,-0.2939348221,-0.3240501285,0.1296465993,-0.1057170257,-0.1006093845,0.2473123968,0.1119014323,-0.1696666926,0.2362319827,-0.1059877202,-0.2554987967,-0.5657853484,0.3649443686,0.23361893,-0.1368011981,-0.0151887229,0.0324518047,0.0685580447,-0.1694662422,0.572822988,-0.2556990087,0.1473989487,-0.095241107,-0.2188861817,0.0466725528,-0.3066892028,0.0719366148,0.2427719384,0.0897350758,0.1054017991,0.1554961056,-0.2294098139,0.1013626903,0.181036979,-0.0353980511,-0.0266640913,0.1002140269,0.5406512618,0.3721492589,-0.1249257624,-0.129481554,0.0568670519,-0.0367878564,0.1227776483,0.0311886594,0.2147913277,-0.3598895669,-0.1131691262,0.0711495131,0.0917075723,-0.1393203884,0.1415489912,0.7215830088,-0.1206659973,0.1398363858,0.0471956655,0.1483438015,-0.1907476187,0.0251497291,-0.0653437078,-0.0253225043,0.1732768714,0.1365409195,0.1921574473,-0.1690635383,0.2207565159,0.1226691455,0.267064184,-0.2381130755,0.0871987194,-0.3716704845,0.1613682657,0.2053639442,0.1392564476,0.2550626695,-0.0422037803,-0.0452656262,-0.0251929462,0.0961957425,-0.3489897847,-0.365759939,-0.2603074908,0.0855558664,-0.0185977984,-0.1519390792,-0.5155616403,-0.4576686323,-0.0135056907,-0.275706321,-0.1152858436,-0.1120437831,0.036160294,-0.4071178436,0.4432790875,0.4497457743,-0.3629339337,0.0974959061,-0.0111646624,0.0004489943,0.0442566611,0.335114181,-0.1948211938,0.09084443,-0.3700456619,-0.1470950842,0.4817884862,-0.6308919191,-0.5266734958,0.4691863954,-0.0156159634,-0.1747933924,0.1873798221,0.1840229332,0.2436390519,-0.0247960705,0.0157400817,0.2771080434,0.1599723846,-0.0193772558,-0.2106706798,-0.3089704812,0.0360030867,0.1118314564,0.1359898448,0.0014546949,0.4625453353,-0.0260967836,0.3786990643,-0.1464327723,0.1524419188,0.3833598197,0.4820323884,0.2073187679,-0.1340682507,0.2457385808,-0.4730626345,-0.0478930026,0.1075474471,-0.4402324557,-0.2094289511,-0.1622176468,-0.0057171565,-0.1584044248,-0.2368806303,0.1601702571,-0.0140387705,0.0610916875,-0.1786529869,-0.1449938715,-0.1914950013,0.3710275292,-0.1508071274,0.0346166715,-0.1332603097,0.6925702095,-0.3196692765,-0.0803304911,0.0977722034,-0.1760411114,0.2887410522,-0.2172831893,-0.2229076326,0.3530237675,-0.2944036424,0.5860389471,-0.1928031445,0.3995559812,0.1996216178,-0.1799443513,0.3573048711,-0.0611427613,0.2429034859,0.0051078419,-0.0665533468,0.2225022167,-0.3444164693,0.3657674789,0.4451170564,-0.1658440083,0.1474837661,-0.0515021794,0.1195932254,-0.010836835,0.2701353729,-0.0191503912,0.2505918145,-0.1071067154,-0.2997033298,0.338786006,0.1614518166,-0.0755201876,0.2802351117,0.1416828334,0.3327289224,-0.1510933936,0.0215278435,0.2114137709,0.350869596,0.1427525729,-0.1889165491,0.2852966785,0.3808850348,-0.290107131,0.3794764578,0.0049454826,0.2021841407,0.4133448005,0.0140298577,-0.0563507676,-0.1360384822,-0.2094858736,-0.0645500645,0.0968624502,-0.5591400266,0.0748847723,-0.330696851,-0.2309482694,-0.3336356878,0.1958571523,-0.0456189476,-0.189805612,-0.3087564707,0.5337558389,0.1978805512,-0.0155970724,-0.0680829212,0.107169278,-0.2461314499,-0.2705364823,-0.2247102708,-0.0855608284,-0.1869094521,-0.0415779278,0.2394335717,0.067908287,0.1281425506,-0.147133559,-0.3475012183,-0.5005226135,-0.025833169,-0.012016207,0.3362388313,0.3599482775,0.1549184173,0.5422621369,0.0101410924,-0.2701672316,0.0528144576,0.0025792038,-0.0369844846,-0.0801247582,-0.0444890447,-0.062526755,0.1591738462,-0.376952976,-0.4660072625,-0.272729218,0.0742559433,-0.1292780489,0.4784614146,0.0407723263,-0.0397594422,0.0824822262,0.0171046741,0.2734913826,-0.104222171,-0.6493803859,0.1882528812,-0.2475812584,-0.2464491576,0.0708665103,0.1661071628,0.3669281304,-0.103027828,-0.7127384543,-0.3805267513,0.1247557551,0.4643068016,-0.2653199136,0.1684736907,0.2085427046,-0.0138028627,0.0622594543,-0.0191278141,-0.1240472794,0.1722651124,0.2472811341,0.2826841772,0.0826563165,0.4393643439,-0.3292027116,0.4117842615,0.2907215655,0.052050855,0.6116373539,-0.0687991828,0.3417039216,-0.1881387979,-0.2586171627,0.1163145974,0.0173529331,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1671","title":"connection issue ","comments":"> `\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))`\r\n\r\nDo you have an internet connection on the machine ? Is there a proxy that might block requests to aws ?\r\n\r\n> I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks\r\n\r\nHF_HOME is used to specify the directory for the cache files of this library.\r\nYou can use save_to_disk and load_from_disk without changing the HF_HOME:\r\n```python\r\nimdb = datasets.load_dataset(\"imdb\")\r\nimdb.save_to_disk(\"\/idiap\/temp\/rkarimi\/hf_datasets\/imdb\")\r\nimdb = datasets.load_from_disk(\"\/idiap\/temp\/rkarimi\/hf_datasets\/imdb\")\r\n```\r\n\r\n> could you tell me how I can make the two datastes the same format\r\n\r\nIndeed they returns different things:\r\n- `load_dataset` returns a `Dataset` object if the split is specified, or a `DatasetDict` if no split is given. Therefore `load_datasets(\"imdb\", split=\"train[10]\")` returns a `Dataset` object containing 10 elements.\r\n- doing `d[\"train\"][:10]` on a DatasetDict \"d\" gets the train split `d[\"train\"]` as a `Dataset` object and then gets the first 10 elements as a dictionary","body":"Hi\r\nI am getting this connection issue, resulting in large failure on cloud, @lhoestq  I appreciate your help on this.\r\n\r\nIf I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks\r\n\r\nI tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks\r\n\r\n```\r\nimdb = datasets.load_dataset(\"imdb\")\r\nimdb.save_to_disk(\"\/idiap\/temp\/rkarimi\/hf_datasets\/imdb\")\r\n>>> os.environ[\"HF_HOME\"]=\"\/idiap\/temp\/rkarimi\/hf_datasets\/\"\r\n>>> imdb = datasets.load_dataset(\"imdb\")\r\nReusing dataset imdb (\/idiap\/temp\/rkarimi\/cache_home_2\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)\r\n```\r\n\r\nI tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still  downloading data. could you tell me how to fix this issue @lhoestq  thanks \r\n\r\nAlso this is on cloud, so I save them in a path, copy it to \"another machine\" to load the data\r\n\r\n### Error stack\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/finetune_t5_trainer.py\", line 344, in <module>\r\n    main()\r\n  File \".\/finetune_t5_trainer.py\", line 232, in main\r\n    for task in data_args.eval_tasks} if training_args.do_test else None\r\n  File \".\/finetune_t5_trainer.py\", line 232, in <dictcomp>\r\n    for task in data_args.eval_tasks} if training_args.do_test else None\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 136, in get_dataset\r\n    split = self.get_sampled_split(split, n_obs)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 64, in get_sampled_split\r\n    dataset = self.load_dataset(split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 454, in load_dataset\r\n    split=split, script_version=\"master\")\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 403, in http_head\r\n    url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 542, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 655, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n```\r\n","comment_length":210,"text":"connection issue  \n Hi\r\nI am getting this connection issue, resulting in large failure on cloud, @lhoestq  I appreciate your help on this.\r\n\r\nIf I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks\r\n\r\nI tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks\r\n\r\n```\r\nimdb = datasets.load_dataset(\"imdb\")\r\nimdb.save_to_disk(\"\/idiap\/temp\/rkarimi\/hf_datasets\/imdb\")\r\n>>> os.environ[\"HF_HOME\"]=\"\/idiap\/temp\/rkarimi\/hf_datasets\/\"\r\n>>> imdb = datasets.load_dataset(\"imdb\")\r\nReusing dataset imdb (\/idiap\/temp\/rkarimi\/cache_home_2\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)\r\n```\r\n\r\nI tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still  downloading data. could you tell me how to fix this issue @lhoestq  thanks \r\n\r\nAlso this is on cloud, so I save them in a path, copy it to \"another machine\" to load the data\r\n\r\n### Error stack\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/finetune_t5_trainer.py\", line 344, in <module>\r\n    main()\r\n  File \".\/finetune_t5_trainer.py\", line 232, in main\r\n    for task in data_args.eval_tasks} if training_args.do_test else None\r\n  File \".\/finetune_t5_trainer.py\", line 232, in <dictcomp>\r\n    for task in data_args.eval_tasks} if training_args.do_test else None\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 136, in get_dataset\r\n    split = self.get_sampled_split(split, n_obs)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 64, in get_sampled_split\r\n    dataset = self.load_dataset(split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 454, in load_dataset\r\n    split=split, script_version=\"master\")\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 403, in http_head\r\n    url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 542, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 655, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n```\r\n \n > `\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))`\r\n\r\nDo you have an internet connection on the machine ? Is there a proxy that might block requests to aws ?\r\n\r\n> I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks\r\n\r\nHF_HOME is used to specify the directory for the cache files of this library.\r\nYou can use save_to_disk and load_from_disk without changing the HF_HOME:\r\n```python\r\nimdb = datasets.load_dataset(\"imdb\")\r\nimdb.save_to_disk(\"\/idiap\/temp\/rkarimi\/hf_datasets\/imdb\")\r\nimdb = datasets.load_from_disk(\"\/idiap\/temp\/rkarimi\/hf_datasets\/imdb\")\r\n```\r\n\r\n> could you tell me how I can make the two datastes the same format\r\n\r\nIndeed they returns different things:\r\n- `load_dataset` returns a `Dataset` object if the split is specified, or a `DatasetDict` if no split is given. Therefore `load_datasets(\"imdb\", split=\"train[10]\")` returns a `Dataset` object containing 10 elements.\r\n- doing `d[\"train\"][:10]` on a DatasetDict \"d\" gets the train split `d[\"train\"]` as a `Dataset` object and then gets the first 10 elements as a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1670","title":"wiki_dpr pre-processing performance","comments":"Hi ! And thanks for the tips :) \r\n\r\nIndeed currently `wiki_dpr` takes some time to be processed.\r\nMultiprocessing for dataset generation is definitely going to speed up things.\r\n\r\nRegarding the index note that for the default configurations, the index is downloaded instead of being built, which avoid spending time on constructing the index. However in other cases it would be awesome to make the construction faster.\r\n\r\nAny contribution that can help things faster are welcome. In particular in you have some code that can build a wiki_dpr IVF PQ index in a sharded GPU setup and would like to share it, we can add it to an `examples` folder. In particular since faiss is becoming the library of reference for dataset indexing for tasks like Open Domain Question Answering.\r\n\r\n","body":"I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).\r\n\r\nI won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.\r\n\r\nThe other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.\r\n\r\nIt looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.\r\n\r\nReferences:\r\n[1] https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wiki_dpr\/wiki_dpr.py\r\n[2] https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/search.py\r\n[3] https:\/\/github.com\/facebookresearch\/faiss\/blob\/master\/benchs\/bench_hnsw.py\r\n[4] https:\/\/github.com\/facebookresearch\/faiss\/issues\/422","comment_length":129,"text":"wiki_dpr pre-processing performance \n I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).\r\n\r\nI won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.\r\n\r\nThe other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.\r\n\r\nIt looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.\r\n\r\nReferences:\r\n[1] https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wiki_dpr\/wiki_dpr.py\r\n[2] https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/search.py\r\n[3] https:\/\/github.com\/facebookresearch\/faiss\/blob\/master\/benchs\/bench_hnsw.py\r\n[4] https:\/\/github.com\/facebookresearch\/faiss\/issues\/422 \n Hi ! And thanks for the tips :) \r\n\r\nIndeed currently `wiki_dpr` takes some time to be processed.\r\nMultiprocessing for dataset generation is definitely going to speed up things.\r\n\r\nRegarding the index note that for the default configurations, the index is downloaded instead of being built, which avoid spending time on constructing the index. However in other cases it would be awesome to make the construction faster.\r\n\r\nAny contribution that can help things faster are welcome. In particular in you have some code that can build a wiki_dpr IVF PQ index in a sharded GPU setup and would like to share it, we can add it to an `examples` folder. In particular since faiss is becoming the library of reference for dataset indexing for tasks like Open Domain Question Answering.\r\n\r\n","embeddings":[-0.2196649015,-0.1832672656,-0.1137228534,0.088132672,-0.1135564297,-0.0815373436,0.0223083887,0.3311191797,0.1895469576,0.0709527209,0.0205211919,-0.1025920883,0.3246052861,0.1452307552,-0.3447281122,-0.2720117271,0.2055481523,0.0610853657,0.1536909789,0.0085712625,-0.2336437255,-0.0478305891,-0.4042540789,-0.0988772362,-0.1302234828,-0.2336065769,-0.0790712684,-0.1159494072,-0.1927477121,-0.4908298254,0.2295529395,0.4961724281,0.1521090567,0.1528985053,-0.000108029,-0.1149050891,0.202179715,0.2288627923,0.1197232381,0.6329683065,0.0736345351,-0.1388030648,0.1686449498,-0.2438187152,0.0160673484,-0.3819811642,-0.1399827302,0.1675919443,0.2192769051,-0.1553871781,0.1405241936,0.0523834117,-0.1207046583,0.1294268966,-0.051441852,-0.1134570464,-0.1975087225,-0.2734824419,0.1838988364,-0.1971022189,-0.1978050768,0.5801024437,-0.2147762626,0.074397549,0.2620584369,0.1684101373,0.2045363486,-0.2048115581,0.1987678111,0.5227435231,0.2674139738,0.1252056807,-0.2163727283,-0.2284141034,-0.0392408296,-0.3450886905,0.2761880159,-0.2448882461,-0.178367734,0.0715071261,0.0493536294,0.0162273142,0.0047916044,-0.0439381488,0.0087107196,0.4345324636,0.1707940251,-0.0183573328,0.2114876658,-0.1735899448,-0.1470002234,-0.1979638934,-0.0012505206,0.2027682811,-0.3930261433,-0.0495891608,0.1050152108,-0.1647939384,0.1057785749,-0.172263056,-0.3507420719,0.4661415815,0.1852914244,-0.0895521417,0.1366778612,-0.0003281521,-0.3072820306,-0.1281069666,0.3641516566,0.0541132689,-0.1369956583,0.2023543417,0.2030726075,-0.110679388,-0.1670619398,-0.3106616735,-0.2953485847,-0.1063729525,0.2886683941,0.1519557238,0.1150512993,-0.2484764755,-0.0148870749,0.5208369493,-0.1647288203,0.5626708269,0.1504448801,-0.1578185707,-0.3267688453,0.2533043027,-0.0347385146,-0.1732836664,-0.1848648787,0.2981399,0.0767066926,0.0865779296,-0.0797210634,0.0952973589,0.1707717776,-0.0256922953,-0.0198636167,-0.0553642437,0.2363325804,0.2633557916,-0.1075721607,0.1206818521,-0.2376253307,-0.0400305279,-0.2965534925,-0.1210606694,-0.0640706867,-0.3717763424,-0.1162206233,0.2038687766,0.1534664482,0.0098027978,-0.0241119564,0.3252814412,0.0509022176,0.007238368,-0.0024153227,0.0057540494,-0.1513900757,-0.1015185714,0.4997426867,0.2514150143,-0.0847324133,0.1747264266,0.025348749,0.1252768636,0.1669304371,0.4507260323,-0.1114014834,0.1618746668,0.0395002887,0.2550393641,0.0195264257,-0.3203860819,-0.2272886336,0.378285557,0.163028419,0.0185769629,0.1553283632,0.01525026,0.2176691741,0.0091753835,-0.065287143,0.4854988456,0.1750310212,0.1409177184,-0.4725474119,-0.4832615852,-0.0594813675,0.3461815417,0.1197740883,-0.226567924,-0.2047182173,-0.1846186817,0.3050087392,-0.3265382349,0.098593615,0.1026033312,0.1075408608,0.2157734931,0.2896379232,-0.0459009036,-0.1346040815,0.2787211239,-0.3080227077,0.3445780575,0.277059257,-0.289414227,0.1209242195,0.030660117,-0.1056921631,-0.1341904551,0.1402307153,-0.1129245162,0.0192971174,0.0391548127,-0.1006057411,-0.0919625461,-0.2119908631,-0.0181858987,0.0214301459,-0.0155423917,-0.3207993209,0.0573524646,0.0892536417,0.2041374445,0.0866511241,0.1444229335,-0.0217300933,0.0549624152,0.0868354291,-0.0066821561,0.520585835,0.2361232042,0.061384175,-0.1906127334,-0.0632872432,-0.0609438457,0.0114322649,-0.2304022908,-0.2520048618,0.5083364844,0.0967105404,0.672422111,0.0362220407,-0.1513773799,0.1983903944,-0.092809923,0.3236225545,0.1621481329,0.096767351,0.0861774608,-0.15423356,0.1096738502,-0.2470085621,0.3957998157,0.0847710297,-0.1390085518,-0.1460458934,0.3455000222,0.1476966441,-0.4026895761,0.1203723028,-0.3250585198,-0.1167353988,0.234021306,0.0778978541,-0.0932746679,-0.0612068288,-0.1905838549,0.2561009526,0.0626242831,-0.1506821215,0.1598194391,0.0484115444,0.1712654829,-0.0974179357,-0.2836385965,-0.1396322846,0.0632073507,-0.0014035341,0.0198498406,-0.0967805237,-0.0367061719,0.4275031984,0.0264048632,-0.4450348914,-0.3590585291,0.2395977229,-0.1972917616,-0.1552642286,0.2693766952,-0.0099309999,0.0478624813,-0.105079107,-0.2813078761,-0.453338474,-0.3079473972,-0.2773779333,0.0130231027,0.1892403513,0.6149399281,0.2304699421,0.2074116468,-0.232235074,-0.0473324023,-0.4137030542,0.1432667375,-0.0947487503,0.0748724937,0.0620125197,0.1043494195,-0.2676288784,-0.2927021086,0.0963432938,-0.1845993698,0.073781468,-0.1074340865,-0.2597098947,-0.002387204,0.0752095431,-0.3678357601,-0.0981878564,-0.2722530067,0.1236127838,-0.1237497404,0.1488938332,-0.2418092638,0.0643003285,0.1365865469,-0.1227324083,0.0969901755,-0.1594769806,-0.0740229934,0.3361534476,0.0502667464,-0.2920596302,-0.0997658446,-0.1421093345,-0.0260732789,0.0583991557,-0.4986907244,0.1020059213,-0.4466369152,0.4375715554,0.1235352606,0.2664425075,0.3579042554,0.0905895829,-0.2238862216,0.0039343988,-0.0138119599,0.0772024393,-0.2698499262,-0.0257808845,-0.0899664909,0.027619997,-0.2029920518,1.1685949564,0.1803689152,0.060284365,0.2578519285,0.2749099433,0.1021383107,-0.1524999142,0.0782125816,0.1474120915,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1670","title":"wiki_dpr pre-processing performance","comments":"I'd be happy to contribute something when I get the time, probably adding multiprocessing and \/ or cython support to wiki_dpr. I've written cythonized apache beam code before as well.\r\n\r\nFor sharded index building, I used the FAISS example code for indexing 1 billion vectors as a start. I'm sure you're aware that the documentation isn't great, but the source code is fairly easy to follow.","body":"I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).\r\n\r\nI won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.\r\n\r\nThe other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.\r\n\r\nIt looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.\r\n\r\nReferences:\r\n[1] https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wiki_dpr\/wiki_dpr.py\r\n[2] https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/search.py\r\n[3] https:\/\/github.com\/facebookresearch\/faiss\/blob\/master\/benchs\/bench_hnsw.py\r\n[4] https:\/\/github.com\/facebookresearch\/faiss\/issues\/422","comment_length":66,"text":"wiki_dpr pre-processing performance \n I've been working with wiki_dpr and noticed that the dataset processing is seriously impaired in performance [1]. It takes about 12h to process the entire dataset. Most of this time is simply loading and processing the data, but the actual indexing is also quite slow (3h).\r\n\r\nI won't repeat the concerns around multiprocessing as they are addressed in other issues (#786), but this is the first obvious thing to do. Using cython to speed up the text manipulation may be also help. Loading and processing a dataset of this size in under 15 minutes does not seem unreasonable on a modern multi-core machine. I have hit such targets myself on similar tasks. Would love to see this improve.\r\n\r\nThe other issue is that it takes 3h to construct the FAISS index. If only we could use GPUs with HNSW, but we can't. My sharded GPU indexing code can build an IVF + PQ index in 10 minutes on 20 million vectors. Still, 3h seems slow even for the CPU.\r\n\r\nIt looks like HF is adding only 1000 vectors at a time by default [2], whereas the faiss benchmarks adds 1 million vectors at a time (effectively) [3]. It's possible the runtime could be reduced with a larger batch. Also, it looks like project dependencies ultimately use OpenBLAS, but this is known to have issues when combined with OpenMP, which HNSW does [3]. A workaround is to set the environment variable `OMP_WAIT_POLICY=PASSIVE` via `os.environ` or similar.\r\n\r\nReferences:\r\n[1] https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wiki_dpr\/wiki_dpr.py\r\n[2] https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/search.py\r\n[3] https:\/\/github.com\/facebookresearch\/faiss\/blob\/master\/benchs\/bench_hnsw.py\r\n[4] https:\/\/github.com\/facebookresearch\/faiss\/issues\/422 \n I'd be happy to contribute something when I get the time, probably adding multiprocessing and \/ or cython support to wiki_dpr. I've written cythonized apache beam code before as well.\r\n\r\nFor sharded index building, I used the FAISS example code for indexing 1 billion vectors as a start. I'm sure you're aware that the documentation isn't great, but the source code is fairly easy to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1662","title":"Arrow file is too large when saving vector data","comments":"Hi !\r\nThe arrow file size is due to the embeddings. Indeed if they're stored as float32 then the total size of the embeddings is\r\n\r\n20 000 000 vectors * 768 dimensions * 4 bytes per dimension ~= 60GB\r\n\r\nIf you want to reduce the size you can consider using quantization for example, or maybe using dimension reduction techniques.\r\n","body":"I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file?","comment_length":59,"text":"Arrow file is too large when saving vector data \n I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file? \n Hi !\r\nThe arrow file size is due to the embeddings. Indeed if they're stored as float32 then the total size of the embeddings is\r\n\r\n20 000 000 vectors * 768 dimensions * 4 bytes per dimension ~= 60GB\r\n\r\nIf you want to reduce the size you can consider using quantization for example, or maybe using dimension reduction techniques.\r\n","embeddings":[0.1137216091,-0.3316557705,-0.0589662418,0.4534536302,0.1343118995,-0.116157122,-0.1786454469,0.4608655572,-0.4080996215,0.3328985274,0.1982362866,-0.0884741992,-0.1221771985,-0.1876727194,-0.1028836071,-0.1468020082,0.1206523329,0.4042373896,0.0463363416,-0.0668317229,0.2086952031,0.2305034697,0.0889814273,0.0583834723,-0.3092935979,-0.2625985146,-0.1549984366,0.0315525122,-0.3289449811,-0.1841223538,-0.1933373958,-0.1647046059,0.3871693313,0.4107970595,-0.000129293,-0.1663684547,-0.0067866617,-0.080024071,-0.1275971979,0.3673682809,0.0773602054,-0.3972461224,0.01280786,-0.262186408,0.2411106825,-0.3113117516,-0.1806944609,-0.2490857095,0.5573136806,0.0042652586,0.0689282045,-0.2304158509,0.0921434909,0.347211659,0.2535005212,-0.0635205135,-0.0713259727,0.1529286802,0.471544981,0.2860766947,-0.1823883057,0.363199383,0.110103935,-0.208829388,0.3755971491,-0.2341856956,0.6091362238,-0.0399567485,0.2837225199,0.3255839348,0.6376829743,-0.1370803267,0.0512443036,0.0781831965,0.3942013085,-0.0722277611,0.0660198182,0.2971102297,-0.0139591889,-0.0113798045,-0.0376826189,-0.6596640944,-0.3486739397,0.0186890159,0.1498577446,-0.2296135724,-0.1608579159,-0.1307564974,0.3024173975,-0.3091194928,0.0180879068,0.0056584124,-0.1944341063,-0.0326657034,-0.2165947109,-0.2044761628,-0.6254200935,-0.0533948205,-0.0757654458,-0.1932907104,0.6419781446,-0.1503009349,0.0239941832,0.0240580477,0.1890589446,0.4034417272,-0.1562243998,0.2484546602,0.0352215841,0.0753098577,-0.0337957032,-0.1493660361,-0.4900302887,-0.1498427391,0.2052189112,-0.3869850338,-0.280672282,-0.0732591748,0.2934067249,0.1845793724,-0.4840515852,0.2669833899,0.0975444838,0.1559239924,0.1078798324,0.2910142243,-0.0011189451,0.0923160613,-0.065930061,0.0094854841,0.0277631208,-0.0981323868,-0.142045781,0.0060584713,-0.0782943964,-0.0065724929,0.1483317316,-0.3339111209,-0.0072672791,-0.3509913385,0.0287624039,-0.3294256628,0.2498751283,-0.0001296597,-0.1208896637,0.1071232408,-0.272860378,0.1755198091,-0.1996482164,0.2861877084,-0.3546923101,-0.1992547512,-0.4079980254,0.0020834215,-0.070258759,0.2342661321,-0.0766559169,0.0782729685,0.2531573474,-0.1458364427,0.0639629588,-0.1947598904,0.019837359,-0.1918919384,0.203684628,0.0059584631,-0.5826382637,0.0113032684,-0.213216126,0.2590471208,0.2356853485,0.6174716949,-0.0363893546,0.0585219972,0.1287312806,0.4063420296,0.1356898844,0.0480834953,-0.5367757678,0.217118755,0.2386541963,-0.1011692807,0.1859214008,0.2929548621,0.0892949253,0.0071523362,-0.2879393399,0.1310657859,0.1717647612,0.1636817306,-0.2564821541,-0.441157192,-0.2164209038,-0.0965436473,-0.3174477816,-0.6281605363,-0.0682073161,0.025035454,0.272800386,-0.2577063739,0.2252342701,0.492847234,0.2753368914,-0.000695367,0.2238850147,0.1669015735,-0.1212274358,-0.1580065787,-0.2481136918,-0.4369781613,-0.2673237324,-0.198346287,-0.012754973,-0.1155380681,0.0847476721,0.2097585201,-0.0684809238,-0.4338859022,-0.2588705719,-0.2044482231,-0.0028295205,-0.1649833918,-0.1741110981,0.0226179603,-0.0813772157,-0.0307607241,-0.038439855,-0.2457405776,0.247255221,0.1347584128,-0.1484475136,0.1164523661,-0.2407489568,0.1598006934,-0.167587474,0.4313790202,0.1129634604,0.0386708044,-0.020548841,-0.64373523,0.7238839269,0.1546060443,0.1889218539,0.1275749654,-0.2429232001,-0.349193722,0.0800835863,0.2482022792,0.093988277,-0.22779046,-0.1839208454,0.067819491,-0.1057577431,-0.1484805644,0.1197732911,0.2603482902,0.34256953,0.4101367593,0.1127658933,-0.1909984201,0.343357563,0.0198469441,0.1349350959,0.5249637961,-0.6002112627,-0.3416878581,0.0518216789,-0.1772211194,-0.0939612314,0.126302734,0.0625123084,-0.125511691,0.4896761477,-0.0078556342,0.3011786342,0.3242177963,0.4192395508,0.1767469794,-0.0108457673,0.0837734193,-0.1029767692,0.062662974,-0.2192377597,0.0466163233,-0.2451952845,0.2424612343,-0.2749293149,-0.2344910651,-0.1921862066,-0.0173116568,-0.1019467115,0.0515865199,0.2451206446,-0.1880639195,-0.1591486186,-0.211703971,0.1817723513,0.4403665066,0.2060803175,0.0918668434,0.1535120904,-0.0351310559,-0.1502384543,-0.0109650763,0.0438867137,-0.014695243,0.1125260592,0.1260084361,0.1070844159,-0.1678998917,-0.1243059039,-0.0153395627,-0.0361870378,-0.1662033349,-0.1303281933,0.0919687748,-0.786903739,-0.0398700088,-0.2734321952,-0.0855514109,-0.1463751346,0.1608956605,-0.2135525942,-0.0653852299,0.1312352121,-0.1547039151,-0.0369184352,-0.2339600474,0.2178565115,-0.0222987738,0.1768089086,-0.0442196913,0.3882011771,0.1545167118,0.0012794459,0.0640717447,0.2359921187,-0.3928713799,0.3850260675,0.0214982945,-0.1801554859,0.0799703673,0.0457315855,0.1141679585,0.0209659413,-0.3639511764,0.2030226737,-0.2756900787,-0.278595984,-0.3433789909,0.2026297897,0.0911812708,0.054971572,-0.1060471535,0.1742748171,0.1105966941,0.2940917611,0.4214516878,0.4746117592,-0.1692388207,-0.2786592543,0.0714132786,0.3714794517,0.6862313747,0.1074137762,0.1681974679,0.3925536573,0.1168598086,-0.0548362769,0.2649499476,-0.0390816852,0.0188127756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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1662","title":"Arrow file is too large when saving vector data","comments":"Thanks for your reply @lhoestq.\r\nI want to save original embedding for these sentences for subsequent calculations. So does arrow have a way to save in a compressed format to reduce the size of the file?","body":"I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file?","comment_length":36,"text":"Arrow file is too large when saving vector data \n I computed the sentence embedding of each sentence of bookcorpus data using bert base and saved them to disk. I used 20M sentences and the obtained arrow file is about 59GB while the original text file is only about 1.3GB. Are there any ways to reduce the size of the arrow file? \n Thanks for your reply @lhoestq.\r\nI want to save original embedding for these sentences for subsequent calculations. So does arrow have a way to save in a compressed format to reduce the size of the file?","embeddings":[0.067263931,-0.2825369835,-0.0641458035,0.402374655,0.0808355585,-0.053776551,-0.2692085207,0.4793400168,-0.5607516766,0.3315005004,0.1089846343,0.1340743154,-0.1180463359,-0.2116098851,0.0485850386,-0.0961459577,0.0595268831,0.4329222441,0.0893179998,0.0133465035,0.2631244659,0.3559788764,0.0939318687,0.0067862286,-0.3308284283,-0.1569064856,-0.1461255252,0.0108346129,-0.1975939274,-0.2436818033,-0.111749433,-0.1628140807,0.3664756417,0.2997963428,-0.0001282434,-0.155340448,0.0799876302,-0.1324873716,-0.1543596387,0.350638628,0.0955116302,-0.5090038776,0.0873981565,-0.3085873425,0.1737430841,-0.402608633,-0.1041352078,-0.2626860142,0.6798275709,-0.0405709147,0.0581341572,-0.187691167,0.0452428348,0.2361843884,0.1947540641,-0.0782135054,-0.1130747423,0.1938004643,0.3574740291,0.3629294634,-0.1065315455,0.2919212282,0.1914874613,-0.2800036669,0.2227589339,-0.2258037478,0.4473464489,0.0007933857,0.225431934,0.3514633477,0.5140762925,-0.1759474128,-0.0603622533,0.0637928993,0.4757856131,-0.2109968364,0.0174834989,0.286449641,-0.0265951119,0.0579873584,0.0427703559,-0.6632637978,-0.3350250721,-0.0129562095,0.1566061527,-0.2324803472,-0.243476972,-0.2268908918,0.3395007849,-0.2392470539,0.029350739,-0.0688615665,-0.1880541891,-0.0309015494,-0.1763373017,-0.2596206665,-0.77453053,-0.1786165833,-0.0796045065,-0.2344761342,0.6635527015,-0.0720998943,0.1642229855,-0.00721636,0.1766322851,0.3590497673,0.0140034854,0.2010472417,0.0712022483,0.0396765769,0.0171178654,-0.1618068665,-0.5301873684,-0.0396627933,0.1944205314,-0.3796878755,-0.328019321,-0.0312103201,0.3187498152,0.1300549507,-0.335705936,0.228883937,0.0295665171,0.0492363684,0.1042785197,0.3126418293,-0.0208850373,0.0403178036,0.0544417799,0.0019847983,0.0394293331,-0.1343961507,-0.0781032443,0.0259398296,-0.0275111292,-0.2190253884,0.2604770958,-0.3533632457,-0.0077571361,-0.2964896262,0.0408075601,-0.2585963011,0.2016054541,-0.0302536003,0.0025413448,0.0752286091,-0.3014023304,0.1610336155,-0.1245599538,0.3366652131,-0.4297147691,-0.1650011241,-0.3299642205,-0.0179977175,-0.1047722399,0.1742520183,0.0003865594,0.1255943328,0.2257331014,-0.1661272943,0.0993145332,-0.1296684444,0.0494081452,-0.2312420905,0.2432983816,-0.0923740864,-0.6731775403,-0.0857579261,-0.2777484357,0.1920726895,0.1400735378,0.6400219202,-0.0744174495,-0.0322014168,0.0783581287,0.3584970236,0.3260307312,0.0667536333,-0.4791609347,0.1774730831,0.2246381789,-0.0786180794,0.1037978977,0.2265954316,0.1492664814,-0.0604442693,-0.2285675853,0.0938980207,0.1577383876,0.0399236791,-0.1941211522,-0.3479957879,-0.1874046624,-0.1616005301,-0.3704382479,-0.556866169,-0.0573177338,0.0993908495,0.2825308442,-0.2271603346,0.225230366,0.4774558246,0.1413949728,-0.0760712996,0.3271079659,0.1416174024,-0.0021055548,-0.1257630587,-0.3621923327,-0.5235086083,-0.1756133139,-0.190572843,0.0293155946,-0.121326156,0.0939254388,0.1542140841,-0.0850109085,-0.4176620245,-0.1665883511,-0.1945740879,-0.1212180406,-0.1485397071,-0.1625646353,0.0435752422,-0.0093543902,-0.0929440409,0.035087578,-0.2618329525,0.1685757786,0.049347043,-0.2110937685,0.0334855728,-0.2219132781,0.1685773581,-0.1188398153,0.4782136381,0.076688461,-0.1507988572,0.0236453805,-0.5871305466,0.5806174874,0.1016194671,0.1639539897,0.1385890245,-0.4002934992,-0.2357888073,0.1655118316,0.0865475535,0.1155845299,-0.2047315091,-0.1576061398,0.0299940892,-0.1912243962,-0.2066265792,0.0113874953,0.2525856197,0.3043104708,0.2930154502,0.1326592267,-0.2342439294,0.3456522822,0.0127602592,0.1384345591,0.5068286061,-0.590945363,-0.3025445342,-0.0011266111,-0.1128183678,-0.0976115465,0.1313309968,0.0698328018,-0.1697295159,0.5162374377,-0.0031064451,0.3938286304,0.270829916,0.5881363153,0.2482156903,0.0130917123,0.1224994883,-0.1683842391,0.2059627771,-0.123657085,-0.1146202683,-0.1343303025,0.1422505081,-0.3604365885,-0.1974012852,-0.1886174828,0.0823270231,-0.0327368937,0.0508299693,0.2275571376,-0.2626132071,-0.0950481668,-0.1746382415,0.1507802308,0.3636558354,0.1913493723,-0.0163355395,0.2036825567,-0.0591201037,-0.1144365445,0.0136475526,-0.064595215,-0.1314064264,0.0420030691,0.0426589921,0.1085365787,-0.1422220021,-0.1445227712,0.084627755,0.0082741491,-0.2499080151,-0.1291313916,0.0014617112,-0.7448284626,0.1219720989,-0.2711542547,-0.1041727141,-0.2187893093,0.0720855296,-0.1498136371,-0.0517308488,0.1437551379,-0.1157066822,-0.0283922385,-0.1755820364,0.291978091,0.0340435579,0.1302584112,-0.0711229295,0.3743300736,0.2452189028,-0.0035659089,-0.0189086702,0.2167963386,-0.3478161395,0.4853478968,-0.0247741044,-0.2298325151,0.0000074246,0.0339117311,0.0755403712,0.1155659705,-0.2431470305,0.2363770157,-0.1075066254,-0.2725660801,-0.3093037307,0.178979218,0.1244450286,-0.058327686,-0.1150035635,0.0773663744,0.1102449819,0.3835187256,0.4883324504,0.5294553041,-0.1333200783,-0.24173069,0.0422669388,0.3603091836,0.6346899867,0.120903194,0.1444972456,0.2817271948,0.1929074973,0.0391993187,0.4585183859,-0.1066624895,0.0548391752,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1647","title":"NarrativeQA fails to load with `load_dataset`","comments":"Hi @eric-mitchell,\r\nI think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","body":"When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https:\/\/huggingface.co\/datasets\/narrativeqa), I receive a cascade of exceptions, ending with\r\n\r\n    FileNotFoundError: Couldn't find file locally at narrativeqa\/narrativeqa.py, or remotely at \r\n        https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/narrativeqa\/narrativeqa.py or \r\n        https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/narrativeqa\/narrativeqa.py\r\n\r\nWorkaround: manually copy the `narrativeqa.py` builder into my local directory with \r\n\r\n    curl https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/narrativeqa\/narrativeqa.py -o narrativeqa.py\r\n\r\nand load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10.","comment_length":55,"text":"NarrativeQA fails to load with `load_dataset` \n When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https:\/\/huggingface.co\/datasets\/narrativeqa), I receive a cascade of exceptions, ending with\r\n\r\n    FileNotFoundError: Couldn't find file locally at narrativeqa\/narrativeqa.py, or remotely at \r\n        https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/narrativeqa\/narrativeqa.py or \r\n        https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/narrativeqa\/narrativeqa.py\r\n\r\nWorkaround: manually copy the `narrativeqa.py` builder into my local directory with \r\n\r\n    curl https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/narrativeqa\/narrativeqa.py -o narrativeqa.py\r\n\r\nand load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10. \n Hi @eric-mitchell,\r\nI think the issue might be that this dataset was added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","embeddings":[-0.2821941078,0.1054382026,0.0326368958,0.2439089417,0.190005973,0.1799186468,0.1333401948,0.0412888601,-0.1444729418,0.0441531092,-0.014136171,-0.0756954849,-0.0687526986,0.3853098452,0.0838763937,-0.1709612459,0.0442917012,0.0163581055,0.0471326895,0.0996936783,-0.2275328636,0.6004674435,-0.3060184121,0.0204679333,-0.3162917495,-0.0214897413,-0.1606631875,0.3630981743,-0.1869076639,-0.3998726904,0.6071943045,-0.0712309107,0.2232357115,0.5504921079,-0.00011022,0.0608154461,0.4106917381,-0.1400963366,-0.2848300636,-0.545355022,0.2669924498,-0.2112297714,0.2129349858,0.1392676383,-0.1671685725,-0.3874749839,0.2078933418,-0.0572460517,0.321726203,0.0858349651,0.1936791837,0.3349677324,0.2189273536,-0.1860184222,-0.1006725505,0.4217249751,-0.2878890634,0.5078809261,0.3055493236,-0.4217514992,0.1246747151,0.1989541054,-0.0314400643,-0.005937275,0.3751299381,-0.0012227655,0.0501069874,-0.2876365185,0.1067650765,0.3626172245,0.6540010571,-0.3735683262,-0.5450147986,-0.183354333,0.1010366306,-0.084696874,0.2412417382,0.2746928334,-0.2394714504,0.1392699778,-0.1500250399,-0.116059199,-0.1332204044,0.2248403281,-0.1042077318,-0.1651473194,-0.2865085304,0.0356239192,0.0348373204,-0.2221505195,-0.1596003324,0.1280231774,-0.2268224061,0.2955132127,-0.2854912579,0.1019492745,0.0155473538,0.225426659,0.1659238935,-0.2129214704,0.028150741,-0.0490586534,-0.0764428303,0.1599115729,0.2491157055,0.1045944691,0.3280425072,0.0166725665,0.0025238984,0.4532367289,-0.0647722185,-0.2219322473,-0.0902573466,-0.1459997892,-0.4528244436,-0.2933596671,0.4718654156,-0.30325827,-0.3878256679,-0.1083442271,-0.187420994,0.0374947675,0.0771065801,0.4981051683,0.0121543976,0.1539297551,0.0155747412,0.3006159663,-0.2579495609,0.1018595099,-0.2480658889,0.0056528724,-0.0319857895,0.1371661723,0.2997520268,-0.3352165818,0.3940825164,-0.0288877748,-0.0616462827,0.1528032422,-0.0062926491,0.0956953615,-0.2000017166,0.2249585986,-0.1231740341,0.0305550043,0.2513726354,-0.0039111096,-0.0630689114,-0.060995277,-0.1491312832,-0.2974407375,-0.0398866013,0.1490037739,-0.1069887877,-0.2064623535,-0.0754367337,0.1805878431,0.0128380544,-0.127744019,-0.088431038,0.0661217421,-0.0049029607,-0.0903493464,0.2024713159,0.6703977585,-0.2659667432,-0.3223662674,0.0665737167,-0.2062622905,-0.002002259,0.205178231,-0.2598896027,0.0763844475,-0.2033912838,-0.1399738789,0.0594103932,-0.4468630552,-0.2169282287,0.2421035022,-0.0552593172,0.409137398,0.0252109431,-0.0529107004,-0.0884344503,-0.1937901825,0.1179099977,0.2477221787,0.041410502,-0.180747062,-0.0769385174,-0.1732111126,0.1643526256,0.2965083718,-0.2560911775,0.0753234625,0.1443372518,-0.1822345108,0.1934351921,-0.0239516459,-0.0791850686,0.2923217118,0.2021273822,0.1244123653,0.0860616416,0.1987719983,-0.5315409899,0.2751492262,-0.2863561213,-0.0349830911,-0.1951927692,-0.0762917846,-0.1019935831,0.0727367327,-0.3476805091,-0.3338475525,0.0303023178,0.2231727242,0.051065281,0.1007084996,-0.3703853488,0.3647746444,-0.2149709016,0.1742464453,-0.5457684398,0.0198504608,0.059093833,-0.109520033,0.122496672,0.2437279373,0.0530007482,-0.2464805096,0.0897159576,0.4100808501,-0.2697848976,0.4130957425,0.0533893108,0.0024124011,0.1878376901,-0.3031235933,0.1404956579,-0.0539183393,0.0428540744,0.0091652777,-0.0933393985,0.2232495397,-0.1476233006,0.3639097214,0.0349284671,0.1885386556,0.2245209068,0.0839198828,-0.2038252205,-0.2347534746,0.434181571,-0.1314287931,0.3960675895,-0.0928729326,-0.3104823828,0.0197391361,0.1395178586,0.1080144867,-0.0624740012,0.0629142523,-0.3400149047,0.1228983179,0.087337561,-0.149281919,0.4034076035,0.1285380274,-0.0878837779,0.1276677847,-0.0151850637,-0.3860321939,0.1712200493,0.0358905382,-0.288698107,0.0302725509,0.0978229865,0.0187041406,-0.361423403,0.1283961535,-0.0121853743,0.0879093707,-0.2980452776,0.2551460266,-0.0776269361,-0.3964503109,-0.1199690253,-0.4994731545,-0.2983438969,-0.225864917,-0.1354241669,0.113485992,-0.0269375853,0.2017488629,0.3073233962,0.0679067969,-0.2341177016,-0.1906663924,-0.072332561,-0.2339591384,-0.1884654313,0.0955380574,0.296767801,-0.1169552803,0.2403816581,-0.3985351026,0.0019971929,-0.2876364291,-0.2706886232,0.1007398292,-0.2863255739,0.4284830689,0.0129717411,0.2201460004,-0.0836322457,0.0230730586,0.439858377,-0.0221727323,-0.1318078339,0.1779700667,-0.186746195,-0.1351217628,-0.1410122067,-0.1960711926,-0.30781129,-0.4054515958,0.3266366422,0.052409023,-0.1396427751,0.637440145,-0.0049181371,0.1109821573,-0.4007156491,0.0691167936,-0.0332454443,-0.5922862291,0.238506645,-0.3008897901,-0.2814723253,0.3023081422,0.1887840033,0.1527570635,0.2165230811,-0.4129439294,-0.1828762442,0.0208987184,0.0596235693,0.1238240898,0.1198371798,0.268009901,-0.1689155102,0.0583419874,0.0049574403,-0.1630619317,0.0573053621,-0.0954310149,0.2994501889,-0.1553699225,0.4328054786,-0.0993204266,0.5152155757,0.4010253847,0.0893512145,0.4676293433,-0.0574609526,0.5533753633,-0.1913194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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1647","title":"NarrativeQA fails to load with `load_dataset`","comments":"Update: HuggingFace did an intermediate release yesterday just before the v2.0.\r\n\r\nTo load it you can just update `datasets`\r\n\r\n`pip install --upgrade datasets`","body":"When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https:\/\/huggingface.co\/datasets\/narrativeqa), I receive a cascade of exceptions, ending with\r\n\r\n    FileNotFoundError: Couldn't find file locally at narrativeqa\/narrativeqa.py, or remotely at \r\n        https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/narrativeqa\/narrativeqa.py or \r\n        https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/narrativeqa\/narrativeqa.py\r\n\r\nWorkaround: manually copy the `narrativeqa.py` builder into my local directory with \r\n\r\n    curl https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/narrativeqa\/narrativeqa.py -o narrativeqa.py\r\n\r\nand load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10.","comment_length":23,"text":"NarrativeQA fails to load with `load_dataset` \n When loading the NarrativeQA dataset with `load_dataset('narrativeqa')` as given in the documentation [here](https:\/\/huggingface.co\/datasets\/narrativeqa), I receive a cascade of exceptions, ending with\r\n\r\n    FileNotFoundError: Couldn't find file locally at narrativeqa\/narrativeqa.py, or remotely at \r\n        https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/narrativeqa\/narrativeqa.py or \r\n        https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/narrativeqa\/narrativeqa.py\r\n\r\nWorkaround: manually copy the `narrativeqa.py` builder into my local directory with \r\n\r\n    curl https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/narrativeqa\/narrativeqa.py -o narrativeqa.py\r\n\r\nand load the dataset as `load_dataset('narrativeqa.py')` everything works fine. I'm on datasets v1.1.3 using Python 3.6.10. \n Update: HuggingFace did an intermediate release yesterday just before the v2.0.\r\n\r\nTo load it you can just update `datasets`\r\n\r\n`pip install --upgrade datasets`","embeddings":[-0.2199551761,0.0480084457,0.0608192459,0.312422812,0.1940941066,0.1517978162,0.1400994509,0.0211620592,-0.1197655275,0.0751328319,0.0060817758,-0.1055012122,-0.0284701269,0.328783989,0.1128825396,-0.1749626398,0.0848928317,0.0000286327,-0.0132892365,0.1011296585,-0.2477790415,0.5799874067,-0.3045037091,0.0495427139,-0.3111803234,0.0400928035,-0.1827201545,0.4143431485,-0.134498775,-0.3618732095,0.5955119133,-0.0901765451,0.1628862619,0.5966639519,-0.0001159591,0.0845015123,0.3932443559,-0.1201084405,-0.2960406244,-0.5694472194,0.2616091669,-0.1980767101,0.2148328125,0.1369700134,-0.1391425133,-0.3809946775,0.2064242959,-0.015644094,0.3636644483,0.0311052669,0.1502898186,0.3551585078,0.1913443357,-0.1960780472,-0.1232333258,0.4534870386,-0.2778141499,0.5499457717,0.3091154397,-0.4308938682,0.0927215219,0.1659913957,-0.0754026026,-0.0536760539,0.39754951,-0.024482822,0.0316641182,-0.2200985551,0.1294304579,0.3337748051,0.5657204986,-0.3766566217,-0.5654232502,-0.2516533732,0.0828977525,-0.0521907918,0.3052110076,0.2050047815,-0.2722261846,0.1463726908,-0.2035996914,-0.1753699481,-0.1267252564,0.1935858279,-0.1112736091,-0.2434892654,-0.2949478626,0.0435590893,0.0719450936,-0.1950484067,-0.1617371589,0.0534055047,-0.238451764,0.2782220542,-0.2544952929,0.1104443818,0.0095407376,0.2557695806,0.1553553045,-0.2342424244,0.0359687246,-0.0622371808,-0.0856734887,0.1938934624,0.2793362737,0.1482362747,0.3574495912,-0.0253055226,-0.0095609967,0.4405895472,-0.0695300847,-0.208296597,-0.0765138119,-0.1682606637,-0.4591735303,-0.3255860507,0.4318531752,-0.3057953417,-0.4052734077,-0.0722541139,-0.2397596389,0.0862227082,0.1374139935,0.4865350425,0.0081579303,0.1582750976,0.0058746026,0.2865971923,-0.2645154595,0.0871506706,-0.2418975085,-0.0022812472,-0.0693875924,0.1570147574,0.2398714125,-0.3612253666,0.3871788979,-0.017591022,-0.0449736305,0.0816111639,-0.0984736979,0.1081791595,-0.2023358643,0.2186914086,-0.1624392867,0.0317286365,0.2772150636,-0.0326874964,-0.0554952323,-0.0622329935,-0.137747556,-0.3022783101,-0.0104777804,0.1069138348,-0.0946805701,-0.1699189395,-0.1125650629,0.1576721817,-0.0130356383,-0.1991377473,-0.0738989785,0.0706882998,0.0106178634,-0.073409833,0.2454290986,0.7580022812,-0.2056699097,-0.312558651,0.071137473,-0.1893505454,0.0001482788,0.2615221441,-0.2317508012,0.0680976883,-0.2531420588,-0.1607623249,0.0935508609,-0.4468418658,-0.1955764443,0.2799386978,-0.0556045361,0.4567216039,0.0581239238,-0.0801238641,-0.1273096502,-0.2414393574,0.1230968684,0.1893331259,0.024409499,-0.206517607,-0.0782089755,-0.1571550071,0.1286076456,0.2847905159,-0.2935614586,0.077220507,0.145658657,-0.1646689028,0.1315743774,-0.0165994577,-0.1360207945,0.2849665582,0.1409733444,0.1329290271,0.0182084814,0.2053834796,-0.5579439998,0.2882403731,-0.2570711672,-0.0820594132,-0.1659240127,-0.0840731114,-0.0773310065,0.0983150154,-0.3395567238,-0.3217682838,-0.012640723,0.2045415044,0.0194325894,0.1464295685,-0.3868191838,0.3574392796,-0.2472709864,0.2067177594,-0.5592110753,-0.0302885082,0.0747719556,-0.1133629307,0.0375892036,0.26366961,0.0972482264,-0.2482202649,0.0932326689,0.459361881,-0.2942497432,0.4402934313,0.0197736491,-0.0383595861,0.230896011,-0.2425242513,0.0496424139,-0.0545340925,-0.009617541,0.0093187159,-0.1185193583,0.1728489101,-0.1332919747,0.4073511362,0.0459632091,0.181058377,0.1797826737,0.0661977828,-0.181844309,-0.2293711901,0.4912464023,-0.1180183291,0.4162700772,-0.0020716565,-0.3156051636,-0.017534731,0.1378815174,0.0692394525,-0.0539511181,0.079073526,-0.3096742332,0.1229100451,0.1191897839,-0.140136838,0.3699494004,0.0957033783,-0.0847904533,0.1523884386,-0.0509501025,-0.3968065381,0.2081427425,0.0215498786,-0.3026605844,0.048985716,0.0666019022,0.0369160734,-0.3345046937,0.0931314453,-0.0384076945,0.0460374355,-0.3374765217,0.2550656497,-0.1003413945,-0.4144649804,-0.1569963396,-0.4590072334,-0.3220498264,-0.2167787254,-0.1421779394,0.19984442,-0.0557115711,0.1832412779,0.3134799898,0.0883676261,-0.2705671489,-0.2283976227,-0.1064613387,-0.1779920757,-0.2053411752,0.0222982261,0.3676596284,-0.1473156363,0.2259963453,-0.4360030293,-0.071560286,-0.2555087805,-0.2175281793,0.1356736869,-0.2151544541,0.431124419,0.0441277251,0.1951391399,-0.1098990589,-0.0055347406,0.5068662167,0.0420540832,-0.1580023468,0.2077817768,-0.1208435148,-0.1133539528,-0.1543784589,-0.1071836203,-0.2348463386,-0.4226069748,0.3171221316,-0.0450610556,-0.116146937,0.6448138356,0.0387850404,0.1422766447,-0.4684151113,0.0847676247,0.0091073811,-0.5962807536,0.2181991339,-0.2305508703,-0.2328947932,0.2591850162,0.2447499633,0.0769027323,0.2677282095,-0.3939247429,-0.2405212373,0.0683698058,0.0462512821,0.1119177192,0.1560761034,0.2483342141,-0.1363087296,0.0755938292,0.0432069562,-0.1572255045,0.0981369168,-0.1003512517,0.3137177229,-0.1538291126,0.4582180083,-0.1005096957,0.5343384147,0.4117761254,0.0967059731,0.4817893803,-0.0264523011,0.581859529,-0.2475986183,-0.4075126052,0.0939726904,0.017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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1644","title":"HoVeR dataset fails to load","comments":"Hover was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `hover` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"hover\")\r\n```","body":"Hi! I'm getting an error when trying to load **HoVeR** dataset. Another one (**SQuAD**) does work for me. I'm using the latest (1.1.3) version of the library.\r\n\r\nSteps to reproduce the error:\r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"hover\")\r\nTraceback (most recent call last):\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/hover\/hover.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/hover\/hover.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at hover\/hover.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/hover\/hover.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/hover\/hover.py\r\n```","comment_length":40,"text":"HoVeR dataset fails to load \n Hi! I'm getting an error when trying to load **HoVeR** dataset. Another one (**SQuAD**) does work for me. I'm using the latest (1.1.3) version of the library.\r\n\r\nSteps to reproduce the error:\r\n\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"hover\")\r\nTraceback (most recent call last):\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/hover\/hover.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/hover\/hover.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/Users\/urikz\/anaconda\/envs\/mentionmemory\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at hover\/hover.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/hover\/hover.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/hover\/hover.py\r\n``` \n Hover was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `hover` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"hover\")\r\n```","embeddings":[-0.221469745,0.0588316135,0.0173894744,0.2946339846,0.2871959507,0.1036297306,0.2778086066,0.2113136351,0.055386655,0.0441018417,-0.1638761312,-0.0258904286,0.0066259452,-0.1750875711,0.3238515556,-0.1725585759,-0.1127176136,-0.0135882031,-0.2122376859,0.1584800482,-0.0319676921,0.2331239581,-0.1512842625,0.2855141759,-0.034938287,0.188546896,-0.0476546511,0.1102088764,-0.0013451164,-0.5571446419,0.6485419273,0.1964526772,0.2534165084,0.6113681793,-0.0001130312,0.1879943311,0.427631259,0.0710939392,-0.4378682077,-0.4516066909,0.1970393956,-0.0665755048,0.401270926,0.088631399,-0.1916379929,-0.1743660718,-0.0482023507,-0.356459558,0.1199604869,0.1515654474,0.2370973378,0.5524005294,-0.0181829743,-0.2322163582,0.2086297274,-0.0932899341,-0.1618075073,0.4859958291,0.277926445,0.0000181067,0.0415287912,0.1254346818,-0.1783881336,0.1609940231,0.4485349655,-0.0253838282,-0.1275161803,-0.0778705627,0.1246726066,0.2353181243,0.5451827049,-0.2534771264,-0.3022951782,-0.173381567,0.0765558109,0.1363622248,0.2944325209,-0.2126211375,-0.0716271028,0.1803832352,-0.1028136313,-0.1887427866,-0.0607598387,0.2107258141,0.1004542783,-0.158973828,-0.2308696061,0.2097773254,0.1637531519,-0.05227549,0.0160596594,-0.0153824734,0.0535579473,0.2114631385,-0.396317631,0.0593051873,0.0366261862,0.1347655654,0.2433893085,0.3089216948,0.3861995041,-0.0397093259,-0.2499678433,0.1359069943,0.4187658131,0.0782540217,-0.0268121343,-0.3456539214,0.439147234,0.2833787799,0.1257042885,-0.0463790931,-0.0763063729,-0.0516994074,-0.1337563246,-0.1697103679,0.2196262628,-0.2770833075,-0.2052009851,0.1551857591,-0.1102024615,-0.0619326904,0.1705599576,0.3384833932,-0.1790276617,0.2826485932,0.2799422443,0.2793718576,0.0097070495,-0.1391048133,-0.2306847572,0.3470246792,-0.1135188118,0.1684673876,0.4636680186,-0.3708824217,0.2146412134,0.0527159609,0.2620734274,0.0118495738,-0.064015612,-0.1308800727,-0.2860842049,0.262001574,0.0375623852,0.3730460107,0.3032679558,-0.3780884445,-0.0785978958,0.1303363591,-0.1046970189,-0.2736743093,-0.1131843925,0.140702635,-0.5425629616,-0.1690017283,-0.7952097058,-0.1158957928,-0.0374030024,-0.1901670247,-0.1757319272,-0.2521235943,0.1131115183,-0.1844933033,0.2569434643,0.5731605291,-0.4683733881,-0.1417627186,-0.3210857809,-0.2450504303,-0.1895079464,0.0499389432,-0.2290991247,0.0145849064,-0.3759831786,0.2342487425,0.201632306,-0.5678400397,-0.3043695986,0.1881448478,-0.2737540305,0.1391416043,-0.1096155122,-0.0431161709,0.0342822187,-0.1298496127,-0.2751350701,0.3710016608,0.0552587844,-0.1252245307,-0.152625978,-0.0876119956,0.0242702495,0.481423825,-0.0050684409,-0.0346196033,0.1112635955,0.4048300385,0.1238478646,0.0082403999,-0.002646581,0.2363563478,0.1557277441,0.0660671145,0.0265987385,-0.2205318511,-0.5138401985,0.279047966,0.2804960012,0.0934022292,-0.0520986617,0.1098806933,-0.3560436666,-0.0623568818,-0.4687760472,-0.257620424,0.042077519,0.2640281022,-0.1119287014,-0.0139558166,-0.2272306979,0.1317779124,-0.1492676586,0.2208481133,-0.2036609352,0.351572752,-0.1274198592,-0.1394954026,0.1126090959,0.1909775883,0.0127466023,-0.2036845237,-0.1120589375,0.5353549719,-0.19174546,-0.1702150106,0.1466422528,-0.0516479574,0.1855684668,-0.3324547708,0.019998746,0.0225046352,0.0527466536,0.0093191722,-0.1755173802,-0.0428609997,-0.0302115995,0.422205627,0.1818193346,0.1183508858,0.3142014444,0.0689993054,-0.0676742047,-0.1829338819,0.3034820855,0.062292099,0.4900861979,-0.0120340073,-0.197725758,0.0360332131,-0.0401958451,-0.007314716,0.1719882339,0.2215848565,-0.2188159376,-0.0418814793,-0.076336585,0.1008972302,0.5481566787,0.09765248,-0.0673388541,-0.1769091785,0.0582956001,-0.1067657098,0.1759354621,-0.1082828864,-0.0781492665,0.0818073526,0.1608298421,-0.0328737162,-0.2620770931,-0.4119637311,-0.0328710265,0.1852684617,-0.2394344509,0.1062231287,-0.3376254737,-0.1877980381,-0.0378028154,0.0568638258,-0.4906938374,-0.3384881616,-0.2438513488,0.2719503939,0.3034607172,0.0520159602,-0.0791255087,0.2371322662,0.0541921705,0.0087981354,-0.1003618538,0.0227770153,-0.2968294621,0.0321643203,0.1251295209,-0.1656052917,0.1294487268,0.0444601886,0.1059454307,-0.3842816651,-0.1393769234,0.2893945277,-0.0943957046,0.4804418087,0.2228505611,0.3556439877,0.0432693847,0.0097817592,0.5540043116,-0.0970824137,-0.0247857142,0.1292891353,0.061432723,0.0392577611,-0.1232213378,-0.0799865648,-0.4014402926,-0.3176139891,-0.003122418,-0.0119200535,-0.1667927057,0.4903976619,-0.0261619668,0.2140633762,-0.1069947779,0.1902738214,-0.2419166118,-0.4892966151,0.200207904,-0.2261955738,-0.2358217239,-0.0773115382,0.2203531563,0.3298978508,-0.1658444703,-0.5135371089,-0.1725959629,-0.0367091224,0.1087123677,-0.0219955295,0.1146963835,0.0110178953,-0.0514286198,-0.0525038578,-0.1449035555,-0.1751174331,-0.1460386962,-0.1188498512,0.2608271539,0.0687822402,0.0731951147,0.1224943027,0.6985940337,-0.1189664379,-0.2699800432,0.5415757895,0.0427996106,0.492747426,-0.0116330767,-0.5201570988,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1641","title":"muchocine dataset cannot be dowloaded","comments":"I have encountered the same error with `v1.0.1` and `v1.0.2` on both Windows and Linux environments. However, cloning the repo and using the path to the dataset's root directory worked for me. Even after having the dataset cached - passing the path is the only way (for now) to load the dataset.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"squad\")                      # Works\r\ndataset = load_dataset(\"code_search_net\", \"python\")  # Error\r\ndataset = load_dataset(\"covid_qa_deepset\")           # Error\r\n\r\npath = \"\/huggingface\/datasets\/datasets\/{}\/\"\r\ndataset = load_dataset(path.format(\"code_search_net\"), \"python\")  # Works\r\ndataset = load_dataset(path.format(\"covid_qa_deepset\"))           # Works\r\n```\r\n\r\n","body":"```python\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n\r\n7 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    281                 raise FileNotFoundError(\r\n    282                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 283                         combined_path, github_file_path, file_path\r\n    284                     )\r\n    285                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at muchocine\/muchocine.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n```","comment_length":88,"text":"muchocine dataset cannot be dowloaded \n ```python\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n\r\n7 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    281                 raise FileNotFoundError(\r\n    282                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 283                         combined_path, github_file_path, file_path\r\n    284                     )\r\n    285                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at muchocine\/muchocine.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n``` \n I have encountered the same error with `v1.0.1` and `v1.0.2` on both Windows and Linux environments. However, cloning the repo and using the path to the dataset's root directory worked for me. Even after having the dataset cached - passing the path is the only way (for now) to load the dataset.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"squad\")                      # Works\r\ndataset = load_dataset(\"code_search_net\", \"python\")  # Error\r\ndataset = load_dataset(\"covid_qa_deepset\")           # Error\r\n\r\npath = \"\/huggingface\/datasets\/datasets\/{}\/\"\r\ndataset = load_dataset(path.format(\"code_search_net\"), \"python\")  # Works\r\ndataset = load_dataset(path.format(\"covid_qa_deepset\"))           # Works\r\n```\r\n\r\n","embeddings":[-0.3657995164,-0.1491084993,-0.0535814576,0.3329214454,0.4308770597,0.1230888665,0.359962672,0.3173546791,0.3176317215,0.0625982285,-0.2380269468,0.0012010086,-0.088910602,0.0610442795,0.0386018343,0.0196577273,-0.09693712,0.0420849808,-0.0913112238,0.0183463041,-0.2103825361,0.0953873545,-0.0354201607,-0.1008452177,-0.1107043624,-0.1373711079,-0.0456292927,0.3429242671,-0.4638158977,-0.2475145757,0.3933928609,-0.0597475134,0.234281376,0.4329293668,-0.0001103552,0.1410666555,0.3332580626,-0.1318264008,-0.4365814328,-0.5427050591,-0.2340316921,-0.2482806295,0.1217606962,-0.1392974555,-0.0860487893,-0.2626752853,0.0757315606,-0.1615948975,0.3259954154,0.4596894383,0.2739565372,0.380763948,0.1729074568,-0.2033828199,0.4229698777,-0.0247138757,-0.1236245483,0.4684586823,0.0693040863,-0.024407642,0.0798502043,0.0708083808,-0.188991338,0.1367580295,0.1010280177,-0.1283799112,-0.0986570567,-0.3918550313,0.1986587644,0.2060496807,0.2593174577,-0.1449202597,-0.4517106712,-0.0636464879,0.0942918658,-0.4788383842,0.0918764025,0.2396257669,0.1592647433,0.1096555591,0.0373005494,-0.249765873,-0.1381266117,0.3669583201,-0.1173515543,0.0664340779,-0.2460633069,-0.1064698994,0.0680627376,-0.1647121012,-0.1510896981,0.1155014038,-0.2062194943,0.102867052,-0.2802644968,-0.056664519,-0.0772117227,-0.0013284244,0.0971117243,0.2565974891,0.0405130051,-0.0053874771,0.0977683812,0.0714682937,0.0552441515,0.1962952316,-0.0038632948,0.1495791376,0.0931143016,0.3699893057,-0.0203259401,-0.0598866008,-0.4647661746,-0.250232935,-0.2243047506,-0.2275567502,0.3014701009,-0.2856255472,-0.3109283149,-0.0650905594,-0.0692585632,-0.0173054188,0.2589639723,0.591776371,-0.2199613154,0.1064362228,0.0758352801,0.3028856218,-0.0017310816,0.2429312021,-0.2484750748,0.4639939368,-0.1429483891,-0.0564340316,0.251064837,-0.4133730233,0.4145965874,-0.1181406304,0.0109985182,-0.1353993267,-0.0377918668,-0.1631853282,0.1514472365,0.224788323,-0.0626443401,0.0758576468,0.2453983724,0.0033513503,-0.1945626736,-0.2132005095,-0.1482447833,-0.4352903068,-0.1609933972,0.2583007216,-0.0089885276,-0.1736529022,-0.1358133107,-0.366612941,0.1069539264,-0.0593503825,-0.0898369849,-0.2057666928,-0.1339452416,-0.2123783231,0.4348729551,0.6249145865,0.0577635914,-0.1927994341,-0.1481889635,0.1223766208,0.1011742428,0.1879488081,-0.256937474,0.1325680017,-0.3040416241,-0.2483960986,0.5892069936,-0.478657186,-0.3480915129,0.4911933541,-0.2445544153,0.0593723133,0.1439642012,-0.1001187265,0.2385188937,0.0485155918,0.5149019957,0.1525217742,0.0060279611,-0.0757718012,-0.2067523301,-0.3548433185,-0.1694717556,0.2668253481,0.2473154366,0.1203408763,0.2451995462,0.3110216558,0.0581675619,-0.0235748794,-0.0413322337,0.251663208,0.2244619727,0.0511546023,-0.2082608342,-0.060526751,-0.373261869,0.2185180634,-0.0019837443,0.0943156704,-0.1907487065,0.1080496237,-0.5253341198,-0.041457817,-0.2475919873,-0.1638884544,0.163851127,0.2125468105,0.256457001,0.1356508881,-0.2132635266,0.3509989381,-0.1266469955,0.0677978545,-0.2135928869,0.0486418754,-0.169808507,-0.1537032425,0.1298379898,0.1843456626,0.2591026723,-0.2886403799,0.0465948954,0.2431535423,-0.0469079241,-0.2477146685,0.040527422,0.2363331914,0.2393021435,-0.0464564115,0.1544012427,-0.0636134222,0.1630853117,0.0505272746,0.1560126543,0.3476131558,-0.1160445437,0.2653279901,0.2960418165,-0.1287916303,0.3048322797,0.0371037014,0.1048189625,-0.2303738743,0.2754022777,0.1103917286,0.4532395899,0.0878859088,-0.244653523,-0.0362577438,0.1542772949,0.1417571306,-0.100424476,0.088071011,-0.1078079715,0.0503145568,-0.1643220484,0.1259839535,0.4394570589,0.3152256012,-0.003013884,-0.1307429522,0.1457129568,-0.1901173443,0.22852467,0.0828294829,0.1319265068,0.0903955325,0.2431523353,0.0604094751,-0.2865849137,-0.4136480987,0.0604158528,0.2175486982,-0.1233721375,0.0543118194,-0.3415805101,-0.4350299239,-0.1832307875,-0.0812561661,-0.1189615279,-0.2224486619,-0.1093107909,0.3850286901,0.0820262358,0.1743492037,-0.3616304398,0.1311659068,0.0034895537,-0.463675499,-0.0208469816,-0.0498597361,0.0238630641,0.0916467384,0.3101963997,-0.3293982744,0.2989204228,-0.2584398687,-0.3787842393,-0.3437911272,-0.0524499752,0.1639781594,0.0416437387,0.2919322252,0.0584816337,0.2683333457,-0.0478225909,-0.1814966053,0.2785207331,-0.0333806127,-0.101678744,-0.0568195209,-0.0152252857,0.004719927,0.1111131161,-0.3484595418,-0.3846930861,-0.5702346563,0.001228385,0.0241096672,0.0976283625,0.1633743942,0.0823181942,0.1395433843,-0.2771349549,0.2637175024,0.0028176897,-0.6356604695,0.3077563643,-0.3354674876,-0.4740182459,0.0636546239,0.0719056129,0.1483119577,0.0598436333,-0.3255211115,-0.1184874848,-0.2935323417,0.0969748423,0.0113175409,0.1488799453,0.1564039141,-0.0627485886,-0.1523783356,-0.0394820422,-0.0744168758,0.0284218621,0.0755534172,0.1952203214,-0.1556702703,0.3054393232,-0.1872801781,0.8105006814,-0.0446907543,-0.2567236423,0.6232312918,-0.3263625205,0.543845892,-0.1952953637,-0.5162585378,-0.088937521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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1641","title":"muchocine dataset cannot be dowloaded","comments":"Hi @mrm8488 and @amoux!\r\n The datasets you are trying to load have been added to the library during the community sprint for v2 last month. They will be available with the v2 release!\r\nFor now, there are still a couple of solutions to load the datasets:\r\n1. As suggested by @amoux, you can clone the git repo and pass the local path to the script\r\n2. You can also install the latest (master) version of `datasets` using pip: `pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","body":"```python\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n\r\n7 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    281                 raise FileNotFoundError(\r\n    282                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 283                         combined_path, github_file_path, file_path\r\n    284                     )\r\n    285                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at muchocine\/muchocine.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n```","comment_length":81,"text":"muchocine dataset cannot be dowloaded \n ```python\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n\r\n7 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    281                 raise FileNotFoundError(\r\n    282                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 283                         combined_path, github_file_path, file_path\r\n    284                     )\r\n    285                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at muchocine\/muchocine.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n``` \n Hi @mrm8488 and @amoux!\r\n The datasets you are trying to load have been added to the library during the community sprint for v2 last month. They will be available with the v2 release!\r\nFor now, there are still a couple of solutions to load the datasets:\r\n1. As suggested by @amoux, you can clone the git repo and pass the local path to the script\r\n2. You can also install the latest (master) version of `datasets` using pip: `pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","embeddings":[-0.3657995164,-0.1491084993,-0.0535814576,0.3329214454,0.4308770597,0.1230888665,0.359962672,0.3173546791,0.3176317215,0.0625982285,-0.2380269468,0.0012010086,-0.088910602,0.0610442795,0.0386018343,0.0196577273,-0.09693712,0.0420849808,-0.0913112238,0.0183463041,-0.2103825361,0.0953873545,-0.0354201607,-0.1008452177,-0.1107043624,-0.1373711079,-0.0456292927,0.3429242671,-0.4638158977,-0.2475145757,0.3933928609,-0.0597475134,0.234281376,0.4329293668,-0.0001103552,0.1410666555,0.3332580626,-0.1318264008,-0.4365814328,-0.5427050591,-0.2340316921,-0.2482806295,0.1217606962,-0.1392974555,-0.0860487893,-0.2626752853,0.0757315606,-0.1615948975,0.3259954154,0.4596894383,0.2739565372,0.380763948,0.1729074568,-0.2033828199,0.4229698777,-0.0247138757,-0.1236245483,0.4684586823,0.0693040863,-0.024407642,0.0798502043,0.0708083808,-0.188991338,0.1367580295,0.1010280177,-0.1283799112,-0.0986570567,-0.3918550313,0.1986587644,0.2060496807,0.2593174577,-0.1449202597,-0.4517106712,-0.0636464879,0.0942918658,-0.4788383842,0.0918764025,0.2396257669,0.1592647433,0.1096555591,0.0373005494,-0.249765873,-0.1381266117,0.3669583201,-0.1173515543,0.0664340779,-0.2460633069,-0.1064698994,0.0680627376,-0.1647121012,-0.1510896981,0.1155014038,-0.2062194943,0.102867052,-0.2802644968,-0.056664519,-0.0772117227,-0.0013284244,0.0971117243,0.2565974891,0.0405130051,-0.0053874771,0.0977683812,0.0714682937,0.0552441515,0.1962952316,-0.0038632948,0.1495791376,0.0931143016,0.3699893057,-0.0203259401,-0.0598866008,-0.4647661746,-0.250232935,-0.2243047506,-0.2275567502,0.3014701009,-0.2856255472,-0.3109283149,-0.0650905594,-0.0692585632,-0.0173054188,0.2589639723,0.591776371,-0.2199613154,0.1064362228,0.0758352801,0.3028856218,-0.0017310816,0.2429312021,-0.2484750748,0.4639939368,-0.1429483891,-0.0564340316,0.251064837,-0.4133730233,0.4145965874,-0.1181406304,0.0109985182,-0.1353993267,-0.0377918668,-0.1631853282,0.1514472365,0.224788323,-0.0626443401,0.0758576468,0.2453983724,0.0033513503,-0.1945626736,-0.2132005095,-0.1482447833,-0.4352903068,-0.1609933972,0.2583007216,-0.0089885276,-0.1736529022,-0.1358133107,-0.366612941,0.1069539264,-0.0593503825,-0.0898369849,-0.2057666928,-0.1339452416,-0.2123783231,0.4348729551,0.6249145865,0.0577635914,-0.1927994341,-0.1481889635,0.1223766208,0.1011742428,0.1879488081,-0.256937474,0.1325680017,-0.3040416241,-0.2483960986,0.5892069936,-0.478657186,-0.3480915129,0.4911933541,-0.2445544153,0.0593723133,0.1439642012,-0.1001187265,0.2385188937,0.0485155918,0.5149019957,0.1525217742,0.0060279611,-0.0757718012,-0.2067523301,-0.3548433185,-0.1694717556,0.2668253481,0.2473154366,0.1203408763,0.2451995462,0.3110216558,0.0581675619,-0.0235748794,-0.0413322337,0.251663208,0.2244619727,0.0511546023,-0.2082608342,-0.060526751,-0.373261869,0.2185180634,-0.0019837443,0.0943156704,-0.1907487065,0.1080496237,-0.5253341198,-0.041457817,-0.2475919873,-0.1638884544,0.163851127,0.2125468105,0.256457001,0.1356508881,-0.2132635266,0.3509989381,-0.1266469955,0.0677978545,-0.2135928869,0.0486418754,-0.169808507,-0.1537032425,0.1298379898,0.1843456626,0.2591026723,-0.2886403799,0.0465948954,0.2431535423,-0.0469079241,-0.2477146685,0.040527422,0.2363331914,0.2393021435,-0.0464564115,0.1544012427,-0.0636134222,0.1630853117,0.0505272746,0.1560126543,0.3476131558,-0.1160445437,0.2653279901,0.2960418165,-0.1287916303,0.3048322797,0.0371037014,0.1048189625,-0.2303738743,0.2754022777,0.1103917286,0.4532395899,0.0878859088,-0.244653523,-0.0362577438,0.1542772949,0.1417571306,-0.100424476,0.088071011,-0.1078079715,0.0503145568,-0.1643220484,0.1259839535,0.4394570589,0.3152256012,-0.003013884,-0.1307429522,0.1457129568,-0.1901173443,0.22852467,0.0828294829,0.1319265068,0.0903955325,0.2431523353,0.0604094751,-0.2865849137,-0.4136480987,0.0604158528,0.2175486982,-0.1233721375,0.0543118194,-0.3415805101,-0.4350299239,-0.1832307875,-0.0812561661,-0.1189615279,-0.2224486619,-0.1093107909,0.3850286901,0.0820262358,0.1743492037,-0.3616304398,0.1311659068,0.0034895537,-0.463675499,-0.0208469816,-0.0498597361,0.0238630641,0.0916467384,0.3101963997,-0.3293982744,0.2989204228,-0.2584398687,-0.3787842393,-0.3437911272,-0.0524499752,0.1639781594,0.0416437387,0.2919322252,0.0584816337,0.2683333457,-0.0478225909,-0.1814966053,0.2785207331,-0.0333806127,-0.101678744,-0.0568195209,-0.0152252857,0.004719927,0.1111131161,-0.3484595418,-0.3846930861,-0.5702346563,0.001228385,0.0241096672,0.0976283625,0.1633743942,0.0823181942,0.1395433843,-0.2771349549,0.2637175024,0.0028176897,-0.6356604695,0.3077563643,-0.3354674876,-0.4740182459,0.0636546239,0.0719056129,0.1483119577,0.0598436333,-0.3255211115,-0.1184874848,-0.2935323417,0.0969748423,0.0113175409,0.1488799453,0.1564039141,-0.0627485886,-0.1523783356,-0.0394820422,-0.0744168758,0.0284218621,0.0755534172,0.1952203214,-0.1556702703,0.3054393232,-0.1872801781,0.8105006814,-0.0446907543,-0.2567236423,0.6232312918,-0.3263625205,0.543845892,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1641","title":"muchocine dataset cannot be dowloaded","comments":"If you don't want to clone entire `datasets` repo, just download the `muchocine` directory and pass the local path to the directory. Cheers!","body":"```python\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n\r\n7 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    281                 raise FileNotFoundError(\r\n    282                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 283                         combined_path, github_file_path, file_path\r\n    284                     )\r\n    285                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at muchocine\/muchocine.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n```","comment_length":23,"text":"muchocine dataset cannot be dowloaded \n ```python\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n\r\n7 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    281                 raise FileNotFoundError(\r\n    282                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 283                         combined_path, github_file_path, file_path\r\n    284                     )\r\n    285                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at muchocine\/muchocine.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n``` \n If you don't want to clone entire `datasets` repo, just download the `muchocine` directory and pass the local path to the directory. Cheers!","embeddings":[-0.3657995164,-0.1491084993,-0.0535814576,0.3329214454,0.4308770597,0.1230888665,0.359962672,0.3173546791,0.3176317215,0.0625982285,-0.2380269468,0.0012010086,-0.088910602,0.0610442795,0.0386018343,0.0196577273,-0.09693712,0.0420849808,-0.0913112238,0.0183463041,-0.2103825361,0.0953873545,-0.0354201607,-0.1008452177,-0.1107043624,-0.1373711079,-0.0456292927,0.3429242671,-0.4638158977,-0.2475145757,0.3933928609,-0.0597475134,0.234281376,0.4329293668,-0.0001103552,0.1410666555,0.3332580626,-0.1318264008,-0.4365814328,-0.5427050591,-0.2340316921,-0.2482806295,0.1217606962,-0.1392974555,-0.0860487893,-0.2626752853,0.0757315606,-0.1615948975,0.3259954154,0.4596894383,0.2739565372,0.380763948,0.1729074568,-0.2033828199,0.4229698777,-0.0247138757,-0.1236245483,0.4684586823,0.0693040863,-0.024407642,0.0798502043,0.0708083808,-0.188991338,0.1367580295,0.1010280177,-0.1283799112,-0.0986570567,-0.3918550313,0.1986587644,0.2060496807,0.2593174577,-0.1449202597,-0.4517106712,-0.0636464879,0.0942918658,-0.4788383842,0.0918764025,0.2396257669,0.1592647433,0.1096555591,0.0373005494,-0.249765873,-0.1381266117,0.3669583201,-0.1173515543,0.0664340779,-0.2460633069,-0.1064698994,0.0680627376,-0.1647121012,-0.1510896981,0.1155014038,-0.2062194943,0.102867052,-0.2802644968,-0.056664519,-0.0772117227,-0.0013284244,0.0971117243,0.2565974891,0.0405130051,-0.0053874771,0.0977683812,0.0714682937,0.0552441515,0.1962952316,-0.0038632948,0.1495791376,0.0931143016,0.3699893057,-0.0203259401,-0.0598866008,-0.4647661746,-0.250232935,-0.2243047506,-0.2275567502,0.3014701009,-0.2856255472,-0.3109283149,-0.0650905594,-0.0692585632,-0.0173054188,0.2589639723,0.591776371,-0.2199613154,0.1064362228,0.0758352801,0.3028856218,-0.0017310816,0.2429312021,-0.2484750748,0.4639939368,-0.1429483891,-0.0564340316,0.251064837,-0.4133730233,0.4145965874,-0.1181406304,0.0109985182,-0.1353993267,-0.0377918668,-0.1631853282,0.1514472365,0.224788323,-0.0626443401,0.0758576468,0.2453983724,0.0033513503,-0.1945626736,-0.2132005095,-0.1482447833,-0.4352903068,-0.1609933972,0.2583007216,-0.0089885276,-0.1736529022,-0.1358133107,-0.366612941,0.1069539264,-0.0593503825,-0.0898369849,-0.2057666928,-0.1339452416,-0.2123783231,0.4348729551,0.6249145865,0.0577635914,-0.1927994341,-0.1481889635,0.1223766208,0.1011742428,0.1879488081,-0.256937474,0.1325680017,-0.3040416241,-0.2483960986,0.5892069936,-0.478657186,-0.3480915129,0.4911933541,-0.2445544153,0.0593723133,0.1439642012,-0.1001187265,0.2385188937,0.0485155918,0.5149019957,0.1525217742,0.0060279611,-0.0757718012,-0.2067523301,-0.3548433185,-0.1694717556,0.2668253481,0.2473154366,0.1203408763,0.2451995462,0.3110216558,0.0581675619,-0.0235748794,-0.0413322337,0.251663208,0.2244619727,0.0511546023,-0.2082608342,-0.060526751,-0.373261869,0.2185180634,-0.0019837443,0.0943156704,-0.1907487065,0.1080496237,-0.5253341198,-0.041457817,-0.2475919873,-0.1638884544,0.163851127,0.2125468105,0.256457001,0.1356508881,-0.2132635266,0.3509989381,-0.1266469955,0.0677978545,-0.2135928869,0.0486418754,-0.169808507,-0.1537032425,0.1298379898,0.1843456626,0.2591026723,-0.2886403799,0.0465948954,0.2431535423,-0.0469079241,-0.2477146685,0.040527422,0.2363331914,0.2393021435,-0.0464564115,0.1544012427,-0.0636134222,0.1630853117,0.0505272746,0.1560126543,0.3476131558,-0.1160445437,0.2653279901,0.2960418165,-0.1287916303,0.3048322797,0.0371037014,0.1048189625,-0.2303738743,0.2754022777,0.1103917286,0.4532395899,0.0878859088,-0.244653523,-0.0362577438,0.1542772949,0.1417571306,-0.100424476,0.088071011,-0.1078079715,0.0503145568,-0.1643220484,0.1259839535,0.4394570589,0.3152256012,-0.003013884,-0.1307429522,0.1457129568,-0.1901173443,0.22852467,0.0828294829,0.1319265068,0.0903955325,0.2431523353,0.0604094751,-0.2865849137,-0.4136480987,0.0604158528,0.2175486982,-0.1233721375,0.0543118194,-0.3415805101,-0.4350299239,-0.1832307875,-0.0812561661,-0.1189615279,-0.2224486619,-0.1093107909,0.3850286901,0.0820262358,0.1743492037,-0.3616304398,0.1311659068,0.0034895537,-0.463675499,-0.0208469816,-0.0498597361,0.0238630641,0.0916467384,0.3101963997,-0.3293982744,0.2989204228,-0.2584398687,-0.3787842393,-0.3437911272,-0.0524499752,0.1639781594,0.0416437387,0.2919322252,0.0584816337,0.2683333457,-0.0478225909,-0.1814966053,0.2785207331,-0.0333806127,-0.101678744,-0.0568195209,-0.0152252857,0.004719927,0.1111131161,-0.3484595418,-0.3846930861,-0.5702346563,0.001228385,0.0241096672,0.0976283625,0.1633743942,0.0823181942,0.1395433843,-0.2771349549,0.2637175024,0.0028176897,-0.6356604695,0.3077563643,-0.3354674876,-0.4740182459,0.0636546239,0.0719056129,0.1483119577,0.0598436333,-0.3255211115,-0.1184874848,-0.2935323417,0.0969748423,0.0113175409,0.1488799453,0.1564039141,-0.0627485886,-0.1523783356,-0.0394820422,-0.0744168758,0.0284218621,0.0755534172,0.1952203214,-0.1556702703,0.3054393232,-0.1872801781,0.8105006814,-0.0446907543,-0.2567236423,0.6232312918,-0.3263625205,0.543845892,-0.1952953637,-0.5162585378,-0.088937521,0.0012898825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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1641","title":"muchocine dataset cannot be dowloaded","comments":"Muchocine was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `muchocine` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"muchocine\", split=\"train\")\r\n```","body":"```python\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n\r\n7 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    281                 raise FileNotFoundError(\r\n    282                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 283                         combined_path, github_file_path, file_path\r\n    284                     )\r\n    285                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at muchocine\/muchocine.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n```","comment_length":41,"text":"muchocine dataset cannot be dowloaded \n ```python\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n\r\n7 frames\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    281                 raise FileNotFoundError(\r\n    282                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 283                         combined_path, github_file_path, file_path\r\n    284                     )\r\n    285                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at muchocine\/muchocine.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/muchocine\/muchocine.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/muchocine\/muchocine.py\r\n``` \n Muchocine was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `muchocine` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"muchocine\", split=\"train\")\r\n```","embeddings":[-0.3657995164,-0.1491084993,-0.0535814576,0.3329214454,0.4308770597,0.1230888665,0.359962672,0.3173546791,0.3176317215,0.0625982285,-0.2380269468,0.0012010086,-0.088910602,0.0610442795,0.0386018343,0.0196577273,-0.09693712,0.0420849808,-0.0913112238,0.0183463041,-0.2103825361,0.0953873545,-0.0354201607,-0.1008452177,-0.1107043624,-0.1373711079,-0.0456292927,0.3429242671,-0.4638158977,-0.2475145757,0.3933928609,-0.0597475134,0.234281376,0.4329293668,-0.0001103552,0.1410666555,0.3332580626,-0.1318264008,-0.4365814328,-0.5427050591,-0.2340316921,-0.2482806295,0.1217606962,-0.1392974555,-0.0860487893,-0.2626752853,0.0757315606,-0.1615948975,0.3259954154,0.4596894383,0.2739565372,0.380763948,0.1729074568,-0.2033828199,0.4229698777,-0.0247138757,-0.1236245483,0.4684586823,0.0693040863,-0.024407642,0.0798502043,0.0708083808,-0.188991338,0.1367580295,0.1010280177,-0.1283799112,-0.0986570567,-0.3918550313,0.1986587644,0.2060496807,0.2593174577,-0.1449202597,-0.4517106712,-0.0636464879,0.0942918658,-0.4788383842,0.0918764025,0.2396257669,0.1592647433,0.1096555591,0.0373005494,-0.249765873,-0.1381266117,0.3669583201,-0.1173515543,0.0664340779,-0.2460633069,-0.1064698994,0.0680627376,-0.1647121012,-0.1510896981,0.1155014038,-0.2062194943,0.102867052,-0.2802644968,-0.056664519,-0.0772117227,-0.0013284244,0.0971117243,0.2565974891,0.0405130051,-0.0053874771,0.0977683812,0.0714682937,0.0552441515,0.1962952316,-0.0038632948,0.1495791376,0.0931143016,0.3699893057,-0.0203259401,-0.0598866008,-0.4647661746,-0.250232935,-0.2243047506,-0.2275567502,0.3014701009,-0.2856255472,-0.3109283149,-0.0650905594,-0.0692585632,-0.0173054188,0.2589639723,0.591776371,-0.2199613154,0.1064362228,0.0758352801,0.3028856218,-0.0017310816,0.2429312021,-0.2484750748,0.4639939368,-0.1429483891,-0.0564340316,0.251064837,-0.4133730233,0.4145965874,-0.1181406304,0.0109985182,-0.1353993267,-0.0377918668,-0.1631853282,0.1514472365,0.224788323,-0.0626443401,0.0758576468,0.2453983724,0.0033513503,-0.1945626736,-0.2132005095,-0.1482447833,-0.4352903068,-0.1609933972,0.2583007216,-0.0089885276,-0.1736529022,-0.1358133107,-0.366612941,0.1069539264,-0.0593503825,-0.0898369849,-0.2057666928,-0.1339452416,-0.2123783231,0.4348729551,0.6249145865,0.0577635914,-0.1927994341,-0.1481889635,0.1223766208,0.1011742428,0.1879488081,-0.256937474,0.1325680017,-0.3040416241,-0.2483960986,0.5892069936,-0.478657186,-0.3480915129,0.4911933541,-0.2445544153,0.0593723133,0.1439642012,-0.1001187265,0.2385188937,0.0485155918,0.5149019957,0.1525217742,0.0060279611,-0.0757718012,-0.2067523301,-0.3548433185,-0.1694717556,0.2668253481,0.2473154366,0.1203408763,0.2451995462,0.3110216558,0.0581675619,-0.0235748794,-0.0413322337,0.251663208,0.2244619727,0.0511546023,-0.2082608342,-0.060526751,-0.373261869,0.2185180634,-0.0019837443,0.0943156704,-0.1907487065,0.1080496237,-0.5253341198,-0.041457817,-0.2475919873,-0.1638884544,0.163851127,0.2125468105,0.256457001,0.1356508881,-0.2132635266,0.3509989381,-0.1266469955,0.0677978545,-0.2135928869,0.0486418754,-0.169808507,-0.1537032425,0.1298379898,0.1843456626,0.2591026723,-0.2886403799,0.0465948954,0.2431535423,-0.0469079241,-0.2477146685,0.040527422,0.2363331914,0.2393021435,-0.0464564115,0.1544012427,-0.0636134222,0.1630853117,0.0505272746,0.1560126543,0.3476131558,-0.1160445437,0.2653279901,0.2960418165,-0.1287916303,0.3048322797,0.0371037014,0.1048189625,-0.2303738743,0.2754022777,0.1103917286,0.4532395899,0.0878859088,-0.244653523,-0.0362577438,0.1542772949,0.1417571306,-0.100424476,0.088071011,-0.1078079715,0.0503145568,-0.1643220484,0.1259839535,0.4394570589,0.3152256012,-0.003013884,-0.1307429522,0.1457129568,-0.1901173443,0.22852467,0.0828294829,0.1319265068,0.0903955325,0.2431523353,0.0604094751,-0.2865849137,-0.4136480987,0.0604158528,0.2175486982,-0.1233721375,0.0543118194,-0.3415805101,-0.4350299239,-0.1832307875,-0.0812561661,-0.1189615279,-0.2224486619,-0.1093107909,0.3850286901,0.0820262358,0.1743492037,-0.3616304398,0.1311659068,0.0034895537,-0.463675499,-0.0208469816,-0.0498597361,0.0238630641,0.0916467384,0.3101963997,-0.3293982744,0.2989204228,-0.2584398687,-0.3787842393,-0.3437911272,-0.0524499752,0.1639781594,0.0416437387,0.2919322252,0.0584816337,0.2683333457,-0.0478225909,-0.1814966053,0.2785207331,-0.0333806127,-0.101678744,-0.0568195209,-0.0152252857,0.004719927,0.1111131161,-0.3484595418,-0.3846930861,-0.5702346563,0.001228385,0.0241096672,0.0976283625,0.1633743942,0.0823181942,0.1395433843,-0.2771349549,0.2637175024,0.0028176897,-0.6356604695,0.3077563643,-0.3354674876,-0.4740182459,0.0636546239,0.0719056129,0.1483119577,0.0598436333,-0.3255211115,-0.1184874848,-0.2935323417,0.0969748423,0.0113175409,0.1488799453,0.1564039141,-0.0627485886,-0.1523783356,-0.0394820422,-0.0744168758,0.0284218621,0.0755534172,0.1952203214,-0.1556702703,0.3054393232,-0.1872801781,0.8105006814,-0.0446907543,-0.2567236423,0.6232312918,-0.3263625205,0.543845892,-0.1952953637,-0.5162585378,-0.088937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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1639","title":"bug with sst2 in glue ","comments":"Maybe you can use nltk's treebank detokenizer ?\r\n```python\r\nfrom nltk.tokenize.treebank import TreebankWordDetokenizer\r\n\r\nTreebankWordDetokenizer().detokenize(\"it 's a charming and often affecting journey . \".split())\r\n# \"it's a charming and often affecting journey.\"\r\n```","body":"Hi\r\nI am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below.\r\nIs there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq \r\n \r\n```\r\n>>> a =  datasets.load_dataset('glue', 'sst2', split=\"validation\", script_version=\"master\")\r\nReusing dataset glue (\/julia\/datasets\/glue\/sst2\/1.0.0\/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)\r\n>>> a[:10]\r\n{'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': [\"it 's a charming and often affecting journey . \", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', \"the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . \", \"it 's slow -- very , very slow . \", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', \"or doing last year 's taxes with your ex-wife . \", \"you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . \", \"in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . \"]}\r\n\r\n```","comment_length":32,"text":"bug with sst2 in glue  \n Hi\r\nI am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below.\r\nIs there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq \r\n \r\n```\r\n>>> a =  datasets.load_dataset('glue', 'sst2', split=\"validation\", script_version=\"master\")\r\nReusing dataset glue (\/julia\/datasets\/glue\/sst2\/1.0.0\/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)\r\n>>> a[:10]\r\n{'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': [\"it 's a charming and often affecting journey . \", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', \"the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . \", \"it 's slow -- very , very slow . \", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', \"or doing last year 's taxes with your ex-wife . \", \"you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . \", \"in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . \"]}\r\n\r\n``` \n Maybe you can use nltk's treebank detokenizer ?\r\n```python\r\nfrom nltk.tokenize.treebank import TreebankWordDetokenizer\r\n\r\nTreebankWordDetokenizer().detokenize(\"it 's a charming and often affecting journey . \".split())\r\n# \"it's a charming and often affecting journey.\"\r\n```","embeddings":[0.1141937822,-0.191114828,0.0535292141,0.1571243256,0.1562406421,-0.3605676293,0.1134419814,0.4331327975,-0.0932659656,0.0230266862,-0.1225385964,0.1391696036,-0.0974969193,0.0586754978,-0.16858913,-0.1118808985,0.2775104642,0.0810054019,-0.3122582436,-0.2152646184,0.1465487629,0.3253771961,-0.2217946947,0.1962158382,-0.3749184012,0.0005402695,-0.0025017459,-0.1653343439,-0.147021547,-0.1796660125,-0.361616075,0.1227448359,0.142877385,0.2076182663,-0.0001284339,-0.2215033621,0.2694629729,-0.2194067836,0.0018538549,0.2560395002,0.0565842278,-0.5237965584,0.0025379027,-0.3194332123,-0.3669329584,0.1096748784,-0.0938306451,-0.2007995695,0.5336482525,-0.1431433856,0.076762639,0.2225267589,-0.0619566552,0.1098598987,0.1986176372,0.0025218893,-0.0819646716,0.0560987182,0.0948231965,-0.0120578278,0.1472665966,0.2358227223,-0.124583222,0.0914944485,0.0435767211,-0.0318556316,-0.3331217766,-0.3354297876,0.1253537089,0.3483810127,0.295286864,-0.0750595182,-0.5162137747,-0.2952619195,-0.0524900481,0.2702248991,-0.1150405854,0.058658164,0.1640500277,0.4188830256,-0.1041115001,-0.2002945542,-0.0514230505,-0.0067006447,0.1387176812,0.3399919868,0.0612359568,0.1504879743,-0.1737190634,-0.2166426033,-0.4400324523,0.1399834752,-0.5158091187,-0.0077790557,-0.464527607,-0.1171266735,-0.3323483467,0.0888856053,-0.094950065,0.2479356527,-0.2073272169,0.1058496088,0.3114411533,-0.2439594269,0.155563429,0.2846984863,0.2777003348,0.3676304817,0.1604797393,-0.4486633539,-0.1351236403,-0.0723751634,-0.1148506701,0.0376185514,-0.104485102,0.0774470791,-0.078829743,-0.2549705803,-0.375463903,0.0849398673,-0.525841713,0.2092369944,-0.3027280867,-0.0012374806,-0.1326924413,0.1751765609,0.0157193076,0.2238351852,-0.0487246774,-0.5905393958,-0.0669179112,0.0601003617,-0.0542647317,0.0192328487,-0.1725622267,0.1348020285,0.0291421395,-0.0153351994,-0.2141216993,-0.0353840515,-0.24141936,-0.3270127177,0.1687343866,-0.0320347399,-0.2492668033,0.3267421722,-0.1242565215,-0.3437110186,0.1826789826,-0.0952266902,-0.2481368035,0.0048111398,-0.3740372956,-0.072741501,-0.7066127658,-0.0345190354,0.0738087818,0.3483802676,-0.0170580912,0.3711566925,0.056876231,-0.1334564388,-0.0393762775,-0.0836961716,0.3642608821,0.5312820673,-0.7039783597,-0.1444991976,0.2193377018,0.037804082,0.4260115027,0.5988708138,0.3259574771,0.3910284042,-0.1165050268,0.6638131738,0.0409003161,-0.1775779426,-0.4084926248,0.3672143817,-0.0454975516,0.0683856681,0.1460836977,-0.0276188366,0.6220766902,0.0999988765,0.13522695,0.1166748703,0.2677462995,-0.0580955893,-0.3883869946,-0.1797920465,0.2969615161,-0.0649921969,-0.0748581663,-0.1737874299,-0.0679684281,-0.0006405268,0.1131375358,-0.0443205461,0.1762990952,0.1299658269,0.2252661288,0.3682638407,0.0285546631,0.0050624097,0.0588335507,-0.0166372955,-0.106705755,0.2543140352,-0.1692504883,-0.0433502607,0.0708888695,-0.1402979493,0.2227377146,-0.0166765843,0.0620166548,-0.0265861265,-0.097810708,0.1673720181,-0.0492919907,0.1474598199,0.1114816144,0.1355089992,0.1175305471,0.4481175244,0.2730052471,-0.5152079463,0.0349517204,0.2944254577,0.2193368226,-0.3180552423,-0.1498562545,0.4196158051,-0.2714055479,0.170766592,-0.1787165105,0.2072255611,-0.1719323099,-0.3247873783,0.1816176623,0.2580245137,0.0087032681,0.1195492223,0.1745596975,0.2186061442,0.2944153249,0.0248743445,-0.2141067535,0.0806526095,-0.0175328758,-0.0525173694,-0.3154951334,-0.2962321639,0.2288537174,-0.2663196623,0.0239960402,0.0651253909,-0.2167958617,0.2262640297,0.3028301895,-0.0247993115,0.3780799806,-0.0241712723,-0.4174866676,0.0829204246,0.1892524064,0.2949672043,0.0403658897,0.1943738759,-0.1516488045,-0.0976008922,0.3454198539,-0.0168022793,0.2071207911,0.1002804637,-0.2213463336,0.1448145807,0.1498964429,0.1462793648,-0.0215860773,0.4351988137,-0.2174084038,0.2660402954,-0.5064103603,-0.2188868523,-0.5575281978,-0.5232078433,0.0320059061,0.270314455,-0.0204196051,-0.0448600911,0.0462543927,-0.3525875211,0.0211024024,0.2890893519,0.1296738833,0.2662609518,-0.0789649412,0.0987234786,-0.0341484174,0.1235249415,-0.3036597669,-0.1553719491,0.0000800888,0.0990958661,0.1361237764,-0.207561478,-0.0551052243,-0.4055820405,-0.2890824974,0.1528496295,-0.2902615368,0.3968253732,0.3595591784,0.0558852814,-0.2514448166,0.0870464295,0.2250032425,0.0028077357,-0.0131191052,0.1350421757,-0.1564460397,-0.2393328846,-0.1441880018,-0.0708739907,-0.1651630253,0.1086726934,0.3374187648,-0.3074238598,-0.0873105377,-0.1631095707,-0.425998956,-0.3015058041,-0.2955172956,-0.1434105188,0.0432185121,-0.3151704371,0.4570654631,0.243942067,-0.2985363007,-0.2668834925,0.0724310428,-0.369666487,-0.3661431074,-0.0754311308,-0.1193569973,-0.4101173878,0.0520401448,-0.160611555,-0.1784392148,0.1711137891,-0.0700922534,0.0546809845,-0.0876444876,-0.4567007422,0.62574929,0.2299714088,-0.004340386,-0.0116407303,0.5590554476,0.5441452861,-0.0169792324,0.1982869655,-0.1012941003,0.1787251234,-0.1645834297,0.1216292232,0.0102444943,-0.1149952784,0.420328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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1639","title":"bug with sst2 in glue ","comments":"I don't know if there exists a detokenized version somewhere. Even the version on kaggle is tokenized","body":"Hi\r\nI am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below.\r\nIs there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq \r\n \r\n```\r\n>>> a =  datasets.load_dataset('glue', 'sst2', split=\"validation\", script_version=\"master\")\r\nReusing dataset glue (\/julia\/datasets\/glue\/sst2\/1.0.0\/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)\r\n>>> a[:10]\r\n{'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': [\"it 's a charming and often affecting journey . \", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', \"the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . \", \"it 's slow -- very , very slow . \", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', \"or doing last year 's taxes with your ex-wife . \", \"you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . \", \"in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . \"]}\r\n\r\n```","comment_length":17,"text":"bug with sst2 in glue  \n Hi\r\nI am getting very low accuracy on SST2 I investigate this and observe that for this dataset sentences are tokenized, while this is correct for the other datasets in GLUE, please see below.\r\nIs there any alternatives I could get untokenized sentences? I am unfortunately under time pressure to report some results on this dataset. thank you for your help. @lhoestq \r\n \r\n```\r\n>>> a =  datasets.load_dataset('glue', 'sst2', split=\"validation\", script_version=\"master\")\r\nReusing dataset glue (\/julia\/datasets\/glue\/sst2\/1.0.0\/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4)\r\n>>> a[:10]\r\n{'idx': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 'label': [1, 0, 1, 1, 0, 1, 0, 0, 1, 0], 'sentence': [\"it 's a charming and often affecting journey . \", 'unflinchingly bleak and desperate ', 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', \"the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . \", \"it 's slow -- very , very slow . \", 'although laced with humor and a few fanciful touches , the film is a refreshingly serious look at young women . ', 'a sometimes tedious film . ', \"or doing last year 's taxes with your ex-wife . \", \"you do n't have to know about music to appreciate the film 's easygoing blend of comedy and romance . \", \"in exactly 89 minutes , most of which passed as slowly as if i 'd been sitting naked on an igloo , formula 51 sank from quirky to jerky to utter turkey . \"]}\r\n\r\n``` \n I don't know if there exists a detokenized version somewhere. Even the version on kaggle is tokenized","embeddings":[0.1053772271,-0.1916711181,0.0535599329,0.0827101097,0.1684059799,-0.3281565011,0.1854297668,0.4321678579,-0.0919407979,0.0309742317,-0.0943492949,0.1462736279,-0.1057705283,0.138015911,-0.1469752043,-0.0554611683,0.3053624332,0.0720337108,-0.2480497807,-0.1800416261,0.1838430911,0.3005387485,-0.2079133391,0.1841128916,-0.358858794,0.0333110765,0.0494763404,-0.2354058623,-0.1768406481,-0.2107705027,-0.3141484857,0.1596002728,0.193716079,0.2096603662,-0.0001275378,-0.2242546529,0.2872391939,-0.2060788572,0.0091374535,0.281778127,0.0366854705,-0.5301072598,-0.0000769759,-0.2940465212,-0.3653716445,0.1080832183,-0.0872452185,-0.235310331,0.5179437399,-0.1901039183,0.0725806132,0.2578645349,-0.101898618,0.0968488604,0.2016626596,-0.0233195685,-0.1244882122,0.1061551198,0.08858715,0.0119531509,0.1987287104,0.3125153482,-0.0967756957,0.0525541343,0.0309715979,-0.0561690629,-0.3840512931,-0.3257555664,0.1482466757,0.3792476654,0.3166408837,-0.0897192881,-0.5367329121,-0.2675744593,-0.041843567,0.2583253682,-0.1118208319,0.0156590901,0.1824868917,0.4119680226,-0.064618364,-0.2049472332,-0.0397313088,-0.0683754832,0.1081719175,0.3712735176,0.0445146784,0.1453737766,-0.1497977674,-0.2369308472,-0.447389096,0.1808105707,-0.4920337498,-0.0555570088,-0.4540756941,-0.1429215968,-0.38015908,0.0319992192,-0.0619642287,0.2108943164,-0.182887435,0.1540302038,0.3370283246,-0.2426043302,0.203800261,0.3650320768,0.318313688,0.3222047091,0.1798518449,-0.4099115431,-0.1268843263,-0.069605425,-0.1418879032,0.0408858433,-0.1073176712,0.0882590786,-0.0875706896,-0.2446332276,-0.4116359055,0.064416036,-0.5661545396,0.223309651,-0.3138051331,0.0089959437,-0.1119709462,0.1845390052,-0.0280314293,0.2074243277,-0.0727613941,-0.6290407777,-0.0687368512,-0.0062103234,-0.063937746,0.0341500528,-0.1824687868,0.0688116848,0.0377292112,-0.0475334823,-0.1774316877,-0.0282412209,-0.2062680423,-0.2804603279,0.1551904082,-0.0316387527,-0.2349665314,0.3630231321,-0.0941834971,-0.3709600568,0.1779542863,-0.0937622339,-0.2195090353,0.0055769766,-0.3337346613,-0.0651310906,-0.6949193478,-0.0588202439,0.0399165191,0.3861969411,-0.0074766479,0.3310188651,0.0459608547,-0.1881844252,0.0179588348,-0.0791794211,0.3777060807,0.5376538038,-0.762662828,-0.1581082791,0.1939786673,0.0355225392,0.4186126292,0.5911073089,0.3239365816,0.3647040725,-0.1242012754,0.6969619989,0.0042047054,-0.208952114,-0.4275865555,0.3860729933,-0.0376090258,0.090931505,0.1503320485,-0.0861776471,0.6578813791,0.0823523477,0.103084825,0.0325747095,0.2396647483,-0.1090309694,-0.3792620301,-0.1749333441,0.3303101659,-0.0758963674,-0.097798191,-0.1917981803,-0.0626145527,-0.0028709034,0.128158316,0.021715872,0.2007224709,0.0928254351,0.2245758623,0.3747392297,0.0294538494,0.0179387014,0.0467925519,-0.0380257629,-0.0738161653,0.2513394654,-0.1488924623,-0.0840390846,0.0735711753,-0.1498223394,0.210893631,-0.066608876,0.0683752224,-0.0589382276,-0.1446881741,0.1409900934,-0.0863773301,0.1108239144,0.1083044484,0.1027767733,0.1052518189,0.3852163553,0.221386373,-0.5122485757,0.0479111709,0.2678563297,0.214848876,-0.3405073583,-0.1625143439,0.3817720711,-0.2230755389,0.15265508,-0.1253413111,0.2494989187,-0.1301655173,-0.3278444409,0.1481739581,0.2530640662,-0.0347637199,0.1585804969,0.1489637941,0.2186182886,0.2874464989,-0.0190626495,-0.2203672677,0.1157481819,0.051628083,-0.0309161991,-0.3549937308,-0.3772973716,0.1641123891,-0.3063129187,-0.0073033432,0.0423836485,-0.2061954141,0.2887103558,0.300026834,-0.0405451059,0.3853990734,-0.0378463641,-0.4604098201,0.057044223,0.2201635838,0.2649001181,-0.0403721631,0.1799645871,-0.1132788137,-0.1288619488,0.3557143807,-0.034090627,0.171519354,0.0668156967,-0.2446060181,0.1565224379,0.134263441,0.1531965882,-0.0122299809,0.4428392947,-0.1954577863,0.2174531668,-0.4741294384,-0.2130085677,-0.587084353,-0.455011785,0.0164153036,0.2930877805,-0.0381961986,-0.0149812931,0.0942262709,-0.3488217592,0.0119788041,0.3152786195,0.1148794293,0.2802421451,-0.1082459837,0.0956882536,-0.0202123653,0.097623378,-0.3434951603,-0.1455566436,-0.0335753523,0.0611251183,0.1160847321,-0.2009541839,-0.0082516456,-0.4096842408,-0.3374337852,0.1583040059,-0.2983544469,0.4141184986,0.3482485414,0.063403815,-0.2890954018,0.1293140501,0.2046280503,-0.0259066932,-0.0365047194,0.0962841585,-0.159734115,-0.2362699062,-0.1160138696,-0.0897850245,-0.1597997397,0.1439817995,0.3131771982,-0.3221341968,-0.1053250507,-0.2012376636,-0.3947537541,-0.3288001418,-0.2766459584,-0.2073532045,0.0142798573,-0.3016605377,0.4418472052,0.2269349098,-0.3200713992,-0.2176027,0.0909380838,-0.3419412374,-0.3723222911,-0.0544716083,-0.0980043411,-0.3579964638,0.0681325346,-0.1384385675,-0.2349672019,0.2289199829,-0.0151854921,0.044205185,-0.0976490602,-0.5201345086,0.6407052875,0.2494034916,0.0104425531,-0.0207645502,0.50195086,0.5810190439,0.0396907292,0.2045617402,-0.105859749,0.1814359576,-0.1387355477,0.1529069096,-0.0077936118,-0.0941909626,0.4422138333,0.0720098615,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1636","title":"winogrande cannot be dowloaded ","comments":"I have same issue for other datasets (`myanmar_news` in my case).\r\n\r\nA version of `datasets` runs correctly on my local machine (**without GPU**) which looking for the dataset at \r\n```\r\nhttps:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/myanmar_news\/myanmar_news.py\r\n```\r\n\r\nMeanwhile, other version runs on Colab (**with GPU**) failed to download the dataset. It try to find the dataset at `1.1.3` instead of `master` . If I disable GPU on my Colab, the code can load the dataset without any problem.\r\n\r\nMaybe there is some version missmatch with the GPU and CPU version of code for these datasets?","body":"Hi,\r\nI am getting this error when trying to run the codes on the cloud.  Thank you for any suggestion and help on this @lhoestq \r\n\r\n```\r\n File \".\/finetune_trainer.py\", line 318, in <module>\r\n    main()\r\n  File \".\/finetune_trainer.py\", line 148, in main\r\n    for task in data_args.tasks]\r\n  File \".\/finetune_trainer.py\", line 148, in <listcomp>\r\n    for task in data_args.tasks]\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 65, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 466, in load_dataset\r\n    return datasets.load_dataset('winogrande', 'winogrande_l', split=split)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 487, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/winogrande\/winogrande.py\r\nyo\/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):\r\n  File \"\/usr\/lib\/python3.6\/runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"\/usr\/lib\/python3.6\/runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/distributed\/launch.py\", line 260, in <module>\r\n    main()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/distributed\/launch.py\", line 256, in main\r\n    cmd=cmd)\r\n```","comment_length":90,"text":"winogrande cannot be dowloaded  \n Hi,\r\nI am getting this error when trying to run the codes on the cloud.  Thank you for any suggestion and help on this @lhoestq \r\n\r\n```\r\n File \".\/finetune_trainer.py\", line 318, in <module>\r\n    main()\r\n  File \".\/finetune_trainer.py\", line 148, in main\r\n    for task in data_args.tasks]\r\n  File \".\/finetune_trainer.py\", line 148, in <listcomp>\r\n    for task in data_args.tasks]\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 65, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 466, in load_dataset\r\n    return datasets.load_dataset('winogrande', 'winogrande_l', split=split)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 487, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/winogrande\/winogrande.py\r\nyo\/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):\r\n  File \"\/usr\/lib\/python3.6\/runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"\/usr\/lib\/python3.6\/runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/distributed\/launch.py\", line 260, in <module>\r\n    main()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/distributed\/launch.py\", line 256, in main\r\n    cmd=cmd)\r\n``` \n I have same issue for other datasets (`myanmar_news` in my case).\r\n\r\nA version of `datasets` runs correctly on my local machine (**without GPU**) which looking for the dataset at \r\n```\r\nhttps:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/myanmar_news\/myanmar_news.py\r\n```\r\n\r\nMeanwhile, other version runs on Colab (**with GPU**) failed to download the dataset. It try to find the dataset at `1.1.3` instead of `master` . If I disable GPU on my Colab, the code can load the dataset without any problem.\r\n\r\nMaybe there is some version missmatch with the GPU and CPU version of code for these datasets?","embeddings":[-0.368696928,0.1048511565,-0.0660381317,0.1727429926,0.2931744158,0.0646404102,0.6664779782,0.0742833689,0.32062307,0.0376309268,-0.1209185869,0.0811130106,-0.0274400972,0.3085554242,0.0915508866,-0.1352519244,-0.1551791877,0.0680280253,-0.3883652091,-0.1035755351,-0.3014270067,0.1449964643,-0.3381091654,0.1980768144,-0.1758435071,-0.3244913816,-0.0872360617,0.2303459346,-0.4127731025,-0.1883580089,0.3303743899,-0.1787249595,0.2364586294,0.1999069154,-0.0001119528,0.0268156249,0.2326923311,-0.0847722366,-0.1867612749,-0.1960292011,-0.0253983475,0.3051585853,0.2187183499,-0.184618175,-0.0229243319,0.1664848775,-0.1624972969,0.0773649812,0.3128141165,0.5773444772,0.1898547262,0.3975589275,0.2045686692,0.0331234634,-0.4235072732,-0.3424312174,0.0212687682,0.6150701642,0.1908356696,-0.4338017404,0.2548056841,0.0014486369,0.0207589474,0.1185219586,0.0675943941,-0.1765601337,0.2509324253,-0.419539243,-0.1311502308,-0.0256385114,0.5311209559,0.0215759054,-0.1296834201,0.2047387362,0.1120898128,-0.2739987075,0.284229666,0.1477161348,-0.1760560274,0.1649956256,-0.3080173731,-0.0580678545,-0.3582025766,0.3218635321,0.1481005102,0.1265269965,0.0242350139,0.0446994193,0.2552797198,0.0882350504,0.1398637444,0.0488179848,0.0544819944,0.0351311862,-0.4112992287,-0.0904320851,-0.2612808347,0.2552453876,-0.3865052164,0.1501704752,0.1091204733,-0.1255415678,0.4431173503,0.1739905626,0.1946264058,0.3714409769,-0.2129581422,0.2366685122,0.0951329246,0.0697603747,0.1420715451,0.082355991,-0.6819787025,-0.5539950728,-0.1986275911,0.1179601252,0.1390322596,-0.2845466435,-0.2745520771,-0.0962962508,0.3059588969,0.0497980826,0.3026941717,0.2752325535,-0.0745164827,-0.2629697025,0.277310878,0.2931385636,-0.1387893111,-0.0831434205,-0.0040009543,0.2816756666,-0.1500656903,-0.1428893209,0.2244484723,0.1805662811,0.3548044562,-0.0919252411,0.3388037384,-0.2103509307,0.0394848287,-0.2153775543,-0.2308950871,0.0822293311,-0.1234867275,-0.1133310646,0.3215297163,0.0159397442,-0.0242304523,0.1006711274,-0.3189349174,-0.5324395895,-0.148290664,0.1527325511,0.0759669244,-0.1154205725,-0.6399577856,-0.1232644543,0.0951323062,-0.046619948,0.0554771423,-0.4480449557,-0.0278314501,-0.097169362,0.2478317022,0.4105527699,-0.3679771721,-0.1377865523,0.0975870043,-0.1613768488,-0.0650710016,0.1746538728,-0.3203060925,0.4273764491,-0.4233532846,-0.1221769452,0.4045239985,-0.6057487726,-0.3403845727,0.3660116792,-0.1654238105,-0.1204435304,-0.054276295,-0.1046243161,0.4172097147,-0.1382526308,0.3981581926,0.4014757574,0.0502186231,-0.1129810363,-0.103532888,-0.2852769196,-0.0202987473,0.3621865809,0.2654350102,0.1293463856,0.0755940005,-0.0660735965,0.060531009,0.0940140858,-0.2598439157,0.3106851578,-0.0612095036,0.1996018887,-0.0799068734,0.2906145453,-0.1260471642,0.1627796441,-0.0035591011,-0.0161473919,0.042138584,0.1411819458,-0.1098405421,-0.1448151171,-0.3546047807,-0.108858645,0.0833577812,-0.0719759688,0.3594585955,-0.1155302525,-0.1014227644,0.2762686014,-0.2571201921,-0.0231026448,-0.0483524762,0.1581780165,-0.2223320305,-0.2964906693,0.1315133125,-0.0264314245,0.2973249257,-0.1454710066,-0.3102459013,0.3024873137,-0.2499793172,0.1710686535,-0.0369232446,0.2473673075,0.1887207776,-0.1569518745,0.1100813076,0.3358827531,0.1203901619,-0.0491737686,0.5043795109,0.1778320223,-0.2820903063,0.3455861211,0.0722008571,0.1410001516,-0.0977449194,0.0539246649,-0.0214269403,0.1489199549,0.326510191,0.069836095,0.3296034038,-0.2740300596,-0.081678547,-0.0127635589,0.1321129948,-0.0582861416,0.1922532618,0.0241464414,-0.058877904,0.1225888729,-0.138240248,-0.3534490764,0.6288217902,-0.0779441819,-0.1627549827,0.2168244272,0.0880993605,-0.3022451401,0.4201553464,0.1474983692,0.2916156054,0.1335139573,-0.1542398781,-0.2613516152,-0.2166075557,-0.251067549,0.1798330992,0.0996108577,-0.2337482125,0.1668332368,-0.2003485709,0.0094736833,-0.2737947404,-0.2217485309,0.0794080794,0.1521431506,0.1278292239,0.3142968118,0.1968609989,0.1366688311,-0.0264539234,0.3919760585,0.0437615626,-0.1345431209,-0.1614870876,-0.2623594999,-0.1333007216,0.0577557385,0.1983505785,-0.4033934176,0.3068819344,-0.1677616835,-0.1851496696,0.0981961116,0.1332039982,0.0097874077,0.110046871,0.6873757243,-0.0991813987,0.3886087835,-0.1293687224,0.0391853452,0.2474743873,-0.1137061566,-0.0018731067,-0.2590671182,0.1835538596,-0.0399589501,0.0559465811,-0.4450828731,-0.6970521212,-0.2890126407,-0.0726742893,-0.2989792824,0.28790766,-0.134353295,0.1210230514,0.0026091747,0.2019261122,0.0667655617,-0.1324052066,-0.4622768164,0.2771128118,-0.3519118428,-0.3357568979,0.1141091138,0.395082891,0.2223625481,-0.4351142049,-0.2844531834,-0.3437049687,0.0159265492,0.0304134078,-0.1782898754,-0.1346721351,0.3508553803,-0.1107115448,-0.03814926,-0.0219648555,0.024923183,0.0567572452,-0.0207183138,0.2247359157,0.1840579212,0.3671400547,-0.1862891912,0.6083549261,0.0779509991,-0.1369999945,0.1451140046,-0.1870760173,-0.0541699491,-0.0692810565,-0.4518500268,0.4742793739,-0.11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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1636","title":"winogrande cannot be dowloaded ","comments":"It looks like they're two different issues\r\n\r\n----------\r\n\r\nFirst for `myanmar_news`: \r\n\r\nIt must come from the way you installed `datasets`.\r\nIf you install `datasets` from source, then the `myanmar_news` script will be loaded from `master`.\r\nHowever if you install from `pip` it will get it using the version of the lib (here `1.1.3`) and `myanmar_news` is not available in `1.1.3`.\r\n\r\nThe difference between your GPU and CPU executions must be the environment, one seems to have installed `datasets` from source and not the other.\r\n\r\n----------\r\n\r\nThen for `winogrande`:\r\n\r\nThe errors says that the url https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/winogrande\/winogrande.py is not reachable.\r\nHowever it works fine on my side.\r\n\r\nDoes your machine have an internet connection ? Are connections to github blocked by some sort of proxy ?\r\nCan you also try again in case github had issues when you tried the first time ?\r\n","body":"Hi,\r\nI am getting this error when trying to run the codes on the cloud.  Thank you for any suggestion and help on this @lhoestq \r\n\r\n```\r\n File \".\/finetune_trainer.py\", line 318, in <module>\r\n    main()\r\n  File \".\/finetune_trainer.py\", line 148, in main\r\n    for task in data_args.tasks]\r\n  File \".\/finetune_trainer.py\", line 148, in <listcomp>\r\n    for task in data_args.tasks]\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 65, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 466, in load_dataset\r\n    return datasets.load_dataset('winogrande', 'winogrande_l', split=split)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 487, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/winogrande\/winogrande.py\r\nyo\/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):\r\n  File \"\/usr\/lib\/python3.6\/runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"\/usr\/lib\/python3.6\/runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/distributed\/launch.py\", line 260, in <module>\r\n    main()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/distributed\/launch.py\", line 256, in main\r\n    cmd=cmd)\r\n```","comment_length":141,"text":"winogrande cannot be dowloaded  \n Hi,\r\nI am getting this error when trying to run the codes on the cloud.  Thank you for any suggestion and help on this @lhoestq \r\n\r\n```\r\n File \".\/finetune_trainer.py\", line 318, in <module>\r\n    main()\r\n  File \".\/finetune_trainer.py\", line 148, in main\r\n    for task in data_args.tasks]\r\n  File \".\/finetune_trainer.py\", line 148, in <listcomp>\r\n    for task in data_args.tasks]\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 65, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 466, in load_dataset\r\n    return datasets.load_dataset('winogrande', 'winogrande_l', split=split)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 487, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/winogrande\/winogrande.py\r\nyo\/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):\r\n  File \"\/usr\/lib\/python3.6\/runpy.py\", line 193, in _run_module_as_main\r\n    \"__main__\", mod_spec)\r\n  File \"\/usr\/lib\/python3.6\/runpy.py\", line 85, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/distributed\/launch.py\", line 260, in <module>\r\n    main()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/distributed\/launch.py\", line 256, in main\r\n    cmd=cmd)\r\n``` \n It looks like they're two different issues\r\n\r\n----------\r\n\r\nFirst for `myanmar_news`: \r\n\r\nIt must come from the way you installed `datasets`.\r\nIf you install `datasets` from source, then the `myanmar_news` script will be loaded from `master`.\r\nHowever if you install from `pip` it will get it using the version of the lib (here `1.1.3`) and `myanmar_news` is not available in `1.1.3`.\r\n\r\nThe difference between your GPU and CPU executions must be the environment, one seems to have installed `datasets` from source and not the other.\r\n\r\n----------\r\n\r\nThen for `winogrande`:\r\n\r\nThe errors says that the url https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/winogrande\/winogrande.py is not reachable.\r\nHowever it works fine on my side.\r\n\r\nDoes your machine have an internet connection ? Are connections to github blocked by some sort of proxy ?\r\nCan you also try again in case github had issues when you tried the first time ?\r\n","embeddings":[-0.368696928,0.1048511565,-0.0660381317,0.1727429926,0.2931744158,0.0646404102,0.6664779782,0.0742833689,0.32062307,0.0376309268,-0.1209185869,0.0811130106,-0.0274400972,0.3085554242,0.0915508866,-0.1352519244,-0.1551791877,0.0680280253,-0.3883652091,-0.1035755351,-0.3014270067,0.1449964643,-0.3381091654,0.1980768144,-0.1758435071,-0.3244913816,-0.0872360617,0.2303459346,-0.4127731025,-0.1883580089,0.3303743899,-0.1787249595,0.2364586294,0.1999069154,-0.0001119528,0.0268156249,0.2326923311,-0.0847722366,-0.1867612749,-0.1960292011,-0.0253983475,0.3051585853,0.2187183499,-0.184618175,-0.0229243319,0.1664848775,-0.1624972969,0.0773649812,0.3128141165,0.5773444772,0.1898547262,0.3975589275,0.2045686692,0.0331234634,-0.4235072732,-0.3424312174,0.0212687682,0.6150701642,0.1908356696,-0.4338017404,0.2548056841,0.0014486369,0.0207589474,0.1185219586,0.0675943941,-0.1765601337,0.2509324253,-0.419539243,-0.1311502308,-0.0256385114,0.5311209559,0.0215759054,-0.1296834201,0.2047387362,0.1120898128,-0.2739987075,0.284229666,0.1477161348,-0.1760560274,0.1649956256,-0.3080173731,-0.0580678545,-0.3582025766,0.3218635321,0.1481005102,0.1265269965,0.0242350139,0.0446994193,0.2552797198,0.0882350504,0.1398637444,0.0488179848,0.0544819944,0.0351311862,-0.4112992287,-0.0904320851,-0.2612808347,0.2552453876,-0.3865052164,0.1501704752,0.1091204733,-0.1255415678,0.4431173503,0.1739905626,0.1946264058,0.3714409769,-0.2129581422,0.2366685122,0.0951329246,0.0697603747,0.1420715451,0.082355991,-0.6819787025,-0.5539950728,-0.1986275911,0.1179601252,0.1390322596,-0.2845466435,-0.2745520771,-0.0962962508,0.3059588969,0.0497980826,0.3026941717,0.2752325535,-0.0745164827,-0.2629697025,0.277310878,0.2931385636,-0.1387893111,-0.0831434205,-0.0040009543,0.2816756666,-0.1500656903,-0.1428893209,0.2244484723,0.1805662811,0.3548044562,-0.0919252411,0.3388037384,-0.2103509307,0.0394848287,-0.2153775543,-0.2308950871,0.0822293311,-0.1234867275,-0.1133310646,0.3215297163,0.0159397442,-0.0242304523,0.1006711274,-0.3189349174,-0.5324395895,-0.148290664,0.1527325511,0.0759669244,-0.1154205725,-0.6399577856,-0.1232644543,0.0951323062,-0.046619948,0.0554771423,-0.4480449557,-0.0278314501,-0.097169362,0.2478317022,0.4105527699,-0.3679771721,-0.1377865523,0.0975870043,-0.1613768488,-0.0650710016,0.1746538728,-0.3203060925,0.4273764491,-0.4233532846,-0.1221769452,0.4045239985,-0.6057487726,-0.3403845727,0.3660116792,-0.1654238105,-0.1204435304,-0.054276295,-0.1046243161,0.4172097147,-0.1382526308,0.3981581926,0.4014757574,0.0502186231,-0.1129810363,-0.103532888,-0.2852769196,-0.0202987473,0.3621865809,0.2654350102,0.1293463856,0.0755940005,-0.0660735965,0.060531009,0.0940140858,-0.2598439157,0.3106851578,-0.0612095036,0.1996018887,-0.0799068734,0.2906145453,-0.1260471642,0.1627796441,-0.0035591011,-0.0161473919,0.042138584,0.1411819458,-0.1098405421,-0.1448151171,-0.3546047807,-0.108858645,0.0833577812,-0.0719759688,0.3594585955,-0.1155302525,-0.1014227644,0.2762686014,-0.2571201921,-0.0231026448,-0.0483524762,0.1581780165,-0.2223320305,-0.2964906693,0.1315133125,-0.0264314245,0.2973249257,-0.1454710066,-0.3102459013,0.3024873137,-0.2499793172,0.1710686535,-0.0369232446,0.2473673075,0.1887207776,-0.1569518745,0.1100813076,0.3358827531,0.1203901619,-0.0491737686,0.5043795109,0.1778320223,-0.2820903063,0.3455861211,0.0722008571,0.1410001516,-0.0977449194,0.0539246649,-0.0214269403,0.1489199549,0.326510191,0.069836095,0.3296034038,-0.2740300596,-0.081678547,-0.0127635589,0.1321129948,-0.0582861416,0.1922532618,0.0241464414,-0.058877904,0.1225888729,-0.138240248,-0.3534490764,0.6288217902,-0.0779441819,-0.1627549827,0.2168244272,0.0880993605,-0.3022451401,0.4201553464,0.1474983692,0.2916156054,0.1335139573,-0.1542398781,-0.2613516152,-0.2166075557,-0.251067549,0.1798330992,0.0996108577,-0.2337482125,0.1668332368,-0.2003485709,0.0094736833,-0.2737947404,-0.2217485309,0.0794080794,0.1521431506,0.1278292239,0.3142968118,0.1968609989,0.1366688311,-0.0264539234,0.3919760585,0.0437615626,-0.1345431209,-0.1614870876,-0.2623594999,-0.1333007216,0.0577557385,0.1983505785,-0.4033934176,0.3068819344,-0.1677616835,-0.1851496696,0.0981961116,0.1332039982,0.0097874077,0.110046871,0.6873757243,-0.0991813987,0.3886087835,-0.1293687224,0.0391853452,0.2474743873,-0.1137061566,-0.0018731067,-0.2590671182,0.1835538596,-0.0399589501,0.0559465811,-0.4450828731,-0.6970521212,-0.2890126407,-0.0726742893,-0.2989792824,0.28790766,-0.134353295,0.1210230514,0.0026091747,0.2019261122,0.0667655617,-0.1324052066,-0.4622768164,0.2771128118,-0.3519118428,-0.3357568979,0.1141091138,0.395082891,0.2223625481,-0.4351142049,-0.2844531834,-0.3437049687,0.0159265492,0.0304134078,-0.1782898754,-0.1346721351,0.3508553803,-0.1107115448,-0.03814926,-0.0219648555,0.024923183,0.0567572452,-0.0207183138,0.2247359157,0.1840579212,0.3671400547,-0.1862891912,0.6083549261,0.0779509991,-0.1369999945,0.1451140046,-0.1870760173,-0.0541699491,-0.0692810565,-0.4518500268,0.4742793739,-0.113183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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1634","title":"Inspecting datasets per category","comments":"That's interesting, can you tell me what you think would be useful to access to inspect a dataset?\r\n\r\nYou can filter them in the hub with the search by the way: https:\/\/huggingface.co\/datasets have you seen it?","body":"Hi\r\nIs there a way I could get all NLI datasets\/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq ","comment_length":36,"text":"Inspecting datasets per category \n Hi\r\nIs there a way I could get all NLI datasets\/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq  \n That's interesting, can you tell me what you think would be useful to access to inspect a dataset?\r\n\r\nYou can filter them in the hub with the search by the way: https:\/\/huggingface.co\/datasets have you seen it?","embeddings":[-0.2455319911,-0.1529337466,-0.2294568419,0.3999654353,0.0459513701,0.2248052955,0.0731056929,0.4707452953,-0.0032283345,-0.1497346461,-0.5485332608,-0.1118844971,-0.1011159196,0.3258757293,0.0880427063,0.1338709444,-0.0260168537,0.0386467539,-0.0946159959,0.0354207307,-0.0761443749,-0.1488868296,-0.2528313398,0.239694044,0.0124907149,-0.0431006625,-0.3931308985,-0.1143400446,-0.2368001491,-0.3348473608,0.1663315743,0.395935297,0.3783767521,0.0771230236,-0.000107972,-0.0072599016,0.0652775094,0.046406921,-0.1809976399,0.1047304571,-0.6043121815,-0.2638504207,0.1473354548,-0.1237352937,0.0415516011,-0.0603904389,0.4771701992,-0.2552815676,-0.2283760458,-0.0516712368,0.1873449236,0.2058766037,-0.4735043347,0.078137584,-0.1382701844,0.2351609468,-0.198034063,-0.0474467836,0.5839135647,-0.0409656614,-0.177018851,0.3836557269,0.1003370807,0.0608108565,0.1391247511,-0.0921693072,-0.0813433826,-0.4509172738,0.0876605883,0.5685508251,0.5253902078,-0.0804104581,-0.4150145352,-0.2234537899,-0.184940502,0.1121361852,-0.4191016257,0.850507617,-0.0754681379,0.1847095788,-0.1760089695,-0.0661096498,-0.2954183519,0.0319227092,-0.1430185437,0.2825137973,-0.0627331883,-0.1289217621,0.2206001282,-0.0508234985,0.3368180692,-0.0682560429,-0.3944413364,0.1516827345,-0.6407146454,-0.2385390848,0.3341816962,0.6969444156,0.1839789003,0.175811708,0.2753145993,0.2173420489,-0.230701074,-0.1931372434,0.461650908,0.0655522794,0.2504457533,0.0461897664,0.303167522,-0.0581082106,-0.0202748384,0.0779221654,-0.1189201623,0.170808807,-0.2324444503,-0.0415991992,0.1922338754,-0.1545951813,-0.1994924843,-0.3903557658,-0.1095198095,-0.4240267277,0.0493930168,0.3936800659,0.1353563666,-0.0321975946,-0.3744388223,0.2982469797,-0.1149066612,-0.5472370386,-0.1787673533,0.035287071,0.0234185643,-0.0174971763,0.1506324261,-0.1031354144,-0.0708575845,-0.1618688554,0.0054196925,0.0382021777,-0.029030351,-0.0673473254,0.0550171286,0.0873191357,0.312037617,-0.1972889751,0.0786146894,0.2358674854,-0.4207357764,-0.1280501336,-0.0361292511,-0.4050108194,-0.0450908951,0.1007256955,-0.3277039528,-0.2192318887,-0.2970399559,0.611412406,-0.4679879248,0.2038843781,0.0732191131,0.314232111,0.283782959,-0.1606057286,-0.1994332671,0.4373907149,-0.1983653009,-0.0861080885,-0.0749574229,-0.247760281,-0.104261741,0.1070460677,-0.1139393598,0.2101263404,-0.3110682964,0.0409238376,0.8389814496,-0.2249023318,-0.3771826029,0.1156929955,0.3103725612,-0.1505962312,0.1415734738,0.3947982192,-0.0614907369,-0.010832048,-0.2910554707,0.2192099243,-0.0396326594,-0.0137523348,0.0243359152,-0.315728128,0.1325859278,0.1902198046,0.0364495404,0.2422332019,0.2564565241,-0.1025398523,0.4131573439,-0.114271149,-0.1415675431,-0.0168976523,0.1025638655,-0.1194425002,0.0969595686,-0.300057292,-0.3621153831,0.1579020172,-0.0509960502,0.1799060702,0.0329711325,-0.2369339913,0.0066283979,0.0663223639,-0.1448626816,-0.0071046934,0.2586657405,0.178842634,0.0613704473,0.1345415562,-0.2937826514,0.2766747475,0.0193650555,0.0965427384,-0.1030542329,0.3132304847,0.1412937194,0.3934921324,0.2139265984,0.1543788016,-0.2115135789,-0.0644509569,0.0090086861,-0.3272518814,-0.2054938525,0.1604975015,0.8943871856,0.6694521904,0.2533872724,-0.5422837138,0.2767968178,-0.1760485619,-0.025960017,0.0198597591,-0.2345683128,0.3427531421,-0.2071764767,0.4622580707,-0.0255446602,-0.0318100527,0.0727981925,0.036438819,-0.1948776096,0.0417967588,0.4725963175,0.2244651914,0.0703530535,0.1599472314,-0.2708268166,0.280457288,0.3801903129,-0.1596799642,0.1027686074,0.0950903222,-0.145575583,-0.0148924505,0.2546900809,0.0104940627,0.0758028328,0.1760607362,0.0171193983,-0.1715416163,0.0746806264,-0.0338230766,-0.0092220474,0.1070100516,-0.0957994685,-0.2957440615,0.397996366,-0.0150660882,-0.2960931957,-0.1552005261,-0.1319240034,0.1509987265,-0.046348989,0.290843606,-0.0595920347,-0.5412447453,0.3073117435,-0.29486233,-0.1921954006,-0.2989366949,0.1141650677,-0.2823266089,0.130208984,-0.2304908782,-0.4811024368,0.6867519021,-0.0976978093,0.2228114754,-0.0934261531,-0.1628004313,-0.1883012056,0.1311374158,0.440828681,0.216014877,0.3410702646,-0.143998161,0.5487811565,-0.1458551288,-0.3076657951,0.1404660493,0.0481414795,0.1991781592,-0.2155126631,0.1595045626,-0.0369566306,0.0478237718,0.0600176752,0.2438788712,-0.089751035,0.2800734043,-0.2317492962,0.0928029045,0.23717013,-0.1616496146,-0.4624883831,-0.1963436902,0.1247851551,-0.3123081028,0.3161909282,-0.2666243911,-0.3154160082,-0.1647447795,-0.1834567785,-0.1416295916,-0.0881386176,-0.2221992016,0.0457377024,-0.0412664115,-0.1307436228,-0.1385354847,-0.2107693255,0.3759195209,0.0217984933,-0.399569273,-0.1029036418,-0.1500617713,0.0271741692,0.1704886258,-0.1327169836,0.0620967411,-0.1057753041,0.0755798146,0.1335565746,0.0940785483,-0.1578190923,-0.1179904714,0.2207147777,0.0705570206,-0.1607826948,0.2513234615,0.3187564015,0.203243196,0.1024870798,0.2634471655,-0.1615879089,0.1351609081,0.319488287,-0.4736805558,-0.0829159766,0.1024240404,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1634","title":"Inspecting datasets per category","comments":"Hi @thomwolf \r\nthank you, I was not aware of this, I was looking into the data viewer linked into readme page. \r\n\r\nThis is exactly what I was looking for, but  this does not work currently, please see the attached \r\nI am selecting to see all nli datasets in english and it retrieves none. thanks\r\n\r\n![5tarDHn9CP6ngeM](https:\/\/user-images.githubusercontent.com\/53898419\/103107612-1509aa80-4638-11eb-85b5-0c995a189969.png)\r\n\r\n\r\n\r\n","body":"Hi\r\nIs there a way I could get all NLI datasets\/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq ","comment_length":55,"text":"Inspecting datasets per category \n Hi\r\nIs there a way I could get all NLI datasets\/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq  \n Hi @thomwolf \r\nthank you, I was not aware of this, I was looking into the data viewer linked into readme page. \r\n\r\nThis is exactly what I was looking for, but  this does not work currently, please see the attached \r\nI am selecting to see all nli datasets in english and it retrieves none. thanks\r\n\r\n![5tarDHn9CP6ngeM](https:\/\/user-images.githubusercontent.com\/53898419\/103107612-1509aa80-4638-11eb-85b5-0c995a189969.png)\r\n\r\n\r\n\r\n","embeddings":[-0.3020195365,0.0937979147,-0.1714936346,0.4588537514,0.0671875924,0.2000089139,0.1474443525,0.49897331,-0.1237441152,-0.0721698329,-0.6115688682,-0.0818746164,-0.0379927382,0.2360660881,0.131478712,0.0705120787,0.065998964,0.0717614144,-0.2098444998,-0.0730781108,-0.059058927,-0.0043414631,-0.2953975797,0.2639480531,0.1482008398,-0.0464194864,-0.4075677693,-0.291372925,-0.2740218639,-0.3585765958,0.164096415,0.2567878962,0.4316548407,0.0354354978,-0.000108613,-0.0164748654,0.1057887301,0.0324192271,-0.0531503595,0.0628628582,-0.4935716391,-0.3013846874,0.2357198596,-0.1952267736,0.0598739646,-0.2024279088,0.5626852512,-0.3242330253,-0.1774676293,0.1138124242,0.1750924736,-0.0224909093,-0.3953582644,0.1345527172,-0.1644510925,0.1767985076,-0.1878391653,-0.130546689,0.4642978609,-0.0359445103,-0.2221885026,0.3846780062,0.1228795126,0.0805994868,-0.0227045789,-0.0394640118,0.0611899346,-0.4473687112,0.1216736063,0.6197764277,0.6888504028,-0.0595749803,-0.4407017827,-0.0740549341,-0.1628539264,0.1237755194,-0.3919146657,0.9289369583,-0.1156804115,0.1182618216,-0.1310912371,-0.056632258,-0.3187266588,0.0347070284,-0.294816345,0.3741955459,-0.0890563503,-0.0939147621,0.3317596912,-0.1761251241,0.3616896272,0.0232480131,-0.4417388439,0.1799140722,-0.7394194007,-0.1312709004,0.2512081861,0.54157722,0.2040117234,-0.0172321368,0.2887922823,0.2197982669,-0.2134324163,-0.1720743775,0.4058315158,0.1329377294,0.3010050356,0.0407841131,0.1693354994,-0.1552112699,-0.0337218195,0.0742785707,-0.2333092541,0.0204621889,-0.3318940103,-0.0512507595,0.0436876714,-0.1987778246,-0.2195052356,-0.3356381655,-0.1315139681,-0.4507161677,-0.0831772387,0.3694804907,0.1268960685,-0.0060849669,-0.3100359738,0.3781314194,-0.2511692941,-0.6250892282,-0.0985437706,0.0786972567,-0.0395732038,-0.1394738555,0.1178268567,-0.132292375,-0.0797973722,-0.1720879674,0.0024927517,0.0442238301,-0.0058483463,-0.0924382731,0.0156469904,-0.037989933,0.4044882953,-0.1612757593,0.1049123332,-0.0051472778,-0.3377614319,0.0264738481,0.087710388,-0.2825858891,-0.1999628544,0.1168851554,-0.2446359694,-0.2116881758,-0.1490552574,0.6655653715,-0.3820025921,0.1058220938,0.0460244417,0.1420821995,0.2311930209,-0.1116814986,-0.1651346236,0.4212861657,-0.4193429053,-0.0928275809,-0.1446888745,-0.3321814835,-0.035393361,0.1578119099,-0.0548250973,0.0212567579,-0.3922074735,0.0473742113,0.8782119155,-0.138991788,-0.2263593376,0.2655929029,0.3861093223,-0.2313533574,0.2574380636,0.2039435059,0.0125827817,0.0419330858,-0.3922658563,0.1225627586,-0.0020044029,0.0424103327,0.0268573072,-0.4239691496,0.3247992992,0.179589808,0.0849992931,0.1655385047,0.3775900602,-0.1253252923,0.587793529,-0.1668925136,-0.0504017398,0.0300414674,-0.0740754157,-0.0916089043,0.1358509511,-0.2105307132,-0.3396768868,0.119444333,-0.082640782,0.320548296,0.0602466837,-0.1397473067,0.1110084727,-0.0298251733,-0.2207383364,-0.0601396561,0.2412538528,0.2704812586,0.0336975642,0.215193823,-0.2410713285,0.2090810388,0.0245016143,-0.0129749887,-0.0265538804,0.2569160461,0.0907474309,0.3026961684,0.2979464233,0.126738295,-0.1749521941,-0.0169488527,-0.0762933418,-0.3363853395,-0.1274318248,0.1786785573,0.7207256556,0.5630325675,0.2103105187,-0.4626798034,0.3300400674,-0.0529621504,-0.0632888079,0.0695943236,-0.3170131147,0.3304572403,-0.0975014716,0.4795435667,0.0679147243,-0.0239531547,0.219502598,0.0179052707,-0.1856084466,-0.1168108657,0.48088485,0.2276680171,0.0520079918,0.1034202427,-0.3596718013,0.3000658154,0.4525820911,-0.1030228212,0.0546753742,0.0290706661,-0.0871543735,0.0506488159,0.2534509003,0.0919288173,0.0513923354,0.2142790705,0.1353226453,-0.1168498173,0.1431589127,-0.0556243286,0.0240946878,0.0016845705,-0.0039028097,-0.1994135529,0.3179620504,-0.1397604346,-0.3216177821,-0.1213301495,-0.0041835429,0.1333161741,-0.068133086,0.1726482958,-0.2653362751,-0.7509955168,0.0605957881,-0.2273226529,-0.1888329834,-0.2517419755,-0.0104628066,-0.4002918303,0.1387111992,-0.1137303039,-0.5434058309,0.509698689,-0.1051074937,0.3512748182,-0.070585914,-0.1407811642,-0.3351511359,0.1513425857,0.4208589792,0.1502161175,0.1985864341,-0.2169165909,0.4200828671,-0.0584665202,-0.3545227051,0.2315334976,-0.0147799132,0.209756434,-0.3728648424,0.0447465964,-0.1688536257,0.0584379993,0.0314166956,0.2289773673,-0.123162955,0.2000934482,-0.1428049058,0.0288113058,0.1368291974,-0.3826674223,-0.5265018344,-0.1817942262,0.0331892557,-0.2954965532,0.438386023,-0.2491925061,-0.3484262526,-0.1118429676,-0.107559368,-0.0286989696,-0.0434639677,-0.0734534934,0.1195354685,-0.129232794,-0.2260616124,0.0163660757,-0.1137905791,0.3740551472,-0.0836806074,-0.442250371,0.0434216335,-0.1405605525,-0.0144093074,0.1422806531,-0.0484289639,0.0285579599,-0.0508260615,0.0869196057,0.0409819409,0.0735117123,-0.0676587075,-0.1629754603,0.1693504751,0.0401130021,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1634","title":"Inspecting datasets per category","comments":"I see 4 results for NLI in English but indeed some are not tagged yet and missing (GLUE), we will focus on that in January (cc @yjernite): https:\/\/huggingface.co\/datasets?filter=task_ids:natural-language-inference,languages:en","body":"Hi\r\nIs there a way I could get all NLI datasets\/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq ","comment_length":28,"text":"Inspecting datasets per category \n Hi\r\nIs there a way I could get all NLI datasets\/all QA datasets to get some understanding of available datasets per category? this is hard for me to inspect the datasets one by one in the webpage, thanks for the suggestions @lhoestq  \n I see 4 results for NLI in English but indeed some are not tagged yet and missing (GLUE), we will focus on that in January (cc @yjernite): https:\/\/huggingface.co\/datasets?filter=task_ids:natural-language-inference,languages:en","embeddings":[-0.1127985418,0.0156888273,-0.2197402567,0.4125655591,-0.0079149175,0.026991684,0.0843824744,0.4735390544,-0.0621107072,-0.1604093611,-0.5733580589,-0.1227079108,0.0521350764,0.3426798284,0.1644427776,0.0418968275,0.100888744,-0.0833265334,-0.2709667981,-0.1761794686,-0.0469946191,0.178943187,-0.2701209188,0.3549456298,0.0659168661,-0.0939937755,-0.4046341181,-0.3289308548,-0.2377311885,-0.2400817424,0.1890156269,0.2508487105,0.4176180065,-0.0365298539,-0.0001102242,-0.0850438923,0.0039951331,-0.0407811664,-0.0730079189,0.0611323453,-0.4303300381,-0.3196310103,0.1286224574,-0.1766109169,-0.0106974281,-0.1142883599,0.4511528611,-0.3239992261,-0.1636002958,0.0433886871,0.1818676591,0.0093082245,-0.4813211262,0.146073699,-0.1470403373,0.1292658448,-0.0827775225,-0.0604550317,0.5571928024,-0.2251417935,-0.1827837378,0.4445842206,0.1540631205,-0.0277672932,-0.1510073245,0.0132741947,-0.0464177206,-0.5090123415,0.0752446428,0.6124581099,0.496126622,-0.0473681651,-0.5170997977,-0.1746857762,-0.1840706319,0.0752101094,-0.4121730328,0.8895875216,-0.1419305205,0.2088404447,0.0084068812,-0.0851003453,-0.2214701623,0.1187308356,-0.1582270563,0.4387899637,-0.0738752633,-0.117742613,0.2705903947,-0.2010828406,0.062749058,0.0608076528,-0.3862090111,0.1860885322,-0.69509691,-0.1772442013,0.3227952421,0.5855038762,0.2710989714,0.0167680308,0.1947613806,0.1557922065,-0.1826265454,-0.161041379,0.4567234814,0.1354044229,0.2885792255,0.0037540917,0.1845114082,-0.1134006605,-0.0974414125,0.0830178782,-0.1895136833,0.1046670973,-0.2143543214,0.0113174664,0.0283199977,-0.2254145145,-0.2909221947,-0.3071687222,-0.1820036918,-0.4849576652,-0.1087528318,0.339627713,0.1163031757,0.0348418914,-0.2204786688,0.4193568528,-0.2193921953,-0.6845995784,-0.0882566422,0.0998279229,-0.0802271515,-0.0805768818,0.0993035138,-0.0153533192,-0.057511773,-0.2019354403,-0.0699019358,0.1084723249,-0.0063275839,-0.189262554,0.1160751283,-0.0741073564,0.2871043682,-0.109026581,0.0747684762,0.0665938407,-0.4490146935,-0.1377215832,0.1082337275,-0.236370638,-0.1890600473,0.0896724612,-0.2355096787,-0.2154624313,-0.2315912545,0.8258110881,-0.3214225769,0.2188818455,0.1348439902,0.1498854905,0.2046007663,-0.1159629449,-0.1072126552,0.3968374729,-0.3240875602,-0.2145777345,-0.0769570172,-0.2934623361,0.0621811412,0.3232209086,-0.0426837839,0.1701610684,-0.3040454388,0.1332400739,0.8199773431,-0.1311862916,-0.2302034944,0.1967497021,0.3711154163,-0.1904722005,0.2296781242,0.1832924336,0.0072219879,0.0040391102,-0.2037837356,0.3367217481,0.0340469331,-0.0324148946,0.0162279028,-0.4255031645,0.3276370466,0.1472854465,0.0769338682,0.1007180735,0.2246593535,-0.2018427551,0.385843724,-0.1028623506,-0.0476812311,-0.0138876671,0.0274659079,-0.1208754182,0.0670092627,-0.272803992,-0.3843850195,0.1236685589,-0.082649067,0.4163319468,-0.073247388,-0.1229216382,0.1007919684,-0.0096191,-0.1153100729,-0.0703625381,0.2301851064,0.3147184849,0.118111603,0.2442222238,-0.3133840561,0.2815248668,-0.0372196063,0.0223637763,-0.1091490313,0.2844394445,0.087567918,0.1831196249,0.2248633206,0.2734616995,-0.2931275666,-0.0311005656,-0.0402636416,-0.4010964632,-0.2481615692,0.1165764853,0.8087966442,0.5512917042,0.174339518,-0.5052917004,0.2770757377,-0.133137241,-0.0335992761,0.0797795355,-0.0714485496,0.3099631071,-0.0887234509,0.5227497816,0.0742839053,-0.0779778659,0.1878208667,0.1012386903,-0.2409105301,-0.0713264123,0.53509444,0.0795138851,0.0601890832,0.161670208,-0.43609339,0.2146095634,0.532804966,-0.1646728814,0.149516046,0.101268664,-0.0416984819,-0.0310222078,0.2356841117,0.0367941149,0.1024970114,0.2288898677,0.1723770499,-0.2392042577,0.1234094128,-0.0517314635,0.073348321,-0.0261238907,-0.0974049121,-0.2387784123,0.3937008083,-0.0026428597,-0.3572147191,-0.066080302,-0.1198119298,0.1046438217,0.0034508468,0.2326081246,-0.2002726346,-0.8213554621,0.1187007576,-0.2470667958,-0.2600858808,-0.1919220239,-0.0024958323,-0.569118619,0.1178916544,-0.1062088087,-0.4092338979,0.4692082703,-0.2063668817,0.1681923121,-0.1286978126,-0.2273899615,-0.3320856094,0.1356230974,0.3242715597,0.1927656978,0.2986070216,-0.3433547616,0.2622004747,0.0103463726,-0.3482649326,0.186990872,-0.0681907609,0.2572000921,-0.2491106987,0.102082409,-0.0885111466,0.0174125433,0.1467038542,0.2707752287,-0.1872179955,0.1284037828,-0.2476276308,0.0773552582,0.1484889835,-0.4460300207,-0.5780335665,-0.1683107913,0.0925660804,-0.357511431,0.3129068017,-0.149653405,-0.4064166248,-0.1207008511,-0.2696771622,0.0610913672,-0.0928570107,-0.0928908065,0.0211014841,0.0906309932,-0.111561358,-0.1502605677,-0.1687968969,0.4150243998,-0.0158124343,-0.3585648537,-0.022690298,-0.1867684275,0.1630617082,0.1342711896,0.0555054843,0.1085530892,-0.0928499475,0.0801583007,0.0759951547,-0.0502451286,0.0357467681,-0.0165054202,0.2407966256,0.0827601105,0.0190385152,0.23616229,0.3050002456,0.2867231369,0.0190422051,0.37180021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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1633","title":"social_i_qa wrong format of labels","comments":"@lhoestq, should I raise a PR for this? Just a minor change while reading labels text file","body":"Hi,\r\nthere is extra \"\\n\" in labels of social_i_qa datasets, no big deal, but I was wondering if you could remove it to make it consistent.\r\nso label is 'label': '1\\n', not '1'\r\nthanks\r\n\r\n```\r\n>>> import datasets \r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\r\n...    'social_i_qa')\r\ncahce dir  \/julia\/cache\/datasets\r\nDownloading: 4.72kB [00:00, 3.52MB\/s]                                                                                                  \r\ncahce dir \/julia\/cache\/datasets\r\nDownloading: 2.19kB [00:00, 1.81MB\/s]                                                                                                  \r\nUsing custom data configuration default\r\nReusing dataset social_i_qa (\/julia\/datasets\/social_i_qa\/default\/0.1.0\/4a4190cc2d2482d43416c2167c0c5dccdd769d4482e84893614bd069e5c3ba06)\r\n>>> dataset['train'][0]\r\n{'answerA': 'like attending', 'answerB': 'like staying home', 'answerC': 'a good friend to have', 'context': 'Cameron decided to have a barbecue and gathered her friends together.', 'label': '1\\n', 'question': 'How would Others feel as a result?'}\r\n\r\n```\r\n\r\n","comment_length":17,"text":"social_i_qa wrong format of labels \n Hi,\r\nthere is extra \"\\n\" in labels of social_i_qa datasets, no big deal, but I was wondering if you could remove it to make it consistent.\r\nso label is 'label': '1\\n', not '1'\r\nthanks\r\n\r\n```\r\n>>> import datasets \r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\r\n...    'social_i_qa')\r\ncahce dir  \/julia\/cache\/datasets\r\nDownloading: 4.72kB [00:00, 3.52MB\/s]                                                                                                  \r\ncahce dir \/julia\/cache\/datasets\r\nDownloading: 2.19kB [00:00, 1.81MB\/s]                                                                                                  \r\nUsing custom data configuration default\r\nReusing dataset social_i_qa (\/julia\/datasets\/social_i_qa\/default\/0.1.0\/4a4190cc2d2482d43416c2167c0c5dccdd769d4482e84893614bd069e5c3ba06)\r\n>>> dataset['train'][0]\r\n{'answerA': 'like attending', 'answerB': 'like staying home', 'answerC': 'a good friend to have', 'context': 'Cameron decided to have a barbecue and gathered her friends together.', 'label': '1\\n', 'question': 'How would Others feel as a result?'}\r\n\r\n```\r\n\r\n \n @lhoestq, should I raise a PR for this? Just a minor change while reading labels text 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1630","title":"Adding UKP Argument Aspect Similarity Corpus","comments":"Adding a link to the guide on adding a dataset if someone want to give it a try: https:\/\/github.com\/huggingface\/datasets#add-a-new-dataset-to-the-hub\r\n\r\nwe should add this guide to the issue template @lhoestq ","body":"Hi, this would be great to have this dataset included.\r\n\r\n## Adding a Dataset\r\n- **Name:** UKP Argument Aspect Similarity Corpus\r\n- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either \u201chigh similarity\u201d, \u201csome similarity\u201d, \u201cno similarity\u201d or \u201cnot related\u201d with respect to the topic.\r\n- **Paper:** https:\/\/www.aclweb.org\/anthology\/P19-1054\/\r\n- **Data:** https:\/\/tudatalib.ulb.tu-darmstadt.de\/handle\/tudatalib\/1998\r\n- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https:\/\/arxiv.org\/pdf\/2005.00247.pdf \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nThank you","comment_length":29,"text":"Adding UKP Argument Aspect Similarity Corpus \n Hi, this would be great to have this dataset included.\r\n\r\n## Adding a Dataset\r\n- **Name:** UKP Argument Aspect Similarity Corpus\r\n- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either \u201chigh similarity\u201d, \u201csome similarity\u201d, \u201cno similarity\u201d or \u201cnot related\u201d with respect to the topic.\r\n- **Paper:** https:\/\/www.aclweb.org\/anthology\/P19-1054\/\r\n- **Data:** https:\/\/tudatalib.ulb.tu-darmstadt.de\/handle\/tudatalib\/1998\r\n- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https:\/\/arxiv.org\/pdf\/2005.00247.pdf \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nThank you \n Adding a link to the guide on adding a dataset if someone want to give it a try: https:\/\/github.com\/huggingface\/datasets#add-a-new-dataset-to-the-hub\r\n\r\nwe should add this guide to the issue template @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1630","title":"Adding UKP Argument Aspect Similarity Corpus","comments":"thanks @thomwolf , this is added now. The template is correct, sorry my mistake not to include it. ","body":"Hi, this would be great to have this dataset included.\r\n\r\n## Adding a Dataset\r\n- **Name:** UKP Argument Aspect Similarity Corpus\r\n- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either \u201chigh similarity\u201d, \u201csome similarity\u201d, \u201cno similarity\u201d or \u201cnot related\u201d with respect to the topic.\r\n- **Paper:** https:\/\/www.aclweb.org\/anthology\/P19-1054\/\r\n- **Data:** https:\/\/tudatalib.ulb.tu-darmstadt.de\/handle\/tudatalib\/1998\r\n- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https:\/\/arxiv.org\/pdf\/2005.00247.pdf \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nThank you","comment_length":18,"text":"Adding UKP Argument Aspect Similarity Corpus \n Hi, this would be great to have this dataset included.\r\n\r\n## Adding a Dataset\r\n- **Name:** UKP Argument Aspect Similarity Corpus\r\n- **Description:** The UKP Argument Aspect Similarity Corpus (UKP ASPECT) includes 3,595 sentence pairs over 28 controversial topics. Each sentence pair was annotated via crowdsourcing as either \u201chigh similarity\u201d, \u201csome similarity\u201d, \u201cno similarity\u201d or \u201cnot related\u201d with respect to the topic.\r\n- **Paper:** https:\/\/www.aclweb.org\/anthology\/P19-1054\/\r\n- **Data:** https:\/\/tudatalib.ulb.tu-darmstadt.de\/handle\/tudatalib\/1998\r\n- **Motivation:** this is one of the datasets currently used frequently in recent adapter papers like https:\/\/arxiv.org\/pdf\/2005.00247.pdf \r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\r\nThank you \n thanks @thomwolf , this is added now. The template is correct, sorry my mistake not to include it. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1627","title":"`Dataset.map` disable progress bar","comments":"Progress bar can be disabled like this:\r\n```python\r\nfrom datasets.utils.logging import set_verbosity_error\r\nset_verbosity_error()\r\n```\r\n\r\nThere is this line in `Dataset.map`:\r\n```python\r\nnot_verbose = bool(logger.getEffectiveLevel() > WARNING)\r\n```\r\n\r\nSo any logging level higher than `WARNING` turns off the progress bar.","body":"I can't find anything to turn off the `tqdm` progress bars while running a preprocessing function using `Dataset.map`. I want to do akin to `disable_tqdm=True` in the case of `transformers`. Is there something like that?","comment_length":39,"text":"`Dataset.map` disable progress bar \n I can't find anything to turn off the `tqdm` progress bars while running a preprocessing function using `Dataset.map`. I want to do akin to `disable_tqdm=True` in the case of `transformers`. Is there something like that? \n Progress bar can be disabled like this:\r\n```python\r\nfrom datasets.utils.logging import set_verbosity_error\r\nset_verbosity_error()\r\n```\r\n\r\nThere is this line in `Dataset.map`:\r\n```python\r\nnot_verbose = bool(logger.getEffectiveLevel() > WARNING)\r\n```\r\n\r\nSo any logging level higher than `WARNING` turns off the progress bar.","embeddings":[-0.345834583,-0.3181484342,-0.0507016964,-0.1878176481,0.3254140317,-0.0002219211,0.2311783284,0.1533992589,-0.3758892715,0.2149729431,0.1782131493,0.6198928952,-0.2077849209,0.0813507065,-0.1250561923,0.0541477911,-0.0937978029,0.2880425155,-0.4447112978,0.1668906212,-0.1205287501,-0.0268635228,-0.4199817181,0.1133120134,-0.098049596,-0.0874668136,0.4577662945,-0.2100177258,0.1111780331,-0.5028156042,0.1255543977,0.5249206424,-0.1022467762,0.2963654995,-0.0001341136,0.024378268,0.3334862888,0.1161456704,0.0576809719,0.2084132433,-0.3663836718,-0.3946353495,0.3381592035,-0.1342763007,0.0732083544,-0.172539711,-0.2612757683,-0.5232918262,0.4219367802,-0.0776959732,-0.0365383662,0.503811419,-0.467764616,0.5086688399,-0.0745022893,0.0810276791,-0.3309802115,-0.0999305546,0.5109071136,-0.0141434502,-0.2387829423,0.3522944152,-0.2959550023,0.1542491317,0.2584664524,-0.0826860368,0.605276823,-0.5217351317,0.2537859976,0.1346681714,0.5032689571,-0.5142654777,-0.103938736,-0.1459460407,-0.0485781766,0.0544987172,0.1487227976,-0.3223780096,-0.3646895587,0.1625011861,-0.9887856245,-0.3662439585,-0.0918549299,-0.0601705424,-0.1202919558,0.0436481424,-0.0014366576,0.0460398644,0.1100329161,-0.0151390852,0.2032525688,0.0090326387,-0.0887708217,0.2580164075,-0.255309701,-0.3512195051,0.1488700509,-0.0420721844,0.0288523752,0.367156446,-0.1467290372,0.2798498869,0.0295170005,0.1189122275,0.1812521815,0.0157953911,0.4012874961,-0.0109180687,0.3395793438,-0.4546201527,0.050877247,0.3440840244,0.4707216322,-0.5852707028,0.575140059,0.3569725454,0.2022000998,-0.109085694,0.1236839592,0.0169166792,0.2879556119,-0.0798742622,0.2036167681,0.0256176069,0.267922312,-0.1138722375,-0.2338978052,0.0312828198,-0.1787957549,-0.1378449798,0.1107528955,-0.2916526496,-0.2107667178,0.1103599146,0.0275119357,0.2005032748,-0.0148581173,0.0525610186,0.0456209332,0.0795346051,0.452668488,-0.0510114431,0.5489702225,0.622191608,-0.2569610476,0.1888652593,0.0575466119,0.2622515261,-0.4132283032,0.479164958,0.1666655838,-0.2782751918,-0.0373225622,-0.1018301398,-0.1409644932,0.178478837,-0.4876033962,0.2415321469,0.2238913774,0.0873818621,0.38781479,-0.1732172519,-0.3146266937,-0.0629522726,0.2620423138,0.4065777957,-0.3015166223,-0.2856298387,-0.1173471361,-0.241612196,0.0504980199,-0.1054368392,-0.2011386901,0.2815577686,-0.0167808644,-0.0703995004,0.3614420295,-0.2343790382,-0.3531353176,0.4110332727,-0.0478603989,-0.071903035,-0.0759115368,0.2448171228,0.0474888794,-0.0725209787,-0.1860623509,0.0920645967,0.0307610128,0.0773212016,0.1497756541,-0.1875065714,0.2341778874,-0.0015334329,0.1241452917,0.2763523459,0.1399574131,-0.420553863,0.1893345863,0.2317565829,0.1503296643,0.355152905,-0.1651833951,-0.4015514851,-0.1150018945,-0.2628898919,-0.2299821228,0.0786335915,-0.0617304593,-0.1473404765,-0.1688000858,-0.2442699522,-0.0655416101,0.0673422515,-0.1299708635,0.2982673049,-0.1344028115,-0.5122274756,0.0367587544,-0.1499243528,-0.3869235814,0.2167724669,0.1945154071,0.1012107059,0.1696923226,0.0502697118,0.459985137,-0.1136488244,-0.0803473443,0.1388212293,0.04870243,0.0829548314,-0.0290944334,0.3649711013,0.1031373292,0.252364248,0.1710756421,0.3974097371,0.037687175,-0.224794358,-0.1037115678,0.2083734721,-0.4188669622,0.1820928901,0.1995804161,0.1599497646,-0.1794397086,0.234629944,-0.1804733127,0.1407560408,0.1444905102,-0.0888667107,-0.4731482565,-0.3031069338,0.068484731,0.3422614038,0.3420707881,-0.0974302292,0.2473237365,-0.0083739804,0.3244614601,0.1424059421,0.2136680335,-0.1983314902,-0.3455728889,0.2194939256,-0.045962207,0.1714564711,0.4166837037,0.0125212241,0.3845880926,0.2453301251,-0.1530783921,0.0026161363,-0.0621065646,0.3627313673,0.0730372146,0.0545571297,-0.0962882191,-0.1259753853,-0.226972416,-0.2155729979,-0.1228719503,0.0408077799,-0.1454324424,0.1795247644,-0.3290635943,-0.0077629453,0.0007107481,-0.1432679594,-0.480457902,-0.1354398131,0.2090540528,0.0530926064,-0.0528867468,0.5098966956,0.1242874414,0.1399916708,0.0301752854,-0.2715519071,-0.2854590416,-0.2685713172,0.3451518714,-0.1722872555,-0.0865654498,-0.205834344,0.1100154966,-0.1852139533,0.1396550983,-0.2164420784,0.0435309522,0.1771795452,0.0013085809,0.0881358832,-0.0593979508,0.1097072065,0.0103697209,0.5180321932,-0.0942613408,-0.0591810234,0.0995153561,-0.1263444573,-0.0315817818,0.0843847543,-0.1217757612,0.3325320482,-0.1045642644,-0.099418819,-0.083496742,-0.3843751848,0.0502827391,0.1206876636,-0.1646551937,-0.0517021455,-0.1406535804,-0.1926172823,0.1343061328,-0.4898988605,0.3091023266,-0.1470202953,0.1182011515,0.0801663697,0.167426616,0.1182433292,0.263187319,-0.3549214602,0.0283561442,-0.1727309078,-0.0023675128,-0.2131805718,0.0833469704,0.3554988205,0.1952704191,0.0604897067,-0.0558646135,-0.1040972248,-0.0567384958,0.0631672069,0.0698353723,0.0344594829,-0.0473815687,-0.2366431057,0.6141837239,0.0919991732,-0.0919125304,-0.1033864692,0.0090510743,0.1637837589,0.1960847825,-0.0705555379,-0.5262030959,0.0143890986,-0.0459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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1624","title":"Cannot download ade_corpus_v2","comments":"Hi @him1411, the dataset you are trying to load has been added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","body":"I tried this to get the dataset following this url : https:\/\/huggingface.co\/datasets\/ade_corpus_v2\r\n\r\nbut received this error : \r\n\r\n`Traceback (most recent call last):\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/ade_corpus_v2\/ade_corpus_v2.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/ade_corpus_v2\/ade_corpus_v2.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at ade_corpus_v2\/ade_corpus_v2.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/ade_corpus_v2\/ade_corpus_v2.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/ade_corpus_v2\/ade_corpus_v2.py`\r\n\r\n\r\n","comment_length":54,"text":"Cannot download ade_corpus_v2 \n I tried this to get the dataset following this url : https:\/\/huggingface.co\/datasets\/ade_corpus_v2\r\n\r\nbut received this error : \r\n\r\n`Traceback (most recent call last):\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/ade_corpus_v2\/ade_corpus_v2.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/ade_corpus_v2\/ade_corpus_v2.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at ade_corpus_v2\/ade_corpus_v2.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/ade_corpus_v2\/ade_corpus_v2.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/ade_corpus_v2\/ade_corpus_v2.py`\r\n\r\n\r\n \n Hi @him1411, the dataset you are trying to load has been added during the community sprint and has not been released yet. It will be available with the v2 of `datasets`.\r\nFor now, you should be able to load the datasets after installing the latest (master) version of `datasets` using pip:\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","embeddings":[-0.0399101153,-0.1982669234,-0.113394618,0.2741905153,0.2603805959,0.2967451215,-0.0410111733,0.3299029171,0.0405992754,-0.1682073772,-0.2708159387,-0.2759156823,0.0790571347,-0.2060552537,-0.0267115999,-0.323489368,-0.1370057613,0.0029619159,-0.0265697669,-0.0274784267,-0.1152140573,0.3363958001,-0.2073079646,-0.0367349312,0.2109981328,0.0620413721,-0.1915316135,0.0425680354,-0.2808462083,-0.3533932865,0.53807652,-0.1105325148,0.2315054089,0.4590536654,-0.0001128967,0.159000352,0.2790457606,-0.0251458976,-0.365804255,-0.5255252123,-0.0857978389,0.0248197503,-0.0926860869,-0.3844043911,-0.1996401548,-0.3515524268,0.1897516251,-0.121204555,0.1625540704,0.300662756,0.2591804862,-0.0057054446,0.2623307705,-0.4079148769,-0.0430632941,0.0092382785,-0.0960070938,0.1479903311,0.075496003,0.2773635089,0.5707489848,0.2245631963,-0.2179099917,0.1162523031,-0.0670321286,0.100350976,0.1089014634,-0.3214739263,0.3586633801,-0.0397363305,0.6245003939,-0.1722650677,-0.5032495856,0.0513363592,0.0833838657,-0.1121204123,0.1236503422,0.0861939713,-0.2576685548,0.2251090109,-0.271217525,-0.2157211602,-0.2600765824,0.0914617851,0.0068647084,-0.0785092935,-0.1740344763,-0.019697912,-0.0309119318,0.080880858,-0.3524926901,0.0483626872,-0.1184762642,0.3002521396,0.0673744977,-0.0911049545,-0.1955265105,-0.0883468688,0.1906267405,0.0668392628,0.2124758512,-0.2406171709,-0.0119449897,-0.0469680093,-0.0392432958,0.2032940686,0.1763388067,-0.0667514876,0.2336608768,0.4772131443,0.1515923887,-0.2167411596,-0.1939044446,-0.2442658097,-0.2289280444,0.1582281142,0.2803454697,-0.447412163,-0.2276506722,-0.0553199723,-0.0535495766,0.197368741,0.0759611502,0.2929617763,-0.2096658796,-0.0176630169,-0.0392605364,0.2517108321,-0.0915941373,-0.0751967505,-0.1726430207,0.0985154733,-0.1267765015,-0.071235925,0.4334601164,-0.172145322,0.5568087697,-0.01730459,-0.165155232,0.0689437911,-0.0026068252,-0.2857436836,-0.1562635452,0.4075728059,0.2570572495,0.4013083279,0.0511066392,-0.1140500754,-0.0957691148,-0.2148584425,-0.1625625491,-0.2975975871,0.1640312821,0.2230684161,-0.0380365215,-0.0300294422,-0.2216090858,-0.06868653,0.1481539458,-0.0852437839,-0.0049362229,0.1350704432,-0.572783649,-0.2564504445,0.2576949894,0.3892453015,-0.1078387275,0.0925006121,-0.1284802705,-0.0780445039,-0.0828075334,0.1136744544,-0.1936371028,0.1439759731,-0.3953800201,0.0960064754,0.3923988938,-0.9619325995,-0.5316289663,0.3074181378,-0.0790592358,-0.0476625413,0.2594870925,-0.193416208,0.3689076602,-0.3970071971,0.0991809666,0.1445134729,0.0960305706,-0.1170032397,0.0447865799,-0.045579128,-0.124108091,0.054028295,-0.0139340973,0.190086633,0.1639372259,0.0328419097,0.3211568594,0.1087778062,0.181635797,0.3467017114,0.1078858748,-0.1950686425,0.1039923355,-0.1141309366,-0.3088447154,0.3227958381,-0.0806961358,0.0740596354,-0.2461928576,-0.1048443615,-0.4744685292,-0.1219155565,-0.1964444965,-0.4550785422,0.0906680822,0.0183877759,0.5066975355,0.1317300051,-0.0604760759,0.3836167157,-0.0854322091,0.142460525,-0.1503279358,0.0147880698,0.0733482689,0.053287223,-0.0068445369,-0.0800044015,0.1633701622,0.0168759283,-0.0752949789,0.1261767894,-0.014680774,0.2781453431,0.1799918562,-0.0110479174,0.2677811384,-0.3115859032,0.0727299973,-0.0469257645,0.2421076,0.0392748155,0.0623304248,-0.1299505532,0.1001603454,0.1143912748,0.3588016033,0.1484081596,0.3363650739,-0.1169097647,0.141220659,-0.1831562817,0.2509795129,0.1245312616,0.114884451,-0.154750973,-0.0881509483,0.274064064,0.1330340058,0.1747714728,-0.0855381563,0.0776381493,-0.1877564043,0.1635003835,-0.0824789479,0.45902583,0.4656788111,0.2134691328,0.3276383281,0.1585112959,0.0019474645,-0.1873863041,0.324993223,0.0360771008,0.1105055809,0.1557202041,0.1118210033,0.146860078,-0.0375960097,-0.1002386212,0.2401796877,0.2203896046,-0.131401822,-0.1094390303,-0.1590308398,-0.4430620968,-0.2277200371,-0.1068865955,0.0623177551,0.0080609368,-0.0241542794,0.424263984,0.1728570908,-0.0849471092,-0.3658924997,-0.0858860016,-0.0495841317,-0.1270220429,-0.0928509459,0.2467514724,-0.2537060678,0.1124774516,0.14385432,0.1789725572,0.3550393283,-0.2721133232,0.0480044447,-0.3824070096,-0.080881089,0.2435293049,0.0341095068,0.3125128746,0.1089713648,0.2086796612,-0.1823421866,-0.0530172512,0.2419634461,-0.087568678,-0.4155235291,-0.0122696823,-0.0029781247,-0.0665701255,-0.1537419707,-0.3599541783,-0.2867087424,-0.251372695,-0.2509933114,0.0242556222,0.1879399717,0.4431188405,-0.2806210816,0.0617797747,0.2239062786,0.2829473913,-0.2250178158,-0.2212397009,0.361212194,-0.5924842954,-0.3680436611,0.1045943275,0.1452607214,0.4514105618,0.2817083299,-0.4592278302,-0.2388827056,-0.0816094652,-0.2589484155,0.0334601477,-0.1563779563,0.3027187884,-0.2187416255,-0.0749977157,0.0001980195,0.0595103949,0.2751667202,-0.0508786924,0.1851986796,-0.0113794683,0.3512956798,0.1126058176,0.4582701921,0.6416583657,0.0769231766,0.5418627262,-0.1050295159,0.1122363955,-0.0519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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1624","title":"Cannot download ade_corpus_v2","comments":"`ade_corpus_v2` was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `ade_corpus_v2` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"ade_corpus_v2\", \"Ade_corpos_v2_drug_ade_relation\")\r\n```\r\n\r\n(looks like there is a typo in the configuration name, we'll fix it for the v2.0 release of `datasets` soon)","body":"I tried this to get the dataset following this url : https:\/\/huggingface.co\/datasets\/ade_corpus_v2\r\n\r\nbut received this error : \r\n\r\n`Traceback (most recent call last):\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/ade_corpus_v2\/ade_corpus_v2.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/ade_corpus_v2\/ade_corpus_v2.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at ade_corpus_v2\/ade_corpus_v2.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/ade_corpus_v2\/ade_corpus_v2.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/ade_corpus_v2\/ade_corpus_v2.py`\r\n\r\n\r\n","comment_length":61,"text":"Cannot download ade_corpus_v2 \n I tried this to get the dataset following this url : https:\/\/huggingface.co\/datasets\/ade_corpus_v2\r\n\r\nbut received this error : \r\n\r\n`Traceback (most recent call last):\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/ade_corpus_v2\/ade_corpus_v2.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 278, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 486, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/ade_corpus_v2\/ade_corpus_v2.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/opt\/anaconda3\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 282, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at ade_corpus_v2\/ade_corpus_v2.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/ade_corpus_v2\/ade_corpus_v2.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/ade_corpus_v2\/ade_corpus_v2.py`\r\n\r\n\r\n \n `ade_corpus_v2` was added recently, that's why it wasn't available yet.\r\n\r\nTo load it you can just update `datasets`\r\n```\r\npip install --upgrade datasets\r\n```\r\n\r\nand then you can load `ade_corpus_v2` with\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"ade_corpus_v2\", \"Ade_corpos_v2_drug_ade_relation\")\r\n```\r\n\r\n(looks like there is a typo in the configuration name, we'll fix it for the v2.0 release of `datasets` soon)","embeddings":[-0.0399101153,-0.1982669234,-0.113394618,0.2741905153,0.2603805959,0.2967451215,-0.0410111733,0.3299029171,0.0405992754,-0.1682073772,-0.2708159387,-0.2759156823,0.0790571347,-0.2060552537,-0.0267115999,-0.323489368,-0.1370057613,0.0029619159,-0.0265697669,-0.0274784267,-0.1152140573,0.3363958001,-0.2073079646,-0.0367349312,0.2109981328,0.0620413721,-0.1915316135,0.0425680354,-0.2808462083,-0.3533932865,0.53807652,-0.1105325148,0.2315054089,0.4590536654,-0.0001128967,0.159000352,0.2790457606,-0.0251458976,-0.365804255,-0.5255252123,-0.0857978389,0.0248197503,-0.0926860869,-0.3844043911,-0.1996401548,-0.3515524268,0.1897516251,-0.121204555,0.1625540704,0.300662756,0.2591804862,-0.0057054446,0.2623307705,-0.4079148769,-0.0430632941,0.0092382785,-0.0960070938,0.1479903311,0.075496003,0.2773635089,0.5707489848,0.2245631963,-0.2179099917,0.1162523031,-0.0670321286,0.100350976,0.1089014634,-0.3214739263,0.3586633801,-0.0397363305,0.6245003939,-0.1722650677,-0.5032495856,0.0513363592,0.0833838657,-0.1121204123,0.1236503422,0.0861939713,-0.2576685548,0.2251090109,-0.271217525,-0.2157211602,-0.2600765824,0.0914617851,0.0068647084,-0.0785092935,-0.1740344763,-0.019697912,-0.0309119318,0.080880858,-0.3524926901,0.0483626872,-0.1184762642,0.3002521396,0.0673744977,-0.0911049545,-0.1955265105,-0.0883468688,0.1906267405,0.0668392628,0.2124758512,-0.2406171709,-0.0119449897,-0.0469680093,-0.0392432958,0.2032940686,0.1763388067,-0.0667514876,0.2336608768,0.4772131443,0.1515923887,-0.2167411596,-0.1939044446,-0.2442658097,-0.2289280444,0.1582281142,0.2803454697,-0.447412163,-0.2276506722,-0.0553199723,-0.0535495766,0.197368741,0.0759611502,0.2929617763,-0.2096658796,-0.0176630169,-0.0392605364,0.2517108321,-0.0915941373,-0.0751967505,-0.1726430207,0.0985154733,-0.1267765015,-0.071235925,0.4334601164,-0.172145322,0.5568087697,-0.01730459,-0.165155232,0.0689437911,-0.0026068252,-0.2857436836,-0.1562635452,0.4075728059,0.2570572495,0.4013083279,0.0511066392,-0.1140500754,-0.0957691148,-0.2148584425,-0.1625625491,-0.2975975871,0.1640312821,0.2230684161,-0.0380365215,-0.0300294422,-0.2216090858,-0.06868653,0.1481539458,-0.0852437839,-0.0049362229,0.1350704432,-0.572783649,-0.2564504445,0.2576949894,0.3892453015,-0.1078387275,0.0925006121,-0.1284802705,-0.0780445039,-0.0828075334,0.1136744544,-0.1936371028,0.1439759731,-0.3953800201,0.0960064754,0.3923988938,-0.9619325995,-0.5316289663,0.3074181378,-0.0790592358,-0.0476625413,0.2594870925,-0.193416208,0.3689076602,-0.3970071971,0.0991809666,0.1445134729,0.0960305706,-0.1170032397,0.0447865799,-0.045579128,-0.124108091,0.054028295,-0.0139340973,0.190086633,0.1639372259,0.0328419097,0.3211568594,0.1087778062,0.181635797,0.3467017114,0.1078858748,-0.1950686425,0.1039923355,-0.1141309366,-0.3088447154,0.3227958381,-0.0806961358,0.0740596354,-0.2461928576,-0.1048443615,-0.4744685292,-0.1219155565,-0.1964444965,-0.4550785422,0.0906680822,0.0183877759,0.5066975355,0.1317300051,-0.0604760759,0.3836167157,-0.0854322091,0.142460525,-0.1503279358,0.0147880698,0.0733482689,0.053287223,-0.0068445369,-0.0800044015,0.1633701622,0.0168759283,-0.0752949789,0.1261767894,-0.014680774,0.2781453431,0.1799918562,-0.0110479174,0.2677811384,-0.3115859032,0.0727299973,-0.0469257645,0.2421076,0.0392748155,0.0623304248,-0.1299505532,0.1001603454,0.1143912748,0.3588016033,0.1484081596,0.3363650739,-0.1169097647,0.141220659,-0.1831562817,0.2509795129,0.1245312616,0.114884451,-0.154750973,-0.0881509483,0.274064064,0.1330340058,0.1747714728,-0.0855381563,0.0776381493,-0.1877564043,0.1635003835,-0.0824789479,0.45902583,0.4656788111,0.2134691328,0.3276383281,0.1585112959,0.0019474645,-0.1873863041,0.324993223,0.0360771008,0.1105055809,0.1557202041,0.1118210033,0.146860078,-0.0375960097,-0.1002386212,0.2401796877,0.2203896046,-0.131401822,-0.1094390303,-0.1590308398,-0.4430620968,-0.2277200371,-0.1068865955,0.0623177551,0.0080609368,-0.0241542794,0.424263984,0.1728570908,-0.0849471092,-0.3658924997,-0.0858860016,-0.0495841317,-0.1270220429,-0.0928509459,0.2467514724,-0.2537060678,0.1124774516,0.14385432,0.1789725572,0.3550393283,-0.2721133232,0.0480044447,-0.3824070096,-0.080881089,0.2435293049,0.0341095068,0.3125128746,0.1089713648,0.2086796612,-0.1823421866,-0.0530172512,0.2419634461,-0.087568678,-0.4155235291,-0.0122696823,-0.0029781247,-0.0665701255,-0.1537419707,-0.3599541783,-0.2867087424,-0.251372695,-0.2509933114,0.0242556222,0.1879399717,0.4431188405,-0.2806210816,0.0617797747,0.2239062786,0.2829473913,-0.2250178158,-0.2212397009,0.361212194,-0.5924842954,-0.3680436611,0.1045943275,0.1452607214,0.4514105618,0.2817083299,-0.4592278302,-0.2388827056,-0.0816094652,-0.2589484155,0.0334601477,-0.1563779563,0.3027187884,-0.2187416255,-0.0749977157,0.0001980195,0.0595103949,0.2751667202,-0.0508786924,0.1851986796,-0.0113794683,0.3512956798,0.1126058176,0.4582701921,0.6416583657,0.0769231766,0.5418627262,-0.1050295159,0.1122363955,-0.0519141667,-0.5397056937,-0.1024657413,0.1521128267,0.2426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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1618","title":"Can't filter language:EN on https:\/\/huggingface.co\/datasets","comments":"Cool @mapmeld ! My 2 cents (for a next iteration), it would be cool to have a small search widget in the filter dropdown as you have a ton of languages now here! Closing this in the meantime.","body":"When visiting https:\/\/huggingface.co\/datasets, I don't see an obvious way to filter only English datasets. This is unexpected for me, am I missing something? I'd expect English to be selectable in the language widget. This problem reproduced on Mozilla Firefox and MS Edge:\r\n\r\n![screenshot](https:\/\/user-images.githubusercontent.com\/4547987\/102792244-892e1f00-43a8-11eb-9e89-4826ca201a87.png)\r\n","comment_length":38,"text":"Can't filter language:EN on https:\/\/huggingface.co\/datasets \n When visiting https:\/\/huggingface.co\/datasets, I don't see an obvious way to filter only English datasets. This is unexpected for me, am I missing something? I'd expect English to be selectable in the language widget. This problem reproduced on Mozilla Firefox and MS Edge:\r\n\r\n![screenshot](https:\/\/user-images.githubusercontent.com\/4547987\/102792244-892e1f00-43a8-11eb-9e89-4826ca201a87.png)\r\n \n Cool @mapmeld ! My 2 cents (for a next iteration), it would be cool to have a small search widget in the filter dropdown as you have a ton of languages now here! Closing this in the meantime.","embeddings":[0.0697203875,-0.1904691607,-0.0992743149,-0.1423217207,0.3043785393,-0.1967637241,-0.0242899098,0.2436978966,0.2299478501,0.0734315589,-0.3851575255,-0.0264967307,0.1065776572,0.3587076068,0.0037252805,-0.0293827187,0.0082300613,-0.242115587,-0.0193542466,-0.3352008164,-0.0219079703,0.2305799723,0.0144517198,0.1968016028,0.0344665274,0.1441680491,0.2395025939,0.1189103574,-0.336981833,0.161104992,0.4430655837,0.0839436278,-0.2019207776,0.0722684786,-0.0001132097,0.1843021512,0.2821063697,-0.2375727147,-0.3122716844,-0.4901600182,0.0833079889,0.0298773609,0.0978154019,0.1604104936,-0.1911302358,-0.0888534859,0.0164860357,-0.5040478706,0.1358761489,0.2542887032,0.1379289478,0.2267963141,-0.230095014,0.182113722,0.1706558913,0.2834809422,-0.1793498844,-0.2151714116,0.614452064,0.0103617357,-0.024525905,0.3743491471,0.189716965,-0.2065367103,-0.0197540335,-0.0695601776,-0.0864234343,-0.3363251388,0.4389978051,0.4179053307,0.3462484777,0.1578739285,-0.369800508,-0.034940578,-0.2311831117,0.2896886468,0.2406505644,0.2826818526,-0.4962031245,0.292322427,-0.1496741176,-0.622476697,0.1252483577,0.4565009475,-0.1259596497,0.8481764793,0.0109492494,0.0648580492,0.3868456185,-0.2022313327,0.0874971673,-0.1736609042,0.1670818478,0.2750412822,0.0258528944,0.2047601789,0.0499382615,0.5662867427,0.1952952743,-0.068339631,-0.5048609376,0.4946347475,-0.0847920105,-0.0357238911,0.2893125713,-0.1108626351,0.3451492786,-0.0452296995,0.4507942498,-0.0567076541,-0.0216337461,-0.030431971,0.1588544399,-0.0109640537,-0.4000426233,-0.3252842724,0.1004615277,-0.4018624723,-0.2509342134,-0.0828103423,0.0253898688,-0.5085737705,0.372757256,0.2321229726,0.2330646813,0.0200557187,-0.0536398739,0.3188359141,-0.2947303355,-0.3379003704,-0.0659277886,0.0888256207,-0.3805269599,-0.2679069042,0.0916711017,-0.4386591911,0.1977417767,0.0660373792,0.042881541,-0.0982045755,-0.1369184852,0.1586972028,0.1299182922,0.2043980062,-0.1609536558,0.259979248,0.503883481,-0.7266700268,-0.0265471209,-0.0072138193,-0.3502404988,0.039247971,-0.4442353845,0.0769849792,-0.1553951055,-0.1846591979,0.2390430719,0.6273361444,-0.0862734243,-0.0393217131,0.0991693884,0.2599027455,-0.3728788495,0.2773992717,0.1285035014,0.1500095427,-0.5414364338,-0.3975726664,-0.1406737268,-0.204844594,0.1602687091,0.1611751318,-0.1280632168,-0.4564213455,-0.0561366789,0.1231921762,0.2289880216,-0.0248813666,-0.325927943,0.025333602,0.0851873457,0.0772140548,-0.1757469028,-0.1210657507,-0.1756032258,-0.0099734394,0.0850169137,0.1311845332,-0.1131326109,-0.181431964,-0.1108245179,-0.023086803,0.6042264104,0.2240638286,0.2061372846,-0.1574541479,0.1637067944,0.0172632169,0.2774575651,-0.2118106484,-0.0415724181,0.1440002471,0.2356201708,0.1567976475,0.1479810029,-0.1542041749,-0.2422254682,-0.0708123371,0.0586463436,0.3345796466,-0.0909061357,-0.0999172032,-0.1584774703,0.055048652,-0.2936167717,-0.2847729623,0.1034499854,0.0707144216,-0.2660553753,0.2952750027,-0.1530410647,0.003563449,-0.0228449423,0.012876993,-0.1686631292,0.1423533708,-0.0322412215,0.0786100999,-0.2693579197,0.1288791001,0.0960844383,-0.090056181,-0.1398057938,-0.1038971171,0.3578149378,-0.0511831976,0.5464028716,0.0946927369,0.3221946359,-0.2679735124,-0.0512743555,0.2520977557,0.0933540463,0.0747131258,-0.4248587489,0.2352568805,0.2121915817,0.3593796194,0.1562715471,0.1533129811,0.477858156,0.0400025323,-0.3378351927,-0.4407450259,0.4604885578,-0.0364161246,0.1299155354,0.0974904224,-0.4817985594,0.1562607884,0.3481703401,0.2584572732,0.4431127608,0.4171910882,0.0544425547,-0.159989208,0.1677643061,0.0575118698,-0.0727854818,0.1639977396,0.3393564224,0.045149561,0.1474932879,-0.2835951447,0.0294036139,0.0779328346,-0.0032880101,-0.1536536217,0.2630381882,-0.388395071,-0.7758849859,0.3943957984,-0.1288506389,-0.1343992651,0.0731743425,0.2974516749,-0.6716725826,-0.4274066389,0.1434940249,-0.0696998909,-0.6090425253,-0.3300592899,0.3609874547,-0.1772851497,-0.0729263127,0.087815918,0.0043422952,0.1457873434,-0.2441672832,0.0668262616,-0.3475687504,-0.2859016657,-0.2279474437,0.0368862636,0.3765652776,0.2082701027,0.2132933736,-0.0707803145,0.0950175598,-0.1185419708,-0.8488957882,0.1651747227,0.043694146,0.4854832292,-0.0589543432,0.1918137968,-0.336092025,0.0960922316,0.4884258807,-0.034146715,-0.2212391794,0.1713183373,0.1258920282,0.1197915524,-0.0225212928,-0.2072440684,-0.4753740728,-0.0032552248,-0.3889010847,-0.1890711635,-0.0813273713,0.0611331128,-0.1392064393,-0.1334471852,-0.4243969321,-0.0804809481,-0.4438278675,-0.1025070325,0.2734499276,-0.0141879087,-0.0718323663,0.1732351482,0.2118355781,-0.0628603697,-0.0226121489,-0.252859503,-0.0648088232,-0.1417482942,0.0499465913,0.1261299998,0.0976682827,0.2344747782,-0.0770998448,0.0769518837,-0.2106241882,-0.24988392,-0.0035374023,-0.1133511737,0.3706026673,-0.0966546908,0.3994728327,0.2778926194,0.4070092738,-0.0409842841,0.3978936076,0.729614675,0.3698035181,0.3786163628,-0.1808141023,-0.3395781815,0.1518745571,-0.0530514792,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"Hi @SapirWeissbuch,\r\nWhen you are saying it freezes, at that time it is unzipping the file from the zip file it downloaded. Since it's a very heavy file it'll take some time. It was taking ~11GB after unzipping when it started reading examples for me. Hope that helps!\r\n![Screenshot 2020-12-21 at 23 40 52](https:\/\/user-images.githubusercontent.com\/19718818\/102808355-3b380c00-43e6-11eb-81ab-c31019ae6322.png)\r\n","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":54,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n Hi @SapirWeissbuch,\r\nWhen you are saying it freezes, at that time it is unzipping the file from the zip file it downloaded. Since it's a very heavy file it'll take some time. It was taking ~11GB after unzipping when it started reading examples for me. Hope that helps!\r\n![Screenshot 2020-12-21 at 23 40 52](https:\/\/user-images.githubusercontent.com\/19718818\/102808355-3b380c00-43e6-11eb-81ab-c31019ae6322.png)\r\n","embeddings":[-0.0419084467,0.172738567,-0.0584622957,0.448946923,0.0830925331,0.2269361615,0.2345205098,0.0880642906,0.1740850508,-0.000837622,-0.2182780355,0.2595328987,0.0018136572,0.0818539709,-0.0989711285,-0.0069779027,-0.0974632129,-0.1411154121,0.209971875,0.1532503814,-0.1402238458,0.5456249118,-0.2876877189,-0.3265475035,-0.1779135317,-0.056436412,0.0590684079,0.2066374421,-0.0397573076,-0.2740011215,0.5494995117,0.2960214317,0.0283417329,0.5103057623,-0.0001163505,-0.2048004419,0.3131906688,-0.0038417126,-0.4972884059,-0.2235812694,-0.3429577351,-0.0255894195,0.057130184,0.0733717233,0.0538625382,0.4552014768,-0.1131191179,-0.6822659969,0.0399417654,0.0720884874,0.1493647844,-0.0646007583,-0.199761346,-0.1022181883,0.1219624579,-0.1928976029,-0.2828271389,0.4720593989,0.2885800898,0.0657839403,0.2345456928,0.2593853474,0.0046880711,0.3377595544,0.0988686234,0.0581915826,-0.325982064,-0.3113313019,0.4224762917,0.2790928483,0.8703471422,-0.2963435948,-0.175425455,-0.0423670523,0.0715548322,-0.1038353592,0.2577138245,0.088854,-0.4103254676,-0.0335079283,-0.1232032478,-0.1644373834,-0.0424618758,0.0381719917,-0.1927553713,-0.2027969658,-0.1956015676,-0.0244732946,0.121129334,0.0154822152,0.3752960861,-0.266582936,-0.2203110307,0.1416900307,-0.6825615764,0.0889082029,-0.1432446986,0.1520679891,0.1661089212,0.1773971468,0.2389192879,0.250829041,-0.2416718155,-0.0300459806,0.2960170209,0.4848723114,-0.0671037883,-0.0906197503,0.3128862679,0.2891587019,-0.116367057,0.0646981373,-0.11278864,-0.0540562049,-0.0088184932,-0.1395270377,0.1751488596,-0.3751081228,-0.3067250848,0.0658895671,0.0155868111,0.1677224189,0.0349553004,0.3543732464,-0.3763412237,0.2156981528,-0.1120009422,-0.045582965,-0.1661397219,-0.041979067,-0.086148724,-0.1404378414,0.0543522239,0.1692944914,0.411878109,-0.1329503953,0.0746368617,-0.2131661922,0.172381416,-0.0418972,0.0963757411,-0.456872046,-0.0322256424,0.19248043,0.0370418951,0.4138689041,-0.2077854425,-0.1593095809,-0.1645265073,0.2517725527,-0.2718759179,-0.1436472535,-0.007459823,0.0797771662,-0.4005433023,-0.0722330213,-0.168295905,0.123660475,0.1961122453,-0.0432695486,-0.0236979295,0.0501558706,-0.33690238,-0.2600096762,0.245839864,0.6521677971,-0.4494019747,0.3058450818,-0.3420861661,0.1299992949,0.0804680139,0.2561940253,-0.0010044767,0.0337726139,-0.4061591327,0.0149713336,0.3212281764,-0.3201260567,-0.7326216698,0.3826341033,-0.0256310049,0.251427412,0.0303474572,0.2256506681,0.1226950809,-0.0749238282,0.1661731154,0.4375931621,-0.1711920202,-0.031693168,-0.4060000777,-0.27152583,0.1005339772,0.1699432135,-0.1659566909,0.0679287687,-0.036284972,0.1187874749,0.1556513757,0.3864189982,0.0588685088,0.3071848452,0.2669268847,0.00021677,0.0709671304,-0.067124255,-0.8073760271,0.4343363047,-0.0653368309,-0.1522076726,-0.19800587,-0.0142491395,-0.4346853793,-0.1675317585,-0.2741286755,-0.2488716394,-0.0109109906,0.0987044871,0.3192574084,0.2353910059,0.1014146581,0.648337841,0.3068910837,-0.0480939932,-0.4378825426,0.0892025158,0.0519032031,-0.1164015979,-0.0646075159,-0.0341096781,0.0176020544,-0.2008464038,-0.21785447,0.2084423602,0.1733133793,0.1311374009,-0.0172833204,0.0190534666,0.2716843486,0.0193918105,0.286211133,0.0508091412,0.3185540438,-0.2622424662,-0.1959272921,-0.0148142194,-0.4580947161,0.2177153826,-0.1105609089,-0.0237456206,0.2106559426,0.0273159984,0.2022928298,-0.0464965068,0.296946913,0.1381292492,0.3851886988,-0.1820930392,0.0159382094,0.3154764771,0.2811936736,0.1673019528,-0.0332994573,0.1528603882,-0.1099497899,-0.0577406734,0.1584849656,0.4288981557,0.6317715049,0.0146721471,0.1970623732,-0.0221591387,0.2154348344,-0.2755388916,0.0512860119,0.082552731,-0.1301777363,0.1544675082,0.1960978359,-0.0384207219,0.1795618236,-0.1035709754,0.1096571609,0.1532823741,-0.0308224317,0.2952177227,-0.2102186233,-0.191859901,0.0659354851,-0.0963198915,-0.0047730547,-0.4441174865,-0.1619956195,0.3785782158,0.2477114648,-0.0651289448,-0.0693964809,0.2523093522,0.1083593071,-0.5406515002,-0.3403006196,-0.2089052647,-0.2180715799,0.0151600912,0.271680057,0.0632411689,0.2063985914,-0.0864794552,-0.0475741588,-0.181358844,-0.1395511925,-0.0201594122,-0.0543301664,0.4290530384,0.0674380362,0.3619537354,-0.1399614513,0.2467644066,0.0067677926,0.0304791257,-0.0865390971,0.0126491021,0.0340264253,0.1970240325,-0.1144118533,-0.3764761388,-0.3245883286,-0.4204921424,0.2019141614,-0.0184990447,0.003930144,0.3973693252,0.0829567239,0.0703602433,0.2658996582,-0.1668619066,-0.3439247608,-0.3762113452,0.6043846011,-0.1868122518,-0.1460451782,0.0393626504,-0.0498542413,0.3061037064,0.1134551391,-0.5427657366,0.0421469919,-0.5677765608,0.5096668005,-0.1733064055,-0.1721161604,0.2683787644,-0.221430406,0.0925935358,-0.0034911465,-0.1249541789,0.0168372486,0.2290721536,0.2733463645,0.1916391999,0.1165311038,0.1638864726,0.5300636292,0.1157094315,0.1098057777,0.36005628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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"Hi @bhavitvyamalik \r\nThanks for the reply!\r\nActually I let it run for 30 minutes before I killed the process. In this time, 30GB were extracted (much more than 11GB), I checked the size of the destination directory.\r\n\r\nWhat version of Datasets are you using?\r\n","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":44,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n Hi @bhavitvyamalik \r\nThanks for the reply!\r\nActually I let it run for 30 minutes before I killed the process. In this time, 30GB were extracted (much more than 11GB), I checked the size of the destination directory.\r\n\r\nWhat version of Datasets are you using?\r\n","embeddings":[-0.072755143,0.1626463085,-0.0521008894,0.4159293771,0.0719526932,0.1767252088,0.1985662133,0.0667587072,0.1333532035,-0.0039923522,-0.2109427154,0.2015078813,-0.0472181328,0.0925029591,-0.1151683033,0.0005983744,-0.1870140433,-0.0596627817,0.1579323113,0.1352991462,-0.1677170992,0.439342469,-0.2720969319,-0.3291651607,-0.2195433378,0.0065108202,0.0340620987,0.1153706238,-0.0439226031,-0.2515452802,0.5903285146,0.1817964613,0.2022955716,0.5596300364,-0.00011643,-0.2690275013,0.3349490762,-0.0813676193,-0.5700404644,-0.2561136186,-0.4124467671,0.0133202085,0.0350640342,0.0512670875,0.0637827441,0.4205184281,-0.1521245837,-0.64205724,0.0961365029,0.184820652,0.2039233148,-0.1425318271,-0.1532195061,-0.105651319,0.1025357097,-0.0433299318,-0.2304594815,0.4613931775,0.1876121461,0.0937481448,0.2468427271,0.3056149483,-0.0021749551,0.3733741343,0.1243720353,0.0224541258,-0.2359964401,-0.3585821986,0.4380189478,0.2638860345,0.8583325744,-0.2614651024,-0.2454858124,-0.1408300549,0.0629529282,-0.1338534206,0.2592305541,0.2633804381,-0.3231261969,-0.0073956484,-0.1510874331,-0.1000832841,-0.0636823475,-0.0040893252,-0.2076932192,-0.2849427164,-0.1990710497,0.0020443622,0.0280593559,-0.00062262,0.3906316459,-0.1917218268,-0.1724092066,0.3107558787,-0.5941225886,0.1101470888,-0.0551612638,0.1852333695,0.2100424469,0.173067838,0.160839662,0.08328861,-0.3457117677,-0.0585267134,0.2925242186,0.5779297948,-0.0692556798,-0.0797537342,0.1991256326,0.2562425733,-0.0700737685,0.0698362142,-0.0884934738,-0.0246249195,0.0572537109,-0.1104035899,0.2593982816,-0.3535263538,-0.3831744194,0.0682486594,0.0211931132,0.2233143002,-0.0483936593,0.2390223891,-0.2393819839,0.2680348158,-0.0848907307,0.060208302,-0.1209563091,-0.1047899053,-0.1099950448,-0.0689461753,0.0120426118,0.1089615747,0.4129388332,-0.1468844563,0.0874883607,-0.1852960438,0.1007984355,-0.0752597973,0.0441004112,-0.4850211143,-0.0442705639,0.285319984,0.1247140467,0.3971720636,-0.2388545573,-0.138188079,-0.1328911185,0.3041514158,-0.3151180744,-0.2827414274,-0.0892390534,0.1152573451,-0.4591413438,-0.0980610847,-0.3268722594,0.1572990417,0.2685288191,0.1059336066,-0.0093497122,-0.0360065624,-0.3958993554,-0.2581654191,0.2020711601,0.5899381042,-0.4222159386,0.2609266043,-0.3011381328,0.1883919388,0.1586330533,0.2318543792,0.0258223563,0.1076005399,-0.4511807561,0.0197257139,0.2745524943,-0.3494895101,-0.7386118174,0.3036574721,0.021286672,0.2213606387,0.0366427563,0.0763042793,0.1528085172,-0.0280256998,0.16202043,0.4609229863,-0.1844620705,-0.0038157732,-0.385767132,-0.3302130401,0.0818016604,0.1815673113,-0.0305189472,0.0676134229,0.0258463323,0.0914006531,0.2147233635,0.3795999885,0.1587375998,0.2579649389,0.3154444396,-0.0450445525,0.0312176943,-0.0865430459,-0.7296493053,0.4170327187,-0.1016145498,-0.1146012545,-0.1999589801,-0.0041276789,-0.5134187937,-0.0837907493,-0.2601849139,-0.2858148217,-0.0128261345,0.0724398345,0.3398637474,0.1317500025,0.0976457074,0.6660851836,0.2577014863,-0.12178348,-0.4322471023,0.0490944795,0.0339934193,-0.102925472,-0.0327064544,0.010017273,0.0939169005,-0.1624750495,-0.2578213811,0.1050464064,0.1943413317,0.0221010577,0.0371774621,0.0491048954,0.2568796873,0.0424103886,0.2400223911,0.0523945093,0.2376604378,-0.1321962029,-0.1441477388,-0.0882766098,-0.3817676008,0.2373189926,-0.2045960426,0.0101193106,0.2657052577,-0.0246239956,0.2844458818,-0.0999696851,0.2773302495,0.1908300519,0.2834511101,-0.1209324971,0.1273938566,0.2658622563,0.1088988706,0.1941094548,-0.0323126167,0.2162722945,-0.1747235805,-0.1451085657,0.1201923266,0.4297387004,0.6090689898,0.0016629801,0.270540148,-0.0489554182,0.3418198228,-0.1985937655,0.0410673879,0.0859616771,-0.1470722109,0.187978819,0.2925009727,0.0380579941,0.197683081,-0.0623431243,0.1066617742,0.2068859041,-0.0509885959,0.1892718971,-0.1957104355,-0.1634073853,-0.0388464779,-0.1722293645,0.0784078911,-0.3618616462,-0.1269526333,0.3402609229,0.2416095138,-0.0549681224,-0.1024231687,0.1973516792,0.1123743206,-0.478464663,-0.2655420601,-0.1195397303,-0.2441501468,0.044734966,0.1455067992,-0.0701164529,0.2637217641,-0.0968733057,-0.034589503,-0.213289842,-0.1277239025,-0.0152927311,0.0476173051,0.3991951942,0.1074263006,0.2642818093,-0.0586503781,0.2124877572,-0.0502059944,-0.0096272482,-0.0813302696,0.0515185595,-0.0399384424,0.2434220463,-0.1355899274,-0.326969862,-0.3629831374,-0.3707721531,0.1907312572,0.1076776683,0.0279355422,0.4600449502,0.0399975292,-0.0275215711,0.3603964746,-0.1745766401,-0.4181405008,-0.3557340503,0.6125190854,-0.1771862209,-0.1476271003,0.1279090047,-0.0705701038,0.3535722792,0.1714600772,-0.5831279159,0.0055774353,-0.4850946069,0.5077188015,-0.1956724972,-0.1334520131,0.1899163276,-0.3022510409,0.1054920405,0.076634936,-0.1188502386,-0.0458650179,0.2866661251,0.3172294199,0.1094697714,0.1482848525,0.1944676489,0.4785459638,0.215719983,0.1037300304,0.3804444671,0.179491818,0.3754736483,-0.3020776808,-0.08651568,0.0627321377,-0.1200739145,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"I'm using datasets version: 1.1.3. I think you should drop `cache_dir` and use only\r\n`dataset = datasets.load_dataset(\"trivia_qa\", \"rc\")`\r\n\r\nTried that on colab and it's working there too\r\n![image](https:\/\/user-images.githubusercontent.com\/19718818\/102814269-4db74300-43f0-11eb-8f26-ecfcf4632002.png)\r\n","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":28,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n I'm using datasets version: 1.1.3. I think you should drop `cache_dir` and use only\r\n`dataset = datasets.load_dataset(\"trivia_qa\", \"rc\")`\r\n\r\nTried that on colab and it's working there too\r\n![image](https:\/\/user-images.githubusercontent.com\/19718818\/102814269-4db74300-43f0-11eb-8f26-ecfcf4632002.png)\r\n","embeddings":[-0.0324455127,0.2749699056,-0.0218249429,0.3239808977,0.1333954036,0.1968196183,0.2799459994,0.0583024845,0.0948531032,-0.059717834,-0.2450279444,0.2710150778,-0.0008884075,0.1060801074,-0.1267104149,0.0433528572,-0.114684917,-0.0377161428,0.2132201493,0.1566438973,-0.1349406689,0.4377724528,-0.2820023,-0.3223251998,-0.2458768636,0.0691145957,0.0668900087,0.1003829762,0.0343146771,-0.2480177581,0.67411834,0.3076746166,0.149097681,0.5383524895,-0.0001176828,-0.1858210415,0.3122101426,-0.0681562498,-0.625824213,-0.2935509086,-0.4505560398,0.0806286782,0.0823827982,0.0722627118,-0.0465550199,0.4582152367,-0.0952111408,-0.6741397977,0.0192471445,0.1569029987,0.1905343533,-0.1172520518,-0.2548100948,-0.0953706503,0.1114436388,-0.0803015232,-0.27206406,0.5007556677,0.2071802467,0.0817496479,0.2020538896,0.3206925094,-0.0337509513,0.39436692,0.1626695246,0.0672909617,-0.2473103404,-0.307952553,0.4526152015,0.2340932935,0.9158679843,-0.3237604201,-0.2919473052,-0.1021097675,0.0687192604,-0.1473658085,0.2955060601,0.1430480182,-0.2731516361,-0.0047070812,-0.1451757252,-0.1480061263,-0.0216660183,-0.0137277143,-0.1773999482,-0.2071225792,-0.1470057219,-0.004651607,0.041708041,0.0171908811,0.510862112,-0.2119361758,-0.1754100323,0.248605594,-0.623901844,0.1116386428,-0.0553300567,0.2207639962,0.1355824471,0.1886218488,0.1547271311,0.1280135959,-0.3434439898,-0.0555103943,0.2494443655,0.5355216861,-0.0092455531,-0.0484632105,0.2394342124,0.2919661403,-0.0908755213,0.0781818926,-0.074564226,-0.0653682649,0.0330923907,-0.0501569174,0.2361414284,-0.3740792274,-0.3454495072,-0.0093842046,-0.0154698594,0.2531176805,0.0263857655,0.2701820433,-0.2840004861,0.2326665819,-0.0908644348,0.0841919929,-0.1722839177,-0.0141151398,-0.0864259601,-0.1260414422,0.0389911756,0.1875640303,0.4296388626,-0.1550474912,0.1072456837,-0.1846923828,0.1514116973,-0.0643315837,0.0191757977,-0.4474502802,-0.1141708121,0.2350038588,0.0871080831,0.4529133737,-0.1869063079,-0.1543507725,-0.0992894471,0.2812709212,-0.3120135665,-0.2740344405,-0.0483691655,0.0841600969,-0.4793226719,-0.1381507814,-0.3207099736,0.1803421527,0.2674741447,0.0174977332,-0.0052148467,-0.0017862897,-0.4459944665,-0.2575539649,0.2131515294,0.6501175165,-0.4160538614,0.192986995,-0.3035592139,0.186270237,0.1453388035,0.1869918108,-0.0152987568,0.0502583906,-0.3998034596,-0.0738630965,0.2610940635,-0.3984422088,-0.7728948593,0.2378232479,0.0084293252,0.188441962,0.0210103337,0.1339071393,0.0947505683,-0.0753481686,0.1275687516,0.455789268,-0.1201919094,0.0158131346,-0.3521460891,-0.3146685362,0.0798838809,0.2370212227,-0.0563240834,0.1390461177,-0.0251109377,0.0512885526,0.1590341032,0.3394579291,0.1158736944,0.2126523256,0.3286348581,-0.0262288544,0.0397684798,-0.0002226138,-0.8115261197,0.4374570847,-0.0522571355,-0.1002551541,-0.2450171113,-0.040324863,-0.467741698,-0.1434410214,-0.2980220318,-0.2695590854,-0.0153903868,0.1407362819,0.3109505177,0.1854406297,0.087342687,0.6520835161,0.307155937,-0.1022906676,-0.3502542973,0.0763698146,0.0567293838,-0.0485360622,-0.027627591,0.0433520973,0.1094926298,-0.1354460567,-0.2275478542,0.1687919796,0.1888411194,0.0848039016,0.0694317594,0.0469981581,0.2705690265,0.0723999366,0.179262653,0.0418834873,0.248191461,-0.1535512954,-0.1856684387,-0.0135972165,-0.3767162859,0.2876800001,-0.2158856541,0.0418824516,0.2819383144,0.0126301255,0.2349714637,-0.0830989033,0.2622601688,0.2052707374,0.3273557127,-0.1094087288,0.0862701535,0.3412249684,0.1120816469,0.2141619921,-0.0404642113,0.1691351235,-0.1454675496,-0.139097482,0.1596773714,0.4336571097,0.6067399979,-0.0081361635,0.2251293361,-0.0391937681,0.3126021028,-0.2367356867,0.0741251335,0.0563662015,-0.1426462978,0.1152948439,0.3039670885,-0.0187689774,0.2213625312,-0.0872809589,0.1258670688,0.1848024279,-0.0884196013,0.2492524534,-0.2403630763,-0.1852182299,-0.0158719402,-0.2151351273,0.0807840154,-0.3866450489,-0.1256521344,0.3427274227,0.2241571248,0.0063512516,-0.106487155,0.2327479124,0.080698289,-0.6001936197,-0.316981703,-0.2200912535,-0.2548022866,0.0200473219,0.1552534848,-0.0421639681,0.2343332171,-0.1184985191,-0.0251719654,-0.2593436241,-0.1221707091,-0.0266620032,-0.0059835552,0.3980239928,0.0872639567,0.206764549,-0.1229046881,0.1910785586,-0.0037700424,0.0013773274,-0.0179857742,-0.0032086673,-0.0109097874,0.2183505148,-0.1473802626,-0.3007587194,-0.3201138079,-0.3525672257,0.147317782,0.0582233183,0.04454704,0.5137864947,0.0451366603,-0.015032338,0.3702687621,-0.203423664,-0.3940540552,-0.374451369,0.5996718407,-0.184115991,-0.1274546832,0.1135366485,-0.0431589969,0.3891008198,0.1707431227,-0.5868318677,-0.0655888245,-0.4273253679,0.5127469301,-0.1010312513,-0.1719828397,0.1673006564,-0.2365752459,0.1099241376,0.0389212146,-0.1907941401,-0.0395424888,0.253788799,0.303168118,0.154148221,0.1072200984,0.1426324397,0.506226182,0.1756164283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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"Train, Validation, and Test splits contain 138384, 18669, and 17210 samples respectively. It takes some time to read the samples. Even in your colab notebook it was reading the samples before you killed the process. Let me know if it works now!","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":42,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n Train, Validation, and Test splits contain 138384, 18669, and 17210 samples respectively. It takes some time to read the samples. Even in your colab notebook it was reading the samples before you killed the process. Let me know if it works now!","embeddings":[-0.0954272225,0.2150017768,-0.0282702781,0.3146703243,0.0880519301,0.1548192501,0.2518567741,0.0587237068,0.1317174435,0.0135483062,-0.2907213271,0.294244945,-0.063027285,0.1593239903,-0.1379980147,0.0480366312,-0.1314404756,-0.0800191611,0.2093431652,0.1527232826,-0.1440872252,0.4468840361,-0.3239098787,-0.327598691,-0.2357051075,-0.0038085489,0.0367073528,0.0954877064,-0.0514701568,-0.2540023625,0.6315826774,0.2810488939,0.1258323491,0.5134747028,-0.000117358,-0.1675780565,0.3026215732,-0.0567497648,-0.5882295966,-0.2423142344,-0.4385442436,0.0669240281,0.0713514611,0.0721423477,-0.0678216144,0.468113631,-0.0136968521,-0.5675046444,0.1036921069,0.1793596745,0.180880487,-0.0697262362,-0.1773590595,-0.0844901055,0.1710763574,-0.0346744396,-0.2726801932,0.5722362399,0.2749502957,0.0593251102,0.1192120835,0.3345854282,-0.013206346,0.3771262467,0.0707204193,0.0227241423,-0.2844106555,-0.3562527001,0.397049129,0.2216862887,0.8158916831,-0.2651734948,-0.1943282187,-0.1256033629,0.0726431385,-0.1700063646,0.3319043517,0.1924577057,-0.3744276762,0.0152338324,-0.1312677264,-0.0496278256,-0.0233993158,-0.0152375614,-0.1556309611,-0.1486995816,-0.1854819208,-0.0202173553,0.1402362436,0.0790977105,0.5242319703,-0.1135060042,-0.1165755615,0.2436380982,-0.6248546839,0.0691105053,-0.1014846414,0.1613452584,0.1315224171,0.1775840819,0.2185835987,0.0994748026,-0.2863441706,-0.0031777616,0.2560694814,0.4834381342,-0.0459775254,-0.0941640139,0.203132093,0.3059603572,-0.1675646305,0.0793081895,-0.1169579551,-0.0772604048,-0.0377766639,-0.0337748528,0.2018983662,-0.3512710929,-0.3899106085,0.0125035541,-0.0930636674,0.2361389697,0.0449043214,0.2959346473,-0.34680444,0.148555547,-0.0739104077,-0.038953416,-0.2628845274,-0.0872745365,-0.0986863896,-0.0980872884,-0.0486942939,0.1933555901,0.4289930165,-0.1102925837,0.1828455329,-0.2092844397,0.1033675447,-0.1057472751,0.0984404087,-0.5431810617,-0.0812800452,0.3238000274,0.1397364885,0.3871344328,-0.2562746406,-0.20229581,-0.0877487361,0.3268471062,-0.3405276239,-0.2281249613,0.0071293581,0.0892666951,-0.4724737406,-0.1162059009,-0.3092406988,0.1435221285,0.2485707402,-0.0102978908,-0.0947474614,-0.0128461253,-0.4346059263,-0.2804276347,0.2533690631,0.5699442625,-0.4258940816,0.230786249,-0.334975183,0.224328354,0.193610996,0.1867098957,-0.0140071502,0.1114510074,-0.4291025698,-0.0067005889,0.291716218,-0.2781499624,-0.7794819474,0.2389283627,0.0014892045,0.2033474594,0.0229181163,0.1304776371,0.1507204622,-0.043587327,0.1604697257,0.4117088914,-0.1476098746,0.0172978677,-0.3770851791,-0.3359038532,0.0895883515,0.2272990346,0.0135471635,0.0313052759,-0.0042720078,0.0734458417,0.2714964151,0.3763743341,0.0417548977,0.207447201,0.3754951358,-0.0400265343,0.0129543627,0.005521643,-0.6925061941,0.4320077598,-0.0775319412,-0.087106064,-0.2032730877,0.0011354347,-0.49914819,-0.113199681,-0.3160092235,-0.2926142514,-0.0279086549,0.1228660718,0.2305107713,0.2348573804,0.1045904905,0.6910942197,0.1962856203,-0.0885030776,-0.393810153,0.0615453273,0.0296903159,-0.1168797836,-0.0716760606,0.0280773155,0.0582787357,-0.161901325,-0.2185112536,0.1395226568,0.2593931854,0.0189049225,0.0082036965,0.0197802801,0.3181707263,0.0067134425,0.1502089798,0.0071472749,0.2437195778,-0.152905494,-0.1781631857,0.0367755331,-0.473328203,0.1957154125,-0.2445640862,0.0079243025,0.2596302629,0.0045232824,0.2119437605,0.0076272758,0.3155985475,0.1694469899,0.3344169855,-0.109226048,0.0524763614,0.2959811091,0.1642036289,0.220667541,-0.0794057846,0.1246937364,-0.151683569,-0.1268700212,0.2336960435,0.4344563484,0.6323289275,-0.035062246,0.2059133053,0.0076473556,0.2383215129,-0.2561874986,0.0199435093,0.0333449021,-0.0229340717,0.1256630719,0.2581071854,-0.0045014932,0.1622480154,-0.0430843793,0.1665069461,0.1929653585,-0.0923789516,0.2687755227,-0.1619881988,-0.1913446933,-0.0973138362,-0.1816344708,0.1139667332,-0.3931698799,-0.1319476366,0.3868918419,0.2352635413,0.0197710879,-0.0125679215,0.2920116186,0.0494219176,-0.471886009,-0.2409401685,-0.178058207,-0.2163993865,0.0519582927,0.1794566512,0.0317140371,0.2411491871,-0.1188347191,-0.1186477244,-0.1004496217,-0.1711639315,0.0169870779,-0.0109774359,0.4662133753,0.0267574321,0.2249595821,-0.1297753751,0.0577979274,-0.0152402101,-0.0280416422,-0.0870885849,-0.0635520816,-0.0419487357,0.2378916889,-0.128480196,-0.3079329431,-0.3237249851,-0.3039703369,0.1589350402,0.0231039766,0.01735349,0.4270168543,0.0706182793,0.0454905815,0.3967775106,-0.2290724069,-0.349501282,-0.378993541,0.5690661073,-0.2083933949,-0.1274505258,0.1044181138,-0.0623076111,0.4087072611,0.1532644331,-0.6130222082,-0.0492451154,-0.4274726808,0.4506584108,-0.0927331895,-0.2406839877,0.1335648596,-0.2650046647,0.1155035496,0.0462870076,-0.1447690129,-0.0226460733,0.2541779876,0.3913129568,0.0966663212,0.0932568237,0.162793681,0.578135252,0.1583214402,0.0928006098,0.3064733446,0.1476077139,0.3610422909,-0.2567945123,-0.1881389767,0.0833066329,-0.1589289606,-0.2403326929,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"Hi, it works on colab but it still doesn't work on my computer, same problem as before - overly large and long extraction process.\r\nI have to use a custom 'cache_dir' because I don't have any space left in my home directory where it is defaulted, maybe this could be the issue?","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":52,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n Hi, it works on colab but it still doesn't work on my computer, same problem as before - overly large and long extraction process.\r\nI have to use a custom 'cache_dir' because I don't have any space left in my home directory where it is defaulted, maybe this could be the issue?","embeddings":[-0.1124394983,0.2039625049,-0.0727101192,0.4566599131,0.1053852066,0.1380977482,0.2412602454,0.0399661735,0.1459222138,0.0728044286,-0.2571244538,0.1797399968,-0.0175125841,0.0835775211,-0.1670748293,0.0181838013,-0.2205189466,-0.0765432492,0.262817055,0.0581117049,-0.1664710045,0.4603987336,-0.2236673981,-0.2790875435,-0.2222800702,-0.0290817581,0.0343550332,0.0978260487,0.0028531079,-0.1748681068,0.6022046804,0.2330117822,0.1949602365,0.6118329167,-0.0001210657,-0.2649900019,0.2710088491,-0.0766073689,-0.5360262394,-0.1091710255,-0.3738023341,0.061139483,0.1372760236,-0.0116485972,0.0458871648,0.4654710591,-0.1074189246,-0.6454579234,0.104428418,0.1348217726,0.1602918208,-0.148973316,-0.3102101982,-0.089912802,0.0827999935,0.0267981123,-0.2206467539,0.4412756562,0.2664740384,0.0577256456,0.2510488033,0.2638855577,-0.0377928242,0.3813094497,0.1449037641,0.1518656909,-0.2558335364,-0.4166747332,0.4611064196,0.2921358943,0.7200582027,-0.133162111,-0.2939246595,-0.1821670234,0.0125792157,-0.2129628509,0.3156765997,0.2628422976,-0.3349860609,0.0770834088,-0.11117284,-0.1183197349,-0.1295943558,-0.0991460308,-0.2954618633,-0.2909482121,-0.1909498721,-0.0468303934,0.1284815967,0.0477972813,0.4253875613,-0.2764372826,-0.0477600619,0.1911709905,-0.5783612728,0.1121393219,0.0455096029,0.245426178,0.0799599513,0.2400475591,0.2162092477,0.1265865862,-0.283814162,-0.1221358627,0.2870098948,0.5951668024,-0.0842282921,-0.1412150711,0.2576093972,0.2235172093,-0.145800665,0.0932792574,-0.1343962103,0.0148859564,0.019345643,-0.1112164333,0.1985664368,-0.3478776813,-0.3684642017,0.0084945308,-0.0260760728,0.2039990574,-0.0027995019,0.2565117478,-0.2200749069,0.270526737,-0.2517562509,0.0077803265,-0.1173054054,-0.0626892522,-0.0948332027,-0.0068682325,-0.0089578992,0.1656866521,0.4055348635,-0.1726150811,-0.039167393,-0.2994236648,0.0978527963,-0.0339586474,-0.000242449,-0.5300260186,0.0057029636,0.2260296345,0.1368425339,0.4079750776,-0.1774229258,-0.1665692478,-0.2452620715,0.2282978892,-0.2902781367,-0.3212238848,0.0549395941,0.0653731301,-0.4587558508,-0.0608705282,-0.3371566534,0.0665167496,0.2378190458,0.1256387234,-0.0948304906,-0.0256254077,-0.3592674136,-0.2082239985,0.1178154424,0.5704817176,-0.4226481318,0.350644201,-0.3547403812,0.2162886262,0.2554175556,0.2924048305,0.0121941697,0.1714260131,-0.3983627856,0.0105082095,0.2924824357,-0.386695981,-0.8326298594,0.3234568834,-0.0628117323,0.2581585646,0.1022337005,0.0824794322,0.1330956668,-0.0432955921,0.1874770522,0.5307177305,-0.1809251904,-0.0270353146,-0.3147310615,-0.4350322187,0.130693242,0.1489083171,-0.0150489034,0.1042971686,0.0000300174,0.091236718,0.1730369031,0.2804522812,0.0889832824,0.3110314012,0.3881545365,-0.037390966,-0.0309918877,0.0304189995,-0.5355272889,0.432238847,-0.0747448355,-0.0441823192,-0.2751703858,-0.0550684109,-0.3800143301,-0.0545448512,-0.3257084787,-0.2278085053,-0.0647570565,0.1403909773,0.4081245959,0.083685495,0.0989000276,0.6743146777,0.3070165515,-0.1923600733,-0.4451985061,-0.007311421,0.0292270388,-0.0255627371,-0.0624024756,-0.1033241823,0.0079640774,-0.2448357195,-0.1993350238,0.0276979785,0.2593225837,-0.0258349907,0.0311506726,0.0664681941,0.2851371467,0.0224811938,0.3190886378,0.0880980417,0.2208914012,-0.1117255092,-0.2005067468,-0.0054202103,-0.3680719733,0.3170793056,-0.1383224577,-0.0302100535,0.3640990555,-0.0329875723,0.3663946092,-0.1630755514,0.2493323386,0.2290354222,0.3185803294,-0.1538075209,0.037300881,0.3991546035,0.1025302187,0.2353081554,-0.0605215542,0.2296677381,-0.0669093058,-0.1127091646,0.1428695619,0.4477927983,0.6377922297,-0.0017876205,0.2312705666,-0.0116687426,0.4445112944,-0.2531865835,0.1265708655,0.1394788176,-0.1144221053,0.1315434724,0.3815363944,0.1249604672,0.1835181713,-0.1551882774,0.0522776879,0.1829489022,0.0505098701,0.3114051223,-0.2933896482,-0.1412698328,0.0102272574,-0.1402571499,0.1915502697,-0.3343356848,-0.0997165963,0.2889537513,0.1851547062,-0.0220518783,-0.0802043006,0.2349568307,0.1068214029,-0.6577318311,-0.1860103756,-0.12593548,-0.1675988287,-0.0205535926,0.16597794,-0.0659078807,0.1569801271,-0.1125473902,-0.0452650748,-0.2311018854,-0.1205702573,0.1382140517,0.1124632284,0.3400293589,0.1230643764,0.1951078773,-0.0293242093,0.0919511989,-0.0122229662,-0.0432704799,-0.0923534036,0.0983579084,-0.0017712895,0.2034452111,-0.1427102983,-0.218968913,-0.3988292217,-0.3529396951,0.1966072619,0.1087290347,0.1134831756,0.5008627176,-0.0045686108,-0.0660050139,0.3374961913,-0.1798048168,-0.3153896034,-0.2892059684,0.6500527859,-0.1588740349,-0.1606778353,0.0356204323,-0.1235070154,0.3259694576,0.311514765,-0.596796453,0.0108579295,-0.5298214555,0.5150713921,-0.1468387395,-0.150575608,0.2410836965,-0.2570917308,0.0925663486,0.1877712309,-0.1138511747,-0.0390502475,0.361372143,0.324747324,0.1278100461,0.0414549448,0.1006369293,0.4964287281,0.181912452,0.1965619922,0.2644200623,0.2189560533,0.3408922255,-0.310798347,-0.1178690642,0.0881003141,-0.1094503775,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"I tried running this again - More details of the problem:\r\nCode:\r\n```\r\ndatasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir=\"\/path\/to\/cache\")\r\n```\r\n\r\nThe output:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to path\/to\/cache\/trivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                          \r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.67G\/2.67G [03:38<00:00, 12.2MB\/s]\r\n\r\n```\r\nThe process continues (no progress bar is visible).\r\nI tried `du -sh .` in `path\/to\/cache`, and the size keeps increasing, reached 35G before I killed the process.\r\n\r\nGoogle Colab with custom `cache_dir` has same issue.\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing#scrollTo=2G2O0AeNIXan","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":81,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n I tried running this again - More details of the problem:\r\nCode:\r\n```\r\ndatasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir=\"\/path\/to\/cache\")\r\n```\r\n\r\nThe output:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to path\/to\/cache\/trivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                          \r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.67G\/2.67G [03:38<00:00, 12.2MB\/s]\r\n\r\n```\r\nThe process continues (no progress bar is visible).\r\nI tried `du -sh .` in `path\/to\/cache`, and the size keeps increasing, reached 35G before I killed the process.\r\n\r\nGoogle Colab with custom `cache_dir` has same issue.\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing#scrollTo=2G2O0AeNIXan","embeddings":[-0.0191132333,0.2346220762,-0.0358678177,0.3387272656,0.1412011832,0.1956146508,0.2685050666,0.0284198001,0.1188834086,-0.0826994851,-0.2390524,0.2821522653,0.0216848385,0.0726570189,-0.0835466832,0.0234450512,-0.1319268644,-0.077235058,0.1958685517,0.1156656966,-0.1322735101,0.4592790306,-0.274974972,-0.317925185,-0.2377310246,0.0502318218,0.0789388195,0.1147859767,0.0367471986,-0.2417893857,0.6353093982,0.2854878306,0.1081413254,0.5510926843,-0.0001176656,-0.1716504246,0.3202268481,-0.0672563165,-0.6127192974,-0.2736315131,-0.4003522098,0.0468358658,0.0461337902,0.0724672452,0.0060809413,0.4613676965,-0.1097799614,-0.7131503224,0.0690336972,0.1353898197,0.1855592728,-0.1125454381,-0.2207427323,-0.0838005543,0.0829930454,-0.0740477368,-0.2689909935,0.4667278826,0.1987545192,0.0861581787,0.2101964206,0.260833323,0.0145356264,0.384326309,0.1283073723,0.0625407398,-0.2769593894,-0.3085980713,0.4427739084,0.2299291641,0.8865922093,-0.3260815144,-0.3149341345,-0.1055868566,0.0569770783,-0.1421446353,0.2633228004,0.1837041527,-0.2723793387,-0.0140484553,-0.1401576847,-0.0825646371,-0.0307545867,-0.0464234352,-0.2129700482,-0.241199702,-0.1517520696,0.0083343768,0.0067779361,0.0274224039,0.4106859267,-0.1950242072,-0.1808835566,0.2906986475,-0.6349016428,0.1571294069,-0.0411919281,0.1883413643,0.1735039949,0.1483285576,0.2045854181,0.1414367259,-0.3397989273,-0.0378533378,0.2626331449,0.531612277,-0.0408639833,-0.0834982246,0.2299902588,0.2655797899,-0.1089846715,0.0714503825,-0.0711207166,-0.047081247,0.0569608398,-0.0621099919,0.241721943,-0.3457325101,-0.4039240777,0.0383145735,-0.0017094251,0.2466624677,0.0015263157,0.2440172732,-0.275067836,0.2244942486,-0.0682820752,0.074724786,-0.1628013402,-0.0360300541,-0.0955939367,-0.1428514123,0.0032888087,0.1493494064,0.4283840954,-0.1551055759,0.1066986248,-0.156065464,0.1478950381,-0.1040507704,0.0231474452,-0.4320104122,-0.107814461,0.2410247624,0.0746769011,0.4414884746,-0.2072996646,-0.1470959932,-0.0880824551,0.2640244067,-0.293947041,-0.2577490509,-0.0445576347,0.0855401382,-0.4759041071,-0.1131555066,-0.294577986,0.1743015945,0.303640455,0.0474472009,-0.0126435179,-0.0173471384,-0.4301814437,-0.2669342756,0.2036341727,0.6221860051,-0.4207316041,0.1967634559,-0.3082355261,0.194241032,0.1443872899,0.179546684,0.0068101212,0.0972253382,-0.4354678392,-0.0398846604,0.2702302337,-0.3908962309,-0.7491314411,0.2808628082,-0.0086194472,0.2237785906,0.0303814951,0.1010896787,0.1038411781,-0.0577310324,0.1362883002,0.4570526183,-0.1250881404,-0.0197001416,-0.3529679775,-0.2770241201,0.0781979263,0.2064096481,-0.0646447986,0.1350308359,0.0128663061,0.0768369287,0.2177930772,0.3736059666,0.1321482807,0.1880967021,0.2888700962,-0.0331768543,0.0478501543,0.0071891216,-0.8005840778,0.4807349145,-0.0945226029,-0.1170418188,-0.259799242,-0.0258311182,-0.5113695264,-0.0753076971,-0.2906000018,-0.283392936,-0.0104413424,0.1654554307,0.2727052271,0.1715573221,0.0851592645,0.6717213988,0.2998747826,-0.1202849969,-0.3922044337,0.069436118,0.0767221674,-0.0590650626,-0.0433744974,0.0797144324,0.1047338471,-0.1470641196,-0.203613326,0.1653554142,0.1932532787,0.075868085,0.0367464796,0.0325158127,0.253762424,0.0919819102,0.1649685204,0.0511354506,0.2706748545,-0.1777030528,-0.1767133027,-0.0247356147,-0.3629616499,0.2979874611,-0.2117595077,0.0285086688,0.2780199051,0.0143422978,0.2297800183,-0.0664697737,0.282533139,0.1815014631,0.3434978724,-0.1430892646,0.0949134603,0.3221319616,0.1214525625,0.1996028721,-0.0438179523,0.1478548944,-0.1202018857,-0.1116029993,0.1134591326,0.4167696834,0.6022350192,-0.0363141373,0.2517687678,-0.0315059014,0.2953365445,-0.2402058989,0.0384855978,0.058603812,-0.1457096338,0.1686426252,0.3002696037,-0.0121428287,0.2025443316,-0.0837552994,0.1454544961,0.1655578762,-0.1184707209,0.2592589557,-0.2133530974,-0.1156928539,-0.0370280258,-0.203744024,0.0560552962,-0.4093008339,-0.1734046042,0.3552066982,0.246805802,-0.0204446856,-0.107994929,0.2236550301,0.0996357426,-0.5879987478,-0.299007237,-0.1832721829,-0.2583683133,0.0222613122,0.1391981989,-0.0598681718,0.2214749008,-0.0970888883,-0.0285948794,-0.2190076858,-0.1292646676,-0.0258197282,-0.0072490936,0.4299786985,0.0988122672,0.203066349,-0.0951739252,0.2066628933,0.0094749322,-0.0003330787,-0.0543339662,0.0226713307,0.0008212873,0.1979881078,-0.15129219,-0.309789598,-0.3121215999,-0.35847193,0.1870757043,0.0355167873,0.0285188444,0.4856404662,0.0236524437,-0.012974075,0.3645753562,-0.2017696798,-0.4081791639,-0.3453952968,0.5892272592,-0.2119027674,-0.1583936363,0.1197955683,-0.0461748429,0.4122824967,0.1915090084,-0.583807528,0.0116565665,-0.4313183129,0.5346263647,-0.1392471045,-0.1815200895,0.1536774337,-0.262444973,0.1151670367,0.0053054686,-0.1958691627,0.002066188,0.2577970922,0.2908815145,0.1448958218,0.1264071465,0.1457356662,0.4955901206,0.1865119487,0.0836846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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"1) You can clear the huggingface folder in your `.cache` directory to use default directory for datasets. Speed of extraction and loading of samples depends a lot on your machine's configurations too.\r\n\r\n2) I tried on colab `dataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")`. After memory usage reached around 42GB (starting from 32GB used already), the dataset was loaded in the memory. Even Your colab notebook shows \r\n![image](https:\/\/user-images.githubusercontent.com\/19718818\/102852229-c7c4e780-4443-11eb-91d6-bf21024358a3.png)\r\nwhich means it's loaded now.","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":73,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n 1) You can clear the huggingface folder in your `.cache` directory to use default directory for datasets. Speed of extraction and loading of samples depends a lot on your machine's configurations too.\r\n\r\n2) I tried on colab `dataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")`. After memory usage reached around 42GB (starting from 32GB used already), the dataset was loaded in the memory. Even Your colab notebook shows \r\n![image](https:\/\/user-images.githubusercontent.com\/19718818\/102852229-c7c4e780-4443-11eb-91d6-bf21024358a3.png)\r\nwhich means it's loaded now.","embeddings":[-0.0378204137,0.1969360858,-0.0213918537,0.3770355284,0.0714803264,0.1753475517,0.2409699857,0.0573944151,0.127870515,-0.0225050412,-0.2598694861,0.2617431879,-0.0099751623,0.1539277285,-0.0962107927,0.0181324072,-0.1629698277,-0.0272244308,0.1827304214,0.1249765903,-0.1456334442,0.4732732773,-0.3000766337,-0.3055313528,-0.2189928889,0.0209773611,0.0766963586,0.104297854,-0.0106252572,-0.2406463474,0.6824563146,0.2984650433,0.1582715958,0.5067254305,-0.000116904,-0.1950888783,0.2863745391,-0.0882358924,-0.5817727447,-0.2903612852,-0.4384790659,0.0601245612,0.0849269405,0.0675119832,-0.0535828397,0.4907841086,-0.0729141086,-0.6632893085,0.0589521229,0.1665002257,0.1875519454,-0.0474640839,-0.2352565974,-0.1112776622,0.1040510833,-0.0874529034,-0.2666222751,0.4951730669,0.2078435719,0.1018992737,0.2289200723,0.3050989807,-0.0203275681,0.3870891333,0.1716640741,0.0868210569,-0.2917646468,-0.3670197725,0.4509593248,0.2974137664,0.8650926352,-0.2715186775,-0.2730097473,-0.0695945993,0.0907623842,-0.1313936114,0.307498157,0.1710153371,-0.2946821749,-0.0152117237,-0.1419036239,-0.1120753363,-0.0633661151,-0.0124609992,-0.1693012416,-0.2273414433,-0.2012782097,-0.0119249281,0.0618782379,0.0034097272,0.4565645158,-0.2102435231,-0.1713304818,0.266010046,-0.6071332097,0.1296246797,-0.1088141128,0.2369006276,0.1592016965,0.1369526237,0.1727169454,0.0987171531,-0.3527310193,-0.0351227634,0.2121552825,0.5477255583,-0.0109770065,-0.1223005727,0.2560369372,0.3002017438,-0.1371740848,0.0925453752,-0.0757906809,-0.0648038387,-0.0123179266,-0.081881091,0.2244823426,-0.3794716001,-0.3778353333,-0.0424906127,-0.0378495008,0.2366098315,0.0364250094,0.2636562288,-0.2741851509,0.1741034091,-0.0838901401,0.0420697518,-0.1603948772,-0.0573036633,-0.0917438492,-0.1155974492,0.0423483029,0.1906615347,0.4011397064,-0.2065199465,0.1264312863,-0.1874910593,0.1777374744,-0.0363082439,0.0199493393,-0.4155361652,-0.0973082781,0.247795105,0.1213410646,0.4248805344,-0.2058926374,-0.1840034872,-0.1319413632,0.23598589,-0.3165967166,-0.2726558745,-0.0210115407,0.0798255131,-0.4653993547,-0.1361622065,-0.3558708727,0.1462778002,0.234274298,0.0311608352,-0.0463752747,-0.0202396959,-0.4285145998,-0.2600781322,0.2213883847,0.6070357561,-0.398234427,0.2053857297,-0.2950755358,0.2050085664,0.1462543756,0.2117609382,-0.0300156977,0.0446040221,-0.4265945852,-0.0395600498,0.2726883888,-0.4085456133,-0.7721036673,0.202782169,0.033973407,0.2119552642,0.0570143573,0.12341737,0.1194112375,-0.0648046583,0.1202156469,0.4723507166,-0.1216243804,0.0043084258,-0.3479326069,-0.3304363787,0.0952135995,0.1849820465,-0.057031922,0.1299722642,-0.0222084355,0.0487578362,0.170876801,0.3199008107,0.0690201372,0.1946125329,0.3535335958,-0.0481612496,0.0248623658,0.0106180422,-0.7797961831,0.4660256207,-0.083256565,-0.0681072921,-0.2493628114,-0.0339063071,-0.4600460827,-0.1263608634,-0.3008000851,-0.3012436926,-0.0207808614,0.1813722551,0.3229856789,0.1746608317,0.0593355633,0.7220692635,0.2963217497,-0.0923906565,-0.3953096271,0.0675364062,0.0664163604,-0.0736004114,-0.0174641833,0.0514175482,0.0939883366,-0.1732202768,-0.2242990881,0.1480604112,0.1868628263,0.0319901146,0.0709332675,0.0308086295,0.2856915593,0.0174240693,0.1968009472,0.0230444875,0.2305075079,-0.1218319982,-0.1542066336,0.0039639054,-0.4010272026,0.2766900957,-0.2161407024,0.0326379389,0.2775684595,0.0468800366,0.2360722125,-0.0684322044,0.2617351115,0.2082517892,0.3502916694,-0.1108467206,0.0505608879,0.3242493272,0.1505035907,0.2410300076,-0.0496642962,0.1662349999,-0.1415006369,-0.1254716367,0.1596951485,0.412473768,0.5925504565,-0.0173770096,0.2083378136,-0.0246682446,0.2961340845,-0.2416315228,0.0521872267,0.0349838287,-0.1199700162,0.0931526795,0.3018989563,0.01543862,0.1826259345,-0.083641395,0.084378846,0.1672872901,-0.0572505742,0.2478214055,-0.2375026941,-0.2205661237,-0.0380853713,-0.19256109,0.0287783667,-0.341162622,-0.1547432393,0.3447343409,0.2560048103,-0.0188446585,-0.0518012792,0.2708902061,0.0623524971,-0.5690119863,-0.278600961,-0.1567381173,-0.2715739012,0.0196659621,0.1388840377,-0.0198112577,0.2030585706,-0.1676170975,-0.0171669684,-0.2220652252,-0.1390314549,0.0090787802,0.0060735,0.4389398396,0.1061840579,0.229515478,-0.1326919794,0.1424076855,0.0056625861,-0.0073766881,-0.0655236989,-0.0197632238,-0.0305116735,0.2000202984,-0.1168845668,-0.3033442795,-0.3647994697,-0.335850507,0.1801334172,0.0498668514,0.0223348122,0.4906466901,0.0020801595,0.0206448827,0.330388248,-0.148971647,-0.3603318036,-0.4174559712,0.6014065742,-0.2016437352,-0.1822627932,0.1378254443,-0.0615997352,0.40658921,0.1509123296,-0.586907208,-0.0809577405,-0.4267997444,0.5569659472,-0.0758911967,-0.1767404228,0.1463646293,-0.2511541545,0.1215645447,0.0279136822,-0.2074700445,-0.0166781601,0.2717612386,0.3219144344,0.1806330234,0.0880651399,0.1262785047,0.5181487799,0.2077556401,0.0797469616,0.3638018966,0.1753033996,0.3964679837,-0.2793215811,-0.1735205352,0.0575630032,-0.0832296237,-0.2034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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"Facing the same issue.\r\nI am able to download datasets without `cache_dir`, however, when I specify the `cache_dir`, the process hangs indefinitely after partial download. \r\nTried for `data = load_dataset(\"cnn_dailymail\", \"3.0.0\")`","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":31,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n Facing the same issue.\r\nI am able to download datasets without `cache_dir`, however, when I specify the `cache_dir`, the process hangs indefinitely after partial download. \r\nTried for `data = load_dataset(\"cnn_dailymail\", \"3.0.0\")`","embeddings":[0.0042780945,0.2504116297,-0.0458030961,0.3203591704,0.174059093,0.2203368247,0.2745952606,0.0188670121,0.1395895034,-0.0835989192,-0.2411101013,0.2049974203,-0.0157355629,0.0229584035,-0.1130747646,0.0706264004,-0.0831784979,-0.012375203,0.214881748,0.1076202914,-0.1432205886,0.4405174851,-0.2540305853,-0.3808916807,-0.2399228662,0.0780592263,0.0524286777,0.1447728127,0.0600571446,-0.2211668193,0.6519641876,0.2406462729,0.0253143925,0.502795577,-0.0001188678,-0.1076321304,0.350839287,-0.0716238022,-0.7364218235,-0.3258126974,-0.3748835921,0.0870155171,0.109717764,0.0362841338,0.0253028739,0.408542335,-0.0688275471,-0.6611969471,0.0648548454,0.1356102377,0.1606152356,-0.1279445589,-0.3012706637,-0.0829707533,0.0818593428,-0.1440077573,-0.2933000326,0.4838713408,0.1657514572,0.0694014207,0.1291466802,0.2706472576,0.0797717944,0.4111890197,0.0540815964,0.0604026616,-0.3164115548,-0.359775126,0.4886941016,0.2121761292,0.8598021269,-0.2700321376,-0.3039605021,-0.1749399304,0.0525521673,-0.1236264482,0.290151149,0.1497558802,-0.3275518417,-0.0069505651,-0.1564629227,-0.0704139322,-0.0106756752,0.0054445537,-0.2916892171,-0.2443006337,-0.1538294554,-0.0073771616,-0.0015822977,0.0826448426,0.4729681313,-0.2602844834,-0.0807422027,0.2202804536,-0.5595542789,0.2158766687,0.000559893,0.1892478913,0.1013273299,0.1822555214,0.1477422714,0.2065310478,-0.3083066344,-0.087026976,0.2530344725,0.4764888585,-0.0430285707,-0.0697925165,0.283046782,0.2299452722,-0.0114323553,0.0710697472,-0.1208941266,0.0165324863,0.0011087955,-0.0767835081,0.1764149815,-0.2972975075,-0.3234760165,0.0074924449,0.0409277193,0.3652978241,0.0059952508,0.2722024024,-0.242434144,0.2520879805,-0.1457788795,0.013629158,-0.1482284367,0.0827375427,-0.0643170774,-0.1548316777,0.0538986698,0.0969780013,0.4505031407,-0.1375050992,0.1490903497,-0.1958867311,0.2819207311,-0.0487325191,0.0776979998,-0.4769000411,-0.0573143624,0.3119814396,0.1081625447,0.5107375979,-0.1896294802,-0.0414041132,-0.0710497648,0.2285241187,-0.1788782328,-0.263349086,0.0674281269,0.0627503768,-0.4993799925,-0.0282171965,-0.2152943164,0.1369773746,0.2798576057,0.0653764382,0.0029331285,-0.0338038728,-0.392280668,-0.2411888838,0.2537250519,0.6359077692,-0.3161544502,0.2139904648,-0.2323968112,0.1923343241,0.0928999335,0.2078724802,-0.0724156275,-0.0071817767,-0.4010176957,-0.0495647304,0.2674063146,-0.3931474686,-0.7098671794,0.3313200176,0.0407892279,0.2903725207,0.0077557415,0.0894190222,0.0465986431,-0.0027359091,0.1709143072,0.500800252,-0.0923290178,0.0558370352,-0.3301295936,-0.2972788215,0.0386329032,0.2795317769,-0.0485461131,0.1419136673,-0.1011564583,0.1120294109,0.2013295293,0.3345919847,0.1605563164,0.231125623,0.2642463148,-0.036222633,0.0119830258,-0.060543254,-0.8455967903,0.4726104736,0.0385342017,-0.1076812297,-0.297590822,0.0029712343,-0.422159344,-0.1490445584,-0.2707059979,-0.2411905378,-0.0184294619,0.1848862022,0.3131714165,0.1435331255,0.0976728126,0.6807877421,0.3323359191,-0.0728507414,-0.35404706,0.0430621877,0.1104802936,-0.0161763746,-0.0810256675,0.0414480492,0.0675494671,-0.1951831579,-0.2127973586,0.1842820942,0.1314559579,0.0822755545,0.0119864093,0.0230521932,0.2591760755,0.1992135793,0.1444220096,0.0551135689,0.2881848514,-0.1542217284,-0.1795338243,0.0172716696,-0.310582757,0.3332156539,-0.1253064424,0.0291084964,0.3228703141,-0.007037492,0.1806444973,-0.0623966157,0.3442015052,0.1316409558,0.4150342643,-0.1498939544,0.0717741624,0.2736897171,0.1089301705,0.1691252887,-0.052160766,0.169222787,-0.1906673163,-0.0682865903,0.0960837007,0.4361806512,0.5844067335,-0.0283588301,0.3492489159,-0.1001049206,0.2558563948,-0.2869028747,0.1058244258,0.1019388735,-0.1843168885,0.2019687444,0.2924491167,-0.0843551606,0.1706735343,-0.0542840846,0.221521154,0.0816672668,-0.0835195929,0.2589274347,-0.199834764,-0.1345647722,0.0375201106,-0.2513227463,0.1366075277,-0.4426152706,-0.1470975578,0.3263929188,0.2022882551,-0.026781654,-0.1754199564,0.2357199043,0.0906463861,-0.6479483247,-0.3592618704,-0.1837377101,-0.1817581654,0.0170165244,0.1409485936,-0.0831934884,0.1492794305,-0.0941857696,-0.1126950458,-0.2637678385,-0.0968256369,-0.0063732327,-0.0246003587,0.4230521023,0.1201583222,0.2072814405,-0.0478807241,0.2065472007,0.0156726111,0.0309749153,0.0067942669,-0.0120712314,0.0788661838,0.2587018013,-0.1997960657,-0.2379235774,-0.3741104901,-0.3802778125,0.2016575485,-0.0252621844,0.0028096561,0.4357881844,-0.0093410453,-0.068602711,0.3549776673,-0.2090492696,-0.3691793978,-0.3391586244,0.6691077352,-0.2349963933,-0.1425837725,0.0567806512,-0.0644834638,0.2819382548,0.2827350199,-0.5972637534,-0.0077203391,-0.5064714551,0.5216751099,-0.1362667531,-0.2705992162,0.1317769438,-0.2185649872,0.0977876112,0.0000025939,-0.1688382477,-0.0128682489,0.2656877637,0.2363733947,0.1391280144,0.0743553862,0.1826934665,0.571531117,0.1059387997,0.0792105943,0.2879718542,0.22063905,0.4312893152,-0.299883455,-0.0697754622,0.0152765382,-0.1323608756,-0.14771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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"Hi @ashutoshml,\r\nI tried this and it worked for me:\r\n`data = load_dataset(\"cnn_dailymail\", \"3.0.0\", cache_dir=\".\/dummy\")`\r\n\r\nI'm using datasets==1.8.0. It took around 3-4 mins for dataset to unpack and start loading examples.","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":31,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n Hi @ashutoshml,\r\nI tried this and it worked for me:\r\n`data = load_dataset(\"cnn_dailymail\", \"3.0.0\", cache_dir=\".\/dummy\")`\r\n\r\nI'm using datasets==1.8.0. It took around 3-4 mins for dataset to unpack and start loading examples.","embeddings":[-0.0161708575,0.2297847122,-0.0322397873,0.3434302211,0.1108229533,0.2220182866,0.3055529296,0.0372812152,0.10845723,-0.0921418965,-0.2221646756,0.2408726811,-0.0385525823,0.1162008494,-0.1133522615,0.0309860595,-0.1359189302,-0.0631945282,0.2353850603,0.1180652082,-0.1302544326,0.4372630119,-0.319630444,-0.3554959893,-0.2387880683,0.0237787813,0.0636806637,0.0957122222,0.0047329515,-0.2345732152,0.6385351419,0.2791397274,0.1181462184,0.5157641172,-0.0001169941,-0.215261206,0.3333494067,-0.0684954599,-0.6077932715,-0.2789219916,-0.3930041492,0.0457532331,0.0335161351,0.0610926859,-0.0509885475,0.4967774451,-0.1270866692,-0.68250072,0.0614344291,0.1803266108,0.1823161244,-0.0502500981,-0.171939522,-0.0725494921,0.0638539717,-0.0905586258,-0.268150121,0.4946600199,0.1551376581,0.0975516513,0.2529839873,0.331369698,0.0248196889,0.4136205614,0.1185360849,0.0480060801,-0.2777655423,-0.3346879184,0.4164537489,0.2303195447,0.8650121689,-0.2543489635,-0.3269252479,-0.0918510035,0.0841074139,-0.1494254023,0.2977922261,0.1900098473,-0.3102540672,-0.016460225,-0.1508080512,-0.0905476511,-0.0704411194,0.007946115,-0.2361735106,-0.2268900275,-0.1544542313,0.0180781335,0.019396238,-0.0038164356,0.456076026,-0.2130610645,-0.1223457232,0.3066767156,-0.65750283,0.1063818857,-0.0774628073,0.217916131,0.1605318338,0.1575927585,0.1439424455,0.1163089126,-0.3457915783,-0.0678958967,0.2134515345,0.5641543865,-0.0443470739,-0.0618443526,0.2127415836,0.2808054388,-0.0879347697,0.0965339541,-0.0884197056,-0.0386992842,0.0230213664,-0.056780152,0.2750973701,-0.354660511,-0.3671809733,0.0226325095,-0.0311452299,0.2224421203,0.0147286085,0.2650328875,-0.3251701891,0.2021937072,-0.0550872535,0.0201080665,-0.1727339774,-0.0391677469,-0.0853923783,-0.110216707,0.0527203865,0.1796500236,0.4412157536,-0.1889049858,0.0968768895,-0.205994308,0.1617501974,-0.0732570812,0.0391553342,-0.4348950386,-0.1178796068,0.225325644,0.0690706298,0.4374957085,-0.225779146,-0.1089572459,-0.1188760772,0.2386876792,-0.2894028127,-0.2850323915,-0.0544328168,0.0862685591,-0.4735613763,-0.1438089013,-0.2476587296,0.1591524482,0.23349154,0.0608700961,-0.0520559698,0.0331652872,-0.4323675931,-0.2124741673,0.2255028188,0.6246807575,-0.4017665386,0.2280920893,-0.2959651947,0.2312543094,0.1291996092,0.1825995743,-0.0365856327,0.0507646762,-0.4023651481,-0.066472508,0.2884465754,-0.3448900878,-0.7637765408,0.2402676046,-0.0072369417,0.2049037218,0.0244494379,0.1251177788,0.0759882927,-0.0541138202,0.1253979355,0.514295578,-0.1276306808,0.0129121635,-0.3770835102,-0.3079400361,0.1181101054,0.2464576662,-0.0579772703,0.1241133362,-0.039152056,0.0778740719,0.1895922422,0.3612080812,0.1256436855,0.2161608934,0.3133903444,-0.0379740372,0.056541618,0.0117612993,-0.8077424169,0.4555340707,-0.027855536,-0.0420433581,-0.2352332771,-0.0102761416,-0.5106800795,-0.1290507764,-0.2872656882,-0.2603490055,-0.0208017975,0.1453473717,0.340796113,0.1737203747,0.0690539032,0.6878806949,0.3122401834,-0.1150005162,-0.4065687358,0.0771846324,0.0412027799,-0.0303805787,-0.0158531889,0.0304849856,0.1075851917,-0.1671617627,-0.2501544356,0.1136160642,0.1644426733,0.0490662605,0.0501779951,0.013094632,0.2476552874,0.0753425658,0.2009117603,0.0724595711,0.2859911323,-0.1650985926,-0.2105876356,0.002755848,-0.3375557065,0.2851551771,-0.2091512531,0.0070916349,0.2657751441,0.0062512872,0.2497428805,-0.0618720166,0.294426322,0.1491237879,0.3727253079,-0.1142761484,0.0861810446,0.3334768713,0.1487441063,0.1839720011,-0.0698969737,0.1951130182,-0.1243645325,-0.1425950825,0.1091438308,0.4017100036,0.5841244459,-0.0246835425,0.2626039684,-0.0332573317,0.2991403937,-0.2309496254,0.0736985207,0.0413556881,-0.1135909557,0.1331713796,0.2872654498,-0.015570784,0.1911846995,-0.1201843098,0.1731726527,0.2000922263,-0.0830413252,0.2018013746,-0.1967657208,-0.1593468338,-0.0198100135,-0.2326493114,0.0836899877,-0.3867376149,-0.1762474328,0.3189530075,0.2630099356,0.0070880526,-0.1054377481,0.2337634414,0.1556800455,-0.5326211452,-0.3184814155,-0.1940667778,-0.2282195836,0.0227809176,0.1533803791,-0.0598246381,0.2215584666,-0.1137655601,-0.0823396742,-0.2419231832,-0.152606979,-0.0322496705,0.0191484652,0.4340485036,0.0852257311,0.2672777176,-0.0650587976,0.2055649906,0.0122110434,0.0226376634,-0.0593248978,0.0130007574,-0.0156129673,0.217313841,-0.0895089135,-0.3338562548,-0.3593615294,-0.3331201077,0.1772400141,0.0283102542,0.0318437628,0.4842742383,0.0203257129,-0.0261697695,0.3580590189,-0.1635661125,-0.3964845836,-0.3687490225,0.6486810446,-0.2061018348,-0.151373595,0.1052231714,-0.0556890629,0.3705451787,0.1249329001,-0.585927546,-0.0531539619,-0.4565603733,0.4831457138,-0.1305647343,-0.2152395695,0.1574800164,-0.2882137597,0.1117240041,0.0590288602,-0.1880036443,-0.0513585582,0.251760602,0.28122437,0.1736371666,0.164906323,0.1558852047,0.4896213114,0.2271452844,0.0765749663,0.3385466337,0.1747327149,0.3446930051,-0.26328969,-0.1565910876,0.038693402,-0.0647285581,-0.1784442365,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1615","title":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir`","comments":"Ok. I waited for 20-30 mins, and it still is stuck.\r\nI am using datasets==1.8.0.\r\n\r\nIs there anyway to check what is happening? like a` --verbose` flag?\r\n\r\n![Screenshot 2021-06-25 at 6 37 43 PM](https:\/\/user-images.githubusercontent.com\/2375919\/123429653-cdfb7280-d5e4-11eb-9fa7-ff295800cc86.png)\r\n","body":"Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n","comment_length":34,"text":"Bug: Can't download TriviaQA with `load_dataset` - custom `cache_dir` \n Hello,\r\nI'm having issue downloading TriviaQA dataset with `load_dataset`.\r\n\r\n## Environment info\r\n- `datasets` version: 1.1.3\r\n- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1\r\n- Python version: 3.7.3\r\n\r\n## The code I'm running:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset(\"trivia_qa\", \"rc\", cache_dir = \".\/datasets\")\r\n```\r\n\r\n## The output:\r\n1. Download begins:\r\n```\r\nDownloading and preparing dataset trivia_qa\/rc (download: 2.48 GiB, generated: 14.92 GiB, post-processed: Unknown size, total: 17.40 GiB) to \/cs\/labs\/gabis\/sapirweissbuch\/tr\r\nivia_qa\/rc\/1.1.0\/e734e28133f4d9a353af322aa52b9f266f6f27cbf2f072690a1694e577546b0d...                                                                                         \r\nDownloading:  17%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589                                                                                                   | 446M\/2.67G [00:37<04:45, 7.77MB\/s]\r\n```\r\n2. 100% is reached\r\n3. It got stuck here for about an hour, and added additional 30G of data to \".\/datasets\" directory. I killed the process eventually.\r\n\r\nA similar issue can be observed in Google Colab:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/1nn1Lw02GhfGFylzbS2j6yksGjPo7kkN-?usp=sharing\r\n\r\n## Expected behaviour:\r\nThe dataset \"TriviaQA\" should be successfully downloaded.\r\n \n Ok. I waited for 20-30 mins, and it still is stuck.\r\nI am using datasets==1.8.0.\r\n\r\nIs there anyway to check what is happening? like a` --verbose` flag?\r\n\r\n![Screenshot 2021-06-25 at 6 37 43 PM](https:\/\/user-images.githubusercontent.com\/2375919\/123429653-cdfb7280-d5e4-11eb-9fa7-ff295800cc86.png)\r\n","embeddings":[-0.0052458039,0.1308883131,-0.0646162108,0.3441570997,0.0718581229,0.210291639,0.17244111,0.1310920417,0.1391692013,-0.0710647553,-0.1699336171,0.231231302,-0.0192254949,0.0066203936,-0.1691107601,0.2085393071,-0.1640118062,-0.0868696421,0.2699991167,0.0834092572,-0.038244091,0.4004627764,-0.2224357128,-0.2988501787,-0.1695676297,-0.0254348591,0.0705305934,0.0405973941,0.0147904633,-0.3218882978,0.6169593334,0.3028764427,0.009877827,0.521084547,-0.0001201023,-0.1916544735,0.4338795245,0.0159098525,-0.7354334593,-0.2126133889,-0.411706686,0.0140906833,0.0877757818,0.0253632683,0.0474618189,0.4686435461,-0.0080202678,-0.680444181,0.0924841166,0.0909667015,0.1437713504,-0.0238936823,-0.2291480601,-0.0375164934,0.0284584425,0.0096831163,-0.2325122207,0.4369439483,0.2176711112,0.0913062915,0.2389549613,0.252281189,-0.0492004231,0.3928609788,-0.0071388446,0.0374720767,-0.1860148758,-0.3192746043,0.4707184732,0.3660711348,0.8030222058,-0.1876770109,-0.3021990061,-0.1832547039,0.1051823273,-0.0451491773,0.413316071,0.1583818048,-0.336879909,-0.0022909432,-0.1966359764,-0.0813699439,0.0054737031,-0.0495399833,-0.2693914771,-0.261174351,-0.1570450515,-0.0208111741,0.0786024854,0.0757898912,0.331874609,-0.2209260613,-0.1263227463,0.3372460604,-0.6835750341,0.1361633986,0.072355479,0.2225887775,0.1222133413,0.1436494887,0.2464012951,0.1499664932,-0.3367521167,-0.0158412661,0.2920416594,0.4770641625,0.0216687676,-0.1471895725,0.2946625352,0.2768577933,-0.0165046696,0.1851952672,-0.1194929481,-0.1138872355,0.1638812125,-0.1195146367,0.3472663462,-0.420897752,-0.4582388997,0.0693760514,-0.1012671068,0.2368401289,0.0996522456,0.2810336053,-0.3059815466,0.1489553154,-0.0852394775,-0.0406089798,-0.176011309,0.0588091128,-0.0030846773,-0.1624728441,0.0150796827,0.1091506332,0.4115763903,-0.2335549593,-0.0193372164,-0.1226827353,0.1655345261,-0.1065096632,0.040022023,-0.4161472917,-0.0327929854,0.3291257918,0.0912063345,0.4472205341,-0.1924474239,-0.1869480759,-0.1487813592,0.1830757111,-0.1687164009,-0.1786273271,-0.0225004498,0.0721458644,-0.5353237391,-0.1041093096,-0.2382825613,0.1389842033,0.1975210309,0.093503885,0.0546241254,-0.0165530462,-0.3146316707,-0.2055378258,0.2628719211,0.7534264922,-0.4006408155,0.2732146978,-0.3736734986,0.197200492,0.0929380357,0.2091330439,0.0395619236,0.1090814918,-0.4702808559,-0.0468017869,0.2117950022,-0.3955647647,-0.5958545804,0.3525334001,-0.0282600336,0.2908311486,0.0996687785,0.0646447912,-0.0339887924,-0.0744108632,0.1817499697,0.3799595833,-0.0818123296,-0.030382704,-0.3953439593,-0.2925280333,0.0339508913,0.2108902037,-0.095669277,-0.0097001679,-0.0184888877,0.0432756208,0.1957745552,0.384322077,0.038434092,0.1697260439,0.3557586074,0.0017097529,-0.0794348642,-0.0463985428,-0.8227305412,0.4676510394,0.0079893582,-0.1329741031,-0.286468029,-0.1050078943,-0.5263269544,0.0180345941,-0.3432105482,-0.1726970226,-0.0191411693,0.2503947318,0.3605091274,0.255169481,0.0629131943,0.6656355262,0.2315622419,-0.1129195243,-0.3988396227,0.0818557218,0.176239863,-0.0596291497,0.0037791485,0.0058503971,0.0960794389,-0.2225623876,-0.1633274108,0.1464251727,0.1269235909,0.1480647177,0.0653381199,0.0636171326,0.2154255956,0.1169756204,0.0703701675,0.0574338362,0.2074352652,-0.1087787524,-0.2665083706,-0.0270735361,-0.3863425255,0.315282613,-0.1466709822,-0.034661144,0.2687528729,-0.0348880775,0.1267798692,-0.0633670911,0.4413505495,0.2584002614,0.3805348575,-0.053692665,0.040112108,0.2626843154,0.2711616158,0.118067421,-0.012823306,0.1971620172,-0.005204225,-0.0706369504,0.177028209,0.3518933654,0.4828983545,-0.0416873321,0.2759874761,-0.0799083486,0.1331025064,-0.1560465991,0.0334436744,0.1082303226,-0.1268747151,0.1741719693,0.309795171,-0.0264489166,0.0924227014,-0.1649825573,0.1287864298,0.1788528413,-0.1485517621,0.198604241,-0.2390971035,-0.0132886302,-0.0422030911,-0.2888913751,0.1038679853,-0.493181169,-0.1106578112,0.2955605984,0.283478111,0.0019748365,-0.0721168146,0.2240708172,0.0732660294,-0.5757681131,-0.4541899264,-0.1719468981,-0.2614944279,-0.0203753747,0.19481574,-0.0408428162,0.250739783,-0.0865891799,-0.10126701,-0.200471431,-0.2505178452,0.0091868388,0.0438208655,0.481872797,0.0933851972,0.289922148,0.007487243,0.2269599438,-0.0196238793,0.0244032089,-0.0774511546,-0.0027823437,0.0303157717,0.2225931287,-0.1063723639,-0.2903220952,-0.3319783509,-0.4047456682,0.2436876744,-0.0214492511,0.0139278583,0.4712770879,0.0456215478,0.0825299621,0.2933071554,-0.143687427,-0.3843383491,-0.3096329272,0.6168893576,-0.1876232028,-0.0910336152,0.0997093245,-0.0327564068,0.2840221226,0.1509723514,-0.6188516021,0.0646927357,-0.6073617935,0.5238555074,-0.1325420737,-0.2632633746,0.213538304,-0.1811937243,0.1117006019,0.0977270678,-0.1261819601,-0.0444909185,0.2625546753,0.3519196212,0.0681914166,0.1534047872,0.183074683,0.494686693,0.1345431656,0.022358112,0.2706026435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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1611","title":"shuffle with torch generator ","comments":"Is there a way one can convert the two generator? not sure overall what alternatives I could have to shuffle the datasets with a torch generator, thanks ","body":"Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq ","comment_length":27,"text":"shuffle with torch generator  \n Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq  \n Is there a way one can convert the two generator? not sure overall what alternatives I could have to shuffle the datasets with a torch generator, thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1611","title":"shuffle with torch generator ","comments":"@lhoestq  let me please expalin in more details, maybe you could help me suggesting an alternative to solve the issue for now, I have multiple large datasets using huggingface library, then I need to define a distributed sampler on top of it, for this I need to shard the datasets and give each shard to each core, but before sharding I need to shuffle the dataset, if you are familiar with distributed sampler in pytorch, this needs to be done based on seed+epoch generator to make it consistent across the cores they do it through defining a torch generator, I was wondering if you could tell me how I can shuffle the data for now, I am unfortunately blocked by this and have a limited time left, and I greatly appreciate your help on this. thanks ","body":"Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq ","comment_length":136,"text":"shuffle with torch generator  \n Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq  \n @lhoestq  let me please expalin in more details, maybe you could help me suggesting an alternative to solve the issue for now, I have multiple large datasets using huggingface library, then I need to define a distributed sampler on top of it, for this I need to shard the datasets and give each shard to each core, but before sharding I need to shuffle the dataset, if you are familiar with distributed sampler in pytorch, this needs to be done based on seed+epoch generator to make it consistent across the cores they do it through defining a torch generator, I was wondering if you could tell me how I can shuffle the data for now, I am unfortunately blocked by this and have a limited time left, and I greatly appreciate your help on this. thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1611","title":"shuffle with torch generator ","comments":"@lhoestq  Is there a way I could shuffle the datasets from this library with a custom defined shuffle function? thanks for your help on this. ","body":"Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq ","comment_length":25,"text":"shuffle with torch generator  \n Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq  \n @lhoestq  Is there a way I could shuffle the datasets from this library with a custom defined shuffle function? thanks for your help on this. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1611","title":"shuffle with torch generator ","comments":"Right now the shuffle method only accepts the `seed` (optional int) or `generator` (optional `np.random.Generator`) parameters.\r\n\r\nHere is a suggestion to shuffle the data using your own shuffle method using `select`.\r\n`select` can be used to re-order the dataset samples or simply pick a few ones if you want.\r\nIt's what is used under the hood when you call `dataset.shuffle`.\r\n\r\nTo use `select` you must have the list of re-ordered indices of your samples.\r\n\r\nLet's say you have a `shuffle` methods that you want to use. Then you can first build your shuffled list of indices:\r\n```python\r\nshuffled_indices = shuffle(range(len(dataset)))\r\n```\r\n\r\nThen you can shuffle your dataset using the shuffled indices with \r\n```python\r\nshuffled_dataset = dataset.select(shuffled_indices)\r\n```\r\n\r\nHope that helps","body":"Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq ","comment_length":120,"text":"shuffle with torch generator  \n Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq  \n Right now the shuffle method only accepts the `seed` (optional int) or `generator` (optional `np.random.Generator`) parameters.\r\n\r\nHere is a suggestion to shuffle the data using your own shuffle method using `select`.\r\n`select` can be used to re-order the dataset samples or simply pick a few ones if you want.\r\nIt's what is used under the hood when you call `dataset.shuffle`.\r\n\r\nTo use `select` you must have the list of re-ordered indices of your samples.\r\n\r\nLet's say you have a `shuffle` methods that you want to use. Then you can first build your shuffled list of indices:\r\n```python\r\nshuffled_indices = shuffle(range(len(dataset)))\r\n```\r\n\r\nThen you can shuffle your dataset using the shuffled indices with \r\n```python\r\nshuffled_dataset = dataset.select(shuffled_indices)\r\n```\r\n\r\nHope that helps","embeddings":[-0.2320701778,-0.1594077945,-0.0132597713,0.249286592,0.1917155385,0.016128635,0.086092934,0.1256412417,-0.0738925636,0.5062565804,-0.0565603077,0.6657477021,-0.3163603842,-0.3614477515,-0.1684427559,-0.2553729117,0.0437459536,-0.1630821377,-0.2135148495,0.032560911,-0.2393629104,-0.2892496884,-0.2646623254,-0.005998516,-0.119662039,0.0604129359,-0.0957779735,0.0697435588,-0.2313453108,-0.034571778,-0.1720061898,0.5989302397,0.1985050887,0.4059646726,-0.0001133756,-0.1537085474,0.2525965571,-0.1660947502,-0.2985817492,-0.2896046937,0.3812659681,0.308853507,-0.4335247278,0.0859259665,-0.2295082361,-0.3008211851,0.1807852536,-0.3355771005,-0.018414218,0.0614014305,0.1672649235,0.0012047766,0.2648460567,-0.0670845434,0.519961834,0.3149352074,0.093722187,-0.0753147975,0.5529654026,0.1821962595,-0.1812990904,-0.0414466672,-0.0807733834,0.176660493,0.3287064135,-0.2766603827,-0.5377693772,-0.1196920723,0.1335413605,0.4067071974,0.2531914413,-0.1997767985,-0.2101837546,-0.3013878465,-0.0292875133,-0.0357886665,0.1097449958,0.2107415795,-0.3999198973,-0.1496413797,-0.2572130561,-0.0833966285,0.1118390039,-0.1043697894,0.2718878984,0.6229640841,0.0889731348,0.140069589,0.4378804564,-0.1311396956,0.81343472,-0.0863590613,-0.0097718071,0.2210773677,-0.2670795321,0.0049428302,-0.0320762694,0.3473730683,0.377025038,-0.0491665564,0.1465638578,0.1273645312,0.042808637,0.0636321902,0.1232161373,-0.1424030364,-0.1858207136,0.3928990662,0.2217474431,-0.1759120822,0.1167247891,-0.0850033313,-0.1812994629,-0.5007681847,-0.1929596961,0.2007648051,-0.4478093982,0.0526513308,0.0036981783,-0.4550374448,0.0964477584,-0.2533772886,0.2330251038,0.2721402943,-0.068879813,0.1646943837,-0.028656682,0.0458342843,0.0906960294,-0.2706475258,-0.0352261774,-0.1291973442,-0.1892329454,0.0456854962,0.0670174509,-0.2744113207,0.1627708077,0.083790347,0.2174981833,0.4782420099,-0.0006481794,-0.0075603076,0.1372971684,-0.1494775414,0.2428740412,0.0184350647,-0.1708400697,0.3659123778,-0.1772079319,0.4577911794,-0.295635134,-0.2572662532,0.0998008177,0.1069459841,-0.1186270118,0.0358435139,-0.2069556117,0.0063397097,0.0167940035,-0.3003845215,-0.0010501485,-0.218586117,-0.0986286253,-0.238034457,0.05922959,-0.056848567,0.0386081189,0.0080923988,-0.1043094099,0.1638102382,0.3211078644,0.1710610986,-0.1124110669,0.1007732973,-0.2374942154,-0.521967411,0.3435090184,0.047422938,-0.2410604656,-0.2898803651,0.0686896443,-0.047338672,0.2372686714,0.238971889,0.0942459255,-0.2064083368,0.4309085608,0.4226894677,-0.2441977412,0.1156652942,-0.2087157071,0.0345320404,0.3860126436,0.5716748238,0.0606420599,-0.0480869561,-0.0235483851,-0.1555312872,0.1788862944,-0.0860240087,-0.0381914563,0.0053408854,-0.232880339,0.2788631618,-0.0127549833,-0.0245501604,0.0993406177,0.0880826935,0.1925429702,-0.0531476997,0.1646049172,-0.0536928214,0.2090454847,-0.318884939,-0.1554498076,-0.1669819504,0.0440988317,0.1306563318,0.3118703961,-0.2316394448,-0.0347861759,-0.0988282785,-0.1887603551,-0.144592911,-0.6924346685,0.0650368854,-0.1370727122,-0.2032377869,0.1183274239,0.108606644,0.0162565354,0.0743982419,-0.0976934433,0.2368776798,-0.0716199726,-0.2531092763,-0.0759782195,-0.0043030628,-0.0251436271,-0.0400895551,0.1345903277,0.1320821196,0.1572430879,-0.0739689916,0.0745791867,0.5301996469,-0.716584444,0.0497625172,0.0402512364,-0.1974244267,-0.0040386356,0.2728999555,-0.0175354853,0.4752105176,0.5115851164,0.2074907571,0.1819639653,-0.0543831028,-0.5250878334,-0.11700131,0.2466132939,0.0588052645,-0.0901359543,-0.0052354415,-0.2030215859,0.1712268889,-0.0845850706,-0.0675661564,0.3254946768,0.2940306664,0.0934721157,-0.042188257,0.3154894412,-0.1702660918,-0.0833766237,-0.1547855437,-0.3174166679,0.1140315831,0.190901354,-0.1367492974,-0.0972053558,-0.1878259629,0.1252653897,0.324699074,-0.0931765214,-0.001537644,-0.1604825407,-0.0584675893,-0.2479928583,-0.2115260661,-0.191160053,-0.0813388899,0.1489317566,0.5911383629,-0.0183113813,0.1634088308,0.6033519506,0.093340449,0.1049074903,-0.0179258101,-0.135517329,-0.2551341355,-0.0939079821,0.1050561219,0.1692867279,-0.1076337695,0.3447979987,-0.1817201525,-0.183020696,-0.1967984587,0.2760646641,-0.0145381819,-0.0120235104,-0.113359943,0.1915734708,-0.2135903388,0.130221799,-0.2264458537,0.2502763867,-0.5216255784,0.1627506167,0.2632404566,-0.0664846972,0.0604910627,0.0069141998,-0.1209042743,-0.2186222076,-0.4105161428,-0.0653211474,0.0126492381,0.173568204,-0.1661871225,0.2061451226,-0.3581282496,0.0035157301,-0.2108099163,-0.1508387625,-0.8749733567,0.518968761,-0.2251122594,-0.1516726315,-0.2170840651,-0.0929623693,0.0123957107,0.7811891437,0.320733577,0.3386201859,-0.0524125248,0.1517129689,-0.1009292752,0.5109634995,0.2728753686,-0.050905861,0.1122866794,0.0777172372,0.0903858095,-0.0663493872,-0.0504682288,0.5741662383,0.4247804284,0.22136347,-0.0017520887,0.8899260759,-0.2905632854,-0.26383847,0.042600777,-0.0307404492,0.2523863316,-0.1464753896,-0.1405351311,0.3846739233,0.1928543895,-0.262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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1611","title":"shuffle with torch generator ","comments":"thank you @lhoestq  thank you very much for responding to my question, this greatly helped me and remove the blocking for continuing my work, thanks. ","body":"Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq ","comment_length":25,"text":"shuffle with torch generator  \n Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq  \n thank you @lhoestq  thank you very much for responding to my question, this greatly helped me and remove the blocking for continuing my work, thanks. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1611","title":"shuffle with torch generator ","comments":"@lhoestq  could you confirm the method proposed does not bring the whole data into memory? thanks ","body":"Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq ","comment_length":16,"text":"shuffle with torch generator  \n Hi\r\nI need to shuffle mutliple large datasets with `generator = torch.Generator()` for a distributed sampler which needs to make sure datasets are consistent across different cores, for this, this is really necessary for me to use  torch generator, based on documentation this generator is not supported with datasets, I really need to make shuffle work with this generator and I was wondering what I can do about this issue, thanks for your help \r\n\r\n@lhoestq  \n @lhoestq  could you confirm the method proposed does not bring the whole data into memory? thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1610","title":"shuffle does not accept seed ","comments":"Hi Thomas\r\nthanks for reponse, yes, I did checked it, but this does not work for me please see \r\n\r\n```\r\n(internship) rkarimi@italix17:\/idiap\/user\/rkarimi\/dev$ python \r\nPython 3.7.9 (default, Aug 31 2020, 12:42:55) \r\n[GCC 7.3.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import datasets \r\n2020-12-20 01:48:50.766004: W tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2020-12-20 01:48:50.766029: I tensorflow\/stream_executor\/cuda\/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n>>> data = datasets.load_dataset(\"scitail\", \"snli_format\")\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nReusing dataset scitail (\/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/scitail\/snli_format\/1.1.0\/fd8ccdfc3134ce86eb4ef10ba7f21ee2a125c946e26bb1dd3625fe74f48d3b90)\r\n>>> data.shuffle(seed=2)\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nTypeError: shuffle() got an unexpected keyword argument 'seed'\r\n\r\n```\r\n\r\ndatasets version\r\n`datasets                  1.1.2                     <pip>\r\n`\r\n","body":"Hi\r\nI need to shuffle the dataset, but this needs to be based on epoch+seed to be consistent across the cores, when I pass seed to shuffle, this does not accept seed, could you assist me with this? thanks  @lhoestq\r\n ","comment_length":134,"text":"shuffle does not accept seed  \n Hi\r\nI need to shuffle the dataset, but this needs to be based on epoch+seed to be consistent across the cores, when I pass seed to shuffle, this does not accept seed, could you assist me with this? thanks  @lhoestq\r\n  \n Hi Thomas\r\nthanks for reponse, yes, I did checked it, but this does not work for me please see \r\n\r\n```\r\n(internship) rkarimi@italix17:\/idiap\/user\/rkarimi\/dev$ python \r\nPython 3.7.9 (default, Aug 31 2020, 12:42:55) \r\n[GCC 7.3.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import datasets \r\n2020-12-20 01:48:50.766004: W tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2020-12-20 01:48:50.766029: I tensorflow\/stream_executor\/cuda\/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n>>> data = datasets.load_dataset(\"scitail\", \"snli_format\")\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nReusing dataset scitail (\/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/scitail\/snli_format\/1.1.0\/fd8ccdfc3134ce86eb4ef10ba7f21ee2a125c946e26bb1dd3625fe74f48d3b90)\r\n>>> data.shuffle(seed=2)\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nTypeError: shuffle() got an unexpected keyword argument 'seed'\r\n\r\n```\r\n\r\ndatasets version\r\n`datasets                  1.1.2                     <pip>\r\n`\r\n","embeddings":[-0.3611408174,-0.2253305167,-0.0523433983,0.1002608612,0.2563570142,-0.1110154986,0.1722029597,0.0852967724,-0.0057133436,0.3612514436,0.144108519,0.3828558326,-0.2792423368,0.4236404002,-0.317094028,0.1824320108,0.1758906841,0.0246543158,0.0966961905,-0.2187662274,-0.2138340175,-0.1767721623,-0.3849220574,-0.392226547,-0.0966786593,0.0997234434,-0.0631705746,-0.0549211204,-0.2333814502,0.0829104856,0.0291087646,0.4695254266,0.2254402041,0.543418169,-0.0001184209,0.031107679,0.1028720811,-0.1222021952,-0.1623677164,-0.2037837803,-0.4092410505,0.7001438737,-0.2987084985,-0.1451266557,-0.4335159957,0.0217807833,-0.0585794561,-0.3505462706,0.2419661433,0.0192649774,0.0743444338,-0.0877100602,0.1874300539,0.045032803,0.3148187399,0.1693404168,-0.0267277192,0.1806753725,0.1170011461,0.141771391,0.1314733326,-0.0136718452,0.0271484889,0.2115646452,0.4446219504,-0.2895041108,0.0597491972,-0.3515516818,-0.022754604,0.2394785285,0.6474811435,-0.1044927686,-0.3328814507,0.027288055,-0.1217833981,-0.7533959746,-0.1434610784,0.2448544502,-0.2657306492,-0.0243245978,0.0012280716,0.0514400005,-0.1524673551,0.06739714,0.1754340529,0.6841983795,0.0115244631,0.1180583239,0.4326361716,-0.0639571697,0.3201989532,0.032385245,-0.057937406,0.1822149307,-0.3543589115,-0.189820081,-0.3696711063,0.1263028532,0.1526526362,0.1987504214,0.0989194363,0.2543503344,-0.0179729294,0.2990306616,-0.0273408722,0.1894387454,0.1938605756,0.1103519574,-0.0203140359,-0.0033378631,0.0162443109,0.0666703433,-0.1197569966,-0.1333681792,-0.2709949315,0.065532282,-0.1789503247,-0.1597332656,-0.1167848408,-0.4980922341,-0.3183217049,-0.383685261,0.4276821613,0.3747380078,-0.1726719737,0.1342258751,0.0090161609,0.2096813023,0.0860612616,-0.2013346702,-0.267860502,0.0493237115,-0.2658182085,0.2053087056,0.0714615434,-0.2550654411,0.2359534204,0.2998144329,0.180451721,-0.0260684006,-0.0820793882,0.0033828055,-0.0434186012,0.0275081135,-0.0385255069,0.0666897446,0.1341362596,-0.0235903002,0.0407774411,0.4544708729,-0.5137068629,-0.0959360525,0.1449231654,0.080949977,-0.031147819,-0.1751510203,-0.5291618109,0.0693709999,0.0980649665,0.0767359361,-0.0567197688,-0.1430936456,-0.2210722417,-0.2535941601,0.4845531285,-0.2019007206,-0.1611191332,-0.0025037813,0.1482612342,0.2340067476,0.3288626075,0.4299624562,-0.0824116766,-0.2007815838,0.0891031995,-0.0327073224,0.2788090706,-0.111896567,-0.2866583467,-0.371278882,0.0071844622,-0.2699796557,-0.0005191008,0.2127477378,0.0968917906,-0.3004891276,0.2491336912,0.4734845161,-0.0651877597,0.1062624678,-0.2993537784,-0.0737838373,0.0894062892,0.5021672845,-0.1099241674,0.0022002284,0.0911847427,-0.1518818885,0.4024082124,-0.0993448198,-0.0192164183,0.0537746586,0.4163772762,0.1382247359,0.0518619418,0.050076507,0.0253946539,0.0496197604,0.4589367807,0.0498924553,0.3490516245,0.0063804165,-0.2450549603,-0.2303178012,-0.2283510715,0.0235861186,-0.0238635521,0.0090243444,0.1661869437,-0.0394502543,-0.1085365787,0.0519927889,-0.5093948841,0.0178662818,-0.3958610892,0.1027719229,-0.0856780186,-0.3175879717,0.0648345202,0.0358296186,0.1814566553,0.0059541785,-0.0573778749,0.3696957231,0.1335140914,-0.2687822282,-0.3451565504,0.2437442392,0.0348926447,0.0060798237,0.0194668155,-0.363627255,0.1999331564,-0.2298474908,0.2633122802,0.3140081465,-0.2565154135,-0.114737764,0.0411611907,-0.2615285218,0.2098861635,0.0645635426,-0.1613117605,0.1803571582,0.3249889314,0.0004540306,0.0708319843,-0.0369634517,-0.3684694767,-0.0874771997,0.2278015167,0.0122382622,-0.1194051877,-0.1054250896,-0.1213169619,0.106050767,0.1194289252,0.2121357769,0.2475169301,0.1334799826,0.0652236938,-0.0148238074,0.1242563277,-0.3287729323,-0.1415364444,-0.1527764797,0.3554347157,-0.1534022093,0.1107402891,-0.0885211974,-0.1478292793,-0.2959602773,0.2944551706,0.4305765033,-0.1968322843,-0.1009759232,-0.3533166647,-0.0418738499,-0.1448265016,-0.3894093931,-0.0849636048,0.0953142941,-0.0746296123,0.7804245353,0.2613789737,0.1790030599,0.4629921913,0.2231993973,-0.0111896936,0.008618732,-0.1971976906,-0.2141981274,0.1154349595,0.094418481,0.309376359,0.1184532344,0.2321745753,-0.0511785261,-0.2368722111,0.1078239232,-0.2652058005,-0.1191261634,-0.0676183403,0.1537304074,-0.5521233082,0.2170722187,-0.2828325033,-0.4466594756,0.3089562058,-0.1998510212,-0.1039057001,0.0575454906,0.0287181959,-0.2272478938,-0.2291841209,-0.1263979375,-0.1747358739,-0.3220356107,0.1404100657,0.1458332539,-0.0682677403,0.438867718,0.278580755,-0.382552892,0.0910977125,-0.2483300716,-0.3010384142,-0.5564925075,0.3111891747,-0.1494143158,-0.1545503885,-0.0209704097,-0.1006934345,0.2367001176,0.7145299315,0.0014578539,0.2914760411,-0.0760957226,-0.148301065,-0.0383760892,0.0375671387,0.5418398976,-0.2448954731,0.2498478144,0.1140450686,-0.1856481433,0.1597103328,0.2866020799,0.4009343386,0.2772111595,0.6139820218,0.1146798804,0.8872416019,-0.323028028,-0.3530137241,0.2038476914,0.0597041175,0.0461004451,-0.1533634067,-0.004342542,0.4281462431,0.4057964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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1610","title":"shuffle does not accept seed ","comments":"Thanks for reporting ! \r\n\r\nIndeed it looks like an issue with `suffle` on `DatasetDict`. We're going to fix that.\r\nIn the meantime you can shuffle each split (train, validation, test) separately:\r\n```python\r\nshuffled_train_dataset = data[\"train\"].shuffle(seed=42)\r\n```\r\n","body":"Hi\r\nI need to shuffle the dataset, but this needs to be based on epoch+seed to be consistent across the cores, when I pass seed to shuffle, this does not accept seed, could you assist me with this? thanks  @lhoestq\r\n ","comment_length":36,"text":"shuffle does not accept seed  \n Hi\r\nI need to shuffle the dataset, but this needs to be based on epoch+seed to be consistent across the cores, when I pass seed to shuffle, this does not accept seed, could you assist me with this? thanks  @lhoestq\r\n  \n Thanks for reporting ! \r\n\r\nIndeed it looks like an issue with `suffle` on `DatasetDict`. We're going to fix that.\r\nIn the meantime you can shuffle each split (train, validation, test) separately:\r\n```python\r\nshuffled_train_dataset = data[\"train\"].shuffle(seed=42)\r\n```\r\n","embeddings":[-0.1347804815,-0.2200724334,-0.0721425191,0.0236785691,0.2914151549,0.0666157305,0.1913048029,0.1325144321,-0.1194050908,0.3138949275,0.134742856,0.3167942166,-0.3220253885,0.3731395304,-0.2299949974,0.0033440839,0.1122753695,-0.0262981746,0.1395083368,-0.0770237893,-0.2660176158,-0.1038577631,-0.4424047172,-0.3410610557,-0.2854548097,0.0956010371,-0.1741223484,0.1295313388,-0.2702615559,-0.0985725001,-0.0670805499,0.3628925979,-0.009106623,0.5489265919,-0.0001074182,-0.0270830337,-0.0419341661,-0.1488695443,-0.1034156308,0.0203134511,-0.3263738453,0.5454821587,-0.4160846472,-0.1584909707,-0.4424951673,-0.037389271,-0.1593659818,-0.069139488,0.3702379167,0.110146746,0.2067461908,0.0331087261,0.0294005517,-0.0273064841,0.2497342974,0.0464598499,0.0437573493,0.0516144149,0.0109907668,0.0839249492,-0.0199910924,-0.0127108265,0.2320576608,0.2207108587,0.2102143914,-0.2231942415,0.0342869908,-0.253064394,-0.0665040687,0.1607753336,0.2795592844,-0.1354098469,-0.3697174489,-0.1066208705,-0.0143135777,-0.6579858661,-0.0505812541,0.3136821985,-0.0247074813,0.0323528349,-0.0712482333,0.1344084442,-0.0298695043,-0.0848142356,0.0765891373,0.699716568,0.0725433081,0.0611053966,0.423551172,0.0133899152,0.1375877261,0.0112705929,-0.0726978183,0.1279779822,-0.3236722052,-0.2280355841,-0.2975527644,0.2766510844,0.1149020791,0.1078327671,-0.0889401585,0.2085072398,0.0864677727,0.1739783436,0.1651337892,0.2276163697,0.0355324335,0.3068234026,-0.0154891489,0.0969886109,-0.1259662658,0.1423597634,0.0188584886,-0.2953653634,-0.0575607941,0.1596961915,-0.2405930758,-0.1067030132,-0.2917694747,-0.3292226195,-0.2995627224,-0.2655608356,0.2093190402,0.3032734394,-0.0809738189,0.0591247566,-0.0797601491,0.1136507764,-0.0517724566,-0.2500488758,-0.43158108,0.0078702839,-0.3319699466,0.1614773124,0.0961486921,-0.2158621848,0.3259159029,0.3171091974,0.0427013934,-0.1045705527,-0.0938387662,-0.0080860965,0.0616393201,0.0213751812,0.0275105126,-0.0125426231,0.146946013,-0.0121893724,0.10842143,0.2525421679,-0.3640213013,-0.2342913747,0.4208706021,0.2410050035,-0.1537894607,-0.0020534673,-0.3313791156,0.0717510432,0.1327168792,0.1020409539,-0.1301130503,-0.1859665215,-0.2507869601,-0.1024178416,0.4589520097,-0.1903232634,-0.0930913761,-0.0459462591,0.1666830033,0.0458181128,0.2529376745,0.5030997396,-0.0493666828,-0.0394233875,-0.0561052486,0.0591851585,0.1244835407,0.0668547079,-0.2905207872,-0.2616107166,-0.1367256492,-0.1420549899,-0.096235238,0.0116049051,0.1021196693,-0.2010557652,0.3246784508,0.3828471303,-0.2970031798,0.1307126433,-0.4009949863,0.0544542857,0.1685847044,0.4805800915,0.0144611215,-0.0717338324,-0.1493746787,-0.1211038083,0.4762872159,0.0101417443,-0.029344473,0.0018905971,0.2415610105,0.1914938241,0.1795387268,-0.0784265473,0.2410700917,0.1030149981,0.3657622933,0.0524096079,0.3203825057,-0.1304528713,-0.3920786679,-0.2696313262,-0.3058220148,-0.1071614623,0.1618437171,-0.170145154,0.2359348238,-0.1054814756,-0.0968539566,-0.135586217,-0.2334574312,0.0112656048,-0.3087714911,0.0612226799,-0.135071665,-0.292391181,0.0529397465,0.1395294517,0.102069214,0.0419107638,-0.1187908277,0.409160018,0.3247660398,-0.4278866053,-0.3233824372,0.0829123929,-0.0699155778,0.1035030857,-0.1071636528,-0.0716485009,0.1234953627,-0.2072857916,0.1270310432,0.4635509551,-0.3934854865,-0.1097532064,0.0302342214,-0.1540511549,0.2201206684,0.058988221,-0.2735131383,0.0595620312,0.333858788,-0.2367123961,0.1785191596,-0.0354736522,-0.2713895142,-0.1414781213,0.3098217845,-0.0125956545,-0.1193342432,-0.2523328364,-0.1314130127,0.1176525056,-0.0989534184,0.291110158,0.2756367326,0.2380652279,-0.0021677529,0.0973027647,-0.023164466,-0.2529582083,-0.2076808959,-0.125868991,0.2291233391,0.0015448589,0.0062876199,-0.0961295441,-0.1727616638,-0.0996162519,0.2327681184,0.3319702148,-0.2466630042,-0.1438867301,-0.3990576267,0.1052869111,-0.3117464185,-0.274704963,-0.1441822797,-0.1001659483,0.180894807,0.4816630781,-0.0424368717,0.2302291691,0.2112178504,0.0923637897,0.021320127,0.2123742253,-0.0577039495,-0.2570916414,0.0187981017,0.1791387647,0.2132530212,0.1310414821,0.3282648325,-0.09263096,-0.2903418541,0.1300692111,-0.151954487,-0.1913879663,-0.181830138,0.1780179441,-0.330005765,0.1186374649,-0.1865497977,-0.3050548732,0.3582506776,-0.2353467494,-0.1327146739,0.0911906138,0.1785441935,-0.175164476,-0.1757030934,-0.2797611654,-0.1637222916,-0.2218892872,0.1413851082,-0.126843065,-0.126042828,0.1422865391,0.0657179356,-0.4844720066,-0.0367817581,-0.3342334628,-0.2686417401,-0.4810955822,0.1757566631,-0.1975926012,-0.1893901229,0.0030248165,-0.0851678997,0.0662170053,0.4775412977,0.0966284424,0.2267088592,-0.1116108,-0.1041982174,-0.1159769818,0.0072134668,0.4805997908,-0.1754934341,0.1418778747,0.0142328534,-0.2586209774,0.2727104425,0.2904882729,0.3311959207,-0.0986543447,0.4549436271,0.1734048277,0.9153655171,-0.234760657,-0.0158350207,0.074536182,0.0564704128,-0.0724040046,-0.1511095315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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1609","title":"Not able to use 'jigsaw_toxicity_pred' dataset","comments":"Hi @jassimran,\r\nThe `jigsaw_toxicity_pred` dataset has not been released yet, it will be available with version 2 of `datasets`, coming soon.\r\nYou can still access it by installing the master (unreleased) version of datasets directly :\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`\r\nPlease let me know if this helps","body":" When trying to use jigsaw_toxicity_pred dataset, like this in a [colab](https:\/\/colab.research.google.com\/drive\/1LwO2A5M2X5dvhkAFYE4D2CUT3WUdWnkn?usp=sharing):\r\n```\r\nfrom datasets import list_datasets, list_metrics, load_dataset, load_metric\r\n\r\nds = load_dataset(\"jigsaw_toxicity_pred\")\r\n```\r\n \r\nI see below error:\r\n\r\n> FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    280                 raise FileNotFoundError(\r\n    281                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 282                         combined_path, github_file_path, file_path\r\n    283                     )\r\n    284                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py","comment_length":46,"text":"Not able to use 'jigsaw_toxicity_pred' dataset \n  When trying to use jigsaw_toxicity_pred dataset, like this in a [colab](https:\/\/colab.research.google.com\/drive\/1LwO2A5M2X5dvhkAFYE4D2CUT3WUdWnkn?usp=sharing):\r\n```\r\nfrom datasets import list_datasets, list_metrics, load_dataset, load_metric\r\n\r\nds = load_dataset(\"jigsaw_toxicity_pred\")\r\n```\r\n \r\nI see below error:\r\n\r\n> FileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nFileNotFoundError                         Traceback (most recent call last)\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    280                 raise FileNotFoundError(\r\n    281                     \"Couldn't find file locally at {}, or remotely at {} or {}\".format(\r\n--> 282                         combined_path, github_file_path, file_path\r\n    283                     )\r\n    284                 )\r\n\r\nFileNotFoundError: Couldn't find file locally at jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/jigsaw_toxicity_pred\/jigsaw_toxicity_pred.py \n Hi @jassimran,\r\nThe `jigsaw_toxicity_pred` dataset has not been released yet, it will be available with version 2 of `datasets`, coming soon.\r\nYou can still access it by installing the master (unreleased) version of datasets directly :\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`\r\nPlease let me know if this helps","embeddings":[-0.1765043288,-0.07363718,-0.0608391762,0.2410436869,0.3765500188,0.2648481727,0.1960246563,0.0077844937,-0.1372358799,0.1830359697,-0.2765671909,0.3002050519,-0.0954562724,0.0872111022,-0.1608526856,0.1532540321,0.0479022898,0.2332412452,-0.1677392572,0.2624707222,-0.1808016002,0.1098075956,-0.2976607084,0.1541490108,-0.4638727307,-0.1733397245,0.0785701647,0.4033997655,-0.1453522146,-0.3795202076,0.5730249286,-0.0468719266,0.1658125073,0.7571409941,-0.0001131657,0.3079266548,0.403950721,-0.07764256,-0.3698799014,-0.5823313594,-0.5913619995,-0.1218554154,0.1567143202,-0.1497648954,-0.3388918042,-0.0401686765,0.1544191539,-0.3331000805,0.3245493472,0.3865208328,0.2066322863,0.3872398436,0.1222632974,-0.3384933174,-0.0258551668,0.2666294873,-0.1972867548,0.6044800878,0.3443886936,-0.043817237,0.2820273042,0.0967959613,-0.0518094562,0.1000471711,0.0944557488,0.0319421031,-0.14020437,-0.3953693509,0.3337980807,0.4867743552,0.2882891893,-0.3954355717,-0.2368726581,-0.1654328406,-0.0092283944,-0.2622863352,-0.0164637957,0.0009507873,0.0711009502,0.1785696,-0.02291191,0.1373260766,-0.1632054299,0.2048930526,0.2333356142,0.3115946352,-0.2750920057,0.0986382738,0.1140073687,-0.4757558703,0.1821375787,0.053063944,-0.055582568,0.0987114236,-0.3599222004,0.1672172993,-0.1756782979,0.2006390989,0.2447078824,-0.4047555923,0.0635226369,-0.154194504,-0.0503103919,0.0883153006,0.1189346239,0.0482748933,0.144855082,0.4108527601,0.2634599507,0.5451515913,-0.2810816169,-0.1524787694,-0.1094568223,-0.2906466126,-0.0365780257,-0.2321513444,0.468872577,-0.3515856564,-0.4451787174,-0.3080203533,-0.1883712411,0.1652941406,0.3322097659,0.4939045906,0.1570606083,-0.1572115421,-0.0750433728,0.3428778648,-0.0558868386,0.1293441951,-0.2999328971,0.2289604694,-0.2291098982,0.0647563487,0.1287426651,-0.4026706815,0.4692715704,-0.1230796278,0.1983292699,-0.0562339425,-0.1611506492,-0.1195021868,-0.1380105764,0.248663187,0.0260710046,0.0224840399,0.0836298689,-0.0628136694,-0.0675119013,0.0591872223,-0.3778591454,-0.4255342782,0.0368446857,0.1483312547,-0.4374786913,-0.1637428552,0.0553643554,-0.1182392314,0.1565728635,-0.1337582022,-0.1645391881,-0.0758990571,-0.4998377264,-0.2826627493,0.1935234666,0.4830069244,-0.2996079922,0.042469319,-0.3080744743,-0.0867858678,0.1806526482,0.1393012106,-0.3239135444,0.0703095943,-0.2412335426,-0.0015097894,0.3545083404,-0.5732682943,-0.2533680201,0.1079819202,0.0063852407,0.0841249228,0.1747066528,-0.3414479792,-0.2044880688,-0.1492318958,0.1607513875,0.0345812254,-0.0251700934,-0.1039276347,0.0745938495,-0.300321579,-0.0428007878,0.2549883425,-0.0014975984,0.178963989,0.337190181,-0.0854072347,0.1417766362,-0.1695163995,-0.0791049078,0.1477522999,0.004492451,0.0200523064,-0.0354111455,-0.0347735882,-0.2954501212,0.2822983265,-0.1452663541,-0.0098495539,-0.3215234876,-0.061694432,-0.5691983104,0.0971442834,-0.2326286733,-0.0552996621,0.1148718297,0.0202192422,0.0741456002,0.2099332213,-0.0664540157,0.303404808,-0.1201207116,0.0736225247,-0.3704358041,0.1775779873,-0.1285678893,-0.3357838392,0.1636983454,0.2939181924,0.2214094847,-0.1230493411,-0.0515593998,0.2560582161,0.007353568,0.0841370597,0.2337305546,0.2524306178,0.1431983262,-0.1125041172,0.2018995136,-0.0870922133,0.3635498285,-0.0120474827,0.1510169804,0.401984036,-0.2108748257,0.2795546353,0.0922547653,-0.0128316488,0.2691123188,-0.0919803232,0.0392131172,-0.3684935868,0.1300258487,-0.0170000475,0.1427501142,0.118689537,-0.3156521916,0.0353442915,0.1817990243,0.0676587746,0.026217714,0.005977266,-0.1093193814,0.0335148275,0.2066028118,0.076037623,0.5343503952,0.2244418263,0.0346319638,0.073210299,0.032673452,-0.3638688922,0.0351182409,0.1861788481,0.4019531012,0.1055076569,0.179616183,-0.1125291809,0.0497633927,-0.0733935162,0.0133888135,0.3247944713,-0.2611782849,0.4062215984,-0.6884021759,-0.849063158,0.0187445264,-0.1318324208,-0.0035038979,-0.1434836835,-0.2425691336,-0.0528350025,0.0637289807,0.2590059638,0.0430169031,0.0015332315,-0.1681162566,-0.2226403505,-0.0675634071,-0.2243787944,-0.2410758287,0.1178557202,0.2741014063,0.0301817339,0.3007182777,-0.2623437047,-0.0789944902,-0.1063176543,-0.0491989404,0.1852882355,0.0297127925,0.5172261,0.2643103302,0.0573489554,-0.232159853,-0.3317301571,0.3848645389,-0.230599001,0.0403684676,-0.0265582688,-0.4671980143,-0.0780436993,0.0726623535,0.0134151634,-0.3836221397,-0.3464674652,-0.2247263789,0.1520386636,-0.0058445763,0.4043499231,0.1393468082,0.3607227504,-0.0079633249,0.1871718466,-0.1175756231,-0.7591186166,0.3459428549,-0.1522512734,-0.1781123281,0.2786597908,-0.1888881624,0.3604364097,0.0063017262,-0.2204868048,-0.3749679625,-0.0712205693,0.0595394559,0.2680722177,0.1431832612,-0.0766409934,-0.2826042175,0.0295414124,-0.2402922511,-0.3323746324,0.1549118608,-0.0324948691,0.4485782683,0.2557002902,0.2735060155,0.0265375283,0.5264333487,0.2063240707,-0.310410291,0.560850203,-0.2335514724,0.5399410129,-0.2854922712,-0.3986561596,-0.0295694638,0.2363219857,0.11102235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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1600","title":"AttributeError: 'DatasetDict' object has no attribute 'train_test_split'","comments":"Hi @david-waterworth!\r\n\r\nAs indicated in the error message, `load_dataset(\"csv\")` returns a `DatasetDict` object, which is mapping of `str` to `Dataset` objects. I believe in this case the behavior is to return a `train` split with all the data.\r\n`train_test_split` is a method of the `Dataset` object, so you will need to do something like this:\r\n```python\r\ndataset_dict = load_dataset(`'csv', data_files='data.txt')\r\ndataset = dataset_dict['split name, eg train']\r\ndataset.train_test_split(test_size=0.1)\r\n```\r\n\r\nPlease let me know if this helps. \ud83d\ude42 ","body":"The following code fails with \"'DatasetDict' object has no attribute 'train_test_split'\" - am I doing something wrong?\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='data.txt')\r\ndataset = dataset.train_test_split(test_size=0.1)\r\n```\r\n\r\n> AttributeError: 'DatasetDict' object has no attribute 'train_test_split'","comment_length":76,"text":"AttributeError: 'DatasetDict' object has no attribute 'train_test_split' \n The following code fails with \"'DatasetDict' object has no attribute 'train_test_split'\" - am I doing something wrong?\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='data.txt')\r\ndataset = dataset.train_test_split(test_size=0.1)\r\n```\r\n\r\n> AttributeError: 'DatasetDict' object has no attribute 'train_test_split' \n Hi @david-waterworth!\r\n\r\nAs indicated in the error message, `load_dataset(\"csv\")` returns a `DatasetDict` object, which is mapping of `str` to `Dataset` objects. I believe in this case the behavior is to return a `train` split with all the data.\r\n`train_test_split` is a method of the `Dataset` object, so you will need to do something like this:\r\n```python\r\ndataset_dict = load_dataset(`'csv', data_files='data.txt')\r\ndataset = dataset_dict['split name, eg train']\r\ndataset.train_test_split(test_size=0.1)\r\n```\r\n\r\nPlease let me know if this helps. \ud83d\ude42 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1600","title":"AttributeError: 'DatasetDict' object has no attribute 'train_test_split'","comments":"Thanks, that's working - the same issue also tripped me up with training. \r\n\r\nI also agree https:\/\/github.com\/huggingface\/datasets\/issues\/767 would be a useful addition. ","body":"The following code fails with \"'DatasetDict' object has no attribute 'train_test_split'\" - am I doing something wrong?\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='data.txt')\r\ndataset = dataset.train_test_split(test_size=0.1)\r\n```\r\n\r\n> AttributeError: 'DatasetDict' object has no attribute 'train_test_split'","comment_length":22,"text":"AttributeError: 'DatasetDict' object has no attribute 'train_test_split' \n The following code fails with \"'DatasetDict' object has no attribute 'train_test_split'\" - am I doing something wrong?\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='data.txt')\r\ndataset = dataset.train_test_split(test_size=0.1)\r\n```\r\n\r\n> AttributeError: 'DatasetDict' object has no attribute 'train_test_split' \n Thanks, that's working - the same issue also tripped me up with training. \r\n\r\nI also agree https:\/\/github.com\/huggingface\/datasets\/issues\/767 would be a useful addition. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1600","title":"AttributeError: 'DatasetDict' object has no attribute 'train_test_split'","comments":"> ```python\r\n> dataset_dict = load_dataset(`'csv', data_files='data.txt')\r\n> dataset = dataset_dict['split name, eg train']\r\n> dataset.train_test_split(test_size=0.1)\r\n> ```\r\n\r\nI am getting error like\r\nKeyError: 'split name, eg train'\r\nCould you please tell me how to solve this?","body":"The following code fails with \"'DatasetDict' object has no attribute 'train_test_split'\" - am I doing something wrong?\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='data.txt')\r\ndataset = dataset.train_test_split(test_size=0.1)\r\n```\r\n\r\n> AttributeError: 'DatasetDict' object has no attribute 'train_test_split'","comment_length":37,"text":"AttributeError: 'DatasetDict' object has no attribute 'train_test_split' \n The following code fails with \"'DatasetDict' object has no attribute 'train_test_split'\" - am I doing something wrong?\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='data.txt')\r\ndataset = dataset.train_test_split(test_size=0.1)\r\n```\r\n\r\n> AttributeError: 'DatasetDict' object has no attribute 'train_test_split' \n > ```python\r\n> dataset_dict = load_dataset(`'csv', data_files='data.txt')\r\n> dataset = dataset_dict['split name, eg train']\r\n> dataset.train_test_split(test_size=0.1)\r\n> ```\r\n\r\nI am getting error like\r\nKeyError: 'split name, eg train'\r\nCould you please tell me how to solve this?","embeddings":[-0.1205269769,-0.2044595033,-0.1178737432,0.3427510262,0.2748533487,0.2128635794,0.417621702,0.2791910172,0.3849134147,0.1766941845,0.2599661648,0.2442348003,-0.3558097184,0.4015853405,-0.1800408959,-0.3566969633,0.067672275,0.2050315142,0.2042681277,-0.0092861699,-0.2286288738,0.1024326384,-0.3420259058,0.0578026101,-0.3522693217,-0.0670242831,-0.3373372853,0.0235142987,-0.0220198687,-0.3306672275,0.3673844337,-0.1288262606,0.0630214587,0.4926139414,-0.0001131172,0.0144109484,0.0952198729,-0.0939696506,-0.3082743287,-0.2538853586,-0.4034256935,-0.202546224,0.1784944981,-0.5090008974,-0.102444768,-0.0878219306,-0.1878918558,-0.3301984966,0.4390422702,0.4318709075,0.2212600708,-0.0175378602,0.1199533343,-0.1634230316,0.0466196314,0.1382140666,0.0279129241,0.1376452893,-0.1803663224,-0.0400673151,0.3375974894,0.0766585469,-0.0616211817,0.3274958134,0.2132835984,0.1494387984,-0.1793500632,-0.3218154609,-0.0956685841,0.1868734211,0.5323076844,-0.2772233486,-0.4459287822,-0.1072070524,0.1357379705,-0.4340752065,-0.0066019399,0.0991728157,0.103386566,0.0961878225,0.0116508557,-0.2029306293,-0.1861786246,0.0810089484,-0.355663836,0.019007111,-0.1421079338,0.1209091917,0.0314170085,-0.017612258,-0.0384897254,-0.1144160181,0.0046987622,0.0224142,-0.2375529706,-0.2133391798,-0.2970763743,-0.2553686202,-0.019230146,0.0633097738,0.0608688407,-0.2469666153,0.1673274487,0.2162225097,0.3056078255,0.2904831469,0.2199985683,0.5732904673,0.0975123495,-0.09575665,-0.3294288218,-0.2312819809,-0.1072048396,-0.28599298,0.2528921068,0.276391089,0.4462272823,-0.0980492383,-0.3808518648,0.0926919058,-0.2468934953,-0.0697228089,-0.011388368,0.3455750048,-0.0857682526,0.1073075607,-0.0358099639,0.0881662741,-0.1349001974,-0.2533355951,-0.2457509339,0.4074082971,-0.0367090926,-0.0669158176,0.0501543581,-0.0713475645,0.183567673,-0.2005105764,0.0712040439,-0.2747604549,-0.089260377,-0.3202910721,-0.1533060521,0.2038680315,0.0455179252,0.1852240264,0.2966268659,-0.3811807632,-0.101030305,0.102444075,-0.4939915836,-0.2768363059,-0.0385558307,0.2153871953,-0.1205421016,-0.1385895908,-0.3266150355,-0.0319312476,0.1544391662,-0.0379985981,-0.0203255415,-0.2732340991,-0.144125089,-0.3367162049,0.2728694975,0.442786485,-0.4894809723,-0.1112404764,-0.0402708277,-0.1182367355,0.1037686318,0.0835876912,-0.0242834799,0.4687138796,-0.1057143733,0.0184721779,0.3932589293,-0.3447049856,-0.3340929747,0.2828659117,-0.0146962339,-0.0924784318,0.1201412156,-0.0028033424,0.2520149648,0.0232443549,0.0854704529,0.2322946787,-0.1012347639,-0.0957083106,0.0600435659,-0.059937641,0.4805958569,0.2306594998,0.1884990036,0.0880542323,-0.0794905797,0.0212223791,0.0340896733,-0.0553408042,-0.0318096951,0.1547145545,0.3268216252,0.0971263945,0.0214630086,-0.3522357047,-0.2091632932,0.1463080943,0.4027765393,-0.1277881414,-0.1587322205,-0.0898962542,-0.3355984688,0.0228168685,-0.4687238038,-0.2829129398,0.1041628271,-0.2288238406,0.0968894362,0.0673707202,-0.3220150769,0.3304789662,-0.1484997272,0.2350497693,-0.0644603446,0.3642419279,-0.2054459155,-0.0867250413,-0.1168430969,0.1835987419,0.1139998958,-0.0121547868,-0.1291647702,0.412443608,0.2695295215,-0.265586704,0.1130643785,-0.150993228,0.1742063761,-0.2123697698,-0.1195155904,0.1874687821,0.2570874989,-0.075355716,0.0458243713,0.4258392155,-0.0667375922,0.3461900353,-0.0190855768,0.2911891043,0.0023090953,-0.1240794659,0.037286561,-0.0840156674,0.1021381319,-0.4604661763,0.2323112488,-0.2532217801,-0.0918207392,-0.2488137037,0.4830524623,0.0224960204,0.1657025814,-0.0495697223,-0.2577195168,0.3827397823,-0.1473414004,0.4524562955,0.488855511,0.1363449991,-0.3093397021,0.0952892229,0.0685566887,0.1056234762,0.194423005,0.0804859027,0.2588699758,0.085517548,-0.3087680936,0.0358704738,-0.027666593,-0.5531837344,-0.0048054676,0.3986990452,-0.3466000855,0.0871452391,-0.141471684,-0.0603525266,-0.1292672753,-0.2361915112,0.0036394603,-0.3927210569,-0.1627770215,-0.1968063116,0.0466178469,-0.0247741044,-0.2414561212,-0.2356132716,0.1663375497,-0.2057171762,0.2398035675,-0.3715010285,-0.0335458443,0.1028845385,0.0890767947,0.0590357147,0.1915899068,-0.1959491819,0.0938040093,0.1138881072,0.0716952235,0.0083815558,-0.2420751154,0.1781362742,0.2741517723,0.3571283817,-0.0458537564,-0.3544014692,0.3528320789,-0.0043780128,-0.0839072242,0.1576317996,0.260635078,-0.1917724758,-0.0259466432,-0.5728279948,-0.4099226296,-0.2271383256,0.0313870013,0.0239049792,0.1485964954,0.1456320733,0.3056466281,0.1615990549,0.2220598161,0.107079722,0.0387223028,-0.1193662509,0.3027025461,-0.2321112007,-0.2960785627,0.1044882387,-0.0693777278,0.1490538567,-0.1352611929,-0.3425974548,-0.08992102,-0.1535870433,0.1381921172,-0.1096958742,0.0668885857,0.1864421219,0.1027308181,-0.0582427457,-0.0311725661,-0.0990062281,0.0555013791,-0.012225708,0.0967250541,-0.1267628968,0.3843730092,-0.1644410491,0.5157351494,0.1411608458,-0.1086243689,0.1097046956,-0.3059410751,0.0143279238,-0.0158294626,-0.6153257489,-0.0605167411,-0.0618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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1594","title":"connection error ","comments":"This happen quite often when they are too many concurrent requests to github.\r\n\r\ni can understand it\u2019s a bit cumbersome to handle on the user side. Maybe we should try a few times in the lib (eg with timeout) before failing, what do you think @lhoestq ?","body":"Hi\r\nI am hitting to this error, thanks \r\n\r\n```\r\n> Traceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 379, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 208, in main\r\n    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO\r\n  File \"finetune_t5_trainer.py\", line 207, in <dictcomp>\r\n    for task in data_args.eval_tasks}\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 70, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 66, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split, script_version=\"master\")\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 487, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/boolq\/boolq.py\r\nel\/0 I1217 01:11:33.898849 354161 main shadow.py:210 Current job status: FINISHED\r\n```","comment_length":47,"text":"connection error  \n Hi\r\nI am hitting to this error, thanks \r\n\r\n```\r\n> Traceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 379, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 208, in main\r\n    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO\r\n  File \"finetune_t5_trainer.py\", line 207, in <dictcomp>\r\n    for task in data_args.eval_tasks}\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 70, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 66, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split, script_version=\"master\")\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 487, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/boolq\/boolq.py\r\nel\/0 I1217 01:11:33.898849 354161 main shadow.py:210 Current job status: FINISHED\r\n``` \n This happen quite often when they are too many concurrent requests to github.\r\n\r\ni can understand it\u2019s a bit cumbersome to handle on the user side. Maybe we should try a few times in the lib (eg with timeout) before failing, what do you think @lhoestq ?","embeddings":[-0.3293083012,-0.1345852762,-0.2016925067,0.3231747746,0.5001865029,-0.1886005998,0.1602738351,0.2997075617,-0.3055787683,0.1561734229,0.012656942,-0.0056440663,0.2251010835,0.1642649472,-0.0455740355,-0.0660552904,-0.2141919881,0.0516924225,-0.1668691933,0.1017805934,-0.0759902298,0.0589256845,-0.135654822,0.1066663116,-0.0014342469,-0.0925262198,0.0482695475,0.0914029479,-0.1770411283,-0.3981225789,0.3847735524,-0.0475312918,-0.0878206268,0.2440319657,-0.0001026009,0.2239751518,0.1840706468,0.0613851659,-0.2191357613,-0.165497303,-0.1825285554,-0.1764973998,0.3466186523,-0.2045548558,-0.323461026,0.156537205,0.0194423646,0.0245129764,0.6095243096,0.5391385555,0.3651290834,0.1239325255,0.2508780658,-0.1263591349,0.0121593978,-0.4649489522,-0.0548876636,0.2500353456,0.2293919772,-0.1382567436,0.0677758455,0.0419034436,0.0411453992,-0.0182585083,0.0719028115,-0.1040173098,0.1631603092,-0.3608938754,0.2469529659,0.1152732894,0.5638700128,0.0516682491,-0.1345327944,0.2436355948,0.0145922545,-0.2167304009,0.2574052811,0.0059991763,-0.2366169691,0.1825777292,0.1746409535,-0.1708101779,-0.5097798705,0.229768768,-0.093413502,0.2104021162,-0.0483462103,0.0594062284,0.0371313393,-0.2034060955,0.3239794672,0.2822365165,0.0639957413,0.095603019,-0.4873182774,-0.0258345623,-0.0517093278,-0.1743869781,-0.0484157056,0.1437137574,0.2108507156,0.0826344863,0.1433905512,0.0551506393,0.0877403691,0.1378038824,-0.1206299141,0.2517746687,0.2280283868,0.3546999991,-0.1444411874,-0.0109692346,-0.1517023295,-0.524538219,0.0227191225,0.3155859113,0.3285774887,-0.0800363272,-0.2814172208,0.1608621478,-0.073479332,0.0374105051,-0.0147579722,0.5862597227,-0.4222016037,0.1364524961,-0.0385238677,0.0206818804,-0.1698478311,0.0816259533,-0.2103554457,0.128688857,-0.1109872386,-0.2046239078,0.1267408729,-0.0979281366,0.1036819816,-0.1254515946,0.0796652734,-0.1540387422,0.2098276913,-0.3071586192,0.0403931066,0.2144149095,-0.0093317134,-0.0397677943,0.256238997,0.0868284404,0.1426610798,-0.0583050475,-0.3986396194,-0.4699076712,-0.0246357843,0.3337431848,0.1888823807,-0.1018986925,0.242503643,-0.239442572,0.1015270948,-0.1859617233,-0.1586317867,-0.3791605234,-0.1898315698,-0.2785962224,0.3494767845,0.2411410213,-0.2269233316,-0.0555184782,-0.0063581206,-0.2299630344,-0.0841925368,0.1094194576,-0.1654981077,0.1853645891,-0.2126497775,-0.0556343347,0.6239518523,-0.6403186321,-0.5884609222,0.451880306,-0.2488524467,-0.2172074914,0.0026223192,0.0128350044,0.31572631,0.1871753633,0.2534630299,0.2531638145,-0.0963229612,-0.0373811014,-0.1279286742,-0.2783178389,0.210431397,0.2529270649,0.338386029,-0.0542290099,0.1496915519,-0.2224408686,0.2122184187,0.1233668029,0.055118911,0.2695868015,0.2858492136,0.1266420037,-0.0151618924,0.2609181404,-0.2238556445,0.1011382937,-0.0823274553,0.1316879541,-0.4020649791,0.2130479068,-0.3046886325,-0.070966728,-0.4243715107,-0.1110660285,0.3290539384,0.0198725909,0.1009851322,0.0605011731,-0.0466322526,0.2943363786,-0.151177302,0.1532868445,-0.0333023034,0.2780037224,-0.3268547356,-0.0552035421,0.0937715843,0.0030752169,0.3442077935,-0.1848721802,-0.1886288971,0.2643786371,-0.159370333,0.1300932914,0.2049147338,0.0657288283,0.11827223,-0.448369503,-0.0238029417,0.0437438563,0.1285386086,-0.0440290347,0.4191423953,0.3036993146,0.029638866,0.3536557257,0.1969596446,0.1627426744,0.30114609,0.0441382863,-0.1183543652,0.1699621975,0.1193344668,0.1104473621,0.2826167047,-0.2836562991,-0.0844766945,0.0332247652,0.164361313,-0.0335939415,0.0535354093,0.0687814355,0.2974520922,-0.0559393056,-0.1830288321,0.1009347588,0.4425370097,0.0588133782,-0.1450980455,0.2595957816,0.0428838842,-0.1643870771,0.3133176267,0.0137783522,0.231696859,0.3095141351,0.0246753413,-0.0696165934,-0.1814823747,-0.2767195404,-0.1199273095,0.334752351,-0.1648125499,0.2214969695,-0.0891913995,-0.1461733282,0.0719188824,-0.1294409186,-0.1530590802,-0.2924027145,-0.0076095946,-0.0767282844,0.1505491138,0.2648813426,-0.1862804443,0.2516584396,0.3692764044,-0.1475120485,0.0229872093,-0.3701071739,-0.1531626284,0.2031988502,-0.0022343313,-0.3685530126,0.2077259868,-0.1924225092,-0.218137607,-0.1249535531,-0.1477749348,0.023245059,0.0012742316,0.0432976708,0.256049335,0.3906775117,0.1269900203,-0.0520312451,0.4064903259,-0.0884209946,-0.0913150236,0.0280723516,-0.0236177891,0.0279100221,0.0800108612,-0.6284663677,-0.5869358182,-0.294239372,0.1182904765,0.1431990862,0.1980281472,0.24892205,0.1683931798,0.1541638076,0.0365752652,0.318130374,-0.2569623291,-0.5267910361,0.1774822176,-0.1706408262,-0.4868075848,0.2212446034,0.1338598132,0.5196148753,-0.0392800197,-0.4721224606,-0.4179493487,0.0700986385,0.1188787967,-0.316524297,-0.1002306342,0.2164641619,-0.0834572911,-0.1960401684,0.0654206201,-0.0279408097,0.178632319,-0.000116027,0.2409600466,-0.1367491931,0.4993528426,-0.0066893827,0.4120452404,-0.0015904063,0.0416391492,0.173962459,-0.2517149448,0.1825367659,0.0320855603,-0.3973609209,0.1971779168,-0.0506810173,-0.1291242838,0.0730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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1594","title":"connection error ","comments":"Hi @lhoestq thank you for the modification, I will use`script_version=\"master\"` for now  :), to my experience, also setting timeout to a larger number like 3*60 which I normally use helps a lot on this.\r\n","body":"Hi\r\nI am hitting to this error, thanks \r\n\r\n```\r\n> Traceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 379, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 208, in main\r\n    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO\r\n  File \"finetune_t5_trainer.py\", line 207, in <dictcomp>\r\n    for task in data_args.eval_tasks}\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 70, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 66, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split, script_version=\"master\")\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 487, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/boolq\/boolq.py\r\nel\/0 I1217 01:11:33.898849 354161 main shadow.py:210 Current job status: FINISHED\r\n```","comment_length":34,"text":"connection error  \n Hi\r\nI am hitting to this error, thanks \r\n\r\n```\r\n> Traceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 379, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 208, in main\r\n    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO\r\n  File \"finetune_t5_trainer.py\", line 207, in <dictcomp>\r\n    for task in data_args.eval_tasks}\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 70, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 66, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split, script_version=\"master\")\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 267, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 487, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/master\/datasets\/boolq\/boolq.py\r\nel\/0 I1217 01:11:33.898849 354161 main shadow.py:210 Current job status: FINISHED\r\n``` \n Hi @lhoestq thank you for the modification, I will use`script_version=\"master\"` for now  :), to my experience, also setting timeout to a larger number like 3*60 which I normally use helps a lot on this.\r\n","embeddings":[-0.3293083012,-0.1345852762,-0.2016925067,0.3231747746,0.5001865029,-0.1886005998,0.1602738351,0.2997075617,-0.3055787683,0.1561734229,0.012656942,-0.0056440663,0.2251010835,0.1642649472,-0.0455740355,-0.0660552904,-0.2141919881,0.0516924225,-0.1668691933,0.1017805934,-0.0759902298,0.0589256845,-0.135654822,0.1066663116,-0.0014342469,-0.0925262198,0.0482695475,0.0914029479,-0.1770411283,-0.3981225789,0.3847735524,-0.0475312918,-0.0878206268,0.2440319657,-0.0001026009,0.2239751518,0.1840706468,0.0613851659,-0.2191357613,-0.165497303,-0.1825285554,-0.1764973998,0.3466186523,-0.2045548558,-0.323461026,0.156537205,0.0194423646,0.0245129764,0.6095243096,0.5391385555,0.3651290834,0.1239325255,0.2508780658,-0.1263591349,0.0121593978,-0.4649489522,-0.0548876636,0.2500353456,0.2293919772,-0.1382567436,0.0677758455,0.0419034436,0.0411453992,-0.0182585083,0.0719028115,-0.1040173098,0.1631603092,-0.3608938754,0.2469529659,0.1152732894,0.5638700128,0.0516682491,-0.1345327944,0.2436355948,0.0145922545,-0.2167304009,0.2574052811,0.0059991763,-0.2366169691,0.1825777292,0.1746409535,-0.1708101779,-0.5097798705,0.229768768,-0.093413502,0.2104021162,-0.0483462103,0.0594062284,0.0371313393,-0.2034060955,0.3239794672,0.2822365165,0.0639957413,0.095603019,-0.4873182774,-0.0258345623,-0.0517093278,-0.1743869781,-0.0484157056,0.1437137574,0.2108507156,0.0826344863,0.1433905512,0.0551506393,0.0877403691,0.1378038824,-0.1206299141,0.2517746687,0.2280283868,0.3546999991,-0.1444411874,-0.0109692346,-0.1517023295,-0.524538219,0.0227191225,0.3155859113,0.3285774887,-0.0800363272,-0.2814172208,0.1608621478,-0.073479332,0.0374105051,-0.0147579722,0.5862597227,-0.4222016037,0.1364524961,-0.0385238677,0.0206818804,-0.1698478311,0.0816259533,-0.2103554457,0.128688857,-0.1109872386,-0.2046239078,0.1267408729,-0.0979281366,0.1036819816,-0.1254515946,0.0796652734,-0.1540387422,0.2098276913,-0.3071586192,0.0403931066,0.2144149095,-0.0093317134,-0.0397677943,0.256238997,0.0868284404,0.1426610798,-0.0583050475,-0.3986396194,-0.4699076712,-0.0246357843,0.3337431848,0.1888823807,-0.1018986925,0.242503643,-0.239442572,0.1015270948,-0.1859617233,-0.1586317867,-0.3791605234,-0.1898315698,-0.2785962224,0.3494767845,0.2411410213,-0.2269233316,-0.0555184782,-0.0063581206,-0.2299630344,-0.0841925368,0.1094194576,-0.1654981077,0.1853645891,-0.2126497775,-0.0556343347,0.6239518523,-0.6403186321,-0.5884609222,0.451880306,-0.2488524467,-0.2172074914,0.0026223192,0.0128350044,0.31572631,0.1871753633,0.2534630299,0.2531638145,-0.0963229612,-0.0373811014,-0.1279286742,-0.2783178389,0.210431397,0.2529270649,0.338386029,-0.0542290099,0.1496915519,-0.2224408686,0.2122184187,0.1233668029,0.055118911,0.2695868015,0.2858492136,0.1266420037,-0.0151618924,0.2609181404,-0.2238556445,0.1011382937,-0.0823274553,0.1316879541,-0.4020649791,0.2130479068,-0.3046886325,-0.070966728,-0.4243715107,-0.1110660285,0.3290539384,0.0198725909,0.1009851322,0.0605011731,-0.0466322526,0.2943363786,-0.151177302,0.1532868445,-0.0333023034,0.2780037224,-0.3268547356,-0.0552035421,0.0937715843,0.0030752169,0.3442077935,-0.1848721802,-0.1886288971,0.2643786371,-0.159370333,0.1300932914,0.2049147338,0.0657288283,0.11827223,-0.448369503,-0.0238029417,0.0437438563,0.1285386086,-0.0440290347,0.4191423953,0.3036993146,0.029638866,0.3536557257,0.1969596446,0.1627426744,0.30114609,0.0441382863,-0.1183543652,0.1699621975,0.1193344668,0.1104473621,0.2826167047,-0.2836562991,-0.0844766945,0.0332247652,0.164361313,-0.0335939415,0.0535354093,0.0687814355,0.2974520922,-0.0559393056,-0.1830288321,0.1009347588,0.4425370097,0.0588133782,-0.1450980455,0.2595957816,0.0428838842,-0.1643870771,0.3133176267,0.0137783522,0.231696859,0.3095141351,0.0246753413,-0.0696165934,-0.1814823747,-0.2767195404,-0.1199273095,0.334752351,-0.1648125499,0.2214969695,-0.0891913995,-0.1461733282,0.0719188824,-0.1294409186,-0.1530590802,-0.2924027145,-0.0076095946,-0.0767282844,0.1505491138,0.2648813426,-0.1862804443,0.2516584396,0.3692764044,-0.1475120485,0.0229872093,-0.3701071739,-0.1531626284,0.2031988502,-0.0022343313,-0.3685530126,0.2077259868,-0.1924225092,-0.218137607,-0.1249535531,-0.1477749348,0.023245059,0.0012742316,0.0432976708,0.256049335,0.3906775117,0.1269900203,-0.0520312451,0.4064903259,-0.0884209946,-0.0913150236,0.0280723516,-0.0236177891,0.0279100221,0.0800108612,-0.6284663677,-0.5869358182,-0.294239372,0.1182904765,0.1431990862,0.1980281472,0.24892205,0.1683931798,0.1541638076,0.0365752652,0.318130374,-0.2569623291,-0.5267910361,0.1774822176,-0.1706408262,-0.4868075848,0.2212446034,0.1338598132,0.5196148753,-0.0392800197,-0.4721224606,-0.4179493487,0.0700986385,0.1188787967,-0.316524297,-0.1002306342,0.2164641619,-0.0834572911,-0.1960401684,0.0654206201,-0.0279408097,0.178632319,-0.000116027,0.2409600466,-0.1367491931,0.4993528426,-0.0066893827,0.4120452404,-0.0015904063,0.0416391492,0.173962459,-0.2517149448,0.1825367659,0.0320855603,-0.3973609209,0.1971779168,-0.0506810173,-0.129124283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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1593","title":"Access to key in DatasetDict map","comments":"Indeed that would be cool\r\n\r\nAlso FYI right now the easiest way to do this is\r\n```python\r\ndataset_dict[\"train\"] = dataset_dict[\"train\"].map(my_transform_for_the_train_set)\r\ndataset_dict[\"test\"] = dataset_dict[\"test\"].map(my_transform_for_the_test_set)\r\n```","body":"It is possible that we want to do different things in the `map` function (and possibly other functions too) of a `DatasetDict`, depending on the key. I understand that `DatasetDict.map` is a really thin wrapper of `Dataset.map`, so it is easy to directly implement this functionality in the client code. Still, it'd be nice if there can be a flag, similar to `with_indices`, that allows the callable to know the key inside `DatasetDict`.","comment_length":24,"text":"Access to key in DatasetDict map \n It is possible that we want to do different things in the `map` function (and possibly other functions too) of a `DatasetDict`, depending on the key. I understand that `DatasetDict.map` is a really thin wrapper of `Dataset.map`, so it is easy to directly implement this functionality in the client code. Still, it'd be nice if there can be a flag, similar to `with_indices`, that allows the callable to know the key inside `DatasetDict`. \n Indeed that would be cool\r\n\r\nAlso FYI right now the easiest way to do this is\r\n```python\r\ndataset_dict[\"train\"] = dataset_dict[\"train\"].map(my_transform_for_the_train_set)\r\ndataset_dict[\"test\"] = dataset_dict[\"test\"].map(my_transform_for_the_test_set)\r\n```","embeddings":[0.0123616559,-0.0980538875,-0.1504415572,0.0315879881,0.1485255063,0.0519785024,0.2157814354,0.1856265515,0.2782222033,0.1137610897,0.0187772661,0.6971397996,-0.194628492,0.3624713123,0.0075963112,0.1261140257,-0.082267195,0.1726022512,0.259760946,0.0200805254,-0.5007085204,-0.2413183302,-0.2991085351,0.1724794954,0.1813092083,-0.058190126,-0.0412486903,-0.1254830509,0.0863479972,-0.4971494079,0.3150211871,0.3500140011,-0.0713424459,0.0319679268,-0.0001151014,0.165341422,0.1797243357,-0.0615737364,-0.2317584604,-0.2660992742,-0.4753479064,-0.2223521471,-0.0665304959,-0.648195982,0.0736432299,-0.2658869922,0.0899404213,-0.3869361579,0.1715967357,0.1491442025,0.1471835971,0.1689081043,-0.0654482991,0.2037469,-0.0100793177,0.0594722889,-0.0677720681,-0.1251585186,0.0909942687,-0.1609252095,0.1930097193,0.0897944868,-0.0347207636,-0.1856620163,0.4800027609,0.1962268353,-0.2003143132,-0.2015486062,-0.035012912,0.1073151156,0.257601738,-0.464533478,-0.4607774317,-0.349897176,0.0422466584,0.0605236515,0.1202891022,-0.1899610758,0.1220527738,0.2078469992,-0.0567756705,-0.0148770856,-0.1210499257,0.069634445,-0.1026101559,0.3232737184,0.0501525626,0.3766308129,0.1448031515,0.1034575552,-0.050728824,-0.1709432006,0.0642506629,0.4460495412,-0.0070257471,-0.1497470886,0.2523940206,-0.1559841633,0.2075985819,-0.3478935063,0.1729162782,0.1256625801,-0.4662780166,0.3151428699,0.382566154,0.1909714043,0.3423776031,0.4960179925,0.4341569245,-0.2414720207,-0.1015854925,0.0000163147,0.0529405624,-0.0984004512,0.3259883821,0.449416995,0.4494536519,0.1942183673,-0.0887192115,-0.142272234,-0.1836024076,-0.2230855525,0.2525042892,0.1441774219,0.1953484714,0.1295474321,-0.0843947977,-0.1063770875,0.1183463037,0.0830751881,-0.1132612005,-0.1972928047,0.1457876861,-0.0385118201,0.0284653343,-0.0229258575,0.0479106344,-0.1424472928,0.1269001067,0.1206097007,0.2216482311,-0.1108534709,0.4448646903,0.0949500576,-0.3591142893,-0.02338765,0.1648881137,-0.2535071373,-0.5074768066,0.0631086677,-0.2506268322,-0.3553037345,0.5641079545,0.1127244756,0.1348879039,0.0448333025,-0.1656806767,0.4262408614,0.0385414772,-0.1489331275,0.2924417555,-0.1452793479,-0.6354404688,-0.4432385862,-0.1224961877,0.0715213642,-0.4225706458,-0.2193415612,0.193688795,-0.2955155671,-0.2337773442,-0.1400133073,-0.4305417538,0.1845209599,-0.1133115068,0.0842298418,0.5983136892,-0.5417661667,-0.7054085732,-0.0194750242,-0.0734470561,-0.0909807459,0.1061515138,0.1983310878,0.3027149737,-0.1075788587,0.3345128894,0.2482724488,0.0704048499,0.0739826337,0.3476862907,-0.0775430724,0.0440010428,-0.1386542767,-0.2733567357,0.012396723,-0.0209333003,-0.1570063531,0.1605323255,-0.0444436334,0.1319596767,-0.0316115208,0.1468590945,-0.014786616,0.0897356346,-0.0011813468,-0.1574274004,0.4363519549,-0.0688039362,-0.1161404923,-0.3282233477,-0.5135014653,0.0457432643,0.3376213014,-0.1675526947,0.0446512923,0.1876629889,-0.0961411968,0.0183243807,-0.0490369909,-0.3668048382,0.0269409791,-0.0686387569,-0.1688911319,0.0345465615,-0.1081574336,0.2327875048,0.2283022553,0.0057086465,0.1308414042,0.1189313233,0.1284094006,-0.1436486691,-0.0352761261,0.3456194699,-0.0780207291,-0.1358102262,0.4973068535,0.1956263185,0.0363544971,0.0547037758,0.2495283335,0.1709625572,-0.1297852248,-0.142611891,0.5926755071,0.0285720006,0.5155330896,-0.0098347757,0.0482867248,0.0550444834,0.057674747,-0.4270036817,-0.3303960562,-0.4731330574,-0.1918272525,0.2141078115,0.0192024149,0.1139845699,0.1446620822,0.6021998525,0.0569199324,0.1399388462,-0.2470755428,-0.1606605053,0.1173473224,-0.0280296579,0.0974755585,0.229315713,0.1352882832,-0.0288372599,0.0935410336,-0.1350356638,0.1313690245,0.1300598681,0.3321389854,-0.1751943827,-0.285826683,0.0560939834,-0.0637323633,-0.0282833148,-0.3308681846,-0.0558249801,-0.0974879339,-0.1905638427,0.241342634,-0.0426226109,-0.0978617445,0.0608884171,-0.2186288387,0.0325925685,-0.3761589825,-0.0131312925,0.0083226291,-0.2312106341,0.1049044132,-0.1630046368,0.1837065965,0.1399992853,-0.3912048936,0.0317251533,-0.4720563293,0.036917083,0.0322630927,0.0279489849,-0.3296690881,0.3956876993,0.2419899553,0.0166119263,-0.1163709685,-0.2459565401,-0.005321688,-0.1730002463,-0.2134752423,0.3398188353,0.1414757967,-0.175737828,0.1314511448,0.1284939945,0.0330686271,-0.2387274206,-0.1916492283,0.055913426,-0.0331564508,0.0056467326,-0.0744236708,-0.2525494993,-0.3577523232,0.5741587877,-0.1613992602,0.2467688471,-0.0077684666,-0.0785122663,0.1846992224,0.1403214037,0.1610547751,-0.2782910168,0.2995181084,0.1700734049,-0.1171790063,-0.1032219529,-0.111432977,-0.2328278869,0.2313754708,0.1477987915,-0.1864003241,-0.1199128479,-0.0949169323,0.3826450408,-0.1426097453,0.0625523701,0.3247343898,0.3371560574,0.0288054533,-0.1656000465,-0.2728478014,-0.1100341082,0.3427269757,-0.3026394248,0.4693041444,0.3372187018,-0.0239047296,0.4323866665,-0.0281220581,-0.0266241487,0.086345084,-0.0989653617,0.0983515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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1591","title":"IWSLT-17 Link Broken","comments":"Sorry, this is a duplicate of #1287. Not sure why it didn't come up when I searched `iwslt` in the issues list.","body":"```\r\nFileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnmted\/\/texts\/DeEnItNlRo\/DeEnItNlRo\/DeEnItNlRo-DeEnItNlRo.tgz\r\n```","comment_length":22,"text":"IWSLT-17 Link Broken \n ```\r\nFileNotFoundError: Couldn't find file at https:\/\/wit3.fbk.eu\/archive\/2017-01-trnmted\/\/texts\/DeEnItNlRo\/DeEnItNlRo\/DeEnItNlRo-DeEnItNlRo.tgz\r\n``` \n Sorry, this is a duplicate of #1287. Not sure why it didn't come up when I searched `iwslt` in the issues list.","embeddings":[0.0006844614,-0.4732919931,-0.0473029353,-0.0252460092,-0.072372213,-0.1377999783,0.4260438979,0.2196717709,0.0861584172,-0.0510080159,0.1014911458,0.01117827,0.234124288,0.1399892271,0.068584688,0.3315440118,0.1696381569,-0.0591772646,-0.2145912647,0.0649079904,-0.2930266559,0.4922843874,-0.1860420853,-0.0230990555,-0.4022633433,0.1943532526,-0.0546754338,0.3688046932,0.0660205707,-0.31805709,0.0928245783,-0.2664248943,-0.1155584902,0.0801602453,-0.0001027269,0.0725963414,0.3312967718,0.0182316564,-0.039724309,0.0038765541,-0.4141123891,-0.360758245,-0.3401444852,-0.2679913342,0.083196342,-0.1541734785,0.0127533665,0.4328042269,0.4594027698,0.2857708037,0.2857938707,0.0177020784,0.3464299738,-0.2142918855,-0.033776205,-0.1964155138,-0.1482028365,0.0175156519,-0.0572566092,-0.0490614623,0.0739294812,0.4202610552,-0.0014970835,-0.0906919241,0.0832032412,-0.1663054526,0.6395931244,-0.2843967378,0.3459231853,0.0326452889,0.3625123501,0.0956142023,-0.024690453,0.239191696,-0.0066234395,0.1453074068,0.4166468084,-0.0576201826,-0.108288981,0.1479462981,0.0997009128,-0.1229253262,0.0443260074,0.1056374758,0.0061146948,0.709802866,-0.0680117458,-0.0042495984,0.2167512625,-0.2968645692,-0.0850264803,0.3304434121,-0.2750071287,0.0375195071,0.0305321943,-0.1025233194,0.1772165745,0.0931896865,-0.156649217,-0.1070362926,-0.1556742787,0.0075665819,-0.0228822865,-0.0098473709,-0.1456732303,0.0217749588,0.0329333246,-0.1880739331,0.3502298892,0.2299733311,-0.0562805794,0.0538175255,0.0640300959,-0.2839123607,-0.2731635571,0.0800906494,0.0603784025,0.014891156,-0.3269964159,-0.0366399214,-0.0750796422,0.1032091603,0.0396595038,0.1052277684,0.1486850977,0.1083868593,0.0737453252,-0.1670612395,-0.145693019,-0.1165530309,-0.0839784294,0.0587396324,-0.0097535206,0.0754460767,0.1167550012,0.2683935165,0.1553176641,-0.0396597013,-0.3994235992,-0.3470971584,-0.3023123443,0.2189502269,0.0932866335,0.2179436833,-0.1534749269,0.1959988624,0.1381669492,-0.2337029725,0.091553919,0.1750638038,-0.2198525071,-0.3070573211,-0.3303204775,0.3187909424,-0.0890317112,-0.062058486,0.592000246,0.1136392131,-0.2392677814,-0.2184087783,-0.0538383052,0.2799759209,-0.5356158614,-0.0395276621,0.1645094007,0.2866088748,-0.4959416389,-0.2172528803,-0.157035768,-0.045078706,0.0142990323,0.2759904265,-0.098097913,0.2701850832,-0.3180533946,0.3719313741,0.211175859,-0.1238324866,-0.3806768358,0.2075423598,-0.2282713056,-0.10474433,-0.1508671492,0.1886084825,-0.0305274352,-0.2770000696,-0.2378554344,0.023651721,0.1344772577,0.1217098087,-0.4252086282,-0.4194178581,0.1552657634,-0.1698845029,0.0007901921,-0.2586677969,0.1822284013,0.424702704,0.1566957235,-0.0342610441,-0.0651527345,0.179021433,0.476087451,0.1283302903,0.3319918215,0.0866797343,-0.0711061656,0.1365967393,0.2252643555,0.1361791939,0.1692947149,0.062154945,-0.2857600451,-0.0129033951,-0.2341265231,-0.2812821865,0.299508661,0.1546766013,-0.1314084977,0.0246637166,-0.1131095663,-0.0352985673,-0.3365405202,-0.0846760347,0.0395616442,0.3213646114,-0.3308702111,-0.1463459283,-0.0502646044,0.2350209206,0.3047770262,-0.0263919551,-0.1382350773,0.1381839514,-0.2247166932,0.104634434,0.0036255436,0.3050383031,0.1095009297,-0.6218362451,-0.1884242296,0.5265060067,0.010966639,-0.0426728055,0.1724851727,-0.3564175367,0.1630452871,0.1086338684,-0.0718908906,-0.0120034106,-0.0416823737,-0.3572949767,0.0865869522,-0.1812565029,0.1133780628,-0.1891230643,-0.3709328473,-0.10582757,0.2016046196,0.2312986255,0.3363108337,0.0031455364,0.2152808756,0.1793357432,0.0183640625,-0.015806498,-0.1573385894,0.1023871675,0.220795989,0.0757260323,-0.040155828,-0.0022344652,-0.0290193856,-0.1879840642,0.3390098214,0.0486641303,-0.0258562863,0.3497512341,0.1588130295,0.1261734515,-0.2462342978,0.1571489275,0.0975352302,0.0104038026,-0.2005771846,-0.0918560997,-0.3729525805,-0.6177487969,-0.2585009933,0.1413275152,-0.1600207984,-0.2828134596,0.2161189318,0.0731555,-0.1082731038,0.3150978088,0.2780673206,0.4048838615,0.0364424214,0.5076473951,-0.2202726007,-0.0938652083,-0.3835863173,0.1270001978,0.4048323333,-0.052804023,0.1483353674,-0.1700387895,-0.0775309503,-0.6530392766,-0.5673181415,0.048985064,0.0108172055,0.2896001935,0.0804569051,0.3780201972,-0.1454787254,-0.3218047619,0.3558204174,-0.0894986987,0.1128537282,-0.279014051,-0.081680648,0.0054657566,-0.2410790175,-0.0255379099,-0.1625329852,-0.4413508773,0.1885261834,-0.3920689821,0.0677083731,0.1095629111,0.0386846252,-0.0286509693,0.0545981824,-0.0528109483,-0.0466461331,0.258187741,0.2488073856,-0.1049061567,-0.3156597018,0.2583633363,0.2335182428,0.1036950126,-0.5245766044,-0.5797256231,-0.1467759013,0.0488854237,0.0952193588,-0.1783047616,0.0995092615,0.0492230803,0.0556763858,-0.24199754,-0.1582617462,-0.0889321789,0.065396443,-0.0556306615,0.4127615094,0.1255225241,0.113470301,0.1550671607,0.1015897989,0.1160734743,0.0212453716,0.1573708504,-0.0480968505,-0.0487801097,0.0341104269,-0.3927622736,0.2581166625,-0.0589644164,0.0655069351,0.409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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1590","title":"Add helper to resolve namespace collision","comments":"I was thinking about using something like [importlib](https:\/\/docs.python.org\/3\/library\/importlib.html#importing-a-source-file-directly) to over-ride the collision. \r\n\r\n**Reason requested**: I use the [following template](https:\/\/github.com\/jramapuram\/ml_base\/) repo where I house all my datasets as a submodule.","body":"Many projects use a module called `datasets`, however this is incompatible with huggingface datasets. It would be great if there if there was some helper or similar function to resolve such a common conflict. ","comment_length":29,"text":"Add helper to resolve namespace collision \n Many projects use a module called `datasets`, however this is incompatible with huggingface datasets. It would be great if there if there was some helper or similar function to resolve such a common conflict.  \n I was thinking about using something like [importlib](https:\/\/docs.python.org\/3\/library\/importlib.html#importing-a-source-file-directly) to over-ride the collision. \r\n\r\n**Reason requested**: I use the [following template](https:\/\/github.com\/jramapuram\/ml_base\/) repo where I house all my datasets as a submodule.","embeddings":[-0.156945169,0.085805431,-0.0459537581,0.2750368118,0.1614482999,-0.0471068732,0.1879473031,0.2351552397,0.1399725974,0.0676217303,-0.1060155556,0.0726316646,-0.379468441,0.365108043,0.1478723288,-0.0800853819,0.1018497273,0.0871386304,-0.1015021279,0.2270358056,-0.2017204762,0.1321813911,-0.1773248911,0.0692506805,-0.1361082792,0.2808799446,0.2181895971,0.0411920734,0.0783126503,-0.3763766289,0.6635086536,0.2178044617,0.3690737188,0.6192420721,-0.0001174822,0.1117214039,0.3730975389,-0.0607055351,-0.1470485926,-0.1178849488,-0.0896531269,-0.398647964,0.157278046,-0.0462405048,-0.1469103098,-0.1982926875,-0.250744313,-0.3956302106,0.2224406898,0.1046413779,0.1570375562,0.3138197362,0.3482822776,0.1955201179,0.4042695761,0.0580365136,-0.103069976,0.2518261075,0.3102469146,-0.2774194777,0.1863786131,0.2999771833,-0.4233086109,-0.1609926224,0.5737731457,0.0806408972,-0.133542031,-0.1649817675,-0.3388842046,0.2239961177,0.3205664456,-0.3809448779,-0.2733329535,-0.6625205278,-0.0953679755,-0.3431422114,0.3581660986,-0.2587684691,-0.0489988141,0.1162643433,-0.0102240816,-0.3572150171,-0.2172235548,0.1401239187,0.0621522479,0.265432477,-0.118245177,0.0382897072,0.4718239605,-0.0413214862,0.1391025037,-0.1224656329,0.1292885244,0.0249687508,-0.3898670971,-0.2220895588,-0.0659959465,0.1947240829,-0.0027468349,0.0927888975,-0.1214878112,0.1434396654,-0.2469825,0.089137055,0.1438131034,-0.0759093091,0.0228997152,-0.0074788737,0.717302084,0.278201133,0.0356224775,0.1428511143,-0.0309190508,-0.279659003,-0.1277398765,-0.0557538271,0.3128400445,-0.083722353,-0.1144183874,0.1310545057,-0.0295079686,-0.01024203,0.3119973242,-0.0674568936,-0.1261175126,0.2743070722,0.2009346187,-0.014787293,-0.0914753675,-0.3621776998,0.0768510923,-0.0200122334,-0.1118421853,-0.1172181293,-0.0236462485,0.1487340629,-0.1232327819,-0.1212419197,0.4266929924,0.0588852316,0.1227620766,0.2128577232,-0.0646680519,0.130825609,-0.1384952068,0.0264550764,0.0455058515,-0.2981049418,-0.2503246367,0.0837171525,-0.2816407681,-0.1105155125,0.0168741271,0.1137429625,0.0872917548,0.0983204395,-0.3846315742,0.066032514,-0.1101949885,-0.3452208936,0.045744326,0.0482117534,-0.174320519,-0.2597379982,0.2053050697,0.1476268619,-0.3792133629,-0.0783925578,-0.3599071801,0.0991626084,-0.1537526548,-0.0157151036,0.0686499849,-0.1013021022,-0.3450179994,-0.1573765278,0.1345909834,-0.4595775902,-0.4601137936,-0.0651671961,-0.005425611,0.1478833109,0.1906592399,0.5217299461,-0.3122331798,-0.2124907225,0.0728029087,0.5595843196,-0.2102899998,-0.0309661087,0.1056246608,-0.1280076951,0.1873516887,0.175775826,0.0800944716,-0.1775848716,-0.0847342461,0.0296681896,0.0435900353,-0.2440781742,0.0962440521,0.2909335792,0.3882429004,0.2304106057,-0.048839692,-0.4740625024,-0.5224717259,0.0260031708,0.0659537911,0.1904855072,-0.1272553951,-0.1919789612,0.1316481978,0.0562363416,-0.0914121121,0.1116883308,-0.023463849,-0.1359507591,0.3357867599,-0.211732924,-0.2838371396,0.3958899379,-0.1835471839,0.4851572216,-0.473492682,0.3816845417,0.1658197939,-0.0012573909,0.0393790901,0.5233588815,-0.0207728297,-0.0889599398,0.0956602693,0.3063693643,0.0091867987,0.2391800135,-0.0697517172,0.3499189913,0.1179017425,-0.0932851136,-0.1358383149,0.0945345461,-0.0933089778,0.1238581985,0.1265600771,0.4233871996,-0.2325821668,0.277510941,0.0566695891,0.0093641998,-0.0860435218,-0.1140576079,-0.1919590831,-0.0608415939,0.2643036842,-0.0066962368,0.5013024211,0.1276096553,-0.0960039794,-0.0228632148,0.1501436234,0.0308485869,0.1486248672,0.1569752544,-0.3729528487,0.3065495491,-0.0669645965,0.3952649832,0.2192809135,0.1973306686,-0.2224758267,0.3172088563,-0.0031693033,-0.0333979614,0.2044757456,0.1071964651,-0.0041245315,-0.1295863539,0.1589461863,-0.0098833358,0.011780642,0.1770435423,-0.3526941538,0.1459255069,-0.398136586,0.059504699,-0.3560387194,-0.5332534909,-0.3027786314,-0.3312542737,-0.6207664609,-0.2648798525,0.0466144644,0.3321264982,-0.3618955314,0.1726870686,0.215398252,0.3085461855,0.014952302,-0.2485610843,0.2147975713,-0.2350459099,-0.2394423932,0.0423847996,0.4659856558,-0.1626095027,0.2937952876,-0.1141601205,-0.0810242593,-0.2225541025,-0.6898431778,0.0872551426,-0.0175350346,0.227743268,0.3318365514,0.1090879366,0.3108024001,-0.1535188705,-0.0013428192,-0.2860572934,-0.0153256878,-0.0740682483,0.1047858819,0.2111226767,-0.0123377629,0.1076954082,-0.2220643461,-0.4446206689,0.1081407964,-0.1349276304,-0.0310644787,0.2012324035,-0.0743323788,0.1763153821,-0.2543161511,-0.0639796034,0.1600046009,-0.3523983657,0.1451933831,-0.0190420412,-0.0227381904,-0.098684676,-0.0657074824,0.0990856513,-0.0748306289,-0.2034688443,-0.4110997617,-0.1161350384,0.2301383615,0.3544764817,0.2806236148,0.0717646703,0.152112022,0.1068637371,-0.2129294872,-0.0888511017,0.0074360888,0.0716136098,0.0171761755,0.4399723411,0.0482326634,-0.2288437635,0.2650896907,0.1216901988,-0.1875744164,0.3132732511,0.1984449625,0.5431454182,0.1646975875,-0.247422725,-0.1733193249,-0.019950788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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1590","title":"Add helper to resolve namespace collision","comments":"Alternatively huggingface could consider some submodule type structure like:\r\n\r\n`import huggingface.datasets`\r\n`import huggingface.transformers`\r\n\r\n`datasets` is a very common module in ML and should be an end-user decision and not scope all of python \u00af\\_(\u30c4)_\/\u00af \r\n","body":"Many projects use a module called `datasets`, however this is incompatible with huggingface datasets. It would be great if there if there was some helper or similar function to resolve such a common conflict. ","comment_length":34,"text":"Add helper to resolve namespace collision \n Many projects use a module called `datasets`, however this is incompatible with huggingface datasets. It would be great if there if there was some helper or similar function to resolve such a common conflict.  \n Alternatively huggingface could consider some submodule type structure like:\r\n\r\n`import huggingface.datasets`\r\n`import huggingface.transformers`\r\n\r\n`datasets` is a very common module in ML and should be an end-user decision and not scope all of python \u00af\\_(\u30c4)_\/\u00af \r\n","embeddings":[-0.0202236325,-0.2652241588,-0.0927194953,0.1554595232,0.3644935787,-0.1625663787,0.2206045687,0.1671950966,0.0891767815,0.2554261982,-0.2716357112,0.1697729826,-0.2384689003,0.2573799193,0.0592853911,-0.2396575511,0.0753815696,0.197005704,-0.3191300631,0.0870092362,-0.2288310677,0.1971452683,-0.1359622777,0.0589765124,-0.2770675421,0.0928416848,0.2053655386,-0.0865936726,0.0594990812,-0.3789724708,0.4213656783,0.1152880341,0.0546915084,0.5897495151,-0.0001011381,0.1318942457,0.337695092,-0.0408486165,-0.0935960487,-0.0902457908,0.0646301135,-0.2467926443,0.2434570044,-0.0631534308,-0.2757797539,-0.1726580709,-0.0201392658,-0.1908741295,0.4177950025,0.0541873388,0.346364826,0.5181908011,0.233932972,0.0629720017,0.2500138879,0.1026623249,-0.1660955846,0.115399763,0.191190809,-0.1256443113,0.0923160687,0.4077009261,-0.2057329267,-0.1077073067,0.4733327329,0.1147729009,-0.1144275665,-0.3207325935,-0.1828877181,0.2236241102,0.1393297613,-0.4643296003,-0.3387849629,-0.4886345267,-0.1247690469,-0.1406293362,0.3330661952,-0.1330355108,-0.04382772,0.1750166118,-0.0336099826,-0.0629154816,-0.1290286928,0.088739343,0.0112887789,0.3285007775,-0.2876746058,0.0572053492,0.2873640954,-0.1634868681,-0.1059212163,0.0114281531,0.1962403804,-0.0631782189,-0.23037377,-0.2078415751,0.0271324832,0.2500008941,0.0400675647,0.0164244473,-0.1680965573,0.2096388489,0.1057278141,0.1243800223,0.0933449343,-0.0037212193,-0.1013072059,-0.1184755191,0.5116112232,0.103482753,-0.0130224088,0.0168297067,0.017249098,-0.224902004,-0.1072269008,-0.1891673058,0.3918105066,-0.0000874776,-0.1438305229,-0.098392278,0.084255442,0.1389573067,0.2741027176,0.3232211471,-0.0582580082,0.1231165156,0.1442242414,0.050936278,-0.1939392984,-0.1619465649,-0.0936441422,-0.0890825838,-0.2535675168,-0.1062750742,0.021815002,0.1464740187,0.1363381892,-0.082087256,0.3387714028,0.0901669711,0.122593388,0.2884990573,0.0387651324,0.2551595569,-0.0981007591,0.0819577947,0.0615825318,-0.1290495694,-0.2916733623,-0.255592376,-0.2272693068,-0.0853667483,-0.0852701813,0.2785585821,0.0571323857,0.1005708426,-0.0861962512,0.0224285461,-0.0613554418,-0.2259920388,0.0058093392,0.0954289213,-0.137312457,-0.2678747177,0.1736903787,0.1147441566,0.0881103128,-0.221149683,-0.1986528337,-0.1133023798,-0.0881039575,0.1867656857,-0.0536876321,-0.1450605243,-0.2509948611,-0.1305901259,0.0392693505,-0.3478722572,-0.4469206929,0.003473382,-0.0512945242,0.0921060294,0.2216733098,0.2875139415,-0.2845281363,-0.1318760365,0.1497124135,0.334582448,-0.1078119054,0.1419370323,-0.0702395961,-0.1103658304,0.0854812488,0.0956269726,-0.0715063512,-0.0109138954,-0.1561855972,0.0679795146,0.0651495233,-0.1832594573,0.012015162,0.3451623619,0.4077172577,0.2324431092,0.0379570313,-0.3982560039,-0.5612103343,0.032327652,-0.2237790078,0.1317364872,-0.102172792,-0.381174773,-0.0273611676,-0.0074822535,-0.101243414,-0.0579985864,0.2481876612,-0.033184994,0.2494141012,-0.1152242944,-0.1502001137,0.2772931755,0.026908299,0.293605715,-0.5201870203,0.2467380464,-0.0133098299,-0.0575284772,0.1687585562,0.4031574428,-0.0246705879,-0.1086483672,0.0968585834,0.3235653341,0.0206217505,0.2406048775,-0.1278152019,0.3849263489,0.0608320311,-0.1427503079,-0.0769751817,0.2066701651,-0.0725471973,0.176276207,0.0158786476,0.4241325557,-0.1905089021,0.1394430697,0.1381799281,0.0197811555,0.1021703109,-0.1923259348,-0.313311398,-0.2394379079,0.0874391869,-0.0588207208,0.521186769,0.27946648,-0.2153863162,-0.1565445662,0.3494957089,0.1497510225,0.2577457428,0.1061117202,-0.4559352398,0.2515173554,-0.0359121971,0.2682374418,0.1603031605,0.3987917006,-0.159025833,0.2306382805,-0.0639001951,-0.1053712294,0.1615660042,0.2154632956,-0.0500359945,-0.0688724667,0.0756266043,-0.0285415687,-0.2224746048,0.0728974417,-0.3403604329,-0.0364852473,-0.3240096569,0.1397470832,-0.2611646354,-0.3896709979,-0.4315490425,-0.1077602357,-0.5112162828,-0.1372930259,0.0964360014,0.1642341763,-0.3801087737,0.3237053454,0.2867611051,0.2110137194,-0.0020325056,-0.1493339837,-0.0526552461,-0.1076910049,-0.2154489309,0.1281558126,0.2769778967,-0.030529527,0.391313374,-0.1259329319,0.0550007448,-0.1950947493,-0.6819356084,0.0630114228,-0.0705563426,0.1962787956,0.3726926744,0.0421325117,0.3110900819,-0.3523676395,0.1790613234,-0.2390560806,-0.1668489277,-0.0133356946,-0.0222005192,-0.0204774402,-0.140968889,-0.0746177286,-0.1765633076,-0.5822033286,0.3250768185,-0.1797237992,-0.0627534762,0.3977240026,-0.0689526424,0.1572034359,-0.3618356884,0.2050260305,0.0232381057,-0.2114356011,0.2111343443,-0.1115906388,-0.1141852885,-0.1968887597,-0.1522673517,0.2833880186,-0.1439309716,-0.1671087444,-0.3801899552,-0.2101474404,0.0034125147,0.327509284,0.2715934515,0.1351822764,0.2047846019,-0.1520042866,-0.2211496085,-0.1301624477,-0.0582694225,-0.0515990667,0.1095560119,0.1124327257,0.1236451566,-0.2242598236,0.278770715,0.2801963091,-0.2398181558,0.252494812,0.0529353768,0.4887318909,-0.0350748785,-0.3629176319,-0.0896551386,-0.0427206457,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1590","title":"Add helper to resolve namespace collision","comments":"It also wasn't initially obvious to me that the samples which contain `import datasets` were in fact importing a huggingface library (in fact all the huggingface imports are very generic - transformers, tokenizers, datasets...)","body":"Many projects use a module called `datasets`, however this is incompatible with huggingface datasets. It would be great if there if there was some helper or similar function to resolve such a common conflict. ","comment_length":34,"text":"Add helper to resolve namespace collision \n Many projects use a module called `datasets`, however this is incompatible with huggingface datasets. It would be great if there if there was some helper or similar function to resolve such a common conflict.  \n It also wasn't initially obvious to me that the samples which contain `import datasets` were in fact importing a huggingface library (in fact all the huggingface imports are very generic - transformers, tokenizers, datasets...)","embeddings":[-0.1716234088,-0.054331854,-0.1060904935,0.1661169678,0.3200764358,-0.2631010115,0.2233148962,0.2214922905,0.1152144894,0.3468360901,-0.2177070975,0.0769941732,-0.2270479798,0.2291831821,0.0510888733,-0.096799396,0.1414095759,0.1882733256,-0.3230443299,0.0956738368,-0.2504667938,0.367636472,-0.1152618155,0.0489351638,-0.2631996274,0.1357953101,0.1781104803,0.0242974274,-0.1031204388,-0.3912395537,0.4888105392,0.2592075467,0.0982602835,0.5347704887,-0.0001008511,0.0321501456,0.3329060674,-0.0276504848,0.0007692393,-0.047589425,0.0364767201,-0.1708824188,0.2306132913,-0.0239406116,-0.1686529517,-0.2056214064,-0.1198736578,-0.2970339954,0.2763868868,0.0841663331,0.3598596156,0.4617402256,0.1313858181,-0.0768034458,0.3627577722,0.0963326097,-0.2227722704,0.057724949,0.3163791895,-0.1041614413,0.0468180887,0.3070700169,-0.1991894394,-0.0762047842,0.4549307525,0.0259640291,-0.3586925566,-0.3287334144,-0.1528097689,0.1908170134,0.2731131315,-0.2819491923,-0.1536628753,-0.4264019728,-0.1069417149,-0.0959383398,0.3473276496,-0.3036998808,-0.0107707921,0.1086404622,-0.1284351051,-0.1054658592,-0.2067374438,0.0488111526,0.1202847734,0.182044819,-0.2933821082,0.0484388955,0.3704205453,-0.1188665405,-0.1089816317,0.0120951794,0.0670721531,-0.1090289876,-0.3166467845,-0.1150623709,0.0921506062,0.237753734,0.1597691774,0.1767258346,-0.2038028091,0.2182209194,0.0010576987,0.0623374693,-0.000859837,0.0542277135,-0.0342781059,-0.198603794,0.5527970791,0.2132434249,0.0499669015,0.0854636878,-0.0799074247,-0.2244858742,-0.1833886206,-0.2480139881,0.2999303341,-0.129007861,-0.0830086395,-0.0633308589,0.2499461174,0.1599763185,0.167051509,0.2875520289,-0.1070812419,0.0924921855,0.1625744551,-0.0009797555,-0.1402504891,-0.2982375622,-0.1280475706,-0.0877862871,-0.2294624746,-0.1101402715,0.0930651426,0.001677885,0.1425856054,-0.0730218738,0.3274430335,0.1099218428,0.1255504787,0.387673676,0.1581718028,0.2085342705,-0.1047998071,0.1603885442,0.0640211031,-0.1535737664,-0.2043193579,-0.1815986931,-0.3285942674,-0.1244390756,-0.2427878231,0.2783993185,-0.0005783685,0.0742766187,-0.1776985228,0.0787390396,-0.2354187071,-0.3015482426,-0.0025794331,0.089835383,-0.1285718232,-0.1933573037,0.2595249116,0.1862837672,-0.0397231281,-0.15157184,-0.2312023938,-0.2090185732,-0.0258405879,0.1009816453,-0.0768338814,-0.0912891179,-0.4290794134,0.0888910294,0.0363239795,-0.341076076,-0.3444636464,-0.0266798232,-0.122072041,0.0966459885,0.1180794463,0.2478375137,-0.0789285526,-0.0897482783,0.0506904311,0.3743130863,-0.0806017816,0.1113695726,-0.0837374702,-0.1473541558,0.0619588085,0.1496371627,0.0322673842,-0.0506884307,0.0818423778,0.0967354923,0.0244633369,-0.2688745856,0.051268708,0.3549134433,0.2928172648,0.1757862419,0.0566872731,-0.2884209454,-0.5094377995,0.0490486473,-0.1337141693,0.1777652055,0.0579022653,-0.2778173685,-0.1193551496,-0.0210711509,-0.1724146456,-0.132930249,0.2425019145,0.0880356133,0.2063600868,-0.1213769615,-0.1532645673,0.3962278962,-0.0709776133,0.2890957594,-0.5446171761,0.136016503,-0.0133889224,-0.0801835358,0.2109015733,0.3362109363,-0.0737625286,-0.1314787269,0.012851377,0.4666207135,0.0388001427,0.2169883698,-0.0610328466,0.3250129819,0.0382029973,-0.0809074268,-0.1348622888,-0.0163693149,-0.1158625707,0.1263958216,0.0752323493,0.323456794,-0.1517747045,0.1348322332,0.1206180081,0.0085516339,0.0611655228,-0.1266064495,-0.2937129438,-0.2063525468,0.1784841269,-0.0399721339,0.5010519624,0.2095853388,-0.1630304307,-0.0904012397,0.3006502092,0.1256808788,0.1690942347,0.1850461513,-0.3328076303,0.1235525161,0.0621022321,0.3123052716,0.1769826561,0.3749838471,-0.1040768772,0.1340055466,-0.0802904889,-0.194326207,0.1071156189,0.0810026154,-0.0310398173,-0.0732616782,0.0953732356,-0.0209314171,-0.4374803007,0.004406306,-0.3678159714,-0.0123512559,-0.3567407131,0.0487865619,-0.3243841231,-0.4119811952,-0.2333052158,-0.0788863972,-0.4529622197,-0.2149646431,0.2001776993,0.1844459027,-0.3822817504,0.3398858905,0.2563820481,0.3452131152,-0.1577738971,0.0183630418,-0.096030958,-0.0979340076,-0.2653641701,0.1727079153,0.2459769547,0.0696047544,0.4489150047,-0.1880033165,0.2625869811,-0.2494304478,-0.7585305572,0.0928101167,-0.01130387,0.2021751255,0.3293008804,0.0585579537,0.2586821616,-0.3670679033,0.0069151097,-0.3343408108,-0.1845338941,0.0125687122,-0.1436916888,-0.162294507,-0.1993564963,-0.1186818257,-0.0368571021,-0.5511009693,0.2229225039,-0.203947112,-0.1150979176,0.3167098463,-0.0235494208,0.2907909155,-0.4422486126,0.1673290431,-0.0310630128,-0.40988639,0.2175301015,-0.2191780061,-0.2563723624,-0.0489579104,0.0027548892,0.2813417912,-0.1618327051,-0.238123253,-0.2436077297,-0.1488197148,0.1200854182,0.3816463351,0.1798692048,0.2096943408,0.1286325753,-0.168921262,-0.2520650923,-0.1157061458,0.0651172996,-0.202627629,0.0197580419,0.0236343928,-0.0603592545,-0.2330673635,0.204626888,0.1164685562,-0.1896945834,0.3827630877,0.080948256,0.6727414727,-0.1006569639,-0.2928420603,0.0680517405,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1585","title":"FileNotFoundError for `amazon_polarity`","comments":"Hi @phtephanx , the `amazon_polarity` dataset has not been released yet. It will be available in the coming soon v2of `datasets` :) \r\n\r\nYou can still access it now if you want, but you will need to install datasets via the master branch:\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","body":"Version: `datasets==v1.1.3`\r\n\r\n### Reproduction\r\n```python\r\nfrom datasets import load_dataset\r\ndata = load_dataset(\"amazon_polarity\")\r\n```\r\ncrashes with\r\n```bash\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/amazon_polarity\/amazon_polarity.py\r\n```\r\nand \r\n```bash\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/amazon_polarity\/amazon_polarity.py\r\n```\r\nand\r\n```bash\r\nFileNotFoundError: Couldn't find file locally at amazon_polarity\/amazon_polarity.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/amazon_polarity\/amazon_polarity.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/amazon_polarity\/amazon_polarity.py\r\n```","comment_length":45,"text":"FileNotFoundError for `amazon_polarity` \n Version: `datasets==v1.1.3`\r\n\r\n### Reproduction\r\n```python\r\nfrom datasets import load_dataset\r\ndata = load_dataset(\"amazon_polarity\")\r\n```\r\ncrashes with\r\n```bash\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/amazon_polarity\/amazon_polarity.py\r\n```\r\nand \r\n```bash\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/amazon_polarity\/amazon_polarity.py\r\n```\r\nand\r\n```bash\r\nFileNotFoundError: Couldn't find file locally at amazon_polarity\/amazon_polarity.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/amazon_polarity\/amazon_polarity.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/amazon_polarity\/amazon_polarity.py\r\n``` \n Hi @phtephanx , the `amazon_polarity` dataset has not been released yet. It will be available in the coming soon v2of `datasets` :) \r\n\r\nYou can still access it now if you want, but you will need to install datasets via the master branch:\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","embeddings":[-0.3479880095,-0.4079693556,-0.1594295353,0.1468008608,0.3409609795,0.1245037466,0.1368293166,0.0708829165,-0.0147829289,0.2100149393,-0.0304772444,-0.0856967419,-0.2752964795,0.0079954294,-0.083372511,-0.1797067821,0.0836886317,0.1474182904,-0.1062064916,0.144836992,-0.1857416183,0.3676777184,-0.2011100203,-0.0363301374,-0.1535672098,0.0321417078,-0.1244175658,0.2561988235,-0.3571479619,-0.1719460189,0.6051847339,-0.1438151598,0.116646491,0.4674164355,-0.0001094239,0.121839717,0.2970665395,0.1170058623,-0.2840379775,-0.6816424727,0.1893191934,-0.1137111932,0.1002502143,-0.0300899651,-0.1724277139,-0.4273685515,0.3060815334,0.06626679,0.4094986022,0.0599564649,0.2611253858,0.4023949802,0.4682300389,-0.1297983825,0.4718745947,0.3050636947,-0.1289897412,0.1152159944,0.0195885096,-0.0436794125,0.2055599838,0.3719202876,0.0113057951,-0.0789840817,0.2118726075,-0.0042474139,0.1750297099,-0.1100075245,0.0232831724,0.2148030698,0.2996555865,-0.3209213614,-0.5227025151,-0.0029204008,0.011715604,-0.3397398591,0.1643906385,0.1410713345,-0.0013033786,-0.1067062169,0.0194250494,-0.2168499976,-0.3099757731,0.394634515,-0.074210383,0.5269249678,-0.3228904307,0.0313689671,0.1816498935,-0.4286199808,-0.3890161514,0.1138272062,-0.1071820781,0.1441517919,-0.2899423242,-0.075633727,-0.1392741054,0.0748889819,0.1399799585,0.2753765583,-0.074021332,-0.0055774814,-0.0318000428,0.170882374,0.077100262,0.034131851,0.1519277245,0.3041283786,0.1158187687,0.3378837705,0.1673315763,-0.0622062683,-0.0027534203,-0.3101986945,-0.0584132709,-0.1008433327,0.4976038337,-0.1935555488,-0.4300061166,-0.0161387399,-0.2263162285,0.1012486294,0.1638518721,0.4274002314,0.0445426889,-0.028920725,0.1809092015,0.1616847813,-0.2305804193,0.2029237449,-0.2381457984,0.2560457885,-0.1458518952,-0.0051119602,0.1202393174,-0.1230172813,0.4229930043,-0.1286453754,-0.0903867185,0.0102752652,0.1358660012,0.0473022275,0.0937853083,0.342635721,-0.1503624171,-0.075939998,0.1274428368,0.0928332508,-0.1470044255,-0.1799183488,0.0203040726,-0.0959174559,-0.2991919518,0.207498163,-0.2199320644,-0.1230285391,0.0789890736,-0.024935998,-0.1744746268,-0.3944456577,0.0188542735,-0.0501719341,-0.153336212,-0.1192642674,0.2974745631,0.6852934361,-0.2487232983,-0.2089466453,-0.3055853546,-0.2532973588,0.1533036679,0.0427145138,-0.0661377087,-0.131085366,-0.3379172683,-0.2395652235,0.1273248941,-0.34221071,-0.219497785,0.1426003575,-0.39196527,0.1090528369,0.2609383166,0.1072728559,-0.4844020605,-0.0023038345,0.072755076,-0.2347019166,0.1089569405,0.0022045132,-0.0262717772,-0.3088288307,-0.0118465014,0.1687119305,-0.010352876,-0.1097878367,0.239464134,0.1723675132,0.1868828386,-0.015090609,-0.1329808533,0.4036785662,0.430098176,0.1812001914,-0.043866355,-0.4173109531,-0.5634769797,0.0696375445,-0.1297064722,-0.0143243913,-0.1950017214,-0.1236344129,-0.1820705384,-0.1635348201,-0.0530653857,-0.1849985868,0.1603649855,0.1631918401,0.3020811379,0.0290639997,-0.1731580943,0.1303827018,-0.2044846565,0.2718700469,-0.5783797503,0.0628575236,-0.1096294373,-0.0123150293,0.148976624,0.1735637784,0.0894442797,-0.2606093287,0.2154633403,0.4597685933,-0.2463010401,0.4114013314,0.1603762209,0.174153924,0.2682320476,-0.0960373655,-0.0961564556,-0.0080652973,0.0273449682,0.1278957278,-0.0698075444,0.385148108,-0.1351651549,0.1483459175,0.1445954889,0.0045578103,0.1259079725,0.2072737366,-0.2886961699,-0.3195874393,0.3829951286,-0.0956356525,0.2078200877,-0.2757167518,-0.3405966461,-0.0480875447,0.1322781593,-0.0102751087,0.164276734,-0.099009566,-0.2142263502,0.1930595189,0.1184575707,0.4621918201,0.4799415469,0.3050818443,-0.0855378881,0.0466704704,-0.1272161007,-0.160594821,0.4043017924,0.2406167239,0.0038172002,0.0504072271,-0.1336296946,-0.0189378634,-0.2433249801,-0.3935017288,-0.2533627152,0.1306104809,-0.2505778074,-0.0321471058,-0.4261734784,-0.2258952856,-0.3069345355,0.1278160065,-0.1757679582,-0.1779222339,-0.1088575646,0.2513082922,-0.1686687618,0.0835060179,-0.0559361838,0.0933208317,-0.0435148589,-0.3251518011,-0.2229113281,-0.1641744226,-0.0613541678,0.1425081193,0.2137884796,-0.1246764585,0.4876343012,-0.2830376029,0.0688867271,-0.3899007142,-0.4924218059,0.0189632233,-0.2072217911,0.0949290171,0.4582244754,0.1032193527,-0.2475157976,-0.0739440322,0.1684570312,-0.1575738043,-0.2392317504,-0.0062979213,-0.0697318763,-0.1733554006,-0.0253236219,-0.402853936,-0.1508603245,-0.337118268,0.2300270051,0.148672834,-0.0006279053,0.1806941628,0.1901656538,0.3676950336,-0.3491116762,0.2490089387,0.0143668177,-0.2989014089,0.2959597111,-0.2435079366,-0.2643984556,0.2790344954,0.1617137939,0.0167356525,0.0499413833,-0.2836008668,-0.06305857,-0.12184567,-0.1174788252,0.0111594694,0.3160513639,0.3380795121,0.0302935038,-0.100764282,0.0467141345,-0.1240636781,-0.1301065385,-0.2254658639,0.3550337851,-0.2552389801,0.076704964,-0.2182062417,0.6340251565,0.0623792075,-0.1936589777,0.0910418853,-0.1483147144,0.490024000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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1581","title":"Installing datasets and transformers in a tensorflow docker image throws Permission Error on 'import transformers'","comments":"Thanks for reporting !\r\nYou can override the directory in which cache file are stored using for example\r\n```\r\nENV HF_HOME=\"\/root\/cache\/hf_cache_home\"\r\n```\r\n\r\nThis way both `transformers` and `datasets` will use this directory instead of the default `.cache`","body":"I am using a docker container, based on latest tensorflow-gpu image, to run transformers and datasets (4.0.1 and 1.1.3 respectively - Dockerfile attached below). Importing transformers throws a Permission Error to access `\/.cache`:\r\n\r\n```\r\n$ docker run --gpus=all --rm -it -u $(id -u):$(id -g) -v $(pwd)\/data:\/root\/data -v $(pwd):\/root -v $(pwd)\/models\/:\/root\/models -v $(pwd)\/saved_models\/:\/root\/saved_models -e \"HOST_HOSTNAME=$(hostname)\" hf-error:latest \/bin\/bash\r\n\r\n________                               _______________                \r\n___  __\/__________________________________  ____\/__  \/________      __\r\n__  \/  _  _ \\_  __ \\_  ___\/  __ \\_  ___\/_  \/_   __  \/_  __ \\_ | \/| \/ \/\r\n_  \/   \/  __\/  \/ \/ \/(__  )\/ \/_\/ \/  \/   _  __\/   _  \/ \/ \/_\/ \/_ |\/ |\/ \/ \r\n\/_\/    \\___\/\/_\/ \/_\/\/____\/ \\____\/\/_\/    \/_\/      \/_\/  \\____\/____\/|__\/\r\n\r\n\r\nYou are running this container as user with ID 1000 and group 1000,\r\nwhich should map to the ID and group for your user on the Docker host. Great!\r\n\r\ntf-docker \/root > python\r\nPython 3.6.9 (default, Oct  8 2020, 12:12:24) \r\n[GCC 8.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import transformers\r\n2020-12-15 23:53:21.165827: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/__init__.py\", line 22, in <module>\r\n    from .integrations import (  # isort:skip\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/integrations.py\", line 5, in <module>\r\n    from .trainer_utils import EvaluationStrategy\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/trainer_utils.py\", line 25, in <module>\r\n    from .file_utils import is_tf_available, is_torch_available, is_torch_tpu_available\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/file_utils.py\", line 88, in <module>\r\n    import datasets  # noqa: F401\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/__init__.py\", line 26, in <module>\r\n    from .arrow_dataset import Dataset, concatenate_datasets\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py\", line 40, in <module>\r\n    from .arrow_reader import ArrowReader\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 31, in <module>\r\n    from .utils import cached_path, logging\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/download_manager.py\", line 25, in <module>\r\n    from .file_utils import HF_DATASETS_CACHE, cached_path, get_from_cache, hash_url_to_filename\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 118, in <module>\r\n    os.makedirs(HF_MODULES_CACHE, exist_ok=True)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nPermissionError: [Errno 13] Permission denied: '\/.cache'\r\n```\r\nI've pinned the problem to `RUN pip install datasets`, and by commenting it you can actually import transformers correctly. Another workaround I've found is creating the directory and giving permissions to it directly on the Dockerfile.\r\n\r\n```\r\nFROM tensorflow\/tensorflow:latest-gpu-jupyter\r\nWORKDIR \/root\r\n\r\nEXPOSE 80\r\nEXPOSE 8888\r\nEXPOSE 6006\r\n\r\nENV SHELL \/bin\/bash\r\nENV PATH=\"\/root\/.local\/bin:${PATH}\"\r\n\r\nENV CUDA_CACHE_PATH=\"\/root\/cache\/cuda\"\r\nENV CUDA_CACHE_MAXSIZE=\"4294967296\"\r\n\r\nENV TFHUB_CACHE_DIR=\"\/root\/cache\/tfhub\"\r\n\r\nRUN pip install --upgrade pip\r\n\r\nRUN apt update -y && apt upgrade -y\r\n\r\nRUN pip install transformers\r\n\r\n#Installing datasets will throw the error, try commenting and rebuilding\r\nRUN pip install datasets\r\n\r\n#Another workaround is creating the directory and give permissions explicitly\r\n#RUN mkdir \/.cache\r\n#RUN chmod 777 \/.cache\r\n```\r\n","comment_length":37,"text":"Installing datasets and transformers in a tensorflow docker image throws Permission Error on 'import transformers' \n I am using a docker container, based on latest tensorflow-gpu image, to run transformers and datasets (4.0.1 and 1.1.3 respectively - Dockerfile attached below). Importing transformers throws a Permission Error to access `\/.cache`:\r\n\r\n```\r\n$ docker run --gpus=all --rm -it -u $(id -u):$(id -g) -v $(pwd)\/data:\/root\/data -v $(pwd):\/root -v $(pwd)\/models\/:\/root\/models -v $(pwd)\/saved_models\/:\/root\/saved_models -e \"HOST_HOSTNAME=$(hostname)\" hf-error:latest \/bin\/bash\r\n\r\n________                               _______________                \r\n___  __\/__________________________________  ____\/__  \/________      __\r\n__  \/  _  _ \\_  __ \\_  ___\/  __ \\_  ___\/_  \/_   __  \/_  __ \\_ | \/| \/ \/\r\n_  \/   \/  __\/  \/ \/ \/(__  )\/ \/_\/ \/  \/   _  __\/   _  \/ \/ \/_\/ \/_ |\/ |\/ \/ \r\n\/_\/    \\___\/\/_\/ \/_\/\/____\/ \\____\/\/_\/    \/_\/      \/_\/  \\____\/____\/|__\/\r\n\r\n\r\nYou are running this container as user with ID 1000 and group 1000,\r\nwhich should map to the ID and group for your user on the Docker host. Great!\r\n\r\ntf-docker \/root > python\r\nPython 3.6.9 (default, Oct  8 2020, 12:12:24) \r\n[GCC 8.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import transformers\r\n2020-12-15 23:53:21.165827: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/__init__.py\", line 22, in <module>\r\n    from .integrations import (  # isort:skip\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/integrations.py\", line 5, in <module>\r\n    from .trainer_utils import EvaluationStrategy\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/trainer_utils.py\", line 25, in <module>\r\n    from .file_utils import is_tf_available, is_torch_available, is_torch_tpu_available\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/file_utils.py\", line 88, in <module>\r\n    import datasets  # noqa: F401\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/__init__.py\", line 26, in <module>\r\n    from .arrow_dataset import Dataset, concatenate_datasets\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py\", line 40, in <module>\r\n    from .arrow_reader import ArrowReader\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 31, in <module>\r\n    from .utils import cached_path, logging\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/download_manager.py\", line 25, in <module>\r\n    from .file_utils import HF_DATASETS_CACHE, cached_path, get_from_cache, hash_url_to_filename\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 118, in <module>\r\n    os.makedirs(HF_MODULES_CACHE, exist_ok=True)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nPermissionError: [Errno 13] Permission denied: '\/.cache'\r\n```\r\nI've pinned the problem to `RUN pip install datasets`, and by commenting it you can actually import transformers correctly. Another workaround I've found is creating the directory and giving permissions to it directly on the Dockerfile.\r\n\r\n```\r\nFROM tensorflow\/tensorflow:latest-gpu-jupyter\r\nWORKDIR \/root\r\n\r\nEXPOSE 80\r\nEXPOSE 8888\r\nEXPOSE 6006\r\n\r\nENV SHELL \/bin\/bash\r\nENV PATH=\"\/root\/.local\/bin:${PATH}\"\r\n\r\nENV CUDA_CACHE_PATH=\"\/root\/cache\/cuda\"\r\nENV CUDA_CACHE_MAXSIZE=\"4294967296\"\r\n\r\nENV TFHUB_CACHE_DIR=\"\/root\/cache\/tfhub\"\r\n\r\nRUN pip install --upgrade pip\r\n\r\nRUN apt update -y && apt upgrade -y\r\n\r\nRUN pip install transformers\r\n\r\n#Installing datasets will throw the error, try commenting and rebuilding\r\nRUN pip install datasets\r\n\r\n#Another workaround is creating the directory and give permissions explicitly\r\n#RUN mkdir \/.cache\r\n#RUN chmod 777 \/.cache\r\n```\r\n \n Thanks for reporting !\r\nYou can override the directory in which cache file are stored using for example\r\n```\r\nENV HF_HOME=\"\/root\/cache\/hf_cache_home\"\r\n```\r\n\r\nThis way both `transformers` and `datasets` will use this directory instead of the default `.cache`","embeddings":[-0.2601940632,0.1537577212,-0.087916173,0.1361187547,0.0533822998,0.0593192391,0.656006515,0.1960314065,0.161277622,-0.0136698019,-0.3051161468,-0.168390587,-0.0324441306,-0.5109063387,-0.0259655211,-0.2055929005,0.0551733933,-0.0676345825,-0.3101544976,-0.1253348142,-0.0913535804,-0.0168972444,-0.0075819497,0.1020792797,-0.3513800204,0.0235478356,0.333840996,-0.0979835615,0.0049274717,-0.1136808172,0.3865947425,0.1387621909,0.1959901601,0.8908079863,-0.0001138685,0.364698112,0.1204629838,0.0754132047,0.0866398886,-0.2086414546,-0.1387084424,-0.2477509379,-0.1201189086,-0.0817605555,0.0790471286,0.0090726782,0.0140740611,-0.1214024574,0.6044265628,0.3587148786,0.2027944475,0.4903935492,0.0908189341,0.123684071,0.2537278831,-0.0316032395,-0.2483440191,-0.0738279372,-0.1243278161,-0.2798888981,-0.0047014416,0.2760694027,-0.0540417656,0.1426951587,0.2170650065,-0.1940569878,0.1225119233,-0.3570998013,-0.0412276238,0.0453185365,0.5151111484,-0.065642722,-0.5028495193,-0.1864654869,-0.1189291701,0.0584088117,0.302632153,0.3046842217,-0.0361773148,0.2790159285,-0.2144559771,-0.1514513791,-0.4730093479,0.095054619,-0.2394710332,0.3513744175,0.0620445088,0.1947925389,-0.0911417976,-0.0715904012,0.6296569109,-0.0060653305,0.1474893838,0.2979938984,-0.3239389062,0.2316237688,0.0331782922,-0.5658233166,-0.3128330112,0.0865982324,-0.2314409018,-0.0924056843,-0.0704433471,0.3829046488,-0.2021469325,0.4120383263,-0.3363805413,0.7340712547,0.3856590092,-0.3047202826,0.0332622379,-0.0432148613,-0.0374102555,-0.0881388262,-0.1260698587,-0.0319224447,0.14577277,0.0939131603,-0.098433651,-0.034681011,0.086896874,-0.0100169806,0.1854218394,0.3815259933,0.1036055908,0.0991566405,0.3901361525,0.1196964458,-0.0152244633,0.0574717671,-0.2500608265,0.072026372,-0.0946181267,-0.0641494319,0.1987671703,0.0866814032,0.0298909303,-0.1037014425,0.2303165644,-0.1406204551,0.4395029545,-0.2077358812,0.1142411977,0.7253692746,0.1236809492,0.040582139,0.3252632618,-0.1411764622,0.0385678783,0.1115270332,-0.3354057968,-0.1929328144,0.0483054183,0.1385359615,-0.133828178,0.2708508074,-0.2460403144,-0.1343725771,0.3796409369,-0.0594122037,-0.0387408696,-0.2568915188,0.0189277288,-0.1345490813,-0.0994921178,0.2476488799,-0.1799386889,-0.1785385013,-0.0476372056,0.0107344426,-0.0975554064,0.4373539984,-0.1490976363,0.3612090945,-0.012258118,0.1215736493,0.0213417336,-0.5040411353,-0.4507939517,-0.0658406541,-0.0317620635,0.0552451983,0.0770501271,0.0080055622,0.3263442218,-0.2391067594,-0.2724581957,0.3224855959,0.2141270787,0.0887082145,0.22979559,-0.1883960515,-0.0167393964,0.0299266316,0.5327509046,0.2355925739,0.0445311628,0.4013054967,-0.1830300242,-0.342936188,0.1383527666,0.4372718632,0.3134897947,0.0098209782,0.0195712894,-0.0359986238,-0.3422741592,0.3768794537,-0.1361479759,0.1983713806,-0.0884066597,-0.001343661,-0.1373440027,0.3042966723,-0.0974787921,-0.0624615848,0.1574099958,0.0914994776,-0.0297624823,-0.0523535758,0.2131533921,0.4122335017,0.0303778984,0.1924372464,-0.013811538,0.4103766978,-0.2020863146,-0.0439700596,-0.0946270898,-0.0712999925,0.1517301798,-0.1395329535,-0.205512628,0.1627992392,-0.0285273809,0.1827243418,-0.5095719695,0.427361697,0.2366541922,-0.0472982004,0.0571585074,0.1110836491,-0.002362475,-0.0089515029,-0.0438567549,0.0467418507,0.0703111067,0.0665785149,-0.0663850605,0.1159160361,0.2043628991,0.0928915516,0.0580297783,-0.0351178497,0.0987340659,0.1005920395,-0.0032767237,-0.1262062788,0.3256442547,-0.2848223448,0.2149302065,0.1594939679,0.0941943005,0.0378742032,-0.0761301368,0.1714250296,0.3254772723,0.3470335901,0.2231839895,0.0314969905,0.0246453322,-0.0549993627,-0.0549255013,-0.0206400882,-0.1116272211,0.1299436986,0.1952213347,0.0732089356,0.0617158674,0.0350541733,-0.1838503778,-0.2796123028,0.0911719501,0.3946083188,-0.3553246856,0.1200247556,-0.039686121,0.0555186421,0.1301530749,0.0189663973,-0.4570333958,-0.2215031236,-0.2029660195,0.4210834503,-0.1194587797,0.1644146591,-0.0810745582,0.0509676039,0.2963262796,-0.1255412996,-0.259331286,0.104335174,0.0625970736,0.0425717719,0.1925008893,-0.5198504925,0.1900203079,0.1750378907,0.1842620224,-0.0083790794,-0.5007292032,0.0480755009,-0.1537050158,0.1523056179,-0.1455800682,0.2838713229,0.0492252409,-0.2909571528,-0.0210785959,-0.1372650564,-0.0680153519,-0.0453203991,-0.0619840845,-0.2530847788,-0.1954331398,-0.069822371,-0.2933194339,-0.4212827682,0.1225009188,0.371108681,0.2529848516,0.4407685995,0.1377109438,0.0921132565,0.2690692544,0.0994600207,-0.0383629762,-0.107421346,0.2451769412,-0.3104769289,-0.2563972175,0.0066135097,0.0111053875,0.2837287486,0.337452203,-0.3564890623,-0.1421988308,-0.312304467,-0.0041876035,-0.0811958015,0.1192051098,0.045397982,-0.0257927254,0.1307452321,-0.1638926119,0.0347888954,-0.0795593485,0.1042242572,-0.0728223324,0.0976093784,0.0997843817,0.2115115076,0.6869980693,-0.1291385889,0.1413394809,0.018308932,0.2151246369,0.4513024092,0.0434456803,-0.2967938781,0.0668857172,0.0837084129,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1581","title":"Installing datasets and transformers in a tensorflow docker image throws Permission Error on 'import transformers'","comments":"> Thanks for reporting !\r\n> You can override the directory in which cache file are stored using for example\r\n> \r\n> ```\r\n> ENV HF_HOME=\"\/root\/cache\/hf_cache_home\"\r\n> ```\r\n> \r\n> This way both `transformers` and `datasets` will use this directory instead of the default `.cache`\r\n\r\ncan we disable caching directly?","body":"I am using a docker container, based on latest tensorflow-gpu image, to run transformers and datasets (4.0.1 and 1.1.3 respectively - Dockerfile attached below). Importing transformers throws a Permission Error to access `\/.cache`:\r\n\r\n```\r\n$ docker run --gpus=all --rm -it -u $(id -u):$(id -g) -v $(pwd)\/data:\/root\/data -v $(pwd):\/root -v $(pwd)\/models\/:\/root\/models -v $(pwd)\/saved_models\/:\/root\/saved_models -e \"HOST_HOSTNAME=$(hostname)\" hf-error:latest \/bin\/bash\r\n\r\n________                               _______________                \r\n___  __\/__________________________________  ____\/__  \/________      __\r\n__  \/  _  _ \\_  __ \\_  ___\/  __ \\_  ___\/_  \/_   __  \/_  __ \\_ | \/| \/ \/\r\n_  \/   \/  __\/  \/ \/ \/(__  )\/ \/_\/ \/  \/   _  __\/   _  \/ \/ \/_\/ \/_ |\/ |\/ \/ \r\n\/_\/    \\___\/\/_\/ \/_\/\/____\/ \\____\/\/_\/    \/_\/      \/_\/  \\____\/____\/|__\/\r\n\r\n\r\nYou are running this container as user with ID 1000 and group 1000,\r\nwhich should map to the ID and group for your user on the Docker host. Great!\r\n\r\ntf-docker \/root > python\r\nPython 3.6.9 (default, Oct  8 2020, 12:12:24) \r\n[GCC 8.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import transformers\r\n2020-12-15 23:53:21.165827: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/__init__.py\", line 22, in <module>\r\n    from .integrations import (  # isort:skip\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/integrations.py\", line 5, in <module>\r\n    from .trainer_utils import EvaluationStrategy\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/trainer_utils.py\", line 25, in <module>\r\n    from .file_utils import is_tf_available, is_torch_available, is_torch_tpu_available\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/file_utils.py\", line 88, in <module>\r\n    import datasets  # noqa: F401\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/__init__.py\", line 26, in <module>\r\n    from .arrow_dataset import Dataset, concatenate_datasets\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py\", line 40, in <module>\r\n    from .arrow_reader import ArrowReader\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 31, in <module>\r\n    from .utils import cached_path, logging\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/download_manager.py\", line 25, in <module>\r\n    from .file_utils import HF_DATASETS_CACHE, cached_path, get_from_cache, hash_url_to_filename\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 118, in <module>\r\n    os.makedirs(HF_MODULES_CACHE, exist_ok=True)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nPermissionError: [Errno 13] Permission denied: '\/.cache'\r\n```\r\nI've pinned the problem to `RUN pip install datasets`, and by commenting it you can actually import transformers correctly. Another workaround I've found is creating the directory and giving permissions to it directly on the Dockerfile.\r\n\r\n```\r\nFROM tensorflow\/tensorflow:latest-gpu-jupyter\r\nWORKDIR \/root\r\n\r\nEXPOSE 80\r\nEXPOSE 8888\r\nEXPOSE 6006\r\n\r\nENV SHELL \/bin\/bash\r\nENV PATH=\"\/root\/.local\/bin:${PATH}\"\r\n\r\nENV CUDA_CACHE_PATH=\"\/root\/cache\/cuda\"\r\nENV CUDA_CACHE_MAXSIZE=\"4294967296\"\r\n\r\nENV TFHUB_CACHE_DIR=\"\/root\/cache\/tfhub\"\r\n\r\nRUN pip install --upgrade pip\r\n\r\nRUN apt update -y && apt upgrade -y\r\n\r\nRUN pip install transformers\r\n\r\n#Installing datasets will throw the error, try commenting and rebuilding\r\nRUN pip install datasets\r\n\r\n#Another workaround is creating the directory and give permissions explicitly\r\n#RUN mkdir \/.cache\r\n#RUN chmod 777 \/.cache\r\n```\r\n","comment_length":50,"text":"Installing datasets and transformers in a tensorflow docker image throws Permission Error on 'import transformers' \n I am using a docker container, based on latest tensorflow-gpu image, to run transformers and datasets (4.0.1 and 1.1.3 respectively - Dockerfile attached below). Importing transformers throws a Permission Error to access `\/.cache`:\r\n\r\n```\r\n$ docker run --gpus=all --rm -it -u $(id -u):$(id -g) -v $(pwd)\/data:\/root\/data -v $(pwd):\/root -v $(pwd)\/models\/:\/root\/models -v $(pwd)\/saved_models\/:\/root\/saved_models -e \"HOST_HOSTNAME=$(hostname)\" hf-error:latest \/bin\/bash\r\n\r\n________                               _______________                \r\n___  __\/__________________________________  ____\/__  \/________      __\r\n__  \/  _  _ \\_  __ \\_  ___\/  __ \\_  ___\/_  \/_   __  \/_  __ \\_ | \/| \/ \/\r\n_  \/   \/  __\/  \/ \/ \/(__  )\/ \/_\/ \/  \/   _  __\/   _  \/ \/ \/_\/ \/_ |\/ |\/ \/ \r\n\/_\/    \\___\/\/_\/ \/_\/\/____\/ \\____\/\/_\/    \/_\/      \/_\/  \\____\/____\/|__\/\r\n\r\n\r\nYou are running this container as user with ID 1000 and group 1000,\r\nwhich should map to the ID and group for your user on the Docker host. Great!\r\n\r\ntf-docker \/root > python\r\nPython 3.6.9 (default, Oct  8 2020, 12:12:24) \r\n[GCC 8.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import transformers\r\n2020-12-15 23:53:21.165827: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/__init__.py\", line 22, in <module>\r\n    from .integrations import (  # isort:skip\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/integrations.py\", line 5, in <module>\r\n    from .trainer_utils import EvaluationStrategy\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/trainer_utils.py\", line 25, in <module>\r\n    from .file_utils import is_tf_available, is_torch_available, is_torch_tpu_available\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/file_utils.py\", line 88, in <module>\r\n    import datasets  # noqa: F401\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/__init__.py\", line 26, in <module>\r\n    from .arrow_dataset import Dataset, concatenate_datasets\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py\", line 40, in <module>\r\n    from .arrow_reader import ArrowReader\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 31, in <module>\r\n    from .utils import cached_path, logging\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/download_manager.py\", line 25, in <module>\r\n    from .file_utils import HF_DATASETS_CACHE, cached_path, get_from_cache, hash_url_to_filename\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 118, in <module>\r\n    os.makedirs(HF_MODULES_CACHE, exist_ok=True)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nPermissionError: [Errno 13] Permission denied: '\/.cache'\r\n```\r\nI've pinned the problem to `RUN pip install datasets`, and by commenting it you can actually import transformers correctly. Another workaround I've found is creating the directory and giving permissions to it directly on the Dockerfile.\r\n\r\n```\r\nFROM tensorflow\/tensorflow:latest-gpu-jupyter\r\nWORKDIR \/root\r\n\r\nEXPOSE 80\r\nEXPOSE 8888\r\nEXPOSE 6006\r\n\r\nENV SHELL \/bin\/bash\r\nENV PATH=\"\/root\/.local\/bin:${PATH}\"\r\n\r\nENV CUDA_CACHE_PATH=\"\/root\/cache\/cuda\"\r\nENV CUDA_CACHE_MAXSIZE=\"4294967296\"\r\n\r\nENV TFHUB_CACHE_DIR=\"\/root\/cache\/tfhub\"\r\n\r\nRUN pip install --upgrade pip\r\n\r\nRUN apt update -y && apt upgrade -y\r\n\r\nRUN pip install transformers\r\n\r\n#Installing datasets will throw the error, try commenting and rebuilding\r\nRUN pip install datasets\r\n\r\n#Another workaround is creating the directory and give permissions explicitly\r\n#RUN mkdir \/.cache\r\n#RUN chmod 777 \/.cache\r\n```\r\n \n > Thanks for reporting !\r\n> You can override the directory in which cache file are stored using for example\r\n> \r\n> ```\r\n> ENV HF_HOME=\"\/root\/cache\/hf_cache_home\"\r\n> ```\r\n> \r\n> This way both `transformers` and `datasets` will use this directory instead of the default `.cache`\r\n\r\ncan we disable caching directly?","embeddings":[-0.2601940632,0.1537577212,-0.087916173,0.1361187547,0.0533822998,0.0593192391,0.656006515,0.1960314065,0.161277622,-0.0136698019,-0.3051161468,-0.168390587,-0.0324441306,-0.5109063387,-0.0259655211,-0.2055929005,0.0551733933,-0.0676345825,-0.3101544976,-0.1253348142,-0.0913535804,-0.0168972444,-0.0075819497,0.1020792797,-0.3513800204,0.0235478356,0.333840996,-0.0979835615,0.0049274717,-0.1136808172,0.3865947425,0.1387621909,0.1959901601,0.8908079863,-0.0001138685,0.364698112,0.1204629838,0.0754132047,0.0866398886,-0.2086414546,-0.1387084424,-0.2477509379,-0.1201189086,-0.0817605555,0.0790471286,0.0090726782,0.0140740611,-0.1214024574,0.6044265628,0.3587148786,0.2027944475,0.4903935492,0.0908189341,0.123684071,0.2537278831,-0.0316032395,-0.2483440191,-0.0738279372,-0.1243278161,-0.2798888981,-0.0047014416,0.2760694027,-0.0540417656,0.1426951587,0.2170650065,-0.1940569878,0.1225119233,-0.3570998013,-0.0412276238,0.0453185365,0.5151111484,-0.065642722,-0.5028495193,-0.1864654869,-0.1189291701,0.0584088117,0.302632153,0.3046842217,-0.0361773148,0.2790159285,-0.2144559771,-0.1514513791,-0.4730093479,0.095054619,-0.2394710332,0.3513744175,0.0620445088,0.1947925389,-0.0911417976,-0.0715904012,0.6296569109,-0.0060653305,0.1474893838,0.2979938984,-0.3239389062,0.2316237688,0.0331782922,-0.5658233166,-0.3128330112,0.0865982324,-0.2314409018,-0.0924056843,-0.0704433471,0.3829046488,-0.2021469325,0.4120383263,-0.3363805413,0.7340712547,0.3856590092,-0.3047202826,0.0332622379,-0.0432148613,-0.0374102555,-0.0881388262,-0.1260698587,-0.0319224447,0.14577277,0.0939131603,-0.098433651,-0.034681011,0.086896874,-0.0100169806,0.1854218394,0.3815259933,0.1036055908,0.0991566405,0.3901361525,0.1196964458,-0.0152244633,0.0574717671,-0.2500608265,0.072026372,-0.0946181267,-0.0641494319,0.1987671703,0.0866814032,0.0298909303,-0.1037014425,0.2303165644,-0.1406204551,0.4395029545,-0.2077358812,0.1142411977,0.7253692746,0.1236809492,0.040582139,0.3252632618,-0.1411764622,0.0385678783,0.1115270332,-0.3354057968,-0.1929328144,0.0483054183,0.1385359615,-0.133828178,0.2708508074,-0.2460403144,-0.1343725771,0.3796409369,-0.0594122037,-0.0387408696,-0.2568915188,0.0189277288,-0.1345490813,-0.0994921178,0.2476488799,-0.1799386889,-0.1785385013,-0.0476372056,0.0107344426,-0.0975554064,0.4373539984,-0.1490976363,0.3612090945,-0.012258118,0.1215736493,0.0213417336,-0.5040411353,-0.4507939517,-0.0658406541,-0.0317620635,0.0552451983,0.0770501271,0.0080055622,0.3263442218,-0.2391067594,-0.2724581957,0.3224855959,0.2141270787,0.0887082145,0.22979559,-0.1883960515,-0.0167393964,0.0299266316,0.5327509046,0.2355925739,0.0445311628,0.4013054967,-0.1830300242,-0.342936188,0.1383527666,0.4372718632,0.3134897947,0.0098209782,0.0195712894,-0.0359986238,-0.3422741592,0.3768794537,-0.1361479759,0.1983713806,-0.0884066597,-0.001343661,-0.1373440027,0.3042966723,-0.0974787921,-0.0624615848,0.1574099958,0.0914994776,-0.0297624823,-0.0523535758,0.2131533921,0.4122335017,0.0303778984,0.1924372464,-0.013811538,0.4103766978,-0.2020863146,-0.0439700596,-0.0946270898,-0.0712999925,0.1517301798,-0.1395329535,-0.205512628,0.1627992392,-0.0285273809,0.1827243418,-0.5095719695,0.427361697,0.2366541922,-0.0472982004,0.0571585074,0.1110836491,-0.002362475,-0.0089515029,-0.0438567549,0.0467418507,0.0703111067,0.0665785149,-0.0663850605,0.1159160361,0.2043628991,0.0928915516,0.0580297783,-0.0351178497,0.0987340659,0.1005920395,-0.0032767237,-0.1262062788,0.3256442547,-0.2848223448,0.2149302065,0.1594939679,0.0941943005,0.0378742032,-0.0761301368,0.1714250296,0.3254772723,0.3470335901,0.2231839895,0.0314969905,0.0246453322,-0.0549993627,-0.0549255013,-0.0206400882,-0.1116272211,0.1299436986,0.1952213347,0.0732089356,0.0617158674,0.0350541733,-0.1838503778,-0.2796123028,0.0911719501,0.3946083188,-0.3553246856,0.1200247556,-0.039686121,0.0555186421,0.1301530749,0.0189663973,-0.4570333958,-0.2215031236,-0.2029660195,0.4210834503,-0.1194587797,0.1644146591,-0.0810745582,0.0509676039,0.2963262796,-0.1255412996,-0.259331286,0.104335174,0.0625970736,0.0425717719,0.1925008893,-0.5198504925,0.1900203079,0.1750378907,0.1842620224,-0.0083790794,-0.5007292032,0.0480755009,-0.1537050158,0.1523056179,-0.1455800682,0.2838713229,0.0492252409,-0.2909571528,-0.0210785959,-0.1372650564,-0.0680153519,-0.0453203991,-0.0619840845,-0.2530847788,-0.1954331398,-0.069822371,-0.2933194339,-0.4212827682,0.1225009188,0.371108681,0.2529848516,0.4407685995,0.1377109438,0.0921132565,0.2690692544,0.0994600207,-0.0383629762,-0.107421346,0.2451769412,-0.3104769289,-0.2563972175,0.0066135097,0.0111053875,0.2837287486,0.337452203,-0.3564890623,-0.1421988308,-0.312304467,-0.0041876035,-0.0811958015,0.1192051098,0.045397982,-0.0257927254,0.1307452321,-0.1638926119,0.0347888954,-0.0795593485,0.1042242572,-0.0728223324,0.0976093784,0.0997843817,0.2115115076,0.6869980693,-0.1291385889,0.1413394809,0.018308932,0.2151246369,0.4513024092,0.0434456803,-0.2967938781,0.0668857172,0.0837084129,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1581","title":"Installing datasets and transformers in a tensorflow docker image throws Permission Error on 'import transformers'","comments":"Hi ! Unfortunately no since we need this directory to load datasets.\r\nWhen you load a dataset, it downloads the raw data files in the cache directory inside <cache_dir>\/downloads. Then it builds the dataset and saves it as arrow data inside <cache_dir>\/<dataset_name>.\r\n\r\nHowever you can specify the directory of your choice, and it can be a temporary directory if you want to clean everything up at one point.","body":"I am using a docker container, based on latest tensorflow-gpu image, to run transformers and datasets (4.0.1 and 1.1.3 respectively - Dockerfile attached below). Importing transformers throws a Permission Error to access `\/.cache`:\r\n\r\n```\r\n$ docker run --gpus=all --rm -it -u $(id -u):$(id -g) -v $(pwd)\/data:\/root\/data -v $(pwd):\/root -v $(pwd)\/models\/:\/root\/models -v $(pwd)\/saved_models\/:\/root\/saved_models -e \"HOST_HOSTNAME=$(hostname)\" hf-error:latest \/bin\/bash\r\n\r\n________                               _______________                \r\n___  __\/__________________________________  ____\/__  \/________      __\r\n__  \/  _  _ \\_  __ \\_  ___\/  __ \\_  ___\/_  \/_   __  \/_  __ \\_ | \/| \/ \/\r\n_  \/   \/  __\/  \/ \/ \/(__  )\/ \/_\/ \/  \/   _  __\/   _  \/ \/ \/_\/ \/_ |\/ |\/ \/ \r\n\/_\/    \\___\/\/_\/ \/_\/\/____\/ \\____\/\/_\/    \/_\/      \/_\/  \\____\/____\/|__\/\r\n\r\n\r\nYou are running this container as user with ID 1000 and group 1000,\r\nwhich should map to the ID and group for your user on the Docker host. Great!\r\n\r\ntf-docker \/root > python\r\nPython 3.6.9 (default, Oct  8 2020, 12:12:24) \r\n[GCC 8.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import transformers\r\n2020-12-15 23:53:21.165827: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/__init__.py\", line 22, in <module>\r\n    from .integrations import (  # isort:skip\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/integrations.py\", line 5, in <module>\r\n    from .trainer_utils import EvaluationStrategy\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/trainer_utils.py\", line 25, in <module>\r\n    from .file_utils import is_tf_available, is_torch_available, is_torch_tpu_available\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/file_utils.py\", line 88, in <module>\r\n    import datasets  # noqa: F401\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/__init__.py\", line 26, in <module>\r\n    from .arrow_dataset import Dataset, concatenate_datasets\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py\", line 40, in <module>\r\n    from .arrow_reader import ArrowReader\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 31, in <module>\r\n    from .utils import cached_path, logging\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/download_manager.py\", line 25, in <module>\r\n    from .file_utils import HF_DATASETS_CACHE, cached_path, get_from_cache, hash_url_to_filename\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 118, in <module>\r\n    os.makedirs(HF_MODULES_CACHE, exist_ok=True)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nPermissionError: [Errno 13] Permission denied: '\/.cache'\r\n```\r\nI've pinned the problem to `RUN pip install datasets`, and by commenting it you can actually import transformers correctly. Another workaround I've found is creating the directory and giving permissions to it directly on the Dockerfile.\r\n\r\n```\r\nFROM tensorflow\/tensorflow:latest-gpu-jupyter\r\nWORKDIR \/root\r\n\r\nEXPOSE 80\r\nEXPOSE 8888\r\nEXPOSE 6006\r\n\r\nENV SHELL \/bin\/bash\r\nENV PATH=\"\/root\/.local\/bin:${PATH}\"\r\n\r\nENV CUDA_CACHE_PATH=\"\/root\/cache\/cuda\"\r\nENV CUDA_CACHE_MAXSIZE=\"4294967296\"\r\n\r\nENV TFHUB_CACHE_DIR=\"\/root\/cache\/tfhub\"\r\n\r\nRUN pip install --upgrade pip\r\n\r\nRUN apt update -y && apt upgrade -y\r\n\r\nRUN pip install transformers\r\n\r\n#Installing datasets will throw the error, try commenting and rebuilding\r\nRUN pip install datasets\r\n\r\n#Another workaround is creating the directory and give permissions explicitly\r\n#RUN mkdir \/.cache\r\n#RUN chmod 777 \/.cache\r\n```\r\n","comment_length":68,"text":"Installing datasets and transformers in a tensorflow docker image throws Permission Error on 'import transformers' \n I am using a docker container, based on latest tensorflow-gpu image, to run transformers and datasets (4.0.1 and 1.1.3 respectively - Dockerfile attached below). Importing transformers throws a Permission Error to access `\/.cache`:\r\n\r\n```\r\n$ docker run --gpus=all --rm -it -u $(id -u):$(id -g) -v $(pwd)\/data:\/root\/data -v $(pwd):\/root -v $(pwd)\/models\/:\/root\/models -v $(pwd)\/saved_models\/:\/root\/saved_models -e \"HOST_HOSTNAME=$(hostname)\" hf-error:latest \/bin\/bash\r\n\r\n________                               _______________                \r\n___  __\/__________________________________  ____\/__  \/________      __\r\n__  \/  _  _ \\_  __ \\_  ___\/  __ \\_  ___\/_  \/_   __  \/_  __ \\_ | \/| \/ \/\r\n_  \/   \/  __\/  \/ \/ \/(__  )\/ \/_\/ \/  \/   _  __\/   _  \/ \/ \/_\/ \/_ |\/ |\/ \/ \r\n\/_\/    \\___\/\/_\/ \/_\/\/____\/ \\____\/\/_\/    \/_\/      \/_\/  \\____\/____\/|__\/\r\n\r\n\r\nYou are running this container as user with ID 1000 and group 1000,\r\nwhich should map to the ID and group for your user on the Docker host. Great!\r\n\r\ntf-docker \/root > python\r\nPython 3.6.9 (default, Oct  8 2020, 12:12:24) \r\n[GCC 8.4.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import transformers\r\n2020-12-15 23:53:21.165827: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/__init__.py\", line 22, in <module>\r\n    from .integrations import (  # isort:skip\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/integrations.py\", line 5, in <module>\r\n    from .trainer_utils import EvaluationStrategy\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/trainer_utils.py\", line 25, in <module>\r\n    from .file_utils import is_tf_available, is_torch_available, is_torch_tpu_available\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/file_utils.py\", line 88, in <module>\r\n    import datasets  # noqa: F401\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/__init__.py\", line 26, in <module>\r\n    from .arrow_dataset import Dataset, concatenate_datasets\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py\", line 40, in <module>\r\n    from .arrow_reader import ArrowReader\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_reader.py\", line 31, in <module>\r\n    from .utils import cached_path, logging\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/download_manager.py\", line 25, in <module>\r\n    from .file_utils import HF_DATASETS_CACHE, cached_path, get_from_cache, hash_url_to_filename\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 118, in <module>\r\n    os.makedirs(HF_MODULES_CACHE, exist_ok=True)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 210, in makedirs\r\n    makedirs(head, mode, exist_ok)\r\n  File \"\/usr\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nPermissionError: [Errno 13] Permission denied: '\/.cache'\r\n```\r\nI've pinned the problem to `RUN pip install datasets`, and by commenting it you can actually import transformers correctly. Another workaround I've found is creating the directory and giving permissions to it directly on the Dockerfile.\r\n\r\n```\r\nFROM tensorflow\/tensorflow:latest-gpu-jupyter\r\nWORKDIR \/root\r\n\r\nEXPOSE 80\r\nEXPOSE 8888\r\nEXPOSE 6006\r\n\r\nENV SHELL \/bin\/bash\r\nENV PATH=\"\/root\/.local\/bin:${PATH}\"\r\n\r\nENV CUDA_CACHE_PATH=\"\/root\/cache\/cuda\"\r\nENV CUDA_CACHE_MAXSIZE=\"4294967296\"\r\n\r\nENV TFHUB_CACHE_DIR=\"\/root\/cache\/tfhub\"\r\n\r\nRUN pip install --upgrade pip\r\n\r\nRUN apt update -y && apt upgrade -y\r\n\r\nRUN pip install transformers\r\n\r\n#Installing datasets will throw the error, try commenting and rebuilding\r\nRUN pip install datasets\r\n\r\n#Another workaround is creating the directory and give permissions explicitly\r\n#RUN mkdir \/.cache\r\n#RUN chmod 777 \/.cache\r\n```\r\n \n Hi ! Unfortunately no since we need this directory to load datasets.\r\nWhen you load a dataset, it downloads the raw data files in the cache directory inside <cache_dir>\/downloads. Then it builds the dataset and saves it as arrow data inside <cache_dir>\/<dataset_name>.\r\n\r\nHowever you can specify the directory of your choice, and it can be a temporary directory if you want to clean everything up at one point.","embeddings":[-0.2601940632,0.1537577212,-0.087916173,0.1361187547,0.0533822998,0.0593192391,0.656006515,0.1960314065,0.161277622,-0.0136698019,-0.3051161468,-0.168390587,-0.0324441306,-0.5109063387,-0.0259655211,-0.2055929005,0.0551733933,-0.0676345825,-0.3101544976,-0.1253348142,-0.0913535804,-0.0168972444,-0.0075819497,0.1020792797,-0.3513800204,0.0235478356,0.333840996,-0.0979835615,0.0049274717,-0.1136808172,0.3865947425,0.1387621909,0.1959901601,0.8908079863,-0.0001138685,0.364698112,0.1204629838,0.0754132047,0.0866398886,-0.2086414546,-0.1387084424,-0.2477509379,-0.1201189086,-0.0817605555,0.0790471286,0.0090726782,0.0140740611,-0.1214024574,0.6044265628,0.3587148786,0.2027944475,0.4903935492,0.0908189341,0.123684071,0.2537278831,-0.0316032395,-0.2483440191,-0.0738279372,-0.1243278161,-0.2798888981,-0.0047014416,0.2760694027,-0.0540417656,0.1426951587,0.2170650065,-0.1940569878,0.1225119233,-0.3570998013,-0.0412276238,0.0453185365,0.5151111484,-0.065642722,-0.5028495193,-0.1864654869,-0.1189291701,0.0584088117,0.302632153,0.3046842217,-0.0361773148,0.2790159285,-0.2144559771,-0.1514513791,-0.4730093479,0.095054619,-0.2394710332,0.3513744175,0.0620445088,0.1947925389,-0.0911417976,-0.0715904012,0.6296569109,-0.0060653305,0.1474893838,0.2979938984,-0.3239389062,0.2316237688,0.0331782922,-0.5658233166,-0.3128330112,0.0865982324,-0.2314409018,-0.0924056843,-0.0704433471,0.3829046488,-0.2021469325,0.4120383263,-0.3363805413,0.7340712547,0.3856590092,-0.3047202826,0.0332622379,-0.0432148613,-0.0374102555,-0.0881388262,-0.1260698587,-0.0319224447,0.14577277,0.0939131603,-0.098433651,-0.034681011,0.086896874,-0.0100169806,0.1854218394,0.3815259933,0.1036055908,0.0991566405,0.3901361525,0.1196964458,-0.0152244633,0.0574717671,-0.2500608265,0.072026372,-0.0946181267,-0.0641494319,0.1987671703,0.0866814032,0.0298909303,-0.1037014425,0.2303165644,-0.1406204551,0.4395029545,-0.2077358812,0.1142411977,0.7253692746,0.1236809492,0.040582139,0.3252632618,-0.1411764622,0.0385678783,0.1115270332,-0.3354057968,-0.1929328144,0.0483054183,0.1385359615,-0.133828178,0.2708508074,-0.2460403144,-0.1343725771,0.3796409369,-0.0594122037,-0.0387408696,-0.2568915188,0.0189277288,-0.1345490813,-0.0994921178,0.2476488799,-0.1799386889,-0.1785385013,-0.0476372056,0.0107344426,-0.0975554064,0.4373539984,-0.1490976363,0.3612090945,-0.012258118,0.1215736493,0.0213417336,-0.5040411353,-0.4507939517,-0.0658406541,-0.0317620635,0.0552451983,0.0770501271,0.0080055622,0.3263442218,-0.2391067594,-0.2724581957,0.3224855959,0.2141270787,0.0887082145,0.22979559,-0.1883960515,-0.0167393964,0.0299266316,0.5327509046,0.2355925739,0.0445311628,0.4013054967,-0.1830300242,-0.342936188,0.1383527666,0.4372718632,0.3134897947,0.0098209782,0.0195712894,-0.0359986238,-0.3422741592,0.3768794537,-0.1361479759,0.1983713806,-0.0884066597,-0.001343661,-0.1373440027,0.3042966723,-0.0974787921,-0.0624615848,0.1574099958,0.0914994776,-0.0297624823,-0.0523535758,0.2131533921,0.4122335017,0.0303778984,0.1924372464,-0.013811538,0.4103766978,-0.2020863146,-0.0439700596,-0.0946270898,-0.0712999925,0.1517301798,-0.1395329535,-0.205512628,0.1627992392,-0.0285273809,0.1827243418,-0.5095719695,0.427361697,0.2366541922,-0.0472982004,0.0571585074,0.1110836491,-0.002362475,-0.0089515029,-0.0438567549,0.0467418507,0.0703111067,0.0665785149,-0.0663850605,0.1159160361,0.2043628991,0.0928915516,0.0580297783,-0.0351178497,0.0987340659,0.1005920395,-0.0032767237,-0.1262062788,0.3256442547,-0.2848223448,0.2149302065,0.1594939679,0.0941943005,0.0378742032,-0.0761301368,0.1714250296,0.3254772723,0.3470335901,0.2231839895,0.0314969905,0.0246453322,-0.0549993627,-0.0549255013,-0.0206400882,-0.1116272211,0.1299436986,0.1952213347,0.0732089356,0.0617158674,0.0350541733,-0.1838503778,-0.2796123028,0.0911719501,0.3946083188,-0.3553246856,0.1200247556,-0.039686121,0.0555186421,0.1301530749,0.0189663973,-0.4570333958,-0.2215031236,-0.2029660195,0.4210834503,-0.1194587797,0.1644146591,-0.0810745582,0.0509676039,0.2963262796,-0.1255412996,-0.259331286,0.104335174,0.0625970736,0.0425717719,0.1925008893,-0.5198504925,0.1900203079,0.1750378907,0.1842620224,-0.0083790794,-0.5007292032,0.0480755009,-0.1537050158,0.1523056179,-0.1455800682,0.2838713229,0.0492252409,-0.2909571528,-0.0210785959,-0.1372650564,-0.0680153519,-0.0453203991,-0.0619840845,-0.2530847788,-0.1954331398,-0.069822371,-0.2933194339,-0.4212827682,0.1225009188,0.371108681,0.2529848516,0.4407685995,0.1377109438,0.0921132565,0.2690692544,0.0994600207,-0.0383629762,-0.107421346,0.2451769412,-0.3104769289,-0.2563972175,0.0066135097,0.0111053875,0.2837287486,0.337452203,-0.3564890623,-0.1421988308,-0.312304467,-0.0041876035,-0.0811958015,0.1192051098,0.045397982,-0.0257927254,0.1307452321,-0.1638926119,0.0347888954,-0.0795593485,0.1042242572,-0.0728223324,0.0976093784,0.0997843817,0.2115115076,0.6869980693,-0.1291385889,0.1413394809,0.018308932,0.2151246369,0.4513024092,0.0434456803,-0.2967938781,0.0668857172,0.0837084129,-0.206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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1541","title":"connection issue while downloading data","comments":"could you tell me how I can avoid download, by pre-downloading the data first, put them in a folder so the code does not try to redownload? could you tell me the path to put the downloaded data, and how to do it? thanks\r\n@lhoestq ","body":"Hi\r\nI am running my codes on google cloud, and I am getting this error resulting in the failure of the codes when trying to download the data, could you assist me to solve this? also as a temporary solution, could you tell me how I can increase the number of retries and timeout to at least let the models run for now. thanks \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 361, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 269, in main\r\n    add_prefix=False if training_args.train_adapters else True)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 70, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 306, in load_dataset\r\n    return datasets.load_dataset('glue', 'cola', split=split)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 403, in http_head\r\n    url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 542, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 655, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f47db511e80>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n```","comment_length":45,"text":"connection issue while downloading data \n Hi\r\nI am running my codes on google cloud, and I am getting this error resulting in the failure of the codes when trying to download the data, could you assist me to solve this? also as a temporary solution, could you tell me how I can increase the number of retries and timeout to at least let the models run for now. thanks \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 361, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 269, in main\r\n    add_prefix=False if training_args.train_adapters else True)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 70, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 306, in load_dataset\r\n    return datasets.load_dataset('glue', 'cola', split=split)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 403, in http_head\r\n    url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 542, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 655, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f47db511e80>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n``` \n could you tell me how I can avoid download, by pre-downloading the data first, put them in a folder so the code does not try to redownload? could you tell me the path to put the downloaded data, and how to do it? thanks\r\n@lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1541","title":"connection issue while downloading data","comments":"Does your instance have an internet connection ?\r\n\r\nIf you don't have an internet connection you'll need to have the dataset on the instance disk.\r\nTo do so first download the dataset on another machine using `load_dataset` and then you can save it in a folder using `my_dataset.save_to_disk(\"path\/to\/folder\")`. Once the folder is copied on your instance you can reload the dataset with `datasets.load_from_disk(\"path\/to\/folder\")`","body":"Hi\r\nI am running my codes on google cloud, and I am getting this error resulting in the failure of the codes when trying to download the data, could you assist me to solve this? also as a temporary solution, could you tell me how I can increase the number of retries and timeout to at least let the models run for now. thanks \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 361, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 269, in main\r\n    add_prefix=False if training_args.train_adapters else True)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 70, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 306, in load_dataset\r\n    return datasets.load_dataset('glue', 'cola', split=split)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 403, in http_head\r\n    url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 542, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 655, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f47db511e80>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n```","comment_length":63,"text":"connection issue while downloading data \n Hi\r\nI am running my codes on google cloud, and I am getting this error resulting in the failure of the codes when trying to download the data, could you assist me to solve this? also as a temporary solution, could you tell me how I can increase the number of retries and timeout to at least let the models run for now. thanks \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 361, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 269, in main\r\n    add_prefix=False if training_args.train_adapters else True)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 70, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/workdir\/seq2seq\/data\/tasks.py\", line 306, in load_dataset\r\n    return datasets.load_dataset('glue', 'cola', split=split)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 589, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py\", line 263, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py\", line 403, in http_head\r\n    url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 542, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/sessions.py\", line 655, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/requests\/adapters.py\", line 504, in send\r\n    raise ConnectTimeout(e, request=request)\r\nrequests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/glue\/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f47db511e80>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))\r\n``` \n Does your instance have an internet connection ?\r\n\r\nIf you don't have an internet connection you'll need to have the dataset on the instance disk.\r\nTo do so first download the dataset on another machine using `load_dataset` and then you can save it in a folder using `my_dataset.save_to_disk(\"path\/to\/folder\")`. Once the folder is copied on your instance you can reload the dataset with `datasets.load_from_disk(\"path\/to\/folder\")`","embeddings":[-0.2348409742,-0.0264184847,-0.1684961021,0.3393738568,0.4391576052,-0.1407120377,0.1563979387,0.3509828448,-0.2138073742,0.0685087293,-0.0208971277,-0.1096859127,0.0704352781,0.536990881,-0.1515039504,-0.2388876677,-0.1877327263,0.0280617326,-0.2314403802,0.0420351811,-0.1295740306,0.2590333223,-0.0621024258,0.1730599999,0.0358291566,-0.1131441817,0.1226589531,0.1459693611,-0.2801611722,-0.3838599026,0.1542091817,0.1745480001,-0.0517693125,0.2062220573,-0.0001088944,0.1076903939,0.307946831,-0.1070469543,-0.2203330994,-0.1559585482,0.0400469601,0.0956171006,0.1934548467,-0.0438331701,-0.1365971714,0.434663713,0.0030512204,0.0237636659,0.4515209794,0.4471628666,0.2572606802,0.1820829362,0.3130951524,-0.1857011616,0.0406049751,-0.4782786965,0.0231450051,0.3593066633,0.262865752,-0.2473660111,0.2174865901,0.121585317,0.0678002685,-0.1103276387,0.0881747901,-0.0993106067,0.0909342691,-0.3702736497,0.0434348211,0.1779972017,0.6764319539,0.0420952141,-0.1345541775,0.2067839056,-0.0563103072,-0.202206403,0.3282392919,-0.0293237772,-0.1838054359,0.1603696644,-0.0458533056,-0.0682923198,-0.3555369377,0.2799238265,-0.0877680257,0.0889927521,0.0597958229,0.1290601194,-0.0441392027,-0.0850264803,0.1178375557,0.1198882908,-0.0155313239,-0.0139372749,-0.4044622779,-0.0382161178,-0.0493910164,-0.1649002284,0.0893062726,0.1916986555,0.386172086,-0.1656158417,0.1851050854,0.195149079,0.3030425608,0.0251457244,-0.1535613388,0.2802621722,0.485332787,0.358232379,0.1091378555,-0.1168528646,-0.0793488696,-0.4436267912,-0.1070655286,0.1931964457,0.1481758207,-0.1713293791,-0.2580232322,0.1838609725,-0.1554706842,0.2243509144,0.1914636046,0.4051600397,-0.2933759391,-0.1206312254,0.205328241,0.0556207299,-0.1346567273,-0.2475837469,-0.177614972,0.1270141453,0.0032318893,-0.1961533576,0.112988241,-0.1806070954,0.3181100488,-0.135443598,0.1654633135,-0.1547960788,-0.0770795271,-0.3587570786,-0.4265533388,0.342864722,-0.0515845008,-0.1006182507,0.1217820197,-0.0271780584,0.1396220624,-0.1921745092,-0.2594857812,-0.4317109585,-0.2148144096,0.3007487655,0.0068481578,-0.0460898913,-0.0009702805,-0.1341415346,0.1635116786,-0.39072451,0.0341531858,-0.3428096473,0.016795313,-0.3032230735,0.2672646046,0.5000346899,-0.4822481871,0.00113251,-0.0824278891,-0.2504619658,0.1914057732,0.2989630699,-0.24371849,0.1051212549,-0.1535787433,0.0678506047,0.3929648101,-0.3507179916,-0.4748890698,0.3805271983,-0.1470362097,-0.2910526693,-0.0379408561,0.064364858,0.2132600844,0.0781288892,0.1432295442,0.3018902838,-0.0660439059,-0.2896078825,-0.2765758634,-0.2182384431,0.0853394121,0.4488564432,0.3002042472,0.0365872607,0.303275764,-0.3245083392,-0.0462646373,0.0777715594,0.0712761208,0.3233808875,0.3449003398,0.1176356673,-0.0203552675,0.3761102557,-0.549890995,0.173010692,-0.4278171957,0.2051826715,-0.3752169609,0.1459923834,-0.1822524518,-0.0354656875,-0.1066942215,-0.0441699438,0.2470875382,-0.311899066,0.0967386141,0.1461656541,0.1997203827,0.4978630841,-0.2475582361,0.1445532143,-0.0677045658,0.5404032469,-0.3357788622,-0.2070756704,0.3209988177,-0.287119031,0.2573584616,-0.0747268349,-0.2176730037,0.3465878367,-0.2453757226,0.3602873087,0.0862292126,0.2653048635,0.1509155035,-0.1417225152,-0.0060187192,0.2202348262,0.3463457227,-0.1375163347,0.2603257,0.2672841549,0.0849163458,0.1005876735,0.1340834051,0.1851221174,0.290214479,0.055423528,-0.0036834383,0.1190352961,0.2799320817,0.1297676265,0.1541454643,-0.2395042926,-0.3208917081,0.0304419827,0.187446937,-0.1364736408,0.0050887545,0.095593743,0.3231284916,-0.2867569923,-0.1466925889,-0.0483226217,0.3849936426,-0.0679269657,-0.2181104124,0.3391186297,0.1424878389,-0.3514521122,0.3687647581,-0.0148211624,0.1530308723,0.4343566298,0.0197932944,-0.1724939644,-0.2720381916,-0.5410823822,0.172705397,0.5017488003,-0.3676473796,0.028473923,-0.0609808527,-0.1288914084,0.1670016497,0.1663617939,-0.2979949117,-0.302639693,0.0902758762,0.1386961788,-0.1017941013,0.0400378779,0.0291711539,0.2307890654,0.306073457,-0.0174340829,-0.1619219929,-0.293936342,-0.1299986094,0.0691568255,0.0803927258,-0.2212884873,0.1464639306,-0.0845270455,-0.0798363164,-0.1683973521,-0.1975148916,0.0711754262,0.0564199798,0.3333255649,0.3781257868,0.4529694021,-0.0027468328,-0.0465435982,0.2666623294,-0.201058805,-0.1093119979,0.0565123335,-0.0399197973,-0.0880911946,-0.1407164335,-0.4213065803,-0.6614133716,-0.2270002961,-0.018772373,-0.1047685444,0.202976346,0.2386614531,0.168742612,0.0918304995,0.2343424708,-0.0431979001,-0.3158206642,-0.4506627917,0.258895874,-0.1679927409,-0.2692593634,0.1085924357,0.3187679946,0.4288701117,-0.0719965622,-0.4718382359,-0.2920773327,-0.0196224786,0.131521821,-0.2031797171,-0.1841822714,0.328887105,-0.1671674997,-0.1352888495,0.0198434796,0.1486324817,0.3061666489,0.3233553171,0.1663650423,-0.0350661464,0.6165557504,-0.1657438129,0.3962852657,0.199892655,0.0060246023,0.2777493,-0.0252536535,-0.0822132155,-0.0938235968,-0.0131384525,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1514","title":"how to get all the options of a property in datasets ","comments":"In a dataset, labels correspond to the `ClassLabel` feature that has the `names` property that returns string represenation of the integer classes (or `num_classes` to get the number of different classes).","body":"Hi\r\ncould you tell me how I can get all unique options of a property of dataset?\r\nfor instance in case of boolq, if the user wants to know which unique labels it has, is there a way to access unique labels without getting all training data lables and then forming a set i mean? thanks","comment_length":31,"text":"how to get all the options of a property in datasets  \n Hi\r\ncould you tell me how I can get all unique options of a property of dataset?\r\nfor instance in case of boolq, if the user wants to know which unique labels it has, is there a way to access unique labels without getting all training data lables and then forming a set i mean? thanks \n In a dataset, labels correspond to the `ClassLabel` feature that has the `names` property that returns string represenation of the integer classes (or `num_classes` to get the number of different classes).","embeddings":[-0.3508508503,-0.5276566148,-0.0328901485,0.3197903633,-0.0088524511,0.1327080727,0.0669215247,0.0627553165,-0.1925359815,0.2703049183,-0.2775320113,0.2990584671,-0.2552965283,0.1220931783,-0.0826280192,0.2548773885,-0.1904511899,0.1400496662,-0.09147764,-0.3434121013,-0.4865431786,-0.2970269322,-0.458103627,0.0147145968,-0.1832594424,-0.4297350049,-0.1620201468,-0.3819602132,-0.2474734187,-0.4416976869,-0.1803476363,0.3577092886,-0.0052560666,-0.1465243846,-0.0001120292,-0.1863492727,-0.0877825618,0.1195253432,-0.2179146856,-0.1170703992,-0.208410114,0.0798479095,0.0001605893,-0.2902169824,-0.2380115986,-0.1694646478,0.1302454025,-0.3067198396,-0.2818312943,0.0357822031,0.1677719653,-0.1580674499,-0.1943156421,0.0313468911,0.2919986844,0.3407337368,-0.1350935102,-0.1522563994,0.5592805743,0.2760846317,0.1553929299,0.1595231146,0.1639227718,-0.065399915,-0.1125929207,0.0601310804,-0.0172084011,-0.0513155013,0.1700330079,0.4939925671,0.8889746666,-0.0780139416,-0.555224359,-0.222472623,0.3864808679,-0.2008572221,-0.072684668,0.4160019159,-0.021555854,0.0239764266,-0.5343723893,-0.1047019809,-0.2602104545,0.0041006017,0.0951181576,0.4499118328,-0.0597374104,0.0480396077,0.0435346849,-0.1944862306,0.3572511375,0.0081248814,-0.0555034131,0.2131772637,-0.180676043,-0.1613455266,-0.0790354833,0.3115596771,0.4610341489,0.2292971611,-0.0018088394,-0.2025976181,-0.2045291215,0.0713876858,0.1316974461,0.1638012081,0.2778801322,0.4300645292,-0.090524666,-0.2591495514,-0.5156908035,-0.0436422862,-0.1627069116,0.5808580518,0.0987301767,0.0906743333,0.1428923756,-0.0987817422,0.0261776708,-0.1224273294,-0.5973362327,-0.1279473752,0.3690081239,0.0653480664,-0.1663045436,-0.2808000743,-0.1604654193,-0.3047666848,0.0698339045,-0.5663423538,-0.24556835,0.0211142674,-0.0447985902,0.0290551689,0.1502798051,0.0854732171,0.2885451913,0.2838986814,-0.0270618312,-0.0989094749,0.1929654628,-0.1295313537,0.073515676,-0.2468218803,-0.0755354539,-0.1078964993,-0.0959062129,-0.0326659568,-0.4610232115,-0.0920462757,0.0755554736,-0.3649215102,0.0379907489,0.1456158459,-0.1221426204,-0.1351405084,-0.234095186,0.5512105227,-0.1914868504,0.2397634685,0.131430462,-0.098591961,-0.2369734794,-0.0555083305,-0.1372011453,0.1909402013,-0.4381872118,-0.1115924791,0.1313178986,-0.1416656375,-0.1695653647,-0.0368135758,-0.3678408563,0.2495279312,-0.198859483,-0.0420435667,0.2656773329,0.0845837221,-0.4063229859,-0.0453337878,0.3962123394,-0.3897976279,0.2347951531,0.3541610837,0.5342296362,0.155602932,0.0012972433,-0.0104314303,-0.1367512047,-0.4786080718,0.2135133296,-0.2491564751,0.0555046983,-0.127150178,-0.2984972596,0.0940545499,0.2261770964,0.0374370739,0.1945082843,0.0008146421,-0.2040582448,0.3154881597,0.1634713411,0.3308371305,-0.140871644,-0.5227249265,-0.4604741931,0.2434815466,-0.1847603023,-0.1982658803,0.2715202868,-0.3839793801,0.2311086953,0.1584577709,0.0020248869,0.0699903071,0.1261916459,0.1834160835,0.0030262142,-0.2639645636,-0.3475838602,0.1739324182,-0.1795786023,-0.0859248266,-0.0596419498,-0.2804452181,0.3700778484,0.1967259049,-0.0314778648,0.0921080634,0.1555160284,0.1380991042,-0.0922473669,-0.0289695244,0.0102938851,-0.0202162024,0.1972585469,0.2296967357,0.1364670694,-0.2346062958,-0.1057953537,-0.0488548726,0.0834585354,0.0277628601,-0.2555168569,0.2668005228,-0.1034759358,0.2608881593,-0.0779846907,0.1833365858,0.0540484488,-0.0457128584,-0.0940100104,-0.1271288991,0.1622812301,-0.1134361476,0.4942336679,0.3270045817,-0.4405329525,0.1776648015,0.2505425811,-0.1502178609,0.0749085695,-0.2717399001,0.1221662238,0.121959269,0.4038164914,0.1231876388,0.2708517015,0.2157386839,0.0594006851,0.1966955811,-0.1114786863,0.1793223321,0.2144340128,0.0014691475,-0.2242041379,-0.3802837431,-0.0141311381,0.100709416,-0.032080587,-0.3933856487,0.2156148404,0.0209182147,-0.0542869009,-0.0450572558,-0.411819607,-0.0780554116,0.1107792109,-0.0728577822,-0.0198096782,-0.1078429073,0.1396297365,-0.089996092,0.0984444246,0.1316862255,-0.491967082,0.4692613482,-0.0703241378,0.0017363622,0.372818768,-0.1602380574,-0.317358017,0.0002689914,-0.3346790671,0.1726289988,0.4815166295,0.2825987041,0.2387952656,-0.4937898517,-0.0780083463,0.1100169718,0.1204605103,-0.0002427356,0.0564519651,-0.1279259473,-0.3248132467,0.3949852586,0.1154254153,0.2966908514,-0.0409834608,-0.1411793232,-0.1113710031,-0.2185892016,0.0414262228,-0.4756484032,-0.509113431,-0.0658925474,0.1078628227,-0.2145976722,0.2966996729,-0.0095512038,-0.4133852422,0.1338648647,0.0358329788,0.4845369756,-0.1738816351,-0.4474420249,0.0916540176,0.0210226625,0.0016238815,-0.2051277757,-0.200714916,0.0482299402,0.2721456885,-0.1739082634,-0.2373665273,-0.2317432761,0.0977828205,-0.1227781028,0.0688818768,0.1421620697,0.115131326,0.0753470436,-0.0545673594,-0.3315775394,0.1224296242,-0.2906486988,0.3100847304,0.3690904677,-0.0175023321,0.0651027709,0.2933643758,0.5332478881,-0.03682952,-0.0299321152,-0.4662999213,0.1133903638,0.4116409719,-0.454364121,-0.12257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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1514","title":"how to get all the options of a property in datasets ","comments":"I think the `features` attribute of the dataset object is what you are looking for:\r\n```\r\n>>> dataset.features\r\n{'sentence1': Value(dtype='string', id=None),\r\n 'sentence2': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\r\n 'idx': Value(dtype='int32', id=None)\r\n}\r\n>>> dataset.features[\"label\"].names\r\n['not_equivalent', 'equivalent']\r\n```\r\n\r\nFor reference: https:\/\/huggingface.co\/docs\/datasets\/exploring.html","body":"Hi\r\ncould you tell me how I can get all unique options of a property of dataset?\r\nfor instance in case of boolq, if the user wants to know which unique labels it has, is there a way to access unique labels without getting all training data lables and then forming a set i mean? thanks","comment_length":42,"text":"how to get all the options of a property in datasets  \n Hi\r\ncould you tell me how I can get all unique options of a property of dataset?\r\nfor instance in case of boolq, if the user wants to know which unique labels it has, is there a way to access unique labels without getting all training data lables and then forming a set i mean? thanks \n I think the `features` attribute of the dataset object is what you are looking for:\r\n```\r\n>>> dataset.features\r\n{'sentence1': Value(dtype='string', id=None),\r\n 'sentence2': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\r\n 'idx': Value(dtype='int32', id=None)\r\n}\r\n>>> dataset.features[\"label\"].names\r\n['not_equivalent', 'equivalent']\r\n```\r\n\r\nFor reference: https:\/\/huggingface.co\/docs\/datasets\/exploring.html","embeddings":[-0.2287427336,-0.7055018544,0.0021907324,0.3662371039,0.0509751029,0.1418879181,0.0267284531,0.0608512163,-0.1547129899,0.2690688372,-0.4087378383,0.2562119663,-0.2511259019,0.4104970396,-0.0019404892,0.1533517241,-0.1557465792,0.1335611194,-0.1536472142,-0.2861343324,-0.4006635249,-0.1075738594,-0.4153287113,0.0467985049,-0.1314859986,-0.3693220019,-0.2243348658,-0.2647702992,-0.3092392087,-0.4690890908,-0.1003361195,0.4063610137,-0.0191017445,-0.0985037237,-0.0001168394,-0.0966578275,-0.08514373,0.0530237928,-0.195506528,-0.1642183661,-0.0560531989,0.0330166593,0.0604154281,-0.3104787171,-0.2448531687,-0.2659602463,0.063094683,-0.2815676033,-0.0964833125,0.0834135562,0.1124279872,0.122878924,-0.1639308035,0.0523825288,0.2354943752,0.3953170478,-0.1452857256,-0.1424498409,0.3674568236,0.2163593024,0.2054813504,0.1736009419,0.1753453612,0.0197265018,0.0648147613,0.1097645015,-0.1048912853,-0.1086171567,0.1151849255,0.6177591681,0.7410152555,-0.0802450255,-0.612547338,-0.3371491432,0.265163064,-0.2187315077,-0.0421202779,0.3485732079,-0.0364700854,0.0196287557,-0.6166024804,-0.1469857246,-0.2099895477,0.0341836624,0.0878430605,0.3314309418,-0.2299049497,0.107028015,0.1994532347,-0.2394700199,0.1162179559,-0.0532050356,-0.160334751,0.2908092439,-0.2628741264,-0.2033747286,0.0598603152,0.3661664128,0.6192520261,0.2419130355,0.0272459202,-0.2319880575,-0.2885713279,0.0393390879,0.2170250863,0.2005427778,0.2487373948,0.3153836429,0.0167148542,-0.0292649139,-0.3435151577,-0.0755878538,-0.081313394,0.4947544038,-0.0487851687,0.0495078489,0.3242346644,-0.1381803751,0.0299292915,-0.1983109564,-0.5271268487,-0.095401071,0.3363982737,0.0749922022,-0.135993138,-0.2295092493,-0.0980432183,-0.1476190835,-0.0068864855,-0.4394291937,-0.2158149183,0.0091785034,0.0450276509,0.0627499819,0.1190925166,-0.1027028337,0.3910476565,0.2135322392,-0.0078681912,-0.1423297822,0.0254099611,-0.0637543425,0.0469572209,-0.1330059916,-0.0796604156,-0.0924228877,-0.0602715649,-0.1388602257,-0.460755825,-0.1905695349,0.1057441831,-0.4433948994,-0.028986495,0.097121276,-0.3224673867,-0.1415637434,-0.4366815984,0.6044265628,-0.2416905761,0.350016892,0.1379347593,0.017984312,-0.3908011019,-0.046833761,-0.0240165386,0.3638699055,-0.3369419873,-0.2405359,0.179744035,-0.2132232487,-0.3713692725,0.0807779655,-0.3572637141,0.2071002722,-0.2416037768,-0.0352623798,0.2858682573,0.0241249148,-0.331443131,0.0052651698,0.3683824539,-0.2202334404,0.1870829165,0.2353427708,0.5210514069,0.1221040264,-0.0985236019,0.1144925132,-0.0286738817,-0.3989966214,0.2246572524,-0.2133475691,0.0700233206,-0.145232439,-0.3617038429,0.1528536528,0.0965439603,-0.1897106767,0.2468205839,0.0048565585,-0.2093896568,0.3276514113,0.2211042941,0.3890689313,-0.1036694273,-0.5267808437,-0.6104329824,0.3131175041,0.0081161158,-0.1598109752,0.2112653106,-0.4473211765,0.1214880645,0.1537570804,-0.0376690403,-0.0609103665,0.0337237902,0.1989069581,-0.000506168,-0.1429089159,-0.4254854023,0.272126466,-0.1248877794,0.0505873822,-0.1900824159,-0.1255626231,0.4010555446,0.2112405896,0.0548178665,0.0028917007,0.2383458763,-0.010320913,-0.0924656242,0.06456732,-0.033551883,0.1128497869,0.2294691205,0.2720689774,0.2494476438,-0.2392003387,-0.1886919737,-0.0828718841,0.1910863072,0.018258119,-0.3324782252,0.3907878101,-0.0463843308,0.3015041649,-0.0743616149,0.1733385921,0.0144688906,-0.030882081,-0.1836419255,-0.1196030751,0.1472086459,-0.0567021742,0.3591713607,0.3756254017,-0.4823968112,0.1143965274,0.3670990467,-0.1303100586,-0.1336413771,-0.2044655383,0.0917301923,0.0992022231,0.3935653269,0.0503240153,0.2244663686,0.18861489,0.0470325425,0.2774070799,0.0206078831,0.2673248649,0.1599066257,0.0343190916,-0.1635104567,-0.3330229223,0.1298903525,0.067790553,-0.1989113688,-0.2625770271,0.1614474952,0.0883533433,-0.1697712094,0.0232223105,-0.3889833391,-0.0831419975,0.0753609017,-0.0631954893,-0.0673457459,-0.0844076052,0.0233297255,0.0526544079,0.1011319757,0.0764356479,-0.3535858989,0.392444104,-0.080462046,0.0092259431,0.2691920996,-0.0380385555,-0.2051478773,-0.0644438863,-0.3056202531,0.2183968872,0.4517791271,0.1415783763,0.1047298834,-0.5595074296,-0.0770265386,0.0939003527,0.0703040957,0.2038977295,0.1465806514,-0.0607118271,-0.2809715867,0.3665855527,0.1948534995,0.272393167,-0.1599522233,-0.1031919196,-0.0639202073,-0.1811526269,0.0288371444,-0.4219209254,-0.4215579629,-0.0707494467,0.3276548386,-0.1999600977,0.2525439262,-0.0054208282,-0.2850001454,0.0456598215,-0.1100159883,0.3793177009,-0.1815898418,-0.5349197388,0.1260641515,0.0119428365,-0.0821237266,-0.1585401595,-0.1444892585,-0.1269701123,0.1906870306,-0.2452409863,-0.3869085312,-0.3368161917,0.2491073757,-0.0860304832,0.1027117223,0.2500685155,0.0352764949,0.1486334801,-0.0380931124,-0.5036994219,0.1496802419,-0.0694487318,0.2438976616,0.3529318571,0.0748306811,0.0213789437,0.3612077236,0.5248460174,0.010234491,0.0907141715,-0.5891011357,0.2321334332,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1478","title":"Inconsistent argument names.","comments":"Also for the `Accuracy` metric the `accuracy_score` method should have its args in the opposite order so `accuracy_score(predictions, references,,,)`.","body":"Just find it a wee bit odd that in the transformers library `predictions` are those made by the model:\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/trainer_utils.py#L51-L61\r\n\r\nWhile in many datasets metrics they are the ground truth labels:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c3f53792a744ede18d748a1133b6597fdd2d8d18\/metrics\/accuracy\/accuracy.py#L31-L40\r\n\r\nDo you think predictions & references should be swapped? I'd be willing to do some refactoring here if you agree.","comment_length":19,"text":"Inconsistent argument names. \n Just find it a wee bit odd that in the transformers library `predictions` are those made by the model:\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/trainer_utils.py#L51-L61\r\n\r\nWhile in many datasets metrics they are the ground truth labels:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c3f53792a744ede18d748a1133b6597fdd2d8d18\/metrics\/accuracy\/accuracy.py#L31-L40\r\n\r\nDo you think predictions & references should be swapped? I'd be willing to do some refactoring here if you agree. \n Also for the `Accuracy` metric the `accuracy_score` method should have its args in the opposite order so `accuracy_score(predictions, references,,,)`.","embeddings":[0.2731813788,-0.3669873476,0.0105247274,0.2615756392,0.4064151645,-0.2207668722,0.2435519993,-0.032928925,-0.1960491836,0.0497912131,-0.1712593883,0.110400103,0.0239894912,0.1010815725,-0.013367855,-0.3983656764,0.0884952918,-0.1208069101,-0.2717051208,-0.2669148743,-0.0417980738,0.3241396844,-0.1340851486,0.1431115568,-0.3410859108,0.1306581944,0.0284888018,-0.2280272394,-0.165961355,-0.3751151264,0.2316489071,0.1122537553,-0.2013053298,0.5761030912,-0.0001090249,-0.0986292958,0.4503882229,0.0429984331,0.0979532823,-0.1389461309,0.2040537149,0.0040316018,0.3800654411,-0.2709213793,-0.1811490357,0.0455643758,-0.1698185354,0.3196667433,0.5733880997,0.2023383826,0.2764056921,0.4994625151,0.0267404187,-0.1820530742,-0.0167505071,0.3684441447,-0.1435261369,0.2194007188,-0.0225852784,0.1702657491,-0.2521291077,0.2552764118,0.2196521759,-0.079987891,0.4930861592,0.0928412154,0.0441884212,0.0080637373,-0.2042267025,0.0753111169,0.098084718,-0.2048594058,-0.5751951337,-0.1471262276,-0.1671092808,-0.2236678004,0.0386000089,-0.0628711656,-0.0837079734,-0.0743604824,-0.3173147142,0.0666565821,-0.2484065741,-0.1054518223,-0.0565710105,0.3778792024,-0.0511569865,0.0255960952,0.2035983503,-0.0259750765,0.0071266093,-0.0220366679,0.0458586961,0.2064843774,-0.2466256917,-0.2352699488,0.0978803411,0.3341541886,0.0830831751,-0.0604761653,-0.050915353,0.133210063,-0.0257559661,-0.0544035584,0.2670994103,0.4839111567,0.1295625716,0.1448741704,0.1113019958,0.1941492856,0.0352858938,0.0035420808,0.3475992978,-0.0608533807,0.1952458024,-0.0376509018,-0.2405876368,-0.4538148642,0.0780746415,-0.0467881598,0.1735589057,-0.2127188295,0.3083499372,0.2447806597,-0.4023400843,0.1304409951,0.1004997417,0.1457679123,-0.1560167223,0.0451313332,-0.2794976532,-0.0354639553,-0.5579994321,0.2265981734,-0.0161454044,0.1510367841,0.4049934447,0.0445037112,0.4226751924,-0.116089344,-0.2303492129,0.0658566579,0.1480440795,0.0695276335,-0.3464034498,-0.0202824045,0.0759003237,-0.1423481405,-0.1665621996,-0.1238345876,-0.1915263236,-0.2033853084,0.0824014917,0.2798464596,-0.3662946522,-0.0239016935,-0.1684896648,0.1240849942,0.032805346,-0.005280144,0.0329590328,-0.1358790845,-0.0155637451,-0.1864932179,0.279056251,0.2494159192,0.1906177998,-0.3905467391,0.1428603381,-0.1148656607,-0.1832009256,0.3622815013,-0.2361149937,-0.022618385,-0.0717781708,-0.0623340681,0.1299925148,-0.3559307456,-0.1099891067,0.0228483547,0.2844336927,0.1188532338,-0.0488430411,-0.0386589468,-0.0623236038,-0.24523516,0.0481164716,-0.133120209,0.1778406799,0.0154769756,-0.5007194281,-0.2730275989,-0.1432960182,0.097972095,0.1249622032,0.1286139488,-0.2446158379,0.1608840972,0.3392624557,-0.0987039059,-0.3262064457,0.1028044671,0.230823487,-0.1984651685,0.0530900508,0.0304738618,-0.4228440225,-0.0428095423,-0.2905333042,0.4193040133,0.2434006184,-0.2294383794,-0.1947897822,0.0939320251,-0.021168163,-0.1322898269,0.2385233492,-0.0693557039,0.1061500758,-0.0104011251,-0.1873704791,0.0234177262,-0.2420466542,0.2245829403,-0.2961933911,0.1095297933,-0.1609722972,-0.1399498731,0.0054599759,0.2729823589,0.1944747567,-0.1046131551,-0.1219412461,0.4222339094,0.02305682,0.215087831,-0.147157684,0.5913020968,0.3448300064,-0.1929062158,-0.0175549462,0.1209854111,-0.028342478,-0.2437526584,-0.3149511814,0.4001962543,-0.085733138,0.1407819837,0.3331146538,-0.1448747814,-0.0783898383,0.0735543594,-0.3948216438,-0.0057423394,0.2205551118,-0.161967352,0.2159049809,0.1072520688,-0.251216948,-0.1485432684,0.3663340807,-0.0666086376,-0.1350828707,0.2656883597,-0.0148499086,-0.0443814471,-0.1563386768,0.1039333865,0.4005812109,0.2021436393,-0.0817384645,0.1937269717,-0.1493364125,-0.1747569293,0.1467334032,0.3263317347,-0.3253344595,-0.0154467486,0.0735313147,0.1794380099,-0.3161493242,-0.056428954,-0.3818185329,0.0026232249,-0.1877410263,0.1746571958,0.0561089478,0.0912809893,-0.4408505857,-0.4194336236,-0.5085701346,-0.37309587,0.0834481493,-0.0125853643,-0.1168553606,0.2910577655,0.0102692302,0.2725937665,-0.2012775242,0.3011476696,-0.231815815,-0.1809814721,-0.1449623257,0.0649188533,-0.0005280174,-0.1864445359,0.3517945111,-0.1220342591,-0.0329939984,0.18036744,-0.7035816312,0.1564162225,-0.059343271,0.4357468486,0.2694195211,-0.0466163754,0.0246974695,-0.2172188014,0.472784698,-0.3604517877,0.1338212788,-0.0346326418,0.0381666161,-0.3561862409,-0.1619342715,-0.2841198146,-0.023173647,-0.221237272,0.1652252823,0.0890472084,0.0157862101,0.6461840868,-0.0748782903,-0.0009122782,-0.1254460216,0.2407364547,-0.1744231582,-0.3264143765,0.3574675024,-0.1375958025,-0.3368236721,-0.2210949808,0.0389712229,0.2903655171,-0.2625148296,-0.2988057137,-0.528434813,-0.1652683169,-0.2844391167,0.1087446958,0.2394404262,0.0225937013,0.0692526102,-0.0820013359,-0.082464464,-0.538801074,0.2279700786,0.0763646513,0.3385048807,-0.1704762876,0.0478972048,-0.0700189695,0.6143711209,0.2681386471,-0.3585923016,-0.0221968349,0.1695961803,0.0751660764,-0.1357620806,-0.3061009943,0.08764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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1478","title":"Inconsistent argument names.","comments":"Thanks for pointing this out ! \ud83d\udd75\ud83c\udffb \r\nPredictions and references should indeed be swapped in the docstring.\r\nHowever, the call to `accuracy_score` should not be changed, it [signature](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score) being:\r\n```\r\nsklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None)\r\n```\r\n\r\nFeel free to open a PR if you want to fix this :)","body":"Just find it a wee bit odd that in the transformers library `predictions` are those made by the model:\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/trainer_utils.py#L51-L61\r\n\r\nWhile in many datasets metrics they are the ground truth labels:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c3f53792a744ede18d748a1133b6597fdd2d8d18\/metrics\/accuracy\/accuracy.py#L31-L40\r\n\r\nDo you think predictions & references should be swapped? I'd be willing to do some refactoring here if you agree.","comment_length":49,"text":"Inconsistent argument names. \n Just find it a wee bit odd that in the transformers library `predictions` are those made by the model:\r\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/master\/src\/transformers\/trainer_utils.py#L51-L61\r\n\r\nWhile in many datasets metrics they are the ground truth labels:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/c3f53792a744ede18d748a1133b6597fdd2d8d18\/metrics\/accuracy\/accuracy.py#L31-L40\r\n\r\nDo you think predictions & references should be swapped? I'd be willing to do some refactoring here if you agree. \n Thanks for pointing this out ! \ud83d\udd75\ud83c\udffb \r\nPredictions and references should indeed be swapped in the docstring.\r\nHowever, the call to `accuracy_score` should not be changed, it [signature](https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score) being:\r\n```\r\nsklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None)\r\n```\r\n\r\nFeel free to open a PR if you want to fix this :)","embeddings":[0.256931603,-0.439550519,-0.0103000738,0.1632704586,0.4465913773,-0.2115409821,0.1434418708,-0.0334956869,0.0414287522,0.0814790502,-0.0405135155,0.1803880632,-0.0548593551,0.1349070221,0.0187775791,-0.4100299776,0.1643256396,-0.1507949233,-0.2882173657,-0.2812667489,-0.0857049823,0.316467911,-0.1572450399,0.100552015,-0.4445046186,0.0573824234,0.0731789321,-0.2022777051,-0.1855972856,-0.3112768531,0.2928377688,0.0348808169,-0.1435786635,0.5684481263,-0.0001043543,-0.0801726133,0.5083734989,-0.0048199133,0.0420004018,-0.1177941635,0.1701010466,0.0062208744,0.2916088998,-0.3434048891,-0.193949163,0.2135120183,-0.1863261312,0.1779732704,0.4698330164,0.2248388529,0.3030235767,0.440297097,0.064599596,-0.1933786869,-0.0585102588,0.2556340694,-0.0898000821,0.1844703853,-0.1508141309,0.1044708788,-0.2257791758,0.3445389271,0.1872504205,-0.1968569905,0.568767786,0.1029587388,0.079935573,-0.1289537251,-0.0912590697,0.0444573052,0.0709270686,-0.2305466086,-0.4193950593,-0.1462265998,-0.1154459268,-0.2439677417,0.0441442579,-0.003800323,-0.1105913967,-0.1083739847,-0.3040143251,0.004342108,-0.2384107411,-0.0871796086,-0.1200482175,0.4932813644,-0.044805076,0.0596698895,0.2030075341,-0.0181639157,0.0755194277,-0.0440237559,-0.0175675377,0.1420896649,-0.2674137056,-0.2524872422,-0.0743403137,0.2494470328,0.0487836823,-0.1247599721,0.018226834,0.117621921,-0.0264747404,-0.0782871321,0.2774725556,0.5153080821,0.0807188824,0.2178994119,0.0439514443,0.2514868379,0.0921024159,-0.0154798897,0.2969269753,-0.1088856757,0.1562731415,-0.0452339128,-0.1819580346,-0.2827305198,0.0063982559,0.0453011133,0.0737396255,-0.0733467042,0.2071209252,0.1893324256,-0.3304433525,0.2138773203,0.2055527419,0.1191916168,-0.0907178596,0.0011589869,-0.3113301098,0.0857705697,-0.5817627311,0.2180779576,0.0201008264,0.2139526904,0.3889667094,0.0482910089,0.3449585438,-0.17496714,-0.2742282748,0.0641731992,0.1296645105,0.0910407528,-0.371928215,0.0573801994,0.0904335752,-0.0964154452,-0.1146554649,-0.002281853,-0.2376356274,-0.1269274205,0.1172142848,0.3066257536,-0.3674982786,-0.0712011233,-0.1644512117,0.1566270888,0.0240525641,0.1329238862,0.0971584097,-0.1436061561,-0.071237579,-0.1228671968,0.3704476058,0.1328169107,0.2067985237,-0.316463232,0.1525969803,-0.0030898147,-0.2281825244,0.2791340053,-0.2480566353,0.0794885308,-0.0573107414,-0.0417285301,0.0855614096,-0.4337061048,-0.1464656889,-0.023914637,0.1614613533,0.017851403,-0.1277337372,-0.0667634234,-0.0342098922,-0.1476313025,-0.0077677043,-0.033803694,0.1741591841,0.0677702725,-0.6005351543,-0.3513386846,-0.1266983151,-0.0123309819,0.2001012862,0.1121135131,-0.2058453709,0.3587814867,0.3848200142,-0.060632389,-0.2027910352,0.2178660184,0.3127883673,-0.1990872622,0.0022054054,0.0562240593,-0.4301520288,-0.0479111075,-0.2720617652,0.4051241875,0.1613992751,-0.2514888644,-0.2848739326,0.1218406782,-0.0194588695,-0.2196539789,0.3248638809,-0.0722801909,0.2226168513,-0.011103617,-0.0927924961,-0.0237485468,-0.2221786082,0.1867496073,-0.3372329175,0.2868709564,-0.2119273245,-0.0809812248,0.0009326966,0.2739035487,0.2689195871,-0.0639837906,-0.0738747865,0.4159818292,-0.1009960026,0.1458866596,-0.1311028749,0.3805533051,0.3150107861,-0.2862721682,-0.0108403536,0.0776697546,-0.0136617944,-0.1669187099,-0.2959807217,0.2807634771,-0.2125167102,0.1141026914,0.1887623817,-0.0884110928,-0.1432165056,0.043011412,-0.290368408,-0.078977339,0.0140888663,-0.2077496052,0.10352391,0.0311889369,-0.1021187678,-0.1167837977,0.4021645486,-0.1101825386,-0.1710701734,0.1190947667,-0.1837718785,-0.0675297305,-0.1294608265,0.1501441598,0.3384487033,0.1897508353,-0.1371939331,0.1450781077,-0.1647858769,-0.1246198788,0.15455392,0.3616389632,-0.2243347466,0.0703088567,0.1447581798,0.1423943937,-0.3193593323,0.0456405729,-0.2093979418,0.0649728402,-0.1356942207,0.1865713,0.0880624801,0.2206481546,-0.4930603802,-0.3447504044,-0.3960666955,-0.4616573751,0.0575411543,0.0512602292,-0.0770540014,0.270311296,0.0260869879,0.1901890337,-0.0972842425,0.1863812506,-0.1831385791,-0.2294822335,-0.1294647306,0.1167707443,-0.0222530197,-0.1021947637,0.2585438192,-0.0462663807,-0.0047253119,0.089859955,-0.6740859747,0.0580762029,-0.1147524938,0.3619232178,0.2027481794,-0.0060335714,-0.077190198,-0.3147086501,0.4627213478,-0.3848543167,0.0855311975,0.0152146835,0.1265775859,-0.2489892691,-0.1569399685,-0.282684654,0.0228785723,-0.1770999432,0.0959240124,0.0997364223,-0.1777557433,0.6479942203,0.0956636742,-0.0052459999,-0.2033057064,0.1182725951,-0.2391469032,-0.2335713953,0.356430769,-0.0545614548,-0.4124352932,-0.2016922235,-0.0423714966,0.3321449161,-0.2471004725,-0.3998714387,-0.4859279096,-0.136632219,-0.1886876822,0.0765183568,0.188956067,0.041314967,0.0390404686,-0.0667268336,-0.1327357292,-0.4137021601,0.2914661467,0.0208934862,0.2645951807,-0.1987661272,0.0895240083,-0.0390896089,0.7449858189,0.2926265299,-0.3071352839,-0.0790222287,0.2613869905,0.1781193465,-0.0992988944,-0.2288461924,0.0927711129,0.001460645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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1452","title":"SNLI dataset contains labels with value -1","comments":"I believe the `-1` label is used for missing\/NULL data as per HuggingFace Dataset conventions. If I recall correctly SNLI has some entries with no (gold) labels in the dataset.","body":"```\r\nimport datasets\r\nnli_data = datasets.load_dataset(\"snli\")\r\ntrain_data = nli_data['train']\r\ntrain_labels = train_data['label']\r\nlabel_set = set(train_labels)\r\nprint(label_set)\r\n```\r\n\r\n**Output:**\r\n`{0, 1, 2, -1}`","comment_length":30,"text":"SNLI dataset contains labels with value -1 \n ```\r\nimport datasets\r\nnli_data = datasets.load_dataset(\"snli\")\r\ntrain_data = nli_data['train']\r\ntrain_labels = train_data['label']\r\nlabel_set = set(train_labels)\r\nprint(label_set)\r\n```\r\n\r\n**Output:**\r\n`{0, 1, 2, -1}` \n I believe the `-1` label is used for missing\/NULL data as per HuggingFace Dataset conventions. If I recall correctly SNLI has some entries with no (gold) labels in the dataset.","embeddings":[0.2351298779,-0.490634948,-0.1780145317,0.3363342583,0.2337820381,0.0211006664,0.3015899956,0.2003286481,0.0807732418,0.2864296734,-0.2351152003,0.3536292017,-0.2061203569,0.2780169249,0.0497128963,0.0089706723,0.2335757613,0.3703630269,0.0620434359,-0.4029215276,-0.3136185408,0.2042293549,-0.3197530508,0.1720517725,-0.4605438709,0.2223438323,-0.0642018318,-0.0900376439,-0.0218581893,-0.4933305979,0.16226013,-0.0321304351,-0.0364017226,0.0682409629,-0.000104066,-0.2530908883,0.0445113033,-0.027761437,-0.4121533334,-0.2150870562,-0.1446629316,-0.1115380302,-0.0711202994,-0.2451922894,-0.2267181277,-0.1674472392,0.1702845544,-0.0743094236,-0.0091543021,0.2677800059,0.2388068587,0.2588718534,-0.2726499438,0.0377832241,0.3642984629,-0.2887417376,0.1562072635,-0.0347504877,0.0894331336,0.0672424585,0.4728746712,0.5562069416,-0.137286514,-0.0182487294,0.0932062343,0.2765094638,0.0019010649,-0.3113327324,0.194612965,0.3709362745,0.1806552559,-0.2979120314,-0.2675397396,-0.0996704921,0.0889209583,-0.400216639,-0.0638480633,0.2426854521,0.1563144922,0.2327328622,0.0338694304,0.1923460215,-0.2313928604,0.1665087938,-0.0847164243,0.7823176384,-0.1968450695,0.1007970646,0.1636362374,0.0839669183,-0.4554844499,-0.0083741294,0.1143384129,0.4739844203,-0.3202089965,-0.0803492367,-0.0371239595,0.0814103708,0.0021936407,0.1008560136,-0.0091487048,0.0005345253,-0.0671000481,0.2667031884,-0.0283777863,-0.003456393,0.3293767273,0.1094885617,0.1501355916,-0.1802954674,0.2476408631,-0.0512554944,-0.2885903418,-0.1748425812,0.4545010328,-0.0278989784,0.1671315432,-0.1204602346,-0.4925311208,0.1128047779,-0.2256963253,0.0788223892,0.0458180904,0.1962345541,-0.0339996964,0.1010887474,-0.0013508926,0.0699644536,-0.0285988636,-0.2663111985,-0.2864689529,0.1551371664,-0.0046351412,-0.255432874,0.0295977704,-0.2740984261,0.2123831958,0.0571984798,-0.2523680329,-0.1367340088,0.1693802029,-0.0879595131,0.3424265981,0.3663026989,-0.224009648,0.3934674263,0.0625335276,-0.2190621793,-0.1465875208,-0.0780828521,-0.0950909108,-0.1828194708,-0.1656190008,0.2921012044,-0.1998002529,-0.0757763386,0.1041484401,0.0385775976,-0.0655758008,0.1044519171,0.2772592306,-0.1649906337,-0.0727408752,-0.0392621532,0.258243829,0.1227393299,-0.3179697096,-0.2274758369,0.1154534742,-0.384016186,0.2018665224,0.2267106622,0.1190630421,-0.1606129408,-0.1722411662,0.2534030676,0.3473176956,-0.4435730278,-0.2762626708,-0.0471902117,-0.161497131,-0.3439723551,0.116528824,0.4465350807,-0.1905210316,0.2841313779,0.2110200822,-0.0879750624,0.1887966096,-0.0729888156,-0.2380004674,-0.0798481405,0.3521511555,0.1141519696,0.003062258,-0.0117154438,-0.2967057824,-0.0361137688,0.3701810241,-0.0732097775,0.116582036,0.1806010157,0.3337184191,0.1263370663,-0.0010266864,-0.2666113675,-0.1928448379,0.2538926005,0.0759872124,0.4210845232,0.0298257601,-0.1766707599,-0.3001585901,-0.0803937614,-0.3594002128,-0.1270229965,0.2832456529,0.2410756797,0.2093353719,0.0433067717,-0.2880734205,0.1890072227,-0.1809074283,0.0934363306,-0.3956659436,0.1654046476,0.0461279452,0.0933330059,-0.1129261702,0.4331927001,0.2286773175,0.1468732506,0.2520022988,0.2583920062,-0.2196794748,-0.1835345328,0.2187638432,0.276529938,0.0116540277,-0.4192703366,-0.0756351277,0.3144541681,0.2531535625,0.0568554029,-0.1340450197,0.4621799886,-0.0223456342,0.0066585857,-0.1698778123,0.1676912904,-0.0149603896,-0.0020620213,-0.2303067148,-0.4399383664,0.0580249019,-0.391689837,0.2669937611,0.1903838515,-0.6285805106,0.0602436475,0.3843151331,-0.3520504832,0.117489174,-0.0791908652,-0.2204506546,0.117962271,0.1759145558,0.1723509878,0.0378966071,0.3321821988,-0.0567628257,0.0690156668,-0.323064208,-0.1077095866,0.0968855023,0.0975665525,-0.0577555373,-0.054227829,0.2304429859,0.0467052869,-0.2609626651,-0.2921113372,-0.2107824236,0.2805331349,-0.3731403649,0.1426354945,-0.0735427737,-0.2771177292,-0.3835356832,-0.1542982608,-0.1928972304,-0.2834320962,0.0728442371,-0.2848047018,0.0335918926,-0.0416482314,0.056503389,0.1506259441,0.0116777811,-0.0249490235,-0.3300718069,-0.3428486884,-0.2137782276,0.1240366548,-0.3182232976,0.3774763942,0.3344863355,-0.3859687448,-0.0977402106,-0.0646956787,-0.2464907765,0.0238352492,-0.2468114942,-0.0295265559,0.3473882079,0.1475397497,-0.1745041013,0.2958403528,0.1671305448,-0.0695272759,-0.1272739172,0.005984548,-0.1463558078,-0.0574937053,-0.3506551385,-0.2518244982,-0.4022501111,-0.0249263868,0.1182148606,-0.0469041616,0.1613894701,0.4060355425,0.0036259489,0.3636782467,0.1491140574,0.2072501779,-0.2078464627,-0.2352870703,0.2357077748,0.0460595004,-0.2393185943,0.0106626861,0.094345361,-0.0006302742,-0.2364697605,-0.2919704616,-0.1215140894,-0.0245993957,0.0083640981,0.1204760596,0.1074016318,0.0959753916,0.0974492133,-0.0904043391,-0.230564937,0.0119007919,0.2191186845,-0.2883580327,0.0082270857,-0.081275098,0.1632949114,0.0013201949,0.0399134755,0.3995077312,-0.1092988849,0.0766125172,-0.2272634357,0.2812047303,0.0771833584,-0.0067515741,0.0989886969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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1444","title":"FileNotFound remotly, can't load a dataset","comments":"This dataset will be available in version-2 of the library. If you want to use this dataset now, install datasets from `master` branch rather.\r\n\r\nCommand to install datasets from `master` branch:\r\n`!pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","body":"```py\r\n!pip install datasets\r\nimport datasets as ds\r\n\r\ncorpus = ds.load_dataset('large_spanish_corpus')\r\n```\r\ngives the error\r\n\r\n> FileNotFoundError: Couldn't find file locally at large_spanish_corpus\/large_spanish_corpus.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/large_spanish_corpus\/large_spanish_corpus.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/large_spanish_corpus\/large_spanish_corpus.py\r\n\r\nnot just `large_spanish_corpus`,  `zest` too, but `squad` is available. \r\n\r\nthis was using colab and localy ","comment_length":34,"text":"FileNotFound remotly, can't load a dataset \n ```py\r\n!pip install datasets\r\nimport datasets as ds\r\n\r\ncorpus = ds.load_dataset('large_spanish_corpus')\r\n```\r\ngives the error\r\n\r\n> FileNotFoundError: Couldn't find file locally at large_spanish_corpus\/large_spanish_corpus.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/large_spanish_corpus\/large_spanish_corpus.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/large_spanish_corpus\/large_spanish_corpus.py\r\n\r\nnot just `large_spanish_corpus`,  `zest` too, but `squad` is available. \r\n\r\nthis was using colab and localy  \n This dataset will be available in version-2 of the library. If you want to use this dataset now, install datasets from `master` branch rather.\r\n\r\nCommand to install datasets from `master` branch:\r\n`!pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`","embeddings":[-0.3270967305,-0.3888709247,-0.0211854372,0.3421646357,0.3611239195,0.053929776,-0.0934886783,0.212861225,-0.0617659502,0.2608962655,-0.3553176522,0.1730859429,0.0300486814,-0.1594371796,0.0316852406,0.0206086189,-0.0119563369,0.0681066439,0.0414810441,-0.0984659716,-0.1051172316,0.337292552,-0.2512364686,-0.050593365,-0.1271476299,0.0490329787,-0.1710327119,0.1196063608,-0.3393884599,-0.1975401193,0.3731856644,-0.0539984293,0.3303165436,0.7346161604,-0.0001053096,0.0331307463,0.3021110892,-0.165072754,-0.1891661733,-0.5733721852,0.2214238346,-0.5341050625,-0.0140152387,-0.1214286312,-0.3000083864,-0.1858647615,0.2292091548,-0.0646855161,0.4581916332,0.138992846,0.2760677934,0.1265496016,0.1662908792,-0.3696480691,0.2097802907,0.1956458986,-0.1781926453,0.2711293101,0.5124987364,0.0008453406,0.3474407494,0.2254715711,-0.1975982338,-0.0633731633,0.0224851724,-0.0391207598,-0.1415897161,-0.397159338,0.2629119158,0.2328353524,0.3422170281,-0.3914035559,-0.2908690572,-0.1270831078,0.0680985674,-0.0180698503,-0.0308835004,0.195157066,-0.0137036536,0.1463201791,0.1868037134,-0.1229998842,-0.047105141,0.3313314319,-0.0886648819,0.2586908042,-0.3090187013,0.0099390605,0.1905119717,-0.178924948,-0.2507298589,0.0302959979,-0.0183176827,0.4132662117,-0.0670698136,0.0363565609,0.1135698333,0.0308769792,0.3723825514,-0.1460781097,-0.0953407064,0.0370077156,-0.1044247374,0.2302518487,0.2692061365,0.045740705,0.1354756653,0.1478246897,0.1838281304,0.2538388968,-0.0949297026,-0.1023403108,-0.2020095885,-0.2282737941,-0.3279549778,-0.1099150777,0.4180825353,-0.368394345,-0.1520365477,-0.1110243574,-0.0217322372,0.035854809,0.2241002768,0.6009091139,-0.1057141572,0.0214474481,-0.2264181376,0.1999215186,-0.1387862265,-0.0065258481,-0.2208006829,0.1473014504,-0.2257985026,-0.0462417379,0.2515354156,-0.5578830838,0.393538475,-0.0472707152,0.1960410029,-0.0681858808,-0.1338357925,-0.301745981,0.0997979343,0.229762733,0.101319164,0.0338174775,0.2995767891,-0.2491473556,-0.2520236969,0.0262057353,-0.2204843909,-0.1265044808,-0.3007340133,0.1876771152,-0.3505807221,-0.0759404302,-0.3589547575,0.2076333761,-0.0608204342,-0.4315320253,-0.000035826,-0.0217535459,-0.2501894236,-0.1095410511,0.425739795,0.7670357227,-0.2865989804,-0.1230983064,0.0340257213,-0.126667127,0.101105772,0.126527518,-0.1167581826,-0.0218859594,-0.265375644,0.1027911976,0.2992968559,-0.615336597,-0.3468686044,0.2056128085,-0.0500283204,0.1586273313,-0.0413157791,-0.1025643721,-0.0431875326,0.050346978,0.0029872649,0.0049375533,0.0312955864,0.0295400694,-0.0962504521,-0.1457029581,-0.1982443333,0.3119064271,0.1383856982,0.0675422177,0.3202099502,0.0876349062,0.0316906422,0.0958209634,-0.0984545723,0.2816782892,0.3037556708,0.1534382701,0.0837896764,-0.2247743607,-0.3634960353,0.2498729378,0.0457058363,0.2284103036,-0.2949457169,-0.188501969,-0.1586822271,-0.0201391019,-0.2509643435,-0.1564370543,0.1302569211,0.0791035146,0.1900039166,0.1363538951,-0.2076636851,0.1021811813,-0.1985594481,0.0608884916,-0.3810701668,0.0536024906,-0.1443722248,-0.0908582732,0.0266818665,0.3134473264,-0.0061015519,-0.2961701751,0.1275138259,0.2339981198,-0.0410703793,0.0831171274,0.0651024356,-0.1028961241,0.1492845863,-0.1187017262,0.1305786818,0.0326571129,0.1909237206,-0.0021715867,-0.1348509341,0.138798058,0.0411254317,0.0962456986,0.1917950064,-0.2008479834,0.3672035336,0.112716496,-0.2047113925,-0.1698101759,0.3535322249,0.0969964117,0.1955730617,0.0430092216,-0.3212142587,-0.1043168306,0.3177223802,0.1372816265,0.1213165373,0.2048687339,-0.2800172865,0.186897859,0.003449304,0.0588296801,0.4702047706,0.3391547799,-0.0764484555,0.0293206442,0.1730826646,-0.0994678661,0.0943906382,0.1055547893,0.1211164296,0.2762360275,0.0952636003,0.111055091,-0.3392531872,-0.1590596288,-0.2432276756,0.2127058059,-0.0921130925,0.0312307067,-0.1918958724,-0.5405955911,-0.2883024216,-0.1157423779,-0.3506484628,0.1294652373,-0.0584437922,-0.0211829785,0.2439412177,0.1399305463,0.0428650416,0.1373578906,-0.1882007718,-0.4878160954,-0.0153020732,-0.2103477567,-0.126777187,0.0949553549,0.373341918,-0.0983640179,0.3098777235,-0.2427178919,0.0662054196,-0.1999077499,-0.318315357,0.120759435,-0.196837157,0.2978352308,0.0523591004,0.2083314359,-0.3186392784,-0.0556888133,0.3241010904,-0.2407051027,-0.1146929413,-0.0510720573,-0.2177354693,-0.2655118704,-0.0859508514,-0.4589136243,-0.5289609432,-0.447973907,0.1381776482,0.3176063001,-0.0419272594,0.4903953373,0.2172504216,0.4897038043,-0.1627669334,0.2261385322,-0.1228660271,-0.4091244936,0.2950487733,-0.2791319191,-0.3444777429,0.1522465646,0.0878732055,0.2448349744,-0.3050942421,-0.4055993855,-0.2700473964,-0.0187662374,-0.0090881865,-0.0477787703,0.328910172,0.2568496764,-0.2854099572,-0.0629229397,-0.0179242268,-0.1917268783,-0.1979177296,0.0373480804,0.3480443954,-0.2784844339,0.1747761518,-0.0510753207,0.5100226402,0.1080506817,-0.0539683104,0.4590839148,-0.2125363797,0.535427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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1422","title":"Can't map dataset (loaded from csv)","comments":"Please could you post the whole script? I can't reproduce your issue. After updating the feature names\/labels to match with the data, everything works fine for me. Try to update datasets\/transformers to the newest version.","body":"Hello! I am trying to load single csv file with two columns: ('label': str, 'text' str), where is label is str of two possible classes.\r\n\r\nBelow steps are similar with [this notebook](https:\/\/colab.research.google.com\/drive\/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing), where bert model and tokenizer are used to classify lmdb loaded dataset. Only one difference it is the dataset loaded from .csv file.\r\nHere is how I load it:\r\n\r\n```python\r\ndata_path = 'data.csv'\r\ndata = pd.read_csv(data_path)\r\n\r\n# process class name to indices\r\nclasses = ['neg', 'pos']\r\nclass_to_idx = { cl: i for i, cl in enumerate(classes) }\r\n\r\n# now data is like {'label': int, 'text' str}\r\ndata['label'] = data['label'].apply(lambda x: class_to_idx[x])\r\n\r\n# load dataset and map it with defined `tokenize` function\r\nfeatures = Features({\r\n  target: ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None),\r\n  feature: Value(dtype='string', id=None),\r\n})\r\ndataset = Dataset.from_pandas(data, features=features)\r\ndataset.map(tokenize, batched=True, batch_size=len(dataset))\r\n```\r\n\r\nIt ruins on the last line with following error:\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-112-32b6275ce418> in <module>()\r\n      9 })\r\n     10 dataset = Dataset.from_pandas(data, features=features)\r\n---> 11 dataset.map(tokenizer, batched=True, batch_size=len(dataset))\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1237         test_inputs = self[:2] if batched else self[0]\r\n   1238         test_indices = [0, 1] if batched else 0\r\n-> 1239         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1240         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1241 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1208             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1209             processed_inputs = (\r\n-> 1210                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1211             )\r\n   1212             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\r\n   2281             )\r\n   2282         ), (\r\n-> 2283             \"text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \"\r\n   2284             \"or `List[List[str]]` (batch of pretokenized examples).\"\r\n   2285         )\r\n\r\nAssertionError: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).\r\n```\r\n\r\nwhich I think is not expected. I also tried the same steps using `Dataset.from_csv` which resulted in the same error.\r\n\r\nFor reproducing this, I used [this dataset from kaggle](https:\/\/www.kaggle.com\/team-ai\/spam-text-message-classification)","comment_length":35,"text":"Can't map dataset (loaded from csv) \n Hello! I am trying to load single csv file with two columns: ('label': str, 'text' str), where is label is str of two possible classes.\r\n\r\nBelow steps are similar with [this notebook](https:\/\/colab.research.google.com\/drive\/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing), where bert model and tokenizer are used to classify lmdb loaded dataset. Only one difference it is the dataset loaded from .csv file.\r\nHere is how I load it:\r\n\r\n```python\r\ndata_path = 'data.csv'\r\ndata = pd.read_csv(data_path)\r\n\r\n# process class name to indices\r\nclasses = ['neg', 'pos']\r\nclass_to_idx = { cl: i for i, cl in enumerate(classes) }\r\n\r\n# now data is like {'label': int, 'text' str}\r\ndata['label'] = data['label'].apply(lambda x: class_to_idx[x])\r\n\r\n# load dataset and map it with defined `tokenize` function\r\nfeatures = Features({\r\n  target: ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None),\r\n  feature: Value(dtype='string', id=None),\r\n})\r\ndataset = Dataset.from_pandas(data, features=features)\r\ndataset.map(tokenize, batched=True, batch_size=len(dataset))\r\n```\r\n\r\nIt ruins on the last line with following error:\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-112-32b6275ce418> in <module>()\r\n      9 })\r\n     10 dataset = Dataset.from_pandas(data, features=features)\r\n---> 11 dataset.map(tokenizer, batched=True, batch_size=len(dataset))\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1237         test_inputs = self[:2] if batched else self[0]\r\n   1238         test_indices = [0, 1] if batched else 0\r\n-> 1239         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1240         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1241 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1208             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1209             processed_inputs = (\r\n-> 1210                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1211             )\r\n   1212             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\r\n   2281             )\r\n   2282         ), (\r\n-> 2283             \"text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \"\r\n   2284             \"or `List[List[str]]` (batch of pretokenized examples).\"\r\n   2285         )\r\n\r\nAssertionError: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).\r\n```\r\n\r\nwhich I think is not expected. I also tried the same steps using `Dataset.from_csv` which resulted in the same error.\r\n\r\nFor reproducing this, I used [this dataset from kaggle](https:\/\/www.kaggle.com\/team-ai\/spam-text-message-classification) \n Please could you post the whole script? I can't reproduce your issue. After updating the feature names\/labels to match with the data, everything works fine for me. Try to update datasets\/transformers to the newest 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1422","title":"Can't map dataset (loaded from csv)","comments":"Actually, the problem was how `tokenize` function was defined. This was completely my side mistake, so there are really no needs in this issue anymore","body":"Hello! I am trying to load single csv file with two columns: ('label': str, 'text' str), where is label is str of two possible classes.\r\n\r\nBelow steps are similar with [this notebook](https:\/\/colab.research.google.com\/drive\/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing), where bert model and tokenizer are used to classify lmdb loaded dataset. Only one difference it is the dataset loaded from .csv file.\r\nHere is how I load it:\r\n\r\n```python\r\ndata_path = 'data.csv'\r\ndata = pd.read_csv(data_path)\r\n\r\n# process class name to indices\r\nclasses = ['neg', 'pos']\r\nclass_to_idx = { cl: i for i, cl in enumerate(classes) }\r\n\r\n# now data is like {'label': int, 'text' str}\r\ndata['label'] = data['label'].apply(lambda x: class_to_idx[x])\r\n\r\n# load dataset and map it with defined `tokenize` function\r\nfeatures = Features({\r\n  target: ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None),\r\n  feature: Value(dtype='string', id=None),\r\n})\r\ndataset = Dataset.from_pandas(data, features=features)\r\ndataset.map(tokenize, batched=True, batch_size=len(dataset))\r\n```\r\n\r\nIt ruins on the last line with following error:\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-112-32b6275ce418> in <module>()\r\n      9 })\r\n     10 dataset = Dataset.from_pandas(data, features=features)\r\n---> 11 dataset.map(tokenizer, batched=True, batch_size=len(dataset))\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1237         test_inputs = self[:2] if batched else self[0]\r\n   1238         test_indices = [0, 1] if batched else 0\r\n-> 1239         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1240         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1241 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1208             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1209             processed_inputs = (\r\n-> 1210                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1211             )\r\n   1212             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\r\n   2281             )\r\n   2282         ), (\r\n-> 2283             \"text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \"\r\n   2284             \"or `List[List[str]]` (batch of pretokenized examples).\"\r\n   2285         )\r\n\r\nAssertionError: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).\r\n```\r\n\r\nwhich I think is not expected. I also tried the same steps using `Dataset.from_csv` which resulted in the same error.\r\n\r\nFor reproducing this, I used [this dataset from kaggle](https:\/\/www.kaggle.com\/team-ai\/spam-text-message-classification)","comment_length":25,"text":"Can't map dataset (loaded from csv) \n Hello! I am trying to load single csv file with two columns: ('label': str, 'text' str), where is label is str of two possible classes.\r\n\r\nBelow steps are similar with [this notebook](https:\/\/colab.research.google.com\/drive\/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing), where bert model and tokenizer are used to classify lmdb loaded dataset. Only one difference it is the dataset loaded from .csv file.\r\nHere is how I load it:\r\n\r\n```python\r\ndata_path = 'data.csv'\r\ndata = pd.read_csv(data_path)\r\n\r\n# process class name to indices\r\nclasses = ['neg', 'pos']\r\nclass_to_idx = { cl: i for i, cl in enumerate(classes) }\r\n\r\n# now data is like {'label': int, 'text' str}\r\ndata['label'] = data['label'].apply(lambda x: class_to_idx[x])\r\n\r\n# load dataset and map it with defined `tokenize` function\r\nfeatures = Features({\r\n  target: ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None),\r\n  feature: Value(dtype='string', id=None),\r\n})\r\ndataset = Dataset.from_pandas(data, features=features)\r\ndataset.map(tokenize, batched=True, batch_size=len(dataset))\r\n```\r\n\r\nIt ruins on the last line with following error:\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<ipython-input-112-32b6275ce418> in <module>()\r\n      9 })\r\n     10 dataset = Dataset.from_pandas(data, features=features)\r\n---> 11 dataset.map(tokenizer, batched=True, batch_size=len(dataset))\r\n\r\n2 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1237         test_inputs = self[:2] if batched else self[0]\r\n   1238         test_indices = [0, 1] if batched else 0\r\n-> 1239         update_data = does_function_return_dict(test_inputs, test_indices)\r\n   1240         logger.info(\"Testing finished, running the mapping function on the dataset\")\r\n   1241 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in does_function_return_dict(inputs, indices)\r\n   1208             fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n   1209             processed_inputs = (\r\n-> 1210                 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n   1211             )\r\n   1212             does_return_dict = isinstance(processed_inputs, Mapping)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/tokenization_utils_base.py in __call__(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, is_split_into_words, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose, **kwargs)\r\n   2281             )\r\n   2282         ), (\r\n-> 2283             \"text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) \"\r\n   2284             \"or `List[List[str]]` (batch of pretokenized examples).\"\r\n   2285         )\r\n\r\nAssertionError: text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) or `List[List[str]]` (batch of pretokenized examples).\r\n```\r\n\r\nwhich I think is not expected. I also tried the same steps using `Dataset.from_csv` which resulted in the same error.\r\n\r\nFor reproducing this, I used [this dataset from kaggle](https:\/\/www.kaggle.com\/team-ai\/spam-text-message-classification) \n Actually, the problem was how `tokenize` function was defined. This was completely my side mistake, so there are really no needs in this issue 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1324","title":"\u2753 Sharing ElasticSearch indexed dataset ","comments":"Hello @pietrolesci , I am not sure to understand what you are trying to do here.\r\n\r\nIf you're looking for ways to save a dataset on disk, you can you the `save_to_disk` method:\r\n```python\r\n>>> import datasets\r\n>>> loaded_dataset = datasets.load(\"dataset_name\")\r\n>>> loaded_dataset.save_to_disk(\"\/path\/on\/your\/disk\")\r\n```\r\n\r\nThe saved dataset can later be retrieved using:\r\n```python\r\n>>> loaded_dataset = datasets.Dataset.load_from_disk(\"\/path\/on\/your\/disk\")\r\n```\r\n\r\nAlso, I'd recommend posting your question directly in the issue section of the [elasticsearch repo](https:\/\/github.com\/elastic\/elasticsearch)","body":"Hi there,\r\n\r\nFirst of all, thank you very much for this amazing library. Datasets have become my preferred data structure for basically everything I am currently doing.\r\n\r\n**Question:** I'm working with a dataset and I have an elasticsearch container running at localhost:9200. I added an elasticsearch index and I was wondering\r\n\r\n- how can I know where it has been saved?\r\n\r\n- how can I share the indexed dataset with others?\r\n\r\nI tried to dig into the docs, but could not find anything about that.\r\n\r\nThank you very much for your help.\r\n\r\nBest,\r\nPietro\r\n\r\nEdit: apologies for the wrong label","comment_length":73,"text":"\u2753 Sharing ElasticSearch indexed dataset  \n Hi there,\r\n\r\nFirst of all, thank you very much for this amazing library. Datasets have become my preferred data structure for basically everything I am currently doing.\r\n\r\n**Question:** I'm working with a dataset and I have an elasticsearch container running at localhost:9200. I added an elasticsearch index and I was wondering\r\n\r\n- how can I know where it has been saved?\r\n\r\n- how can I share the indexed dataset with others?\r\n\r\nI tried to dig into the docs, but could not find anything about that.\r\n\r\nThank you very much for your help.\r\n\r\nBest,\r\nPietro\r\n\r\nEdit: apologies for the wrong label \n Hello @pietrolesci , I am not sure to understand what you are trying to do here.\r\n\r\nIf you're looking for ways to save a dataset on disk, you can you the `save_to_disk` method:\r\n```python\r\n>>> import datasets\r\n>>> loaded_dataset = datasets.load(\"dataset_name\")\r\n>>> loaded_dataset.save_to_disk(\"\/path\/on\/your\/disk\")\r\n```\r\n\r\nThe saved dataset can later be retrieved using:\r\n```python\r\n>>> loaded_dataset = datasets.Dataset.load_from_disk(\"\/path\/on\/your\/disk\")\r\n```\r\n\r\nAlso, I'd recommend posting your question directly in the issue section of the [elasticsearch repo](https:\/\/github.com\/elastic\/elasticsearch)","embeddings":[-0.1429361105,-0.0638304427,-0.1649869233,0.115781866,-0.2390129417,0.181620121,0.2375487387,0.0645310879,0.1375180483,0.2632859945,-0.2542998195,0.1102019325,-0.0403488576,-0.1499138623,0.1162049919,0.2783807516,0.1407323331,-0.0522752032,0.1609809846,-0.1157200262,-0.0960236788,-0.119999066,0.0747434869,-0.0017549342,-0.0378025025,-0.1324211657,-0.4750579894,-0.1286243647,0.0660043433,-0.2219629884,0.5905970931,0.228190884,0.1744388193,0.3492886424,-0.00011476,0.0371787362,0.3044726849,-0.1059916615,-0.0600125231,-0.3337877989,-0.581566155,-0.215335533,0.3577898443,-0.5136823654,-0.0529217497,-0.2876566947,0.215880692,-0.6586778164,-0.0800799802,-0.0880682096,0.1715499014,0.1409371942,-0.1088337451,-0.0197396595,0.1892026067,0.1232945025,-0.0214467831,-0.0853259489,0.0034694511,0.0862663165,0.3268643916,0.1831186116,-0.1671927869,0.0605216324,0.1701910049,0.1723883599,-0.5219464302,-0.2311159968,-0.071456939,-0.0918339714,0.8283691406,-0.2656334937,-0.2670591772,0.0779282004,-0.0186363161,-0.1364419609,0.0233307462,0.1480533183,0.1877560019,0.1393417418,-0.2092095912,-0.4387285113,-0.4489777386,0.1656543314,-0.0322407037,0.5990275145,-0.1216457784,0.1211185828,-0.1504866928,0.0379112922,0.3560683131,-0.0478119478,0.1332057416,0.1248650327,0.2179226428,-0.1643845588,-0.152307421,-0.3114429414,-0.0014166407,0.1400372088,-0.1719859093,-0.2091900259,0.2432661802,0.3475384414,0.3548472822,0.2206200361,0.1119060665,0.3693484664,0.2211711854,-0.3995965719,-0.1336420625,-0.3954393864,-0.002178137,0.2996359468,0.4795609117,-0.384178102,0.1183941662,-0.1556719542,-0.0499291383,-0.1355164498,-0.2635773122,-0.2346781343,0.1576255262,0.2230813056,-0.0426803678,-0.4526020885,0.0704217851,0.053011056,0.0606575049,0.0951057225,-0.1951247007,-0.1827514917,0.1014507264,0.3022234738,0.1282158643,-0.431622237,0.1091167778,-0.2733577192,0.2553037405,-0.0140679283,0.429731071,0.1854222566,0.2484382838,0.3468284905,0.3137482703,0.1941066235,0.0212832112,-0.1582435519,-0.4444412589,-0.0081882961,-0.2976227403,-0.4269990623,-0.2789047062,0.0796810836,-0.14677082,0.1225128844,-0.2636526823,0.1629920751,0.0278550591,0.1676827371,0.0675702393,0.3594226241,-0.3890078664,-0.3475756347,0.4555885196,-0.1132136956,-0.1483667642,-0.1401285827,-0.4394765794,0.3204064667,-0.1318344772,-0.0364204086,-0.2487839162,0.1561017185,0.0096513871,0.3899398744,0.3576794565,-0.2793350816,-0.4653582871,0.0308838598,-0.0632729381,-0.4568527043,-0.090642795,0.2196332067,0.5229578614,-0.1691964865,-0.0543145537,0.5200255513,0.130307585,-0.1105512753,-0.2245105654,-0.105370082,-0.100602217,-0.0674336106,-0.2246749103,-0.0784225389,0.4058630764,0.083795689,-0.1331441849,0.3465709686,0.2486115694,0.2592419088,0.6578143239,0.3961037695,0.0544923209,-0.000192329,-0.1552846283,-0.0264302511,0.1335355639,-0.325779438,-0.0197524186,-0.4639935195,-0.1371489465,0.2006537616,-0.0217944086,0.0898777023,0.1105008945,0.0291883945,0.0281217005,0.3715507388,-0.2713485658,-0.1345423907,0.0544304997,-0.1252729148,-0.1900705695,0.4088031948,-0.0426318161,0.055150643,-0.1099947318,-0.0405121073,0.2740743756,-0.0997938141,-0.1949898452,0.4314921796,-0.4871744215,0.4711259902,0.3968756199,0.4983282089,0.0694006085,-0.6583535075,0.5281495452,-0.0021941918,-0.1115332916,0.1143557876,-0.3864212334,0.2820076048,-0.3749865294,0.1623958796,-0.1657324582,-0.0703583956,-0.1311714053,0.009526629,-0.3378591239,0.0164274033,-0.1932197362,-0.2723450959,-0.1785287708,0.1191431507,-0.1280243248,0.3031448424,0.0438921526,-0.0066568959,0.213763088,0.1184215173,0.044545982,-0.2419227511,-0.0614567101,-0.0233433712,0.3800972402,0.1158345491,0.3460180461,-0.1429543495,0.2170419097,0.1167357787,0.2452485263,0.1727616191,0.0757868513,-0.3122872114,0.1125931963,0.0014003376,0.3942690194,-0.1093649566,-0.2946109772,-0.0856119469,0.068510212,0.4260074794,0.0198859684,-0.0122362422,-0.0640035197,-0.1772958189,-0.2049411535,-0.2950291038,-0.0858226418,0.3247539401,0.1016401574,-0.1656903923,-0.4062299728,0.3377700746,0.0262661129,-0.1283356398,-0.102193132,-0.0353158005,0.0038677722,-0.0308065824,0.1300443113,0.3277303576,0.4591417909,0.3788124025,0.3379550874,-0.0890286043,0.0154948421,-0.1455993354,0.1245422736,0.083466284,-0.0886008218,0.1689879596,-0.7019357681,-0.2014822364,-0.0496284701,0.0304674767,0.0253383685,-0.1356609464,0.1490632892,0.1728926003,0.3836007714,-0.359167397,-0.4527559876,-0.0289978329,0.1434897929,-0.138441056,0.1597138494,-0.2611454427,-0.2167722583,0.0661258474,0.2811373174,-0.2025665492,0.0066708107,-0.6452115774,0.1128072739,-0.4477564692,-0.0002353656,0.2146393508,0.0456143841,0.0255773813,0.0032441698,-0.3282503486,0.3077257872,0.1301650107,-0.0031391599,0.0891865417,0.2140133083,0.3354142904,0.0677713454,0.0188867711,0.0027593495,-0.2675281465,-0.0721446946,0.428008765,0.1089506745,0.2220333368,-0.0767017007,0.0200661104,0.713788569,0.1559154987,-0.0385167301,0.1857172102,0.1338333189,0.2979231477,-0.026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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1324","title":"\u2753 Sharing ElasticSearch indexed dataset ","comments":"Hi @SBrandeis,\n\nThanks a lot for picking up my request. \n\nMaybe I can clarify my use-case with a bit of context. Say I have the IMDb dataset. I create an ES index on it. Now I can save and reload the dataset from disk normally. Once I reload the dataset, it is easy to retrieve the ES index on my machine. I was wondering: is there a way I can share the (now) indexed version of the IMDb dataset with my colleagues without requiring them to re-index it?\n\nThanks a lot in advance for your consideration.\n\nBest,\n\nPietro","body":"Hi there,\r\n\r\nFirst of all, thank you very much for this amazing library. Datasets have become my preferred data structure for basically everything I am currently doing.\r\n\r\n**Question:** I'm working with a dataset and I have an elasticsearch container running at localhost:9200. I added an elasticsearch index and I was wondering\r\n\r\n- how can I know where it has been saved?\r\n\r\n- how can I share the indexed dataset with others?\r\n\r\nI tried to dig into the docs, but could not find anything about that.\r\n\r\nThank you very much for your help.\r\n\r\nBest,\r\nPietro\r\n\r\nEdit: apologies for the wrong label","comment_length":98,"text":"\u2753 Sharing ElasticSearch indexed dataset  \n Hi there,\r\n\r\nFirst of all, thank you very much for this amazing library. Datasets have become my preferred data structure for basically everything I am currently doing.\r\n\r\n**Question:** I'm working with a dataset and I have an elasticsearch container running at localhost:9200. I added an elasticsearch index and I was wondering\r\n\r\n- how can I know where it has been saved?\r\n\r\n- how can I share the indexed dataset with others?\r\n\r\nI tried to dig into the docs, but could not find anything about that.\r\n\r\nThank you very much for your help.\r\n\r\nBest,\r\nPietro\r\n\r\nEdit: apologies for the wrong label \n Hi @SBrandeis,\n\nThanks a lot for picking up my request. \n\nMaybe I can clarify my use-case with a bit of context. Say I have the IMDb dataset. I create an ES index on it. Now I can save and reload the dataset from disk normally. Once I reload the dataset, it is easy to retrieve the ES index on my machine. I was wondering: is there a way I can share the (now) indexed version of the IMDb dataset with my colleagues without requiring them to re-index it?\n\nThanks a lot in advance for your consideration.\n\nBest,\n\nPietro","embeddings":[-0.1988601536,0.1247475296,-0.119677715,0.0339192636,-0.3991583884,0.1631872207,0.2689709365,0.0641190484,0.0988282338,0.2021004707,-0.228173539,0.1584118009,0.0233792495,-0.0414418392,-0.0148405479,0.3394413888,0.0893101469,0.0631404296,0.11034666,-0.1242943257,0.0559722446,-0.2349125296,0.0433592908,-0.0645673648,-0.0652778074,-0.061832089,-0.4033026993,-0.1812634468,0.0436657816,-0.2119496018,0.5912082791,0.301091224,0.2538352609,0.2416616231,-0.0001154477,0.059577629,0.276428014,-0.0930100605,-0.1243460774,-0.3499150574,-0.6613681316,-0.1312082708,0.2852573693,-0.4554820657,-0.0966447666,-0.3421029449,0.2402936816,-0.7295071483,-0.0450430326,-0.2401281297,0.1476607323,0.0412187912,-0.1752968878,-0.0152987074,0.1940662861,0.113657333,0.0129568912,-0.1093859896,0.1031234711,0.1652024686,0.205419451,0.1775828004,-0.0940437019,0.0174312349,0.1710152775,0.0823680609,-0.524279058,-0.1088333279,-0.0281775277,0.0563650094,0.9450519681,-0.1793226451,-0.419952482,0.0407501198,0.0339218043,-0.0398226306,-0.0109042032,-0.0083920714,0.2608441114,0.1329615861,-0.2057773024,-0.4527464509,-0.3319205642,0.0893896967,0.0239438452,0.5131623149,-0.054500781,0.0551777445,-0.166105777,-0.0404138342,0.4516308308,-0.0163610075,0.0697255582,0.1077305153,0.2860757709,-0.1387107968,-0.1411644071,-0.290079236,-0.0777222589,0.1675433367,-0.1779172421,-0.1583892852,0.229165405,0.2637395859,0.4209385812,0.2178203613,0.1772520393,0.3229680657,0.1465878785,-0.352927804,-0.1640387475,-0.3661541939,-0.0132499412,0.3466744125,0.4428035915,-0.3139209151,0.0692416802,-0.1900915205,-0.074898243,-0.2175788134,-0.2848870456,-0.1972461045,0.1211084202,0.0965382755,-0.0309478715,-0.372071743,0.0214540251,-0.0606534444,-0.0201938078,-0.0292197559,-0.1957124025,-0.2964936495,0.0171738043,0.3570494652,0.1961147338,-0.4609553516,0.0394107066,-0.244851768,0.223120898,0.0160479229,0.3973346949,0.2916308343,0.2900517583,0.1884032786,0.257761538,0.2254975289,-0.0337665081,-0.2146922797,-0.4397146106,0.0227637663,-0.2895736992,-0.4273771048,-0.2026579678,0.0500161946,-0.1111699566,0.1029620916,-0.1753638536,0.2210798413,-0.0205312632,0.0416545309,0.0754981786,0.3739998341,-0.3952652216,-0.2210346758,0.414886713,0.0035593063,-0.2440735847,-0.2064417303,-0.5053339601,0.3462714255,-0.2634888887,0.0357699208,-0.280257076,0.0588382967,0.0382040925,0.3766159713,0.3078480065,-0.2098412663,-0.6120325327,0.0018124729,-0.0938809216,-0.3875820637,-0.004830536,0.2987863123,0.5886089206,-0.1760359704,-0.0411486402,0.4830717742,0.0590430647,-0.0885216147,-0.1775696427,-0.1968394965,-0.0563885197,-0.1380819231,-0.0941395834,-0.0182277765,0.3711380363,0.1541195214,-0.1286540627,0.3143368363,0.2648582757,0.1926955879,0.486101687,0.427141726,0.0616721921,0.0193928163,-0.258890003,0.0219211411,0.0880433843,-0.3332216144,0.1101880223,-0.5423186421,-0.132173717,0.1099899933,0.0572945066,0.0492972583,0.0710489675,0.0199016817,-0.0125617124,0.313713342,-0.2198410183,-0.1813590527,0.1798437834,-0.1857519895,-0.3203570545,0.3934904933,-0.0204347074,0.0663026199,-0.1220833287,0.0164788682,0.191543445,-0.1148840338,-0.1754519641,0.3342700303,-0.519467473,0.4946865737,0.3578430116,0.500210762,0.0858272761,-0.6622859836,0.5527696609,-0.0361749306,-0.1229287684,0.0886336714,-0.3847316802,0.2940207422,-0.4438501298,0.1080007255,-0.1244286001,-0.1022992879,-0.1508060396,-0.0758102834,-0.3517902195,-0.0726294816,-0.2458980531,-0.3034408092,-0.20773004,0.1918961555,-0.0563424863,0.3178856075,-0.0210104138,0.1341428459,0.1603891701,0.0969604477,-0.038572602,-0.2863000333,-0.0457849167,-0.1007416099,0.2109859884,0.1735736579,0.3986305296,-0.1023572609,0.3224239051,0.0732471496,0.1670353711,0.0395460837,0.1103518903,-0.2544102073,0.107952252,0.04026898,0.3734403551,-0.0160953645,-0.1876020432,-0.128962934,0.0510351732,0.3918848336,-0.0940178856,-0.0854631141,-0.0150619606,-0.1260554045,-0.1579370052,-0.2407260388,-0.0270373318,0.4399486482,0.0583895408,-0.0824674368,-0.4287155569,0.3352515697,-0.0003864482,-0.1406266838,-0.0541082919,-0.1411371976,0.0395008922,-0.0201627724,0.1621989608,0.3113367558,0.4181275666,0.3823670447,0.3871317804,-0.1832802743,-0.0685769245,-0.1460180283,0.1803927422,0.1241778135,-0.136332795,0.054550156,-0.6782448292,-0.2409548163,-0.1003120914,-0.0597910695,0.0098857796,-0.2054486573,0.1269374043,0.184761256,0.4511450231,-0.4822726846,-0.2808845639,0.0073809791,0.2037153989,-0.2172985673,0.0744234696,-0.1621911675,-0.1661675572,-0.0636928603,0.3251665831,-0.248437956,-0.0824173763,-0.6632475257,0.0341141559,-0.4512145519,0.0057522156,0.0873015672,-0.0184708405,-0.0174825881,0.1385416985,-0.3052280545,0.4069462419,0.1929593682,0.0120888986,0.0669228211,0.089193657,0.3754009008,0.0703555495,-0.0106692379,0.032443732,-0.2659999728,-0.0522957966,0.4143597186,0.1227937266,0.1801627576,-0.0679911599,0.0717385486,0.7623034716,0.1164719164,-0.1460179687,0.1448879689,0.1330832243,0.3890991807,-0.0884660035,-0.233131513,-0.23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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1324","title":"\u2753 Sharing ElasticSearch indexed dataset ","comments":"Thanks for the clarification.\r\n\r\nI am not familiar with ElasticSearch, but if I understand well you're trying to migrate your data along with the ES index.\r\nMy advice would be to check out ES documentation, for instance, this might help you: https:\/\/www.elastic.co\/guide\/en\/cloud\/current\/ec-migrate-data.html\r\n\r\nLet me know if it helps","body":"Hi there,\r\n\r\nFirst of all, thank you very much for this amazing library. Datasets have become my preferred data structure for basically everything I am currently doing.\r\n\r\n**Question:** I'm working with a dataset and I have an elasticsearch container running at localhost:9200. I added an elasticsearch index and I was wondering\r\n\r\n- how can I know where it has been saved?\r\n\r\n- how can I share the indexed dataset with others?\r\n\r\nI tried to dig into the docs, but could not find anything about that.\r\n\r\nThank you very much for your help.\r\n\r\nBest,\r\nPietro\r\n\r\nEdit: apologies for the wrong label","comment_length":48,"text":"\u2753 Sharing ElasticSearch indexed dataset  \n Hi there,\r\n\r\nFirst of all, thank you very much for this amazing library. Datasets have become my preferred data structure for basically everything I am currently doing.\r\n\r\n**Question:** I'm working with a dataset and I have an elasticsearch container running at localhost:9200. I added an elasticsearch index and I was wondering\r\n\r\n- how can I know where it has been saved?\r\n\r\n- how can I share the indexed dataset with others?\r\n\r\nI tried to dig into the docs, but could not find anything about that.\r\n\r\nThank you very much for your help.\r\n\r\nBest,\r\nPietro\r\n\r\nEdit: apologies for the wrong label \n Thanks for the clarification.\r\n\r\nI am not familiar with ElasticSearch, but if I understand well you're trying to migrate your data along with the ES index.\r\nMy advice would be to check out ES documentation, for instance, this might help you: https:\/\/www.elastic.co\/guide\/en\/cloud\/current\/ec-migrate-data.html\r\n\r\nLet me know if it helps","embeddings":[-0.1457756609,-0.045346763,-0.1310747564,0.0862376168,-0.3029541075,0.1440629959,0.168963179,0.0394937918,0.0905099213,0.167836979,-0.2247992307,0.1182387918,0.0383169986,-0.0483014807,0.0813873261,0.2858945429,0.2005696893,-0.0846778378,0.1571799964,-0.1761487871,-0.0924894065,-0.1813904643,0.0924829692,0.0360959023,-0.0428278074,-0.0231426042,-0.4308625162,-0.1304083616,0.0299442615,-0.1795429438,0.469704628,0.1927620769,0.275839597,0.3492526412,-0.0001151063,0.0133273397,0.2313718647,-0.1313443929,-0.0700168684,-0.3784717023,-0.7095398903,-0.1439011991,0.3159016967,-0.4703968465,-0.0717966259,-0.3100250661,0.1885525137,-0.7246231437,-0.0082336534,0.0296246633,0.1764687002,0.0073751709,-0.0613887832,-0.0677640513,0.2456240207,0.0809686035,-0.0013593545,-0.1211186722,0.0571740121,0.1488161236,0.2379551381,0.158067733,-0.0959754065,-0.0015726967,0.173545748,0.1030583009,-0.4810886979,-0.1708822548,-0.0100431833,-0.0526020043,0.7975552082,-0.1531261057,-0.2209452242,0.1287153512,-0.0147749521,-0.0849731192,0.0635086447,0.0574337319,0.2169608623,0.1254877448,-0.236580953,-0.4179345965,-0.4528800547,0.0952967554,-0.1337144673,0.6397081614,-0.1087928787,0.0641462654,-0.1296021342,0.0166655034,0.5582042336,0.0055199042,0.1231711358,0.1779037416,0.2077921331,-0.1689664871,-0.1721054316,-0.3097271025,-0.0356601365,0.1470437199,-0.1803037226,-0.1733400375,0.2875542343,0.3189651668,0.3117184341,0.2659611106,0.1532383412,0.2794008553,0.0862537697,-0.4532053471,-0.0932770297,-0.3655295372,-0.0941490158,0.2714438438,0.2927916944,-0.3570107222,0.0569864549,-0.2542733252,-0.0886999667,-0.1678356379,-0.1851098984,-0.2368090302,0.1837714016,0.1748078614,0.0455106199,-0.4377539754,0.0412700698,-0.009010422,0.0657930449,-0.0398498476,-0.2080461383,-0.1812649071,-0.0013803255,0.2258405834,0.0353704728,-0.3625747561,0.0773466825,-0.2569189966,0.2154798508,-0.0168701336,0.4023520648,0.1661077589,0.315841943,0.2647255063,0.2203661352,0.1481296122,0.0576660708,-0.1004638523,-0.4170539379,-0.0847444758,-0.2948832214,-0.4469672441,-0.3006444275,0.0753790811,-0.121927321,0.023230629,-0.1892858297,0.320091933,-0.04891764,0.0919416696,0.0651453212,0.429769218,-0.3482821286,-0.2637747824,0.4185822308,-0.0818905458,-0.2158023268,-0.1309787184,-0.4038163722,0.3640606999,-0.0214126687,0.0175250582,-0.2373889089,0.0326789208,0.0393478908,0.4336847365,0.2714959085,-0.1292854548,-0.4007872939,0.0380572714,-0.1345510632,-0.5735340714,0.0136672873,0.1823256463,0.5256351829,-0.29685691,-0.071812667,0.5366872549,0.0097737322,-0.0651436225,-0.2112269253,-0.0803556964,-0.0552819371,-0.1397622228,-0.226126045,-0.1280879676,0.399525106,0.1132630184,-0.2694829702,0.3338492811,0.271224618,0.235200122,0.5771109462,0.3885474503,0.0084548704,0.1116839498,-0.1991685778,-0.0603856705,0.0679599941,-0.3469825983,0.0607089736,-0.4277478456,-0.2286389172,0.1216201857,-0.0791684166,0.0404194891,0.1097247973,0.0403867438,-0.0493519753,0.3112525642,-0.2652309537,-0.2971449494,0.0204566047,-0.1574488729,-0.2572397292,0.4892600477,0.0032491321,0.0899046957,-0.1495745331,-0.0277398415,0.2485863119,-0.1627816409,-0.3001868427,0.3699037731,-0.5102575421,0.4789199233,0.4329916835,0.5023630857,0.0516019203,-0.7361007929,0.6079818606,-0.0727884397,-0.1887968481,0.0689285696,-0.4780113697,0.1741653681,-0.5054827332,0.1937195808,-0.1432692409,-0.0824300125,-0.1361819357,-0.0746347681,-0.2095844895,0.0672513098,-0.1215950623,-0.230892688,-0.1964156926,0.2698990107,-0.0475250818,0.3268631399,0.0483636893,0.1000007242,0.1638967544,0.093992956,-0.066106841,-0.3816815019,0.0605910309,-0.0777859837,0.336887002,0.1360581219,0.4463493526,-0.1025030836,0.2812378109,0.0636640787,0.2965095639,0.0862451717,0.0999866426,-0.3449760675,0.0289299879,0.0542095788,0.3567815125,-0.0990595073,-0.2287160009,-0.006872871,0.0799518079,0.4823937714,0.0181276258,-0.0609977134,-0.0829006955,-0.1063559651,-0.1488537788,-0.2547786534,0.0012727914,0.3403769433,0.0274672173,-0.1734492779,-0.4013466239,0.3015756309,0.0434722342,-0.081888251,-0.1868399382,-0.173001498,-0.0350471511,-0.0165757686,0.2086957693,0.348998487,0.3991431594,0.4420820773,0.3904934525,-0.1034120172,-0.1730882674,-0.1159180552,0.1397185773,0.0154737933,-0.2044383436,0.1714712828,-0.6772081256,-0.1900226325,-0.0780407786,-0.039935112,-0.0449576154,-0.0345212407,0.0807467028,0.1899700165,0.5178708434,-0.4853645265,-0.4031346738,-0.0117790578,0.104527235,-0.2234055102,0.1328673512,-0.2687136233,-0.1638313532,-0.0073705218,0.2812463939,-0.2817280293,-0.0349922329,-0.6651854515,0.0611876808,-0.4426783323,0.0827700421,0.2310274094,0.0671742782,0.0836090073,0.0312215593,-0.2561567426,0.383764267,0.2040835172,0.0096310116,0.0569279604,0.1633331627,0.41004318,0.0706089437,-0.019218307,0.0783297271,-0.189073801,-0.0556713231,0.4821393788,0.164823547,0.161321491,-0.0689434782,0.0577240959,0.7453504205,0.1732260734,-0.115282096,0.1567568779,0.2003351897,0.197094366,-0.0396700427,-0.2997998595,-0.0960258842,0.3181846142,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1299","title":"can't load \"german_legal_entity_recognition\" dataset","comments":"Please if you could tell me more about the error? \r\n\r\n1. Please check the directory you've been working on\r\n2. Check for any typos","body":"FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition\/german_legal_entity_recognition.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py\r\n","comment_length":24,"text":"can't load \"german_legal_entity_recognition\" dataset \n FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition\/german_legal_entity_recognition.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py\r\n \n Please if you could tell me more about the error? \r\n\r\n1. Please check the directory you've been working on\r\n2. Check for any typos","embeddings":[-0.2461109459,-0.2954854667,-0.040122088,0.4629744589,0.134741649,0.0751197487,0.1829098016,0.2415550202,0.2743229866,0.084339641,-0.1621886194,-0.2467778772,-0.0410388298,0.2271589786,0.1816771328,-0.3239541352,-0.022687709,0.043106351,0.1395513117,-0.2421332002,-0.1837922037,0.3086111248,-0.1309448034,0.2035576105,-0.2785853744,0.1524153352,-0.1371282935,0.4542097747,-0.3045515418,-0.2061940581,0.243312344,-0.2026706636,0.3079298735,0.4991836846,-0.0001100421,0.0578923151,0.2836090922,-0.0098897405,-0.2552669048,-0.4077288806,-0.000673596,-0.2990614176,-0.1864303499,-0.1148855016,-0.2259489447,-0.2796554267,0.1619674116,0.1354253888,0.2634932995,0.1191348284,0.2091219276,0.2212058753,0.2193627506,-0.3371452987,-0.1168966964,0.2205763161,-0.0645754561,0.5331406593,0.1345074773,-0.2835020423,0.2549849451,0.227091521,-0.0557186753,-0.0208476763,0.3751378357,-0.0840708092,0.3238506317,-0.3081605136,0.2847310305,0.234388113,0.580249548,-0.0297926161,-0.4290724099,-0.0293356739,0.2880429029,0.0057340362,0.2407151312,0.3006296158,-0.0669369847,0.1814675182,-0.1373806745,-0.4443282783,-0.3213987947,0.2860621214,-0.2091830373,0.0494364537,-0.2200017869,-0.0653783605,0.2275342345,-0.2309976071,-0.4672821164,0.02005703,0.0080825547,0.5115160942,-0.1440299898,0.026818607,-0.0979861096,0.3844959438,0.1937473714,0.1407542825,-0.0220368207,-0.081481725,-0.1746383458,0.4402425885,0.0594136417,0.3439055383,0.1706577986,0.2345796674,0.1332697272,0.1937059164,-0.1103216931,0.0529637858,-0.4952788651,-0.3095989525,-0.35193488,-0.2179227024,0.3497495055,-0.3086481988,-0.2129980773,0.0519950278,-0.188149184,-0.0218673814,0.34618783,0.5144165754,-0.064978011,-0.2256052792,-0.1134259552,0.173306644,-0.1949893981,-0.0240159798,-0.2273389846,0.2044352293,-0.2663430274,0.1077500731,0.1484897137,-0.4823882282,0.4201607704,-0.2502062023,0.0515125133,0.0119270589,-0.1076055914,-0.07985273,-0.1945853382,0.2724869847,-0.2817009687,-0.1495464295,0.2713314295,-0.2432877421,-0.1272886097,-0.0838614032,-0.2914420664,-0.3538348675,-0.3816328645,0.1404944211,-0.1598204821,-0.1504817158,-0.1611048132,0.1011340767,-0.1878173947,-0.3983779848,0.003076813,0.0209349561,-0.1749327779,-0.1412834227,0.3550225496,0.660187006,-0.1571733057,-0.1841322929,0.0235803425,-0.2447856367,0.1131006628,0.7056491971,-0.23356691,0.0617510192,-0.4061587155,0.2985373139,0.2582632899,-0.5748142004,-0.242413938,0.3591725528,-0.2599228621,0.055071637,-0.0022604682,-0.0761059597,-0.2731065154,-0.1170467883,-0.0694173649,0.1008342132,0.0887652189,-0.2138914913,-0.1731622517,-0.1359585822,0.1755654216,0.3268742859,0.1123142168,0.102979809,0.2103891224,-0.0414946526,-0.0276233666,-0.0393747576,-0.1953698993,0.3031049669,0.2408164889,0.3216609657,0.0204128716,-0.1257183254,-0.3142140508,0.3031443954,-0.3899177015,0.2161759436,-0.2050553113,-0.1065893546,-0.2555696368,-0.0077495999,-0.3642155826,-0.1737731546,0.0880405232,0.0162563585,0.2212073952,0.1731007546,-0.0889178962,0.1511776149,-0.0848262981,0.1308608204,-0.3367919922,0.2155641615,-0.2358353138,0.1930098683,-0.0413667262,0.1421066225,0.1962637007,-0.2131726146,-0.0544220507,0.3109876215,-0.2180805802,0.2646730244,0.4681869745,0.3286012113,0.2611548603,-0.26838094,0.3497458696,-0.0667929128,0.1807490736,0.1038902849,-0.1683859229,0.083073236,0.1250580996,0.0417994745,0.0687506497,-0.0625735596,0.0969749838,-0.0657422021,-0.0689738393,-0.0130336583,0.4850594103,0.0714358389,0.3537172675,-0.0129299164,-0.375918895,-0.2800389528,0.2990945876,-0.1732295603,0.1164980382,0.2613541484,-0.2369155884,0.0857469961,0.0461429916,0.0166967437,0.3218719661,0.1582996845,-0.1274445355,0.0714788437,-0.0126155028,-0.1146031097,0.0880259499,-0.0003688995,0.0651855022,0.240393728,-0.107860893,0.0802324042,-0.4135636985,-0.3385950923,-0.0826898366,0.2061855793,-0.2287418395,0.0982242972,-0.2093147635,-0.478164494,-0.2285084724,0.0613498837,-0.5594624281,0.0963179693,0.066031158,-0.033255972,0.0308852997,0.1123059765,0.039493341,0.2607886195,-0.184775278,-0.2399189472,-0.2281203717,-0.1024246737,-0.2071873993,0.0067287451,0.5578662753,0.196608901,0.430419445,-0.2647414207,0.0509952605,-0.3066093028,-0.4186324477,0.0406331159,-0.1041649207,0.2540677786,0.0432715751,0.2298954725,-0.3527813554,-0.1797481328,0.5061764717,-0.1521884501,-0.2286915183,0.0058346274,-0.0436440408,-0.2822234631,0.0267798062,-0.1938976645,-0.4402248859,-0.3264966607,0.4318302572,0.0639394745,0.0518777966,0.3492003083,-0.0634190589,0.3247578442,-0.1961455494,0.1281203479,-0.1535876542,-0.4176025689,0.2414850891,-0.3585348427,-0.3183611631,0.0801431462,0.1686861813,0.2564223111,-0.0917546004,-0.173003152,-0.2125993967,0.005323017,-0.0363134146,-0.0836634859,0.1677423567,0.1912681609,-0.1686710268,-0.0399982147,0.1529936194,-0.0419996828,0.0001621202,0.1548144817,0.198603943,-0.0571862459,0.2133805752,-0.1873622388,0.670863688,0.1992785782,0.0671118051,0.4373435378,-0.0646627024,0.2162336409,-0.1551831663,-0.4231314957,-0.0120128877,0.1890667379,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1299","title":"can't load \"german_legal_entity_recognition\" dataset","comments":"> Please if you could tell me more about the error?\r\n> \r\n> 1. Please check the directory you've been working on\r\n> 2. Check for any typos\r\n\r\nError happens during the execution of this line:\r\ndataset = load_dataset(\"german_legal_entity_recognition\")\r\n\r\nAlso, when I try to open mentioned links via Opera I have errors \"404: Not Found\" and \"This XML file does not appear to have any style information associated with it. The document tree is shown below.\" respectively.","body":"FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition\/german_legal_entity_recognition.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py\r\n","comment_length":77,"text":"can't load \"german_legal_entity_recognition\" dataset \n FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition\/german_legal_entity_recognition.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py\r\n \n > Please if you could tell me more about the error?\r\n> \r\n> 1. Please check the directory you've been working on\r\n> 2. Check for any typos\r\n\r\nError happens during the execution of this line:\r\ndataset = load_dataset(\"german_legal_entity_recognition\")\r\n\r\nAlso, when I try to open mentioned links via Opera I have errors \"404: Not Found\" and \"This XML file does not appear to have any style information associated with it. The document tree is shown below.\" respectively.","embeddings":[-0.1284616888,-0.0970662758,-0.0301719233,0.52423352,0.2877425551,0.138086766,0.1956101209,0.2292719632,0.1878093183,0.1808798313,-0.3449552059,-0.1354647428,0.0454051569,0.238665849,0.1107380167,-0.3510169089,-0.1016481742,0.0041541751,0.1530084312,-0.1696580201,-0.1890911311,0.3157928884,-0.1670924276,0.2493717968,-0.12845397,0.2280391455,-0.1583690494,0.5008804202,-0.267236799,-0.3464731872,0.1775410622,-0.1727831811,0.1381816566,0.5028778911,-0.0001160817,0.1684517264,0.3584832847,0.0463054292,-0.3028357327,-0.4721885622,-0.0444821008,0.011026741,-0.1089340225,-0.1386529654,-0.1597964764,-0.2255044729,0.0787687302,0.0283097997,0.1838006675,0.3876415789,0.1323372275,0.2076338083,0.2036084682,-0.239934504,0.112439163,0.069475919,-0.0881248787,0.3908723891,0.1431781501,0.0015836535,0.3734642267,0.2545952499,-0.214456439,0.0117633203,0.3610550165,-0.0947234556,0.2621243894,-0.4173006713,0.3253712952,0.3081182539,0.8236352801,-0.0829197094,-0.4649599791,-0.0205962099,0.1881633401,-0.0181164816,0.3648922443,0.2760870755,-0.0488722064,0.1934266239,-0.1533073336,-0.3488994837,-0.289683938,0.2990824878,-0.1846633554,0.0604278706,-0.1255752444,-0.0613287054,0.3649992645,-0.1658501327,-0.3809635341,-0.1153136492,0.0675977021,0.5025035739,-0.0852106288,0.1173983365,-0.0282755103,0.3216753006,0.1948667914,0.1033321321,0.0905859619,0.0120888771,-0.2064005136,0.4004949331,0.0734971538,0.2989094853,0.030573098,0.006263718,0.2821767926,0.1956744492,-0.0332635939,0.1224434227,-0.4561715126,-0.3617556095,-0.4565536082,-0.1500506103,0.4977904856,-0.4203483164,-0.3818337321,0.1402867287,-0.2901342511,-0.1732571274,0.335816592,0.6531698108,-0.2122738212,-0.1192939207,-0.1761870831,0.1861924231,-0.3083966374,-0.0409370661,-0.1233855113,0.1253173202,-0.245634079,0.2390463352,0.144863382,-0.3951359689,0.2847658098,-0.2692220807,0.0242194589,-0.1080353558,-0.1189616099,-0.2255274355,-0.249380976,0.3539412618,-0.1349590868,-0.1933986098,0.2049238831,-0.3587966561,-0.1289900541,0.0766790211,-0.327272743,-0.4347092807,-0.2450088412,0.1028737351,-0.3333546519,-0.1754341424,-0.1302225441,0.0702588037,-0.1435457915,-0.3391142488,-0.0810110644,0.0006705662,-0.2801930308,-0.1255639642,0.3089601099,0.6876109838,-0.0440485701,-0.1589181423,0.1731986552,-0.2280242145,0.1038119122,0.6274731159,-0.191130802,0.1403751969,-0.319752574,0.1875180453,0.4359926283,-0.6256730556,-0.2637548149,0.2893291414,-0.1598046869,0.0991236046,-0.065715462,-0.1781258434,-0.159752056,0.074345462,-0.0614383258,0.0533809662,0.1809496582,-0.0987008363,-0.1542083323,-0.1613453031,-0.0043838653,0.3532508314,0.1352611035,0.0834900662,0.2695771456,-0.286185056,0.0958966538,-0.1564672887,-0.1349140555,0.4070647955,0.1309380829,0.5612075329,0.1535349339,-0.1372198015,-0.1892559379,0.2605710626,-0.2483993024,0.250526607,-0.1847560704,-0.1427255273,-0.3887182772,-0.0137939975,-0.4441559017,-0.1686789691,-0.0096325669,0.0590864867,0.1441983134,0.2651419342,-0.1881757528,0.1842205971,0.0520941168,0.2091677934,-0.4159216881,0.3306803405,-0.2947822213,0.1462970525,-0.1381468624,0.2533885837,0.1048906371,-0.201987505,-0.1726142317,0.3779194057,0.0250423625,0.1596824527,0.3278082013,0.471762985,0.2786517739,-0.4465465248,0.24975577,0.0216959529,0.2192718834,0.1120481789,-0.2551671863,-0.0079791378,0.1175501049,0.1259956658,0.2405890971,-0.0571400039,0.0639624596,-0.0355957896,-0.1666634381,0.0263212621,0.5831589103,-0.0020470028,0.4861436486,-0.0022115733,-0.4058570862,-0.2668928504,0.2205027193,-0.1580255777,0.1402618587,0.2830582261,-0.2300815582,0.0876422971,-0.0906660259,0.1340228319,0.2167288959,0.1192997843,-0.1595410854,0.2266142219,0.0307773091,-0.185721457,0.0778872073,0.1208221912,0.1685411632,0.1628404707,-0.0735765547,0.1814069301,-0.3839359283,-0.4361386597,-0.0667828843,0.0940121189,-0.2802446485,0.2051865309,-0.1672077477,-0.3744663894,-0.1607932895,0.0722543374,-0.485195905,0.0286906119,0.0245605502,0.1296552122,-0.037622828,0.0515218414,-0.1298662275,0.2387040704,-0.1413996965,-0.2614262402,-0.0420084484,-0.0587313473,-0.2014176548,-0.0789254531,0.4222655296,0.1445723325,0.17905505,-0.3623304963,0.1587977111,-0.2472373098,-0.3430608213,0.1865047961,0.0980219468,0.1405782253,-0.1274766922,0.102063328,-0.2902251184,-0.108414501,0.5380041599,0.0940812379,-0.1029296443,0.0803398043,0.008339758,-0.2383141369,0.0861875042,-0.2965488434,-0.5001032948,-0.3831173778,0.2841564715,-0.1005473286,0.2432362735,0.4525517523,-0.0493821613,0.3326973319,-0.0926731676,0.2460079044,-0.1583628803,-0.4093944132,0.3129261136,-0.4540614784,-0.3857223094,0.0652327985,0.1502108574,0.2502662838,-0.0103789754,-0.3031935394,-0.0848824158,-0.0673022941,-0.0191881657,-0.1619923711,-0.018712664,0.2730546892,-0.027946746,-0.0371688157,0.0137552675,-0.0009872422,-0.0250708554,0.1494068205,0.2231205553,0.0141198635,0.2424313426,-0.1294840574,0.6173750758,0.1517210007,0.1059064865,0.5841574669,-0.170213908,0.2498150766,-0.0573802926,-0.352294594,-0.0659816042,-0.02659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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1299","title":"can't load \"german_legal_entity_recognition\" dataset","comments":"Hello @nataly-obr, the `german_legal_entity_recognition` dataset has not yet been released (it is part of the coming soon v2 release).\r\n\r\nYou can still access it now if you want, but you will need to install `datasets` via the master branch:\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`\r\n\r\nPlease let me know if it solves the issue :) ","body":"FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition\/german_legal_entity_recognition.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py\r\n","comment_length":52,"text":"can't load \"german_legal_entity_recognition\" dataset \n FileNotFoundError: Couldn't find file locally at german_legal_entity_recognition\/german_legal_entity_recognition.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/german_legal_entity_recognition\/german_legal_entity_recognition.py\r\n \n Hello @nataly-obr, the `german_legal_entity_recognition` dataset has not yet been released (it is part of the coming soon v2 release).\r\n\r\nYou can still access it now if you want, but you will need to install `datasets` via the master branch:\r\n`pip install git+https:\/\/github.com\/huggingface\/datasets.git@master`\r\n\r\nPlease let me know if it solves the issue :) ","embeddings":[-0.287135601,-0.2293046713,-0.0558743365,0.3209625185,0.1926872134,0.1035037786,0.1227492914,0.239558354,0.2547089756,-0.0033335367,-0.1283259392,-0.1231847852,-0.0620686971,0.3180630505,0.1877844334,-0.3089747429,0.0005011986,0.015175079,0.0721771196,-0.129286781,-0.1097700596,0.3460334539,-0.0871673822,0.1794844419,-0.2606520057,0.1178169921,-0.0188958384,0.2385766953,-0.4208890796,-0.3372544944,0.2726183534,-0.0110955201,0.2925595641,0.371425271,-0.0001043722,0.0022043132,0.3241093755,-0.0111567322,-0.2839604914,-0.3169110417,-0.0197815206,-0.3420846462,-0.1262253225,-0.0518512838,-0.3168023825,-0.2389598191,0.1567593962,0.1090816408,0.2710792124,0.1588666737,0.2833471596,0.1991866529,0.270620048,-0.360001564,-0.0701991469,0.0936058387,-0.1158882603,0.4267020822,0.1928993613,-0.2007417381,0.2586779892,0.2370307893,0.116937682,0.0021750489,0.3039171398,-0.024705613,0.3294273913,-0.2710103095,0.2744709849,0.2596640587,0.4311144054,-0.123146072,-0.4543117583,-0.101801075,0.2980352342,-0.0944480523,0.2075906694,0.2430980504,-0.0850081593,0.2237718999,-0.1252354681,-0.3121505082,-0.2083556652,0.2568840683,-0.2121196538,0.10334263,-0.2279229611,-0.063815482,0.305175215,-0.2178628445,-0.4415865242,0.1773997098,-0.0384146236,0.5027360916,-0.0967360586,0.1253807396,0.0440346785,0.348957032,0.281717509,0.1656108946,-0.0340988412,0.0228382517,-0.0802756473,0.4368696809,0.0577675477,0.3527481556,0.0837927386,0.2032633126,0.1320182085,0.280441016,0.0159925073,0.0769498572,-0.4701928198,-0.170616135,-0.3189519644,-0.1559458673,0.2127619684,-0.2779736519,-0.2171692252,0.0097234305,-0.0927772149,0.0084325718,0.3076982796,0.4883945286,-0.0327314734,-0.2342407852,-0.0934844688,0.1717046797,-0.1615845859,0.0802362263,-0.2343033999,0.0346785411,-0.2837478518,-0.0286612548,0.2533976436,-0.4500764012,0.4466090202,-0.1697286069,0.0600390323,0.1076340079,-0.0652886629,0.0629625693,-0.1449688226,0.2517574131,-0.1662921458,-0.1733265817,0.1900547892,-0.3055607378,-0.1252636909,-0.1787098497,-0.2166444659,-0.3605930507,-0.37529248,0.2259646505,-0.2043678761,-0.187016964,-0.1884661317,0.1423495114,-0.1045726389,-0.4150547385,-0.0911060497,-0.015621705,-0.0996251032,-0.0927589387,0.3819972575,0.4307042062,-0.1753313094,-0.2079688311,-0.0456437804,-0.2844352722,0.1065340787,0.4751934111,-0.2280706912,-0.0386946984,-0.396861434,0.2115337998,0.1432289928,-0.453361243,-0.3421435356,0.2101932317,-0.1873482615,0.0749401674,0.0014123755,-0.1085762084,-0.2418076843,-0.0224312004,-0.1778215766,0.1832603663,0.1322078854,-0.1387587935,-0.0946837962,-0.1494649351,0.0830519348,0.1617780328,0.0679191053,0.1183405146,0.1734056771,0.0127754696,0.0263376106,0.0217664279,-0.1108661219,0.2096807808,0.224819839,0.3467616439,0.0571014285,-0.1895009875,-0.2203349471,0.307705611,-0.4281440973,0.2901538312,-0.3179882467,-0.2025410235,-0.3226408362,0.0154769868,-0.3389259875,-0.2649016976,0.1795160919,0.1342784911,0.2389125377,0.1872345358,-0.0360569693,0.0982850417,0.1055332795,0.1050726026,-0.402998805,0.1303628236,-0.2962359488,0.1427173615,0.0135174822,0.1573440433,0.083754845,-0.1933580935,-0.0413600132,0.291136831,-0.2500871718,0.3001385927,0.288407445,0.3958922923,0.2157092541,-0.2101268321,0.2361978441,-0.1107795089,0.1215829477,0.0962872505,-0.2546933889,0.1321818978,0.0794701874,0.0011697293,0.051285997,0.043703761,0.1992012411,0.0104948403,-0.0914536193,-0.1404322237,0.2713919282,-0.0491027795,0.3081584871,-0.1044256687,-0.2544832826,-0.1351487786,0.387834996,-0.020589333,0.1426635832,0.2112210989,-0.2736790478,0.0250289291,0.1215223297,0.0918626711,0.2886164188,0.2752539814,-0.0311059374,0.0427798927,-0.0110768965,-0.0889173299,0.0887093395,-0.0508761816,-0.0482559986,0.1743098646,-0.0122377193,0.0207016468,-0.4914883375,-0.1776544154,-0.0759711117,0.2441278845,-0.1945655346,0.0935321674,-0.2653297782,-0.410626471,-0.1424117684,-0.0438923091,-0.5049240589,0.0443081111,0.1313380003,0.0712512136,0.0490951054,0.129050374,0.0700280741,0.1916438043,-0.1662600338,-0.134924069,-0.310583204,-0.0739901289,-0.2353853434,0.1117409691,0.3545113504,0.2079512775,0.4717747271,-0.1894608289,0.1324892938,-0.3272043169,-0.600632906,0.0623441152,-0.2352684885,0.3087221384,0.1546491235,0.1451974809,-0.3562120497,-0.1046805456,0.4863615036,-0.1898590624,-0.2475607246,-0.0297015887,-0.1127689481,-0.3060136139,-0.041085545,-0.1921435744,-0.4251380563,-0.384382993,0.548238337,0.1713629663,-0.0013478778,0.3274297416,-0.088318333,0.2806099355,-0.1597571969,0.1602456421,-0.2694223523,-0.3390863538,0.2167554349,-0.4446284473,-0.3358726501,0.0180750545,0.1143524498,0.3700516224,-0.0964458957,-0.1132557914,-0.2231542468,-0.0799965337,-0.0062224856,-0.0623166449,0.1297807693,0.2979497612,-0.0672560036,-0.0971203595,0.0130105522,-0.0781469196,-0.0373245329,0.0681313425,0.2425166667,-0.0593306758,0.1630978733,0.0247505642,0.6636180878,0.219872117,0.0549555272,0.3495468199,-0.1756163687,0.2357822061,-0.1428213269,-0.4567848444,-0.019317124,0.1067793369,0.11343611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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1290","title":"imdb dataset cannot be downloaded","comments":"Hi @rabeehk , I am unable to reproduce your problem locally.\r\nCan you try emptying the cache (removing the content of `\/idiap\/temp\/rkarimi\/cache_home_1\/datasets`) and retry ?","body":"hi\r\nplease find error below getting imdb train spli:\r\nthanks\r\n\r\n`\r\ndatasets.load_dataset>>> datasets.load_dataset(\"imdb\", split=\"train\")`\r\n\r\n\r\nerrors\r\n\r\n\r\n```\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=7486451, num_examples=5628, dataset_name='imdb')}]\r\n\r\n\r\n```","comment_length":25,"text":"imdb dataset cannot be downloaded \n hi\r\nplease find error below getting imdb train spli:\r\nthanks\r\n\r\n`\r\ndatasets.load_dataset>>> datasets.load_dataset(\"imdb\", split=\"train\")`\r\n\r\n\r\nerrors\r\n\r\n\r\n```\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=7486451, num_examples=5628, dataset_name='imdb')}]\r\n\r\n\r\n``` \n Hi @rabeehk , I am unable to reproduce your problem locally.\r\nCan you try emptying the cache (removing the content of `\/idiap\/temp\/rkarimi\/cache_home_1\/datasets`) and retry ?","embeddings":[-0.4860101342,0.0299127121,-0.1837317646,0.2636173666,0.3486504555,0.3077562451,0.3157056868,0.4439661801,0.2162813097,0.0212673135,-0.0990766734,-0.0724211112,-0.0315420739,0.1580998898,-0.0292219277,-0.1209031865,-0.1767851561,0.0127698407,-0.1587827206,0.0179681294,-0.1283203512,0.238350302,-0.1618407369,-0.0335202925,-0.4301397502,-0.3121137321,0.139201194,0.0827983245,-0.3193941116,-0.4591375291,0.392133534,-0.0082366345,0.2423053384,0.2826945782,-0.0001035062,-0.036567647,0.5334970951,-0.1074083596,-0.3835625947,-0.2877946198,-0.327385217,0.0555791669,-0.002764019,-0.2751792669,0.0047641462,-0.0253852848,0.0113665806,-0.3959704638,0.037413571,0.4941405356,0.2971445024,-0.1297646463,0.2451955676,-0.2180922627,0.2423908561,0.0137036452,0.0529719256,0.2488452196,0.140880987,0.2723523378,0.2486406863,0.1801100224,-0.3034388125,-0.0220966898,0.2865758538,-0.0972823352,0.1653281152,-0.4706068635,0.1722737402,0.2280539721,0.7382758856,-0.2388616651,-0.3810375333,0.1447506249,0.3771938384,-0.1702625602,0.100001283,0.2182770073,-0.1827754229,0.101738967,-0.2199486643,-0.0662822351,-0.1369595826,0.1590718776,0.0886127204,-0.1605072618,0.0495887101,-0.0400308669,0.0987498984,-0.1344026029,0.2517719865,0.0384320244,-0.0021123588,0.236170128,-0.260240078,-0.1002123132,-0.2237588018,0.0192964952,0.0649492294,0.2488008887,-0.1534933597,-0.1331486851,0.016815085,0.1392866224,0.182145372,0.1841432154,-0.058251448,0.3144685328,0.1950910091,0.4538915157,-0.1490833759,-0.1017040089,-0.2541640997,-0.1346600354,-0.0824786797,0.143608585,0.0311763398,-0.1240337119,-0.4782600105,0.0448355265,-0.2012896836,0.1291317046,0.2305186391,0.3140892088,-0.1347789168,0.1046995968,0.2271656245,0.252812922,0.1159728169,-0.1042450368,-0.246147573,0.3043960631,-0.0936458483,-0.0565775447,0.3721653223,-0.1996798813,0.3377612829,0.0696253031,0.0036384759,-0.2105993181,0.2095436156,-0.3007852435,0.0287867971,0.3584664762,0.2556399703,0.2001560777,-0.0840682834,0.1235398501,-0.0588877834,0.0461486988,-0.2743842006,-0.319922477,-0.0881592631,0.3507601321,0.1262279451,-0.1656855792,-0.3235688508,-0.1998392344,0.09139698,-0.2130101174,0.0047630412,-0.1729651242,-0.025870176,-0.133760795,0.3516489267,0.4913713336,-0.2694042921,0.0458030626,-0.4400700331,-0.1443544328,0.0440970734,0.3456625044,-0.1699273437,0.3116580248,-0.32995224,0.1360130161,0.2640047073,-0.6436082721,-0.5091253519,0.325425595,-0.0387009382,-0.11843279,0.2331247181,0.2476876527,0.3442249298,0.0431956872,0.1554581672,0.3124530315,-0.153066501,-0.0956452191,-0.1574309617,-0.2806973457,-0.0521216802,0.1857506186,0.3627323508,0.1378489584,0.114368625,0.2967046201,0.2887882888,0.1775026917,0.1041831821,0.3223493993,0.2164294571,0.1731072664,-0.1532247961,-0.1303550899,-0.2971837521,0.080735229,0.2190829068,-0.0312121324,-0.2718612254,-0.2509310544,-0.3407040238,-0.2066206634,-0.1483213454,0.0180350691,0.2382639647,-0.0485067032,-0.0493651181,-0.0633319318,-0.2919754088,0.4999574423,-0.2259841561,0.0901971832,-0.292301327,0.1588745862,-0.2442293465,-0.1025532484,0.124649033,-0.0284797642,0.1251573414,-0.0823221952,-0.0637026131,0.3320347369,0.0375415012,0.0506807417,0.0673423484,-0.0951315463,0.1241538003,-0.3400791883,0.1844294965,-0.2295352072,0.0710089952,-0.0017823211,-0.0044872374,-0.0572311543,-0.2801706791,0.0628418326,0.2860819995,0.0964290351,0.298142463,-0.1603615433,0.0415811948,-0.4865525067,0.0784966424,0.0981659293,0.2395064682,-0.0675733835,-0.2537468672,0.0527260862,0.3675588369,0.1426952928,0.0361450277,0.1218582988,-0.0939497575,0.0451115519,-0.1279809475,0.2976512313,0.1854215115,0.1711168885,0.0062498222,-0.1330317408,0.228055194,-0.2165435851,0.2388888299,-0.0532691255,0.0778323933,0.3355535567,-0.092541486,0.0923875868,-0.1577505767,-0.3601544201,0.085387975,0.2455344051,-0.2284465432,-0.2190803885,-0.3301956058,-0.2610275745,-0.0584802814,-0.1618005931,0.0648657903,-0.173955366,-0.1136664376,0.3175649643,0.252427876,0.0781725198,-0.287586838,-0.0884352028,0.0608519055,-0.2129155397,-0.1243227869,-0.1231795996,-0.0458455533,0.2266675681,0.3349539936,-0.021135442,0.2578156292,-0.4402718544,-0.0080837542,-0.2770047784,0.0826189965,0.0398202352,0.0108164065,0.095336467,0.124990724,0.4773280323,0.1202947125,-0.3079587817,0.0788394511,-0.0808625892,-0.1750845313,-0.0007010662,-0.0925777853,-0.0714502633,0.0025721136,-0.4792709947,-0.4157936573,-0.316506058,-0.0713702664,0.0571880192,0.0436627604,0.0977312848,0.2333915681,0.2032420188,0.3070598245,0.1822692305,-0.0370333716,-0.3868349493,0.302156955,-0.2015400529,-0.2068994343,0.2725228369,-0.0110130245,0.321393162,0.0716894716,-0.6101097465,0.1016692817,-0.0240966566,0.3465483487,-0.098723419,-0.109618932,0.0603595115,-0.065568395,-0.1339580268,-0.0619546995,-0.0375211053,-0.0004061266,-0.0901715308,0.1974919438,-0.2317510247,0.5183964968,-0.0380557179,0.3936097324,0.327231586,-0.1218411848,0.4882543683,-0.0589160919,0.2292048335,-0.2034323961,-0.4554421902,0.0337610655,-0.0116053149,-0.1714819819,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1290","title":"imdb dataset cannot be downloaded","comments":"Hi,\r\nthanks, I did remove the cache and still the same error here\r\n\r\n```\r\n>>> a = datasets.load_dataset(\"imdb\", split=\"train\")\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=4902716, num_examples=3680, dataset_name='imdb')}]\r\n```\r\n\r\ndatasets version\r\n```\r\ndatasets                  1.1.2                     <pip>\r\ntensorflow-datasets       4.1.0                     <pip>\r\n\r\n```","body":"hi\r\nplease find error below getting imdb train spli:\r\nthanks\r\n\r\n`\r\ndatasets.load_dataset>>> datasets.load_dataset(\"imdb\", split=\"train\")`\r\n\r\n\r\nerrors\r\n\r\n\r\n```\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=7486451, num_examples=5628, dataset_name='imdb')}]\r\n\r\n\r\n```","comment_length":115,"text":"imdb dataset cannot be downloaded \n hi\r\nplease find error below getting imdb train spli:\r\nthanks\r\n\r\n`\r\ndatasets.load_dataset>>> datasets.load_dataset(\"imdb\", split=\"train\")`\r\n\r\n\r\nerrors\r\n\r\n\r\n```\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=7486451, num_examples=5628, dataset_name='imdb')}]\r\n\r\n\r\n``` \n Hi,\r\nthanks, I did remove the cache and still the same error here\r\n\r\n```\r\n>>> a = datasets.load_dataset(\"imdb\", split=\"train\")\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown size, total: 207.28 MiB) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/imdb\/plain_text\/1.0.0\/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=4902716, num_examples=3680, dataset_name='imdb')}]\r\n```\r\n\r\ndatasets version\r\n```\r\ndatasets                  1.1.2                     <pip>\r\ntensorflow-datasets       4.1.0                     <pip>\r\n\r\n```","embeddings":[-0.4860101342,0.0299127121,-0.1837317646,0.2636173666,0.3486504555,0.3077562451,0.3157056868,0.4439661801,0.2162813097,0.0212673135,-0.0990766734,-0.0724211112,-0.0315420739,0.1580998898,-0.0292219277,-0.1209031865,-0.1767851561,0.0127698407,-0.1587827206,0.0179681294,-0.1283203512,0.238350302,-0.1618407369,-0.0335202925,-0.4301397502,-0.3121137321,0.139201194,0.0827983245,-0.3193941116,-0.4591375291,0.392133534,-0.0082366345,0.2423053384,0.2826945782,-0.0001035062,-0.036567647,0.5334970951,-0.1074083596,-0.3835625947,-0.2877946198,-0.327385217,0.0555791669,-0.002764019,-0.2751792669,0.0047641462,-0.0253852848,0.0113665806,-0.3959704638,0.037413571,0.4941405356,0.2971445024,-0.1297646463,0.2451955676,-0.2180922627,0.2423908561,0.0137036452,0.0529719256,0.2488452196,0.140880987,0.2723523378,0.2486406863,0.1801100224,-0.3034388125,-0.0220966898,0.2865758538,-0.0972823352,0.1653281152,-0.4706068635,0.1722737402,0.2280539721,0.7382758856,-0.2388616651,-0.3810375333,0.1447506249,0.3771938384,-0.1702625602,0.100001283,0.2182770073,-0.1827754229,0.101738967,-0.2199486643,-0.0662822351,-0.1369595826,0.1590718776,0.0886127204,-0.1605072618,0.0495887101,-0.0400308669,0.0987498984,-0.1344026029,0.2517719865,0.0384320244,-0.0021123588,0.236170128,-0.260240078,-0.1002123132,-0.2237588018,0.0192964952,0.0649492294,0.2488008887,-0.1534933597,-0.1331486851,0.016815085,0.1392866224,0.182145372,0.1841432154,-0.058251448,0.3144685328,0.1950910091,0.4538915157,-0.1490833759,-0.1017040089,-0.2541640997,-0.1346600354,-0.0824786797,0.143608585,0.0311763398,-0.1240337119,-0.4782600105,0.0448355265,-0.2012896836,0.1291317046,0.2305186391,0.3140892088,-0.1347789168,0.1046995968,0.2271656245,0.252812922,0.1159728169,-0.1042450368,-0.246147573,0.3043960631,-0.0936458483,-0.0565775447,0.3721653223,-0.1996798813,0.3377612829,0.0696253031,0.0036384759,-0.2105993181,0.2095436156,-0.3007852435,0.0287867971,0.3584664762,0.2556399703,0.2001560777,-0.0840682834,0.1235398501,-0.0588877834,0.0461486988,-0.2743842006,-0.319922477,-0.0881592631,0.3507601321,0.1262279451,-0.1656855792,-0.3235688508,-0.1998392344,0.09139698,-0.2130101174,0.0047630412,-0.1729651242,-0.025870176,-0.133760795,0.3516489267,0.4913713336,-0.2694042921,0.0458030626,-0.4400700331,-0.1443544328,0.0440970734,0.3456625044,-0.1699273437,0.3116580248,-0.32995224,0.1360130161,0.2640047073,-0.6436082721,-0.5091253519,0.325425595,-0.0387009382,-0.11843279,0.2331247181,0.2476876527,0.3442249298,0.0431956872,0.1554581672,0.3124530315,-0.153066501,-0.0956452191,-0.1574309617,-0.2806973457,-0.0521216802,0.1857506186,0.3627323508,0.1378489584,0.114368625,0.2967046201,0.2887882888,0.1775026917,0.1041831821,0.3223493993,0.2164294571,0.1731072664,-0.1532247961,-0.1303550899,-0.2971837521,0.080735229,0.2190829068,-0.0312121324,-0.2718612254,-0.2509310544,-0.3407040238,-0.2066206634,-0.1483213454,0.0180350691,0.2382639647,-0.0485067032,-0.0493651181,-0.0633319318,-0.2919754088,0.4999574423,-0.2259841561,0.0901971832,-0.292301327,0.1588745862,-0.2442293465,-0.1025532484,0.124649033,-0.0284797642,0.1251573414,-0.0823221952,-0.0637026131,0.3320347369,0.0375415012,0.0506807417,0.0673423484,-0.0951315463,0.1241538003,-0.3400791883,0.1844294965,-0.2295352072,0.0710089952,-0.0017823211,-0.0044872374,-0.0572311543,-0.2801706791,0.0628418326,0.2860819995,0.0964290351,0.298142463,-0.1603615433,0.0415811948,-0.4865525067,0.0784966424,0.0981659293,0.2395064682,-0.0675733835,-0.2537468672,0.0527260862,0.3675588369,0.1426952928,0.0361450277,0.1218582988,-0.0939497575,0.0451115519,-0.1279809475,0.2976512313,0.1854215115,0.1711168885,0.0062498222,-0.1330317408,0.228055194,-0.2165435851,0.2388888299,-0.0532691255,0.0778323933,0.3355535567,-0.092541486,0.0923875868,-0.1577505767,-0.3601544201,0.085387975,0.2455344051,-0.2284465432,-0.2190803885,-0.3301956058,-0.2610275745,-0.0584802814,-0.1618005931,0.0648657903,-0.173955366,-0.1136664376,0.3175649643,0.252427876,0.0781725198,-0.287586838,-0.0884352028,0.0608519055,-0.2129155397,-0.1243227869,-0.1231795996,-0.0458455533,0.2266675681,0.3349539936,-0.021135442,0.2578156292,-0.4402718544,-0.0080837542,-0.2770047784,0.0826189965,0.0398202352,0.0108164065,0.095336467,0.124990724,0.4773280323,0.1202947125,-0.3079587817,0.0788394511,-0.0808625892,-0.1750845313,-0.0007010662,-0.0925777853,-0.0714502633,0.0025721136,-0.4792709947,-0.4157936573,-0.316506058,-0.0713702664,0.0571880192,0.0436627604,0.0977312848,0.2333915681,0.2032420188,0.3070598245,0.1822692305,-0.0370333716,-0.3868349493,0.302156955,-0.2015400529,-0.2068994343,0.2725228369,-0.0110130245,0.321393162,0.0716894716,-0.6101097465,0.1016692817,-0.0240966566,0.3465483487,-0.098723419,-0.109618932,0.0603595115,-0.065568395,-0.1339580268,-0.0619546995,-0.0375211053,-0.0004061266,-0.0901715308,0.1974919438,-0.2317510247,0.5183964968,-0.0380557179,0.3936097324,0.327231586,-0.1218411848,0.4882543683,-0.0589160919,0.2292048335,-0.2034323961,-0.4554421902,0.0337610655,-0.0116053149,-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-0.4206544757,0.4592054188,-0.0953678638,0.0193864685,-0.1915466785,0.2458002567,0.2035212517,-0.0267859176,-0.4084336758,0.2136357129,0.3001485467,-0.1244565621,-0.1870332062,0.1222754568,0.0408127569,0.2679310143,-0.0382403135,-0.0211121086,0.0068067512,-0.189632833,-0.2512019277,-0.1015275717]}
{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1287","title":"'iwslt2017-ro-nl', cannot be downloaded ","comments":"Looks like the data has been moved from its original location to google drive\r\n\r\nNew url: https:\/\/drive.google.com\/u\/0\/uc?id=12ycYSzLIG253AFN35Y6qoyf9wtkOjakp&export=download","body":"Hi\r\nI am trying \r\n`>>> datasets.load_dataset(\"iwslt2017\", 'iwslt2017-ro-nl', split=\"train\")`\r\n\r\ngetting this error thank you for your help\r\n```\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nDownloading and preparing dataset iwsl_t217\/iwslt2017-ro-nl (download: 314.07 MiB, generated: 39.92 MiB, post-processed: Unknown size, total: 354.00 MiB) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/iwsl_t217\/iwslt2017-ro-nl\/1.0.0\/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/iwslt2017\/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd\/iwslt2017.py\", line 118, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(MULTI_URL)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 477, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/wit3.fbk.eu\/archive\/2017-01-trnmted\/\/texts\/DeEnItNlRo\/DeEnItNlRo\/DeEnItNlRo-DeEnItNlRo.tgz\r\n\r\n```","comment_length":17,"text":"'iwslt2017-ro-nl', cannot be downloaded  \n Hi\r\nI am trying \r\n`>>> datasets.load_dataset(\"iwslt2017\", 'iwslt2017-ro-nl', split=\"train\")`\r\n\r\ngetting this error thank you for your help\r\n```\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nDownloading and preparing dataset iwsl_t217\/iwslt2017-ro-nl (download: 314.07 MiB, generated: 39.92 MiB, post-processed: Unknown size, total: 354.00 MiB) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/iwsl_t217\/iwslt2017-ro-nl\/1.0.0\/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/iwslt2017\/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd\/iwslt2017.py\", line 118, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(MULTI_URL)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 477, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/wit3.fbk.eu\/archive\/2017-01-trnmted\/\/texts\/DeEnItNlRo\/DeEnItNlRo\/DeEnItNlRo-DeEnItNlRo.tgz\r\n\r\n``` \n Looks like the data has been moved from its original location to google drive\r\n\r\nNew url: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1286","title":"[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0):  terminate called after throwing an instance of 'google::protobuf::FatalException'   what():  CHECK failed: (index) >= (0):  Aborted","comments":"I remember also getting the same issue for several other translation datasets like all the iwslt2017 group, this is blokcing me and I really need to fix it and I was wondering if you have an idea on this. @lhoestq  thanks,. ","body":"Hi\r\nI am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help\r\n\r\n{'epoch': 20.0}                                                                                                                                             \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20\/20 [00:16<00:00,  1.22it\/s]\r\n12\/08\/2020 10:41:19 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/experiment\/joint\/finetune\/lr-2e-5\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   *** Evaluate ***\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.utils.utils -   using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Num examples = 1998\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32\/32 [00:37<00:00,  1.19s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n","comment_length":41,"text":"[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0):  terminate called after throwing an instance of 'google::protobuf::FatalException'   what():  CHECK failed: (index) >= (0):  Aborted \n Hi\r\nI am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help\r\n\r\n{'epoch': 20.0}                                                                                                                                             \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20\/20 [00:16<00:00,  1.22it\/s]\r\n12\/08\/2020 10:41:19 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/experiment\/joint\/finetune\/lr-2e-5\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   *** Evaluate ***\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.utils.utils -   using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Num examples = 1998\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32\/32 [00:37<00:00,  1.19s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n \n I remember also getting the same issue for several other translation datasets like all the iwslt2017 group, this is blokcing me and I really need to fix it and I was wondering if you have an idea on this. @lhoestq  thanks,. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1286","title":"[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0):  terminate called after throwing an instance of 'google::protobuf::FatalException'   what():  CHECK failed: (index) >= (0):  Aborted","comments":"maybe there is an empty line or something inside these datasets? could you tell me why this is happening? thanks ","body":"Hi\r\nI am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help\r\n\r\n{'epoch': 20.0}                                                                                                                                             \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20\/20 [00:16<00:00,  1.22it\/s]\r\n12\/08\/2020 10:41:19 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/experiment\/joint\/finetune\/lr-2e-5\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   *** Evaluate ***\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.utils.utils -   using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Num examples = 1998\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32\/32 [00:37<00:00,  1.19s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n","comment_length":20,"text":"[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0):  terminate called after throwing an instance of 'google::protobuf::FatalException'   what():  CHECK failed: (index) >= (0):  Aborted \n Hi\r\nI am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help\r\n\r\n{'epoch': 20.0}                                                                                                                                             \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20\/20 [00:16<00:00,  1.22it\/s]\r\n12\/08\/2020 10:41:19 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/experiment\/joint\/finetune\/lr-2e-5\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   *** Evaluate ***\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.utils.utils -   using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Num examples = 1998\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32\/32 [00:37<00:00,  1.19s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n \n maybe there is an empty line or something inside these datasets? could you tell me why this is happening? thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1286","title":"[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0):  terminate called after throwing an instance of 'google::protobuf::FatalException'   what():  CHECK failed: (index) >= (0):  Aborted","comments":"I just checked and the wmt16 en-ro doesn't have empty lines\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nlen(d)  # 610320\r\nlen(d.filter(lambda x: len(x[\"translation\"][\"en\"].strip()) > 0))  # 610320\r\nlen(d.filter(lambda x: len(x[\"translation\"][\"ro\"].strip()) > 0))  # 610320\r\n# also tested for split=\"validation\" and \"test\"\r\n```\r\n\r\nCan you open an issue on the `transformers` repo ? also cc @sgugger ","body":"Hi\r\nI am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help\r\n\r\n{'epoch': 20.0}                                                                                                                                             \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20\/20 [00:16<00:00,  1.22it\/s]\r\n12\/08\/2020 10:41:19 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/experiment\/joint\/finetune\/lr-2e-5\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   *** Evaluate ***\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.utils.utils -   using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Num examples = 1998\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32\/32 [00:37<00:00,  1.19s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n","comment_length":59,"text":"[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0):  terminate called after throwing an instance of 'google::protobuf::FatalException'   what():  CHECK failed: (index) >= (0):  Aborted \n Hi\r\nI am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help\r\n\r\n{'epoch': 20.0}                                                                                                                                             \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20\/20 [00:16<00:00,  1.22it\/s]\r\n12\/08\/2020 10:41:19 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/experiment\/joint\/finetune\/lr-2e-5\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   *** Evaluate ***\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.utils.utils -   using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Num examples = 1998\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32\/32 [00:37<00:00,  1.19s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n \n I just checked and the wmt16 en-ro doesn't have empty lines\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nlen(d)  # 610320\r\nlen(d.filter(lambda x: len(x[\"translation\"][\"en\"].strip()) > 0))  # 610320\r\nlen(d.filter(lambda x: len(x[\"translation\"][\"ro\"].strip()) > 0))  # 610320\r\n# also tested for split=\"validation\" and \"test\"\r\n```\r\n\r\nCan you open an issue on the `transformers` repo ? also cc @sgugger 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1286","title":"[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0):  terminate called after throwing an instance of 'google::protobuf::FatalException'   what():  CHECK failed: (index) >= (0):  Aborted","comments":"Hi @lhoestq \r\nI am not really sure which part is causing this, to me this is more related to dataset library as this is happening for some of the datassets below please find the information to reprodcue the bug, this is really blocking me and I appreciate your help\r\n\r\n\r\n## Environment info\r\n- `transformers` version: 3.5.1\r\n- Platform: GPU\r\n- Python version: 3.7 \r\n- PyTorch version (GPU?): 1.0.4\r\n- Tensorflow version (GPU?): - \r\n- Using GPU in script?: - \r\n- Using distributed or parallel set-up in script?: - \r\n\r\n### Who can help\r\n tokenizers: @mfuntowicz\r\n Trainer: @sgugger\r\n TextGeneration: @TevenLeScao \r\n nlp datasets: [different repo](https:\/\/github.com\/huggingface\/nlp)\r\n rust tokenizers: [different repo](https:\/\/github.com\/huggingface\/tokenizers)\r\n examples\/seq2seq: @patil-suraj\r\n\r\n## Information\r\nHi\r\nI am testing seq2seq model with T5 on different datasets and this is always getting the following bug, this is really blocking me as this fails for many datasets. could you have a look please? thanks  \r\n\r\n```\r\n[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n\r\n```\r\n\r\nTo reproduce the error please run on 1 GPU:\r\n```\r\ngit clone git@github.com:rabeehk\/debug-seq2seq.git\r\npython setup.py develop \r\ncd seq2seq \r\npython finetune_t5_trainer.py temp.json\r\n\r\n```\r\n\r\nFull output of the program:\r\n\r\n```\r\n(internship) rkarimi@vgnh008:\/idiap\/user\/rkarimi\/dev\/debug-seq2seq\/seq2seq$ python finetune_t5_trainer.py temp.json \r\n2020-12-12 15:38:16.234542: W tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2020-12-12 15:38:16.234598: I tensorflow\/stream_executor\/cuda\/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n12\/12\/2020 15:38:32 - WARNING - __main__ -   Process rank: -1, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False\r\n12\/12\/2020 15:38:32 - INFO - __main__ -   Training\/evaluation parameters Seq2SeqTrainingArguments(output_dir='outputs\/test', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=False, evaluation_strategy=<EvaluationStrategy.NO: 'no'>, prediction_loss_only=False, per_device_train_batch_size=64, per_device_eval_batch_size=64, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=0.01, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=2, max_steps=-1, warmup_steps=500, logging_dir='runs\/Dec12_15-38-32_vgnh008', logging_first_step=True, logging_steps=200, save_steps=200, save_total_limit=1, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=200, dataloader_num_workers=0, past_index=-1, run_name='outputs\/test', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.1, sortish_sampler=False, predict_with_generate=True, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear', fixed_length_emb=None, encoder_projection=None, encoder_pooling=None, projection_length=None, only_projection_bottleneck=False, concat_projection_token=False, gcs_bucket='ruse-xcloud-bucket', temperature=10, train_adapters=True, do_finetune=True, parametric_task_embedding=False, eval_output_dir='outputs\/finetune-adapter\/test-n-1-lr-1e-02-e-20')\r\nSome weights of T5ForConditionalGeneration were not initialized from the model checkpoint at t5-small and are newly initialized: ['encoder.block.0.layer.0.adapter_controller.meta_up_sampler.weight_generator.0.weight', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.weight_generator.0.bias', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.weight_generator.1.weight', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.weight_generator.1.bias', 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'decoder.block.5.layer.0.adapter_controller.meta_down_sampler.bias_generator.0.bias', 'decoder.block.5.layer.0.adapter_controller.meta_down_sampler.bias_generator.1.weight', 'decoder.block.5.layer.0.adapter_controller.meta_down_sampler.bias_generator.1.bias', 'decoder.block.5.layer.0.adapter_controller.post_layer_norm.weight', 'decoder.block.5.layer.0.adapter_controller.post_layer_norm.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.post_layer_norm.weight', 'decoder.block.5.layer.2.adapter_controller.post_layer_norm.bias']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140079090376272 acquired on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140079090376272 released on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\nUsing custom data configuration default\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140082549312272 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140082549312272 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140082549365648 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nReusing dataset boolq (\/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534)\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140082549365648 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nLoading cached processed dataset at \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534\/cache-6810ece2a440c3be.arrow\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549560848 acquired on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549560848 released on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\nUsing custom data configuration default\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549560848 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549560848 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549365200 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nReusing dataset boolq (\/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534)\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549365200 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nLoading cached processed dataset at \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534\/cache-9a2822394a3a4e34.arrow\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.metrics.metrics -   selected metric <function build_compute_metrics_fn.<locals>.classification_metrics at 0x7f66b464cc20> for task boolq\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -   ***** Running training *****\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Num examples = 10\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Num Epochs = 2\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Instantaneous batch size per device = 64\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Total train batch size (w. parallel, distributed & accumulation) = 64\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Gradient Accumulation steps = 1\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Total optimization steps = 2\r\n{'loss': 529.79443359375, 'learning_rate': 2e-05, 'epoch': 1.0}                                                                                                                                           \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  2.37it\/s]12\/12\/2020 15:38:46 - INFO - seq2seq.trainers.trainer -   \r\n\r\nTraining completed. Do not forget to share your model on huggingface.co\/models =)\r\n\r\n\r\n{'epoch': 2.0}                                                                                                                                                                                            \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  2.43it\/s]\r\n12\/12\/2020 15:38:46 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/test\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079084929680 acquired on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079084929680 released on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\nUsing custom data configuration default\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079084929360 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079084929360 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079085355216 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nReusing dataset boolq (\/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534)\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079085355216 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nLoading cached processed dataset at \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534\/cache-164dd1d57e9fa69a.arrow\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.metrics.metrics -   selected metric <function build_compute_metrics_fn.<locals>.classification_metrics at 0x7f66b40c67a0> for task boolq\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -   ***** Running training *****\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Num examples = 1\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Num Epochs = 2\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Instantaneous batch size per device = 64\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Total train batch size (w. parallel, distributed & accumulation) = 64\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Gradient Accumulation steps = 1\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Total optimization steps = 2\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Continuing training from checkpoint, will skip to saved global_step\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Continuing training from epoch 2\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Continuing training from global step 2\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Will skip the first 0 steps in the first epoch\r\n  0%|                                                                                                                                                                               | 0\/2 [00:00<?, ?it\/s]12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -   \r\n\r\nTraining completed. Do not forget to share your model on huggingface.co\/models =)\r\n\r\n\r\n{'epoch': 2.0}                                                                                                                                                                                            \r\n  0%|                                                                                                                                                                               | 0\/2 [00:00<?, ?it\/s]\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/finetune-adapter\/test-n-1-lr-1e-02-e-20\/boolq\r\n12\/12\/2020 15:39:07 - INFO - seq2seq.utils.utils -   using task specific params for boolq: {'max_length': 3}\r\n12\/12\/2020 15:39:07 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/12\/2020 15:39:07 - INFO - seq2seq.trainers.trainer -     Num examples = 3269\r\n12\/12\/2020 15:39:07 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 52\/52 [00:12<00:00,  4.86it\/s][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n```\r\n\r\n\r\n\r\n","body":"Hi\r\nI am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help\r\n\r\n{'epoch': 20.0}                                                                                                                                             \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20\/20 [00:16<00:00,  1.22it\/s]\r\n12\/08\/2020 10:41:19 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/experiment\/joint\/finetune\/lr-2e-5\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   *** Evaluate ***\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.utils.utils -   using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Num examples = 1998\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32\/32 [00:37<00:00,  1.19s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n","comment_length":1524,"text":"[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0):  terminate called after throwing an instance of 'google::protobuf::FatalException'   what():  CHECK failed: (index) >= (0):  Aborted \n Hi\r\nI am getting this error when evaluating on wmt16-ro-en using finetune_trainer.py of huggingface repo. thank for your help\r\n\r\n{'epoch': 20.0}                                                                                                                                             \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20\/20 [00:16<00:00,  1.22it\/s]\r\n12\/08\/2020 10:41:19 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/experiment\/joint\/finetune\/lr-2e-5\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   {'wmt16-en-ro': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1998), 'qnli': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 5462), 'scitail': Dataset(features: {'src_texts': Value(dtype='string', id=None), 'task': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 1303)}\r\n12\/08\/2020 10:41:24 - INFO - __main__ -   *** Evaluate ***\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.utils.utils -   using task specific params for wmt16-en-ro: {'max_length': 300, 'num_beams': 4}\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Num examples = 1998\r\n12\/08\/2020 10:41:24 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32\/32 [00:37<00:00,  1.19s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n \n Hi @lhoestq \r\nI am not really sure which part is causing this, to me this is more related to dataset library as this is happening for some of the datassets below please find the information to reprodcue the bug, this is really blocking me and I appreciate your help\r\n\r\n\r\n## Environment info\r\n- `transformers` version: 3.5.1\r\n- Platform: GPU\r\n- Python version: 3.7 \r\n- PyTorch version (GPU?): 1.0.4\r\n- Tensorflow version (GPU?): - \r\n- Using GPU in script?: - \r\n- Using distributed or parallel set-up in script?: - \r\n\r\n### Who can help\r\n tokenizers: @mfuntowicz\r\n Trainer: @sgugger\r\n TextGeneration: @TevenLeScao \r\n nlp datasets: [different repo](https:\/\/github.com\/huggingface\/nlp)\r\n rust tokenizers: [different repo](https:\/\/github.com\/huggingface\/tokenizers)\r\n examples\/seq2seq: @patil-suraj\r\n\r\n## Information\r\nHi\r\nI am testing seq2seq model with T5 on different datasets and this is always getting the following bug, this is really blocking me as this fails for many datasets. could you have a look please? thanks  \r\n\r\n```\r\n[libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n\r\n```\r\n\r\nTo reproduce the error please run on 1 GPU:\r\n```\r\ngit clone git@github.com:rabeehk\/debug-seq2seq.git\r\npython setup.py develop \r\ncd seq2seq \r\npython finetune_t5_trainer.py temp.json\r\n\r\n```\r\n\r\nFull output of the program:\r\n\r\n```\r\n(internship) rkarimi@vgnh008:\/idiap\/user\/rkarimi\/dev\/debug-seq2seq\/seq2seq$ python finetune_t5_trainer.py temp.json \r\n2020-12-12 15:38:16.234542: W tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n2020-12-12 15:38:16.234598: I tensorflow\/stream_executor\/cuda\/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n12\/12\/2020 15:38:32 - WARNING - __main__ -   Process rank: -1, device: cuda:0, n_gpu: 1, distributed training: False, 16-bits training: False\r\n12\/12\/2020 15:38:32 - INFO - __main__ -   Training\/evaluation parameters Seq2SeqTrainingArguments(output_dir='outputs\/test', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluate_during_training=False, evaluation_strategy=<EvaluationStrategy.NO: 'no'>, prediction_loss_only=False, per_device_train_batch_size=64, per_device_eval_batch_size=64, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=0.01, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=2, max_steps=-1, warmup_steps=500, logging_dir='runs\/Dec12_15-38-32_vgnh008', logging_first_step=True, logging_steps=200, save_steps=200, save_total_limit=1, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=200, dataloader_num_workers=0, past_index=-1, run_name='outputs\/test', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, label_smoothing=0.1, sortish_sampler=False, predict_with_generate=True, adafactor=False, encoder_layerdrop=None, decoder_layerdrop=None, dropout=None, attention_dropout=None, lr_scheduler='linear', fixed_length_emb=None, encoder_projection=None, encoder_pooling=None, projection_length=None, only_projection_bottleneck=False, concat_projection_token=False, gcs_bucket='ruse-xcloud-bucket', temperature=10, train_adapters=True, do_finetune=True, parametric_task_embedding=False, eval_output_dir='outputs\/finetune-adapter\/test-n-1-lr-1e-02-e-20')\r\nSome weights of T5ForConditionalGeneration were not initialized from the model checkpoint at t5-small and are newly initialized: ['encoder.block.0.layer.0.adapter_controller.meta_up_sampler.weight_generator.0.weight', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.weight_generator.0.bias', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.weight_generator.1.weight', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.weight_generator.1.bias', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.bias_generator.0.weight', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.bias_generator.0.bias', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.bias_generator.1.weight', 'encoder.block.0.layer.0.adapter_controller.meta_up_sampler.bias_generator.1.bias', 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'decoder.block.5.layer.0.adapter_controller.meta_down_sampler.bias_generator.1.bias', 'decoder.block.5.layer.0.adapter_controller.post_layer_norm.weight', 'decoder.block.5.layer.0.adapter_controller.post_layer_norm.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.weight_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_up_sampler.bias_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.weight_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.0.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.0.bias', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.1.weight', 'decoder.block.5.layer.2.adapter_controller.meta_down_sampler.bias_generator.1.bias', 'decoder.block.5.layer.2.adapter_controller.post_layer_norm.weight', 'decoder.block.5.layer.2.adapter_controller.post_layer_norm.bias']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140079090376272 acquired on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140079090376272 released on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\nUsing custom data configuration default\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140082549312272 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140082549312272 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140082549365648 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nReusing dataset boolq (\/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534)\r\n12\/12\/2020 15:38:44 - INFO - filelock -   Lock 140082549365648 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nLoading cached processed dataset at \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534\/cache-6810ece2a440c3be.arrow\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549560848 acquired on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549560848 released on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\nUsing custom data configuration default\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549560848 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549560848 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549365200 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nReusing dataset boolq (\/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534)\r\n12\/12\/2020 15:38:45 - INFO - filelock -   Lock 140082549365200 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nLoading cached processed dataset at \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534\/cache-9a2822394a3a4e34.arrow\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.metrics.metrics -   selected metric <function build_compute_metrics_fn.<locals>.classification_metrics at 0x7f66b464cc20> for task boolq\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -   ***** Running training *****\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Num examples = 10\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Num Epochs = 2\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Instantaneous batch size per device = 64\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Total train batch size (w. parallel, distributed & accumulation) = 64\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Gradient Accumulation steps = 1\r\n12\/12\/2020 15:38:45 - INFO - seq2seq.trainers.trainer -     Total optimization steps = 2\r\n{'loss': 529.79443359375, 'learning_rate': 2e-05, 'epoch': 1.0}                                                                                                                                           \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  2.37it\/s]12\/12\/2020 15:38:46 - INFO - seq2seq.trainers.trainer -   \r\n\r\nTraining completed. Do not forget to share your model on huggingface.co\/models =)\r\n\r\n\r\n{'epoch': 2.0}                                                                                                                                                                                            \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00,  2.43it\/s]\r\n12\/12\/2020 15:38:46 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/test\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079084929680 acquired on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079084929680 released on \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/4c7b1146606607c193d1ef601d8d0c134521b2ac59f61ee98c09119be925ee16.7ad892de9d7f1b4f9dfc598ef31e4a398a7224176bc9a3110e0e2075ff943e8f.py.lock\r\nUsing custom data configuration default\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079084929360 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079084929360 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079085355216 acquired on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nReusing dataset boolq (\/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534)\r\n12\/12\/2020 15:38:59 - INFO - filelock -   Lock 140079085355216 released on \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/_idiap_temp_rkarimi_cache_home_1_datasets_boolq_default_0.1.0_1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534.lock\r\nLoading cached processed dataset at \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/default\/0.1.0\/1fcfdc6f36dc89a2245ffbbd5248ab33890594b50396731ebc78411bdd2ca534\/cache-164dd1d57e9fa69a.arrow\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.metrics.metrics -   selected metric <function build_compute_metrics_fn.<locals>.classification_metrics at 0x7f66b40c67a0> for task boolq\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -   ***** Running training *****\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Num examples = 1\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Num Epochs = 2\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Instantaneous batch size per device = 64\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Total train batch size (w. parallel, distributed & accumulation) = 64\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Gradient Accumulation steps = 1\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Total optimization steps = 2\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Continuing training from checkpoint, will skip to saved global_step\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Continuing training from epoch 2\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Continuing training from global step 2\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -     Will skip the first 0 steps in the first epoch\r\n  0%|                                                                                                                                                                               | 0\/2 [00:00<?, ?it\/s]12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -   \r\n\r\nTraining completed. Do not forget to share your model on huggingface.co\/models =)\r\n\r\n\r\n{'epoch': 2.0}                                                                                                                                                                                            \r\n  0%|                                                                                                                                                                               | 0\/2 [00:00<?, ?it\/s]\r\n12\/12\/2020 15:38:59 - INFO - seq2seq.trainers.trainer -   Saving model checkpoint to outputs\/finetune-adapter\/test-n-1-lr-1e-02-e-20\/boolq\r\n12\/12\/2020 15:39:07 - INFO - seq2seq.utils.utils -   using task specific params for boolq: {'max_length': 3}\r\n12\/12\/2020 15:39:07 - INFO - seq2seq.trainers.trainer -   ***** Running Evaluation *****\r\n12\/12\/2020 15:39:07 - INFO - seq2seq.trainers.trainer -     Num examples = 3269\r\n12\/12\/2020 15:39:07 - INFO - seq2seq.trainers.trainer -     Batch size = 64\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 52\/52 [00:12<00:00,  4.86it\/s][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nAborted\r\n```\r\n\r\n\r\n\r\n","embeddings":[-0.0197352897,-0.7191827893,0.0039322353,0.349368006,0.484241426,0.0076734996,0.2424444556,0.1216879562,-0.2513463497,0.3279392719,-0.0821483955,-0.1225553527,-0.1213336438,0.3986013532,0.024262283,-0.2556790411,-0.0995945483,0.0782158896,-0.3232808411,0.1850418597,-0.0975957587,0.2666340768,-0.3790644109,0.2954924107,-0.077346921,0.0884474367,0.1073483229,0.0845447779,-0.0165824965,-0.3517342508,0.0590415671,-0.0919298232,-0.3089157045,0.3623276055,-0.0001231714,-0.1029601544,0.2742366493,0.044334691,-0.0298559833,-0.1064275429,0.264336437,0.5919190049,-0.1285633147,0.155628711,-0.1199958175,0.0390061848,0.131960839,-0.02621934,0.2547330558,0.2040431052,0.1139890254,0.3572780192,0.4076769948,0.0077055944,-0.0644371659,-0.047120966,-0.2066649497,0.1545071602,0.0657213703,-0.1996357739,0.1090153903,-0.0004496211,0.1906781793,-0.0334959961,-0.2198651433,-0.3123973608,-0.0370121896,-0.457115531,0.0150993029,0.4477550387,-0.0995786712,-0.2989667356,-0.1321324855,0.2411860675,0.0361542404,-0.7871572971,0.0136458101,0.1679371297,-0.0502248146,0.2885320783,-0.137509048,0.1263263226,-0.2918356359,0.0164490547,-0.0907559618,0.296466738,-0.1985564381,0.2684500217,0.3229812086,0.1940049678,-0.3849190474,0.1892252117,0.0081450799,0.1069084778,-0.3058028519,-0.2175878286,0.1347346306,0.0532117598,0.291143328,0.1250610203,-0.0492140464,0.0266518034,-0.0314312987,0.1974624097,0.2514950335,-0.0322238952,0.0891965702,0.1261572838,0.157536611,0.5037890673,0.053735029,0.2031769902,0.0966271609,-0.2018554062,0.0816629007,-0.0702104494,0.5441356897,-0.1696140319,-0.5968497992,0.4957826138,-0.4942743778,-0.0085916603,0.2324092388,0.4670405984,-0.1604339182,0.0753958225,0.251483798,-0.1050434783,0.033637356,0.03451325,-0.0803707466,0.2207057327,0.1228926927,0.2221151143,-0.1487662196,-0.4933043122,0.1599145234,-0.1897882819,0.2903176546,-0.6953832507,-0.2288500816,-0.366612047,-0.0604084767,0.1137853563,-0.1019294262,-0.1594460607,0.2681770623,0.0848985463,-0.013057163,0.0232961178,0.2334747612,-0.3981198967,0.1256912202,0.1727491468,-0.1056274921,0.1610930264,0.0997461826,-0.0547138155,-0.037544772,0.2110037208,0.0817849934,-0.023355253,-0.5620622039,0.0634886622,0.2465737909,0.3047662675,0.0822414607,-0.0770296752,0.1512786895,-0.0502387509,0.054323215,0.4209915996,-0.0538993366,0.4664910734,-0.2711001635,0.1770830005,0.277297765,-0.3652850688,-0.0003139204,0.1826036125,-0.2375145853,0.1117426082,-0.2652013898,-0.4663338959,-0.1654590964,-0.1183409244,0.1349778473,-0.0789419264,-0.2356961071,-0.0736637264,-0.3157056272,-0.0500996858,0.2459145337,0.1108266786,0.0505057685,-0.2129725069,0.1019253507,-0.0896832198,0.2275961339,0.0058478625,-0.2316600084,0.11156746,0.4407880902,-0.0369520411,-0.0522972122,-0.1141529381,-0.116719678,0.1146957576,-0.0951117575,0.2058560997,0.2640617192,-0.0140006486,-0.222789079,0.3215104342,-0.2788105011,-0.2408593893,0.1297049522,-0.0126401894,-0.1174058467,0.0466670133,0.0669539571,-0.0052326033,-0.5102657676,0.1701666266,0.1292891949,0.047954496,-0.4098981023,-0.0345081277,-0.0219770689,0.1678383499,0.0930051282,-0.2305410653,-0.0614778958,0.4744261503,-0.0609407164,0.1962468177,-0.0178826489,-0.2281215787,-0.019167576,-0.6979162097,-0.3184904456,-0.0092402529,0.1823446751,-0.1330951899,0.4334768355,0.2950298786,0.1894212216,-0.0313181132,0.1306529492,-0.1452960372,-0.1054134294,-0.0241774917,-0.1970705837,-0.0412810817,0.4775482118,0.0791641027,-0.083314769,-0.0907501876,-0.5611173511,-0.1841323078,0.3183330894,-0.2006358951,0.0832459405,0.2185398936,0.2986201346,-0.1066382155,-0.174405396,-0.2389483154,0.4070246518,-0.0666783303,-0.0943469256,0.3965403438,-0.1829442829,0.1772942096,0.30615291,0.1469939947,0.2547303438,0.3355357349,0.0759050399,0.0548323095,-0.3406339884,0.1482571512,-0.1149554327,0.2089336812,-0.2045271844,0.2236363739,-0.2222569287,0.1700025946,-0.2625818849,-0.2926515341,-0.1523296684,-0.0743206516,0.020759508,0.3121842742,0.1147071868,0.2348940372,0.3013081253,0.2710024714,0.1796744317,0.24606888,0.0581639707,0.1148115098,-0.2809874415,-0.0713953227,0.409381628,-0.3983355463,0.437820226,0.0031497572,-0.3976996541,-0.1845485568,-0.3869085014,0.2667299807,-0.3574424982,0.4122446179,0.23498936,0.0989026725,0.2883796394,-0.0820783675,0.2549666166,-0.1147719994,-0.2736467421,0.1317070425,0.0489208996,-0.0504887998,-0.1470930874,-0.1372155547,-0.076008074,-0.1587286741,0.4361136556,0.0166942198,0.2049041837,0.3152825236,0.0969091505,0.2270750254,-0.0253577381,0.1294412464,-0.3533500433,-0.2036488354,-0.0627004877,0.0351996385,-0.2237658501,-0.0332907401,0.0955420956,0.3138401806,-0.115697816,-0.3986230791,-0.2539067566,0.2589060962,-0.4011817276,0.0286345966,-0.0587614216,0.2250745445,-0.0227604751,0.0023586736,0.1034687683,-0.1657687128,0.2917427123,0.0014571624,0.4208348393,-0.0582709499,0.4073520303,0.1907383204,0.2507788241,0.2218789309,-0.1074185222,0.2332936674,-0.0760882199,0.2300221622,0.0626940429,-0.2186158597,0.122598998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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1285","title":"boolq does not work ","comments":"here is the minimal code to reproduce\r\n\r\n`datasets>>> datasets.load_dataset(\"boolq\", \"train\")\r\n\r\nthe errors\r\n\r\n```\r\n`cahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nUsing custom data configuration train\r\nDownloading and preparing dataset boolq\/train (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/train\/0.1.0\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 149, in download_custom\r\n    custom_download(url, path)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/tensorflow\/python\/lib\/io\/file_io.py\", line 516, in copy_v2\r\n    compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)\r\n\r\n\r\n\r\n```","body":"Hi\r\nI am getting this error when trying to load boolq, thanks for your help\r\n\r\nts_boolq_default_0.1.0_2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11.lock\r\nTraceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 274, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 147, in main\r\n    for task in data_args.tasks]\r\n  File \"finetune_t5_trainer.py\", line 147, in <listcomp>\r\n    for task in data_args.tasks]\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/ruse\/seq2seq\/tasks\/tasks.py\", line 58, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/ruse\/seq2seq\/tasks\/tasks.py\", line 54, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 149, in download_custom\r\n    custom_download(url, path)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/tensorflow\/python\/lib\/io\/file_io.py\", line 516, in copy_v2\r\n    compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)\r\ntensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists\r\n\r\n","comment_length":115,"text":"boolq does not work  \n Hi\r\nI am getting this error when trying to load boolq, thanks for your help\r\n\r\nts_boolq_default_0.1.0_2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11.lock\r\nTraceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 274, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 147, in main\r\n    for task in data_args.tasks]\r\n  File \"finetune_t5_trainer.py\", line 147, in <listcomp>\r\n    for task in data_args.tasks]\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/ruse\/seq2seq\/tasks\/tasks.py\", line 58, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/ruse\/seq2seq\/tasks\/tasks.py\", line 54, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 149, in download_custom\r\n    custom_download(url, path)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/tensorflow\/python\/lib\/io\/file_io.py\", line 516, in copy_v2\r\n    compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)\r\ntensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists\r\n\r\n \n here is the minimal code to reproduce\r\n\r\n`datasets>>> datasets.load_dataset(\"boolq\", \"train\")\r\n\r\nthe errors\r\n\r\n```\r\n`cahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\nUsing custom data configuration train\r\nDownloading and preparing dataset boolq\/train (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/boolq\/train\/0.1.0\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11...\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\r\ncahce dir  \/idiap\/temp\/rkarimi\/cache_home_1\/datasets\/downloads\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 149, in download_custom\r\n    custom_download(url, path)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/tensorflow\/python\/lib\/io\/file_io.py\", line 516, in copy_v2\r\n    compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)\r\n\r\n\r\n\r\n```","embeddings":[-0.2191431969,-0.205223009,-0.1021828577,0.056804508,0.0433856584,0.0777705982,0.3575468659,0.2529199123,0.2545483708,-0.0290935356,-0.137118265,0.3180469871,-0.2989818454,0.435959518,-0.0375271626,-0.0104442453,-0.2001055479,0.0617069267,0.0194576196,0.1253176033,-0.3767187595,0.4527184665,-0.3950281441,0.1717271507,0.039792046,-0.1584234387,0.0690807477,0.2183280289,-0.3045058548,-0.367679894,0.1875551343,-0.0160979461,0.1602543443,0.1953685731,-0.000100287,0.0124218855,0.3434242904,0.0293650366,-0.1709919572,-0.3191271424,-0.1750642061,-0.3502523601,0.2896553278,-0.0837975815,-0.0092108911,0.0844620094,-0.040360827,0.1825928986,0.3829545379,0.3910404146,0.3393365741,0.0961863548,0.1874352694,-0.2147234976,0.2771983445,-0.5643305779,0.0346739218,0.1041202247,0.2713558376,-0.1368377209,0.066627875,0.1479776949,-0.2208089978,-0.2162980139,-0.1051368415,-0.2081234902,-0.0779546276,-0.3245044649,0.1794871092,0.0790971294,0.6055165529,-0.0848833472,-0.0521295182,0.1929166764,0.2385609597,-0.1072742119,0.1638332754,0.0137318,0.0599494018,0.0544823632,0.2340850532,-0.3852249384,-0.0247666705,0.0413612984,-0.0779064223,0.2063310295,-0.0665245429,0.0390985645,0.2114246339,-0.2090814412,0.1663435102,0.508505702,-0.109657988,-0.0038654527,-0.6627800465,0.024767397,0.2779552937,0.107672438,-0.0527212545,0.0032308488,-0.265878886,0.0674191788,0.1694182903,0.1585515887,-0.1650961787,0.3916872144,0.2289465219,0.2797166705,0.0727470815,0.2224866748,-0.2273650616,0.2074246705,-0.2910070717,-0.492138654,-0.0160816219,0.1348418295,0.3629411459,-0.0850622207,-0.2391541153,-0.1036297455,-0.1959228367,-0.0248908307,0.3913449347,0.7891902924,0.1488980204,-0.3131633401,0.1699921936,0.0756845623,-0.2671214938,-0.1868343651,-0.3410372734,0.2802948356,0.0750981793,-0.1892602742,0.1544688791,-0.3493214548,0.1605904698,0.1638125777,-0.0757626295,-0.1015292183,0.2639566064,-0.1807930171,-0.2796195447,0.2034140676,-0.1660426855,0.0412304625,0.222171858,0.0821633413,-0.063733995,0.2537777126,-0.3442661166,-0.4547116458,-0.1617778689,0.3301042616,0.1894203126,-0.27367872,0.1826002151,0.2261167169,0.1871689409,-0.1125649959,-0.1956937462,-0.3212908208,-0.2962062657,-0.1822833419,0.2448646575,0.3433954418,-0.4542224705,-0.0933891684,-0.2166294158,-0.2461134046,-0.054044459,0.1466912478,-0.2197429091,0.398642987,-0.1403478235,0.0188561901,0.1012149081,-0.2950314879,-0.4969398975,0.2633554637,-0.2121250033,-0.1468689889,-0.2692807019,0.0484414436,0.1941547692,0.1539271921,0.0051376866,0.0565571487,0.2050610185,-0.2150689512,-0.1294436157,-0.0946026072,0.2298936993,0.3611728549,0.1907626092,-0.0996231213,-0.0038339908,0.0660987124,0.0545602627,0.2124175131,-0.2959643602,0.2606880069,0.2629840374,-0.1679650694,0.0497377403,0.0490054712,-0.3281366825,0.1711824983,-0.1773874164,0.1284162402,0.0088677797,0.2201396525,-0.3857020736,0.124791272,-0.2456311733,0.0568337888,0.2308734208,-0.2148270011,-0.0974920318,0.0471501648,-0.1539621353,0.2841616571,-0.1739644557,0.055631686,-0.071144715,-0.0564676598,-0.3174751997,-0.1609033346,0.2278515995,0.1587961763,0.1742808223,0.0216859486,0.0050521316,0.3554823399,0.0803116187,-0.0317420475,-0.2111491263,-0.4885710478,-0.0345933922,-0.1139063314,0.0384387746,0.102731511,0.1070292294,0.1205408573,0.4304286242,0.2363624573,-0.1677540839,-0.0515252911,-0.0633639246,0.1917677373,0.1616193652,0.0279592704,-0.0829842314,-0.0570030771,0.4093349576,0.0435367897,-0.0650580376,-0.0590648204,-0.0195259042,0.014713522,0.4898835123,0.1344736814,0.0625171438,-0.1088181809,0.2517293394,0.1401916444,0.0302508697,0.1328377724,0.277749151,0.1064233333,-0.4235241711,-0.0366860069,-0.0264514554,-0.2671252787,0.0989147648,-0.3150690496,-0.1128067076,0.1915855706,-0.0246155076,-0.162846759,-0.1511110663,-0.2533967495,0.1131062135,0.344355613,-0.1373842359,-0.0577376187,-0.1854242533,-0.2737466395,0.3402540386,-0.0681999996,0.0371059217,-0.316401571,0.2217026651,-0.0770749301,0.138763383,0.3388938606,-0.0285247955,0.1094074845,0.3005981147,-0.2708330452,-0.0180830099,-0.2160334438,-0.1804675907,0.1515997946,0.111972183,-0.2137085795,0.0305303,-0.1087111309,-0.0169140063,-0.004822446,-0.3049965501,-0.1138391271,-0.3007988334,0.3057751358,0.2515809238,0.0596834607,-0.3211912513,-0.0032180632,0.1792205125,-0.2130450308,-0.0801892728,0.1580676436,0.1449140161,-0.0205494296,-0.204799369,-0.4926880896,-0.5303481817,-0.4888476729,-0.0292072166,-0.0471295975,0.1707663089,0.3728390336,-0.0082199024,0.3335877657,0.1069243327,0.1427049488,-0.0587089285,-0.2668074667,0.2721175849,0.0249675456,-0.3096513748,0.1576294154,0.1298577338,0.3517134786,-0.2375917286,-0.1885915101,0.1306701601,0.0096215066,0.0643238276,-0.2209012657,-0.1836156547,0.0529978499,-0.0396089479,-0.0411025397,0.0911995918,-0.2069228888,0.2017320096,0.0643187687,0.1448442787,0.0772933438,0.2657338977,-0.0761375427,0.5265434384,0.0392807871,0.1855731457,0.2640739977,0.130462423,0.134970814,-0.0506642237,-0.5398096442,-0.067186132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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1285","title":"boolq does not work ","comments":"This has been fixed by #881 \r\nthis fix will be available in the next release soon.\r\n\r\nIf you don't want to wait for the release you can actually load the latest version of boolq by specifying `script_version=\"master\"` in `load_dataset`","body":"Hi\r\nI am getting this error when trying to load boolq, thanks for your help\r\n\r\nts_boolq_default_0.1.0_2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11.lock\r\nTraceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 274, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 147, in main\r\n    for task in data_args.tasks]\r\n  File \"finetune_t5_trainer.py\", line 147, in <listcomp>\r\n    for task in data_args.tasks]\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/ruse\/seq2seq\/tasks\/tasks.py\", line 58, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/ruse\/seq2seq\/tasks\/tasks.py\", line 54, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 149, in download_custom\r\n    custom_download(url, path)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/tensorflow\/python\/lib\/io\/file_io.py\", line 516, in copy_v2\r\n    compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)\r\ntensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists\r\n\r\n","comment_length":39,"text":"boolq does not work  \n Hi\r\nI am getting this error when trying to load boolq, thanks for your help\r\n\r\nts_boolq_default_0.1.0_2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11.lock\r\nTraceback (most recent call last):\r\n  File \"finetune_t5_trainer.py\", line 274, in <module>\r\n    main()\r\n  File \"finetune_t5_trainer.py\", line 147, in main\r\n    for task in data_args.tasks]\r\n  File \"finetune_t5_trainer.py\", line 147, in <listcomp>\r\n    for task in data_args.tasks]\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/ruse\/seq2seq\/tasks\/tasks.py\", line 58, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/ruse\/seq2seq\/tasks\/tasks.py\", line 54, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 149, in download_custom\r\n    custom_download(url, path)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/tensorflow\/python\/lib\/io\/file_io.py\", line 516, in copy_v2\r\n    compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)\r\ntensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists\r\n\r\n \n This has been fixed by #881 \r\nthis fix will be available in the next release soon.\r\n\r\nIf you don't want to wait for the release you can actually load the latest version of boolq by specifying `script_version=\"master\"` in `load_dataset`","embeddings":[-0.2191431969,-0.205223009,-0.1021828577,0.056804508,0.0433856584,0.0777705982,0.3575468659,0.2529199123,0.2545483708,-0.0290935356,-0.137118265,0.3180469871,-0.2989818454,0.435959518,-0.0375271626,-0.0104442453,-0.2001055479,0.0617069267,0.0194576196,0.1253176033,-0.3767187595,0.4527184665,-0.3950281441,0.1717271507,0.039792046,-0.1584234387,0.0690807477,0.2183280289,-0.3045058548,-0.367679894,0.1875551343,-0.0160979461,0.1602543443,0.1953685731,-0.000100287,0.0124218855,0.3434242904,0.0293650366,-0.1709919572,-0.3191271424,-0.1750642061,-0.3502523601,0.2896553278,-0.0837975815,-0.0092108911,0.0844620094,-0.040360827,0.1825928986,0.3829545379,0.3910404146,0.3393365741,0.0961863548,0.1874352694,-0.2147234976,0.2771983445,-0.5643305779,0.0346739218,0.1041202247,0.2713558376,-0.1368377209,0.066627875,0.1479776949,-0.2208089978,-0.2162980139,-0.1051368415,-0.2081234902,-0.0779546276,-0.3245044649,0.1794871092,0.0790971294,0.6055165529,-0.0848833472,-0.0521295182,0.1929166764,0.2385609597,-0.1072742119,0.1638332754,0.0137318,0.0599494018,0.0544823632,0.2340850532,-0.3852249384,-0.0247666705,0.0413612984,-0.0779064223,0.2063310295,-0.0665245429,0.0390985645,0.2114246339,-0.2090814412,0.1663435102,0.508505702,-0.109657988,-0.0038654527,-0.6627800465,0.024767397,0.2779552937,0.107672438,-0.0527212545,0.0032308488,-0.265878886,0.0674191788,0.1694182903,0.1585515887,-0.1650961787,0.3916872144,0.2289465219,0.2797166705,0.0727470815,0.2224866748,-0.2273650616,0.2074246705,-0.2910070717,-0.492138654,-0.0160816219,0.1348418295,0.3629411459,-0.0850622207,-0.2391541153,-0.1036297455,-0.1959228367,-0.0248908307,0.3913449347,0.7891902924,0.1488980204,-0.3131633401,0.1699921936,0.0756845623,-0.2671214938,-0.1868343651,-0.3410372734,0.2802948356,0.0750981793,-0.1892602742,0.1544688791,-0.3493214548,0.1605904698,0.1638125777,-0.0757626295,-0.1015292183,0.2639566064,-0.1807930171,-0.2796195447,0.2034140676,-0.1660426855,0.0412304625,0.222171858,0.0821633413,-0.063733995,0.2537777126,-0.3442661166,-0.4547116458,-0.1617778689,0.3301042616,0.1894203126,-0.27367872,0.1826002151,0.2261167169,0.1871689409,-0.1125649959,-0.1956937462,-0.3212908208,-0.2962062657,-0.1822833419,0.2448646575,0.3433954418,-0.4542224705,-0.0933891684,-0.2166294158,-0.2461134046,-0.054044459,0.1466912478,-0.2197429091,0.398642987,-0.1403478235,0.0188561901,0.1012149081,-0.2950314879,-0.4969398975,0.2633554637,-0.2121250033,-0.1468689889,-0.2692807019,0.0484414436,0.1941547692,0.1539271921,0.0051376866,0.0565571487,0.2050610185,-0.2150689512,-0.1294436157,-0.0946026072,0.2298936993,0.3611728549,0.1907626092,-0.0996231213,-0.0038339908,0.0660987124,0.0545602627,0.2124175131,-0.2959643602,0.2606880069,0.2629840374,-0.1679650694,0.0497377403,0.0490054712,-0.3281366825,0.1711824983,-0.1773874164,0.1284162402,0.0088677797,0.2201396525,-0.3857020736,0.124791272,-0.2456311733,0.0568337888,0.2308734208,-0.2148270011,-0.0974920318,0.0471501648,-0.1539621353,0.2841616571,-0.1739644557,0.055631686,-0.071144715,-0.0564676598,-0.3174751997,-0.1609033346,0.2278515995,0.1587961763,0.1742808223,0.0216859486,0.0050521316,0.3554823399,0.0803116187,-0.0317420475,-0.2111491263,-0.4885710478,-0.0345933922,-0.1139063314,0.0384387746,0.102731511,0.1070292294,0.1205408573,0.4304286242,0.2363624573,-0.1677540839,-0.0515252911,-0.0633639246,0.1917677373,0.1616193652,0.0279592704,-0.0829842314,-0.0570030771,0.4093349576,0.0435367897,-0.0650580376,-0.0590648204,-0.0195259042,0.014713522,0.4898835123,0.1344736814,0.0625171438,-0.1088181809,0.2517293394,0.1401916444,0.0302508697,0.1328377724,0.277749151,0.1064233333,-0.4235241711,-0.0366860069,-0.0264514554,-0.2671252787,0.0989147648,-0.3150690496,-0.1128067076,0.1915855706,-0.0246155076,-0.162846759,-0.1511110663,-0.2533967495,0.1131062135,0.344355613,-0.1373842359,-0.0577376187,-0.1854242533,-0.2737466395,0.3402540386,-0.0681999996,0.0371059217,-0.316401571,0.2217026651,-0.0770749301,0.138763383,0.3388938606,-0.0285247955,0.1094074845,0.3005981147,-0.2708330452,-0.0180830099,-0.2160334438,-0.1804675907,0.1515997946,0.111972183,-0.2137085795,0.0305303,-0.1087111309,-0.0169140063,-0.004822446,-0.3049965501,-0.1138391271,-0.3007988334,0.3057751358,0.2515809238,0.0596834607,-0.3211912513,-0.0032180632,0.1792205125,-0.2130450308,-0.0801892728,0.1580676436,0.1449140161,-0.0205494296,-0.204799369,-0.4926880896,-0.5303481817,-0.4888476729,-0.0292072166,-0.0471295975,0.1707663089,0.3728390336,-0.0082199024,0.3335877657,0.1069243327,0.1427049488,-0.0587089285,-0.2668074667,0.2721175849,0.0249675456,-0.3096513748,0.1576294154,0.1298577338,0.3517134786,-0.2375917286,-0.1885915101,0.1306701601,0.0096215066,0.0643238276,-0.2209012657,-0.1836156547,0.0529978499,-0.0396089479,-0.0411025397,0.0911995918,-0.2069228888,0.2017320096,0.0643187687,0.1448442787,0.0772933438,0.2657338977,-0.0761375427,0.5265434384,0.0392807871,0.1855731457,0.2640739977,0.130462423,0.134970814,-0.0506642237,-0.5398096442,-0.0671861321,0.4233142436,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1167","title":"\u2753 On-the-fly tokenization with datasets, tokenizers, and torch Datasets and Dataloaders","comments":"We're working on adding on-the-fly transforms in datasets.\r\nCurrently the only on-the-fly functions that can be applied are in `set_format` in which we transform the data in either numpy\/torch\/tf tensors or pandas.\r\nFor example\r\n```python\r\ndataset.set_format(\"torch\")\r\n```\r\napplies `torch.Tensor` to the dataset entries on-the-fly.\r\n\r\nWe plan to extend this to user-defined formatting transforms.\r\nFor example\r\n```python\r\ndataset.set_format(transform=tokenize)\r\n```\r\n\r\nWhat do you think ?","body":"Hi there,\r\n\r\nI have a question regarding \"on-the-fly\" tokenization. This question was elicited by reading the \"How to train a new language model from scratch using Transformers and Tokenizers\" [here](https:\/\/huggingface.co\/blog\/how-to-train). Towards the end there is this sentence: \"If your dataset is very large, you can opt to load and tokenize examples on the fly, rather than as a preprocessing step\". I've tried coming up with a solution that would combine both `datasets` and `tokenizers`, but did not manage to find a good pattern.\r\n\r\nI guess the solution would entail wrapping a dataset into a Pytorch dataset.\r\n\r\nAs a concrete example from the [docs](https:\/\/huggingface.co\/transformers\/custom_datasets.html)\r\n\r\n```python\r\nimport torch\r\n\r\nclass SquadDataset(torch.utils.data.Dataset):\r\n    def __init__(self, encodings):\r\n        # instead of doing this beforehand, I'd like to do tokenization on the fly\r\n        self.encodings = encodings \r\n\r\n    def __getitem__(self, idx):\r\n        return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\r\n\r\n    def __len__(self):\r\n        return len(self.encodings.input_ids)\r\n\r\ntrain_dataset = SquadDataset(train_encodings)\r\n```\r\n\r\nHow would one implement this with \"on-the-fly\" tokenization exploiting the vectorized capabilities of tokenizers?\r\n\r\n\r\n----\r\n\r\nEdit: I have come up with this solution. It does what I want, but I feel it's not very elegant\r\n\r\n```python\r\nclass CustomPytorchDataset(Dataset):\r\n    def __init__(self):\r\n        self.dataset = some_hf_dataset(...)\r\n        self.tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\")\r\n\r\n    def __getitem__(self, batch_idx):\r\n        instance = self.dataset[text_col][batch_idx]\r\n        tokenized_text = self.tokenizer(instance, truncation=True, padding=True)\r\n        return tokenized_text\r\n\r\n    def __len__(self):\r\n        return len(self.dataset)\r\n\r\n    @staticmethod\r\n    def collate_fn(batch):\r\n        # batch is a list, however it will always contain 1 item because we should not use the\r\n        # batch_size argument as batch_size is controlled by the sampler\r\n        return {k: torch.tensor(v) for k, v in batch[0].items()}\r\n\r\ntorch_ds = CustomPytorchDataset()\r\n\r\n# NOTE: batch_sampler returns list of integers and since here we have SequentialSampler\r\n# it returns: [1, 2, 3], [4, 5, 6], etc. - check calling `list(batch_sampler)`\r\nbatch_sampler = BatchSampler(SequentialSampler(torch_ds), batch_size=3, drop_last=True)\r\n\r\n# NOTE: no `batch_size` as now the it is controlled by the sampler!\r\ndl = DataLoader(dataset=torch_ds, sampler=batch_sampler, collate_fn=torch_ds.collate_fn)\r\n```","comment_length":63,"text":"\u2753 On-the-fly tokenization with datasets, tokenizers, and torch Datasets and Dataloaders \n Hi there,\r\n\r\nI have a question regarding \"on-the-fly\" tokenization. This question was elicited by reading the \"How to train a new language model from scratch using Transformers and Tokenizers\" [here](https:\/\/huggingface.co\/blog\/how-to-train). Towards the end there is this sentence: \"If your dataset is very large, you can opt to load and tokenize examples on the fly, rather than as a preprocessing step\". I've tried coming up with a solution that would combine both `datasets` and `tokenizers`, but did not manage to find a good pattern.\r\n\r\nI guess the solution would entail wrapping a dataset into a Pytorch dataset.\r\n\r\nAs a concrete example from the [docs](https:\/\/huggingface.co\/transformers\/custom_datasets.html)\r\n\r\n```python\r\nimport torch\r\n\r\nclass SquadDataset(torch.utils.data.Dataset):\r\n    def __init__(self, encodings):\r\n        # instead of doing this beforehand, I'd like to do tokenization on the fly\r\n        self.encodings = encodings \r\n\r\n    def __getitem__(self, idx):\r\n        return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\r\n\r\n    def __len__(self):\r\n        return len(self.encodings.input_ids)\r\n\r\ntrain_dataset = SquadDataset(train_encodings)\r\n```\r\n\r\nHow would one implement this with \"on-the-fly\" tokenization exploiting the vectorized capabilities of tokenizers?\r\n\r\n\r\n----\r\n\r\nEdit: I have come up with this solution. It does what I want, but I feel it's not very elegant\r\n\r\n```python\r\nclass CustomPytorchDataset(Dataset):\r\n    def __init__(self):\r\n        self.dataset = some_hf_dataset(...)\r\n        self.tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\")\r\n\r\n    def __getitem__(self, batch_idx):\r\n        instance = self.dataset[text_col][batch_idx]\r\n        tokenized_text = self.tokenizer(instance, truncation=True, padding=True)\r\n        return tokenized_text\r\n\r\n    def __len__(self):\r\n        return len(self.dataset)\r\n\r\n    @staticmethod\r\n    def collate_fn(batch):\r\n        # batch is a list, however it will always contain 1 item because we should not use the\r\n        # batch_size argument as batch_size is controlled by the sampler\r\n        return {k: torch.tensor(v) for k, v in batch[0].items()}\r\n\r\ntorch_ds = CustomPytorchDataset()\r\n\r\n# NOTE: batch_sampler returns list of integers and since here we have SequentialSampler\r\n# it returns: [1, 2, 3], [4, 5, 6], etc. - check calling `list(batch_sampler)`\r\nbatch_sampler = BatchSampler(SequentialSampler(torch_ds), batch_size=3, drop_last=True)\r\n\r\n# NOTE: no `batch_size` as now the it is controlled by the sampler!\r\ndl = DataLoader(dataset=torch_ds, sampler=batch_sampler, collate_fn=torch_ds.collate_fn)\r\n``` \n We're working on adding on-the-fly transforms in datasets.\r\nCurrently the only on-the-fly functions that can be applied are in `set_format` in which we transform the data in either numpy\/torch\/tf tensors or pandas.\r\nFor example\r\n```python\r\ndataset.set_format(\"torch\")\r\n```\r\napplies `torch.Tensor` to the dataset entries on-the-fly.\r\n\r\nWe plan to extend this to user-defined formatting transforms.\r\nFor example\r\n```python\r\ndataset.set_format(transform=tokenize)\r\n```\r\n\r\nWhat do you think ?","embeddings":[-0.0571537055,-0.0378616676,0.0511645302,-0.0909028575,0.1780218631,-0.0221669544,0.5987785459,0.0828894675,-0.3151782453,-0.0732152164,0.1041466147,0.2624972165,-0.2840220034,0.0880036131,0.1834016889,-0.0820697471,0.2904809117,0.0297533553,0.1173341721,0.1771769375,0.1671274304,-0.3500298262,-0.2116543353,0.0744798779,-0.4641672373,-0.3110953271,-0.2085753232,-0.3593772352,-0.1359574348,-0.4139526784,0.1105546877,0.321094662,0.5333424807,0.5197013021,-0.0001220133,0.0060748109,-0.0021984389,-0.003848559,-0.2255186588,-0.0814791322,0.5955740213,-0.2086483538,0.0416740924,-0.1967036575,0.1241504401,-0.3029721975,-0.0499146953,-0.3180963397,0.4491649866,-0.1373936236,0.0502252616,0.3842606843,-0.1409321427,0.1993175298,-0.0296621621,0.234946698,-0.0849569961,-0.2966648936,0.3195873201,-0.1833726019,-0.2352818847,0.0610689744,-0.1143956408,-0.3018173277,0.3582299948,-0.089004688,-0.4720630944,-0.2796250582,-0.1780117452,0.0847608447,0.1384000778,-0.4279212952,-0.5727862716,-0.4943040013,-0.0471200868,0.0320064016,-0.3290809393,0.0610616878,-0.0465172306,-0.0024522992,-0.4008657336,-0.1909391135,-0.0380677842,0.1802811027,-0.1636813432,0.3774628043,-0.0297228619,0.2500401437,-0.186584264,-0.241131261,0.3104632497,-0.3286831677,0.1768815666,0.4461234212,-0.2475443184,-0.1767549068,-0.0567022003,-0.388520658,0.2258034199,-0.0604092032,-0.1110222861,0.1192571223,-0.1659703404,-0.0158327799,0.075269565,0.2692531943,0.1779618114,0.2152022719,0.1101557836,-0.4173893631,0.0087661706,0.0452198572,-0.3886713386,0.2294880897,0.0866203457,0.0366172791,-0.3295232952,0.1210333109,0.194229424,-0.0327317379,-0.3979840875,0.0642564446,0.0989573076,-0.0077737379,-0.1188002005,0.0148154534,-0.0021530192,0.1808408201,0.0536755659,-0.0783503056,0.0499117672,0.004377903,-0.1358769089,0.0813597217,-0.0152883288,0.1048120558,-0.0366410352,0.1655039936,0.1909096092,0.0099893352,0.2136267424,-0.0565729141,0.1137497649,-0.1060923561,-0.0452968143,0.1164471805,0.1570580602,0.0914212018,-0.2916375995,0.2853478193,-0.0932707563,-0.2645080686,0.2515757382,0.0332041085,-0.3945994675,-0.2396771014,-0.3217060566,0.3632381856,0.1968056411,0.0486058928,0.0922082514,-0.0612006113,0.1684160531,0.0173153542,0.3184633851,0.2123426944,-0.2312633395,-0.3678816259,0.6276065111,0.1510909647,0.3359800875,0.0259950589,-0.4712953568,0.4807199538,0.0094875405,0.2042948157,0.3170516491,-0.3109449744,-0.0013000687,0.2392292023,-0.1839888245,-0.0250468124,-0.0423632786,0.021418795,0.4540833831,0.096150361,0.0890528262,0.5082175136,-0.0558253229,0.0745907426,-0.0885538831,-0.2710236609,0.3134452403,0.089934729,0.1590591669,0.0109740319,-0.0871456414,0.2193920761,-0.0712971911,-0.1872736961,0.178572759,-0.1451717019,-0.1254775524,0.1383187026,0.019939715,-0.0620585792,-0.1841981709,0.2015856802,-0.0898046345,0.1972798556,-0.1764650941,-0.2715864778,-0.0019044264,0.1634167284,-0.0177414566,-0.0604430363,-0.034226656,-0.217196703,-0.0614103936,0.0688114613,-0.4198598862,-0.0446384512,-0.3517662287,0.1824168563,-0.0726610571,0.1820559502,0.1786254346,-0.0246587824,-0.2389157861,0.1298804581,0.0365737751,-0.2153056264,0.0737867802,0.2548118532,0.0257010628,0.0530781597,-0.1508049965,0.4075115621,0.2054130733,0.2179683,0.064851746,0.0144562926,0.0228054821,-0.011162349,-0.0500633828,0.6243644953,0.1738144457,0.4188345075,0.0587214492,-0.0847589746,0.1361261606,0.1478969902,-0.2176665515,0.1060156077,-0.1907912642,0.1404688805,0.6474385262,-0.0946735144,-0.0212153215,-0.0418491513,-0.1164316386,-0.1920311451,0.0326263793,0.0857618302,-0.4482505023,-0.1540810168,0.0124109471,-0.2460067868,-0.039687328,0.0382439047,0.1952546984,-0.0384145379,0.0311054252,0.0237772651,0.1196404099,0.2907427251,-0.095278427,-0.0264544506,0.0945417434,0.3982783258,-0.1978990138,-0.2764557004,0.1561102569,0.0813878402,-0.0881064311,0.2645244002,-0.1462126374,0.0312485229,-0.0986711904,-0.5169739127,-0.4592525661,-0.0376530886,0.0611969307,-0.1395250559,0.1332175732,0.3374570906,-0.1167318895,0.6314311028,0.0603885092,-0.2420101464,0.2137720436,-0.2343087494,-0.0964066461,-0.0841436535,0.2045982033,-0.3338748217,0.2849550843,0.2845368385,0.1369119436,-0.1833368987,-0.4072165787,0.3090746701,0.0721441135,0.1843277961,-0.0469122864,-0.348541677,-0.0848900974,-0.2920381129,-0.0587293468,-0.2009663731,-0.0947476774,-0.0253325906,-0.0770390704,0.0565334223,0.0295351259,0.089028582,-0.3233362436,0.0183351617,0.0809076726,-0.0284002218,0.0759999156,-0.0228999108,-0.2220890075,0.1977068633,-0.2201230973,-0.061893113,0.0560037978,-0.4251791835,0.3280382752,0.0323309265,-0.1505910605,-0.2655514181,-0.2750265598,0.146408543,0.4522670805,-0.2196106166,-0.0494643524,-0.1388583183,0.1196654662,-0.2595689893,0.1129310578,0.3263929188,0.0525319725,0.1158334464,0.0058208667,0.2495071441,-0.0138157206,-0.255254209,0.0529872328,0.2193771899,0.3225408792,0.2826948464,1.0907583237,-0.2653211951,-0.4417159557,0.1172583252,-0.0038027915,0.0831062868,-0.0358712934,-0.2238188386,-0.1119775623,-0.38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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1110","title":"Using a feature named \"_type\" fails with certain operations","comments":"Thanks for reporting !\r\n\r\nIndeed this is a keyword in the library that is used to encode\/decode features to a python dictionary that we can save\/load to json.\r\nWe can probably change `_type` to something that is less likely to collide with user feature names.\r\nIn this case we would want something backward compatible though.\r\n\r\nFeel free to try a fix and open a PR, and to ping me if I can help :) ","body":"A column named `_type` leads to a `TypeError: unhashable type: 'dict'` for certain operations:\r\n```python\r\nfrom datasets import Dataset, concatenate_datasets\r\n\r\nds = Dataset.from_dict({\"_type\": [\"whatever\"]}).map()\r\nconcatenate_datasets([ds])\r\n# or simply\r\nDataset(ds._data)\r\n```\r\nContext: We are using datasets to persist data coming from elasticsearch to feed to our pipeline, and elasticsearch has a `_type` field, hence the strange name of the column.\r\n\r\nNot sure if you wish to support this specific column name, but if you do i would be happy to try a fix and provide a PR. I already had a look into it and i think the culprit is the `datasets.features.generate_from_dict` function. It uses the hard coded `_type` string to figure out if it reached the end of the nested feature object from a serialized dict.\r\n\r\nBest wishes and keep up the awesome work!","comment_length":74,"text":"Using a feature named \"_type\" fails with certain operations \n A column named `_type` leads to a `TypeError: unhashable type: 'dict'` for certain operations:\r\n```python\r\nfrom datasets import Dataset, concatenate_datasets\r\n\r\nds = Dataset.from_dict({\"_type\": [\"whatever\"]}).map()\r\nconcatenate_datasets([ds])\r\n# or simply\r\nDataset(ds._data)\r\n```\r\nContext: We are using datasets to persist data coming from elasticsearch to feed to our pipeline, and elasticsearch has a `_type` field, hence the strange name of the column.\r\n\r\nNot sure if you wish to support this specific column name, but if you do i would be happy to try a fix and provide a PR. I already had a look into it and i think the culprit is the `datasets.features.generate_from_dict` function. It uses the hard coded `_type` string to figure out if it reached the end of the nested feature object from a serialized dict.\r\n\r\nBest wishes and keep up the awesome work! \n Thanks for reporting !\r\n\r\nIndeed this is a keyword in the library that is used to encode\/decode features to a python dictionary that we can save\/load to json.\r\nWe can probably change `_type` to something that is less likely to collide with user feature names.\r\nIn this case we would want something backward compatible though.\r\n\r\nFeel free to try a fix and open a PR, and to ping me if I can help :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1103","title":"Add support to download kaggle datasets","comments":"Hey, I think this is great idea. Any plan to integrate kaggle private datasets loading to `datasets`?","body":"We can use API key","comment_length":17,"text":"Add support to download kaggle datasets \n We can use API key \n Hey, I think this is great idea. Any plan to integrate kaggle private datasets loading to `datasets`?","embeddings":[-0.0929649547,-0.053584218,-0.3102498949,-0.0035680523,0.1154249161,-0.0199873969,0.2132152468,0.1274612695,0.4255777001,0.1014183462,-0.2042617798,0.6420328021,-0.0812081993,0.8870463967,0.0586065575,0.0357894786,0.1376734525,0.1102098078,0.1666734815,0.201852873,-0.1626320183,0.0076732179,-0.1302725375,-0.1597166955,0.1356511265,-0.2504248619,0.017365545,-0.1539834887,-0.3501514792,-0.4008308351,0.2865821719,0.2992354333,0.2788437009,0.1203535944,-0.0000982298,-0.1139250547,0.3490458429,0.0557616614,-0.210727945,0.1748960465,-0.5735141039,-0.2588055432,0.016866181,-0.1636401713,-0.1123944819,-0.2678085566,-0.1713749319,-0.3603084087,0.2351503372,-0.1250749379,0.2732905746,0.006384558,0.0885759369,-0.1921589077,0.1979539096,0.1911397725,-0.1841082871,-0.1639204174,0.4762825668,0.0860301107,0.0310169384,0.2202863246,0.2117949575,-0.1668585539,0.2060696185,-0.0416042879,-0.2298645228,-0.5424913168,0.0190903172,0.0220291577,1.0136518478,-0.1241830066,-0.2577559054,-0.3066722155,0.032376729,0.0141826691,-0.067809701,0.0927636996,-0.0220896937,0.1796204895,0.1324836463,-0.6074897051,-0.2174334824,0.1237905174,0.1043906137,0.5533359051,-0.0011595344,-0.0430266485,0.1158502027,-0.1654866487,0.435978353,-0.1371934712,-0.1044681221,0.1234971806,-0.1767945886,-0.2896084785,-0.0022104767,0.1693391353,0.3592155576,0.3478613794,0.0657119006,0.3563125134,-0.1603371799,-0.0845618248,0.4710317254,0.0696583688,0.2438338548,-0.0263642445,0.4169931412,0.0564147197,0.2899772525,0.0078798831,-0.0219127778,0.0729435608,-0.275755316,-0.1811768115,0.0029459349,-0.0364105552,0.0814337209,-0.1260784417,0.1859476119,-0.1600484848,-0.1126899794,0.1078306511,-0.1048745662,0.1617765427,-0.1908884645,0.043846406,0.2076887339,-0.0576501302,-0.1485186964,0.0058379769,0.0369170681,0.1093908548,0.0658129975,-0.1096902937,0.1760620326,-0.1862022728,0.277864486,0.19964917,0.1841573417,0.3225196898,-0.2182249427,0.0646085739,0.0479777977,-0.1586374342,-0.305888176,-0.1642429233,-0.087016955,0.1231410578,-0.2252591997,-0.2828967571,-0.1214038357,0.3159552217,-0.329862982,-0.2243256718,-0.5454100966,0.3330480456,-0.1434221566,-0.1627181321,0.0098491665,0.3126566112,-0.0290235244,-0.1342370063,0.0432077125,-0.0810189322,-0.10948053,-0.2283750623,-0.1753743589,-0.1984549761,-0.0973824561,0.0002355361,-0.2214530706,0.094040364,-0.2998362184,0.2068417221,0.6396182775,-0.4268696606,-0.3617960215,0.2395075113,-0.1071419567,-0.1990328133,0.0805609971,0.2385319322,0.253870219,0.0339549147,0.0562470555,0.3365939856,-0.0096011767,-0.0481802523,-0.2135483921,-0.4054665864,-0.0370904468,0.2970249653,0.0025687772,-0.0871418267,0.3029793203,-0.1463075727,0.1826406121,0.0095972428,0.1907448471,-0.1902959645,0.4004264176,-0.1466522664,0.1918909699,0.0919801444,-0.3843583167,0.1946885139,-0.3316366374,0.0416459739,-0.0040103919,-0.345194757,-0.1721148938,0.0983393714,-0.3835199475,0.026156133,0.2060831636,-0.2966518402,0.0946116075,0.1269972771,-0.2230920494,0.2289740443,0.2138634473,0.0540144034,-0.175079897,0.2178053409,-0.2335838228,0.2678087056,0.0801347867,0.0497058854,-0.0649685934,-0.1727432609,0.2545925379,-0.0741074383,0.162464872,0.4052411616,0.4697453678,-0.0727103949,0.4805855453,-0.0431595147,0.1689442396,0.0748146027,0.1771881133,-0.0979500487,-0.2475565374,0.3408476114,0.0691556334,-0.0139367543,-0.176382035,-0.0046359641,0.1185565814,0.0576760508,-0.1215807647,-0.1760537326,-0.0643194169,-0.3711441159,-0.0425792746,-0.1604275256,-0.1076765433,0.4074072242,0.0366183445,-0.1014718637,0.1294517666,0.0963313803,-0.0978769809,-0.1545976698,0.3012616932,0.2223402709,0.2467843145,0.3052209616,-0.0063447845,-0.1875217706,0.4511363506,-0.1555742174,0.1747367829,0.2000945359,0.1127392054,0.2868580818,0.0697410628,-0.2519512773,0.0697324872,0.0750761554,0.1321231574,0.1331682503,-0.0068098707,-0.1906313449,-0.105003342,-0.4177584052,0.2547333241,-0.0595765784,0.2357586026,-0.4169063866,0.188446641,-0.0759593993,-0.0838453621,0.1074507907,-0.2154809386,0.4759658277,-0.1823241413,-0.1267191619,-0.2750762999,-0.3052017689,-0.1172137558,0.3365138471,0.2783371508,-0.212425366,0.5011542439,0.131085366,0.2197770774,-0.3135256767,-0.5158466101,0.0511482544,0.008959285,-0.0700863972,0.053938549,0.0763895363,0.1957163066,-0.0425221622,-0.0450359359,0.0193727016,-0.3334476352,-0.3106675446,-0.0499325395,-0.0490736254,-0.1771842837,-0.3822337687,-0.4056794941,-0.3214925528,0.3853613734,0.113257356,-0.0648361221,-0.2443984598,0.1801357716,0.0929392651,0.0071948017,-0.3171281517,-0.2774167359,-0.1570271999,0.28554672,-0.2521851957,-0.3377675414,0.3224461973,-0.1721737832,0.1879161745,-0.2725309134,-0.3888732791,-0.2476118207,0.1317628771,0.3319598138,0.0334973037,-0.0380640849,0.0672867671,0.1975274533,-0.0695056766,-0.1006402075,-0.2368372083,-0.1417509913,0.0269679315,0.0733378157,0.04553454,-0.1579207778,0.0893397182,0.5135681629,0.0981259048,0.140764907,0.3335723579,0.1886803061,0.1842944771,0.0722925067,-0.1296096146,0.0371074416,-0.102124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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1064","title":"Not support links with 302 redirect ","comments":"> Hi !\r\n> This kind of links is now supported by the library since #1316\r\n\r\nI updated links in TLC datasets to be the github links in this pull request \r\n https:\/\/github.com\/huggingface\/datasets\/pull\/1737\r\n\r\nEverything works now. Thank you.","body":"I have an issue adding this download link https:\/\/github.com\/jitkapat\/thailitcorpus\/releases\/download\/v.2.0\/tlc_v.2.0.tar.gz\r\n\r\nit might be because it is not a direct link (it returns 302 and redirects to aws that returns 403 for head requests). \r\n\r\n```\r\nr.head(\"https:\/\/github.com\/jitkapat\/thailitcorpus\/releases\/download\/v.2.0\/tlc_v.2.0.tar.gz\", allow_redirects=True)                                      \r\n# <Response [403]>\r\n```","comment_length":37,"text":"Not support links with 302 redirect  \n I have an issue adding this download link https:\/\/github.com\/jitkapat\/thailitcorpus\/releases\/download\/v.2.0\/tlc_v.2.0.tar.gz\r\n\r\nit might be because it is not a direct link (it returns 302 and redirects to aws that returns 403 for head requests). \r\n\r\n```\r\nr.head(\"https:\/\/github.com\/jitkapat\/thailitcorpus\/releases\/download\/v.2.0\/tlc_v.2.0.tar.gz\", allow_redirects=True)                                      \r\n# <Response [403]>\r\n``` \n > Hi !\r\n> This kind of links is now supported by the library since #1316\r\n\r\nI updated links in TLC datasets to be the github links in this pull request \r\n https:\/\/github.com\/huggingface\/datasets\/pull\/1737\r\n\r\nEverything works now. Thank you.","embeddings":[-0.0889685974,-0.2517869771,-0.0088746836,-0.1504719406,0.1692062467,-0.0146814175,-0.0734717399,0.35192433,0.0370435566,-0.0853566155,-0.1462335736,0.1753974855,-0.0085471682,0.0927394256,0.1950139403,-0.2360422313,-0.0524225272,-0.0261497889,-0.0826820955,0.2028121501,-0.027272163,0.5130646825,-0.0248572268,-0.0624030828,-0.068671234,0.0774284601,0.1210468486,0.2031793594,0.0683747903,-0.2401624471,0.303055048,0.1178529337,0.1594067365,0.5492524505,-0.0001215735,0.1579644233,0.3548876941,0.072055988,-0.2901379168,-0.5894262195,-0.2536681294,-0.2439851612,0.0339293927,0.032435026,-0.1306387931,0.5072250962,-0.0778939947,0.1441019475,-0.0753327459,0.1765078306,0.1485421062,0.2375597805,0.2008063793,-0.0810438469,0.9201811552,0.0543805584,-0.1180588827,0.3259595335,-0.0304333828,0.1545787603,0.3328635097,0.177852422,0.095285818,0.0808029175,-0.111031048,-0.1084039807,-0.0450997874,-0.2801686823,-0.15075472,0.2552185953,0.6863910556,0.0206972808,-0.5041068792,0.1242092252,-0.1229810044,-0.2610161602,0.3652248383,0.2766008675,0.1618135124,0.0424441136,0.0220192727,-0.4948576689,-0.1582905501,0.1913100332,0.1813697368,0.1272976249,0.079785414,0.0317876004,0.2093795091,0.1482400596,0.2320011407,-0.1184864119,-0.3121515214,-0.0051235668,-0.2185737342,-0.079031229,-0.1069963127,0.1383845359,0.2005695701,0.3603540957,0.0971251801,0.0609982759,-0.2779536545,0.1389889568,-0.1637238413,0.0455287695,-0.4307024777,0.1138571426,0.599773109,0.1345745623,0.1671667397,-0.1014247984,0.025814455,-0.3618370593,-0.640655756,-0.0050231772,0.021018425,0.0484001972,-0.0703640431,-0.0238336902,-0.3518248796,-0.1699009836,-0.1177654788,0.2023970634,-0.0567761026,0.0609716028,0.5130020976,0.0460665971,0.0043183723,0.1508783102,-0.0211402364,0.0231467597,-0.130186066,-0.1388564408,0.076084815,-0.0486892276,0.1044013426,-0.0494075343,0.2970653474,-0.1429970115,0.1065954491,0.0482982174,0.0091796611,0.5238859057,0.0449464433,0.2545099258,-0.1337096244,-0.1347231716,0.1427950412,0.3535699844,-0.6124945283,-0.2109988928,-0.1807133853,0.0326900445,-0.1545538604,-0.2067750841,-0.1573908031,-0.0028978975,-0.1474408358,-0.5722486377,-0.1322476417,0.06036878,-0.0241044015,-0.1116486788,0.0952816308,0.3426842093,-0.4351748228,0.0094628846,-0.0647440851,0.2282375544,-0.012002646,0.3981320262,-0.1556462944,-0.0729114115,-0.3013798892,-0.1341858655,0.7303339839,-0.4807989001,-0.3789023459,0.4282314181,-0.2898419797,-0.1877360195,-0.1061226353,0.2756073773,-0.3474136591,-0.1764629483,-0.1268268824,0.1685846895,0.2764305174,0.0358418599,-0.2897286713,-0.1738007516,-0.0571221299,-0.0227623209,-0.0404700972,0.1606975347,0.1272218972,-0.3260907829,0.3235673904,0.0820758864,0.2460441887,0.1628433168,0.2186272591,0.083060056,0.0801443234,0.0311642233,0.0336822607,-0.0903039277,0.001823253,0.3663538694,-0.0354037546,-0.093159847,-0.2621215284,-0.2167806327,0.0052463999,0.1010594741,-0.0144658852,0.0691929385,-0.0824376494,0.3779104948,-0.1585894376,0.0074779531,0.1893827915,0.3136532009,-0.0861897618,0.5394979119,-0.2678464353,0.1029390469,0.1966937482,-0.0428085066,0.1874131113,-0.1979300976,-0.1212188601,0.3034728169,-0.3177319169,0.1804010719,0.6118975282,0.0786386654,0.2613998353,-0.0922379345,-0.313267231,0.3064296544,0.2189106494,0.110688135,-0.1312217265,0.1056995988,0.1801846623,0.0462584086,0.0018849198,0.2894602716,0.2616717219,0.0509676673,-0.227001518,0.2264629304,0.1595851183,0.0993027538,-0.7005435228,-0.574280262,-0.2411242723,-0.0756931528,0.2490627617,-0.2395289093,0.1468952596,0.183181271,-0.0438629016,0.0771969482,0.1577172577,0.8859192729,0.1208991036,0.2381839305,0.0884258673,0.0439855233,-0.0307360832,-0.2622562647,0.3470580876,0.0624578409,-0.2946636379,0.1126286536,0.1000957862,0.0410520211,-0.2012776285,-0.4085431695,0.2061423808,0.14872168,-0.2893052399,-0.0932014212,-0.2989703119,-0.2179988623,0.1421817988,-0.2177566439,-0.0476185866,-0.2104533017,0.082964927,0.3161398768,-0.1778657734,0.1747334749,0.1694303453,0.0775181353,0.2099795938,-0.4418687224,-0.2040369511,-0.1694379896,-0.1004686952,0.081806533,0.1153391376,-0.5353226066,0.1564164609,-0.138467893,0.2705561221,-0.2036029696,-0.3495527506,-0.0268721711,-0.0887323618,-0.1053681001,0.182529673,0.1798851341,-0.1700104028,0.0821453407,0.235492155,0.194015041,0.1734012365,-0.1229033992,0.1601561159,-0.1496061981,-0.2317112237,-0.3762661517,0.0774012581,-0.347966373,-0.0618770272,0.1576521695,0.1705688983,-0.046214655,0.2099301964,-0.0879655629,0.2394791543,-0.2012473792,-0.1868759692,-0.4467816949,0.5848569274,-0.4474649727,-0.487768203,0.4066593349,0.0196296908,0.2492921203,0.082568787,-0.4886935651,-0.1169576198,-0.0007140478,-0.2425427735,0.0936522782,0.4416886568,0.1985341609,0.2711295784,-0.1463364661,0.0718564466,-0.6122390628,-0.043817915,0.2205577493,0.2329094857,0.1641921699,0.3945307732,0.2477852404,0.4924861193,0.0896543935,0.1346900165,0.5550949574,0.0060134712,0.2173865288,0.0753515065,0.1182252541,0.0517434254,0.0758493543,0.1876437515,0.12329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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1046","title":"Dataset.map() turns tensors into lists?","comments":"A solution is to have the tokenizer return a list instead of a tensor, and then use `dataset_tok.set_format(type = 'torch')` to convert that list into a tensor. Still not sure if bug.","body":"I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!\r\n\r\n```import datasets\r\nimport torch  \r\nfrom datasets import load_dataset                                                                                                                 \r\nprint(\"version datasets\", datasets.__version__)\r\n\r\ndataset = load_dataset(\"snli\", split='train[0:50]')  \r\n\r\ndef tokenizer_fn(example):\r\n    # actually uses a tokenizer which does something like:\r\n    return {'input_ids': torch.tensor([[0, 1, 2]])}\r\n\r\nprint(\"First item in dataset:\\n\", dataset[0])\r\ntokenized = tokenizer_fn(dataset[0])\r\nprint(\"Tokenized hyp:\\n\", tokenized)\r\ndataset_tok = dataset.map(tokenizer_fn, batched=False,\r\n        remove_columns=['label', 'premise', 'hypothesis'])\r\nprint(\"Tokenized using map:\\n\", dataset_tok[0])\r\nprint(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))\r\ndataset_tok = dataset.map(tokenizer_fn, batched=False,\r\n                          remove_columns=['label', 'premise', 'hypothesis'])\r\nprint(\"Tokenized using map:\\n\", dataset_tok[0])\r\nprint(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))\r\n```\r\n\r\nThe output is:\r\n\r\n```\r\nversion datasets 1.1.3\r\nReusing dataset snli (\/home\/tom\/.cache\/huggingface\/datasets\/snli\/plain_text\/1.0.0\/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)\r\nFirst item in dataset:\r\n {'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}\r\nTokenized hyp:\r\n {'input_ids': tensor([[0, 1, 2]])}\r\nLoading cached processed dataset at \/home\/tom\/.cache\/huggingface\/datasets\/snli\/plain_text\/1.0.0\/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c\/cache-fe38f449fe9ac46f.arrow\r\nTokenized using map:\r\n {'input_ids': [[0, 1, 2]]}\r\n<class 'torch.Tensor'> <class 'list'>\r\n```\r\n\r\nOr am I doing something wrong?\r\n","comment_length":32,"text":"Dataset.map() turns tensors into lists? \n I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!\r\n\r\n```import datasets\r\nimport torch  \r\nfrom datasets import load_dataset                                                                                                                 \r\nprint(\"version datasets\", datasets.__version__)\r\n\r\ndataset = load_dataset(\"snli\", split='train[0:50]')  \r\n\r\ndef tokenizer_fn(example):\r\n    # actually uses a tokenizer which does something like:\r\n    return {'input_ids': torch.tensor([[0, 1, 2]])}\r\n\r\nprint(\"First item in dataset:\\n\", dataset[0])\r\ntokenized = tokenizer_fn(dataset[0])\r\nprint(\"Tokenized hyp:\\n\", tokenized)\r\ndataset_tok = dataset.map(tokenizer_fn, batched=False,\r\n        remove_columns=['label', 'premise', 'hypothesis'])\r\nprint(\"Tokenized using map:\\n\", dataset_tok[0])\r\nprint(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))\r\ndataset_tok = dataset.map(tokenizer_fn, batched=False,\r\n                          remove_columns=['label', 'premise', 'hypothesis'])\r\nprint(\"Tokenized using map:\\n\", dataset_tok[0])\r\nprint(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))\r\n```\r\n\r\nThe output is:\r\n\r\n```\r\nversion datasets 1.1.3\r\nReusing dataset snli (\/home\/tom\/.cache\/huggingface\/datasets\/snli\/plain_text\/1.0.0\/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)\r\nFirst item in dataset:\r\n {'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}\r\nTokenized hyp:\r\n {'input_ids': tensor([[0, 1, 2]])}\r\nLoading cached processed dataset at \/home\/tom\/.cache\/huggingface\/datasets\/snli\/plain_text\/1.0.0\/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c\/cache-fe38f449fe9ac46f.arrow\r\nTokenized using map:\r\n {'input_ids': [[0, 1, 2]]}\r\n<class 'torch.Tensor'> <class 'list'>\r\n```\r\n\r\nOr am I doing something wrong?\r\n \n A solution is to have the tokenizer return a list instead of a tensor, and then use `dataset_tok.set_format(type = 'torch')` to convert that list into a tensor. Still not sure if bug.","embeddings":[-0.0905342773,-0.1736622602,-0.1339069456,0.2130297422,0.2162035555,0.2193657011,0.4914948344,0.3712697923,0.257444948,-0.0263979062,-0.1461347193,0.680251956,-0.10030251,-0.4164583683,0.1840583086,0.0253589209,0.294393152,0.0346806832,-0.0507990569,-0.2774459422,-0.1831274182,-0.0675765797,-0.1957766712,0.0791181624,-0.1412031651,-0.1547512561,0.1446099281,-0.3113888204,-0.0627612844,-0.2368612587,0.1946785748,0.0335779414,0.1022556797,0.446719557,-0.0001160152,0.1183271185,-0.037234392,0.2079562694,-0.0176475514,-0.1034466103,0.0076182238,-0.1327908486,0.1470107734,-0.1844788641,-0.0054996279,-0.1479554027,-0.0391307659,-0.6350414753,0.179096505,0.3810082972,0.1443395168,0.2552904189,-0.1439363956,0.2130863816,0.3324943781,0.2167777121,-0.2084973902,-0.2550541461,0.1627887785,-0.0757924542,0.0668914467,0.1129236296,-0.4984409213,-0.0192023627,0.2370801419,0.2221133709,-0.1869863868,-0.5936381817,0.0203761477,0.1143103391,0.346229285,-0.3974611461,-0.1002945304,-0.1565368474,-0.2213947773,0.0423909165,-0.0511560366,0.0540001467,0.0545048267,-0.0863507465,-0.2528419197,-0.0814966038,0.0649670362,0.2210161388,-0.5532370806,0.2875820696,-0.0781859979,0.2344746888,0.1804774553,-0.0505854003,0.1507671028,-0.3792155981,0.0853365734,0.1579334438,-0.3272643685,-0.1862962246,0.1454360634,-0.4385431707,0.0561810508,-0.1962929219,0.1501737237,0.2216709554,-0.397954762,0.1758711338,0.1768113673,0.1449903399,-0.0155394245,0.680030942,-0.0505105294,-0.4166106284,-0.0770037621,0.183756128,0.0966400057,-0.0125526022,0.0351255648,-0.0809561312,0.1311855167,0.0802533701,-0.1056552529,0.1230914518,-0.5634173751,0.1094647199,0.002619667,0.0384143218,-0.186223343,0.1880778372,0.0646297261,-0.0047181626,0.0791275799,0.2578691542,-0.0984069183,-0.2119452506,-0.1304918379,-0.0817815512,-0.0984928384,-0.0916112587,0.0619072318,0.092382431,0.1118398234,-0.0017336064,0.2803901732,-0.0927288532,0.5385236144,0.1707926393,-0.1122141108,0.4933412373,0.2230668664,-0.0651279017,-0.2487444878,0.2211106271,-0.3083320856,0.1185796559,0.0200917516,0.1291921139,-0.0472025983,-0.0984765962,-0.2833484411,0.1163356304,0.2763032913,-0.0582993142,0.2532229424,-0.3349198103,-0.132949248,-0.1963020861,0.1043847948,-0.0451547392,-0.118641369,-0.0467285588,0.044093363,0.0888606682,0.0959444866,0.3504171371,-0.2296563685,0.3976287246,-0.1970900148,0.3188100755,0.3553226292,-0.1605848074,-0.4590785503,0.0325200707,-0.0414366685,0.2008669823,-0.4211300015,0.2581838071,0.5175717473,0.0115962904,0.1081317067,-0.0138313118,0.0394521281,0.0712760985,0.0759738982,0.0893174186,0.6063425541,-0.1942304671,0.0912254751,0.4368440509,-0.0899207368,0.1663245708,0.2264939547,-0.0353974365,-0.0143849719,0.2562342286,-0.0984062627,0.1964918971,0.0711438432,-0.1331272125,-0.3207593262,-0.0673473179,0.2024287283,0.0814435259,-0.0857608393,-0.2460781038,0.0505057834,0.1824002415,-0.1853736043,0.0724808052,0.1081486642,-0.0400793664,-0.2167155892,0.0308499429,-0.0139192659,-0.2510787547,0.1745028645,0.204618454,-0.4255838692,0.2819700539,0.1628724337,0.1375444531,-0.4352928102,0.1270138621,-0.0553200878,-0.0830142573,0.0988000557,0.0349606797,0.1280521005,0.0265865475,-0.0954571441,0.5214632154,0.2866054773,-0.2311563343,0.0751397461,0.4937567711,0.0238809288,-0.229771331,0.1961795241,0.1395006329,0.2197048515,0.3267371356,-0.4468351305,0.2423465103,0.036944773,0.2038594931,-0.3506708741,0.1286530793,-0.4682184458,0.2203543335,0.1738942266,0.1489115953,-0.1910985857,0.0657252818,0.4890259206,-0.1033508554,0.0994033739,-0.0586623512,-0.5665890574,-0.0065713013,0.2327365875,-0.2958332598,0.1299310476,-0.078820765,0.3676331341,0.0134283369,-0.0215824917,0.1092869341,0.0924237296,0.351996392,0.0202198178,-0.0760973096,0.0611620396,0.065281257,-0.3260114789,0.0977090001,-0.0819270909,0.1087761745,-0.1746973693,0.215378195,-0.2485268414,0.114612475,-0.0772436485,-0.4351750314,-0.1941461265,-0.1610156745,-0.0932937786,0.1621305048,0.0375246331,0.1928770095,0.3247022033,0.0016953748,0.3418312073,-0.225582853,-0.1251525581,-0.2449374646,-0.1197988689,0.0738311112,0.0181270093,-0.4264046252,0.083983548,-0.0697512254,-0.0664159432,-0.7368848324,-0.2431028038,0.1734049171,-0.2962734401,-0.2769447267,0.3027134836,0.2004784793,-0.3066086471,0.0971379206,-0.0186878163,0.1475964338,0.0467998534,0.0844613612,-0.1103945002,0.1160804108,-0.1308739036,-0.3476417065,-0.0094152419,-0.0166204292,0.2716757655,0.0838909224,0.1525645256,0.0774168074,0.3029705882,0.1268658638,0.0299354363,-0.1717898846,-0.1643576324,-0.2441188097,0.3611247838,-0.0520239957,-0.22654441,-0.0212237015,-0.0030592845,0.1346479952,0.5783620477,-0.3174059987,0.0344276801,-0.0420033671,0.2637937367,0.0175747406,0.1347250193,0.3775294721,0.2478036731,0.1076655388,-0.0430456065,-0.0288084429,-0.0538102761,-0.0161097497,0.3160053194,0.1498702914,0.3397210538,0.1404833645,0.6328849196,0.0862662196,-0.5870518684,-0.2141459286,-0.3498601615,0.2165563256,-0.1520948708,-0.3102630079,-0.1093504801,-0.28600806,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1046","title":"Dataset.map() turns tensors into lists?","comments":"It is expected behavior, you should set the format to `\"torch\"` as you mentioned to get pytorch tensors back.\r\nBy default datasets returns pure python objects.","body":"I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!\r\n\r\n```import datasets\r\nimport torch  \r\nfrom datasets import load_dataset                                                                                                                 \r\nprint(\"version datasets\", datasets.__version__)\r\n\r\ndataset = load_dataset(\"snli\", split='train[0:50]')  \r\n\r\ndef tokenizer_fn(example):\r\n    # actually uses a tokenizer which does something like:\r\n    return {'input_ids': torch.tensor([[0, 1, 2]])}\r\n\r\nprint(\"First item in dataset:\\n\", dataset[0])\r\ntokenized = tokenizer_fn(dataset[0])\r\nprint(\"Tokenized hyp:\\n\", tokenized)\r\ndataset_tok = dataset.map(tokenizer_fn, batched=False,\r\n        remove_columns=['label', 'premise', 'hypothesis'])\r\nprint(\"Tokenized using map:\\n\", dataset_tok[0])\r\nprint(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))\r\ndataset_tok = dataset.map(tokenizer_fn, batched=False,\r\n                          remove_columns=['label', 'premise', 'hypothesis'])\r\nprint(\"Tokenized using map:\\n\", dataset_tok[0])\r\nprint(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))\r\n```\r\n\r\nThe output is:\r\n\r\n```\r\nversion datasets 1.1.3\r\nReusing dataset snli (\/home\/tom\/.cache\/huggingface\/datasets\/snli\/plain_text\/1.0.0\/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)\r\nFirst item in dataset:\r\n {'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}\r\nTokenized hyp:\r\n {'input_ids': tensor([[0, 1, 2]])}\r\nLoading cached processed dataset at \/home\/tom\/.cache\/huggingface\/datasets\/snli\/plain_text\/1.0.0\/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c\/cache-fe38f449fe9ac46f.arrow\r\nTokenized using map:\r\n {'input_ids': [[0, 1, 2]]}\r\n<class 'torch.Tensor'> <class 'list'>\r\n```\r\n\r\nOr am I doing something wrong?\r\n","comment_length":26,"text":"Dataset.map() turns tensors into lists? \n I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!\r\n\r\n```import datasets\r\nimport torch  \r\nfrom datasets import load_dataset                                                                                                                 \r\nprint(\"version datasets\", datasets.__version__)\r\n\r\ndataset = load_dataset(\"snli\", split='train[0:50]')  \r\n\r\ndef tokenizer_fn(example):\r\n    # actually uses a tokenizer which does something like:\r\n    return {'input_ids': torch.tensor([[0, 1, 2]])}\r\n\r\nprint(\"First item in dataset:\\n\", dataset[0])\r\ntokenized = tokenizer_fn(dataset[0])\r\nprint(\"Tokenized hyp:\\n\", tokenized)\r\ndataset_tok = dataset.map(tokenizer_fn, batched=False,\r\n        remove_columns=['label', 'premise', 'hypothesis'])\r\nprint(\"Tokenized using map:\\n\", dataset_tok[0])\r\nprint(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))\r\ndataset_tok = dataset.map(tokenizer_fn, batched=False,\r\n                          remove_columns=['label', 'premise', 'hypothesis'])\r\nprint(\"Tokenized using map:\\n\", dataset_tok[0])\r\nprint(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))\r\n```\r\n\r\nThe output is:\r\n\r\n```\r\nversion datasets 1.1.3\r\nReusing dataset snli (\/home\/tom\/.cache\/huggingface\/datasets\/snli\/plain_text\/1.0.0\/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)\r\nFirst item in dataset:\r\n {'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}\r\nTokenized hyp:\r\n {'input_ids': tensor([[0, 1, 2]])}\r\nLoading cached processed dataset at \/home\/tom\/.cache\/huggingface\/datasets\/snli\/plain_text\/1.0.0\/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c\/cache-fe38f449fe9ac46f.arrow\r\nTokenized using map:\r\n {'input_ids': [[0, 1, 2]]}\r\n<class 'torch.Tensor'> <class 'list'>\r\n```\r\n\r\nOr am I doing something wrong?\r\n \n It is expected behavior, you should set the format to `\"torch\"` as you mentioned to get pytorch tensors back.\r\nBy default datasets returns pure python objects.","embeddings":[-0.0905342773,-0.1736622602,-0.1339069456,0.2130297422,0.2162035555,0.2193657011,0.4914948344,0.3712697923,0.257444948,-0.0263979062,-0.1461347193,0.680251956,-0.10030251,-0.4164583683,0.1840583086,0.0253589209,0.294393152,0.0346806832,-0.0507990569,-0.2774459422,-0.1831274182,-0.0675765797,-0.1957766712,0.0791181624,-0.1412031651,-0.1547512561,0.1446099281,-0.3113888204,-0.0627612844,-0.2368612587,0.1946785748,0.0335779414,0.1022556797,0.446719557,-0.0001160152,0.1183271185,-0.037234392,0.2079562694,-0.0176475514,-0.1034466103,0.0076182238,-0.1327908486,0.1470107734,-0.1844788641,-0.0054996279,-0.1479554027,-0.0391307659,-0.6350414753,0.179096505,0.3810082972,0.1443395168,0.2552904189,-0.1439363956,0.2130863816,0.3324943781,0.2167777121,-0.2084973902,-0.2550541461,0.1627887785,-0.0757924542,0.0668914467,0.1129236296,-0.4984409213,-0.0192023627,0.2370801419,0.2221133709,-0.1869863868,-0.5936381817,0.0203761477,0.1143103391,0.346229285,-0.3974611461,-0.1002945304,-0.1565368474,-0.2213947773,0.0423909165,-0.0511560366,0.0540001467,0.0545048267,-0.0863507465,-0.2528419197,-0.0814966038,0.0649670362,0.2210161388,-0.5532370806,0.2875820696,-0.0781859979,0.2344746888,0.1804774553,-0.0505854003,0.1507671028,-0.3792155981,0.0853365734,0.1579334438,-0.3272643685,-0.1862962246,0.1454360634,-0.4385431707,0.0561810508,-0.1962929219,0.1501737237,0.2216709554,-0.397954762,0.1758711338,0.1768113673,0.1449903399,-0.0155394245,0.680030942,-0.0505105294,-0.4166106284,-0.0770037621,0.183756128,0.0966400057,-0.0125526022,0.0351255648,-0.0809561312,0.1311855167,0.0802533701,-0.1056552529,0.1230914518,-0.5634173751,0.1094647199,0.002619667,0.0384143218,-0.186223343,0.1880778372,0.0646297261,-0.0047181626,0.0791275799,0.2578691542,-0.0984069183,-0.2119452506,-0.1304918379,-0.0817815512,-0.0984928384,-0.0916112587,0.0619072318,0.092382431,0.1118398234,-0.0017336064,0.2803901732,-0.0927288532,0.5385236144,0.1707926393,-0.1122141108,0.4933412373,0.2230668664,-0.0651279017,-0.2487444878,0.2211106271,-0.3083320856,0.1185796559,0.0200917516,0.1291921139,-0.0472025983,-0.0984765962,-0.2833484411,0.1163356304,0.2763032913,-0.0582993142,0.2532229424,-0.3349198103,-0.132949248,-0.1963020861,0.1043847948,-0.0451547392,-0.118641369,-0.0467285588,0.044093363,0.0888606682,0.0959444866,0.3504171371,-0.2296563685,0.3976287246,-0.1970900148,0.3188100755,0.3553226292,-0.1605848074,-0.4590785503,0.0325200707,-0.0414366685,0.2008669823,-0.4211300015,0.2581838071,0.5175717473,0.0115962904,0.1081317067,-0.0138313118,0.0394521281,0.0712760985,0.0759738982,0.0893174186,0.6063425541,-0.1942304671,0.0912254751,0.4368440509,-0.0899207368,0.1663245708,0.2264939547,-0.0353974365,-0.0143849719,0.2562342286,-0.0984062627,0.1964918971,0.0711438432,-0.1331272125,-0.3207593262,-0.0673473179,0.2024287283,0.0814435259,-0.0857608393,-0.2460781038,0.0505057834,0.1824002415,-0.1853736043,0.0724808052,0.1081486642,-0.0400793664,-0.2167155892,0.0308499429,-0.0139192659,-0.2510787547,0.1745028645,0.204618454,-0.4255838692,0.2819700539,0.1628724337,0.1375444531,-0.4352928102,0.1270138621,-0.0553200878,-0.0830142573,0.0988000557,0.0349606797,0.1280521005,0.0265865475,-0.0954571441,0.5214632154,0.2866054773,-0.2311563343,0.0751397461,0.4937567711,0.0238809288,-0.229771331,0.1961795241,0.1395006329,0.2197048515,0.3267371356,-0.4468351305,0.2423465103,0.036944773,0.2038594931,-0.3506708741,0.1286530793,-0.4682184458,0.2203543335,0.1738942266,0.1489115953,-0.1910985857,0.0657252818,0.4890259206,-0.1033508554,0.0994033739,-0.0586623512,-0.5665890574,-0.0065713013,0.2327365875,-0.2958332598,0.1299310476,-0.078820765,0.3676331341,0.0134283369,-0.0215824917,0.1092869341,0.0924237296,0.351996392,0.0202198178,-0.0760973096,0.0611620396,0.065281257,-0.3260114789,0.0977090001,-0.0819270909,0.1087761745,-0.1746973693,0.215378195,-0.2485268414,0.114612475,-0.0772436485,-0.4351750314,-0.1941461265,-0.1610156745,-0.0932937786,0.1621305048,0.0375246331,0.1928770095,0.3247022033,0.0016953748,0.3418312073,-0.225582853,-0.1251525581,-0.2449374646,-0.1197988689,0.0738311112,0.0181270093,-0.4264046252,0.083983548,-0.0697512254,-0.0664159432,-0.7368848324,-0.2431028038,0.1734049171,-0.2962734401,-0.2769447267,0.3027134836,0.2004784793,-0.3066086471,0.0971379206,-0.0186878163,0.1475964338,0.0467998534,0.0844613612,-0.1103945002,0.1160804108,-0.1308739036,-0.3476417065,-0.0094152419,-0.0166204292,0.2716757655,0.0838909224,0.1525645256,0.0774168074,0.3029705882,0.1268658638,0.0299354363,-0.1717898846,-0.1643576324,-0.2441188097,0.3611247838,-0.0520239957,-0.22654441,-0.0212237015,-0.0030592845,0.1346479952,0.5783620477,-0.3174059987,0.0344276801,-0.0420033671,0.2637937367,0.0175747406,0.1347250193,0.3775294721,0.2478036731,0.1076655388,-0.0430456065,-0.0288084429,-0.0538102761,-0.0161097497,0.3160053194,0.1498702914,0.3397210538,0.1404833645,0.6328849196,0.0862662196,-0.5870518684,-0.2141459286,-0.3498601615,0.2165563256,-0.1520948708,-0.3102630079,-0.1093504801,-0.28600806,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1004","title":"how large datasets are handled under the hood ","comments":"This library uses Apache Arrow under the hood to store datasets on disk.\r\nThe advantage of Apache Arrow is that it allows to memory map the dataset. This allows to load datasets bigger than memory and with almost no RAM usage. It also offers excellent I\/O speed.\r\n\r\nFor example when you access one element or one batch\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nsquad = load_dataset(\"squad\", split=\"train\")\r\nfirst_element = squad[0]\r\none_batch = squad[:8]\r\n```\r\n\r\nthen only this element\/batch is loaded in memory, while the rest of the dataset is memory mapped.","body":"Hi\r\nI want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks ","comment_length":90,"text":"how large datasets are handled under the hood  \n Hi\r\nI want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks  \n This library uses Apache Arrow under the hood to store datasets on disk.\r\nThe advantage of Apache Arrow is that it allows to memory map the dataset. This allows to load datasets bigger than memory and with almost no RAM usage. It also offers excellent I\/O speed.\r\n\r\nFor example when you access one element or one batch\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nsquad = load_dataset(\"squad\", split=\"train\")\r\nfirst_element = squad[0]\r\none_batch = squad[:8]\r\n```\r\n\r\nthen only this element\/batch is loaded in memory, while the rest of the dataset is memory mapped.","embeddings":[-0.0588864759,-0.4006500244,-0.0841394514,0.4953401983,-0.0051892721,-0.0410410501,0.2562442422,0.082638748,0.4370158017,0.1028491035,-0.1519440413,0.0303455573,-0.1056119353,-0.0217184518,0.1242289692,0.061641559,0.039550595,-0.1121024564,-0.6995661855,0.1022263393,-0.2493334413,-0.3557489812,-0.1300130635,-0.3272945881,-0.3039337397,-0.3051180542,-0.1829487383,-0.0901290253,0.0206644628,-0.2042738348,-0.0386582613,-0.0815087706,0.2003152072,0.7517547011,-0.0001189604,-0.0367895141,0.1360406429,0.0017194661,0.0376435071,0.2387624979,-0.2333405614,-0.3989806473,-0.0278325807,-0.1681381911,0.2054624259,-0.3424188793,0.0848583505,-0.616220355,0.3852132559,-0.1248321608,0.1235423461,-0.2621011436,-0.0925903544,0.2763326168,0.3111933172,0.1433324367,0.0195619334,-0.1486619264,0.6805699468,-0.0344118811,-0.2302269489,-0.1037611589,0.1673593074,-0.1966172457,0.0919266045,-0.201681003,0.1234455183,-0.3608862162,-0.0673593357,0.1288553774,0.5153530836,-0.1416386366,-0.3262237012,-0.2012563944,-0.2796247602,-0.0023358271,-0.1063352972,0.4049009085,-0.0822648257,0.1108777225,-0.3683152795,-0.3405007124,-0.3337821364,-0.0366813987,-0.3001170754,0.1452180445,-0.062937662,0.0894539431,0.1375580728,-0.2816590667,0.2963627577,-0.4750454724,0.1944923401,0.2549041808,-0.1021594629,-0.2395146191,-0.1099360362,0.0570476651,0.4679745138,0.1335092336,0.2479615808,0.1755761802,-0.2547267079,0.0770526528,0.3933933973,0.1047822088,-0.2252570242,-0.0755659118,0.4390206337,-0.359421432,-0.1419167966,-0.1065338179,-0.062414363,-0.0423801355,-0.0418439098,-0.4785649776,-0.078477487,-0.2720260918,0.2171857357,-0.0676897094,-0.1527037919,-0.1355276257,0.1241803691,0.3209927976,-0.1514753848,0.2444750816,-0.0954088345,-0.0181139726,0.0559465848,0.121969372,-0.0666993782,0.0123048499,-0.3038210869,0.1711558253,0.1640012115,0.0028020029,0.1232638881,0.2098841369,-0.0685096681,-0.0771990791,0.2681392729,-0.2920338213,0.3498936296,0.2049546391,-0.0418092422,0.1418164074,-0.072726801,0.3579365611,-0.2949612737,0.4229598641,-0.4347180128,-0.2101899087,0.4934930205,0.0568359867,-0.1956836134,0.0365932435,-0.5651928782,0.0108646322,0.1202700511,0.005338362,0.0754910111,0.1426695585,-0.0617287979,-0.3355418146,-0.0220437553,0.1639938504,-0.5725691915,0.095140487,0.0164686739,0.1323545277,0.0291997623,0.4890319407,-0.2978397012,0.0071913837,-0.1534463763,-0.178645879,0.4220184684,-0.214909941,-0.4589166343,0.4631193578,0.362381041,0.1702606231,0.2086673528,0.188361302,0.0513136126,-0.1379454285,-0.1611617357,0.6018933058,-0.1137406304,0.1777114868,-0.0230638571,0.0201796833,-0.0609261394,0.0680001155,0.0143553047,-0.2378221154,0.3191002309,-0.2142076045,0.1988714933,-0.0789626762,0.1503863782,0.1449685544,0.0309887771,0.0816588029,0.0755418167,-0.0170645695,-0.4261789918,0.2068165243,-0.1172283739,-0.3329842985,0.0282216612,-0.0069538574,0.2477512956,0.0752912015,-0.0171729643,0.1064273342,-0.1028251648,-0.1765655875,-0.2068213373,-0.0904372931,-0.2467716485,0.2182663381,0.0694199577,0.2246511132,-0.3101911545,0.1304001063,0.2957300246,-0.1103137061,-0.195648402,-0.0994968191,-0.188249588,-0.1264005005,0.0898182988,0.0821364373,0.2953771353,-0.0338168219,0.0958972201,0.3368049562,0.4200541675,-0.1791235209,0.4618538618,-0.0391623415,0.1382342428,-0.3454586565,-0.2119730264,0.3631180823,-0.2875338793,0.5229017138,0.1235602126,-0.1351252198,0.0095715178,0.0218977872,-0.0130687412,0.2025154829,0.1118588671,0.2873811722,0.6247633696,0.2490035146,0.0394603796,-0.2190214843,0.2453068048,-0.0015913476,0.2007402629,0.0249179639,-0.4273910522,-0.0144782541,0.2521755099,-0.0906086937,0.2695960104,0.2748970687,0.1174039394,-0.1638914794,0.4040957689,-0.0336422846,0.1366795748,0.1346002966,0.3476347923,-0.0030691165,0.0086476887,-0.2951585352,0.0046866145,-0.2069446892,0.0752809346,-0.1986514181,-0.1057801768,0.1440148503,-0.2291140109,-0.2856042385,-0.2744174898,-0.2355762571,-0.4793237746,-0.0293898005,-0.2364707589,0.0844094828,0.0439957567,0.0969997048,0.027675895,0.6954143643,-0.0482198,-0.1201428846,0.0571183935,-0.029637143,-0.0915001631,0.0601225682,0.4931307137,-0.2695896626,0.3946196735,0.4713744521,0.0249749385,-0.132005766,-0.0868253186,0.0459545739,0.0567706898,0.0622712336,-0.3290964961,0.1470323205,0.0561653152,0.3827188313,-0.0509234108,0.0700895265,-0.054986503,0.0320945084,0.0162867215,0.3869161904,0.201121524,-0.1517053992,-0.2286734581,-0.3820271492,0.3547494113,0.1659956127,0.1937939376,0.0476233959,0.1845691651,-0.0287558027,-0.0829225555,-0.0636627525,0.1533194631,-0.4200549126,0.4128156006,-0.1698292345,-0.0909518301,-0.0368246101,0.1147509962,0.0962599665,0.4367291033,-0.2818885148,0.1796182841,-0.2459022701,-0.1230282784,0.0438913926,0.6782795191,0.2842325568,-0.1847536266,-0.0193747841,-0.0482371226,0.0945092142,-0.2058205903,-0.2530432343,0.4445666075,0.3438854516,0.3572461307,0.1479998529,0.7315521836,0.0413678251,0.1667902768,0.1807873696,0.2659292519,0.0448410548,-0.0270311162,0.059310481,-0.2050995678,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1004","title":"how large datasets are handled under the hood ","comments":"How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.\r\n\r\nEDIT:\r\nMy fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.","body":"Hi\r\nI want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks ","comment_length":68,"text":"how large datasets are handled under the hood  \n Hi\r\nI want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks  \n How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.\r\n\r\nEDIT:\r\nMy fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.","embeddings":[-0.1215671673,-0.4438164532,-0.1115840673,0.4983528852,0.067883864,-0.1806738824,0.197150439,-0.0075913304,0.4332483113,0.0732882097,-0.1071146503,0.2367630154,-0.1338435113,0.0458660387,-0.0414698794,-0.0014341233,0.0602205321,-0.0184701513,-0.6697032452,0.06769678,-0.1225022003,-0.2739754319,-0.2129993737,-0.2707862258,-0.4327622652,-0.3156321049,-0.0948926508,-0.0439279675,-0.0171791948,-0.1848109812,0.0234301481,-0.1452039033,0.1936464906,0.7684065104,-0.0001201546,-0.1148603484,0.1988204718,0.0495708138,0.1518303752,0.4270415008,-0.2958095372,-0.3880932331,0.0579504557,-0.1263776124,0.1963099837,-0.3373782337,-0.0204911698,-0.3247038424,0.4007909596,-0.0438235365,0.1147308648,-0.3078790605,-0.0085201412,0.2554507554,0.1298278868,0.0812880397,-0.0228819046,0.0341698378,0.6602918506,0.0674037188,-0.3207933307,0.0198224075,0.3828050196,-0.0876138061,0.0810198709,-0.2135655135,0.1960029751,-0.3160541058,-0.1036865786,0.2220429629,0.5307355523,0.0745657608,-0.2137930095,-0.1574528664,-0.1965864152,-0.1126702949,0.0260423962,0.1900892407,-0.1245145053,0.0925197676,-0.4205059111,-0.384206444,-0.3212280273,-0.1058504358,-0.1592171639,0.0602541268,-0.144015789,0.0976159871,0.2512976825,-0.2444238365,0.3467597663,-0.2242797315,0.0637847334,0.2126442492,-0.3167775571,-0.3181909919,-0.3145974576,0.0072492859,0.2140951008,0.0262053572,0.2432012856,0.2331735939,-0.2770914733,0.0579361878,0.373039335,0.1893132627,-0.2583979964,-0.0292445906,0.2394621968,-0.3323382139,-0.2076711804,-0.0278383736,-0.1254138649,-0.1924134493,0.0290484224,-0.45896402,-0.3164376318,-0.22271806,0.1845161468,-0.1283962131,-0.0210293718,-0.2380319089,0.1576445848,0.3425010741,-0.1521487832,0.4732321203,-0.1909871697,-0.1226953715,0.0920948312,0.0799302161,-0.0951354131,-0.0252468791,-0.444096148,0.0891451836,0.1055520698,0.1422843188,0.1316375732,0.1794971377,-0.2031678706,-0.0553410649,0.1711278856,-0.3524627984,0.2662963271,0.085853599,-0.1850575507,0.0771692842,-0.1993013173,0.5173634291,-0.1493874937,0.3545512259,-0.6022629738,-0.2095153332,0.3533734977,0.0607273914,0.0541064776,-0.0319280885,-0.5426729321,0.061998006,0.1068783551,0.0489877872,0.1851813942,0.0778620765,0.0058571994,-0.2848335803,-0.0387132503,-0.0621048734,-0.5078706145,0.0839684904,0.0214739274,0.0379009359,0.3106988072,0.5393097401,-0.334410727,0.0553935207,-0.1138814688,-0.1326754838,0.4459807277,-0.2488920093,-0.3662179112,0.5615829825,0.21416834,0.0004851517,0.253995657,0.170931682,-0.0130625069,-0.0812172294,-0.1040080711,0.4598284662,-0.0227198713,0.1220827773,0.0063937814,-0.054606162,-0.1883065999,0.1092321277,-0.0403846428,-0.2716549933,0.2416463643,-0.208726719,0.2332989424,-0.0532250181,0.0906439573,0.1668754965,-0.017967917,-0.0557855554,0.0768469647,0.1827198118,-0.4248358607,0.1400219202,-0.1323659122,-0.4478213787,0.2587947547,0.1581343561,0.2468056828,0.0421299823,0.0565526597,0.0603841655,-0.1120461002,-0.1753149778,-0.1937236339,-0.2523959875,-0.229202196,0.2160687596,-0.0651035309,0.1158558875,-0.0814065859,0.0038226172,0.3082476854,-0.1531687677,-0.2304404527,-0.1268645078,-0.2324857861,-0.1005854234,-0.0570703186,0.158270821,0.2823875546,0.0536024198,0.2251091003,0.25341326,0.3548586965,-0.2406089157,0.3344473839,-0.065612942,0.0892825872,-0.28173244,-0.2128619254,0.1794786751,-0.3186481595,0.4223964512,0.0850994959,-0.1555410326,0.0558321327,-0.0452408716,0.0845040828,0.252423048,0.1671889126,0.1530891806,0.5587385297,0.3262904286,-0.1059882715,-0.265664041,0.2755075991,0.1176057309,0.0361893289,0.0962628946,-0.4059443772,-0.0126217231,0.3432349861,-0.0570628755,0.322545141,0.270318687,0.1609900147,-0.2113861889,0.4023603499,-0.0841270611,0.153287217,0.043371018,0.5052736402,0.1157467887,-0.0274701584,-0.3022116125,0.0172642786,-0.1175325066,0.161785394,-0.1426556259,-0.0345931686,0.0774320811,-0.1431572139,-0.15403153,-0.2296726108,-0.2347118258,-0.226842016,0.1399940699,-0.2304358035,0.0682878867,0.0375845134,0.1580303162,-0.0489989631,0.6598153114,-0.1480918676,0.0952270404,0.0034780821,0.0267199781,-0.1317080408,0.0593353435,0.357062906,-0.3354431093,0.2742570341,0.4707393646,-0.0013770198,-0.075243555,0.0400803909,0.050390631,0.165208593,-0.0751269087,-0.330455184,0.0211023819,-0.1063793525,0.2256067842,-0.0876437351,-0.1321447343,-0.1368320137,-0.0243298598,-0.0768870562,0.3532197773,0.2285236567,-0.1721686125,-0.2935732901,-0.37499246,0.5281118155,0.1634026319,0.1652739793,0.0051041623,0.2578337193,0.0530497581,-0.1767074615,-0.1510028094,0.1319056451,-0.4742539525,0.3149018586,-0.0819231942,-0.0689846054,-0.1042475328,0.1330755502,0.2088453472,0.488294512,-0.2809638083,0.1956727058,-0.2383020371,-0.1123147234,-0.0271476898,0.5863160491,0.3648005724,-0.2229548246,-0.116202265,-0.0678480715,0.1707149744,0.0376973338,-0.1658644378,0.47704795,0.2928766906,0.2151965797,0.0553326942,0.7928889394,0.0288615357,0.0240357071,0.2115834206,0.2363714278,-0.0143146822,0.0673245713,0.0543309934,-0.0071293735,-0.1185246259,0.13473294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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/1004","title":"how large datasets are handled under the hood ","comments":"> How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.\r\n\r\nLoading arrow data from disk is done with memory-mapping. This allows to load huge datasets without filling your RAM.\r\nMemory mapping is almost instantaneous and is done within one process.\r\n\r\nThen, the speed of querying examples from the dataset is I\/O bounded depending on your disk. If it's an SSD then fetching examples from the dataset will be very fast.\r\nBut since the I\/O speed of an SSD is lower than the one of RAM it's expected to be slower to fetch data from disk than from memory.\r\nStill, if you load the dataset in different processes then it can be faster but there will still be the I\/O bottleneck of the disk.\r\n\r\n> EDIT:\r\n> My fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.\r\n\r\nOk let me know if that helps !\r\n","body":"Hi\r\nI want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks ","comment_length":192,"text":"how large datasets are handled under the hood  \n Hi\r\nI want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks  \n > How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.\r\n\r\nLoading arrow data from disk is done with memory-mapping. This allows to load huge datasets without filling your RAM.\r\nMemory mapping is almost instantaneous and is done within one process.\r\n\r\nThen, the speed of querying examples from the dataset is I\/O bounded depending on your disk. If it's an SSD then fetching examples from the dataset will be very fast.\r\nBut since the I\/O speed of an SSD is lower than the one of RAM it's expected to be slower to fetch data from disk than from memory.\r\nStill, if you load the dataset in different processes then it can be faster but there will still be the I\/O bottleneck of the disk.\r\n\r\n> EDIT:\r\n> My fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.\r\n\r\nOk let me know if that helps !\r\n","embeddings":[-0.1094140559,-0.4308580458,-0.0871282145,0.4903816283,0.0192652438,-0.2002978623,0.1710616499,0.0366569124,0.4349535108,0.0388859324,-0.1141780913,0.255189091,-0.1232734472,0.0230295472,-0.0365431942,-0.0497361943,0.078658551,-0.032821767,-0.6193565726,0.1037578136,-0.1304108351,-0.2858450413,-0.1606623381,-0.2703860998,-0.374741137,-0.3324694037,-0.0884396583,-0.038149666,-0.0140343411,-0.2108242363,0.0616952069,-0.0937612951,0.2144096345,0.7233284712,-0.0001186684,-0.0918641016,0.1741971672,0.0445404537,0.0798384473,0.3599433601,-0.2897218168,-0.3927341998,0.0601457208,-0.1590089202,0.2115675211,-0.3300986886,0.0054535544,-0.406844914,0.3743906021,-0.0250430312,0.0991554707,-0.2730448246,-0.0589687005,0.2336074263,0.086818032,0.0922659338,-0.010505178,0.0626730323,0.6923478246,0.0525821187,-0.3609701991,0.0302438959,0.3175122142,0.0123824496,0.1165365651,-0.2114973217,0.1849644631,-0.2949663401,-0.1062445641,0.2516591251,0.5105578303,0.0641362891,-0.2390613556,-0.2338179797,-0.1869091243,-0.1160810515,0.029436959,0.2002780735,-0.1415206939,0.0633397698,-0.4293504357,-0.3593241572,-0.2917678356,-0.0438303649,-0.0944022983,0.0374131799,-0.1127544418,0.1373373419,0.2806222737,-0.2216241658,0.3802333474,-0.3126363456,0.1021855101,0.1682952344,-0.34418872,-0.2493542582,-0.3483252227,-0.0298017878,0.262118876,0.0415903516,0.2865476608,0.2560191751,-0.2070029378,0.0754499957,0.3970377147,0.1765200049,-0.2426723391,0.006630295,0.1881208271,-0.2431785613,-0.189987272,0.0183487572,-0.1382554471,-0.1635321677,0.0175347459,-0.4711621404,-0.3381839097,-0.1440287977,0.1884435117,-0.1743113846,-0.0635789186,-0.2412644923,0.1829616427,0.3198435605,-0.1800623834,0.4577937722,-0.1897554398,-0.0614738278,0.0486692786,0.0233896598,-0.0542968847,-0.0407357588,-0.4293087721,0.1759207398,0.1483029872,0.1976806521,0.1073697433,0.1569950879,-0.2365114242,-0.0819059536,0.1956626773,-0.3922347426,0.2660459876,0.0276158527,-0.1685373634,0.114251256,-0.1680119485,0.4740410149,-0.1923685223,0.374212712,-0.5957955718,-0.1878307909,0.3516031504,0.0461341552,-0.0175210331,-0.0504430383,-0.6256650686,0.1540378779,0.1236738339,0.0126045831,0.1617602557,0.0536949225,-0.0882831141,-0.2674564421,0.0252437443,-0.0110351825,-0.4720745087,0.0524091311,-0.00689208,0.1041392237,0.2835587561,0.5517659187,-0.4106472433,0.1135390922,-0.1192628443,-0.1173923016,0.417812109,-0.2137856632,-0.4303643107,0.5667306781,0.209075138,0.1083019972,0.2623871565,0.1652846038,0.0832058266,-0.0930705816,-0.059861958,0.4544083178,0.0080702081,0.1396607757,-0.0041866316,-0.0941515863,-0.159570545,0.1432994306,-0.0489722975,-0.2116727382,0.1907656938,-0.2539235055,0.2201652676,-0.0919652134,0.1123252735,0.1764955819,-0.0526342914,-0.0457840785,0.0468089059,0.2086643279,-0.4808402658,0.2109634727,-0.1099825725,-0.40636608,0.2123639286,0.19595927,0.2658372223,0.0164994448,0.0127253914,0.0896878764,-0.1028103232,-0.1301660538,-0.1081871614,-0.2483939976,-0.1687362045,0.2720019519,-0.0546696186,0.0887358636,-0.1681142151,0.0489379764,0.3127925098,-0.1338700354,-0.231592834,-0.1520959586,-0.2393630892,-0.1030456945,-0.0203980822,0.1859755516,0.23611027,0.0516227223,0.1734180748,0.33000651,0.3617977202,-0.2407522947,0.331685245,-0.059569817,0.1335672885,-0.2932128012,-0.1862156838,0.205185473,-0.2873652577,0.4769780338,0.0904592946,-0.1368916482,0.0462920889,-0.0474574305,0.0434842855,0.256268084,0.1648508459,0.1016660333,0.5824890137,0.292388469,-0.1364488006,-0.287222743,0.2512176037,0.1232348084,0.0444282554,0.1085529923,-0.3892177343,-0.0254145283,0.3360786438,-0.0132308826,0.3325226903,0.2566536963,0.1349617839,-0.2326954603,0.351439476,-0.0757604092,0.1290671974,0.1013587564,0.5716062188,0.0419178791,-0.0346123725,-0.3070819676,0.0590891354,-0.179525584,0.1923035085,-0.1513794363,-0.0766243339,0.1248512045,-0.125127703,-0.1016531289,-0.2586200833,-0.2021533549,-0.2168625146,0.1597631723,-0.2158708721,0.1099507287,0.0296053234,0.1242319793,-0.0525167212,0.5798897147,-0.1888747811,-0.0299105234,0.0334783942,-0.0032975054,-0.1046979055,0.0513123013,0.4138850868,-0.2695017457,0.2428399622,0.403031826,0.0128974235,-0.0930936933,0.0647097081,0.0429363176,0.1651882082,-0.0084680505,-0.3586981595,0.0244836155,-0.1535338759,0.1645013243,-0.0560683608,-0.1228211522,-0.0865571648,-0.0029996345,-0.0506532155,0.3680099249,0.2494110614,-0.1850355566,-0.2905768454,-0.3868479431,0.4570654631,0.1317229271,0.2138234079,-0.0320347212,0.313857168,0.0434479751,-0.1027893052,-0.0727140978,0.1427296996,-0.4498758912,0.3469192982,-0.0438542403,-0.1049363017,-0.1490645409,0.0617099963,0.1873238981,0.5508527756,-0.3123829961,0.2569415569,-0.2491494268,-0.1047134995,-0.0540878586,0.5024775267,0.3338576555,-0.2245719731,-0.0651558042,-0.0494262464,0.1075888574,0.0588983372,-0.1344208568,0.4623301029,0.362972647,0.207198292,0.1127268076,0.8515409231,0.0474615395,-0.0225949753,0.2076239735,0.2847183943,-0.0164002404,0.0514184609,0.0425158739,-0.0239238963,-0.1738159508,0.1217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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/996","title":"NotADirectoryError while loading the CNN\/Dailymail dataset","comments":"Looks like the google drive download failed.\r\nI'm getting a `Google Drive - Quota exceeded` error while looking at the downloaded file.\r\n\r\nWe should consider finding a better host than google drive for this dataset imo\r\nrelated : #873 #864 ","body":"\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'","comment_length":40,"text":"NotADirectoryError while loading the CNN\/Dailymail dataset \n \r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories' \n Looks like the google drive download failed.\r\nI'm getting a `Google Drive - Quota exceeded` error while looking at the downloaded file.\r\n\r\nWe should consider finding a better host than google drive for this dataset imo\r\nrelated : #873 #864 ","embeddings":[-0.1502866894,0.013875572,0.0027152719,0.2634353042,0.4774574339,0.0598452203,0.4901823997,0.3076288998,-0.2510559261,0.2567766011,-0.3245876431,-0.0494811945,-0.4837743342,-0.1979143322,0.0145921567,0.0012005472,-0.1768816113,0.1828713715,-0.1539244652,-0.0876947716,-0.1727184951,0.321044147,-0.0058411579,0.1479859501,-0.5002063513,-0.1975877583,-0.0039216476,0.5093349218,-0.1271757334,-0.2311133146,0.2591030598,-0.2564248741,0.0552018695,0.7316402197,-0.0001171105,0.3691133261,0.3056399524,-0.0785469413,-0.5518268347,-0.4986005127,0.2059125155,-0.2117751688,0.0436902605,-0.2601816654,-0.1276563704,0.0635365993,0.1162231341,0.314124018,0.3930333555,0.4601137042,0.2285237312,0.2302971035,0.2858363986,0.0685345754,-0.1296327561,0.3916880488,-0.1658728719,0.5677597523,-0.4966021776,0.1812898517,0.0389438532,0.305713594,-0.1901411414,-0.0401899554,0.0673795491,0.0520442091,0.2945782244,-0.4586854279,0.0862284899,0.1943815053,0.2952353358,-0.3140712976,-0.352039814,-0.0179468803,0.1240581274,-0.3361694813,0.2545648515,0.2885335088,-0.2310854793,0.0986495689,-0.1670768112,-0.1167311221,-0.3022927642,0.4389296472,-0.1841000915,0.2932372689,-0.1138128117,0.0412949026,0.1631835103,-0.1387998015,0.1639585048,-0.0754344985,0.3370934725,0.0137136988,0.1517517567,-0.1500609815,0.0298330132,-0.2463437021,-0.1883456111,0.1450426131,-0.2204875052,-0.3060177863,-0.0318602733,0.0074542542,-0.0541749112,0.0959956795,-0.1966276169,0.6701014638,0.2965373397,0.5027940869,0.032012783,-0.0403410904,-0.1909714937,-0.2045384943,-0.0870549977,-0.0306533463,0.6625394821,-0.0383561254,-0.3331961334,-0.0131454738,-0.4197606444,0.0783914253,0.3010311425,0.5060918927,0.0743204281,-0.0245896969,0.0031736968,0.1511423886,-0.0656605288,0.3835736215,-0.2604818046,0.3102358878,-0.1780570149,-0.0055621727,0.3036935627,-0.3783838153,0.2278976589,-0.0782359242,0.1879086792,-0.1120337173,0.0400563292,-0.1671946496,-0.0153000206,0.6389673948,-0.3143552542,0.122172676,0.2448890358,-0.0823115781,-0.2242325693,0.1375702471,-0.1204497367,-0.5571912527,0.0483228825,0.0999315456,0.1062155142,-0.0634725392,0.3532803357,-0.3217015862,0.0260888226,-0.1006018892,0.0767360628,-0.0643335655,-0.1164478585,-0.1373271048,0.3215051293,0.5865563154,-0.1676753461,-0.1125146374,-0.1753574014,-0.1381154209,0.2370170057,0.4046929777,-0.0672517568,0.2623784244,-0.283016324,-0.2667835057,0.3034884334,-0.3377234638,-0.4008953273,0.0308544394,-0.2147400379,0.2598990798,0.0527489483,0.1346762031,-0.3762584031,-0.0294961911,0.0617878698,0.1520974636,-0.0357308947,0.0904737264,-0.2340044081,-0.1187213138,0.087981984,0.3039717376,0.4382177889,0.0894154683,0.0313502997,0.2226996422,0.0503849052,-0.2450427115,-0.0719153062,0.3450480998,0.4285292625,-0.0693802163,-0.1211159676,0.1079738438,-0.3771114051,0.0220831204,-0.0822666511,0.3187367916,-0.2467507422,0.0032257261,-0.345186919,-0.1285056919,-0.2752381563,-0.1792377532,0.1339013726,0.1903796345,0.0196383093,0.2382143587,-0.214954406,0.2647375464,-0.1341636926,0.3000068367,-0.328188777,0.3138391674,-0.2469952852,-0.0395054705,-0.0923118144,0.4096641243,0.1486414522,-0.2167054117,-0.063395448,0.2275290042,-0.0740630478,-0.0576760061,0.0219573081,-0.0339813121,0.2657676935,-0.0904394239,0.1729007512,0.4206444323,0.0139150154,0.0184680186,-0.035002321,0.4468706846,0.0479335338,-0.0523554198,0.1870311946,-0.0533488803,0.0944574773,-0.0551234968,-0.0113500115,-0.0475617275,0.2278446257,-0.1467844099,0.5949158072,-0.0756230205,-0.2936546504,-0.498267144,-0.017299084,-0.2320438623,0.1143534184,0.2787705362,-0.2667388916,0.0841669738,-0.181010142,0.181104362,0.475499481,0.056991227,-0.0085043851,0.1831954718,-0.1056299657,-0.1482228041,0.3001134098,0.122813046,0.0768738464,0.2936602235,0.0724526346,0.0703333616,-0.5287972093,-0.3453274369,0.0061527174,0.3515328765,-0.3705409467,-0.1018935218,-0.1132583693,-0.2290444076,-0.031764362,-0.4700891078,-0.2261396796,-0.1386552751,0.0434038565,0.3248308003,0.0040019695,0.164022252,-0.02154289,-0.0796546414,0.1637674123,-0.1426153183,-0.225755021,0.1100877151,0.1099090129,0.0368247889,0.4638560116,-0.3609945476,0.245059073,-0.0506406166,-0.0929013863,-0.3026795685,-0.4553116262,-0.065082185,0.0196095668,0.263384372,0.1738888323,0.353010118,-0.1059392542,-0.3259163201,0.2694613934,0.1050129607,-0.0333887413,0.4007444382,-0.2799060941,0.0285295025,-0.030528551,-0.3856055737,-0.4965451062,-0.2660867572,0.0952375159,-0.0068388553,0.1661554724,0.6047119498,0.1591120362,0.1782280058,-0.2618115246,0.173198998,-0.0134630306,-0.1314901114,0.3829728365,-0.3403701782,-0.2306490541,-0.0014361457,-0.066587925,0.2421359271,-0.313904047,-0.5207077265,-0.2967836559,-0.0093218395,-0.2617911696,-0.0441968888,-0.1472934484,0.1218888909,-0.0831382871,-0.0681381524,0.2673321962,-0.1342415065,-0.1002488732,0.0882345513,0.0051644589,-0.2591240108,0.4475921392,-0.0936799422,0.7807009816,0.096185118,0.0188385956,0.4052089155,-0.016006697,0.4341351092,-0.2269287109,-0.4035912752,0.0421542041,0.35869300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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/996","title":"NotADirectoryError while loading the CNN\/Dailymail dataset","comments":"It is working now, thank you. \r\n\r\nShould I leave this issue open to address the Quota-exceeded error?","body":"\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'","comment_length":17,"text":"NotADirectoryError while loading the CNN\/Dailymail dataset \n \r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories' \n It is working now, thank you. \r\n\r\nShould I leave this issue open to address the Quota-exceeded error?","embeddings":[-0.1502866894,0.013875572,0.0027152719,0.2634353042,0.4774574339,0.0598452203,0.4901823997,0.3076288998,-0.2510559261,0.2567766011,-0.3245876431,-0.0494811945,-0.4837743342,-0.1979143322,0.0145921567,0.0012005472,-0.1768816113,0.1828713715,-0.1539244652,-0.0876947716,-0.1727184951,0.321044147,-0.0058411579,0.1479859501,-0.5002063513,-0.1975877583,-0.0039216476,0.5093349218,-0.1271757334,-0.2311133146,0.2591030598,-0.2564248741,0.0552018695,0.7316402197,-0.0001171105,0.3691133261,0.3056399524,-0.0785469413,-0.5518268347,-0.4986005127,0.2059125155,-0.2117751688,0.0436902605,-0.2601816654,-0.1276563704,0.0635365993,0.1162231341,0.314124018,0.3930333555,0.4601137042,0.2285237312,0.2302971035,0.2858363986,0.0685345754,-0.1296327561,0.3916880488,-0.1658728719,0.5677597523,-0.4966021776,0.1812898517,0.0389438532,0.305713594,-0.1901411414,-0.0401899554,0.0673795491,0.0520442091,0.2945782244,-0.4586854279,0.0862284899,0.1943815053,0.2952353358,-0.3140712976,-0.352039814,-0.0179468803,0.1240581274,-0.3361694813,0.2545648515,0.2885335088,-0.2310854793,0.0986495689,-0.1670768112,-0.1167311221,-0.3022927642,0.4389296472,-0.1841000915,0.2932372689,-0.1138128117,0.0412949026,0.1631835103,-0.1387998015,0.1639585048,-0.0754344985,0.3370934725,0.0137136988,0.1517517567,-0.1500609815,0.0298330132,-0.2463437021,-0.1883456111,0.1450426131,-0.2204875052,-0.3060177863,-0.0318602733,0.0074542542,-0.0541749112,0.0959956795,-0.1966276169,0.6701014638,0.2965373397,0.5027940869,0.032012783,-0.0403410904,-0.1909714937,-0.2045384943,-0.0870549977,-0.0306533463,0.6625394821,-0.0383561254,-0.3331961334,-0.0131454738,-0.4197606444,0.0783914253,0.3010311425,0.5060918927,0.0743204281,-0.0245896969,0.0031736968,0.1511423886,-0.0656605288,0.3835736215,-0.2604818046,0.3102358878,-0.1780570149,-0.0055621727,0.3036935627,-0.3783838153,0.2278976589,-0.0782359242,0.1879086792,-0.1120337173,0.0400563292,-0.1671946496,-0.0153000206,0.6389673948,-0.3143552542,0.122172676,0.2448890358,-0.0823115781,-0.2242325693,0.1375702471,-0.1204497367,-0.5571912527,0.0483228825,0.0999315456,0.1062155142,-0.0634725392,0.3532803357,-0.3217015862,0.0260888226,-0.1006018892,0.0767360628,-0.0643335655,-0.1164478585,-0.1373271048,0.3215051293,0.5865563154,-0.1676753461,-0.1125146374,-0.1753574014,-0.1381154209,0.2370170057,0.4046929777,-0.0672517568,0.2623784244,-0.283016324,-0.2667835057,0.3034884334,-0.3377234638,-0.4008953273,0.0308544394,-0.2147400379,0.2598990798,0.0527489483,0.1346762031,-0.3762584031,-0.0294961911,0.0617878698,0.1520974636,-0.0357308947,0.0904737264,-0.2340044081,-0.1187213138,0.087981984,0.3039717376,0.4382177889,0.0894154683,0.0313502997,0.2226996422,0.0503849052,-0.2450427115,-0.0719153062,0.3450480998,0.4285292625,-0.0693802163,-0.1211159676,0.1079738438,-0.3771114051,0.0220831204,-0.0822666511,0.3187367916,-0.2467507422,0.0032257261,-0.345186919,-0.1285056919,-0.2752381563,-0.1792377532,0.1339013726,0.1903796345,0.0196383093,0.2382143587,-0.214954406,0.2647375464,-0.1341636926,0.3000068367,-0.328188777,0.3138391674,-0.2469952852,-0.0395054705,-0.0923118144,0.4096641243,0.1486414522,-0.2167054117,-0.063395448,0.2275290042,-0.0740630478,-0.0576760061,0.0219573081,-0.0339813121,0.2657676935,-0.0904394239,0.1729007512,0.4206444323,0.0139150154,0.0184680186,-0.035002321,0.4468706846,0.0479335338,-0.0523554198,0.1870311946,-0.0533488803,0.0944574773,-0.0551234968,-0.0113500115,-0.0475617275,0.2278446257,-0.1467844099,0.5949158072,-0.0756230205,-0.2936546504,-0.498267144,-0.017299084,-0.2320438623,0.1143534184,0.2787705362,-0.2667388916,0.0841669738,-0.181010142,0.181104362,0.475499481,0.056991227,-0.0085043851,0.1831954718,-0.1056299657,-0.1482228041,0.3001134098,0.122813046,0.0768738464,0.2936602235,0.0724526346,0.0703333616,-0.5287972093,-0.3453274369,0.0061527174,0.3515328765,-0.3705409467,-0.1018935218,-0.1132583693,-0.2290444076,-0.031764362,-0.4700891078,-0.2261396796,-0.1386552751,0.0434038565,0.3248308003,0.0040019695,0.164022252,-0.02154289,-0.0796546414,0.1637674123,-0.1426153183,-0.225755021,0.1100877151,0.1099090129,0.0368247889,0.4638560116,-0.3609945476,0.245059073,-0.0506406166,-0.0929013863,-0.3026795685,-0.4553116262,-0.065082185,0.0196095668,0.263384372,0.1738888323,0.353010118,-0.1059392542,-0.3259163201,0.2694613934,0.1050129607,-0.0333887413,0.4007444382,-0.2799060941,0.0285295025,-0.030528551,-0.3856055737,-0.4965451062,-0.2660867572,0.0952375159,-0.0068388553,0.1661554724,0.6047119498,0.1591120362,0.1782280058,-0.2618115246,0.173198998,-0.0134630306,-0.1314901114,0.3829728365,-0.3403701782,-0.2306490541,-0.0014361457,-0.066587925,0.2421359271,-0.313904047,-0.5207077265,-0.2967836559,-0.0093218395,-0.2617911696,-0.0441968888,-0.1472934484,0.1218888909,-0.0831382871,-0.0681381524,0.2673321962,-0.1342415065,-0.1002488732,0.0882345513,0.0051644589,-0.2591240108,0.4475921392,-0.0936799422,0.7807009816,0.096185118,0.0188385956,0.4052089155,-0.016006697,0.4341351092,-0.2269287109,-0.4035912752,0.0421542041,0.35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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/996","title":"NotADirectoryError while loading the CNN\/Dailymail dataset","comments":"I've looked into it and couldn't find a solution. This looks like a Google Drive limitation..\r\nPlease try to use other hosts when possible","body":"\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'","comment_length":24,"text":"NotADirectoryError while loading the CNN\/Dailymail dataset \n \r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories' \n I've looked into it and couldn't find a solution. This looks like a Google Drive limitation..\r\nPlease try to use other hosts when possible","embeddings":[-0.1502866894,0.013875572,0.0027152719,0.2634353042,0.4774574339,0.0598452203,0.4901823997,0.3076288998,-0.2510559261,0.2567766011,-0.3245876431,-0.0494811945,-0.4837743342,-0.1979143322,0.0145921567,0.0012005472,-0.1768816113,0.1828713715,-0.1539244652,-0.0876947716,-0.1727184951,0.321044147,-0.0058411579,0.1479859501,-0.5002063513,-0.1975877583,-0.0039216476,0.5093349218,-0.1271757334,-0.2311133146,0.2591030598,-0.2564248741,0.0552018695,0.7316402197,-0.0001171105,0.3691133261,0.3056399524,-0.0785469413,-0.5518268347,-0.4986005127,0.2059125155,-0.2117751688,0.0436902605,-0.2601816654,-0.1276563704,0.0635365993,0.1162231341,0.314124018,0.3930333555,0.4601137042,0.2285237312,0.2302971035,0.2858363986,0.0685345754,-0.1296327561,0.3916880488,-0.1658728719,0.5677597523,-0.4966021776,0.1812898517,0.0389438532,0.305713594,-0.1901411414,-0.0401899554,0.0673795491,0.0520442091,0.2945782244,-0.4586854279,0.0862284899,0.1943815053,0.2952353358,-0.3140712976,-0.352039814,-0.0179468803,0.1240581274,-0.3361694813,0.2545648515,0.2885335088,-0.2310854793,0.0986495689,-0.1670768112,-0.1167311221,-0.3022927642,0.4389296472,-0.1841000915,0.2932372689,-0.1138128117,0.0412949026,0.1631835103,-0.1387998015,0.1639585048,-0.0754344985,0.3370934725,0.0137136988,0.1517517567,-0.1500609815,0.0298330132,-0.2463437021,-0.1883456111,0.1450426131,-0.2204875052,-0.3060177863,-0.0318602733,0.0074542542,-0.0541749112,0.0959956795,-0.1966276169,0.6701014638,0.2965373397,0.5027940869,0.032012783,-0.0403410904,-0.1909714937,-0.2045384943,-0.0870549977,-0.0306533463,0.6625394821,-0.0383561254,-0.3331961334,-0.0131454738,-0.4197606444,0.0783914253,0.3010311425,0.5060918927,0.0743204281,-0.0245896969,0.0031736968,0.1511423886,-0.0656605288,0.3835736215,-0.2604818046,0.3102358878,-0.1780570149,-0.0055621727,0.3036935627,-0.3783838153,0.2278976589,-0.0782359242,0.1879086792,-0.1120337173,0.0400563292,-0.1671946496,-0.0153000206,0.6389673948,-0.3143552542,0.122172676,0.2448890358,-0.0823115781,-0.2242325693,0.1375702471,-0.1204497367,-0.5571912527,0.0483228825,0.0999315456,0.1062155142,-0.0634725392,0.3532803357,-0.3217015862,0.0260888226,-0.1006018892,0.0767360628,-0.0643335655,-0.1164478585,-0.1373271048,0.3215051293,0.5865563154,-0.1676753461,-0.1125146374,-0.1753574014,-0.1381154209,0.2370170057,0.4046929777,-0.0672517568,0.2623784244,-0.283016324,-0.2667835057,0.3034884334,-0.3377234638,-0.4008953273,0.0308544394,-0.2147400379,0.2598990798,0.0527489483,0.1346762031,-0.3762584031,-0.0294961911,0.0617878698,0.1520974636,-0.0357308947,0.0904737264,-0.2340044081,-0.1187213138,0.087981984,0.3039717376,0.4382177889,0.0894154683,0.0313502997,0.2226996422,0.0503849052,-0.2450427115,-0.0719153062,0.3450480998,0.4285292625,-0.0693802163,-0.1211159676,0.1079738438,-0.3771114051,0.0220831204,-0.0822666511,0.3187367916,-0.2467507422,0.0032257261,-0.345186919,-0.1285056919,-0.2752381563,-0.1792377532,0.1339013726,0.1903796345,0.0196383093,0.2382143587,-0.214954406,0.2647375464,-0.1341636926,0.3000068367,-0.328188777,0.3138391674,-0.2469952852,-0.0395054705,-0.0923118144,0.4096641243,0.1486414522,-0.2167054117,-0.063395448,0.2275290042,-0.0740630478,-0.0576760061,0.0219573081,-0.0339813121,0.2657676935,-0.0904394239,0.1729007512,0.4206444323,0.0139150154,0.0184680186,-0.035002321,0.4468706846,0.0479335338,-0.0523554198,0.1870311946,-0.0533488803,0.0944574773,-0.0551234968,-0.0113500115,-0.0475617275,0.2278446257,-0.1467844099,0.5949158072,-0.0756230205,-0.2936546504,-0.498267144,-0.017299084,-0.2320438623,0.1143534184,0.2787705362,-0.2667388916,0.0841669738,-0.181010142,0.181104362,0.475499481,0.056991227,-0.0085043851,0.1831954718,-0.1056299657,-0.1482228041,0.3001134098,0.122813046,0.0768738464,0.2936602235,0.0724526346,0.0703333616,-0.5287972093,-0.3453274369,0.0061527174,0.3515328765,-0.3705409467,-0.1018935218,-0.1132583693,-0.2290444076,-0.031764362,-0.4700891078,-0.2261396796,-0.1386552751,0.0434038565,0.3248308003,0.0040019695,0.164022252,-0.02154289,-0.0796546414,0.1637674123,-0.1426153183,-0.225755021,0.1100877151,0.1099090129,0.0368247889,0.4638560116,-0.3609945476,0.245059073,-0.0506406166,-0.0929013863,-0.3026795685,-0.4553116262,-0.065082185,0.0196095668,0.263384372,0.1738888323,0.353010118,-0.1059392542,-0.3259163201,0.2694613934,0.1050129607,-0.0333887413,0.4007444382,-0.2799060941,0.0285295025,-0.030528551,-0.3856055737,-0.4965451062,-0.2660867572,0.0952375159,-0.0068388553,0.1661554724,0.6047119498,0.1591120362,0.1782280058,-0.2618115246,0.173198998,-0.0134630306,-0.1314901114,0.3829728365,-0.3403701782,-0.2306490541,-0.0014361457,-0.066587925,0.2421359271,-0.313904047,-0.5207077265,-0.2967836559,-0.0093218395,-0.2617911696,-0.0441968888,-0.1472934484,0.1218888909,-0.0831382871,-0.0681381524,0.2673321962,-0.1342415065,-0.1002488732,0.0882345513,0.0051644589,-0.2591240108,0.4475921392,-0.0936799422,0.7807009816,0.096185118,0.0188385956,0.4052089155,-0.016006697,0.4341351092,-0.2269287109,-0.4035912752,0.0421542041,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/996","title":"NotADirectoryError while loading the CNN\/Dailymail dataset","comments":"The original links are google drive links.  Would it be feasible for HF to maintain their own servers for this? Also, I think the same issue must also exist with TFDS.","body":"\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'","comment_length":31,"text":"NotADirectoryError while loading the CNN\/Dailymail dataset \n \r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories' \n The original links are google drive links.  Would it be feasible for HF to maintain their own servers for this? Also, I think the same issue must also exist with TFDS.","embeddings":[-0.1502866894,0.013875572,0.0027152719,0.2634353042,0.4774574339,0.0598452203,0.4901823997,0.3076288998,-0.2510559261,0.2567766011,-0.3245876431,-0.0494811945,-0.4837743342,-0.1979143322,0.0145921567,0.0012005472,-0.1768816113,0.1828713715,-0.1539244652,-0.0876947716,-0.1727184951,0.321044147,-0.0058411579,0.1479859501,-0.5002063513,-0.1975877583,-0.0039216476,0.5093349218,-0.1271757334,-0.2311133146,0.2591030598,-0.2564248741,0.0552018695,0.7316402197,-0.0001171105,0.3691133261,0.3056399524,-0.0785469413,-0.5518268347,-0.4986005127,0.2059125155,-0.2117751688,0.0436902605,-0.2601816654,-0.1276563704,0.0635365993,0.1162231341,0.314124018,0.3930333555,0.4601137042,0.2285237312,0.2302971035,0.2858363986,0.0685345754,-0.1296327561,0.3916880488,-0.1658728719,0.5677597523,-0.4966021776,0.1812898517,0.0389438532,0.305713594,-0.1901411414,-0.0401899554,0.0673795491,0.0520442091,0.2945782244,-0.4586854279,0.0862284899,0.1943815053,0.2952353358,-0.3140712976,-0.352039814,-0.0179468803,0.1240581274,-0.3361694813,0.2545648515,0.2885335088,-0.2310854793,0.0986495689,-0.1670768112,-0.1167311221,-0.3022927642,0.4389296472,-0.1841000915,0.2932372689,-0.1138128117,0.0412949026,0.1631835103,-0.1387998015,0.1639585048,-0.0754344985,0.3370934725,0.0137136988,0.1517517567,-0.1500609815,0.0298330132,-0.2463437021,-0.1883456111,0.1450426131,-0.2204875052,-0.3060177863,-0.0318602733,0.0074542542,-0.0541749112,0.0959956795,-0.1966276169,0.6701014638,0.2965373397,0.5027940869,0.032012783,-0.0403410904,-0.1909714937,-0.2045384943,-0.0870549977,-0.0306533463,0.6625394821,-0.0383561254,-0.3331961334,-0.0131454738,-0.4197606444,0.0783914253,0.3010311425,0.5060918927,0.0743204281,-0.0245896969,0.0031736968,0.1511423886,-0.0656605288,0.3835736215,-0.2604818046,0.3102358878,-0.1780570149,-0.0055621727,0.3036935627,-0.3783838153,0.2278976589,-0.0782359242,0.1879086792,-0.1120337173,0.0400563292,-0.1671946496,-0.0153000206,0.6389673948,-0.3143552542,0.122172676,0.2448890358,-0.0823115781,-0.2242325693,0.1375702471,-0.1204497367,-0.5571912527,0.0483228825,0.0999315456,0.1062155142,-0.0634725392,0.3532803357,-0.3217015862,0.0260888226,-0.1006018892,0.0767360628,-0.0643335655,-0.1164478585,-0.1373271048,0.3215051293,0.5865563154,-0.1676753461,-0.1125146374,-0.1753574014,-0.1381154209,0.2370170057,0.4046929777,-0.0672517568,0.2623784244,-0.283016324,-0.2667835057,0.3034884334,-0.3377234638,-0.4008953273,0.0308544394,-0.2147400379,0.2598990798,0.0527489483,0.1346762031,-0.3762584031,-0.0294961911,0.0617878698,0.1520974636,-0.0357308947,0.0904737264,-0.2340044081,-0.1187213138,0.087981984,0.3039717376,0.4382177889,0.0894154683,0.0313502997,0.2226996422,0.0503849052,-0.2450427115,-0.0719153062,0.3450480998,0.4285292625,-0.0693802163,-0.1211159676,0.1079738438,-0.3771114051,0.0220831204,-0.0822666511,0.3187367916,-0.2467507422,0.0032257261,-0.345186919,-0.1285056919,-0.2752381563,-0.1792377532,0.1339013726,0.1903796345,0.0196383093,0.2382143587,-0.214954406,0.2647375464,-0.1341636926,0.3000068367,-0.328188777,0.3138391674,-0.2469952852,-0.0395054705,-0.0923118144,0.4096641243,0.1486414522,-0.2167054117,-0.063395448,0.2275290042,-0.0740630478,-0.0576760061,0.0219573081,-0.0339813121,0.2657676935,-0.0904394239,0.1729007512,0.4206444323,0.0139150154,0.0184680186,-0.035002321,0.4468706846,0.0479335338,-0.0523554198,0.1870311946,-0.0533488803,0.0944574773,-0.0551234968,-0.0113500115,-0.0475617275,0.2278446257,-0.1467844099,0.5949158072,-0.0756230205,-0.2936546504,-0.498267144,-0.017299084,-0.2320438623,0.1143534184,0.2787705362,-0.2667388916,0.0841669738,-0.181010142,0.181104362,0.475499481,0.056991227,-0.0085043851,0.1831954718,-0.1056299657,-0.1482228041,0.3001134098,0.122813046,0.0768738464,0.2936602235,0.0724526346,0.0703333616,-0.5287972093,-0.3453274369,0.0061527174,0.3515328765,-0.3705409467,-0.1018935218,-0.1132583693,-0.2290444076,-0.031764362,-0.4700891078,-0.2261396796,-0.1386552751,0.0434038565,0.3248308003,0.0040019695,0.164022252,-0.02154289,-0.0796546414,0.1637674123,-0.1426153183,-0.225755021,0.1100877151,0.1099090129,0.0368247889,0.4638560116,-0.3609945476,0.245059073,-0.0506406166,-0.0929013863,-0.3026795685,-0.4553116262,-0.065082185,0.0196095668,0.263384372,0.1738888323,0.353010118,-0.1059392542,-0.3259163201,0.2694613934,0.1050129607,-0.0333887413,0.4007444382,-0.2799060941,0.0285295025,-0.030528551,-0.3856055737,-0.4965451062,-0.2660867572,0.0952375159,-0.0068388553,0.1661554724,0.6047119498,0.1591120362,0.1782280058,-0.2618115246,0.173198998,-0.0134630306,-0.1314901114,0.3829728365,-0.3403701782,-0.2306490541,-0.0014361457,-0.066587925,0.2421359271,-0.313904047,-0.5207077265,-0.2967836559,-0.0093218395,-0.2617911696,-0.0441968888,-0.1472934484,0.1218888909,-0.0831382871,-0.0681381524,0.2673321962,-0.1342415065,-0.1002488732,0.0882345513,0.0051644589,-0.2591240108,0.4475921392,-0.0936799422,0.7807009816,0.096185118,0.0188385956,0.4052089155,-0.016006697,0.4341351092,-0.2269287109,-0.4035912752,0.0421542041,0.358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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/996","title":"NotADirectoryError while loading the CNN\/Dailymail dataset","comments":"It's possible to host data on our side but we should ask the authors. TFDS has the same issue and doesn't have a solution either afaik.\r\nOtherwise you can use the google drive link, but it it's not that convenient because of this quota issue.","body":"\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'","comment_length":45,"text":"NotADirectoryError while loading the CNN\/Dailymail dataset \n \r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories' \n It's possible to host data on our side but we should ask the authors. TFDS has the same issue and doesn't have a solution either afaik.\r\nOtherwise you can use the google drive link, but it it's not that convenient because of this quota issue.","embeddings":[-0.1502866894,0.013875572,0.0027152719,0.2634353042,0.4774574339,0.0598452203,0.4901823997,0.3076288998,-0.2510559261,0.2567766011,-0.3245876431,-0.0494811945,-0.4837743342,-0.1979143322,0.0145921567,0.0012005472,-0.1768816113,0.1828713715,-0.1539244652,-0.0876947716,-0.1727184951,0.321044147,-0.0058411579,0.1479859501,-0.5002063513,-0.1975877583,-0.0039216476,0.5093349218,-0.1271757334,-0.2311133146,0.2591030598,-0.2564248741,0.0552018695,0.7316402197,-0.0001171105,0.3691133261,0.3056399524,-0.0785469413,-0.5518268347,-0.4986005127,0.2059125155,-0.2117751688,0.0436902605,-0.2601816654,-0.1276563704,0.0635365993,0.1162231341,0.314124018,0.3930333555,0.4601137042,0.2285237312,0.2302971035,0.2858363986,0.0685345754,-0.1296327561,0.3916880488,-0.1658728719,0.5677597523,-0.4966021776,0.1812898517,0.0389438532,0.305713594,-0.1901411414,-0.0401899554,0.0673795491,0.0520442091,0.2945782244,-0.4586854279,0.0862284899,0.1943815053,0.2952353358,-0.3140712976,-0.352039814,-0.0179468803,0.1240581274,-0.3361694813,0.2545648515,0.2885335088,-0.2310854793,0.0986495689,-0.1670768112,-0.1167311221,-0.3022927642,0.4389296472,-0.1841000915,0.2932372689,-0.1138128117,0.0412949026,0.1631835103,-0.1387998015,0.1639585048,-0.0754344985,0.3370934725,0.0137136988,0.1517517567,-0.1500609815,0.0298330132,-0.2463437021,-0.1883456111,0.1450426131,-0.2204875052,-0.3060177863,-0.0318602733,0.0074542542,-0.0541749112,0.0959956795,-0.1966276169,0.6701014638,0.2965373397,0.5027940869,0.032012783,-0.0403410904,-0.1909714937,-0.2045384943,-0.0870549977,-0.0306533463,0.6625394821,-0.0383561254,-0.3331961334,-0.0131454738,-0.4197606444,0.0783914253,0.3010311425,0.5060918927,0.0743204281,-0.0245896969,0.0031736968,0.1511423886,-0.0656605288,0.3835736215,-0.2604818046,0.3102358878,-0.1780570149,-0.0055621727,0.3036935627,-0.3783838153,0.2278976589,-0.0782359242,0.1879086792,-0.1120337173,0.0400563292,-0.1671946496,-0.0153000206,0.6389673948,-0.3143552542,0.122172676,0.2448890358,-0.0823115781,-0.2242325693,0.1375702471,-0.1204497367,-0.5571912527,0.0483228825,0.0999315456,0.1062155142,-0.0634725392,0.3532803357,-0.3217015862,0.0260888226,-0.1006018892,0.0767360628,-0.0643335655,-0.1164478585,-0.1373271048,0.3215051293,0.5865563154,-0.1676753461,-0.1125146374,-0.1753574014,-0.1381154209,0.2370170057,0.4046929777,-0.0672517568,0.2623784244,-0.283016324,-0.2667835057,0.3034884334,-0.3377234638,-0.4008953273,0.0308544394,-0.2147400379,0.2598990798,0.0527489483,0.1346762031,-0.3762584031,-0.0294961911,0.0617878698,0.1520974636,-0.0357308947,0.0904737264,-0.2340044081,-0.1187213138,0.087981984,0.3039717376,0.4382177889,0.0894154683,0.0313502997,0.2226996422,0.0503849052,-0.2450427115,-0.0719153062,0.3450480998,0.4285292625,-0.0693802163,-0.1211159676,0.1079738438,-0.3771114051,0.0220831204,-0.0822666511,0.3187367916,-0.2467507422,0.0032257261,-0.345186919,-0.1285056919,-0.2752381563,-0.1792377532,0.1339013726,0.1903796345,0.0196383093,0.2382143587,-0.214954406,0.2647375464,-0.1341636926,0.3000068367,-0.328188777,0.3138391674,-0.2469952852,-0.0395054705,-0.0923118144,0.4096641243,0.1486414522,-0.2167054117,-0.063395448,0.2275290042,-0.0740630478,-0.0576760061,0.0219573081,-0.0339813121,0.2657676935,-0.0904394239,0.1729007512,0.4206444323,0.0139150154,0.0184680186,-0.035002321,0.4468706846,0.0479335338,-0.0523554198,0.1870311946,-0.0533488803,0.0944574773,-0.0551234968,-0.0113500115,-0.0475617275,0.2278446257,-0.1467844099,0.5949158072,-0.0756230205,-0.2936546504,-0.498267144,-0.017299084,-0.2320438623,0.1143534184,0.2787705362,-0.2667388916,0.0841669738,-0.181010142,0.181104362,0.475499481,0.056991227,-0.0085043851,0.1831954718,-0.1056299657,-0.1482228041,0.3001134098,0.122813046,0.0768738464,0.2936602235,0.0724526346,0.0703333616,-0.5287972093,-0.3453274369,0.0061527174,0.3515328765,-0.3705409467,-0.1018935218,-0.1132583693,-0.2290444076,-0.031764362,-0.4700891078,-0.2261396796,-0.1386552751,0.0434038565,0.3248308003,0.0040019695,0.164022252,-0.02154289,-0.0796546414,0.1637674123,-0.1426153183,-0.225755021,0.1100877151,0.1099090129,0.0368247889,0.4638560116,-0.3609945476,0.245059073,-0.0506406166,-0.0929013863,-0.3026795685,-0.4553116262,-0.065082185,0.0196095668,0.263384372,0.1738888323,0.353010118,-0.1059392542,-0.3259163201,0.2694613934,0.1050129607,-0.0333887413,0.4007444382,-0.2799060941,0.0285295025,-0.030528551,-0.3856055737,-0.4965451062,-0.2660867572,0.0952375159,-0.0068388553,0.1661554724,0.6047119498,0.1591120362,0.1782280058,-0.2618115246,0.173198998,-0.0134630306,-0.1314901114,0.3829728365,-0.3403701782,-0.2306490541,-0.0014361457,-0.066587925,0.2421359271,-0.313904047,-0.5207077265,-0.2967836559,-0.0093218395,-0.2617911696,-0.0441968888,-0.1472934484,0.1218888909,-0.0831382871,-0.0681381524,0.2673321962,-0.1342415065,-0.1002488732,0.0882345513,0.0051644589,-0.2591240108,0.4475921392,-0.0936799422,0.7807009816,0.096185118,0.0188385956,0.4052089155,-0.016006697,0.4341351092,-0.2269287109,-0.4035912752,0.0421542041,0.35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0.4074542522,-0.4818341732,0.6003141999,-0.2737822831,-0.096901536,-0.0200534146,0.1188859493,-0.0998277366,0.0116760274,-0.3960606158,0.3837617338,0.2735113204,0.1435285211,-0.1272129565,0.0563923828,0.1020406485,0.000462132,0.071871601,0.3017484546,0.177706942,-0.3659015894,0.0227029789,-0.2111171633]}
{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/996","title":"NotADirectoryError while loading the CNN\/Dailymail dataset","comments":"Okay. I imagine asking every author who shares their dataset on Google Drive will also be cumbersome.","body":"\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'","comment_length":17,"text":"NotADirectoryError while loading the CNN\/Dailymail dataset \n \r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories' \n Okay. I imagine asking every author who shares their dataset on Google Drive will also be cumbersome.","embeddings":[-0.1502866894,0.013875572,0.0027152719,0.2634353042,0.4774574339,0.0598452203,0.4901823997,0.3076288998,-0.2510559261,0.2567766011,-0.3245876431,-0.0494811945,-0.4837743342,-0.1979143322,0.0145921567,0.0012005472,-0.1768816113,0.1828713715,-0.1539244652,-0.0876947716,-0.1727184951,0.321044147,-0.0058411579,0.1479859501,-0.5002063513,-0.1975877583,-0.0039216476,0.5093349218,-0.1271757334,-0.2311133146,0.2591030598,-0.2564248741,0.0552018695,0.7316402197,-0.0001171105,0.3691133261,0.3056399524,-0.0785469413,-0.5518268347,-0.4986005127,0.2059125155,-0.2117751688,0.0436902605,-0.2601816654,-0.1276563704,0.0635365993,0.1162231341,0.314124018,0.3930333555,0.4601137042,0.2285237312,0.2302971035,0.2858363986,0.0685345754,-0.1296327561,0.3916880488,-0.1658728719,0.5677597523,-0.4966021776,0.1812898517,0.0389438532,0.305713594,-0.1901411414,-0.0401899554,0.0673795491,0.0520442091,0.2945782244,-0.4586854279,0.0862284899,0.1943815053,0.2952353358,-0.3140712976,-0.352039814,-0.0179468803,0.1240581274,-0.3361694813,0.2545648515,0.2885335088,-0.2310854793,0.0986495689,-0.1670768112,-0.1167311221,-0.3022927642,0.4389296472,-0.1841000915,0.2932372689,-0.1138128117,0.0412949026,0.1631835103,-0.1387998015,0.1639585048,-0.0754344985,0.3370934725,0.0137136988,0.1517517567,-0.1500609815,0.0298330132,-0.2463437021,-0.1883456111,0.1450426131,-0.2204875052,-0.3060177863,-0.0318602733,0.0074542542,-0.0541749112,0.0959956795,-0.1966276169,0.6701014638,0.2965373397,0.5027940869,0.032012783,-0.0403410904,-0.1909714937,-0.2045384943,-0.0870549977,-0.0306533463,0.6625394821,-0.0383561254,-0.3331961334,-0.0131454738,-0.4197606444,0.0783914253,0.3010311425,0.5060918927,0.0743204281,-0.0245896969,0.0031736968,0.1511423886,-0.0656605288,0.3835736215,-0.2604818046,0.3102358878,-0.1780570149,-0.0055621727,0.3036935627,-0.3783838153,0.2278976589,-0.0782359242,0.1879086792,-0.1120337173,0.0400563292,-0.1671946496,-0.0153000206,0.6389673948,-0.3143552542,0.122172676,0.2448890358,-0.0823115781,-0.2242325693,0.1375702471,-0.1204497367,-0.5571912527,0.0483228825,0.0999315456,0.1062155142,-0.0634725392,0.3532803357,-0.3217015862,0.0260888226,-0.1006018892,0.0767360628,-0.0643335655,-0.1164478585,-0.1373271048,0.3215051293,0.5865563154,-0.1676753461,-0.1125146374,-0.1753574014,-0.1381154209,0.2370170057,0.4046929777,-0.0672517568,0.2623784244,-0.283016324,-0.2667835057,0.3034884334,-0.3377234638,-0.4008953273,0.0308544394,-0.2147400379,0.2598990798,0.0527489483,0.1346762031,-0.3762584031,-0.0294961911,0.0617878698,0.1520974636,-0.0357308947,0.0904737264,-0.2340044081,-0.1187213138,0.087981984,0.3039717376,0.4382177889,0.0894154683,0.0313502997,0.2226996422,0.0503849052,-0.2450427115,-0.0719153062,0.3450480998,0.4285292625,-0.0693802163,-0.1211159676,0.1079738438,-0.3771114051,0.0220831204,-0.0822666511,0.3187367916,-0.2467507422,0.0032257261,-0.345186919,-0.1285056919,-0.2752381563,-0.1792377532,0.1339013726,0.1903796345,0.0196383093,0.2382143587,-0.214954406,0.2647375464,-0.1341636926,0.3000068367,-0.328188777,0.3138391674,-0.2469952852,-0.0395054705,-0.0923118144,0.4096641243,0.1486414522,-0.2167054117,-0.063395448,0.2275290042,-0.0740630478,-0.0576760061,0.0219573081,-0.0339813121,0.2657676935,-0.0904394239,0.1729007512,0.4206444323,0.0139150154,0.0184680186,-0.035002321,0.4468706846,0.0479335338,-0.0523554198,0.1870311946,-0.0533488803,0.0944574773,-0.0551234968,-0.0113500115,-0.0475617275,0.2278446257,-0.1467844099,0.5949158072,-0.0756230205,-0.2936546504,-0.498267144,-0.017299084,-0.2320438623,0.1143534184,0.2787705362,-0.2667388916,0.0841669738,-0.181010142,0.181104362,0.475499481,0.056991227,-0.0085043851,0.1831954718,-0.1056299657,-0.1482228041,0.3001134098,0.122813046,0.0768738464,0.2936602235,0.0724526346,0.0703333616,-0.5287972093,-0.3453274369,0.0061527174,0.3515328765,-0.3705409467,-0.1018935218,-0.1132583693,-0.2290444076,-0.031764362,-0.4700891078,-0.2261396796,-0.1386552751,0.0434038565,0.3248308003,0.0040019695,0.164022252,-0.02154289,-0.0796546414,0.1637674123,-0.1426153183,-0.225755021,0.1100877151,0.1099090129,0.0368247889,0.4638560116,-0.3609945476,0.245059073,-0.0506406166,-0.0929013863,-0.3026795685,-0.4553116262,-0.065082185,0.0196095668,0.263384372,0.1738888323,0.353010118,-0.1059392542,-0.3259163201,0.2694613934,0.1050129607,-0.0333887413,0.4007444382,-0.2799060941,0.0285295025,-0.030528551,-0.3856055737,-0.4965451062,-0.2660867572,0.0952375159,-0.0068388553,0.1661554724,0.6047119498,0.1591120362,0.1782280058,-0.2618115246,0.173198998,-0.0134630306,-0.1314901114,0.3829728365,-0.3403701782,-0.2306490541,-0.0014361457,-0.066587925,0.2421359271,-0.313904047,-0.5207077265,-0.2967836559,-0.0093218395,-0.2617911696,-0.0441968888,-0.1472934484,0.1218888909,-0.0831382871,-0.0681381524,0.2673321962,-0.1342415065,-0.1002488732,0.0882345513,0.0051644589,-0.2591240108,0.4475921392,-0.0936799422,0.7807009816,0.096185118,0.0188385956,0.4052089155,-0.016006697,0.4341351092,-0.2269287109,-0.4035912752,0.0421542041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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/996","title":"NotADirectoryError while loading the CNN\/Dailymail dataset","comments":"Not as long as the data is stored on GG drive unfortunately.\r\nMaybe we can ask if there's a mirror ?\r\n\r\nHi @JafferWilson is there a download link to get cnn dailymail from another host than GG drive ?\r\n\r\nTo give you some context, this library provides tools to download and process datasets. For CNN DailyMail the data are downloaded from the link you provide on your github repository. Unfortunately because of GG drive quotas, many users are not able to load this dataset.","body":"\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'","comment_length":84,"text":"NotADirectoryError while loading the CNN\/Dailymail dataset \n \r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories' \n Not as long as the data is stored on GG drive unfortunately.\r\nMaybe we can ask if there's a mirror ?\r\n\r\nHi @JafferWilson is there a download link to get cnn dailymail from another host than GG drive ?\r\n\r\nTo give you some context, this library provides tools to download and process datasets. For CNN DailyMail the data are downloaded from the link you provide on your github repository. Unfortunately because of GG drive quotas, many users are not able to load this dataset.","embeddings":[-0.1502866894,0.013875572,0.0027152719,0.2634353042,0.4774574339,0.0598452203,0.4901823997,0.3076288998,-0.2510559261,0.2567766011,-0.3245876431,-0.0494811945,-0.4837743342,-0.1979143322,0.0145921567,0.0012005472,-0.1768816113,0.1828713715,-0.1539244652,-0.0876947716,-0.1727184951,0.321044147,-0.0058411579,0.1479859501,-0.5002063513,-0.1975877583,-0.0039216476,0.5093349218,-0.1271757334,-0.2311133146,0.2591030598,-0.2564248741,0.0552018695,0.7316402197,-0.0001171105,0.3691133261,0.3056399524,-0.0785469413,-0.5518268347,-0.4986005127,0.2059125155,-0.2117751688,0.0436902605,-0.2601816654,-0.1276563704,0.0635365993,0.1162231341,0.314124018,0.3930333555,0.4601137042,0.2285237312,0.2302971035,0.2858363986,0.0685345754,-0.1296327561,0.3916880488,-0.1658728719,0.5677597523,-0.4966021776,0.1812898517,0.0389438532,0.305713594,-0.1901411414,-0.0401899554,0.0673795491,0.0520442091,0.2945782244,-0.4586854279,0.0862284899,0.1943815053,0.2952353358,-0.3140712976,-0.352039814,-0.0179468803,0.1240581274,-0.3361694813,0.2545648515,0.2885335088,-0.2310854793,0.0986495689,-0.1670768112,-0.1167311221,-0.3022927642,0.4389296472,-0.1841000915,0.2932372689,-0.1138128117,0.0412949026,0.1631835103,-0.1387998015,0.1639585048,-0.0754344985,0.3370934725,0.0137136988,0.1517517567,-0.1500609815,0.0298330132,-0.2463437021,-0.1883456111,0.1450426131,-0.2204875052,-0.3060177863,-0.0318602733,0.0074542542,-0.0541749112,0.0959956795,-0.1966276169,0.6701014638,0.2965373397,0.5027940869,0.032012783,-0.0403410904,-0.1909714937,-0.2045384943,-0.0870549977,-0.0306533463,0.6625394821,-0.0383561254,-0.3331961334,-0.0131454738,-0.4197606444,0.0783914253,0.3010311425,0.5060918927,0.0743204281,-0.0245896969,0.0031736968,0.1511423886,-0.0656605288,0.3835736215,-0.2604818046,0.3102358878,-0.1780570149,-0.0055621727,0.3036935627,-0.3783838153,0.2278976589,-0.0782359242,0.1879086792,-0.1120337173,0.0400563292,-0.1671946496,-0.0153000206,0.6389673948,-0.3143552542,0.122172676,0.2448890358,-0.0823115781,-0.2242325693,0.1375702471,-0.1204497367,-0.5571912527,0.0483228825,0.0999315456,0.1062155142,-0.0634725392,0.3532803357,-0.3217015862,0.0260888226,-0.1006018892,0.0767360628,-0.0643335655,-0.1164478585,-0.1373271048,0.3215051293,0.5865563154,-0.1676753461,-0.1125146374,-0.1753574014,-0.1381154209,0.2370170057,0.4046929777,-0.0672517568,0.2623784244,-0.283016324,-0.2667835057,0.3034884334,-0.3377234638,-0.4008953273,0.0308544394,-0.2147400379,0.2598990798,0.0527489483,0.1346762031,-0.3762584031,-0.0294961911,0.0617878698,0.1520974636,-0.0357308947,0.0904737264,-0.2340044081,-0.1187213138,0.087981984,0.3039717376,0.4382177889,0.0894154683,0.0313502997,0.2226996422,0.0503849052,-0.2450427115,-0.0719153062,0.3450480998,0.4285292625,-0.0693802163,-0.1211159676,0.1079738438,-0.3771114051,0.0220831204,-0.0822666511,0.3187367916,-0.2467507422,0.0032257261,-0.345186919,-0.1285056919,-0.2752381563,-0.1792377532,0.1339013726,0.1903796345,0.0196383093,0.2382143587,-0.214954406,0.2647375464,-0.1341636926,0.3000068367,-0.328188777,0.3138391674,-0.2469952852,-0.0395054705,-0.0923118144,0.4096641243,0.1486414522,-0.2167054117,-0.063395448,0.2275290042,-0.0740630478,-0.0576760061,0.0219573081,-0.0339813121,0.2657676935,-0.0904394239,0.1729007512,0.4206444323,0.0139150154,0.0184680186,-0.035002321,0.4468706846,0.0479335338,-0.0523554198,0.1870311946,-0.0533488803,0.0944574773,-0.0551234968,-0.0113500115,-0.0475617275,0.2278446257,-0.1467844099,0.5949158072,-0.0756230205,-0.2936546504,-0.498267144,-0.017299084,-0.2320438623,0.1143534184,0.2787705362,-0.2667388916,0.0841669738,-0.181010142,0.181104362,0.475499481,0.056991227,-0.0085043851,0.1831954718,-0.1056299657,-0.1482228041,0.3001134098,0.122813046,0.0768738464,0.2936602235,0.0724526346,0.0703333616,-0.5287972093,-0.3453274369,0.0061527174,0.3515328765,-0.3705409467,-0.1018935218,-0.1132583693,-0.2290444076,-0.031764362,-0.4700891078,-0.2261396796,-0.1386552751,0.0434038565,0.3248308003,0.0040019695,0.164022252,-0.02154289,-0.0796546414,0.1637674123,-0.1426153183,-0.225755021,0.1100877151,0.1099090129,0.0368247889,0.4638560116,-0.3609945476,0.245059073,-0.0506406166,-0.0929013863,-0.3026795685,-0.4553116262,-0.065082185,0.0196095668,0.263384372,0.1738888323,0.353010118,-0.1059392542,-0.3259163201,0.2694613934,0.1050129607,-0.0333887413,0.4007444382,-0.2799060941,0.0285295025,-0.030528551,-0.3856055737,-0.4965451062,-0.2660867572,0.0952375159,-0.0068388553,0.1661554724,0.6047119498,0.1591120362,0.1782280058,-0.2618115246,0.173198998,-0.0134630306,-0.1314901114,0.3829728365,-0.3403701782,-0.2306490541,-0.0014361457,-0.066587925,0.2421359271,-0.313904047,-0.5207077265,-0.2967836559,-0.0093218395,-0.2617911696,-0.0441968888,-0.1472934484,0.1218888909,-0.0831382871,-0.0681381524,0.2673321962,-0.1342415065,-0.1002488732,0.0882345513,0.0051644589,-0.2591240108,0.4475921392,-0.0936799422,0.7807009816,0.096185118,0.0188385956,0.4052089155,-0.016006697,0.4341351092,-0.2269287109,-0.4035912752,0.0421542041,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/996","title":"NotADirectoryError while loading the CNN\/Dailymail dataset","comments":"Thanks for the link @mrazizi !\r\n\r\nApparently the original authors don't host the dataset themselves (\"for legal reasons\", source [here](https:\/\/github.com\/abisee\/cnn-dailymail\/issues\/9)).","body":"\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'","comment_length":20,"text":"NotADirectoryError while loading the CNN\/Dailymail dataset \n \r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories' \n Thanks for the link @mrazizi !\r\n\r\nApparently the original authors don't host the dataset themselves (\"for legal reasons\", source [here](https:\/\/github.com\/abisee\/cnn-dailymail\/issues\/9)).","embeddings":[-0.1502866894,0.013875572,0.0027152719,0.2634353042,0.4774574339,0.0598452203,0.4901823997,0.3076288998,-0.2510559261,0.2567766011,-0.3245876431,-0.0494811945,-0.4837743342,-0.1979143322,0.0145921567,0.0012005472,-0.1768816113,0.1828713715,-0.1539244652,-0.0876947716,-0.1727184951,0.321044147,-0.0058411579,0.1479859501,-0.5002063513,-0.1975877583,-0.0039216476,0.5093349218,-0.1271757334,-0.2311133146,0.2591030598,-0.2564248741,0.0552018695,0.7316402197,-0.0001171105,0.3691133261,0.3056399524,-0.0785469413,-0.5518268347,-0.4986005127,0.2059125155,-0.2117751688,0.0436902605,-0.2601816654,-0.1276563704,0.0635365993,0.1162231341,0.314124018,0.3930333555,0.4601137042,0.2285237312,0.2302971035,0.2858363986,0.0685345754,-0.1296327561,0.3916880488,-0.1658728719,0.5677597523,-0.4966021776,0.1812898517,0.0389438532,0.305713594,-0.1901411414,-0.0401899554,0.0673795491,0.0520442091,0.2945782244,-0.4586854279,0.0862284899,0.1943815053,0.2952353358,-0.3140712976,-0.352039814,-0.0179468803,0.1240581274,-0.3361694813,0.2545648515,0.2885335088,-0.2310854793,0.0986495689,-0.1670768112,-0.1167311221,-0.3022927642,0.4389296472,-0.1841000915,0.2932372689,-0.1138128117,0.0412949026,0.1631835103,-0.1387998015,0.1639585048,-0.0754344985,0.3370934725,0.0137136988,0.1517517567,-0.1500609815,0.0298330132,-0.2463437021,-0.1883456111,0.1450426131,-0.2204875052,-0.3060177863,-0.0318602733,0.0074542542,-0.0541749112,0.0959956795,-0.1966276169,0.6701014638,0.2965373397,0.5027940869,0.032012783,-0.0403410904,-0.1909714937,-0.2045384943,-0.0870549977,-0.0306533463,0.6625394821,-0.0383561254,-0.3331961334,-0.0131454738,-0.4197606444,0.0783914253,0.3010311425,0.5060918927,0.0743204281,-0.0245896969,0.0031736968,0.1511423886,-0.0656605288,0.3835736215,-0.2604818046,0.3102358878,-0.1780570149,-0.0055621727,0.3036935627,-0.3783838153,0.2278976589,-0.0782359242,0.1879086792,-0.1120337173,0.0400563292,-0.1671946496,-0.0153000206,0.6389673948,-0.3143552542,0.122172676,0.2448890358,-0.0823115781,-0.2242325693,0.1375702471,-0.1204497367,-0.5571912527,0.0483228825,0.0999315456,0.1062155142,-0.0634725392,0.3532803357,-0.3217015862,0.0260888226,-0.1006018892,0.0767360628,-0.0643335655,-0.1164478585,-0.1373271048,0.3215051293,0.5865563154,-0.1676753461,-0.1125146374,-0.1753574014,-0.1381154209,0.2370170057,0.4046929777,-0.0672517568,0.2623784244,-0.283016324,-0.2667835057,0.3034884334,-0.3377234638,-0.4008953273,0.0308544394,-0.2147400379,0.2598990798,0.0527489483,0.1346762031,-0.3762584031,-0.0294961911,0.0617878698,0.1520974636,-0.0357308947,0.0904737264,-0.2340044081,-0.1187213138,0.087981984,0.3039717376,0.4382177889,0.0894154683,0.0313502997,0.2226996422,0.0503849052,-0.2450427115,-0.0719153062,0.3450480998,0.4285292625,-0.0693802163,-0.1211159676,0.1079738438,-0.3771114051,0.0220831204,-0.0822666511,0.3187367916,-0.2467507422,0.0032257261,-0.345186919,-0.1285056919,-0.2752381563,-0.1792377532,0.1339013726,0.1903796345,0.0196383093,0.2382143587,-0.214954406,0.2647375464,-0.1341636926,0.3000068367,-0.328188777,0.3138391674,-0.2469952852,-0.0395054705,-0.0923118144,0.4096641243,0.1486414522,-0.2167054117,-0.063395448,0.2275290042,-0.0740630478,-0.0576760061,0.0219573081,-0.0339813121,0.2657676935,-0.0904394239,0.1729007512,0.4206444323,0.0139150154,0.0184680186,-0.035002321,0.4468706846,0.0479335338,-0.0523554198,0.1870311946,-0.0533488803,0.0944574773,-0.0551234968,-0.0113500115,-0.0475617275,0.2278446257,-0.1467844099,0.5949158072,-0.0756230205,-0.2936546504,-0.498267144,-0.017299084,-0.2320438623,0.1143534184,0.2787705362,-0.2667388916,0.0841669738,-0.181010142,0.181104362,0.475499481,0.056991227,-0.0085043851,0.1831954718,-0.1056299657,-0.1482228041,0.3001134098,0.122813046,0.0768738464,0.2936602235,0.0724526346,0.0703333616,-0.5287972093,-0.3453274369,0.0061527174,0.3515328765,-0.3705409467,-0.1018935218,-0.1132583693,-0.2290444076,-0.031764362,-0.4700891078,-0.2261396796,-0.1386552751,0.0434038565,0.3248308003,0.0040019695,0.164022252,-0.02154289,-0.0796546414,0.1637674123,-0.1426153183,-0.225755021,0.1100877151,0.1099090129,0.0368247889,0.4638560116,-0.3609945476,0.245059073,-0.0506406166,-0.0929013863,-0.3026795685,-0.4553116262,-0.065082185,0.0196095668,0.263384372,0.1738888323,0.353010118,-0.1059392542,-0.3259163201,0.2694613934,0.1050129607,-0.0333887413,0.4007444382,-0.2799060941,0.0285295025,-0.030528551,-0.3856055737,-0.4965451062,-0.2660867572,0.0952375159,-0.0068388553,0.1661554724,0.6047119498,0.1591120362,0.1782280058,-0.2618115246,0.173198998,-0.0134630306,-0.1314901114,0.3829728365,-0.3403701782,-0.2306490541,-0.0014361457,-0.066587925,0.2421359271,-0.313904047,-0.5207077265,-0.2967836559,-0.0093218395,-0.2617911696,-0.0441968888,-0.1472934484,0.1218888909,-0.0831382871,-0.0681381524,0.2673321962,-0.1342415065,-0.1002488732,0.0882345513,0.0051644589,-0.2591240108,0.4475921392,-0.0936799422,0.7807009816,0.096185118,0.0188385956,0.4052089155,-0.016006697,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/993","title":"Problem downloading amazon_reviews_multi","comments":"Hi @hfawaz ! This is working fine for me. Is it a repeated occurence? Have you tried from the latest verion?","body":"Thanks for adding the dataset. \r\nAfter trying to load the dataset, I am getting the following error: \r\n`ConnectionError: Couldn't reach https:\/\/amazon-reviews-ml.s3-us-west-2.amazonaws.com\/json\/train\/dataset_fr_train.json\r\n`\r\nI used the following code to load the dataset: \r\n`load_dataset(\r\n            dataset_name,\r\n            \"all_languages\",\r\n            cache_dir=\".data\"\r\n        )`\r\n\r\nI am using version 1.1.3 of `datasets`\r\n\r\nNote that I can perform a successfull `wget https:\/\/amazon-reviews-ml.s3-us-west-2.amazonaws.com\/json\/train\/dataset_fr_train.json`","comment_length":21,"text":"Problem downloading amazon_reviews_multi \n Thanks for adding the dataset. \r\nAfter trying to load the dataset, I am getting the following error: \r\n`ConnectionError: Couldn't reach https:\/\/amazon-reviews-ml.s3-us-west-2.amazonaws.com\/json\/train\/dataset_fr_train.json\r\n`\r\nI used the following code to load the dataset: \r\n`load_dataset(\r\n            dataset_name,\r\n            \"all_languages\",\r\n            cache_dir=\".data\"\r\n        )`\r\n\r\nI am using version 1.1.3 of `datasets`\r\n\r\nNote that I can perform a successfull `wget https:\/\/amazon-reviews-ml.s3-us-west-2.amazonaws.com\/json\/train\/dataset_fr_train.json` \n Hi @hfawaz ! This is working fine for me. Is it a repeated occurence? Have you tried from the latest verion?","embeddings":[-0.3760583997,-0.0687171593,-0.1512161046,0.3995557427,0.2218290865,0.0871828422,0.2271226645,0.0012161636,-0.195425272,-0.1700538546,-0.1386815906,0.0604705513,0.1707975268,0.0332740657,0.0018724314,0.0242816843,0.0877761245,-0.1517885327,-0.085342288,0.0247635096,-0.1652069837,0.1678636968,-0.1811462194,-0.0880342796,-0.2276556343,-0.0458784476,-0.063408047,-0.0876697153,-0.3202602863,-0.3831088543,0.4728878736,0.2383781224,0.2648138702,0.3811822236,-0.0001174623,-0.1535703838,0.3675583899,-0.1078284085,-0.2808185518,-0.6039906144,-0.3533784449,-0.2391920239,0.2802509964,0.0287721436,0.0832013786,-0.0687334239,-0.0326403342,-0.2118874639,0.1221739128,0.2553088963,0.1851304471,0.1339330375,0.4523338675,-0.0434270166,0.2750202417,-0.1569391638,-0.0335237533,0.3968005478,0.0909879431,-0.0359309576,0.2279304117,0.1416745931,0.0797833428,0.3172475994,0.1299233735,-0.0447500683,0.2263744026,-0.1134047061,0.091087617,0.422377795,1.0506269932,-0.10850247,-0.5078006387,0.2045578659,0.158776626,-0.0387851559,0.1576090306,0.2874030173,-0.1353876442,-0.1331919432,-0.429851532,-0.3206474781,-0.288641721,0.5616419911,-0.0451895967,0.3056504726,-0.0740531236,0.2216133922,0.3126436174,-0.0657328293,0.5485727787,-0.0852166191,-0.0937298164,0.2087666541,-0.8067884445,0.0056742691,-0.2236727774,-0.1762273163,0.0434960127,0.312823087,0.0840657279,0.0621921755,-0.028283149,0.2298112959,0.4412487447,-0.1112852916,-0.0073343068,0.300162673,0.0472344235,0.4499185979,0.1058038995,-0.1061760634,-0.2239825875,-0.213673383,0.0520493872,0.0940273702,0.0255822558,-0.2895092666,-0.3205344081,0.0411056243,-0.5326039195,-0.1666151434,0.0506155863,0.2480783015,-0.2394607812,0.2531683743,-0.0434296466,0.3617746234,-0.0829924047,0.0746374875,-0.1123440489,0.0865691081,-0.177681908,-0.0614344738,0.2400965989,0.1313207597,0.2572076917,-0.1772686243,0.0769190118,-0.3000606894,0.1120905504,-0.129009515,-0.2409229726,0.2902305126,0.3096740544,0.1613355726,0.0355424248,0.0597349256,0.056611605,0.0393190756,-0.0957612991,-0.1534672529,-0.1657355577,0.1485721022,-0.2256349325,-0.2716696858,-0.2497368306,-0.0944566056,0.0449420847,-0.1431613266,-0.1663995385,-0.0688501447,0.1288469732,-0.0877747387,0.1445049942,0.3304733336,-0.3380998671,0.0293947477,-0.4909790754,-0.1468683332,0.0422965735,0.3341827691,-0.3259694874,-0.0363804698,-0.1774045676,-0.0283069517,0.3972617686,-0.2358672768,-0.391702652,0.5111751556,-0.2891079187,-0.1156889498,-0.0255055241,0.2173869014,-0.114275001,0.1892291307,-0.0317801088,0.2984339297,-0.0319019072,-0.1459318846,-0.0540253595,-0.2005337924,0.2504793704,0.3099494576,0.1478506327,-0.0288840756,0.198020786,0.1882623732,0.3265378177,0.2143276632,0.0855998024,0.3521366715,0.353977561,0.0481052995,0.0110343955,-0.0713915974,-0.5713795424,0.0172049403,-0.0972928256,-0.1698230058,-0.1361767948,-0.0581827387,-0.2476274967,-0.0544587187,-0.2091049403,0.1748756021,0.0531651042,0.1519445628,-0.0390002467,-0.0467892438,-0.0379904509,0.1853621453,0.0951345563,0.1475638151,-0.2620975375,0.2204957902,-0.1911636591,0.1527510732,0.275311172,-0.1027089506,0.2127271593,-0.1334378719,-0.0645937249,0.3175309002,0.0101136528,0.1606999934,0.2440329045,0.1258067489,-0.0531193279,-0.1045050919,0.0152933765,0.259087503,0.2428422123,-0.099616766,-0.2561759651,0.1772407889,0.1053444445,0.2681096196,0.0212707371,0.0990921259,0.2801519632,0.0281583741,-0.0041362522,-0.0167243909,0.3990073502,-0.0466886424,-0.1435838342,-0.3875207901,-0.0578988157,0.009158507,0.1139790118,-0.0975529924,0.1503444016,0.109780401,0.0100891581,-0.2266483903,-0.0137330126,0.4280416369,0.4721235931,0.0101010324,0.215177834,0.0595326573,0.0701398328,-0.1609264612,0.1694002599,0.0959650576,0.3051463366,0.3443479538,-0.0558003485,-0.0531795509,-0.0502258539,-0.1583517045,0.3277203441,0.2827937603,-0.250895381,0.1228301823,-0.482427001,-0.0901075229,-0.2425749749,-0.1869426072,-0.3196102977,-0.3992118537,-0.2374681234,0.3306756318,0.1338935047,-0.0416912399,0.0084600495,-0.0954337195,0.1509998888,-0.037251085,0.0951510444,-0.2162163556,-0.2899645567,0.1005893871,0.2935492992,-0.1091830805,0.1274592131,0.0831154659,0.1468355358,-0.3079131246,-0.1269351244,-0.1508038789,0.1781183779,0.1312027425,0.231682241,0.3100517392,-0.1224841774,0.0356893577,0.0782066062,0.1118622348,0.0035301854,0.125541091,-0.0680983886,-0.175469175,0.2824950218,-0.5468952656,-0.1960801929,-0.1828793883,0.1689725816,-0.2596386969,0.019116411,-0.4372482598,0.0934619159,0.1319027841,-0.1245440021,0.0840198025,-0.2682675123,-0.2069098651,0.2822828293,-0.0779304877,-0.3567316234,0.3560471535,0.2730462849,0.2037633955,0.1154053062,-0.7272118926,0.0916247591,0.2344181687,-0.2698007822,0.1553618312,0.0154137164,0.1727865785,-0.2322534621,-0.0533687733,0.1976912171,-0.0629869029,-0.160548225,0.1174262986,0.7222898006,-0.1894682646,0.2288447469,0.1139828637,0.3751296699,0.2665195763,-0.0288639143,0.2219634205,0.1663761586,0.1095670313,0.2133678049,-0.2673486769,0.0439313017,-0.345387876,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/988","title":"making sure datasets are not loaded in memory and distributed training of them","comments":"my implementation of sharding per TPU core: https:\/\/github.com\/google-research\/ruse\/blob\/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c\/seq2seq\/trainers\/t5_trainer.py#L316 \r\nmy implementation of dataloader for this case https:\/\/github.com\/google-research\/ruse\/blob\/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c\/seq2seq\/tasks\/tasks.py#L496 ","body":"Hi\r\nI am dealing with large-scale datasets which I need to train distributedly, I used the shard function to divide the dataset across the cores, without any sampler, this does not work for distributed training and does not become any faster than 1 TPU core. 1) how I can make sure data is not loaded in memory 2) in case of distributed training with iterative datasets which measures needs to be taken? Is this all sharding the data only. I was wondering if there can be possibility for me to discuss this with someone with distributed training with iterative datasets using dataset library. thanks ","comment_length":16,"text":"making sure datasets are not loaded in memory and distributed training of them \n Hi\r\nI am dealing with large-scale datasets which I need to train distributedly, I used the shard function to divide the dataset across the cores, without any sampler, this does not work for distributed training and does not become any faster than 1 TPU core. 1) how I can make sure data is not loaded in memory 2) in case of distributed training with iterative datasets which measures needs to be taken? Is this all sharding the data only. I was wondering if there can be possibility for me to discuss this with someone with distributed training with iterative datasets using dataset library. thanks  \n my implementation of sharding per TPU core: https:\/\/github.com\/google-research\/ruse\/blob\/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c\/seq2seq\/trainers\/t5_trainer.py#L316 \r\nmy implementation of dataloader for this case https:\/\/github.com\/google-research\/ruse\/blob\/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c\/seq2seq\/tasks\/tasks.py#L496 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/961","title":"sample multiple datasets ","comments":"here I share my dataloader currently for multiple tasks: https:\/\/gist.github.com\/rabeehkarimimahabadi\/39f9444a4fb6f53dcc4fca5d73bf8195 \r\n\r\nI need to train my model distributedly with this dataloader, \"MultiTasksataloader\", currently this does not work in distributed fasion,\r\nto save on memory I tried to use iterative datasets, could you have a look in this dataloader and tell me if this is indeed the case? not sure how to make datasets being iterative to not load them in memory, then I remove the sampler for dataloader, and shard the data per core, could you tell me please how I should implement this case in datasets library? and how do you find my implementation in terms of correctness? thanks \r\n","body":"Hi\r\nI am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is: \r\n-  I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it\r\n\r\nsub-questions:\r\n- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?\r\n- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help. ","comment_length":109,"text":"sample multiple datasets  \n Hi\r\nI am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is: \r\n-  I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it\r\n\r\nsub-questions:\r\n- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?\r\n- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help.  \n here I share my dataloader currently for multiple tasks: https:\/\/gist.github.com\/rabeehkarimimahabadi\/39f9444a4fb6f53dcc4fca5d73bf8195 \r\n\r\nI need to train my model distributedly with this dataloader, \"MultiTasksataloader\", currently this does not work in distributed fasion,\r\nto save on memory I tried to use iterative datasets, could you have a look in this dataloader and tell me if this is indeed the case? not sure how to make datasets being iterative to not load them in memory, then I remove the sampler for dataloader, and shard the data per core, could you tell me please how I should implement this case in datasets library? and how do you find my implementation in terms of correctness? thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/937","title":"Local machine\/cluster Beam Datasets example\/tutorial","comments":"I tried to make it run once on the SparkRunner but it seems that this runner has some issues when it is run locally.\r\nFrom my experience the DirectRunner is fine though, even if it's clearly not memory efficient.\r\n\r\nIt would be awesome though to make it work locally on a SparkRunner !\r\nDid you manage to make your processing work ?","body":"Hi,\r\n\r\nI'm wondering if https:\/\/huggingface.co\/docs\/datasets\/beam_dataset.html has an non-GCP or non-Dataflow version example\/tutorial? I tried to migrate it to run on DirectRunner and SparkRunner, however, there were way too many runtime errors that I had to fix during the process, and even so I wasn't able to get either runner correctly producing the desired output.\r\n\r\nThanks!\r\nShang","comment_length":62,"text":"Local machine\/cluster Beam Datasets example\/tutorial \n Hi,\r\n\r\nI'm wondering if https:\/\/huggingface.co\/docs\/datasets\/beam_dataset.html has an non-GCP or non-Dataflow version example\/tutorial? I tried to migrate it to run on DirectRunner and SparkRunner, however, there were way too many runtime errors that I had to fix during the process, and even so I wasn't able to get either runner correctly producing the desired output.\r\n\r\nThanks!\r\nShang \n I tried to make it run once on the SparkRunner but it seems that this runner has some issues when it is run locally.\r\nFrom my experience the DirectRunner is fine though, even if it's clearly not memory efficient.\r\n\r\nIt would be awesome though to make it work locally on a SparkRunner !\r\nDid you manage to make your processing work ?","embeddings":[-0.3029166758,-0.2597852647,-0.0041649756,0.0888215601,0.0611687601,-0.2294601947,0.2730875015,-0.1252957582,0.1471410692,0.2653598189,0.1793320179,0.292142719,-0.4641712606,0.6944869757,0.2492861748,-0.5270981789,0.3334576786,-0.1199868992,0.0216181409,-0.2315687686,0.2499720454,0.346807003,-0.3306990564,-0.0525378808,-0.0932485908,-0.0328033715,-0.1721203774,0.2677879333,-0.2200568318,0.0841011629,0.2658599615,-0.1088843346,0.1863445193,0.9052969813,-0.0001201803,-0.0497153252,0.1546807736,-0.0539860837,0.0199465435,-0.1867879331,0.1566442847,-0.0154153006,0.1619346738,-0.0727845058,-0.2123981416,-0.0957708508,0.0908535048,-0.0055826623,0.588593483,0.169653371,0.1072054803,0.3378794789,-0.1923525631,-0.1925451756,-0.0544641316,0.3821781278,-0.3049484789,0.4146491885,0.4382084608,-0.0001665803,-0.2150050253,0.2646872401,-0.1692704409,-0.1197135076,0.274279952,-0.1783319712,-0.4092798531,-0.2647215724,0.0094662057,0.3803465664,0.6444175243,-0.3084756732,-0.1788963377,-0.1481508464,0.077381447,0.0355662592,0.1662692428,0.1155457944,-0.2511981428,0.1777534932,-0.7257139683,-0.2467558533,-0.2392481565,-0.2115032971,-0.1159090698,0.3887881935,-0.1884066612,0.0926894322,0.1613622606,0.0715748742,0.4279718101,0.0208769329,0.1395020485,-0.1157980636,-0.4014292955,-0.1895822436,-0.0212012138,0.6500042081,-0.0850925595,0.1844476461,0.1049814001,-0.1101926491,0.0840262249,0.1532219052,-0.1201360673,0.2642698586,0.2677609622,0.0173277669,0.0258307029,-0.2055127919,0.205679372,0.1432573199,-0.451302588,-0.1956639588,-0.7054801583,-0.0198629946,-0.2233604938,-0.1397652924,-0.0853938982,-0.0715436935,-0.114061147,0.0226786807,0.2285529524,0.160407275,0.2587019205,0.1301407516,-0.0917088538,0.0659475401,-0.0490657575,-0.3933473527,-0.0686852261,0.3721349239,-0.5328849554,-0.0297052823,0.0960214064,-0.0564032793,0.0813707113,-0.0479136147,0.1972567439,0.3849090934,0.0602352731,0.1320077926,0.0568427555,0.1362747699,0.0219949577,0.1173633784,0.1448400319,0.1398748755,-0.2038722634,-0.1034587696,-0.2237517089,-0.3094114959,0.0077487589,-0.0295714587,-0.0920754969,-0.232178852,-0.5076836944,0.4833564162,-0.2039633542,0.0057094223,0.2567739785,-0.1527157426,-0.2808699608,-0.2357541919,0.3215939105,0.4583259821,-0.526817739,0.0077194958,0.091197826,-0.3155311048,0.2618651986,0.2542577684,0.0717829168,0.2218497843,-0.3367223144,-0.0444209725,0.3111922443,-0.7092143297,0.1847455055,0.667861104,-0.2511822581,-0.1177572757,-0.0484736189,-0.1916433424,0.0642129108,-0.0097681554,-0.0884431899,0.2391255647,-0.1667373478,0.128468737,-0.0637892038,-0.2918200791,-0.1378310919,0.2049366534,-0.0137708057,0.1322251558,-0.0084460527,-0.1825434417,0.2063750327,-0.0425384566,0.0986142755,0.4210444689,0.0658791214,-0.1860312968,-0.0873794854,-0.0554307215,-0.0324145593,0.1529463977,0.2787356079,-0.0447169393,0.5172027946,-0.1820726544,-0.2667183876,-0.0303762518,0.0600623675,-0.2148182988,-0.0125031192,-0.250058502,0.3179764748,-0.1634434909,-0.3365817666,0.3091777861,-0.2486633211,-0.0815386027,-0.2981159091,0.4468592107,0.1138235703,-0.3273065984,-0.0089702997,-0.1782461256,0.113416031,-0.3471775949,-0.0641544238,0.4955254197,-0.250857234,0.4583969414,0.3250461519,0.5605686903,0.3120700717,-0.0244113207,0.30259642,-0.3043553531,-0.1911411732,0.2306416035,-0.3735177219,0.1239517108,-0.0207574945,0.109043628,0.2474950999,0.4676242173,-0.091564931,0.0088479323,-0.007127889,0.3194851577,0.4472433031,-0.1027063057,0.1383283436,-0.1776744276,-0.5297761559,0.1255438775,0.2470868677,-0.1524432003,0.0014616583,0.0039344076,-0.3945119977,0.0936572403,0.6196673512,0.348968327,0.1256736368,0.2109438479,0.2386404127,0.132665351,0.2032565325,-0.3326336741,0.1098635644,-0.1469198614,0.1641789526,0.0269414857,0.0126337865,0.134936437,-0.2734863162,-0.0991846547,0.0476974174,0.0586126298,-0.1351984292,0.2164726257,-0.3170535266,-0.30358091,-0.2953271866,0.3783070445,-0.1633469611,-0.1004698053,-0.0179955438,0.3528321385,-0.0808843747,0.1908670515,0.3830774426,0.3397631049,-0.2224692851,-0.180575192,-0.3251306117,-0.1658750176,-0.2335842401,0.0170451067,0.2264356017,0.1227668151,0.2665676773,-0.1184477955,0.0159032196,-0.4890853465,-0.2099977732,0.4201814532,-0.0344737321,0.0625678971,0.2083222568,0.0580512509,-0.7962208986,-0.0000914092,0.0758332908,-0.5537876487,-0.2558651567,-0.1597559303,0.1086098775,0.0087943813,-0.2054430246,0.114836596,-0.4072747827,-0.3363671601,0.4378926754,0.0404328108,0.2076618671,-0.3011609912,0.09281075,0.1540543586,-0.0330746472,-0.2489912957,0.128828764,-0.5100076795,-0.0225219335,-0.4240023494,-0.3663930893,0.0597414263,0.1786950827,0.0822686553,0.2870673239,-0.2168940604,-0.2206926197,0.0196215548,-0.1551414579,0.198210299,0.1966361105,0.5560917258,-0.2582683265,0.1138668954,0.0125354026,-0.0091435118,0.0828745365,0.0091216229,0.0329649597,0.3433301449,0.1300072819,-0.0158585813,0.7973929048,-0.0741805136,0.0728162602,-0.1025688499,-0.1284381002,0.2782167792,-0.1638895273,0.0173636209,0.4576426744,0.1414338052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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/919","title":"wrong length with datasets ","comments":"Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs. ","body":"Hi\r\nI have a MRPC dataset which I convert it to seq2seq format, then this is of this format:\r\n\r\n`Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10)\r\n`\r\n\r\nI feed it to a dataloader:\r\n```\r\ndataloader = DataLoader(\r\n            train_dataset,\r\n            batch_size=self.args.train_batch_size,\r\n            sampler=train_sampler,\r\n            collate_fn=self.data_collator,\r\n            drop_last=self.args.dataloader_drop_last,\r\n            num_workers=self.args.dataloader_num_workers,\r\n        )\r\n```\r\n\r\nnow if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks \r\n","comment_length":26,"text":"wrong length with datasets  \n Hi\r\nI have a MRPC dataset which I convert it to seq2seq format, then this is of this format:\r\n\r\n`Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10)\r\n`\r\n\r\nI feed it to a dataloader:\r\n```\r\ndataloader = DataLoader(\r\n            train_dataset,\r\n            batch_size=self.args.train_batch_size,\r\n            sampler=train_sampler,\r\n            collate_fn=self.data_collator,\r\n            drop_last=self.args.dataloader_drop_last,\r\n            num_workers=self.args.dataloader_num_workers,\r\n        )\r\n```\r\n\r\nnow if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks \r\n \n Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/915","title":"Shall we change the hashing to encoding to reduce potential replicated cache files?","comments":"This is an interesting idea !\r\nDo you have ideas about how to approach the decoding and the normalization ?","body":"Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.\r\n\r\nIf you have interest in this, I'd love to help :).","comment_length":20,"text":"Shall we change the hashing to encoding to reduce potential replicated cache files? \n Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.\r\n\r\nIf you have interest in this, I'd love to help :). \n This is an interesting idea !\r\nDo you have ideas about how to approach the decoding and the normalization 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/915","title":"Shall we change the hashing to encoding to reduce potential replicated cache files?","comments":"@lhoestq\r\nI think we first need to save the transformation chain to a list in `self._fingerprint`. Then we can\r\n- decode all the current saved datasets to see if there is already one that is equivalent to the transformation we need now.\r\n- or, calculate all the possible hash value of the current chain for comparison so that we could continue to use hashing.\r\nIf we find one, we can adjust the list in `self._fingerprint` to it.\r\n\r\nAs for the transformation reordering rules, we can just start with some manual rules, like two sort on the same column should merge to one, filter and select can change orders.\r\n\r\nAnd for encoding and decoding, we can just manually specify `sort` is 0, `shuffling` is 2 and create a base-n number or use some general algorithm like `base64.urlsafe_b64encode`.\r\n\r\nBecause we are not doing lazy evaluation now, we may not be able to normalize the transformation to its minimal form. If we want to support that, we can provde a `Sequential` api and let user input a list or transformation, so that user would not use the intermediate datasets. This would look like tf.data.Dataset.","body":"Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.\r\n\r\nIf you have interest in this, I'd love to help :).","comment_length":191,"text":"Shall we change the hashing to encoding to reduce potential replicated cache files? \n Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.\r\n\r\nIf you have interest in this, I'd love to help :). \n @lhoestq\r\nI think we first need to save the transformation chain to a list in `self._fingerprint`. Then we can\r\n- decode all the current saved datasets to see if there is already one that is equivalent to the transformation we need now.\r\n- or, calculate all the possible hash value of the current chain for comparison so that we could continue to use hashing.\r\nIf we find one, we can adjust the list in `self._fingerprint` to it.\r\n\r\nAs for the transformation reordering rules, we can just start with some manual rules, like two sort on the same column should merge to one, filter and select can change orders.\r\n\r\nAnd for encoding and decoding, we can just manually specify `sort` is 0, `shuffling` is 2 and create a base-n number or use some general algorithm like `base64.urlsafe_b64encode`.\r\n\r\nBecause we are not doing lazy evaluation now, we may not be able to normalize the transformation to its minimal form. If we want to support that, we can provde a `Sequential` api and let user input a list or transformation, so that user would not use the intermediate datasets. This would look like 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/897","title":"Dataset viewer issues","comments":"Thanks for reporting !\r\ncc @srush for the empty feature list issue and the encoding issue\r\ncc @julien-c maybe we can update the url and just have a redirection from the old url to the new one ?","body":"I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues\/bugs though:\r\n\r\n- the URL is still under `nlp`, perhaps an alias for `datasets` can be made\r\n- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user\r\n\r\n```bash\r\nIndexError: list index out of range\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 316, in <module>\r\n    st.table(style)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 122, in wrapped_method\r\n    return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 367, in _enqueue_new_element_delta\r\n    rv = marshall_element(msg.delta.new_element)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 120, in marshall_element\r\n    return method(dg, element, *args, **kwargs)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 2944, in table\r\n    data_frame_proto.marshall_data_frame(data, element.table)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/elements\/data_frame_proto.py\", line 54, in marshall_data_frame\r\n    _marshall_styles(proto_df.style, df, styler)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/elements\/data_frame_proto.py\", line 73, in _marshall_styles\r\n    translated_style = styler._translate()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/pandas\/io\/formats\/style.py\", line 351, in _translate\r\n    * (len(clabels[0]) - len(hidden_columns))\r\n```\r\n\r\n- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wmt19&config=cs-en). This problem goes away when you enable \"List view\", because then some syntax highlighteris used, and the special characters are coded correctly.\r\n","comment_length":38,"text":"Dataset viewer issues \n I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues\/bugs though:\r\n\r\n- the URL is still under `nlp`, perhaps an alias for `datasets` can be made\r\n- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user\r\n\r\n```bash\r\nIndexError: list index out of range\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 316, in <module>\r\n    st.table(style)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 122, in wrapped_method\r\n    return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 367, in _enqueue_new_element_delta\r\n    rv = marshall_element(msg.delta.new_element)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 120, in marshall_element\r\n    return method(dg, element, *args, **kwargs)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 2944, in table\r\n    data_frame_proto.marshall_data_frame(data, element.table)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/elements\/data_frame_proto.py\", line 54, in marshall_data_frame\r\n    _marshall_styles(proto_df.style, df, styler)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/elements\/data_frame_proto.py\", line 73, in _marshall_styles\r\n    translated_style = styler._translate()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/pandas\/io\/formats\/style.py\", line 351, in _translate\r\n    * (len(clabels[0]) - len(hidden_columns))\r\n```\r\n\r\n- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wmt19&config=cs-en). This problem goes away when you enable \"List view\", because then some syntax highlighteris used, and the special characters are coded correctly.\r\n \n Thanks for reporting !\r\ncc @srush for the empty feature list issue and the encoding issue\r\ncc @julien-c maybe we can update the url and just have a redirection from the old url to the new one ?","embeddings":[-0.2186932266,0.2789271474,-0.026432747,0.2816513479,-0.0023903828,0.1115044877,0.2981258929,0.2988632917,-0.1956236809,0.0647588521,-0.0076386253,0.2664061487,-0.3950020671,0.0988750979,-0.1122838408,-0.2007787526,0.0220978931,-0.0146634579,0.0232188925,-0.0943433568,-0.1718729287,-0.2318453193,-0.3681103587,0.0472469144,0.1730451733,-0.1287799329,-0.0984149203,-0.0248546954,-0.1657533944,-0.6458482146,0.0453227796,0.2832351029,0.1305533051,0.4277637899,-0.0001141002,-0.0952439457,0.5284169316,0.2207832187,-0.4246532321,-0.1656228304,-0.1231052652,-0.3184152842,0.4010361731,-0.1876373738,0.2975274324,-0.34791255,0.0207551885,-0.1845204532,0.0901871175,0.469602257,0.1956328005,0.1108126119,-0.1084176004,0.1752130687,0.3652741909,0.2491191179,-0.2797198892,-0.1015249267,0.1632782519,-0.0791658387,-0.025709914,0.3140527904,-0.1640222073,-0.0861883536,0.3573250473,-0.1882148087,-0.0757901296,-0.4510447383,-0.0953087807,0.2241510004,0.7972296476,-0.3495242298,-0.3797179759,-0.3601836562,0.0131202983,-0.1577013135,0.1619466543,0.0079309354,-0.1782668978,0.0668451935,-0.2203083187,-0.3423940837,-0.1549729556,0.0965098739,-0.2113793194,0.6253469586,0.1779181063,0.0807632133,0.1139025018,-0.1835037321,0.5544168949,-0.1228946298,-0.1087577194,0.1986276656,-0.2692966461,-0.1737065315,0.461104244,-0.1622395366,-0.1637828648,0.4153255224,0.2714703977,0.0435075462,-0.3141144812,0.009632552,0.4544065893,0.1671871543,0.147337541,0.1572187394,0.3739819229,-0.038006708,0.3260328472,0.1013467759,0.0978917032,-0.0086792316,-0.2973321676,-0.116244182,0.4545963407,-0.2330589294,-0.303227216,0.1526127011,-0.1009672284,-0.263060987,0.1532100588,0.219112128,-0.0706097782,0.0922878385,-0.1988677233,0.1861469299,-0.2814712524,-0.1896958649,-0.1201233566,-0.1157088652,-0.1896719784,-0.3266671598,0.1634646803,-0.2876240313,0.119710207,-0.1003617421,-0.1547210664,0.194593668,0.0908226743,-0.1636734307,-0.0009145842,0.0688804686,-0.2302842587,0.1360697597,0.1197247058,-0.2654082775,0.0479442663,0.2560563684,0.2586598098,-0.1782319695,-0.1489072144,0.1788307279,-0.2246099263,0.0703034028,0.0440880843,0.3644196093,-0.2849558592,-0.2395537049,-0.1893162578,-0.2350794524,-0.1941615939,-0.187835604,0.1720422506,0.5530490279,-0.4830772579,-0.0465624295,-0.4554136395,-0.0557204336,0.0420507528,0.059990108,-0.1993482709,0.1053986698,-0.3029534817,-0.0673145503,0.5750009418,-0.219153285,-0.4035130143,0.2735553682,-0.172700122,0.0276703667,0.0239073969,0.1319134533,0.1925355643,-0.3088919222,-0.3873092234,-0.0334397294,-0.0646378994,-0.2213612795,-0.3128040135,-0.1074304804,0.5276122689,0.036488194,0.13503021,-0.0540118702,0.2751266658,0.0094174203,0.3650549352,0.1358383149,0.0362217166,0.0742885768,-0.0089339912,0.1816091686,-0.0343146659,-0.1788937896,-0.370862782,0.0983280316,0.1578837186,0.0319679044,0.0578098372,-0.0395850949,-0.0441822745,0.176623553,-0.2002382427,0.0091595007,0.1059279591,0.0371709205,-0.228386879,0.150171414,-0.3601384461,-0.3355703354,-0.0756464526,0.1033099219,-0.2184205651,0.4458243251,-0.0775557607,-0.0546324924,0.2532230914,0.3302969038,0.2176555693,-0.1338980347,-0.1547198892,0.225530237,0.3120882213,0.0964817703,0.2038223445,0.2202825546,0.4631510973,-0.4166270792,0.4512762427,0.4248676896,0.0157839339,-0.0111361668,-0.4655109346,0.1208941489,-0.4010038972,0.3006582558,0.1231233552,0.0035366071,0.3432372808,0.0029793673,0.0387485735,-0.1294725239,0.2690471411,0.0817266405,-0.0146125136,-0.0484843403,-0.4041103125,0.2343586981,-0.1110977903,0.142692402,-0.2079056054,0.1828098595,-0.2422186732,-0.1872489154,0.2078428715,0.1649779528,0.1381793618,0.1974901557,0.00771394,0.1722227633,0.1840904504,-0.1922398806,0.2213065922,0.2634327114,-0.2253491729,0.1050514877,0.11574696,0.0659233034,-0.2038090527,-0.3943380713,0.1596047878,0.3535887003,-0.2750309408,-0.172228083,-0.383197397,-0.708920002,0.0878656879,-0.0594570749,-0.3682579696,-0.550560534,0.1306333691,0.3110088706,-0.3960461915,0.2758713663,-0.5261030793,0.0930711403,-0.0833637863,0.377148807,0.029483242,-0.315015018,-0.1494927406,0.1138873547,0.3704943359,0.3368929923,0.3391319811,0.2285357863,0.0902660713,-0.5124884248,-0.3946644664,0.1821422726,-0.0811305791,0.3599928916,0.0334619135,-0.0304460786,-0.0033648976,-0.0145208053,0.1848327667,-0.0334529392,0.0808227807,0.0377494097,0.2265972346,-0.0568405129,0.0043872967,-0.2615486085,-0.1642236412,-0.5408262014,0.0232084766,0.1773352772,0.2984770536,0.2128200233,-0.110032782,0.0811095014,0.1700314879,-0.1284519434,0.08159215,0.2522673607,0.2351938635,-0.1482935697,-0.3752676547,0.1431304067,0.1678874493,-0.1806288958,-0.0289230738,-0.6907394528,0.2365356535,0.0004746777,0.0933537111,0.0852360874,-0.1197598875,0.2346420884,0.3941362798,-0.0285920184,0.0604873337,-0.1572939903,-0.1623924971,-0.0272981729,0.2487436384,0.0613057353,0.2927386463,-0.0788261741,0.4196695387,0.1608722657,0.1330552101,0.3300288022,-0.1292558014,0.3863185942,0.0880506858,-0.364426285,0.3801051974,0.2277207226,-0.2473003566,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/897","title":"Dataset viewer issues","comments":"Ok, I redirected on our side to a new url. \u26a0\ufe0f @srush: if you update the Streamlit config too to `\/datasets\/viewer`, let me know because I'll need to change our nginx config at the same time","body":"I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues\/bugs though:\r\n\r\n- the URL is still under `nlp`, perhaps an alias for `datasets` can be made\r\n- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user\r\n\r\n```bash\r\nIndexError: list index out of range\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 316, in <module>\r\n    st.table(style)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 122, in wrapped_method\r\n    return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 367, in _enqueue_new_element_delta\r\n    rv = marshall_element(msg.delta.new_element)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 120, in marshall_element\r\n    return method(dg, element, *args, **kwargs)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 2944, in table\r\n    data_frame_proto.marshall_data_frame(data, element.table)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/elements\/data_frame_proto.py\", line 54, in marshall_data_frame\r\n    _marshall_styles(proto_df.style, df, styler)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/elements\/data_frame_proto.py\", line 73, in _marshall_styles\r\n    translated_style = styler._translate()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/pandas\/io\/formats\/style.py\", line 351, in _translate\r\n    * (len(clabels[0]) - len(hidden_columns))\r\n```\r\n\r\n- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wmt19&config=cs-en). This problem goes away when you enable \"List view\", because then some syntax highlighteris used, and the special characters are coded correctly.\r\n","comment_length":36,"text":"Dataset viewer issues \n I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues\/bugs though:\r\n\r\n- the URL is still under `nlp`, perhaps an alias for `datasets` can be made\r\n- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user\r\n\r\n```bash\r\nIndexError: list index out of range\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 316, in <module>\r\n    st.table(style)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 122, in wrapped_method\r\n    return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 367, in _enqueue_new_element_delta\r\n    rv = marshall_element(msg.delta.new_element)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 120, in marshall_element\r\n    return method(dg, element, *args, **kwargs)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/DeltaGenerator.py\", line 2944, in table\r\n    data_frame_proto.marshall_data_frame(data, element.table)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/elements\/data_frame_proto.py\", line 54, in marshall_data_frame\r\n    _marshall_styles(proto_df.style, df, styler)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/elements\/data_frame_proto.py\", line 73, in _marshall_styles\r\n    translated_style = styler._translate()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/pandas\/io\/formats\/style.py\", line 351, in _translate\r\n    * (len(clabels[0]) - len(hidden_columns))\r\n```\r\n\r\n- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wmt19&config=cs-en). This problem goes away when you enable \"List view\", because then some syntax highlighteris used, and the special characters are coded correctly.\r\n \n Ok, I redirected on our side to a new url. \u26a0\ufe0f @srush: if you update the Streamlit config too to `\/datasets\/viewer`, let me know because I'll need to change our nginx config at the same 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/888","title":"Nested lists are zipped unexpectedly","comments":"Yes following the Tensorflow Datasets convention, objects with type `Sequence of a Dict` are actually stored as a `dictionary of lists`.\r\nSee the [documentation](https:\/\/huggingface.co\/docs\/datasets\/features.html?highlight=features) for more details","body":"I might misunderstand something, but I expect that if I define:\r\n```python\r\n\"top\": datasets.features.Sequence({\r\n  \"middle\": datasets.features.Sequence({\r\n    \"bottom\": datasets.Value(\"int32\")\r\n  })\r\n})\r\n```\r\n\r\nAnd I then create an example:\r\n```python\r\nyield 1, {\r\n  \"top\": [{\r\n    \"middle\": [\r\n      {\"bottom\": 1},\r\n      {\"bottom\": 2}\r\n    ]\r\n  }]\r\n}\r\n```\r\n\r\nI then load my dataset:\r\n```python\r\ntrain = load_dataset(\"my dataset\")[\"train\"]\r\n```\r\n\r\nand expect to be able to access `data[0][\"top\"][0][\"middle\"][0]`.\r\n\r\nThat is not the case. Here is `data[0]` as JSON:\r\n\r\n```json\r\n{\"top\": {\"middle\": [{\"bottom\": [1, 2]}]}}\r\n```\r\n\r\nClearly different than the thing I inputted.\r\n```json\r\n{\"top\": [{\"middle\": [{\"bottom\": 1},{\"bottom\": 2}]}]}\r\n```","comment_length":27,"text":"Nested lists are zipped unexpectedly \n I might misunderstand something, but I expect that if I define:\r\n```python\r\n\"top\": datasets.features.Sequence({\r\n  \"middle\": datasets.features.Sequence({\r\n    \"bottom\": datasets.Value(\"int32\")\r\n  })\r\n})\r\n```\r\n\r\nAnd I then create an example:\r\n```python\r\nyield 1, {\r\n  \"top\": [{\r\n    \"middle\": [\r\n      {\"bottom\": 1},\r\n      {\"bottom\": 2}\r\n    ]\r\n  }]\r\n}\r\n```\r\n\r\nI then load my dataset:\r\n```python\r\ntrain = load_dataset(\"my dataset\")[\"train\"]\r\n```\r\n\r\nand expect to be able to access `data[0][\"top\"][0][\"middle\"][0]`.\r\n\r\nThat is not the case. Here is `data[0]` as JSON:\r\n\r\n```json\r\n{\"top\": {\"middle\": [{\"bottom\": [1, 2]}]}}\r\n```\r\n\r\nClearly different than the thing I inputted.\r\n```json\r\n{\"top\": [{\"middle\": [{\"bottom\": 1},{\"bottom\": 2}]}]}\r\n``` \n Yes following the Tensorflow Datasets convention, objects with type `Sequence of a Dict` are actually stored as a `dictionary of lists`.\r\nSee the [documentation](https:\/\/huggingface.co\/docs\/datasets\/features.html?highlight=features) for more details","embeddings":[0.2298384905,-0.4676939547,-0.1091862842,0.3930577338,0.0112235183,-0.0073927417,0.2414543033,0.0436257534,0.1704822183,0.1277209669,-0.1480687261,0.4359344244,0.2361756712,0.2945679426,-0.0669526309,-0.0988031253,0.2758648992,-0.0747677162,-0.1930995435,-0.1146473587,-0.3136231005,0.2664879262,-0.3605991602,0.0551948212,0.0843064338,-0.0568167605,-0.1831727624,0.1451124549,0.1657565385,-0.0842644498,0.5213048458,0.0379811637,-0.0886196643,0.5056124926,-0.0001140757,0.2123831511,0.1992186159,-0.0585278496,-0.2496746927,-0.3481115401,0.0021727781,-0.4233009219,0.1910582185,-0.2739611268,-0.0578965172,-0.2668310702,-0.027284259,-0.4505228996,0.5443418026,0.0647106767,0.2045087218,0.2837791443,0.0236122441,0.0481437519,0.2327332348,0.0205130894,-0.1190875769,0.0153840426,0.1555838287,0.0709386021,0.1159702092,-0.0186715648,0.0423263907,0.0694481134,0.2614141405,0.4643959403,-0.2956536412,-0.4514957666,-0.3163237274,0.2530359328,0.0560796261,-0.1317227781,-0.385786891,-0.5079496503,-0.3157915473,-0.4794180989,0.2364197224,0.1448518634,-0.0973740146,0.2124937028,-0.1825155169,0.1744580567,-0.0692022443,0.3213108182,-0.1747741252,0.3411718011,-0.2386367768,0.1546947062,-0.1007574424,-0.1128670499,0.1495955139,-0.4561371207,0.2408811301,0.1273013055,-0.1851060539,-0.0407877788,0.0541589707,-0.3483132124,0.0675065592,0.104037106,-0.0081319045,0.1614092886,-0.3872076869,-0.0663330257,0.2979258597,0.2695173025,-0.0865092576,-0.0295973197,-0.1483498067,0.296833396,0.1175824329,0.0159885343,0.2882220745,0.0699860975,-0.1379981041,-0.0307000056,0.3500602841,0.1445537806,-0.336206913,0.101761207,-0.4738681316,-0.0749661848,-0.0336969607,0.0638875291,-0.3206653297,0.1525014639,-0.0071569891,0.2471447438,-0.134557724,0.3446595669,-0.2371739298,-0.1904279739,-0.0652847886,0.0663499311,-0.0420984179,-0.0953788757,0.165487349,0.2125787437,0.1812343299,-0.267642647,0.039244622,0.0152299171,0.3261408508,0.1474461704,-0.1008216143,0.3499751091,0.2230881602,-0.181228146,-0.1014808938,0.0566560216,-0.5195723176,-0.0518508218,0.1581280828,0.133820042,-0.4378278255,0.0968860388,-0.0804307014,0.1471621841,0.3309530914,0.3669150174,0.0343742371,0.0342494622,-0.508404851,-0.2293902636,0.0003260465,-0.073894307,-0.2071796358,-0.0262299776,-0.101927571,0.0716583878,0.2804749012,0.3513951302,-0.1295993477,0.2629665136,-0.3151412308,0.2650033236,0.1025323644,0.0171958786,-0.3716728389,0.216081202,-0.1512710452,0.5964806676,0.0167001151,0.0097154761,0.0972158089,-0.1860406846,0.4022799134,0.1987628192,0.3353220224,-0.2042614371,-0.1204482689,-0.0844999179,0.4401874244,-0.2315003872,-0.5066453815,0.0658121705,-0.2022055387,-0.0535663217,0.2737059295,-0.0846181214,-0.022978913,0.426255703,-0.050876908,0.1780268103,0.0066015054,0.057343334,-0.5211535692,0.1728512347,-0.1369978935,-0.1579048485,-0.3792924881,-0.1082615778,-0.1152307987,0.3877604902,-0.1813657284,-0.0864765346,0.1596705765,0.1504893452,-0.0718193054,-0.126271829,-0.1037976593,0.1233520582,0.0714035407,0.0906172246,-0.6953419447,0.3920565248,0.2789391875,0.0548487045,-0.2176354676,0.2317307889,-0.0456582345,-0.2940310836,0.0523777828,0.2249441743,0.23389557,-0.1588659734,-0.4249760807,0.0298046209,0.3272547722,-0.0199340992,-0.1682194918,0.4148456156,-0.0735301897,-0.0860977098,-0.4220732152,0.4835673273,-0.0940231383,0.4029453993,-0.1599096209,-0.0625059307,0.0480617508,0.1892828047,-0.203675285,-0.2096896619,-0.0529219136,0.2995162308,0.1877167523,0.2752779722,-0.243581444,0.4470909238,0.7448371053,0.0350882672,-0.0720682368,0.2781617045,-0.0195389204,-0.24792023,-0.0599321611,0.0952594429,0.3652765453,0.0646614954,0.2216386199,0.115891628,-0.1229742169,-0.2536591589,-0.0077809398,0.2639900446,0.1566016376,-0.0100727268,0.2818867564,-0.0750234798,-0.25094679,0.1176291183,-0.0121422997,0.0026538027,-0.1509614885,0.3371892571,-0.3686792254,0.0113473218,0.0587112792,-0.5471538901,-0.094660528,-0.314432025,-0.1246987954,0.2293175161,-0.0363037214,-0.0210861433,0.5416872501,0.0074516344,0.2658097446,0.0552181304,-0.0712552443,0.0766024962,-0.2392053306,0.0723662525,-0.0737971365,-0.2192000002,-0.0197297335,0.2536250353,-0.2125402242,-0.2188146561,-0.20200409,0.0020514855,-0.4176460207,0.2868644893,0.124497503,0.3577858508,0.0863532647,-0.1363054365,0.0286891311,0.7422843575,-0.2904424369,0.0333607905,0.1572047919,-0.0130648529,-0.2704990804,-0.3338178396,-0.2083853483,-0.0691349134,0.6958264112,0.2305352539,0.1344957203,0.1984081417,-0.0606457628,-0.2014248818,0.3159995675,-0.1195729449,-0.2812665403,-0.3180609345,0.290892154,-0.150292635,-0.4329236448,-0.1716616005,-0.2320409268,0.1632248759,-0.0522457212,-0.2677069902,-0.0519596711,-0.3353410065,0.2634570897,0.1147342622,-0.0183236897,0.3230091929,0.0054677785,0.0031281051,-0.0969485864,-0.0848343,0.2513024211,0.0311405659,0.1827379018,0.1495402455,0.2881786227,0.0694663823,0.3716077805,0.4487586319,-0.0575874783,0.0708739385,-0.2050477564,0.0590154119,-0.3577525318,-0.0285773091,-0.3248988688,-0.0913258642,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/888","title":"Nested lists are zipped unexpectedly","comments":"Thanks.\r\nThis is a bit (very) confusing, but I guess if its intended, I'll just work with it as if its how my data was originally structured :) \r\n","body":"I might misunderstand something, but I expect that if I define:\r\n```python\r\n\"top\": datasets.features.Sequence({\r\n  \"middle\": datasets.features.Sequence({\r\n    \"bottom\": datasets.Value(\"int32\")\r\n  })\r\n})\r\n```\r\n\r\nAnd I then create an example:\r\n```python\r\nyield 1, {\r\n  \"top\": [{\r\n    \"middle\": [\r\n      {\"bottom\": 1},\r\n      {\"bottom\": 2}\r\n    ]\r\n  }]\r\n}\r\n```\r\n\r\nI then load my dataset:\r\n```python\r\ntrain = load_dataset(\"my dataset\")[\"train\"]\r\n```\r\n\r\nand expect to be able to access `data[0][\"top\"][0][\"middle\"][0]`.\r\n\r\nThat is not the case. Here is `data[0]` as JSON:\r\n\r\n```json\r\n{\"top\": {\"middle\": [{\"bottom\": [1, 2]}]}}\r\n```\r\n\r\nClearly different than the thing I inputted.\r\n```json\r\n{\"top\": [{\"middle\": [{\"bottom\": 1},{\"bottom\": 2}]}]}\r\n```","comment_length":28,"text":"Nested lists are zipped unexpectedly \n I might misunderstand something, but I expect that if I define:\r\n```python\r\n\"top\": datasets.features.Sequence({\r\n  \"middle\": datasets.features.Sequence({\r\n    \"bottom\": datasets.Value(\"int32\")\r\n  })\r\n})\r\n```\r\n\r\nAnd I then create an example:\r\n```python\r\nyield 1, {\r\n  \"top\": [{\r\n    \"middle\": [\r\n      {\"bottom\": 1},\r\n      {\"bottom\": 2}\r\n    ]\r\n  }]\r\n}\r\n```\r\n\r\nI then load my dataset:\r\n```python\r\ntrain = load_dataset(\"my dataset\")[\"train\"]\r\n```\r\n\r\nand expect to be able to access `data[0][\"top\"][0][\"middle\"][0]`.\r\n\r\nThat is not the case. Here is `data[0]` as JSON:\r\n\r\n```json\r\n{\"top\": {\"middle\": [{\"bottom\": [1, 2]}]}}\r\n```\r\n\r\nClearly different than the thing I inputted.\r\n```json\r\n{\"top\": [{\"middle\": [{\"bottom\": 1},{\"bottom\": 2}]}]}\r\n``` \n Thanks.\r\nThis is a bit (very) confusing, but I guess if its intended, I'll just work with it as if its how my data was originally structured :) \r\n","embeddings":[0.2184679955,-0.3172184825,-0.1545520872,0.4096399546,0.0198083632,0.0022643409,0.1813217551,0.1284821928,0.12042135,0.1107622907,-0.0894270167,0.5233137608,0.330974251,0.2051908076,-0.109711796,-0.0653213114,0.3139220476,-0.0546331406,-0.0289824065,-0.0713038146,-0.3160270452,0.2633286417,-0.3739173114,0.0464868322,0.1322630197,-0.052191142,-0.257597208,0.3128820956,0.1092591286,-0.0722756535,0.4127613306,-0.0793377608,-0.1082954109,0.4087022543,-0.0001130303,0.1514425874,0.2343340516,-0.1096897945,-0.2292565107,-0.2896882892,0.1124610975,-0.4846199155,0.1144394651,-0.254737854,-0.0396972746,-0.2845827341,-0.1264148057,-0.6383929849,0.4745340943,-0.1257840693,0.2245387584,0.2374026626,0.066508241,0.0422530994,0.2652522027,-0.0834824145,-0.0373349898,0.1073116064,0.2651552558,-0.0105168568,0.1435327828,-0.0846476629,-0.1027298719,0.0434745848,0.142403692,0.3884346187,-0.3656547666,-0.4055756927,-0.31646505,0.1316068769,0.1293771714,-0.080391638,-0.3327471316,-0.4394020736,-0.3403945863,-0.4365845323,0.1443232149,0.2944245338,-0.0351609923,0.2033887357,-0.1429800838,0.1454972625,0.026514465,0.3943552077,-0.0928072035,0.5669482946,-0.1781391799,0.2075331062,-0.1415402293,-0.0978918895,0.3217330575,-0.5392986536,0.1195542812,0.0827537253,-0.0811452046,0.0289473999,-0.0254871789,-0.2950782776,0.0209243447,0.2430077046,0.0097585898,0.0969810039,-0.2464405894,-0.0963637009,0.3439159393,0.230506897,-0.1567772627,0.0480112173,-0.1993891001,0.2459215224,0.0642057806,-0.0743023232,0.3313893378,-0.0181406587,0.0041822796,0.0940826386,0.3247089982,-0.006613283,-0.3000763655,-0.0172231663,-0.6085450053,-0.1270057708,-0.0157090407,-0.021211097,-0.218605265,0.2345955968,-0.066056788,0.3846606612,-0.1284175813,0.2158971429,-0.1964947134,-0.1969259232,-0.1403606832,0.0498289317,-0.0228432324,-0.0731570721,0.1988915205,0.3086185753,0.0985997394,-0.3232749999,0.0642548949,0.0588632487,0.299834162,0.0770312697,0.04181787,0.3301210701,0.2961995006,-0.1713803411,-0.0828469694,0.1917276978,-0.6464284658,-0.0623244494,0.1348978579,0.153404057,-0.4481498897,0.0445792265,0.0383603871,-0.0374039449,0.3544823825,0.2428795695,0.0145594385,0.0994814262,-0.3080238998,-0.1964315772,-0.1299649328,-0.061015334,-0.2842946351,0.1140612587,-0.2470975369,0.1278833151,0.4151358306,0.1677791476,-0.1544118971,0.2156068981,-0.3377684653,0.2923148572,0.1259207875,0.134586677,-0.4144970775,0.3028047085,-0.1807539761,0.5522437096,0.0382716246,-0.0058981394,-0.1193735525,-0.1312676668,0.4894069135,0.1428713351,0.3969982266,-0.1976763159,-0.2331183404,-0.0353870355,0.4713866413,-0.1843084842,-0.5262537599,0.1478794217,-0.1520498842,-0.1689659506,0.3451805711,-0.127233699,0.0350561962,0.4289444685,0.0043613054,-0.0003774321,-0.0241305437,0.0816469565,-0.3355894983,0.0989226699,-0.2258457243,-0.2860853672,-0.4463803172,-0.0183767788,-0.1477211714,0.2564418614,-0.1700654775,-0.0308613479,0.1736151874,0.2090605497,-0.1163886189,-0.3199609518,-0.0105879959,0.0195043832,0.3265604079,0.0976136401,-0.6830198765,0.4054053724,0.287594229,-0.0113849714,-0.1458497047,0.0695441589,0.1524824053,-0.2553286552,0.0180172846,0.3207508028,0.1593113393,-0.1835816354,-0.3426262736,-0.1545603424,0.152085945,-0.0245116409,0.0242283363,0.5047954321,-0.0688403696,-0.037720222,-0.2710852027,0.5477904677,-0.2554773986,0.4314802885,-0.2103954256,-0.0547998399,-0.0019476638,0.1101335883,-0.1451644599,-0.1782767326,-0.0519486926,0.3697811365,0.1320610195,0.2217051834,-0.127253592,0.5368802547,0.6226235628,0.0149432728,-0.1085233986,0.2366561741,-0.0172710251,-0.2339497209,-0.0827031359,0.1318300515,0.4769052267,0.1663245559,0.2383282781,0.0633735284,-0.0466573946,-0.2988387048,0.0415332839,0.294847697,0.1417426616,0.0189413652,0.2471589893,-0.1103716344,-0.1890186518,0.1310887337,-0.0676205233,-0.0555508547,-0.1847279221,0.4284857213,-0.4148808122,0.1286701411,0.008959488,-0.6086062193,-0.1259289086,-0.367431581,-0.0962766632,0.1630174369,-0.0332720689,-0.0793730319,0.3183373809,-0.050881777,0.2074662745,0.0344276726,-0.0979534984,0.0549232662,-0.3040453494,0.1165449396,-0.0992763788,-0.1788055301,-0.1328109205,0.2860119045,-0.0628293157,-0.0983806923,-0.1677879244,-0.0613331199,-0.3298401833,0.4358928502,-0.0053036991,0.2740277946,0.1420147717,-0.16843234,-0.0573893785,0.8114554286,-0.2242003679,-0.0530012213,0.1051107273,-0.1221103892,-0.2619016171,-0.2897172868,-0.2417907417,-0.0876349509,0.5448132753,0.2349026799,0.0652129278,0.0126600154,-0.0915844589,-0.4041164815,0.4660033584,-0.2062158734,-0.2937187254,-0.362541914,0.3837715983,-0.1704190671,-0.307312429,-0.141105026,-0.2317969948,0.2073756009,-0.0551498756,-0.2060276866,0.0694100633,-0.2908574641,0.177852422,0.0170119833,0.0154639743,0.252792716,0.1166265607,-0.0228671432,-0.1234941036,-0.0833317712,0.1336052865,-0.1174999177,0.3187896907,-0.0142855374,0.1677422523,0.0150169525,0.2484380007,0.3785092831,0.1109360531,0.175862506,-0.1604481488,0.041153077,-0.2754459083,-0.0336143374,-0.4888683558,-0.063897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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"Yes right now `ArrayXD` can only be used as a column feature type, not a subtype.\r\nWith the current Arrow limitations I don't think we'll be able to make it work as a subtype, however it should be possible to allow dimensions of dynamic sizes (`Array3D(shape=(None, 137, 2), dtype=\"float32\")` for example since the [underlying arrow  type](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L236) allows dynamic sizes.\r\n\r\nFor now I'd suggest the use of nested `Sequence` types. Once we have the dynamic sizes you can update the dataset.\r\nWhat do you think ?","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":85,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n Yes right now `ArrayXD` can only be used as a column feature type, not a subtype.\r\nWith the current Arrow limitations I don't think we'll be able to make it work as a subtype, however it should be possible to allow dimensions of dynamic sizes (`Array3D(shape=(None, 137, 2), dtype=\"float32\")` for example since the [underlying arrow  type](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/features.py#L236) allows dynamic sizes.\r\n\r\nFor now I'd suggest the use of nested `Sequence` types. Once we have the dynamic sizes you can update the dataset.\r\nWhat do you think ?","embeddings":[-0.1616059095,0.0108419182,-0.066619046,0.1772314459,0.3133355081,0.0057711992,0.5568307638,0.1153363064,-0.1397421658,0.0790204331,0.1618123502,0.0611892194,-0.2482876331,0.2882796824,0.1433374882,-0.4168105423,0.1650112569,0.257226795,0.0553811863,0.1130094305,-0.1897820085,-0.2030609548,-0.2229629159,0.1924257725,-0.2032823563,-0.1325577945,-0.231277436,-0.1746644974,-0.2154562771,-0.4873974323,0.3294047117,0.1431981474,0.2426173389,0.1376820207,-0.0001154924,-0.0107514635,0.3926018178,0.0132955424,-0.1953313798,0.0277076904,0.0773622021,-0.4880388379,0.1142667159,-0.3838246763,0.1063902453,-0.9378988147,-0.0416392684,-0.089719303,0.1050876603,0.0500929318,0.1922921687,0.1715771407,0.3899645805,-0.0105450368,0.4836086333,0.3242554367,-0.2707018256,-0.0386977233,0.3831052482,0.1397040933,0.0771353468,-0.1748768538,-0.0284891389,0.0574248694,0.4720866382,0.1713972539,0.2727361321,-0.1684577018,-0.1026954353,0.4105275571,0.4952409863,-0.2319118083,-0.3075928688,-0.3115698397,0.0032654202,-0.1753804535,-0.107309036,0.213774845,-0.1296340227,-0.0140746748,-0.153499797,0.1548554152,-0.4418312311,0.3143471777,0.107572265,0.1329267472,0.0735217705,0.2417716831,-0.2476113737,-0.5056387782,0.4316933155,-0.2360915989,-0.0270423125,0.1407017559,0.007250621,-0.0080920141,-0.1698652208,-0.3400796354,0.4770568311,-0.1000495404,0.3479025066,0.0111999288,0.1662002504,0.0931350738,0.0409678333,0.3405994475,-0.1212562546,-0.274788022,-0.1511282027,0.1171398014,0.1713352799,0.0727716759,-0.2241659313,-0.1283685118,0.2919893861,-0.1056135818,0.2182835788,0.3313554227,0.0621668063,-0.0202995911,-0.5062046051,-0.1305086911,-0.0367953256,0.1205356345,0.0674209297,0.134990871,-0.0809327886,0.3865687251,0.1314042807,-0.005581412,-0.096765779,-0.1221151724,-0.1639343798,-0.0432264544,-0.0311276615,0.266010344,-0.0362326242,-0.0843537003,0.1206867918,0.151942417,-0.0673132837,0.1036842763,0.1126583666,0.0875582546,-0.1935007125,-0.2413788289,-0.2568321824,0.0092555583,-0.2337166965,0.3463324308,0.0196913555,-0.2524262071,-0.4545671642,0.1253437996,-0.2485116571,-0.0374918729,-0.2303138226,0.2204686403,0.2725118697,0.0192481298,0.1092601791,-0.0748157352,0.0403708667,-0.4584237337,-0.0161776636,-0.0629338846,-0.279412061,0.1130259931,0.0052774181,-0.0103954813,-0.1350494027,-0.0232585538,-0.1822223067,0.0009784076,-0.0583328903,0.199472785,0.4691610038,0.2001069784,-0.044287011,0.0683372617,0.0993991643,-0.184038803,0.3197771907,0.0600170381,-0.0458100401,-0.1721129268,-0.2826547027,0.3145986497,-0.0964618996,-0.169590503,-0.0800119117,-0.5706949234,0.3061468303,0.1484428793,0.0984555408,-0.0734474957,0.0419839844,-0.4109460711,0.2504271865,-0.4095450342,0.2487358302,-0.0755418465,0.2601501048,-0.0907999352,0.1170577183,-0.278133601,-0.8328418136,0.2076082975,-0.3454922438,-0.1067001,0.0295050927,-0.1889729053,-0.1704300195,0.1456988603,-0.1304069459,0.1564953774,0.0873014778,0.003468764,0.056150198,-0.2968775928,-0.2516316772,-0.3527005613,-0.0608819425,-0.0207408238,-0.4056304097,0.4219984412,0.1468448043,-0.2653646767,-0.2945557535,0.2871393263,-0.0391316265,-0.0812378004,-0.0405562706,0.2517087162,-0.0943240002,0.0002011115,-0.5369089246,0.1763547063,0.0346698388,-0.1743862033,0.3629363775,0.0060334634,0.0265163053,-0.1310422868,0.2302036434,0.4568025768,-0.00220561,0.4285460413,-0.1744944006,-0.0404056124,-0.0721176341,0.1969555914,-0.0736600012,-0.1658544093,-0.2773365378,0.2361840755,0.1594388634,-0.0260374323,-0.202352047,0.1595479995,0.2601911128,-0.1021904275,0.0460606888,0.1400887966,-0.2551288307,0.0729365945,0.1663903445,0.1524063498,0.1684515923,0.2051693499,-0.1223241985,-0.0231505018,-0.1620201916,-0.0988160968,0.2174188793,0.1760276556,0.4924125671,0.0949758366,0.4513032734,-0.1852277368,-0.1904083043,-0.0040292214,0.0273078512,0.0714368448,-0.1541869342,0.0563471504,-0.095658578,-0.0615262166,-0.0848443359,-0.6617180109,-0.0479578748,0.0101695135,0.0034061878,0.0023362103,-0.1764207333,0.2182947099,0.0707682073,-0.1285052747,0.2033283412,-0.1506586671,-0.1665368378,0.0061287363,-0.0245495308,0.063650392,0.2478206456,-0.0018959439,0.0085574938,0.2448384613,0.0823839381,-0.331317693,-0.31513533,-0.008297408,-0.1528569609,0.232638672,0.431912303,-0.0495447852,0.1705277413,-0.3125085533,0.3104337454,0.0129286656,-0.2742746472,0.1388646811,-0.2198174149,0.1748547554,-0.1059625894,0.007749442,-0.1792992055,-0.3620901704,0.3248399198,0.4152484536,0.1981838644,0.2461229265,0.3520291448,0.171993956,-0.0005932474,0.0170385297,0.1446620226,0.2558503151,0.3828643262,-0.0266483743,-0.1819356531,-0.1355111152,-0.9175083041,0.0974397585,0.3725898564,0.0600478686,-0.1703146398,-0.1563492268,0.2750999928,0.0853943676,-0.0136378491,0.4456554055,0.1230401099,0.0161780324,-0.0962931663,-0.0493287668,0.3227924705,0.2541518509,-0.0290216487,0.2366008013,0.3795795441,-0.116150856,0.6229423285,0.0335682631,-0.4343720973,0.3152050376,-0.2801405191,-0.1684267521,-0.1951777041,-0.0541303828,-0.1407594681,-0.030709805,-0.0905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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"> Yes right now ArrayXD can only be used as a column feature type, not a subtype. \r\n\r\nMeaning it can't be nested under `Sequence`?\r\nIf so, for now I'll just make it a python list and make it with the nested `Sequence` type you suggested.","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":45,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n > Yes right now ArrayXD can only be used as a column feature type, not a subtype. \r\n\r\nMeaning it can't be nested under `Sequence`?\r\nIf so, for now I'll just make it a python list and make it with the nested `Sequence` type you suggested.","embeddings":[-0.1496909857,0.0466833152,-0.0832266435,0.1190683097,0.2730078399,0.1201471463,0.6236190796,0.1325292736,0.0004305769,0.1169990227,0.2726795971,0.2137202024,-0.148215428,0.0873367488,0.1494974643,-0.3523188233,0.2304175645,0.3752020895,0.1984266639,0.0808257163,-0.1512219608,-0.096542038,-0.2307991832,0.3625943065,-0.2430000156,-0.1153224781,-0.1199117154,-0.083274208,-0.1397193968,-0.5142061114,0.400708884,0.0255213007,0.2322932035,0.1146209612,-0.0001181501,-0.0146637578,0.4348638058,0.0591648482,-0.1229830682,-0.0048914473,0.1011948138,-0.4042523205,0.2644628286,-0.4277358651,-0.0240509287,-0.9039796591,0.0427302048,0.0066637788,0.1252934635,0.2079314888,0.1442271024,0.1297579557,0.4764501154,-0.1806749701,0.4600847661,0.1405650675,-0.294495523,-0.0807835236,0.5120022297,0.1438986361,0.0992064923,-0.3135210872,-0.1309571117,0.070738256,0.4406239986,0.341997236,0.2460750192,-0.258565098,-0.2623868287,0.3397233784,0.4132310152,-0.1750988066,-0.1234787703,-0.1843845248,0.035751652,-0.2876292765,0.0165764224,0.108738631,-0.2263008356,0.0154627264,-0.0096945362,0.1207381189,-0.4009537697,0.3173990548,0.167513445,0.2928384542,0.0707616508,0.2630764842,-0.1044254303,-0.3481960297,0.5756608844,-0.2738814652,0.1003337279,0.2177470624,-0.0703516454,0.0229680296,-0.1750971228,-0.3643649518,0.4070279598,-0.0397160687,0.2340948582,-0.0654597878,0.262444824,0.1516705006,-0.0850233287,0.3085604012,-0.0135916527,-0.2896936536,-0.0956176296,0.135140568,0.069339782,0.0423803516,-0.2960188091,-0.0798206255,0.2877986133,-0.0363064967,0.2192562371,0.2294505835,0.0576040633,0.0001428305,-0.4910034239,-0.2163024694,-0.0469095036,0.2278482318,0.1335302591,-0.0912338495,-0.1209771037,0.3931637108,0.1145647317,0.0539095663,-0.1054858863,-0.0203385726,-0.1385608315,0.0261314381,-0.0925683603,0.149864018,0.1324961931,-0.0177042931,0.1824238598,0.1176808625,0.1183420047,0.1672090739,0.1050793156,0.1216110811,-0.0390887484,0.0213914569,0.0607711226,-0.0322818048,-0.2146696597,0.3445396423,-0.1502037346,-0.2812199295,-0.4611911476,0.0951247141,-0.2679341137,0.0268113408,-0.0855621696,0.0531741716,0.3206283748,-0.0092907809,0.0700603575,0.0084868576,0.0941199511,-0.3910106719,0.0102527263,-0.2070121318,-0.3107109666,0.1050077155,-0.18326886,-0.0511369854,-0.1033404246,-0.0341761559,-0.1830640733,0.211112693,-0.2438436449,0.3546352983,0.4485312104,0.1238437071,0.0761409104,0.0937400237,0.0458837189,-0.1258334666,0.2754472494,0.1234984547,-0.0420816056,-0.2332231998,-0.1978224665,0.13695544,-0.0650237277,-0.3227218688,0.0924911499,-0.3614104986,0.4532861412,0.0346874967,-0.0556673594,0.1533393115,-0.145034492,-0.3850358129,0.1831584722,-0.4505046904,0.1036106423,0.1198770404,0.2142672837,-0.0773840323,0.0693560019,-0.3480018079,-0.7058081627,0.1983203292,-0.331862241,-0.0510807261,-0.0323284976,-0.1590729654,-0.2161258608,0.1869294643,-0.2282211334,0.123631902,0.0654980689,-0.0101885274,0.1269925386,-0.2844240367,-0.3284108341,-0.3324742615,-0.027413439,-0.0462289862,-0.5676476359,0.4337337911,-0.0152600538,-0.3834446073,-0.2677718699,0.2581305206,0.0449853316,-0.0654633492,0.0735676512,0.2084098458,-0.0886835456,-0.1237079054,-0.5375239849,0.0925365314,-0.0326056667,-0.4755496085,0.148510769,0.1747869402,0.0008851513,-0.2654135227,0.1246099547,0.3514433205,0.2007004917,0.5677340031,-0.162301302,0.0468121879,-0.0505783074,0.1966940612,-0.0660024434,-0.082470648,-0.0508012064,0.2496977746,0.0501037538,-0.0416222885,-0.1632547975,0.2374595553,0.3121314943,-0.0719148219,0.0712543279,0.0841196105,-0.1205454245,0.0141477119,0.1988032311,-0.226331979,0.1835254133,0.1453007609,-0.1091471016,0.1152717471,-0.3458954692,-0.0947077125,0.1184055805,0.0416260101,0.4965810776,0.0626622066,0.2471486926,-0.216076836,-0.1896668822,-0.1980236322,-0.0887104049,0.1040930152,-0.2519029975,0.0450727008,-0.2113893479,-0.0883209705,0.0093189608,-0.5655494332,0.0671063662,-0.0881417468,-0.1248616129,0.1353561282,-0.0841241106,0.2511493564,-0.0522039458,-0.2421683669,0.2563661337,-0.0964112952,-0.253352344,-0.0717587173,0.026772311,0.0615802445,0.1554649621,0.0690751523,-0.0449589565,0.2691813111,-0.0338182524,-0.3550164402,-0.2752417028,0.0499213599,-0.2036171556,0.2274008393,0.4706684947,0.1386533529,0.0706332773,-0.5315939188,0.3367731869,0.0535758436,-0.3053088486,0.2561750412,-0.1780288666,0.0555123426,-0.0216427539,-0.0892763287,-0.138226822,-0.3535017371,0.1806749254,0.5079566836,0.2637710571,0.0704964027,0.2626504898,0.2246547639,0.0375612006,-0.0808727145,0.181336835,0.2828784287,0.459724158,-0.174524352,-0.2502916753,-0.0227665305,-0.8539394736,0.03768811,0.1683426648,0.1771163195,-0.1344535053,-0.1827836931,0.1993626356,0.1049528271,-0.0358290114,0.366969198,0.1059952453,0.0051781307,-0.1473027319,-0.0526276715,0.4297555089,0.0648394153,0.0059892545,0.2077502757,0.3540019095,-0.2904821336,0.4884471297,0.2339154929,-0.2760825455,0.3280070722,-0.2727936804,-0.1918717474,-0.3395034969,-0.0488788709,-0.259424895,0.0347659551,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"Yea unfortunately..\r\nThat's a current limitation with Arrow ExtensionTypes that can't be used in the default Arrow Array objects.\r\nWe already have an ExtensionArray that allows us to use them as column types but not for subtypes.\r\nMaybe we can extend it, I haven't experimented with that yet","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":48,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n Yea unfortunately..\r\nThat's a current limitation with Arrow ExtensionTypes that can't be used in the default Arrow Array objects.\r\nWe already have an ExtensionArray that allows us to use them as column types but not for subtypes.\r\nMaybe we can extend it, I haven't experimented with that yet","embeddings":[-0.0212208629,0.1621364951,0.0191255398,0.1933747083,0.3534964919,0.14655599,0.6769052148,0.1001415253,-0.1353637576,0.169750154,0.1754417866,0.2483867407,-0.2187324613,-0.0489068329,0.1682210267,-0.4851907492,0.1346689463,0.301538527,0.0228541978,0.0704353228,-0.0469424687,-0.1379909664,-0.2132520974,0.2988721728,-0.3213111758,0.0069461325,-0.0415838324,-0.2609827816,-0.2811144292,-0.5396837592,0.4548950791,-0.1367884874,0.2157600969,0.0541257896,-0.0001180573,0.1391656101,0.4717529714,0.1158182248,-0.2676101625,-0.0728083625,0.11657352,-0.3765859902,0.2254805863,-0.2949297726,0.0746296048,-0.8747572899,-0.0137047069,-0.0480413474,-0.0390084349,0.2231349796,0.1390294582,0.1530657113,0.3917603493,-0.1380421072,0.5562932491,0.202924341,-0.3445002139,-0.033176966,0.2575600147,0.1419353336,0.0736942366,-0.2520451844,0.0317469165,0.1535783261,0.3329479098,0.3048067093,0.3153316975,-0.214182049,-0.0936915204,0.358394742,0.4189319015,-0.1743405461,-0.2196032852,-0.1580248773,0.0351172648,-0.0825668797,-0.0719243437,0.1402474791,-0.1435849816,-0.0595130511,-0.0816430151,0.234032765,-0.4148140252,0.2877807319,0.1884303987,0.0627020672,0.1177320853,0.2468917817,-0.1241737679,-0.2687945068,0.3728429973,-0.2962972522,-0.0879029259,0.1672547311,0.0104683796,0.1018036157,-0.17798087,-0.295091331,0.3978040218,-0.0078654578,0.262419045,0.0521328934,0.2533870041,0.1738923937,-0.0227853209,0.2581659257,-0.0524807721,-0.0677274689,-0.1461710781,-0.0010091361,0.0735208914,0.0296862274,-0.26029405,-0.1566109955,0.4041567445,-0.1065996066,0.1935001612,0.3161326945,0.0838794187,-0.0544140004,-0.5178289413,-0.126952678,0.0819739774,0.0538603775,0.1410972327,-0.0200638101,-0.0995899886,0.4002565444,0.0466507711,-0.0912169069,-0.1167473197,-0.0064966544,-0.2495865077,-0.1215882674,0.0517685488,0.1578974575,-0.0178110804,-0.060043335,0.1222077906,0.1848501265,0.1443341523,0.0823199823,0.1457604319,0.1453525871,-0.117805317,-0.1048186645,0.0018953665,-0.0324768573,-0.2453708351,0.1876764297,-0.035435237,-0.2638182938,-0.5372961164,0.0882840231,-0.2288085073,0.0409309901,-0.302485615,-0.0318419971,0.176060617,0.0075882496,0.076031886,-0.1696111411,0.2447959632,-0.3598736525,0.1270972341,-0.1972584873,-0.2495065182,0.0388288461,-0.2804268897,-0.0530519858,-0.0234772153,-0.0567796342,-0.2708203197,0.0650436729,-0.1856776327,0.3091666698,0.535697639,0.3405401111,0.0292397663,-0.1499933153,0.1001170278,-0.1054030806,0.2279826403,0.0375015363,-0.0667348355,-0.1072203442,-0.2658880651,0.2532443702,-0.1829283386,-0.2341534942,0.0166667402,-0.4594129026,0.2168775946,0.0980007425,-0.0008795772,0.0604747236,0.0081520816,-0.4529452324,0.0485944264,-0.4307850301,0.2427630126,-0.0922999978,0.3001604676,0.0703751594,0.1670927554,-0.3774493337,-0.6568467021,0.2559567392,-0.3469610512,0.0396645591,0.0744251609,-0.1671468765,-0.1128228977,0.2092345059,-0.0401513577,0.212995261,0.0444583595,-0.0040363651,0.1563403308,-0.2169308066,-0.2036586553,-0.4877384901,-0.0552401952,0.0020829339,-0.4064585865,0.4541429877,-0.0363356397,-0.2464057058,-0.3527885973,0.2259245664,-0.1012142375,-0.0033876027,0.0250052679,0.170704633,0.0352447741,-0.1194292605,-0.4558752477,0.0306929294,-0.0753997043,-0.3779909015,0.1492902189,0.1526350528,0.0179340709,-0.1737755835,0.1094519496,0.3874122202,0.2484331727,0.5152691007,-0.0953628421,0.1582252085,-0.031422928,0.2399685532,-0.1496783346,-0.1498088986,-0.1329908222,0.1323253363,0.0241526403,0.0498933308,-0.0974258333,0.0543422885,0.2293841243,-0.0608253032,0.0662455857,0.1479123682,-0.2643353045,0.1247423813,0.2334783971,-0.0696420521,0.0761213675,0.1735021472,0.0079988455,-0.0898217037,-0.3006952703,-0.1095322669,0.1824299991,0.1665159762,0.5337377191,0.0058295014,0.3214497268,-0.1366489083,-0.1477596313,0.0287922323,-0.0188665222,0.0288343504,-0.2176436186,0.0899148285,-0.2739956379,0.0458775051,-0.0408506729,-0.5973742008,0.167632103,-0.0625094175,-0.108652994,-0.0140208555,-0.2081925869,0.2753811479,0.0204892047,-0.1851257086,0.1897025704,-0.1098251715,-0.1761683077,-0.0481249504,-0.1175917536,0.0242518932,0.2732262313,-0.0044652619,-0.0865117088,0.2622177601,-0.07133995,-0.3759306669,-0.3747505546,0.0583744124,-0.1334777772,0.2712836266,0.6011125445,-0.088197656,0.05690483,-0.487717241,0.3660814464,-0.049885422,-0.2371715307,0.4311589301,-0.2244034261,0.1103974655,-0.101535663,-0.0475695133,-0.0823078677,-0.2928113043,0.2672373354,0.4423721731,0.2078857571,0.1900187135,0.367729336,0.2935208976,-0.0595102161,-0.0418136157,0.210139513,0.3851242661,0.5554254651,-0.1326040775,-0.2390658557,0.0469727889,-0.91827178,0.0598658696,0.2745571733,0.0965736359,-0.053191565,-0.166264385,0.246754691,0.151778698,0.0734153092,0.236335665,0.0730143115,0.0306824204,-0.0971598029,-0.0720546618,0.3589542508,0.0592509732,-0.0544411652,0.0599308871,0.2749343216,-0.2500484288,0.4926742613,0.032344237,-0.3953185081,0.2900138795,-0.2813062072,-0.1571723223,-0.2730333507,-0.0383777022,-0.2033661604,0.0202534366,-0.0747557804,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"Cool\r\nSo please consider this issue as a feature request for:\r\n```\r\nArray3D(shape=(None, 137, 2), dtype=\"float32\")\r\n```\r\n\r\nits a way to represent videos, poses, and other cool sequences","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":28,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n Cool\r\nSo please consider this issue as a feature request for:\r\n```\r\nArray3D(shape=(None, 137, 2), dtype=\"float32\")\r\n```\r\n\r\nits a way to represent videos, poses, and other cool sequences","embeddings":[-0.1195075139,0.2424518317,-0.0825230032,0.0961937606,0.3023026884,-0.0807917342,0.6678081155,0.1274508834,-0.4523451328,0.2668219209,0.3651503921,0.1533161998,-0.2551758289,0.1488408297,0.2744103074,-0.4257724583,0.137148574,0.5718728304,0.1896610856,0.1134701744,-0.0811235532,-0.0910715684,-0.1586165726,0.1871943772,-0.3031569421,-0.0180845149,-0.1779543012,-0.329739064,-0.1345255077,-0.4514861703,0.2623895109,0.0389636084,0.1677659899,0.1494594365,-0.0001254906,-0.0167882256,0.5140053034,0.0055480083,-0.1965205371,0.0710476041,0.0390492938,-0.5094625354,0.2315442562,-0.261415422,-0.0486275256,-0.5891057849,-0.0373304486,-0.0661952943,-0.0366015285,0.1377487034,0.0999122933,0.2449054867,0.1895420998,-0.06588687,0.5248126388,0.610652566,-0.4015274942,-0.0032514634,0.4093408287,0.0484816991,0.0090829208,-0.0446680859,-0.1138379797,0.1528004557,0.3713417053,0.1253393888,0.415722698,-0.3516628444,-0.1197506264,0.3804672956,0.5132594109,-0.2269854546,-0.1633286476,-0.182523191,0.1375932992,-0.1191869304,-0.1681579649,0.2776908875,-0.3058533072,-0.1067285091,-0.3200063407,0.1812176108,-0.4519753158,0.4415179193,0.2319643945,-0.0324618071,0.0265332405,0.1550103873,-0.1543131471,-0.209668532,0.6051813364,-0.3144930005,0.1713800877,0.0266731288,0.2315782309,0.0007797396,-0.1781047583,-0.2584551573,0.2240985185,0.0240561981,0.1896695644,0.0009135625,0.3361068964,0.0549349301,-0.2350885868,0.1551093906,0.0196652636,-0.2865149081,-0.1086950898,0.1641214192,0.0523839481,0.0812797323,-0.0928827748,-0.1581509113,0.2144554853,0.1445916593,0.2246973515,0.332344532,0.0989628211,-0.0212183725,-0.6472465992,-0.1190168485,0.0493511483,0.1676117927,0.1162116081,0.0217959359,-0.1420206875,0.4201963842,0.0413306095,-0.1068359762,-0.1327309608,0.0284793191,-0.2020907849,0.0345518552,0.1474196017,0.2719275951,0.0682382137,-0.109878473,0.2920950949,0.11498826,0.2518875003,0.1304156184,0.2680571973,0.1843969524,-0.1766609102,-0.0291833673,-0.0108279614,0.0239786617,-0.201913923,0.2528160214,0.0359996557,-0.2911665738,-0.4017091393,0.067572996,-0.1332117319,0.114795804,0.0110675618,0.0392675735,0.1291614175,-0.0920967236,0.0919329748,-0.0653633699,0.0920566171,-0.4038434625,0.1100912169,0.0587433577,-0.3762679994,-0.006013969,-0.178534314,0.059890978,-0.0581475683,-0.0933830068,-0.2455004007,0.1491202414,-0.2815334499,0.0978553221,0.595564425,0.0683713183,-0.0665700361,0.1286036968,0.0778094828,-0.1911045015,0.2700201869,0.1773519665,-0.0744975433,-0.0917330533,-0.3189812899,0.3159507811,-0.1740139723,-0.2062491626,0.0853026509,-0.4805605412,0.5045505762,0.1227777302,0.0911306366,-0.0121926675,-0.0017257027,-0.3954717815,0.1035296321,-0.5231149197,0.226359427,0.0648657605,0.3272498846,-0.1017292067,0.0142545803,-0.2904191017,-0.7621379495,0.111858502,-0.3817775846,0.032397341,-0.0928597674,-0.0941649526,-0.0098006679,0.1435677707,-0.1588366777,0.1365599483,0.0186942574,0.2234544903,0.1755224764,-0.0927112252,-0.1479680538,-0.4886057973,0.1605400294,-0.0935594961,-0.3995197415,0.6104867458,-0.0304089449,-0.3412742615,-0.2333664894,0.3026459217,0.1116786525,-0.1268317699,0.0759551004,0.0232768264,-0.0467142202,-0.0072606667,-0.5716792345,0.145181343,0.1695394218,-0.5125933886,0.3018430769,0.2380748391,-0.0208656117,-0.2693944573,0.2650481761,0.5354461074,0.0164004322,0.5599130988,-0.1488922536,0.1069105789,-0.0834948793,0.3042312562,-0.171058476,-0.2814382017,-0.077081494,0.3215998113,0.2348310798,-0.0816382766,-0.1202621683,0.1673327982,0.3450403512,-0.1063046157,0.1170570776,0.1044826806,-0.167239517,0.0305953287,0.2194487154,-0.2486446351,-0.0095984172,0.1133675948,-0.1509042084,-0.0167223141,-0.1790010631,-0.1775204539,0.2399976403,0.1978987306,0.5377187133,0.1734980047,0.2530478239,-0.2342146337,-0.2563255131,-0.2838896215,0.0782159492,-0.0029493568,-0.1662907004,0.0819261819,-0.0985972136,-0.1753582805,-0.0538215861,-0.8407202363,-0.054962907,-0.0161434188,-0.1332004666,-0.0183025189,-0.1250449717,0.2904634476,0.13499704,-0.1161116585,0.188772127,-0.1279420555,-0.2394805998,-0.0208785441,0.0010634139,-0.0478813611,0.1938603371,0.1102154404,-0.0285726655,0.0893598944,0.1956140846,-0.4436903,-0.4140672684,0.076156199,-0.054647658,0.3043238223,0.6237299442,-0.028849924,0.1416362822,-0.4335573018,0.3720016479,0.0307335332,-0.1980158538,0.2701029778,-0.1913002133,0.1311505884,-0.0774391294,0.1274390668,-0.0436305888,-0.2877110243,0.1742714942,0.4535890222,0.2285217792,0.0646868199,0.3853501379,0.3064377606,0.0319394469,0.0617693998,0.2502890527,0.3139868975,0.3440012336,-0.1534818113,-0.1516786218,-0.079578571,-0.81973207,-0.0414601192,0.1995376348,0.0056264522,-0.3204469383,-0.1587313265,0.2815923393,0.0212312546,0.0466254018,0.3734765351,0.1232596338,0.1193044111,-0.0999317393,-0.247372061,0.320019871,0.1477468759,-0.1601671278,0.0104148667,0.423867017,-0.2608790696,0.6008725762,-0.0103291906,-0.365965575,0.3163512945,-0.168075338,-0.0308003966,-0.2145516574,-0.0248499848,-0.1443897337,0.0874093398,-0.096813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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"@lhoestq well, so sequence of sequences doesn't work either...\r\n\r\n```\r\npyarrow.lib.ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\n\r\n\r\n","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":23,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n @lhoestq well, so sequence of sequences doesn't work either...\r\n\r\n```\r\npyarrow.lib.ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\n\r\n\r\n","embeddings":[-0.1600914001,0.1551725566,-0.1365799904,0.2011847347,0.2378053665,0.0024272436,0.5615333319,0.0813985392,-0.1561107635,0.1868873239,0.3679437339,0.1477245986,-0.1952098012,-0.0751516744,0.1982174069,-0.3740194738,0.1051326022,0.4515889585,0.0958153531,0.08156012,-0.1296787113,-0.1000082046,-0.2531840801,0.2315312177,-0.254568696,-0.1155647039,-0.1887791753,-0.2444766909,-0.06532906,-0.6357085109,0.2661866248,0.0415746868,0.1111066714,0.2200846672,-0.0001201842,-0.0430640951,0.4993039668,0.0553212203,-0.186354965,-0.0483908392,-0.0165861212,-0.5161793232,0.2466993332,-0.2830962837,0.0256015882,-0.6335045099,-0.0507780239,-0.1111695543,0.0991790667,0.2162883282,0.1357580125,0.1012992039,0.2948573232,-0.137925908,0.4939403832,0.1856482029,-0.2645460367,-0.0064463499,0.4243836999,0.007798085,0.0039838003,-0.2260001004,-0.0914998204,0.1946606338,0.3239576519,0.254425168,0.1728006154,-0.2684161663,-0.1167435572,0.4633981884,0.5487830043,-0.1992025524,-0.2062028348,-0.1623517722,0.0251365583,-0.2495518625,-0.0168566722,0.2230231464,-0.2625147998,-0.0679104626,-0.2960222065,0.2252050191,-0.363474369,0.3561138809,0.2739037275,0.0741850212,0.0362597667,0.1922661662,-0.0426773988,-0.1601042449,0.4481900334,-0.2737982273,0.0411335751,0.2004844248,-0.0477827378,0.0812530294,-0.178754583,-0.2598590553,0.3866290748,0.1006254405,0.2853740156,-0.0497447625,0.2407920212,0.0112909209,-0.1565422118,0.2287863046,-0.0354375876,-0.1955488473,-0.1534389257,0.1481019109,0.1131824628,0.1070677564,-0.0841125026,-0.2671606541,0.342169106,-0.0100373942,0.1621028483,0.2628884912,-0.0015815891,0.1584796607,-0.7266274095,-0.227558881,-0.0102352845,0.234435901,0.067370452,-0.0787664577,-0.1443527788,0.320643723,0.069072336,0.0175569374,-0.2220751792,-0.0282847118,-0.1574119329,0.1480099559,-0.0056370324,0.1456133872,0.1314304173,-0.0021543058,0.1279088408,0.0469316244,0.2403779626,-0.0591416769,0.1808516383,0.1860529482,-0.1897394061,0.0794378445,-0.0132834725,-0.1411844492,-0.2069685161,0.3373622596,-0.0748132393,-0.2489276081,-0.2396021038,0.1195377484,-0.246433869,0.1436358094,-0.0567751117,0.0221291538,0.3504425287,0.0329586603,0.0972870141,-0.0463144891,0.1343153715,-0.3639497161,0.0316104814,0.029170379,-0.4040642977,0.1027748659,-0.0170007963,0.0420777649,0.0010878013,0.0002231555,-0.2483725697,0.316739589,-0.3398919702,0.3151932657,0.4771946669,0.2314243913,-0.1476486921,0.0780263394,0.0087051541,-0.1805401891,0.2836229205,0.1247067675,-0.0706822649,-0.1788303554,-0.2814875245,0.1967943013,-0.0682694316,-0.2683036625,-0.0474518985,-0.3563322127,0.3282252252,0.0095096314,-0.030009063,-0.0579109564,-0.0611507185,-0.3634927571,0.0984668285,-0.3985061944,0.1682198346,0.31026721,0.2540777922,-0.107006073,0.0180024803,-0.2215562314,-0.6752679944,0.1367509216,-0.3360004723,-0.0714466199,-0.1608760059,-0.0421856195,-0.1771296114,0.2556402981,-0.2550107837,0.1528830677,0.0767279789,0.1722394973,0.2379298359,-0.2839889526,-0.129172653,-0.2462118715,0.0235670023,-0.0346851312,-0.4833646715,0.6345498562,-0.0643043891,-0.3637027442,-0.2196398973,0.134125039,0.0457702875,-0.1106671616,0.0889573693,0.2254216522,0.063523598,-0.1646242142,-0.4993397295,0.0038288655,0.1241193116,-0.5690355301,0.0915195569,0.2302256972,0.0061184354,-0.2539628148,0.1037810966,0.3309844732,0.009149394,0.5892533064,-0.1080661267,-0.0582965054,-0.0223790538,0.2074624002,-0.1052798182,-0.1419218183,0.0479141511,0.364918381,0.3332372308,-0.0593755059,-0.0809152201,0.0729771703,0.2331445366,-0.0524769053,0.0030045197,0.0871590674,-0.1870843023,-0.0404211357,0.1665886045,-0.2382638752,0.1528133303,0.1505664587,-0.1506057233,-0.0140659194,-0.3169110119,-0.2260744423,0.1385513544,0.1558432877,0.5817113519,0.1286941022,0.3334386051,-0.1319292635,-0.1932003796,-0.37912485,0.0576524436,0.1512158662,-0.1938259155,0.1737959981,-0.1454391927,0.0365481786,0.0281192232,-0.6938303113,0.0446573012,-0.0963609591,-0.1944794655,0.053863287,-0.1052140892,0.216292426,0.120070532,-0.2124529779,0.2809821069,-0.0610840246,-0.1656045765,-0.0961052477,-0.0122706117,0.0360585079,0.2140233964,0.0470585413,0.0029294493,0.1734709293,-0.0841613412,-0.4023230672,-0.3385605514,0.072231628,-0.0891835764,0.207783699,0.5260062814,0.0793336332,-0.0433284454,-0.5001631379,0.3418544531,0.171259746,-0.2787890136,0.3581455946,-0.2093979269,0.1884738058,0.0264000911,-0.0835735872,-0.0133491298,-0.3030293882,0.2011641413,0.5015564561,0.2515633106,0.2823497057,0.3574547768,0.2633704841,-0.0483494699,-0.0047425479,0.1647399962,0.3353207409,0.3585027158,-0.1083962098,-0.2562660575,-0.1058744043,-0.8942086101,0.0657701492,0.3306562304,-0.0534470007,-0.2664452791,-0.2960111499,0.2652873993,-0.0444679111,0.1249464676,0.3958416879,0.1515047103,-0.0277538504,-0.0751148835,-0.0660347939,0.4360360801,0.0146460207,0.0330353975,0.0122602638,0.3850975037,-0.1809913367,0.6154463291,0.2386411726,-0.2948965132,0.2468746454,-0.2687768042,-0.0160998311,-0.3804985583,0.1405596286,-0.214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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"Working with Arrow can be quite fun sometimes.\r\nYou can fix this issue by trying to reduce the writer batch size (same trick than the one used to reduce the RAM usage in https:\/\/github.com\/huggingface\/datasets\/issues\/741).\r\n\r\nLet me know if it works.\r\nI haven't investigated yet on https:\/\/github.com\/huggingface\/datasets\/issues\/741 since I was preparing this week's sprint to add datasets but this is in my priority list for early next week.","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":67,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n Working with Arrow can be quite fun sometimes.\r\nYou can fix this issue by trying to reduce the writer batch size (same trick than the one used to reduce the RAM usage in https:\/\/github.com\/huggingface\/datasets\/issues\/741).\r\n\r\nLet me know if it works.\r\nI haven't investigated yet on https:\/\/github.com\/huggingface\/datasets\/issues\/741 since I was preparing this week's sprint to add datasets but this is in my priority list for early next week.","embeddings":[-0.2213128656,0.1979756653,-0.0157015547,0.364423275,0.3536314368,0.0121849468,0.5489980578,0.1765438765,-0.2264501303,0.2063268274,0.2152808607,0.2304821461,-0.1810271591,0.0037143859,0.2849797308,-0.3763326705,0.152505219,0.3805985153,0.0335737132,0.1360763907,-0.039774064,-0.0720350146,-0.1213939786,0.1114183366,-0.4059671164,-0.0048565464,-0.174713254,-0.1200739592,-0.2310266197,-0.5286946297,0.1810473055,0.0685255677,0.0716023594,0.2211990356,-0.0001212513,0.087012656,0.5184174776,0.0738940537,-0.3144264519,-0.025351407,0.1473908573,-0.4104197919,0.2535746694,-0.2232877612,0.0524131991,-0.6484952569,0.0717870221,-0.0252834801,0.1690187305,0.2796278894,0.1555815488,0.2720468044,0.3203712106,-0.1293947846,0.4958250821,0.4110741317,-0.2912885845,-0.0375179946,0.2439015657,0.0963079259,-0.2048285753,-0.0812970996,-0.0461048447,0.1272631735,0.4374938905,0.2534179091,0.1801085174,-0.2713246047,0.1292905211,0.3118749857,0.5142441392,-0.2984479666,-0.168679297,-0.2406481653,0.0696059838,-0.1214201823,0.0500409193,0.2096702605,-0.3758709431,-0.1750664711,-0.2559000552,0.0531944819,-0.3825436234,0.1938744485,0.3322298229,-0.0729309767,-0.0771721229,0.1957787424,0.0948959142,-0.0844441727,0.4489100277,-0.327493161,-0.0603616014,0.1682132185,-0.0260267965,0.1914516836,-0.0618921444,-0.2489123344,0.3557296395,-0.0146377338,0.0907957405,-0.0260442365,0.4090178311,-0.0300462581,-0.1666974127,0.2424432188,0.0268453769,-0.0564475209,-0.0126626221,0.1458721906,0.1368909776,0.0023782705,-0.3036974669,-0.3543183804,0.4389374256,-0.1597704291,0.2262702584,0.2719847262,0.0580549575,0.0527417213,-0.3872817159,-0.1634101421,-0.0512050129,0.2605142593,0.0079418793,-0.1695230752,-0.0749827847,0.3475945294,-0.1134954691,-0.0771844387,-0.2006068528,0.014128658,-0.1872189641,-0.0355556086,0.1856772751,-0.0006738221,0.1712860465,-0.1005151942,0.1102587208,0.0364879072,0.2294089049,-0.003272105,0.0745189786,0.2431539297,-0.1059862971,0.0759956688,-0.1007448137,-0.1035404727,-0.1938961297,0.3595401347,-0.0072725825,-0.3163636327,-0.360494107,0.0677369311,-0.1884798706,0.1095320061,-0.333424747,0.0241517015,0.2464962453,0.1929247975,0.0468096547,-0.0788814947,0.1197319925,-0.4186955988,0.255220294,0.1506827325,-0.3296794593,-0.0764193609,-0.1774583757,-0.1453157067,0.014685709,0.140763998,-0.2277489454,0.316801995,-0.1969990581,0.1646838039,0.589875102,0.1421812475,-0.1942748576,0.0489282757,-0.0359419808,-0.1330453902,0.273894757,0.1337053478,-0.0858033672,0.0003619304,-0.2265037745,0.2427281588,-0.2060781866,-0.1284230053,-0.102871716,-0.4257862568,0.2570198774,0.1826580465,0.1883201748,-0.2002343237,-0.0763456523,-0.4452912211,0.1509934217,-0.4555953145,0.3199744821,0.2012659609,0.2738685608,-0.0686623827,0.0919819102,-0.1971551031,-0.6865983605,0.2245500386,-0.3239831626,0.1136917248,-0.1173114628,-0.038934432,-0.0470360108,0.2342349738,-0.1890112758,0.0169434957,0.0304380786,0.0079185683,0.2468836457,-0.1551266462,-0.1208433658,-0.3237175345,-0.0724272728,0.0879729837,-0.4804650843,0.3551844358,-0.0268486403,-0.325533092,-0.2417758107,0.1261120737,-0.0964428559,-0.0699635223,-0.0347927138,0.1379459947,-0.1718249172,0.0094862087,-0.4415675104,0.0668799579,0.0340707116,-0.3649432659,0.3318942189,0.0976708978,0.0095571307,-0.2681993842,0.023220323,0.3331259191,0.1053549051,0.5135986805,-0.0469888523,-0.0121937897,-0.0900004432,0.2590038776,-0.0001805388,-0.0954834595,0.0781409889,0.1912837923,0.3023990393,0.1007427201,-0.0900859535,0.018186193,0.3622381687,-0.0750440434,-0.0154785076,0.2164575905,-0.3582879603,-0.0443061814,0.1146616265,-0.2236914337,0.0958220214,0.1896508783,-0.0962158144,-0.0030712364,-0.1216244921,-0.2456038296,0.2308207005,0.1090443581,0.4995390177,0.0859795809,0.2474468946,-0.2025018185,-0.295427382,-0.1155417562,0.1100827605,0.211879313,-0.3103191555,-0.0364510939,0.0036005985,-0.0263739657,-0.0472575314,-0.4187700748,-0.0772805512,-0.0021739975,-0.1159006581,0.0387827083,-0.139152199,0.2060878724,0.0957118347,-0.1754145473,0.1691356301,-0.0827374086,-0.1555674076,-0.0700948238,-0.0628747791,0.0273880363,0.4325107038,-0.0062161619,0.1022777036,0.0694009662,0.007533317,-0.386561662,-0.3180573881,0.0014939316,-0.1885012686,0.3855703175,0.5089985728,0.0129553992,0.0704964399,-0.2351247966,0.4283284843,-0.0620499067,-0.2789503932,0.2951141894,-0.2495291084,0.0132227372,-0.0754204839,-0.0625773817,0.0871267617,-0.3013954461,0.4161198735,0.389233321,0.2587939799,0.2775218785,0.4537557662,0.3612791598,-0.1578188688,0.0766769573,0.2665534019,0.0884502903,0.4034970403,-0.0317742452,-0.2591501772,-0.0606958941,-0.6752923131,0.0791358799,0.1878122836,-0.1335006803,-0.0855476558,-0.3491368592,0.3653412759,-0.0175711326,0.2512726486,0.320278734,0.1309190243,0.0220842119,-0.0302399304,-0.153926909,0.4097852707,-0.0565460175,-0.116648674,-0.0444961376,0.3685621619,-0.2781518102,0.7187972069,0.1594279408,-0.1908810735,0.1735194027,-0.1888525188,0.0868747234,-0.2385756671,-0.0506617054,0.0356894806,0.08391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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"The batch size fix doesn't work... not for #741 and not for this dataset I'm trying (DGS corpus)\r\nLoading the DGS corpus takes 400GB of RAM, which is fine with me as my machine is large enough\r\n","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":37,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n The batch size fix doesn't work... not for #741 and not for this dataset I'm trying (DGS corpus)\r\nLoading the DGS corpus takes 400GB of RAM, which is fine with me as my machine is large enough\r\n","embeddings":[-0.3745762408,0.1280584186,-0.0476197153,0.2082384676,0.2405514866,-0.0033118401,0.498398006,0.1474630982,-0.2932840288,0.1866018623,0.2814418674,-0.0539645143,-0.1909173727,-0.1175011471,0.2229365259,-0.2851468623,0.1425438225,0.3723789752,0.0049171061,0.0282003712,-0.0874818787,-0.1740174294,-0.2596130073,-0.1141795963,-0.2867548764,-0.1924320161,-0.2385251522,-0.1416583657,-0.1820704788,-0.5740127563,0.1513036191,0.002134026,0.2676509023,0.41988343,-0.0001258403,0.0140420245,0.5327542424,-0.0296705477,-0.3078179061,0.1804806888,0.1160388887,-0.5670775771,0.0755023509,-0.276815325,0.0667396337,-0.6155688763,0.0792861879,-0.0935679004,0.081132412,0.268692255,0.0758143887,0.114766039,0.2332258224,-0.2019833624,0.45135355,0.2915461063,-0.193495363,0.0987177491,0.479788363,0.2244311869,-0.1344648898,-0.0934096575,-0.2257816494,0.278609246,0.2249961346,0.1619924605,0.502917707,-0.2946100831,0.1333659887,0.3447367847,0.5489017963,-0.1115242913,-0.2093063295,-0.2974889278,-0.0686773434,-0.1175813824,-0.2431563884,0.2419317216,-0.2852624655,-0.1375110447,-0.2829046547,0.1252440214,-0.3688933253,0.3038164377,0.2715136111,0.0086959489,0.0880789384,0.2428213358,-0.0325247385,-0.147288844,0.3926494122,-0.1554118097,-0.1184123755,0.1431395561,0.0588827133,0.1550054699,-0.0380720459,-0.2888388336,0.4321164489,-0.1063440517,0.0138710439,-0.0473383963,0.5867905021,-0.0926304832,-0.1287658811,0.3904908001,-0.197296083,-0.0416502208,0.104577817,0.1069969684,-0.0620186403,0.2088286281,-0.2877637148,-0.4063176513,0.4093298316,-0.1046169549,0.0710790157,0.1694402844,0.0670005009,0.1073107868,-0.3212196827,-0.0598258451,-0.1791732311,0.3538230062,-0.0039107813,-0.0253145602,-0.067893751,0.2610295117,-0.0380551033,-0.0714618713,-0.1640246958,-0.0808365121,-0.2309175581,-0.0260763094,0.2522598505,0.1547982097,0.2087245136,-0.2251219302,0.2305276841,0.0526594669,0.1099237651,-0.1192197204,0.2041054815,0.3068583906,-0.0317756459,0.0919987112,-0.0652691722,0.0603402667,-0.2398638576,0.4755078852,-0.1224317998,-0.2511698008,-0.2756825387,0.0325488411,-0.234231621,0.0859296247,-0.3321462274,0.0839202031,0.4152879715,0.0884541422,0.1165011972,-0.1233680993,0.0668459609,-0.4432203174,0.0363993905,0.1962483078,-0.3947774768,0.0962427631,-0.017629914,0.1693833172,0.2834205925,0.1204254329,-0.0769041181,0.2557766438,-0.1914673746,0.244873032,0.5503516197,0.037440028,-0.3248765469,0.2341147214,-0.0235389136,-0.279196918,0.3945928514,0.1149656698,0.0964222103,-0.0518599004,-0.1384651363,0.3612681031,-0.2345642298,0.073998265,-0.1379666179,-0.5268688798,0.0947980955,0.1622679532,0.3325763345,-0.1004156545,0.0353725329,-0.3560432196,0.164801836,-0.4008250535,0.3126735985,0.1492701322,0.2288143933,-0.1841290146,0.2083989829,-0.1813565493,-0.6962065697,0.2876124978,-0.5014748573,0.0739164948,0.0391726717,-0.0429699719,-0.1620541066,0.0703147948,-0.06924849,0.0174353831,-0.0559540018,0.0669509843,0.3730543256,-0.1842379272,-0.0645482466,-0.3771052957,0.0573678315,-0.0027467362,-0.3606955409,0.204564333,0.0023691175,-0.4256267846,-0.1967066675,0.07872767,-0.0671877041,-0.1091160476,-0.0298987515,0.1906698495,-0.1057278588,-0.1265948117,-0.5945059657,0.1133438274,-0.0075300592,-0.1709633023,0.3066866398,0.1203050762,-0.0383864716,-0.2761614025,0.1548981369,0.2250410914,0.0956047401,0.4443617761,-0.0483179651,-0.1787980348,0.0536852293,0.1091341153,0.0232802946,-0.1872337908,0.0573429167,0.1311627775,0.2436996251,0.017907491,-0.1004728675,-0.0674117953,0.2330175042,-0.0413283221,0.1955351233,0.1248196363,-0.3651072979,-0.0716262013,0.1410181373,-0.2624342144,0.1857797205,0.2264382243,0.0025504262,0.0076846564,-0.0249636211,-0.2826313972,0.1699322462,0.0370539054,0.5360158086,0.2710428834,0.2685303688,-0.1529060304,-0.259624213,-0.0702255815,0.1242118999,0.1814390123,-0.2733660638,0.0103877597,0.0000498165,-0.1357454062,-0.0412943549,-0.39041695,0.0741709098,0.0874913856,-0.03729425,0.0703357086,-0.1533456594,0.1652028412,-0.0397780389,-0.0418311208,0.2344289571,-0.2710824609,-0.093743071,-0.0093460726,-0.2818738222,-0.0323999524,0.3056126237,-0.0038354909,0.1419288516,0.0992972031,0.0966010988,-0.2507053018,-0.2788382769,-0.0341021232,0.0217122845,0.3230953217,0.4574573636,0.1499737054,0.1179577932,-0.1752307415,0.3148290813,-0.1135184541,-0.2176906615,0.1710073501,-0.3160590231,0.0326278284,-0.12103533,-0.0494649559,0.0112170922,-0.3729518056,0.0696581304,0.4055189788,0.2205289751,0.3084494472,0.4828367829,0.2367551923,0.1243309602,0.1658313274,0.1984265894,0.2151188701,0.4572960436,-0.0293485895,-0.2974740267,-0.1902780086,-0.6981368661,0.0184096936,0.3738331199,-0.2694817185,-0.0245692376,-0.3942727447,0.1062362418,0.0583179519,0.2845515907,0.3782826662,-0.0474195741,-0.0254100319,-0.070316568,-0.0584218055,0.2494764179,0.1239886656,0.1173087806,-0.030874934,0.4462179244,-0.1945865005,0.7147114277,0.0947718918,-0.3038935959,0.2707913518,-0.1401407123,0.0170624387,-0.2703028619,0.0372008979,0.0961655378,0.1277515888,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"Not yet, I've been pretty busy with the dataset sprint lately but this is something that's been asked several times already. So I'll definitely work on this as soon as I'm done with the sprint and with the RAM issue you reported.","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":42,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n Not yet, I've been pretty busy with the dataset sprint lately but this is something that's been asked several times already. So I'll definitely work on this as soon as I'm done with the sprint and with the RAM issue you reported.","embeddings":[-0.1500287801,0.2067138553,-0.0467009619,0.2796334326,0.4458692074,-0.0149022257,0.6093963981,0.1298004836,-0.3220183551,0.1937067658,0.2713231444,0.3150215149,-0.1662629992,-0.1340918243,0.284039706,-0.3528666794,0.19875063,0.4137451351,-0.026127778,0.0539427474,-0.0769715011,-0.1808713377,-0.2342819422,0.1500301063,-0.4875097275,0.0162202325,-0.2609251738,-0.0898402855,-0.2083282918,-0.6032204032,0.254519701,0.0392498374,0.0257065631,0.2163897157,-0.0001187129,0.0717893392,0.5233740211,0.100117974,-0.3757022321,-0.0207123328,0.1298973262,-0.4313429296,0.292747587,-0.3118649125,-0.0444274358,-0.6730905175,-0.0052330205,-0.0925311223,0.0851977468,0.2867689133,0.1531415731,0.1496409923,0.2610460818,-0.2167924196,0.5045078993,0.3086963296,-0.2349268496,-0.0965385884,0.2660935819,0.0270716902,-0.086784184,-0.141119346,-0.0499659292,0.2337395102,0.3484751582,0.3037255406,0.2073807418,-0.2104097009,0.0256282426,0.3553407788,0.5374174118,-0.2118532956,-0.1425718665,-0.1533841938,0.0074660117,-0.135996148,-0.0367606618,0.2109224051,-0.3065979481,-0.0860812441,-0.272334069,0.1510215849,-0.3894477487,0.2965713441,0.1956054121,0.0183366071,0.0433375016,0.2343143076,-0.0197654553,-0.1694684625,0.520832777,-0.2911010981,-0.0225759018,0.1274242401,0.0479965396,0.159519285,-0.0755240172,-0.309481889,0.3864966333,-0.0554514565,0.1326502264,0.0440819077,0.3791761994,0.0212110206,-0.155546248,0.2336513996,-0.1281942576,-0.0899409801,0.0055330736,0.0701004863,0.0022220521,0.047043819,-0.2628852427,-0.2715009749,0.4232479632,-0.0934406668,0.2694977522,0.1873334795,0.027152203,0.0392056406,-0.5091846585,-0.1334914267,-0.0782195926,0.2353959531,0.0806331336,-0.0823203847,-0.0616264939,0.3054972291,-0.117213659,-0.1475103796,-0.2355750948,0.016607523,-0.2514528334,-0.0253880639,0.1708405167,0.2288855016,0.1449002475,-0.0470355786,0.0650947466,0.1135158092,0.2895406187,-0.0105913784,0.1357876062,0.1952750534,-0.2065051794,0.0302209035,-0.0129400818,-0.1132825986,-0.1792579889,0.383002609,0.0182225816,-0.3663841188,-0.287332505,0.0946431831,-0.2151788771,0.0613822043,-0.0709390715,0.1718257815,0.2112199366,0.1510830671,0.0608303361,-0.0333512835,0.0417920388,-0.4401707947,0.2193191946,0.1395140141,-0.4058801234,0.0554630384,-0.16317752,-0.0664396361,-0.0156456679,-0.024564052,-0.2742761672,0.2772711217,-0.1955377012,0.2298749089,0.5872292519,0.1661988199,-0.2438392788,-0.036898382,0.003327277,-0.1664946824,0.2262385786,0.1600516289,-0.1171486527,0.0681513846,-0.1770196408,0.3029491007,-0.1542997658,-0.1596271843,-0.1055895463,-0.3996223211,0.3469384313,0.2101228684,0.1483471245,-0.1116844714,-0.1029537246,-0.3682799041,0.1174836382,-0.452626735,0.2587610781,0.131894663,0.3844100237,-0.159466669,0.0933059677,-0.2613682449,-0.6854189634,0.2985851765,-0.2492964268,0.1730052531,-0.0901348516,0.0227009468,-0.0997130573,0.1692358255,-0.2032765299,-0.0019785985,0.0679667667,0.0506005473,0.2336383909,-0.1973650455,-0.1905950308,-0.3107894957,-0.0612241738,0.0008915419,-0.3421287537,0.5485239029,-0.0172521248,-0.3967372775,-0.3598187268,0.0743663013,-0.0085200118,-0.045928441,-0.0238599069,0.192988649,-0.0130882757,-0.1298053563,-0.4362171888,0.1046651453,0.0309572704,-0.3927151859,0.2777435482,0.2070194632,-0.0343700536,-0.2954361439,0.040611852,0.3032567203,0.0700180531,0.5143806338,-0.0675568953,0.0170665272,0.0247860625,0.2132641077,-0.0073072454,-0.1172537357,-0.0014551623,0.2322852463,0.2657836676,0.0138674714,-0.0900225639,0.0586162247,0.3243603408,-0.0800146908,0.0507921055,0.0836850479,-0.2945884168,-0.0193244703,0.2128085494,-0.2105057836,0.1979473382,0.2074630111,-0.0482013412,0.1102696732,-0.2756565809,-0.148309499,0.2355589122,0.1429792047,0.5560609102,0.0222477783,0.2309238911,-0.2626541853,-0.2959458828,-0.1668593436,0.1668504477,0.2569403648,-0.3018983305,-0.0585598759,-0.1459394544,-0.0343174115,-0.0355405957,-0.4733985066,0.0393411368,0.0014645186,-0.1478918493,0.0221957322,-0.1176974103,0.2584047318,0.0480031744,-0.0906381011,0.2260757089,-0.0672281161,-0.1370675266,-0.1112420186,-0.1663928926,0.0230181832,0.3057001531,0.0607741438,-0.0255741272,0.1304396689,0.0086157033,-0.3728959858,-0.3643664122,0.0700161085,-0.0963850766,0.3734642565,0.4846831858,0.0340089314,0.0707842708,-0.2421431392,0.4771783352,-0.0035104363,-0.2795153856,0.3224967718,-0.2145095915,0.0431248248,-0.0884386525,-0.2002503425,0.0433548018,-0.338442862,0.2144085169,0.3770569861,0.2181912661,0.2402545661,0.4826954901,0.3846856654,-0.0262592789,0.0725125968,0.1978988498,0.2519872487,0.3446090519,-0.0619258769,-0.2083565742,-0.088966094,-0.7822172046,0.0658636242,0.3343043029,-0.132839486,-0.1330103725,-0.3091554344,0.368486762,-0.0566361919,0.1405151188,0.378064692,0.1708713621,-0.0217635259,-0.0804595947,-0.1139997169,0.3861759305,0.0908233076,-0.0778868273,-0.0079130335,0.422504127,-0.1440055519,0.67773664,0.098717466,-0.2711271644,0.2037224323,-0.2157235891,0.0048970757,-0.2505938709,0.0265117045,-0.1217844039,0.0888629109,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"Hi @lhoestq,\r\nAny chance you have some updates on the supporting `ArrayXD` as a subtype or support of dynamic sized arrays?\r\n\r\ne.g.:\r\n`datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))`\r\n`Array3D(shape=(None, 137, 2), dtype=\"float32\")`","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":29,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n Hi @lhoestq,\r\nAny chance you have some updates on the supporting `ArrayXD` as a subtype or support of dynamic sized arrays?\r\n\r\ne.g.:\r\n`datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))`\r\n`Array3D(shape=(None, 137, 2), dtype=\"float32\")`","embeddings":[-0.2084440738,0.0039850092,-0.0901311412,0.0411780179,0.3527282774,-0.0363005772,0.6182537675,0.0921264216,-0.1956912279,0.1928671002,0.2420260608,0.0475220531,-0.279655844,0.0491814986,0.2426549196,-0.3445935845,0.2176743299,0.4056478441,0.1302125007,0.0160453673,-0.1540563405,-0.1357140988,-0.3179493546,0.2507115304,-0.1650814414,-0.0063748928,-0.2581344247,-0.119082734,-0.0953019857,-0.479681313,0.4352333546,0.1003657132,0.2172332257,0.1735836864,-0.0001256443,-0.0151909478,0.4540738761,0.0053071789,-0.227201283,0.1061744466,0.0516524911,-0.4238549471,0.1683063805,-0.3253711462,0.0930252373,-1.0158853531,0.037417639,-0.1650810093,-0.0047545969,-0.0251165349,0.0971729681,0.11753501,0.574431777,-0.0544573478,0.4038918316,0.3394396007,-0.2964442074,0.0017574386,0.6183679104,0.1803751737,0.1633254141,-0.3020035326,-0.1555918902,0.108475998,0.3668763041,0.090071328,0.2777218521,-0.1963364184,-0.103932485,0.3525259197,0.4946058095,-0.2098657191,-0.2475662529,-0.2931713462,0.0062063825,-0.0968711451,-0.1634802818,0.1315524876,-0.2634223998,-0.0381583236,-0.2493409365,0.0278524123,-0.4791485667,0.3923698962,0.1832090616,0.3168774843,0.0780540109,0.2102219164,-0.1305958778,-0.4713119566,0.5399202108,-0.194673717,-0.0393474698,0.1770695597,-0.0109686926,0.0382324494,-0.1436097324,-0.3914031684,0.3296518028,-0.1347400844,0.2233513296,0.0036931592,0.293533504,0.1309708953,-0.1120029986,0.4439811409,-0.0050440785,-0.2573388517,-0.1000133753,0.0176749211,0.1494630575,0.1185548976,-0.3885904849,-0.0665612966,0.2135720402,-0.0072955526,0.1125636622,0.3052555621,0.0839365646,-0.0825337246,-0.5444916487,-0.1952552646,-0.0342573859,0.1130028889,0.1320813447,0.0644626394,-0.0958098546,0.3358771801,0.0190796219,-0.0274252389,-0.0492887571,-0.0743581057,-0.1696981192,-0.2090104669,-0.0742272735,0.242568031,0.0230555441,-0.2454911023,0.2963340878,0.2589597404,-0.100914754,0.1745902151,0.1355239749,0.0865416005,-0.1246086657,-0.1421930939,-0.1530184895,0.0659832358,-0.2035204768,0.4575885832,0.0349642374,-0.2632192671,-0.519004643,0.0634810776,-0.2189638764,-0.0599216819,-0.0134624243,0.1540388167,0.2465145588,-0.0262611266,0.0705753192,-0.1021020785,0.1038182303,-0.3753688335,0.0550073683,-0.1984353215,-0.4159475565,0.0520624742,0.0362304784,0.0176687427,-0.2496100962,-0.1657966375,-0.1368048042,0.1306780875,-0.1476325989,0.1554267555,0.4196239114,0.0659340173,0.0262140669,0.1470692605,0.1074054241,-0.2000729293,0.2734329104,0.0588099398,0.0231610555,-0.0664216205,-0.3663851917,0.2728359997,-0.1010288522,-0.2770082951,0.0349043347,-0.5597991943,0.3022977412,0.2005216032,0.1087435186,0.0053187301,0.0642158836,-0.3475567997,0.3698147833,-0.4612921476,0.2046328038,-0.045162335,0.3303063214,-0.1753949374,0.1733091474,-0.3176588714,-0.7671214342,0.2427631915,-0.3001486063,-0.0834676474,0.1464914978,0.0340713076,-0.1214431673,0.1729816794,-0.2208021134,0.0893120319,-0.0316909812,-0.0686633363,0.1665673107,-0.2547790408,-0.2116602212,-0.4258862436,0.0152585693,-0.0417573117,-0.5153887868,0.3571023345,0.119301036,-0.3739496768,-0.3116869032,0.2690061033,-0.1038860083,-0.2080880255,-0.1168521494,0.2687589824,-0.108457692,0.0143572027,-0.5090368986,0.1212968975,-0.0007314586,-0.3114989996,0.4452406764,0.1880426556,0.0074210605,-0.3056252897,0.2982736826,0.4102921784,0.1303010136,0.4531944692,-0.1391331255,-0.0143084815,-0.0755283907,0.2140805274,0.0493239611,-0.1115580276,-0.1117410287,0.28411448,0.1176197901,-0.0952014923,-0.1254863739,0.1526382864,0.1783764213,-0.0880279541,0.061760243,0.156608209,-0.1742018312,0.1263123602,0.1600427032,0.0594885349,0.1768950969,0.1391759664,-0.2293741256,0.0531166196,-0.1666702628,-0.1580823064,0.2809893787,0.0568841062,0.4773663878,0.1018739268,0.2846907377,-0.2070886791,-0.1774286032,-0.1415572464,-0.032472983,0.080820322,-0.1407948434,0.0441586338,-0.1467220485,-0.1975245476,0.0022291201,-0.6770448685,0.0133736972,-0.023938952,-0.1055031866,0.0230710264,-0.0805765614,0.2785808146,-0.0787080303,-0.1286106259,0.2623370588,-0.153232947,-0.1476906389,0.0146163749,-0.0939416289,-0.0071486169,0.1849808991,-0.0640511289,-0.1356012374,0.23879309,0.1510271579,-0.1945213377,-0.325247705,-0.0284230523,-0.0677536502,0.2654829323,0.4885601401,-0.1751176864,0.1404495686,-0.3283764124,0.3227990568,-0.1191454157,-0.249133423,0.1754204929,-0.1840374321,0.010525967,-0.102536954,0.0885618776,-0.1960027218,-0.2493997961,0.2090706229,0.4657217264,0.2165712565,0.2988514602,0.456590414,0.2107476443,-0.0091061844,-0.1195418462,0.3286369443,0.2301234156,0.4304123521,-0.0030972138,-0.1493749022,-0.1261469573,-0.8431602716,0.1197349057,0.3775215447,0.1666362584,-0.1006572992,-0.1861110777,0.1386382729,0.0683850944,0.0345074013,0.3450479209,0.0837456211,0.0808112323,-0.208127141,-0.0666603222,0.3870494068,0.2004280537,-0.0055897529,0.2823007107,0.3034777641,-0.1905017644,0.6329155564,-0.0674083084,-0.3457585871,0.2688483298,-0.157796964,-0.1502286792,-0.1161775738,-0.2045220733,-0.071985945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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"Hi ! We haven't worked in this lately and it's not in our very short-term roadmap since it requires a bit a work to make it work with arrow. Though this will definitely be added at one point.","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":38,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n Hi ! We haven't worked in this lately and it's not in our very short-term roadmap since it requires a bit a work to make it work with arrow. Though this will definitely be added at one point.","embeddings":[-0.098730363,0.2157691866,-0.036189571,0.1980810314,0.3335241973,-0.0148143889,0.6729161739,0.0941125751,-0.2653336525,0.1920526028,0.3300935626,0.2847850323,-0.1851584017,-0.0952648148,0.2423299104,-0.3900923729,0.1759411246,0.4636602998,0.0826879442,0.0519561619,-0.0520088933,-0.1384513229,-0.2443876714,0.291831702,-0.3977594078,-0.0074219075,-0.2625413537,-0.2283953875,-0.1936278641,-0.5090429187,0.3147083819,-0.0006440361,0.1157892793,0.2204848826,-0.0001211161,0.0560126528,0.4980730712,0.0563406199,-0.2676818371,-0.1266442239,0.0979174301,-0.3960363865,0.2492384017,-0.3438536227,-0.0336581133,-0.8026284575,0.0193171967,0.022197593,0.0784239694,0.2514157891,0.1347982287,0.1327703595,0.2914691865,-0.1992772669,0.4904192388,0.3490455449,-0.2730592489,-0.0843790248,0.3083592057,0.0497198217,-0.019599501,-0.2024357468,-0.0691877753,0.1527654529,0.3510464132,0.3043106496,0.3325360119,-0.2280716896,-0.0403411388,0.4299192131,0.5088960528,-0.1575274915,-0.1708115935,-0.1280062795,0.0862528756,-0.0945466086,-0.0624217018,0.23521173,-0.2872251272,-0.070620805,-0.1919656992,0.1625069529,-0.4509160817,0.3606544733,0.2118666619,0.0809227228,-0.011420276,0.224977985,-0.0935353264,-0.2292497754,0.5371082425,-0.3507045805,0.0374394953,0.2259349227,0.1172810718,0.1355792731,-0.1068528518,-0.2385307401,0.4191494882,-0.0049818107,0.1828813404,-0.0639445111,0.3001132309,0.1084017679,-0.1901930124,0.2092389464,-0.0208750684,-0.1174462512,-0.079273425,0.1655405909,0.0466907024,0.0618224926,-0.2769748271,-0.2844107449,0.4106661081,-0.070956558,0.2312156707,0.2673474848,0.0908107162,0.029430246,-0.5808674097,-0.1497225612,-0.0254861526,0.2121632546,0.0922296867,-0.1061180383,-0.1419930756,0.4051830173,0.0228594691,-0.0956141427,-0.1998205781,0.0070374119,-0.2048146278,-0.0589816011,0.1027753428,0.1123301014,0.1261414587,-0.0109216636,0.0562070161,0.1010504514,0.2348321527,0.0499260314,0.1728387773,0.1402012706,-0.1890899837,-0.0646460578,-0.033547692,-0.1814259142,-0.2189383358,0.3710366189,-0.074490577,-0.2519074678,-0.4225301147,0.0955078527,-0.2765147984,0.0651352033,-0.0594705343,0.0752608404,0.2303730994,0.062844269,0.040109355,-0.0310946181,0.2473570555,-0.3945892751,0.187360853,-0.0015768883,-0.317786932,-0.0184286162,-0.147383675,-0.0497858599,-0.0766056105,-0.08500579,-0.2564000189,0.2750615478,-0.2200451642,0.2653540075,0.6616533995,0.2300476432,-0.027522454,0.0093542282,0.0203854386,-0.1692549139,0.2073658705,0.0774759054,-0.1663610935,-0.037582919,-0.3147429228,0.2284341455,-0.1725175679,-0.2374633849,-0.0014495193,-0.3701037169,0.4319508374,0.1311580986,0.0757123306,-0.025696978,-0.0593152493,-0.4242015183,0.0617760122,-0.4753856063,0.2388728857,0.0956538469,0.3680794835,-0.1174954548,0.1033731177,-0.1987990141,-0.6633157134,0.1479938328,-0.3269465566,0.0684972331,-0.1035158411,-0.0127287619,-0.116716668,0.1993882358,-0.1957894266,0.0680562183,0.0465568937,0.0832817182,0.1992708445,-0.1604417413,-0.22584261,-0.4588735998,0.0166465584,-0.0229313765,-0.3702343404,0.5743254423,-0.0487583391,-0.3515618443,-0.3371126056,0.1342283189,0.0599165261,-0.0795552805,0.0301119033,0.1492354721,-0.0830535591,-0.0787754729,-0.4900359809,0.086402446,0.0316077992,-0.473190099,0.2193963528,0.1813652962,-0.0090986267,-0.2457866669,0.0635049418,0.3747840226,0.0879565179,0.5813252926,-0.126971975,0.0660553575,-0.0166170597,0.1873176247,-0.0637854636,-0.0988989323,-0.0413624495,0.1875483096,0.2195616513,-0.0412394181,-0.0814983547,0.0124033531,0.2831691802,-0.0763910264,0.0485755615,0.0943504274,-0.2622021139,0.024129929,0.2071482092,-0.2704463303,0.0933376402,0.1391813755,-0.074250184,0.0506263003,-0.2858203053,-0.1754847914,0.1957800239,0.0846200436,0.5732079148,0.1150958687,0.2738003433,-0.2092379332,-0.2014389485,-0.176131919,0.0851689354,0.150538072,-0.2391188145,0.0694116428,-0.0960010886,0.0080925869,-0.0627086237,-0.5734021664,0.0562896356,-0.0410986617,-0.1251586378,-0.0071785632,-0.1493553221,0.2693190575,0.0940229446,-0.1845419854,0.2232490033,-0.1377822757,-0.2320767939,-0.0810120031,-0.1397719979,0.0229129959,0.1893306226,0.031169109,-0.0940592363,0.0999367386,0.0354998484,-0.4118802845,-0.4540100992,0.0996510759,-0.1142487898,0.3344694078,0.5833544135,-0.0149075463,0.0700243488,-0.3229906559,0.4294372499,-0.0011500459,-0.2277644128,0.351760596,-0.2792389095,0.078852579,-0.1059107035,-0.0940299928,0.0376971699,-0.2489798814,0.1979440749,0.4887146056,0.2029841542,0.2031731009,0.4810780883,0.3484107554,-0.080846265,0.0429802574,0.2867523134,0.2725436985,0.4623089731,-0.1302213222,-0.2261158228,-0.0421507992,-0.9024546146,0.0799669623,0.2271867096,0.0157992914,-0.1956704706,-0.1324646175,0.3634037971,-0.0211039837,0.1189388111,0.2944111526,0.1568292975,0.0415894873,-0.1517076492,-0.0737201124,0.4391624928,0.1008504704,-0.0229888428,-0.0276293308,0.2658874094,-0.1881069988,0.6245250106,0.1190605685,-0.3782255352,0.1852205098,-0.2673841417,-0.0269855857,-0.2219008505,0.0707336441,-0.1824989468,0.1632906348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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"@lhoestq, thanks for the update.\r\n\r\nI actually tried to modify some piece of code to make it work. Can you please tell if I missing anything here?\r\nI think that for vast majority of cases it's enough to make first dimension of the array dynamic i.e. `shape=(None, 100, 100)`. For that, it's enough to modify class [ArrayExtensionArray](https:\/\/github.com\/huggingface\/datasets\/blob\/9ca24250ea44e7611c4dabd01ecf9415a7f0be6c\/src\/datasets\/features.py#L397) to output list of arrays of different sizes instead of list of arrays of same sizes (current version)\r\nBelow are my modifications of this class.\r\n\r\n```\r\nclass ArrayExtensionArray(pa.ExtensionArray):\r\n    def __array__(self):\r\n        zero_copy_only = _is_zero_copy_only(self.storage.type)\r\n        return self.to_numpy(zero_copy_only=zero_copy_only)\r\n\r\n    def __getitem__(self, i):\r\n        return self.storage[i]\r\n\r\n    def to_numpy(self, zero_copy_only=True):\r\n        storage: pa.ListArray = self.storage\r\n        size = 1\r\n        for i in range(self.type.ndims):\r\n            size *= self.type.shape[i]\r\n            storage = storage.flatten()\r\n        numpy_arr = storage.to_numpy(zero_copy_only=zero_copy_only)\r\n        numpy_arr = numpy_arr.reshape(len(self), *self.type.shape)\r\n        return numpy_arr\r\n\r\n    def to_list_of_numpy(self, zero_copy_only=True):\r\n        storage: pa.ListArray = self.storage\r\n        shape = self.type.shape\r\n        arrays = []\r\n        for dim in range(1, self.type.ndims):\r\n            assert shape[dim] is not None, f\"Support only dynamic size on first dimension. Got: {shape}\"\r\n\r\n        first_dim_offsets = np.array([off.as_py() for off in storage.offsets])\r\n        for i in range(len(storage)):\r\n            storage_el = storage[i:i+1]\r\n            first_dim = first_dim_offsets[i+1] - first_dim_offsets[i]\r\n            # flatten storage\r\n            for dim in range(self.type.ndims):\r\n                storage_el = storage_el.flatten()\r\n\r\n            numpy_arr = storage_el.to_numpy(zero_copy_only=zero_copy_only)\r\n            arrays.append(numpy_arr.reshape(first_dim, *shape[1:]))\r\n\r\n        return arrays\r\n\r\n    def to_pylist(self):\r\n        zero_copy_only = _is_zero_copy_only(self.storage.type)\r\n        if self.type.shape[0] is None:\r\n            return self.to_list_of_numpy(zero_copy_only=zero_copy_only)\r\n        else:\r\n            return self.to_numpy(zero_copy_only=zero_copy_only).tolist()\r\n```\r\n\r\nI ran few tests and it works as expected. Let me know what you think.","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":224,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n @lhoestq, thanks for the update.\r\n\r\nI actually tried to modify some piece of code to make it work. Can you please tell if I missing anything here?\r\nI think that for vast majority of cases it's enough to make first dimension of the array dynamic i.e. `shape=(None, 100, 100)`. For that, it's enough to modify class [ArrayExtensionArray](https:\/\/github.com\/huggingface\/datasets\/blob\/9ca24250ea44e7611c4dabd01ecf9415a7f0be6c\/src\/datasets\/features.py#L397) to output list of arrays of different sizes instead of list of arrays of same sizes (current version)\r\nBelow are my modifications of this class.\r\n\r\n```\r\nclass ArrayExtensionArray(pa.ExtensionArray):\r\n    def __array__(self):\r\n        zero_copy_only = _is_zero_copy_only(self.storage.type)\r\n        return self.to_numpy(zero_copy_only=zero_copy_only)\r\n\r\n    def __getitem__(self, i):\r\n        return self.storage[i]\r\n\r\n    def to_numpy(self, zero_copy_only=True):\r\n        storage: pa.ListArray = self.storage\r\n        size = 1\r\n        for i in range(self.type.ndims):\r\n            size *= self.type.shape[i]\r\n            storage = storage.flatten()\r\n        numpy_arr = storage.to_numpy(zero_copy_only=zero_copy_only)\r\n        numpy_arr = numpy_arr.reshape(len(self), *self.type.shape)\r\n        return numpy_arr\r\n\r\n    def to_list_of_numpy(self, zero_copy_only=True):\r\n        storage: pa.ListArray = self.storage\r\n        shape = self.type.shape\r\n        arrays = []\r\n        for dim in range(1, self.type.ndims):\r\n            assert shape[dim] is not None, f\"Support only dynamic size on first dimension. Got: {shape}\"\r\n\r\n        first_dim_offsets = np.array([off.as_py() for off in storage.offsets])\r\n        for i in range(len(storage)):\r\n            storage_el = storage[i:i+1]\r\n            first_dim = first_dim_offsets[i+1] - first_dim_offsets[i]\r\n            # flatten storage\r\n            for dim in range(self.type.ndims):\r\n                storage_el = storage_el.flatten()\r\n\r\n            numpy_arr = storage_el.to_numpy(zero_copy_only=zero_copy_only)\r\n            arrays.append(numpy_arr.reshape(first_dim, *shape[1:]))\r\n\r\n        return arrays\r\n\r\n    def to_pylist(self):\r\n        zero_copy_only = _is_zero_copy_only(self.storage.type)\r\n        if self.type.shape[0] is None:\r\n            return self.to_list_of_numpy(zero_copy_only=zero_copy_only)\r\n        else:\r\n            return self.to_numpy(zero_copy_only=zero_copy_only).tolist()\r\n```\r\n\r\nI ran few tests and it works as expected. Let me know what you think.","embeddings":[0.0081767803,0.1172873601,-0.1061084569,0.2801861167,0.4025137126,-0.0643157065,0.5933448672,0.1092592403,-0.2332408428,0.2207361907,0.1275905818,0.3022358418,-0.2472839952,-0.021195896,0.2019220144,-0.3135286272,0.0890397951,0.5067413449,0.0488858111,-0.027319083,-0.0532906801,-0.0695483908,-0.1607426554,0.1468552947,-0.2513417602,0.068744354,-0.2443502545,-0.2243448496,-0.1773382425,-0.4697862864,0.2879074514,0.1888695657,0.0287835617,0.0859840885,-0.0001230811,0.0541417636,0.3321122825,-0.000784546,-0.271482408,-0.0819979534,0.2015638053,-0.4400664866,0.2384080887,-0.3255049884,-0.0139813889,-0.8362496495,-0.0494648106,-0.1110906601,0.0708753094,0.2057912499,0.1044426039,0.0736096427,0.2843008041,-0.0789362416,0.469932735,0.3682914674,-0.2882514894,-0.1275915951,0.3975459039,0.1431577504,-0.0400086902,-0.2101088613,-0.1313411742,0.1417175084,0.3835519552,0.2264853567,0.21871306,-0.2353864908,0.0733531862,0.3944592178,0.5115253925,-0.2453649491,-0.1943132877,-0.2347998172,0.0897710398,-0.0467550159,-0.0856359825,0.3283034563,-0.2202517986,-0.0487610213,-0.3536956906,0.228640601,-0.5123696923,0.3541974127,0.1902458519,0.0773997903,-0.0146469232,0.2034287453,-0.1073710397,-0.2308212966,0.5585191846,-0.3572503328,0.0319317989,0.2399896532,0.0688930228,0.1028445438,-0.0690795258,-0.4354409575,0.3606022298,-0.0460961498,0.1935750544,-0.0213646404,0.2743874788,0.0304053184,-0.1026975885,0.2985353172,-0.0004932333,-0.0596963055,-0.2731168568,0.1101117358,0.1455025822,0.0948338583,-0.1704499573,-0.1599857956,0.4061376154,-0.0382157266,0.2606358528,0.2152996212,0.0485563874,0.01908659,-0.7071174383,-0.1580285132,-0.0421594679,0.1278482974,0.1143367141,0.0015046305,-0.0514350832,0.3788039982,0.0120271919,-0.130340308,-0.1523470432,-0.0806081817,-0.1551577151,0.0050094398,0.0776838809,0.242716983,0.0285711735,0.0373322554,0.1099322289,0.0145733897,0.0978115797,0.0748503357,0.2582392693,0.1224490479,-0.1893648952,-0.0665747374,-0.0766481608,-0.0325968675,-0.2722557485,0.4397105277,-0.0544199869,-0.2422068566,-0.3447396457,0.0649461374,-0.2117099464,0.118435584,-0.1155644432,0.0989569426,0.243027553,0.0399205908,0.0517658889,-0.0657433197,0.1976664066,-0.4457695484,0.1396119446,0.0001105489,-0.4662371278,0.0617593825,-0.0835714191,-0.0497667231,-0.0255610775,-0.1201092005,-0.2007638514,0.1728928983,-0.2748225033,0.3207069933,0.5103976727,0.3285774887,-0.1169237345,0.0830684155,0.0326129645,-0.0853299946,0.2711330354,0.0790556371,-0.1332363039,0.0536823235,-0.3695650697,0.1610988081,-0.0928770304,-0.1600335687,-0.0935422108,-0.4882471263,0.3123804331,0.1447314769,0.1749598235,0.0073294099,-0.0438041948,-0.4492785633,0.2552002966,-0.5342483521,0.2359112054,0.1078927666,0.3874282539,-0.0732296407,0.0397920944,-0.1910843849,-0.6725085974,0.1904433221,-0.4558854997,0.048146721,-0.0301428977,-0.0505475476,-0.1543769836,0.1480968446,-0.173468411,0.1258011758,0.0503978841,0.1491036564,0.2341405749,-0.2919446528,-0.0407898687,-0.3832749724,-0.1340226382,-0.1048874259,-0.5120505095,0.6572319865,0.0724064708,-0.2904246747,-0.2972910106,0.1471006125,0.0280328784,-0.0370860957,-0.1188070551,0.1229283139,-0.0658304915,-0.113117449,-0.470267117,0.1696010679,-0.0339044966,-0.2905175388,0.2976221144,0.1144750789,-0.0782995895,-0.1841138899,0.1907569319,0.5881355405,0.0851957574,0.5646958351,-0.1479355544,-0.0133569725,-0.123678565,0.1781947762,-0.0113226762,-0.1554098874,-0.1618798077,0.230711773,0.2857266963,-0.0325835124,-0.1471522748,0.1662853062,0.2829980254,-0.0573412813,-0.0830609724,0.0654538125,-0.1417272687,0.068353273,0.1191514879,-0.1040920317,0.2394607067,0.1961164773,-0.1819210649,-0.0090215402,-0.1736881733,-0.1677853614,0.2829214633,0.1613493711,0.5691132545,0.140334785,0.3767085373,-0.3264777064,-0.2785777748,-0.1017967686,0.0143978046,0.1471296251,-0.2031050473,0.0580046773,-0.1077750176,-0.0069000553,-0.1237576082,-0.5938737988,-0.0299383029,0.0092836525,-0.0754430518,-0.00920224,-0.1901368946,0.1717361659,0.1932961196,-0.1336842775,0.2393604219,-0.0947372764,-0.1887859106,0.0840677246,-0.1397699714,-0.006381908,0.2208733112,0.0207979623,-0.09176559,0.1303753853,-0.0254858453,-0.4571220875,-0.3635931313,0.105608426,-0.0876275823,0.282962501,0.5301013589,-0.0609327778,0.0696673542,-0.2881963849,0.3887398541,0.0011954469,-0.2087062746,0.2950402498,-0.2056496292,0.1029592007,-0.0882220715,-0.0808877349,-0.0251198038,-0.2163183838,0.2204162329,0.3758439422,0.2948091328,0.2302731723,0.4909337461,0.2809514701,-0.0408370122,0.0959874541,0.1638640463,0.2621394992,0.3690192997,-0.0638757721,-0.1603313237,-0.1058410406,-0.8136040568,0.087740168,0.3370889127,-0.0335913338,-0.3418251276,-0.0918516293,0.1671680957,-0.0780736059,0.0688280165,0.3388420939,0.0913430005,0.0552683398,-0.0304660685,-0.044307258,0.414275825,0.1505993754,-0.0082934257,0.0106465947,0.2831360102,-0.198879391,0.5975487828,0.0907361731,-0.2871260941,0.1008346081,-0.2542867661,-0.0541666187,-0.2191137969,-0.0126045523,-0.1798820794,0.129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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/887","title":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comments":"Thanks for diving into this !\r\n\r\nIndeed focusing on making the first dimensions dynamic make total sense (and users could still re-order their dimensions to match this constraint).\r\nYour code looks great :) I think it can even be extended to support several dynamic dimensions if we want to.\r\n\r\nFeel free to open a PR to include these changes, then we can update our test suite to make sure it works in all use cases.\r\nIn particular I think we might need a few tweaks to allow it to be converted to pandas (though I haven't tested yet):\r\n\r\n```python\r\nfrom datasets import Dataset, Features, Array3D\r\n\r\n# this works\r\nmatrix = [[1, 0], [0, 1]]\r\nfeatures = Features({\"a\": Array3D(dtype=\"int32\", shape=(1, 2, 2))})\r\nd = Dataset.from_dict({\"a\": [[matrix], [matrix]]})\r\nprint(d.to_pandas())\r\n\r\n# this should work as well\r\nmatrix = [[1, 0], [0, 1]]\r\nfeatures = Features({\"a\": Array3D(dtype=\"int32\", shape=(None, 2, 2))})\r\nd = Dataset.from_dict({\"a\": [[matrix], [matrix] * 2]})\r\nprint(d.to_pandas())\r\n```\r\n\r\nI'll be happy to help you on this :)","body":"I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>","comment_length":164,"text":"pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) \r\n\r\n```python\r\n    def _info(self):\r\n        return datasets.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            # This defines the different columns of the dataset and their types\r\n            features=datasets.Features(\r\n                {\r\n                    \"pose\": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype=\"float32\"))\r\n                }\r\n            ),\r\n            homepage=_HOMEPAGE,\r\n            citation=_CITATION,\r\n        )\r\n    def _generate_examples(self):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        yield 1, {\r\n            \"pose\": [np.zeros(shape=(137, 2), dtype=np.float32)]\r\n        }\r\n```\r\n\r\nBut this doesn't work -\r\n> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> \n Thanks for diving into this !\r\n\r\nIndeed focusing on making the first dimensions dynamic make total sense (and users could still re-order their dimensions to match this constraint).\r\nYour code looks great :) I think it can even be extended to support several dynamic dimensions if we want to.\r\n\r\nFeel free to open a PR to include these changes, then we can update our test suite to make sure it works in all use cases.\r\nIn particular I think we might need a few tweaks to allow it to be converted to pandas (though I haven't tested yet):\r\n\r\n```python\r\nfrom datasets import Dataset, Features, Array3D\r\n\r\n# this works\r\nmatrix = [[1, 0], [0, 1]]\r\nfeatures = Features({\"a\": Array3D(dtype=\"int32\", shape=(1, 2, 2))})\r\nd = Dataset.from_dict({\"a\": [[matrix], [matrix]]})\r\nprint(d.to_pandas())\r\n\r\n# this should work as well\r\nmatrix = [[1, 0], [0, 1]]\r\nfeatures = Features({\"a\": Array3D(dtype=\"int32\", shape=(None, 2, 2))})\r\nd = Dataset.from_dict({\"a\": [[matrix], [matrix] * 2]})\r\nprint(d.to_pandas())\r\n```\r\n\r\nI'll be happy to help you on this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/883","title":"Downloading\/caching only a part of a datasets' dataset.","comments":"I think it would be a very helpful feature, because sometimes one only wants to evaluate models on the dev set, and the whole training data may be many times bigger.\r\nThis makes the task impossible with limited memory resources.","body":"Hi,\r\nI want to use the validation data *only* (of natural question).\r\nI don't want to have the whole dataset cached in my machine, just the dev set.\r\nIs this possible? I can't find a way to do it in the docs.\r\n\r\nThank you,\r\nSapir","comment_length":40,"text":"Downloading\/caching only a part of a datasets' dataset. \n Hi,\r\nI want to use the validation data *only* (of natural question).\r\nI don't want to have the whole dataset cached in my machine, just the dev set.\r\nIs this possible? I can't find a way to do it in the docs.\r\n\r\nThank you,\r\nSapir \n I think it would be a very helpful feature, because sometimes one only wants to evaluate models on the dev set, and the whole training data may be many times bigger.\r\nThis makes the task impossible with limited memory resources.","embeddings":[-0.2596469522,0.0176629163,-0.1444136202,0.0560275577,0.0052094632,0.1932338327,0.0714360848,0.424452126,-0.2096630186,0.1225835308,-0.1673961878,-0.3112493753,0.0382805541,0.3431017399,0.2733544707,0.3157155216,-0.0449594185,0.4445394576,-0.013587472,0.1797878444,0.1321712285,-0.3596689105,0.0900435597,0.1798119247,-0.0343141593,-0.0130379051,0.3524547517,-0.1729032844,-0.3025227785,-0.2646118999,0.507656455,0.1568506509,0.098767817,-0.3624978662,-0.0001216029,0.0493053906,0.0157325901,-0.259304136,-0.1948634237,0.1747968346,-0.3646432161,0.2211199254,-0.1016264111,-0.1109533757,-0.2629934251,0.1496164203,0.0997373387,-0.1148649603,0.3868528008,0.3819859028,0.0726678595,0.2527618706,-0.2582719028,0.0818846226,-0.4386486113,-0.120596759,-0.1113695875,0.2293516546,0.3401831985,0.1742604077,0.1508786678,0.3609142303,0.203758955,0.3365459442,0.5206016898,-0.105901733,0.2213973999,-0.5290890932,0.3528997898,0.3634751141,0.8179839849,-0.2111045569,-0.1709374338,0.0381576978,-0.1108286008,-0.324495703,-0.0276461076,0.3498649001,-0.3038274348,0.5577995777,-0.6025673747,-0.5060825944,-0.4514364898,0.0211165417,-0.0719960853,-0.1111353412,0.0267906878,-0.0793382227,0.2038470954,0.3404905498,0.4757578373,-0.1010633186,-0.3275689483,0.2050732076,-0.1556142569,-0.4630132914,-0.4531757832,0.1124547273,0.2769366503,0.2156210542,-0.0281268787,0.1360063851,0.0196452886,0.0326850899,0.3324066103,0.2571540177,0.2097781897,0.3000056744,0.126241982,-0.2836258709,0.0435491428,-0.0383794494,0.0166266803,0.1793973297,-0.0352480635,0.2005891949,-0.1922214776,-0.57342875,0.0282574501,-0.162422657,-0.026856754,-0.1042123809,0.0117447656,0.3720725477,-0.2337694913,-0.0338172726,-0.414483577,0.1290370375,-0.0244037621,-0.7614021897,-0.1353474855,0.2001847923,-0.1518820971,0.1537026912,0.3087083995,-0.2576307654,0.2571436763,-0.2327064127,-0.0536342002,-0.0144959623,0.3822110593,-0.2405206561,0.3597934842,0.3008718193,-0.1003428549,0.1436013728,-0.0037028547,0.0677643716,-0.2702770233,0.2140781879,-0.0240437184,-0.7160574198,0.132977128,0.0306058656,-0.291547507,0.0498247035,-0.0186125264,0.2691911757,0.1003063768,-0.0424363464,-0.028077485,0.1042831391,-0.0931085125,0.0569567047,0.3539173901,0.0054562553,-0.3496731222,-0.0455453433,0.0757640079,-0.2223428041,-0.0782371387,0.179689303,-0.3526026309,0.2832117081,-0.0349932089,-0.0515899062,0.6107296348,-0.033008486,-0.341673106,0.0503783599,0.0374969244,-0.3896382749,-0.107169047,0.1974391341,0.1391672492,0.0782243237,-0.5371806026,0.4953878224,-0.0045664981,-0.2572207153,-0.0902148783,-0.1335968673,0.0509743616,-0.0344618112,0.3187843263,0.5672199726,0.093451567,0.2109842896,-0.0654322132,-0.1826470941,0.1779056787,0.1441976428,0.1808380336,-0.2567463815,-0.1036055014,-0.2643276751,-0.3934879899,0.2896128893,0.0190090984,-0.6332793832,0.5450124741,-0.1466409117,-0.2126225233,-0.2811501026,0.1985191107,-0.3874562681,-0.0683606789,-0.0787717327,0.5183345079,-0.2221153527,-0.4332073927,0.2459861338,-0.0388423614,-0.0085976645,-0.0347101055,-0.0773481131,-0.0136265745,0.2332274318,-0.0780256838,-0.2326035351,-0.1438825428,-0.0770930946,0.0010289117,0.2435720712,-0.1894330233,-0.0382388569,-0.0579957701,0.4480165541,0.1133444309,-0.1220167428,0.0389686115,-0.1264074892,0.037879277,0.0592170879,-0.2691627443,0.445589602,-0.0425802842,0.1304669082,0.0402546898,-0.0788122565,0.2034360319,-0.3745237887,0.0144097637,-0.2602585554,0.0538863651,-0.1644445062,0.335213393,-0.038239155,-0.2119158804,-0.1100293547,0.1415116936,0.0741901323,0.0180712882,-0.1057825983,-0.1695173681,-0.0013913501,0.1838152558,0.187406376,0.3733811378,0.0554433055,0.3742053807,0.1727607846,0.0835776925,-0.0322370641,0.0589875244,-0.2422545105,-0.1820131689,-0.03363695,-0.3563263416,-0.1824299991,-0.1795852184,0.4001220763,0.1201160178,-0.0454229973,-0.1748720109,0.0162675995,-0.4271829426,-0.0491978042,-0.0482637696,0.1393751502,-0.1046889052,0.2175350934,0.063642472,0.1797725111,-0.0330221541,0.0858021379,-0.2827939391,0.4851481616,-0.3380614221,0.0753311589,-0.0537016839,-0.1475924999,-0.069603391,0.101518549,0.1213861555,0.1434981525,0.3591328263,-0.0873147398,-0.1552483439,-0.3242338002,0.1137347221,-0.020188326,0.4564116299,-0.0426918343,-0.1663059741,0.513420701,0.16451253,0.1184290349,-0.180262655,-0.2282163352,-0.3151454329,-0.1713221967,0.2047231942,0.2288086414,-0.1834838986,-0.2206598818,-0.3584470153,-0.1597438604,0.1136493832,-0.1076956987,0.1931927055,0.2225603163,-0.0169393755,-0.2397378832,-0.3206496239,0.0403390974,-0.2267978936,-0.3674768507,0.2391419262,-0.041413974,-0.2598355114,0.1799654961,-0.0711047426,0.0322008021,0.3513366282,-0.3724110126,-0.1565231383,0.0650557131,-0.0956741422,-0.1702403426,-0.0697042122,0.2953811288,-0.5932688713,0.1054323688,0.0074931323,-0.0587730929,-0.0502597913,0.0245569292,0.2246670425,0.0605238751,0.1298893839,0.350484848,0.8538551927,-0.1514459699,-0.1626930386,0.0154224522,0.0880191401,0.4468539357,-0.0379184447,-0.0748696849,0.2885404825,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/880","title":"Add SQA","comments":"I\u2019ll take this one to test the workflow for the sprint next week cc @yjernite @lhoestq ","body":"## Adding a Dataset\r\n- **Name:** SQA (Sequential Question Answering) by Microsoft. \r\n- **Description:** The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.\r\n- **Paper:** https:\/\/www.microsoft.com\/en-us\/research\/publication\/search-based-neural-structured-learning-sequential-question-answering\/\r\n- **Data:** https:\/\/www.microsoft.com\/en-us\/download\/details.aspx?id=54253\r\n- **Motivation:** currently, the [Tapas](https:\/\/ai.googleblog.com\/2020\/04\/using-neural-networks-to-find-answers.html) algorithm by Google AI is being added to the Transformers library (see https:\/\/github.com\/huggingface\/transformers\/pull\/8113). It would be great to use that model in combination with this dataset, on which it achieves SOTA results (average question accuracy of 0.71).\r\n\r\nNote 1: this dataset actually consists of 2 types of files: \r\n1) TSV files, containing the questions, answer coordinates and answer texts (for training, dev and test)\r\n2) a folder of csv files, which contain the actual tabular data\r\n\r\nNote 2: if you download the dataset straight from the download link above, then you will see that the `answer_coordinates` and `answer_text` columns are string lists of string tuples and strings respectively, which is not ideal. It would be better to make them true Python lists of tuples and strings respectively (using `ast.literal_eval`), before uploading them to the HuggingFace hub.\r\n\r\nAdding this would be great! Then we could possibly also add [WTQ (WikiTable Questions)](https:\/\/github.com\/ppasupat\/WikiTableQuestions) and [TabFact (Tabular Fact Checking)](https:\/\/github.com\/wenhuchen\/Table-Fact-Checking) on which TAPAS also achieves state-of-the-art results. Note that the TAPAS algorithm requires these datasets to first be converted into the SQA format.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":16,"text":"Add SQA \n ## Adding a Dataset\r\n- **Name:** SQA (Sequential Question Answering) by Microsoft. \r\n- **Description:** The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.\r\n- **Paper:** https:\/\/www.microsoft.com\/en-us\/research\/publication\/search-based-neural-structured-learning-sequential-question-answering\/\r\n- **Data:** https:\/\/www.microsoft.com\/en-us\/download\/details.aspx?id=54253\r\n- **Motivation:** currently, the [Tapas](https:\/\/ai.googleblog.com\/2020\/04\/using-neural-networks-to-find-answers.html) algorithm by Google AI is being added to the Transformers library (see https:\/\/github.com\/huggingface\/transformers\/pull\/8113). It would be great to use that model in combination with this dataset, on which it achieves SOTA results (average question accuracy of 0.71).\r\n\r\nNote 1: this dataset actually consists of 2 types of files: \r\n1) TSV files, containing the questions, answer coordinates and answer texts (for training, dev and test)\r\n2) a folder of csv files, which contain the actual tabular data\r\n\r\nNote 2: if you download the dataset straight from the download link above, then you will see that the `answer_coordinates` and `answer_text` columns are string lists of string tuples and strings respectively, which is not ideal. It would be better to make them true Python lists of tuples and strings respectively (using `ast.literal_eval`), before uploading them to the HuggingFace hub.\r\n\r\nAdding this would be great! Then we could possibly also add [WTQ (WikiTable Questions)](https:\/\/github.com\/ppasupat\/WikiTableQuestions) and [TabFact (Tabular Fact Checking)](https:\/\/github.com\/wenhuchen\/Table-Fact-Checking) on which TAPAS also achieves state-of-the-art results. Note that the TAPAS algorithm requires these datasets to first be converted into the SQA format.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n I\u2019ll take this one to test the workflow for the sprint next week cc @yjernite @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/880","title":"Add SQA","comments":"@thomwolf here's a slightly adapted version of the code from the [official Tapas repository](https:\/\/github.com\/google-research\/tapas\/blob\/master\/tapas\/utils\/interaction_utils.py) that is used to turn the `answer_coordinates` and `answer_texts` columns into true Python lists of tuples\/strings:\r\n\r\n```\r\nimport pandas as pd\r\nimport ast\r\n\r\ndata = pd.read_csv(\"\/content\/sqa_data\/random-split-1-dev.tsv\", sep='\\t')\r\n\r\ndef _parse_answer_coordinates(answer_coordinate_str):\r\n  \"\"\"Parses the answer_coordinates of a question.\r\n  Args:\r\n    answer_coordinate_str: A string representation of a Python list of tuple\r\n      strings.\r\n      For example: \"['(1, 4)','(1, 3)', ...]\"\r\n  \"\"\"\r\n\r\n  try:\r\n    answer_coordinates = []\r\n    # make a list of strings\r\n    coords = ast.literal_eval(answer_coordinate_str)\r\n    # parse each string as a tuple\r\n    for row_index, column_index in sorted(\r\n        ast.literal_eval(coord) for coord in coords):\r\n      answer_coordinates.append((row_index, column_index))\r\n  except SyntaxError:\r\n    raise ValueError('Unable to evaluate %s' % answer_coordinate_str)\r\n  \r\n  return answer_coordinates\r\n\r\n\r\ndef _parse_answer_text(answer_text):\r\n  \"\"\"Populates the answer_texts field of `answer` by parsing `answer_text`.\r\n  Args:\r\n    answer_text: A string representation of a Python list of strings.\r\n      For example: \"[u'test', u'hello', ...]\"\r\n  \"\"\"\r\n  try:\r\n    answer = []\r\n    for value in ast.literal_eval(answer_text):\r\n      answer.append(value)\r\n  except SyntaxError:\r\n    raise ValueError('Unable to evaluate %s' % answer_text)\r\n\r\n  return answer\r\n\r\ndata['answer_coordinates'] = data['answer_coordinates'].apply(lambda coords_str: _parse_answer_coordinates(coords_str))\r\ndata['answer_text'] = data['answer_text'].apply(lambda txt: _parse_answer_text(txt))\r\n```\r\n\r\nHere I'm using Pandas to read in one of the TSV files (the dev set). \r\n\r\n","body":"## Adding a Dataset\r\n- **Name:** SQA (Sequential Question Answering) by Microsoft. \r\n- **Description:** The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.\r\n- **Paper:** https:\/\/www.microsoft.com\/en-us\/research\/publication\/search-based-neural-structured-learning-sequential-question-answering\/\r\n- **Data:** https:\/\/www.microsoft.com\/en-us\/download\/details.aspx?id=54253\r\n- **Motivation:** currently, the [Tapas](https:\/\/ai.googleblog.com\/2020\/04\/using-neural-networks-to-find-answers.html) algorithm by Google AI is being added to the Transformers library (see https:\/\/github.com\/huggingface\/transformers\/pull\/8113). It would be great to use that model in combination with this dataset, on which it achieves SOTA results (average question accuracy of 0.71).\r\n\r\nNote 1: this dataset actually consists of 2 types of files: \r\n1) TSV files, containing the questions, answer coordinates and answer texts (for training, dev and test)\r\n2) a folder of csv files, which contain the actual tabular data\r\n\r\nNote 2: if you download the dataset straight from the download link above, then you will see that the `answer_coordinates` and `answer_text` columns are string lists of string tuples and strings respectively, which is not ideal. It would be better to make them true Python lists of tuples and strings respectively (using `ast.literal_eval`), before uploading them to the HuggingFace hub.\r\n\r\nAdding this would be great! Then we could possibly also add [WTQ (WikiTable Questions)](https:\/\/github.com\/ppasupat\/WikiTableQuestions) and [TabFact (Tabular Fact Checking)](https:\/\/github.com\/wenhuchen\/Table-Fact-Checking) on which TAPAS also achieves state-of-the-art results. Note that the TAPAS algorithm requires these datasets to first be converted into the SQA format.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":185,"text":"Add SQA \n ## Adding a Dataset\r\n- **Name:** SQA (Sequential Question Answering) by Microsoft. \r\n- **Description:** The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.\r\n- **Paper:** https:\/\/www.microsoft.com\/en-us\/research\/publication\/search-based-neural-structured-learning-sequential-question-answering\/\r\n- **Data:** https:\/\/www.microsoft.com\/en-us\/download\/details.aspx?id=54253\r\n- **Motivation:** currently, the [Tapas](https:\/\/ai.googleblog.com\/2020\/04\/using-neural-networks-to-find-answers.html) algorithm by Google AI is being added to the Transformers library (see https:\/\/github.com\/huggingface\/transformers\/pull\/8113). It would be great to use that model in combination with this dataset, on which it achieves SOTA results (average question accuracy of 0.71).\r\n\r\nNote 1: this dataset actually consists of 2 types of files: \r\n1) TSV files, containing the questions, answer coordinates and answer texts (for training, dev and test)\r\n2) a folder of csv files, which contain the actual tabular data\r\n\r\nNote 2: if you download the dataset straight from the download link above, then you will see that the `answer_coordinates` and `answer_text` columns are string lists of string tuples and strings respectively, which is not ideal. It would be better to make them true Python lists of tuples and strings respectively (using `ast.literal_eval`), before uploading them to the HuggingFace hub.\r\n\r\nAdding this would be great! Then we could possibly also add [WTQ (WikiTable Questions)](https:\/\/github.com\/ppasupat\/WikiTableQuestions) and [TabFact (Tabular Fact Checking)](https:\/\/github.com\/wenhuchen\/Table-Fact-Checking) on which TAPAS also achieves state-of-the-art results. Note that the TAPAS algorithm requires these datasets to first be converted into the SQA format.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n @thomwolf here's a slightly adapted version of the code from the [official Tapas repository](https:\/\/github.com\/google-research\/tapas\/blob\/master\/tapas\/utils\/interaction_utils.py) that is used to turn the `answer_coordinates` and `answer_texts` columns into true Python lists of tuples\/strings:\r\n\r\n```\r\nimport pandas as pd\r\nimport ast\r\n\r\ndata = pd.read_csv(\"\/content\/sqa_data\/random-split-1-dev.tsv\", sep='\\t')\r\n\r\ndef _parse_answer_coordinates(answer_coordinate_str):\r\n  \"\"\"Parses the answer_coordinates of a question.\r\n  Args:\r\n    answer_coordinate_str: A string representation of a Python list of tuple\r\n      strings.\r\n      For example: \"['(1, 4)','(1, 3)', ...]\"\r\n  \"\"\"\r\n\r\n  try:\r\n    answer_coordinates = []\r\n    # make a list of strings\r\n    coords = ast.literal_eval(answer_coordinate_str)\r\n    # parse each string as a tuple\r\n    for row_index, column_index in sorted(\r\n        ast.literal_eval(coord) for coord in coords):\r\n      answer_coordinates.append((row_index, column_index))\r\n  except SyntaxError:\r\n    raise ValueError('Unable to evaluate %s' % answer_coordinate_str)\r\n  \r\n  return answer_coordinates\r\n\r\n\r\ndef _parse_answer_text(answer_text):\r\n  \"\"\"Populates the answer_texts field of `answer` by parsing `answer_text`.\r\n  Args:\r\n    answer_text: A string representation of a Python list of strings.\r\n      For example: \"[u'test', u'hello', ...]\"\r\n  \"\"\"\r\n  try:\r\n    answer = []\r\n    for value in ast.literal_eval(answer_text):\r\n      answer.append(value)\r\n  except SyntaxError:\r\n    raise ValueError('Unable to evaluate %s' % answer_text)\r\n\r\n  return answer\r\n\r\ndata['answer_coordinates'] = data['answer_coordinates'].apply(lambda coords_str: _parse_answer_coordinates(coords_str))\r\ndata['answer_text'] = data['answer_text'].apply(lambda txt: _parse_answer_text(txt))\r\n```\r\n\r\nHere I'm using Pandas to read in one of the TSV files (the dev set). \r\n\r\n","embeddings":[-0.0640092716,-0.2312078327,-0.2099176347,-0.1210695952,0.0999142751,-0.1975832283,0.0337971561,0.3069660664,0.1483602226,0.0562699474,-0.1675558388,-0.002632767,-0.0712603852,0.5443822742,0.1500162035,-0.1045532525,0.0272174533,0.1428327858,-0.2944614291,0.0711858869,-0.196293667,0.0914863199,0.0215712246,0.031229699,-0.2368902415,-0.0844550505,-0.1073180363,-0.1923377961,-0.1904111356,-0.1972694844,0.0054088389,0.072247088,0.1686420888,0.2730415761,-0.0001059325,-0.1343121231,-0.0751999691,-0.0975328833,-0.0939193591,0.0506609641,-0.1607964188,0.0926124603,-0.1110516489,-0.0771295801,-0.5252555013,0.2041900754,-0.0748102888,-0.3007471859,0.5820878148,0.2628435194,0.2186131328,0.0928574726,0.0751176327,-0.2174711227,0.3752230704,0.0049580853,-0.3586767614,-0.0065896218,0.4021380246,-0.348051697,0.0548400171,0.4530360699,0.265696764,-0.2276320606,0.1525066942,0.1255038083,0.2736350596,0.0334795527,0.0674656704,0.2694988251,0.3335775435,-0.0927536935,-0.1821043491,-0.1886140257,0.0144181484,0.1235252768,-0.2833782434,0.118624717,-0.3490382135,0.0167793985,-0.1807740331,-0.2453861386,-0.349671483,0.101798743,0.0179150905,0.5167180896,-0.0192335062,-0.1948671192,-0.0831076577,-0.1331273913,-0.0427691936,-0.0472527817,0.2443597317,0.2530235648,-0.5382785201,-0.2720508277,0.2136117667,0.1150445491,0.2435956448,0.1921216249,0.4720071256,-0.0723904297,0.0314646773,-0.2965055406,0.1672351956,0.4781259894,-0.003513505,0.0426871032,-0.1635618806,0.1145740077,0.069656536,0.0185354222,0.0113641415,0.4373466372,-0.2889096141,0.0332337134,0.121735774,0.0777647123,-0.3187230825,0.2000280917,-0.2216603905,-0.0858714953,-0.0461771674,0.3067342937,-0.2722217441,0.0963958725,0.1617859751,0.1525750607,0.0376236141,-0.1205838397,-0.0992913097,-0.1381375194,-0.1389065683,0.327624768,0.2592454553,0.0756572187,-0.118240878,-0.1105898842,0.1236674637,0.0671117604,-0.1190187186,-0.1257779002,0.2719293535,0.0063779862,-0.1196178198,-0.2474647164,0.006988266,-0.1855606288,-0.0826086476,0.2046098411,0.086625129,0.0822594985,-0.0458444618,0.2935351431,-0.0351122767,-0.3378953934,0.0560649224,0.3847602308,0.0544066653,-0.0786112845,0.1612036526,0.0770690739,-0.1640680432,-0.0699188039,0.2680043876,0.1623664647,0.0437387638,0.0179177821,-0.0602712929,0.2952128351,-0.4049222767,0.1558105201,0.1623992175,0.4924613237,0.1605460048,0.4469804764,-0.3020619452,0.042209927,-0.051084321,-0.1068252027,-0.0611654036,0.2619523108,-0.0233064312,0.222698912,0.1795921922,-0.0981692225,0.124762632,0.2569143176,-0.243451342,-0.0364894234,-0.2605640292,-0.320944041,-0.1499199122,0.5569957495,-0.0081572412,-0.0947261527,-0.1503633857,-0.2269732505,0.0897196904,-0.2418155223,-0.1053456739,-0.202632457,0.418138355,-0.1816864908,0.0669904649,-0.5491580367,-0.5756059885,-0.1482614726,-0.1463883817,0.2786490321,0.0339066721,-0.4509789348,-0.3733327091,0.1327016652,0.0075063659,-0.0343044698,0.2479703277,0.1869211048,0.2428165525,-0.0500193909,-0.0937463418,0.1550153494,-0.0424523093,-0.1147119105,-0.1329626888,0.198689729,-0.1225987598,0.0456116945,-0.0185674727,0.314791292,-0.1338375211,0.1751381606,0.3347406685,0.1550746709,-0.13191697,-0.2625659704,0.0805614889,0.4734991193,0.0326889567,-0.0527552143,-0.1923760474,0.4993881285,0.1238283738,-0.0980774164,-0.2853282094,0.4043613374,-0.1816473901,0.2681885958,0.1508674324,0.0346501954,-0.0117136929,-0.0824667439,-0.131069541,0.1497388333,0.4146442711,0.1367218494,-0.2725359797,-0.0683397576,-0.3361949921,0.3972062767,0.2918215394,-0.2563173175,0.3381398618,0.0930465311,-0.0448115245,-0.099624306,0.0174711868,0.1898336112,0.1904856265,0.2655975521,-0.1340573281,0.132565096,-0.0351311676,-0.1304928362,0.0047888304,0.0729855746,-0.1076816693,0.3593825102,0.3060602844,0.1129134521,-0.0875147507,-0.081166327,-0.2993248403,-0.0468394086,-0.0217849854,0.1793799549,0.2182229459,-0.2264639288,0.0334007703,-0.3110869825,0.0157671552,0.0116476575,0.6521463394,-0.1362127811,-0.1908007115,0.2219664752,0.074260965,0.910795629,-0.1697585285,-0.0477678366,-0.2234660834,-0.2786903083,0.1275860667,0.3061983287,0.0774174184,-0.0461224802,0.5052061081,-0.1818101406,0.3880173266,-0.0880887881,-0.3654263318,0.0920002386,-0.0208264478,0.0868983418,-0.0135212597,0.0519566387,-0.2462124228,-0.311757654,0.0962447152,-0.2440950423,-0.1063671485,-0.0403895676,0.2175629735,-0.090775229,-0.1126224622,-0.4963790476,-0.0617724843,-0.3034152091,0.4760821164,0.2297993004,-0.0288607217,-0.0194287114,-0.0775105655,0.0343687013,-0.1526608765,0.1068070829,-0.2156098038,0.1638543606,0.2227652371,-0.1240256354,-0.211823523,-0.4943149984,-0.3110443056,-0.0237979498,0.0763129145,-0.1710079461,-0.0684284568,-0.2200554162,0.0027596692,-0.1519955397,0.1327385157,0.4515957832,-0.0766791403,-0.2075061649,-0.0513131246,0.2085881829,0.5510808229,0.0616792701,-0.1596296579,0.1330932677,0.2265027463,0.0326379724,0.6488921046,0.0589807928,0.0846870765,0.2585291862,0.0645583197,-0.11768765,-0.2783655524,0.0752346739,-0.1934869885,-0.157743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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/879","title":"boolq does not load ","comments":"Hi ! It runs on my side without issues. I tried\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"boolq\")\r\n```\r\n\r\nWhat version of datasets and tensorflow are your runnning ?\r\nAlso if you manage to get a minimal reproducible script (on google colab for example) that would be useful.","body":"Hi\r\nI am getting these errors trying to load boolq thanks \r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 5, in <module>\r\n    data = AutoTask().get(\"boolq\").get_dataset(\"train\", n_obs=10)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 42, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 38, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 150, in download_custom\r\n    get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 472, in get_from_cache\r\n    f\"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been\"\r\nFileNotFoundError: Cannot find the requested files in the cached path at \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.\r\n","comment_length":47,"text":"boolq does not load  \n Hi\r\nI am getting these errors trying to load boolq thanks \r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 5, in <module>\r\n    data = AutoTask().get(\"boolq\").get_dataset(\"train\", n_obs=10)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 42, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 38, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 150, in download_custom\r\n    get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 472, in get_from_cache\r\n    f\"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been\"\r\nFileNotFoundError: Cannot find the requested files in the cached path at \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.\r\n \n Hi ! It runs on my side without issues. I tried\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"boolq\")\r\n```\r\n\r\nWhat version of datasets and tensorflow are your runnning ?\r\nAlso if you manage to get a minimal reproducible script (on google colab for example) that would be useful.","embeddings":[-0.3142423332,-0.1296663433,-0.1398147941,0.2243659049,0.0898041949,0.0106670726,0.4253870547,0.3031967878,0.4457686841,-0.1245959029,-0.086008586,0.3309848905,-0.1855678558,0.4824474752,0.0417104885,-0.0254136994,-0.1236411631,0.0837756172,-0.0132116294,-0.0677919537,-0.4335164726,0.3662593663,-0.4190507531,0.1728812605,-0.1200258434,-0.2764460444,-0.0112306615,0.2354567647,-0.3227624893,-0.3402307034,0.129193157,0.0399898887,0.2798753381,0.1975854039,-0.0001004217,0.04742755,0.4136013389,0.0912821293,-0.2201259285,-0.290461421,-0.1725928336,-0.3197637498,0.1770071685,-0.1704840362,-0.0781291723,-0.054515522,-0.0554295033,0.0769731924,0.4125249982,0.4600992799,0.3143282831,0.0757981241,0.1170568392,-0.1951514184,0.1931924373,-0.1626982242,0.1515527815,0.2015698403,0.0419378281,-0.2938958704,0.1587524712,0.2637588382,-0.183339566,-0.246029079,0.0121768303,-0.2074029446,-0.1017384678,-0.3344747722,0.2351708412,0.0141455783,0.5186159015,-0.1224103197,-0.2733826637,-0.056684956,0.1429380774,-0.1925470382,0.1893985271,0.0914991349,0.0534336679,-0.0635306165,0.1850988269,-0.4896081984,-0.0376428179,-0.0354207493,-0.1383907795,0.2759750187,-0.1215392649,0.051884301,0.1912807226,-0.1711804718,0.1133473963,0.2080259323,-0.152289167,0.1584958732,-0.5179923177,0.0358057208,0.1561286449,-0.0271783192,-0.0363988988,0.129627496,-0.189453423,0.0187248792,-0.091720216,0.2068092227,0.0046222373,0.5135238171,0.3842213452,0.2736550868,0.1583575159,0.1075289547,-0.2233469784,0.0342296846,-0.5201335549,-0.3621052206,-0.1302121133,0.2492164373,0.2366273552,-0.0848474279,-0.3538365662,-0.1739944071,-0.3224677145,-0.1347561181,0.3432625234,0.6987859011,0.0430136062,-0.1265625507,0.0525118038,0.2696886957,-0.3483470976,-0.0706117153,-0.2877412438,0.3017656803,-0.0307151303,-0.1554742754,0.2976013422,-0.1689217985,0.352542758,0.0606402606,-0.249807626,-0.0990925953,0.2741260231,-0.2546375394,-0.373259753,0.2511183619,0.0047492059,0.0151632084,0.2008347362,0.2303849608,-0.0411107466,0.2055432796,-0.2143663764,-0.2891750336,0.0416452251,0.3372689188,-0.0229818821,-0.2775761187,-0.1418446451,0.1758444458,0.1150734574,-0.1472827941,-0.2639817595,-0.327144146,-0.1046774015,-0.2185691744,0.3993265629,0.4546142817,-0.3941679597,-0.264450103,-0.3861935735,-0.2117394805,0.005800161,0.2955780327,-0.1370882541,0.5168119073,-0.2074039429,0.0861331522,0.2646962404,-0.2111936361,-0.1844504923,0.1970254332,-0.0149663696,-0.0348743387,-0.2750920653,-0.044528529,0.1203418449,0.1673647314,0.1317070127,0.1415139586,0.2968439162,-0.2430085838,-0.1465197951,-0.1223876178,0.0710638985,0.3457807302,0.2301300913,-0.0535534918,-0.0584518202,-0.4113751054,0.0685673952,0.1532153785,-0.2991673946,0.1951564103,0.3404016793,-0.1151792407,0.1968043298,0.0254542511,-0.3794722259,0.1544528753,-0.2339846492,-0.0378329121,-0.062096227,0.1533769965,-0.326677829,0.0734995157,-0.1862191111,-0.0524057746,0.1931232661,-0.1655142456,-0.0569789149,0.0857940465,-0.308848381,0.2691085041,-0.0235381071,0.1346359849,-0.2137397379,0.1054314226,-0.1465704441,-0.0927517265,0.2968972921,0.2993911803,0.1980737299,0.0299892873,-0.0212003067,0.2432890236,0.1288769841,0.1043745577,-0.0412735492,-0.5058262348,0.1112553775,-0.0384226665,0.307051599,0.1330125481,0.0742572993,0.0808739066,0.0900950283,0.2717500031,-0.0859902874,0.1937272102,0.1444682777,0.1817198992,0.2400854379,0.0292750653,-0.006893957,-0.1292017549,0.4810894728,-0.0400450267,-0.0255702455,0.0285188742,-0.1477422416,0.0691427216,0.4380694628,0.1077947244,0.122457467,0.0266056359,-0.1225930899,0.1235340685,0.1176418364,0.1531540751,0.2710778117,0.2076154053,-0.4852933288,-0.1021019965,-0.0421196967,-0.2693491876,0.1733107269,-0.1938354522,0.0288225207,0.2061602622,-0.0693069771,-0.1248023659,-0.1851713657,-0.2250053734,0.09095148,0.2774633765,-0.1973669082,-0.0649487823,-0.0835349858,-0.3896842897,0.191729635,-0.1083577797,-0.0304460432,-0.35120821,0.1287216693,-0.029635014,-0.0343716405,0.2476255149,0.0612640604,0.3148314655,0.107014671,-0.0850824639,-0.1364320368,-0.2685342133,-0.2266000211,0.1310845762,0.1870205998,0.0818480328,0.2532462776,-0.0990140066,0.0883723646,-0.0534891039,-0.2225491405,-0.08780545,-0.0944324955,0.3347641528,0.1184832379,0.0666116774,-0.0564614795,-0.2238010466,0.3523494303,-0.1633935571,-0.0211878568,0.1725939363,0.1716831923,-0.0578616336,-0.220501855,-0.5058780313,-0.5898679495,-0.4911761284,0.0963694528,0.0636325926,0.1966700107,0.3625305295,-0.0294897985,0.1960614175,0.0712995976,-0.0495073088,0.0055860337,-0.1599078923,0.262078315,-0.0651985332,-0.2783256769,0.0434393212,-0.0880052298,0.4329284132,-0.2539993227,-0.3081473708,0.003639091,-0.0644266978,-0.0977022052,-0.0620605685,-0.1536531448,-0.1169783846,0.0611614808,0.0138719501,0.0663510412,-0.3257706761,0.227166757,0.0191833954,0.1968749166,-0.0655772835,0.3469649255,-0.3650504053,0.6071892977,0.1826376915,0.1984719187,0.2066986561,0.2602954507,0.0882441327,0.022590179,-0.7111612558,0.0119960429,0.1972316355,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/879","title":"boolq does not load ","comments":"hey\ni do the exact same commands. for me it fails i guess might be issues with\ncaching maybe?\nthanks\nbest\nrabeeh\n\nOn Tue, Nov 24, 2020, 10:24 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hi ! It runs on my side without issues. I tried\n>\n> from datasets import load_datasetload_dataset(\"boolq\")\n>\n> What version of datasets and tensorflow are your runnning ?\n> Also if you manage to get a minimal reproducible script (on google colab\n> for example) that would be useful.\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/879#issuecomment-732769114>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABP4ZCGGDR2FUMRKZTIY5CTSRN3VXANCNFSM4T7R3U6A>\n> .\n>\n","body":"Hi\r\nI am getting these errors trying to load boolq thanks \r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 5, in <module>\r\n    data = AutoTask().get(\"boolq\").get_dataset(\"train\", n_obs=10)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 42, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 38, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 150, in download_custom\r\n    get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 472, in get_from_cache\r\n    f\"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been\"\r\nFileNotFoundError: Cannot find the requested files in the cached path at \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.\r\n","comment_length":117,"text":"boolq does not load  \n Hi\r\nI am getting these errors trying to load boolq thanks \r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 5, in <module>\r\n    data = AutoTask().get(\"boolq\").get_dataset(\"train\", n_obs=10)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 42, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 38, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 150, in download_custom\r\n    get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 472, in get_from_cache\r\n    f\"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been\"\r\nFileNotFoundError: Cannot find the requested files in the cached path at \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.\r\n \n hey\ni do the exact same commands. for me it fails i guess might be issues with\ncaching maybe?\nthanks\nbest\nrabeeh\n\nOn Tue, Nov 24, 2020, 10:24 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hi ! It runs on my side without issues. I tried\n>\n> from datasets import load_datasetload_dataset(\"boolq\")\n>\n> What version of datasets and tensorflow are your runnning ?\n> Also if you manage to get a minimal reproducible script (on google colab\n> for example) that would be useful.\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/879#issuecomment-732769114>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABP4ZCGGDR2FUMRKZTIY5CTSRN3VXANCNFSM4T7R3U6A>\n> .\n>\n","embeddings":[-0.3142423332,-0.1296663433,-0.1398147941,0.2243659049,0.0898041949,0.0106670726,0.4253870547,0.3031967878,0.4457686841,-0.1245959029,-0.086008586,0.3309848905,-0.1855678558,0.4824474752,0.0417104885,-0.0254136994,-0.1236411631,0.0837756172,-0.0132116294,-0.0677919537,-0.4335164726,0.3662593663,-0.4190507531,0.1728812605,-0.1200258434,-0.2764460444,-0.0112306615,0.2354567647,-0.3227624893,-0.3402307034,0.129193157,0.0399898887,0.2798753381,0.1975854039,-0.0001004217,0.04742755,0.4136013389,0.0912821293,-0.2201259285,-0.290461421,-0.1725928336,-0.3197637498,0.1770071685,-0.1704840362,-0.0781291723,-0.054515522,-0.0554295033,0.0769731924,0.4125249982,0.4600992799,0.3143282831,0.0757981241,0.1170568392,-0.1951514184,0.1931924373,-0.1626982242,0.1515527815,0.2015698403,0.0419378281,-0.2938958704,0.1587524712,0.2637588382,-0.183339566,-0.246029079,0.0121768303,-0.2074029446,-0.1017384678,-0.3344747722,0.2351708412,0.0141455783,0.5186159015,-0.1224103197,-0.2733826637,-0.056684956,0.1429380774,-0.1925470382,0.1893985271,0.0914991349,0.0534336679,-0.0635306165,0.1850988269,-0.4896081984,-0.0376428179,-0.0354207493,-0.1383907795,0.2759750187,-0.1215392649,0.051884301,0.1912807226,-0.1711804718,0.1133473963,0.2080259323,-0.152289167,0.1584958732,-0.5179923177,0.0358057208,0.1561286449,-0.0271783192,-0.0363988988,0.129627496,-0.189453423,0.0187248792,-0.091720216,0.2068092227,0.0046222373,0.5135238171,0.3842213452,0.2736550868,0.1583575159,0.1075289547,-0.2233469784,0.0342296846,-0.5201335549,-0.3621052206,-0.1302121133,0.2492164373,0.2366273552,-0.0848474279,-0.3538365662,-0.1739944071,-0.3224677145,-0.1347561181,0.3432625234,0.6987859011,0.0430136062,-0.1265625507,0.0525118038,0.2696886957,-0.3483470976,-0.0706117153,-0.2877412438,0.3017656803,-0.0307151303,-0.1554742754,0.2976013422,-0.1689217985,0.352542758,0.0606402606,-0.249807626,-0.0990925953,0.2741260231,-0.2546375394,-0.373259753,0.2511183619,0.0047492059,0.0151632084,0.2008347362,0.2303849608,-0.0411107466,0.2055432796,-0.2143663764,-0.2891750336,0.0416452251,0.3372689188,-0.0229818821,-0.2775761187,-0.1418446451,0.1758444458,0.1150734574,-0.1472827941,-0.2639817595,-0.327144146,-0.1046774015,-0.2185691744,0.3993265629,0.4546142817,-0.3941679597,-0.264450103,-0.3861935735,-0.2117394805,0.005800161,0.2955780327,-0.1370882541,0.5168119073,-0.2074039429,0.0861331522,0.2646962404,-0.2111936361,-0.1844504923,0.1970254332,-0.0149663696,-0.0348743387,-0.2750920653,-0.044528529,0.1203418449,0.1673647314,0.1317070127,0.1415139586,0.2968439162,-0.2430085838,-0.1465197951,-0.1223876178,0.0710638985,0.3457807302,0.2301300913,-0.0535534918,-0.0584518202,-0.4113751054,0.0685673952,0.1532153785,-0.2991673946,0.1951564103,0.3404016793,-0.1151792407,0.1968043298,0.0254542511,-0.3794722259,0.1544528753,-0.2339846492,-0.0378329121,-0.062096227,0.1533769965,-0.326677829,0.0734995157,-0.1862191111,-0.0524057746,0.1931232661,-0.1655142456,-0.0569789149,0.0857940465,-0.308848381,0.2691085041,-0.0235381071,0.1346359849,-0.2137397379,0.1054314226,-0.1465704441,-0.0927517265,0.2968972921,0.2993911803,0.1980737299,0.0299892873,-0.0212003067,0.2432890236,0.1288769841,0.1043745577,-0.0412735492,-0.5058262348,0.1112553775,-0.0384226665,0.307051599,0.1330125481,0.0742572993,0.0808739066,0.0900950283,0.2717500031,-0.0859902874,0.1937272102,0.1444682777,0.1817198992,0.2400854379,0.0292750653,-0.006893957,-0.1292017549,0.4810894728,-0.0400450267,-0.0255702455,0.0285188742,-0.1477422416,0.0691427216,0.4380694628,0.1077947244,0.122457467,0.0266056359,-0.1225930899,0.1235340685,0.1176418364,0.1531540751,0.2710778117,0.2076154053,-0.4852933288,-0.1021019965,-0.0421196967,-0.2693491876,0.1733107269,-0.1938354522,0.0288225207,0.2061602622,-0.0693069771,-0.1248023659,-0.1851713657,-0.2250053734,0.09095148,0.2774633765,-0.1973669082,-0.0649487823,-0.0835349858,-0.3896842897,0.191729635,-0.1083577797,-0.0304460432,-0.35120821,0.1287216693,-0.029635014,-0.0343716405,0.2476255149,0.0612640604,0.3148314655,0.107014671,-0.0850824639,-0.1364320368,-0.2685342133,-0.2266000211,0.1310845762,0.1870205998,0.0818480328,0.2532462776,-0.0990140066,0.0883723646,-0.0534891039,-0.2225491405,-0.08780545,-0.0944324955,0.3347641528,0.1184832379,0.0666116774,-0.0564614795,-0.2238010466,0.3523494303,-0.1633935571,-0.0211878568,0.1725939363,0.1716831923,-0.0578616336,-0.220501855,-0.5058780313,-0.5898679495,-0.4911761284,0.0963694528,0.0636325926,0.1966700107,0.3625305295,-0.0294897985,0.1960614175,0.0712995976,-0.0495073088,0.0055860337,-0.1599078923,0.262078315,-0.0651985332,-0.2783256769,0.0434393212,-0.0880052298,0.4329284132,-0.2539993227,-0.3081473708,0.003639091,-0.0644266978,-0.0977022052,-0.0620605685,-0.1536531448,-0.1169783846,0.0611614808,0.0138719501,0.0663510412,-0.3257706761,0.227166757,0.0191833954,0.1968749166,-0.0655772835,0.3469649255,-0.3650504053,0.6071892977,0.1826376915,0.1984719187,0.2066986561,0.2602954507,0.0882441327,0.022590179,-0.7111612558,0.0119960429,0.1972316355,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/879","title":"boolq does not load ","comments":"Could you check if it works on the master branch ?\r\nYou can use `load_dataset(\"boolq\", script_version=\"master\")` to do so.\r\nWe did some changes recently in boolq to remove the TF dependency and we changed the way the data files are downloaded in https:\/\/github.com\/huggingface\/datasets\/pull\/881","body":"Hi\r\nI am getting these errors trying to load boolq thanks \r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 5, in <module>\r\n    data = AutoTask().get(\"boolq\").get_dataset(\"train\", n_obs=10)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 42, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 38, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 150, in download_custom\r\n    get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 472, in get_from_cache\r\n    f\"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been\"\r\nFileNotFoundError: Cannot find the requested files in the cached path at \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.\r\n","comment_length":43,"text":"boolq does not load  \n Hi\r\nI am getting these errors trying to load boolq thanks \r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 5, in <module>\r\n    data = AutoTask().get(\"boolq\").get_dataset(\"train\", n_obs=10)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 42, in get_dataset\r\n    dataset = self.load_dataset(split=split)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks\/tasks.py\", line 38, in load_dataset\r\n    return datasets.load_dataset(self.task.name, split=split)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \" \/idiap\/home\/rkarimi\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/boolq\/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11\/boolq.py\", line 74, in _split_generators\r\n    downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/download_manager.py\", line 150, in download_custom\r\n    get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 472, in get_from_cache\r\n    f\"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been\"\r\nFileNotFoundError: Cannot find the requested files in the cached path at \/idiap\/home\/rkarimi\/.cache\/huggingface\/datasets\/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.\r\n \n Could you check if it works on the master branch ?\r\nYou can use `load_dataset(\"boolq\", script_version=\"master\")` to do so.\r\nWe did some changes recently in boolq to remove the TF dependency and we changed the way the data files are downloaded in https:\/\/github.com\/huggingface\/datasets\/pull\/881","embeddings":[-0.3142423332,-0.1296663433,-0.1398147941,0.2243659049,0.0898041949,0.0106670726,0.4253870547,0.3031967878,0.4457686841,-0.1245959029,-0.086008586,0.3309848905,-0.1855678558,0.4824474752,0.0417104885,-0.0254136994,-0.1236411631,0.0837756172,-0.0132116294,-0.0677919537,-0.4335164726,0.3662593663,-0.4190507531,0.1728812605,-0.1200258434,-0.2764460444,-0.0112306615,0.2354567647,-0.3227624893,-0.3402307034,0.129193157,0.0399898887,0.2798753381,0.1975854039,-0.0001004217,0.04742755,0.4136013389,0.0912821293,-0.2201259285,-0.290461421,-0.1725928336,-0.3197637498,0.1770071685,-0.1704840362,-0.0781291723,-0.054515522,-0.0554295033,0.0769731924,0.4125249982,0.4600992799,0.3143282831,0.0757981241,0.1170568392,-0.1951514184,0.1931924373,-0.1626982242,0.1515527815,0.2015698403,0.0419378281,-0.2938958704,0.1587524712,0.2637588382,-0.183339566,-0.246029079,0.0121768303,-0.2074029446,-0.1017384678,-0.3344747722,0.2351708412,0.0141455783,0.5186159015,-0.1224103197,-0.2733826637,-0.056684956,0.1429380774,-0.1925470382,0.1893985271,0.0914991349,0.0534336679,-0.0635306165,0.1850988269,-0.4896081984,-0.0376428179,-0.0354207493,-0.1383907795,0.2759750187,-0.1215392649,0.051884301,0.1912807226,-0.1711804718,0.1133473963,0.2080259323,-0.152289167,0.1584958732,-0.5179923177,0.0358057208,0.1561286449,-0.0271783192,-0.0363988988,0.129627496,-0.189453423,0.0187248792,-0.091720216,0.2068092227,0.0046222373,0.5135238171,0.3842213452,0.2736550868,0.1583575159,0.1075289547,-0.2233469784,0.0342296846,-0.5201335549,-0.3621052206,-0.1302121133,0.2492164373,0.2366273552,-0.0848474279,-0.3538365662,-0.1739944071,-0.3224677145,-0.1347561181,0.3432625234,0.6987859011,0.0430136062,-0.1265625507,0.0525118038,0.2696886957,-0.3483470976,-0.0706117153,-0.2877412438,0.3017656803,-0.0307151303,-0.1554742754,0.2976013422,-0.1689217985,0.352542758,0.0606402606,-0.249807626,-0.0990925953,0.2741260231,-0.2546375394,-0.373259753,0.2511183619,0.0047492059,0.0151632084,0.2008347362,0.2303849608,-0.0411107466,0.2055432796,-0.2143663764,-0.2891750336,0.0416452251,0.3372689188,-0.0229818821,-0.2775761187,-0.1418446451,0.1758444458,0.1150734574,-0.1472827941,-0.2639817595,-0.327144146,-0.1046774015,-0.2185691744,0.3993265629,0.4546142817,-0.3941679597,-0.264450103,-0.3861935735,-0.2117394805,0.005800161,0.2955780327,-0.1370882541,0.5168119073,-0.2074039429,0.0861331522,0.2646962404,-0.2111936361,-0.1844504923,0.1970254332,-0.0149663696,-0.0348743387,-0.2750920653,-0.044528529,0.1203418449,0.1673647314,0.1317070127,0.1415139586,0.2968439162,-0.2430085838,-0.1465197951,-0.1223876178,0.0710638985,0.3457807302,0.2301300913,-0.0535534918,-0.0584518202,-0.4113751054,0.0685673952,0.1532153785,-0.2991673946,0.1951564103,0.3404016793,-0.1151792407,0.1968043298,0.0254542511,-0.3794722259,0.1544528753,-0.2339846492,-0.0378329121,-0.062096227,0.1533769965,-0.326677829,0.0734995157,-0.1862191111,-0.0524057746,0.1931232661,-0.1655142456,-0.0569789149,0.0857940465,-0.308848381,0.2691085041,-0.0235381071,0.1346359849,-0.2137397379,0.1054314226,-0.1465704441,-0.0927517265,0.2968972921,0.2993911803,0.1980737299,0.0299892873,-0.0212003067,0.2432890236,0.1288769841,0.1043745577,-0.0412735492,-0.5058262348,0.1112553775,-0.0384226665,0.307051599,0.1330125481,0.0742572993,0.0808739066,0.0900950283,0.2717500031,-0.0859902874,0.1937272102,0.1444682777,0.1817198992,0.2400854379,0.0292750653,-0.006893957,-0.1292017549,0.4810894728,-0.0400450267,-0.0255702455,0.0285188742,-0.1477422416,0.0691427216,0.4380694628,0.1077947244,0.122457467,0.0266056359,-0.1225930899,0.1235340685,0.1176418364,0.1531540751,0.2710778117,0.2076154053,-0.4852933288,-0.1021019965,-0.0421196967,-0.2693491876,0.1733107269,-0.1938354522,0.0288225207,0.2061602622,-0.0693069771,-0.1248023659,-0.1851713657,-0.2250053734,0.09095148,0.2774633765,-0.1973669082,-0.0649487823,-0.0835349858,-0.3896842897,0.191729635,-0.1083577797,-0.0304460432,-0.35120821,0.1287216693,-0.029635014,-0.0343716405,0.2476255149,0.0612640604,0.3148314655,0.107014671,-0.0850824639,-0.1364320368,-0.2685342133,-0.2266000211,0.1310845762,0.1870205998,0.0818480328,0.2532462776,-0.0990140066,0.0883723646,-0.0534891039,-0.2225491405,-0.08780545,-0.0944324955,0.3347641528,0.1184832379,0.0666116774,-0.0564614795,-0.2238010466,0.3523494303,-0.1633935571,-0.0211878568,0.1725939363,0.1716831923,-0.0578616336,-0.220501855,-0.5058780313,-0.5898679495,-0.4911761284,0.0963694528,0.0636325926,0.1966700107,0.3625305295,-0.0294897985,0.1960614175,0.0712995976,-0.0495073088,0.0055860337,-0.1599078923,0.262078315,-0.0651985332,-0.2783256769,0.0434393212,-0.0880052298,0.4329284132,-0.2539993227,-0.3081473708,0.003639091,-0.0644266978,-0.0977022052,-0.0620605685,-0.1536531448,-0.1169783846,0.0611614808,0.0138719501,0.0663510412,-0.3257706761,0.227166757,0.0191833954,0.1968749166,-0.0655772835,0.3469649255,-0.3650504053,0.6071892977,0.1826376915,0.1984719187,0.2066986561,0.2602954507,0.0882441327,0.02259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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"> neat feature\r\n\r\nI dint get these clearly, can you please elaborate like how to work on these ","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":18,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n > neat feature\r\n\r\nI dint get these clearly, can you please elaborate like how to work on these 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"It could maybe work almost out of the box just by using `cached_path` in the text\/csv\/json scripts, no?","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":18,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n It could maybe work almost out of the box just by using `cached_path` in the text\/csv\/json scripts, no?","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059,-0.2900656164,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"Thanks thomwolf and julien-c\r\n\r\nI'm still confusion on what you guys said, \r\n\r\nI have solved the problem as follows:\r\n\r\n1. read the csv file using pandas from s3 \r\n2. Convert to dictionary key as column name and values as list column data\r\n3. convert it to Dataset using \r\n`from datasets import Dataset`\r\n`train_dataset = Dataset.from_dict(train_dict)`","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":55,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n Thanks thomwolf and julien-c\r\n\r\nI'm still confusion on what you guys said, \r\n\r\nI have solved the problem as follows:\r\n\r\n1. read the csv file using pandas from s3 \r\n2. Convert to dictionary key as column name and values as list column data\r\n3. convert it to Dataset using \r\n`from datasets import Dataset`\r\n`train_dataset = Dataset.from_dict(train_dict)`","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"We were brainstorming around your use-case.\r\n\r\nLet's keep the issue open for now, I think this is an interesting question to think about.","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":23,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n We were brainstorming around your use-case.\r\n\r\nLet's keep the issue open for now, I think this is an interesting question to think about.","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059,-0.2900656164,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"> We were brainstorming around your use-case.\r\n> \r\n> Let's keep the issue open for now, I think this is an interesting question to think about.\r\n\r\nSure thomwolf, Thanks for your concern ","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":32,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n > We were brainstorming around your use-case.\r\n> \r\n> Let's keep the issue open for now, I think this is an interesting question to think about.\r\n\r\nSure thomwolf, Thanks for your concern 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"I agree it would be cool to have that feature. Also that's good to know that pandas supports this.\r\nFor the moment I'd suggest to first download the files locally as thom suggested and then load the dataset by providing paths to the local files","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":45,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n I agree it would be cool to have that feature. Also that's good to know that pandas supports this.\r\nFor the moment I'd suggest to first download the files locally as thom suggested and then load the dataset by providing paths to the local files","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059,-0.2900656164,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"Any updates on this issue?\r\nI face a similar issue. I have many parquet files in S3 and I would like to train on them. \r\nTo be honest I even face issues with only getting the last layer embedding out of them.","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":42,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n Any updates on this issue?\r\nI face a similar issue. I have many parquet files in S3 and I would like to train on them. \r\nTo be honest I even face issues with only getting the last layer embedding out of them.","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059,-0.2900656164,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"Hi dorlavie, \r\nYou can find one solution that i have mentioned above, that can help you. \r\nAnd there is one more solution also which is downloading files locally\r\n","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":28,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n Hi dorlavie, \r\nYou can find one solution that i have mentioned above, that can help you. \r\nAnd there is one more solution also which is downloading files locally\r\n","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059,-0.290065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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"> Hi dorlavie,\r\n> You can find one solution that i have mentioned above, that can help you.\r\n> And there is one more solution also which is downloading files locally\r\n\r\nmahesh1amour, thanks for the fast reply\r\n\r\nUnfortunately, in my case I can not read with pandas. The dataset is too big (50GB). \r\nIn addition, due to security concerns I am not allowed to save the data locally","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":68,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n > Hi dorlavie,\r\n> You can find one solution that i have mentioned above, that can help you.\r\n> And there is one more solution also which is downloading files locally\r\n\r\nmahesh1amour, thanks for the fast reply\r\n\r\nUnfortunately, in my case I can not read with pandas. The dataset is too big (50GB). \r\nIn addition, due to security concerns I am not allowed to save the data locally","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059,-0.2900656164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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"@dorlavie could use `boto3` to download the data to your local machine and then load it with `dataset`\r\n\r\nboto3 example [documentation](https:\/\/boto3.amazonaws.com\/v1\/documentation\/api\/latest\/guide\/s3-example-download-file.html)\r\n```python\r\nimport boto3\r\n\r\ns3 = boto3.client('s3')\r\ns3.download_file('BUCKET_NAME', 'OBJECT_NAME', 'FILE_NAME')\r\n```\r\n\r\ndatasets example [documentation](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html)\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files=['my_file_1.csv', 'my_file_2.csv', 'my_file_3.csv'])\r\n```\r\n","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":46,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n @dorlavie could use `boto3` to download the data to your local machine and then load it with `dataset`\r\n\r\nboto3 example [documentation](https:\/\/boto3.amazonaws.com\/v1\/documentation\/api\/latest\/guide\/s3-example-download-file.html)\r\n```python\r\nimport boto3\r\n\r\ns3 = boto3.client('s3')\r\ns3.download_file('BUCKET_NAME', 'OBJECT_NAME', 'FILE_NAME')\r\n```\r\n\r\ndatasets example [documentation](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html)\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files=['my_file_1.csv', 'my_file_2.csv', 'my_file_3.csv'])\r\n```\r\n","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.166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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"Thanks @philschmid for the suggestion.\r\nAs I mentioned in the previous comment, due to security issues I can not save the data locally.\r\nI need to read it from S3 and process it directly.\r\n\r\nI guess that many other people try to train \/ fit those models on huge datasets (e.g entire Wiki), what is the best practice in those cases?","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":61,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n Thanks @philschmid for the suggestion.\r\nAs I mentioned in the previous comment, due to security issues I can not save the data locally.\r\nI need to read it from S3 and process it directly.\r\n\r\nI guess that many other people try to train \/ fit those models on huge datasets (e.g entire Wiki), what is the best practice in those cases?","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059,-0.2900656164,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"If I understand correctly you're not allowed to write data on disk that you downloaded from S3 for example ?\r\nOr is it the use of the `boto3` library that is not allowed in your case ?","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":37,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n If I understand correctly you're not allowed to write data on disk that you downloaded from S3 for example ?\r\nOr is it the use of the `boto3` library that is not allowed in your case ?","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059,-0.2900656164,0.148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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"@lhoestq yes you are correct.\r\nI am not allowed to save the \"raw text\" locally - The \"raw text\" must be saved only on S3.\r\nI am allowed to save the output of any model locally. \r\nIt doesn't matter how I do it boto3\/pandas\/pyarrow, it is forbidden","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":47,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n @lhoestq yes you are correct.\r\nI am not allowed to save the \"raw text\" locally - The \"raw text\" must be saved only on S3.\r\nI am allowed to save the output of any model locally. \r\nIt doesn't matter how I do it boto3\/pandas\/pyarrow, it is forbidden","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059,-0.29006561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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/878","title":"Loading Data From S3 Path in Sagemaker","comments":"@dorlavie are you using sagemaker for training too? Then you could use S3 URI, for example `s3:\/\/my-bucket\/my-training-data` and pass it within the `.fit()` function when you start the sagemaker training job. Sagemaker would then download the data from s3 into the training runtime and you could load it from disk\r\n\r\n**sagemaker start training job**\r\n```python\r\npytorch_estimator.fit({'train':'s3:\/\/my-bucket\/my-training-data','eval':'s3:\/\/my-bucket\/my-evaluation-data'})\r\n```\r\n\r\n**in the train.py script**\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ntrain_dataset = load_from_disk(os.environ['SM_CHANNEL_TRAIN'])\r\n```\r\n\r\nI have created an example of how to use transformers and datasets with sagemaker. \r\nhttps:\/\/github.com\/philschmid\/huggingface-sagemaker-example\/tree\/main\/03_huggingface_sagemaker_trainer_with_data_from_s3\r\n\r\nThe example contains a jupyter notebook `sagemaker-example.ipynb` and an `src\/` folder. The sagemaker-example is a jupyter notebook that is used to create the training job on AWS Sagemaker. The `src\/` folder contains the `train.py`, our training script, and `requirements.txt` for additional dependencies.\r\n\r\n","body":"In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load","comment_length":127,"text":"Loading Data From S3 Path in Sagemaker \n In Sagemaker Im tring to load the data set from S3 path as follows\r\n\r\n`train_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/train.csv'\r\n    valid_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/validation.csv'\r\n    test_path = 's3:\/\/xxxxxxxxxx\/xxxxxxxxxx\/test.csv'\r\n    \r\n    data_files = {}\r\n    data_files[\"train\"] = train_path\r\n    data_files[\"validation\"] = valid_path\r\n    data_files[\"test\"] = test_path\r\n    extension = train_path.split(\".\")[-1]\r\n    datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)\r\n    print(datasets)`\r\n\r\n\r\nI getting an error of\r\n\r\n`algo-1-7plil_1  |   File \"main.py\", line 21, in <module>\r\nalgo-1-7plil_1  |     datasets = load_dataset(extension, data_files=data_files)\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 603, in load_dataset\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 155, in __init__\r\nalgo-1-7plil_1  |     **config_kwargs,\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 305, in _create_builder_config\r\nalgo-1-7plil_1  |     m.update(str(os.path.getmtime(data_file)))\r\nalgo-1-7plil_1  |   File \"\/opt\/conda\/lib\/python3.6\/genericpath.py\", line 55, in getmtime\r\nalgo-1-7plil_1  |     return os.stat(filename).st_mtime\r\nalgo-1-7plil_1  | FileNotFoundError: [Errno 2] No such file or directory: 's3:\/\/lsmv-sagemaker\/pubmedbert\/test.csv`\r\n\r\nBut when im trying with pandas , it is able to load from S3\r\n\r\nDoes the datasets library support S3 path to load \n @dorlavie are you using sagemaker for training too? Then you could use S3 URI, for example `s3:\/\/my-bucket\/my-training-data` and pass it within the `.fit()` function when you start the sagemaker training job. Sagemaker would then download the data from s3 into the training runtime and you could load it from disk\r\n\r\n**sagemaker start training job**\r\n```python\r\npytorch_estimator.fit({'train':'s3:\/\/my-bucket\/my-training-data','eval':'s3:\/\/my-bucket\/my-evaluation-data'})\r\n```\r\n\r\n**in the train.py script**\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ntrain_dataset = load_from_disk(os.environ['SM_CHANNEL_TRAIN'])\r\n```\r\n\r\nI have created an example of how to use transformers and datasets with sagemaker. \r\nhttps:\/\/github.com\/philschmid\/huggingface-sagemaker-example\/tree\/main\/03_huggingface_sagemaker_trainer_with_data_from_s3\r\n\r\nThe example contains a jupyter notebook `sagemaker-example.ipynb` and an `src\/` folder. The sagemaker-example is a jupyter notebook that is used to create the training job on AWS Sagemaker. The `src\/` folder contains the `train.py`, our training script, and `requirements.txt` for additional dependencies.\r\n\r\n","embeddings":[-0.2508122325,-0.2407749146,-0.0531521216,0.5038032532,0.2426776439,0.0379796848,0.4234098494,0.230222702,0.0534563586,0.0122716874,-0.0213181283,0.3855955303,-0.236418426,0.408721298,-0.0733910277,0.1519470513,0.0036897035,0.1174415499,0.241575554,0.1663119793,-0.0428829901,0.2773869038,-0.0571360439,0.1090650633,-0.1840185374,0.0136175472,0.0146284783,0.3506195247,-0.075076893,-0.5084875822,0.010816888,-0.0190767832,0.4783017635,0.3833350539,-0.0001139213,-0.0286531113,0.3157631457,0.0450190939,-0.422922045,-0.384821564,-0.5591976643,0.1110935286,0.1526586562,-0.0374609157,-0.3507244587,-0.0653413385,-0.3116801381,-0.2656855285,0.4028450549,0.6623779535,0.1782039702,0.1914703399,-0.073105447,-0.3078445792,0.2821199894,0.0203729924,0.0018274108,0.2220327705,-0.2715070248,0.1779980212,0.2951638997,0.2709996998,-0.4019239247,0.1139450222,0.5365952253,0.0304873791,0.3513378203,0.0784727633,0.127464205,0.1705706865,0.5800996423,-0.1361254901,-0.4543988407,-0.184919998,-0.2141834646,-0.2824923396,0.1915267557,0.0894623622,0.0374890529,0.028242277,-0.0464704409,-0.0466800258,-0.0988201797,0.2466035932,0.0339196883,0.440559864,-0.278003633,0.0633563548,0.2615089417,0.0703382492,0.3421872258,-0.1132721528,0.0181237478,0.0466640703,-0.2519853413,0.16740264,-0.0153579842,0.2122866958,-0.0699794069,0.1938651204,0.3034305573,-0.0739920959,0.3996638358,0.2401907742,0.3883311152,-0.1680295616,0.0913824365,0.0003885696,-0.0994113535,0.4869207144,0.0785982981,-0.1934865415,-0.3133118749,-0.5367276073,0.1240956336,0.268817991,0.3971948624,-0.3510361314,-0.2658778727,-0.0860199854,-0.1192131713,-0.1965766996,0.2694832683,0.4972227812,-0.3471298218,-0.1139851063,-0.1179710552,0.2110883445,-0.2143538892,0.0854033828,-0.1983184814,0.2434528023,-0.319057256,0.0227509346,0.148680076,-0.5799320936,0.5168523192,-0.0738639161,0.0051261182,-0.3440077305,0.1492902786,-0.2164640427,0.1777615249,0.181439817,0.1935322434,0.2570756674,-0.0537814647,-0.0760165527,-0.1820334494,0.0855752379,-0.1849054843,-0.4797708988,0.0412797295,0.2240626961,-0.2657831907,-0.1602720469,-0.0594209172,-0.1859486103,0.2781676948,-0.0984312296,-0.3886997104,0.0453326479,-0.1181020886,-0.2207810432,0.4690745473,0.3192168772,-0.3537423909,0.1085963026,-0.471652329,0.113847062,0.0835102424,0.21051687,-0.3659238815,0.4489594996,-0.3048421741,0.1328942031,0.5610044003,-0.2565937042,0.0060540144,0.0853612125,-0.0421512462,-0.1606775224,0.1342204362,-0.0630417317,-0.1340707988,-0.1085644215,0.0167824328,0.3441396952,0.0382880904,0.0388525389,-0.1566989571,-0.0247691069,0.1257411838,0.2443982661,0.1100840494,-0.0220544655,-0.0373709686,-0.1964025348,-0.0556683019,-0.1184008718,-0.0434881225,0.4679802656,0.273306489,0.301110357,-0.0028269866,0.1516498625,-0.2742566764,0.1333042681,0.0448954403,-0.2772715986,-0.0668589994,0.0106648421,-0.2895232141,0.0034126535,-0.4520739615,0.0989072919,0.1053083465,0.3138039708,0.2326270342,-0.0971172452,-0.1190223917,-0.305985868,0.3305369616,-0.0151232248,-0.3088082671,0.113530919,-0.4051382542,-0.0548965894,0.0869040713,0.0439935476,0.1716851592,0.1176360101,0.0150821852,0.1787704825,0.1243599057,0.2274493873,-0.2144693881,0.1562106162,-0.1199650764,0.030693043,0.1797361076,-0.1334500164,0.1356184781,0.0835730806,-0.044440113,0.4771928191,-0.0267096646,0.189440012,0.1397075802,0.0150925107,0.2696094215,-0.1694880128,-0.0204154514,-0.13537094,0.0476758145,-0.1200017855,0.1146692112,0.0762749314,-0.0729279891,-0.129548803,0.3834590316,0.0959832966,-0.0108043309,-0.2696371973,-0.2296590656,-0.1449694782,0.065362744,0.495529741,0.3665240705,0.22105892,0.0965472981,-0.035579741,-0.0539808832,-0.1855220199,0.2379354239,0.4237586856,0.4380967915,0.3556293249,-0.3781282902,-0.0747246221,-0.2402070016,-0.1607340276,0.1642100364,0.2822508812,-0.4002408981,0.1890033185,-0.0598931015,-0.1696401238,-0.298271358,-0.354865402,-0.1032038108,-0.2554685473,-0.1192265376,0.2769101858,-0.2668563426,-0.027291365,0.0960367247,0.0128378468,-0.0740059763,-0.0552989915,-0.3418391049,-0.2471806109,-0.1704055071,0.0550118871,0.2183091938,0.0958847925,0.2658967972,-0.1865487844,0.1083012894,-0.463339597,-0.0825944766,-0.0176458135,-0.00809725,0.3024953902,0.2742291689,0.0802076086,0.2997995019,-0.2465780228,0.4653754234,-0.012098128,0.0204350967,0.2364755422,-0.0213384144,-0.1121434867,-0.0333958156,-0.3809584379,0.0699729174,-0.4534810483,-0.3755663633,0.0112922871,0.1440520436,0.2753456831,0.3328946829,0.2299250364,-0.2099558264,0.3361966312,-0.2096320391,-0.2037051469,0.1267781556,-0.2523744106,-0.2639803588,0.3304711878,-0.0450135954,0.8056797385,-0.1035317406,-0.2581118941,-0.028181551,0.054154627,-0.3135477901,-0.1422804147,-0.2467391789,0.3080139756,0.208803907,-0.0856468529,-0.0078174854,-0.3258495927,0.0911117196,-0.2148199975,0.2122523487,-0.2017768174,0.8904483914,-0.0051942291,0.4429087043,0.1470208913,-0.2118462175,0.4529938698,-0.1063801572,0.1824462712,-0.1048233956,-0.2602479756,-0.1180830076,0.1664527059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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/877","title":"DataLoader(datasets) become more and more slowly within iterations","comments":"Hi ! Thanks for reporting.\r\nDo you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\nIt would be nice to know whether it comes from the dataloader or not","body":"Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly!\r\n```\r\ndataset = load_from_disk(dataset_path)  # around 21,000,000 lines\r\n\r\nlineloader = tqdm(DataLoader(dataset, batch_size=1))\r\nfor idx, line in enumerate(lineloader):\r\n     # do some thing for each line\r\n```\r\nIn the begining, the loading speed is around 2000it\/s, but after 1 minutes later, the speed is much slower, just around 800it\/s.\r\n\r\nAnd when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it\/s.\r\n\r\nCould you please help me with this problem?\r\nThanks a lot!","comment_length":38,"text":"DataLoader(datasets) become more and more slowly within iterations \n Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly!\r\n```\r\ndataset = load_from_disk(dataset_path)  # around 21,000,000 lines\r\n\r\nlineloader = tqdm(DataLoader(dataset, batch_size=1))\r\nfor idx, line in enumerate(lineloader):\r\n     # do some thing for each line\r\n```\r\nIn the begining, the loading speed is around 2000it\/s, but after 1 minutes later, the speed is much slower, just around 800it\/s.\r\n\r\nAnd when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it\/s.\r\n\r\nCould you please help me with this problem?\r\nThanks a lot! \n Hi ! Thanks for reporting.\r\nDo you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\nIt would be nice to know whether it comes from the dataloader or 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/877","title":"DataLoader(datasets) become more and more slowly within iterations","comments":"> Hi ! Thanks for reporting.\r\n> Do you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\n> It would be nice to know whether it comes from the dataloader or not\r\n\r\nI did not iter data from raw dataset, maybe I will test later. Now I iter all files directly from `open(file)`,  around 20000it\/s.","body":"Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly!\r\n```\r\ndataset = load_from_disk(dataset_path)  # around 21,000,000 lines\r\n\r\nlineloader = tqdm(DataLoader(dataset, batch_size=1))\r\nfor idx, line in enumerate(lineloader):\r\n     # do some thing for each line\r\n```\r\nIn the begining, the loading speed is around 2000it\/s, but after 1 minutes later, the speed is much slower, just around 800it\/s.\r\n\r\nAnd when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it\/s.\r\n\r\nCould you please help me with this problem?\r\nThanks a lot!","comment_length":64,"text":"DataLoader(datasets) become more and more slowly within iterations \n Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly!\r\n```\r\ndataset = load_from_disk(dataset_path)  # around 21,000,000 lines\r\n\r\nlineloader = tqdm(DataLoader(dataset, batch_size=1))\r\nfor idx, line in enumerate(lineloader):\r\n     # do some thing for each line\r\n```\r\nIn the begining, the loading speed is around 2000it\/s, but after 1 minutes later, the speed is much slower, just around 800it\/s.\r\n\r\nAnd when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it\/s.\r\n\r\nCould you please help me with this problem?\r\nThanks a lot! \n > Hi ! Thanks for reporting.\r\n> Do you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\n> It would be nice to know whether it comes from the dataloader or not\r\n\r\nI did not iter data from raw dataset, maybe I will test later. Now I iter all files directly from `open(file)`,  around 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/876","title":"imdb dataset cannot be loaded ","comments":"It looks like there was an issue while building the imdb dataset.\r\nCould you provide more information about your OS and the version of python and `datasets` ?\r\n\r\nAlso could you try again with \r\n```python\r\ndataset = datasets.load_dataset(\"imdb\", split=\"train\", download_mode=\"force_redownload\")\r\n```\r\nto make sure it's not a corrupted file issue ?","body":"Hi\r\nI am trying to load the imdb train dataset\r\n\r\n`dataset = datasets.load_dataset(\"imdb\", split=\"train\")`\r\n\r\ngetting following errors, thanks for your help \r\n```\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=32660064, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=26476338, num_examples=20316, dataset_name='imdb')}, {'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n>>> dataset = datasets.load_dataset(\"imdb\", split=\"train\")\r\n\r\n```\r\n","comment_length":51,"text":"imdb dataset cannot be loaded  \n Hi\r\nI am trying to load the imdb train dataset\r\n\r\n`dataset = datasets.load_dataset(\"imdb\", split=\"train\")`\r\n\r\ngetting following errors, thanks for your help \r\n```\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=32660064, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=26476338, num_examples=20316, dataset_name='imdb')}, {'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n>>> dataset = datasets.load_dataset(\"imdb\", split=\"train\")\r\n\r\n```\r\n \n It looks like there was an issue while building the imdb dataset.\r\nCould you provide more information about your OS and the version of python and `datasets` ?\r\n\r\nAlso could you try again with \r\n```python\r\ndataset = datasets.load_dataset(\"imdb\", split=\"train\", download_mode=\"force_redownload\")\r\n```\r\nto make sure it's not a corrupted file issue 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/876","title":"imdb dataset cannot be loaded ","comments":"Hi ! I just tried in 1.8.0 and it worked fine. Can you try again ? Maybe the dataset host had some issues that are fixed now","body":"Hi\r\nI am trying to load the imdb train dataset\r\n\r\n`dataset = datasets.load_dataset(\"imdb\", split=\"train\")`\r\n\r\ngetting following errors, thanks for your help \r\n```\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=32660064, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=26476338, num_examples=20316, dataset_name='imdb')}, {'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n>>> dataset = datasets.load_dataset(\"imdb\", split=\"train\")\r\n\r\n```\r\n","comment_length":27,"text":"imdb dataset cannot be loaded  \n Hi\r\nI am trying to load the imdb train dataset\r\n\r\n`dataset = datasets.load_dataset(\"imdb\", split=\"train\")`\r\n\r\ngetting following errors, thanks for your help \r\n```\r\nTraceback (most recent call last):        \r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 558, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 73, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=32660064, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=26476338, num_examples=20316, dataset_name='imdb')}, {'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n>>> dataset = datasets.load_dataset(\"imdb\", split=\"train\")\r\n\r\n```\r\n \n Hi ! I just tried in 1.8.0 and it worked fine. Can you try again ? Maybe the dataset host had some issues that are fixed now","embeddings":[-0.5215904713,-0.0397978574,-0.1209275573,0.4138287604,0.3087592721,0.3442183137,0.4162842035,0.4116177261,0.1893083304,-0.1259101331,-0.2459378541,-0.0363801979,-0.2387997508,0.1189185232,-0.1006124243,-0.1933890283,-0.1093897745,-0.0083492799,0.0082321288,0.0248102322,-0.1954829097,0.2631110549,-0.4074177742,-0.0073810266,-0.5533467531,-0.2076044679,0.0664415807,0.2464725524,-0.1878924072,-0.3777330518,0.4374702871,0.0338272862,0.2985222936,0.4265396595,-0.0001084289,0.0661215633,0.5560089946,-0.1408037841,-0.3377951086,-0.2855277359,-0.2910868526,-0.0734336898,0.2554953098,-0.2121904343,-0.0610393658,-0.2735612988,-0.0876790956,-0.4681127667,0.3000148833,0.4594834447,0.2551243603,-0.0305228662,0.076037012,-0.1456561089,0.0994302854,0.1825692356,0.009692302,0.3030386865,0.0760118738,0.0173887219,0.2145227194,0.0415253453,-0.2540514171,-0.0379882827,0.2962742746,0.0274888687,0.067300871,-0.4677696824,0.0347380377,0.2485592067,0.6612049937,-0.0836040899,-0.3514135182,-0.0572897382,0.1594483256,-0.2821145952,0.1125696599,0.2813580036,-0.1202666163,0.1045412347,-0.4093435705,-0.066220209,-0.0858248621,0.1770364344,0.0300608855,-0.0702761412,0.0590686873,0.0750905573,0.2223216593,-0.1004089639,0.3060327768,0.0287062842,0.0114890561,0.1601479799,-0.2722389698,0.0616545677,-0.309935689,0.0013539037,-0.0696112737,0.2496410906,-0.2069759518,-0.0210834984,-0.0292110536,0.2595463991,0.2834863961,0.1375840008,0.0388361663,0.4064239264,0.1448393166,0.2383134514,-0.2738069892,-0.1059613079,-0.2260224223,-0.1501700431,0.0980359986,0.1302989721,0.0258586034,-0.1813311875,-0.5039566755,-0.0179434661,-0.1861053258,0.0018605727,0.3757007718,0.3940535188,-0.1082345173,0.1711023748,0.1874571294,0.3105209172,-0.1181692928,-0.063041687,-0.1855241805,0.2731193006,-0.1848761886,0.0785208493,0.4066706896,-0.1432269812,0.4070621133,0.0138801718,0.0988444313,-0.2059773356,0.1962580979,-0.3265180588,0.0474708453,0.3069472015,0.2882384956,0.1749825925,0.1750798076,-0.0222159196,-0.1219259053,0.0434901752,-0.2463379949,-0.4131351709,-0.1829316765,0.2787935436,-0.0036364328,-0.0550396219,-0.4261500239,-0.0292973146,0.0431886576,-0.1881007403,-0.1758461297,-0.3051405251,-0.1755626351,-0.1236034855,0.4283623397,0.4742207825,-0.2819431126,-0.0677016973,-0.3545875847,-0.1019600853,0.2071660906,0.2522236705,-0.3016596138,0.2245974392,-0.3644298613,0.1026928723,0.3114507794,-0.4896250367,-0.1315897405,0.307010144,0.1519509554,0.1178987846,0.0510700904,0.1178216711,0.3234882951,0.0152814304,0.2373962253,0.4272217155,-0.0333052389,-0.1566784978,-0.0633250624,-0.2016087919,0.1954357326,0.3601022959,0.3082616925,0.00211513,0.0012145919,0.2097969502,0.2742595971,0.117574349,-0.0408722498,0.2063197494,-0.0526263677,0.3531843424,-0.0541510358,-0.1990605146,-0.4411433637,0.1967228204,0.2666048408,-0.0465099066,-0.1574426889,-0.1011871099,-0.275845468,-0.1793883443,-0.2391406745,0.0034423762,0.1849972755,-0.0831256956,-0.0139205651,0.058201801,-0.437218219,0.396702677,-0.1066199988,0.1430695206,-0.3454021513,0.3588088751,-0.1727034897,-0.2236957699,0.1571170837,0.1187447309,0.1863096654,-0.2496664673,-0.0465861037,0.4384959638,0.3271714747,-0.028225705,-0.0712756068,-0.2227297127,0.1980135292,-0.3737979829,0.1602865607,-0.1303503811,0.0517517813,-0.1354398578,-0.0591690466,0.1639060974,-0.2817702293,0.2440556437,0.2280354053,0.1386639625,0.2511508763,-0.0994582698,-0.0211281814,-0.440608412,0.1827893555,-0.0190275162,0.018330995,-0.0145888012,-0.2629710436,-0.2127719522,0.4115481675,0.1476501226,-0.0533790998,0.1388183981,-0.1717075109,0.1022975519,-0.0761989653,0.1512963623,0.2494870126,0.1761391312,-0.0901043043,-0.073893562,0.1371012181,-0.1112428233,0.2687686086,0.063081257,0.1386984587,0.3271507621,-0.1574336737,-0.0432163775,-0.3005732298,-0.3626558483,0.1164298654,0.266146481,-0.3761330843,-0.0721555799,-0.3137325346,-0.1708401293,-0.0889626667,-0.3290142715,-0.0407060795,-0.3265829384,-0.0876567066,0.3447710276,0.3259420991,0.1502281427,-0.2954137623,-0.1498266309,-0.0587890856,-0.0474688821,-0.0757709593,-0.2140558362,-0.1474243701,0.1250645518,0.4519128203,0.1995556653,0.2789734304,-0.3943610191,-0.0972729251,0.0210898109,0.15831393,-0.0527739674,-0.1372921914,0.1901933104,0.1164055914,0.2480758727,0.1137991101,-0.5119875669,0.3313744664,-0.1434600055,0.0764830038,0.1595186889,0.0767459869,-0.0790889338,0.0146051319,-0.47806409,-0.4822545052,-0.3378787637,-0.0881939232,0.1694431752,0.0430157892,0.2023380548,0.136626929,0.1154308394,0.318382442,0.1276842356,-0.0295815244,-0.3274250031,0.3202656507,-0.2303406298,-0.1603825837,0.2412955314,0.1050819606,0.1461465657,0.0087437248,-0.414042294,0.0979003534,-0.0617495067,0.1573085785,-0.0945536718,-0.146949634,-0.0430615917,0.0699405968,-0.0419445448,0.0470371619,-0.0949973911,-0.03719889,-0.0491980128,0.1601986736,-0.1255298108,0.5187346935,-0.3258313537,0.5013358593,0.2936226428,-0.2053146213,0.3749496639,-0.192737937,0.1645800173,-0.2501033247,-0.6196154952,-0.1546026468,-0.12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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/873","title":"load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error","comments":"I see the issue happening again today - \r\n\r\n[nltk_data] Downloading package stopwords to \/root\/nltk_data...\r\n[nltk_data]   Package stopwords is already up-to-date!\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\nCan someone please take a look ?","body":"```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('cnn_dailymail', '3.0.0')\r\n```\r\nStack trace:\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-6-2e06a8332652> in <module>()\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')\r\n\r\n5 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    608         download_config=download_config,\r\n    609         download_mode=download_mode,\r\n--> 610         ignore_verifications=ignore_verifications,\r\n    611     )\r\n    612 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    513                     if not downloaded_from_gcs:\r\n    514                         self._download_and_prepare(\r\n--> 515                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    516                         )\r\n    517                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    568         split_dict = SplitDict(dataset_name=self.name)\r\n    569         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 570         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    571 \r\n    572         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\nI have ran the code on Google Colab","comment_length":108,"text":"load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error \n ```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('cnn_dailymail', '3.0.0')\r\n```\r\nStack trace:\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-6-2e06a8332652> in <module>()\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')\r\n\r\n5 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    608         download_config=download_config,\r\n    609         download_mode=download_mode,\r\n--> 610         ignore_verifications=ignore_verifications,\r\n    611     )\r\n    612 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    513                     if not downloaded_from_gcs:\r\n    514                         self._download_and_prepare(\r\n--> 515                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    516                         )\r\n    517                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    568         split_dict = SplitDict(dataset_name=self.name)\r\n    569         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 570         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    571 \r\n    572         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\nI have ran the code on Google Colab \n I see the issue happening again today - \r\n\r\n[nltk_data] Downloading package stopwords to \/root\/nltk_data...\r\n[nltk_data]   Package stopwords is already up-to-date!\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n     22 \r\n     23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\nCan someone please take a look ?","embeddings":[-0.224293083,0.1419460326,-0.0225758813,0.2384324819,0.3819946051,0.1261650622,0.6168980598,0.2147594988,-0.0231766459,0.1334128678,-0.163756609,0.0582367294,-0.3759562671,-0.123077713,0.0085837077,0.114968501,-0.0964015499,0.1582960486,-0.2051095515,-0.1422860175,-0.3901512027,0.1864792407,-0.0768181756,0.0159925465,-0.2544403374,-0.2072729915,-0.1191695929,0.4782720804,-0.2281916291,-0.3415712714,0.291224122,-0.1937130839,0.2596129179,0.6446203589,-0.0001103697,0.1348124146,0.257455349,-0.0459846482,-0.5990345478,-0.6107162833,-0.1344918758,-0.1810776293,0.2224275321,-0.2857463658,-0.0545736887,0.1120457277,0.0625397936,0.019521933,0.3589360118,0.5676545501,0.2382051796,0.2500875592,-0.0214656368,-0.1423246413,-0.1394215375,0.3917793632,-0.08329615,0.5298015475,-0.3784783185,-0.0074536237,0.3009919226,0.2560061216,-0.3467578292,0.2481284142,0.3105844557,0.0931708962,0.2014932483,-0.318579495,0.0102561293,0.052064389,0.462816,-0.377645731,-0.2139322609,-0.0260961484,0.0874807909,-0.4519509375,0.3440143466,0.3034414649,-0.1303296834,0.0531827994,-0.146174401,-0.1438385248,-0.1814096421,0.4520489275,-0.1718828231,0.2167272717,-0.0570031144,0.0317016952,0.0401946232,0.0549006499,0.5230491161,-0.2012925595,0.1109838113,0.1689763218,-0.0259168874,0.0619172007,0.200421989,-0.1987337023,-0.1959023029,0.1629692465,-0.0814728886,-0.2725152671,-0.0308208447,0.0420363732,0.0562603734,0.1875984669,-0.0870422944,0.5960316658,0.2050020099,0.2587226927,-0.0126039628,-0.1288865656,-0.3524653614,-0.249772191,0.1027377322,0.0556450076,0.6368337274,0.0808701962,-0.262622416,-0.1706470996,-0.2875089049,0.0266700387,0.1824934632,0.3770372868,0.0879202038,-0.0015779705,0.1018526927,0.2980035841,-0.0611399859,0.3207466304,-0.2227891982,0.2121761739,-0.1194054484,-0.03893435,0.270580411,-0.2202292234,0.1217843741,-0.1732522994,-0.0137373274,-0.0063968683,0.1550607979,-0.1759008318,0.074225083,0.5078181028,-0.1087166741,0.2788080275,0.270821631,0.0265562665,-0.3172303438,0.1326729208,-0.1793326437,-0.4073957503,-0.0890357345,0.191819638,0.0433196686,-0.1540216804,0.0512391925,-0.2044744641,-0.1510185897,-0.1646996737,-0.0049300003,-0.1914943755,-0.0878131837,-0.3597047925,0.294339478,0.6152353883,-0.3911423981,0.0004790114,-0.4250386953,-0.1540962905,0.1708229333,0.2825909257,-0.1810852587,0.418251425,-0.2191811651,-0.2109978348,0.4664492309,-0.4039042294,-0.4069173932,0.1619243324,-0.1690849811,-0.0231488161,0.0143251596,0.1950932592,-0.1637684554,0.0874652714,0.0317935087,0.3440898061,-0.0140823061,0.0791541114,-0.095202975,-0.2891000509,0.1494426131,0.1394836754,0.2537146509,0.2065376937,0.0017706892,0.057444334,0.0345365591,-0.1340152472,-0.0199387986,0.193029359,0.4896597266,0.0706251934,-0.3033567369,-0.0080354828,-0.5152108073,0.1154644713,0.0506806336,0.2688595951,-0.2443437576,0.0475811958,-0.2807430923,-0.1213214844,-0.2518719435,-0.0720805973,0.1413440108,0.362624079,0.0602164939,0.06581752,-0.2875800133,0.2650077939,0.0555366054,0.1482611448,-0.1362416446,0.4147193134,-0.1979314238,-0.0624308884,-0.0436080284,0.1352329403,0.2345956862,-0.1446532458,-0.1301756352,0.3645673692,-0.1508782059,-0.0773257464,0.0398339257,-0.1054766253,0.0736475289,-0.0363827124,0.2342951298,0.3620508015,0.1599349976,-0.0431439169,-0.1002390012,0.2904100418,0.161951825,0.0390076637,0.1538697779,-0.0371430255,0.0395891629,-0.0555200279,0.1082931161,-0.041275423,0.2116338313,0.0997428,0.4739992917,0.0227582809,-0.1307650357,-0.3711324632,0.2705956399,-0.134705767,0.1444204599,0.1506156623,-0.2888510227,0.0992454216,-0.1900030524,0.1472333819,0.6052938104,0.1164713353,0.0325042419,0.1258146018,-0.0870899335,-0.0704523772,0.2077787369,0.0883645713,0.2569261789,0.2160902619,0.0695267469,0.0620280765,-0.3969856501,-0.2876626253,0.0426680557,0.4161446691,-0.3931626678,0.059591908,-0.1244833395,-0.2166050375,-0.0977737382,-0.5352479219,-0.0786898881,-0.3155716062,-0.1555617154,0.1735693216,0.1367603093,0.1662632525,-0.1210425571,-0.1262292862,0.1058900803,-0.4859184921,-0.2068847418,-0.0484767519,-0.0174885709,0.0651339069,0.2804500163,-0.1032850295,0.2476293594,-0.1312977076,-0.1217798218,-0.1863985956,-0.0907368809,-0.0835310295,-0.1042266935,0.2084366828,0.2644519508,0.3542820513,0.0467904173,-0.1947212368,0.3619304001,-0.1828137487,0.0223103501,0.3297629058,-0.1260294914,-0.0134168044,-0.0603099614,-0.5458139777,-0.5458031297,-0.3574637175,-0.1193348393,0.128277272,0.1643208861,0.439052999,0.149543047,0.1939637363,-0.1065731868,0.0041575646,-0.0272339098,-0.1540678293,0.4063127637,-0.2877522409,-0.2439707518,0.1713533998,-0.0405313782,0.1901890039,-0.1609371156,-0.4939940274,-0.2556856275,-0.0731515959,0.038228672,-0.1987717301,-0.051362209,0.2149719,0.0962023139,-0.100954771,0.1519619524,-0.1301398873,-0.0496761128,0.1687473506,0.0203161482,-0.2199278027,0.5267876983,-0.2912866473,0.5844215155,0.1298206747,-0.0976593196,0.3287144303,-0.2017037868,0.435521692,-0.2107226998,-0.5223431587,-0.0787455291,0.2892427742,-0.1790048331,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/873","title":"load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error","comments":"> atal(\"Unsupported publisher: %s\", publisher) --> 134 files = sorted(os.listdir(top_dir)) 135 136 ret_files = []\r\n> \r\n> NotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n> \r\n> Can someone please take a look ?\r\n\r\n2 short-term workarounds:\r\n\r\n1.  Use this line instead `dataset = load_dataset('ccdv\/cnn_dailymail', '3.0.0')`. [In a related issue](https:\/\/github.com\/huggingface\/datasets\/issues\/996#issuecomment-997343101), this person mentioned another data source copy that just works.\r\n2.  Use the same data source, but edit the urls.  Instead of google drive quota problems mentioned in #996, I was getting the \"can't scan this file for viruses\" problem, which results in that prompted html getting downloaded instead of the files.  You can get around this by:\r\n    1. Look at the traceback and find out where `cnn_dailymail.py` is on your computer.\r\n    2. Edit the `cnn_stories` and `dm_stories` url's by adding the following to the end of them `&confirm=t`.  This should be around line 67.\r\n    3. You may have to remove those confirmation html files in your download directory (`~\/.cache\/huggingface\/datasets\/downloads` for me) so that they don't get in the way of the new download attempts.\r\n\r\nEither method works for me.  I would've made a PR, but not sure if they want to go with the new ccdv\/cnn_dailymail source or not.","body":"```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('cnn_dailymail', '3.0.0')\r\n```\r\nStack trace:\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-6-2e06a8332652> in <module>()\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')\r\n\r\n5 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    608         download_config=download_config,\r\n    609         download_mode=download_mode,\r\n--> 610         ignore_verifications=ignore_verifications,\r\n    611     )\r\n    612 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    513                     if not downloaded_from_gcs:\r\n    514                         self._download_and_prepare(\r\n--> 515                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    516                         )\r\n    517                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    568         split_dict = SplitDict(dataset_name=self.name)\r\n    569         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 570         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    571 \r\n    572         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\nI have ran the code on Google Colab","comment_length":199,"text":"load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error \n ```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('cnn_dailymail', '3.0.0')\r\n```\r\nStack trace:\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-6-2e06a8332652> in <module>()\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')\r\n\r\n5 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    608         download_config=download_config,\r\n    609         download_mode=download_mode,\r\n--> 610         ignore_verifications=ignore_verifications,\r\n    611     )\r\n    612 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    513                     if not downloaded_from_gcs:\r\n    514                         self._download_and_prepare(\r\n--> 515                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    516                         )\r\n    517                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    568         split_dict = SplitDict(dataset_name=self.name)\r\n    569         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 570         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    571 \r\n    572         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\nI have ran the code on Google Colab \n > atal(\"Unsupported publisher: %s\", publisher) --> 134 files = sorted(os.listdir(top_dir)) 135 136 ret_files = []\r\n> \r\n> NotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n> \r\n> Can someone please take a look ?\r\n\r\n2 short-term workarounds:\r\n\r\n1.  Use this line instead `dataset = load_dataset('ccdv\/cnn_dailymail', '3.0.0')`. [In a related issue](https:\/\/github.com\/huggingface\/datasets\/issues\/996#issuecomment-997343101), this person mentioned another data source copy that just works.\r\n2.  Use the same data source, but edit the urls.  Instead of google drive quota problems mentioned in #996, I was getting the \"can't scan this file for viruses\" problem, which results in that prompted html getting downloaded instead of the files.  You can get around this by:\r\n    1. Look at the traceback and find out where `cnn_dailymail.py` is on your computer.\r\n    2. Edit the `cnn_stories` and `dm_stories` url's by adding the following to the end of them `&confirm=t`.  This should be around line 67.\r\n    3. You may have to remove those confirmation html files in your download directory (`~\/.cache\/huggingface\/datasets\/downloads` for me) so that they don't get in the way of the new download attempts.\r\n\r\nEither method works for me.  I would've made a PR, but not sure if they want to go with the new ccdv\/cnn_dailymail source or not.","embeddings":[-0.224293083,0.1419460326,-0.0225758813,0.2384324819,0.3819946051,0.1261650622,0.6168980598,0.2147594988,-0.0231766459,0.1334128678,-0.163756609,0.0582367294,-0.3759562671,-0.123077713,0.0085837077,0.114968501,-0.0964015499,0.1582960486,-0.2051095515,-0.1422860175,-0.3901512027,0.1864792407,-0.0768181756,0.0159925465,-0.2544403374,-0.2072729915,-0.1191695929,0.4782720804,-0.2281916291,-0.3415712714,0.291224122,-0.1937130839,0.2596129179,0.6446203589,-0.0001103697,0.1348124146,0.257455349,-0.0459846482,-0.5990345478,-0.6107162833,-0.1344918758,-0.1810776293,0.2224275321,-0.2857463658,-0.0545736887,0.1120457277,0.0625397936,0.019521933,0.3589360118,0.5676545501,0.2382051796,0.2500875592,-0.0214656368,-0.1423246413,-0.1394215375,0.3917793632,-0.08329615,0.5298015475,-0.3784783185,-0.0074536237,0.3009919226,0.2560061216,-0.3467578292,0.2481284142,0.3105844557,0.0931708962,0.2014932483,-0.318579495,0.0102561293,0.052064389,0.462816,-0.377645731,-0.2139322609,-0.0260961484,0.0874807909,-0.4519509375,0.3440143466,0.3034414649,-0.1303296834,0.0531827994,-0.146174401,-0.1438385248,-0.1814096421,0.4520489275,-0.1718828231,0.2167272717,-0.0570031144,0.0317016952,0.0401946232,0.0549006499,0.5230491161,-0.2012925595,0.1109838113,0.1689763218,-0.0259168874,0.0619172007,0.200421989,-0.1987337023,-0.1959023029,0.1629692465,-0.0814728886,-0.2725152671,-0.0308208447,0.0420363732,0.0562603734,0.1875984669,-0.0870422944,0.5960316658,0.2050020099,0.2587226927,-0.0126039628,-0.1288865656,-0.3524653614,-0.249772191,0.1027377322,0.0556450076,0.6368337274,0.0808701962,-0.262622416,-0.1706470996,-0.2875089049,0.0266700387,0.1824934632,0.3770372868,0.0879202038,-0.0015779705,0.1018526927,0.2980035841,-0.0611399859,0.3207466304,-0.2227891982,0.2121761739,-0.1194054484,-0.03893435,0.270580411,-0.2202292234,0.1217843741,-0.1732522994,-0.0137373274,-0.0063968683,0.1550607979,-0.1759008318,0.074225083,0.5078181028,-0.1087166741,0.2788080275,0.270821631,0.0265562665,-0.3172303438,0.1326729208,-0.1793326437,-0.4073957503,-0.0890357345,0.191819638,0.0433196686,-0.1540216804,0.0512391925,-0.2044744641,-0.1510185897,-0.1646996737,-0.0049300003,-0.1914943755,-0.0878131837,-0.3597047925,0.294339478,0.6152353883,-0.3911423981,0.0004790114,-0.4250386953,-0.1540962905,0.1708229333,0.2825909257,-0.1810852587,0.418251425,-0.2191811651,-0.2109978348,0.4664492309,-0.4039042294,-0.4069173932,0.1619243324,-0.1690849811,-0.0231488161,0.0143251596,0.1950932592,-0.1637684554,0.0874652714,0.0317935087,0.3440898061,-0.0140823061,0.0791541114,-0.095202975,-0.2891000509,0.1494426131,0.1394836754,0.2537146509,0.2065376937,0.0017706892,0.057444334,0.0345365591,-0.1340152472,-0.0199387986,0.193029359,0.4896597266,0.0706251934,-0.3033567369,-0.0080354828,-0.5152108073,0.1154644713,0.0506806336,0.2688595951,-0.2443437576,0.0475811958,-0.2807430923,-0.1213214844,-0.2518719435,-0.0720805973,0.1413440108,0.362624079,0.0602164939,0.06581752,-0.2875800133,0.2650077939,0.0555366054,0.1482611448,-0.1362416446,0.4147193134,-0.1979314238,-0.0624308884,-0.0436080284,0.1352329403,0.2345956862,-0.1446532458,-0.1301756352,0.3645673692,-0.1508782059,-0.0773257464,0.0398339257,-0.1054766253,0.0736475289,-0.0363827124,0.2342951298,0.3620508015,0.1599349976,-0.0431439169,-0.1002390012,0.2904100418,0.161951825,0.0390076637,0.1538697779,-0.0371430255,0.0395891629,-0.0555200279,0.1082931161,-0.041275423,0.2116338313,0.0997428,0.4739992917,0.0227582809,-0.1307650357,-0.3711324632,0.2705956399,-0.134705767,0.1444204599,0.1506156623,-0.2888510227,0.0992454216,-0.1900030524,0.1472333819,0.6052938104,0.1164713353,0.0325042419,0.1258146018,-0.0870899335,-0.0704523772,0.2077787369,0.0883645713,0.2569261789,0.2160902619,0.0695267469,0.0620280765,-0.3969856501,-0.2876626253,0.0426680557,0.4161446691,-0.3931626678,0.059591908,-0.1244833395,-0.2166050375,-0.0977737382,-0.5352479219,-0.0786898881,-0.3155716062,-0.1555617154,0.1735693216,0.1367603093,0.1662632525,-0.1210425571,-0.1262292862,0.1058900803,-0.4859184921,-0.2068847418,-0.0484767519,-0.0174885709,0.0651339069,0.2804500163,-0.1032850295,0.2476293594,-0.1312977076,-0.1217798218,-0.1863985956,-0.0907368809,-0.0835310295,-0.1042266935,0.2084366828,0.2644519508,0.3542820513,0.0467904173,-0.1947212368,0.3619304001,-0.1828137487,0.0223103501,0.3297629058,-0.1260294914,-0.0134168044,-0.0603099614,-0.5458139777,-0.5458031297,-0.3574637175,-0.1193348393,0.128277272,0.1643208861,0.439052999,0.149543047,0.1939637363,-0.1065731868,0.0041575646,-0.0272339098,-0.1540678293,0.4063127637,-0.2877522409,-0.2439707518,0.1713533998,-0.0405313782,0.1901890039,-0.1609371156,-0.4939940274,-0.2556856275,-0.0731515959,0.038228672,-0.1987717301,-0.051362209,0.2149719,0.0962023139,-0.100954771,0.1519619524,-0.1301398873,-0.0496761128,0.1687473506,0.0203161482,-0.2199278027,0.5267876983,-0.2912866473,0.5844215155,0.1298206747,-0.0976593196,0.3287144303,-0.2017037868,0.435521692,-0.2107226998,-0.5223431587,-0.0787455291,0.2892427742,-0.1790048331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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/873","title":"load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error","comments":"experience the same problem, ccdv\/cnn_dailymail not working either. \r\n\r\nSolve this problem by installing datasets library from the master branch:\r\npython -m pip install git+https:\/\/github.com\/huggingface\/datasets.git@master","body":"```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('cnn_dailymail', '3.0.0')\r\n```\r\nStack trace:\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-6-2e06a8332652> in <module>()\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')\r\n\r\n5 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    608         download_config=download_config,\r\n    609         download_mode=download_mode,\r\n--> 610         ignore_verifications=ignore_verifications,\r\n    611     )\r\n    612 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    513                     if not downloaded_from_gcs:\r\n    514                         self._download_and_prepare(\r\n--> 515                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    516                         )\r\n    517                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    568         split_dict = SplitDict(dataset_name=self.name)\r\n    569         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 570         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    571 \r\n    572         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\nI have ran the code on Google Colab","comment_length":24,"text":"load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error \n ```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('cnn_dailymail', '3.0.0')\r\n```\r\nStack trace:\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError                        Traceback (most recent call last)\r\n\r\n<ipython-input-6-2e06a8332652> in <module>()\r\n      1 from datasets import load_dataset\r\n----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')\r\n\r\n5 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    608         download_config=download_config,\r\n    609         download_mode=download_mode,\r\n--> 610         ignore_verifications=ignore_verifications,\r\n    611     )\r\n    612 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    513                     if not downloaded_from_gcs:\r\n    514                         self._download_and_prepare(\r\n--> 515                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    516                         )\r\n    517                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    568         split_dict = SplitDict(dataset_name=self.name)\r\n    569         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 570         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    571 \r\n    572         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n```\r\nI have ran the code on Google Colab \n experience the same problem, ccdv\/cnn_dailymail not working either. \r\n\r\nSolve this problem by installing datasets library from the master branch:\r\npython -m pip install git+https:\/\/github.com\/huggingface\/datasets.git@master","embeddings":[-0.224293083,0.1419460326,-0.0225758813,0.2384324819,0.3819946051,0.1261650622,0.6168980598,0.2147594988,-0.0231766459,0.1334128678,-0.163756609,0.0582367294,-0.3759562671,-0.123077713,0.0085837077,0.114968501,-0.0964015499,0.1582960486,-0.2051095515,-0.1422860175,-0.3901512027,0.1864792407,-0.0768181756,0.0159925465,-0.2544403374,-0.2072729915,-0.1191695929,0.4782720804,-0.2281916291,-0.3415712714,0.291224122,-0.1937130839,0.2596129179,0.6446203589,-0.0001103697,0.1348124146,0.257455349,-0.0459846482,-0.5990345478,-0.6107162833,-0.1344918758,-0.1810776293,0.2224275321,-0.2857463658,-0.0545736887,0.1120457277,0.0625397936,0.019521933,0.3589360118,0.5676545501,0.2382051796,0.2500875592,-0.0214656368,-0.1423246413,-0.1394215375,0.3917793632,-0.08329615,0.5298015475,-0.3784783185,-0.0074536237,0.3009919226,0.2560061216,-0.3467578292,0.2481284142,0.3105844557,0.0931708962,0.2014932483,-0.318579495,0.0102561293,0.052064389,0.462816,-0.377645731,-0.2139322609,-0.0260961484,0.0874807909,-0.4519509375,0.3440143466,0.3034414649,-0.1303296834,0.0531827994,-0.146174401,-0.1438385248,-0.1814096421,0.4520489275,-0.1718828231,0.2167272717,-0.0570031144,0.0317016952,0.0401946232,0.0549006499,0.5230491161,-0.2012925595,0.1109838113,0.1689763218,-0.0259168874,0.0619172007,0.200421989,-0.1987337023,-0.1959023029,0.1629692465,-0.0814728886,-0.2725152671,-0.0308208447,0.0420363732,0.0562603734,0.1875984669,-0.0870422944,0.5960316658,0.2050020099,0.2587226927,-0.0126039628,-0.1288865656,-0.3524653614,-0.249772191,0.1027377322,0.0556450076,0.6368337274,0.0808701962,-0.262622416,-0.1706470996,-0.2875089049,0.0266700387,0.1824934632,0.3770372868,0.0879202038,-0.0015779705,0.1018526927,0.2980035841,-0.0611399859,0.3207466304,-0.2227891982,0.2121761739,-0.1194054484,-0.03893435,0.270580411,-0.2202292234,0.1217843741,-0.1732522994,-0.0137373274,-0.0063968683,0.1550607979,-0.1759008318,0.074225083,0.5078181028,-0.1087166741,0.2788080275,0.270821631,0.0265562665,-0.3172303438,0.1326729208,-0.1793326437,-0.4073957503,-0.0890357345,0.191819638,0.0433196686,-0.1540216804,0.0512391925,-0.2044744641,-0.1510185897,-0.1646996737,-0.0049300003,-0.1914943755,-0.0878131837,-0.3597047925,0.294339478,0.6152353883,-0.3911423981,0.0004790114,-0.4250386953,-0.1540962905,0.1708229333,0.2825909257,-0.1810852587,0.418251425,-0.2191811651,-0.2109978348,0.4664492309,-0.4039042294,-0.4069173932,0.1619243324,-0.1690849811,-0.0231488161,0.0143251596,0.1950932592,-0.1637684554,0.0874652714,0.0317935087,0.3440898061,-0.0140823061,0.0791541114,-0.095202975,-0.2891000509,0.1494426131,0.1394836754,0.2537146509,0.2065376937,0.0017706892,0.057444334,0.0345365591,-0.1340152472,-0.0199387986,0.193029359,0.4896597266,0.0706251934,-0.3033567369,-0.0080354828,-0.5152108073,0.1154644713,0.0506806336,0.2688595951,-0.2443437576,0.0475811958,-0.2807430923,-0.1213214844,-0.2518719435,-0.0720805973,0.1413440108,0.362624079,0.0602164939,0.06581752,-0.2875800133,0.2650077939,0.0555366054,0.1482611448,-0.1362416446,0.4147193134,-0.1979314238,-0.0624308884,-0.0436080284,0.1352329403,0.2345956862,-0.1446532458,-0.1301756352,0.3645673692,-0.1508782059,-0.0773257464,0.0398339257,-0.1054766253,0.0736475289,-0.0363827124,0.2342951298,0.3620508015,0.1599349976,-0.0431439169,-0.1002390012,0.2904100418,0.161951825,0.0390076637,0.1538697779,-0.0371430255,0.0395891629,-0.0555200279,0.1082931161,-0.041275423,0.2116338313,0.0997428,0.4739992917,0.0227582809,-0.1307650357,-0.3711324632,0.2705956399,-0.134705767,0.1444204599,0.1506156623,-0.2888510227,0.0992454216,-0.1900030524,0.1472333819,0.6052938104,0.1164713353,0.0325042419,0.1258146018,-0.0870899335,-0.0704523772,0.2077787369,0.0883645713,0.2569261789,0.2160902619,0.0695267469,0.0620280765,-0.3969856501,-0.2876626253,0.0426680557,0.4161446691,-0.3931626678,0.059591908,-0.1244833395,-0.2166050375,-0.0977737382,-0.5352479219,-0.0786898881,-0.3155716062,-0.1555617154,0.1735693216,0.1367603093,0.1662632525,-0.1210425571,-0.1262292862,0.1058900803,-0.4859184921,-0.2068847418,-0.0484767519,-0.0174885709,0.0651339069,0.2804500163,-0.1032850295,0.2476293594,-0.1312977076,-0.1217798218,-0.1863985956,-0.0907368809,-0.0835310295,-0.1042266935,0.2084366828,0.2644519508,0.3542820513,0.0467904173,-0.1947212368,0.3619304001,-0.1828137487,0.0223103501,0.3297629058,-0.1260294914,-0.0134168044,-0.0603099614,-0.5458139777,-0.5458031297,-0.3574637175,-0.1193348393,0.128277272,0.1643208861,0.439052999,0.149543047,0.1939637363,-0.1065731868,0.0041575646,-0.0272339098,-0.1540678293,0.4063127637,-0.2877522409,-0.2439707518,0.1713533998,-0.0405313782,0.1901890039,-0.1609371156,-0.4939940274,-0.2556856275,-0.0731515959,0.038228672,-0.1987717301,-0.051362209,0.2149719,0.0962023139,-0.100954771,0.1519619524,-0.1301398873,-0.0496761128,0.1687473506,0.0203161482,-0.2199278027,0.5267876983,-0.2912866473,0.5844215155,0.1298206747,-0.0976593196,0.3287144303,-0.2017037868,0.435521692,-0.2107226998,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/871","title":"terminate called after throwing an instance of 'google::protobuf::FatalException'","comments":"Loading the iwslt2017-en-nl config of iwslt2017 works fine on my side. \r\nMaybe you can open an issue on transformers as well ? And also add more details about your environment (OS, python version, version of transformers and datasets etc.)","body":"Hi\r\nI am using the dataset \"iwslt2017-en-nl\", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks \r\n\r\n\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 63\/63 [02:47<00:00,  2.18s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nrun_t5_base_eval.sh: line 19:  5795 Aborted ","comment_length":39,"text":"terminate called after throwing an instance of 'google::protobuf::FatalException' \n Hi\r\nI am using the dataset \"iwslt2017-en-nl\", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks \r\n\r\n\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 63\/63 [02:47<00:00,  2.18s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nrun_t5_base_eval.sh: line 19:  5795 Aborted  \n Loading the iwslt2017-en-nl config of iwslt2017 works fine on my side. \r\nMaybe you can open an issue on transformers as well ? And also add more details about your environment (OS, python version, version of transformers and datasets etc.)","embeddings":[-0.2756947875,-0.6896218061,0.1162040085,0.3686614335,0.3344204426,-0.0612295717,0.3348029554,0.1536107957,-0.2083397657,0.1179825291,-0.0654007196,-0.3324613571,-0.0295326822,0.4058206081,-0.1832788736,-0.2990669608,0.0189545434,0.1540884376,-0.4621327221,-0.0021639555,-0.229054451,0.3899804056,-0.2134581506,0.3483884335,-0.1130772457,0.1178065538,0.0829797462,0.0127721429,-0.0949840471,-0.2316043675,0.2211651206,-0.0909958556,0.16676566,0.3702025115,-0.0001311232,-0.0367903896,0.2655953467,0.0570688918,-0.144013986,0.1104688868,-0.0511542857,0.1461777091,0.0536198951,0.0361517482,0.0598792806,-0.2049441636,0.2089884281,0.231264174,0.4897398949,0.0151624773,0.0531786382,0.3063324094,0.3312010467,0.0816985816,-0.0270447396,-0.1165635586,-0.2888156772,-0.0628107935,-0.1118589938,-0.0160784628,0.3052893579,0.0109918034,0.0765329003,-0.1810525209,-0.165804401,-0.046485927,0.4919874072,-0.3176007271,0.2732107639,0.3026655614,0.5659933686,-0.2089445889,-0.320820719,0.2825088203,-0.162963748,-0.663364172,0.029516371,0.0157640278,0.0368270725,0.2872439623,-0.1765947342,-0.0747157559,-0.35607481,0.0618637055,-0.2483198345,0.3457750976,-0.0724097714,0.3870842457,0.1909076124,0.0661901161,-0.3279819787,0.2797852159,-0.1643176973,0.0391894393,-0.3566974699,-0.2677064836,-0.07975474,0.1459319293,0.1702948064,0.1213659793,-0.0884903446,0.0574843735,0.250775665,0.1139543504,0.4480936527,0.0888853446,-0.0909259915,0.0955569446,0.263713032,0.1925384849,-0.0199964084,0.0608912818,0.1707051396,-0.2056468129,0.2166097313,-0.0390899517,0.5442737341,-0.4371444881,-0.3980947733,0.3282663822,-0.5851676464,0.0734328553,0.1095122471,0.4647295177,-0.0218743067,0.1591077894,0.2288995832,0.0017637751,-0.2170954794,-0.3298825622,-0.0325273424,0.1248777285,0.013593358,0.0792466924,-0.0938060656,-0.1105452105,0.0189604871,-0.1687659621,0.0570333302,-0.4175057113,-0.0315140933,-0.2017413676,-0.2151169777,0.0476760156,0.0230274014,0.0913014933,0.0626819059,0.2338235974,-0.108844474,-0.0194759425,0.2328732312,-0.4418900907,-0.1168154776,0.0209349543,-0.3718620241,0.0700107217,0.0511764847,0.1008900106,0.0117201507,0.0647251308,0.0112039009,-0.1178448871,-0.601600647,0.0955598652,0.2137631625,0.5098158121,-0.2983199954,-0.28317222,0.0214242265,-0.2185611278,0.1330840141,0.3510248661,-0.1074053645,0.4740375578,-0.1680228561,0.0771273598,0.4050397873,-0.0754855275,-0.4074357152,0.2993320227,-0.1043747813,-0.0264380835,-0.2313578129,-0.3312267959,-0.2108638287,-0.0910302997,-0.4682708383,0.0834774077,0.0042564496,0.0229098741,-0.3265223205,-0.2786833346,0.2017011046,0.1292589903,0.084895663,-0.1155647039,0.0439050086,0.5326945782,0.0079243751,-0.1599257588,-0.0717023686,0.2296440005,0.3724676967,0.0957855359,0.0016297285,-0.0008592252,-0.3926899433,0.1919946223,-0.1509628445,0.3499168158,-0.0339801721,0.1316320002,-0.3984308839,0.2384351492,-0.3872943819,-0.2096565515,0.0423250608,0.1670191139,-0.1135266274,-0.0217385683,-0.0022783617,0.2230875194,-0.3238983154,0.1648083925,-0.1945526004,0.2180779129,-0.1290382594,-0.1867525429,-0.0920874253,0.2005738467,0.1242625192,-0.2472160012,-0.0812027007,0.5033457279,-0.4497034252,0.3622533381,0.1256987303,0.2714719474,-0.1072640643,-0.7133020163,-0.1491653323,0.0605793186,0.0442922227,0.0842485502,0.2721653879,0.0209644437,0.36870116,0.1002801135,0.0422450602,-0.1372859478,0.0769415498,-0.1270656139,-0.3090764582,-0.0567719229,0.196080789,0.0975053906,-0.097971715,0.092575267,-0.3171671331,-0.1859455705,0.5001758337,-0.3895013034,0.1055529118,0.1940921694,0.221313715,0.0158279464,-0.0167704094,-0.3721936345,0.4321252108,-0.1188456342,-0.0447104648,0.2925679684,-0.2349233478,0.3068990409,0.2109820098,0.2268821746,-0.0990797579,0.2470541894,0.1511300802,0.1275759935,-0.2835476398,0.1347997189,-0.0292089619,0.3193389773,-0.3493123651,0.3340668976,-0.2501600385,0.0029524611,-0.0844357237,0.1695369184,-0.2327766865,-0.1175029948,-0.0777457878,0.3478622139,0.2846853137,0.2375227958,0.136313796,0.2873690426,0.3123163581,0.0504829735,0.0776460469,0.0606766343,-0.3715503216,-0.1625667959,0.2789064944,-0.0084201917,0.1755388528,-0.0387269333,-0.2875962555,-0.1948439926,-0.2629094422,0.4366433024,-0.2573680282,0.3862488568,0.2549961507,0.3238667548,0.0508058593,-0.2321354598,0.3769733608,-0.0804985315,-0.1112752408,0.1476732641,0.0080181323,0.0012472833,-0.1322028935,-0.1545511931,-0.0031232389,-0.2535025775,0.3643809855,-0.1252323091,-0.0454624519,0.1597647965,0.0816032141,0.0978151113,0.0324481204,0.0944973305,-0.2424069941,0.071293667,0.1028503701,0.170868665,-0.1095625237,0.0842222348,0.1628800929,0.4006054103,0.0270047523,-0.5209590197,-0.0805877149,-0.0152732851,-0.2542364895,-0.0713413805,0.1167048663,0.33683303,0.0828818902,0.163786754,0.1131120771,-0.0577146374,0.3626656234,0.1209022775,0.4364433587,0.1704380363,0.6243668199,0.03596754,0.4471463859,0.2275732607,-0.094705753,0.3119553328,0.093693763,0.0500957854,-0.0640045106,-0.2120324373,0.0056936136,-0.1506774127,-0.1362486482,0.321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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/871","title":"terminate called after throwing an instance of 'google::protobuf::FatalException'","comments":"closing now, figured out this is because the max length of decoder was set smaller than the input_dimensions. thanks ","body":"Hi\r\nI am using the dataset \"iwslt2017-en-nl\", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks \r\n\r\n\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 63\/63 [02:47<00:00,  2.18s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nrun_t5_base_eval.sh: line 19:  5795 Aborted ","comment_length":19,"text":"terminate called after throwing an instance of 'google::protobuf::FatalException' \n Hi\r\nI am using the dataset \"iwslt2017-en-nl\", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks \r\n\r\n\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 63\/63 [02:47<00:00,  2.18s\/it][libprotobuf FATAL \/sentencepiece\/src\/..\/third_party\/protobuf-lite\/google\/protobuf\/repeated_field.h:1505] CHECK failed: (index) >= (0): \r\nterminate called after throwing an instance of 'google::protobuf::FatalException'\r\n  what():  CHECK failed: (index) >= (0): \r\nrun_t5_base_eval.sh: line 19:  5795 Aborted  \n closing now, figured out this is because the max length of decoder was set smaller than the input_dimensions. thanks ","embeddings":[-0.1825481504,-0.7557119727,0.0836407691,0.556730926,0.3312155604,-0.0441040806,0.2286126763,0.2201018035,-0.3796162307,0.3128049076,-0.0100409836,-0.4250835478,0.0028816557,0.5060398579,-0.2287921458,-0.4332334399,-0.0710474998,0.3094232976,-0.3874340355,0.1011399552,-0.2264460176,0.418671906,-0.2191978246,0.1852833182,0.0081758648,0.1957097799,-0.0577186234,-0.0667141601,-0.3531189263,-0.3468537331,0.0182195511,-0.2039844096,0.0581342317,0.3137004673,-0.0001306237,-0.0284277499,0.2318881005,-0.0366353914,-0.0024561405,0.1110143065,-0.0404468663,0.0954321474,-0.0432409495,0.0571707711,-0.0214736089,-0.2490238994,0.1340977252,0.0824278742,0.4000383317,0.1778144687,0.0598794147,0.0708278567,0.5268979669,0.1011874452,0.1303852797,-0.1859424114,-0.2451243103,-0.0544138551,-0.0872224346,0.254393518,0.2519197166,0.0127651393,0.2343612909,-0.10397394,-0.1582995653,-0.1667215228,0.4694933891,-0.3594505787,0.3739234507,0.5036091208,0.4249293506,-0.1596073657,-0.2389924079,0.1978807002,-0.0463761911,-0.7821172476,0.0214621071,0.1177389845,-0.0314023718,0.3177411556,-0.1956370324,0.0250428114,-0.3913386464,-0.0535499491,-0.3420303464,0.1048160717,-0.1563069522,0.2693475485,0.3107014596,-0.012363188,-0.3689581454,0.4468303621,-0.2233463228,0.0046288338,-0.3167666197,-0.2840055227,-0.087674655,0.0885874704,0.3425703347,0.0983420759,0.0299788564,-0.044186756,0.3686290681,-0.0731782168,0.4484470189,0.0698964819,-0.1750067025,-0.047461804,-0.0304171108,0.2391154468,-0.0833925828,0.0399034657,0.0878183767,-0.386467576,0.0334503576,-0.0612443797,0.5407340527,-0.4030388296,-0.3742876649,0.5508589745,-0.5646084547,0.1382816583,-0.0152639402,0.3928346932,0.0368884876,0.3064093888,0.1208616048,-0.025470525,-0.1815385073,-0.4603445232,-0.0324585438,0.0571843795,0.068704173,-0.0381941721,-0.2290016264,-0.1918761581,0.0274250377,-0.1162856147,0.2301355898,-0.4824358821,-0.0526085831,-0.3860574365,-0.1272218376,0.0582951158,0.1070084795,-0.0941504464,0.0039941398,0.1228219047,-0.136877954,0.1021425724,0.2030085027,-0.3419500291,-0.2019581199,0.0443180725,-0.2757993937,0.2491314113,0.0974390805,0.2676698267,0.0701395497,0.1285983175,0.037547905,-0.2970994115,-0.5591106415,0.0333798081,0.2109642178,0.3538913429,-0.4521012306,-0.1101317555,0.1289368123,-0.162707895,0.2067390829,0.3624174297,0.0659419522,0.2911633551,-0.2080686092,0.1842984855,0.3846414685,0.0041619176,-0.4475169182,0.4741547704,-0.1718921214,0.0268619098,-0.0815850273,-0.5575348139,-0.0763312876,0.0625408515,-0.3875132203,0.0609403141,-0.050428085,-0.1020278111,-0.3786960244,-0.3348831534,0.2869663835,0.0521849319,0.0931096971,-0.2644413114,-0.0289088674,0.3470962942,0.1054541841,-0.1164799556,-0.0546410456,0.2943658829,0.1599955708,-0.0123672755,0.0500191785,-0.0715421513,-0.059885744,0.1266557723,-0.1815430522,0.2302283794,0.1811086833,0.0477650389,-0.4110559523,0.2406524271,-0.1958540082,-0.1214052588,0.0813282952,0.0905070826,0.0031745289,0.0335053764,0.0377168022,0.0373364352,-0.279001534,0.1965822279,-0.1421760172,0.0688369796,-0.2133826017,-0.1122256517,0.0000704748,0.2261541337,0.0785465315,-0.1326743513,-0.13583529,0.4077900052,-0.1459801346,0.1788313091,-0.0823335871,0.0799966678,-0.1061856225,-0.6050518155,0.0727640837,0.0006517002,-0.0793634504,0.0802322552,0.4008986652,0.0033712611,0.4482319057,0.1287207454,0.0407877825,-0.2953241169,0.0407613441,-0.0307979509,-0.2658254504,-0.1130768657,0.2272973061,0.0492021553,0.0264593475,0.0901864544,-0.3722547591,0.0433302931,0.5868806243,-0.3244768381,-0.0204519518,0.3520979285,0.1912356168,-0.0975401103,-0.1523132473,-0.2997414768,0.2898995876,-0.0278740041,0.1170780435,0.2232637703,-0.1730557382,0.1739894599,0.2037848681,0.2063530385,-0.0424582995,0.3433316946,0.1607792675,0.0747347847,-0.194274202,0.1378559768,-0.0842173025,0.2802475095,-0.2942640781,0.2067055255,-0.3464756608,-0.1027304381,-0.036015369,0.2028561831,-0.1097783148,-0.0775026605,-0.0121806301,0.1491119415,0.2092546076,0.0943256617,0.0159660671,0.3456902802,0.3015296757,0.3043162525,0.1930469275,0.2721781135,-0.3297426701,-0.0792557523,0.2436175197,-0.09949781,0.1018210649,-0.0045349356,-0.2779570222,0.047878366,-0.3190452158,0.3975229859,-0.2260155231,0.4261747599,0.2910575271,0.3495925069,0.162409544,-0.1286505461,0.2713697255,0.1310101002,-0.2149520963,0.1189751178,0.0433747992,0.1060262918,-0.1928852499,0.0047683287,0.0756090879,-0.3440089822,0.597823441,0.0725695342,0.0980785564,0.1002419367,0.2643187344,0.1980092078,0.1320688128,0.2236942202,-0.3274862468,-0.0945414826,0.070234336,0.0601463728,-0.1019025594,0.0203388724,0.1251716018,0.552069962,0.0094988542,-0.6542897224,-0.0641237572,-0.0393796414,-0.3225176632,-0.0548027568,0.0118522821,0.2194900066,-0.0144388573,0.1663329154,0.1242133677,0.0081098229,0.2320028096,0.1027007401,0.3276295662,0.1301310956,0.6841685176,0.1434123963,0.4717115164,0.3242788911,0.0999211371,0.2292104363,-0.0142609617,0.1526381522,-0.1874094158,0.0287796371,0.2010574043,-0.1487109363,-0.228917703,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/870","title":"[Feature Request] Add optional parameter in text loading script to preserve linebreaks","comments":"Hi ! Thanks for your message.\r\nIndeed it's a free feature we can add and that can be useful.\r\nIf you want to contribute, feel free to open a PR to add it to the text dataset script :)","body":"I'm working on a project about rhyming verse using phonetic poetry and song lyrics, and line breaks are a vital part of the data. \r\n\r\nI recently switched over to use the datasets library when my various corpora grew larger than my computer's memory. And so far, it is SO great. \r\n\r\nBut the first time I processed all of my data into a dataset, I hadn't realized the text loader script was processing the source files line-by-line and stripping off the newlines. \r\n\r\nOnce I caught the issue, I made my own data loader by modifying one line in the default text loader (changing `batch = batch.splitlines()` to `batch = batch.splitlines(True)` inside `_generate_tables`). And so I'm all set as far as my project is concerned.\r\n\r\nBut if my use case is more general, it seems like it'd be pretty trivial to add a kwarg to the default text loader called keeplinebreaks or something, which would default to False and get passed to `splitlines()`. ","comment_length":39,"text":"[Feature Request] Add optional parameter in text loading script to preserve linebreaks \n I'm working on a project about rhyming verse using phonetic poetry and song lyrics, and line breaks are a vital part of the data. \r\n\r\nI recently switched over to use the datasets library when my various corpora grew larger than my computer's memory. And so far, it is SO great. \r\n\r\nBut the first time I processed all of my data into a dataset, I hadn't realized the text loader script was processing the source files line-by-line and stripping off the newlines. \r\n\r\nOnce I caught the issue, I made my own data loader by modifying one line in the default text loader (changing `batch = batch.splitlines()` to `batch = batch.splitlines(True)` inside `_generate_tables`). And so I'm all set as far as my project is concerned.\r\n\r\nBut if my use case is more general, it seems like it'd be pretty trivial to add a kwarg to the default text loader called keeplinebreaks or something, which would default to False and get passed to `splitlines()`.  \n Hi ! Thanks for your message.\r\nIndeed it's a free feature we can add and that can be useful.\r\nIf you want to contribute, feel free to open a PR to add it to the text dataset script :)","embeddings":[-0.4354711771,0.2950844765,0.0567216426,-0.2441309839,-0.0972463861,-0.2072373778,0.3331260979,0.1758846045,0.298045516,0.3277394772,0.503433764,0.2391882837,-0.0176374037,0.1306846887,-0.0111618992,-0.1541550457,-0.16735439,0.3164179623,0.0948439389,0.0943592563,-0.1742428243,0.2289418578,-0.235174939,-0.3131567836,-0.0942863822,0.1758474857,0.0615477115,-0.1762807816,0.0486642383,-0.2417834848,0.0536904819,0.4617238045,-0.2017268538,-0.0629813299,-0.0001233125,-0.1519173682,0.1834264398,-0.1822315753,-0.6022976637,-0.1147578061,-0.3729275763,-0.2914463282,-0.0992282629,-0.2274686992,0.0622691773,0.3424104452,-0.0458786301,-0.4362998009,0.2800565064,0.3078560531,0.0495161414,0.1074072942,-0.5713555813,-0.0663225502,0.5268213749,0.2910938561,-0.0629604682,0.0548023283,0.0162094031,0.0578812249,-0.4066486061,0.3086510599,-0.3627738655,-0.2398989648,0.3153908551,0.0333261937,-0.1040520519,0.0191558599,0.1258228421,0.6455950141,0.60443151,-0.5228234529,-0.1811380684,-0.5374609232,0.3158695698,-0.2599709928,0.0773754343,0.0897537693,-0.4800499976,0.1028653905,-0.3286261261,-0.5799759626,-0.2724969685,0.0282563232,0.0515560955,0.0071636853,0.0852803141,-0.1294995248,0.2139755487,0.2667275369,-0.1155733764,-0.3966223001,-0.1672593951,-0.0120494142,0.186608389,-0.1383511573,-0.1339076161,0.0295246951,0.2464732081,0.1903955638,0.2343104035,-0.0728933737,0.1659848839,-0.0096090594,-0.1466227919,0.0010832801,0.6396608949,-0.1263293624,0.3962599933,-0.0598768182,0.2541896701,-0.0147522138,0.3621482253,-0.0553385541,0.2506429851,0.2168622315,0.5353254676,0.0507847294,-0.1015287712,-0.0084517095,0.1577020437,-0.2738271654,0.0231101047,0.0231772289,0.2529730201,0.2497906089,0.0939324647,0.339779228,-0.0245637652,-0.4520849586,-0.011584973,-0.0739066601,0.0690934211,0.4735104442,0.4798795879,-0.1322537512,0.0188573785,0.3098440468,-0.3272877038,0.2125819623,-0.2599174976,0.0107391179,-0.0624036416,0.0897311941,0.0502958484,-0.0514470898,-0.2781385481,0.1292174906,-0.1442714781,0.3982302845,-0.2081502825,-0.2423781157,-0.0120961415,0.1186780483,-0.1746381819,-0.1164849475,-0.119011566,0.3408353925,0.2329739481,-0.2190415263,0.3950002193,-0.0677531585,-0.4148365855,-0.1905252039,0.1208384708,0.6844409108,-0.5018539429,-0.3941883147,-0.2446784824,-0.161777854,0.1972188801,-0.0845851451,-0.2083405107,0.3146696985,-0.0943591967,0.4131858051,0.5370357037,0.2126027495,-0.2113257647,0.4115028977,0.1002037302,-0.026850421,0.302057296,0.1166809946,0.4532318711,0.0867427513,-0.3238539994,0.505616188,0.1082096696,0.2016529441,0.0791892409,-0.1329990327,0.3330931067,0.1691438109,-0.1261501461,-0.0380360596,-0.0463945642,-0.0436888225,0.1735132337,-0.1540231556,0.0981511027,-0.0570635386,0.1134195551,0.2344139367,-0.0318807997,-0.0984189659,-0.5309476852,-0.1450162679,0.1449734271,-0.1053956747,-0.2306468189,-0.3572915196,0.1532925963,0.0899948329,0.0845068693,0.3819425404,-0.0544856638,0.076240249,-0.4913656712,0.1855785102,-0.2050102353,0.1258591115,-0.059617836,0.2973459065,-0.0412375517,0.1600450277,0.4320734441,-0.1415886432,-0.0284115095,0.2037593275,0.0357373431,0.0757633299,-0.1862323135,0.5891005993,0.2256730944,0.3013256192,-0.35499385,-0.3149200082,-0.0073471186,0.078584373,0.0901804417,0.0804409906,-0.1342437565,-0.1915708333,-0.2021723688,0.3526658714,-0.1503386796,0.335311085,-0.1181824058,-0.1217780635,-0.2494113445,-0.3147170246,-0.2909689844,-0.4226794541,0.2018985897,-0.1425284743,0.3798127174,0.2567830086,-0.0651082024,-0.0855781361,0.579664588,-0.1368783265,0.3069625795,0.3238114715,0.0211424418,-0.1402036101,0.1488457471,0.2698791921,0.3828237057,0.2634972036,-0.1487598717,0.066809997,-0.0059781624,-0.384434104,0.2802663445,0.2170626372,-0.0857429132,0.373426646,0.0165433735,-0.1488485634,-0.3234377503,-0.0239392072,0.021003224,-0.0865441561,-0.3517746031,-0.1167265996,-0.2842797637,-0.2587321103,-0.1788249165,-0.2389427423,-0.3589533269,-0.2414561361,0.1967178732,-0.1388000548,-0.3496547043,0.183488369,-0.2629195452,0.7408051491,-0.1833309233,-0.5621784925,0.0269698948,-0.1942996532,0.0812375247,-0.0659784749,-0.0138453506,0.1965544224,0.2640571594,-0.0036518862,0.0909801573,-0.069748193,0.1031589136,0.0497970171,0.0387121104,-0.2021922767,0.4928569496,0.1726311892,0.3285779655,-0.2593170106,-0.1793032438,-0.2124928087,0.0418997332,0.1382410973,0.11571455,-0.0770108402,-0.3744924068,-0.1818037778,0.0186165143,-0.2738801837,-0.0273665339,-0.4108988047,0.0061276481,0.2384626418,0.0889581889,0.0378395692,-0.0128541291,-0.0526321232,0.0465871394,0.1360524893,0.3777827024,0.018419534,0.1504377127,-0.0623602904,-0.0568684004,0.001330325,0.3807697594,-0.4775208533,0.2629410625,-0.120035246,0.495228976,-0.122098282,-0.0351204202,0.3664514124,0.13640064,0.0815182477,-0.2246571034,-0.0147726443,0.2787934244,-0.3612312675,0.0486786589,0.2344342768,0.5663764477,-0.0025382354,0.3888105452,-0.0068308953,0.1409278512,0.2664133608,-0.1572481096,0.3855630159,0.3046710491,-0.2800631225,-0.3151492476,0.079511039,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/866","title":"OSCAR from Inria group","comments":"PR is already open here : #348 \r\nThe only thing remaining is to compute the metadata of each subdataset (one per language + shuffled\/unshuffled).\r\nAs soon as #863 is merged we can start computing them. This will take a bit of time though","body":"## Adding a Dataset\r\n- **Name:** *OSCAR* (Open Super-large Crawled ALMAnaCH coRpus), multilingual parsing of Common Crawl (separate crawls for many different languages), [here](https:\/\/oscar-corpus.com\/).\r\n- **Description:** *OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.*\r\n- **Paper:** *[here](https:\/\/hal.inria.fr\/hal-02148693)*\r\n- **Data:** *[here](https:\/\/oscar-corpus.com\/)*\r\n- **Motivation:** *useful for unsupervised tasks in separate languages. In an ideal world, your team would be able to obtain the unshuffled version, that could be used to train GPT-2-like models (the shuffled version, I suppose, could be used for translation).*\r\n\r\nI am aware that you do offer the \"colossal\" Common Crawl dataset already, but this has the advantage to be available in many subcorpora for different languages.\r\n","comment_length":43,"text":"OSCAR from Inria group \n ## Adding a Dataset\r\n- **Name:** *OSCAR* (Open Super-large Crawled ALMAnaCH coRpus), multilingual parsing of Common Crawl (separate crawls for many different languages), [here](https:\/\/oscar-corpus.com\/).\r\n- **Description:** *OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.*\r\n- **Paper:** *[here](https:\/\/hal.inria.fr\/hal-02148693)*\r\n- **Data:** *[here](https:\/\/oscar-corpus.com\/)*\r\n- **Motivation:** *useful for unsupervised tasks in separate languages. In an ideal world, your team would be able to obtain the unshuffled version, that could be used to train GPT-2-like models (the shuffled version, I suppose, could be used for translation).*\r\n\r\nI am aware that you do offer the \"colossal\" Common Crawl dataset already, but this has the advantage to be available in many subcorpora for different languages.\r\n \n PR is already open here : #348 \r\nThe only thing remaining is to compute the metadata of each subdataset (one per language + shuffled\/unshuffled).\r\nAs soon as #863 is merged we can start computing them. This will take a bit of time though","embeddings":[-0.0789998397,-0.0689798594,-0.0829853863,0.0613858551,-0.0888234153,0.0940621272,-0.0122487498,0.2985754311,0.0025459034,0.0863161162,-0.5969402194,-0.0512048565,-0.2450727522,0.0754724219,0.1506562084,-0.3321431279,0.0520217121,-0.1897595078,-0.2447093278,-0.2940576375,-0.199752599,0.1900043935,0.1084448248,-0.070598796,0.2079501152,-0.2153771073,-0.434027344,-0.1118402407,-0.1303550303,-0.1936031133,-0.0030159694,0.2002108693,0.2905132473,0.6354877949,-0.0000960657,-0.1057058871,-0.0594696738,-0.239071697,0.0947269425,-0.1166666821,0.2534609437,-0.0826734975,-0.1983726919,-0.3360138237,-0.4045934379,-0.3702588379,-0.0277564283,-0.4808144867,0.1629257798,0.1797285974,0.280683279,-0.227109924,-0.1751101911,-0.2170567811,-0.0248893499,-0.2869288921,-0.3192919791,0.0610993914,0.5420654416,-0.0749456733,0.0386708193,0.380599916,0.1031532437,-0.2377462238,-0.1179090589,-0.0452904068,0.0805145651,-0.3604671061,0.1537543535,0.337295413,0.01977247,-0.342140913,-0.3169185221,-0.1763942689,-0.049127724,-0.0205627326,-0.2518903911,0.3864118159,0.0364644006,0.2546266019,0.2060189247,-0.2413934618,-0.1193951666,0.1010256633,0.031734243,0.5971215963,0.0801759139,-0.1237552091,-0.0247247443,0.0153800361,-0.0398108587,-0.0067003742,0.0270718131,0.1904661953,-0.3079119325,-0.3250470757,0.2066055089,-0.1593383849,0.4159701467,-0.1509889215,0.2482505888,0.1740213186,-0.1795057654,0.1214326546,0.2831611335,-0.0899356827,-0.1744023114,-0.1156539544,0.0723321214,-0.4601128399,0.082082212,0.1743639112,-0.1357317269,0.1313313097,-0.2064629942,0.0541563295,-0.0775346607,-0.0458718017,0.0806471184,-0.053738445,-0.229937017,0.0137936892,-0.0986439809,0.0768383518,-0.0258921981,0.0120433979,-0.0909743235,0.067946665,-0.1071033701,-0.2761722207,-0.2034252882,0.217635721,-0.3801766038,0.128845647,0.172749579,0.0190114211,0.0119643249,-0.2340984046,0.2328408957,0.2133850306,0.3611582518,-0.320294857,0.1609599441,-0.1494008154,0.2117096037,-0.2598241866,0.0502515323,-0.2192108184,-0.4714737535,-0.02956287,0.0450573191,-0.3023353219,-0.1582363695,0.2464541197,-0.231522426,-0.1368895769,-0.2449243069,0.7459754348,0.007385409,0.1247611344,-0.0491997711,-0.0011200133,-0.3634642661,-0.0202040933,0.0891351327,0.2649673522,0.1860181391,-0.0088046864,0.0476065725,0.1841085404,0.1200442836,0.3375632167,-0.1249205098,-0.1493848115,0.0020371596,0.1753962636,0.2010474354,-0.2509192228,0.0566660091,0.0413590707,0.0060923966,-0.0718067288,0.1888000369,-0.1069103032,0.3648880124,-0.3289222419,0.1453191936,0.6325167418,-0.246833697,0.1829524487,-0.2372281253,-0.0358664356,-0.0515489802,0.266964227,0.3047321737,-0.3177728355,0.1110945493,-0.3176100552,0.1368640959,-0.1907478273,0.2715631127,-0.019909827,0.1988722235,0.0542283766,0.2405931354,-0.4054969251,-0.0055573247,0.0321984887,-0.0676580444,0.5373077989,-0.0850452781,-0.3493946791,-0.1995297819,-0.2065919489,-0.0381338969,-0.0213455074,0.2677493393,0.3026916981,0.0885429606,0.0233812835,-0.1373600811,-0.0861660987,-0.1342484504,-0.3086870313,-0.0033678757,0.3649312854,-0.092291303,-0.0811056048,0.2129443586,0.4055532217,-0.1997703165,-0.2372059375,0.1052786708,0.1152985618,-0.1646990031,0.0917293653,0.5061905384,0.3447551131,0.1893038005,-0.5001240969,0.1349046379,0.114430137,0.1833438426,-0.0649386346,-0.1615116894,0.2966725528,0.1831851155,-0.0219672918,0.2072990388,-0.0891565457,0.3712120354,-0.0440208018,-0.0954973996,-0.0847255811,0.0343358442,-0.01430998,0.0468290932,0.1122884527,-0.5806649327,0.1995316148,0.5494887829,0.0785557106,0.3099135458,0.1553822309,-0.0023567595,-0.1778399795,-0.0857951343,0.0435632914,0.1141144857,0.3094471693,0.3973644674,0.0615208037,0.1920935512,-0.0087894751,0.173266083,-0.1201455519,0.2182255983,0.2752735019,0.2165376097,0.0397535898,-0.2015638053,-0.1670654267,0.086631842,0.1049868613,0.3335330784,0.1044118851,0.162094906,-0.2809582949,-0.2595311105,-0.1624915302,-0.1026665196,-0.0025337348,0.2718780935,-0.28386724,-0.1953326166,0.25302881,-0.0899115428,0.3012341261,-0.1137074754,-0.0297509097,0.1293421686,-0.4761375785,-0.0963706374,0.2489105463,0.2857629955,0.0243882071,0.4065279365,0.1019363329,-0.0001941636,-0.0554574728,-0.4803338349,0.197908029,-0.2193891853,-0.1993477046,-0.0839121789,-0.3282859921,-0.0089863446,-0.2560660541,0.1163565591,-0.1906362772,-0.1358748525,-0.02395395,-0.1252659708,-0.0306453183,-0.1082518473,-0.6249163151,-0.5535086989,-0.3931676745,0.1162367687,-0.0869350731,0.1905634701,-0.2222539932,-0.2236909419,-0.0532186143,0.2386676222,0.1823453456,-0.2849721313,-0.055927936,0.2615026534,-0.3868191838,-0.2336463481,-0.2682065368,-0.3120085001,0.2669037282,0.1276145428,-0.0902532935,0.0230467692,-0.0722815096,-0.3246203661,0.2294089049,-0.0798891932,0.2951599956,0.2661080062,-0.2085749805,-0.0683946311,-0.0480041467,0.0669269115,0.1893723011,-0.0352248847,-0.069174476,0.2103768736,0.241316095,0.3895183802,-0.0755582377,0.1789115518,0.3257670999,0.3153468966,0.0676872954,-0.0875455365,-0.200642854,0.1844980568,0.185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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/865","title":"Have Trouble importing `datasets`","comments":"I'm sorry, this was a problem with my environment.\r\nNow that I have identified the cause of environmental dependency, I would like to fix it and try it.\r\nExcuse me for making a noise.","body":"I'm failing to import transformers (v4.0.0-dev), and tracing the cause seems to be failing to import datasets.\r\n\r\nI cloned the newest version of datasets (master branch), and do `pip install -e .`.\r\n\r\nThen, `import datasets` causes the error below.\r\n\r\n```\r\n~\/workspace\/Clone\/datasets\/src\/datasets\/utils\/file_utils.py in <module>\r\n    116 sys.path.append(str(HF_MODULES_CACHE))\r\n    117 \r\n--> 118 os.makedirs(HF_MODULES_CACHE, exist_ok=True)\r\n    119 if not os.path.exists(os.path.join(HF_MODULES_CACHE, \"__init__.py\")):\r\n    120     with open(os.path.join(HF_MODULES_CACHE, \"__init__.py\"), \"w\"):\r\n\r\n~\/.pyenv\/versions\/anaconda3-2020.07\/lib\/python3.8\/os.py in makedirs(name, mode, exist_ok)\r\n    221             return\r\n    222     try:\r\n--> 223         mkdir(name, mode)\r\n    224     except OSError:\r\n    225         # Cannot rely on checking for EEXIST, since the operating system \r\n\r\nFileNotFoundError: [Errno 2] No such file or directory: '<MY_HOME_DIRECTORY>\/.cache\/huggingface\/modules'\r\n```\r\n\r\nThe error occurs in `os.makedirs` in `file_utils.py`, even though `exist_ok = True` option is set.\r\n(I use Python 3.8, so `exist_ok` is expected to work.)\r\n\r\nI've checked some environment variables, and they are set as below.\r\n\r\n```\r\n*** NameError: name 'HF_MODULES_CACHE' is not defined\r\n*** NameError: name 'hf_cache_home' is not defined\r\n*** NameError: name 'XDG_CACHE_HOME' is not defined\r\n```\r\n\r\nShould I set some environment variables before using this library?\r\nAnd, do you have any idea why \"No such file or directory\" occurs even though the `exist_ok = True` option is set?\r\n\r\nThank you in advance.","comment_length":34,"text":"Have Trouble importing `datasets` \n I'm failing to import transformers (v4.0.0-dev), and tracing the cause seems to be failing to import datasets.\r\n\r\nI cloned the newest version of datasets (master branch), and do `pip install -e .`.\r\n\r\nThen, `import datasets` causes the error below.\r\n\r\n```\r\n~\/workspace\/Clone\/datasets\/src\/datasets\/utils\/file_utils.py in <module>\r\n    116 sys.path.append(str(HF_MODULES_CACHE))\r\n    117 \r\n--> 118 os.makedirs(HF_MODULES_CACHE, exist_ok=True)\r\n    119 if not os.path.exists(os.path.join(HF_MODULES_CACHE, \"__init__.py\")):\r\n    120     with open(os.path.join(HF_MODULES_CACHE, \"__init__.py\"), \"w\"):\r\n\r\n~\/.pyenv\/versions\/anaconda3-2020.07\/lib\/python3.8\/os.py in makedirs(name, mode, exist_ok)\r\n    221             return\r\n    222     try:\r\n--> 223         mkdir(name, mode)\r\n    224     except OSError:\r\n    225         # Cannot rely on checking for EEXIST, since the operating system \r\n\r\nFileNotFoundError: [Errno 2] No such file or directory: '<MY_HOME_DIRECTORY>\/.cache\/huggingface\/modules'\r\n```\r\n\r\nThe error occurs in `os.makedirs` in `file_utils.py`, even though `exist_ok = True` option is set.\r\n(I use Python 3.8, so `exist_ok` is expected to work.)\r\n\r\nI've checked some environment variables, and they are set as below.\r\n\r\n```\r\n*** NameError: name 'HF_MODULES_CACHE' is not defined\r\n*** NameError: name 'hf_cache_home' is not defined\r\n*** NameError: name 'XDG_CACHE_HOME' is not defined\r\n```\r\n\r\nShould I set some environment variables before using this library?\r\nAnd, do you have any idea why \"No such file or directory\" occurs even though the `exist_ok = True` option is set?\r\n\r\nThank you in advance. \n I'm sorry, this was a problem with my environment.\r\nNow that I have identified the cause of environmental dependency, I would like to fix it and try it.\r\nExcuse me for making a noise.","embeddings":[-0.2681194544,0.1308584958,0.0091978218,0.1722479761,0.2753852904,0.0738350973,0.2072007209,0.1833841354,0.1528996229,-0.103595838,-0.2849372625,-0.1424152702,-0.1180940643,-0.3181892335,0.030698495,0.1511705667,0.2635430694,0.3381048739,-0.3553359807,-0.2657526731,-0.2365347594,0.1956792325,0.0699337795,0.0383725874,-0.3075052202,0.1586766541,-0.1923209578,0.3546025157,-0.0874144137,-0.3079354763,0.312802583,0.0767817199,0.2757256031,0.7110565901,-0.0001215231,0.1625322849,0.1903069764,-0.0628716424,-0.4191234708,-0.3236556053,-0.089015618,-0.3284656107,0.1100215167,-0.1870281249,0.1990170926,-0.2734282017,0.0801438391,-0.2908483744,0.499045819,0.3661040962,0.1287855655,0.3562702835,0.158652395,-0.0425901823,0.1484103799,0.4451641142,-0.0781542957,-0.0663885251,-0.0085933041,-0.1054819077,0.2778817713,0.0019224144,-0.1829766482,0.0140808029,0.4155776203,-0.0752482414,0.0377338007,-0.3184424639,0.0598807037,0.2000163943,0.7275674343,-0.4121959507,-0.7552509308,-0.1253420413,-0.1917922646,-0.1638842523,0.2845663726,-0.0826183408,0.0983769298,0.2858668268,0.0040534018,-0.344030112,-0.1661034375,0.3062963784,-0.0775494576,0.1118347645,0.0083073387,0.0248556882,0.2521101534,-0.189615652,0.3435187638,-0.34113428,0.0948805362,0.3831880391,-0.3209574819,-0.0606265664,0.0867931843,0.1025330126,-0.066327408,0.0996712446,-0.4005199969,-0.0226232875,-0.2901917994,0.1602175683,0.1484983265,0.2043939829,-0.0641826391,0.3525402546,0.2106776237,-0.1175974756,0.1762677282,-0.0752191767,-0.2436424047,-0.2041593492,0.0367049463,-0.07335186,0.1752852947,-0.3583666682,-0.0762620121,0.0499932393,-0.0009142692,0.0625434071,-0.0196596943,0.3692307174,-0.0863489211,0.1779126227,0.1272700727,0.336876899,-0.2937982082,0.1131299511,-0.1551246345,0.3129038215,-0.2285295278,0.0236860625,0.292273283,-0.0684456974,0.2617128491,-0.120366618,0.3084441125,0.246784091,0.0894458815,-0.2138560414,0.0618057363,0.600042522,-0.2023255676,0.4967263043,0.3052872717,-0.2590166032,-0.1908972561,-0.0503731258,-0.1729454249,-0.2369495183,-0.1975753754,0.090082705,-0.4204051793,0.0599871017,-0.1369484663,-0.4408088326,0.287068665,-0.2488075197,-0.0929095671,-0.274868995,0.2901176512,-0.1786625534,0.163043052,0.7134696841,-0.4537554085,-0.0906329527,-0.4043835998,-0.0830353349,-0.1028399915,0.3096979558,-0.1342532337,0.1072888374,-0.2472750247,-0.1418412924,0.2915894985,-0.4425392151,-0.3745623231,-0.0354103893,0.0048852852,0.0591549091,0.1987728775,-0.0395493209,0.1241699755,-0.2175826728,-0.1797391921,0.2130536437,0.030654503,-0.1623436958,-0.1035052165,-0.2806387842,0.0464729294,0.3110309839,0.4101851583,0.0462240204,0.0579210073,0.1423965693,-0.0716323927,-0.0558816716,0.1470709443,0.5463572741,0.4187244177,0.2150306851,0.1226576716,-0.0435194634,-0.2793141007,0.3250387311,0.0944105759,-0.0312895589,-0.3206380308,0.0869084224,-0.1255264282,0.0822432265,-0.2658863664,-0.0612874404,0.0229207948,0.0132843051,0.042952776,0.0041286382,-0.1376996487,0.5887189507,0.0841866434,0.2985939384,-0.3340809643,0.4976209402,-0.2197433263,-0.0243328996,-0.2272533327,0.2132330835,0.0356290005,-0.09588743,-0.1653893888,0.2277174294,0.0509410389,0.0403989963,-0.0894364864,0.4067900479,0.1685875058,-0.3530105352,0.3245938718,0.2188595831,0.0722006634,0.0125812823,-0.1360511929,-0.1728676111,-0.1118316501,0.2684084773,0.0629780516,0.0753322914,0.159749493,-0.0182622466,-0.0926279575,-0.297341764,0.2764791846,0.1400915086,0.5950938463,0.0195134785,0.2839184999,-0.0588102527,0.0648782477,-0.0461956933,0.0504941344,0.029605398,-0.2449672669,0.0941413566,0.2393054068,0.3468308449,0.5564562082,0.1072781309,-0.1874959618,0.104786694,-0.1752255857,0.0151931047,0.0236833971,0.1832286865,0.1447095424,0.1225250736,-0.1115251556,0.0541960895,-0.1200408712,-0.5026333332,0.0624662638,0.3078273833,-0.5334969163,0.214196831,-0.3632020056,-0.1724309027,-0.1713004112,-0.0200897194,-0.2282012403,-0.3269524276,-0.1747828424,0.1958208531,-0.0569681153,0.1376822889,-0.0231506284,-0.1850917339,-0.1616015285,-0.5011033416,-0.1654448956,-0.1189043298,-0.0615781955,-0.0852289796,0.1452154219,-0.0186589975,0.1159555316,-0.106114693,0.1091464683,-0.3176578581,-0.3510705531,0.0525581427,0.0505925156,0.3023043573,0.125305593,0.2575555146,0.1992651969,-0.2578475773,0.1370526105,0.0607150346,-0.0799539387,0.0862371251,0.0300319437,-0.3280070126,-0.1532604992,-0.1224608868,-0.4666886032,-0.4357229471,-0.2288575917,0.0394917913,0.070153892,0.6503170729,0.2038682848,0.1293322593,0.108779259,-0.0912211537,-0.0634966046,-0.0144536514,0.5791630149,-0.2120685279,-0.2635461092,0.0121335378,-0.1841260195,0.2727899849,0.1857423633,-0.2984972298,-0.0178074744,-0.1328408718,0.138414979,0.0182921272,0.3956168294,0.4145404994,0.1749931872,0.0981788114,-0.2287124693,0.0139991287,-0.1211025566,0.1775335371,0.3473450243,-0.1626528949,0.3198258877,-0.293930918,0.8454340696,0.0806806609,0.0163488109,0.416377306,0.1902275085,0.7380416989,-0.0096346894,-0.473370105,-0.0255538244,-0.0094589153,-0.23999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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/864","title":"Unable to download cnn_dailymail dataset","comments":"Same here! My kaggle notebook stopped working like yesterday. It's strange because I have fixed version of datasets==1.1.2","body":"### Script to reproduce the error\r\n```\r\nfrom datasets import load_dataset\r\n\r\ntrain_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\nvalid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n```\r\n\r\n\r\n### Error\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError                        Traceback (most recent call last)\r\n<ipython-input-8-47c39c228935> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 train_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\n      4 valid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n\r\n5 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    469                     if not downloaded_from_gcs:\r\n    470                         self._download_and_prepare(\r\n--> 471                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    472                         )\r\n    473                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    524         split_dict = SplitDict(dataset_name=self.name)\r\n    525         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 526         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    527 \r\n    528         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\n```\r\n\r\nThanks for any suggestions.","comment_length":18,"text":"Unable to download cnn_dailymail dataset \n ### Script to reproduce the error\r\n```\r\nfrom datasets import load_dataset\r\n\r\ntrain_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\nvalid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n```\r\n\r\n\r\n### Error\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError                        Traceback (most recent call last)\r\n<ipython-input-8-47c39c228935> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 train_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\n      4 valid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n\r\n5 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    469                     if not downloaded_from_gcs:\r\n    470                         self._download_and_prepare(\r\n--> 471                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    472                         )\r\n    473                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    524         split_dict = SplitDict(dataset_name=self.name)\r\n    525         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 526         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    527 \r\n    528         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\n```\r\n\r\nThanks for any suggestions. \n Same here! My kaggle notebook stopped working like yesterday. It's strange because I have fixed version of 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/864","title":"Unable to download cnn_dailymail dataset","comments":"I couldn't reproduce unfortunately. I tried\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"cnn_dailymail\", \"3.0.0\", download_mode=\"force_redownload\")\r\n```\r\nand it worked fine on both my env (python 3.7.2) and colab (python 3.6.9)\r\n\r\nMaybe there was an issue with the google drive download link of the dataset ?\r\nAre you still having the issue ? If so could your give me more info about your python and requests version ?","body":"### Script to reproduce the error\r\n```\r\nfrom datasets import load_dataset\r\n\r\ntrain_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\nvalid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n```\r\n\r\n\r\n### Error\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError                        Traceback (most recent call last)\r\n<ipython-input-8-47c39c228935> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 train_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\n      4 valid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n\r\n5 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    469                     if not downloaded_from_gcs:\r\n    470                         self._download_and_prepare(\r\n--> 471                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    472                         )\r\n    473                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    524         split_dict = SplitDict(dataset_name=self.name)\r\n    525         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 526         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    527 \r\n    528         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\n```\r\n\r\nThanks for any suggestions.","comment_length":66,"text":"Unable to download cnn_dailymail dataset \n ### Script to reproduce the error\r\n```\r\nfrom datasets import load_dataset\r\n\r\ntrain_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\nvalid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n```\r\n\r\n\r\n### Error\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError                        Traceback (most recent call last)\r\n<ipython-input-8-47c39c228935> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 train_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\n      4 valid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n\r\n5 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    469                     if not downloaded_from_gcs:\r\n    470                         self._download_and_prepare(\r\n--> 471                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    472                         )\r\n    473                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    524         split_dict = SplitDict(dataset_name=self.name)\r\n    525         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 526         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    527 \r\n    528         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\n```\r\n\r\nThanks for any suggestions. \n I couldn't reproduce unfortunately. I tried\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"cnn_dailymail\", \"3.0.0\", download_mode=\"force_redownload\")\r\n```\r\nand it worked fine on both my env (python 3.7.2) and colab (python 3.6.9)\r\n\r\nMaybe there was an issue with the google drive download link of the dataset ?\r\nAre you still having the issue ? If so could your give me more info about your python and requests version ?","embeddings":[-0.3053483963,0.2343734205,-0.0792272016,0.1706803292,0.3605651855,0.1738087684,0.5607900023,0.2436558902,-0.1719244719,0.0751596391,-0.1179388687,0.0767754763,-0.2897081375,-0.037752647,-0.0130565865,-0.029159829,-0.2127254903,0.1067626104,-0.1973434091,0.0291639101,-0.1067140326,0.0961273983,-0.152200684,-0.0697495267,-0.2929644287,-0.1320359707,0.0363866985,0.1792989373,-0.3712784052,-0.3521047533,0.3593557477,-0.0825211555,0.1873489618,0.4354524016,-0.0001112958,0.2995791733,0.3638949096,-0.0500410311,-0.4619030058,-0.4736372828,-0.179699868,-0.1351321191,0.0417744182,-0.2966795862,-0.0017572836,0.0122687863,-0.0355194137,0.1481749117,0.3508039415,0.6557962298,0.2708093822,0.4034875631,0.3544582725,-0.1669905931,-0.2214082927,0.1594429463,-0.0742193013,0.5186958313,-0.4013257325,0.1032769531,0.2056567669,0.1207840592,-0.1278402507,0.1728732437,0.0507546216,0.0189495403,0.1369116306,-0.4564376771,0.1248682067,0.2598636746,0.4185577333,-0.2014641464,-0.2431451827,0.1196377352,0.1434075534,-0.2662436664,0.1472920924,0.3098779321,-0.1643135697,0.0582009703,-0.2776976824,0.0111331921,-0.2915741205,0.4140602648,-0.2303673029,0.1816995293,0.0403025933,0.074070029,0.2228647918,-0.1520415843,0.4015417099,-0.2636485696,0.2471809238,0.0726078078,-0.0523370765,-0.1367853284,0.0545635037,-0.2483980805,0.0137203373,0.013428431,-0.1899162829,-0.2480668873,0.0034492572,0.0725217238,-0.0031762752,0.0848950818,-0.2378142029,0.6527371407,0.1373988241,0.4710148573,-0.1147683114,0.0423018746,-0.231807664,-0.1314209402,0.1342493296,0.1773545742,0.5618001223,-0.1913481504,-0.4141942859,0.2526679039,-0.5342707038,0.0010653652,0.1967067122,0.3225836754,-0.1696945727,0.0354947671,0.0004754911,0.1084765121,0.0281692632,0.0804795772,-0.269304961,0.2859299481,-0.1196614057,-0.0974230468,0.3719027638,-0.3562030494,0.1796881706,-0.022808535,0.1048488617,-0.1313501447,0.1455949098,-0.2708338499,-0.1464384943,0.4110406041,-0.1056434289,0.1318189353,0.0854265168,0.0459011495,-0.1711771935,0.1168934256,-0.0236167368,-0.39915061,0.0757410899,0.2404704243,-0.0782731622,-0.3346488178,0.1967336833,-0.2113056481,-0.0441895574,0.009028675,-0.1316437125,-0.274735719,-0.0488870777,-0.1960804462,0.3570636213,0.6024298072,-0.4387532175,0.0300037079,-0.1770686358,-0.221620962,0.1865835041,0.2859837115,-0.3314290345,0.286572963,-0.0801039413,-0.2004273683,0.4411747754,-0.4087324739,-0.5403160453,0.2019555569,-0.0457448848,0.0747404993,-0.0577663146,0.0530315936,0.0841376334,0.0774072558,0.0953814834,0.4915386736,-0.1939899623,-0.0277162641,-0.1510462016,-0.329621315,0.1893856525,0.3296568394,0.4307672381,0.1475800574,-0.0082081985,0.1020928398,0.1281474233,0.0709251389,-0.0517404899,0.1613204777,0.3219523132,-0.0353380404,-0.1970408112,-0.0764020458,-0.3915072083,0.1054196283,0.1194396839,0.1985470504,-0.0941805393,-0.0577527583,-0.4808587134,-0.1143843681,-0.2268015891,0.0408287831,0.2129354179,0.2092737854,0.1277357638,0.0437873676,-0.2601549923,0.3888755739,-0.0502965711,0.206739068,-0.1509432048,0.4017488956,-0.2926222682,-0.0586007535,0.0658875853,0.0477816202,0.2527953982,-0.1254135668,-0.1062384844,0.1132924706,0.0106233535,-0.126910314,-0.0642558783,-0.0739669502,0.0886368826,-0.1603420526,0.0321264453,0.3180967569,0.0794667676,0.0362760201,-0.0129773337,0.2884828448,0.1633599401,-0.042649705,0.1151296869,0.1497045159,0.2333556563,-0.2054045051,0.0016976002,-0.0741698295,0.25011307,-0.0294035058,0.3837204874,-0.1167592481,-0.3227631152,-0.2946901619,0.2018496543,-0.223611176,-0.0194849409,0.0601015314,-0.1977682114,0.104586333,-0.2133276165,0.3237728477,0.4441053867,0.0796773285,0.0288690459,0.1061182842,-0.2178812474,-0.1060986742,0.1595469862,0.2282201052,0.1528194398,0.1916666478,-0.0428625122,0.2319728434,-0.2976652682,-0.4345216155,0.2509975433,0.4884727299,-0.3115290105,-0.029125981,-0.3028720617,-0.1064913571,-0.1622152328,-0.4730811119,-0.0811016932,-0.2360571027,-0.1359013468,0.2614663541,0.0638850853,0.0723204911,-0.3064315617,-0.1044544727,0.1325635612,-0.322935015,-0.0856684521,0.0551765487,-0.0396370254,0.1476238221,0.3995283544,-0.0969782919,0.3422371447,-0.0154319182,-0.1305357367,-0.2630587816,-0.1927981675,-0.0004327538,0.0221684016,0.2420882732,0.1672898382,0.430962652,0.0226662755,-0.193605423,0.1404919475,0.0584454164,-0.026869243,0.2278819531,-0.2574721575,-0.1057041809,-0.010171094,-0.4860118926,-0.5390897393,-0.326920718,-0.1599415839,-0.0482898466,0.19598943,0.423708111,0.116052188,0.1385355592,-0.1566384435,0.0500074252,-0.1222570613,-0.1248526126,0.4896658361,-0.3741426468,-0.3817529976,0.1049950793,-0.1745428294,0.2857010067,-0.089311935,-0.4778813422,-0.2060608715,-0.1840455681,-0.0779386759,-0.1294920743,-0.2238213867,0.1238588989,-0.0531830192,-0.0448054187,0.121457316,-0.1170100793,-0.0215034578,0.1581927538,0.0776544288,-0.1886182427,0.5733031034,0.0269378722,0.6518713236,0.2029567659,-0.2608668506,0.3176005781,-0.0788142756,0.2220961154,-0.0406563729,-0.4731614292,-0.0460523441,0.2134987712,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/864","title":"Unable to download cnn_dailymail dataset","comments":"No, It's working fine now. Very strange. Here are my python and request versions\r\n\r\nrequests         2.24.0\r\nPython 3.8.2","body":"### Script to reproduce the error\r\n```\r\nfrom datasets import load_dataset\r\n\r\ntrain_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\nvalid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n```\r\n\r\n\r\n### Error\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError                        Traceback (most recent call last)\r\n<ipython-input-8-47c39c228935> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 train_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\n      4 valid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n\r\n5 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    469                     if not downloaded_from_gcs:\r\n    470                         self._download_and_prepare(\r\n--> 471                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    472                         )\r\n    473                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    524         split_dict = SplitDict(dataset_name=self.name)\r\n    525         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 526         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    527 \r\n    528         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\n```\r\n\r\nThanks for any suggestions.","comment_length":18,"text":"Unable to download cnn_dailymail dataset \n ### Script to reproduce the error\r\n```\r\nfrom datasets import load_dataset\r\n\r\ntrain_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\nvalid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n```\r\n\r\n\r\n### Error\r\n```\r\n---------------------------------------------------------------------------\r\nNotADirectoryError                        Traceback (most recent call last)\r\n<ipython-input-8-47c39c228935> in <module>()\r\n      1 from datasets import load_dataset\r\n      2 \r\n----> 3 train_dataset = load_dataset(\"cnn_dailymail\", \"3.0.0\", split= 'train[:10%')\r\n      4 valid_dataset = load_dataset(\"cnn_dailymail\",\"3.0.0\", split=\"validation[:5%]\")\r\n\r\n5 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    469                     if not downloaded_from_gcs:\r\n    470                         self._download_and_prepare(\r\n--> 471                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    472                         )\r\n    473                     # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    524         split_dict = SplitDict(dataset_name=self.name)\r\n    525         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 526         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    527 \r\n    528         # Checksums verification\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _split_generators(self, dl_manager)\r\n    252     def _split_generators(self, dl_manager):\r\n    253         dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n--> 254         train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)\r\n    255         # Generate shared vocabulary\r\n    256 \r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _subset_filenames(dl_paths, split)\r\n    153     else:\r\n    154         logging.fatal(\"Unsupported split: %s\", split)\r\n--> 155     cnn = _find_files(dl_paths, \"cnn\", urls)\r\n    156     dm = _find_files(dl_paths, \"dm\", urls)\r\n    157     return cnn + dm\r\n\r\n\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/cnn_dailymail\/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n    132     else:\r\n    133         logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134     files = sorted(os.listdir(top_dir))\r\n    135 \r\n    136     ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '\/root\/.cache\/huggingface\/datasets\/downloads\/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\/cnn\/stories'\r\n\r\n```\r\n\r\nThanks for any suggestions. \n No, It's working fine now. Very strange. Here are my python and request versions\r\n\r\nrequests         2.24.0\r\nPython 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/861","title":"Possible Bug: Small training\/dataset file creates gigantic output","comments":"The preprocessing tokenizes the input text. Tokenization outputs `input_ids`, `attention_mask`, `token_type_ids` and `special_tokens_mask`. All those are of length`max_seq_length` because of padding. Therefore for each sample it generate 4 *`max_seq_length` integers. Currently they're all saved as int64. This is why the tokenization takes so much space.\r\n\r\nI'm sure we can optimize that though\r\nWhat do you think @sgugger ?","body":"Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n","comment_length":58,"text":"Possible Bug: Small training\/dataset file creates gigantic output \n Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n \n The preprocessing tokenizes the input text. Tokenization outputs `input_ids`, `attention_mask`, `token_type_ids` and `special_tokens_mask`. All those are of length`max_seq_length` because of padding. Therefore for each sample it generate 4 *`max_seq_length` integers. Currently they're all saved as int64. This is why the tokenization takes so much space.\r\n\r\nI'm sure we can optimize that though\r\nWhat do you think @sgugger ?","embeddings":[-0.1432341933,-0.3876414895,0.1133134663,0.3227741718,0.5927131176,-0.1981907338,0.2874234021,0.3909299076,-0.3091718554,0.1720577329,-0.0339277796,-0.1797885746,-0.3273913562,0.1897948235,0.1685712636,-0.0655538663,0.1466006935,0.116178818,-0.0373176374,-0.2512279153,0.0122301606,0.0331541412,0.2052446604,0.2363262922,-0.5892032385,0.116232723,-0.0976605043,0.0225226693,-0.4522168636,-0.0920315832,0.0688617229,-0.3121908009,0.1892007589,0.5060113072,-0.000128655,-0.0801775157,0.1424444616,-0.0695752501,0.0205701813,0.2090911865,0.2144756615,-0.0632570237,-0.2133893222,0.1053180844,0.215238288,-0.1143107042,-0.0709828362,-0.0340408124,0.3668509126,0.1840243191,0.0656992346,0.1355391443,0.0148969349,0.1036687791,0.2429664731,0.3396823704,-0.0371804014,-0.0100553706,0.2122915089,-0.1989565939,0.0148402276,0.1700541526,0.0983981639,-0.1982290298,0.3284576833,0.211391449,-0.0749181658,-0.4113597274,0.2120729089,0.3883528709,-0.0406982489,-0.3539565206,-0.3082945943,-0.3972704113,0.2411764115,-0.5351740122,0.0933327749,0.1817525029,-0.1299932003,0.0571895428,-0.2757267952,-0.4147567749,-0.2204075903,-0.0975380242,-0.2143557817,0.0497394353,-0.2284279913,-0.0945073366,-0.0247093644,-0.1514883786,-0.3679102659,-0.0261253119,-0.0072875586,0.1992163956,-0.0928649604,-0.324272722,-0.1126561165,-0.3281558454,0.2717485428,-0.4419617057,-0.2412327826,-0.4030223191,-0.1295828819,-0.4189319313,0.3993144631,0.45951581,-0.3760156333,0.1799371392,0.129983902,0.0144979181,-0.1388451308,-0.0527608991,-0.1634542048,0.1329072416,0.0710657537,-0.4047501087,0.0313030481,-0.2290522456,-0.1003042832,0.4591183364,-0.3784509897,0.2226309627,0.0510771945,0.3274946213,-0.0760810301,0.1053190678,0.0109074526,0.051346615,-0.4180237353,-0.0177939795,-0.0611290112,0.0742200837,-0.2151125818,0.1869876385,-0.0288434997,0.1044516861,0.021352781,-0.0179804843,0.3231276572,-0.099340573,0.0395014994,-0.2510479093,0.1726971269,0.1685138196,-0.2115899771,0.4402742386,-0.032869909,0.0573067553,-0.057333827,0.0731936917,-0.2025119215,-0.4613577127,-0.0575413331,0.0954975709,-0.0324038714,0.3140635192,-0.0619527102,0.3278669119,0.4565284252,0.0198546946,0.0250289068,-0.2284952551,-0.2265143692,0.0091150738,0.3995687664,0.3902240396,-0.484636277,-0.1758751422,-0.2054727226,0.3122827709,0.3894993663,0.5888673663,0.0077837189,0.5783097744,-0.2811872363,0.2357924432,-0.2949523628,0.2546403706,-0.4308692813,0.3521903157,-0.0578786768,-0.0545474775,0.0877296999,-0.0587389097,0.1824905276,0.0208971072,0.3823952079,0.0333050042,-0.1517231613,0.0272625796,-0.3604698479,-0.4016648233,-0.2655518949,0.0143587757,0.1724800318,-0.1572998762,-0.3328549564,0.5829175711,0.1297849119,-0.1000100225,0.1598347723,0.3174715042,0.0870409384,-0.038315177,0.0524265729,-0.0793230012,-0.1097159609,-0.0088055804,-0.307941258,0.2040724009,0.1063894778,-0.2711715996,-0.0457132347,0.0320105255,0.0785083622,-0.206470862,-0.0180337559,-0.1957940906,-0.1108462065,0.0106169786,-0.0733258352,-0.1052491441,-0.0842952281,0.0720166713,-0.4379627407,-0.0457763746,-0.0231428873,-0.1467807889,-0.0945440531,0.360575676,-0.1125401333,0.0251169559,-0.0708715022,0.1594514549,0.1305894256,0.0670296475,-0.0969280303,0.2377001047,0.2820757926,0.0817722231,0.1023699269,0.1841624379,0.0422055312,-0.0524386503,-0.1178935096,0.2136938721,0.2088421732,0.1476619989,0.241948694,-0.2180727869,-0.1030561328,0.1184765771,-0.044392433,-0.2775182724,0.3078273535,0.0634442493,0.2897989154,0.1936533898,-0.199255988,-0.0915348753,0.2885442376,-0.1878994107,0.0241267122,0.4312776625,-0.3436952829,-0.1197063774,-0.1063292995,-0.2838623822,0.2830734253,0.0803629383,0.1233097166,0.1092917994,0.1420129687,-0.0746385753,0.2112692147,0.2753331065,-0.1940709949,0.1610940397,0.1346510351,0.0592170246,-0.1989697665,0.0861475393,-0.1964784116,0.3385744989,-0.4070677757,0.3386442363,0.0577274486,0.0137761049,-0.3941378295,-0.2747454345,-0.1809253544,0.0264378972,0.1926738918,-0.2345577329,-0.1110428274,-0.052663371,0.3810812831,0.2989987433,0.1577854604,0.2653697729,0.0186494663,0.1821882874,-0.3386882246,-0.0294872876,0.1913497746,-0.1869173199,0.1518467218,0.0983013734,-0.2298426032,-0.0564885587,-0.3236949444,0.2155354023,-0.1799170524,0.279482007,0.2024963349,0.1363641471,-0.4355506599,0.094604522,0.0197804589,-0.0027538224,-0.1579777598,-0.014179674,-0.1234321445,0.1277320832,-0.0837785304,-0.2641698122,-0.0601658933,0.0195474718,0.3254421949,-0.0098445956,0.0360103473,0.211654976,0.1585821211,0.1004245207,-0.0514147468,-0.1972807795,-0.0010389336,-0.1854279786,0.2125998437,0.0062263282,-0.1962724924,-0.3562273085,-0.053124927,0.0255862307,-0.1569030285,-0.5457936525,0.0518393554,-0.4050366879,-0.3002316356,-0.2334708869,0.3785063624,0.1156734899,-0.107770294,0.0849830359,0.0984227061,0.1134190038,-0.0752596483,-0.2457754016,0.2301739305,-0.4163112342,0.3523648381,-0.0792582184,0.878788054,0.3173368573,-0.0099722948,-0.0350926481,-0.0326418132,0.399187237,-0.4704627395,-0.1981317103,0.2872303426,-0.05922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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/861","title":"Possible Bug: Small training\/dataset file creates gigantic output","comments":"First I think we should disable padding in the dataset processing and let the data collator do it.\r\n\r\nThen I'm wondering if you need attention_mask and token_type_ids at this point ?\r\n\r\nFinally we can also specify the output feature types at this line https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py#L280 to use more optimized integer precisions for the output. Maybe something like:\r\n- input_ids: uint16 or uint32\r\n- token_type_ids: uint8 or bool\r\n- attention_mask: bool\r\n- special_tokens_mask: bool\r\n\r\nAlso IMO these changes are all on the `transformers` side. Maybe we should discuss on the `transformers` repo","body":"Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n","comment_length":90,"text":"Possible Bug: Small training\/dataset file creates gigantic output \n Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n \n First I think we should disable padding in the dataset processing and let the data collator do it.\r\n\r\nThen I'm wondering if you need attention_mask and token_type_ids at this point ?\r\n\r\nFinally we can also specify the output feature types at this line https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py#L280 to use more optimized integer precisions for the output. Maybe something like:\r\n- input_ids: uint16 or uint32\r\n- token_type_ids: uint8 or bool\r\n- attention_mask: bool\r\n- special_tokens_mask: bool\r\n\r\nAlso IMO these changes are all on the `transformers` side. Maybe we should discuss on the `transformers` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/861","title":"Possible Bug: Small training\/dataset file creates gigantic output","comments":"> First I think we should disable padding in the dataset processing and let the data collator do it.\r\n\r\nNo, you can't do that on TPUs as dynamic shapes will result in a very slow training. The script can however be tweaked to use the `PaddingDataCollator` with a fixed max length instead of dynamic batching.\r\n\r\nFor the other optimizations, they can be done by changing the script directly for each user's use case. Not sure we can find something that is general enough to be in transformers or the examples script.","body":"Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n","comment_length":91,"text":"Possible Bug: Small training\/dataset file creates gigantic output \n Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n \n > First I think we should disable padding in the dataset processing and let the data collator do it.\r\n\r\nNo, you can't do that on TPUs as dynamic shapes will result in a very slow training. The script can however be tweaked to use the `PaddingDataCollator` with a fixed max length instead of dynamic batching.\r\n\r\nFor the other optimizations, they can be done by changing the script directly for each user's use case. Not sure we can find something that is general enough to be in transformers or the examples 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/861","title":"Possible Bug: Small training\/dataset file creates gigantic output","comments":"Oh yes right..\r\nDo you think that a lazy map feature on the `datasets` side could help to avoid storing padded tokenized texts then ?","body":"Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n","comment_length":25,"text":"Possible Bug: Small training\/dataset file creates gigantic output \n Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n \n Oh yes right..\r\nDo you think that a lazy map feature on the `datasets` side could help to avoid storing padded tokenized texts then ?","embeddings":[-0.1584593505,-0.322750479,0.1142651811,0.2783169448,0.6050810218,-0.0893944949,0.3665043712,0.3757137358,-0.2504401803,0.1644323915,-0.0610655993,-0.0809407011,-0.3737191558,0.1472707838,0.1473814845,0.0324401334,0.1391411573,0.1354866177,-0.0773729756,-0.2471373975,0.0194773749,-0.0309278648,0.2424664944,0.1948526949,-0.5476983786,0.0997936502,-0.0319239534,-0.0307262652,-0.4810985029,-0.1251824498,0.1005678922,-0.2483758777,0.1287238598,0.424087584,-0.0001295393,-0.0181988981,0.1412843764,-0.0814446658,0.0510074385,0.1926465183,0.0713473484,-0.0778828338,-0.1597842276,0.0811405629,0.2066449672,-0.1023944914,0.0354038849,-0.079418689,0.3247146308,0.1447516978,0.063819319,0.1143500283,-0.0348902978,0.0747108832,0.3187438548,0.2770513296,-0.0755639598,0.0055221049,0.1870386451,-0.2282137126,-0.0746483877,0.235629946,0.0775171071,-0.2015115768,0.3477509022,0.2555222213,-0.0118268114,-0.3913118243,0.2150571942,0.3343567848,-0.0059385668,-0.369189173,-0.281550616,-0.4759232402,0.1507344693,-0.3983701468,0.0676338822,0.1264641881,-0.0999671966,0.1164510176,-0.3256670237,-0.4626322091,-0.2151950002,-0.0421484858,-0.1925977319,0.0499017164,-0.2066259682,-0.1162705347,-0.0609063059,-0.1237846464,-0.2689934075,-0.0728556141,0.0181449167,0.2470308542,0.0355407819,-0.3357768357,-0.0486233234,-0.400313288,0.3179071546,-0.5198046565,-0.1670588255,-0.3840146661,-0.2822938859,-0.3092981875,0.4190124869,0.4008267522,-0.3786330521,0.2864236236,0.147435993,0.0143901473,-0.0981879681,-0.0872362033,-0.1584558487,0.1529669315,0.0889035314,-0.366333425,0.0794737265,-0.2428524643,-0.11945007,0.4220166206,-0.3422700167,0.2382763326,0.0211726893,0.3271232843,-0.1095505804,0.0815232024,-0.0186989699,0.0428276025,-0.3623221517,0.069616802,-0.0575441085,-0.0081365826,-0.1674923748,0.1830439121,-0.0043521412,0.0869902298,0.0923545286,-0.0637386516,0.2453142405,-0.0269418415,0.074988164,-0.2383559346,0.2199847698,0.1590303481,-0.2199200541,0.4460888505,-0.0356886499,0.0270396918,-0.06633766,0.1065040454,-0.2845102549,-0.5047079921,-0.0448392741,0.0848665237,0.01339396,0.2874855995,-0.1068297699,0.3798874617,0.4936864674,-0.0647113547,0.0465578251,-0.1642074883,-0.3196155131,-0.0120980768,0.3641358614,0.3811829984,-0.4837587774,-0.1946118027,-0.1694867313,0.2451892048,0.2841442823,0.6231352687,-0.0581989922,0.5801355243,-0.2897653282,0.2861112654,-0.260382086,0.2289155871,-0.4580262303,0.3011760712,-0.0231810678,-0.0101977661,0.0377300903,-0.1035020128,0.2780748606,-0.0187087432,0.4887704849,-0.0405674987,-0.1937264651,0.048390232,-0.3471644223,-0.3909625411,-0.2233014405,-0.0301923789,0.1755296439,-0.1729311347,-0.2678878307,0.5790894628,0.1735107601,-0.0645666271,0.2249490172,0.3249577284,0.1268682331,-0.0376047418,0.0377345607,-0.1063976064,-0.1840966493,0.000745947,-0.2624854147,0.1973665208,0.0605289787,-0.3366334438,-0.0279029291,-0.0303604268,0.0522277951,-0.1490053236,-0.0096160518,-0.2432951331,-0.1688456088,0.1220776215,-0.0486761443,-0.1587258875,-0.0792643651,0.0563898794,-0.3761591017,-0.0059617641,0.0287979059,-0.1611763984,-0.0967428312,0.3295545876,-0.1723220646,-0.0049660024,-0.0797172859,0.1359841675,0.1103423238,0.1088261977,-0.1641490757,0.3137359917,0.3106914163,0.0941727832,0.0482255332,0.2186592072,0.0120981317,-0.0983668417,-0.1908928603,0.1803031266,0.208724305,0.0652376935,0.1750696898,-0.2777831554,-0.0779210106,0.1187643185,-0.0812474042,-0.3290862441,0.2585925758,0.047444284,0.2946568429,0.1983209252,-0.1492181271,-0.075496234,0.3165712655,-0.1617223918,0.0371678807,0.3789635301,-0.3250766397,-0.0943503231,-0.0045814626,-0.3105895519,0.2905078232,0.0462883227,0.1345673352,0.0940559953,0.1150725111,-0.0617256872,0.1856593937,0.2106611729,-0.1262027025,0.2095306963,0.192082569,-0.0009269142,-0.2589702904,0.1365609318,-0.1572662592,0.2898987234,-0.3594797254,0.2282149941,0.0755934566,0.0265272297,-0.3885186911,-0.2284081131,-0.1528207213,0.0331248231,0.2542636096,-0.1518006921,-0.151860863,-0.0158998445,0.3029646575,0.3114877939,0.2414972484,0.2074394822,0.0153080598,0.1233328879,-0.2894179225,-0.031588655,0.1572476476,-0.2357091755,0.1222896278,0.1127193421,-0.2993734181,-0.0817451477,-0.2953290343,0.2014806569,-0.1648557335,0.196104303,0.2442874312,0.125231877,-0.3874123991,0.1048204973,0.0128359301,0.0034779874,-0.1348334402,-0.1136595905,-0.0770547688,0.1489091218,-0.080621928,-0.2752304077,-0.0724572614,-0.0015945281,0.3967808187,-0.0382245816,0.0059397155,0.198438257,0.2022285312,0.0955072492,-0.0675063059,-0.1960124373,-0.0069532352,-0.2221462876,0.2693296671,-0.0200770982,-0.1803517789,-0.3509810567,-0.0367721766,-0.0391584337,-0.1439628601,-0.5498438478,0.1042803302,-0.3475675583,-0.2124298364,-0.1869568676,0.3236688375,0.162125513,-0.0278233867,0.0954016596,0.0770545453,0.0741135105,-0.046077922,-0.153002888,0.2259287834,-0.367066294,0.3084632754,-0.0015540273,0.8413866758,0.258456707,0.0591780581,0.0175117403,-0.0364140868,0.4315395355,-0.4216063023,-0.1953714192,0.2533493936,-0.1266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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/861","title":"Possible Bug: Small training\/dataset file creates gigantic output","comments":"I think I can do the tweak mentioned above with the data collator as short fix (but fully focused on v4 right now so that will be for later this week, beginning of next week :-) ).\r\nIf it doesn't hurt performance to tokenize on the fly, that would clearly be the long-term solution however!","body":"Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n","comment_length":55,"text":"Possible Bug: Small training\/dataset file creates gigantic output \n Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n \n I think I can do the tweak mentioned above with the data collator as short fix (but fully focused on v4 right now so that will be for later this week, beginning of next week :-) ).\r\nIf it doesn't hurt performance to tokenize on the fly, that would clearly be the long-term solution however!","embeddings":[-0.2022947818,-0.2920219898,0.1189169437,0.2300961465,0.6037492156,-0.1722533703,0.315689683,0.3898852468,-0.2658645213,0.18251656,-0.0329228267,-0.147778973,-0.3427737355,0.2524911165,0.1107931584,-0.0713135824,0.1064804345,0.1568867862,-0.0434261188,-0.2282034308,0.0541994162,0.0182766169,0.2426915318,0.2236170024,-0.5773249865,0.093116641,-0.0897391662,0.0289621837,-0.4984022975,-0.1051620916,0.0200684927,-0.2774215639,0.2171355188,0.488702625,-0.0001255141,-0.0723906457,0.1390117407,-0.0683199093,0.0042039501,0.1563264132,0.203809917,-0.0963017419,-0.1436719298,0.1250668019,0.2383717299,-0.1081832573,-0.0434214957,-0.0000365168,0.3469856083,0.169578895,0.0850071013,0.1917706728,0.0978405029,0.0513320677,0.2168005258,0.2441504598,-0.0491969772,0.0492714457,0.2134055197,-0.2436895669,-0.0475986265,0.1766494215,0.1195391566,-0.1437061578,0.3066144884,0.192255035,-0.0794912949,-0.411546886,0.1700009108,0.4091199338,-0.0398110822,-0.3814209998,-0.2852917016,-0.3907469213,0.2280966341,-0.5098490119,0.0361433998,0.1651209593,-0.1153219119,0.0947942585,-0.2568562329,-0.391516,-0.2216070294,-0.0695686936,-0.2767575085,0.0675148293,-0.2188895941,-0.0988881961,-0.0032573708,-0.0955056176,-0.3700415492,0.0121714342,-0.0498173758,0.1920453161,-0.1057280302,-0.3005862236,-0.093510747,-0.3335511088,0.2913614511,-0.4924598038,-0.2083922178,-0.4083329141,-0.1743303239,-0.3648548722,0.4070279896,0.4913434088,-0.4073669612,0.1588030905,0.1524096578,0.036944177,-0.1634155959,-0.0812346712,-0.1595999002,0.1207202673,0.016142847,-0.3720576167,0.0639492795,-0.2456080317,-0.1266844869,0.3839028776,-0.3510911465,0.2377408147,0.0550510064,0.3134867251,-0.0650647357,0.1613825709,0.0347572714,0.041105397,-0.4139357805,-0.0494072996,-0.0716322884,0.0595841482,-0.1605283618,0.1914116293,-0.0187105779,0.100606747,0.0448962785,-0.0284892917,0.3421201706,-0.0739843398,0.0721718669,-0.255746454,0.1020974442,0.1747199595,-0.1739896983,0.4763528407,0.0112040155,0.0109734619,-0.0122268423,0.0318185911,-0.1611828953,-0.5222705007,-0.0953116566,0.1208966076,-0.0136681022,0.2884064913,-0.0481704585,0.3760944009,0.395816952,0.0022844994,-0.0270233732,-0.2176161557,-0.1882951558,-0.0019311025,0.4034152627,0.3783380091,-0.5633426905,-0.1548845172,-0.2199615538,0.284835279,0.3699565232,0.6364403963,-0.0472258776,0.5527011156,-0.2490289807,0.2100589722,-0.3150477707,0.2535911202,-0.4138020277,0.3480509222,-0.1281874627,-0.072096169,0.0379310586,-0.0920354947,0.247273311,0.0253312513,0.4554362297,0.0424429737,-0.1859478354,0.0485504866,-0.3573648632,-0.3754622042,-0.2417564243,0.0276660081,0.1375680268,-0.1434081644,-0.2986965477,0.5591665506,0.1224410012,-0.1235059127,0.1712397784,0.3022281826,0.1504045725,-0.0457573496,0.0347059667,-0.0240929928,-0.1081381887,-0.0064385473,-0.2265459001,0.2554838955,0.0755123645,-0.3105176985,-0.0878991336,-0.0096517168,0.10226053,-0.1911879331,0.0169620737,-0.221573621,-0.1310765445,0.0813742056,-0.0826464519,-0.1217534319,-0.1119900718,0.0957406461,-0.4077610672,-0.0668910071,-0.0375665426,-0.1556822062,-0.0447244309,0.3314082921,-0.1087693125,0.022935316,-0.0908818096,0.2122023255,0.0596889518,0.0822513476,-0.0839946866,0.232918784,0.2856232226,0.1345306933,0.1035177633,0.1799343228,0.0648086518,-0.0836119652,-0.0658560619,0.2375005335,0.231471926,0.112771228,0.2546750605,-0.2077405453,-0.0510378331,0.1223885641,-0.0303190965,-0.2649427354,0.2836706042,0.0495792888,0.2589974105,0.1513907462,-0.190586105,-0.0424954854,0.2515276074,-0.1678776145,0.0247898456,0.3735732138,-0.3690002561,-0.0784586444,-0.0579495206,-0.2319737077,0.2978201509,0.0852164626,0.1362763196,0.120152764,0.0970116854,-0.0916482285,0.1554721147,0.282751292,-0.1316686273,0.1620893776,0.2225505114,0.0028682395,-0.2590218186,0.0297040921,-0.2065397054,0.3683235943,-0.367923528,0.29502666,0.0566736832,0.0482108817,-0.3758403361,-0.276639998,-0.2192476839,0.0057147471,0.2062768638,-0.1668457985,-0.1179317534,-0.0931925625,0.3834417164,0.263286829,0.1543856561,0.3253230453,0.0347320177,0.0900794715,-0.3657518327,-0.0053994451,0.1525135487,-0.1867826581,0.1173915789,0.1443026811,-0.2773666978,-0.0515350252,-0.3002146184,0.1842243075,-0.1805002689,0.2566883266,0.2256781608,0.1575404704,-0.4216123223,0.0344796591,0.0324531607,-0.0248775072,-0.1283336878,-0.0536395386,-0.1409126818,0.1573622078,-0.0745352432,-0.2841409445,-0.1479458958,-0.0517506041,0.361946106,-0.0264673084,-0.0296914093,0.1829232126,0.1623683572,0.0922787413,0.0150932139,-0.2145968676,0.0251060873,-0.1410413831,0.1920464486,0.013445612,-0.1889305711,-0.3331333995,-0.0168802962,0.0008043608,-0.1568006575,-0.5277712941,0.0539353974,-0.434366107,-0.2976064682,-0.2196919769,0.34762308,0.0865405574,-0.0860693753,0.0464070737,0.1269993782,0.1192756593,-0.0778975412,-0.2258176357,0.2165210247,-0.4335952401,0.4292582273,-0.0345524959,0.8483142257,0.2267303169,-0.0550652854,0.0104465755,-0.0209994148,0.3937900364,-0.4736229479,-0.2124661505,0.3288615942,-0.1232093722,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/861","title":"Possible Bug: Small training\/dataset file creates gigantic output","comments":"> Hey guys,\r\n> \r\n> I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n> \r\n> I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n> \r\n> I've used the following CMD:\r\n> `python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\nIt's actually because of the parameter 'preprocessing_num_worker' when using TPU. \r\nI am also planning to have my model trained on the google TPU with a 11gb text corpus. With x8 cores enabled, each TPU core has its own dataset.  When not using distributed training, the preprocessed file is about 77gb. On the opposite, if enable xla, the file produced will easily consume all my free space(more than 220gb, I think it will be, in the end, around 600gb ). \r\nSo I think that's maybe where the problem came from. \r\n\r\nIs there any possibility that all of the cores share the same preprocess dataset?\r\n\r\n@sgugger @RammMaschine ","body":"Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n","comment_length":273,"text":"Possible Bug: Small training\/dataset file creates gigantic output \n Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n \n > Hey guys,\r\n> \r\n> I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n> \r\n> I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n> \r\n> I've used the following CMD:\r\n> `python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\nIt's actually because of the parameter 'preprocessing_num_worker' when using TPU. \r\nI am also planning to have my model trained on the google TPU with a 11gb text corpus. With x8 cores enabled, each TPU core has its own dataset.  When not using distributed training, the preprocessed file is about 77gb. On the opposite, if enable xla, the file produced will easily consume all my free space(more than 220gb, I think it will be, in the end, around 600gb ). \r\nSo I think that's maybe where the problem came from. \r\n\r\nIs there any possibility that all of the cores share the same preprocess dataset?\r\n\r\n@sgugger @RammMaschine 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/861","title":"Possible Bug: Small training\/dataset file creates gigantic output","comments":"Hi @NebelAI, we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs.","body":"Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n","comment_length":34,"text":"Possible Bug: Small training\/dataset file creates gigantic output \n Hey guys,\r\n\r\nI was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n\r\nI've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n\r\nI've used the following CMD:\r\n`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\n \n Hi @NebelAI, we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs.","embeddings":[-0.2338341922,-0.3642718196,0.1098738834,0.327167213,0.5802890658,-0.1227116883,0.2948497832,0.405277729,-0.1822131574,0.1775166541,-0.0482908227,-0.1247077584,-0.3384362161,0.2299035192,0.0976041406,-0.0641692579,0.1237757429,0.1533718109,-0.104493238,-0.2381588221,0.0190756135,0.0444056056,0.2407272607,0.2485654652,-0.5509336591,0.0961136147,-0.0530444272,0.065711841,-0.4600453079,-0.0649351627,0.0930358097,-0.3468028009,0.218477875,0.5662022829,-0.0001273608,-0.0424782932,0.158666268,-0.0371259227,-0.036269132,0.151588738,0.1211322546,-0.0592242442,-0.1560094357,0.1039409339,0.2607800663,-0.0881411657,-0.0348140784,-0.0693408921,0.3328700364,0.2546795309,0.0841450766,0.1687070429,0.0530854799,0.0208149012,0.2235208601,0.277895391,-0.0392047167,0.0236582737,0.1884796172,-0.1935082525,-0.0342083648,0.2210821062,0.1108082533,-0.112880215,0.3315674961,0.1934318841,-0.0573355258,-0.3944152594,0.2410113364,0.3753354549,0.0406103432,-0.4069215953,-0.2597789168,-0.4071704149,0.2153960615,-0.4601975381,0.0786264762,0.1565641165,-0.1745781153,0.0869323239,-0.2822416127,-0.433242768,-0.2624081075,-0.0644187182,-0.1952349842,-0.0204993654,-0.2531405985,-0.0878002122,0.03168533,-0.0513358004,-0.3014719188,0.001802813,-0.0131004192,0.1772996336,-0.0929567218,-0.2810669541,-0.0840085447,-0.3132231832,0.2787923813,-0.5234856606,-0.1843097955,-0.4352833331,-0.2017560005,-0.3546653688,0.4170531631,0.472019732,-0.396017313,0.1702489257,0.166572541,0.0548957549,-0.1536719203,-0.0758734569,-0.1547504961,0.0704373196,0.0707222223,-0.4381209016,0.129961893,-0.2795091271,-0.1710017174,0.389624536,-0.2968327403,0.203411743,0.0244191531,0.3456146717,-0.0571381301,0.1504266262,0.0753576159,0.0295034647,-0.4357024431,-0.0140295429,-0.0874336883,0.0690611601,-0.187155813,0.1723109186,-0.0044256677,0.0444649197,0.0975162014,-0.0309927966,0.2879881859,-0.0614806116,0.0658553243,-0.2731784284,0.1098375544,0.1949203312,-0.1669240147,0.4897101223,-0.0056195566,0.0467573926,0.0041758274,0.0890709981,-0.2138061821,-0.5087085366,-0.1334372163,0.1073392332,-0.0225376859,0.270331651,-0.1141858697,0.3702563047,0.4396837354,-0.0440382175,-0.0226347893,-0.1944944859,-0.2229566425,-0.0126110548,0.3754176795,0.4236915112,-0.5573843122,-0.1374476403,-0.2831021845,0.2760488689,0.3170405924,0.6052901745,-0.0366573595,0.5938890576,-0.2680997849,0.1369596124,-0.2820636332,0.2830556929,-0.4317550957,0.3631533384,-0.0558017679,-0.0812832415,0.0675941855,-0.0509304926,0.1601727456,0.0494954251,0.4206693172,0.0105388872,-0.1910239309,0.0278292429,-0.3491926789,-0.3933972418,-0.3029136062,0.0391489714,0.2074746937,-0.1426802725,-0.2428856492,0.5842377543,0.1493231952,-0.1176010892,0.1443619877,0.4034582973,0.1061514243,-0.0489939563,0.0309899431,-0.1546383053,-0.152195558,0.0177603085,-0.2466843575,0.1808103472,0.0917116851,-0.2361067832,-0.0985658318,-0.0183471236,0.0784693137,-0.1859901994,-0.0025018868,-0.1868593842,-0.1341238618,0.0562628359,-0.0534956567,-0.0828902647,-0.1111693531,0.0557697192,-0.4824645817,-0.021635972,-0.0438356549,-0.1878069937,-0.0588857941,0.3047100008,-0.1567812711,0.0496466309,-0.1076477319,0.2003514916,0.0655244663,0.0901458561,-0.0104257148,0.1849321127,0.264739275,0.0936757922,0.1331100613,0.1993817389,0.0828021467,-0.0678429306,-0.14168109,0.1253068745,0.1942510158,0.1309911758,0.2353687584,-0.238740474,-0.0969014019,0.1526818573,-0.0067462875,-0.2505146265,0.2871016264,0.0482300855,0.2550387383,0.1577132046,-0.1824488491,-0.0940706655,0.3434639573,-0.1666010469,0.0557361171,0.4261432886,-0.3578202128,-0.0915519223,-0.0463074259,-0.1888587028,0.3729067147,0.0803322345,0.1341005117,0.1067790464,0.0644714683,-0.0672613978,0.1499306411,0.2874512672,-0.117579177,0.1728916913,0.1884485483,-0.0295738932,-0.255150497,0.0547025092,-0.2172520757,0.3717318475,-0.3848313391,0.3306095004,0.094664447,0.032725431,-0.4031649232,-0.2052146494,-0.1755549014,0.0127516724,0.1642492265,-0.1578899324,-0.0829308257,-0.0999551937,0.3912376165,0.2013758719,0.1962990761,0.3108693659,0.033097636,0.138301909,-0.3434259295,-0.0045561604,0.1663041711,-0.1633000374,0.1363902241,0.1126272157,-0.2667165399,-0.062426392,-0.2402979136,0.1260363311,-0.1923039705,0.2783963084,0.2113269866,0.2293050587,-0.3828122318,0.0926501378,0.0435232259,-0.0196592789,-0.1070588678,-0.0421098135,-0.1186993942,0.1387941241,-0.0932442546,-0.3279576302,-0.1222926825,-0.0531825721,0.3685314655,-0.0001866183,-0.0081697125,0.2316599637,0.2695368826,0.132894665,0.0196138173,-0.1554873884,0.0083823735,-0.1846472472,0.1803590357,0.0460972153,-0.1881256253,-0.2821445167,0.0419535525,-0.0011371947,-0.1777221411,-0.5618310571,0.0184513349,-0.4612651169,-0.2686636448,-0.2251045257,0.4086788595,0.1059861481,-0.0596177578,0.0595238023,0.0867204517,0.0746403039,-0.0934038609,-0.2141722739,0.2236485779,-0.4218251705,0.4128449261,-0.0376652479,0.8130553961,0.3064289987,-0.068814449,-0.0068479721,-0.0082486682,0.4584790468,-0.455973357,-0.2210274935,0.2777840793,-0.1431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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/854","title":"wmt16 does not download ","comments":"Hi,I also posted it to the forum, but this is a bug, perhaps it needs to be reported here? thanks ","body":"Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz","comment_length":20,"text":"wmt16 does not download  \n Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz \n Hi,I also posted it to the forum, but this is a bug, perhaps it needs to be reported here? thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/854","title":"wmt16 does not download ","comments":"It looks like the official OPUS server for WMT16 doesn't provide the data files anymore (503 error).\r\nI searched a bit and couldn't find a mirror except maybe http:\/\/nlp.ffzg.hr\/resources\/corpora\/setimes\/ (the data are a cleaned version of the original ones though)\r\nShould we consider replacing the old urls with these ones even though it's not the exact same data ?","body":"Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz","comment_length":59,"text":"wmt16 does not download  \n Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz \n It looks like the official OPUS server for WMT16 doesn't provide the data files anymore (503 error).\r\nI searched a bit and couldn't find a mirror except maybe http:\/\/nlp.ffzg.hr\/resources\/corpora\/setimes\/ (the data are a cleaned version of the original ones though)\r\nShould we consider replacing the old urls with these ones even though it's not the exact same data ?","embeddings":[-0.444955498,-0.4224762022,-0.0815040842,0.4179413617,0.5046890974,0.1244975701,0.1281453669,0.2139965892,0.2786702514,0.001354157,0.0727131143,-0.110461086,-0.2351618558,0.097207725,0.2837482691,-0.0405813567,-0.1238627285,-0.002883099,-0.4859333932,0.0390384048,-0.2367140353,0.1167636067,-0.1118449643,-0.0652594045,-0.3488698602,-0.0671071485,-0.1394528598,0.0584430955,-0.360514909,-0.2884312868,0.3591679037,0.0484933145,0.2195616364,0.4216263294,-0.0001045874,-0.0344254412,0.2609169781,-0.1103322431,-0.1643058062,-0.3919683993,-0.3435014486,-0.1982588619,-0.0863412246,-0.0891985819,-0.0184502378,-0.1176216602,-0.1129943654,-0.3425154686,0.2524401546,0.3540210128,0.3467800915,0.2697255611,0.3545668721,-0.1914296448,0.1382765472,0.1699798554,0.0555037595,0.1284032315,-0.0070466073,-0.1400783062,0.1166838035,0.1394858658,-0.0372346565,0.2416531146,0.104287751,-0.1586447954,0.0307699218,-0.254928261,0.2687593997,0.1392050236,0.3950155675,-0.1157429069,-0.1314078271,0.1513412595,0.065062128,-0.5738540292,0.2200950086,0.5009623766,-0.1863734424,0.1921614856,-0.2714767158,-0.0870952085,0.0238122586,0.1413062066,-0.2197024077,0.2921693921,-0.213550061,0.0183895659,0.1849008203,-0.1017094776,0.0880924761,-0.0139018279,0.1450838894,0.2526480556,-0.2700490355,0.1261817366,-0.1145764664,0.1292577088,0.0806544647,0.0086127445,-0.2520152926,-0.1507218033,0.2881473005,-0.0259473715,0.0269001275,0.1888409704,-0.2194881141,0.0834902674,0.0834250972,0.1954315603,-0.0369618386,-0.0546250939,-0.3145231903,-0.2925898135,0.0326478705,-0.0937117338,0.1151885688,0.0685743988,-0.2826316357,0.0161389802,0.0049903886,-0.1779205352,0.0023902771,0.3310005367,-0.2150157988,0.148575604,0.2978157997,0.0600120388,-0.0970932543,-0.106148757,-0.2628639042,-0.0803785697,-0.1983224154,-0.056407325,0.3051498234,-0.0921546817,0.3761104941,0.0229630414,-0.179779619,-0.1802678704,0.0199732501,-0.1830584705,0.1112451032,0.4338179529,0.0693689287,0.237574324,0.0718808621,0.254779309,-0.0013334155,0.2246186733,-0.0462478027,-0.4587911963,0.1238833144,0.370313406,-0.1037134379,-0.115419358,0.0248291045,-0.1448108256,0.0704493821,0.2599559426,-0.1440329552,-0.2033017129,-0.1653168947,-0.2190619856,0.5277351141,0.393963933,0.0342416279,-0.0421035253,-0.4067505896,-0.2453195155,0.3608443141,0.2823581398,0.0014633266,0.1401390284,-0.3047563136,0.0974318907,0.2519396544,-0.2148155421,-0.2565926909,0.211815387,-0.2131187767,-0.0246797614,-0.0519014411,0.0910042152,0.0106451157,0.0145032462,0.267085135,0.1857897788,-0.3312119544,0.0249924865,-0.1970980912,-0.3113334775,0.4462002218,0.2143390477,0.2483334541,-0.0707214475,0.0157011021,0.114467077,0.3708969951,0.2580592632,0.0792705491,-0.0338332728,0.2125306726,0.0079512121,-0.2015179247,-0.1089143082,-0.2836967707,0.1032906994,0.2451010346,-0.0778647214,0.0508962013,-0.0072366488,-0.4273627698,0.0413815603,-0.168958351,-0.1493412256,0.1657439917,-0.0761240497,0.1448943615,-0.0492947139,-0.0764248297,-0.0518814288,-0.2385359704,0.0705099478,0.1058792993,-0.0204066541,-0.3056635261,-0.0091247633,0.2450969666,-0.0581195839,0.1331098229,-0.0372367017,-0.135418728,0.2523603141,0.123330079,0.2528036237,-0.0802929029,0.0251827948,0.1523814946,-0.452506125,0.0430596173,0.0419443399,0.1044071913,-0.1576236039,0.2183504254,-0.0314418413,-0.0910328627,0.0418949425,0.174456507,0.1563680768,0.1477839649,-0.024936242,0.0901833549,-0.1331830174,0.3215939999,-0.0938108116,0.2526182532,-0.0654185861,-0.0649184287,-0.1171566918,0.4020731449,0.152085036,0.0896184817,0.044230748,-0.3025979102,0.0285023507,-0.2157823443,0.3114795089,0.4959137142,0.1446499079,0.1639534533,0.1199491099,0.1569378823,-0.2816541195,0.4310475886,-0.040102955,0.2351522595,0.2522007525,-0.1961644143,-0.0563999377,-0.2167151421,0.2778975368,0.0175765604,0.2078112811,-0.1578265578,-0.2426187694,-0.2548194826,-0.0673511997,-0.4623289704,-0.5436559319,-0.2178747207,-0.1321282089,-0.2382991165,0.0240342431,-0.0367041901,0.1426422745,-0.0420636795,-0.1025992855,0.0817945004,0.1268719584,0.0026955113,-0.0534274839,-0.0635396317,0.1841251254,0.2524006665,-0.3535847664,0.5754960179,-0.3072552681,0.0299873985,-0.2428164035,-0.1333234608,-0.0243879445,-0.0370058864,0.0867503658,0.3703727424,0.3781490922,0.2589347661,-0.0355772786,0.369383812,-0.2827515304,-0.0627758875,-0.121466957,0.1225737482,0.0374437831,-0.2173156589,-0.562472403,-0.4598293304,-0.4110196233,-0.0917023495,0.0304594226,0.1562970281,0.1512025297,0.0725564435,0.0382239632,0.1439446807,-0.0554896332,-0.3277630508,-0.3739012778,0.3905153573,-0.2670379281,-0.4456063807,0.2986950874,0.0598832257,0.4613804221,0.2046910077,-0.4112800956,0.3908862174,-0.1190442592,-0.0669095516,0.0473832563,0.1677639037,0.1566010565,-0.0419885479,-0.1558275223,-0.1271512806,-0.1122654378,-0.1918485314,-0.1983194798,0.5375465751,-0.1967826039,0.337072134,0.0668314472,0.7139240503,0.141764015,-0.167829141,0.2005022764,-0.10505981,0.1060315967,0.1251528114,-0.4757870138,0.1009271666,-0.1338579655,0.17134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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/854","title":"wmt16 does not download ","comments":"The data storage is down at the moment. Sorry. Hopefully, it will come back soon. Apologies for the inconvenience ...","body":"Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz","comment_length":20,"text":"wmt16 does not download  \n Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz \n The data storage is down at the moment. Sorry. Hopefully, it will come back soon. Apologies for the inconvenience ...","embeddings":[-0.444955498,-0.4224762022,-0.0815040842,0.4179413617,0.5046890974,0.1244975701,0.1281453669,0.2139965892,0.2786702514,0.001354157,0.0727131143,-0.110461086,-0.2351618558,0.097207725,0.2837482691,-0.0405813567,-0.1238627285,-0.002883099,-0.4859333932,0.0390384048,-0.2367140353,0.1167636067,-0.1118449643,-0.0652594045,-0.3488698602,-0.0671071485,-0.1394528598,0.0584430955,-0.360514909,-0.2884312868,0.3591679037,0.0484933145,0.2195616364,0.4216263294,-0.0001045874,-0.0344254412,0.2609169781,-0.1103322431,-0.1643058062,-0.3919683993,-0.3435014486,-0.1982588619,-0.0863412246,-0.0891985819,-0.0184502378,-0.1176216602,-0.1129943654,-0.3425154686,0.2524401546,0.3540210128,0.3467800915,0.2697255611,0.3545668721,-0.1914296448,0.1382765472,0.1699798554,0.0555037595,0.1284032315,-0.0070466073,-0.1400783062,0.1166838035,0.1394858658,-0.0372346565,0.2416531146,0.104287751,-0.1586447954,0.0307699218,-0.254928261,0.2687593997,0.1392050236,0.3950155675,-0.1157429069,-0.1314078271,0.1513412595,0.065062128,-0.5738540292,0.2200950086,0.5009623766,-0.1863734424,0.1921614856,-0.2714767158,-0.0870952085,0.0238122586,0.1413062066,-0.2197024077,0.2921693921,-0.213550061,0.0183895659,0.1849008203,-0.1017094776,0.0880924761,-0.0139018279,0.1450838894,0.2526480556,-0.2700490355,0.1261817366,-0.1145764664,0.1292577088,0.0806544647,0.0086127445,-0.2520152926,-0.1507218033,0.2881473005,-0.0259473715,0.0269001275,0.1888409704,-0.2194881141,0.0834902674,0.0834250972,0.1954315603,-0.0369618386,-0.0546250939,-0.3145231903,-0.2925898135,0.0326478705,-0.0937117338,0.1151885688,0.0685743988,-0.2826316357,0.0161389802,0.0049903886,-0.1779205352,0.0023902771,0.3310005367,-0.2150157988,0.148575604,0.2978157997,0.0600120388,-0.0970932543,-0.106148757,-0.2628639042,-0.0803785697,-0.1983224154,-0.056407325,0.3051498234,-0.0921546817,0.3761104941,0.0229630414,-0.179779619,-0.1802678704,0.0199732501,-0.1830584705,0.1112451032,0.4338179529,0.0693689287,0.237574324,0.0718808621,0.254779309,-0.0013334155,0.2246186733,-0.0462478027,-0.4587911963,0.1238833144,0.370313406,-0.1037134379,-0.115419358,0.0248291045,-0.1448108256,0.0704493821,0.2599559426,-0.1440329552,-0.2033017129,-0.1653168947,-0.2190619856,0.5277351141,0.393963933,0.0342416279,-0.0421035253,-0.4067505896,-0.2453195155,0.3608443141,0.2823581398,0.0014633266,0.1401390284,-0.3047563136,0.0974318907,0.2519396544,-0.2148155421,-0.2565926909,0.211815387,-0.2131187767,-0.0246797614,-0.0519014411,0.0910042152,0.0106451157,0.0145032462,0.267085135,0.1857897788,-0.3312119544,0.0249924865,-0.1970980912,-0.3113334775,0.4462002218,0.2143390477,0.2483334541,-0.0707214475,0.0157011021,0.114467077,0.3708969951,0.2580592632,0.0792705491,-0.0338332728,0.2125306726,0.0079512121,-0.2015179247,-0.1089143082,-0.2836967707,0.1032906994,0.2451010346,-0.0778647214,0.0508962013,-0.0072366488,-0.4273627698,0.0413815603,-0.168958351,-0.1493412256,0.1657439917,-0.0761240497,0.1448943615,-0.0492947139,-0.0764248297,-0.0518814288,-0.2385359704,0.0705099478,0.1058792993,-0.0204066541,-0.3056635261,-0.0091247633,0.2450969666,-0.0581195839,0.1331098229,-0.0372367017,-0.135418728,0.2523603141,0.123330079,0.2528036237,-0.0802929029,0.0251827948,0.1523814946,-0.452506125,0.0430596173,0.0419443399,0.1044071913,-0.1576236039,0.2183504254,-0.0314418413,-0.0910328627,0.0418949425,0.174456507,0.1563680768,0.1477839649,-0.024936242,0.0901833549,-0.1331830174,0.3215939999,-0.0938108116,0.2526182532,-0.0654185861,-0.0649184287,-0.1171566918,0.4020731449,0.152085036,0.0896184817,0.044230748,-0.3025979102,0.0285023507,-0.2157823443,0.3114795089,0.4959137142,0.1446499079,0.1639534533,0.1199491099,0.1569378823,-0.2816541195,0.4310475886,-0.040102955,0.2351522595,0.2522007525,-0.1961644143,-0.0563999377,-0.2167151421,0.2778975368,0.0175765604,0.2078112811,-0.1578265578,-0.2426187694,-0.2548194826,-0.0673511997,-0.4623289704,-0.5436559319,-0.2178747207,-0.1321282089,-0.2382991165,0.0240342431,-0.0367041901,0.1426422745,-0.0420636795,-0.1025992855,0.0817945004,0.1268719584,0.0026955113,-0.0534274839,-0.0635396317,0.1841251254,0.2524006665,-0.3535847664,0.5754960179,-0.3072552681,0.0299873985,-0.2428164035,-0.1333234608,-0.0243879445,-0.0370058864,0.0867503658,0.3703727424,0.3781490922,0.2589347661,-0.0355772786,0.369383812,-0.2827515304,-0.0627758875,-0.121466957,0.1225737482,0.0374437831,-0.2173156589,-0.562472403,-0.4598293304,-0.4110196233,-0.0917023495,0.0304594226,0.1562970281,0.1512025297,0.0725564435,0.0382239632,0.1439446807,-0.0554896332,-0.3277630508,-0.3739012778,0.3905153573,-0.2670379281,-0.4456063807,0.2986950874,0.0598832257,0.4613804221,0.2046910077,-0.4112800956,0.3908862174,-0.1190442592,-0.0669095516,0.0473832563,0.1677639037,0.1566010565,-0.0419885479,-0.1558275223,-0.1271512806,-0.1122654378,-0.1918485314,-0.1983194798,0.5375465751,-0.1967826039,0.337072134,0.0668314472,0.7139240503,0.141764015,-0.167829141,0.2005022764,-0.10505981,0.1060315967,0.1251528114,-0.4757870138,0.1009271666,-0.1338579655,0.171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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/854","title":"wmt16 does not download ","comments":"Dear great huggingface team, this is not working yet, I really appreciate some temporary fix on this, I need this for my project and this is time sensitive and I will be grateful for your help on this. ","body":"Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz","comment_length":38,"text":"wmt16 does not download  \n Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz \n Dear great huggingface team, this is not working yet, I really appreciate some temporary fix on this, I need this for my project and this is time sensitive and I will be grateful for your help on this. ","embeddings":[-0.444955498,-0.4224762022,-0.0815040842,0.4179413617,0.5046890974,0.1244975701,0.1281453669,0.2139965892,0.2786702514,0.001354157,0.0727131143,-0.110461086,-0.2351618558,0.097207725,0.2837482691,-0.0405813567,-0.1238627285,-0.002883099,-0.4859333932,0.0390384048,-0.2367140353,0.1167636067,-0.1118449643,-0.0652594045,-0.3488698602,-0.0671071485,-0.1394528598,0.0584430955,-0.360514909,-0.2884312868,0.3591679037,0.0484933145,0.2195616364,0.4216263294,-0.0001045874,-0.0344254412,0.2609169781,-0.1103322431,-0.1643058062,-0.3919683993,-0.3435014486,-0.1982588619,-0.0863412246,-0.0891985819,-0.0184502378,-0.1176216602,-0.1129943654,-0.3425154686,0.2524401546,0.3540210128,0.3467800915,0.2697255611,0.3545668721,-0.1914296448,0.1382765472,0.1699798554,0.0555037595,0.1284032315,-0.0070466073,-0.1400783062,0.1166838035,0.1394858658,-0.0372346565,0.2416531146,0.104287751,-0.1586447954,0.0307699218,-0.254928261,0.2687593997,0.1392050236,0.3950155675,-0.1157429069,-0.1314078271,0.1513412595,0.065062128,-0.5738540292,0.2200950086,0.5009623766,-0.1863734424,0.1921614856,-0.2714767158,-0.0870952085,0.0238122586,0.1413062066,-0.2197024077,0.2921693921,-0.213550061,0.0183895659,0.1849008203,-0.1017094776,0.0880924761,-0.0139018279,0.1450838894,0.2526480556,-0.2700490355,0.1261817366,-0.1145764664,0.1292577088,0.0806544647,0.0086127445,-0.2520152926,-0.1507218033,0.2881473005,-0.0259473715,0.0269001275,0.1888409704,-0.2194881141,0.0834902674,0.0834250972,0.1954315603,-0.0369618386,-0.0546250939,-0.3145231903,-0.2925898135,0.0326478705,-0.0937117338,0.1151885688,0.0685743988,-0.2826316357,0.0161389802,0.0049903886,-0.1779205352,0.0023902771,0.3310005367,-0.2150157988,0.148575604,0.2978157997,0.0600120388,-0.0970932543,-0.106148757,-0.2628639042,-0.0803785697,-0.1983224154,-0.056407325,0.3051498234,-0.0921546817,0.3761104941,0.0229630414,-0.179779619,-0.1802678704,0.0199732501,-0.1830584705,0.1112451032,0.4338179529,0.0693689287,0.237574324,0.0718808621,0.254779309,-0.0013334155,0.2246186733,-0.0462478027,-0.4587911963,0.1238833144,0.370313406,-0.1037134379,-0.115419358,0.0248291045,-0.1448108256,0.0704493821,0.2599559426,-0.1440329552,-0.2033017129,-0.1653168947,-0.2190619856,0.5277351141,0.393963933,0.0342416279,-0.0421035253,-0.4067505896,-0.2453195155,0.3608443141,0.2823581398,0.0014633266,0.1401390284,-0.3047563136,0.0974318907,0.2519396544,-0.2148155421,-0.2565926909,0.211815387,-0.2131187767,-0.0246797614,-0.0519014411,0.0910042152,0.0106451157,0.0145032462,0.267085135,0.1857897788,-0.3312119544,0.0249924865,-0.1970980912,-0.3113334775,0.4462002218,0.2143390477,0.2483334541,-0.0707214475,0.0157011021,0.114467077,0.3708969951,0.2580592632,0.0792705491,-0.0338332728,0.2125306726,0.0079512121,-0.2015179247,-0.1089143082,-0.2836967707,0.1032906994,0.2451010346,-0.0778647214,0.0508962013,-0.0072366488,-0.4273627698,0.0413815603,-0.168958351,-0.1493412256,0.1657439917,-0.0761240497,0.1448943615,-0.0492947139,-0.0764248297,-0.0518814288,-0.2385359704,0.0705099478,0.1058792993,-0.0204066541,-0.3056635261,-0.0091247633,0.2450969666,-0.0581195839,0.1331098229,-0.0372367017,-0.135418728,0.2523603141,0.123330079,0.2528036237,-0.0802929029,0.0251827948,0.1523814946,-0.452506125,0.0430596173,0.0419443399,0.1044071913,-0.1576236039,0.2183504254,-0.0314418413,-0.0910328627,0.0418949425,0.174456507,0.1563680768,0.1477839649,-0.024936242,0.0901833549,-0.1331830174,0.3215939999,-0.0938108116,0.2526182532,-0.0654185861,-0.0649184287,-0.1171566918,0.4020731449,0.152085036,0.0896184817,0.044230748,-0.3025979102,0.0285023507,-0.2157823443,0.3114795089,0.4959137142,0.1446499079,0.1639534533,0.1199491099,0.1569378823,-0.2816541195,0.4310475886,-0.040102955,0.2351522595,0.2522007525,-0.1961644143,-0.0563999377,-0.2167151421,0.2778975368,0.0175765604,0.2078112811,-0.1578265578,-0.2426187694,-0.2548194826,-0.0673511997,-0.4623289704,-0.5436559319,-0.2178747207,-0.1321282089,-0.2382991165,0.0240342431,-0.0367041901,0.1426422745,-0.0420636795,-0.1025992855,0.0817945004,0.1268719584,0.0026955113,-0.0534274839,-0.0635396317,0.1841251254,0.2524006665,-0.3535847664,0.5754960179,-0.3072552681,0.0299873985,-0.2428164035,-0.1333234608,-0.0243879445,-0.0370058864,0.0867503658,0.3703727424,0.3781490922,0.2589347661,-0.0355772786,0.369383812,-0.2827515304,-0.0627758875,-0.121466957,0.1225737482,0.0374437831,-0.2173156589,-0.562472403,-0.4598293304,-0.4110196233,-0.0917023495,0.0304594226,0.1562970281,0.1512025297,0.0725564435,0.0382239632,0.1439446807,-0.0554896332,-0.3277630508,-0.3739012778,0.3905153573,-0.2670379281,-0.4456063807,0.2986950874,0.0598832257,0.4613804221,0.2046910077,-0.4112800956,0.3908862174,-0.1190442592,-0.0669095516,0.0473832563,0.1677639037,0.1566010565,-0.0419885479,-0.1558275223,-0.1271512806,-0.1122654378,-0.1918485314,-0.1983194798,0.5375465751,-0.1967826039,0.337072134,0.0668314472,0.7139240503,0.141764015,-0.167829141,0.2005022764,-0.10505981,0.1060315967,0.1251528114,-0.4757870138,0.1009271666,-0.1338579655,0.171341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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/854","title":"wmt16 does not download ","comments":"We have reached out to the OPUS team which is currently working on making the data available again. Cc @jorgtied ","body":"Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz","comment_length":20,"text":"wmt16 does not download  \n Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz \n We have reached out to the OPUS team which is currently working on making the data available again. Cc @jorgtied 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/854","title":"wmt16 does not download ","comments":"Hi, this is still down, I would be really grateful if you could ping them one more time. thank you so much. ","body":"Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz","comment_length":22,"text":"wmt16 does not download  \n Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz \n Hi, this is still down, I would be really grateful if you could ping them one more time. thank you so much. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/854","title":"wmt16 does not download ","comments":"Hi\r\nI am trying with multiple setting of wmt datasets and all failed so far, I need to have at least one dataset working for testing somecodes, and this is really time sensitive, I greatly appreciate letting me know of one translation datasets currently working. thanks ","body":"Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz","comment_length":46,"text":"wmt16 does not download  \n Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz \n Hi\r\nI am trying with multiple setting of wmt datasets and all failed so far, I need to have at least one dataset working for testing somecodes, and this is really time sensitive, I greatly appreciate letting me know of one translation datasets currently working. thanks ","embeddings":[-0.444955498,-0.4224762022,-0.0815040842,0.4179413617,0.5046890974,0.1244975701,0.1281453669,0.2139965892,0.2786702514,0.001354157,0.0727131143,-0.110461086,-0.2351618558,0.097207725,0.2837482691,-0.0405813567,-0.1238627285,-0.002883099,-0.4859333932,0.0390384048,-0.2367140353,0.1167636067,-0.1118449643,-0.0652594045,-0.3488698602,-0.0671071485,-0.1394528598,0.0584430955,-0.360514909,-0.2884312868,0.3591679037,0.0484933145,0.2195616364,0.4216263294,-0.0001045874,-0.0344254412,0.2609169781,-0.1103322431,-0.1643058062,-0.3919683993,-0.3435014486,-0.1982588619,-0.0863412246,-0.0891985819,-0.0184502378,-0.1176216602,-0.1129943654,-0.3425154686,0.2524401546,0.3540210128,0.3467800915,0.2697255611,0.3545668721,-0.1914296448,0.1382765472,0.1699798554,0.0555037595,0.1284032315,-0.0070466073,-0.1400783062,0.1166838035,0.1394858658,-0.0372346565,0.2416531146,0.104287751,-0.1586447954,0.0307699218,-0.254928261,0.2687593997,0.1392050236,0.3950155675,-0.1157429069,-0.1314078271,0.1513412595,0.065062128,-0.5738540292,0.2200950086,0.5009623766,-0.1863734424,0.1921614856,-0.2714767158,-0.0870952085,0.0238122586,0.1413062066,-0.2197024077,0.2921693921,-0.213550061,0.0183895659,0.1849008203,-0.1017094776,0.0880924761,-0.0139018279,0.1450838894,0.2526480556,-0.2700490355,0.1261817366,-0.1145764664,0.1292577088,0.0806544647,0.0086127445,-0.2520152926,-0.1507218033,0.2881473005,-0.0259473715,0.0269001275,0.1888409704,-0.2194881141,0.0834902674,0.0834250972,0.1954315603,-0.0369618386,-0.0546250939,-0.3145231903,-0.2925898135,0.0326478705,-0.0937117338,0.1151885688,0.0685743988,-0.2826316357,0.0161389802,0.0049903886,-0.1779205352,0.0023902771,0.3310005367,-0.2150157988,0.148575604,0.2978157997,0.0600120388,-0.0970932543,-0.106148757,-0.2628639042,-0.0803785697,-0.1983224154,-0.056407325,0.3051498234,-0.0921546817,0.3761104941,0.0229630414,-0.179779619,-0.1802678704,0.0199732501,-0.1830584705,0.1112451032,0.4338179529,0.0693689287,0.237574324,0.0718808621,0.254779309,-0.0013334155,0.2246186733,-0.0462478027,-0.4587911963,0.1238833144,0.370313406,-0.1037134379,-0.115419358,0.0248291045,-0.1448108256,0.0704493821,0.2599559426,-0.1440329552,-0.2033017129,-0.1653168947,-0.2190619856,0.5277351141,0.393963933,0.0342416279,-0.0421035253,-0.4067505896,-0.2453195155,0.3608443141,0.2823581398,0.0014633266,0.1401390284,-0.3047563136,0.0974318907,0.2519396544,-0.2148155421,-0.2565926909,0.211815387,-0.2131187767,-0.0246797614,-0.0519014411,0.0910042152,0.0106451157,0.0145032462,0.267085135,0.1857897788,-0.3312119544,0.0249924865,-0.1970980912,-0.3113334775,0.4462002218,0.2143390477,0.2483334541,-0.0707214475,0.0157011021,0.114467077,0.3708969951,0.2580592632,0.0792705491,-0.0338332728,0.2125306726,0.0079512121,-0.2015179247,-0.1089143082,-0.2836967707,0.1032906994,0.2451010346,-0.0778647214,0.0508962013,-0.0072366488,-0.4273627698,0.0413815603,-0.168958351,-0.1493412256,0.1657439917,-0.0761240497,0.1448943615,-0.0492947139,-0.0764248297,-0.0518814288,-0.2385359704,0.0705099478,0.1058792993,-0.0204066541,-0.3056635261,-0.0091247633,0.2450969666,-0.0581195839,0.1331098229,-0.0372367017,-0.135418728,0.2523603141,0.123330079,0.2528036237,-0.0802929029,0.0251827948,0.1523814946,-0.452506125,0.0430596173,0.0419443399,0.1044071913,-0.1576236039,0.2183504254,-0.0314418413,-0.0910328627,0.0418949425,0.174456507,0.1563680768,0.1477839649,-0.024936242,0.0901833549,-0.1331830174,0.3215939999,-0.0938108116,0.2526182532,-0.0654185861,-0.0649184287,-0.1171566918,0.4020731449,0.152085036,0.0896184817,0.044230748,-0.3025979102,0.0285023507,-0.2157823443,0.3114795089,0.4959137142,0.1446499079,0.1639534533,0.1199491099,0.1569378823,-0.2816541195,0.4310475886,-0.040102955,0.2351522595,0.2522007525,-0.1961644143,-0.0563999377,-0.2167151421,0.2778975368,0.0175765604,0.2078112811,-0.1578265578,-0.2426187694,-0.2548194826,-0.0673511997,-0.4623289704,-0.5436559319,-0.2178747207,-0.1321282089,-0.2382991165,0.0240342431,-0.0367041901,0.1426422745,-0.0420636795,-0.1025992855,0.0817945004,0.1268719584,0.0026955113,-0.0534274839,-0.0635396317,0.1841251254,0.2524006665,-0.3535847664,0.5754960179,-0.3072552681,0.0299873985,-0.2428164035,-0.1333234608,-0.0243879445,-0.0370058864,0.0867503658,0.3703727424,0.3781490922,0.2589347661,-0.0355772786,0.369383812,-0.2827515304,-0.0627758875,-0.121466957,0.1225737482,0.0374437831,-0.2173156589,-0.562472403,-0.4598293304,-0.4110196233,-0.0917023495,0.0304594226,0.1562970281,0.1512025297,0.0725564435,0.0382239632,0.1439446807,-0.0554896332,-0.3277630508,-0.3739012778,0.3905153573,-0.2670379281,-0.4456063807,0.2986950874,0.0598832257,0.4613804221,0.2046910077,-0.4112800956,0.3908862174,-0.1190442592,-0.0669095516,0.0473832563,0.1677639037,0.1566010565,-0.0419885479,-0.1558275223,-0.1271512806,-0.1122654378,-0.1918485314,-0.1983194798,0.5375465751,-0.1967826039,0.337072134,0.0668314472,0.7139240503,0.141764015,-0.167829141,0.2005022764,-0.10505981,0.1060315967,0.1251528114,-0.4757870138,0.1009271666,-0.1338579655,0.171341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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/854","title":"wmt16 does not download ","comments":"It is still down, unfortunately. I'm sorry for that. It should come up again later today or tomorrow at the latest if no additional complications will happen.","body":"Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz","comment_length":27,"text":"wmt16 does not download  \n Hi, I appreciate your help with the following error, thanks \r\n\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset(\"wmt16\", \"ro-en\", split=\"train\")\r\nDownloading and preparing dataset wmt16\/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wmt16\/ro-en\/1.0.0\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/root\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wmt16\/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308\/wmt_utils.py\", line 755, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract(urls_to_download)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in _single_map_nested\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 181, in <listcomp>\r\n    mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/root\/anaconda3\/envs\/pytorch\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/opus.nlpl.eu\/download.php?f=SETIMES\/v2\/tmx\/en-ro.tmx.gz \n It is still down, unfortunately. I'm sorry for that. It should come up again later today or tomorrow at the latest if no additional complications will happen.","embeddings":[-0.444955498,-0.4224762022,-0.0815040842,0.4179413617,0.5046890974,0.1244975701,0.1281453669,0.2139965892,0.2786702514,0.001354157,0.0727131143,-0.110461086,-0.2351618558,0.097207725,0.2837482691,-0.0405813567,-0.1238627285,-0.002883099,-0.4859333932,0.0390384048,-0.2367140353,0.1167636067,-0.1118449643,-0.0652594045,-0.3488698602,-0.0671071485,-0.1394528598,0.0584430955,-0.360514909,-0.2884312868,0.3591679037,0.0484933145,0.2195616364,0.4216263294,-0.0001045874,-0.0344254412,0.2609169781,-0.1103322431,-0.1643058062,-0.3919683993,-0.3435014486,-0.1982588619,-0.0863412246,-0.0891985819,-0.0184502378,-0.1176216602,-0.1129943654,-0.3425154686,0.2524401546,0.3540210128,0.3467800915,0.2697255611,0.3545668721,-0.1914296448,0.1382765472,0.1699798554,0.0555037595,0.1284032315,-0.0070466073,-0.1400783062,0.1166838035,0.1394858658,-0.0372346565,0.2416531146,0.104287751,-0.1586447954,0.0307699218,-0.254928261,0.2687593997,0.1392050236,0.3950155675,-0.1157429069,-0.1314078271,0.1513412595,0.065062128,-0.5738540292,0.2200950086,0.5009623766,-0.1863734424,0.1921614856,-0.2714767158,-0.0870952085,0.0238122586,0.1413062066,-0.2197024077,0.2921693921,-0.213550061,0.0183895659,0.1849008203,-0.1017094776,0.0880924761,-0.0139018279,0.1450838894,0.2526480556,-0.2700490355,0.1261817366,-0.1145764664,0.1292577088,0.0806544647,0.0086127445,-0.2520152926,-0.1507218033,0.2881473005,-0.0259473715,0.0269001275,0.1888409704,-0.2194881141,0.0834902674,0.0834250972,0.1954315603,-0.0369618386,-0.0546250939,-0.3145231903,-0.2925898135,0.0326478705,-0.0937117338,0.1151885688,0.0685743988,-0.2826316357,0.0161389802,0.0049903886,-0.1779205352,0.0023902771,0.3310005367,-0.2150157988,0.148575604,0.2978157997,0.0600120388,-0.0970932543,-0.106148757,-0.2628639042,-0.0803785697,-0.1983224154,-0.056407325,0.3051498234,-0.0921546817,0.3761104941,0.0229630414,-0.179779619,-0.1802678704,0.0199732501,-0.1830584705,0.1112451032,0.4338179529,0.0693689287,0.237574324,0.0718808621,0.254779309,-0.0013334155,0.2246186733,-0.0462478027,-0.4587911963,0.1238833144,0.370313406,-0.1037134379,-0.115419358,0.0248291045,-0.1448108256,0.0704493821,0.2599559426,-0.1440329552,-0.2033017129,-0.1653168947,-0.2190619856,0.5277351141,0.393963933,0.0342416279,-0.0421035253,-0.4067505896,-0.2453195155,0.3608443141,0.2823581398,0.0014633266,0.1401390284,-0.3047563136,0.0974318907,0.2519396544,-0.2148155421,-0.2565926909,0.211815387,-0.2131187767,-0.0246797614,-0.0519014411,0.0910042152,0.0106451157,0.0145032462,0.267085135,0.1857897788,-0.3312119544,0.0249924865,-0.1970980912,-0.3113334775,0.4462002218,0.2143390477,0.2483334541,-0.0707214475,0.0157011021,0.114467077,0.3708969951,0.2580592632,0.0792705491,-0.0338332728,0.2125306726,0.0079512121,-0.2015179247,-0.1089143082,-0.2836967707,0.1032906994,0.2451010346,-0.0778647214,0.0508962013,-0.0072366488,-0.4273627698,0.0413815603,-0.168958351,-0.1493412256,0.1657439917,-0.0761240497,0.1448943615,-0.0492947139,-0.0764248297,-0.0518814288,-0.2385359704,0.0705099478,0.1058792993,-0.0204066541,-0.3056635261,-0.0091247633,0.2450969666,-0.0581195839,0.1331098229,-0.0372367017,-0.135418728,0.2523603141,0.123330079,0.2528036237,-0.0802929029,0.0251827948,0.1523814946,-0.452506125,0.0430596173,0.0419443399,0.1044071913,-0.1576236039,0.2183504254,-0.0314418413,-0.0910328627,0.0418949425,0.174456507,0.1563680768,0.1477839649,-0.024936242,0.0901833549,-0.1331830174,0.3215939999,-0.0938108116,0.2526182532,-0.0654185861,-0.0649184287,-0.1171566918,0.4020731449,0.152085036,0.0896184817,0.044230748,-0.3025979102,0.0285023507,-0.2157823443,0.3114795089,0.4959137142,0.1446499079,0.1639534533,0.1199491099,0.1569378823,-0.2816541195,0.4310475886,-0.040102955,0.2351522595,0.2522007525,-0.1961644143,-0.0563999377,-0.2167151421,0.2778975368,0.0175765604,0.2078112811,-0.1578265578,-0.2426187694,-0.2548194826,-0.0673511997,-0.4623289704,-0.5436559319,-0.2178747207,-0.1321282089,-0.2382991165,0.0240342431,-0.0367041901,0.1426422745,-0.0420636795,-0.1025992855,0.0817945004,0.1268719584,0.0026955113,-0.0534274839,-0.0635396317,0.1841251254,0.2524006665,-0.3535847664,0.5754960179,-0.3072552681,0.0299873985,-0.2428164035,-0.1333234608,-0.0243879445,-0.0370058864,0.0867503658,0.3703727424,0.3781490922,0.2589347661,-0.0355772786,0.369383812,-0.2827515304,-0.0627758875,-0.121466957,0.1225737482,0.0374437831,-0.2173156589,-0.562472403,-0.4598293304,-0.4110196233,-0.0917023495,0.0304594226,0.1562970281,0.1512025297,0.0725564435,0.0382239632,0.1439446807,-0.0554896332,-0.3277630508,-0.3739012778,0.3905153573,-0.2670379281,-0.4456063807,0.2986950874,0.0598832257,0.4613804221,0.2046910077,-0.4112800956,0.3908862174,-0.1190442592,-0.0669095516,0.0473832563,0.1677639037,0.1566010565,-0.0419885479,-0.1558275223,-0.1271512806,-0.1122654378,-0.1918485314,-0.1983194798,0.5375465751,-0.1967826039,0.337072134,0.0668314472,0.7139240503,0.141764015,-0.167829141,0.2005022764,-0.10505981,0.1060315967,0.1251528114,-0.4757870138,0.1009271666,-0.1338579655,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/853","title":"concatenate_datasets support axis=0 or 1 \uff1f","comments":"Unfortunately `concatenate_datasets` only supports concatenating the rows, while what you want to achieve is concatenate the columns.\r\nCurrently to add more columns to a dataset, one must use `map`.\r\nWhat you can do is somehting like this:\r\n```python\r\n# suppose you have datasets d1, d2, d3\r\ndef add_columns(example, index):\r\n    example.update(d2[index])\r\n    example.update(d3[index])\r\n    return example\r\n\r\nfull_dataset = d1.map(add_columns, with_indices=True)\r\n```","body":"I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n","comment_length":58,"text":"concatenate_datasets support axis=0 or 1 \uff1f \n I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n \n Unfortunately `concatenate_datasets` only supports concatenating the rows, while what you want to achieve is concatenate the columns.\r\nCurrently to add more columns to a dataset, one must use `map`.\r\nWhat you can do is somehting like this:\r\n```python\r\n# suppose you have datasets d1, d2, d3\r\ndef add_columns(example, index):\r\n    example.update(d2[index])\r\n    example.update(d3[index])\r\n    return example\r\n\r\nfull_dataset = d1.map(add_columns, with_indices=True)\r\n```","embeddings":[-0.4226087928,-0.0862603486,-0.1545989662,0.0781621411,0.1073093116,0.4110481143,0.3115082681,0.4648504257,0.11830201,0.1705961972,-0.1758232564,0.3464903831,-0.0750733614,0.5408211946,-0.2195973098,-0.2845952511,0.158336103,0.5231662393,-0.5183831453,0.0794194117,-0.436337471,-0.114001967,-0.3252285123,-0.3519526124,-0.330509007,0.2272312343,-0.3263256848,0.0496895649,-0.2420312911,-0.2773002088,0.1845785081,0.1565621346,0.173753351,0.1870673746,-0.0001018656,-0.0065462426,-0.014074605,-0.0987925455,-0.2388120592,-0.2386993021,-0.4274660349,-0.5326142907,0.0191351399,-0.1855815798,0.00708585,-0.54960531,-0.1515820473,-0.1952258646,-0.0363274477,0.2691266239,0.2765330672,-0.0024136822,0.1438422054,-0.2205383331,-0.032708846,0.0477842875,-0.1488682181,-0.2383367568,-0.1028846502,-0.0304823443,0.2678528428,0.0924807116,-0.0786667019,-0.0955473408,0.023014795,0.242949158,0.3289216161,-0.4637416899,0.2783742845,0.3595761955,0.5390436053,-0.1761268824,-0.3088946939,-0.0107760755,0.2730102837,-0.2251131088,-0.1153179258,0.1722409278,0.1841141433,0.1055816859,0.0051624589,-0.0969100967,-0.23845011,0.3530945182,-0.1239473671,0.5723182559,0.0937582031,0.1246292293,0.1647108048,-0.2323429585,0.2123437822,-0.23055996,-0.0263885669,0.3826236725,-0.5797169805,-0.0849746242,-0.1941434741,-0.2402858585,0.1860107481,0.0934349298,0.0127488635,-0.0230270587,-0.1148875579,0.2161030173,0.1035968661,0.1430449039,0.1178156883,0.3845241368,-0.1129865721,-0.2188611925,0.3609628081,-0.0352086946,-0.3074696958,-0.4276658595,-0.0928756297,-0.0125978654,0.0828213617,0.2491242439,0.0911288038,-0.1014141664,-0.1131042466,-0.0271373149,-0.0245385617,0.0541519746,0.1044036224,0.7092756629,-0.0496406145,0.2461360544,0.2259809971,0.0167850908,-0.2002800703,0.3086336255,0.1093921065,-0.0477167293,0.1133465692,-0.202474311,-0.0465146936,0.3516930938,0.1328005195,0.1754063815,0.0056137159,-0.2470530123,0.1815116853,0.2198554426,0.1323290914,-0.04526278,0.1345876306,-0.1955263466,0.0179670826,0.3099166751,-0.1724102497,-0.0889016166,-0.5252352357,0.2082276791,-0.0423238724,-0.0700515583,-0.3134088218,0.362336725,-0.0771497115,0.053949222,0.1155345589,-0.0474661477,0.0621779561,-0.1661249548,0.2009577751,0.1113442332,-0.2669385672,0.008026164,0.1536028087,-0.1589449644,0.144599691,-0.060014613,0.1177267805,-0.2345389277,-0.0067726024,0.005962213,0.3625376821,-0.3298892677,-0.092698358,-0.0321074724,-0.119783029,-0.0059565259,0.2458178103,0.0969633609,0.1838972867,0.2839229703,0.2015565485,0.048203133,0.03500719,-0.0099596903,0.0151676936,-0.1204555184,0.4265736043,0.2494770288,-0.0432004891,0.2766696513,-0.0150050763,-0.458845675,0.3301061988,-0.3999517858,0.2924720943,0.1318642795,0.1267351508,-0.2035218477,0.0067223655,-0.1177159026,-0.5746800303,0.0519519411,0.2862970233,0.2127360255,0.0785789639,-0.1286859661,-0.1028125957,-0.1928750426,0.0099845137,0.0941524804,0.1835021228,0.041317977,0.0519455858,-0.2493308485,-0.3810605705,0.3606599867,0.0950973034,-0.043297831,-0.150848031,0.2130998522,0.1889882833,0.0291418806,-0.0165353753,0.2718974352,0.094373554,-0.1173461825,0.1646354496,0.3794598281,-0.3452474475,0.0082483059,0.0471475646,0.2545304894,0.1226964965,0.2721693516,-0.1793629527,-0.3759014606,0.1136744767,0.0921133831,-0.0816449523,0.4765714705,-0.2874643803,0.2087194175,-0.0956577808,0.1742875129,0.2577313781,-0.0465931073,-0.2239287049,-0.3543518484,-0.0163601711,0.2291424125,-0.1916588992,-0.023656385,-0.0687824488,0.1239473447,0.2656056583,0.0214643311,-0.1022831798,-0.0943579972,-0.1167930365,0.1243985221,0.2267947644,0.1167610437,0.2644933164,0.3999997377,-0.1086042523,0.2540391684,-0.2752259374,0.0746818334,0.0916333795,0.037317358,-0.077711314,0.4481419325,0.2226282358,-0.0754357949,-0.0185630322,-0.2296893299,0.1100940779,0.5023943782,-0.0948243812,-0.1374462992,-0.2418773025,-0.0132372165,0.0900166482,-0.3467955887,0.0228301026,-0.2478994131,-0.1796188504,0.1272639781,-0.1465993971,0.3609495461,-0.3012213111,0.130200386,-0.0414414369,-0.023027841,-0.0322708227,0.071534358,0.1925762147,0.1740308702,-0.1611603647,-0.08097969,-0.0969500318,-0.119551599,0.2034372389,-0.1111137196,0.0296608675,0.0209119581,-0.1271328479,-0.4854318798,0.0185908508,0.0776493698,-0.2564775348,-0.1758208275,0.1687879264,0.3261306882,0.0632399768,0.1794681698,0.0166821964,0.0185164176,-0.1775396615,-0.2074855417,-0.2333163023,-0.4139026701,0.1558990926,0.2039990872,0.2865823507,-0.038900733,0.4930993617,-0.0612504482,0.0392677672,-0.0590723567,-0.1255935133,-0.336705327,0.4871846437,-0.1394262612,-0.0531917028,0.1305378675,-0.2593684494,0.1299346089,0.281085521,0.0949886888,-0.2102482617,-0.0393762663,0.1496009827,-0.3974593878,0.2691262066,0.4252674878,0.3284216821,-0.1030907184,0.0182091072,-0.0089028059,0.0823037326,0.2164365053,-0.2202183753,-0.2070922703,0.1523664743,-0.0258811712,-0.1935560107,0.4212956131,0.0882193223,0.17858693,-0.2154992223,0.3437775671,0.0687642395,-0.2970072329,-0.242259264,0.09271304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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/853","title":"concatenate_datasets support axis=0 or 1 \uff1f","comments":"That's not really difficult to add, though, no?\r\nI think it can be done without copy.\r\nMaybe let's add it to the roadmap?","body":"I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n","comment_length":23,"text":"concatenate_datasets support axis=0 or 1 \uff1f \n I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n \n That's not really difficult to add, though, no?\r\nI think it can be done without copy.\r\nMaybe let's add it to the roadmap?","embeddings":[-0.5632335544,0.1566246152,-0.1052774861,-0.0830112472,0.0874820575,0.1624974906,0.3028231859,0.4194337726,-0.224942416,0.3868966103,-0.1537489593,0.2858173251,-0.1336852908,0.4265962541,-0.3774040043,-0.1137892231,0.1690777391,0.6831735969,-0.4871610105,-0.0461216047,-0.2769603431,0.0618332699,-0.1913713217,-0.3543604612,-0.5695503354,0.2912069857,-0.1852046996,0.1471563578,-0.3962182403,-0.1970464885,0.2927441597,0.3368051052,-0.0439014137,0.1087513044,-0.0001123584,0.0200279392,0.0133769969,-0.158865124,-0.2239809185,-0.2859130204,-0.4628483057,-0.3097318411,-0.0573883057,-0.0591023043,0.1034685671,-0.1293453574,0.0893115848,-0.23392196,0.0351497196,0.1296929866,0.1886484325,-0.0460463874,-0.1494620144,-0.3029183149,0.0352646708,0.3872572184,-0.1596915424,-0.1119441763,0.036842376,0.0510122664,0.1473965943,0.1967135519,0.0847492069,-0.1048685014,-0.0312238317,0.127853781,0.2851846814,-0.5738030672,0.1894932836,0.4373262525,0.3858180642,-0.2070233077,-0.2310138941,-0.106377244,0.1256219,-0.0378942862,-0.0085131256,0.2486876398,0.0003842789,0.2998109758,-0.1266189814,-0.3214491308,-0.3337571323,0.219253853,0.0035825574,0.5095900893,0.1821104288,0.1283189505,0.247975871,-0.0896193013,0.1514654011,-0.1121116281,-0.1872598082,0.3319087327,-0.3466578126,-0.1094931662,-0.42353037,-0.3127010465,0.3242367506,0.3647496104,0.2044358253,-0.0514718108,-0.2810693681,0.2224729955,0.1368426979,-0.0281804111,0.2627055049,0.2246279866,0.1004936025,-0.4747583866,0.4068492949,0.1419351399,-0.2541023493,-0.6352835894,-0.3118085861,-0.0495493114,0.0398108363,0.1275728941,-0.0244059376,-0.2763108909,-0.0880281478,0.0547675602,-0.1994536072,-0.0870600417,0.1845135838,0.5468416214,-0.0291948318,0.1838525087,0.1613679528,-0.2034956664,-0.1228639185,0.1137353703,0.2657358646,0.1090382114,0.0932540372,-0.3103024662,-0.0534344316,0.101030387,0.4630501568,0.2935289145,0.0899410695,-0.112579301,0.1572803408,0.2680854797,0.1508649588,-0.1060420647,0.0711883977,-0.2159586251,-0.1277613044,0.3148569763,-0.2388080806,-0.078952089,-0.6027963758,0.1250679642,-0.1908835173,-0.1287215203,-0.2735530734,0.401488781,-0.108865045,0.0152949858,0.1486211866,0.1504068524,0.1207897589,-0.1274710149,0.1286760271,0.1386974752,-0.2228637338,-0.125365898,0.1494992673,-0.3760162294,-0.0257564671,-0.0534549877,-0.0917768329,-0.3919664025,-0.1096220985,0.238018766,0.5789764524,-0.3724956214,-0.0461445339,-0.0229790378,-0.3579958677,-0.1780030578,0.3134596646,0.2745227218,0.182815522,0.0905676335,0.0883903801,0.285232842,-0.1022025645,0.0373513959,0.0420043692,-0.2569622695,0.2918095887,0.2295566052,-0.0237759054,0.1568799764,0.2578094304,-0.5314164162,0.2531365454,-0.4744189382,0.1922059208,0.065575771,0.4364443719,-0.458805263,-0.027983278,0.0510564335,-0.4954914749,0.0699076429,0.1260803938,0.2886126041,0.1175459027,-0.3634769917,0.0863496736,-0.1281976551,-0.0332222879,0.0490106083,0.0638270751,-0.0172972493,-0.0022057504,-0.095239453,-0.3850867748,0.4718657434,0.1025447175,-0.0925958082,-0.1465745121,0.2257517725,0.0452562012,0.122484073,-0.0722053275,0.2096060961,-0.0679520518,-0.2650655806,0.2195994556,0.5704522729,-0.4314958155,0.1467092335,0.2623591721,0.1093144417,0.266836971,-0.1554703563,-0.1848710626,-0.5273680687,-0.0256932471,-0.0429723822,-0.0176549312,0.5327882767,-0.3147445917,0.030716097,0.0439040363,0.2274836302,0.3138510883,-0.0138027193,-0.3958470523,-0.3787468076,-0.0729582459,0.4171087444,0.0160726439,0.1849319935,-0.189302817,0.1207722872,0.344766587,0.0078030094,-0.0184842981,-0.0078622419,-0.1470701247,0.1143816933,0.2931669652,0.027650645,0.3205238283,0.3793960214,-0.1209535748,0.1799788475,-0.2484185994,-0.0468228161,0.1409377009,-0.0139968572,0.2253265232,0.3300787807,0.3414494395,-0.0892062932,-0.2700644135,-0.3984596431,0.0046033515,0.2516726255,-0.1310018152,-0.2252396643,-0.2523091435,0.0098989401,0.0092809079,-0.2826526165,-0.241720289,-0.2430527955,0.128206104,0.1327527463,-0.0508079194,0.570241034,-0.2493341863,0.5388227105,-0.2218421847,-0.0355609357,-0.1266869754,0.1360903531,0.2055342644,0.0919156224,-0.1181469411,-0.3078886271,0.096773915,-0.1567498296,0.2157622278,-0.2193115652,-0.2720019221,0.092466116,0.0982192457,-0.5494916439,0.1595659405,0.0025911804,-0.1303557754,-0.0801043808,0.0279196408,0.3592705131,-0.2882347405,-0.003315567,-0.2200141698,0.0260150228,-0.4219441712,0.0597501583,-0.2398126274,-0.3552928865,0.2629978359,0.3009651601,0.2693175077,0.1727238595,0.5507326722,-0.1992111206,-0.0610369667,-0.1994949132,-0.0663796365,-0.4897853136,0.5394616127,-0.0980456769,0.0153267551,0.0874516964,-0.1252419502,0.0777762309,0.4468548298,-0.0799492449,-0.3376495242,0.1285665631,0.3639609218,-0.3346345723,0.1178630292,0.4186065793,0.4637767375,-0.047855366,-0.1099586934,0.1922911853,0.2692982852,0.1375145763,-0.2730713785,-0.2173589319,-0.1211927906,0.3359744847,0.0764826462,0.2960463166,-0.0274231024,0.2046491653,-0.2270864844,0.5847343206,0.2745037675,-0.0230415035,-0.001479847,0.3178342879,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/853","title":"concatenate_datasets support axis=0 or 1 \uff1f","comments":"Actually it's doable but requires to update the `Dataset._data_files` schema to support this.\r\nI'm re-opening this since we may want to add this in the future","body":"I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n","comment_length":26,"text":"concatenate_datasets support axis=0 or 1 \uff1f \n I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n \n Actually it's doable but requires to update the `Dataset._data_files` schema to support this.\r\nI'm re-opening this since we may want to add this in the future","embeddings":[-0.6200821996,0.1132390276,-0.1497742683,0.1142357141,0.1257235706,0.203243047,0.3956650198,0.3959012628,-0.003955096,0.2456055582,-0.2038999349,0.382650435,-0.0755913034,0.5006220341,-0.4717248976,-0.0797574967,0.1835412532,0.7113130093,-0.5424261093,-0.0117648598,-0.2366658449,0.0113508236,-0.339661628,-0.3187690377,-0.5360070467,0.231191203,-0.0610606186,0.0987927616,-0.3007909656,-0.2530787289,0.3102920949,0.3644462526,0.0571097322,0.1387692094,-0.0001069398,0.0727541521,-0.0874109194,-0.1837273687,-0.268895328,-0.2693293095,-0.5620741248,-0.2944664955,-0.0757344291,-0.0576736629,-0.0406845585,-0.3894323409,0.0240067728,-0.3615780473,-0.0405684486,0.1573858559,0.2330461591,-0.019054044,-0.1294506192,-0.2183726579,-0.0201024469,0.318313092,-0.0768049359,-0.1730319262,-0.1243906766,-0.0206792969,-0.0104138469,0.1871278286,0.1481388658,-0.1268914491,0.0444814377,0.1418748647,0.3391287923,-0.4659833014,0.1633160859,0.4161893725,0.465918988,-0.1859806478,-0.2017848194,-0.0357140675,0.0992443562,-0.1593410969,0.063998051,0.2853879035,0.1368727088,0.3058355749,-0.0108416639,-0.1034041122,-0.3154926598,0.2407753915,-0.2459397018,0.5976780057,0.1353767961,0.092905879,0.0829346702,-0.2302642018,0.1642215401,-0.1872150451,-0.1841837913,0.3217138648,-0.541516304,-0.1199429631,-0.3733865917,-0.1350853443,0.3298960924,0.2463191748,0.2044765651,0.0405509956,-0.1924936622,0.2452629358,0.2398508042,0.0245794728,0.4051347375,0.1368649751,0.0434726961,-0.2887252569,0.219042778,0.0950889662,-0.3077245653,-0.6520453095,-0.3247913718,0.0252014529,0.0389828049,0.1931320876,-0.1030317545,-0.143552348,-0.0916431695,0.1084327847,0.0559918582,-0.0860122368,0.2221773416,0.7616563439,-0.0534542948,0.192138344,0.1734066308,-0.2751089931,-0.1755377501,0.1871213317,0.1160215512,0.0174796227,0.1904693991,-0.1842016429,-0.0449708812,0.314483881,0.33914572,0.1827120632,0.1533740461,-0.2209471911,0.2149470448,0.2782842815,0.0714068413,-0.0948672742,0.0562412553,-0.168221429,-0.014185952,0.3145468235,-0.1227612421,0.0353895612,-0.4891705513,0.2089896053,-0.200812608,-0.0463645123,-0.2499530911,0.3336359859,-0.1040295139,-0.0500911064,0.0291792974,-0.0175051223,0.0748191103,-0.1127745807,0.1682960838,0.106441766,-0.2814204097,-0.0691355243,0.1847222745,-0.4150511324,-0.0711692423,-0.0134752281,-0.0360353626,-0.3152643144,-0.1420895606,0.2441543192,0.4369239807,-0.4175728261,-0.0972495824,-0.0296995398,-0.2238773406,-0.1285273433,0.2407663614,0.1232236624,0.1626571566,0.1246631965,0.1226341277,0.1622515619,0.0343255587,0.0087945648,-0.0435843021,-0.2390632927,0.3819728494,0.1639908552,-0.0783886909,0.1224677637,0.1311313808,-0.4837779701,0.4867559373,-0.3978932798,0.2967704833,0.1409301013,0.2017504126,-0.4265710413,-0.0140514541,-0.0838253051,-0.5214062333,0.0832028612,0.0862970725,0.2248170972,0.0663842112,-0.2890563011,0.0348978378,-0.2273228317,-0.0602604412,0.0564953536,0.2055269331,-0.004535391,-0.1216683313,-0.1537702531,-0.3518807292,0.376627475,0.1587485224,-0.1154476553,-0.117099151,0.2699806392,0.1768978089,0.13533023,-0.0552103892,0.2202481329,-0.0084873801,-0.1499313563,0.2518579066,0.5035156012,-0.2917832732,0.0624000318,0.2550005317,0.207112357,0.0982297361,0.0081180157,-0.1666467339,-0.4425747097,0.0584744625,0.0448831022,-0.1063391268,0.6201360226,-0.3699854612,0.0314688012,-0.014405868,0.1793006361,0.3834830523,-0.0434765667,-0.4043259323,-0.3500224948,-0.0427922569,0.3511182666,-0.1354760677,0.2653811872,-0.1887793243,0.110713914,0.2804255486,0.0704487041,-0.0874780715,-0.2053280771,-0.2089479268,0.127779603,0.3520625532,0.1063212454,0.3652781844,0.3536134362,-0.1055600941,0.160537228,-0.2328376472,-0.0367885716,0.1600503922,-0.1095190272,0.1294082999,0.5694359541,0.3345155418,-0.1547619253,-0.2027948201,-0.3446968496,0.054579135,0.3210361898,-0.1644705981,-0.1237167418,-0.3376923203,-0.0289667007,-0.0205559023,-0.2282090187,-0.1973005384,-0.1747505516,0.0097843781,0.120202966,-0.0833294019,0.5366846323,-0.2834307849,0.3671422899,-0.1475298107,0.0455883108,-0.0810189918,0.1155287623,0.1502861232,0.1537218839,-0.0949315578,-0.0600872748,-0.0542418137,-0.1520798057,0.1689743996,-0.2086485624,-0.0228293501,0.0750807822,0.0956626236,-0.4917036295,0.1373925656,0.0581366643,-0.1652506292,-0.031514287,0.1185321361,0.4089565873,-0.1036080047,-0.0026436097,-0.1406343281,0.1124781594,-0.3971733451,-0.0300063509,-0.2158603221,-0.3724113405,0.2106262147,0.1946964711,0.2574153244,0.132876575,0.4780389369,-0.1940965056,-0.0909743607,-0.1697713584,-0.03095611,-0.4245989323,0.4616805613,-0.0298407376,-0.0371070504,0.1607216746,-0.1619771123,0.1664905548,0.4515373707,0.0763074011,-0.2016095966,0.1102224067,0.237504676,-0.3646521568,0.0101901228,0.3265556097,0.3919829726,-0.0877716616,-0.1209307536,0.1506007016,0.3027242124,0.0087928418,-0.2549477518,-0.2459114194,-0.187236473,0.2631952465,-0.1249638051,0.3277338743,-0.1082651168,0.1255526096,-0.1994958669,0.4069361687,0.3109946549,-0.0276982412,-0.1179938987,0.2570595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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/853","title":"concatenate_datasets support axis=0 or 1 \uff1f","comments":"Hi @lhoestq, I would love to help and add this feature if still needed. My plan is to add an axis variable in the `concatenate_datasets` function in `arrow_dataset.py` and when that is set to 1 concatenate columns instead of rows. ","body":"I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n","comment_length":40,"text":"concatenate_datasets support axis=0 or 1 \uff1f \n I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n \n Hi @lhoestq, I would love to help and add this feature if still needed. My plan is to add an axis variable in the `concatenate_datasets` function in `arrow_dataset.py` and when that is set to 1 concatenate columns instead of rows. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/853","title":"concatenate_datasets support axis=0 or 1 \uff1f","comments":"Hi ! I would love to see this feature implemented as well :) Thank you for proposing your help !\r\n\r\nHere is a few things about the current implementation:\r\n- A dataset object is a wrapper of one `pyarrow.Table` that contains the data\r\n- Pyarrow offers an API that allows to transform Table objects. For example there are functions like `concat_tables`, `Table.rename_columns`, `Table.add_column` etc.\r\n\r\nTherefore adding columns from another dataset is possible thanks to the pyarrow API and in particular `Table.add_column` :) \r\n\r\nHowever this breaks some features we have regarding pickle. A dataset object can be pickled and unpickled without loading all the data in memory. It is useful for multiprocessing for example. Pickling a dataset object is possible thanks to the `Dataset._data_files` which defines the list of arrow files that will be used to form the final Table (basically all the data from each files are concatenated on axis 0).\r\n\r\nTherefore to be able to add columns to a Dataset and still be able to work with it in a multiprocessing setup, we need to extend this last aspect to be able to reconstruct a Table object from multiple arrow files that are combined in both axis 0 and 1. Currently this reconstruction mechanism only supports axis 0.\r\n\r\nI'm sure we can figure something out that enables users to add columns from another dataset while keeping the multiprocessing support.","body":"I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n","comment_length":230,"text":"concatenate_datasets support axis=0 or 1 \uff1f \n I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n \n Hi ! I would love to see this feature implemented as well :) Thank you for proposing your help !\r\n\r\nHere is a few things about the current implementation:\r\n- A dataset object is a wrapper of one `pyarrow.Table` that contains the data\r\n- Pyarrow offers an API that allows to transform Table objects. For example there are functions like `concat_tables`, `Table.rename_columns`, `Table.add_column` etc.\r\n\r\nTherefore adding columns from another dataset is possible thanks to the pyarrow API and in particular `Table.add_column` :) \r\n\r\nHowever this breaks some features we have regarding pickle. A dataset object can be pickled and unpickled without loading all the data in memory. It is useful for multiprocessing for example. Pickling a dataset object is possible thanks to the `Dataset._data_files` which defines the list of arrow files that will be used to form the final Table (basically all the data from each files are concatenated on axis 0).\r\n\r\nTherefore to be able to add columns to a Dataset and still be able to work with it in a multiprocessing setup, we need to extend this last aspect to be able to reconstruct a Table object from multiple arrow files that are combined in both axis 0 and 1. Currently this reconstruction mechanism only supports axis 0.\r\n\r\nI'm sure we can figure something out that enables users to add columns from another dataset while keeping the multiprocessing support.","embeddings":[-0.5330207348,0.3033456802,-0.0301639233,0.0675024018,-0.0625750944,-0.0582219549,0.2094470412,0.4255707562,-0.0745848566,0.1827805936,-0.1869088858,0.7275902033,-0.1599380672,0.5724496841,-0.2195644528,-0.2587185502,0.1644585729,0.6102408171,-0.5832628608,0.2564370036,-0.2590903342,-0.1035273075,-0.3908792138,-0.2035314292,-0.479586333,0.2016779631,-0.2737205327,-0.0490148738,-0.2796463966,-0.3253386915,0.3125083447,0.2397160828,0.1502655894,0.2005506605,-0.0001199671,0.1369438618,-0.1574162394,-0.1425992846,-0.2051132619,-0.3425643742,-0.2079406083,-0.4237650633,0.1109852642,0.0214358438,0.0509263799,-0.3994389176,0.0115839047,-0.2094816118,-0.084717989,0.0106306169,0.106538333,0.2030961365,0.1404246688,-0.0321108177,0.1800280213,0.2532159686,-0.2782604396,-0.0167417303,0.1530687958,-0.2789544463,-0.0680217445,0.0427841656,0.0780213028,-0.1596894115,-0.1154882833,0.2264118791,-0.017162431,-0.4759059548,0.00997792,0.3201736808,0.5716279149,-0.4608500004,-0.3677833676,-0.2338828146,0.2534549832,-0.2218376845,0.0001270876,0.3750638664,0.0167706646,0.2371695787,0.1775349975,-0.0572856665,-0.339548111,0.241171658,-0.0190362707,0.4345147014,0.191094175,0.2638529241,0.2380313724,0.0664346367,0.1477770507,-0.0472184569,0.0093137389,0.3843119144,-0.5604681969,-0.0574878342,-0.0918807611,-0.2331230193,0.3174954355,0.3900786936,0.1542620808,0.1787298173,-0.2172387391,0.1800462157,0.3443218768,0.0806481913,0.1206246316,0.1088648587,0.0886459723,-0.35766536,0.2269135863,0.1656297147,-0.2526624203,-0.4635510743,-0.1971271634,0.008335501,0.0876557231,0.3238953352,0.0359356739,-0.1849306822,-0.1439826936,-0.0253474452,-0.0368295535,-0.0154865012,0.0473193936,0.6687628031,-0.0877524465,0.354726851,0.0581446216,-0.2263710499,0.0241897553,0.1549757868,-0.1172148809,0.0183615871,0.2820980251,-0.2173440307,-0.1762043983,0.3253812492,0.2179025859,0.3356548846,0.1447256207,-0.3521080613,0.208059147,0.2606727183,0.0259189941,-0.2390604615,0.1745023578,-0.2554290593,-0.112465091,0.507558465,-0.0809986293,-0.1647176743,-0.5950244069,-0.0452269912,-0.2724877,0.0383551754,-0.4368534386,0.4114597142,-0.1237173229,-0.1192981228,-0.0507039875,-0.0770139992,0.1889921278,-0.3673183322,0.1948676109,0.1620839685,-0.4798499644,0.0667726472,0.2755307257,-0.2665080726,0.1613331735,-0.0931987539,-0.136908859,-0.1783189774,-0.025109157,0.4001131058,0.3169251978,-0.1422618926,-0.1286444962,-0.2277919054,-0.1653326452,0.0983732268,0.2961516678,0.1404905915,0.0227770675,0.2862357795,0.030266637,0.1991989464,0.0826271102,0.0302405972,-0.0266129151,-0.2715085745,0.4335519671,0.230537042,-0.0026214032,0.0165927131,0.2031267136,-0.8441972136,0.4641341567,-0.3334346116,0.3690649867,-0.1239213347,0.2949068248,-0.2296962589,-0.0010065569,-0.2239664048,-0.5165885687,0.2283263057,0.0090546226,0.306769222,-0.0804099813,-0.2215365618,0.233523488,-0.0020000336,-0.0799393803,0.0808485299,-0.0164996739,-0.054875873,0.169963032,-0.1656709611,-0.481654197,0.2382019907,0.1083152145,-0.0715284348,-0.0773396641,0.3713358641,0.1570248157,-0.0870870426,-0.1743693799,0.3615190685,-0.0181467645,-0.0206053741,0.3296248913,0.4635334313,-0.1377492249,-0.1679189503,0.1001984477,0.1087247431,-0.0209423788,0.0529409871,-0.027797332,-0.624504745,0.1870194972,0.0502292551,0.0945491716,0.7123307586,-0.3065618575,0.0765161961,0.0154870041,0.0544215031,0.2053551823,0.0487106331,-0.2358563393,-0.2040682584,-0.0640039369,0.4038100541,-0.1281858236,0.1696962565,-0.5598098636,0.1875688583,0.331804961,0.0576810651,0.0605530925,-0.0258674938,-0.0626168251,-0.0101345079,0.2175338119,-0.0440558381,0.4475494325,0.2890944183,-0.0274714101,0.0343553089,-0.3288681507,0.0302778147,0.1246185154,-0.1124888659,0.1410586089,0.4801807404,0.3467616439,-0.0932761654,-0.0006989819,-0.2700641155,0.1327155381,0.2761547565,-0.2611801624,-0.1646780521,-0.546102047,0.1018718928,-0.1480001807,-0.4510300159,-0.0061001205,-0.0652492046,-0.0083421925,0.1660646498,-0.1294544041,0.3992390335,-0.3121035993,0.3103449941,-0.4550032616,0.0091779148,-0.1386479139,-0.0753175393,0.0793842375,0.0230257344,0.0066539124,-0.1259336025,-0.1095997095,0.0941338688,0.1009102687,-0.1167372316,-0.0924804211,0.04725435,-0.1176140606,-0.2593384683,0.1583619267,-0.0850253329,-0.1846009344,-0.1618708521,0.0596693531,0.2899071276,-0.1702336967,0.1635434926,-0.0480611362,0.141576767,-0.2636120915,-0.1541386396,-0.3244239688,-0.2585850656,0.4762250483,0.081650205,0.2547367215,0.0297357086,0.4941406846,-0.0699383467,0.0058190618,-0.0565872081,0.0685968623,-0.2480999827,0.4400029778,-0.0790254474,-0.1334135085,0.0509739965,-0.2691562772,0.0301825721,0.3969061375,0.0643414259,-0.0442860685,-0.0039547672,0.5810075998,-0.2796831131,0.2266358882,0.4895676672,0.5526223779,0.0019504568,-0.0365976393,-0.0019033587,0.1531602591,0.214816317,-0.3528675735,-0.0444393679,-0.20587641,0.2725043595,0.223283112,0.0741394758,-0.0553186908,0.4579606652,-0.2235922217,0.4109124541,0.1331116557,-0.1093586162,-0.1176005602,-0.193219855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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/853","title":"concatenate_datasets support axis=0 or 1 \uff1f","comments":"@lhoestq, we have two Pull Requests to implement:\r\n- Dataset.add_item: #1870\r\n- Dataset.add_column: #2145\r\nwhich add a single row or column, repectively.\r\n\r\nThe request here is to implement the concatenation of *multiple* rows\/columns. Am I right?\r\n\r\nWe should agree on the API:\r\n- `concatenate_datasets` with `axis`?\r\n- other Dataset method name?","body":"I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n","comment_length":51,"text":"concatenate_datasets support axis=0 or 1 \uff1f \n I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n \n @lhoestq, we have two Pull Requests to implement:\r\n- Dataset.add_item: #1870\r\n- Dataset.add_column: #2145\r\nwhich add a single row or column, repectively.\r\n\r\nThe request here is to implement the concatenation of *multiple* rows\/columns. Am I right?\r\n\r\nWe should agree on the API:\r\n- `concatenate_datasets` with `axis`?\r\n- other Dataset method name?","embeddings":[-0.2659380436,-0.0951724425,-0.0820606872,0.0408310741,-0.2382574379,0.1075186431,0.2930018604,0.2532637119,0.0705212206,0.163655743,-0.0048813536,0.3495405316,-0.0274940208,0.506087184,-0.0220796485,-0.0006613127,0.2938616872,0.5536045432,-0.258451134,0.0008193748,-0.5191210508,0.0612342656,-0.4006459713,-0.2011353225,-0.1511581987,0.1710902005,-0.4236633182,-0.092278868,-0.3538229465,-0.0868456736,0.3892123699,0.1871355176,0.0247681364,0.2834436893,-0.0001184696,-0.0120507609,0.0087371422,-0.122200653,0.0314648636,-0.2648957372,-0.4881327152,-0.503651917,0.0892496109,-0.176972419,0.1351442635,-0.4490820169,0.1138924435,-0.3092614412,0.0400503054,0.1761848927,0.1028396562,-0.2188607305,0.0002189216,-0.1063739136,0.0210726708,0.3390488625,-0.0424190722,-0.2795723081,0.1721025556,0.1112681478,0.0802450702,0.0127466992,0.1822037399,-0.0479208753,0.1250523925,0.1816170216,0.0806788653,-0.4402333498,0.1858932525,0.2143524438,0.7385683656,-0.1596595347,-0.1948232204,0.1284783781,0.3589209914,-0.0335197598,-0.1378203481,0.3503243625,0.0804880932,0.016247917,-0.1092110351,-0.3514394164,-0.2742884755,0.4549536109,-0.2489991784,0.4160044789,0.0517960228,0.2449574769,-0.1435271204,-0.2731948793,0.1568623036,-0.2587737441,-0.0133946165,0.393481344,-0.7762804031,-0.0654555783,-0.0358732305,-0.172175318,0.352060318,0.2876513898,0.2437452674,0.0126422876,-0.1284730881,0.1759790182,0.1850314587,0.1046386287,0.3357360065,0.1796384752,0.0372422561,-0.0798572674,0.3408164382,0.1116783842,-0.3545680642,-0.2287241668,-0.2912501097,-0.0033484448,0.1097575054,0.219615221,-0.0571818277,-0.1750199497,-0.4987612665,-0.2761734128,-0.0422026739,-0.2724586427,0.3980245888,0.7039450407,0.0774429813,0.1547517627,0.460747838,-0.026079705,-0.1366769224,0.3382978439,0.0647643954,0.1297107935,0.1435089111,-0.3264356256,-0.1615809798,0.3666673899,0.2250947058,-0.0098874541,0.2446798235,-0.2090383172,-0.02384842,0.261744976,0.1008512378,-0.2530786693,0.0368566662,-0.1425586194,-0.1416334063,0.5437421799,-0.455088228,-0.0620337501,-0.3632274866,0.0947867706,0.1142199412,0.0765894502,-0.3987362683,0.5860467553,0.0410762765,-0.0614060536,0.2056023479,0.06568297,0.2846110761,-0.2194785476,0.1627058238,0.112755008,-0.3627958894,-0.0764591545,-0.0711542815,-0.1191039681,0.0906974301,0.1103571653,-0.3048974872,-0.0153968679,-0.1455277652,0.3676785827,0.7453736663,-0.5776740313,0.0677253753,-0.0179841202,-0.1502817422,-0.2375476807,0.3531585932,-0.0343224369,0.2178145498,0.211429894,0.4623912573,-0.0348031484,-0.1017920449,-0.1677041203,0.1560549289,-0.4027188718,0.2917557359,0.0737123415,-0.080206044,0.093509309,0.106322147,-0.5713726878,0.3373341262,-0.2997717261,0.1135528386,-0.1664852202,0.1833057106,-0.425771445,-0.0045941123,-0.0861032158,-0.7237885594,-0.1307581961,0.053054437,0.2005598247,0.218866244,-0.2594639659,0.051768396,-0.2207985669,0.0086278226,0.2065585405,0.050066229,0.1679950505,-0.0919817537,-0.2912178934,-0.5517224669,0.2889827192,0.0675501004,0.0585341826,-0.2895825803,0.2176003307,0.2340444773,0.1096145958,-0.0363398381,0.1233324409,0.0766651556,-0.2965348065,0.3771733046,0.4547506869,-0.2495809346,-0.1006749198,0.2262954265,0.2702122927,0.2350303382,0.402156204,0.0361059047,-0.6281508803,0.2069537342,-0.1773712784,-0.0195848253,0.257620275,-0.1827200502,0.0336572975,-0.112759538,0.0090819988,0.1381788999,0.15898785,-0.4199895561,-0.2442210019,0.0635697916,0.4059892297,-0.0317375101,0.0387631282,-0.2467802465,0.0434407927,0.5327420235,-0.141611129,-0.0219513848,0.1453937143,0.0774851739,-0.1550809443,0.2595832646,0.1713085771,0.5673298836,0.1690229475,-0.1723489165,0.0535901785,0.0660697222,-0.0508411601,0.147713989,0.0979552716,0.1257692724,0.2814124227,0.4114007652,-0.1037703753,-0.0634790808,-0.355222106,0.3785521984,0.1729941666,0.0084063746,-0.1615497917,-0.2478617877,0.0271673221,-0.1061720178,-0.3447228372,-0.0536144786,-0.2709853351,-0.1538495868,-0.2257503718,0.0189992078,0.4302059412,-0.3312753141,0.5477534533,-0.3603266478,-0.06783171,-0.0629120544,0.2237073183,0.1445543766,0.0330756679,-0.2828635871,-0.2202087194,0.161260739,-0.2060239166,0.3179126978,-0.1629032046,-0.1811393052,0.1974108368,-0.1518647671,-0.5007161498,0.3635136783,-0.0827935189,-0.1428014636,-0.154132247,0.0581260435,0.3354324698,-0.1542829424,-0.0468135811,-0.0196095519,0.1223357692,-0.3268173039,-0.1804267764,-0.2262930125,-0.2809428573,0.2300112098,0.1175999716,0.2725910842,-0.1568998545,-0.0127175162,-0.0007027829,0.1691474915,-0.1735597998,-0.1779267341,-0.4013747573,0.2534333169,0.0225359574,-0.0000554147,0.0847217068,0.0013698537,0.1821198165,0.5443748236,0.0448849164,-0.5154663324,0.0607238635,0.4005960524,-0.182083562,0.05265899,0.642696619,0.3233398199,-0.0403671376,-0.0673003197,-0.2063944042,0.2793902457,0.4032505453,-0.3642904758,0.2013050318,0.2751306593,0.0929688215,0.0622564219,0.5054622889,0.0393628962,0.2922310531,-0.0776181668,0.199205339,0.1672183573,-0.136530146,-0.1519128382,0.1184378117,0.3083663285,-0.04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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/853","title":"concatenate_datasets support axis=0 or 1 \uff1f","comments":"For the API, I like `concatenate_datasets` with `axis` personally :)\r\nFrom a list of `Dataset` objects, it would concatenate them to a new `Dataset` object backed by a `ConcatenationTable`, that is the concatenation of the tables of each input dataset. The concatenation is either on axis=0 (append rows) or on axis=1 (append columns).\r\n\r\nRegarding what we need to implement:\r\nThe axis=0 is already supported and is the current behavior of `concatenate_datasets`.\r\nAlso `add_item` is not needed to implement axis=1 (though it's an awesome addition to this library).\r\n\r\nTo implement axis=1, we either need `add_column` or a `ConcatenationTable` constructor to concatenate tables horizontally.\r\nI have a preference for using a `ConcatenationTable` constructor because this way we can end up with a `ConcatenationTable` with only 1 additional block per table, while `add_column` would add 1 block per new column.\r\n\r\nMaybe we can simply have an equivalent of `ConcatenationTable.from_tables` but for axis=1 ?\r\n`axis` could also be an argument of `ConcatenationTable.from_tables`","body":"I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n","comment_length":158,"text":"concatenate_datasets support axis=0 or 1 \uff1f \n I want to achieve the following result\r\n![image](https:\/\/user-images.githubusercontent.com\/12437751\/99207426-f0c8db80-27f8-11eb-820a-4d9f7287b742.png)\r\n \n For the API, I like `concatenate_datasets` with `axis` personally :)\r\nFrom a list of `Dataset` objects, it would concatenate them to a new `Dataset` object backed by a `ConcatenationTable`, that is the concatenation of the tables of each input dataset. The concatenation is either on axis=0 (append rows) or on axis=1 (append columns).\r\n\r\nRegarding what we need to implement:\r\nThe axis=0 is already supported and is the current behavior of `concatenate_datasets`.\r\nAlso `add_item` is not needed to implement axis=1 (though it's an awesome addition to this library).\r\n\r\nTo implement axis=1, we either need `add_column` or a `ConcatenationTable` constructor to concatenate tables horizontally.\r\nI have a preference for using a `ConcatenationTable` constructor because this way we can end up with a `ConcatenationTable` with only 1 additional block per table, while `add_column` would add 1 block per new column.\r\n\r\nMaybe we can simply have an equivalent of `ConcatenationTable.from_tables` but for axis=1 ?\r\n`axis` could also be an argument of `ConcatenationTable.from_tables`","embeddings":[-0.3482715786,0.0645761117,-0.0892973468,0.1464073062,-0.0047059562,0.0687122494,0.1781945527,0.4301660955,-0.0550013296,0.1688200533,-0.0407027751,0.5322623849,-0.1796880811,0.445717454,-0.1766492575,-0.1298689544,0.2669727206,0.6772677898,-0.5987274647,0.1521145254,-0.2508710027,0.0997436792,-0.3959632218,-0.3213891685,-0.2488771528,0.1981462091,-0.3766765893,-0.1862781644,-0.3892164826,-0.2522885203,0.4810886681,0.4199801087,0.1927005947,0.2435198277,-0.0001130875,0.1142905056,-0.1581038088,-0.1443047673,-0.1761360765,-0.4564717412,-0.3388725519,-0.4732193351,0.0550094843,-0.0381946638,-0.0079040946,-0.4393771887,-0.0983280316,-0.4240880311,-0.1667995751,0.067817919,0.1913835555,-0.1983626038,-0.0467882939,-0.1468116045,0.0341700539,0.2906013131,-0.2897627354,-0.3481404483,0.0273524895,-0.0086381566,0.1154628769,-0.0258336309,-0.0689706877,-0.1557124555,0.2706198692,0.0808820054,0.1776652187,-0.4447636604,0.0939608514,0.343719542,0.6369028687,-0.4048181474,-0.2229255438,-0.0605255365,0.2747723758,-0.1232588738,-0.0366237573,0.2869488001,0.206185624,0.1165096238,0.0360166915,-0.1216584221,-0.4184626341,0.4105969965,-0.0651765838,0.5333595872,0.0679957345,0.1643005311,0.2092807889,-0.1621576995,0.1780517995,-0.305097878,-0.1483661681,0.4382909536,-0.598508656,-0.0461570807,0.0286519378,-0.1468940675,0.221202448,0.2496879697,0.2240441889,0.0773980767,-0.2034948766,0.124492839,0.3155554533,0.2442362607,0.3259379864,0.1582871377,0.0588031709,-0.0576618239,0.4282644689,0.0426718444,-0.3903122544,-0.2664996684,-0.3373597562,-0.0756568685,0.2000364661,0.1580793411,0.0427088365,-0.0701212063,-0.1635327041,0.0718540326,0.1799079776,-0.0388043784,0.1471805274,0.8159406185,0.1902590543,0.1429032832,0.222755,-0.049682945,-0.0719223693,0.2015489042,0.0534341633,-0.015657058,0.2549004853,-0.2117113769,-0.2964214385,0.3922960758,0.3107230663,0.201591745,0.1524663419,-0.2357833087,0.272909075,0.2159264982,-0.0233637188,-0.1713620275,0.1085500717,-0.1895643473,-0.1724314541,0.52756989,-0.1246685386,-0.0434619971,-0.4908125997,0.0946800932,-0.2702818215,0.1111802086,-0.324477762,0.5614660978,-0.2449736446,-0.1677212864,0.0688402876,0.0336241573,0.1603230089,-0.1857771873,0.2226322889,0.1458013803,-0.3933649659,-0.0158849023,0.2394766212,-0.2605885267,-0.0569060519,-0.1669490188,-0.1067134887,-0.1044214815,-0.0215456896,0.1586321592,0.389970392,-0.2733126283,-0.1256055385,-0.0999528766,-0.1640653759,0.0103304069,0.1373250484,0.1862504929,0.2845201492,0.1511421502,0.2225961536,0.1819189638,0.0488866791,0.0880408809,-0.0409181193,-0.2720239162,0.4463224113,0.1754126102,-0.2321843356,0.1784433573,0.0155665707,-0.4601062834,0.323685497,-0.3310925364,0.2429708987,-0.0784440413,0.2711414695,-0.3207565844,0.0282503609,-0.070944123,-0.5652903914,0.0586757548,0.0997295529,0.3890404999,0.1061437726,-0.2458092123,0.3670020401,-0.0809095055,-0.0522159934,0.2115101367,0.1009573936,-0.1020019799,0.0345257558,-0.1555290669,-0.4602502584,0.3170199394,0.0911786109,0.0120880818,-0.3484279513,0.2383431494,0.2912606299,0.122279644,-0.0443316698,0.3409978151,-0.0028972286,-0.1761046499,0.2134356052,0.4273332655,-0.2389496714,-0.1755453348,0.0812274441,0.4070699215,0.1399201155,0.3254657388,0.0133524667,-0.6059426069,0.1656654775,-0.005250657,0.0453851297,0.7022058964,-0.3599378467,0.1289962977,0.0048014605,0.1523850262,0.2226151377,0.1210525185,-0.4439998567,-0.3551975191,0.0241794195,0.3818527162,-0.3455274105,0.0015524292,-0.1329493076,0.1672097296,0.2806382477,-0.036435362,0.0220139213,-0.0107343197,0.0183313824,0.0007221641,0.341783762,0.1739911288,0.4883176982,0.2531154156,-0.1698952168,0.1381896138,-0.2280168682,0.0345700458,0.2451861054,0.0025361185,-0.0327581502,0.1938692927,0.2984042466,-0.0517041683,0.0257179327,-0.5200487971,0.0546143986,0.4540601373,-0.1175141931,-0.1232595444,-0.4112281501,-0.0146782696,0.0131857749,-0.5087953806,-0.0605513453,-0.1007205024,-0.0289612841,-0.0368107595,-0.1229571477,0.3387333155,-0.2723270357,0.4934965074,-0.5590958595,-0.0255259685,-0.0199112277,0.0346751064,0.1099816561,0.0477813445,-0.031433776,0.0257086307,0.030616682,-0.0037529406,0.2519108653,0.0522177108,-0.1747587174,0.1148420125,-0.0004915578,-0.5256105065,0.173251465,-0.0341614597,-0.109292224,-0.0681305975,0.050896287,0.1972895563,-0.0247644298,0.1738230735,-0.2129161805,-0.1244230717,-0.4129621387,0.0100566652,-0.1492166966,-0.2941409051,0.3062925041,0.2049193531,0.276792109,-0.0660553724,0.3649310172,0.0285790451,0.1179981604,0.0248038545,0.0412423164,-0.3984266818,0.3619605601,-0.0162214395,0.0040751467,0.1638403833,-0.1292437911,0.231779024,0.3739016652,-0.0349322706,-0.1287055761,0.0903959796,0.321022898,-0.2032411993,0.1163638383,0.5452889204,0.4964998364,0.0389782526,-0.0805067495,-0.1451353282,0.2195563167,0.1137303486,-0.409794271,-0.0398146175,0.0830532014,0.2609753609,-0.1843200475,0.3280723691,-0.0582596287,0.348760128,-0.0385393165,0.4031772316,0.0968077183,-0.0685751215,-0.2857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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/849","title":"Load amazon dataset","comments":"Thanks for reporting !\r\nWe plan to show information about the different configs of the datasets on the website, with the corresponding `load_dataset` calls.\r\n\r\nAlso I think the bullet points formatting has been fixed","body":"Hi,\r\nI was going through amazon_us_reviews dataset and found that example API usage given on website is different from the API usage while loading dataset. \r\n\r\nEg. what API usage is on the [website](https:\/\/huggingface.co\/datasets\/amazon_us_reviews) \r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"amazon_us_reviews\")\r\n```\r\nHow it is when I tried (the error generated does point me to the right direction though)\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"amazon_us_reviews\", 'Books_v1_00')\r\n``` \r\nAlso, there is some issue with formatting as it's not showing bullet list in description with new line. Can I work on it?","comment_length":34,"text":"Load amazon dataset \n Hi,\r\nI was going through amazon_us_reviews dataset and found that example API usage given on website is different from the API usage while loading dataset. \r\n\r\nEg. what API usage is on the [website](https:\/\/huggingface.co\/datasets\/amazon_us_reviews) \r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"amazon_us_reviews\")\r\n```\r\nHow it is when I tried (the error generated does point me to the right direction though)\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"amazon_us_reviews\", 'Books_v1_00')\r\n``` \r\nAlso, there is some issue with formatting as it's not showing bullet list in description with new line. Can I work on it? \n Thanks for reporting !\r\nWe plan to show information about the different configs of the datasets on the website, with the corresponding `load_dataset` calls.\r\n\r\nAlso I think the bullet points formatting has been fixed","embeddings":[-0.0789317265,-0.1892752051,-0.2111489326,0.5182409286,0.1775332242,0.2812187374,0.2807130814,0.1095822901,0.1075348184,-0.2891573012,-0.1656707525,0.1336098462,0.3731780946,0.3808050156,0.1173837855,-0.0786431283,-0.0114683844,0.0120945619,-0.0104108388,0.0372485481,-0.0519340113,0.3416516781,-0.4777825177,-0.1001210883,0.0012968603,-0.1514359862,0.0161404442,0.0154884551,-0.0385072865,-0.3352609277,0.2822167873,0.1228939518,0.2670375109,0.4127134681,-0.000120029,-0.1779257655,0.4665377736,-0.1020854488,-0.2560844719,-0.2737341821,-0.2082238495,-0.3046475351,0.4594644904,-0.3098630607,-0.0713830516,-0.5172998905,-0.3546373248,-0.262871176,0.323926121,0.2284378558,0.1925453097,0.1057268605,-0.0450246073,-0.1168593541,0.0473390706,0.2259967178,0.1323072016,0.1022474021,0.2680190206,0.151785329,-0.0303034801,0.2312604487,0.0532070957,0.2929938138,0.3227801621,-0.0937235281,-0.16267021,-0.1474439055,0.4255650342,0.2147620618,1.3290979862,-0.2534140348,-0.2807647288,-0.1864213943,-0.2302003056,-0.1689096987,-0.016869057,0.3455489874,-0.007727148,0.0436370149,-0.2335562557,-0.2231645286,-0.186931625,0.3701874614,0.059278477,0.063065514,-0.3054240942,0.1380738467,-0.1763880998,-0.1328852624,0.1571980864,-0.1911980659,0.0317064747,0.1797028482,-0.5834522247,0.0873515531,0.0808451697,0.1540183574,0.06331826,0.4426725507,0.3057207763,0.2691638768,-0.0215794686,0.1393393576,0.5947414041,0.2712326646,0.3216514289,0.0154353594,-0.0527829342,0.3021752536,-0.0381989889,-0.1961116791,-0.0054876166,-0.1151610389,0.097782284,-0.2972743511,0.237949118,-0.5695005059,-0.2049052119,-0.0927838236,-0.2847169638,-0.3555089235,0.0098460838,0.4803138971,-0.1018202677,0.1047831625,0.2056426853,0.2912029028,-0.1113749444,-0.1213556975,-0.1241604239,-0.0199554022,-0.2060885578,0.0916363001,-0.0304607861,-0.0041035721,0.358540833,0.0954123884,-0.001444246,-0.3614833057,0.3334643543,-0.1039520055,0.0891807377,0.0643302128,0.4270684123,0.2381878495,0.1438618004,0.1221372634,-0.2038876414,0.270342797,-0.3434373736,-0.2165079266,-0.183988899,0.1285462826,-0.3012284636,-0.30059129,-0.5243891478,0.1205802262,0.1504328996,-0.1128383353,-0.0485668294,0.1281505376,-0.1047201306,-0.1302075237,0.0923537239,0.4687439799,-0.4306244254,-0.2646574676,-0.3125124574,-0.1004269049,0.2145790309,0.2239419222,-0.3063053787,0.354963094,-0.1731179953,0.0351228304,0.559214294,-0.3355660141,-0.1034140214,0.4995976686,0.216415599,-0.0355051793,0.0961955935,-0.2372708172,-0.1126170531,0.1777271032,0.1222919226,0.2151847035,0.3439027667,0.127610296,-0.2076399773,-0.2062740177,0.2997550368,0.1547664702,-0.1266524643,-0.0012366591,-0.0236076526,-0.1026685312,0.3608762026,-0.0464179069,-0.1341619045,0.3027929366,-0.0695767403,0.2714541554,0.1779069304,0.1316058636,-0.4466254711,-0.0358647406,0.000468363,0.1481631696,0.1683010608,-0.1922745109,-0.5272638798,0.0090979794,-0.1991000026,-0.1610389799,0.0777426958,0.0320809744,0.0412667245,-0.0335679464,-0.0199565012,-0.0856475905,0.0163855385,0.0283043571,-0.4079386592,0.165841192,0.0833528191,0.0614559948,0.2739598155,0.0993565172,0.2622644603,-0.011710952,-0.1645417064,0.2622707188,0.0659875572,0.6024624705,0.151269421,0.302249521,0.0752668306,0.0555782318,0.2502038777,0.277800709,0.3070844412,-0.1422146708,-0.3737635016,0.3783277571,0.0334415659,0.4875419438,0.1482855678,-0.1668079346,0.2705942392,0.0136275319,-0.0556318685,-0.2422356755,0.2620974779,0.0789038315,0.6111764312,-0.1333584934,-0.1279371083,0.2783301771,0.1441027522,-0.0556796454,0.0739202648,0.0867314711,-0.2402981073,-0.4528817832,0.5009841919,-0.0215052739,0.2161624581,0.2434825152,0.0983895957,0.1076892763,0.2769545317,-0.2266352177,0.302154839,0.3179554045,0.2120098323,-0.1057116687,0.1732845455,-0.166939795,-0.1414024532,-0.1537816226,0.0471223928,0.2280318588,-0.5924049616,0.0342507362,-0.3620118201,-0.4074659646,-0.3450549841,-0.131496191,0.0816858485,-0.3198181093,-0.2920684218,-0.0485499203,-0.04713016,0.0176134277,-0.1387409121,0.0507323742,-0.1904655844,0.054074578,-0.2068430781,-0.2030698508,-0.1652269661,0.0597145557,0.473749429,0.2471573204,0.3921639919,-0.1970732659,0.092662245,0.0477438457,-0.2660235465,0.2005766779,0.1971172392,0.1751429141,-0.1349904239,0.055997923,0.1437902302,0.1499388069,0.1842006296,0.119969666,0.1417253464,-0.0464305691,-0.1063116565,-0.1500596702,0.1808391362,-0.5233334303,-0.1948813796,-0.3031464517,-0.0537587479,0.2007278651,0.2991801798,-0.1856728494,-0.0715600774,0.1559025347,0.3398942351,0.2962896526,-0.3032110631,-0.4763090611,0.161378935,-0.2892211676,-0.275064826,0.1589958966,0.3177672625,0.242174089,0.0612025522,-0.2267962992,-0.0413053893,-0.0851614624,-0.1112039015,-0.0724887326,0.1672209352,0.2617268264,0.0364038274,-0.1051956266,0.0054173698,-0.2416447401,-0.0716852769,-0.1896223277,0.0281640124,-0.0354814641,0.1203640103,-0.1563309282,0.5662083626,0.255155921,-0.0545405857,0.3984052241,-0.103493534,0.1799929589,-0.0499388427,-0.0158129949,-0.3308517337,-0.1166108623,0.004270799,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/848","title":"Error when concatenate_datasets","comments":"As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n\r\nThe indices mapping correspond to a mapping on top of the data table that is used to re-order\/select a sample of the original data table. For example if you do `dataset.train_test_split`, then the resulting train and test datasets will have both an indices mapping to tell which examples are in train and which ones in test.\r\n\r\nBefore saving your datasets on disk, you should call `dataset.flatten_indices()` to remove the indices mapping. It should fix your issue. Under the hood it will create a new data table using the indices mapping. The new data table is going to be a subset of the old one (for example taking only the test set examples), and since the indices mapping will be gone you'll be able to concatenate your datasets.\r\n","body":"Hello, when I concatenate two dataset loading  from disk, I encountered a problem:\r\n```\r\ntest_dataset = load_from_disk('data\/test_dataset')\r\ntrn_dataset = load_from_disk('data\/train_dataset')\r\n\r\ntrain_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n```\r\nAnd it reported ValueError blow:\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-38-74fa525512ca> in <module>\r\n----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n\r\n\/opt\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in concatenate_datasets(dsets, info, split)\r\n   2547                 \"However datasets' indices {} come from memory and datasets' indices {} come from disk.\".format(\r\n   2548                     [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],\r\n-> 2549                     [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],\r\n   2550                 )\r\n   2551             )\r\n\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\n\r\nBut it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:\r\n```\r\ntrn_dataset._data_files\r\n# output\r\n[{'filename': 'data\/train_dataset\/csv-train.arrow', 'skip': 0, 'take': 593264}]\r\n\r\ntest_dataset._data_files\r\n# output\r\n[{'filename': 'data\/test_dataset\/csv-test.arrow', 'skip': 0, 'take': 424383}]\r\n\r\nprint([not dset._data_files for dset in [trn_dataset, test_dataset]])\r\n# [False, False]\r\n\r\n# And I tested the code the same as arrow_dataset, but nothing happened\r\ndsets = [trn_dataset, test_dataset]\r\ndsets_in_memory = [not dset._data_files for dset in dsets]\r\nif any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):\r\n    raise ValueError(\r\n        \"Datasets should ALL come from memory, or should ALL come from disk.\\n\"\r\n        \"However datasets {} come from memory and datasets {} come from disk.\".format(\r\n            [i for i in range(len(dsets)) if dsets_in_memory[i]],\r\n            [i for i in range(len(dsets)) if not dsets_in_memory[i]],\r\n        )\r\n    )\r\n```\r\n\r\nAny suggestions would be greatly appreciated! \r\nThanks!","comment_length":172,"text":"Error when concatenate_datasets \n Hello, when I concatenate two dataset loading  from disk, I encountered a problem:\r\n```\r\ntest_dataset = load_from_disk('data\/test_dataset')\r\ntrn_dataset = load_from_disk('data\/train_dataset')\r\n\r\ntrain_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n```\r\nAnd it reported ValueError blow:\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-38-74fa525512ca> in <module>\r\n----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n\r\n\/opt\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in concatenate_datasets(dsets, info, split)\r\n   2547                 \"However datasets' indices {} come from memory and datasets' indices {} come from disk.\".format(\r\n   2548                     [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],\r\n-> 2549                     [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],\r\n   2550                 )\r\n   2551             )\r\n\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\n\r\nBut it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:\r\n```\r\ntrn_dataset._data_files\r\n# output\r\n[{'filename': 'data\/train_dataset\/csv-train.arrow', 'skip': 0, 'take': 593264}]\r\n\r\ntest_dataset._data_files\r\n# output\r\n[{'filename': 'data\/test_dataset\/csv-test.arrow', 'skip': 0, 'take': 424383}]\r\n\r\nprint([not dset._data_files for dset in [trn_dataset, test_dataset]])\r\n# [False, False]\r\n\r\n# And I tested the code the same as arrow_dataset, but nothing happened\r\ndsets = [trn_dataset, test_dataset]\r\ndsets_in_memory = [not dset._data_files for dset in dsets]\r\nif any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):\r\n    raise ValueError(\r\n        \"Datasets should ALL come from memory, or should ALL come from disk.\\n\"\r\n        \"However datasets {} come from memory and datasets {} come from disk.\".format(\r\n            [i for i in range(len(dsets)) if dsets_in_memory[i]],\r\n            [i for i in range(len(dsets)) if not dsets_in_memory[i]],\r\n        )\r\n    )\r\n```\r\n\r\nAny suggestions would be greatly appreciated! \r\nThanks! \n As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n\r\nThe indices mapping correspond to a mapping on top of the data table that is used to re-order\/select a sample of the original data table. For example if you do `dataset.train_test_split`, then the resulting train and test datasets will have both an indices mapping to tell which examples are in train and which ones in test.\r\n\r\nBefore saving your datasets on disk, you should call `dataset.flatten_indices()` to remove the indices mapping. It should fix your issue. Under the hood it will create a new data table using the indices mapping. The new data table is going to be a subset of the old one (for example taking only the test set examples), and since the indices mapping will be gone you'll be able to concatenate your datasets.\r\n","embeddings":[-0.0911899135,-0.1483132094,-0.0447487496,0.6809360385,0.1068756282,0.2218791395,0.3129051626,0.2291087657,-0.1234838143,0.1246045008,-0.1083224565,0.2736025453,-0.0816895962,-0.1234090403,-0.30136621,-0.0974017531,0.1984925121,0.1485707313,-0.5059524179,-0.031675972,-0.3506394327,0.1602455676,-0.3358776569,0.0398673192,-0.4097848237,0.049245391,-0.1317905039,0.2761821151,-0.0312033258,-0.1838645488,0.4585869312,-0.3488870859,0.1864263117,0.5364499688,-0.0001176379,0.1700864136,0.2740953863,-0.1723825485,-0.3783839941,-0.256719321,-0.3621298075,-0.1004148722,-0.0029407758,-0.1234583333,0.207650885,-0.1481209397,-0.2583855689,-0.5043874979,0.33071509,0.2264483124,0.1104794741,0.3941425681,0.0912052542,-0.0980597213,0.0378851555,0.1533982009,0.0659453794,0.4203861058,-0.1898589581,-0.0617092587,0.2108293176,0.0670610666,-0.1246672496,0.1450287402,0.0780371949,0.3099108636,0.1576000452,-0.3968754411,-0.1252778769,0.1079114303,0.525313437,-0.3321693838,-0.3178471923,-0.0861591324,0.1388193816,-0.3995229304,0.217888236,0.2979132831,-0.147090584,0.1174623221,-0.0907389298,0.0771459118,-0.2191733122,0.1846294105,-0.1806519479,0.114519842,-0.1610969752,0.2407015562,0.138170585,-0.0730411187,0.5286436677,-0.3571856916,-0.1269245148,0.1591957361,-0.3422968984,0.0321878828,-0.1896209419,-0.704597652,0.0890091434,0.055820778,0.1037015319,-0.2204585522,0.0773697644,0.2818921804,0.4017777145,0.1492893547,-0.037381921,0.5497024655,-0.0202757102,-0.0616969094,0.199370116,0.009052719,-0.2867643833,-0.2114093006,0.1154302359,-0.1646887511,0.0836554542,-0.0013334746,-0.4971266091,-0.1518924385,-0.1278606951,-0.1404941082,0.1965599209,0.0301942825,0.123899661,0.5308156013,0.130738169,0.4409759939,0.217348516,0.0174215604,-0.226509124,0.0222139601,-0.0451169945,0.2104356736,0.103413932,0.0044444473,0.0821225643,0.1775865853,0.1087558568,0.006450789,0.0768852085,-0.4948595166,0.0583267137,0.3114701509,0.0211642738,0.0750039294,0.2738741934,-0.0503142029,-0.1439029574,0.3170154095,-0.4437072873,-0.2742709517,-0.513563931,0.0968817398,0.0989558399,0.2329158783,-0.3203627765,-0.1113318652,0.6928232908,0.1045668423,-0.0683576986,-0.0662886873,-0.1984153092,-0.3195584714,0.3204246461,0.2007118911,-0.2164676934,-0.0330451019,-0.0184931085,-0.0677970722,0.3604027629,0.5054668188,-0.3153637946,0.1992003024,-0.3855018318,-0.0331822969,0.3084054589,-0.4366770387,-0.1402199119,0.2154022306,0.0032159954,-0.073586531,0.2376121879,0.0082833301,0.1314229518,0.1472160071,0.8280869722,0.3132544458,0.0639004782,-0.1781871915,-0.097612448,-0.1383359432,0.2659764588,0.1000099033,-0.2323941588,0.0350794122,-0.1362989247,-0.4177925885,0.3237215579,-0.1016945988,0.0396771654,0.3689118922,0.0351548083,-0.343180269,-0.1154859886,0.0461800434,-0.1954729706,0.0483524054,0.0379616804,-0.0852145106,-0.2998000979,0.0915440768,-0.1228940859,0.0769214854,-0.072335422,0.0498545468,0.0176120866,0.235278517,-0.1725991517,-0.2480318099,-0.1653319597,0.4237135053,-0.2777830064,-0.026918387,-0.2073603868,0.5686868429,-0.0645174757,-0.0320977345,-0.1763465405,-0.0393736251,0.0589343719,0.1164020374,-0.0541011542,0.5149708986,0.0520663075,-0.1684773117,0.025941968,-0.2057821304,0.2290216684,-0.0772498548,0.139911443,-0.1201847419,0.1309275776,-0.0969888195,0.0723317713,0.4227220416,-0.1323702931,0.2828258872,-0.0215705317,0.0900893733,0.1695275009,0.1452977657,-0.058325775,-0.4177126288,-0.0745221898,0.1675421298,0.2188819051,0.2504171431,-0.4655806124,-0.2347148806,0.338824898,0.0246771201,0.0422085784,0.1224695444,-0.3912708461,-0.0406097658,-0.0284233205,0.3491848409,0.7707187533,0.1276440471,0.0334592573,-0.1203363314,-0.0359245241,0.0076954216,0.3499154747,0.0637109801,0.3463784754,0.3114224076,-0.0173665993,0.0689949468,-0.0166558977,-0.0879927427,0.0875865072,0.0622759163,-0.403681308,0.0528634526,-0.1899863631,-0.0543130003,-0.304048568,-0.246319145,-0.1040120274,-0.2119144052,-0.409406215,0.227412343,-0.0545448326,0.195634678,-0.1933861077,0.0545213036,0.150928095,-0.0917485282,0.0003248953,0.0094417734,-0.2065684795,-0.0326042064,0.2210373729,-0.1053390354,0.036282707,-0.1857272536,0.030104639,-0.1180914491,0.1154467016,0.0138302073,-0.0758283138,0.1963147074,0.0516775548,0.2055033743,-0.0473079644,-0.6516988873,0.1352370232,0.5362832546,-0.2745609879,0.3799952567,0.1490535438,-0.0947601125,-0.1053224504,-0.2667202652,-0.4566354156,-0.3636389971,-0.065149948,-0.1452127844,0.2417424619,0.2856628299,0.2368689626,0.0091699623,0.1459453553,0.1538192779,-0.0961045325,-0.2391644567,0.4765647054,0.0441288911,-0.1764593273,-0.1454289705,-0.0685137585,0.2939199209,0.2050095499,-0.3704074025,0.1182424426,-0.0839300379,0.0721743554,-0.3724434078,0.0816907659,0.0955052301,0.3071263731,-0.0495688766,-0.1056536734,-0.1409310997,0.1213522106,0.0509188175,0.3641844094,-0.026990803,0.3982563913,-0.0897532776,0.4547502995,0.2624274492,-0.1617570817,0.2130926996,0.1749910712,0.5188266635,0.009832982,-0.4894423485,-0.3085491657,-0.1053094938,-0.0222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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/848","title":"Error when concatenate_datasets","comments":"> As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n> \r\n> The indices mapping correspond to a mapping on top of the data table that is used to re-order\/select a sample of the original data table. For example if you do `dataset.train_test_split`, then the resulting train and test datasets will have both an indices mapping to tell which examples are in train and which ones in test.\r\n> \r\n> Before saving your datasets on disk, you should call `dataset.flatten_indices()` to remove the indices mapping. It should fix your issue. Under the hood it will create a new data table using the indices mapping. The new data table is going to be a subset of the old one (for example taking only the test set examples), and since the indices mapping will be gone you'll be able to concatenate your datasets.\r\n\r\n`dataset.flatten_indices()` solved my problem, thanks so much!","body":"Hello, when I concatenate two dataset loading  from disk, I encountered a problem:\r\n```\r\ntest_dataset = load_from_disk('data\/test_dataset')\r\ntrn_dataset = load_from_disk('data\/train_dataset')\r\n\r\ntrain_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n```\r\nAnd it reported ValueError blow:\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-38-74fa525512ca> in <module>\r\n----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n\r\n\/opt\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in concatenate_datasets(dsets, info, split)\r\n   2547                 \"However datasets' indices {} come from memory and datasets' indices {} come from disk.\".format(\r\n   2548                     [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],\r\n-> 2549                     [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],\r\n   2550                 )\r\n   2551             )\r\n\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\n\r\nBut it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:\r\n```\r\ntrn_dataset._data_files\r\n# output\r\n[{'filename': 'data\/train_dataset\/csv-train.arrow', 'skip': 0, 'take': 593264}]\r\n\r\ntest_dataset._data_files\r\n# output\r\n[{'filename': 'data\/test_dataset\/csv-test.arrow', 'skip': 0, 'take': 424383}]\r\n\r\nprint([not dset._data_files for dset in [trn_dataset, test_dataset]])\r\n# [False, False]\r\n\r\n# And I tested the code the same as arrow_dataset, but nothing happened\r\ndsets = [trn_dataset, test_dataset]\r\ndsets_in_memory = [not dset._data_files for dset in dsets]\r\nif any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):\r\n    raise ValueError(\r\n        \"Datasets should ALL come from memory, or should ALL come from disk.\\n\"\r\n        \"However datasets {} come from memory and datasets {} come from disk.\".format(\r\n            [i for i in range(len(dsets)) if dsets_in_memory[i]],\r\n            [i for i in range(len(dsets)) if not dsets_in_memory[i]],\r\n        )\r\n    )\r\n```\r\n\r\nAny suggestions would be greatly appreciated! \r\nThanks!","comment_length":184,"text":"Error when concatenate_datasets \n Hello, when I concatenate two dataset loading  from disk, I encountered a problem:\r\n```\r\ntest_dataset = load_from_disk('data\/test_dataset')\r\ntrn_dataset = load_from_disk('data\/train_dataset')\r\n\r\ntrain_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n```\r\nAnd it reported ValueError blow:\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-38-74fa525512ca> in <module>\r\n----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n\r\n\/opt\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in concatenate_datasets(dsets, info, split)\r\n   2547                 \"However datasets' indices {} come from memory and datasets' indices {} come from disk.\".format(\r\n   2548                     [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],\r\n-> 2549                     [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],\r\n   2550                 )\r\n   2551             )\r\n\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\n\r\nBut it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:\r\n```\r\ntrn_dataset._data_files\r\n# output\r\n[{'filename': 'data\/train_dataset\/csv-train.arrow', 'skip': 0, 'take': 593264}]\r\n\r\ntest_dataset._data_files\r\n# output\r\n[{'filename': 'data\/test_dataset\/csv-test.arrow', 'skip': 0, 'take': 424383}]\r\n\r\nprint([not dset._data_files for dset in [trn_dataset, test_dataset]])\r\n# [False, False]\r\n\r\n# And I tested the code the same as arrow_dataset, but nothing happened\r\ndsets = [trn_dataset, test_dataset]\r\ndsets_in_memory = [not dset._data_files for dset in dsets]\r\nif any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):\r\n    raise ValueError(\r\n        \"Datasets should ALL come from memory, or should ALL come from disk.\\n\"\r\n        \"However datasets {} come from memory and datasets {} come from disk.\".format(\r\n            [i for i in range(len(dsets)) if dsets_in_memory[i]],\r\n            [i for i in range(len(dsets)) if not dsets_in_memory[i]],\r\n        )\r\n    )\r\n```\r\n\r\nAny suggestions would be greatly appreciated! \r\nThanks! \n > As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n> \r\n> The indices mapping correspond to a mapping on top of the data table that is used to re-order\/select a sample of the original data table. For example if you do `dataset.train_test_split`, then the resulting train and test datasets will have both an indices mapping to tell which examples are in train and which ones in test.\r\n> \r\n> Before saving your datasets on disk, you should call `dataset.flatten_indices()` to remove the indices mapping. It should fix your issue. Under the hood it will create a new data table using the indices mapping. The new data table is going to be a subset of the old one (for example taking only the test set examples), and since the indices mapping will be gone you'll be able to concatenate your datasets.\r\n\r\n`dataset.flatten_indices()` solved my problem, thanks so much!","embeddings":[-0.0911899135,-0.1483132094,-0.0447487496,0.6809360385,0.1068756282,0.2218791395,0.3129051626,0.2291087657,-0.1234838143,0.1246045008,-0.1083224565,0.2736025453,-0.0816895962,-0.1234090403,-0.30136621,-0.0974017531,0.1984925121,0.1485707313,-0.5059524179,-0.031675972,-0.3506394327,0.1602455676,-0.3358776569,0.0398673192,-0.4097848237,0.049245391,-0.1317905039,0.2761821151,-0.0312033258,-0.1838645488,0.4585869312,-0.3488870859,0.1864263117,0.5364499688,-0.0001176379,0.1700864136,0.2740953863,-0.1723825485,-0.3783839941,-0.256719321,-0.3621298075,-0.1004148722,-0.0029407758,-0.1234583333,0.207650885,-0.1481209397,-0.2583855689,-0.5043874979,0.33071509,0.2264483124,0.1104794741,0.3941425681,0.0912052542,-0.0980597213,0.0378851555,0.1533982009,0.0659453794,0.4203861058,-0.1898589581,-0.0617092587,0.2108293176,0.0670610666,-0.1246672496,0.1450287402,0.0780371949,0.3099108636,0.1576000452,-0.3968754411,-0.1252778769,0.1079114303,0.525313437,-0.3321693838,-0.3178471923,-0.0861591324,0.1388193816,-0.3995229304,0.217888236,0.2979132831,-0.147090584,0.1174623221,-0.0907389298,0.0771459118,-0.2191733122,0.1846294105,-0.1806519479,0.114519842,-0.1610969752,0.2407015562,0.138170585,-0.0730411187,0.5286436677,-0.3571856916,-0.1269245148,0.1591957361,-0.3422968984,0.0321878828,-0.1896209419,-0.704597652,0.0890091434,0.055820778,0.1037015319,-0.2204585522,0.0773697644,0.2818921804,0.4017777145,0.1492893547,-0.037381921,0.5497024655,-0.0202757102,-0.0616969094,0.199370116,0.009052719,-0.2867643833,-0.2114093006,0.1154302359,-0.1646887511,0.0836554542,-0.0013334746,-0.4971266091,-0.1518924385,-0.1278606951,-0.1404941082,0.1965599209,0.0301942825,0.123899661,0.5308156013,0.130738169,0.4409759939,0.217348516,0.0174215604,-0.226509124,0.0222139601,-0.0451169945,0.2104356736,0.103413932,0.0044444473,0.0821225643,0.1775865853,0.1087558568,0.006450789,0.0768852085,-0.4948595166,0.0583267137,0.3114701509,0.0211642738,0.0750039294,0.2738741934,-0.0503142029,-0.1439029574,0.3170154095,-0.4437072873,-0.2742709517,-0.513563931,0.0968817398,0.0989558399,0.2329158783,-0.3203627765,-0.1113318652,0.6928232908,0.1045668423,-0.0683576986,-0.0662886873,-0.1984153092,-0.3195584714,0.3204246461,0.2007118911,-0.2164676934,-0.0330451019,-0.0184931085,-0.0677970722,0.3604027629,0.5054668188,-0.3153637946,0.1992003024,-0.3855018318,-0.0331822969,0.3084054589,-0.4366770387,-0.1402199119,0.2154022306,0.0032159954,-0.073586531,0.2376121879,0.0082833301,0.1314229518,0.1472160071,0.8280869722,0.3132544458,0.0639004782,-0.1781871915,-0.097612448,-0.1383359432,0.2659764588,0.1000099033,-0.2323941588,0.0350794122,-0.1362989247,-0.4177925885,0.3237215579,-0.1016945988,0.0396771654,0.3689118922,0.0351548083,-0.343180269,-0.1154859886,0.0461800434,-0.1954729706,0.0483524054,0.0379616804,-0.0852145106,-0.2998000979,0.0915440768,-0.1228940859,0.0769214854,-0.072335422,0.0498545468,0.0176120866,0.235278517,-0.1725991517,-0.2480318099,-0.1653319597,0.4237135053,-0.2777830064,-0.026918387,-0.2073603868,0.5686868429,-0.0645174757,-0.0320977345,-0.1763465405,-0.0393736251,0.0589343719,0.1164020374,-0.0541011542,0.5149708986,0.0520663075,-0.1684773117,0.025941968,-0.2057821304,0.2290216684,-0.0772498548,0.139911443,-0.1201847419,0.1309275776,-0.0969888195,0.0723317713,0.4227220416,-0.1323702931,0.2828258872,-0.0215705317,0.0900893733,0.1695275009,0.1452977657,-0.058325775,-0.4177126288,-0.0745221898,0.1675421298,0.2188819051,0.2504171431,-0.4655806124,-0.2347148806,0.338824898,0.0246771201,0.0422085784,0.1224695444,-0.3912708461,-0.0406097658,-0.0284233205,0.3491848409,0.7707187533,0.1276440471,0.0334592573,-0.1203363314,-0.0359245241,0.0076954216,0.3499154747,0.0637109801,0.3463784754,0.3114224076,-0.0173665993,0.0689949468,-0.0166558977,-0.0879927427,0.0875865072,0.0622759163,-0.403681308,0.0528634526,-0.1899863631,-0.0543130003,-0.304048568,-0.246319145,-0.1040120274,-0.2119144052,-0.409406215,0.227412343,-0.0545448326,0.195634678,-0.1933861077,0.0545213036,0.150928095,-0.0917485282,0.0003248953,0.0094417734,-0.2065684795,-0.0326042064,0.2210373729,-0.1053390354,0.036282707,-0.1857272536,0.030104639,-0.1180914491,0.1154467016,0.0138302073,-0.0758283138,0.1963147074,0.0516775548,0.2055033743,-0.0473079644,-0.6516988873,0.1352370232,0.5362832546,-0.2745609879,0.3799952567,0.1490535438,-0.0947601125,-0.1053224504,-0.2667202652,-0.4566354156,-0.3636389971,-0.065149948,-0.1452127844,0.2417424619,0.2856628299,0.2368689626,0.0091699623,0.1459453553,0.1538192779,-0.0961045325,-0.2391644567,0.4765647054,0.0441288911,-0.1764593273,-0.1454289705,-0.0685137585,0.2939199209,0.2050095499,-0.3704074025,0.1182424426,-0.0839300379,0.0721743554,-0.3724434078,0.0816907659,0.0955052301,0.3071263731,-0.0495688766,-0.1056536734,-0.1409310997,0.1213522106,0.0509188175,0.3641844094,-0.026990803,0.3982563913,-0.0897532776,0.4547502995,0.2624274492,-0.1617570817,0.2130926996,0.1749910712,0.5188266635,0.009832982,-0.4894423485,-0.3085491657,-0.1053094938,-0.0222776867,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/848","title":"Error when concatenate_datasets","comments":"@lhoestq we can add a mention of `dataset.flatten_indices()` in the error message (no rush, just put it on your TODO list or I can do it when I come at it)","body":"Hello, when I concatenate two dataset loading  from disk, I encountered a problem:\r\n```\r\ntest_dataset = load_from_disk('data\/test_dataset')\r\ntrn_dataset = load_from_disk('data\/train_dataset')\r\n\r\ntrain_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n```\r\nAnd it reported ValueError blow:\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-38-74fa525512ca> in <module>\r\n----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n\r\n\/opt\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in concatenate_datasets(dsets, info, split)\r\n   2547                 \"However datasets' indices {} come from memory and datasets' indices {} come from disk.\".format(\r\n   2548                     [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],\r\n-> 2549                     [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],\r\n   2550                 )\r\n   2551             )\r\n\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\n\r\nBut it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:\r\n```\r\ntrn_dataset._data_files\r\n# output\r\n[{'filename': 'data\/train_dataset\/csv-train.arrow', 'skip': 0, 'take': 593264}]\r\n\r\ntest_dataset._data_files\r\n# output\r\n[{'filename': 'data\/test_dataset\/csv-test.arrow', 'skip': 0, 'take': 424383}]\r\n\r\nprint([not dset._data_files for dset in [trn_dataset, test_dataset]])\r\n# [False, False]\r\n\r\n# And I tested the code the same as arrow_dataset, but nothing happened\r\ndsets = [trn_dataset, test_dataset]\r\ndsets_in_memory = [not dset._data_files for dset in dsets]\r\nif any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):\r\n    raise ValueError(\r\n        \"Datasets should ALL come from memory, or should ALL come from disk.\\n\"\r\n        \"However datasets {} come from memory and datasets {} come from disk.\".format(\r\n            [i for i in range(len(dsets)) if dsets_in_memory[i]],\r\n            [i for i in range(len(dsets)) if not dsets_in_memory[i]],\r\n        )\r\n    )\r\n```\r\n\r\nAny suggestions would be greatly appreciated! \r\nThanks!","comment_length":31,"text":"Error when concatenate_datasets \n Hello, when I concatenate two dataset loading  from disk, I encountered a problem:\r\n```\r\ntest_dataset = load_from_disk('data\/test_dataset')\r\ntrn_dataset = load_from_disk('data\/train_dataset')\r\n\r\ntrain_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n```\r\nAnd it reported ValueError blow:\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-38-74fa525512ca> in <module>\r\n----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])\r\n\r\n\/opt\/miniconda3\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in concatenate_datasets(dsets, info, split)\r\n   2547                 \"However datasets' indices {} come from memory and datasets' indices {} come from disk.\".format(\r\n   2548                     [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],\r\n-> 2549                     [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],\r\n   2550                 )\r\n   2551             )\r\n\r\nValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.\r\nHowever datasets' indices [1] come from memory and datasets' indices [0] come from disk.\r\n```\r\n\r\nBut it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:\r\n```\r\ntrn_dataset._data_files\r\n# output\r\n[{'filename': 'data\/train_dataset\/csv-train.arrow', 'skip': 0, 'take': 593264}]\r\n\r\ntest_dataset._data_files\r\n# output\r\n[{'filename': 'data\/test_dataset\/csv-test.arrow', 'skip': 0, 'take': 424383}]\r\n\r\nprint([not dset._data_files for dset in [trn_dataset, test_dataset]])\r\n# [False, False]\r\n\r\n# And I tested the code the same as arrow_dataset, but nothing happened\r\ndsets = [trn_dataset, test_dataset]\r\ndsets_in_memory = [not dset._data_files for dset in dsets]\r\nif any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):\r\n    raise ValueError(\r\n        \"Datasets should ALL come from memory, or should ALL come from disk.\\n\"\r\n        \"However datasets {} come from memory and datasets {} come from disk.\".format(\r\n            [i for i in range(len(dsets)) if dsets_in_memory[i]],\r\n            [i for i in range(len(dsets)) if not dsets_in_memory[i]],\r\n        )\r\n    )\r\n```\r\n\r\nAny suggestions would be greatly appreciated! \r\nThanks! \n @lhoestq we can add a mention of `dataset.flatten_indices()` in the error message (no rush, just put it on your TODO list or I can do it when I come at it)","embeddings":[-0.0911899135,-0.1483132094,-0.0447487496,0.6809360385,0.1068756282,0.2218791395,0.3129051626,0.2291087657,-0.1234838143,0.1246045008,-0.1083224565,0.2736025453,-0.0816895962,-0.1234090403,-0.30136621,-0.0974017531,0.1984925121,0.1485707313,-0.5059524179,-0.031675972,-0.3506394327,0.1602455676,-0.3358776569,0.0398673192,-0.4097848237,0.049245391,-0.1317905039,0.2761821151,-0.0312033258,-0.1838645488,0.4585869312,-0.3488870859,0.1864263117,0.5364499688,-0.0001176379,0.1700864136,0.2740953863,-0.1723825485,-0.3783839941,-0.256719321,-0.3621298075,-0.1004148722,-0.0029407758,-0.1234583333,0.207650885,-0.1481209397,-0.2583855689,-0.5043874979,0.33071509,0.2264483124,0.1104794741,0.3941425681,0.0912052542,-0.0980597213,0.0378851555,0.1533982009,0.0659453794,0.4203861058,-0.1898589581,-0.0617092587,0.2108293176,0.0670610666,-0.1246672496,0.1450287402,0.0780371949,0.3099108636,0.1576000452,-0.3968754411,-0.1252778769,0.1079114303,0.525313437,-0.3321693838,-0.3178471923,-0.0861591324,0.1388193816,-0.3995229304,0.217888236,0.2979132831,-0.147090584,0.1174623221,-0.0907389298,0.0771459118,-0.2191733122,0.1846294105,-0.1806519479,0.114519842,-0.1610969752,0.2407015562,0.138170585,-0.0730411187,0.5286436677,-0.3571856916,-0.1269245148,0.1591957361,-0.3422968984,0.0321878828,-0.1896209419,-0.704597652,0.0890091434,0.055820778,0.1037015319,-0.2204585522,0.0773697644,0.2818921804,0.4017777145,0.1492893547,-0.037381921,0.5497024655,-0.0202757102,-0.0616969094,0.199370116,0.009052719,-0.2867643833,-0.2114093006,0.1154302359,-0.1646887511,0.0836554542,-0.0013334746,-0.4971266091,-0.1518924385,-0.1278606951,-0.1404941082,0.1965599209,0.0301942825,0.123899661,0.5308156013,0.130738169,0.4409759939,0.217348516,0.0174215604,-0.226509124,0.0222139601,-0.0451169945,0.2104356736,0.103413932,0.0044444473,0.0821225643,0.1775865853,0.1087558568,0.006450789,0.0768852085,-0.4948595166,0.0583267137,0.3114701509,0.0211642738,0.0750039294,0.2738741934,-0.0503142029,-0.1439029574,0.3170154095,-0.4437072873,-0.2742709517,-0.513563931,0.0968817398,0.0989558399,0.2329158783,-0.3203627765,-0.1113318652,0.6928232908,0.1045668423,-0.0683576986,-0.0662886873,-0.1984153092,-0.3195584714,0.3204246461,0.2007118911,-0.2164676934,-0.0330451019,-0.0184931085,-0.0677970722,0.3604027629,0.5054668188,-0.3153637946,0.1992003024,-0.3855018318,-0.0331822969,0.3084054589,-0.4366770387,-0.1402199119,0.2154022306,0.0032159954,-0.073586531,0.2376121879,0.0082833301,0.1314229518,0.1472160071,0.8280869722,0.3132544458,0.0639004782,-0.1781871915,-0.097612448,-0.1383359432,0.2659764588,0.1000099033,-0.2323941588,0.0350794122,-0.1362989247,-0.4177925885,0.3237215579,-0.1016945988,0.0396771654,0.3689118922,0.0351548083,-0.343180269,-0.1154859886,0.0461800434,-0.1954729706,0.0483524054,0.0379616804,-0.0852145106,-0.2998000979,0.0915440768,-0.1228940859,0.0769214854,-0.072335422,0.0498545468,0.0176120866,0.235278517,-0.1725991517,-0.2480318099,-0.1653319597,0.4237135053,-0.2777830064,-0.026918387,-0.2073603868,0.5686868429,-0.0645174757,-0.0320977345,-0.1763465405,-0.0393736251,0.0589343719,0.1164020374,-0.0541011542,0.5149708986,0.0520663075,-0.1684773117,0.025941968,-0.2057821304,0.2290216684,-0.0772498548,0.139911443,-0.1201847419,0.1309275776,-0.0969888195,0.0723317713,0.4227220416,-0.1323702931,0.2828258872,-0.0215705317,0.0900893733,0.1695275009,0.1452977657,-0.058325775,-0.4177126288,-0.0745221898,0.1675421298,0.2188819051,0.2504171431,-0.4655806124,-0.2347148806,0.338824898,0.0246771201,0.0422085784,0.1224695444,-0.3912708461,-0.0406097658,-0.0284233205,0.3491848409,0.7707187533,0.1276440471,0.0334592573,-0.1203363314,-0.0359245241,0.0076954216,0.3499154747,0.0637109801,0.3463784754,0.3114224076,-0.0173665993,0.0689949468,-0.0166558977,-0.0879927427,0.0875865072,0.0622759163,-0.403681308,0.0528634526,-0.1899863631,-0.0543130003,-0.304048568,-0.246319145,-0.1040120274,-0.2119144052,-0.409406215,0.227412343,-0.0545448326,0.195634678,-0.1933861077,0.0545213036,0.150928095,-0.0917485282,0.0003248953,0.0094417734,-0.2065684795,-0.0326042064,0.2210373729,-0.1053390354,0.036282707,-0.1857272536,0.030104639,-0.1180914491,0.1154467016,0.0138302073,-0.0758283138,0.1963147074,0.0516775548,0.2055033743,-0.0473079644,-0.6516988873,0.1352370232,0.5362832546,-0.2745609879,0.3799952567,0.1490535438,-0.0947601125,-0.1053224504,-0.2667202652,-0.4566354156,-0.3636389971,-0.065149948,-0.1452127844,0.2417424619,0.2856628299,0.2368689626,0.0091699623,0.1459453553,0.1538192779,-0.0961045325,-0.2391644567,0.4765647054,0.0441288911,-0.1764593273,-0.1454289705,-0.0685137585,0.2939199209,0.2050095499,-0.3704074025,0.1182424426,-0.0839300379,0.0721743554,-0.3724434078,0.0816907659,0.0955052301,0.3071263731,-0.0495688766,-0.1056536734,-0.1409310997,0.1213522106,0.0509188175,0.3641844094,-0.026990803,0.3982563913,-0.0897532776,0.4547502995,0.2624274492,-0.1617570817,0.2130926996,0.1749910712,0.5188266635,0.009832982,-0.4894423485,-0.3085491657,-0.1053094938,-0.0222776867,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/847","title":"multiprocessing in dataset map \"can only test a child process\"","comments":"It looks like an issue with wandb\/tqdm here.\r\nWe're using the `multiprocess` library instead of the `multiprocessing` builtin python package to support various types of mapping functions. Maybe there's some sort of incompatibility.\r\n\r\nCould you make a minimal script to reproduce or a google colab ?","body":"Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n```","comment_length":46,"text":"multiprocessing in dataset map \"can only test a child process\" \n Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n``` \n It looks like an issue with wandb\/tqdm here.\r\nWe're using the `multiprocess` library instead of the `multiprocessing` builtin python package to support various types of mapping functions. Maybe there's some sort of incompatibility.\r\n\r\nCould you make a minimal script to reproduce or a google colab ?","embeddings":[-0.3956997097,0.0156290922,-0.1538687646,-0.1850299388,0.141797632,-0.0578565337,0.5068988204,0.3400265574,-0.0834040865,0.1681774557,-0.0184273254,0.3370628655,0.0475015864,0.0918332115,-0.2264437228,0.1021468788,-0.0123605253,0.0946920067,0.1290109754,0.0473636836,-0.1753095239,0.1040760726,-0.2302506417,0.2859587967,-0.4516958296,-0.285730809,-0.0163212903,-0.0623164959,-0.2643237412,-0.4696436226,-0.149113372,0.2851142585,-0.1861115098,0.6030860543,-0.0001044559,0.0936724842,0.2627740204,0.0446500145,-0.054140687,-0.030070236,0.095131956,-0.0515859984,-0.0310001038,-0.4035981894,-0.0044792355,-0.2674306333,0.1779136211,-0.4431346357,0.2878316641,0.3189612627,0.2665320337,0.5655280352,-0.2092227638,-0.0047211065,-0.201201424,-0.0783623531,-0.0357206613,-0.1625539958,0.2167165279,-0.2403003126,-0.3567937613,0.2491746992,-0.0430285074,0.1743139625,-0.3290867805,0.0007867881,0.277649343,-0.237424776,0.3172336221,0.079235658,0.1507846713,-0.2674021721,-0.3465930223,-0.1820102185,-0.2675664425,-0.1380905807,-0.0896170512,0.0866119117,-0.3090318441,0.0237945151,-0.231428802,0.0673615932,-0.0069425311,-0.0971344262,-0.20974648,0.6421571374,0.0204102993,0.2258118987,-0.058426097,0.0421580598,0.1321844459,-0.1236515045,0.1550007761,-0.0283196401,-0.1767590791,-0.1339012235,0.1732696593,-0.3608185351,0.1934940219,-0.0518135242,-0.2702677846,0.1569519043,0.1210899875,0.1657909304,0.1612778902,0.0505134016,0.1559104919,0.2796486914,0.2925396562,-0.196995303,-0.3908066452,-0.0121860905,0.0276292767,-0.2749152184,0.19010818,0.1587806493,0.1859900355,0.0203872453,-0.316894412,-0.0674285442,-0.1310207993,-0.0011093016,0.0109488498,0.4655701518,-0.0625771657,0.210119307,-0.1869856268,0.2766140103,-0.1955184191,-0.1335654557,-0.2674530447,0.1599751413,-0.105241254,-0.0912755728,0.0864879191,0.2284782082,0.3958972394,0.057422515,0.0732819885,-0.0852145255,0.4325413704,-0.219634518,0.2467176765,0.0557839759,0.068621248,0.1359149069,0.26744017,-0.3098887801,-0.1797690243,-0.0748893917,0.0085688289,-0.1883713305,-0.0060650436,0.2934336662,0.0234699752,0.0294385776,-0.1553809643,0.1325114965,0.2363231629,0.0417060144,0.0084723802,-0.1836157143,-0.2125665992,-0.1611631811,0.1659645736,0.2332042754,-0.1246359199,-0.0227329582,0.0209111851,-0.1451625675,0.2747372985,0.2273871899,-0.0913355052,0.6480855346,-0.1730043739,0.5878829956,0.1861093491,-0.5310037136,-0.1901540011,0.2092227191,-0.3177124858,-0.0111948214,0.1317908615,-0.1686787903,0.3851614892,0.080286935,0.1898801774,0.2464483976,-0.1104762852,0.2880237997,-0.2860673666,-0.0096036047,0.0849479064,-0.071969904,0.2393062264,-0.2704798877,-0.0424101911,-0.1621235162,0.2372229397,-0.1482038051,0.2230832577,-0.0443854667,0.0625272393,0.0818433464,-0.0392007828,-0.1215575859,0.0788098946,0.2157844901,0.0433690101,0.0418133661,-0.1905940175,-0.1984119862,-0.0963724405,0.2760661244,-0.2941963971,-0.3430737555,0.3505134284,0.3571797907,-0.0198514294,-0.0224457197,-0.0754135773,0.1201187074,0.1530826986,0.0062083742,0.0715006441,-0.0785518736,-0.1991024315,-0.1974657774,-0.0115251811,0.0726843029,0.1600964069,0.003486624,-0.0784358084,0.4022406936,0.2385911644,0.1009370983,0.2098905593,-0.1776235253,-0.0964289233,0.0071378755,-0.0728402957,0.111530818,0.0388300754,-0.0264948942,0.1444305331,0.4932917356,0.2365600765,0.1932385713,0.2239498198,-0.0101361321,0.2388229668,-0.1069113389,-0.0609275028,-0.1341272146,0.057798285,0.0031344036,0.1119551882,-0.1359223723,-0.2041955888,0.1788984239,0.314935714,-0.0856286287,0.1352984309,-0.1058834121,-0.0881043524,-0.1929530203,0.0814027041,0.1926788986,0.3943273127,0.2004811913,0.1856659055,-0.0386409611,-0.0774591267,-0.0734680966,0.1406773478,0.0299724508,0.1926366687,0.2765003741,0.2475029826,-0.2172683924,-0.3548972011,-0.2435459346,0.1768790036,0.1148345843,-0.1390989274,0.0299772602,-0.3175421357,0.0918787643,-0.0323445313,-0.0665194169,0.2071175724,-0.3488472402,0.1094204113,0.1852676868,-0.1694183499,0.3220192492,-0.1171384901,0.0898978636,-0.0134830419,-0.0217510052,-0.0988093317,-0.1474416703,-0.1254175901,0.1327666491,0.1662156433,0.1449765265,0.4963173568,0.0013359481,-0.2189733684,-0.5480391383,-0.2451718748,0.0971291885,-0.1633996367,0.1954907775,0.2778953016,0.0427568778,-0.0250689685,-0.3409390152,0.3002497554,-0.4462933838,-0.373233676,-0.2133507133,0.0948979482,-0.1722041965,-0.0335693397,-0.3764518201,-0.3236232698,-0.1813369691,0.3523524106,-0.0931495354,0.1716113091,-0.155457288,-0.1054516658,0.1455910206,-0.0203673467,0.1121426821,-0.2187279463,-0.1448161304,-0.0194471348,-0.1510415971,-0.283069551,-0.1560443342,-0.140906021,0.4374369085,-0.10340821,-0.1784181744,-0.0407508872,-0.1612140536,0.4367106259,0.0242293067,-0.0490040779,0.3442159593,0.2696896195,-0.348139286,-0.0873824731,-0.16058743,0.0496429652,-0.2982184887,-0.1103075147,-0.185685128,0.4214314222,0.2078024745,0.5972415209,0.028514469,-0.0613885559,0.1324319392,-0.4377176166,-0.0172750056,-0.0337198712,-0.4259907305,0.1626303345,-0.266784489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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/847","title":"multiprocessing in dataset map \"can only test a child process\"","comments":"hi facing the same issue here - \r\n\r\n`AssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 996, in emit\r\n    stream.write(msg)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/lib\/redirect.py\", line 100, in new_write\r\n    cb(name, data)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/wandb_run.py\", line 723, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 153, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 158, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 456, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/_utils\/worker.py\", line 198, in _worker_loop\r\n    data = fetcher.fetch(index)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"<ipython-input-8-a4d9a08d114e>\", line 20, in __getitem__\r\n    return_token_type_ids=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/tokenization_utils_base.py\", line 2405, in encode_plus\r\n    **kwargs,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/tokenization_utils_base.py\", line 2125, in _get_padding_truncation_strategies\r\n    \"Truncation was not explicitly activated but `max_length` is provided a specific value, \"\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1320, in warning\r\n    self._log(WARNING, msg, args, **kwargs)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1444, in _log\r\n    self.handle(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1454, in handle\r\n    self.callHandlers(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1516, in callHandlers\r\n    hdlr.handle(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 865, in handle\r\n    self.emit(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1000, in emit\r\n    self.handleError(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 917, in handleError\r\n    sys.stderr.write('--- Logging error ---\\n')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/lib\/redirect.py\", line 100, in new_write\r\n    cb(name, data)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/wandb_run.py\", line 723, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 153, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 158, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 456, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process`\r\n","body":"Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n```","comment_length":293,"text":"multiprocessing in dataset map \"can only test a child process\" \n Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n``` \n hi facing the same issue here - \r\n\r\n`AssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 996, in emit\r\n    stream.write(msg)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/lib\/redirect.py\", line 100, in new_write\r\n    cb(name, data)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/wandb_run.py\", line 723, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 153, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 158, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 456, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/_utils\/worker.py\", line 198, in _worker_loop\r\n    data = fetcher.fetch(index)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"<ipython-input-8-a4d9a08d114e>\", line 20, in __getitem__\r\n    return_token_type_ids=True\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/tokenization_utils_base.py\", line 2405, in encode_plus\r\n    **kwargs,\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/transformers\/tokenization_utils_base.py\", line 2125, in _get_padding_truncation_strategies\r\n    \"Truncation was not explicitly activated but `max_length` is provided a specific value, \"\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1320, in warning\r\n    self._log(WARNING, msg, args, **kwargs)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1444, in _log\r\n    self.handle(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1454, in handle\r\n    self.callHandlers(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1516, in callHandlers\r\n    hdlr.handle(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 865, in handle\r\n    self.emit(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 1000, in emit\r\n    self.handleError(record)\r\n  File \"\/usr\/lib\/python3.6\/logging\/__init__.py\", line 917, in handleError\r\n    sys.stderr.write('--- Logging error ---\\n')\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/lib\/redirect.py\", line 100, in new_write\r\n    cb(name, data)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/wandb_run.py\", line 723, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 153, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 158, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/wandb\/sdk\/interface\/interface.py\", line 456, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process`\r\n","embeddings":[-0.3956997097,0.0156290922,-0.1538687646,-0.1850299388,0.141797632,-0.0578565337,0.5068988204,0.3400265574,-0.0834040865,0.1681774557,-0.0184273254,0.3370628655,0.0475015864,0.0918332115,-0.2264437228,0.1021468788,-0.0123605253,0.0946920067,0.1290109754,0.0473636836,-0.1753095239,0.1040760726,-0.2302506417,0.2859587967,-0.4516958296,-0.285730809,-0.0163212903,-0.0623164959,-0.2643237412,-0.4696436226,-0.149113372,0.2851142585,-0.1861115098,0.6030860543,-0.0001044559,0.0936724842,0.2627740204,0.0446500145,-0.054140687,-0.030070236,0.095131956,-0.0515859984,-0.0310001038,-0.4035981894,-0.0044792355,-0.2674306333,0.1779136211,-0.4431346357,0.2878316641,0.3189612627,0.2665320337,0.5655280352,-0.2092227638,-0.0047211065,-0.201201424,-0.0783623531,-0.0357206613,-0.1625539958,0.2167165279,-0.2403003126,-0.3567937613,0.2491746992,-0.0430285074,0.1743139625,-0.3290867805,0.0007867881,0.277649343,-0.237424776,0.3172336221,0.079235658,0.1507846713,-0.2674021721,-0.3465930223,-0.1820102185,-0.2675664425,-0.1380905807,-0.0896170512,0.0866119117,-0.3090318441,0.0237945151,-0.231428802,0.0673615932,-0.0069425311,-0.0971344262,-0.20974648,0.6421571374,0.0204102993,0.2258118987,-0.058426097,0.0421580598,0.1321844459,-0.1236515045,0.1550007761,-0.0283196401,-0.1767590791,-0.1339012235,0.1732696593,-0.3608185351,0.1934940219,-0.0518135242,-0.2702677846,0.1569519043,0.1210899875,0.1657909304,0.1612778902,0.0505134016,0.1559104919,0.2796486914,0.2925396562,-0.196995303,-0.3908066452,-0.0121860905,0.0276292767,-0.2749152184,0.19010818,0.1587806493,0.1859900355,0.0203872453,-0.316894412,-0.0674285442,-0.1310207993,-0.0011093016,0.0109488498,0.4655701518,-0.0625771657,0.210119307,-0.1869856268,0.2766140103,-0.1955184191,-0.1335654557,-0.2674530447,0.1599751413,-0.105241254,-0.0912755728,0.0864879191,0.2284782082,0.3958972394,0.057422515,0.0732819885,-0.0852145255,0.4325413704,-0.219634518,0.2467176765,0.0557839759,0.068621248,0.1359149069,0.26744017,-0.3098887801,-0.1797690243,-0.0748893917,0.0085688289,-0.1883713305,-0.0060650436,0.2934336662,0.0234699752,0.0294385776,-0.1553809643,0.1325114965,0.2363231629,0.0417060144,0.0084723802,-0.1836157143,-0.2125665992,-0.1611631811,0.1659645736,0.2332042754,-0.1246359199,-0.0227329582,0.0209111851,-0.1451625675,0.2747372985,0.2273871899,-0.0913355052,0.6480855346,-0.1730043739,0.5878829956,0.1861093491,-0.5310037136,-0.1901540011,0.2092227191,-0.3177124858,-0.0111948214,0.1317908615,-0.1686787903,0.3851614892,0.080286935,0.1898801774,0.2464483976,-0.1104762852,0.2880237997,-0.2860673666,-0.0096036047,0.0849479064,-0.071969904,0.2393062264,-0.2704798877,-0.0424101911,-0.1621235162,0.2372229397,-0.1482038051,0.2230832577,-0.0443854667,0.0625272393,0.0818433464,-0.0392007828,-0.1215575859,0.0788098946,0.2157844901,0.0433690101,0.0418133661,-0.1905940175,-0.1984119862,-0.0963724405,0.2760661244,-0.2941963971,-0.3430737555,0.3505134284,0.3571797907,-0.0198514294,-0.0224457197,-0.0754135773,0.1201187074,0.1530826986,0.0062083742,0.0715006441,-0.0785518736,-0.1991024315,-0.1974657774,-0.0115251811,0.0726843029,0.1600964069,0.003486624,-0.0784358084,0.4022406936,0.2385911644,0.1009370983,0.2098905593,-0.1776235253,-0.0964289233,0.0071378755,-0.0728402957,0.111530818,0.0388300754,-0.0264948942,0.1444305331,0.4932917356,0.2365600765,0.1932385713,0.2239498198,-0.0101361321,0.2388229668,-0.1069113389,-0.0609275028,-0.1341272146,0.057798285,0.0031344036,0.1119551882,-0.1359223723,-0.2041955888,0.1788984239,0.314935714,-0.0856286287,0.1352984309,-0.1058834121,-0.0881043524,-0.1929530203,0.0814027041,0.1926788986,0.3943273127,0.2004811913,0.1856659055,-0.0386409611,-0.0774591267,-0.0734680966,0.1406773478,0.0299724508,0.1926366687,0.2765003741,0.2475029826,-0.2172683924,-0.3548972011,-0.2435459346,0.1768790036,0.1148345843,-0.1390989274,0.0299772602,-0.3175421357,0.0918787643,-0.0323445313,-0.0665194169,0.2071175724,-0.3488472402,0.1094204113,0.1852676868,-0.1694183499,0.3220192492,-0.1171384901,0.0898978636,-0.0134830419,-0.0217510052,-0.0988093317,-0.1474416703,-0.1254175901,0.1327666491,0.1662156433,0.1449765265,0.4963173568,0.0013359481,-0.2189733684,-0.5480391383,-0.2451718748,0.0971291885,-0.1633996367,0.1954907775,0.2778953016,0.0427568778,-0.0250689685,-0.3409390152,0.3002497554,-0.4462933838,-0.373233676,-0.2133507133,0.0948979482,-0.1722041965,-0.0335693397,-0.3764518201,-0.3236232698,-0.1813369691,0.3523524106,-0.0931495354,0.1716113091,-0.155457288,-0.1054516658,0.1455910206,-0.0203673467,0.1121426821,-0.2187279463,-0.1448161304,-0.0194471348,-0.1510415971,-0.283069551,-0.1560443342,-0.140906021,0.4374369085,-0.10340821,-0.1784181744,-0.0407508872,-0.1612140536,0.4367106259,0.0242293067,-0.0490040779,0.3442159593,0.2696896195,-0.348139286,-0.0873824731,-0.16058743,0.0496429652,-0.2982184887,-0.1103075147,-0.185685128,0.4214314222,0.2078024745,0.5972415209,0.028514469,-0.0613885559,0.1324319392,-0.4377176166,-0.0172750056,-0.0337198712,-0.4259907305,0.1626303345,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/847","title":"multiprocessing in dataset map \"can only test a child process\"","comments":"It looks like this warning : \r\n\"Truncation was not explicitly activated but max_length is provided a specific value, \"\r\nis not handled well by wandb.\r\n\r\nThe error occurs when calling the tokenizer.\r\nMaybe you can try to specify `truncation=True` when calling the tokenizer to remove the warning ?\r\nOtherwise I don't know why wandb would fail on a warning. Maybe one of its logging handlers have some issues with the logging of tokenizers. Maybe @n1t0 knows more about this ?","body":"Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n```","comment_length":80,"text":"multiprocessing in dataset map \"can only test a child process\" \n Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n``` \n It looks like this warning : \r\n\"Truncation was not explicitly activated but max_length is provided a specific value, \"\r\nis not handled well by wandb.\r\n\r\nThe error occurs when calling the tokenizer.\r\nMaybe you can try to specify `truncation=True` when calling the tokenizer to remove the warning ?\r\nOtherwise I don't know why wandb would fail on a warning. Maybe one of its logging handlers have some issues with the logging of tokenizers. Maybe @n1t0 knows more about this ?","embeddings":[-0.3956997097,0.0156290922,-0.1538687646,-0.1850299388,0.141797632,-0.0578565337,0.5068988204,0.3400265574,-0.0834040865,0.1681774557,-0.0184273254,0.3370628655,0.0475015864,0.0918332115,-0.2264437228,0.1021468788,-0.0123605253,0.0946920067,0.1290109754,0.0473636836,-0.1753095239,0.1040760726,-0.2302506417,0.2859587967,-0.4516958296,-0.285730809,-0.0163212903,-0.0623164959,-0.2643237412,-0.4696436226,-0.149113372,0.2851142585,-0.1861115098,0.6030860543,-0.0001044559,0.0936724842,0.2627740204,0.0446500145,-0.054140687,-0.030070236,0.095131956,-0.0515859984,-0.0310001038,-0.4035981894,-0.0044792355,-0.2674306333,0.1779136211,-0.4431346357,0.2878316641,0.3189612627,0.2665320337,0.5655280352,-0.2092227638,-0.0047211065,-0.201201424,-0.0783623531,-0.0357206613,-0.1625539958,0.2167165279,-0.2403003126,-0.3567937613,0.2491746992,-0.0430285074,0.1743139625,-0.3290867805,0.0007867881,0.277649343,-0.237424776,0.3172336221,0.079235658,0.1507846713,-0.2674021721,-0.3465930223,-0.1820102185,-0.2675664425,-0.1380905807,-0.0896170512,0.0866119117,-0.3090318441,0.0237945151,-0.231428802,0.0673615932,-0.0069425311,-0.0971344262,-0.20974648,0.6421571374,0.0204102993,0.2258118987,-0.058426097,0.0421580598,0.1321844459,-0.1236515045,0.1550007761,-0.0283196401,-0.1767590791,-0.1339012235,0.1732696593,-0.3608185351,0.1934940219,-0.0518135242,-0.2702677846,0.1569519043,0.1210899875,0.1657909304,0.1612778902,0.0505134016,0.1559104919,0.2796486914,0.2925396562,-0.196995303,-0.3908066452,-0.0121860905,0.0276292767,-0.2749152184,0.19010818,0.1587806493,0.1859900355,0.0203872453,-0.316894412,-0.0674285442,-0.1310207993,-0.0011093016,0.0109488498,0.4655701518,-0.0625771657,0.210119307,-0.1869856268,0.2766140103,-0.1955184191,-0.1335654557,-0.2674530447,0.1599751413,-0.105241254,-0.0912755728,0.0864879191,0.2284782082,0.3958972394,0.057422515,0.0732819885,-0.0852145255,0.4325413704,-0.219634518,0.2467176765,0.0557839759,0.068621248,0.1359149069,0.26744017,-0.3098887801,-0.1797690243,-0.0748893917,0.0085688289,-0.1883713305,-0.0060650436,0.2934336662,0.0234699752,0.0294385776,-0.1553809643,0.1325114965,0.2363231629,0.0417060144,0.0084723802,-0.1836157143,-0.2125665992,-0.1611631811,0.1659645736,0.2332042754,-0.1246359199,-0.0227329582,0.0209111851,-0.1451625675,0.2747372985,0.2273871899,-0.0913355052,0.6480855346,-0.1730043739,0.5878829956,0.1861093491,-0.5310037136,-0.1901540011,0.2092227191,-0.3177124858,-0.0111948214,0.1317908615,-0.1686787903,0.3851614892,0.080286935,0.1898801774,0.2464483976,-0.1104762852,0.2880237997,-0.2860673666,-0.0096036047,0.0849479064,-0.071969904,0.2393062264,-0.2704798877,-0.0424101911,-0.1621235162,0.2372229397,-0.1482038051,0.2230832577,-0.0443854667,0.0625272393,0.0818433464,-0.0392007828,-0.1215575859,0.0788098946,0.2157844901,0.0433690101,0.0418133661,-0.1905940175,-0.1984119862,-0.0963724405,0.2760661244,-0.2941963971,-0.3430737555,0.3505134284,0.3571797907,-0.0198514294,-0.0224457197,-0.0754135773,0.1201187074,0.1530826986,0.0062083742,0.0715006441,-0.0785518736,-0.1991024315,-0.1974657774,-0.0115251811,0.0726843029,0.1600964069,0.003486624,-0.0784358084,0.4022406936,0.2385911644,0.1009370983,0.2098905593,-0.1776235253,-0.0964289233,0.0071378755,-0.0728402957,0.111530818,0.0388300754,-0.0264948942,0.1444305331,0.4932917356,0.2365600765,0.1932385713,0.2239498198,-0.0101361321,0.2388229668,-0.1069113389,-0.0609275028,-0.1341272146,0.057798285,0.0031344036,0.1119551882,-0.1359223723,-0.2041955888,0.1788984239,0.314935714,-0.0856286287,0.1352984309,-0.1058834121,-0.0881043524,-0.1929530203,0.0814027041,0.1926788986,0.3943273127,0.2004811913,0.1856659055,-0.0386409611,-0.0774591267,-0.0734680966,0.1406773478,0.0299724508,0.1926366687,0.2765003741,0.2475029826,-0.2172683924,-0.3548972011,-0.2435459346,0.1768790036,0.1148345843,-0.1390989274,0.0299772602,-0.3175421357,0.0918787643,-0.0323445313,-0.0665194169,0.2071175724,-0.3488472402,0.1094204113,0.1852676868,-0.1694183499,0.3220192492,-0.1171384901,0.0898978636,-0.0134830419,-0.0217510052,-0.0988093317,-0.1474416703,-0.1254175901,0.1327666491,0.1662156433,0.1449765265,0.4963173568,0.0013359481,-0.2189733684,-0.5480391383,-0.2451718748,0.0971291885,-0.1633996367,0.1954907775,0.2778953016,0.0427568778,-0.0250689685,-0.3409390152,0.3002497554,-0.4462933838,-0.373233676,-0.2133507133,0.0948979482,-0.1722041965,-0.0335693397,-0.3764518201,-0.3236232698,-0.1813369691,0.3523524106,-0.0931495354,0.1716113091,-0.155457288,-0.1054516658,0.1455910206,-0.0203673467,0.1121426821,-0.2187279463,-0.1448161304,-0.0194471348,-0.1510415971,-0.283069551,-0.1560443342,-0.140906021,0.4374369085,-0.10340821,-0.1784181744,-0.0407508872,-0.1612140536,0.4367106259,0.0242293067,-0.0490040779,0.3442159593,0.2696896195,-0.348139286,-0.0873824731,-0.16058743,0.0496429652,-0.2982184887,-0.1103075147,-0.185685128,0.4214314222,0.2078024745,0.5972415209,0.028514469,-0.0613885559,0.1324319392,-0.4377176166,-0.0172750056,-0.0337198712,-0.4259907305,0.1626303345,-0.266784489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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/847","title":"multiprocessing in dataset map \"can only test a child process\"","comments":"I'm having a similar issue but when I try to do multiprocessing with the `DataLoader`\r\n\r\nCode to reproduce:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='\/home\/ad\/Desktop\/bookcorpus', split='train[:1%]')\r\nbook_corpus = book_corpus.map(encode, batched=True, num_proc=20, load_from_cache_file=True, batch_size=5000)\r\nbook_corpus.set_format(type='torch', columns=['text', \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\nfrom transformers import DataCollatorForWholeWordMask\r\nfrom transformers import Trainer, TrainingArguments\r\n\r\ndata_collator = DataCollatorForWholeWordMask(\r\n    tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/mobile_linear_att_8L_128_128_03layerdrop_shared\",\r\n    overwrite_output_dir=True,\r\n    num_train_epochs=1,\r\n    per_device_train_batch_size=64,\r\n    save_steps=50,\r\n    save_total_limit=2,\r\n    logging_first_step=True,\r\n    warmup_steps=100,\r\n    logging_steps=50,\r\n    gradient_accumulation_steps=1,\r\n    fp16=True,\r\n    **dataloader_num_workers=10**,\r\n)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=book_corpus,\r\n    tokenizer=tokenizer)\r\n\r\ntrainer.train()\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<timed eval> in <module>\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/transformers\/trainer.py in train(self, model_path, trial)\r\n    869             self.control = self.callback_handler.on_epoch_begin(self.args, self.state, self.control)\r\n    870 \r\n--> 871             for step, inputs in enumerate(epoch_iterator):\r\n    872 \r\n    873                 # Skip past any already trained steps if resuming training\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    433         if self._sampler_iter is None:\r\n    434             self._reset()\r\n--> 435         data = self._next_data()\r\n    436         self._num_yielded += 1\r\n    437         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n   1083             else:\r\n   1084                 del self._task_info[idx]\r\n-> 1085                 return self._process_data(data)\r\n   1086 \r\n   1087     def _try_put_index(self):\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in _process_data(self, data)\r\n   1109         self._try_put_index()\r\n   1110         if isinstance(data, ExceptionWrapper):\r\n-> 1111             data.reraise()\r\n   1112         return data\r\n   1113 \r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/_utils.py in reraise(self)\r\n    426             # have message field\r\n    427             raise self.exc_type(message=msg)\r\n--> 428         raise self.exc_type(msg)\r\n    429 \r\n    430 \r\n\r\nAssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/worker.py\", line 198, in _worker_loop\r\n    data = fetcher.fetch(index)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1087, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1074, in _getitem\r\n    format_kwargs=format_kwargs,\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 890, in _convert_outputs\r\n    v = map_nested(command, v, **map_nested_kwargs)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 851, in command\r\n    return torch.tensor(x, **format_kwargs)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/warnings.py\", line 101, in _showwarnmsg\r\n    _showwarnmsg_impl(msg)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/warnings.py\", line 30, in _showwarnmsg_impl\r\n    file.write(text)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 100, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 723, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 153, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 158, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 456, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n```\r\n\r\nAs a workaround I have commented line 456 and 457 in `\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py`","body":"Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n```","comment_length":383,"text":"multiprocessing in dataset map \"can only test a child process\" \n Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n``` \n I'm having a similar issue but when I try to do multiprocessing with the `DataLoader`\r\n\r\nCode to reproduce:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='\/home\/ad\/Desktop\/bookcorpus', split='train[:1%]')\r\nbook_corpus = book_corpus.map(encode, batched=True, num_proc=20, load_from_cache_file=True, batch_size=5000)\r\nbook_corpus.set_format(type='torch', columns=['text', \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\nfrom transformers import DataCollatorForWholeWordMask\r\nfrom transformers import Trainer, TrainingArguments\r\n\r\ndata_collator = DataCollatorForWholeWordMask(\r\n    tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/mobile_linear_att_8L_128_128_03layerdrop_shared\",\r\n    overwrite_output_dir=True,\r\n    num_train_epochs=1,\r\n    per_device_train_batch_size=64,\r\n    save_steps=50,\r\n    save_total_limit=2,\r\n    logging_first_step=True,\r\n    warmup_steps=100,\r\n    logging_steps=50,\r\n    gradient_accumulation_steps=1,\r\n    fp16=True,\r\n    **dataloader_num_workers=10**,\r\n)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=book_corpus,\r\n    tokenizer=tokenizer)\r\n\r\ntrainer.train()\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<timed eval> in <module>\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/transformers\/trainer.py in train(self, model_path, trial)\r\n    869             self.control = self.callback_handler.on_epoch_begin(self.args, self.state, self.control)\r\n    870 \r\n--> 871             for step, inputs in enumerate(epoch_iterator):\r\n    872 \r\n    873                 # Skip past any already trained steps if resuming training\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    433         if self._sampler_iter is None:\r\n    434             self._reset()\r\n--> 435         data = self._next_data()\r\n    436         self._num_yielded += 1\r\n    437         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n   1083             else:\r\n   1084                 del self._task_info[idx]\r\n-> 1085                 return self._process_data(data)\r\n   1086 \r\n   1087     def _try_put_index(self):\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in _process_data(self, data)\r\n   1109         self._try_put_index()\r\n   1110         if isinstance(data, ExceptionWrapper):\r\n-> 1111             data.reraise()\r\n   1112         return data\r\n   1113 \r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/_utils.py in reraise(self)\r\n    426             # have message field\r\n    427             raise self.exc_type(message=msg)\r\n--> 428         raise self.exc_type(msg)\r\n    429 \r\n    430 \r\n\r\nAssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/worker.py\", line 198, in _worker_loop\r\n    data = fetcher.fetch(index)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1087, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1074, in _getitem\r\n    format_kwargs=format_kwargs,\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 890, in _convert_outputs\r\n    v = map_nested(command, v, **map_nested_kwargs)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 851, in command\r\n    return torch.tensor(x, **format_kwargs)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/warnings.py\", line 101, in _showwarnmsg\r\n    _showwarnmsg_impl(msg)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/warnings.py\", line 30, in _showwarnmsg_impl\r\n    file.write(text)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 100, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 723, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 153, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 158, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 456, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n```\r\n\r\nAs a workaround I have commented line 456 and 457 in `\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py`","embeddings":[-0.3956997097,0.0156290922,-0.1538687646,-0.1850299388,0.141797632,-0.0578565337,0.5068988204,0.3400265574,-0.0834040865,0.1681774557,-0.0184273254,0.3370628655,0.0475015864,0.0918332115,-0.2264437228,0.1021468788,-0.0123605253,0.0946920067,0.1290109754,0.0473636836,-0.1753095239,0.1040760726,-0.2302506417,0.2859587967,-0.4516958296,-0.285730809,-0.0163212903,-0.0623164959,-0.2643237412,-0.4696436226,-0.149113372,0.2851142585,-0.1861115098,0.6030860543,-0.0001044559,0.0936724842,0.2627740204,0.0446500145,-0.054140687,-0.030070236,0.095131956,-0.0515859984,-0.0310001038,-0.4035981894,-0.0044792355,-0.2674306333,0.1779136211,-0.4431346357,0.2878316641,0.3189612627,0.2665320337,0.5655280352,-0.2092227638,-0.0047211065,-0.201201424,-0.0783623531,-0.0357206613,-0.1625539958,0.2167165279,-0.2403003126,-0.3567937613,0.2491746992,-0.0430285074,0.1743139625,-0.3290867805,0.0007867881,0.277649343,-0.237424776,0.3172336221,0.079235658,0.1507846713,-0.2674021721,-0.3465930223,-0.1820102185,-0.2675664425,-0.1380905807,-0.0896170512,0.0866119117,-0.3090318441,0.0237945151,-0.231428802,0.0673615932,-0.0069425311,-0.0971344262,-0.20974648,0.6421571374,0.0204102993,0.2258118987,-0.058426097,0.0421580598,0.1321844459,-0.1236515045,0.1550007761,-0.0283196401,-0.1767590791,-0.1339012235,0.1732696593,-0.3608185351,0.1934940219,-0.0518135242,-0.2702677846,0.1569519043,0.1210899875,0.1657909304,0.1612778902,0.0505134016,0.1559104919,0.2796486914,0.2925396562,-0.196995303,-0.3908066452,-0.0121860905,0.0276292767,-0.2749152184,0.19010818,0.1587806493,0.1859900355,0.0203872453,-0.316894412,-0.0674285442,-0.1310207993,-0.0011093016,0.0109488498,0.4655701518,-0.0625771657,0.210119307,-0.1869856268,0.2766140103,-0.1955184191,-0.1335654557,-0.2674530447,0.1599751413,-0.105241254,-0.0912755728,0.0864879191,0.2284782082,0.3958972394,0.057422515,0.0732819885,-0.0852145255,0.4325413704,-0.219634518,0.2467176765,0.0557839759,0.068621248,0.1359149069,0.26744017,-0.3098887801,-0.1797690243,-0.0748893917,0.0085688289,-0.1883713305,-0.0060650436,0.2934336662,0.0234699752,0.0294385776,-0.1553809643,0.1325114965,0.2363231629,0.0417060144,0.0084723802,-0.1836157143,-0.2125665992,-0.1611631811,0.1659645736,0.2332042754,-0.1246359199,-0.0227329582,0.0209111851,-0.1451625675,0.2747372985,0.2273871899,-0.0913355052,0.6480855346,-0.1730043739,0.5878829956,0.1861093491,-0.5310037136,-0.1901540011,0.2092227191,-0.3177124858,-0.0111948214,0.1317908615,-0.1686787903,0.3851614892,0.080286935,0.1898801774,0.2464483976,-0.1104762852,0.2880237997,-0.2860673666,-0.0096036047,0.0849479064,-0.071969904,0.2393062264,-0.2704798877,-0.0424101911,-0.1621235162,0.2372229397,-0.1482038051,0.2230832577,-0.0443854667,0.0625272393,0.0818433464,-0.0392007828,-0.1215575859,0.0788098946,0.2157844901,0.0433690101,0.0418133661,-0.1905940175,-0.1984119862,-0.0963724405,0.2760661244,-0.2941963971,-0.3430737555,0.3505134284,0.3571797907,-0.0198514294,-0.0224457197,-0.0754135773,0.1201187074,0.1530826986,0.0062083742,0.0715006441,-0.0785518736,-0.1991024315,-0.1974657774,-0.0115251811,0.0726843029,0.1600964069,0.003486624,-0.0784358084,0.4022406936,0.2385911644,0.1009370983,0.2098905593,-0.1776235253,-0.0964289233,0.0071378755,-0.0728402957,0.111530818,0.0388300754,-0.0264948942,0.1444305331,0.4932917356,0.2365600765,0.1932385713,0.2239498198,-0.0101361321,0.2388229668,-0.1069113389,-0.0609275028,-0.1341272146,0.057798285,0.0031344036,0.1119551882,-0.1359223723,-0.2041955888,0.1788984239,0.314935714,-0.0856286287,0.1352984309,-0.1058834121,-0.0881043524,-0.1929530203,0.0814027041,0.1926788986,0.3943273127,0.2004811913,0.1856659055,-0.0386409611,-0.0774591267,-0.0734680966,0.1406773478,0.0299724508,0.1926366687,0.2765003741,0.2475029826,-0.2172683924,-0.3548972011,-0.2435459346,0.1768790036,0.1148345843,-0.1390989274,0.0299772602,-0.3175421357,0.0918787643,-0.0323445313,-0.0665194169,0.2071175724,-0.3488472402,0.1094204113,0.1852676868,-0.1694183499,0.3220192492,-0.1171384901,0.0898978636,-0.0134830419,-0.0217510052,-0.0988093317,-0.1474416703,-0.1254175901,0.1327666491,0.1662156433,0.1449765265,0.4963173568,0.0013359481,-0.2189733684,-0.5480391383,-0.2451718748,0.0971291885,-0.1633996367,0.1954907775,0.2778953016,0.0427568778,-0.0250689685,-0.3409390152,0.3002497554,-0.4462933838,-0.373233676,-0.2133507133,0.0948979482,-0.1722041965,-0.0335693397,-0.3764518201,-0.3236232698,-0.1813369691,0.3523524106,-0.0931495354,0.1716113091,-0.155457288,-0.1054516658,0.1455910206,-0.0203673467,0.1121426821,-0.2187279463,-0.1448161304,-0.0194471348,-0.1510415971,-0.283069551,-0.1560443342,-0.140906021,0.4374369085,-0.10340821,-0.1784181744,-0.0407508872,-0.1612140536,0.4367106259,0.0242293067,-0.0490040779,0.3442159593,0.2696896195,-0.348139286,-0.0873824731,-0.16058743,0.0496429652,-0.2982184887,-0.1103075147,-0.185685128,0.4214314222,0.2078024745,0.5972415209,0.028514469,-0.0613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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/847","title":"multiprocessing in dataset map \"can only test a child process\"","comments":"Isn't it more the pytorch warning on the use of non-writable memory for tensor that trigger this here @lhoestq? (since it seems to be a warning triggered in `torch.tensor()`","body":"Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n```","comment_length":29,"text":"multiprocessing in dataset map \"can only test a child process\" \n Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n``` \n Isn't it more the pytorch warning on the use of non-writable memory for tensor that trigger this here @lhoestq? (since it seems to be a warning triggered in `torch.tensor()`","embeddings":[-0.3956997097,0.0156290922,-0.1538687646,-0.1850299388,0.141797632,-0.0578565337,0.5068988204,0.3400265574,-0.0834040865,0.1681774557,-0.0184273254,0.3370628655,0.0475015864,0.0918332115,-0.2264437228,0.1021468788,-0.0123605253,0.0946920067,0.1290109754,0.0473636836,-0.1753095239,0.1040760726,-0.2302506417,0.2859587967,-0.4516958296,-0.285730809,-0.0163212903,-0.0623164959,-0.2643237412,-0.4696436226,-0.149113372,0.2851142585,-0.1861115098,0.6030860543,-0.0001044559,0.0936724842,0.2627740204,0.0446500145,-0.054140687,-0.030070236,0.095131956,-0.0515859984,-0.0310001038,-0.4035981894,-0.0044792355,-0.2674306333,0.1779136211,-0.4431346357,0.2878316641,0.3189612627,0.2665320337,0.5655280352,-0.2092227638,-0.0047211065,-0.201201424,-0.0783623531,-0.0357206613,-0.1625539958,0.2167165279,-0.2403003126,-0.3567937613,0.2491746992,-0.0430285074,0.1743139625,-0.3290867805,0.0007867881,0.277649343,-0.237424776,0.3172336221,0.079235658,0.1507846713,-0.2674021721,-0.3465930223,-0.1820102185,-0.2675664425,-0.1380905807,-0.0896170512,0.0866119117,-0.3090318441,0.0237945151,-0.231428802,0.0673615932,-0.0069425311,-0.0971344262,-0.20974648,0.6421571374,0.0204102993,0.2258118987,-0.058426097,0.0421580598,0.1321844459,-0.1236515045,0.1550007761,-0.0283196401,-0.1767590791,-0.1339012235,0.1732696593,-0.3608185351,0.1934940219,-0.0518135242,-0.2702677846,0.1569519043,0.1210899875,0.1657909304,0.1612778902,0.0505134016,0.1559104919,0.2796486914,0.2925396562,-0.196995303,-0.3908066452,-0.0121860905,0.0276292767,-0.2749152184,0.19010818,0.1587806493,0.1859900355,0.0203872453,-0.316894412,-0.0674285442,-0.1310207993,-0.0011093016,0.0109488498,0.4655701518,-0.0625771657,0.210119307,-0.1869856268,0.2766140103,-0.1955184191,-0.1335654557,-0.2674530447,0.1599751413,-0.105241254,-0.0912755728,0.0864879191,0.2284782082,0.3958972394,0.057422515,0.0732819885,-0.0852145255,0.4325413704,-0.219634518,0.2467176765,0.0557839759,0.068621248,0.1359149069,0.26744017,-0.3098887801,-0.1797690243,-0.0748893917,0.0085688289,-0.1883713305,-0.0060650436,0.2934336662,0.0234699752,0.0294385776,-0.1553809643,0.1325114965,0.2363231629,0.0417060144,0.0084723802,-0.1836157143,-0.2125665992,-0.1611631811,0.1659645736,0.2332042754,-0.1246359199,-0.0227329582,0.0209111851,-0.1451625675,0.2747372985,0.2273871899,-0.0913355052,0.6480855346,-0.1730043739,0.5878829956,0.1861093491,-0.5310037136,-0.1901540011,0.2092227191,-0.3177124858,-0.0111948214,0.1317908615,-0.1686787903,0.3851614892,0.080286935,0.1898801774,0.2464483976,-0.1104762852,0.2880237997,-0.2860673666,-0.0096036047,0.0849479064,-0.071969904,0.2393062264,-0.2704798877,-0.0424101911,-0.1621235162,0.2372229397,-0.1482038051,0.2230832577,-0.0443854667,0.0625272393,0.0818433464,-0.0392007828,-0.1215575859,0.0788098946,0.2157844901,0.0433690101,0.0418133661,-0.1905940175,-0.1984119862,-0.0963724405,0.2760661244,-0.2941963971,-0.3430737555,0.3505134284,0.3571797907,-0.0198514294,-0.0224457197,-0.0754135773,0.1201187074,0.1530826986,0.0062083742,0.0715006441,-0.0785518736,-0.1991024315,-0.1974657774,-0.0115251811,0.0726843029,0.1600964069,0.003486624,-0.0784358084,0.4022406936,0.2385911644,0.1009370983,0.2098905593,-0.1776235253,-0.0964289233,0.0071378755,-0.0728402957,0.111530818,0.0388300754,-0.0264948942,0.1444305331,0.4932917356,0.2365600765,0.1932385713,0.2239498198,-0.0101361321,0.2388229668,-0.1069113389,-0.0609275028,-0.1341272146,0.057798285,0.0031344036,0.1119551882,-0.1359223723,-0.2041955888,0.1788984239,0.314935714,-0.0856286287,0.1352984309,-0.1058834121,-0.0881043524,-0.1929530203,0.0814027041,0.1926788986,0.3943273127,0.2004811913,0.1856659055,-0.0386409611,-0.0774591267,-0.0734680966,0.1406773478,0.0299724508,0.1926366687,0.2765003741,0.2475029826,-0.2172683924,-0.3548972011,-0.2435459346,0.1768790036,0.1148345843,-0.1390989274,0.0299772602,-0.3175421357,0.0918787643,-0.0323445313,-0.0665194169,0.2071175724,-0.3488472402,0.1094204113,0.1852676868,-0.1694183499,0.3220192492,-0.1171384901,0.0898978636,-0.0134830419,-0.0217510052,-0.0988093317,-0.1474416703,-0.1254175901,0.1327666491,0.1662156433,0.1449765265,0.4963173568,0.0013359481,-0.2189733684,-0.5480391383,-0.2451718748,0.0971291885,-0.1633996367,0.1954907775,0.2778953016,0.0427568778,-0.0250689685,-0.3409390152,0.3002497554,-0.4462933838,-0.373233676,-0.2133507133,0.0948979482,-0.1722041965,-0.0335693397,-0.3764518201,-0.3236232698,-0.1813369691,0.3523524106,-0.0931495354,0.1716113091,-0.155457288,-0.1054516658,0.1455910206,-0.0203673467,0.1121426821,-0.2187279463,-0.1448161304,-0.0194471348,-0.1510415971,-0.283069551,-0.1560443342,-0.140906021,0.4374369085,-0.10340821,-0.1784181744,-0.0407508872,-0.1612140536,0.4367106259,0.0242293067,-0.0490040779,0.3442159593,0.2696896195,-0.348139286,-0.0873824731,-0.16058743,0.0496429652,-0.2982184887,-0.1103075147,-0.185685128,0.4214314222,0.2078024745,0.5972415209,0.028514469,-0.0613885559,0.1324319392,-0.4377176166,-0.0172750056,-0.0337198712,-0.4259907305,0.16263033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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/847","title":"multiprocessing in dataset map \"can only test a child process\"","comments":"Yep this time this is a warning from pytorch that causes wandb to not work properly.\r\nCould this by a wandb issue ?","body":"Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n```","comment_length":23,"text":"multiprocessing in dataset map \"can only test a child process\" \n Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n``` \n Yep this time this is a warning from pytorch that causes wandb to not work properly.\r\nCould this by a wandb issue 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/847","title":"multiprocessing in dataset map \"can only test a child process\"","comments":"Hi @timothyjlaurent @gaceladri \r\nIf you're running `transformers` from `master` you can try setting the env var `WAND_DISABLE=true` (from https:\/\/github.com\/huggingface\/transformers\/pull\/9896) and try again ?\r\nThis issue might be related to https:\/\/github.com\/huggingface\/transformers\/issues\/9623 ","body":"Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n```","comment_length":30,"text":"multiprocessing in dataset map \"can only test a child process\" \n Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n``` \n Hi @timothyjlaurent @gaceladri \r\nIf you're running `transformers` from `master` you can try setting the env var `WAND_DISABLE=true` (from https:\/\/github.com\/huggingface\/transformers\/pull\/9896) and try again ?\r\nThis issue might be related to https:\/\/github.com\/huggingface\/transformers\/issues\/9623 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/847","title":"multiprocessing in dataset map \"can only test a child process\"","comments":"I have commented the lines that cause my code break. I'm now seeing my reports on Wandb and my code does not break. I am training now, so I will check probably in 6 hours. I suppose that setting wandb disable will work as well.","body":"Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n```","comment_length":45,"text":"multiprocessing in dataset map \"can only test a child process\" \n Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.\r\n\r\n``` \r\ndef tokenizer_fn(example):\r\n    return tokenizer.batch_encode_plus(example['text'])\r\n\r\nds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])\r\n```\r\n\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nRemoteTraceback                           Traceback (most recent call last)\r\nRemoteTraceback: \r\n\"\"\"\r\nTraceback (most recent call last):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/multiprocess\/pool.py\", line 119, in worker\r\n    result = (True, func(*args, **kwds))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 156, in wrapper\r\n    out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1510, in _map_single\r\n    for i in pbar:\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 228, in __iter__\r\n    for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1186, in __iter__\r\n    self.close()\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/notebook.py\", line 251, in close\r\n    super(tqdm_notebook, self).close(*args, **kwargs)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1291, in close\r\n    fp_write('')\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/tqdm\/std.py\", line 1288, in fp_write\r\n    self.fp.write(_unicode(s))\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 91, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 598, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 146, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 151, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/jovyan\/share\/users\/tlaurent\/invitae-bert\/ve\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 431, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/usr\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\"\"\"\r\n``` \n I have commented the lines that cause my code break. I'm now seeing my reports on Wandb and my code does not break. I am training now, so I will check probably in 6 hours. I suppose that setting wandb disable will work as well.","embeddings":[-0.3956997097,0.0156290922,-0.1538687646,-0.1850299388,0.141797632,-0.0578565337,0.5068988204,0.3400265574,-0.0834040865,0.1681774557,-0.0184273254,0.3370628655,0.0475015864,0.0918332115,-0.2264437228,0.1021468788,-0.0123605253,0.0946920067,0.1290109754,0.0473636836,-0.1753095239,0.1040760726,-0.2302506417,0.2859587967,-0.4516958296,-0.285730809,-0.0163212903,-0.0623164959,-0.2643237412,-0.4696436226,-0.149113372,0.2851142585,-0.1861115098,0.6030860543,-0.0001044559,0.0936724842,0.2627740204,0.0446500145,-0.054140687,-0.030070236,0.095131956,-0.0515859984,-0.0310001038,-0.4035981894,-0.0044792355,-0.2674306333,0.1779136211,-0.4431346357,0.2878316641,0.3189612627,0.2665320337,0.5655280352,-0.2092227638,-0.0047211065,-0.201201424,-0.0783623531,-0.0357206613,-0.1625539958,0.2167165279,-0.2403003126,-0.3567937613,0.2491746992,-0.0430285074,0.1743139625,-0.3290867805,0.0007867881,0.277649343,-0.237424776,0.3172336221,0.079235658,0.1507846713,-0.2674021721,-0.3465930223,-0.1820102185,-0.2675664425,-0.1380905807,-0.0896170512,0.0866119117,-0.3090318441,0.0237945151,-0.231428802,0.0673615932,-0.0069425311,-0.0971344262,-0.20974648,0.6421571374,0.0204102993,0.2258118987,-0.058426097,0.0421580598,0.1321844459,-0.1236515045,0.1550007761,-0.0283196401,-0.1767590791,-0.1339012235,0.1732696593,-0.3608185351,0.1934940219,-0.0518135242,-0.2702677846,0.1569519043,0.1210899875,0.1657909304,0.1612778902,0.0505134016,0.1559104919,0.2796486914,0.2925396562,-0.196995303,-0.3908066452,-0.0121860905,0.0276292767,-0.2749152184,0.19010818,0.1587806493,0.1859900355,0.0203872453,-0.316894412,-0.0674285442,-0.1310207993,-0.0011093016,0.0109488498,0.4655701518,-0.0625771657,0.210119307,-0.1869856268,0.2766140103,-0.1955184191,-0.1335654557,-0.2674530447,0.1599751413,-0.105241254,-0.0912755728,0.0864879191,0.2284782082,0.3958972394,0.057422515,0.0732819885,-0.0852145255,0.4325413704,-0.219634518,0.2467176765,0.0557839759,0.068621248,0.1359149069,0.26744017,-0.3098887801,-0.1797690243,-0.0748893917,0.0085688289,-0.1883713305,-0.0060650436,0.2934336662,0.0234699752,0.0294385776,-0.1553809643,0.1325114965,0.2363231629,0.0417060144,0.0084723802,-0.1836157143,-0.2125665992,-0.1611631811,0.1659645736,0.2332042754,-0.1246359199,-0.0227329582,0.0209111851,-0.1451625675,0.2747372985,0.2273871899,-0.0913355052,0.6480855346,-0.1730043739,0.5878829956,0.1861093491,-0.5310037136,-0.1901540011,0.2092227191,-0.3177124858,-0.0111948214,0.1317908615,-0.1686787903,0.3851614892,0.080286935,0.1898801774,0.2464483976,-0.1104762852,0.2880237997,-0.2860673666,-0.0096036047,0.0849479064,-0.071969904,0.2393062264,-0.2704798877,-0.0424101911,-0.1621235162,0.2372229397,-0.1482038051,0.2230832577,-0.0443854667,0.0625272393,0.0818433464,-0.0392007828,-0.1215575859,0.0788098946,0.2157844901,0.0433690101,0.0418133661,-0.1905940175,-0.1984119862,-0.0963724405,0.2760661244,-0.2941963971,-0.3430737555,0.3505134284,0.3571797907,-0.0198514294,-0.0224457197,-0.0754135773,0.1201187074,0.1530826986,0.0062083742,0.0715006441,-0.0785518736,-0.1991024315,-0.1974657774,-0.0115251811,0.0726843029,0.1600964069,0.003486624,-0.0784358084,0.4022406936,0.2385911644,0.1009370983,0.2098905593,-0.1776235253,-0.0964289233,0.0071378755,-0.0728402957,0.111530818,0.0388300754,-0.0264948942,0.1444305331,0.4932917356,0.2365600765,0.1932385713,0.2239498198,-0.0101361321,0.2388229668,-0.1069113389,-0.0609275028,-0.1341272146,0.057798285,0.0031344036,0.1119551882,-0.1359223723,-0.2041955888,0.1788984239,0.314935714,-0.0856286287,0.1352984309,-0.1058834121,-0.0881043524,-0.1929530203,0.0814027041,0.1926788986,0.3943273127,0.2004811913,0.1856659055,-0.0386409611,-0.0774591267,-0.0734680966,0.1406773478,0.0299724508,0.1926366687,0.2765003741,0.2475029826,-0.2172683924,-0.3548972011,-0.2435459346,0.1768790036,0.1148345843,-0.1390989274,0.0299772602,-0.3175421357,0.0918787643,-0.0323445313,-0.0665194169,0.2071175724,-0.3488472402,0.1094204113,0.1852676868,-0.1694183499,0.3220192492,-0.1171384901,0.0898978636,-0.0134830419,-0.0217510052,-0.0988093317,-0.1474416703,-0.1254175901,0.1327666491,0.1662156433,0.1449765265,0.4963173568,0.0013359481,-0.2189733684,-0.5480391383,-0.2451718748,0.0971291885,-0.1633996367,0.1954907775,0.2778953016,0.0427568778,-0.0250689685,-0.3409390152,0.3002497554,-0.4462933838,-0.373233676,-0.2133507133,0.0948979482,-0.1722041965,-0.0335693397,-0.3764518201,-0.3236232698,-0.1813369691,0.3523524106,-0.0931495354,0.1716113091,-0.155457288,-0.1054516658,0.1455910206,-0.0203673467,0.1121426821,-0.2187279463,-0.1448161304,-0.0194471348,-0.1510415971,-0.283069551,-0.1560443342,-0.140906021,0.4374369085,-0.10340821,-0.1784181744,-0.0407508872,-0.1612140536,0.4367106259,0.0242293067,-0.0490040779,0.3442159593,0.2696896195,-0.348139286,-0.0873824731,-0.16058743,0.0496429652,-0.2982184887,-0.1103075147,-0.185685128,0.4214314222,0.2078024745,0.5972415209,0.028514469,-0.0613885559,0.1324319392,-0.4377176166,-0.0172750056,-0.0337198712,-0.4259907305,0.1626303345,-0.26678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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/846","title":"Add HoVer multi-hop fact verification dataset","comments":"Hi @yjernite  I'm new but wanted to contribute. Has anyone already taken this problem and do you think it is suitable for newbies?","body":"## Adding a Dataset\r\n- **Name:** HoVer\r\n- **Description:** https:\/\/twitter.com\/YichenJiang9\/status\/1326954363806429186 contains 20K claim verification examples\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2011.03088\r\n- **Data:** https:\/\/hover-nlp.github.io\/\r\n- **Motivation:** There are still few multi-hop information extraction benchmarks (HotpotQA, which dataset wase based off, notwithstanding)\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":23,"text":"Add HoVer multi-hop fact verification dataset \n ## Adding a Dataset\r\n- **Name:** HoVer\r\n- **Description:** https:\/\/twitter.com\/YichenJiang9\/status\/1326954363806429186 contains 20K claim verification examples\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2011.03088\r\n- **Data:** https:\/\/hover-nlp.github.io\/\r\n- **Motivation:** There are still few multi-hop information extraction benchmarks (HotpotQA, which dataset wase based off, notwithstanding)\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n Hi @yjernite  I'm new but wanted to contribute. Has anyone already taken this problem and do you think it is suitable for newbies?","embeddings":[-0.2272989005,-0.0823319703,0.0003385669,0.1499359757,-0.4059508145,-0.034600284,0.1919758469,0.1057019457,0.0961782411,-0.1447629035,-0.0471815988,-0.1035467982,-0.2635313869,0.2344664782,0.2132848501,-0.0595738515,-0.1592912972,-0.194790706,0.1115603521,0.009324884,-0.0119550144,0.0130747324,-0.2015615404,0.0045057177,-0.5718672276,0.0330752693,-0.2534197867,-0.1548031569,0.085829176,-0.3601928651,0.2060377002,0.6282486916,0.1120479181,0.5196282864,-0.0001165368,-0.3225642741,0.1629251093,0.0532238297,-0.2220108807,-0.0875658318,-0.23451294,0.0085838782,0.0981310904,-0.1646174341,0.0027158612,0.0003690502,-0.3450667858,0.0169514809,0.2611086667,0.1450025737,0.1791539639,0.2080793083,-0.1005195454,0.0022978534,0.2288694829,-0.0532467775,0.0240708869,0.5337884426,0.1278332323,0.0082168104,-0.3305610418,0.2556033432,-0.0482346192,0.0952407345,0.0296689831,-0.2165821046,0.2072823197,-0.0248800274,-0.1847686172,0.6821916103,0.1113199666,-0.1358933747,-0.3626839221,-0.1780867875,0.1415117979,0.1910554618,0.0108547676,0.0837788507,-0.3718945682,0.2523231208,-0.2055734992,-0.4319069088,-0.0509221964,0.0619594045,0.3457696438,0.1788576692,-0.0137493173,0.1511368901,0.2833220661,0.1685571223,0.1721879393,-0.3932555318,0.1634166688,-0.0612804182,-0.2462203205,-0.3360615373,0.3016603291,0.4368488193,0.3293212354,0.408415705,0.1978220791,0.1490226984,-0.3179811835,0.0085508069,0.1067508161,-0.1359966099,-0.365855515,-0.4572265744,0.3480314314,0.0410776399,0.1714973897,0.0878176093,0.089294225,0.2867174149,-0.3137625158,0.1096377447,0.1290668398,-0.2129604816,-0.1475043297,0.1170882434,-0.4780736566,-0.4129661322,0.1277894229,-0.1038535833,-0.0501338355,0.3127092421,0.2355261147,0.1694095731,0.1646916717,-0.3821560442,-0.1032017544,0.3580859303,-0.0851727203,0.1719325185,0.2503606379,0.1378546506,-0.1598970145,-0.0078856191,0.2475170344,0.1192437336,0.1588481665,-0.2928923666,-0.0950281546,-0.2073489875,-0.2217977345,-0.0861623213,0.040520221,-0.1773965061,-0.0580103956,0.1205490008,0.1289972961,-0.2766111791,-0.0955953449,0.113270469,-0.5047513247,-0.076026611,-0.4323448241,0.5820619464,-0.0895041451,0.2344906628,-0.0069962996,0.1698775291,0.0283586793,-0.0424429923,0.0730624348,0.4558426142,0.0693438277,-0.0632667169,-0.1861064583,-0.0203272663,-0.1498075277,-0.1419146657,-0.0407370627,0.3462034762,-0.186228931,0.074893102,0.1125301272,-0.4129200876,-0.0766671523,0.2678289115,-0.3645820618,0.0779021084,0.3461769223,0.1696006507,0.0150969187,-0.3515820205,-0.2433547378,0.5789194703,-0.0191441067,0.0034195944,-0.457888335,-0.0963017642,0.0129377283,0.2265038788,0.0787469819,-0.3378924131,-0.0535279885,0.3043085337,0.3111548126,-0.0393290445,0.2238323092,-0.0580610447,0.2108476311,-0.6122569442,-0.0277998205,-0.1950296462,-0.1623071879,-0.0837937295,0.2959181964,0.4300269783,0.3091977835,-0.1348819733,-0.1295538247,-0.0997592211,-0.2761282623,-0.4181807339,0.1464835405,0.3087125719,0.1575443745,0.0618326738,-0.0519811809,-0.0381782576,0.0885732621,0.0160052553,0.1007430702,0.4683502913,-0.0116548184,0.0949854255,0.008958105,0.3744881749,-0.1441869289,-0.2735956013,0.0491674617,0.4318183064,-0.2593183219,-0.0692741424,0.7123370767,0.2589343488,0.3165448904,-0.4310328662,0.2104399949,-0.0167118199,0.1257604659,-0.1446498036,-0.2468677312,0.345398128,0.1053264886,0.1579203159,-0.0618477389,0.1428497583,0.0796139091,-0.14160873,-0.288089484,-0.0173713434,0.4400113523,0.0126462849,0.2279019505,-0.2999400496,-0.0706615523,0.2726605535,-0.0812865794,0.0399397314,0.2333283722,0.1611472666,-0.0433438905,-0.2864331603,-0.0294447709,0.1646221578,0.3358062208,0.1740187705,0.2849569321,-0.0659761131,0.0629301071,-0.0905710459,0.209753409,-0.1903973669,-0.2169682235,0.0436335318,0.062459413,0.0254511628,-0.1361393481,-0.4984517992,0.2366655469,0.3610859811,-0.1582579017,-0.1105240285,-0.1688098758,-0.307507813,-0.1922272742,-0.341814816,-0.2188437879,-0.4317831695,0.0095069604,0.0510069206,0.2749845088,0.361848712,0.2475446165,0.4854172468,-0.0482300669,0.4010957479,-0.1088409349,-0.1563973129,-0.0341116935,0.1446636766,0.2256753743,0.141429171,0.4724613428,0.0736723244,0.1783833057,-0.2351920009,-0.3586514294,0.2105641514,-0.0159761328,0.2286697179,0.2341108918,0.0499635339,0.2000391185,0.2142780572,0.1577644348,0.0015248955,-0.1417124718,0.1305211782,-0.1321739703,0.0097809955,-0.1054687798,0.1849376857,-0.2513540387,-0.1153514013,0.3771843016,0.0288773943,-0.0870286673,0.0213880111,-0.114605166,0.324867785,-0.1924210936,0.0294510014,-0.287555486,-0.5863478184,-0.0235058051,-0.081751585,-0.0255939867,-0.3916584849,-0.3142075539,0.1521116644,-0.1375472695,-0.5253841877,-0.3399840891,0.0022224619,0.2595570683,0.1521728635,0.0431391187,0.2412624806,-0.1067442372,-0.1025691703,0.0414261408,0.0672295243,0.0882845446,-0.1683748215,0.3496380746,0.2270532101,-0.1118773222,0.5127782822,0.4072456658,0.106169574,0.063926585,0.2201278061,-0.0686427876,0.0777291283,0.1895662546,-0.5597390532,0.4821096361,-0.031890139,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/846","title":"Add HoVer multi-hop fact verification dataset","comments":"Hi @tenjjin! This dataset is still up for grabs! Here's the link with the guide to add it. You should play around with the library first (download and look at a few datasets), then follow the steps here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md","body":"## Adding a Dataset\r\n- **Name:** HoVer\r\n- **Description:** https:\/\/twitter.com\/YichenJiang9\/status\/1326954363806429186 contains 20K claim verification examples\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2011.03088\r\n- **Data:** https:\/\/hover-nlp.github.io\/\r\n- **Motivation:** There are still few multi-hop information extraction benchmarks (HotpotQA, which dataset wase based off, notwithstanding)\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":39,"text":"Add HoVer multi-hop fact verification dataset \n ## Adding a Dataset\r\n- **Name:** HoVer\r\n- **Description:** https:\/\/twitter.com\/YichenJiang9\/status\/1326954363806429186 contains 20K claim verification examples\r\n- **Paper:** https:\/\/arxiv.org\/abs\/2011.03088\r\n- **Data:** https:\/\/hover-nlp.github.io\/\r\n- **Motivation:** There are still few multi-hop information extraction benchmarks (HotpotQA, which dataset wase based off, notwithstanding)\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n Hi @tenjjin! This dataset is still up for grabs! Here's the link with the guide to add it. You should play around with the library first (download and look at a few datasets), then follow the steps here:\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md","embeddings":[-0.2867436111,-0.2775808871,-0.0820240155,0.0229686629,-0.2790857255,-0.1241937578,0.1645177305,0.114708744,0.0981632844,0.1487334222,-0.0963040292,0.0598273389,-0.0950224623,0.2981519401,0.2122938484,-0.1031539589,-0.1313484758,-0.0484599657,-0.0788757727,0.0388446972,0.033641424,0.1323769987,-0.1098015383,-0.0514863804,-0.3951320946,-0.011697568,-0.1528687775,-0.1421919912,0.1767664999,-0.347092092,0.0790029094,0.4557312131,-0.0380147658,0.352250129,-0.0001060055,-0.1624501348,0.1336018741,0.0436690412,-0.2459265739,0.0830911249,-0.080947347,0.0728994682,0.1475116313,-0.097418502,-0.1465436071,-0.0416963473,-0.2358896434,0.137357682,0.3529121578,0.151205942,0.2692036629,0.3744874299,-0.0909703895,-0.1768823266,0.1466149986,-0.1151665598,0.0339635424,0.4747840166,0.0599792004,-0.0117558846,-0.4476830065,0.4065052867,0.0009208469,0.1696484983,-0.1179278716,-0.1064522639,0.1529274732,-0.0746432841,-0.0437307246,0.6290504932,-0.1349589825,-0.146775946,-0.3264868855,-0.2491106391,0.0428118929,0.1343468279,0.0443006642,0.1490805745,-0.2735669315,0.2022499293,-0.2149324715,-0.0940118581,-0.0298706461,0.052622743,0.2132790685,0.1690588295,-0.1091086045,-0.0234358348,0.3585716486,0.1595171243,-0.035753388,-0.2431963384,0.1687718183,-0.1860033572,-0.0606004298,-0.2975720167,0.2898409963,0.3649698496,0.3671050668,0.3012627363,0.265204519,0.1582503766,-0.2483098954,0.0941536129,-0.0687469319,-0.0953088403,-0.2874856293,-0.5833560824,0.3475053012,0.2314156741,0.1296987236,0.0780098587,0.1783207357,0.2515965402,-0.3900646865,0.0836937949,-0.1611859053,-0.1440847665,-0.1861333102,0.106196709,-0.3253612816,-0.1250450313,0.0548867285,0.0711383373,-0.100818716,0.3313430846,0.1849272549,0.0917672366,0.0834687352,-0.3077295721,-0.2107056379,0.3382160366,-0.0327012278,0.1933873296,0.2491162419,0.0862507001,0.1802133322,0.1143538803,0.221690774,0.0304584447,0.1632221341,-0.2651650608,0.0008842012,-0.0709963441,-0.0951058418,-0.2264544666,0.0763855502,-0.1893618554,-0.1272076815,-0.1004316881,0.10269209,-0.1465356201,-0.1066593155,0.2135045826,-0.3886550665,-0.1913703233,-0.2606023848,0.4759401679,-0.1730584055,0.2680049837,0.0965695009,0.2344008088,0.1171851829,0.0083590727,0.1605289727,0.368747443,0.1212783903,-0.2506896257,0.0331295729,-0.0858479887,-0.1767145693,-0.0707864091,-0.0486028716,0.2160945088,-0.2160632759,0.1856982559,-0.0379081219,-0.3163784444,-0.1167093366,0.0720605105,-0.3015666902,0.0878662542,0.2680449188,0.0802268386,0.0543415174,-0.1052969322,-0.166125074,0.5198565125,0.096768342,0.1907670647,-0.3441918194,-0.1677937806,-0.2322195321,0.1796374023,-0.0135582099,-0.320284158,0.011876368,0.2393240035,0.3109693825,-0.0990885794,0.1330364794,0.0712411627,0.1044498459,-0.4205801487,-0.1068930253,-0.2172247916,-0.0872856006,-0.1155741513,0.0521446429,0.4067137837,0.1621482968,-0.2223690748,-0.2138919085,-0.0603819042,-0.1263387203,-0.418602109,0.3150726259,0.4057452381,0.1784080714,0.1347752362,0.0722763464,-0.0547275767,0.0883349776,0.1120368987,0.1067325398,0.286919117,-0.0464472398,-0.0571552925,0.2328546941,0.2236227095,-0.1332375705,-0.2088885605,0.1052233577,0.4635672569,-0.2398419827,0.1055540591,0.550093472,0.3264041841,0.172630921,-0.3131439388,0.1095872596,0.0789438784,0.0664948821,0.0865849629,-0.3467356861,0.4270170927,0.2828143835,0.0556462407,0.0929596871,0.1265733242,0.2059932351,-0.1193459108,-0.2036150545,-0.17944628,0.363371402,-0.025536593,0.2383512706,-0.2011653781,-0.1728343815,0.269005388,0.0963138416,0.1139984503,0.3939310908,0.1579205692,-0.3027701676,-0.3743290305,0.0889573619,0.0322211869,0.0094886217,0.2932662666,0.3736000955,-0.1231614351,-0.063248381,-0.234593749,0.2791765928,-0.2151177377,-0.2966951132,0.1072228476,0.175737381,-0.0794863477,-0.4073752761,-0.411487788,-0.0374751128,0.0819425881,-0.0628039539,-0.0727675557,-0.1820836216,-0.3367846906,-0.1073109284,-0.205092743,-0.2028737217,-0.3738683164,0.1655452102,0.184712708,0.079681173,0.3150455654,0.1682979465,0.5256705284,0.048397731,0.4300487041,-0.1132222041,-0.1184878275,-0.0463088639,0.2272655666,-0.0351149738,0.1383318752,0.4301140606,0.0323062539,0.1055170074,-0.134631902,-0.3381764889,0.3274391294,0.0189793892,0.0784658045,0.249945581,0.0561926737,0.235697642,0.1594017595,0.1428957134,-0.1569969803,-0.1630942971,0.1478888392,-0.1265541911,0.0657547563,-0.2258962095,0.172743544,-0.1313379556,-0.1951169372,0.4276386499,0.0411098972,-0.0647484288,0.1986626685,-0.0287462622,0.3513500988,-0.3559431434,0.2393335849,-0.2662965059,-0.5580601692,0.0462880805,-0.2378893197,-0.0749971643,-0.4700532854,-0.3998803794,0.1343472302,-0.1288474947,-0.4724076688,-0.4922143221,-0.2050073892,0.233960703,0.1317834705,0.0074967425,0.3199823499,-0.1177764162,-0.2865698934,-0.0135590257,0.0779701322,0.1763718575,-0.2182677537,0.1791594476,0.0688348636,-0.1795098037,0.5941285491,0.3341059387,0.0510026067,-0.0625781491,0.22476013,-0.161502137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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/843","title":"use_custom_baseline still produces errors for bertscore","comments":"Thanks for reporting ! That's a bug indeed\r\nIf you want to contribute, feel free to fix this issue and open a PR :)","body":"`metric = load_metric('bertscore')`\r\n`a1 = \"random sentences\"`\r\n`b1 = \"random sentences\"`\r\n`metric.compute(predictions = [a1], references = [b1], lang = 'en')`\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/stephen_chan\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bertscore\/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363\/bertscore.py\", line 108, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`\r\n\r\nAdding 'use_custom_baseline = False' as an argument produces this error\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\nTypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`\r\n\r\nThis is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2","comment_length":24,"text":"use_custom_baseline still produces errors for bertscore \n `metric = load_metric('bertscore')`\r\n`a1 = \"random sentences\"`\r\n`b1 = \"random sentences\"`\r\n`metric.compute(predictions = [a1], references = [b1], lang = 'en')`\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/stephen_chan\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bertscore\/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363\/bertscore.py\", line 108, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`\r\n\r\nAdding 'use_custom_baseline = False' as an argument produces this error\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\nTypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`\r\n\r\nThis is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2 \n Thanks for reporting ! That's a bug indeed\r\nIf you want to contribute, feel free to fix this issue and open a PR :)","embeddings":[-0.0237094034,0.1297198385,0.1280906647,0.0422289521,0.1812677085,-0.0610478111,0.4962110817,0.0543297529,-0.0781167671,0.1093732566,0.0923896208,0.2173016816,-0.2150024921,-0.0573218279,-0.3110678792,-0.2098553926,-0.1186002046,0.2547813654,0.0980242491,-0.0632236227,-0.5826330781,0.0948995575,-0.5061203837,-0.215433374,-0.35657686,0.0306723919,-0.1378583461,0.0974576175,-0.1915955991,-0.4349971116,0.3886699378,0.2518529296,-0.0504473448,0.4357790351,-0.0001216621,0.0103914374,0.1038189605,-0.2130182087,0.0852774009,-0.2657131553,-0.3200367689,0.2057951838,-0.1055373028,-0.2700328529,-0.0187032651,-0.0896984786,-0.3070335388,-0.2126887143,0.2421084493,0.4283301532,0.1078686416,0.1945304722,-0.0735884681,-0.3550617695,0.0813606381,-0.3017669916,-0.1456321925,0.4351503253,-0.4142250419,0.0377761535,-0.1057628989,0.0243913326,0.0382915474,0.2849423885,0.4474446476,0.1052068099,0.1581571102,0.038551081,0.1076110601,0.3610633314,-0.0474509597,-0.2185909897,-0.2287302911,0.0338249914,0.1287768334,-0.7086322904,0.0648087263,-0.2146535367,-0.1373078823,0.0183905475,-0.1703177691,-0.2219579816,-0.3350681961,0.1999560148,0.0099008847,-0.0556113496,-0.1081215888,-0.0112577705,0.5676791072,-0.0950888395,0.1574656516,-0.2250990719,0.0535053648,0.0574578121,-0.2062042654,-0.1097372621,0.12056005,0.0120413145,0.054992035,-0.4186506271,0.2580140531,0.0277164709,-0.0427124538,0.0072117271,0.0113446796,0.4146568477,0.0075685591,0.0421947539,0.0975388438,0.191309765,0.0582834631,0.0420947596,0.1064490676,-0.2987893522,0.4436658919,0.1426459551,0.0499458313,-0.0650884509,-0.0797337145,-0.0866107643,-0.3678119183,-0.1821782291,0.123251684,0.1614996195,0.1791384518,0.3282141685,-0.2257362157,0.3017466664,-0.0212723557,-0.0919406414,-0.1466501504,0.1026794463,-0.4289891124,0.2630151808,0.3088880479,-0.231842652,0.338822633,0.3215659857,0.3396804333,0.0668376684,0.0925773531,-0.2689784467,0.1435692757,-0.1173270866,-0.0588499606,-0.1988247335,0.1535756886,0.2831663787,-0.1306258589,0.1588048041,-0.2481677085,-0.2222669274,0.2047489434,0.0787288472,-0.2645999789,-0.0504632033,-0.4324563146,0.1234535053,0.2307007313,-0.0401564389,-0.1669592857,-0.212514475,-0.5594500899,-0.1909614503,0.2443027347,0.1580370814,-0.6430358887,0.0087558338,-0.327750057,0.3320314586,-0.0049079126,0.0230638236,-0.1867527962,0.524061203,-0.1446579248,0.0781480223,0.1206864864,-0.3428070843,-0.3460653424,-0.3002776504,0.2792723775,-0.0698436797,-0.0094367675,-0.0923496783,0.1101372689,-0.0958160833,0.2726924419,-0.2665002644,0.2159829736,0.048586145,-0.1672807783,-0.1360288858,-0.0962842554,-0.0488965586,0.0996153727,-0.0844486505,-0.081754446,0.2319123149,-0.2356778234,-0.279645294,-0.0172303971,0.1953890473,0.1852337569,-0.3643390834,0.2012469918,-0.033185754,-0.0130387833,0.2165409625,-0.0341511108,0.3013232946,0.1570087224,-0.0586365052,-0.414134711,-0.1540224254,-0.1648738533,-0.1367589384,0.020873813,0.052580867,0.0798623934,0.0717615858,-0.2632439435,-0.6167683601,-0.2063766569,-0.0747958198,-0.1205357388,0.1412699074,0.0095126759,-0.111390762,0.2324660718,0.4710415006,0.1814607084,0.1329210252,-0.1492077559,0.3699563742,0.0696664825,-0.0689894408,0.1675907969,0.2383378595,-0.0214524176,-0.3603651524,0.1581872851,0.0130310114,0.0414637662,-0.0580951311,0.0373458974,0.4591887891,0.1649273485,0.2114328593,-0.1492764056,-0.0983438566,0.0560314693,-0.0891301706,-0.0203597043,-0.4252582192,0.0593474247,0.0462406427,0.1611888856,-0.0780128688,0.1707072854,-0.1522732377,0.6477606297,0.2304110974,0.3287092149,0.2288495749,0.0277477484,-0.1844192892,-0.2881701887,-0.2612560987,0.6102736592,0.1198472455,-0.1168618053,0.300278455,-0.0058482424,-0.1454414725,0.1483454257,0.2962324023,-0.151988253,-0.0261059254,0.2358247638,0.0336825959,-0.0323423557,0.0176370312,-0.0364517421,0.2214458287,-0.4314708114,0.1298580766,-0.3040905297,0.1482007354,0.0089214835,-0.4882210791,-0.0718791708,-0.2311621457,0.0693006143,-0.0674946308,0.0923007056,0.5150328875,0.1193876714,0.7285951972,0.2180926949,-0.0683989078,-0.0394961499,-0.355155766,-0.0989579856,0.01308465,-0.2014216632,-0.1084744856,0.243563354,-0.195292443,-0.1802336425,0.1880682409,-0.3187426627,0.028164126,-0.0842724815,0.3037753999,0.521917522,-0.1194943562,-0.1522707939,0.1433849931,0.2648014426,0.0178211331,0.2042805403,0.0669968277,-0.2079891413,0.0298557431,-0.0894269943,-0.2768165767,0.3283346295,-0.1472149789,-0.1486426443,0.0162934661,-0.0162279829,-0.0395782441,0.3637632728,0.2727850378,0.1033838913,-0.2550582886,-0.1482810676,-0.6655891538,0.4515167773,-0.2910750508,-0.1359456033,0.0890357345,-0.2381780744,0.547031045,0.0908265561,-0.2057355195,-0.5882261992,0.0714426935,0.2592980862,-0.0418433808,0.0959583148,0.1591807753,0.0861903727,0.0080002183,-0.1678431779,-0.1875739247,0.1888466477,-0.3293327689,0.1942361444,0.1593272686,0.2722424567,0.133979097,0.8989217877,0.2725177705,-0.273058027,0.4387993217,-0.0669018626,0.3824104071,-0.394960165,-0.3603945374,-0.1386916637,0.1852064878,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/843","title":"use_custom_baseline still produces errors for bertscore","comments":"This error is because of a mismatch between `datasets` and `bert_score`. With `datasets=1.1.2` and `bert_score>=0.3.6` it works ok. So `pip install -U bert_score` should fix the problem. ","body":"`metric = load_metric('bertscore')`\r\n`a1 = \"random sentences\"`\r\n`b1 = \"random sentences\"`\r\n`metric.compute(predictions = [a1], references = [b1], lang = 'en')`\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/stephen_chan\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bertscore\/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363\/bertscore.py\", line 108, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`\r\n\r\nAdding 'use_custom_baseline = False' as an argument produces this error\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\nTypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`\r\n\r\nThis is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2","comment_length":27,"text":"use_custom_baseline still produces errors for bertscore \n `metric = load_metric('bertscore')`\r\n`a1 = \"random sentences\"`\r\n`b1 = \"random sentences\"`\r\n`metric.compute(predictions = [a1], references = [b1], lang = 'en')`\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/stephen_chan\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bertscore\/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363\/bertscore.py\", line 108, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`\r\n\r\nAdding 'use_custom_baseline = False' as an argument produces this error\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\nTypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`\r\n\r\nThis is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2 \n This error is because of a mismatch between `datasets` and `bert_score`. With `datasets=1.1.2` and `bert_score>=0.3.6` it works ok. So `pip install -U bert_score` should fix the problem. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/843","title":"use_custom_baseline still produces errors for bertscore","comments":"Hello everyone,\r\n\r\nI think the  problem is not solved: \r\n\r\n```\r\nfrom datasets import load_metric\r\nmetric=load_metric('bertscore')\r\nmetric.compute(\r\n    predictions=predictions,\r\n    references=references,\r\n    lang='fr',\r\n    rescale_with_baseline=True\r\n)\r\nTypeError: get_hash() missing 2 required positional arguments: 'use_custom_baseline' and 'use_fast_tokenizer'\r\n```\r\nThis code is produced using `Python 3.6.9 datasets==1.1.2 and bert_score==0.3.10`","body":"`metric = load_metric('bertscore')`\r\n`a1 = \"random sentences\"`\r\n`b1 = \"random sentences\"`\r\n`metric.compute(predictions = [a1], references = [b1], lang = 'en')`\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/stephen_chan\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bertscore\/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363\/bertscore.py\", line 108, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`\r\n\r\nAdding 'use_custom_baseline = False' as an argument produces this error\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\nTypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`\r\n\r\nThis is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2","comment_length":42,"text":"use_custom_baseline still produces errors for bertscore \n `metric = load_metric('bertscore')`\r\n`a1 = \"random sentences\"`\r\n`b1 = \"random sentences\"`\r\n`metric.compute(predictions = [a1], references = [b1], lang = 'en')`\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/stephen_chan\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bertscore\/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363\/bertscore.py\", line 108, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`\r\n\r\nAdding 'use_custom_baseline = False' as an argument produces this error\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\nTypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`\r\n\r\nThis is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2 \n Hello everyone,\r\n\r\nI think the  problem is not solved: \r\n\r\n```\r\nfrom datasets import load_metric\r\nmetric=load_metric('bertscore')\r\nmetric.compute(\r\n    predictions=predictions,\r\n    references=references,\r\n    lang='fr',\r\n    rescale_with_baseline=True\r\n)\r\nTypeError: get_hash() missing 2 required positional arguments: 'use_custom_baseline' and 'use_fast_tokenizer'\r\n```\r\nThis code is produced using `Python 3.6.9 datasets==1.1.2 and bert_score==0.3.10`","embeddings":[0.0139922751,0.0406964272,0.0927704349,0.0760170147,0.1578616649,-0.0484319031,0.4595745802,0.0406530872,-0.0702814609,0.0988220498,0.057240773,0.248223722,-0.2429824471,-0.091258958,-0.3137350082,-0.2044166178,-0.1234866977,0.2484205514,0.1250584424,-0.0682218224,-0.6134792566,0.1149746105,-0.5267378688,-0.2245671898,-0.3061106801,-0.0073447996,-0.1297489554,0.0940015838,-0.1675110012,-0.4430608451,0.3566359282,0.2593745887,-0.0273150597,0.5043865442,-0.0001236519,0.0151397856,0.0650538281,-0.2212443501,0.1006062552,-0.2754280567,-0.3003541529,0.2056778818,-0.1089255288,-0.3084519506,-0.0567066446,-0.0974938422,-0.3291552365,-0.2222906947,0.2909580171,0.4247182906,0.0997443795,0.1678161025,-0.0701237246,-0.3143260777,0.0707339644,-0.3041083813,-0.132868439,0.4761042893,-0.3822268844,0.0905164182,-0.0475983918,0.0151075721,0.0084916111,0.275585413,0.4619062841,0.0610387363,0.1685637087,0.0290288627,0.1067181006,0.3407550752,-0.0626366436,-0.1964442432,-0.1896905005,0.0891402587,0.1015939116,-0.7081446648,0.0732918009,-0.2468524128,-0.1026242673,0.0150160482,-0.1463925391,-0.2310788929,-0.3840640485,0.2057585269,0.0964826643,-0.0224360973,-0.0936609283,-0.010377693,0.5541225672,-0.1331993788,0.2133925557,-0.2867097855,0.0761622712,0.0739566684,-0.2188491374,-0.0774903074,0.1112971604,-0.0142223453,0.0371265672,-0.4702761471,0.2807561159,0.0519439876,0.0180908851,0.0164009593,-0.0885795504,0.4158307016,0.0310362186,0.064934589,0.0825008005,0.2168201357,-0.0032398945,0.0564349405,0.0582802892,-0.3148384094,0.4590700567,0.2077643722,0.0701418519,-0.0728394613,-0.0429898798,-0.1028405949,-0.4027306736,-0.1958101392,0.1490715593,0.17960006,0.1914663017,0.3129924834,-0.1987798959,0.2753800154,-0.0125335623,-0.0601523481,-0.1233491302,0.1453828961,-0.38491714,0.2618605494,0.2926675975,-0.260150671,0.3255074918,0.30894804,0.3424107432,-0.0041429466,0.0734393448,-0.2821101844,0.1013380215,-0.1857380718,-0.0533379838,-0.2254092246,0.1736967713,0.2982823551,-0.1498628855,0.1796702445,-0.2692430317,-0.2601077855,0.2153955698,0.0630152002,-0.2491658181,-0.0688862652,-0.4689850807,0.1305702031,0.237238735,0.0045827674,-0.1681928933,-0.1690101027,-0.5841546655,-0.186730504,0.2247252613,0.1487060636,-0.6226087213,0.0103871049,-0.2680073082,0.3499619961,0.0292986743,0.0206672512,-0.1841612458,0.5567415357,-0.1364111602,0.0483741127,0.1785369813,-0.4073660076,-0.3432235122,-0.3021988869,0.3096629381,-0.1078904644,0.0489362925,-0.1057818085,0.118581593,-0.0633806363,0.2802256048,-0.2763059139,0.2693374157,0.049765192,-0.1741134077,-0.1504568309,-0.1033492908,-0.0554999784,0.1072779223,-0.0930515602,-0.0682105869,0.2482923865,-0.2434185147,-0.2698500454,-0.0465709418,0.2116320878,0.194063589,-0.3016175926,0.1966770291,0.0066732019,-0.0459150858,0.2049425393,-0.085631296,0.3094464839,0.1192712188,-0.0633344203,-0.4761702716,-0.1659834236,-0.1861745715,-0.1153101027,0.0121974889,0.0760244727,0.1235741228,0.0723399892,-0.255281955,-0.6563645005,-0.2443365902,-0.0664544851,-0.1146557406,0.1561340839,-0.0055224099,-0.1433352381,0.2601358891,0.460088104,0.2633832395,0.0992789343,-0.1747127175,0.35264045,0.0372519083,-0.0551031791,0.1486088187,0.2489783615,-0.0254417956,-0.4127577245,0.1762646288,0.0228236318,0.0518608727,-0.0410667434,0.113983281,0.4497624934,0.1684157401,0.2445272952,-0.1405307204,-0.13733311,0.0315355137,-0.0921100304,0.0284693372,-0.4044124484,0.1003224105,0.069021903,0.1901570112,-0.0592134222,0.2248275429,-0.1967794895,0.7020145655,0.2295797765,0.3146533966,0.2003013492,0.0612428933,-0.1737088114,-0.3064579368,-0.275839597,0.5699107647,0.1226788387,-0.1319181621,0.3084480464,0.0509160534,-0.1044948846,0.152882427,0.2951426506,-0.1105292141,0.0119593767,0.1853530556,0.0484520607,0.002255708,-0.0102937007,-0.0005142058,0.276298821,-0.4457083941,0.1012327224,-0.2893078327,0.1160955131,0.0153841209,-0.4470415413,-0.0500866547,-0.2175884396,0.0222832039,-0.1053767353,0.1690021008,0.5220665336,0.1414784938,0.7223024368,0.226603508,-0.1214421019,-0.0468008667,-0.3608556986,-0.0696485639,-0.0147932395,-0.1647163928,-0.0889836773,0.2091378272,-0.2166952789,-0.1947316378,0.2374855876,-0.3260768652,0.0331060514,-0.0415863246,0.2737569809,0.4932181835,-0.1144722924,-0.1439746171,0.1149252057,0.2683179975,-0.019764347,0.2143016309,0.0747958496,-0.2519958913,0.0253388956,-0.0508959368,-0.31077829,0.3045133352,-0.1827806085,-0.198859036,0.0107530486,0.0226688292,-0.0432986468,0.3582718372,0.3304134607,0.1229671165,-0.2094842196,-0.1147248447,-0.6629092693,0.438267529,-0.2981931269,-0.1130236089,0.1229114309,-0.254455626,0.5852690935,0.039028883,-0.203691557,-0.6184414625,0.0393268503,0.2566467226,-0.0809669569,0.0743938088,0.0919255465,0.0779964924,0.0086050136,-0.1452542841,-0.2045074403,0.222530663,-0.3108867705,0.1650232971,0.2436050326,0.3170932233,0.1539705098,0.9103382826,0.2706152499,-0.2894462049,0.4798105955,-0.0593984537,0.3143587708,-0.3554131687,-0.3691224754,-0.16725339,0.2391471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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/843","title":"use_custom_baseline still produces errors for bertscore","comments":"Hi ! This has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2770, we'll do a new release soon to make the fix available :)\r\n\r\nIn the meantime please use an older version of `bert_score`","body":"`metric = load_metric('bertscore')`\r\n`a1 = \"random sentences\"`\r\n`b1 = \"random sentences\"`\r\n`metric.compute(predictions = [a1], references = [b1], lang = 'en')`\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/stephen_chan\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bertscore\/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363\/bertscore.py\", line 108, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`\r\n\r\nAdding 'use_custom_baseline = False' as an argument produces this error\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\nTypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`\r\n\r\nThis is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2","comment_length":30,"text":"use_custom_baseline still produces errors for bertscore \n `metric = load_metric('bertscore')`\r\n`a1 = \"random sentences\"`\r\n`b1 = \"random sentences\"`\r\n`metric.compute(predictions = [a1], references = [b1], lang = 'en')`\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\n  File \"\/home\/stephen_chan\/.cache\/huggingface\/modules\/datasets_modules\/metrics\/bertscore\/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363\/bertscore.py\", line 108, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`\r\n\r\nAdding 'use_custom_baseline = False' as an argument produces this error\r\n\r\n`Traceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/stephen_chan\/.local\/lib\/python3.6\/site-packages\/datasets\/metric.py\", line 393, in compute\r\n    output = self._compute(predictions=predictions, references=references, **kwargs)\r\nTypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`\r\n\r\nThis is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2 \n Hi ! This has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2770, we'll do a new release soon to make the fix available :)\r\n\r\nIn the meantime please use an older version of `bert_score`","embeddings":[-0.065399088,0.1055707484,0.1252271235,0.0791326985,0.1758203357,-0.1042810902,0.4214691222,0.0186607651,-0.0667726249,0.118460685,0.030669719,0.2369848192,-0.1979214549,0.030478498,-0.3029206395,-0.2030736357,-0.1065387726,0.2290034145,0.0602318011,-0.0018443018,-0.5456944108,0.1240426227,-0.4763626754,-0.1773430258,-0.3232955635,0.0166038554,-0.0931779444,0.0963591561,-0.1938426793,-0.4644752443,0.3707922101,0.2937927246,-0.0783435032,0.4273879826,-0.0001202581,-0.0173141062,0.0906589329,-0.1919967681,0.0823990405,-0.3026836216,-0.3079280853,0.2507736683,-0.0756759867,-0.1881365031,-0.0568038225,-0.0254872702,-0.277130127,-0.2293009609,0.2610580921,0.3748821318,0.1189197302,0.248761192,-0.0141959656,-0.3966115117,0.1391546875,-0.285646826,-0.1170233935,0.4653153718,-0.4163216352,0.0323894061,-0.1525924653,0.0655753314,0.0691507012,0.2131879628,0.4943492413,0.1090632901,0.0412167087,0.0574481115,0.1284718812,0.3740675449,-0.050068941,-0.2580922544,-0.268222183,-0.0349042676,0.1512615681,-0.6960839033,0.08812695,-0.298895061,-0.0544674546,0.0102438703,-0.213337943,-0.1494624466,-0.3620051742,0.1846598238,0.0540706404,-0.1190564036,-0.1737605929,-0.0061601447,0.6393912435,-0.1262257397,0.0312464442,-0.160253346,-0.0081798183,0.0703401491,-0.2226918787,-0.1142823473,0.1105880961,-0.0201596767,0.0919506475,-0.360073328,0.2725138068,0.030992236,-0.0768296495,0.0010996127,0.0304140672,0.4342128336,0.0872670263,-0.0564139113,0.1268499494,0.2700208724,0.1153942868,0.0258651953,0.0955619141,-0.2423228472,0.3633945584,0.1154833809,0.0006458133,-0.1070115343,-0.1145095229,-0.048915565,-0.2870645225,-0.1943377703,0.1714108884,0.2480600476,0.1765980273,0.2997084558,-0.2521529198,0.2725555301,0.0030375775,-0.0681247488,-0.1418930292,0.0871898234,-0.3522244394,0.2969470918,0.3173727393,-0.2396328151,0.355274111,0.2791146636,0.3951649666,0.0837464109,0.0532858297,-0.2689545155,0.1519267857,-0.1623131782,-0.0842929482,-0.2084585726,0.1395534575,0.2767295539,-0.1474154145,0.1042362824,-0.2027218491,-0.2549835443,0.2148388773,0.091043137,-0.2995553613,-0.0900899097,-0.4372721612,0.1694345325,0.1358772367,-0.094205454,-0.1295930594,-0.1712069809,-0.5282446146,-0.1445283443,0.3029254973,0.2183142304,-0.5863320231,-0.0307365377,-0.2681738734,0.2509887815,-0.0577307492,0.0461670123,-0.1169253811,0.4635560811,-0.0958911777,0.09203621,0.0361051597,-0.3284228742,-0.3902496099,-0.3276280165,0.3103570938,-0.0577141792,0.0274140816,-0.09317334,0.031585183,-0.0997008607,0.2024668157,-0.2675183713,0.1949836761,0.0600165091,-0.1490323842,-0.1651210934,-0.1675140411,-0.0138966935,0.0807847232,-0.1848622262,-0.0777184218,0.3040081561,-0.2377139926,-0.2887169421,-0.0250663795,0.1667808443,0.1623036414,-0.295412451,0.178111583,-0.0408695564,-0.0651674643,0.2309506088,-0.084703967,0.2836992443,0.1728590578,-0.0925022289,-0.4605918825,-0.1143953279,-0.1628159136,-0.1544780731,0.0337178856,0.0203069765,0.1430742592,0.1113379225,-0.2873969078,-0.6143479347,-0.2077045888,-0.055125121,-0.1873973012,0.0940355137,0.0287742354,-0.1254122704,0.2686022818,0.5271698833,0.1975507736,0.1344726682,-0.1280670911,0.4088070095,0.0464214347,-0.0431450307,0.1315714419,0.2197876722,0.0151624121,-0.344604671,0.0824566633,0.0367514528,0.0169554148,0.0046336357,-0.0471122898,0.4674701989,0.0854474008,0.2155937552,-0.0656195581,-0.1301858872,0.0143371029,-0.0257110745,-0.0032305294,-0.4469573796,0.1062192619,-0.0156325996,0.1746184677,-0.1073334962,0.1675826609,-0.1494366825,0.5737681985,0.1685099155,0.3206823766,0.2710583806,0.0261102356,-0.203624621,-0.2102162242,-0.3017736971,0.5065487027,0.1265647411,-0.1132989973,0.2986457944,0.0085949944,-0.1254892051,0.1335160434,0.2480135262,-0.1131374687,-0.0160017945,0.2157008797,0.0475166589,-0.0509593226,0.0090919938,-0.1127000153,0.253324002,-0.4297865331,0.1104360744,-0.3132552803,0.0675640702,-0.002046505,-0.4873185754,-0.1534600854,-0.2266827524,0.1109821647,-0.0218976811,0.0506076179,0.4909879565,0.1721702218,0.6954312325,0.1758468151,-0.0201313645,-0.0392874293,-0.4039442241,-0.0806418955,0.0235443413,-0.1637841314,-0.1326147914,0.2936668694,-0.2023090869,-0.2084415406,0.1652985364,-0.3238097131,0.0610867515,-0.0988676026,0.3026757836,0.5101534724,-0.1756213307,-0.248159796,0.1170610934,0.2749007046,-0.0380744934,0.2095594704,0.0698156431,-0.2160654664,0.066987589,-0.0378154255,-0.2673087418,0.3557494581,-0.125671044,0.0374212675,0.0129873194,0.0083139688,0.034192998,0.3668732345,0.3727366924,0.0499798581,-0.1662344933,-0.176174894,-0.6425845027,0.3792101741,-0.3096800745,-0.1714020222,0.0285001304,-0.1941157877,0.4604353607,0.0138922427,-0.1628634483,-0.6429674625,0.0338853747,0.2201700956,-0.0202882253,0.1086328477,0.1279480308,0.0701922327,-0.0310323592,-0.1363003552,-0.1708536446,0.2175342739,-0.4094260633,0.1471411288,0.1625185311,0.2354807109,0.1228960231,0.851375699,0.2858025134,-0.1723722219,0.4137632549,-0.0692917556,0.4069245756,-0.4315741658,-0.3084430397,-0.0779511333,0.15194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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/842","title":"How to enable `.map()` pre-processing pipelines to support multi-node parallelism?","comments":"Right now multiprocessing only runs on single node.\r\n\r\nHowever it's probably possible to extend it to support multi nodes. Indeed we're using the `multiprocess` library from the `pathos` project to do multiprocessing in `datasets`, and `pathos` is made to support parallelism on several nodes. More info about pathos [on the pathos repo](https:\/\/github.com\/uqfoundation\/pathos).\r\n\r\nIf you're familiar with pathos or if you want to give it a try, it could be a nice addition to the library :)","body":"Hi,\r\n\r\nCurrently, multiprocessing can be enabled for the `.map()` stages on a single node. However, in the case of multi-node training, (since more than one node would be available) I'm wondering if it's possible to extend the parallel processing among nodes, instead of only 1 node running the `.map()` while the other node is waiting for it to finish?\r\n\r\nThanks!","comment_length":76,"text":"How to enable `.map()` pre-processing pipelines to support multi-node parallelism? \n Hi,\r\n\r\nCurrently, multiprocessing can be enabled for the `.map()` stages on a single node. However, in the case of multi-node training, (since more than one node would be available) I'm wondering if it's possible to extend the parallel processing among nodes, instead of only 1 node running the `.map()` while the other node is waiting for it to finish?\r\n\r\nThanks! \n Right now multiprocessing only runs on single node.\r\n\r\nHowever it's probably possible to extend it to support multi nodes. Indeed we're using the `multiprocess` library from the `pathos` project to do multiprocessing in `datasets`, and `pathos` is made to support parallelism on several nodes. More info about pathos [on the pathos repo](https:\/\/github.com\/uqfoundation\/pathos).\r\n\r\nIf you're familiar with pathos or if you want to give it a try, it could be a nice addition to the library :)","embeddings":[-0.3369057477,-0.31968382,-0.1686351299,-0.0785499811,-0.1322283894,0.0334650092,0.0507945754,-0.0447819233,0.23892124,0.2358291,0.254429847,0.5422918797,-0.157852158,0.322792232,0.0088176709,-0.4016127288,0.0359969996,-0.0369444191,-0.0063834358,0.2683166265,-0.0824367628,0.0330719277,-0.3741092682,0.0420585163,-0.0999500751,-0.1551183015,-0.1367379725,-0.0298173353,-0.0436844453,-0.2958486378,-0.2203299701,0.3011392653,0.0312907211,0.3520682752,-0.0001225286,0.0020044774,0.2021490186,0.1364621222,0.2638634145,-0.2030464858,-0.2922287583,-0.1116604879,0.207213819,-0.141263485,-0.0572501421,0.1294005662,0.2296898514,-0.4878687263,0.0606242493,-0.1914961189,0.0673572645,0.3336282372,0.0711796507,0.2239813656,-0.5225394368,0.1676170975,-0.0575988702,-0.1018259302,0.6528367996,-0.3768672943,-0.1162374765,-0.0478885807,0.0505254529,0.1481447369,0.0178733636,-0.2197743803,0.5481138229,-0.6776022911,-0.136391893,0.1981161386,-0.0967679918,0.0911058486,-0.2966392934,-0.0853985548,-0.3229728639,-0.2332477868,-0.1775843799,-0.0001219448,0.0031385261,-0.1174954176,-0.5662781596,-0.4102555811,0.0705289096,0.2957291305,0.1834556013,0.6211115718,0.2272422761,0.2678335011,0.3702197969,0.1326148659,0.0390931107,0.0203601159,0.3610491753,0.2500997484,-0.3338703215,0.0637047216,0.0589844957,-0.1692203581,-0.2133903801,-0.2581843734,0.2163925916,0.2993526757,-0.2664533854,0.4457849562,0.1577317864,-0.1810770035,0.087563701,0.361533612,0.1224431545,-0.2412858903,-0.0981195047,0.0740630776,-0.036469657,-0.4829166234,-0.4006948173,0.3822456598,-0.3058327436,0.3852480948,0.3548592031,-0.3782683611,-0.1410485357,-0.2402350605,0.3023352325,-0.21192348,0.44224751,0.3982145786,0.063173674,0.2084145248,-0.1993974596,-0.2483129799,0.0638077632,-0.0258909427,-0.1408359259,0.1586971581,-0.0259823594,0.0150852073,0.0209516305,-0.1133035198,0.1074199229,-0.0729566813,0.4114916921,0.0906192586,0.0863064602,0.3343430459,0.0364167951,-0.0127970399,0.0399881005,0.6484883428,-0.1837859601,0.2720239758,-0.459177047,-0.3027990162,0.2662397325,-0.0613082014,0.2564393878,0.0969900116,-0.3469530642,0.1863732338,0.3003367782,-0.2851771116,0.269323945,-0.1820218712,-0.6244847775,-0.1700150669,0.3108026981,0.020038614,0.1818705648,-0.023772506,-0.1502303481,-0.0657917038,0.2736561894,0.0446197055,-0.2955271006,0.1507144123,0.1166545451,0.3167734146,0.065670602,-0.3526523113,0.4236588776,0.3865174949,-0.3203398287,0.0235287379,-0.2047762722,0.0141240684,-0.1535019875,0.1601706147,-0.0434379615,0.2964762747,-0.0211057644,0.0839059427,-0.1153804138,-0.2237194777,0.1503989398,0.0188092142,0.229651615,0.1636524647,-0.0292276144,-0.4754525423,0.3516525626,-0.0050842268,0.3887659907,0.0353224427,-0.2026080489,0.1578772813,-0.15698798,-0.2968689799,0.2742410898,0.2267602682,-0.3845553398,-0.2667267025,0.1944010258,-0.0281353705,0.658357203,-0.0203725323,0.0132354638,-0.0133071709,0.0218380522,-0.1558619142,-0.1386217475,-0.2770654559,-0.1314100325,0.1661051363,0.1653211266,-0.0123661188,-0.1140139401,0.2490856946,0.0076229563,0.1042492613,-0.3774382472,0.0114934901,0.0409500487,-0.0934703946,0.0581272058,0.2879985571,0.139239341,0.3358106017,0.2157691866,0.3359017372,0.3863933086,-0.1082795709,-0.0061820247,-0.1044881791,-0.1360841393,-0.252900213,0.466763556,0.2141513228,-0.0421636961,0.263661176,0.0598158538,0.1984933019,-0.0791036189,0.1946500689,-0.1260084361,0.159426868,0.1235274523,0.0014310838,-0.1964187473,-0.1850953996,-0.2523138821,-0.0365091898,-0.1916247606,0.0773274526,-0.1236756593,-0.1794439703,0.0905999169,0.1549218446,0.2646670043,-0.2878614068,0.2626061738,-0.108295761,0.3512683213,0.080504477,0.0600422546,-0.0556469597,-0.0219643414,0.1413629204,0.4599143267,-0.1237834543,0.0615026392,-0.0394493081,0.1551724821,-0.1666533202,0.3832467794,-0.1452605277,-0.08250238,0.3208311796,-0.2554083169,0.0117312223,0.0800678134,-0.4001930654,0.0283896513,0.0566958003,0.0959389582,0.2010514438,-0.1413591504,0.2351785004,0.2716403008,0.1544082314,-0.1668892801,-0.2972889841,0.1566224247,-0.0717362985,0.3108200133,-0.0209002178,0.0974181816,0.0560167171,-0.1543952078,0.3162545562,-0.172154665,-0.1625145078,-0.1071026102,-0.1085598096,0.1113179922,-0.1270810813,-0.0457958728,-0.1687175333,0.0315191001,-0.1978416145,0.1965869218,-0.2807557881,-0.0775249004,-0.1319009513,0.0424582027,-0.3148796856,-0.289426744,-0.0161795262,-0.3274700642,-0.2385780662,0.5306853652,-0.113502562,0.2678406537,-0.3076747954,0.0607952587,-0.2777210772,-0.2799748182,-0.2050078809,0.306293577,-0.4266135097,0.0779460371,-0.2861332297,0.0420378782,0.0888571739,0.1036917716,0.3386378884,0.422760278,0.1103589237,0.2011974454,0.1311051697,0.1691947728,0.0740518644,0.0106884902,0.4992435575,0.0048932615,0.1549522281,0.0984938592,0.1453436613,0.2018730789,0.3149352968,-0.0234792847,0.3149955571,-0.0544724055,0.0026231229,0.6437373757,0.1863555312,-0.0420929976,0.1919645518,0.0683293268,-0.3384121358,0.1102453917,-0.1958668083,-0.0822985396,-0.2924408615,-0.2481647581,0.02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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/841","title":"Can not reuse datasets already downloaded","comments":"It seems the process needs '\/datasets.huggingface.co\/datasets\/datasets\/wikipedia\/wikipedia.py'\r\nWhere and how to assign this ```wikipedia.py``` after I manually download it ?","body":"Hello,\r\nI need to connect to a frontal node (with http proxy, no gpu) before connecting to a gpu node (but no http proxy, so can not use wget so on).\r\nI successfully downloaded and reuse the wikipedia datasets in a frontal node. \r\nWhen I connect to the gpu node, I supposed to use the downloaded datasets from cache, but failed and end with time out error.\r\n\r\nOn frontal node:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('wikipedia', '20200501.en')\r\nReusing dataset wikipedia (\/linkhome\/rech\/genini01\/uua34ms\/.cache\/huggingface\/datasets\/wikipedia\/20200501.en\/1.0.0\/f92599dfccab29832c442b82870fa8f6983e5b4ebbf5e6e2dcbe894e325339cd)\r\n\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/torch\/cuda\/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http:\/\/www.nvidia.com\/Download\/index.aspx (Triggered internally at  \/pytorch\/c10\/cuda\/CUDAFunctions.cpp:100.)\r\n  return torch._C._cuda_getDeviceCount() > 0\r\n```\r\n\r\nOn gpu node:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('wikipedia', '20200501.en')\r\nTraceback (most recent call last):\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 160, in _new_conn\r\n    (self._dns_host, self.port), self.timeout, **extra_kw\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py\", line 84, in create_connection\r\n    raise err\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py\", line 74, in create_connection\r\n    sock.connect(sa)\r\nTimeoutError: [Errno 110] Connection timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 677, in urlopen\r\n    chunked=chunked,\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 381, in _make_request\r\n    self._validate_conn(conn)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 978, in _validate_conn\r\n    conn.connect()\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 309, in connect\r\n    conn = self._new_conn()\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 172, in _new_conn\r\n    self, \"Failed to establish a new connection: %s\" % e\r\nurllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/adapters.py\", line 449, in send\r\n    timeout=timeout\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 727, in urlopen\r\n    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py\", line 446, in increment\r\n    raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/wikipedia\/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 590, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 264, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return requests.head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/sessions.py\", line 530, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/sessions.py\", line 643, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/adapters.py\", line 516, in send\r\n    raise ConnectionError(e, request=request)\r\nrequests.exceptions.ConnectionError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/wikipedia\/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))\r\n\r\n```\r\n\r\nAny advice?Thanks!\r\n","comment_length":19,"text":"Can not reuse datasets already downloaded \n Hello,\r\nI need to connect to a frontal node (with http proxy, no gpu) before connecting to a gpu node (but no http proxy, so can not use wget so on).\r\nI successfully downloaded and reuse the wikipedia datasets in a frontal node. \r\nWhen I connect to the gpu node, I supposed to use the downloaded datasets from cache, but failed and end with time out error.\r\n\r\nOn frontal node:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('wikipedia', '20200501.en')\r\nReusing dataset wikipedia (\/linkhome\/rech\/genini01\/uua34ms\/.cache\/huggingface\/datasets\/wikipedia\/20200501.en\/1.0.0\/f92599dfccab29832c442b82870fa8f6983e5b4ebbf5e6e2dcbe894e325339cd)\r\n\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/torch\/cuda\/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http:\/\/www.nvidia.com\/Download\/index.aspx (Triggered internally at  \/pytorch\/c10\/cuda\/CUDAFunctions.cpp:100.)\r\n  return torch._C._cuda_getDeviceCount() > 0\r\n```\r\n\r\nOn gpu node:\r\n```\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('wikipedia', '20200501.en')\r\nTraceback (most recent call last):\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 160, in _new_conn\r\n    (self._dns_host, self.port), self.timeout, **extra_kw\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py\", line 84, in create_connection\r\n    raise err\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py\", line 74, in create_connection\r\n    sock.connect(sa)\r\nTimeoutError: [Errno 110] Connection timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 677, in urlopen\r\n    chunked=chunked,\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 381, in _make_request\r\n    self._validate_conn(conn)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 978, in _validate_conn\r\n    conn.connect()\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 309, in connect\r\n    conn = self._new_conn()\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 172, in _new_conn\r\n    self, \"Failed to establish a new connection: %s\" % e\r\nurllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/adapters.py\", line 449, in send\r\n    timeout=timeout\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 727, in urlopen\r\n    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py\", line 446, in increment\r\n    raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/wikipedia\/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 590, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 264, in prepare_module\r\n    head_hf_s3(path, filename=name, dataset=dataset)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 200, in head_hf_s3\r\n    return requests.head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/api.py\", line 104, in head\r\n    return request('head', url, **kwargs)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/api.py\", line 61, in request\r\n    return session.request(method=method, url=url, **kwargs)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/sessions.py\", line 530, in request\r\n    resp = self.send(prep, **send_kwargs)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/sessions.py\", line 643, in send\r\n    r = adapter.send(request, **kwargs)\r\n  File \"\/linkhome\/rech\/genini01\/uua34ms\/work\/anaconda3\/envs\/pytorch_pip170_cuda102\/lib\/python3.6\/site-packages\/requests\/adapters.py\", line 516, in send\r\n    raise ConnectionError(e, request=request)\r\nrequests.exceptions.ConnectionError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/wikipedia\/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))\r\n\r\n```\r\n\r\nAny advice?Thanks!\r\n \n It seems the process needs '\/datasets.huggingface.co\/datasets\/datasets\/wikipedia\/wikipedia.py'\r\nWhere and how to assign this ```wikipedia.py``` after I manually download it ?","embeddings":[-0.1548725963,-0.237658456,-0.0935400799,0.247308448,0.3116977513,0.0938448086,0.2028710246,0.0549309067,0.4361793399,-0.1159729883,0.0571257547,-0.0696771219,0.4684705436,-0.0135196988,0.0379944071,-0.0326445065,-0.1300401986,-0.1004902571,0.043639604,-0.1173638478,-0.067842707,0.0512391962,0.098733671,-0.1973604411,-0.5170297027,-0.0366622321,0.0695867091,0.0257131644,0.1279812306,-0.3292092085,0.4963114858,0.1519474685,0.3188914657,0.3908818066,-0.0001227586,0.0331632793,0.030148387,-0.0086721852,-0.2577647865,-0.2175235897,-0.0583401732,-0.0221800189,0.2983060181,-0.3426147997,-0.3325791955,0.101670213,0.1009476855,-0.5310968757,0.5326627493,0.0265148096,0.0990964547,-0.2245471179,0.1964668036,-0.1530518681,-0.1416291595,0.1641683578,0.0021096452,0.1725648493,0.1944148242,-0.0351392329,0.240674302,0.0909061506,-0.0575655103,0.292018801,0.4881071448,-0.041038204,0.1593190581,-0.4436327815,-0.0056614853,0.1268529743,0.7601932287,-0.1796581596,-0.0926003754,-0.1281972378,-0.2547182739,-0.2147118151,-0.0247817393,0.0598493181,-0.2936771214,0.1041671634,-0.0512807183,-0.3261173368,-0.1129007787,0.3657031059,0.0524880961,0.0805803537,0.2111367285,0.2992266715,0.1300238222,0.0384743251,0.1402243525,-0.200508222,0.1321433336,-0.0326064713,-0.4005512297,0.0403882489,0.0198649764,-0.019808352,0.0059408899,0.4356174171,-0.0049160216,0.1558227986,0.0689676479,0.0348820761,-0.1209750921,0.0979312733,0.0121463193,0.06792593,0.2295104861,0.0980924442,-0.3003791869,-0.1996251047,0.214803949,-0.0826328993,-0.1759484708,0.045841869,0.2712121308,-0.1657319218,-0.0148507813,0.0250686854,-0.1129281819,-0.0103006111,-0.0408026613,0.3777107,-0.1183998212,0.2515930831,0.3486973643,0.0073920749,-0.2008040547,-0.1518076509,-0.0998892114,-0.0000952289,-0.1795948297,0.2472524494,0.3716264069,-0.02905076,0.1782625765,-0.0607331134,0.1072972268,-0.1576452255,0.5114760995,-0.2132519633,0.1165531278,-0.0030914692,0.1830020398,0.4008692503,0.2319138795,0.0943756551,0.0228220876,0.0235882774,-0.2802010179,-0.3750861585,0.0085785277,0.1051453203,0.1237072051,0.1330554336,0.0396871641,-0.1955207735,0.3279145062,-0.2095109075,-0.0007530835,-0.0024339631,-0.416903764,-0.2892304063,0.2441191375,0.5860952139,-0.4188292325,0.0107058454,0.1581711322,0.1157769933,0.1240282804,0.6433213353,-0.3510301709,0.1319203973,-0.1617866009,-0.1739807278,0.2686309516,-0.5118985176,-0.3102938235,0.1303102374,-0.0157408956,0.1743181646,0.0765928924,0.2913169861,0.078338787,0.0681622103,0.1149008647,0.4836628437,-0.0171200335,-0.2183826119,-0.1767849475,-0.2592068017,0.3600456715,-0.100005284,0.1763042957,0.130079329,0.054526329,0.1995591968,0.2683362961,-0.1596238613,0.2831754684,0.231387049,0.1733906716,0.2606773973,0.0332907476,0.0171749331,-0.4030640721,0.3302550912,-0.2461273968,0.0058934791,-0.2333052158,-0.3232369423,-0.1935282648,-0.2746445835,-0.003013182,-0.0217538923,0.0367822424,0.2466294467,0.2087594718,0.1449085325,0.2628288567,0.4253348708,-0.2703686059,0.04579065,-0.3987375796,0.3691842854,-0.2255245149,0.0189247094,-0.2020832896,-0.0271259695,0.3046060205,0.0082265893,0.2096119374,0.0491676554,-0.1407170594,0.3016272485,0.0209527593,0.1291457713,0.3272279203,-0.3400678337,0.0546231046,0.3461517692,0.3550490737,-0.3573242426,-0.1212392151,0.0814285874,0.2531200349,0.1774753183,-0.0738468692,0.0116527285,0.1081959307,-0.0070220106,0.1595490128,-0.0215573218,0.1896750331,0.4042560756,-0.2426960766,-0.0646580383,-0.2098242044,0.2700272202,0.0560824908,0.1939546764,0.0376007371,-0.1223338842,0.0595591776,-0.2091467232,0.3500083387,-0.3235579431,-0.0773916319,0.0955946445,0.3279133141,0.1028089672,-0.1100912392,-0.1628506631,-0.0124461604,-0.1816113442,0.2727844715,-0.0513619222,0.01331932,-0.0920112804,0.0433616899,-0.0553808548,0.3955430388,0.1600077003,-0.3600956798,0.139388293,-0.3536379039,-0.4845449924,0.1353780627,-0.172681585,-0.0969468877,-0.3000202477,-0.1184427962,0.5529574752,0.2341260016,0.2936708629,-0.0186400749,0.2825274765,0.24666439,-0.2299642414,-0.2326804996,-0.3672699332,0.0690651238,0.0263998099,0.3404126465,0.199619025,0.2153250128,-0.1309523284,-0.2550795078,-0.4913638532,-0.08555682,-0.0909787863,0.1733043194,0.1972339004,-0.3326894939,0.349003315,-0.0626110807,-0.2103337348,0.19828628,-0.0772249624,0.0963688567,-0.2657545507,-0.2588169873,0.1132068262,-0.0567036606,-0.346736908,-0.3991658986,-0.3085103929,-0.0470423885,0.1668826342,0.0726270974,-0.068381764,0.178662762,0.0022527329,0.1858491302,0.2069580704,-0.3619476259,-0.3047581613,0.2433561087,-0.1396142542,-0.3152446151,0.1756567806,0.1172586679,-0.0212443564,0.471175909,-0.4773329496,-0.3291167021,-0.0574132986,0.3427009284,0.0243760739,-0.1468636394,0.3705311716,-0.3419768512,-0.0292724743,0.0876091123,0.0092929425,0.0055176825,-0.307690084,0.4178673625,0.1014434099,0.3604357243,0.0691400543,0.9451534152,0.1241173595,-0.2306533456,0.3185611665,-0.1047408879,0.2298144251,-0.103616789,-0.1561472863,0.0744232088,-0.3390268087,0.2327618897,-0.3644365072,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/836","title":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas","comments":"Which version of pyarrow do you have ? Could you try to update pyarrow and try again ?","body":"Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n","comment_length":18,"text":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas \n Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n \n Which version of pyarrow do you have ? Could you try to update pyarrow and try again ?","embeddings":[-0.24118343,-0.3198650777,-0.0681558475,0.4316074848,0.3625251055,0.0191550385,0.5210206509,0.437713474,0.2949123979,0.0327753536,0.0169624221,-0.1187045723,0.0905919969,0.1802892983,0.0350663252,0.1130092442,0.0730282664,0.3988213837,0.0317200907,0.0699720979,-0.218860805,0.1208694875,0.005867396,0.0575151071,-0.1244225353,0.0723639429,-0.008208585,0.1169146225,-0.2343517244,-0.4013206661,0.4197234511,0.0784542188,0.3328505158,0.5207661986,-0.0001169051,0.1564733684,0.2654486001,-0.1856259704,-0.0441993587,-0.5195806026,0.0301294662,-0.5329574347,0.2776663601,-0.1672024429,-0.0144887799,-0.1786803156,-0.1109988168,-0.0358052403,0.3014124632,0.3175454736,0.1804958135,-0.2607497573,0.0598921739,-0.0100186532,0.2052922398,0.0371788666,-0.0156855155,0.4175470769,0.1173958927,-0.1267608404,-0.0487308204,-0.193666935,-0.2392427474,0.3581996262,0.3196350932,0.1399399787,0.0709672496,-0.1089668348,0.2680699527,0.1055052057,0.6067355871,-0.307642132,-0.1485474855,-0.0154003808,-0.0202969182,-0.4886516333,0.2558154762,0.404494822,-0.1521599442,0.1173331738,-0.2122125626,0.2484468818,-0.2663929462,0.2028620988,-0.0673474893,-0.3531901836,-0.2522074878,0.2693178952,0.2523397803,0.0737477168,0.3336401582,-0.0937501863,0.0268538464,0.1806966811,-0.5556491017,0.2796493471,-0.1176608056,-0.0904364362,0.1637407988,0.096218653,0.4014219046,0.1370620281,0.0423445031,0.1946978569,0.3191299736,0.1861328632,-0.0428146571,0.0691050887,0.0273857322,0.3462172449,-0.2297836244,-0.2338579148,-0.3881369233,-0.5049571991,0.2381427437,-0.1538383663,0.2674439549,-0.2704333961,-0.2479329556,-0.0960200727,-0.0361324735,-0.0094233667,-0.0528344922,0.5738207698,0.215833962,0.3802042603,-0.054209996,0.2572092116,-0.2112757564,-0.09865468,-0.0803525597,0.0201157145,-0.0576021709,0.0522914641,0.3777484894,-0.2166886032,0.1214607805,0.1762953401,-0.043517001,-0.2938742042,-0.1455057859,-0.4177222252,0.0367241055,0.283308208,0.2024739832,0.0979529768,0.2341527939,-0.2812092602,-0.0606879592,0.3805413246,-0.2301190495,-0.1685689539,0.0972641706,0.0963025913,-0.0661173835,0.1656782478,-0.3956311643,0.1563510746,0.3519503474,-0.4384936988,0.0334676392,-0.2134845853,-0.2411233634,-0.3524810374,0.2018900961,0.2790597677,-0.7854661942,0.3429474831,0.0539065748,-0.0317488685,0.1149988398,0.4237150252,-0.1483171433,0.0611786693,-0.2709869146,0.0921530128,0.1406241804,0.007542707,-0.6293674707,0.3307564855,-0.04602734,0.0895455927,0.3120644391,0.0645528063,0.0512152202,0.0891771689,-0.1080223322,0.0185540114,0.0231345072,0.0440054424,-0.2607476711,-0.1291924864,0.3187506497,0.142625019,-0.0647743195,-0.0676729232,0.2388889641,-0.4087391198,0.2025453746,-0.0180878602,0.1416161656,0.1752468795,0.1791311353,-0.1968236417,0.0349740647,0.0104333451,-0.4493494034,0.1433825642,0.2848802209,-0.1687007099,-0.2159446925,-0.0797307715,-0.384118408,-0.0297010913,-0.2681604922,-0.1241994351,-0.0246524885,0.0725153983,0.0595197789,-0.1440529078,-0.1857402176,0.1161194369,-0.2237771302,-0.0112759126,-0.0158436764,0.1422190964,0.058799319,-0.1249890551,0.1064720228,-0.0629007593,0.207167998,-0.0483339541,-0.2763522565,0.2495912611,0.1456864476,0.0456155054,-0.2857958674,-0.1747876853,0.0830201879,-0.2415053844,-0.0872012973,0.0237385109,0.3663716614,-0.1825469434,-0.3392808735,0.2706229687,-0.1567003578,0.3015136421,0.1879752129,-0.3580714464,0.3065021634,0.016720105,0.1781937331,0.0704920888,0.1380855441,0.0736546293,0.4947431087,0.2995411456,-0.0968417376,-0.1562502384,0.5009906292,-0.0470440127,-0.2185632885,0.3112592995,-0.1181778163,-0.1945351362,0.0684309453,0.0887451172,0.3798215091,0.1293763965,-0.1915307492,0.0146643994,0.1435023844,-0.2500295639,0.1204057485,-0.2204343826,0.1473618895,0.3965650201,0.1270093769,-0.1272856295,-0.195144549,0.0377438515,0.0875855386,0.3257235587,-0.4119557142,-0.0675459281,-0.0734540001,-0.0052061281,-0.1802845895,0.0986031592,-0.0555794202,0.0971230865,-0.3996109366,0.3328239024,0.2897825539,-0.0790665746,-0.2431259155,0.08863388,0.2158128768,-0.3370558321,-0.0034607018,-0.0489910357,-0.2919403613,-0.0261766538,0.3307105303,0.1098190099,-0.0008771374,-0.0217662286,0.0280251838,-0.0548448637,-0.0925327018,-0.0439247601,-0.0732297674,0.0209216662,-0.0511663184,0.4193309844,0.0076079252,-0.1446308047,0.4017484784,-0.2536608279,-0.0489250831,0.350135982,0.0636930913,-0.0958797708,-0.1087071598,-0.3498898149,-0.139888227,-0.4293371439,0.3910627365,0.0847561061,0.0419315621,0.0926315561,0.4331049323,-0.0260847546,0.3013546467,-0.0312164128,-0.0848050937,-0.2977054417,0.3645742834,-0.0561163239,-0.4676565826,0.011941432,0.0828354955,0.3591901362,0.1638266891,-0.4252471328,0.3267471194,-0.15090774,0.1628198922,0.0011049408,-0.0242274385,0.0609546043,-0.0872276798,-0.0218234472,0.0364301093,-0.2417160571,-0.0141803464,-0.0320445746,0.1475835294,0.0513685606,0.458429724,-0.0371929593,0.6513361931,0.0874645486,-0.0882313102,0.4855556488,-0.308239907,0.3818774223,-0.2096708715,-0.3737454712,-0.0855747312,-0.3519817293,-0.192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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/836","title":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas","comments":"Thanks for the fast response. I have the latest version '2.0.0' (I tried to update)\r\nI am working with Python 3.8.5","body":"Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n","comment_length":21,"text":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas \n Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n \n Thanks for the fast response. I have the latest version '2.0.0' (I tried to update)\r\nI am working with Python 3.8.5","embeddings":[-0.24118343,-0.3198650777,-0.0681558475,0.4316074848,0.3625251055,0.0191550385,0.5210206509,0.437713474,0.2949123979,0.0327753536,0.0169624221,-0.1187045723,0.0905919969,0.1802892983,0.0350663252,0.1130092442,0.0730282664,0.3988213837,0.0317200907,0.0699720979,-0.218860805,0.1208694875,0.005867396,0.0575151071,-0.1244225353,0.0723639429,-0.008208585,0.1169146225,-0.2343517244,-0.4013206661,0.4197234511,0.0784542188,0.3328505158,0.5207661986,-0.0001169051,0.1564733684,0.2654486001,-0.1856259704,-0.0441993587,-0.5195806026,0.0301294662,-0.5329574347,0.2776663601,-0.1672024429,-0.0144887799,-0.1786803156,-0.1109988168,-0.0358052403,0.3014124632,0.3175454736,0.1804958135,-0.2607497573,0.0598921739,-0.0100186532,0.2052922398,0.0371788666,-0.0156855155,0.4175470769,0.1173958927,-0.1267608404,-0.0487308204,-0.193666935,-0.2392427474,0.3581996262,0.3196350932,0.1399399787,0.0709672496,-0.1089668348,0.2680699527,0.1055052057,0.6067355871,-0.307642132,-0.1485474855,-0.0154003808,-0.0202969182,-0.4886516333,0.2558154762,0.404494822,-0.1521599442,0.1173331738,-0.2122125626,0.2484468818,-0.2663929462,0.2028620988,-0.0673474893,-0.3531901836,-0.2522074878,0.2693178952,0.2523397803,0.0737477168,0.3336401582,-0.0937501863,0.0268538464,0.1806966811,-0.5556491017,0.2796493471,-0.1176608056,-0.0904364362,0.1637407988,0.096218653,0.4014219046,0.1370620281,0.0423445031,0.1946978569,0.3191299736,0.1861328632,-0.0428146571,0.0691050887,0.0273857322,0.3462172449,-0.2297836244,-0.2338579148,-0.3881369233,-0.5049571991,0.2381427437,-0.1538383663,0.2674439549,-0.2704333961,-0.2479329556,-0.0960200727,-0.0361324735,-0.0094233667,-0.0528344922,0.5738207698,0.215833962,0.3802042603,-0.054209996,0.2572092116,-0.2112757564,-0.09865468,-0.0803525597,0.0201157145,-0.0576021709,0.0522914641,0.3777484894,-0.2166886032,0.1214607805,0.1762953401,-0.043517001,-0.2938742042,-0.1455057859,-0.4177222252,0.0367241055,0.283308208,0.2024739832,0.0979529768,0.2341527939,-0.2812092602,-0.0606879592,0.3805413246,-0.2301190495,-0.1685689539,0.0972641706,0.0963025913,-0.0661173835,0.1656782478,-0.3956311643,0.1563510746,0.3519503474,-0.4384936988,0.0334676392,-0.2134845853,-0.2411233634,-0.3524810374,0.2018900961,0.2790597677,-0.7854661942,0.3429474831,0.0539065748,-0.0317488685,0.1149988398,0.4237150252,-0.1483171433,0.0611786693,-0.2709869146,0.0921530128,0.1406241804,0.007542707,-0.6293674707,0.3307564855,-0.04602734,0.0895455927,0.3120644391,0.0645528063,0.0512152202,0.0891771689,-0.1080223322,0.0185540114,0.0231345072,0.0440054424,-0.2607476711,-0.1291924864,0.3187506497,0.142625019,-0.0647743195,-0.0676729232,0.2388889641,-0.4087391198,0.2025453746,-0.0180878602,0.1416161656,0.1752468795,0.1791311353,-0.1968236417,0.0349740647,0.0104333451,-0.4493494034,0.1433825642,0.2848802209,-0.1687007099,-0.2159446925,-0.0797307715,-0.384118408,-0.0297010913,-0.2681604922,-0.1241994351,-0.0246524885,0.0725153983,0.0595197789,-0.1440529078,-0.1857402176,0.1161194369,-0.2237771302,-0.0112759126,-0.0158436764,0.1422190964,0.058799319,-0.1249890551,0.1064720228,-0.0629007593,0.207167998,-0.0483339541,-0.2763522565,0.2495912611,0.1456864476,0.0456155054,-0.2857958674,-0.1747876853,0.0830201879,-0.2415053844,-0.0872012973,0.0237385109,0.3663716614,-0.1825469434,-0.3392808735,0.2706229687,-0.1567003578,0.3015136421,0.1879752129,-0.3580714464,0.3065021634,0.016720105,0.1781937331,0.0704920888,0.1380855441,0.0736546293,0.4947431087,0.2995411456,-0.0968417376,-0.1562502384,0.5009906292,-0.0470440127,-0.2185632885,0.3112592995,-0.1181778163,-0.1945351362,0.0684309453,0.0887451172,0.3798215091,0.1293763965,-0.1915307492,0.0146643994,0.1435023844,-0.2500295639,0.1204057485,-0.2204343826,0.1473618895,0.3965650201,0.1270093769,-0.1272856295,-0.195144549,0.0377438515,0.0875855386,0.3257235587,-0.4119557142,-0.0675459281,-0.0734540001,-0.0052061281,-0.1802845895,0.0986031592,-0.0555794202,0.0971230865,-0.3996109366,0.3328239024,0.2897825539,-0.0790665746,-0.2431259155,0.08863388,0.2158128768,-0.3370558321,-0.0034607018,-0.0489910357,-0.2919403613,-0.0261766538,0.3307105303,0.1098190099,-0.0008771374,-0.0217662286,0.0280251838,-0.0548448637,-0.0925327018,-0.0439247601,-0.0732297674,0.0209216662,-0.0511663184,0.4193309844,0.0076079252,-0.1446308047,0.4017484784,-0.2536608279,-0.0489250831,0.350135982,0.0636930913,-0.0958797708,-0.1087071598,-0.3498898149,-0.139888227,-0.4293371439,0.3910627365,0.0847561061,0.0419315621,0.0926315561,0.4331049323,-0.0260847546,0.3013546467,-0.0312164128,-0.0848050937,-0.2977054417,0.3645742834,-0.0561163239,-0.4676565826,0.011941432,0.0828354955,0.3591901362,0.1638266891,-0.4252471328,0.3267471194,-0.15090774,0.1628198922,0.0011049408,-0.0242274385,0.0609546043,-0.0872276798,-0.0218234472,0.0364301093,-0.2417160571,-0.0141803464,-0.0320445746,0.1475835294,0.0513685606,0.458429724,-0.0371929593,0.6513361931,0.0874645486,-0.0882313102,0.4855556488,-0.308239907,0.3818774223,-0.2096708715,-0.3737454712,-0.0855747312,-0.3519817293,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/836","title":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas","comments":"I think that the issue is similar to this one:https:\/\/issues.apache.org\/jira\/browse\/ARROW-9612\r\nThe problem is in arrow when the column data contains long strings.\r\nAny ideas on how to bypass this?","body":"Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n","comment_length":29,"text":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas \n Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n \n I think that the issue is similar to this one:https:\/\/issues.apache.org\/jira\/browse\/ARROW-9612\r\nThe problem is in arrow when the column data contains long strings.\r\nAny ideas on how to bypass this?","embeddings":[-0.24118343,-0.3198650777,-0.0681558475,0.4316074848,0.3625251055,0.0191550385,0.5210206509,0.437713474,0.2949123979,0.0327753536,0.0169624221,-0.1187045723,0.0905919969,0.1802892983,0.0350663252,0.1130092442,0.0730282664,0.3988213837,0.0317200907,0.0699720979,-0.218860805,0.1208694875,0.005867396,0.0575151071,-0.1244225353,0.0723639429,-0.008208585,0.1169146225,-0.2343517244,-0.4013206661,0.4197234511,0.0784542188,0.3328505158,0.5207661986,-0.0001169051,0.1564733684,0.2654486001,-0.1856259704,-0.0441993587,-0.5195806026,0.0301294662,-0.5329574347,0.2776663601,-0.1672024429,-0.0144887799,-0.1786803156,-0.1109988168,-0.0358052403,0.3014124632,0.3175454736,0.1804958135,-0.2607497573,0.0598921739,-0.0100186532,0.2052922398,0.0371788666,-0.0156855155,0.4175470769,0.1173958927,-0.1267608404,-0.0487308204,-0.193666935,-0.2392427474,0.3581996262,0.3196350932,0.1399399787,0.0709672496,-0.1089668348,0.2680699527,0.1055052057,0.6067355871,-0.307642132,-0.1485474855,-0.0154003808,-0.0202969182,-0.4886516333,0.2558154762,0.404494822,-0.1521599442,0.1173331738,-0.2122125626,0.2484468818,-0.2663929462,0.2028620988,-0.0673474893,-0.3531901836,-0.2522074878,0.2693178952,0.2523397803,0.0737477168,0.3336401582,-0.0937501863,0.0268538464,0.1806966811,-0.5556491017,0.2796493471,-0.1176608056,-0.0904364362,0.1637407988,0.096218653,0.4014219046,0.1370620281,0.0423445031,0.1946978569,0.3191299736,0.1861328632,-0.0428146571,0.0691050887,0.0273857322,0.3462172449,-0.2297836244,-0.2338579148,-0.3881369233,-0.5049571991,0.2381427437,-0.1538383663,0.2674439549,-0.2704333961,-0.2479329556,-0.0960200727,-0.0361324735,-0.0094233667,-0.0528344922,0.5738207698,0.215833962,0.3802042603,-0.054209996,0.2572092116,-0.2112757564,-0.09865468,-0.0803525597,0.0201157145,-0.0576021709,0.0522914641,0.3777484894,-0.2166886032,0.1214607805,0.1762953401,-0.043517001,-0.2938742042,-0.1455057859,-0.4177222252,0.0367241055,0.283308208,0.2024739832,0.0979529768,0.2341527939,-0.2812092602,-0.0606879592,0.3805413246,-0.2301190495,-0.1685689539,0.0972641706,0.0963025913,-0.0661173835,0.1656782478,-0.3956311643,0.1563510746,0.3519503474,-0.4384936988,0.0334676392,-0.2134845853,-0.2411233634,-0.3524810374,0.2018900961,0.2790597677,-0.7854661942,0.3429474831,0.0539065748,-0.0317488685,0.1149988398,0.4237150252,-0.1483171433,0.0611786693,-0.2709869146,0.0921530128,0.1406241804,0.007542707,-0.6293674707,0.3307564855,-0.04602734,0.0895455927,0.3120644391,0.0645528063,0.0512152202,0.0891771689,-0.1080223322,0.0185540114,0.0231345072,0.0440054424,-0.2607476711,-0.1291924864,0.3187506497,0.142625019,-0.0647743195,-0.0676729232,0.2388889641,-0.4087391198,0.2025453746,-0.0180878602,0.1416161656,0.1752468795,0.1791311353,-0.1968236417,0.0349740647,0.0104333451,-0.4493494034,0.1433825642,0.2848802209,-0.1687007099,-0.2159446925,-0.0797307715,-0.384118408,-0.0297010913,-0.2681604922,-0.1241994351,-0.0246524885,0.0725153983,0.0595197789,-0.1440529078,-0.1857402176,0.1161194369,-0.2237771302,-0.0112759126,-0.0158436764,0.1422190964,0.058799319,-0.1249890551,0.1064720228,-0.0629007593,0.207167998,-0.0483339541,-0.2763522565,0.2495912611,0.1456864476,0.0456155054,-0.2857958674,-0.1747876853,0.0830201879,-0.2415053844,-0.0872012973,0.0237385109,0.3663716614,-0.1825469434,-0.3392808735,0.2706229687,-0.1567003578,0.3015136421,0.1879752129,-0.3580714464,0.3065021634,0.016720105,0.1781937331,0.0704920888,0.1380855441,0.0736546293,0.4947431087,0.2995411456,-0.0968417376,-0.1562502384,0.5009906292,-0.0470440127,-0.2185632885,0.3112592995,-0.1181778163,-0.1945351362,0.0684309453,0.0887451172,0.3798215091,0.1293763965,-0.1915307492,0.0146643994,0.1435023844,-0.2500295639,0.1204057485,-0.2204343826,0.1473618895,0.3965650201,0.1270093769,-0.1272856295,-0.195144549,0.0377438515,0.0875855386,0.3257235587,-0.4119557142,-0.0675459281,-0.0734540001,-0.0052061281,-0.1802845895,0.0986031592,-0.0555794202,0.0971230865,-0.3996109366,0.3328239024,0.2897825539,-0.0790665746,-0.2431259155,0.08863388,0.2158128768,-0.3370558321,-0.0034607018,-0.0489910357,-0.2919403613,-0.0261766538,0.3307105303,0.1098190099,-0.0008771374,-0.0217662286,0.0280251838,-0.0548448637,-0.0925327018,-0.0439247601,-0.0732297674,0.0209216662,-0.0511663184,0.4193309844,0.0076079252,-0.1446308047,0.4017484784,-0.2536608279,-0.0489250831,0.350135982,0.0636930913,-0.0958797708,-0.1087071598,-0.3498898149,-0.139888227,-0.4293371439,0.3910627365,0.0847561061,0.0419315621,0.0926315561,0.4331049323,-0.0260847546,0.3013546467,-0.0312164128,-0.0848050937,-0.2977054417,0.3645742834,-0.0561163239,-0.4676565826,0.011941432,0.0828354955,0.3591901362,0.1638266891,-0.4252471328,0.3267471194,-0.15090774,0.1628198922,0.0011049408,-0.0242274385,0.0609546043,-0.0872276798,-0.0218234472,0.0364301093,-0.2417160571,-0.0141803464,-0.0320445746,0.1475835294,0.0513685606,0.458429724,-0.0371929593,0.6513361931,0.0874645486,-0.0882313102,0.4855556488,-0.308239907,0.3818774223,-0.2096708715,-0.3737454712,-0.0855747312,-0.3519817293,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/836","title":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas","comments":"We should expose the [`block_size` argument](https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.csv.ReadOptions.html#pyarrow.csv.ReadOptions) of Apache Arrow csv `ReadOptions` in the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/csv\/csv.py).\r\n\r\n\r\nIn the meantime you can specify yourself the `ReadOptions` config like this:\r\n```python\r\nimport pyarrow.csv as pac   # PyArrow is installed with `datasets`\r\n\r\nread_options = pac.ReadOptions(block_size=1e9)  # try to find the right value for your use-case\r\ndataset = load_dataset('csv', data_files=files, read_options=read_options)\r\n```\r\n","body":"Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n","comment_length":56,"text":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas \n Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n \n We should expose the [`block_size` argument](https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.csv.ReadOptions.html#pyarrow.csv.ReadOptions) of Apache Arrow csv `ReadOptions` in the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/csv\/csv.py).\r\n\r\n\r\nIn the meantime you can specify yourself the `ReadOptions` config like this:\r\n```python\r\nimport pyarrow.csv as pac   # PyArrow is installed with `datasets`\r\n\r\nread_options = pac.ReadOptions(block_size=1e9)  # try to find the right value for your use-case\r\ndataset = load_dataset('csv', data_files=files, read_options=read_options)\r\n```\r\n","embeddings":[-0.24118343,-0.3198650777,-0.0681558475,0.4316074848,0.3625251055,0.0191550385,0.5210206509,0.437713474,0.2949123979,0.0327753536,0.0169624221,-0.1187045723,0.0905919969,0.1802892983,0.0350663252,0.1130092442,0.0730282664,0.3988213837,0.0317200907,0.0699720979,-0.218860805,0.1208694875,0.005867396,0.0575151071,-0.1244225353,0.0723639429,-0.008208585,0.1169146225,-0.2343517244,-0.4013206661,0.4197234511,0.0784542188,0.3328505158,0.5207661986,-0.0001169051,0.1564733684,0.2654486001,-0.1856259704,-0.0441993587,-0.5195806026,0.0301294662,-0.5329574347,0.2776663601,-0.1672024429,-0.0144887799,-0.1786803156,-0.1109988168,-0.0358052403,0.3014124632,0.3175454736,0.1804958135,-0.2607497573,0.0598921739,-0.0100186532,0.2052922398,0.0371788666,-0.0156855155,0.4175470769,0.1173958927,-0.1267608404,-0.0487308204,-0.193666935,-0.2392427474,0.3581996262,0.3196350932,0.1399399787,0.0709672496,-0.1089668348,0.2680699527,0.1055052057,0.6067355871,-0.307642132,-0.1485474855,-0.0154003808,-0.0202969182,-0.4886516333,0.2558154762,0.404494822,-0.1521599442,0.1173331738,-0.2122125626,0.2484468818,-0.2663929462,0.2028620988,-0.0673474893,-0.3531901836,-0.2522074878,0.2693178952,0.2523397803,0.0737477168,0.3336401582,-0.0937501863,0.0268538464,0.1806966811,-0.5556491017,0.2796493471,-0.1176608056,-0.0904364362,0.1637407988,0.096218653,0.4014219046,0.1370620281,0.0423445031,0.1946978569,0.3191299736,0.1861328632,-0.0428146571,0.0691050887,0.0273857322,0.3462172449,-0.2297836244,-0.2338579148,-0.3881369233,-0.5049571991,0.2381427437,-0.1538383663,0.2674439549,-0.2704333961,-0.2479329556,-0.0960200727,-0.0361324735,-0.0094233667,-0.0528344922,0.5738207698,0.215833962,0.3802042603,-0.054209996,0.2572092116,-0.2112757564,-0.09865468,-0.0803525597,0.0201157145,-0.0576021709,0.0522914641,0.3777484894,-0.2166886032,0.1214607805,0.1762953401,-0.043517001,-0.2938742042,-0.1455057859,-0.4177222252,0.0367241055,0.283308208,0.2024739832,0.0979529768,0.2341527939,-0.2812092602,-0.0606879592,0.3805413246,-0.2301190495,-0.1685689539,0.0972641706,0.0963025913,-0.0661173835,0.1656782478,-0.3956311643,0.1563510746,0.3519503474,-0.4384936988,0.0334676392,-0.2134845853,-0.2411233634,-0.3524810374,0.2018900961,0.2790597677,-0.7854661942,0.3429474831,0.0539065748,-0.0317488685,0.1149988398,0.4237150252,-0.1483171433,0.0611786693,-0.2709869146,0.0921530128,0.1406241804,0.007542707,-0.6293674707,0.3307564855,-0.04602734,0.0895455927,0.3120644391,0.0645528063,0.0512152202,0.0891771689,-0.1080223322,0.0185540114,0.0231345072,0.0440054424,-0.2607476711,-0.1291924864,0.3187506497,0.142625019,-0.0647743195,-0.0676729232,0.2388889641,-0.4087391198,0.2025453746,-0.0180878602,0.1416161656,0.1752468795,0.1791311353,-0.1968236417,0.0349740647,0.0104333451,-0.4493494034,0.1433825642,0.2848802209,-0.1687007099,-0.2159446925,-0.0797307715,-0.384118408,-0.0297010913,-0.2681604922,-0.1241994351,-0.0246524885,0.0725153983,0.0595197789,-0.1440529078,-0.1857402176,0.1161194369,-0.2237771302,-0.0112759126,-0.0158436764,0.1422190964,0.058799319,-0.1249890551,0.1064720228,-0.0629007593,0.207167998,-0.0483339541,-0.2763522565,0.2495912611,0.1456864476,0.0456155054,-0.2857958674,-0.1747876853,0.0830201879,-0.2415053844,-0.0872012973,0.0237385109,0.3663716614,-0.1825469434,-0.3392808735,0.2706229687,-0.1567003578,0.3015136421,0.1879752129,-0.3580714464,0.3065021634,0.016720105,0.1781937331,0.0704920888,0.1380855441,0.0736546293,0.4947431087,0.2995411456,-0.0968417376,-0.1562502384,0.5009906292,-0.0470440127,-0.2185632885,0.3112592995,-0.1181778163,-0.1945351362,0.0684309453,0.0887451172,0.3798215091,0.1293763965,-0.1915307492,0.0146643994,0.1435023844,-0.2500295639,0.1204057485,-0.2204343826,0.1473618895,0.3965650201,0.1270093769,-0.1272856295,-0.195144549,0.0377438515,0.0875855386,0.3257235587,-0.4119557142,-0.0675459281,-0.0734540001,-0.0052061281,-0.1802845895,0.0986031592,-0.0555794202,0.0971230865,-0.3996109366,0.3328239024,0.2897825539,-0.0790665746,-0.2431259155,0.08863388,0.2158128768,-0.3370558321,-0.0034607018,-0.0489910357,-0.2919403613,-0.0261766538,0.3307105303,0.1098190099,-0.0008771374,-0.0217662286,0.0280251838,-0.0548448637,-0.0925327018,-0.0439247601,-0.0732297674,0.0209216662,-0.0511663184,0.4193309844,0.0076079252,-0.1446308047,0.4017484784,-0.2536608279,-0.0489250831,0.350135982,0.0636930913,-0.0958797708,-0.1087071598,-0.3498898149,-0.139888227,-0.4293371439,0.3910627365,0.0847561061,0.0419315621,0.0926315561,0.4331049323,-0.0260847546,0.3013546467,-0.0312164128,-0.0848050937,-0.2977054417,0.3645742834,-0.0561163239,-0.4676565826,0.011941432,0.0828354955,0.3591901362,0.1638266891,-0.4252471328,0.3267471194,-0.15090774,0.1628198922,0.0011049408,-0.0242274385,0.0609546043,-0.0872276798,-0.0218234472,0.0364301093,-0.2417160571,-0.0141803464,-0.0320445746,0.1475835294,0.0513685606,0.458429724,-0.0371929593,0.6513361931,0.0874645486,-0.0882313102,0.4855556488,-0.308239907,0.3818774223,-0.2096708715,-0.373745471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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/836","title":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas","comments":"This did help to load the data. But the problem now is that I get:\r\nArrowInvalid: CSV parse error: Expected 5 columns, got 187\r\n\r\nIt seems that this change the parsing so I changed the table to tab-separated and tried to load it directly from pyarrow\r\nBut I got a similar error, again it loaded fine in pandas so I am not sure what to do.\r\n\r\n\r\n\r\n","body":"Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n","comment_length":66,"text":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas \n Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n \n This did help to load the data. But the problem now is that I get:\r\nArrowInvalid: CSV parse error: Expected 5 columns, got 187\r\n\r\nIt seems that this change the parsing so I changed the table to tab-separated and tried to load it directly from pyarrow\r\nBut I got a similar error, again it loaded fine in pandas so I am not sure what to do.\r\n\r\n\r\n\r\n","embeddings":[-0.24118343,-0.3198650777,-0.0681558475,0.4316074848,0.3625251055,0.0191550385,0.5210206509,0.437713474,0.2949123979,0.0327753536,0.0169624221,-0.1187045723,0.0905919969,0.1802892983,0.0350663252,0.1130092442,0.0730282664,0.3988213837,0.0317200907,0.0699720979,-0.218860805,0.1208694875,0.005867396,0.0575151071,-0.1244225353,0.0723639429,-0.008208585,0.1169146225,-0.2343517244,-0.4013206661,0.4197234511,0.0784542188,0.3328505158,0.5207661986,-0.0001169051,0.1564733684,0.2654486001,-0.1856259704,-0.0441993587,-0.5195806026,0.0301294662,-0.5329574347,0.2776663601,-0.1672024429,-0.0144887799,-0.1786803156,-0.1109988168,-0.0358052403,0.3014124632,0.3175454736,0.1804958135,-0.2607497573,0.0598921739,-0.0100186532,0.2052922398,0.0371788666,-0.0156855155,0.4175470769,0.1173958927,-0.1267608404,-0.0487308204,-0.193666935,-0.2392427474,0.3581996262,0.3196350932,0.1399399787,0.0709672496,-0.1089668348,0.2680699527,0.1055052057,0.6067355871,-0.307642132,-0.1485474855,-0.0154003808,-0.0202969182,-0.4886516333,0.2558154762,0.404494822,-0.1521599442,0.1173331738,-0.2122125626,0.2484468818,-0.2663929462,0.2028620988,-0.0673474893,-0.3531901836,-0.2522074878,0.2693178952,0.2523397803,0.0737477168,0.3336401582,-0.0937501863,0.0268538464,0.1806966811,-0.5556491017,0.2796493471,-0.1176608056,-0.0904364362,0.1637407988,0.096218653,0.4014219046,0.1370620281,0.0423445031,0.1946978569,0.3191299736,0.1861328632,-0.0428146571,0.0691050887,0.0273857322,0.3462172449,-0.2297836244,-0.2338579148,-0.3881369233,-0.5049571991,0.2381427437,-0.1538383663,0.2674439549,-0.2704333961,-0.2479329556,-0.0960200727,-0.0361324735,-0.0094233667,-0.0528344922,0.5738207698,0.215833962,0.3802042603,-0.054209996,0.2572092116,-0.2112757564,-0.09865468,-0.0803525597,0.0201157145,-0.0576021709,0.0522914641,0.3777484894,-0.2166886032,0.1214607805,0.1762953401,-0.043517001,-0.2938742042,-0.1455057859,-0.4177222252,0.0367241055,0.283308208,0.2024739832,0.0979529768,0.2341527939,-0.2812092602,-0.0606879592,0.3805413246,-0.2301190495,-0.1685689539,0.0972641706,0.0963025913,-0.0661173835,0.1656782478,-0.3956311643,0.1563510746,0.3519503474,-0.4384936988,0.0334676392,-0.2134845853,-0.2411233634,-0.3524810374,0.2018900961,0.2790597677,-0.7854661942,0.3429474831,0.0539065748,-0.0317488685,0.1149988398,0.4237150252,-0.1483171433,0.0611786693,-0.2709869146,0.0921530128,0.1406241804,0.007542707,-0.6293674707,0.3307564855,-0.04602734,0.0895455927,0.3120644391,0.0645528063,0.0512152202,0.0891771689,-0.1080223322,0.0185540114,0.0231345072,0.0440054424,-0.2607476711,-0.1291924864,0.3187506497,0.142625019,-0.0647743195,-0.0676729232,0.2388889641,-0.4087391198,0.2025453746,-0.0180878602,0.1416161656,0.1752468795,0.1791311353,-0.1968236417,0.0349740647,0.0104333451,-0.4493494034,0.1433825642,0.2848802209,-0.1687007099,-0.2159446925,-0.0797307715,-0.384118408,-0.0297010913,-0.2681604922,-0.1241994351,-0.0246524885,0.0725153983,0.0595197789,-0.1440529078,-0.1857402176,0.1161194369,-0.2237771302,-0.0112759126,-0.0158436764,0.1422190964,0.058799319,-0.1249890551,0.1064720228,-0.0629007593,0.207167998,-0.0483339541,-0.2763522565,0.2495912611,0.1456864476,0.0456155054,-0.2857958674,-0.1747876853,0.0830201879,-0.2415053844,-0.0872012973,0.0237385109,0.3663716614,-0.1825469434,-0.3392808735,0.2706229687,-0.1567003578,0.3015136421,0.1879752129,-0.3580714464,0.3065021634,0.016720105,0.1781937331,0.0704920888,0.1380855441,0.0736546293,0.4947431087,0.2995411456,-0.0968417376,-0.1562502384,0.5009906292,-0.0470440127,-0.2185632885,0.3112592995,-0.1181778163,-0.1945351362,0.0684309453,0.0887451172,0.3798215091,0.1293763965,-0.1915307492,0.0146643994,0.1435023844,-0.2500295639,0.1204057485,-0.2204343826,0.1473618895,0.3965650201,0.1270093769,-0.1272856295,-0.195144549,0.0377438515,0.0875855386,0.3257235587,-0.4119557142,-0.0675459281,-0.0734540001,-0.0052061281,-0.1802845895,0.0986031592,-0.0555794202,0.0971230865,-0.3996109366,0.3328239024,0.2897825539,-0.0790665746,-0.2431259155,0.08863388,0.2158128768,-0.3370558321,-0.0034607018,-0.0489910357,-0.2919403613,-0.0261766538,0.3307105303,0.1098190099,-0.0008771374,-0.0217662286,0.0280251838,-0.0548448637,-0.0925327018,-0.0439247601,-0.0732297674,0.0209216662,-0.0511663184,0.4193309844,0.0076079252,-0.1446308047,0.4017484784,-0.2536608279,-0.0489250831,0.350135982,0.0636930913,-0.0958797708,-0.1087071598,-0.3498898149,-0.139888227,-0.4293371439,0.3910627365,0.0847561061,0.0419315621,0.0926315561,0.4331049323,-0.0260847546,0.3013546467,-0.0312164128,-0.0848050937,-0.2977054417,0.3645742834,-0.0561163239,-0.4676565826,0.011941432,0.0828354955,0.3591901362,0.1638266891,-0.4252471328,0.3267471194,-0.15090774,0.1628198922,0.0011049408,-0.0242274385,0.0609546043,-0.0872276798,-0.0218234472,0.0364301093,-0.2417160571,-0.0141803464,-0.0320445746,0.1475835294,0.0513685606,0.458429724,-0.0371929593,0.6513361931,0.0874645486,-0.0882313102,0.4855556488,-0.308239907,0.3818774223,-0.2096708715,-0.3737454712,-0.0855747312,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/836","title":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas","comments":"Got almost the same error loading a ~5GB TSV file, first got the same error as OP, then tried giving it my own ReadOptions and also got the same CSV parse error.","body":"Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n","comment_length":32,"text":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas \n Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n \n Got almost the same error loading a ~5GB TSV file, first got the same error as OP, then tried giving it my own ReadOptions and also got the same CSV parse error.","embeddings":[-0.24118343,-0.3198650777,-0.0681558475,0.4316074848,0.3625251055,0.0191550385,0.5210206509,0.437713474,0.2949123979,0.0327753536,0.0169624221,-0.1187045723,0.0905919969,0.1802892983,0.0350663252,0.1130092442,0.0730282664,0.3988213837,0.0317200907,0.0699720979,-0.218860805,0.1208694875,0.005867396,0.0575151071,-0.1244225353,0.0723639429,-0.008208585,0.1169146225,-0.2343517244,-0.4013206661,0.4197234511,0.0784542188,0.3328505158,0.5207661986,-0.0001169051,0.1564733684,0.2654486001,-0.1856259704,-0.0441993587,-0.5195806026,0.0301294662,-0.5329574347,0.2776663601,-0.1672024429,-0.0144887799,-0.1786803156,-0.1109988168,-0.0358052403,0.3014124632,0.3175454736,0.1804958135,-0.2607497573,0.0598921739,-0.0100186532,0.2052922398,0.0371788666,-0.0156855155,0.4175470769,0.1173958927,-0.1267608404,-0.0487308204,-0.193666935,-0.2392427474,0.3581996262,0.3196350932,0.1399399787,0.0709672496,-0.1089668348,0.2680699527,0.1055052057,0.6067355871,-0.307642132,-0.1485474855,-0.0154003808,-0.0202969182,-0.4886516333,0.2558154762,0.404494822,-0.1521599442,0.1173331738,-0.2122125626,0.2484468818,-0.2663929462,0.2028620988,-0.0673474893,-0.3531901836,-0.2522074878,0.2693178952,0.2523397803,0.0737477168,0.3336401582,-0.0937501863,0.0268538464,0.1806966811,-0.5556491017,0.2796493471,-0.1176608056,-0.0904364362,0.1637407988,0.096218653,0.4014219046,0.1370620281,0.0423445031,0.1946978569,0.3191299736,0.1861328632,-0.0428146571,0.0691050887,0.0273857322,0.3462172449,-0.2297836244,-0.2338579148,-0.3881369233,-0.5049571991,0.2381427437,-0.1538383663,0.2674439549,-0.2704333961,-0.2479329556,-0.0960200727,-0.0361324735,-0.0094233667,-0.0528344922,0.5738207698,0.215833962,0.3802042603,-0.054209996,0.2572092116,-0.2112757564,-0.09865468,-0.0803525597,0.0201157145,-0.0576021709,0.0522914641,0.3777484894,-0.2166886032,0.1214607805,0.1762953401,-0.043517001,-0.2938742042,-0.1455057859,-0.4177222252,0.0367241055,0.283308208,0.2024739832,0.0979529768,0.2341527939,-0.2812092602,-0.0606879592,0.3805413246,-0.2301190495,-0.1685689539,0.0972641706,0.0963025913,-0.0661173835,0.1656782478,-0.3956311643,0.1563510746,0.3519503474,-0.4384936988,0.0334676392,-0.2134845853,-0.2411233634,-0.3524810374,0.2018900961,0.2790597677,-0.7854661942,0.3429474831,0.0539065748,-0.0317488685,0.1149988398,0.4237150252,-0.1483171433,0.0611786693,-0.2709869146,0.0921530128,0.1406241804,0.007542707,-0.6293674707,0.3307564855,-0.04602734,0.0895455927,0.3120644391,0.0645528063,0.0512152202,0.0891771689,-0.1080223322,0.0185540114,0.0231345072,0.0440054424,-0.2607476711,-0.1291924864,0.3187506497,0.142625019,-0.0647743195,-0.0676729232,0.2388889641,-0.4087391198,0.2025453746,-0.0180878602,0.1416161656,0.1752468795,0.1791311353,-0.1968236417,0.0349740647,0.0104333451,-0.4493494034,0.1433825642,0.2848802209,-0.1687007099,-0.2159446925,-0.0797307715,-0.384118408,-0.0297010913,-0.2681604922,-0.1241994351,-0.0246524885,0.0725153983,0.0595197789,-0.1440529078,-0.1857402176,0.1161194369,-0.2237771302,-0.0112759126,-0.0158436764,0.1422190964,0.058799319,-0.1249890551,0.1064720228,-0.0629007593,0.207167998,-0.0483339541,-0.2763522565,0.2495912611,0.1456864476,0.0456155054,-0.2857958674,-0.1747876853,0.0830201879,-0.2415053844,-0.0872012973,0.0237385109,0.3663716614,-0.1825469434,-0.3392808735,0.2706229687,-0.1567003578,0.3015136421,0.1879752129,-0.3580714464,0.3065021634,0.016720105,0.1781937331,0.0704920888,0.1380855441,0.0736546293,0.4947431087,0.2995411456,-0.0968417376,-0.1562502384,0.5009906292,-0.0470440127,-0.2185632885,0.3112592995,-0.1181778163,-0.1945351362,0.0684309453,0.0887451172,0.3798215091,0.1293763965,-0.1915307492,0.0146643994,0.1435023844,-0.2500295639,0.1204057485,-0.2204343826,0.1473618895,0.3965650201,0.1270093769,-0.1272856295,-0.195144549,0.0377438515,0.0875855386,0.3257235587,-0.4119557142,-0.0675459281,-0.0734540001,-0.0052061281,-0.1802845895,0.0986031592,-0.0555794202,0.0971230865,-0.3996109366,0.3328239024,0.2897825539,-0.0790665746,-0.2431259155,0.08863388,0.2158128768,-0.3370558321,-0.0034607018,-0.0489910357,-0.2919403613,-0.0261766538,0.3307105303,0.1098190099,-0.0008771374,-0.0217662286,0.0280251838,-0.0548448637,-0.0925327018,-0.0439247601,-0.0732297674,0.0209216662,-0.0511663184,0.4193309844,0.0076079252,-0.1446308047,0.4017484784,-0.2536608279,-0.0489250831,0.350135982,0.0636930913,-0.0958797708,-0.1087071598,-0.3498898149,-0.139888227,-0.4293371439,0.3910627365,0.0847561061,0.0419315621,0.0926315561,0.4331049323,-0.0260847546,0.3013546467,-0.0312164128,-0.0848050937,-0.2977054417,0.3645742834,-0.0561163239,-0.4676565826,0.011941432,0.0828354955,0.3591901362,0.1638266891,-0.4252471328,0.3267471194,-0.15090774,0.1628198922,0.0011049408,-0.0242274385,0.0609546043,-0.0872276798,-0.0218234472,0.0364301093,-0.2417160571,-0.0141803464,-0.0320445746,0.1475835294,0.0513685606,0.458429724,-0.0371929593,0.6513361931,0.0874645486,-0.0882313102,0.4855556488,-0.308239907,0.3818774223,-0.2096708715,-0.3737454712,-0.0855747312,-0.3519817293,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/836","title":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas","comments":"> We should expose the [`block_size` argument](https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.csv.ReadOptions.html#pyarrow.csv.ReadOptions) of Apache Arrow csv `ReadOptions` in the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/csv\/csv.py).\r\n> \r\n> In the meantime you can specify yourself the `ReadOptions` config like this:\r\n> \r\n> ```python\r\n> import pyarrow.csv as pac   # PyArrow is installed with `datasets`\r\n> \r\n> read_options = pac.ReadOptions(block_size=1e9)  # try to find the right value for your use-case\r\n> dataset = load_dataset('csv', data_files=files, read_options=read_options)\r\n> ```\r\n\r\nThis did not work for me, I got\r\n`TypeError: __init__() got an unexpected keyword argument 'read_options'`","body":"Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n","comment_length":82,"text":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas \n Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n \n > We should expose the [`block_size` argument](https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.csv.ReadOptions.html#pyarrow.csv.ReadOptions) of Apache Arrow csv `ReadOptions` in the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/csv\/csv.py).\r\n> \r\n> In the meantime you can specify yourself the `ReadOptions` config like this:\r\n> \r\n> ```python\r\n> import pyarrow.csv as pac   # PyArrow is installed with `datasets`\r\n> \r\n> read_options = pac.ReadOptions(block_size=1e9)  # try to find the right value for your use-case\r\n> dataset = load_dataset('csv', data_files=files, read_options=read_options)\r\n> ```\r\n\r\nThis did not work for me, I got\r\n`TypeError: __init__() got an unexpected keyword argument 'read_options'`","embeddings":[-0.24118343,-0.3198650777,-0.0681558475,0.4316074848,0.3625251055,0.0191550385,0.5210206509,0.437713474,0.2949123979,0.0327753536,0.0169624221,-0.1187045723,0.0905919969,0.1802892983,0.0350663252,0.1130092442,0.0730282664,0.3988213837,0.0317200907,0.0699720979,-0.218860805,0.1208694875,0.005867396,0.0575151071,-0.1244225353,0.0723639429,-0.008208585,0.1169146225,-0.2343517244,-0.4013206661,0.4197234511,0.0784542188,0.3328505158,0.5207661986,-0.0001169051,0.1564733684,0.2654486001,-0.1856259704,-0.0441993587,-0.5195806026,0.0301294662,-0.5329574347,0.2776663601,-0.1672024429,-0.0144887799,-0.1786803156,-0.1109988168,-0.0358052403,0.3014124632,0.3175454736,0.1804958135,-0.2607497573,0.0598921739,-0.0100186532,0.2052922398,0.0371788666,-0.0156855155,0.4175470769,0.1173958927,-0.1267608404,-0.0487308204,-0.193666935,-0.2392427474,0.3581996262,0.3196350932,0.1399399787,0.0709672496,-0.1089668348,0.2680699527,0.1055052057,0.6067355871,-0.307642132,-0.1485474855,-0.0154003808,-0.0202969182,-0.4886516333,0.2558154762,0.404494822,-0.1521599442,0.1173331738,-0.2122125626,0.2484468818,-0.2663929462,0.2028620988,-0.0673474893,-0.3531901836,-0.2522074878,0.2693178952,0.2523397803,0.0737477168,0.3336401582,-0.0937501863,0.0268538464,0.1806966811,-0.5556491017,0.2796493471,-0.1176608056,-0.0904364362,0.1637407988,0.096218653,0.4014219046,0.1370620281,0.0423445031,0.1946978569,0.3191299736,0.1861328632,-0.0428146571,0.0691050887,0.0273857322,0.3462172449,-0.2297836244,-0.2338579148,-0.3881369233,-0.5049571991,0.2381427437,-0.1538383663,0.2674439549,-0.2704333961,-0.2479329556,-0.0960200727,-0.0361324735,-0.0094233667,-0.0528344922,0.5738207698,0.215833962,0.3802042603,-0.054209996,0.2572092116,-0.2112757564,-0.09865468,-0.0803525597,0.0201157145,-0.0576021709,0.0522914641,0.3777484894,-0.2166886032,0.1214607805,0.1762953401,-0.043517001,-0.2938742042,-0.1455057859,-0.4177222252,0.0367241055,0.283308208,0.2024739832,0.0979529768,0.2341527939,-0.2812092602,-0.0606879592,0.3805413246,-0.2301190495,-0.1685689539,0.0972641706,0.0963025913,-0.0661173835,0.1656782478,-0.3956311643,0.1563510746,0.3519503474,-0.4384936988,0.0334676392,-0.2134845853,-0.2411233634,-0.3524810374,0.2018900961,0.2790597677,-0.7854661942,0.3429474831,0.0539065748,-0.0317488685,0.1149988398,0.4237150252,-0.1483171433,0.0611786693,-0.2709869146,0.0921530128,0.1406241804,0.007542707,-0.6293674707,0.3307564855,-0.04602734,0.0895455927,0.3120644391,0.0645528063,0.0512152202,0.0891771689,-0.1080223322,0.0185540114,0.0231345072,0.0440054424,-0.2607476711,-0.1291924864,0.3187506497,0.142625019,-0.0647743195,-0.0676729232,0.2388889641,-0.4087391198,0.2025453746,-0.0180878602,0.1416161656,0.1752468795,0.1791311353,-0.1968236417,0.0349740647,0.0104333451,-0.4493494034,0.1433825642,0.2848802209,-0.1687007099,-0.2159446925,-0.0797307715,-0.384118408,-0.0297010913,-0.2681604922,-0.1241994351,-0.0246524885,0.0725153983,0.0595197789,-0.1440529078,-0.1857402176,0.1161194369,-0.2237771302,-0.0112759126,-0.0158436764,0.1422190964,0.058799319,-0.1249890551,0.1064720228,-0.0629007593,0.207167998,-0.0483339541,-0.2763522565,0.2495912611,0.1456864476,0.0456155054,-0.2857958674,-0.1747876853,0.0830201879,-0.2415053844,-0.0872012973,0.0237385109,0.3663716614,-0.1825469434,-0.3392808735,0.2706229687,-0.1567003578,0.3015136421,0.1879752129,-0.3580714464,0.3065021634,0.016720105,0.1781937331,0.0704920888,0.1380855441,0.0736546293,0.4947431087,0.2995411456,-0.0968417376,-0.1562502384,0.5009906292,-0.0470440127,-0.2185632885,0.3112592995,-0.1181778163,-0.1945351362,0.0684309453,0.0887451172,0.3798215091,0.1293763965,-0.1915307492,0.0146643994,0.1435023844,-0.2500295639,0.1204057485,-0.2204343826,0.1473618895,0.3965650201,0.1270093769,-0.1272856295,-0.195144549,0.0377438515,0.0875855386,0.3257235587,-0.4119557142,-0.0675459281,-0.0734540001,-0.0052061281,-0.1802845895,0.0986031592,-0.0555794202,0.0971230865,-0.3996109366,0.3328239024,0.2897825539,-0.0790665746,-0.2431259155,0.08863388,0.2158128768,-0.3370558321,-0.0034607018,-0.0489910357,-0.2919403613,-0.0261766538,0.3307105303,0.1098190099,-0.0008771374,-0.0217662286,0.0280251838,-0.0548448637,-0.0925327018,-0.0439247601,-0.0732297674,0.0209216662,-0.0511663184,0.4193309844,0.0076079252,-0.1446308047,0.4017484784,-0.2536608279,-0.0489250831,0.350135982,0.0636930913,-0.0958797708,-0.1087071598,-0.3498898149,-0.139888227,-0.4293371439,0.3910627365,0.0847561061,0.0419315621,0.0926315561,0.4331049323,-0.0260847546,0.3013546467,-0.0312164128,-0.0848050937,-0.2977054417,0.3645742834,-0.0561163239,-0.4676565826,0.011941432,0.0828354955,0.3591901362,0.1638266891,-0.4252471328,0.3267471194,-0.15090774,0.1628198922,0.0011049408,-0.0242274385,0.0609546043,-0.0872276798,-0.0218234472,0.0364301093,-0.2417160571,-0.0141803464,-0.0320445746,0.1475835294,0.0513685606,0.458429724,-0.0371929593,0.6513361931,0.0874645486,-0.0882313102,0.4855556488,-0.308239907,0.3818774223,-0.2096708715,-0.3737454712,-0.0855747312,-0.351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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/836","title":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas","comments":"Hi ! Yes because of issues with PyArrow's CSV reader we switched to using the Pandas CSV reader. In particular the `read_options` argument is not supported anymore, but you can pass any parameter of Pandas' `read_csv` function (see the list here in [Pandas documentation](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_csv.html))","body":"Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n","comment_length":44,"text":"load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas \n Hi All\r\nI am trying to load a custom dataset  and I am trying to load a single file to make sure the file is loading correctly:\r\ndataset = load_dataset('csv', data_files=files)\r\nWhen I run it I get:\r\n\r\nDownloading and preparing dataset csv\/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache\/huggingface\/datasets\/csv\/default-35575a1051604c88\/0.0.0\/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...\r\n\r\nI am getting this error:\r\n6a4ac4\/csv.py in _generate_tables(self, files)\r\n     78     def _generate_tables(self, files):\r\n     79         for i, file in enumerate(files):\r\n---> 80             pa_table = pac.read_csv(\r\n     81                 file,\r\n     82                 read_options=self.config.pa_read_options,\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/anaconda2\/envs\/nlp\/lib\/python3.8\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\n**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\n\r\n\r\nThe size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.\r\nThere is no issue reading the file with pandas. any idea what could be the issue?\r\nWhen I am running a different CSV I do not get  this line:\r\n (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)\r\n\r\nAny ideas?\r\n \n Hi ! Yes because of issues with PyArrow's CSV reader we switched to using the Pandas CSV reader. In particular the `read_options` argument is not supported anymore, but you can pass any parameter of Pandas' `read_csv` function (see the list here in [Pandas documentation](https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.read_csv.html))","embeddings":[-0.24118343,-0.3198650777,-0.0681558475,0.4316074848,0.3625251055,0.0191550385,0.5210206509,0.437713474,0.2949123979,0.0327753536,0.0169624221,-0.1187045723,0.0905919969,0.1802892983,0.0350663252,0.1130092442,0.0730282664,0.3988213837,0.0317200907,0.0699720979,-0.218860805,0.1208694875,0.005867396,0.0575151071,-0.1244225353,0.0723639429,-0.008208585,0.1169146225,-0.2343517244,-0.4013206661,0.4197234511,0.0784542188,0.3328505158,0.5207661986,-0.0001169051,0.1564733684,0.2654486001,-0.1856259704,-0.0441993587,-0.5195806026,0.0301294662,-0.5329574347,0.2776663601,-0.1672024429,-0.0144887799,-0.1786803156,-0.1109988168,-0.0358052403,0.3014124632,0.3175454736,0.1804958135,-0.2607497573,0.0598921739,-0.0100186532,0.2052922398,0.0371788666,-0.0156855155,0.4175470769,0.1173958927,-0.1267608404,-0.0487308204,-0.193666935,-0.2392427474,0.3581996262,0.3196350932,0.1399399787,0.0709672496,-0.1089668348,0.2680699527,0.1055052057,0.6067355871,-0.307642132,-0.1485474855,-0.0154003808,-0.0202969182,-0.4886516333,0.2558154762,0.404494822,-0.1521599442,0.1173331738,-0.2122125626,0.2484468818,-0.2663929462,0.2028620988,-0.0673474893,-0.3531901836,-0.2522074878,0.2693178952,0.2523397803,0.0737477168,0.3336401582,-0.0937501863,0.0268538464,0.1806966811,-0.5556491017,0.2796493471,-0.1176608056,-0.0904364362,0.1637407988,0.096218653,0.4014219046,0.1370620281,0.0423445031,0.1946978569,0.3191299736,0.1861328632,-0.0428146571,0.0691050887,0.0273857322,0.3462172449,-0.2297836244,-0.2338579148,-0.3881369233,-0.5049571991,0.2381427437,-0.1538383663,0.2674439549,-0.2704333961,-0.2479329556,-0.0960200727,-0.0361324735,-0.0094233667,-0.0528344922,0.5738207698,0.215833962,0.3802042603,-0.054209996,0.2572092116,-0.2112757564,-0.09865468,-0.0803525597,0.0201157145,-0.0576021709,0.0522914641,0.3777484894,-0.2166886032,0.1214607805,0.1762953401,-0.043517001,-0.2938742042,-0.1455057859,-0.4177222252,0.0367241055,0.283308208,0.2024739832,0.0979529768,0.2341527939,-0.2812092602,-0.0606879592,0.3805413246,-0.2301190495,-0.1685689539,0.0972641706,0.0963025913,-0.0661173835,0.1656782478,-0.3956311643,0.1563510746,0.3519503474,-0.4384936988,0.0334676392,-0.2134845853,-0.2411233634,-0.3524810374,0.2018900961,0.2790597677,-0.7854661942,0.3429474831,0.0539065748,-0.0317488685,0.1149988398,0.4237150252,-0.1483171433,0.0611786693,-0.2709869146,0.0921530128,0.1406241804,0.007542707,-0.6293674707,0.3307564855,-0.04602734,0.0895455927,0.3120644391,0.0645528063,0.0512152202,0.0891771689,-0.1080223322,0.0185540114,0.0231345072,0.0440054424,-0.2607476711,-0.1291924864,0.3187506497,0.142625019,-0.0647743195,-0.0676729232,0.2388889641,-0.4087391198,0.2025453746,-0.0180878602,0.1416161656,0.1752468795,0.1791311353,-0.1968236417,0.0349740647,0.0104333451,-0.4493494034,0.1433825642,0.2848802209,-0.1687007099,-0.2159446925,-0.0797307715,-0.384118408,-0.0297010913,-0.2681604922,-0.1241994351,-0.0246524885,0.0725153983,0.0595197789,-0.1440529078,-0.1857402176,0.1161194369,-0.2237771302,-0.0112759126,-0.0158436764,0.1422190964,0.058799319,-0.1249890551,0.1064720228,-0.0629007593,0.207167998,-0.0483339541,-0.2763522565,0.2495912611,0.1456864476,0.0456155054,-0.2857958674,-0.1747876853,0.0830201879,-0.2415053844,-0.0872012973,0.0237385109,0.3663716614,-0.1825469434,-0.3392808735,0.2706229687,-0.1567003578,0.3015136421,0.1879752129,-0.3580714464,0.3065021634,0.016720105,0.1781937331,0.0704920888,0.1380855441,0.0736546293,0.4947431087,0.2995411456,-0.0968417376,-0.1562502384,0.5009906292,-0.0470440127,-0.2185632885,0.3112592995,-0.1181778163,-0.1945351362,0.0684309453,0.0887451172,0.3798215091,0.1293763965,-0.1915307492,0.0146643994,0.1435023844,-0.2500295639,0.1204057485,-0.2204343826,0.1473618895,0.3965650201,0.1270093769,-0.1272856295,-0.195144549,0.0377438515,0.0875855386,0.3257235587,-0.4119557142,-0.0675459281,-0.0734540001,-0.0052061281,-0.1802845895,0.0986031592,-0.0555794202,0.0971230865,-0.3996109366,0.3328239024,0.2897825539,-0.0790665746,-0.2431259155,0.08863388,0.2158128768,-0.3370558321,-0.0034607018,-0.0489910357,-0.2919403613,-0.0261766538,0.3307105303,0.1098190099,-0.0008771374,-0.0217662286,0.0280251838,-0.0548448637,-0.0925327018,-0.0439247601,-0.0732297674,0.0209216662,-0.0511663184,0.4193309844,0.0076079252,-0.1446308047,0.4017484784,-0.2536608279,-0.0489250831,0.350135982,0.0636930913,-0.0958797708,-0.1087071598,-0.3498898149,-0.139888227,-0.4293371439,0.3910627365,0.0847561061,0.0419315621,0.0926315561,0.4331049323,-0.0260847546,0.3013546467,-0.0312164128,-0.0848050937,-0.2977054417,0.3645742834,-0.0561163239,-0.4676565826,0.011941432,0.0828354955,0.3591901362,0.1638266891,-0.4252471328,0.3267471194,-0.15090774,0.1628198922,0.0011049408,-0.0242274385,0.0609546043,-0.0872276798,-0.0218234472,0.0364301093,-0.2417160571,-0.0141803464,-0.0320445746,0.1475835294,0.0513685606,0.458429724,-0.0371929593,0.6513361931,0.0874645486,-0.0882313102,0.4855556488,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/835","title":"Wikipedia postprocessing","comments":"Hi @bminixhofer ! Parsing WikiMedia is notoriously difficult: this processing used [mwparserfromhell](https:\/\/github.com\/earwig\/mwparserfromhell) which is pretty good but not perfect.\r\n\r\nAs an alternative, you can also use the Wiki40b dataset which was pre-processed using an un-released Google internal tool","body":"Hi, thanks for this library!\r\n\r\nRunning this code:\r\n\r\n```py\r\nimport datasets\r\nwikipedia = datasets.load_dataset(\"wikipedia\", \"20200501.de\")\r\nprint(wikipedia['train']['text'][0])\r\n```\r\n\r\nI get:\r\n\r\n```\r\nmini|Ricardo Flores Mag\u00f3n\r\nmini|Mexikanische Revolution\u00e4re, Mag\u00f3n in der Mitte anf\u00fchrend, gegen die Diktatur von Porfirio Diaz, Ausschnitt des Gem\u00e4lde \u201eTierra y Libertad\u201c von Idelfonso Carrara (?) von 1930.\r\n\r\nRicardo Flores Mag\u00f3n (* 16. September 1874 in San Antonio Eloxochitl\u00e1n im mexikanischen Bundesstaat Oaxaca; \u2020 22. November 1922 im Bundesgef\u00e4ngnis Leavenworth im US-amerikanischen Bundesstaat Kansas) war als Journalist, Gewerkschafter und Literat ein f\u00fchrender anarchistischer Theoretiker und Aktivist, der die revolution\u00e4re mexikanische Bewegung radikal beeinflusste. Mag\u00f3n war Gr\u00fcnder der Partido Liberal Mexicano und Mitglied der Industrial Workers of the World.\r\n\r\nPolitische Biografie \r\nJournalistisch und politisch k\u00e4mpfte er und sein Bruder sehr kompromisslos gegen die Diktatur Porfirio Diaz. Philosophisch und politisch orientiert an radikal anarchistischen Idealen und den Erfahrungen seiner indigenen Vorfahren bei der gemeinschaftlichen Bewirtschaftung des Gemeindelandes, machte er die Forderung \u201eLand und Freiheit\u201c (Tierra y Libertad) popul\u00e4r. Besonders Francisco Villa und Emiliano Zapata griffen die Forderung Land und Freiheit auf. Seine Philosophie hatte gro\u00dfen Einfluss auf die Landarbeiter. 1904 floh er in die USA und gr\u00fcndete 1906 die Partido Liberal Mexicano. Im Exil lernte er u. a. Emma Goldman kennen. Er verbrachte die meiste Zeit seines Lebens in Gef\u00e4ngnissen und im Exil und wurde 1918 in den USA wegen \u201eBehinderung der Kriegsanstrengungen\u201c zu zwanzig Jahren Gef\u00e4ngnis verurteilt. Zu seinem Tod gibt es drei verschiedene Theorien. Offiziell starb er an Herzversagen. Librado Rivera, der die Leiche mit eigenen Augen gesehen hat, geht davon aus, dass Mag\u00f3n von einem Mitgefangenen erdrosselt wurde. Die staatstreue Gewerkschaftszeitung CROM ver\u00f6ffentlichte 1923 einen Beitrag, nachdem Mag\u00f3n von einem Gef\u00e4ngnisw\u00e4rter erschlagen wurde.\r\nmini|Die Br\u00fcder Ricardo (links) und Enrique Flores Mag\u00f3n (rechts) vor dem Los Angeles County Jail, 1917\r\n\r\n[...]\r\n```\r\n\r\nso some Markup like `mini|` is still left. Should I run another parser on this text before feeding it to an ML model or is this a known imperfection of parsing Wiki markup?\r\n\r\nApologies if this has been asked before.","comment_length":38,"text":"Wikipedia postprocessing \n Hi, thanks for this library!\r\n\r\nRunning this code:\r\n\r\n```py\r\nimport datasets\r\nwikipedia = datasets.load_dataset(\"wikipedia\", \"20200501.de\")\r\nprint(wikipedia['train']['text'][0])\r\n```\r\n\r\nI get:\r\n\r\n```\r\nmini|Ricardo Flores Mag\u00f3n\r\nmini|Mexikanische Revolution\u00e4re, Mag\u00f3n in der Mitte anf\u00fchrend, gegen die Diktatur von Porfirio Diaz, Ausschnitt des Gem\u00e4lde \u201eTierra y Libertad\u201c von Idelfonso Carrara (?) von 1930.\r\n\r\nRicardo Flores Mag\u00f3n (* 16. September 1874 in San Antonio Eloxochitl\u00e1n im mexikanischen Bundesstaat Oaxaca; \u2020 22. November 1922 im Bundesgef\u00e4ngnis Leavenworth im US-amerikanischen Bundesstaat Kansas) war als Journalist, Gewerkschafter und Literat ein f\u00fchrender anarchistischer Theoretiker und Aktivist, der die revolution\u00e4re mexikanische Bewegung radikal beeinflusste. Mag\u00f3n war Gr\u00fcnder der Partido Liberal Mexicano und Mitglied der Industrial Workers of the World.\r\n\r\nPolitische Biografie \r\nJournalistisch und politisch k\u00e4mpfte er und sein Bruder sehr kompromisslos gegen die Diktatur Porfirio Diaz. Philosophisch und politisch orientiert an radikal anarchistischen Idealen und den Erfahrungen seiner indigenen Vorfahren bei der gemeinschaftlichen Bewirtschaftung des Gemeindelandes, machte er die Forderung \u201eLand und Freiheit\u201c (Tierra y Libertad) popul\u00e4r. Besonders Francisco Villa und Emiliano Zapata griffen die Forderung Land und Freiheit auf. Seine Philosophie hatte gro\u00dfen Einfluss auf die Landarbeiter. 1904 floh er in die USA und gr\u00fcndete 1906 die Partido Liberal Mexicano. Im Exil lernte er u. a. Emma Goldman kennen. Er verbrachte die meiste Zeit seines Lebens in Gef\u00e4ngnissen und im Exil und wurde 1918 in den USA wegen \u201eBehinderung der Kriegsanstrengungen\u201c zu zwanzig Jahren Gef\u00e4ngnis verurteilt. Zu seinem Tod gibt es drei verschiedene Theorien. Offiziell starb er an Herzversagen. Librado Rivera, der die Leiche mit eigenen Augen gesehen hat, geht davon aus, dass Mag\u00f3n von einem Mitgefangenen erdrosselt wurde. Die staatstreue Gewerkschaftszeitung CROM ver\u00f6ffentlichte 1923 einen Beitrag, nachdem Mag\u00f3n von einem Gef\u00e4ngnisw\u00e4rter erschlagen wurde.\r\nmini|Die Br\u00fcder Ricardo (links) und Enrique Flores Mag\u00f3n (rechts) vor dem Los Angeles County Jail, 1917\r\n\r\n[...]\r\n```\r\n\r\nso some Markup like `mini|` is still left. Should I run another parser on this text before feeding it to an ML model or is this a known imperfection of parsing Wiki markup?\r\n\r\nApologies if this has been asked before. \n Hi @bminixhofer ! Parsing WikiMedia is notoriously difficult: this processing used [mwparserfromhell](https:\/\/github.com\/earwig\/mwparserfromhell) which is pretty good but not perfect.\r\n\r\nAs an alternative, you can also use the Wiki40b dataset which was pre-processed using an un-released Google internal 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/834","title":"[GEM] add WikiLingua cross-lingual abstractive summarization dataset","comments":"Hey @yjernite. This is a very interesting dataset. Would love to work on adding it but I see that the link to the data is to a gdrive folder. Can I just confirm wether dlmanager can handle gdrive urls or would this have to be a manual dl?","body":"## Adding a Dataset\r\n- **Name:** WikiLingua\r\n- **Description:** The dataset includes ~770k article and summary pairs in 18 languages from WikiHow. The gold-standard article-summary alignments across languages were extracted by aligning the images that are used to describe each how-to step in an article.\r\n- **Paper:** https:\/\/arxiv.org\/pdf\/2010.03093.pdf\r\n- **Data:** https:\/\/github.com\/esdurmus\/Wikilingua\r\n- **Motivation:** Included in the GEM shared task. Multilingual.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":48,"text":"[GEM] add WikiLingua cross-lingual abstractive summarization dataset \n ## Adding a Dataset\r\n- **Name:** WikiLingua\r\n- **Description:** The dataset includes ~770k article and summary pairs in 18 languages from WikiHow. The gold-standard article-summary alignments across languages were extracted by aligning the images that are used to describe each how-to step in an article.\r\n- **Paper:** https:\/\/arxiv.org\/pdf\/2010.03093.pdf\r\n- **Data:** https:\/\/github.com\/esdurmus\/Wikilingua\r\n- **Motivation:** Included in the GEM shared task. Multilingual.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n Hey @yjernite. This is a very interesting dataset. Would love to work on adding it but I see that the link to the data is to a gdrive folder. Can I just confirm wether dlmanager can handle gdrive urls or would this have to be a manual dl?","embeddings":[-0.2052895278,0.319988668,0.0722148195,0.5395573378,0.1300372928,0.2193807662,0.1277997494,-0.1864241362,-0.0289536398,-0.0699264407,0.2743760049,0.0008884232,-0.4298549891,0.2057960778,0.3081344664,-0.2922178805,0.0054489104,-0.2033134252,0.020582499,-0.1820490211,-0.1673908681,0.4652565718,0.1199145317,0.0503368936,-0.4685993195,-0.0266561564,-0.221419096,0.4315164089,-0.0926346853,-0.1953790188,-0.0345566086,0.039644599,0.1316577494,0.4354016185,-0.0001247795,-0.157182321,0.2606930733,-0.2081924975,-0.0927850381,-0.2235533744,-0.3300047517,-0.1996063143,-0.1916079819,-0.1280442029,0.175924316,0.128310591,-0.0804150552,0.1652437001,-0.2902439833,0.0426329561,0.1184955612,-0.0546926484,0.177083537,-0.0281240065,0.3376811445,-0.2312233597,-0.0272024591,0.5546996593,-0.0733492374,0.1199260205,-0.1226686612,0.4144370556,-0.1018918827,-0.1896880269,0.0083972318,0.2109396905,0.0017848539,-0.0774430335,0.1375340819,0.2818035483,0.816493392,-0.23187159,-0.0623929389,-0.1128107756,-0.1559919119,-0.1765374243,0.4124311805,0.161280483,-0.0271767341,0.0534955487,0.0535362288,-0.5035980344,-0.1352059394,0.2228758782,-0.0204972029,0.4011122882,0.065344125,0.0235374309,0.4362442195,0.2480707169,-0.1870647073,-0.096568279,-0.3789028823,0.1565636396,0.0187848099,-0.2509078085,0.0238386672,0.2325567156,0.114207454,-0.0979505479,0.0392102003,0.0481207408,-0.0954174697,0.0808626562,0.3494334817,0.2302668393,-0.2853442132,-0.0323552191,0.2578540742,-0.1746076494,0.1903271526,-0.0247632414,-0.1124453545,-0.1519078612,-0.3518644869,-0.1581168622,-0.3366399705,0.1224362776,0.0758734867,0.1092616916,-0.224985078,-0.2128604949,0.1006018296,0.0871745571,0.2883945405,-0.2428627759,-0.0612188093,0.3998378217,0.2928891778,0.1777856797,0.0584300682,0.2694648504,-0.0828705728,0.2098586559,-0.1889800131,0.084674865,0.124035351,-0.085408546,-0.2084121704,-0.1505706161,0.0108843315,0.0665663704,-0.183389768,0.1558983624,0.3799422681,0.3868770897,0.0069782999,-0.6324939132,-0.3096870482,0.1332536936,-0.171611011,-0.3060252368,-0.2727497518,-0.0214119256,-0.2076001018,-0.3338611126,-0.2829799056,0.5032006502,0.0978399888,-0.3781301975,0.0974009335,0.425876081,0.0381935351,-0.1741587222,-0.2004806399,0.3255687058,-0.2859554291,0.0491206385,-0.2233881056,-0.0063545676,0.3607916832,0.2327745706,-0.0493528396,0.0860492662,-0.2695146501,0.4979097545,0.2523216903,-0.1120953634,-0.1419781446,0.1514694542,-0.4574857056,0.0134938266,0.192811653,0.353248328,-0.07077948,-0.1276389062,-0.1220430434,0.2550140917,0.1740980744,0.0441155881,-0.3670468628,-0.436099261,0.227699846,0.1296114326,0.2096010894,-0.141670242,0.3847234547,0.3507202268,0.2013375163,-0.25459373,0.1432922035,0.1356197894,0.2010868341,0.0470831208,-0.070095621,0.0878504664,-0.4258346856,0.0398113281,0.484170258,0.4287417233,0.2425023317,-0.0437106267,0.1110672206,-0.0640633255,-0.21433492,-0.1703340262,-0.0034962082,0.39950791,-0.0788112357,0.2376496047,-0.0732419491,-0.2341186255,-0.2306239307,-0.1214824617,0.0381963402,0.3170720041,-0.1187933236,0.0798900649,-0.1165798977,0.2972486317,-0.2057968974,-0.0472994857,-0.0345139541,0.1623503566,-0.1037028581,0.3616391122,0.6724396348,0.7141319513,0.0822548419,-0.2385071814,0.3458833992,-0.0627142712,0.2449794859,-0.1494480968,-0.0729832724,0.2659508288,-0.0039396649,0.0668013692,0.0737567842,0.2355251014,0.2932620049,-0.019925477,-0.0440586656,-0.3305786848,0.3704812825,0.2180639505,-0.2417214364,0.1267597973,-0.2977869809,0.0076664374,0.4142896533,-0.0502846688,0.4785454273,0.218574062,-0.3042879999,-0.3060279489,-0.0249244981,0.610376358,0.3237772584,0.0808550045,0.378413111,0.0617275313,0.1076251939,-0.1882869005,0.365808636,0.0872485787,-0.0782542452,0.5172120333,-0.1013433263,-0.0657736734,-0.2807046473,0.1297214925,0.0327551328,0.2047456503,-0.149987638,-0.0216756314,-0.0512133278,-0.1157659367,-0.1355130523,-0.7797605991,-0.6879051328,-0.3252259791,0.1452034861,-0.1766125262,-0.1035038456,0.0233518053,0.1382453591,0.1964084208,-0.4809713662,-0.3989130259,-0.087769106,-0.4259753823,-0.1295738816,-0.0361106209,0.4199083149,-0.0313053317,0.291648984,0.019221358,0.1689298153,-0.2439244241,-0.4900278747,0.3283202648,-0.1685169786,0.2541861534,0.0756706521,-0.1180241033,0.0898889452,-0.4692749083,0.1869605184,0.255725354,0.1016540229,-0.0497036763,0.0046757781,-0.0229755584,0.045001559,0.0411935225,-0.294365108,-0.2600292265,-0.0858576968,0.0822593868,0.1040048003,-0.2154649347,-0.0400587469,0.2880854905,0.0635915697,-0.1582787782,-0.3831860423,-0.1378221214,0.3207927346,-0.1182926521,-0.246581316,0.272031188,-0.2453189045,0.3434216082,-0.0815165266,-0.4744024277,-0.2974785864,0.1456634253,-0.0116080921,0.4878692925,0.1211170778,0.3266866505,0.1165993288,-0.0131175695,-0.0340050459,-0.2883881927,0.1065922976,0.3647940159,0.2817761898,0.1308726817,0.3322602212,0.1952828765,0.6535807848,-0.0746081769,0.23401995,0.2576474547,-0.2271134704,0.3407444358,-0.0939052701,-0.101073496,-0.0377458967,-0.1238515228,-0.4158323705,0.31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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/834","title":"[GEM] add WikiLingua cross-lingual abstractive summarization dataset","comments":"Hi @KMFODA ! A version of WikiLingua is actually already accessible in the [GEM dataset](https:\/\/huggingface.co\/datasets\/gem)\r\n\r\nYou can use it for example to load the French to English translation with:\r\n```python\r\nfrom datasets import load_dataset\r\nwikilingua = load_dataset(\"gem\", \"wiki_lingua_french_fr\")\r\n```\r\n\r\nClosed by https:\/\/github.com\/huggingface\/datasets\/pull\/1807","body":"## Adding a Dataset\r\n- **Name:** WikiLingua\r\n- **Description:** The dataset includes ~770k article and summary pairs in 18 languages from WikiHow. The gold-standard article-summary alignments across languages were extracted by aligning the images that are used to describe each how-to step in an article.\r\n- **Paper:** https:\/\/arxiv.org\/pdf\/2010.03093.pdf\r\n- **Data:** https:\/\/github.com\/esdurmus\/Wikilingua\r\n- **Motivation:** Included in the GEM shared task. Multilingual.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":42,"text":"[GEM] add WikiLingua cross-lingual abstractive summarization dataset \n ## Adding a Dataset\r\n- **Name:** WikiLingua\r\n- **Description:** The dataset includes ~770k article and summary pairs in 18 languages from WikiHow. The gold-standard article-summary alignments across languages were extracted by aligning the images that are used to describe each how-to step in an article.\r\n- **Paper:** https:\/\/arxiv.org\/pdf\/2010.03093.pdf\r\n- **Data:** https:\/\/github.com\/esdurmus\/Wikilingua\r\n- **Motivation:** Included in the GEM shared task. Multilingual.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n Hi @KMFODA ! A version of WikiLingua is actually already accessible in the [GEM dataset](https:\/\/huggingface.co\/datasets\/gem)\r\n\r\nYou can use it for example to load the French to English translation with:\r\n```python\r\nfrom datasets import load_dataset\r\nwikilingua = load_dataset(\"gem\", \"wiki_lingua_french_fr\")\r\n```\r\n\r\nClosed by https:\/\/github.com\/huggingface\/datasets\/pull\/1807","embeddings":[-0.2764140666,0.0026880144,-0.1001025662,0.3107095659,-0.1134149954,0.1402342021,0.0106717795,0.2736697793,-0.0497234203,0.142544508,-0.0666124299,0.2388452888,0.0332172699,0.3290542066,0.1007773429,-0.5720511079,0.0124257961,0.0506400205,-0.189469412,-0.0938588232,0.0496565029,0.2917680144,-0.1541137397,0.034482073,-0.0742902011,0.1673282683,-0.2920971811,-0.0621645786,0.0580645055,-0.1694517136,-0.049450133,0.1079933122,0.1147315726,0.1120782569,-0.0000970769,-0.235713914,0.1173490658,-0.0934773833,-0.0832296833,-0.3354904652,0.0568946898,-0.4867471457,-0.0898417607,-0.152729094,-0.0286800899,-0.0799354091,-0.0225389898,0.3618356287,0.1711872816,-0.0163235459,0.30158481,-0.2413649559,0.1877074689,-0.0982502475,0.0409793444,-0.3352949619,-0.187696889,0.1640687734,-0.0243242867,-0.0562591292,-0.2111863643,0.5608845353,-0.0509362333,-0.2531584799,-0.2900612652,-0.0126946662,-0.1052725241,-0.0649602041,0.1165637448,0.4845497906,0.529859364,-0.1681496799,-0.2733721137,-0.0119785434,-0.1028654203,-0.1336417496,-0.0689065158,0.1995877922,0.0682092905,0.2821740508,0.1197725609,-0.25279212,-0.1005490199,0.2251769602,0.1247845069,0.4762441218,-0.0582085103,-0.0441962518,0.2988864183,-0.1675044298,-0.5653138161,0.1359974742,-0.107461445,0.2373199612,-0.0855726525,-0.1235274225,0.1528774947,0.0799808875,0.1966228336,-0.2533028424,-0.152831316,0.4855698049,0.0506215021,0.1805730313,0.2176923007,0.175059244,0.0759467632,-0.0547261015,-0.0148441475,0.1278166026,-0.0411525331,0.0292952079,-0.0243418477,0.0643674061,-0.2445610017,-0.0819883272,-0.4378207028,0.0686352327,0.2060537487,0.2339699119,0.1268783659,-0.1293237656,-0.1584433019,0.3530856967,0.0957632139,0.2372762561,-0.0781104341,0.2695079446,-0.2140510082,-0.1261201203,-0.123630695,0.2545527816,-0.1758843064,0.1946148276,-0.1769746095,0.0841224939,0.2207692266,0.0119759105,0.2674368322,-0.191990748,0.0756382048,0.0522559807,-0.0806918144,-0.2325192541,0.2944804132,0.0688738376,0.253816098,-0.4139411151,-0.2517581582,-0.1296180189,-0.2286814153,-0.0899842232,-0.3403100967,0.2785220444,0.0378572978,-0.2425411195,-0.2779177129,0.7183287144,0.1214745641,-0.3903105557,0.0970283002,0.2184337378,-0.2372532934,0.02628747,0.1079149693,0.4262573719,-0.0857505426,-0.1368868351,-0.0094694961,-0.0878258795,0.0817494914,0.1771855205,-0.0713140368,-0.1179260164,-0.1324967295,0.4285255075,0.1194244251,-0.1509241015,-0.1736158282,0.0201856829,-0.1053154841,0.2885639369,-0.0036446585,0.0764425918,0.0263945311,-0.0283590816,0.1301962733,0.1024601087,0.1560315788,0.1353257,-0.2220541686,-0.327780813,0.0901897177,0.1936070323,0.0324617438,-0.1908340007,0.2195291519,0.1991110891,0.2937733829,-0.2792626619,0.1236492842,0.2130652368,-0.1529013813,0.1465049088,0.0546806157,-0.2641773224,0.0251854416,0.1113230437,0.3206298053,0.3576340377,0.2289250493,-0.2382941544,-0.0464861356,-0.1705206931,-0.3059718907,-0.2131436914,0.3340744972,0.1207557693,0.0298732203,0.3504917026,0.0571480058,-0.2722002268,-0.3950471282,-0.2795887887,-0.0788680017,0.0020740025,-0.1584918946,-0.0642237365,-0.0997120813,0.3893690705,-0.2712367773,0.0983206928,0.0154334633,0.1517489552,-0.0550842546,0.4982587397,0.4569424391,0.2020403147,-0.0717084259,-0.5005124807,0.1014631912,-0.1269534379,0.0405575447,0.0141751384,0.0026556414,0.4599095285,0.2683020234,0.1284926683,0.1728938073,0.077689968,0.438174516,0.064910844,-0.1373220682,-0.3299251497,0.3048838079,0.0782761425,-0.4333210886,-0.122109659,-0.1712439656,0.1732613593,0.5744444132,0.1377800256,0.5119507909,0.2164384127,-0.0974679589,-0.204381749,-0.0144237485,-0.0558188856,-0.173418045,0.2701702416,0.3225536346,0.1384960711,0.1292924732,0.0410367325,0.2512614727,-0.0856256187,0.0452633686,0.1973093301,0.0913208649,-0.1187370792,-0.3444887996,0.0685833469,-0.2377658188,-0.0307473838,0.1356806457,0.1075929031,-0.0717532188,-0.0445475504,-0.1838493645,-0.3029926717,-0.2344042808,-0.161650151,0.2162353396,-0.2200760543,-0.240059346,0.128452763,0.1167839691,-0.1129344478,-0.2529805005,-0.1024315432,-0.01384304,-0.5788555741,-0.2555541098,0.1801484823,0.048232194,-0.0450079776,0.3994951248,-0.4012573063,-0.1552133411,0.0356057584,-0.6449292898,0.2720446587,-0.1622140706,0.020054061,-0.0106310388,-0.0697962418,-0.3605292141,-0.3130759299,0.3076897562,0.0325359292,-0.0289600249,0.1261402518,0.0377021208,0.0117119923,-0.035280969,-0.5705169439,-0.1980738342,-0.407093972,0.3077458143,0.0545626767,0.1486679316,-0.3104326427,0.2955928445,0.1908233464,-0.1759011447,0.3428914249,-0.3420238495,-0.0923003629,0.1120934337,-0.1527304798,-0.3790644407,0.0731656253,-0.2005584538,0.3650987446,-0.1057616845,-0.2588934004,-0.1058176011,0.179116264,0.0326339118,0.1819611192,0.2368079126,0.2737877071,0.1304390579,-0.2074815631,0.0725155026,0.0377643704,-0.0318166912,-0.1206884682,0.0855492949,-0.2100822926,0.3565778136,0.2325437665,0.6987831593,0.1690097153,0.3995890915,0.3031805456,-0.1493190527,0.121412836,-0.372786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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/827","title":"[GEM] MultiWOZ dialogue dataset","comments":"Hi @yjernite can I help in adding this dataset? \r\n\r\nI am excited about this because this will be my first contribution to the datasets library as well as to hugginface.","body":"## Adding a Dataset\r\n- **Name:** MultiWOZ (Multi-Domain Wizard-of-Oz)\r\n- **Description:** 10k annotated human-human dialogues. Each dialogue consists of a goal, multiple user and system utterances as well as a belief state. Only system utterances are annotated with dialogue acts \u2013 there are no annotations from the user side.\r\n- **Paper:** https:\/\/arxiv.org\/pdf\/2007.12720.pdf\r\n- **Data:** https:\/\/github.com\/budzianowski\/multiwoz\r\n- **Motivation:** Will likely be part of the GEM shared task\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":30,"text":"[GEM] MultiWOZ dialogue dataset \n ## Adding a Dataset\r\n- **Name:** MultiWOZ (Multi-Domain Wizard-of-Oz)\r\n- **Description:** 10k annotated human-human dialogues. Each dialogue consists of a goal, multiple user and system utterances as well as a belief state. Only system utterances are annotated with dialogue acts \u2013 there are no annotations from the user side.\r\n- **Paper:** https:\/\/arxiv.org\/pdf\/2007.12720.pdf\r\n- **Data:** https:\/\/github.com\/budzianowski\/multiwoz\r\n- **Motivation:** Will likely be part of the GEM shared task\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n Hi @yjernite can I help in adding this dataset? \r\n\r\nI am excited about this because this will be my first contribution to the datasets library as well as to hugginface.","embeddings":[-0.0495593362,-0.1261757463,0.0484424531,0.5216318965,0.0280474238,0.2442046106,0.2257897556,-0.129513517,-0.0764305368,-0.0352300182,-0.3937834501,0.0283759013,-0.4157284796,0.3712361455,0.2753158212,-0.4878649712,-0.0401315726,-0.1847281158,-0.0573805906,-0.0662159994,0.0544462986,0.2652646005,-0.0837500691,-0.0141651276,-0.7082762122,-0.2576463521,-0.2459636778,0.5358034968,-0.0593674779,-0.2135654688,-0.1025887504,-0.0253923126,-0.1053599641,0.4787712991,-0.0001166607,-0.2765088081,0.2415370196,-0.0704951137,-0.158460483,-0.1901423484,-0.303958714,-0.1010894477,-0.1578002423,0.0485258624,-0.0672851503,0.1110400036,-0.2357476354,0.072080344,0.1161287352,0.2136203647,0.1436246634,0.1713851988,0.229274109,-0.1413156241,-0.0795614347,0.1754795611,-0.1360081434,0.1217542589,0.1660677642,0.1298537403,0.13394095,0.2862460911,-0.2868770659,-0.3387582302,0.0378700607,-0.1565734595,-0.1137382835,-0.2021169215,-0.1208337992,0.6377723813,0.782586813,-0.0460340902,-0.4587578773,-0.1823887378,0.1662186831,0.1890712678,-0.0151091991,0.2074808776,-0.1902395189,0.0880809948,-0.499795258,-0.3024016023,-0.1117864102,0.1520489454,0.1694430709,0.4894135296,-0.0048377253,0.1498208344,0.0406989902,-0.0129358061,-0.6592090726,0.0461831838,-0.1632288098,-0.0782562196,-0.5269982815,-0.2313212007,0.0054978612,0.2764656842,0.2792702913,0.2418639213,0.1933353096,0.0343860462,-0.4858166873,-0.0963608399,0.3565450907,-0.0357551947,0.2355093807,-0.4836921692,-0.0768123865,-0.3012522161,0.2371323407,0.1238581017,-0.2489851564,0.0568620972,-0.0454359427,-0.191259414,0.0778548494,0.0098750964,0.1015383452,-0.0046854638,-0.1881654263,-0.1904006451,0.3987607658,0.3084577918,-0.2176786363,0.0310238171,-0.2466755211,0.5173609853,0.214096576,-0.275044769,-0.1006629393,0.20847027,-0.0185947064,0.2577991784,0.0416524336,-0.1372463703,0.3089649081,0.0768450275,0.4037554562,0.0251382645,0.0357506275,0.0723679066,0.0309202764,0.0611653328,0.0265694745,0.2006107271,0.1279187799,0.1385942101,-0.0259206686,0.0555952191,-0.0541707613,-0.4240857959,-0.8219958544,0.0419629551,-0.3433516324,-0.4522158206,-0.0966314226,0.2801698446,0.0758952275,-0.1119188294,0.0898485929,0.1927165836,-0.2006132454,-0.2603945136,0.1181053296,0.4660356641,-0.0739355162,0.0199907217,-0.0051787714,0.0489323474,0.1514672339,0.2265951782,-0.2079218924,0.0020900264,0.0370475911,0.5209835768,0.0974760428,-0.1354452372,-0.0091419891,0.1261940747,-0.1579438746,0.455842495,0.2269834727,0.4218324125,0.0401256382,-0.0735240206,0.2379349768,0.2138475478,0.0857971832,0.0093416246,-0.1274498552,-0.2149721831,0.354528904,0.1450643539,-0.1484981477,-0.271540463,0.0218770802,-0.0533044823,0.3994185627,-0.0728087649,0.214067772,0.1887446791,-0.0459010825,0.3278737664,0.0064844755,-0.3588349223,-0.0401546247,0.0463247076,0.4060170949,0.3642266393,0.2133593559,-0.0913456082,-0.0593798496,0.0209012106,-0.1971767396,-0.3014435172,-0.0209976994,0.3040892184,0.0192148145,-0.2110359818,-0.3215515018,0.6143251657,0.2347304672,-0.0238543488,-0.3885957897,0.1535160542,0.0642829984,-0.0954658687,-0.0493885837,0.4657987952,-0.1018567234,-0.1015431583,0.0256350245,0.3633405268,-0.3810467422,-0.0191664658,0.2527374923,0.211559996,0.1455547661,-0.0376944318,0.3808533847,-0.3574172854,0.0935362279,-0.1666322201,-0.0316772014,0.0237861816,0.0688074902,-0.1138606817,-0.3425866663,0.3334616721,0.1323535591,-0.0600715019,-0.2378442287,-0.0501989573,0.2843133211,-0.3968089819,-0.2087936252,0.0861881599,-0.5717890859,0.2553270161,0.2885805368,-0.2681139708,0.3120868206,-0.0075467913,-0.2782268226,-0.2280256003,-0.0677193478,-0.2392433584,0.1310912669,0.3073764741,0.0268663969,-0.2262406647,0.0077417539,0.0461377352,0.1179578006,0.1477182209,0.1421055347,0.2155932486,0.1317403018,0.1953988522,-0.0600356273,-0.1858831495,0.0917344838,0.0612705499,-0.2202705741,-0.3777098358,-0.2315757275,0.0206582509,-0.0031795367,-0.8598554134,-0.3776175082,0.0210866872,0.1854080409,-0.208636567,0.1864878833,0.140997529,0.1710365862,0.090553388,-0.1613760293,-0.0967269167,-0.2473046035,-0.474103272,0.0599152967,0.070634678,0.1254862398,-0.0712724626,0.3366229832,0.1479424685,0.158692345,-0.1248507723,-0.3355082273,-0.0475235246,-0.3214035332,0.3632976115,0.1861050874,0.0045008925,0.1736129075,-0.5391616821,0.0940193236,0.0636401251,-0.1032526642,0.0796101019,-0.0586554632,-0.3287820816,0.0343878902,-0.1330083609,-0.2042253911,-0.446413964,0.2932297885,-0.0822253153,0.068107672,0.1388568729,-0.2183091044,0.3125792742,-0.228241384,0.2930274308,-0.2374432385,-0.4391099513,0.386726439,-0.1543562263,-0.1579540074,-0.0408226177,-0.2597220838,0.27492854,-0.2068925351,-0.1263917089,-0.2367945462,-0.1196150109,-0.1223814338,0.1548680514,0.0074252626,0.3379835784,0.1504380554,-0.1130660027,-0.0759113729,-0.4116906524,0.10021016,0.4797812402,0.3220478594,-0.0224163495,0.0151808262,0.027829811,0.4263081849,0.259511143,0.1940469444,0.1456073821,-0.0416595452,-0.050338082,-0.1276602894,-0.6021878123,0.3620354831,-0.10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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/824","title":"Discussion using datasets in offline mode","comments":"I think it would be very cool. I'm currently working on a cluster from Compute Canada, and I have internet access only when I'm not in the nodes where I run the scripts. So I was expecting to be able to use the wmt14 dataset until I realized I needed internet connection even if I downloaded the data already. I'm going to try option 2 you mention for now though! Thanks ;)","body":"`datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n","comment_length":72,"text":"Discussion using datasets in offline mode \n `datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n \n I think it would be very cool. I'm currently working on a cluster from Compute Canada, and I have internet access only when I'm not in the nodes where I run the scripts. So I was expecting to be able to use the wmt14 dataset until I realized I needed internet connection even if I downloaded the data already. I'm going to try option 2 you mention for now though! Thanks 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/824","title":"Discussion using datasets in offline mode","comments":"Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\r\n\r\n@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?","body":"`datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n","comment_length":57,"text":"Discussion using datasets in offline mode \n `datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n \n Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\r\n\r\n@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?","embeddings":[-0.4731812477,0.2457838953,-0.0126306163,0.1412141472,0.2833554745,-0.1470207572,0.6012274623,0.0127757993,0.2687810659,0.1312778145,-0.0240368135,-0.0239853207,-0.0246622115,0.4021067917,-0.0912403315,-0.1021380574,-0.1722334921,0.0388772562,-0.1540046185,0.1325951368,-0.1408108175,-0.1281190962,-0.3727094233,-0.0282363463,-0.1628144085,-0.1865869015,0.009141881,0.1420212984,-0.157231614,-0.2762964368,0.2854104936,0.1805244088,0.2238958478,0.4791246653,-0.0001042308,0.1437665671,0.0545553416,-0.0134886838,-0.2069027573,-0.3381868601,-0.1917754114,-0.3979368508,0.2337578535,-0.4007375836,-0.2150847316,0.0365372971,-0.0734726861,-0.395838052,0.3150739074,0.3079384267,0.2526885569,0.6633725762,-0.14806135,-0.3228698373,-0.1723310798,0.010202216,-0.2392340451,0.3925467134,0.0617909804,-0.0708869025,-0.1110986471,0.1163179576,-0.119291313,0.0119442893,0.3592294753,0.0990151986,-0.0436064601,-0.0926574692,0.1286549419,0.4549899101,0.3857674897,-0.2256464809,-0.3233912885,-0.4115032256,-0.1213282347,-0.3992854655,0.0777640641,0.0915819705,-0.0518016815,0.2859633863,-0.4508610964,-0.2770608366,-0.3688122928,0.0866334587,-0.3436934948,0.129058823,-0.2209807485,0.0518599302,-0.0594523624,0.1764986664,-0.1783763617,-0.2810511887,0.1342646629,-0.131892249,-0.2653567493,-0.0710257813,0.1660244018,0.0489503071,0.1325022876,0.0835703537,0.245603919,0.0134868138,0.0630616322,0.0728721172,0.4931008518,0.2701188326,-0.066404894,-0.1654520333,0.590166986,0.1680280119,0.0154071264,-0.2984622121,0.34388116,-0.247408554,0.0511436015,-0.0044536674,0.3934408128,-0.2904764712,-0.0705588907,-0.028938571,0.2609572411,-0.0422502495,0.2302477062,0.2531334758,-0.200735867,0.0239093136,0.0023878447,0.3707030416,-0.2583317161,-0.287779361,-0.0691777095,-0.2514779568,-0.1524575353,0.0909147337,0.1942048073,0.0988478512,0.0931645483,-0.0352786593,0.2799161375,0.0193171445,0.2784254849,-0.0613344237,-0.0014951745,0.3023600876,0.1102334112,-0.0681090355,0.1214372739,-0.1861234307,0.0257518515,0.0565423518,-0.0653337613,-0.4622017443,0.1761785448,0.219720602,-0.6732071042,-0.2223996818,-0.2730343044,0.1555661261,-0.2369358987,-0.2124957442,0.0235868525,0.018460894,-0.2118948549,-0.211705938,0.2878770828,0.5173381567,-0.4603885412,0.0129500646,0.0447110832,-0.2136073858,-0.2071649134,0.1019181535,-0.414919436,0.6289384961,0.0107537126,-0.3816751838,0.5595380068,-0.3048187792,-0.3140288293,0.2726414204,-0.1605727226,-0.0045871525,-0.0534324944,0.1087546423,0.4603524208,0.084867388,-0.0908238813,0.2521170378,-0.128145203,-0.0130102765,-0.2615109086,-0.317256242,0.1956141591,0.3260177076,0.3225422502,0.170808658,0.2575958967,-0.1861371994,-0.0085249459,-0.1622692347,0.1202049702,0.0423874669,0.2255617529,0.0671963319,-0.0102309389,-0.1653408855,-0.5502773523,0.2796308994,-0.0287088715,0.2188271582,-0.2158027589,-0.210777849,-0.027634386,-0.1952420026,-0.181086868,-0.0795596987,0.1171106398,-0.1978259683,0.0859222189,0.0982890129,-0.2900714576,0.1137299016,-0.011981139,0.0217427425,-0.1584365219,0.3012230992,-0.0498387516,0.0836622119,-0.1551096439,0.0562893637,-0.0545988455,-0.1622434705,-0.0954587534,0.5110428333,-0.02388593,0.0563261919,0.2349392772,0.5590957999,0.3679666519,0.0043761171,0.495628953,-0.050213553,0.3213089108,-0.1114702821,-0.3984197676,0.7357347608,-0.144050926,0.2574732006,0.0989937186,-0.0164562874,0.2910700142,-0.0265043136,-0.4488827586,-0.0220366903,0.0183654837,0.0145753147,0.2139872313,-0.0351547152,-0.3072082996,0.0073910407,0.3660686314,0.0653327256,0.1509138197,-0.0016662013,-0.0714008063,0.0618565455,-0.0847105533,0.0961386189,0.5059415102,0.222510621,-0.0020846452,0.222269088,-0.0752183199,-0.2918922305,0.1622450352,0.2784020007,0.0117790736,0.0948213711,0.1106504574,-0.0135444626,-0.2606648505,-0.0851194039,0.0104148071,0.0521303751,-0.3171434402,0.3023553789,-0.192141518,0.0446793325,0.0116658304,0.1038789749,-0.503385365,-0.1916292906,0.1738934666,0.0866980851,-0.2768389881,0.1611506343,-0.0816928521,0.5301485658,-0.1862419546,-0.172270909,0.1785868406,-0.587223351,-0.0618414544,0.0992661193,0.3291293085,0.031592764,0.4697214663,0.1105318367,-0.100488849,-0.2267502248,-0.2593652904,0.0775288045,-0.0444324464,0.3455691338,0.0793805569,0.0427691117,0.0755403414,0.2921114266,0.4025548398,-0.2950235307,0.2515189648,0.1761236638,-0.124143742,-0.0401732139,-0.3764849007,-0.432044059,-0.3803494871,-0.4162005484,0.2004627436,-0.0402469784,-0.0421941429,0.2894975543,0.1804338396,0.0699566826,-0.0712620169,0.0905176699,-0.1396664679,-0.4403935373,0.2246841788,-0.1572482735,-0.5371839404,0.208421275,0.1666212529,0.1439089775,0.2761147022,-0.5148002505,-0.3084290028,0.0591682941,0.3579508364,0.1813364923,0.2270392478,0.3989451826,-0.1022249237,-0.0224113967,-0.0809667036,0.069430992,0.2551833987,0.0415008217,-0.0146295261,0.0407630838,0.3204085529,0.0124633554,0.4728292227,-0.065537177,0.0620023683,0.2407371551,-0.0948777795,0.6474258304,-0.2596338689,-0.1485426873,-0.0732285753,-0.0469047278,-0.34583833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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/824","title":"Discussion using datasets in offline mode","comments":"here is my way to load a dataset offline, but it **requires** an online machine\r\n1. (online machine)\r\n```\r\nimport datasets\r\ndata = datasets.load_dataset(...)\r\ndata.save_to_disk(\/YOUR\/DATASET\/DIR)\r\n```\r\n2. copy the dir from online to the offline machine\r\n3. (offline machine)\r\n```\r\nimport datasets\r\ndata = datasets.load_from_disk(\/SAVED\/DATA\/DIR)\r\n```\r\n\r\nHTH.","body":"`datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n","comment_length":47,"text":"Discussion using datasets in offline mode \n `datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n \n here is my way to load a dataset offline, but it **requires** an online machine\r\n1. (online machine)\r\n```\r\nimport datasets\r\ndata = datasets.load_dataset(...)\r\ndata.save_to_disk(\/YOUR\/DATASET\/DIR)\r\n```\r\n2. copy the dir from online to the offline machine\r\n3. (offline machine)\r\n```\r\nimport datasets\r\ndata = datasets.load_from_disk(\/SAVED\/DATA\/DIR)\r\n```\r\n\r\nHTH.","embeddings":[-0.4902576208,0.2288961858,-0.0332210772,0.138873294,0.2363725007,-0.0891150385,0.5481942892,0.0673723891,0.2955965102,0.2450511903,-0.0101743611,-0.0694922432,0.0276259091,0.3827281892,-0.1057184339,-0.0218463782,-0.1530392021,0.0536614954,-0.1772286743,0.0896224678,-0.1652288735,-0.0921332538,-0.4337235987,-0.0704064891,-0.0181331802,-0.1504255235,-0.0832125545,0.219438836,-0.1661252081,-0.2807519138,0.238820821,0.1633139551,0.2291972488,0.475061357,-0.0001033812,0.0263173375,0.0082090031,-0.0013799195,-0.2269736379,-0.4025881588,-0.2158048302,-0.5285046697,0.1796488315,-0.4550701976,-0.2032037377,-0.0582043938,-0.0745971277,-0.4929271638,0.3164803088,0.3498669565,0.2593776882,0.5757408142,-0.1358263642,-0.2754445672,-0.1606792808,-0.1514874995,-0.1493050307,0.4366157353,-0.0145771503,-0.0082134157,-0.0025773346,0.0952334329,-0.1961562037,0.0413857102,0.3163404763,0.0586813726,-0.1016099751,-0.1018178985,0.2198021561,0.3930848241,0.4261620939,-0.2737801671,-0.2470030934,-0.339004308,-0.0023413347,-0.4567470551,0.0788485259,0.1836712658,0.0242283177,0.224884212,-0.3797212541,-0.3182393014,-0.3313245773,0.1638136953,-0.32146281,0.0111691775,-0.2536790669,0.0693017393,-0.0612447411,0.0913351476,-0.1768202484,-0.28515324,0.073911421,-0.0619464032,-0.2594501376,0.0268554222,0.1183790341,0.0684494004,0.1312052011,0.0652054697,0.2398014665,-0.018818114,0.0791069493,0.0497344695,0.4407645464,0.2978351414,0.0075793457,-0.176871866,0.5290125012,0.091033712,-0.0241946559,-0.29890728,0.2565964162,-0.3073076606,0.1321211159,0.0137979519,0.3654094338,-0.3048083484,-0.158787936,-0.0139988959,0.1392609626,-0.0424629487,0.1689319164,0.2522922754,-0.2716249526,0.0451420024,0.0697742701,0.3969351351,-0.2989778817,-0.2657657862,-0.0589482971,-0.1259195954,-0.1128110662,0.0637708753,0.178990826,0.0000603757,0.0520434454,-0.0411280394,0.2421651185,-0.0735087693,0.2981470525,-0.0951727554,0.0823780447,0.2353083044,0.2750588059,-0.0874181688,0.0769162402,-0.2003214508,0.0379160382,0.038790673,-0.1426270306,-0.3582569659,0.170656845,0.2765568197,-0.6289308071,-0.1753666699,-0.321626395,0.0367598273,-0.3161136806,-0.1942735314,-0.0308537446,-0.0616714284,-0.1780046523,-0.2453662008,0.3240109086,0.5668384433,-0.383969754,0.1050266996,0.0844061449,-0.160434112,-0.1781923324,0.023398824,-0.3148404658,0.5233699083,-0.0130562568,-0.3010381162,0.5681838393,-0.3550915122,-0.2651605904,0.2487461865,-0.1917605847,-0.105965592,-0.1038612276,0.0901149809,0.440076679,0.0750485212,-0.0843940079,0.289010942,-0.0351713262,0.0004676559,-0.2186113894,-0.2369781137,0.1673176885,0.2837593257,0.255562067,0.1129005253,0.3047425449,-0.1972569376,0.0391629934,-0.2821209729,0.1191658303,0.1654856652,0.1697198749,0.1023969799,-0.044208128,-0.1828145236,-0.48932302,0.2718419433,0.0478837043,0.1571957916,-0.199201867,-0.1379158646,-0.020388905,-0.1831310242,-0.1769697517,0.0272823572,0.1410640925,-0.1107874289,0.1724625081,0.1293686181,-0.2598986626,0.1782922298,0.0107880915,0.002460724,-0.1300739944,0.2903401554,-0.0492371321,0.0646001175,-0.0289290342,0.0881469399,0.0581811368,-0.1937225014,-0.1185834855,0.5259695649,-0.0300909895,0.0616296493,0.26878497,0.3818560541,0.306464076,-0.0120455539,0.5338500142,-0.0427261144,0.3334380388,-0.0925104991,-0.3228542805,0.7542521954,-0.1525749713,0.2171272337,0.1025260761,0.0183239989,0.2599797547,0.001278999,-0.4040777385,-0.0359521806,0.1184480637,0.0022458544,0.181137234,-0.0850736648,-0.3064594567,-0.0152912978,0.3312467039,-0.0188398156,0.1778849959,-0.0660747066,-0.0304849781,0.1306045651,-0.0831728801,0.0887685046,0.516928196,0.2506965101,0.0519572198,0.2298254222,-0.0313782431,-0.2562146187,0.132566765,0.2321359664,0.0492413528,0.0684337541,0.1004821807,-0.0237272847,-0.2063807398,-0.052083157,-0.0547190756,0.0232721549,-0.2868103087,0.2843016386,-0.2516606748,-0.0163154081,0.0470518991,0.1107893586,-0.4484026432,-0.2002999336,0.0647204295,0.077696614,-0.2318820804,0.1336179227,-0.1268714517,0.3907523155,-0.2403125763,-0.2388580739,0.2622436583,-0.4872460365,-0.1227765456,0.1072288454,0.3064813912,0.1311014891,0.5087970495,0.0214199591,-0.0971539766,-0.3305742443,-0.1572176069,0.0155851031,-0.065936245,0.2778904438,0.0809208751,0.0578192733,-0.0453803204,0.3248309493,0.3875049353,-0.3374926746,0.2036764622,0.1821809709,-0.0564875826,-0.0917128548,-0.3432077765,-0.431303978,-0.4415458739,-0.3976587355,0.1741933823,0.0349772051,-0.0529258959,0.1827891618,0.1519728601,0.1029269621,0.0166772045,0.1853231043,-0.182843402,-0.4674518108,0.1784860343,-0.1808056384,-0.6038360596,0.2909515798,0.2311562598,0.1528016329,0.3490706384,-0.4436715245,-0.3765623868,0.0681066141,0.4156066775,0.1085773632,0.3436664641,0.343449086,-0.0702814162,-0.0382861346,-0.0923792347,0.0928403884,0.1769469231,0.0589718558,-0.1342711449,0.0336064994,0.2543356419,-0.0294251926,0.3790815473,-0.0375484191,0.0918622464,0.2364589721,-0.157876581,0.6929191351,-0.1827820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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/824","title":"Discussion using datasets in offline mode","comments":"> here is my way to load a dataset offline, but it **requires** an online machine\n> \n> 1. (online machine)\n> \n> ```\n> \n> import datasets\n> \n> data = datasets.load_dataset(...)\n> \n> data.save_to_disk(\/YOUR\/DATASET\/DIR)\n> \n> ```\n> \n> 2. copy the dir from online to the offline machine\n> \n> 3. (offline machine)\n> \n> ```\n> \n> import datasets\n> \n> data = datasets.load_from_disk(\/SAVED\/DATA\/DIR)\n> \n> ```\n> \n> \n> \n> HTH.\n\n","body":"`datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n","comment_length":76,"text":"Discussion using datasets in offline mode \n `datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n \n > here is my way to load a dataset offline, but it **requires** an online machine\n> \n> 1. (online machine)\n> \n> ```\n> \n> import datasets\n> \n> data = datasets.load_dataset(...)\n> \n> data.save_to_disk(\/YOUR\/DATASET\/DIR)\n> \n> ```\n> \n> 2. copy the dir from online to the offline machine\n> \n> 3. (offline machine)\n> \n> ```\n> \n> import datasets\n> \n> data = datasets.load_from_disk(\/SAVED\/DATA\/DIR)\n> \n> ```\n> \n> \n> \n> HTH.\n\n","embeddings":[-0.4992605448,0.2269978523,-0.0324693248,0.1418719441,0.2369505018,-0.1029172167,0.5442941189,0.0744111389,0.2753627598,0.2442882061,-0.0088339234,-0.0665396452,0.0280860681,0.3756225705,-0.0988998637,-0.0419552475,-0.1623334885,0.0563546792,-0.183036536,0.1076543331,-0.1633780748,-0.0869517475,-0.4333003163,-0.0625077635,-0.0159387887,-0.1606817096,-0.0798981264,0.2115724087,-0.1717491895,-0.2845186889,0.2436344028,0.1665008962,0.1988918632,0.4731360674,-0.0001034733,0.0370239355,0.0119215008,0.0104895122,-0.2277320027,-0.3849576116,-0.2243474573,-0.5146489739,0.1638061702,-0.4546643198,-0.2070107609,-0.0379570685,-0.0688914806,-0.5084757209,0.3095602393,0.3518121243,0.25706622,0.5904164314,-0.1282708943,-0.2792427242,-0.1558488756,-0.15226309,-0.1556978971,0.4283836484,-0.0172956381,0.0013684349,-0.0222441554,0.1041298062,-0.1752347797,0.0346602872,0.3222211003,0.0672788918,-0.1097400188,-0.0845898315,0.2206331044,0.3967526555,0.4247180521,-0.2884344757,-0.25191921,-0.3396162391,-0.0075891577,-0.4678492844,0.0960984454,0.1565518826,0.0217524823,0.2291809618,-0.3944315314,-0.3091626763,-0.3443603218,0.1524468064,-0.3242220581,0.0023712981,-0.2609032691,0.0710143372,-0.0611248761,0.0929871574,-0.1979121715,-0.2828268111,0.0606515519,-0.0742605627,-0.2604793012,0.0141299367,0.1201808229,0.0637012869,0.1402420402,0.058837384,0.2486954182,-0.0098348763,0.0979686975,0.0510386862,0.4422774613,0.2957024574,0.0074487762,-0.18953906,0.5416590571,0.0993699357,-0.0075667072,-0.2980296016,0.2820797563,-0.3101067841,0.1336489916,0.0073752431,0.3609673977,-0.2954883873,-0.1674440801,-0.0125406291,0.1405458748,-0.0426560454,0.1835252494,0.248358503,-0.2873734534,0.0299902372,0.0637684539,0.3856902122,-0.2997954786,-0.2546656728,-0.0616325028,-0.1359128952,-0.1291581094,0.0727540553,0.1864255965,0.0080418475,0.0445535854,-0.0334911197,0.2402406931,-0.0691183209,0.3054921031,-0.1039336249,0.0785027891,0.2336844802,0.2520386577,-0.0957668126,0.0624969415,-0.1969115734,0.0382462256,0.041058708,-0.1074667722,-0.3596408069,0.178369388,0.2726848423,-0.6226016879,-0.1710650325,-0.3079641163,0.0581852198,-0.306432128,-0.1984430552,-0.0153495949,-0.0460300557,-0.193595171,-0.2405310273,0.3376948535,0.5559729338,-0.398294121,0.0968975052,0.0791635215,-0.1771891415,-0.1863670498,0.0195536334,-0.3082571626,0.5199754834,-0.0187853388,-0.2941873968,0.5648753643,-0.3655905724,-0.2701430023,0.2469820678,-0.1869648099,-0.1120411009,-0.0934418514,0.0912888423,0.444742173,0.0852880627,-0.0959229767,0.2809376121,-0.0458619855,-0.0005543638,-0.2211628258,-0.2394605875,0.1544378698,0.2881146371,0.2723816335,0.0969029889,0.3091706038,-0.1952723414,0.0280246921,-0.2787548006,0.1350964159,0.1634455919,0.1533000767,0.0920097008,-0.0438158251,-0.1929366887,-0.4884028733,0.2647079527,0.041078344,0.1632880867,-0.2042591572,-0.1388626993,-0.0210518315,-0.1890713125,-0.1622339189,0.0133329192,0.1397613436,-0.1002759784,0.1611437947,0.1137790605,-0.2563475668,0.1799523532,0.0201561693,0.0074801487,-0.138511315,0.269367069,-0.0576035604,0.0668179542,-0.0368147902,0.0932565108,0.0424022302,-0.1958928406,-0.1061084792,0.529029727,-0.0309691075,0.0468532704,0.2650736272,0.3803910315,0.3248943985,-0.011337583,0.5266875029,-0.0440164357,0.3241901398,-0.0970292091,-0.3409502506,0.7555660009,-0.1524325758,0.2127019763,0.1112290621,0.0115109617,0.265591383,0.0024921908,-0.4044695199,-0.0297388695,0.1032060906,0.0016839154,0.1952834725,-0.0857538134,-0.3268732727,-0.0021086955,0.3489390612,-0.0138921747,0.1751540899,-0.0613358766,-0.0318150036,0.1201424897,-0.0734539181,0.0859299004,0.5219720006,0.2544083893,0.0612387247,0.2307022661,-0.0414699912,-0.2559099793,0.136794433,0.2250438631,0.0342362486,0.0703126267,0.0828345418,-0.0354552269,-0.2182721943,-0.0521009415,-0.0413502678,0.0165150035,-0.2833811343,0.2871290445,-0.2482628822,-0.028776994,0.0571506321,0.1327002645,-0.4499962926,-0.1922508031,0.0758107826,0.0834263191,-0.2347825021,0.1367863566,-0.1147691905,0.4157854319,-0.2417856604,-0.2162092179,0.2557033598,-0.489272207,-0.1254084259,0.1139194816,0.2904767692,0.1211071759,0.5174885988,0.018219661,-0.0972281322,-0.3355351686,-0.1610652059,0.0355822183,-0.0712829605,0.2829785645,0.0933507159,0.0530732386,-0.067163527,0.3253516853,0.3937312961,-0.3271920383,0.1934456229,0.1792645156,-0.0497172922,-0.0871885717,-0.3529951572,-0.4282158017,-0.432069838,-0.3919803202,0.192620337,0.0132437786,-0.0574363023,0.1898374707,0.1482215226,0.116527997,0.0058472878,0.1888998151,-0.1883189976,-0.4760995209,0.1688492447,-0.189299643,-0.6016700268,0.2806211114,0.2295092046,0.1657632887,0.3582723439,-0.4448154569,-0.3781495988,0.0732920542,0.4401058555,0.110242337,0.3342652023,0.3533639014,-0.0593588874,-0.0430468284,-0.0973874703,0.0781206414,0.1909287125,0.0516173132,-0.1234366819,0.0378183462,0.2629092932,-0.0286334138,0.4023732245,-0.0331205502,0.0967799574,0.2285624743,-0.1566263735,0.7003893256,-0.1845089942,-0.1487710476,-0.0188667681,-0.05123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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/824","title":"Discussion using datasets in offline mode","comments":"I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\r\n\r\nLet me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :) \r\n\r\nI already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\r\n\r\n----------\r\n\r\n> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\r\n\r\nIndeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\r\nFor example if you have a dataset script at `.\/my_dataset\/my_dataset.py` then you can do\r\n```python\r\nload_dataset(\".\/my_dataset\")\r\n```\r\nand the dataset script will generate your dataset once and for all.\r\n\r\n----------\r\n\r\nAbout I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\r\ncf #1724 ","body":"`datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n","comment_length":179,"text":"Discussion using datasets in offline mode \n `datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n \n I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\r\n\r\nLet me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :) \r\n\r\nI already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\r\n\r\n----------\r\n\r\n> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\r\n\r\nIndeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\r\nFor example if you have a dataset script at `.\/my_dataset\/my_dataset.py` then you can do\r\n```python\r\nload_dataset(\".\/my_dataset\")\r\n```\r\nand the dataset script will generate your dataset once and for all.\r\n\r\n----------\r\n\r\nAbout I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\r\ncf #1724 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/824","title":"Discussion using datasets in offline mode","comments":"The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon","body":"`datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n","comment_length":38,"text":"Discussion using datasets in offline mode \n `datasets.load_dataset(\"csv\", ...)` breaks if you have no connection (There is already this issue https:\/\/github.com\/huggingface\/datasets\/issues\/761 about it). It seems to be the same for metrics too.\r\n\r\nI create this ticket to discuss a bit and gather what you have in mind or other propositions.\r\n\r\nHere are some points to open discussion:\r\n- if you want to prepare your code\/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.\r\n- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset(\"MY_PATH\/csv.py\", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.\r\n- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV\/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least \"freeze\" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.\r\n \r\nWDYT? (thks)\r\n\r\n \n The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon","embeddings":[-0.4490848482,0.2095090002,-0.0598193891,0.129349485,0.2621940374,-0.1312862486,0.5469678044,0.0949262381,0.3154391944,0.2294326127,0.0510399193,-0.0031574785,0.0479425304,0.4036746323,-0.1032212973,-0.1191765517,-0.1096814498,0.0806255639,-0.170926407,0.0925025642,-0.1816644222,-0.0897327736,-0.3750758767,-0.0255609434,-0.104983367,-0.1746587455,-0.1071397066,0.1566956937,-0.2103743553,-0.3327201605,0.2491134703,0.1175882071,0.1634298414,0.4241681695,-0.0000999822,0.0421361551,0.1069040447,0.0379941911,-0.2203476429,-0.419916749,-0.1981761009,-0.4793075919,0.2418385744,-0.40180251,-0.1757818609,-0.1275016814,-0.0598839149,-0.4807877541,0.2974709272,0.3426465392,0.2918207049,0.5356094241,-0.08034008,-0.2813901603,-0.1216623858,-0.0586182959,-0.1778581738,0.4252844453,0.0733571202,-0.0123791518,-0.0838829204,0.0888442323,-0.1531640589,0.0956401974,0.3111796975,0.0713066906,-0.0924655721,-0.1528321356,0.2028551847,0.3944026828,0.4603175521,-0.3109174371,-0.2949747741,-0.3411391079,-0.0479014292,-0.4361881316,0.1274563819,0.1441726834,0.0090271737,0.233983919,-0.3632723987,-0.2975689471,-0.3418593109,0.169493258,-0.2816976607,0.0181066487,-0.2516751885,0.0413689315,-0.0832428709,0.0752860978,-0.1551486254,-0.3205278516,0.098621361,-0.0793635026,-0.1888277978,-0.0165316593,0.1534282714,0.008187145,0.1217974946,0.0751493126,0.1525371522,-0.0060451161,0.0926281363,0.0680228099,0.4427141249,0.3181465268,0.0188554749,-0.2447724193,0.5638321638,0.0607491359,-0.0046479688,-0.2882612944,0.3297551274,-0.2873273194,0.0361570455,-0.016085932,0.3358884156,-0.2712710202,-0.0916830376,0.0604369603,0.1495210975,-0.0821654052,0.1536692828,0.2071387023,-0.2203721106,0.0295776036,0.0346205868,0.3958796859,-0.2415048033,-0.2347968221,-0.1089479625,-0.1066183075,-0.1055866778,0.0744700357,0.1888174713,-0.0131090106,0.1214088798,-0.0402394049,0.2105225325,-0.0341542251,0.278637141,-0.1574495286,0.0479996912,0.2500382364,0.1872593313,-0.106234327,0.0567811839,-0.2355721295,0.0381199308,-0.0035781907,-0.0436274111,-0.404987365,0.0963111818,0.2798317969,-0.5623047352,-0.1680555195,-0.2548446953,0.1297697127,-0.2823038101,-0.0971270129,0.0206746962,-0.0097434577,-0.1449509412,-0.1983079612,0.239082709,0.5462014675,-0.4452953041,0.0876879916,0.1217823178,-0.1871830672,-0.17599307,0.0642042682,-0.343101263,0.5343926549,0.051759094,-0.2778843641,0.5229828954,-0.3582603335,-0.2345213741,0.2888397574,-0.1610287726,-0.1441398114,-0.0667064488,0.0498279259,0.3899838328,0.1192973927,0.0001437401,0.2430983931,-0.0564485043,0.0195687898,-0.2536431253,-0.323527962,0.14072375,0.2494011074,0.3065206707,0.1239916235,0.2792335451,-0.2578680515,-0.0405986905,-0.2623461485,0.165442884,0.1579164416,0.2099319845,0.1023168489,-0.0483671613,-0.2059078664,-0.4760749638,0.2252617329,0.0337006971,0.2320879996,-0.230220288,-0.1496614814,-0.008372074,-0.179708451,-0.1597651243,-0.0425286368,0.1691499352,-0.1388658583,0.1849676222,0.1760512888,-0.3189759254,0.1439083815,-0.0562663488,0.0259863827,-0.1155401766,0.2796734273,-0.0809465349,0.0232010745,0.0426407829,0.0838658288,-0.0070740283,-0.1859913468,-0.1073740348,0.4920392036,-0.0600119606,0.050861761,0.315263927,0.361379236,0.3560869098,0.0397625342,0.5095092654,-0.1218395978,0.3111970127,-0.0770422667,-0.3356893063,0.6987401247,-0.1456841528,0.2038489729,0.0956835374,0.0029137544,0.3142004907,-0.0028089855,-0.3935572803,-0.0653379932,0.0797696859,-0.044440072,0.2148961127,-0.0678070262,-0.3889352679,-0.0229981318,0.3756242096,-0.0001716741,0.1845305413,-0.0001421404,0.0194953308,0.062744543,-0.0596937574,0.0841197968,0.5141689777,0.2386389077,0.0379683487,0.2522847354,-0.0703066364,-0.3152319491,0.1395313442,0.2154895812,0.0092474939,0.0789768994,0.089975588,-0.003388139,-0.3099704087,-0.0535693206,-0.0292135775,0.0814710259,-0.2326799631,0.2459449321,-0.3154218197,-0.0520201139,0.0422418192,0.111955598,-0.4437055588,-0.2312417775,0.0868251696,0.0358285643,-0.2842279077,0.1475006491,-0.0897418633,0.4524809718,-0.2049701959,-0.138762638,0.1279346496,-0.5095137954,-0.1033318937,0.133870855,0.3391273618,0.1479579806,0.4992528558,0.0086958241,-0.0772898719,-0.2907142639,-0.1874965131,0.0661994219,-0.0880849659,0.3189185262,0.0668629855,0.0481230058,-0.0487571284,0.2254338264,0.4193473756,-0.3061971366,0.1707129776,0.085586451,-0.0520983674,-0.1553375125,-0.3459178209,-0.487205416,-0.4130060375,-0.4035458863,0.2704867423,0.0267385636,-0.0292487256,0.1243112534,0.158038646,0.1001027748,-0.0176117718,0.2050555199,-0.2188558429,-0.4933129549,0.1240409464,-0.2316770852,-0.5781071782,0.2055875212,0.1968098283,0.1813315898,0.2394980788,-0.4866481721,-0.374794066,0.0422560088,0.3973865807,0.1870754659,0.3177512586,0.3660067022,-0.0246600416,-0.076631479,-0.0784322396,0.0246607829,0.2000732422,0.0979012176,-0.1254032403,0.0741901919,0.3168487847,-0.0595339797,0.4317931533,-0.0111093363,0.0496152043,0.2642003894,-0.1114155203,0.6812868118,-0.2694692016,-0.1440427005,0.0132880118,-0.0587873459,-0.2512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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/823","title":"how processing in batch works in datasets ","comments":"Hi I don\u2019t think this is a request for a dataset like you labeled it.\r\n\r\nI also think this would be better suited for the forum at https:\/\/discuss.huggingface.co. we try to keep the issue for the repo for bug reports and new features\/dataset requests and have usage questions discussed on the forum. Thanks.","body":"Hi,\r\nI need to process my datasets before it is passed to dataloader in batch, \r\nhere is my codes \r\n\r\n```\r\nclass AbstractTask(ABC):\r\n    task_name: str = NotImplemented\r\n    preprocessor: Callable = NotImplemented\r\n    split_to_data_split: Mapping[str, str] = NotImplemented\r\n    tokenizer: Callable = NotImplemented\r\n    max_source_length: str = NotImplemented\r\n    max_target_length: str = NotImplemented\r\n    # TODO: should not be a task item, but cannot see other ways.\r\n    tpu_num_cores: int = None\r\n\r\n    # The arguments set are for all tasks and needs to be kept common.\r\n    def __init__(self, config):\r\n        self.max_source_length = config['max_source_length']\r\n        self.max_target_length = config['max_target_length']\r\n        self.tokenizer = config['tokenizer']\r\n        self.tpu_num_cores = config['tpu_num_cores']\r\n\r\n    def _encode(self, batch) -> Dict[str, torch.Tensor]:\r\n        batch_encoding = self.tokenizer.prepare_seq2seq_batch(\r\n            [x[\"src_texts\"] for x in batch],\r\n            tgt_texts=[x[\"tgt_texts\"] for x in batch],\r\n            max_length=self.max_source_length,\r\n            max_target_length=self.max_target_length,\r\n            padding=\"max_length\" if self.tpu_num_cores is not None else \"longest\",  # TPU hack\r\n            return_tensors=\"pt\"\r\n        )\r\n        return batch_encoding.data\r\n\r\n\r\n    def data_split(self, split):\r\n        return self.split_to_data_split[split]\r\n\r\n    def get_dataset(self, split, n_obs=None):\r\n        split = self.data_split(split)\r\n        if n_obs is not None:\r\n            split = split+\"[:{}]\".format(n_obs)\r\n        dataset = load_dataset(self.task_name, split=split)\r\n        dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names)\r\n        dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n        dataset.set_format(type=\"torch\", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])\r\n        return dataset\r\n\r\n```\r\n\r\nI call it like \r\n\r\n`AutoTask.get(task, train_dataset_config).get_dataset(split=\"train\", n_obs=data_args.n_train) \r\n`\r\n\r\nThis gives the following error, to me because the data inside the   dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks \r\n\r\n  File \"finetune_multitask_trainer.py\", line 192, in main\r\n    if training_args.do_train else None\r\n  File \"finetune_multitask_trainer.py\", line 191, in <dictcomp>\r\n    split=\"train\", n_obs=data_args.n_train) for task in data_args.task}\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in get_dataset\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1236, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1207, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in <lambda>\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in _encode\r\n    [x[\"src_texts\"] for x in batch],\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in <listcomp>\r\n    [x[\"src_texts\"] for x in batch],\r\nTypeError: string indices must be integers\r\n\r\n","comment_length":53,"text":"how processing in batch works in datasets  \n Hi,\r\nI need to process my datasets before it is passed to dataloader in batch, \r\nhere is my codes \r\n\r\n```\r\nclass AbstractTask(ABC):\r\n    task_name: str = NotImplemented\r\n    preprocessor: Callable = NotImplemented\r\n    split_to_data_split: Mapping[str, str] = NotImplemented\r\n    tokenizer: Callable = NotImplemented\r\n    max_source_length: str = NotImplemented\r\n    max_target_length: str = NotImplemented\r\n    # TODO: should not be a task item, but cannot see other ways.\r\n    tpu_num_cores: int = None\r\n\r\n    # The arguments set are for all tasks and needs to be kept common.\r\n    def __init__(self, config):\r\n        self.max_source_length = config['max_source_length']\r\n        self.max_target_length = config['max_target_length']\r\n        self.tokenizer = config['tokenizer']\r\n        self.tpu_num_cores = config['tpu_num_cores']\r\n\r\n    def _encode(self, batch) -> Dict[str, torch.Tensor]:\r\n        batch_encoding = self.tokenizer.prepare_seq2seq_batch(\r\n            [x[\"src_texts\"] for x in batch],\r\n            tgt_texts=[x[\"tgt_texts\"] for x in batch],\r\n            max_length=self.max_source_length,\r\n            max_target_length=self.max_target_length,\r\n            padding=\"max_length\" if self.tpu_num_cores is not None else \"longest\",  # TPU hack\r\n            return_tensors=\"pt\"\r\n        )\r\n        return batch_encoding.data\r\n\r\n\r\n    def data_split(self, split):\r\n        return self.split_to_data_split[split]\r\n\r\n    def get_dataset(self, split, n_obs=None):\r\n        split = self.data_split(split)\r\n        if n_obs is not None:\r\n            split = split+\"[:{}]\".format(n_obs)\r\n        dataset = load_dataset(self.task_name, split=split)\r\n        dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names)\r\n        dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n        dataset.set_format(type=\"torch\", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])\r\n        return dataset\r\n\r\n```\r\n\r\nI call it like \r\n\r\n`AutoTask.get(task, train_dataset_config).get_dataset(split=\"train\", n_obs=data_args.n_train) \r\n`\r\n\r\nThis gives the following error, to me because the data inside the   dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks \r\n\r\n  File \"finetune_multitask_trainer.py\", line 192, in main\r\n    if training_args.do_train else None\r\n  File \"finetune_multitask_trainer.py\", line 191, in <dictcomp>\r\n    split=\"train\", n_obs=data_args.n_train) for task in data_args.task}\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in get_dataset\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1236, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1207, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in <lambda>\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in _encode\r\n    [x[\"src_texts\"] for x in batch],\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in <listcomp>\r\n    [x[\"src_texts\"] for x in batch],\r\nTypeError: string indices must be integers\r\n\r\n \n Hi I don\u2019t think this is a request for a dataset like you labeled it.\r\n\r\nI also think this would be better suited for the forum at https:\/\/discuss.huggingface.co. we try to keep the issue for the repo for bug reports and new features\/dataset requests and have usage questions discussed on the forum. Thanks.","embeddings":[-0.5023794174,-0.1743950695,-0.229502216,0.1301572621,0.1780355573,0.0317946561,0.2955617607,0.1713047624,-0.1484707892,0.1780768037,0.0091214189,0.1247809753,0.0641202703,0.2456238419,-0.0561650358,-0.0358917713,-0.0745975822,0.0483772866,0.1096090525,0.0072343689,-0.3676092625,0.0689976066,-0.404307127,0.0522343665,-0.2230468243,-0.2693564892,-0.061245136,-0.0392570309,-0.0044130813,-0.1525889933,-0.0022256428,0.2334949821,0.2088395506,0.4326501787,-0.0001049565,0.0785311237,0.246775642,0.0393823534,-0.2903354466,0.2226282358,-0.0494002365,-0.1695528775,-0.106298469,-0.3042087555,-0.100836426,-0.0860008001,-0.1385592818,-0.3374301493,0.3848311007,0.1753834486,0.2844721079,0.2802553177,-0.1476055533,0.141417712,0.0610040873,0.071453087,0.085112974,-0.3902092874,0.0682323202,0.147927478,-0.0484061763,0.149620384,-0.2720299363,0.0859410837,-0.2550970316,0.0218879282,0.0260984451,-0.3847026229,0.0884854123,-0.1349758655,0.2846595347,-0.2123625129,-0.244074598,-0.3842577636,-0.2724657655,-0.4653172493,-0.0269787684,0.1131320298,-0.0684819147,0.1012712568,-0.0785877854,0.2408464402,-0.0737230629,-0.1666946113,0.1114271581,0.256811142,-0.2119238675,0.2198818326,0.0700407103,0.0887886882,0.1648149788,-0.2350720912,-0.0004340044,0.2723601758,-0.490796864,-0.0871194154,-0.111774452,-0.1519913375,-0.1408956349,0.0541603342,-0.1284128726,0.1272303164,0.3161275685,0.1564184427,-0.0295962766,0.1643566489,0.1187213957,0.2562070191,-0.077118583,-0.3050593734,-0.2260431051,0.0277573764,-0.0785889626,-0.3298466206,0.1471393257,0.3610413373,0.2499244362,0.0302780811,-0.4871647358,-0.090392217,-0.0190420989,-0.0580975227,0.1420931816,0.2845916152,-0.2407628298,0.2508462965,-0.0810260475,-0.1523166597,-0.2926350832,-0.0461959541,-0.3027382791,-0.1152567863,-0.1496196091,0.0702788755,0.1349772662,-0.1721600443,0.3239180446,0.0755829066,0.0962865055,-0.1746606827,0.5014520288,-0.3949423134,0.2224124223,0.2599134743,0.1795815676,0.2426499426,0.0532012805,0.2097658068,0.0399616584,0.07378117,-0.6257678866,-0.1791180819,0.2474631667,0.3138492405,-0.050551679,0.0014612001,-0.5935842991,-0.0869221464,0.3213269711,0.0906271413,0.1197098047,-0.1754258871,-0.1859953701,-0.1946797967,0.2003967762,0.0852582231,-0.4504095614,0.067932725,-0.1153643429,-0.3227443099,0.5454286933,0.1242681667,-0.1138667688,0.6199551225,-0.3095553815,0.378302753,0.1937190294,-0.318939209,-0.2815083861,0.5390690565,-0.2575122714,0.0180868749,0.0398210399,0.0325068124,0.558662951,-0.0602873936,0.0362305008,0.3787432015,-0.1192286387,0.0902983919,-0.0995698944,0.1156369671,0.0859533623,-0.017408317,0.103893064,0.0438636467,-0.2167430967,-0.0445535667,0.1737817377,0.0534989499,0.2965995669,0.2496741712,0.0114939697,0.2087981552,0.1932462901,-0.045412153,-0.2964651883,0.0957323313,-0.0203324519,-0.1832336485,0.0405643061,-0.3565836847,-0.1427385509,0.1464838088,-0.4080851972,-0.170314461,0.2187933773,0.0912246779,0.0223609656,-0.3133271337,0.0051412471,0.3289259076,-0.018437,0.0320123062,-0.1938534528,-0.0220454745,0.0078472476,-0.1507639438,-0.0613341182,-0.0791995898,0.2228215635,0.0074299281,-0.0493339114,0.3533655703,0.2747455239,0.0639955476,-0.0365163907,-0.1238466576,-0.010248973,0.1141474769,-0.0905264094,-0.0358541906,0.2171731889,-0.1256070137,0.2478310466,0.4252819717,-0.0469338372,0.2657586038,-0.0382558815,-0.2736453712,0.2264523953,-0.0702164769,-0.0428678021,0.1976911724,-0.0458724052,0.0015260566,0.3408337235,0.0440044962,-0.0559479967,0.1770871282,0.427303493,-0.0043448089,-0.1692032218,-0.0192307252,-0.2083047777,0.1080089733,0.1594958156,-0.0258413963,0.1219083592,0.1693717986,0.1196489036,-0.1023140699,0.104523696,-0.0379634686,0.0820302144,0.3170749545,0.180519715,0.1618741006,-0.0916261226,-0.0561815985,-0.0036794732,-0.1100691184,0.1913622171,0.2398844659,-0.3654334843,0.2132201493,-0.145929724,-0.08981058,-0.1511178762,-0.0314461552,0.1613285244,-0.1398269385,-0.0764070004,0.2173885703,-0.1582910717,0.0283092428,-0.0862446353,0.0393373929,0.2715219557,-0.3807459176,-0.1608629972,-0.0596176609,-0.1818827242,0.2018623352,0.0678684339,0.1080862805,0.4683231413,0.0514345989,0.0387846157,-0.1155499518,-0.1522178799,0.0778788254,-0.1976525038,0.1767100245,0.0169449784,0.1401751041,-0.1079600304,-0.3839942813,0.1391080171,-0.1839413941,-0.3313497603,-0.0855730399,-0.0173469968,0.040500477,-0.2087071538,-0.4318087101,-0.2330217361,-0.1791113019,0.0216235537,-0.0695498586,0.2032481134,-0.0093688983,-0.0178895891,0.1497216076,0.2623847723,-0.1572469622,-0.0877216086,-0.4881262481,0.1246121675,-0.2836306691,-0.0197322294,-0.0922569484,-0.1024708152,0.4223552346,0.3086383641,-0.1903934777,0.1180599406,-0.0889263675,0.2408317477,-0.0977540761,-0.0265352037,0.1954074502,-0.0789332092,-0.099664703,-0.1173348725,0.0736229196,0.1176979691,-0.2414382696,0.0647149384,-0.017784683,0.6492415667,-0.0192825571,0.9116159081,0.0870948359,-0.0874783471,0.132925272,0.0044225426,-0.1579394042,-0.0129763782,-0.3009167314,0.0907453895,0.0279778596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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/823","title":"how processing in batch works in datasets ","comments":"Hi Thomas,\nwhat I do not get from documentation is that why when you set batched=True,\nthis is processed in batch, while data is not divided to batched\nbeforehand, basically this is a question on the documentation and I do not\nget the batched=True, but sure, if you think this is more appropriate in\nforum I will post it there.\nthanks\nBest\nRabeeh\n\nOn Tue, Nov 10, 2020 at 12:21 PM Thomas Wolf <notifications@github.com>\nwrote:\n\n> Hi I don\u2019t think this is a request for a dataset like you labeled it.\n>\n> I also think this would be better suited for the forum at\n> https:\/\/discuss.huggingface.co. we try to keep the issue for the repo for\n> bug reports and new features\/dataset requests and have usage questions\n> discussed on the forum. Thanks.\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/823#issuecomment-724639476>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ARPXHH4FIPFHVVUHANAE4F3SPEO2JANCNFSM4TQQVEXQ>\n> .\n>\n","body":"Hi,\r\nI need to process my datasets before it is passed to dataloader in batch, \r\nhere is my codes \r\n\r\n```\r\nclass AbstractTask(ABC):\r\n    task_name: str = NotImplemented\r\n    preprocessor: Callable = NotImplemented\r\n    split_to_data_split: Mapping[str, str] = NotImplemented\r\n    tokenizer: Callable = NotImplemented\r\n    max_source_length: str = NotImplemented\r\n    max_target_length: str = NotImplemented\r\n    # TODO: should not be a task item, but cannot see other ways.\r\n    tpu_num_cores: int = None\r\n\r\n    # The arguments set are for all tasks and needs to be kept common.\r\n    def __init__(self, config):\r\n        self.max_source_length = config['max_source_length']\r\n        self.max_target_length = config['max_target_length']\r\n        self.tokenizer = config['tokenizer']\r\n        self.tpu_num_cores = config['tpu_num_cores']\r\n\r\n    def _encode(self, batch) -> Dict[str, torch.Tensor]:\r\n        batch_encoding = self.tokenizer.prepare_seq2seq_batch(\r\n            [x[\"src_texts\"] for x in batch],\r\n            tgt_texts=[x[\"tgt_texts\"] for x in batch],\r\n            max_length=self.max_source_length,\r\n            max_target_length=self.max_target_length,\r\n            padding=\"max_length\" if self.tpu_num_cores is not None else \"longest\",  # TPU hack\r\n            return_tensors=\"pt\"\r\n        )\r\n        return batch_encoding.data\r\n\r\n\r\n    def data_split(self, split):\r\n        return self.split_to_data_split[split]\r\n\r\n    def get_dataset(self, split, n_obs=None):\r\n        split = self.data_split(split)\r\n        if n_obs is not None:\r\n            split = split+\"[:{}]\".format(n_obs)\r\n        dataset = load_dataset(self.task_name, split=split)\r\n        dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names)\r\n        dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n        dataset.set_format(type=\"torch\", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])\r\n        return dataset\r\n\r\n```\r\n\r\nI call it like \r\n\r\n`AutoTask.get(task, train_dataset_config).get_dataset(split=\"train\", n_obs=data_args.n_train) \r\n`\r\n\r\nThis gives the following error, to me because the data inside the   dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks \r\n\r\n  File \"finetune_multitask_trainer.py\", line 192, in main\r\n    if training_args.do_train else None\r\n  File \"finetune_multitask_trainer.py\", line 191, in <dictcomp>\r\n    split=\"train\", n_obs=data_args.n_train) for task in data_args.task}\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in get_dataset\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1236, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1207, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in <lambda>\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in _encode\r\n    [x[\"src_texts\"] for x in batch],\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in <listcomp>\r\n    [x[\"src_texts\"] for x in batch],\r\nTypeError: string indices must be integers\r\n\r\n","comment_length":167,"text":"how processing in batch works in datasets  \n Hi,\r\nI need to process my datasets before it is passed to dataloader in batch, \r\nhere is my codes \r\n\r\n```\r\nclass AbstractTask(ABC):\r\n    task_name: str = NotImplemented\r\n    preprocessor: Callable = NotImplemented\r\n    split_to_data_split: Mapping[str, str] = NotImplemented\r\n    tokenizer: Callable = NotImplemented\r\n    max_source_length: str = NotImplemented\r\n    max_target_length: str = NotImplemented\r\n    # TODO: should not be a task item, but cannot see other ways.\r\n    tpu_num_cores: int = None\r\n\r\n    # The arguments set are for all tasks and needs to be kept common.\r\n    def __init__(self, config):\r\n        self.max_source_length = config['max_source_length']\r\n        self.max_target_length = config['max_target_length']\r\n        self.tokenizer = config['tokenizer']\r\n        self.tpu_num_cores = config['tpu_num_cores']\r\n\r\n    def _encode(self, batch) -> Dict[str, torch.Tensor]:\r\n        batch_encoding = self.tokenizer.prepare_seq2seq_batch(\r\n            [x[\"src_texts\"] for x in batch],\r\n            tgt_texts=[x[\"tgt_texts\"] for x in batch],\r\n            max_length=self.max_source_length,\r\n            max_target_length=self.max_target_length,\r\n            padding=\"max_length\" if self.tpu_num_cores is not None else \"longest\",  # TPU hack\r\n            return_tensors=\"pt\"\r\n        )\r\n        return batch_encoding.data\r\n\r\n\r\n    def data_split(self, split):\r\n        return self.split_to_data_split[split]\r\n\r\n    def get_dataset(self, split, n_obs=None):\r\n        split = self.data_split(split)\r\n        if n_obs is not None:\r\n            split = split+\"[:{}]\".format(n_obs)\r\n        dataset = load_dataset(self.task_name, split=split)\r\n        dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names)\r\n        dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n        dataset.set_format(type=\"torch\", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])\r\n        return dataset\r\n\r\n```\r\n\r\nI call it like \r\n\r\n`AutoTask.get(task, train_dataset_config).get_dataset(split=\"train\", n_obs=data_args.n_train) \r\n`\r\n\r\nThis gives the following error, to me because the data inside the   dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks \r\n\r\n  File \"finetune_multitask_trainer.py\", line 192, in main\r\n    if training_args.do_train else None\r\n  File \"finetune_multitask_trainer.py\", line 191, in <dictcomp>\r\n    split=\"train\", n_obs=data_args.n_train) for task in data_args.task}\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in get_dataset\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1236, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1207, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in <lambda>\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in _encode\r\n    [x[\"src_texts\"] for x in batch],\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in <listcomp>\r\n    [x[\"src_texts\"] for x in batch],\r\nTypeError: string indices must be integers\r\n\r\n \n Hi Thomas,\nwhat I do not get from documentation is that why when you set batched=True,\nthis is processed in batch, while data is not divided to batched\nbeforehand, basically this is a question on the documentation and I do not\nget the batched=True, but sure, if you think this is more appropriate in\nforum I will post it there.\nthanks\nBest\nRabeeh\n\nOn Tue, Nov 10, 2020 at 12:21 PM Thomas Wolf <notifications@github.com>\nwrote:\n\n> Hi I don\u2019t think this is a request for a dataset like you labeled it.\n>\n> I also think this would be better suited for the forum at\n> https:\/\/discuss.huggingface.co. we try to keep the issue for the repo for\n> bug reports and new features\/dataset requests and have usage questions\n> discussed on the forum. Thanks.\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/823#issuecomment-724639476>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ARPXHH4FIPFHVVUHANAE4F3SPEO2JANCNFSM4TQQVEXQ>\n> .\n>\n","embeddings":[-0.5023794174,-0.1743950695,-0.229502216,0.1301572621,0.1780355573,0.0317946561,0.2955617607,0.1713047624,-0.1484707892,0.1780768037,0.0091214189,0.1247809753,0.0641202703,0.2456238419,-0.0561650358,-0.0358917713,-0.0745975822,0.0483772866,0.1096090525,0.0072343689,-0.3676092625,0.0689976066,-0.404307127,0.0522343665,-0.2230468243,-0.2693564892,-0.061245136,-0.0392570309,-0.0044130813,-0.1525889933,-0.0022256428,0.2334949821,0.2088395506,0.4326501787,-0.0001049565,0.0785311237,0.246775642,0.0393823534,-0.2903354466,0.2226282358,-0.0494002365,-0.1695528775,-0.106298469,-0.3042087555,-0.100836426,-0.0860008001,-0.1385592818,-0.3374301493,0.3848311007,0.1753834486,0.2844721079,0.2802553177,-0.1476055533,0.141417712,0.0610040873,0.071453087,0.085112974,-0.3902092874,0.0682323202,0.147927478,-0.0484061763,0.149620384,-0.2720299363,0.0859410837,-0.2550970316,0.0218879282,0.0260984451,-0.3847026229,0.0884854123,-0.1349758655,0.2846595347,-0.2123625129,-0.244074598,-0.3842577636,-0.2724657655,-0.4653172493,-0.0269787684,0.1131320298,-0.0684819147,0.1012712568,-0.0785877854,0.2408464402,-0.0737230629,-0.1666946113,0.1114271581,0.256811142,-0.2119238675,0.2198818326,0.0700407103,0.0887886882,0.1648149788,-0.2350720912,-0.0004340044,0.2723601758,-0.490796864,-0.0871194154,-0.111774452,-0.1519913375,-0.1408956349,0.0541603342,-0.1284128726,0.1272303164,0.3161275685,0.1564184427,-0.0295962766,0.1643566489,0.1187213957,0.2562070191,-0.077118583,-0.3050593734,-0.2260431051,0.0277573764,-0.0785889626,-0.3298466206,0.1471393257,0.3610413373,0.2499244362,0.0302780811,-0.4871647358,-0.090392217,-0.0190420989,-0.0580975227,0.1420931816,0.2845916152,-0.2407628298,0.2508462965,-0.0810260475,-0.1523166597,-0.2926350832,-0.0461959541,-0.3027382791,-0.1152567863,-0.1496196091,0.0702788755,0.1349772662,-0.1721600443,0.3239180446,0.0755829066,0.0962865055,-0.1746606827,0.5014520288,-0.3949423134,0.2224124223,0.2599134743,0.1795815676,0.2426499426,0.0532012805,0.2097658068,0.0399616584,0.07378117,-0.6257678866,-0.1791180819,0.2474631667,0.3138492405,-0.050551679,0.0014612001,-0.5935842991,-0.0869221464,0.3213269711,0.0906271413,0.1197098047,-0.1754258871,-0.1859953701,-0.1946797967,0.2003967762,0.0852582231,-0.4504095614,0.067932725,-0.1153643429,-0.3227443099,0.5454286933,0.1242681667,-0.1138667688,0.6199551225,-0.3095553815,0.378302753,0.1937190294,-0.318939209,-0.2815083861,0.5390690565,-0.2575122714,0.0180868749,0.0398210399,0.0325068124,0.558662951,-0.0602873936,0.0362305008,0.3787432015,-0.1192286387,0.0902983919,-0.0995698944,0.1156369671,0.0859533623,-0.017408317,0.103893064,0.0438636467,-0.2167430967,-0.0445535667,0.1737817377,0.0534989499,0.2965995669,0.2496741712,0.0114939697,0.2087981552,0.1932462901,-0.045412153,-0.2964651883,0.0957323313,-0.0203324519,-0.1832336485,0.0405643061,-0.3565836847,-0.1427385509,0.1464838088,-0.4080851972,-0.170314461,0.2187933773,0.0912246779,0.0223609656,-0.3133271337,0.0051412471,0.3289259076,-0.018437,0.0320123062,-0.1938534528,-0.0220454745,0.0078472476,-0.1507639438,-0.0613341182,-0.0791995898,0.2228215635,0.0074299281,-0.0493339114,0.3533655703,0.2747455239,0.0639955476,-0.0365163907,-0.1238466576,-0.010248973,0.1141474769,-0.0905264094,-0.0358541906,0.2171731889,-0.1256070137,0.2478310466,0.4252819717,-0.0469338372,0.2657586038,-0.0382558815,-0.2736453712,0.2264523953,-0.0702164769,-0.0428678021,0.1976911724,-0.0458724052,0.0015260566,0.3408337235,0.0440044962,-0.0559479967,0.1770871282,0.427303493,-0.0043448089,-0.1692032218,-0.0192307252,-0.2083047777,0.1080089733,0.1594958156,-0.0258413963,0.1219083592,0.1693717986,0.1196489036,-0.1023140699,0.104523696,-0.0379634686,0.0820302144,0.3170749545,0.180519715,0.1618741006,-0.0916261226,-0.0561815985,-0.0036794732,-0.1100691184,0.1913622171,0.2398844659,-0.3654334843,0.2132201493,-0.145929724,-0.08981058,-0.1511178762,-0.0314461552,0.1613285244,-0.1398269385,-0.0764070004,0.2173885703,-0.1582910717,0.0283092428,-0.0862446353,0.0393373929,0.2715219557,-0.3807459176,-0.1608629972,-0.0596176609,-0.1818827242,0.2018623352,0.0678684339,0.1080862805,0.4683231413,0.0514345989,0.0387846157,-0.1155499518,-0.1522178799,0.0778788254,-0.1976525038,0.1767100245,0.0169449784,0.1401751041,-0.1079600304,-0.3839942813,0.1391080171,-0.1839413941,-0.3313497603,-0.0855730399,-0.0173469968,0.040500477,-0.2087071538,-0.4318087101,-0.2330217361,-0.1791113019,0.0216235537,-0.0695498586,0.2032481134,-0.0093688983,-0.0178895891,0.1497216076,0.2623847723,-0.1572469622,-0.0877216086,-0.4881262481,0.1246121675,-0.2836306691,-0.0197322294,-0.0922569484,-0.1024708152,0.4223552346,0.3086383641,-0.1903934777,0.1180599406,-0.0889263675,0.2408317477,-0.0977540761,-0.0265352037,0.1954074502,-0.0789332092,-0.099664703,-0.1173348725,0.0736229196,0.1176979691,-0.2414382696,0.0647149384,-0.017784683,0.6492415667,-0.0192825571,0.9116159081,0.0870948359,-0.0874783471,0.132925272,0.0044225426,-0.1579394042,-0.0129763782,-0.3009167314,0.0907453895,0.0279778596,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/823","title":"how processing in batch works in datasets ","comments":"Yes the forum is perfect for that. You can post in the `datasets` section.\r\nThanks a lot!","body":"Hi,\r\nI need to process my datasets before it is passed to dataloader in batch, \r\nhere is my codes \r\n\r\n```\r\nclass AbstractTask(ABC):\r\n    task_name: str = NotImplemented\r\n    preprocessor: Callable = NotImplemented\r\n    split_to_data_split: Mapping[str, str] = NotImplemented\r\n    tokenizer: Callable = NotImplemented\r\n    max_source_length: str = NotImplemented\r\n    max_target_length: str = NotImplemented\r\n    # TODO: should not be a task item, but cannot see other ways.\r\n    tpu_num_cores: int = None\r\n\r\n    # The arguments set are for all tasks and needs to be kept common.\r\n    def __init__(self, config):\r\n        self.max_source_length = config['max_source_length']\r\n        self.max_target_length = config['max_target_length']\r\n        self.tokenizer = config['tokenizer']\r\n        self.tpu_num_cores = config['tpu_num_cores']\r\n\r\n    def _encode(self, batch) -> Dict[str, torch.Tensor]:\r\n        batch_encoding = self.tokenizer.prepare_seq2seq_batch(\r\n            [x[\"src_texts\"] for x in batch],\r\n            tgt_texts=[x[\"tgt_texts\"] for x in batch],\r\n            max_length=self.max_source_length,\r\n            max_target_length=self.max_target_length,\r\n            padding=\"max_length\" if self.tpu_num_cores is not None else \"longest\",  # TPU hack\r\n            return_tensors=\"pt\"\r\n        )\r\n        return batch_encoding.data\r\n\r\n\r\n    def data_split(self, split):\r\n        return self.split_to_data_split[split]\r\n\r\n    def get_dataset(self, split, n_obs=None):\r\n        split = self.data_split(split)\r\n        if n_obs is not None:\r\n            split = split+\"[:{}]\".format(n_obs)\r\n        dataset = load_dataset(self.task_name, split=split)\r\n        dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names)\r\n        dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n        dataset.set_format(type=\"torch\", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])\r\n        return dataset\r\n\r\n```\r\n\r\nI call it like \r\n\r\n`AutoTask.get(task, train_dataset_config).get_dataset(split=\"train\", n_obs=data_args.n_train) \r\n`\r\n\r\nThis gives the following error, to me because the data inside the   dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks \r\n\r\n  File \"finetune_multitask_trainer.py\", line 192, in main\r\n    if training_args.do_train else None\r\n  File \"finetune_multitask_trainer.py\", line 191, in <dictcomp>\r\n    split=\"train\", n_obs=data_args.n_train) for task in data_args.task}\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in get_dataset\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1236, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1207, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in <lambda>\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in _encode\r\n    [x[\"src_texts\"] for x in batch],\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in <listcomp>\r\n    [x[\"src_texts\"] for x in batch],\r\nTypeError: string indices must be integers\r\n\r\n","comment_length":17,"text":"how processing in batch works in datasets  \n Hi,\r\nI need to process my datasets before it is passed to dataloader in batch, \r\nhere is my codes \r\n\r\n```\r\nclass AbstractTask(ABC):\r\n    task_name: str = NotImplemented\r\n    preprocessor: Callable = NotImplemented\r\n    split_to_data_split: Mapping[str, str] = NotImplemented\r\n    tokenizer: Callable = NotImplemented\r\n    max_source_length: str = NotImplemented\r\n    max_target_length: str = NotImplemented\r\n    # TODO: should not be a task item, but cannot see other ways.\r\n    tpu_num_cores: int = None\r\n\r\n    # The arguments set are for all tasks and needs to be kept common.\r\n    def __init__(self, config):\r\n        self.max_source_length = config['max_source_length']\r\n        self.max_target_length = config['max_target_length']\r\n        self.tokenizer = config['tokenizer']\r\n        self.tpu_num_cores = config['tpu_num_cores']\r\n\r\n    def _encode(self, batch) -> Dict[str, torch.Tensor]:\r\n        batch_encoding = self.tokenizer.prepare_seq2seq_batch(\r\n            [x[\"src_texts\"] for x in batch],\r\n            tgt_texts=[x[\"tgt_texts\"] for x in batch],\r\n            max_length=self.max_source_length,\r\n            max_target_length=self.max_target_length,\r\n            padding=\"max_length\" if self.tpu_num_cores is not None else \"longest\",  # TPU hack\r\n            return_tensors=\"pt\"\r\n        )\r\n        return batch_encoding.data\r\n\r\n\r\n    def data_split(self, split):\r\n        return self.split_to_data_split[split]\r\n\r\n    def get_dataset(self, split, n_obs=None):\r\n        split = self.data_split(split)\r\n        if n_obs is not None:\r\n            split = split+\"[:{}]\".format(n_obs)\r\n        dataset = load_dataset(self.task_name, split=split)\r\n        dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names)\r\n        dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n        dataset.set_format(type=\"torch\", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])\r\n        return dataset\r\n\r\n```\r\n\r\nI call it like \r\n\r\n`AutoTask.get(task, train_dataset_config).get_dataset(split=\"train\", n_obs=data_args.n_train) \r\n`\r\n\r\nThis gives the following error, to me because the data inside the   dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks \r\n\r\n  File \"finetune_multitask_trainer.py\", line 192, in main\r\n    if training_args.do_train else None\r\n  File \"finetune_multitask_trainer.py\", line 191, in <dictcomp>\r\n    split=\"train\", n_obs=data_args.n_train) for task in data_args.task}\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in get_dataset\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1236, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/idiap\/user\/rkarimi\/libs\/anaconda3\/envs\/internship\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py\", line 1207, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 56, in <lambda>\r\n    dataset = dataset.map(lambda batch: self._encode(batch), batched=True)\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in _encode\r\n    [x[\"src_texts\"] for x in batch],\r\n  File \"\/remote\/idiap.svm\/user.active\/rkarimi\/dev\/internship\/seq2seq\/tasks.py\", line 37, in <listcomp>\r\n    [x[\"src_texts\"] for x in batch],\r\nTypeError: string indices must be integers\r\n\r\n \n Yes the forum is perfect for that. You can post in the `datasets` section.\r\nThanks a lot!","embeddings":[-0.5023794174,-0.1743950695,-0.229502216,0.1301572621,0.1780355573,0.0317946561,0.2955617607,0.1713047624,-0.1484707892,0.1780768037,0.0091214189,0.1247809753,0.0641202703,0.2456238419,-0.0561650358,-0.0358917713,-0.0745975822,0.0483772866,0.1096090525,0.0072343689,-0.3676092625,0.0689976066,-0.404307127,0.0522343665,-0.2230468243,-0.2693564892,-0.061245136,-0.0392570309,-0.0044130813,-0.1525889933,-0.0022256428,0.2334949821,0.2088395506,0.4326501787,-0.0001049565,0.0785311237,0.246775642,0.0393823534,-0.2903354466,0.2226282358,-0.0494002365,-0.1695528775,-0.106298469,-0.3042087555,-0.100836426,-0.0860008001,-0.1385592818,-0.3374301493,0.3848311007,0.1753834486,0.2844721079,0.2802553177,-0.1476055533,0.141417712,0.0610040873,0.071453087,0.085112974,-0.3902092874,0.0682323202,0.147927478,-0.0484061763,0.149620384,-0.2720299363,0.0859410837,-0.2550970316,0.0218879282,0.0260984451,-0.3847026229,0.0884854123,-0.1349758655,0.2846595347,-0.2123625129,-0.244074598,-0.3842577636,-0.2724657655,-0.4653172493,-0.0269787684,0.1131320298,-0.0684819147,0.1012712568,-0.0785877854,0.2408464402,-0.0737230629,-0.1666946113,0.1114271581,0.256811142,-0.2119238675,0.2198818326,0.0700407103,0.0887886882,0.1648149788,-0.2350720912,-0.0004340044,0.2723601758,-0.490796864,-0.0871194154,-0.111774452,-0.1519913375,-0.1408956349,0.0541603342,-0.1284128726,0.1272303164,0.3161275685,0.1564184427,-0.0295962766,0.1643566489,0.1187213957,0.2562070191,-0.077118583,-0.3050593734,-0.2260431051,0.0277573764,-0.0785889626,-0.3298466206,0.1471393257,0.3610413373,0.2499244362,0.0302780811,-0.4871647358,-0.090392217,-0.0190420989,-0.0580975227,0.1420931816,0.2845916152,-0.2407628298,0.2508462965,-0.0810260475,-0.1523166597,-0.2926350832,-0.0461959541,-0.3027382791,-0.1152567863,-0.1496196091,0.0702788755,0.1349772662,-0.1721600443,0.3239180446,0.0755829066,0.0962865055,-0.1746606827,0.5014520288,-0.3949423134,0.2224124223,0.2599134743,0.1795815676,0.2426499426,0.0532012805,0.2097658068,0.0399616584,0.07378117,-0.6257678866,-0.1791180819,0.2474631667,0.3138492405,-0.050551679,0.0014612001,-0.5935842991,-0.0869221464,0.3213269711,0.0906271413,0.1197098047,-0.1754258871,-0.1859953701,-0.1946797967,0.2003967762,0.0852582231,-0.4504095614,0.067932725,-0.1153643429,-0.3227443099,0.5454286933,0.1242681667,-0.1138667688,0.6199551225,-0.3095553815,0.378302753,0.1937190294,-0.318939209,-0.2815083861,0.5390690565,-0.2575122714,0.0180868749,0.0398210399,0.0325068124,0.558662951,-0.0602873936,0.0362305008,0.3787432015,-0.1192286387,0.0902983919,-0.0995698944,0.1156369671,0.0859533623,-0.017408317,0.103893064,0.0438636467,-0.2167430967,-0.0445535667,0.1737817377,0.0534989499,0.2965995669,0.2496741712,0.0114939697,0.2087981552,0.1932462901,-0.045412153,-0.2964651883,0.0957323313,-0.0203324519,-0.1832336485,0.0405643061,-0.3565836847,-0.1427385509,0.1464838088,-0.4080851972,-0.170314461,0.2187933773,0.0912246779,0.0223609656,-0.3133271337,0.0051412471,0.3289259076,-0.018437,0.0320123062,-0.1938534528,-0.0220454745,0.0078472476,-0.1507639438,-0.0613341182,-0.0791995898,0.2228215635,0.0074299281,-0.0493339114,0.3533655703,0.2747455239,0.0639955476,-0.0365163907,-0.1238466576,-0.010248973,0.1141474769,-0.0905264094,-0.0358541906,0.2171731889,-0.1256070137,0.2478310466,0.4252819717,-0.0469338372,0.2657586038,-0.0382558815,-0.2736453712,0.2264523953,-0.0702164769,-0.0428678021,0.1976911724,-0.0458724052,0.0015260566,0.3408337235,0.0440044962,-0.0559479967,0.1770871282,0.427303493,-0.0043448089,-0.1692032218,-0.0192307252,-0.2083047777,0.1080089733,0.1594958156,-0.0258413963,0.1219083592,0.1693717986,0.1196489036,-0.1023140699,0.104523696,-0.0379634686,0.0820302144,0.3170749545,0.180519715,0.1618741006,-0.0916261226,-0.0561815985,-0.0036794732,-0.1100691184,0.1913622171,0.2398844659,-0.3654334843,0.2132201493,-0.145929724,-0.08981058,-0.1511178762,-0.0314461552,0.1613285244,-0.1398269385,-0.0764070004,0.2173885703,-0.1582910717,0.0283092428,-0.0862446353,0.0393373929,0.2715219557,-0.3807459176,-0.1608629972,-0.0596176609,-0.1818827242,0.2018623352,0.0678684339,0.1080862805,0.4683231413,0.0514345989,0.0387846157,-0.1155499518,-0.1522178799,0.0778788254,-0.1976525038,0.1767100245,0.0169449784,0.1401751041,-0.1079600304,-0.3839942813,0.1391080171,-0.1839413941,-0.3313497603,-0.0855730399,-0.0173469968,0.040500477,-0.2087071538,-0.4318087101,-0.2330217361,-0.1791113019,0.0216235537,-0.0695498586,0.2032481134,-0.0093688983,-0.0178895891,0.1497216076,0.2623847723,-0.1572469622,-0.0877216086,-0.4881262481,0.1246121675,-0.2836306691,-0.0197322294,-0.0922569484,-0.1024708152,0.4223552346,0.3086383641,-0.1903934777,0.1180599406,-0.0889263675,0.2408317477,-0.0977540761,-0.0265352037,0.1954074502,-0.0789332092,-0.099664703,-0.1173348725,0.0736229196,0.1176979691,-0.2414382696,0.0647149384,-0.017784683,0.6492415667,-0.0192825571,0.9116159081,0.0870948359,-0.0874783471,0.132925272,0.0044225426,-0.1579394042,-0.0129763782,-0.3009167314,0.0907453895,0.0279778596,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/822","title":"datasets freezes ","comments":"Pytorch is unable to convert strings to tensors unfortunately.\r\nYou can use `set_format(type=\"torch\")` on columns that can be converted to tensors, such as token ids.\r\n\r\nThis makes me think that we should probably raise an error or at least a warning when one tries to create pytorch tensors out of text columns","body":"Hi, I want to load these two datasets and convert them to Dataset format in torch and the code freezes for me, could you have a look please? thanks \r\n\r\ndataset1 = load_dataset(\"squad\", split=\"train[:10]\")\r\ndataset1 = dataset1.set_format(type='torch', columns=['context', 'answers', 'question'])\r\n\r\ndataset2 = load_dataset(\"imdb\", split=\"train[:10]\")\r\ndataset2 = dataset2.set_format(type=\"torch\", columns=[\"text\", \"label\"])\r\nprint(len(dataset1))\r\n","comment_length":52,"text":"datasets freezes  \n Hi, I want to load these two datasets and convert them to Dataset format in torch and the code freezes for me, could you have a look please? thanks \r\n\r\ndataset1 = load_dataset(\"squad\", split=\"train[:10]\")\r\ndataset1 = dataset1.set_format(type='torch', columns=['context', 'answers', 'question'])\r\n\r\ndataset2 = load_dataset(\"imdb\", split=\"train[:10]\")\r\ndataset2 = dataset2.set_format(type=\"torch\", columns=[\"text\", \"label\"])\r\nprint(len(dataset1))\r\n \n Pytorch is unable to convert strings to tensors unfortunately.\r\nYou can use `set_format(type=\"torch\")` on columns that can be converted to tensors, such as token ids.\r\n\r\nThis makes me think that we should probably raise an error or at least a warning when one tries to create pytorch tensors out of text columns","embeddings":[-0.1619098037,-0.3347924054,-0.0465797186,0.5367450714,0.3581036329,0.2125209719,0.507258594,0.3903239369,-0.0151281459,0.1207219064,-0.2165184468,0.2929005921,-0.1185355037,-0.163084209,-0.1131302863,-0.5349528193,0.1154309735,-0.0951829627,-0.3404101431,0.099049516,0.0500869565,0.0638206452,-0.1515897661,-0.1478492171,-0.4358990192,0.0075123678,0.0953070819,0.0503921248,-0.1359757781,-0.0677020252,0.1145445183,-0.0718163922,0.0865185112,0.7834653854,-0.0001211927,0.0531437434,0.304533422,0.1574761122,-0.1029468924,-0.1995556355,0.3838464916,-0.1363200545,0.1754233837,-0.2181679606,-0.13459225,0.010250886,-0.2882260978,-0.695276022,0.3244414032,0.4434750676,0.0937087387,0.5506082177,0.0422830433,0.0736018717,0.3115720153,-0.0100868698,-0.209312886,-0.1175049171,0.3612826467,0.1951310337,0.1336320788,0.2532553673,-0.4077238142,-0.0682554767,0.0949572027,0.073289074,-0.6647979617,-0.4644117653,0.0864163265,0.3095062077,0.5834822059,-0.459107399,-0.1308524907,-0.1118449643,0.0003552453,0.0143291298,0.0995948836,0.1460790634,-0.1873875856,-0.0970856175,-0.3416729867,-0.1862387955,-0.244726643,0.2388877869,-0.1802271158,-0.1747247279,0.0037158506,0.1924472749,0.1358708888,-0.0270289164,0.4467356503,-0.3042343855,0.1853563637,-0.0435124636,-0.3819194436,-0.0245090611,-0.5991339684,-0.3232549131,-0.0219162125,-0.0832411498,-0.0257245861,0.1993785948,0.0594956875,0.1835234612,-0.0041054711,0.2055943012,0.0165229831,0.3655209541,0.1646527648,-0.4713938236,-0.0830719993,0.1285180002,-0.0653167292,-0.5972931981,-0.0566868968,-0.1479667127,0.1452046931,-0.2821500599,-0.2286584377,0.0399436429,-0.2544407845,0.0160606895,0.1840375215,0.3908998072,-0.2808794975,0.2990472019,0.3615897,0.1452337205,-0.2739693224,-0.0120231789,-0.0326119326,0.1297482997,-0.1249793321,-0.0447367541,0.2628989518,-0.4223605096,-0.0743070692,0.3290851712,0.1034980193,0.1094256118,0.2540254295,-0.243856743,0.2168108374,0.0404201262,0.0672834069,0.2015821189,0.0806757435,0.3792657554,-0.0123764686,0.3443981409,-0.2613652945,0.1523290128,-0.2981989384,0.0603747405,0.0591149107,0.0105317133,0.0715914145,0.1019400582,0.3803873658,-0.136848405,0.213163957,-0.2690629065,-0.122754328,-0.0398496948,0.1607143283,0.3656430542,-0.2027439773,0.080682762,0.5047172904,0.0049646464,-0.0276198518,0.3383366168,-0.166179657,-0.0340987258,0.0272393059,-0.1163115501,0.3451951146,-0.2792834044,0.0360599644,0.082947053,-0.0611122884,-0.0769845322,0.1463359892,0.1638709307,0.1401912272,-0.0225377791,0.2774900198,0.1859399676,-0.1286250055,0.0115317963,-0.1566434503,-0.0132685956,0.5077599287,0.0647813752,0.0822657272,-0.0090032117,-0.254273206,0.2098658383,-0.062928237,-0.1881982088,0.0560245216,0.4037212729,-0.3440684676,0.0713159442,0.0562156551,-0.4236242771,-0.4435555041,0.0634101629,0.2090815157,0.154961288,0.1222557649,-0.010005923,-0.0060081375,-0.1404091865,0.0333748497,0.2474482059,-0.0741904899,-0.3382622898,0.0578820817,0.0056778826,-0.0865047723,0.3033380806,-0.1110071093,0.254724741,-0.2904978395,0.3031731248,-0.056193836,-0.3207971752,-0.0883349702,0.2030960917,0.0999128968,-0.0701328889,-0.0903616995,0.2229854614,-0.0041757375,0.0624920875,-0.4371133149,-0.1511400491,0.2752560377,-0.1057287306,0.3364081085,0.0070383325,-0.0124842981,-0.1347418278,-0.1144411191,0.1100080237,-0.1437335908,0.2196324319,-0.0382271335,0.0104148416,-0.2166306674,0.1087430939,-0.1233973876,0.037359409,0.3091332912,-0.1157442108,0.2992348969,0.0862626061,-0.6952055097,-0.0541114882,0.3723339438,-0.0515537821,0.0357348733,0.0595976487,-0.2202146947,0.1280035228,0.1781383604,-0.3011554778,0.237342909,0.1993241459,0.0374270044,-0.0060503324,-0.3070225716,-0.1291005611,0.2157094181,0.1472150981,0.0153890336,0.0693671256,0.0225959811,-0.1770961136,-0.0239738394,0.1697230041,0.107528761,0.0790479556,-0.3709747493,0.1485443711,-0.0851233751,0.0055117817,-0.2897947133,-0.0226648543,-0.2290740311,-0.2103304267,-0.0848406702,0.3650323451,0.2018142045,0.0982009545,0.1191345453,-0.2002101839,0.2110097259,-0.164093554,-0.1957546026,-0.2156160027,-0.1579663157,-0.0199520588,0.4141778648,-0.0392508619,0.2661200166,-0.0810979456,0.1195303053,-0.3094357848,-0.1037665382,0.1392344981,-0.2589545846,-0.1265085787,-0.1212610528,0.2966690063,-0.017536452,-0.0650339201,0.1117540449,0.0458006449,-0.0447583608,0.2886176407,0.0960974023,-0.0350133441,-0.0744962767,-0.5995054841,-0.2112913281,-0.1445088536,-0.0621451512,-0.3863690197,-0.0292695723,-0.120325923,0.460427165,0.2548903227,0.0141043458,0.1126452461,-0.029850319,-0.2954290211,0.5208236575,-0.2749049962,-0.4628773332,-0.0779241547,0.2729069293,-0.0477543809,0.3154073954,-0.1507891417,0.0254723467,-0.4094622731,0.045414146,-0.1422857046,-0.0549072437,0.4183910787,0.1074017733,0.0682937577,-0.084711656,0.0460318625,-0.0318503417,-0.1916361302,0.0971079543,-0.0728925318,0.3707443476,0.0162022281,0.4566970766,-0.1325985938,-0.477281332,0.1253287792,-0.1024665684,0.3399639726,-0.0632666945,-0.4580647051,0.0325973742,-0.1638097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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/816","title":"[Caching] Dill globalvars() output order is not deterministic and can cause cache issues.","comments":"To show the issue:\r\n```\r\npython -c \"from datasets.fingerprint import Hasher; a=[]; func = lambda : len(a); print(Hasher.hash(func))\"\r\n```\r\ndoesn't always return the same ouput since `globs` is a dictionary with \"a\" and \"len\" as keys but sometimes not in the same order","body":"Dill uses `dill.detect.globalvars` to get the globals used by a function in a recursive dump. `globalvars` returns a dictionary of all the globals that a dumped function needs. However the order of the keys in this dict is not deterministic and can cause caching issues.\r\n\r\nTo fix that one could register an implementation of dill's `save_function` in the `datasets` pickler that sorts the globals keys before dumping a function.","comment_length":43,"text":"[Caching] Dill globalvars() output order is not deterministic and can cause cache issues. \n Dill uses `dill.detect.globalvars` to get the globals used by a function in a recursive dump. `globalvars` returns a dictionary of all the globals that a dumped function needs. However the order of the keys in this dict is not deterministic and can cause caching issues.\r\n\r\nTo fix that one could register an implementation of dill's `save_function` in the `datasets` pickler that sorts the globals keys before dumping a function. \n To show the issue:\r\n```\r\npython -c \"from datasets.fingerprint import Hasher; a=[]; func = lambda : len(a); print(Hasher.hash(func))\"\r\n```\r\ndoesn't always return the same ouput since `globs` is a dictionary with \"a\" and \"len\" as keys but sometimes not in the same 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/815","title":"Is dataset iterative or not?","comments":"Hello !\r\nCould you give more details ?\r\n\r\nIf you mean iter through one dataset then yes, `Dataset` object does implement the `__iter__` method so you can use \r\n```python\r\nfor example in dataset:\r\n    # do something\r\n```\r\n\r\nIf you want to iter through several datasets you can first concatenate them\r\n```python\r\nfrom datasets import concatenate_datasets\r\n\r\nnew_dataset = concatenate_datasets([dataset1, dataset2])\r\n```\r\nLet me know if this helps !","body":"Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks","comment_length":67,"text":"Is dataset iterative or not? \n Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks \n Hello !\r\nCould you give more details ?\r\n\r\nIf you mean iter through one dataset then yes, `Dataset` object does implement the `__iter__` method so you can use \r\n```python\r\nfor example in dataset:\r\n    # do something\r\n```\r\n\r\nIf you want to iter through several datasets you can first concatenate them\r\n```python\r\nfrom datasets import concatenate_datasets\r\n\r\nnew_dataset = concatenate_datasets([dataset1, dataset2])\r\n```\r\nLet me know if this helps !","embeddings":[-0.2620126605,-0.2916283309,-0.1804153472,0.1139712036,0.0606512427,-0.0265371073,0.2819753587,0.1687740684,0.1109021083,0.0845518932,0.1554597318,0.2481859922,-0.3554365635,0.348952949,0.1711002141,-0.0656197593,0.0205348637,0.136970818,-0.6233143806,-0.0821494088,0.0825396925,-0.1274391562,-0.2666273415,-0.1741281897,0.0523192361,-0.3682792485,-0.3957517743,-0.142244488,-0.2242219597,-0.3630472422,0.2143723816,0.2408522069,0.4066430032,0.554397583,-0.000103148,-0.1346856058,0.1404353678,-0.0824473649,-0.1723602265,-0.2457790971,-0.1541205347,-0.2673062086,0.0849707127,-0.4197580814,-0.0327179879,-0.1310338676,0.0873626098,-0.3289755881,0.2696433663,0.1327248216,0.2795183361,-0.2198545337,0.1302630305,-0.0211496111,-0.031763576,0.0726195425,-0.0583847053,0.220533058,0.1717952639,0.1565460712,0.121111922,0.028726371,0.1655883044,-0.003596331,0.3876419663,-0.0965651274,-0.0442575067,-0.4276583195,-0.1730998605,0.0660026446,0.8125045896,-0.4412543774,-0.3938426971,0.0490858927,0.0430765748,-0.5168247819,-0.152706787,0.1491913646,0.0641432405,0.0586709566,-0.1348238289,-0.2621481419,-0.2698768973,0.1240683198,-0.1202691644,0.1766415834,-0.1396183521,0.0977383852,0.1554127485,-0.1787713915,0.3191452026,-0.0058359746,0.1843648553,0.2849902213,-0.56351614,-0.3246938884,0.0088891173,0.3077014387,0.3426326215,0.3845371902,0.0610330328,-0.0531119891,-0.1638740897,0.2153864801,0.5052185655,-0.0575832464,-0.0967656747,0.0773621872,0.1371305436,-0.074623026,-0.1787652373,-0.0850120857,-0.2036229223,-0.2351184189,0.1354383528,-0.1673399955,-0.0108033558,-0.0898151025,-0.0006082836,-0.2141327262,-0.2479062378,-0.2665740252,0.1642467827,0.235846296,-0.2475819737,0.1832304746,-0.271872133,-0.1247223839,0.0381813161,0.2522245646,-0.1004244685,0.4507007003,-0.3770989776,0.0321811177,-0.0641270578,-0.3635902405,0.0666291267,0.0946309268,0.1086149588,-0.0891537145,0.1355137974,-0.2728854418,0.3756600916,-0.003879535,-0.0490063578,-0.0022548011,-0.1385650188,0.0565218776,-0.4262951016,0.0191203542,-0.3938416243,-0.0465501696,-0.0372907184,0.2154168636,-0.1831136048,-0.2176530361,0.079800874,0.1534947306,0.0734745041,-0.112355262,0.0158816241,-0.1077264473,-0.0338811763,-0.2993712425,0.0883344263,0.1641410291,-0.35705176,0.0776655599,0.0804291293,0.0491958223,0.1362267435,0.2218888253,-0.2568956912,0.2136597186,-0.212691471,-0.0707973689,0.5765584707,-0.2613782585,-0.1435530037,0.2921441495,0.0746691748,-0.0027985352,0.1206455603,0.1994356066,-0.0524061844,-0.0169341825,-0.1686793566,0.2380015701,-0.011463956,-0.1006743237,-0.0052550002,0.1009077653,0.1932469457,0.0401523896,0.0893867463,0.2351981401,0.0582371019,0.1127639264,0.455421716,-0.2984415591,0.108294256,0.2104291022,0.3090749681,0.114899829,0.1462791264,-0.3679890633,0.1484366357,0.0986365899,0.2785969377,0.0520479679,0.2340695709,-0.069348231,-0.0442877933,0.0764071643,-0.2159165442,-0.0578871146,0.1884937882,0.1673408449,0.1742748469,-0.1189610735,-0.5899415016,0.218558684,-0.2163225114,-0.118447803,-0.5084574819,0.2147316337,0.1542232484,-0.0108168935,0.0965031907,0.116957441,-0.1846950948,-0.434378773,0.2691549659,0.2770812511,0.0441000424,-0.0713122487,0.0792737752,0.3585073054,0.2771754563,-0.1215388402,0.0665503368,-0.1913222969,0.3170205057,-0.0304633435,0.244586587,0.4754381776,-0.1599055827,0.1892339438,0.4515470266,-0.1398740113,0.0697892085,-0.0783411115,-0.3781239092,0.1251234412,-0.1108881757,-0.164718017,0.432398051,-0.1913584173,-0.4387002289,-0.2600629032,0.4845893979,-0.0010402949,-0.0046966672,0.0654750764,-0.073386617,0.0072842892,-0.0527888536,0.1374273598,0.3874976337,0.0551788807,0.2038333267,0.0640795827,0.0119077144,0.1259277463,0.1537786573,0.0687423125,0.3130619526,-0.0194043946,0.0347604007,0.0564903691,-0.163587302,-0.3934365511,-0.0763192996,0.1618542373,-0.021135876,0.3687207401,-0.3297156096,-0.3494110107,-0.3988712132,-0.2384474277,0.1140254661,0.0361569747,-0.2951516509,-0.0410355069,0.3395088315,0.1355220973,0.14926745,0.1712168604,0.0856718794,0.0247138273,0.0559491813,-0.0249677356,-0.0153802279,0.1965170205,0.2742664218,-0.1589580178,0.5873372555,0.0007301636,-0.0804165229,-0.3680339158,-0.1861910671,0.1703963727,-0.0128429551,0.0351405255,0.2513346076,-0.0045819697,-0.048999127,-0.0918228626,0.0235354751,-0.3367699981,0.1729476899,-0.2204838693,-0.1017430574,0.1009223163,0.0909778923,-0.2924436629,-0.5083232522,-0.2475090325,0.2429561317,0.4355327189,0.2685111463,0.1604335457,0.0689342543,0.2123544365,0.1988351494,0.0773472935,0.0226623565,-0.299783498,0.18126297,-0.3456683755,-0.2615365088,-0.0005994889,-0.0124733187,0.4389668107,0.1449292451,-0.2703377306,-0.1131626964,0.0078622205,-0.016493693,0.069344826,0.5041059852,0.1270898581,-0.2268275321,-0.083556585,-0.0664393157,0.0531284325,-0.066472061,-0.143053785,0.2096247226,0.4548493922,0.3592880964,-0.0460021123,0.3433163762,0.2341688424,-0.4142976105,0.3587226868,-0.3138966858,-0.1621472687,-0.0567742474,-0.2992808819,0.0955502167,-0.328994751,-0.2866768241,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/815","title":"Is dataset iterative or not?","comments":"Hi Huggingface\/Datasets team,\nI want to use the datasets inside Seq2SeqDataset here\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py\nand there I need to return back each line from the datasets and I am not\nsure how to access each line and implement this?\nIt seems it also has get_item attribute? so I was not sure if this is\niterative dataset? or if this is non-iterable datasets?\nthanks.\n\n\n\nOn Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hello !\n> Could you give more details ?\n>\n> If you mean iter through one dataset then yes, Dataset object does\n> implement the __iter__ method so you can use\n>\n> for example in dataset:\n>     # do something\n>\n> If you want to iter through several datasets you can first concatenate them\n>\n> from datasets import concatenate_datasets\n> new_dataset = concatenate_datasets([dataset1, dataset2])\n>\n> Let me know if this helps !\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/815#issuecomment-723881199>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA>\n> .\n>\n","body":"Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks","comment_length":185,"text":"Is dataset iterative or not? \n Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks \n Hi Huggingface\/Datasets team,\nI want to use the datasets inside Seq2SeqDataset here\nhttps:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py\nand there I need to return back each line from the datasets and I am not\nsure how to access each line and implement this?\nIt seems it also has get_item attribute? so I was not sure if this is\niterative dataset? or if this is non-iterable datasets?\nthanks.\n\n\n\nOn Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hello !\n> Could you give more details ?\n>\n> If you mean iter through one dataset then yes, Dataset object does\n> implement the __iter__ method so you can use\n>\n> for example in dataset:\n>     # do something\n>\n> If you want to iter through several datasets you can first concatenate them\n>\n> from datasets import concatenate_datasets\n> new_dataset = concatenate_datasets([dataset1, dataset2])\n>\n> Let me know if this helps !\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/815#issuecomment-723881199>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA>\n> .\n>\n","embeddings":[-0.0225438979,-0.3792024851,-0.071719721,0.3793151379,0.0794595927,-0.1549821049,0.2763752937,0.0828172937,0.0711623803,-0.1428877562,-0.064362593,0.0111193135,-0.2454968393,0.3429043889,0.1579365581,-0.0702992827,-0.1000306979,0.0074630883,-0.7639597058,-0.0446302965,0.0603365041,0.2321568131,-0.2711442709,-0.0109251523,-0.0369475,-0.2411496639,-0.326510787,-0.062773332,-0.2232872099,-0.3062738776,0.3705847859,0.2676627934,0.3039007485,0.5059751868,-0.0001149827,-0.0865792185,-0.0136720212,-0.1119051799,-0.260540694,-0.2602420747,-0.2470697761,-0.1210107729,0.1278484464,-0.3559494913,-0.04556977,0.037800625,0.1667557806,-0.3080901802,0.6174594164,0.1739711314,0.163666904,0.1257338077,0.0991589129,-0.0202110875,-0.0610699952,0.2306855172,0.0680697858,0.1013273224,0.2460479736,0.161670208,0.0873173177,0.1419124752,0.1690963805,-0.1322489083,0.2284310758,0.0251351129,-0.1097571179,-0.3999998271,-0.0769667327,0.2307674885,0.7655302286,-0.4036310911,-0.493411839,-0.2389011681,-0.0845850855,-0.3491231799,-0.0607267022,0.087834686,0.061579302,0.2074181736,-0.2750288248,-0.1799227744,-0.2871975899,-0.0714753866,-0.0842036381,-0.1233078465,-0.3571073711,0.0375433192,0.1368417293,-0.2389593422,-0.1284435242,0.1822870374,0.111900121,0.3583672643,-0.6574568152,-0.1644616723,0.0324652568,0.2509856522,0.5292328596,0.5199728608,0.0708161891,-0.0728314742,-0.059563607,0.1355994046,0.3547314107,0.2167765945,0.1332976073,0.014836791,-0.0468348674,-0.0156760421,0.0080921166,-0.1874767244,-0.1975459754,-0.263307482,-0.0288977232,-0.1812646687,0.1243735775,-0.1571443975,-0.0886652917,-0.1423647404,-0.2186679989,-0.2480700761,0.2155747712,0.1797961891,0.0147842159,-0.0092717009,-0.1977039576,0.1596263349,0.0233290289,0.1618883312,-0.08999376,0.293906033,-0.3323544264,0.3278651536,-0.106336765,-0.516843617,0.1241302714,-0.0498067364,-0.0145229921,-0.1895644367,0.0131153511,-0.1561181247,0.2389704734,0.0416877829,-0.135061428,0.1681132317,-0.1038133204,-0.006343523,-0.4026481211,-0.3620334864,-0.2837927043,-0.2724316716,-0.0047699902,0.0828314647,-0.2892344296,-0.0493358523,0.0017668679,0.2995201349,0.042851679,-0.0531391948,0.1576129645,-0.0021380899,-0.2632719576,-0.0274778921,0.1387465,0.5703583956,-0.5171878338,-0.1693527848,0.1901704669,-0.0613867976,0.0647265315,0.4910531044,-0.1845665574,0.2792844176,-0.2950097322,0.1451631933,0.4489835203,-0.1201361865,-0.3379767239,0.3601585329,-0.0514758937,0.1209129244,0.2169084847,-0.0967275575,0.1411954463,0.0771336481,-0.1310061663,0.0307137091,0.101206392,-0.2649412751,-0.0493882522,-0.076473698,0.1218608767,-0.0727327168,-0.0181674603,0.3623619676,0.080653362,0.1890706122,0.4062772989,-0.2153889239,0.2619225383,0.2413795739,0.1841720939,0.1566097438,0.1263941675,-0.3120626807,-0.2084096819,0.1497214884,0.1003217399,-0.0974685103,-0.0270919185,-0.0313607678,-0.2901252508,0.0899075717,-0.2494388521,-0.2793979645,0.1004119292,0.2482919246,0.3779501319,-0.0617013089,-0.4771336019,0.5368739963,0.0398987681,0.1137709469,-0.820933938,0.2269504964,0.2916396856,-0.0419201218,0.0092738755,0.2299204469,-0.0443532504,-0.3739892244,0.4070418477,0.2918508053,-0.2554703653,0.1345612854,0.1751822829,0.413870424,0.2163657695,-0.0493225977,-0.0306187849,-0.3302228451,0.2782608271,-0.0702292398,0.1413688362,0.4477908909,-0.2019180506,0.4001391828,0.2018059194,-0.0399867333,-0.0388885327,-0.0268118605,-0.6704294682,0.1082902253,0.0874225572,-0.1647338122,0.4941927493,-0.13775222,-0.276053071,-0.1087179184,0.4138204455,-0.0944038481,-0.0200498216,0.2225458175,-0.1451559663,-0.0746287853,0.0574685447,-0.0752753913,0.2247612029,-0.0499064773,0.2173964232,0.0197042469,0.152012229,0.0490660034,0.0343685336,0.1375614554,0.2676272094,-0.063083604,0.0223439671,0.0916071907,-0.1821548045,-0.3705839217,-0.1461513489,0.035750553,-0.2060860544,0.4430924356,-0.2589530051,-0.2633575797,-0.4498876631,-0.0666277185,0.0132170264,0.0169128012,-0.3413527906,-0.0877528265,0.3287211359,0.0274921115,0.1229677051,0.3827255666,0.2354795486,0.0622025579,-0.2230383456,-0.0625786334,0.140451476,0.0504576527,0.1174330488,-0.1744202971,0.4147769511,-0.0110876467,0.0203940421,-0.3350255191,-0.2326262146,0.3629404604,-0.0303245783,0.2029055059,0.2526981533,0.0050781057,-0.0727120712,-0.2006012499,-0.0021753481,-0.1680450141,-0.024172185,0.0551481694,-0.0846828222,0.222228542,0.1161014065,-0.2689585388,-0.3376810253,-0.1086261794,0.7068123221,0.0823258534,0.1625310481,0.2160707414,-0.1467021406,0.3742017448,-0.1792030782,0.2425270379,-0.0822483376,-0.4794171154,0.2723617554,-0.3086797595,-0.2070455551,-0.0615933686,-0.111495778,0.5452197194,0.0474165976,-0.2879161239,-0.2418958098,-0.0889611468,-0.0576903969,0.1043202877,0.2547335625,0.3147212863,-0.218001619,0.0530307032,0.0392929614,-0.0398627333,0.1431075484,-0.029665783,0.1179028824,0.3721633554,0.6116313934,0.2033930123,0.5283328295,0.5597794056,0.0441531688,0.1339010894,-0.203017965,-0.1482434124,-0.0539473034,-0.4497843087,0.1008930057,-0.3520355225,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/815","title":"Is dataset iterative or not?","comments":"could you tell me please if datasets also has __getitem__ any idea on how\nto integrate it with Seq2SeqDataset is appreciated thanks\n\nOn Mon, Nov 9, 2020 at 10:22 AM Rabeeh Karimi Mahabadi <rabeeh@google.com>\nwrote:\n\n> Hi Huggingface\/Datasets team,\n> I want to use the datasets inside Seq2SeqDataset here\n> https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py\n> and there I need to return back each line from the datasets and I am not\n> sure how to access each line and implement this?\n> It seems it also has get_item attribute? so I was not sure if this is\n> iterative dataset? or if this is non-iterable datasets?\n> thanks.\n>\n>\n>\n> On Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest <notifications@github.com>\n> wrote:\n>\n>> Hello !\n>> Could you give more details ?\n>>\n>> If you mean iter through one dataset then yes, Dataset object does\n>> implement the __iter__ method so you can use\n>>\n>> for example in dataset:\n>>     # do something\n>>\n>> If you want to iter through several datasets you can first concatenate\n>> them\n>>\n>> from datasets import concatenate_datasets\n>> new_dataset = concatenate_datasets([dataset1, dataset2])\n>>\n>> Let me know if this helps !\n>>\n>> \u2014\n>> You are receiving this because you authored the thread.\n>> Reply to this email directly, view it on GitHub\n>> <https:\/\/github.com\/huggingface\/datasets\/issues\/815#issuecomment-723881199>,\n>> or unsubscribe\n>> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA>\n>> .\n>>\n>\n","body":"Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks","comment_length":236,"text":"Is dataset iterative or not? \n Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks \n could you tell me please if datasets also has __getitem__ any idea on how\nto integrate it with Seq2SeqDataset is appreciated thanks\n\nOn Mon, Nov 9, 2020 at 10:22 AM Rabeeh Karimi Mahabadi <rabeeh@google.com>\nwrote:\n\n> Hi Huggingface\/Datasets team,\n> I want to use the datasets inside Seq2SeqDataset here\n> https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py\n> and there I need to return back each line from the datasets and I am not\n> sure how to access each line and implement this?\n> It seems it also has get_item attribute? so I was not sure if this is\n> iterative dataset? or if this is non-iterable datasets?\n> thanks.\n>\n>\n>\n> On Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest <notifications@github.com>\n> wrote:\n>\n>> Hello !\n>> Could you give more details ?\n>>\n>> If you mean iter through one dataset then yes, Dataset object does\n>> implement the __iter__ method so you can use\n>>\n>> for example in dataset:\n>>     # do something\n>>\n>> If you want to iter through several datasets you can first concatenate\n>> them\n>>\n>> from datasets import concatenate_datasets\n>> new_dataset = concatenate_datasets([dataset1, dataset2])\n>>\n>> Let me know if this helps !\n>>\n>> \u2014\n>> You are receiving this because you authored the thread.\n>> Reply to this email directly, view it on GitHub\n>> <https:\/\/github.com\/huggingface\/datasets\/issues\/815#issuecomment-723881199>,\n>> or unsubscribe\n>> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA>\n>> .\n>>\n>\n","embeddings":[-0.1425883025,-0.3471777141,-0.1183143407,0.351855129,0.0803463459,-0.0877772421,0.223645106,0.2374293953,0.0332362615,-0.1268832535,-0.0385066755,0.0500974245,-0.2673036456,0.3630913198,0.0759754926,-0.0330848284,-0.0817347839,-0.0110200336,-0.7120335102,-0.0541849993,0.0637001842,0.2144561112,-0.278190881,-0.0741706416,-0.0047890185,-0.2388824522,-0.3776046038,-0.0251137242,-0.2068046629,-0.3644319177,0.2285363674,0.259321481,0.2990615964,0.5007333159,-0.0001083229,-0.1844812483,0.0241037235,-0.0698913485,-0.3096701801,-0.2250895202,-0.2824634612,-0.2135332972,0.0689910948,-0.3665153384,-0.0540316068,0.0273773614,0.1581238508,-0.3098548055,0.5706965923,0.1549828649,0.2261646688,0.135545373,0.0920443982,-0.0505273603,-0.0746740252,0.1321913302,0.0607593432,0.107791394,0.2328056991,0.1501888037,0.0214257874,0.1485862136,0.0968904793,-0.119904913,0.2620963752,-0.0519879013,-0.1156976596,-0.3440950513,-0.0209760815,0.1512996405,0.7606995106,-0.3858964145,-0.5661017895,-0.3009552956,-0.0400618352,-0.4118648171,-0.0546102487,0.1472489387,0.0820052549,0.1540421695,-0.2121725231,-0.1637293696,-0.2180507481,-0.138769567,-0.0414787196,-0.0969732553,-0.3794801235,-0.0211918242,0.0490676872,-0.2237613946,-0.1534072012,0.1326575428,0.1087393835,0.3603591025,-0.6488003731,-0.2567496002,0.0532763414,0.3321332037,0.5177853107,0.5682513118,0.0786654949,-0.0219275337,-0.0438606963,0.1380574256,0.36561656,0.1424448043,0.1061025709,-0.0619186237,-0.0528920069,0.0391137227,0.0796753243,-0.1260007024,-0.1406913102,-0.2216004282,-0.0771193728,-0.1565037966,0.0832067356,-0.1770687997,-0.102325581,-0.1652231067,-0.1618963778,-0.216027841,0.114815414,0.1496847272,-0.1081649587,-0.0370219424,-0.2981789112,0.0841588825,-0.0231003538,0.1277363598,-0.1204520166,0.3016714156,-0.2454103082,0.2856014073,-0.1388820857,-0.5510953665,0.1653596759,0.0034501124,-0.0500821993,-0.0972503349,-0.0386770107,-0.2035280168,0.133711949,0.0107535673,-0.0971942544,0.175756678,-0.1511567682,-0.0482361279,-0.4053089619,-0.3546987474,-0.2803290188,-0.1745544076,0.0428336449,0.1455173641,-0.2266248018,-0.0543788932,0.0425452627,0.343660742,0.0179279372,-0.1024764851,0.1268294603,0.0494637229,-0.3142506778,-0.0543956235,0.1424339116,0.5140600204,-0.447607398,-0.2488467246,0.193901211,-0.0189503431,0.0248288605,0.446115315,-0.1179488525,0.2248840034,-0.2795751691,0.1410651207,0.367336154,-0.1072311252,-0.2978147864,0.3310321867,-0.1225782707,0.1277452707,0.2151694298,-0.1188951135,0.1472864747,0.0209828187,-0.1125127152,0.0276411753,0.0095553789,-0.2397180051,-0.1244156882,-0.0534946397,0.0133727426,-0.0317953303,-0.0155017227,0.2256134152,0.1291157305,0.1652711183,0.4320666194,-0.1759159714,0.2170918435,0.2566938102,0.222090289,0.0498518646,0.0886928588,-0.2924880981,-0.26236099,0.1929330379,0.1236289218,-0.0143335843,-0.0734221488,-0.017405197,-0.2349372804,0.0079411762,-0.2194015086,-0.3230405152,0.1734453142,0.2221041918,0.3988912404,-0.053792119,-0.4686821401,0.5173083544,0.0457804874,0.1597618163,-0.8009337187,0.1625424325,0.2433849126,-0.0265652779,0.0728494972,0.1985827833,-0.0902727172,-0.3639824688,0.3573990166,0.2823091745,-0.1799612343,0.1475442648,0.1906578988,0.4468785524,0.2049324363,0.025963014,-0.0192388874,-0.3458394408,0.2969488502,-0.0436750725,0.0461218357,0.4764792621,-0.1672108173,0.3203335404,0.2574346662,-0.0481926575,0.0035503039,-0.0402696878,-0.5831554532,0.1241913959,0.1108983904,0.0170067437,0.4671846032,-0.1074706391,-0.2827883363,-0.0736338049,0.3593038917,-0.1044112071,-0.0366754904,0.1507020593,-0.1847528368,-0.1070031226,0.107776314,-0.0739282519,0.2477795929,0.0068557751,0.2435870022,0.0406483598,0.1717321277,0.021413913,0.0621021688,0.1553298533,0.1925956756,-0.0365203433,0.0750450119,0.1274712533,-0.155453071,-0.3518773317,-0.1105525941,0.057045754,-0.1778412014,0.3865460157,-0.2391075045,-0.2566678822,-0.3890227079,0.1047529653,-0.0565256588,0.0039648698,-0.2241475731,-0.074211061,0.3099526763,0.0557001233,0.114118889,0.4600075483,0.2347175181,0.0880562961,-0.1795245409,-0.0620844886,0.154369235,0.1218862236,0.1859986782,-0.1343820393,0.5079180002,-0.0053067748,-0.0006818027,-0.3153592646,-0.2890056968,0.3099226952,-0.1156775504,0.232262373,0.1817793101,-0.0603307225,-0.0614840649,-0.132613495,-0.0467311852,-0.1158417314,-0.0954230651,-0.040212974,-0.0446160547,0.2140877694,0.1073508188,-0.2305363864,-0.2717323005,-0.2117005438,0.7207010388,0.1371042579,0.2246389687,0.1908034682,-0.1179903522,0.3346859217,-0.1943351775,0.3252695501,-0.0625202358,-0.4787774384,0.3021920323,-0.3026356101,-0.1847880334,-0.1038351059,-0.1253803819,0.6247452497,0.0419879518,-0.3094746768,-0.2259999067,-0.05416568,0.0536925234,0.1094723418,0.2407761961,0.3135147989,-0.1626960039,-0.032929875,0.037129119,0.0101286797,0.0426246934,-0.04738383,0.0893518254,0.3445410132,0.5481717587,0.2190328091,0.5223556161,0.5356540084,0.0138916271,0.1400536597,-0.0983513892,-0.0980428457,-0.0277523268,-0.4531847239,0.1973807812,-0.346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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/815","title":"Is dataset iterative or not?","comments":"`datasets.Dataset` objects implement indeed `__getitem__`. It returns a dictionary with one field per column.\r\n\r\nWe've not added the integration of the datasets library for the seq2seq utilities yet. The current seq2seq utilities are based on text files.\r\n\r\nHowever as soon as you have a `datasets.Dataset` with columns \"tgt_texts\" (str), \"src_texts\" (str), and \"id\" (int) you should be able to implement your own Seq2SeqDataset class that wraps your dataset object. Does that make sense to you ?","body":"Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks","comment_length":76,"text":"Is dataset iterative or not? \n Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks \n `datasets.Dataset` objects implement indeed `__getitem__`. It returns a dictionary with one field per column.\r\n\r\nWe've not added the integration of the datasets library for the seq2seq utilities yet. The current seq2seq utilities are based on text files.\r\n\r\nHowever as soon as you have a `datasets.Dataset` with columns \"tgt_texts\" (str), \"src_texts\" (str), and \"id\" (int) you should be able to implement your own Seq2SeqDataset class that wraps your dataset object. Does that make sense to you ?","embeddings":[-0.1060156971,-0.0653585941,-0.0744617656,0.2728957236,0.0212031826,-0.0095434887,0.2350491583,0.2607481778,0.0178500302,-0.1811223626,0.2007772624,0.2117641866,-0.3875654638,0.19634749,0.0909050927,0.0099149477,-0.1221981049,0.0177189317,-0.6584701538,-0.1331171393,-0.0125520015,0.1090283915,-0.3733929098,-0.0125812609,-0.0573374778,-0.2997436523,-0.3547765613,-0.0941223279,-0.3227387667,-0.3790437877,0.1680089086,0.3244619071,0.4530280828,0.3990264237,-0.000104547,-0.3836460412,0.0271534603,-0.0992269292,-0.2510547936,-0.1770192385,-0.2945179641,-0.2617836297,0.1364851445,-0.4748810828,-0.0804626793,-0.0584186241,0.0909327939,-0.3703354597,0.2763306499,0.0961010903,0.2760343254,-0.424844265,-0.0344233364,-0.0648907349,0.0225603599,0.049664747,-0.0703690499,-0.0418418087,0.3111680746,0.3059273064,-0.0155926682,-0.0311851595,0.1218771935,-0.1981253922,0.3405269384,-0.0772939995,-0.0879147723,-0.3361750245,0.0023595735,0.0840367898,1.1303713322,-0.2262818068,-0.306194514,0.0234375037,-0.015231166,-0.4028499424,-0.1640854478,0.29465729,0.1456294358,0.1291965842,-0.2035063654,-0.3969239295,-0.2363087386,-0.1007134318,-0.126223281,-0.0489157997,-0.1681756973,0.0840758383,-0.0203479566,-0.2884940207,0.1532578617,0.1695320457,0.0152814379,0.2250156701,-0.6152793169,-0.3189679086,0.0359667391,0.3883178532,0.4129840136,0.3702382445,0.0624873154,0.0317518041,0.002943628,0.2047530264,0.4332893193,0.0117595373,0.0783103853,0.0479110144,0.0488203801,-0.1845354289,-0.205026418,-0.0807033256,-0.2166119516,-0.1597901881,0.1061509401,-0.164549768,-0.1567519009,-0.1790119857,-0.0017529721,0.0000938252,-0.2421870977,-0.1376674771,0.1298843771,0.1093622819,-0.0902501866,-0.0274752136,-0.1273261607,0.1498544961,0.1142807603,-0.0207859315,-0.0622128136,0.474968195,-0.2980031371,0.0847253576,-0.0686666444,-0.3925188482,0.0734553486,0.0530897193,-0.1771769673,-0.0755128488,0.0849153474,-0.2322214246,0.1744033247,-0.1072451621,-0.168242231,0.1184785441,-0.1914855093,-0.1652150154,-0.4168971181,-0.1530209631,-0.3378536105,-0.1186842546,-0.1220381483,0.1512054056,-0.1720935106,-0.190055728,0.1995152831,0.4862645566,-0.0187182296,-0.1701807976,0.04553831,-0.1141480654,-0.2942237258,-0.1201193109,-0.0273045525,0.4135944843,-0.7152242064,0.112702921,0.0177283678,0.1990044415,0.0440053158,0.3204875886,-0.1433266252,0.2875398695,-0.1735821366,0.0832265913,0.4689073563,0.0261819661,-0.2216172963,0.1995010227,-0.1015404016,-0.1679414064,0.1796113551,0.0820492953,0.2748194933,-0.0586654022,-0.113525264,0.0100040371,0.0261893589,-0.2152169049,-0.1232686713,-0.0396792367,0.1559778601,0.0144439712,0.0755374804,0.2684825361,0.1918976307,0.2799932361,0.2079637647,-0.1532561034,0.2448382527,0.0787206218,0.3107308149,-0.0092805242,0.1097956449,-0.182276234,-0.0991682038,0.1989352703,0.3425956368,-0.0336224139,0.0973454714,0.0093338443,-0.1203001216,-0.0338788666,-0.1323507577,-0.1853948683,0.193635568,-0.0194624607,0.3257060051,-0.1215667352,-0.361246258,0.2648516297,-0.0278109107,-0.071654588,-0.635613203,0.1184780896,0.2372146845,-0.0631385371,0.1188148335,0.1731708795,-0.1699392051,-0.2727327943,0.3001478314,0.3359160125,-0.0359949581,-0.0734905005,0.2832366228,0.5574188828,0.1177820191,0.0026029074,0.2196706831,-0.4144702256,0.2916940749,0.0419196561,-0.0218471196,0.4437411129,-0.1784731895,0.1090888977,0.1369903237,-0.0261689257,0.074157767,0.0160719939,-0.5682403445,0.0849422514,-0.2583654225,0.1411114633,0.253107518,-0.0921327323,-0.3893495798,-0.1187008172,0.4135907888,0.024784619,0.0798389092,0.1774080992,-0.111607179,-0.1065041423,-0.0296097398,-0.0075327279,0.3253003657,-0.0390895978,0.1885775924,-0.0195131265,0.0985074043,0.0713533461,0.1269074082,0.073755078,0.1753561497,-0.1058323383,0.0945662931,0.0835992545,-0.1805022657,-0.2936784327,0.0505964272,0.2180814445,-0.0557901785,0.3049521744,-0.4185894132,-0.1184837818,-0.3756096363,0.2154709846,0.2625839412,0.087883763,-0.0428275131,-0.1942445636,0.40513134,0.0120761963,-0.0770671666,0.5587694645,0.2348256409,0.0640678704,-0.0663646385,-0.1118966937,0.0384163037,0.1578057408,0.1735128313,-0.0893606544,0.5077780485,0.0487739891,0.2402329594,-0.4060040414,-0.3096460104,0.3122244179,-0.0110638198,0.2463253886,0.2539362311,-0.1717471629,-0.0561871156,-0.1773165464,-0.033527866,-0.2286292464,0.0861981958,0.0113951238,-0.0456722192,0.0788047239,0.0978551954,-0.3836063445,-0.1660971045,-0.1279617548,0.4235076606,0.2191938311,0.3020019829,-0.0476010256,-0.0579888113,0.2834451795,-0.0312266331,0.1959004402,-0.1284116507,-0.4388583899,0.3763958514,-0.2919609845,-0.1978156716,0.0604937226,-0.10550908,0.6279435158,0.0679761171,-0.2470659614,0.1047998741,0.1767358631,0.1371100247,0.252369225,0.1300071329,0.266561836,-0.1598167568,-0.0430794172,0.0375887752,-0.1077144817,0.2246945947,0.2038492709,0.2265102714,0.3720066249,0.6611022949,0.036840599,0.4294275641,0.3031008244,-0.2027745992,0.229659453,-0.0535757914,-0.2370571792,-0.0487279333,-0.2013127208,0.2020185143,-0.3556555808,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/815","title":"Is dataset iterative or not?","comments":"Hi\nI am sorry for asking it multiple times but I am not getting the dataloader\ntype, could you confirm if the dataset library returns back an iterable\ntype dataloader or a mapping type one where one has access to __getitem__,\nin the former case, one can iterate with __iter__, and how I can configure\nit to return the data back as the iterative type? I am dealing with\nlarge-scale datasets and  I do not want to bring all in memory\nthanks for your help\nBest regards\nRabeeh\n\nOn Mon, Nov 9, 2020 at 11:17 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> datasets.Dataset objects implement indeed __getitem__. It returns a\n> dictionary with one field per column.\n>\n> We've not added the integration of the datasets library for the seq2seq\n> utilities yet. The current seq2seq utilities are based on text files.\n>\n> However as soon as you have a datasets.Dataset with columns \"tgt_texts\"\n> (str), \"src_texts\" (str), and \"id\" (int) you should be able to implement\n> your own Seq2SeqDataset class that wraps your dataset object. Does that\n> make sense ?\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/815#issuecomment-723915556>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ARPXHHYOC22EM7F666BZSOTSO66R3ANCNFSM4TPB7OWA>\n> .\n>\n","body":"Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks","comment_length":217,"text":"Is dataset iterative or not? \n Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks \n Hi\nI am sorry for asking it multiple times but I am not getting the dataloader\ntype, could you confirm if the dataset library returns back an iterable\ntype dataloader or a mapping type one where one has access to __getitem__,\nin the former case, one can iterate with __iter__, and how I can configure\nit to return the data back as the iterative type? I am dealing with\nlarge-scale datasets and  I do not want to bring all in memory\nthanks for your help\nBest regards\nRabeeh\n\nOn Mon, Nov 9, 2020 at 11:17 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> datasets.Dataset objects implement indeed __getitem__. It returns a\n> dictionary with one field per column.\n>\n> We've not added the integration of the datasets library for the seq2seq\n> utilities yet. The current seq2seq utilities are based on text files.\n>\n> However as soon as you have a datasets.Dataset with columns \"tgt_texts\"\n> (str), \"src_texts\" (str), and \"id\" (int) you should be able to implement\n> your own Seq2SeqDataset class that wraps your dataset object. Does that\n> make sense ?\n>\n> \u2014\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/815#issuecomment-723915556>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ARPXHHYOC22EM7F666BZSOTSO66R3ANCNFSM4TPB7OWA>\n> .\n>\n","embeddings":[-0.1699585021,-0.1714058369,-0.0689312667,0.29238832,0.1009495035,-0.0845248029,0.3338168263,0.2466901839,0.179701373,-0.1265341341,0.068009831,0.1442486793,-0.3788862526,0.412889421,0.1700596809,-0.0318089277,-0.0919883251,0.0115778297,-0.6868837476,-0.1174983755,0.0290254131,0.1487227678,-0.2845766842,-0.039639689,-0.0925771147,-0.2904197276,-0.2832887471,0.0088117104,-0.3676557541,-0.3626948595,0.3347042799,0.2895670235,0.494183898,0.5043627024,-0.000105784,-0.2772066593,0.0528096072,-0.1300345659,-0.2246703207,-0.2106571794,-0.2412293106,-0.2051681876,0.1279392838,-0.3685281277,-0.044530645,-0.085979417,0.0379860997,-0.3243009448,0.3284459412,0.07557372,0.2515381873,-0.2350952774,0.0400567502,-0.0884437189,-0.0125634493,0.1745035052,-0.0649378449,0.0631418675,0.2634143531,0.1847220361,-0.1005844921,-0.0201257877,0.1368308514,-0.120177418,0.3518400192,-0.1117137149,-0.1345442384,-0.3502725661,-0.0687426254,0.1795235276,1.1247750521,-0.2266197652,-0.3315274715,-0.1051943004,-0.1077040732,-0.384303391,0.0519696921,0.1613957435,0.1376384348,0.0931484699,-0.2176344097,-0.2320619076,-0.2463390976,-0.0989342481,-0.1977272481,-0.218505457,-0.3083755374,0.0284801517,0.1063963398,-0.3117009699,-0.0058239074,0.1124444008,0.1506894529,0.284599334,-0.6823297739,-0.3560846746,-0.0164678078,0.2891712487,0.4668389559,0.3724458814,0.0903385282,-0.0460713729,-0.1429632455,0.1361737102,0.4857326448,0.1536505073,0.091422677,-0.0331862569,0.0548191965,-0.0040666908,0.0111732017,-0.0994318202,-0.2033264786,-0.1721980423,0.0453471877,-0.1871928871,-0.0844531655,-0.2436035872,0.0123192566,-0.0572085008,-0.0694758371,-0.1390123516,0.1348976046,0.1547203958,-0.0590074472,0.1231463104,-0.112008132,0.0874819085,0.0184106734,0.0013463529,-0.0628624037,0.3680286705,-0.3997497857,0.1252013147,-0.0149138141,-0.4068548381,0.0481101125,0.1276565492,-0.194136247,0.0171589013,-0.0430658795,-0.2238801867,0.1048341841,-0.0725570247,-0.2010730803,0.2333637476,-0.2167590559,0.0247235019,-0.3907789588,-0.1400688738,-0.3842135966,-0.0907068476,-0.1005614698,0.1538315862,-0.2043938637,-0.230973646,-0.1431592256,0.3670678735,-0.0654408634,-0.1174171567,0.0704515204,-0.1015896052,-0.2147193104,-0.1085753292,0.0136708729,0.4157608151,-0.7381816506,-0.0744674206,0.0410949215,-0.0164117254,0.1620524973,0.4142245948,-0.147715345,0.2711117566,-0.2453812361,0.1480748802,0.3742136061,0.0069465423,-0.2698088884,0.2698873281,-0.0603346266,0.0054400959,0.1192872524,0.0076572672,0.2325147092,-0.0825404748,-0.1676668227,0.1485361308,0.080385901,-0.1396999657,-0.1522193402,-0.1051234007,0.1615737826,0.0591262095,0.0720561668,0.2700530887,0.1509655267,0.2775504589,0.2225801945,-0.1702853441,0.2985440195,0.0976856202,0.294963181,0.0548568815,0.0704542994,-0.2696400583,-0.2262954265,0.2209364027,0.1445796192,-0.0016945195,0.1903557777,0.0931611657,-0.1611178815,-0.0879651383,-0.2035260648,-0.1719710231,0.176610738,0.0991632566,0.284912318,-0.0842207223,-0.4242133498,0.3340341747,-0.1232871562,-0.0024321913,-0.7031782269,0.1637267619,0.3130322397,-0.0872310475,0.0873475075,0.1098693162,-0.1471638083,-0.2829943597,0.2350479662,0.3226821125,-0.0962361544,0.1296237409,0.2510299683,0.4881451428,0.1757104993,0.0233464204,0.2103522718,-0.4388235807,0.2086124569,-0.0578279197,0.0817303732,0.4162714481,-0.2419126928,0.1187950522,0.1114322618,-0.0289150774,0.0098034479,-0.0222697835,-0.4933255613,0.2212381065,-0.0613504834,0.0618069582,0.2879597247,-0.0901600569,-0.2579862773,-0.1866322458,0.4187476039,-0.0632652566,0.0046470636,0.2310644686,-0.2082143426,-0.0975415632,0.0669202954,-0.033318989,0.3083370328,-0.0466552228,0.2622880638,-0.0572665557,0.1278201789,0.0857335702,0.1289166063,0.0169786401,0.2707156241,0.0105032828,0.0580160804,0.0779035836,-0.2738215029,-0.219468981,-0.0109609812,0.1313216537,-0.0874417871,0.1833934635,-0.4029295146,-0.1049549431,-0.3309220076,0.1238508001,0.0387210064,0.0642085597,-0.118480444,-0.0965833813,0.4058251679,0.0656917691,-0.0229723603,0.5276075006,0.2944499552,0.0137874298,-0.1694500744,0.0357918106,0.0432924218,0.1796344221,0.2374723703,-0.2035424262,0.4684326351,0.0262028147,0.0783145949,-0.4120460153,-0.2755690515,0.3278045058,-0.1076430827,0.2515527606,0.3064386547,0.0044726939,-0.0206184741,-0.0463249534,0.0341679901,-0.3438085616,-0.0050206608,-0.0264046974,-0.0640740618,0.2453982681,0.0882140025,-0.4056174755,-0.2220086604,-0.2096966058,0.5581911802,0.2181431651,0.2245459855,0.1635544896,-0.0632682517,0.3151679635,-0.0617828034,0.2107881606,-0.2140313387,-0.5315765142,0.387829423,-0.2561224997,-0.2005531937,0.0406557843,-0.0047406703,0.5716431737,0.0240638386,-0.3571440279,0.0166935623,0.0419940725,0.1119483784,0.1873508692,0.2401827276,0.2688873708,-0.2422915995,-0.0287387297,0.0596929789,-0.0924317911,0.158922255,0.1621715128,0.3196926713,0.4330098927,0.5967088938,0.0411427133,0.5284713507,0.3443607986,-0.1735187322,0.2482471615,-0.0325032473,-0.2144196332,-0.1131134257,-0.2672350407,0.2950757444,-0.345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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/815","title":"Is dataset iterative or not?","comments":"`datasets.Dataset` objects are both iterative and mapping types: it has both `__iter__` and `__getitem__`\r\nFor example you can do\r\n```python\r\nfor example in dataset:\r\n    # do something\r\n```\r\nor\r\n```python\r\nfor i in range(len(dataset)):\r\n    example = dataset[i]\r\n    # do something\r\n```\r\nWhen you do that, one and only one example is loaded into memory at a time.","body":"Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks","comment_length":57,"text":"Is dataset iterative or not? \n Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks \n `datasets.Dataset` objects are both iterative and mapping types: it has both `__iter__` and `__getitem__`\r\nFor example you can do\r\n```python\r\nfor example in dataset:\r\n    # do something\r\n```\r\nor\r\n```python\r\nfor i in range(len(dataset)):\r\n    example = dataset[i]\r\n    # do something\r\n```\r\nWhen you do that, one and only one example is loaded into memory at a time.","embeddings":[-0.1967024505,-0.4120611846,-0.1522585452,0.1939141899,0.0503938869,0.0091604656,0.2521877587,0.0610394888,0.1378219873,-0.0013328588,0.2277509272,0.2708903253,-0.4278086424,0.1769471169,0.2024409622,-0.088955082,0.0129831638,0.0472291596,-0.6187960505,-0.0078373151,0.1369362026,-0.0778279081,-0.3001040816,-0.1672134846,0.0571090728,-0.4246666431,-0.310051322,-0.1855567694,-0.1906789541,-0.4267381132,0.2655524015,0.1786016971,0.4127590954,0.5190775394,-0.0001068608,-0.2087260485,0.1724295765,-0.0724189803,-0.1570537984,-0.2525888681,-0.1945832968,-0.1432822496,-0.0132758319,-0.3930830359,-0.0778554678,-0.2569579482,0.1618881971,-0.3726496398,0.2177618146,0.0823897272,0.2613908052,-0.2673082352,0.0140859205,-0.0343472809,0.0291362312,0.1420092136,-0.1895926595,0.3146386445,0.2581835389,0.2592709064,0.0707307905,-0.0838772431,0.1835328341,-0.0293516405,0.5176247358,-0.1810382158,-0.0284579135,-0.3781050742,-0.1316801459,0.1800668538,0.9738004804,-0.3325405419,-0.180568099,0.092509523,0.07839486,-0.4161494374,-0.0789299309,0.2087811977,0.0914726183,0.1689748615,-0.3774550557,-0.2158584297,-0.2636704743,0.15692693,-0.0114712836,0.0202188417,-0.1483395249,0.1454471201,0.1469690949,-0.2227167934,0.3494539261,-0.0374085978,0.095672451,0.2934467196,-0.354888618,-0.3146653175,-0.0452603213,0.4222278595,0.4786589742,0.3207339346,0.2023726702,-0.1110485569,-0.2233713269,0.2842499614,0.4751828015,0.0039901678,-0.0489843749,0.0934275985,0.1161991507,-0.0192664526,-0.1462731361,-0.0210601743,-0.2141522914,-0.078951031,0.3076207042,-0.2920466959,-0.1230150163,-0.0697307587,0.0273173954,-0.1093935445,-0.2524139285,-0.1942876875,0.2612032592,0.2329009324,-0.1484914273,0.1340863705,-0.3233181238,-0.0912753865,0.1094379202,0.2910441458,-0.0449736118,0.4422123134,-0.5005634427,0.0238534585,-0.0357486568,-0.2278592587,0.2085994482,0.1532032192,-0.1350090951,-0.107849583,0.1483562887,-0.3669768274,0.4331787527,-0.0354758203,-0.1922498643,0.0064364015,-0.2999882102,0.1547482908,-0.3955385685,0.0480349436,-0.2260352522,0.0580670498,-0.0429606624,0.1644735336,-0.2056888938,-0.2527205944,-0.0254761502,0.3109799922,0.121580705,-0.1092042178,0.1143796593,-0.2127530575,-0.1631635427,-0.3115358651,0.0765097141,0.2239909023,-0.3857717216,0.1330495179,0.0314944386,0.0469468571,0.1477143019,0.2904067338,-0.2406658679,0.2274698466,-0.1616248339,-0.118223168,0.418687731,-0.1354309469,-0.1791634858,0.3351472616,0.201632306,-0.0980946347,0.0681597963,0.29716748,-0.0122090001,0.0224211458,-0.2033890784,0.2134364694,0.0243260618,-0.1471536309,-0.0023533842,-0.0215294175,0.1723941714,0.0104990965,0.0406940207,0.2140854299,0.0902528614,0.1390072256,0.2731764019,-0.2391561717,0.1900429875,0.2386129946,0.3220140636,0.0492032021,0.0945582017,-0.4822582603,0.0254878756,0.155633688,0.186152339,-0.1578350812,0.2956633568,-0.0067588552,-0.1262248307,0.0798645169,-0.1618826538,-0.056561932,0.125680536,0.0663363412,0.2056292892,-0.0901576057,-0.5047391057,0.0998199582,-0.2030547559,-0.2080510706,-0.5096104145,0.1290673614,0.2270847112,-0.0779467896,0.0327608138,0.015286563,-0.2983108163,-0.497923255,0.2777377367,0.3969455063,0.1560833603,-0.007922153,0.1019600481,0.3819927275,0.3212147653,-0.1746238023,0.1462098658,-0.1991002262,0.2882044315,-0.0181854982,0.1007042602,0.4194480777,-0.2194589972,0.0578731149,0.3707637489,-0.1672394127,0.0571276546,-0.0573669299,-0.4465524256,0.1243522912,-0.1040654853,-0.1959182024,0.4206411839,-0.1265025288,-0.3818365335,-0.3234954476,0.4590500295,-0.0309022348,0.0291666482,0.0501575768,-0.1086960807,-0.0667860508,0.0552263297,0.0299283322,0.3643385172,-0.0167238824,0.2228473276,0.0211025793,0.0027691489,0.1936891824,0.1838862598,-0.021365894,0.3514572382,-0.167028293,-0.0330475196,0.023264816,-0.2684541643,-0.3299869895,0.0165297929,0.1501984149,0.0575455762,0.2963264883,-0.4058513641,-0.1907162964,-0.3694969416,-0.1114165112,0.2052114159,0.1090968251,-0.3205469847,0.042428676,0.4008634686,0.1220249757,0.0850847438,0.2148122936,0.058388222,0.0247809552,-0.0603260696,0.0091481637,-0.1000981703,0.1860867292,0.287830472,-0.2173032165,0.6166129708,0.0909842849,-0.0911396667,-0.4059427977,-0.1621184647,0.1468191445,0.0502733923,0.0921218991,0.2490787804,-0.0362272821,-0.1754505932,-0.0418330804,0.0035600148,-0.4967641532,0.1590124071,-0.1841693074,-0.1201962382,0.0413623303,0.0619924329,-0.1926466972,-0.3554748595,-0.1489828676,0.3245443404,0.394795239,0.179288134,0.1702627689,0.0526030511,0.3306016028,0.1137496009,0.0347363278,-0.0312977396,-0.3998983204,0.1823969632,-0.3657858968,-0.2789263725,0.0530234016,0.0544123314,0.4394408166,0.1919809729,-0.3579131067,-0.026459612,-0.0615793876,0.0533848852,0.0929170847,0.394934237,0.0913500488,-0.2753692567,-0.0266986489,-0.0448271036,-0.0533393584,0.0777137876,-0.0599412173,0.3174922168,0.5418897867,0.3369597793,-0.0612844191,0.5106337667,0.3213931024,-0.4664492011,0.381926477,-0.2949079573,-0.2295010686,-0.1006760746,-0.1804185808,0.1238948107,-0.253370434,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/815","title":"Is dataset iterative or not?","comments":"Hi there, \r\nHere is what I am trying, this is not working for me in map-style datasets, could you please tell me how to use datasets with being able to access ___getitem__ ? could you assist me please correcting this example? I need map-style datasets which is formed from concatenation of two datasets from your library. thanks \r\n\r\n\r\n```\r\nimport datasets\r\ndataset1 = load_dataset(\"squad\", split=\"train[:10]\")\r\ndataset1 = dataset1.map(lambda example: {\"src_texts\": \"question: {0} context: {1} \".format(\r\n    example[\"question\"], example[\"context\"]),\r\n    \"tgt_texts\": example[\"answers\"][\"text\"][0]}, remove_columns=dataset1.column_names)\r\ndataset2 = load_dataset(\"imdb\", split=\"train[:10]\")\r\ndataset2 = dataset2.map(lambda example: {\"src_texts\": \"imdb: \" + example[\"text\"],\r\n            \"tgt_texts\": str(example[\"label\"])}, remove_columns=dataset2.column_names)\r\ntrain_dataset = datasets.concatenate_datasets([dataset1, dataset2])\r\ntrain_dataset.set_format(type='torch', columns=['src_texts', 'tgt_texts'])\r\ndataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)\r\nfor id, batch in enumerate(dataloader):\r\n    print(batch)\r\n\r\n```","body":"Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks","comment_length":113,"text":"Is dataset iterative or not? \n Hi\r\nI want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?\r\ncould you provide me with example how I can use datasets as iterative datasets?\r\nthanks \n Hi there, \r\nHere is what I am trying, this is not working for me in map-style datasets, could you please tell me how to use datasets with being able to access ___getitem__ ? could you assist me please correcting this example? I need map-style datasets which is formed from concatenation of two datasets from your library. thanks \r\n\r\n\r\n```\r\nimport datasets\r\ndataset1 = load_dataset(\"squad\", split=\"train[:10]\")\r\ndataset1 = dataset1.map(lambda example: {\"src_texts\": \"question: {0} context: {1} \".format(\r\n    example[\"question\"], example[\"context\"]),\r\n    \"tgt_texts\": example[\"answers\"][\"text\"][0]}, remove_columns=dataset1.column_names)\r\ndataset2 = load_dataset(\"imdb\", split=\"train[:10]\")\r\ndataset2 = dataset2.map(lambda example: {\"src_texts\": \"imdb: \" + example[\"text\"],\r\n            \"tgt_texts\": str(example[\"label\"])}, remove_columns=dataset2.column_names)\r\ntrain_dataset = datasets.concatenate_datasets([dataset1, dataset2])\r\ntrain_dataset.set_format(type='torch', columns=['src_texts', 'tgt_texts'])\r\ndataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)\r\nfor id, batch in enumerate(dataloader):\r\n    print(batch)\r\n\r\n```","embeddings":[-0.3016710877,-0.3916234076,-0.1244997233,0.404502511,0.1218660474,0.0815260336,0.2345808595,0.1949106157,-0.044544179,-0.0258444548,0.0440256149,0.3346882463,-0.3416885436,0.019357549,-0.0354218148,-0.1522007585,-0.1350665241,-0.1091265082,-0.5712974668,-0.0941947401,0.164425537,-0.0607798435,-0.3945014477,-0.1852220446,-0.0154445795,-0.4847174585,-0.2174984962,0.0037641965,-0.1276628673,-0.3302404881,0.1825329959,0.0282143466,0.4424453378,0.6334045529,-0.0001107979,-0.1362617463,0.1161465049,-0.1768816561,-0.1535315514,-0.3136429191,-0.0109618222,-0.1165110692,-0.0174039751,-0.4819852412,-0.1775075346,-0.129210785,0.0617713556,-0.5960788131,0.3605302274,0.174767822,0.1965260357,-0.1608913243,0.0373760648,-0.0517343171,0.1932127476,0.1550517082,-0.088112399,0.2196961045,0.4312990904,0.2817698121,-0.015033531,0.0472457521,0.0915912762,-0.0721105114,0.1525731236,0.0000492522,-0.2120264024,-0.4927812517,-0.0110825384,0.1845875531,0.9366436005,-0.2746395469,-0.2969922721,-0.0351296812,0.016817661,-0.1741650999,-0.0689116716,0.2412091196,0.0703459904,0.1441503614,-0.3334394693,-0.1051563323,-0.1860531867,0.2216388881,0.0751473308,0.2418799847,0.0030301763,0.2312335819,0.0462208837,-0.2195009887,0.4654043615,-0.1329292804,0.0892364904,0.3992209733,-0.4541883767,-0.1844446361,-0.0349401422,0.2144804597,0.4375275671,0.261620611,0.1651032269,-0.1079955995,-0.2650260627,0.2307070792,0.2453352213,0.0775087997,-0.0132842967,0.2721357644,0.0922990367,-0.1410165727,-0.010839927,0.0221032053,-0.2615159154,-0.4489365816,0.0846842527,0.0368227996,-0.1015720591,0.0349570476,-0.0030915847,-0.2123387456,-0.2890574336,-0.2483493537,0.3508866429,0.2104179114,-0.211345911,0.0223543681,-0.1101625934,0.1763292104,0.2847446799,0.3477299511,-0.0633524805,0.4023623466,-0.4622748196,-0.0274971724,0.0432804003,-0.3923403323,0.1161287427,0.1986756623,-0.1707923263,0.0677612275,0.1151254922,-0.3227116168,0.4243600965,-0.1387716681,0.0229630731,0.0071933516,-0.0757018775,0.2180230319,-0.3226849139,0.0705003589,-0.4101574123,-0.0100664478,-0.1824006885,0.1245952249,-0.0685806647,-0.1054999307,-0.1791855246,0.1854764372,0.2596293688,-0.144537881,0.2509702146,0.0507896431,-0.1488247514,-0.2530644536,0.0971306488,0.3796824217,-0.3504396975,0.0436405204,0.3202689886,0.1515729278,0.154348731,0.3483071029,-0.3904181421,0.2539191842,-0.3402405083,-0.1352609396,0.7417567372,-0.363509506,-0.1803584248,0.2364930212,-0.0599689409,-0.2498018891,0.3100492656,0.0061494638,-0.0729998127,0.0717940256,0.192838043,0.1711915284,0.0321159698,-0.160643518,0.0101480642,-0.0285592396,0.3351587951,-0.025440501,0.0790984035,0.1570300907,0.2761316895,-0.0619236082,0.4041700065,-0.2901450396,0.1881703138,0.2839535177,0.0180364363,-0.1514426917,0.051079087,-0.1703205407,-0.2152455151,0.2737480998,0.0589009672,-0.1949934661,0.110076502,0.016197836,0.0288132206,-0.1400697231,-0.2077917606,-0.0491939299,0.0650475249,0.123596929,0.2050937563,-0.2752660811,-0.3432314694,0.0536302775,-0.2269364744,-0.1522424072,-0.5957727432,0.2830938101,0.1569339335,-0.1839175373,0.0886456445,0.1052316949,-0.0220429972,-0.3450835049,0.1771088541,0.3911457062,-0.0403560884,0.1695431471,0.1876871288,0.250310719,0.3719840646,-0.043576315,0.1544352621,-0.3036029041,0.2019958645,-0.1644172966,0.1088795736,0.4175557792,-0.178999722,0.1390865594,0.2945196033,-0.162000522,0.0500433519,0.0338672101,-0.3456018567,0.095110774,-0.1028606743,0.0285397004,0.4338846505,-0.2218484432,-0.4662246704,-0.2694395185,0.3125096262,0.0944652632,0.0498280376,-0.0394720137,-0.1615007073,0.0070869224,0.2433333844,-0.2581147552,0.4800901115,0.0130743021,0.2423738688,0.0065550869,0.1503530294,0.0643304437,0.1935148835,0.0452768989,0.3508191407,0.1702409238,0.2046918422,0.1170993447,-0.0401206091,-0.3137160242,0.0163484886,0.0010911541,-0.1328433156,0.2385167927,-0.2977777123,0.0241626892,-0.4486703873,-0.1598946303,0.1747912467,0.0770025253,-0.3366315067,0.0492514819,0.4261993468,0.1866766065,0.1212245077,0.0822213888,0.3265725672,-0.1102489829,-0.0354747139,-0.1402110308,0.0021169186,0.146857366,0.2564623952,-0.3135670125,0.51144135,0.0465419665,-0.0001485249,-0.5351338983,-0.1205405667,0.3685362935,-0.1301970929,-0.0973266214,0.293846637,-0.1601385474,-0.1209717691,-0.1281894147,0.0786629468,-0.2453061193,0.0866097286,-0.1101557016,-0.2211600393,-0.0738406703,0.0910160169,-0.334584415,-0.513856709,-0.2599232495,0.2739360332,0.3042732179,0.1782877147,0.1947641522,0.1513937712,0.4163945913,0.2766897976,0.1746632308,-0.1345507801,-0.5078074932,0.4365925789,-0.443395555,-0.3223357201,-0.0671115518,-0.0891141891,0.4015447795,0.2927371264,-0.2275419384,-0.0919914469,0.064163357,0.2196813971,-0.0644264668,0.4492762685,0.3778802454,-0.1747938544,-0.0332650058,-0.0464871079,0.1320675015,-0.018378688,0.0769087747,0.5140583515,0.4622370303,0.3192089796,0.056447994,0.7233349681,0.236876145,-0.4231692851,0.4203618467,-0.291253835,-0.2060451359,-0.1417576224,-0.3200324178,0.1163159609,-0.26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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/813","title":"How to implement DistributedSampler with datasets ","comments":"Hi Apparently I need to shard the data and give one host a chunk, could you provide me please with examples on how to do it? I want to use it jointly with finetune_trainer.py in huggingface repo seq2seq examples. thanks. ","body":"Hi,\r\nI am using your datasets to define my dataloaders, and I am training finetune_trainer.py in huggingface repo on them.\r\nI need a distributedSampler to be able to train the models on TPUs being able to distribute the load across the TPU cores. Could you tell me how I can implement the distribued sampler when using datasets in which datasets are iterative? To give you more context, I have multiple of datasets and I need to write sampler for this case. thanks. ","comment_length":40,"text":"How to implement DistributedSampler with datasets  \n Hi,\r\nI am using your datasets to define my dataloaders, and I am training finetune_trainer.py in huggingface repo on them.\r\nI need a distributedSampler to be able to train the models on TPUs being able to distribute the load across the TPU cores. Could you tell me how I can implement the distribued sampler when using datasets in which datasets are iterative? To give you more context, I have multiple of datasets and I need to write sampler for this case. thanks.  \n Hi Apparently I need to shard the data and give one host a chunk, could you provide me please with examples on how to do it? I want to use it jointly with finetune_trainer.py in huggingface repo seq2seq examples. thanks. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/812","title":"Too much logging ","comments":"Hi ! Thanks for reporting :) \r\nI agree these one should be hidden when the logging level is warning, we'll fix that","body":"I'm doing this in the beginning of my script:\r\n\r\nfrom datasets.utils import logging as datasets_logging\r\ndatasets_logging.set_verbosity_warning()\r\n\r\nbut I'm still getting these logs:\r\n\r\n[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\n[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\nusing datasets version = 1.1.2","comment_length":22,"text":"Too much logging  \n I'm doing this in the beginning of my script:\r\n\r\nfrom datasets.utils import logging as datasets_logging\r\ndatasets_logging.set_verbosity_warning()\r\n\r\nbut I'm still getting these logs:\r\n\r\n[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\n[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\nusing datasets version = 1.1.2 \n Hi ! Thanks for reporting :) \r\nI agree these one should be hidden when the logging level is warning, we'll fix that","embeddings":[-0.0939558521,-0.1382928491,-0.110982053,0.1179051399,0.3646460772,0.1386169642,0.3069940209,0.5121560693,0.1688756645,-0.0100423116,0.0955123603,0.0601680726,-0.4101086259,0.0572798029,-0.2360527515,0.2184815109,0.0992703438,-0.1177201867,-0.231096819,-0.1383837909,-0.0463390015,-0.0264285505,-0.0634166002,0.0502451658,-0.7488369346,-0.1222361922,0.2166901678,0.0171178672,-0.1799691617,-0.465010494,-0.0918776095,0.2049916536,0.1833454669,0.2377516925,-0.0001085636,-0.20621261,0.3049531281,0.0561281964,-0.2948624492,0.3130479455,-0.1765792817,-0.4536098838,0.264634788,-0.1287505031,-0.0320021063,-0.5764784813,0.1200485975,-0.5471832156,0.1971037537,0.2420777082,0.2531926036,0.1721661538,-0.1171072647,0.1836513132,0.1604662985,0.1954940259,-0.0464435145,-0.0426941253,0.2407290637,-0.1449797302,-0.2897666097,0.5247582197,-0.2106172442,-0.093664065,0.2563079298,0.0742956996,-0.0066664438,-0.4359248877,0.2002311647,0.2293611169,0.2505168915,-0.4348904788,-0.2396631539,-0.5395965576,0.1525105983,-0.2023011297,-0.0393041484,0.1188085526,0.0259445962,0.2967513204,-0.3168110251,-0.1253167987,0.0727278888,-0.1946348846,0.2814538479,0.0904524699,-0.0954422504,-0.0199597813,0.242654562,-0.2031737417,-0.005536681,0.0190858953,-0.1287662536,0.0733042508,-0.368399024,-0.1760264486,0.2714226842,0.4546540976,-0.0106762527,0.1569292098,-0.5877223611,0.0308099519,0.1449273527,-0.0687160939,0.2483414859,0.0682765171,0.3815250397,0.4842720628,0.0695502087,0.0023660441,0.2182523906,0.1636326611,0.3279185295,-0.3859716058,0.5891591907,0.0934159234,0.4714000821,-0.243321389,-0.0503412001,-0.0017934728,-0.0907608345,-0.1449495852,0.0998376757,0.2186496705,0.257361263,-0.112491928,-0.0209494438,0.1804551929,-0.1787154227,-0.1113092601,-0.3608377874,0.2265654355,-0.0386886448,0.1814368069,0.1555698663,-0.0132067855,-0.0337050185,0.1645651013,-0.0999963954,-0.0064752758,0.2545646429,-0.0888237432,0.1127234027,0.4412614405,-0.2111655325,0.1293465942,0.0744356066,-0.1091437489,-0.2172542065,-0.0095765982,0.0266517997,-0.344096452,-0.117465958,0.2434980124,-0.3380615711,0.0631735772,0.0415719822,0.0605500452,0.2659909129,0.2676562965,0.2104022354,-0.1764074117,-0.1235794947,-0.160682857,0.046920523,0.7568493485,-0.1010416821,-0.1524249017,-0.1544768959,-0.34261325,0.3341272175,0.2843775749,0.0356809534,0.2831241786,-0.31321612,0.1170730367,-0.072375685,-0.3900976181,-0.0120040989,0.3230906129,-0.2881067395,0.1007575542,0.5861548781,0.1028016508,0.0012948104,-0.2383528054,0.009368659,-0.1693769395,0.0935047865,-0.057938844,-0.2075730115,-0.1830702871,-0.0157597847,0.1808310598,-0.1068214923,-0.2202105224,-0.0464311466,0.0053200815,0.3438035548,-0.1056678444,0.0014571475,0.2216452956,0.2044258267,-0.1354949027,-0.1259181052,-0.1356040984,-0.3863966763,-0.0477210879,-0.0831304491,0.0528704599,-0.4278614521,-0.5052879453,0.0825892389,0.0003729831,-0.1854661852,-0.2281099409,0.1308967173,0.0035779478,-0.2127634585,-0.1032315567,-0.2443549931,0.3279069662,-0.0935143903,0.2245454639,-0.3384838402,-0.3394961655,0.0967411026,-0.123986423,0.3080798388,0.0880163983,0.3376420736,0.0235521495,0.2304944992,0.279966712,-0.0227968302,0.6912989616,0.0908789188,0.3066889644,0.051868137,0.1061581969,0.1213443875,-0.091763407,-0.2069844604,-0.0415721275,0.0172877163,0.3455808163,0.090901643,0.0299074426,-0.2113509476,-0.0423978716,-0.1206108779,-0.072812587,-0.124246195,-0.5477374196,0.3308843672,0.0519312583,0.0606073514,0.2649485469,0.0394691005,0.336020112,0.4038300812,-0.2107298374,0.1244593039,0.08450073,0.0159387402,0.0463408493,0.1757119298,0.1541772634,0.1422488838,0.3217694163,0.0495252907,0.2399304509,0.2244525552,-0.2068880647,0.2346292585,0.0124481553,-0.1978023648,-0.0443737507,-0.2063257992,-0.2044216543,-0.1408551931,-0.1601755619,-0.3429880142,0.2901280224,-0.3985350728,0.0035822436,-0.3858218491,-0.2859234214,-0.1935075521,0.0809366927,0.0804829001,-0.2955826819,0.2598420084,-0.0968647227,-0.3056240678,0.2036596239,-0.1606090814,0.060657829,0.1127745435,0.1230950132,-0.183965072,0.1099292412,-0.0364184715,0.0853303894,0.0721569955,-0.0706835389,0.4171567559,-0.1239209101,-0.0536239594,-0.197516039,-0.2242767662,-0.0322817005,-0.2084121108,0.0298854411,0.2623637021,0.0961000696,0.1462890059,-0.0539728366,-0.3215299845,0.029573366,0.1425770223,-0.0307257473,-0.1538363844,0.0601688214,-0.1685929298,-0.3298369646,-0.2052047402,-0.2479126751,0.1057197899,-0.0258241557,0.0914529413,0.3211137354,-0.1065982133,0.1568311304,-0.3661248386,0.3012650013,-0.028612826,-0.2743656337,0.2313615531,0.0794463158,-0.0137224877,0.0065798308,0.000336167,-0.3040707111,-0.2560657263,-0.5025682449,-0.102837719,-0.4806154668,-0.081789583,-0.1125073209,0.1075822636,0.2545316815,0.2832551301,-0.2098629475,0.0634870753,0.1032883972,-0.0417309552,-0.4492866099,0.1921845526,-0.2249032706,0.2574488521,0.1716227233,0.2437792718,0.6294993162,0.0763812959,-0.0625268146,-0.066285871,0.5207753778,-0.0275091138,-0.275418371,-0.0688880458,0.2339812964,-0.273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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/812","title":"Too much logging ","comments":"+1, the amount of logging is excessive.\r\n\r\nMost of it indeed comes from `filelock.py`, though there are occasionally messages from other sources too. Below is an example (all of these messages were logged after I already called `datasets.logging.set_verbosity_error()`)\r\n\r\n```\r\nI1109 21:26:01.742688 139785006901056 filelock.py:318] Lock 139778216292192 released on \/home\/kitaev\/.cache\/huggingface\/datasets\/9ed4f2e133395826175a892c70611f68522c7bc61a35476e8b51a31afb76e4bf.e6f3e3f3e3875a07469d1cfd32e16e1d06b149616b11eef2d081c43d515b492d.py.lock\r\nI1109 21:26:01.747898 139785006901056 filelock.py:274] Lock 139778216290176 acquired on \/home\/kitaev\/.cache\/huggingface\/datasets\/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748258 139785006901056 filelock.py:318] Lock 139778216290176 released on \/home\/kitaev\/.cache\/huggingface\/datasets\/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748412 139785006901056 filelock.py:274] Lock 139778215853024 acquired on \/home\/kitaev\/.cache\/huggingface\/datasets\/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748497 139785006901056 filelock.py:318] Lock 139778215853024 released on \/home\/kitaev\/.cache\/huggingface\/datasets\/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:07:17.029001 140301730502464 filelock.py:274] Lock 140289479304360 acquired on \/home\/kitaev\/.cache\/huggingface\/datasets\/b16d3a04bf2cad1346896852bf120ba846ea1bebb1cd60255bb3a1a2bbcc3a67.ec871b06a00118091ec63eff0a641fddcb8d3c7cd52e855bbb2be28944df4b82.py.lock\r\nI1109 21:07:17.029341 140301730502464 filelock.py:318] Lock 140289479304360 released on \/home\/kitaev\/.cache\/huggingface\/datasets\/b16d3a04bf2cad1346896852bf120ba846ea1bebb1cd60255bb3a1a2bbcc3a67.ec871b06a00118091ec63eff0a641fddcb8d3c7cd52e855bbb2be28944df4b82.py.lock\r\nI1109 21:07:17.058964 140301730502464 filelock.py:274] Lock 140251889388120 acquired on \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.060933 140301730502464 filelock.py:318] Lock 140251889388120 released on \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.061067 140301730502464 filelock.py:274] Lock 140296072521488 acquired on \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.069736 140301730502464 metric.py:400] Removing \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow\r\nI1109 21:07:17.069949 140301730502464 filelock.py:318] Lock 140296072521488 released on \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow.lock\r\n```","body":"I'm doing this in the beginning of my script:\r\n\r\nfrom datasets.utils import logging as datasets_logging\r\ndatasets_logging.set_verbosity_warning()\r\n\r\nbut I'm still getting these logs:\r\n\r\n[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\n[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\nusing datasets version = 1.1.2","comment_length":145,"text":"Too much logging  \n I'm doing this in the beginning of my script:\r\n\r\nfrom datasets.utils import logging as datasets_logging\r\ndatasets_logging.set_verbosity_warning()\r\n\r\nbut I'm still getting these logs:\r\n\r\n[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\n[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\nusing datasets version = 1.1.2 \n +1, the amount of logging is excessive.\r\n\r\nMost of it indeed comes from `filelock.py`, though there are occasionally messages from other sources too. Below is an example (all of these messages were logged after I already called `datasets.logging.set_verbosity_error()`)\r\n\r\n```\r\nI1109 21:26:01.742688 139785006901056 filelock.py:318] Lock 139778216292192 released on \/home\/kitaev\/.cache\/huggingface\/datasets\/9ed4f2e133395826175a892c70611f68522c7bc61a35476e8b51a31afb76e4bf.e6f3e3f3e3875a07469d1cfd32e16e1d06b149616b11eef2d081c43d515b492d.py.lock\r\nI1109 21:26:01.747898 139785006901056 filelock.py:274] Lock 139778216290176 acquired on \/home\/kitaev\/.cache\/huggingface\/datasets\/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748258 139785006901056 filelock.py:318] Lock 139778216290176 released on \/home\/kitaev\/.cache\/huggingface\/datasets\/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748412 139785006901056 filelock.py:274] Lock 139778215853024 acquired on \/home\/kitaev\/.cache\/huggingface\/datasets\/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748497 139785006901056 filelock.py:318] Lock 139778215853024 released on \/home\/kitaev\/.cache\/huggingface\/datasets\/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:07:17.029001 140301730502464 filelock.py:274] Lock 140289479304360 acquired on \/home\/kitaev\/.cache\/huggingface\/datasets\/b16d3a04bf2cad1346896852bf120ba846ea1bebb1cd60255bb3a1a2bbcc3a67.ec871b06a00118091ec63eff0a641fddcb8d3c7cd52e855bbb2be28944df4b82.py.lock\r\nI1109 21:07:17.029341 140301730502464 filelock.py:318] Lock 140289479304360 released on \/home\/kitaev\/.cache\/huggingface\/datasets\/b16d3a04bf2cad1346896852bf120ba846ea1bebb1cd60255bb3a1a2bbcc3a67.ec871b06a00118091ec63eff0a641fddcb8d3c7cd52e855bbb2be28944df4b82.py.lock\r\nI1109 21:07:17.058964 140301730502464 filelock.py:274] Lock 140251889388120 acquired on \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.060933 140301730502464 filelock.py:318] Lock 140251889388120 released on \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.061067 140301730502464 filelock.py:274] Lock 140296072521488 acquired on \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.069736 140301730502464 metric.py:400] Removing \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow\r\nI1109 21:07:17.069949 140301730502464 filelock.py:318] Lock 140296072521488 released on \/home\/kitaev\/.cache\/huggingface\/metrics\/glue\/mnli\/default_experiment-1-0.arrow.lock\r\n```","embeddings":[-0.0745943114,-0.0315374509,-0.051372882,0.1314180642,0.2844710648,0.2179289162,0.261808306,0.4838854373,0.0517462902,-0.1740336418,0.10809917,0.013671496,-0.4307381511,-0.0791786313,-0.1534981877,0.1896948665,0.0866655931,-0.2138257474,-0.2665444911,-0.0811803937,0.0343078077,0.0029079311,0.0677583516,0.0736543462,-0.8763465285,-0.1577260941,0.1634814441,0.0411982201,-0.1345582753,-0.3958104551,-0.1170414612,0.2172026038,0.1443828791,0.2253048569,-0.0001129851,-0.2467205822,0.2506766617,0.0904491022,-0.3506259024,0.3797374666,-0.0783602819,-0.4808190465,0.2577619851,-0.0923956782,0.11044994,-0.5984712243,0.1523717493,-0.5589926839,0.2384316325,0.2718780041,0.183426097,0.1516076922,0.0547525808,0.0827177241,0.1596712321,0.3057552278,0.0517393015,0.0208021719,0.3077190518,-0.122384347,-0.2875323892,0.4429542422,-0.1540036052,-0.1464923471,0.3328672349,-0.0242481306,-0.0548646934,-0.3220509887,0.2032457441,0.303786397,0.253004998,-0.4261766374,-0.3371694684,-0.6361056566,0.1488002837,-0.1059594676,-0.0772842988,0.1271360219,-0.0251316931,0.3121364117,-0.1953437924,-0.1719778031,0.1232566983,-0.2216964662,0.2597734928,-0.0053385668,-0.0639947951,-0.0270456579,0.3266189694,-0.1445752531,-0.0301965568,0.0324756689,-0.1313667446,-0.04204401,-0.4276967943,-0.0721251965,0.3259894252,0.3903737664,0.0073607657,0.1643509418,-0.5164461136,0.0500925705,0.1268525273,-0.1393424422,0.3606739342,0.0856678858,0.2658696175,0.5355189443,0.011528274,-0.077660352,0.1887794137,0.1849962026,0.3528625071,-0.4127818942,0.6333889365,-0.0439091772,0.3881655931,-0.1832707226,-0.056088537,0.0786956623,-0.1461774707,-0.0709004775,0.1323032528,0.1157304496,0.189390257,-0.1116280556,-0.0326280668,0.2161399722,-0.1818705797,-0.0952515155,-0.2570808232,0.2912867963,-0.0619749203,0.237243861,0.1315514147,-0.0919226632,-0.1203432009,0.1508066207,-0.1984521151,-0.1433123052,0.1449324787,-0.0918433368,0.0544101372,0.2532407343,-0.1634290814,0.1583191007,0.1093846411,-0.0930436179,-0.2132111937,-0.1570729762,-0.0029680019,-0.4197719395,-0.1508747041,0.1769854575,-0.3609291315,0.1228154525,0.0573315248,0.0382686593,0.2015438676,0.2192128897,0.1270129085,-0.1874304712,0.047402408,-0.0919263363,0.068847388,0.8149482608,-0.1338517964,-0.114942193,-0.1992361248,-0.2495595217,0.211494863,0.3670786619,0.0883117616,0.453595072,-0.2758108377,0.1275876164,-0.1310860217,-0.3799781203,-0.0529377274,0.260345459,-0.3433253169,0.1315962523,0.5908987522,0.1757712066,0.0965019837,-0.1554005593,0.195778802,-0.1910143644,0.125952825,-0.1410339177,-0.132521987,-0.1077744141,-0.1486362815,0.2916974127,-0.1531303078,-0.358929038,0.0563393645,0.1525655985,0.2883655429,-0.1712488532,-0.0581985675,0.3116374016,0.1786777973,0.0267673787,-0.1798519492,-0.1012191325,-0.3404593468,-0.0530149043,-0.122672379,-0.0383121595,-0.4733019173,-0.3894544542,0.0773143247,0.08421202,-0.1481729448,-0.1231567934,0.0824628696,0.1649744511,-0.2223937958,-0.1105730385,-0.173396185,0.537933588,-0.2286855727,0.2063637227,-0.3914621472,-0.3222681284,0.1621666551,-0.2050782144,0.3768683672,0.2321710289,0.1237566248,-0.0448712409,0.3035086989,0.2935694754,0.1105188653,0.7735791802,0.1506030709,0.2828683257,0.0450517088,0.0371117815,0.0559085794,-0.1027713045,-0.2085751444,-0.0622852445,-0.1143948883,0.2769285142,0.0641203597,0.0131075913,-0.1927532107,0.017503988,-0.0604067445,-0.0479669385,-0.1571537703,-0.4694757462,0.3884260654,0.0354194604,0.1513438672,0.3073644042,0.0933588371,0.2709863782,0.3807005584,-0.3204109967,0.2158672959,0.1895162165,-0.0337895788,-0.0532429703,0.0926937237,0.1728969663,0.1485681534,0.2563018203,0.0606386848,0.2134078592,0.2705394328,-0.1500982791,0.2070964128,-0.0006093183,-0.2361578047,-0.0310836062,-0.1758471876,-0.1036933511,-0.0950704217,-0.1284287721,-0.3363715708,0.3529498875,-0.4624248147,0.0696306601,-0.4349549413,-0.1110905111,-0.2090227604,0.2291614264,0.0648022369,-0.4513124526,0.1271877289,-0.0272102673,-0.2379029542,0.0569796674,-0.0679085702,0.0843495801,0.2101684809,0.0175130218,-0.0816577002,-0.088481389,-0.1031881422,0.0086281681,0.1440275013,-0.0204745978,0.37675789,-0.179528594,0.0304742623,-0.2623321116,-0.2368261367,-0.0319889188,-0.1196215153,0.1111499295,0.3146394789,0.1130473241,0.0980317444,-0.1044176146,-0.3348392546,0.0784268156,0.1560448259,0.0541005991,-0.2534635365,-0.0549040586,0.0237653293,-0.2233605385,-0.2945464253,-0.2709878385,0.2620931566,-0.0507086478,0.0254697483,0.3799551427,-0.0819750875,0.2585391104,-0.485540539,0.4720431864,0.0324536301,-0.1948691607,0.2467245758,0.157118544,-0.0181050766,-0.0250264369,0.0343120098,-0.360928297,-0.1947155744,-0.5805231333,-0.0669255108,-0.4184484482,-0.0779099762,-0.054952912,0.1938615143,0.1924898922,0.2107670456,-0.1480478197,0.1688166708,0.0595319569,0.0140637951,-0.3548664749,0.1852608472,-0.2460646629,0.1779491901,0.2643882036,0.1877130568,0.7277320027,0.0353037119,-0.0418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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/812","title":"Too much logging ","comments":"In the latest version of the lib the logs about locks are at the DEBUG level so you won't see them by default.\r\nAlso `set_verbosity_warning` does take into account these logs now.\r\nCan you try to update the lib ?\r\n```\r\npip install --upgrade datasets\r\n```","body":"I'm doing this in the beginning of my script:\r\n\r\nfrom datasets.utils import logging as datasets_logging\r\ndatasets_logging.set_verbosity_warning()\r\n\r\nbut I'm still getting these logs:\r\n\r\n[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\n[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\nusing datasets version = 1.1.2","comment_length":46,"text":"Too much logging  \n I'm doing this in the beginning of my script:\r\n\r\nfrom datasets.utils import logging as datasets_logging\r\ndatasets_logging.set_verbosity_warning()\r\n\r\nbut I'm still getting these logs:\r\n\r\n[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\n[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\nusing datasets version = 1.1.2 \n In the latest version of the lib the logs about locks are at the DEBUG level so you won't see them by default.\r\nAlso `set_verbosity_warning` does take into account these logs now.\r\nCan you try to update the lib ?\r\n```\r\npip install --upgrade datasets\r\n```","embeddings":[-0.3842003942,-0.1210572794,-0.0782302096,0.1213070825,0.3027052581,0.0185212623,0.1953247935,0.3992016315,0.2092471421,-0.0036375662,0.0869948938,0.2293002307,-0.3797929883,-0.0543083921,-0.1946718246,0.1247082725,0.0595848374,-0.0120906904,-0.296502471,-0.0548450947,-0.0047584106,0.1723802239,-0.0426926166,-0.0422268845,-0.6513229609,-0.0713218078,0.2296185344,0.0982798114,-0.3177295625,-0.5123000741,0.0021837505,0.3213411868,0.0933954567,0.3480690718,-0.0001114561,-0.200807631,0.3850497007,0.0813151449,-0.3874275386,0.1584529728,-0.1273747236,-0.6889070868,0.3180123866,-0.1518696845,-0.0132954363,-0.3914661407,0.1741176695,-0.4228611588,0.3276722729,0.2203636169,0.2372916043,0.2560481131,0.195072785,-0.0656680092,0.1696548164,0.1276334077,-0.1257700175,0.0613509566,0.395629108,0.0216282569,-0.2153387815,0.481187582,-0.2172435373,-0.0182276201,0.1597385854,0.0684910342,-0.1942418665,-0.2886531353,0.2093299776,0.3352796733,0.297159642,-0.4165317714,-0.4168954194,-0.49858284,0.1036061794,-0.1400542855,0.01207841,0.0399671569,-0.0432757102,0.2632344067,-0.2367275953,-0.2274615765,0.1178487316,-0.0880375504,0.3674965203,0.0152489664,-0.1200143695,-0.0643429831,0.3860506415,-0.1922379583,-0.0838978812,0.2320225686,-0.1648689359,-0.0848813429,-0.3075753152,-0.0059677563,0.2617579699,0.5459194183,0.0173493121,0.2318050414,-0.4413781762,0.1197401211,0.1755082458,-0.0772828385,0.1295022219,0.0951479599,0.2877977788,0.4277957082,0.1300466657,0.1100871563,0.0666892081,0.0680088252,0.3630872965,-0.4080975056,0.486507982,0.0096886158,0.4269075096,-0.2582693398,0.0138986977,0.0117206462,0.0211146027,-0.140564844,0.0885047466,0.3236785233,0.0389576517,-0.1044979841,0.0325594507,0.1472505033,-0.3150570095,-0.0125614721,-0.2725060284,0.1832267046,-0.1048332155,0.1756085008,0.1836132258,-0.1565751731,0.2222394794,0.2806104124,-0.0470447801,-0.0109790359,0.080832094,0.0555732138,-0.0364440866,0.3621894717,-0.1134297177,0.1094324514,-0.0041413005,-0.1216425374,-0.161744833,-0.0908530354,0.0200258847,-0.3312015235,-0.2264630497,0.1802598089,-0.3200673759,0.0009690747,0.1256016046,0.0405621193,0.1751790643,0.0196825489,0.1185606271,-0.2449597716,0.0220148806,-0.1212942973,0.1891782135,0.7124804258,-0.1160551086,-0.1059542596,-0.2146634758,-0.2846231461,0.1850438267,0.2023046762,0.077396296,0.1450667828,-0.161702469,0.0507675409,-0.0373744033,-0.4824723601,-0.2755785286,0.1520289034,-0.1536350995,0.0235888343,0.5440300107,0.148409456,0.1424225867,-0.0662036836,-0.0997482762,-0.2115711123,0.193767935,-0.1589092463,-0.1838151515,-0.1617066115,-0.1949259788,0.1878437847,-0.1351660937,-0.2437298298,0.0579747744,0.2588115335,0.2267549187,-0.1032233164,-0.1416472644,0.3722043335,0.3256737292,0.1242635623,-0.1412417442,-0.2158426493,-0.4205309451,0.0231691673,-0.0652115867,0.0638560727,-0.3778139353,-0.3627589345,-0.0519610122,0.0710157827,-0.1508546025,-0.2055117786,0.1037454158,0.1855564564,-0.0640465394,-0.008680488,-0.2316903919,0.4891944528,-0.1248555556,0.2356144786,-0.4816490114,-0.2807700336,-0.0231933761,-0.2656398714,0.2748295665,0.2632425725,0.1486994773,-0.0271445643,0.175705567,0.3804515302,0.0298848189,0.642467618,-0.013993212,0.1570060998,0.0326066762,0.1046307236,0.1406440735,-0.1251662672,-0.2521862984,0.030247815,-0.0197069347,0.2708444595,0.0190177541,0.0220347401,-0.1444375366,-0.0326223113,-0.0307910256,-0.1097344682,-0.1263531148,-0.4837382734,0.4376625419,-0.0284979697,0.2203833312,0.1991180629,-0.0573324375,0.1922785342,0.5509569645,-0.1026852354,0.2122148424,0.1574656218,0.0063915611,0.0618393235,0.1904491186,0.1744951308,0.2084707916,0.3503152132,0.0116701359,0.2073846161,0.1112729833,-0.1860414147,0.1864984185,-0.0509207323,-0.3102308214,0.0356198326,-0.1938985735,-0.1716840267,-0.2291991562,-0.0639284998,-0.4013713598,0.3197189867,-0.3461950123,-0.0856330395,-0.4943998456,-0.0459201634,-0.1702392399,-0.0059617315,0.0087136477,-0.3549367487,0.2253211886,0.0546040572,-0.1508049667,0.2046785206,-0.0311180037,-0.0979538187,0.0676514283,0.0417160057,-0.1886265278,-0.016510332,-0.1442102343,0.0259317867,0.0992549062,0.0115960026,0.3698418438,-0.1825221628,0.0071974611,-0.2161287814,-0.332898736,-0.086498335,-0.3166137636,0.2226391882,0.3451011181,0.2186995894,0.1186477467,-0.0733521432,-0.0937786549,-0.0263979044,0.0352336876,-0.0686816052,-0.1568099409,-0.0560356006,-0.1092876494,-0.4498687685,-0.2963137627,-0.406060338,0.3338097632,-0.0031321058,0.0861224458,0.3134583533,0.0736862049,0.2679020464,-0.4539487958,0.4992460608,0.0025510641,-0.2084095478,0.2432997972,0.0237041265,-0.0945892259,-0.0157246515,0.1261738837,-0.203374356,-0.2979955971,-0.4741601646,-0.2152491212,-0.4398393929,0.0482872352,-0.0393980779,0.2068232894,0.3207935393,0.1873241067,-0.1971336901,-0.0205138903,0.0013091677,-0.0330726765,-0.3698883951,0.206555292,-0.1743269265,0.2902251482,0.0709295571,0.2890484631,0.6024174094,-0.0093561644,0.0520627424,-0.188550666,0.6065215468,-0.2101409137,-0.4003718197,-0.140446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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/812","title":"Too much logging ","comments":"Thanks. For some reason I have to use the older version. Is that possible I can fix this by some surface-level trick?\r\n\r\nI'm still using 1.13 version datasets.","body":"I'm doing this in the beginning of my script:\r\n\r\nfrom datasets.utils import logging as datasets_logging\r\ndatasets_logging.set_verbosity_warning()\r\n\r\nbut I'm still getting these logs:\r\n\r\n[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\n[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\nusing datasets version = 1.1.2","comment_length":28,"text":"Too much logging  \n I'm doing this in the beginning of my script:\r\n\r\nfrom datasets.utils import logging as datasets_logging\r\ndatasets_logging.set_verbosity_warning()\r\n\r\nbut I'm still getting these logs:\r\n\r\n[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\n[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on \/home\/username\/.cache\/huggingface\/datasets\/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock\r\n\r\nusing datasets version = 1.1.2 \n Thanks. For some reason I have to use the older version. Is that possible I can fix this by some surface-level trick?\r\n\r\nI'm still using 1.13 version datasets.","embeddings":[-0.1845477223,-0.0496453494,-0.062777333,0.1152714267,0.3269049823,0.223525539,0.2482613325,0.5285163522,0.0655084327,-0.1148342267,0.0331851132,0.0734856799,-0.3805565834,0.0945336744,-0.1415010095,0.0496978089,0.0995935127,-0.134514451,-0.2368705869,-0.0040861694,0.0368101299,-0.0554204546,-0.0283102822,-0.0276657771,-0.6987543106,-0.057560008,0.1499656737,-0.0849991739,-0.1258638203,-0.4327806532,0.0435505845,0.2825653851,0.347121954,0.2078094333,-0.0001142335,-0.172021687,0.4031152725,0.091663219,-0.3014632165,0.3300531507,-0.0656788871,-0.4610651135,0.2376310527,-0.0499527194,-0.0254255533,-0.5238725543,0.1842403263,-0.4831919372,0.3442483842,0.145630613,0.2353027761,0.1066778302,0.0380543396,0.0139943054,0.0903736874,0.3014698625,-0.0019081363,0.0512065217,0.346989274,-0.134948045,-0.1935210079,0.4127218127,-0.1736093909,-0.104301393,0.3182494938,0.0128091397,-0.0742239431,-0.2959928513,0.3224023581,0.2155657709,0.2681922019,-0.3811312616,-0.3995742798,-0.5169018507,0.0863062814,-0.2460647076,-0.0615998879,0.0412022956,-0.0013887151,0.3254362345,-0.2808995247,-0.3249802887,0.1087714955,-0.1722458899,0.2617557943,0.0027784535,-0.0368907973,-0.0374573171,0.3654305935,-0.1419900358,0.0854225233,0.2243274599,-0.186613366,-0.1186130047,-0.4022973776,-0.0864095986,0.2720977664,0.5041313767,-0.0741478279,0.2894924581,-0.6020694971,0.0333600156,0.1272958368,-0.2467955947,0.2720279694,0.0735769942,0.373291105,0.5536032915,0.0765608624,-0.0217355452,0.1850701272,0.0759661347,0.2913796008,-0.4003849924,0.629802227,-0.0334342271,0.4733229578,-0.2939380109,0.1994019747,-0.1041646525,-0.0936498716,-0.0824798197,0.0064308448,0.2301989943,0.2105492204,-0.1513177454,0.0179953892,0.2042636275,-0.1932067126,-0.2217479199,-0.2325340956,0.2259944081,0.0028698135,0.2076526731,0.2626849711,-0.1796609014,-0.144416824,0.1959083974,-0.2313893288,-0.0759731308,0.1347895414,-0.070946984,-0.0010419055,0.2148303539,-0.1866118759,0.1901022941,-0.0359654501,-0.1311169416,-0.2601169944,-0.0466415808,-0.0500824451,-0.3688070476,-0.1743072122,0.1516400874,-0.2515847087,0.0005147704,-0.0462866426,0.1082532927,0.1905434579,0.2031271458,0.058156319,-0.205994308,-0.2135687172,-0.0273733623,0.0847042352,0.7543078065,-0.1837232411,-0.1074110493,-0.3210379779,-0.244593218,0.1847121269,0.2475205213,0.1200597882,0.3297619522,-0.2883557379,0.0214670021,-0.0718074515,-0.4368183911,-0.215370357,0.1418339163,-0.1801400334,0.0469524451,0.5603656769,0.14654167,0.2135159224,-0.2289565057,0.0564918034,-0.177875936,0.0707222447,-0.1444955915,-0.1745427698,-0.1741807461,-0.0578502901,0.2597694099,-0.0377275422,-0.1251596361,0.010942257,0.0561600626,0.301389575,-0.2052971572,0.0062508853,0.3207732737,0.2069016695,-0.1019963995,-0.1961970329,-0.1875396073,-0.4523920119,-0.0530892164,-0.0805827156,0.0437465049,-0.3083762228,-0.5036979318,-0.0187294688,-0.028476391,-0.1009290442,-0.1177567244,0.038482707,0.1760460734,-0.0963705406,-0.1906676888,-0.0979926139,0.3941920996,-0.1013596356,0.1274788827,-0.4881790876,-0.2294750959,0.0685021281,-0.1784319133,0.4208115637,0.1858517081,0.187051788,-0.0038346234,0.2205534279,0.2407078147,-0.0329660252,0.7907809019,0.0644707307,0.337153852,-0.0882408917,0.1161919013,0.0621470176,-0.0583439618,-0.2887877822,-0.0298366267,-0.0413102433,0.4230466783,0.2049775571,0.1118844748,-0.1287044883,0.0438720994,-0.1169309765,-0.150756672,-0.0841367841,-0.50324893,0.4043030739,-0.0699333474,0.1761554778,0.2005905062,-0.0713346675,0.3584004343,0.4714872539,-0.2176748961,0.2357916981,0.2873411179,-0.0208740979,0.0213779658,0.1399319023,0.1880250573,0.1088555008,0.2872410119,0.0148908999,0.3430375755,0.3121269941,-0.0554930381,0.300398469,-0.0318197086,-0.244365558,-0.1732458025,-0.0298624411,-0.1476175487,-0.0066398615,0.0383540168,-0.4003477991,0.3763830364,-0.4999678731,0.0595466942,-0.6568230987,-0.2970498204,-0.2544571459,0.1902570277,0.039268665,-0.3844943941,0.1392332911,0.0904712453,-0.145260185,0.1740479767,-0.0440053269,0.1154812723,0.0897040889,0.1575214565,-0.3321352303,-0.0229365006,-0.1803100407,0.0299765542,0.2657260895,-0.005944049,0.4097063541,-0.200605467,-0.051324062,-0.2849327624,-0.3416579366,-0.0627895817,-0.1004976705,0.1743464172,0.2368714213,0.1116315499,0.0671489239,0.0330287442,-0.1913238019,-0.0941478387,0.0959202647,-0.0322462767,-0.2914229035,0.1275749952,-0.0760287493,-0.327640295,-0.2210877985,-0.2438619435,0.3612037003,-0.0425977185,0.1103075221,0.3691274524,-0.1180830151,0.0815081075,-0.413200736,0.422019273,-0.0115909111,-0.2237794101,0.254720211,0.0998540521,-0.0389037132,-0.0693699345,0.0822910964,-0.3534398973,-0.2201089859,-0.5487343669,-0.2341194451,-0.4150407314,-0.0939439908,-0.0649746358,0.185556367,0.2899772227,0.1923263669,-0.0875974,0.1645504981,-0.0551616661,-0.0478195921,-0.3874906301,0.1405472755,-0.0966754854,0.2842620909,0.1976348758,0.3441001773,0.5574746132,0.0424780734,-0.0754356161,-0.2141036689,0.5718830228,-0.2818314731,-0.3039311767,-0.1314197332,0.11560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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/809","title":"Add Google Taskmaster dataset","comments":"Hey @yjernite. Was going to start working on this but found taskmaster 1,2 & 3 in the datasets library already so think this can be closed now?","body":"## Adding a Dataset\r\n- **Name:** Taskmaster\r\n- **Description:** A large dataset of task-oriented dialogue with annotated goals (55K dialogues covering entertainment and travel reservations)\r\n- **Paper:** https:\/\/arxiv.org\/abs\/1909.05358\r\n- **Data:** https:\/\/github.com\/google-research-datasets\/Taskmaster\r\n- **Motivation:** One of few annotated datasets of this size for goal-oriented dialogue\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":27,"text":"Add Google Taskmaster dataset \n ## Adding a Dataset\r\n- **Name:** Taskmaster\r\n- **Description:** A large dataset of task-oriented dialogue with annotated goals (55K dialogues covering entertainment and travel reservations)\r\n- **Paper:** https:\/\/arxiv.org\/abs\/1909.05358\r\n- **Data:** https:\/\/github.com\/google-research-datasets\/Taskmaster\r\n- **Motivation:** One of few annotated datasets of this size for goal-oriented dialogue\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n Hey @yjernite. Was going to start working on this but found taskmaster 1,2 & 3 in the datasets library already so think this can be closed now?","embeddings":[-0.2614889145,0.0552474037,-0.1595136374,0.2156586349,0.1674283445,-0.0379510708,0.3336213231,0.0670085549,0.132318899,0.1240423247,-0.2129537016,0.2662527859,-0.48644045,0.5283167362,0.1895260811,-0.0497275516,0.0831428096,-0.087164484,0.1714651287,0.1660844684,-0.1717765331,0.0900838003,-0.1450598985,0.023761902,-0.1984927356,-0.2001335025,0.027907839,-0.0120169437,-0.1924094111,-0.3131717443,-0.030132398,0.3213685751,-0.0966233164,0.7043562531,-0.0001007273,-0.3078626096,0.1542027891,-0.1997465938,-0.3623100519,-0.0376223251,-0.1035003513,0.0397048853,-0.1769136488,-0.0062243836,-0.2134982497,-0.1862116456,0.0373942629,0.0631220043,0.1247560009,0.4792369604,0.2868831754,0.0314498469,0.0237882473,-0.3139028549,-0.0892795473,0.1673174053,-0.325029403,0.1835339516,0.2450786382,0.0302086752,-0.1205400303,0.3014375567,-0.1818485111,-0.1398768127,0.205429107,-0.2605779767,-0.1797824502,-0.2628004551,0.1561324,0.3817877769,0.578802824,-0.1634546965,-0.129009068,-0.4247144163,-0.0288697425,-0.1403267533,-0.1643438786,0.2142509669,-0.1045960262,0.1321996301,-0.2390735745,-0.3919066191,-0.0509699248,0.0245202649,0.0453483388,0.1810941994,-0.2719650269,-0.1165957153,0.1177231744,-0.2911083996,-0.1091829017,0.0246927124,-0.1397085935,-0.0661734268,-0.5199308991,-0.3846782446,0.3971691728,0.248669222,0.3383380771,0.1867453456,0.1201629192,-0.0119864037,0.0094055673,-0.061529085,0.4166882336,0.1291999519,0.0020518445,-0.0619577542,0.1798332483,0.2364335507,0.1209973842,0.2560901642,-0.172796011,-0.0051418608,-0.0672673434,-0.0164523423,-0.0275292192,0.1008455828,-0.3065927327,0.2058406472,-0.1520988345,0.0444887653,0.1388910264,0.2740323842,-0.2231860757,0.0721610114,-0.0989489108,0.1962263733,0.0454923175,-0.0089532947,-0.0678549334,0.1156092584,-0.28828758,0.3044955134,0.1619835049,0.224510029,0.0406731032,-0.1672491878,-0.0642289221,-0.094134897,0.1624991298,-0.1709158123,-0.0306092966,-0.1118817329,0.0123427762,-0.3497440219,-0.2084514499,0.0089686243,-0.1720944494,-0.0218165051,0.184971571,-0.2141790539,-0.1163008586,0.2941468954,-0.159095183,-0.2060000747,-0.3234015107,0.7503315806,-0.0713446587,-0.0585385524,0.2222454846,0.2418702245,-0.3724497855,-0.1413063854,-0.0511270277,0.3489097655,0.0621128753,-0.0115338294,-0.1397532374,-0.1462148875,-0.4228977561,0.3107075989,0.0636393279,0.4232715368,-0.1566381156,0.0577303506,-0.0616485551,-0.2638650537,-0.2786205709,0.1703373045,-0.5045643449,0.0620137304,0.2612501681,0.1390815079,0.4737354517,-0.4069030583,0.5480496287,0.235035345,-0.1516464949,0.02583923,-0.1084815115,-0.3002912104,0.0096795065,0.3743725121,0.1809770018,-0.2353051156,-0.1113969535,-0.1721189767,0.0077166096,-0.1511417925,0.0177359395,0.1571633518,0.3781970143,0.1448082924,-0.0746482313,-0.35207811,-0.4401951134,-0.2714777887,0.0580170304,0.1237808242,0.2761168778,-0.4302568138,-0.163715601,-0.0106257489,-0.0130670462,-0.146705851,0.2628631294,0.1287089437,0.0623064376,-0.1343763471,-0.1763136238,0.5124223828,-0.1791616082,-0.0888966843,0.1272492558,0.0461914353,0.0050449008,-0.052316092,0.2747652829,0.4075893462,-0.0309089236,0.1027138978,0.2287934124,0.1635100693,-0.0730133355,0.2508262694,0.2525398135,0.4002345502,-0.0040863007,-0.2029565871,-0.0149969924,0.2142480314,0.0235825907,-0.0960364863,-0.2542673647,0.268188864,0.2542662621,0.0910768807,0.0821855739,0.1735062152,0.1134506762,-0.209338516,-0.1832897663,0.0297998786,0.1873932481,0.213800773,-0.0630107,0.1144268438,-0.691962719,0.1650878191,0.3536649942,-0.0179386362,0.4091228545,0.183029905,-0.1275765747,-0.3061305285,-0.0035227491,-0.3534864485,0.104459025,0.4246437848,0.0630821511,-0.0029152632,0.2378720194,-0.1665383875,0.1324298829,0.0892466307,0.0002087655,0.2678321302,0.2840036154,-0.0470410027,-0.2962804437,-0.2191294581,-0.0053095068,0.0315587185,-0.1969404519,-0.199775815,0.1438695788,-0.2817783356,-0.0144887408,-0.0204758402,-0.0104614664,-0.0066536441,0.2491914332,0.1484094262,-0.0625197962,0.1598895639,0.2966145277,0.5640652776,-0.2380785495,0.0970344841,-0.1008222625,-0.5919599533,0.003471192,0.2379782796,0.1573915333,-0.2912105918,0.5987552404,-0.0476847626,0.1234845892,-0.5213546157,-0.4303777218,0.2126313001,-0.3136650622,0.2960092425,0.1633021832,0.066306673,-0.0380839072,0.0635015443,-0.2227798551,0.1621677428,-0.06548962,-0.2262498736,-0.1913326383,0.0895263776,-0.0155229745,-0.314037919,-0.079275474,-0.6464050412,0.5182401538,-0.1646692306,0.1542172432,0.3452112377,-0.1528852731,0.1042081341,-0.1129702479,0.0673101693,-0.3176529706,0.1281550825,0.1276369393,-0.2890620232,-0.1419183612,-0.2176164985,-0.2709980309,0.1267265081,-0.0954340026,-0.3981805146,0.1359130889,-0.0661595017,0.095359318,-0.0531415045,-0.2207897604,0.2568977177,-0.016948536,-0.2660772502,-0.0653651655,-0.3890554309,0.1412203312,0.2797262073,0.0679741129,0.0680811852,0.0780960247,0.0350673497,0.2983905077,-0.0397373848,0.2279607505,0.1530387551,0.0089080166,0.3236825168,-0.0899442136,-0.2734912038,0.5499250889,0.2022094429,-0.0500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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/807","title":"load_dataset for LOCAL CSV files report CONNECTION ERROR","comments":"Hi !\r\nThe url works on my side.\r\n\r\nIs the url working in your navigator ?\r\nAre you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?","body":"## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n```","comment_length":30,"text":"load_dataset for LOCAL CSV files report CONNECTION ERROR \n ## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n``` \n Hi !\r\nThe url works on my side.\r\n\r\nIs the url working in your navigator ?\r\nAre you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?","embeddings":[-0.324712038,0.0161048565,-0.1088049561,0.0260043461,0.2420431376,0.0198685937,0.7173692584,0.3756367266,0.2509218454,0.1337042451,-0.0342321098,0.1497044712,0.0797994062,0.0211837217,-0.1269313991,-0.1772823036,-0.1271574497,0.2743321061,-0.4187993109,0.0144969616,-0.1248850673,-0.0023575695,-0.0349380858,0.1405339241,-0.2024638206,-0.0062384652,0.1157821938,0.1312774718,-0.0276923925,-0.493919909,0.3365218639,0.0402496904,0.323772788,0.3632176518,-0.0001060309,0.2135237306,0.2517406344,-0.0288667344,-0.4279088378,-0.4611105323,-0.1089922264,-0.3690283597,0.4228135347,-0.3986177444,0.0005737995,-0.1198977008,-0.0673584118,-0.3161996007,0.4313887954,0.4785009623,0.2786398232,0.4154756367,0.0165828858,-0.1110344306,0.0718218759,0.0555340908,-0.0624923073,0.4061276317,0.0516244806,-0.1849907339,-0.242678538,0.0846120864,-0.1926984191,0.3955322206,0.1906039566,0.0830963105,0.0377710871,-0.1966335922,0.1252265424,0.107683368,0.4764897823,-0.2314554602,-0.2321467549,-0.0584997945,-0.1994815022,-0.1881569773,0.1660740525,0.0867301002,-0.1683380306,0.1474405825,-0.2638413012,0.0990802348,-0.3907521665,0.1494552046,-0.1105123758,0.1547572166,-0.067184031,0.1947448403,0.158264637,-0.056483563,0.4036888182,-0.2070344985,0.1916099489,0.1276624054,-0.3377639651,0.1311576068,0.1394052505,-0.3861347735,-0.1064571813,0.0498938598,-0.0361735485,-0.0535907373,0.1335505396,0.2916464508,0.1983619481,0.0791434944,-0.1308648437,0.2947734892,0.2687248588,0.1325622052,0.042261485,-0.0624847747,-0.0800571069,-0.3558065295,0.1634977609,0.1999797225,0.4894587696,-0.0474283807,-0.2816003859,0.1326408088,0.0129102021,-0.1073727086,0.0189691093,0.3589312434,-0.0879683271,0.2043156773,0.0424528793,0.0136886993,-0.2235278487,0.0845431536,-0.2120842189,0.0379552618,-0.1193305477,-0.0695323423,0.3813319206,-0.0790898874,0.0466334634,-0.0146686006,0.0989310592,0.0503491461,0.2611702383,-0.2932826877,0.0491831452,0.4129322171,0.0561541021,0.2478840053,0.4095256925,-0.1842343062,-0.1060605422,0.0865907371,-0.2045428902,-0.3371692896,-0.3385475874,0.2402119339,0.0368901119,0.1089414582,-0.0184665043,-0.3302738965,0.0986591801,-0.2747161686,-0.1541592777,-0.3237557411,-0.0389051214,-0.2159913927,0.3081872761,0.6463362575,-0.4303185344,0.0429565385,0.0566363595,-0.1299165338,0.1414598227,0.2508479655,-0.3076960742,0.0851259753,-0.2284607738,-0.0966767818,0.2314821184,-0.3093754947,-0.3032815158,0.5005193353,-0.2373457253,-0.072943598,0.2169772685,0.0126159824,0.291056484,0.1858281493,0.2615151703,0.3000478446,-0.1007597595,0.1175532788,-0.0799232721,-0.222560361,0.1973579973,0.1164559647,0.0098163048,0.2231837362,0.3249672055,-0.2579794228,0.1741140336,-0.0768722668,0.1447919756,0.1345079839,0.1739715636,0.0419842489,-0.0606005527,-0.021664409,-0.508518219,0.1381557435,0.1310921013,0.0903006941,-0.4376563728,-0.0162693374,-0.3771235943,0.0051721488,-0.2671605647,-0.030241888,0.2100175917,0.3415310383,-0.0191593226,-0.0523174517,-0.0488567986,0.3256911337,-0.1166130304,-0.0192557331,-0.4156301022,0.3395552039,-0.3544683456,-0.0690031797,0.052354306,-0.0314197689,0.1626497358,-0.1825555712,-0.2719682157,0.356123805,-0.0955856889,0.2182246149,0.0170497447,-0.0044389288,0.0526204556,-0.2450993061,0.0323775932,0.0311139729,0.1533319503,0.0431826673,-0.261313647,0.3525983989,-0.0953487977,0.163369596,0.0506540611,0.0704118088,0.4514212012,0.0119942082,-0.1140478626,-0.1085091978,0.1054950804,0.2389213145,0.2262360007,0.0491035245,-0.1802719235,-0.1886238307,0.2018595934,-0.1532273442,0.0116079831,0.0923530832,-0.1086221784,-0.0442817397,0.214684695,0.2319191694,0.5149133801,0.1026022732,-0.1526015699,-0.0183210652,-0.0503232926,-0.1338230819,0.1102777719,-0.0386922434,0.0448774025,0.3272635043,0.0759821385,0.1069803238,-0.2410078943,-0.3630864322,-0.0419490486,0.2638888955,-0.3493005633,0.2479265183,-0.1645680964,0.0182821918,-0.2659018636,-0.0503300428,-0.1105000004,-0.178074345,-0.3695805371,0.3607570827,-0.0763827115,0.2372517288,-0.279001534,-0.0516414121,0.3879840374,-0.4498327672,-0.0730597377,-0.2081662267,-0.1601461768,0.0924806148,0.0935094729,-0.1568920612,0.303164959,-0.0989919826,0.0205262117,-0.16106233,-0.0238344092,-0.0821166858,0.0125281876,0.1283056736,0.1878541559,0.4475226104,0.1206022948,-0.5035608411,0.3184547424,-0.0834972411,0.0481938235,0.1964564323,-0.0537022725,-0.2032553554,0.0006876013,-0.6345347762,-0.5991461873,-0.469863981,0.1375343353,0.0665089488,0.2132315189,0.295524776,0.2269022167,0.2099236399,0.0866580531,0.0757485032,-0.0596129522,-0.1591841877,0.3499985933,-0.2349603325,-0.3570283055,-0.1133962646,-0.1152546331,0.1219444349,0.1364867091,-0.444775492,-0.0236161184,-0.1746839583,0.3565398753,-0.0467238724,0.0517050326,0.4662779868,0.1686569452,-0.1706291884,0.1126605943,-0.244187057,-0.1408112943,0.1942563504,0.0843700692,0.0044104136,0.4593695402,-0.158959344,0.653288424,-0.0798089057,-0.1897096932,0.2884309888,-0.1362253278,0.342966646,-0.1845075488,-0.6308505535,-0.0917780697,-0.3062294424,-0.20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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/807","title":"load_dataset for LOCAL CSV files report CONNECTION ERROR","comments":"> Hi !\r\n> The url works on my side.\r\n> \r\n> Is the url working in your navigator ?\r\n> Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n\r\nI tried another server, it's working now. Thanks a lot.\r\n\r\nAnd I'm curious about why download things from \"github\" when I load dataset from local files ?  Dose datasets work if my network crashed?","body":"## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n```","comment_length":69,"text":"load_dataset for LOCAL CSV files report CONNECTION ERROR \n ## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n``` \n > Hi !\r\n> The url works on my side.\r\n> \r\n> Is the url working in your navigator ?\r\n> Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n\r\nI tried another server, it's working now. Thanks a lot.\r\n\r\nAnd I'm curious about why download things from \"github\" when I load dataset from local files ?  Dose datasets work if my network crashed?","embeddings":[-0.324712038,0.0161048565,-0.1088049561,0.0260043461,0.2420431376,0.0198685937,0.7173692584,0.3756367266,0.2509218454,0.1337042451,-0.0342321098,0.1497044712,0.0797994062,0.0211837217,-0.1269313991,-0.1772823036,-0.1271574497,0.2743321061,-0.4187993109,0.0144969616,-0.1248850673,-0.0023575695,-0.0349380858,0.1405339241,-0.2024638206,-0.0062384652,0.1157821938,0.1312774718,-0.0276923925,-0.493919909,0.3365218639,0.0402496904,0.323772788,0.3632176518,-0.0001060309,0.2135237306,0.2517406344,-0.0288667344,-0.4279088378,-0.4611105323,-0.1089922264,-0.3690283597,0.4228135347,-0.3986177444,0.0005737995,-0.1198977008,-0.0673584118,-0.3161996007,0.4313887954,0.4785009623,0.2786398232,0.4154756367,0.0165828858,-0.1110344306,0.0718218759,0.0555340908,-0.0624923073,0.4061276317,0.0516244806,-0.1849907339,-0.242678538,0.0846120864,-0.1926984191,0.3955322206,0.1906039566,0.0830963105,0.0377710871,-0.1966335922,0.1252265424,0.107683368,0.4764897823,-0.2314554602,-0.2321467549,-0.0584997945,-0.1994815022,-0.1881569773,0.1660740525,0.0867301002,-0.1683380306,0.1474405825,-0.2638413012,0.0990802348,-0.3907521665,0.1494552046,-0.1105123758,0.1547572166,-0.067184031,0.1947448403,0.158264637,-0.056483563,0.4036888182,-0.2070344985,0.1916099489,0.1276624054,-0.3377639651,0.1311576068,0.1394052505,-0.3861347735,-0.1064571813,0.0498938598,-0.0361735485,-0.0535907373,0.1335505396,0.2916464508,0.1983619481,0.0791434944,-0.1308648437,0.2947734892,0.2687248588,0.1325622052,0.042261485,-0.0624847747,-0.0800571069,-0.3558065295,0.1634977609,0.1999797225,0.4894587696,-0.0474283807,-0.2816003859,0.1326408088,0.0129102021,-0.1073727086,0.0189691093,0.3589312434,-0.0879683271,0.2043156773,0.0424528793,0.0136886993,-0.2235278487,0.0845431536,-0.2120842189,0.0379552618,-0.1193305477,-0.0695323423,0.3813319206,-0.0790898874,0.0466334634,-0.0146686006,0.0989310592,0.0503491461,0.2611702383,-0.2932826877,0.0491831452,0.4129322171,0.0561541021,0.2478840053,0.4095256925,-0.1842343062,-0.1060605422,0.0865907371,-0.2045428902,-0.3371692896,-0.3385475874,0.2402119339,0.0368901119,0.1089414582,-0.0184665043,-0.3302738965,0.0986591801,-0.2747161686,-0.1541592777,-0.3237557411,-0.0389051214,-0.2159913927,0.3081872761,0.6463362575,-0.4303185344,0.0429565385,0.0566363595,-0.1299165338,0.1414598227,0.2508479655,-0.3076960742,0.0851259753,-0.2284607738,-0.0966767818,0.2314821184,-0.3093754947,-0.3032815158,0.5005193353,-0.2373457253,-0.072943598,0.2169772685,0.0126159824,0.291056484,0.1858281493,0.2615151703,0.3000478446,-0.1007597595,0.1175532788,-0.0799232721,-0.222560361,0.1973579973,0.1164559647,0.0098163048,0.2231837362,0.3249672055,-0.2579794228,0.1741140336,-0.0768722668,0.1447919756,0.1345079839,0.1739715636,0.0419842489,-0.0606005527,-0.021664409,-0.508518219,0.1381557435,0.1310921013,0.0903006941,-0.4376563728,-0.0162693374,-0.3771235943,0.0051721488,-0.2671605647,-0.030241888,0.2100175917,0.3415310383,-0.0191593226,-0.0523174517,-0.0488567986,0.3256911337,-0.1166130304,-0.0192557331,-0.4156301022,0.3395552039,-0.3544683456,-0.0690031797,0.052354306,-0.0314197689,0.1626497358,-0.1825555712,-0.2719682157,0.356123805,-0.0955856889,0.2182246149,0.0170497447,-0.0044389288,0.0526204556,-0.2450993061,0.0323775932,0.0311139729,0.1533319503,0.0431826673,-0.261313647,0.3525983989,-0.0953487977,0.163369596,0.0506540611,0.0704118088,0.4514212012,0.0119942082,-0.1140478626,-0.1085091978,0.1054950804,0.2389213145,0.2262360007,0.0491035245,-0.1802719235,-0.1886238307,0.2018595934,-0.1532273442,0.0116079831,0.0923530832,-0.1086221784,-0.0442817397,0.214684695,0.2319191694,0.5149133801,0.1026022732,-0.1526015699,-0.0183210652,-0.0503232926,-0.1338230819,0.1102777719,-0.0386922434,0.0448774025,0.3272635043,0.0759821385,0.1069803238,-0.2410078943,-0.3630864322,-0.0419490486,0.2638888955,-0.3493005633,0.2479265183,-0.1645680964,0.0182821918,-0.2659018636,-0.0503300428,-0.1105000004,-0.178074345,-0.3695805371,0.3607570827,-0.0763827115,0.2372517288,-0.279001534,-0.0516414121,0.3879840374,-0.4498327672,-0.0730597377,-0.2081662267,-0.1601461768,0.0924806148,0.0935094729,-0.1568920612,0.303164959,-0.0989919826,0.0205262117,-0.16106233,-0.0238344092,-0.0821166858,0.0125281876,0.1283056736,0.1878541559,0.4475226104,0.1206022948,-0.5035608411,0.3184547424,-0.0834972411,0.0481938235,0.1964564323,-0.0537022725,-0.2032553554,0.0006876013,-0.6345347762,-0.5991461873,-0.469863981,0.1375343353,0.0665089488,0.2132315189,0.295524776,0.2269022167,0.2099236399,0.0866580531,0.0757485032,-0.0596129522,-0.1591841877,0.3499985933,-0.2349603325,-0.3570283055,-0.1133962646,-0.1152546331,0.1219444349,0.1364867091,-0.444775492,-0.0236161184,-0.1746839583,0.3565398753,-0.0467238724,0.0517050326,0.4662779868,0.1686569452,-0.1706291884,0.1126605943,-0.244187057,-0.1408112943,0.1942563504,0.0843700692,0.0044104136,0.4593695402,-0.158959344,0.653288424,-0.0798089057,-0.1897096932,0.2884309888,-0.1362253278,0.342966646,-0.1845075488,-0.6308505535,-0.0917780697,-0.306229442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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/807","title":"load_dataset for LOCAL CSV files report CONNECTION ERROR","comments":"\r\n\r\n\r\n> > Hi !\r\n> > The url works on my side.\r\n> > Is the url working in your navigator ?\r\n> > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> \r\n> I tried another server, it's working now. Thanks a lot.\r\n> \r\n> And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n\r\nI download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f \r\n\r\nThanks :D","body":"## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n```","comment_length":103,"text":"load_dataset for LOCAL CSV files report CONNECTION ERROR \n ## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n``` \n \r\n\r\n\r\n> > Hi !\r\n> > The url works on my side.\r\n> > Is the url working in your navigator ?\r\n> > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> \r\n> I tried another server, it's working now. Thanks a lot.\r\n> \r\n> And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n\r\nI download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f \r\n\r\nThanks :D","embeddings":[-0.324712038,0.0161048565,-0.1088049561,0.0260043461,0.2420431376,0.0198685937,0.7173692584,0.3756367266,0.2509218454,0.1337042451,-0.0342321098,0.1497044712,0.0797994062,0.0211837217,-0.1269313991,-0.1772823036,-0.1271574497,0.2743321061,-0.4187993109,0.0144969616,-0.1248850673,-0.0023575695,-0.0349380858,0.1405339241,-0.2024638206,-0.0062384652,0.1157821938,0.1312774718,-0.0276923925,-0.493919909,0.3365218639,0.0402496904,0.323772788,0.3632176518,-0.0001060309,0.2135237306,0.2517406344,-0.0288667344,-0.4279088378,-0.4611105323,-0.1089922264,-0.3690283597,0.4228135347,-0.3986177444,0.0005737995,-0.1198977008,-0.0673584118,-0.3161996007,0.4313887954,0.4785009623,0.2786398232,0.4154756367,0.0165828858,-0.1110344306,0.0718218759,0.0555340908,-0.0624923073,0.4061276317,0.0516244806,-0.1849907339,-0.242678538,0.0846120864,-0.1926984191,0.3955322206,0.1906039566,0.0830963105,0.0377710871,-0.1966335922,0.1252265424,0.107683368,0.4764897823,-0.2314554602,-0.2321467549,-0.0584997945,-0.1994815022,-0.1881569773,0.1660740525,0.0867301002,-0.1683380306,0.1474405825,-0.2638413012,0.0990802348,-0.3907521665,0.1494552046,-0.1105123758,0.1547572166,-0.067184031,0.1947448403,0.158264637,-0.056483563,0.4036888182,-0.2070344985,0.1916099489,0.1276624054,-0.3377639651,0.1311576068,0.1394052505,-0.3861347735,-0.1064571813,0.0498938598,-0.0361735485,-0.0535907373,0.1335505396,0.2916464508,0.1983619481,0.0791434944,-0.1308648437,0.2947734892,0.2687248588,0.1325622052,0.042261485,-0.0624847747,-0.0800571069,-0.3558065295,0.1634977609,0.1999797225,0.4894587696,-0.0474283807,-0.2816003859,0.1326408088,0.0129102021,-0.1073727086,0.0189691093,0.3589312434,-0.0879683271,0.2043156773,0.0424528793,0.0136886993,-0.2235278487,0.0845431536,-0.2120842189,0.0379552618,-0.1193305477,-0.0695323423,0.3813319206,-0.0790898874,0.0466334634,-0.0146686006,0.0989310592,0.0503491461,0.2611702383,-0.2932826877,0.0491831452,0.4129322171,0.0561541021,0.2478840053,0.4095256925,-0.1842343062,-0.1060605422,0.0865907371,-0.2045428902,-0.3371692896,-0.3385475874,0.2402119339,0.0368901119,0.1089414582,-0.0184665043,-0.3302738965,0.0986591801,-0.2747161686,-0.1541592777,-0.3237557411,-0.0389051214,-0.2159913927,0.3081872761,0.6463362575,-0.4303185344,0.0429565385,0.0566363595,-0.1299165338,0.1414598227,0.2508479655,-0.3076960742,0.0851259753,-0.2284607738,-0.0966767818,0.2314821184,-0.3093754947,-0.3032815158,0.5005193353,-0.2373457253,-0.072943598,0.2169772685,0.0126159824,0.291056484,0.1858281493,0.2615151703,0.3000478446,-0.1007597595,0.1175532788,-0.0799232721,-0.222560361,0.1973579973,0.1164559647,0.0098163048,0.2231837362,0.3249672055,-0.2579794228,0.1741140336,-0.0768722668,0.1447919756,0.1345079839,0.1739715636,0.0419842489,-0.0606005527,-0.021664409,-0.508518219,0.1381557435,0.1310921013,0.0903006941,-0.4376563728,-0.0162693374,-0.3771235943,0.0051721488,-0.2671605647,-0.030241888,0.2100175917,0.3415310383,-0.0191593226,-0.0523174517,-0.0488567986,0.3256911337,-0.1166130304,-0.0192557331,-0.4156301022,0.3395552039,-0.3544683456,-0.0690031797,0.052354306,-0.0314197689,0.1626497358,-0.1825555712,-0.2719682157,0.356123805,-0.0955856889,0.2182246149,0.0170497447,-0.0044389288,0.0526204556,-0.2450993061,0.0323775932,0.0311139729,0.1533319503,0.0431826673,-0.261313647,0.3525983989,-0.0953487977,0.163369596,0.0506540611,0.0704118088,0.4514212012,0.0119942082,-0.1140478626,-0.1085091978,0.1054950804,0.2389213145,0.2262360007,0.0491035245,-0.1802719235,-0.1886238307,0.2018595934,-0.1532273442,0.0116079831,0.0923530832,-0.1086221784,-0.0442817397,0.214684695,0.2319191694,0.5149133801,0.1026022732,-0.1526015699,-0.0183210652,-0.0503232926,-0.1338230819,0.1102777719,-0.0386922434,0.0448774025,0.3272635043,0.0759821385,0.1069803238,-0.2410078943,-0.3630864322,-0.0419490486,0.2638888955,-0.3493005633,0.2479265183,-0.1645680964,0.0182821918,-0.2659018636,-0.0503300428,-0.1105000004,-0.178074345,-0.3695805371,0.3607570827,-0.0763827115,0.2372517288,-0.279001534,-0.0516414121,0.3879840374,-0.4498327672,-0.0730597377,-0.2081662267,-0.1601461768,0.0924806148,0.0935094729,-0.1568920612,0.303164959,-0.0989919826,0.0205262117,-0.16106233,-0.0238344092,-0.0821166858,0.0125281876,0.1283056736,0.1878541559,0.4475226104,0.1206022948,-0.5035608411,0.3184547424,-0.0834972411,0.0481938235,0.1964564323,-0.0537022725,-0.2032553554,0.0006876013,-0.6345347762,-0.5991461873,-0.469863981,0.1375343353,0.0665089488,0.2132315189,0.295524776,0.2269022167,0.2099236399,0.0866580531,0.0757485032,-0.0596129522,-0.1591841877,0.3499985933,-0.2349603325,-0.3570283055,-0.1133962646,-0.1152546331,0.1219444349,0.1364867091,-0.444775492,-0.0236161184,-0.1746839583,0.3565398753,-0.0467238724,0.0517050326,0.4662779868,0.1686569452,-0.1706291884,0.1126605943,-0.244187057,-0.1408112943,0.1942563504,0.0843700692,0.0044104136,0.4593695402,-0.158959344,0.653288424,-0.0798089057,-0.1897096932,0.2884309888,-0.1362253278,0.342966646,-0.1845075488,-0.6308505535,-0.0917780697,-0.3062294424,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/807","title":"load_dataset for LOCAL CSV files report CONNECTION ERROR","comments":"hello, how did you solve this problems?\r\n\r\n> > > Hi !\r\n> > > The url works on my side.\r\n> > > Is the url working in your navigator ?\r\n> > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > \r\n> > \r\n> > I tried another server, it's working now. Thanks a lot.\r\n> > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> \r\n> I download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f\r\n> \r\n> Thanks :D\r\n\r\nhello, I tried this. but it still failed. how do you fix this error?","body":"## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n```","comment_length":136,"text":"load_dataset for LOCAL CSV files report CONNECTION ERROR \n ## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n``` \n hello, how did you solve this problems?\r\n\r\n> > > Hi !\r\n> > > The url works on my side.\r\n> > > Is the url working in your navigator ?\r\n> > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > \r\n> > \r\n> > I tried another server, it's working now. Thanks a lot.\r\n> > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> \r\n> I download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f\r\n> \r\n> Thanks :D\r\n\r\nhello, I tried this. but it still failed. how do you fix this error?","embeddings":[-0.324712038,0.0161048565,-0.1088049561,0.0260043461,0.2420431376,0.0198685937,0.7173692584,0.3756367266,0.2509218454,0.1337042451,-0.0342321098,0.1497044712,0.0797994062,0.0211837217,-0.1269313991,-0.1772823036,-0.1271574497,0.2743321061,-0.4187993109,0.0144969616,-0.1248850673,-0.0023575695,-0.0349380858,0.1405339241,-0.2024638206,-0.0062384652,0.1157821938,0.1312774718,-0.0276923925,-0.493919909,0.3365218639,0.0402496904,0.323772788,0.3632176518,-0.0001060309,0.2135237306,0.2517406344,-0.0288667344,-0.4279088378,-0.4611105323,-0.1089922264,-0.3690283597,0.4228135347,-0.3986177444,0.0005737995,-0.1198977008,-0.0673584118,-0.3161996007,0.4313887954,0.4785009623,0.2786398232,0.4154756367,0.0165828858,-0.1110344306,0.0718218759,0.0555340908,-0.0624923073,0.4061276317,0.0516244806,-0.1849907339,-0.242678538,0.0846120864,-0.1926984191,0.3955322206,0.1906039566,0.0830963105,0.0377710871,-0.1966335922,0.1252265424,0.107683368,0.4764897823,-0.2314554602,-0.2321467549,-0.0584997945,-0.1994815022,-0.1881569773,0.1660740525,0.0867301002,-0.1683380306,0.1474405825,-0.2638413012,0.0990802348,-0.3907521665,0.1494552046,-0.1105123758,0.1547572166,-0.067184031,0.1947448403,0.158264637,-0.056483563,0.4036888182,-0.2070344985,0.1916099489,0.1276624054,-0.3377639651,0.1311576068,0.1394052505,-0.3861347735,-0.1064571813,0.0498938598,-0.0361735485,-0.0535907373,0.1335505396,0.2916464508,0.1983619481,0.0791434944,-0.1308648437,0.2947734892,0.2687248588,0.1325622052,0.042261485,-0.0624847747,-0.0800571069,-0.3558065295,0.1634977609,0.1999797225,0.4894587696,-0.0474283807,-0.2816003859,0.1326408088,0.0129102021,-0.1073727086,0.0189691093,0.3589312434,-0.0879683271,0.2043156773,0.0424528793,0.0136886993,-0.2235278487,0.0845431536,-0.2120842189,0.0379552618,-0.1193305477,-0.0695323423,0.3813319206,-0.0790898874,0.0466334634,-0.0146686006,0.0989310592,0.0503491461,0.2611702383,-0.2932826877,0.0491831452,0.4129322171,0.0561541021,0.2478840053,0.4095256925,-0.1842343062,-0.1060605422,0.0865907371,-0.2045428902,-0.3371692896,-0.3385475874,0.2402119339,0.0368901119,0.1089414582,-0.0184665043,-0.3302738965,0.0986591801,-0.2747161686,-0.1541592777,-0.3237557411,-0.0389051214,-0.2159913927,0.3081872761,0.6463362575,-0.4303185344,0.0429565385,0.0566363595,-0.1299165338,0.1414598227,0.2508479655,-0.3076960742,0.0851259753,-0.2284607738,-0.0966767818,0.2314821184,-0.3093754947,-0.3032815158,0.5005193353,-0.2373457253,-0.072943598,0.2169772685,0.0126159824,0.291056484,0.1858281493,0.2615151703,0.3000478446,-0.1007597595,0.1175532788,-0.0799232721,-0.222560361,0.1973579973,0.1164559647,0.0098163048,0.2231837362,0.3249672055,-0.2579794228,0.1741140336,-0.0768722668,0.1447919756,0.1345079839,0.1739715636,0.0419842489,-0.0606005527,-0.021664409,-0.508518219,0.1381557435,0.1310921013,0.0903006941,-0.4376563728,-0.0162693374,-0.3771235943,0.0051721488,-0.2671605647,-0.030241888,0.2100175917,0.3415310383,-0.0191593226,-0.0523174517,-0.0488567986,0.3256911337,-0.1166130304,-0.0192557331,-0.4156301022,0.3395552039,-0.3544683456,-0.0690031797,0.052354306,-0.0314197689,0.1626497358,-0.1825555712,-0.2719682157,0.356123805,-0.0955856889,0.2182246149,0.0170497447,-0.0044389288,0.0526204556,-0.2450993061,0.0323775932,0.0311139729,0.1533319503,0.0431826673,-0.261313647,0.3525983989,-0.0953487977,0.163369596,0.0506540611,0.0704118088,0.4514212012,0.0119942082,-0.1140478626,-0.1085091978,0.1054950804,0.2389213145,0.2262360007,0.0491035245,-0.1802719235,-0.1886238307,0.2018595934,-0.1532273442,0.0116079831,0.0923530832,-0.1086221784,-0.0442817397,0.214684695,0.2319191694,0.5149133801,0.1026022732,-0.1526015699,-0.0183210652,-0.0503232926,-0.1338230819,0.1102777719,-0.0386922434,0.0448774025,0.3272635043,0.0759821385,0.1069803238,-0.2410078943,-0.3630864322,-0.0419490486,0.2638888955,-0.3493005633,0.2479265183,-0.1645680964,0.0182821918,-0.2659018636,-0.0503300428,-0.1105000004,-0.178074345,-0.3695805371,0.3607570827,-0.0763827115,0.2372517288,-0.279001534,-0.0516414121,0.3879840374,-0.4498327672,-0.0730597377,-0.2081662267,-0.1601461768,0.0924806148,0.0935094729,-0.1568920612,0.303164959,-0.0989919826,0.0205262117,-0.16106233,-0.0238344092,-0.0821166858,0.0125281876,0.1283056736,0.1878541559,0.4475226104,0.1206022948,-0.5035608411,0.3184547424,-0.0834972411,0.0481938235,0.1964564323,-0.0537022725,-0.2032553554,0.0006876013,-0.6345347762,-0.5991461873,-0.469863981,0.1375343353,0.0665089488,0.2132315189,0.295524776,0.2269022167,0.2099236399,0.0866580531,0.0757485032,-0.0596129522,-0.1591841877,0.3499985933,-0.2349603325,-0.3570283055,-0.1133962646,-0.1152546331,0.1219444349,0.1364867091,-0.444775492,-0.0236161184,-0.1746839583,0.3565398753,-0.0467238724,0.0517050326,0.4662779868,0.1686569452,-0.1706291884,0.1126605943,-0.244187057,-0.1408112943,0.1942563504,0.0843700692,0.0044104136,0.4593695402,-0.158959344,0.653288424,-0.0798089057,-0.1897096932,0.2884309888,-0.1362253278,0.342966646,-0.1845075488,-0.6308505535,-0.0917780697,-0.3062294424,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/807","title":"load_dataset for LOCAL CSV files report CONNECTION ERROR","comments":"> hello, how did you solve this problems?\r\n> \r\n> > > > Hi !\r\n> > > > The url works on my side.\r\n> > > > Is the url working in your navigator ?\r\n> > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > > \r\n> > > \r\n> > > I tried another server, it's working now. Thanks a lot.\r\n> > > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> > \r\n> > \r\n> > I download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f\r\n> > Thanks :D\r\n> \r\n> hello, I tried this. but it still failed. how do you fix this error?\r\n\r\n\u4f60\u628a\u90a3\u4e2a\u811a\u672c\u4e0b\u8f7d\u5230\u4f60\u672c\u5730\u5b89\u88c5\u76ee\u5f55\u4e0b\uff0c\u7136\u540e `load_dataset(csv_script_path, data_fiels)`\r\n\r\n","body":"## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n```","comment_length":155,"text":"load_dataset for LOCAL CSV files report CONNECTION ERROR \n ## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n``` \n > hello, how did you solve this problems?\r\n> \r\n> > > > Hi !\r\n> > > > The url works on my side.\r\n> > > > Is the url working in your navigator ?\r\n> > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > > \r\n> > > \r\n> > > I tried another server, it's working now. Thanks a lot.\r\n> > > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> > \r\n> > \r\n> > I download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f\r\n> > Thanks :D\r\n> \r\n> hello, I tried this. but it still failed. how do you fix this error?\r\n\r\n\u4f60\u628a\u90a3\u4e2a\u811a\u672c\u4e0b\u8f7d\u5230\u4f60\u672c\u5730\u5b89\u88c5\u76ee\u5f55\u4e0b\uff0c\u7136\u540e `load_dataset(csv_script_path, data_fiels)`\r\n\r\n","embeddings":[-0.324712038,0.0161048565,-0.1088049561,0.0260043461,0.2420431376,0.0198685937,0.7173692584,0.3756367266,0.2509218454,0.1337042451,-0.0342321098,0.1497044712,0.0797994062,0.0211837217,-0.1269313991,-0.1772823036,-0.1271574497,0.2743321061,-0.4187993109,0.0144969616,-0.1248850673,-0.0023575695,-0.0349380858,0.1405339241,-0.2024638206,-0.0062384652,0.1157821938,0.1312774718,-0.0276923925,-0.493919909,0.3365218639,0.0402496904,0.323772788,0.3632176518,-0.0001060309,0.2135237306,0.2517406344,-0.0288667344,-0.4279088378,-0.4611105323,-0.1089922264,-0.3690283597,0.4228135347,-0.3986177444,0.0005737995,-0.1198977008,-0.0673584118,-0.3161996007,0.4313887954,0.4785009623,0.2786398232,0.4154756367,0.0165828858,-0.1110344306,0.0718218759,0.0555340908,-0.0624923073,0.4061276317,0.0516244806,-0.1849907339,-0.242678538,0.0846120864,-0.1926984191,0.3955322206,0.1906039566,0.0830963105,0.0377710871,-0.1966335922,0.1252265424,0.107683368,0.4764897823,-0.2314554602,-0.2321467549,-0.0584997945,-0.1994815022,-0.1881569773,0.1660740525,0.0867301002,-0.1683380306,0.1474405825,-0.2638413012,0.0990802348,-0.3907521665,0.1494552046,-0.1105123758,0.1547572166,-0.067184031,0.1947448403,0.158264637,-0.056483563,0.4036888182,-0.2070344985,0.1916099489,0.1276624054,-0.3377639651,0.1311576068,0.1394052505,-0.3861347735,-0.1064571813,0.0498938598,-0.0361735485,-0.0535907373,0.1335505396,0.2916464508,0.1983619481,0.0791434944,-0.1308648437,0.2947734892,0.2687248588,0.1325622052,0.042261485,-0.0624847747,-0.0800571069,-0.3558065295,0.1634977609,0.1999797225,0.4894587696,-0.0474283807,-0.2816003859,0.1326408088,0.0129102021,-0.1073727086,0.0189691093,0.3589312434,-0.0879683271,0.2043156773,0.0424528793,0.0136886993,-0.2235278487,0.0845431536,-0.2120842189,0.0379552618,-0.1193305477,-0.0695323423,0.3813319206,-0.0790898874,0.0466334634,-0.0146686006,0.0989310592,0.0503491461,0.2611702383,-0.2932826877,0.0491831452,0.4129322171,0.0561541021,0.2478840053,0.4095256925,-0.1842343062,-0.1060605422,0.0865907371,-0.2045428902,-0.3371692896,-0.3385475874,0.2402119339,0.0368901119,0.1089414582,-0.0184665043,-0.3302738965,0.0986591801,-0.2747161686,-0.1541592777,-0.3237557411,-0.0389051214,-0.2159913927,0.3081872761,0.6463362575,-0.4303185344,0.0429565385,0.0566363595,-0.1299165338,0.1414598227,0.2508479655,-0.3076960742,0.0851259753,-0.2284607738,-0.0966767818,0.2314821184,-0.3093754947,-0.3032815158,0.5005193353,-0.2373457253,-0.072943598,0.2169772685,0.0126159824,0.291056484,0.1858281493,0.2615151703,0.3000478446,-0.1007597595,0.1175532788,-0.0799232721,-0.222560361,0.1973579973,0.1164559647,0.0098163048,0.2231837362,0.3249672055,-0.2579794228,0.1741140336,-0.0768722668,0.1447919756,0.1345079839,0.1739715636,0.0419842489,-0.0606005527,-0.021664409,-0.508518219,0.1381557435,0.1310921013,0.0903006941,-0.4376563728,-0.0162693374,-0.3771235943,0.0051721488,-0.2671605647,-0.030241888,0.2100175917,0.3415310383,-0.0191593226,-0.0523174517,-0.0488567986,0.3256911337,-0.1166130304,-0.0192557331,-0.4156301022,0.3395552039,-0.3544683456,-0.0690031797,0.052354306,-0.0314197689,0.1626497358,-0.1825555712,-0.2719682157,0.356123805,-0.0955856889,0.2182246149,0.0170497447,-0.0044389288,0.0526204556,-0.2450993061,0.0323775932,0.0311139729,0.1533319503,0.0431826673,-0.261313647,0.3525983989,-0.0953487977,0.163369596,0.0506540611,0.0704118088,0.4514212012,0.0119942082,-0.1140478626,-0.1085091978,0.1054950804,0.2389213145,0.2262360007,0.0491035245,-0.1802719235,-0.1886238307,0.2018595934,-0.1532273442,0.0116079831,0.0923530832,-0.1086221784,-0.0442817397,0.214684695,0.2319191694,0.5149133801,0.1026022732,-0.1526015699,-0.0183210652,-0.0503232926,-0.1338230819,0.1102777719,-0.0386922434,0.0448774025,0.3272635043,0.0759821385,0.1069803238,-0.2410078943,-0.3630864322,-0.0419490486,0.2638888955,-0.3493005633,0.2479265183,-0.1645680964,0.0182821918,-0.2659018636,-0.0503300428,-0.1105000004,-0.178074345,-0.3695805371,0.3607570827,-0.0763827115,0.2372517288,-0.279001534,-0.0516414121,0.3879840374,-0.4498327672,-0.0730597377,-0.2081662267,-0.1601461768,0.0924806148,0.0935094729,-0.1568920612,0.303164959,-0.0989919826,0.0205262117,-0.16106233,-0.0238344092,-0.0821166858,0.0125281876,0.1283056736,0.1878541559,0.4475226104,0.1206022948,-0.5035608411,0.3184547424,-0.0834972411,0.0481938235,0.1964564323,-0.0537022725,-0.2032553554,0.0006876013,-0.6345347762,-0.5991461873,-0.469863981,0.1375343353,0.0665089488,0.2132315189,0.295524776,0.2269022167,0.2099236399,0.0866580531,0.0757485032,-0.0596129522,-0.1591841877,0.3499985933,-0.2349603325,-0.3570283055,-0.1133962646,-0.1152546331,0.1219444349,0.1364867091,-0.444775492,-0.0236161184,-0.1746839583,0.3565398753,-0.0467238724,0.0517050326,0.4662779868,0.1686569452,-0.1706291884,0.1126605943,-0.244187057,-0.1408112943,0.1942563504,0.0843700692,0.0044104136,0.4593695402,-0.158959344,0.653288424,-0.0798089057,-0.1897096932,0.2884309888,-0.1362253278,0.342966646,-0.1845075488,-0.6308505535,-0.0917780697,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/807","title":"load_dataset for LOCAL CSV files report CONNECTION ERROR","comments":"> > hello, how did you solve this problems?\r\n> > > > > Hi !\r\n> > > > > The url works on my side.\r\n> > > > > Is the url working in your navigator ?\r\n> > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > > > \r\n> > > > \r\n> > > > I tried another server, it's working now. Thanks a lot.\r\n> > > > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> > > \r\n> > > \r\n> > > I download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f\r\n> > > Thanks :D\r\n> > \r\n> > \r\n> > hello, I tried this. but it still failed. how do you fix this error?\r\n> \r\n> \u4f60\u628a\u90a3\u4e2a\u811a\u672c\u4e0b\u8f7d\u5230\u4f60\u672c\u5730\u5b89\u88c5\u76ee\u5f55\u4e0b\uff0c\u7136\u540e `load_dataset(csv_script_path, data_fiels)`\r\n\r\n\u597d\u7684\u597d\u7684\uff01\u89e3\u51b3\u4e86\uff0c\u611f\u8c22\u611f\u8c22\uff01\uff01\uff01","body":"## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n```","comment_length":174,"text":"load_dataset for LOCAL CSV files report CONNECTION ERROR \n ## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n``` \n > > hello, how did you solve this problems?\r\n> > > > > Hi !\r\n> > > > > The url works on my side.\r\n> > > > > Is the url working in your navigator ?\r\n> > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > > > \r\n> > > > \r\n> > > > I tried another server, it's working now. Thanks a lot.\r\n> > > > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> > > \r\n> > > \r\n> > > I download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f\r\n> > > Thanks :D\r\n> > \r\n> > \r\n> > hello, I tried this. but it still failed. how do you fix this error?\r\n> \r\n> \u4f60\u628a\u90a3\u4e2a\u811a\u672c\u4e0b\u8f7d\u5230\u4f60\u672c\u5730\u5b89\u88c5\u76ee\u5f55\u4e0b\uff0c\u7136\u540e `load_dataset(csv_script_path, data_fiels)`\r\n\r\n\u597d\u7684\u597d\u7684\uff01\u89e3\u51b3\u4e86\uff0c\u611f\u8c22\u611f\u8c22\uff01\uff01\uff01","embeddings":[-0.324712038,0.0161048565,-0.1088049561,0.0260043461,0.2420431376,0.0198685937,0.7173692584,0.3756367266,0.2509218454,0.1337042451,-0.0342321098,0.1497044712,0.0797994062,0.0211837217,-0.1269313991,-0.1772823036,-0.1271574497,0.2743321061,-0.4187993109,0.0144969616,-0.1248850673,-0.0023575695,-0.0349380858,0.1405339241,-0.2024638206,-0.0062384652,0.1157821938,0.1312774718,-0.0276923925,-0.493919909,0.3365218639,0.0402496904,0.323772788,0.3632176518,-0.0001060309,0.2135237306,0.2517406344,-0.0288667344,-0.4279088378,-0.4611105323,-0.1089922264,-0.3690283597,0.4228135347,-0.3986177444,0.0005737995,-0.1198977008,-0.0673584118,-0.3161996007,0.4313887954,0.4785009623,0.2786398232,0.4154756367,0.0165828858,-0.1110344306,0.0718218759,0.0555340908,-0.0624923073,0.4061276317,0.0516244806,-0.1849907339,-0.242678538,0.0846120864,-0.1926984191,0.3955322206,0.1906039566,0.0830963105,0.0377710871,-0.1966335922,0.1252265424,0.107683368,0.4764897823,-0.2314554602,-0.2321467549,-0.0584997945,-0.1994815022,-0.1881569773,0.1660740525,0.0867301002,-0.1683380306,0.1474405825,-0.2638413012,0.0990802348,-0.3907521665,0.1494552046,-0.1105123758,0.1547572166,-0.067184031,0.1947448403,0.158264637,-0.056483563,0.4036888182,-0.2070344985,0.1916099489,0.1276624054,-0.3377639651,0.1311576068,0.1394052505,-0.3861347735,-0.1064571813,0.0498938598,-0.0361735485,-0.0535907373,0.1335505396,0.2916464508,0.1983619481,0.0791434944,-0.1308648437,0.2947734892,0.2687248588,0.1325622052,0.042261485,-0.0624847747,-0.0800571069,-0.3558065295,0.1634977609,0.1999797225,0.4894587696,-0.0474283807,-0.2816003859,0.1326408088,0.0129102021,-0.1073727086,0.0189691093,0.3589312434,-0.0879683271,0.2043156773,0.0424528793,0.0136886993,-0.2235278487,0.0845431536,-0.2120842189,0.0379552618,-0.1193305477,-0.0695323423,0.3813319206,-0.0790898874,0.0466334634,-0.0146686006,0.0989310592,0.0503491461,0.2611702383,-0.2932826877,0.0491831452,0.4129322171,0.0561541021,0.2478840053,0.4095256925,-0.1842343062,-0.1060605422,0.0865907371,-0.2045428902,-0.3371692896,-0.3385475874,0.2402119339,0.0368901119,0.1089414582,-0.0184665043,-0.3302738965,0.0986591801,-0.2747161686,-0.1541592777,-0.3237557411,-0.0389051214,-0.2159913927,0.3081872761,0.6463362575,-0.4303185344,0.0429565385,0.0566363595,-0.1299165338,0.1414598227,0.2508479655,-0.3076960742,0.0851259753,-0.2284607738,-0.0966767818,0.2314821184,-0.3093754947,-0.3032815158,0.5005193353,-0.2373457253,-0.072943598,0.2169772685,0.0126159824,0.291056484,0.1858281493,0.2615151703,0.3000478446,-0.1007597595,0.1175532788,-0.0799232721,-0.222560361,0.1973579973,0.1164559647,0.0098163048,0.2231837362,0.3249672055,-0.2579794228,0.1741140336,-0.0768722668,0.1447919756,0.1345079839,0.1739715636,0.0419842489,-0.0606005527,-0.021664409,-0.508518219,0.1381557435,0.1310921013,0.0903006941,-0.4376563728,-0.0162693374,-0.3771235943,0.0051721488,-0.2671605647,-0.030241888,0.2100175917,0.3415310383,-0.0191593226,-0.0523174517,-0.0488567986,0.3256911337,-0.1166130304,-0.0192557331,-0.4156301022,0.3395552039,-0.3544683456,-0.0690031797,0.052354306,-0.0314197689,0.1626497358,-0.1825555712,-0.2719682157,0.356123805,-0.0955856889,0.2182246149,0.0170497447,-0.0044389288,0.0526204556,-0.2450993061,0.0323775932,0.0311139729,0.1533319503,0.0431826673,-0.261313647,0.3525983989,-0.0953487977,0.163369596,0.0506540611,0.0704118088,0.4514212012,0.0119942082,-0.1140478626,-0.1085091978,0.1054950804,0.2389213145,0.2262360007,0.0491035245,-0.1802719235,-0.1886238307,0.2018595934,-0.1532273442,0.0116079831,0.0923530832,-0.1086221784,-0.0442817397,0.214684695,0.2319191694,0.5149133801,0.1026022732,-0.1526015699,-0.0183210652,-0.0503232926,-0.1338230819,0.1102777719,-0.0386922434,0.0448774025,0.3272635043,0.0759821385,0.1069803238,-0.2410078943,-0.3630864322,-0.0419490486,0.2638888955,-0.3493005633,0.2479265183,-0.1645680964,0.0182821918,-0.2659018636,-0.0503300428,-0.1105000004,-0.178074345,-0.3695805371,0.3607570827,-0.0763827115,0.2372517288,-0.279001534,-0.0516414121,0.3879840374,-0.4498327672,-0.0730597377,-0.2081662267,-0.1601461768,0.0924806148,0.0935094729,-0.1568920612,0.303164959,-0.0989919826,0.0205262117,-0.16106233,-0.0238344092,-0.0821166858,0.0125281876,0.1283056736,0.1878541559,0.4475226104,0.1206022948,-0.5035608411,0.3184547424,-0.0834972411,0.0481938235,0.1964564323,-0.0537022725,-0.2032553554,0.0006876013,-0.6345347762,-0.5991461873,-0.469863981,0.1375343353,0.0665089488,0.2132315189,0.295524776,0.2269022167,0.2099236399,0.0866580531,0.0757485032,-0.0596129522,-0.1591841877,0.3499985933,-0.2349603325,-0.3570283055,-0.1133962646,-0.1152546331,0.1219444349,0.1364867091,-0.444775492,-0.0236161184,-0.1746839583,0.3565398753,-0.0467238724,0.0517050326,0.4662779868,0.1686569452,-0.1706291884,0.1126605943,-0.244187057,-0.1408112943,0.1942563504,0.0843700692,0.0044104136,0.4593695402,-0.158959344,0.653288424,-0.0798089057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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/807","title":"load_dataset for LOCAL CSV files report CONNECTION ERROR","comments":"> \r\n> \r\n> > hello, how did you solve this problems?\r\n> > > > > Hi !\r\n> > > > > The url works on my side.\r\n> > > > > Is the url working in your navigator ?\r\n> > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > > > \r\n> > > > \r\n> > > > I tried another server, it's working now. Thanks a lot.\r\n> > > > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> > > \r\n> > > \r\n> > > I download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f\r\n> > > Thanks :D\r\n> > \r\n> > \r\n> > hello, I tried this. but it still failed. how do you fix this error?\r\n> \r\n> \u4f60\u628a\u90a3\u4e2a\u811a\u672c\u4e0b\u8f7d\u5230\u4f60\u672c\u5730\u5b89\u88c5\u76ee\u5f55\u4e0b\uff0c\u7136\u540e `load_dataset(csv_script_path, data_fiels)`\r\n\r\n\u6211\u7167\u7740\u505a\u4e86\uff0c\u7136\u540e\u62a5\u9519\u3002\r\nValueError: unable to parse C:\/Software\/Anaconda\/envs\/ptk_gpu2\/Lib\/site-packages\/datasets\\dataset_infos.json as a URL or as a local path\r\n\r\n`---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-5-fd2106a3f053> in <module>\r\n----> 1 dataset = load_dataset('C:\/Software\/Anaconda\/envs\/ptk_gpu2\/Lib\/site-packages\/datasets\/csv.py', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\nC:\\Software\\Anaconda\\envs\\ptk_gpu2\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\nC:\\Software\\Anaconda\\envs\\ptk_gpu2\\lib\\site-packages\\datasets\\load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    296         local_dataset_infos_path = cached_path(\r\n    297             dataset_infos,\r\n--> 298             download_config=download_config,\r\n    299         )\r\n    300     except (FileNotFoundError, ConnectionError):\r\n\r\nC:\\Software\\Anaconda\\envs\\ptk_gpu2\\lib\\site-packages\\datasets\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    316     else:\r\n    317         # Something unknown\r\n--> 318         raise ValueError(\"unable to parse {} as a URL or as a local path\".format(url_or_filename))\r\n    319 \r\n    320     if download_config.extract_compressed_file and output_path is not None:\r\n\r\nValueError: unable to parse C:\/Software\/Anaconda\/envs\/ptk_gpu2\/Lib\/site-packages\/datasets\\dataset_infos.json as a URL or as a local path\r\n\r\n`","body":"## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n```","comment_length":316,"text":"load_dataset for LOCAL CSV files report CONNECTION ERROR \n ## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n``` \n > \r\n> \r\n> > hello, how did you solve this problems?\r\n> > > > > Hi !\r\n> > > > > The url works on my side.\r\n> > > > > Is the url working in your navigator ?\r\n> > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > > > \r\n> > > > \r\n> > > > I tried another server, it's working now. Thanks a lot.\r\n> > > > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> > > \r\n> > > \r\n> > > I download the scripts `https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py` and move it to the package dir `*\/datasets\/` solved the problem. Could you please put the file `datasets\/datasets\/csv\/csv.py` to `datasets\/src\/datasets\/`\uff1f\r\n> > > Thanks :D\r\n> > \r\n> > \r\n> > hello, I tried this. but it still failed. how do you fix this error?\r\n> \r\n> \u4f60\u628a\u90a3\u4e2a\u811a\u672c\u4e0b\u8f7d\u5230\u4f60\u672c\u5730\u5b89\u88c5\u76ee\u5f55\u4e0b\uff0c\u7136\u540e `load_dataset(csv_script_path, data_fiels)`\r\n\r\n\u6211\u7167\u7740\u505a\u4e86\uff0c\u7136\u540e\u62a5\u9519\u3002\r\nValueError: unable to parse C:\/Software\/Anaconda\/envs\/ptk_gpu2\/Lib\/site-packages\/datasets\\dataset_infos.json as a URL or as a local path\r\n\r\n`---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-5-fd2106a3f053> in <module>\r\n----> 1 dataset = load_dataset('C:\/Software\/Anaconda\/envs\/ptk_gpu2\/Lib\/site-packages\/datasets\/csv.py', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\nC:\\Software\\Anaconda\\envs\\ptk_gpu2\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\nC:\\Software\\Anaconda\\envs\\ptk_gpu2\\lib\\site-packages\\datasets\\load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    296         local_dataset_infos_path = cached_path(\r\n    297             dataset_infos,\r\n--> 298             download_config=download_config,\r\n    299         )\r\n    300     except (FileNotFoundError, ConnectionError):\r\n\r\nC:\\Software\\Anaconda\\envs\\ptk_gpu2\\lib\\site-packages\\datasets\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    316     else:\r\n    317         # Something unknown\r\n--> 318         raise ValueError(\"unable to parse {} as a URL or as a local path\".format(url_or_filename))\r\n    319 \r\n    320     if download_config.extract_compressed_file and output_path is not None:\r\n\r\nValueError: unable to parse C:\/Software\/Anaconda\/envs\/ptk_gpu2\/Lib\/site-packages\/datasets\\dataset_infos.json as a URL or as a local path\r\n\r\n`","embeddings":[-0.324712038,0.0161048565,-0.1088049561,0.0260043461,0.2420431376,0.0198685937,0.7173692584,0.3756367266,0.2509218454,0.1337042451,-0.0342321098,0.1497044712,0.0797994062,0.0211837217,-0.1269313991,-0.1772823036,-0.1271574497,0.2743321061,-0.4187993109,0.0144969616,-0.1248850673,-0.0023575695,-0.0349380858,0.1405339241,-0.2024638206,-0.0062384652,0.1157821938,0.1312774718,-0.0276923925,-0.493919909,0.3365218639,0.0402496904,0.323772788,0.3632176518,-0.0001060309,0.2135237306,0.2517406344,-0.0288667344,-0.4279088378,-0.4611105323,-0.1089922264,-0.3690283597,0.4228135347,-0.3986177444,0.0005737995,-0.1198977008,-0.0673584118,-0.3161996007,0.4313887954,0.4785009623,0.2786398232,0.4154756367,0.0165828858,-0.1110344306,0.0718218759,0.0555340908,-0.0624923073,0.4061276317,0.0516244806,-0.1849907339,-0.242678538,0.0846120864,-0.1926984191,0.3955322206,0.1906039566,0.0830963105,0.0377710871,-0.1966335922,0.1252265424,0.107683368,0.4764897823,-0.2314554602,-0.2321467549,-0.0584997945,-0.1994815022,-0.1881569773,0.1660740525,0.0867301002,-0.1683380306,0.1474405825,-0.2638413012,0.0990802348,-0.3907521665,0.1494552046,-0.1105123758,0.1547572166,-0.067184031,0.1947448403,0.158264637,-0.056483563,0.4036888182,-0.2070344985,0.1916099489,0.1276624054,-0.3377639651,0.1311576068,0.1394052505,-0.3861347735,-0.1064571813,0.0498938598,-0.0361735485,-0.0535907373,0.1335505396,0.2916464508,0.1983619481,0.0791434944,-0.1308648437,0.2947734892,0.2687248588,0.1325622052,0.042261485,-0.0624847747,-0.0800571069,-0.3558065295,0.1634977609,0.1999797225,0.4894587696,-0.0474283807,-0.2816003859,0.1326408088,0.0129102021,-0.1073727086,0.0189691093,0.3589312434,-0.0879683271,0.2043156773,0.0424528793,0.0136886993,-0.2235278487,0.0845431536,-0.2120842189,0.0379552618,-0.1193305477,-0.0695323423,0.3813319206,-0.0790898874,0.0466334634,-0.0146686006,0.0989310592,0.0503491461,0.2611702383,-0.2932826877,0.0491831452,0.4129322171,0.0561541021,0.2478840053,0.4095256925,-0.1842343062,-0.1060605422,0.0865907371,-0.2045428902,-0.3371692896,-0.3385475874,0.2402119339,0.0368901119,0.1089414582,-0.0184665043,-0.3302738965,0.0986591801,-0.2747161686,-0.1541592777,-0.3237557411,-0.0389051214,-0.2159913927,0.3081872761,0.6463362575,-0.4303185344,0.0429565385,0.0566363595,-0.1299165338,0.1414598227,0.2508479655,-0.3076960742,0.0851259753,-0.2284607738,-0.0966767818,0.2314821184,-0.3093754947,-0.3032815158,0.5005193353,-0.2373457253,-0.072943598,0.2169772685,0.0126159824,0.291056484,0.1858281493,0.2615151703,0.3000478446,-0.1007597595,0.1175532788,-0.0799232721,-0.222560361,0.1973579973,0.1164559647,0.0098163048,0.2231837362,0.3249672055,-0.2579794228,0.1741140336,-0.0768722668,0.1447919756,0.1345079839,0.1739715636,0.0419842489,-0.0606005527,-0.021664409,-0.508518219,0.1381557435,0.1310921013,0.0903006941,-0.4376563728,-0.0162693374,-0.3771235943,0.0051721488,-0.2671605647,-0.030241888,0.2100175917,0.3415310383,-0.0191593226,-0.0523174517,-0.0488567986,0.3256911337,-0.1166130304,-0.0192557331,-0.4156301022,0.3395552039,-0.3544683456,-0.0690031797,0.052354306,-0.0314197689,0.1626497358,-0.1825555712,-0.2719682157,0.356123805,-0.0955856889,0.2182246149,0.0170497447,-0.0044389288,0.0526204556,-0.2450993061,0.0323775932,0.0311139729,0.1533319503,0.0431826673,-0.261313647,0.3525983989,-0.0953487977,0.163369596,0.0506540611,0.0704118088,0.4514212012,0.0119942082,-0.1140478626,-0.1085091978,0.1054950804,0.2389213145,0.2262360007,0.0491035245,-0.1802719235,-0.1886238307,0.2018595934,-0.1532273442,0.0116079831,0.0923530832,-0.1086221784,-0.0442817397,0.214684695,0.2319191694,0.5149133801,0.1026022732,-0.1526015699,-0.0183210652,-0.0503232926,-0.1338230819,0.1102777719,-0.0386922434,0.0448774025,0.3272635043,0.0759821385,0.1069803238,-0.2410078943,-0.3630864322,-0.0419490486,0.2638888955,-0.3493005633,0.2479265183,-0.1645680964,0.0182821918,-0.2659018636,-0.0503300428,-0.1105000004,-0.178074345,-0.3695805371,0.3607570827,-0.0763827115,0.2372517288,-0.279001534,-0.0516414121,0.3879840374,-0.4498327672,-0.0730597377,-0.2081662267,-0.1601461768,0.0924806148,0.0935094729,-0.1568920612,0.303164959,-0.0989919826,0.0205262117,-0.16106233,-0.0238344092,-0.0821166858,0.0125281876,0.1283056736,0.1878541559,0.4475226104,0.1206022948,-0.5035608411,0.3184547424,-0.0834972411,0.0481938235,0.1964564323,-0.0537022725,-0.2032553554,0.0006876013,-0.6345347762,-0.5991461873,-0.469863981,0.1375343353,0.0665089488,0.2132315189,0.295524776,0.2269022167,0.2099236399,0.0866580531,0.0757485032,-0.0596129522,-0.1591841877,0.3499985933,-0.2349603325,-0.3570283055,-0.1133962646,-0.1152546331,0.1219444349,0.1364867091,-0.444775492,-0.0236161184,-0.1746839583,0.3565398753,-0.0467238724,0.0517050326,0.4662779868,0.1686569452,-0.1706291884,0.1126605943,-0.244187057,-0.1408112943,0.1942563504,0.0843700692,0.0044104136,0.4593695402,-0.158959344,0.653288424,-0.0798089057,-0.1897096932,0.2884309888,-0.1362253278,0.342966646,-0.1845075488,-0.6308505535,-0.0917780697,-0.3062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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/807","title":"load_dataset for LOCAL CSV files report CONNECTION ERROR","comments":"I also experienced this issue this morning. Looks like something specific to windows.\r\nI'm working on a fix","body":"## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n```","comment_length":18,"text":"load_dataset for LOCAL CSV files report CONNECTION ERROR \n ## load_dataset for LOCAL CSV files report CONNECTION ERROR\r\n- **Description:** \r\nA local demo csv file:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom datasets import load_dataset\r\nimport torch\r\nimport transformers\r\n\r\ndf = pd.DataFrame(np.arange(1200).reshape(300,4))\r\ndf.to_csv('test.csv', header=False, index=False)\r\n\r\nprint('datasets version: ', datasets.__version__)\r\nprint('pytorch version: ', torch.__version__)\r\nprint('transformers version: ', transformers.__version__)\r\n\r\n# output:\r\ndatasets version:  1.1.2\r\npytorch version:  1.5.0\r\ntransformers version:  3.2.0\r\n```\r\n\r\nwhen I load data through `dataset`:\r\n```\r\ndataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n```\r\nError infos:\r\n```\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-17-bbdadb9a0c78> in <module>\r\n----> 1 dataset = load_dataset('csv', data_files='.\/test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    588     # Download\/copy dataset processing script\r\n    589     module_path, hash = prepare_module(\r\n--> 590         path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n    591     )\r\n    592 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n    266         file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)\r\n    267         try:\r\n--> 268             local_path = cached_path(file_path, download_config=download_config)\r\n    269         except FileNotFoundError:\r\n    270             if script_version is not None:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    306             user_agent=download_config.user_agent,\r\n    307             local_files_only=download_config.local_files_only,\r\n--> 308             use_etag=download_config.use_etag,\r\n    309         )\r\n    310     elif os.path.exists(url_or_filename):\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\r\n```\r\n\r\nAnd I try to connect to the site with requests:\r\n```\r\nimport requests\r\n\r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n```\r\n\r\nSimilarly Error occurs:\r\n```\r\n---------------------------------------------------------------------------\r\nConnectionRefusedError                    Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    159             conn = connection.create_connection(\r\n--> 160                 (self._dns_host, self.port), self.timeout, **extra_kw\r\n    161             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     83     if err is not None:\r\n---> 84         raise err\r\n     85 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py in create_connection(address, timeout, source_address, socket_options)\r\n     73                 sock.bind(source_address)\r\n---> 74             sock.connect(sa)\r\n     75             return sock\r\n\r\nConnectionRefusedError: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nNewConnectionError                        Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    676                 headers=headers,\r\n--> 677                 chunked=chunked,\r\n    678             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)\r\n    380         try:\r\n--> 381             self._validate_conn(conn)\r\n    382         except (SocketTimeout, BaseSSLError) as e:\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in _validate_conn(self, conn)\r\n    975         if not getattr(conn, \"sock\", None):  # AppEngine might not have  `.sock`\r\n--> 976             conn.connect()\r\n    977 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in connect(self)\r\n    307         # Add certificate verification\r\n--> 308         conn = self._new_conn()\r\n    309         hostname = self.host\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connection.py in _new_conn(self)\r\n    171             raise NewConnectionError(\r\n--> 172                 self, \"Failed to establish a new connection: %s\" % e\r\n    173             )\r\n\r\nNewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nMaxRetryError                             Traceback (most recent call last)\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    448                     retries=self.max_retries,\r\n--> 449                     timeout=timeout\r\n    450                 )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\r\n    724             retries = retries.increment(\r\n--> 725                 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n    726             )\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)\r\n    438         if new_retry.is_exhausted():\r\n--> 439             raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\n    440 \r\n\r\nMaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-20-18cc3eb4a049> in <module>\r\n      1 import requests\r\n      2 \r\n----> 3 requests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py\")\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in head(url, **kwargs)\r\n    102 \r\n    103     kwargs.setdefault('allow_redirects', False)\r\n--> 104     return request('head', url, **kwargs)\r\n    105 \r\n    106 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/api.py in request(method, url, **kwargs)\r\n     59     # cases, and look like a memory leak in others.\r\n     60     with sessions.Session() as session:\r\n---> 61         return session.request(method=method, url=url, **kwargs)\r\n     62 \r\n     63 \r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\r\n    528         }\r\n    529         send_kwargs.update(settings)\r\n--> 530         resp = self.send(prep, **send_kwargs)\r\n    531 \r\n    532         return resp\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/sessions.py in send(self, request, **kwargs)\r\n    641 \r\n    642         # Send the request\r\n--> 643         r = adapter.send(request, **kwargs)\r\n    644 \r\n    645         # Total elapsed time of the request (approximately)\r\n\r\n~\/.conda\/envs\/py36\/lib\/python3.6\/site-packages\/requests\/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)\r\n    514                 raise SSLError(e, request=request)\r\n    515 \r\n--> 516             raise ConnectionError(e, request=request)\r\n    517 \r\n    518         except ClosedPoolError as e:\r\n\r\nConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: \/huggingface\/datasets\/1.1.2\/datasets\/csv\/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))\r\n``` \n I also experienced this issue this morning. Looks like something specific to windows.\r\nI'm working on a fix","embeddings":[-0.324712038,0.0161048565,-0.1088049561,0.0260043461,0.2420431376,0.0198685937,0.7173692584,0.3756367266,0.2509218454,0.1337042451,-0.0342321098,0.1497044712,0.0797994062,0.0211837217,-0.1269313991,-0.1772823036,-0.1271574497,0.2743321061,-0.4187993109,0.0144969616,-0.1248850673,-0.0023575695,-0.0349380858,0.1405339241,-0.2024638206,-0.0062384652,0.1157821938,0.1312774718,-0.0276923925,-0.493919909,0.3365218639,0.0402496904,0.323772788,0.3632176518,-0.0001060309,0.2135237306,0.2517406344,-0.0288667344,-0.4279088378,-0.4611105323,-0.1089922264,-0.3690283597,0.4228135347,-0.3986177444,0.0005737995,-0.1198977008,-0.0673584118,-0.3161996007,0.4313887954,0.4785009623,0.2786398232,0.4154756367,0.0165828858,-0.1110344306,0.0718218759,0.0555340908,-0.0624923073,0.4061276317,0.0516244806,-0.1849907339,-0.242678538,0.0846120864,-0.1926984191,0.3955322206,0.1906039566,0.0830963105,0.0377710871,-0.1966335922,0.1252265424,0.107683368,0.4764897823,-0.2314554602,-0.2321467549,-0.0584997945,-0.1994815022,-0.1881569773,0.1660740525,0.0867301002,-0.1683380306,0.1474405825,-0.2638413012,0.0990802348,-0.3907521665,0.1494552046,-0.1105123758,0.1547572166,-0.067184031,0.1947448403,0.158264637,-0.056483563,0.4036888182,-0.2070344985,0.1916099489,0.1276624054,-0.3377639651,0.1311576068,0.1394052505,-0.3861347735,-0.1064571813,0.0498938598,-0.0361735485,-0.0535907373,0.1335505396,0.2916464508,0.1983619481,0.0791434944,-0.1308648437,0.2947734892,0.2687248588,0.1325622052,0.042261485,-0.0624847747,-0.0800571069,-0.3558065295,0.1634977609,0.1999797225,0.4894587696,-0.0474283807,-0.2816003859,0.1326408088,0.0129102021,-0.1073727086,0.0189691093,0.3589312434,-0.0879683271,0.2043156773,0.0424528793,0.0136886993,-0.2235278487,0.0845431536,-0.2120842189,0.0379552618,-0.1193305477,-0.0695323423,0.3813319206,-0.0790898874,0.0466334634,-0.0146686006,0.0989310592,0.0503491461,0.2611702383,-0.2932826877,0.0491831452,0.4129322171,0.0561541021,0.2478840053,0.4095256925,-0.1842343062,-0.1060605422,0.0865907371,-0.2045428902,-0.3371692896,-0.3385475874,0.2402119339,0.0368901119,0.1089414582,-0.0184665043,-0.3302738965,0.0986591801,-0.2747161686,-0.1541592777,-0.3237557411,-0.0389051214,-0.2159913927,0.3081872761,0.6463362575,-0.4303185344,0.0429565385,0.0566363595,-0.1299165338,0.1414598227,0.2508479655,-0.3076960742,0.0851259753,-0.2284607738,-0.0966767818,0.2314821184,-0.3093754947,-0.3032815158,0.5005193353,-0.2373457253,-0.072943598,0.2169772685,0.0126159824,0.291056484,0.1858281493,0.2615151703,0.3000478446,-0.1007597595,0.1175532788,-0.0799232721,-0.222560361,0.1973579973,0.1164559647,0.0098163048,0.2231837362,0.3249672055,-0.2579794228,0.1741140336,-0.0768722668,0.1447919756,0.1345079839,0.1739715636,0.0419842489,-0.0606005527,-0.021664409,-0.508518219,0.1381557435,0.1310921013,0.0903006941,-0.4376563728,-0.0162693374,-0.3771235943,0.0051721488,-0.2671605647,-0.030241888,0.2100175917,0.3415310383,-0.0191593226,-0.0523174517,-0.0488567986,0.3256911337,-0.1166130304,-0.0192557331,-0.4156301022,0.3395552039,-0.3544683456,-0.0690031797,0.052354306,-0.0314197689,0.1626497358,-0.1825555712,-0.2719682157,0.356123805,-0.0955856889,0.2182246149,0.0170497447,-0.0044389288,0.0526204556,-0.2450993061,0.0323775932,0.0311139729,0.1533319503,0.0431826673,-0.261313647,0.3525983989,-0.0953487977,0.163369596,0.0506540611,0.0704118088,0.4514212012,0.0119942082,-0.1140478626,-0.1085091978,0.1054950804,0.2389213145,0.2262360007,0.0491035245,-0.1802719235,-0.1886238307,0.2018595934,-0.1532273442,0.0116079831,0.0923530832,-0.1086221784,-0.0442817397,0.214684695,0.2319191694,0.5149133801,0.1026022732,-0.1526015699,-0.0183210652,-0.0503232926,-0.1338230819,0.1102777719,-0.0386922434,0.0448774025,0.3272635043,0.0759821385,0.1069803238,-0.2410078943,-0.3630864322,-0.0419490486,0.2638888955,-0.3493005633,0.2479265183,-0.1645680964,0.0182821918,-0.2659018636,-0.0503300428,-0.1105000004,-0.178074345,-0.3695805371,0.3607570827,-0.0763827115,0.2372517288,-0.279001534,-0.0516414121,0.3879840374,-0.4498327672,-0.0730597377,-0.2081662267,-0.1601461768,0.0924806148,0.0935094729,-0.1568920612,0.303164959,-0.0989919826,0.0205262117,-0.16106233,-0.0238344092,-0.0821166858,0.0125281876,0.1283056736,0.1878541559,0.4475226104,0.1206022948,-0.5035608411,0.3184547424,-0.0834972411,0.0481938235,0.1964564323,-0.0537022725,-0.2032553554,0.0006876013,-0.6345347762,-0.5991461873,-0.469863981,0.1375343353,0.0665089488,0.2132315189,0.295524776,0.2269022167,0.2099236399,0.0866580531,0.0757485032,-0.0596129522,-0.1591841877,0.3499985933,-0.2349603325,-0.3570283055,-0.1133962646,-0.1152546331,0.1219444349,0.1364867091,-0.444775492,-0.0236161184,-0.1746839583,0.3565398753,-0.0467238724,0.0517050326,0.4662779868,0.1686569452,-0.1706291884,0.1126605943,-0.244187057,-0.1408112943,0.1942563504,0.0843700692,0.0044104136,0.4593695402,-0.158959344,0.653288424,-0.0798089057,-0.1897096932,0.2884309888,-0.1362253278,0.342966646,-0.1845075488,-0.6308505535,-0.0917780697,-0.3062294424,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/806","title":"Quail dataset urls are out of date","comments":"Hi ! Thanks for reporting.\r\nWe should fix the urls and use quail 1.3.\r\nIf you want to contribute feel free to fix the urls and open a PR :) ","body":"<h3>Code<\/h3>\r\n\r\n```\r\nfrom datasets import load_dataset\r\nquail = load_dataset('quail')\r\n```\r\n\r\n<h3>Error<\/h3>\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/text-machine-lab\/quail\/master\/quail_v1.2\/xml\/ordered\/quail_1.2_train.xml\r\n```\r\n\r\n\r\nAs per [quail v1.3 commit](https:\/\/github.com\/text-machine-lab\/quail\/commit\/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/quail\/quail.py#L52-L58](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/quail\/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore.","comment_length":30,"text":"Quail dataset urls are out of date \n <h3>Code<\/h3>\r\n\r\n```\r\nfrom datasets import load_dataset\r\nquail = load_dataset('quail')\r\n```\r\n\r\n<h3>Error<\/h3>\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/text-machine-lab\/quail\/master\/quail_v1.2\/xml\/ordered\/quail_1.2_train.xml\r\n```\r\n\r\n\r\nAs per [quail v1.3 commit](https:\/\/github.com\/text-machine-lab\/quail\/commit\/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/quail\/quail.py#L52-L58](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/quail\/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore. \n Hi ! Thanks for reporting.\r\nWe should fix the urls and use quail 1.3.\r\nIf you want to contribute feel free to fix the urls and open a PR :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/806","title":"Quail dataset urls are out of date","comments":"Done! PR [https:\/\/github.com\/huggingface\/datasets\/pull\/820](https:\/\/github.com\/huggingface\/datasets\/pull\/820)\r\n\r\nUpdated links and also regenerated the metadata and dummy data for v1.3 in order to pass verifications as described here: [https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#adding-tests-and-metadata-to-the-dataset](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#adding-tests-and-metadata-to-the-dataset). ","body":"<h3>Code<\/h3>\r\n\r\n```\r\nfrom datasets import load_dataset\r\nquail = load_dataset('quail')\r\n```\r\n\r\n<h3>Error<\/h3>\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/text-machine-lab\/quail\/master\/quail_v1.2\/xml\/ordered\/quail_1.2_train.xml\r\n```\r\n\r\n\r\nAs per [quail v1.3 commit](https:\/\/github.com\/text-machine-lab\/quail\/commit\/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/quail\/quail.py#L52-L58](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/quail\/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore.","comment_length":24,"text":"Quail dataset urls are out of date \n <h3>Code<\/h3>\r\n\r\n```\r\nfrom datasets import load_dataset\r\nquail = load_dataset('quail')\r\n```\r\n\r\n<h3>Error<\/h3>\r\n\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/text-machine-lab\/quail\/master\/quail_v1.2\/xml\/ordered\/quail_1.2_train.xml\r\n```\r\n\r\n\r\nAs per [quail v1.3 commit](https:\/\/github.com\/text-machine-lab\/quail\/commit\/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/quail\/quail.py#L52-L58](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/quail\/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore. \n Done! PR [https:\/\/github.com\/huggingface\/datasets\/pull\/820](https:\/\/github.com\/huggingface\/datasets\/pull\/820)\r\n\r\nUpdated links and also regenerated the metadata and dummy data for v1.3 in order to pass verifications as described here: [https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#adding-tests-and-metadata-to-the-dataset](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#adding-tests-and-metadata-to-the-dataset). 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/805","title":"On loading a metric from datasets, I get the following error","comments":"Hi ! We support only pyarrow > 0.17.1 so that we have access to the `PyExtensionType` object.\r\nCould you update pyarrow and try again ?\r\n```\r\npip install --upgrade pyarrow\r\n```","body":"`from datasets import load_metric`\r\n\r\n`metric = load_metric('bleurt')`\r\n\r\nTraceback:\r\n210 class _ArrayXDExtensionType(pa.PyExtensionType):\r\n    211 \r\n    212     ndims: int = None\r\n\r\nAttributeError: module 'pyarrow' has no attribute 'PyExtensionType'\r\n\r\nAny help will be appreciated. Thank you. ","comment_length":31,"text":"On loading a metric from datasets, I get the following error \n `from datasets import load_metric`\r\n\r\n`metric = load_metric('bleurt')`\r\n\r\nTraceback:\r\n210 class _ArrayXDExtensionType(pa.PyExtensionType):\r\n    211 \r\n    212     ndims: int = None\r\n\r\nAttributeError: module 'pyarrow' has no attribute 'PyExtensionType'\r\n\r\nAny help will be appreciated. Thank you.  \n Hi ! We support only pyarrow > 0.17.1 so that we have access to the `PyExtensionType` object.\r\nCould you update pyarrow and try again ?\r\n```\r\npip install --upgrade pyarrow\r\n```","embeddings":[-0.4477339387,0.0432356484,-0.0295897797,0.4208563864,0.5251889825,0.1035732403,0.1647764891,0.1940906644,0.1096883938,0.1362633109,-0.1096516475,0.3368699849,-0.0627767667,-0.1668589115,0.1410338879,-0.2463784814,-0.0271944776,0.1920602918,0.0922093615,-0.0036332002,-0.3685493469,-0.1364815086,-0.3558763266,0.1456970572,-0.1629061401,0.2254023403,0.1931549758,0.0090093873,-0.398291707,-0.7526149154,0.3747636974,-0.1119994223,0.3816016912,0.3474768698,-0.0001240613,-0.053221643,0.469494462,0.0690529719,-0.0325647779,-0.1555888951,-0.0472494997,-0.5924620628,0.4746727645,-0.2663748264,0.1151427776,-0.3819155693,-0.0803709626,0.0106822941,0.0000424386,0.3255780041,0.1146777421,0.262067914,0.1725226194,-0.0434034839,0.2312049717,-0.2000409216,-0.0261914544,0.7022871375,0.3933603764,-0.0741885453,0.0453938395,-0.1326176524,-0.2683163583,0.3054684401,0.6438337564,0.0471058302,0.7276001573,0.1125335544,-0.0025623052,0.2077432424,0.5605256557,-0.3992996216,-0.1872151494,-0.0055953818,0.1198050156,-0.5990043879,-0.005785937,0.1554776579,-0.0863231942,-0.0297744703,-0.1559571475,-0.2771827579,-0.3049781024,0.3827385306,-0.4256869256,0.1999412924,-0.0028616376,0.1904506534,0.0421300307,0.1036916971,0.3612973988,0.1235068142,-0.0343147144,0.3206458986,-0.1970831752,0.3396155238,-0.0133926738,0.0045510773,0.1632942855,0.0130464789,-0.1188294515,0.01834419,0.2028711885,0.3485454321,0.1614825428,0.3077957034,0.167246446,0.0110294614,0.0559720658,-0.0040248726,0.0830651447,-0.0670681298,-0.216040954,-0.4661629498,0.5968541503,0.066800788,0.301246047,-0.1160360202,-0.4341298044,-0.1129990816,-0.0865911841,-0.0943883881,0.0497982688,0.2513809502,-0.1997378767,0.091239281,0.067767866,0.3696974516,-0.2501341403,-0.1857693344,-0.195272699,0.106611982,-0.2625359595,0.000029409,0.0891798884,-0.0568173267,0.0568081327,0.0030256815,0.0976196378,-0.1197636873,-0.0368676521,0.055588901,-0.3641157746,0.2943823338,-0.0207618456,0.0673542768,0.155113399,-0.2252222896,-0.2445803434,0.4579228759,-0.6061689258,-0.2087029815,-0.1568139642,0.1332367659,-0.3736676574,-0.1092547998,-0.3528388441,0.0116287572,0.0077765966,-0.4129943848,-0.0713235363,-0.311319828,-0.1520630121,-0.3541125059,0.2045817971,-0.0227164198,-0.520308733,-0.3517714143,-0.0129674748,-0.2946338356,0.2751324475,-0.0584545434,-0.1662703305,0.3013266623,0.0159817003,0.1851962358,0.6382400393,-0.4752629399,-0.3443492949,-0.1950326562,-0.0009902224,-0.2643800974,-0.1600805372,0.0409099795,0.1501144022,0.0641754866,0.2980703712,0.2062173188,-0.0031768342,-0.1594294161,-0.1733082384,-0.2832826376,0.4397923946,0.3413206339,0.3044853508,-0.0591276512,0.169913739,-0.1738015413,0.1014139801,-0.0718534738,-0.1542688012,0.1848923564,0.3217561841,-0.035723228,0.0550460368,-0.4485323727,-0.1258915663,0.1892145276,-0.3106262982,-0.1679628491,0.0042414125,0.1094830856,-0.4346903563,0.2367288172,-0.1108597293,0.3339415193,-0.0271152575,-0.2589212358,-0.2148229182,0.1870917529,-0.2771652937,-0.1958680898,-0.0949810445,0.1448462158,0.0309311766,0.3782994747,-0.0787290782,-0.2414984405,-0.0593729168,0.256108284,0.3077491224,0.0221542884,-0.153763026,0.2603686154,0.1600511521,0.0808942989,-0.2138433903,0.3093136251,0.182526201,-0.4685382545,0.1197991669,-0.0668745339,0.0494302139,-0.0901229233,0.4214505255,0.3279112577,0.2351516187,0.1385500431,-0.2111467719,0.0676800162,-0.0596081987,0.1601516604,0.1424189508,-0.0854180828,0.2626128495,0.0419947952,0.1380295008,-0.1082081273,0.2867133915,-0.3000459969,0.289277941,-0.1676278859,0.3101268113,0.0500693247,-0.4673430324,-0.049384214,-0.0922976583,-0.4501459002,0.3805409968,0.1928175837,-0.1406975836,0.2333019227,-0.0476165079,-0.0548883602,0.1000475436,0.1550489217,0.1539150923,0.2955878377,0.137259081,-0.0522830151,-0.2871318161,0.0765533745,0.007502296,0.3762489557,-0.4939357638,0.002011366,-0.2372105122,0.3236038983,0.1041835174,-0.6289691329,-0.2238147259,-0.2210235596,-0.008297204,0.0773931369,0.2758723795,0.2762283683,0.0756507516,0.0263556875,0.1077225134,-0.3396800458,-0.0453541875,-0.4383217096,-0.2347650528,-0.0768533647,0.0777075887,0.119253017,0.209879443,-0.3348416388,0.2730451524,-0.1901924014,-0.2635513842,-0.1309440881,0.0219958052,0.4793193042,0.1622716486,0.172949627,0.0853078142,0.1829311997,0.5639775991,-0.3476026058,-0.0682466552,0.3482559025,-0.4411282837,-0.0449686944,0.0662383065,-0.0566097982,-0.3273491263,-0.2449299246,-0.1256792247,0.222770974,0.0177656729,0.0613550209,0.2782217264,0.2475695908,0.1272646785,0.0482373424,-0.2141440064,-0.0603616834,0.2959457338,-0.1059392169,-0.3464952111,0.2384506315,0.0630744696,0.1560287178,0.1219247431,-0.207655713,-0.5688388944,0.0659226999,0.4641953409,-0.1530641466,0.0602020621,0.2451774925,0.0405853204,0.0811165944,-0.1358038485,-0.1196376011,-0.1869875491,0.0505612865,0.202673614,-0.2258238047,0.078733027,-0.3294830918,0.5184357762,-0.0982304513,-0.0923547223,0.285625577,-0.2308761477,0.2227141857,0.0457650647,-0.2934775651,0.1164181307,0.084576539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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/804","title":"Empty output\/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa')","comments":"Yes: TriviaQA has a private test set for the leaderboard [here](https:\/\/competitions.codalab.org\/competitions\/17208)\r\n\r\nFor the KILT training and validation portions, you need to link the examples from the TriviaQA dataset as detailed here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md","body":"# The issue\r\n\r\nIt's all in the title, it appears to be fine on the train and validation sets.\r\n\r\nIs there some kind of mapping to do like for the questions (see https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md) ? \r\n\r\n# How to reproduce\r\n```py\r\nfrom datasets import load_dataset\r\nkilt_tasks = load_dataset(\"kilt_tasks\")\r\ntrivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')\r\n# both in \"kilt_tasks\"\r\nIn [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']])                                                                                                                        \r\nOut[18]: False\r\n# and \"trivia_qa\"\r\nIn [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']])                                                                                                                        \r\nOut[13]: True\r\n# appears to be fine on the train and validation sets.\r\nIn [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']])                                                                                                                       \r\nOut[14]: False\r\n\r\nIn [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']])                                                                                                                  \r\nOut[15]: False\r\n\r\nIn [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']])                                                                                                                       \r\nOut[16]: True\r\n\r\nIn [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']])                                                                                                                  \r\nOut[17]: True\r\n\r\n```","comment_length":32,"text":"Empty output\/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa') \n # The issue\r\n\r\nIt's all in the title, it appears to be fine on the train and validation sets.\r\n\r\nIs there some kind of mapping to do like for the questions (see https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md) ? \r\n\r\n# How to reproduce\r\n```py\r\nfrom datasets import load_dataset\r\nkilt_tasks = load_dataset(\"kilt_tasks\")\r\ntrivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')\r\n# both in \"kilt_tasks\"\r\nIn [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']])                                                                                                                        \r\nOut[18]: False\r\n# and \"trivia_qa\"\r\nIn [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']])                                                                                                                        \r\nOut[13]: True\r\n# appears to be fine on the train and validation sets.\r\nIn [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']])                                                                                                                       \r\nOut[14]: False\r\n\r\nIn [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']])                                                                                                                  \r\nOut[15]: False\r\n\r\nIn [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']])                                                                                                                       \r\nOut[16]: True\r\n\r\nIn [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']])                                                                                                                  \r\nOut[17]: True\r\n\r\n``` \n Yes: TriviaQA has a private test set for the leaderboard [here](https:\/\/competitions.codalab.org\/competitions\/17208)\r\n\r\nFor the KILT training and validation portions, you need to link the examples from the TriviaQA dataset as detailed here:\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md","embeddings":[0.4346867502,-0.3452418447,-0.0812816396,0.1217559502,0.2600176632,-0.079768993,0.4260964692,0.4618622661,0.13298136,0.2688274384,0.0946825445,0.2613138556,-0.0670852587,0.5240859985,0.0683326572,0.2598775327,-0.1134255379,0.0808698758,-0.1446951777,-0.2219866514,-0.2116925418,0.0270461477,-0.2168559432,-0.1244957,-0.3129106164,0.0097973114,-0.0458583422,-0.0411906838,0.048802264,-0.3793427348,0.2050960362,0.0018789937,-0.4776950479,0.250802815,-0.0001265892,-0.0889239535,0.1993868202,-0.1674762517,-0.4454841018,-0.1441676319,-0.6783222556,0.0141277779,0.170403555,-0.2482234389,-0.3704464138,0.332406044,0.0045207287,-0.3541144133,0.3715062439,0.4303570986,0.0710942596,0.3091676831,-0.3334417641,-0.1123591363,-0.0177280717,0.078342855,-0.2280900925,0.0803658217,0.219762668,-0.1270766258,0.0533198975,0.330270946,0.1531341374,0.1341962814,-0.0423491672,0.1115850955,0.1451227814,-0.5243434906,0.2711947858,0.4891029,0.1355248094,-0.0259978324,-0.4715607762,-0.3702868521,-0.0004940171,-0.2080955058,0.2873293459,0.3626281917,-0.2731277049,-0.0013897181,-0.3779554665,0.0880932957,-0.1987210512,-0.0318931043,-0.6022466421,0.2828427255,-0.0419524498,0.056307897,-0.2972771823,0.0585988387,-0.1214954033,-0.2579809427,0.0553128049,0.2168537378,-0.5435726047,-0.0804850832,0.2416573912,-0.1061613038,0.021168476,-0.1563095897,0.0860012099,0.0318802781,-0.247733444,0.133972764,0.4812690318,0.4112280011,0.4215257168,0.1192411184,0.1123541594,0.0562162548,-0.1539959162,0.0744720474,0.0348182209,0.1464500576,-0.1980704218,0.0675947815,0.335577786,-0.4146649241,-0.5860321522,0.1421547383,-0.3326154053,0.0864446908,-0.1067222133,0.320563525,-0.2858104408,0.2536134124,-0.1336442977,0.4243001938,-0.2777159512,0.0795583054,-0.1509981602,-0.0046977056,-0.2762502432,0.1931227595,0.224704355,0.0346412919,0.1216206253,0.1096358076,0.0274162982,-0.0919776112,0.1531841308,-0.2339354455,0.4344162047,0.188072145,0.1408801973,0.1189044341,0.2922724783,-0.1410393864,0.0318254307,0.4630865157,-0.0387344584,-0.1531048566,0.2196510732,0.1176871657,-0.53619802,0.0612278245,-0.0697553232,0.3681988418,0.2204560041,0.0522886068,0.0639485046,-0.17597422,-0.0918827653,-0.1526447237,0.2948548198,0.1581320167,-0.4779413342,-0.107231468,0.2162587047,0.0987294242,-0.1795674562,0.0834397152,-0.0059622596,0.3908629119,-0.2375229746,0.0595924743,0.1495541036,-0.5808662772,-0.049199108,-0.2524282932,0.010969785,0.3100386262,-0.0182249602,-0.1082027107,0.1928690821,0.1797241122,0.0380761921,0.0307709333,-0.0385318212,-0.1282064915,-0.2770301104,-0.0048107761,0.2065397203,0.1226963475,0.2405065149,-0.0155215608,-0.1409450322,0.0033424345,0.1804189235,0.0150427595,0.1349632591,0.0477367826,0.415969938,-0.0334042497,-0.0810122117,0.1435109526,-0.2264648527,0.2795407772,-0.0904851481,0.4886126518,-0.0579417124,-0.2643123567,-0.2712050676,-0.100305073,-0.5140523911,-0.6296908259,0.0657849908,0.042335704,0.0234564636,0.0934630856,-0.178200841,0.0506216809,0.0379335284,-0.1656878889,-0.3817005455,0.2512590885,0.0596449114,0.0076799048,-0.3221111894,0.5083587766,-0.0573302358,0.0728075057,0.1012719348,-0.0902488902,0.0823971555,-0.0617132597,0.1081170887,0.3362760544,0.3323972225,-0.1298761666,-0.2818245292,0.3482183516,0.0935201496,-0.2479854822,-0.5444375277,0.5965662599,0.0071430248,0.4199711978,0.0516379662,0.0222856048,-0.0676893592,-0.2766861916,-0.218831867,-0.1478293985,-0.0463503338,-0.2262968719,0.1864347905,0.1002461314,-0.2956453562,0.2027985752,0.4188897908,0.0210408829,0.2085370421,-0.3053345382,0.0180465262,-0.0307684503,0.184289664,0.0231524054,0.4413944483,0.084580645,0.2386827767,0.0726953074,0.1604368389,-0.2435355335,0.152713567,-0.0230280645,0.1729786992,0.3898729682,0.3559095263,-0.1646558493,-0.0903521478,0.3359053135,0.0750357136,0.1385468841,-0.3268065453,-0.1666371226,-0.1502793282,-0.0419876017,-0.1752014607,0.0523343533,0.0248411261,-0.4100830853,0.3285831213,-0.0310880356,-0.2622547448,0.2447648793,-0.0283536986,0.1885126978,-0.0595245399,-0.3672983944,-0.0129037043,-0.5210822821,-0.2430393249,0.1383781731,-0.1649945974,-0.1913993061,0.1399034113,-0.1132650226,-0.5428164005,-0.2194762528,-0.429346025,0.1460483223,-0.2055821866,0.3748728931,0.0741121471,0.4747892022,-0.3547728956,-0.0322884209,0.5146630406,0.021866139,-0.3692892492,0.012095049,0.157763347,0.0929032192,-0.1327739358,-0.7486938834,-0.1053432226,-0.2937638462,0.266032517,-0.086060524,0.1985882968,0.3975977898,-0.1153228208,-0.039976459,-0.1941642463,-0.1932611018,-0.1947054118,0.0486537777,0.1662123948,-0.1380502284,-0.3828946948,0.0876780897,-0.2003528923,0.5565998554,-0.0043731234,-0.4171013534,-0.4424279928,0.0046635936,0.5091130733,0.2116530091,-0.1150768623,0.2026955187,0.0264127888,-0.0459441505,-0.1555680037,0.0131564932,0.5017895699,0.3555322886,0.107933417,0.0652409568,0.0247997716,0.3510317802,0.641597569,0.3786245286,0.3638949096,0.1289847493,-0.249352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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/804","title":"Empty output\/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa')","comments":"Oh ok, I guess I read the paper too fast \ud83d\ude05, thank you for your answer!","body":"# The issue\r\n\r\nIt's all in the title, it appears to be fine on the train and validation sets.\r\n\r\nIs there some kind of mapping to do like for the questions (see https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md) ? \r\n\r\n# How to reproduce\r\n```py\r\nfrom datasets import load_dataset\r\nkilt_tasks = load_dataset(\"kilt_tasks\")\r\ntrivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')\r\n# both in \"kilt_tasks\"\r\nIn [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']])                                                                                                                        \r\nOut[18]: False\r\n# and \"trivia_qa\"\r\nIn [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']])                                                                                                                        \r\nOut[13]: True\r\n# appears to be fine on the train and validation sets.\r\nIn [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']])                                                                                                                       \r\nOut[14]: False\r\n\r\nIn [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']])                                                                                                                  \r\nOut[15]: False\r\n\r\nIn [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']])                                                                                                                       \r\nOut[16]: True\r\n\r\nIn [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']])                                                                                                                  \r\nOut[17]: True\r\n\r\n```","comment_length":16,"text":"Empty output\/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa') \n # The issue\r\n\r\nIt's all in the title, it appears to be fine on the train and validation sets.\r\n\r\nIs there some kind of mapping to do like for the questions (see https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md) ? \r\n\r\n# How to reproduce\r\n```py\r\nfrom datasets import load_dataset\r\nkilt_tasks = load_dataset(\"kilt_tasks\")\r\ntrivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')\r\n# both in \"kilt_tasks\"\r\nIn [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']])                                                                                                                        \r\nOut[18]: False\r\n# and \"trivia_qa\"\r\nIn [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']])                                                                                                                        \r\nOut[13]: True\r\n# appears to be fine on the train and validation sets.\r\nIn [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']])                                                                                                                       \r\nOut[14]: False\r\n\r\nIn [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']])                                                                                                                  \r\nOut[15]: False\r\n\r\nIn [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']])                                                                                                                       \r\nOut[16]: True\r\n\r\nIn [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']])                                                                                                                  \r\nOut[17]: True\r\n\r\n``` \n Oh ok, I guess I read the paper too fast \ud83d\ude05, thank you for your answer!","embeddings":[0.4681178033,-0.4883505404,-0.1169174835,0.0633333772,0.3016143143,-0.0618819781,0.5027799606,0.441729635,0.1443300396,0.2951969802,0.150508225,0.2478302419,-0.1058168337,0.4204440415,0.0343295373,0.204022795,-0.0182283055,0.134105131,-0.2242505699,-0.2463575304,-0.2494607121,-0.0017767396,-0.2310018092,-0.1340512782,-0.3148827255,0.0192815457,-0.018374715,-0.0441296548,-0.0066700438,-0.2820155323,0.2964689136,0.0416831449,-0.5087098479,0.2796152532,-0.0001253978,0.0176601093,0.165543437,-0.1627589613,-0.3021452427,-0.2690975666,-0.5443492532,0.003459824,0.2009383291,-0.2457512021,-0.3536373079,0.2595878839,-0.0016623527,-0.2429096997,0.3208558857,0.4414460063,0.0885331482,0.3402518332,-0.2983184457,-0.0976915359,0.1007717326,0.1736653894,-0.2127652615,0.1006111056,0.3349828124,-0.1839226484,0.066155158,0.4312537313,0.115748547,0.0765572712,-0.0095213763,0.0850534961,0.192847997,-0.5020107627,0.2259553075,0.4908399284,0.1194553599,0.0117059685,-0.461663574,-0.3616985679,-0.0677119046,-0.1615849137,0.2391583323,0.2563347518,-0.3226281404,0.0545236282,-0.3806765079,0.0091280751,-0.1806761771,-0.0156914108,-0.5306880474,0.3866957426,-0.0629000738,0.0666511431,-0.234006092,0.0058015864,-0.0944485217,-0.2278283834,0.106599234,0.1917997748,-0.4877149761,-0.0786596537,0.2468911409,-0.1749946177,0.0170607883,-0.1575658619,0.1409419477,-0.0101393433,-0.2379292548,0.1809385568,0.4561161399,0.371773392,0.3836920559,0.1170968637,0.1253914237,0.024882203,-0.1779976189,0.068300955,-0.0132526876,0.104945302,-0.173080191,0.0444740392,0.3815128803,-0.3759962618,-0.5460976362,0.1028343514,-0.3343901634,0.0862018541,-0.1280481815,0.4136289358,-0.2671588957,0.323705703,-0.1753737032,0.484808147,-0.2423684746,0.1622779816,-0.1694838703,0.0083823279,-0.2954889536,0.0338302106,0.1820193827,0.0345722064,0.1677876711,0.0791904926,-0.0682363361,-0.0843573734,0.1491902024,-0.29161641,0.5289235711,0.2174315751,0.1086589694,0.1371990144,0.3113140762,-0.1387363672,-0.0306898654,0.4569903016,-0.1073429361,-0.0885391235,0.1621474773,0.1351391673,-0.4702080488,0.0000834229,0.0173491593,0.3994839787,0.2739620209,0.0624445751,0.1275006235,-0.1487631649,-0.0352493897,-0.1899267733,0.205567047,0.158592999,-0.3715784848,-0.1733774841,0.3192159832,0.0479408093,-0.1288811713,0.0926913023,-0.0432998426,0.4183746576,-0.2419098616,0.0545514785,0.1526187062,-0.6342750192,-0.0166913364,-0.2027662247,-0.0080077397,0.3960766196,0.0161043759,-0.1464105844,0.1910933554,0.2044422776,0.2436323762,-0.0492489189,-0.0163895898,-0.101412721,-0.2839249671,-0.0278917216,0.2803300619,0.0384866111,0.2653223872,-0.0381516963,-0.1428515464,-0.0613704324,0.2356456667,-0.0408080593,0.0899868831,0.1494654715,0.4336160123,-0.0053794263,-0.0390727557,0.0198617131,-0.2733375728,0.1653865129,-0.0145297125,0.439550519,-0.0143588698,-0.2540389895,-0.2323454916,-0.1023680419,-0.4545465708,-0.5898591876,0.0748970881,0.014747561,-0.0367927887,0.0872119442,-0.1551977843,0.0207577292,0.0445901267,-0.1249775887,-0.3414300084,0.3159354031,0.058935713,0.028890267,-0.3574270606,0.3923297524,-0.0711159855,0.0870083347,0.1109094843,-0.0962955803,0.1215808392,-0.0367224552,0.0171239562,0.3487404585,0.3160446286,-0.1571756154,-0.3339691758,0.2780619562,0.1288728863,-0.2391919643,-0.5651096702,0.5706455112,-0.015483479,0.4227933586,0.1110909656,0.0158536006,0.0158130974,-0.3055428267,-0.2595428228,-0.1548806876,0.0180579703,-0.2967331111,0.1731819212,0.1353041381,-0.3907076418,0.1122884527,0.4177013934,0.0491052642,0.2202721685,-0.2979328632,-0.0175317377,-0.0396430343,0.1566833109,-0.0367524624,0.4276127815,0.1694587767,0.2256353348,0.0127891097,0.2090923935,-0.2430087328,0.1855034232,-0.0402710624,0.2182824463,0.4549237192,0.354709506,-0.1246863976,-0.1934615076,0.2973785102,0.0703052655,0.138393566,-0.3260527849,-0.2424041778,-0.1273448914,0.0182369594,-0.1301850528,0.0313257873,0.0962652564,-0.3991000056,0.3158450723,0.0024841891,-0.3320240676,0.2252082974,-0.1030021459,0.163310796,-0.0388344564,-0.3680047691,-0.0482127853,-0.4899754822,-0.2169424444,0.1613847911,-0.1952280253,-0.1368522346,0.1412716061,-0.1272917092,-0.531288743,-0.1544178277,-0.5428310037,0.1698828191,-0.222117722,0.3141136169,0.1305209547,0.4695051014,-0.3395379484,-0.0461444035,0.4725041687,0.0025181998,-0.4022099972,0.0161709189,0.1601449996,0.0788671598,-0.1604304463,-0.7739167809,-0.0642809197,-0.3372270763,0.286716193,-0.1638684422,0.1809030026,0.448207885,-0.0710506216,-0.0587882176,-0.2128799707,-0.1925424933,-0.1631088406,0.0021028754,0.1418824196,-0.0721007437,-0.3668020666,0.007778734,-0.2205589116,0.3654289842,0.093249701,-0.400913775,-0.410728097,-0.0315597616,0.4304032922,0.2287753969,-0.0975355133,0.2679589093,-0.0008596504,-0.0774438679,-0.2450527549,0.0018970593,0.5727344155,0.3348303735,0.0469684862,0.1178127527,-0.0271696839,0.3268133998,0.5415535569,0.3504087329,0.3235523701,0.1822449267,-0.2408680916,0.3834410012,-0.2624072433,-0.3972393274,0.2047518343,0.1588831544,-0.1285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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/801","title":"How to join two datasets?","comments":"Hi ! Currently the only way to add new fields to a dataset is by using `.map` and picking items from the other dataset\r\n","body":"Hi,\r\n\r\nI'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels? \r\n\r\nI'm currently trying to create paired sentences for BERT from `wikipedia\/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence.\r\n\r\nThanks!","comment_length":24,"text":"How to join two datasets? \n Hi,\r\n\r\nI'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels? \r\n\r\nI'm currently trying to create paired sentences for BERT from `wikipedia\/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence.\r\n\r\nThanks! \n Hi ! Currently the only way to add new fields to a dataset is by using `.map` and picking items from the other dataset\r\n","embeddings":[-0.0807135701,-0.0231983922,-0.0087042591,0.0758321956,-0.0804641396,0.1938629001,-0.0964456275,0.1502997428,0.0917028189,-0.1372491866,-0.156401813,0.2632109523,0.2652062774,0.0553944521,0.0318246484,-0.1623478681,0.2753705978,0.1366879791,-0.1497536302,0.0182720907,0.0281484146,0.0739849955,-0.1760732085,-0.0964749157,-0.2948448658,0.0638359413,-0.1693757772,-0.2055224776,-0.007480918,-0.1788524538,0.1081667691,0.2946743965,0.0237622503,0.5012590885,-0.0001200251,-0.182182014,0.148232311,-0.1188671067,-0.0978056639,-0.5917367339,-0.4379109144,-0.1937709898,-0.0372159071,-0.2641343176,-0.1709364951,-0.2597176135,-0.0092760064,-0.3962280154,0.1879961193,-0.0077911085,0.0517811179,-0.2181003094,0.3178336918,-0.2899117172,0.368132025,0.0600768179,0.231391415,0.204635486,0.4667863846,0.0924607143,0.35362342,0.0318278708,-0.2515158951,-0.1778289527,0.2656972706,0.1065583304,-0.2725177407,-0.5039494634,0.1312267184,0.5253686905,0.8458815813,-0.1958146393,-0.2037546039,-0.1436982155,0.2615859509,0.1677378267,-0.1626797467,0.065555878,0.0571829155,-0.1085786298,-0.0172955748,-0.4783712029,-0.0146891614,0.1460682899,-0.0518698655,0.4198278785,0.1197788194,0.1123552471,-0.1201640666,-0.3536275029,-0.0021790173,-0.1028894931,-0.2314326614,0.3964161575,-0.1357270479,-0.3579379618,-0.4266589582,-0.207686156,0.2877579927,-0.2325144559,-0.109399341,-0.1312533915,-0.3093343377,0.2262607515,0.4135226309,-0.1765180826,0.4025959671,0.1679807901,-0.1497799158,-0.2919568717,-0.6388095617,-0.0956266075,0.0734440163,-0.2497820556,-0.0123184929,-0.1612832695,0.123229228,-0.0954735726,0.1156069934,0.1166566536,-0.3803674877,-0.1796399355,-0.4582118988,-0.0172274187,0.0473739319,-0.1791682541,-0.0947115719,0.3156735897,0.0625576377,-0.2051766515,0.0225611832,-0.0225598402,0.0753076598,0.1359440684,-0.3133123219,0.0678401217,0.2115236372,0.2421742678,0.1165313795,-0.0914426222,0.1404797584,-0.3214638233,0.0882012248,-0.2670930028,0.2662129402,0.3648284078,0.0332867876,-0.3661639392,-0.077468127,0.3192604482,-0.3424720168,0.0919076502,-0.1811608523,0.0503414199,-0.0347732008,-0.3690575063,0.1503404826,0.4734448195,0.2010741085,-0.379218936,-0.0435686931,0.1888428777,-0.4432769418,-0.1435356885,0.0995916724,0.0958330482,-0.610889852,-0.246776849,0.130536139,0.18384628,-0.2199200839,0.2735758126,0.0045823003,0.4709973633,0.0193588324,0.0979583412,0.2940160334,0.0065495647,-0.1389174908,0.238259837,-0.061020188,-0.1116313562,-0.0856147856,0.3941873908,0.3761968315,-0.1148844287,0.2615608871,0.1333845556,-0.205403015,-0.2404527366,0.1724509895,-0.2500698864,0.2003563643,-0.3064526021,-0.0209771879,-0.0451242886,0.2041331381,0.0940571874,0.1203064397,-0.2302704453,0.2388392687,0.361251533,0.0425797552,0.4038475156,-0.1544989944,-0.3849770129,-0.357668519,-0.0640172362,-0.1553385556,0.2091977,0.3712136447,-0.2315183133,-0.1918838769,-0.3699703515,0.3332959712,-0.0095565738,0.0069703101,-0.1931199729,-0.1151965261,-0.1268462539,-0.1134818792,-0.0729124695,0.2275792062,-0.1046024486,-0.2657743096,0.27956146,0.1391690969,-0.0043341462,-0.249317944,0.3853373528,0.08726307,0.1868847311,0.1661546379,0.0304406304,-0.0998971909,0.3716405332,0.249361068,-0.2330416441,0.0095545622,-0.5110837817,-0.1398496926,-0.2362093776,-0.0440366454,-0.265645206,-0.2391246706,0.1430874914,-0.0139346393,0.2417303473,0.1318977028,-0.0250237696,0.1806058735,-0.0407013409,-0.0761865079,-0.2563690543,-0.050618533,0.1348457336,0.1929733455,0.3609315157,-0.2296914309,0.0628190935,0.0342490375,0.0914989114,0.1899907589,-0.2439336926,-0.4683243334,0.0066163177,-0.2882340848,0.0960114896,0.1645246297,0.3466561139,0.1398829222,0.0730472878,-0.0421125665,-0.0236181002,0.0177689642,0.0443218946,0.3303335607,0.109584108,0.256780237,0.0742078945,0.3293254972,0.2420234382,0.1224824488,-0.1384059936,-0.1348122507,-0.2841987014,0.1067584902,-0.2484176904,-0.4215637147,-0.1875657886,-0.1845408529,-0.1230121776,-0.0149282655,0.1865074635,-0.0561988056,0.1403868496,0.0554290675,0.1559760273,-0.1527743936,-0.0191225801,0.1331458986,-0.5160618424,0.2592284381,0.1565908492,0.1139577702,0.1209732369,0.472194165,-0.1159626096,-0.2033684999,-0.5184364915,-0.5585831404,0.0389996357,-0.1104806066,0.2622460127,-0.1279797405,0.1481089741,-0.1493074,-0.0289613381,0.2102096677,0.2862325907,0.0190500263,-0.3370529115,-0.1956279725,0.0766984373,0.259927839,-0.4277597368,-0.180271104,0.0028607389,0.187552616,0.0680031925,0.0265829805,-0.4053646326,0.1125639677,-0.122062929,-0.1676352471,0.0574354641,-0.0919366777,-0.3873731196,0.2161987722,-0.0784542337,-0.2595170438,0.1928527057,-0.0159029327,-0.0558186658,-0.2051000446,0.1267775893,0.1307677478,-0.1578683704,0.1032423452,0.2249044776,0.1547768265,0.1833477914,0.1117331535,0.0685983002,-0.1756863147,-0.1481736153,0.268507719,0.0928248242,0.1118733361,-0.0606018007,0.2222197205,-0.3472218812,0.3986665905,0.5254830718,0.1192845628,-0.059492372,-0.2288450301,0.3587059081,-0.245824635,-0.105610311,-0.2335343659,0.1408099234,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/801","title":"How to join two datasets?","comments":"Closing this one. Feel free to re-open if you have other questions about this issue.\r\n\r\nAlso linking another discussion about joining datasets: #853 ","body":"Hi,\r\n\r\nI'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels? \r\n\r\nI'm currently trying to create paired sentences for BERT from `wikipedia\/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence.\r\n\r\nThanks!","comment_length":23,"text":"How to join two datasets? \n Hi,\r\n\r\nI'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels? \r\n\r\nI'm currently trying to create paired sentences for BERT from `wikipedia\/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence.\r\n\r\nThanks! \n Closing this one. Feel free to re-open if you have other questions about this issue.\r\n\r\nAlso linking another discussion about joining datasets: #853 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/798","title":"Cannot load TREC dataset: ConnectionError","comments":"Hi ! Indeed there's an issue with those links.\r\nWe should probably use the target urls of the redirections instead","body":"## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here. ","comment_length":20,"text":"Cannot load TREC dataset: ConnectionError \n ## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here.  \n Hi ! Indeed there's an issue with those links.\r\nWe should probably use the target urls of the redirections 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/798","title":"Cannot load TREC dataset: ConnectionError","comments":"Hi, the same issue here, could you tell me how to download it through datasets? thanks ","body":"## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here. ","comment_length":16,"text":"Cannot load TREC dataset: ConnectionError \n ## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here.  \n Hi, the same issue here, could you tell me how to download it through datasets? thanks ","embeddings":[-0.1691725552,0.0168359298,0.0626651496,0.3633992374,0.2918767631,-0.1796784997,0.2199582756,-0.0434447527,-0.1838140339,0.0629585609,-0.3238992393,0.1360173374,0.0722290277,0.1577587724,-0.0579137243,-0.0945270881,-0.1901099831,-0.2000556588,-0.0688836575,0.2535367906,-0.1328548044,0.4753988683,-0.2073137164,0.2046911418,-0.4112499952,0.0120672574,0.2174304873,0.4217193425,-0.2242174745,-0.6152897477,0.4672029912,0.0918213353,0.2583209872,0.630194068,-0.0001212188,0.0438775904,0.3747947514,-0.0809114501,-0.3682197928,-0.5027552843,-0.5078126788,-0.0790523142,0.2648388743,0.2292865217,-0.3196886182,0.4285567403,0.0791531503,-0.325060308,0.3382033706,0.6332944036,0.0820835829,-0.0768375695,0.0945120901,-0.1623590142,0.3016184568,-0.1488979459,-0.0706101805,0.4869538546,0.2155133337,-0.0386384055,0.0549374111,-0.0216429308,-0.0271049347,-0.0117052123,0.1798272282,-0.0742924362,-0.1101848111,-0.4340969324,0.4241297245,0.352265209,0.6402148008,-0.1733739376,-0.2272690535,0.1454129517,-0.081264168,-0.4302360415,0.3003183305,0.3217078149,-0.1221241057,0.1870257258,-0.4078291059,-0.2597643733,-0.3686292768,0.0043727914,-0.416408807,0.1270349473,-0.2663984001,0.131530717,0.2910034359,0.0152950874,0.3388897479,0.2237246931,-0.4470455348,-0.0075067496,-0.6929902434,0.1829680651,-0.1482103318,-0.0363469273,0.0934938565,0.3697319627,0.3174377084,0.0368965492,-0.2863053977,0.4065686166,0.0869918466,0.3421801031,0.0432393365,0.1872974336,0.3242341876,0.1837179959,-0.0132212332,-0.0930793434,-0.195083186,-0.2066059262,-0.1330894083,0.1809051484,0.0973416939,-0.2702093422,-0.391297996,0.1465577036,-0.4139411747,0.009193588,0.1225641072,0.4541309178,-0.2755913138,-0.0242432654,-0.0385249332,0.2208224833,-0.0965710506,-0.1626555324,-0.066012606,0.2261447608,-0.2326459587,0.1317702383,0.3381001651,-0.1813087612,0.1413071007,-0.164917931,-0.066844523,-0.2156009972,-0.0086505944,-0.2676545978,-0.1067192107,0.4307493269,0.0776933208,0.0562675633,0.1243787333,-0.3719575107,-0.0352643579,0.0133798691,-0.5714361072,-0.3518609107,0.0621165261,0.0705782101,-0.1204286665,0.0219029319,-0.3443544507,0.0012689885,-0.0043544434,-0.1800432205,-0.1748710871,-0.2704450488,-0.3334649503,-0.3149197698,0.3458762765,0.451456517,-0.5566045642,-0.0270752627,0.0968400612,0.0743105635,0.1674599946,0.3314805627,-0.1907550693,-0.0951525122,-0.2181226611,-0.1000668108,0.0956019834,-0.3112884164,-0.2985938787,-0.0033583376,-0.0194073506,-0.0483571663,-0.2369234562,-0.1034136266,0.013775479,0.0616033152,0.6465601921,0.2157903463,-0.128316775,-0.1761939228,-0.1502933949,-0.276214689,-0.2542467415,0.0869560763,0.1214700714,0.0059731621,0.2363967299,-0.4764141738,0.070228681,0.041881226,-0.0073060701,0.3011595905,0.3711313903,-0.0534163527,-0.1965114772,0.2541643977,-0.4512370229,0.3701867163,-0.0718612894,0.2864062488,-0.2534539998,0.1539354026,-0.2667068243,0.1449401677,-0.0538807735,-0.1338416189,-0.0591719225,0.0979989544,0.2250677347,0.2401248366,-0.1199853867,0.5677842498,0.135291785,0.1387476027,-0.2335021943,0.2586082816,0.0218197275,0.0688075498,0.1329162866,0.0106323007,0.2142108232,-0.2170554101,-0.0641218349,0.318633914,0.2173629254,0.0417961814,0.057461638,0.1577191204,0.2542841733,-0.3891916275,-0.0997398421,0.0472882427,-0.0086243441,-0.0252697486,-0.0405539423,0.2425730079,0.0648595691,0.4205703437,0.1400236785,0.0894204974,0.3215745389,0.0298587233,-0.0160045065,0.179474324,0.1751997471,0.2178916037,0.0230622217,-0.0838506073,-0.3602313399,0.1493657082,-0.1642055213,0.0495590158,-0.1129640341,0.1247549057,-0.1547979712,-0.0220802911,0.0700044334,0.3708671927,0.2201276124,0.0490480177,-0.2005204111,0.1874097735,0.0605272613,-0.2058147639,-0.0445009433,0.1096539572,0.3646667898,0.2655833662,0.426687777,-0.0487633571,-0.095108144,-0.3787639439,-0.0358973593,0.2614510655,-0.27078861,0.4008783698,-0.2434709668,-0.3584270775,0.0938047096,-0.3161964118,-0.3449476063,-0.2649385929,-0.1177102774,0.4375137389,0.1163083985,0.0623287521,-0.2111103535,0.3716548681,0.0254304223,-0.5300382972,-0.023688294,-0.2573983371,-0.3198367953,0.015924165,0.3333905041,-0.2213886827,0.0698618591,-0.2547039092,0.0297967102,-0.1255459189,-0.2030438036,0.161564365,0.1523073763,0.3011630476,-0.0669810548,0.2267765105,-0.2425041497,0.0940599963,0.2750999629,0.067961067,0.1023404077,0.048934143,-0.0400866605,0.0441079624,0.0249122102,-0.2555710673,-0.4800460041,-0.3582805991,0.1784227639,0.0196136292,0.3458847404,0.2960084677,0.0382493213,-0.0866623893,0.0641809925,-0.0769640952,-0.1533749849,-0.3865545392,0.1724119484,-0.2786794007,-0.2431341708,0.2509688139,0.0747392997,0.384760499,0.0763949454,-0.6094400883,-0.1564110816,-0.1264603585,0.1048995107,0.0258263517,-0.2825921476,0.3334425092,-0.0882083923,-0.0051477673,0.216064468,-0.4404521585,0.0323337875,0.3356822431,0.3913239837,0.2737467587,0.3707469404,-0.1709450483,0.4239256084,0.2026095539,0.0485433601,0.3296657801,-0.1572235525,0.3341207802,-0.0892179385,-0.2969244123,-0.2172842175,-0.2103471756,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/798","title":"Cannot load TREC dataset: ConnectionError","comments":"Actually it's already fixed on the master branch since #740 \r\nI'll do the 1.1.3 release soon","body":"## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here. ","comment_length":16,"text":"Cannot load TREC dataset: ConnectionError \n ## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here.  \n Actually it's already fixed on the master branch since #740 \r\nI'll do the 1.1.3 release 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/798","title":"Cannot load TREC dataset: ConnectionError","comments":"Hi\nthanks, but I did tried to install from the pip install git+... and it does\nnot work for me,. thanks for the help. I have the same issue with wmt16,\n\"ro-en\"\nthanks.\nBest\nRabeeh\n\nOn Mon, Nov 16, 2020 at 10:29 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Actually it's already fixed on the master branch since #740\n> <https:\/\/github.com\/huggingface\/datasets\/pull\/740>\n> I'll do the 1.1.3 release soon\n>\n> \u2014\n> You are receiving this because you commented.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/798#issuecomment-727854736>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABP4ZCEUBJKPOCLABXCKMPDSQDWH3ANCNFSM4TJBUKSA>\n> .\n>\n","body":"## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here. ","comment_length":98,"text":"Cannot load TREC dataset: ConnectionError \n ## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here.  \n Hi\nthanks, but I did tried to install from the pip install git+... and it does\nnot work for me,. thanks for the help. I have the same issue with wmt16,\n\"ro-en\"\nthanks.\nBest\nRabeeh\n\nOn Mon, Nov 16, 2020 at 10:29 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Actually it's already fixed on the master branch since #740\n> <https:\/\/github.com\/huggingface\/datasets\/pull\/740>\n> I'll do the 1.1.3 release soon\n>\n> \u2014\n> You are receiving this because you commented.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/datasets\/issues\/798#issuecomment-727854736>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABP4ZCEUBJKPOCLABXCKMPDSQDWH3ANCNFSM4TJBUKSA>\n> .\n>\n","embeddings":[-0.1691725552,0.0168359298,0.0626651496,0.3633992374,0.2918767631,-0.1796784997,0.2199582756,-0.0434447527,-0.1838140339,0.0629585609,-0.3238992393,0.1360173374,0.0722290277,0.1577587724,-0.0579137243,-0.0945270881,-0.1901099831,-0.2000556588,-0.0688836575,0.2535367906,-0.1328548044,0.4753988683,-0.2073137164,0.2046911418,-0.4112499952,0.0120672574,0.2174304873,0.4217193425,-0.2242174745,-0.6152897477,0.4672029912,0.0918213353,0.2583209872,0.630194068,-0.0001212188,0.0438775904,0.3747947514,-0.0809114501,-0.3682197928,-0.5027552843,-0.5078126788,-0.0790523142,0.2648388743,0.2292865217,-0.3196886182,0.4285567403,0.0791531503,-0.325060308,0.3382033706,0.6332944036,0.0820835829,-0.0768375695,0.0945120901,-0.1623590142,0.3016184568,-0.1488979459,-0.0706101805,0.4869538546,0.2155133337,-0.0386384055,0.0549374111,-0.0216429308,-0.0271049347,-0.0117052123,0.1798272282,-0.0742924362,-0.1101848111,-0.4340969324,0.4241297245,0.352265209,0.6402148008,-0.1733739376,-0.2272690535,0.1454129517,-0.081264168,-0.4302360415,0.3003183305,0.3217078149,-0.1221241057,0.1870257258,-0.4078291059,-0.2597643733,-0.3686292768,0.0043727914,-0.416408807,0.1270349473,-0.2663984001,0.131530717,0.2910034359,0.0152950874,0.3388897479,0.2237246931,-0.4470455348,-0.0075067496,-0.6929902434,0.1829680651,-0.1482103318,-0.0363469273,0.0934938565,0.3697319627,0.3174377084,0.0368965492,-0.2863053977,0.4065686166,0.0869918466,0.3421801031,0.0432393365,0.1872974336,0.3242341876,0.1837179959,-0.0132212332,-0.0930793434,-0.195083186,-0.2066059262,-0.1330894083,0.1809051484,0.0973416939,-0.2702093422,-0.391297996,0.1465577036,-0.4139411747,0.009193588,0.1225641072,0.4541309178,-0.2755913138,-0.0242432654,-0.0385249332,0.2208224833,-0.0965710506,-0.1626555324,-0.066012606,0.2261447608,-0.2326459587,0.1317702383,0.3381001651,-0.1813087612,0.1413071007,-0.164917931,-0.066844523,-0.2156009972,-0.0086505944,-0.2676545978,-0.1067192107,0.4307493269,0.0776933208,0.0562675633,0.1243787333,-0.3719575107,-0.0352643579,0.0133798691,-0.5714361072,-0.3518609107,0.0621165261,0.0705782101,-0.1204286665,0.0219029319,-0.3443544507,0.0012689885,-0.0043544434,-0.1800432205,-0.1748710871,-0.2704450488,-0.3334649503,-0.3149197698,0.3458762765,0.451456517,-0.5566045642,-0.0270752627,0.0968400612,0.0743105635,0.1674599946,0.3314805627,-0.1907550693,-0.0951525122,-0.2181226611,-0.1000668108,0.0956019834,-0.3112884164,-0.2985938787,-0.0033583376,-0.0194073506,-0.0483571663,-0.2369234562,-0.1034136266,0.013775479,0.0616033152,0.6465601921,0.2157903463,-0.128316775,-0.1761939228,-0.1502933949,-0.276214689,-0.2542467415,0.0869560763,0.1214700714,0.0059731621,0.2363967299,-0.4764141738,0.070228681,0.041881226,-0.0073060701,0.3011595905,0.3711313903,-0.0534163527,-0.1965114772,0.2541643977,-0.4512370229,0.3701867163,-0.0718612894,0.2864062488,-0.2534539998,0.1539354026,-0.2667068243,0.1449401677,-0.0538807735,-0.1338416189,-0.0591719225,0.0979989544,0.2250677347,0.2401248366,-0.1199853867,0.5677842498,0.135291785,0.1387476027,-0.2335021943,0.2586082816,0.0218197275,0.0688075498,0.1329162866,0.0106323007,0.2142108232,-0.2170554101,-0.0641218349,0.318633914,0.2173629254,0.0417961814,0.057461638,0.1577191204,0.2542841733,-0.3891916275,-0.0997398421,0.0472882427,-0.0086243441,-0.0252697486,-0.0405539423,0.2425730079,0.0648595691,0.4205703437,0.1400236785,0.0894204974,0.3215745389,0.0298587233,-0.0160045065,0.179474324,0.1751997471,0.2178916037,0.0230622217,-0.0838506073,-0.3602313399,0.1493657082,-0.1642055213,0.0495590158,-0.1129640341,0.1247549057,-0.1547979712,-0.0220802911,0.0700044334,0.3708671927,0.2201276124,0.0490480177,-0.2005204111,0.1874097735,0.0605272613,-0.2058147639,-0.0445009433,0.1096539572,0.3646667898,0.2655833662,0.426687777,-0.0487633571,-0.095108144,-0.3787639439,-0.0358973593,0.2614510655,-0.27078861,0.4008783698,-0.2434709668,-0.3584270775,0.0938047096,-0.3161964118,-0.3449476063,-0.2649385929,-0.1177102774,0.4375137389,0.1163083985,0.0623287521,-0.2111103535,0.3716548681,0.0254304223,-0.5300382972,-0.023688294,-0.2573983371,-0.3198367953,0.015924165,0.3333905041,-0.2213886827,0.0698618591,-0.2547039092,0.0297967102,-0.1255459189,-0.2030438036,0.161564365,0.1523073763,0.3011630476,-0.0669810548,0.2267765105,-0.2425041497,0.0940599963,0.2750999629,0.067961067,0.1023404077,0.048934143,-0.0400866605,0.0441079624,0.0249122102,-0.2555710673,-0.4800460041,-0.3582805991,0.1784227639,0.0196136292,0.3458847404,0.2960084677,0.0382493213,-0.0866623893,0.0641809925,-0.0769640952,-0.1533749849,-0.3865545392,0.1724119484,-0.2786794007,-0.2431341708,0.2509688139,0.0747392997,0.384760499,0.0763949454,-0.6094400883,-0.1564110816,-0.1264603585,0.1048995107,0.0258263517,-0.2825921476,0.3334425092,-0.0882083923,-0.0051477673,0.216064468,-0.4404521585,0.0323337875,0.3356822431,0.3913239837,0.2737467587,0.3707469404,-0.1709450483,0.4239256084,0.2026095539,0.0485433601,0.3296657801,-0.1572235525,0.3341207802,-0.0892179385,-0.2969244123,-0.2172842175,-0.210347175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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/798","title":"Cannot load TREC dataset: ConnectionError","comments":"I just tested on google colab using\r\n```python\r\n!pip install git+https:\/\/github.com\/huggingface\/datasets.git\r\nfrom datasets import load_dataset\r\nload_dataset(\"trec\")\r\n```\r\nand it works.\r\nCan you detail how you got the issue even when using the latest version on master ?\r\n\r\nAlso about wmt we'll look into it, thanks for reporting !","body":"## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here. ","comment_length":48,"text":"Cannot load TREC dataset: ConnectionError \n ## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here.  \n I just tested on google colab using\r\n```python\r\n!pip install git+https:\/\/github.com\/huggingface\/datasets.git\r\nfrom datasets import load_dataset\r\nload_dataset(\"trec\")\r\n```\r\nand it works.\r\nCan you detail how you got the issue even when using the latest version on master ?\r\n\r\nAlso about wmt we'll look into it, thanks for reporting 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/798","title":"Cannot load TREC dataset: ConnectionError","comments":"I think the new url with .edu is also broken:\r\n```\r\nConnectionError: Couldn't reach https:\/\/cogcomp.seas.upenn.edu\/Data\/QA\/QC\/train_5500.label\r\n```\r\nCant download the dataset anymore.","body":"## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here. ","comment_length":21,"text":"Cannot load TREC dataset: ConnectionError \n ## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here.  \n I think the new url with .edu is also broken:\r\n```\r\nConnectionError: Couldn't reach https:\/\/cogcomp.seas.upenn.edu\/Data\/QA\/QC\/train_5500.label\r\n```\r\nCant download the dataset anymore.","embeddings":[-0.1691725552,0.0168359298,0.0626651496,0.3633992374,0.2918767631,-0.1796784997,0.2199582756,-0.0434447527,-0.1838140339,0.0629585609,-0.3238992393,0.1360173374,0.0722290277,0.1577587724,-0.0579137243,-0.0945270881,-0.1901099831,-0.2000556588,-0.0688836575,0.2535367906,-0.1328548044,0.4753988683,-0.2073137164,0.2046911418,-0.4112499952,0.0120672574,0.2174304873,0.4217193425,-0.2242174745,-0.6152897477,0.4672029912,0.0918213353,0.2583209872,0.630194068,-0.0001212188,0.0438775904,0.3747947514,-0.0809114501,-0.3682197928,-0.5027552843,-0.5078126788,-0.0790523142,0.2648388743,0.2292865217,-0.3196886182,0.4285567403,0.0791531503,-0.325060308,0.3382033706,0.6332944036,0.0820835829,-0.0768375695,0.0945120901,-0.1623590142,0.3016184568,-0.1488979459,-0.0706101805,0.4869538546,0.2155133337,-0.0386384055,0.0549374111,-0.0216429308,-0.0271049347,-0.0117052123,0.1798272282,-0.0742924362,-0.1101848111,-0.4340969324,0.4241297245,0.352265209,0.6402148008,-0.1733739376,-0.2272690535,0.1454129517,-0.081264168,-0.4302360415,0.3003183305,0.3217078149,-0.1221241057,0.1870257258,-0.4078291059,-0.2597643733,-0.3686292768,0.0043727914,-0.416408807,0.1270349473,-0.2663984001,0.131530717,0.2910034359,0.0152950874,0.3388897479,0.2237246931,-0.4470455348,-0.0075067496,-0.6929902434,0.1829680651,-0.1482103318,-0.0363469273,0.0934938565,0.3697319627,0.3174377084,0.0368965492,-0.2863053977,0.4065686166,0.0869918466,0.3421801031,0.0432393365,0.1872974336,0.3242341876,0.1837179959,-0.0132212332,-0.0930793434,-0.195083186,-0.2066059262,-0.1330894083,0.1809051484,0.0973416939,-0.2702093422,-0.391297996,0.1465577036,-0.4139411747,0.009193588,0.1225641072,0.4541309178,-0.2755913138,-0.0242432654,-0.0385249332,0.2208224833,-0.0965710506,-0.1626555324,-0.066012606,0.2261447608,-0.2326459587,0.1317702383,0.3381001651,-0.1813087612,0.1413071007,-0.164917931,-0.066844523,-0.2156009972,-0.0086505944,-0.2676545978,-0.1067192107,0.4307493269,0.0776933208,0.0562675633,0.1243787333,-0.3719575107,-0.0352643579,0.0133798691,-0.5714361072,-0.3518609107,0.0621165261,0.0705782101,-0.1204286665,0.0219029319,-0.3443544507,0.0012689885,-0.0043544434,-0.1800432205,-0.1748710871,-0.2704450488,-0.3334649503,-0.3149197698,0.3458762765,0.451456517,-0.5566045642,-0.0270752627,0.0968400612,0.0743105635,0.1674599946,0.3314805627,-0.1907550693,-0.0951525122,-0.2181226611,-0.1000668108,0.0956019834,-0.3112884164,-0.2985938787,-0.0033583376,-0.0194073506,-0.0483571663,-0.2369234562,-0.1034136266,0.013775479,0.0616033152,0.6465601921,0.2157903463,-0.128316775,-0.1761939228,-0.1502933949,-0.276214689,-0.2542467415,0.0869560763,0.1214700714,0.0059731621,0.2363967299,-0.4764141738,0.070228681,0.041881226,-0.0073060701,0.3011595905,0.3711313903,-0.0534163527,-0.1965114772,0.2541643977,-0.4512370229,0.3701867163,-0.0718612894,0.2864062488,-0.2534539998,0.1539354026,-0.2667068243,0.1449401677,-0.0538807735,-0.1338416189,-0.0591719225,0.0979989544,0.2250677347,0.2401248366,-0.1199853867,0.5677842498,0.135291785,0.1387476027,-0.2335021943,0.2586082816,0.0218197275,0.0688075498,0.1329162866,0.0106323007,0.2142108232,-0.2170554101,-0.0641218349,0.318633914,0.2173629254,0.0417961814,0.057461638,0.1577191204,0.2542841733,-0.3891916275,-0.0997398421,0.0472882427,-0.0086243441,-0.0252697486,-0.0405539423,0.2425730079,0.0648595691,0.4205703437,0.1400236785,0.0894204974,0.3215745389,0.0298587233,-0.0160045065,0.179474324,0.1751997471,0.2178916037,0.0230622217,-0.0838506073,-0.3602313399,0.1493657082,-0.1642055213,0.0495590158,-0.1129640341,0.1247549057,-0.1547979712,-0.0220802911,0.0700044334,0.3708671927,0.2201276124,0.0490480177,-0.2005204111,0.1874097735,0.0605272613,-0.2058147639,-0.0445009433,0.1096539572,0.3646667898,0.2655833662,0.426687777,-0.0487633571,-0.095108144,-0.3787639439,-0.0358973593,0.2614510655,-0.27078861,0.4008783698,-0.2434709668,-0.3584270775,0.0938047096,-0.3161964118,-0.3449476063,-0.2649385929,-0.1177102774,0.4375137389,0.1163083985,0.0623287521,-0.2111103535,0.3716548681,0.0254304223,-0.5300382972,-0.023688294,-0.2573983371,-0.3198367953,0.015924165,0.3333905041,-0.2213886827,0.0698618591,-0.2547039092,0.0297967102,-0.1255459189,-0.2030438036,0.161564365,0.1523073763,0.3011630476,-0.0669810548,0.2267765105,-0.2425041497,0.0940599963,0.2750999629,0.067961067,0.1023404077,0.048934143,-0.0400866605,0.0441079624,0.0249122102,-0.2555710673,-0.4800460041,-0.3582805991,0.1784227639,0.0196136292,0.3458847404,0.2960084677,0.0382493213,-0.0866623893,0.0641809925,-0.0769640952,-0.1533749849,-0.3865545392,0.1724119484,-0.2786794007,-0.2431341708,0.2509688139,0.0747392997,0.384760499,0.0763949454,-0.6094400883,-0.1564110816,-0.1264603585,0.1048995107,0.0258263517,-0.2825921476,0.3334425092,-0.0882083923,-0.0051477673,0.216064468,-0.4404521585,0.0323337875,0.3356822431,0.3913239837,0.2737467587,0.3707469404,-0.1709450483,0.4239256084,0.2026095539,0.0485433601,0.3296657801,-0.1572235525,0.3341207802,-0.0892179385,-0.2969244123,-0.2172842175,-0.2103471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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/798","title":"Cannot load TREC dataset: ConnectionError","comments":"Hi ! The URL seems to work fine on my side, can you try again ?","body":"## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here. ","comment_length":16,"text":"Cannot load TREC dataset: ConnectionError \n ## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here.  \n Hi ! The URL seems to work fine on my side, can you try again 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/798","title":"Cannot load TREC dataset: ConnectionError","comments":"Forgot to update, i wrote an email to the webmaster of seas.upenn.edu because i couldnt reach the url on any machine. This was the answer:\r\n```\r\nThank you for your report. The server was offline for maintenance and is now available again.\r\n```\r\nGuess all back to normal now \ud83d\ude42 ","body":"## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here. ","comment_length":50,"text":"Cannot load TREC dataset: ConnectionError \n ## Problem\r\nI cannot load \"trec\" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label')` returns <Response [302]>. \r\n* `requests.head('http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`\r\n* Opening `http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label' in a browser works, but opens a different address\r\n* Increasing max_redirects to 100 doesn't help\r\n\r\nAlso, while debugging I've seen that requesting 'https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/trec\/default\/1.1.0\/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.\r\n\r\n* datasets.__version__ == '1.1.2'\r\n* requests.__version__ == '2.24.0'\r\n\r\n## Error trace\r\n```\r\n>>> import datasets\r\n>>> datasets.__version__\r\n'1.1.2'\r\n>>> dataset = load_dataset(\"trec\", split=\"train\")\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/home\/przemyslaw\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/przemyslaw\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/trec\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7\/trec.py\", line 140, in _split_generators\r\n    dl_files = dl_manager.download_and_extract(_URLs)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/download_manager.py\", line 179, in download\r\n    num_proc=download_config.num_proc,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in map_nested\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/home\/przemyslaw\/.local\/lib\/python3.6\/site-packages\/datasets\/utils\/file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n```\r\n\r\nI would appreciate some suggestions here.  \n Forgot to update, i wrote an email to the webmaster of seas.upenn.edu because i couldnt reach the url on any machine. This was the answer:\r\n```\r\nThank you for your report. The server was offline for maintenance and is now available again.\r\n```\r\nGuess all back to normal now \ud83d\ude42 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/796","title":"Seq2Seq Metrics QOL: Bleu, Rouge","comments":"Hi ! Thanks for letting us know your experience :) \r\nWe should at least improve the error messages indeed","body":"Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand:\r\n\r\n+ Bleu expects tokenization, can I just kwarg it like sacrebleu?\r\n+ different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py#L61\r\n\r\n\r\n#### What I tried\r\n\r\n\r\nRouge experience:\r\n```python\r\n\r\nrouge = load_metric('rouge')\r\nrouge.add_batch(['hi im sam'], ['im daniel']) # fails\r\nrouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works\r\nrouge.compute() # huge messy output, but reasonable. Not worth integrating b\/c don't want to rewrite all the postprocessing.\r\n```\r\n\r\nBLEU experience:\r\n```python\r\nbleu = load_metric('bleu')\r\nbleu.add_batch(predictions=['hi im sam'], references=['im daniel'])\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n```\r\nAll of these raise `ValueError: Got a string but expected a list instead: 'im daniel'`\r\n\r\n#### Doc Typo\r\nThis says `dataset=load_metric(...)` which seems wrong, will cause `NameError`\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png)\r\n\r\ncc @lhoestq, feel free to ignore.","comment_length":19,"text":"Seq2Seq Metrics QOL: Bleu, Rouge \n Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand:\r\n\r\n+ Bleu expects tokenization, can I just kwarg it like sacrebleu?\r\n+ different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py#L61\r\n\r\n\r\n#### What I tried\r\n\r\n\r\nRouge experience:\r\n```python\r\n\r\nrouge = load_metric('rouge')\r\nrouge.add_batch(['hi im sam'], ['im daniel']) # fails\r\nrouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works\r\nrouge.compute() # huge messy output, but reasonable. Not worth integrating b\/c don't want to rewrite all the postprocessing.\r\n```\r\n\r\nBLEU experience:\r\n```python\r\nbleu = load_metric('bleu')\r\nbleu.add_batch(predictions=['hi im sam'], references=['im daniel'])\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n```\r\nAll of these raise `ValueError: Got a string but expected a list instead: 'im daniel'`\r\n\r\n#### Doc Typo\r\nThis says `dataset=load_metric(...)` which seems wrong, will cause `NameError`\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png)\r\n\r\ncc @lhoestq, feel free to ignore. \n Hi ! Thanks for letting us know your experience :) \r\nWe should at least improve the error messages indeed","embeddings":[0.1769534498,-0.0281658582,-0.0853118747,0.0595532954,0.1316637546,-0.2772905231,0.0935510099,0.1194606274,-0.0715432093,0.0205882788,-0.2373280972,0.0115100741,-0.1035928577,-0.0028772415,0.0880227238,-0.231206879,-0.0192949343,0.0564785786,0.022084957,-0.209247455,-0.2625801265,0.3527042568,-0.0217220634,0.2763134837,-0.2073199898,0.2331521213,-0.0639110953,0.2056541741,-0.2941870987,-0.1854021698,0.0096485056,0.3292575777,0.0186899304,0.2050482631,-0.0001137066,-0.3125211596,0.0184408836,-0.1469954848,-0.1234472394,-0.1045938656,-0.1109406278,-0.195944801,0.0969132185,-0.1325443089,-0.0355381072,0.1201137006,-0.318225652,-0.0693274662,0.5164248943,-0.0813493282,0.168647632,-0.0962301493,-0.1875781119,-0.1486988664,0.3641577363,-0.1070586815,0.0575782731,0.004555345,0.2955908477,-0.0346819237,-0.1117430776,0.2009179592,0.0118584456,-0.214055717,0.1996009201,0.0452660359,0.2193309963,-0.1476968676,-0.1681335866,0.2540251315,0.1309775263,-0.0490120389,-0.4720951617,0.0694950223,-0.0954873562,-0.7001909018,-0.0471167974,-0.2837664187,0.1515046805,-0.1515443474,-0.1255643219,0.0069697043,0.1390461624,-0.2734936476,-0.136483863,0.0243614838,-0.1411269158,-0.0774002075,0.1040128618,-0.1568869799,-0.6167755127,0.2696429491,-0.1904052645,0.0336680152,-0.5840451121,0.0813066885,0.0431840122,0.1194143519,-0.1405089945,0.4723142385,0.1637546718,0.0697114617,0.1093147695,0.0862753019,-0.3274796307,0.8337019682,0.087496534,0.0176879056,0.1399512142,-0.0052242139,0.1916358024,-0.0466981567,0.0865587071,-0.5624172091,-0.0675756931,0.3009655178,-0.2086916715,-0.4418406785,-0.4798848629,0.1379334778,-0.2506815195,-0.1557816118,0.3023255765,0.3863398731,-0.335867852,0.0597458482,0.40995875,0.3697021306,-0.2531629503,-0.1009762734,-0.1679190695,0.0602376014,-0.2565958202,0.225626573,0.0209779311,0.0801946297,0.3454347551,0.0971023589,0.3335015774,0.004242064,0.1434872895,0.0575459935,-0.2455943674,-0.1922012866,-0.1943170577,0.0088598132,0.0848355591,-0.2524261475,-0.0161993299,-0.382820487,-0.3244469762,-0.1584510505,0.0603077821,0.1470858306,-0.1753459871,0.1231860295,-0.0035436349,0.3194283247,-0.0393082947,-0.1132980362,0.005361788,0.1049269363,-0.2670657039,-0.0042275405,0.493555367,-0.0255884975,-0.3216415644,-0.3817739487,0.4146699309,0.1683200598,-0.0606814697,0.270088762,0.0352221616,0.2871845067,0.0276340041,-0.1804422289,0.3904258907,-0.5985485911,-0.0689604282,-0.0158594176,-0.0115918266,-0.2362428457,0.2013259977,-0.1834621727,0.3161441982,-0.1831455827,0.4822333455,0.1010600477,0.0653936863,-0.2801431715,-0.2663798928,-0.1388558894,0.1392518878,-0.0844184905,0.1085980386,0.1279085279,-0.2155474573,0.3492257893,0.0701876506,-0.2282511145,0.0856477097,0.0376856215,0.1824711561,0.0331669003,0.2077793777,0.3472885191,0.1417553872,0.1336536705,-0.697609067,0.1592135727,0.0037085325,0.0860563889,-0.3027497232,-0.2085744739,0.0227134824,-0.4090459943,0.1354869604,-0.2492710054,0.2036932856,0.1237623915,0.001924585,0.1244986206,0.2435036302,0.182470277,-0.2680424452,-0.1919852942,0.202580899,0.0015646351,0.0111755775,0.4812694788,0.2334780693,0.1712279469,0.04375894,0.2832264006,0.0035683014,0.1227550358,0.1778895706,0.2196926922,0.2059095204,0.0626964644,0.1532783359,-0.0062190397,0.1477894634,-0.2205672115,-0.0119060511,0.455041945,-0.4066660404,0.1981160939,-0.2130469829,-0.0134293316,-0.0060455082,0.027414592,-0.3535484672,0.0184353963,0.325185895,0.1578260958,0.4982276857,-0.2068050951,0.2832622528,-0.1557312608,0.0057616415,-0.1006078348,0.0947469696,-0.040629521,0.1436415911,-0.0996471047,-0.0770215541,-0.4216920137,0.0819059685,0.1051404104,-0.1054541767,0.0385688841,-0.0031587586,-0.09889777,-0.1584773511,-0.0599150099,-0.2916539907,0.037877731,0.2082577944,0.1982465535,0.2047153264,-0.3442966044,-0.2024778128,-0.0576463677,-0.3000858426,0.1449369639,-0.0344874784,0.539752841,0.2608511448,0.1027044579,-0.2336456478,-0.3465655744,0.3593018949,-0.039825689,0.0450821519,0.1441108286,0.0771829188,0.4756244421,0.038900096,-0.0621122345,-0.0366739109,-0.1377660632,0.0031252198,0.0776271671,-0.3016474545,0.1216313019,0.1167247444,-0.3648528755,0.2510655224,0.1978717893,-0.5468524098,0.124114424,-0.1195817515,0.5138995051,0.3033767939,-0.301456511,0.023169307,-0.0888207555,0.2371552736,-0.0323478281,-0.2406721413,0.3399661481,-0.0452200957,0.1214821339,-0.0695555136,-0.1393463463,0.0858451575,-0.2094722241,0.3407860696,-0.0766743347,-0.2421108335,-0.1812707037,-0.0949681625,0.2206552625,0.1995648444,0.4270272851,-0.1554479897,-0.2086057812,0.3116022646,-0.1577573866,-0.2221233696,-0.2294902205,-0.1876649112,0.491013974,-0.1489214301,-0.0511640161,-0.3940578103,0.1894480735,-0.1001680195,0.2220212966,-0.0307293721,0.1455084085,0.1064650342,-0.1404236406,-0.0989815742,-0.1612590551,0.191022709,0.1008730531,0.4178522527,-0.2088980824,0.339697659,-0.0117482059,0.6103149056,0.1115433425,0.2892510593,0.1768062711,0.3831200898,0.1211188436,-0.0213042125,-0.2075167298,0.0879762396,-0.0352794267,-0.29334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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/796","title":"Seq2Seq Metrics QOL: Bleu, Rouge","comments":"prediction = [['Hey', 'how', 'are', 'you', '?']]  \r\nreference=[['Hey', 'how', 'are', 'you', '?']]\r\nbleu.compute(predictions=prediction,references=reference)\r\n\r\nalso tried this kind of things lol\r\nI definitely need help too","body":"Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand:\r\n\r\n+ Bleu expects tokenization, can I just kwarg it like sacrebleu?\r\n+ different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py#L61\r\n\r\n\r\n#### What I tried\r\n\r\n\r\nRouge experience:\r\n```python\r\n\r\nrouge = load_metric('rouge')\r\nrouge.add_batch(['hi im sam'], ['im daniel']) # fails\r\nrouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works\r\nrouge.compute() # huge messy output, but reasonable. Not worth integrating b\/c don't want to rewrite all the postprocessing.\r\n```\r\n\r\nBLEU experience:\r\n```python\r\nbleu = load_metric('bleu')\r\nbleu.add_batch(predictions=['hi im sam'], references=['im daniel'])\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n```\r\nAll of these raise `ValueError: Got a string but expected a list instead: 'im daniel'`\r\n\r\n#### Doc Typo\r\nThis says `dataset=load_metric(...)` which seems wrong, will cause `NameError`\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png)\r\n\r\ncc @lhoestq, feel free to ignore.","comment_length":25,"text":"Seq2Seq Metrics QOL: Bleu, Rouge \n Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand:\r\n\r\n+ Bleu expects tokenization, can I just kwarg it like sacrebleu?\r\n+ different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py#L61\r\n\r\n\r\n#### What I tried\r\n\r\n\r\nRouge experience:\r\n```python\r\n\r\nrouge = load_metric('rouge')\r\nrouge.add_batch(['hi im sam'], ['im daniel']) # fails\r\nrouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works\r\nrouge.compute() # huge messy output, but reasonable. Not worth integrating b\/c don't want to rewrite all the postprocessing.\r\n```\r\n\r\nBLEU experience:\r\n```python\r\nbleu = load_metric('bleu')\r\nbleu.add_batch(predictions=['hi im sam'], references=['im daniel'])\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n```\r\nAll of these raise `ValueError: Got a string but expected a list instead: 'im daniel'`\r\n\r\n#### Doc Typo\r\nThis says `dataset=load_metric(...)` which seems wrong, will cause `NameError`\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png)\r\n\r\ncc @lhoestq, feel free to ignore. \n prediction = [['Hey', 'how', 'are', 'you', '?']]  \r\nreference=[['Hey', 'how', 'are', 'you', '?']]\r\nbleu.compute(predictions=prediction,references=reference)\r\n\r\nalso tried this kind of things lol\r\nI definitely need help too","embeddings":[0.1769534498,-0.0281658582,-0.0853118747,0.0595532954,0.1316637546,-0.2772905231,0.0935510099,0.1194606274,-0.0715432093,0.0205882788,-0.2373280972,0.0115100741,-0.1035928577,-0.0028772415,0.0880227238,-0.231206879,-0.0192949343,0.0564785786,0.022084957,-0.209247455,-0.2625801265,0.3527042568,-0.0217220634,0.2763134837,-0.2073199898,0.2331521213,-0.0639110953,0.2056541741,-0.2941870987,-0.1854021698,0.0096485056,0.3292575777,0.0186899304,0.2050482631,-0.0001137066,-0.3125211596,0.0184408836,-0.1469954848,-0.1234472394,-0.1045938656,-0.1109406278,-0.195944801,0.0969132185,-0.1325443089,-0.0355381072,0.1201137006,-0.318225652,-0.0693274662,0.5164248943,-0.0813493282,0.168647632,-0.0962301493,-0.1875781119,-0.1486988664,0.3641577363,-0.1070586815,0.0575782731,0.004555345,0.2955908477,-0.0346819237,-0.1117430776,0.2009179592,0.0118584456,-0.214055717,0.1996009201,0.0452660359,0.2193309963,-0.1476968676,-0.1681335866,0.2540251315,0.1309775263,-0.0490120389,-0.4720951617,0.0694950223,-0.0954873562,-0.7001909018,-0.0471167974,-0.2837664187,0.1515046805,-0.1515443474,-0.1255643219,0.0069697043,0.1390461624,-0.2734936476,-0.136483863,0.0243614838,-0.1411269158,-0.0774002075,0.1040128618,-0.1568869799,-0.6167755127,0.2696429491,-0.1904052645,0.0336680152,-0.5840451121,0.0813066885,0.0431840122,0.1194143519,-0.1405089945,0.4723142385,0.1637546718,0.0697114617,0.1093147695,0.0862753019,-0.3274796307,0.8337019682,0.087496534,0.0176879056,0.1399512142,-0.0052242139,0.1916358024,-0.0466981567,0.0865587071,-0.5624172091,-0.0675756931,0.3009655178,-0.2086916715,-0.4418406785,-0.4798848629,0.1379334778,-0.2506815195,-0.1557816118,0.3023255765,0.3863398731,-0.335867852,0.0597458482,0.40995875,0.3697021306,-0.2531629503,-0.1009762734,-0.1679190695,0.0602376014,-0.2565958202,0.225626573,0.0209779311,0.0801946297,0.3454347551,0.0971023589,0.3335015774,0.004242064,0.1434872895,0.0575459935,-0.2455943674,-0.1922012866,-0.1943170577,0.0088598132,0.0848355591,-0.2524261475,-0.0161993299,-0.382820487,-0.3244469762,-0.1584510505,0.0603077821,0.1470858306,-0.1753459871,0.1231860295,-0.0035436349,0.3194283247,-0.0393082947,-0.1132980362,0.005361788,0.1049269363,-0.2670657039,-0.0042275405,0.493555367,-0.0255884975,-0.3216415644,-0.3817739487,0.4146699309,0.1683200598,-0.0606814697,0.270088762,0.0352221616,0.2871845067,0.0276340041,-0.1804422289,0.3904258907,-0.5985485911,-0.0689604282,-0.0158594176,-0.0115918266,-0.2362428457,0.2013259977,-0.1834621727,0.3161441982,-0.1831455827,0.4822333455,0.1010600477,0.0653936863,-0.2801431715,-0.2663798928,-0.1388558894,0.1392518878,-0.0844184905,0.1085980386,0.1279085279,-0.2155474573,0.3492257893,0.0701876506,-0.2282511145,0.0856477097,0.0376856215,0.1824711561,0.0331669003,0.2077793777,0.3472885191,0.1417553872,0.1336536705,-0.697609067,0.1592135727,0.0037085325,0.0860563889,-0.3027497232,-0.2085744739,0.0227134824,-0.4090459943,0.1354869604,-0.2492710054,0.2036932856,0.1237623915,0.001924585,0.1244986206,0.2435036302,0.182470277,-0.2680424452,-0.1919852942,0.202580899,0.0015646351,0.0111755775,0.4812694788,0.2334780693,0.1712279469,0.04375894,0.2832264006,0.0035683014,0.1227550358,0.1778895706,0.2196926922,0.2059095204,0.0626964644,0.1532783359,-0.0062190397,0.1477894634,-0.2205672115,-0.0119060511,0.455041945,-0.4066660404,0.1981160939,-0.2130469829,-0.0134293316,-0.0060455082,0.027414592,-0.3535484672,0.0184353963,0.325185895,0.1578260958,0.4982276857,-0.2068050951,0.2832622528,-0.1557312608,0.0057616415,-0.1006078348,0.0947469696,-0.040629521,0.1436415911,-0.0996471047,-0.0770215541,-0.4216920137,0.0819059685,0.1051404104,-0.1054541767,0.0385688841,-0.0031587586,-0.09889777,-0.1584773511,-0.0599150099,-0.2916539907,0.037877731,0.2082577944,0.1982465535,0.2047153264,-0.3442966044,-0.2024778128,-0.0576463677,-0.3000858426,0.1449369639,-0.0344874784,0.539752841,0.2608511448,0.1027044579,-0.2336456478,-0.3465655744,0.3593018949,-0.039825689,0.0450821519,0.1441108286,0.0771829188,0.4756244421,0.038900096,-0.0621122345,-0.0366739109,-0.1377660632,0.0031252198,0.0776271671,-0.3016474545,0.1216313019,0.1167247444,-0.3648528755,0.2510655224,0.1978717893,-0.5468524098,0.124114424,-0.1195817515,0.5138995051,0.3033767939,-0.301456511,0.023169307,-0.0888207555,0.2371552736,-0.0323478281,-0.2406721413,0.3399661481,-0.0452200957,0.1214821339,-0.0695555136,-0.1393463463,0.0858451575,-0.2094722241,0.3407860696,-0.0766743347,-0.2421108335,-0.1812707037,-0.0949681625,0.2206552625,0.1995648444,0.4270272851,-0.1554479897,-0.2086057812,0.3116022646,-0.1577573866,-0.2221233696,-0.2294902205,-0.1876649112,0.491013974,-0.1489214301,-0.0511640161,-0.3940578103,0.1894480735,-0.1001680195,0.2220212966,-0.0307293721,0.1455084085,0.1064650342,-0.1404236406,-0.0989815742,-0.1612590551,0.191022709,0.1008730531,0.4178522527,-0.2088980824,0.339697659,-0.0117482059,0.6103149056,0.1115433425,0.2892510593,0.1768062711,0.3831200898,0.1211188436,-0.0213042125,-0.2075167298,0.0879762396,-0.0352794267,-0.29334884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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/796","title":"Seq2Seq Metrics QOL: Bleu, Rouge","comments":"Hi !\r\n\r\nAs described in the documentation for `bleu`:\r\n```\r\nArgs:\r\n    predictions: list of translations to score.\r\n        Each translation should be tokenized into a list of tokens.\r\n    references: list of lists of references for each translation.\r\n        Each reference should be tokenized into a list of tokens.\r\n```\r\n\r\nTherefore you can use this metric this way:\r\n```python\r\nfrom datasets import load_metric\r\n\r\npredictions = [\r\n    [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\r\n    [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\r\n]\r\nreferences = [\r\n    [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\r\n    [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\r\n]\r\n\r\nbleu = load_metric(\"bleu\")\r\nbleu.compute(predictions=predictions, references=references)\r\n# Or you can also add batches before calling compute()\r\n# bleu.add_batch(predictions=predictions, references=references)\r\n# bleu.compute()\r\n```\r\n\r\nHope this helps :)","body":"Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand:\r\n\r\n+ Bleu expects tokenization, can I just kwarg it like sacrebleu?\r\n+ different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py#L61\r\n\r\n\r\n#### What I tried\r\n\r\n\r\nRouge experience:\r\n```python\r\n\r\nrouge = load_metric('rouge')\r\nrouge.add_batch(['hi im sam'], ['im daniel']) # fails\r\nrouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works\r\nrouge.compute() # huge messy output, but reasonable. Not worth integrating b\/c don't want to rewrite all the postprocessing.\r\n```\r\n\r\nBLEU experience:\r\n```python\r\nbleu = load_metric('bleu')\r\nbleu.add_batch(predictions=['hi im sam'], references=['im daniel'])\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n```\r\nAll of these raise `ValueError: Got a string but expected a list instead: 'im daniel'`\r\n\r\n#### Doc Typo\r\nThis says `dataset=load_metric(...)` which seems wrong, will cause `NameError`\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png)\r\n\r\ncc @lhoestq, feel free to ignore.","comment_length":142,"text":"Seq2Seq Metrics QOL: Bleu, Rouge \n Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand:\r\n\r\n+ Bleu expects tokenization, can I just kwarg it like sacrebleu?\r\n+ different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/utils.py#L61\r\n\r\n\r\n#### What I tried\r\n\r\n\r\nRouge experience:\r\n```python\r\n\r\nrouge = load_metric('rouge')\r\nrouge.add_batch(['hi im sam'], ['im daniel']) # fails\r\nrouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works\r\nrouge.compute() # huge messy output, but reasonable. Not worth integrating b\/c don't want to rewrite all the postprocessing.\r\n```\r\n\r\nBLEU experience:\r\n```python\r\nbleu = load_metric('bleu')\r\nbleu.add_batch(predictions=['hi im sam'], references=['im daniel'])\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n\r\nbleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])\r\n```\r\nAll of these raise `ValueError: Got a string but expected a list instead: 'im daniel'`\r\n\r\n#### Doc Typo\r\nThis says `dataset=load_metric(...)` which seems wrong, will cause `NameError`\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png)\r\n\r\ncc @lhoestq, feel free to ignore. \n Hi !\r\n\r\nAs described in the documentation for `bleu`:\r\n```\r\nArgs:\r\n    predictions: list of translations to score.\r\n        Each translation should be tokenized into a list of tokens.\r\n    references: list of lists of references for each translation.\r\n        Each reference should be tokenized into a list of tokens.\r\n```\r\n\r\nTherefore you can use this metric this way:\r\n```python\r\nfrom datasets import load_metric\r\n\r\npredictions = [\r\n    [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\r\n    [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\r\n]\r\nreferences = [\r\n    [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\r\n    [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\r\n]\r\n\r\nbleu = load_metric(\"bleu\")\r\nbleu.compute(predictions=predictions, references=references)\r\n# Or you can also add batches before calling compute()\r\n# bleu.add_batch(predictions=predictions, references=references)\r\n# bleu.compute()\r\n```\r\n\r\nHope this helps :)","embeddings":[0.1769534498,-0.0281658582,-0.0853118747,0.0595532954,0.1316637546,-0.2772905231,0.0935510099,0.1194606274,-0.0715432093,0.0205882788,-0.2373280972,0.0115100741,-0.1035928577,-0.0028772415,0.0880227238,-0.231206879,-0.0192949343,0.0564785786,0.022084957,-0.209247455,-0.2625801265,0.3527042568,-0.0217220634,0.2763134837,-0.2073199898,0.2331521213,-0.0639110953,0.2056541741,-0.2941870987,-0.1854021698,0.0096485056,0.3292575777,0.0186899304,0.2050482631,-0.0001137066,-0.3125211596,0.0184408836,-0.1469954848,-0.1234472394,-0.1045938656,-0.1109406278,-0.195944801,0.0969132185,-0.1325443089,-0.0355381072,0.1201137006,-0.318225652,-0.0693274662,0.5164248943,-0.0813493282,0.168647632,-0.0962301493,-0.1875781119,-0.1486988664,0.3641577363,-0.1070586815,0.0575782731,0.004555345,0.2955908477,-0.0346819237,-0.1117430776,0.2009179592,0.0118584456,-0.214055717,0.1996009201,0.0452660359,0.2193309963,-0.1476968676,-0.1681335866,0.2540251315,0.1309775263,-0.0490120389,-0.4720951617,0.0694950223,-0.0954873562,-0.7001909018,-0.0471167974,-0.2837664187,0.1515046805,-0.1515443474,-0.1255643219,0.0069697043,0.1390461624,-0.2734936476,-0.136483863,0.0243614838,-0.1411269158,-0.0774002075,0.1040128618,-0.1568869799,-0.6167755127,0.2696429491,-0.1904052645,0.0336680152,-0.5840451121,0.0813066885,0.0431840122,0.1194143519,-0.1405089945,0.4723142385,0.1637546718,0.0697114617,0.1093147695,0.0862753019,-0.3274796307,0.8337019682,0.087496534,0.0176879056,0.1399512142,-0.0052242139,0.1916358024,-0.0466981567,0.0865587071,-0.5624172091,-0.0675756931,0.3009655178,-0.2086916715,-0.4418406785,-0.4798848629,0.1379334778,-0.2506815195,-0.1557816118,0.3023255765,0.3863398731,-0.335867852,0.0597458482,0.40995875,0.3697021306,-0.2531629503,-0.1009762734,-0.1679190695,0.0602376014,-0.2565958202,0.225626573,0.0209779311,0.0801946297,0.3454347551,0.0971023589,0.3335015774,0.004242064,0.1434872895,0.0575459935,-0.2455943674,-0.1922012866,-0.1943170577,0.0088598132,0.0848355591,-0.2524261475,-0.0161993299,-0.382820487,-0.3244469762,-0.1584510505,0.0603077821,0.1470858306,-0.1753459871,0.1231860295,-0.0035436349,0.3194283247,-0.0393082947,-0.1132980362,0.005361788,0.1049269363,-0.2670657039,-0.0042275405,0.493555367,-0.0255884975,-0.3216415644,-0.3817739487,0.4146699309,0.1683200598,-0.0606814697,0.270088762,0.0352221616,0.2871845067,0.0276340041,-0.1804422289,0.3904258907,-0.5985485911,-0.0689604282,-0.0158594176,-0.0115918266,-0.2362428457,0.2013259977,-0.1834621727,0.3161441982,-0.1831455827,0.4822333455,0.1010600477,0.0653936863,-0.2801431715,-0.2663798928,-0.1388558894,0.1392518878,-0.0844184905,0.1085980386,0.1279085279,-0.2155474573,0.3492257893,0.0701876506,-0.2282511145,0.0856477097,0.0376856215,0.1824711561,0.0331669003,0.2077793777,0.3472885191,0.1417553872,0.1336536705,-0.697609067,0.1592135727,0.0037085325,0.0860563889,-0.3027497232,-0.2085744739,0.0227134824,-0.4090459943,0.1354869604,-0.2492710054,0.2036932856,0.1237623915,0.001924585,0.1244986206,0.2435036302,0.182470277,-0.2680424452,-0.1919852942,0.202580899,0.0015646351,0.0111755775,0.4812694788,0.2334780693,0.1712279469,0.04375894,0.2832264006,0.0035683014,0.1227550358,0.1778895706,0.2196926922,0.2059095204,0.0626964644,0.1532783359,-0.0062190397,0.1477894634,-0.2205672115,-0.0119060511,0.455041945,-0.4066660404,0.1981160939,-0.2130469829,-0.0134293316,-0.0060455082,0.027414592,-0.3535484672,0.0184353963,0.325185895,0.1578260958,0.4982276857,-0.2068050951,0.2832622528,-0.1557312608,0.0057616415,-0.1006078348,0.0947469696,-0.040629521,0.1436415911,-0.0996471047,-0.0770215541,-0.4216920137,0.0819059685,0.1051404104,-0.1054541767,0.0385688841,-0.0031587586,-0.09889777,-0.1584773511,-0.0599150099,-0.2916539907,0.037877731,0.2082577944,0.1982465535,0.2047153264,-0.3442966044,-0.2024778128,-0.0576463677,-0.3000858426,0.1449369639,-0.0344874784,0.539752841,0.2608511448,0.1027044579,-0.2336456478,-0.3465655744,0.3593018949,-0.039825689,0.0450821519,0.1441108286,0.0771829188,0.4756244421,0.038900096,-0.0621122345,-0.0366739109,-0.1377660632,0.0031252198,0.0776271671,-0.3016474545,0.1216313019,0.1167247444,-0.3648528755,0.2510655224,0.1978717893,-0.5468524098,0.124114424,-0.1195817515,0.5138995051,0.3033767939,-0.301456511,0.023169307,-0.0888207555,0.2371552736,-0.0323478281,-0.2406721413,0.3399661481,-0.0452200957,0.1214821339,-0.0695555136,-0.1393463463,0.0858451575,-0.2094722241,0.3407860696,-0.0766743347,-0.2421108335,-0.1812707037,-0.0949681625,0.2206552625,0.1995648444,0.4270272851,-0.1554479897,-0.2086057812,0.3116022646,-0.1577573866,-0.2221233696,-0.2294902205,-0.1876649112,0.491013974,-0.1489214301,-0.0511640161,-0.3940578103,0.1894480735,-0.1001680195,0.2220212966,-0.0307293721,0.1455084085,0.1064650342,-0.1404236406,-0.0989815742,-0.1612590551,0.191022709,0.1008730531,0.4178522527,-0.2088980824,0.339697659,-0.0117482059,0.6103149056,0.1115433425,0.2892510593,0.1768062711,0.3831200898,0.1211188436,-0.0213042125,-0.2075167298,0.0879762396,-0.0352794267,-0.293348848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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/792","title":"KILT dataset: empty string in triviaqa input field","comments":"Just found out about https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md\r\n(Not very clear in https:\/\/huggingface.co\/datasets\/kilt_tasks links to http:\/\/github.com\/huggingface\/datasets\/datasets\/kilt_tasks\/README.md which is dead, closing the issue though :))","body":"# What happened\r\nBoth train and test splits of the triviaqa dataset (part of the KILT benchmark) seem to have empty string in their input field (unlike the natural questions dataset, part of the same benchmark)\r\n\r\n# Versions\r\nKILT version is `1.0.0`\r\n`datasets` version is `1.1.2`\r\n[more here](https:\/\/gist.github.com\/PaulLerner\/3768c8d25f723edbac20d99b6a4056c1)\r\n\r\n# How to reproduce\r\n```py\r\nIn [1]: from datasets import load_dataset\r\nIn [4]: dataset = load_dataset(\"kilt_tasks\")                                                                                                                                                                \r\n# everything works fine, removed output for a better readibility\r\nDataset kilt_tasks downloaded and prepared to \/people\/lerner\/.cache\/huggingface\/datasets\/kilt_tasks\/all_tasks\/1.0.0\/821c4295a2c35db2847585918d9c47d7f028f1a26b78825d8e77cd3aeb2621a1. Subsequent calls will reuse this data.\r\n\r\n# empty string in triviaqa input field\r\nIn [36]: dataset['train_triviaqa'][0]                                                                                                                                                                       \r\nOut[36]: \r\n{'id': 'dpql_5197',\r\n 'input': '',\r\n 'meta': {'left_context': '',\r\n  'mention': '',\r\n  'obj_surface': {'text': []},\r\n  'partial_evidence': {'end_paragraph_id': [],\r\n   'meta': [],\r\n   'section': [],\r\n   'start_paragraph_id': [],\r\n   'title': [],\r\n   'wikipedia_id': []},\r\n  'right_context': '',\r\n  'sub_surface': {'text': []},\r\n  'subj_aliases': {'text': []},\r\n  'template_questions': {'text': []}},\r\n 'output': {'answer': ['five  \u00a3', '5 \u00a3', '\u00a35', 'five \u00a3'],\r\n  'meta': [],\r\n  'provenance': [{'bleu_score': [1.0],\r\n    'end_character': [248],\r\n    'end_paragraph_id': [30],\r\n    'meta': [],\r\n    'section': ['Section::::Question of legal tender.\\n'],\r\n    'start_character': [246],\r\n    'start_paragraph_id': [30],\r\n    'title': ['Banknotes of the pound sterling'],\r\n    'wikipedia_id': ['270680']}]}}\r\nIn [35]: dataset['train_triviaqa']['input'][:10]                                                                                                                                                            \r\nOut[35]: ['', '', '', '', '', '', '', '', '', '']\r\n# same with test set \r\nIn [37]: dataset['test_triviaqa']['input'][:10]                                                                                                                                                             \r\nOut[37]: ['', '', '', '', '', '', '', '', '', '']\r\n# works fine with natural questions\r\nIn [34]: dataset['train_nq']['input'][:10]                                                                                                                                                                  \r\nOut[34]: \r\n['how i.met your mother who is the mother',\r\n 'who had the most wins in the nfl',\r\n 'who played mantis guardians of the galaxy 2',\r\n 'what channel is the premier league on in france',\r\n \"god's not dead a light in the darkness release date\",\r\n 'who is the current president of un general assembly',\r\n 'when do the eclipse supposed to take place',\r\n 'what is the name of the sea surrounding dubai',\r\n 'who holds the nba record for most points in a career',\r\n 'when did the new maze runner movie come out']\r\n```\r\n\r\nStay safe :)","comment_length":21,"text":"KILT dataset: empty string in triviaqa input field \n # What happened\r\nBoth train and test splits of the triviaqa dataset (part of the KILT benchmark) seem to have empty string in their input field (unlike the natural questions dataset, part of the same benchmark)\r\n\r\n# Versions\r\nKILT version is `1.0.0`\r\n`datasets` version is `1.1.2`\r\n[more here](https:\/\/gist.github.com\/PaulLerner\/3768c8d25f723edbac20d99b6a4056c1)\r\n\r\n# How to reproduce\r\n```py\r\nIn [1]: from datasets import load_dataset\r\nIn [4]: dataset = load_dataset(\"kilt_tasks\")                                                                                                                                                                \r\n# everything works fine, removed output for a better readibility\r\nDataset kilt_tasks downloaded and prepared to \/people\/lerner\/.cache\/huggingface\/datasets\/kilt_tasks\/all_tasks\/1.0.0\/821c4295a2c35db2847585918d9c47d7f028f1a26b78825d8e77cd3aeb2621a1. Subsequent calls will reuse this data.\r\n\r\n# empty string in triviaqa input field\r\nIn [36]: dataset['train_triviaqa'][0]                                                                                                                                                                       \r\nOut[36]: \r\n{'id': 'dpql_5197',\r\n 'input': '',\r\n 'meta': {'left_context': '',\r\n  'mention': '',\r\n  'obj_surface': {'text': []},\r\n  'partial_evidence': {'end_paragraph_id': [],\r\n   'meta': [],\r\n   'section': [],\r\n   'start_paragraph_id': [],\r\n   'title': [],\r\n   'wikipedia_id': []},\r\n  'right_context': '',\r\n  'sub_surface': {'text': []},\r\n  'subj_aliases': {'text': []},\r\n  'template_questions': {'text': []}},\r\n 'output': {'answer': ['five  \u00a3', '5 \u00a3', '\u00a35', 'five \u00a3'],\r\n  'meta': [],\r\n  'provenance': [{'bleu_score': [1.0],\r\n    'end_character': [248],\r\n    'end_paragraph_id': [30],\r\n    'meta': [],\r\n    'section': ['Section::::Question of legal tender.\\n'],\r\n    'start_character': [246],\r\n    'start_paragraph_id': [30],\r\n    'title': ['Banknotes of the pound sterling'],\r\n    'wikipedia_id': ['270680']}]}}\r\nIn [35]: dataset['train_triviaqa']['input'][:10]                                                                                                                                                            \r\nOut[35]: ['', '', '', '', '', '', '', '', '', '']\r\n# same with test set \r\nIn [37]: dataset['test_triviaqa']['input'][:10]                                                                                                                                                             \r\nOut[37]: ['', '', '', '', '', '', '', '', '', '']\r\n# works fine with natural questions\r\nIn [34]: dataset['train_nq']['input'][:10]                                                                                                                                                                  \r\nOut[34]: \r\n['how i.met your mother who is the mother',\r\n 'who had the most wins in the nfl',\r\n 'who played mantis guardians of the galaxy 2',\r\n 'what channel is the premier league on in france',\r\n \"god's not dead a light in the darkness release date\",\r\n 'who is the current president of un general assembly',\r\n 'when do the eclipse supposed to take place',\r\n 'what is the name of the sea surrounding dubai',\r\n 'who holds the nba record for most points in a career',\r\n 'when did the new maze runner movie come out']\r\n```\r\n\r\nStay safe :) \n Just found out about https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/kilt_tasks\/README.md\r\n(Not very clear in https:\/\/huggingface.co\/datasets\/kilt_tasks links to http:\/\/github.com\/huggingface\/datasets\/datasets\/kilt_tasks\/README.md which is dead, closing the issue though :))","embeddings":[0.4154247642,-0.3093399704,-0.0309624653,0.0308451131,0.2169784904,0.0481552258,0.6097394824,0.4196387529,0.1841181815,0.1952489913,0.3654612899,0.5179553032,-0.0468075871,0.443081677,0.0452616364,0.0297371373,0.0262947138,0.0816094503,0.2449792027,-0.1349078119,-0.070260793,0.1185156778,-0.5049002171,-0.3920861781,-0.6020743251,0.0715131238,-0.1471185088,0.1056134403,0.1308469176,-0.3451221585,0.2047567815,-0.1966237426,-0.2979108095,0.234799698,-0.0001233155,-0.151816383,0.4405516088,-0.1911161393,-0.5569390655,-0.2748937905,-0.5155441761,0.0414867885,0.1126812473,-0.3192623258,-0.0520286188,0.3783489466,-0.0556893386,-0.5345303416,0.1037189364,0.3284580708,0.0828188136,0.2482213229,-0.427097708,-0.0155318491,0.0678356588,0.2325283736,-0.0963372067,-0.1887251586,-0.0307343956,0.0090678968,-0.0292269066,0.3749616146,0.0552502051,0.0512047894,-0.0871716738,0.1463882774,-0.1052402556,-0.5381202698,0.4375944436,0.4902975857,0.0022711277,-0.0752102211,-0.3428653777,-0.3614497185,0.074659653,0.0352610387,0.1127898246,0.4587815702,-0.1910824776,-0.128039673,-0.0515876785,0.0197217278,-0.0616242215,-0.0467875451,-0.557240665,0.3227185309,0.0275498293,-0.0352287889,-0.118647553,-0.079226315,0.0615168065,-0.3293149769,-0.2264239937,0.1363897473,-0.4327518642,-0.0133391628,0.0735009536,0.0046049915,0.0714485347,-0.0606742166,0.0952832028,0.1380965114,0.0809555203,0.0362243317,0.3264836371,0.2131464481,0.606731236,-0.2336505055,-0.1019396335,-0.0146458298,-0.289934814,0.0237061009,0.015417112,0.2261016667,-0.1452406347,-0.1047834158,0.5170952082,-0.1638511717,-0.432489872,0.1947111189,-0.3115721047,0.0000848518,-0.251018554,0.1180457547,-0.2242430896,0.4553079605,-0.2690626383,0.3509604037,-0.1322484165,-0.3637267947,-0.2435650229,0.0825486705,0.0769863799,0.0719891563,0.1621539891,-0.3181550205,0.247709319,0.1627962738,-0.0797332376,-0.1926609129,0.3062608838,-0.2796264589,0.164816618,0.2342649996,0.1821506172,0.1010709777,0.2721576095,-0.1779694855,0.1281515658,0.4900316596,-0.2172444761,-0.159130156,-0.1640295833,0.0902906805,-0.5352227688,-0.1692023426,0.0077250013,0.3015995026,-0.0443646833,-0.0157206971,-0.0389804244,-0.0784888789,-0.0181758441,-0.1367528588,0.3983205855,0.4051298201,-0.6290889382,-0.0194267798,-0.1245946959,0.4337553084,0.0284347795,-0.0846947059,0.002301543,0.4501265883,0.0450163893,0.4883404076,0.1594999433,-0.6424002051,0.1234371215,0.1109090075,0.0765333474,0.2729054689,0.0170958098,-0.152042836,0.2271609455,0.3121139407,0.1535473913,0.0757146105,-0.1106448695,-0.0357699879,-0.4687983692,-0.2415012717,0.3733985126,0.1570773721,0.0501448363,-0.1175259948,-0.0163034443,0.3112974465,0.1443440318,0.0159370303,0.1407600641,0.2728122473,0.4098913372,-0.1147982776,-0.0665833578,0.051839523,-0.4112521112,0.1828290373,0.02530648,0.4265533388,0.0540686511,-0.2753804326,-0.3356623948,-0.0422768667,-0.366150707,-0.5387892127,0.0638962165,-0.0977963582,0.0266561024,0.2791167796,-0.2297948003,0.0499987155,-0.068345502,0.0233631264,-0.4188793898,0.1438494474,0.1057638526,-0.0649687797,-0.3397308886,0.447250247,0.0299084373,-0.0277109426,0.0762488991,0.2349160463,0.3358787596,0.0155418394,0.3939433992,-0.1505122185,0.2231674641,-0.1009625569,-0.0389687121,0.2608843148,0.2673651278,-0.2981997728,-0.5522263646,0.4946863353,0.0474158712,0.1908845305,-0.1122226864,0.0049033449,-0.2117701322,-0.17569682,-0.1037322879,-0.0738084316,-0.0485363267,-0.4181039631,0.0915501639,0.0548916347,-0.2473305315,0.2331138253,0.3761444986,0.1827236712,0.1425498724,-0.1430834234,-0.273860991,-0.1377910078,0.1052863598,0.190239951,0.2923644483,0.1332271397,0.0850077569,-0.198642984,0.2707414925,-0.1987440884,0.2556269765,0.2737278938,0.0222738199,0.3003895581,0.2671950459,0.0109333806,-0.0619636998,0.2227361798,0.2534605563,0.1360461414,-0.1222360283,-0.0501957685,-0.2849498987,-0.1190199703,-0.0362408496,-0.0462100692,-0.114528209,-0.5389600396,-0.0615606979,-0.1473469436,-0.0517592132,0.0327147618,-0.1225998476,0.2000129819,0.1589460969,-0.4246274829,-0.2907564938,-0.4309128523,-0.2542892694,0.0144867217,0.0569267049,0.0548986532,-0.0078120911,0.0646720976,-0.1538101584,-0.1130127236,-0.5093662143,-0.0147382505,-0.3350986242,0.4982024729,0.0131052872,0.496679455,-0.3786387444,-0.0884267762,0.3220568001,0.0506732464,-0.2215313762,0.1099406332,0.1743040979,0.1307162195,-0.4416171908,-0.7334843874,-0.0226352513,-0.096458964,0.0566737242,0.0583065376,-0.1101087928,0.402231127,-0.0306141581,0.3108605444,0.0632256418,-0.3287021816,-0.1513582766,-0.1570688188,0.3093299866,-0.0767925158,-0.3370245099,0.2594738305,-0.2156599313,0.4304480553,-0.1713768244,-0.4541926086,-0.1187740266,-0.0140375998,0.4953697622,0.1077462509,-0.2205437124,0.1702052504,-0.0410149284,0.0568225347,-0.1034188047,0.0223851092,0.2761441171,0.2957688272,0.2035993785,-0.0116870571,0.125397265,0.2966403961,0.4599235654,0.0736202821,0.3123475909,0.1346438378,-0.3639746606,0.278357178,-0.2543643415,-0.0631811023,0.2531127036,0.0127647556,0.0002652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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/790","title":"Error running pip install -e \".[dev]\" on MacOS 10.13.6: faiss\/python does not exist","comments":"I saw that `faiss-cpu` 1.6.4.post2 was released recently to fix the installation on macos. It should work now","body":"I was following along with https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#adding-tests-and-metadata-to-the-dataset when I ran into this error.\r\n\r\n```sh\r\ngit clone https:\/\/github.com\/huggingface\/datasets\r\ncd datasets\r\nvirtualenv venv -p python3 --system-site-packages\r\nsource venv\/bin\/activate\r\npip install -e \".[dev]\"\r\n```\r\n\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/59632\/97868518-72871800-1cd5-11eb-9cd2-37d4e9d20b39.png)\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/59632\/97868592-977b8b00-1cd5-11eb-8f3c-0c409616149c.png)\r\n\r\nPython 3.7.7\r\n","comment_length":18,"text":"Error running pip install -e \".[dev]\" on MacOS 10.13.6: faiss\/python does not exist \n I was following along with https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html#adding-tests-and-metadata-to-the-dataset when I ran into this error.\r\n\r\n```sh\r\ngit clone https:\/\/github.com\/huggingface\/datasets\r\ncd datasets\r\nvirtualenv venv -p python3 --system-site-packages\r\nsource venv\/bin\/activate\r\npip install -e \".[dev]\"\r\n```\r\n\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/59632\/97868518-72871800-1cd5-11eb-9cd2-37d4e9d20b39.png)\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/59632\/97868592-977b8b00-1cd5-11eb-8f3c-0c409616149c.png)\r\n\r\nPython 3.7.7\r\n \n I saw that `faiss-cpu` 1.6.4.post2 was released recently to fix the installation on macos. It should work now","embeddings":[-0.0769137219,-0.4744410813,-0.1149005964,-0.0313249752,0.0470356792,0.0747042671,-0.2394820005,0.2321131378,0.3420989513,0.0246700086,-0.0713810101,0.2375308126,0.0024278027,0.066258654,0.0126584889,0.0398926735,0.4198972881,0.2260307074,-0.0316481218,-0.2329014987,0.0014593964,0.3070571423,-0.0191779342,0.0570393763,-0.1384629309,0.0629520267,-0.2245894074,0.0982487574,-0.2784340382,-0.4324710667,0.1324747354,-0.1166830063,0.0231352206,0.4486960769,-0.0001248198,0.1516883075,0.2149834633,0.0974139795,-0.2460971624,-0.3716697395,-0.1054948047,-0.307315439,0.1879502684,-0.0491277799,0.15651232,-0.0496589951,0.0473566465,-0.1054647192,0.2997088134,0.1950040609,0.1107113287,0.459600836,0.3656337261,-0.1996756494,0.0875823796,0.1747200787,-0.2323464006,0.1902423799,0.2368948609,0.1777076125,0.5242687464,0.1032261401,-0.0240859278,-0.049831178,0.0052525774,-0.0410645306,0.2971595526,-0.2509606481,-0.1255887747,0.0883223712,-0.0781328827,-0.2779022753,-0.4838891327,-0.1123396754,0.1949889213,-0.1475775391,0.2691460848,0.2406691313,-0.2661559582,0.2806168795,-0.0845102519,-0.5820125937,-0.111558266,0.0773854405,0.1476298571,0.0403320938,-0.1895345747,0.0433260575,0.2459104508,0.0419017933,-0.3036502302,0.0213176515,0.2408441901,0.2564199567,-0.2195349485,-0.1184682548,-0.0794374645,-0.0824062452,0.0761013553,-0.2071825862,-0.6648669839,-0.3138446808,-0.2615450919,0.2230482548,-0.0323677324,-0.0902222246,-0.0856781825,-0.0056550601,-0.0009375344,0.023171328,0.2461711615,0.0166704543,-0.1541399956,-0.1352241337,-0.7007260323,-0.3548826277,0.2498062551,-0.1318226755,-0.4621340632,0.1604498178,0.0744873956,0.0655441955,0.0822720677,0.3852746189,0.0288051385,-0.105658479,0.1026321799,0.2561386824,-0.2377709746,0.1440687478,-0.0480729602,0.1899397224,-0.1215004027,-0.0111378739,0.121295549,-0.3222525418,0.3431439102,0.1137485206,0.0346564241,0.136552915,-0.2019346952,0.1111452505,-0.2067697644,0.5875923038,0.0539902113,0.1794100255,0.1431519389,0.1792557985,-0.1760357022,0.1573632658,-0.2410850674,-0.244509086,-0.2892553508,0.069831565,-0.2390052378,-0.1153463125,-0.0618102364,-0.2575477362,-0.089860037,0.1062235609,0.0646146536,-0.0425125323,0.1820692867,-0.1051482931,0.1427769065,0.0718968287,-0.1721661985,-0.2253894359,-0.251116693,-0.1698783338,-0.093329452,0.3640250564,0.2783349752,0.2200587541,-0.26883322,-0.1268996149,0.435151279,-0.5085304976,0.0122087738,0.2115213573,0.0271888059,-0.1193129569,0.1028826088,-0.1287745684,-0.4710634053,0.10203477,-0.0978003442,0.0465045869,-0.0181763005,-0.2232404053,-0.1655162275,-0.5149326921,-0.0163664259,0.3023715615,0.1532721221,-0.0726735517,-0.0383706912,-0.3819718063,0.1565912813,-0.0442993604,0.0273570158,0.4160861969,0.8438920379,0.5213233829,-0.0873947442,-0.366656661,0.2548884749,0.219048664,0.0821953192,0.0615975261,-0.4336166084,-0.1164684817,-0.1909641922,0.4214517474,0.0527328514,-0.1817916334,-0.0493554324,-0.0084572975,0.220620513,0.258086592,-0.3192430735,0.2147803754,-0.0341657884,0.3442053199,-0.4534943998,0.1650847197,-0.2749231458,-0.0954194516,-0.1468310356,0.3459373713,-0.001902262,-0.0620544888,0.0765880048,0.2894162536,0.0595697723,-0.2615385652,0.2932676971,-0.0617162138,0.0749857128,-0.2080413699,0.150472194,-0.0534184948,-0.0314483494,0.396376282,0.0543086417,0.0109212417,-0.026063066,0.1357116699,0.2305481583,0.2402281165,0.2736192644,0.0662929937,0.0941658393,-0.2988328636,0.3442799449,0.0353484787,0.3008334339,-0.1841428131,0.119016014,-0.0637628064,0.1355283558,-0.0006024914,0.0433985926,0.2118345201,-0.096296154,0.2498711348,0.0397552438,-0.3664267659,0.5468246341,0.0831656232,-0.2761651278,0.1839732379,-0.055418361,-0.048827488,0.2700510621,0.007651459,0.0988890007,-0.1048019901,-0.0599188283,-0.086297743,-0.2169666588,-0.4104287326,-0.0999905542,0.2072316557,-0.3612687588,-0.0186494756,-0.0393618122,0.076958321,-0.1209061742,-0.3665560782,-0.4147287011,-0.160359323,0.2663107216,0.0446308106,0.0994489416,0.2024787217,-0.0282269716,0.0350032188,0.0785415098,-0.1282932609,-0.2449283451,-0.073949948,0.0042509297,-0.0038533323,-0.1449314505,0.0087598739,0.3355324864,-0.1896279454,-0.1491673738,-0.3814901412,-0.3965193331,0.2551424205,-0.4297249019,0.4415021837,0.3835313022,0.0721150935,-0.0986621678,0.0896866322,0.2708003521,-0.3785279989,-0.3092986047,-0.055280529,0.0339288302,-0.2750465572,-0.1942581683,-0.2093297988,-0.3411789834,-0.095007807,0.3561174572,-0.0179157313,-0.0884842947,0.1934183836,0.3617049158,0.10972251,-0.0950982943,0.0694146231,-0.3011163175,-0.3425506949,0.0825262889,-0.2646844089,-0.2384745479,0.3331691325,0.1514116526,0.4445262253,0.0719862357,-0.3710216284,-0.2489838153,-0.2236432582,0.4790898561,-0.1239096299,0.6306265593,0.1899009347,0.008816855,0.0424109809,-0.1913038641,0.1098376364,-0.1345633417,-0.1592764854,0.2964021564,-0.1310562193,0.4414909184,-0.1539944857,0.8269540668,0.3067979217,0.0285531003,0.2114059031,-0.2127243578,0.6402563453,-0.0050484003,-0.2645453811,0.1621617675,0.3845385015,0.0281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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/786","title":"feat(dataset): multiprocessing _generate_examples","comments":"I agree that would be cool :)\r\nRight now the only distributed dataset builder is based on Apache Beam so you can use distributed processing frameworks like Dataflow, Spark, Flink etc. to build your dataset but it's not really well suited for single-worker parallel processing afaik","body":"forking this out of #741, this issue is only regarding multiprocessing\r\n\r\nI'd love if there was a dataset configuration parameter `workers`, where when it is `1` it behaves as it does right now, and when its `>1` maybe `_generate_examples` can also get the `pool` and return an iterable using the pool.\r\n\r\nIn my use case, I would instead of:\r\n```python\r\nfor datum in data:\r\n     yield self.load_datum(datum)\r\n```\r\ndo:\r\n```python\r\nreturn pool.map(self.load_datum, data)\r\n```\r\n\r\nAs the dataset in question, as an example, has **only** 7000 rows, and takes 10 seconds to load each row on average, it takes almost 20 hours to load the entire dataset.\r\nIf this was a larger dataset (and many such datasets exist), it would take multiple days to complete.\r\n\r\nUsing multiprocessing, for example, 40 cores, could speed it up dramatically. For this dataset, hopefully to fully load in under an hour.","comment_length":46,"text":"feat(dataset): multiprocessing _generate_examples \n forking this out of #741, this issue is only regarding multiprocessing\r\n\r\nI'd love if there was a dataset configuration parameter `workers`, where when it is `1` it behaves as it does right now, and when its `>1` maybe `_generate_examples` can also get the `pool` and return an iterable using the pool.\r\n\r\nIn my use case, I would instead of:\r\n```python\r\nfor datum in data:\r\n     yield self.load_datum(datum)\r\n```\r\ndo:\r\n```python\r\nreturn pool.map(self.load_datum, data)\r\n```\r\n\r\nAs the dataset in question, as an example, has **only** 7000 rows, and takes 10 seconds to load each row on average, it takes almost 20 hours to load the entire dataset.\r\nIf this was a larger dataset (and many such datasets exist), it would take multiple days to complete.\r\n\r\nUsing multiprocessing, for example, 40 cores, could speed it up dramatically. For this dataset, hopefully to fully load in under an hour. \n I agree that would be cool :)\r\nRight now the only distributed dataset builder is based on Apache Beam so you can use distributed processing frameworks like Dataflow, Spark, Flink etc. to build your dataset but it's not really well suited for single-worker parallel processing afaik","embeddings":[-0.5645692348,-0.1257423609,-0.1173854023,-0.166632995,-0.0713058785,-0.0848814175,0.3084898293,0.345071286,0.1016154811,0.4008847475,0.2586757839,0.1760060638,0.0299800057,0.1242403463,0.1066480726,0.0938062146,0.0195515174,0.1313365996,-0.2605703473,-0.0488939695,-0.3563632071,-0.0217418987,-0.0285441708,-0.1431766152,-0.3056107759,-0.1653455794,-0.0860954672,0.0822234824,0.0034817467,-0.2213504016,0.1820405722,0.5444226265,0.2789001465,0.412689507,-0.0001032015,-0.0390751399,0.2969443798,0.0052962429,-0.3610217869,-0.1338654608,-0.1356738806,-0.3160059154,0.0205591172,-0.2597048283,0.0211865883,-0.187975958,0.246158734,-0.4733569026,0.0596728586,-0.1250498146,0.2303161472,0.3476171196,-0.2190504968,-0.2033669204,-0.1540288478,-0.0212983526,-0.1601623893,0.1414245963,0.5995049477,-0.3429674506,-0.1796414107,0.1689000428,-0.0268125031,0.3214696348,0.258869797,0.0280346218,0.2928673923,-0.4736996889,-0.0322284326,0.2586959898,0.3223201632,-0.0653719604,-0.4411685467,-0.4137692153,0.1503238827,-0.1780725271,0.098107338,0.4271344543,-0.3625374436,0.1794993281,-0.3271012902,0.0309631135,-0.0444259606,-0.0228912085,0.0459823161,0.4582173228,0.1388121396,0.16112037,0.2576366365,0.0965296403,0.3622563779,-0.1368422061,0.3720157444,0.0939120203,-0.3785074353,-0.0503505878,0.1009605825,-0.176166445,0.2498613,-0.0211286955,0.2596452832,-0.0036759903,-0.0463215411,0.2976036072,0.3191949129,-0.3273790181,-0.1877823919,-0.0794773921,0.2441907078,-0.1838876605,-0.0110040419,0.0401750095,-0.0484521315,-0.2198261321,0.2778868973,0.0812505409,0.0187796224,0.162140891,-0.1537270546,-0.2023274153,0.0209043548,-0.0703686997,0.0725858733,0.0811427906,-0.0497736,0.5781404376,-0.1443649977,0.0651179478,-0.2028383017,-0.1945530623,-0.0828361958,0.0311295856,-0.2206898481,0.2142316848,0.3574272692,0.035415668,0.096330516,0.2090110183,0.2967506647,-0.0109740403,0.4788134396,-0.1122947857,0.1474232823,0.1567318588,0.0870099813,-0.1228972971,-0.1640131027,-0.1195179448,-0.2253357172,0.1451944113,-0.0699561089,-0.4257085323,-0.1593768001,0.2495116144,-0.055490911,0.1053343341,-0.1263075471,0.2614251673,0.1310819238,0.1803332418,-0.0915223584,-0.1575553715,-0.2608850896,-0.0602515563,0.325807035,0.2831625342,-0.1300890595,0.2780073881,0.0442510545,0.0856357887,0.1165908054,0.1238762811,-0.1102689132,0.2128217965,-0.1414506733,-0.052149646,0.2380713224,-0.1173013002,-0.0812401026,0.3136304617,-0.0579978116,0.0389036834,0.1617939472,0.1347354949,0.2970617115,-0.0906533971,0.242137447,0.4703310132,-0.3758898377,0.2193625569,-0.2116719484,-0.1738851219,-0.168848291,0.2470009327,0.1568025053,0.2567701936,-0.0740778744,-0.2490072846,0.485902667,-0.2836574018,0.2944054008,-0.0935088843,-0.0824324414,0.1030864865,0.1391343772,-0.4160619676,-0.2420691699,0.3760923147,0.1697995067,-0.0567646362,0.0959128216,-0.1960014552,0.0544757992,0.1322979927,-0.0876032412,0.1565964222,0.1157926917,0.1026672497,-0.0603532344,-0.441342175,-0.3751507998,0.1957874298,-0.0584253892,-0.1846154928,-0.1708138883,0.1369488835,0.2126252949,-0.2457502931,0.1578003317,0.0827546641,-0.0318877399,-0.1456852853,0.052638445,0.2507564723,-0.0995185301,0.3289547265,0.1844166666,0.3626998067,0.1138872877,0.1689618677,0.05482715,0.0197907388,0.0313610248,-0.0820285678,-0.1565284431,0.4870259166,-0.3659533858,0.3455126882,0.1757371128,0.1759542376,0.4313523173,0.1570576578,-0.1642201096,0.0836196616,0.085631378,-0.0001003791,0.33670187,-0.0109573444,-0.4514661729,0.3199449778,0.1571628004,-0.182391867,0.0331963301,0.0422897488,0.3300043344,-0.1101244017,0.1448792666,0.2291253358,0.522993803,0.2154854387,0.2187856734,-0.1615592539,-0.1944313496,-0.0688633695,0.1502071619,-0.0102946041,0.3209049404,0.0703947023,0.13579005,-0.2369163185,-0.3364010155,-0.3829324245,0.1167751253,0.0366262235,-0.0017936444,0.1108320504,-0.0108287456,0.1982709169,-0.2554686368,0.2144106328,-0.0470128655,-0.3205996752,-0.0406120308,0.5437672734,-0.09951704,0.1028357148,-0.0570215955,0.2325585037,-0.1785197109,0.1215940192,-0.1859091073,-0.0988349915,-0.0075362287,0.0907950774,-0.0857532024,0.0673558488,0.4577212632,-0.0036933012,-0.104328461,-0.423586607,0.1313679963,0.0695388839,0.0352151692,0.1806467772,0.2100236416,0.0348645896,0.1539574116,-0.2294790596,0.1865983903,-0.2671281695,0.1010825038,-0.1395892799,-0.1501678973,-0.0437807031,-0.078035444,-0.0934929401,-0.3203550279,-0.4222767651,0.1101503074,-0.0202856138,0.236502856,-0.2105718702,0.0797268078,-0.1382808834,0.3285043538,-0.0509297289,-0.1423629522,-0.6931467056,0.283370167,-0.2654668093,-0.1514147371,-0.1869240552,-0.1685637981,0.0771837831,0.4729092717,-0.1621153057,-0.0578048453,-0.2334820032,0.190585658,-0.1642941982,0.0998540297,0.236958757,0.1101685539,-0.0786666349,-0.191914022,-0.1630766541,0.0809303373,0.0563186631,-0.1001630276,0.1788741648,0.2567817569,0.0413926095,0.77250278,0.2194131911,0.1988105178,0.2402208596,0.0078342538,-0.0833366066,0.0221496802,-0.2844358087,0.0830958486,-0.2348846048,-0.0329468921,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/784","title":"Issue with downloading Wikipedia data for low resource language","comments":"Hello, maybe you could ty to use another date for the wikipedia dump (see the available [dates](https:\/\/dumps.wikimedia.org\/jvwiki) here for `jv`) ?","body":"Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet\r\n```\r\njv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner')\r\nsu_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner')\r\n```\r\nAnd I get the following error for these two languages:\r\nJavanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nSundanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nI found from https:\/\/github.com\/huggingface\/datasets\/issues\/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json` and `https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json` are no longer valid.\r\n\r\n Any suggestions on how to handle this issue? Thanks!","comment_length":21,"text":"Issue with downloading Wikipedia data for low resource language \n Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet\r\n```\r\njv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner')\r\nsu_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner')\r\n```\r\nAnd I get the following error for these two languages:\r\nJavanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nSundanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nI found from https:\/\/github.com\/huggingface\/datasets\/issues\/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json` and `https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json` are no longer valid.\r\n\r\n Any suggestions on how to handle this issue? Thanks! \n Hello, maybe you could ty to use another date for the wikipedia dump (see the available [dates](https:\/\/dumps.wikimedia.org\/jvwiki) here for `jv`) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/784","title":"Issue with downloading Wikipedia data for low resource language","comments":"@lhoestq\r\n\r\nI've tried `load_dataset('wikipedia', '20200501.zh', beam_runner='DirectRunner')` and got the same `FileNotFoundError` as @SamuelCahyawijaya.\r\n\r\nAlso, using another date (e.g. `load_dataset('wikipedia', '20201120.zh', beam_runner='DirectRunner')`) will give the following error message.\r\n\r\n```\r\nValueError: BuilderConfig 20201120.zh not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']\r\n```\r\n\r\nI am pretty sure that `https:\/\/dumps.wikimedia.org\/enwiki\/20201120\/dumpstatus.json` exists.","body":"Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet\r\n```\r\njv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner')\r\nsu_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner')\r\n```\r\nAnd I get the following error for these two languages:\r\nJavanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nSundanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nI found from https:\/\/github.com\/huggingface\/datasets\/issues\/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json` and `https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json` are no longer valid.\r\n\r\n Any suggestions on how to handle this issue? Thanks!","comment_length":342,"text":"Issue with downloading Wikipedia data for low resource language \n Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet\r\n```\r\njv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner')\r\nsu_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner')\r\n```\r\nAnd I get the following error for these two languages:\r\nJavanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nSundanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nI found from https:\/\/github.com\/huggingface\/datasets\/issues\/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json` and `https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json` are no longer valid.\r\n\r\n Any suggestions on how to handle this issue? Thanks! \n @lhoestq\r\n\r\nI've tried `load_dataset('wikipedia', '20200501.zh', beam_runner='DirectRunner')` and got the same `FileNotFoundError` as @SamuelCahyawijaya.\r\n\r\nAlso, using another date (e.g. `load_dataset('wikipedia', '20201120.zh', beam_runner='DirectRunner')`) will give the following error message.\r\n\r\n```\r\nValueError: BuilderConfig 20201120.zh not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']\r\n```\r\n\r\nI am pretty sure that `https:\/\/dumps.wikimedia.org\/enwiki\/20201120\/dumpstatus.json` exists.","embeddings":[0.0533478856,0.0374849103,0.0597833283,0.4869290888,0.170536682,0.0233596079,0.093230091,0.4074784219,0.1670001149,0.1014821902,-0.180892691,-0.1391495764,0.4188603461,-0.1732718199,-0.0940446556,-0.495067656,0.1877741963,0.0399843417,0.0126653081,-0.0655117556,-0.2115678489,-0.0480744801,-0.1926138699,0.0627112836,-0.3057098985,-0.0056309984,0.0016467076,-0.0364647955,-0.3302689195,-0.2426016182,0.2610393167,0.1820007414,0.2705383003,0.414835304,-0.0001278781,-0.0497068129,0.5732793212,-0.1151508689,-0.5644016862,0.088967219,-0.2109943181,-0.4145148098,-0.1965319067,-0.3739251792,0.0392361134,-0.1627885997,0.234076649,-0.2813850939,0.2931888998,-0.0290631242,0.0679255798,0.1302415282,0.1000647396,-0.2170469314,0.3911059499,0.1138225943,0.0463740714,0.0177767929,-0.0791449174,0.0869459212,0.0281140544,0.215274632,-0.2443998754,-0.0413640589,0.211182043,-0.0207845569,-0.2214757651,-0.5394378901,0.4275811613,0.5065014362,0.9873847961,-0.0341156051,-0.5420722365,0.0208634101,-0.0884430483,0.1717970371,0.2517359555,0.0773003176,-0.0216554496,0.0064187408,0.1908419132,-0.3481397629,-0.1752084047,0.5972660184,-0.0458157919,0.3435457349,-0.0293708425,0.1244830489,-0.1325082183,-0.4068507254,-0.196453318,-0.4271188676,-0.0505387969,0.4434016645,0.0007426016,0.0057797455,-0.1001872495,0.0649934188,0.1602074206,-0.2895126045,-0.0883787498,0.0405808762,-0.1443123817,0.1057483703,0.2951692939,-0.2529043257,0.1639977545,-0.1666972041,0.6046065092,0.1877863258,0.3268435895,-0.0606869645,0.0451207832,-0.2781887949,-0.5877600908,-0.1193340272,0.2341959774,-0.3487767875,-0.0475725196,0.1888379306,-0.2759740651,-0.1863067895,-0.149003014,0.240567416,-0.2268441468,0.4528813958,0.3064103425,0.2869040668,-0.3019836545,-0.2889588773,-0.0044378703,0.2404556721,0.0577961206,0.1191031039,0.1557949781,0.0076080612,0.2810476422,0.1295275837,-0.3599660993,-0.1727759838,-0.2213955969,-0.2736275196,-0.328809917,0.1263093203,0.1419122815,0.5926673412,-0.1778511405,-0.1102340296,-0.1915977746,0.059934184,-0.3075916469,0.0290118828,-0.0388549939,0.0066995448,-0.3058397472,0.1448859721,-0.1023367941,0.2594457269,0.0931876302,-0.1298283637,-0.0810647085,0.1333623379,-0.4865556359,-0.2310166508,0.4018515944,0.6183560491,-0.3419445157,-0.2550344169,-0.2246883363,-0.0379407294,0.3226376474,0.2405833751,-0.1962711364,0.5838981271,-0.1156998426,0.1431093365,0.3990396261,-0.0626019314,-0.245179534,0.3416454196,0.1305207908,-0.0190299917,0.1855218709,0.0644040853,0.2784171104,-0.1964817494,0.0487367027,0.407776475,0.1818939,-0.0078275073,-0.3822375834,-0.2369911075,0.1355568171,0.074671343,0.4870291948,-0.0940967575,0.2486828268,0.8490917087,0.2026953995,-0.0856895447,0.3260680437,0.4646955431,-0.3252147436,-0.0939502642,0.0796766654,0.0160330478,-0.0819027573,0.2273687869,-0.0334083624,0.044444833,0.0221871193,-0.0124820368,-0.3875280917,-0.1059804931,0.0293927956,0.2333385944,0.0024630348,0.0668149292,0.1838377267,0.3588463366,0.3215928078,-0.1883118004,0.0001829717,-0.0873958021,-0.2711069286,0.4411354959,-0.0971421897,0.1178860739,0.1242606044,-0.0658245459,0.4569521844,0.037395373,-0.0878356472,0.1333163679,0.1280237138,0.2300779521,0.09697669,-0.1452700645,0.4124618769,-0.0279227216,0.3956124485,0.5580114722,0.2673858702,-0.1624314636,-0.0820200741,0.4217672944,-0.0235179048,0.0680852756,-0.0762696937,0.0021344603,0.3455212712,-0.0045456886,0.2148859799,-0.1222809702,0.2748683393,0.3064382374,-0.1189002544,0.1761002094,-0.4096235335,-0.140421316,0.3270944655,-0.1661039889,0.2058768868,-0.0424847528,-0.2158710212,-0.0264976043,0.250402689,-0.0422125235,0.0219621379,0.093374446,0.1414162815,-0.2690082192,0.185656935,-0.1909544021,0.117873475,0.2861928344,0.361367017,0.2144424319,0.0326790139,0.0631784126,-0.0983968005,0.270280093,0.0177615173,0.2583781481,-0.1833215505,-0.1620476097,-0.3506138921,-0.3741486073,-0.287103951,0.0854783803,-0.3898091018,-0.3666174412,0.1491402388,0.1553399563,-0.2938445508,0.132437095,-0.1568155736,-0.1100076362,-0.0155158909,-0.0630766451,-0.2139376104,-0.2459361553,-0.2690545022,-0.1106629074,0.4098207057,0.1954005808,0.0575194061,-0.1600296199,-0.384940505,-0.5117307901,-0.194742322,0.1738581508,0.0831749812,0.178712666,-0.0822415948,0.5658929348,0.0030056352,-0.0505431034,0.1183449998,0.0240438879,-0.0823360533,0.1118144244,-0.1079549119,0.1611085385,-0.1291732192,-0.2555248737,-0.1848215312,-0.354953289,-0.1326247156,0.0402967595,0.1444684267,-0.1802950799,0.1903788745,-0.0165350512,0.3194538653,0.2845262885,-0.1832271665,-0.141690895,0.5626681447,-0.0918639824,-0.4536193013,0.1298017651,-0.1563428491,-0.0148345353,0.1588099748,-0.3009743094,0.2276768535,0.1487456262,0.1069899201,0.248714596,0.0975349173,0.0849275887,-0.2770103514,0.1548021734,-0.0610632561,0.1394795179,-0.1391167939,-0.1906077415,0.4748793244,0.4391957521,0.4500229359,-0.0618055724,0.7416902184,0.1354653686,0.2463316023,0.3136912286,0.3680102527,0.2478992045,0.0193921328,0.0227028877,0.1015528291,0.127037257,0.04435708,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/784","title":"Issue with downloading Wikipedia data for low resource language","comments":"For posterity, here's how I got the data I needed:  I needed Bengali, so I had to check which dumps are available here: https:\/\/dumps.wikimedia.org\/bnwiki\/ , then I ran:\r\n```\r\nload_dataset(\"wikipedia\", language=\"bn\", date=\"20211101\",\r\n                        beam_runner=\"DirectRunner\")\r\n```","body":"Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet\r\n```\r\njv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner')\r\nsu_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner')\r\n```\r\nAnd I get the following error for these two languages:\r\nJavanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nSundanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nI found from https:\/\/github.com\/huggingface\/datasets\/issues\/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json` and `https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json` are no longer valid.\r\n\r\n Any suggestions on how to handle this issue? Thanks!","comment_length":34,"text":"Issue with downloading Wikipedia data for low resource language \n Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet\r\n```\r\njv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner')\r\nsu_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner')\r\n```\r\nAnd I get the following error for these two languages:\r\nJavanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nSundanese\r\n```\r\nFileNotFoundError: Couldn't find file at https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json\r\n```\r\n\r\nI found from https:\/\/github.com\/huggingface\/datasets\/issues\/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https:\/\/dumps.wikimedia.org\/jvwiki\/20200501\/dumpstatus.json` and `https:\/\/dumps.wikimedia.org\/suwiki\/20200501\/dumpstatus.json` are no longer valid.\r\n\r\n Any suggestions on how to handle this issue? Thanks! \n For posterity, here's how I got the data I needed:  I needed Bengali, so I had to check which dumps are available here: https:\/\/dumps.wikimedia.org\/bnwiki\/ , then I ran:\r\n```\r\nload_dataset(\"wikipedia\", language=\"bn\", date=\"20211101\",\r\n                        beam_runner=\"DirectRunner\")\r\n```","embeddings":[0.1842239499,-0.058177527,0.0112094563,0.4411030114,0.1695390344,0.0318268128,0.038634263,0.4088579416,0.0813780129,-0.0029667765,-0.2646800578,-0.1834713817,0.3728420138,-0.2051679045,-0.0723536089,-0.5031830668,0.2173893303,0.0228397418,0.0646551773,-0.0733708441,-0.2014415562,-0.0422712415,-0.189521417,0.0464784652,-0.2509059906,-0.0048594866,0.007204297,-0.022863416,-0.3308116794,-0.1949871629,0.3061787188,0.1961012334,0.2438401431,0.359372437,-0.0001229589,-0.0544023924,0.5495913625,-0.0978219584,-0.4912759662,0.0040135388,-0.249503091,-0.4454336762,-0.2279457152,-0.3935523629,0.1122202575,-0.1835831702,0.2740871012,-0.2496966273,0.2385211587,-0.0558048934,0.1210738346,0.1338965744,-0.0121219484,-0.210670501,0.4513134956,0.2375527471,0.0554547347,-0.0125311958,-0.0984668583,0.0656897724,0.0268156491,0.2345267385,-0.2863785028,-0.0447690003,0.1163128763,-0.0015278956,-0.346881479,-0.4977549613,0.4794636071,0.4737910628,0.8442746997,0.0046013468,-0.5384098291,0.0331674069,-0.1113920361,0.1112131625,0.2351546884,0.1124716103,0.0228219703,0.0690400898,0.1927201599,-0.2556403279,-0.134741351,0.5621658564,-0.0524955988,0.271771431,-0.0574954934,0.0598690212,-0.1328205168,-0.3688194752,-0.1932855844,-0.3507808149,-0.0657767355,0.3892231584,-0.0562022552,0.0623253249,-0.0210057199,0.0524956249,0.191798225,-0.3067708015,-0.1169767305,0.0669132397,-0.162106514,0.0626747385,0.3260498941,-0.3407634497,0.173858121,-0.0778598338,0.570299983,0.1926374286,0.3161953986,-0.0916833803,0.1249182671,-0.2366685867,-0.5126219988,-0.1394292861,0.3146567345,-0.3560967743,-0.0724518597,0.2883739173,-0.2865231037,-0.145389542,-0.1233630255,0.2036998719,-0.1339174807,0.5179424882,0.2982182205,0.3038111329,-0.327816695,-0.2540660501,-0.0211675763,0.2039021999,0.0231280141,0.1362651139,0.1199013144,0.1104364544,0.3128022552,0.1107422486,-0.3744211793,-0.1586309671,-0.1913691759,-0.39212358,-0.2933775485,0.1261716783,0.1726806611,0.6361405253,-0.1900323778,-0.0666211098,-0.1566776037,0.033959046,-0.3267123699,0.0550521575,0.0151450746,0.0605409928,-0.3273277581,0.1586176455,-0.1109121069,0.337690711,0.1173297837,-0.1049227044,-0.0082248254,0.1274604946,-0.4978198707,-0.2138524354,0.3126987219,0.5953320265,-0.3135938346,-0.1851133406,-0.2915330231,-0.0376538932,0.4077916741,0.3056116998,-0.2188565433,0.5591301918,-0.1177204326,0.3358739913,0.3951779306,0.0223925263,-0.2623561919,0.2925558984,0.1239210293,0.0408451483,0.1288021505,0.0077932822,0.3497218788,-0.2055131495,0.0945705995,0.3914226294,0.1877188981,-0.0109730447,-0.4024474621,-0.1710332036,0.1444333643,0.062378712,0.5195034742,-0.2034367025,0.2514818609,0.8731659055,0.1831893027,-0.0938259438,0.3391295075,0.4350098073,-0.2459397763,-0.0714771077,0.0526009388,-0.0483216643,-0.0314383581,0.2111267298,-0.0581176393,0.0967830941,-0.0800024867,-0.0128291342,-0.4011957347,-0.1340049207,0.0800124258,0.2215912193,0.0806807578,0.0674153343,0.0719257072,0.3699811399,0.3369247913,-0.2911366522,0.0421007611,-0.095737271,-0.2730644047,0.4055638909,-0.0528754406,0.0807261318,0.0991795063,-0.0846429616,0.4339187145,0.0891039595,-0.0881897956,0.1434890628,0.14928253,0.2337628603,0.1963637471,-0.1992730945,0.4255385399,-0.0939613506,0.3312918246,0.4810966253,0.303281486,-0.1782429665,-0.0555083267,0.3592755198,-0.0610150285,-0.0042801565,-0.1060571298,0.024104083,0.4284397364,0.0023234996,0.2195865959,-0.1164311245,0.2630824745,0.3267830312,-0.1534005255,0.184998706,-0.4041655064,-0.1354708821,0.2976037264,-0.1896167994,0.2538270652,-0.048617065,-0.2241096348,-0.0419012271,0.2603784502,-0.0601934604,0.1425139308,0.0995978937,0.2203071713,-0.3262496293,0.2310304344,-0.2356618345,0.1177074015,0.2514900565,0.2810695767,0.1933813244,0.1026960015,-0.0038804256,-0.1905676275,0.3351738453,0.0125430413,0.2888711989,-0.1263795793,-0.1277299076,-0.3452961445,-0.2878985405,-0.221977219,0.0644990504,-0.3378767669,-0.3940708935,0.1214786023,0.069769159,-0.3693054616,0.0206666179,-0.2239887118,-0.2044777721,-0.0090069845,-0.0406451747,-0.1930989921,-0.240086928,-0.2265384644,-0.0512092263,0.4371563792,0.3024928868,0.0755090639,-0.114371255,-0.414750874,-0.512003541,-0.1660685539,0.0826227441,0.0502364077,0.1143775284,-0.0543530248,0.5414711833,-0.1068582386,-0.0764500648,0.1659405679,0.0484978966,-0.0798498169,0.1636716574,-0.0498482063,0.1700673103,-0.1134487092,-0.2822913527,-0.1761109978,-0.3117098808,-0.096222274,-0.0335819051,0.1293968111,-0.2546256185,0.2074756175,-0.0002653608,0.2809408009,0.212745145,-0.2763458788,-0.0757427886,0.5759851933,-0.0614576116,-0.4606327415,0.1226703227,-0.115218848,0.0097037693,0.2343766242,-0.2742626667,0.1766473353,0.125610292,0.163523972,0.2701587379,0.1058924422,0.0477923192,-0.2758006454,0.0989546925,-0.043912977,0.2063164115,-0.1759673357,-0.1896717846,0.4950906634,0.3496008813,0.3793680966,-0.0057834941,0.7121015787,0.0569735058,0.2938870788,0.297991395,0.462381959,0.2255829275,0.1247130856,-0.0573583394,0.0323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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/778","title":"Unexpected behavior when loading cached csv file?","comments":"Hi ! Thanks for reporting.\r\nThe same issue was reported in #730 (but with the encodings instead of the delimiter). It was fixed by #770 .\r\nThe fix will be available in the next release :)","body":"I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode=\"force_redownload\"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset.\r\n\r\nSmall snippet to reproduce the behavior:\r\n```python\r\nimport datasets\r\n\r\nwith open(\"dummy_data.csv\", \"w\") as file:\r\n    file.write(\"test,this;text\\n\")\r\n\r\nprint(datasets.load_dataset(\"csv\", data_files=\"dummy_data.csv\", split=\"train\").column_names)\r\n# [\"test\", \"this;text\"]\r\n\r\nprint(datasets.load_dataset(\"csv\", data_files=\"dummy_data.csv\", split=\"train\", delimiter=\";\").column_names)\r\n# still [\"test\", \"this;text\"]\r\n```\r\n\r\nBy the way, thanks a lot for this amazing library! :)","comment_length":36,"text":"Unexpected behavior when loading cached csv file? \n I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode=\"force_redownload\"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset.\r\n\r\nSmall snippet to reproduce the behavior:\r\n```python\r\nimport datasets\r\n\r\nwith open(\"dummy_data.csv\", \"w\") as file:\r\n    file.write(\"test,this;text\\n\")\r\n\r\nprint(datasets.load_dataset(\"csv\", data_files=\"dummy_data.csv\", split=\"train\").column_names)\r\n# [\"test\", \"this;text\"]\r\n\r\nprint(datasets.load_dataset(\"csv\", data_files=\"dummy_data.csv\", split=\"train\", delimiter=\";\").column_names)\r\n# still [\"test\", \"this;text\"]\r\n```\r\n\r\nBy the way, thanks a lot for this amazing library! :) \n Hi ! Thanks for reporting.\r\nThe same issue was reported in #730 (but with the encodings instead of the delimiter). It was fixed by #770 .\r\nThe fix will be available in the next release :)","embeddings":[0.1203237697,-0.1472451985,-0.1446630508,0.3973078132,0.0596904233,0.1563190073,0.7675094604,-0.1010695621,0.3270575106,0.0822042897,-0.0324487649,-0.0013563952,0.1088031158,-0.2761227489,-0.1162617058,0.1431807727,0.1974260807,0.1384890378,-0.0753118917,-0.0711717606,-0.2949311733,0.2251483202,-0.1458898336,-0.1837260425,-0.0212593339,0.3673403859,-0.0063925441,0.2704255283,0.1843010187,-0.3755665123,0.4452941418,0.204180479,0.2709940076,0.3557014763,-0.0001234261,-0.0847295299,0.0421750657,-0.2460636199,-0.1824997067,-0.2576674223,-0.3444088697,-0.3255288303,0.1659789383,-0.640501976,-0.0831279233,0.4193374217,0.0104722306,-0.3650924265,0.0418628417,0.289988786,0.1642397046,-0.2764177918,-0.4180568159,0.3066487312,0.2418942451,-0.1092139035,-0.2015373558,0.1077693254,-0.0016694436,0.2170140743,-0.0673587844,0.0988658071,-0.2695139349,0.3476351798,0.3233605027,0.2655831873,-0.1112889498,0.1209461093,0.2490369827,0.0957703143,0.5434871316,-0.0160900671,-0.1401772052,-0.3993289471,-0.1829320341,-0.9000109434,0.4474709034,0.1172697395,0.2949036956,0.2847469151,-0.1221222505,0.174939245,0.0017118829,-0.0270429906,-0.2510727048,0.0042830245,-0.3040367961,0.1181052923,-0.1376942694,0.0799042359,0.2939622998,-0.3672236502,-0.0892909467,0.0888362452,-0.2034035623,0.2997893393,-0.1713486165,0.052713871,-0.1950713694,0.051088877,0.1519305706,-0.0921810642,0.0524523109,0.0886828452,-0.0951909423,0.2707783282,0.134291783,0.1440228224,0.1321519762,0.0277069639,-0.36105299,-0.1131923497,-0.040470168,-0.0574660264,0.7582945228,-0.0240596309,-0.1336691678,-0.1446204334,-0.2497699261,0.1310025305,-0.0539615043,-0.1943701506,-0.310503155,0.1582721621,-0.0937401354,0.0074114646,-0.2572183013,0.1283313781,-0.1247400343,-0.1262661517,0.013780483,-0.2658877671,-0.2747731507,-0.0999717265,0.1676563174,-0.0354341939,0.1349232346,0.0876384303,-0.2041487396,-0.3977515399,-0.0401847735,-0.040093936,0.4941184521,0.1790121496,-0.0341455601,0.3948505223,0.2257198989,-0.7923141718,-0.1296625137,0.2836402655,-0.4482902586,-0.1099786162,0.0770924613,0.0487055443,-0.4173682034,-0.0573835187,-0.240827471,0.1323028803,0.4306553006,-0.2343614995,0.0749179125,-0.279586494,-0.3188855052,-0.4057266712,-0.1243101358,0.5804481506,-0.6152841449,-0.1354853958,-0.2932513654,0.0454964712,0.0796200335,0.2107527107,-0.0280726515,-0.3229223788,-0.4087453783,0.0544598401,0.1152574718,-0.1735103428,-0.5145955682,-0.0387589894,-0.1684631109,0.5907817483,0.3413653672,0.0757716,0.4065316021,-0.0589029714,0.1259220839,0.2793928385,0.2539744377,0.020392511,-0.1249643192,-0.062951766,0.3729768991,-0.0367324986,-0.0246832166,0.2701401114,0.3431218266,-0.4709174037,0.2977422178,0.1017794907,-0.1056778878,0.1167057529,-0.1657940447,0.1936220974,0.2169577777,-0.0856490135,-0.4177284539,0.1580454558,0.1372050792,-0.3520218134,-0.1161354929,-0.0502136871,-0.2620417178,-0.1570705622,-0.1790102124,-0.0666497722,0.0170435309,0.2542367578,0.022816889,-0.0022813804,0.0898884535,0.4797988236,0.006873731,0.1321927905,-0.2679718137,-0.2331027985,0.0207112674,-0.0352903008,-0.5559324026,-0.0199753065,0.2322299033,-0.1991242319,-0.2429983616,0.4130986035,0.212196663,0.3052728474,-0.0656284466,0.1739781946,0.02716472,0.2331711203,0.1174948886,-0.008733904,0.1551889032,-0.1189426407,0.1674257666,0.5678290725,0.0782879218,0.2709801793,-0.2086476386,-0.18897973,0.3382747769,-0.0512223542,0.0360137224,-0.3398999274,0.1569098234,0.0934806317,0.0617582798,0.2420657575,-0.1884282529,-0.0971823782,0.2958268225,-0.1709136963,-0.0693111792,-0.0697167441,-0.3157299459,0.0640438199,0.1098499596,0.4225114286,0.5408830643,0.0355893523,-0.0270284247,-0.1226385534,0.0877398625,-0.3477230966,0.2842814028,0.0567690432,-0.0782581866,0.2036445439,0.2016819417,-0.0129735712,-0.3612755537,0.2047887892,0.1962146461,-0.3082534075,-0.3972662985,0.2769383788,-0.4201709628,0.0169204082,-0.2480187863,-0.0532403365,-0.1965268105,-0.2177442312,-0.257578522,0.3369186819,0.1936834753,-0.1185887307,-0.3190780878,0.4337733686,-0.1746449322,-0.5059754252,0.1377483159,-0.0415904298,-0.2272444069,-0.051291991,-0.0573533028,-0.1890227199,-0.0249267872,-0.1329493076,0.0726283267,0.0894688666,-0.1508388519,-0.1577429622,0.1279167384,0.2775491476,-0.1862485558,0.0429017358,0.0478855073,-0.0051693525,-0.0678490251,0.2459173352,0.2164950073,-0.0260622688,0.1768840998,-0.0759266093,-0.0018829505,-0.2840174437,-0.3081313074,0.0247397479,0.0312394556,0.0404082946,0.1132464781,-0.001486378,-0.3966306746,-0.1957970709,0.1762916297,0.1216729656,-0.4113669097,-0.3755255044,0.4756684005,-0.2218316644,-0.0878486708,0.1451059282,-0.0337921306,-0.0669071376,-0.043367561,-0.3657758534,0.4310222864,-0.2626770437,0.2542522252,0.2346964031,-0.2392585725,0.2565965652,-0.0234662108,0.2168571353,-0.2194843441,-0.392771095,0.1954289824,0.1232841164,0.3100214899,0.1594948024,0.1339705437,-0.0279372744,0.7450904846,-0.1718225926,-0.0983114094,0.3850101531,0.0750990808,0.3412340581,-0.0181278549,-0.0712050423,-0.0378132388,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/778","title":"Unexpected behavior when loading cached csv file?","comments":"Thanks for the prompt reply and terribly sorry for the spam! \r\nLooking forward to the new release! ","body":"I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode=\"force_redownload\"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset.\r\n\r\nSmall snippet to reproduce the behavior:\r\n```python\r\nimport datasets\r\n\r\nwith open(\"dummy_data.csv\", \"w\") as file:\r\n    file.write(\"test,this;text\\n\")\r\n\r\nprint(datasets.load_dataset(\"csv\", data_files=\"dummy_data.csv\", split=\"train\").column_names)\r\n# [\"test\", \"this;text\"]\r\n\r\nprint(datasets.load_dataset(\"csv\", data_files=\"dummy_data.csv\", split=\"train\", delimiter=\";\").column_names)\r\n# still [\"test\", \"this;text\"]\r\n```\r\n\r\nBy the way, thanks a lot for this amazing library! :)","comment_length":17,"text":"Unexpected behavior when loading cached csv file? \n I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode=\"force_redownload\"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset.\r\n\r\nSmall snippet to reproduce the behavior:\r\n```python\r\nimport datasets\r\n\r\nwith open(\"dummy_data.csv\", \"w\") as file:\r\n    file.write(\"test,this;text\\n\")\r\n\r\nprint(datasets.load_dataset(\"csv\", data_files=\"dummy_data.csv\", split=\"train\").column_names)\r\n# [\"test\", \"this;text\"]\r\n\r\nprint(datasets.load_dataset(\"csv\", data_files=\"dummy_data.csv\", split=\"train\", delimiter=\";\").column_names)\r\n# still [\"test\", \"this;text\"]\r\n```\r\n\r\nBy the way, thanks a lot for this amazing library! :) \n Thanks for the prompt reply and terribly sorry for the spam! \r\nLooking forward to the new release! 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/773","title":"Adding CC-100: Monolingual Datasets from Web Crawl Data","comments":"These dataset files are no longer available.  https:\/\/data.statmt.org\/cc-100\/ files provided in this link are no longer available. Can anybody fix that issue?\r\n@abhishekkrthakur @yjernite ","body":"## Adding a Dataset\r\n- **Name:** CC-100: Monolingual Datasets from Web Crawl Data\r\n- **Description:** https:\/\/twitter.com\/alex_conneau\/status\/1321507120848625665\r\n- **Paper:** https:\/\/arxiv.org\/abs\/1911.02116\r\n- **Data:** http:\/\/data.statmt.org\/cc-100\/\r\n- **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how \"Wikipedia-like\" it is, hopefully helping avoid some of the worst parts of the common crawl.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":24,"text":"Adding CC-100: Monolingual Datasets from Web Crawl Data \n ## Adding a Dataset\r\n- **Name:** CC-100: Monolingual Datasets from Web Crawl Data\r\n- **Description:** https:\/\/twitter.com\/alex_conneau\/status\/1321507120848625665\r\n- **Paper:** https:\/\/arxiv.org\/abs\/1911.02116\r\n- **Data:** http:\/\/data.statmt.org\/cc-100\/\r\n- **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how \"Wikipedia-like\" it is, hopefully helping avoid some of the worst parts of the common crawl.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n These dataset files are no longer available.  https:\/\/data.statmt.org\/cc-100\/ files provided in this link are no longer available. Can anybody fix that issue?\r\n@abhishekkrthakur @yjernite 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/773","title":"Adding CC-100: Monolingual Datasets from Web Crawl Data","comments":"Hi ! Can you open an issue to report this problem ? This will help keep track of the fix :)","body":"## Adding a Dataset\r\n- **Name:** CC-100: Monolingual Datasets from Web Crawl Data\r\n- **Description:** https:\/\/twitter.com\/alex_conneau\/status\/1321507120848625665\r\n- **Paper:** https:\/\/arxiv.org\/abs\/1911.02116\r\n- **Data:** http:\/\/data.statmt.org\/cc-100\/\r\n- **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how \"Wikipedia-like\" it is, hopefully helping avoid some of the worst parts of the common crawl.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n","comment_length":21,"text":"Adding CC-100: Monolingual Datasets from Web Crawl Data \n ## Adding a Dataset\r\n- **Name:** CC-100: Monolingual Datasets from Web Crawl Data\r\n- **Description:** https:\/\/twitter.com\/alex_conneau\/status\/1321507120848625665\r\n- **Paper:** https:\/\/arxiv.org\/abs\/1911.02116\r\n- **Data:** http:\/\/data.statmt.org\/cc-100\/\r\n- **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how \"Wikipedia-like\" it is, hopefully helping avoid some of the worst parts of the common crawl.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/huggingface.co\/docs\/datasets\/share_dataset.html).\r\n \n Hi ! Can you open an issue to report this problem ? This will help keep track of the fix :)","embeddings":[0.1544764191,-0.331720382,-0.0355252139,0.1143348664,0.2201696336,-0.0529687889,0.0626521781,0.2461550236,0.2492931038,0.2353866994,-0.1515418589,-0.00162655,0.0217748899,0.342597872,0.1175908521,-0.049415525,-0.0165672563,-0.0350248963,-0.0640942603,-0.1299632937,-0.1167746261,0.1430407315,0.0728053078,-0.1182486117,-0.2972140014,-0.0989400148,-0.0724316612,0.1410130262,-0.1905927956,-0.2651711702,0.1144541949,0.1881307065,0.0445753783,0.374725014,-0.0000976586,-0.0619985983,0.4788953662,-0.065050602,-0.3430996239,-0.1394644529,-0.0265204944,-0.1203629524,0.0997158661,-0.05124842,-0.1890167147,0.148096174,-0.1711701155,-0.2212578058,0.3092139661,0.0740131587,0.3614701927,0.1639526337,-0.1256863773,-0.2227277309,-0.0686930418,0.0428945012,-0.267085135,0.4324517846,0.3458734155,0.050427366,0.0100415582,0.5570169091,0.0185088534,-0.1130130291,-0.0012547596,-0.2366215289,-0.0558189824,-0.2102650404,0.4367349148,0.3899993002,0.3366006017,-0.0959221274,-0.0438727438,-0.0799355879,0.0073724142,-0.2987826765,0.1824219078,0.2458913475,0.0026518928,0.2719819546,-0.1589512527,-0.3112980723,0.0888898447,0.1671681106,-0.0443404093,0.2347389311,-0.0934470668,-0.1299387962,0.2801299989,-0.1750249565,-0.1541330069,-0.0829594433,-0.0400883481,0.0193588622,-0.2392418087,-0.0613593347,0.2938683033,0.196993351,0.4528532624,0.0105885109,0.210651502,0.2379251868,-0.2296323031,0.1046387479,0.1180360392,0.1976435781,0.014494352,-0.0833213404,0.3579638004,0.0750383288,0.0060856324,-0.0561915189,0.1276345253,0.0473655239,-0.1408659518,-0.1276860684,0.1903383285,0.0388197377,-0.1963489801,0.2449398786,-0.1830483079,-0.0061255423,0.0023100008,0.3142811656,0.3077406585,0.115309611,0.0410344265,0.1868220568,-0.3224706352,-0.2680364847,-0.2916021049,0.0340238027,-0.2265994102,0.1067793369,0.1750347167,-0.1907668859,0.023225978,0.0528872684,0.1941225231,-0.1378630549,-0.1062109992,0.0872075036,-0.1279254556,0.1094638258,0.1117617786,0.2106419802,0.045273561,-0.4011797309,-0.0773494691,0.1075307876,-0.2536940575,-0.137210086,-0.4139119983,0.3120906949,0.0041394136,-0.069101125,-0.046219375,0.4707403183,0.1508844048,0.2313072532,0.0794980079,0.173027575,-0.4203982055,0.0277040638,0.0044684997,0.4736387134,-0.0805184394,-0.2188910395,-0.0935302824,0.2399051338,0.3127405345,0.4468441606,-0.1640070528,-0.0186538342,-0.1194109991,0.2767609656,-0.138249293,-0.1932726651,-0.2801223397,0.1645392329,-0.1911209375,0.0692133307,-0.035408821,-0.2007774264,-0.0299808793,0.1532711983,0.060169559,0.1694424897,0.142462343,0.2070702165,-0.4301701784,-0.126870051,0.303040266,-0.0743625388,-0.0227963924,-0.1919902712,0.0915429965,-0.3222585917,0.2378896475,-0.2990086675,0.2261382192,0.1435044855,0.2850301266,0.1037716195,0.2992837131,-0.0732242987,-0.2300023437,0.1574401259,0.0301185828,0.5092732906,-0.1853250712,-0.3524045348,-0.3688170612,-0.0623934828,-0.3548546433,-0.2307553887,0.2728114724,0.0363259576,-0.1927677393,0.2357797474,0.0804114193,0.1522642374,-0.158807829,-0.0946358219,-0.0983532369,0.2980970144,-0.3324442208,0.1344971061,0.0551556088,0.2945711315,0.1546881497,0.1238822266,-0.1341070533,0.1321913749,-0.3487960696,0.4126078784,0.4023534358,-0.1597121954,0.4559774697,-0.6156455278,0.0521805584,0.2166351676,0.2248237133,-0.1251291633,-0.085517101,0.0461259969,0.1275739223,0.0526224263,0.0393036529,0.0815496594,0.4952328205,-0.1259028316,-0.0285167787,-0.2938542366,0.2935526669,0.1635688245,0.2990805209,-0.0191755928,-0.4025942087,0.2592789531,0.2829764187,0.0254195668,0.3407048583,0.2989010513,-0.2099195123,-0.3161809742,0.0528427996,-0.0466612056,-0.114604488,0.1900642663,0.1370703131,0.0412654877,0.0804193914,-0.2977386117,0.1307420433,-0.0242474806,0.0049719461,0.2312336415,0.3575288653,-0.0911848098,-0.7218768597,-0.0259743202,-0.1040824056,0.022853164,-0.246636942,-0.0172213223,-0.3554459512,-0.2354427874,-0.2414975464,-0.1941313893,-0.3846525848,-0.2973053753,0.2084015161,-0.0763889402,-0.255135417,0.185566932,-0.0138984816,0.2664170563,0.0493561998,0.3118392229,-0.1573338807,-0.4071156085,-0.0744326711,0.1964383423,0.0885920376,-0.0249706935,0.2962003648,-0.1402249783,0.1040707156,-0.2028906196,-0.6040535569,0.2483885288,-0.1528865695,-0.0354620926,-0.1043260619,0.0530945174,-0.3024432957,-0.2278032601,0.2467625886,-0.0458334573,-0.1353027076,0.1722771674,-0.0616682805,0.0402957685,-0.2539198995,-0.2524586022,-0.2055338621,-0.3358537257,0.0212398451,0.0191929974,0.0583551899,0.3860044479,0.0435920879,-0.0426139943,0.0532755777,0.1464029998,-0.5650014281,-0.0730603114,0.1875834018,-0.4068383574,-0.3654285073,-0.1462317407,-0.093188338,0.1389493942,-0.0996960104,-0.1306019276,0.152779147,-0.2422309667,-0.0395450667,0.1642457992,0.0223565884,0.2108968943,0.0686408058,-0.2700365484,-0.1652226448,-0.0260805152,-0.0807283446,0.0939944834,0.2247562408,-0.0374530293,0.1365589201,0.2274735868,0.6573122144,0.1176539212,0.2458179891,0.4829921722,0.1512058824,0.4032595158,-0.2040252388,-0.410323292,0.1169382781,-0.1088200584,0.039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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/771","title":"Using `Dataset.map` with `n_proc>1` print multiple progress bars","comments":"Yes it allows to monitor the speed of each process. Currently each process takes care of one shard of the dataset.\r\n\r\nAt one point we can consider using streaming batches to a pool of processes instead of sharding the dataset in `num_proc` parts. At that point it will be easy to use only one progress bar","body":"When using `Dataset.map` with `n_proc > 1`, only one of the processes should print a progress bar (to make the output readable). Right now, `n_proc` progress bars are printed.","comment_length":56,"text":"Using `Dataset.map` with `n_proc>1` print multiple progress bars \n When using `Dataset.map` with `n_proc > 1`, only one of the processes should print a progress bar (to make the output readable). Right now, `n_proc` progress bars are printed. \n Yes it allows to monitor the speed of each process. Currently each process takes care of one shard of the dataset.\r\n\r\nAt one point we can consider using streaming batches to a pool of processes instead of sharding the dataset in `num_proc` parts. At that point it will be easy to use only one progress 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/769","title":"How to choose proper download_mode in function load_dataset?","comments":"`download_mode=datasets.GenerateMode.FORCE_REDOWNLOAD` should work.\r\nThis makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing","body":"Hi, I am a beginner to datasets and I try to use datasets to load my csv file.\r\nmy csv file looks like this\r\n\r\n``` \r\ntext,label\r\n\"Effective but too-tepid biopic\",3\r\n\"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .\",4\r\n\"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .\",5\r\n```\r\n\r\nFirst I try to use this command to load my csv file .  \r\n\r\n``` python\r\ndataset=load_dataset('csv', data_files=['sst_test.csv'])\r\n```\r\n\r\nIt seems good, but when i try to overwrite the convert_options to convert  'label' columns from int64 to float32 like this.\r\n\r\n``` python\r\nimport pyarrow as pa\r\nfrom pyarrow import csv\r\nread_options = csv.ReadOptions(block_size=1024*1024)\r\nparse_options = csv.ParseOptions()\r\nconvert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,\r\n                       parse_options=parse_options, convert_options=convert_options)\r\n```\r\n\r\nIt keeps the same:\r\n\r\n```shell\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)\r\n```\r\n\r\nI think this issue is caused by the parameter \"download_mode\" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.\r\n\r\nIs it a bug? How to choose proper download_mode to avoid this issue?\r\n","comment_length":17,"text":"How to choose proper download_mode in function load_dataset? \n Hi, I am a beginner to datasets and I try to use datasets to load my csv file.\r\nmy csv file looks like this\r\n\r\n``` \r\ntext,label\r\n\"Effective but too-tepid biopic\",3\r\n\"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .\",4\r\n\"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .\",5\r\n```\r\n\r\nFirst I try to use this command to load my csv file .  \r\n\r\n``` python\r\ndataset=load_dataset('csv', data_files=['sst_test.csv'])\r\n```\r\n\r\nIt seems good, but when i try to overwrite the convert_options to convert  'label' columns from int64 to float32 like this.\r\n\r\n``` python\r\nimport pyarrow as pa\r\nfrom pyarrow import csv\r\nread_options = csv.ReadOptions(block_size=1024*1024)\r\nparse_options = csv.ParseOptions()\r\nconvert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,\r\n                       parse_options=parse_options, convert_options=convert_options)\r\n```\r\n\r\nIt keeps the same:\r\n\r\n```shell\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)\r\n```\r\n\r\nI think this issue is caused by the parameter \"download_mode\" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.\r\n\r\nIs it a bug? How to choose proper download_mode to avoid this issue?\r\n \n `download_mode=datasets.GenerateMode.FORCE_REDOWNLOAD` should work.\r\nThis makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/769","title":"How to choose proper download_mode in function load_dataset?","comments":"Indeed you should use `features` in this case. \r\n```python\r\nfeatures = Features({'text': Value('string'), 'label': Value('float32')})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], features=features)\r\n```\r\nNote that because of an issue with the caching when you change the features (see #750 ) you still need to specify the `FORCE_REDOWNLOAD ` flag. I'm working on a fix for this one","body":"Hi, I am a beginner to datasets and I try to use datasets to load my csv file.\r\nmy csv file looks like this\r\n\r\n``` \r\ntext,label\r\n\"Effective but too-tepid biopic\",3\r\n\"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .\",4\r\n\"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .\",5\r\n```\r\n\r\nFirst I try to use this command to load my csv file .  \r\n\r\n``` python\r\ndataset=load_dataset('csv', data_files=['sst_test.csv'])\r\n```\r\n\r\nIt seems good, but when i try to overwrite the convert_options to convert  'label' columns from int64 to float32 like this.\r\n\r\n``` python\r\nimport pyarrow as pa\r\nfrom pyarrow import csv\r\nread_options = csv.ReadOptions(block_size=1024*1024)\r\nparse_options = csv.ParseOptions()\r\nconvert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,\r\n                       parse_options=parse_options, convert_options=convert_options)\r\n```\r\n\r\nIt keeps the same:\r\n\r\n```shell\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)\r\n```\r\n\r\nI think this issue is caused by the parameter \"download_mode\" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.\r\n\r\nIs it a bug? How to choose proper download_mode to avoid this issue?\r\n","comment_length":55,"text":"How to choose proper download_mode in function load_dataset? \n Hi, I am a beginner to datasets and I try to use datasets to load my csv file.\r\nmy csv file looks like this\r\n\r\n``` \r\ntext,label\r\n\"Effective but too-tepid biopic\",3\r\n\"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .\",4\r\n\"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .\",5\r\n```\r\n\r\nFirst I try to use this command to load my csv file .  \r\n\r\n``` python\r\ndataset=load_dataset('csv', data_files=['sst_test.csv'])\r\n```\r\n\r\nIt seems good, but when i try to overwrite the convert_options to convert  'label' columns from int64 to float32 like this.\r\n\r\n``` python\r\nimport pyarrow as pa\r\nfrom pyarrow import csv\r\nread_options = csv.ReadOptions(block_size=1024*1024)\r\nparse_options = csv.ParseOptions()\r\nconvert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,\r\n                       parse_options=parse_options, convert_options=convert_options)\r\n```\r\n\r\nIt keeps the same:\r\n\r\n```shell\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)\r\n```\r\n\r\nI think this issue is caused by the parameter \"download_mode\" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.\r\n\r\nIs it a bug? How to choose proper download_mode to avoid this issue?\r\n \n Indeed you should use `features` in this case. \r\n```python\r\nfeatures = Features({'text': Value('string'), 'label': Value('float32')})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], features=features)\r\n```\r\nNote that because of an issue with the caching when you change the features (see #750 ) you still need to specify the `FORCE_REDOWNLOAD ` flag. I'm working on a fix for this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/769","title":"How to choose proper download_mode in function load_dataset?","comments":"https:\/\/github.com\/huggingface\/datasets\/issues\/769#issuecomment-717837832\r\n> This makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing\r\n\r\n@lhoestq do you still think we should rename it?\r\n","body":"Hi, I am a beginner to datasets and I try to use datasets to load my csv file.\r\nmy csv file looks like this\r\n\r\n``` \r\ntext,label\r\n\"Effective but too-tepid biopic\",3\r\n\"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .\",4\r\n\"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .\",5\r\n```\r\n\r\nFirst I try to use this command to load my csv file .  \r\n\r\n``` python\r\ndataset=load_dataset('csv', data_files=['sst_test.csv'])\r\n```\r\n\r\nIt seems good, but when i try to overwrite the convert_options to convert  'label' columns from int64 to float32 like this.\r\n\r\n``` python\r\nimport pyarrow as pa\r\nfrom pyarrow import csv\r\nread_options = csv.ReadOptions(block_size=1024*1024)\r\nparse_options = csv.ParseOptions()\r\nconvert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,\r\n                       parse_options=parse_options, convert_options=convert_options)\r\n```\r\n\r\nIt keeps the same:\r\n\r\n```shell\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)\r\n```\r\n\r\nI think this issue is caused by the parameter \"download_mode\" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.\r\n\r\nIs it a bug? How to choose proper download_mode to avoid this issue?\r\n","comment_length":25,"text":"How to choose proper download_mode in function load_dataset? \n Hi, I am a beginner to datasets and I try to use datasets to load my csv file.\r\nmy csv file looks like this\r\n\r\n``` \r\ntext,label\r\n\"Effective but too-tepid biopic\",3\r\n\"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .\",4\r\n\"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .\",5\r\n```\r\n\r\nFirst I try to use this command to load my csv file .  \r\n\r\n``` python\r\ndataset=load_dataset('csv', data_files=['sst_test.csv'])\r\n```\r\n\r\nIt seems good, but when i try to overwrite the convert_options to convert  'label' columns from int64 to float32 like this.\r\n\r\n``` python\r\nimport pyarrow as pa\r\nfrom pyarrow import csv\r\nread_options = csv.ReadOptions(block_size=1024*1024)\r\nparse_options = csv.ParseOptions()\r\nconvert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,\r\n                       parse_options=parse_options, convert_options=convert_options)\r\n```\r\n\r\nIt keeps the same:\r\n\r\n```shell\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)\r\n```\r\n\r\nI think this issue is caused by the parameter \"download_mode\" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.\r\n\r\nIs it a bug? How to choose proper download_mode to avoid this issue?\r\n \n https:\/\/github.com\/huggingface\/datasets\/issues\/769#issuecomment-717837832\r\n> This makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing\r\n\r\n@lhoestq do you still think we should rename it?\r\n","embeddings":[-0.2798766196,-0.197881192,-0.0330595225,0.2058907449,0.3862631619,-0.0532018282,0.3852045536,0.0819179937,0.2267123461,-0.0746655837,-0.2240721136,0.1203780994,0.2853154242,0.1946470141,-0.031751249,-0.0357661806,0.1483395696,0.0477411747,-0.3576507568,0.0579626076,-0.4304966033,0.0155731253,-0.2335753143,-0.222875759,0.1358538419,0.0957420617,0.1488026828,0.1619443893,-0.142266795,-0.3561889827,0.4395989478,0.0853912979,0.4502349198,0.0591915585,-0.0001246051,0.0087077618,0.2521946728,-0.34062621,-0.1852662265,-0.3258290887,-0.1933569163,-0.2350030541,0.2867307961,-0.3290708661,0.0063424702,-0.140569821,-0.0195055287,-0.1120356023,0.1878169775,0.3316867352,0.1174970791,0.1792927831,-0.0977366343,0.1619236767,0.1509307623,0.0706969053,-0.04567305,0.4510048926,0.249895528,0.2817792594,0.1527323574,0.0376645811,-0.2944643795,0.1153796613,0.4248175025,0.0952471122,0.4449944198,-0.2206556648,0.0240305029,-0.0046069603,0.7648963332,-0.1914554238,-0.1510395259,-0.2834732533,0.0180371106,-0.36372298,0.221909076,0.039506454,0.0190862175,0.2825434506,-0.3408091962,-0.0291572195,-0.0210831761,0.3526394069,-0.0439543836,-0.0532963164,-0.2429080307,0.3271569014,0.311626792,0.0589103661,0.1056567803,-0.4572940469,0.1735612452,0.0586836487,-0.4151707292,0.124826327,-0.1340853274,0.2951500714,0.239169687,0.2448859364,-0.1533808857,-0.0919527188,-0.1065980494,0.0483951792,0.3560338616,0.1230935603,0.1135601997,-0.306673497,0.0083554462,0.1074333042,0.2101042271,0.0549981855,-0.0106370589,-0.2197756171,0.4889347553,-0.0421051718,0.183354333,-0.1487325728,-0.2604842782,-0.1201855168,-0.0979047343,-0.0321298316,-0.0049804547,0.3651891053,-0.0390867032,0.1556200087,-0.1059520617,0.3933193088,-0.2662840784,-0.1034472957,-0.0409734324,-0.185214743,-0.1128127128,-0.1452270597,0.3013894856,-0.3332737684,0.2803798616,0.3818292022,-0.1894675493,-0.1511897594,-0.0680520982,-0.0948725715,-0.0116732595,0.4627521038,0.1288736314,0.2757651806,0.2283206135,-0.1082012653,-0.0296051949,0.5129961967,-0.3226845562,-0.3870906532,0.0265755337,0.0711318329,-0.3539587855,-0.0015816399,-0.4478651583,0.1447370648,0.0387932956,-0.2455806434,0.100901641,-0.321157068,-0.0744450316,-0.3019759059,0.0069100126,0.6977206469,-0.4630517066,-0.0244423877,-0.1951680332,-0.2860996127,0.3574333787,0.1216734797,-0.3739616275,-0.0325677656,-0.2235721499,-0.0849588364,0.381148994,-0.2314783633,-0.5355520844,0.250411272,0.0280001238,0.103340745,0.305683285,0.2288891077,0.2014803439,-0.156583041,-0.2518615425,0.2928071618,0.0946625173,0.154952541,-0.0883871317,-0.1852508783,0.26674366,0.0442553759,-0.2757135928,0.1762919277,-0.0571449809,-0.2143395543,0.1828951538,0.0625466332,-0.1157342866,0.0514296256,0.1139402166,0.1710984409,-0.1588857323,0.0615908168,-0.7496747375,-0.0414651036,0.2590666413,-0.2823925912,-0.2711191475,-0.1315847337,-0.4229800701,-0.1340055168,-0.2985756397,0.0937905535,-0.0454473346,0.1822706163,0.2255943418,0.0006144412,-0.0911719427,0.2795013487,-0.202673316,0.2758002877,-0.0866622031,0.2260611206,0.1173140109,-0.0509412289,0.1398920566,-0.258785367,0.338754952,-0.2362071723,-0.167184338,0.5111985207,0.1960523576,0.2534099221,-0.5042049885,0.0759891272,0.1977402866,0.2034174055,-0.026535457,-0.0884581506,0.2453965098,0.0594476834,-0.3105703294,0.1903271526,-0.2030623108,0.1081635207,-0.134680599,-0.1777808964,0.3459452093,0.0982834324,-0.1056152135,-0.1349871755,-0.0823600516,0.0695214197,0.254296571,0.2243035138,-0.2490677238,0.0909964293,0.3637663126,-0.2461998165,0.1216967553,0.1667250395,-0.0227196235,-0.2220357507,0.3906045556,0.0392669961,0.5417053103,0.0809040815,-0.0352124982,-0.088793464,-0.1267450005,-0.2366363704,0.4187597334,-0.0936526805,0.3716505468,0.1068071425,0.0432007834,-0.3191596866,-0.3765233755,0.202642262,0.2935577035,0.2588933408,-0.4908259213,0.0454124399,-0.366132468,-0.2729247808,-0.1284401715,0.0704621375,0.1323935539,-0.2257123291,-0.2988414764,0.3510197699,0.069026731,0.1826914102,-0.2203528881,-0.15423958,0.1302873492,-0.7033341527,0.0349128731,0.0517541952,-0.322653681,-0.0889523253,0.2682000697,-0.1609915048,0.1523504555,-0.2811252475,-0.0360434875,-0.3939154446,0.0524652749,0.0081823459,0.0208721682,0.2632745802,0.0224778987,0.4223606586,-0.0321840644,0.173306793,0.2474169135,0.008925274,0.1123766229,0.2064726353,-0.0879385918,0.1196221858,-0.1147773862,-0.6103790402,-0.3059312105,-0.1651434898,-0.0890946388,0.0204335768,-0.0532194115,0.1091903672,0.2968273461,-0.1718129665,0.0366906747,0.1219293624,-0.3866273761,-0.1621594727,0.5011813641,0.1229358315,-0.2017390281,-0.0746706426,0.0295099039,-0.0029822998,0.2423738837,-0.2988707125,-0.3291435242,-0.1323128343,0.4280744791,-0.0478325561,0.328389734,0.3014459312,-0.0700722039,0.1480008811,0.0011775111,-0.1766247153,0.0465115421,0.1394319385,0.1297917813,0.2723116577,0.4223999679,-0.175067395,0.4972099066,0.1787394434,-0.0587198921,0.3751194775,-0.161529839,0.3235297501,0.1146289036,-0.120274134,-0.0625772998,-0.1784730405,-0.0005942145,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/769","title":"How to choose proper download_mode in function load_dataset?","comments":"It's no big deal, but since it can be confusing to users I think it's worth renaming it, and deprecate `GenerateMode` until `datasets` 2.0 at least. IMO it's confusing to have `download_mode=GenerateMode.something`","body":"Hi, I am a beginner to datasets and I try to use datasets to load my csv file.\r\nmy csv file looks like this\r\n\r\n``` \r\ntext,label\r\n\"Effective but too-tepid biopic\",3\r\n\"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .\",4\r\n\"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .\",5\r\n```\r\n\r\nFirst I try to use this command to load my csv file .  \r\n\r\n``` python\r\ndataset=load_dataset('csv', data_files=['sst_test.csv'])\r\n```\r\n\r\nIt seems good, but when i try to overwrite the convert_options to convert  'label' columns from int64 to float32 like this.\r\n\r\n``` python\r\nimport pyarrow as pa\r\nfrom pyarrow import csv\r\nread_options = csv.ReadOptions(block_size=1024*1024)\r\nparse_options = csv.ParseOptions()\r\nconvert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,\r\n                       parse_options=parse_options, convert_options=convert_options)\r\n```\r\n\r\nIt keeps the same:\r\n\r\n```shell\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)\r\n```\r\n\r\nI think this issue is caused by the parameter \"download_mode\" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.\r\n\r\nIs it a bug? How to choose proper download_mode to avoid this issue?\r\n","comment_length":32,"text":"How to choose proper download_mode in function load_dataset? \n Hi, I am a beginner to datasets and I try to use datasets to load my csv file.\r\nmy csv file looks like this\r\n\r\n``` \r\ntext,label\r\n\"Effective but too-tepid biopic\",3\r\n\"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .\",4\r\n\"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .\",5\r\n```\r\n\r\nFirst I try to use this command to load my csv file .  \r\n\r\n``` python\r\ndataset=load_dataset('csv', data_files=['sst_test.csv'])\r\n```\r\n\r\nIt seems good, but when i try to overwrite the convert_options to convert  'label' columns from int64 to float32 like this.\r\n\r\n``` python\r\nimport pyarrow as pa\r\nfrom pyarrow import csv\r\nread_options = csv.ReadOptions(block_size=1024*1024)\r\nparse_options = csv.ParseOptions()\r\nconvert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})\r\ndataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,\r\n                       parse_options=parse_options, convert_options=convert_options)\r\n```\r\n\r\nIt keeps the same:\r\n\r\n```shell\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)\r\n```\r\n\r\nI think this issue is caused by the parameter \"download_mode\" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.\r\n\r\nIs it a bug? How to choose proper download_mode to avoid this issue?\r\n \n It's no big deal, but since it can be confusing to users I think it's worth renaming it, and deprecate `GenerateMode` until `datasets` 2.0 at least. IMO it's confusing to have 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/768","title":"Add a `lazy_map` method to `Dataset` and `DatasetDict`","comments":"This is cool! I think some aspects to think about and decide in terms of API are:\r\n- do we allow several methods (chained i guess)\r\n- how do we inspect the currently set method(s)\r\n- how do we control\/reset them","body":"The library is great, but it would be even more awesome with a `lazy_map` method implemented on `Dataset` and `DatasetDict`. This would apply a function on a give item but when the item is requested. Two use cases:\r\n\r\n1. load image on the fly\r\n2. apply a random function and get different outputs at each epoch (like data augmentation or randomly masking a part of a sentence for BERT-like objectives).","comment_length":41,"text":"Add a `lazy_map` method to `Dataset` and `DatasetDict` \n The library is great, but it would be even more awesome with a `lazy_map` method implemented on `Dataset` and `DatasetDict`. This would apply a function on a give item but when the item is requested. Two use cases:\r\n\r\n1. load image on the fly\r\n2. apply a random function and get different outputs at each epoch (like data augmentation or randomly masking a part of a sentence for BERT-like objectives). \n This is cool! I think some aspects to think about and decide in terms of API are:\r\n- do we allow several methods (chained i guess)\r\n- how do we inspect the currently set method(s)\r\n- how do we control\/reset 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/767","title":"Add option for named splits when using ds.train_test_split","comments":"Yes definitely we should give more flexibility to control the name of the splits outputted by `train_test_split`.\r\n\r\nRelated is the very interesting feedback from @bramvanroy on how we should improve this method: https:\/\/discuss.huggingface.co\/t\/how-to-split-main-dataset-into-train-dev-test-as-datasetdict\/1090\/5\r\n\r\nAnd in particular that it should advantageously be able to split in 3 splits as well instead of just 2 like we copied from sklearn.","body":"### Feature Request \ud83d\ude80 \r\n\r\nCan we add a way to name your splits when using the `.train_test_split` function?\r\n\r\nIn almost every use case I've come across, I have a `train` and a `test` split in my `DatasetDict`, and I want to create a `validation` split. Therefore, its kinda useless to get a `test` split back from `train_test_split`, as it'll just overwrite my real `test` split that I intended to keep.\r\n\r\n### Workaround\r\n\r\nthis is my hack for dealin with this, for now :slightly_smiling_face:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\u200b\r\n\u200b\r\nds = load_dataset('imdb')\r\nds['train'], ds['validation'] = ds['train'].train_test_split(.1).values()\r\n```\r\n","comment_length":58,"text":"Add option for named splits when using ds.train_test_split \n ### Feature Request \ud83d\ude80 \r\n\r\nCan we add a way to name your splits when using the `.train_test_split` function?\r\n\r\nIn almost every use case I've come across, I have a `train` and a `test` split in my `DatasetDict`, and I want to create a `validation` split. Therefore, its kinda useless to get a `test` split back from `train_test_split`, as it'll just overwrite my real `test` split that I intended to keep.\r\n\r\n### Workaround\r\n\r\nthis is my hack for dealin with this, for now :slightly_smiling_face:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\u200b\r\n\u200b\r\nds = load_dataset('imdb')\r\nds['train'], ds['validation'] = ds['train'].train_test_split(.1).values()\r\n```\r\n \n Yes definitely we should give more flexibility to control the name of the splits outputted by `train_test_split`.\r\n\r\nRelated is the very interesting feedback from @bramvanroy on how we should improve this method: https:\/\/discuss.huggingface.co\/t\/how-to-split-main-dataset-into-train-dev-test-as-datasetdict\/1090\/5\r\n\r\nAnd in particular that it should advantageously be able to split in 3 splits as well instead of just 2 like we copied from sklearn.","embeddings":[0.0725738257,-0.0852903724,-0.1180252507,-0.045865681,0.15466851,0.2035396695,0.6252444386,0.1981317103,0.1147508919,0.3940248191,0.0419959575,0.3692273498,-0.2435895652,0.2782408893,0.191498816,-0.4520206153,0.0777252316,-0.2225460112,0.2795640826,0.1441616565,-0.1701510102,0.0290893447,-0.10989476,0.2488474548,-0.4482490122,0.0521879084,-0.0278837271,-0.0407919213,0.0729061589,-0.4732066393,0.0694575384,0.5114654303,-0.0923280418,0.4597217441,-0.0001201279,-0.2272785604,0.3921885788,-0.1634059697,-0.3604239523,-0.2719935775,-0.2721001208,-0.0508755185,0.191066891,-0.3920917809,-0.0140885385,-0.0546336323,-0.0692299753,-0.2750483453,0.1217140108,0.2252164781,0.1275219619,-0.0359692238,-0.1207832396,-0.0023297553,0.0822661072,0.4533889294,-0.3355133832,0.1411752254,0.0616262667,-0.0589847378,0.2678494453,0.035397239,0.0078432718,-0.17598553,-0.0337530002,0.068655543,-0.1521918923,-0.2227631807,-0.0949150696,0.400662899,0.3223872483,-0.3291953206,-0.407809943,-0.4338805974,0.1580452025,-0.2891124785,-0.0043494841,-0.0610060431,0.00684661,0.2569349408,-0.1519077867,-0.0383322202,-0.1391979456,0.1504184306,0.167988494,0.2730539441,0.1757129133,0.1539538652,0.4608617723,0.0318741426,0.2671248317,-0.275901258,0.1429983675,0.0135914311,-0.1820885837,-0.2353997082,-0.3166776597,-0.4003146589,-0.0385823026,0.2578307092,-0.089904584,0.0029252549,-0.1185064763,-0.036200352,0.1213353276,0.2042189091,0.1922021061,0.7993481755,0.1940679997,0.1425664276,-0.0509872809,-0.0838256404,-0.0732704252,-0.2198163271,0.1319383532,0.11735183,-0.1480457783,0.0972706452,-0.2157282531,-0.1622211337,-0.3056786954,-0.160752967,0.2621247172,0.2394130081,0.0350664519,0.0869063213,-0.0795196444,0.0568442196,0.1468181014,-0.0548607074,-0.1057783291,0.0489816442,-0.252707839,0.1430670917,0.2551349103,-0.0202106182,0.0251753498,-0.1186656654,0.2427727133,-0.2523259223,-0.0712954998,-0.0101479227,0.3710731268,0.1764828712,-0.2565138936,-0.0895712972,-0.2148702592,-0.0413802266,-0.1269136667,0.0241174344,-0.183362633,-0.4484345317,0.0184313636,0.0609599091,-0.1824507713,0.0002761522,-0.0470030382,0.2932840586,0.2690389752,0.0702133179,0.1655514985,0.034612231,-0.1679075807,-0.0667347088,0.389613241,0.087421909,-0.3150944114,-0.1640779525,-0.337852329,0.0888542384,0.0349265188,0.003133609,-0.0557395108,0.2230695784,-0.0702297017,0.240098834,0.3350121081,-0.1714231819,0.0440316312,0.0458580256,0.0866497979,-0.0092960801,0.2888076603,-0.155959785,0.1066175625,-0.03297792,-0.1210659072,0.1750892252,-0.324523747,-0.197215274,0.0226151776,-0.0928212106,0.3992806375,0.1736496389,0.0248096939,-0.1548391134,-0.2240208536,0.2639464438,0.4356185496,-0.0077086515,0.0810170174,-0.2269656956,0.3232060671,0.4250838459,0.126909703,-0.43144539,-0.1730945557,0.1214078814,0.1779445559,-0.1334395111,-0.1241060197,-0.3705472946,-0.3022031486,-0.1747015566,-0.0957440734,0.002574404,0.0565087125,-0.1349787116,0.1169960871,-0.0912767351,-0.3370675743,0.0810627416,-0.4117369652,0.2173947245,-0.0233671255,0.3630812764,0.3540457487,-0.0059769913,-0.4311004579,0.4017511606,0.1534221172,-0.1506371498,-0.1249693185,0.4914858341,0.5371119976,-0.1535212845,-0.1335526854,-0.0277760606,0.0029967285,-0.1223408952,0.0540128276,0.2383948565,-0.1113675386,-0.1098863631,-0.1630325466,0.3751423955,-0.4445958734,0.3816928566,-0.1641297787,0.0651184618,-0.1421230435,-0.224709183,-0.4564577341,-0.3743859529,-0.284876734,-0.5980314016,0.1653639525,-0.3122050464,-0.2530174553,-0.2069655508,0.3411161005,-0.1814827323,0.0084395213,-0.1815954745,0.2201620042,0.187446177,-0.0904239565,0.4492094219,0.3208989501,0.1303464621,-0.0994146317,-0.1213775352,-0.0566756092,-0.0879041404,0.1856535524,0.3309950531,-0.0504077822,0.1269751042,-0.2277490497,-0.2841596305,0.0142253274,-0.1667642742,0.2953528166,-0.2170961648,-0.2728697956,-0.0041689798,-0.314860642,-0.1530627459,-0.344350189,-0.1824578047,-0.1817972213,-0.3793221414,0.0949562863,0.095782958,-0.3661894202,0.3272184134,-0.093679972,0.1565567106,-0.3529231548,-0.3120750487,0.0425678864,-0.19191809,0.0191946402,0.0373649485,-0.0636336803,0.0886320546,0.4473897219,0.0682875365,-0.2181128263,-0.2077765465,-0.218667239,-0.088274762,-0.1306657046,0.0344410539,0.3023154438,-0.057647936,0.2030570805,-0.3977272511,-0.018455863,0.1599649042,-0.0437739454,-0.3815746605,0.392447859,0.1830078065,-0.2294371575,-0.3533361852,-0.326146394,-0.1929230988,0.4223927259,-0.5255027413,-0.0241620224,0.0357887708,-0.2785822451,0.1127695441,0.1251918674,-0.1200393364,-0.1015919223,0.3104766309,0.0435890779,-0.2384750396,0.2072957605,0.1077677086,-0.2218754888,0.0217224658,-0.3567278981,0.0503735952,0.0290435776,0.0944005325,0.277043432,0.1614022702,-0.0828790963,0.0974330902,0.0858204812,0.1860565543,-0.0896666199,-0.2755860984,0.1496620476,0.2805896997,0.2331838012,0.2243523002,0.2292753458,-0.063043341,0.5140989423,0.20800744,0.0556261316,-0.104147993,-0.0233318843,-0.1820419431,0.00504759,-0.4781915545,-0.0282481294,-0.1414563209,-0.304308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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/761","title":"Downloaded datasets are not usable offline","comments":"Yes currently you need an internet connection because the lib tries to check for the etag of the dataset script online to see if you don't have it locally already.\r\n\r\nIf we add a way to store the etag\/hash locally after the first download, it would allow users to first download the dataset with an internet connection, and still have it working without an internet connection.\r\n\r\nI'll let you know when we add this feature.","body":"I've been trying to use the IMDB dataset offline, but after downloading it and turning off the internet it still raises  an error from the ```requests``` library trying to reach for the online dataset.\r\nIs this the intended behavior ?\r\n(Sorry, I wrote the the first version of this issue while still on nlp 0.3.0).","comment_length":75,"text":"Downloaded datasets are not usable offline \n I've been trying to use the IMDB dataset offline, but after downloading it and turning off the internet it still raises  an error from the ```requests``` library trying to reach for the online dataset.\r\nIs this the intended behavior ?\r\n(Sorry, I wrote the the first version of this issue while still on nlp 0.3.0). \n Yes currently you need an internet connection because the lib tries to check for the etag of the dataset script online to see if you don't have it locally already.\r\n\r\nIf we add a way to store the etag\/hash locally after the first download, it would allow users to first download the dataset with an internet connection, and still have it working without an internet connection.\r\n\r\nI'll let you know when we add this feature.","embeddings":[-0.2098324746,0.2670384049,-0.0017677535,0.1898869723,-0.0595396459,-0.0748802498,0.3903472424,-0.0172846429,0.1707941145,-0.0225018989,0.1237998679,0.0126209315,-0.0386806279,0.0756761879,0.0625319555,0.1871642321,-0.1557508409,0.2216936052,-0.1252215505,-0.0784502327,-0.1236611605,0.1148991734,-0.4275744855,-0.0501417667,-0.1870036274,-0.3171657324,-0.0034036417,-0.0439859852,-0.1552307904,-0.2724395096,0.4218712747,0.0848470479,0.2210721076,-0.1517106444,-0.0001220542,-0.2454779446,0.4161519706,-0.1439789981,-0.4349360764,-0.3407869637,-0.275238812,-0.130433321,0.2988806665,-0.5066426992,0.0441146605,0.1309671402,0.3384900391,-0.4647698104,-0.1303227693,0.1508988738,0.0914961025,0.1822045296,-0.0067803198,0.0909337997,0.4891792238,0.0798255131,-0.2148955464,0.1665281504,0.1152893752,0.0191016942,0.1782421768,0.0184437726,-0.1545058042,0.0909674913,0.3861879408,0.1950401664,-0.1535570621,-0.264256984,-0.0379818715,0.3704021275,0.5538470745,-0.1437790245,-0.2343484908,-0.3610733449,0.0763947517,-0.2982944846,0.1947353929,0.2515519261,-0.0451297611,0.1533563435,-0.3593324721,-0.4431596398,-0.1371422261,0.5251787901,0.0860984623,0.2136146724,-0.1137967482,0.1065247357,0.0008924592,0.0111899041,0.0522401333,-0.3040782809,0.1637428999,-0.095231548,0.2223513871,-0.0641377941,0.1375614554,0.4724583626,-0.1811282486,0.3028615415,0.1037672609,-0.0921731666,-0.1592959911,0.2099303901,0.5247608423,0.3245604634,0.3381833136,-0.2607774436,0.5273810625,0.2759962082,-0.0852581114,-0.1607044637,0.1783451736,0.1802045852,0.0469965003,-0.1126272827,0.283922106,-0.3313288689,-0.1025822237,-0.1161459088,-0.0363384522,0.0267836638,0.2100475878,0.0840593576,-0.2664492726,-0.1593923569,0.0865489244,0.2714380324,-0.182312429,-0.4581656456,0.0118206562,-0.2709528208,-0.1404256076,-0.0769108534,0.4374878705,0.0036356177,0.3053943515,-0.2534536123,-0.09133441,-0.0206222814,0.4746436775,-0.2036610395,0.3540556431,0.2140980214,0.3410397768,0.0610174984,-0.0455781445,-0.0827937499,-0.0744462088,0.2547931671,-0.0524521917,-0.4573020637,0.0692087784,0.0919585526,-0.4702423215,-0.3144265413,-0.2976517379,-0.0881111026,0.0970745608,-0.5006312132,0.0282156132,0.0839831457,-0.1873994172,0.0166355651,0.024963472,0.4703622162,-0.3674836159,-0.065719232,-0.4391421974,-0.1759605259,-0.0243706182,0.2671213746,-0.425421983,0.3243421316,-0.3204441965,0.1207296401,0.6827148199,-0.1885617673,-0.6827122569,0.3701702356,-0.0995323807,-0.1788527668,-0.0034115152,0.3794584572,0.4256163836,-0.0089578917,-0.4156363606,0.526925683,0.0929822326,0.0212123487,-0.2327948958,-0.1643290818,0.2605100572,-0.0170766078,0.0112660453,0.3139621913,0.3630164564,0.1670653373,0.1006167307,0.3052434623,0.3341993093,-0.1018294767,0.0742243305,0.3801196814,-0.1144180223,-0.0251530949,-0.496630609,0.0201001372,-0.1487763673,0.0987895653,-0.5461813211,-0.4241181016,-0.0341123566,-0.2377707958,-0.0445402674,0.2871575952,-0.0388877802,0.1060238928,0.0273684487,0.1294902116,-0.3869740367,-0.0304966494,-0.0898807198,-0.0513926893,-0.5284494758,0.2566904724,-0.1686318219,0.0119885337,-0.1151870266,-0.08566311,-0.0241342541,-0.1413842738,-0.1097180918,0.3671101928,-0.2565329671,0.0871693268,0.1891967803,0.2710218728,0.2855142653,-0.3220532537,0.4973919094,-0.0081949756,0.2154596895,-0.0381353423,-0.3263987005,0.4724673033,-0.0683582947,0.2161989808,0.1441254616,0.0053595509,0.3300654888,-0.1547808796,-0.251830101,-0.582813859,0.0283664428,0.2074073851,0.0082336636,-0.0725473017,-0.1747859716,0.1800655276,0.5578531027,0.2726838887,0.2287419587,0.0783198699,0.0125849415,-0.2767373323,0.0853907838,0.1471385062,0.318325758,0.1985955089,0.3152204454,0.1368162483,-0.0074230353,-0.4224086106,-0.0591056459,0.08879558,-0.0197892617,-0.0263547413,0.0225592107,-0.0377119742,-0.3606550097,-0.0614188686,0.2296983153,0.2251725644,-0.2382855415,-0.0716267601,-0.6653861403,-0.7250332236,0.0148601914,-0.0738799125,-0.4821614325,-0.2052155584,0.0278104208,0.4159468114,-0.1507524848,0.0593610033,-0.2272671759,0.5010195971,-0.3477635682,-0.2220639735,-0.0665618703,-0.3527732193,-0.2486335337,-0.0017851433,0.3669335544,0.1794337332,0.4034474194,-0.1142761111,-0.1231461391,-0.2343118489,0.0056437459,0.0564889796,0.3319588304,0.0723532587,-0.0066338349,-0.107201986,-0.1433563381,0.0088691209,0.1476003081,-0.3277746439,0.0985141546,-0.049735833,-0.0195473451,0.0176217649,-0.3477735519,-0.4379716218,-0.2635905147,-0.1216423288,0.1585334092,0.1391206235,0.1520963162,0.0390637219,-0.1248000041,0.2505188882,-0.1376264691,0.0951220021,-0.0327172242,-0.4872590005,0.1624234766,-0.3436885476,-0.2576625347,0.4373550415,0.0587734766,0.1389320642,0.2758118808,-0.6511327028,0.0503680073,0.0381959118,0.169741258,0.3652427793,0.0016047496,0.5040283203,-0.2468517274,0.0363385603,0.0136324381,0.0794102326,0.3829860091,0.1882002801,0.2508431673,0.2320654541,0.3819538355,0.1213085353,0.1898973733,-0.1730674356,0.1170406044,0.6550409198,0.0645186529,0.4792505801,-0.3082836568,0.0291578453,-0.11741549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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"Does this HEAD request return 200 on your machine ?\r\n```python\r\nimport requests                                                                                                                                                                                                         \r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\")\r\n```\r\n\r\nIf it returns 200, could you try again to load the dataset ?","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":28,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n Does this HEAD request return 200 on your machine ?\r\n```python\r\nimport requests                                                                                                                                                                                                         \r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\")\r\n```\r\n\r\nIf it returns 200, could you try again to load the dataset ?","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.4104644656,0.2627967,-0.0999407768,0.430472672,-0.1017871127,0.298207432,-0.0337928012,-0.5136099458,-0.039039325,-0.0248874258,-0.049359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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"Thank you very much for your response.\r\nWhen I run \r\n``` \r\nimport requests                                                                                                                                                                                                         \r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\")\r\n```\r\nIt returns 200.\r\n\r\nAnd I try again to load the dataset. I got the following errors again. \r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 475, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"C:\\Users\\666666\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\cnn_dailymail\\0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\\cnn_dailymail.py\", line 253, in _split_generators\r\n    dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\download_manager.py\", line 175, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 224, in map_nested\r\n    mapped = [\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ\r\n\r\nConnection error happened but the url was different.\r\n\r\nI add the following code.\r\n```\r\nrequests.head(\"https:\/\/drive.google.com\/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ\")\r\n```\r\nThis  didn't  return 200\r\nIt returned like this:\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connection.py\", line 159, in _new_conn\r\n    conn = connection.create_connection(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\util\\connection.py\", line 84, in create_connection\r\n    raise err\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\util\\connection.py\", line 74, in create_connection\r\n    sock.connect(sa)\r\nTimeoutError: [WinError 10060] \r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connectionpool.py\", line 670, in urlopen\r\n    httplib_response = self._make_request(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connectionpool.py\", line 381, in _make_request\r\n    self._validate_conn(conn)\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connectionpool.py\", line 978, in _validate_conn\r\n    conn.connect()\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connection.py\", line 309, in connect\r\n    conn = self._new_conn()\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connection.py\", line 171, in _new_conn\r\n    raise NewConnectionError(\r\nurllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x000001F6060618E0>: Failed to establish a new connection: [WinError 10060] ","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":272,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n Thank you very much for your response.\r\nWhen I run \r\n``` \r\nimport requests                                                                                                                                                                                                         \r\nrequests.head(\"https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\")\r\n```\r\nIt returns 200.\r\n\r\nAnd I try again to load the dataset. I got the following errors again. \r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 475, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 531, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"C:\\Users\\666666\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\cnn_dailymail\\0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\\cnn_dailymail.py\", line 253, in _split_generators\r\n    dl_paths = dl_manager.download_and_extract(_DL_URLS)\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\download_manager.py\", line 254, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\download_manager.py\", line 175, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 224, in map_nested\r\n    mapped = [\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 225, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\py_utils.py\", line 163, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 300, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\", line 475, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/drive.google.com\/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ\r\n\r\nConnection error happened but the url was different.\r\n\r\nI add the following code.\r\n```\r\nrequests.head(\"https:\/\/drive.google.com\/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ\")\r\n```\r\nThis  didn't  return 200\r\nIt returned like this:\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connection.py\", line 159, in _new_conn\r\n    conn = connection.create_connection(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\util\\connection.py\", line 84, in create_connection\r\n    raise err\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\util\\connection.py\", line 74, in create_connection\r\n    sock.connect(sa)\r\nTimeoutError: [WinError 10060] \r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connectionpool.py\", line 670, in urlopen\r\n    httplib_response = self._make_request(\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connectionpool.py\", line 381, in _make_request\r\n    self._validate_conn(conn)\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connectionpool.py\", line 978, in _validate_conn\r\n    conn.connect()\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connection.py\", line 309, in connect\r\n    conn = self._new_conn()\r\n  File \"C:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\urllib3\\connection.py\", line 171, in _new_conn\r\n    raise NewConnectionError(\r\nurllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x000001F6060618E0>: Failed to establish a new connection: [WinError 10060] ","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.4104644656,0.2627967,-0.0999407768,0.430472672,-0.1017871127,0.298207432,-0.0337928012,-0.5136099458,-0.039039325,-0.0248874258,-0.0493591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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"I can browse the google drive through google chrome. It's weird. I can download the dataset through google drive manually.","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":20,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n I can browse the google drive through google chrome. It's weird. I can download the dataset through google drive manually.","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.4104644656,0.2627967,-0.0999407768,0.430472672,-0.1017871127,0.298207432,-0.0337928012,-0.5136099458,-0.039039325,-0.0248874258,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"Is it possible I download the dataset manually from google drive and use it for further test ? How can I do this ? I want to reproduce the model in this link https:\/\/huggingface.co\/patrickvonplaten\/bert2bert-cnn_dailymail-fp16. But I can't download the dataset through load_dataset method . I have tried many times  and the connection error always happens .\r\n","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":56,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n Is it possible I download the dataset manually from google drive and use it for further test ? How can I do this ? I want to reproduce the model in this link https:\/\/huggingface.co\/patrickvonplaten\/bert2bert-cnn_dailymail-fp16. But I can't download the dataset through load_dataset method . I have tried many times  and the connection error always happens .\r\n","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.4104644656,0.2627967,-0.0999407768,0.430472672,-0.1017871127,0.298207432,-0.0337928012,-0.5136099458,-0.039039325,-0.0248874258,-0.04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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"The head request should definitely work, not sure what's going on on your side.\r\nIf you find a way to make it work, please post it here since other users might encounter the same issue.\r\n\r\nIf you don't manage to fix it you can use `load_dataset` on google colab and then save it using `dataset.save_to_disk(\"path\/to\/dataset\")`.\r\nThen you can download the directory on your machine and do\r\n```python\r\nfrom datasets import load_from_disk\r\ndataset = load_from_disk(\"path\/to\/local\/dataset\")\r\n```","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":75,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n The head request should definitely work, not sure what's going on on your side.\r\nIf you find a way to make it work, please post it here since other users might encounter the same issue.\r\n\r\nIf you don't manage to fix it you can use `load_dataset` on google colab and then save it using `dataset.save_to_disk(\"path\/to\/dataset\")`.\r\nThen you can download the directory on your machine and do\r\n```python\r\nfrom datasets import load_from_disk\r\ndataset = load_from_disk(\"path\/to\/local\/dataset\")\r\n```","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.4104644656,0.2627967,-0.0999407768,0.430472672,-0.1017871127,0.298207432,-0.0337928012,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"Hi\r\nI want to know if this problem has been solved because I encountered a similar issue. Thanks.\r\n`train_data = datasets.load_dataset(\"xsum\", `split=\"train\")`\r\n`ConnectionError:` Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/xsum\/xsum.py`","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":26,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n Hi\r\nI want to know if this problem has been solved because I encountered a similar issue. Thanks.\r\n`train_data = datasets.load_dataset(\"xsum\", `split=\"train\")`\r\n`ConnectionError:` Couldn't reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.3\/datasets\/xsum\/xsum.py`","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.4104644656,0.2627967,-0.0999407768,0.430472672,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"Hi @smile0925 ! Do you have an internet connection ? Are you using some kind of proxy that may block the access to this file ?\r\n\r\nOtherwise you can try to update `datasets` since we introduced retries for http requests in the 1.2.0 version\r\n```\r\npip install --upgrade datasets\r\n```\r\nLet me know if that helps.","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":56,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n Hi @smile0925 ! Do you have an internet connection ? Are you using some kind of proxy that may block the access to this file ?\r\n\r\nOtherwise you can try to update `datasets` since we introduced retries for http requests in the 1.2.0 version\r\n```\r\npip install --upgrade datasets\r\n```\r\nLet me know if that helps.","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.4104644656,0.2627967,-0.0999407768,0.430472672,-0.1017871127,0.298207432,-0.0337928012,-0.5136099458,-0.039039325,-0.0248874258,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"Hi @lhoestq \r\nOh, may be you are right. I find that my server uses some kind of proxy that block the access to this file.\r\n![image](https:\/\/user-images.githubusercontent.com\/46243662\/106456211-2ca24180-64c8-11eb-831e-47e9b40e7da4.png)\r\n\r\n","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":26,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n Hi @lhoestq \r\nOh, may be you are right. I find that my server uses some kind of proxy that block the access to this file.\r\n![image](https:\/\/user-images.githubusercontent.com\/46243662\/106456211-2ca24180-64c8-11eb-831e-47e9b40e7da4.png)\r\n\r\n","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.410464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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"> Hi @lhoestq\r\n> Oh, may be you are right. I find that my server uses some kind of proxy that block the access to this file.\r\n> ![image](https:\/\/user-images.githubusercontent.com\/46243662\/106456211-2ca24180-64c8-11eb-831e-47e9b40e7da4.png)\r\n\r\nI have the same problem, have you solved it? Many thanks","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":40,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n > Hi @lhoestq\r\n> Oh, may be you are right. I find that my server uses some kind of proxy that block the access to this file.\r\n> ![image](https:\/\/user-images.githubusercontent.com\/46243662\/106456211-2ca24180-64c8-11eb-831e-47e9b40e7da4.png)\r\n\r\nI have the same problem, have you solved it? Many thanks","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.4104644656,0.2627967,-0.0999407768,0.430472672,-0.1017871127,0.298207432,-0.0337928012,-0.5136099458,-0.039039325,-0.0248874258,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/759","title":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py","comments":"Hi  @ZhengxiangShi \r\nYou can first try whether your network can access these files. I need to use VPN to access these files, so I download the files that cannot be accessed to the local in advance, and then use them in the code. Like this,\r\n`train_data = datasets.load_dataset(\"xsum.py\", split=\"train\")`","body":"Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ?","comment_length":49,"text":"(Load dataset failure) ConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py \n Hey, I want to load the cnn-dailymail dataset for fine-tune.\r\nI write the code like this\r\nfrom datasets import load_dataset\r\n\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctrain\u201d)\r\n\r\nAnd I got the following errors.\r\n\r\nTraceback (most recent call last):\r\nFile \u201ctest.py\u201d, line 7, in\r\ntest_dataset = load_dataset(\u201ccnn_dailymail\u201d, \u201c3.0.0\u201d, split=\u201ctest\u201d)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 589, in load_dataset\r\nmodule_path, hash = prepare_module(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\u201d, line 268, in prepare_module\r\nlocal_path = cached_path(file_path, download_config=download_config)\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 300, in cached_path\r\noutput_path = get_from_cache(\r\nFile \u201cC:\\Users\\666666\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\utils\\file_utils.py\u201d, line 475, in get_from_cache\r\nraise ConnectionError(\u201cCouldn\u2019t reach {}\u201d.format(url))\r\nConnectionError: Couldn\u2019t reach https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.1.2\/datasets\/cnn_dailymail\/cnn_dailymail.py\r\n\r\nHow can I fix this ? \n Hi  @ZhengxiangShi \r\nYou can first try whether your network can access these files. I need to use VPN to access these files, so I download the files that cannot be accessed to the local in advance, and then use them in the code. Like this,\r\n`train_data = datasets.load_dataset(\"xsum.py\", split=\"train\")`","embeddings":[-0.1726205349,-0.0845809281,-0.0973370373,0.1288862824,0.4222565293,0.2019445449,0.3426600695,0.0621942766,-0.0575587004,0.0043574101,-0.1423110366,0.0824962258,-0.0660601109,0.2489195168,0.1123090535,-0.1254215389,-0.0808455199,-0.1146305054,-0.2291170061,0.0067147403,-0.2098105252,0.1440933049,-0.2096494436,0.0432117768,-0.4213045835,-0.1652373374,0.086121805,0.3555387259,-0.0301731415,-0.2586031556,0.3710347414,0.0136734368,0.0507145114,0.4067729115,-0.0001109412,0.2821536362,0.4214935005,-0.0077436431,-0.4700695574,-0.4119871855,0.0286166817,-0.2524267137,0.203611657,-0.1215822548,-0.08969336,-0.0993359014,-0.0485146157,-0.2351501137,0.4430734515,0.4244684279,0.2697091103,0.2736960649,0.3110575974,-0.0948241577,-0.3082554638,-0.2242909074,0.0436809249,0.5262338519,-0.3041549921,-0.0550037548,0.2369493842,0.1852680892,-0.1102643609,0.0394290015,0.1027930453,0.0368396975,-0.0811182633,-0.3631623089,0.0167236421,0.0721938908,0.2434540987,-0.2260054797,-0.4658710957,-0.1638950258,0.0304690413,-0.352312237,0.2059648484,0.072493799,-0.1310377121,0.1562265158,-0.4081345499,-0.1662252545,-0.2331795692,0.4767994583,-0.0834709108,-0.0691660941,-0.1013452783,0.1568791717,0.2081463486,-0.0147652691,0.2456072122,0.0163964685,0.3304591179,0.2135430425,-0.4810218215,0.115841113,0.0163757335,0.1395893097,0.135859251,0.2361456752,0.0839775056,-0.0570870079,-0.1238575578,0.0851527154,0.0606347546,0.2453833222,0.0089363651,0.1387266517,0.1872473806,0.305845052,-0.0122886831,0.0348720662,-0.1371922344,-0.2628456056,-0.08476042,0.1266726404,0.4672960341,-0.0668150634,-0.1457813829,0.2286665291,-0.2497208267,-0.0773124471,0.1255286932,0.5078197718,-0.2459857464,0.1030792743,-0.0911056921,0.2443113178,-0.0984816253,0.0281397756,-0.2782628238,0.1583172977,-0.1469017863,0.1858578175,0.4209389389,-0.4283124804,0.3650208414,-0.0827948749,0.1328939646,-0.2651251256,0.0727618709,-0.0731363744,-0.2295966446,0.3315565586,-0.0128483977,0.1878604293,0.3255273104,0.219899714,-0.0086199995,-0.0932961255,-0.2572560012,-0.5359469652,0.2843953967,0.2346502244,-0.1325822622,-0.2377674282,-0.1039849222,-0.0701877475,-0.0855135694,0.0499906503,-0.2254374325,-0.0758890808,-0.0001020563,-0.1851737052,0.4579845369,0.4795647562,-0.3562659025,-0.131882593,-0.0889203772,-0.1993902177,0.1022753343,0.2505486906,-0.2102564871,0.267824322,-0.0300623551,-0.200193122,0.3244513571,-0.2567795813,-0.4303721189,0.2295979559,-0.1415840089,0.0772204101,-0.159794271,-0.0198278166,-0.230043903,0.0976685733,0.2224139124,0.4544759095,-0.2219419777,-0.0075087207,-0.0761365741,-0.2887578607,0.1971730888,0.3495088518,0.1905551255,0.0837119222,0.112290211,-0.1229663789,0.1389329135,0.0792715624,-0.1288710833,0.2222590446,-0.0730446875,0.0600969754,0.005793402,0.1931337863,-0.4456617534,0.2171620876,0.2575122118,0.2054394931,-0.1591732353,0.0718008503,-0.3314308226,0.064478755,-0.3276307583,-0.1017315835,0.1681315899,0.1100925952,0.1149900779,0.1487406194,-0.3477537334,0.5853907466,-0.2807269692,0.1544780582,-0.3642602861,0.3336731791,-0.137807101,0.0208101701,0.0532537736,0.1948694438,0.2270952165,-0.2124796659,-0.1230695471,0.3350651264,-0.0970880687,0.1396979541,0.1005691662,0.0206223,0.0887210742,-0.2732220292,-0.0075916527,0.1752086878,-0.0484225005,-0.0636551455,0.2221704721,0.2624588609,-0.0824369416,0.3350197375,0.1653448045,-0.0167164225,0.155774191,0.0822781697,-0.0725755841,-0.080437921,0.348846674,-0.282363534,0.4914526939,-0.1779355407,-0.1883532107,-0.214296937,0.1073015034,-0.2060754895,0.0367991664,0.1807588488,-0.1527466178,-0.1959124207,-0.1415822357,0.2285894006,0.3831121027,0.1266689599,0.0174037032,0.3330525756,-0.1124231219,-0.1255553067,0.2139613777,0.1642096043,0.253416568,0.125690341,-0.1063401401,0.0876753926,-0.3335894048,-0.2977259159,0.0457495488,0.2941001952,-0.3505651951,-0.0266053155,-0.2348111719,-0.1075837016,0.0410018079,-0.4958513677,-0.1831308901,-0.4478105903,-0.1630501598,-0.0245089773,0.10010802,0.1913428754,-0.1383036971,0.2042535245,0.2681008875,-0.1757077277,-0.1990249157,-0.1413165927,-0.1222066134,0.042547036,0.1909830719,-0.2497787029,0.2615085244,-0.1237755716,-0.1931108236,-0.1338968277,-0.0432524495,0.0448913872,-0.0807589814,0.4253887534,0.0415047854,0.5178230405,0.1474954188,-0.0527280755,0.3786589503,-0.0750784799,0.0525866598,0.0137069914,-0.0126736257,0.1713650823,0.1054245755,-0.4197510779,-0.5957531929,-0.3186041415,0.1332792342,-0.140416503,0.160090819,0.4751754701,0.1390486509,0.0728231594,-0.0277652182,0.3243475854,-0.295239687,-0.5784931779,0.3427504003,-0.426258415,-0.3999854922,0.0517781228,0.2337378412,0.1585310996,-0.136862874,-0.4911381602,-0.2912989259,-0.0210318696,-0.2082522511,-0.1408883333,0.0342517607,-0.0817868337,-0.1706445515,-0.0807222873,0.0528550036,-0.215396449,0.0207666811,-0.1897796839,0.1059555113,0.0540594012,0.5800537467,-0.2069241405,0.4104644656,0.2627967,-0.0999407768,0.430472672,-0.1017871127,0.298207432,-0.0337928012,-0.5136099458,-0.039039325,-0.0248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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/758","title":"Process 0 very slow when using num_procs with map to tokenizer","comments":"Hi ! Thanks for reporting.\r\nIs the distribution of text length of your data evenly distributed across your dataset ? I mean, could it be because the examples in the first part of your dataset are slower to process ?\r\nAlso could how many CPUs can you use for multiprocessing ?\r\n```python\r\nimport multiprocessing\r\nprint(multiprocessing.cpu_count())\r\n```\r\nWhich tokenizer are you using ?","body":"<img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n","comment_length":62,"text":"Process 0 very slow when using num_procs with map to tokenizer \n <img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n \n Hi ! Thanks for reporting.\r\nIs the distribution of text length of your data evenly distributed across your dataset ? I mean, could it be because the examples in the first part of your dataset are slower to process ?\r\nAlso could how many CPUs can you use for multiprocessing ?\r\n```python\r\nimport multiprocessing\r\nprint(multiprocessing.cpu_count())\r\n```\r\nWhich tokenizer are you using 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/758","title":"Process 0 very slow when using num_procs with map to tokenizer","comments":"Using pre trained HF tokenizer. The result is the same with tokenizer multiprocessing off and on.\r\nI have (absolutely) no idea about the distribution, but since this issue occurs on all of my datasets(regardless of files), I don't think distribution is the problems.\r\n\r\nI can use up to 16 cores.","body":"<img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n","comment_length":50,"text":"Process 0 very slow when using num_procs with map to tokenizer \n <img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n \n Using pre trained HF tokenizer. The result is the same with tokenizer multiprocessing off and on.\r\nI have (absolutely) no idea about the distribution, but since this issue occurs on all of my datasets(regardless of files), I don't think distribution is the problems.\r\n\r\nI can use up to 16 cores.","embeddings":[-0.4506952465,-0.3895408213,-0.0472824574,0.1629320234,0.1149342209,-0.2815493047,0.2656876743,0.2587329149,-0.3923633397,0.1888355911,0.0629082173,0.3718959093,-0.089070864,-0.2105110139,-0.5482542515,0.2360698283,0.0839328393,-0.0310935583,0.5445747375,-0.1181110516,-0.1754408777,0.177372694,-0.2817248106,0.0584396273,-0.5508942604,-0.1075035185,0.0991637409,0.2062431872,-0.0079598241,-0.3365160823,-0.6048396826,-0.0062392484,-0.0067063249,0.2380303293,-0.0001208894,-0.0261882041,-0.0501437411,0.3045714498,0.1773629338,0.1757730395,0.5087664127,-0.2677305043,-0.2065653354,-0.0814368874,0.071761854,-0.1732417494,0.1646704227,0.1108722761,0.3598265648,0.3113973737,0.1259145588,0.3464170992,-0.2695309818,0.1674762368,-0.3548649848,0.1909781843,0.0127337407,-0.1612080932,0.1026980132,0.1404281855,-0.0731380284,0.1114659607,-0.206951499,0.0112679061,0.1181672961,-0.0765307769,0.3340560496,-0.5317914486,0.3109235168,0.1555503756,-0.1304652095,0.2259662002,-0.0447759517,-0.2271244377,-0.1173180416,-0.4069034457,-0.0659299865,0.1536652893,-0.2233647555,-0.2492575049,-0.2870896161,0.2072083056,0.1220583245,-0.1853976846,-0.1608427912,0.1784252673,0.2193142921,0.1878379583,0.0917399302,-0.0794800594,-0.3464467525,-0.189996779,0.0932571292,0.1641352475,-0.2648397088,0.0863497406,0.0162886754,-0.2284547687,-0.1790191531,-0.0498304591,-0.4908621907,0.5438275933,-0.0379492939,0.1100388244,-0.0540822633,-0.0507318787,0.0711289942,0.107380487,0.3145228326,-0.0370388553,-0.2704118788,0.0606514923,-0.1888573617,-0.2157375216,0.1592000425,-0.1182655767,-0.4101752043,-0.0712799132,-0.3443175852,-0.0421151891,0.06168871,0.2465012819,0.1357420832,0.2784369588,0.1164710522,0.60796386,-0.2985524535,-0.1146471873,-0.4788651466,0.0308691394,-0.065696761,-0.0983158946,-0.1533274055,0.1412196904,-0.0580749735,0.0928089991,-0.0424256958,0.1374468058,0.2228023261,-0.0566168576,0.1432552338,-0.3872431517,0.1344203055,0.1969755739,-0.0905412212,0.3274353147,0.0075174361,-0.1662386656,-0.3650250435,0.1921859384,-0.5542647243,-0.1544521153,-0.1172215492,0.0300147496,-0.0256938469,0.3321104944,-0.1265891045,0.065926671,0.0601636171,-0.0067125461,-0.1519165933,-0.4198389351,-0.5100108385,-0.1309079677,0.0354381427,0.2284735143,0.0076896199,-0.0061620814,0.4147745073,0.1895893365,0.6329798698,0.640367806,0.0365301333,0.0456745885,-0.2062079906,0.4334468842,0.0078639546,-0.1366570741,-0.3977273107,0.2765939534,-0.2492134571,-0.2269993275,0.1242460683,0.083922565,0.2745398581,-0.0625268966,0.1646543741,0.0608774461,0.1374640614,0.3524214029,-0.3490796983,0.0038467364,-0.0185448434,0.0742785409,-0.0408974811,-0.1899458319,-0.0398687422,-0.2290841043,0.0277229939,-0.1095842272,0.0088469079,0.2146681696,0.1113792136,-0.1869167089,0.0424843207,0.1480467767,0.2814905941,0.3698045313,-0.2151997536,0.2599093616,-0.0825202018,-0.0297249015,0.2806521356,0.1298107505,-0.1157191023,-0.2477763742,-0.003191859,-0.1168284938,-0.2420949936,0.0901817456,0.0670359358,0.3922367692,0.1027478278,-0.0189812109,0.2864282429,-0.0789332911,-0.1034206524,-0.2746038735,-0.2179026753,-0.0040626163,0.3598762453,0.020688016,-0.0209823065,0.1538755745,0.297991693,-0.102121003,-0.2200790495,0.1073790118,0.3305101693,0.0649032518,0.1737664342,-0.132722348,-0.1296590418,-0.1472826004,0.2585277259,0.4864607751,0.233011663,0.2920912504,0.1635444164,-0.0365977846,0.0878182575,0.1875586957,0.4652343392,0.1040159687,0.1991215497,0.6450462341,0.1737261266,0.2268470228,-0.10093683,0.4220349193,0.3750983179,0.0383585021,-0.2353627384,0.3845766485,-0.1413104832,-0.0957228616,0.3067465425,-0.5021467209,0.2544321418,0.0890975595,-0.0221233293,-0.0148402629,-0.24618119,-0.0257795248,-0.0438657589,0.1368954033,0.1569816917,0.1807011962,0.1568026394,0.217647776,-0.3013871908,-0.320089221,0.1313546896,0.2890082002,-0.1799802184,0.0986567512,-0.2343864441,0.5450378656,0.0290588476,0.1898016632,-0.2035871744,-0.3212849796,0.1369773895,0.3257724047,0.2375264913,0.0229058005,-0.0110994456,0.0008424884,0.0958475396,0.012094615,-0.1002325043,0.0772534236,-0.4060472548,-0.1035230905,0.4133099914,-0.0200189874,0.1561088413,0.0215083845,-0.1393605322,-0.1392398626,-0.385617286,0.0294844713,0.0673709363,0.0429110453,-0.0571673959,-0.0194588322,-0.2499811947,-0.0217318404,0.2867519557,-0.3686539829,-0.1650758386,0.1257063746,-0.2890628278,-0.3887500465,-0.0040579909,0.3380643427,-0.2690812051,0.0169429854,0.076980032,0.0362953693,0.3447990119,-0.0609910861,-0.0147451404,0.0760455728,0.217759192,-0.1731932014,-0.04938481,-0.1005866528,0.2364498824,-0.0065876739,-0.1847718954,-0.1687864661,0.0533468314,-0.059252739,0.1908752024,-0.0932112858,-0.0675301999,-0.279248327,0.5511071086,-0.2200596333,-0.1358428746,0.3535012901,0.2189850658,-0.2078604996,0.1770895869,-0.0537383817,0.1026754677,-0.2086285204,-0.0796081871,-0.022159813,0.1777781993,0.2634583414,0.656558454,0.1694375575,-0.5546483397,0.4604192078,-0.1575149447,-0.1610368192,0.0329057723,-0.0412072316,0.3707009554,0.107076600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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/758","title":"Process 0 very slow when using num_procs with map to tokenizer","comments":"Ok weird, I don't manage to reproduce this issue on my side.\r\nDoes it happen even with `num_proc=2` for example ?\r\nAlso could you provide more details about your OS and the versions of tokenizers\/datasets\/multiprocess that you're using ?","body":"<img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n","comment_length":39,"text":"Process 0 very slow when using num_procs with map to tokenizer \n <img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n \n Ok weird, I don't manage to reproduce this issue on my side.\r\nDoes it happen even with `num_proc=2` for example ?\r\nAlso could you provide more details about your OS and the versions of tokenizers\/datasets\/multiprocess that you're using ?","embeddings":[-0.4499242008,-0.3384662867,-0.0881828442,0.1553920507,0.0886590257,-0.303383559,0.2761160433,0.248826772,-0.4145074189,0.1561545581,0.2132177651,0.3615123332,-0.2245154679,-0.1062326878,-0.5425727963,0.2070079744,0.0648821518,0.0284322016,0.497579217,0.0339274369,-0.2033851445,0.1207496226,-0.373627156,0.0247500073,-0.579837501,-0.1454626024,0.1142511964,0.1221130416,-0.1154534519,-0.3299306929,-0.6470974088,-0.0766082034,-0.0318846926,0.3341849744,-0.0001155862,-0.0463386886,0.1090576127,0.3409170806,0.059888225,0.0577889681,0.3721919358,-0.356223613,-0.1680819839,-0.1338618249,0.065941833,-0.1426615119,0.1589431912,0.0322805718,0.3406347036,0.4026558697,0.1911497116,0.3179595768,-0.270878762,0.2543511689,-0.2134602964,0.1684640348,-0.0395519584,-0.2626494169,0.1379910856,0.2332983911,0.0755674392,0.171354115,-0.2536707819,-0.1796937585,0.0796079189,-0.0737387091,0.3969673514,-0.5955144763,0.3338231742,0.0688479915,-0.0498033278,0.2084262371,-0.1226272732,-0.2446288168,-0.1194128171,-0.3561387062,-0.0315729678,0.1177541912,-0.145931378,-0.1997264475,-0.4144126475,0.1733220965,0.0927784368,-0.0780107528,-0.2313115299,0.1342223734,0.1872867495,0.1875690073,0.0374248996,-0.2165006995,-0.2947055697,-0.2338628024,0.046458371,0.2533203065,-0.2309342772,0.0660237148,0.0152793219,-0.130366236,-0.2016302198,0.0667562559,-0.4654318392,0.4875847101,0.0327738598,0.1434691548,-0.1114958152,0.028573351,0.170927912,0.1275348812,0.3001178801,-0.2543088794,-0.2343515307,0.0001800376,-0.1543804854,-0.1821833402,0.2154282629,-0.0612580925,-0.2682190239,-0.0233409647,-0.3308350146,-0.0115478653,0.0873355493,0.2619652152,0.2996673286,0.2002889067,0.06225859,0.5214753747,-0.2556758523,-0.1215853095,-0.4594755769,0.1133482605,-0.0815635771,-0.1647175997,-0.1865520477,0.1378510892,-0.0613127537,0.177166447,-0.0054514324,0.0253582746,0.1104048416,0.0252898894,0.1823234558,-0.3223147392,0.1147368103,0.2022774369,-0.067110002,0.3035331666,-0.1029452011,-0.1617824137,-0.3697654605,0.2381415516,-0.5384536982,-0.1524564475,-0.1589490175,0.0934766233,-0.1279311031,0.2658382952,-0.2042573839,0.0647621751,0.0831181258,-0.0184914488,-0.1679270715,-0.3296148479,-0.5520940423,-0.1329564005,-0.0274989866,0.1706850529,0.0621937141,-0.0064918748,0.4061864018,0.1375060827,0.5677406788,0.476406157,0.080736883,-0.0067321165,-0.1896296293,0.4975835383,0.1461083293,-0.0847519487,-0.3920003176,0.2247670889,-0.343560338,-0.2591389716,0.1443513781,0.1934028417,0.2961143255,-0.0813638046,0.1891617924,0.1684509665,0.1706659049,0.4236152172,-0.3447260559,0.1150199845,0.0540287793,0.0510680936,-0.1712332666,-0.2077021897,-0.1022623032,-0.2073799968,-0.075518772,-0.1466234326,0.0673182905,0.2143203318,0.0944850072,-0.1933384538,0.0748752281,0.0299660843,0.1912508309,0.3252852559,-0.101033181,0.1861101836,-0.133623302,-0.0344640464,0.259673357,0.1267644167,-0.1248527467,-0.146664843,0.0586642399,-0.1872172356,-0.2034932226,0.1582451314,0.1260849386,0.3644276857,0.0801934525,-0.0583211333,0.2455107272,-0.0991178155,-0.1141911075,-0.2937938869,-0.1932035387,0.0026086592,0.3814733028,0.0125687579,-0.0252914242,0.2470211834,0.381334722,0.0205002334,-0.2337235808,0.1055138111,0.4118440449,0.0603031777,0.2026038766,-0.0950057581,-0.0862973481,-0.1775080264,0.2709073424,0.5127896667,0.2094573975,0.2903516293,0.1432604343,0.0554517657,0.0688226894,0.1647234708,0.242582202,0.0216359366,0.1735888273,0.6222238541,0.2241018862,0.2362848967,-0.0152292065,0.3324324191,0.3139469028,0.0017893401,-0.1696559787,0.3111461997,-0.184545666,-0.0576296039,0.2842711508,-0.4943203628,0.2353970706,0.2080538273,-0.0235128459,-0.0208482351,-0.199622348,-0.0781733841,0.0861452892,0.1754349172,0.1429576427,0.0968637988,0.2540300488,0.1578579992,-0.2692854404,-0.2531763315,0.0884561986,0.3354820609,-0.1019076928,0.1001666933,-0.168754667,0.401204437,0.0715172142,0.0944321156,-0.1741177738,-0.317132175,0.1304926872,0.2831198573,0.1585765928,0.1246608719,-0.0297836568,0.0023998972,0.19020091,0.0668637529,-0.082956627,0.0202425588,-0.3556158543,-0.0494644456,0.4389982224,-0.0162160955,0.2940421999,-0.009238421,0.0038734111,-0.2317297608,-0.455683887,0.0031739497,0.041392073,0.025513377,-0.0680909976,-0.062108703,-0.2358986437,0.0194190461,0.248058036,-0.3583425283,-0.2180262059,0.1097904369,-0.2262161374,-0.3725872338,-0.0686939433,0.3364560902,-0.2102839351,0.075750798,0.1448751837,0.0660199672,0.3742148876,-0.0085043246,0.1516018957,0.1291854978,0.2616471052,-0.1156107262,-0.0668545216,-0.0575702265,0.2586344779,-0.0262191463,-0.1741725057,-0.1098842695,0.0847538635,-0.111498557,0.2365138978,-0.0075679761,-0.205554381,-0.3503775299,0.4797764122,-0.1803678423,-0.156106472,0.3318628669,0.2220036238,-0.2312347293,0.1933972389,-0.0816725567,-0.053440515,-0.2416557372,-0.0583512411,-0.015322376,0.1631850004,0.2545320094,0.5217760205,0.1270966381,-0.5550823808,0.2751224041,-0.241236642,-0.1587435007,0.0387795419,-0.1154578179,0.3504100144,0.2130519897,0.0414600782,0.171665743,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/758","title":"Process 0 very slow when using num_procs with map to tokenizer","comments":"Yes, I can confirm it also happens with ```num_proc=2```.\r\n```\r\ntokenizers               0.9.2\r\ndatasets                 1.1.2\r\nmultiprocess             0.70.10\r\n```\r\n```\r\nLinux nipa2020-0629 4.4.0-178-generic #208-Ubuntu SMP Sun Apr 5 23:45:10 UTC 2020 x86_64 x86_64 x86_64 GNU\/Linux\r\n```","body":"<img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n","comment_length":34,"text":"Process 0 very slow when using num_procs with map to tokenizer \n <img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n \n Yes, I can confirm it also happens with ```num_proc=2```.\r\n```\r\ntokenizers               0.9.2\r\ndatasets                 1.1.2\r\nmultiprocess             0.70.10\r\n```\r\n```\r\nLinux nipa2020-0629 4.4.0-178-generic #208-Ubuntu SMP Sun Apr 5 23:45:10 UTC 2020 x86_64 x86_64 x86_64 GNU\/Linux\r\n```","embeddings":[-0.4147121906,-0.2900132239,-0.140239194,0.1775934547,0.0856034979,-0.3098762035,0.3165899217,0.2695382237,-0.4015316069,0.1938359886,0.1718184054,0.4401447177,-0.1775085926,-0.1734138131,-0.4684062302,0.1972351074,0.0476887487,0.0190271009,0.4751037061,0.0531506315,-0.2164152861,0.2401550859,-0.3768131137,0.0269497838,-0.4960835874,-0.098242946,0.1989468336,0.1213030815,-0.1391700208,-0.3674746752,-0.6068891287,-0.1121927947,-0.056367632,0.2119784504,-0.0001163927,-0.0296951998,0.064612627,0.2730703056,0.1113588661,0.0883958414,0.3482666612,-0.3627751768,-0.2092968374,-0.1784971654,0.0755575001,-0.1697577238,0.1654167473,0.0946597606,0.3169885278,0.3889237344,0.1781579107,0.3117571175,-0.2135271281,0.2594029605,-0.2771312296,0.1019631773,-0.0687709749,-0.2140848786,0.0303773228,0.24990201,0.0425839722,0.2547703981,-0.2161941975,-0.1262565553,0.0159442481,-0.0336816646,0.3718964159,-0.5546597242,0.2848030031,0.0327237323,-0.1297376305,0.214637503,-0.0601828024,-0.1541355848,-0.188558042,-0.3445382118,-0.0453153625,0.1204939932,-0.2108682245,-0.2227787226,-0.3741155565,0.1841550767,0.0840206817,-0.070907712,-0.1827478707,0.1372275054,0.1799198687,0.2410597801,0.0588663109,-0.225803718,-0.2731664479,-0.2224009186,0.1319557726,0.2197855115,-0.2365812212,0.0364632607,0.0125438012,-0.1788612455,-0.2265859991,0.061087925,-0.3848721981,0.479562819,0.0413384214,0.2088337541,-0.2314832211,-0.0097556654,0.183086291,0.1602279395,0.2368336618,-0.1341395229,-0.172889486,0.0311940089,-0.1120954826,-0.1960592866,0.2113963664,0.013027939,-0.2358991206,0.0171142239,-0.3119621873,0.0140123181,0.090544939,0.199494049,0.2683678567,0.2375326306,0.1195797473,0.4252529144,-0.2917689085,-0.175411433,-0.4300312698,0.1039827317,-0.0970262513,-0.1099700704,-0.11935734,0.0670904741,-0.0476063192,0.21875453,0.0155169098,-0.044767268,0.153735131,0.0297058746,0.1537378579,-0.3857297301,0.1910876781,0.237996161,-0.0603851639,0.2763994336,-0.0695460215,-0.1646362692,-0.3798218966,0.2488522083,-0.4449457228,-0.0545314811,-0.0999007523,0.1063408852,-0.0132189747,0.2420077324,-0.0977023616,0.0103092529,0.1538992524,-0.1459278613,-0.0659778565,-0.2974435389,-0.5686413646,-0.1623958498,-0.0126863318,0.1474449039,0.0966638029,-0.0690117702,0.4687348902,0.1338403225,0.633321166,0.4759652317,0.0957222506,-0.0273743309,-0.1826714426,0.5199816823,0.2056587487,-0.0890081972,-0.4167572856,0.1860774606,-0.2954647243,-0.3388358653,0.1845507324,0.1925991625,0.2760441303,-0.0736087337,0.152774483,0.1710793078,0.2181038409,0.3887585998,-0.3401806653,0.0432673022,0.057945244,0.0052662301,-0.143991366,-0.2692907155,-0.0136551736,-0.1358415335,-0.1160059199,-0.108673729,0.0707351044,0.1403703988,0.0405127108,-0.2887232006,0.035770189,0.0396937765,0.1362640411,0.267501384,-0.20482786,0.1992261261,-0.1795548052,-0.0568981692,0.213009119,0.110573329,-0.0963906348,-0.2223664671,0.0520984493,-0.1409859061,-0.0806370601,0.127523005,0.1280655414,0.3340401947,-0.0087754494,-0.0142836347,0.2048281729,-0.0602168515,-0.1882988065,-0.274857074,-0.2319175303,0.0195146333,0.381421119,-0.0707417205,-0.0461662859,0.2635863423,0.2881624103,0.0682804883,-0.252323091,0.0121111022,0.3966016769,0.0402958468,0.1687045097,-0.0676355585,-0.0248788558,-0.12055365,0.2534329891,0.4735650718,0.1911372542,0.2915652394,0.2026228756,-0.0064282566,0.0646826103,0.1991950274,0.2518364489,-0.1087460592,0.0632869005,0.7362861633,0.1480686069,0.2410648912,0.014350378,0.3129008412,0.4191803634,0.043556422,-0.1877629012,0.3188773692,-0.1470636427,-0.0989779457,0.299633503,-0.4021481276,0.2188609242,0.211352855,0.0284018014,-0.0751190335,-0.1632568836,-0.1046004221,0.1323328763,0.1673816592,0.1391259134,0.1631523818,0.239171654,0.159887284,-0.362634182,-0.2156475931,0.1362676919,0.2745377719,-0.0113989646,0.0717987195,-0.1668399572,0.3841632605,0.1069370657,0.0126776444,-0.0979371518,-0.2654924393,0.1053292006,0.2229979634,0.1926709563,0.1570714861,-0.0243053958,-0.0396595411,0.2687875032,0.0549105778,-0.1527597904,0.1180010661,-0.2908624709,-0.0477984622,0.3717545867,-0.0466328934,0.3291921616,-0.0740024522,-0.04973628,-0.2217082977,-0.44871521,0.0687726289,0.0453884266,-0.0049517802,-0.0374320187,-0.0763192698,-0.2055060714,0.0315735191,0.2338883132,-0.3088588119,-0.2734475732,0.0143820895,-0.192863524,-0.2959949076,-0.0909073353,0.2817852199,-0.2543785274,-0.0103561897,0.0863191411,0.1400083601,0.4790601432,0.0132499197,0.0994248912,0.1158333048,0.2477497458,-0.064958401,-0.1200689673,-0.0427146442,0.2542879283,-0.0453790873,-0.1241725832,-0.1803464293,0.0653553382,-0.0297835805,0.1603381187,0.0569864884,-0.2868960798,-0.3649917841,0.5504561067,-0.158702746,-0.1019768193,0.4046473801,0.2637842,-0.2704053223,0.2251262516,-0.0688944533,-0.0361203142,-0.2399604619,-0.1305760443,0.0161142368,0.2189335376,0.2953400612,0.4784649611,0.1764711291,-0.6153280735,0.3364477158,-0.2446139455,-0.1234952807,0.0053260941,-0.1004122645,0.3539540172,0.2251975685,0.0864546224,0.118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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/758","title":"Process 0 very slow when using num_procs with map to tokenizer","comments":"I can't reproduce on my side unfortunately with the same versions.\r\n\r\nDo you have issues when doing multiprocessing with python ?\r\n```python\r\nfrom tqdm.auto import tqdm\r\nfrom multiprocess import Pool, RLock\r\n\r\ndef process_data(shard):\r\n    # implement\r\n\r\nnum_proc = 8\r\nshards = [] # implement, this must be a list of size num_proc\r\n\r\nwith Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool:\r\n    results = [pool.apply_async(process_data, shard=shard) for shard in shards]\r\n    transformed_shards = [r.get() for r in results]\r\n```","body":"<img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n","comment_length":73,"text":"Process 0 very slow when using num_procs with map to tokenizer \n <img width=\"721\" alt=\"image\" src=\"https:\/\/user-images.githubusercontent.com\/17930170\/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png\">\r\nThe code I am using is\r\n```\r\n\r\n        dataset = load_dataset(\"text\", data_files=[file_path], split='train')\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), num_proc=8)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        dataset.save_to_disk(file_path+'.arrow')\r\n```\r\n \n I can't reproduce on my side unfortunately with the same versions.\r\n\r\nDo you have issues when doing multiprocessing with python ?\r\n```python\r\nfrom tqdm.auto import tqdm\r\nfrom multiprocess import Pool, RLock\r\n\r\ndef process_data(shard):\r\n    # implement\r\n\r\nnum_proc = 8\r\nshards = [] # implement, this must be a list of size num_proc\r\n\r\nwith Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool:\r\n    results = [pool.apply_async(process_data, shard=shard) for shard in shards]\r\n    transformed_shards = [r.get() for r in results]\r\n```","embeddings":[-0.3621354103,-0.5087301135,-0.1394249797,0.074978061,0.0402247943,-0.3560976386,0.3881988823,0.1418159902,-0.4626859426,0.2230350375,0.0311758332,0.3382255137,-0.2334183604,-0.087559782,-0.4083906412,0.1022370458,0.0076495558,-0.1594914347,0.2531303465,0.0326251537,-0.1771969795,0.2042715698,-0.3319115341,-0.0055946107,-0.6348926425,-0.1702251732,0.0586432293,0.2909309566,0.0616428033,-0.2190775126,-0.6123360395,0.0709186122,-0.1117318198,0.3896766603,-0.0001135372,-0.0468342192,0.0159822349,0.2007141858,0.2702916861,0.2192208916,0.3434351683,-0.3710708916,-0.1273700297,-0.2034926414,0.0797320679,0.069500722,0.0555393435,0.1417084634,0.4168218672,0.1280537099,0.2039487511,0.5180537105,-0.1577168554,0.3365240991,-0.2163354009,-0.1263161302,-0.117036894,-0.2264710963,0.3812011778,0.2920641601,-0.0488046855,0.4048925936,-0.2574481666,0.0048076492,-0.0755035281,-0.0256644879,0.3905305564,-0.6222294569,0.2957596183,0.0915026441,-0.1952246875,0.1449882686,-0.2645458281,-0.1054365262,-0.1821681559,-0.446723491,-0.0244202148,0.1261487007,-0.1433109194,-0.1415111274,-0.5341367722,0.0735078827,0.1497888565,-0.203086257,-0.1841876954,0.1338226795,0.1804544777,0.1738160998,-0.0526166447,-0.2131351382,-0.2869664133,-0.1519455761,0.2450894564,0.0610678084,-0.3582467139,-0.0217568148,0.0150993718,-0.159471184,-0.2735793293,0.0945431963,-0.3346866071,0.6400344372,0.1284670681,0.1383740455,-0.2279019356,-0.1701498777,0.0366427638,-0.0220989808,0.2077170461,-0.3187614083,-0.2897849977,0.0965854004,0.0173377823,-0.2958787084,0.2639737427,0.073942475,-0.191046387,0.0637174249,-0.17707856,-0.0365515612,0.2190357596,0.2024123073,0.1830807924,0.2976763844,0.1855953187,0.4982711375,-0.3031582236,0.0021319415,-0.4452203214,-0.1513814181,-0.1632452756,-0.1266250312,-0.0921153203,0.0348824263,-0.1104717851,0.2267440557,-0.1102699265,0.0132167479,0.4914779663,-0.0523906201,0.2614605129,-0.2932785153,0.1446408033,0.3133213222,-0.0084735081,0.1547652781,-0.0946155265,-0.2155823112,-0.2673734128,0.1971883774,-0.475502789,-0.1252941638,-0.0142284892,0.1151569262,0.0019123899,0.3380803466,-0.0697642937,0.0178970899,0.2934842408,0.2063705176,-0.1937802434,-0.3681376278,-0.4264445007,-0.2172588855,0.0611192584,0.1044013947,0.1582095623,0.0513375401,0.3336831331,0.127746895,0.4713522792,0.5424495935,0.1154186204,0.0417913795,-0.0256374981,0.4444888234,0.2932839096,-0.1252952367,-0.3540532589,0.2891217768,-0.2604304254,-0.2326419502,0.2232977003,0.206240043,0.3754859567,-0.0470183007,0.2187004685,0.1550389677,0.0404801555,0.4499570429,-0.3326165378,0.0179055501,-0.0288358591,0.0906401426,0.1248635128,-0.2718461156,-0.0265716035,-0.340380609,-0.0024562262,-0.0861337557,0.0460289195,0.1847891361,0.0244432371,-0.2166291773,-0.0092744613,-0.0815287903,0.057569433,0.3631345332,-0.1907765418,0.2156136483,-0.1590053886,0.0149135282,0.3050498366,0.101495944,-0.2248282284,-0.1282920092,0.1212932244,-0.0976703092,-0.0827974081,-0.0777845308,0.0200119726,0.2593530715,0.1543824375,-0.0542898774,0.2893970013,0.0681320652,-0.0803182423,-0.4340829849,-0.2544192672,0.0692352504,0.3470928371,-0.0687739551,0.0581307113,0.260525614,0.2913330793,0.0728142411,0.0302236136,0.1023276299,0.3324845135,0.0144097013,0.1362325251,-0.070233576,-0.1539847255,-0.1466698349,0.3840997219,0.5922679901,0.0784226432,0.2981658578,0.1087239757,0.1097748578,0.0894447118,0.0530198216,0.203907147,0.0658640489,0.1775876284,0.5067195296,-0.0242265761,0.0737212077,-0.1616682261,0.3329648972,0.3019838631,0.0647395104,-0.1266432703,0.1125895381,0.0120350504,0.0771588981,0.1308263093,-0.3062311709,0.2695827484,0.2243545949,-0.0358175673,-0.0356405452,-0.2221834064,-0.1087189838,0.0581647791,0.2047241926,0.0137569401,0.2202899903,0.2550688684,0.0539958254,-0.1373395175,-0.2441286892,0.1931726635,0.1583592892,-0.0990221649,0.1024988592,-0.1780764759,0.5981843472,0.0530727021,0.0544366315,-0.1519256681,-0.39184618,0.1261687726,0.2221514881,0.0655586496,0.1626021862,0.0847128332,-0.1941848695,0.1956421137,0.0266870055,-0.0524273999,0.0328526124,-0.1817349941,-0.055635374,0.4671281278,-0.0657507256,0.3467688859,-0.2140849233,-0.1339963526,-0.31273669,-0.3596593738,0.0138718383,-0.0464475006,0.1118478775,-0.084439449,-0.1321330518,-0.2101774663,-0.0616514646,0.3838855028,-0.24511756,-0.1824629903,0.1848247498,-0.1050725877,-0.4156938791,-0.0517984852,0.1632776558,-0.2007575929,0.0415546224,0.0667129979,0.0860705003,0.4626763463,-0.1805195212,0.0466812328,0.08330331,0.1142295077,-0.0243651867,-0.0521159321,-0.0890167505,0.2752712071,-0.0479844846,-0.2823193967,-0.0810268074,-0.0195017513,0.0616551191,0.2946303785,0.0788054541,-0.2685387433,-0.2180759609,0.419676125,-0.0750634745,-0.0371207371,0.2952258885,0.2299128771,-0.2754557133,0.2846280038,0.0524856336,-0.1074728891,-0.1358467638,-0.0922812894,0.0228650309,0.1707631648,0.2578331232,0.8335025311,0.1053365096,-0.3736720979,0.1181062013,-0.2024864405,-0.2785546184,0.0446266867,-0.136722073,0.2043661028,0.03397000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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/757","title":"CUDA out of memory","comments":"```python\r\ntokenizer = FunnelTokenizer.from_pretrained('funnel-transformer\/small')\r\n\r\ndef tokenize(batch):\r\n    return tokenizer(batch['text'], padding='max_length', truncation=True,max_length=512)\r\n\r\ndataset = load_dataset(\"bookcorpus\",split='train[:1000]').shuffle()\r\ndataset = dataset.map(tokenize, batched=True, batch_size=512)\r\n\r\n# dataset = LineByLineTextDataset(\r\n#     tokenizer=tokenizer,\r\n#     file_path=\".\/wiki1000.txt\",\r\n#     block_size=128\r\n# )\r\n\r\ndata_collator = DataCollatorForLanguageModeling(\r\n    tokenizer=tokenizer, mlm=True, mlm_probability=0.15\r\n)\r\n\r\nconfig=FunnelConfig(\r\n    return_dict=True\r\n)\r\n\r\nmodel= FunnelForMaskedLM(config=config)\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/checkpoints\",\r\n    overwrite_output_dir=True,\r\n    do_train=True,\r\n    num_train_epochs=1,\r\n    per_device_train_batch_size=16,\r\n    per_device_eval_batch_size=16,\r\n    save_steps=10000,\r\n    logging_dir='.\/ptlogs'\r\n)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=dataset,\r\n)\r\ntrainer.train()\r\n```","body":"In your dataset ,cuda run out of memory as long as the trainer begins:\r\nhowever, without changing any other element\/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.\r\n","comment_length":64,"text":"CUDA out of memory \n In your dataset ,cuda run out of memory as long as the trainer begins:\r\nhowever, without changing any other element\/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.\r\n \n ```python\r\ntokenizer = FunnelTokenizer.from_pretrained('funnel-transformer\/small')\r\n\r\ndef tokenize(batch):\r\n    return tokenizer(batch['text'], padding='max_length', truncation=True,max_length=512)\r\n\r\ndataset = load_dataset(\"bookcorpus\",split='train[:1000]').shuffle()\r\ndataset = dataset.map(tokenize, batched=True, batch_size=512)\r\n\r\n# dataset = LineByLineTextDataset(\r\n#     tokenizer=tokenizer,\r\n#     file_path=\".\/wiki1000.txt\",\r\n#     block_size=128\r\n# )\r\n\r\ndata_collator = DataCollatorForLanguageModeling(\r\n    tokenizer=tokenizer, mlm=True, mlm_probability=0.15\r\n)\r\n\r\nconfig=FunnelConfig(\r\n    return_dict=True\r\n)\r\n\r\nmodel= FunnelForMaskedLM(config=config)\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/checkpoints\",\r\n    overwrite_output_dir=True,\r\n    do_train=True,\r\n    num_train_epochs=1,\r\n    per_device_train_batch_size=16,\r\n    per_device_eval_batch_size=16,\r\n    save_steps=10000,\r\n    logging_dir='.\/ptlogs'\r\n)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=dataset,\r\n)\r\ntrainer.train()\r\n```","embeddings":[-0.3100788295,-0.129237324,-0.1342045665,0.1175396442,0.4688266218,-0.144197613,0.2783528566,0.0998793095,-0.2433777303,0.3591911495,0.0829845816,-0.0730310678,-0.1192087159,0.3212790191,-0.1076370701,-0.1704480499,0.0092369532,-0.0881269574,0.0153183658,0.0442902222,0.0046651633,0.0154469348,-0.2489062846,-0.0319562852,-0.2206834406,-0.1371230483,0.098574169,-0.0503237098,-0.1877260506,-0.3946231604,0.0375735722,0.1146164834,0.2151951045,0.4167882502,-0.0001027996,0.100179024,0.0244873706,-0.1405445188,-0.420407176,-0.044092454,0.2414928675,-0.2798286676,0.0506280325,-0.2537201047,-0.217214182,0.2278189063,-0.137842834,0.1720422953,0.4584761858,0.3797530234,0.3435774744,0.1088275239,0.0585624166,-0.0294913892,0.0538175181,0.0755457133,-0.094014205,0.0066134301,0.1109111086,-0.1369544417,0.0238310844,0.3242061734,0.0113588162,-0.0089151151,-0.0367027931,-0.0341678485,0.2736227214,-0.2479079366,0.1470226943,-0.0891442969,0.1329367161,-0.303568095,-0.0373581164,-0.154106468,-0.0806474909,-0.4634771049,0.104373008,0.1010245979,-0.4107734263,0.0172619764,-0.060389284,-0.0050989529,-0.0138990106,0.0261216536,-0.0662517399,0.5225920081,-0.105364047,-0.0356148928,0.1708458811,-0.1355095953,0.4120981097,0.0853057355,-0.1276610792,0.0237403847,-0.2587937713,-0.1875814199,-0.0713279247,-0.1986297518,0.0711449012,-0.1124361977,0.1579910815,-0.0060249884,0.485934943,0.0080661094,0.0980062783,0.3584094346,-0.1202131212,0.3187831044,0.036064826,0.1946543455,-0.2176494598,-0.1244595945,-0.1859422475,-0.3380061984,0.0242839288,0.0381148756,-0.0462808795,0.0161911696,-0.1271799654,0.1684389859,-0.2535944283,0.273471266,0.1817775816,0.4570909142,-0.0964738876,0.2302170694,0.1769119948,0.039496012,-0.2093317509,-0.0818557143,-0.2425555587,0.1164505482,-0.3404504359,-0.1421803385,0.1027912721,0.2205390334,0.325173974,-0.1931248605,0.039561864,-0.3057804406,0.0956942514,-0.2383188754,0.005653997,0.120123364,0.3433279693,0.0877017304,0.0954148024,-0.2783067524,0.1181035638,0.2075963616,-0.2924019396,-0.2291511446,0.2030310035,0.3269710243,0.2827208042,0.1686135232,-0.3689680994,-0.0063974787,0.2053465843,0.1965654343,0.0562733859,-0.3184034526,-0.217391789,-0.1352854371,0.2781272531,0.3030323982,-0.1902775317,-0.2108455002,0.0214644112,-0.0315198824,0.3774225116,0.3584521413,-0.3117789626,0.458008647,-0.0675834194,-0.0518542342,0.5453791618,-0.5652977228,-0.2127517611,0.1543048024,-0.1909199059,0.0454589501,-0.2386426181,-0.2231659591,0.4829888046,0.0969781056,0.3733592331,0.2380772829,-0.0351438671,0.1896499544,-0.2864001989,0.0090552494,-0.0297914427,-0.0055249878,0.048713401,-0.0527741164,-0.2343461215,0.1280329823,0.2338199168,-0.0104771908,0.2677803338,0.3582985401,0.3073626459,0.116965957,-0.1215274259,0.1936755478,0.1673586965,-0.011587088,-0.0309378542,0.1239467412,-0.0786432847,-0.2058527172,-0.0455837846,0.082807377,-0.0156984944,-0.359300375,0.3384872079,0.0375348926,0.0266572926,0.019130975,0.12357115,0.2986924946,-0.3541118503,0.2222425044,0.2377438098,0.0852836743,-0.188553378,-0.1800913662,0.0524535179,0.1183927581,0.2418252528,0.0297301617,-0.09869048,0.2562616467,-0.0663084537,-0.0891714245,-0.093724899,-0.1130933166,0.0153399678,0.0903212279,-0.0647252128,-0.0206237342,0.1655088067,0.0199047942,0.351183176,0.2012838572,0.3272457421,0.1273166686,0.20463714,-0.0206221342,0.2332151383,-0.0857417285,0.1878331602,0.0592325293,0.2610836327,-0.0133153806,0.1250681132,0.0058278828,-0.2236133516,0.1084053963,0.2264301479,0.0882090405,-0.0231090486,0.2589934468,-0.2839830816,0.011871255,-0.0420016795,0.0582629777,0.0772991255,0.2125343382,0.1205851436,0.02303656,0.0700101703,-0.198204115,0.2579903603,0.0380883589,-0.0887699425,0.0096041206,0.1740602255,-0.1931758523,-0.3281930387,-0.1449996084,0.1208069474,0.4868705869,-0.1175011918,0.0966288596,-0.0272011105,-0.1130644977,-0.2430841476,-0.0940625593,0.1485604197,-0.0184286889,0.043626897,0.4213282168,-0.0010324048,0.3150789142,0.3121339977,0.2186200023,0.1445562392,-0.0231679399,0.0133008128,-0.1335752606,-0.2880644798,0.2566813529,0.2196992189,0.0427814312,0.1171905473,0.0628623143,-0.0104829054,-0.0288040359,-0.0449764356,-0.016239509,0.0228475034,0.1572996229,-0.165305838,0.2963692546,-0.2617641687,-0.4079509079,0.1808433533,-0.060446579,-0.2805642784,-0.1905061454,-0.0001221336,-0.2679699361,-0.2092573792,-0.216757074,-0.2778628469,-0.4542639256,-0.0103372186,0.0486837067,0.0854049921,-0.0741245449,0.2338189036,0.2066883296,0.2127250135,0.0140997944,-0.0412977263,-0.2158882469,0.0728689283,-0.2318145186,-0.1928956658,0.0099314637,-0.1537414342,0.3478007019,0.1561032534,-0.4876750708,0.3729537427,-0.0741802976,-0.271235466,-0.1000492945,-0.0658702105,0.3611463308,0.1052038521,-0.2213782966,0.1072303504,-0.1624223888,-0.0407600775,-0.2125340253,0.2935686707,-0.3540355861,0.6404054761,0.1446554661,0.8162136078,-0.0796792433,0.0347614996,0.1796438992,-0.0052892175,0.0840145051,-0.1534436494,-0.27903112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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/757","title":"CUDA out of memory","comments":"`RuntimeError: CUDA out of memory. Tried to allocate 954.00 MiB (GPU 0; 15.90 GiB total capacity; 14.35 GiB already allocated; 753.75 MiB free; 14.39 GiB reserved in total by PyTorch)\r\nException raised from malloc at \/pytorch\/c10\/cuda\/CUDACachingAllocator.cpp:272 (most recent call first):`\r\n\r\npart of error output","body":"In your dataset ,cuda run out of memory as long as the trainer begins:\r\nhowever, without changing any other element\/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.\r\n","comment_length":44,"text":"CUDA out of memory \n In your dataset ,cuda run out of memory as long as the trainer begins:\r\nhowever, without changing any other element\/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.\r\n \n `RuntimeError: CUDA out of memory. Tried to allocate 954.00 MiB (GPU 0; 15.90 GiB total capacity; 14.35 GiB already allocated; 753.75 MiB free; 14.39 GiB reserved in total by PyTorch)\r\nException raised from malloc at \/pytorch\/c10\/cuda\/CUDACachingAllocator.cpp:272 (most recent call first):`\r\n\r\npart of error output","embeddings":[-0.2118009329,-0.1436042786,-0.1251238734,0.3756681681,0.4871861339,0.0221625883,0.2372636348,0.1879336387,-0.0591316633,0.3615151942,0.203222096,0.0899497569,-0.1103607565,0.0615564361,-0.0812174976,-0.2425248623,-0.1598561704,-0.0211354885,-0.2000565976,0.0185324159,-0.0547572672,0.0996781886,-0.2718253136,0.0186517034,-0.4758870304,-0.2184392959,0.0810014233,0.1155068204,0.1469694525,-0.2040057778,0.0754881054,-0.259085238,0.0466847345,0.5353175402,-0.0000942944,-0.0917870924,0.0012609041,-0.0493776798,-0.1668550968,-0.0935569406,0.2256605923,-0.0973295197,0.169995144,-0.3079277277,-0.2159549147,0.1849445999,-0.1372168511,-0.2383294702,0.2873457968,0.4498963654,0.3909004629,-0.0431755222,0.0861904696,-0.1659832597,-0.0875586346,-0.1732179672,-0.0146951824,0.0005477721,0.138158083,-0.0281157382,-0.003598714,0.1856382936,0.0475580357,0.0354331508,0.0543559715,0.0449393056,-0.0404999256,-0.216718182,0.0772738829,-0.0306700766,0.11783804,-0.2315409631,0.0958704725,-0.0387620963,0.0093952063,-0.413179785,0.1378812343,0.1859952062,-0.3922763169,0.0164076183,-0.2586274743,-0.0303190649,-0.0934135765,0.0631963611,-0.1956590414,0.0080889892,-0.1182058156,0.0632062629,0.2467140555,-0.0173122883,0.2066463828,0.091149509,0.0142326057,-0.0221963245,-0.282905221,-0.0232094135,-0.1080686375,-0.3290325403,0.0293364115,-0.1882707328,0.2276498377,0.00415518,0.1609884501,0.0526035205,0.1466838717,0.3722299337,-0.3857885301,0.0974769741,0.0332972854,0.0865174532,-0.3276966214,-0.2392992377,-0.0550302528,-0.3216640651,0.0693228245,-0.0124912187,0.0669356212,-0.132598713,-0.1062621623,0.2111896425,-0.2619428933,0.2171588987,0.1159809604,0.2579928935,-0.140743345,0.1431768537,0.1726024449,-0.0793246031,-0.1121673137,-0.0890850127,-0.3556940854,0.1650259197,-0.3696238399,-0.0468623042,0.1952004582,0.0122246621,0.3212688863,-0.1181160659,0.202634424,-0.4704040587,0.0320863761,-0.3434745371,0.0106649967,0.0647375137,0.1504801065,0.0602492169,0.0967096463,-0.0062753516,0.2173627913,0.3751479089,-0.2610060871,-0.3041603863,-0.0635333136,0.3546237648,0.2509842217,0.119456321,0.0315395556,0.0001186078,0.2730512023,0.0621277541,0.0361014679,-0.1540497541,-0.0545561686,-0.1836684197,0.3471758962,0.188801229,-0.2707559168,0.0562871993,0.219892785,0.0580805875,0.1811884195,0.4126347303,-0.3015066087,0.1460334212,-0.1990502477,-0.1652383357,0.2115194798,-0.1684204489,-0.2656309605,0.0336978994,-0.0929884985,-0.1253866553,-0.195714578,0.0292374343,0.299109906,0.223430112,0.2497170269,0.1406296939,-0.256942302,0.0725273117,-0.3540612459,-0.1874581575,0.1888196319,0.0551340654,0.3159446716,-0.0105230073,-0.2614680231,0.1066229418,0.1901454926,-0.0579619519,-0.0212716665,0.1260482669,0.3186985552,-0.0929767191,-0.0987953618,-0.0475212559,0.0558381975,0.0396544635,0.066314213,0.1411865354,-0.1820722669,0.004149775,-0.0556848384,-0.0952627361,0.0390261151,-0.2896197736,0.3484506011,0.057923045,-0.1184399426,-0.0052982415,-0.0020452042,0.3104230762,-0.4857845008,0.0727449581,0.0900419578,0.17615062,-0.2769615352,-0.2281685323,0.0018621,0.1827633232,0.1517068446,-0.0721077621,-0.1934701204,0.2518944144,-0.0209872313,-0.2037340254,-0.1180720702,-0.0767886937,0.1183430925,-0.0845875293,-0.0492652506,0.0054363911,0.1151401699,-0.03707885,0.1277406514,0.0058230055,0.1121657416,0.0126649579,0.0891129151,-0.0318649225,0.1537890434,-0.0675355941,0.3522224426,-0.036048077,0.0746686235,0.2333031744,0.2196899951,0.0484552234,-0.3491225243,-0.0526744314,0.3689388335,0.1791172028,-0.0128230657,0.2499065548,-0.1032726541,0.0485810079,-0.0381968692,-0.1489265114,-0.0114671318,0.2289577425,0.0813973173,0.1702391207,-0.0014853079,-0.1016810462,0.1454582661,0.1066214219,0.232187286,-0.119015418,0.0962334499,-0.034775693,-0.2055913657,-0.1354940832,0.2554323077,0.4957813025,-0.181113556,-0.0380417854,-0.1817548573,-0.1290147156,-0.1607402414,-0.0649124607,-0.0140387565,-0.0476050861,0.0061219838,0.2428815961,0.0315907039,0.3472577035,0.1770738065,0.2584714592,0.121574305,0.152034685,0.2021979392,-0.2745970488,-0.1216749623,0.2975054681,0.2598125637,-0.0124936262,0.1879578084,0.168282941,0.0326671712,0.1049642041,-0.0073334319,-0.1140931472,0.0201444961,0.1777545959,-0.2008757293,0.1997723132,-0.1199362651,-0.2861014605,0.2192390561,0.0083032688,-0.1159449965,-0.0851343721,0.1891164929,-0.1369484961,-0.1508421749,-0.2441036254,-0.113745153,-0.359675616,-0.0707698762,-0.0618628114,0.120352298,-0.1065524518,0.3134255409,0.382746309,0.0811476856,0.0531449839,-0.1908714473,-0.1974184364,-0.0866251662,-0.1350986958,-0.224124074,0.0129015716,0.1013907343,0.4695522785,0.015268201,-0.6003519297,0.0291695036,-0.0662457421,-0.0540731959,-0.0895547271,-0.1722446978,0.4207229614,0.0196295939,-0.2582046092,0.1793518513,-0.1991150528,0.060993474,-0.1229906529,0.1664449573,-0.245826304,0.615635097,0.2445935607,0.5803915858,0.0293949954,-0.1628429443,0.1924736649,-0.1467560381,0.14566423,-0.2136935145,-0.337397486,0.3285893202,-0.2230758071,-0.3722511232,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/757","title":"CUDA out of memory","comments":"from funnel model to bert model : error still happened\r\n\r\nfrom your dataset to LineByLineTextDataset : error disapeared","body":"In your dataset ,cuda run out of memory as long as the trainer begins:\r\nhowever, without changing any other element\/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.\r\n","comment_length":18,"text":"CUDA out of memory \n In your dataset ,cuda run out of memory as long as the trainer begins:\r\nhowever, without changing any other element\/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.\r\n \n from funnel model to bert model : error still happened\r\n\r\nfrom your dataset to LineByLineTextDataset : error disapeared","embeddings":[-0.2429937124,-0.0383692868,-0.0662143826,0.19402197,0.5733138323,-0.096849665,0.3483140171,0.0940258205,-0.0914314687,0.4046832919,0.0462234057,0.0135813393,-0.0812719017,0.3208122551,-0.1960563958,-0.3946526647,-0.0328095481,0.0102433218,-0.1957312971,-0.0121695027,-0.0123564731,-0.0033930612,-0.1592791975,0.0870993361,-0.1410438865,-0.03772806,0.002121479,0.0090349838,-0.1163757145,-0.2628358305,0.0889436752,-0.0642799363,0.1672817916,0.3281091452,-0.0001007679,0.1339539886,0.1042969301,-0.1116643324,-0.2635507286,-0.1729589105,-0.1789835095,-0.1399423927,0.2102268338,-0.2823681831,-0.1664161384,0.4420573413,-0.0516923405,0.03373098,0.5577896237,0.4128130376,0.335689038,0.1212375909,0.1510000527,-0.2648308277,0.2176375985,-0.0050718458,-0.0425598994,-0.040778853,0.1038939878,-0.0698297545,-0.0774987489,0.3561424315,0.1329948902,0.1485108882,0.1547268331,-0.0627807453,0.1188859418,-0.2141754925,0.1081306785,0.1221961603,0.1534587294,-0.1914740056,-0.0599850528,0.0532139763,0.0153939854,-0.3182945848,0.1467740089,-0.0166956671,-0.3548562527,0.0946364105,-0.1052352041,-0.2175528854,-0.1025776491,-0.1140932888,-0.1665934473,0.1490675509,-0.03719173,-0.0587535836,0.2903490067,-0.1254053712,0.3205078244,0.1537186056,0.0427101962,-0.0738143772,-0.2277662158,-0.1170416698,-0.0533464141,-0.1595803052,0.0571266636,-0.3830276728,0.2500422895,-0.1162567958,0.1959917843,0.035354618,0.3170464039,0.286280036,-0.3701386452,0.2391137481,0.0503853895,0.1746375561,-0.3613769114,-0.2272451073,-0.1112045571,-0.3267977834,-0.0800593197,-0.0327487327,0.0792790577,-0.1619876474,-0.2722689211,0.1725315154,-0.254881978,0.2640634179,0.2559896111,0.4726040959,0.0060154749,0.2089016885,0.132183224,0.0104280077,-0.0713887066,-0.0696887374,-0.2688013911,0.0624106675,-0.3850221336,-0.1537840962,0.1105828807,0.0035166475,0.2379930913,-0.1026343405,0.0520373024,-0.1890207976,0.0747663528,-0.3209008873,0.0013835065,0.1301466972,0.3464878798,0.0360809378,0.020009907,-0.0249867551,0.2250242531,0.2758370936,-0.4562953115,-0.3984077275,0.0950752571,0.2998204231,0.3816589415,0.0991173759,-0.1765723526,0.1599657834,0.1963942051,-0.049138803,-0.0305246413,-0.2006733567,-0.0990436524,-0.1039260626,0.3158841431,0.391621232,-0.4216264486,-0.1140593961,-0.1328940541,-0.0142196221,0.3896189034,0.5055039525,-0.4047174752,0.1558742225,-0.0543605424,0.0269637015,0.3355779648,-0.273036778,-0.2073615491,0.1021931693,0.0825998113,-0.0646452382,-0.2675142288,-0.2481892258,0.3915963769,0.0264495462,0.250297159,0.0818310231,-0.2580812573,0.1815284491,-0.2347031683,-0.0493548736,0.1143747196,-0.0836780816,0.2223152965,0.0171532445,-0.2288851738,0.3926403522,0.0965137258,-0.1182944849,0.1400403231,0.2874973416,0.337926209,0.0090108616,-0.0741014928,0.1534021348,-0.0264252778,-0.0433663316,-0.0966737047,0.0782344192,-0.0732004866,-0.1002274454,-0.0470795892,0.0761650726,0.0329090543,-0.2485426068,0.3355541229,0.0063603483,-0.245523721,-0.071964182,0.0238838587,0.0617828108,-0.4273528457,0.1283402741,0.253841579,0.1770500988,-0.3252508342,-0.0646981448,0.0731215477,0.1953135729,0.1215332672,0.0226774067,-0.1938195527,0.2192295194,-0.0949145108,-0.0056969658,0.1321394742,-0.2101243883,0.0761880875,-0.1818877459,-0.1388998777,-0.0490953922,0.2414444685,0.0396053456,0.0389331654,-0.0427992903,0.2721196413,0.1030704603,0.27469486,0.0665912852,0.3181394339,0.030663345,0.2861680984,-0.1076054946,0.1705531627,0.1305482984,0.3169270158,0.0225526094,-0.341042608,0.0226457827,0.4099620283,0.2349964529,0.0324318856,0.2992371917,-0.1192656383,-0.1215240508,-0.056027934,-0.0252685305,0.0644885451,0.1640000939,0.1957211643,0.0184660032,-0.0664573759,-0.1216934174,0.2433014661,0.094361268,0.0452732779,-0.1018354744,0.177628085,-0.1306377202,-0.2493300736,-0.0861566141,0.2323688567,0.4989029765,-0.2284733653,0.062990576,-0.0876248479,-0.0317607522,-0.1993570775,-0.345600605,-0.0594019629,-0.0510536805,0.1763631999,0.2323435545,-0.0868498236,0.3003810048,0.3504382968,0.291087091,0.0862325281,0.3323001564,-0.0694113746,-0.2761394382,-0.1076880619,0.2419588864,0.291364938,0.0424761139,0.0451027155,0.0642616153,0.06966912,0.0128549114,-0.1309442371,0.0154970288,0.0609394014,0.2333246171,-0.00944856,0.3017679155,-0.2326458097,-0.3526659906,0.2210343927,0.0122787775,-0.2054139525,-0.0669541359,0.0479299575,-0.1271324009,-0.1160670593,-0.2654438615,-0.1898758858,-0.4146497548,-0.1663347185,-0.1074047536,0.0727820992,-0.273742348,0.2912783921,0.3617987037,0.2805692554,-0.010073415,-0.0860195458,-0.1375756711,-0.1062793583,-0.2191572338,-0.2372312248,-0.0587483384,0.1296009719,0.3659161031,0.0628581122,-0.4608674943,0.2224972993,-0.0445925705,-0.3251991868,0.033326745,-0.1258550584,0.3331925571,0.1592571884,-0.2517319024,0.0018177774,-0.0933884457,0.0826418847,-0.2295024395,0.2506913543,-0.4444752038,0.6022287607,0.0526815727,0.8941935897,-0.1279267222,-0.0536749102,0.3798964918,0.0599210672,0.1554176509,-0.3653232157,-0.2811015844,0.2810940742,-0.1616919041,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/757","title":"CUDA out of memory","comments":"Since you're using a data collator you don't need to tokenizer the dataset using `map`. Could you try not to use `map` and only the data collator instead ? The data collator is supposed to pad to the longest sequence in each batch afaik, instead of padding to 512.\r\n\r\nAlso cc @sgugger ","body":"In your dataset ,cuda run out of memory as long as the trainer begins:\r\nhowever, without changing any other element\/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.\r\n","comment_length":52,"text":"CUDA out of memory \n In your dataset ,cuda run out of memory as long as the trainer begins:\r\nhowever, without changing any other element\/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.\r\n \n Since you're using a data collator you don't need to tokenizer the dataset using `map`. Could you try not to use `map` and only the data collator instead ? The data collator is supposed to pad to the longest sequence in each batch afaik, instead of padding to 512.\r\n\r\nAlso cc @sgugger 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/751","title":"Error loading ms_marco v2.1 using load_dataset()","comments":"There was a similar issue in #294 \r\nClearing the cache and download again the dataset did the job. Could you try to clear your cache and download the dataset again ?","body":"Code:\r\n`dataset = load_dataset('ms_marco', 'v2.1')`\r\n\r\nError:\r\n```\r\n`---------------------------------------------------------------------------\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-16-34378c057212> in <module>()\r\n      9 \r\n     10 # Downloading and loading a dataset\r\n---> 11 dataset = load_dataset('ms_marco', 'v2.1')\r\n\r\n10 frames\r\n\/usr\/lib\/python3.6\/json\/decoder.py in raw_decode(self, s, idx)\r\n    353         \"\"\"\r\n    354         try:\r\n--> 355             obj, end = self.scan_once(s, idx)\r\n    356         except StopIteration as err:\r\n    357             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660)\r\n`\r\n```","comment_length":31,"text":"Error loading ms_marco v2.1 using load_dataset() \n Code:\r\n`dataset = load_dataset('ms_marco', 'v2.1')`\r\n\r\nError:\r\n```\r\n`---------------------------------------------------------------------------\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-16-34378c057212> in <module>()\r\n      9 \r\n     10 # Downloading and loading a dataset\r\n---> 11 dataset = load_dataset('ms_marco', 'v2.1')\r\n\r\n10 frames\r\n\/usr\/lib\/python3.6\/json\/decoder.py in raw_decode(self, s, idx)\r\n    353         \"\"\"\r\n    354         try:\r\n--> 355             obj, end = self.scan_once(s, idx)\r\n    356         except StopIteration as err:\r\n    357             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660)\r\n`\r\n``` \n There was a similar issue in #294 \r\nClearing the cache and download again the dataset did the job. Could you try to clear your cache and download the dataset again ?","embeddings":[0.010368593,0.1111898348,-0.1000250876,0.3606889546,0.4273394346,0.051915016,0.2879604995,0.5654810071,0.0125486925,0.3217283189,-0.0122815529,0.5649032593,-0.0228346568,0.2471558452,-0.1524105668,-0.3327514231,-0.0393442959,0.3240633905,0.130652234,0.1089469045,-0.0823314264,0.2334581763,-0.1433686763,0.234962821,0.0887256414,0.1099612266,0.0444128998,0.1186437234,-0.3717558682,-0.6237437129,0.2395816296,-0.0147982743,0.3841393888,0.4122720063,-0.0001079264,0.2034292966,0.4274071157,-0.1497963965,-0.328352809,-0.6941396594,-0.5283315182,-0.0733867437,0.330268383,-0.0713751391,-0.0157223493,0.0518315434,-0.2022550702,-0.4683452547,0.7235790491,0.3449121118,0.2332075983,0.0984003097,0.2272278368,-0.0567691959,0.1948496848,0.1532230824,0.0622134432,0.2472892404,-0.1464816779,0.2154764235,0.2918561697,0.2464020252,-0.1134709418,0.3241589069,0.1573538631,0.1225416735,0.3648796678,-0.4494427443,0.3054109216,0.1438313425,0.6902774572,-0.1550350785,-0.2551623285,0.076748848,0.1294802725,-0.4748290479,0.3319576681,0.0643206164,-0.2432660609,0.1752771735,-0.0887585357,0.0134896645,-0.2744025886,0.0100894226,-0.1931982934,0.0089537222,-0.1390662938,0.1527302563,0.3419303894,-0.2617520988,0.0862084851,0.0477184989,-0.2760582864,0.1573705971,-0.193217203,-0.1091299579,-0.1569622159,-0.4858180881,-0.0533559658,0.0962756202,0.2548743784,0.0609164909,0.3814300299,0.0362790786,0.6026726365,0.0255676787,-0.1211960316,0.0345732681,0.1222294345,0.0011011522,0.0688749999,-0.080301553,-0.2731036544,0.0020022115,0.1748583615,0.1447976679,0.3112102151,-0.2800496817,-0.338255614,0.1514056474,-0.5848353505,-0.2422351837,-0.2254050672,0.3102692664,0.0200363994,0.243372798,0.0605809614,0.2738599777,0.0199087597,-0.345985949,-0.0432902537,-0.151647523,-0.034664046,-0.1455734074,0.2728931308,-0.0849440917,0.1361089945,0.0872814953,0.1812023669,-0.3393514752,0.3095952272,-0.2254335731,0.0995342582,0.322071135,0.2754564881,0.0525145791,0.1816451102,-0.1673370153,-0.0530319996,0.3141585588,-0.2938114405,-0.2666989565,-0.272241056,0.2071204484,0.0013436769,-0.1711638719,-0.2540102899,0.0457333252,-0.0178011265,-0.0603865162,-0.3778084219,-0.166707918,0.0880220011,-0.1780563593,0.3932457268,0.2161404043,-0.3279469013,0.0394064449,-0.0959152654,-0.1318071783,0.2154319882,0.1643887758,-0.3142090142,0.5403991342,-0.0388913788,-0.1691236645,0.0986142009,-0.3066726029,-0.2937805653,0.3694313169,0.0046840599,0.1591133624,0.0017894151,-0.2512532771,0.3732081652,0.1588671207,0.1945876777,0.0618088804,-0.1427410543,0.091400139,-0.0991631299,-0.2575182319,0.0906345546,0.3011377454,0.0775809586,0.0796924233,-0.0985772461,0.2593408823,0.1528244913,-0.1152997166,0.125785172,0.1634820998,-0.1097195745,0.163519904,0.0510325208,0.1401446462,-0.2065712661,0.0699192733,0.041585654,0.2281046808,-0.3960620165,0.0385790132,-0.3178732395,0.0104322629,-0.3651974499,0.1068749055,0.2487116605,0.1227117181,0.0558310673,0.1275044233,-0.3013746142,-0.0274712723,0.3916778266,0.1682389826,-0.0519139394,0.2938364148,-0.1912588775,-0.2941619158,0.1120572463,-0.0442084298,0.0378200486,-0.1330128312,-0.3254247904,0.237740919,0.1895778179,0.2034036815,-0.1667607874,-0.1456785649,0.1348054856,-0.3300461173,0.083804056,0.5397526622,0.1680045724,-0.0221341699,-0.1026687026,0.1981106699,-0.0855673701,0.1137717143,-0.0782797113,-0.0304033551,0.3267214596,0.0816080049,-0.175970912,-0.2072336674,0.262455821,-0.0087886006,0.0951394513,-0.1814563274,-0.2600827813,-0.2177453041,0.2487670183,-0.078285411,-0.0508634597,0.1320009679,-0.0949898735,0.0630510747,-0.2078442574,0.0244821422,0.2064062655,0.0302203465,-0.2265804112,0.1264622509,0.1852026284,-0.0127630159,0.2005794495,0.1897395104,0.3189218044,0.273775816,-0.0581553727,0.0075665601,-0.2234509587,-0.1390782744,-0.110349752,0.450409919,-0.2590261102,0.1004737243,-0.2818918526,-0.0750758201,-0.1346456259,-0.4714370966,-0.0931167752,-0.0985968709,-0.2331870943,0.1405063719,0.0831357315,0.0812237486,-0.102752991,0.070249185,0.1847392172,0.0335136652,-0.1212211922,-0.3000424504,-0.2222504616,0.0501412116,0.3008953631,-0.3179346919,-0.0289157107,-0.402251184,0.0792943239,0.2344600856,-0.2420565039,0.2829415202,-0.2433229536,0.1071463674,0.0976634547,0.3463149965,-0.0807698891,-0.1787151247,0.2980033457,-0.033628013,-0.0701223835,0.2351943105,-0.024764929,0.0044142492,-0.0345524028,-0.6003323793,-0.2259482443,-0.1773615479,0.084404625,0.0945687369,0.0720440298,0.2071624398,0.2518556118,0.1329909265,0.1408315301,0.2209936231,-0.2139878869,-0.4691381752,0.1890761405,-0.1856191903,-0.288592279,0.3380995095,0.1319410056,0.3926619589,-0.3039434254,-0.3875097334,0.311933279,0.1138057634,-0.1046722755,-0.4057462215,-0.3225399256,0.1243585423,0.1816706359,0.0121639222,-0.0981237888,-0.2578531802,0.2355495989,-0.0515550934,0.1400612742,-0.0608264059,0.8555181623,-0.0773985311,0.328720808,0.2119135261,0.1571446508,0.2867904603,-0.0260023903,0.0474580079,-0.0931168646,-0.1263145804,-0.0346654542,-0.1132034808,-0.1697463691,0.10504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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/751","title":"Error loading ms_marco v2.1 using load_dataset()","comments":"I was able to load the dataset successfully, I'm pretty sure it's just a cache issue that you have.\r\nLet me know if clearing your cache fixes the problem","body":"Code:\r\n`dataset = load_dataset('ms_marco', 'v2.1')`\r\n\r\nError:\r\n```\r\n`---------------------------------------------------------------------------\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-16-34378c057212> in <module>()\r\n      9 \r\n     10 # Downloading and loading a dataset\r\n---> 11 dataset = load_dataset('ms_marco', 'v2.1')\r\n\r\n10 frames\r\n\/usr\/lib\/python3.6\/json\/decoder.py in raw_decode(self, s, idx)\r\n    353         \"\"\"\r\n    354         try:\r\n--> 355             obj, end = self.scan_once(s, idx)\r\n    356         except StopIteration as err:\r\n    357             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660)\r\n`\r\n```","comment_length":29,"text":"Error loading ms_marco v2.1 using load_dataset() \n Code:\r\n`dataset = load_dataset('ms_marco', 'v2.1')`\r\n\r\nError:\r\n```\r\n`---------------------------------------------------------------------------\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-16-34378c057212> in <module>()\r\n      9 \r\n     10 # Downloading and loading a dataset\r\n---> 11 dataset = load_dataset('ms_marco', 'v2.1')\r\n\r\n10 frames\r\n\/usr\/lib\/python3.6\/json\/decoder.py in raw_decode(self, s, idx)\r\n    353         \"\"\"\r\n    354         try:\r\n--> 355             obj, end = self.scan_once(s, idx)\r\n    356         except StopIteration as err:\r\n    357             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660)\r\n`\r\n``` \n I was able to load the dataset successfully, I'm pretty sure it's just a cache issue that you have.\r\nLet me know if clearing your cache fixes the problem","embeddings":[-0.0129359569,0.1030107811,-0.1228333712,0.3122574687,0.3983422816,0.0521115921,0.2968255281,0.5685597658,0.0205572154,0.3210778832,-0.0469787084,0.5231743455,0.0632516816,0.1900868565,-0.1655188352,-0.2574036717,-0.0236327332,0.3507929146,0.0692266896,0.0745202303,-0.0900942162,0.1957165897,-0.1244621351,0.2141236961,0.0591254495,0.0740995929,0.0389386192,0.1152041405,-0.3439752758,-0.6365219951,0.1919737756,-0.0079323296,0.3155696392,0.4043069184,-0.0001020064,0.1777641773,0.4454354048,-0.0689876229,-0.3125953078,-0.582008779,-0.5053097606,-0.117103003,0.3165388107,-0.1282913983,-0.1209806576,0.1134021059,-0.1850688607,-0.4382014573,0.6948314309,0.3881035447,0.3028866053,0.1351647079,0.2301937789,-0.1009389684,0.2550452352,0.1251532286,0.0138985058,0.2112888247,-0.1272655278,0.2033331394,0.2619906366,0.2876456976,-0.0855982825,0.3251772821,0.1399059892,0.1585076749,0.3314511776,-0.3463332355,0.2681953311,0.1036736369,0.6430297494,-0.1307166368,-0.2762633264,0.0868243799,0.079673253,-0.5477735996,0.3189710081,0.0929363742,-0.2067407519,0.1782433689,-0.0895706862,0.0375676341,-0.1832725704,0.0019572945,-0.2085376978,0.0816446841,-0.1336175948,0.0782065094,0.3002834916,-0.2424610406,0.0746838823,0.0812309012,-0.2699528635,0.1097850651,-0.1803840995,-0.0948034674,-0.0430226736,-0.4446944594,-0.1199472025,0.1145335287,0.2236438096,0.0987935066,0.3771330118,-0.0008453242,0.463940829,0.0501384921,-0.1559505463,0.0771147609,0.1845827252,0.0444036573,-0.0029853527,-0.10394191,-0.2111139148,0.0192598104,0.1602069736,0.1095812023,0.3138606846,-0.2204987258,-0.2839262187,0.0783530921,-0.4800007939,-0.1939749122,-0.1975454837,0.3207300603,-0.0632954314,0.2304754704,0.0567353852,0.2245039493,-0.0679012388,-0.2256979644,-0.1210092083,-0.131233871,-0.0852742717,-0.1201667413,0.2747131586,-0.0853872821,0.1471427232,0.0709505007,0.1519605517,-0.2827930152,0.3410668373,-0.2363017797,0.1135585755,0.3298206329,0.2770091295,0.0701734424,0.181936115,-0.1325307488,-0.096114397,0.2491235584,-0.2524724007,-0.2802442014,-0.1876838207,0.2855273485,0.0113282856,-0.1512570083,-0.2134167403,-0.0533782616,0.0100037782,-0.0807726085,-0.3593396842,-0.1714255065,0.0606306829,-0.2019232213,0.3621022999,0.2705246508,-0.2723309696,0.0733098388,-0.1354018748,-0.0922605991,0.1877469867,0.2017704844,-0.3014396727,0.4362956285,-0.0464412123,-0.1566222161,0.1402229518,-0.3202365041,-0.324088335,0.3028960526,-0.0329794586,0.0878275633,0.025868902,-0.1924054474,0.3647069633,0.1537065655,0.1992154568,0.0544421673,-0.0356813632,0.123538658,-0.1245329753,-0.2586768568,0.0181741659,0.2463353723,0.0644575283,0.0442390405,-0.1041809767,0.2037008852,0.1403645724,-0.1245170459,0.1137161776,0.2085631043,-0.1304852813,0.1713306755,0.0299089886,0.1364278197,-0.1399660259,0.1214684993,0.025929274,0.1900323033,-0.2726621628,0.0064861607,-0.3014554679,0.0316114724,-0.368709594,0.1013638079,0.3043015003,0.1964470446,0.1233296543,0.1316632479,-0.2749223411,-0.0110125663,0.4417962432,0.1070485935,-0.1180402935,0.261908859,-0.2515699863,-0.2915503979,0.1108597741,-0.0367101543,0.0580888987,-0.0801604316,-0.3036938608,0.1816572249,0.2036078274,0.2495737076,-0.1795324236,-0.1363855153,0.0791234225,-0.2600405812,0.1992523521,0.5228087306,0.120222576,-0.024972599,-0.0676704645,0.2387714833,-0.0229306649,0.1125961915,-0.0246520545,-0.0547818206,0.3964394331,0.0283731315,-0.1547789574,-0.2609422505,0.2471832335,0.0534547232,0.1135942638,-0.1151223704,-0.3128592968,-0.1558890343,0.3872981369,-0.036732804,0.0159019101,0.1358817816,-0.1428260207,0.0166943911,-0.2165794075,-0.0299253613,0.1951276213,0.088865526,-0.1606716514,0.0816550702,0.1995746195,-0.0875950977,0.2196178734,0.2029738724,0.2804163694,0.1726022512,0.035534434,-0.0301926322,-0.2262343466,-0.0232357215,-0.1176520064,0.3725837469,-0.2375440896,0.084952496,-0.3510678709,-0.0531920083,-0.1461604536,-0.5117496848,-0.0538696796,-0.1060151458,-0.1435576379,0.1756749004,0.0639039576,0.1328894496,-0.0974535942,0.0262348205,0.1760558635,-0.0128644807,-0.1240892857,-0.3575330675,-0.2978409827,0.1148166433,0.2390877455,-0.3049159646,0.0696581602,-0.4312526882,0.0471266545,0.185514003,-0.2506832182,0.257073313,-0.1656760126,0.0676144883,0.0843776315,0.2562079728,-0.0387876891,-0.1973778456,0.3516507447,-0.1273055673,-0.1333613694,0.2202691287,-0.0234848429,0.0086861402,-0.0370711125,-0.6399906278,-0.223705247,-0.2604730129,0.1010658741,0.1509131491,0.0356118344,0.1985674798,0.1864263117,0.1326588094,0.0938302279,0.2327876091,-0.2737099826,-0.4300875366,0.2375005633,-0.2297223955,-0.3239218891,0.3098138273,0.0468384735,0.3736924231,-0.3118451238,-0.4044915438,0.2070856541,0.0206048284,-0.0509722754,-0.3278871477,-0.2940309048,0.2029005736,0.1398703456,-0.0699764043,-0.0671648607,-0.2701518834,0.1653960049,-0.0801305249,0.1338672191,-0.1019447222,0.850292027,-0.1222684085,0.408911556,0.1821502596,0.1215497926,0.2841959894,-0.0286712442,0.0892631635,-0.1776071936,-0.1607208401,-0.0405220464,-0.088667966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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/749","title":"[XGLUE] Adding new dataset","comments":"Small poll  @thomwolf @yjernite @lhoestq @JetRunner @qiweizhen .\r\n\r\nAs stated in the XGLUE paper: https:\/\/arxiv.org\/pdf\/2004.01401.pdf , for each of the 11 down-stream tasks training data is only available in English, whereas development and test data is available in multiple different language *cf.* here: \r\n\r\n![Screenshot from 2020-11-04 15-02-17](https:\/\/user-images.githubusercontent.com\/23423619\/98120893-d7499a80-1eae-11eb-9d0b-57dfe5d4ee68.png)\r\n\r\nSo, I'd suggest to have exactly 11 \"language-independent\" configs: \"ner\", \"pos\", ... and give the sample in each dataset in the config a \"language\" label being one of \"ar\", \"bg\", .... => To me this makes more sense than making languaga specific config, *e.g.* \"ner-de\", ...especially because training data is only available in English. Do you guys agree? ","body":"XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance ","comment_length":105,"text":"[XGLUE] Adding new dataset \n XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance  \n Small poll  @thomwolf @yjernite @lhoestq @JetRunner @qiweizhen .\r\n\r\nAs stated in the XGLUE paper: https:\/\/arxiv.org\/pdf\/2004.01401.pdf , for each of the 11 down-stream tasks training data is only available in English, whereas development and test data is available in multiple different language *cf.* here: \r\n\r\n![Screenshot from 2020-11-04 15-02-17](https:\/\/user-images.githubusercontent.com\/23423619\/98120893-d7499a80-1eae-11eb-9d0b-57dfe5d4ee68.png)\r\n\r\nSo, I'd suggest to have exactly 11 \"language-independent\" configs: \"ner\", \"pos\", ... and give the sample in each dataset in the config a \"language\" label being one of \"ar\", \"bg\", .... => To me this makes more sense than making languaga specific config, *e.g.* \"ner-de\", ...especially because training data is only available in English. Do you guys agree? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/749","title":"[XGLUE] Adding new dataset","comments":"In this case we should have named splits, so config `ner` has splits `train`, `validation`, `test-en`, `test-ar`, `test-bg`, etc...\r\n\r\nThis is more in the spirit of the task afaiu, and will avoid making users do the filtering step themselves when testing different models or different configurations of the same model.","body":"XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance ","comment_length":50,"text":"[XGLUE] Adding new dataset \n XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance  \n In this case we should have named splits, so config `ner` has splits `train`, `validation`, `test-en`, `test-ar`, `test-bg`, etc...\r\n\r\nThis is more in the spirit of the task afaiu, and will avoid making users do the filtering step themselves when testing different models or different configurations of the same model.","embeddings":[-0.4026003182,-0.0958493128,-0.1003199592,-0.1407158375,-0.1486179531,-0.1347500682,0.2657963932,0.3144331574,0.0133637274,0.1866730452,-0.17175138,0.0889778659,-0.0634423941,0.341416955,0.3362111151,-0.0489127561,-0.0718723834,0.1033512279,0.2111007869,-0.0516765267,-0.068702355,0.2674790621,0.0762642696,0.1808439344,-0.1707201898,-0.396427691,-0.0069606001,0.1819518954,-0.0863823593,-0.3037915528,-0.0648695454,0.2361162007,-0.2647854984,0.2227278203,-0.0001021953,0.0186399277,0.0620974973,-0.1218779758,0.0950857401,-0.0929759815,-0.2374880761,-0.2317353487,-0.0698724464,-0.0005482199,-0.390712589,0.1390844733,-0.0086090108,-0.3298976719,0.326613456,0.3445443511,0.237161845,0.1790290177,-0.1479173452,-0.1607901305,0.225808233,-0.0567048341,-0.0942573696,0.1117315516,0.42716676,0.1156872138,-0.2336812317,0.4810343981,0.3760601282,-0.1239869222,-0.1967464983,-0.0698347092,0.116224058,-0.2355744094,-0.22294949,0.6634970307,-0.0900202766,-0.18455486,-0.404548645,-0.1773203313,-0.0388085395,-0.2991451621,0.1057845727,0.2563796341,-0.1572853923,0.0980824232,0.2095357627,-0.5304242373,-0.101264745,-0.0068214149,-0.1744519323,0.5518788695,0.0126464935,-0.0335026868,0.1698779166,0.0689379275,-0.1111647189,-0.0333246812,-0.1056858152,-0.1299832463,-0.3020146787,-0.1565746516,-0.0306329112,0.1455106884,0.1133949161,0.0860124677,0.1005492434,0.2182037681,0.1289458871,0.1670177728,-0.1857685447,0.0559552126,0.2164851725,0.019334862,0.2798151076,-0.166944474,-0.1872885674,0.0404679291,0.1680810004,-0.074464418,-0.4383301437,-0.0118650096,-0.1712932289,-0.1781162918,-0.3314332664,0.024797691,-0.0068879151,-0.1436261386,0.2092060745,0.2793003321,0.0224499423,-0.1478819102,-0.1934198886,0.1645053774,-0.2525926828,-0.3452326357,-0.1880884916,0.4269104302,-0.2951265275,-0.0494643971,0.0286358763,-0.0868245885,0.1208114997,0.0471362844,-0.1909853667,0.0883193612,0.362795651,-0.0257854871,0.0616963431,0.1848794222,0.0155417407,-0.108741723,0.0349667706,-0.1832040399,-0.3418172002,-0.2202503234,0.0020331701,-0.2498936206,-0.2068327516,0.2593634725,-0.1927213967,-0.1097695604,0.501901269,0.7477043271,-0.1138684452,-0.3097315729,0.0103494432,0.0722304881,-0.1551324427,-0.1781288683,0.142340228,0.2348195016,-0.7939331532,-0.4151142538,-0.1149807125,-0.37403813,0.2320399284,0.0306626987,-0.1746026278,-0.179068774,-0.067640312,0.5377990603,0.4789310992,-0.0326394998,-0.150624916,0.1968884468,-0.3211741149,-0.0246893503,0.3203324676,0.1018750742,0.0402509756,-0.0075318152,-0.1099249423,0.5292525887,-0.3676404953,-0.3368195891,-0.2437324375,0.0249286927,0.3988683224,0.1669460535,0.2497586906,-0.3429686427,0.1850258559,0.0071820016,0.0768774003,-0.2135211527,0.0529179722,0.1174665317,0.3316627443,-0.1416356713,-0.1905556768,-0.1798572838,-0.1615664065,0.0233864971,-0.4569210708,0.3356417418,0.2028000057,-0.1793420315,-0.0773236156,-0.2438413352,-0.2253038138,-0.256334573,0.2326025069,0.2952122986,0.1288315207,0.1328589767,-0.1517306715,0.348810941,-0.2325498164,0.1285135746,-0.3362828493,0.1396402419,-0.1186682731,-0.185119763,-0.1478704065,0.1360009462,-0.1948617548,-0.2928481996,0.0158039331,0.4303457737,0.1253077984,-0.0231997836,0.2271459699,0.0917504504,0.2911508083,-0.0336008631,0.117426835,-0.0836509094,-0.0812103897,0.0772666857,-0.0643262267,0.5886890292,0.2394322455,-0.1911157072,0.189277336,0.0202029254,0.3595643044,-0.1796154678,-0.1154732704,-0.2785170972,0.3284752965,-0.0679824799,0.1604867578,0.1114509106,-0.4790667593,-0.0094287936,0.4859450758,0.0590871796,0.3648809493,-0.0237304829,0.1297607273,-0.1083734483,0.0682213306,0.4315474927,0.1073834673,0.2848714292,0.2683021128,-0.0328877755,-0.377515614,-0.2526907027,0.1884729117,-0.1644174308,-0.0211052727,0.2314586639,-0.178697601,-0.4338044226,-0.4670210779,-0.1032855213,0.2745408714,-0.0487776957,-0.1409630477,-0.1502785385,-0.1997580081,-0.2132203281,-0.3502658308,-0.097134158,-0.3459997773,-0.1867788583,0.5182228684,-0.2924462557,-0.1760054976,0.3785411119,-0.1598130018,0.307507813,-0.2044347972,-0.0438871458,0.0040378701,-0.0985976011,-0.3283260763,0.2513146996,0.1908450872,0.2258790731,0.3649789095,-0.0743880644,0.0986498892,0.0716004074,-0.7308645844,0.1624788344,-0.1577544957,0.2988201678,0.1267984957,-0.1182792559,0.1131324768,-0.2868728042,0.2103506625,-0.240805313,-0.1970635355,0.0792607963,-0.040716324,0.0391069427,-0.2071117312,-0.8082507849,-0.4853231013,-0.3589679003,-0.1158563197,-0.1579067409,0.1148273051,0.1002138779,0.0731069371,0.1114850715,-0.2805955708,0.007613312,-0.151039198,-0.123407498,0.1163697094,-0.2014593482,-0.1243724003,-0.0786941051,-0.20791623,0.1581918746,0.0877095088,-0.1286592036,-0.0306887534,-0.0390449129,0.1243704483,0.4112747014,-0.0139590818,0.3051303625,-0.0276305657,-0.1860746443,-0.2051464021,-0.0044551315,0.3356563449,0.0322706066,0.1974271834,-0.120324634,0.3349353373,0.1339778304,0.3058947623,-0.0827450231,-0.0348390378,0.3734642267,0.0061591254,0.1780783236,-0.0371430963,-0.1299080104,0.222192198,0.04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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/749","title":"[XGLUE] Adding new dataset","comments":"I see your point! \r\n\r\nI think this would be quite feasible to do and makes sense to me as well! In the paper results are reported per language, so it seems more natural to do it this way. \r\n\r\nGood for me @yjernite ! What do the others think? @lhoestq \r\n","body":"XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance ","comment_length":49,"text":"[XGLUE] Adding new dataset \n XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance  \n I see your point! \r\n\r\nI think this would be quite feasible to do and makes sense to me as well! In the paper results are reported per language, so it seems more natural to do it this way. \r\n\r\nGood for me @yjernite ! What do the others think? @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/749","title":"[XGLUE] Adding new dataset","comments":"Okey actually not that easy to add things like `test-de` to `datasets` => this would be the first dataset to have this.\r\nSee: https:\/\/github.com\/huggingface\/datasets\/pull\/802","body":"XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance ","comment_length":24,"text":"[XGLUE] Adding new dataset \n XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance  \n Okey actually not that easy to add things like `test-de` to `datasets` => this would be the first dataset to have this.\r\nSee: https:\/\/github.com\/huggingface\/datasets\/pull\/802","embeddings":[-0.1846334934,-0.2418597192,-0.1847652048,-0.1303508282,-0.0518171452,0.0069968733,0.2505635023,0.1113591269,0.1802298576,0.1449396908,-0.1167486832,0.1012669802,0.0485733636,0.3905768692,0.6209658384,-0.178280443,0.1267161667,0.0511756316,-0.0147035746,-0.0934559926,-0.1027881503,0.2167291492,0.1080801263,-0.0527150519,-0.1677287966,-0.2435363978,-0.28423962,0.1435454786,-0.0887115151,-0.3312167525,-0.0131604495,0.2882205844,-0.1768379807,0.1054641306,-0.0001009602,-0.1696676761,0.1100735739,-0.0895158276,-0.0204101969,-0.0245752875,-0.2445190102,-0.4427774549,-0.143860206,-0.0647223443,-0.1366013885,-0.0817899257,0.1576744914,-0.511418581,0.0948295146,0.1543592066,0.2733411491,0.2456284314,-0.0281457156,-0.235783875,0.3271295726,0.0656340495,-0.1248783842,0.2170629948,0.7011628747,0.1104023978,0.0426903777,0.2003490627,0.1661870927,-0.0251015443,-0.0942072794,0.0687786862,-0.1166618764,-0.1290900409,-0.0265672635,0.6269078255,0.1902135313,-0.4049749374,-0.4105316699,-0.1999892145,0.0986906812,-0.052923277,0.089781791,0.0726022571,0.1213970259,0.0318280049,0.1945581585,-0.4966183007,-0.0597505048,0.1288611144,0.0036636719,0.2533256412,-0.154023096,-0.0024558634,0.1759861559,0.0424560644,-0.1609525979,0.0287617818,-0.2005242556,0.1064115092,-0.096863158,-0.0444806516,0.0238624662,0.1938484758,0.4159869552,0.0361158326,0.0447767302,0.2233369946,-0.2803298533,0.1791000217,-0.1463030428,0.0760989711,0.2867692411,-0.4129454195,0.3636011779,-0.2374961525,0.2125659436,-0.0065588597,-0.0568500049,0.0141510293,-0.3175007999,-0.008249063,-0.0268741958,-0.050289616,-0.1470522881,0.0037651628,0.04865136,-0.1039883718,-0.0061707301,0.2225236148,0.0996550471,-0.1233040839,-0.1621115953,0.3138157427,-0.0012074781,-0.3761256635,-0.0911941528,0.3496199548,-0.269058466,0.1013754681,-0.0004975646,-0.1953832209,0.1036227047,0.1846352816,0.0611572266,0.0986920446,0.2261886001,0.0720733181,0.1173693091,-0.1074078232,-0.0736710206,0.0463803634,0.0098902006,-0.3629238904,-0.3809714913,-0.2105388939,0.0378625728,0.0682305396,-0.5171687603,0.2253204435,-0.1216332614,-0.3320922852,0.0610728674,0.7353149652,-0.2060721964,-0.1873333007,0.3742213845,0.1848040372,0.0606342368,-0.182090655,0.1231317297,0.3011280596,-0.7084853649,-0.3878898919,0.0161899347,-0.3116719425,0.015024215,-0.007212868,-0.0522164777,-0.1488976926,-0.2193853557,0.3566614985,0.3074155748,-0.2631284893,-0.1048190519,0.1527160853,-0.1363544017,0.0324215107,0.1493159831,0.2461178303,0.0076194662,0.0819620118,-0.228445515,0.4928712845,-0.1376854926,-0.0693279281,-0.0171897598,-0.1178915724,0.1163965687,0.2341865301,-0.017736556,-0.1847666949,0.3748797774,-0.0480547622,0.0143025937,-0.2178456634,0.0823105574,0.2338499725,0.2585249543,0.0419751368,-0.1527703702,-0.3222052455,-0.3203323483,0.117840603,-0.0965881869,0.23034814,0.1352093667,-0.2790805995,-0.0196157284,-0.0212945659,-0.205671683,-0.2783054709,0.1957814693,0.3253358305,0.1568973511,0.3068617284,-0.1636470705,0.2912967801,0.0270691346,0.0393407121,-0.382066071,0.1652247906,0.1009419039,-0.0487701856,-0.2471422702,0.2708751261,-0.2948076129,-0.3454168439,0.0475604273,0.4005276263,-0.0562770739,0.3210214078,0.4340528548,0.3132542372,0.3498570919,-0.0516707823,0.2166532725,-0.1477486938,0.0593076646,-0.0179117396,0.0332002603,0.3806247413,0.2867743075,-0.1806423515,0.0645806566,0.0515145473,0.3325469494,-0.0578965135,-0.0247553792,-0.2705189884,0.3214475513,-0.0682074577,0.462057054,-0.0891146958,-0.4519284666,-0.0041489908,0.5400702357,-0.2069958597,0.5007694364,-0.038568195,-0.2486328334,-0.1093044207,0.2232329845,0.2447239757,0.1789746583,0.2782121897,0.2959163487,-0.0094852336,-0.1759129912,-0.2030114532,0.1238840893,-0.2936118245,-0.2068038434,0.0600175858,0.0018413918,-0.2386073917,-0.6312516928,-0.1454912871,0.0645731837,-0.0627526641,0.0172268339,-0.1164710149,-0.2509187758,-0.3461498022,-0.2239780277,-0.2117842436,-0.4119899273,-0.2713960111,0.3582319021,-0.3860490918,-0.101405561,0.2287199944,0.1626435518,0.3069648445,-0.3231985271,-0.2430468202,-0.3107090294,-0.2273840308,-0.1320769638,0.2030258328,0.0638731644,0.0810738206,0.5975708365,-0.1493800133,0.3434199393,-0.3206497133,-0.6411496401,0.1712259054,-0.2336112112,0.3255996704,0.227258727,-0.1397531629,0.1338934749,-0.0364695564,0.1930484474,-0.373699069,-0.1994298249,-0.1343020648,-0.1690985262,0.0130697396,-0.1824685633,-0.4047191441,-0.4013569653,-0.3103552163,0.3401172757,-0.1355203837,-0.1033565253,0.0624947473,-0.1144509241,0.4457504153,-0.1651321352,0.0383789837,-0.3390419483,-0.3150513172,0.0420127362,-0.3130304515,-0.2071701586,-0.1308929175,-0.0832276568,0.1937596798,-0.104283832,-0.2162323743,-0.2951601148,-0.2080211788,0.304359287,0.2436639667,0.2603614032,0.2636737823,-0.0056840666,-0.1536726058,-0.1816168427,-0.1033957005,0.1106702387,0.1836391836,-0.1427585334,-0.0462077335,0.2588562071,0.0358119942,0.316762507,-0.1313972026,-0.0619766638,0.4659319222,-0.1548800617,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/749","title":"[XGLUE] Adding new dataset","comments":"IMO we should have one config per language. That's what we're doing for xnli, xtreme etc.\r\nHaving split names that depend on the language seems wrong. We should try to avoid split names that are not train\/val\/test.\r\nSorry for late response on this one","body":"XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance ","comment_length":44,"text":"[XGLUE] Adding new dataset \n XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance  \n IMO we should have one config per language. That's what we're doing for xnli, xtreme etc.\r\nHaving split names that depend on the language seems wrong. We should try to avoid split names that are not train\/val\/test.\r\nSorry for late response on this one","embeddings":[-0.1462257057,-0.1761126667,-0.0905722529,0.0123518491,-0.0241257884,-0.1703824699,0.4644282162,0.2293003947,0.0064150211,0.1897848248,-0.0784216374,-0.0123210121,0.063715063,0.3173589706,0.3393554986,-0.147364676,0.064874202,-0.120767951,0.1857912093,-0.1410236955,-0.1970852613,0.164675653,0.141678527,0.2563765943,-0.3061516285,-0.2510169446,-0.1212384701,0.1231266856,-0.0533281155,-0.26364398,-0.1327727735,0.0994950905,-0.1483211666,0.1550781876,-0.0000984329,-0.1763023585,0.1570734084,-0.1662795395,0.3259849548,0.0369416624,-0.3540754318,-0.4925276935,-0.2166533321,-0.2867809832,-0.0726134628,0.0749670267,0.0203127936,-0.4325012267,0.0292520337,0.0513051972,0.2826530933,-0.0485953242,-0.1415353864,-0.0816421062,0.2933580875,-0.2213200927,-0.1784569621,0.002078576,0.5535991788,0.2362468839,-0.1021504775,0.3790180683,0.0996620655,-0.1741277128,-0.0846165493,0.0462375432,-0.0509669743,-0.3432504833,-0.0554944761,0.6024343967,0.0136226807,-0.2653130889,-0.1561050266,-0.2016578913,0.0942969546,-0.2133093178,0.3548102379,0.1433286816,0.1954391599,-0.0333734304,0.313854903,-0.4838635325,0.0441258475,0.0608955212,0.0301384553,0.6080756783,-0.0815607458,0.2101168036,0.0818187073,-0.0044063856,0.055607412,0.018105261,-0.1166885123,-0.1805522591,-0.278193444,0.0348439664,-0.133814171,0.0385017693,0.1513521671,-0.0915089622,-0.1049464494,0.1773038059,0.1033563018,0.1640123576,-0.0740814656,0.0739103556,0.3556385934,-0.1418625563,0.3384209275,-0.3893523216,0.0308436155,0.0682724193,0.0302113201,-0.1210897565,-0.3905890882,-0.0484874696,-0.2725441158,-0.0133919427,-0.0945795998,0.0500221699,-0.08578033,-0.202482149,0.1805745661,0.1018227637,0.0545383655,-0.0015527862,-0.2859128416,0.2533295751,-0.0977325439,-0.3352463841,-0.155273214,0.3696979582,-0.4024463296,0.0887354165,-0.0924175307,0.0822391734,0.0640990734,0.1878533512,-0.0102433059,-0.0425229743,0.2050230652,-0.053944096,-0.0014978915,-0.0343507156,0.0269424915,0.0131021254,-0.0330026187,-0.165032953,-0.4244991839,-0.215992257,-0.2676058412,-0.1515643597,-0.1938266903,0.2565615773,-0.0661879331,-0.1259263158,0.3019111156,0.6030246019,0.0658158287,-0.0055519342,0.2308276743,0.0461863801,0.0359373204,-0.3438422382,-0.0265953317,0.1573953778,-0.560788691,-0.2898280919,0.0050518517,-0.2177070975,0.2166292816,0.1000580415,-0.191809684,-0.2419541031,-0.1731529683,0.4972555935,0.4736790657,-0.240694955,0.0017588803,0.139537856,-0.2263592631,-0.2233425677,0.3514536917,0.1564726084,0.1404679567,-0.0067435224,-0.0991059467,0.3637696803,-0.2389213145,-0.1682415456,-0.179953143,-0.3024235964,0.0140931718,0.2203172147,-0.0066400929,-0.4272894263,0.1903276891,-0.0931604877,0.0925678834,-0.1461269557,0.1340751648,0.151235491,0.2007117122,0.0784001276,-0.1671226025,0.0491228178,-0.2886480093,0.0849829912,-0.1902270615,0.2473544925,0.3146641552,-0.1260802597,0.1242845282,-0.4120953381,-0.307710737,-0.2251723856,0.246767953,0.3807688951,-0.0058764932,0.0702522174,-0.3039516211,0.4216486514,-0.0748284236,0.0373808108,-0.3291956484,-0.006822926,-0.0320982449,-0.1438957751,-0.1064084172,0.2375395447,-0.1356835812,-0.3920332789,0.0525945835,0.4568064809,0.0867202207,0.0595481545,0.2669987082,0.1823760569,0.3151248991,0.0306577925,0.4122351706,-0.3238601089,-0.1963976473,-0.2101339102,0.2165523916,0.4376225471,0.1735754609,-0.0046036956,-0.097738795,0.0390911475,0.272116214,-0.1845210046,-0.1671703011,-0.2693996429,0.1353587359,0.033449512,0.4870150089,0.1121011898,-0.537476778,0.0372514836,0.7857860327,-0.0592366755,0.2184426934,-0.0784685388,0.1288003623,0.0427695811,0.1004865319,0.4957652092,0.1081290841,0.2882475555,0.0216908045,-0.1542692631,-0.2354768068,-0.2799210846,0.0978767425,-0.1289460361,0.0073645664,-0.0057402877,-0.254562676,-0.2525956929,-0.3949924111,-0.1950167716,0.1849539876,-0.1311198175,0.1120041385,-0.1308682561,-0.1418882012,-0.3241763711,-0.3846465647,-0.2120252252,-0.6176679134,-0.264865011,0.2558196783,-0.658339262,-0.1273485273,0.3772856891,0.0395399369,0.0717264414,-0.390676409,-0.3015213311,-0.0437556952,-0.0720791221,-0.4109976292,0.171017468,0.1405231208,0.0250371359,0.4922403395,-0.0793910399,0.1855514944,-0.074500598,-0.5724858046,0.2775664032,-0.4036213756,0.0977482796,0.1708719283,-0.3082919717,0.0878910348,-0.3493945897,0.3390375972,-0.1168491021,0.1387781352,-0.0910428986,0.0008447964,-0.1255896091,-0.0018690528,-0.6168110371,-0.5302234888,-0.3577502966,0.0649172217,-0.2152603716,0.0530571081,0.0140573578,-0.2009966522,0.2050771862,-0.2021887004,-0.0735040605,-0.1877882183,-0.3085239828,0.0390862003,-0.179997474,-0.1100341082,0.0209620614,0.0351652466,0.4360462725,-0.0134434495,-0.1864354312,0.0974280834,-0.1578869224,0.2900023758,0.4185093641,0.23428002,0.1832298338,0.1810414344,-0.1444858015,-0.0840473995,-0.126610592,0.2406166345,0.1379864216,0.2461365461,0.1285828501,0.188356325,-0.0293926969,0.256842047,-0.0068727173,0.105248943,0.4177756906,0.0217579138,0.1973075569,-0.2238929123,-0.2783102095,0.4182807803,-0.0201440472,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/749","title":"[XGLUE] Adding new dataset","comments":"@lhoestq agreed on having one config per language, but we also need to be able to have different split names and people are going to want to use hyphens, so we should at the very least warn them why it's failing :) E.g. for ANLI with different stages of data (currently using underscores) or https:\/\/www.tau-nlp.org\/commonsenseqa with their train-sanity or dev-sanity splits","body":"XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance ","comment_length":61,"text":"[XGLUE] Adding new dataset \n XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance  \n @lhoestq agreed on having one config per language, but we also need to be able to have different split names and people are going to want to use hyphens, so we should at the very least warn them why it's failing :) E.g. for ANLI with different stages of data (currently using underscores) or https:\/\/www.tau-nlp.org\/commonsenseqa with their train-sanity or dev-sanity splits","embeddings":[-0.2000913322,-0.1522804648,-0.1069084406,-0.112353228,-0.0792811289,-0.2000014931,0.5592647791,0.1603520364,0.1297950149,0.1972673535,-0.0472673699,0.0549896657,0.0048433915,0.3074243367,0.300499469,-0.2359765619,0.0617931373,-0.0328123979,0.1070400625,-0.0768814161,-0.144819051,0.1187251955,0.1501040757,0.06426806,-0.2914257348,-0.3140029907,-0.2087100595,0.1164670661,-0.0417363569,-0.2866325676,-0.1831393391,0.2181471586,-0.1481360495,0.0460788421,-0.0000994877,-0.1674608588,0.1176850125,-0.1754718572,0.2268093526,-0.0554833598,-0.4276823997,-0.5850602388,-0.1877811253,-0.1727513075,-0.0764963478,0.0747532323,0.1615252197,-0.3223183751,0.1621859074,-0.016851211,0.2486219853,-0.035681095,0.0158526208,-0.0933979824,0.2921179235,-0.1258252263,-0.1625911295,0.1111362278,0.5344116688,0.0421693847,-0.1167415977,0.2462140024,0.0771799386,-0.1617516875,-0.2693609297,-0.0677690282,-0.069693692,-0.2593415976,-0.0977884457,0.6707048416,0.0018181055,-0.2552489638,-0.2341094613,-0.2518298328,0.1429242492,-0.1198723614,0.2260702699,0.2438061237,0.0440104827,0.00164947,0.3162364066,-0.490437448,-0.0035794664,0.2545977235,0.1498419195,0.4481433332,0.0083515672,0.0914842039,0.1814766526,-0.0291998815,-0.1529129446,0.1295476258,-0.0814182982,-0.1340005696,-0.3194148242,-0.0192977302,-0.0664459243,-0.0460366867,0.1971566081,-0.1193258241,-0.0815122426,0.2506907284,-0.1215141863,0.2826818824,-0.0358217359,0.0253942981,0.2486378103,-0.1062540635,0.353379041,-0.2628124654,0.0464551337,0.0820845738,-0.0214688182,-0.1186307743,-0.4098235965,0.0041323812,-0.2621773779,0.0790798292,-0.1390137225,-0.0721227899,-0.1061048582,-0.0694446415,0.0607370846,0.0999145284,0.1349299848,-0.0380974673,-0.269852519,0.3025579751,-0.10853789,-0.3179698288,-0.0852049142,0.340692848,-0.3904686868,0.2825713456,-0.0011110291,0.1071576998,0.0306002367,0.2661173344,-0.2616833746,-0.0092200674,0.2431921214,-0.025525311,0.1116447374,-0.0860612392,0.0445116684,-0.0805907995,-0.1204369143,-0.3179932535,-0.4474070668,-0.1331004947,-0.1393804103,-0.0474322326,-0.5369630456,0.2319704592,-0.1346972287,-0.2424329072,0.3213674426,0.702580452,0.1054731533,-0.1079343036,0.3025411665,0.0773232505,0.0856088176,-0.283187449,-0.085445933,0.2961887121,-0.583910346,-0.3671292663,-0.0423091203,-0.1428717077,0.1852105558,0.2240878642,-0.0939926431,-0.2408581227,-0.0905022025,0.5788308978,0.2949937582,-0.0800256431,-0.0437667929,0.0483726077,-0.2129266411,-0.2295107394,0.4102588296,0.2193477154,0.1522818506,-0.1028388068,-0.1026312709,0.5329892635,-0.1997340322,-0.19203116,-0.2687574029,-0.3714087307,-0.0669597909,0.2908557355,-0.1108097062,-0.4445999265,0.2499771267,-0.1283310354,0.0342377983,-0.2165666968,0.1971199811,0.1866378486,0.1733614653,0.1198027804,-0.1649485379,-0.1606724113,-0.2717201114,-0.0421764106,-0.1458329409,0.3109076321,0.1103108302,-0.2175168842,0.0918080211,-0.3954267502,-0.2288378477,-0.0556196719,0.2442547977,0.3357694745,-0.0485513173,0.2046450227,-0.2657516003,0.3758607805,0.0656333193,0.0459028296,-0.2742502987,0.1087707132,-0.0235380586,-0.1934509426,-0.041371569,0.4167004228,-0.1104963869,-0.273794353,0.1936376244,0.4694652259,-0.0133421598,0.2094618231,0.2545446455,0.1569453776,0.3164723217,0.0097726109,0.4155246317,-0.3414929509,-0.1877511889,-0.1871416122,0.1694924384,0.3568233848,0.2377646416,-0.0222448669,0.0605877154,0.0200414062,0.2959116697,-0.2497529536,-0.0891408026,-0.3473980129,0.214688614,-0.129070431,0.3781507313,0.0310075227,-0.5096861124,0.1320846528,0.7999991179,-0.039274741,0.5096131563,-0.074428916,-0.0220924933,-0.0267020129,0.092434369,0.3850877285,0.0828881487,0.3525207937,0.0537568927,-0.2341546863,-0.2267881632,-0.365375936,0.1503828019,-0.2305915207,-0.0933856666,0.1149935424,-0.1172457561,-0.2532336712,-0.4707606137,-0.1996025294,0.0749980062,-0.1893981099,0.1460272074,-0.1899925172,-0.1876575351,-0.4007490873,-0.3989224732,-0.3629322052,-0.5685099363,-0.260099262,0.3544929624,-0.5420058966,-0.1989032328,0.4133724272,0.0591998659,0.1492994428,-0.4386818111,-0.2791981697,-0.0675601438,-0.2322134823,-0.3018974662,0.1828038394,0.1581467837,-0.0470945872,0.4090433717,0.0335788988,0.0718934089,-0.0733387247,-0.5325052738,0.2297291905,-0.2416845262,0.0931292176,0.1921675056,-0.2887467444,0.1889669448,-0.4706890881,0.2868530452,-0.0119840391,0.0934711918,-0.0222060978,-0.0688297302,-0.0162252896,-0.1070671231,-0.4447098076,-0.4915530086,-0.4010550976,0.1596658826,-0.3207647502,-0.0119162686,0.0915309861,-0.0613051131,0.1787116677,-0.1809263676,-0.0398546308,-0.2107488513,-0.177683562,0.0296898,-0.2467573285,-0.0975076184,-0.0743637308,-0.0030583297,0.2528924644,0.0278940778,-0.1953131706,0.0266535431,-0.2030615658,0.1726285368,0.3333505094,0.2352800071,0.2445562035,0.1802091599,-0.2039491236,-0.0323086977,-0.1615832895,0.1644463092,0.0807995498,0.2233292311,0.0131587051,0.167428866,0.2213269621,0.2721554637,-0.0015126242,0.0654091761,0.4926971793,0.0336894207,0.2852627039,-0.1993743479,-0.152240023,0.35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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/749","title":"[XGLUE] Adding new dataset","comments":"Really cool dataset \ud83d\udc4d btw. does Transformers support all 11 tasks \ud83e\udd14 would be awesome to have a xglue script (like the \"normal\" glue one)","body":"XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance ","comment_length":25,"text":"[XGLUE] Adding new dataset \n XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance  \n Really cool dataset \ud83d\udc4d btw. does Transformers support all 11 tasks \ud83e\udd14 would be awesome to have a xglue script (like the \"normal\" glue one)","embeddings":[-0.3609884679,-0.2430483103,-0.1333388388,-0.1087900698,-0.0488198325,-0.1585718393,0.4023332894,0.1078701392,0.1723619401,0.0442129448,-0.0323640816,0.0167611782,-0.1374104619,0.3313932717,0.5176857114,-0.2384225577,0.1370011419,-0.0414835922,-0.2136694044,-0.1239042655,-0.2755305469,0.1441282779,-0.0130416006,0.0020683771,-0.3241946101,-0.3382702172,-0.2491800189,0.0200917218,0.0855365247,-0.0490060486,-0.1051613837,0.2372089475,-0.1000937745,0.1456027627,-0.0001029017,-0.2386933267,0.1524556726,-0.0855204463,0.1233778894,0.1465126723,-0.4335066676,-0.5227155685,-0.284322232,-0.126483649,-0.0534855463,0.1218110025,0.2125723958,-0.1292340308,0.2736060917,0.0016656595,0.283796519,0.2364493906,0.0420415588,-0.1583895683,0.3880887926,0.0325444713,-0.0888498947,-0.1677185297,0.8024855852,0.0244995225,-0.14155595,0.1799804419,-0.1484936625,-0.3167272508,-0.1134844422,-0.1171215773,-0.1555602401,-0.2585976422,-0.0800477117,0.5484515429,0.1892139465,-0.231271863,-0.3534981012,-0.1925369203,-0.1706315428,0.1413885057,-0.1134962738,0.2801836133,0.1149859503,-0.1105148345,0.2131234705,-0.4164280891,-0.0800762028,0.0479947254,0.0213420168,0.5528358221,-0.1332029998,0.080798246,0.2142960131,0.0414795391,0.038148731,-0.0028293354,-0.0836118534,0.118608281,-0.3118964434,-0.1651511639,0.0856062397,0.0546375215,0.1615312397,0.1043573245,0.1731624156,0.3329555094,-0.0175849013,0.247294113,0.0191554707,0.0790556371,-0.0230050124,-0.0928765535,0.2940820158,-0.4682030082,0.1120841727,0.0056076217,-0.130060941,0.0141564878,-0.3059464991,0.1412544101,-0.0782055631,0.0545207597,-0.0553626716,-0.0613579415,0.2052805424,-0.0639327466,-0.0996132717,0.132367,0.1529039443,0.0511071682,-0.1864060313,0.2986691594,0.0478519164,-0.2754711211,-0.0506898053,0.33980304,-0.1761929542,-0.085260354,0.0325038694,0.2464813441,-0.1237427145,0.2510396838,-0.1529009789,0.0991487503,0.4014096856,-0.0238538552,0.1298343241,0.1362227947,-0.1011758074,0.0525082201,-0.050817918,-0.1687996835,-0.366877526,-0.0154537763,0.0796928033,-0.0815573633,-0.3735295534,0.2293701023,-0.2696936429,-0.344999373,0.086140506,0.628996253,-0.0880313143,0.0049777371,0.2904132307,0.105007343,0.2124749422,-0.3345422447,-0.0929958671,0.3001294136,-0.4730134904,-0.3338137567,-0.0290499087,-0.2311615497,-0.0021570779,0.052013617,-0.0440511741,-0.0961744413,-0.0073740389,0.2651539445,0.288978368,-0.264759481,-0.207452938,0.2098602355,-0.193057999,-0.0273487438,0.2175819427,0.4717178345,0.2743147016,-0.126091823,-0.3171695471,0.6667872071,-0.1519976258,0.0343477428,-0.0801531523,-0.2497335672,0.1098253578,0.3647827804,0.2909832597,-0.2167263329,0.1439425796,0.0396679081,-0.043223124,-0.1433188021,0.141574949,0.2905838788,0.2136435956,-0.0768426359,-0.0356039442,-0.3173741698,-0.5034196973,-0.0827657282,-0.1377798319,0.2258930206,0.0800666064,-0.1218504906,0.1599663794,-0.0953992382,-0.1808453798,-0.2863193154,0.2205733657,0.1641145796,0.1397455037,-0.0252317581,-0.2686023414,0.4233843684,0.3195320666,0.0075033177,-0.3495750427,-0.0012606267,0.0281364378,-0.0337243862,-0.0981782749,0.1319551021,-0.0795587972,-0.4043676853,0.1795045882,0.2557623088,-0.1880486906,0.3382915258,0.2561551332,0.6219782233,0.3133595586,-0.01916508,0.3991294205,0.069849506,0.0090057161,-0.1501580775,0.0675852597,0.390750885,0.1409379095,0.0182825942,-0.0826657861,0.2094757855,0.2510527968,-0.1255118102,-0.1356682628,0.0638031587,0.3023630381,0.0520361476,0.3860325515,-0.0337325558,-0.1731179357,0.1216315255,0.6009011865,-0.1979758292,0.5357917547,-0.0756154731,-0.376876384,0.099514477,0.0751873255,0.4574151933,-0.0352169909,0.3183375895,0.1094111428,-0.0901598483,-0.0832100883,-0.1180773377,0.1242982745,-0.1158265397,-0.1036301926,0.0889113769,-0.094376348,-0.2065753192,-0.4662317932,-0.2011834979,0.0638101771,-0.0539061241,0.1487091184,-0.0733557269,0.1418774575,-0.2242625952,-0.3412419558,-0.2206401527,-0.3772154748,-0.2209184915,0.2375549078,-0.3334909976,-0.2636536658,0.2999638617,0.0928904191,0.3302301764,-0.3172257245,-0.258176446,-0.2700491846,-0.2920682728,-0.0477995761,0.2156932801,0.0847446918,-0.0897062272,0.3635438681,0.033396434,0.3812054098,-0.0919457227,-0.5689717531,0.1191645637,-0.1226817593,0.0685385242,0.1305507272,-0.1295525879,0.146707505,-0.2462765574,0.1192120165,-0.2974481583,0.0594247691,-0.0277623404,-0.1027413979,-0.1354483515,-0.1127604693,-0.2742823064,-0.4515147805,-0.399027735,0.4014700949,-0.1385790855,0.0809628367,0.0772029981,-0.0410939939,0.1876616925,-0.0107725887,-0.1963230968,-0.1856986582,-0.1812002808,0.2295367122,-0.1653044373,-0.2328504175,-0.3834370077,-0.3701706827,0.1409291625,0.1719186455,-0.0434372872,0.0923865736,-0.4001675546,-0.0342918821,0.3072669208,0.407863915,0.3317818046,0.0975070447,-0.115450874,-0.1843906492,0.0087157516,0.0510748439,0.2351146638,-0.1191026345,0.0116807008,0.1188951582,0.094940871,0.3151741624,-0.2041479349,0.0975989923,0.3520654142,0.0841611773,0.2209134698,-0.0726856291,-0.2704918087,0.1274237037,-0.1682581604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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/749","title":"[XGLUE] Adding new dataset","comments":"Just to make sure this is what we want here. If we add one config per language, \r\n\r\nthis means that this dataset ends up with well over 100 different configs most of which will have the same `train` split. The train split is always in English. Also, I'm not sure whether it's better for the user to be honest. \r\n\r\nI think it could be quite confusing for the user to have\r\n\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner-de\", split=\"train\")\r\n```\r\n\r\nin English even though it's `ner-de`.\r\n\r\nTo be honest, I'd prefer:\r\n\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner\", split=\"train\")\r\ntest_dataset_de = load_dataset(\"xglue\", \"ner\", split=\"test-de\")\r\ntest_dataset_fr = load_dataset(\"xglue\", \"ner\", split=\"test-fr\")\r\n```\r\n\r\nhere","body":"XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance ","comment_length":107,"text":"[XGLUE] Adding new dataset \n XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance  \n Just to make sure this is what we want here. If we add one config per language, \r\n\r\nthis means that this dataset ends up with well over 100 different configs most of which will have the same `train` split. The train split is always in English. Also, I'm not sure whether it's better for the user to be honest. \r\n\r\nI think it could be quite confusing for the user to have\r\n\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner-de\", split=\"train\")\r\n```\r\n\r\nin English even though it's `ner-de`.\r\n\r\nTo be honest, I'd prefer:\r\n\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner\", split=\"train\")\r\ntest_dataset_de = load_dataset(\"xglue\", \"ner\", split=\"test-de\")\r\ntest_dataset_fr = load_dataset(\"xglue\", \"ner\", split=\"test-fr\")\r\n```\r\n\r\nhere","embeddings":[-0.1910263747,0.05320004,-0.0776923746,0.0108434688,0.038727738,-0.0806754977,0.5347980261,0.0377284102,-0.0204801112,0.14164415,-0.1144325361,0.068052195,-0.18432872,0.2480089068,0.3943488598,-0.1348956078,0.0209186338,-0.1578741223,0.2138730437,-0.1265163422,-0.1170296818,0.1462667137,0.2255790681,0.0706288889,-0.3789371252,-0.2115485519,-0.1750750542,0.0153059065,0.0442247875,-0.2807618976,0.026356373,0.0578123778,-0.048457969,0.1628393084,-0.000103672,0.1411678642,-0.0540858395,-0.0771505013,0.1324319243,-0.1961729825,-0.2736229002,-0.4907403886,-0.0753002167,-0.1089987531,-0.1303656399,-0.0766442493,0.0178500637,-0.4681305885,0.1797653586,0.1816543788,0.2313515693,-0.098832339,-0.4710571766,-0.0546997786,0.154390797,-0.1809719205,-0.0108413668,0.2446625829,0.2024462968,0.1673575342,-0.2104752809,0.4398652017,0.1683361977,-0.2313731462,-0.1968753934,0.1649381816,-0.1484714895,-0.236178726,-0.1522667706,0.567723453,0.0808059573,-0.3228999972,-0.39285478,-0.2704529762,0.1999154389,-0.288887471,0.0518833436,0.211303696,0.2176040113,0.1751316488,0.2317298204,-0.6836288571,0.0540126339,0.1356295347,-0.0971826985,0.513705492,-0.0015301312,0.2439488322,0.0815680474,0.1402274966,0.020834947,-0.007843568,-0.0897864774,-0.1002551019,-0.240918234,0.106680952,-0.1642866284,-0.0150391851,0.0946135595,-0.0935695246,0.0727476105,0.1536908448,0.1089784577,0.2050895989,0.1776739359,0.1180366501,0.2366352975,0.0627688617,0.2887322307,-0.4710458517,-0.077805832,0.019506963,0.0746848956,-0.1912410408,-0.172714293,-0.0459464751,0.026984781,0.0663522854,-0.0959774479,0.1052939743,-0.2061987072,-0.2935826778,0.0990042239,0.144900769,0.1277307272,0.1413604468,-0.2030745447,0.3725821972,-0.2662522793,-0.2571800649,-0.2277455777,0.3409709036,-0.3748532534,0.0921020061,-0.202175647,-0.0808232799,-0.0476233251,0.3292251229,-0.1071465835,-0.1580495834,0.0915870816,-0.1337833554,0.0519525707,-0.1247290224,0.0125548588,0.158018738,0.0570185855,-0.3566270173,-0.4626468122,-0.0300364345,-0.2527826428,-0.2549269497,-0.2875261307,0.2056722492,-0.1694247574,-0.2321385145,0.2545635998,0.7391151786,0.1273803115,-0.1175596789,0.2827657163,-0.1243179813,-0.1663301587,-0.2764618695,-0.0702709928,0.2362784445,-0.5743151903,-0.4132932127,0.2253564447,-0.1976189017,0.2069160342,0.1966434866,-0.3473395705,0.0109948069,-0.1177361086,0.353420794,0.5970428586,-0.292347014,-0.1303872913,0.2141420841,-0.2635833323,0.0889391527,0.2357155979,0.2031340897,0.102727823,0.1982204914,0.0678457916,0.4630224407,-0.0053035812,-0.1573871821,-0.1402194053,-0.1902844608,0.3542566597,0.2323129326,-0.0381515399,-0.2863727212,0.1947164685,0.0899080932,0.2390928119,-0.14268291,0.0964538679,0.2986013293,0.0468662828,0.0179023575,-0.1953507513,-0.070842877,-0.3336547315,0.1470629275,0.03686722,0.3827440143,0.2917511761,-0.266276747,0.1779703349,-0.3421458006,-0.4258976281,-0.3459898531,0.1614493877,0.2230120301,-0.06804052,0.2329872996,-0.2800186276,0.5938700438,0.0390464291,0.1116575375,-0.3817915618,0.0530276559,0.0686305612,-0.1893719882,-0.2376475036,0.2393784821,-0.1718095094,-0.240515247,-0.0398931168,0.4583503008,0.2323985696,0.0488370694,-0.0718663484,0.1190843657,0.3836656511,0.1094573438,0.181332618,-0.2294235229,-0.1822823733,-0.1237252206,0.0827196315,0.5217264891,0.3244667053,0.1004315913,-0.1075853109,0.0357733592,0.1910133958,-0.0415985622,-0.3569164872,-0.2650184631,0.0556679294,0.0556831546,0.4347801805,0.1592078805,-0.5791935325,-0.071252428,0.7180764675,0.0097676571,0.169363454,-0.0693663284,0.0558392256,0.1011972576,0.1895572692,0.520197928,0.212292105,0.2389431447,0.2982468307,-0.0165144466,-0.1940762103,-0.1828314662,-0.105938226,-0.237099722,0.079501465,-0.0362919569,-0.2030076832,-0.2575028539,-0.408272028,-0.1509490013,0.1471932828,-0.0292324778,-0.0294269305,0.0245032161,-0.3005194366,-0.180154115,-0.5986668468,-0.301335603,-0.6115759015,-0.1811361611,0.2447134852,-0.6047827005,-0.0467013642,0.4229427278,-0.1425320357,-0.0406717062,-0.4698758125,-0.5025680065,0.2565771937,-0.042105563,-0.4064779878,0.0918783247,0.1579806954,-0.0327246822,0.2512928843,-0.1421437413,-0.0645048916,0.1113217846,-0.4008816779,0.1825397164,-0.206215471,0.1482295245,0.1005489975,-0.3347789347,-0.0572909713,-0.3021550775,0.3210506439,0.0150025142,0.145675987,-0.0646712929,0.0767859817,0.0475415513,-0.065370813,-0.720400095,-0.5405853987,-0.1982033402,0.0103970701,-0.1640195251,0.1104089469,0.2534588873,0.0890554488,0.0119740162,-0.1456250101,-0.004497014,-0.2257570624,-0.382006228,-0.0291856434,-0.109988898,-0.1568063796,-0.0304675456,-0.0716736317,0.2077922523,0.1767010987,-0.1378644258,0.0166729614,-0.19390513,0.2275470346,0.0353257358,0.4253292978,0.1598967463,0.154330641,-0.0326043293,-0.0435756557,-0.1585964561,0.3780442774,0.2033304721,0.0127778435,-0.0691448152,0.0634470582,0.0594621636,0.2326344848,0.1773548722,0.0641298592,0.2799993455,-0.3380756676,0.3977771997,-0.1409880519,-0.2067123204,0.218290031,-0.0323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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/749","title":"[XGLUE] Adding new dataset","comments":"Oh yes right I didn't notice the train set was always in english sorry.\r\nMoreover it seems that the way this dataset is used is to pick a pretrained multilingual model, fine-tune it on the english train set and then evaluate on each test set (one per language).\r\nSo to better fit the usual usage of this dataset, I agree that it's better to have one test split per language. \r\n\r\nSomething like your latest example patrick is fine imo :\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner\", split=\"train\")\r\ntest_dataset_de = load_dataset(\"xglue\", \"ner\", split=\"test.de\")\r\n```\r\n\r\nI just replace test-de with test.de since `-` is not allowed for split names (it has to follow the `\\w+` regex), and usually we specify the language after a point. ","body":"XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance ","comment_length":122,"text":"[XGLUE] Adding new dataset \n XGLUE is a multilingual GLUE like dataset propesed in this [paper](https:\/\/arxiv.org\/pdf\/2004.01401.pdf).\r\n\r\nI'm planning on adding the dataset to the library myself in a couple of weeks.\r\nAlso tagging @JetRunner @qiweizhen in case I need some guidance  \n Oh yes right I didn't notice the train set was always in english sorry.\r\nMoreover it seems that the way this dataset is used is to pick a pretrained multilingual model, fine-tune it on the english train set and then evaluate on each test set (one per language).\r\nSo to better fit the usual usage of this dataset, I agree that it's better to have one test split per language. \r\n\r\nSomething like your latest example patrick is fine imo :\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner\", split=\"train\")\r\ntest_dataset_de = load_dataset(\"xglue\", \"ner\", split=\"test.de\")\r\n```\r\n\r\nI just replace test-de with test.de since `-` is not allowed for split names (it has to follow the `\\w+` regex), and usually we specify the language after a point. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Thank you !\r\nCould you provide a csv file that reproduces the error ?\r\nIt doesn't have to be one of your dataset. As long as it reproduces the error\r\nThat would help a lot !","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":36,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Thank you !\r\nCould you provide a csv file that reproduces the error ?\r\nIt doesn't have to be one of your dataset. As long as it reproduces the error\r\nThat would help a lot !","embeddings":[-0.0531706028,0.0708665848,-0.0034636373,0.3629573286,0.2447588295,0.2201897651,0.4253186285,0.3036738336,0.3109277785,0.0509861857,-0.0670640767,0.2354821861,-0.0957939476,-0.0218962226,-0.274692744,-0.1102110669,0.0493473858,0.0534440912,-0.3843253851,0.037524797,-0.3989782333,0.3356210291,-0.342168659,-0.0064872117,-0.1416352987,0.2397978306,-0.0553938709,0.3360723555,-0.1290960461,-0.3701969385,0.4902819991,-0.0949141309,0.5313909054,0.2242039144,-0.0001188278,0.0561855994,0.3267244995,-0.0927152708,-0.2536969185,-0.5014423728,-0.0495749563,-0.4797810912,0.3480935395,-0.0440137945,-0.0695052668,0.0655863881,-0.1130333692,-0.3628052771,0.3677662909,0.5194334984,0.1816963255,-0.2161453515,-0.0732249096,-0.0899274051,0.5836966634,0.0712949336,-0.1410909891,0.2741574347,0.0311340336,-0.2326374799,-0.0960092619,0.1110259891,-0.0724671558,0.2592616379,0.2572398484,0.2152855694,-0.1385260969,-0.1685495973,0.0878666192,0.1150503457,0.7295114398,-0.1114873886,-0.1594744027,-0.0453408323,-0.0405998491,-0.0944409966,0.3628718555,0.2874688208,0.0556214824,0.349548161,-0.0392498896,0.0313880295,-0.2870233655,0.1172613874,0.0713907778,-0.0096609592,-0.1667080075,0.3307066858,0.1022032648,-0.0038514612,0.2687622905,-0.0411292315,-0.1087320149,0.2209747434,-0.7359090447,0.3105952442,-0.145863384,-0.0289481953,-0.0242926143,0.1971954703,0.2464096844,-0.0232908204,-0.0816031247,0.3690752387,0.2595978379,0.0325677507,0.0150489165,0.2488965839,0.2425163686,-0.2127750814,0.0344926678,-0.1823337972,-0.3340726197,-0.5727440715,-0.0450330302,0.172230944,0.2509998083,-0.2274134457,-0.2134582698,0.1509696543,-0.265511781,0.0042609028,-0.1363606155,0.2583585382,0.1820407212,0.3217639625,0.102238223,0.3233686984,-0.0812660158,-0.4609397948,-0.0293140858,0.1431435496,-0.4125258923,-0.04699637,0.4920209944,-0.1724782735,0.1270473599,0.2477502078,-0.1354368776,-0.3477976024,-0.0136513198,-0.2814347446,0.0046409201,0.3041781187,0.0724007636,0.2251592278,0.3489328623,-0.202039063,-0.0728904754,0.3434776366,-0.4518966079,-0.2065818906,-0.1280949712,0.0978843123,-0.2282010019,0.0720365569,-0.339265883,0.071510911,0.0557156317,-0.1955105513,-0.1711066067,-0.319401443,-0.2678619921,-0.2312421948,0.0226150919,0.4551670849,-0.8168234825,0.2262132317,-0.0098615317,0.0500961393,0.0799537003,0.2039691955,-0.26779145,0.0344988219,-0.2549441755,0.1157383472,0.1402722597,-0.2161636353,-0.2937296331,0.1842556894,-0.0505548716,-0.0749084279,0.0845157504,0.0922507197,0.3441407084,-0.0177558176,-0.2049922645,0.1499433815,0.0094183013,0.0440510996,-0.2377330959,-0.1822585315,0.244654566,0.2318156809,0.0159404799,0.1241321713,0.2280937433,-0.6181377769,0.3143367767,-0.0560143255,0.2522957623,0.1251528263,0.2919839323,0.0680332705,0.331485182,0.0560596585,-0.5451503992,0.19358778,0.4534468949,-0.1884105951,-0.090702869,-0.0167362671,-0.4643526971,0.0068462631,-0.1820607185,-0.0329604782,-0.0002793333,0.0497451872,-0.0883560553,-0.1819704771,-0.115388982,0.4194991291,-0.2807845473,0.0929733589,-0.3300335109,0.280695945,0.1371117383,-0.0622213967,0.2043175697,-0.0787182376,0.315700233,-0.0505178608,-0.1529254764,0.2792815268,0.3547394872,-0.0294624101,0.1198978052,-0.1979084909,0.0191887449,-0.2967647016,-0.009896161,0.2180438787,0.3470375836,-0.0931992084,-0.484100908,0.324023366,-0.3189892173,0.2089032382,-0.2283426225,0.1850348562,0.2576622367,-0.0349268019,-0.0827434361,0.0283303466,0.0569879189,0.0253285822,0.2225339562,0.0946080312,-0.1744120866,-0.141436547,0.0628362969,0.181490019,-0.2388355881,0.2301218361,-0.1126146689,-0.0576637127,0.3364206254,0.2839767933,0.4159408808,0.0849344209,-0.2692225575,-0.0180044211,0.0707981363,-0.1863808781,0.0902066603,0.0085394103,0.0635168999,0.1984077096,0.2047156543,0.0949885398,-0.1371750087,-0.1268930137,0.1822054684,0.2504083812,-0.6004428864,-0.0170621369,-0.1977541596,0.3690707386,-0.231453076,-0.0469768457,0.1054718569,-0.2846023142,-0.2780268788,0.3058357239,-0.1332714558,0.0533150509,-0.2049994469,0.2518928945,0.1134851575,-0.1292214096,0.0387605093,-0.1857044101,-0.2578786016,0.0051694517,0.0914032385,-0.02278913,-0.113893643,0.1033109799,0.0897010043,-0.3063434064,-0.0327222049,0.1250150055,0.0054551079,0.0982463285,0.1273556799,-0.0856845826,-0.0653377175,-0.1550449729,0.2096203864,0.0910362974,0.0384264775,0.4184431136,0.2405577749,-0.1648762971,-0.1206735224,-0.6375058293,-0.2401450276,-0.2200728506,0.0447856039,-0.1669529229,-0.0543237068,0.0653824583,0.3040782213,-0.0523246713,0.2384745777,-0.1168534085,-0.1043981314,-0.2288609892,0.5683006048,-0.2417068183,-0.2770061493,0.2639984488,-0.0347369425,0.0478000417,0.3508776724,-0.396723628,0.2066193074,-0.0642232522,0.0257207323,-0.0835890323,0.0371143781,0.0664877445,-0.03237468,0.0934540704,-0.0514092781,-0.1807925254,0.2330056578,-0.0544732921,0.04972969,0.1682844162,0.556370616,-0.2356366813,0.6716985106,0.1087843254,0.1012935862,0.3297623992,-0.0753615946,0.2564425766,0.171028018,-0.224067837,0.0610778555,-0.1236017346,-0.333802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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"I think another good example is the following:\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sts-dev.csv\"], delimiter=\"\\t\", column_names=[\"one\", \"two\", \"three\", \"four\",  \"score\", \"sentence1\", \"sentence2\"], script_version=\"master\")`\r\n`\r\n\r\nDisplayed error `CSV parse error: Expected 7 columns, got 6` even tough I put 7 columns. First four columns from the csv don't have a name, so I've named them by default. The csv file is the .dev file from STSb benchmark dataset.\r\n\r\n","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":72,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n I think another good example is the following:\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sts-dev.csv\"], delimiter=\"\\t\", column_names=[\"one\", \"two\", \"three\", \"four\",  \"score\", \"sentence1\", \"sentence2\"], script_version=\"master\")`\r\n`\r\n\r\nDisplayed error `CSV parse error: Expected 7 columns, got 6` even tough I put 7 columns. First four columns from the csv don't have a name, so I've named them by default. The csv file is the .dev file from STSb benchmark dataset.\r\n\r\n","embeddings":[-0.0901070535,0.0171127245,0.0167961176,0.3881013691,0.2925541103,0.2115879506,0.3847278059,0.229759112,0.2224226147,0.089858532,-0.1013280302,0.2565506101,-0.0943530872,0.0382322073,-0.2512097657,-0.1036800966,0.0104466416,0.0889976621,-0.2961676121,0.0908844918,-0.3902235329,0.3168067336,-0.3827799261,-0.0461566821,-0.1766609251,0.2577837706,0.0003086621,0.2659320533,-0.0958688855,-0.4407778382,0.5010362267,0.0035070458,0.5712614655,0.3400994837,-0.000120307,0.0167349447,0.3593454361,-0.1578391045,-0.2551825643,-0.4866746068,-0.0636008605,-0.4186861515,0.3337731659,-0.1273178309,-0.1325138211,0.0567032024,-0.0902742967,-0.5262620449,0.3351167738,0.4816686213,0.1698999405,-0.1670440137,-0.1962601095,0.012288156,0.6344578266,0.0383948833,-0.1703587919,0.2307640314,0.060028255,-0.0478319153,-0.077239953,0.0414571948,-0.0999131575,0.2801174223,0.2486495972,0.2107812464,-0.2227522135,-0.1397711188,0.1007597297,0.1845953166,0.7821591496,-0.1575741917,-0.160560146,-0.0145655898,-0.0244933534,-0.088375546,0.3267967999,0.3057576716,0.0497207083,0.3230619133,-0.1105097607,0.1278137118,-0.2835086882,0.0646023676,0.0731800869,-0.0285636242,-0.2363406867,0.3204714358,0.0431455448,-0.0127093485,0.3014170825,-0.128121689,-0.1516457647,0.1567753404,-0.7318518162,0.2994091809,-0.1890997142,-0.0831176937,-0.0139113748,0.1470643878,0.2610993981,0.0409128815,-0.0495255105,0.3694851696,0.1844192296,0.0623423234,0.074477002,0.2123364657,0.2606661618,-0.1923152953,-0.0245101713,-0.1213865876,-0.2915119529,-0.4715484083,-0.1039494053,0.1235364452,0.2158168554,-0.2365605086,-0.2136596739,0.1357294023,-0.2236691117,0.0182397123,-0.1153503507,0.287561208,0.0961308181,0.2370782644,0.0844724104,0.1946082264,-0.0593218282,-0.4193832576,-0.0320638455,0.0889588967,-0.396494329,-0.0089434134,0.4253871143,-0.2059131116,0.155105412,0.2090531737,-0.1807143092,-0.3763448596,-0.1051809862,-0.29985255,0.1227002218,0.2350853831,0.0722876862,0.22527197,0.1758594811,-0.2096450329,-0.0948163494,0.3743303716,-0.4765123725,-0.203762114,-0.1129608005,0.0870465264,-0.3702276945,0.042812489,-0.4717314839,0.0912087634,0.0114765568,-0.1476510614,-0.1846654564,-0.3796912134,-0.3459873796,-0.2003361583,0.0562690981,0.4900042713,-0.7549694777,0.2011175901,0.0685681775,0.0840682685,0.1757277995,0.1387699544,-0.2232251018,0.0998104513,-0.2925262749,0.0760307387,0.117212534,-0.1124075651,-0.3250758946,0.1897105426,-0.0210658405,-0.0349105038,0.1909705698,0.0766447634,0.4279769659,-0.0000178403,-0.2978307605,0.1610570997,0.0182893444,0.0978719965,-0.2641838193,-0.2243623137,0.21959728,0.2429786772,0.0428958684,0.0870601982,0.2906083465,-0.4773252904,0.2948067486,-0.0837217569,0.2005083561,0.1304012984,0.1938507706,-0.0031297801,0.3042298853,-0.0209543332,-0.5490700603,0.2013014704,0.5036683083,-0.1273956448,-0.054686565,-0.0433550999,-0.4341191947,-0.0346208662,-0.1757203788,0.0496488176,-0.0256375745,0.1051591933,-0.0783729777,-0.1835370809,-0.0745789483,0.3672889173,-0.3182172179,0.0592879392,-0.3288670182,0.3052717745,0.1356878728,-0.0979554504,0.2334704846,-0.0340172574,0.2635864615,-0.1565463692,-0.1594044268,0.2825955749,0.3972230554,-0.0089151813,0.0568526611,-0.1653582603,0.0449949019,-0.2886992395,-0.039644707,0.2083660811,0.3289196491,-0.052529674,-0.388286978,0.3406111598,-0.3962658346,0.2122683227,-0.185850665,0.0636317208,0.2840827405,-0.0627926365,-0.0625529289,0.0368210375,0.0639444813,0.0541221611,0.2319158167,0.1155366451,-0.1605666876,-0.0800897554,0.2399234921,0.1933390945,-0.2646180987,0.2249322534,-0.1317929924,-0.0654701069,0.3131316006,0.2746325135,0.3821977377,0.1115466356,-0.2823289633,-0.0077096047,0.1524128914,-0.2335685045,0.1316001713,0.103461735,0.0353699438,0.2109541148,0.1988956332,0.104850173,-0.1725974083,-0.1863415837,0.1725758761,0.2190569788,-0.5805425644,0.0048966892,-0.2296882421,0.3273848593,-0.2782441974,0.0585424565,0.1733689457,-0.2118965089,-0.3372685611,0.2768916786,-0.1294859648,0.0467706956,-0.1542281657,0.3068214059,0.1176594198,-0.2467561811,0.046924077,-0.2137753069,-0.3152797222,-0.0071447152,0.104252696,0.0340959989,-0.1043617055,-0.037348751,0.0876660049,-0.24026151,-0.0827936083,0.1639792025,0.0137606245,0.1385041326,0.1075599939,-0.1262121946,-0.0486987755,-0.15883407,0.1437249035,0.0194418002,0.0146210454,0.3827898204,0.2163419425,-0.209561646,-0.1090568677,-0.6926314831,-0.1795829237,-0.2248694301,0.025014244,-0.1732673943,-0.0428306684,0.047063034,0.3236681819,-0.0238800831,0.2033355534,-0.0535822399,-0.0562276058,-0.3528072536,0.5881902575,-0.2043498456,-0.3377241194,0.2926166058,-0.0908306092,0.0103339078,0.3109973967,-0.4887527227,0.2925721109,-0.055077184,0.0720270127,-0.0035766959,0.0297304932,0.0222400259,-0.0356193259,0.1201732904,-0.0546184741,-0.2449769825,0.197209239,-0.075742051,0.119669348,0.2698403597,0.434009552,-0.2318444848,0.6490834951,0.0943914577,0.006586032,0.2863881886,-0.0369606726,0.3239893317,0.1193069592,-0.1768079102,0.1326798797,-0.0976964459,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi, seems I also can't read csv file. I was trying with a dummy csv with only three rows.\r\n\r\n```\r\ntext,label\r\nI hate google,negative\r\nI love Microsoft,positive\r\nI don't like you,negative\r\n```\r\nI was using the HuggingFace image in Paperspace Gradient (datasets==1.1.3). The following code doesn't work:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', script_version=\"master\", data_files=['test_data.csv'], delimiter=\",\")\r\n```\r\nIt outputs the following:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv\/default-3b6254ff4dd403e5 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/csv\/default-3b6254ff4dd403e5\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nDataset csv downloaded and prepared to \/root\/.cache\/huggingface\/datasets\/csv\/default-3b6254ff4dd403e5\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2. Subsequent calls will reuse this data.\r\n```\r\nBut `len(dataset)` gives `1` and I can't access rows with indexing `dataset[0]` (it gives `KeyError: 0`).\r\n\r\nHowever, loading from pandas dataframe is working.\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\ndf = pd.read_csv('test_data.csv')\r\ndataset = Dataset.from_pandas(df)\r\n```\r\n\r\n","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":141,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi, seems I also can't read csv file. I was trying with a dummy csv with only three rows.\r\n\r\n```\r\ntext,label\r\nI hate google,negative\r\nI love Microsoft,positive\r\nI don't like you,negative\r\n```\r\nI was using the HuggingFace image in Paperspace Gradient (datasets==1.1.3). The following code doesn't work:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', script_version=\"master\", data_files=['test_data.csv'], delimiter=\",\")\r\n```\r\nIt outputs the following:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv\/default-3b6254ff4dd403e5 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/csv\/default-3b6254ff4dd403e5\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nDataset csv downloaded and prepared to \/root\/.cache\/huggingface\/datasets\/csv\/default-3b6254ff4dd403e5\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2. Subsequent calls will reuse this data.\r\n```\r\nBut `len(dataset)` gives `1` and I can't access rows with indexing `dataset[0]` (it gives `KeyError: 0`).\r\n\r\nHowever, loading from pandas dataframe is working.\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\ndf = pd.read_csv('test_data.csv')\r\ndataset = Dataset.from_pandas(df)\r\n```\r\n\r\n","embeddings":[-0.0118629886,0.0808789656,-0.0193711855,0.4624471068,0.2891317606,0.1691015065,0.3954825103,0.2487924546,0.2549251318,0.1220230907,-0.1881923825,0.2273677438,-0.1354269832,-0.0153029179,-0.2497221678,-0.0935218707,0.0260975901,0.0599039607,-0.3732225597,-0.0090303589,-0.3582966924,0.3465511799,-0.3180826902,-0.0767824054,-0.100431487,0.1366493404,-0.0739043728,0.3516087532,-0.2565934062,-0.2810692787,0.4602858126,0.027672179,0.5446310043,0.3226149082,-0.0001188028,0.1157893538,0.257464081,-0.125779599,-0.1716525704,-0.6218040586,0.0017178034,-0.4847868681,0.2545427382,-0.1264301389,-0.1261842698,0.0441867076,-0.0974872708,-0.3808397949,0.4975135028,0.5468569994,0.1608204097,-0.2178964913,-0.002659607,0.0565558858,0.67342031,0.047714334,-0.1692203432,0.270789206,0.0260531921,-0.0865285397,-0.0880396217,0.1368653774,-0.0883378312,0.2905078828,0.2541857958,0.2660015523,-0.1604845822,-0.2315118462,0.0664864704,0.1838555038,0.6319200993,-0.0759829357,-0.1848375648,-0.0473118685,0.0015072487,-0.2142893523,0.3849784434,0.32181862,-0.0536062904,0.3348769844,-0.1593370736,0.0491260625,-0.4021936655,0.1563876867,0.0565393716,0.0167305879,-0.2711093724,0.2473977357,0.1620324701,0.0409820639,0.3063595295,0.0125093972,-0.0958893001,0.2550705969,-0.6877479553,0.3242422044,-0.1610052288,-0.0471008494,-0.0373417251,0.1841595173,0.3348793387,0.018604733,-0.100939855,0.3323724568,0.1696489453,0.0724511296,-0.0694890693,0.3101442754,0.3111258745,-0.1418602616,0.0889902264,-0.1957607716,-0.3813566566,-0.5510748625,-0.1370717734,0.153520748,0.2152677923,-0.191373378,-0.1348076761,0.0824970827,-0.29584378,0.0412005521,-0.1037042812,0.2584906816,0.1181269959,0.1929664612,0.1120182201,0.217828393,-0.0669630021,-0.3518299758,-0.0048816982,0.1507574767,-0.4125787318,-0.0420589931,0.4580542147,-0.1998315305,0.1430309713,0.1652851403,-0.0317209847,-0.3051397204,-0.027504025,-0.345038265,0.1042648628,0.3114556968,0.0919667482,0.1780766696,0.2598017156,-0.2742810249,-0.0650035962,0.2549707592,-0.4401567578,-0.1146177351,-0.0722140744,0.0712279826,-0.2610419691,0.1687274575,-0.5450232625,0.1131039113,-0.0171524789,-0.1490582228,-0.1941609979,-0.3184810877,-0.2939890623,-0.2533073127,0.0877587721,0.4105604887,-0.7388890386,0.1863694638,-0.0079962043,0.0084585734,0.1423375905,0.2571278811,-0.2166781574,-0.0076674805,-0.2400081605,0.0525184162,0.1994749755,-0.2259487063,-0.4637648165,0.1456199139,0.0125081893,-0.0459893011,0.1068215147,0.0247449912,0.4188020527,0.0953659117,-0.2324724197,0.2021092921,0.0218010601,0.0537071675,-0.2709424198,-0.2555734217,0.2727690637,0.2312987149,0.0567305461,0.0727248862,0.2554844618,-0.5654004812,0.3175981641,-0.0955061093,0.2537537217,0.103790231,0.305829823,0.0119233225,0.2585232258,0.0209247191,-0.5154770017,0.1781058162,0.3431998193,-0.1195503771,-0.1280687302,-0.0305286143,-0.4309688807,-0.1388527304,-0.2483625561,-0.1230339333,0.0097857034,0.0791332871,0.0067214277,-0.176499486,-0.1114899814,0.4863345623,-0.3433071971,0.1073693335,-0.3811258078,0.2562277913,0.1100255027,-0.0257717185,0.2472402304,-0.0620501488,0.2698598504,-0.1324793398,-0.1780457497,0.2074373513,0.348687917,-0.0601232275,0.0584408008,-0.1465489864,0.0486193746,-0.278768152,-0.0639291406,0.1557741612,0.3600311279,-0.0460514463,-0.5185007453,0.3144533634,-0.3182805181,0.1841373742,-0.1650368422,0.1002310738,0.2616048455,0.011042295,-0.1340304017,0.006500219,0.054611396,0.1028838083,0.3047707677,0.0061566592,-0.0917265415,-0.169329986,0.0866777003,0.1697233468,-0.2521165609,0.209491834,-0.078904435,-0.0527838208,0.3029036522,0.3002995253,0.3695094585,0.0668440759,-0.2557020485,0.0274065994,0.0987148434,-0.2053614408,0.0438876525,0.0450873449,0.1305779815,0.167106539,0.2208328098,0.1397261918,-0.1462558806,-0.1437519342,0.1935928166,0.2692375183,-0.5836182237,0.0046391208,-0.2520117164,0.2815494239,-0.177628994,0.0017936039,0.1016970277,-0.2104956359,-0.3504090011,0.2913831472,-0.0326991342,0.0169307422,-0.0212115459,0.3473856151,0.1048926115,-0.0763040558,-0.0240837857,-0.1735054851,-0.2351216227,0.0199389532,0.0443579592,0.0041387249,-0.0721165389,0.0316162929,0.0195567273,-0.2486414164,-0.1216644198,0.1549575031,-0.0072373631,0.18079032,0.0941220745,-0.0761066079,-0.1042506322,-0.0307540875,0.171576336,0.1048674658,-0.0070715062,0.2781068087,0.203579098,-0.1809631735,-0.0832260624,-0.5839006305,-0.3633822799,-0.2267879546,0.0676118359,-0.1146416292,-0.0052905944,0.0991690531,0.2561677396,0.0274770912,0.2174453735,-0.0561032481,-0.1361311823,-0.3465022147,0.5890855789,-0.2951643169,-0.3459739089,0.2518579364,-0.0580747612,0.0655628592,0.454408139,-0.4482577145,0.1498956829,-0.0319364406,0.0750386342,0.0224172082,0.0262204837,0.0370181464,-0.0896322504,0.1411664933,-0.0410196222,-0.1947057843,0.1235551015,-0.0017070151,0.1028957069,0.248731181,0.441820085,-0.1863634586,0.7564931512,0.1680112332,0.0771736503,0.303815335,-0.1413715333,0.3919916451,0.1962672919,-0.2521838546,0.1308351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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"This is because load_dataset without `split=` returns a dictionary of split names (train\/validation\/test) to dataset.\r\nYou can do\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', script_version=\"master\", data_files=['test_data.csv'], delimiter=\",\")\r\nprint(dataset[\"train\"][0])\r\n```\r\n\r\nOr if you want to directly get the train split:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', script_version=\"master\", data_files=['test_data.csv'], delimiter=\",\", split=\"train\")\r\nprint(dataset[0])\r\n```\r\n","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":55,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n This is because load_dataset without `split=` returns a dictionary of split names (train\/validation\/test) to dataset.\r\nYou can do\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', script_version=\"master\", data_files=['test_data.csv'], delimiter=\",\")\r\nprint(dataset[\"train\"][0])\r\n```\r\n\r\nOr if you want to directly get the train split:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', script_version=\"master\", data_files=['test_data.csv'], delimiter=\",\", split=\"train\")\r\nprint(dataset[0])\r\n```\r\n","embeddings":[-0.0920246169,0.0564349107,0.0039141956,0.372161001,0.2419901937,0.2112514675,0.5301389098,0.3948995173,0.2474182695,0.0687352568,-0.1450405717,0.2728796601,-0.1780427992,0.0853817388,-0.2621557415,-0.1945452243,0.0322136991,0.0497310571,-0.3552699387,0.0039880648,-0.3571982682,0.3788069189,-0.3598428667,-0.0790998042,-0.2235179991,0.1674229652,-0.1466455311,0.341891259,-0.0646044761,-0.3069737852,0.4642286599,-0.1027503386,0.4105321765,0.3379975855,-0.0001209265,0.091597721,0.2450603694,-0.1091812253,-0.2504522502,-0.6010637283,-0.0259371456,-0.3921331465,0.3510513306,-0.1411182433,-0.1290298402,0.0570398457,-0.1692730635,-0.3528496027,0.4674951434,0.5159787536,0.1531329453,-0.2059019059,-0.1971256584,-0.0124462983,0.5450271368,0.1082914993,-0.1457669139,0.288138032,-0.0139438249,-0.2046414018,-0.111815691,0.06374906,0.0106420731,0.2190205753,0.1316032112,0.2965677381,-0.2615711093,-0.2017144263,0.0166659206,0.194961369,0.570548296,-0.0731799155,-0.1748564541,-0.1613171101,-0.059986379,-0.2130065262,0.3124711812,0.3574937284,-0.0169693641,0.3381771445,-0.1206576899,0.0709901527,-0.2584077418,0.0994611308,-0.0562138148,0.0654799715,-0.1489541382,0.2812530398,0.2017697394,0.1015310362,0.2514773607,-0.0579952151,-0.0673245639,0.1580315381,-0.6834616661,0.2036809176,-0.2168770581,-0.1526712775,-0.054263711,0.2458858341,0.1760437191,-0.0476836078,-0.0708104372,0.3254227638,0.3645863235,0.0074674669,-0.0258980151,0.4044125676,0.2407534719,-0.1334234476,-0.0388788767,-0.1857776791,-0.2934213877,-0.5016217828,-0.086302571,0.2012825906,0.1611034721,-0.1534703076,-0.2344414443,0.0494668446,-0.3282530606,-0.0160041694,-0.1057873964,0.3072037101,0.0792054757,0.3233473003,0.0002639497,0.2762194574,-0.1499179155,-0.3500113487,-0.0380696207,0.1538398564,-0.4294225276,-0.0667094663,0.4375000298,-0.1740852147,0.1433625072,0.1928385347,-0.0566435307,-0.3830625713,-0.0729662925,-0.3045082986,0.127741605,0.2792576253,0.1017231271,0.1716285944,0.2939251661,-0.1996124834,-0.0871599913,0.2777005136,-0.4890449941,-0.2541227639,-0.0913462788,0.1075182632,-0.2549365759,0.1419746131,-0.3131116331,0.0468015783,0.0950297117,-0.1052417681,-0.1675722599,-0.3225544095,-0.3190791607,-0.2440020442,0.0466859564,0.3962135911,-0.7064715028,0.1609789878,-0.0436272025,0.0166498329,0.1765231192,0.2286920249,-0.2605005205,0.0781636834,-0.2938950658,0.1257948726,0.2624050379,-0.21647802,-0.3129369318,0.2795172334,-0.0455346778,-0.0195158757,0.1386790872,0.0048780697,0.4192972481,0.0327761024,-0.1222806796,0.2695268393,-0.0665015802,0.0513628274,-0.2043424994,-0.2208314836,0.230395481,0.2533374131,0.09017317,0.064632073,0.2520318627,-0.4523490071,0.4311442971,-0.0185215902,0.2009960264,0.0600047223,0.2045498639,0.0673958138,0.2995598316,-0.0448753238,-0.5501888394,0.1751102954,0.4230570197,-0.1836449653,-0.1712397188,-0.0816116408,-0.4569071531,-0.0527785569,-0.2632133961,-0.0353595838,-0.0295918472,-0.0453094617,-0.0110517554,-0.1825748086,-0.1791177988,0.4217712879,-0.261186868,0.1721967459,-0.3425503671,0.3586449921,0.1426158845,-0.0517234728,0.1552652717,0.0561741702,0.2390625179,-0.1527739614,-0.1483723372,0.3356363773,0.4766589999,-0.0618811175,-0.0393589959,-0.2567850351,0.1200383306,-0.2125388384,-0.0765184611,0.2014890015,0.266081214,-0.0892454609,-0.4791173935,0.3983052075,-0.3870799243,0.3109741211,-0.1254491508,0.1998694092,0.241813615,-0.1007643268,-0.1040568799,-0.0747825727,0.0224811137,-0.0448641144,0.2591088712,0.0762873888,-0.1619548649,-0.1972352862,0.3197233379,0.1151808053,-0.2512791157,0.1153136343,-0.0970705897,-0.0526020266,0.2553263307,0.388995707,0.4357036948,0.083102718,-0.2003983855,0.0272413809,0.1205655038,-0.2420069575,0.1677270532,0.0737355798,0.0803622305,0.2283989042,0.1371799409,0.0185588915,-0.1221626997,-0.1420411319,0.2180795521,0.1531413943,-0.5580414534,0.0672471225,-0.2492441237,0.3542293012,-0.3054797351,-0.088093169,0.1177534387,-0.3376854062,-0.2673149109,0.2258105576,-0.1555700451,0.0849401802,-0.1968522966,0.168894276,0.0460411496,-0.2257519066,0.127748847,-0.2152117789,-0.262229979,0.0265395846,0.0546971373,0.0048142457,-0.0604922995,0.0089467121,-0.0171862077,-0.1233489215,-0.0182160083,0.1867986768,-0.070273146,-0.0006798789,0.1853316277,-0.1570403576,-0.0293877814,-0.2066927999,0.1623373479,0.077324219,0.056724295,0.3653017879,0.293117404,-0.0645011738,-0.1795730293,-0.7377924919,-0.2892294824,-0.2201963365,0.0692026615,-0.1671292484,-0.0350531973,0.0936679617,0.2560247779,-0.0321816392,0.2291259617,-0.1719628274,-0.1385293454,-0.1858995855,0.5807957649,-0.2469512373,-0.2713826299,0.2195544392,-0.1315249503,0.007083829,0.3676760197,-0.4218454957,0.2262401134,-0.0697562769,0.0749832019,-0.0520796403,0.0243040752,0.0830063075,-0.0699172094,0.0978282839,-0.0387605727,-0.1052255258,0.2508596182,-0.0496933125,0.1034532785,0.1427065581,0.5326666832,-0.0993343964,0.8147948384,0.0513601415,0.059384767,0.1929327548,-0.1644670963,0.2508680522,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Good point\r\n\r\nDesign question for us, though: should `load_dataset` when no split is specified and only one split is present in the dataset (common use case with CSV\/text\/JSON datasets) return a `Dataset` instead of a `DatsetDict`? I feel like it's often what the user is expecting. I break a bit the paradigm of a unique return type but since this library is designed for widespread DS people more than CS people usage I would tend to think that UX should take precedence over CS reasons. What do you think?","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":89,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Good point\r\n\r\nDesign question for us, though: should `load_dataset` when no split is specified and only one split is present in the dataset (common use case with CSV\/text\/JSON datasets) return a `Dataset` instead of a `DatsetDict`? I feel like it's often what the user is expecting. I break a bit the paradigm of a unique return type but since this library is designed for widespread DS people more than CS people usage I would tend to think that UX should take precedence over CS reasons. What do you think?","embeddings":[-0.0537754819,0.1033459008,0.0190047286,0.3612280488,0.1807834059,-0.0042513018,0.5432186127,0.222499162,0.3372097313,-0.003618543,-0.0237615574,0.3069189489,-0.0317640789,0.118917942,-0.2025142312,-0.0749718025,-0.012215076,0.1207054928,-0.3683388829,-0.0174310263,-0.4309170544,0.292850107,-0.2213966399,-0.0585704185,-0.1151770204,0.1571623236,0.0017044473,0.3660930991,-0.1084838063,-0.257276535,0.6280568838,0.0230042394,0.4292282462,0.2245847285,-0.0001190717,0.0388116241,0.3117853403,-0.1329061836,-0.2897416055,-0.5877243876,-0.027259687,-0.3956184685,0.3377561569,-0.1040745974,-0.0616714507,0.172775656,-0.1652778685,-0.5165011287,0.3123306334,0.3730696142,0.1405084878,-0.2834521234,-0.2309973836,0.0034147301,0.5908178687,0.2067524493,-0.1971361637,0.2810762227,0.1669810265,-0.1015687287,-0.1598022133,0.083155334,-0.1043233722,0.1894381344,0.29018116,0.2623083591,-0.2809916139,-0.11749015,-0.0169992391,0.1899370998,0.8341629505,-0.0735759661,-0.2151966095,-0.2079991996,-0.0091483137,-0.1942547411,0.2959364951,0.224168852,-0.0256013237,0.2741404772,-0.039895419,0.0002723018,-0.2295822501,0.0643773004,-0.1193104833,0.0102746459,-0.1569630653,0.3779331744,0.0456255935,0.1549314559,0.3429839015,-0.1116522849,-0.1162602082,0.0835307017,-0.567054987,0.1575641781,0.0551394075,-0.1592389643,0.0174478833,0.3070752323,0.3632949889,0.0668959916,-0.17210491,0.2512286603,0.434494853,0.0817990974,0.0020746593,0.2576614916,0.3648816347,-0.2776294947,0.013783399,-0.1647897661,-0.1719340533,-0.2707709968,-0.0575214885,0.1610938907,0.1682668328,-0.1684322953,-0.2216254175,0.0732909441,-0.2644221187,-0.0547595546,-0.1519445032,0.2221849561,0.1522415429,0.5024954677,0.0156444684,0.1761675328,-0.0987721533,-0.3069808483,0.0024105534,0.0743475333,-0.4379191697,-0.0022643732,0.4775822163,-0.1238090396,0.0213834923,0.1910146922,-0.146714136,-0.2925012708,-0.0691041574,-0.3121488094,0.1865879744,0.2201448679,0.0137046082,0.2126200795,0.204693377,-0.2328411341,-0.2364430428,0.3585527539,-0.536218226,-0.3046457469,-0.0398351848,0.0985862017,-0.2729690969,0.2291877419,-0.3323957026,0.1283838004,0.0017088264,-0.1008917093,-0.0892250985,-0.3063386679,-0.2946177125,-0.33223176,-0.0385905392,0.5090972185,-0.8320771456,0.2264830768,-0.0860439837,0.1219893396,0.0956524983,0.344486624,-0.3025426567,0.096642442,-0.3494292498,0.0400712192,0.3277105987,-0.2119710743,-0.3828731477,0.2315531373,-0.1052042544,0.0784768239,0.164193958,0.1649427414,0.444454968,-0.043529626,-0.0862467512,0.2531380653,-0.1244701743,0.0795064718,-0.2437729388,-0.2483042628,0.2499010712,0.170079872,0.1038125083,0.0540584698,0.1944538951,-0.4752427936,0.358985275,-0.0931508318,0.1257806569,-0.1334753633,0.3010072112,0.0260031652,0.2912549078,-0.0078927856,-0.6777339578,0.1792105287,0.4068228602,-0.1115447357,-0.0382764116,-0.1820578724,-0.3484876454,-0.0155588202,-0.169530496,0.0278336145,-0.0358911566,0.0391564444,-0.1085351482,-0.1549387872,-0.2077755928,0.4025168419,-0.2128464878,0.0848495588,-0.3851655424,0.3566335142,0.2739728093,0.0097316541,0.1406469643,0.1305159926,0.2248567194,-0.1097731665,-0.0581612885,0.2158618718,0.357062906,-0.0946864858,-0.0116337584,-0.1943476945,0.0966617912,-0.1202944145,-0.0475928932,0.2353375703,0.3100091517,-0.165341869,-0.5367754698,0.4306187034,-0.4352806211,0.2406532615,-0.1465645581,0.0563365296,0.1764354557,-0.0112934625,-0.1437526196,-0.0274792295,-0.1477054954,0.0337617807,0.2478099763,0.1867637783,-0.1355113536,-0.0533684976,0.3618720472,0.1317256093,-0.1845165491,0.2107610554,0.0488578379,-0.237596035,0.3247870803,0.3543029428,0.6315694451,0.095159784,-0.1953668892,-0.1066858545,0.0825454518,-0.2646082938,0.1412470192,-0.0092707984,0.0298803374,0.2075575739,0.1890493482,0.0394458137,-0.1802781969,-0.1906566024,0.0667323321,0.0999250039,-0.5104933381,0.0682827085,-0.1229088902,0.2668240666,-0.2863724232,-0.0286341738,0.0715720952,-0.3165184855,-0.2467340976,0.1903038323,-0.1523376703,0.0006298273,-0.1428436786,0.3498927057,-0.0687937289,-0.2591992617,-0.0169770215,-0.1245573983,-0.271569401,0.0175173245,0.0291548818,0.0485817418,0.0630102456,0.0337894745,0.0646716133,-0.2758508325,-0.1107280478,0.2082014233,0.027093282,0.0583995543,0.3743036985,-0.1855602264,0.0611077026,-0.1489398926,0.1078897193,0.1560414881,0.1085718945,0.2716521621,0.1851983666,0.0015301723,-0.2390160114,-0.7112779617,-0.1844305545,-0.2410863042,0.1508251876,-0.1209753156,0.0023745727,-0.0167912655,0.1608787179,-0.1565036476,0.2552342117,-0.0619459786,-0.1892142594,-0.2331595719,0.4649218023,-0.1569713652,-0.2644332051,0.1217835993,-0.1825101972,0.1514082551,0.4018861651,-0.4829933643,0.1828479171,0.0471073501,0.1323951334,-0.0508755594,0.14494133,0.0866822302,0.0103522362,0.1132815108,-0.0133335805,-0.1951068789,0.2277120352,-0.0892839804,0.0242395401,0.2536148131,0.3969816864,-0.138894558,0.705930233,0.0036074312,0.015542306,0.2021128535,0.0485987701,0.4244733751,0.0127241453,-0.1967828274,0.096319139,-0.1831023842,-0.3161441088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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications.\r\nI'm ok with returning the dataset object if no split specifications are given for text\/json\/csv\/pandas.\r\n\r\nFor the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think ?","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":73,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications.\r\nI'm ok with returning the dataset object if no split specifications are given for text\/json\/csv\/pandas.\r\n\r\nFor the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Thanks for your quick response! I'm fine with specifying the split as @lhoestq suggested. My only concern is when I'm loading from python dict or pandas, the library returns a dataset instead of a dictionary of datasets when no split is specified. I know that they use a different function `Dataset.from_dict` or `Dataset.from_pandas` but the text\/csv files use `load_dataset()`. However, to the user, they do the same task and we probably expect them to have the same behavior.","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":78,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Thanks for your quick response! I'm fine with specifying the split as @lhoestq suggested. My only concern is when I'm loading from python dict or pandas, the library returns a dataset instead of a dictionary of datasets when no split is specified. I know that they use a different function `Dataset.from_dict` or `Dataset.from_pandas` but the text\/csv files use `load_dataset()`. However, to the user, they do the same task and we probably expect them to have the same 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='.\/amazon_data\/Video_Games_5.csv', delimiter=\",\", split=['train', 'test'])\r\n```\r\nI was running the above line, but got this error.\r\n\r\n```ValueError: Unknown split \"test\". Should be one of ['train'].```\r\n\r\nThe data is amazon product data. I load the Video_Games_5.json.gz data into pandas and save it as csv file. and then load the csv file using the above code. I thought, ```split=['train', 'test']``` would split the data into train and test. did I misunderstood?\r\n\r\nThank you!\r\n\r\n","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":78,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n ```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='.\/amazon_data\/Video_Games_5.csv', delimiter=\",\", split=['train', 'test'])\r\n```\r\nI was running the above line, but got this error.\r\n\r\n```ValueError: Unknown split \"test\". Should be one of ['train'].```\r\n\r\nThe data is amazon product data. I load the Video_Games_5.json.gz data into pandas and save it as csv file. and then load the csv file using the above code. I thought, ```split=['train', 'test']``` would split the data into train and test. did I misunderstood?\r\n\r\nThank you!\r\n\r\n","embeddings":[0.0013099385,0.05171258,-0.019382393,0.4013643861,0.2351075709,0.2222292572,0.5522068739,0.3211097121,0.2396999747,-0.0363929719,-0.146097675,0.2243235707,-0.025056662,0.102229014,-0.3207229376,-0.1825466752,0.0453077927,0.0672884807,-0.2340873033,0.0617866963,-0.3590757251,0.3422645926,-0.2727225721,-0.0144880507,-0.2078830004,0.0124817826,-0.1053493991,0.296968013,-0.1483759135,-0.2648254633,0.4790059626,-0.1716454923,0.5383775234,0.3396247923,-0.0001182179,0.0470696352,0.2971403897,-0.1246482879,-0.2083082646,-0.5637422204,-0.056119509,-0.3337765634,0.3093161285,-0.1384035796,-0.0306093525,0.0026258738,-0.1647102088,-0.37167117,0.5169056058,0.5258500576,0.1700556278,-0.2575105131,-0.1054589003,0.010397505,0.5956737995,0.0796283782,-0.1261375397,0.2239545137,0.0111493096,-0.128290683,-0.1261236072,0.0465885103,-0.0409378149,0.3501431644,0.1306047738,0.2808638811,-0.2173999995,-0.2594374418,0.0357642174,0.1646072417,0.6517999172,-0.0907546654,-0.25573349,-0.0893641934,-0.0458670929,-0.1906019151,0.2356330603,0.4025241733,-0.0143940095,0.2629038095,-0.0302205607,0.0621254407,-0.2307951003,0.143907845,-0.0291519202,0.0988970771,-0.2419938594,0.3104786575,0.0748927519,0.0376325175,0.2845781446,-0.0803926289,-0.0853785947,0.1097130626,-0.73577106,0.1972242743,-0.2606303692,-0.1393675655,-0.1818331033,0.2301413119,0.1986734569,-0.048055131,-0.0711116493,0.3267121017,0.3619975746,0.1196252257,-0.0493249558,0.3038940132,0.2154174745,-0.1192011908,-0.0823735744,-0.189896971,-0.2964744866,-0.5364337564,-0.0690400451,0.1694035679,0.2778784931,-0.2056666315,-0.3103758693,0.0415622666,-0.3661009669,-0.0593541749,-0.0650034547,0.3313742578,0.0484936647,0.1800385416,-0.0199314784,0.3329245448,-0.1016789228,-0.2537936866,-0.0099349301,0.2129421234,-0.3322185278,-0.0475763343,0.4394691885,-0.1399374455,0.169890821,0.1438711435,-0.1016609594,-0.3589423001,-0.0439114124,-0.3201140761,0.0598089546,0.2911030948,0.0977208391,0.2263792157,0.2708276212,-0.2004960328,-0.1134681404,0.3200462759,-0.4528761506,-0.1949418187,-0.1236148328,0.0638868064,-0.2958910167,0.0516121313,-0.3975146711,0.0070914431,0.1372073293,-0.1690459847,-0.1107430756,-0.2598054111,-0.223449856,-0.2329428494,-0.0297738779,0.3591510355,-0.729249835,0.2099155039,-0.1305401772,0.139788568,0.204263553,0.2783480585,-0.2136737108,0.0838187039,-0.1957585216,0.1398439556,0.1922795922,-0.1948812455,-0.2584933043,0.3368588388,-0.0548021421,0.0118795587,0.14804326,0.0079602227,0.2661395669,0.0118092205,-0.2148744017,0.277939707,-0.1107130125,0.0532059409,-0.2178413868,-0.2958856225,0.393114984,0.2233066708,0.0809110627,0.0637842864,0.219349429,-0.4887855649,0.2911222577,-0.0074691433,0.213099435,0.1266507357,0.2399577498,-0.0118248109,0.3064186573,-0.0468057133,-0.5506250858,0.1211448312,0.2973080575,-0.0805335045,-0.2491194308,-0.0293331854,-0.4990498126,-0.1263436526,-0.1931822002,-0.1471881717,-0.0113766007,0.0028395574,-0.0695435628,-0.1811055839,-0.1231717244,0.4104182422,-0.1968202293,0.1574802846,-0.420506835,0.3484317362,0.0685198754,-0.046662081,0.232258305,-0.0470527746,0.317230612,-0.1737196296,-0.172609672,0.2992421985,0.3452042341,0.0514555573,0.0920722485,-0.2689474523,0.0843897611,-0.1345872581,-0.07657893,0.1975729764,0.3707106113,-0.090206176,-0.4967188537,0.3981715739,-0.373051554,0.3407868743,-0.1548341811,0.1214029342,0.2545317709,-0.0342797935,-0.0573217347,0.0154310679,-0.0674974099,-0.0414524674,0.1813625395,0.1204914972,-0.1412470639,-0.0947611406,0.2932521701,0.1242887378,-0.2171434313,0.1496353447,-0.0212595519,-0.0907215476,0.3234220147,0.4788161814,0.4989160895,0.0934970304,-0.2257995307,-0.0502079949,0.090594776,-0.2129285783,0.1495766789,0.1314753294,0.1568460763,0.1497189403,0.1046723127,0.0309833139,-0.0564096943,-0.153122142,0.2575722635,0.2865161896,-0.5545143485,0.0832569972,-0.2742894888,0.251234889,-0.2948322296,-0.1245409399,0.1124371514,-0.3302797377,-0.2978978753,0.2424979359,-0.1188882664,0.0766470656,-0.1887726784,0.1858246028,0.0945126414,-0.1874366701,0.0460106358,-0.2297289073,-0.1853150278,0.0440204144,0.1608080864,0.1147634089,-0.1593758166,0.0924839228,0.1120204851,-0.1131626442,-0.0734895021,0.1205578297,-0.0692943409,0.1066164449,0.0611006469,-0.0971011519,-0.1153244749,-0.3006359041,0.0956041068,0.0619500428,0.0524068102,0.3358862698,0.3010444641,-0.1343676746,-0.0637167245,-0.771294415,-0.292863667,-0.2254818976,0.1364150345,-0.0603985898,-0.0800451189,-0.009466528,0.2729968131,-0.046676036,0.3181810975,-0.0819869265,-0.1524343342,-0.3559525907,0.6282113194,-0.33587569,-0.2506924868,0.212349385,-0.0891900733,0.0134996045,0.3305257559,-0.3797052801,0.2495916933,-0.0787978917,-0.0502249151,-0.0006279607,0.0238592699,0.0380966552,-0.0071674823,0.081921041,-0.0103262225,-0.1273985505,0.2148552835,0.0483199209,0.1261241436,0.104697682,0.4444700181,-0.1454232782,0.832663238,0.0092135007,0.0208379421,0.2185450792,-0.1146018207,0.2767150104,0.1338882148,-0.2511597872,0.0126347225,-0.1921897978,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi ! the `split` argument in `load_dataset` is used to select the splits you want among the available splits.\r\nHowever when loading a csv with a single file as you did, only a `train` split is available by default.\r\n\r\nIndeed since `data_files='.\/amazon_data\/Video_Games_5.csv'` is equivalent to `data_files={\"train\": '.\/amazon_data\/Video_Games_5.csv'}`, you can get a dataset with \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='.\/amazon_data\/Video_Games_5.csv', delimiter=\",\", split=\"train\")\r\n```\r\n\r\nAnd then to get both a train and test split you can do\r\n```python\r\ndataset = dataset.train_test_split()\r\nprint(dataset.keys())\r\n# ['train', 'test']\r\n```\r\n\r\n\r\nAlso note that a csv dataset may have several available splits if it is defined this way:\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={\r\n    \"train\": '.\/amazon_data\/Video_Games_5_train.csv',\r\n    \"test\": '.\/amazon_data\/Video_Games_5_test.csv'\r\n})\r\nprint(dataset.keys())\r\n# ['train', 'test']\r\n```\r\n","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":123,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi ! the `split` argument in `load_dataset` is used to select the splits you want among the available splits.\r\nHowever when loading a csv with a single file as you did, only a `train` split is available by default.\r\n\r\nIndeed since `data_files='.\/amazon_data\/Video_Games_5.csv'` is equivalent to `data_files={\"train\": '.\/amazon_data\/Video_Games_5.csv'}`, you can get a dataset with \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='.\/amazon_data\/Video_Games_5.csv', delimiter=\",\", split=\"train\")\r\n```\r\n\r\nAnd then to get both a train and test split you can do\r\n```python\r\ndataset = dataset.train_test_split()\r\nprint(dataset.keys())\r\n# ['train', 'test']\r\n```\r\n\r\n\r\nAlso note that a csv dataset may have several available splits if it is defined this way:\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={\r\n    \"train\": '.\/amazon_data\/Video_Games_5_train.csv',\r\n    \"test\": '.\/amazon_data\/Video_Games_5_test.csv'\r\n})\r\nprint(dataset.keys())\r\n# ['train', 'test']\r\n```\r\n","embeddings":[-0.1236993298,0.0545275696,0.0046684053,0.3440138996,0.2238630652,0.2072241902,0.5557584763,0.3320873976,0.2292669863,0.0723564923,-0.1219850779,0.1893656254,-0.0988404006,0.0580993779,-0.3150721788,-0.194993943,0.0256498456,0.0631211326,-0.2891602218,0.0247312747,-0.2967126966,0.3495420814,-0.2854318023,-0.1041691303,-0.2474516779,0.1247463822,-0.1284908503,0.2887857258,-0.1067125648,-0.2828909755,0.5074794292,-0.0799292624,0.4618830681,0.3443642855,-0.0001203501,0.0970003307,0.2799856365,-0.091465354,-0.2787306607,-0.5799635053,-0.0071041742,-0.375300616,0.3846252859,-0.1235786229,-0.1427274942,0.0628602952,-0.0909830704,-0.3723733723,0.4926059544,0.502761364,0.1562837213,-0.2277879417,-0.2041125447,-0.051319059,0.6431521177,0.1048247665,-0.1537516266,0.270380944,0.0426355526,-0.1800077707,-0.1540324092,0.0384248085,0.0187865905,0.2721130848,0.1503915191,0.3210097253,-0.1926092505,-0.2060050964,0.0234964862,0.1783809215,0.5856808424,-0.0617553107,-0.1998890489,-0.1086915657,-0.0031236117,-0.2112372667,0.2918922901,0.3944924474,-0.0140921967,0.2673090696,-0.0840748101,0.0562876873,-0.2715720832,0.1967208087,-0.0411109254,0.0441895053,-0.160697028,0.3093575537,0.1828568578,0.1352512985,0.2616449594,-0.0672460869,-0.0468277894,0.1808257997,-0.7518927455,0.2322711051,-0.2511095703,-0.1547812074,-0.0936986431,0.2901218832,0.2221346498,-0.0328100696,-0.0704159141,0.3143113554,0.3604484797,0.0580574311,-0.0649092644,0.373855114,0.2827087641,-0.0966242552,-0.0495880581,-0.1580700725,-0.2829408944,-0.5059507489,-0.0812057331,0.1843688339,0.2114668787,-0.1583938897,-0.2342969477,0.0050778058,-0.3175958097,-0.0391786583,-0.0982536599,0.324622035,0.122901082,0.2582788169,0.0182131343,0.3555499017,-0.1680651605,-0.3251541555,-0.0390207842,0.163063094,-0.3775836825,-0.0187384021,0.527097404,-0.2026496828,0.1497711837,0.2378042489,-0.0695297271,-0.3647531271,-0.0183723271,-0.3357921243,0.060033977,0.2678999603,0.1478130966,0.2357051224,0.2901499271,-0.1314113438,-0.0991395116,0.3206919432,-0.4503082037,-0.2298634648,-0.0609409958,0.0792621821,-0.2566174567,0.1285786182,-0.3197812736,0.1215938702,0.1233536229,-0.1894128919,-0.1974068284,-0.2858693004,-0.3190152645,-0.2252106816,0.0287518781,0.4265123904,-0.7253031731,0.1600687057,-0.0735779107,0.0818117931,0.1762104481,0.2192542106,-0.3011444211,0.0285338406,-0.2588867843,0.1298175007,0.2128348202,-0.1452604532,-0.3053944111,0.3130154312,-0.0814771727,-0.0150277643,0.1646915376,0.0735130981,0.3808694184,0.0346264243,-0.1928317547,0.273281008,-0.0599818751,0.0648037419,-0.2179353982,-0.2626712918,0.268104732,0.2398951948,0.121989511,0.0886864811,0.2000192255,-0.4222188294,0.3684153557,0.0018666971,0.2421445996,0.0944420248,0.2172833234,0.0069560236,0.2594904304,-0.0468955226,-0.5880161524,0.175081268,0.3549012542,-0.1456279159,-0.1272691637,-0.0827832744,-0.4457899332,-0.1092281342,-0.2193202376,-0.0531642027,-0.0301740468,0.0546009205,-0.0905675292,-0.2170437425,-0.150492087,0.3589515984,-0.2253829837,0.1345571727,-0.347008884,0.3731137514,0.1203849763,-0.074222669,0.1797977686,-0.0364786349,0.3094663024,-0.1655763984,-0.1550948173,0.3368910849,0.4324690402,-0.0177731831,-0.0070517296,-0.2337273061,0.1350266635,-0.1509144008,-0.0988640636,0.2633002996,0.2995477617,-0.1329545379,-0.6124714613,0.3689530492,-0.34813115,0.3461934924,-0.1082850099,0.1398616582,0.254413873,-0.0694497824,-0.0920359567,-0.0502822772,-0.0246854499,-0.0507279113,0.2192940414,0.0786125585,-0.1820709705,-0.1413666159,0.1904687732,0.1454722136,-0.2642039955,0.1631590277,-0.0574991964,-0.0909903049,0.286041081,0.3959853947,0.4706317782,0.0796952099,-0.1567980647,-0.0353182107,0.074081555,-0.2415788472,0.1159654185,0.0701689273,0.0640580058,0.2292760313,0.2091118246,0.0643702894,-0.087200731,-0.1415751725,0.2427642196,0.1857290268,-0.5693569779,0.0414618142,-0.2665552199,0.3180040419,-0.3426658511,-0.056278944,0.1317123622,-0.3142736554,-0.2392712831,0.2791632712,-0.1528598368,0.0895879045,-0.1620828956,0.241129145,0.0410924926,-0.189879939,0.1409055889,-0.1960645169,-0.273843199,0.0282651931,0.1539470106,0.0585999936,-0.1277996004,0.1029053107,-0.0550785661,-0.1706884354,-0.0086257122,0.1619700938,-0.0035848648,0.0908206999,0.138238579,-0.1053299382,-0.0646063834,-0.2693961263,0.1504160911,0.0771211237,0.0710023791,0.314376086,0.2691509128,-0.0828716531,-0.179314509,-0.780608654,-0.2570376992,-0.2282651514,0.0682769269,-0.0943248793,-0.0680395365,-0.0079498822,0.2537840307,-0.0882224739,0.3137325346,-0.1078761145,-0.1132512689,-0.2240658998,0.5512241721,-0.2445445955,-0.3344505131,0.2084482312,-0.1100612208,-0.0290145148,0.3913619816,-0.4320285916,0.2069480866,-0.0562909469,-0.0276837908,-0.0692590624,0.0554977842,0.0666981637,-0.06051654,0.0955885202,-0.0226425305,-0.1251135468,0.2517670393,-0.020024037,0.1037029028,0.0690994859,0.4551238418,-0.1279479712,0.840536654,0.0934606194,0.0382797495,0.2161407471,-0.1279042959,0.2627877295,0.1161440983,-0.2052368075,0.049135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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"> In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications.\r\n> I'm ok with returning the dataset object if no split specifications are given for text\/json\/csv\/pandas.\r\n> \r\n> For the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think ?\r\n\r\nYes maybe this would be good. I think having to select 'train' from the resulting object why the user gave no split information is a confusing and not intuitive behavior.","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":107,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n > In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications.\r\n> I'm ok with returning the dataset object if no split specifications are given for text\/json\/csv\/pandas.\r\n> \r\n> For the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think ?\r\n\r\nYes maybe this would be good. I think having to select 'train' from the resulting object why the user gave no split information is a confusing and not intuitive behavior.","embeddings":[-0.0685335547,0.0198079459,0.0123501066,0.4163457453,0.1547634006,0.1310773194,0.5161136985,0.3348332942,0.3283668458,0.0745705143,-0.1174225211,0.2238826901,-0.1303579956,0.0772807971,-0.3574971259,-0.1409811527,-0.0161414035,0.1667791605,-0.3396335244,-0.0541298836,-0.3720598519,0.3022883534,-0.3071189523,-0.103884697,-0.2333146483,0.1547114998,-0.1243326813,0.2636172473,-0.1306831241,-0.3113133609,0.5264427662,-0.0348026864,0.4296829104,0.1796253175,-0.0001202436,0.0683241561,0.2098931521,-0.1570206881,-0.2832562327,-0.5928282738,-0.0948916599,-0.5000761151,0.371696502,-0.1824131757,-0.1818093508,0.0587633513,-0.055730477,-0.3692515492,0.4264984727,0.4404452145,0.1417728961,-0.2375429273,-0.2223368734,0.0053099506,0.5322136283,0.1271093339,-0.159385249,0.2628558576,0.0841457024,-0.1708380729,-0.146781832,0.0727015436,-0.0378269888,0.250505805,0.1527512074,0.2516296506,-0.2149782777,-0.2802566588,-0.0473094918,0.2274475098,0.6906309724,-0.0038556212,-0.2016994506,-0.2366113514,-0.0083019249,-0.1586325616,0.2352561206,0.3178546727,-0.0731082335,0.3024133444,-0.1214098781,0.0119989626,-0.2797518969,0.1332839131,-0.0117351543,0.1140175834,-0.159745574,0.3371417224,0.0914066955,0.1130064726,0.2736600935,-0.1419514567,-0.050943207,0.1469705999,-0.6654461026,0.1463200301,-0.0904512107,-0.0697755963,-0.0807317495,0.1399653256,0.2095887661,0.0222028363,-0.0940629467,0.2417105883,0.4279995859,0.0152157107,0.0089434115,0.2975203991,0.2508084476,-0.1638221741,-0.0027235139,-0.0982123688,-0.2451073229,-0.3989139199,-0.1590597928,0.1913882792,0.2666960657,-0.1663199514,-0.2446895391,0.0393525884,-0.3511972725,0.0108301658,-0.1631431282,0.2812631726,-0.0030980257,0.306346029,0.0556032732,0.2700705826,-0.1631046236,-0.4479640126,-0.0052000051,0.1509830803,-0.3910146654,-0.0152986366,0.460850805,-0.0783595219,0.0757180154,0.1967018694,-0.098565042,-0.3172414303,-0.0278438255,-0.3539455533,0.1186480522,0.2464787811,0.1359597892,0.2408735752,0.3258979023,-0.2979562879,-0.1760974228,0.3370364904,-0.510586381,-0.2835355997,-0.0214070193,0.0790357664,-0.3109275103,0.216049701,-0.2705379725,0.2318892628,0.0025535435,-0.0811033621,-0.2396937907,-0.3311726749,-0.3782068789,-0.3062495589,-0.0184656214,0.5380911231,-0.7937141061,0.2113918662,-0.0439789854,0.0304449033,0.0645339489,0.2373640984,-0.3146848977,0.145540446,-0.3004151285,0.0451257639,0.2184087038,-0.1572796106,-0.3521279097,0.2249472886,-0.0438504554,0.1430745125,0.1946258098,0.1160269603,0.4747645855,0.0054960218,-0.1496740729,0.1738155186,-0.103760235,0.0363456868,-0.2112027258,-0.2809379995,0.2493326515,0.1949699521,0.1628943235,0.1472988278,0.1863788366,-0.4255873561,0.3947057426,-0.0318465978,0.217958495,0.0551677607,0.2408977449,0.0201239865,0.2374704331,0.001313109,-0.6431271434,0.2445382327,0.4549826384,-0.058966089,-0.0715787113,-0.1860950738,-0.3736642301,-0.085830465,-0.2312476635,-0.0666555539,-0.0176788997,0.0032555866,-0.0478532016,-0.1757822484,-0.1272353232,0.477886498,-0.2450702637,0.1183171272,-0.3356822729,0.3488133848,0.1532374322,0.0226258691,0.1873706132,-0.0584682524,0.2424250394,-0.1430356652,-0.0515100509,0.2048112005,0.3868688047,-0.1422166824,-0.0124562914,-0.1445665807,0.0729097873,-0.2080978006,-0.0453578681,0.1602115184,0.3658777177,-0.1580575109,-0.5012956262,0.4670168757,-0.2943073213,0.263843447,-0.1589294821,0.1330959797,0.233153671,-0.0610940196,-0.1029713675,-0.0019982792,-0.1220468134,-0.006638811,0.2533269227,0.1580813676,-0.2474706471,-0.1264657676,0.3704720736,0.2474065572,-0.2348777205,0.1483876258,-0.0330001377,-0.0938444063,0.2924904823,0.342839241,0.4789253175,0.1217185855,-0.2401447594,-0.0546514429,0.1157442108,-0.1957455575,0.0661826879,-0.015107044,0.1794178486,0.2044955045,0.084171623,0.0573599301,-0.0746098384,-0.1888594031,0.2203994244,0.1704453975,-0.5285298228,-0.006785084,-0.2358623892,0.2502968013,-0.3065753281,-0.0481774397,0.0667919666,-0.2848746777,-0.2406479418,0.2247037143,-0.129499495,0.0832543299,-0.1042544395,0.2468931675,-0.0310731418,-0.1913963109,0.1393317878,-0.1290226877,-0.2795024216,0.0142542943,0.0443121567,-0.0355804302,-0.0780696049,0.034434963,-0.0694945976,-0.2210642248,-0.0055247238,0.2116847932,-0.0453424938,0.0798723474,0.1930060089,-0.0438242257,0.0246098042,-0.1626574993,0.1271110326,0.2256253064,0.0464623943,0.220476836,0.2706637383,-0.0505867973,-0.1775081009,-0.764139533,-0.2559477091,-0.1818013638,0.0870506093,-0.1146430597,-0.0085540516,-0.0022579252,0.3254054189,-0.0912772119,0.2516673505,-0.1356215328,-0.0944925025,-0.2183258682,0.5146271586,-0.1794226319,-0.3070169091,0.2129276395,-0.1601766795,0.0302102249,0.3554811478,-0.4325180054,0.225927785,-0.0241388604,0.0817532241,-0.0706900135,-0.0414355844,0.1300362349,-0.0349057168,0.1392338574,-0.0643521845,-0.1901704669,0.3100559413,-0.0989008397,0.098874338,0.1584572494,0.4907527566,-0.1198672578,0.8878324032,0.0415000953,0.0302721038,0.1754058301,-0.0502931029,0.3468176126,0.1235480085,-0.2270592302,0.00121144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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"> Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n> \r\n> `from datasets import load_dataset`\r\n> `dataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")`\r\n> \r\n> Displayed error:\r\n> `... ArrowInvalid: CSV parse error: Expected 2 columns, got 1`\r\n\r\nI'm also facing the same issue when trying to load from a csv file locally:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('csv', data_files='sample_data.csv')\r\n```\r\n\r\nError when executed from Google Colab:\r\n```python\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-34-79a8d4f65ed6> in <module>()\r\n      1 from nlp import load_dataset\r\n----> 2 dataset = load_dataset('csv', data_files='sample_data.csv')\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    547     # Download and prepare data\r\n    548     builder_instance.download_and_prepare(\r\n--> 549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n    551 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    461                 if not downloaded_from_gcs:\r\n    462                     self._download_and_prepare(\r\n--> 463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n    465                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    535             try:\r\n    536                 # Prepare split will record examples associated to the split\r\n--> 537                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    538             except OSError:\r\n    539                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    863 \r\n    864         generator = self._generate_tables(**split_generator.gen_kwargs)\r\n--> 865         for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n    866             writer.write_table(table)\r\n    867         num_examples, num_bytes = writer.finalize()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/tqdm\/notebook.py in __iter__(self, *args, **kwargs)\r\n    213     def __iter__(self, *args, **kwargs):\r\n    214         try:\r\n--> 215             for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n    216                 # return super(tqdm...) will not catch exception\r\n    217                 yield obj\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/tqdm\/std.py in __iter__(self)\r\n   1102                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\r\n   1103 \r\n-> 1104         for obj in iterable:\r\n   1105             yield obj\r\n   1106             # Update and possibly print the progressbar.\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/datasets\/csv\/ede98314803c971fef04bcee45d660c62f3332e8a74491e0b876106f3d99bd9b\/csv.py in _generate_tables(self, files)\r\n     78                 read_options=self.config.pa_read_options,\r\n     79                 parse_options=self.config.pa_parse_options,\r\n---> 80                 convert_options=self.config.convert_options,\r\n     81             )\r\n     82             yield i, pa_table\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: CSV parse error: Expected 1 columns, got 8\r\n```\r\n\r\nVersion:\r\n```\r\nnlp==0.4.0\r\n```","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":319,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n > Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n> \r\n> `from datasets import load_dataset`\r\n> `dataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")`\r\n> \r\n> Displayed error:\r\n> `... ArrowInvalid: CSV parse error: Expected 2 columns, got 1`\r\n\r\nI'm also facing the same issue when trying to load from a csv file locally:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('csv', data_files='sample_data.csv')\r\n```\r\n\r\nError when executed from Google Colab:\r\n```python\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-34-79a8d4f65ed6> in <module>()\r\n      1 from nlp import load_dataset\r\n----> 2 dataset = load_dataset('csv', data_files='sample_data.csv')\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    547     # Download and prepare data\r\n    548     builder_instance.download_and_prepare(\r\n--> 549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n    551 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    461                 if not downloaded_from_gcs:\r\n    462                     self._download_and_prepare(\r\n--> 463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n    465                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    535             try:\r\n    536                 # Prepare split will record examples associated to the split\r\n--> 537                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    538             except OSError:\r\n    539                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    863 \r\n    864         generator = self._generate_tables(**split_generator.gen_kwargs)\r\n--> 865         for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n    866             writer.write_table(table)\r\n    867         num_examples, num_bytes = writer.finalize()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/tqdm\/notebook.py in __iter__(self, *args, **kwargs)\r\n    213     def __iter__(self, *args, **kwargs):\r\n    214         try:\r\n--> 215             for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n    216                 # return super(tqdm...) will not catch exception\r\n    217                 yield obj\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/tqdm\/std.py in __iter__(self)\r\n   1102                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\r\n   1103 \r\n-> 1104         for obj in iterable:\r\n   1105             yield obj\r\n   1106             # Update and possibly print the progressbar.\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/nlp\/datasets\/csv\/ede98314803c971fef04bcee45d660c62f3332e8a74491e0b876106f3d99bd9b\/csv.py in _generate_tables(self, files)\r\n     78                 read_options=self.config.pa_read_options,\r\n     79                 parse_options=self.config.pa_parse_options,\r\n---> 80                 convert_options=self.config.convert_options,\r\n     81             )\r\n     82             yield i, pa_table\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: CSV parse error: Expected 1 columns, got 8\r\n```\r\n\r\nVersion:\r\n```\r\nnlp==0.4.0\r\n```","embeddings":[-0.0300141871,0.1694913059,0.0235360079,0.2714547515,0.2753945589,0.1793165207,0.4045257568,0.27959463,0.2873067856,0.0150397848,-0.0485231131,0.3499826193,-0.1212732792,-0.068123661,-0.1520148665,-0.1536566764,-0.0122299837,0.1196543425,-0.4033187032,-0.0239689164,-0.3553748131,0.3309987187,-0.2975638509,0.0998900831,-0.0714402348,0.1982583553,-0.0852632672,0.3722810149,-0.0867927074,-0.3423321545,0.5356943607,-0.0946969464,0.5112503767,0.2374755144,-0.000117985,0.0406568088,0.3306674957,-0.1105561927,-0.2995376289,-0.6809139252,0.0257156584,-0.5392317772,0.4017808139,-0.1466551125,-0.0527872182,0.0457104594,-0.0339156613,-0.4215278625,0.3402782679,0.4766380191,0.1821974665,-0.0922983885,-0.1318363994,0.0157459285,0.4828670025,0.073752366,-0.1259507537,0.376285553,0.0207763575,-0.239270404,-0.1518488377,0.0073804525,-0.0787402168,0.255281359,0.2491716594,0.2138702422,-0.2403781712,-0.1153684705,0.01403144,0.1130977795,0.6585769057,-0.1782788783,-0.1866249293,-0.0933595672,0.0274395104,-0.2190636396,0.3825586736,0.411262095,0.009916761,0.3105875552,-0.1560350358,0.056958098,-0.2717018127,0.2379808724,0.1065417454,-0.0040133619,-0.1554982364,0.311968416,0.0586880073,0.0225891359,0.2661928236,-0.1402395517,-0.0138087049,0.2710196674,-0.6992922425,0.3220380843,-0.0580023006,-0.0070773927,-0.0701758564,0.143386364,0.3351231813,-0.0486308858,-0.0925272033,0.3708167374,0.2820994854,0.1138467863,-0.0387672856,0.2824321389,0.2630428374,-0.2030360848,0.0290505793,-0.1195268556,-0.278203696,-0.509021163,-0.1248255,0.224749893,0.2519869506,-0.2572544217,-0.1120413765,0.0733333454,-0.2260328829,0.0147897396,-0.0744889155,0.2470282465,0.0601703785,0.3175560236,0.0203008205,0.2700637579,-0.1704099178,-0.3315723538,-0.0171890836,0.1262030602,-0.3891074359,-0.0880584419,0.5497084856,-0.1239738017,0.1671730429,0.1720090955,-0.1229457706,-0.2391269058,-0.0686041415,-0.2752360702,0.048061531,0.2800689936,0.1143449247,0.1900819838,0.3180615008,-0.1938027889,-0.1355801076,0.2522106469,-0.385945946,-0.2324071527,-0.195453614,0.090378508,-0.321360141,0.0626311451,-0.3915347159,0.015699001,0.0457716361,-0.2120894641,-0.1808320135,-0.3092979789,-0.3017327785,-0.2416182309,0.0150502138,0.439994216,-0.6876297593,0.202816084,-0.1004167125,0.0604222976,0.1709206104,0.2772907615,-0.298163563,0.1015653536,-0.2844896615,0.0790061653,0.2385806441,-0.2647067308,-0.3350452483,0.359580934,-0.0717722252,-0.0879997015,0.085076347,0.0455226339,0.3238097727,0.0071840598,-0.1462555081,0.3527291119,-0.0047068587,0.1135263741,-0.238440603,-0.1901738048,0.2679091096,0.2576404512,-0.0618824847,0.1129786223,0.2957235277,-0.4787016213,0.3347389102,-0.1028870493,0.2226001024,0.1331427842,0.1572675556,0.0099994345,0.218157202,0.0542478301,-0.6148228049,0.1521190852,0.2315641493,-0.0791522712,-0.2202433795,-0.0017674118,-0.3930812478,-0.0305651203,-0.2295912206,0.0227669589,-0.0195139833,0.0806080624,0.0607920885,-0.1256914139,-0.0473370999,0.3860825002,-0.2677580118,0.0173374768,-0.4094599485,0.306309551,0.0902065188,-0.0382477455,0.2489789575,0.0863740221,0.2496588975,-0.1442183256,-0.2104988992,0.2916672528,0.2853559256,0.0471600108,0.0695529282,-0.2708469331,0.0942480564,-0.213366732,0.0322463699,0.2261844873,0.3391343653,-0.0313163921,-0.5053218007,0.397185266,-0.3178347647,0.2437009811,-0.1135058179,0.1233608425,0.345356822,-0.0892557874,-0.0802449137,-0.0036903089,0.1109050438,0.1402174681,0.2556707561,0.1141963229,-0.2472608835,-0.1746401638,0.1861750036,0.1400503814,-0.1960377693,0.213381201,0.0154024968,-0.1066111475,0.3125395775,0.2956349254,0.4571997523,0.087733224,-0.2595733404,0.0056093941,-0.0213326346,-0.2613018453,0.0891569406,-0.0073952861,0.0612976886,0.2624716759,0.2842783034,0.0976949036,-0.1761980355,-0.1439563781,0.1858266294,0.2496437132,-0.5318591595,0.045227129,-0.3609481156,0.1762495041,-0.2729723752,0.0102986777,0.0426914208,-0.2635087967,-0.3743283749,0.250363946,-0.1324359477,0.0816713944,-0.1343074292,0.2072207183,0.0467177778,-0.2297455966,0.1525468528,-0.2799641192,-0.3581955731,0.0062141931,0.1478461474,0.0675947666,-0.0600930229,0.0656328052,-0.0216071587,-0.2102790177,-0.0601661056,0.1272987425,0.0478489771,0.1065663546,0.1082932204,-0.1527350247,-0.1061812192,-0.1949448586,0.2128024846,0.0481399782,-0.032766521,0.3588487506,0.2019795477,-0.1589808315,-0.2023465782,-0.630129993,-0.401289463,-0.2943170667,0.1171190888,-0.1430850029,0.0297290646,0.1440817416,0.2027333081,0.0192646347,0.2147850841,-0.0837452039,-0.0446465909,-0.2041229755,0.5625194907,-0.3179401159,-0.4271067381,0.1390203238,-0.0806052983,0.0882797316,0.3525590897,-0.4951308072,0.1878040284,-0.1395660788,0.0535571463,0.0482543707,0.0520809889,0.0155839585,-0.0864479989,0.0881131589,-0.0037086506,-0.1890086681,0.1955624372,0.035877265,0.0729663149,0.2429578751,0.4747933149,-0.2480227947,0.5842331052,0.0097642895,0.0339329801,0.3762377799,-0.100591749,0.3321916759,0.1103129387,-0.2510380149,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi @kauvinlucas\r\n\r\nYou can use the latest versions of `datasets` to do this.\r\nTo do so, just `pip install datasets` instead of `nlp` (the library was renamed) and then\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='sample_data.csv')","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":38,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi @kauvinlucas\r\n\r\nYou can use the latest versions of `datasets` to do this.\r\nTo do so, just `pip install datasets` instead of `nlp` (the library was renamed) and then\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='sample_data.csv')","embeddings":[-0.0319091491,0.0998910815,0.0016058994,0.3416559994,0.2253115177,0.1857520193,0.3781754375,0.2843493819,0.3076332808,-0.0046042749,-0.0736300051,0.3338021338,-0.1202119589,-0.036175251,-0.2130927294,-0.0587122701,0.0360811427,0.0771625936,-0.3771968484,0.0088428417,-0.3649479747,0.3057532012,-0.3254077137,-0.0012915325,-0.0824024007,0.2179437876,-0.0744925812,0.3262184858,-0.1025668606,-0.3898982704,0.4821476638,-0.080058597,0.4992660582,0.2201852053,-0.0001179857,0.0123486947,0.3326276839,-0.0737969577,-0.2836776376,-0.5951529145,0.0139686335,-0.5699070096,0.3534557819,-0.115568459,-0.0387592912,0.0617986023,-0.0708740726,-0.3653351963,0.3587920666,0.5203021765,0.1995920837,-0.2032059133,-0.0811686441,-0.0118461316,0.5204197764,0.0610235557,-0.1259580702,0.2708447278,0.0428973995,-0.2233492732,-0.0964197516,0.0951562971,-0.1126321852,0.2678712606,0.2468886822,0.2386394143,-0.1517352313,-0.1456529647,0.0518766902,0.1153152809,0.6635107398,-0.1355136186,-0.176729992,-0.1062554643,-0.0070097786,-0.1568265557,0.3422959745,0.3546644747,-0.0102011804,0.3242204189,-0.0250324197,0.0301253237,-0.2691831589,0.1886892468,0.1149083152,0.027992826,-0.1570668072,0.3057209253,0.114892371,0.0086925505,0.2700324059,-0.0869944915,-0.0580076687,0.2113421112,-0.6860401034,0.3148464859,-0.1057037562,-0.0131408172,-0.0355927013,0.2131419182,0.2888286412,-0.0168030355,-0.087785773,0.370107919,0.2730760276,0.0119341025,-0.0131305298,0.2724969983,0.2526120842,-0.2041951269,0.0607698746,-0.1565530896,-0.2978444397,-0.5129746199,-0.0728966221,0.1486257911,0.2486549616,-0.1994403005,-0.181474179,0.1242232621,-0.2517411113,-0.0170060508,-0.1177749485,0.260204196,0.0609210394,0.3025761843,0.0645253956,0.2838415802,-0.1195834279,-0.3835787475,-0.0249955859,0.1081288904,-0.384224385,-0.0725212768,0.5409681797,-0.1791391969,0.1588603854,0.2351213247,-0.1358587444,-0.3058106601,-0.0346773267,-0.2870726883,0.0495085083,0.2614042759,0.0752800852,0.2058995217,0.3096886575,-0.2199794948,-0.0928175375,0.3182603121,-0.4290420711,-0.1496896595,-0.1494023353,0.1122689098,-0.2766397595,0.019795645,-0.3394761384,0.0994089991,0.0399336889,-0.2738412917,-0.1835940629,-0.2900147736,-0.2794313133,-0.2576267421,0.0108211935,0.4110125899,-0.7598640919,0.2014146149,-0.0385610051,0.063506037,0.1321390569,0.2012669444,-0.2380695045,-0.0085036838,-0.2470524311,0.1170615256,0.2001416832,-0.2251778692,-0.3695258498,0.2039723098,-0.0277520828,-0.0621384047,0.1256328523,0.0767565146,0.3356224895,-0.0383712798,-0.1593500674,0.2200727016,0.020736536,0.0405360498,-0.2515647113,-0.2080706507,0.2807472646,0.2186114639,-0.017313784,0.0922422409,0.2300778776,-0.596401751,0.328918159,-0.0431947187,0.2586236,0.0781924874,0.2754150033,0.0668581799,0.276987046,0.0377102606,-0.5350408554,0.1619918793,0.4143816233,-0.1803888232,-0.1539355814,-0.0427342318,-0.3967543244,-0.0268209055,-0.1542127728,-0.0362929888,0.0212248582,0.0724720359,-0.0212220177,-0.16581586,-0.1091084406,0.3955930769,-0.3020714223,0.0671919063,-0.368129313,0.2836926281,0.1172193065,-0.0429604799,0.2385161221,0.0490527786,0.2982416749,-0.0532653332,-0.1485976726,0.2183762789,0.2961485684,-0.0237372331,0.0841621459,-0.2446893901,0.0538063347,-0.239152804,-0.0022596212,0.2839276791,0.3712147474,-0.0852704495,-0.5303786993,0.3305390477,-0.2939954698,0.2343984544,-0.1652853191,0.1570280343,0.30540061,-0.0682469532,-0.1116520762,0.0074358834,0.1065215394,0.0153380921,0.251903981,0.1161820367,-0.2326779962,-0.1420400143,0.105782263,0.1679900587,-0.2043682039,0.2531194687,-0.0756668299,-0.11570815,0.3229525387,0.2924413681,0.4490132928,0.1327143162,-0.2260429859,-0.0074027041,0.0159655362,-0.2307155132,0.0959510878,-0.0015275988,0.0104517154,0.2142614424,0.2492847443,0.1078535989,-0.1244596541,-0.0946541205,0.1780858785,0.2668474913,-0.5732170939,0.0274307393,-0.2653020322,0.275398016,-0.2149259597,-0.0302288085,0.1330321133,-0.2786577046,-0.3122953773,0.3066865206,-0.0924999565,0.0600423031,-0.1841714382,0.2245592475,0.0431744792,-0.1701749712,0.0359234363,-0.2332860082,-0.3239174187,0.0234433077,0.1193632409,0.0211772751,-0.009588981,0.1299151629,0.0245018937,-0.2708138525,-0.061675638,0.081739895,0.0176403709,0.100418292,0.1105277836,-0.1548156738,-0.0887109637,-0.1159626618,0.2029960454,0.0429197177,0.045604378,0.3967782259,0.2087472975,-0.149520278,-0.1410145611,-0.6391364336,-0.3571048975,-0.2650282681,0.0800263733,-0.1071311012,-0.0271581337,0.1139522865,0.277887851,-0.0299414881,0.19760032,-0.0752973184,-0.1388437152,-0.2092313468,0.58896029,-0.2381600291,-0.3440220654,0.2079728991,-0.077859439,0.0502680503,0.3534047604,-0.451241076,0.1194554567,-0.0669652373,0.0515395552,-0.0284980275,0.0880324692,0.0203698091,-0.0561100505,0.076953657,-0.0587273389,-0.1870900691,0.1889620125,-0.0395994671,0.0470475703,0.2158955038,0.4871893227,-0.17796655,0.5998911858,0.1483035535,0.0594503656,0.3233929276,-0.0991083905,0.319134295,0.1864452958,-0.2664167881,0.0995654166,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi \r\nI'm having a different problem with loading local csv. \r\n```Python\r\nfrom datasets import load_dataset  \r\ndataset = load_dataset('csv', data_files='sample.csv')  \r\n```  \r\n\r\ngives `ValueError: Specified named and prefix; you can only specify one.` error  \r\n\r\nversions:  \r\n- datasets: 1.1.3   \r\n- python: 3.9.6  \r\n- pyarrow: 2.0.0 ","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":42,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi \r\nI'm having a different problem with loading local csv. \r\n```Python\r\nfrom datasets import load_dataset  \r\ndataset = load_dataset('csv', data_files='sample.csv')  \r\n```  \r\n\r\ngives `ValueError: Specified named and prefix; you can only specify one.` error  \r\n\r\nversions:  \r\n- datasets: 1.1.3   \r\n- python: 3.9.6  \r\n- pyarrow: 2.0.0 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Oh.. I figured it out. According to issue #[42387](https:\/\/github.com\/pandas-dev\/pandas\/issues\/42387) from pandas, this new version does not accept None for both parameters (which was being done by the repo I'm testing). Dowgrading Pandas==1.0.4 and Python==3.8 worked","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":35,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Oh.. I figured it out. According to issue #[42387](https:\/\/github.com\/pandas-dev\/pandas\/issues\/42387) from pandas, this new version does not accept None for both parameters (which was being done by the repo I'm testing). Dowgrading Pandas==1.0.4 and Python==3.8 worked","embeddings":[0.0241772942,0.12562038,0.0306745227,0.319699347,0.2818748057,0.1347274333,0.470521152,0.3493146896,0.2720313966,-0.0102589251,-0.1092102081,0.2939046621,-0.044851318,-0.0265678819,-0.3008044064,-0.1147533208,0.0091162156,0.0720298886,-0.4082455635,0.0407130383,-0.4287291169,0.284797132,-0.3110737801,-0.0556099638,-0.152755484,0.1461137533,-0.1490888745,0.2877849042,-0.1212894768,-0.399774164,0.4710716605,-0.0675764605,0.4576898217,0.2826908827,-0.0001190207,0.0470557921,0.3922930956,-0.1001775339,-0.2630170882,-0.5131826997,-0.0591822937,-0.4227257073,0.3733281195,-0.0852224305,-0.046688281,0.0004197524,-0.1162619591,-0.2997397184,0.3223545551,0.5077928901,0.197917968,-0.1602941602,-0.0620777011,-0.0502253436,0.4172463417,0.1050913036,-0.1397542655,0.2756552696,0.0337564647,-0.1532249302,-0.180978328,0.0272843409,-0.1014828458,0.2782543898,0.1798395813,0.2050615847,-0.0613027588,-0.2106291652,0.1289485842,0.2036833167,0.6754380465,-0.078778483,-0.2856331468,-0.1039790958,-0.0243786722,-0.1393211037,0.3564254642,0.2502124608,0.0016349813,0.3509472907,0.0232161768,0.0296683796,-0.2598004043,0.1540524513,0.0373894386,0.0158521775,-0.1935314983,0.2507592142,0.0340466313,-0.0823894143,0.3526718318,-0.1788177937,-0.0623942763,0.197615087,-0.6042397618,0.2810893059,-0.0836109594,-0.0234648902,-0.1294538528,0.2142701298,0.2488881201,-0.0499460101,-0.1401538253,0.2775976658,0.252376765,0.0532183312,-0.0278310105,0.2208503634,0.3117902875,-0.1449721903,0.1107845604,-0.1051988527,-0.222037822,-0.5018793344,-0.0677434355,0.0968574733,0.3531314433,-0.1722039282,-0.2210885882,0.1006415039,-0.2622009814,0.0220399573,-0.1620692015,0.2267710119,0.0502322465,0.4545515478,0.0701662973,0.2115022689,-0.0344944336,-0.3089322746,-0.0769338682,0.2031660825,-0.3034771979,-0.0684907511,0.4976694286,-0.2253912538,0.1549333036,0.3578968942,-0.1139468104,-0.2443407625,-0.1136397198,-0.3365449309,0.0902300701,0.3289268613,0.0469029807,0.2032093704,0.2896735072,-0.2509704232,0.0028574821,0.3258141279,-0.4054673314,-0.156913951,-0.0999016613,0.1198122278,-0.2537450194,0.0574827828,-0.3325140476,0.1230519116,-0.0078405384,-0.151597634,-0.1227682382,-0.2970442176,-0.2000421733,-0.2799708545,0.0212287959,0.5202496648,-0.7918003201,0.2299589813,0.0292756967,0.1033632308,0.0896141902,0.1875173002,-0.2415774614,-0.1062611863,-0.2405188531,0.0625411719,0.1165735126,-0.237686798,-0.3421730995,0.2998892069,0.0453347303,-0.0108899875,0.1983411163,-0.0019309281,0.2845632434,-0.04353882,-0.1288126856,0.126778692,-0.0160801522,0.0224632267,-0.2836357951,-0.2488529831,0.2401542813,0.1519769132,0.130537197,0.094353117,0.1428275257,-0.6783123612,0.3488560617,-0.0634345189,0.2517107129,0.1308165491,0.3389266133,0.0228330884,0.2688421905,0.0505939238,-0.5418136716,0.1698775589,0.5113579035,-0.1673701853,-0.1205927134,-0.0629021823,-0.3830545843,-0.0392312631,-0.099480696,-0.0518742502,0.017235551,0.0323562659,-0.0781595409,-0.1198081449,-0.1356744468,0.4858939946,-0.2923885882,0.0885697305,-0.258349508,0.256650269,0.1534156799,-0.1027728841,0.275652498,-0.0653968081,0.3416048288,-0.0444978252,-0.1651556939,0.2488504946,0.2802524865,-0.0365780033,0.0584948584,-0.2135780156,0.0895586982,-0.129989624,-0.0512957759,0.2908011973,0.3992747664,-0.0659131631,-0.4376945198,0.4224687815,-0.3204883039,0.2275464833,-0.0841154754,0.1403484344,0.3445750177,-0.0801549181,-0.152662009,0.0182648096,-0.0319590047,0.0388623103,0.3248752356,0.1877735257,-0.3184331059,-0.1537049115,0.0780854449,0.1375251859,-0.2164755464,0.2041727304,-0.0594577938,-0.1062221378,0.3829270005,0.3608201444,0.4717500806,0.1498554945,-0.2529484928,-0.0557890125,-0.01006338,-0.2269156724,0.1954914182,-0.0310393851,0.0481213443,0.1908816695,0.1868442446,0.0977427289,-0.1696167439,-0.0430323705,0.2352081537,0.2486244291,-0.6367043853,-0.0294911135,-0.2627077103,0.3036528826,-0.1164851189,-0.0980645567,0.1143851429,-0.3141474128,-0.3436886072,0.3097485006,-0.1371875107,0.0977562815,-0.3261144757,0.2158664763,0.0691104531,-0.1033549011,-0.0268367529,-0.1415639222,-0.2339593023,0.0365678035,0.0974037871,-0.0330904983,-0.0662379935,0.1117405221,0.0317247063,-0.288046211,-0.0658863038,0.0880052224,-0.0089438949,0.1357587576,0.1609303057,-0.0979804546,-0.1344276816,-0.1008068323,0.1499018073,0.0083998181,0.0724000111,0.2714380026,0.1862328798,-0.1533819735,-0.1734954864,-0.6161032915,-0.2489308417,-0.2709670365,0.0619678237,-0.1247730777,-0.058524888,0.135047093,0.365917027,0.021160759,0.3091208637,-0.0635993406,-0.1574359089,-0.2968179882,0.6772490144,-0.1667860597,-0.3600411713,0.2659082413,-0.0987677053,0.0851714388,0.343912065,-0.3236020803,0.0558186956,-0.117518723,0.0973820686,0.0033491177,0.1036384255,0.0825691447,-0.0110409744,0.0334409028,-0.0814219564,-0.1823910028,0.1710588038,-0.0009193884,-0.0323532522,0.2558740377,0.563256979,-0.1767581552,0.6726998091,0.0456445329,0.0922084004,0.3408700228,-0.0584755018,0.4342031777,0.2090545893,-0.2655888498,-0.0067741885,-0.0711784288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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi, \r\nI got an `OSError: Cannot find data file. ` when I tried to use load_dataset with tsv files. I have checked the paths, and they are correct.  \r\n\r\nversions\r\n- python: 3.7.9\r\n- datasets: 1.1.3\r\n- pyarrow: 2.0.0\r\n- transformers: 4.2.2\r\n\r\n~~~\r\ndata_files = {\"train\": \"train.tsv\", \"test\",: \"test.tsv\"}\r\ndatasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n~~~\r\n\r\nThe entire Error message is on below:\r\n\r\n```08\/14\/2021 16:55:44 - INFO - __main__ -   load a local file for train: \/project\/media-framing\/transformer4\/data\/0\/val\/label1.tsv\r\n08\/14\/2021 16:55:44 - INFO - __main__ -   load a local file for test: \/project\/media-framing\/transformer4\/data\/unlabel\/test.tsv\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv\/default-00a4200ae8507533 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/usr4\/cs542sp\/hey1\/.cache\/huggingface\/datasets\/csv\/default-00a4200ae8507533\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nTraceback (most recent call last):\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 592, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 944, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 307, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_glue.py\", line 484, in <module>\r\n    main()\r\n  File \"run_glue.py\", line 243, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 515, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 594, in _download_and_prepare\r\n    raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\nOSError: Cannot find data file. ```","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":229,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi, \r\nI got an `OSError: Cannot find data file. ` when I tried to use load_dataset with tsv files. I have checked the paths, and they are correct.  \r\n\r\nversions\r\n- python: 3.7.9\r\n- datasets: 1.1.3\r\n- pyarrow: 2.0.0\r\n- transformers: 4.2.2\r\n\r\n~~~\r\ndata_files = {\"train\": \"train.tsv\", \"test\",: \"test.tsv\"}\r\ndatasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n~~~\r\n\r\nThe entire Error message is on below:\r\n\r\n```08\/14\/2021 16:55:44 - INFO - __main__ -   load a local file for train: \/project\/media-framing\/transformer4\/data\/0\/val\/label1.tsv\r\n08\/14\/2021 16:55:44 - INFO - __main__ -   load a local file for test: \/project\/media-framing\/transformer4\/data\/unlabel\/test.tsv\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv\/default-00a4200ae8507533 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/usr4\/cs542sp\/hey1\/.cache\/huggingface\/datasets\/csv\/default-00a4200ae8507533\/0.0.0\/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nTraceback (most recent call last):\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 592, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 944, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 307, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_glue.py\", line 484, in <module>\r\n    main()\r\n  File \"run_glue.py\", line 243, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 610, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 515, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 594, in _download_and_prepare\r\n    raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\nOSError: Cannot find data file. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi ! It looks like the error stacktrace doesn't match with your code snippet.\r\n\r\nWhat error do you get when running this ?\r\n```\r\ndata_files = {\"train\": \"train.tsv\", \"test\",: \"test.tsv\"}\r\ndatasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n```\r\ncan you check that both tsv files are in the same folder as the current working directory of your shell ?","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":57,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi ! It looks like the error stacktrace doesn't match with your code snippet.\r\n\r\nWhat error do you get when running this ?\r\n```\r\ndata_files = {\"train\": \"train.tsv\", \"test\",: \"test.tsv\"}\r\ndatasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n```\r\ncan you check that both tsv files are in the same folder as the current working directory of your shell ?","embeddings":[-0.1632082611,0.1201281324,-0.0203899965,0.3711948693,0.2908929586,0.1906737834,0.3376256526,0.308806628,0.2558354139,0.1050071195,-0.1869195849,0.2054682523,-0.1593434066,0.061179474,-0.1956521869,-0.0624763109,0.0268127378,0.0686473697,-0.4645629525,0.0177216865,-0.3333188891,0.3470653892,-0.2780237794,-0.0542951822,-0.0767822489,0.2361236066,-0.0599401705,0.3367495239,-0.081656985,-0.3559603095,0.5194858909,-0.0635318682,0.560716033,0.5292677283,-0.0001137903,0.129445225,0.2825890183,-0.1467752755,-0.1785620749,-0.6393190026,0.0613104552,-0.4359839857,0.2645874918,-0.1104227826,-0.1284557283,0.0712635145,-0.0708982721,-0.4720506668,0.4504335225,0.5279279947,0.2148371339,-0.0864070952,-0.0118197463,-0.0221919268,0.7886179686,0.0594406687,-0.1394074708,0.2724424303,0.0125369942,-0.0810968876,-0.0302768908,0.0458028689,-0.0359600559,0.1779315323,0.1814219505,0.296615392,-0.1023824066,-0.1386378556,-0.0735867172,0.2100553215,0.7554205656,-0.1943683177,-0.1298263371,0.0654361546,-0.1378994882,-0.1567752808,0.406347245,0.2865529954,0.0064115208,0.3294068873,-0.1184835657,0.1312605739,-0.256188333,0.0950852111,0.098347187,-0.0493261069,-0.1786335856,0.2593528926,0.1806005985,0.0034185105,0.3713912666,-0.0517851748,-0.058962699,0.2235426456,-0.7684729695,0.3118459284,-0.0455594212,-0.1471838802,0.014965063,0.179177165,0.2139286548,-0.0393063612,-0.1528491527,0.3345857263,0.2363773733,0.1051969528,-0.1414199769,0.2154708654,0.2190301716,-0.1684966683,-0.102421768,-0.1876339912,-0.2559287846,-0.5735869408,-0.0645407438,0.2073688507,0.3200314641,-0.3114317954,-0.1733118594,0.0734636188,-0.2722924352,-0.0609482974,-0.109194845,0.2909167111,0.2314547151,0.2941367328,0.1858211011,0.1946026683,-0.1058751419,-0.2749941051,-0.0832266957,0.1312164217,-0.403624326,-0.1082926467,0.4074251354,-0.1435656697,0.0499262922,0.209153235,-0.044293914,-0.2685553432,-0.035612233,-0.3195300698,0.0450207554,0.2549913824,0.1352955699,0.1181483567,0.3289474249,-0.1746667027,-0.1117878854,0.3744624555,-0.4798728526,-0.1793120503,-0.0459833853,0.1310596317,-0.2370796353,0.0573173016,-0.289082557,0.0589945316,0.000939445,-0.1820271462,-0.2509436011,-0.3599371612,-0.2920160294,-0.2913584113,0.1218943521,0.4260993302,-0.8851076961,0.2679671943,-0.0419623069,-0.0201598052,0.1030402482,0.2525891662,-0.286568433,0.1934656799,-0.2951545119,0.0659349412,0.1927287132,-0.3063805699,-0.226077646,0.1231190339,-0.118889071,-0.0308844969,0.0815995261,-0.0159799103,0.2922246456,0.0229680575,-0.2450550199,0.1886671185,-0.0248834491,0.0727707818,-0.2346982211,-0.1997045279,0.2740665078,0.2147894949,-0.0159138534,0.0792820826,0.2235260457,-0.5767694712,0.3881706893,-0.1135374233,0.2037299871,0.1781434715,0.3660393059,0.1063058153,0.2167533636,0.0133437794,-0.4905884266,0.1591786593,0.3609518707,-0.1155055836,-0.0092602121,-0.0075382371,-0.450137645,-0.0408641472,-0.1863635927,0.0030228717,0.0847011283,0.0825764611,0.0114414012,-0.1034900472,-0.0824752748,0.3019010425,-0.1349658966,0.0457387194,-0.2683048546,0.3771105111,0.0310081206,-0.1467919499,0.2174808681,-0.0860394463,0.3234868646,-0.1837220341,-0.1866134852,0.3333313465,0.3146055341,-0.0872737169,0.0854247138,-0.159656167,-0.0496572256,-0.2291757762,-0.0167078115,0.2488171458,0.314687252,-0.1180462316,-0.4590246975,0.2613494396,-0.4094178975,0.206133008,-0.1435100138,0.0847721547,0.2101373971,-0.14181602,0.0210019778,0.0874420106,0.1191504151,0.0604729094,0.1988692731,0.005549375,-0.0911601782,-0.1056592017,0.119756341,0.1689518839,-0.2727974355,0.1789902747,-0.1137832776,-0.0162422266,0.242197454,0.357165277,0.4763866663,0.1523467749,-0.1742680818,0.0455711745,0.1098353192,-0.1504481435,0.0936007947,-0.0194585808,0.1371385455,0.2552471459,0.1668246537,0.0839905292,-0.1430705041,-0.2369464487,0.0455449112,0.2538726926,-0.5752357244,0.0083326316,-0.249729678,0.3108226061,-0.3310995996,-0.0051644901,0.1356990635,-0.2337109447,-0.3300172985,0.2485228926,-0.1445584297,0.0756820589,-0.1767652184,0.1835032552,0.1014495492,-0.1629744172,0.0509379804,-0.173088938,-0.3325943947,0.0628696084,0.1289216727,-0.0283848252,-0.061428573,0.0472806394,0.1133723781,-0.2062104195,-0.069977954,0.1670141667,-0.0763246343,0.0880631581,0.1179210618,-0.0030940298,-0.0499594063,-0.1978800446,0.246868819,-0.0530651137,-0.0031851677,0.3039884865,0.2936697304,-0.1829455346,-0.1477911621,-0.7467257977,-0.3022550941,-0.2975016534,0.0628438219,-0.0657057986,0.0176544636,0.1362267286,0.3105674088,0.0593861453,0.1433230191,-0.0918809026,-0.0467757061,-0.3576166034,0.5338152051,-0.2907546759,-0.2972563803,0.2983312905,-0.0351573192,0.0133842342,0.3558810651,-0.4415941238,0.1364328116,-0.0556880422,-0.0080279978,-0.06351161,0.1525565982,0.0533665977,0.019340897,0.0493189469,-0.0812957287,-0.2418559194,0.2346500009,-0.0742264614,0.1467011869,0.1442626268,0.5319949389,-0.2617813051,0.6968776584,0.0899335667,0.0690586641,0.3502433598,-0.1133134142,0.3590984643,0.1277568191,-0.2564528286,0.091887854,-0.0979034901,-0.3763469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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi @lhoestq, Below is the entire error message after I move both tsv files to the same directory. It's the same with I got before.\r\n```\r\n\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/torch\/cuda\/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http:\/\/www.nvidia.com\/Download\/index.aspx (Triggered internally at  \/pytorch\/c10\/cuda\/CUDAFunctions.cpp:100.)\r\n  return torch._C._cuda_getDeviceCount() > 0\r\n08\/29\/2021 22:56:43 - WARNING - __main__ -   Process rank: -1, device: cpu, n_gpu: 0distributed training: False, 16-bits training: False\r\n08\/29\/2021 22:56:43 - INFO - __main__ -   Training\/evaluation parameters TrainingArguments(output_dir=\/projectnb\/media-framing\/pred_result\/label1\/, overwrite_output_dir=True, do_train=True, do_eval=False, do_predict=True, evaluation_strategy=EvaluationStrategy.NO, prediction_loss_only=False, per_device_train_batch_size=8, per_device_eval_batch_size=8, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=8.0, max_steps=-1, lr_scheduler_type=SchedulerType.LINEAR, warmup_steps=0, logging_dir=runs\/Aug29_22-56-43_scc1, logging_first_step=False, logging_steps=500, save_steps=3000, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level=O1, fp16_backend=auto, local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name=\/projectnb\/media-framing\/pred_result\/label1\/, disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, ignore_data_skip=False, sharded_ddp=False, deepspeed=None, label_smoothing_factor=0.0, adafactor=False, _n_gpu=0)\r\n08\/29\/2021 22:56:43 - INFO - __main__ -   load a local file for train: \/project\/media-framing\/transformer4\/temp_train.tsv\r\n08\/29\/2021 22:56:43 - INFO - __main__ -   load a local file for test: \/project\/media-framing\/transformer4\/temp_test.tsv\r\n08\/29\/2021 22:56:43 - WARNING - datasets.builder -   Using custom data configuration default-df627c23ac0e98ec\r\nDownloading and preparing dataset csv\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/usr4\/cs542sp\/hey1\/.cache\/huggingface\/datasets\/csv\/default-df627c23ac0e98ec\/0.0.0\/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff...\r\nTraceback (most recent call last):\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1166, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 428, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_glue.py\", line 487, in <module>\r\n    main()\r\n  File \"run_glue.py\", line 244, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 852, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 616, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 699, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file. \r\nOriginal error:\r\nerror closing file\r\n```","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":311,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi @lhoestq, Below is the entire error message after I move both tsv files to the same directory. It's the same with I got before.\r\n```\r\n\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/torch\/cuda\/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http:\/\/www.nvidia.com\/Download\/index.aspx (Triggered internally at  \/pytorch\/c10\/cuda\/CUDAFunctions.cpp:100.)\r\n  return torch._C._cuda_getDeviceCount() > 0\r\n08\/29\/2021 22:56:43 - WARNING - __main__ -   Process rank: -1, device: cpu, n_gpu: 0distributed training: False, 16-bits training: False\r\n08\/29\/2021 22:56:43 - INFO - __main__ -   Training\/evaluation parameters TrainingArguments(output_dir=\/projectnb\/media-framing\/pred_result\/label1\/, overwrite_output_dir=True, do_train=True, do_eval=False, do_predict=True, evaluation_strategy=EvaluationStrategy.NO, prediction_loss_only=False, per_device_train_batch_size=8, per_device_eval_batch_size=8, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=8.0, max_steps=-1, lr_scheduler_type=SchedulerType.LINEAR, warmup_steps=0, logging_dir=runs\/Aug29_22-56-43_scc1, logging_first_step=False, logging_steps=500, save_steps=3000, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level=O1, fp16_backend=auto, local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name=\/projectnb\/media-framing\/pred_result\/label1\/, disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, ignore_data_skip=False, sharded_ddp=False, deepspeed=None, label_smoothing_factor=0.0, adafactor=False, _n_gpu=0)\r\n08\/29\/2021 22:56:43 - INFO - __main__ -   load a local file for train: \/project\/media-framing\/transformer4\/temp_train.tsv\r\n08\/29\/2021 22:56:43 - INFO - __main__ -   load a local file for test: \/project\/media-framing\/transformer4\/temp_test.tsv\r\n08\/29\/2021 22:56:43 - WARNING - datasets.builder -   Using custom data configuration default-df627c23ac0e98ec\r\nDownloading and preparing dataset csv\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/usr4\/cs542sp\/hey1\/.cache\/huggingface\/datasets\/csv\/default-df627c23ac0e98ec\/0.0.0\/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff...\r\nTraceback (most recent call last):\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1166, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 428, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"run_glue.py\", line 487, in <module>\r\n    main()\r\n  File \"run_glue.py\", line 244, in main\r\n    datasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 852, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 616, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 699, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file. \r\nOriginal error:\r\nerror closing file\r\n```","embeddings":[-0.0713824928,0.0363221169,-0.024686452,0.5199025869,0.3269370794,0.1928398013,0.44621858,0.3241172433,0.224331066,0.0428135805,-0.019542044,0.2665969729,-0.0372168943,0.0397648402,-0.2458034754,-0.1460843086,0.0505351909,0.0280245766,-0.3117779493,0.0664772168,-0.260904789,0.3204184771,-0.1558368057,-0.0361693315,-0.2530987263,0.119819276,0.0454959869,0.2727533579,0.0326381475,-0.3402159512,0.4851877093,-0.1499628276,0.533010304,0.4523147643,-0.0001191331,0.1187679619,0.2267004997,-0.1120318621,-0.2227353454,-0.6003681421,0.0691084787,-0.4721171856,0.3808331192,-0.1278465241,-0.169731915,0.068624936,-0.1462235749,-0.4836004674,0.3415564597,0.4694939256,0.1677398831,-0.2340410054,-0.0738900751,0.0044538765,0.4453818202,0.1310156137,-0.1876753718,0.2258012593,0.009724549,-0.1359496862,-0.0403806679,0.1108501628,-0.130706951,0.2922811806,0.2499799132,0.2974606156,-0.2419434488,-0.2736930251,0.0225309189,0.1213904694,0.6164861917,-0.1500841528,-0.1400368065,-0.0326985009,-0.0828981847,-0.1896711886,0.3366042972,0.3559082747,-0.1302392781,0.2394926399,-0.1136730015,0.1308840364,-0.2241668999,0.0715673938,0.049326621,-0.0001854788,-0.1302859634,0.3417437673,0.0915791616,0.0901505724,0.3912831545,-0.004260757,0.002390295,0.1033155695,-0.7571657896,0.3397616744,-0.0897243097,-0.17062895,-0.162389636,0.1441668272,0.3156573772,-0.0900196284,-0.0864366218,0.3288353682,0.0821671486,0.0484148823,-0.0442836992,0.2279284298,0.2494887561,-0.1326309741,0.0859453157,-0.188307777,-0.3141408861,-0.6196153164,-0.1500588059,0.2270557433,0.3016443551,-0.1968029141,-0.2878167033,0.0655969828,-0.2806815207,0.066669859,-0.1228434145,0.2648940682,0.0883032531,0.3031728566,0.1720811129,0.2169495225,-0.0404541604,-0.2523911595,-0.0274142977,0.1094366089,-0.3116878569,-0.0032872735,0.5225704908,-0.2512436807,0.1504862458,0.2206335515,-0.0773041472,-0.3231442273,0.0181409009,-0.3295076489,0.1302096099,0.2901154757,0.1380326301,0.3126874268,0.3299174607,-0.1744098216,-0.070775561,0.3949204981,-0.4245900214,-0.1792442054,-0.2038639933,0.0775322244,-0.0885883346,0.1009734198,-0.2871201932,0.0694979653,0.1445493251,-0.2312690765,-0.0790209547,-0.3212262392,-0.3405198455,-0.3129277527,0.1117309853,0.4026996195,-0.8818349242,0.2132673413,0.198320508,0.0556938276,0.0214947499,0.3038444221,-0.2842591107,-0.0352121368,-0.2930731475,-0.0345344506,0.0590712763,-0.288402468,-0.2493695915,0.1552080065,-0.0890525058,-0.0228530131,0.2429944128,0.1101588085,0.3556645215,-0.0237174034,-0.0928946137,0.1914544106,-0.0591227002,0.0131877027,-0.2130649835,-0.1901592016,0.3738617003,0.1716047227,-0.0081138918,0.0118381307,0.1692466736,-0.6463864446,0.3319563568,-0.1069131419,0.1779621542,0.0905336738,0.325034976,0.0173670687,0.2009603232,0.1837183982,-0.6111759543,0.1801491976,0.4992318749,-0.1896237284,-0.2479818463,-0.1043635309,-0.3395034671,-0.0565126985,-0.1028170288,-0.1103380173,-0.0110080214,0.1839656383,-0.1009614468,-0.0751741454,-0.040860422,0.4944821894,-0.4295209944,0.0704878941,-0.3189311028,0.3528833091,0.0992905796,-0.1022058576,0.141086325,-0.0712023675,0.27628842,-0.1578126103,-0.1376788318,0.2136023492,0.3686512113,-0.0230407659,-0.0386369973,-0.2215279043,0.1488551795,-0.1720895022,-0.0323602557,0.2876801789,0.358145684,-0.0749094114,-0.4160434902,0.3269958198,-0.4082435071,0.1859025806,-0.1869416833,0.0978934839,0.3414917588,0.0086155161,0.0448844656,0.0606090277,-0.0055956342,0.1934436113,0.1597741842,0.1372384876,-0.2749932408,-0.0690765604,0.1137360707,0.2358391583,-0.2924256027,0.1657490879,-0.1018801779,-0.0168215632,0.439864397,0.2854737341,0.4559783638,0.0421452485,-0.2504726052,-0.0080843512,0.0931468531,-0.2148771435,0.0924219713,-0.0473628677,0.0956739485,0.2528899312,0.196509093,0.1560171694,-0.0759197176,-0.1987670809,0.2110615969,0.2683174312,-0.6494820118,0.0274201259,-0.2144494951,0.2339817286,-0.2603882849,-0.104509525,0.103598237,-0.1728931814,-0.3434310555,0.344153285,-0.0421172939,0.1579322219,-0.0993181616,0.1854149103,0.0730421916,-0.1689814329,0.0334094986,-0.2104582936,-0.2221073508,-0.008325018,0.1756947488,0.0255544335,-0.0517407879,0.0312438663,0.075098142,-0.2419181615,-0.0653229058,0.0522311255,-0.0377469473,0.1385564357,0.035073515,-0.0259483196,-0.0233432185,-0.3175126016,0.1683665216,0.072531648,-0.0024671736,0.2256510705,0.2033078521,-0.2221161574,-0.1577876061,-0.6343611479,-0.3415049911,-0.2504074872,-0.0175146479,-0.0438977964,-0.034607254,-0.0959560424,0.3808245063,-0.0116766794,0.2445736825,-0.0465579517,-0.0434968397,-0.2414994985,0.5401979089,-0.1761843115,-0.3055940568,0.2881653011,0.0057944898,0.0812152103,0.401751399,-0.4659741223,0.1516272277,0.0125736389,0.1310024559,-0.0981328413,0.00196495,0.1370166242,0.0259656571,0.085618265,0.0137890568,-0.2664068043,0.1253763139,-0.0736640915,0.1062660664,0.1298653036,0.6638559699,-0.1733186543,0.6843754649,0.131282866,-0.1560296118,0.2848717868,-0.1524146497,0.3640497029,0.166402027,-0.3182913363,0.1269710064,-0.2186773717,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi !\r\nCan you try running this into a python shell directly ?\r\n\r\n```python\r\nimport os\r\nfrom datasets import load_dataset\r\n\r\ndata_files = {\"train\": \"train.tsv\", \"test\": \"test.tsv\"}\r\nassert all(os.path.isfile(data_file) for data_file in data_files.values()), \"Couln't find files\"\r\n\r\ndatasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\nprint(\"success !\")\r\n```\r\n\r\nThis way all the code from `run_glue.py` doesn't interfere with our tests :)","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":56,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi !\r\nCan you try running this into a python shell directly ?\r\n\r\n```python\r\nimport os\r\nfrom datasets import load_dataset\r\n\r\ndata_files = {\"train\": \"train.tsv\", \"test\": \"test.tsv\"}\r\nassert all(os.path.isfile(data_file) for data_file in data_files.values()), \"Couln't find files\"\r\n\r\ndatasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\nprint(\"success !\")\r\n```\r\n\r\nThis way all the code from `run_glue.py` doesn't interfere with our tests :)","embeddings":[-0.0062861182,0.0618332289,0.0004360501,0.3439050019,0.2524879575,0.1751339436,0.5055190325,0.32783705,0.3839304149,0.0075207599,-0.094731316,0.2648680508,-0.0998279154,-0.0093660085,-0.2245774418,-0.039557457,0.0509862155,0.0297241844,-0.3710769117,0.0725511983,-0.3521109223,0.2782054842,-0.3021057844,-0.0440652966,-0.1426056921,0.1825702786,-0.1116087809,0.3334528208,-0.099801138,-0.4000122845,0.4402317405,-0.0391456969,0.4721477032,0.3309651613,-0.0001206773,0.0893450528,0.3573922217,-0.0702851191,-0.2225422859,-0.5089859962,-0.0338167772,-0.4686772525,0.296729207,-0.1405784786,-0.1099878922,0.1247854009,-0.1471688002,-0.4223869145,0.3362388313,0.4916538596,0.1666339934,-0.0498269834,-0.0776987374,-0.0487935245,0.6013208628,0.0008950081,-0.107385233,0.3473229408,0.0976693556,-0.2549462914,-0.0749159977,0.0784208179,-0.0796423927,0.2506426871,0.1551815718,0.2411040068,-0.1426447928,-0.1929208934,0.0080289897,0.1800806522,0.5383203626,-0.1723931134,-0.2085453123,-0.0655025765,-0.0718722492,-0.0408947691,0.3501514196,0.3825588822,0.029311765,0.3788618147,0.013485969,0.0541821085,-0.1889536381,0.126449421,0.0808381736,-0.000396442,-0.1469158232,0.2984919548,0.1597528756,0.0262267981,0.1689083278,-0.1150744557,-0.0622056946,0.1477849483,-0.7149015069,0.3251987696,-0.1247542053,-0.0206366517,0.0187079646,0.2130948007,0.2478446364,-0.042283155,-0.0525682718,0.3610035777,0.2418078333,0.0896460861,-0.0427161828,0.2770175934,0.2427061945,-0.1332805008,-0.0287302025,-0.1192627922,-0.3003031015,-0.5950941443,-0.0516846143,0.1479632556,0.307525605,-0.1655176282,-0.2107231319,0.0764237642,-0.3221824467,0.0662977695,-0.1481997073,0.229479,0.1425035149,0.2334895581,0.0762687698,0.3107706904,-0.1320535988,-0.3083095551,-0.0419163592,0.2396093458,-0.379576385,-0.0501148328,0.4993746877,-0.2040912956,0.1153782755,0.1919148713,-0.1458258927,-0.3528422415,0.0152910464,-0.2772711217,0.0987497717,0.3070719838,0.0310322456,0.1829850376,0.3100918233,-0.3143515885,-0.0652119443,0.2924493551,-0.3839842379,-0.1958357692,-0.073478803,0.0947331712,-0.3603913784,0.0796126425,-0.3193351626,0.025692692,-0.008764972,-0.1769020706,-0.1282196492,-0.356578052,-0.2364251912,-0.2965958416,0.1110925674,0.4650505185,-0.7945942879,0.238329649,-0.0096505545,0.0674593449,0.0469612703,0.2029931396,-0.175192818,0.0009627892,-0.3269457817,0.1846571863,0.1312337667,-0.3535791039,-0.3629861176,0.2135502994,-0.1101615503,-0.0140276952,0.1743032336,0.0087105827,0.2934425771,-0.0421954803,-0.175726518,0.1522732228,0.008166681,0.0231556855,-0.3212611377,-0.2146085203,0.2824979722,0.1791924387,0.0050034234,0.0181187242,0.2591667473,-0.7052285075,0.3372290432,-0.0257217083,0.2324234694,0.0988066494,0.372282356,0.0113708097,0.301420778,-0.0037520607,-0.5378309488,0.2384977788,0.4775056541,-0.1331087351,-0.1889230758,-0.0681338608,-0.4203745723,0.0107433051,-0.2168693542,-0.1097213328,0.0102444394,0.0357291028,-0.0001164881,-0.1082693115,-0.0550721921,0.4337472022,-0.2398032397,0.1292284727,-0.3495222628,0.3247340322,0.1120966896,-0.059616562,0.1662975848,0.037718799,0.2580135763,-0.1628742814,-0.1263890266,0.3396655619,0.3961007595,-0.0519712567,0.160907954,-0.1690751165,0.0690016448,-0.2459465712,-0.0559245907,0.2450754493,0.3397670686,-0.0777557641,-0.4732547104,0.2924112678,-0.3288089037,0.3145468235,-0.1321841329,0.1685900688,0.2230485827,-0.0696802065,-0.1975671649,0.0641156211,0.0427139364,0.0154422,0.3271248341,0.1262814254,-0.1261104643,-0.1338541657,0.1003465354,0.1134780571,-0.1979324669,0.1449226886,-0.1365266144,-0.0266337264,0.3255553544,0.4348104894,0.4938296676,0.1152940765,-0.1612459123,-0.0280058421,0.0757928044,-0.1865972131,0.1406206936,0.0464963689,0.0275778249,0.1959311217,0.2476542741,0.0444588624,-0.0910918713,-0.1231984198,0.1277632713,0.185345605,-0.6318135262,-0.0364054255,-0.2342247665,0.3816649616,-0.2050330043,-0.0471458361,0.1352360547,-0.2819332182,-0.3157407641,0.203558296,-0.1404105872,0.0973091722,-0.2060460597,0.2179884166,0.1297790706,-0.2205867618,0.0024759686,-0.171497345,-0.2777157128,-0.0176119246,0.0986566097,0.0389547199,0.0045793718,0.1216914579,0.0442261361,-0.2933131158,-0.1359817088,0.1183634251,-0.0273984317,0.1899541467,0.2141971886,-0.1362731904,-0.0025947772,-0.1741008759,0.2184861451,0.0330374613,0.0418562219,0.4006383121,0.2771764994,-0.1021996513,-0.1422503591,-0.6783524752,-0.3884107769,-0.2795219123,0.1429558396,-0.1724932641,-0.1111443713,0.1216984391,0.3000505567,0.0373916067,0.1272048652,-0.1077364758,-0.117249079,-0.2421749532,0.5630997419,-0.2089006603,-0.3398690522,0.1851096898,-0.1722486913,0.0041179014,0.3509646058,-0.3908100128,0.018076485,-0.0999999195,0.0693459138,-0.0278682131,0.1542191803,0.0238924045,-0.047691077,0.1044527367,-0.0720873252,-0.1617171317,0.2585965693,-0.0316148922,-0.0129348328,0.2223136127,0.4758070409,-0.1225692928,0.5804255009,0.0619642809,0.050454434,0.2805772424,-0.1669808328,0.3939423263,0.1707340628,-0.2829434574,0.0277966596,-0.1335334331,-0.31849619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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi @lhoestq, \r\n\r\nBelow is what I got from terminal after I copied and run your code. I think the files themselves are good since there is no assertion error. \r\n\r\n```\r\nUsing custom data configuration default-df627c23ac0e98ec\r\nDownloading and preparing dataset csv\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/usr4\/cs542sp\/hey1\/.cache\/huggingface\/datasets\/csv\/default-df627c23ac0e98ec\/0.0.0\/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff...\r\nTraceback (most recent call last):\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1166, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 428, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 7, in <module>\r\n    datasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 852, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 616, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 699, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file. \r\nOriginal error:\r\nerror closing file\r\n```","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":160,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi @lhoestq, \r\n\r\nBelow is what I got from terminal after I copied and run your code. I think the files themselves are good since there is no assertion error. \r\n\r\n```\r\nUsing custom data configuration default-df627c23ac0e98ec\r\nDownloading and preparing dataset csv\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/usr4\/cs542sp\/hey1\/.cache\/huggingface\/datasets\/csv\/default-df627c23ac0e98ec\/0.0.0\/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff...\r\nTraceback (most recent call last):\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 693, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1166, in _prepare_split\r\n    num_examples, num_bytes = writer.finalize()\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/arrow_writer.py\", line 428, in finalize\r\n    self.stream.close()\r\n  File \"pyarrow\/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n  File \"pyarrow\/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"test.py\", line 7, in <module>\r\n    datasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 852, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 616, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/projectnb2\/media-framing\/env-trans4\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 699, in _download_and_prepare\r\n    + str(e)\r\nOSError: Cannot find data file. \r\nOriginal error:\r\nerror closing file\r\n```","embeddings":[-0.0766204745,0.0920656696,-0.0017136495,0.4262596369,0.260334909,0.1757656038,0.385474503,0.2841121852,0.2640709579,0.0310262423,-0.1189905927,0.2788829803,-0.0557524003,0.0436067656,-0.3021000326,-0.0312552042,0.0004868807,0.0566506386,-0.3885477185,0.0410138145,-0.3662183285,0.3667454123,-0.3187191486,-0.0609998479,-0.1914399117,0.2263685465,-0.0368314795,0.3382658362,-0.1301845312,-0.3756915331,0.5021184683,-0.0521184951,0.5287908316,0.3145392835,-0.0001166436,0.0442261696,0.3172171116,-0.080038406,-0.2472346574,-0.4812021852,-0.0732618049,-0.4360754192,0.2785712481,-0.0932627395,-0.0746381879,0.0831260681,-0.1838671267,-0.4044435322,0.3599595428,0.4905599058,0.2041939795,-0.2266175747,-0.0533966236,-0.0375091098,0.4672858119,0.1351828426,-0.1646743715,0.2667809427,0.033427421,-0.1499333084,-0.0587946437,0.0766809732,-0.0680063516,0.2834093571,0.2143716961,0.2197063267,-0.1669077724,-0.1799285561,0.1194262952,0.1464961022,0.7298074365,-0.1525365412,-0.1637503803,-0.0346819237,-0.0223762766,-0.0578420088,0.3883640468,0.3147871494,0.0142923724,0.3722792864,-0.0827065781,0.0316018648,-0.263974905,0.0726018175,0.0781468526,-0.0138204424,-0.2187384963,0.3078764081,0.0906548798,0.0069222543,0.297133714,-0.1402279586,-0.0977815613,0.1826354414,-0.6836587191,0.2746154666,-0.1902481765,-0.008679051,-0.0350687504,0.1952123642,0.2596012354,-0.043297451,-0.1149725541,0.2992675304,0.1880345345,0.0414905027,0.0026777333,0.2720786631,0.2288470268,-0.1444371492,0.0412311144,-0.1318569779,-0.3197771311,-0.5077925324,-0.0904954299,0.1599707305,0.2539120913,-0.243753776,-0.227644816,0.1201614067,-0.2313852608,-0.0067400448,-0.1025092751,0.2435493618,0.1126939952,0.2905176282,0.0676930919,0.2179534733,-0.0906946287,-0.4313392341,-0.0451537371,0.1183533147,-0.3720602393,-0.046401687,0.5200387836,-0.2410795689,0.1423213035,0.2660549879,-0.1297727227,-0.3421305418,-0.0322260521,-0.3169589639,0.0239527002,0.2362485677,0.0389745571,0.2405245155,0.2866297066,-0.1781049669,-0.0325141177,0.3158904016,-0.443251431,-0.1592340767,-0.1350477934,0.1337426007,-0.27512604,0.05435827,-0.4043934047,0.1153675318,0.0411192514,-0.1912349463,-0.1712235659,-0.281470269,-0.3073675036,-0.2589139342,0.0480230786,0.4904466867,-0.8519433141,0.2261208892,0.0183286071,0.0025323441,0.0836948082,0.1709737182,-0.2485905588,0.0157358274,-0.2890161574,0.14831613,0.0495959707,-0.1857823282,-0.3911417127,0.2297905535,-0.0328218825,-0.0420922972,0.1136223748,0.0496373698,0.369251281,-0.071498096,-0.2215430439,0.176014185,0.0029329371,0.0403850414,-0.2676356137,-0.1843561232,0.1896206588,0.1752882749,0.007474679,0.1149878353,0.2790457308,-0.60149014,0.3060942888,-0.0593442731,0.2580801249,0.1458770484,0.2768695652,0.0048321439,0.3109204173,0.068410866,-0.5979942679,0.2334529459,0.4385319352,-0.2033963799,-0.1050681099,-0.0430174991,-0.4723057747,-0.0249618161,-0.2017359883,-0.0651664287,0.025429761,0.1114392206,-0.0386273377,-0.1682164371,-0.1144261211,0.4684047401,-0.3507110775,0.0618633665,-0.3040837348,0.3389953077,0.1605005413,-0.0358106717,0.2233442068,-0.0775785223,0.3259297907,-0.1029166803,-0.1839357167,0.2597768605,0.3249568343,0.0075665405,0.1412291378,-0.2065003663,0.0305013545,-0.2821558714,-0.0077941343,0.200310424,0.3678487539,-0.1124740839,-0.439716965,0.2634721398,-0.308039844,0.2148465365,-0.1841071844,0.0894398615,0.2627619505,-0.0551595129,-0.0341272652,-0.0108826719,0.0296714623,0.0668125376,0.2504251003,0.166002214,-0.1503114998,-0.1154987141,0.131458804,0.1737294793,-0.2715708017,0.25199458,-0.0939705297,-0.0774245486,0.3294499218,0.3344382048,0.4893704653,0.1043431237,-0.2505175471,0.0342538357,0.1167054474,-0.1767628938,0.0952144936,-0.0147623774,0.0734598115,0.2274675816,0.1784396619,0.1246469542,-0.1151839346,-0.1555411816,0.2122543454,0.2461774647,-0.5974342823,-0.0291215237,-0.2400234342,0.3107037246,-0.2130826265,-0.0169079825,0.1156351641,-0.2395446748,-0.3577504754,0.2877122462,-0.075320594,0.0539753176,-0.177641809,0.2452251613,0.0701454207,-0.1850723028,-0.0241964459,-0.1246553212,-0.2909143567,0.0290511791,0.1494376808,0.019947404,-0.0702546462,0.0626022816,0.0362962075,-0.3233754933,-0.0476093441,0.1449241936,0.0297123641,0.1076415479,0.1203675121,-0.1193226203,-0.046053648,-0.1017050594,0.1734133959,0.0696248338,0.0196209364,0.346560806,0.2277445346,-0.170257777,-0.1075497717,-0.6430516243,-0.2873028219,-0.2208728194,0.0694131702,-0.1359775513,-0.0641472712,0.0360071994,0.2975532413,-0.0357256941,0.2472857982,-0.1144540161,-0.1707125902,-0.2853313982,0.5567880273,-0.2298516631,-0.2987788022,0.2550551593,-0.0699987933,0.0933172256,0.3623686731,-0.4278604984,0.1757393926,-0.0856653899,0.0414425917,-0.0948990434,0.0472871326,0.0213367194,-0.0696596652,0.0943147987,-0.0596975833,-0.2065607756,0.2107337117,-0.0503792353,0.0644159466,0.1991274804,0.482239157,-0.1926071197,0.5956169367,0.1381940395,0.0244416744,0.3114423454,-0.07276728,0.3431052566,0.1681955904,-0.2420172542,0.1264448911,-0.0926905498,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Hi, could this be a permission error ? I think it fails to close the arrow file that contains the data from your CSVs in the cache.\r\n\r\nBy default datasets are cached in `~\/.cache\/huggingface\/datasets`, could you check that you have the right permissions ?\r\nYou can also try to change the cache directory by passing `cache_dir=\"path\/to\/my\/cache\/dir\"` to `load_dataset`.","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":58,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Hi, could this be a permission error ? I think it fails to close the arrow file that contains the data from your CSVs in the cache.\r\n\r\nBy default datasets are cached in `~\/.cache\/huggingface\/datasets`, could you check that you have the right permissions ?\r\nYou can also try to change the cache directory by passing `cache_dir=\"path\/to\/my\/cache\/dir\"` to `load_dataset`.","embeddings":[-0.0871088356,0.1020701975,0.005998747,0.4314921498,0.2185793817,0.2184780687,0.4292903244,0.2389688194,0.3231713176,0.0248068217,-0.1687801331,0.2384760678,-0.1101290509,-0.0933418646,-0.2372374684,-0.0756902173,0.0261753611,0.0410047062,-0.3811805844,-0.0062905271,-0.4014434218,0.3093370497,-0.3083968759,-0.0182189606,-0.1635810286,0.1486214399,-0.0188286174,0.3629311025,-0.088870883,-0.3489400744,0.4900049269,-0.0543516316,0.5426036716,0.2987747788,-0.0001183373,0.0226844866,0.3176947236,-0.0851583183,-0.2275812477,-0.4275220931,-0.0256141461,-0.4985882044,0.3423484564,-0.06707187,-0.0731020123,0.0908294916,-0.0877925381,-0.4491149187,0.4408088923,0.5118373036,0.2004705667,-0.1390616298,-0.0094959829,0.0101438593,0.6154857874,-0.001861185,-0.1703196168,0.2800339758,0.0369507,-0.1588282287,-0.0883734897,0.1460739523,-0.0622680411,0.2507012188,0.31141904,0.1999147385,-0.2324631065,-0.2419393361,0.1065781191,0.0923586562,0.8474333882,-0.1498607844,-0.2260136008,-0.0473056883,-0.0552015752,-0.1341855377,0.3748573661,0.3546611965,0.0664210916,0.3369226456,-0.0629092678,-0.0407476351,-0.2813122272,0.1430380195,0.1430392563,-0.0449890979,-0.1894115806,0.2800143361,0.1055740714,-0.0342199914,0.3509489298,-0.0848069414,-0.1161021888,0.2580316663,-0.7147852182,0.3244916201,-0.1518708318,0.0887799039,-0.0345322937,0.2629768252,0.2805000842,0.022332605,-0.1302015036,0.3442869186,0.2256664336,0.1672879308,-0.0359066837,0.2805074751,0.3097871244,-0.2277148366,-0.0528800599,-0.1950272769,-0.2916230559,-0.5650448203,-0.0099357637,0.1524816453,0.2901365757,-0.2326605916,-0.136371091,0.124290213,-0.2214371413,0.0102816029,-0.090347901,0.3146436214,0.0904793143,0.3160606623,0.1083630994,0.2409510314,-0.1057104468,-0.3624810576,-0.0699226111,0.0720898286,-0.3807951212,0.038262289,0.4703016877,-0.1982618421,0.1263877898,0.1747371256,-0.0393530875,-0.2941902578,0.0493728518,-0.291264385,0.0694968998,0.3317597508,0.0307913981,0.2064109147,0.3324394524,-0.1748835742,-0.0883184075,0.2969932258,-0.4597531259,-0.2354933172,-0.0657947138,0.0806414336,-0.2515916526,0.132902354,-0.3708431125,-0.0102759385,0.1059380248,-0.1918373853,-0.1255963147,-0.2630356252,-0.2816921473,-0.3016434908,0.0190750826,0.5431545377,-0.7934693694,0.2218711078,-0.0641072839,0.1017690822,0.0040062084,0.2952950299,-0.2567481995,0.0294366553,-0.2967207432,0.0368544273,0.1628824174,-0.2294470072,-0.4436288476,0.1812134981,-0.0252471622,-0.0147933764,0.0866156742,0.0915315077,0.362511158,-0.0462300591,-0.2393852919,0.18926467,-0.0175592806,0.0613049679,-0.2599571645,-0.2040599734,0.224815473,0.1709872037,0.0435030572,0.1520606279,0.2611731589,-0.6375333071,0.2761252224,-0.0541094914,0.2460305691,0.1802657694,0.2484291196,0.0611739159,0.3364658654,0.0358324423,-0.596761167,0.2510197461,0.3427164853,-0.2168759406,-0.1212083325,-0.0457303263,-0.4000038207,0.0011727435,-0.2293895036,-0.0621970631,0.0090293186,0.0889609978,-0.0153363999,-0.1442162246,-0.0935701355,0.5005298257,-0.2158488333,0.081880495,-0.3989738524,0.300879091,0.1314293295,-0.0771669298,0.1612426043,-0.0463612601,0.3139698803,-0.0904841274,-0.2068674117,0.2543218434,0.3026152551,-0.0388666764,0.1233864725,-0.1583100259,0.0368795432,-0.3185792267,0.0433271006,0.1833157688,0.3288411796,-0.1012388244,-0.4540030956,0.2778144479,-0.3211692274,0.2337656766,-0.2180206031,0.156018585,0.2118747532,-0.0045894478,-0.0888199434,0.0230344869,0.0618801862,0.0452033319,0.3459661305,0.0847883001,-0.1154410541,-0.1102720797,0.1374965459,0.1963553876,-0.1975062042,0.2129110694,-0.1349883527,-0.0453927144,0.353230685,0.2573804855,0.4387530684,0.0898240507,-0.2252526581,-0.0238307621,0.1509096622,-0.152304545,0.0165314153,-0.0135874683,0.1102258638,0.16401577,0.1770403087,0.1246003509,-0.1302484274,-0.0883391201,0.1637058109,0.2491808832,-0.4925572872,0.0511966832,-0.1800087392,0.3748138547,-0.1793498993,-0.0850353241,0.0108298818,-0.2172825634,-0.31294626,0.3145366907,-0.0917653516,0.1111260876,-0.1890403479,0.2836256027,0.131011039,-0.1542268991,-0.0150205409,-0.1804295033,-0.2538110912,-0.0217656139,0.1527909189,-0.0590267442,-0.0866953731,0.0797144324,0.1314535141,-0.3408510387,-0.0016875594,0.0904464573,0.0303205382,0.1180033013,0.0525206178,-0.0170974117,-0.0492991284,-0.1359845102,0.1895927191,0.0173037294,0.0275386497,0.3591513038,0.2666297257,-0.1454355121,-0.0984572247,-0.6051182747,-0.2989300191,-0.3046963215,0.1061419174,-0.1025243104,-0.0014811994,0.116023019,0.2876195908,-0.0665228218,0.2434743196,-0.0484236106,-0.1217130795,-0.2950877845,0.5952745676,-0.2911517918,-0.3286848366,0.268263489,-0.0384926982,0.0919636637,0.3597333133,-0.4090351164,0.1694013774,-0.0824309289,0.0561728328,-0.0561686978,0.0685708374,0.0811326206,-0.0554329008,0.1393038779,-0.0295770783,-0.1968939304,0.1694541276,-0.0910888836,0.0490142629,0.2071580291,0.4857744575,-0.2566995025,0.7927243114,0.0675841272,0.1144968942,0.3349134922,0.0031341482,0.3701060116,0.1599618047,-0.2955479026,0.0845650658,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/743","title":"load_dataset for CSV files not working","comments":"Thank you!! @lhoestq\r\n\r\nFor some reason, I don't have the default path for datasets to cache, maybe because I work from a remote system.  The issue solved after I pass the `cache_dir` argument to the function. Thank you very much!!","body":"Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you","comment_length":40,"text":"load_dataset for CSV files not working \n Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\".\/sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")\r\n`\r\n\r\nDisplayed error:\r\n`\r\n...\r\nArrowInvalid: CSV parse error: Expected 2 columns, got 1\r\n`\r\n\r\nI should mention that when I've tried to read data from `https:\/\/github.com\/lhoestq\/transformers\/tree\/custom-dataset-in-rag-retriever\/examples\/rag\/test_data\/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with \/r character, so I've removed them from the custom dataset, but the problem still remains.\r\n\r\nI've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.\r\nhttps:\/\/colab.research.google.com\/drive\/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing\r\n\r\nAre there any work around for it ?\r\nThank you \n Thank you!! @lhoestq\r\n\r\nFor some reason, I don't have the default path for datasets to cache, maybe because I work from a remote system.  The issue solved after I pass the `cache_dir` argument to the function. Thank you very much!!","embeddings":[-0.0859662145,0.0921201184,0.0054692053,0.3930594325,0.2793588638,0.1994730979,0.4239506125,0.2563423812,0.3121513426,0.00980769,-0.1000402272,0.2833045721,-0.0902011842,-0.0692626685,-0.2416357994,-0.0534538962,0.0487427153,0.0339308605,-0.4194591641,0.0217577126,-0.398001343,0.3447437286,-0.3417994976,-0.0670136884,-0.1728119552,0.2312749773,-0.0274074618,0.3173514903,-0.0774215236,-0.3483394682,0.49772048,-0.1169507429,0.500279367,0.3159280419,-0.0001189884,0.0335704125,0.3231101632,-0.1217699423,-0.2798947692,-0.5001267195,-0.0860216245,-0.4523081481,0.3036141992,-0.078889586,-0.0746764019,0.1003823653,-0.0871309862,-0.4603987932,0.3343280852,0.5259562135,0.1874039024,-0.2362770885,-0.1149116457,0.0203485657,0.5270505548,0.0821966901,-0.1715659946,0.2562561035,-0.0119706914,-0.1819378287,-0.0650214925,0.1478487253,-0.0790288895,0.2778861821,0.2819365561,0.2120058984,-0.2090995163,-0.1924725622,0.1150583476,0.0516182072,0.7780841589,-0.1751658916,-0.2040853649,-0.0703984648,-0.0710535273,-0.0905073807,0.3608498275,0.2796316147,0.0471044742,0.3526415825,-0.0739563033,0.0198418032,-0.2478520125,0.1096846163,0.1472314298,-0.0370750353,-0.1845748425,0.3111932576,0.0819834694,0.0278162509,0.3614430428,-0.1226573363,-0.0612086058,0.2232882828,-0.6913048029,0.3598780632,-0.1117809787,0.0476231165,-0.0488829464,0.2159273475,0.253226757,-0.0178384166,-0.0982983708,0.3449589312,0.2084871233,0.0942893401,0.03507635,0.249590531,0.2458130419,-0.240046218,-0.0242090952,-0.1735283583,-0.3206408918,-0.4924867153,0.0513884798,0.1816558838,0.211786285,-0.2026416957,-0.1311562359,0.1114847362,-0.1737065762,-0.0094566187,-0.0949874967,0.3192047179,0.0954763293,0.3276444077,0.0360649265,0.2327312231,-0.1163586378,-0.3847432733,-0.0511320345,0.1306393594,-0.3966401219,-0.0078006592,0.499743551,-0.2116759121,0.1611486226,0.2013804168,-0.1022118405,-0.3209100664,0.0098241894,-0.3409103155,0.1178882346,0.2810696959,0.0596529432,0.2322187573,0.310836643,-0.1735278219,-0.0792894661,0.3202610612,-0.4694957733,-0.2254518718,-0.053723976,0.0856733099,-0.2701482773,0.1136537343,-0.3645306528,0.0592239499,0.0930911601,-0.2261726111,-0.148012653,-0.2921352684,-0.3328604698,-0.2874190509,0.0227104947,0.527285099,-0.7632331848,0.2065825462,0.0069278814,0.0596400052,0.077546671,0.201359719,-0.2320704013,-0.0131589593,-0.3008173406,0.084708415,0.1511888057,-0.2414093018,-0.4044985175,0.1948494166,-0.0091790361,-0.0367273539,0.1076726317,0.072963208,0.3510636687,-0.0444780849,-0.1565225273,0.2063654363,0.0127947778,0.079222329,-0.2519009113,-0.1841476113,0.2559945285,0.1668462902,0.0265599005,0.1279062033,0.2115993351,-0.6256981492,0.2878684402,-0.026080206,0.2456631958,0.1336244941,0.240569219,0.020431878,0.3675477803,0.0579219759,-0.5920143127,0.2176810503,0.4573203027,-0.2367344201,-0.12829566,-0.0426198766,-0.4006431401,0.0049440595,-0.1802303642,-0.051015377,0.0053335573,0.1431531161,0.0037192472,-0.166486457,-0.1181761697,0.5300241709,-0.2569983602,0.0464286283,-0.3279723525,0.2669471502,0.1607712805,-0.0713458657,0.1533521861,-0.0794750452,0.3291767538,-0.0821014568,-0.2026355863,0.2568963766,0.3679184616,-0.0227024481,0.0851090699,-0.1917208433,-0.0070678876,-0.2358018756,-0.0132546611,0.2161183953,0.3630660474,-0.0771617219,-0.4703349769,0.282397002,-0.329693377,0.2206449211,-0.1862371415,0.1222787425,0.2683041692,-0.0067571732,-0.0762864873,0.0306818839,0.0430371426,-0.016078135,0.3278394043,0.1356023103,-0.1543986797,-0.0686571375,0.104084596,0.2219552994,-0.1962908655,0.2119081914,-0.1135942265,-0.1372815222,0.3695862293,0.2924639583,0.4771190882,0.1017706245,-0.2151629329,-0.03321575,0.1260908991,-0.1481108814,0.0860050619,-0.0315163843,0.0596535169,0.1926266849,0.2122889012,0.0930455029,-0.1251711696,-0.1155806929,0.17658481,0.2235135734,-0.5865730047,0.0359858647,-0.2217503041,0.3033283353,-0.1915452182,-0.0284086727,0.1247221753,-0.2356092483,-0.3277724385,0.3035749495,-0.0688414797,0.0889437273,-0.1954190433,0.2737675011,0.0884293616,-0.2449583858,-0.0298119988,-0.1432175487,-0.2710935771,-0.0190364961,0.0947937593,0.0005641778,-0.1103101224,0.0575547032,0.1049836054,-0.3393681943,0.0112823285,0.138859272,0.0470259152,0.1042666659,0.1056636572,-0.1229335219,-0.0585625321,-0.100685738,0.1599597633,0.0252639428,0.0772768706,0.3538436592,0.229650408,-0.1431204975,-0.1136703938,-0.6079529524,-0.3110757172,-0.2347203642,0.0422148593,-0.1346933544,-0.0265995637,0.0542938448,0.2813584208,-0.0934910253,0.2276720554,-0.0884653553,-0.1698265374,-0.3080235124,0.6029754877,-0.2578561306,-0.2866512835,0.2364552021,-0.0638770014,0.0657695159,0.3948900402,-0.4063292742,0.1408141255,-0.0391779132,0.0357070789,-0.0525728613,0.0657720342,0.0635105297,-0.0665663034,0.1175326779,-0.0782246068,-0.2430461198,0.2015887648,-0.0742934272,0.0763092563,0.2349738181,0.4599756002,-0.196335122,0.6576650143,0.1088631675,0.0870276839,0.3282864988,-0.0926542878,0.3686032593,0.1682643145,-0.2678069174,0.0895845443,-0.0932174847,-0.36705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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"Thanks for reporting.\r\nIn theory since the dataset script is just made to yield examples to write them into an arrow file, it's not supposed to create memory issues.\r\n\r\nCould you please try to run this exact same loop in a separate script to see if it's not an issue with `PIL` ?\r\nYou can just copy paste what's inside `_generate_examples` and remove all the code for `datasets` (remove yield).\r\n\r\nIf the RAM usage stays low after 600 examples it means that it comes from some sort of memory leak in the library, or with pyarrow.","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":96,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n Thanks for reporting.\r\nIn theory since the dataset script is just made to yield examples to write them into an arrow file, it's not supposed to create memory issues.\r\n\r\nCould you please try to run this exact same loop in a separate script to see if it's not an issue with `PIL` ?\r\nYou can just copy paste what's inside `_generate_examples` and remove all the code for `datasets` (remove yield).\r\n\r\nIf the RAM usage stays low after 600 examples it means that it comes from some sort of memory leak in the library, or with pyarrow.","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.25744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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"Here's an equivalent loading code:\r\n```python\r\nimages_path = \"PHOENIX-2014-T-release-v3\/PHOENIX-2014-T\/features\/fullFrame-210x260px\/train\"\r\n\r\nfor dir_path in tqdm(os.listdir(images_path)):\r\n    frames_path = os.path.join(images_path, dir_path)\r\n    np_frames = []\r\n    for frame_name in os.listdir(frames_path):\r\n        frame_path = os.path.join(frames_path, frame_name)\r\n        im = Image.open(frame_path)\r\n        np_frames.append(np.asarray(im))\r\n        im.close()\r\n```\r\n\r\nThe process takes 0.3% of memory, even after 1000 examples on the small machine with 120GB RAM.\r\n\r\nI guess something in the datasets library doesn't release the reference to the objects I'm yielding, but no idea how to test for this","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":75,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n Here's an equivalent loading code:\r\n```python\r\nimages_path = \"PHOENIX-2014-T-release-v3\/PHOENIX-2014-T\/features\/fullFrame-210x260px\/train\"\r\n\r\nfor dir_path in tqdm(os.listdir(images_path)):\r\n    frames_path = os.path.join(images_path, dir_path)\r\n    np_frames = []\r\n    for frame_name in os.listdir(frames_path):\r\n        frame_path = os.path.join(frames_path, frame_name)\r\n        im = Image.open(frame_path)\r\n        np_frames.append(np.asarray(im))\r\n        im.close()\r\n```\r\n\r\nThe process takes 0.3% of memory, even after 1000 examples on the small machine with 120GB RAM.\r\n\r\nI guess something in the datasets library doesn't release the reference to the objects I'm yielding, but no idea how to test for this","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.257440209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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"I've had similar issues with Arrow once. I'll investigate...\r\n\r\nFor now maybe we can simply use the images paths in the dataset you want to add. I don't expect to fix this memory issue until 1-2 weeks unfortunately. Then we can just update the dataset with the images. What do you think ?","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":53,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n I've had similar issues with Arrow once. I'll investigate...\r\n\r\nFor now maybe we can simply use the images paths in the dataset you want to add. I don't expect to fix this memory issue until 1-2 weeks unfortunately. Then we can just update the dataset with the images. What do you think ?","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.2574402094,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"If it's just 1-2 weeks, I think it's best if we wait. I don't think it is very urgent to add it, and it will be much more useful with the images loaded rather than not (the images are low resolution, and thus papers using this dataset actually fit the entire video into memory anyway)\r\n\r\nI'll keep working on other datasets in the meanwhile :) ","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":65,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n If it's just 1-2 weeks, I think it's best if we wait. I don't think it is very urgent to add it, and it will be much more useful with the images loaded rather than not (the images are low resolution, and thus papers using this dataset actually fit the entire video into memory anyway)\r\n\r\nI'll keep working on other datasets in the meanwhile :) ","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.2574402094,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"Ok found the issue. This is because the batch size used by the writer is set to 10 000 elements by default so it would load your full dataset in memory (the writer has a buffer that flushes only after each batch). Moreover to write in Apache Arrow we have to use python objects so what's stored inside the ArrowWriter's buffer is actually python integers (32 bits).\r\n\r\nLowering the batch size to 10 should do the job.\r\n\r\nI will add a flag to the DatasetBuilder class of dataset scripts, so that we can customize the batch size.","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":97,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n Ok found the issue. This is because the batch size used by the writer is set to 10 000 elements by default so it would load your full dataset in memory (the writer has a buffer that flushes only after each batch). Moreover to write in Apache Arrow we have to use python objects so what's stored inside the ArrowWriter's buffer is actually python integers (32 bits).\r\n\r\nLowering the batch size to 10 should do the job.\r\n\r\nI will add a flag to the DatasetBuilder class of dataset scripts, so that we can customize the batch size.","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.25744020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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"Thanks, that's awesome you managed to find the problem.\r\n\r\nAbout the 32 bits - really? there isn't a way to serialize the numpy array somehow? 32 bits would take 4 times the memory \/ disk space needed to store these videos.\r\n\r\nPlease let me know when the batch size is customizable and I'll try again!","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":55,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n Thanks, that's awesome you managed to find the problem.\r\n\r\nAbout the 32 bits - really? there isn't a way to serialize the numpy array somehow? 32 bits would take 4 times the memory \/ disk space needed to store these videos.\r\n\r\nPlease let me know when the batch size is customizable and I'll try again!","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.2574402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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"The 32 bit integrers are only used in the writer's buffer because Arrow doesn't take numpy arrays correctly as input. On disk it's stored as uint8 in arrow format ;)","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":30,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n The 32 bit integrers are only used in the writer's buffer because Arrow doesn't take numpy arrays correctly as input. On disk it's stored as uint8 in arrow format ;)","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.2574402094,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"> I don't expect to fix this memory issue until 1-2 weeks unfortunately.\r\n\r\nHi @lhoestq \r\nnot to rush of course, but I was wondering if you have a new timeline so I know how to plan my work around this :) ","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":41,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n > I don't expect to fix this memory issue until 1-2 weeks unfortunately.\r\n\r\nHi @lhoestq \r\nnot to rush of course, but I was wondering if you have a new timeline so I know how to plan my work around this :) ","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.2574402094,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"Alright it should be good now.\r\nYou just have to specify `_writer_batch_size = 10` for example as a class attribute of the dataset builder class.","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":25,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n Alright it should be good now.\r\nYou just have to specify `_writer_batch_size = 10` for example as a class attribute of the dataset builder class.","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.2574402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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"I added it, but still it consumes as much memory\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/722\/files#diff-2e0d865dd4a60dedd1861d6f8c5ed281ded71508467908e1e0b1dbe7d2d420b1R66\r\n\r\nDid I not do it correctly?","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":17,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n I added it, but still it consumes as much memory\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/722\/files#diff-2e0d865dd4a60dedd1861d6f8c5ed281ded71508467908e1e0b1dbe7d2d420b1R66\r\n\r\nDid I not do it correctly?","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.257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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"Yes you did it right.\r\nDid you rebase to include the changes of #828 ?\r\n\r\nEDIT: looks like you merged from master in the PR. Not sure why you still have an issue then, I will investigate","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":37,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n Yes you did it right.\r\nDid you rebase to include the changes of #828 ?\r\n\r\nEDIT: looks like you merged from master in the PR. Not sure why you still have an issue then, I will investigate","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.25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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"Sorry for the delay, I was busy with the dataset sprint and the incredible amount of contributions to the library ^^'\r\n\r\nWhat you can try to do to find what's wrong is check at which frequency the arrow writer writes all the examples from its in-memory buffer on disk. This happens [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L257-L258) in the code.\r\n\r\nThe idea is that `write_on_file` writes the examples every `writer_batch_size` examples and clear the buffer `self. current_rows`. As soon as `writer_batch_size` is small enough you shouldn't have memory issues in theory.\r\n\r\nLet me know if you have questions or if I can help.\r\n\r\nSince the dataset sprint is over and I will also be done with all the PRs soon I will be able to go back at it and take a look.","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":128,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n Sorry for the delay, I was busy with the dataset sprint and the incredible amount of contributions to the library ^^'\r\n\r\nWhat you can try to do to find what's wrong is check at which frequency the arrow writer writes all the examples from its in-memory buffer on disk. This happens [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/arrow_writer.py#L257-L258) in the code.\r\n\r\nThe idea is that `write_on_file` writes the examples every `writer_batch_size` examples and clear the buffer `self. current_rows`. As soon as `writer_batch_size` is small enough you shouldn't have memory issues in theory.\r\n\r\nLet me know if you have questions or if I can help.\r\n\r\nSince the dataset sprint is over and I will also be done with all the PRs soon I will be able to go back at it and take a look.","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0.415009141,0.1420457959,0.3436701,-0.0614233911,0.4706937373,-0.2932085395,-0.1800679713,0.2319211662,-0.1787169725,-0.25744020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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"I had the same issue. It works for me by setting `DEFAULT_WRITER_BATCH_SIZE = 10` of my dataset builder class. (And not `_writer_batch_size` as previously mentioned).  I guess this is because `_writer_batch_size` is overwritten in `__init__` (see [here](https:\/\/github.com\/huggingface\/datasets\/blob\/0e2563e5d5c2fc193ea27d7c24607bb35607f2d5\/src\/datasets\/builder.py#L934))","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":37,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n I had the same issue. It works for me by setting `DEFAULT_WRITER_BATCH_SIZE = 10` of my dataset builder class. (And not `_writer_batch_size` as previously mentioned).  I guess this is because `_writer_batch_size` is overwritten in `__init__` (see [here](https:\/\/github.com\/huggingface\/datasets\/blob\/0e2563e5d5c2fc193ea27d7c24607bb35607f2d5\/src\/datasets\/builder.py#L934))","embeddings":[-0.2472248971,-0.0484526679,-0.0161761772,0.2615477145,0.1814279854,0.2023240179,0.1410058886,0.2836433053,-0.0936915278,0.2016249001,0.4972395897,0.1490257382,-0.234624356,0.0067335241,-0.0158069357,-0.3033439517,0.0866438746,0.0552453026,-0.1493512541,-0.0251512229,-0.4015744925,-0.0364088193,0.0979415551,-0.2667103112,-0.5073342323,-0.2018489987,-0.0731501654,0.0787201747,-0.1270601451,-0.2315939218,-0.1274705678,-0.0400048792,0.1177601293,0.3480851054,-0.0001107187,-0.0142071992,0.2528391778,0.056058187,-0.2610005736,0.2551356554,0.0024836487,-0.2779872119,-0.1727146357,-0.0488479808,0.2572742403,-0.300396353,0.1659740806,-0.3587361276,0.2852363586,0.3103289604,0.1906799227,0.2333978266,0.0225876123,-0.0645367801,0.0283546913,0.1364507228,-0.2712320983,-0.0043950318,-0.0322477482,-0.1391170472,-0.1249336302,0.3213855624,0.1251342595,0.0555912778,0.3767856956,-0.0258995909,-0.3605363071,-0.4826664329,0.1298619807,0.2105497569,0.4798392355,-0.2130855024,-0.1907441765,-0.3127378821,-0.0608719289,-0.2809078097,-0.0386913605,0.5055589676,-0.4797238708,-0.156840533,-0.4373593628,0.0112411054,-0.0739322454,-0.0789558962,0.2725889087,-0.4267039895,-0.0361702442,0.2112913728,0.4411447942,0.0069410964,0.1683401465,-0.1735475063,0.0405645072,-0.0908014327,-0.2532210648,-0.0523642302,0.2876224518,-0.1453632712,0.3291010559,-0.0412678234,0.024514053,-0.2715124786,0.1530065686,-0.0058941585,0.0967000052,0.2201670259,-0.4858937263,-0.1767417938,0.2278588116,0.0444847383,0.0899148658,-0.159083873,-0.0274225194,-0.1531633884,0.2210888267,-0.3335966468,0.1902000308,0.2805503607,-0.2625469565,0.2027172446,-0.2653440237,0.1029191092,-0.146130681,0.1159540489,0.0655315965,0.1141222045,-0.1104514003,0.1520260572,-0.2346293479,-0.2064009309,-0.2843567729,0.0219008811,-0.2518551946,0.201895386,0.133929655,0.2431201041,0.217063278,0.0080225421,0.0536194853,-0.0079490449,0.4056237936,-0.2521450818,-0.006455563,0.2825173438,0.1068824902,0.1090517938,-0.0290899146,0.1394088417,-0.0898926258,0.2382693589,0.0490416102,-0.4122443199,0.0999208838,0.1508389413,0.095170334,0.2297330797,-0.4434710443,0.2292865813,0.1947136968,0.3888747096,-0.054538507,-0.0666551888,-0.3439253271,-0.2636183798,0.1964847147,0.7139770985,-0.3620707393,0.2919789255,-0.1818291992,0.1198833138,0.1442041844,0.4414608181,-0.0528309084,0.4882627726,-0.2243760228,-0.2710960507,-0.0880410895,-0.094117634,-0.2132639587,0.2518543005,-0.1075511724,0.1572639942,0.3729579151,0.2033801377,-0.0334945098,-0.0882051438,0.0497728027,0.2308515012,-0.3586278558,0.124964945,-0.1097932085,-0.389754653,-0.0506447367,-0.001079825,0.1469896585,0.0434499905,-0.1591925323,0.0015705117,0.1117232442,-0.3314083517,0.1802747697,0.2826015949,0.1433033198,-0.138225913,-0.0929224715,0.0918074995,-0.2511996031,0.2500662208,0.2217899859,0.1579179615,-0.0372050181,-0.1576395929,0.1384074986,0.0796204284,-0.040574912,-0.1741780937,0.0987476259,0.0408518016,0.2424800396,-0.1990905702,0.0407792144,0.4570713639,-0.2844359577,0.036405541,-0.6800735593,-0.0023433422,-0.0597890802,-0.1311700791,-0.0204953104,0.0292914025,-0.0755665004,-0.0433139056,-0.0115973717,0.298815459,0.2784372568,0.0222370271,-0.0605900697,0.1867603958,0.1487032622,-0.1612704545,0.3168522716,0.0585198477,0.1633767188,-0.071909681,-0.2061856836,0.0668226629,-0.095822297,0.3077232838,0.2266746014,0.0122988485,0.0099700829,0.2215041071,0.1509661227,-0.050812643,0.0896444991,0.2920495272,0.1799527705,0.1877627522,-0.3006313741,0.0827777237,0.1254699379,-0.0749200955,-0.1055606827,0.2994289696,-0.1035914943,-0.2416673452,0.0810325369,0.2266719788,0.5017876625,0.1558399349,-0.1444334984,0.0478165336,0.0574757233,-0.2169308364,0.1586061567,0.1800119579,0.3289294541,-0.1385491341,0.0901937708,-0.0619816333,-0.4440478981,-0.0912801176,0.184844926,0.2858141065,-0.1865232587,0.2504453957,0.0615522042,-0.0788814276,-0.0309186876,0.4246840179,-0.0496711768,-0.2127269059,-0.1706473529,0.2725588679,-0.018957546,-0.0283497255,0.0066530071,0.261056304,0.2244727314,-0.02329541,0.0941695124,-0.0079309605,-0.0817143396,0.1298800558,0.1777150482,-0.2123888582,0.4530031681,-0.170789659,-0.0871114284,-0.2084838599,-0.0723169595,0.1136234626,-0.2280815989,0.5252376199,0.1035802439,0.252276659,-0.117324926,-0.1160969213,0.0819555968,-0.0663691983,-0.1613080353,0.0102339908,-0.0259694383,-0.114658013,-0.0101680011,-0.1553606391,0.0795831308,-0.3436994553,0.356094867,0.1022500247,0.1887512654,0.5033752918,0.4098785222,-0.0466410108,0.1624164879,0.1523554623,-0.1796501279,-0.3409413695,0.1021591201,-0.3160607517,-0.33717224,0.0466640629,-0.227466464,0.2909381688,0.4419275224,-0.5405673385,-0.2555706799,-0.512783587,0.178343758,-0.0151668554,0.2652284801,0.090331912,-0.0019471067,-0.1002620608,0.0065976339,-0.1557332873,-0.0046922737,0.0445123166,0.1287520081,0.2151253819,0.59130162,0.0606980622,0.7865701914,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/741","title":"Creating dataset consumes too much memory","comments":"Yes the class attribute you can change is `DEFAULT_WRITER_BATCH_SIZE`.\r\nOtherwise in `load_dataset` you can specify `writer_batch_size=`","body":"Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n","comment_length":16,"text":"Creating dataset consumes too much memory \n Moving this issue from https:\/\/github.com\/huggingface\/datasets\/pull\/722 here, because it seems like a general issue.\r\n\r\nGiven the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):\r\n```python\r\n    def _generate_examples(self, base_path, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n\r\n        filepath = os.path.join(base_path, \"annotations\", \"manual\", \"PHOENIX-2014-T.\" + split + \".corpus.csv\")\r\n        images_path = os.path.join(base_path, \"features\", \"fullFrame-210x260px\", split)\r\n\r\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\r\n            data = csv.DictReader(f, delimiter=\"|\", quoting=csv.QUOTE_NONE)\r\n            for row in data:\r\n                frames_path = os.path.join(images_path, row[\"video\"])[:-7]\r\n                np_frames = []\r\n                for frame_name in os.listdir(frames_path):\r\n                    frame_path = os.path.join(frames_path, frame_name)\r\n                    im = Image.open(frame_path)\r\n                    np_frames.append(np.asarray(im))\r\n                    im.close()\r\n\r\n                yield row[\"name\"], {\"video\": np_frames}\r\n```\r\n\r\nThe dataset creation process goes out of memory on a machine with 500GB RAM.\r\nI was under the impression that the \"generator\" here is exactly for that, to avoid memory constraints.\r\n\r\n\r\nHowever, even if you want the entire dataset in memory, it would be in the worst case\r\n`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes\r\nSo I'm not sure why it's taking more than 500GB.\r\n\r\nAnd the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.\r\n\r\n\r\n---\r\n\r\n## Info that might help:\r\nIterating over examples is extremely slow.\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png)\r\nIf I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples\/sec\r\n\r\nAnd you can see at this state it is using 94% of the memory:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png)\r\n\r\nAnd it is only using one CPU core, which is probably why it's so slow:\r\n![image](https:\/\/user-images.githubusercontent.com\/5757359\/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)\r\n \n Yes the class attribute you can change is `DEFAULT_WRITER_BATCH_SIZE`.\r\nOtherwise in `load_dataset` you can specify 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/737","title":"Trec Dataset Connection Error","comments":"Thanks for reporting.\r\nThat's because the download url has changed. The old url now redirects to the new one but we don't support redirection for downloads.\r\n\r\nI'm opening a PR to update the url","body":"**Datasets Version:**\r\n1.1.2\r\n\r\n**Python Version:**\r\n3.6\/3.7\r\n\r\n\r\n**Code:**\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"trec\")\r\n```\r\n\r\n**Expected behavior:**\r\nDownload Trec dataset and load Dataset object\r\n\r\n**Current Behavior:**\r\nGet a connection error saying it couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label (but the link doesn't seem broken)\r\n\r\n<details>\r\n  <summary>Error Logs<\/summary>\r\n \r\n\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/root\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\n---------------------------------------------------------------------------\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-8-66bf1242096e> in <module>()\r\n----> 1 load_dataset(\"trec\")\r\n\r\n10 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n\r\n<\/details>","comment_length":34,"text":"Trec Dataset Connection Error \n **Datasets Version:**\r\n1.1.2\r\n\r\n**Python Version:**\r\n3.6\/3.7\r\n\r\n\r\n**Code:**\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"trec\")\r\n```\r\n\r\n**Expected behavior:**\r\nDownload Trec dataset and load Dataset object\r\n\r\n**Current Behavior:**\r\nGet a connection error saying it couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label (but the link doesn't seem broken)\r\n\r\n<details>\r\n  <summary>Error Logs<\/summary>\r\n \r\n\r\nUsing custom data configuration default\r\nDownloading and preparing dataset trec\/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to \/root\/.cache\/huggingface\/datasets\/trec\/default\/1.1.0\/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...\r\n---------------------------------------------------------------------------\r\nConnectionError                           Traceback (most recent call last)\r\n<ipython-input-8-66bf1242096e> in <module>()\r\n----> 1 load_dataset(\"trec\")\r\n\r\n10 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)\r\n    473         elif response is not None and response.status_code == 404:\r\n    474             raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\n--> 475         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    476 \r\n    477     # Try a second time\r\n\r\nConnectionError: Couldn't reach http:\/\/cogcomp.org\/Data\/QA\/QC\/train_5500.label\r\n\r\n<\/details> \n Thanks for reporting.\r\nThat's because the download url has changed. The old url now redirects to the new one but we don't support redirection for downloads.\r\n\r\nI'm opening a PR to update the url","embeddings":[-0.2436395139,0.1074598357,-0.014969049,0.1505164951,0.3800676465,-0.1288013607,0.2745419741,0.3322343528,-0.2046611756,0.1162951291,-0.1912941933,0.2581116557,0.072281599,-0.1658235937,-0.1021717414,0.0497933179,-0.1717509627,0.0062029338,-0.2832172811,0.0877527371,-0.0442226492,0.2602857649,-0.0055203196,0.2427942008,-0.5984306335,-0.1884526461,0.1804316342,0.4515568316,-0.2506002486,-0.5709514618,0.3888585269,0.0727966577,0.12875247,0.5149000287,-0.0001109371,0.056604173,0.2422578931,-0.0557584353,-0.5349962115,-0.4334150553,-0.5455427766,-0.0273083281,0.1403693557,-0.1366654336,-0.2754541934,0.4765296876,0.0609516352,-0.4809705913,0.231019482,0.6710780263,0.2249374539,0.2940627038,0.0680481568,-0.2650927603,0.2843118906,-0.1481320709,-0.1591581553,0.4663556218,0.1364138126,-0.0583354458,-0.0214009155,0.1513233185,0.0624634773,0.1721279323,0.1745289564,0.0565684251,0.1772675514,-0.3218351305,0.2633388937,0.2760293186,0.4948776364,-0.3989416659,-0.2013637871,0.0997701138,-0.0143683497,-0.658031702,0.2096267641,-0.0679786652,-0.1465303004,0.2543896139,-0.3509271145,0.0848670676,-0.3491250277,0.1558761001,-0.2747200131,-0.1051498055,-0.200848341,-0.0068778759,-0.1133388355,-0.0278355852,0.1749705821,0.0200294312,-0.2384963632,0.0650303811,-0.5116367936,0.0560323447,0.0803835317,-0.1478816718,-0.0281517096,0.3335969448,0.0427142084,0.038103085,-0.1636677086,0.2506561577,0.0588728264,0.2197348475,-0.1769096851,0.3202084899,0.2743516266,0.233279407,-0.1748762131,-0.1759629995,-0.1833797991,-0.0463008061,0.1591341943,0.1637717783,0.3319268525,-0.1655625403,-0.5211207867,0.2541392148,-0.1025256291,0.1115969643,0.0097520873,0.4247652888,-0.1067638621,0.0908646733,-0.1643857658,0.0531059578,-0.2054545432,-0.058853671,-0.1864492446,0.019791089,-0.2384144664,0.0471161604,0.3205462098,0.0270043239,0.1940556467,-0.0945624411,0.0846998394,-0.1832986772,0.1642670035,-0.324323386,0.0265539382,0.3521924019,-0.0864366814,0.1659188271,0.2515583634,-0.2422627509,-0.0546777807,-0.0281223208,-0.4812555611,-0.4549756646,-0.0710661337,0.2084080875,0.1548659056,-0.0179841239,0.1349477768,-0.2025086731,0.0613218844,-0.2018948942,-0.0231011249,-0.2384263724,-0.1747210324,-0.2204871029,0.3749778867,0.3168279529,-0.5557841659,0.0769451335,-0.0761044174,0.1106991619,0.1391872168,0.2035178989,-0.1795076132,0.149582848,-0.2587057054,-0.0297243446,0.3721457422,-0.430886507,-0.5118092895,0.0736469775,-0.1449172944,0.1010434702,-0.1864410043,0.163426578,0.2461973429,0.0745620281,0.3918343782,0.0501541793,-0.2675366104,-0.1172212064,-0.2557188869,-0.1203596294,-0.2273943573,-0.0058596572,0.0968563557,0.0362855792,0.2212335765,-0.153466776,0.2883461416,0.1403520256,0.0544888899,0.2114231437,0.5641490221,-0.0762912184,-0.2149109542,-0.0440055393,-0.2572885752,0.274630338,0.0434309617,0.1699548513,-0.3841554821,-0.0731172785,-0.5478708744,0.0116442656,-0.1969336122,-0.2449869663,0.1305416524,0.2496086359,0.1088530198,0.1462856084,-0.1167913079,0.4556647539,0.0960789695,0.104242757,0.0182092991,0.249475345,-0.0983027369,-0.0271586217,-0.1154528931,0.2011945695,0.1797367781,-0.1083229259,-0.2002046257,0.4768730998,0.2418258041,-0.0474010147,-0.094438456,0.1279828101,0.2421502769,-0.1477313191,0.0017794613,-0.0935001001,0.1546034962,0.0253841225,-0.0430585183,0.3091504574,-0.0131638292,0.1504990309,0.1926895827,-0.0952668861,0.2412174493,0.0416728891,-0.0225719586,0.0657677054,0.018250579,0.2073457837,0.1549005806,-0.1155516282,-0.2918310463,0.1244803667,0.342433244,0.0697862878,-0.0812748522,0.0232161861,-0.2145894468,0.0729293078,0.1710629165,0.2937608063,0.4095773995,0.1543966532,0.0596784391,0.123982057,-0.2317830026,-0.1342634559,-0.0668331683,0.0030505019,0.2552390993,0.3379247785,0.2496048361,0.0391560681,-0.0740778521,-0.3382083774,-0.0782374963,0.2021334469,-0.3116461337,0.2916006148,-0.280492276,-0.2236142904,-0.0499674231,-0.4288078547,-0.0789081827,-0.3910887241,-0.104875356,0.4562005103,0.0663824975,0.1978205442,-0.4925712347,0.0761058256,0.1892468929,-0.4692899883,0.0011927318,-0.1383705437,-0.314391762,0.1267779171,0.0829908624,-0.1739951372,0.251747787,-0.2204298377,0.0104240412,-0.2139101774,-0.2533704042,0.0877238587,0.1492300034,0.1897948533,0.0066279345,0.0844305828,-0.1221543923,-0.0734417811,0.3308883309,0.0803055614,0.1345987469,0.0250462424,-0.0844899788,-0.0403569527,-0.028842343,-0.5540796518,-0.4347090423,-0.4233095646,0.0265906602,0.0278892498,0.2145307213,0.3089239299,-0.0137016736,0.1361341476,-0.1069938466,-0.0539860278,-0.13827461,-0.4636726379,0.2503553629,-0.3218648434,-0.3025492132,0.1655755937,-0.0695523843,0.5321611762,0.0086490298,-0.4650232196,-0.2676859498,-0.0982105434,0.6345692277,0.0915184766,-0.2353428453,0.2610230744,-0.0646533519,-0.1607629508,0.0247910656,-0.1128008366,-0.0059721055,0.0834122896,0.3514324427,-0.0541349128,0.4497286975,-0.0431193672,0.5627840757,0.1353463531,-0.1065158844,0.4253454208,-0.3066239357,0.5101931095,-0.2793849409,-0.2797890902,-0.1413617134,-0.2360912561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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/730","title":"Possible caching bug","comments":"Thanks for reporting. That's a bug indeed.\r\nApparently only the `data_files` parameter is taken into account right now in `DatasetBuilder._create_builder_config` but it should also be the case for `config_kwargs` (or at least the instantiated `builder_config`)","body":"The following code with `test1.txt` containing just \"\ud83e\udd17\ud83e\udd17\ud83e\udd17\":\r\n```\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"latin_1\")\r\nprint(dataset[0])\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"utf-8\")\r\nprint(dataset[0])\r\n``` \r\nproduces this output:\r\n```\r\nDownloading and preparing dataset text\/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...\r\nDataset text downloaded and prepared to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.\r\n{'text': '\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97'}\r\nUsing custom data configuration default\r\nReusing dataset text (\/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)\r\n{'text': '\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97'}\r\n```\r\nJust changing the order (and deleting the temp files):\r\n```\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"utf-8\")\r\nprint(dataset[0])\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"latin_1\")\r\nprint(dataset[0])\r\n```\r\nproduces this:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset text\/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...\r\nDataset text downloaded and prepared to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.\r\n{'text': '\ud83e\udd17\ud83e\udd17\ud83e\udd17'}\r\nUsing custom data configuration default\r\nReusing dataset text (\/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)\r\n{'text': '\ud83e\udd17\ud83e\udd17\ud83e\udd17'}\r\n```\r\n\r\nIs it intended that the cache path does not depend on the config entries?\r\n\r\ntested with datasets==1.1.2 and python==3.8.5","comment_length":35,"text":"Possible caching bug \n The following code with `test1.txt` containing just \"\ud83e\udd17\ud83e\udd17\ud83e\udd17\":\r\n```\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"latin_1\")\r\nprint(dataset[0])\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"utf-8\")\r\nprint(dataset[0])\r\n``` \r\nproduces this output:\r\n```\r\nDownloading and preparing dataset text\/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...\r\nDataset text downloaded and prepared to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.\r\n{'text': '\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97'}\r\nUsing custom data configuration default\r\nReusing dataset text (\/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)\r\n{'text': '\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97'}\r\n```\r\nJust changing the order (and deleting the temp files):\r\n```\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"utf-8\")\r\nprint(dataset[0])\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"latin_1\")\r\nprint(dataset[0])\r\n```\r\nproduces this:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset text\/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...\r\nDataset text downloaded and prepared to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.\r\n{'text': '\ud83e\udd17\ud83e\udd17\ud83e\udd17'}\r\nUsing custom data configuration default\r\nReusing dataset text (\/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)\r\n{'text': '\ud83e\udd17\ud83e\udd17\ud83e\udd17'}\r\n```\r\n\r\nIs it intended that the cache path does not depend on the config entries?\r\n\r\ntested with datasets==1.1.2 and python==3.8.5 \n Thanks for reporting. That's a bug indeed.\r\nApparently only the `data_files` parameter is taken into account right now in `DatasetBuilder._create_builder_config` but it should also be the case for `config_kwargs` (or at least the instantiated 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/730","title":"Possible caching bug","comments":"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/json\/default-c1e124ad488911b8\/0.0.0\/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`","body":"The following code with `test1.txt` containing just \"\ud83e\udd17\ud83e\udd17\ud83e\udd17\":\r\n```\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"latin_1\")\r\nprint(dataset[0])\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"utf-8\")\r\nprint(dataset[0])\r\n``` \r\nproduces this output:\r\n```\r\nDownloading and preparing dataset text\/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...\r\nDataset text downloaded and prepared to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.\r\n{'text': '\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97'}\r\nUsing custom data configuration default\r\nReusing dataset text (\/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)\r\n{'text': '\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97'}\r\n```\r\nJust changing the order (and deleting the temp files):\r\n```\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"utf-8\")\r\nprint(dataset[0])\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"latin_1\")\r\nprint(dataset[0])\r\n```\r\nproduces this:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset text\/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...\r\nDataset text downloaded and prepared to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.\r\n{'text': '\ud83e\udd17\ud83e\udd17\ud83e\udd17'}\r\nUsing custom data configuration default\r\nReusing dataset text (\/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)\r\n{'text': '\ud83e\udd17\ud83e\udd17\ud83e\udd17'}\r\n```\r\n\r\nIs it intended that the cache path does not depend on the config entries?\r\n\r\ntested with datasets==1.1.2 and python==3.8.5","comment_length":63,"text":"Possible caching bug \n The following code with `test1.txt` containing just \"\ud83e\udd17\ud83e\udd17\ud83e\udd17\":\r\n```\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"latin_1\")\r\nprint(dataset[0])\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"utf-8\")\r\nprint(dataset[0])\r\n``` \r\nproduces this output:\r\n```\r\nDownloading and preparing dataset text\/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...\r\nDataset text downloaded and prepared to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.\r\n{'text': '\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97'}\r\nUsing custom data configuration default\r\nReusing dataset text (\/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)\r\n{'text': '\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97\u00f0\\x9f\u00a4\\x97'}\r\n```\r\nJust changing the order (and deleting the temp files):\r\n```\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"utf-8\")\r\nprint(dataset[0])\r\ndataset = datasets.load_dataset('text', data_files=['test1.txt'], split=\"train\", encoding=\"latin_1\")\r\nprint(dataset[0])\r\n```\r\nproduces this:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset text\/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...\r\nDataset text downloaded and prepared to \/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.\r\n{'text': '\ud83e\udd17\ud83e\udd17\ud83e\udd17'}\r\nUsing custom data configuration default\r\nReusing dataset text (\/home\/arne\/.cache\/huggingface\/datasets\/text\/default-15600e4d83254059\/0.0.0\/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)\r\n{'text': '\ud83e\udd17\ud83e\udd17\ud83e\udd17'}\r\n```\r\n\r\nIs it intended that the cache path does not depend on the config entries?\r\n\r\ntested with datasets==1.1.2 and python==3.8.5 \n Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data\/train-v2.0.json --output_dir .\/re_trained_model\/`\r\n\r\nchange the dateset to load json by refering to https:\/\/huggingface.co\/docs\/datasets\/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/726","title":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset","comments":"Hi try, to provide more information please.\r\n\r\nExample code in a colab to reproduce the error, details on what you are trying to do and what you were expected and details on your environment (OS, PyPi packages version).","body":"Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake.","comment_length":38,"text":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset \n Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake. \n Hi try, to provide more information please.\r\n\r\nExample code in a colab to reproduce the error, details on what you are trying to do and what you were expected and details on your environment (OS, PyPi packages 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/726","title":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset","comments":"> Hi try, to provide more information please.\r\n> \r\n> Example code in a colab to reproduce the error, details on what you are trying to do and what you were expected and details on your environment (OS, PyPi packages version).\r\n\r\nI have update the description, sorry for the incomplete issue by mistake.","body":"Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake.","comment_length":53,"text":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset \n Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake. \n > Hi try, to provide more information please.\r\n> \r\n> Example code in a colab to reproduce the error, details on what you are trying to do and what you were expected and details on your environment (OS, PyPi packages version).\r\n\r\nI have update the description, sorry for the incomplete issue by mistake.","embeddings":[-0.4920600653,0.0389875546,-0.0594882704,0.3662634194,0.2443764508,-0.0337870046,0.13654387,0.550494194,-0.00061072,-0.0602816343,-0.0666541532,-0.0310110506,0.0238088965,0.3648073375,-0.2299020737,0.2352990061,-0.1055581272,0.0833998173,-0.3995411098,-0.0401990227,-0.2967323363,0.1188192815,-0.1012961715,-0.1545280963,0.0459318496,0.0663644373,0.2705113292,0.1403153092,-0.2775344551,-0.2145338356,0.1424371004,-0.1216851696,-0.1503286809,0.324788034,-0.0001102053,0.2582596838,0.3656147122,-0.2235869169,-0.2715173662,-0.1560243964,-0.4222053289,-0.1340990663,-0.1875573993,-0.1528116018,0.0739320889,-0.2589229047,-0.1242828369,-0.4580081105,0.0629987568,0.2549592257,0.2594358921,0.5298959017,0.4289398491,0.1028628424,0.2806044221,-0.0724724978,0.0572922044,0.1174185649,0.1282486916,0.1596287191,0.0161918625,-0.0816258788,-0.1522659957,0.1627645791,0.1623219252,-0.3617706895,0.0090026567,-0.1375187486,0.1970816851,0.3654464483,0.7156856656,-0.1815347672,-0.3790054619,-0.0936338827,-0.1986480951,0.0984760225,0.3893739879,0.3778521121,-0.0309308544,0.0653221011,-0.5064879656,0.1339263022,0.1091979817,0.0512661226,0.0404527225,0.0759120733,0.0293370411,0.008689425,-0.1022983715,-0.1159981489,0.3256934285,-0.2656748891,-0.3963989913,0.137359947,-0.3631493449,0.0508111529,-0.1439990103,0.0500055067,0.366363883,0.6212907434,0.2462342829,0.0420111604,-0.308077246,0.0778114572,0.0899420232,0.3613916039,-0.0054691094,0.1346786171,0.1948350817,0.10071899,0.0852302238,0.0521176606,0.0023271991,-0.542937696,0.1448046714,0.0951064378,0.3429888189,-0.3038514555,-0.4549231231,0.047217641,0.1378372461,-0.1998882294,0.1437949091,-0.0076497574,-0.0868444666,0.0802833959,0.3436453938,0.0880122259,-0.1021173596,-0.2038995028,-0.2310939729,-0.2034747303,-0.0948625132,-0.0629801005,0.1900294423,-0.2003930807,0.6136524081,-0.0917051286,0.0911761746,-0.1107355282,0.0129413661,0.046453923,0.0409467779,0.321911335,-0.1849273741,0.0135516413,0.1199886575,0.1528467536,-0.0941035226,0.2584348321,-0.2343892604,-0.3227925301,-0.1861532629,0.219078213,-0.3183863163,0.0472868122,-0.1684523225,-0.3878966272,0.3156642318,-0.2622767985,-0.0602091402,-0.2444446683,-0.1759607047,-0.138668865,0.3337224126,0.2220809311,-0.1910729706,0.126049161,0.1682969481,-0.1456349194,0.0463749357,0.2319472134,-0.1313233376,0.0905488282,-0.2481278926,-0.0242459103,0.0881310552,-0.5387325287,-0.4547849596,-0.0014980707,0.2015110701,0.0489920415,0.1314415038,-0.1612880677,-0.0724657029,-0.2714241743,-0.2563689649,0.258287251,-0.034631554,0.389313817,-0.2438358366,-0.2965556383,0.0666808411,0.11800237,-0.0492598414,-0.0816775784,0.1693636179,-0.0095629962,0.205123961,0.0532166623,0.0325854793,0.108541891,0.1249753609,-0.1738582253,-0.0426240675,-0.0249788035,-0.1966191828,0.2551011145,-0.0707978681,0.1320103109,0.0627517551,0.178278774,-0.4489194155,-0.1798519492,-0.2049353272,0.0868078247,0.1547742933,0.1201993003,-0.1345562041,0.0315445885,0.097442545,0.1682491302,-0.1691212803,0.0556032844,-0.2289179713,0.2009186,-0.0244281776,-0.1356206685,0.4263866842,0.0483330823,-0.0907463729,0.0649090111,-0.4173122048,0.4893443882,-0.0621393695,0.0347024985,0.0946347788,0.4519070983,0.0079325,-0.3528214693,0.0029319238,0.0297744963,0.1392337382,-0.1301709712,0.2713800669,-0.0893520936,0.0362150632,-0.1035486832,-0.1582131386,-0.1385202408,0.0254937224,-0.0858455822,-0.0036497831,-0.1547219008,0.036334835,0.2815303206,0.2090259939,0.1824414283,0.3501009941,-0.2496142536,0.3917295635,0.0523996353,0.0041363831,0.1314524859,-0.0301868841,-0.0547485016,-0.123130776,0.2424137145,0.4796056449,0.0881364644,0.0710710213,0.1027916372,0.0828066617,-0.0666035786,0.2418119758,0.1984371692,0.3564124107,0.4244209528,-0.1783008277,-0.1144582033,-0.2286888808,0.1582078785,0.1266210526,0.296477139,-0.5625895262,-0.2332416624,-0.3110375106,0.1262899935,-0.0553843863,0.0316452757,-0.3143775761,-0.1802434623,-0.1689627618,0.1081855819,-0.0475393161,0.1951696426,-0.1458839178,0.0477331541,0.3103329241,0.0443977341,0.1516741216,-0.0028465451,-0.1123988777,0.0865353346,0.6746875644,-0.1140086427,0.3397882283,-0.477501303,0.1271569282,-0.3800797462,0.0426583849,-0.0863100588,-0.0893176869,0.2315170616,0.2202654928,0.2755811214,0.0788401291,-0.2032470554,0.0359021463,0.0523619428,-0.4162954092,0.0309536979,-0.0636543036,-0.1320037097,0.1025646105,-0.0423322469,0.0432340465,-0.3893762231,-0.0724421144,-0.0345688462,0.1005237103,0.0962934569,0.0817191377,-0.0508541204,0.0996042937,0.2866059244,-0.1770675182,-0.8880888224,0.3540101349,-0.1670295596,-0.3587040901,-0.0214136578,0.1422802508,0.2374749631,0.0288894922,-0.4529916644,-0.1852105707,-0.2191690356,-0.0274334569,0.0638383254,0.1969694644,0.1162543371,0.0691954195,-0.0823741183,-0.3447431326,-0.2273833901,0.1099680588,-0.2630299628,0.5279319286,-0.0101229204,0.062973924,0.0090144631,0.1534843743,0.411018759,-0.1777416617,0.424354881,0.2426645756,0.3268184364,-0.2149715424,-0.1142090335,0.0165318102,-0.1049812809,0.333722651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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/726","title":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset","comments":"Hi, I have manually downloaded the compressed dataset `openwebtext.tar.xz' and use the following command to preprocess the examples:\r\n```\r\n>>> dataset = load_dataset('\/home\/admin\/workspace\/datasets\/datasets-master\/datasets-master\/datasets\/openwebtext', data_dir='\/home\/admin\/workspace\/datasets')\r\nUsing custom data configuration default\r\nDownloading and preparing dataset openwebtext\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/default\/0.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nDataset openwebtext downloaded and prepared to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/default\/0.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02. Subsequent calls will reuse this data.\r\n>>> len(dataset['train'])\r\n74571\r\n>>>\r\n```\r\nThe size of the pre-processed example file is only 354MB, however the processed bookcorpus dataset is 4.6g. Are there any problems?","body":"Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake.","comment_length":87,"text":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset \n Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake. \n Hi, I have manually downloaded the compressed dataset `openwebtext.tar.xz' and use the following command to preprocess the examples:\r\n```\r\n>>> dataset = load_dataset('\/home\/admin\/workspace\/datasets\/datasets-master\/datasets-master\/datasets\/openwebtext', data_dir='\/home\/admin\/workspace\/datasets')\r\nUsing custom data configuration default\r\nDownloading and preparing dataset openwebtext\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/default\/0.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nDataset openwebtext downloaded and prepared to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/default\/0.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02. Subsequent calls will reuse this data.\r\n>>> len(dataset['train'])\r\n74571\r\n>>>\r\n```\r\nThe size of the pre-processed example file is only 354MB, however the processed bookcorpus dataset is 4.6g. Are there any problems?","embeddings":[-0.4920600653,0.0389875546,-0.0594882704,0.3662634194,0.2443764508,-0.0337870046,0.13654387,0.550494194,-0.00061072,-0.0602816343,-0.0666541532,-0.0310110506,0.0238088965,0.3648073375,-0.2299020737,0.2352990061,-0.1055581272,0.0833998173,-0.3995411098,-0.0401990227,-0.2967323363,0.1188192815,-0.1012961715,-0.1545280963,0.0459318496,0.0663644373,0.2705113292,0.1403153092,-0.2775344551,-0.2145338356,0.1424371004,-0.1216851696,-0.1503286809,0.324788034,-0.0001102053,0.2582596838,0.3656147122,-0.2235869169,-0.2715173662,-0.1560243964,-0.4222053289,-0.1340990663,-0.1875573993,-0.1528116018,0.0739320889,-0.2589229047,-0.1242828369,-0.4580081105,0.0629987568,0.2549592257,0.2594358921,0.5298959017,0.4289398491,0.1028628424,0.2806044221,-0.0724724978,0.0572922044,0.1174185649,0.1282486916,0.1596287191,0.0161918625,-0.0816258788,-0.1522659957,0.1627645791,0.1623219252,-0.3617706895,0.0090026567,-0.1375187486,0.1970816851,0.3654464483,0.7156856656,-0.1815347672,-0.3790054619,-0.0936338827,-0.1986480951,0.0984760225,0.3893739879,0.3778521121,-0.0309308544,0.0653221011,-0.5064879656,0.1339263022,0.1091979817,0.0512661226,0.0404527225,0.0759120733,0.0293370411,0.008689425,-0.1022983715,-0.1159981489,0.3256934285,-0.2656748891,-0.3963989913,0.137359947,-0.3631493449,0.0508111529,-0.1439990103,0.0500055067,0.366363883,0.6212907434,0.2462342829,0.0420111604,-0.308077246,0.0778114572,0.0899420232,0.3613916039,-0.0054691094,0.1346786171,0.1948350817,0.10071899,0.0852302238,0.0521176606,0.0023271991,-0.542937696,0.1448046714,0.0951064378,0.3429888189,-0.3038514555,-0.4549231231,0.047217641,0.1378372461,-0.1998882294,0.1437949091,-0.0076497574,-0.0868444666,0.0802833959,0.3436453938,0.0880122259,-0.1021173596,-0.2038995028,-0.2310939729,-0.2034747303,-0.0948625132,-0.0629801005,0.1900294423,-0.2003930807,0.6136524081,-0.0917051286,0.0911761746,-0.1107355282,0.0129413661,0.046453923,0.0409467779,0.321911335,-0.1849273741,0.0135516413,0.1199886575,0.1528467536,-0.0941035226,0.2584348321,-0.2343892604,-0.3227925301,-0.1861532629,0.219078213,-0.3183863163,0.0472868122,-0.1684523225,-0.3878966272,0.3156642318,-0.2622767985,-0.0602091402,-0.2444446683,-0.1759607047,-0.138668865,0.3337224126,0.2220809311,-0.1910729706,0.126049161,0.1682969481,-0.1456349194,0.0463749357,0.2319472134,-0.1313233376,0.0905488282,-0.2481278926,-0.0242459103,0.0881310552,-0.5387325287,-0.4547849596,-0.0014980707,0.2015110701,0.0489920415,0.1314415038,-0.1612880677,-0.0724657029,-0.2714241743,-0.2563689649,0.258287251,-0.034631554,0.389313817,-0.2438358366,-0.2965556383,0.0666808411,0.11800237,-0.0492598414,-0.0816775784,0.1693636179,-0.0095629962,0.205123961,0.0532166623,0.0325854793,0.108541891,0.1249753609,-0.1738582253,-0.0426240675,-0.0249788035,-0.1966191828,0.2551011145,-0.0707978681,0.1320103109,0.0627517551,0.178278774,-0.4489194155,-0.1798519492,-0.2049353272,0.0868078247,0.1547742933,0.1201993003,-0.1345562041,0.0315445885,0.097442545,0.1682491302,-0.1691212803,0.0556032844,-0.2289179713,0.2009186,-0.0244281776,-0.1356206685,0.4263866842,0.0483330823,-0.0907463729,0.0649090111,-0.4173122048,0.4893443882,-0.0621393695,0.0347024985,0.0946347788,0.4519070983,0.0079325,-0.3528214693,0.0029319238,0.0297744963,0.1392337382,-0.1301709712,0.2713800669,-0.0893520936,0.0362150632,-0.1035486832,-0.1582131386,-0.1385202408,0.0254937224,-0.0858455822,-0.0036497831,-0.1547219008,0.036334835,0.2815303206,0.2090259939,0.1824414283,0.3501009941,-0.2496142536,0.3917295635,0.0523996353,0.0041363831,0.1314524859,-0.0301868841,-0.0547485016,-0.123130776,0.2424137145,0.4796056449,0.0881364644,0.0710710213,0.1027916372,0.0828066617,-0.0666035786,0.2418119758,0.1984371692,0.3564124107,0.4244209528,-0.1783008277,-0.1144582033,-0.2286888808,0.1582078785,0.1266210526,0.296477139,-0.5625895262,-0.2332416624,-0.3110375106,0.1262899935,-0.0553843863,0.0316452757,-0.3143775761,-0.1802434623,-0.1689627618,0.1081855819,-0.0475393161,0.1951696426,-0.1458839178,0.0477331541,0.3103329241,0.0443977341,0.1516741216,-0.0028465451,-0.1123988777,0.0865353346,0.6746875644,-0.1140086427,0.3397882283,-0.477501303,0.1271569282,-0.3800797462,0.0426583849,-0.0863100588,-0.0893176869,0.2315170616,0.2202654928,0.2755811214,0.0788401291,-0.2032470554,0.0359021463,0.0523619428,-0.4162954092,0.0309536979,-0.0636543036,-0.1320037097,0.1025646105,-0.0423322469,0.0432340465,-0.3893762231,-0.0724421144,-0.0345688462,0.1005237103,0.0962934569,0.0817191377,-0.0508541204,0.0996042937,0.2866059244,-0.1770675182,-0.8880888224,0.3540101349,-0.1670295596,-0.3587040901,-0.0214136578,0.1422802508,0.2374749631,0.0288894922,-0.4529916644,-0.1852105707,-0.2191690356,-0.0274334569,0.0638383254,0.1969694644,0.1162543371,0.0691954195,-0.0823741183,-0.3447431326,-0.2273833901,0.1099680588,-0.2630299628,0.5279319286,-0.0101229204,0.062973924,0.0090144631,0.1534843743,0.411018759,-0.1777416617,0.424354881,0.2426645756,0.3268184364,-0.2149715424,-0.1142090335,0.0165318102,-0.1049812809,0.33372265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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/726","title":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset","comments":"NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n\r\ni got this issue when i try to work on my own datasets kindly tell me, from where i can get checksums of train and dev file in my github repo","body":"Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake.","comment_length":39,"text":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset \n Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake. \n NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n\r\ni got this issue when i try to work on my own datasets kindly tell me, from where i can get checksums of train and dev file in my github 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/726","title":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset","comments":"Hi,  I got the similar issue for xnli dataset while working on colab with python3.7.  \r\n\r\n`nlp.load_dataset(path = 'xnli')`\r\n\r\nThe above command resulted in following issue : \r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']\r\n```\r\n\r\nAny idea how to fix this  ?","body":"Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake.","comment_length":44,"text":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset \n Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake. \n Hi,  I got the similar issue for xnli dataset while working on colab with python3.7.  \r\n\r\n`nlp.load_dataset(path = 'xnli')`\r\n\r\nThe above command resulted in following issue : \r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']\r\n```\r\n\r\nAny idea how to fix this  ?","embeddings":[-0.4920600653,0.0389875546,-0.0594882704,0.3662634194,0.2443764508,-0.0337870046,0.13654387,0.550494194,-0.00061072,-0.0602816343,-0.0666541532,-0.0310110506,0.0238088965,0.3648073375,-0.2299020737,0.2352990061,-0.1055581272,0.0833998173,-0.3995411098,-0.0401990227,-0.2967323363,0.1188192815,-0.1012961715,-0.1545280963,0.0459318496,0.0663644373,0.2705113292,0.1403153092,-0.2775344551,-0.2145338356,0.1424371004,-0.1216851696,-0.1503286809,0.324788034,-0.0001102053,0.2582596838,0.3656147122,-0.2235869169,-0.2715173662,-0.1560243964,-0.4222053289,-0.1340990663,-0.1875573993,-0.1528116018,0.0739320889,-0.2589229047,-0.1242828369,-0.4580081105,0.0629987568,0.2549592257,0.2594358921,0.5298959017,0.4289398491,0.1028628424,0.2806044221,-0.0724724978,0.0572922044,0.1174185649,0.1282486916,0.1596287191,0.0161918625,-0.0816258788,-0.1522659957,0.1627645791,0.1623219252,-0.3617706895,0.0090026567,-0.1375187486,0.1970816851,0.3654464483,0.7156856656,-0.1815347672,-0.3790054619,-0.0936338827,-0.1986480951,0.0984760225,0.3893739879,0.3778521121,-0.0309308544,0.0653221011,-0.5064879656,0.1339263022,0.1091979817,0.0512661226,0.0404527225,0.0759120733,0.0293370411,0.008689425,-0.1022983715,-0.1159981489,0.3256934285,-0.2656748891,-0.3963989913,0.137359947,-0.3631493449,0.0508111529,-0.1439990103,0.0500055067,0.366363883,0.6212907434,0.2462342829,0.0420111604,-0.308077246,0.0778114572,0.0899420232,0.3613916039,-0.0054691094,0.1346786171,0.1948350817,0.10071899,0.0852302238,0.0521176606,0.0023271991,-0.542937696,0.1448046714,0.0951064378,0.3429888189,-0.3038514555,-0.4549231231,0.047217641,0.1378372461,-0.1998882294,0.1437949091,-0.0076497574,-0.0868444666,0.0802833959,0.3436453938,0.0880122259,-0.1021173596,-0.2038995028,-0.2310939729,-0.2034747303,-0.0948625132,-0.0629801005,0.1900294423,-0.2003930807,0.6136524081,-0.0917051286,0.0911761746,-0.1107355282,0.0129413661,0.046453923,0.0409467779,0.321911335,-0.1849273741,0.0135516413,0.1199886575,0.1528467536,-0.0941035226,0.2584348321,-0.2343892604,-0.3227925301,-0.1861532629,0.219078213,-0.3183863163,0.0472868122,-0.1684523225,-0.3878966272,0.3156642318,-0.2622767985,-0.0602091402,-0.2444446683,-0.1759607047,-0.138668865,0.3337224126,0.2220809311,-0.1910729706,0.126049161,0.1682969481,-0.1456349194,0.0463749357,0.2319472134,-0.1313233376,0.0905488282,-0.2481278926,-0.0242459103,0.0881310552,-0.5387325287,-0.4547849596,-0.0014980707,0.2015110701,0.0489920415,0.1314415038,-0.1612880677,-0.0724657029,-0.2714241743,-0.2563689649,0.258287251,-0.034631554,0.389313817,-0.2438358366,-0.2965556383,0.0666808411,0.11800237,-0.0492598414,-0.0816775784,0.1693636179,-0.0095629962,0.205123961,0.0532166623,0.0325854793,0.108541891,0.1249753609,-0.1738582253,-0.0426240675,-0.0249788035,-0.1966191828,0.2551011145,-0.0707978681,0.1320103109,0.0627517551,0.178278774,-0.4489194155,-0.1798519492,-0.2049353272,0.0868078247,0.1547742933,0.1201993003,-0.1345562041,0.0315445885,0.097442545,0.1682491302,-0.1691212803,0.0556032844,-0.2289179713,0.2009186,-0.0244281776,-0.1356206685,0.4263866842,0.0483330823,-0.0907463729,0.0649090111,-0.4173122048,0.4893443882,-0.0621393695,0.0347024985,0.0946347788,0.4519070983,0.0079325,-0.3528214693,0.0029319238,0.0297744963,0.1392337382,-0.1301709712,0.2713800669,-0.0893520936,0.0362150632,-0.1035486832,-0.1582131386,-0.1385202408,0.0254937224,-0.0858455822,-0.0036497831,-0.1547219008,0.036334835,0.2815303206,0.2090259939,0.1824414283,0.3501009941,-0.2496142536,0.3917295635,0.0523996353,0.0041363831,0.1314524859,-0.0301868841,-0.0547485016,-0.123130776,0.2424137145,0.4796056449,0.0881364644,0.0710710213,0.1027916372,0.0828066617,-0.0666035786,0.2418119758,0.1984371692,0.3564124107,0.4244209528,-0.1783008277,-0.1144582033,-0.2286888808,0.1582078785,0.1266210526,0.296477139,-0.5625895262,-0.2332416624,-0.3110375106,0.1262899935,-0.0553843863,0.0316452757,-0.3143775761,-0.1802434623,-0.1689627618,0.1081855819,-0.0475393161,0.1951696426,-0.1458839178,0.0477331541,0.3103329241,0.0443977341,0.1516741216,-0.0028465451,-0.1123988777,0.0865353346,0.6746875644,-0.1140086427,0.3397882283,-0.477501303,0.1271569282,-0.3800797462,0.0426583849,-0.0863100588,-0.0893176869,0.2315170616,0.2202654928,0.2755811214,0.0788401291,-0.2032470554,0.0359021463,0.0523619428,-0.4162954092,0.0309536979,-0.0636543036,-0.1320037097,0.1025646105,-0.0423322469,0.0432340465,-0.3893762231,-0.0724421144,-0.0345688462,0.1005237103,0.0962934569,0.0817191377,-0.0508541204,0.0996042937,0.2866059244,-0.1770675182,-0.8880888224,0.3540101349,-0.1670295596,-0.3587040901,-0.0214136578,0.1422802508,0.2374749631,0.0288894922,-0.4529916644,-0.1852105707,-0.2191690356,-0.0274334569,0.0638383254,0.1969694644,0.1162543371,0.0691954195,-0.0823741183,-0.3447431326,-0.2273833901,0.1099680588,-0.2630299628,0.5279319286,-0.0101229204,0.062973924,0.0090144631,0.1534843743,0.411018759,-0.1777416617,0.424354881,0.2426645756,0.3268184364,-0.2149715424,-0.1142090335,0.0165318102,-0.1049812809,0.333722651,0.2043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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/726","title":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset","comments":"Says fixed but I'm still getting it. \r\n\r\ncommand:\r\n\r\n dataset = load_dataset(\"ted_talks_iwslt\", language_pair=(\"en\", \"es\"), year=\"2014\",download_mode=\"force_redownload\")\r\n\r\ngot:\r\n\r\nUsing custom data configuration en_es_2014-35a2d3350a0f9823\r\nDownloading and preparing dataset ted_talks_iwslt\/en_es_2014 (download: 2.15 KiB, generated: Unknown size, post-processed: Unknown size, total: 2.15 KiB) to \/home\/ken\/.cache\/huggingface\/datasets\/ted_talks_iwslt\/en_es_2014-35a2d3350a0f9823\/1.1.0\/43935b3fe470c753a023642e1f54b068c590847f9928bd3f2ec99f15702ad6a6...\r\nDownloading:\r\n2.21k\/? [00:00<00:00, 141kB\/s]\r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download']","body":"Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake.","comment_length":52,"text":"\"Checksums didn't match for dataset source files\" error while loading openwebtext dataset \n Hi,\r\nI have encountered this problem during loading the openwebtext dataset:\r\n```\r\n>>> dataset = load_dataset('openwebtext')\r\nDownloading and preparing dataset openwebtext\/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to \/home\/admin\/.cache\/huggingface\/datasets\/openwebtext\/plain_text\/1.0.0\/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 476, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 536, in _download_and_prepare\r\n    self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n  File \"\/home\/admin\/workspace\/anaconda3\/envs\/torch1.6-py3.7\/lib\/python3.7\/site-packages\/datasets\/utils\/info_utils.py\", line 39, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/zenodo.org\/record\/3834942\/files\/openwebtext.tar.xz']\r\n```\r\nI think this problem is caused because the released dataset has changed. Or I should download the dataset manually?\r\n\r\nSorry for release the unfinised issue by mistake. \n Says fixed but I'm still getting it. \r\n\r\ncommand:\r\n\r\n dataset = load_dataset(\"ted_talks_iwslt\", language_pair=(\"en\", \"es\"), year=\"2014\",download_mode=\"force_redownload\")\r\n\r\ngot:\r\n\r\nUsing custom data configuration en_es_2014-35a2d3350a0f9823\r\nDownloading and preparing dataset ted_talks_iwslt\/en_es_2014 (download: 2.15 KiB, generated: Unknown size, post-processed: Unknown size, total: 2.15 KiB) to \/home\/ken\/.cache\/huggingface\/datasets\/ted_talks_iwslt\/en_es_2014-35a2d3350a0f9823\/1.1.0\/43935b3fe470c753a023642e1f54b068c590847f9928bd3f2ec99f15702ad6a6...\r\nDownloading:\r\n2.21k\/? [00:00<00:00, 141kB\/s]\r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/u\/0\/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download']","embeddings":[-0.4920600653,0.0389875546,-0.0594882704,0.3662634194,0.2443764508,-0.0337870046,0.13654387,0.550494194,-0.00061072,-0.0602816343,-0.0666541532,-0.0310110506,0.0238088965,0.3648073375,-0.2299020737,0.2352990061,-0.1055581272,0.0833998173,-0.3995411098,-0.0401990227,-0.2967323363,0.1188192815,-0.1012961715,-0.1545280963,0.0459318496,0.0663644373,0.2705113292,0.1403153092,-0.2775344551,-0.2145338356,0.1424371004,-0.1216851696,-0.1503286809,0.324788034,-0.0001102053,0.2582596838,0.3656147122,-0.2235869169,-0.2715173662,-0.1560243964,-0.4222053289,-0.1340990663,-0.1875573993,-0.1528116018,0.0739320889,-0.2589229047,-0.1242828369,-0.4580081105,0.0629987568,0.2549592257,0.2594358921,0.5298959017,0.4289398491,0.1028628424,0.2806044221,-0.0724724978,0.0572922044,0.1174185649,0.1282486916,0.1596287191,0.0161918625,-0.0816258788,-0.1522659957,0.1627645791,0.1623219252,-0.3617706895,0.0090026567,-0.1375187486,0.1970816851,0.3654464483,0.7156856656,-0.1815347672,-0.3790054619,-0.0936338827,-0.1986480951,0.0984760225,0.3893739879,0.3778521121,-0.0309308544,0.0653221011,-0.5064879656,0.1339263022,0.1091979817,0.0512661226,0.0404527225,0.0759120733,0.0293370411,0.008689425,-0.1022983715,-0.1159981489,0.3256934285,-0.2656748891,-0.3963989913,0.137359947,-0.3631493449,0.0508111529,-0.1439990103,0.0500055067,0.366363883,0.6212907434,0.2462342829,0.0420111604,-0.308077246,0.0778114572,0.0899420232,0.3613916039,-0.0054691094,0.1346786171,0.1948350817,0.10071899,0.0852302238,0.0521176606,0.0023271991,-0.542937696,0.1448046714,0.0951064378,0.3429888189,-0.3038514555,-0.4549231231,0.047217641,0.1378372461,-0.1998882294,0.1437949091,-0.0076497574,-0.0868444666,0.0802833959,0.3436453938,0.0880122259,-0.1021173596,-0.2038995028,-0.2310939729,-0.2034747303,-0.0948625132,-0.0629801005,0.1900294423,-0.2003930807,0.6136524081,-0.0917051286,0.0911761746,-0.1107355282,0.0129413661,0.046453923,0.0409467779,0.321911335,-0.1849273741,0.0135516413,0.1199886575,0.1528467536,-0.0941035226,0.2584348321,-0.2343892604,-0.3227925301,-0.1861532629,0.219078213,-0.3183863163,0.0472868122,-0.1684523225,-0.3878966272,0.3156642318,-0.2622767985,-0.0602091402,-0.2444446683,-0.1759607047,-0.138668865,0.3337224126,0.2220809311,-0.1910729706,0.126049161,0.1682969481,-0.1456349194,0.0463749357,0.2319472134,-0.1313233376,0.0905488282,-0.2481278926,-0.0242459103,0.0881310552,-0.5387325287,-0.4547849596,-0.0014980707,0.2015110701,0.0489920415,0.1314415038,-0.1612880677,-0.0724657029,-0.2714241743,-0.2563689649,0.258287251,-0.034631554,0.389313817,-0.2438358366,-0.2965556383,0.0666808411,0.11800237,-0.0492598414,-0.0816775784,0.1693636179,-0.0095629962,0.205123961,0.0532166623,0.0325854793,0.108541891,0.1249753609,-0.1738582253,-0.0426240675,-0.0249788035,-0.1966191828,0.2551011145,-0.0707978681,0.1320103109,0.0627517551,0.178278774,-0.4489194155,-0.1798519492,-0.2049353272,0.0868078247,0.1547742933,0.1201993003,-0.1345562041,0.0315445885,0.097442545,0.1682491302,-0.1691212803,0.0556032844,-0.2289179713,0.2009186,-0.0244281776,-0.1356206685,0.4263866842,0.0483330823,-0.0907463729,0.0649090111,-0.4173122048,0.4893443882,-0.0621393695,0.0347024985,0.0946347788,0.4519070983,0.0079325,-0.3528214693,0.0029319238,0.0297744963,0.1392337382,-0.1301709712,0.2713800669,-0.0893520936,0.0362150632,-0.1035486832,-0.1582131386,-0.1385202408,0.0254937224,-0.0858455822,-0.0036497831,-0.1547219008,0.036334835,0.2815303206,0.2090259939,0.1824414283,0.3501009941,-0.2496142536,0.3917295635,0.0523996353,0.0041363831,0.1314524859,-0.0301868841,-0.0547485016,-0.123130776,0.2424137145,0.4796056449,0.0881364644,0.0710710213,0.1027916372,0.0828066617,-0.0666035786,0.2418119758,0.1984371692,0.3564124107,0.4244209528,-0.1783008277,-0.1144582033,-0.2286888808,0.1582078785,0.1266210526,0.296477139,-0.5625895262,-0.2332416624,-0.3110375106,0.1262899935,-0.0553843863,0.0316452757,-0.3143775761,-0.1802434623,-0.1689627618,0.1081855819,-0.0475393161,0.1951696426,-0.1458839178,0.0477331541,0.3103329241,0.0443977341,0.1516741216,-0.0028465451,-0.1123988777,0.0865353346,0.6746875644,-0.1140086427,0.3397882283,-0.477501303,0.1271569282,-0.3800797462,0.0426583849,-0.0863100588,-0.0893176869,0.2315170616,0.2202654928,0.2755811214,0.0788401291,-0.2032470554,0.0359021463,0.0523619428,-0.4162954092,0.0309536979,-0.0636543036,-0.1320037097,0.1025646105,-0.0423322469,0.0432340465,-0.3893762231,-0.0724421144,-0.0345688462,0.1005237103,0.0962934569,0.0817191377,-0.0508541204,0.0996042937,0.2866059244,-0.1770675182,-0.8880888224,0.3540101349,-0.1670295596,-0.3587040901,-0.0214136578,0.1422802508,0.2374749631,0.0288894922,-0.4529916644,-0.1852105707,-0.2191690356,-0.0274334569,0.0638383254,0.1969694644,0.1162543371,0.0691954195,-0.0823741183,-0.3447431326,-0.2273833901,0.1099680588,-0.2630299628,0.5279319286,-0.0101229204,0.062973924,0.0090144631,0.1534843743,0.411018759,-0.1777416617,0.4243548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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/724","title":"need to redirect \/nlp to \/datasets and remove outdated info","comments":"Should be fixed now: \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/35882\/95917301-040b0600-0d78-11eb-9655-c4ac0e788089.png)\r\n\r\nNot sure I understand what you mean by the second part?\r\n","body":"It looks like the website still has all the `nlp` data, e.g.: https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wikihow&config=all\r\n\r\nshould probably redirect to: https:\/\/huggingface.co\/datasets\/wikihow\r\n\r\nalso for some reason the new information is slightly borked. If you look at the old one it was nicely formatted and had the links marked up, the new one is just a jumble of text in one chunk and no markup for links (i.e. not clickable).","comment_length":16,"text":"need to redirect \/nlp to \/datasets and remove outdated info \n It looks like the website still has all the `nlp` data, e.g.: https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wikihow&config=all\r\n\r\nshould probably redirect to: https:\/\/huggingface.co\/datasets\/wikihow\r\n\r\nalso for some reason the new information is slightly borked. If you look at the old one it was nicely formatted and had the links marked up, the new one is just a jumble of text in one chunk and no markup for links (i.e. not clickable). \n Should be fixed now: \r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/35882\/95917301-040b0600-0d78-11eb-9655-c4ac0e788089.png)\r\n\r\nNot sure I understand what you mean by the second part?\r\n","embeddings":[0.2948436141,0.110556297,-0.0633747578,0.1094637066,-0.0713283047,0.0353955403,-0.260299772,0.6290499568,-0.2917530835,-0.4541862309,-0.1458730847,0.2195103467,0.2947607338,0.0850115344,0.1424258053,-0.3171723187,0.0133651607,0.0606414154,-0.0077770688,-0.1089684814,-0.186075002,0.0806226283,-0.403195709,0.116498366,-0.0495447777,0.0418990068,-0.2173669338,0.3226778805,-0.0874300599,-0.42714867,0.1224372461,0.0153161846,-0.0209404994,0.1970620006,-0.0001060939,-0.3152425885,0.3930561543,0.1965439171,-0.3718169928,-0.0397977009,-0.2657049596,-0.5014023781,-0.0838163868,-0.1341035664,0.0758307502,-0.1319982708,0.0686012283,0.3240991533,0.4053322673,0.4329928458,0.2685536742,-0.2723818123,-0.1567654908,0.1352109313,0.0452874862,0.3373916447,0.1736284792,-0.1308058947,0.1029123887,-0.0435013175,-0.1377879679,0.6198434234,-0.1445967555,-0.272770226,0.0824484453,-0.0814060494,0.0386756659,-0.1081278846,-0.0297006462,0.1601059139,0.5080628991,-0.1142880544,-0.0372500196,-0.1996771693,0.002060547,-0.2207480073,0.2206597626,0.1426458508,-0.155316025,0.1439509392,-0.2525389194,-0.644289434,-0.2582044899,0.3472139537,0.0650897548,0.5357505679,0.0393282659,-0.050986398,0.0237035751,-0.0212874264,0.2273884416,0.001841528,-0.151028946,0.2054466754,0.2761593759,-0.1788726002,-0.0123925805,0.4630789459,0.0318617187,-0.0050338199,-0.005989654,0.1522022784,-0.2559991777,-0.060855072,0.1496139318,0.0887265354,0.1551512778,-0.0099968156,0.1412384659,0.2226877958,0.1227829456,0.1127246916,0.2170561105,-0.0220430028,-0.4140966833,0.0599268787,0.3093501925,-0.3077695072,-0.0351443216,0.010313062,-0.0311403442,-0.156090647,0.046028506,0.0781720728,-0.2404728979,0.2759421468,0.2424458712,0.0025035948,-0.2576318681,-0.1979256719,0.0392020196,0.1484769732,-0.5195070505,-0.0037097603,0.1793030053,0.3031880558,0.1963305771,0.2088835537,-0.4914647341,-0.0103206355,-0.1324130148,0.0879880488,0.3486702144,-0.0020263144,-0.1399039179,0.1751738936,-0.0648551062,-0.1758546531,-0.1601034403,0.1516988873,-0.3903276026,-0.1063865125,-0.1017534062,0.2205360234,0.0132216113,-0.0695660561,0.1046729833,0.522659421,-0.0152454171,-0.2866698802,0.0681395009,0.249386549,-0.0525919236,-0.160615474,0.0980929881,0.4052127004,0.0794249922,-0.0646125898,-0.0768415034,-0.0301984455,-0.0066010901,0.1782200783,-0.137875095,0.0530477688,-0.1463340968,-0.2217721194,0.6264216304,-0.3208308518,-0.0289240386,0.2638678253,-0.0541572273,0.1031716317,-0.2276762724,0.0109351939,-0.1501184702,-0.2054261863,-0.077296935,0.2869209647,0.1903690845,-0.0708118603,-0.5612444282,-0.1219867617,0.0364453308,-0.145921424,-0.118674621,0.2481559962,-0.128270492,0.1427189112,0.5751604438,0.1089756563,0.2362707108,0.0461571813,-0.0069270916,0.2162114084,-0.0232686903,0.1955585331,-0.3031808436,-0.2531629801,-0.2206091434,0.2218620479,0.3798179924,-0.2203883529,-0.2938579917,-0.3824447095,-0.1047745347,-0.4056072235,0.2608014643,0.0412185416,0.0370696932,0.1344967782,-0.2119551301,0.106041573,0.0213722773,0.1596153378,-0.281570524,0.2219356149,-0.2116109282,0.1130198613,0.0817909911,0.3167993128,0.3062698841,0.0317722075,0.0738056153,0.3021827042,-0.2413655072,0.2140286416,0.3557474315,0.0032243282,0.3906036615,-0.3758459985,0.13474828,0.2379235178,-0.2560378313,0.1230783686,-0.3258954287,0.014580911,0.1396802366,0.3352673948,0.0611242428,0.2751969993,0.0969419181,-0.4506769776,0.0199287087,0.0352055691,0.035097219,0.1807038337,-0.1705889702,0.0479946248,-0.2274552584,0.5184099078,-0.1327476501,-0.0902360827,-0.1630612463,0.4421414137,-0.1375371665,-0.4395441413,0.1946420968,0.1848457158,-0.2379846722,0.2428795099,0.1344380826,0.2076068968,0.0131123681,-0.1935088336,0.3632593453,0.1367352903,0.013725807,-0.1369606555,0.0938369855,0.2238072306,-0.1558355689,-0.1874810159,-0.1377896518,0.3766480684,-0.200019002,-0.0217866879,-0.2344863266,-0.8449617624,-0.266662091,-0.0810860023,-0.49774459,-0.4589166939,-0.0094369287,-0.019099053,-0.2085675299,0.1083395779,0.0919973403,0.2354967743,-0.2721153796,0.3169374168,-0.535997808,-0.3892775774,-0.1888776273,0.1811912209,0.1817516685,0.3827549219,0.1627145112,-0.0475809872,0.0194041189,-0.2012942135,-0.6216056347,-0.0174883623,0.135520786,-0.0172693375,0.0692636371,0.3452521265,-0.1315455288,-0.0921540037,0.1151114926,-0.0690847859,0.0220816936,-0.2542694807,0.0454222411,0.2809758782,-0.0525238067,-0.2910253704,-0.2359342873,-0.3109239042,0.021790389,0.1182686761,0.3605513871,0.4227543175,0.0719411299,-0.2329274565,-0.1364896595,0.1704229563,-0.3226844966,-0.2482672483,0.0414103344,-0.1580933034,-0.1972006261,-0.0252275281,0.064657025,0.0775933489,-0.049986288,-0.6025821567,-0.1269472986,-0.1313961595,-0.1286750287,0.0709887221,0.1048422158,0.283921659,0.2563126683,-0.3228650391,0.1037822217,-0.1825142652,-0.1681531817,0.007123149,0.4162358046,-0.0056421845,-0.0713063926,0.2042766809,0.2970851362,0.1327783018,0.2455477268,0.1495489776,0.2490274608,0.3893796802,0.1003492847,-0.2246986032,0.1389163136,-0.0300456882,0.19689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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/724","title":"need to redirect \/nlp to \/datasets and remove outdated info","comments":"Thank you!\r\n\r\n> Not sure I understand what you mean by the second part?\r\n\r\nCompare the 2:\r\n* https:\/\/huggingface.co\/datasets\/wikihow\r\n* https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wikihow&config=all\r\nCan you see the difference? 2nd has formatting, 1st doesn't.\r\n","body":"It looks like the website still has all the `nlp` data, e.g.: https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wikihow&config=all\r\n\r\nshould probably redirect to: https:\/\/huggingface.co\/datasets\/wikihow\r\n\r\nalso for some reason the new information is slightly borked. If you look at the old one it was nicely formatted and had the links marked up, the new one is just a jumble of text in one chunk and no markup for links (i.e. not clickable).","comment_length":31,"text":"need to redirect \/nlp to \/datasets and remove outdated info \n It looks like the website still has all the `nlp` data, e.g.: https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wikihow&config=all\r\n\r\nshould probably redirect to: https:\/\/huggingface.co\/datasets\/wikihow\r\n\r\nalso for some reason the new information is slightly borked. If you look at the old one it was nicely formatted and had the links marked up, the new one is just a jumble of text in one chunk and no markup for links (i.e. not clickable). \n Thank you!\r\n\r\n> Not sure I understand what you mean by the second part?\r\n\r\nCompare the 2:\r\n* https:\/\/huggingface.co\/datasets\/wikihow\r\n* https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wikihow&config=all\r\nCan you see the difference? 2nd has formatting, 1st doesn't.\r\n","embeddings":[0.3347691298,-0.031311173,-0.0245432202,0.122392565,-0.1146651059,0.0453228764,-0.3248898685,0.6779083014,-0.4332506657,-0.5133858323,-0.1581162512,0.0308898725,0.2777537107,0.0370305218,0.0912264884,-0.3001837134,0.083900176,0.068306528,0.108894676,-0.0865245089,-0.185257107,0.076251559,-0.4062897265,0.112150088,-0.0114804832,0.1721997857,-0.2518543899,0.2978983521,-0.0522664674,-0.4027541578,0.2221582085,-0.0079392707,-0.0050460813,0.3382590413,-0.0001157864,-0.4116526842,0.4116846025,0.1879996806,-0.4584499002,-0.0305531826,-0.4210923314,-0.4628536999,-0.0959183425,-0.0723487809,0.0769471079,-0.0744698048,-0.0712039396,0.3125736713,0.344877243,0.4643400609,0.1784186959,-0.2903703153,-0.2416379601,0.1717674881,0.0189941525,0.420421809,0.2907690108,-0.1033793986,0.1266770363,0.0224710423,0.0177703183,0.6001993418,-0.2633239031,-0.2864018083,0.0878261626,-0.1737945527,0.01004091,0.018770678,0.0099457484,0.1783487201,0.5071709156,-0.1586164236,-0.0345927998,-0.1617970318,-0.0723986849,-0.3024497628,0.1925821304,0.0792358518,-0.1341700256,0.1910063475,-0.274197489,-0.6634989381,-0.2567833662,0.371929884,0.0737498626,0.5255467892,0.0017782624,-0.0485953577,-0.0253332872,0.0524695925,0.3299182653,-0.1322030276,-0.149890855,0.1076483727,0.3520326912,-0.2275372893,-0.1650887281,0.3888019919,-0.0739497617,0.0860262662,-0.0450351946,0.1166939363,-0.2130912244,-0.0696077719,0.0730750188,0.0922397524,0.2408150136,0.0387114286,0.0163716115,0.2204917669,0.0733710229,0.0563520864,0.2624738812,0.0330741331,-0.3904784322,0.1451823413,0.3424996138,-0.3545633256,-0.1334958076,-0.0107321003,-0.130911991,-0.2028384209,0.1030148193,0.0233705062,-0.2182503939,0.239734292,0.2594830394,0.0784377381,-0.2327984571,-0.2440481782,0.1151465923,0.1520035565,-0.4599367976,-0.0848177969,0.1431110054,0.2702641189,0.1501922607,0.2429584712,-0.5883119106,-0.0873502269,-0.1672285497,0.0308704153,0.4714208841,-0.021943152,-0.213327229,0.254794389,0.0051450529,-0.0993284062,-0.2255294174,0.2119438797,-0.3909611702,-0.1834077388,-0.0727772787,0.1527855247,0.0505205728,-0.0792206377,0.0594301336,0.5098864436,0.0243786406,-0.3396795392,0.1360300928,0.245713532,-0.0578637496,-0.1810048223,0.1157800257,0.5219936371,0.0678177252,-0.053227894,-0.1457304209,0.0465020239,0.1242175773,0.1956205517,-0.0921932757,0.0991828442,-0.0882086456,-0.3709585369,0.6637300849,-0.351118505,-0.0227153935,0.3351544142,-0.0081379581,0.1604323983,-0.2453326732,-0.0492001548,-0.1875029504,-0.2134551257,-0.0744737983,0.224197045,0.3232367337,-0.1579634398,-0.5133376122,-0.1274110973,0.0353278667,-0.2628821135,-0.1963531822,0.2379519343,-0.1808660626,0.0626831427,0.6130368114,0.2045864165,0.3268550038,0.0366968066,-0.0764690936,0.2783766091,-0.0655194446,0.164321959,-0.2536492646,-0.3068724275,-0.2187329978,0.0782774016,0.3773010671,-0.1764589995,-0.3194611371,-0.4541701078,-0.1285852939,-0.370475024,0.1609469652,0.0367840119,0.0669013262,0.0918622017,-0.2161007077,0.1195017323,0.122188963,0.1626618803,-0.1556651294,0.234363243,-0.1073224619,0.1185677797,0.0680757836,0.3675707281,0.3903980255,0.0310998894,0.033188317,0.3623469174,-0.2315732539,0.1050688922,0.3430273831,-0.0005020927,0.2701265514,-0.3319279253,0.1148291528,0.3078564703,-0.2995312512,0.1157678738,-0.2941272855,-0.0970844701,0.1080173403,0.4377196729,0.0486137755,0.2380438447,-0.0427469164,-0.4784863889,0.0134781944,0.012924836,0.0927122831,0.1475967914,-0.1852423102,0.1496761441,-0.1700379252,0.489030242,-0.2432168871,-0.2002646476,-0.1469666064,0.4490039349,-0.2449116707,-0.5029028058,0.1238067895,0.1460931897,-0.1699922383,0.1770005077,0.1778144538,0.1586057395,0.1773630828,-0.1300580055,0.391132921,0.1921301335,-0.1077755764,-0.1821141541,0.0611279532,0.2805323601,0.0116043091,-0.2150404304,-0.1216224656,0.3242534399,-0.1866368651,0.0014892109,-0.3264100552,-0.8657703996,-0.3520624638,-0.2004303336,-0.5172258019,-0.4104232788,-0.0949900299,-0.1143295988,-0.1342767477,0.0450775363,0.1265208274,0.2379600853,-0.2683819234,0.1645227522,-0.5306436419,-0.2966865897,-0.2325037271,0.0822637007,0.1692272127,0.4310949147,0.1023434475,-0.0135781635,0.1317180097,-0.2384172827,-0.5461174846,-0.0772489086,0.1511229873,-0.1104475632,0.0499580279,0.3271476328,-0.0515405722,-0.0389799103,0.1058775336,0.0922546014,0.0479799062,-0.1891593039,0.1006740928,0.3573777676,0.0506377891,-0.1992050409,-0.2320473641,-0.2316874117,-0.1065358222,0.0011562388,0.303037852,0.3275019526,0.0424165204,-0.2985092103,-0.1533736587,0.1980222911,-0.2720221877,-0.2821151614,-0.0865133554,-0.0520646535,-0.0480839573,-0.0406525321,0.1638768762,-0.0399586968,0.0063309935,-0.5437178612,0.0172786396,-0.1721352935,-0.2262423187,0.0743889436,0.0549087524,0.3278782964,0.2925352156,-0.2564878464,0.1380576938,-0.221404165,-0.2019814253,-0.0032891636,0.470210284,0.0355643556,-0.080441989,0.2707927525,0.224577263,0.1259835213,0.24359034,0.0709466189,0.3061742187,0.3696184158,0.0687897056,-0.2160182595,0.1123954654,-0.0352278799,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/724","title":"need to redirect \/nlp to \/datasets and remove outdated info","comments":"For context, those are two different pages (not an old vs new one), one is from the dataset viewer (you can browse data inside the datasets) while the other is just a basic reference page displayed some metadata about the dataset.\r\n\r\nFor the second one, we'll move to markdown parsing soon, so it'll be formatted better.","body":"It looks like the website still has all the `nlp` data, e.g.: https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wikihow&config=all\r\n\r\nshould probably redirect to: https:\/\/huggingface.co\/datasets\/wikihow\r\n\r\nalso for some reason the new information is slightly borked. If you look at the old one it was nicely formatted and had the links marked up, the new one is just a jumble of text in one chunk and no markup for links (i.e. not clickable).","comment_length":56,"text":"need to redirect \/nlp to \/datasets and remove outdated info \n It looks like the website still has all the `nlp` data, e.g.: https:\/\/huggingface.co\/nlp\/viewer\/?dataset=wikihow&config=all\r\n\r\nshould probably redirect to: https:\/\/huggingface.co\/datasets\/wikihow\r\n\r\nalso for some reason the new information is slightly borked. If you look at the old one it was nicely formatted and had the links marked up, the new one is just a jumble of text in one chunk and no markup for links (i.e. not clickable). \n For context, those are two different pages (not an old vs new one), one is from the dataset viewer (you can browse data inside the datasets) while the other is just a basic reference page displayed some metadata about the dataset.\r\n\r\nFor the second one, we'll move to markdown parsing soon, so it'll be formatted better.","embeddings":[0.1384783983,0.0236901566,-0.0980000347,0.0289668012,-0.0519955009,0.0458978377,-0.1316044331,0.4961518943,-0.1375634372,-0.2072739899,-0.1407679468,0.3467579484,0.2411794066,0.0714614987,0.1461839974,-0.1521984339,-0.0214515217,0.1132967472,-0.0513905771,-0.1351284236,-0.1931079328,0.0566270165,-0.3132694066,0.1716675758,-0.0621245429,0.0175958984,-0.1403496563,0.2805624008,-0.1343233436,-0.4249670506,0.102028273,0.0087717818,-0.0224362407,0.1715869755,-0.0000975793,-0.1550674587,0.4177809656,0.2299157381,-0.4386451542,0.0474657714,-0.1592706442,-0.4129195213,0.0570607409,-0.2196607888,-0.0308993682,-0.2214241624,0.137592271,0.2655303478,0.2472458333,0.3672331572,0.3661076427,-0.019929383,-0.2039603144,0.0462491661,0.0675611421,0.2029665858,0.0472302809,-0.1386793703,0.1671683341,-0.1134862825,-0.2322293669,0.5095509887,-0.0722998306,-0.2618313134,0.0562584512,-0.0017119646,-0.0014855193,-0.070292905,0.046875637,0.1706978679,0.436910212,-0.0547419526,-0.0492673069,-0.2271544784,-0.0232388247,-0.1028957665,0.2928943634,0.1422806829,-0.1384128332,0.0905143023,-0.1495300829,-0.4678625762,-0.1713795066,0.3049802184,-0.0220149513,0.56427598,0.0080763763,-0.1040589735,0.074239254,-0.0737466514,0.1059833243,0.0995106027,-0.0752474815,0.0970747918,0.1928911507,-0.0765538886,0.2435375452,0.4252410531,0.0815108493,-0.0695508495,0.0161836036,0.1859911829,-0.198638469,-0.0451902598,0.1165728718,0.068919152,0.0976125598,-0.1040062606,0.2782568038,0.3188299239,0.077490069,0.1003954634,0.2027646154,-0.0784591734,-0.2845477164,-0.1333402097,0.1968569905,-0.2208771259,-0.0664087161,-0.0128305396,0.0212563667,-0.1261846721,0.0431220122,0.2340467721,-0.1883869618,0.1729913354,0.0916701928,0.0043306686,-0.3476365805,-0.2004378885,-0.1041787714,0.062396165,-0.5295287371,0.0361767597,0.2182329595,0.2483716607,0.222904548,0.2349675894,-0.4926007986,0.0033514709,-0.1259416193,0.2126781195,0.2112226337,0.0128597161,0.0014351723,0.2291309386,-0.0713291466,-0.2442645431,-0.0399351902,0.0254284684,-0.3115333617,-0.1752976775,-0.1647272855,0.3262220323,0.0564432628,-0.0685154498,0.18339324,0.397426784,-0.036995735,-0.2568757236,-0.0134542286,0.301879108,-0.170118615,-0.1396078616,0.0991517305,0.3828622699,0.0952413604,-0.1544090509,-0.0409387872,-0.0012377891,0.0293821972,0.173140496,-0.2121288776,0.0950792581,-0.218935281,-0.102346234,0.5547258854,-0.2279092371,-0.1078956276,0.2369597107,-0.1232387871,0.052433379,-0.1402726769,-0.0220217165,-0.1591455042,-0.0985369757,0.0036777037,0.1784611493,0.268003732,0.0771942139,-0.6268885136,-0.114068307,0.070186913,-0.0578800477,-0.1109447777,0.1748309284,-0.0566855557,0.1209659502,0.5310482383,-0.0093563842,0.1440632641,0.136564821,0.0343695357,0.0989690274,-0.052304022,0.0686732009,-0.2810053825,-0.1475200951,-0.2399805039,0.3287532926,0.326877594,-0.2241638899,-0.403958559,-0.2527482808,-0.1470331401,-0.4105485678,0.3645327091,0.1858108342,0.040363986,0.2435142398,-0.1546339989,0.1094995886,-0.0035772857,0.094842501,-0.3514446616,0.1881889552,-0.279169172,0.0763323531,0.1151818782,0.2385628968,0.2698487043,0.0877145231,0.0782882199,0.2478946298,-0.2400452644,0.2538331151,0.3343015313,0.0537943766,0.3653838336,-0.3246807754,0.0520896465,0.2069708407,-0.1764696091,0.1793634892,-0.3303443193,0.1332094222,0.1781615019,0.2474444807,0.1418089867,0.2600362003,0.2022663504,-0.4204209745,0.0664190575,-0.0762854293,0.0819931701,0.1620307565,-0.0552762561,0.0285124127,-0.2853654921,0.5206699967,0.0286518987,0.0097688157,-0.0239305515,0.3777434528,-0.1655337811,-0.3923074007,0.154936716,0.2252096236,-0.1950206906,0.2894262969,0.2376935482,0.2057400793,-0.1825958043,-0.2519817948,0.31046018,0.0524475761,0.0171946883,-0.0813839436,0.1677494943,0.1088386849,-0.3862136006,-0.064042598,-0.190724194,0.3152854145,-0.2420074344,-0.0095195165,-0.2421172112,-0.6865228415,-0.1989766806,-0.0486601219,-0.3761569262,-0.5674775839,0.1000581011,0.0890862197,-0.2643342018,0.096295163,0.0220667068,0.2123070061,-0.26298967,0.403229773,-0.4363816381,-0.3189541996,-0.2448745221,0.2402091324,0.1480138302,0.4297671318,0.177730605,-0.066489473,0.0116419494,-0.2758164108,-0.5960994363,0.0688938275,0.1598372906,0.065273501,0.0897851214,0.2754555345,-0.1754969805,-0.1561299413,0.1922322065,-0.1071819812,0.0250942484,-0.1592062861,-0.0489173345,0.1280827224,-0.0721236542,-0.4142029881,-0.130512476,-0.3529879153,0.0151670761,0.0577152446,0.3492697477,0.3905658126,-0.0092571238,-0.1279177517,-0.1278903782,0.2130807787,-0.3561644852,-0.1776023656,0.1717917323,-0.2524499893,-0.3447203338,-0.0921413898,0.0265414398,0.1429200619,-0.1665219814,-0.5557922125,-0.1072855741,-0.2250916362,-0.0492843576,0.0959306881,0.0670157894,0.2911284268,0.2124027312,-0.3828006685,0.0761632994,-0.2007709295,-0.1852182299,-0.0926346481,0.3297071457,-0.1473432481,-0.0259679593,0.1464907229,0.2578772306,0.1349194646,0.1953126341,0.2204305083,0.0854305848,0.419657737,0.011008135,-0.2900315523,0.0634929314,0.0090660583,0.1195359826,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/723","title":"Adding pseudo-labels to datasets","comments":"Nice ! :)\r\nIt's indeed the first time we have such contributions so we'll have to figure out the appropriate way to integrate them.\r\nCould you add details on what they could be used for ?\r\n","body":"I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n","comment_length":36,"text":"Adding pseudo-labels to datasets \n I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n \n Nice ! :)\r\nIt's indeed the first time we have such contributions so we'll have to figure out the appropriate way to integrate them.\r\nCould you add details on what they could be used for ?\r\n","embeddings":[0.35087201,-0.1339654475,0.0640572533,0.0146746505,0.1172191575,0.0425939336,0.2484151274,-0.055692628,-0.1454755217,-0.0428759754,-0.227577284,0.0762309954,-0.1649908125,0.5450238585,0.4980666637,-0.1480551064,0.3547057807,-0.1910561919,0.1890376806,-0.1643102169,-0.3369725645,-0.2637389898,0.1954767555,-0.177405566,-0.3335675597,-0.1520867795,-0.188161701,0.0017013031,0.0816946998,-0.5430544615,0.0081283478,0.3411814272,0.2522218525,0.6251304746,-0.0001249106,-0.1294019818,-0.0920366049,-0.1204193681,-0.1105555296,0.190760687,-0.0204500537,-0.2631741762,-0.214708522,0.196944952,-0.09029378,-0.4064389765,-0.1335061342,0.0102537191,0.1772880256,-0.0072268839,0.0163613167,0.1366703957,0.2723228633,0.1299423873,0.5037116408,0.3426888287,-0.2014466077,0.3047901094,0.3386514485,0.3808413744,0.4996564984,0.4813934267,-0.0260894764,-0.1911639571,0.5817210078,0.0361270718,-0.0280091148,-0.2036755383,-0.3807860315,0.1567308158,0.4856583178,-0.2559436262,-0.6600388288,-0.4705187678,0.0994573832,-0.2843776643,-0.1884535402,-0.1961780787,0.4040839374,-0.0619082637,-0.0100463936,-0.2337230146,-0.0850889012,0.2030259669,0.1202186421,0.5472038984,-0.2007035315,0.2139021754,0.0618340038,-0.1964243501,-0.0501444563,-0.2513433397,0.1982958913,0.1462722868,0.1533986032,-0.1464244574,-0.0554305874,0.035822615,0.2769576609,0.306052506,-0.0804551244,-0.1082585827,-0.2618288696,-0.0918944031,-0.0831924751,0.1469036788,0.0197892208,0.2458151579,0.2622636557,0.0032124117,0.3767152131,-0.0831515342,-0.4829546213,0.5400187373,-0.0798437819,-0.184470728,0.0166599695,-0.1186084896,-0.0216396227,-0.0099645751,0.1242481619,-0.1875851005,0.078972511,-0.113677375,0.0045646601,0.073093608,-0.0961935148,0.3804770708,0.2330143303,-0.0041363514,-0.0311319679,-0.1038940996,-0.0761066303,0.3563047945,0.0114585357,-0.3125945926,0.2468480021,-0.2488931119,0.198173508,-0.2102049887,0.0347838104,0.1707411706,-0.0479497649,0.2335938662,-0.1842186451,0.0526870042,0.0468658768,0.0510809645,-0.2296123803,-0.0847219676,-0.0553180501,-0.1274133474,-0.3076143861,-0.007403973,-0.3845643401,-0.3234862685,-0.3623290658,0.3362720907,0.146713376,-0.014857064,0.0895832852,0.1612404734,-0.1204106584,-0.119428359,0.3341804743,0.1501198262,-0.3646391928,-0.2879036665,0.1363932937,0.027432194,0.1304156631,0.2103500217,0.0395956598,0.1638571173,0.2064767182,-0.0699356869,-0.118732132,-0.4139882326,-0.1851709634,-0.0824119002,-0.0383956283,-0.0672680587,-0.0235427301,0.447196722,-0.0290771779,-0.2683320642,-0.0834478214,0.0239267237,-0.0027650036,-0.0937337726,-0.0517457947,0.009491222,-0.1063969806,0.5546265244,-0.0790554658,0.0317616798,-0.0460544899,-0.1878689229,-0.1235040873,-0.0379969329,0.112446934,-0.0244041886,0.4308757782,0.4505261779,-0.0841156617,-0.3351357281,-0.4225297272,0.0643078163,0.3131957352,0.1518680602,0.2139699608,-0.3543727398,-0.2606374025,-0.1056091785,-0.1188807711,-0.0032252385,-0.1172124222,0.1667604148,0.21048522,0.0392617024,-0.351926595,-0.1949398965,0.0344020948,0.2529699504,-0.0170265827,0.1207967103,0.1968993843,0.0700706169,-0.218046084,0.5762590766,0.0122700259,-0.0774033144,0.2291522622,0.2382849604,-0.0247948989,-0.0827146396,0.1057941392,0.3093137443,0.4576770365,-0.1437068284,0.1279944032,-0.305983603,0.0114496276,-0.1778569967,-0.134126693,0.0533383228,0.1222603694,0.1460716277,-0.3488898277,-0.1220633239,-0.064772293,0.1816105545,-0.1966081113,0.0349039547,-0.0665336326,0.1509809941,0.4198540449,0.0485272035,-0.1534426212,0.3250514567,-0.017040005,-0.3731516302,0.1307826489,0.1720673442,-0.4888537526,0.2132398188,-0.0017561347,0.3827134967,-0.0193849653,0.0638613105,0.2843587399,0.0112366378,-0.0600267053,0.0600339547,-0.1200994998,0.313303262,0.0610037185,0.047300674,0.0482840054,0.1464210451,0.1569345295,0.0194783043,-0.0098285247,-0.0560709536,-0.255022794,-0.070810616,0.2456015944,-0.0945197418,-0.2045022398,-0.6011893749,-0.4497777224,-0.245485127,0.0487111993,-0.134419933,-0.1453191191,0.1909579039,0.1226615235,0.4898834825,-0.3512583077,-0.2254018039,0.1413585842,-0.1690039635,0.1628854871,-0.1073578298,0.1542331129,-0.1214861125,0.4159196317,-0.1388185173,0.1517219543,-0.1876494437,-0.5756072402,0.0636467338,-0.4807730317,0.1864727437,0.2330859452,-0.2272555083,0.3000864089,-0.2512019873,0.0596793853,0.1218449026,0.1086961329,-0.2782357037,-0.1743098348,0.0284928493,-0.3839595318,0.130328998,-0.4349294305,-0.1555609256,0.3866901696,0.1300320923,0.0219939239,0.2576771379,-0.273460269,0.1378861368,-0.058666721,0.008496304,-0.0351490192,-0.5451734662,0.4454016089,-0.0228166711,0.0330391526,0.1718705148,-0.1100521907,-0.0759961978,-0.0923953131,-0.1332699805,-0.3692465723,0.0426127203,-0.0050614271,0.35817945,0.3633756638,0.0498868078,0.0678379014,0.1727570444,-0.1507345587,-0.3482174575,0.1096350178,-0.0155550726,0.2649582624,-0.114940092,0.1408982128,-0.2226189524,0.627032876,0.176966846,-0.2345686555,0.1262998134,0.2979913652,0.2040144354,0.2684484422,0.0368236713,0.1379386485,0.0083837947,0.18789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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/723","title":"Adding pseudo-labels to datasets","comments":"A new configuration for those datasets should do the job then.\r\nNote that until now datasets like xsum only had one configuration. It means that users didn't have to specify the configuration name when loading the dataset. If we add new configs, users that update the lib will have to update their code to specify the default\/standard configuration name (not the one with pseudo labels).","body":"I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n","comment_length":65,"text":"Adding pseudo-labels to datasets \n I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n \n A new configuration for those datasets should do the job then.\r\nNote that until now datasets like xsum only had one configuration. It means that users didn't have to specify the configuration name when loading the dataset. If we add new configs, users that update the lib will have to update their code to specify the default\/standard configuration name (not the one with pseudo labels).","embeddings":[0.2998081446,-0.1344268322,0.1064767763,-0.042103827,0.1416293383,0.0385911241,0.3353704512,-0.0611251034,-0.1213262454,0.0122373067,-0.2308751792,0.1057627127,-0.1279500425,0.5004864931,0.4318877757,-0.072646223,0.3291837275,-0.1554969102,0.2026746273,-0.154693082,-0.3575718999,-0.2737482488,0.2698325813,-0.1786535531,-0.320311904,-0.1530624032,-0.1499094367,-0.0244253874,0.1104237139,-0.5956424475,0.03784661,0.4156611264,0.2552714348,0.5836076736,-0.0001237342,-0.089877069,-0.1095676944,-0.1492246985,-0.1526645422,0.186216265,-0.0167718735,-0.2705730498,-0.103080228,0.1222240925,-0.1497649848,-0.3686211109,-0.0767853931,-0.0310839936,0.1511062682,-0.0925186798,0.0303302091,0.1451057494,0.1626512855,0.1272767633,0.3680979013,0.2810037732,-0.2382881194,0.3393152356,0.2810574472,0.4066390097,0.4596785903,0.495606482,-0.0231238827,-0.0686169863,0.5873855352,0.0751341805,0.0005662205,-0.1729782373,-0.3276819587,0.2396902293,0.4566649497,-0.3164331615,-0.6837278605,-0.5047792196,0.186757043,-0.3624064624,-0.0957857668,-0.2155633718,0.3606551588,-0.0346914567,0.0314153694,-0.2650181949,-0.0182420593,0.1895082146,0.0594545864,0.5780060887,-0.1692253798,0.2320201695,0.0101037323,-0.2667593062,-0.0918954983,-0.266084522,0.1848731637,0.0811003968,0.109753862,-0.1387264729,-0.1134848222,-0.0457396507,0.2438456714,0.2950548232,-0.0437582806,-0.0620150231,-0.2086888105,-0.1097071916,-0.1035718396,0.120954074,0.0874914825,0.2561557293,0.2847910523,0.0440343879,0.3656741679,-0.0779900178,-0.4416409433,0.5037554502,-0.1021510139,-0.1318588704,0.0779781863,-0.0652201772,0.0415383391,-0.0243347026,0.1696671098,-0.2140185833,0.0382408239,-0.0785938352,0.0153718702,0.0808275193,-0.0493975282,0.3345745802,0.1719156206,0.0234977677,-0.0198372286,-0.1612987965,-0.0988189727,0.364339143,0.0371232517,-0.3528376818,0.2718880177,-0.2289155722,0.1966086477,-0.2133773714,0.0106298393,0.2160994411,-0.0613005795,0.22638309,-0.2178615183,0.1044051498,0.0036458736,-0.0238881763,-0.2566460669,-0.1206147447,-0.0953300893,-0.0889450163,-0.324175477,0.0147484932,-0.4072446823,-0.3032717109,-0.3384978473,0.3656526208,0.1131786928,-0.0525872856,0.0730779171,0.1723829359,-0.0328396074,-0.134907037,0.2840846479,0.2005071789,-0.3355906308,-0.3381416798,0.1224144846,0.0566466339,0.078985855,0.1605279148,0.0321430862,0.0978841782,0.2206550688,-0.0889733806,-0.067524299,-0.382614553,-0.1448272616,-0.0675379038,-0.0896222368,-0.0237168763,0.0551170558,0.4480420351,-0.0472856201,-0.2814720869,-0.1423392445,-0.0717054978,0.0374272801,-0.0787703171,-0.0332069062,-0.0672871247,-0.144183591,0.4287656546,-0.0894117802,0.0541012548,-0.0772441998,-0.1134878173,-0.0747608393,-0.0645027384,0.1017478332,-0.0114808753,0.4220195115,0.4557925463,-0.0916468725,-0.2570293248,-0.4810330868,0.1187712476,0.3459650278,0.1877480745,0.2587982714,-0.3725942075,-0.2178406566,-0.18145293,-0.1556061953,-0.0693745464,-0.1133408993,0.2501719892,0.1705455929,-0.0530524775,-0.3825318217,-0.1964839101,0.0427181721,0.2877751589,-0.1020270437,0.0700213835,0.1953106821,0.0370659307,-0.2225669771,0.4910910726,0.0013808043,-0.1018630192,0.1985341907,0.3084602654,-0.0033228048,-0.0713012293,0.0395375341,0.3227552176,0.4791212678,-0.0398230702,0.153262645,-0.2845877707,-0.0183797833,-0.0789748728,-0.1487845182,0.0462142751,0.2128414512,0.1702634394,-0.3061312139,-0.1273018569,-0.0200702492,0.1623200178,-0.2193689942,-0.0518594421,-0.0748730451,0.1818132997,0.4083220065,0.0816129372,-0.2233512402,0.2831476629,0.0431273393,-0.3308486938,0.0959251076,0.1368115544,-0.4195856452,0.2700432837,0.0196660664,0.437004745,-0.0176327806,0.0807090253,0.2086809725,0.0106297815,-0.1043510288,0.0108252103,-0.1265753955,0.2933960557,0.1004739404,0.0300719496,0.0111526167,0.1413501799,0.1606981009,-0.0773590207,0.0576363541,-0.1008791625,-0.2513286769,-0.1020588428,0.1794400215,-0.0913679004,-0.3065832257,-0.5622990131,-0.5115970969,-0.2192707658,0.0635916889,-0.1512479633,-0.2140166014,0.3226282001,0.0542418063,0.3781419098,-0.3508310318,-0.2711749673,0.1680888981,-0.1181780621,0.1399009079,-0.123668462,0.0609191768,-0.2141324133,0.4188081026,-0.1850165576,0.1274848729,-0.2075192034,-0.5780903101,0.1039367914,-0.4823778272,0.2204030305,0.2675939798,-0.1833579689,0.3257483244,-0.2888326645,0.1066211313,0.1330201775,0.1489219964,-0.3089151382,-0.1425167471,0.0886036679,-0.320543617,0.0195567682,-0.3618177474,-0.1463156641,0.3072495162,0.1251452565,0.0840290487,0.3436688483,-0.24739483,0.0715096071,-0.0873192921,0.0665148124,-0.0324894264,-0.5351750255,0.3863258064,-0.0446912907,0.1066346914,0.1578733027,-0.1220841855,-0.0349110253,-0.0149162663,-0.0934661776,-0.4165671766,0.055048883,0.0668338463,0.3598328531,0.3506225348,0.0857239813,0.1384339929,0.1219525859,-0.1305479556,-0.4114057422,0.1381793916,-0.0073749246,0.2347899228,-0.0767000094,0.1047660783,-0.2347685695,0.6055535674,0.1378824413,-0.2848439813,0.1233670413,0.213933453,0.2773593366,0.2527821362,0.0802338943,0.1231472194,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/723","title":"Adding pseudo-labels to datasets","comments":"Oh yes why not. I'm more in favor of this actually since pseudo labels are things that users (not dataset authors in general) can compute by themselves and share with the community","body":"I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n","comment_length":32,"text":"Adding pseudo-labels to datasets \n I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n \n Oh yes why not. I'm more in favor of this actually since pseudo labels are things that users (not dataset authors in general) can compute by themselves and share with the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/723","title":"Adding pseudo-labels to datasets","comments":"![image](https:\/\/user-images.githubusercontent.com\/6045025\/96045248-b528a380-0e3f-11eb-9124-bd55afa031bb.png)\r\n\r\nI assume I should (for example) rename the xsum dir, change the URL, and put the modified dir somewhere in S3?","body":"I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n","comment_length":22,"text":"Adding pseudo-labels to datasets \n I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n \n ![image](https:\/\/user-images.githubusercontent.com\/6045025\/96045248-b528a380-0e3f-11eb-9124-bd55afa031bb.png)\r\n\r\nI assume I should (for example) rename the xsum dir, change the URL, and put the modified dir somewhere in S3?","embeddings":[0.3824186623,-0.0590515547,0.0575784147,0.0074140299,0.1342783868,0.0131380288,0.2963197231,-0.1217540801,-0.2198833525,-0.0435279682,-0.2787754536,0.0804837942,-0.0839418247,0.5122793913,0.4707950652,-0.0267684255,0.3672170043,-0.1611523479,0.1646211743,-0.1702684611,-0.3577936292,-0.284829855,0.2717745602,-0.2073100656,-0.3152383268,-0.1203107312,-0.2245425135,0.0721220225,0.0589686148,-0.5473186374,-0.0266391244,0.4308969676,0.2439986616,0.6618462801,-0.0001245916,-0.1142086759,-0.156179741,-0.1502090394,-0.1823928207,0.2175573707,-0.0315193571,-0.1962168813,-0.206166178,0.1632058173,-0.1114674062,-0.324881196,-0.0985213965,0.0733948052,0.1952245086,0.0326190107,0.0204025283,0.1139785945,0.2299670279,0.1345407367,0.4793930948,0.3306820393,-0.1912674457,0.3493455648,0.2104496062,0.3990181386,0.494325161,0.4715925157,-0.0191405583,-0.1135742515,0.6187542677,0.042759899,-0.0338233523,-0.1695554852,-0.3249826729,0.121823445,0.5115076303,-0.2819126844,-0.731289506,-0.4637393355,0.1253407747,-0.3203245103,-0.1134286523,-0.2277140617,0.4337185323,-0.0508428477,-0.0439432859,-0.3035870194,-0.0564605072,0.2161291689,0.0429830402,0.5357024074,-0.1913152039,0.1910958886,-0.0058261519,-0.2352779508,-0.0667269155,-0.2758331895,0.1633462906,0.124404572,0.1628944129,-0.1206509471,-0.0619253442,-0.0050701685,0.2346730381,0.3252992332,0.0175860897,-0.1301683635,-0.2440437675,-0.1120035872,-0.0779500678,0.1155245751,-0.0518663377,0.289770931,0.2308941483,0.0532464907,0.3382211626,-0.12401364,-0.4614148438,0.4557311237,-0.1177974343,-0.1218679026,0.07338617,-0.0636416078,0.0258405544,-0.0516759418,0.1071726456,-0.1597567052,0.0295777787,-0.0548921265,-0.0067709219,0.0393005647,-0.0814270675,0.371589303,0.177459389,0.0856072679,-0.0314126387,-0.132732302,-0.0919188261,0.3518421054,-0.0154538229,-0.2775564194,0.2595612705,-0.2489346564,0.1895795465,-0.2381272912,0.0119780172,0.2015544623,-0.0093984995,0.2288381755,-0.1336815953,0.130899027,-0.0534304976,-0.0058628661,-0.2272228897,-0.1346719563,-0.077406317,-0.1108798161,-0.3310883939,0.0051318696,-0.4340772331,-0.3205754459,-0.3698263466,0.2506905496,0.1282762885,-0.0645630881,0.109578602,0.187093854,-0.0930071101,-0.1487048268,0.3043155968,0.1734105051,-0.3290264308,-0.2644236684,0.1126336604,0.0758917406,0.0661482587,0.2349952459,0.0647127107,0.1130079031,0.165698573,-0.0372064486,-0.054179702,-0.4142211378,-0.2015166879,-0.0602946915,-0.1206694692,-0.0934125409,0.0243772902,0.4403903186,-0.0290453583,-0.324778378,-0.1446695179,0.0162284821,0.0528157912,-0.053754624,-0.032078281,0.0505713373,-0.1820052713,0.4311705232,-0.1165945381,0.0583196804,-0.0444591381,-0.1440145969,-0.1113424227,-0.0915574953,0.1040965617,0.0736880377,0.500715971,0.5515341759,-0.1131003052,-0.2475200295,-0.4533876181,0.0532470718,0.315992862,0.1733866483,0.2216348052,-0.3751897216,-0.2680237293,-0.1474182904,-0.1475027502,-0.0535799675,-0.0952903405,0.24758479,0.2361048758,-0.019332435,-0.2998919487,-0.2160845101,0.1283097118,0.2848452628,-0.0539394021,0.1205325648,0.1492785066,0.020002516,-0.1491583139,0.5127786398,0.0104662608,-0.1040401086,0.2029866576,0.3039376736,-0.0074415477,-0.0276306737,0.1016785055,0.3263528347,0.4377632439,-0.0990099385,0.1382637471,-0.2529663742,-0.0132120606,-0.0791060477,-0.1793911606,0.0229728557,0.109579958,0.13348037,-0.2986077964,-0.152113378,0.0372588933,0.1393225491,-0.1947154403,0.0079036588,-0.1108312383,0.2330360413,0.3945525885,0.1087793857,-0.095689103,0.3215187192,-0.004888718,-0.3789289594,0.1060980111,0.1068026647,-0.4911374152,0.1963323653,0.0301518682,0.4219139516,-0.0347625203,0.0619035214,0.340346843,0.0125119602,-0.04759432,0.02531977,-0.127601251,0.3111085892,0.0907710344,0.082946822,0.1214504987,0.1007056013,0.1734576672,-0.0229759179,0.0254100412,-0.0625023395,-0.2944380045,-0.063615337,0.2560421824,-0.1195860878,-0.1983718425,-0.5820183754,-0.4939034879,-0.2003488243,0.0969549939,-0.0954896659,-0.2112884223,0.2024484128,0.1277593374,0.3231777549,-0.3230867088,-0.2451108992,0.1739901751,-0.1317127943,0.1823119223,-0.1230627373,0.1306600571,-0.1697481722,0.3952826858,-0.1792241335,0.1420127302,-0.2615374029,-0.5440930128,0.0700345561,-0.4652977884,0.1871996373,0.3011380732,-0.1650559604,0.2846772373,-0.2040569633,0.1087174714,0.1524674445,0.1083435714,-0.2670251131,-0.1588961631,0.0674753636,-0.366787225,0.1237903461,-0.2899271548,-0.1475366652,0.3569642007,0.1526903063,0.027100997,0.2999839187,-0.1780371964,0.0791995302,-0.0334858112,0.0238806754,-0.0537361987,-0.5200936198,0.4123340547,-0.0438693166,0.1034152433,0.2109449059,-0.1105611175,-0.0498708598,-0.0476945788,-0.0883577839,-0.4446310997,0.0703269467,0.0244686529,0.3464213908,0.391292125,0.1474480629,0.0580122955,0.1238531247,-0.1444465071,-0.3642102182,0.0896709338,0.0311482977,0.2948604822,-0.1509866714,0.165262714,-0.2183775902,0.6097123623,0.1429276019,-0.2653751671,0.1522255242,0.2022414953,0.2568854094,0.261213392,0.1293950826,0.1242536306,0.0688544661,0.0794270113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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/723","title":"Adding pseudo-labels to datasets","comments":"You can use the `datasets-cli` to upload the folder with your version of xsum with the pseudo labels.\r\n\r\n```\r\ndatasets-cli upload_dataset path\/to\/xsum\r\n```","body":"I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n","comment_length":23,"text":"Adding pseudo-labels to datasets \n I recently [uploaded pseudo-labels](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/seq2seq\/precomputed_pseudo_labels.md) for CNN\/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.\r\nSince pseudo-labels are just a large model's generations on an existing dataset, what is the right way to structure this contribution.\r\nI read https:\/\/huggingface.co\/docs\/datasets\/add_dataset.html, but it doesn't really cover this type of contribution.\r\n\r\nI could, for example, make a new directory, `xsum_bart_pseudolabels` for each set of pseudolabels or add some sort of parametrization to `xsum.py`: https:\/\/github.com\/huggingface\/datasets\/blob\/5f4c6e830f603830117877b8990a0e65a2386aa6\/datasets\/xsum\/xsum.py\r\n\r\nWhat do you think @lhoestq ?\r\n\r\n\r\n \n You can use the `datasets-cli` to upload the folder with your version of xsum with the pseudo labels.\r\n\r\n```\r\ndatasets-cli upload_dataset path\/to\/xsum\r\n```","embeddings":[0.3005567491,-0.0865521207,0.0789877996,0.0127475159,0.1379013658,0.042592112,0.2965248227,-0.0892710388,-0.1311770082,0.0045273653,-0.2615566254,0.1170213744,-0.101874873,0.5030103326,0.4647361934,-0.0501749516,0.3400839269,-0.1612035632,0.1209385246,-0.1879306287,-0.3377030492,-0.2674909234,0.2434321344,-0.1668701917,-0.3428444862,-0.1463990957,-0.1756635904,0.044133652,0.0913106054,-0.5311963558,0.0352854356,0.4109178782,0.2603910565,0.644187808,-0.0001249052,-0.0960011184,-0.1164314598,-0.1558603644,-0.1917866468,0.1872811019,-0.0406610146,-0.1984737664,-0.1967243254,0.1530397087,-0.1072224453,-0.374322921,-0.0886982307,-0.0048337351,0.1889227629,-0.0055733151,0.016215466,0.2126973718,0.2619461417,0.11458157,0.3966299593,0.312405467,-0.2199382186,0.329834342,0.2574898601,0.4000386298,0.4975061417,0.4654204845,0.0034594834,-0.1363222152,0.5766840577,0.0685753152,0.0213656109,-0.1638678908,-0.3478551209,0.1882938147,0.5000043511,-0.2947636247,-0.7588800788,-0.4253388643,0.1248204783,-0.2834514081,-0.1056061387,-0.2003812939,0.3972685337,-0.0533891357,-0.0065780366,-0.254371047,-0.084383741,0.1836412847,0.0637215823,0.5415856838,-0.1714508235,0.2019059211,0.0627435893,-0.2626826465,-0.0453341231,-0.2511155009,0.156747669,0.1438652277,0.1374804229,-0.1627710462,-0.1111432239,0.0124162966,0.2875876725,0.2554081678,-0.0147288051,-0.1271378547,-0.2798385322,-0.090706788,-0.0495130159,0.1478044987,0.0205173716,0.2564308047,0.2562672198,0.0137418807,0.3419875801,-0.1323289275,-0.5306587219,0.5164095163,-0.0874370411,-0.1615372896,0.0084345825,-0.1053058356,-0.0154332751,-0.0224043578,0.1680036783,-0.1824706197,0.0620614626,-0.0694893748,0.0375250652,0.071760796,-0.0921622962,0.3676926792,0.1835538894,-0.0092500374,-0.052627679,-0.0945297033,-0.087111637,0.3537300229,-0.0076326779,-0.3353779018,0.2623464465,-0.2569543421,0.2294719517,-0.1936970204,0.01232043,0.1991736293,-0.0730665103,0.2315552235,-0.1415062994,0.150870949,-0.0122410776,-0.0095902886,-0.2297956347,-0.1294585466,-0.0831670314,-0.0762726963,-0.3556428552,0.0113501186,-0.4253790379,-0.3329553902,-0.4054366052,0.2301377952,0.1485272944,-0.0096587623,0.0667015091,0.1540709883,-0.1053178608,-0.1127883121,0.314979285,0.1690998822,-0.4170022011,-0.2718285024,0.1106524616,0.0382295586,0.0504448488,0.2552046478,0.0666076764,0.1342077553,0.1700593382,-0.05008642,-0.0997086689,-0.4665155113,-0.2023460865,-0.0561346672,-0.0590186566,-0.0885888413,0.0227516871,0.4571012557,-0.015280202,-0.3111627996,-0.13169384,-0.0294852275,0.0568469949,-0.0687908083,-0.0553259291,-0.0011320733,-0.1273282021,0.4534283876,-0.1470853388,0.0185575504,-0.0733295158,-0.1068966165,-0.0800819919,-0.0740582123,0.0727354959,-0.0007414462,0.4443889558,0.4343406558,-0.1196610183,-0.3397579491,-0.4548968077,0.1086860523,0.2950422168,0.1677487046,0.240214631,-0.3612698019,-0.2660116851,-0.1327229589,-0.1280923039,-0.0307635013,-0.1144382805,0.1807572991,0.2044485956,-0.0083762901,-0.3424597979,-0.1521357596,0.0372333564,0.2448761463,-0.0409918167,0.1239593923,0.1826606095,0.0440870188,-0.1847420186,0.5197100043,0.0191852208,-0.1109805927,0.1982563585,0.3092309535,-0.0159006752,-0.0633957684,0.1213110313,0.2998844385,0.4853625298,-0.0897499472,0.1092524379,-0.2770066559,-0.0257104132,-0.0954571813,-0.1917815059,0.0236201603,0.186128661,0.1155374423,-0.2611899674,-0.1196405664,0.0220842585,0.2011290044,-0.1726160049,-0.0360716879,-0.0711005554,0.2465389818,0.4118812382,0.0672813281,-0.1039957628,0.3385364115,0.0379811786,-0.3380328119,0.1061590761,0.1020345539,-0.5140880346,0.2836023867,-0.0069670076,0.4016209841,-0.0132528748,0.0652671307,0.2741593719,-0.0282700229,-0.0877167657,0.0564490482,-0.1335628182,0.2707892358,0.0627320856,0.043914482,0.0381421298,0.119860895,0.1434423029,0.0160628892,0.0239307471,-0.045829013,-0.2845480144,-0.1033421978,0.2123590261,-0.1288749278,-0.2173863053,-0.5689035654,-0.4772747159,-0.2290362269,0.0598107427,-0.0901624858,-0.1786348373,0.2162619531,0.0645548552,0.3908275068,-0.2997992635,-0.2319872826,0.1835287809,-0.1345534623,0.1550382972,-0.119854793,0.1175880209,-0.1475571245,0.3982762694,-0.1288672388,0.1292864233,-0.238943696,-0.5371047258,0.0983880237,-0.4517771602,0.1908956766,0.2576667666,-0.1502099931,0.3111432791,-0.2268038839,0.1120988876,0.1109325811,0.0671593472,-0.2997234464,-0.1118497998,0.0367011577,-0.3677363098,0.0979162678,-0.3780786991,-0.1553580761,0.3291868567,0.1468504518,0.0413799137,0.3024629653,-0.2576722205,0.0737459138,-0.0114810914,-0.0000506818,-0.0530079231,-0.5784045458,0.4582704306,-0.0525679588,0.0255833659,0.1926742941,-0.1010329127,-0.0518296175,-0.0418860801,-0.0808490962,-0.4202512801,0.0469216332,0.0205524266,0.3903393149,0.3359698057,0.1172429323,0.0368375517,0.1378154904,-0.1517869532,-0.3558728099,0.1650114954,0.0210258663,0.2545069158,-0.1433811337,0.1291761696,-0.2391848415,0.5956507921,0.2005895078,-0.2929969132,0.1209338978,0.2484630793,0.2504304647,0.2333758771,0.0773001164,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/721","title":"feat(dl_manager): add support for ftp downloads","comments":"We only support http by default for downloading.\r\nIf you really need to use ftp, then feel free to use a library that allows to download through ftp in your dataset script (I see that you've started working on #722 , that's awesome !). The users will get a message to install the extra library when they load the dataset.\r\n\r\nTo make the download_manager work with a custom downloader, you can call `download_manager.download_custom` instead of `download_manager.download_and_extract`. The expected arguments are the following:\r\n```\r\nurl_or_urls: url or `list`\/`dict` of urls to download and extract. Each\r\n        url is a `str`.\r\ncustom_download: Callable with signature (src_url: str, dst_path: str) -> Any\r\n        as for example `tf.io.gfile.copy`, that lets you download from google storage\r\n```\r\n","body":"I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n","comment_length":120,"text":"feat(dl_manager): add support for ftp downloads \n I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n \n We only support http by default for downloading.\r\nIf you really need to use ftp, then feel free to use a library that allows to download through ftp in your dataset script (I see that you've started working on #722 , that's awesome !). The users will get a message to install the extra library when they load the dataset.\r\n\r\nTo make the download_manager work with a custom downloader, you can call `download_manager.download_custom` instead of `download_manager.download_and_extract`. The expected arguments are the following:\r\n```\r\nurl_or_urls: url or `list`\/`dict` of urls to download and extract. Each\r\n        url is a `str`.\r\ncustom_download: Callable with signature (src_url: str, dst_path: str) -> Any\r\n        as for example `tf.io.gfile.copy`, that lets you download from google storage\r\n```\r\n","embeddings":[-0.2777805328,0.0965271667,-0.0294328295,0.2827260196,-0.0488570444,0.0626954138,-0.2193705291,0.4863711894,0.2671810389,0.0046167555,-0.0645394847,0.1767717898,-0.1338637918,0.0276905168,0.1833811402,-0.3053059578,-0.2551793158,0.1110800207,0.0832186565,0.1619187891,-0.3100489378,0.2241034508,0.1274141669,0.0247967672,0.1058143452,-0.0016975444,-0.0335105099,0.2009846121,-0.3600776196,-0.5725451708,0.159604311,0.199882552,0.5002796054,0.3012334704,-0.0001155282,-0.0273309927,0.2771560252,-0.0509039946,-0.1939808726,-0.4507175088,-0.5581098199,-0.3901635706,0.1760960817,-0.0818983316,0.1434405744,-0.3247947991,-0.065694958,0.1504253596,0.1654934138,0.4937476218,0.2138060778,0.2187065184,0.0942479074,-0.1992247105,0.3822670281,0.0258258972,-0.0190181285,0.2136030048,0.2988850772,-0.0795222893,-0.0127831465,-0.2919237018,-0.1655909419,0.2839197814,0.3653366864,0.169727549,0.102001138,-0.4725706279,-0.3483140767,0.2249996811,0.6980264783,-0.0426879153,-0.1774830669,-0.0982262641,-0.1084202006,-0.0485818088,0.1637655646,0.0252196565,-0.38479653,0.1930247247,0.134944737,-0.2261709273,-0.2653417289,0.3329018354,-0.1544879824,0.4816545844,0.0088161603,-0.1243843883,0.3409364223,0.2407482266,-0.1731476039,-0.1758915633,0.0956520438,0.0276539046,0.2391528636,-0.0896978527,0.1219808757,-0.3390654325,-0.045642782,0.3806220591,0.0886813626,-0.0634947419,-0.1267754883,0.2225989997,0.0764587149,0.2798477113,-0.1293370426,0.0132238781,0.4056569636,0.3186863065,0.2450589091,0.0259217806,-0.1233363152,-0.1067257673,-0.3545540869,0.0777444541,0.2355566323,-0.1232114583,-0.1206975356,-0.0501870885,-0.121394664,-0.2151160538,-0.066038765,0.1542132646,-0.1493148357,0.1724778563,0.0286382735,0.1730007231,0.1612342,-0.3008628488,0.0119847218,0.0084588053,-0.0474399365,-0.1504227221,0.217641905,0.1390340924,-0.0804110244,-0.1865072399,0.0279484205,0.1799260825,-0.0033731801,0.0043380656,0.129010573,0.3257303834,0.030877959,0.2170122266,0.0210642368,-0.2257748991,-0.2357034087,0.2544204593,-0.0345582105,-0.3946982324,-0.3626570702,0.15627186,-0.2903930247,-0.4475151896,-0.3203414083,0.1746923178,-0.3312831521,-0.2136016488,-0.1230311543,0.0842836201,-0.232864663,-0.195668593,0.1440956295,0.4552592039,-0.492256403,0.060594257,-0.3768833876,-0.3115023077,0.4496421218,0.0502946638,-0.124092795,0.3914755583,-0.3554272652,0.4173650742,0.8397292495,-0.4267210364,-0.3148742318,0.6333284974,-0.2937240303,0.1136790961,0.2245716155,-0.0514887236,0.4169696271,-0.285752058,-0.6332723498,0.5020344853,-0.0726740584,0.0090590129,-0.2345491052,-0.199662298,0.2574515343,0.0535952002,0.2425493449,0.3365590274,0.3952041268,-0.0429333262,0.3353668153,0.3670587838,0.2471549511,0.0626695752,0.2879798114,0.2796064615,-0.1349156648,-0.2456129342,-0.2629238665,0.1651069224,0.0862020552,0.169700712,-0.1856204569,0.1597983092,-0.2809397578,-0.0604899079,-0.2156412601,-0.0157647207,0.0051061544,0.1655586362,0.2290703654,0.0236069784,-0.1031404659,0.1136211827,0.2875776887,0.307384938,0.1115076169,0.1563326418,-0.1724861264,0.0395464599,0.0204862412,-0.0457603186,0.2629824579,-0.3486967683,-0.158597663,0.1335842758,-0.123903431,0.0464619994,0.3054710925,0.1961762905,0.2601018846,0.1200301051,0.0739792585,0.4652787745,0.2284341604,-0.0791872293,-0.0061101671,-0.0467151813,0.3204243481,0.2881264389,0.0336574428,0.1974166185,0.2212958783,-0.1061479598,0.0918356776,-0.1773681641,0.0986497253,-0.098504819,-0.179042086,-0.2212844491,-0.0927878693,0.1402365565,-0.0492130965,-0.0503070131,0.1286207139,0.3818585277,0.1158388779,-0.0050424086,0.0836059898,0.8543784022,0.3756823242,0.1835933626,0.306401521,0.1088297367,0.1027726009,-0.2426906973,0.2653562129,0.1198403314,-0.3955549896,0.3678884208,-0.0257276297,0.1617210805,0.03200287,-0.2243326455,0.1551394314,0.3608131409,-0.2279530466,-0.0266593248,-0.6431180835,-0.5907304883,-0.1313005239,-0.1926848292,-0.1272398382,-0.4519247413,-0.1167864427,0.3211928904,-0.1044618934,0.0826620832,-0.284847945,-0.0026747747,-0.2534915507,-0.3995335102,-0.2082609534,0.1707179397,-0.192368567,0.0894376114,0.3211081624,-0.0662640333,0.3892353177,-0.0055792588,0.1255531013,-0.5770410299,0.0153351827,0.2387178242,-0.1064808145,-0.0664174706,0.2190169543,0.0265400391,0.337253809,-0.0493886471,0.2074355632,-0.1061513349,-0.123633936,-0.1736947596,0.1422599256,-0.0082121305,-0.1790382564,-0.2249698639,-0.3690969944,-0.6310892105,0.1111525744,0.1660155654,0.1625747383,-0.0175437406,-0.2038410306,0.2674894035,-0.2053000182,-0.1185505614,0.0029458227,-0.1617115736,0.4682974517,-0.3175647259,-0.3787608147,0.2251977623,0.0455160663,-0.136271134,0.1256070286,-0.35362342,-0.2745190263,-0.0791602433,0.3072172701,-0.1262131631,0.2914076746,0.2416874766,-0.0288196709,-0.0462423675,0.0606233366,-0.0025995267,-0.0029891578,0.4793727994,0.3309399188,0.2338623703,0.3097515106,-0.0318109356,0.6106972694,-0.0915334746,0.2176847756,0.5523766875,-0.0916210413,0.1654037833,0.1803812534,-0.238364473,0.074733071,-0.1104736999,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/721","title":"feat(dl_manager): add support for ftp downloads","comments":"Also maybe it coud be interesting to have a direct support of ftp inside the `datasets` library. Do you know any good libraries that we might consider adding as a (optional ?) dependency ?","body":"I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n","comment_length":34,"text":"feat(dl_manager): add support for ftp downloads \n I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n \n Also maybe it coud be interesting to have a direct support of ftp inside the `datasets` library. Do you know any good libraries that we might consider adding as a (optional ?) dependency ?","embeddings":[-0.3274756074,0.0598696545,-0.0164928641,0.2534114122,-0.0806325227,0.0911129639,-0.3827839494,0.4749818444,0.2600127161,-0.012885185,0.0683740675,0.0932430774,-0.1218968257,0.061171934,0.2366616279,-0.2707274258,-0.1249640808,0.167685017,0.0703390911,0.1570510566,-0.3043116629,0.2058279514,0.1108845472,0.0007483974,0.0134709068,-0.0703274235,-0.0722677484,0.1834948659,-0.2807340622,-0.4544092715,0.1355403811,0.1415678263,0.4248403609,0.257706821,-0.0001219638,-0.0764313564,0.2710136175,-0.0649453849,-0.145114854,-0.4268159568,-0.5726474524,-0.4681797624,0.2224383056,-0.0011480418,0.1972098202,-0.3350039124,-0.0732626393,0.0971458182,0.0442159399,0.4594890773,0.1673590541,0.2992135882,0.0093542505,-0.1778156161,0.4375674129,0.0231842883,-0.0504501872,0.153066352,0.3597561121,-0.1422450393,0.0555440299,-0.3372543752,-0.1355669498,0.17609936,0.3862240016,0.1103014052,0.0883987769,-0.4388636351,-0.3785873353,0.1965651363,0.8066228032,-0.0113757458,-0.2615829706,-0.1113187373,-0.0873063803,0.0143470513,0.1853109151,0.0202660579,-0.3324308693,0.1831110269,0.1143505871,-0.3629404008,-0.252194494,0.4006340504,-0.1277102828,0.5490372181,0.1106914505,-0.1825642735,0.2825981975,0.2046848089,-0.1684248596,-0.1567012221,0.0916963443,-0.0014251804,0.2913459241,-0.1304873973,0.1080868021,-0.263481915,-0.0174486358,0.3301560879,0.0243162625,-0.0238242205,-0.2186668217,0.2281159759,0.0460556336,0.229918018,-0.1359343827,-0.0047339913,0.5143220425,0.1797383577,0.3002929091,0.0320505537,-0.1394660324,-0.0609621108,-0.4214181006,0.0173486751,0.1475913525,-0.1914305687,-0.1164726242,0.0196850393,-0.1824108958,-0.0891887099,-0.0083674528,0.1318999231,-0.1650015265,0.264490068,0.0364155918,0.1938411891,0.1065606028,-0.3565620482,0.0130211208,0.0204761699,-0.0704325736,-0.1268787384,0.190478757,0.1413714439,-0.0339485891,-0.1371247917,0.0703350455,0.2175328285,0.0164672732,0.0222992525,0.071576938,0.4026202857,-0.0590976477,0.1796619445,-0.0069614006,-0.1225644648,-0.2095035166,0.2328031212,-0.0161455404,-0.3932777047,-0.4267930686,0.0802333504,-0.3305881917,-0.4670033455,-0.275801599,0.1919849217,-0.4273515642,-0.3020412624,-0.125728175,0.1029542983,-0.1907224655,-0.2069382668,0.1016146988,0.3802587688,-0.4875880182,-0.0005755357,-0.3736157119,-0.2895914912,0.4132218957,0.1140806377,-0.1112021208,0.3359066546,-0.3094168603,0.3546554148,0.8370084167,-0.4016798437,-0.2758085132,0.6220941544,-0.2426191568,0.0267043393,0.1759946644,0.0300458558,0.2986840606,-0.2243283987,-0.6506483555,0.5972316265,-0.0663956851,0.0441403799,-0.1612779647,-0.2266774178,0.3191091418,0.1394774914,0.3316980004,0.2728152871,0.418646276,-0.0235842355,0.2792365849,0.3195464313,0.1983102113,0.1458649486,0.2886610627,0.3233473003,-0.1175427511,-0.3403542042,-0.2940297127,0.0807635486,0.1218628213,0.145616442,-0.1783807725,0.1935077459,-0.122542657,-0.0336224549,-0.0697396398,0.0909017399,-0.0722767636,0.181281209,0.1942894012,0.0414849557,-0.1086733341,0.12853688,0.2902016938,0.318875879,0.1650923342,0.2465964407,-0.1522357166,0.0011203651,0.1121921167,0.0614286289,0.2203390896,-0.3315290511,-0.1351704597,0.092469655,-0.1107333153,0.0936811343,0.4048472345,0.2875028849,0.3451217115,0.1375146806,0.0838565826,0.4250737131,0.1754143536,-0.0993347764,0.0376974717,-0.014303389,0.2977044582,0.217909053,0.0552522168,0.2402647734,0.1647406667,-0.0411328562,0.0723397285,-0.1136398315,0.2451276779,-0.0986617208,-0.157007724,-0.1735036671,-0.1777079999,0.0258666202,-0.1199384108,-0.0781918764,0.1950016618,0.3921463192,0.1654786468,-0.0244768504,0.2006291151,0.7358022928,0.3375622034,0.2228246927,0.2597005367,-0.004251441,0.0336218365,-0.2456354797,0.1866513938,0.1149216965,-0.4359814227,0.4135332406,-0.0206876155,0.1957435459,-0.0184401404,-0.1528042108,0.1500848979,0.3519281745,-0.1117857918,-0.0641986206,-0.6870807409,-0.6479420066,-0.0983839184,-0.1577105969,-0.2821214199,-0.4398795664,-0.1192172542,0.2665982544,-0.0242006164,0.1208501086,-0.2807169557,0.0926330313,-0.38007164,-0.4477750063,-0.267439127,0.1894757897,-0.1792530268,0.0328132771,0.344219923,-0.0277355686,0.4250334799,0.0987618342,0.1917737126,-0.6420844197,-0.0550661869,0.1851613373,-0.0226981044,-0.0905228183,0.1784199625,-0.0125826104,0.3820634782,-0.0953880697,0.1900233775,-0.1293125004,-0.11783766,-0.1879602373,0.08741723,-0.1212427691,-0.1198334172,-0.0994132608,-0.4218278229,-0.6161510348,0.077366069,0.1289636642,0.0838463902,0.0494505242,-0.2287164181,0.2352489531,-0.1928627938,-0.0224560052,0.0970234796,-0.0697934255,0.4639669359,-0.2383593023,-0.42216748,0.2020000815,0.0862178504,-0.1741467863,0.2055050582,-0.3952102065,-0.3099576831,-0.1374028027,0.3529423773,-0.1657400131,0.2932446301,0.2597908378,0.0002999851,0.0142573928,0.0154755171,-0.0873998627,-0.0309707597,0.4773543477,0.3319088817,0.1926017851,0.2289376259,-0.0619901903,0.5613712668,-0.0307525713,0.1898088902,0.5820297599,-0.0288637355,0.1707128584,0.2009478211,-0.2395555526,0.0938971043,-0.0521430336,0.1095566303,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/721","title":"feat(dl_manager): add support for ftp downloads","comments":"Downloading an `ftp` file is as simple as:\r\n```python\r\nimport urllib \r\nurllib.urlretrieve('ftp:\/\/server\/path\/to\/file', 'file')\r\n```\r\n\r\nI believe this should be supported by the library, as its not using any dependency and is trivial amount of code.","body":"I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n","comment_length":35,"text":"feat(dl_manager): add support for ftp downloads \n I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n \n Downloading an `ftp` file is as simple as:\r\n```python\r\nimport urllib \r\nurllib.urlretrieve('ftp:\/\/server\/path\/to\/file', 'file')\r\n```\r\n\r\nI believe this should be supported by the library, as its not using any dependency and is trivial amount of code.","embeddings":[-0.2455563694,-0.0056827385,0.0114010274,0.2680145502,-0.0810396895,0.0563570522,-0.3472591639,0.4960227907,0.210584417,0.0559276417,0.0054219542,0.1040770933,-0.1624985635,-0.0003008154,0.2173724473,-0.3047148883,-0.1787896901,0.1610744596,0.1181761697,0.1564905196,-0.3281778991,0.2467977852,0.0930166245,0.043319881,0.036886517,-0.0102594495,-0.0181726627,0.231210947,-0.3351477981,-0.4214291871,0.1100811958,0.0631712005,0.4437259138,0.1806788743,-0.0001204356,-0.0327388421,0.2680413425,-0.1002686322,-0.104510501,-0.4727423191,-0.4627041221,-0.4231175184,0.2234623879,-0.0714606047,0.1594662964,-0.2432890236,-0.0140075972,0.2061714381,0.0663921311,0.4829704463,0.1702732891,0.3050340414,0.0770800337,-0.1257840693,0.4577258229,-0.0401502363,-0.0512701757,0.248623386,0.2742137909,-0.0884236693,0.0582275316,-0.3440451622,-0.1427604556,0.252089411,0.3116759062,0.1652508527,0.1236155704,-0.4026138186,-0.3681646883,0.1997761279,0.63075459,-0.0083609242,-0.2014279366,-0.0267409869,-0.1026719362,-0.0145607283,0.1862136722,0.0254227892,-0.3613951802,0.1787406057,0.1856765449,-0.3242506385,-0.2554449737,0.3719015419,-0.1366192997,0.5931152105,0.0658612102,-0.1004417092,0.2225957811,0.2331712842,-0.2562013268,-0.0763917342,0.1533754021,0.0574023239,0.342338115,-0.0806174129,0.0626318008,-0.32030195,0.0128945205,0.3063532114,0.1055897027,-0.0480534174,-0.1900885701,0.2983517647,0.0536059104,0.3081001043,-0.17290093,-0.0186953414,0.5094934106,0.2821047008,0.2624134719,0.0560330637,-0.1178547964,-0.1194995865,-0.462259084,0.054779388,0.2014470696,-0.1590370089,-0.1283294559,-0.0145571018,-0.2463677675,-0.0972510129,0.0239095557,0.1393690854,-0.2065571994,0.1906870902,0.1232686862,0.1359563023,0.0505211502,-0.2750120759,0.0420158543,0.0029452278,-0.0010131976,-0.155328095,0.1617364585,0.1152453199,-0.0066440976,-0.1665710807,0.0627628565,0.1509384364,0.0086926483,-0.0056089424,0.1514650434,0.3983240128,0.0141673498,0.1728252769,0.0121454336,-0.1598490328,-0.243770197,0.2962782979,-0.1052594557,-0.3810894787,-0.4174828231,0.0826406702,-0.2794828713,-0.4609342813,-0.2328967601,0.1762951463,-0.3809169233,-0.3419992924,-0.102248162,0.0753771663,-0.2319997251,-0.212921828,0.1630425602,0.3832987845,-0.3571052551,0.007798316,-0.2994357646,-0.298350215,0.5073300004,0.1147064492,-0.1210711449,0.3324902356,-0.3670815527,0.4251608253,0.8384565711,-0.3629837632,-0.2417307049,0.6288360357,-0.2879821956,0.0591514036,0.1531856209,-0.0149559369,0.2839210927,-0.2343828976,-0.5873812437,0.5538029671,-0.0242186021,0.0745490938,-0.2459794879,-0.185032025,0.3185222745,0.0530269071,0.2675327063,0.3087219894,0.4214221239,0.0938497633,0.3757402897,0.3142935336,0.2340296209,0.1025049537,0.3178471029,0.2957000732,-0.0954593495,-0.3132984042,-0.2004291862,0.0692326352,0.0264658313,0.194797948,-0.1536310762,0.1266146898,-0.1657294184,-0.0594669282,-0.1859846115,0.0433253907,-0.0508963764,0.1662818044,0.1799157411,0.1008666158,-0.0685695037,0.0021913652,0.2449868172,0.3067437112,0.1160969734,0.1882428378,-0.1808428913,0.0640043169,0.0556332879,0.0540285408,0.2409528196,-0.3728549182,-0.1152889803,0.1083641201,-0.0711680353,0.0535149314,0.3289978206,0.2119719535,0.3346286118,0.1550185978,0.0265759416,0.5274912715,0.2497471571,-0.0545664579,0.0958918482,0.0023235921,0.3467338383,0.2445716113,0.0871291831,0.2104832679,0.1962825507,-0.0432377532,0.0579768345,-0.1230782047,0.1969663203,-0.066023387,-0.2364279628,-0.2281231135,-0.1946508437,-0.0071621803,-0.0965443328,-0.0657776818,0.2163253576,0.4139409363,0.2313465625,-0.0124053061,0.1543519199,0.6730174422,0.2891684473,0.1565618217,0.3078869581,0.1071752459,0.0316325985,-0.253988862,0.2010782361,0.1231395602,-0.389958173,0.4704409838,0.0261115432,0.1698914617,-0.0346687436,-0.139299795,0.1403881162,0.3499347866,-0.1492049992,-0.0147421025,-0.6423718333,-0.6558085084,-0.1747983396,-0.1938220263,-0.2456446439,-0.4119878709,-0.1122543067,0.2524807751,-0.0747434944,0.0646307245,-0.1988042891,-0.002335784,-0.2858401537,-0.4750635922,-0.183858797,0.2437696606,-0.2298301756,0.0236968584,0.3109210432,-0.0177714881,0.3862735629,0.0264210552,0.1282990426,-0.6088418961,-0.1003479213,0.2114328593,-0.0697606131,-0.1035302505,0.1966163367,-0.0041562659,0.3545367718,-0.0289515182,0.2459471375,-0.1386429369,-0.1292089224,-0.1795073897,0.1364054531,-0.0362183787,-0.2145157307,-0.1819323599,-0.4030963778,-0.6090556979,0.1008850932,0.1400819123,0.1651463211,0.0233957283,-0.2871911228,0.2522779405,-0.1295570284,-0.0499394946,0.0600947663,-0.0067992806,0.4296649992,-0.3297210634,-0.4547201693,0.2211284041,0.0919658989,-0.158678636,0.1415458918,-0.3535096049,-0.3210872412,-0.0745454282,0.2627055645,-0.132836625,0.3481777012,0.2570135295,-0.0688443854,0.0271192361,0.0475400575,-0.0865596011,0.0000654736,0.514926672,0.3293808699,0.2759384811,0.2812847793,-0.0223778728,0.5285961628,-0.0103555033,0.2793345153,0.6546480656,-0.1272286177,0.1461856961,0.1091930568,-0.1454851478,0.1041269079,-0.101027526,0.0010329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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/721","title":"feat(dl_manager): add support for ftp downloads","comments":"I know its unorthodox, but I added `ftp` download support to `file_utils` in the same PR https:\/\/github.com\/huggingface\/datasets\/pull\/722\r\nSo its possible to understand the interaction of the download component with the ftp download ability","body":"I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n","comment_length":33,"text":"feat(dl_manager): add support for ftp downloads \n I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n \n I know its unorthodox, but I added `ftp` download support to `file_utils` in the same PR https:\/\/github.com\/huggingface\/datasets\/pull\/722\r\nSo its possible to understand the interaction of the download component with the ftp download ability","embeddings":[-0.2079318464,-0.0998929814,-0.0202530045,0.3010161221,0.0044642645,0.038478259,-0.3553130329,0.3622789085,0.1900351942,-0.0035547365,-0.0368139669,0.0217449348,0.0155007364,-0.0038925642,0.2229107469,-0.3022278845,-0.1582735032,0.0847345889,0.0404505357,0.1436005384,-0.3097785711,0.2319472432,0.1025383323,0.0332681201,-0.0504573658,-0.014419958,0.0126235299,0.216467768,-0.3532263339,-0.4517711997,0.1148509458,0.0617914759,0.3427506685,0.1893779635,-0.000120639,0.0197103098,0.3356402516,-0.0973493159,-0.2229550034,-0.4683343768,-0.5454901457,-0.411131382,0.1993215382,0.0049577951,0.1873623431,-0.2422083467,-0.0317680351,0.2030040175,0.0860505104,0.4672448039,0.1789339334,0.3690392673,0.0318196863,-0.1910261363,0.3959606588,0.0568572283,-0.0408926457,0.2149498016,0.3350120485,-0.0349815078,0.0446458422,-0.3629597723,-0.0632884279,0.2762863636,0.3633669019,0.2289074808,0.1841487586,-0.4370957613,-0.2981275022,0.1590768248,0.699360013,0.0777133629,-0.2732343376,-0.1167283952,-0.0868948326,0.0861626565,0.2511809468,-0.079638727,-0.3449171782,0.1491218954,0.1730009615,-0.2589472532,-0.170397833,0.3341841996,-0.197999388,0.5212568045,0.0061053308,-0.1265738457,0.2468764633,0.2629101872,-0.2788716853,-0.1374629885,0.1785530448,0.0504078344,0.3377340138,-0.0213782303,0.1397557408,-0.2761086524,-0.0083406651,0.3188734949,0.1086752042,-0.111659728,-0.2271296084,0.2703460753,0.0646889135,0.3599967659,-0.1743905246,-0.0469691567,0.3693583608,0.3174996674,0.2482461184,0.006688117,-0.0072048814,-0.1123003885,-0.4965158105,-0.0217914972,0.2988591492,-0.1590376794,-0.2438595444,-0.0120566264,-0.1646545678,-0.1358545572,-0.054800421,0.15742217,-0.1514691859,0.1966957748,-0.0269040912,0.2070281357,0.082897678,-0.2286737561,-0.0190481041,-0.0415509604,-0.0312383361,-0.2148872614,0.161039114,0.1214091107,0.0064423801,-0.1272520274,0.0971123502,0.0847668126,-0.041205138,0.0134572312,0.1249888688,0.4334996343,-0.0127799418,0.2451587021,0.0644340813,-0.2286069989,-0.2067031562,0.2412199974,-0.1036896259,-0.4144616127,-0.3034451008,0.1017002612,-0.3632735312,-0.3726772368,-0.1595049798,0.1136647165,-0.3486055434,-0.315356493,-0.086832732,0.11650341,-0.1531251967,-0.2190992236,0.1667493433,0.383581847,-0.4564204514,-0.047297094,-0.2854032815,-0.3417429924,0.506298244,0.1772540808,-0.1460350007,0.3434446156,-0.353361845,0.4306581914,0.8133015037,-0.3829745352,-0.3716633618,0.705005765,-0.4238267839,0.2211805135,0.2233175039,-0.0965155438,0.2633066177,-0.2493056655,-0.5300371051,0.4649730027,-0.0029226474,-0.0015167119,-0.2496085465,-0.1350483745,0.2841676474,-0.0408967622,0.2322322428,0.337898463,0.3964929879,0.051944647,0.3205003142,0.3770959973,0.30320099,0.0766608566,0.2393833399,0.2546314597,-0.0940833017,-0.2554593384,-0.2583701611,0.1295386851,-0.0381314419,0.166278705,-0.1916953474,0.1113921329,-0.2765848637,-0.0130323609,-0.1457217634,-0.0526321158,-0.0306963269,0.216698125,0.1879994869,0.0291851554,-0.0721330196,0.0595457926,0.2788746357,0.2985241711,0.1559013724,0.1172475964,-0.2246597409,0.0273785274,-0.0366569459,0.00244577,0.2337848097,-0.4035803378,-0.1247583181,0.1427869946,-0.1231002286,0.1376376897,0.2947228253,0.1800485849,0.3189501762,0.2410545051,-0.0431585833,0.4741290808,0.2841287851,-0.0888023451,0.073186405,-0.0395187065,0.2465720922,0.3004812002,0.0893413648,0.1786063462,0.2016614676,-0.0689590946,0.0651522204,-0.1590226442,0.2348520756,-0.1180081218,-0.0782875642,-0.1455991417,-0.1292728931,0.0702390149,-0.1378353238,-0.0682510808,0.1008945331,0.3550207019,0.1246390864,-0.056308914,0.1041949987,0.7412886024,0.3876671195,0.160544917,0.4062668085,0.0733299777,0.0268041044,-0.2988274097,0.2523026466,0.1610683352,-0.4967062473,0.5382865667,0.0496946275,0.2061890364,0.0334009193,-0.0575258844,0.2096959054,0.3144170344,-0.2221792191,-0.0278006662,-0.6374946833,-0.600946188,-0.1810956895,-0.1889698505,-0.2641664147,-0.4526263177,-0.1407168359,0.2260042727,-0.1081146449,0.0873988718,-0.218007341,0.0166965518,-0.3855964839,-0.3432736397,-0.2249444276,0.2820158601,-0.1479971409,0.0275822561,0.2968629599,0.0026488476,0.3359049261,0.0013950104,0.0883508176,-0.5908955932,-0.0373855606,0.2585214376,-0.0719510689,-0.0291324556,0.151628077,-0.0415065326,0.3573758006,-0.088770628,0.2683821619,-0.094729282,-0.1510519981,-0.1963443905,0.1135050207,-0.0697748587,-0.1697794497,-0.1321094483,-0.3559782505,-0.5992090702,0.2074672729,0.0885049328,0.1327043623,-0.0062963073,-0.3061015606,0.2599484622,-0.1653517187,-0.0363086089,-0.0331934504,-0.0868117213,0.3976346254,-0.3162903786,-0.4053558111,0.1422792077,0.1079493091,-0.1221241355,0.2139302045,-0.3075752556,-0.2922318578,-0.1043406725,0.263728112,-0.1654445529,0.3404821754,0.3213359714,-0.1051378548,-0.0344130695,-0.0144899851,0.0042766188,0.0218883287,0.5916693807,0.3955231011,0.144984588,0.2034229934,-0.013806208,0.6329036355,-0.0128569389,0.3049511313,0.6129640341,-0.0857623369,0.1866588295,0.114027299,-0.1501547843,0.1227124929,-0.1731449813,-0.000850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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/721","title":"feat(dl_manager): add support for ftp downloads","comments":"@hoanganhpham1006 yes.\r\nSee pull request https:\/\/github.com\/huggingface\/datasets\/pull\/722 , it has a loader for this dataset, mostly ready.\r\nThere's one issue that delays it being merged - https:\/\/github.com\/huggingface\/datasets\/issues\/741 - regarding memory consumption.","body":"I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n","comment_length":30,"text":"feat(dl_manager): add support for ftp downloads \n I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n \n @hoanganhpham1006 yes.\r\nSee pull request https:\/\/github.com\/huggingface\/datasets\/pull\/722 , it has a loader for this dataset, mostly ready.\r\nThere's one issue that delays it being merged - https:\/\/github.com\/huggingface\/datasets\/issues\/741 - regarding memory consumption.","embeddings":[-0.207817927,-0.1362151951,0.0223399419,0.368414551,-0.0426028669,0.0941679403,-0.4160577655,0.4587387145,0.246598646,-0.0219785664,-0.104866907,0.0549300723,-0.1739564091,0.0406175293,0.2238930464,-0.2853588164,-0.109582372,0.0470968671,0.0764123052,0.1486932337,-0.3671719432,0.3155606389,0.1266240329,-0.0520574041,-0.0608695634,0.0682501197,-0.1144394055,0.2525817156,-0.3424279988,-0.4832611382,0.1144609973,0.1468002796,0.3998353779,0.2815961838,-0.0001227519,-0.0169627946,0.3013392985,-0.0854624659,-0.205602482,-0.4361374676,-0.4879086912,-0.4381598532,0.204580158,0.0155038713,0.1532450169,-0.2656704485,-0.0692233518,0.1311786622,0.1514955461,0.4167751074,0.148070544,0.3967960179,0.0261271615,-0.2171597779,0.3806223869,0.0019360805,-0.0103933439,0.3392916918,0.2438274771,-0.1254053265,0.0567795187,-0.2961394489,-0.0569786802,0.2207807004,0.3716031015,0.133394137,0.0937364474,-0.4013227522,-0.3160227835,0.1644095778,0.5963615179,-0.0194317419,-0.2410590053,-0.1115393192,-0.1419954151,-0.0060443142,0.2234905064,-0.0008247739,-0.4154690504,0.1532354802,0.1061660573,-0.3735143542,-0.2633183599,0.333652854,-0.1387932599,0.5295559168,0.0314591974,-0.1647163182,0.3249212801,0.2675722837,-0.302785486,-0.1141962782,0.1184461117,0.074788712,0.2175052762,-0.1066058502,0.0439071506,-0.3347759545,0.0518928729,0.3083164096,0.1205500737,-0.0901618153,-0.1433764398,0.2116677016,0.1183404401,0.3491191566,-0.1774645597,-0.1086019799,0.384899199,0.2491233498,0.3148803413,0.0080584977,-0.1638400704,-0.0860047117,-0.5304250121,-0.0754911304,0.1751984954,-0.159505263,-0.171575442,0.0380557142,-0.1565726846,-0.1459298581,-0.0113843987,0.2451413125,-0.1559437513,0.2249639034,0.0938650295,0.1684688926,0.0178118125,-0.2756283581,-0.0035254352,-0.0146278683,-0.0804342479,-0.1210728586,0.1708238721,0.113258563,0.0096824868,-0.2057599723,0.1116544381,0.115702495,-0.0857889056,0.021078255,0.1708335727,0.4054081738,-0.0006323688,0.1629956961,0.0858401209,-0.1381656677,-0.1729673594,0.2869307697,-0.0179809872,-0.4325714707,-0.3796412051,0.0643770173,-0.354464978,-0.4458071887,-0.2828682363,0.219019264,-0.311722517,-0.2606828809,-0.1746931523,0.1834027022,-0.268594861,-0.1940467954,0.1424164921,0.4873999357,-0.3776033223,-0.0931542367,-0.2818639278,-0.2958835661,0.4628069699,0.243124485,-0.1338122785,0.3007458448,-0.3882220685,0.3272667825,0.7001002431,-0.347008884,-0.2477435619,0.6614431143,-0.3661957979,0.1240254492,0.227305904,-0.0370961502,0.2169185728,-0.2122607529,-0.4664954841,0.4712201953,-0.0262596216,0.0468968265,-0.2975867987,-0.2159231901,0.303047061,0.0292146616,0.2969394624,0.2930310369,0.3287850022,0.0340177156,0.3883121014,0.3318313062,0.2062091827,0.1013031751,0.3124403358,0.2461340278,-0.1173017249,-0.2410794497,-0.3073039651,0.1516397446,-0.048524417,0.2113685459,-0.116776526,0.1303155869,-0.2362406254,-0.0487335362,-0.1344463974,-0.0297987927,-0.0940584615,0.150647074,0.2303199023,0.0588666275,-0.1300015002,0.1075513363,0.1942079663,0.3531340063,0.057989005,0.2031669766,-0.1665961146,0.0836378112,0.0449276343,0.0117370198,0.1984714717,-0.3616105914,-0.1048683971,0.1805507243,-0.1400326341,0.1049061343,0.3677375913,0.1984399706,0.3461498916,0.1714134663,0.0351369902,0.430300802,0.2232325971,-0.1132473797,0.0082803071,0.0375885889,0.2606927454,0.3046484888,0.1443363875,0.1958293468,0.1838881373,-0.0570907667,0.0616536327,-0.1389453709,0.2580167055,-0.0735475942,-0.150655508,-0.154685542,-0.1694375575,0.0279493257,-0.1301021725,-0.0157377254,0.0681838617,0.4152185023,0.0894243643,-0.025441831,0.1282014549,0.7226035595,0.377538681,0.2367203683,0.2860601842,0.1250470132,0.0694644451,-0.2483469546,0.2356323004,0.1740339249,-0.4175153375,0.4896562397,0.0088774413,0.1388650239,-0.0630019829,-0.0210741013,0.2118410021,0.3253067136,-0.2296219617,-0.1021391749,-0.5599485636,-0.6452051401,-0.2360999286,-0.1311146617,-0.3341293931,-0.5082860589,-0.1180110127,0.2917005718,-0.0588609204,0.0850161538,-0.2439475209,0.1025520191,-0.3336437047,-0.4053463042,-0.2126259208,0.1854860038,-0.112926513,0.0057555819,0.384722054,-0.091664359,0.4335967898,-0.022649806,0.1536938548,-0.5495795608,-0.1093697846,0.2470646948,-0.0960324779,-0.1344262809,0.087641567,0.0862911567,0.3288471997,-0.076649785,0.286080569,-0.1717059612,-0.1637559533,-0.1800730526,0.1500083655,-0.0488474518,-0.154743284,-0.210155949,-0.3709218502,-0.6213084459,0.1715507805,0.1312792152,0.106965594,0.1341509819,-0.2162243426,0.2566540837,-0.1929634959,-0.1720329076,-0.0204367358,-0.1622503847,0.3975332081,-0.2929755151,-0.4353757799,0.1951984614,0.1112825125,-0.0704709291,0.1163327768,-0.3959720135,-0.242349878,-0.0722930208,0.2193657607,-0.1033337042,0.3142015636,0.3255229592,-0.0736394301,-0.0287296567,0.0735493973,-0.0482596606,0.0058852532,0.4612645209,0.397813201,0.2045585215,0.3464250863,0.0365738049,0.7163600326,0.047580786,0.3232117891,0.6261866093,-0.1124065295,0.2358408719,0.1207485348,-0.2355271578,0.0711995214,-0.1708737761,0.062237843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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/721","title":"feat(dl_manager): add support for ftp downloads","comments":"The problem which I have now is that this dataset seems does not allow to download? Can you share it with me pls","body":"I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n","comment_length":23,"text":"feat(dl_manager): add support for ftp downloads \n I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n \n The problem which I have now is that this dataset seems does not allow to download? Can you share it with me pls","embeddings":[-0.2195911556,-0.1120689511,-0.0246193595,0.3834899366,-0.0526028275,0.1177402362,-0.3828471899,0.4139447808,0.2322627753,0.0223058201,-0.0140955709,0.0171899106,-0.1022191644,0.0319089331,0.2101533264,-0.2765799165,-0.2073134631,0.0704654902,0.1246696562,0.0944500268,-0.3448491991,0.2155543268,0.0538635552,-0.0150338225,-0.002244324,-0.0836158395,-0.0123813748,0.1466105282,-0.4219306409,-0.4405503869,0.0988955647,0.0674532875,0.4428101778,0.2283713818,-0.0001201383,-0.0678524375,0.2262694538,-0.1244195849,-0.1119161248,-0.4976867139,-0.6040620804,-0.4267119765,0.167746067,-0.0655230507,0.1008527204,-0.2254551649,-0.0777876675,0.1828066707,0.1271830946,0.5547147989,0.1909677982,0.2840135694,0.0148847355,-0.1946382225,0.3984506726,-0.0345649943,-0.0255294144,0.2603332996,0.21910505,-0.0397336334,0.1848991066,-0.3369786739,-0.0732683539,0.2586836517,0.2793957889,0.133928299,0.1869349927,-0.5500770211,-0.2737672031,0.1698972583,0.7219539285,0.0584876686,-0.2320967019,0.0399339609,-0.0457105376,-0.0285104048,0.1964208037,0.0886953101,-0.4111626148,0.2304250598,0.1812592298,-0.2941674888,-0.2398548722,0.3223291337,-0.2248306572,0.5259870887,-0.0105329342,-0.1534703523,0.1897880733,0.2882221341,-0.1858109981,-0.1448328793,0.1183310673,0.1023713499,0.3044013083,-0.0678838044,0.0628744289,-0.2980289757,-0.0205402896,0.3111588359,0.1126253605,-0.1349289864,-0.1364929974,0.227102384,0.0349793658,0.2347702235,-0.1673434228,-0.0229942743,0.4096495211,0.2589091957,0.2554894686,-0.0043367213,-0.1848020852,-0.0966101885,-0.4551785886,0.0259516574,0.2356131971,-0.1696947515,-0.190508455,0.0792635083,-0.2015102059,-0.1778980196,-0.0338972397,0.2074545771,-0.1466837823,0.1321310848,0.0629355684,0.2153571844,0.1690015048,-0.3285966814,-0.0077877478,0.0101578021,-0.0384752452,-0.1748359054,0.1545878202,0.2050777227,-0.0165672414,-0.1299769878,0.0299464501,0.0974588618,0.021439692,-0.0664843172,0.2248134017,0.4637010992,0.0701338872,0.2412337959,0.0547637045,-0.1556604058,-0.1665830016,0.3274118602,-0.0776167214,-0.3467562497,-0.3879059255,0.1058626175,-0.3641716838,-0.4292062223,-0.218113035,0.1445102841,-0.3195294738,-0.2302254885,-0.0655010268,0.1692455411,-0.1796378195,-0.2509208322,0.1736604422,0.3953446746,-0.4349624813,0.0290004183,-0.3200612962,-0.3286038637,0.5275195241,0.1898436397,-0.1224430203,0.3176210225,-0.3467461765,0.4102366865,0.8917118311,-0.3346193433,-0.3704231083,0.624605298,-0.3008839786,0.0145958727,0.2337301373,-0.0524067543,0.3178453147,-0.2185449153,-0.5774485469,0.539021492,-0.0026435682,-0.0013851926,-0.2533585727,-0.1590520591,0.3335995972,-0.0122704068,0.3183053434,0.3677804768,0.3756348789,0.0217861347,0.3828482032,0.3941229582,0.2284238338,0.141210556,0.3604916632,0.2412090451,-0.0930865705,-0.2692519128,-0.1937125921,0.0958233774,-0.0300673023,0.2068408728,-0.1162710264,0.1539364159,-0.267205894,-0.0368832499,-0.2007843405,-0.0366823301,-0.0223183408,0.1322454214,0.1591792703,0.0649762377,-0.1109997332,0.0250935741,0.244552508,0.2543138564,0.1832391471,0.2468275428,-0.2353002429,0.0370694473,-0.0002029525,-0.0345541649,0.2976437211,-0.3468154967,-0.1460515261,0.0344424136,-0.1146792397,0.0226303823,0.3501186073,0.2540035248,0.2828947902,0.0655724183,0.0102714244,0.4430164099,0.2658160925,-0.0549040698,0.0487808958,-0.0302391443,0.2986435592,0.218571946,0.0507325009,0.2505043149,0.2344325036,-0.0882805213,0.0875615254,-0.1297133118,0.2024669945,-0.0549488068,-0.1953208447,-0.1999290735,-0.1733127981,0.0314071402,-0.1219292954,-0.0436027795,0.2009671479,0.3435566723,0.1428687423,-0.0367139727,0.1076911986,0.7519909739,0.2806704938,0.1639990062,0.3033875227,0.0694541931,0.0829764307,-0.2507296503,0.2020807266,0.1029977277,-0.3249638677,0.521641016,0.0256121531,0.192514345,0.0141275153,-0.0935264826,0.2667743564,0.3767357171,-0.1761631072,-0.090033412,-0.5962128639,-0.6407173276,-0.1288952976,-0.1327850521,-0.3110176921,-0.4138788879,-0.1716987044,0.2639714777,0.0401951931,0.0294970591,-0.2321714312,0.0237820316,-0.2966636121,-0.3578520417,-0.2559229434,0.193116799,-0.1150306612,0.0433514565,0.320225805,0.0060025943,0.3827809691,-0.0271581486,0.11095386,-0.6365141273,0.0093950564,0.2352179587,-0.0255296826,-0.0233493689,0.128038317,0.1464680582,0.3535769582,0.0205632225,0.2245682627,-0.0904154778,-0.1382988393,-0.2259079516,0.055093877,-0.0535068735,-0.1188602298,-0.1805927753,-0.4567272067,-0.5308476686,0.0875444487,0.1722155809,0.1441112012,0.0104325507,-0.2622845173,0.2387209684,-0.1256548464,-0.1229226217,-0.0297259446,-0.1118735224,0.4354454875,-0.3324555755,-0.5053565502,0.2403377295,0.1019918621,-0.1038701534,0.0684414655,-0.3555796146,-0.2570869625,-0.124401249,0.3066698909,-0.1409659386,0.2711115479,0.3191989362,-0.1667193323,0.0051958007,-0.0055935071,-0.0693926588,0.0037146129,0.5728480816,0.4226831496,0.1594146192,0.3276003003,0.0024073636,0.6448269486,-0.0095672924,0.2822215557,0.6291103363,-0.1618081182,0.1219543144,0.1170671433,-0.2406397015,0.0876679868,-0.0754482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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/721","title":"feat(dl_manager): add support for ftp downloads","comments":"The dataset loader is not yet ready, because of that issue.\r\nIf you want to just download the dataset the old-fashioned way, just go to: https:\/\/www-i6.informatik.rwth-aachen.de\/ftp\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz (the ftp link is now broken, and its available over https)","body":"I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n","comment_length":37,"text":"feat(dl_manager): add support for ftp downloads \n I am working on a new dataset (#302) and encounter a problem downloading it.\r\n\r\n```python\r\n# This is the official download link from https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/\r\n_URL = \"ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz\"\r\n\r\ndl_manager.download_and_extract(_URL)\r\n```\r\n\r\nI get an error:\r\n\r\n> ValueError: unable to parse ftp:\/\/wasserstoff.informatik.rwth-aachen.de\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz as a URL or as a local path\r\n\r\nI checked, and indeed you don't consider `ftp` as a remote file.\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/4c2af707a6955cf4b45f83ac67990395327c5725\/src\/datasets\/utils\/file_utils.py#L188\r\n\r\nAdding `ftp` to that list does not immediately solve the issue, so there probably needs to be some extra work.\r\n\r\n\r\n\r\n \n The dataset loader is not yet ready, because of that issue.\r\nIf you want to just download the dataset the old-fashioned way, just go to: https:\/\/www-i6.informatik.rwth-aachen.de\/ftp\/pub\/rwth-phoenix\/2016\/phoenix-2014-T.v3.tar.gz (the ftp link is now broken, and its available over https)","embeddings":[-0.2821508646,-0.0215412136,-0.0087811993,0.2353300899,-0.0750115365,0.0881375074,-0.2813771665,0.4609623253,0.2376454324,0.021310145,0.0197128095,0.0793237612,-0.1120400652,0.0288054869,0.2074834853,-0.2595874369,-0.1745924205,0.0500782542,0.0860737637,0.1392570436,-0.3367642462,0.2255442142,0.093302615,0.0308642723,0.0250631385,-0.0268508308,-0.0471630022,0.2105636001,-0.3284516335,-0.4584114254,0.1427454054,0.0775095671,0.4306091368,0.2409657538,-0.0001195454,0.0194303878,0.3105322719,-0.0597625934,-0.1921466589,-0.4733149707,-0.5679838061,-0.4453574717,0.1962510943,-0.0225957353,0.1427641064,-0.3135254979,-0.0587531067,0.1872797608,0.1010844186,0.5034633875,0.1821435839,0.2758154571,0.0917264596,-0.1938150674,0.330616802,-0.0564118177,-0.0530907661,0.2648175359,0.2582599819,-0.1079685166,0.0960217267,-0.3503322005,-0.0996441245,0.2647417486,0.2908240557,0.1488873512,0.1582138538,-0.4356733561,-0.3246853948,0.2026762217,0.7000156045,0.0620673262,-0.2714015245,-0.0443456843,-0.0864064172,-0.0254671145,0.1995577365,0.0327339061,-0.3621903062,0.1935665607,0.1648830622,-0.3147110343,-0.2591263354,0.3636896312,-0.1913252026,0.5927678943,0.0355499908,-0.1313801706,0.2586606145,0.2421822101,-0.136722967,-0.1332246512,0.1057113633,0.070227176,0.2541283369,-0.0678744242,0.0353642888,-0.3570435047,-0.0415239222,0.3222969174,0.1360559165,-0.0897942558,-0.17428267,0.2492266446,0.0741091147,0.3009946048,-0.1796060205,-0.0477748848,0.4765815437,0.2672050595,0.326230973,0.0576015227,-0.1351092011,-0.1535451263,-0.4611136913,0.0714939982,0.214524135,-0.1443417519,-0.1893423796,0.0324055701,-0.1543548256,-0.1804030836,-0.0766993761,0.1669009775,-0.1812235713,0.2234870046,0.0402884074,0.1987622976,0.0476837568,-0.3713335395,0.0091267638,-0.0419542864,-0.0121170115,-0.179774493,0.1604952365,0.189798817,-0.0275741052,-0.1526712626,-0.0065940763,0.1284850985,-0.0272581037,-0.014349116,0.1219643056,0.4336204827,0.0577198267,0.2097714841,0.0633033961,-0.1816210151,-0.1494152844,0.3578703105,-0.1170712635,-0.421894908,-0.4223869741,0.0920756534,-0.3440444171,-0.4904932976,-0.3125094175,0.1632703543,-0.3456213176,-0.2232789397,-0.1560643315,0.1160735041,-0.225792706,-0.1811288744,0.135245055,0.3909862936,-0.4929123223,0.0246767756,-0.3338528275,-0.2905285954,0.4726332724,0.1250493228,-0.1509480625,0.3816462457,-0.37542817,0.3525737524,0.8150752187,-0.3507859707,-0.3017644584,0.6629589796,-0.3365699947,0.0677952841,0.164497152,-0.051824335,0.3105320036,-0.2412600815,-0.5735133886,0.558311522,-0.0212691035,0.0276219603,-0.2257877886,-0.1839017421,0.3385206461,0.0228705481,0.2872436345,0.3535701334,0.381776005,-0.0145400017,0.3758553267,0.3502211869,0.1977120787,0.1511360556,0.2737631202,0.2791815996,-0.1216490045,-0.1910120696,-0.3182948828,0.1219421923,0.0116811609,0.1984686106,-0.1835459471,0.2057807297,-0.2299393415,-0.0293844622,-0.1918578893,-0.0055028312,-0.0523436628,0.1304647923,0.1405320466,0.0675436109,-0.0822006017,0.0480408706,0.3051608801,0.3396253884,0.1168485433,0.2010666132,-0.2154401094,0.0572634675,0.0254720505,-0.0420174003,0.2511591613,-0.3942451179,-0.1590341926,0.1220752448,-0.0913945138,0.0655208975,0.3380198181,0.2291091084,0.3188152313,0.1212419569,0.0016420583,0.4901714921,0.1867331266,-0.062870793,0.048834227,0.0486768298,0.2936161458,0.292632252,0.0477472395,0.239598617,0.2429680079,-0.0954048187,0.0971740559,-0.0827437937,0.1909264624,-0.0649102256,-0.2271276116,-0.215315327,-0.1315472424,0.0321932882,-0.1921300739,-0.0864678919,0.1772977114,0.3546396494,0.1660159528,-0.0113036698,0.1358819306,0.7480919957,0.3015371561,0.1720658541,0.2561052442,0.0626670718,0.0420245714,-0.2632793784,0.2398954779,0.1317509264,-0.3243546188,0.4762621224,-0.00190047,0.1587664187,-0.0111282719,-0.085818626,0.2294829488,0.3678600788,-0.2246294022,-0.0351526439,-0.6205755472,-0.5866054296,-0.1442284584,-0.1357115805,-0.2736100852,-0.5019877553,-0.1304797381,0.2951658666,-0.0787130445,0.0650274754,-0.3146880567,0.02385398,-0.2574695647,-0.376431644,-0.1914370954,0.2212374359,-0.1721339226,0.0412393324,0.3339522779,-0.0831134692,0.3308106363,0.0350064225,0.1513211876,-0.6007010341,-0.0285297129,0.2332389355,-0.0630536303,-0.0624087676,0.1022353992,0.1170671955,0.3741820157,-0.0691222548,0.2739335299,-0.1158630326,-0.1350189,-0.1439898759,0.092330955,-0.0261781756,-0.1369401067,-0.2131223232,-0.4117681384,-0.5828337669,0.0274788514,0.1414267421,0.1413069218,0.0885490924,-0.1958159655,0.1616010666,-0.0886616111,-0.155405432,0.0077403332,-0.1080392748,0.4505591393,-0.3225883245,-0.4660054147,0.2123737782,0.0972791016,-0.1651213914,0.0707212165,-0.4019480944,-0.3196632564,-0.0820868537,0.2933103144,-0.1356044412,0.2699176371,0.2686161399,-0.1009520516,-0.0188567005,0.0767827481,-0.0001750693,-0.0275560711,0.4820994139,0.4234930873,0.2095014453,0.3233124912,0.0368335731,0.6444626451,-0.0771290287,0.2494202256,0.6027050018,-0.1445818543,0.1560005993,0.1535471529,-0.1989412159,0.0645653382,-0.1352654397,0.10365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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/720","title":"OSError: Cannot find data file when not using the dummy dataset in RAG","comments":"Same issue here. I will be digging further, but it looks like the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wiki_dpr\/wiki_dpr.py#L132) is attempting to open a file that is not downloaded yet. \r\n\r\n```\r\n99dcbca09109e58502e6b9271d4d3f3791b43f61f3161a76b25d2775ab1a4498.lock\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnpicklingError                           Traceback (most recent call last)\r\n~\/anaconda3\/envs\/eqa\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    446             try:\r\n--> 447                 return pickle.load(fid, **pickle_kwargs)\r\n    448             except Exception:\r\n\r\nUnpicklingError: pickle data was truncated\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n~\/src\/datasets\/src\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    559 \r\n--> 560         if verify_infos:\r\n    561             verify_splits(self.info.splits, split_dict)\r\n\r\n~\/src\/datasets\/src\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n    847                 writer.write(example)\r\n--> 848         finally:\r\n    849             num_examples, num_bytes = writer.finalize()\r\n\r\n~\/anaconda3\/envs\/eqa\/lib\/python3.7\/site-packages\/tqdm\/notebook.py in __iter__(self, *args, **kwargs)\r\n    227         try:\r\n--> 228             for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n    229                 # return super(tqdm...) will not catch exception\r\n\r\n~\/anaconda3\/envs\/eqa\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1132         try:\r\n-> 1133             for obj in iterable:\r\n   1134                 yield obj\r\n\r\n\/hdd\/rag\/cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_dpr\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2\/wiki_dpr.py in _generate_examples(self, data_file, vectors_files)\r\n    131                         break\r\n--> 132                     vecs = np.load(open(vectors_files.pop(0), \"rb\"), allow_pickle=True)\r\n    133                     vec_idx = 0\r\n\r\n~\/anaconda3\/envs\/eqa\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    449                 raise IOError(\r\n--> 450                     \"Failed to interpret file %s as a pickle\" % repr(file))\r\n    451 \r\n\r\nOSError: Failed to interpret file <_io.BufferedReader name='\/hdd\/rag\/downloads\/99dcbca09109e58502e6b9271d4d3f3791b43f61f3161a76b25d2775ab1a4498'> as a pickle\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-8-24351ff8ce44> in <module>\r\n      4 retriever = RagRetriever.from_pretrained(\"facebook\/rag-sequence-nq\", \r\n      5                                          index_name=\"exact\",\r\n----> 6                                          use_dummy_dataset=False)\r\n\r\n~\/src\/transformers\/src\/transformers\/retrieval_rag.py in from_pretrained(cls, retriever_name_or_path, **kwargs)\r\n    321         generator_tokenizer = rag_tokenizer.generator\r\n    322         return cls(\r\n--> 323             config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer\r\n    324         )\r\n    325 \r\n\r\n~\/src\/transformers\/src\/transformers\/retrieval_rag.py in __init__(self, config, question_encoder_tokenizer, generator_tokenizer)\r\n    310         self.config = config\r\n    311         if self._init_retrieval:\r\n--> 312             self.init_retrieval()\r\n    313 \r\n    314     @classmethod\r\n\r\n~\/src\/transformers\/src\/transformers\/retrieval_rag.py in init_retrieval(self)\r\n    338 \r\n    339         logger.info(\"initializing retrieval\")\r\n--> 340         self.index.init_index()\r\n    341 \r\n    342     def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):\r\n\r\n~\/src\/transformers\/src\/transformers\/retrieval_rag.py in init_index(self)\r\n    248                 split=self.dataset_split,\r\n    249                 index_name=self.index_name,\r\n--> 250                 dummy=self.use_dummy_dataset,\r\n    251             )\r\n    252             self.dataset.set_format(\"numpy\", columns=[\"embeddings\"], output_all_columns=True)\r\n\r\n~\/src\/datasets\/src\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    615     builder_instance.download_and_prepare(\r\n    616         download_config=download_config,\r\n--> 617         download_mode=download_mode,\r\n    618         ignore_verifications=ignore_verifications,\r\n    619     )\r\n\r\n~\/src\/datasets\/src\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    481                     # Sync info\r\n    482                     self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n--> 483                     self.info.download_checksums = dl_manager.get_recorded_sizes_checksums()\r\n    484                     self.info.size_in_bytes = self.info.dataset_size + self.info.download_size\r\n    485                     # Save info\r\n\r\n~\/src\/datasets\/src\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    560         if verify_infos:\r\n    561             verify_splits(self.info.splits, split_dict)\r\n--> 562 \r\n    563         # Update the info object with the splits.\r\n    564         self.info.splits = split_dict\r\n\r\nOSError: Cannot find data file.\r\n```\r\n\r\nThank you.","body":"## Environment info\r\n\r\n    transformers version: 3.3.1\r\n    Platform: Linux-4.19\r\n    Python version: 3.7.7\r\n    PyTorch version (GPU?): 1.6.0\r\n    Tensorflow version (GPU?): No\r\n    Using GPU in script?: Yes\r\n    Using distributed or parallel set-up in script?: No\r\n\r\n## To reproduce\r\n\r\nSteps to reproduce the behaviour:\r\n```\r\nimport os\r\nos.environ['HF_DATASETS_CACHE'] = '\/workspace\/notebooks\/POCs\/cache'\r\n\r\nfrom transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n\r\ntokenizer = RagTokenizer.from_pretrained(\"facebook\/rag-token-nq\")\r\nretriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=False) \r\n```\r\n\r\nPlese note that I'm using the whole dataset: **use_dummy_dataset=False**\r\nAfter around 4 hours (downloading and some other things) this is returned:\r\n\r\n```\r\nDownloading and preparing dataset wiki_dpr\/psgs_w100.nq.exact (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/workspace\/notebooks\/POCs\/cache\/wiki_dpr\/psgs_w100.nq.exact\/0.0.0\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2...\r\n\r\n---------------------------------------------------------------------------\r\nUnpicklingError                           Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    459             try:\r\n--> 460                 return pickle.load(fid, **pickle_kwargs)\r\n    461             except Exception:\r\n\r\nUnpicklingError: pickle data was truncated\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    552                 # Prepare split will record examples associated to the split\r\n--> 553                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    554             except OSError:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n    840             for key, record in utils.tqdm(\r\n--> 841                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    842             ):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/tqdm\/notebook.py in __iter__(self, *args, **kwargs)\r\n    217         try:\r\n--> 218             for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n    219                 # return super(tqdm...) will not catch exception\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1128         try:\r\n-> 1129             for obj in iterable:\r\n   1130                 yield obj\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_dpr\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2\/wiki_dpr.py in _generate_examples(self, data_file, vectors_files)\r\n    131                         break\r\n--> 132                     vecs = np.load(open(vectors_files.pop(0), \"rb\"), allow_pickle=True)\r\n    133                     vec_idx = 0\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    462                 raise IOError(\r\n--> 463                     \"Failed to interpret file %s as a pickle\" % repr(file))\r\n    464     finally:\r\n\r\nOSError: Failed to interpret file <_io.BufferedReader name='\/workspace\/notebooks\/POCs\/cache\/downloads\/f34d5f091294259b4ca90e813631e69a6ded660d71b6cbedf89ddba50df94448'> as a pickle\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-10-f28df370ac47> in <module>\r\n      1 # ln -s \/workspace\/notebooks\/POCs\/cache \/root\/.cache\/huggingface\/datasets\r\n----> 2 retriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in from_pretrained(cls, retriever_name_or_path, **kwargs)\r\n    307         generator_tokenizer = rag_tokenizer.generator\r\n    308         return cls(\r\n--> 309             config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer\r\n    310         )\r\n    311 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in __init__(self, config, question_encoder_tokenizer, generator_tokenizer)\r\n    298         self.config = config\r\n    299         if self._init_retrieval:\r\n--> 300             self.init_retrieval()\r\n    301 \r\n    302     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in init_retrieval(self)\r\n    324 \r\n    325         logger.info(\"initializing retrieval\")\r\n--> 326         self.index.init_index()\r\n    327 \r\n    328     def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in init_index(self)\r\n    238                 split=self.dataset_split,\r\n    239                 index_name=self.index_name,\r\n--> 240                 dummy=self.use_dummy_dataset,\r\n    241             )\r\n    242             self.dataset.set_format(\"numpy\", columns=[\"embeddings\"], output_all_columns=True)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    474                     if not downloaded_from_gcs:\r\n    475                         self._download_and_prepare(\r\n--> 476                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    477                         )\r\n    478                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    553                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    554             except OSError:\r\n--> 555                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n    556 \r\n    557         if verify_infos:\r\n\r\nOSError: Cannot find data file. \r\n\r\n```\r\n\r\nThanks \r\n","comment_length":387,"text":"OSError: Cannot find data file when not using the dummy dataset in RAG \n ## Environment info\r\n\r\n    transformers version: 3.3.1\r\n    Platform: Linux-4.19\r\n    Python version: 3.7.7\r\n    PyTorch version (GPU?): 1.6.0\r\n    Tensorflow version (GPU?): No\r\n    Using GPU in script?: Yes\r\n    Using distributed or parallel set-up in script?: No\r\n\r\n## To reproduce\r\n\r\nSteps to reproduce the behaviour:\r\n```\r\nimport os\r\nos.environ['HF_DATASETS_CACHE'] = '\/workspace\/notebooks\/POCs\/cache'\r\n\r\nfrom transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n\r\ntokenizer = RagTokenizer.from_pretrained(\"facebook\/rag-token-nq\")\r\nretriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=False) \r\n```\r\n\r\nPlese note that I'm using the whole dataset: **use_dummy_dataset=False**\r\nAfter around 4 hours (downloading and some other things) this is returned:\r\n\r\n```\r\nDownloading and preparing dataset wiki_dpr\/psgs_w100.nq.exact (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/workspace\/notebooks\/POCs\/cache\/wiki_dpr\/psgs_w100.nq.exact\/0.0.0\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2...\r\n\r\n---------------------------------------------------------------------------\r\nUnpicklingError                           Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    459             try:\r\n--> 460                 return pickle.load(fid, **pickle_kwargs)\r\n    461             except Exception:\r\n\r\nUnpicklingError: pickle data was truncated\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    552                 # Prepare split will record examples associated to the split\r\n--> 553                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    554             except OSError:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n    840             for key, record in utils.tqdm(\r\n--> 841                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    842             ):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/tqdm\/notebook.py in __iter__(self, *args, **kwargs)\r\n    217         try:\r\n--> 218             for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n    219                 # return super(tqdm...) will not catch exception\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1128         try:\r\n-> 1129             for obj in iterable:\r\n   1130                 yield obj\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_dpr\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2\/wiki_dpr.py in _generate_examples(self, data_file, vectors_files)\r\n    131                         break\r\n--> 132                     vecs = np.load(open(vectors_files.pop(0), \"rb\"), allow_pickle=True)\r\n    133                     vec_idx = 0\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    462                 raise IOError(\r\n--> 463                     \"Failed to interpret file %s as a pickle\" % repr(file))\r\n    464     finally:\r\n\r\nOSError: Failed to interpret file <_io.BufferedReader name='\/workspace\/notebooks\/POCs\/cache\/downloads\/f34d5f091294259b4ca90e813631e69a6ded660d71b6cbedf89ddba50df94448'> as a pickle\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-10-f28df370ac47> in <module>\r\n      1 # ln -s \/workspace\/notebooks\/POCs\/cache \/root\/.cache\/huggingface\/datasets\r\n----> 2 retriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in from_pretrained(cls, retriever_name_or_path, **kwargs)\r\n    307         generator_tokenizer = rag_tokenizer.generator\r\n    308         return cls(\r\n--> 309             config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer\r\n    310         )\r\n    311 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in __init__(self, config, question_encoder_tokenizer, generator_tokenizer)\r\n    298         self.config = config\r\n    299         if self._init_retrieval:\r\n--> 300             self.init_retrieval()\r\n    301 \r\n    302     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in init_retrieval(self)\r\n    324 \r\n    325         logger.info(\"initializing retrieval\")\r\n--> 326         self.index.init_index()\r\n    327 \r\n    328     def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in init_index(self)\r\n    238                 split=self.dataset_split,\r\n    239                 index_name=self.index_name,\r\n--> 240                 dummy=self.use_dummy_dataset,\r\n    241             )\r\n    242             self.dataset.set_format(\"numpy\", columns=[\"embeddings\"], output_all_columns=True)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    474                     if not downloaded_from_gcs:\r\n    475                         self._download_and_prepare(\r\n--> 476                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    477                         )\r\n    478                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    553                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    554             except OSError:\r\n--> 555                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n    556 \r\n    557         if verify_infos:\r\n\r\nOSError: Cannot find data file. \r\n\r\n```\r\n\r\nThanks \r\n \n Same issue here. I will be digging further, but it looks like the [script](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wiki_dpr\/wiki_dpr.py#L132) is attempting to open a file that is not downloaded yet. \r\n\r\n```\r\n99dcbca09109e58502e6b9271d4d3f3791b43f61f3161a76b25d2775ab1a4498.lock\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnpicklingError                           Traceback (most recent call last)\r\n~\/anaconda3\/envs\/eqa\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    446             try:\r\n--> 447                 return pickle.load(fid, **pickle_kwargs)\r\n    448             except Exception:\r\n\r\nUnpicklingError: pickle data was truncated\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n~\/src\/datasets\/src\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    559 \r\n--> 560         if verify_infos:\r\n    561             verify_splits(self.info.splits, split_dict)\r\n\r\n~\/src\/datasets\/src\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n    847                 writer.write(example)\r\n--> 848         finally:\r\n    849             num_examples, num_bytes = writer.finalize()\r\n\r\n~\/anaconda3\/envs\/eqa\/lib\/python3.7\/site-packages\/tqdm\/notebook.py in __iter__(self, *args, **kwargs)\r\n    227         try:\r\n--> 228             for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n    229                 # return super(tqdm...) will not catch exception\r\n\r\n~\/anaconda3\/envs\/eqa\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1132         try:\r\n-> 1133             for obj in iterable:\r\n   1134                 yield obj\r\n\r\n\/hdd\/rag\/cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_dpr\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2\/wiki_dpr.py in _generate_examples(self, data_file, vectors_files)\r\n    131                         break\r\n--> 132                     vecs = np.load(open(vectors_files.pop(0), \"rb\"), allow_pickle=True)\r\n    133                     vec_idx = 0\r\n\r\n~\/anaconda3\/envs\/eqa\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    449                 raise IOError(\r\n--> 450                     \"Failed to interpret file %s as a pickle\" % repr(file))\r\n    451 \r\n\r\nOSError: Failed to interpret file <_io.BufferedReader name='\/hdd\/rag\/downloads\/99dcbca09109e58502e6b9271d4d3f3791b43f61f3161a76b25d2775ab1a4498'> as a pickle\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-8-24351ff8ce44> in <module>\r\n      4 retriever = RagRetriever.from_pretrained(\"facebook\/rag-sequence-nq\", \r\n      5                                          index_name=\"exact\",\r\n----> 6                                          use_dummy_dataset=False)\r\n\r\n~\/src\/transformers\/src\/transformers\/retrieval_rag.py in from_pretrained(cls, retriever_name_or_path, **kwargs)\r\n    321         generator_tokenizer = rag_tokenizer.generator\r\n    322         return cls(\r\n--> 323             config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer\r\n    324         )\r\n    325 \r\n\r\n~\/src\/transformers\/src\/transformers\/retrieval_rag.py in __init__(self, config, question_encoder_tokenizer, generator_tokenizer)\r\n    310         self.config = config\r\n    311         if self._init_retrieval:\r\n--> 312             self.init_retrieval()\r\n    313 \r\n    314     @classmethod\r\n\r\n~\/src\/transformers\/src\/transformers\/retrieval_rag.py in init_retrieval(self)\r\n    338 \r\n    339         logger.info(\"initializing retrieval\")\r\n--> 340         self.index.init_index()\r\n    341 \r\n    342     def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):\r\n\r\n~\/src\/transformers\/src\/transformers\/retrieval_rag.py in init_index(self)\r\n    248                 split=self.dataset_split,\r\n    249                 index_name=self.index_name,\r\n--> 250                 dummy=self.use_dummy_dataset,\r\n    251             )\r\n    252             self.dataset.set_format(\"numpy\", columns=[\"embeddings\"], output_all_columns=True)\r\n\r\n~\/src\/datasets\/src\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    615     builder_instance.download_and_prepare(\r\n    616         download_config=download_config,\r\n--> 617         download_mode=download_mode,\r\n    618         ignore_verifications=ignore_verifications,\r\n    619     )\r\n\r\n~\/src\/datasets\/src\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    481                     # Sync info\r\n    482                     self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n--> 483                     self.info.download_checksums = dl_manager.get_recorded_sizes_checksums()\r\n    484                     self.info.size_in_bytes = self.info.dataset_size + self.info.download_size\r\n    485                     # Save info\r\n\r\n~\/src\/datasets\/src\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    560         if verify_infos:\r\n    561             verify_splits(self.info.splits, split_dict)\r\n--> 562 \r\n    563         # Update the info object with the splits.\r\n    564         self.info.splits = split_dict\r\n\r\nOSError: Cannot find data file.\r\n```\r\n\r\nThank you.","embeddings":[-0.2258688062,-0.0487169065,0.0123973489,0.1052551121,0.3706667423,-0.0698539317,0.3323160708,0.2692305744,0.0155129815,0.325091511,-0.13942644,0.217653051,-0.0637350604,-0.3219818473,-0.0156813674,0.0387514979,0.0088648982,0.176944688,-0.2071304321,-0.2274689972,-0.2748771906,0.0712658539,-0.2206947207,-0.0241290927,-0.6810176969,0.1146390289,-0.05781601,0.3083193898,-0.1954237521,-0.251630187,0.202607438,-0.3225424588,0.2263191193,0.7035835981,-0.0001225423,0.2990083098,0.1429332048,-0.2735655606,-0.2608669698,-0.278689146,0.4701996744,-0.0022001471,-0.1336365491,-0.2349150628,-0.3879041076,-0.0179219656,0.1504863203,-0.2608039379,0.1146472842,0.4453887343,0.1201401874,0.5135034323,-0.0789261982,-0.1666063219,0.4416405857,-0.1321118772,-0.2456288487,0.2880708873,0.1355750412,-0.3839831054,-0.2244360745,0.3043579757,0.0357949734,0.214489162,0.0499641933,0.1509962678,-0.331099242,-0.1621896625,0.2034894675,0.1592778862,0.4668168724,-0.1024601981,-0.4101263881,0.1078529805,-0.1492986828,-0.01189222,0.3080828786,0.250741303,-0.2307934165,0.3093445301,-0.1880170703,0.1411207914,-0.1838113368,0.3203998804,-0.1091684997,0.5142392516,-0.0597856715,0.1333447248,0.0515007935,0.266982913,-0.1275482178,-0.0532716103,0.2568569183,0.1107259393,-0.4334582984,-0.0851476789,-0.141758278,-0.2281454057,0.0090549998,0.283882916,0.119040519,-0.0872798711,-0.050617218,0.0559016839,-0.0747926906,0.0031038059,-0.4398024082,0.3263707459,0.1369172037,0.0633962378,-0.0617391281,-0.2556205094,0.0124686137,-0.0752046928,-0.1377577782,-0.0830673054,0.2952482104,-0.1419144869,-0.3792672455,-0.1376513541,-0.2718304992,0.044915434,-0.0649191588,0.5335163474,-0.1762444675,0.0942436904,0.2233512551,0.2114507407,-0.3598036766,-0.2135165185,-0.189420566,0.1914025992,-0.2589720488,-0.0695585534,0.336157769,0.1515605152,0.5469791293,-0.0422237702,-0.0395792834,0.0757380351,0.5255907774,-0.3452492356,0.1852508485,0.3671411276,-0.0957375243,0.1022145227,0.3197237849,0.0107704932,-0.0048441472,0.1602138281,-0.134794578,-0.4402830601,-0.0159310251,0.123059392,-0.096447885,-0.0022720867,0.5216624737,-0.3452539146,0.0150453327,-0.0186987352,0.1125785187,-0.0878424272,-0.3588058054,-0.177587077,0.1657052636,0.3699544072,-0.4192665219,-0.0607249737,0.0974297151,0.1354825199,0.272669822,0.407297045,-0.2612980008,0.5780154467,-0.3673042655,-0.0979130566,0.2546078265,-0.3039738536,-0.4831534326,-0.0294232927,-0.2176701427,0.3169541955,0.3235011697,0.1196951345,0.0952945575,0.1186122298,0.0603864826,0.2023348361,-0.0112885842,-0.1898554116,-0.3436117768,-0.36027354,0.3072978556,0.3618117571,0.3364816904,0.0394932702,-0.0466328859,0.2523000836,0.2554249763,-0.1765722781,0.0135172177,0.3977082074,0.2658633292,0.0652284846,0.1295245886,-0.2036168128,-0.0927456841,0.2144518346,0.0100941388,0.1594747603,0.0780224055,-0.2786820233,-0.2695260644,-0.1588254571,-0.2466272712,-0.4860778153,0.0125515806,0.3241280615,0.2520541549,-0.0928548723,-0.2521234155,0.4377958477,-0.1952871829,0.2539069653,-0.3626701534,0.3693114519,-0.1394768953,-0.1955277026,-0.1041209176,0.0695498437,0.0412254259,-0.1709701717,0.0008806916,0.3422067165,0.2573977411,-0.3763899207,0.2662567496,0.057855133,0.0825240463,-0.119228363,-0.0103514362,0.4316679239,0.228631705,0.0311591905,-0.1992195249,0.2342782319,-0.0837530121,-0.0150448382,-0.0632191002,-0.0380300283,0.2054506838,-0.2455834001,0.0543307029,0.0540948063,0.2960953116,0.0623819977,0.5005347133,-0.086363323,-0.1532837451,-0.0471563451,0.4012603462,-0.0439240448,0.0086467201,0.1421054751,-0.1760937274,0.100363411,-0.0817503482,-0.1644160599,0.589443326,0.1907703578,0.0153245684,0.0057594245,0.0197617151,-0.1352092624,-0.0579379462,0.0057319207,0.1119224355,0.2939564884,0.010454841,0.0019570047,-0.204659909,-0.2126511633,0.1033594385,0.2841060758,-0.086158067,0.0140030636,-0.229308635,0.2530737817,-0.1107315645,0.0085567124,-0.2427500635,-0.1266786009,0.0411275737,0.2089068741,-0.0423332155,0.3041032553,0.092355594,0.0221105833,0.2015033662,-0.195492208,-0.1071512103,-0.1214265823,-0.2365635782,-0.0093308585,-0.0095360149,-0.075437203,0.2127888501,-0.2755419016,-0.0830216929,-0.5109938383,-0.2276017517,0.0538613498,0.2870907784,0.2047031373,0.1335327327,0.2145299315,-0.062615633,-0.0509736314,0.2801601589,-0.2143454701,-0.1684692353,0.0525324866,-0.0789887682,0.2073695064,0.0410677455,-0.6410495043,-0.3373051286,-0.2971862853,-0.1248568967,-0.0847932026,-0.0033860109,-0.1192247272,0.1342246085,0.2047805488,0.0360465646,-0.0473384112,-0.123831518,-0.2134780288,0.5039732456,-0.0896632299,-0.2254339159,0.1115513816,-0.2123592645,0.0627879202,0.2687756121,-0.5052886009,-0.5236566663,-0.091148667,0.3369216025,-0.1908483356,0.089464426,0.4921669066,-0.3213092685,-0.0286873244,-0.0757975057,-0.048251491,0.1265075505,-0.1775063425,0.4617244005,-0.0131942481,0.2616951168,0.2069703788,1.1618537903,0.221200183,-0.0264962409,0.0756130442,-0.0601709187,0.3890325725,-0.2206432372,-0.1952376217,0.169987455,-0.0849358365,-0.0233410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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/720","title":"OSError: Cannot find data file when not using the dummy dataset in RAG","comments":"An update on my end. This seems like a transient issue. Reran the script from scratch overnight with no errors. ","body":"## Environment info\r\n\r\n    transformers version: 3.3.1\r\n    Platform: Linux-4.19\r\n    Python version: 3.7.7\r\n    PyTorch version (GPU?): 1.6.0\r\n    Tensorflow version (GPU?): No\r\n    Using GPU in script?: Yes\r\n    Using distributed or parallel set-up in script?: No\r\n\r\n## To reproduce\r\n\r\nSteps to reproduce the behaviour:\r\n```\r\nimport os\r\nos.environ['HF_DATASETS_CACHE'] = '\/workspace\/notebooks\/POCs\/cache'\r\n\r\nfrom transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n\r\ntokenizer = RagTokenizer.from_pretrained(\"facebook\/rag-token-nq\")\r\nretriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=False) \r\n```\r\n\r\nPlese note that I'm using the whole dataset: **use_dummy_dataset=False**\r\nAfter around 4 hours (downloading and some other things) this is returned:\r\n\r\n```\r\nDownloading and preparing dataset wiki_dpr\/psgs_w100.nq.exact (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/workspace\/notebooks\/POCs\/cache\/wiki_dpr\/psgs_w100.nq.exact\/0.0.0\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2...\r\n\r\n---------------------------------------------------------------------------\r\nUnpicklingError                           Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    459             try:\r\n--> 460                 return pickle.load(fid, **pickle_kwargs)\r\n    461             except Exception:\r\n\r\nUnpicklingError: pickle data was truncated\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    552                 # Prepare split will record examples associated to the split\r\n--> 553                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    554             except OSError:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n    840             for key, record in utils.tqdm(\r\n--> 841                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    842             ):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/tqdm\/notebook.py in __iter__(self, *args, **kwargs)\r\n    217         try:\r\n--> 218             for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n    219                 # return super(tqdm...) will not catch exception\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1128         try:\r\n-> 1129             for obj in iterable:\r\n   1130                 yield obj\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_dpr\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2\/wiki_dpr.py in _generate_examples(self, data_file, vectors_files)\r\n    131                         break\r\n--> 132                     vecs = np.load(open(vectors_files.pop(0), \"rb\"), allow_pickle=True)\r\n    133                     vec_idx = 0\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    462                 raise IOError(\r\n--> 463                     \"Failed to interpret file %s as a pickle\" % repr(file))\r\n    464     finally:\r\n\r\nOSError: Failed to interpret file <_io.BufferedReader name='\/workspace\/notebooks\/POCs\/cache\/downloads\/f34d5f091294259b4ca90e813631e69a6ded660d71b6cbedf89ddba50df94448'> as a pickle\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-10-f28df370ac47> in <module>\r\n      1 # ln -s \/workspace\/notebooks\/POCs\/cache \/root\/.cache\/huggingface\/datasets\r\n----> 2 retriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in from_pretrained(cls, retriever_name_or_path, **kwargs)\r\n    307         generator_tokenizer = rag_tokenizer.generator\r\n    308         return cls(\r\n--> 309             config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer\r\n    310         )\r\n    311 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in __init__(self, config, question_encoder_tokenizer, generator_tokenizer)\r\n    298         self.config = config\r\n    299         if self._init_retrieval:\r\n--> 300             self.init_retrieval()\r\n    301 \r\n    302     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in init_retrieval(self)\r\n    324 \r\n    325         logger.info(\"initializing retrieval\")\r\n--> 326         self.index.init_index()\r\n    327 \r\n    328     def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in init_index(self)\r\n    238                 split=self.dataset_split,\r\n    239                 index_name=self.index_name,\r\n--> 240                 dummy=self.use_dummy_dataset,\r\n    241             )\r\n    242             self.dataset.set_format(\"numpy\", columns=[\"embeddings\"], output_all_columns=True)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    474                     if not downloaded_from_gcs:\r\n    475                         self._download_and_prepare(\r\n--> 476                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    477                         )\r\n    478                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    553                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    554             except OSError:\r\n--> 555                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n    556 \r\n    557         if verify_infos:\r\n\r\nOSError: Cannot find data file. \r\n\r\n```\r\n\r\nThanks \r\n","comment_length":20,"text":"OSError: Cannot find data file when not using the dummy dataset in RAG \n ## Environment info\r\n\r\n    transformers version: 3.3.1\r\n    Platform: Linux-4.19\r\n    Python version: 3.7.7\r\n    PyTorch version (GPU?): 1.6.0\r\n    Tensorflow version (GPU?): No\r\n    Using GPU in script?: Yes\r\n    Using distributed or parallel set-up in script?: No\r\n\r\n## To reproduce\r\n\r\nSteps to reproduce the behaviour:\r\n```\r\nimport os\r\nos.environ['HF_DATASETS_CACHE'] = '\/workspace\/notebooks\/POCs\/cache'\r\n\r\nfrom transformers import RagTokenizer, RagRetriever, RagTokenForGeneration\r\n\r\ntokenizer = RagTokenizer.from_pretrained(\"facebook\/rag-token-nq\")\r\nretriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=False) \r\n```\r\n\r\nPlese note that I'm using the whole dataset: **use_dummy_dataset=False**\r\nAfter around 4 hours (downloading and some other things) this is returned:\r\n\r\n```\r\nDownloading and preparing dataset wiki_dpr\/psgs_w100.nq.exact (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/workspace\/notebooks\/POCs\/cache\/wiki_dpr\/psgs_w100.nq.exact\/0.0.0\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2...\r\n\r\n---------------------------------------------------------------------------\r\nUnpicklingError                           Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    459             try:\r\n--> 460                 return pickle.load(fid, **pickle_kwargs)\r\n    461             except Exception:\r\n\r\nUnpicklingError: pickle data was truncated\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    552                 # Prepare split will record examples associated to the split\r\n--> 553                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    554             except OSError:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n    840             for key, record in utils.tqdm(\r\n--> 841                 generator, unit=\" examples\", total=split_info.num_examples, leave=False, disable=not_verbose\r\n    842             ):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/tqdm\/notebook.py in __iter__(self, *args, **kwargs)\r\n    217         try:\r\n--> 218             for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n    219                 # return super(tqdm...) will not catch exception\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1128         try:\r\n-> 1129             for obj in iterable:\r\n   1130                 yield obj\r\n\r\n~\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wiki_dpr\/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2\/wiki_dpr.py in _generate_examples(self, data_file, vectors_files)\r\n    131                         break\r\n--> 132                     vecs = np.load(open(vectors_files.pop(0), \"rb\"), allow_pickle=True)\r\n    133                     vec_idx = 0\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/numpy\/lib\/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n    462                 raise IOError(\r\n--> 463                     \"Failed to interpret file %s as a pickle\" % repr(file))\r\n    464     finally:\r\n\r\nOSError: Failed to interpret file <_io.BufferedReader name='\/workspace\/notebooks\/POCs\/cache\/downloads\/f34d5f091294259b4ca90e813631e69a6ded660d71b6cbedf89ddba50df94448'> as a pickle\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-10-f28df370ac47> in <module>\r\n      1 # ln -s \/workspace\/notebooks\/POCs\/cache \/root\/.cache\/huggingface\/datasets\r\n----> 2 retriever = RagRetriever.from_pretrained(\"facebook\/rag-token-nq\", index_name=\"exact\", use_dummy_dataset=False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in from_pretrained(cls, retriever_name_or_path, **kwargs)\r\n    307         generator_tokenizer = rag_tokenizer.generator\r\n    308         return cls(\r\n--> 309             config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer\r\n    310         )\r\n    311 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in __init__(self, config, question_encoder_tokenizer, generator_tokenizer)\r\n    298         self.config = config\r\n    299         if self._init_retrieval:\r\n--> 300             self.init_retrieval()\r\n    301 \r\n    302     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in init_retrieval(self)\r\n    324 \r\n    325         logger.info(\"initializing retrieval\")\r\n--> 326         self.index.init_index()\r\n    327 \r\n    328     def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/transformers\/retrieval_rag.py in init_index(self)\r\n    238                 split=self.dataset_split,\r\n    239                 index_name=self.index_name,\r\n--> 240                 dummy=self.use_dummy_dataset,\r\n    241             )\r\n    242             self.dataset.set_format(\"numpy\", columns=[\"embeddings\"], output_all_columns=True)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    609         download_config=download_config,\r\n    610         download_mode=download_mode,\r\n--> 611         ignore_verifications=ignore_verifications,\r\n    612     )\r\n    613 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    474                     if not downloaded_from_gcs:\r\n    475                         self._download_and_prepare(\r\n--> 476                             dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    477                         )\r\n    478                     # Sync info\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    553                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    554             except OSError:\r\n--> 555                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n    556 \r\n    557         if verify_infos:\r\n\r\nOSError: Cannot find data file. \r\n\r\n```\r\n\r\nThanks \r\n \n An update on my end. This seems like a transient issue. Reran the script from scratch overnight with no errors. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/709","title":"How to use similarity settings  other then \"BM25\" in Elasticsearch index ?","comments":"Datasets does not use elasticsearch API to define custom similarity. If you want to use a custom similarity, the best would be to run a curl request directly to your elasticsearch instance (see sample hereafter, directly from ES documentation), then you should be able to use `my_similarity` in your configuration passed to datasets\r\n\r\n```\r\ncurl -X PUT \"localhost:9200\/index?pretty\" -H 'Content-Type: application\/json' -d'\r\n{\r\n  \"settings\": {\r\n    \"index\": {\r\n      \"similarity\": {\r\n        \"my_similarity\": {\r\n          \"type\": \"DFR\",\r\n          \"basic_model\": \"g\",\r\n          \"after_effect\": \"l\",\r\n          \"normalization\": \"h2\",\r\n          \"normalization.h2.c\": \"3.0\"\r\n        }\r\n      }\r\n    }\r\n  }\r\n}\r\n'\r\n\r\n```","body":"**QUESTION : How should we use other similarity algorithms supported by Elasticsearch other than \"BM25\"  ?**\r\n**ES Reference**\r\nhttps:\/\/www.elastic.co\/guide\/en\/elasticsearch\/reference\/current\/index-modules-similarity.html\r\n**HF doc reference:**\r\nhttps:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html\r\n\r\n**context :**\r\n========\r\n\r\nI used the latest Elasticsearch server  version 7.9.2\r\nWhen I set DFR  which is one of the other similarity algorithms supported by elasticsearch  in the mapping, I get an error\r\n\r\nFor example DFR that I had tried in the first instance in mappings as below.,\r\n`\"mappings\": {\"properties\": {\"text\": {\"type\": \"text\", \"analyzer\": \"standard\", \"similarity\": \"DFR\"}}},`\r\n\r\nI get the following error \r\nRequestError: RequestError(400, 'mapper_parsing_exception', 'Unknown Similarity type [DFR] for field [text]')\r\n\r\nThe other thing as another option I had tried was to declare \"similarity\": \"my_similarity\" within settings and then assigning \"my_similarity\" inside the mappings as below \r\n\r\n`es_config = {\r\n        \"settings\": {\r\n            \"number_of_shards\": 1,\r\n             **\"similarity\":  \"my_similarity\"**: {\r\n          \"type\": \"DFR\",\r\n          \"basic_model\": \"g\",\r\n          \"after_effect\": \"l\",\r\n          \"normalization\": \"h2\",\r\n          \"normalization.h2.c\": \"3.0\"\r\n        } ,\r\n            \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n            \r\n        },\r\n        \"mappings\": {\"properties\": {\"text\": {\"type\": \"text\", \"analyzer\": \"standard\", \"similarity\": \"my_similarity\"}}},\r\n    }`\r\n\r\nFor this , I got the following error\r\nRequestError: RequestError(400, 'illegal_argument_exception', 'unknown setting [index.similarity] please check that any required plugins are installed, or check the breaking changes documentation for removed settings')\r\n\r\n","comment_length":88,"text":"How to use similarity settings  other then \"BM25\" in Elasticsearch index ? \n **QUESTION : How should we use other similarity algorithms supported by Elasticsearch other than \"BM25\"  ?**\r\n**ES Reference**\r\nhttps:\/\/www.elastic.co\/guide\/en\/elasticsearch\/reference\/current\/index-modules-similarity.html\r\n**HF doc reference:**\r\nhttps:\/\/huggingface.co\/docs\/datasets\/faiss_and_ea.html\r\n\r\n**context :**\r\n========\r\n\r\nI used the latest Elasticsearch server  version 7.9.2\r\nWhen I set DFR  which is one of the other similarity algorithms supported by elasticsearch  in the mapping, I get an error\r\n\r\nFor example DFR that I had tried in the first instance in mappings as below.,\r\n`\"mappings\": {\"properties\": {\"text\": {\"type\": \"text\", \"analyzer\": \"standard\", \"similarity\": \"DFR\"}}},`\r\n\r\nI get the following error \r\nRequestError: RequestError(400, 'mapper_parsing_exception', 'Unknown Similarity type [DFR] for field [text]')\r\n\r\nThe other thing as another option I had tried was to declare \"similarity\": \"my_similarity\" within settings and then assigning \"my_similarity\" inside the mappings as below \r\n\r\n`es_config = {\r\n        \"settings\": {\r\n            \"number_of_shards\": 1,\r\n             **\"similarity\":  \"my_similarity\"**: {\r\n          \"type\": \"DFR\",\r\n          \"basic_model\": \"g\",\r\n          \"after_effect\": \"l\",\r\n          \"normalization\": \"h2\",\r\n          \"normalization.h2.c\": \"3.0\"\r\n        } ,\r\n            \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n            \r\n        },\r\n        \"mappings\": {\"properties\": {\"text\": {\"type\": \"text\", \"analyzer\": \"standard\", \"similarity\": \"my_similarity\"}}},\r\n    }`\r\n\r\nFor this , I got the following error\r\nRequestError: RequestError(400, 'illegal_argument_exception', 'unknown setting [index.similarity] please check that any required plugins are installed, or check the breaking changes documentation for removed settings')\r\n\r\n \n Datasets does not use elasticsearch API to define custom similarity. If you want to use a custom similarity, the best would be to run a curl request directly to your elasticsearch instance (see sample hereafter, directly from ES documentation), then you should be able to use `my_similarity` in your configuration passed to datasets\r\n\r\n```\r\ncurl -X PUT \"localhost:9200\/index?pretty\" -H 'Content-Type: application\/json' -d'\r\n{\r\n  \"settings\": {\r\n    \"index\": {\r\n      \"similarity\": {\r\n        \"my_similarity\": {\r\n          \"type\": \"DFR\",\r\n          \"basic_model\": \"g\",\r\n          \"after_effect\": \"l\",\r\n          \"normalization\": \"h2\",\r\n          \"normalization.h2.c\": \"3.0\"\r\n        }\r\n      }\r\n    }\r\n  }\r\n}\r\n'\r\n\r\n```","embeddings":[-0.1099796966,-0.6802694798,-0.0450149104,-0.0399373472,-0.1662632376,0.0516545996,-0.1838181019,0.2227956951,0.445256263,0.1382717043,-0.3413906097,-0.1468974054,0.0635201782,-0.2089974731,-0.3592832088,-0.0928407833,0.0289196949,0.0444094092,0.104161717,0.1085229665,-0.1743733436,0.1254539192,-0.000062598,-0.0005490784,-0.1480967999,0.187689364,0.0392793305,-0.2065735906,0.1528002173,-0.3148995042,0.1453121305,-0.2109477669,0.1577213705,0.044019714,-0.0001227613,0.0690059215,0.369327873,-0.0321302786,0.4254214466,-0.4718561471,-0.0608858466,0.216519013,0.1834836155,-0.177677691,-0.2151821107,-0.3694826066,0.0328343771,-0.4300846457,0.2202268839,0.3700291514,0.1151460186,-0.1253225058,0.178447336,-0.0453588031,0.1369128078,0.0639811084,0.0044003711,-0.2802915871,0.3826874793,0.4209572971,0.3343570828,-0.0454553254,-0.2023601979,0.3465203643,0.3322952092,0.1597774178,0.3609420955,-0.2127847522,0.2631377578,0.1443555504,0.1755000949,0.1586993188,0.1121972054,0.0569028407,-0.1709520221,-0.0436668098,0.0706234202,-0.2081927657,0.147807315,0.0993624851,-0.1951645613,-0.1788298786,-0.1105849594,0.2574418783,-0.0947720185,0.1925845742,-0.0943944752,-0.060371127,0.256309092,-0.4657059312,-0.1320724636,0.0108752036,0.1735929549,0.1292192787,-0.0163315777,-0.2779355049,-0.3406863213,-0.1888903081,0.3027116358,-0.1236504167,-0.075883396,0.1966890097,0.1552668363,0.2615856826,-0.3191659153,0.2049432248,0.066407159,0.4087871909,0.0873084664,-0.2203004509,-0.1675471514,0.0207611546,0.4790687561,-0.0699595362,0.0530895814,-0.4394777119,-0.2394437939,-0.4781885147,0.1970800012,-0.1506071687,-0.2655495703,-0.2916416824,0.1298919767,0.1182668284,0.0235036481,-0.4387791157,0.0350998938,0.086308904,0.0781803951,-0.0199933443,-0.0445248969,-0.0094926823,0.0087711113,0.3264604509,0.3060792685,-0.4353673458,0.5557736754,0.118113786,0.0581613109,0.3656066954,0.2970832288,0.0120175947,0.3168718219,-0.0040030503,-0.2208718807,0.0614858866,0.181742534,-0.0633637607,-0.3740391731,-0.0667839721,-0.5649577379,-0.2988860309,-0.2933887541,0.1480261087,-0.3074215949,-0.2548411787,-0.2832580209,0.2734139264,-0.2297459394,-0.1462838948,0.0989792868,-0.0884415507,-0.2237434983,0.0443157107,0.0850663036,-0.1622759551,-0.0286308713,-0.309638828,-0.0529246293,0.4652359486,0.1320650578,0.1934707612,-0.1054061055,-0.6684598923,0.0378974527,0.5693466663,0.5097745657,0.0055426504,-0.1204319596,0.1143238619,0.2543828189,-0.0709597841,0.2039704621,-0.1388183385,0.200619638,0.1326102912,0.1450300664,0.2731208801,0.2092545331,-0.2835346162,-0.3051760793,-0.2141604125,-0.0925754905,0.0189207196,-0.0333237126,0.0647436008,0.3815340698,-0.1603909731,-0.3229284883,-0.1779347062,-0.1087432355,0.0910640359,0.177333802,0.2237422019,0.2579643428,-0.1902689487,0.259560734,0.176634118,-0.3559484184,0.0818277076,0.2356043905,0.1936783195,-0.0839837566,-0.3139104247,-0.0714864954,-0.076721184,0.0441464931,-0.219810918,-0.1627634317,-0.0379004888,-0.1630102098,-0.255543828,-0.0120575782,-0.0245708581,-0.175784722,0.5014539361,-0.2597070634,0.032013379,-0.0591114424,-0.1223389208,0.2278618217,-0.0852190033,-0.1969066113,0.0806867406,-0.1826835275,0.3506126702,0.3075753152,0.7557079792,0.0849625394,-0.4385830462,0.3527544439,-0.008834363,-0.2499325126,0.0982048661,0.0114404131,-0.038631659,0.2910941541,0.3620179892,0.0191407558,-0.2755416036,-0.0913378075,-0.0194800962,0.241254732,-0.3768110871,0.2612398267,-0.3031679094,-0.1589258015,0.1838589758,0.1624084562,-0.161703825,0.0292062797,0.1390403211,0.2721504271,0.2021351606,0.022936523,-0.1996165365,-0.0208406579,-0.1506257057,0.2454142272,0.1411077678,-0.074645862,0.1552448422,-0.1430294216,-0.1715416163,0.3471221626,0.1294501126,-0.028844336,-0.0878201574,0.0389396362,0.0593524985,0.0525161289,-0.387117058,-0.0891268551,-0.102802895,-0.1088057458,0.1004484147,-0.5472633839,0.1111528501,-0.1829553545,0.4247772992,-0.2955296934,-0.036486432,0.0299583022,0.179770276,-0.0549660586,0.1705628783,-0.2438575327,-0.1432878375,-0.1753666252,-0.1681888252,-0.2786498368,0.2655235529,-0.2386914045,-0.0093539348,0.0260451511,0.3414477408,0.0146750361,0.0368778296,0.0348646119,-0.3051835001,-0.6715813875,0.1528566778,0.3799234927,0.2668421566,0.2583189309,-0.3530448675,-0.4748586714,-0.2644218206,0.3256278634,-0.282907933,0.1872529984,-0.0278585479,-0.3894019127,0.3343360126,0.3319964409,-0.294306308,-0.095977217,-0.2260111719,-0.1961698383,0.0739624947,0.1944969147,0.3149732649,-0.1686628014,0.0810831338,0.0829168931,0.3201363981,-0.1368821263,0.1280291229,0.5260477662,-0.1801885217,0.2626565099,0.0549673811,-0.0480560251,0.2496026605,0.394746244,-0.0880166888,-0.0321652628,-0.005675585,-0.417722702,0.0833182558,0.1446457058,-0.1467420906,0.0077060866,-0.085375607,0.1881073862,0.1078551039,0.0606455915,0.1781775355,0.3725463152,0.3807065189,0.1436383873,-0.0966634154,0.8414549232,-0.0144099146,0.0652125701,0.3936395347,0.1621803492,-0.2119904459,-0.1389683634,-0.1302311569,-0.218854174,0.394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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/708","title":"Datasets performance slow? - 6.4x slower than in memory dataset","comments":"Facing a similar issue here. My model using SQuAD dataset takes about 1h to process with in memory data and more than 2h with datasets directly.","body":"I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n","comment_length":26,"text":"Datasets performance slow? - 6.4x slower than in memory dataset \n I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n \n Facing a similar issue here. My model using SQuAD dataset takes about 1h to process with in memory data and more than 2h with datasets directly.","embeddings":[-0.380666554,-0.0424089059,-0.0314095877,0.4377960861,0.1005082354,0.1263741553,0.0136552053,0.3071820736,0.0914101228,-0.0730988383,-0.0662421435,0.3011278808,-0.0139888329,-0.2742891014,-0.0800373852,0.0623485856,0.2097818255,0.0289249532,-0.2366964072,-0.1054769978,-0.2640983164,-0.2973698974,-0.0698852018,-0.2284685522,-0.3223609924,-0.1434253901,0.0818204358,-0.0005959411,-0.3450991511,-0.403732717,0.3832427859,0.0766954646,0.0686847046,0.5503901243,-0.0001125017,-0.0448605046,0.2153707594,0.1636437178,-0.1740950942,0.3116467595,-0.0444426648,-0.2415705472,-0.0513389893,-0.1154529974,-0.0132120242,-0.1297846586,-0.0774296299,-0.7160379887,0.0181460567,0.1087614447,0.1370815635,0.319951117,-0.3872780502,0.2173005193,0.1421412528,0.3198285103,-0.4389561415,-0.103455998,0.3924718201,-0.1402355283,-0.0548132025,0.2295684218,-0.182692647,0.2396481335,0.4582098126,-0.0244686864,-0.1892895699,-0.2351143211,0.0883351192,0.4672665894,0.1863689572,-0.1389111727,-0.6342912316,-0.3808341622,-0.2253736407,-0.1158320382,-0.0066045467,0.2012721598,-0.1965836883,0.1110184193,-0.5031599998,0.0621067099,0.0210670028,-0.0106447944,-0.1593937129,0.3560089469,0.108682014,0.2212632298,0.3045376241,0.1173553243,0.395116508,-0.3474166691,-0.0426575504,0.0569677539,-0.6727506518,0.0735261813,0.1322349459,0.1327432245,0.2916539013,0.0192395188,0.1375171244,0.2773087919,-0.0180944502,-0.1378564835,0.2188938707,0.132766515,-0.3069685102,0.4844334424,0.3089217246,-0.2571935356,0.1131687313,0.2347921282,-0.0429064818,-0.1230405346,0.0808739588,-0.2456587255,0.0628576428,-0.2480963022,-0.3668070436,-0.1015461758,-0.128623262,-0.0355535038,-0.0565834977,0.1639846861,-0.2934108377,0.2284448445,-0.1210528836,0.0039493861,-0.2586508393,0.0483607389,-0.0533004217,-0.0860240832,-0.3140702248,0.3556382954,0.2916277051,-0.0728289783,0.1457336843,-0.0316702574,0.0242215451,0.2429368049,0.3603523374,-0.38301754,0.2523589134,0.1450635195,-0.2260443121,0.5183621645,0.1015642062,0.4579477608,-0.2209920734,0.3493163586,-0.0600941256,-0.2040511966,0.2610084414,0.1124157831,-0.264804244,-0.1167250723,-0.4379530251,0.2817272842,0.1080345884,-0.0582337268,-0.0100165736,-0.3681587577,-0.1956059635,-0.1200666726,0.3318262398,0.3998510838,-0.5381987095,0.1778304726,0.060519293,0.211497277,0.2493430674,0.6614067554,-0.3634275496,0.2380728871,-0.1285901368,-0.2693578005,-0.1169592217,-0.1960741431,-0.5835575461,0.150880143,0.0044011883,0.137756452,0.103662096,0.4255777299,0.3806003928,0.1373888403,0.1205449998,0.2378032357,0.0506333634,0.1219747737,-0.3522131741,-0.266743809,0.5058071017,0.2659254074,-0.018459741,-0.1692710668,-0.1304339319,-0.2034452409,0.171515435,-0.1723211259,0.1123998016,0.2761603296,-0.0154160084,-0.1323151141,0.1253483295,0.0962624475,-0.2414122373,0.4494385421,0.0475798473,0.0823373869,-0.0722553283,-0.0690130517,0.2340127826,0.1237764359,-0.2757113576,-0.0913971737,-0.020273624,-0.1243479028,0.0506436378,-0.2392936796,-0.1698194146,0.3928050995,-0.2049032599,-0.0397737734,-0.2208188623,0.0434946306,0.0289860237,-0.0810197294,-0.0130326152,-0.1096339822,-0.1200399175,-0.0273582377,-0.1362688243,0.1346836239,0.2414696813,0.260176301,0.1974770576,0.3538561463,0.1384093165,-0.3115014434,0.3684659004,0.282561928,0.1614504755,-0.1878308803,-0.3582589626,0.5080374479,0.0408374928,0.6212487817,0.131858483,-0.2736033797,0.0354713872,0.161772728,-0.152790606,0.361810714,0.2422434837,0.183134377,0.5993636847,0.3990736306,-0.2270187736,0.1293455511,0.3130761981,0.0727399737,-0.1557809412,-0.0534361303,-0.2871873081,-0.2821799815,0.2410202771,-0.0649421513,0.5271029472,0.2901991308,0.1248301491,-0.1665140986,-0.0623956509,-0.088564761,0.0004647589,0.2956546247,0.2021181136,0.0236077383,0.1321617812,-0.0330443047,-0.2369342744,-0.1259484142,-0.013615571,0.0920877904,-0.1836669147,0.1966559887,-0.048241172,0.0431790762,-0.1185244098,0.0486888587,-0.1458606869,-0.2508894205,-0.07871975,-0.1703781933,0.1408747733,0.0997162461,-0.0557448044,0.297817409,-0.0532311983,-0.3437313437,-0.1330957562,-0.1097652093,-0.2056207806,0.0347893834,0.0790492743,0.201562956,0.3894380927,0.2333966941,0.0630549118,-0.2507633567,-0.1281210929,-0.081134595,-0.1727131903,0.2470872104,-0.3129540384,0.1722493172,0.0450521857,0.0761780441,0.0374998301,-0.3253332973,0.0282385517,-0.1393776536,-0.1856903732,-0.095603399,0.094167456,-0.283816576,-0.2353224307,-0.231665045,0.1122174487,0.1856211126,-0.1038918123,-0.0777875558,0.2100544274,0.1400318295,-0.0230241437,-0.0323164165,0.0010908976,-0.2395755649,0.5963212252,0.1307837963,-0.2874099314,-0.1484392285,0.0325048938,0.0926051661,0.4593246579,-0.4363919497,0.3887852132,-0.4268547595,0.0659126043,-0.0283237677,0.1497395784,0.3875692785,-0.04899057,-0.0553492531,-0.0899968445,-0.2898074687,-0.2084778547,-0.2657877803,0.1750806719,0.0703375936,0.2047269493,0.1540494412,0.7702897191,0.2816254497,-0.1282120496,-0.0700765774,-0.0199760105,0.3221504092,-0.3042885363,-0.1336632073,0.0767839104,-0.2323928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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/708","title":"Datasets performance slow? - 6.4x slower than in memory dataset","comments":"Thanks for the tip @thomwolf ! I did not see that flag in the docs. I'll try with that.","body":"I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n","comment_length":19,"text":"Datasets performance slow? - 6.4x slower than in memory dataset \n I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n \n Thanks for the tip @thomwolf ! I did not see that flag in the docs. I'll try with that.","embeddings":[-0.380666554,-0.0424089059,-0.0314095877,0.4377960861,0.1005082354,0.1263741553,0.0136552053,0.3071820736,0.0914101228,-0.0730988383,-0.0662421435,0.3011278808,-0.0139888329,-0.2742891014,-0.0800373852,0.0623485856,0.2097818255,0.0289249532,-0.2366964072,-0.1054769978,-0.2640983164,-0.2973698974,-0.0698852018,-0.2284685522,-0.3223609924,-0.1434253901,0.0818204358,-0.0005959411,-0.3450991511,-0.403732717,0.3832427859,0.0766954646,0.0686847046,0.5503901243,-0.0001125017,-0.0448605046,0.2153707594,0.1636437178,-0.1740950942,0.3116467595,-0.0444426648,-0.2415705472,-0.0513389893,-0.1154529974,-0.0132120242,-0.1297846586,-0.0774296299,-0.7160379887,0.0181460567,0.1087614447,0.1370815635,0.319951117,-0.3872780502,0.2173005193,0.1421412528,0.3198285103,-0.4389561415,-0.103455998,0.3924718201,-0.1402355283,-0.0548132025,0.2295684218,-0.182692647,0.2396481335,0.4582098126,-0.0244686864,-0.1892895699,-0.2351143211,0.0883351192,0.4672665894,0.1863689572,-0.1389111727,-0.6342912316,-0.3808341622,-0.2253736407,-0.1158320382,-0.0066045467,0.2012721598,-0.1965836883,0.1110184193,-0.5031599998,0.0621067099,0.0210670028,-0.0106447944,-0.1593937129,0.3560089469,0.108682014,0.2212632298,0.3045376241,0.1173553243,0.395116508,-0.3474166691,-0.0426575504,0.0569677539,-0.6727506518,0.0735261813,0.1322349459,0.1327432245,0.2916539013,0.0192395188,0.1375171244,0.2773087919,-0.0180944502,-0.1378564835,0.2188938707,0.132766515,-0.3069685102,0.4844334424,0.3089217246,-0.2571935356,0.1131687313,0.2347921282,-0.0429064818,-0.1230405346,0.0808739588,-0.2456587255,0.0628576428,-0.2480963022,-0.3668070436,-0.1015461758,-0.128623262,-0.0355535038,-0.0565834977,0.1639846861,-0.2934108377,0.2284448445,-0.1210528836,0.0039493861,-0.2586508393,0.0483607389,-0.0533004217,-0.0860240832,-0.3140702248,0.3556382954,0.2916277051,-0.0728289783,0.1457336843,-0.0316702574,0.0242215451,0.2429368049,0.3603523374,-0.38301754,0.2523589134,0.1450635195,-0.2260443121,0.5183621645,0.1015642062,0.4579477608,-0.2209920734,0.3493163586,-0.0600941256,-0.2040511966,0.2610084414,0.1124157831,-0.264804244,-0.1167250723,-0.4379530251,0.2817272842,0.1080345884,-0.0582337268,-0.0100165736,-0.3681587577,-0.1956059635,-0.1200666726,0.3318262398,0.3998510838,-0.5381987095,0.1778304726,0.060519293,0.211497277,0.2493430674,0.6614067554,-0.3634275496,0.2380728871,-0.1285901368,-0.2693578005,-0.1169592217,-0.1960741431,-0.5835575461,0.150880143,0.0044011883,0.137756452,0.103662096,0.4255777299,0.3806003928,0.1373888403,0.1205449998,0.2378032357,0.0506333634,0.1219747737,-0.3522131741,-0.266743809,0.5058071017,0.2659254074,-0.018459741,-0.1692710668,-0.1304339319,-0.2034452409,0.171515435,-0.1723211259,0.1123998016,0.2761603296,-0.0154160084,-0.1323151141,0.1253483295,0.0962624475,-0.2414122373,0.4494385421,0.0475798473,0.0823373869,-0.0722553283,-0.0690130517,0.2340127826,0.1237764359,-0.2757113576,-0.0913971737,-0.020273624,-0.1243479028,0.0506436378,-0.2392936796,-0.1698194146,0.3928050995,-0.2049032599,-0.0397737734,-0.2208188623,0.0434946306,0.0289860237,-0.0810197294,-0.0130326152,-0.1096339822,-0.1200399175,-0.0273582377,-0.1362688243,0.1346836239,0.2414696813,0.260176301,0.1974770576,0.3538561463,0.1384093165,-0.3115014434,0.3684659004,0.282561928,0.1614504755,-0.1878308803,-0.3582589626,0.5080374479,0.0408374928,0.6212487817,0.131858483,-0.2736033797,0.0354713872,0.161772728,-0.152790606,0.361810714,0.2422434837,0.183134377,0.5993636847,0.3990736306,-0.2270187736,0.1293455511,0.3130761981,0.0727399737,-0.1557809412,-0.0534361303,-0.2871873081,-0.2821799815,0.2410202771,-0.0649421513,0.5271029472,0.2901991308,0.1248301491,-0.1665140986,-0.0623956509,-0.088564761,0.0004647589,0.2956546247,0.2021181136,0.0236077383,0.1321617812,-0.0330443047,-0.2369342744,-0.1259484142,-0.013615571,0.0920877904,-0.1836669147,0.1966559887,-0.048241172,0.0431790762,-0.1185244098,0.0486888587,-0.1458606869,-0.2508894205,-0.07871975,-0.1703781933,0.1408747733,0.0997162461,-0.0557448044,0.297817409,-0.0532311983,-0.3437313437,-0.1330957562,-0.1097652093,-0.2056207806,0.0347893834,0.0790492743,0.201562956,0.3894380927,0.2333966941,0.0630549118,-0.2507633567,-0.1281210929,-0.081134595,-0.1727131903,0.2470872104,-0.3129540384,0.1722493172,0.0450521857,0.0761780441,0.0374998301,-0.3253332973,0.0282385517,-0.1393776536,-0.1856903732,-0.095603399,0.094167456,-0.283816576,-0.2353224307,-0.231665045,0.1122174487,0.1856211126,-0.1038918123,-0.0777875558,0.2100544274,0.1400318295,-0.0230241437,-0.0323164165,0.0010908976,-0.2395755649,0.5963212252,0.1307837963,-0.2874099314,-0.1484392285,0.0325048938,0.0926051661,0.4593246579,-0.4363919497,0.3887852132,-0.4268547595,0.0659126043,-0.0283237677,0.1497395784,0.3875692785,-0.04899057,-0.0553492531,-0.0899968445,-0.2898074687,-0.2084778547,-0.2657877803,0.1750806719,0.0703375936,0.2047269493,0.1540494412,0.7702897191,0.2816254497,-0.1282120496,-0.0700765774,-0.0199760105,0.3221504092,-0.3042885363,-0.1336632073,0.0767839104,-0.2323928922,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/708","title":"Datasets performance slow? - 6.4x slower than in memory dataset","comments":"We should add it indeed and also maybe a specific section with all the tips for maximal speed. What do you think @lhoestq @SBrandeis @yjernite ?","body":"I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n","comment_length":26,"text":"Datasets performance slow? - 6.4x slower than in memory dataset \n I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n \n We should add it indeed and also maybe a specific section with all the tips for maximal speed. What do you think @lhoestq @SBrandeis @yjernite ?","embeddings":[-0.380666554,-0.0424089059,-0.0314095877,0.4377960861,0.1005082354,0.1263741553,0.0136552053,0.3071820736,0.0914101228,-0.0730988383,-0.0662421435,0.3011278808,-0.0139888329,-0.2742891014,-0.0800373852,0.0623485856,0.2097818255,0.0289249532,-0.2366964072,-0.1054769978,-0.2640983164,-0.2973698974,-0.0698852018,-0.2284685522,-0.3223609924,-0.1434253901,0.0818204358,-0.0005959411,-0.3450991511,-0.403732717,0.3832427859,0.0766954646,0.0686847046,0.5503901243,-0.0001125017,-0.0448605046,0.2153707594,0.1636437178,-0.1740950942,0.3116467595,-0.0444426648,-0.2415705472,-0.0513389893,-0.1154529974,-0.0132120242,-0.1297846586,-0.0774296299,-0.7160379887,0.0181460567,0.1087614447,0.1370815635,0.319951117,-0.3872780502,0.2173005193,0.1421412528,0.3198285103,-0.4389561415,-0.103455998,0.3924718201,-0.1402355283,-0.0548132025,0.2295684218,-0.182692647,0.2396481335,0.4582098126,-0.0244686864,-0.1892895699,-0.2351143211,0.0883351192,0.4672665894,0.1863689572,-0.1389111727,-0.6342912316,-0.3808341622,-0.2253736407,-0.1158320382,-0.0066045467,0.2012721598,-0.1965836883,0.1110184193,-0.5031599998,0.0621067099,0.0210670028,-0.0106447944,-0.1593937129,0.3560089469,0.108682014,0.2212632298,0.3045376241,0.1173553243,0.395116508,-0.3474166691,-0.0426575504,0.0569677539,-0.6727506518,0.0735261813,0.1322349459,0.1327432245,0.2916539013,0.0192395188,0.1375171244,0.2773087919,-0.0180944502,-0.1378564835,0.2188938707,0.132766515,-0.3069685102,0.4844334424,0.3089217246,-0.2571935356,0.1131687313,0.2347921282,-0.0429064818,-0.1230405346,0.0808739588,-0.2456587255,0.0628576428,-0.2480963022,-0.3668070436,-0.1015461758,-0.128623262,-0.0355535038,-0.0565834977,0.1639846861,-0.2934108377,0.2284448445,-0.1210528836,0.0039493861,-0.2586508393,0.0483607389,-0.0533004217,-0.0860240832,-0.3140702248,0.3556382954,0.2916277051,-0.0728289783,0.1457336843,-0.0316702574,0.0242215451,0.2429368049,0.3603523374,-0.38301754,0.2523589134,0.1450635195,-0.2260443121,0.5183621645,0.1015642062,0.4579477608,-0.2209920734,0.3493163586,-0.0600941256,-0.2040511966,0.2610084414,0.1124157831,-0.264804244,-0.1167250723,-0.4379530251,0.2817272842,0.1080345884,-0.0582337268,-0.0100165736,-0.3681587577,-0.1956059635,-0.1200666726,0.3318262398,0.3998510838,-0.5381987095,0.1778304726,0.060519293,0.211497277,0.2493430674,0.6614067554,-0.3634275496,0.2380728871,-0.1285901368,-0.2693578005,-0.1169592217,-0.1960741431,-0.5835575461,0.150880143,0.0044011883,0.137756452,0.103662096,0.4255777299,0.3806003928,0.1373888403,0.1205449998,0.2378032357,0.0506333634,0.1219747737,-0.3522131741,-0.266743809,0.5058071017,0.2659254074,-0.018459741,-0.1692710668,-0.1304339319,-0.2034452409,0.171515435,-0.1723211259,0.1123998016,0.2761603296,-0.0154160084,-0.1323151141,0.1253483295,0.0962624475,-0.2414122373,0.4494385421,0.0475798473,0.0823373869,-0.0722553283,-0.0690130517,0.2340127826,0.1237764359,-0.2757113576,-0.0913971737,-0.020273624,-0.1243479028,0.0506436378,-0.2392936796,-0.1698194146,0.3928050995,-0.2049032599,-0.0397737734,-0.2208188623,0.0434946306,0.0289860237,-0.0810197294,-0.0130326152,-0.1096339822,-0.1200399175,-0.0273582377,-0.1362688243,0.1346836239,0.2414696813,0.260176301,0.1974770576,0.3538561463,0.1384093165,-0.3115014434,0.3684659004,0.282561928,0.1614504755,-0.1878308803,-0.3582589626,0.5080374479,0.0408374928,0.6212487817,0.131858483,-0.2736033797,0.0354713872,0.161772728,-0.152790606,0.361810714,0.2422434837,0.183134377,0.5993636847,0.3990736306,-0.2270187736,0.1293455511,0.3130761981,0.0727399737,-0.1557809412,-0.0534361303,-0.2871873081,-0.2821799815,0.2410202771,-0.0649421513,0.5271029472,0.2901991308,0.1248301491,-0.1665140986,-0.0623956509,-0.088564761,0.0004647589,0.2956546247,0.2021181136,0.0236077383,0.1321617812,-0.0330443047,-0.2369342744,-0.1259484142,-0.013615571,0.0920877904,-0.1836669147,0.1966559887,-0.048241172,0.0431790762,-0.1185244098,0.0486888587,-0.1458606869,-0.2508894205,-0.07871975,-0.1703781933,0.1408747733,0.0997162461,-0.0557448044,0.297817409,-0.0532311983,-0.3437313437,-0.1330957562,-0.1097652093,-0.2056207806,0.0347893834,0.0790492743,0.201562956,0.3894380927,0.2333966941,0.0630549118,-0.2507633567,-0.1281210929,-0.081134595,-0.1727131903,0.2470872104,-0.3129540384,0.1722493172,0.0450521857,0.0761780441,0.0374998301,-0.3253332973,0.0282385517,-0.1393776536,-0.1856903732,-0.095603399,0.094167456,-0.283816576,-0.2353224307,-0.231665045,0.1122174487,0.1856211126,-0.1038918123,-0.0777875558,0.2100544274,0.1400318295,-0.0230241437,-0.0323164165,0.0010908976,-0.2395755649,0.5963212252,0.1307837963,-0.2874099314,-0.1484392285,0.0325048938,0.0926051661,0.4593246579,-0.4363919497,0.3887852132,-0.4268547595,0.0659126043,-0.0283237677,0.1497395784,0.3875692785,-0.04899057,-0.0553492531,-0.0899968445,-0.2898074687,-0.2084778547,-0.2657877803,0.1750806719,0.0703375936,0.2047269493,0.1540494412,0.7702897191,0.2816254497,-0.1282120496,-0.0700765774,-0.0199760105,0.3221504092,-0.3042885363,-0.1336632073,0.0767839104,-0.2323928922,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/708","title":"Datasets performance slow? - 6.4x slower than in memory dataset","comments":"By default the datasets loaded with `load_dataset` live on disk.\r\nIt's possible to load them in memory by using some transforms like `.map(..., keep_in_memory=True)`.\r\n\r\nSmall correction to @thomwolf 's comment above: currently we don't have the `keep_in_memory` parameter for `load_dataset` AFAIK but it would be nice to add it indeed :)","body":"I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n","comment_length":51,"text":"Datasets performance slow? - 6.4x slower than in memory dataset \n I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n \n By default the datasets loaded with `load_dataset` live on disk.\r\nIt's possible to load them in memory by using some transforms like `.map(..., keep_in_memory=True)`.\r\n\r\nSmall correction to @thomwolf 's comment above: currently we don't have the `keep_in_memory` parameter for `load_dataset` AFAIK but it would be nice to add it indeed 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/708","title":"Datasets performance slow? - 6.4x slower than in memory dataset","comments":"Great! Thanks a lot.\r\n\r\nI did a test using `map(..., keep_in_memory=True)` and also a test using in-memory only data.\r\n\r\n```python\r\nfeatures = dataset.map(tokenize, batched=True, remove_columns=dataset['train'].column_names)\r\nfeatures.set_format(type='torch', columns=['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\nfeatures_in_memory = dataset.map(tokenize, batched=True, keep_in_memory=True, remove_columns=dataset['train'].column_names)\r\nfeatures_in_memory.set_format(type='torch', columns=['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\nin_memory = [features['train'][i] for i in range(len(features['train']))]\r\n```\r\n\r\nFor using the features without any tweak, I got **1min17s** for copying the entire DataLoader to CUDA:\r\n\r\n```\r\n%%time\r\n\r\nfor i, batch in enumerate(DataLoader(features['train'], batch_size=16, num_workers=4)):\r\n    batch['input_ids'].to(device)\r\n```\r\n\r\nFor using the features mapped with `keep_in_memory=True`, I also got **1min17s** for copying the entire DataLoader to CUDA:\r\n\r\n```\r\n%%time\r\n\r\nfor i, batch in enumerate(DataLoader(features_in_memory['train'], batch_size=16, num_workers=4)):\r\n    batch['input_ids'].to(device)\r\n```\r\n\r\nAnd for the case using every element in memory, converted from the original dataset, I got **12.5s**:\r\n\r\n```\r\n%%time\r\n\r\nfor i, batch in enumerate(DataLoader(in_memory, batch_size=16, num_workers=4)):\r\n    batch['input_ids'].to(device)\r\n```\r\n\r\nTaking a closer look in my SQuAD code, using a profiler, I see a lot of calls to `posix read` api. It seems that it is really reliying on disk, which results in a very high train time.","body":"I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n","comment_length":170,"text":"Datasets performance slow? - 6.4x slower than in memory dataset \n I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n \n Great! Thanks a lot.\r\n\r\nI did a test using `map(..., keep_in_memory=True)` and also a test using in-memory only data.\r\n\r\n```python\r\nfeatures = dataset.map(tokenize, batched=True, remove_columns=dataset['train'].column_names)\r\nfeatures.set_format(type='torch', columns=['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\nfeatures_in_memory = dataset.map(tokenize, batched=True, keep_in_memory=True, remove_columns=dataset['train'].column_names)\r\nfeatures_in_memory.set_format(type='torch', columns=['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\nin_memory = [features['train'][i] for i in range(len(features['train']))]\r\n```\r\n\r\nFor using the features without any tweak, I got **1min17s** for copying the entire DataLoader to CUDA:\r\n\r\n```\r\n%%time\r\n\r\nfor i, batch in enumerate(DataLoader(features['train'], batch_size=16, num_workers=4)):\r\n    batch['input_ids'].to(device)\r\n```\r\n\r\nFor using the features mapped with `keep_in_memory=True`, I also got **1min17s** for copying the entire DataLoader to CUDA:\r\n\r\n```\r\n%%time\r\n\r\nfor i, batch in enumerate(DataLoader(features_in_memory['train'], batch_size=16, num_workers=4)):\r\n    batch['input_ids'].to(device)\r\n```\r\n\r\nAnd for the case using every element in memory, converted from the original dataset, I got **12.5s**:\r\n\r\n```\r\n%%time\r\n\r\nfor i, batch in enumerate(DataLoader(in_memory, batch_size=16, num_workers=4)):\r\n    batch['input_ids'].to(device)\r\n```\r\n\r\nTaking a closer look in my SQuAD code, using a profiler, I see a lot of calls to `posix read` api. It seems that it is really reliying on disk, which results in a very high train 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/708","title":"Datasets performance slow? - 6.4x slower than in memory dataset","comments":"I am having the same issue here. When loading from memory I can get the GPU up to 70% util but when loading after mapping I can only get 40%.\r\n\r\nIn disk:\r\n```\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='\/home\/ad\/Desktop\/bookcorpus', split='train[:20%]')\r\nbook_corpus = book_corpus.map(encode, batched=True, num_proc=20, load_from_cache_file=True, batch_size=2500)\r\nbook_corpus.set_format(type='torch', columns=['text', \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/mobile_bert_big\",\r\n    overwrite_output_dir=True,\r\n    num_train_epochs=1,\r\n    per_device_train_batch_size=32,\r\n    per_device_eval_batch_size=16,\r\n    save_steps=50,\r\n    save_total_limit=2,\r\n    logging_first_step=True,\r\n    warmup_steps=100,\r\n    logging_steps=50,\r\n    eval_steps=100,\r\n    no_cuda=False,\r\n    gradient_accumulation_steps=16,\r\n    fp16=True)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=book_corpus,\r\n    tokenizer=tokenizer)\r\n```\r\n\r\nIn disk I can only get 0,17 it\/s:\r\n`[ 13\/28907 01:03 < 46:03:27, 0.17 it\/s, Epoch 0.00\/1] `\r\n\r\nIf I load it with torch.utils.data.Dataset()\r\n```\r\nclass BCorpusDataset(torch.utils.data.Dataset):\r\n    def __init__(self, encodings):\r\n        self.encodings = encodings\r\n\r\n    def __getitem__(self, idx):\r\n        item = [torch.tensor(val[idx]) for key, val in self.encodings.items()][0]\r\n        return item\r\n\r\n    def __len__(self):\r\n        length = [len(val) for key, val in self.encodings.items()][0]\r\n        return length\r\n\r\n**book_corpus = book_corpus.select([i for i in range(16*2000)])** # filtering to not have 20% of BC in memory...\r\nbook_corpus = book_corpus(book_corpus)\r\n```\r\nI can get:\r\n` [ 5\/62 00:09 < 03:03, 0.31 it\/s, Epoch 0.06\/1]`\r\n\r\nBut obviously I can not get BookCorpus in memory xD\r\n\r\nEDIT: it is something weird. If i load in disk 1% of bookcorpus:\r\n```\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='\/home\/ad\/Desktop\/bookcorpus', split='train[:1%]')\r\n```\r\n\r\nI can get 0.28 it\/s, (the same that in memory) but if I load 20% of bookcorpus:\r\n```\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='\/home\/ad\/Desktop\/bookcorpus', split='train[:20%]')\r\n```\r\nI get again 0.17 it\/s. \r\n\r\nI am missing something? I think it is something related to size, and not disk or in-memory.","body":"I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n","comment_length":247,"text":"Datasets performance slow? - 6.4x slower than in memory dataset \n I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n \n I am having the same issue here. When loading from memory I can get the GPU up to 70% util but when loading after mapping I can only get 40%.\r\n\r\nIn disk:\r\n```\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='\/home\/ad\/Desktop\/bookcorpus', split='train[:20%]')\r\nbook_corpus = book_corpus.map(encode, batched=True, num_proc=20, load_from_cache_file=True, batch_size=2500)\r\nbook_corpus.set_format(type='torch', columns=['text', \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/mobile_bert_big\",\r\n    overwrite_output_dir=True,\r\n    num_train_epochs=1,\r\n    per_device_train_batch_size=32,\r\n    per_device_eval_batch_size=16,\r\n    save_steps=50,\r\n    save_total_limit=2,\r\n    logging_first_step=True,\r\n    warmup_steps=100,\r\n    logging_steps=50,\r\n    eval_steps=100,\r\n    no_cuda=False,\r\n    gradient_accumulation_steps=16,\r\n    fp16=True)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=book_corpus,\r\n    tokenizer=tokenizer)\r\n```\r\n\r\nIn disk I can only get 0,17 it\/s:\r\n`[ 13\/28907 01:03 < 46:03:27, 0.17 it\/s, Epoch 0.00\/1] `\r\n\r\nIf I load it with torch.utils.data.Dataset()\r\n```\r\nclass BCorpusDataset(torch.utils.data.Dataset):\r\n    def __init__(self, encodings):\r\n        self.encodings = encodings\r\n\r\n    def __getitem__(self, idx):\r\n        item = [torch.tensor(val[idx]) for key, val in self.encodings.items()][0]\r\n        return item\r\n\r\n    def __len__(self):\r\n        length = [len(val) for key, val in self.encodings.items()][0]\r\n        return length\r\n\r\n**book_corpus = book_corpus.select([i for i in range(16*2000)])** # filtering to not have 20% of BC in memory...\r\nbook_corpus = book_corpus(book_corpus)\r\n```\r\nI can get:\r\n` [ 5\/62 00:09 < 03:03, 0.31 it\/s, Epoch 0.06\/1]`\r\n\r\nBut obviously I can not get BookCorpus in memory xD\r\n\r\nEDIT: it is something weird. If i load in disk 1% of bookcorpus:\r\n```\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='\/home\/ad\/Desktop\/bookcorpus', split='train[:1%]')\r\n```\r\n\r\nI can get 0.28 it\/s, (the same that in memory) but if I load 20% of bookcorpus:\r\n```\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='\/home\/ad\/Desktop\/bookcorpus', split='train[:20%]')\r\n```\r\nI get again 0.17 it\/s. \r\n\r\nI am missing something? I think it is something related to size, and not disk or 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/708","title":"Datasets performance slow? - 6.4x slower than in memory dataset","comments":"There is a way to increase the batches read from memory? or multiprocessed it? I think that one of two or it is reading with just 1 core o it is reading very small chunks from disk and left my GPU at 0 between batches","body":"I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n","comment_length":45,"text":"Datasets performance slow? - 6.4x slower than in memory dataset \n I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n \n There is a way to increase the batches read from memory? or multiprocessed it? I think that one of two or it is reading with just 1 core o it is reading very small chunks from disk and left my GPU at 0 between 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/708","title":"Datasets performance slow? - 6.4x slower than in memory dataset","comments":"My fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.","body":"I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n","comment_length":21,"text":"Datasets performance slow? - 6.4x slower than in memory dataset \n I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.\r\n\r\nNow, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.\r\n\r\nFor example, in the `yelp_polarity` dataset (560000 datapoints, or 17500 batches of 32), it was taking me 3:31 to just get process the data and get it on the GPU (no model involved). Whereas, the equivalent in-memory dataset would finish in just 0:33.\r\n\r\nIs this expected? Given that one of the goals of this project is also accelerate dataset processing, this seems a bit slower than I would expect. I understand the advantages of being able to work on datasets that exceed memory, and that's very exciting to me, but thought I'd open this issue to discuss.\r\n\r\nFor reference I'm running a AMD Ryzen Threadripper 1900X 8-Core Processor CPU, with 128 GB of RAM and an NVME SSD Samsung 960 EVO. I'm running with an RTX Titan 24GB GPU.\r\n\r\nI can see with `iotop` that the dataset gets quickly loaded into the system read buffers, and thus doesn't incur any additional IO reads. Thus in theory, all the data *should* be in RAM, but in my benchmark code below it's still 6.4 times slower.\r\n\r\nWhat am I doing wrong? And is there a way to force the datasets to completely load into memory instead of being memory mapped in cases where you want maximum performance?\r\n\r\nAt 3:31 for 17500 batches, that's 12ms per batch. Does this 12ms just become insignificant as a proportion of forward and backward passes in practice, and thus it's not worth worrying about this in practice?\r\n\r\nIn any case, here's my code `benchmark.py`. If you run it with an argument of `memory` it will copy the data into memory before executing the same test.\r\n\r\n``` py\r\nimport sys\r\nfrom datasets import load_dataset\r\nfrom transformers import DataCollatorWithPadding, BertTokenizerFast\r\nfrom torch.utils.data import DataLoader\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\n    collate_fn = DataCollatorWithPadding(tokenizer, padding=True)\r\n\r\n    ds = load_dataset('yelp_polarity')\r\n\r\n    def do_tokenize(x):\r\n        return tokenizer(x['text'], truncation=True)\r\n\r\n    ds = ds.map(do_tokenize, batched=True)\r\n    ds.set_format('torch', ['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\n    if len(sys.argv) == 2 and sys.argv[1] == 'memory':\r\n        # copy to memory - probably a faster way to do this - but demonstrates the point\r\n        # approximately 530 batches per second - 17500 batches in 0:33\r\n        print('using memory')\r\n        _ds = [data for data in tqdm(ds['train'])]\r\n    else:\r\n        # approximately 83 batches per second - 17500 batches in 3:31\r\n        print('using datasets')\r\n        _ds = ds['train']\r\n\r\n    dl = DataLoader(_ds, shuffle=True, collate_fn=collate_fn, batch_size=32, num_workers=4)\r\n\r\n    for data in tqdm(dl):\r\n        for k, v in data.items():\r\n            data[k] = v.to('cuda')\r\n```\r\n\r\nFor reference, my conda environment is [here](https:\/\/gist.github.com\/05b6101518ff70ed42a858b302a0405d)\r\n\r\nOnce again, I'm very excited about this library, and how easy it is to load datasets, and to do so without worrying about system memory constraints.\r\n\r\nThanks for all your great work.\r\n \n My fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.","embeddings":[-0.380666554,-0.0424089059,-0.0314095877,0.4377960861,0.1005082354,0.1263741553,0.0136552053,0.3071820736,0.0914101228,-0.0730988383,-0.0662421435,0.3011278808,-0.0139888329,-0.2742891014,-0.0800373852,0.0623485856,0.2097818255,0.0289249532,-0.2366964072,-0.1054769978,-0.2640983164,-0.2973698974,-0.0698852018,-0.2284685522,-0.3223609924,-0.1434253901,0.0818204358,-0.0005959411,-0.3450991511,-0.403732717,0.3832427859,0.0766954646,0.0686847046,0.5503901243,-0.0001125017,-0.0448605046,0.2153707594,0.1636437178,-0.1740950942,0.3116467595,-0.0444426648,-0.2415705472,-0.0513389893,-0.1154529974,-0.0132120242,-0.1297846586,-0.0774296299,-0.7160379887,0.0181460567,0.1087614447,0.1370815635,0.319951117,-0.3872780502,0.2173005193,0.1421412528,0.3198285103,-0.4389561415,-0.103455998,0.3924718201,-0.1402355283,-0.0548132025,0.2295684218,-0.182692647,0.2396481335,0.4582098126,-0.0244686864,-0.1892895699,-0.2351143211,0.0883351192,0.4672665894,0.1863689572,-0.1389111727,-0.6342912316,-0.3808341622,-0.2253736407,-0.1158320382,-0.0066045467,0.2012721598,-0.1965836883,0.1110184193,-0.5031599998,0.0621067099,0.0210670028,-0.0106447944,-0.1593937129,0.3560089469,0.108682014,0.2212632298,0.3045376241,0.1173553243,0.395116508,-0.3474166691,-0.0426575504,0.0569677539,-0.6727506518,0.0735261813,0.1322349459,0.1327432245,0.2916539013,0.0192395188,0.1375171244,0.2773087919,-0.0180944502,-0.1378564835,0.2188938707,0.132766515,-0.3069685102,0.4844334424,0.3089217246,-0.2571935356,0.1131687313,0.2347921282,-0.0429064818,-0.1230405346,0.0808739588,-0.2456587255,0.0628576428,-0.2480963022,-0.3668070436,-0.1015461758,-0.128623262,-0.0355535038,-0.0565834977,0.1639846861,-0.2934108377,0.2284448445,-0.1210528836,0.0039493861,-0.2586508393,0.0483607389,-0.0533004217,-0.0860240832,-0.3140702248,0.3556382954,0.2916277051,-0.0728289783,0.1457336843,-0.0316702574,0.0242215451,0.2429368049,0.3603523374,-0.38301754,0.2523589134,0.1450635195,-0.2260443121,0.5183621645,0.1015642062,0.4579477608,-0.2209920734,0.3493163586,-0.0600941256,-0.2040511966,0.2610084414,0.1124157831,-0.264804244,-0.1167250723,-0.4379530251,0.2817272842,0.1080345884,-0.0582337268,-0.0100165736,-0.3681587577,-0.1956059635,-0.1200666726,0.3318262398,0.3998510838,-0.5381987095,0.1778304726,0.060519293,0.211497277,0.2493430674,0.6614067554,-0.3634275496,0.2380728871,-0.1285901368,-0.2693578005,-0.1169592217,-0.1960741431,-0.5835575461,0.150880143,0.0044011883,0.137756452,0.103662096,0.4255777299,0.3806003928,0.1373888403,0.1205449998,0.2378032357,0.0506333634,0.1219747737,-0.3522131741,-0.266743809,0.5058071017,0.2659254074,-0.018459741,-0.1692710668,-0.1304339319,-0.2034452409,0.171515435,-0.1723211259,0.1123998016,0.2761603296,-0.0154160084,-0.1323151141,0.1253483295,0.0962624475,-0.2414122373,0.4494385421,0.0475798473,0.0823373869,-0.0722553283,-0.0690130517,0.2340127826,0.1237764359,-0.2757113576,-0.0913971737,-0.020273624,-0.1243479028,0.0506436378,-0.2392936796,-0.1698194146,0.3928050995,-0.2049032599,-0.0397737734,-0.2208188623,0.0434946306,0.0289860237,-0.0810197294,-0.0130326152,-0.1096339822,-0.1200399175,-0.0273582377,-0.1362688243,0.1346836239,0.2414696813,0.260176301,0.1974770576,0.3538561463,0.1384093165,-0.3115014434,0.3684659004,0.282561928,0.1614504755,-0.1878308803,-0.3582589626,0.5080374479,0.0408374928,0.6212487817,0.131858483,-0.2736033797,0.0354713872,0.161772728,-0.152790606,0.361810714,0.2422434837,0.183134377,0.5993636847,0.3990736306,-0.2270187736,0.1293455511,0.3130761981,0.0727399737,-0.1557809412,-0.0534361303,-0.2871873081,-0.2821799815,0.2410202771,-0.0649421513,0.5271029472,0.2901991308,0.1248301491,-0.1665140986,-0.0623956509,-0.088564761,0.0004647589,0.2956546247,0.2021181136,0.0236077383,0.1321617812,-0.0330443047,-0.2369342744,-0.1259484142,-0.013615571,0.0920877904,-0.1836669147,0.1966559887,-0.048241172,0.0431790762,-0.1185244098,0.0486888587,-0.1458606869,-0.2508894205,-0.07871975,-0.1703781933,0.1408747733,0.0997162461,-0.0557448044,0.297817409,-0.0532311983,-0.3437313437,-0.1330957562,-0.1097652093,-0.2056207806,0.0347893834,0.0790492743,0.201562956,0.3894380927,0.2333966941,0.0630549118,-0.2507633567,-0.1281210929,-0.081134595,-0.1727131903,0.2470872104,-0.3129540384,0.1722493172,0.0450521857,0.0761780441,0.0374998301,-0.3253332973,0.0282385517,-0.1393776536,-0.1856903732,-0.095603399,0.094167456,-0.283816576,-0.2353224307,-0.231665045,0.1122174487,0.1856211126,-0.1038918123,-0.0777875558,0.2100544274,0.1400318295,-0.0230241437,-0.0323164165,0.0010908976,-0.2395755649,0.5963212252,0.1307837963,-0.2874099314,-0.1484392285,0.0325048938,0.0926051661,0.4593246579,-0.4363919497,0.3887852132,-0.4268547595,0.0659126043,-0.0283237677,0.1497395784,0.3875692785,-0.04899057,-0.0553492531,-0.0899968445,-0.2898074687,-0.2084778547,-0.2657877803,0.1750806719,0.0703375936,0.2047269493,0.1540494412,0.7702897191,0.2816254497,-0.1282120496,-0.0700765774,-0.0199760105,0.3221504092,-0.3042885363,-0.1336632073,0.0767839104,-0.232392892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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/707","title":"Requirements should specify pyarrow<1","comments":"@punitaojha, certainly. Feel free to work on this. Let me know if you need any help or clarity.","body":"I was looking at the docs on [Perplexity](https:\/\/huggingface.co\/transformers\/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error,\r\n\r\n```\r\nmodule 'pyarrow' has no attribute 'PyExtensionType'\r\n```\r\nI traced it back to datasets having installed PyArrow 1.0.1 but there's not pinning in the setup file. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e86a2a8f869b91654e782c9133d810bb82783200\/setup.py#L68\r\n\r\nDowngrading by installing `pip install \"pyarrow<1\"` resolved the issue.","comment_length":18,"text":"Requirements should specify pyarrow<1 \n I was looking at the docs on [Perplexity](https:\/\/huggingface.co\/transformers\/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error,\r\n\r\n```\r\nmodule 'pyarrow' has no attribute 'PyExtensionType'\r\n```\r\nI traced it back to datasets having installed PyArrow 1.0.1 but there's not pinning in the setup file. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e86a2a8f869b91654e782c9133d810bb82783200\/setup.py#L68\r\n\r\nDowngrading by installing `pip install \"pyarrow<1\"` resolved the issue. \n @punitaojha, certainly. Feel free to work on this. Let me know if you need any help or clarity.","embeddings":[-0.2744113207,-0.2090404034,0.0152528426,0.18003501,0.0473458432,-0.0619748719,0.0390898697,0.1851368546,-0.0446801148,0.0569026507,-0.0508330688,0.2923365533,-0.0050392509,0.0731776655,0.1184344813,-0.3647958636,0.2140396386,0.5875732899,-0.3834571838,-0.0399198346,-0.0928857177,0.26306355,-0.29579404,0.2726375163,-0.3719050586,0.1225018725,0.0235204715,-0.0084750494,-0.1621870846,-0.5378573537,0.4328965843,0.2639689445,0.028079696,0.5115487576,-0.0001171745,0.0794191658,0.1408243179,-0.0046915193,-0.0439403914,-0.0354698151,-0.0304914769,-0.0833085403,0.3720040917,-0.0927432105,-0.2928206027,-0.0792524815,0.0061559151,0.2068326324,0.4049660861,0.1370559633,0.1354582161,0.41404742,0.6308282018,-0.0483633019,0.5168268681,-0.2037336528,-0.2146046609,0.1246313602,0.2182046473,-0.2637349665,0.0977444574,-0.0559179597,0.2176226079,0.0844787881,0.1007053256,0.0064795171,0.2837662995,-0.2852359116,-0.0767906383,0.3960488141,0.3805250525,-0.2011321783,-0.3604101837,-0.1703670174,-0.2085843235,0.0597833134,0.2075708658,-0.232400164,-0.3061923981,0.3499825299,-0.0237948038,-0.1572733521,-0.2329319715,0.0398114808,-0.3779924214,0.61867553,0.1304641664,0.2004017979,0.0787140951,-0.138892293,-0.2523773015,0.2585268319,-0.0129787577,0.0636072531,-0.293458432,-0.1428923458,0.2488572448,0.2419497669,0.2727940083,0.0403146632,-0.1411073655,0.1671097875,0.1981676668,0.2720076442,0.1446544677,0.0335154794,-0.2245220691,0.1350048631,0.3313992918,-0.0000973628,0.1322936565,0.0535429455,-0.0543981716,-0.2799725235,0.0378361493,-0.3460942805,0.2894438207,-0.1931288242,-0.3421807885,-0.1763553172,-0.1166223884,0.1466711164,-0.0318934657,0.1282465607,0.0388669521,0.3341369033,0.0885849744,0.1577054411,-0.2806058228,-0.0087910155,-0.132013455,-0.1841318309,-0.0091545731,0.0849368274,0.3447553217,0.050514672,0.332337141,0.1975707263,0.068075031,0.4564490318,-0.0531207435,0.1364161074,-0.3700836301,0.4508806169,-0.3323380351,0.1976619512,-0.0581384934,0.0628103763,-0.1915748864,0.1300496459,-0.1899691522,0.0482477732,-0.1915132552,0.1574584693,-0.3573656976,-0.082664758,-0.1527121961,0.0702023059,-0.0288152564,-0.4366858304,-0.0195416939,-0.2502210736,-0.0132398773,-0.0616674386,0.2646416128,0.0713782385,-0.362818718,-0.0274630133,0.1427609324,-0.6227787733,-0.1789803058,0.2393549085,-0.2394668162,0.2606437206,-0.1663723737,0.3105495572,0.1664200425,-0.269310981,-0.4029172361,-0.1308529675,-0.2516518235,0.4186939895,-0.0464100912,-0.1123274341,0.0676421225,-0.0331610255,-0.0478268117,0.1581586301,0.1312135458,0.2168271989,-0.3007755578,-0.3674801886,0.5387315154,0.2180080563,0.0704710558,-0.0844723061,-0.1506546885,-0.07358706,0.2024605721,-0.2686364949,0.133695662,0.0354774483,0.4232333302,-0.0971348882,0.2044667453,-0.2488170713,-0.5351415277,0.319075048,-0.094259806,0.2063255608,-0.1566704512,0.038478896,-0.0378696322,-0.0542144403,-0.3329945803,-0.2113429904,0.1806448847,-0.2434691936,0.1964030266,0.1206182241,-0.1091237515,0.0806275234,0.449213028,0.1215978563,0.0134600187,0.3498786092,-0.1768245101,-0.3050769269,0.2239729464,0.0812370852,0.0774515942,-0.0824306607,-0.0425241143,0.286680609,0.164312169,0.3654063642,-0.1780032665,0.3176199496,0.1306525767,-0.5406620502,-0.0758885443,0.4269067347,0.1035571247,-0.1261876523,0.1132316515,0.1425604224,0.2797724009,0.0193208996,0.1519389451,-0.1222834364,0.1331613213,0.0406503417,0.0729066283,-0.0568339936,0.0058672535,0.1114464849,0.1371488273,0.0175449196,-0.0516746566,0.2303841114,0.3417685032,0.0505564809,0.3291549683,0.1389820129,-0.2897283733,-0.1328887045,0.0805216581,-0.5257543921,0.2791210711,0.2369832247,-0.0444273166,0.1000379026,-0.2512210011,-0.1261416823,0.1744200289,-0.0804376379,0.178245008,0.3231774271,0.0959307104,-0.0637741536,-0.3817213476,-0.1507439464,-0.0748539418,0.3306754231,-0.3862912357,-0.2413176,-0.1049693823,0.3216769695,0.272595346,-0.0808423758,-0.3064685464,-0.2201129049,0.2603960037,0.4772414863,0.1165540069,0.1628492922,-0.0737927631,0.0380958728,0.139837876,-0.2964511812,-0.3593039513,-0.2747438252,-0.4682711661,0.1212390661,0.0316688716,0.0594272241,0.2674309015,0.0404028371,0.0869702622,-0.2797509134,-0.5713727474,0.0258007515,-0.2757348418,0.2965800464,0.2656935453,0.3712022603,0.0146423532,-0.1520946175,0.334280163,-0.5331454873,-0.2797227502,0.0561480001,-0.1597094983,-0.0188374985,-0.227671802,-0.200541243,0.0545630939,-0.2094620019,0.487305969,0.0893598571,-0.0777641311,-0.0578465946,0.2512842417,0.1818445325,-0.100018099,0.0056195739,-0.1132392287,-0.2412524372,0.4282823205,-0.1403423101,-0.37278229,-0.0131195094,0.048239544,0.3445105553,0.2113744169,-0.2584074438,-0.2144919336,-0.160062328,0.4973121881,0.1709284335,0.2897213995,0.5415073633,0.0972692147,-0.0025504425,-0.2357560247,-0.2569276989,0.2003496736,0.0552067384,0.0855824277,-0.1022009477,0.0219613146,-0.0871281177,0.7050462961,0.210354358,0.1172728911,0.2652158141,-0.1464131773,0.0713077039,-0.2871912718,-0.0204011016,0.1816247851,0.1265805662,0.1141961813,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/707","title":"Requirements should specify pyarrow<1","comments":"Hello @mathcass \r\n1. I did fork the repository and clone the same on my local system. \r\n\r\n2. Then learnt about how we can publish our package on pypi.org. Also, found some instructions on same in setup.py documentation.\r\n\r\n3. Then I Perplexity document link that you shared above. I created a colab link from there keep both tensorflow and pytorch means a mixed option and tried to run it in colab but I encountered no errors at a point where you mentioned. Can you help me to figure out the issue. \r\n\r\n4.Here is the link of the colab file with my saved responses. \r\nhttps:\/\/colab.research.google.com\/drive\/1hfYz8Ira39FnREbxgwa_goZWpOojp2NH?usp=sharing","body":"I was looking at the docs on [Perplexity](https:\/\/huggingface.co\/transformers\/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error,\r\n\r\n```\r\nmodule 'pyarrow' has no attribute 'PyExtensionType'\r\n```\r\nI traced it back to datasets having installed PyArrow 1.0.1 but there's not pinning in the setup file. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e86a2a8f869b91654e782c9133d810bb82783200\/setup.py#L68\r\n\r\nDowngrading by installing `pip install \"pyarrow<1\"` resolved the issue.","comment_length":103,"text":"Requirements should specify pyarrow<1 \n I was looking at the docs on [Perplexity](https:\/\/huggingface.co\/transformers\/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error,\r\n\r\n```\r\nmodule 'pyarrow' has no attribute 'PyExtensionType'\r\n```\r\nI traced it back to datasets having installed PyArrow 1.0.1 but there's not pinning in the setup file. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e86a2a8f869b91654e782c9133d810bb82783200\/setup.py#L68\r\n\r\nDowngrading by installing `pip install \"pyarrow<1\"` resolved the issue. \n Hello @mathcass \r\n1. I did fork the repository and clone the same on my local system. \r\n\r\n2. Then learnt about how we can publish our package on pypi.org. Also, found some instructions on same in setup.py documentation.\r\n\r\n3. Then I Perplexity document link that you shared above. I created a colab link from there keep both tensorflow and pytorch means a mixed option and tried to run it in colab but I encountered no errors at a point where you mentioned. Can you help me to figure out the issue. \r\n\r\n4.Here is the link of the colab file with my saved responses. \r\nhttps:\/\/colab.research.google.com\/drive\/1hfYz8Ira39FnREbxgwa_goZWpOojp2NH?usp=sharing","embeddings":[-0.2086724043,-0.2492389828,0.0104706734,0.143163085,-0.1351191252,-0.1367644966,0.0601092875,0.1777841598,-0.0722263679,0.1876842231,-0.1349501312,0.3921408951,-0.0105912182,0.2025151551,0.0202153549,-0.3189196289,0.1558381766,0.6317852139,-0.2972459197,-0.0332883708,-0.0148538966,0.2716951072,-0.3856919408,0.1468177438,-0.4066579044,-0.0718952566,0.0602015518,-0.1913313568,-0.1789083779,-0.4104488492,0.5019386411,0.2933356464,0.0380189568,0.5386695266,-0.0001246494,0.0441618152,0.0715481415,-0.058221627,-0.0730772763,-0.4739520848,-0.0124219982,0.0288729072,0.5552842021,-0.1167393997,-0.2710622251,0.1298567802,0.0246236678,0.3614777923,0.4647699296,0.224004969,0.024686344,0.3220967054,0.519674778,0.027610248,0.4959204793,-0.1979997456,-0.2250536829,0.4007034898,0.2845157087,-0.293859601,0.2136534899,0.0722620934,0.1143926382,0.2241774201,0.0857390538,-0.0296407621,0.306420207,-0.543872416,-0.0512404032,0.4704498947,0.1956389546,-0.2339904308,-0.2855395377,-0.1750824153,-0.076056689,-0.0668525696,0.3151905835,-0.19068636,-0.4019597471,0.2506096959,0.0359963775,-0.0399612859,-0.3839387298,0.1969906092,-0.532189548,0.7457957268,0.0781329572,0.140368104,0.1287548691,-0.0064596632,0.0153422831,0.1976995319,0.2052425593,-0.0080036595,-0.2740123272,-0.1842935085,0.0807474703,-0.0599373505,0.186905086,-0.1586042643,-0.1703172773,0.1618908644,0.0550829098,0.3274672925,0.0876166373,0.1712814569,-0.225545615,0.1574649513,0.3345220089,0.10782291,0.0957444534,0.1650237739,-0.0801129118,-0.2739412785,-0.2060223222,-0.3268119991,0.3248876929,-0.1852011532,-0.543025136,-0.2471684366,-0.3868255019,0.1440582424,0.007572154,0.193520993,-0.168186903,0.2706876099,0.1613992006,0.1995216012,-0.2905425429,0.1262453645,-0.0097410362,-0.0157327522,0.0473034531,0.1668775827,0.3879338205,0.0598513223,0.3442662656,0.2795542777,0.2312610149,0.1923202425,0.1490726173,-0.0685155839,-0.1666152626,0.5420348644,-0.238415882,0.1536210626,-0.0455889925,0.1128447428,-0.1617376059,0.2223211378,-0.3807045221,0.0063013029,-0.2397688776,0.0668680519,-0.4700929821,-0.0116190957,-0.2089211196,0.2450740188,-0.0518171676,-0.2779945731,-0.0258288421,-0.4117038846,-0.2063217014,-0.0955633819,0.3335030675,-0.1586707532,-0.4914388955,-0.0222056601,0.1562986374,-0.343288064,-0.0639649332,0.2183389217,-0.3571609557,0.356990993,-0.1360036582,0.2632779777,0.1253128499,-0.2123099864,-0.4445071518,-0.2307714373,-0.1657947898,0.4377110302,-0.0319378376,-0.1881282777,0.2705567181,-0.0057018003,0.3001312315,0.0910482481,0.0968549624,0.1326059699,-0.2460104674,-0.4140643775,0.6402183771,0.1166730896,0.3057157993,-0.1149227023,-0.2733577788,-0.076900214,0.2295586914,-0.3223408163,-0.0021763155,-0.026989311,0.4426197708,0.0258706734,0.0911421254,-0.3212692738,-0.5001301765,0.2173959762,-0.0801016539,0.3355025649,-0.1914384365,0.060521286,-0.0343133807,-0.1909963489,-0.3320446014,-0.2931030691,0.1080883965,-0.3093075454,0.0226568878,0.1018400863,-0.1950880587,0.1308250874,0.2655107379,0.0207538102,0.2158547193,0.4626187682,-0.1991202086,-0.4572329521,0.2032588124,0.1807994395,0.0625622943,-0.0591128655,-0.1452132314,0.093349576,-0.0193592459,0.1822324395,-0.2986144125,0.2605123222,0.2605971694,-0.4232666194,-0.1955486089,0.3503348827,-0.0008110038,-0.0930695385,0.2096849829,0.3274608552,0.2745113671,0.017079927,0.0372881182,-0.2270409912,0.1220123917,-0.0016372741,-0.0083608758,0.0001987793,0.0653400049,0.1265785098,-0.0049333228,0.080294393,-0.1801214367,0.2274035811,0.4201957285,0.0835636333,0.4133821428,0.055138547,-0.2376071066,-0.1505482495,0.1739173383,-0.3802369833,0.2892012,0.1841975749,-0.0777000561,0.0568747893,-0.3004885316,-0.1140455753,0.1123887077,-0.0253327359,0.4185265005,0.2763240635,0.1527326256,-0.024946237,-0.2231233716,-0.1890087128,0.0537125058,0.2534185648,-0.3964975178,-0.245354414,-0.1942079663,0.1345811188,0.1920168251,0.0723449737,-0.1970225722,-0.048667416,0.2232683599,0.653278172,0.2153997421,0.2832365334,0.0743162706,-0.051443357,0.0335617065,-0.3396537304,-0.2768448293,-0.2075626552,-0.3671346009,0.0441854112,0.0132677574,0.133673802,0.230551362,-0.0909765139,-0.1738574952,-0.1247097477,-0.5882374644,0.1316970289,-0.3359529972,0.3475390971,0.0953313559,0.3303355873,-0.2024286836,-0.2611160278,0.2653265297,-0.3258785307,-0.2533763051,-0.0841299966,-0.2031656206,0.1257281452,-0.1894627512,-0.3544028997,-0.0187996104,-0.1409433037,0.2841362059,-0.1004943103,0.0101524582,0.0029494928,0.3139958978,0.2860015035,0.0444627069,0.0526336655,-0.1595649868,-0.1258476228,0.4896460772,-0.1127681807,-0.3425046802,0.1377508044,-0.0884387046,0.4012726247,0.2340296209,-0.3173576295,-0.3688831925,-0.0117837349,0.4804005921,0.1285279393,0.0694005191,0.552575469,0.0312730111,0.1009884104,-0.1931358874,-0.2890688479,0.2264042348,0.1350500584,0.1334231049,-0.0029779437,-0.0167331435,0.0700285733,0.771420598,0.0803975835,0.0786430463,0.1744908541,-0.079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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/707","title":"Requirements should specify pyarrow<1","comments":"Thanks for looking at this @punitaojha and thanks for sharing the notebook. \r\n\r\nI just tried to reproduce this on my own (based on the environment where I had this issue) and I can't reproduce it somehow. If I run into this again, I'll include some steps to reproduce it. I'll close this as invalid. \r\n\r\nThanks again. ","body":"I was looking at the docs on [Perplexity](https:\/\/huggingface.co\/transformers\/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error,\r\n\r\n```\r\nmodule 'pyarrow' has no attribute 'PyExtensionType'\r\n```\r\nI traced it back to datasets having installed PyArrow 1.0.1 but there's not pinning in the setup file. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e86a2a8f869b91654e782c9133d810bb82783200\/setup.py#L68\r\n\r\nDowngrading by installing `pip install \"pyarrow<1\"` resolved the issue.","comment_length":56,"text":"Requirements should specify pyarrow<1 \n I was looking at the docs on [Perplexity](https:\/\/huggingface.co\/transformers\/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error,\r\n\r\n```\r\nmodule 'pyarrow' has no attribute 'PyExtensionType'\r\n```\r\nI traced it back to datasets having installed PyArrow 1.0.1 but there's not pinning in the setup file. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e86a2a8f869b91654e782c9133d810bb82783200\/setup.py#L68\r\n\r\nDowngrading by installing `pip install \"pyarrow<1\"` resolved the issue. \n Thanks for looking at this @punitaojha and thanks for sharing the notebook. \r\n\r\nI just tried to reproduce this on my own (based on the environment where I had this issue) and I can't reproduce it somehow. If I run into this again, I'll include some steps to reproduce it. I'll close this as invalid. \r\n\r\nThanks again. ","embeddings":[-0.2600724101,-0.2294466645,0.044546254,0.2166786045,0.0749733075,-0.0639310703,-0.0022501727,0.1691178232,-0.0127054844,0.0657434016,0.0208967216,0.2901545167,-0.0119958557,0.0165852979,0.1564203203,-0.3912960589,0.2354253829,0.5927878022,-0.3565097153,-0.0511188917,-0.0974970087,0.2818730175,-0.3155817389,0.2457960546,-0.4022749364,0.1330782473,0.0570968278,-0.0212265309,-0.1788376272,-0.5676388741,0.460424453,0.1918254197,-0.0006512939,0.4826521873,-0.0001226647,0.0562133119,0.1434753388,-0.0056539029,-0.0374005176,-0.0778044462,-0.0551672168,-0.1225150526,0.4323880672,-0.0663315579,-0.2351198643,-0.061791569,0.0275407284,0.206842199,0.3814501464,0.1206037775,0.0818247721,0.3831529319,0.6507722139,-0.0118111502,0.5365049839,-0.1792535931,-0.2274965346,0.1885087341,0.2283306569,-0.2860308588,0.0947201476,-0.1229192317,0.1636893749,0.1266192943,0.1389255226,-0.0354292691,0.3300071359,-0.3048428893,-0.0674957037,0.4033079743,0.3782411516,-0.1925992668,-0.3459409773,-0.1335900426,-0.1589212865,0.0724678636,0.2532921433,-0.1948823631,-0.2869555652,0.3625525832,0.0005612909,-0.1138784215,-0.2268296629,0.1020552516,-0.4096747041,0.5994551778,0.1086408868,0.2016237974,0.0570896231,-0.1125908643,-0.2647919357,0.2614644468,-0.0001149078,0.0883189365,-0.3127442002,-0.059696693,0.2298751026,0.2597002983,0.2728823423,0.0418971144,-0.1688748449,0.103894867,0.2539030313,0.2548690736,0.1296690404,0.0837187991,-0.2245155126,0.1010372341,0.2753295302,0.0285481345,0.1286762804,0.1025176048,-0.0497893393,-0.2957265377,0.0719251782,-0.3837428689,0.3628817797,-0.2149203271,-0.3580614328,-0.1517550796,-0.1998100132,0.1401495785,-0.0616697855,0.1240726262,0.0389086828,0.3743239343,0.067445986,0.1658492237,-0.2863715589,-0.01235676,-0.0845561847,-0.1550300866,-0.0043634567,0.0966148898,0.3515407741,0.0227618832,0.3478555381,0.2659069896,0.0562620573,0.3823370039,-0.0564059503,0.1206506863,-0.3797757924,0.5211027861,-0.3667622507,0.2458288372,-0.0599478856,0.0836729333,-0.216229111,0.1611863524,-0.2448174208,0.0681528971,-0.2135563344,0.1039168462,-0.3787121773,-0.0746729821,-0.1572631299,0.0571043417,-0.0352057032,-0.408365041,0.0018554332,-0.3095980287,-0.0211989656,-0.0501910709,0.2657848597,0.0732726231,-0.4110647142,-0.0553536266,0.1464295983,-0.6278679967,-0.0937169343,0.2602693141,-0.2143107951,0.2615125775,-0.171292156,0.3319961429,0.125491783,-0.2687300146,-0.4613823295,-0.097947903,-0.2837933898,0.4334025085,0.0229181219,-0.1663631499,0.0577699468,0.0078358976,-0.0059621758,0.126890704,0.180409357,0.2102524042,-0.2970997393,-0.3904989362,0.5659132004,0.2077561617,0.077319935,-0.0564301051,-0.122668229,-0.0641804934,0.1925192922,-0.2520045638,0.1266434491,0.0647221133,0.4038166404,-0.1244884953,0.1586154401,-0.2392110825,-0.5577841401,0.3368268311,-0.1081323028,0.1521909237,-0.1528612077,0.0712798536,-0.093885377,-0.0258187391,-0.3526058793,-0.2056111097,0.1142313704,-0.2597649097,0.1468533725,0.1433563679,-0.0995418504,0.0853913948,0.4340462983,0.1081717461,0.0264091697,0.3328432739,-0.1912792921,-0.3221775293,0.2326421142,0.0861702338,0.1178943291,-0.0763146281,-0.0634006858,0.2662022114,0.1697747111,0.3685865402,-0.2167717069,0.3443760574,0.1450622827,-0.5561097264,-0.0559157953,0.4561325312,0.1285793483,-0.1256193668,0.1880304813,0.13425605,0.3463529944,0.0614618249,0.1090695262,-0.1019291952,0.0950394198,0.0584769882,0.0756847337,-0.0650201663,0.0488045961,0.1172883436,0.1151427627,0.0534863845,-0.0210108329,0.1688484401,0.33660537,0.0282275639,0.3812072575,0.1301885396,-0.300298661,-0.1400309354,0.1055176184,-0.543846488,0.2666546106,0.1710639149,-0.1121409088,0.0792613775,-0.2599853277,-0.1251826286,0.1742731333,-0.0578921512,0.2054306269,0.3278053701,0.0803237706,-0.0520259328,-0.3668719828,-0.1731809378,-0.0534409061,0.3192165494,-0.448913008,-0.2553728223,-0.1153847054,0.4096748233,0.2679757476,-0.0832600445,-0.3234757483,-0.15157184,0.1907196343,0.487103194,0.1732062846,0.1628544331,-0.0860742256,0.0134216864,0.1226308197,-0.3300580978,-0.3696283698,-0.2419108152,-0.4619723558,0.0492066704,-0.0058045071,0.1037280783,0.1937630028,0.0053327945,0.0835207999,-0.2515048087,-0.5143217444,0.0052005085,-0.290040195,0.3554943204,0.2452578694,0.4037764966,-0.0252845567,-0.1328048855,0.3036792576,-0.4741431475,-0.2775180936,0.0788417161,-0.2127462029,0.003696393,-0.2109628916,-0.1804171801,0.1119275019,-0.1781096458,0.4739205241,0.0716430992,-0.0863906965,-0.0142506277,0.2749122679,0.2249675542,-0.1203625798,0.0545447916,-0.1089293882,-0.2190484703,0.4475554228,-0.0897278339,-0.3637834191,-0.0060731168,0.0331213847,0.3714805245,0.2010559291,-0.3030137718,-0.1785420924,-0.1841654629,0.5038659573,0.1345975399,0.269472152,0.5878297687,0.0813112706,0.0553361773,-0.2243198901,-0.2839870751,0.1419700235,0.0696283057,0.0827935934,-0.0956305712,0.0275062863,-0.0847217739,0.7192692161,0.201434657,0.168365255,0.2772160769,-0.1349285096,0.0249315538,-0.2609725296,-0.0178890284,0.161109671,0.1278343797,0.1197075322,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/707","title":"Requirements should specify pyarrow<1","comments":"I am sorry for hijacking this closed issue, but I believe I was able to reproduce this very issue. Strangely enough, it also turned out that running `pip install \"pyarrow<1\" --upgrade` did indeed fix the issue (PyArrow was installed in version `0.14.1` in my case).\r\n\r\nPlease see the Colab below:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/15QQS3xWjlKW2aK0J74eEcRFuhXUddUST\r\n\r\nThanks!","body":"I was looking at the docs on [Perplexity](https:\/\/huggingface.co\/transformers\/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error,\r\n\r\n```\r\nmodule 'pyarrow' has no attribute 'PyExtensionType'\r\n```\r\nI traced it back to datasets having installed PyArrow 1.0.1 but there's not pinning in the setup file. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e86a2a8f869b91654e782c9133d810bb82783200\/setup.py#L68\r\n\r\nDowngrading by installing `pip install \"pyarrow<1\"` resolved the issue.","comment_length":52,"text":"Requirements should specify pyarrow<1 \n I was looking at the docs on [Perplexity](https:\/\/huggingface.co\/transformers\/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error,\r\n\r\n```\r\nmodule 'pyarrow' has no attribute 'PyExtensionType'\r\n```\r\nI traced it back to datasets having installed PyArrow 1.0.1 but there's not pinning in the setup file. \r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/e86a2a8f869b91654e782c9133d810bb82783200\/setup.py#L68\r\n\r\nDowngrading by installing `pip install \"pyarrow<1\"` resolved the issue. \n I am sorry for hijacking this closed issue, but I believe I was able to reproduce this very issue. Strangely enough, it also turned out that running `pip install \"pyarrow<1\" --upgrade` did indeed fix the issue (PyArrow was installed in version `0.14.1` in my case).\r\n\r\nPlease see the Colab below:\r\n\r\nhttps:\/\/colab.research.google.com\/drive\/15QQS3xWjlKW2aK0J74eEcRFuhXUddUST\r\n\r\nThanks!","embeddings":[-0.2761943936,-0.1401243955,0.0347475186,0.1603855789,0.048561275,-0.0427087359,0.0266228598,0.198436603,-0.0582807027,0.0600505993,0.0015630063,0.2838506997,-0.0044425442,0.0376977585,0.1520177722,-0.3687959611,0.2303959429,0.5788680911,-0.3444227278,-0.022595562,-0.1056371629,0.2532047927,-0.3011270761,0.2348771393,-0.3952791691,0.1669677496,0.0467276238,-0.0211397596,-0.1742847264,-0.5824115276,0.4924414754,0.252129674,0.0277054142,0.456689775,-0.0001201362,0.0909883007,0.1616440862,-0.0273129307,-0.0626678616,-0.0653416291,-0.0663691536,-0.1003902555,0.4112484753,-0.0659435689,-0.258389771,-0.0569337532,0.0501447804,0.1991277039,0.3331044316,0.1226729155,0.1010975912,0.3785631061,0.6543204784,-0.0274976362,0.5464001298,-0.1852430105,-0.2104420215,0.1501597464,0.2215284705,-0.2979403734,0.0968258232,-0.0911414325,0.1513130516,0.1252644062,0.129845202,-0.0355437733,0.3226665258,-0.30716905,-0.0943656042,0.3945658505,0.3904876411,-0.1932222843,-0.3653137684,-0.1660096794,-0.178064093,0.0701816529,0.2186295837,-0.2005637884,-0.2938903272,0.3531870842,0.0228029191,-0.1070260853,-0.2230653763,0.0897029489,-0.4169590473,0.6115364432,0.1379712522,0.2251069099,0.0881054774,-0.1168655008,-0.2416556031,0.2601368427,-0.0086097121,0.0636655018,-0.3093869686,-0.0776964575,0.2582891285,0.2512825727,0.2540597916,0.0727146715,-0.1410643756,0.1450536549,0.2471518666,0.2898202538,0.1449887007,0.0582231358,-0.2342856973,0.1094237864,0.3053756654,-0.0057278415,0.1311250925,0.0875021145,-0.0547012985,-0.2781450152,0.0926290825,-0.3468330503,0.323202163,-0.1806135625,-0.3758249581,-0.1754792035,-0.1947010756,0.1470202655,-0.0443434045,0.1393225342,0.0568133518,0.380368948,0.0832966343,0.1703187823,-0.2854255736,-0.0176735651,-0.097957924,-0.1771339178,0.0434861667,0.058685489,0.3848065436,0.0592871085,0.319157362,0.2018921822,0.0387462042,0.4158566296,-0.0089400476,0.1414913386,-0.3730438054,0.5116958618,-0.3510389328,0.2050296664,-0.0354541093,0.0312404651,-0.2431348413,0.1497157961,-0.2207836062,0.0337291025,-0.2022808641,0.134383589,-0.4136125743,-0.1025794372,-0.1556591094,0.0494392999,-0.0739996061,-0.4285871983,-0.0487005785,-0.3369171321,-0.0042493721,-0.0796426833,0.2585652471,0.0653273463,-0.414026022,-0.0424384996,0.0710194558,-0.6321167946,-0.1191628501,0.2066752166,-0.2231913805,0.2573313117,-0.1579539329,0.3253785372,0.1259886324,-0.281404078,-0.4366946518,-0.1255472153,-0.2708389759,0.4315644503,-0.0321910977,-0.1144108772,0.0957523212,0.0036066687,-0.0575910397,0.1472731382,0.1389974207,0.2289622873,-0.2556209564,-0.3824260831,0.5881329179,0.2423675805,0.0550807193,-0.0834098235,-0.149246335,-0.036235854,0.1523858458,-0.2435113639,0.1465931386,0.0390842929,0.4210374057,-0.0975347385,0.1797184944,-0.2006237507,-0.5595870018,0.323648721,-0.0932345092,0.1747029126,-0.1643192321,0.0497593209,-0.0593132153,-0.0135820657,-0.3554161489,-0.1940595657,0.1503903866,-0.2420448512,0.1908438802,0.1120263934,-0.0961319357,0.0760718584,0.4792812765,0.1153134257,0.0438901447,0.3394732475,-0.1945205033,-0.3024532497,0.2515620589,0.0911321864,0.111483492,-0.0667078868,-0.0520862713,0.2847637236,0.1765264273,0.3445160985,-0.1805079281,0.3716143966,0.1172270104,-0.5627410412,-0.081143111,0.4557859302,0.1287183315,-0.1143580675,0.1628384888,0.1477029622,0.3197205961,0.0652770549,0.1070592999,-0.0887629241,0.1299800873,0.0553453006,0.0559812747,-0.0689043254,-0.002261241,0.131443128,0.1101041585,0.0435634404,-0.0192339346,0.2190407664,0.3266852498,0.0325320363,0.3579244316,0.1216076463,-0.3350586593,-0.1262318641,0.1116462648,-0.507922411,0.2845572531,0.2100426704,-0.0693620592,0.0497773439,-0.2698375583,-0.1088167876,0.1571920663,-0.0635257214,0.2181335837,0.2919237018,0.1006836444,-0.0767323151,-0.3659601808,-0.2055685371,-0.077467829,0.340467751,-0.4137086868,-0.2654786408,-0.138037473,0.381837517,0.2513831556,-0.1123967245,-0.3306830227,-0.2146785855,0.2353895605,0.494522512,0.1171966121,0.1509263068,-0.1158697009,-0.0013261178,0.1205762476,-0.3492171764,-0.3686798513,-0.2633909881,-0.4776780605,0.0790006891,-0.0042073377,0.0857134536,0.2334501892,0.0129469978,0.0899726152,-0.2458569407,-0.5565886497,-0.0041595125,-0.2848057449,0.349306345,0.2681626379,0.394120127,-0.0177511424,-0.1600650102,0.3386487961,-0.4612849057,-0.2450103909,0.0826767311,-0.1957324147,0.009552585,-0.2275137454,-0.1870778799,0.0804662779,-0.1823906004,0.4641319215,0.074658528,-0.0986429751,-0.0391061939,0.2710894644,0.1867854744,-0.0900549516,0.0501333103,-0.1244107485,-0.2010659873,0.4448416829,-0.1024022028,-0.346373111,0.0103552565,0.0043825815,0.3437042832,0.2179702669,-0.2875938714,-0.1910873652,-0.1717970222,0.4668476284,0.174009189,0.2660770416,0.5662255883,0.116081953,0.0288577732,-0.2397851348,-0.2698367238,0.1398894191,0.0620218031,0.1233609915,-0.1146316975,-0.020297626,-0.0600939095,0.7015287876,0.2194091976,0.143311128,0.24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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/705","title":"TypeError: '<' not supported between instances of 'NamedSplit' and 'NamedSplit'","comments":"Hi !\r\nThanks for reporting :) \r\nIndeed this is an issue on the `datasets` side.\r\nI'm creating a PR","body":"## Environment info\r\n<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.\r\n     Don't forget to fill out the missing fields in that output! -->\r\n     \r\n- `transformers` version: 3.3.1 (installed from master)\r\n- `datasets` version: 1.0.2 (installed as a dependency from transformers)\r\n- Platform: Linux-4.15.0-118-generic-x86_64-with-debian-stretch-sid\r\n- Python version: 3.7.9\r\n\r\nI'm testing my own text classification dataset using [this example](https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/text-classification#run-generic-text-classification-script-in-tensorflow) from transformers. The dataset is split into train \/ dev \/ test, and in csv format, containing just a text and a label columns, using comma as sep. Here's a sample:\r\n```\r\ntext,label\r\n\"Registra-se a presen\u00e7a do acad\u00eamico <name> . <REL_SEP> Ao me deparar com a descri\u00e7\u00e3o de dois autores no polo ativo da a\u00e7\u00e3o junto ao PJe , margem esquerda foi informado pela procuradora do reclamante que se trata de uma reclama\u00e7\u00e3o trabalhista individual . <REL_SEP> Diante disso , face a aus\u00eancia injustificada do autor <name> , determina-se o ARQUIVAMENTO do presente processo , com rela\u00e7\u00e3o a este , nos termos do [[ art . 844 da CLT ]] . <REL_SEP> CUSTAS AUTOR - DISPENSADO <REL_SEP> Custas pelo autor no importe de R $326,82 , calculadas sobre R $16.341,03 , dispensadas na forma da lei , em virtude da concess\u00e3o dos benef\u00edcios da Justi\u00e7a Gratuita , ora deferida . <REL_SEP> Cientes os presentes . <REL_SEP> Audi\u00eancia encerrada \u00e0s 8h42min . <REL_SEP> <name> <REL_SEP> Ju\u00edza do Trabalho <REL_SEP> Ata redigida por << <name> >> , Secret\u00e1rio de Audi\u00eancia .\",NO_RELATION\r\n```\r\n\r\nHowever, @Santosh-Gupta reported in #7351 that he had the exact same problem using the ChemProt dataset. His colab notebook is referenced in the following section.\r\n\r\n## To reproduce\r\n\r\nSteps to reproduce the behavior:\r\n\r\n1. Created a new conda environment using conda env -n transformers python=3.7\r\n2. Cloned transformers master, `cd` into it and installed using pip install --editable .  -r examples\/requirements.txt \r\n3. Installed tensorflow with `pip install tensorflow`\r\n3. Ran `run_tf_text_classification.py` with the following parameters:\r\n\r\n```\r\n--train_file <DATASET_PATH>\/train.csv \\\r\n--dev_file <DATASET_PATH>\/dev.csv \\ \r\n--test_file <DATASET_PATH>\/test.csv \\\r\n--label_column_id 1 \\\r\n--model_name_or_path neuralmind\/bert-base-portuguese-cased \\\r\n--output_dir <OUTPUT_PATH> \\\r\n--num_train_epochs 4 \\\r\n--per_device_train_batch_size 4 \\\r\n--per_device_eval_batch_size 4 \\\r\n--do_train \\\r\n--do_eval \\\r\n--do_predict \\\r\n--logging_steps 1000 \\\r\n--evaluate_during_training \\\r\n--save_steps 1000 \\\r\n--overwrite_output_dir \\\r\n--overwrite_cache\r\n```\r\n\r\nI have also copied [@Santosh-Gupta 's colab notebook](https:\/\/colab.research.google.com\/drive\/11APei6GjphCZbH5wD9yVlfGvpIkh8pwr?usp=sharing) as a reference.\r\n\r\n<!-- If you have code snippets, error messages, stack traces please provide them here as well.\r\n     Important! Use code tags to correctly format your code. See https:\/\/help.github.com\/en\/github\/writing-on-github\/creating-and-highlighting-code-blocks#syntax-highlighting\r\n     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->\r\n\r\nHere is the stack trace:\r\n\r\n```\r\n2020-10-02 07:33:41.622011: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n\/media\/discoD\/repositorios\/transformers_pedro\/src\/transformers\/training_args.py:333: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\r\n  FutureWarning,\r\n2020-10-02 07:33:43.471648: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\r\n2020-10-02 07:33:43.471791: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.472664: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1716] Found device 0 with properties: \r\npciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1\r\ncoreClock: 1.7085GHz coreCount: 15 deviceMemorySize: 7.92GiB deviceMemoryBandwidth: 238.66GiB\/s\r\n2020-10-02 07:33:43.472684: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n2020-10-02 07:33:43.472765: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\r\n2020-10-02 07:33:43.472809: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\r\n2020-10-02 07:33:43.472848: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\r\n2020-10-02 07:33:43.474209: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\r\n2020-10-02 07:33:43.474276: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\r\n2020-10-02 07:33:43.561219: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\r\n2020-10-02 07:33:43.561397: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.562345: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.563219: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1858] Adding visible gpu devices: 0\r\n2020-10-02 07:33:43.563595: I tensorflow\/core\/platform\/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2020-10-02 07:33:43.570091: I tensorflow\/core\/platform\/profile_utils\/cpu_utils.cc:104] CPU Frequency: 3591830000 Hz\r\n2020-10-02 07:33:43.570494: I tensorflow\/compiler\/xla\/service\/service.cc:168] XLA service 0x560842432400 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\r\n2020-10-02 07:33:43.570511: I tensorflow\/compiler\/xla\/service\/service.cc:176]   StreamExecutor device (0): Host, Default Version\r\n2020-10-02 07:33:43.570702: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.571599: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1716] Found device 0 with properties: \r\npciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1\r\ncoreClock: 1.7085GHz coreCount: 15 deviceMemorySize: 7.92GiB deviceMemoryBandwidth: 238.66GiB\/s\r\n2020-10-02 07:33:43.571633: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n2020-10-02 07:33:43.571645: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\r\n2020-10-02 07:33:43.571654: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\r\n2020-10-02 07:33:43.571664: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\r\n2020-10-02 07:33:43.571691: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\r\n2020-10-02 07:33:43.571704: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\r\n2020-10-02 07:33:43.571718: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\r\n2020-10-02 07:33:43.571770: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.572641: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.573475: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1858] Adding visible gpu devices: 0\r\n2020-10-02 07:33:47.139227: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\r\n2020-10-02 07:33:47.139265: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1263]      0 \r\n2020-10-02 07:33:47.139272: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1276] 0:   N \r\n2020-10-02 07:33:47.140323: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:47.141248: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:47.142085: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:47.142854: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1402] Created TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:0 with 5371 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)\r\n2020-10-02 07:33:47.146317: I tensorflow\/compiler\/xla\/service\/service.cc:168] XLA service 0x5608b95dc5c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2020-10-02 07:33:47.146336: I tensorflow\/compiler\/xla\/service\/service.cc:176]   StreamExecutor device (0): GeForce GTX 1070, Compute Capability 6.1\r\n10\/02\/2020 07:33:47 - INFO - __main__ -   n_replicas: 1, distributed training: False, 16-bits training: False\r\n10\/02\/2020 07:33:47 - INFO - __main__ -   Training\/evaluation parameters TFTrainingArguments(output_dir='\/media\/discoD\/models\/datalawyer\/pedidos\/transformers_tf', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=True, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=4, per_device_eval_batch_size=4, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=4.0, max_steps=-1, warmup_steps=0, logging_dir='runs\/Oct02_07-33-43_user-XPS-8700', logging_first_step=False, logging_steps=1000, save_steps=1000, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=1000, dataloader_num_workers=0, past_index=-1, run_name='\/media\/discoD\/models\/datalawyer\/pedidos\/transformers_tf', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=False, tpu_name=None, xla=False)\r\n10\/02\/2020 07:33:53 - INFO - filelock -   Lock 140407857405776 acquired on \/home\/user\/.cache\/huggingface\/datasets\/e0f1e9ed46db1e2429189f06b479cbd4075c0976104c1aacf8f77d9a53d2ad87.03756fef6da334f50a7ff73608e21b5018229944ca250416ce7352e25d84a552.py.lock\r\n10\/02\/2020 07:33:53 - INFO - filelock -   Lock 140407857405776 released on \/home\/user\/.cache\/huggingface\/datasets\/e0f1e9ed46db1e2429189f06b479cbd4075c0976104c1aacf8f77d9a53d2ad87.03756fef6da334f50a7ff73608e21b5018229944ca250416ce7352e25d84a552.py.lock\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"run_tf_text_classification.py\", line 283, in <module>\r\n    main()\r\n  File \"run_tf_text_classification.py\", line 222, in main\r\n    max_seq_length=data_args.max_seq_length,\r\n  File \"run_tf_text_classification.py\", line 43, in get_tfds\r\n    ds = datasets.load_dataset(\"csv\", data_files=files)\r\n  File \"\/media\/discoD\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 604, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/media\/discoD\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 158, in __init__\r\n    **config_kwargs,\r\n  File \"\/media\/discoD\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 269, in _create_builder_config\r\n    for key in sorted(data_files.keys()):\r\nTypeError: '<' not supported between instances of 'NamedSplit' and 'NamedSplit'\r\n```\r\n\r\n## Expected behavior\r\n\r\nShould be able to run the text-classification example as described in [https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/text-classification#run-generic-text-classification-script-in-tensorflow](https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/text-classification#run-generic-text-classification-script-in-tensorflow)\r\n\r\nOriginally opened this issue at transformers' repository: [https:\/\/github.com\/huggingface\/transformers\/issues\/7535](https:\/\/github.com\/huggingface\/transformers\/issues\/7535). @jplu instructed me to open here, since according to [this](https:\/\/github.com\/huggingface\/transformers\/issues\/7535#issuecomment-702778885) evidence, the problem is from datasets.\r\n\r\nThanks!","comment_length":19,"text":"TypeError: '<' not supported between instances of 'NamedSplit' and 'NamedSplit' \n ## Environment info\r\n<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.\r\n     Don't forget to fill out the missing fields in that output! -->\r\n     \r\n- `transformers` version: 3.3.1 (installed from master)\r\n- `datasets` version: 1.0.2 (installed as a dependency from transformers)\r\n- Platform: Linux-4.15.0-118-generic-x86_64-with-debian-stretch-sid\r\n- Python version: 3.7.9\r\n\r\nI'm testing my own text classification dataset using [this example](https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/text-classification#run-generic-text-classification-script-in-tensorflow) from transformers. The dataset is split into train \/ dev \/ test, and in csv format, containing just a text and a label columns, using comma as sep. Here's a sample:\r\n```\r\ntext,label\r\n\"Registra-se a presen\u00e7a do acad\u00eamico <name> . <REL_SEP> Ao me deparar com a descri\u00e7\u00e3o de dois autores no polo ativo da a\u00e7\u00e3o junto ao PJe , margem esquerda foi informado pela procuradora do reclamante que se trata de uma reclama\u00e7\u00e3o trabalhista individual . <REL_SEP> Diante disso , face a aus\u00eancia injustificada do autor <name> , determina-se o ARQUIVAMENTO do presente processo , com rela\u00e7\u00e3o a este , nos termos do [[ art . 844 da CLT ]] . <REL_SEP> CUSTAS AUTOR - DISPENSADO <REL_SEP> Custas pelo autor no importe de R $326,82 , calculadas sobre R $16.341,03 , dispensadas na forma da lei , em virtude da concess\u00e3o dos benef\u00edcios da Justi\u00e7a Gratuita , ora deferida . <REL_SEP> Cientes os presentes . <REL_SEP> Audi\u00eancia encerrada \u00e0s 8h42min . <REL_SEP> <name> <REL_SEP> Ju\u00edza do Trabalho <REL_SEP> Ata redigida por << <name> >> , Secret\u00e1rio de Audi\u00eancia .\",NO_RELATION\r\n```\r\n\r\nHowever, @Santosh-Gupta reported in #7351 that he had the exact same problem using the ChemProt dataset. His colab notebook is referenced in the following section.\r\n\r\n## To reproduce\r\n\r\nSteps to reproduce the behavior:\r\n\r\n1. Created a new conda environment using conda env -n transformers python=3.7\r\n2. Cloned transformers master, `cd` into it and installed using pip install --editable .  -r examples\/requirements.txt \r\n3. Installed tensorflow with `pip install tensorflow`\r\n3. Ran `run_tf_text_classification.py` with the following parameters:\r\n\r\n```\r\n--train_file <DATASET_PATH>\/train.csv \\\r\n--dev_file <DATASET_PATH>\/dev.csv \\ \r\n--test_file <DATASET_PATH>\/test.csv \\\r\n--label_column_id 1 \\\r\n--model_name_or_path neuralmind\/bert-base-portuguese-cased \\\r\n--output_dir <OUTPUT_PATH> \\\r\n--num_train_epochs 4 \\\r\n--per_device_train_batch_size 4 \\\r\n--per_device_eval_batch_size 4 \\\r\n--do_train \\\r\n--do_eval \\\r\n--do_predict \\\r\n--logging_steps 1000 \\\r\n--evaluate_during_training \\\r\n--save_steps 1000 \\\r\n--overwrite_output_dir \\\r\n--overwrite_cache\r\n```\r\n\r\nI have also copied [@Santosh-Gupta 's colab notebook](https:\/\/colab.research.google.com\/drive\/11APei6GjphCZbH5wD9yVlfGvpIkh8pwr?usp=sharing) as a reference.\r\n\r\n<!-- If you have code snippets, error messages, stack traces please provide them here as well.\r\n     Important! Use code tags to correctly format your code. See https:\/\/help.github.com\/en\/github\/writing-on-github\/creating-and-highlighting-code-blocks#syntax-highlighting\r\n     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->\r\n\r\nHere is the stack trace:\r\n\r\n```\r\n2020-10-02 07:33:41.622011: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n\/media\/discoD\/repositorios\/transformers_pedro\/src\/transformers\/training_args.py:333: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\r\n  FutureWarning,\r\n2020-10-02 07:33:43.471648: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\r\n2020-10-02 07:33:43.471791: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.472664: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1716] Found device 0 with properties: \r\npciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1\r\ncoreClock: 1.7085GHz coreCount: 15 deviceMemorySize: 7.92GiB deviceMemoryBandwidth: 238.66GiB\/s\r\n2020-10-02 07:33:43.472684: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n2020-10-02 07:33:43.472765: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\r\n2020-10-02 07:33:43.472809: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\r\n2020-10-02 07:33:43.472848: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\r\n2020-10-02 07:33:43.474209: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\r\n2020-10-02 07:33:43.474276: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\r\n2020-10-02 07:33:43.561219: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\r\n2020-10-02 07:33:43.561397: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.562345: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.563219: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1858] Adding visible gpu devices: 0\r\n2020-10-02 07:33:43.563595: I tensorflow\/core\/platform\/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA\r\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2020-10-02 07:33:43.570091: I tensorflow\/core\/platform\/profile_utils\/cpu_utils.cc:104] CPU Frequency: 3591830000 Hz\r\n2020-10-02 07:33:43.570494: I tensorflow\/compiler\/xla\/service\/service.cc:168] XLA service 0x560842432400 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\r\n2020-10-02 07:33:43.570511: I tensorflow\/compiler\/xla\/service\/service.cc:176]   StreamExecutor device (0): Host, Default Version\r\n2020-10-02 07:33:43.570702: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.571599: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1716] Found device 0 with properties: \r\npciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1\r\ncoreClock: 1.7085GHz coreCount: 15 deviceMemorySize: 7.92GiB deviceMemoryBandwidth: 238.66GiB\/s\r\n2020-10-02 07:33:43.571633: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n2020-10-02 07:33:43.571645: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\r\n2020-10-02 07:33:43.571654: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\r\n2020-10-02 07:33:43.571664: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\r\n2020-10-02 07:33:43.571691: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\r\n2020-10-02 07:33:43.571704: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\r\n2020-10-02 07:33:43.571718: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\r\n2020-10-02 07:33:43.571770: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.572641: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:43.573475: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1858] Adding visible gpu devices: 0\r\n2020-10-02 07:33:47.139227: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\r\n2020-10-02 07:33:47.139265: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1263]      0 \r\n2020-10-02 07:33:47.139272: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1276] 0:   N \r\n2020-10-02 07:33:47.140323: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:47.141248: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:47.142085: I tensorflow\/stream_executor\/cuda\/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n2020-10-02 07:33:47.142854: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1402] Created TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:0 with 5371 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)\r\n2020-10-02 07:33:47.146317: I tensorflow\/compiler\/xla\/service\/service.cc:168] XLA service 0x5608b95dc5c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\r\n2020-10-02 07:33:47.146336: I tensorflow\/compiler\/xla\/service\/service.cc:176]   StreamExecutor device (0): GeForce GTX 1070, Compute Capability 6.1\r\n10\/02\/2020 07:33:47 - INFO - __main__ -   n_replicas: 1, distributed training: False, 16-bits training: False\r\n10\/02\/2020 07:33:47 - INFO - __main__ -   Training\/evaluation parameters TFTrainingArguments(output_dir='\/media\/discoD\/models\/datalawyer\/pedidos\/transformers_tf', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=True, evaluate_during_training=True, evaluation_strategy=<EvaluationStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=4, per_device_eval_batch_size=4, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=4.0, max_steps=-1, warmup_steps=0, logging_dir='runs\/Oct02_07-33-43_user-XPS-8700', logging_first_step=False, logging_steps=1000, save_steps=1000, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=1000, dataloader_num_workers=0, past_index=-1, run_name='\/media\/discoD\/models\/datalawyer\/pedidos\/transformers_tf', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=False, tpu_name=None, xla=False)\r\n10\/02\/2020 07:33:53 - INFO - filelock -   Lock 140407857405776 acquired on \/home\/user\/.cache\/huggingface\/datasets\/e0f1e9ed46db1e2429189f06b479cbd4075c0976104c1aacf8f77d9a53d2ad87.03756fef6da334f50a7ff73608e21b5018229944ca250416ce7352e25d84a552.py.lock\r\n10\/02\/2020 07:33:53 - INFO - filelock -   Lock 140407857405776 released on \/home\/user\/.cache\/huggingface\/datasets\/e0f1e9ed46db1e2429189f06b479cbd4075c0976104c1aacf8f77d9a53d2ad87.03756fef6da334f50a7ff73608e21b5018229944ca250416ce7352e25d84a552.py.lock\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"run_tf_text_classification.py\", line 283, in <module>\r\n    main()\r\n  File \"run_tf_text_classification.py\", line 222, in main\r\n    max_seq_length=data_args.max_seq_length,\r\n  File \"run_tf_text_classification.py\", line 43, in get_tfds\r\n    ds = datasets.load_dataset(\"csv\", data_files=files)\r\n  File \"\/media\/discoD\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 604, in load_dataset\r\n    **config_kwargs,\r\n  File \"\/media\/discoD\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 158, in __init__\r\n    **config_kwargs,\r\n  File \"\/media\/discoD\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 269, in _create_builder_config\r\n    for key in sorted(data_files.keys()):\r\nTypeError: '<' not supported between instances of 'NamedSplit' and 'NamedSplit'\r\n```\r\n\r\n## Expected behavior\r\n\r\nShould be able to run the text-classification example as described in [https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/text-classification#run-generic-text-classification-script-in-tensorflow](https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/text-classification#run-generic-text-classification-script-in-tensorflow)\r\n\r\nOriginally opened this issue at transformers' repository: [https:\/\/github.com\/huggingface\/transformers\/issues\/7535](https:\/\/github.com\/huggingface\/transformers\/issues\/7535). @jplu instructed me to open here, since according to [this](https:\/\/github.com\/huggingface\/transformers\/issues\/7535#issuecomment-702778885) evidence, the problem is from datasets.\r\n\r\nThanks! \n Hi !\r\nThanks for reporting :) \r\nIndeed this is an issue on the `datasets` side.\r\nI'm creating a PR","embeddings":[-0.2919273078,-0.6131456494,-0.0538775101,0.0724286065,0.5358543992,0.0512854382,0.5790147781,0.301427871,0.288526386,0.1793083698,-0.0349086858,0.1695188433,-0.0423114263,-0.1191879734,-0.1670343578,-0.2730587125,-0.1069535911,0.1412786692,-0.4372419417,-0.0667978898,0.0061711208,0.1115164608,-0.3358640075,0.0874110833,-0.3546169102,-0.1470186561,0.1937073618,0.0334143899,0.1056338474,-0.3845270872,0.366317004,-0.0270779245,0.1710627675,0.491507113,-0.0001042778,0.081310913,0.4950879514,-0.138119936,-0.0701588094,-0.343044579,-0.4419636428,0.0307981856,0.0160502605,-0.3915827572,-0.2448375076,-0.0110362982,-0.1189130321,-0.0461345613,0.6161757112,0.278801918,0.2397651076,0.2381774187,-0.1034501866,-0.3372361064,-0.1007003114,0.3406432867,-0.1360172033,-0.1092183441,0.1256229579,-0.2894225121,-0.0251126885,0.2308775187,0.0032396459,-0.089165844,0.3362298608,0.126687631,-0.1719463766,0.0904838964,0.01274959,0.2484526038,0.3977201581,-0.191258952,-0.201420784,-0.1912941635,-0.4447488189,-0.3844889998,0.0586855598,0.0405776054,0.0833992809,0.2616128325,-0.0061038891,0.4328720272,-0.2141648531,-0.1010648534,0.0205578785,0.5832354426,-0.1706664115,0.0971066877,0.0810640901,-0.0337578095,0.1662235856,-0.1250212342,0.2540244162,0.0342846215,-0.2468905896,-0.012379894,-0.0421408713,-0.4438541234,-0.214487657,-0.1216932535,0.2730001509,-0.0499446429,0.0718224198,0.2485999912,0.1498123109,0.2958030701,0.0911678746,0.537527442,0.2819210589,0.3849364817,-0.0427861772,-0.1365961283,-0.142474398,-0.158597067,-0.0144823557,0.4171228111,0.0576643199,-0.1039039567,-0.30254969,-0.2212817669,0.0309037361,-0.0660278946,-0.0246453453,0.4157520235,-0.0416241586,0.3547725976,0.2065986395,0.0611954629,-0.0000107179,-0.1164428368,-0.1507170498,0.1743426323,-0.3866131902,-0.2293417454,-0.0172503423,0.2606917918,0.0060736635,-0.1308667958,-0.083957687,-0.0662648901,-0.0675454959,0.0104777887,0.038608443,0.2141883671,-0.1814568043,0.1716786176,0.1413978785,-0.142892912,-0.0168804023,0.2545795739,-0.0376725495,-0.2689933479,0.0494651496,0.1983073056,0.0924026594,0.0822201595,0.0646613985,0.1997675002,0.2888107002,-0.0023917381,0.0389889143,0.0640591905,-0.1613370031,0.1434037834,0.0981706604,0.3937546015,-0.7670513988,-0.2362775803,-0.0231548995,0.0841976553,-0.0441100746,0.4377836287,-0.3081965744,0.2548838556,0.1809004545,0.2804787159,0.1315836161,-0.4054885507,-0.2366541028,-0.0511300489,0.0623557344,-0.1371805072,0.1976591647,-0.1599786133,-0.0393890887,-0.2148528397,-0.2731992602,0.1258901358,-0.0372625664,0.0372501723,-0.3978560269,-0.0586486682,0.1387393624,0.0166069958,0.0908488706,-0.0245048665,-0.0067748767,0.5229411125,0.1247486845,-0.3379059136,0.1744470745,0.2543297112,0.0626068562,0.2155818939,0.018898882,-0.2434887141,-0.2023972422,0.1049191728,-0.0260952972,0.3419173658,-0.2349476367,-0.1734545231,-0.4128475785,0.0400041379,0.0303186756,-0.1581599265,0.1817014217,0.0163617879,0.0562101454,-0.1548790336,0.0504410565,0.0850315765,-0.0169340409,0.1152144745,-0.3652016521,0.205051899,-0.2516756356,-0.0821671262,-0.126055941,0.208872363,0.2595393658,-0.2008835673,-0.1568623483,0.3399887383,-0.2882199287,0.1538384408,-0.3089927435,-0.0751828775,0.2699451447,-0.4763702154,0.0024343291,0.1310064346,0.0902314931,-0.0006721493,-0.4511058927,0.1370696127,0.0112843513,0.1648834497,0.1284329742,0.0007869658,0.1388457268,-0.1832198799,-0.0573632009,-0.1072910875,-0.0001398468,0.1755349934,-0.2161804736,0.0637440309,-0.1466740072,-0.1216330752,0.4897304475,-0.0830696449,0.2121409774,-0.0066706208,-0.2014056146,0.1740379184,0.0147640789,0.2376895696,0.1019209698,0.1063609719,-0.1818083525,0.1594488174,-0.1297113597,-0.1348221153,0.1366052777,0.1401495934,0.1842842102,0.047402259,-0.0023738095,0.0637102649,0.0001989906,-0.3237716556,0.0035916145,0.0375333168,-0.2615197003,0.1092323586,-0.2121478766,-0.1581652462,-0.2769756615,0.241360575,0.1923743635,-0.0711507946,-0.0779944956,0.0264965203,-0.2088043094,0.3075107038,0.1213724911,0.2280253023,0.2589328587,-0.1669288725,-0.1522791386,-0.3805861771,-0.1148757711,0.1299654692,0.1442116201,0.1163031459,-0.0103829578,0.0811195746,0.0132961869,0.4569865167,-0.5159244537,0.0750539526,-0.314931035,-0.2156138271,-0.0008078428,-0.0266429707,0.0513779521,-0.1849368513,0.3361887038,-0.1817682087,0.0587969385,-0.0685050115,0.1174616069,-0.2828928232,-0.1832380444,-0.6926159859,-0.2156877816,-0.4069219828,0.2902898192,-0.1129137129,-0.1000652313,0.4519687295,-0.04145924,-0.1367301047,0.2323179543,-0.0375305265,0.0842413232,0.2739644647,0.3995569944,-0.1479191929,-0.3361201286,-0.037801262,-0.1635225713,0.0331597701,-0.2620622218,-0.1347199827,-0.0583056845,-0.2712892592,-0.1474146694,0.1512570232,-0.1404368728,0.6117979288,0.1744675785,-0.1556137651,-0.1580144018,-0.4322287738,0.2149780095,0.0620988421,0.0518928878,-0.0564923882,0.5008730292,0.1867362559,0.6495117545,0.0560511127,-0.1962005943,-0.0095487041,0.1530277133,0.0647748932,-0.0284662768,-0.2616336942,0.0940554515,-0.115679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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/699","title":"XNLI dataset is not loading ","comments":"also i tried below code to solve checksum error \r\n`datasets-cli test .\/datasets\/xnli --save_infos --all_configs`\r\n\r\nand it shows \r\n\r\n```\r\n2020-10-02 07:06:16.588760: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nTraceback (most recent call last):\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 268, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 474, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/.\/datasets\/xnli\/xnli.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 279, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 474, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/.\/datasets\/xnli\/xnli.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/opt\/conda\/bin\/datasets-cli\", line 36, in <module>\r\n    service.run()\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/commands\/test.py\", line 76, in run\r\n    module_path, hash = prepare_module(path)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 283, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at .\/datasets\/xnli\/xnli.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/.\/datasets\/xnli\/xnli.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/.\/datasets\/xnli\/xnli.py\r\n```\r\n\r\n","body":"`dataset = datasets.load_dataset(path='xnli')`\r\n\r\nshowing below error \r\n```\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']\r\n```\r\n\r\nI think URL is now changed to \"https:\/\/cims.nyu.edu\/~sbowman\/xnli\/XNLI-MT-1.0.zip\"","comment_length":170,"text":"XNLI dataset is not loading  \n `dataset = datasets.load_dataset(path='xnli')`\r\n\r\nshowing below error \r\n```\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']\r\n```\r\n\r\nI think URL is now changed to \"https:\/\/cims.nyu.edu\/~sbowman\/xnli\/XNLI-MT-1.0.zip\" \n also i tried below code to solve checksum error \r\n`datasets-cli test .\/datasets\/xnli --save_infos --all_configs`\r\n\r\nand it shows \r\n\r\n```\r\n2020-10-02 07:06:16.588760: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nTraceback (most recent call last):\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 268, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 474, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/.\/datasets\/xnli\/xnli.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 279, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 308, in cached_path\r\n    use_etag=download_config.use_etag,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/file_utils.py\", line 474, in get_from_cache\r\n    raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/.\/datasets\/xnli\/xnli.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/opt\/conda\/bin\/datasets-cli\", line 36, in <module>\r\n    service.run()\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/commands\/test.py\", line 76, in run\r\n    module_path, hash = prepare_module(path)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 283, in prepare_module\r\n    combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at .\/datasets\/xnli\/xnli.py, or remotely at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.2\/datasets\/.\/datasets\/xnli\/xnli.py or https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/datasets\/datasets\/.\/datasets\/xnli\/xnli.py\r\n```\r\n\r\n","embeddings":[-0.2810523212,0.140100345,-0.0911717787,0.1260449886,0.2266674191,-0.2409968972,0.3072917163,0.4281842411,0.0896520987,-0.0614517219,-0.0190948769,0.4737130105,0.1864162832,0.1176810041,0.1495385915,0.219538033,0.0383550376,0.094383806,-0.0668891147,-0.1008866653,-0.2314456105,0.0685328618,-0.2965807021,-0.1154323369,-0.0837840065,0.1186984032,0.215154022,0.2002559602,-0.0664989576,-0.2786804438,0.3651867807,-0.0079631768,0.263148725,0.2762711346,-0.0001126595,0.2332450598,0.3754191995,-0.1614189744,-0.4101258516,-0.0952022448,-0.4924982488,-0.1245010495,-0.1306751519,-0.2602872849,0.1164859161,0.061470978,-0.0578304157,-0.1681259871,-0.0545159765,0.4935926199,0.1621233076,0.5488842726,0.2709943354,0.2150414437,-0.0674156547,-0.3017497957,0.0076497304,0.4067320824,0.3300507665,0.014924285,0.055706773,0.1214362085,-0.0206639655,0.000889206,0.158074826,-0.1624352485,0.2153967768,-0.0836901218,0.0030177215,0.2048472762,0.397978425,-0.1201249063,-0.4151076376,0.1187436283,0.0617506504,-0.1697445065,0.4212925434,0.1787170917,-0.3158423603,-0.1220314577,-0.3779917061,-0.0248488933,-0.175771445,0.3431745172,-0.0073401993,0.4367713928,-0.0140386047,0.0259262361,0.0205369312,-0.0988183767,0.2914394438,-0.0606681034,0.0233033914,0.1861115694,-0.6986988187,0.0651394948,-0.0342235416,-0.0879542679,0.1597488374,0.395940125,0.3262369931,0.0806987062,-0.2925149202,0.1523523331,-0.1719446629,0.387106508,0.1406884938,0.0790466443,0.2752794623,0.2361992598,-0.0131360972,0.0545628145,-0.300019145,-0.1971398443,-0.0815355331,0.0388958752,0.1401141584,-0.2044802755,-0.2629959583,0.0759377778,-0.0784603208,-0.138380006,0.4358338416,0.4694562554,-0.320874244,0.2498867661,0.0098449374,0.0181956422,-0.0383259542,-0.1316920221,-0.1493628621,0.071669206,-0.2305678427,-0.0721811801,0.2829676867,-0.3151513636,0.4981717169,-0.1555375308,-0.1146094799,-0.3088402748,0.0614730939,0.0018759954,-0.0404581651,0.2428904325,-0.0249700118,-0.033209119,0.072953172,-0.0030836209,-0.0557516925,0.1683819145,-0.3589117229,-0.2988475561,-0.0545134991,0.2073375136,-0.1995881349,-0.1916325092,-0.0024796955,-0.4883229434,0.0660807788,-0.0477588437,-0.1237207875,-0.2153945267,-0.0667610615,-0.209039852,0.5407055616,0.1293569803,-0.3490072489,0.0516496077,-0.1369031072,-0.0557792149,0.0964061096,0.2605536282,-0.1590194851,0.3728075624,-0.3403566778,-0.1178918779,0.5776913762,-0.476293087,-0.4399244487,0.2114343941,-0.1018751934,0.0079044383,-0.1255787462,0.2523783445,-0.1356548816,-0.0352004357,0.0137475841,0.2980307937,0.1415857971,0.1215579435,-0.2224131823,-0.2473589331,0.1558611095,0.3051367104,0.0188241675,0.1345297247,-0.0449265689,0.0668575466,0.3322269022,-0.0833622739,-0.0464372523,0.221496582,0.4732953012,-0.0393512286,0.0370533764,-0.0048491447,-0.4131316543,0.3650059998,-0.0015582185,-0.0114370799,-0.0650833026,0.1789069325,-0.3976843357,-0.024558926,-0.3283422887,-0.1062684953,0.0848114043,0.2809491754,0.1377623379,0.0063051474,-0.3725928068,0.281760335,-0.8748972416,0.045638036,-0.4280187786,0.4332666099,0.0865833908,-0.0375143439,0.0658052489,0.2386031151,-0.1012093499,-0.2264588177,-0.3001839519,0.3219572604,-0.0343309045,-0.0100150499,0.2217055708,0.1605622619,0.0023727182,-0.3698914945,0.0630576536,0.1883188784,0.0396234393,-0.0004497514,-0.057606101,0.2553064227,-0.1893199533,-0.0278984662,0.166231975,0.0318163037,0.4790482521,-0.3187073171,-0.0347367786,-0.1404661089,0.2331364304,0.30008775,0.604013741,0.1275435239,-0.0661315992,-0.2273055166,0.3051785827,0.0821915567,-0.1016039029,0.1684033722,0.0007089683,0.0061235605,0.0291468389,0.306537658,0.3231015801,-0.0140058435,-0.1969237477,0.0177570265,-0.1565663815,-0.1717172265,0.0702453777,-0.1741173416,0.2569725215,0.2882356048,-0.0568803772,-0.0213246997,-0.2173702568,-0.3831168413,0.2945067883,0.3578618765,-0.3981689513,-0.2213178128,-0.226242587,-0.4142237008,-0.3695698977,-0.1048752293,-0.2279290706,-0.2551909685,-0.2312048525,0.3115474582,0.183365792,0.2592550814,-0.1243881136,0.1447745562,0.2544369698,-0.4136667252,0.0239859987,-0.0903487653,-0.1083464026,0.0917411596,0.5014837384,0.1665120721,0.1969742775,-0.2534525394,0.0452200584,-0.5949463248,-0.2944060862,-0.0161856711,0.0371351354,0.0740418658,-0.074483037,0.3668269813,0.1976650357,-0.3344924748,0.3055459857,0.0922012329,-0.1450958401,0.1104357317,-0.1262268722,-0.160679549,0.0007932255,-0.3663280904,-0.1312322766,-0.2736053765,-0.1452491134,-0.1186463833,0.24539873,0.2374483794,0.0201194752,0.1560905874,0.10875085,0.0869797915,0.0980706513,-0.5159553885,0.3297222853,0.0439850315,-0.406933099,0.0055010873,-0.0666834489,0.5552334785,0.2053047568,-0.4230065048,-0.0054273717,-0.1825606525,0.2202462852,0.2052992284,-0.1584539115,0.0227844305,-0.2259042114,-0.0106735984,-0.1840025187,-0.0045016324,0.2184030861,0.1114478484,0.6592665315,0.3477965593,0.2220592499,-0.0564368404,0.11664965,0.5052437782,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/699","title":"XNLI dataset is not loading ","comments":"Hi !\r\nYes the download url changed.\r\nIt's updated on the master branch. I'm doing a release today to fix that :)","body":"`dataset = datasets.load_dataset(path='xnli')`\r\n\r\nshowing below error \r\n```\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']\r\n```\r\n\r\nI think URL is now changed to \"https:\/\/cims.nyu.edu\/~sbowman\/xnli\/XNLI-MT-1.0.zip\"","comment_length":22,"text":"XNLI dataset is not loading  \n `dataset = datasets.load_dataset(path='xnli')`\r\n\r\nshowing below error \r\n```\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']\r\n```\r\n\r\nI think URL is now changed to \"https:\/\/cims.nyu.edu\/~sbowman\/xnli\/XNLI-MT-1.0.zip\" \n Hi !\r\nYes the download url changed.\r\nIt's updated on the master branch. I'm doing a release today to fix that :)","embeddings":[-0.1545367837,0.328220129,-0.0674623325,0.1016497761,0.1184456944,-0.0849034786,0.1139141247,0.3517860472,-0.0838156715,-0.1345048845,-0.0737449005,0.351093322,0.2048691064,0.0506398119,0.1383061111,0.3167421222,0.1006802842,0.0527608953,-0.1457019895,-0.1142641678,-0.3562244773,0.2969622612,-0.3298872411,0.0699843317,-0.161618337,0.1915494651,-0.0262833908,0.2155114561,-0.2241702974,-0.5219001174,0.46504426,-0.0289612003,0.315335989,0.0534079447,-0.0001107943,0.013320378,0.4856797755,-0.0701202005,-0.4200716019,0.0991579592,-0.5813625455,-0.368876636,-0.0407271609,-0.2189436555,0.1747738719,-0.0294561759,0.0603701323,0.0087949587,-0.055560302,0.4349479377,0.2022579461,0.3315931261,0.2125155479,0.1041497588,-0.1241293699,-0.1358566433,0.2027838379,0.4185233712,0.2098928541,0.0184144136,0.0820090547,0.0695741698,-0.1658983529,-0.1311559528,0.0142947109,-0.0650999174,0.1832603663,-0.0951679498,-0.0232734568,0.1045117155,0.4157608747,-0.0431302972,-0.414258033,0.0711860582,0.1402248889,-0.1816168427,0.2897528112,0.1656956822,-0.3068470657,-0.0380344763,-0.3423021138,-0.2752628624,-0.0842999369,0.3529605865,0.005246751,0.5990450978,-0.0411307588,0.05516655,0.0090629691,-0.080049701,0.2823019326,-0.0525373705,-0.1047084928,0.162681073,-0.5495138764,0.0248671025,-0.005983158,0.1834041476,0.2655077577,0.4027745724,0.0387204066,-0.1163020805,-0.2263501734,-0.0094019566,-0.0039071543,0.300869137,0.1394992024,0.1594572216,0.2239145637,0.1344506443,0.0951100215,0.0431366004,-0.1842562854,-0.1783154607,-0.0636249706,0.0801098347,0.1682343632,-0.2972934246,-0.1405249536,-0.0837188363,-0.0799831524,-0.1666853875,0.1680999696,0.2521229684,-0.222952202,0.3372999728,0.0686314031,0.111228697,0.0157314986,-0.2440187335,-0.1695141792,-0.1212179884,-0.2854925692,-0.0604430065,0.2744226158,-0.2481134385,0.463131845,-0.0921317413,-0.1814789921,-0.1822262406,-0.1476952881,0.1697592288,-0.0256858245,0.1520397216,-0.0257621091,0.0911136121,0.1403442919,-0.0117276981,-0.1172379181,0.2947634459,-0.3946950734,-0.2646890283,-0.0457637869,0.1853075027,-0.1605921239,-0.3530971408,0.0199411251,-0.390781939,-0.0066498616,-0.1254160404,-0.067285046,-0.2273852974,0.0255347565,-0.2347317487,0.4058351517,0.3372837305,-0.1800175905,-0.050451234,-0.2473857701,-0.1954921484,0.0923826844,0.2312322855,-0.153695479,0.1757413,-0.4672147334,-0.0179841816,0.6156256199,-0.5054792762,-0.4822386801,0.3668369651,-0.1643920392,-0.093921721,-0.0435516275,0.2854287922,-0.0775715262,0.0263480246,0.0611944646,0.351580292,0.1483877003,0.1347034127,-0.2264336348,-0.3120892644,0.077529259,0.2374334335,-0.1020624042,0.2401536405,-0.0833321661,-0.0964250043,0.6564130783,-0.0033448022,-0.039024163,0.1771044135,0.3496188223,0.0461644456,-0.0561558343,0.1321394145,-0.2743854821,0.2317928523,-0.0811063424,0.0774697065,-0.136914283,0.1522574127,-0.4671771824,-0.0133984918,-0.3502943516,-0.2138036638,0.0977900699,0.3238503039,0.2103271782,0.1386871189,-0.1924282759,0.4510427415,-0.514996171,0.0892236009,-0.4599666297,0.3872051835,0.051192265,0.026924463,0.1402985007,0.222624138,0.0464308262,-0.0905627906,-0.2875969112,0.4172315001,-0.075165838,-0.1176199242,0.3687200546,-0.0515867397,0.0299848393,-0.4615370333,0.2841538191,0.4072540998,-0.0159509163,0.0830567777,0.040477924,0.1762773097,-0.2168430835,-0.030602742,0.0199314468,0.2044681162,0.449331969,-0.2942862809,-0.1360215843,-0.1536149085,0.1517793685,0.3045043945,0.5157181025,0.0725361332,-0.0740447342,-0.138126269,0.3581745625,-0.0692819282,-0.1495406777,0.3571566343,-0.1663306355,-0.0016493857,-0.0482868962,0.5162200332,0.3303979039,0.0800945088,-0.167221114,0.0157247148,-0.0763310641,-0.2316537499,0.1646535546,-0.1718080044,0.1088634431,0.1868586987,-0.069393374,-0.1027749553,-0.2119053304,-0.2347465605,0.0774969235,0.3297291696,-0.3639899194,-0.2274917513,-0.2603933513,-0.6383991241,-0.486920625,-0.1401940286,-0.2508092523,-0.4705287814,-0.2021902204,0.0587797128,0.2062979788,0.3153224885,-0.3141490817,0.0116963238,0.0457248539,-0.3295633495,0.1589161307,-0.1446891129,-0.2713507712,0.1072728038,0.3671357036,0.1439752281,0.100624457,-0.4280472994,0.2426960021,-0.630448103,-0.2325656563,-0.0655870587,-0.0149605162,0.1261070669,0.0270945895,0.2817869186,0.2949373126,-0.1686242223,0.2453976125,-0.0050110687,-0.1012967229,0.1765973568,-0.0970524773,-0.1183386296,0.1169453189,-0.4073301852,-0.2895722091,-0.2114052922,-0.0537784956,-0.0920130759,0.1696119606,0.1500200331,0.1081298739,0.0406973921,-0.0460923538,-0.0529094115,0.0463920347,-0.514942348,0.346663177,0.0684273541,-0.3327194452,-0.027035756,0.0320866592,0.3410561383,-0.0058479393,-0.5295609236,0.0738169402,-0.1447530538,0.3154476583,0.2459883392,-0.0960092843,0.1120342389,-0.1378752589,-0.0642815679,-0.0147826318,-0.1439587176,0.0396769084,0.0106241666,0.6999570727,0.2855210006,0.2176012397,-0.1492902339,0.0831896216,0.5553334951,-0.0592046306,0.2632457614,-0.0163616259,0.1333655417,0.1375619173,-0.3414487839,0.0171845183,-0.0367083661,-0.10978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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/690","title":"XNLI dataset: NonMatchingChecksumError","comments":"Thanks for reporting.\r\nThe data file must have been updated by the host.\r\nI'll update the checksum with the new one.","body":"Hi,\r\nI tried to download \"xnli\" dataset in colab using \r\n`xnli = load_dataset(path='xnli')`\r\nbut got 'NonMatchingChecksumError' error\r\n\r\n`NonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-27-a87bedc82eeb> in <module>()\r\n----> 1 xnli = load_dataset(path='xnli')\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']`\r\n\r\nThe same code worked well several days ago in colab but stopped working now. Thanks!","comment_length":21,"text":"XNLI dataset: NonMatchingChecksumError \n Hi,\r\nI tried to download \"xnli\" dataset in colab using \r\n`xnli = load_dataset(path='xnli')`\r\nbut got 'NonMatchingChecksumError' error\r\n\r\n`NonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-27-a87bedc82eeb> in <module>()\r\n----> 1 xnli = load_dataset(path='xnli')\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']`\r\n\r\nThe same code worked well several days ago in colab but stopped working now. Thanks! \n Thanks for reporting.\r\nThe data file must have been updated by the host.\r\nI'll update the checksum with the new one.","embeddings":[-0.2586137354,0.2562108636,0.0357761383,0.1724495739,-0.0067088124,-0.0366752595,0.0808679238,0.4067072868,0.175513044,0.2336782813,-0.2135459036,0.3052044809,-0.0187664814,-0.0720469877,-0.2150234878,0.5297763944,0.0938087702,0.2160157412,0.0079976609,0.0222377647,-0.2151079029,0.1584314406,-0.2035468668,-0.3193615377,-0.2458490431,0.1520858407,0.1421747804,-0.0535499007,-0.2479522675,-0.2221305817,0.6901740432,0.1887718588,0.258135736,0.0462866314,-0.0001240973,0.0080530262,0.2406711131,-0.1988253295,-0.4517176449,-0.1169145778,-0.8176319003,-0.3818692267,0.0475631207,-0.2078416198,0.1094296798,0.4795917571,0.2667313814,0.1372768134,0.1055045128,0.2807789743,0.111543946,0.420984,0.1347889304,0.143436417,0.2464423776,-0.0521562845,-0.1362707615,0.4895199239,0.4230527282,0.320353359,0.0393180661,0.1135347262,-0.3681002855,0.3269022107,-0.0359629355,-0.0250639338,0.0205162838,-0.2464183569,0.2830494046,0.4693900645,0.2261938304,-0.2822745442,-0.2415455878,-0.0743846595,0.0434021018,-0.2809100449,0.2798109949,0.08360897,-0.3455017507,-0.1063984632,-0.3278510571,-0.0058591957,-0.0017170587,0.1073263437,0.1493442059,0.5423236489,-0.0477058478,-0.0237834807,0.0697466657,-0.1552949101,0.628156364,-0.2680093348,-0.1252770722,0.1798190773,-0.4900614619,-0.0731538981,-0.2064715475,0.5996738672,0.4066444039,0.2380990088,0.2245099545,0.0259249248,-0.3553993702,0.1254947037,-0.2012140751,0.2479915321,0.099644132,0.0318743624,0.2320785224,0.3483143151,-0.0973198339,0.1221057847,-0.2033595145,0.0350357629,0.2290040255,0.2580448091,0.3275788426,-0.5910425782,-0.3378924727,0.0049089938,-0.2603462338,-0.1746095568,0.1753950864,0.1753493994,-0.1699453145,0.1092463359,-0.1756642014,-0.1288086921,-0.0445124619,-0.3918737769,-0.0676398054,-0.0543278456,-0.0956366882,0.1412098557,0.3571409285,-0.2953674197,0.3991790414,-0.2256494612,0.1817737669,0.0179107245,0.057620544,-0.1116041392,-0.0308814608,0.2910017371,-0.0173147395,0.0595719181,-0.0529284775,-0.140993461,-0.1972710192,0.2363901138,-0.3122947812,-0.2167454213,0.0996265188,0.0400579087,-0.5102123618,-0.3753417432,0.0736734644,-0.465560317,0.1015564576,-0.3904392719,-0.0073960521,-0.3887443542,-0.0695823655,-0.1876173466,0.1607968509,0.3164153397,-0.2147251368,0.0343073457,-0.1672418565,0.0257880948,0.4009526074,0.3156618774,0.0544878095,-0.1665255874,-0.2950071394,-0.2161164731,0.3075037897,-0.2204373926,-0.7255297899,0.1216126904,0.0719884932,0.3229758441,0.1239928231,0.2812179029,0.211918965,-0.0167510137,-0.0355769433,0.0872988254,-0.0745189488,-0.0421463996,-0.2804175019,-0.3618970811,0.1204608902,0.141869247,0.1601865441,0.1856274009,-0.0315918252,-0.0567067042,0.4973293841,-0.1714521796,0.0485826321,-0.0227915589,0.6201295853,-0.1340786219,-0.1849189103,-0.0258624982,-0.3418603837,0.3134120703,0.1858926266,0.0887587965,-0.1150415316,0.0676701441,-0.2243721485,-0.1555407196,-0.2370499223,0.0643190593,-0.024208568,0.3354491889,0.2417681813,0.1224176735,-0.0075558894,0.4944452047,-0.3668858409,0.092463173,-0.386790961,0.3697480559,0.0600519702,-0.0740127936,0.0644229129,0.092517592,-0.08177232,-0.0969615579,-0.2909850478,0.1332344562,0.1913929582,-0.2294040024,0.0671633855,0.2875173688,0.0111955991,-0.1594452411,0.1004255265,0.5777589083,0.1238051131,0.0808635429,-0.0785013288,0.4863092303,-0.1833893359,-0.0225315895,-0.1332876831,0.1746011525,0.3575408161,-0.2149170637,-0.1466772556,-0.1746447533,0.3238839507,0.0543514192,0.1948827505,0.111102879,-0.2262816578,0.0082300305,0.3536303937,0.0136982296,-0.1000851393,0.1387248486,-0.0055579841,-0.0261584017,0.2044367939,0.6320056915,0.380002141,0.0317563005,-0.0440924838,0.0113440827,-0.1384483576,-0.2532644272,-0.0992301106,-0.1576065868,0.2100726664,0.2351075262,0.1551225632,-0.1404782832,-0.3958390355,-0.4012742043,-0.0733323544,0.4360639751,-0.2105490565,-0.1275674552,-0.316608876,-0.5170448422,-0.4883892238,-0.2116082311,-0.2422738224,-0.34609285,-0.1825844944,0.3951963484,0.2581498921,0.2322383672,-0.3695223927,0.0123253996,-0.0546117499,-0.3932121396,0.1058228016,-0.155129239,-0.2283204198,-0.0100653842,0.3551742136,0.0392791517,0.253167659,-0.4118210077,0.1813425124,-0.4413237274,-0.1746168733,0.0173400808,-0.1531530917,-0.041174721,0.0783495978,0.2060880214,-0.2134456187,-0.3065015078,0.0666823983,-0.072919488,-0.1403520554,0.1195373833,-0.2479639947,-0.079216063,0.1542877406,0.0388552472,-0.2466491163,-0.0903133154,-0.1552611142,0.0761152282,0.220612824,0.2278158516,-0.0476889312,0.0238214415,0.0273994859,0.0180009548,0.0116397804,-0.6506959796,0.4857341945,0.1974257678,-0.2535623312,0.1648096591,-0.1050902382,0.2708635032,0.2326164544,-0.4453786314,-0.1136724725,-0.2259542197,0.3107627332,0.4475627542,-0.2022423893,0.0723847002,-0.0949512944,0.1288635284,-0.0753970221,-0.3394464552,0.0084449099,0.0520526543,0.7311505675,0.1499949992,0.1265174747,-0.1364470571,0.2031770051,0.417519927,-0.1546080858,0.2104763687,0.0111747365,0.1802267283,0.0195404235,-0.358356595,0.1440388411,0.0228398163,-0.35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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/690","title":"XNLI dataset: NonMatchingChecksumError","comments":"I'll do a release in the next few days to make the fix available for everyone.\r\nIn the meantime you can load `xnli` with\r\n```\r\nxnli = load_dataset('xnli', script_version=\"master\")\r\n```\r\nThis will use the latest version of the xnli script (available on master branch), instead of the old one.","body":"Hi,\r\nI tried to download \"xnli\" dataset in colab using \r\n`xnli = load_dataset(path='xnli')`\r\nbut got 'NonMatchingChecksumError' error\r\n\r\n`NonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-27-a87bedc82eeb> in <module>()\r\n----> 1 xnli = load_dataset(path='xnli')\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']`\r\n\r\nThe same code worked well several days ago in colab but stopped working now. Thanks!","comment_length":49,"text":"XNLI dataset: NonMatchingChecksumError \n Hi,\r\nI tried to download \"xnli\" dataset in colab using \r\n`xnli = load_dataset(path='xnli')`\r\nbut got 'NonMatchingChecksumError' error\r\n\r\n`NonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-27-a87bedc82eeb> in <module>()\r\n----> 1 xnli = load_dataset(path='xnli')\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     37     if len(bad_urls) > 0:\r\n     38         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 39         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     40     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     41 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip']`\r\n\r\nThe same code worked well several days ago in colab but stopped working now. Thanks! \n I'll do a release in the next few days to make the fix available for everyone.\r\nIn the meantime you can load `xnli` with\r\n```\r\nxnli = load_dataset('xnli', script_version=\"master\")\r\n```\r\nThis will use the latest version of the xnli script (available on master branch), instead of the old 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/687","title":"`ArrowInvalid` occurs while running `Dataset.map()` function","comments":"Hi !\r\n\r\nThis is because `encode` expects one single text as input (str), or one tokenized text (List[str]).\r\nI believe that you actually wanted to use `encode_batch` which expects a batch of texts.\r\nHowever this method is only available for our \"fast\" tokenizers (ex: BertTokenizerFast).\r\nBertJapanese is not one of them unfortunately and I don't think it will be added for now (see https:\/\/github.com\/huggingface\/transformers\/pull\/7141)...\r\ncc @thomwolf for confirmation.\r\n\r\nTherefore what I'd suggest for now is disable batching and process one text at a time using `encode`.\r\nNote that you can make it faster by using multiprocessing:\r\n\r\n```python\r\nnum_proc = None  # Specify here the number of processes if you want to use multiprocessing. ex: num_proc = 4\r\nencoded = train_ds.map(\r\n    lambda example: {'tokens': t.encode(example['title'], max_length=1000)}, num_proc=num_proc\r\n)\r\n```\r\n","body":"It seems to fail to process the final batch. This [colab](https:\/\/colab.research.google.com\/drive\/1_byLZRHwGP13PHMkJWo62Wp50S_Z2HMD?usp=sharing) can reproduce the error.\r\n\r\nCode:\r\n\r\n```python\r\n# train_ds = Dataset(features: {\r\n#     'title': Value(dtype='string', id=None), \r\n#     'score': Value(dtype='float64', id=None)\r\n# }, num_rows: 99999)\r\n\r\n# suggested in #665 \r\nclass PicklableTokenizer(BertJapaneseTokenizer):\r\n    def __getstate__(self):\r\n        state = dict(self.__dict__)\r\n        state['do_lower_case'] = self.word_tokenizer.do_lower_case\r\n        state['never_split'] = self.word_tokenizer.never_split\r\n        del state['word_tokenizer']\r\n        return state\r\n    \r\n    def __setstate(self):\r\n        do_lower_case = state.pop('do_lower_case')\r\n        never_split = state.pop('never_split')\r\n        self.__dict__ = state\r\n        self.word_tokenizer = MecabTokenizer(\r\n            do_lower_case=do_lower_case, never_split=never_split\r\n        )\r\n\r\nt = PicklableTokenizer.from_pretrained('bert-base-japanese-whole-word-masking')\r\n\r\nencoded = train_ds.map(\r\n    lambda examples: {'tokens': t.encode(examples['title'], max_length=1000)}, batched=True, batch_size=1000\r\n)\r\n```\r\n\r\nError Message:\r\n\r\n```\r\n 99% 99\/100 [00:22<00:00, 39.07ba\/s]\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    161             # Call actual function\r\n    162 \r\n--> 163             out = func(self, *args, **kwargs)\r\n    164 \r\n    165             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n   1496                     if update_data:\r\n   1497                         batch = cast_to_python_objects(batch)\r\n-> 1498                         writer.write_batch(batch)\r\n   1499             if update_data:\r\n   1500                 writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    271             typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n    272             typed_sequence_examples[col] = typed_sequence\r\n--> 273         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    274         self.write_table(pa_table)\r\n    275 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_arrays()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.validate()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Column 4 named tokens expected length 999 but got length 1000\r\n```\r\n","comment_length":128,"text":"`ArrowInvalid` occurs while running `Dataset.map()` function \n It seems to fail to process the final batch. This [colab](https:\/\/colab.research.google.com\/drive\/1_byLZRHwGP13PHMkJWo62Wp50S_Z2HMD?usp=sharing) can reproduce the error.\r\n\r\nCode:\r\n\r\n```python\r\n# train_ds = Dataset(features: {\r\n#     'title': Value(dtype='string', id=None), \r\n#     'score': Value(dtype='float64', id=None)\r\n# }, num_rows: 99999)\r\n\r\n# suggested in #665 \r\nclass PicklableTokenizer(BertJapaneseTokenizer):\r\n    def __getstate__(self):\r\n        state = dict(self.__dict__)\r\n        state['do_lower_case'] = self.word_tokenizer.do_lower_case\r\n        state['never_split'] = self.word_tokenizer.never_split\r\n        del state['word_tokenizer']\r\n        return state\r\n    \r\n    def __setstate(self):\r\n        do_lower_case = state.pop('do_lower_case')\r\n        never_split = state.pop('never_split')\r\n        self.__dict__ = state\r\n        self.word_tokenizer = MecabTokenizer(\r\n            do_lower_case=do_lower_case, never_split=never_split\r\n        )\r\n\r\nt = PicklableTokenizer.from_pretrained('bert-base-japanese-whole-word-masking')\r\n\r\nencoded = train_ds.map(\r\n    lambda examples: {'tokens': t.encode(examples['title'], max_length=1000)}, batched=True, batch_size=1000\r\n)\r\n```\r\n\r\nError Message:\r\n\r\n```\r\n 99% 99\/100 [00:22<00:00, 39.07ba\/s]\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    161             # Call actual function\r\n    162 \r\n--> 163             out = func(self, *args, **kwargs)\r\n    164 \r\n    165             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n   1496                     if update_data:\r\n   1497                         batch = cast_to_python_objects(batch)\r\n-> 1498                         writer.write_batch(batch)\r\n   1499             if update_data:\r\n   1500                 writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    271             typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n    272             typed_sequence_examples[col] = typed_sequence\r\n--> 273         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    274         self.write_table(pa_table)\r\n    275 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_arrays()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.validate()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Column 4 named tokens expected length 999 but got length 1000\r\n```\r\n \n Hi !\r\n\r\nThis is because `encode` expects one single text as input (str), or one tokenized text (List[str]).\r\nI believe that you actually wanted to use `encode_batch` which expects a batch of texts.\r\nHowever this method is only available for our \"fast\" tokenizers (ex: BertTokenizerFast).\r\nBertJapanese is not one of them unfortunately and I don't think it will be added for now (see https:\/\/github.com\/huggingface\/transformers\/pull\/7141)...\r\ncc @thomwolf for confirmation.\r\n\r\nTherefore what I'd suggest for now is disable batching and process one text at a time using `encode`.\r\nNote that you can make it faster by using multiprocessing:\r\n\r\n```python\r\nnum_proc = None  # Specify here the number of processes if you want to use multiprocessing. ex: num_proc = 4\r\nencoded = train_ds.map(\r\n    lambda example: {'tokens': t.encode(example['title'], max_length=1000)}, num_proc=num_proc\r\n)\r\n```\r\n","embeddings":[-0.3730494082,0.0209990088,-0.1026319936,-0.0166299157,0.1107027829,0.1678620726,0.1268472821,0.3861759603,-0.1962419301,0.116063267,0.2094027549,0.6037945747,-0.1167867631,0.0404954627,-0.3405689895,0.0122450693,-0.0138801886,0.2132770419,0.0759285465,-0.2047145665,-0.4590324759,0.0273315106,-0.3769527078,0.2137223929,-0.2793795764,-0.46280545,0.2793683112,-0.1693802327,-0.2238800675,-0.74639678,0.2083986551,-0.3749470711,-0.0207395498,0.3278741539,-0.0001172259,0.042348668,0.4025927782,0.0688235834,-0.2501145005,-0.0585927963,-0.443045795,0.0925626904,-0.0866485387,-0.1329717785,0.2778750062,-0.0361048207,0.0314678699,-0.4573630393,0.3919546306,0.2821494341,0.1263703704,0.2222543806,0.1442392766,0.0323247947,0.1279150993,-0.1200693548,-0.0969361216,0.1047375202,0.0181517769,-0.2857314944,-0.0512987599,0.5154019594,0.180965513,0.0922654271,-0.14175497,-0.1290374547,0.1971558928,-0.4126629233,0.3031370938,-0.0948614031,0.0813262165,-0.1926683784,-0.4155083001,-0.0138592795,0.1312633455,-0.1198019758,0.0628945976,0.0746485814,-0.0928725153,0.0599402264,-0.0519259274,0.167006433,-0.2132056057,0.0801583529,0.1788284779,0.4855768085,0.0136159519,0.4012263417,0.2158066779,-0.1161201075,0.0043541109,0.0970336497,0.0199738536,0.241735518,-0.2374131531,-0.127425164,-0.264008224,-0.1885884553,0.1048256159,-0.335390538,0.1042770669,0.0072844056,0.1545805335,0.1884318292,0.3311057091,0.0799308792,0.115690656,0.6522192955,-0.1187430173,-0.4918249249,-0.0285963994,0.0985013545,-0.0701802,-0.4311812818,0.2728593647,0.3366879225,0.1505464315,0.1117760018,-0.3864218295,0.1871385127,-0.589379549,-0.0437153503,-0.0479695611,0.1943127215,0.1245247126,0.0586616062,-0.0686166659,-0.0534041934,-0.0046589328,-0.1363479495,-0.0411160998,0.108408913,-0.4678287506,-0.0251717102,0.0634236187,-0.0948988721,0.3233495355,-0.0266724546,0.1230678931,0.0514647625,0.314424336,-0.2968288362,0.444064647,0.0961867496,-0.0872931406,0.3249668777,0.1295829117,0.1602937281,-0.0586597286,0.128712222,-0.1173376068,-0.230542466,0.0751926452,0.1038942412,0.3227936029,-0.0253573768,-0.1583411545,-0.0692773163,0.4308267832,-0.0557872914,0.178976655,-0.3901212513,0.108979933,-0.2312533557,-0.0915208161,0.2688697875,-0.586361587,0.1150185391,0.0958043039,0.2284136713,0.2394196093,0.2511422038,-0.3237181306,0.2681290507,-0.0672967136,0.7344178557,0.0913244188,-0.4536603689,-0.5696929097,0.1651970297,-0.0714170337,-0.2872891724,-0.1635802388,-0.0753729269,0.1052654237,-0.0703787953,0.2810492516,0.1559283584,-0.206625998,0.0811991617,-0.0850825608,-0.1805368215,0.1134609282,0.0650475174,0.1270639449,-0.2152681202,0.0416606329,-0.2121016532,-0.0126661211,-0.0910451859,0.3163724542,0.1728592515,0.1968010515,-0.0793101043,0.1716614217,0.0586773306,-0.0976552442,0.1258585751,0.0379586145,-0.1189674586,-0.4610619247,-0.2379849702,-0.3678559661,0.3181095123,-0.4086672664,-0.022437958,0.1247887909,0.154564321,-0.0500557572,-0.1316203624,-0.2792712748,-0.2015069723,0.0223132372,-0.0179371852,-0.1443542987,-0.0236081909,-0.1345303059,-0.3064616323,0.1208036989,-0.0018816206,0.1851107925,-0.0172275007,-0.2361747921,0.4019815028,0.1110517904,-0.1824735701,0.2651119232,-0.2703411281,0.0067328028,-0.2617128789,-0.1663633138,0.1191149056,0.2205321938,0.1281047016,0.2508363426,0.3558339179,0.1522091031,0.1357205212,-0.1720412374,0.0719726831,0.1325083375,0.1398831606,-0.1958693117,-0.2773173451,-0.2591813207,-0.1319920123,0.5122656226,-0.1623483896,-0.1164862514,0.0374570452,0.3888116777,0.0573440157,0.0788703561,-0.0464808904,-0.2116911262,0.1952369362,0.1356423497,0.2061406076,0.0987049192,0.0820548683,0.0950869471,-0.0831235722,0.0089299865,0.0431402698,0.0941936523,0.1881881356,0.3178276718,0.2141555399,-0.0749313682,0.0543545187,-0.2489284128,-0.1475625336,0.1688107401,0.2606538236,-0.1852554679,0.1725290269,-0.1887763441,-0.0659476593,-0.0683829561,-0.4471708238,0.170828104,-0.2091741413,-0.0630615354,-0.0351297706,-0.1834743172,0.4110240638,0.0006338063,0.1164892316,0.4737392068,-0.0154695604,-0.0733822435,-0.276661396,-0.1933734715,-0.0152658643,0.228355974,-0.1102727801,0.296617955,0.220810011,0.0844046921,-0.2958572507,-0.0957247838,0.2139255553,-0.1807052791,-0.0076032318,0.2341925651,0.0471360534,-0.5811719894,-0.3872248232,0.1693429947,-0.0757252946,-0.2313343287,-0.15086025,-0.002469372,-0.2634731829,0.0694960281,-0.3197098076,-0.2123733908,-0.2388973385,0.1862350702,-0.2556763291,-0.0316172689,0.0654448792,0.2022592425,0.3322208226,-0.081564784,-0.3609610796,-0.1151162907,0.0165958162,0.3085134029,-0.2723379433,0.0018396794,0.029960243,-0.1951613277,0.2619602382,0.2316198945,-0.1621741951,0.1872944087,-0.1391092986,0.2304623574,-0.3229131997,0.2915933132,0.3173620701,0.2290569842,-0.1079699919,-0.2228882611,0.0057419599,0.1587736607,-0.1930841953,0.0635004044,0.0404598899,0.4070858359,0.0578363314,0.9904161692,0.3290010095,-0.184348613,0.1601355821,0.0439409912,-0.130933702,-0.1031865254,-0.2093008757,-0.12853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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/687","title":"`ArrowInvalid` occurs while running `Dataset.map()` function","comments":"Thank you very much for the kind and precise suggestion!\r\nI'm looking forward to seeing BertJapaneseTokenizer built into the \"fast\" tokenizers.\r\n\r\nI tried `map` with multiprocessing as follows, and it worked!\r\n\r\n```python\r\n# There was a Pickle problem if I use `lambda` for multiprocessing\r\ndef encode(examples):\r\n    return {'tokens': t.encode(examples['title'], max_length=1000)}\r\n\r\nnum_proc = 8\r\nencoded = train_ds.map(encode, num_proc=num_proc)\r\n```\r\n\r\nThank you again!","body":"It seems to fail to process the final batch. This [colab](https:\/\/colab.research.google.com\/drive\/1_byLZRHwGP13PHMkJWo62Wp50S_Z2HMD?usp=sharing) can reproduce the error.\r\n\r\nCode:\r\n\r\n```python\r\n# train_ds = Dataset(features: {\r\n#     'title': Value(dtype='string', id=None), \r\n#     'score': Value(dtype='float64', id=None)\r\n# }, num_rows: 99999)\r\n\r\n# suggested in #665 \r\nclass PicklableTokenizer(BertJapaneseTokenizer):\r\n    def __getstate__(self):\r\n        state = dict(self.__dict__)\r\n        state['do_lower_case'] = self.word_tokenizer.do_lower_case\r\n        state['never_split'] = self.word_tokenizer.never_split\r\n        del state['word_tokenizer']\r\n        return state\r\n    \r\n    def __setstate(self):\r\n        do_lower_case = state.pop('do_lower_case')\r\n        never_split = state.pop('never_split')\r\n        self.__dict__ = state\r\n        self.word_tokenizer = MecabTokenizer(\r\n            do_lower_case=do_lower_case, never_split=never_split\r\n        )\r\n\r\nt = PicklableTokenizer.from_pretrained('bert-base-japanese-whole-word-masking')\r\n\r\nencoded = train_ds.map(\r\n    lambda examples: {'tokens': t.encode(examples['title'], max_length=1000)}, batched=True, batch_size=1000\r\n)\r\n```\r\n\r\nError Message:\r\n\r\n```\r\n 99% 99\/100 [00:22<00:00, 39.07ba\/s]\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    161             # Call actual function\r\n    162 \r\n--> 163             out = func(self, *args, **kwargs)\r\n    164 \r\n    165             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n   1496                     if update_data:\r\n   1497                         batch = cast_to_python_objects(batch)\r\n-> 1498                         writer.write_batch(batch)\r\n   1499             if update_data:\r\n   1500                 writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    271             typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n    272             typed_sequence_examples[col] = typed_sequence\r\n--> 273         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    274         self.write_table(pa_table)\r\n    275 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_arrays()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.validate()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Column 4 named tokens expected length 999 but got length 1000\r\n```\r\n","comment_length":61,"text":"`ArrowInvalid` occurs while running `Dataset.map()` function \n It seems to fail to process the final batch. This [colab](https:\/\/colab.research.google.com\/drive\/1_byLZRHwGP13PHMkJWo62Wp50S_Z2HMD?usp=sharing) can reproduce the error.\r\n\r\nCode:\r\n\r\n```python\r\n# train_ds = Dataset(features: {\r\n#     'title': Value(dtype='string', id=None), \r\n#     'score': Value(dtype='float64', id=None)\r\n# }, num_rows: 99999)\r\n\r\n# suggested in #665 \r\nclass PicklableTokenizer(BertJapaneseTokenizer):\r\n    def __getstate__(self):\r\n        state = dict(self.__dict__)\r\n        state['do_lower_case'] = self.word_tokenizer.do_lower_case\r\n        state['never_split'] = self.word_tokenizer.never_split\r\n        del state['word_tokenizer']\r\n        return state\r\n    \r\n    def __setstate(self):\r\n        do_lower_case = state.pop('do_lower_case')\r\n        never_split = state.pop('never_split')\r\n        self.__dict__ = state\r\n        self.word_tokenizer = MecabTokenizer(\r\n            do_lower_case=do_lower_case, never_split=never_split\r\n        )\r\n\r\nt = PicklableTokenizer.from_pretrained('bert-base-japanese-whole-word-masking')\r\n\r\nencoded = train_ds.map(\r\n    lambda examples: {'tokens': t.encode(examples['title'], max_length=1000)}, batched=True, batch_size=1000\r\n)\r\n```\r\n\r\nError Message:\r\n\r\n```\r\n 99% 99\/100 [00:22<00:00, 39.07ba\/s]\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<timed exec> in <module>\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    161             # Call actual function\r\n    162 \r\n--> 163             out = func(self, *args, **kwargs)\r\n    164 \r\n    165             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n   1496                     if update_data:\r\n   1497                         batch = cast_to_python_objects(batch)\r\n-> 1498                         writer.write_batch(batch)\r\n   1499             if update_data:\r\n   1500                 writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    271             typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n    272             typed_sequence_examples[col] = typed_sequence\r\n--> 273         pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n    274         self.write_table(pa_table)\r\n    275 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_arrays()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.validate()\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Column 4 named tokens expected length 999 but got length 1000\r\n```\r\n \n Thank you very much for the kind and precise suggestion!\r\nI'm looking forward to seeing BertJapaneseTokenizer built into the \"fast\" tokenizers.\r\n\r\nI tried `map` with multiprocessing as follows, and it worked!\r\n\r\n```python\r\n# There was a Pickle problem if I use `lambda` for multiprocessing\r\ndef encode(examples):\r\n    return {'tokens': t.encode(examples['title'], max_length=1000)}\r\n\r\nnum_proc = 8\r\nencoded = train_ds.map(encode, num_proc=num_proc)\r\n```\r\n\r\nThank you again!","embeddings":[-0.3730494082,0.0209990088,-0.1026319936,-0.0166299157,0.1107027829,0.1678620726,0.1268472821,0.3861759603,-0.1962419301,0.116063267,0.2094027549,0.6037945747,-0.1167867631,0.0404954627,-0.3405689895,0.0122450693,-0.0138801886,0.2132770419,0.0759285465,-0.2047145665,-0.4590324759,0.0273315106,-0.3769527078,0.2137223929,-0.2793795764,-0.46280545,0.2793683112,-0.1693802327,-0.2238800675,-0.74639678,0.2083986551,-0.3749470711,-0.0207395498,0.3278741539,-0.0001172259,0.042348668,0.4025927782,0.0688235834,-0.2501145005,-0.0585927963,-0.443045795,0.0925626904,-0.0866485387,-0.1329717785,0.2778750062,-0.0361048207,0.0314678699,-0.4573630393,0.3919546306,0.2821494341,0.1263703704,0.2222543806,0.1442392766,0.0323247947,0.1279150993,-0.1200693548,-0.0969361216,0.1047375202,0.0181517769,-0.2857314944,-0.0512987599,0.5154019594,0.180965513,0.0922654271,-0.14175497,-0.1290374547,0.1971558928,-0.4126629233,0.3031370938,-0.0948614031,0.0813262165,-0.1926683784,-0.4155083001,-0.0138592795,0.1312633455,-0.1198019758,0.0628945976,0.0746485814,-0.0928725153,0.0599402264,-0.0519259274,0.167006433,-0.2132056057,0.0801583529,0.1788284779,0.4855768085,0.0136159519,0.4012263417,0.2158066779,-0.1161201075,0.0043541109,0.0970336497,0.0199738536,0.241735518,-0.2374131531,-0.127425164,-0.264008224,-0.1885884553,0.1048256159,-0.335390538,0.1042770669,0.0072844056,0.1545805335,0.1884318292,0.3311057091,0.0799308792,0.115690656,0.6522192955,-0.1187430173,-0.4918249249,-0.0285963994,0.0985013545,-0.0701802,-0.4311812818,0.2728593647,0.3366879225,0.1505464315,0.1117760018,-0.3864218295,0.1871385127,-0.589379549,-0.0437153503,-0.0479695611,0.1943127215,0.1245247126,0.0586616062,-0.0686166659,-0.0534041934,-0.0046589328,-0.1363479495,-0.0411160998,0.108408913,-0.4678287506,-0.0251717102,0.0634236187,-0.0948988721,0.3233495355,-0.0266724546,0.1230678931,0.0514647625,0.314424336,-0.2968288362,0.444064647,0.0961867496,-0.0872931406,0.3249668777,0.1295829117,0.1602937281,-0.0586597286,0.128712222,-0.1173376068,-0.230542466,0.0751926452,0.1038942412,0.3227936029,-0.0253573768,-0.1583411545,-0.0692773163,0.4308267832,-0.0557872914,0.178976655,-0.3901212513,0.108979933,-0.2312533557,-0.0915208161,0.2688697875,-0.586361587,0.1150185391,0.0958043039,0.2284136713,0.2394196093,0.2511422038,-0.3237181306,0.2681290507,-0.0672967136,0.7344178557,0.0913244188,-0.4536603689,-0.5696929097,0.1651970297,-0.0714170337,-0.2872891724,-0.1635802388,-0.0753729269,0.1052654237,-0.0703787953,0.2810492516,0.1559283584,-0.206625998,0.0811991617,-0.0850825608,-0.1805368215,0.1134609282,0.0650475174,0.1270639449,-0.2152681202,0.0416606329,-0.2121016532,-0.0126661211,-0.0910451859,0.3163724542,0.1728592515,0.1968010515,-0.0793101043,0.1716614217,0.0586773306,-0.0976552442,0.1258585751,0.0379586145,-0.1189674586,-0.4610619247,-0.2379849702,-0.3678559661,0.3181095123,-0.4086672664,-0.022437958,0.1247887909,0.154564321,-0.0500557572,-0.1316203624,-0.2792712748,-0.2015069723,0.0223132372,-0.0179371852,-0.1443542987,-0.0236081909,-0.1345303059,-0.3064616323,0.1208036989,-0.0018816206,0.1851107925,-0.0172275007,-0.2361747921,0.4019815028,0.1110517904,-0.1824735701,0.2651119232,-0.2703411281,0.0067328028,-0.2617128789,-0.1663633138,0.1191149056,0.2205321938,0.1281047016,0.2508363426,0.3558339179,0.1522091031,0.1357205212,-0.1720412374,0.0719726831,0.1325083375,0.1398831606,-0.1958693117,-0.2773173451,-0.2591813207,-0.1319920123,0.5122656226,-0.1623483896,-0.1164862514,0.0374570452,0.3888116777,0.0573440157,0.0788703561,-0.0464808904,-0.2116911262,0.1952369362,0.1356423497,0.2061406076,0.0987049192,0.0820548683,0.0950869471,-0.0831235722,0.0089299865,0.0431402698,0.0941936523,0.1881881356,0.3178276718,0.2141555399,-0.0749313682,0.0543545187,-0.2489284128,-0.1475625336,0.1688107401,0.2606538236,-0.1852554679,0.1725290269,-0.1887763441,-0.0659476593,-0.0683829561,-0.4471708238,0.170828104,-0.2091741413,-0.0630615354,-0.0351297706,-0.1834743172,0.4110240638,0.0006338063,0.1164892316,0.4737392068,-0.0154695604,-0.0733822435,-0.276661396,-0.1933734715,-0.0152658643,0.228355974,-0.1102727801,0.296617955,0.220810011,0.0844046921,-0.2958572507,-0.0957247838,0.2139255553,-0.1807052791,-0.0076032318,0.2341925651,0.0471360534,-0.5811719894,-0.3872248232,0.1693429947,-0.0757252946,-0.2313343287,-0.15086025,-0.002469372,-0.2634731829,0.0694960281,-0.3197098076,-0.2123733908,-0.2388973385,0.1862350702,-0.2556763291,-0.0316172689,0.0654448792,0.2022592425,0.3322208226,-0.081564784,-0.3609610796,-0.1151162907,0.0165958162,0.3085134029,-0.2723379433,0.0018396794,0.029960243,-0.1951613277,0.2619602382,0.2316198945,-0.1621741951,0.1872944087,-0.1391092986,0.2304623574,-0.3229131997,0.2915933132,0.3173620701,0.2290569842,-0.1079699919,-0.2228882611,0.0057419599,0.1587736607,-0.1930841953,0.0635004044,0.0404598899,0.4070858359,0.0578363314,0.9904161692,0.3290010095,-0.184348613,0.1601355821,0.0439409912,-0.130933702,-0.1031865254,-0.2093008757,-0.1285343915,-0.2524230182,-0.0792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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/686","title":"Dataset browser url is still https:\/\/huggingface.co\/nlp\/viewer\/","comments":"Yes! might do it with @srush one of these days. Hopefully it won't break too many links (we can always redirect from old url to new)","body":"Might be worth updating to https:\/\/huggingface.co\/datasets\/viewer\/","comment_length":26,"text":"Dataset browser url is still https:\/\/huggingface.co\/nlp\/viewer\/ \n Might be worth updating to https:\/\/huggingface.co\/datasets\/viewer\/ \n Yes! might do it with @srush one of these days. Hopefully it won't break too many links (we can always redirect from old url to new)","embeddings":[-0.1529248506,0.2032147646,-0.0998434573,-0.1728723794,0.1061696857,0.0467374735,0.179426223,0.2390701473,-0.0500670075,-0.0185796227,-0.1565577686,0.30587098,0.1280383468,0.1724736094,0.3105870485,0.0242812056,0.0012544101,-0.0140798306,-0.1639329493,-0.0225632489,-0.1863491833,0.0512106866,-0.195139125,0.180641219,-0.0065673641,0.0746079534,-0.0707541332,0.2305047661,-0.177075997,-0.3989592195,0.1384267807,0.1680058539,-0.0488178357,0.2282091677,-0.0000960609,0.0672143474,0.4233936071,0.2476758063,-0.3446639478,-0.0000749466,-0.3106382787,-0.065295957,0.1053099558,-0.1298185736,-0.1422041506,-0.2468431741,0.3551162481,-0.065297775,0.0736937225,0.2408897728,0.3633951843,0.3327011764,-0.1858961284,-0.089773424,-0.0542072952,0.1208049208,-0.0696513131,0.0434561446,-0.0182185322,0.066160433,-0.239288643,0.561126709,0.2315685153,-0.0279473159,0.200971365,-0.0143021494,-0.353766799,0.0535263084,0.079043135,0.055047933,0.6028001904,-0.1018182784,-0.1742539704,-0.0268567204,-0.0725952238,0.0548230186,0.2638870776,0.1444999427,0.0604988337,0.2288692594,-0.2419080734,-0.5008474588,-0.1607080102,0.1459707916,-0.131855011,0.3666667938,-0.0955795869,-0.0348553769,0.1961886585,-0.1912985295,0.2546566129,0.120418258,-0.0303971823,-0.263392508,0.1133593619,-0.0686037317,0.2350059301,0.4213660955,0.0431765169,0.2189738303,0.154790014,0.1485686898,-0.1884457916,-0.0044194642,0.0471914522,-0.0387113467,0.0920071825,-0.0088646691,0.6043276191,0.2491928041,0.2259243429,-0.0179779641,0.1733002812,-0.3365879655,-0.3934387267,-0.3392239511,0.2704303861,-0.1058420688,-0.162116915,-0.1167059839,0.1140188202,-0.0489502884,-0.0150499037,0.3982775509,-0.1377293169,-0.0157084912,-0.0406846069,-0.1462123841,-0.2074071765,-0.2186151147,-0.3124217391,0.0294859931,-0.3069172204,-0.0521591716,0.2305074185,-0.0377479866,0.1992869228,-0.1417885274,-0.1505475044,0.0472174175,-0.2635045052,0.2486747354,0.0366090983,0.1801086217,0.0523627028,0.044847589,0.0209172051,-0.2001548409,0.0460464954,0.0541520789,-0.2276464999,-0.1882440597,-0.1267290413,0.2991304398,-0.1316419691,-0.3244582117,0.0465979055,0.206476897,-0.252592802,-0.1256885082,-0.088467285,0.2324637026,-0.1503419429,0.0034821709,0.0761694983,0.2025158554,-0.1588964164,-0.2690931261,0.0305513591,-0.2208424509,-0.2299480885,0.2701799273,-0.2201052308,-0.2789732814,-0.3101256192,0.2633773386,0.6101366282,-0.169667244,-0.3256669641,0.1078999639,-0.2597180009,-0.2637858093,-0.087349087,0.1183230802,-0.0149701741,-0.0187621675,-0.014495492,0.1476344615,0.144870162,0.219928056,-0.2966607809,-0.2687580287,0.0122952219,0.085551165,0.0404122174,-0.0115381861,0.1467533261,-0.1777264178,0.4457988739,-0.0647552237,-0.0068596895,0.1231624037,0.3145541847,0.0974895507,-0.0806366727,-0.0708973333,-0.265624702,-0.1158179119,0.0696807653,0.1581618041,0.017750835,-0.2546255291,-0.3468809724,0.0013946585,-0.1944440603,-0.164983511,0.2775953412,0.2337370664,-0.2706082761,0.2563655674,-0.242731601,0.0653673932,0.1290623993,0.2343828082,-0.35971573,0.3342649937,-0.12441127,0.1992957294,0.1714464724,-0.0682586581,0.2153659165,-0.0765552297,0.0236231182,0.3552123308,-0.1429526359,0.333948791,0.4800492227,-0.0572706535,0.3015576303,-0.4770204425,0.1557276249,0.1190635711,-0.160752207,0.3650277555,-0.2998684645,0.0101009971,-0.068044588,0.0613684207,0.1825455427,0.2595223188,0.3625398278,-0.3248929977,-0.2032797784,-0.2347911149,-0.0175856706,0.1589594632,0.1128976122,0.0513959117,-0.4989529848,0.3118694127,0.1154963747,-0.0290463679,-0.0476505943,0.3867293298,-0.2414716333,-0.3074489832,0.2407457083,0.280131489,-0.0437580869,0.3403137922,0.2554681003,0.193293646,-0.0012780592,-0.3980717957,0.1768649668,0.1721522063,0.0027356723,0.0088600554,0.173740834,-0.2945383489,-0.6536637545,-0.0058761858,-0.0075584813,0.0107460693,-0.33941558,0.0236079656,-0.4454859197,-0.6335576773,-0.2669549882,-0.1445803046,-0.3213297427,-0.5371222496,0.1742296368,0.1724796742,-0.3501804471,0.3709135652,-0.3496403992,0.336697042,-0.1685070992,0.2599729002,-0.2313676625,-0.1633770317,-0.2284460366,0.2568376064,0.0770873949,0.1749694496,0.3014180958,-0.0565680042,0.1974834055,-0.6858537197,-0.4707405865,0.1699635386,0.2959243953,0.1817481965,0.1927588433,0.1188537478,0.0080202166,-0.0642928481,0.2318264842,0.0027228142,0.035466224,-0.2211149782,-0.0204486586,0.132950291,0.0118381921,-0.3134480715,-0.1686445475,-0.2678632736,0.0422301255,-0.18909958,0.1295805275,0.1280422658,-0.0340230949,-0.1728270352,-0.1392841786,-0.0964625105,-0.2624019086,-0.3715264201,0.2499987483,-0.4449140728,-0.306435883,0.0055771689,0.1480789334,0.1420973241,-0.0593496151,-0.506249547,-0.2804401815,-0.1307622045,0.0351853259,0.2475958169,0.0029961944,0.1048962176,0.1557350308,-0.3758841455,0.0317999981,-0.3364630044,-0.0537689514,-0.094900772,0.2806492746,0.008397799,0.2198595107,0.2289399207,0.3426800966,0.1634785384,0.0199132524,0.2377622873,-0.1535305381,0.4485100508,0.1821885854,-0.1965880394,0.2710147798,0.0751455203,0.0043195123,0.342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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/678","title":"The download instructions for c4 datasets are not contained in the error message","comments":"Also not that C4 is a dataset that needs an Apache Beam runtime to be generated.\r\nFor example Dataflow, Spark, Flink etc.\r\n\r\nUsually we generate the dataset on our side once and for all, but we haven't done it for C4 yet.\r\nMore info about beam datasets [here](https:\/\/huggingface.co\/docs\/datasets\/beam_dataset.html)\r\n\r\nLet me know if you have any questions","body":"The manual download instructions are not clear \r\n```The dataset c4 with config en requires manual data. \r\n Please follow the manual download instructions: <bound method C4.manual_download_instructions of <datasets_modules.datasets.c4.830b0c218bd41fed439812c8dd19dbd4767d2a3faa385eb695cf8666c982b1b3.c4.C4 object at 0x7ff8c5969760>>. \r\n Manual data can be loaded with `datasets.load_dataset(c4, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\nEither `@property`  could be added to C4.manual_download_instrcutions (or make it a real property), or the manual_download_instructions function needs to be called I think.\r\n\r\nLet me know if you want a PR for this, but I'm not sure which possible fix is the correct one.","comment_length":56,"text":"The download instructions for c4 datasets are not contained in the error message \n The manual download instructions are not clear \r\n```The dataset c4 with config en requires manual data. \r\n Please follow the manual download instructions: <bound method C4.manual_download_instructions of <datasets_modules.datasets.c4.830b0c218bd41fed439812c8dd19dbd4767d2a3faa385eb695cf8666c982b1b3.c4.C4 object at 0x7ff8c5969760>>. \r\n Manual data can be loaded with `datasets.load_dataset(c4, data_dir='<path\/to\/manual\/data>')\r\n```\r\n\r\nEither `@property`  could be added to C4.manual_download_instrcutions (or make it a real property), or the manual_download_instructions function needs to be called I think.\r\n\r\nLet me know if you want a PR for this, but I'm not sure which possible fix is the correct one. \n Also not that C4 is a dataset that needs an Apache Beam runtime to be generated.\r\nFor example Dataflow, Spark, Flink etc.\r\n\r\nUsually we generate the dataset on our side once and for all, but we haven't done it for C4 yet.\r\nMore info about beam datasets [here](https:\/\/huggingface.co\/docs\/datasets\/beam_dataset.html)\r\n\r\nLet me know if you have any questions","embeddings":[-0.1576072276,-0.2520465553,-0.0443442911,0.2419738322,0.3052386045,-0.0173060261,0.0462005883,0.1740138978,-0.0926164016,0.1525098532,0.011208185,-0.0311050825,-0.1280990988,0.579795897,-0.0943659544,-0.2361066043,-0.1385877132,0.1183631122,-0.2195278853,-0.0132202459,-0.0061009866,0.2214009762,-0.1956916749,-0.0220425092,-0.2092388719,-0.1372852623,0.0151895769,0.1767092049,-0.25517115,-0.3560912907,0.3604578972,0.1128410026,0.2981440425,0.1601436138,-0.0000945143,0.0746880248,0.2518620491,-0.302344203,-0.284550488,0.1466871798,-0.0307056513,-0.0956450775,0.0670619756,-0.1968445182,-0.2997147441,-0.1451850832,0.1192829385,-0.1014602035,0.3640060127,0.2599665523,0.372258395,0.4407114983,0.195772022,-0.2333814949,0.0953537077,-0.1508592516,-0.1420059353,0.1691168994,-0.0406183712,0.2747166157,0.0004316543,0.2728057206,0.1778380871,0.0580458343,0.4070051908,-0.2139707059,-0.1956558675,-0.2863411903,0.1214683652,0.0440186225,0.7800216675,-0.1754940301,-0.0318887569,0.0684108809,0.3036718965,-0.0913942531,0.2903752327,0.2816475034,-0.1135262698,0.1321048588,-0.2517784536,-0.1577833593,-0.2102700472,-0.0275425036,0.1293176264,-0.2587459981,-0.1821366996,0.1505683213,0.1448352039,-0.0655170009,-0.0370307676,0.0024909708,-0.2538126707,0.1177524254,-0.2260058373,-0.1340099126,-0.1367894709,0.5243659616,0.1480009854,0.0493390597,0.0552521087,0.0395027064,-0.0958476365,0.0246077385,-0.0875779167,0.1341896057,0.1416077167,-0.0273241121,0.2056231797,0.0090816459,0.1363287568,-0.1324751228,-0.0923999473,-0.3220089972,-0.4273110032,0.1215066835,-0.1025412306,-0.1251258552,-0.2567414343,-0.0216875579,-0.1581317782,0.1170150787,-0.1226818711,0.502887249,0.0319798253,0.0357804447,0.3110114634,0.1301048398,-0.1264162809,-0.4252971411,-0.1609552205,0.0885234997,-0.3406014442,0.1032325551,0.2031144798,-0.0858152285,0.3210023642,-0.0105482349,0.203918308,0.2336816043,0.043708276,0.1382245421,-0.3299477994,0.2939603627,0.0892609581,0.1966176778,0.0503919981,0.0992412493,0.0147278104,-0.242895171,-0.1383904517,-0.38889727,-0.0293127373,0.2899577916,-0.0359732173,0.0604546554,-0.3929697275,-0.1538803875,-0.0779550523,0.0085782697,-0.018533932,-0.1841296703,0.1792027056,-0.3099900186,0.1034423634,0.5316656232,-0.193946287,0.0594609007,0.107191287,-0.3290980458,0.0939717069,0.1096454486,-0.1983128786,0.1528692544,-0.3218123615,-0.0717164576,0.0001487405,-0.2997231185,-0.0671718419,0.2491941899,0.0763702989,-0.1028418913,-0.0841958225,0.0457970314,0.0344794653,0.0471420623,-0.1555431336,0.0964351818,0.0459185466,0.0953812897,-0.2365958691,-0.2664739788,-0.0504299514,-0.0170552824,0.0486117378,0.0009503769,0.1752432585,-0.0223728772,0.2334792614,-0.1215633005,0.2053255886,0.3341152668,0.0897393376,0.0726901889,-0.0986366346,0.270585686,-0.4880263209,0.1984877437,-0.023317311,0.2936452329,0.2374486923,-0.0921918899,-0.2881412506,-0.1170640588,-0.1954953671,-0.392844677,0.233316198,0.0638065636,0.0160966571,-0.1424281895,-0.055114679,0.6949421167,-0.4194718599,0.136083737,-0.1755590439,0.2874920666,-0.2992298901,-0.0015135614,0.2668257058,-0.1448068172,0.2442115694,-0.1558156163,-0.1182781532,0.5188832283,-0.0700506046,0.1831248552,-0.0156595241,0.102499187,0.1684033424,-0.4067897499,0.2226454169,0.0969818383,0.0564252883,0.0465730317,0.1368783414,0.027752284,0.0609656163,-0.0385046788,0.1375764608,0.2114509344,0.3035189509,0.0903565884,-0.0515104011,-0.008439864,0.0460292585,0.0381696224,0.3691146672,-0.1547346562,-0.2686443925,-0.0204406343,0.372458607,0.1492622942,-0.2134382725,-0.0039702882,-0.2015951276,0.018254986,0.4242030084,0.0845953524,0.1815245748,0.3259015977,-0.2572725415,0.1394944936,0.0787117854,-0.1675015539,0.2197905928,-0.2067821026,0.173573032,-0.076530695,-0.0619713515,0.0288041923,-0.2854051292,-0.1263670325,-0.0495843105,0.0670863912,-0.4333347082,-0.1874492019,-0.07151068,-0.133476302,-0.2219575495,0.1057161689,-0.2684016824,-0.2493634522,0.0531169921,0.0519354008,-0.2146594524,0.2211509943,-0.0773784444,0.2021472454,0.1870241612,0.0604863875,-0.2790496349,0.0561805442,-0.3365766704,0.1974057108,0.0763333738,0.1776463687,0.3503883481,-0.2926580012,0.1538708955,-0.4253621697,-0.2524706423,0.2706113458,-0.1242211014,0.0341452211,0.2647260725,0.4457345605,-0.0405968353,-0.1978025287,0.1048382148,-0.2580126226,-0.2056209147,-0.0564710908,-0.0314116552,-0.1052621827,-0.2328078747,-0.4738240838,-0.2691175044,-0.5012937784,0.061664734,0.0238951892,-0.0179634243,0.3495784998,0.1636542827,0.0834817737,0.1862342507,-0.132588014,-0.1996641308,-0.25761801,0.2411109805,-0.4244994521,-0.4918941855,0.2326402962,0.0886932164,0.1810022593,0.1364176273,-0.2883037627,0.1077371091,-0.1272397339,0.2177413702,0.0037632859,0.0890457481,0.4727257192,-0.105982244,-0.0791258886,-0.2712836862,-0.0139193693,0.1867629588,-0.1283687949,0.4082902074,-0.141553849,0.092208609,0.0083458927,0.6587253213,0.0684503093,-0.2738857269,0.3950152695,-0.024522122,0.5483447909,-0.1196088716,-0.0483414382,0.2783370912,-0.1303411275,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/676","title":"train_test_split returns empty dataset item","comments":"Can you reproduce this example in a Colab so we can investigate? (or give more information on your software\/hardware config)","body":"I try to split my dataset by `train_test_split`, but after that the item in `train` and `test` `Dataset` is empty.\r\nThe codes:\r\n```\r\nyelp_data = datasets.load_from_disk('\/home\/ssd4\/huanglianzhe\/test_yelp')\r\n    print(yelp_data[0])\r\n    yelp_data = yelp_data.train_test_split(test_size=0.1)\r\n    print(yelp_data)\r\n    print(yelp_data['test'])\r\n    print(yelp_data['test'][0])\r\n```\r\nThe outputs:\r\n```\r\n{'stars': 2.0, 'text': 'xxxx'}\r\nLoading cached split indices for dataset at \/home\/ssd4\/huanglianzhe\/test_yelp\/cache-f9b22d8b9d5a7346.arrow and \/home\/ssd4\/huanglianzhe\/test_yelp\/cache-4aa26fa4005059d1.arrow\r\nDatasetDict({'train': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 7219009), 'test': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)})\r\nDataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)\r\n{}    # yelp_data['test'][0] is empty\r\n```","comment_length":20,"text":"train_test_split returns empty dataset item \n I try to split my dataset by `train_test_split`, but after that the item in `train` and `test` `Dataset` is empty.\r\nThe codes:\r\n```\r\nyelp_data = datasets.load_from_disk('\/home\/ssd4\/huanglianzhe\/test_yelp')\r\n    print(yelp_data[0])\r\n    yelp_data = yelp_data.train_test_split(test_size=0.1)\r\n    print(yelp_data)\r\n    print(yelp_data['test'])\r\n    print(yelp_data['test'][0])\r\n```\r\nThe outputs:\r\n```\r\n{'stars': 2.0, 'text': 'xxxx'}\r\nLoading cached split indices for dataset at \/home\/ssd4\/huanglianzhe\/test_yelp\/cache-f9b22d8b9d5a7346.arrow and \/home\/ssd4\/huanglianzhe\/test_yelp\/cache-4aa26fa4005059d1.arrow\r\nDatasetDict({'train': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 7219009), 'test': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)})\r\nDataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)\r\n{}    # yelp_data['test'][0] is empty\r\n``` \n Can you reproduce this example in a Colab so we can investigate? (or give more information on your software\/hardware config)","embeddings":[-0.1150352061,-0.0503816865,-0.0381936133,0.375646472,-0.0080879638,0.2310482264,0.6476925015,0.2886788249,-0.0222678706,0.1684438586,-0.0831825659,0.4956986606,-0.1554624736,0.2236279696,-0.1473039687,-0.0612270273,0.0395237468,0.416975975,-0.0837054104,-0.1733156145,-0.1575848758,0.3264073133,-0.5189428329,-0.2928180695,-0.7594946027,-0.2361606956,-0.3129857183,-0.0154720666,-0.1107865497,-0.0165034104,0.5567771196,-0.0819952339,-0.1336539239,0.5849176645,-0.0001244457,0.0981333107,0.3251812458,-0.1256597042,-0.4603216946,-0.4501077235,-0.5075224638,-0.0494799428,0.3426325321,-0.2649562359,-0.2269916832,-0.0226692744,-0.2063624561,-0.2614695132,0.2259005308,0.2926188409,0.0635271966,0.0414758995,-0.2472750545,-0.1242459789,0.2525101304,0.1699924916,-0.3104469478,-0.1098317429,-0.084317632,0.1739827245,0.1159843951,0.2465375662,0.0693301037,0.3401452601,-0.09772975,0.2747443318,-0.442955941,-0.3045037389,0.3036516905,0.2749741077,0.3398500383,-0.0917443037,-0.1515464038,-0.3225497007,0.0415977091,-0.4011600912,0.0713355243,0.3876688778,-0.090194419,0.1396924406,-0.2474467754,0.0599142238,0.0849869326,0.0673935786,-0.34357512,0.6945251822,-0.1937275529,0.2074731737,0.0974512175,0.2137624174,0.5437459946,-0.2621796131,-0.2669441402,0.0290783402,-0.3628301322,-0.1718165725,-0.1199132726,0.088610962,-0.0517791547,0.1024868041,0.196312204,0.0715745986,-0.0790911168,0.1310102344,0.295804441,0.274138689,0.3077515066,0.411904484,-0.0986466035,0.0760244876,-0.3278721273,-0.0480283536,-0.001570637,0.0881480873,0.0448615327,-0.0580453575,0.1930097491,-0.1583079696,-0.3444063663,-0.1840529591,-0.5183025002,-0.0567977987,0.1866762191,0.3126021326,-0.0572762713,-0.0559262596,-0.3544777334,0.1348287761,-0.0955052078,-0.0661771148,-0.2749396265,0.0778156891,-0.2299034297,0.1631309986,0.2406240255,0.0799832791,0.3387073874,-0.1525916606,-0.0916989893,-0.2025912255,0.2280801088,-0.5900961161,0.4591190815,0.3485629261,0.3222778141,0.1913000494,0.0980790704,-0.0664385781,-0.1165150926,0.4029569626,-0.2699317932,-0.3050501645,0.1691551358,0.0849727765,-0.3663966358,0.0794186145,-0.2584615946,0.2686917484,0.2266364843,0.0961481705,0.1163988858,-0.0441446565,-0.4369967878,-0.0590528809,0.3124624789,0.2948292494,-0.408418119,-0.1554810703,-0.1902661473,0.2188366354,0.2140793651,0.0398626626,-0.2395052761,0.1319135129,-0.3153765798,0.3399737179,0.3605463207,-0.0871430263,-0.2835251689,0.2771761417,0.0215708781,0.051359456,0.0451857708,0.0018947049,0.4075783491,0.1792588532,0.328889519,0.1144289449,-0.2773238122,-0.1597403884,-0.2146873772,-0.2300063521,0.1433477104,0.0812680051,0.3348293602,0.019803999,-0.0664328039,-0.0552028492,0.3579739034,0.0629952848,0.2749548852,0.2020434588,0.3893932104,-0.1076148227,0.0098543344,-0.1398951411,-0.2623062134,0.1802488565,0.1984196007,0.0323021151,-0.0946407765,-0.2139585763,-0.276968807,-0.0646537617,-0.3802941442,-0.3407127857,-0.0074102082,0.0401033908,0.1436058581,0.0353596397,-0.1622667164,0.5844069719,0.0103582954,0.2315689772,-0.0459007397,0.5272762775,0.1391587108,-0.1883159131,-0.2542985082,0.2448890358,0.1367236972,-0.2267313153,-0.0985794738,0.3551600277,0.1909105629,-0.1077017635,-0.0189544912,-0.3879105747,0.3679305017,-0.1755170822,-0.0699600354,0.0392054655,0.3265375495,-0.2248434722,-0.1471385509,0.5041164756,-0.2933380604,0.3285697699,-0.0480019301,-0.0602198541,0.0336071253,-0.1547931284,-0.0144889625,-0.2078600675,-0.3048747778,-0.4725946784,0.3259237409,0.1150533184,-0.2302742004,0.3069022894,0.4501276314,0.187895909,0.0188092645,-0.2738700807,-0.3083892763,-0.0857903063,0.3047046661,0.5613517165,0.5491407514,0.1559779942,0.3771387935,-0.1911429465,0.2594592571,-0.1260149032,0.2184940875,0.0867770016,0.4505963624,0.1112086698,0.0040066582,-0.1120923236,-0.0821984336,-0.0164337568,0.2886066437,0.0569797084,-0.0253688525,-0.0290943962,-0.3493950665,-0.1848792881,-0.229042232,-0.3024784923,0.2572642863,-0.3693206906,-0.1748346984,0.0454847775,0.0536181666,0.0645638555,-0.1782749146,-0.0004121346,0.1094598174,-0.0240367036,0.0801911056,-0.0892360955,-0.2991433442,0.0594417863,-0.1319158077,0.2729621828,-0.0352631845,0.0179516766,-0.1311779171,0.0478454605,-0.0884177163,0.3439834416,-0.3098200858,0.2339693457,0.0029196867,0.0047655958,-0.3975249529,-0.1547760516,0.1998274773,0.0130147431,-0.155743286,-0.138789475,0.2697178125,0.014482283,-0.3401608169,-0.3421728611,-0.1796095669,-0.1125278249,-0.1947131455,-0.2104953229,0.0175590981,-0.027923286,0.06466645,0.2902249098,0.3815029562,-0.4983840883,-0.0954140946,-0.204627201,0.4170759618,-0.2486552298,-0.140739724,0.2069021612,-0.2243056148,0.1000042334,0.1204108894,-0.3114476502,0.0275154077,-0.1803904623,0.098407127,-0.0162202697,-0.2345980853,0.2713419199,-0.0120163467,0.0414469205,-0.1543293446,-0.2120657265,0.0987327918,-0.0192148313,0.3380070329,-0.0995228514,0.0485847108,0.0872996375,0.7695263624,0.1302616596,-0.0590402596,0.185683161,-0.0803642944,0.294434607,-0.2148572505,-0.3306841254,0.085235022,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/676","title":"train_test_split returns empty dataset item","comments":"We'll do a release pretty soon to include the fix :)\r\nIn the meantime you can install the lib from source if you want to ","body":"I try to split my dataset by `train_test_split`, but after that the item in `train` and `test` `Dataset` is empty.\r\nThe codes:\r\n```\r\nyelp_data = datasets.load_from_disk('\/home\/ssd4\/huanglianzhe\/test_yelp')\r\n    print(yelp_data[0])\r\n    yelp_data = yelp_data.train_test_split(test_size=0.1)\r\n    print(yelp_data)\r\n    print(yelp_data['test'])\r\n    print(yelp_data['test'][0])\r\n```\r\nThe outputs:\r\n```\r\n{'stars': 2.0, 'text': 'xxxx'}\r\nLoading cached split indices for dataset at \/home\/ssd4\/huanglianzhe\/test_yelp\/cache-f9b22d8b9d5a7346.arrow and \/home\/ssd4\/huanglianzhe\/test_yelp\/cache-4aa26fa4005059d1.arrow\r\nDatasetDict({'train': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 7219009), 'test': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)})\r\nDataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)\r\n{}    # yelp_data['test'][0] is empty\r\n```","comment_length":25,"text":"train_test_split returns empty dataset item \n I try to split my dataset by `train_test_split`, but after that the item in `train` and `test` `Dataset` is empty.\r\nThe codes:\r\n```\r\nyelp_data = datasets.load_from_disk('\/home\/ssd4\/huanglianzhe\/test_yelp')\r\n    print(yelp_data[0])\r\n    yelp_data = yelp_data.train_test_split(test_size=0.1)\r\n    print(yelp_data)\r\n    print(yelp_data['test'])\r\n    print(yelp_data['test'][0])\r\n```\r\nThe outputs:\r\n```\r\n{'stars': 2.0, 'text': 'xxxx'}\r\nLoading cached split indices for dataset at \/home\/ssd4\/huanglianzhe\/test_yelp\/cache-f9b22d8b9d5a7346.arrow and \/home\/ssd4\/huanglianzhe\/test_yelp\/cache-4aa26fa4005059d1.arrow\r\nDatasetDict({'train': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 7219009), 'test': Dataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)})\r\nDataset(features: {'stars': Value(dtype='float64', id=None), 'text': Value(dtype='string', id=None)}, num_rows: 802113)\r\n{}    # yelp_data['test'][0] is empty\r\n``` \n We'll do a release pretty soon to include the fix :)\r\nIn the meantime you can install the lib from source if you want to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/674","title":"load_dataset() won't download in Windows","comments":"I have the same issue. Tried to download a few of them and not a single one is downloaded successfully.\r\n\r\nThis is the output:\r\n```\r\n>>> dataset = load_dataset('blended_skill_talk', split='train')\r\nUsing custom data configuration default               <-- This step never ends\r\n```","body":"I don't know if this is just me or Windows. Maybe other Windows users can chime in if they don't have this problem. I've been trying to get some of the tutorials working on Windows, but when I use the load_dataset() function, it just stalls and the script keeps running indefinitely without downloading anything. I've waited upwards of 18 hours to download the 'multi-news' dataset (which isn't very big), and still nothing. I've tried running it through different IDE's and the command line, but it had the same behavior. I've also tried it with all virus and malware protection turned off. I've made sure python and all IDE's are exceptions to the firewall and all the requisite permissions are enabled.\r\n\r\nAdditionally, I checked to see if other packages could download content such as an nltk corpus, and they could. I've also run the same script using Ubuntu and it downloaded fine (and quickly). When I copied the downloaded datasets from my Ubuntu drive to my Windows .cache folder it worked fine by reusing the already-downloaded dataset, but it's cumbersome to do that for every dataset I want to try in my Windows environment.\r\n\r\nCould this be a bug, or is there something I'm doing wrong or not thinking of?\r\n\r\nThanks.","comment_length":41,"text":"load_dataset() won't download in Windows \n I don't know if this is just me or Windows. Maybe other Windows users can chime in if they don't have this problem. I've been trying to get some of the tutorials working on Windows, but when I use the load_dataset() function, it just stalls and the script keeps running indefinitely without downloading anything. I've waited upwards of 18 hours to download the 'multi-news' dataset (which isn't very big), and still nothing. I've tried running it through different IDE's and the command line, but it had the same behavior. I've also tried it with all virus and malware protection turned off. I've made sure python and all IDE's are exceptions to the firewall and all the requisite permissions are enabled.\r\n\r\nAdditionally, I checked to see if other packages could download content such as an nltk corpus, and they could. I've also run the same script using Ubuntu and it downloaded fine (and quickly). When I copied the downloaded datasets from my Ubuntu drive to my Windows .cache folder it worked fine by reusing the already-downloaded dataset, but it's cumbersome to do that for every dataset I want to try in my Windows environment.\r\n\r\nCould this be a bug, or is there something I'm doing wrong or not thinking of?\r\n\r\nThanks. \n I have the same issue. Tried to download a few of them and not a single one is downloaded successfully.\r\n\r\nThis is the output:\r\n```\r\n>>> dataset = load_dataset('blended_skill_talk', split='train')\r\nUsing custom data configuration default               <-- This step never ends\r\n```","embeddings":[-0.4348374605,0.2638886869,-0.0968250707,0.3132835925,0.2672564983,0.3075503707,0.3540579677,0.1106310487,0.4425596595,-0.0165286697,0.1196889281,-0.1064612344,-0.0671281815,0.2139161676,0.1405703574,-0.1002630293,0.018564146,-0.0331001058,0.0595737882,-0.1480202377,-0.2890079319,0.1217577606,-0.4512639344,-0.1942238808,-0.1455354691,0.1002141535,-0.1550091505,0.0938178152,-0.128532812,-0.2223109603,0.4647398591,0.1681256443,0.2233542353,0.5532972813,-0.0001173147,-0.1689022481,0.4234569371,-0.0814600661,-0.2416763306,-0.2221424282,0.0686217472,-0.2787030935,0.0384051986,-0.043393191,0.0327521302,0.3367926478,0.0414203741,-0.2054827213,-0.0061557875,0.3664603829,0.1425715834,0.0846243128,0.0911352858,0.0108170155,0.0476456061,0.1078762114,-0.1996514201,0.4818440974,0.1704382598,-0.4610636234,0.2528343797,-0.0523954928,-0.2002899647,0.12531811,0.0094174054,0.0281769503,-0.0543065406,-0.5504904985,0.1858800203,0.3046257794,0.6023476124,0.0177001376,-0.1578363329,-0.1204510257,0.2528926432,-0.0260488745,0.2037484646,0.5220000148,-0.4007895291,-0.025911171,-0.3000251949,-0.1521255225,-0.0877072215,0.3059648573,-0.0330718718,0.0428854339,0.0164353736,0.2268963754,0.2488449812,0.0721496046,0.1917523444,-0.1281947941,0.0682789981,0.027903039,-0.1809323728,0.32985425,-0.0504387878,0.2989700735,-0.0418128595,0.1580298245,-0.0601914823,0.1740888655,0.3321507573,0.0459657125,0.2691091597,-0.099441953,0.2820907235,-0.0252827834,0.3245888948,0.121104911,-0.082859233,0.0667497888,-0.501216948,-0.194568336,0.052375149,-0.0869862512,0.1955155581,-0.3377199173,-0.2771073282,0.0688086748,0.1445810199,-0.1822151542,-0.0500968657,0.2808072567,-0.1014573798,0.4788323939,0.1540380716,0.2452641428,-0.2062827498,-0.1190844253,0.1272698939,-0.0692254156,-0.3084921837,0.1779513508,0.7094912529,-0.0529173873,0.0663962662,-0.1264293194,0.1003017649,-0.0624417365,0.0906467587,-0.2585457861,-0.0227721445,0.1866897345,0.1599061638,0.4259392023,0.0041597588,-0.035244897,-0.1602686942,0.2484574318,-0.1696225107,-0.0448931418,0.2376054525,0.0982200801,-0.1863003373,-0.2393067777,-0.1790407002,0.1990797669,-0.0354634225,-0.0408191569,-0.0609379299,-0.2394403219,-0.2222975194,-0.2471050322,0.2509646714,0.6065970063,-0.4733546078,0.1334774196,-0.0128212413,-0.0194936916,-0.0189592876,0.1573492736,-0.2292907238,0.2850937545,-0.3361421227,-0.0334928222,0.0889084116,-0.2796662152,-0.3336099982,0.4279620349,-0.0389764234,0.1260471195,-0.0503798984,0.1954921484,0.1609428674,-0.0208935887,0.1018467024,0.4022238553,-0.0085760411,-0.1034732461,-0.1004038155,-0.1858472526,0.3791280687,0.415197134,-0.0585369058,0.1164777875,-0.0023768656,0.1477486342,0.2085295767,0.2583529651,0.1554789841,0.3921535909,-0.100433819,0.194047302,0.0573909357,-0.0040819296,-0.5138206482,0.2298600376,0.3814336658,-0.1514694244,0.1052555069,-0.0277960431,-0.2260430306,0.0215200167,-0.0843482167,-0.2146155089,-0.0222895797,0.0955006182,0.3066940308,0.0342207812,-0.0121396556,0.4903284907,-0.1665464789,0.0066171535,-0.2134784907,0.2366082519,0.0909736678,-0.2106549144,0.2085710019,-0.0699847564,0.1091529354,-0.0718648434,-0.1570317298,0.2500483394,-0.0906808674,0.0076134386,0.0512502566,0.0115301656,0.0814980119,-0.3572994471,0.205064401,0.392457515,0.2962708473,-0.3245432973,-0.2591126859,0.0580980293,-0.2606731355,0.33670789,-0.005331513,0.0617840737,-0.0374517217,-0.0032223789,-0.1617287099,0.2174919397,0.5736041069,-0.104907535,0.0363553129,-0.1144829094,-0.1393821537,0.1269520819,0.4384441376,-0.1317360848,-0.0689161569,0.1883349121,-0.2489185929,0.1089598984,-0.0910281092,-0.0266572293,0.6324250102,0.1077284664,0.3119974136,0.0097935377,-0.1067500561,-0.3897905052,0.0751158372,0.0054400098,-0.12851955,0.2242888361,-0.2907730937,-0.0353042223,-0.1567242891,-0.0604768358,0.2551260889,0.1556317061,-0.254458487,0.1504667997,-0.1579031944,0.0311158001,-0.1553646028,-0.15486525,-0.2051059902,-0.1602920145,-0.3846489787,0.4122015834,0.2328325957,-0.1524040848,-0.5157061219,0.1887485832,0.1120762229,-0.3764174581,-0.0860777199,-0.0969321728,-0.3495421708,-0.0422155,0.368955493,0.1660794914,0.1740034223,-0.2884266973,-0.0160381626,-0.1641294658,0.1684151888,-0.1408004761,-0.0490464754,0.212425679,-0.1679721773,0.5251682997,-0.0327252001,-0.0148316082,0.163998723,-0.3974747658,0.1488021761,0.1389051676,0.273614943,-0.1533205211,-0.0900415853,-0.3636281192,-0.2869464159,-0.3258349299,0.1719224453,-0.0516751409,-0.0136036305,-0.0170345493,0.1706237793,0.0451981612,0.0707797706,0.0161171295,-0.1132758409,-0.1930204034,0.675116241,-0.1407323778,-0.6298113465,0.4133820236,0.1727474481,-0.112663798,0.2265709341,-0.5824322701,0.4229878485,-0.1837514937,0.1028695479,0.1750198752,0.1629975885,0.0262151174,-0.4262453318,0.1226002872,-0.009976618,-0.335233748,-0.1860522628,0.0036444147,0.3259528577,0.1315894127,0.1866594106,-0.1926443875,0.2649832964,0.1222916096,0.1034580246,0.116020605,-0.0910001919,0.4476820827,0.0732973143,-0.3743332922,0.3017482758,-0.451111853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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/674","title":"load_dataset() won't download in Windows","comments":"This was fixed in #644 \r\nI'll do a new release soon :)\r\n\r\nIn the meantime you can run it by installing from source","body":"I don't know if this is just me or Windows. Maybe other Windows users can chime in if they don't have this problem. I've been trying to get some of the tutorials working on Windows, but when I use the load_dataset() function, it just stalls and the script keeps running indefinitely without downloading anything. I've waited upwards of 18 hours to download the 'multi-news' dataset (which isn't very big), and still nothing. I've tried running it through different IDE's and the command line, but it had the same behavior. I've also tried it with all virus and malware protection turned off. I've made sure python and all IDE's are exceptions to the firewall and all the requisite permissions are enabled.\r\n\r\nAdditionally, I checked to see if other packages could download content such as an nltk corpus, and they could. I've also run the same script using Ubuntu and it downloaded fine (and quickly). When I copied the downloaded datasets from my Ubuntu drive to my Windows .cache folder it worked fine by reusing the already-downloaded dataset, but it's cumbersome to do that for every dataset I want to try in my Windows environment.\r\n\r\nCould this be a bug, or is there something I'm doing wrong or not thinking of?\r\n\r\nThanks.","comment_length":23,"text":"load_dataset() won't download in Windows \n I don't know if this is just me or Windows. Maybe other Windows users can chime in if they don't have this problem. I've been trying to get some of the tutorials working on Windows, but when I use the load_dataset() function, it just stalls and the script keeps running indefinitely without downloading anything. I've waited upwards of 18 hours to download the 'multi-news' dataset (which isn't very big), and still nothing. I've tried running it through different IDE's and the command line, but it had the same behavior. I've also tried it with all virus and malware protection turned off. I've made sure python and all IDE's are exceptions to the firewall and all the requisite permissions are enabled.\r\n\r\nAdditionally, I checked to see if other packages could download content such as an nltk corpus, and they could. I've also run the same script using Ubuntu and it downloaded fine (and quickly). When I copied the downloaded datasets from my Ubuntu drive to my Windows .cache folder it worked fine by reusing the already-downloaded dataset, but it's cumbersome to do that for every dataset I want to try in my Windows environment.\r\n\r\nCould this be a bug, or is there something I'm doing wrong or not thinking of?\r\n\r\nThanks. \n This was fixed in #644 \r\nI'll do a new release soon :)\r\n\r\nIn the meantime you can run it by installing from 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/674","title":"load_dataset() won't download in Windows","comments":"Closing since version 1.1.0 got released with Windows support :) \r\nLet me know if it works for you now","body":"I don't know if this is just me or Windows. Maybe other Windows users can chime in if they don't have this problem. I've been trying to get some of the tutorials working on Windows, but when I use the load_dataset() function, it just stalls and the script keeps running indefinitely without downloading anything. I've waited upwards of 18 hours to download the 'multi-news' dataset (which isn't very big), and still nothing. I've tried running it through different IDE's and the command line, but it had the same behavior. I've also tried it with all virus and malware protection turned off. I've made sure python and all IDE's are exceptions to the firewall and all the requisite permissions are enabled.\r\n\r\nAdditionally, I checked to see if other packages could download content such as an nltk corpus, and they could. I've also run the same script using Ubuntu and it downloaded fine (and quickly). When I copied the downloaded datasets from my Ubuntu drive to my Windows .cache folder it worked fine by reusing the already-downloaded dataset, but it's cumbersome to do that for every dataset I want to try in my Windows environment.\r\n\r\nCould this be a bug, or is there something I'm doing wrong or not thinking of?\r\n\r\nThanks.","comment_length":19,"text":"load_dataset() won't download in Windows \n I don't know if this is just me or Windows. Maybe other Windows users can chime in if they don't have this problem. I've been trying to get some of the tutorials working on Windows, but when I use the load_dataset() function, it just stalls and the script keeps running indefinitely without downloading anything. I've waited upwards of 18 hours to download the 'multi-news' dataset (which isn't very big), and still nothing. I've tried running it through different IDE's and the command line, but it had the same behavior. I've also tried it with all virus and malware protection turned off. I've made sure python and all IDE's are exceptions to the firewall and all the requisite permissions are enabled.\r\n\r\nAdditionally, I checked to see if other packages could download content such as an nltk corpus, and they could. I've also run the same script using Ubuntu and it downloaded fine (and quickly). When I copied the downloaded datasets from my Ubuntu drive to my Windows .cache folder it worked fine by reusing the already-downloaded dataset, but it's cumbersome to do that for every dataset I want to try in my Windows environment.\r\n\r\nCould this be a bug, or is there something I'm doing wrong or not thinking of?\r\n\r\nThanks. \n Closing since version 1.1.0 got released with Windows support :) \r\nLet me know if it works for you now","embeddings":[-0.5312045217,0.2285069823,-0.0575985946,0.2666499317,0.1350667328,0.3239186406,0.2561958432,0.219893232,0.3572812676,0.0436172225,0.1882215887,-0.0625395849,-0.0545900762,0.1764082909,0.1357093602,-0.0816593394,0.0941099972,0.0226964857,0.0226596929,-0.1013214365,-0.3583259881,0.1709131896,-0.4910069406,-0.1853563637,-0.120413743,0.2017234713,-0.2180992812,0.1222668961,-0.1995783895,-0.3122927547,0.4654698968,0.3261946738,0.3328743577,0.4956374168,-0.0001170868,-0.231574297,0.483954668,-0.0690701902,-0.1345821917,-0.2716261744,-0.0077642691,-0.3620634675,0.0949622169,-0.0017414198,0.010947383,0.3178222775,0.1540232897,-0.2169940323,-0.1187456921,0.3016267717,0.1500115842,0.0650988743,0.2076624781,-0.0231751185,0.1106366217,0.099093087,-0.237621218,0.3654014468,0.1448057294,-0.3507416844,0.1638025194,0.0025700624,-0.1906076223,0.0756300464,0.0991751701,-0.0257794689,0.0206769928,-0.4977041483,0.1921181828,0.2145073712,0.7776694894,-0.0320849679,-0.1946044117,0.0488454401,0.1430145055,-0.0328599066,0.2404405475,0.4651777446,-0.312772125,0.0017895943,-0.3549053073,-0.1745453179,-0.2147067785,0.3462227881,-0.0036783491,0.1802301556,0.0305233449,0.2561663985,0.2806198299,0.1594635397,0.2893838584,-0.1119091064,-0.0438714437,0.1055945083,-0.1552415937,0.2997744977,0.0266171321,0.4183351099,0.017508287,0.1570257992,-0.0587561205,0.1947076768,0.3071296215,0.0666199327,0.4180442095,-0.0607035682,0.3588195443,-0.0342604555,0.4944933653,0.096069999,0.0118606184,0.0071075396,-0.4333915412,-0.1531762481,0.0900343508,-0.0854356065,0.2950430512,-0.4243557751,-0.1556747854,0.0356016494,0.1440680921,-0.2066551149,-0.120803602,0.2567278147,-0.0902356654,0.5926252604,0.1480210871,0.1692719907,-0.2379569411,-0.0793555975,0.1535152048,-0.0292146113,-0.3896257877,0.1484386176,0.6411631107,-0.0656778291,0.0258341134,-0.1347650439,0.0625348687,-0.0405454151,0.0859267861,-0.2406888306,-0.0449584574,0.2409293652,0.1386255175,0.4186852872,-0.0617769621,0.0067275236,-0.2271531224,0.2992707789,-0.0781947151,-0.0815833956,0.1264632493,0.0773238465,-0.1531830132,-0.1732040197,-0.1545853019,0.2747504711,-0.1034760997,-0.1443240941,-0.0104165263,-0.1774776131,-0.3123803735,-0.2741759121,0.1735449582,0.6089103222,-0.5470990539,0.2760280669,-0.0341996253,-0.0876644105,-0.0104920547,0.0894189328,-0.141623497,0.1639377326,-0.2924817801,-0.0215298124,0.025942836,-0.3787801862,-0.3475463688,0.4641360044,0.0749896616,0.0911938697,-0.0299300402,0.2377229929,0.1555584818,-0.0865240097,-0.0461787768,0.4049776196,-0.0507781655,-0.0866378769,-0.148676917,-0.1572996527,0.2671688795,0.3516069949,-0.0558429807,0.1907018572,0.0927738845,0.078379117,0.2400555313,0.2020245939,0.14721632,0.40376845,-0.1247166693,0.0438928157,0.1558066905,-0.0253990274,-0.6035884023,0.206732735,0.3425562978,-0.031738732,0.1866748035,-0.0706361905,-0.2475808263,-0.0217133034,-0.0718118697,-0.2302504629,-0.0182543155,0.1467156708,0.2857332826,-0.0163864009,-0.0573725775,0.4816448092,0.0001618297,0.0678999722,-0.3044669628,0.1455879807,-0.0020915668,-0.1764754057,0.1640513688,-0.1513475329,0.120415017,-0.1093366817,-0.1514501274,0.3896580935,-0.0368123241,0.0581553727,0.049190674,0.0184193924,0.0687952787,-0.3777018487,0.2146212012,0.4776518941,0.2862357497,-0.3557849526,-0.3085817993,0.1224041283,-0.292832762,0.26884076,0.0279136244,0.0429542735,0.0413202234,-0.0010225258,-0.1258667111,0.2151589394,0.5775262117,-0.0929219127,-0.0020058907,-0.1643341333,-0.1167262644,0.074873291,0.2431920171,-0.1832413077,-0.0991430879,0.1818745881,-0.2656547129,0.046527721,-0.0512061678,-0.0279067066,0.6937488914,0.1468280107,0.2767846286,0.0139100784,-0.0489187054,-0.3464194536,0.1142464578,-0.0273889545,-0.1994453818,0.1210508719,-0.2485818416,-0.1819965839,-0.2715475261,-0.0218851939,0.1723171473,0.2161433101,-0.2237141579,0.1064540446,-0.0458935462,0.0451414101,-0.0418594889,-0.2032460868,-0.2282697707,-0.2365659475,-0.3288748264,0.5409021974,0.2697987854,-0.1737596095,-0.4869428575,0.2375602275,0.0965301841,-0.1562859416,-0.2288695276,-0.1317171007,-0.3606881201,-0.0115164006,0.3313680887,0.1251847148,0.1712014824,-0.2329801917,0.0551182292,-0.1988323033,0.1272965819,-0.1261771172,-0.0057974411,0.2244524509,-0.0427630991,0.4186484814,-0.1009568349,0.0202344488,0.11982961,-0.4642215967,0.0796953812,0.2274574935,0.1727819443,-0.2189692557,-0.1373376548,-0.2433601916,-0.2774139047,-0.2721554935,0.2006094456,0.0106294835,0.0735635236,0.0224569906,0.114382498,0.0053695044,0.0750945508,-0.0262677576,-0.1641979814,-0.2334053814,0.6322652102,-0.2217449248,-0.6599826813,0.4075287282,0.2974890172,-0.1475100964,0.2126586735,-0.5720703602,0.3467951119,-0.1019633785,0.0610632151,0.2333485782,0.1665354222,-0.0139730619,-0.3323441446,0.1042610928,-0.0081534088,-0.2222926915,-0.2352988571,-0.0378954262,0.2994037271,0.1876711547,0.1272700578,-0.1850459278,0.2182715684,0.1114651412,0.2113908231,0.2606621981,-0.0783335567,0.4497875571,-0.0028928609,-0.3595738709,0.2079339027,-0.4530533254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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":"We should try to regenerate the data using the official script.\r\nBut iirc that's what we used in the first place, so not sure why it didn't match in the first place.\r\n\r\nI'll let you know when the dataset is updated","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":41,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n We should try to regenerate the data using the official script.\r\nBut iirc that's what we used in the first place, so not sure why it didn't match in the first place.\r\n\r\nI'll let you know when the dataset is 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":"Thanks, looking forward to hearing your update on this thread. \r\n\r\nThis is a blocking issue for us; would appreciate any progress on this front. We can also help with the fix, if you deem it appropriately. ","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":36,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n Thanks, looking forward to hearing your update on this thread. \r\n\r\nThis is a blocking issue for us; would appreciate any progress on this front. We can also help with the fix, if you deem it appropriately. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":"I just started the generation on my side, I'll let you know how it goes :) ","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":16,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n I just started the generation on my side, I'll let you know how it goes :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":"Hmm after a first run I'm still missing 136668\/226711 urls.\r\nI'll relaunch it tomorrow to try to get the remaining ones.","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":21,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n Hmm after a first run I'm still missing 136668\/226711 urls.\r\nI'll relaunch it tomorrow to try to get the remaining ones.","embeddings":[-0.0192339066,-0.2787488699,-0.1315880716,0.4514377117,0.3294107616,0.0132473074,0.0834711641,-0.0324111395,0.1632886678,0.25323686,-0.214021951,0.181240797,0.1041179374,0.3433215916,0.1806179285,0.1001857221,-0.0398060568,-0.1415234357,-0.4157710373,-0.3789446652,-0.1317090839,0.1342010051,-0.1089954525,-0.1242683679,-0.611969471,0.0783976391,-0.2388474196,0.3378801346,-0.0854114518,-0.2222675532,0.2241359353,0.0637293831,0.089367874,0.5341081619,-0.0001078297,-0.0220556576,0.1424761713,-0.0170413572,-0.1624374986,0.2538862228,-0.231166482,0.0268421471,0.0482006781,-0.405474782,-0.0263640303,0.1173033118,-0.0946482047,-0.3096888065,0.158078745,0.0959425792,0.1845942587,0.1571917236,0.0893572941,-0.0256532505,0.0051862001,-0.0828036368,0.1446168125,0.2197121382,0.3912556469,0.3032293618,0.0680401847,0.1804811209,0.0612244383,-0.2153697014,0.2798307538,0.098128058,0.2757803202,-0.2437672019,-0.3017998338,0.2616388202,0.797362864,-0.0047415853,-0.2618876398,-0.0269741826,-0.0765680969,-0.1153860837,0.2018673569,0.0557033569,0.3726731539,0.0041704988,-0.3282065392,0.3529005647,-0.2558207512,0.1323099285,-0.1288596541,-0.0443034545,0.0117962733,0.0079704309,-0.2504276931,0.0427734144,0.1230294555,-0.1049915478,0.2351700515,0.2270646989,-0.2158429772,-0.1437883079,0.060984429,0.0052842968,0.247093156,0.2200070769,0.2636894882,0.0973217487,-0.4094589651,0.0266528986,0.2186471969,0.2032875419,0.3677200377,-0.5091800094,0.0934059843,-0.0424155034,-0.0867900103,0.0701019987,-0.3361897171,0.0212913696,-0.0937169343,0.088907592,-0.1943087429,-0.3586162627,-0.1624151319,0.2117015272,0.0353169329,-0.136645779,0.2173826247,-0.1654897928,-0.0143268658,0.4042074382,0.0237145703,0.0212501306,-0.539381206,-0.2602357864,-0.3475570977,0.118217729,-0.0596303716,-0.0100992732,0.125677377,-0.0402502157,0.4135729074,-0.1710354835,0.2352254093,0.0067364755,0.0788006037,0.1520160735,-0.3228069246,0.2311338782,0.088969335,0.0422685146,-0.0792255104,-0.0212146528,-0.0260818787,-0.0076279873,-0.2080206126,-0.2832221091,0.0483163781,0.305248946,-0.3953118324,-0.2976082265,-0.1643511057,-0.0514992774,0.0617100298,0.2127865404,-0.2347178608,-0.0692887083,0.1486749947,-0.123633042,0.2656905055,0.0006675372,-0.4929637015,-0.0078990664,0.0381508395,-0.244260177,0.1114944592,0.0165498052,-0.0232431199,0.2643283308,0.0746211782,0.1598430425,-0.4744036496,-0.4691445529,-0.0426469147,-0.3227778673,-0.1889185607,-0.1445510685,0.0905963853,0.097147204,0.0018750384,-0.1155165285,0.2047923505,0.0755230337,0.0930427089,-0.2053320259,-0.4654321373,-0.1000907123,-0.0738273486,0.3007145822,0.0293438658,-0.299972564,-0.1004413813,0.3271587789,0.2232566029,-0.0668678582,-0.0878526047,0.1660561413,0.2788275182,-0.082377702,0.1109275371,-0.186829254,0.04142588,0.310914129,0.0750924349,0.2592254579,0.6795742512,-0.0216838457,-0.3754805624,-0.2413866818,0.0568287633,-0.1928550452,0.1584153324,-0.153461501,-0.0133115696,-0.1118040532,-0.2205297947,0.0573064238,-0.574752748,0.1063458994,-0.5254189968,0.3792724609,-0.0178775862,0.0787570924,0.1651778072,0.325288862,0.0451645851,-0.3062184453,0.0017507138,0.5623015761,0.160725683,0.0131055173,0.3588773906,0.1996974498,0.4656715095,-0.2428459674,0.0950104818,-0.1435525864,-0.0314964838,-0.2036189735,-0.339433074,0.2086922675,-0.1423111558,0.0569089204,0.0482508242,0.0840907693,0.0055028205,-0.1831810772,-0.0478080325,0.0529193655,0.2395460308,0.1336751729,-0.3054099381,0.0981754065,0.0219700914,0.3735604882,-0.3904986084,-0.2474121898,-0.2403516769,-0.2415396869,-0.2217556387,0.1088662669,0.0645262524,0.0066637751,0.4553330541,0.29851982,0.1484998614,-0.0245345216,-0.1464135349,-0.1921243221,0.0765454248,0.1265975982,-0.1374517083,-0.1376807392,-0.0914110988,0.0538808331,-0.2065616995,-0.0373752303,0.1147436649,0.102457881,-0.4339114428,-0.3744902909,-0.246969372,-0.1158339679,0.2495810986,0.1147520468,0.1498488188,-0.3861026466,0.1743736565,-0.0147843529,-0.1148667932,0.0133326417,-0.4554790258,0.2821137011,0.0333305188,0.0956916511,0.0830486789,-0.2291259021,-0.1364935786,0.1868573427,-0.0149911372,0.3306301236,0.3095939159,-0.1426821798,-0.0014282425,0.0614826344,-0.4463159442,0.0334600657,-0.1677661836,0.2153141201,-0.0946199223,0.0360548645,0.0441393554,-0.0399484448,0.2810697556,0.0453815982,-0.132837519,-0.0603456944,0.1951060146,-0.3132751286,-0.0596587546,-0.5780410767,-0.077090241,-0.0302528366,-0.0871214718,0.4910757542,0.2203983665,0.0963189751,-0.1681658626,0.1959906667,0.2443377525,-0.2565478683,-0.3770159483,-0.853300333,0.3729169071,-0.1395105571,-0.5446469188,0.0230577756,-0.0514396951,0.2916252613,0.1466273665,-0.467515558,-0.3342877328,0.0168569125,-0.1670075804,0.4653496146,0.2231473774,0.210283488,-0.11818441,-0.206025824,-0.249483481,-0.0421271659,0.2474309951,0.3107559383,0.555118978,-0.3252278566,-0.3398382068,-0.2012266815,0.1445459276,0.1646935195,-0.0298970174,0.0222609509,-0.0806642696,-0.1048713624,0.1389025599,-0.0809603781,0.6621094346,-0.1253650188,-0.069608964,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":"So I managed to download them all but when parsing only 226,181\/226,711 worked.\r\nNot sure if it's worth digging and debugging parsing at this point :\/ ","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":26,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n So I managed to download them all but when parsing only 226,181\/226,711 worked.\r\nNot sure if it's worth digging and debugging parsing at this point :\/ 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":"Thanks @lhoestq\r\nIt would be great to improve coverage, but IDs are the really crucial part for us. We'd really appreciate an update to the dataset with IDs either way!","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":30,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n Thanks @lhoestq\r\nIt would be great to improve coverage, but IDs are the really crucial part for us. We'd really appreciate an update to the dataset with IDs either way!","embeddings":[-0.120538637,-0.3434901834,-0.1572831124,0.4010956287,0.3150455356,0.0539410487,0.1680675596,0.0182598215,0.2290308475,0.2051194459,-0.1785497963,0.2475751191,0.1144534126,0.4436739683,0.0983844176,0.1101119295,-0.0065614008,-0.1371245682,-0.3460201621,-0.2986064553,-0.043396119,0.0572821386,-0.1505293101,-0.1744760871,-0.6682879925,0.014502733,-0.1977975965,0.2540426254,-0.10829743,-0.2513684332,0.3075421453,0.0355967507,0.0455216244,0.4934388399,-0.0001066689,-0.0335242599,0.1318748742,-0.0461245961,-0.0730438009,0.2865335941,-0.1540056467,-0.0908150151,0.0087959999,-0.3714568019,-0.0635807887,0.0710184649,-0.1505089402,-0.3604391813,0.1319084018,0.0261039324,0.205384165,0.1816887707,0.1772074401,0.0034406546,-0.0710885674,-0.129971832,0.0547026582,0.2795854807,0.4867466986,0.2233678848,0.0364004858,0.2235723585,0.0667103305,-0.235170275,0.2473031282,0.0893050358,0.3610637188,-0.1546256393,-0.3263548911,0.2234194279,0.8148685098,-0.0138418199,-0.3631150424,-0.0914176404,-0.0141353784,-0.1905049831,0.1663066298,0.0231584311,0.4013923407,-0.0192244574,-0.319467634,0.3984606266,-0.261143297,0.0211198628,-0.1350149065,-0.1003202647,-0.0169938281,-0.0170955136,-0.2322692424,-0.0396247543,0.0964685157,-0.0244267955,0.1179354787,0.1987861246,-0.3089933693,-0.2048525214,-0.0104514612,0.0081872335,0.2604072988,0.1771929264,0.2447296977,0.0415656567,-0.4094988108,0.0169901028,0.2245205492,0.231425643,0.4467464983,-0.4870941639,0.0470451675,-0.0431569442,-0.126160875,0.0585928001,-0.3492003083,0.0123141892,0.0359336436,0.1200719625,-0.1307220161,-0.3616788089,-0.1987831742,0.2999648154,0.0037913849,-0.1758429557,0.1398107111,-0.1497582048,0.0295124333,0.4021392465,0.016287338,0.0602587834,-0.5490458012,-0.3304890096,-0.3623380363,0.0852195844,-0.0705257729,-0.0179139096,0.1263846755,-0.11324431,0.3872211576,-0.1766903996,0.248853758,0.0368418023,0.0897519737,0.1391469389,-0.3680388927,0.2284670472,0.0240021516,0.0156878158,-0.0285932366,0.0518154539,0.0010552821,-0.0023563453,-0.1921722442,-0.2217102498,-0.0001565067,0.2905130386,-0.3537013829,-0.3277324438,-0.0906434059,0.0307502002,0.1729968488,0.1653863788,-0.1721050441,-0.0541453846,0.0885859132,-0.103844218,0.1517871767,0.013269145,-0.5510629416,-0.0082750237,-0.0016376682,-0.2535509169,0.1331641376,-0.0280404631,0.0421098731,0.2410802394,0.1172041595,0.134856075,-0.5617380738,-0.4743942916,-0.1683333814,-0.3735470474,-0.1620114595,-0.1173287705,0.1920337379,0.06438528,0.0327112228,-0.1309922934,0.1321861595,-0.0206760354,0.0525833443,-0.1722007841,-0.4544654191,-0.0696137249,-0.0106009115,0.2401328832,-0.0399660468,-0.3308128417,-0.1268031448,0.3707153201,0.1579273045,0.0076082805,-0.1216362491,0.0300809592,0.3287639916,-0.1603640318,0.0529127829,-0.1468402296,-0.0415217914,0.3014721274,0.1175609529,0.2241449058,0.7303125262,-0.0659259111,-0.3103890121,-0.1799812019,0.019187035,-0.2510041893,0.188114807,-0.1872991025,-0.0368539989,-0.2384052575,-0.1892733723,0.1357982457,-0.6147508621,0.0889656171,-0.4941736758,0.3555936515,-0.0201430395,-0.0115462411,0.0635659918,0.2999169827,-0.0188042,-0.3205814064,-0.06464421,0.5364034772,0.1481600106,0.0586812049,0.2148335725,0.2414090782,0.4149382114,-0.1923774481,0.1212522313,-0.2626796067,-0.0870194286,-0.1991874278,-0.4233083427,0.2120577544,-0.1298015267,0.025815906,-0.1078194827,0.0357511714,-0.0327578261,-0.1688144803,0.0306376815,0.0417337976,0.1496233791,0.1417599767,-0.2297071666,0.0775303245,0.0226610247,0.4194988608,-0.3977731466,-0.1804336607,-0.2100398242,-0.3189482689,-0.1661608368,0.1576022953,0.1537567973,-0.0587063059,0.493157208,0.321742624,0.0985389426,-0.0338733457,-0.039565403,-0.1556171179,0.0235522371,0.0013054968,-0.1736928523,-0.0394245237,-0.0639208779,0.0033622223,-0.1511525512,0.0975713953,0.1108916327,0.1865143627,-0.4474341571,-0.4137154818,-0.2139122039,-0.1277546734,0.2385014743,0.1950375736,0.1759382337,-0.2985452712,0.1582861543,0.0456121601,-0.0374015272,0.0498322174,-0.4003778398,0.2830519378,0.0254464466,0.0708897263,0.0738012269,-0.16967839,-0.1166358516,0.2136633098,0.0037979127,0.2504509091,0.3692833781,-0.0844295844,-0.0666720793,0.1225024089,-0.4055014551,-0.0075853136,-0.1713564694,0.2512726784,-0.1238033548,0.0624052063,0.0091360817,-0.0322459452,0.2944410741,-0.0420788638,-0.2515262067,-0.1448777616,0.180883944,-0.1780132502,-0.0485051982,-0.5882418156,-0.1118778363,-0.1036614701,-0.0437036492,0.4338700771,0.1562176794,0.0717754588,-0.1139168367,0.1505248696,0.2319957614,-0.2218727618,-0.331430763,-0.8559192419,0.3598410487,-0.0937612653,-0.4732737541,-0.0068540871,-0.0225818809,0.3321779966,0.1283280402,-0.3856081665,-0.3623091578,0.0941484645,-0.0910067335,0.5071302652,0.227072686,0.2142199576,-0.1695542037,-0.1697413921,-0.2687189877,-0.070729807,0.2278573811,0.265399009,0.4700402319,-0.3075751364,-0.330424428,-0.2363013029,0.0882734731,0.2966341078,-0.1167368591,0.0679814517,-0.0080630872,-0.1070004106,0.0694277585,-0.2100117654,0.6536984444,-0.09306784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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":"I gave up at an even earlier point. The dataset I use has 204,017 train examples.","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":16,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n I gave up at an even earlier point. The dataset I use has 204,017 train 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":"@lhoestq @sshleifer like @jbragg said earlier, the main issue for us is that the current XSUM dataset (in your package) does not have IDs suggested by the original dataset ([here is the file](https:\/\/raw.githubusercontent.com\/EdinburghNLP\/XSum\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json).) Would appreciate if you update the XSUM dataset to include the instance IDs. \r\n\r\nThe missing instances is also a problem, but likely not worth pursuing given its relatively small scale. ","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":63,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n @lhoestq @sshleifer like @jbragg said earlier, the main issue for us is that the current XSUM dataset (in your package) does not have IDs suggested by the original dataset ([here is the file](https:\/\/raw.githubusercontent.com\/EdinburghNLP\/XSum\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json).) Would appreciate if you update the XSUM dataset to include the instance IDs. \r\n\r\nThe missing instances is also a problem, but likely not worth pursuing given its relatively small scale. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":">So I managed to download them all but when parsing only 226,181\/226,711 worked.\r\n\r\n@lhoestq any chance we could update the HF-hosted dataset with the IDs in your new version? Happy to help if there's something I can do.","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":38,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n >So I managed to download them all but when parsing only 226,181\/226,711 worked.\r\n\r\n@lhoestq any chance we could update the HF-hosted dataset with the IDs in your new version? Happy to help if there's something I can do.","embeddings":[-0.1108686551,-0.2911382914,-0.1378347427,0.4550742209,0.2817803621,0.07282985,0.0544087291,0.0913588926,0.2821643353,0.2655704319,-0.4389287233,0.0682012886,0.2014297694,0.3437097073,0.0978380814,0.098360911,0.0280660875,-0.0688482076,-0.39984712,-0.3520366251,-0.0486405119,0.0790460929,-0.0701801255,-0.1254000813,-0.6150904298,-0.0004532764,-0.2522834837,0.3219082654,-0.0684697777,-0.2613791823,0.2724297941,0.1768980622,0.1588336527,0.585562706,-0.0001079536,-0.0021305003,0.0701949671,-0.0362244174,-0.2027471662,0.2457122058,-0.1254195124,-0.0502531268,-0.104641296,-0.3473931253,-0.041289553,-0.0858429298,-0.1789296269,-0.2968728542,0.2022092491,0.0117187882,0.190333128,0.1982564479,0.1489178538,-0.0657885075,0.0004690879,-0.0772708431,0.1758524776,0.2949224412,0.384034425,0.2338938713,0.0522160083,0.2036968321,0.0793882012,-0.1030988246,0.2630741894,0.0071052993,0.2479682416,-0.1099337339,-0.2626760304,0.3442577422,0.7662885189,-0.008736901,-0.3143163025,-0.0585712045,-0.0513017103,-0.1753983647,0.1823643744,0.0166409016,0.3686720431,-0.0167589299,-0.1219330877,0.2837216258,-0.2416824698,0.083961755,-0.1171373576,-0.0086127352,-0.040566802,0.0076605235,-0.2974540889,-0.0240416266,0.1238147095,-0.136688605,0.1175005734,0.1794935018,-0.3197864294,-0.0899894759,-0.0294952542,-0.0761650801,0.2779528499,0.0087842755,0.1777738482,0.1494703144,-0.4885767996,-0.0050449204,0.2457178682,0.1824131161,0.4060859382,-0.5367921591,0.0424551889,0.0243291669,-0.0892695263,0.0768095329,-0.3691336513,0.0522828586,-0.1361779422,0.0634151101,-0.2079424411,-0.4489310086,-0.243271023,0.2925207913,0.0687269196,-0.11138805,0.10921707,-0.0368537605,0.0220870804,0.4510500431,0.027541237,0.060727492,-0.590329349,-0.4203078747,-0.3149056733,0.1223097816,0.0103718368,-0.0122706341,0.0663291216,-0.1219097227,0.3088372648,-0.1253540367,0.3376320004,-0.0142568406,-0.0368471257,0.0412789471,-0.3466891348,0.2455322295,0.0649793968,0.0139358658,-0.0470767021,0.0491428934,-0.0200307593,-0.0273537301,-0.2576954961,-0.1876059324,-0.0546092503,0.2807949781,-0.2417281121,-0.2601537704,-0.2303065211,0.0229586214,0.0973036513,0.1958481967,-0.2307623178,-0.1498784423,0.102684319,-0.0829291791,0.1686289608,0.0426320396,-0.6364934444,0.0590564907,0.0792019665,-0.245447576,0.0443412624,0.0417248681,-0.0496388674,0.2293722034,0.0753990412,0.1855361462,-0.5582084656,-0.5333790779,-0.1740589887,-0.3230131865,-0.0884199515,-0.0928148702,0.0452460162,0.0335601307,0.0858178064,-0.1337661892,0.0952780396,0.002880926,0.1338361651,-0.2259705216,-0.4646393657,-0.0772188157,-0.1532885283,0.220384568,0.0640577897,-0.2609004378,-0.0373654775,0.4446001947,0.3015833199,-0.0276407544,-0.0262201652,0.1332650185,0.2527402043,-0.0071262028,0.1067680493,-0.1595184207,-0.0529889576,0.2997504473,0.0269468036,0.1723457575,0.7534607649,-0.0607872084,-0.4119504392,-0.2014401853,0.0471296646,-0.1689955145,0.1534011662,-0.1586530507,-0.0525268354,-0.2474772483,-0.1893184781,0.29528144,-0.5699025393,0.0937045962,-0.4114120007,0.4359882176,0.0315888599,0.0111059481,0.1427347958,0.2196141183,0.0349663422,-0.3012127578,-0.1030707881,0.5682691336,0.0972987935,-0.0176486019,0.2086599767,0.1073379517,0.4287082553,-0.1494135261,0.2242486328,-0.2733376026,-0.0340476111,-0.1739271581,-0.3439344168,0.2497535497,-0.0589425489,0.0061067156,-0.0693114549,-0.0251988061,-0.0190039799,-0.1324999183,0.1328186244,-0.0106653543,0.1425020546,0.139212504,-0.2239284813,-0.0485858284,-0.0573377982,0.4266648591,-0.270881027,-0.1722155958,-0.2802174687,-0.2724072933,-0.2439087033,0.1403642148,0.1972509176,-0.1188449189,0.3779392242,0.2638700604,0.0522032343,0.0659703985,-0.0586499833,-0.1196560189,-0.0230520237,0.0211806502,-0.0362943113,-0.0213891231,-0.0011307135,0.0382127762,-0.2089093775,0.0571318232,0.1290062219,0.1677093357,-0.440556258,-0.3460095227,-0.2939581573,-0.1069727689,0.2392285019,0.2096288949,0.0524936654,-0.3409094512,0.1800817102,0.1349406391,-0.0414221548,-0.0222297367,-0.4305756688,0.2545246184,-0.0061297151,0.061091505,0.068220824,-0.0865661651,-0.2389592081,0.1626758426,0.0419396162,0.3007407188,0.3395791352,-0.0517323613,-0.0833902434,0.0332344994,-0.3909251988,0.0608562939,-0.1610302031,0.2292688042,-0.1898724735,0.0489438623,0.0256061349,0.0482384637,0.2835533321,-0.005784139,-0.1941837966,-0.0817633942,0.1162676215,-0.1925490052,0.0099135665,-0.5236006975,-0.1250429451,-0.1238108501,-0.1371329278,0.4118534029,0.2634223998,0.0554481708,-0.1676878631,0.1139132008,0.1504206359,-0.3045866191,-0.3980051875,-0.8720009923,0.3317911029,-0.1492508352,-0.5336837769,0.0280001499,-0.0066597317,0.3115903437,0.0910401195,-0.4845391512,-0.3018910885,0.0788630769,-0.0095073795,0.4274983406,0.1915900409,0.149491176,-0.2446286678,-0.1636426449,-0.2711959779,-0.0776796713,0.2578827441,0.3769550323,0.4586264491,-0.2341265082,-0.269828856,-0.1781576127,0.142458722,0.182148248,-0.0459706411,0.1569336951,0.0226600543,-0.0574579164,-0.000839628,-0.1019757912,0.6396290064,-0.100396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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/672","title":"Questions about XSUM ","comments":"Well I couldn't parse what I downloaded.\r\nUnfortunately I think I won't be able to take a look at it this week.\r\nI can try to send you what I got if you want to give it a shot @jbragg \r\nOtherwise feel free to re-run the xsum download script, maybe you'll be luckier than me","body":"Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n","comment_length":55,"text":"Questions about XSUM  \n Hi there \u270b \r\n\r\nI'm looking into your `xsum` dataset and I have several questions on that. \r\nSo here is how I loaded the data: \r\n```\r\n>>> data = datasets.load_dataset('xsum', version='1.0.1')\r\n>>> data['train']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 204017)\r\n>>> data['test']\r\nDataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, num_rows: 11333)\r\n```\r\n\r\nThe first issue is, the instance counts don\u2019t match what I see on [the dataset's website](https:\/\/github.com\/EdinburghNLP\/XSum\/tree\/master\/XSum-Dataset#what-builds-the-xsum-dataset) (11,333 vs 11,334 for test set; 204,017 vs 204,045 for training set)\r\n```\r\n \u2026 training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) set.\r\n```\r\nAny thoughts why? Perhaps @mariamabarham could help here, since she recently had a PR on this dataaset https:\/\/github.com\/huggingface\/datasets\/pull\/289  (reviewed by @patrickvonplaten) \r\n\r\nAnother issue is that the instances don't seem to have IDs. The original datasets provides IDs for the instances: https:\/\/github.com\/EdinburghNLP\/XSum\/blob\/master\/XSum-Dataset\/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json but to be able to use them, the dataset sizes need to match. \r\n\r\nCC @jbragg \r\n\r\n \n Well I couldn't parse what I downloaded.\r\nUnfortunately I think I won't be able to take a look at it this week.\r\nI can try to send you what I got if you want to give it a shot @jbragg \r\nOtherwise feel free to re-run the xsum download script, maybe you'll be luckier than me","embeddings":[-0.0726336688,-0.4036209285,-0.1623655409,0.5180847049,0.3325610161,0.0285643209,0.1227809936,-0.0330050364,0.2053789347,0.2613717318,-0.2468281239,0.2338201851,0.08773496,0.4263722897,0.1107674837,0.0942095369,-0.0165946502,-0.1213084236,-0.3660371602,-0.309211731,-0.0072388113,0.0955581591,-0.0635270998,-0.1014966741,-0.6309723854,-0.010049222,-0.1623434275,0.2903314233,-0.127583608,-0.2089564949,0.2380593866,0.015602544,0.0412374176,0.5506979227,-0.0001053002,-0.0060283383,0.1273684055,-0.0236889776,-0.0839767903,0.2564916909,-0.1792499125,-0.0246550217,0.0088917082,-0.4071148038,-0.0485546254,0.0278062299,-0.1476608664,-0.3758966327,0.2245383859,0.0081966771,0.2218513936,0.1583904177,0.102225408,-0.0368435942,-0.0639064908,-0.1222761944,0.1160336584,0.2276520431,0.4027490616,0.3057997227,0.0729457438,0.1370126009,0.0607239902,-0.1835125089,0.2962157726,0.0647668242,0.3481562734,-0.2427538484,-0.3049997091,0.3073093593,0.8674853444,-0.0344167873,-0.2513758838,-0.0144357095,-0.0167685356,-0.125263676,0.1652304381,0.08433415,0.3414100111,-0.0318596847,-0.2515144646,0.4186394215,-0.2792619467,0.0277597848,-0.18175973,-0.0714393929,-0.0967195705,0.0065125376,-0.2285345793,-0.0505385026,0.0692124069,-0.1019143388,0.1559629291,0.2080772966,-0.2770183086,-0.1285909116,-0.0161555801,0.0022373509,0.2066086084,0.1455343515,0.2617549002,0.063609913,-0.4012528956,0.028139418,0.2450592816,0.2434323132,0.4585989714,-0.5399466157,-0.0215715785,-0.104981795,-0.0611165352,0.0925377235,-0.3793508708,-0.0102072256,-0.0399882905,0.0393612459,-0.1692170203,-0.3824184239,-0.2278565317,0.2536819875,0.0415282957,-0.1091380641,0.1842543185,-0.1190410331,0.0361397453,0.3936980069,0.0177476201,0.0284670573,-0.5414434075,-0.3072095513,-0.3471832573,0.1222479269,-0.0597676337,-0.0438298061,0.0897295177,-0.0929655731,0.3672470152,-0.1047382131,0.2594784498,-0.0157939512,0.0495465323,0.0980077982,-0.3226242363,0.21496512,0.0601884574,0.001447715,-0.0674227029,0.0415629968,-0.0277159326,0.0168904755,-0.1902839094,-0.2040955424,-0.0045190994,0.3222165406,-0.3959135711,-0.2772588134,-0.190050751,0.0054745013,0.1448358148,0.2303758115,-0.1911048293,-0.1483587325,0.0973166674,-0.1408893615,0.1688584089,-0.0693916529,-0.6325304508,0.008455053,-0.0131408861,-0.3519762754,0.0666960701,-0.0030393882,0.0073680533,0.2608046234,0.1243237108,0.2177847475,-0.5206571221,-0.4857227802,-0.1147995442,-0.342882365,-0.1811395437,-0.1095322073,0.1012083739,0.1172326431,0.0287327711,-0.1006724313,0.1512282938,0.0304637682,0.1303051859,-0.2050456256,-0.4009779692,-0.0654452816,-0.0390540399,0.1878517121,-0.075581044,-0.3016083539,-0.0906671733,0.3413063884,0.2055914253,0.0135360723,-0.1134441122,0.0760913491,0.2956166565,-0.1378966272,0.0739980638,-0.1905764043,-0.0058532055,0.3091693521,0.1006015167,0.2124834657,0.7265692949,-0.0317329057,-0.392546922,-0.2147996426,0.0045553711,-0.1560645103,0.201537624,-0.2022472322,-0.0298810154,-0.2052899301,-0.1852127463,0.1675911546,-0.5990707278,0.0399206355,-0.4966748953,0.3045557141,0.0330085382,0.0258521549,0.0667311251,0.2611116469,-0.0271318741,-0.2616860569,-0.0266548637,0.5314072371,0.1615615189,0.0253946465,0.2021260262,0.178230539,0.36476776,-0.1731300801,0.1725413501,-0.2544903457,-0.0349752009,-0.1862795204,-0.4021555483,0.2673924565,-0.1242140606,0.0038795343,-0.067067638,0.0252663959,0.0050549749,-0.1537599415,0.0060087997,0.0304666776,0.2154404968,0.1709766239,-0.2440113276,0.0506175831,0.0227390714,0.393366456,-0.3404999971,-0.2269741297,-0.2323799133,-0.3499913812,-0.2318678945,0.190097928,0.1133133918,0.0082026757,0.4606724083,0.2917123735,0.0924161077,-0.0096252123,-0.0577662997,-0.144268617,0.0375588201,0.0757214352,-0.0849122852,-0.0752281845,-0.0750228241,0.0251599979,-0.145025298,0.0872807056,0.0913452208,0.1519302875,-0.4459275901,-0.3298214078,-0.2245246619,-0.1108564734,0.2284854352,0.1782716215,0.1340694427,-0.3090158999,0.1391239166,0.0357809365,-0.0282403082,-0.029433988,-0.3591500521,0.3002124131,0.0597737134,0.1229233593,0.1016185582,-0.1588439047,-0.167549029,0.2136395574,0.0532518104,0.3412386179,0.3781739175,-0.094384864,-0.0506140962,0.135437876,-0.4069740474,0.0473472811,-0.2130524218,0.2740540206,-0.1605814695,0.0470891409,0.049077455,-0.0007053016,0.2382680178,-0.0045107426,-0.2650096118,-0.0866273046,0.2043105513,-0.2747786641,-0.0735613704,-0.5671487451,-0.1443151832,-0.0556054525,-0.0253853537,0.4845307767,0.2149478942,0.0677579567,-0.132711947,0.2286450118,0.2270271629,-0.1904092878,-0.369502455,-0.8211238384,0.3219831884,-0.1093275174,-0.527531743,0.044315327,0.0050817188,0.3266919255,0.098662585,-0.3984340131,-0.3188937306,0.0212341603,-0.1178080663,0.4910205305,0.2024039477,0.1742156893,-0.1624936908,-0.169287324,-0.2453816682,-0.0643381551,0.2330094725,0.2933532,0.4438322484,-0.3135499656,-0.3200974464,-0.2105186284,0.0678214356,0.1921275854,-0.1336700916,-0.0430319346,-0.0406823121,-0.1121809408,0.1019943058,-0.118985422,0.6109253168,-0.1167218983,-0.069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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/669","title":"How to skip a example when running dataset.map","comments":"Hi @xixiaoyao,\r\nDepending on what you want to do you can:\r\n- use a first step of `filter` to filter out the invalid examples: https:\/\/huggingface.co\/docs\/datasets\/processing.html#filtering-rows-select-and-filter\r\n- or directly detect the invalid examples inside the callable used with `map` and return them unchanged or even remove them at the same time if you are using `map` in batched mode. Here is an example where we use `map` in batched mode to add new rows on the fly but you can also use it to remove examples on the fly (that's what `filter` actually do under-the-hood): https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset","body":"in processing func, I process examples and detect some invalid examples, which I did not want it to be added into train dataset. However I did not find how to skip this recognized invalid example when doing dataset.map. ","comment_length":95,"text":"How to skip a example when running dataset.map \n in processing func, I process examples and detect some invalid examples, which I did not want it to be added into train dataset. However I did not find how to skip this recognized invalid example when doing dataset.map.  \n Hi @xixiaoyao,\r\nDepending on what you want to do you can:\r\n- use a first step of `filter` to filter out the invalid examples: https:\/\/huggingface.co\/docs\/datasets\/processing.html#filtering-rows-select-and-filter\r\n- or directly detect the invalid examples inside the callable used with `map` and return them unchanged or even remove them at the same time if you are using `map` in batched mode. Here is an example where we use `map` in batched mode to add new rows on the fly but you can also use it to remove examples on the fly (that's what `filter` actually do under-the-hood): https:\/\/huggingface.co\/docs\/datasets\/processing.html#augmenting-the-dataset","embeddings":[-0.3242636323,-0.2246204466,0.031353876,0.0002581653,0.0896153748,0.3332220614,0.0300699323,0.1055706441,0.1422973573,0.2520426214,0.6323099732,0.4480103552,-0.2600845993,0.4908131957,0.0828728154,-0.0856683031,-0.0504853204,0.0724819228,0.1984967291,0.1779389232,-0.3641427457,-0.0961724371,-0.49770239,0.0496413969,-0.3158500195,-0.2935382724,-0.0769769698,0.140801385,-0.0265658572,-0.2878863811,0.0960213989,-0.0466132201,0.0982665867,0.504729867,-0.0001258959,0.1196550578,0.3082424402,-0.0945355967,0.1854922622,-0.3107355237,-0.3292454779,0.1670228541,-0.1726574451,-0.2398432195,-0.0762515888,-0.0612976849,0.0840196982,-0.0997303799,0.4658529162,0.0540934764,0.0529715009,0.2320326865,-0.3186951876,0.0486841574,-0.1116908491,0.409217,0.0898966044,0.2354432493,0.1143718734,-0.0532132871,0.0675040632,0.231179595,-0.1761514395,-0.0097621782,0.1850004047,-0.1228409261,0.1621088386,-0.3764846027,0.0962039158,0.4493292272,0.22457169,-0.126456663,-0.2365824878,-0.3658840358,-0.0463603511,-0.2754533589,-0.016118804,-0.1472744197,-0.015307745,0.4170869887,-0.6674239635,-0.1911608577,-0.1037272587,0.061202269,0.2733073235,-0.179596588,-0.0818134025,0.2964392006,0.329721123,0.1335158646,-0.2808516026,-0.0747990385,0.3179302514,0.3472799659,0.0003408075,-0.3308746219,0.0062255142,0.197625652,0.0249322187,-0.325789988,0.0555800684,0.1102719903,0.1363181025,0.5397353172,0.0191038679,-0.2823091745,0.0233992338,0.3064346015,0.2565121949,0.1218458638,-0.0133249564,0.1166886836,-0.2119525075,-0.1256945282,-0.2269021273,0.2246917337,0.0821375549,-0.1516428441,-0.1834163517,-0.002640435,-0.4451543987,-0.0977713987,0.1742839068,0.491938293,0.0618027374,-0.2413509339,-0.3540638685,0.1922273487,-0.0949275643,-0.1513017118,-0.0967249796,0.0498009995,-0.1144316345,0.1790896356,0.159690544,-0.0792608336,0.4232955277,0.068015404,-0.2709647715,0.1603313386,0.689712286,-0.1699372232,0.1788068265,0.3412056863,-0.0605335385,-0.057755243,0.2295355052,0.3670894206,-0.0047913282,0.35683617,-0.3024683893,-0.4309857786,0.3180296123,-0.0142080281,-0.0673328266,0.2039246112,-0.5382909179,0.346475184,0.2190382332,-0.4069853425,-0.0032906057,0.1302217692,-0.5322643518,-0.1984508038,0.1176695004,0.1650928259,-0.3112281263,-0.2914988697,-0.2650072873,0.0359472446,0.1033580974,-0.0010721558,-0.3380072117,0.480039537,-0.3783349693,0.1835429221,0.2497921884,-0.2121672928,0.0564303584,0.0568875559,-0.1763331294,0.0893616974,-0.3198416233,-0.1334590614,0.3183126152,-0.1414415985,-0.045973625,0.1065726951,-0.3901832104,-0.089728564,0.0181747284,-0.0168863405,0.5136342645,-0.2013853341,-0.2183645666,0.1387318671,-0.2506180406,-0.3810956776,0.2124197483,-0.138144061,-0.103997089,0.1810707599,0.3804059625,-0.3077709079,0.1553096473,-0.3345043361,-0.0102507612,0.1763610542,0.0401231162,-0.3803938627,0.2575185299,-0.5252299905,-0.1265999824,-0.1873489618,-0.0143144531,-0.0288681462,-0.0561559647,-0.3807054758,0.097371012,-0.1782945395,-0.3164161742,0.0432511419,-0.2925288081,0.3105837703,-0.1860637665,-0.025866719,0.2752487659,-0.0649267435,0.0075345528,-0.0372882746,0.2593930662,-0.4081422985,0.0443684459,0.3251231015,0.4812693298,0.1775559485,-0.0790168941,0.0932360664,0.1937626302,-0.3268751204,-0.2090979517,0.0282353032,-0.0245819092,0.0280555412,-0.2407762855,0.3548715115,0.1204803661,0.125122726,0.1026158258,0.1530056596,0.0357429534,-0.1511326283,0.0913808271,-0.1911781132,0.0256277956,0.058819484,0.1974120438,0.1561806053,-0.1957836747,-0.1475310922,0.4238241911,0.0040207244,-0.1040827483,0.2210841328,0.0002531631,-0.0520118102,0.5704846382,0.0326652527,0.0618723854,0.1499921381,0.0290911198,0.0220050458,-0.3287629783,0.2473180741,0.0675081685,0.1988274008,0.4481800199,-0.335295558,0.00959526,0.1830748469,-0.1451720893,-0.057996545,0.3105287254,0.0239243004,-0.3376735747,0.1957659423,-0.2525347173,-0.1397378743,0.1195069104,0.0803660005,-0.1787724644,-0.1134715974,0.0619538501,0.4838314056,-0.2872804701,0.2964291573,0.0118209347,0.12412972,-0.1833330095,-0.2632946372,-0.0218229517,0.1122780442,-0.2322319001,0.061067149,0.1845856011,0.4620588124,0.5137461424,-0.0903162137,-0.1922500432,0.0471119173,-0.5107018948,0.1798775494,-0.0647185072,0.2261238694,0.2627539635,0.2396679968,-0.5291053057,0.0256425068,-0.1005804241,-0.2393880635,-0.1765174717,-0.1198151186,0.025914276,0.3449905217,-0.2298613489,-0.2000429034,-0.0995236337,-0.0450547412,0.1051680222,-0.2208750397,0.1166531295,-0.1009434164,0.0337652154,0.219216004,-0.0252638608,-0.3246285617,-0.0130740255,-0.3057721257,-0.0206060037,-0.2947101891,0.048565127,0.2048910707,-0.3035925031,0.0695242584,0.3210887015,-0.174884811,-0.4121911526,-0.1730241477,0.0444512405,-0.1805033684,-0.3683572114,0.5422813892,0.0257188454,0.073286593,-0.310159713,-0.3047010899,0.2872392237,0.2302464396,0.0790903941,0.4050255418,0.586245358,0.1559972316,0.6632351279,0.3587724268,-0.0044967737,0.2946740687,-0.0173923522,0.110812313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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/667","title":"Loss not decrease with Datasets and Transformers","comments":"Hi did you manage to fix your issue ?\r\n\r\nIf so feel free to share your fix and close this thread","body":"HI,\r\n\r\nThe following script is used to fine-tune a BertForSequenceClassification model on SST2.\r\n\r\nThe script is adapted from [this colab](https:\/\/colab.research.google.com\/github\/huggingface\/datasets\/blob\/master\/notebooks\/Overview.ipynb) that presents an example of fine-tuning BertForQuestionAnswering using squad dataset. In that colab, loss works fine. When I adapt it to SST2, the loss fails to decrease as it should. I attach the adapted script below and appreciate anyone pointing out what I miss?\r\n\r\n```python\r\nimport torch\r\nfrom datasets import load_dataset\r\nfrom transformers import BertForSequenceClassification\r\nfrom transformers import BertTokenizerFast\r\n# Load our training dataset and tokenizer\r\ndataset = load_dataset(\"glue\", 'sst2')\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\ndel dataset[\"test\"] # let's remove it in this demo\r\n\r\n# Tokenize our training dataset\r\ndef convert_to_features(example_batch):\r\n    encodings = tokenizer(example_batch[\"sentence\"])\r\n    encodings.update({\"labels\": example_batch[\"label\"]})\r\n    return encodings\r\n\r\nencoded_dataset = dataset.map(convert_to_features, batched=True)\r\n# Format our dataset to outputs torch.Tensor to train a pytorch model\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'labels']\r\nencoded_dataset.set_format(type='torch', columns=columns)\r\n\r\n# Instantiate a PyTorch Dataloader around our dataset\r\n# Let's do dynamic batching (pad on the fly with our own collate_fn)\r\ndef collate_fn(examples):\r\n    return tokenizer.pad(examples, return_tensors='pt')\r\n\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset['train'], collate_fn=collate_fn, batch_size=8)\r\n# Now let's train our model\r\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\r\n# Let's load a pretrained Bert model and a simple optimizer\r\nmodel = BertForSequenceClassification.from_pretrained('bert-base-cased', return_dict=True)\r\noptimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\r\nmodel.train().to(device)\r\nfor i, batch in enumerate(dataloader):\r\n    batch.to(device)\r\n    outputs = model(**batch)\r\n    loss = outputs.loss\r\n    loss.backward()\r\n    optimizer.step()\r\n    model.zero_grad()\r\n    print(f'Step {i} - loss: {loss:.3}')\r\n\r\n\r\n```\r\nIn case needed.\r\n\r\n- datasets == 1.0.2\r\n- transformers == 3.2.0","comment_length":21,"text":"Loss not decrease with Datasets and Transformers \n HI,\r\n\r\nThe following script is used to fine-tune a BertForSequenceClassification model on SST2.\r\n\r\nThe script is adapted from [this colab](https:\/\/colab.research.google.com\/github\/huggingface\/datasets\/blob\/master\/notebooks\/Overview.ipynb) that presents an example of fine-tuning BertForQuestionAnswering using squad dataset. In that colab, loss works fine. When I adapt it to SST2, the loss fails to decrease as it should. I attach the adapted script below and appreciate anyone pointing out what I miss?\r\n\r\n```python\r\nimport torch\r\nfrom datasets import load_dataset\r\nfrom transformers import BertForSequenceClassification\r\nfrom transformers import BertTokenizerFast\r\n# Load our training dataset and tokenizer\r\ndataset = load_dataset(\"glue\", 'sst2')\r\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')\r\ndel dataset[\"test\"] # let's remove it in this demo\r\n\r\n# Tokenize our training dataset\r\ndef convert_to_features(example_batch):\r\n    encodings = tokenizer(example_batch[\"sentence\"])\r\n    encodings.update({\"labels\": example_batch[\"label\"]})\r\n    return encodings\r\n\r\nencoded_dataset = dataset.map(convert_to_features, batched=True)\r\n# Format our dataset to outputs torch.Tensor to train a pytorch model\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'labels']\r\nencoded_dataset.set_format(type='torch', columns=columns)\r\n\r\n# Instantiate a PyTorch Dataloader around our dataset\r\n# Let's do dynamic batching (pad on the fly with our own collate_fn)\r\ndef collate_fn(examples):\r\n    return tokenizer.pad(examples, return_tensors='pt')\r\n\r\ndataloader = torch.utils.data.DataLoader(encoded_dataset['train'], collate_fn=collate_fn, batch_size=8)\r\n# Now let's train our model\r\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\r\n# Let's load a pretrained Bert model and a simple optimizer\r\nmodel = BertForSequenceClassification.from_pretrained('bert-base-cased', return_dict=True)\r\noptimizer = torch.optim.Adam(model.parameters(), lr=1e-5)\r\nmodel.train().to(device)\r\nfor i, batch in enumerate(dataloader):\r\n    batch.to(device)\r\n    outputs = model(**batch)\r\n    loss = outputs.loss\r\n    loss.backward()\r\n    optimizer.step()\r\n    model.zero_grad()\r\n    print(f'Step {i} - loss: {loss:.3}')\r\n\r\n\r\n```\r\nIn case needed.\r\n\r\n- datasets == 1.0.2\r\n- transformers == 3.2.0 \n Hi did you manage to fix your issue ?\r\n\r\nIf so feel free to share your fix and close this thread","embeddings":[0.0143272402,-0.0178403128,0.0766257048,0.2709603906,0.1909327656,-0.2114582062,0.3216443956,0.1833657622,-0.3186629415,0.1440708935,-0.1105745062,0.2251771837,0.0432985947,-0.3711665571,-0.3412014246,-0.5152077675,0.092767708,0.1171909422,-0.4170707762,-0.3085789084,0.2463035882,0.148686111,-0.5372670889,-0.2332571298,-0.4022983611,0.2082428634,-0.0268376134,0.08499185,-0.1229775921,-0.1508571804,0.4350038171,0.2246644944,0.6234452128,0.5011128783,-0.0001253695,0.0167352278,0.0884683058,-0.1851132661,-0.0964059457,0.1887830347,0.0095063001,-0.0720067918,0.0785151944,-0.2393475473,-0.2013340443,0.5764876008,-0.0589542091,-0.1579336375,0.5676104426,0.0713826045,0.0511577688,0.0994233415,-0.3086315989,0.0978987366,-0.0312880613,0.1299947351,-0.2217846066,-0.1588303745,0.142397061,-0.2430086881,-0.2492354661,0.244248569,-0.0914825723,0.1282944679,0.4990986884,0.2129201293,-0.3659015596,-0.2715988755,0.2734735608,0.0496383347,0.3472603858,-0.4256628454,-0.4216351211,-0.1504353583,0.1279190332,0.0961998925,0.0465043746,-0.1955449581,0.0177183952,0.0964226946,-0.1567200869,-0.2207983583,-0.4685472846,0.0264698416,0.091796726,0.4098714292,-0.0062107272,-0.1192714646,-0.1143856943,-0.3491779566,-0.1651540697,0.0337856859,0.1241988763,0.1486433446,-0.5537847281,-0.1846330315,-0.1117318422,-0.1745158732,-0.027535744,-0.3231819272,0.0780724362,0.101626128,0.0821538866,-0.1564653516,0.1735979915,0.3648107648,0.0943130329,0.4102594852,0.1208897606,0.1123721153,-0.2615919411,0.0032639012,-0.1972545534,-0.1704994887,0.3708522618,0.0576061383,0.3201502264,-0.192386955,-0.4567261934,-0.0554664135,-0.3279343843,-0.2216428518,0.0916512385,0.2830949724,-0.1445198804,0.2163469046,-0.1008516848,0.2693802416,-0.3416719139,0.0706471205,-0.0472738519,-0.0374250114,-0.5051249862,0.0723484084,0.3726485968,0.217414096,0.2313282341,0.0451216027,0.121455878,0.25222525,-0.0618577823,-0.0985316113,0.3288626969,-0.1315164566,-0.2866594195,0.0891968161,-0.2206839323,0.0552942418,0.0595232844,0.1287122816,-0.3794058859,0.2103689462,-0.0756791532,-0.0523015335,-0.0269694477,-0.0535664596,-0.1378819942,0.3341746628,0.1426265985,-0.1533270776,0.1410789341,-0.6094964743,-0.3426013589,-0.0242808796,0.5197733641,0.5062796474,-0.4620134532,-0.3242632151,0.2939985991,0.2669239342,0.5352307558,0.565192163,-0.1079585701,0.0349414162,0.0557246432,0.2512503266,0.1858744025,-0.2093686908,-0.7334695458,-0.0830294788,-0.0441935062,0.0869677886,0.2217092514,-0.1311743557,0.4703834951,-0.2120390534,-0.1244111434,-0.2151397169,-0.1037929729,0.1159090176,-0.2668857276,0.1395716518,0.4136345685,0.0328764208,0.1747836322,-0.0320379063,-0.1260010004,0.6209384799,0.2226442546,-0.1818389893,0.2899461389,0.0643967465,0.1717638671,-0.2174842805,0.2140316069,0.0806370005,-0.3901189268,0.2001280487,-0.1324909627,0.1447695047,0.014918928,0.0626084656,0.0884095952,-0.2703787982,-0.11363592,-0.1285360157,0.0465300828,0.0782191381,-0.1111471504,-0.4716425538,-0.183214739,-0.119209975,0.169731304,0.0719178542,-0.0869846269,0.3454029858,0.02591029,-0.4336415529,0.0098216785,0.1193503886,0.3469047248,-0.0527056307,-0.1413051337,0.19168441,-0.0627031326,0.1314253807,0.2388147414,0.3168720007,0.4315247536,-0.3996690214,0.277092427,0.4996348917,-0.0656077415,0.1053966805,-0.2424380183,-0.0199280195,0.010152882,0.1996157169,-0.0707076713,-0.2372710705,-0.172502771,-0.1236380488,-0.3080772758,0.1097653583,-0.3041394651,-0.1481756121,0.2156849056,0.06784226,-0.1514199674,0.1748232841,0.3401047885,0.0692100078,0.0954236612,0.2409140021,-0.4496682882,-0.2235210687,0.1370737255,-0.016748609,0.4678155184,-0.0258350112,0.1285744309,-0.1111882403,0.2357772738,-0.1046387479,-0.0667033121,0.301696986,-0.1883014292,0.1050804108,0.3168638051,0.3701637387,-0.0432829969,-0.0838378072,-0.0384021848,0.173660785,-0.2928595245,0.3218758106,-0.2582605183,0.06121286,-0.2138753384,-0.0527619421,0.1186532378,0.1473302543,-0.062692143,-0.0735690594,-0.047246471,0.2227206081,0.0347405151,0.3342996836,0.3281894624,-0.218313694,-0.0666736886,-0.0572618097,-0.0280947909,0.0324906334,-0.04106858,0.0276342891,-0.2657498717,-0.0736551881,-0.1680519432,-0.2551649809,-0.3171067834,0.070417881,-0.1802714169,-0.1311718225,0.2053000927,-0.0421734005,-0.3728771806,-0.2056106925,-0.0110588847,-0.3334241807,0.0961776599,0.1580109149,-0.1058861613,-0.2090536803,0.0334984735,-0.3507479429,0.1322992742,0.1283809543,-0.1099347919,-0.1493009031,-0.1089204922,0.3558456004,-0.0532984845,-0.0475715585,0.029614469,0.2345341742,0.1703339964,-0.3580476344,0.4893873334,-0.0219131485,-0.224332422,-0.1758491248,0.0223402623,0.1377399266,-0.0775631294,0.0666945949,-0.2440136224,-0.2023453563,0.1336150765,-0.1854813993,0.3551799953,0.3544382453,0.1523952782,0.110573411,-0.1908690035,-0.4509736896,0.3485206068,0.3832609057,0.4323945642,-0.4216374457,0.1823228896,0.0361409448,0.9876065254,0.1043779552,-0.335876137,0.2449721843,0.1232649684,-0.0912315398,0.040585354,-0.361766547,0.5099081993,-0.0015320354,-0.08531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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/665","title":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects","comments":"Hi !\r\nIt works on my side with both the LongFormerTokenizer and the LongFormerTokenizerFast.\r\n\r\nWhich version of transformers\/datasets are you using ?","body":"I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n","comment_length":22,"text":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects \n I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n \n Hi !\r\nIt works on my side with both the LongFormerTokenizer and the LongFormerTokenizerFast.\r\n\r\nWhich version of transformers\/datasets are you using ?","embeddings":[-0.2387796193,-0.2978491187,-0.019680649,0.2330349237,0.4450323582,-0.1729919016,0.2851519585,0.1309388727,-0.2760912478,0.1103135347,-0.0697863474,0.4891909063,-0.0087260343,-0.046733927,-0.145709604,-0.111473918,0.0535604171,0.207853511,0.0181137323,-0.0561074987,-0.2787823677,0.2074424773,-0.4731775522,0.1585230231,-0.2825988233,-0.2674281299,0.1064047962,-0.1985800564,-0.2693119049,-0.3823218942,-0.1436450779,-0.073365584,-0.0288482774,0.5633369088,-0.0001192604,0.0994183272,0.108954519,-0.145972386,0.0052242242,-0.0495663248,-0.0748076513,-0.3035497069,-0.205873996,-0.4582723975,-0.1556460112,-0.1438439935,0.0111553641,-0.3324888647,0.5145235062,0.2914209962,0.139978081,0.4932267964,0.0206352938,0.0532785431,0.015327774,0.0298241358,-0.1314535141,-0.2608632743,0.1487917304,0.2165469229,-0.1302717179,0.3254893422,-0.1613031477,-0.2627820969,0.0994700417,0.094937481,0.1041127741,-0.4675594568,0.3230481744,-0.002158023,0.2189722508,-0.020810321,-0.2567602098,-0.2679677308,-0.0794852078,0.0625912696,-0.0412523523,-0.2022152543,0.0776130408,0.0512863845,-0.3526940644,-0.1723531634,-0.1798734814,0.108233422,0.0383866839,0.3674840629,-0.2666110098,0.2930521965,0.0967984498,-0.2501663864,-0.1138216779,-0.0991195515,0.2264450341,0.4768724144,-0.3883422315,-0.3008864224,-0.0943802446,-0.409999162,0.3477460146,-0.0384070538,-0.2276796252,0.2554124892,-0.0489559509,0.1893791109,0.0231759958,0.3520210385,0.2632215321,0.3699644506,0.0116726784,-0.1440164447,0.0652595013,-0.04887354,-0.0822229534,-0.2215761691,-0.0569127798,0.2582862377,0.3595126569,-0.0186132845,-0.046238631,0.0278620608,-0.2262803167,0.0427433066,0.1734637767,0.4068900049,0.1080137938,0.2318411618,-0.3183892965,0.0902408659,-0.124280192,0.0253062062,-0.1654862761,0.214023307,-0.0066213412,-0.1393794417,-0.1643196195,0.0698126107,0.2013424933,-0.026837565,0.0632021129,-0.2202773243,0.061538171,-0.1790308952,0.2758253217,-0.0943112522,-0.0502242185,0.1737568676,0.2081712335,-0.40948084,-0.2060563862,0.0352333188,-0.34115237,0.01733163,0.1311888844,0.1185405329,0.0562376231,0.0476904213,-0.3749628663,0.2920556068,0.4146037698,0.0075913849,0.0322678164,-0.1267381161,-0.2016420662,-0.111127086,0.2182486653,0.1899360716,-0.1373286545,-0.2079769075,0.2503727973,-0.002062849,0.1535669416,0.4049597979,-0.0530274957,0.2562201917,-0.1467852145,0.559774518,0.4704823494,-0.6255075336,-0.5309312344,0.1032184213,-0.4468844831,-0.1779434234,-0.035346359,0.0416382365,0.5613888502,0.1241554692,-0.113206543,0.3392548561,0.072295852,0.2676001787,-0.1563941687,-0.1415297985,0.4092992246,0.0926817507,0.21709764,0.116488941,-0.1682532728,0.1971376091,-0.1022469327,-0.1236947551,0.0914442316,0.1902052313,0.1654276252,-0.2108140886,0.0806120411,-0.1629784405,-0.5545789599,0.0651864186,-0.3476859927,0.2234149575,-0.2890501916,-0.194772318,-0.2887653708,0.1541353166,-0.4519188404,-0.0524003021,0.083847262,-0.0306146815,0.1889378428,-0.0136042386,0.0192883443,-0.0761402398,0.1458211094,0.186094299,-0.2552472055,0.2062030882,-0.2569340169,0.021148419,-0.05973031,0.09906286,0.408500433,-0.1759835035,-0.2551455796,0.2755496502,0.1720520854,-0.0848676786,-0.0500598922,-0.0350354798,0.2658101916,-0.2096956968,-0.0644069538,0.1785884053,0.166718334,-0.0287600234,-0.0929355696,0.4431660473,0.224703446,0.3178621531,-0.0191053115,0.1625137478,0.2412854582,-0.10274712,-0.0464949757,-0.1355438232,-0.1764924526,0.0628632903,0.2340398133,-0.0736531839,0.0656425655,-0.0590749644,0.8479928374,-0.0683462247,0.0793827847,0.204474166,-0.116351068,0.0497056618,-0.0788842216,-0.4018104076,0.0417372212,0.0392285287,-0.1734893918,-0.1066208854,0.2546639144,0.1459451169,0.041525092,0.3150502145,0.1146959588,0.2255442888,0.1599868983,0.1129512191,-0.1720335782,-0.1941431463,0.1629729271,0.1452347934,-0.1468617916,-0.0244321302,-0.0460567623,0.0444771908,0.0104709063,-0.0486729778,-0.195032984,-0.2504672706,0.0337895602,-0.0237582624,0.0957349986,0.4495555162,0.3668554127,0.3240292668,0.427821368,-0.1099201962,-0.170441404,-0.1969794482,-0.0876939595,-0.0677347481,0.1707262248,-0.0053565041,-0.0357478224,0.0103845401,0.1024920642,-0.456127733,-0.4291423857,0.2537454963,-0.178284511,0.1860092133,-0.0141354213,0.1210128963,-0.3429493308,-0.2921084464,0.2118502706,-0.2194129676,-0.3013744652,0.0706816763,-0.0610883981,-0.3209867477,0.0511774272,-0.2405867577,-0.3765725493,-0.3376917541,0.363016367,-0.008248332,0.1135794222,0.1541582793,-0.0030258943,0.2885194421,-0.0200715531,-0.0359078087,-0.2440013885,0.0543059148,0.4122084975,-0.1826187968,-0.2826860547,-0.3915768564,-0.1759163439,0.0525789037,-0.1293791682,-0.1020923853,-0.2275468558,-0.2711945474,0.2238460481,-0.1608631462,0.0458878055,0.558042407,-0.0492363572,0.0907655656,0.0115672005,-0.1609315425,0.2720036209,0.1358186603,0.0812894851,-0.0107793091,0.5259879827,0.1519594789,0.8704359531,0.1582149416,-0.2194377482,0.1683554202,-0.1485036314,-0.010637085,-0.0340552628,-0.3892847896,0.1844976842,-0.0443505682,0.1226070747,0.139588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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/665","title":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects","comments":"Then I guess you need to give us more informations on your setup (OS, python, GPU, etc) or a Google Colab reproducing the error for us to be able to debug this error.","body":"I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n","comment_length":33,"text":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects \n I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n \n Then I guess you need to give us more informations on your setup (OS, python, GPU, etc) or a Google Colab reproducing the error for us to be able to debug this error.","embeddings":[-0.2387796193,-0.2978491187,-0.019680649,0.2330349237,0.4450323582,-0.1729919016,0.2851519585,0.1309388727,-0.2760912478,0.1103135347,-0.0697863474,0.4891909063,-0.0087260343,-0.046733927,-0.145709604,-0.111473918,0.0535604171,0.207853511,0.0181137323,-0.0561074987,-0.2787823677,0.2074424773,-0.4731775522,0.1585230231,-0.2825988233,-0.2674281299,0.1064047962,-0.1985800564,-0.2693119049,-0.3823218942,-0.1436450779,-0.073365584,-0.0288482774,0.5633369088,-0.0001192604,0.0994183272,0.108954519,-0.145972386,0.0052242242,-0.0495663248,-0.0748076513,-0.3035497069,-0.205873996,-0.4582723975,-0.1556460112,-0.1438439935,0.0111553641,-0.3324888647,0.5145235062,0.2914209962,0.139978081,0.4932267964,0.0206352938,0.0532785431,0.015327774,0.0298241358,-0.1314535141,-0.2608632743,0.1487917304,0.2165469229,-0.1302717179,0.3254893422,-0.1613031477,-0.2627820969,0.0994700417,0.094937481,0.1041127741,-0.4675594568,0.3230481744,-0.002158023,0.2189722508,-0.020810321,-0.2567602098,-0.2679677308,-0.0794852078,0.0625912696,-0.0412523523,-0.2022152543,0.0776130408,0.0512863845,-0.3526940644,-0.1723531634,-0.1798734814,0.108233422,0.0383866839,0.3674840629,-0.2666110098,0.2930521965,0.0967984498,-0.2501663864,-0.1138216779,-0.0991195515,0.2264450341,0.4768724144,-0.3883422315,-0.3008864224,-0.0943802446,-0.409999162,0.3477460146,-0.0384070538,-0.2276796252,0.2554124892,-0.0489559509,0.1893791109,0.0231759958,0.3520210385,0.2632215321,0.3699644506,0.0116726784,-0.1440164447,0.0652595013,-0.04887354,-0.0822229534,-0.2215761691,-0.0569127798,0.2582862377,0.3595126569,-0.0186132845,-0.046238631,0.0278620608,-0.2262803167,0.0427433066,0.1734637767,0.4068900049,0.1080137938,0.2318411618,-0.3183892965,0.0902408659,-0.124280192,0.0253062062,-0.1654862761,0.214023307,-0.0066213412,-0.1393794417,-0.1643196195,0.0698126107,0.2013424933,-0.026837565,0.0632021129,-0.2202773243,0.061538171,-0.1790308952,0.2758253217,-0.0943112522,-0.0502242185,0.1737568676,0.2081712335,-0.40948084,-0.2060563862,0.0352333188,-0.34115237,0.01733163,0.1311888844,0.1185405329,0.0562376231,0.0476904213,-0.3749628663,0.2920556068,0.4146037698,0.0075913849,0.0322678164,-0.1267381161,-0.2016420662,-0.111127086,0.2182486653,0.1899360716,-0.1373286545,-0.2079769075,0.2503727973,-0.002062849,0.1535669416,0.4049597979,-0.0530274957,0.2562201917,-0.1467852145,0.559774518,0.4704823494,-0.6255075336,-0.5309312344,0.1032184213,-0.4468844831,-0.1779434234,-0.035346359,0.0416382365,0.5613888502,0.1241554692,-0.113206543,0.3392548561,0.072295852,0.2676001787,-0.1563941687,-0.1415297985,0.4092992246,0.0926817507,0.21709764,0.116488941,-0.1682532728,0.1971376091,-0.1022469327,-0.1236947551,0.0914442316,0.1902052313,0.1654276252,-0.2108140886,0.0806120411,-0.1629784405,-0.5545789599,0.0651864186,-0.3476859927,0.2234149575,-0.2890501916,-0.194772318,-0.2887653708,0.1541353166,-0.4519188404,-0.0524003021,0.083847262,-0.0306146815,0.1889378428,-0.0136042386,0.0192883443,-0.0761402398,0.1458211094,0.186094299,-0.2552472055,0.2062030882,-0.2569340169,0.021148419,-0.05973031,0.09906286,0.408500433,-0.1759835035,-0.2551455796,0.2755496502,0.1720520854,-0.0848676786,-0.0500598922,-0.0350354798,0.2658101916,-0.2096956968,-0.0644069538,0.1785884053,0.166718334,-0.0287600234,-0.0929355696,0.4431660473,0.224703446,0.3178621531,-0.0191053115,0.1625137478,0.2412854582,-0.10274712,-0.0464949757,-0.1355438232,-0.1764924526,0.0628632903,0.2340398133,-0.0736531839,0.0656425655,-0.0590749644,0.8479928374,-0.0683462247,0.0793827847,0.204474166,-0.116351068,0.0497056618,-0.0788842216,-0.4018104076,0.0417372212,0.0392285287,-0.1734893918,-0.1066208854,0.2546639144,0.1459451169,0.041525092,0.3150502145,0.1146959588,0.2255442888,0.1599868983,0.1129512191,-0.1720335782,-0.1941431463,0.1629729271,0.1452347934,-0.1468617916,-0.0244321302,-0.0460567623,0.0444771908,0.0104709063,-0.0486729778,-0.195032984,-0.2504672706,0.0337895602,-0.0237582624,0.0957349986,0.4495555162,0.3668554127,0.3240292668,0.427821368,-0.1099201962,-0.170441404,-0.1969794482,-0.0876939595,-0.0677347481,0.1707262248,-0.0053565041,-0.0357478224,0.0103845401,0.1024920642,-0.456127733,-0.4291423857,0.2537454963,-0.178284511,0.1860092133,-0.0141354213,0.1210128963,-0.3429493308,-0.2921084464,0.2118502706,-0.2194129676,-0.3013744652,0.0706816763,-0.0610883981,-0.3209867477,0.0511774272,-0.2405867577,-0.3765725493,-0.3376917541,0.363016367,-0.008248332,0.1135794222,0.1541582793,-0.0030258943,0.2885194421,-0.0200715531,-0.0359078087,-0.2440013885,0.0543059148,0.4122084975,-0.1826187968,-0.2826860547,-0.3915768564,-0.1759163439,0.0525789037,-0.1293791682,-0.1020923853,-0.2275468558,-0.2711945474,0.2238460481,-0.1608631462,0.0458878055,0.558042407,-0.0492363572,0.0907655656,0.0115672005,-0.1609315425,0.2720036209,0.1358186603,0.0812894851,-0.0107793091,0.5259879827,0.1519594789,0.8704359531,0.1582149416,-0.2194377482,0.1683554202,-0.1485036314,-0.010637085,-0.0340552628,-0.3892847896,0.1844976842,-0.0443505682,0.1226070747,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/665","title":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects","comments":"I have the same issue with `transformers\/BertJapaneseTokenizer`.\r\n\r\n\r\n\r\n```python\r\n# train_ds = Dataset(features: {\r\n#     'title': Value(dtype='string', id=None), \r\n#     'score': Value(dtype='float64', id=None)\r\n# }, num_rows: 99999)\r\n\r\nt = BertJapaneseTokenizer.from_pretrained('bert-base-japanese-whole-word-masking')\r\nencoded = train_ds.map(lambda examples: {'tokens': t.encode(examples['title'])}, batched=True)\r\n```\r\n\r\n<details><summary>Error Message<\/summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-35-2b7d66b291c1> in <module>\r\n      2 \r\n      3 encoded = train_ds.map(lambda examples:\r\n----> 4   {'tokens': t.encode(examples['title'])}, batched=True)\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in dump(self, obj)\r\n    407         if self.proto >= 4:\r\n    408             self.framer.start_framing()\r\n--> 409         self.save(obj)\r\n    410         self.write(STOP)\r\n    411         self.framer.end_framing()\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    608         else:\r\n    609             save(func)\r\n--> 610             save(args)\r\n    611             write(REDUCE)\r\n    612 \r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_tuple(self, obj)\r\n    749         write(MARK)\r\n    750         for element in obj:\r\n--> 751             save(element)\r\n    752 \r\n    753         if id(obj) in memo:\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_dict(self, obj)\r\n    819 \r\n    820         self.memoize(obj)\r\n--> 821         self._batch_setitems(obj.items())\r\n    822 \r\n    823     dispatch[dict] = save_dict\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in _batch_setitems(self, items)\r\n    850                 k, v = tmp[0]\r\n    851                 save(k)\r\n--> 852                 save(v)\r\n    853                 write(SETITEM)\r\n    854             # else tmp is empty, and we're done\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    519 \r\n    520         # Save the reduce() output and finally memoize the object\r\n--> 521         self.save_reduce(obj=obj, *rv)\r\n    522 \r\n    523     def persistent_id(self, obj):\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    632 \r\n    633         if state is not None:\r\n--> 634             save(state)\r\n    635             write(BUILD)\r\n    636 \r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_dict(self, obj)\r\n    819 \r\n    820         self.memoize(obj)\r\n--> 821         self._batch_setitems(obj.items())\r\n    822 \r\n    823     dispatch[dict] = save_dict\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in _batch_setitems(self, items)\r\n    845                 for k, v in tmp:\r\n    846                     save(k)\r\n--> 847                     save(v)\r\n    848                 write(SETITEMS)\r\n    849             elif n:\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    519 \r\n    520         # Save the reduce() output and finally memoize the object\r\n--> 521         self.save_reduce(obj=obj, *rv)\r\n    522 \r\n    523     def persistent_id(self, obj):\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    632 \r\n    633         if state is not None:\r\n--> 634             save(state)\r\n    635             write(BUILD)\r\n    636 \r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_dict(self, obj)\r\n    819 \r\n    820         self.memoize(obj)\r\n--> 821         self._batch_setitems(obj.items())\r\n    822 \r\n    823     dispatch[dict] = save_dict\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in _batch_setitems(self, items)\r\n    845                 for k, v in tmp:\r\n    846                     save(k)\r\n--> 847                     save(v)\r\n    848                 write(SETITEMS)\r\n    849             elif n:\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    494             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    495             if reduce is not None:\r\n--> 496                 rv = reduce(self.proto)\r\n    497             else:\r\n    498                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tagger objects\r\n```\r\n\r\n<\/details>\r\n\r\ntrainsformers: 2.10.0\r\ndatasets: 1.0.2\r\ndill: 0.3.2\r\npython: 3.6.8\r\n\r\nOS: ubuntu 16.04 (Docker Image) on [Deep Learning VM](https:\/\/console.cloud.google.com\/marketplace\/details\/click-to-deploy-images\/deeplearning) (GCP)\r\nGPU: Tesla P100 (CUDA 10)\r\n","body":"I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n","comment_length":861,"text":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects \n I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n \n I have the same issue with `transformers\/BertJapaneseTokenizer`.\r\n\r\n\r\n\r\n```python\r\n# train_ds = Dataset(features: {\r\n#     'title': Value(dtype='string', id=None), \r\n#     'score': Value(dtype='float64', id=None)\r\n# }, num_rows: 99999)\r\n\r\nt = BertJapaneseTokenizer.from_pretrained('bert-base-japanese-whole-word-masking')\r\nencoded = train_ds.map(lambda examples: {'tokens': t.encode(examples['title'])}, batched=True)\r\n```\r\n\r\n<details><summary>Error Message<\/summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-35-2b7d66b291c1> in <module>\r\n      2 \r\n      3 encoded = train_ds.map(lambda examples:\r\n----> 4   {'tokens': t.encode(examples['title'])}, batched=True)\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in dump(self, obj)\r\n    407         if self.proto >= 4:\r\n    408             self.framer.start_framing()\r\n--> 409         self.save(obj)\r\n    410         self.write(STOP)\r\n    411         self.framer.end_framing()\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    608         else:\r\n    609             save(func)\r\n--> 610             save(args)\r\n    611             write(REDUCE)\r\n    612 \r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_tuple(self, obj)\r\n    749         write(MARK)\r\n    750         for element in obj:\r\n--> 751             save(element)\r\n    752 \r\n    753         if id(obj) in memo:\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_dict(self, obj)\r\n    819 \r\n    820         self.memoize(obj)\r\n--> 821         self._batch_setitems(obj.items())\r\n    822 \r\n    823     dispatch[dict] = save_dict\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in _batch_setitems(self, items)\r\n    850                 k, v = tmp[0]\r\n    851                 save(k)\r\n--> 852                 save(v)\r\n    853                 write(SETITEM)\r\n    854             # else tmp is empty, and we're done\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    519 \r\n    520         # Save the reduce() output and finally memoize the object\r\n--> 521         self.save_reduce(obj=obj, *rv)\r\n    522 \r\n    523     def persistent_id(self, obj):\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    632 \r\n    633         if state is not None:\r\n--> 634             save(state)\r\n    635             write(BUILD)\r\n    636 \r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_dict(self, obj)\r\n    819 \r\n    820         self.memoize(obj)\r\n--> 821         self._batch_setitems(obj.items())\r\n    822 \r\n    823     dispatch[dict] = save_dict\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in _batch_setitems(self, items)\r\n    845                 for k, v in tmp:\r\n    846                     save(k)\r\n--> 847                     save(v)\r\n    848                 write(SETITEMS)\r\n    849             elif n:\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    519 \r\n    520         # Save the reduce() output and finally memoize the object\r\n--> 521         self.save_reduce(obj=obj, *rv)\r\n    522 \r\n    523     def persistent_id(self, obj):\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    632 \r\n    633         if state is not None:\r\n--> 634             save(state)\r\n    635             write(BUILD)\r\n    636 \r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    474         f = self.dispatch.get(t)\r\n    475         if f is not None:\r\n--> 476             f(self, obj) # Call unbound method with explicit self\r\n    477             return\r\n    478 \r\n\r\n\/usr\/local\/lib\/python3.6\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save_dict(self, obj)\r\n    819 \r\n    820         self.memoize(obj)\r\n--> 821         self._batch_setitems(obj.items())\r\n    822 \r\n    823     dispatch[dict] = save_dict\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in _batch_setitems(self, items)\r\n    845                 for k, v in tmp:\r\n    846                     save(k)\r\n--> 847                     save(v)\r\n    848                 write(SETITEMS)\r\n    849             elif n:\r\n\r\n\/usr\/local\/lib\/python3.6\/pickle.py in save(self, obj, save_persistent_id)\r\n    494             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    495             if reduce is not None:\r\n--> 496                 rv = reduce(self.proto)\r\n    497             else:\r\n    498                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tagger objects\r\n```\r\n\r\n<\/details>\r\n\r\ntrainsformers: 2.10.0\r\ndatasets: 1.0.2\r\ndill: 0.3.2\r\npython: 3.6.8\r\n\r\nOS: ubuntu 16.04 (Docker Image) on [Deep Learning VM](https:\/\/console.cloud.google.com\/marketplace\/details\/click-to-deploy-images\/deeplearning) (GCP)\r\nGPU: Tesla P100 (CUDA 10)\r\n","embeddings":[-0.2387796193,-0.2978491187,-0.019680649,0.2330349237,0.4450323582,-0.1729919016,0.2851519585,0.1309388727,-0.2760912478,0.1103135347,-0.0697863474,0.4891909063,-0.0087260343,-0.046733927,-0.145709604,-0.111473918,0.0535604171,0.207853511,0.0181137323,-0.0561074987,-0.2787823677,0.2074424773,-0.4731775522,0.1585230231,-0.2825988233,-0.2674281299,0.1064047962,-0.1985800564,-0.2693119049,-0.3823218942,-0.1436450779,-0.073365584,-0.0288482774,0.5633369088,-0.0001192604,0.0994183272,0.108954519,-0.145972386,0.0052242242,-0.0495663248,-0.0748076513,-0.3035497069,-0.205873996,-0.4582723975,-0.1556460112,-0.1438439935,0.0111553641,-0.3324888647,0.5145235062,0.2914209962,0.139978081,0.4932267964,0.0206352938,0.0532785431,0.015327774,0.0298241358,-0.1314535141,-0.2608632743,0.1487917304,0.2165469229,-0.1302717179,0.3254893422,-0.1613031477,-0.2627820969,0.0994700417,0.094937481,0.1041127741,-0.4675594568,0.3230481744,-0.002158023,0.2189722508,-0.020810321,-0.2567602098,-0.2679677308,-0.0794852078,0.0625912696,-0.0412523523,-0.2022152543,0.0776130408,0.0512863845,-0.3526940644,-0.1723531634,-0.1798734814,0.108233422,0.0383866839,0.3674840629,-0.2666110098,0.2930521965,0.0967984498,-0.2501663864,-0.1138216779,-0.0991195515,0.2264450341,0.4768724144,-0.3883422315,-0.3008864224,-0.0943802446,-0.409999162,0.3477460146,-0.0384070538,-0.2276796252,0.2554124892,-0.0489559509,0.1893791109,0.0231759958,0.3520210385,0.2632215321,0.3699644506,0.0116726784,-0.1440164447,0.0652595013,-0.04887354,-0.0822229534,-0.2215761691,-0.0569127798,0.2582862377,0.3595126569,-0.0186132845,-0.046238631,0.0278620608,-0.2262803167,0.0427433066,0.1734637767,0.4068900049,0.1080137938,0.2318411618,-0.3183892965,0.0902408659,-0.124280192,0.0253062062,-0.1654862761,0.214023307,-0.0066213412,-0.1393794417,-0.1643196195,0.0698126107,0.2013424933,-0.026837565,0.0632021129,-0.2202773243,0.061538171,-0.1790308952,0.2758253217,-0.0943112522,-0.0502242185,0.1737568676,0.2081712335,-0.40948084,-0.2060563862,0.0352333188,-0.34115237,0.01733163,0.1311888844,0.1185405329,0.0562376231,0.0476904213,-0.3749628663,0.2920556068,0.4146037698,0.0075913849,0.0322678164,-0.1267381161,-0.2016420662,-0.111127086,0.2182486653,0.1899360716,-0.1373286545,-0.2079769075,0.2503727973,-0.002062849,0.1535669416,0.4049597979,-0.0530274957,0.2562201917,-0.1467852145,0.559774518,0.4704823494,-0.6255075336,-0.5309312344,0.1032184213,-0.4468844831,-0.1779434234,-0.035346359,0.0416382365,0.5613888502,0.1241554692,-0.113206543,0.3392548561,0.072295852,0.2676001787,-0.1563941687,-0.1415297985,0.4092992246,0.0926817507,0.21709764,0.116488941,-0.1682532728,0.1971376091,-0.1022469327,-0.1236947551,0.0914442316,0.1902052313,0.1654276252,-0.2108140886,0.0806120411,-0.1629784405,-0.5545789599,0.0651864186,-0.3476859927,0.2234149575,-0.2890501916,-0.194772318,-0.2887653708,0.1541353166,-0.4519188404,-0.0524003021,0.083847262,-0.0306146815,0.1889378428,-0.0136042386,0.0192883443,-0.0761402398,0.1458211094,0.186094299,-0.2552472055,0.2062030882,-0.2569340169,0.021148419,-0.05973031,0.09906286,0.408500433,-0.1759835035,-0.2551455796,0.2755496502,0.1720520854,-0.0848676786,-0.0500598922,-0.0350354798,0.2658101916,-0.2096956968,-0.0644069538,0.1785884053,0.166718334,-0.0287600234,-0.0929355696,0.4431660473,0.224703446,0.3178621531,-0.0191053115,0.1625137478,0.2412854582,-0.10274712,-0.0464949757,-0.1355438232,-0.1764924526,0.0628632903,0.2340398133,-0.0736531839,0.0656425655,-0.0590749644,0.8479928374,-0.0683462247,0.0793827847,0.204474166,-0.116351068,0.0497056618,-0.0788842216,-0.4018104076,0.0417372212,0.0392285287,-0.1734893918,-0.1066208854,0.2546639144,0.1459451169,0.041525092,0.3150502145,0.1146959588,0.2255442888,0.1599868983,0.1129512191,-0.1720335782,-0.1941431463,0.1629729271,0.1452347934,-0.1468617916,-0.0244321302,-0.0460567623,0.0444771908,0.0104709063,-0.0486729778,-0.195032984,-0.2504672706,0.0337895602,-0.0237582624,0.0957349986,0.4495555162,0.3668554127,0.3240292668,0.427821368,-0.1099201962,-0.170441404,-0.1969794482,-0.0876939595,-0.0677347481,0.1707262248,-0.0053565041,-0.0357478224,0.0103845401,0.1024920642,-0.456127733,-0.4291423857,0.2537454963,-0.178284511,0.1860092133,-0.0141354213,0.1210128963,-0.3429493308,-0.2921084464,0.2118502706,-0.2194129676,-0.3013744652,0.0706816763,-0.0610883981,-0.3209867477,0.0511774272,-0.2405867577,-0.3765725493,-0.3376917541,0.363016367,-0.008248332,0.1135794222,0.1541582793,-0.0030258943,0.2885194421,-0.0200715531,-0.0359078087,-0.2440013885,0.0543059148,0.4122084975,-0.1826187968,-0.2826860547,-0.3915768564,-0.1759163439,0.0525789037,-0.1293791682,-0.1020923853,-0.2275468558,-0.2711945474,0.2238460481,-0.1608631462,0.0458878055,0.558042407,-0.0492363572,0.0907655656,0.0115672005,-0.1609315425,0.2720036209,0.1358186603,0.0812894851,-0.0107793091,0.5259879827,0.1519594789,0.8704359531,0.1582149416,-0.2194377482,0.1683554202,-0.1485036314,-0.010637085,-0.0340552628,-0.3892847896,0.1844976842,-0.0443505682,0.1226070747,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/665","title":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects","comments":"> I have the same issue with `transformers\/BertJapaneseTokenizer`.\r\n\r\nIt looks like it this tokenizer is not supported unfortunately.\r\nThis is because `t.word_tokenizer.mecab` is a `fugashi.fugashi.GenericTagger` which is not compatible with pickle nor dill.\r\n\r\nWe need objects passes to `map` to be picklable for our caching system to work properly.\r\nHere it crashes because the caching system is not able to pickle the GenericTagger.\r\n\r\n\\> Maybe you can create an issue on [fugashi](https:\/\/github.com\/polm\/fugashi\/issues) 's repo and ask to make `fugashi.fugashi.GenericTagger` compatible with pickle ?\r\n\r\nWhat you can do in the meantime is use a picklable wrapper of the tokenizer:\r\n\r\n\r\n```python\r\nfrom transformers import BertJapaneseTokenizer, MecabTokenizer\r\n\r\nclass PicklableTokenizer(BertJapaneseTokenizer):\r\n\r\n    def __getstate__(self):\r\n        state = dict(self.__dict__)\r\n        state[\"do_lower_case\"] = self.word_tokenizer.do_lower_case\r\n        state[\"never_split\"] = self.word_tokenizer.never_split \r\n        del state[\"word_tokenizer\"]\r\n        return state\r\n\r\n    def __setstate__(self, state):\r\n        do_lower_case = state.pop(\"do_lower_case\")\r\n        never_split = state.pop(\"never_split\")\r\n        self.__dict__ = state\r\n        self.word_tokenizer = MecabTokenizer(\r\n            do_lower_case=do_lower_case, never_split=never_split)\r\n        )\r\n\r\nt = PicklableTokenizer.from_pretrained(\"cl-tohoku\/bert-base-japanese-whole-word-masking\")\r\nencoded = train_ds.map(lambda examples: {'tokens': t.encode(examples['title'])}, batched=True)  # it works\r\n```","body":"I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n","comment_length":153,"text":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects \n I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n \n > I have the same issue with `transformers\/BertJapaneseTokenizer`.\r\n\r\nIt looks like it this tokenizer is not supported unfortunately.\r\nThis is because `t.word_tokenizer.mecab` is a `fugashi.fugashi.GenericTagger` which is not compatible with pickle nor dill.\r\n\r\nWe need objects passes to `map` to be picklable for our caching system to work properly.\r\nHere it crashes because the caching system is not able to pickle the GenericTagger.\r\n\r\n\\> Maybe you can create an issue on [fugashi](https:\/\/github.com\/polm\/fugashi\/issues) 's repo and ask to make `fugashi.fugashi.GenericTagger` compatible with pickle ?\r\n\r\nWhat you can do in the meantime is use a picklable wrapper of the tokenizer:\r\n\r\n\r\n```python\r\nfrom transformers import BertJapaneseTokenizer, MecabTokenizer\r\n\r\nclass PicklableTokenizer(BertJapaneseTokenizer):\r\n\r\n    def __getstate__(self):\r\n        state = dict(self.__dict__)\r\n        state[\"do_lower_case\"] = self.word_tokenizer.do_lower_case\r\n        state[\"never_split\"] = self.word_tokenizer.never_split \r\n        del state[\"word_tokenizer\"]\r\n        return state\r\n\r\n    def __setstate__(self, state):\r\n        do_lower_case = state.pop(\"do_lower_case\")\r\n        never_split = state.pop(\"never_split\")\r\n        self.__dict__ = state\r\n        self.word_tokenizer = MecabTokenizer(\r\n            do_lower_case=do_lower_case, never_split=never_split)\r\n        )\r\n\r\nt = PicklableTokenizer.from_pretrained(\"cl-tohoku\/bert-base-japanese-whole-word-masking\")\r\nencoded = train_ds.map(lambda examples: {'tokens': t.encode(examples['title'])}, batched=True)  # it works\r\n```","embeddings":[-0.2387796193,-0.2978491187,-0.019680649,0.2330349237,0.4450323582,-0.1729919016,0.2851519585,0.1309388727,-0.2760912478,0.1103135347,-0.0697863474,0.4891909063,-0.0087260343,-0.046733927,-0.145709604,-0.111473918,0.0535604171,0.207853511,0.0181137323,-0.0561074987,-0.2787823677,0.2074424773,-0.4731775522,0.1585230231,-0.2825988233,-0.2674281299,0.1064047962,-0.1985800564,-0.2693119049,-0.3823218942,-0.1436450779,-0.073365584,-0.0288482774,0.5633369088,-0.0001192604,0.0994183272,0.108954519,-0.145972386,0.0052242242,-0.0495663248,-0.0748076513,-0.3035497069,-0.205873996,-0.4582723975,-0.1556460112,-0.1438439935,0.0111553641,-0.3324888647,0.5145235062,0.2914209962,0.139978081,0.4932267964,0.0206352938,0.0532785431,0.015327774,0.0298241358,-0.1314535141,-0.2608632743,0.1487917304,0.2165469229,-0.1302717179,0.3254893422,-0.1613031477,-0.2627820969,0.0994700417,0.094937481,0.1041127741,-0.4675594568,0.3230481744,-0.002158023,0.2189722508,-0.020810321,-0.2567602098,-0.2679677308,-0.0794852078,0.0625912696,-0.0412523523,-0.2022152543,0.0776130408,0.0512863845,-0.3526940644,-0.1723531634,-0.1798734814,0.108233422,0.0383866839,0.3674840629,-0.2666110098,0.2930521965,0.0967984498,-0.2501663864,-0.1138216779,-0.0991195515,0.2264450341,0.4768724144,-0.3883422315,-0.3008864224,-0.0943802446,-0.409999162,0.3477460146,-0.0384070538,-0.2276796252,0.2554124892,-0.0489559509,0.1893791109,0.0231759958,0.3520210385,0.2632215321,0.3699644506,0.0116726784,-0.1440164447,0.0652595013,-0.04887354,-0.0822229534,-0.2215761691,-0.0569127798,0.2582862377,0.3595126569,-0.0186132845,-0.046238631,0.0278620608,-0.2262803167,0.0427433066,0.1734637767,0.4068900049,0.1080137938,0.2318411618,-0.3183892965,0.0902408659,-0.124280192,0.0253062062,-0.1654862761,0.214023307,-0.0066213412,-0.1393794417,-0.1643196195,0.0698126107,0.2013424933,-0.026837565,0.0632021129,-0.2202773243,0.061538171,-0.1790308952,0.2758253217,-0.0943112522,-0.0502242185,0.1737568676,0.2081712335,-0.40948084,-0.2060563862,0.0352333188,-0.34115237,0.01733163,0.1311888844,0.1185405329,0.0562376231,0.0476904213,-0.3749628663,0.2920556068,0.4146037698,0.0075913849,0.0322678164,-0.1267381161,-0.2016420662,-0.111127086,0.2182486653,0.1899360716,-0.1373286545,-0.2079769075,0.2503727973,-0.002062849,0.1535669416,0.4049597979,-0.0530274957,0.2562201917,-0.1467852145,0.559774518,0.4704823494,-0.6255075336,-0.5309312344,0.1032184213,-0.4468844831,-0.1779434234,-0.035346359,0.0416382365,0.5613888502,0.1241554692,-0.113206543,0.3392548561,0.072295852,0.2676001787,-0.1563941687,-0.1415297985,0.4092992246,0.0926817507,0.21709764,0.116488941,-0.1682532728,0.1971376091,-0.1022469327,-0.1236947551,0.0914442316,0.1902052313,0.1654276252,-0.2108140886,0.0806120411,-0.1629784405,-0.5545789599,0.0651864186,-0.3476859927,0.2234149575,-0.2890501916,-0.194772318,-0.2887653708,0.1541353166,-0.4519188404,-0.0524003021,0.083847262,-0.0306146815,0.1889378428,-0.0136042386,0.0192883443,-0.0761402398,0.1458211094,0.186094299,-0.2552472055,0.2062030882,-0.2569340169,0.021148419,-0.05973031,0.09906286,0.408500433,-0.1759835035,-0.2551455796,0.2755496502,0.1720520854,-0.0848676786,-0.0500598922,-0.0350354798,0.2658101916,-0.2096956968,-0.0644069538,0.1785884053,0.166718334,-0.0287600234,-0.0929355696,0.4431660473,0.224703446,0.3178621531,-0.0191053115,0.1625137478,0.2412854582,-0.10274712,-0.0464949757,-0.1355438232,-0.1764924526,0.0628632903,0.2340398133,-0.0736531839,0.0656425655,-0.0590749644,0.8479928374,-0.0683462247,0.0793827847,0.204474166,-0.116351068,0.0497056618,-0.0788842216,-0.4018104076,0.0417372212,0.0392285287,-0.1734893918,-0.1066208854,0.2546639144,0.1459451169,0.041525092,0.3150502145,0.1146959588,0.2255442888,0.1599868983,0.1129512191,-0.1720335782,-0.1941431463,0.1629729271,0.1452347934,-0.1468617916,-0.0244321302,-0.0460567623,0.0444771908,0.0104709063,-0.0486729778,-0.195032984,-0.2504672706,0.0337895602,-0.0237582624,0.0957349986,0.4495555162,0.3668554127,0.3240292668,0.427821368,-0.1099201962,-0.170441404,-0.1969794482,-0.0876939595,-0.0677347481,0.1707262248,-0.0053565041,-0.0357478224,0.0103845401,0.1024920642,-0.456127733,-0.4291423857,0.2537454963,-0.178284511,0.1860092133,-0.0141354213,0.1210128963,-0.3429493308,-0.2921084464,0.2118502706,-0.2194129676,-0.3013744652,0.0706816763,-0.0610883981,-0.3209867477,0.0511774272,-0.2405867577,-0.3765725493,-0.3376917541,0.363016367,-0.008248332,0.1135794222,0.1541582793,-0.0030258943,0.2885194421,-0.0200715531,-0.0359078087,-0.2440013885,0.0543059148,0.4122084975,-0.1826187968,-0.2826860547,-0.3915768564,-0.1759163439,0.0525789037,-0.1293791682,-0.1020923853,-0.2275468558,-0.2711945474,0.2238460481,-0.1608631462,0.0458878055,0.558042407,-0.0492363572,0.0907655656,0.0115672005,-0.1609315425,0.2720036209,0.1358186603,0.0812894851,-0.0107793091,0.5259879827,0.1519594789,0.8704359531,0.1582149416,-0.2194377482,0.1683554202,-0.1485036314,-0.010637085,-0.0340552628,-0.3892847896,0.1844976842,-0.0443505682,0.12260707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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/665","title":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects","comments":"We can also update the `BertJapaneseTokenizer` in `transformers` as you just shown @lhoestq to make it compatible with pickle. It will be faster than asking on fugashi 's repo and good for the other users of `transformers` as well.\r\n\r\nI'm currently working on `transformers` I'll include it in the https:\/\/github.com\/huggingface\/transformers\/pull\/7141 PR and the next release of `transformers`.","body":"I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n","comment_length":57,"text":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects \n I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n \n We can also update the `BertJapaneseTokenizer` in `transformers` as you just shown @lhoestq to make it compatible with pickle. It will be faster than asking on fugashi 's repo and good for the other users of `transformers` as well.\r\n\r\nI'm currently working on `transformers` I'll include it in the https:\/\/github.com\/huggingface\/transformers\/pull\/7141 PR and the next release of `transformers`.","embeddings":[-0.2387796193,-0.2978491187,-0.019680649,0.2330349237,0.4450323582,-0.1729919016,0.2851519585,0.1309388727,-0.2760912478,0.1103135347,-0.0697863474,0.4891909063,-0.0087260343,-0.046733927,-0.145709604,-0.111473918,0.0535604171,0.207853511,0.0181137323,-0.0561074987,-0.2787823677,0.2074424773,-0.4731775522,0.1585230231,-0.2825988233,-0.2674281299,0.1064047962,-0.1985800564,-0.2693119049,-0.3823218942,-0.1436450779,-0.073365584,-0.0288482774,0.5633369088,-0.0001192604,0.0994183272,0.108954519,-0.145972386,0.0052242242,-0.0495663248,-0.0748076513,-0.3035497069,-0.205873996,-0.4582723975,-0.1556460112,-0.1438439935,0.0111553641,-0.3324888647,0.5145235062,0.2914209962,0.139978081,0.4932267964,0.0206352938,0.0532785431,0.015327774,0.0298241358,-0.1314535141,-0.2608632743,0.1487917304,0.2165469229,-0.1302717179,0.3254893422,-0.1613031477,-0.2627820969,0.0994700417,0.094937481,0.1041127741,-0.4675594568,0.3230481744,-0.002158023,0.2189722508,-0.020810321,-0.2567602098,-0.2679677308,-0.0794852078,0.0625912696,-0.0412523523,-0.2022152543,0.0776130408,0.0512863845,-0.3526940644,-0.1723531634,-0.1798734814,0.108233422,0.0383866839,0.3674840629,-0.2666110098,0.2930521965,0.0967984498,-0.2501663864,-0.1138216779,-0.0991195515,0.2264450341,0.4768724144,-0.3883422315,-0.3008864224,-0.0943802446,-0.409999162,0.3477460146,-0.0384070538,-0.2276796252,0.2554124892,-0.0489559509,0.1893791109,0.0231759958,0.3520210385,0.2632215321,0.3699644506,0.0116726784,-0.1440164447,0.0652595013,-0.04887354,-0.0822229534,-0.2215761691,-0.0569127798,0.2582862377,0.3595126569,-0.0186132845,-0.046238631,0.0278620608,-0.2262803167,0.0427433066,0.1734637767,0.4068900049,0.1080137938,0.2318411618,-0.3183892965,0.0902408659,-0.124280192,0.0253062062,-0.1654862761,0.214023307,-0.0066213412,-0.1393794417,-0.1643196195,0.0698126107,0.2013424933,-0.026837565,0.0632021129,-0.2202773243,0.061538171,-0.1790308952,0.2758253217,-0.0943112522,-0.0502242185,0.1737568676,0.2081712335,-0.40948084,-0.2060563862,0.0352333188,-0.34115237,0.01733163,0.1311888844,0.1185405329,0.0562376231,0.0476904213,-0.3749628663,0.2920556068,0.4146037698,0.0075913849,0.0322678164,-0.1267381161,-0.2016420662,-0.111127086,0.2182486653,0.1899360716,-0.1373286545,-0.2079769075,0.2503727973,-0.002062849,0.1535669416,0.4049597979,-0.0530274957,0.2562201917,-0.1467852145,0.559774518,0.4704823494,-0.6255075336,-0.5309312344,0.1032184213,-0.4468844831,-0.1779434234,-0.035346359,0.0416382365,0.5613888502,0.1241554692,-0.113206543,0.3392548561,0.072295852,0.2676001787,-0.1563941687,-0.1415297985,0.4092992246,0.0926817507,0.21709764,0.116488941,-0.1682532728,0.1971376091,-0.1022469327,-0.1236947551,0.0914442316,0.1902052313,0.1654276252,-0.2108140886,0.0806120411,-0.1629784405,-0.5545789599,0.0651864186,-0.3476859927,0.2234149575,-0.2890501916,-0.194772318,-0.2887653708,0.1541353166,-0.4519188404,-0.0524003021,0.083847262,-0.0306146815,0.1889378428,-0.0136042386,0.0192883443,-0.0761402398,0.1458211094,0.186094299,-0.2552472055,0.2062030882,-0.2569340169,0.021148419,-0.05973031,0.09906286,0.408500433,-0.1759835035,-0.2551455796,0.2755496502,0.1720520854,-0.0848676786,-0.0500598922,-0.0350354798,0.2658101916,-0.2096956968,-0.0644069538,0.1785884053,0.166718334,-0.0287600234,-0.0929355696,0.4431660473,0.224703446,0.3178621531,-0.0191053115,0.1625137478,0.2412854582,-0.10274712,-0.0464949757,-0.1355438232,-0.1764924526,0.0628632903,0.2340398133,-0.0736531839,0.0656425655,-0.0590749644,0.8479928374,-0.0683462247,0.0793827847,0.204474166,-0.116351068,0.0497056618,-0.0788842216,-0.4018104076,0.0417372212,0.0392285287,-0.1734893918,-0.1066208854,0.2546639144,0.1459451169,0.041525092,0.3150502145,0.1146959588,0.2255442888,0.1599868983,0.1129512191,-0.1720335782,-0.1941431463,0.1629729271,0.1452347934,-0.1468617916,-0.0244321302,-0.0460567623,0.0444771908,0.0104709063,-0.0486729778,-0.195032984,-0.2504672706,0.0337895602,-0.0237582624,0.0957349986,0.4495555162,0.3668554127,0.3240292668,0.427821368,-0.1099201962,-0.170441404,-0.1969794482,-0.0876939595,-0.0677347481,0.1707262248,-0.0053565041,-0.0357478224,0.0103845401,0.1024920642,-0.456127733,-0.4291423857,0.2537454963,-0.178284511,0.1860092133,-0.0141354213,0.1210128963,-0.3429493308,-0.2921084464,0.2118502706,-0.2194129676,-0.3013744652,0.0706816763,-0.0610883981,-0.3209867477,0.0511774272,-0.2405867577,-0.3765725493,-0.3376917541,0.363016367,-0.008248332,0.1135794222,0.1541582793,-0.0030258943,0.2885194421,-0.0200715531,-0.0359078087,-0.2440013885,0.0543059148,0.4122084975,-0.1826187968,-0.2826860547,-0.3915768564,-0.1759163439,0.0525789037,-0.1293791682,-0.1020923853,-0.2275468558,-0.2711945474,0.2238460481,-0.1608631462,0.0458878055,0.558042407,-0.0492363572,0.0907655656,0.0115672005,-0.1609315425,0.2720036209,0.1358186603,0.0812894851,-0.0107793091,0.5259879827,0.1519594789,0.8704359531,0.1582149416,-0.2194377482,0.1683554202,-0.1485036314,-0.010637085,-0.0340552628,-0.3892847896,0.1844976842,-0.0443505682,0.12260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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/665","title":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects","comments":"Thank you for the rapid and polite response!\r\n\r\n@lhoestq Thanks for the suggestion! I've passed the pickle phase, but another `ArrowInvalid` problem occored. I created another issue #687 .\r\n\r\n@thomwolf Wow, really fast work. I'm looking forward to the next release \ud83e\udd17","body":"I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n","comment_length":42,"text":"runing dataset.map, it raises TypeError: can't pickle Tokenizer objects \n I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.\r\n\r\n```\r\ndef convert_to_features(example):\r\n    # Tokenize contexts and questions (as pairs of inputs)\r\n    input_pairs = [example['question'], example['context']]\r\n    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)\r\n    context_encodings = tokenizer.encode_plus(example['context'])\r\n    \r\n\r\n    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.\r\n    # this will give us the position of answer span in the context text\r\n    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])\r\n    start_positions_context = context_encodings.char_to_token(start_idx)\r\n    end_positions_context = context_encodings.char_to_token(end_idx-1)\r\n\r\n    # here we will compute the start and end position of the answer in the whole example\r\n    # as the example is encoded like this <s> question<\/s><\/s> context<\/s>\r\n    # and we know the postion of the answer in the context\r\n    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)\r\n    # this will give us the position of the answer span in whole example \r\n    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)\r\n    start_positions = start_positions_context + sep_idx + 1\r\n    end_positions = end_positions_context + sep_idx + 1\r\n\r\n    if end_positions > 512:\r\n      start_positions, end_positions = 0, 0\r\n\r\n    encodings.update({'start_positions': start_positions,\r\n                      'end_positions': end_positions,\r\n                      'attention_mask': encodings['attention_mask']})\r\n    return encodings\r\n```\r\n\r\nThen I run `dataset.map(convert_to_features)`, it raise\r\n```\r\nIn [59]: a.map(convert_to_features)                                                                                                                        \r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-59-c453b508761d> in <module>\r\n----> 1 a.map(convert_to_features)\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n   1242                 fn_kwargs=fn_kwargs,\r\n   1243                 new_fingerprint=new_fingerprint,\r\n-> 1244                 update_data=update_data,\r\n   1245             )\r\n   1246         else:\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/arrow_dataset.py in wrapper(*args, **kwargs)\r\n    151             \"output_all_columns\": self._output_all_columns,\r\n    152         }\r\n--> 153         out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n    154         if new_format[\"columns\"] is not None:\r\n    155             new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    156                         kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n    157                         kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158                             self._fingerprint, transform, kwargs_for_fingerprint\r\n    159                         )\r\n    160 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n    103     for key in sorted(transform_args):\r\n    104         hasher.update(key)\r\n--> 105         hasher.update(transform_args[key])\r\n    106     return hasher.hexdigest()\r\n    107 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in update(self, value)\r\n     55     def update(self, value):\r\n     56         self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57         self.m.update(self.hash(value).encode(\"utf-8\"))\r\n     58 \r\n     59     def hexdigest(self):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash(cls, value)\r\n     51             return cls.dispatch[type(value)](cls, value)\r\n     52         else:\r\n---> 53             return cls.hash_default(value)\r\n     54 \r\n     55     def update(self, value):\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/fingerprint.py in hash_default(cls, value)\r\n     44     @classmethod\r\n     45     def hash_default(cls, value):\r\n---> 46         return cls.hash_bytes(dumps(value))\r\n     47 \r\n     48     @classmethod\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dumps(obj)\r\n    365     file = StringIO()\r\n    366     with _no_cache_fields(obj):\r\n--> 367         dump(obj, file)\r\n    368     return file.getvalue()\r\n    369 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/utils\/py_utils.py in dump(obj, file)\r\n    337 def dump(obj, file):\r\n    338     \"\"\"pickle an object to a file\"\"\"\r\n--> 339     Pickler(file, recurse=True).dump(obj)\r\n    340     return\r\n    341 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in dump(self, obj)\r\n    444             raise PicklingError(msg)\r\n    445         else:\r\n--> 446             StockPickler.dump(self, obj)\r\n    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects\r\n    448         return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in dump(self, obj)\r\n    435         if self.proto >= 4:\r\n    436             self.framer.start_framing()\r\n--> 437         self.save(obj)\r\n    438         self.write(STOP)\r\n    439         self.framer.end_framing()\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_function(pickler, obj)\r\n   1436                                 globs, obj.__name__,\r\n   1437                                 obj.__defaults__, obj.__closure__,\r\n-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)\r\n   1439         else:\r\n   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    636         else:\r\n    637             save(func)\r\n--> 638             save(args)\r\n    639             write(REDUCE)\r\n    640 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_tuple(self, obj)\r\n    787         write(MARK)\r\n    788         for element in obj:\r\n--> 789             save(element)\r\n    790 \r\n    791         if id(obj) in memo:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    547 \r\n    548         # Save the reduce() output and finally memoize the object\r\n--> 549         self.save_reduce(obj=obj, *rv)\r\n    550 \r\n    551     def persistent_id(self, obj):\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n    660 \r\n    661         if state is not None:\r\n--> 662             save(state)\r\n    663             write(BUILD)\r\n    664 \r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    502         f = self.dispatch.get(t)\r\n    503         if f is not None:\r\n--> 504             f(self, obj) # Call unbound method with explicit self\r\n    505             return\r\n    506 \r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/dill\/_dill.py in save_module_dict(pickler, obj)\r\n    931             # we only care about session the first pass thru\r\n    932             pickler._session = False\r\n--> 933         StockPickler.save_dict(pickler, obj)\r\n    934         log.info(\"# D2\")\r\n    935     return\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save_dict(self, obj)\r\n    857 \r\n    858         self.memoize(obj)\r\n--> 859         self._batch_setitems(obj.items())\r\n    860 \r\n    861     dispatch[dict] = save_dict\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in _batch_setitems(self, items)\r\n    883                 for k, v in tmp:\r\n    884                     save(k)\r\n--> 885                     save(v)\r\n    886                 write(SETITEMS)\r\n    887             elif n:\r\n\r\n\/opt\/conda\/lib\/python3.7\/pickle.py in save(self, obj, save_persistent_id)\r\n    522             reduce = getattr(obj, \"__reduce_ex__\", None)\r\n    523             if reduce is not None:\r\n--> 524                 rv = reduce(self.proto)\r\n    525             else:\r\n    526                 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tokenizer objects\r\n```\r\n\r\n \n Thank you for the rapid and polite response!\r\n\r\n@lhoestq Thanks for the suggestion! I've passed the pickle phase, but another `ArrowInvalid` problem occored. I created another issue #687 .\r\n\r\n@thomwolf Wow, really fast work. I'm looking forward to the next release \ud83e\udd17","embeddings":[-0.2387796193,-0.2978491187,-0.019680649,0.2330349237,0.4450323582,-0.1729919016,0.2851519585,0.1309388727,-0.2760912478,0.1103135347,-0.0697863474,0.4891909063,-0.0087260343,-0.046733927,-0.145709604,-0.111473918,0.0535604171,0.207853511,0.0181137323,-0.0561074987,-0.2787823677,0.2074424773,-0.4731775522,0.1585230231,-0.2825988233,-0.2674281299,0.1064047962,-0.1985800564,-0.2693119049,-0.3823218942,-0.1436450779,-0.073365584,-0.0288482774,0.5633369088,-0.0001192604,0.0994183272,0.108954519,-0.145972386,0.0052242242,-0.0495663248,-0.0748076513,-0.3035497069,-0.205873996,-0.4582723975,-0.1556460112,-0.1438439935,0.0111553641,-0.3324888647,0.5145235062,0.2914209962,0.139978081,0.4932267964,0.0206352938,0.0532785431,0.015327774,0.0298241358,-0.1314535141,-0.2608632743,0.1487917304,0.2165469229,-0.1302717179,0.3254893422,-0.1613031477,-0.2627820969,0.0994700417,0.094937481,0.1041127741,-0.4675594568,0.3230481744,-0.002158023,0.2189722508,-0.020810321,-0.2567602098,-0.2679677308,-0.0794852078,0.0625912696,-0.0412523523,-0.2022152543,0.0776130408,0.0512863845,-0.3526940644,-0.1723531634,-0.1798734814,0.108233422,0.0383866839,0.3674840629,-0.2666110098,0.2930521965,0.0967984498,-0.2501663864,-0.1138216779,-0.0991195515,0.2264450341,0.4768724144,-0.3883422315,-0.3008864224,-0.0943802446,-0.409999162,0.3477460146,-0.0384070538,-0.2276796252,0.2554124892,-0.0489559509,0.1893791109,0.0231759958,0.3520210385,0.2632215321,0.3699644506,0.0116726784,-0.1440164447,0.0652595013,-0.04887354,-0.0822229534,-0.2215761691,-0.0569127798,0.2582862377,0.3595126569,-0.0186132845,-0.046238631,0.0278620608,-0.2262803167,0.0427433066,0.1734637767,0.4068900049,0.1080137938,0.2318411618,-0.3183892965,0.0902408659,-0.124280192,0.0253062062,-0.1654862761,0.214023307,-0.0066213412,-0.1393794417,-0.1643196195,0.0698126107,0.2013424933,-0.026837565,0.0632021129,-0.2202773243,0.061538171,-0.1790308952,0.2758253217,-0.0943112522,-0.0502242185,0.1737568676,0.2081712335,-0.40948084,-0.2060563862,0.0352333188,-0.34115237,0.01733163,0.1311888844,0.1185405329,0.0562376231,0.0476904213,-0.3749628663,0.2920556068,0.4146037698,0.0075913849,0.0322678164,-0.1267381161,-0.2016420662,-0.111127086,0.2182486653,0.1899360716,-0.1373286545,-0.2079769075,0.2503727973,-0.002062849,0.1535669416,0.4049597979,-0.0530274957,0.2562201917,-0.1467852145,0.559774518,0.4704823494,-0.6255075336,-0.5309312344,0.1032184213,-0.4468844831,-0.1779434234,-0.035346359,0.0416382365,0.5613888502,0.1241554692,-0.113206543,0.3392548561,0.072295852,0.2676001787,-0.1563941687,-0.1415297985,0.4092992246,0.0926817507,0.21709764,0.116488941,-0.1682532728,0.1971376091,-0.1022469327,-0.1236947551,0.0914442316,0.1902052313,0.1654276252,-0.2108140886,0.0806120411,-0.1629784405,-0.5545789599,0.0651864186,-0.3476859927,0.2234149575,-0.2890501916,-0.194772318,-0.2887653708,0.1541353166,-0.4519188404,-0.0524003021,0.083847262,-0.0306146815,0.1889378428,-0.0136042386,0.0192883443,-0.0761402398,0.1458211094,0.186094299,-0.2552472055,0.2062030882,-0.2569340169,0.021148419,-0.05973031,0.09906286,0.408500433,-0.1759835035,-0.2551455796,0.2755496502,0.1720520854,-0.0848676786,-0.0500598922,-0.0350354798,0.2658101916,-0.2096956968,-0.0644069538,0.1785884053,0.166718334,-0.0287600234,-0.0929355696,0.4431660473,0.224703446,0.3178621531,-0.0191053115,0.1625137478,0.2412854582,-0.10274712,-0.0464949757,-0.1355438232,-0.1764924526,0.0628632903,0.2340398133,-0.0736531839,0.0656425655,-0.0590749644,0.8479928374,-0.0683462247,0.0793827847,0.204474166,-0.116351068,0.0497056618,-0.0788842216,-0.4018104076,0.0417372212,0.0392285287,-0.1734893918,-0.1066208854,0.2546639144,0.1459451169,0.041525092,0.3150502145,0.1146959588,0.2255442888,0.1599868983,0.1129512191,-0.1720335782,-0.1941431463,0.1629729271,0.1452347934,-0.1468617916,-0.0244321302,-0.0460567623,0.0444771908,0.0104709063,-0.0486729778,-0.195032984,-0.2504672706,0.0337895602,-0.0237582624,0.0957349986,0.4495555162,0.3668554127,0.3240292668,0.427821368,-0.1099201962,-0.170441404,-0.1969794482,-0.0876939595,-0.0677347481,0.1707262248,-0.0053565041,-0.0357478224,0.0103845401,0.1024920642,-0.456127733,-0.4291423857,0.2537454963,-0.178284511,0.1860092133,-0.0141354213,0.1210128963,-0.3429493308,-0.2921084464,0.2118502706,-0.2194129676,-0.3013744652,0.0706816763,-0.0610883981,-0.3209867477,0.0511774272,-0.2405867577,-0.3765725493,-0.3376917541,0.363016367,-0.008248332,0.1135794222,0.1541582793,-0.0030258943,0.2885194421,-0.0200715531,-0.0359078087,-0.2440013885,0.0543059148,0.4122084975,-0.1826187968,-0.2826860547,-0.3915768564,-0.1759163439,0.0525789037,-0.1293791682,-0.1020923853,-0.2275468558,-0.2711945474,0.2238460481,-0.1608631462,0.0458878055,0.558042407,-0.0492363572,0.0907655656,0.0115672005,-0.1609315425,0.2720036209,0.1358186603,0.0812894851,-0.0107793091,0.5259879827,0.1519594789,0.8704359531,0.1582149416,-0.2194377482,0.1683554202,-0.1485036314,-0.010637085,-0.0340552628,-0.3892847896,0.1844976842,-0.0443505682,0.12260707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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/664","title":"load_dataset from local squad.py, raise error: TypeError: 'NoneType' object is not callable ","comments":"Hi !\r\nThanks for reporting.\r\nIt looks like no object inherits from `datasets.GeneratorBasedBuilder` (or more generally from `datasets.DatasetBuilder`) in your script.\r\n\r\nCould you check that there exist at least one dataset builder class ?","body":"\r\nversion: 1.0.2\r\n\r\n```\r\ntrain_dataset  = datasets.load_dataset('squad') \r\n```\r\n\r\nThe above code can works. However, when I download the squad.py from your server, and saved as `my_squad.py` to local. I run followings raise errors.\r\n```\r\ntrain_dataset  = datasets.load_dataset('.\/my_squad.py')                                                                                                \r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-28-25a84b4d1581> in <module>\r\n----> 1 train_dataset  = nlp.load_dataset('.\/my_squad.py')\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    602         hash=hash,\r\n    603         features=features,\r\n--> 604         **config_kwargs,\r\n    605     )\r\n    606 \r\n\r\nTypeError: 'NoneType' object is not callable\r\n","comment_length":34,"text":"load_dataset from local squad.py, raise error: TypeError: 'NoneType' object is not callable  \n \r\nversion: 1.0.2\r\n\r\n```\r\ntrain_dataset  = datasets.load_dataset('squad') \r\n```\r\n\r\nThe above code can works. However, when I download the squad.py from your server, and saved as `my_squad.py` to local. I run followings raise errors.\r\n```\r\ntrain_dataset  = datasets.load_dataset('.\/my_squad.py')                                                                                                \r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-28-25a84b4d1581> in <module>\r\n----> 1 train_dataset  = nlp.load_dataset('.\/my_squad.py')\r\n\r\n\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n    602         hash=hash,\r\n    603         features=features,\r\n--> 604         **config_kwargs,\r\n    605     )\r\n    606 \r\n\r\nTypeError: 'NoneType' object is not callable\r\n \n Hi !\r\nThanks for reporting.\r\nIt looks like no object inherits from `datasets.GeneratorBasedBuilder` (or more generally from `datasets.DatasetBuilder`) in your script.\r\n\r\nCould you check that there exist at least one dataset builder class ?","embeddings":[-0.2243587077,0.3296630979,0.111471124,0.1580804884,0.3371319771,-0.0254657716,0.6201825738,0.3964118361,-0.1371878237,0.0546794124,-0.0827249959,0.4112722576,-0.124582462,-0.0242635924,0.3396508992,0.0455027483,-0.0610549562,0.2632742226,-0.1969885081,-0.2398484796,-0.2595587373,-0.0362527221,-0.0532293469,0.3426586688,-0.3587331474,0.0167298,-0.0222245716,0.4130075872,-0.2073467672,-0.4058050811,0.4260213673,-0.0520701259,0.4543029666,0.4609367549,-0.000108605,0.0964231789,0.2037233412,-0.0567755476,-0.4298074245,-0.4939341247,0.0174136348,-0.2600156665,0.348030448,-0.4434491098,-0.044265382,0.1277416945,0.1641693115,-0.2078907192,0.4198295176,0.5076459646,0.2114357054,0.4164859951,-0.0478948094,-0.1072460487,0.0606455281,0.2325323373,-0.1264866292,0.4050805569,0.0112330215,-0.2007900476,0.2467612177,-0.0516305193,-0.2405682504,-0.0204578638,0.2836372554,0.1646960825,-0.00388834,-0.3396708667,0.0379902907,0.0254600774,0.3043539226,-0.5623261929,-0.4327322841,0.0522474162,0.2386764288,-0.0903794169,0.0619195513,0.12697649,-0.1391509771,0.1650296748,-0.2995199859,-0.0174529459,-0.34699139,0.2185474634,0.1378310174,0.0882255137,-0.0734710768,0.2105123252,-0.0946086869,0.1174466833,0.1658370942,-0.0670502037,0.2600558996,0.2247153521,-0.1657711715,-0.0563905463,0.1484064311,-0.2879837751,0.0835403949,0.1376603991,0.1994940639,0.0354084782,0.1475709975,0.3584354818,0.3046308756,-0.0103832595,0.4460417926,0.3905521333,0.2092555761,0.0204773471,-0.3075550199,0.0816470981,-0.4033690989,-0.1090946198,0.2758958042,0.076598607,0.6148070693,-0.1758502126,-0.0884732679,-0.0214995984,-0.0159814488,-0.0150975827,0.1789544821,0.3566183746,-0.0046703769,0.1785069406,0.0226463974,0.3013427854,-0.2289461493,-0.3886495233,-0.1576166451,-0.0191612206,-0.1196386144,0.1119067594,0.3236369491,0.0200300962,0.0620407388,-0.0539848804,0.0096883215,0.0857435167,0.3394778669,-0.2605609894,-0.0883493051,0.0893337578,0.1972521245,0.0455686972,0.3239510953,-0.481225729,-0.1657757461,0.0935725421,-0.1421400309,-0.415040493,-0.2359469682,0.1811512709,-0.1541479975,-0.0848482996,-0.0737669617,-0.1494629234,0.222211197,-0.3436722457,-0.0893649682,-0.2118363082,-0.0979256555,-0.3135224283,0.1799425036,0.7318371534,-0.5566922426,-0.1748506129,0.0376518182,-0.1776361465,0.2152796388,0.0091927666,-0.396612227,0.2239100337,-0.0369548351,-0.1163446084,0.8239561319,-0.5646014214,-0.2108430266,0.3148209155,-0.2390918136,-0.1988459229,-0.0661059618,-0.0253550485,0.3400918543,0.092112653,0.2875827849,0.5355546474,-0.0294604786,0.0208227746,-0.1860280484,-0.2029984593,0.0848430544,0.2191347331,0.0231419913,0.3303081989,0.1459700316,0.0864074007,0.0052345647,-0.1767736226,0.1181473657,0.0941681117,0.3387049139,0.0741478726,-0.2490807027,-0.2523498535,-0.6033933163,0.0904372483,-0.3337163031,0.2756749392,-0.2055827975,-0.1493386626,-0.2154192328,0.0664722323,-0.3703392148,0.1264156103,0.1411835849,0.1770204306,0.0798652619,-0.042924922,-0.3529272079,0.0876473486,-0.2050779909,0.2082749009,-0.1766453534,0.2906638384,-0.1883773059,-0.2833380699,0.089140147,0.2023648024,0.2694806457,-0.1818110049,-0.1411184072,0.3598248661,-0.2107282728,-0.0693585575,0.1634406894,-0.2966667116,0.0126889218,-0.0751981437,0.2857248485,-0.0647990257,0.1065602377,-0.0678152293,-0.1150792912,0.5167067647,-0.0031732633,0.1721068323,-0.0526649728,0.0205951426,0.1081378162,0.0752766132,-0.2094512284,-0.1581074595,-0.2273749858,0.2697479427,0.4829780459,0.1188587025,-0.1470673233,0.0532565042,0.5740777254,-0.05465075,0.1372898668,0.0851620138,-0.1313451231,-0.0412323698,0.0708402544,0.1479733884,0.5725581646,0.0996330902,-0.0794533044,-0.0268103015,-0.1564649791,-0.0062203798,-0.033436507,-0.1205940321,0.2183308899,0.0642101169,0.0933713689,0.0356456973,-0.2885571122,-0.1144710928,0.1267339289,0.2433501333,-0.1978192329,0.2767357826,-0.2465389669,0.0061044395,-0.4481367171,-0.0640273765,-0.3408609629,-0.019282598,-0.201014474,0.1224423125,0.1562079191,0.1803924888,-0.1137546077,0.0052955281,0.0849541128,-0.5639901161,0.191577971,-0.07982371,-0.3821257055,-0.0223751832,0.4319239557,0.079372488,0.2485446036,-0.2006807923,-0.0224453732,-0.0097836722,-0.0044241687,-0.0129084401,0.1107249632,0.2616941631,0.0467463396,0.2120769173,-0.1695703864,-0.2411050797,0.5361255407,-0.3284081817,0.0225232262,0.2159548551,-0.1201800406,-0.0991033167,-0.2032280713,-0.6506759524,-0.5834841132,-0.3092330694,0.0982242003,0.3362255692,0.2940899134,0.2489456236,0.3768067658,0.4234935045,0.0208355319,0.107160382,-0.0720120817,-0.3683303893,0.3266993463,-0.3098600805,-0.3858877718,0.0721755922,-0.0773554444,0.1593592018,-0.1642022133,-0.143696934,-0.1241336241,0.1434112042,0.2297299951,-0.0108086048,0.1534465849,0.3795170486,0.0624459572,0.0130371302,-0.0232359711,-0.2466184199,0.130396232,0.2644398212,0.1292032599,0.1152427644,0.4664043784,-0.3106529713,0.6398789883,-0.0654288009,-0.1792743653,0.3221142292,-0.1978886276,0.3280735314,-0.265311271,-0.5119051933,0.0864262208,-0.0068482873,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/657","title":"Squad Metric Description & Feature Mismatch","comments":"Thanks for reporting !\r\nThere indeed a mismatch between the features and the kwargs description\r\n\r\nI believe `answer_start` was added to match the squad dataset format for consistency, even though it is not used in the metric computation. I think I'd rather keep it this way, so that you can just give `references=squad[\"answers\"]` to `.compute()`.\r\nMaybe we can just fix the description then.","body":"The [description](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/squad\/squad.py#L39) doesn't mention `answer_start` in squad. However the `datasets.features` require [it](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/squad\/squad.py#L68). It's also not used in the evaluation.","comment_length":63,"text":"Squad Metric Description & Feature Mismatch \n The [description](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/squad\/squad.py#L39) doesn't mention `answer_start` in squad. However the `datasets.features` require [it](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/squad\/squad.py#L68). It's also not used in the evaluation. \n Thanks for reporting !\r\nThere indeed a mismatch between the features and the kwargs description\r\n\r\nI believe `answer_start` was added to match the squad dataset format for consistency, even though it is not used in the metric computation. I think I'd rather keep it this way, so that you can just give `references=squad[\"answers\"]` to `.compute()`.\r\nMaybe we can just fix the description then.","embeddings":[0.0384348109,-0.1900426894,-0.0537597947,-0.0725321695,0.4192170799,-0.0426003039,0.1088642105,0.0794378519,-0.2159082294,0.1062574461,-0.1819749475,0.4140356183,0.3362865746,-0.0604616292,0.1003780365,0.156886667,0.0473284982,0.083982572,-0.0124868695,-0.139941901,-0.1415313631,0.2984051406,-0.2252353281,0.2715794444,-0.2736495733,-0.0178927816,-0.0506354868,0.1622512788,-0.3746500313,-0.5675835013,0.2677565515,0.0306367315,-0.3266791105,0.2287681401,-0.0001090158,-0.1445931643,0.232779637,-0.1377821714,-0.1771668792,-0.0342071764,0.1800467074,-0.3626684248,0.1918864399,-0.4109576643,-0.0487220921,0.114833653,0.0063604061,-0.0542373285,0.3847958148,0.1295463592,0.2297418267,0.4827030599,0.0115810884,-0.2939109802,-0.06691356,-0.035664212,-0.1096247807,0.2075034827,0.2950330675,-0.182152763,-0.2618212402,0.2340934575,0.0406408608,0.0445102379,0.4658947289,0.0488792248,0.2508768439,-0.1295684427,0.0629555508,0.0557779558,0.2274373919,-0.2071215808,-0.3798899055,-0.1025848687,0.0531466864,-0.0919421762,0.0872523338,0.082811147,-0.1797547489,-0.0123234494,-0.3148842156,0.101958096,-0.1891083568,0.0358035155,-0.0675041601,0.3396831453,-0.1549963802,0.0361524001,0.2265035808,-0.0704884753,-0.133175984,0.0341340005,-0.0735610873,0.2844384611,-0.4198206961,-0.1560457647,0.2228899747,-0.0200363155,0.6190392375,-0.0421511903,0.1646747142,0.0248638578,0.3776242137,0.177577734,0.0793167651,0.499111861,0.5429851413,-0.1149800494,-0.0014036827,0.1581891924,-0.1400866956,0.0006069957,0.0942751169,-0.14679268,0.1362455189,0.0379651599,0.1092147082,-0.1411068738,-0.1879776269,0.1140722781,0.0556677803,-0.0816742182,0.0181318857,0.3469876647,-0.0054648593,-0.0605896153,-0.1710804999,0.2534984052,-0.0183081664,0.1575538665,-0.3258033991,0.1219163984,-0.2628063262,0.0448266417,-0.1285425723,-0.1219590232,0.4747461081,-0.0501228869,0.7316465974,-0.301017642,-0.0586592481,0.0636631474,-0.0390798114,-0.0274202675,-0.0383260511,0.066543363,0.3291544616,-0.3137712181,-0.0523793772,-0.0128110182,-0.2679873407,-0.2070085108,0.0971626937,0.216930747,-0.5295918584,0.0841749236,0.01919806,0.322234869,0.0022982452,-0.0381796472,-0.0678529441,0.0785545856,-0.4157375097,-0.2373695523,0.4349510074,0.1600910276,-0.0744292885,-0.0936143175,0.2863320708,-0.0914693549,-0.2294351906,0.0623862445,-0.0687920302,0.0911994725,-0.1189325377,0.2205138654,0.4820013046,-0.6493625641,-0.3048389852,0.0117189279,-0.1494525969,-0.1385005713,-0.2431694567,-0.0276350416,0.2740222812,0.1774703264,0.0658344254,0.0588019751,0.2066640109,-0.0599471405,-0.3792648017,-0.1584869474,-0.0096754711,-0.049499087,-0.0848823562,0.1371218711,0.0600272268,0.1734498143,0.0599574819,0.0350070633,-0.1196739227,0.0897828713,0.1923914105,-0.1864150614,0.1418641061,-0.2624903619,-0.325923115,0.2314723432,-0.4219742715,0.1186333448,0.1511097252,-0.3517677188,-0.6282202005,0.2035232484,-0.1491964459,-0.2065085024,0.2031186521,-0.3312682807,0.2791960239,0.0093606655,-0.1268214881,-0.1456931084,-0.2963276505,0.0748766959,0.0622849949,0.0990387723,-0.2384701222,0.0805219635,0.1940943003,0.2733443975,0.1319565922,0.1275603324,-0.125713408,0.4458444118,0.0097677987,0.1940330416,0.2939254344,0.0445197187,-0.0508879311,0.007126587,-0.0308100171,0.2197391391,0.0883625671,-0.2323543727,-0.3145397902,0.4246505797,-0.1224974021,0.1658186466,0.0343078412,0.1127615124,0.1513227373,-0.0237647537,-0.2545131743,-0.0314414017,0.0066199945,-0.0501973704,0.054263439,-0.0734683499,-0.0802657232,-0.0812714621,0.6966173649,0.0119204083,0.0642427728,0.1774988472,-0.0329265259,-0.2631054223,-0.1926166117,-0.4333093762,0.3488348722,0.1907119304,0.0782111883,0.0230336208,-0.043449495,-0.0238941554,0.0534150153,0.1939957142,-0.0103237517,0.3542571664,0.3989413083,-0.1800171137,-0.0387113355,0.1886887103,-0.2210490704,0.0798667595,-0.2387335896,-0.00154825,0.240759939,0.0406457782,0.0088517489,-0.1270717084,-0.4600836337,-0.4150730371,0.3634660542,0.0263142101,0.216481477,0.3412558436,0.2149056941,0.1921670884,0.1436503232,0.0168056451,0.1201283485,-0.4616580904,0.1071901992,0.0702019855,-0.162426278,0.1004727408,0.2189997733,-0.3725537062,-0.0299972836,-0.1779542416,-0.4762518406,0.1616972685,-0.0275497325,0.3890663385,0.1912371367,0.1360046715,-0.497957021,-0.107298784,0.4114948213,-0.3360354006,-0.3585233688,-0.1036209464,0.0542120263,-0.1929950267,-0.2370444238,-0.3711310923,0.0113394381,-0.4087290764,0.387157023,0.0803839564,0.0601451471,0.2898188233,-0.0015505245,0.3929220736,-0.1249706298,-0.1458929032,-0.4327925444,-0.3826179504,0.3666389585,-0.0849316046,-0.4884542227,0.0189987086,-0.1149359792,-0.0452607162,-0.3103398383,-0.0352042839,-1.136441946,0.170079723,0.127178207,-0.1813671589,0.1192192808,0.2368253022,-0.560041666,-0.0788351595,-0.3416191638,-0.4113590419,0.3146167994,0.283890456,0.1413577199,-0.2506248951,0.2161833942,0.1242948472,0.8888487816,0.2331058532,0.1609022915,-0.2426312268,-0.1912288368,0.1344807446,0.0041368171,-0.3907559216,0.1829271019,-0.0979354605,0.024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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/657","title":"Squad Metric Description & Feature Mismatch","comments":"But then providing the `answer_start` becomes mandatory since the format of the features is checked against the one provided in the squad [file](https:\/\/github.com\/huggingface\/datasets\/pull\/658\/files).","body":"The [description](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/squad\/squad.py#L39) doesn't mention `answer_start` in squad. However the `datasets.features` require [it](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/squad\/squad.py#L68). It's also not used in the evaluation.","comment_length":23,"text":"Squad Metric Description & Feature Mismatch \n The [description](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/squad\/squad.py#L39) doesn't mention `answer_start` in squad. However the `datasets.features` require [it](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/squad\/squad.py#L68). It's also not used in the evaluation. \n But then providing the `answer_start` becomes mandatory since the format of the features is checked against the one provided in the squad [file](https:\/\/github.com\/huggingface\/datasets\/pull\/658\/files).","embeddings":[0.0714836419,-0.3686691225,-0.0856218711,-0.0963039696,0.409524858,-0.104312025,0.1253436506,0.0395658389,-0.2808919847,0.0161719397,-0.0155542269,0.3508732915,0.2142232358,0.0877162814,-0.1212031543,0.1233918071,0.0228273273,0.1146427095,-0.1461589038,-0.0721072927,-0.1057656407,0.2958207428,-0.3101403713,0.1393026263,-0.3326881826,0.2032144964,-0.0322561525,0.2053763419,-0.5319287181,-0.4879083633,0.3031818569,0.0732051581,-0.2346524149,0.2549627125,-0.0001087786,-0.1570355594,0.1928605288,-0.1502050757,-0.0956225917,-0.1865075678,0.1002465189,-0.301348418,0.208040446,-0.2634674609,-0.1206465289,0.1266855448,-0.0258798562,0.015812831,0.5856692195,0.1773659736,0.2096451968,0.527971983,0.0051359362,-0.2664542496,-0.0079287859,0.0832580253,-0.1241676286,0.2411233634,0.3226965666,-0.009124618,-0.2592707872,0.215482235,0.1822725385,0.090565145,0.415504694,0.0111930734,0.1710323691,-0.1892596632,-0.1101630777,0.0799341425,0.2163025886,-0.1620045155,-0.2368020415,-0.1409005076,0.0533003248,-0.1408354789,0.2025523186,0.0599073395,-0.0294725504,0.0576386154,-0.2525348961,0.0616584718,-0.1925629675,-0.056944292,-0.0384190679,0.1845898181,-0.2179058045,-0.0223872345,0.0588668995,-0.077031076,-0.3285889626,0.0275729075,-0.0039779129,0.1555655897,-0.346353054,-0.2450008839,0.1401151717,-0.012090506,0.5897557139,0.130713135,0.0278300848,-0.0755563453,0.1916167736,0.0966398194,0.0683344454,0.359431833,0.5389780402,-0.260147512,-0.0470285937,0.0979135334,-0.0602265336,0.0362291634,0.2225055546,-0.1853069216,0.1102717891,-0.0105525507,0.1973386556,-0.1863773465,-0.3243863881,0.2362409085,0.0331388563,-0.1849229038,0.1610157937,0.3308874965,0.0394728146,-0.1160728559,-0.1364458352,0.3753709197,-0.0615498088,0.1788348407,-0.2362853438,0.0175781846,-0.1785049289,0.0176158082,0.0770150349,-0.094592005,0.468426168,0.1037268713,0.6205449104,-0.2139698863,0.0900896639,0.0907742456,-0.0086815041,-0.0588397644,-0.0381924137,0.0323758982,0.3155770302,-0.1487368196,-0.0383848026,-0.0121804858,-0.3050554395,-0.1685030907,0.0228985567,0.247195974,-0.4179111123,0.1742908955,0.1866101474,0.1592618972,-0.1578108668,-0.0198884755,0.0972554982,-0.0071580592,-0.2175530791,-0.0871073008,0.4565599561,0.3123025298,-0.0138828969,-0.1896150857,0.4936756194,-0.2422699481,-0.3501276076,0.1747062504,-0.087278381,-0.0625979453,-0.2254831791,0.1653340608,0.4319165349,-0.6663405299,-0.121143572,0.1177966073,-0.4087069035,-0.1348310262,-0.1633995771,-0.1139434278,0.2157614082,0.041427508,0.0647868514,0.1040396988,0.0628902018,-0.0814871639,-0.3227227032,-0.096813947,-0.1105398908,-0.0340452082,-0.2024563849,0.0050684786,-0.0367087238,0.2662734389,-0.0300772581,0.1435508728,-0.2213812768,0.0760081783,0.2579780221,-0.2862824798,0.0779082999,-0.1838898659,-0.3849327564,0.0795555711,-0.3601306379,0.0163337737,0.1635149717,-0.2715017796,-0.542047739,0.0469520278,-0.1306706965,-0.1301482469,0.196409449,-0.2922311127,0.1908030063,-0.0649100021,-0.2068103254,0.0555554591,-0.5343649387,0.2205049247,0.0747225508,-0.039069593,-0.2212112099,0.1489003301,0.1544100344,0.1852428764,0.1026900783,-0.0409526005,-0.1115171164,0.5497416854,-0.0968206823,0.1595625728,0.2671706676,-0.0458367988,0.0607396588,-0.1218105704,-0.1407346427,0.0995274484,-0.0703592896,-0.1978718042,-0.3766762614,0.4630094767,-0.1700356603,0.1172300279,0.1548947841,0.0790314749,0.19189924,-0.1135168746,-0.4225904047,-0.2107368112,0.1589574367,0.0613230653,0.0597392358,0.067355983,-0.2234184444,-0.0676215887,0.7663306594,-0.0128328009,0.0169440471,0.1607962549,0.0620675348,-0.1778208166,-0.1424493641,-0.4926059842,0.5218940973,0.2119178772,0.0149998432,0.0737033784,-0.1531002074,-0.192088604,0.0915806592,0.0498369373,-0.0947701484,0.2592765689,0.2015848011,-0.1314311922,-0.0671300739,0.0983822271,-0.1724919826,-0.1129594594,-0.2569566965,-0.1258196682,0.1275725663,0.0317176543,-0.1661224812,-0.1506333798,-0.3098291159,-0.2745752037,0.3487704396,-0.0223367456,0.2935876548,0.3687449694,0.1583545059,0.336248219,0.0544574857,-0.1106502786,0.1403378695,-0.2051765025,0.1116326004,0.0981991366,-0.1121825799,0.0899347961,0.2285868078,-0.5091598034,-0.0139453905,-0.0505290627,-0.4765910506,0.2807789445,-0.0454615019,0.3085437715,0.0962103009,0.0719201341,-0.3590722382,0.0183290895,0.3224570453,-0.3235698938,-0.3216747344,-0.1864978522,-0.0132516613,-0.2689188123,-0.3647123873,-0.5461171269,0.0952373967,-0.2989224494,0.3766980469,0.1400758773,-0.0273447372,0.2844640315,-0.0148345474,0.3455062509,-0.2718372643,0.006417166,-0.3936613798,-0.4697285295,0.226873219,-0.1384315342,-0.4657115042,0.0588319972,0.0330042243,-0.0363267474,-0.2294948697,-0.0529052913,-1.0586352348,0.0498611033,0.1250767112,-0.2215347439,0.1816776693,0.3187162578,-0.5645013452,-0.0541788153,-0.3160336912,-0.3421313465,0.3988611102,0.2140415609,0.2097068578,-0.3139363527,0.3115652204,0.1362070441,0.9513806105,0.2537688911,0.1607136726,-0.0673191771,-0.0600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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/651","title":"Problem with JSON dataset format","comments":"Currently the `json` dataset doesn't support this format unfortunately.\r\nHowever you could load it with\r\n```python\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\n\r\ndf = pd.read_json(\"path_to_local.json\", orient=\"index\")\r\ndataset = Dataset.from_pandas(df)\r\n```","body":"I have a local json dataset with the following form.\r\n\r\n{\r\n    'id01234': {'key1': value1, 'key2': value2, 'key3': value3},\r\n    'id01235': {'key1': value1, 'key2': value2, 'key3': value3},\r\n    .\r\n    .\r\n    .\r\n    'id09999': {'key1': value1, 'key2': value2, 'key3': value3}\r\n}\r\nNote that instead of a list of records it's basically a dictionary of key value pairs with the keys being the record_ids and the values being the corresponding record.\r\n\r\nReading this with json:\r\n\r\n```\r\ndata = datasets.load('json', data_files='path_to_local.json')\r\n```\r\nThrows an error and asks me to chose a field. What's the right way to handle this?","comment_length":32,"text":"Problem with JSON dataset format \n I have a local json dataset with the following form.\r\n\r\n{\r\n    'id01234': {'key1': value1, 'key2': value2, 'key3': value3},\r\n    'id01235': {'key1': value1, 'key2': value2, 'key3': value3},\r\n    .\r\n    .\r\n    .\r\n    'id09999': {'key1': value1, 'key2': value2, 'key3': value3}\r\n}\r\nNote that instead of a list of records it's basically a dictionary of key value pairs with the keys being the record_ids and the values being the corresponding record.\r\n\r\nReading this with json:\r\n\r\n```\r\ndata = datasets.load('json', data_files='path_to_local.json')\r\n```\r\nThrows an error and asks me to chose a field. What's the right way to handle this? \n Currently the `json` dataset doesn't support this format unfortunately.\r\nHowever you could load it with\r\n```python\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\n\r\ndf = pd.read_json(\"path_to_local.json\", orient=\"index\")\r\ndataset = Dataset.from_pandas(df)\r\n```","embeddings":[0.1458693445,0.1199862286,-0.0658117831,0.380238533,-0.099493064,0.2573181391,0.2378643304,0.4517599046,0.4588271081,-0.0621231049,0.1368803829,0.4856531322,-0.1455082148,0.2548018694,-0.3202168941,-0.1480671614,0.0985162109,0.2186025679,0.2703855634,-0.0470258854,-0.439702332,-0.1153432727,0.0489592962,0.0190584902,0.0482812189,-0.2671149373,-0.0260776114,0.0717845112,-0.1620740592,-0.4581755102,0.156412676,0.1291371733,0.0828994438,0.1049765572,-0.0001129885,0.0956471115,0.1844623536,-0.0450863615,-0.1903440058,-0.299005568,-0.7772290111,-0.185928598,0.3369084001,-0.2603326738,-0.2133954018,-0.8189460039,-0.1771575511,-0.548494637,0.3485087454,0.0538749993,0.1756142378,-0.0457043499,-0.1172699705,0.2295890301,-0.1242401972,0.4465412796,0.0052379719,0.2138409317,0.1540415138,0.2234113812,0.1710988283,-0.1115013063,-0.2714059353,-0.1085489169,0.3714460135,-0.0568574965,-0.0460285805,0.0683584064,0.0254373662,0.2053817064,0.730425477,-0.0766048282,-0.455851227,-0.20778808,-0.070964992,-0.0512549803,0.0490764119,0.1400657892,-0.031733308,0.4423697889,-0.1603931785,-0.1668311059,-0.0914005786,0.4621090591,-0.1848821789,-0.0156272762,-0.0242636725,0.1310406029,-0.0488182642,-0.3260755539,0.0286141243,-0.4399697781,0.1288776398,0.0635579601,-0.0337626673,-0.2168125808,-0.0416974947,-0.3340826035,0.2119557559,-0.0481729284,-0.0074766404,0.1113776192,-0.2421927154,0.3040264249,0.6694840193,-0.2420202792,0.4449410439,0.0218048561,0.0466984026,-0.1306554377,0.2354110777,-0.0629480258,-0.1822791696,0.029254932,-0.2261861563,-0.2180085033,0.094256714,-0.1267885864,-0.1877722442,0.463996768,-0.4986902475,0.038636975,0.0729411542,0.164200291,-0.0591540337,0.4581668079,0.0739175826,0.309589386,0.2918245196,0.2605212927,-0.092348285,0.0517236143,0.298828721,-0.200180918,0.0685544983,-0.0650402978,0.2729404271,0.1133925244,-0.0976343974,-0.1352136433,0.0604379103,0.0339622423,0.03365485,0.3438401818,0.115554899,0.2728944123,0.058720082,-0.7610890269,-0.2815734446,0.3070136011,-0.1231466979,-0.1196510568,-0.2131774276,0.1140166596,-0.214539364,-0.1683910191,-0.5135980844,0.1099394187,-0.0350384787,0.0967568904,-0.1146052927,0.0682745203,-0.0962640792,-0.3119336367,-0.0715676174,0.2158742994,-0.7670142055,0.0624736212,0.2604255378,0.1439981759,-0.0009404705,0.3863136768,-0.3122444153,0.1130622253,0.0466580428,0.2099815607,0.346383214,-0.4240632057,-0.1853321046,0.6589516401,0.131164059,0.0572888851,0.1835733652,0.0641262904,0.0861064792,-0.1249698699,0.4913157225,0.2782319784,-0.1301450729,0.13153328,-0.0413275771,-0.2937499583,0.3844843507,0.1433086097,-0.2987167835,0.0400857553,-0.0638527125,0.1849567145,0.1030333042,-0.1027433276,-0.0346187055,0.2544205189,0.1330920458,-0.2291435748,-0.0917216688,0.1678028256,-0.4931346178,0.0583169647,0.0407988913,-0.2706646919,-0.1732385457,-0.22928828,-0.2658457458,-0.0448416956,-0.3415186107,0.3753657937,0.1029696763,-0.1386786848,-0.0613617785,0.0832327902,-0.1890549809,-0.1287673414,0.1051743925,0.0485650003,-0.3560819626,0.2340255082,0.0891059116,0.1402282566,0.1026291475,0.0952601954,0.1381038129,-0.0816348866,0.049051594,0.2223201394,0.2931486964,-0.0688418895,0.0757608488,0.1965526193,0.2696464062,0.2145307511,-0.0805483013,0.0517453253,0.2669302523,-0.1208436042,-0.4998886585,0.5624603629,-0.0740086138,0.2754159272,-0.2417865694,0.1344882548,0.5070407391,0.1711891741,-0.2448195219,-0.2652659714,-0.1872988194,0.2555713952,0.0550000519,0.1753744334,-0.5730938911,0.0807779431,0.3986865878,-0.1162142083,0.1785604507,0.0774062574,0.0183163974,0.1102359593,0.0166593287,0.3469281793,0.1840494573,0.1019805148,-0.1722500473,-0.1593765467,0.1704727858,-0.1262270063,-0.030276861,0.0629003644,0.1021463275,0.1732898206,0.1294019371,-0.2646361589,0.0097442307,0.1082995757,0.1709943116,0.3211760223,-0.1843876839,0.2181835771,-0.5527148843,-0.1690990031,-0.1915680319,-0.3349094391,0.0057535684,-0.2660440803,0.0143419644,0.0908641964,-0.1945691407,0.0133133223,0.0123492973,0.0773505047,-0.0093088774,-0.4200900793,-0.0577931404,-0.170786798,-0.1814787835,0.0558716878,0.4747773409,0.0712883994,0.1124226898,-0.1644038558,-0.1423163116,-0.0538366847,0.1102759913,0.1040438935,-0.0833402127,0.1146071479,0.1896594167,0.5317562819,-0.3402480483,-0.1636222452,0.2713463604,0.3887449801,-0.1310292035,0.0392323136,0.139456138,0.059592884,0.0794297606,-0.3075087965,-0.1711047143,-0.2167973965,0.435690552,0.1593658328,0.1710596681,-0.0084817931,0.1128979996,-0.0164313708,0.1447687745,-0.1445475072,-0.0614725128,-0.3365549147,0.3744822741,-0.1530004442,-0.3797767758,0.0752773434,-0.0251518954,-0.0309548583,-0.2401718646,-0.099135004,-0.0014947819,0.042244602,0.0657009631,0.016220659,-0.1312144399,0.1923988461,0.1064992845,0.0139339566,-0.0919715241,-0.1125551462,0.2691612244,0.2732815742,0.1071154699,0.3044460416,0.5737470388,0.0091620311,0.1929805726,0.2185966223,-0.0632901192,0.082307376,-0.0631020144,-0.0089376373,-0.081286788,0.0083392365,-0.2582142651,0.02394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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/650","title":"dummy data testing can't test datasets using `dl_manager.extract` in `_split_generators`","comments":"Hi :) \r\nIn your dummy data zip file you can just have `subset000.xz` as directories instead of compressed files.\r\nLet me know if it helps","body":"Hi, I recently want to add a dataset whose source data is like this\r\n```\r\nopenwebtext.tar.xz\r\n  |__ openwebtext\r\n         |__subset000.xz\r\n         |     |__ ....txt\r\n         |     |__ ....txt\r\n         |     ...\r\n         |__ subset001.xz\r\n         |\r\n         ....\r\n```\r\nSo I wrote `openwebtext.py` like this\r\n```\r\n def _split_generators(self, dl_manager):\r\n        dl_dir = dl_manager.download_and_extract(_URL)\r\n        owt_dir = os.path.join(dl_dir, 'openwebtext')\r\n        subset_xzs = [\r\n            os.path.join(owt_dir, file_name) for file_name in os.listdir(owt_dir) if file_name.endswith('xz') # filter out ...xz.lock\r\n        ]\r\n        ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count()*0.75))\r\n        nested_txt_files = [ \r\n          [ \r\n            os.path.join(ex_dir,txt_file_name) for txt_file_name in os.listdir(ex_dir) if txt_file_name.endswith('txt')\r\n          ] for ex_dir in ex_dirs\r\n        ]\r\n        txt_files = chain(*nested_txt_files)\r\n        return [\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TRAIN, gen_kwargs={\"txt_files\": txt_files}\r\n            ),\r\n        ]\r\n```\r\nAll went good, I can load and use real openwebtext, except when I try to test with dummy data. The problem is  `MockDownloadManager.extract` do nothing, so `ex_dirs = dl_manager.extract(subset_xzs)` won't decompress `subset_xxx.xz`s for me.\r\n\r\nHow should I do ? Or you can modify `MockDownloadManager` to make it like a real `DownloadManager` ?","comment_length":25,"text":"dummy data testing can't test datasets using `dl_manager.extract` in `_split_generators` \n Hi, I recently want to add a dataset whose source data is like this\r\n```\r\nopenwebtext.tar.xz\r\n  |__ openwebtext\r\n         |__subset000.xz\r\n         |     |__ ....txt\r\n         |     |__ ....txt\r\n         |     ...\r\n         |__ subset001.xz\r\n         |\r\n         ....\r\n```\r\nSo I wrote `openwebtext.py` like this\r\n```\r\n def _split_generators(self, dl_manager):\r\n        dl_dir = dl_manager.download_and_extract(_URL)\r\n        owt_dir = os.path.join(dl_dir, 'openwebtext')\r\n        subset_xzs = [\r\n            os.path.join(owt_dir, file_name) for file_name in os.listdir(owt_dir) if file_name.endswith('xz') # filter out ...xz.lock\r\n        ]\r\n        ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count()*0.75))\r\n        nested_txt_files = [ \r\n          [ \r\n            os.path.join(ex_dir,txt_file_name) for txt_file_name in os.listdir(ex_dir) if txt_file_name.endswith('txt')\r\n          ] for ex_dir in ex_dirs\r\n        ]\r\n        txt_files = chain(*nested_txt_files)\r\n        return [\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TRAIN, gen_kwargs={\"txt_files\": txt_files}\r\n            ),\r\n        ]\r\n```\r\nAll went good, I can load and use real openwebtext, except when I try to test with dummy data. The problem is  `MockDownloadManager.extract` do nothing, so `ex_dirs = dl_manager.extract(subset_xzs)` won't decompress `subset_xxx.xz`s for me.\r\n\r\nHow should I do ? Or you can modify `MockDownloadManager` to make it like a real `DownloadManager` ? \n Hi :) \r\nIn your dummy data zip file you can just have `subset000.xz` as directories instead of compressed files.\r\nLet me know if it helps","embeddings":[-0.2775548995,0.0534974262,0.0134969996,0.3913993239,-0.072360985,0.1356922984,0.4522341788,0.3349655271,-0.0165996812,0.0843322054,-0.0575094037,0.2236393094,-0.1632839441,-0.0482924581,0.0641588718,-0.1906499267,-0.1669515967,0.2437199652,0.0134650618,0.0311771519,-0.2944734991,0.0815392062,-0.2834986746,-0.0913858116,-0.1371546984,0.0181267131,-0.2095643431,0.307261467,-0.2457742095,-0.2825932205,0.0010185741,0.0183561444,0.0423617736,0.5455899835,-0.0001227712,0.1388189197,0.3071190715,-0.2575384676,-0.3333342373,-0.0337697677,-0.2507253587,-0.2779996395,-0.1756441742,0.021302674,-0.0840833411,-0.2268642932,-0.1854513139,-0.2622463405,0.3548209369,0.2717152238,0.0923322141,0.61311692,-0.0415804572,-0.1280228794,0.2287501246,-0.0264097862,-0.1879369766,0.2233155817,-0.0529861674,-0.0236796904,0.0873368829,-0.1019545943,-0.0011850322,0.4950457215,-0.3201796412,0.119338505,0.1507472992,-0.3189719617,-0.0463720262,0.550545156,0.4802380502,-0.1391587257,-0.3204444051,-0.2336205691,-0.2743136585,-0.1307140291,0.0747650787,0.2180380672,-0.2292438745,0.3225835264,-0.0293132477,-0.0288202334,0.1385269314,0.2705383897,-0.2816978097,0.3059706092,-0.123613447,0.0502374358,0.4046254754,0.3228848279,0.2671747506,-0.2975229323,-0.3519619703,-0.0660928413,-0.1584336311,-0.2081910372,-0.3658329248,-0.4488224387,0.1594887227,0.3530174494,-0.0114581212,0.1824113429,0.0564967133,0.153296262,0.0790388882,0.4907517433,0.2176588327,0.2714265287,0.0527708866,0.1392944902,0.00167685,0.04832891,0.0824967474,-0.3559404314,-0.2401300967,0.1764753908,0.321375221,-0.1328305751,-0.3999723792,-0.1878486127,-0.2729802728,-0.2551985681,-0.003033394,0.3480630219,-0.0552738905,0.152787447,0.12764512,0.472751826,-0.049808234,-0.5964107513,-0.1689494103,0.0743230879,-0.2225880921,0.113905549,0.1182279512,0.0219427533,0.4373009205,0.1158564389,-0.1179253533,-0.0122450339,0.6474174857,-0.0120400097,0.465523392,0.148574084,0.1298144609,-0.068881914,0.1100025848,-0.1474854946,-0.0640193745,0.0963684469,0.0561538599,-0.1661674827,0.0018030383,0.048893597,-0.4305842221,0.2151851058,-0.0022326279,0.1078094617,-0.0936745405,-0.0127065079,-0.0310162008,-0.046401605,-0.0932798758,0.1658296287,0.3068811595,0.4303734601,-0.2990069985,0.1886532307,-0.4357553422,0.105780296,0.5174133778,-0.0811935589,-0.0792279467,0.3894143403,-0.281427592,0.1036903337,0.4281542301,-0.1070776582,-0.1339181811,0.3495474756,-0.0856407434,0.2467711121,0.3035255373,-0.3556995988,-0.1740887761,-0.0409197249,-0.4010946453,0.1216159016,-0.0223300084,-0.0752302706,-0.3159443736,-0.1216393262,0.3932132125,0.0853224248,0.0043444955,0.1314850748,-0.0922577605,0.1189817488,0.2170329243,-0.0133684017,0.0638615638,-0.0933699682,-0.3415872753,-0.3053729534,-0.06134158,0.1441963464,-0.2157241702,0.3868087828,-0.3383391201,0.3813674152,0.3305300772,-0.0956297293,-0.5658615828,-0.0990950167,-0.2402652353,-0.3324826658,0.0340706035,0.3017258644,0.1029655412,0.0747674704,-0.0000563727,-0.2724884152,0.3430287838,0.1322463006,-0.3578069806,0.1702773571,0.0935114697,-0.1067070961,-0.0051657679,0.0438547842,-0.1398245096,-0.4835775197,-0.1919870526,0.2014565617,0.226877287,-0.1514827162,-0.1123245284,-0.2281041443,0.016749477,-0.148781836,0.0856785253,0.4087572396,0.05439239,-0.1391613334,0.1577101201,0.151158154,0.3081716597,-0.0488703065,-0.2156967819,0.0614776537,0.1661095172,-0.3463017642,0.214206472,-0.2726497054,0.0913260654,0.2350987345,0.3804805279,-0.0418399572,0.28460145,0.0348784402,0.285027802,-0.0671256632,0.1439881325,-0.0548982769,-0.3179729879,-0.1246314719,0.2577799857,0.2502178848,0.5530324578,-0.0051692496,0.3902249932,0.1067654788,0.1798737496,-0.1399505287,0.3181187212,0.0894173011,-0.1737308651,0.2202003449,-0.296266228,-0.3673281074,-0.1835876405,0.2700137794,0.3687210679,0.3929932714,-0.3055078983,-0.109008193,-0.418829143,0.1396477818,0.0166623853,-0.2076057792,-0.0101963971,-0.1257264167,-0.0310548041,0.0821425617,-0.231161356,0.0737062767,0.1541530341,0.1014865637,-0.0827399418,-0.0176653601,-0.2265048623,-0.0992558524,-0.3669633567,0.0497560315,0.3964762092,0.0628838018,0.1707433015,-0.256339252,-0.3311189711,-0.0444091037,-0.0038230647,0.283393085,0.0153302336,0.3275274932,0.2695204318,0.0554335229,0.3602997065,-0.1851060838,-0.074597761,-0.1926950365,-0.1971707642,0.1344675422,0.1516901702,0.1327989846,-0.2324692756,-0.5946473479,-0.150921464,-0.2586509287,0.0104379002,0.0821240395,-0.0298236683,0.28400442,0.0449707247,0.0342343412,0.221667245,-0.264914155,-0.2609222531,-0.3637113869,0.289039731,-0.23346138,-0.2668030262,0.0168261491,-0.5399339795,0.1551875323,-0.2124397755,-0.0537144877,-0.0245191772,0.1239686012,-0.1419387758,0.1030271351,0.2029168457,0.1878895611,-0.0453676954,0.0392214358,-0.1372805536,0.1033983901,0.2979875803,0.0868111998,0.5367175937,-0.1488341242,0.1738517433,0.0305474233,0.298599273,0.0357530974,0.0153352041,0.2445664853,-0.1797609329,0.292242676,-0.1095676348,-0.2095204443,-0.3042358756,0.0125172604,0.0374833681,0.286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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/650","title":"dummy data testing can't test datasets using `dl_manager.extract` in `_split_generators`","comments":"Thanks for your comment @lhoestq ,\r\nJust for confirmation, changing dummy data like this won't make dummy test test the functionality to extract `subsetxxx.xz` but actually kind of circumvent it. But since we will test the real data so it is ok ?","body":"Hi, I recently want to add a dataset whose source data is like this\r\n```\r\nopenwebtext.tar.xz\r\n  |__ openwebtext\r\n         |__subset000.xz\r\n         |     |__ ....txt\r\n         |     |__ ....txt\r\n         |     ...\r\n         |__ subset001.xz\r\n         |\r\n         ....\r\n```\r\nSo I wrote `openwebtext.py` like this\r\n```\r\n def _split_generators(self, dl_manager):\r\n        dl_dir = dl_manager.download_and_extract(_URL)\r\n        owt_dir = os.path.join(dl_dir, 'openwebtext')\r\n        subset_xzs = [\r\n            os.path.join(owt_dir, file_name) for file_name in os.listdir(owt_dir) if file_name.endswith('xz') # filter out ...xz.lock\r\n        ]\r\n        ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count()*0.75))\r\n        nested_txt_files = [ \r\n          [ \r\n            os.path.join(ex_dir,txt_file_name) for txt_file_name in os.listdir(ex_dir) if txt_file_name.endswith('txt')\r\n          ] for ex_dir in ex_dirs\r\n        ]\r\n        txt_files = chain(*nested_txt_files)\r\n        return [\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TRAIN, gen_kwargs={\"txt_files\": txt_files}\r\n            ),\r\n        ]\r\n```\r\nAll went good, I can load and use real openwebtext, except when I try to test with dummy data. The problem is  `MockDownloadManager.extract` do nothing, so `ex_dirs = dl_manager.extract(subset_xzs)` won't decompress `subset_xxx.xz`s for me.\r\n\r\nHow should I do ? Or you can modify `MockDownloadManager` to make it like a real `DownloadManager` ?","comment_length":43,"text":"dummy data testing can't test datasets using `dl_manager.extract` in `_split_generators` \n Hi, I recently want to add a dataset whose source data is like this\r\n```\r\nopenwebtext.tar.xz\r\n  |__ openwebtext\r\n         |__subset000.xz\r\n         |     |__ ....txt\r\n         |     |__ ....txt\r\n         |     ...\r\n         |__ subset001.xz\r\n         |\r\n         ....\r\n```\r\nSo I wrote `openwebtext.py` like this\r\n```\r\n def _split_generators(self, dl_manager):\r\n        dl_dir = dl_manager.download_and_extract(_URL)\r\n        owt_dir = os.path.join(dl_dir, 'openwebtext')\r\n        subset_xzs = [\r\n            os.path.join(owt_dir, file_name) for file_name in os.listdir(owt_dir) if file_name.endswith('xz') # filter out ...xz.lock\r\n        ]\r\n        ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count()*0.75))\r\n        nested_txt_files = [ \r\n          [ \r\n            os.path.join(ex_dir,txt_file_name) for txt_file_name in os.listdir(ex_dir) if txt_file_name.endswith('txt')\r\n          ] for ex_dir in ex_dirs\r\n        ]\r\n        txt_files = chain(*nested_txt_files)\r\n        return [\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TRAIN, gen_kwargs={\"txt_files\": txt_files}\r\n            ),\r\n        ]\r\n```\r\nAll went good, I can load and use real openwebtext, except when I try to test with dummy data. The problem is  `MockDownloadManager.extract` do nothing, so `ex_dirs = dl_manager.extract(subset_xzs)` won't decompress `subset_xxx.xz`s for me.\r\n\r\nHow should I do ? Or you can modify `MockDownloadManager` to make it like a real `DownloadManager` ? \n Thanks for your comment @lhoestq ,\r\nJust for confirmation, changing dummy data like this won't make dummy test test the functionality to extract `subsetxxx.xz` but actually kind of circumvent it. But since we will test the real data so it is ok ?","embeddings":[-0.2775548995,0.0534974262,0.0134969996,0.3913993239,-0.072360985,0.1356922984,0.4522341788,0.3349655271,-0.0165996812,0.0843322054,-0.0575094037,0.2236393094,-0.1632839441,-0.0482924581,0.0641588718,-0.1906499267,-0.1669515967,0.2437199652,0.0134650618,0.0311771519,-0.2944734991,0.0815392062,-0.2834986746,-0.0913858116,-0.1371546984,0.0181267131,-0.2095643431,0.307261467,-0.2457742095,-0.2825932205,0.0010185741,0.0183561444,0.0423617736,0.5455899835,-0.0001227712,0.1388189197,0.3071190715,-0.2575384676,-0.3333342373,-0.0337697677,-0.2507253587,-0.2779996395,-0.1756441742,0.021302674,-0.0840833411,-0.2268642932,-0.1854513139,-0.2622463405,0.3548209369,0.2717152238,0.0923322141,0.61311692,-0.0415804572,-0.1280228794,0.2287501246,-0.0264097862,-0.1879369766,0.2233155817,-0.0529861674,-0.0236796904,0.0873368829,-0.1019545943,-0.0011850322,0.4950457215,-0.3201796412,0.119338505,0.1507472992,-0.3189719617,-0.0463720262,0.550545156,0.4802380502,-0.1391587257,-0.3204444051,-0.2336205691,-0.2743136585,-0.1307140291,0.0747650787,0.2180380672,-0.2292438745,0.3225835264,-0.0293132477,-0.0288202334,0.1385269314,0.2705383897,-0.2816978097,0.3059706092,-0.123613447,0.0502374358,0.4046254754,0.3228848279,0.2671747506,-0.2975229323,-0.3519619703,-0.0660928413,-0.1584336311,-0.2081910372,-0.3658329248,-0.4488224387,0.1594887227,0.3530174494,-0.0114581212,0.1824113429,0.0564967133,0.153296262,0.0790388882,0.4907517433,0.2176588327,0.2714265287,0.0527708866,0.1392944902,0.00167685,0.04832891,0.0824967474,-0.3559404314,-0.2401300967,0.1764753908,0.321375221,-0.1328305751,-0.3999723792,-0.1878486127,-0.2729802728,-0.2551985681,-0.003033394,0.3480630219,-0.0552738905,0.152787447,0.12764512,0.472751826,-0.049808234,-0.5964107513,-0.1689494103,0.0743230879,-0.2225880921,0.113905549,0.1182279512,0.0219427533,0.4373009205,0.1158564389,-0.1179253533,-0.0122450339,0.6474174857,-0.0120400097,0.465523392,0.148574084,0.1298144609,-0.068881914,0.1100025848,-0.1474854946,-0.0640193745,0.0963684469,0.0561538599,-0.1661674827,0.0018030383,0.048893597,-0.4305842221,0.2151851058,-0.0022326279,0.1078094617,-0.0936745405,-0.0127065079,-0.0310162008,-0.046401605,-0.0932798758,0.1658296287,0.3068811595,0.4303734601,-0.2990069985,0.1886532307,-0.4357553422,0.105780296,0.5174133778,-0.0811935589,-0.0792279467,0.3894143403,-0.281427592,0.1036903337,0.4281542301,-0.1070776582,-0.1339181811,0.3495474756,-0.0856407434,0.2467711121,0.3035255373,-0.3556995988,-0.1740887761,-0.0409197249,-0.4010946453,0.1216159016,-0.0223300084,-0.0752302706,-0.3159443736,-0.1216393262,0.3932132125,0.0853224248,0.0043444955,0.1314850748,-0.0922577605,0.1189817488,0.2170329243,-0.0133684017,0.0638615638,-0.0933699682,-0.3415872753,-0.3053729534,-0.06134158,0.1441963464,-0.2157241702,0.3868087828,-0.3383391201,0.3813674152,0.3305300772,-0.0956297293,-0.5658615828,-0.0990950167,-0.2402652353,-0.3324826658,0.0340706035,0.3017258644,0.1029655412,0.0747674704,-0.0000563727,-0.2724884152,0.3430287838,0.1322463006,-0.3578069806,0.1702773571,0.0935114697,-0.1067070961,-0.0051657679,0.0438547842,-0.1398245096,-0.4835775197,-0.1919870526,0.2014565617,0.226877287,-0.1514827162,-0.1123245284,-0.2281041443,0.016749477,-0.148781836,0.0856785253,0.4087572396,0.05439239,-0.1391613334,0.1577101201,0.151158154,0.3081716597,-0.0488703065,-0.2156967819,0.0614776537,0.1661095172,-0.3463017642,0.214206472,-0.2726497054,0.0913260654,0.2350987345,0.3804805279,-0.0418399572,0.28460145,0.0348784402,0.285027802,-0.0671256632,0.1439881325,-0.0548982769,-0.3179729879,-0.1246314719,0.2577799857,0.2502178848,0.5530324578,-0.0051692496,0.3902249932,0.1067654788,0.1798737496,-0.1399505287,0.3181187212,0.0894173011,-0.1737308651,0.2202003449,-0.296266228,-0.3673281074,-0.1835876405,0.2700137794,0.3687210679,0.3929932714,-0.3055078983,-0.109008193,-0.418829143,0.1396477818,0.0166623853,-0.2076057792,-0.0101963971,-0.1257264167,-0.0310548041,0.0821425617,-0.231161356,0.0737062767,0.1541530341,0.1014865637,-0.0827399418,-0.0176653601,-0.2265048623,-0.0992558524,-0.3669633567,0.0497560315,0.3964762092,0.0628838018,0.1707433015,-0.256339252,-0.3311189711,-0.0444091037,-0.0038230647,0.283393085,0.0153302336,0.3275274932,0.2695204318,0.0554335229,0.3602997065,-0.1851060838,-0.074597761,-0.1926950365,-0.1971707642,0.1344675422,0.1516901702,0.1327989846,-0.2324692756,-0.5946473479,-0.150921464,-0.2586509287,0.0104379002,0.0821240395,-0.0298236683,0.28400442,0.0449707247,0.0342343412,0.221667245,-0.264914155,-0.2609222531,-0.3637113869,0.289039731,-0.23346138,-0.2668030262,0.0168261491,-0.5399339795,0.1551875323,-0.2124397755,-0.0537144877,-0.0245191772,0.1239686012,-0.1419387758,0.1030271351,0.2029168457,0.1878895611,-0.0453676954,0.0392214358,-0.1372805536,0.1033983901,0.2979875803,0.0868111998,0.5367175937,-0.1488341242,0.1738517433,0.0305474233,0.298599273,0.0357530974,0.0153352041,0.2445664853,-0.1797609329,0.292242676,-0.1095676348,-0.2095204443,-0.3042358756,0.0125172604,0.0374833681,0.2866483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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/650","title":"dummy data testing can't test datasets using `dl_manager.extract` in `_split_generators`","comments":"Yes it's fine for now. We plan to add a job for slow tests.\r\nAnd at one point we'll also do another pass on the dummy data handling and consider extracting files.","body":"Hi, I recently want to add a dataset whose source data is like this\r\n```\r\nopenwebtext.tar.xz\r\n  |__ openwebtext\r\n         |__subset000.xz\r\n         |     |__ ....txt\r\n         |     |__ ....txt\r\n         |     ...\r\n         |__ subset001.xz\r\n         |\r\n         ....\r\n```\r\nSo I wrote `openwebtext.py` like this\r\n```\r\n def _split_generators(self, dl_manager):\r\n        dl_dir = dl_manager.download_and_extract(_URL)\r\n        owt_dir = os.path.join(dl_dir, 'openwebtext')\r\n        subset_xzs = [\r\n            os.path.join(owt_dir, file_name) for file_name in os.listdir(owt_dir) if file_name.endswith('xz') # filter out ...xz.lock\r\n        ]\r\n        ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count()*0.75))\r\n        nested_txt_files = [ \r\n          [ \r\n            os.path.join(ex_dir,txt_file_name) for txt_file_name in os.listdir(ex_dir) if txt_file_name.endswith('txt')\r\n          ] for ex_dir in ex_dirs\r\n        ]\r\n        txt_files = chain(*nested_txt_files)\r\n        return [\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TRAIN, gen_kwargs={\"txt_files\": txt_files}\r\n            ),\r\n        ]\r\n```\r\nAll went good, I can load and use real openwebtext, except when I try to test with dummy data. The problem is  `MockDownloadManager.extract` do nothing, so `ex_dirs = dl_manager.extract(subset_xzs)` won't decompress `subset_xxx.xz`s for me.\r\n\r\nHow should I do ? Or you can modify `MockDownloadManager` to make it like a real `DownloadManager` ?","comment_length":32,"text":"dummy data testing can't test datasets using `dl_manager.extract` in `_split_generators` \n Hi, I recently want to add a dataset whose source data is like this\r\n```\r\nopenwebtext.tar.xz\r\n  |__ openwebtext\r\n         |__subset000.xz\r\n         |     |__ ....txt\r\n         |     |__ ....txt\r\n         |     ...\r\n         |__ subset001.xz\r\n         |\r\n         ....\r\n```\r\nSo I wrote `openwebtext.py` like this\r\n```\r\n def _split_generators(self, dl_manager):\r\n        dl_dir = dl_manager.download_and_extract(_URL)\r\n        owt_dir = os.path.join(dl_dir, 'openwebtext')\r\n        subset_xzs = [\r\n            os.path.join(owt_dir, file_name) for file_name in os.listdir(owt_dir) if file_name.endswith('xz') # filter out ...xz.lock\r\n        ]\r\n        ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count()*0.75))\r\n        nested_txt_files = [ \r\n          [ \r\n            os.path.join(ex_dir,txt_file_name) for txt_file_name in os.listdir(ex_dir) if txt_file_name.endswith('txt')\r\n          ] for ex_dir in ex_dirs\r\n        ]\r\n        txt_files = chain(*nested_txt_files)\r\n        return [\r\n            datasets.SplitGenerator(\r\n                name=datasets.Split.TRAIN, gen_kwargs={\"txt_files\": txt_files}\r\n            ),\r\n        ]\r\n```\r\nAll went good, I can load and use real openwebtext, except when I try to test with dummy data. The problem is  `MockDownloadManager.extract` do nothing, so `ex_dirs = dl_manager.extract(subset_xzs)` won't decompress `subset_xxx.xz`s for me.\r\n\r\nHow should I do ? Or you can modify `MockDownloadManager` to make it like a real `DownloadManager` ? \n Yes it's fine for now. We plan to add a job for slow tests.\r\nAnd at one point we'll also do another pass on the dummy data handling and consider extracting files.","embeddings":[-0.2775548995,0.0534974262,0.0134969996,0.3913993239,-0.072360985,0.1356922984,0.4522341788,0.3349655271,-0.0165996812,0.0843322054,-0.0575094037,0.2236393094,-0.1632839441,-0.0482924581,0.0641588718,-0.1906499267,-0.1669515967,0.2437199652,0.0134650618,0.0311771519,-0.2944734991,0.0815392062,-0.2834986746,-0.0913858116,-0.1371546984,0.0181267131,-0.2095643431,0.307261467,-0.2457742095,-0.2825932205,0.0010185741,0.0183561444,0.0423617736,0.5455899835,-0.0001227712,0.1388189197,0.3071190715,-0.2575384676,-0.3333342373,-0.0337697677,-0.2507253587,-0.2779996395,-0.1756441742,0.021302674,-0.0840833411,-0.2268642932,-0.1854513139,-0.2622463405,0.3548209369,0.2717152238,0.0923322141,0.61311692,-0.0415804572,-0.1280228794,0.2287501246,-0.0264097862,-0.1879369766,0.2233155817,-0.0529861674,-0.0236796904,0.0873368829,-0.1019545943,-0.0011850322,0.4950457215,-0.3201796412,0.119338505,0.1507472992,-0.3189719617,-0.0463720262,0.550545156,0.4802380502,-0.1391587257,-0.3204444051,-0.2336205691,-0.2743136585,-0.1307140291,0.0747650787,0.2180380672,-0.2292438745,0.3225835264,-0.0293132477,-0.0288202334,0.1385269314,0.2705383897,-0.2816978097,0.3059706092,-0.123613447,0.0502374358,0.4046254754,0.3228848279,0.2671747506,-0.2975229323,-0.3519619703,-0.0660928413,-0.1584336311,-0.2081910372,-0.3658329248,-0.4488224387,0.1594887227,0.3530174494,-0.0114581212,0.1824113429,0.0564967133,0.153296262,0.0790388882,0.4907517433,0.2176588327,0.2714265287,0.0527708866,0.1392944902,0.00167685,0.04832891,0.0824967474,-0.3559404314,-0.2401300967,0.1764753908,0.321375221,-0.1328305751,-0.3999723792,-0.1878486127,-0.2729802728,-0.2551985681,-0.003033394,0.3480630219,-0.0552738905,0.152787447,0.12764512,0.472751826,-0.049808234,-0.5964107513,-0.1689494103,0.0743230879,-0.2225880921,0.113905549,0.1182279512,0.0219427533,0.4373009205,0.1158564389,-0.1179253533,-0.0122450339,0.6474174857,-0.0120400097,0.465523392,0.148574084,0.1298144609,-0.068881914,0.1100025848,-0.1474854946,-0.0640193745,0.0963684469,0.0561538599,-0.1661674827,0.0018030383,0.048893597,-0.4305842221,0.2151851058,-0.0022326279,0.1078094617,-0.0936745405,-0.0127065079,-0.0310162008,-0.046401605,-0.0932798758,0.1658296287,0.3068811595,0.4303734601,-0.2990069985,0.1886532307,-0.4357553422,0.105780296,0.5174133778,-0.0811935589,-0.0792279467,0.3894143403,-0.281427592,0.1036903337,0.4281542301,-0.1070776582,-0.1339181811,0.3495474756,-0.0856407434,0.2467711121,0.3035255373,-0.3556995988,-0.1740887761,-0.0409197249,-0.4010946453,0.1216159016,-0.0223300084,-0.0752302706,-0.3159443736,-0.1216393262,0.3932132125,0.0853224248,0.0043444955,0.1314850748,-0.0922577605,0.1189817488,0.2170329243,-0.0133684017,0.0638615638,-0.0933699682,-0.3415872753,-0.3053729534,-0.06134158,0.1441963464,-0.2157241702,0.3868087828,-0.3383391201,0.3813674152,0.3305300772,-0.0956297293,-0.5658615828,-0.0990950167,-0.2402652353,-0.3324826658,0.0340706035,0.3017258644,0.1029655412,0.0747674704,-0.0000563727,-0.2724884152,0.3430287838,0.1322463006,-0.3578069806,0.1702773571,0.0935114697,-0.1067070961,-0.0051657679,0.0438547842,-0.1398245096,-0.4835775197,-0.1919870526,0.2014565617,0.226877287,-0.1514827162,-0.1123245284,-0.2281041443,0.016749477,-0.148781836,0.0856785253,0.4087572396,0.05439239,-0.1391613334,0.1577101201,0.151158154,0.3081716597,-0.0488703065,-0.2156967819,0.0614776537,0.1661095172,-0.3463017642,0.214206472,-0.2726497054,0.0913260654,0.2350987345,0.3804805279,-0.0418399572,0.28460145,0.0348784402,0.285027802,-0.0671256632,0.1439881325,-0.0548982769,-0.3179729879,-0.1246314719,0.2577799857,0.2502178848,0.5530324578,-0.0051692496,0.3902249932,0.1067654788,0.1798737496,-0.1399505287,0.3181187212,0.0894173011,-0.1737308651,0.2202003449,-0.296266228,-0.3673281074,-0.1835876405,0.2700137794,0.3687210679,0.3929932714,-0.3055078983,-0.109008193,-0.418829143,0.1396477818,0.0166623853,-0.2076057792,-0.0101963971,-0.1257264167,-0.0310548041,0.0821425617,-0.231161356,0.0737062767,0.1541530341,0.1014865637,-0.0827399418,-0.0176653601,-0.2265048623,-0.0992558524,-0.3669633567,0.0497560315,0.3964762092,0.0628838018,0.1707433015,-0.256339252,-0.3311189711,-0.0444091037,-0.0038230647,0.283393085,0.0153302336,0.3275274932,0.2695204318,0.0554335229,0.3602997065,-0.1851060838,-0.074597761,-0.1926950365,-0.1971707642,0.1344675422,0.1516901702,0.1327989846,-0.2324692756,-0.5946473479,-0.150921464,-0.2586509287,0.0104379002,0.0821240395,-0.0298236683,0.28400442,0.0449707247,0.0342343412,0.221667245,-0.264914155,-0.2609222531,-0.3637113869,0.289039731,-0.23346138,-0.2668030262,0.0168261491,-0.5399339795,0.1551875323,-0.2124397755,-0.0537144877,-0.0245191772,0.1239686012,-0.1419387758,0.1030271351,0.2029168457,0.1878895611,-0.0453676954,0.0392214358,-0.1372805536,0.1033983901,0.2979875803,0.0868111998,0.5367175937,-0.1488341242,0.1738517433,0.0305474233,0.298599273,0.0357530974,0.0153352041,0.2445664853,-0.1797609329,0.292242676,-0.1095676348,-0.2095204443,-0.3042358756,0.0125172604,0.0374833681,0.28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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/649","title":"Inconsistent behavior in map","comments":"Thanks for reporting !\r\n\r\nThis issue must have appeared when we refactored type inference in `nlp`\r\nBy default the library tries to keep the same feature types when applying `map` but apparently it has troubles with nested structures. I'll try to fix that next week","body":"I'm observing inconsistent behavior when applying .map(). This happens specifically when I'm incrementally adding onto a feature that is a nested dictionary. Here's a simple example that reproduces the problem.\r\n\r\n```python\r\nimport datasets\r\n\r\n# Dataset with a single feature called 'field' consisting of two examples\r\ndataset = datasets.Dataset.from_dict({'field': ['a', 'b']})\r\nprint(dataset[0])\r\n# outputs\r\n{'field': 'a'}\r\n\r\n# Map this dataset to create another feature called 'otherfield', which is a dictionary containing a key called 'capital'\r\ndataset = dataset.map(lambda example: {'otherfield': {'capital': example['field'].capitalize()}})\r\nprint(dataset[0])\r\n# output is okay\r\n{'field': 'a', 'otherfield': {'capital': 'A'}}\r\n\r\n# Now I want to map again to modify 'otherfield', by adding another key called 'append_x' to the dictionary under 'otherfield'\r\nprint(dataset.map(lambda example: {'otherfield': {'append_x': example['field'] + 'x'}})[0])\r\n# printing out the first example after applying the map shows that the new key 'append_x' doesn't get added\r\n# it also messes up the value stored at 'capital'\r\n{'field': 'a', 'otherfield': {'capital': None}}\r\n\r\n# Instead, I try to do the same thing by using a different mapped fn\r\nprint(dataset.map(lambda example:  {'otherfield': {'append_x': example['field'] + 'x', 'capital': example['otherfield']['capital']}})[0])\r\n# this preserves the value under capital, but still no 'append_x'\r\n{'field': 'a', 'otherfield': {'capital': 'A'}}\r\n\r\n# Instead, I try to pass 'otherfield' to remove_columns\r\nprint(dataset.map(lambda example:  {'otherfield': {'append_x': example['field'] + 'x', 'capital': example['otherfield']['capital']}}, remove_columns=['otherfield'])[0])\r\n# this still doesn't fix the problem\r\n{'field': 'a', 'otherfield': {'capital': 'A'}}\r\n\r\n# Alternately, here's what happens if I just directly map both 'capital' and 'append_x' on a fresh dataset.\r\n\r\n# Recreate the dataset\r\ndataset = datasets.Dataset.from_dict({'field': ['a', 'b']})\r\n# Now map the entire 'otherfield' dict directly, instead of incrementally as before\r\nprint(dataset.map(lambda example:  {'otherfield': {'append_x': example['field'] + 'x', 'capital': example['field'].capitalize()}})[0])\r\n# This looks good!\r\n{'field': 'a', 'otherfield': {'append_x': 'ax', 'capital': 'A'}}\r\n```\r\n\r\nThis might be a new issue, because I didn't see this behavior in the `nlp` library. \r\n\r\nAny help is appreciated!","comment_length":45,"text":"Inconsistent behavior in map \n I'm observing inconsistent behavior when applying .map(). This happens specifically when I'm incrementally adding onto a feature that is a nested dictionary. Here's a simple example that reproduces the problem.\r\n\r\n```python\r\nimport datasets\r\n\r\n# Dataset with a single feature called 'field' consisting of two examples\r\ndataset = datasets.Dataset.from_dict({'field': ['a', 'b']})\r\nprint(dataset[0])\r\n# outputs\r\n{'field': 'a'}\r\n\r\n# Map this dataset to create another feature called 'otherfield', which is a dictionary containing a key called 'capital'\r\ndataset = dataset.map(lambda example: {'otherfield': {'capital': example['field'].capitalize()}})\r\nprint(dataset[0])\r\n# output is okay\r\n{'field': 'a', 'otherfield': {'capital': 'A'}}\r\n\r\n# Now I want to map again to modify 'otherfield', by adding another key called 'append_x' to the dictionary under 'otherfield'\r\nprint(dataset.map(lambda example: {'otherfield': {'append_x': example['field'] + 'x'}})[0])\r\n# printing out the first example after applying the map shows that the new key 'append_x' doesn't get added\r\n# it also messes up the value stored at 'capital'\r\n{'field': 'a', 'otherfield': {'capital': None}}\r\n\r\n# Instead, I try to do the same thing by using a different mapped fn\r\nprint(dataset.map(lambda example:  {'otherfield': {'append_x': example['field'] + 'x', 'capital': example['otherfield']['capital']}})[0])\r\n# this preserves the value under capital, but still no 'append_x'\r\n{'field': 'a', 'otherfield': {'capital': 'A'}}\r\n\r\n# Instead, I try to pass 'otherfield' to remove_columns\r\nprint(dataset.map(lambda example:  {'otherfield': {'append_x': example['field'] + 'x', 'capital': example['otherfield']['capital']}}, remove_columns=['otherfield'])[0])\r\n# this still doesn't fix the problem\r\n{'field': 'a', 'otherfield': {'capital': 'A'}}\r\n\r\n# Alternately, here's what happens if I just directly map both 'capital' and 'append_x' on a fresh dataset.\r\n\r\n# Recreate the dataset\r\ndataset = datasets.Dataset.from_dict({'field': ['a', 'b']})\r\n# Now map the entire 'otherfield' dict directly, instead of incrementally as before\r\nprint(dataset.map(lambda example:  {'otherfield': {'append_x': example['field'] + 'x', 'capital': example['field'].capitalize()}})[0])\r\n# This looks good!\r\n{'field': 'a', 'otherfield': {'append_x': 'ax', 'capital': 'A'}}\r\n```\r\n\r\nThis might be a new issue, because I didn't see this behavior in the `nlp` library. \r\n\r\nAny help is appreciated! \n Thanks for reporting !\r\n\r\nThis issue must have appeared when we refactored type inference in `nlp`\r\nBy default the library tries to keep the same feature types when applying `map` but apparently it has troubles with nested structures. I'll try to fix that next 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/647","title":"Cannot download dataset_info.json","comments":"Thanks for reporting !\r\nWe should add support for servers without internet connection indeed\r\nI'll do that early next week","body":"I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/text\/default-53ee3045f07ba8ca\/0.0.0\/dataset_info.json\r\n```\r\n\r\nI tried to open this link manually, but I cannot access this file. How can I download this file and pass it through `dataset.load_dataset()` manually?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0\r\nTensorFlow version 2.3.0\r\ndatasets version: 1.0.1 \r\n","comment_length":20,"text":"Cannot download dataset_info.json \n I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/text\/default-53ee3045f07ba8ca\/0.0.0\/dataset_info.json\r\n```\r\n\r\nI tried to open this link manually, but I cannot access this file. How can I download this file and pass it through `dataset.load_dataset()` manually?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0\r\nTensorFlow version 2.3.0\r\ndatasets version: 1.0.1 \r\n \n Thanks for reporting !\r\nWe should add support for servers without internet connection indeed\r\nI'll do that early next week","embeddings":[-0.2581685483,0.0243737306,-0.0594887361,0.2031503767,0.0788523629,0.1288594604,0.0959735289,0.2360109985,0.1991672516,0.0795687139,0.1454199255,0.2383177727,0.2438558787,0.1896512508,0.1779324859,-0.1044934914,-0.1757180691,0.0602590367,0.0163063873,0.1639957577,-0.2036467642,0.1125223488,-0.0277882684,0.0930874273,-0.1486055255,-0.329529494,0.1726283729,0.1674591303,-0.1995363235,-0.0480165631,0.3676278591,0.1111577004,0.0231310483,0.1800840497,-0.0001172042,0.2482485324,0.3314515352,-0.0468823798,-0.3855315447,-0.6151015162,-0.2775319219,-0.3104656935,0.1326969713,-0.2921256423,0.0545545891,-0.0274387151,0.3667286336,-0.2455900609,0.3066195846,0.3828884363,0.081887275,0.1975744069,0.1668199748,-0.0151023362,-0.079669036,-0.156904757,0.1030239537,0.2234270424,0.0778491721,0.0222973935,0.1356130689,0.2242699414,-0.1415863484,0.1933998466,0.3185082078,0.0770921484,-0.2284037173,-0.2921458483,0.37315467,0.435272038,0.7340177894,-0.258764267,-0.3504030406,-0.0941050202,0.0685781986,0.0049100188,0.4181962609,0.3318796158,-0.2674552202,0.053244397,-0.3854344189,-0.4756149352,-0.5133515596,0.2596521974,-0.043795459,-0.0062728175,-0.0868521333,0.0650681183,0.1307830065,0.0529989488,0.1102165133,-0.0586893819,0.0596241057,0.2255720496,-0.1393045783,0.0642871931,-0.0231556147,-0.3781893849,0.0988889113,0.237310186,0.1910169274,0.0885317624,-0.2979581356,0.2700930536,0.2438108176,-0.019225426,-0.0753265992,0.0507073924,0.3943025768,0.1891427487,0.2085787952,-0.1946888864,-0.0591314472,-0.2078119069,-0.1013944671,0.0001048631,0.2709664404,-0.0075751944,-0.2217639685,0.1743744761,-0.2492113262,0.0577885956,-0.0308442898,0.2868508399,-0.4215427637,0.1188231632,0.3688768148,0.0901182219,-0.0124301109,-0.2255284041,-0.0405955687,0.0507175401,0.1518693566,0.1059540287,0.202420935,-0.0358267985,0.3656516075,-0.0974892154,-0.0094232261,-0.1456918865,0.3619759381,-0.0676196739,-0.3675275445,0.4784257114,0.3598552644,0.0498910509,-0.0304135587,0.0404353812,-0.0042618262,0.1028195098,-0.5772803426,-0.4673503637,-0.1304803342,0.0880941376,-0.1737635732,0.014178697,-0.3551717103,-0.0608476661,-0.2671119869,-0.279756099,-0.1031838655,0.0695324615,-0.167597428,-0.2041427195,0.3580983579,0.3052932918,-0.4322125018,-0.0583983511,-0.0573551022,-0.2180733234,0.0616178811,0.0804652497,-0.2448321134,0.2876029611,-0.1839329302,0.0781300366,0.4764019549,-0.3475132287,-0.7220580578,0.5315420628,-0.2805051804,-0.065564014,0.0445309468,0.209490478,0.1288461387,0.2588056922,0.4079203606,0.4810492992,0.0635359958,-0.0915062875,0.0023360976,-0.3649243712,0.0945882797,0.3108660579,0.0989390314,0.2225619704,0.1619277745,-0.0530178435,0.2041986287,0.0445404164,0.0700532272,0.4481004179,-0.0124171944,0.0885310099,-0.1026969478,-0.1130253449,-0.6764413714,0.1161552668,0.000277022,-0.0813723281,-0.6097717285,-0.0802460089,-0.2205836326,-0.0389364474,0.0883240774,0.2622841597,0.0493855998,0.049601417,0.1081951037,0.1324227154,-0.1000124738,0.4082681239,-0.3110571802,0.1138603836,-0.3475219905,0.1198774353,0.1842234731,0.1942978352,0.1091840714,-0.1518645138,0.0917049199,-0.0541608594,-0.1490963995,0.4382841885,-0.2289565802,0.4687093794,-0.0645764321,0.321454525,0.079349108,-0.0825936943,-0.0710100234,0.0418956243,0.1006268784,-0.0280569661,-0.288522929,0.3034878671,0.197091952,0.2505658567,0.0842370316,0.2263906747,0.2398903221,0.0106029343,-0.1017640233,0.3073266745,0.1692443341,0.1132228673,0.2009826452,-0.1750233918,-0.5216665268,0.1266985089,0.375092864,-0.1219119057,0.1262109131,0.1015705168,-0.1663372666,-0.0890585035,0.1078620851,0.1592134982,0.1848902404,-0.0357064307,-0.0391115434,0.1988347322,-0.0235857163,-0.243300423,-0.0912044868,0.0482785739,0.2365596443,0.0827148408,0.0179685745,-0.0010185915,-0.0160230845,-0.2249884307,0.1684878618,0.2603356838,-0.2508514822,0.2482616603,-0.2147570997,-0.1431507021,0.112368755,0.2177840173,-0.2780341208,-0.1969718337,-0.1126687303,0.4512029886,-0.0530626029,-0.2184859067,-0.2282786816,0.3127138615,0.1134808213,-0.5957485437,-0.0996805057,-0.1528220028,-0.0897533819,0.0087622888,0.1686343253,0.144774437,0.2064512968,-0.1780374944,0.0105940411,-0.4024783671,0.109743543,0.056042172,0.1726340353,0.1398850381,0.1356797367,0.6456850767,-0.1096475273,0.0676192641,0.1128703505,-0.0828174055,-0.1381504536,-0.0323999189,-0.1608317643,0.1464507878,-0.0544036254,-0.5343643427,-0.4285780787,-0.2995085716,0.4056402743,-0.0244592633,0.279620856,0.0601893328,0.0293879211,0.2110872567,0.3687650859,0.1299320012,-0.0944866166,-0.6934444308,0.3646911085,-0.1951303035,-0.3631881475,0.2532722354,0.064514786,0.159446761,0.4275206923,-0.6492444277,-0.1355833262,-0.0504805855,0.2179579884,-0.2815863192,-0.0338860489,0.4939148724,-0.202109769,0.070963569,-0.0619993955,-0.0312898643,0.0147357816,0.1017191708,0.2120196521,0.4684890807,0.538824141,0.1260890961,0.6983602047,-0.1538118869,0.0850010961,-0.0224630684,-0.3815024197,0.2022925913,0.1190600768,-0.122126326,-0.1462517977,-0.0883389637,-0.1300351471,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/647","title":"Cannot download dataset_info.json","comments":"Right now the recommended way is to create the dataset on a server with internet connection and then to save it and copy the serialized dataset to the server without internet connection.","body":"I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/text\/default-53ee3045f07ba8ca\/0.0.0\/dataset_info.json\r\n```\r\n\r\nI tried to open this link manually, but I cannot access this file. How can I download this file and pass it through `dataset.load_dataset()` manually?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0\r\nTensorFlow version 2.3.0\r\ndatasets version: 1.0.1 \r\n","comment_length":32,"text":"Cannot download dataset_info.json \n I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/text\/default-53ee3045f07ba8ca\/0.0.0\/dataset_info.json\r\n```\r\n\r\nI tried to open this link manually, but I cannot access this file. How can I download this file and pass it through `dataset.load_dataset()` manually?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0\r\nTensorFlow version 2.3.0\r\ndatasets version: 1.0.1 \r\n \n Right now the recommended way is to create the dataset on a server with internet connection and then to save it and copy the serialized dataset to the server without internet connection.","embeddings":[-0.2739142179,0.0575308576,-0.0347832106,0.1870901138,0.0935543105,0.1803250462,0.0954198912,0.2747616768,0.08295542,0.0868135244,0.1172922999,0.2410640866,0.2068442702,0.1971997917,0.2280214131,-0.089716047,-0.1748178154,0.0688972697,0.0379331484,0.1446818113,-0.1514797807,0.0732105523,0.0222487431,0.0637850165,-0.1027823091,-0.3270795047,0.1324490309,0.1972536445,-0.2260443866,-0.0828825682,0.3538629115,0.1435671002,0.0654656366,0.190826878,-0.0001164499,0.2554253638,0.2761071026,-0.1264930665,-0.3891871274,-0.5971481204,-0.2190193981,-0.3448017538,0.1283259988,-0.3310894966,0.0493180268,-0.0733997971,0.3594780564,-0.1955469996,0.3597826064,0.3856014907,0.0774665177,0.2254529595,0.1947092861,0.0195436943,-0.1284128875,-0.121993199,0.0888299346,0.3027686775,0.032998383,0.1223273948,0.1463487148,0.1968403608,-0.1225891337,0.2005220503,0.3200191855,0.0434264839,-0.2740691602,-0.3115540445,0.375538975,0.4086201191,0.7346755862,-0.3074289858,-0.3879797459,-0.087109454,0.0589406826,-0.0454806946,0.4212038517,0.3348724842,-0.2379805744,0.1375946403,-0.3929382861,-0.5090522766,-0.5490764976,0.2441609502,-0.0238861237,-0.0689402819,-0.0839277133,0.0655507743,0.0700825527,0.0319329128,0.1131575331,-0.110623464,0.1374396235,0.2582965195,-0.0843182877,0.0147208814,-0.0684517026,-0.4558711052,0.099631235,0.1897504628,0.1806796342,0.0876994133,-0.228460148,0.2355820239,0.1796387732,0.0048505976,-0.0918789655,0.0451234542,0.3965954185,0.1481320113,0.2023462057,-0.1901379824,-0.1607414931,-0.119329311,-0.0338572413,0.0178025067,0.2646448016,-0.0304432567,-0.2177006155,0.2142812908,-0.2792238295,0.0577114783,-0.0448974743,0.3205521703,-0.4517366588,0.0873989239,0.364179343,0.0601880662,-0.041593913,-0.1907902509,-0.0322496891,0.0793243572,0.2298873663,0.0989318192,0.1862661541,-0.0769819319,0.405151397,-0.1058288068,-0.0253475569,-0.1501196474,0.3815007508,-0.0347792208,-0.3565560281,0.4856557548,0.4002937078,0.0500051454,-0.0698555037,0.0087387599,-0.0139075695,0.1168025061,-0.5142011642,-0.464381367,-0.1429093331,0.0872387588,-0.2037421316,0.0277113337,-0.3442267776,-0.0477546416,-0.1650036126,-0.2439986169,-0.1405543685,0.0832347944,-0.1811585277,-0.2239716351,0.4070423543,0.2606799006,-0.4162976444,-0.0523281135,0.009146465,-0.1822409332,0.0957825705,0.0717509538,-0.2450943291,0.3522921503,-0.1390307844,0.0617719106,0.522126615,-0.3030909896,-0.718416214,0.4686596394,-0.2963498235,-0.0514527969,0.0149099668,0.1851375699,0.1693569869,0.1772417724,0.3453245163,0.5167121291,0.0464096777,-0.0720027685,-0.0083472217,-0.3728986084,0.1220355928,0.2592833638,0.0820800662,0.1653037965,0.1632928848,0.0451843999,0.1699097902,0.000917975,0.0579500385,0.464712888,-0.002785559,0.1030352339,-0.1065410674,-0.07079871,-0.7039730549,0.1187509149,-0.0212403256,-0.1210229173,-0.6178978682,-0.123069182,-0.1917593479,-0.0210739579,0.0644884929,0.2469469309,0.0336726531,0.0527782328,0.1737811267,0.1497736871,-0.0895224214,0.4002639055,-0.2856488228,0.128625825,-0.4003409743,0.1032397971,0.1249527037,0.1720953733,0.1340138763,-0.1628952175,0.1166370958,-0.1240883172,-0.1739653945,0.4517517388,-0.2501598299,0.5106280446,-0.1213387698,0.3544617891,0.1358034164,-0.0870265067,-0.0439147204,0.0381285138,0.0842310116,0.0218504556,-0.3699810505,0.3021430671,0.1896648705,0.1218514442,0.1134383604,0.2273875475,0.3066497147,0.0234624948,-0.1310729384,0.2813239992,0.1746363491,0.1272785366,0.2188441008,-0.1274064779,-0.5014268756,0.2014043927,0.4261487722,-0.1354446858,0.1467635483,0.1135954484,-0.1700537205,-0.1370069534,0.0919980928,0.164185226,0.1402626783,-0.065551661,0.0136207957,0.1298469603,-0.044372391,-0.2295364141,-0.1062689498,0.0263654515,0.2623517811,0.0965017453,0.0491826795,0.033413738,0.0066689355,-0.1386484355,0.1981180757,0.2735874653,-0.1948744655,0.2650151849,-0.2267470658,-0.0929268822,0.0412768908,0.1424242407,-0.2560817599,-0.162516728,-0.092506133,0.4689167738,-0.0575848408,-0.1909465194,-0.1897982061,0.2515119612,0.1366597563,-0.526131928,-0.0057425424,-0.0959622636,-0.0684396923,-0.01858804,0.1396002322,0.1354275793,0.2400066406,-0.1796417683,0.0289248973,-0.3744145632,0.0997284129,0.0843668059,0.1751839221,0.1168126389,0.0857619345,0.6367673278,-0.1055241749,0.0176779404,0.0998934954,-0.0474891588,-0.169460088,0.0396545902,-0.1627120972,0.0653252602,-0.0126701249,-0.5224087238,-0.5092214942,-0.2645646036,0.3670660853,-0.0126496078,0.3041170537,0.1039887592,-0.0237409864,0.1928017437,0.3947021663,0.146002695,-0.0952269286,-0.7382696867,0.4059027731,-0.2604086995,-0.4382019937,0.2343431562,0.0621497184,0.1385695189,0.4293504059,-0.6090550423,-0.135950461,-0.0391379707,0.2375534773,-0.2261968553,-0.0720166564,0.5114514828,-0.1925547868,0.0823357478,-0.1040965319,0.0212002713,0.0836036801,0.12908943,0.2441371828,0.4184113443,0.5404879451,0.1681102514,0.7009679675,-0.056370154,0.0604445674,-0.0432019979,-0.3582232893,0.1943772286,0.1316646039,-0.1063024253,-0.1105115563,-0.1030639261,-0.1522717774,-0.161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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/647","title":"Cannot download dataset_info.json","comments":"#652 should allow you to load text\/json\/csv\/pandas datasets without an internet connection **IF** you've the dataset script locally.\r\n\r\nExample: \r\nIf you have `datasets\/text\/text.py` locally, then you can do `load_dataset(\".\/datasets\/text\", data_files=...)`","body":"I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/text\/default-53ee3045f07ba8ca\/0.0.0\/dataset_info.json\r\n```\r\n\r\nI tried to open this link manually, but I cannot access this file. How can I download this file and pass it through `dataset.load_dataset()` manually?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0\r\nTensorFlow version 2.3.0\r\ndatasets version: 1.0.1 \r\n","comment_length":30,"text":"Cannot download dataset_info.json \n I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/text\/default-53ee3045f07ba8ca\/0.0.0\/dataset_info.json\r\n```\r\n\r\nI tried to open this link manually, but I cannot access this file. How can I download this file and pass it through `dataset.load_dataset()` manually?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0\r\nTensorFlow version 2.3.0\r\ndatasets version: 1.0.1 \r\n \n #652 should allow you to load text\/json\/csv\/pandas datasets without an internet connection **IF** you've the dataset script locally.\r\n\r\nExample: \r\nIf you have `datasets\/text\/text.py` locally, then you can do `load_dataset(\".\/datasets\/text\", data_files=...)`","embeddings":[-0.2791209519,0.0578073859,-0.0465700962,0.1835988313,0.1229124293,0.1868670136,0.1547661722,0.2711952031,0.2081411928,0.0419166461,0.0489858687,0.2596798241,0.282694906,0.1925579756,0.1847233623,-0.044526998,-0.2054240704,0.0323137604,0.0241263341,0.1765891016,-0.2130242437,0.0461439006,-0.0344448686,0.1338437498,-0.1162246987,-0.326908946,0.1606549472,0.2022776902,-0.1810084879,-0.0399473011,0.4320088029,0.1607673913,0.058231581,0.1993090361,-0.0001162807,0.2081344724,0.3396979272,-0.0523446836,-0.3972235024,-0.6450169683,-0.2502361536,-0.2693962753,0.1729401201,-0.3017248809,0.0687525049,-0.1249875054,0.3304026425,-0.2659765482,0.2997096479,0.3808624148,0.0811682716,0.186101228,0.1585743576,-0.0045692651,-0.1447033733,-0.1378964782,0.1531009376,0.284047991,0.042373497,-0.0152341686,0.1086691618,0.2042810172,-0.1773227304,0.2587242424,0.3738180995,0.1103390381,-0.2407457829,-0.2773667574,0.4009684324,0.4214972556,0.7367280126,-0.3251824677,-0.3985488713,-0.1495109797,0.0299512371,-0.0038786351,0.3894180059,0.314329654,-0.2539661825,0.0717129856,-0.3475775123,-0.4419797063,-0.5005505681,0.2443239838,-0.0905222371,-0.0712040961,-0.0764266029,0.0694526061,0.1398933977,0.0175054949,0.2089918256,-0.0750415623,0.101654999,0.2641214132,-0.1614484191,0.0680155754,0.0132725742,-0.4202747643,0.0799991563,0.1321251541,0.1859790236,0.1021032482,-0.3240417838,0.2630485594,0.2888851464,0.0031036921,-0.0972453952,0.0537399054,0.411030978,0.2029902637,0.2062124461,-0.1693377793,-0.1028858051,-0.2158842832,-0.0904720947,-0.0342144184,0.2820253372,-0.0254259165,-0.2750402093,0.1628293246,-0.243377164,0.0810471177,0.0057741208,0.3083935976,-0.4633273184,0.1482780725,0.3730780482,0.0677468106,0.0011441385,-0.1491360962,-0.0526331402,0.1320006102,0.1611264646,0.0686709955,0.1769047827,-0.0410545953,0.352696389,-0.1015117466,-0.0355790481,-0.1430633068,0.3078434169,-0.0415491983,-0.3538811803,0.4735928178,0.3906008899,0.0865467116,-0.0025990999,-0.0301783979,-0.0229926612,0.0905554965,-0.5049895644,-0.4248255193,-0.1023380533,0.0907278657,-0.1651343852,0.0188833959,-0.3795021772,-0.054054182,-0.238785997,-0.2132173032,-0.1138018072,0.0657531619,-0.1576316804,-0.1937026978,0.3814212382,0.3426974118,-0.4553879797,-0.042890057,-0.02567669,-0.1867495775,0.0314610787,0.1171082929,-0.2610658407,0.3278618753,-0.1416548938,-0.0006124565,0.4650782049,-0.3743421137,-0.6786803603,0.5116195679,-0.2511117756,-0.0373778529,0.0120726023,0.1940940917,0.0640429854,0.2246436626,0.4172989428,0.5540279746,0.0352414064,-0.0761468932,0.033500459,-0.3742308617,0.1206460968,0.3107518256,0.1238316372,0.1601830423,0.1756913215,-0.0048195873,0.1874802709,0.0122599164,0.0361302681,0.4173101187,-0.0478650033,0.1298313141,-0.0888287127,-0.0869750679,-0.6877627969,0.1259184331,0.0302223749,-0.1560220718,-0.650418222,-0.1148975864,-0.2287505418,-0.0189219993,0.0801192299,0.2297023684,0.0302912556,0.067814514,0.1056918204,0.1507726759,-0.1127461493,0.4634071589,-0.2621696889,0.154511109,-0.364269346,0.0545704216,0.1842841208,0.2045586407,0.1370400786,-0.1432601362,0.0727346539,-0.0753005892,-0.1526921839,0.4997187257,-0.2178864777,0.4904348552,-0.0908864215,0.3165008426,0.1006958261,-0.01965666,-0.0989884287,0.0419103503,0.1325565577,0.00491085,-0.3661691546,0.3218409419,0.1772815138,0.2363182902,0.1218028069,0.2513240576,0.271055162,0.0193479937,-0.0697235316,0.2834273279,0.1404714733,0.0977991149,0.2253127992,-0.1177348197,-0.5117952228,0.1311011016,0.3453787863,-0.1234256998,0.1201108918,0.1088846698,-0.1616204083,-0.1135591492,0.0792017356,0.1813419908,0.1146394908,-0.051586818,-0.0541436784,0.1559963077,-0.0488835797,-0.2223096043,-0.1094931215,0.0705574155,0.1992183477,0.0120597025,0.0031578953,0.023165239,0.0083967345,-0.1761657149,0.1933675557,0.2756211758,-0.2250331193,0.2806057036,-0.242924273,-0.1875534654,0.127223894,0.1716041863,-0.2033437341,-0.1666338444,-0.1529945582,0.4840420485,-0.0454374515,-0.1549713463,-0.1914562136,0.2822308838,0.1641656458,-0.5675438046,-0.0469845086,-0.1555359215,-0.056237042,0.0154019883,0.2038508505,0.1700980365,0.2169905603,-0.1641974896,0.063286148,-0.436412096,0.1209371984,0.0438387468,0.1322410703,0.1461335272,0.1247555315,0.6109936833,-0.1397796273,0.029906109,0.0867849812,-0.0766616538,-0.0999118015,-0.0275352616,-0.157481581,0.0970095545,-0.0283454917,-0.5136215687,-0.4184240997,-0.3151581585,0.4051456153,0.0110967066,0.2497459352,0.142914623,0.0136674857,0.2127899528,0.3999161124,0.1146334857,-0.0925625637,-0.6567987204,0.400585264,-0.2118275911,-0.3886095881,0.1795176864,0.0410543606,0.1734778881,0.3741798699,-0.6029786468,-0.149218455,-0.0957719013,0.2385056466,-0.2502659261,-0.0587994121,0.4799557924,-0.2010459453,0.0503729433,-0.0519972555,-0.0412348285,0.0203936864,0.1076026559,0.2154725343,0.4581021965,0.5635311604,0.1050902009,0.6342528462,-0.1871810406,0.0657904893,-0.0232320502,-0.3957525194,0.2789325118,0.1227038577,-0.1484974176,-0.2132077515,-0.0865143239,-0.1262926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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/643","title":"Caching processed dataset at wrong folder","comments":"Thanks for reporting !\r\nIt uses a temporary file to write the data.\r\nHowever it looks like the temporary file is not placed in the right directory during the processing","body":"Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n```","comment_length":30,"text":"Caching processed dataset at wrong folder \n Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n``` \n Thanks for reporting !\r\nIt uses a temporary file to write the data.\r\nHowever it looks like the temporary file is not placed in the right directory during the processing","embeddings":[-0.0562820248,0.175503239,-0.048730582,0.3908851743,-0.0356466025,-0.0572809353,0.2760777771,-0.0244471896,0.0408122353,0.1573939621,0.0506472811,0.1766866893,0.0519220531,0.3638361692,-0.035657201,0.3218174875,0.2768937051,-0.0544235073,0.0543463118,-0.0435270667,-0.3072208166,0.3765655458,-0.2001904845,-0.0186393708,-0.5099134445,-0.1708419919,-0.2043422461,-0.0366436392,0.0198707394,0.2087092996,0.1927695423,0.0984714553,0.1747972965,0.6240900159,-0.0001294347,-0.1149767116,0.2048154324,-0.266631186,-0.1961358786,-0.1022202075,-0.3119006157,-0.004488559,-0.1497934759,-0.0720397234,-0.1685159355,0.058167927,0.3384491801,-0.5294010639,-0.0324837863,0.197088182,0.0550545305,-0.4087739289,-0.4787145257,0.3768620193,0.1074029505,0.2737840116,-0.127657488,0.2617730498,0.0274086669,-0.1451185495,0.0063617732,0.2977354228,0.0007320868,-0.0749415234,0.281091392,0.3585237861,-0.2941537499,-0.3332058489,0.5126181841,-0.4663319886,0.3986636102,-0.3778884113,-0.2051143348,-0.2074549645,-0.3296646476,-0.2811291814,0.5710793734,0.1723903865,-0.0678301156,0.0381803103,-0.3426438272,-0.2352154851,0.0801221058,0.1073578671,-0.1712417603,0.2584484816,-0.2692752779,0.0078239897,-0.0813757405,0.2511205375,0.6566110849,-0.3971767128,-0.0177655574,0.3113218546,-0.017163083,0.0418484882,-0.2528593838,0.5198611021,-0.2589477003,-0.1941890419,-0.140186429,-0.137127474,-0.1453160644,0.1114479005,-0.0100332508,0.3893760443,0.0142912092,0.1784577966,0.1091897413,0.0854991004,-0.4649645686,-0.1024239138,-0.1018914804,-0.280590117,0.0215530582,0.1529057473,-0.0357462354,-0.2422925383,-0.1104080006,-0.0570120141,-0.3516086042,0.0353413634,0.0664604306,0.2854492366,0.0223693084,-0.1636580378,-0.2073559016,0.2409924716,-0.1883873343,0.3741033077,-0.0681627691,0.0487460122,-0.3213572502,0.4333797097,0.0406439193,-0.2225215286,0.1916906536,0.0157996304,-0.0768359974,-0.3220772743,-0.0095080128,-0.4610790014,0.3809150755,0.2202900946,0.1426358521,0.4319464564,0.001960421,-0.2612242103,-0.1253498644,0.2638800442,-0.5655605197,-0.2583985031,0.2529354692,-0.0420585498,-0.2712312639,-0.0508865528,-0.611823678,0.0973582864,0.4751488864,-0.2496961355,0.0291339941,-0.0071691517,-0.5547562838,-0.2493054271,0.0841405764,0.2138258815,-0.3784083426,0.2292414606,-0.2632399797,0.4557931721,0.4782080352,0.4059207439,0.0980356708,0.3541256785,-0.4703489542,-0.018397823,0.1202925667,-0.3309086561,-0.7670695186,0.2393060923,-0.0553215742,0.0022589867,0.0424810797,0.0430202186,0.1667083353,-0.1728424579,0.1321417242,0.146696642,0.1153548807,0.2249985486,-0.2722533047,-0.1135415733,-0.0317636095,-0.140868187,0.0147976708,0.1906705499,0.0313851163,-0.3111228645,0.1627004296,-0.0954517499,0.1095285341,0.2840329111,0.198729232,-0.1217923686,-0.0088152941,0.2195258588,-0.1978287846,0.078565374,-0.0435933098,-0.1874962896,-0.4925785065,-0.1061089486,-0.0936830565,-0.1442343742,-0.1505865753,-0.2586653531,-0.070561938,0.3078176081,0.1895607859,0.0718136579,-0.0172853749,0.5587958097,0.0425693914,-0.1547439992,-0.0659867153,0.2057202011,-0.110419631,-0.2670923173,-0.1782824546,-0.1684375703,0.2095979601,-0.2671532929,-0.1758679748,0.1820199937,0.2802887857,0.4550450146,0.0352442339,0.2760804296,0.1084619835,0.1304475814,0.2053912729,-0.0924353227,0.0293364003,0.1651969701,-0.0806705952,0.3651889861,-0.1599464864,0.0635770783,-0.0642158464,-0.3164082468,-0.0544167124,0.1412292272,0.2415256202,-0.1335027814,0.1456729174,0.406855911,0.38108024,0.1703892201,0.1708752066,0.3384847045,0.5877079964,0.0830793008,-0.0072799805,0.0868361369,-0.1417501271,-0.2576186359,0.208980903,0.4739125669,0.4611819983,-0.1000749618,0.2996844947,0.1259815693,0.0905264243,-0.1617136598,0.207000196,0.0486471578,0.1792274863,-0.0102858143,0.2194525152,-0.0296279509,-0.0411489606,0.2106044888,0.0995521247,0.2070693374,-0.0999977291,0.5765224695,-0.1749670357,-0.2962671518,-0.251406163,-0.2246739566,0.126494363,-0.1912495643,-0.0676634088,0.2575970292,-0.0080911685,-0.0047091036,0.3018825948,0.1664896458,-0.0958427787,-0.5050302148,-0.1803772151,0.1535266638,-0.2005008459,-0.113820903,0.3516035974,-0.0197491478,0.0268060695,-0.2237724513,0.0200731717,-0.3809759319,-0.0776564702,0.2111560702,-0.1135221496,0.2927731574,-0.2416089624,0.0524750315,-0.3985488117,-0.2574800551,-0.1008351073,-0.0618555062,-0.0682767481,-0.4157515466,-0.0122670354,-0.0417399481,-0.1293983757,0.0482095219,-0.2307545692,-0.0240253564,0.0910839885,-0.0031617447,0.3184533119,-0.0141437128,-0.0393549576,-0.2473150641,0.3702619076,-0.3497532308,-0.3336966038,-0.6516954899,0.2838873565,-0.1492913365,-0.0440371893,-0.0416815057,-0.0779947564,0.0555622615,0.4272268116,-0.42908746,-0.3207131922,-0.1345354468,-0.0098096468,0.2001427561,-0.0815653354,0.3003765941,-0.004548057,0.2103828043,-0.0307016075,-0.3322169483,0.0428318307,0.3315358758,0.3256826103,-0.0178597346,0.1884341687,0.0062960996,0.6751106381,0.2125789672,0.0176498033,0.3855674863,-0.062898986,0.2614315152,-0.2494227588,-0.0922999755,0.22758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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/643","title":"Caching processed dataset at wrong folder","comments":"Well actually I just tested and the temporary file is placed in the same directory, so it should work as expected.\r\nWhich version of `datasets` are you using ?","body":"Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n```","comment_length":29,"text":"Caching processed dataset at wrong folder \n Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n``` \n Well actually I just tested and the temporary file is placed in the same directory, so it should work as expected.\r\nWhich version of `datasets` are you using ?","embeddings":[-0.0980259776,0.188093707,-0.0280870888,0.389010936,-0.015298578,-0.0114677977,0.3272336423,-0.0096284868,0.0338673592,0.2286566049,0.0054276907,0.2275298089,0.0270035882,0.3596076667,-0.0818914026,0.3205984533,0.2760051787,-0.0400124863,0.064423047,-0.0552731417,-0.3491486609,0.3673860729,-0.250793457,-0.0342624858,-0.5128515363,-0.1368786991,-0.179927215,-0.0654983521,-0.05333478,0.2139814794,0.2417699695,0.0878142864,0.2495012879,0.661549449,-0.0001283521,-0.1254281253,0.2665183842,-0.2071329653,-0.2330143303,-0.1373612583,-0.3181043863,-0.014190034,-0.1339170635,-0.067687653,-0.1839046776,0.0559142567,0.272248596,-0.5808059573,-0.025083039,0.2409744561,0.0725319088,-0.4288330972,-0.4749288559,0.3053100109,0.0975685343,0.2478017658,-0.142292276,0.24306871,0.0100712012,-0.1486692727,-0.0139403753,0.3300213516,-0.0270178802,-0.0138578033,0.2550145984,0.3406344652,-0.3194044828,-0.3421723545,0.5276225209,-0.4035201669,0.4292229116,-0.3404000401,-0.1767730862,-0.2474320978,-0.3338007927,-0.2652026117,0.5508043766,0.1417493224,-0.0569063835,0.0546889231,-0.4403166473,-0.193094179,0.1015329212,0.1382195354,-0.238239482,0.2111843079,-0.2950587869,0.0276474431,-0.011653332,0.1779495776,0.6766315103,-0.3875518441,-0.0103655914,0.2630077302,-0.0764711723,0.0587412193,-0.1897370219,0.5381625891,-0.2099022716,-0.1787819415,-0.2083129883,-0.1328071654,-0.1667217165,0.0946199894,0.0385345221,0.4278603494,-0.0002032616,0.2039845288,0.1246508956,0.1027066931,-0.520960331,-0.0831503347,-0.0767244846,-0.2948054373,0.0310732555,0.0969406813,0.0182932839,-0.2827686071,-0.1402125508,-0.037873257,-0.3314204514,0.0076373308,0.1045960113,0.2721991539,0.0197714288,-0.1099094823,-0.2434050143,0.2486744374,-0.2118350863,0.3456625342,-0.087007165,0.0086983796,-0.3563786149,0.4384669363,0.0830166936,-0.2516986728,0.1514321864,0.0271900576,-0.0714058727,-0.2899838686,-0.0525118522,-0.4461802244,0.3620024025,0.1732686162,0.1398664713,0.4446723163,0.0133799035,-0.2829953432,-0.1326734275,0.2426331043,-0.5642427802,-0.2653877735,0.1985358,-0.0222923867,-0.2773581147,-0.0693465993,-0.6552339792,0.1527505368,0.4416986704,-0.2127338499,0.0085248305,-0.0149793057,-0.5274883509,-0.2238634527,0.0653401166,0.2004465312,-0.3652799428,0.2058353871,-0.3317601681,0.4557800591,0.4756256938,0.3935822248,0.060095001,0.2928664982,-0.4934003055,-0.0278436095,0.1094955131,-0.3094343543,-0.8372094035,0.2986419797,-0.0138159357,-0.0036940803,-0.0160198156,-0.0175278224,0.1869792342,-0.1316637397,0.1638331711,0.1282265484,0.1112386957,0.1921883523,-0.2752439678,-0.1077737361,-0.0107337227,-0.1389642209,0.0377673954,0.2096470594,0.041476462,-0.3141260445,0.1381217241,-0.0697862729,0.111001417,0.2728106678,0.2326658368,-0.1298245341,0.0115753245,0.1633971334,-0.2243786305,0.1474627405,0.0282337032,-0.1464349031,-0.4751341343,-0.1171608195,-0.1194390059,-0.1630004793,-0.1702288687,-0.251778841,-0.0769413188,0.3072750866,0.1577135324,0.0284263119,-0.0407034345,0.525129199,0.0626684353,-0.1593219489,-0.0775787085,0.2420241088,-0.1074183509,-0.2392097563,-0.148795411,-0.195563063,0.2205365747,-0.2613439858,-0.2108390331,0.1778932065,0.30595994,0.4031629562,0.0000764468,0.2722767293,0.1150537804,0.1092067435,0.169486776,-0.0730654225,0.0028634975,0.1589085311,-0.0870772079,0.2959052324,-0.1469192654,0.1218437925,-0.1049630567,-0.2768554688,-0.0485453308,0.1285164803,0.2272479832,-0.1897710115,0.0760212541,0.3430959284,0.3831253946,0.1982024014,0.2105258852,0.3193440139,0.6449167132,0.0745252147,-0.0350593664,0.130790621,-0.1365963966,-0.2520191967,0.2219441235,0.5235064626,0.4602493048,-0.0905334949,0.3149169981,0.1629967391,0.057884112,-0.1506624818,0.1834234148,0.0628222823,0.2291888595,-0.0392771289,0.2224123329,-0.0436756536,-0.0493959263,0.2005114406,0.0795630887,0.2584432662,-0.12045075,0.5334354043,-0.2883337736,-0.2838529646,-0.2435619235,-0.2431814522,0.1216463298,-0.2136332393,-0.0976688191,0.283724159,0.0229831152,0.0302868504,0.232142508,0.1779783815,-0.1119576395,-0.4812890887,-0.1549041867,0.1786360294,-0.2874040008,-0.1078283116,0.3575817943,-0.0645390972,-0.0030333495,-0.216862753,0.0499561653,-0.3820486665,-0.1052474976,0.2430865169,-0.146206215,0.2984904051,-0.2173310965,0.0096436944,-0.4151874185,-0.2589137852,-0.0426567122,-0.0731205493,-0.0663106441,-0.3781530261,-0.0269038547,-0.0367619842,-0.0858888701,0.0030577269,-0.1907083094,-0.0507298149,0.0393040031,-0.0032559084,0.2771962881,0.0263257567,-0.0282737389,-0.2060232162,0.3607292175,-0.3355197906,-0.3527882993,-0.6440094709,0.3036522269,-0.1445791572,-0.0496169664,-0.0091056125,-0.080620043,0.0811304525,0.4302971959,-0.4716597199,-0.3017313778,-0.1893893182,0.0136477137,0.2360906452,-0.0786211044,0.3027957976,0.0290112924,0.1964473873,-0.0391066931,-0.4018261731,0.012068999,0.3081807792,0.3579263091,0.0200459044,0.2512149811,0.054270599,0.6362167001,0.3114107549,-0.0254185032,0.4196685851,-0.0525017269,0.2791052759,-0.2759063244,-0.1334952414,0.2206906229,0.0085314624,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/643","title":"Caching processed dataset at wrong folder","comments":"It looks like a pyarrow issue with google colab.\r\nFor some reason this code increases the disk usage of google colab while it actually writes into google drive:\r\n\r\n```python\r\nimport pyarrow as pa\r\n\r\nstream = pa.OSFile(\"\/content\/drive\/My Drive\/path\/to\/file.arrow\", \"wb\")\r\nwriter = pa.RecordBatchStreamWriter(stream, schema=pa.schema({\"text\": pa.string()}))\r\nwriter.write_table(pa.Table.from_pydict({\"text\": [\"a\"*511 + \"\\n\"] * ((1 << 30) \/\/ 512)}))  # 1GiB\r\nwriter.close()\r\nstream.close()\r\n```\r\n\r\nMoreover if I `rm` the file on google drive, it frees disk space on google colab.","body":"Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n```","comment_length":74,"text":"Caching processed dataset at wrong folder \n Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n``` \n It looks like a pyarrow issue with google colab.\r\nFor some reason this code increases the disk usage of google colab while it actually writes into google drive:\r\n\r\n```python\r\nimport pyarrow as pa\r\n\r\nstream = pa.OSFile(\"\/content\/drive\/My Drive\/path\/to\/file.arrow\", \"wb\")\r\nwriter = pa.RecordBatchStreamWriter(stream, schema=pa.schema({\"text\": pa.string()}))\r\nwriter.write_table(pa.Table.from_pydict({\"text\": [\"a\"*511 + \"\\n\"] * ((1 << 30) \/\/ 512)}))  # 1GiB\r\nwriter.close()\r\nstream.close()\r\n```\r\n\r\nMoreover if I `rm` the file on google drive, it frees disk space on google colab.","embeddings":[0.0214205999,0.2508093715,-0.0044861692,0.4046226144,-0.0593296103,-0.0976660177,0.3005953729,0.0051357779,-0.2152584493,0.1271965504,-0.0451286696,0.3386108279,0.1068906263,0.2934122384,0.0605559871,0.223590672,0.2913432419,-0.0080436692,0.1109899879,-0.0101503739,-0.2467477173,0.3662295043,-0.2008794248,-0.0313707292,-0.4776650369,-0.068877548,-0.0705260411,-0.1316779405,-0.032523375,0.1094124839,0.1837549657,0.0785785317,0.150113225,0.5543874502,-0.0001274843,-0.2014526725,0.2319876403,-0.1646427512,-0.1672947705,0.0952369049,-0.0660738647,-0.0158476364,0.0897296444,-0.0740431994,-0.0477135181,0.0367304012,0.2621205449,-0.1535121351,-0.0225636475,0.1620317549,0.0549754538,-0.3191696107,-0.3850808144,0.4330038726,0.3903363049,0.2227914929,-0.1758393198,0.5310472846,0.076160714,-0.1830207855,-0.3298393786,0.2640752494,-0.1264401525,0.110678345,0.3018000126,0.4598481655,-0.2333257943,-0.1505207419,0.4259735644,-0.4988749921,0.3272492886,-0.5334558487,-0.002896925,-0.3041200638,-0.2905580401,-0.360919714,0.514906764,0.2798948586,-0.2408690751,0.0373578258,-0.389555037,-0.1254132688,0.0084921056,0.0503370427,-0.1800255924,0.1711009294,-0.122367993,0.0516222566,0.0287987366,0.3116212785,0.7678453326,-0.3648163378,0.0661570206,0.1391303986,-0.0734800249,0.1814907938,-0.1290129423,0.585472405,-0.1312903464,-0.1340878308,-0.1266849637,-0.1530451328,0.0074453205,0.1737578511,0.1315623969,0.3484873772,-0.2132810354,0.0425846986,0.0428300127,0.0529006533,-0.3369436264,-0.0782317445,0.0316968448,-0.2644616663,0.1953683048,-0.0778724998,0.0231226273,-0.2491862327,-0.1689238101,-0.2116445005,-0.3935553432,-0.0601108558,-0.0610288493,0.2024491578,-0.0228124522,-0.2642136514,-0.2638733089,0.2894004583,-0.2768119872,0.357504189,-0.0012719117,0.0600019097,-0.2810415626,0.5400337577,0.1548738033,-0.179901123,0.0203176383,0.1410446763,-0.0431125313,-0.2354217023,-0.0890352577,-0.4710323215,0.2895697653,0.191419825,0.1892023832,0.3132681549,-0.1785362363,-0.2473843545,-0.1625051349,0.4494559765,-0.5062214732,-0.3123239577,0.1799287349,-0.0252877772,-0.2993509471,-0.0670899823,-0.7675716877,0.1321822405,0.447485894,-0.2971973121,0.0309301317,-0.096560739,-0.4285757542,-0.3032741249,0.0149896126,0.1452413499,-0.359757185,0.0603813492,-0.1958555281,0.4015370607,0.4847250283,0.4746412337,-0.0242268052,0.3547291756,-0.4302553833,0.0213741045,-0.0106692435,-0.1567398459,-0.8236618638,0.1317311972,-0.132392332,0.0695866272,0.1081514135,0.0055570155,0.158888191,-0.0654986799,0.2246363759,0.2069316357,0.0737835467,0.2635890245,-0.3758739531,-0.1832395643,-0.0158579573,-0.0594401397,-0.0387478173,0.020846229,0.0070683332,-0.4193621874,0.2358665317,-0.1587572396,0.1791306287,0.2395592928,0.3115640879,-0.2237321734,0.0086980397,0.2210537195,-0.259278357,0.0565233119,-0.0845233351,-0.1852927953,-0.6225892305,-0.1273642927,-0.1777253449,-0.0660640448,-0.1020753458,-0.192406714,-0.0981779769,0.2356915325,0.1794316024,0.0773903951,0.1373386532,0.3301828504,0.1836962551,-0.1190631017,0.0269475933,0.3751268089,-0.1567516774,-0.3961032331,-0.0702992603,-0.1427293718,0.2049590796,-0.159670487,-0.0452094115,0.0729753524,0.1840246469,0.4450157285,-0.0777895376,0.2858203351,0.1316982657,0.0216108579,0.1732712388,-0.0894047916,0.01382716,0.0375896022,-0.036116533,0.3025759757,-0.0142473662,0.1336650997,-0.1301425695,-0.3485519886,-0.1377633065,0.0490126163,0.3428657651,0.085674867,0.0891671255,0.5186516047,0.4237043262,0.2823845744,0.1714913249,0.3646704853,0.6328815222,-0.0050880318,-0.0541194044,0.257504791,-0.2285197824,-0.3635298908,0.271355778,0.2931195199,0.4082559943,-0.013962456,0.1979498714,0.1831991225,0.1056673825,-0.2301922888,0.2001953125,0.1337825209,0.2639008164,-0.0150433686,0.3801979721,-0.1243999153,-0.1518711299,0.1923526824,0.0603036918,0.1992247552,-0.209303081,0.5275095701,-0.1733888984,-0.2081394345,-0.2764720917,-0.2005175054,0.0451708846,-0.1497702003,-0.0626137704,0.4318827093,0.0306840912,-0.0486451127,0.3401047587,0.1422446668,-0.162133202,-0.5259186625,-0.1383045167,-0.0220133737,-0.3286528587,-0.0774949044,0.4027002752,-0.0441007689,0.0771534592,-0.1384210736,0.0884591714,-0.3343403339,-0.1214695647,0.2740837634,-0.1896559149,0.3587255776,-0.3514130414,-0.1115452647,-0.4793860316,-0.3280753493,-0.1017479524,-0.0617399886,-0.114281103,-0.3009617925,-0.0578712933,0.0006670832,-0.0771662444,0.0741540119,-0.1301366836,0.020014409,0.1932437271,-0.0068307747,0.3806487024,-0.0050653028,-0.0432363115,-0.1388840973,0.3479846418,-0.3142756224,-0.2478320003,-0.4356377721,0.2898030579,-0.0098420447,-0.055216305,-0.0459030308,-0.1516476423,-0.042331405,0.4133680463,-0.5115941763,-0.3616776168,-0.1239698231,0.2218833715,0.1521575153,-0.110673748,0.2670279145,0.1321386248,0.1791777015,0.0847617015,-0.4711603224,-0.1082860529,0.3099479377,0.2349284887,0.0253391843,0.0066287001,0.0264174417,0.6789851189,-0.0198745523,0.0968693197,0.5025283694,-0.0739436969,0.2042860091,-0.1957439482,-0.0140864467,0.1454436928,-0.11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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/643","title":"Caching processed dataset at wrong folder","comments":"Actually I did more tests it doesn't >.<\r\nI'll let you know if I find a way to fix that","body":"Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n```","comment_length":20,"text":"Caching processed dataset at wrong folder \n Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n``` \n Actually I did more tests it doesn't >.<\r\nI'll let you know if I find a way to fix that","embeddings":[-0.1001971662,0.1869404614,-0.0199802201,0.458135426,-0.0414948054,-0.0328615978,0.310751617,0.0137844495,0.0069233319,0.1962369829,0.0152610112,0.2812228799,0.0530100502,0.4453213215,-0.0918111876,0.3288263083,0.2770382762,-0.0716737285,0.0422292575,-0.0833311453,-0.3645289838,0.394906044,-0.2226699889,-0.0297363307,-0.4760729373,-0.1265688539,-0.1334368289,-0.0599123687,-0.0275350381,0.2656660974,0.2077789158,0.0172537975,0.1959412247,0.6396953464,-0.0001290814,-0.1540848464,0.213269189,-0.2452561557,-0.1593503058,-0.114685297,-0.2909755111,0.0105281863,-0.1092145965,-0.0579332449,-0.2169555575,0.1224940047,0.2166627944,-0.5325363278,-0.010078162,0.2681661546,0.0640946105,-0.47394979,-0.480668664,0.3833518326,0.0587677807,0.243763119,-0.1412620544,0.2848828435,-0.0153787006,-0.1163645834,-0.0842502192,0.2958207428,-0.0695914254,0.0128157008,0.2305926979,0.3311593533,-0.304877311,-0.3038527668,0.533562541,-0.4408625066,0.3466918766,-0.3541082442,-0.1448860615,-0.265127331,-0.3225090802,-0.3541128337,0.5375027061,0.1383071244,-0.1107280925,0.0715741888,-0.4810945094,-0.2326908559,0.0656590536,0.0965537429,-0.1904105842,0.31480214,-0.2614330351,0.0345509984,-0.0387202539,0.2343807966,0.6895991564,-0.3677733541,-0.0195311718,0.1927597374,-0.1213608384,0.0573783107,-0.2128070295,0.5516891479,-0.2121066153,-0.1210532933,-0.2416576743,-0.1189972684,-0.1252040416,0.086635828,0.0504911877,0.445263654,-0.0332958363,0.1330261528,0.0818043128,0.0395884551,-0.5268156528,-0.0845654309,-0.027151756,-0.2695402801,-0.0042475048,0.1287916005,-0.0369938202,-0.290201515,-0.0993199348,-0.0460208394,-0.3127284348,-0.013800025,0.0954971015,0.3146099746,-0.0434423462,-0.1035689488,-0.2572100461,0.2454315275,-0.1969503015,0.3418700993,-0.0803738534,0.0207407363,-0.3455398679,0.4818636477,0.0642976835,-0.2464165837,0.1298306137,0.0363991372,-0.0103645306,-0.3284159601,-0.0583500937,-0.4485407472,0.3813793063,0.1436963975,0.1453280002,0.4247841239,0.0019370598,-0.2195819765,-0.1311643571,0.2365234196,-0.6200785637,-0.2533975542,0.1688311994,-0.0321038254,-0.2495457083,-0.067345947,-0.6999961734,0.1224301383,0.4770286679,-0.205056563,-0.0080381455,-0.0043334458,-0.4935464859,-0.2447816879,0.0572936721,0.1244076341,-0.234338522,0.1792040914,-0.3634156585,0.4979801476,0.513926506,0.3871518075,0.075289458,0.2675049007,-0.4960411191,-0.041340895,0.1093340293,-0.2899128497,-0.8469179869,0.318788588,-0.0423517115,-0.0128071886,0.0056163082,-0.0255613904,0.2197386175,-0.0894946158,0.1782181114,0.1592388153,0.0948842615,0.2119773477,-0.2583329678,-0.1464414895,-0.0520190336,-0.1498499811,0.0301358029,0.2176420838,-0.0006994176,-0.3706175983,0.1509187371,-0.1175173745,0.1293628663,0.2562039196,0.2577470541,-0.1585953236,0.0000068447,0.1345375478,-0.2021965235,0.1305844635,0.0093786167,-0.1663395464,-0.5060367584,-0.0681093782,-0.1266943216,-0.164160803,-0.1428063065,-0.2228372693,-0.087469615,0.3029132783,0.1267005801,0.0550165363,-0.0214776266,0.5375711322,0.1039922982,-0.1637997031,-0.0310336314,0.2271191031,-0.1092197001,-0.2241535634,-0.1367475837,-0.1725661457,0.2570614219,-0.2123559117,-0.2030741274,0.1432766765,0.2454458475,0.4036563337,-0.0317771584,0.2653979063,0.0925490707,0.0413970873,0.1671624035,-0.0784718096,-0.0313067324,0.1556563675,-0.0009804078,0.2876313329,-0.0989504233,0.185148105,-0.0919763967,-0.2702074647,-0.0442136116,0.0820063055,0.2814543247,-0.1440525204,0.0922967345,0.3347589076,0.3947918713,0.2348679602,0.2349326611,0.3371480405,0.6447883844,0.119115226,-0.0171494558,0.1671895385,-0.1317229271,-0.2494183779,0.2548364401,0.4952813685,0.4230060875,-0.1029384285,0.2637450397,0.2036377937,0.0837236345,-0.1512748897,0.1672002077,0.0601979904,0.3180698752,0.0076511712,0.2109301984,-0.064953573,-0.0552661307,0.191541478,0.0658398122,0.2555198073,-0.1284782141,0.5481223464,-0.3072597086,-0.2564249933,-0.2413409054,-0.2760952413,0.0781546682,-0.1602677107,-0.1182289571,0.3369998336,0.0332643539,0.050681293,0.2371831685,0.1976035684,-0.1381628215,-0.4697015882,-0.1437356323,0.1527374685,-0.2454112619,-0.1133157238,0.3678135872,-0.0569793619,0.0241775718,-0.1859462559,0.0227216389,-0.3783481717,-0.0986185744,0.2697833478,-0.1195578128,0.2959055901,-0.2688421607,0.0188592281,-0.4218043387,-0.3589331508,-0.0800452754,-0.0346795283,-0.0787686631,-0.4026960135,-0.0332612507,-0.0269002784,-0.0811652839,0.0032078775,-0.1645305008,-0.0334004648,0.0611832626,0.0083255731,0.3051475286,-0.0450579301,-0.0811832994,-0.2303551286,0.3723268211,-0.3175048828,-0.3663457334,-0.622543931,0.2493044585,-0.1268671304,-0.036184106,-0.0311372411,-0.0876350924,0.1213364005,0.3739616275,-0.4566191435,-0.2531041205,-0.1389434189,-0.0384321883,0.2074797302,-0.0992779285,0.2927907407,0.0628481433,0.221025914,-0.0134768542,-0.4279940426,0.0216266587,0.3045812845,0.382042408,0.0778238848,0.2560872138,0.0397081226,0.6177136898,0.2947866321,-0.00883637,0.4568141401,-0.0352963097,0.2197225392,-0.228672266,-0.1513240635,0.2661589682,0.05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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/643","title":"Caching processed dataset at wrong folder","comments":"Actually I also have the issue when writing a regular text file\r\n\r\n```python\r\nf = open(\"\/content\/drive\/My Drive\/path\/to\/file\", \"w\")\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) \/\/ 512))  # 1GiB\r\nf.close()\r\n```\r\n\r\nIs that supposed to happen ?","body":"Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n```","comment_length":37,"text":"Caching processed dataset at wrong folder \n Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n``` \n Actually I also have the issue when writing a regular text file\r\n\r\n```python\r\nf = open(\"\/content\/drive\/My Drive\/path\/to\/file\", \"w\")\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) \/\/ 512))  # 1GiB\r\nf.close()\r\n```\r\n\r\nIs that supposed to happen ?","embeddings":[-0.0809623152,0.150933817,-0.004716095,0.4641732872,-0.0074457242,-0.0837897584,0.3782770634,-0.0160653237,-0.0122853341,0.1514238566,0.0223207176,0.2040352374,0.1237223744,0.459492594,-0.0932221487,0.3096233606,0.2900456488,-0.0745373294,0.0555239506,-0.1031743512,-0.3542973101,0.3774950802,-0.2202057987,-0.0087551316,-0.4959014654,-0.1943451613,-0.1198794842,0.0271139368,-0.007465004,0.2972819805,0.1181405634,0.0325630791,0.1704257727,0.6366829276,-0.0001297842,-0.1508616656,0.2038340122,-0.2284534872,-0.1701294482,-0.1342807263,-0.2650021017,0.0300459415,-0.0569734611,-0.0505863316,-0.1384722888,0.1548907012,0.2752764821,-0.536054492,0.0094062947,0.2767137289,0.0580062494,-0.4531153738,-0.4936726391,0.3889502585,0.1365727186,0.2704545856,-0.1813366562,0.2528256774,-0.0109171104,-0.1303181499,-0.0711399987,0.2785567641,-0.0806724727,0.0198998265,0.2697373927,0.4275933504,-0.3152773082,-0.2621915042,0.5415553451,-0.4134799838,0.3608884215,-0.3902038336,-0.2110204846,-0.2957029343,-0.3344982564,-0.3674358428,0.5965530872,0.1802713871,-0.1347963959,0.0444769673,-0.473382622,-0.2119874805,0.0828591883,0.1022464931,-0.1576583236,0.2304851413,-0.2503764927,0.0505878739,-0.0516202301,0.269718498,0.68134588,-0.4417134225,-0.0237598475,0.203751564,-0.0917869508,0.0940780118,-0.2643795013,0.4885321259,-0.2613624632,-0.1318342984,-0.1807596684,-0.1316577792,-0.1469574571,0.1340814084,0.1013710946,0.3952644467,-0.0123560624,0.1182581708,0.1236552745,0.0157679636,-0.5297678709,-0.1027585492,-0.0362670198,-0.2775636017,0.0065120058,0.0582706071,-0.0306735374,-0.2944576442,-0.1228455454,-0.0581407771,-0.3343105614,0.0305987541,0.0391496532,0.2700135708,-0.0227348469,-0.1497119069,-0.2075528204,0.2387869209,-0.2323745787,0.3985632062,-0.0660966337,0.0024269284,-0.3374920189,0.4626072943,0.0347473547,-0.2426494062,0.1173257008,0.0656713098,-0.0227639284,-0.3494029939,-0.0363068655,-0.4917875826,0.3772360682,0.1531202048,0.1754184663,0.4545671642,-0.016256839,-0.2907529473,-0.0823221579,0.2838455737,-0.5681078434,-0.2272555232,0.1737813503,-0.0337877572,-0.2504401207,-0.0980441272,-0.6474565864,0.0480876006,0.5046129227,-0.2406375706,0.0103453426,-0.0212473385,-0.5231416821,-0.2621290088,0.0020737362,0.1776815802,-0.2439186871,0.2162410021,-0.2958571315,0.5004327297,0.5286247134,0.4728852808,0.091679275,0.2983110249,-0.5009072423,-0.0403814986,0.1156427637,-0.2453132719,-0.7745263577,0.33667171,-0.0865652412,0.020292284,0.0238598604,0.0039782687,0.2072510272,-0.1113534346,0.2035693377,0.1161359698,0.1152517051,0.191468209,-0.2601095736,-0.0995403826,-0.0675879121,-0.1769413054,-0.0160327274,0.1607621759,0.0332443044,-0.3790466189,0.134554252,-0.1305041462,0.1587210149,0.2919359803,0.2254760414,-0.1097867042,0.0023407498,0.1582442671,-0.1636751592,0.0663116649,0.0064320127,-0.1549727917,-0.4688406885,-0.0954162627,-0.0691837147,-0.1704114974,-0.1157703474,-0.2090335041,-0.0990911499,0.3020282984,0.0527632795,0.1034016609,-0.0371871442,0.5598995686,0.0584912188,-0.1376343369,-0.0100797955,0.1996802539,-0.0974129736,-0.2990058362,-0.1839173734,-0.1400810778,0.2100965381,-0.1822098196,-0.2029643208,0.1439807266,0.2620843649,0.4544187784,-0.0678557977,0.2846869826,0.1313074827,0.1178170815,0.1736066788,-0.0596175753,0.0129096676,0.0975575,-0.0596396327,0.2911247015,-0.0939188749,0.1649128646,-0.0630543455,-0.3115476668,-0.1043291241,0.0928654373,0.2769778371,-0.0767954364,0.113763608,0.3560959697,0.3650048077,0.2202543914,0.2212857902,0.2814454138,0.6594775915,0.0796381086,0.0169117618,0.1316612363,-0.1434538066,-0.2267142385,0.252293855,0.4938217103,0.4509129226,-0.1102535054,0.2818646729,0.2308767438,0.0572349876,-0.192055136,0.2052621245,0.0635759383,0.2396275252,0.0193324275,0.1999723911,-0.0383975133,-0.1153916568,0.2090270668,0.0695498139,0.1952136755,-0.169008702,0.5489720702,-0.2330795825,-0.2392109632,-0.3021422625,-0.1950262636,0.0795688555,-0.1342739314,-0.1480572671,0.3275838196,0.0119456863,-0.0300209504,0.2582430243,0.2365769446,-0.1091117188,-0.4411351085,-0.1046401113,0.1154154837,-0.2608535588,-0.1139017045,0.3723790646,-0.0132912602,0.0179705378,-0.2058743536,0.0274478327,-0.3316384256,-0.0950437561,0.2160342634,-0.1245895699,0.3008134067,-0.2806513011,0.0052453275,-0.432366699,-0.323248148,-0.111687772,-0.0163679365,-0.1328595877,-0.4609757364,0.0380086079,-0.0903612152,-0.1600344181,-0.0169567633,-0.1087301373,-0.0706206784,0.1383949816,-0.0190440193,0.3141641319,0.0401264206,-0.1042646319,-0.2069066465,0.3973642886,-0.2842165232,-0.3432782888,-0.5802721381,0.2480850369,-0.1291804761,-0.076583758,0.0040053441,-0.0649519265,0.0509701334,0.4050847292,-0.4348990917,-0.2244686335,-0.1609023958,-0.0584499575,0.196693182,-0.0321757309,0.2875188887,0.0443622544,0.2087786347,-0.0488303602,-0.4330039024,0.1043199301,0.273560226,0.3252649903,0.041188091,0.2671677768,0.0133692743,0.5328205824,0.3042558134,0.0319025181,0.430485934,-0.0257738996,0.2479047179,-0.2646461129,-0.12208049,0.2533507049,-0.0026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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/643","title":"Caching processed dataset at wrong folder","comments":"The code you wrote should write a 1GB file in the Google Drive folder. Doesn't it? ","body":"Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n```","comment_length":16,"text":"Caching processed dataset at wrong folder \n Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n``` \n The code you wrote should write a 1GB file in the Google Drive folder. Doesn't it? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/643","title":"Caching processed dataset at wrong folder","comments":"I could check it and as you say as I write to te Drive disk the colab disk also increases...","body":"Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n```","comment_length":20,"text":"Caching processed dataset at wrong folder \n Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n``` \n I could check it and as you say as I write to te Drive disk the colab disk also increases...","embeddings":[-0.0909002125,0.0898183137,-0.0347917937,0.4908721149,-0.0633708313,-0.0270626657,0.2676787972,0.0049588634,0.0242158957,0.227923736,-0.0148068471,0.2577182651,0.0650781915,0.4686147571,-0.0855880305,0.3258917928,0.2614867985,-0.0908618197,0.0236124657,-0.0889639705,-0.3442029357,0.3814185262,-0.1878898144,-0.0883715078,-0.4606517553,-0.1489057094,-0.1486406177,-0.0555905029,-0.0616812035,0.2551704347,0.1518823057,0.0476347059,0.2141139209,0.658629775,-0.0001266875,-0.2107377946,0.185264498,-0.2339996248,-0.1972978413,-0.0303513687,-0.2913546264,-0.0428781323,-0.0804462582,-0.093984209,-0.1805127263,0.1525765955,0.1875965446,-0.4624527693,0.0369793512,0.2215129435,0.0815562904,-0.4584001601,-0.4723300636,0.3777891099,0.0973054171,0.2221828401,-0.1482014358,0.3222098351,-0.0102290772,-0.0661063343,-0.1228185073,0.2910982072,-0.0457291193,0.0100274635,0.2436986715,0.3522854447,-0.2692084014,-0.338673532,0.6088119149,-0.4549605548,0.3356356919,-0.3624158204,-0.1185456812,-0.2765686512,-0.2856233716,-0.3757875264,0.5314132571,0.1389435977,-0.147815153,0.0686528757,-0.4924881756,-0.2548331022,0.0755867362,0.0571191795,-0.2038838714,0.2674259841,-0.2745117843,0.0091094803,-0.0179538541,0.2676405907,0.7121443152,-0.3867537677,-0.0097836275,0.2255641222,-0.1349747628,0.043014735,-0.176111728,0.5634051561,-0.2399387956,-0.1454800367,-0.2318115383,-0.1449790001,-0.1336895227,0.1113656312,0.0579791106,0.4367699325,-0.0656073838,0.1153593883,0.073388584,0.0440182611,-0.4660323262,-0.0788127035,-0.0530332364,-0.2441565245,0.0195791069,0.0858408213,-0.0243465174,-0.2801483572,-0.1593984067,-0.0670680925,-0.2925985157,0.0194888283,0.0709376782,0.2782577276,-0.0084352372,-0.116373077,-0.2690790892,0.2244533896,-0.1827167273,0.3975844681,-0.0862434953,0.0280139372,-0.3113510311,0.4865098298,0.0744594336,-0.2300519347,0.1022623703,0.0751506388,-0.0056708157,-0.332251966,-0.1181941479,-0.4248935282,0.3353984654,0.1824312806,0.1418440938,0.3487936258,-0.0752956569,-0.2226856798,-0.1101973206,0.2607309222,-0.6085553169,-0.2279908061,0.1420246065,-0.0007225169,-0.2446970195,-0.0833464563,-0.7278817296,0.0973593891,0.452542454,-0.1691073924,0.0063451272,-0.0134537118,-0.4763551354,-0.2047984153,0.0397766382,0.1366304606,-0.20871526,0.1826031953,-0.315536648,0.532546401,0.4800925553,0.4041325748,0.082662724,0.2509357333,-0.5047847629,-0.1302544922,0.068114236,-0.2428762466,-0.8206087947,0.2948431373,-0.0977444127,0.0086052353,0.0828308985,-0.0241562799,0.2168331146,-0.0997235477,0.1954146773,0.1250831038,0.073805742,0.2005304247,-0.3050268292,-0.1327726692,-0.1084555015,-0.1719167829,-0.002899226,0.1939235181,0.0233739186,-0.360453397,0.1965491772,-0.107761234,0.1663274169,0.2490561455,0.2758973837,-0.2232208848,-0.0158251952,0.1475936174,-0.1878914386,0.0984159708,0.030998541,-0.1838445216,-0.4914022386,-0.1147519946,-0.1692594439,-0.1446654797,-0.1153861657,-0.2338029295,-0.0712446496,0.3111445904,0.097951889,0.003481393,-0.0143393446,0.5263148546,0.0860157162,-0.1585880965,-0.0184127595,0.2007098794,-0.0831227675,-0.2301699519,-0.1330531687,-0.1328961551,0.1908378154,-0.2405849844,-0.1907077432,0.0976371467,0.265999496,0.4339229763,0.0383322537,0.2717318237,0.1190792099,0.0998934954,0.2144974321,-0.0961880237,-0.0499377586,0.1104994267,-0.0246756133,0.2426779568,-0.0756841451,0.1617136002,-0.103887789,-0.3116764426,-0.0743965879,0.0543084219,0.315312922,-0.1267091185,0.0170020703,0.3911203444,0.4046420455,0.2553063929,0.2483256757,0.3756074607,0.6352421641,0.0773170888,-0.0819763839,0.2210304886,-0.1640895009,-0.3080351055,0.2351090163,0.5566420555,0.419631511,-0.0789889097,0.3083798885,0.224425599,0.0988956094,-0.1601508856,0.1938725412,0.05116418,0.2598308027,0.0632752404,0.2444454283,-0.0309985504,-0.0644106045,0.1818398237,0.0606858693,0.2382087857,-0.1505656689,0.5132129192,-0.2193128765,-0.2198534012,-0.2537248731,-0.2095603496,0.1171369106,-0.1727666706,-0.1311654598,0.3562390506,0.0576346107,-0.0108536836,0.2667405605,0.2275639474,-0.1232322454,-0.4242453277,-0.1358309388,0.1687889248,-0.2426113337,-0.0781314746,0.3596924543,-0.0710113049,0.086566709,-0.1987028569,0.0729769692,-0.3626462221,-0.1350426078,0.2516106367,-0.1367730647,0.3073520064,-0.2948588133,0.006540122,-0.4329467118,-0.3261017501,-0.1517671794,-0.0480665863,-0.1187816486,-0.4421813488,-0.0419284552,-0.0632458776,-0.1138281301,0.0201725028,-0.1713443249,-0.0622775033,0.0999836847,0.0376602933,0.3234501183,0.0060572359,-0.0844256729,-0.1918919086,0.4517862797,-0.2782467008,-0.3682191968,-0.6094025373,0.2131614536,-0.0835438222,0.0003339312,0.0212867539,-0.1330575198,0.1271721274,0.3549331725,-0.4617934227,-0.3288876116,-0.1489284188,0.0233004652,0.190487355,-0.0547214076,0.2663661838,0.0645634159,0.1716391146,0.0243139062,-0.4264402092,-0.0048502618,0.3264302015,0.393153429,0.0035664367,0.2174666524,0.0104868496,0.5704649687,0.2698986828,-0.0205930695,0.4525624514,-0.0104251318,0.2274507731,-0.2605685294,-0.1322252005,0.2276980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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/643","title":"Caching processed dataset at wrong folder","comments":"To reproduce it: \r\n```bash\r\n!df -h | grep sda1\r\n```\r\n```python\r\nf = open(\"\/content\/drive\/My Drive\/test_to_remove.txt\", \"w\")\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) \/\/ 512))  # 1GiB\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) \/\/ 512))  # 1GiB\r\nf.close()\r\n```\r\n```bash\r\n!ls -lh \/content\/drive\/My\\ Drive\/test_to_remove.txt\r\n\r\n!df -h | grep sda1\r\n\r\n!rm -rf \/content\/drive\/My\\ Drive\/test_to_remove.txt\r\n\r\n```\r\n[Colab](https:\/\/colab.research.google.com\/drive\/1D0UiweCYQwwWZ65EEhuqqbaDDbhJYXfm?usp=sharing)\r\n\r\n\r\n","body":"Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n```","comment_length":56,"text":"Caching processed dataset at wrong folder \n Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n``` \n To reproduce it: \r\n```bash\r\n!df -h | grep sda1\r\n```\r\n```python\r\nf = open(\"\/content\/drive\/My Drive\/test_to_remove.txt\", \"w\")\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) \/\/ 512))  # 1GiB\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) \/\/ 512))  # 1GiB\r\nf.close()\r\n```\r\n```bash\r\n!ls -lh \/content\/drive\/My\\ Drive\/test_to_remove.txt\r\n\r\n!df -h | grep sda1\r\n\r\n!rm -rf \/content\/drive\/My\\ Drive\/test_to_remove.txt\r\n\r\n```\r\n[Colab](https:\/\/colab.research.google.com\/drive\/1D0UiweCYQwwWZ65EEhuqqbaDDbhJYXfm?usp=sharing)\r\n\r\n\r\n","embeddings":[-0.0841225088,0.1945452243,-0.016326312,0.4593132436,-0.0439055972,-0.0728673413,0.3859433532,0.030553896,0.0337211378,0.2072662413,-0.0354525633,0.2843731046,0.047132723,0.4058027267,-0.1138568521,0.3554214239,0.2777641714,-0.125281468,-0.0011570895,-0.1184547693,-0.3421107829,0.381975174,-0.2049852908,-0.0256493818,-0.4567474127,-0.0956494436,-0.1473441124,-0.0011716173,-0.0159747936,0.2834328711,0.1546794027,0.034230385,0.2064488083,0.6668285131,-0.0001267947,-0.1738701314,0.2114759386,-0.2006000876,-0.2727822661,-0.1019956917,-0.3044102192,0.0360213965,-0.1111781448,-0.0811382532,-0.1309183091,0.1151060164,0.1956802756,-0.5906016827,-0.0217093918,0.2646551728,0.0812927112,-0.449357599,-0.5469542742,0.3519179225,0.1121328771,0.2696870863,-0.12555632,0.2454707474,-0.0296040047,-0.1314675957,-0.0899721906,0.3205049932,-0.1109801382,-0.0286945589,0.2242933512,0.3244673014,-0.3142950833,-0.2437792122,0.5524448752,-0.4051456451,0.3221742511,-0.3460015655,-0.1634961665,-0.3033885658,-0.3498371542,-0.3369373977,0.5142972469,0.1225768402,-0.0838442072,0.0870277807,-0.5243647099,-0.1893934309,0.0964756086,0.0881543607,-0.2207656205,0.2885017395,-0.2636702061,0.0324776769,-0.0383785665,0.2351333648,0.6513156295,-0.4771052599,0.0129428105,0.2287504226,-0.119115591,0.1084964201,-0.1420404315,0.4823613763,-0.2183911353,-0.1262344122,-0.2273806334,-0.1387718171,-0.1228205562,0.0968808532,0.0549007989,0.4719401598,-0.0667963997,0.1334535331,0.0674367249,0.0957627892,-0.495754689,-0.0539302304,0.0051828241,-0.2332229614,-0.0355139002,0.1311545521,-0.0165151991,-0.2762652636,-0.2102953345,0.0187313482,-0.370452255,-0.0185113885,0.0934006423,0.2619767785,-0.0247534588,-0.1263897717,-0.2220385373,0.2480699867,-0.2516383529,0.4041092098,-0.1224166825,0.0474488661,-0.3677769005,0.422128737,0.0711667091,-0.2111281604,0.1136209071,0.0618330427,-0.0047403676,-0.3111233115,-0.0762209743,-0.4326264262,0.4141172469,0.1112445965,0.1141543314,0.4161489904,0.0264522098,-0.267082572,-0.0823545903,0.2368801385,-0.5531480312,-0.2045004368,0.1599131525,0.0041203895,-0.2979134917,-0.0669916645,-0.69560498,0.1260885149,0.4034116864,-0.1562398821,0.0185713284,-0.0514682606,-0.4866917431,-0.2335226983,0.0505956858,0.1709021777,-0.2770163417,0.1444514245,-0.3489136398,0.5182402134,0.545022428,0.4078437984,0.0712292492,0.2802247107,-0.5115201473,-0.0195448175,0.0752299428,-0.257096976,-0.7950388193,0.3377696276,-0.0835522711,0.0778091103,-0.0157460086,-0.0586844683,0.1973324269,-0.1049470231,0.1535241008,0.1360723525,0.1003889292,0.1800670177,-0.276997,-0.1262809485,-0.0409163721,-0.173557803,0.0368149169,0.1725338548,0.0476657264,-0.3246938884,0.1804226339,-0.1166001111,0.1493276656,0.2664092481,0.2396847457,-0.1233286113,-0.0224798303,0.1364758909,-0.2220253795,0.15326491,0.0340711325,-0.1538006663,-0.5125191212,-0.1051220745,-0.1252607852,-0.1472769678,-0.1138537824,-0.2297271639,-0.0633671805,0.3790797293,0.0941518098,0.0542890616,-0.0053921985,0.5049632788,0.0877435058,-0.1718437672,-0.0558091924,0.1888891757,-0.1316520125,-0.2515571117,-0.1695169955,-0.1328744888,0.2208893299,-0.2341687232,-0.1833904386,0.1453886926,0.2613753974,0.423281163,-0.0350139476,0.2826690972,0.0854922608,0.1074394956,0.179387182,-0.0734495372,-0.0230706017,0.1655795872,-0.1058244705,0.2817675471,-0.11350099,0.1841135472,-0.1023149267,-0.3243633211,-0.0864485204,0.0631371513,0.2896333039,-0.1316164136,0.0799007788,0.3997071981,0.3467099965,0.2356557399,0.2422431111,0.3499772251,0.6368630528,0.0411725007,-0.0480863377,0.1404183656,-0.0809065253,-0.282887429,0.275200814,0.5394611359,0.4638066292,-0.0988675952,0.3149523735,0.1691281497,0.0862854645,-0.1600850523,0.2028650641,0.0676125661,0.2505049706,0.0337446369,0.2440981418,-0.0421402641,-0.0904779509,0.1334930211,0.04070637,0.223536551,-0.1144714281,0.5990411043,-0.3186650574,-0.2106164545,-0.2102443427,-0.2406889647,0.1269744188,-0.2022586316,-0.1322252899,0.3160975277,-0.0469736718,0.0264324937,0.2152574062,0.193661496,-0.1426065564,-0.4581068158,-0.1588827223,0.1610168815,-0.2578992248,-0.0891753063,0.3661741614,-0.0499390773,0.0422776751,-0.1529658139,0.0076157907,-0.3925890923,-0.1384721994,0.2612594664,-0.1823771447,0.3225194514,-0.2217480987,-0.0241664164,-0.439527452,-0.3129885793,-0.0940911397,-0.0256117694,-0.1019076034,-0.4108661711,-0.0089527834,-0.0493570417,-0.1378780156,0.0002188164,-0.0952606946,-0.0734974742,0.0395640321,-0.014223271,0.2749411762,0.0180198904,-0.0792432204,-0.1839408427,0.4457667768,-0.2575637102,-0.3838847578,-0.5558648109,0.1845136434,-0.0844427496,-0.019376928,-0.0445440747,-0.0801561922,0.1160566211,0.4078014493,-0.4832977653,-0.2445380539,-0.1550945789,-0.0202741269,0.1770356596,-0.0848400071,0.3010421097,0.0698694736,0.1874433309,-0.0501757152,-0.4178805053,0.034913525,0.2792589366,0.3262370825,-0.0075919875,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/643","title":"Caching processed dataset at wrong folder","comments":"Apparently, Colab uses a local cache of the data files read\/written from Google Drive. See:\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/2087#issuecomment-860818457\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/1915#issuecomment-804234540\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/2147#issuecomment-885052636","body":"Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n```","comment_length":21,"text":"Caching processed dataset at wrong folder \n Hi guys, I run this on my Colab (PRO):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='\/content\/corpus.txt', cache_dir='\/content\/drive\/My Drive', split='train')\r\n\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\ndataset = dataset.map(encode, batched=True)\r\n```\r\nThe file is about 4 GB, so I cannot process it on the Colab HD because there is no enough space. So I decided to mount my Google Drive fs and do it on it.\r\nThe dataset is cached in the right place but by processing it (applying `encode` function) seems to use a different folder because Colab HD starts to grow and it crashes when it should be done in the Drive fs.\r\n\r\nWhat gets me crazy, it prints it is processing\/encoding the dataset in the right folder:\r\n```\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nCaching processed dataset at \/content\/drive\/My Drive\/text\/default-ad3e69d6242ee916\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/cache-b16341780a59747d.arrow\r\n``` \n Apparently, Colab uses a local cache of the data files read\/written from Google Drive. See:\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/2087#issuecomment-860818457\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/1915#issuecomment-804234540\r\n- https:\/\/github.com\/googlecolab\/colabtools\/issues\/2147#issuecomment-885052636","embeddings":[-0.0610343702,0.2720839977,-0.0283772945,0.445343405,-0.081531167,-0.0210293997,0.3334376812,0.0359664299,-0.0489293002,0.2337764651,-0.0136982761,0.2756026387,0.0365146138,0.3952312469,-0.082842432,0.3347194195,0.2051368058,-0.0510053635,0.0500107184,-0.08528696,-0.3551539779,0.3671509027,-0.1679540128,-0.0600117296,-0.4700477123,-0.0544971749,-0.1362235546,-0.0494379923,-0.0417689793,0.2399596423,0.2862650454,0.0359595343,0.1236969531,0.5833591223,-0.0001266252,-0.156157583,0.2202801853,-0.212978363,-0.2345525026,-0.1091252565,-0.3383678496,-0.0145685133,-0.0260069631,-0.0600656867,-0.2109531611,0.148144871,0.2697553039,-0.491091609,-0.0131427469,0.2409366667,0.0793206915,-0.470918417,-0.5250526071,0.3897104859,0.1352818012,0.2636302114,-0.1766056865,0.3681114316,0.0485497825,-0.1179831177,-0.1188477501,0.3179059923,-0.056080699,0.1058957279,0.2113447189,0.3340997994,-0.3169411421,-0.2632052004,0.5499747396,-0.4216975868,0.3362530172,-0.3492866457,-0.1351042092,-0.3083275855,-0.3160908222,-0.3311682045,0.5454383492,0.1797975451,-0.1486809701,0.0793514475,-0.5265557766,-0.1687603444,0.0950279683,0.0548878498,-0.2062120438,0.2981136739,-0.2602463365,0.0158503279,-0.0781459883,0.2501655817,0.7252610922,-0.3686472774,0.0660394207,0.2389564961,-0.0488773212,0.0935397968,-0.1292621344,0.632032454,-0.2738451958,-0.1767209917,-0.1868334264,-0.1085796207,-0.1575983316,0.1380197555,0.0188358203,0.4789526463,0.0039042423,0.0920015723,0.075218603,0.0572814047,-0.4851468801,-0.0536839589,-0.0079048416,-0.2523821592,-0.0383134224,0.1286385357,-0.0470281653,-0.319701314,-0.0857711956,0.000260457,-0.3140642047,-0.0209068004,0.1428412497,0.1975008845,-0.0527262017,-0.1274861991,-0.2333370149,0.1353297681,-0.2033347636,0.3468832672,-0.1063610837,0.01069667,-0.2981441319,0.490164876,0.1197805926,-0.2768304944,0.1424653381,-0.0081910528,-0.019696936,-0.2453966737,-0.0582593009,-0.4647102654,0.4115089476,0.1777170151,0.115800038,0.3921353221,0.053856466,-0.2697461843,-0.1676190645,0.2586172819,-0.5754591823,-0.2996767461,0.2304366976,-0.0208045579,-0.2513169944,-0.0653132871,-0.6218788028,0.1069446281,0.481856972,-0.1754592657,0.0321230516,-0.0747874603,-0.6098787785,-0.215555802,0.0196572151,0.2119017988,-0.343580842,0.1560849994,-0.2851770818,0.4931885004,0.5012341738,0.4064074755,0.0037119717,0.2275684923,-0.461155206,-0.0845464244,0.0169548281,-0.2920345068,-0.7751476169,0.3196128309,-0.072775729,0.0074271024,-0.0272702891,-0.0856553763,0.2009661049,-0.1715903431,0.2674723268,0.1972281784,0.045479171,0.230441466,-0.2951917052,-0.1713251173,-0.0874365345,-0.1459074467,0.0176959522,0.175732106,0.0241740309,-0.3182267845,0.0907217562,-0.113160044,0.1602274328,0.2599445879,0.2357992977,-0.1545769572,0.0087158205,0.2336180657,-0.1543001086,0.1625273824,0.003672329,-0.1264241636,-0.4546043873,-0.0886874869,-0.1274770796,-0.1392586082,-0.1500976682,-0.216619581,-0.0460939184,0.3267394006,0.1241033822,0.0900932103,0.0072633005,0.5470097065,0.0962226391,-0.1861605495,-0.0688972026,0.195203051,-0.12871252,-0.2006803304,-0.1603138894,-0.1325293481,0.2135444283,-0.2256243825,-0.2190117389,0.1192047969,0.1958236098,0.4027073681,0.0170838349,0.298030436,0.1268264949,0.0936340392,0.1950585842,-0.1518374532,0.0072076139,0.1414074898,-0.0516694374,0.3032369316,-0.1035405919,0.1649564356,-0.1215218082,-0.2772289515,-0.0143902032,0.062978223,0.2463200092,-0.1730798483,0.1119626835,0.3348494768,0.4395120144,0.2801447213,0.2144912928,0.3809010386,0.5975578427,0.0968182161,-0.0589017011,0.1561948955,-0.1204376519,-0.3311527371,0.2858499587,0.5158726573,0.4232355654,-0.113623701,0.3228374124,0.1980501562,0.0616599172,-0.2252927274,0.11604812,0.0464665592,0.2495325506,-0.0243313983,0.2553370297,-0.0680699572,-0.0644718409,0.2110360563,0.0694479942,0.2028130591,-0.0649563298,0.5757151842,-0.3395918012,-0.2704718113,-0.305832535,-0.3091862798,0.0882460922,-0.1829891354,-0.1151195541,0.3313700855,-0.0017694813,-0.0220329482,0.2284814715,0.2463257462,-0.1572623849,-0.5371960998,-0.1162828729,0.0796749964,-0.2961491942,-0.0847114176,0.3633794188,-0.0289392266,0.0331705287,-0.2750115395,-0.0099125281,-0.277379185,-0.0903323367,0.3296645284,-0.1228605285,0.3179918528,-0.2799589336,-0.0681023374,-0.5162339807,-0.3406288922,-0.0903521329,-0.0407027528,-0.0643921494,-0.44489941,-0.0423481874,-0.0331220739,-0.1272599548,0.0647555962,-0.201914683,0.0180335157,0.1064041853,0.032687176,0.3001042306,-0.0105564734,-0.0835685432,-0.1544340104,0.403321445,-0.2872377336,-0.3533238769,-0.646609664,0.2498881221,-0.1351866126,-0.0283567738,-0.0251943059,-0.0988628939,0.0905016214,0.3466602266,-0.469776839,-0.3415854871,-0.1621461809,-0.0230158493,0.2876148522,-0.115794085,0.3167226315,0.0533325337,0.1837206036,-0.0475870781,-0.4573251009,0.0412996411,0.3344965577,0.3154990077,0.0712056383,0.1656310111,0.0524887107,0.5838848352,0.1963842362,0.0033476658,0.4174218178,0.033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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"Not sure what could cause that on the `datasets` side. Could this be a `Trainer` issue ? cc @julien-c @sgugger ?","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":21,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n Not sure what could cause that on the `datasets` side. Could this be a `Trainer` issue ? cc @julien-c @sgugger 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"There was a memory leak issue fixed recently in master. You should install from source and see if it fixes your problem.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":22,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n There was a memory leak issue fixed recently in master. You should install from source and see if it fixes your problem.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"@lhoestq @sgugger Thanks for your comments. I have install from source code as you told, but the problem is still there.\r\nTo reproduce the issue, just replace [these lines](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L241-L258) with: \r\n(load_dataset and DataCollatorForDatasetsLanguageModeling as [above mentioned](https:\/\/github.com\/huggingface\/datasets\/issues\/633#issue-702440484))\r\n```python\r\n    dataset = load_dataset(\"bookcorpus\")\r\n    dataset = dataset.train_test_split(test_size=0.1)\r\n    train_dataset = dataset['train']\r\n    eval_dataset = dataset['test'] if training_args.do_eval else None\r\n\r\n    data_collator = DataCollatorForDatasetsLanguageModeling(\r\n        tokenizer=tokenizer,\r\n        mlm=data_args.mlm,\r\n        mlm_probability=data_args.mlm_probability,\r\n        block_size=data_args.block_size\r\n    )\r\n```\r\nand run by:\r\n```bash\r\npython run_language_modeling.py\r\n--output_dir=output \\\r\n--model_type=bert \\\r\n--model_name_or_path=bert-base-uncased \\\r\n--do_train \\\r\n--do_eval \\\r\n--mlm \r\n```","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":80,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n @lhoestq @sgugger Thanks for your comments. I have install from source code as you told, but the problem is still there.\r\nTo reproduce the issue, just replace [these lines](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L241-L258) with: \r\n(load_dataset and DataCollatorForDatasetsLanguageModeling as [above mentioned](https:\/\/github.com\/huggingface\/datasets\/issues\/633#issue-702440484))\r\n```python\r\n    dataset = load_dataset(\"bookcorpus\")\r\n    dataset = dataset.train_test_split(test_size=0.1)\r\n    train_dataset = dataset['train']\r\n    eval_dataset = dataset['test'] if training_args.do_eval else None\r\n\r\n    data_collator = DataCollatorForDatasetsLanguageModeling(\r\n        tokenizer=tokenizer,\r\n        mlm=data_args.mlm,\r\n        mlm_probability=data_args.mlm_probability,\r\n        block_size=data_args.block_size\r\n    )\r\n```\r\nand run by:\r\n```bash\r\npython run_language_modeling.py\r\n--output_dir=output \\\r\n--model_type=bert \\\r\n--model_name_or_path=bert-base-uncased \\\r\n--do_train \\\r\n--do_eval \\\r\n--mlm \r\n```","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"Same here. Pre-training on wikitext-103 to do some test. At the end of the training it takes 32GB of RAM + ~30GB of SWAP. I installed dataset==1.1.0, not built from source. I will try uninstalling and building from source when it finish.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":42,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n Same here. Pre-training on wikitext-103 to do some test. At the end of the training it takes 32GB of RAM + ~30GB of SWAP. I installed dataset==1.1.0, not built from source. I will try uninstalling and building from source when it finish.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"This seems to be on the `transformers` library side.\r\n\r\nIf you have more informations (pip env) or even better, a colab reproducing the error we can investigate.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":27,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n This seems to be on the `transformers` library side.\r\n\r\nIf you have more informations (pip env) or even better, a colab reproducing the error we can investigate.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.25937935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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"It seems like it's solved with freshed versions of transformers. I have tried to replicate the error doing a fresh pip install transformers & datasets on colab and the error doesn't continue. On colab it keeps stable on 5GB! (Y)\r\n\r\nEdit: **Thanks for your great work**. Have a good day.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":50,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n It seems like it's solved with freshed versions of transformers. I have tried to replicate the error doing a fresh pip install transformers & datasets on colab and the error doesn't continue. On colab it keeps stable on 5GB! (Y)\r\n\r\nEdit: **Thanks for your great work**. Have a good day.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0.05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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"@gaceladri witch version transformers and datasets  are you using now? I want to try again. Thanks.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":16,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n @gaceladri witch version transformers and datasets  are you using now? I want to try again. Thanks.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"It's happening to me again. After 4 hours of pre-training, my ram memory gets full and the kernel dies. I am using the last transformers version as today. 4.4.0 and the last version of datasets 1.2.1, both installed from master. The memory consumption keeps increasing.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":45,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n It's happening to me again. After 4 hours of pre-training, my ram memory gets full and the kernel dies. I am using the last transformers version as today. 4.4.0 and the last version of datasets 1.2.1, both installed from master. The memory consumption keeps increasing.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.259379357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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"Thanks for the investigation @gaceladri \r\n\r\nApparently this happens when `num_workers>0` and has to do with objects being copied-on-write.\r\nDid you try setting num_workers to 0 @gaceladri ?\r\nIf the issue doesn't happen with `num_workers=0` then this would confirm that it's indeed related to this python\/pytorch issue.\r\n\r\nSince a `Dataset` object is a wrapper of a pyarrow Table, we should investigate if the data being copied comes from the Table itself or from metadata in the `Dataset` object. If it comes from the metadata in the `Dataset` object, we should be able to implement a workaround. But if it comes from the Table, we'll need to see with the pyarrow team what we can do... ","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":114,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n Thanks for the investigation @gaceladri \r\n\r\nApparently this happens when `num_workers>0` and has to do with objects being copied-on-write.\r\nDid you try setting num_workers to 0 @gaceladri ?\r\nIf the issue doesn't happen with `num_workers=0` then this would confirm that it's indeed related to this python\/pytorch issue.\r\n\r\nSince a `Dataset` object is a wrapper of a pyarrow Table, we should investigate if the data being copied comes from the Table itself or from metadata in the `Dataset` object. If it comes from the metadata in the `Dataset` object, we should be able to implement a workaround. But if it comes from the Table, we'll need to see with the pyarrow team what we can do... 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"Hmmm so this might come from another issue...\r\nSince it doesn't seem to be related to multiprocessing it should be easier to investigate though.\r\nDo you have some ideas @gaceladri ?","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":31,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n Hmmm so this might come from another issue...\r\nSince it doesn't seem to be related to multiprocessing it should be easier to investigate though.\r\nDo you have some ideas @gaceladri 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"@lhoestq I looked quickly to a previously spoted bug in my env wandb \/sdk\/interface\/interface.py, because sometimes when I load the dataset I got a multiprocessing error at line 510 in wandb...interface.py\r\n\r\nThis bug is reported here https:\/\/github.com\/huggingface\/datasets\/issues\/847\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<timed eval> in <module>\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/transformers\/trainer.py in train(self, model_path, trial)\r\n    877             print(len(epoch_iterator))\r\n    878 \r\n--> 879             for step, inputs in enumerate(epoch_iterator):\r\n    880 \r\n    881                 start_step = time.time()\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    433         if self._sampler_iter is None:\r\n    434             self._reset()\r\n--> 435         data = self._next_data()\r\n    436         self._num_yielded += 1\r\n    437         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n   1083             else:\r\n   1084                 del self._task_info[idx]\r\n-> 1085                 return self._process_data(data)\r\n   1086 \r\n   1087     def _try_put_index(self):\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in _process_data(self, data)\r\n   1109         self._try_put_index()\r\n   1110         if isinstance(data, ExceptionWrapper):\r\n-> 1111             data.reraise()\r\n   1112         return data\r\n   1113 \r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/_utils.py in reraise(self)\r\n    426             # have message field\r\n    427             raise self.exc_type(message=msg)\r\n--> 428         raise self.exc_type(msg)\r\n    429 \r\n    430 \r\n\r\nAssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/worker.py\", line 198, in _worker_loop\r\n    data = fetcher.fetch(index)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1083, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1070, in _getitem\r\n    format_kwargs=format_kwargs,\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 886, in _convert_outputs\r\n    v = map_nested(command, v, **map_nested_kwargs)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 847, in command\r\n    return torch.tensor(x, **format_kwargs)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/warnings.py\", line 101, in _showwarnmsg\r\n    _showwarnmsg_impl(msg)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/warnings.py\", line 30, in _showwarnmsg_impl\r\n    file.write(text)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 100, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 729, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 186, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 191, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 510, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n```\r\n\r\nMy workaround was to just comment those lines without looking to much into consecuences:\r\n\r\n```\r\ndef _publish(self, record: pb.Record, local: bool = None) -> None:\r\n        #if self._process and not self._process.is_alive():\r\n        #    raise Exception(\"The wandb backend process has shutdown\")\r\n```\r\n\r\nIt worked so far... I need to try running without wandb and see if it could be causing something wrong with multiprocessing. I am going to try to launch the training setting wandb to false and I will let you know again.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":396,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n @lhoestq I looked quickly to a previously spoted bug in my env wandb \/sdk\/interface\/interface.py, because sometimes when I load the dataset I got a multiprocessing error at line 510 in wandb...interface.py\r\n\r\nThis bug is reported here https:\/\/github.com\/huggingface\/datasets\/issues\/847\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError                            Traceback (most recent call last)\r\n<timed eval> in <module>\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/transformers\/trainer.py in train(self, model_path, trial)\r\n    877             print(len(epoch_iterator))\r\n    878 \r\n--> 879             for step, inputs in enumerate(epoch_iterator):\r\n    880 \r\n    881                 start_step = time.time()\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    433         if self._sampler_iter is None:\r\n    434             self._reset()\r\n--> 435         data = self._next_data()\r\n    436         self._num_yielded += 1\r\n    437         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n   1083             else:\r\n   1084                 del self._task_info[idx]\r\n-> 1085                 return self._process_data(data)\r\n   1086 \r\n   1087     def _try_put_index(self):\r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/dataloader.py in _process_data(self, data)\r\n   1109         self._try_put_index()\r\n   1110         if isinstance(data, ExceptionWrapper):\r\n-> 1111             data.reraise()\r\n   1112         return data\r\n   1113 \r\n\r\n~\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/_utils.py in reraise(self)\r\n    426             # have message field\r\n    427             raise self.exc_type(message=msg)\r\n--> 428         raise self.exc_type(msg)\r\n    429 \r\n    430 \r\n\r\nAssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/worker.py\", line 198, in _worker_loop\r\n    data = fetcher.fetch(index)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1083, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1070, in _getitem\r\n    format_kwargs=format_kwargs,\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 886, in _convert_outputs\r\n    v = map_nested(command, v, **map_nested_kwargs)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 847, in command\r\n    return torch.tensor(x, **format_kwargs)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/warnings.py\", line 101, in _showwarnmsg\r\n    _showwarnmsg_impl(msg)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/warnings.py\", line 30, in _showwarnmsg_impl\r\n    file.write(text)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/lib\/redirect.py\", line 100, in new_write\r\n    cb(name, data)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/wandb_run.py\", line 729, in _console_callback\r\n    self._backend.interface.publish_output(name, data)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 186, in publish_output\r\n    self._publish_output(o)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 191, in _publish_output\r\n    self._publish(rec)\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/site-packages\/wandb\/sdk\/interface\/interface.py\", line 510, in _publish\r\n    if self._process and not self._process.is_alive():\r\n  File \"\/home\/ad\/anaconda3\/envs\/tfm\/lib\/python3.6\/multiprocessing\/process.py\", line 134, in is_alive\r\n    assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n```\r\n\r\nMy workaround was to just comment those lines without looking to much into consecuences:\r\n\r\n```\r\ndef _publish(self, record: pb.Record, local: bool = None) -> None:\r\n        #if self._process and not self._process.is_alive():\r\n        #    raise Exception(\"The wandb backend process has shutdown\")\r\n```\r\n\r\nIt worked so far... I need to try running without wandb and see if it could be causing something wrong with multiprocessing. I am going to try to launch the training setting wandb to false and I will let you know again.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"@lhoestq But despite this, I got lost into the [class Dataset()](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/arrow_dataset.html#Dataset) reading the pyarrow files.\r\n\r\nEdit: but you should be rigth, that it does not have to be related to multiprocessing since it keeps happening when `num_workers=0` ","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":37,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n @lhoestq But despite this, I got lost into the [class Dataset()](https:\/\/huggingface.co\/docs\/datasets\/_modules\/datasets\/arrow_dataset.html#Dataset) reading the pyarrow files.\r\n\r\nEdit: but you should be rigth, that it does not have to be related to multiprocessing since it keeps happening when `num_workers=0` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"Or maybe wandb uses multiprocessing ? One process for wandb logging and one for actual training ? If this is the case then even setting `num_workers=0` would cause the process to be forked for wandb and therefore cause the memory issue.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":41,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n Or maybe wandb uses multiprocessing ? One process for wandb logging and one for actual training ? If this is the case then even setting `num_workers=0` would cause the process to be forked for wandb and therefore cause the memory issue.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"@lhoestq could be, but if we set wandb to false this should not happen. I am going to try.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":19,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n @lhoestq could be, but if we set wandb to false this should not happen. I am going to try.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0.05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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"@lhoestq It keeps happening. I have uninstalled wandb from my env, setted `%env WANDB_DISABLED=true` on my notebook, and commented this func:\r\n\r\n```\r\ndef get_available_reporting_integrations():\r\n    integrations = []\r\n    if is_azureml_available():\r\n        integrations.append(\"azure_ml\")\r\n    if is_comet_available():\r\n        integrations.append(\"comet_ml\")\r\n    if is_mlflow_available():\r\n        integrations.append(\"mlflow\")\r\n    if is_tensorboard_available():\r\n        integrations.append(\"tensorboard\")\r\n    # if is_wandb_available():\r\n    #     integrations.append(\"wandb\")\r\n    return integrations\r\n```\r\nAs a fast test and it keeps increasing the ram memory. Wandb could not be the blameworthy here.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":65,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n @lhoestq It keeps happening. I have uninstalled wandb from my env, setted `%env WANDB_DISABLED=true` on my notebook, and commented this func:\r\n\r\n```\r\ndef get_available_reporting_integrations():\r\n    integrations = []\r\n    if is_azureml_available():\r\n        integrations.append(\"azure_ml\")\r\n    if is_comet_available():\r\n        integrations.append(\"comet_ml\")\r\n    if is_mlflow_available():\r\n        integrations.append(\"mlflow\")\r\n    if is_tensorboard_available():\r\n        integrations.append(\"tensorboard\")\r\n    # if is_wandb_available():\r\n    #     integrations.append(\"wandb\")\r\n    return integrations\r\n```\r\nAs a fast test and it keeps increasing the ram memory. Wandb could not be the blameworthy here.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"Thanks for checking @gaceladri . Let's investigate the single process setting then.\r\nIf you have some sort of colab notebook with a minimal code example that shows this behavior feel free to share it @gaceladri so that we can play around with it to find what causes this. Otherwise I'll probably try to reproduce on my side at one point","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":60,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n Thanks for checking @gaceladri . Let's investigate the single process setting then.\r\nIf you have some sort of colab notebook with a minimal code example that shows this behavior feel free to share it @gaceladri so that we can play around with it to find what causes this. Otherwise I'll probably try to reproduce on my side at one 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"@lhoestq sure. Here you have https:\/\/colab.research.google.com\/drive\/1ba09ZOpyHGAOQLcsxiQAHRXl10qnMU5o?usp=sharing let me know if the link works and it reproduces the issue. To me, it reproduces the issue, since if you start the training the ram memory keeps increasing.\r\n\r\nLet me know. Thanks!","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":39,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n @lhoestq sure. Here you have https:\/\/colab.research.google.com\/drive\/1ba09ZOpyHGAOQLcsxiQAHRXl10qnMU5o?usp=sharing let me know if the link works and it reproduces the issue. To me, it reproduces the issue, since if you start the training the ram memory keeps increasing.\r\n\r\nLet me know. Thanks!","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"Could the bug be comming from tokenizers?\r\n\r\nI got this warning at the terminal from my jupyter notebook: \r\n```\r\nhuggingface\/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\r\nTo disable this warning, you can either:\r\n\t- Avoid using `tokenizers` before the fork if possible\r\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\r\n```","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":63,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n Could the bug be comming from tokenizers?\r\n\r\nI got this warning at the terminal from my jupyter notebook: \r\n```\r\nhuggingface\/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\r\nTo disable this warning, you can either:\r\n\t- Avoid using `tokenizers` before the fork if possible\r\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\r\n```","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"I've never experienced memory issues with tokenizers so I don't know\r\nCc @n1t0 are you aware of any issue that would cause memory to keep increasing when the tokenizer is used in the Data Collator for language modeling ?","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":39,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n I've never experienced memory issues with tokenizers so I don't know\r\nCc @n1t0 are you aware of any issue that would cause memory to keep increasing when the tokenizer is used in the Data Collator for language modeling 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"@lhoestq Thanks for pointing to n1t0, just to clarify. That warning was doing fine-tuning, without collator:\r\n```\r\n\r\nfrom datasets import load_dataset, load_metric\r\nimport numpy as np\r\n\r\nGLUE_TASKS = [\r\n    \"cola\",\r\n    \"mnli\",\r\n    \"mnli-mm\",\r\n    \"mrpc\",\r\n    \"qnli\",\r\n    \"qqp\",\r\n    \"rte\",\r\n    \"sst2\",\r\n    \"stsb\",\r\n    \"wnli\",\r\n]\r\ntask = \"mnli\"\r\nactual_task = \"mnli\" if task == \"mnli-mm\" else task\r\ndataset = load_dataset(\"glue\", actual_task)\r\nmetric = load_metric(\"glue\", actual_task)\r\nbatch_size = 16\r\nattention_type = \"linear\"\r\n\r\nfrom transformers.models.mobilebert_mod import (\r\n    MobileBertForSequenceClassification,\r\n    MobileBertTokenizerFast,\r\n)\r\nfrom transformers.models.mobilebert_mod.configuration_mobilebert import (\r\n    MobileBertConfigMod,\r\n)\r\nfrom transformers import TrainingArguments, Trainer\r\n\r\nnum_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\r\ntokenizer = MobileBertTokenizerFast.from_pretrained(\r\n    \"\/media\/ad\/00b5422b-9d54-4449-8b5d-08eab5cdac8c\/training_trfm\/big_linear_layerdrop_shared\/checkpoint-23000\/\",\r\n    max_len=512,\r\n)\r\nmodel = MobileBertForSequenceClassification.from_pretrained(\r\n    \"\/media\/ad\/00b5422b-9d54-4449-8b5d-08eab5cdac8c\/training_trfm\/big_linear_layerdrop_shared\/checkpoint-23000\/\",\r\n    num_labels=num_labels,\r\n)\r\nprint(model.num_parameters())\r\n\r\ntask_to_keys = {\r\n    \"cola\": (\"sentence\", None),\r\n    \"mnli\": (\"premise\", \"hypothesis\"),\r\n    \"mnli-mm\": (\"premise\", \"hypothesis\"),\r\n    \"mrpc\": (\"sentence1\", \"sentence2\"),\r\n    \"qnli\": (\"question\", \"sentence\"),\r\n    \"qqp\": (\"question1\", \"question2\"),\r\n    \"rte\": (\"sentence1\", \"sentence2\"),\r\n    \"sst2\": (\"sentence\", None),\r\n    \"stsb\": (\"sentence1\", \"sentence2\"),\r\n    \"wnli\": (\"sentence1\", \"sentence2\"),\r\n}\r\n\r\nsentence1_key, sentence2_key = task_to_keys[task]\r\nif sentence2_key is None:\r\n    print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\r\nelse:\r\n    print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\r\n    print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")\r\n\r\n\r\ndef preprocess_function(examples):\r\n    if sentence2_key is None:\r\n        return tokenizer(examples[sentence1_key], truncation=True)\r\n    return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\r\n\r\n\r\nencoded_dataset = dataset.map(preprocess_function, batched=True)\r\nmetric_name = (\r\n    \"pearson\"\r\n    if task == \"stsb\"\r\n    else \"matthews_correlation\"\r\n    if task == \"cola\"\r\n    else \"accuracy\"\r\n)\r\n\r\nargs = TrainingArguments(\r\n    f\"test-glue\/{task}_{attention_type}\",\r\n    evaluation_strategy=\"steps\",\r\n    learning_rate=1e-5,\r\n    per_device_train_batch_size=batch_size,\r\n    per_device_eval_batch_size=batch_size,\r\n    logging_steps=200,\r\n    num_train_epochs=5,\r\n    gradient_accumulation_steps=1,\r\n    warmup_steps=10000,\r\n    fp16=True,\r\n    dataloader_num_workers=10,\r\n    weight_decay=0.1,\r\n    load_best_model_at_end=True,\r\n    metric_for_best_model=metric_name,\r\n)\r\n\r\n\r\ndef compute_metrics(eval_pred):\r\n    predictions, labels = eval_pred\r\n    if task != \"stsb\":\r\n        predictions = np.argmax(predictions, axis=1)\r\n    else:\r\n        predictions = predictions[:, 0]\r\n    return metric.compute(predictions=predictions, references=labels)\r\n\r\n\r\nvalidation_key = (\r\n    \"validation_mismatched\"\r\n    if task == \"mnli-mm\"\r\n    else \"validation_matched\"\r\n    if task == \"mnli\"\r\n    else \"validation\"\r\n)\r\n\r\ntrainer = Trainer(\r\n    model,\r\n    args,\r\n    train_dataset=encoded_dataset[\"train\"],\r\n    eval_dataset=encoded_dataset[validation_key],\r\n    tokenizer=tokenizer,\r\n    compute_metrics=compute_metrics,\r\n)\r\n\r\ntrainer.train()\r\n```\r\n\r\nNow, I have come back to pre-training. The changes that I think I have done are: not formatting the dataset to torch: ~~`big_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])`~~ so maybe some column is dropped and not freezed in memory and now I have not setted any validation dataset in the trainer. \r\n\r\nMy validation dataset before:\r\n```\r\nbook_corpus_eval = load_dataset(\r\n    \"bookcorpus\",\r\n    \"plain_text\",\r\n    cache_dir=\"\/home\/ad\/Desktop\/bookcorpus\",\r\n    split=\"train[98:99%]\",\r\n)\r\nbook_corpus_eval = book_corpus_eval.map(encode, batched=True)\r\nbook_corpus_eval.set_format(\r\n    type=\"torch\", columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"]\r\n)\r\n**book_corpus_eval = book_corpus_eval.select([i for i in range(1500)])**\r\n```\r\nMaybe _selecting_ or indexing the dataset before feeding it to the trainer, do something strange.\r\n\r\nMy trainer now:\r\n```\r\n\r\nbig_dataset = load_from_disk(\"\/home\/ad\/Desktop\/35percent_data.arrow\/\")\r\n\r\nfrom transformers import DataCollatorForWholeWordMask\r\n\r\ndata_collator = DataCollatorForWholeWordMask(\r\n    tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\r\n\r\nfrom transformers import Trainer, TrainingArguments\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/big_linear_layerdrop_shared_silu_secondtry\",\r\n    overwrite_output_dir=True,\r\n    per_device_train_batch_size=60,\r\n    per_device_eval_batch_size=60,\r\n    save_steps=500,\r\n    save_total_limit=10,\r\n    logging_first_step=True,\r\n    logging_steps=100,\r\n#     evaluation_strategy='steps',\r\n#     eval_steps=250,\r\n    gradient_accumulation_steps=8,\r\n    fp16=True,\r\n    dataloader_num_workers=10,\r\n    warmup_steps=15000,\r\n    learning_rate=6e-4,\r\n    adam_epsilon=1e-6,\r\n    adam_beta2=0.98,\r\n    weight_decay=0.01,\r\n    max_grad_norm=1.0,\r\n    max_steps=500000, \r\n)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=big_dataset,\r\n#     eval_dataset=book_corpus_eval,\r\n    tokenizer=tokenizer)\r\n\r\nimport wandb\r\nwandb.login()\r\n\r\ntrainer.train()\r\n```\r\n\r\nAnd surprisingly, the ram now keeps going up and down. The training is up now for 12h without collapse the ram. I don't know what could cause the leakage. :mag: \r\n\r\nEdit: I didn't see the swap memory, that keeps increasing. So the problem persist. ","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":468,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n @lhoestq Thanks for pointing to n1t0, just to clarify. That warning was doing fine-tuning, without collator:\r\n```\r\n\r\nfrom datasets import load_dataset, load_metric\r\nimport numpy as np\r\n\r\nGLUE_TASKS = [\r\n    \"cola\",\r\n    \"mnli\",\r\n    \"mnli-mm\",\r\n    \"mrpc\",\r\n    \"qnli\",\r\n    \"qqp\",\r\n    \"rte\",\r\n    \"sst2\",\r\n    \"stsb\",\r\n    \"wnli\",\r\n]\r\ntask = \"mnli\"\r\nactual_task = \"mnli\" if task == \"mnli-mm\" else task\r\ndataset = load_dataset(\"glue\", actual_task)\r\nmetric = load_metric(\"glue\", actual_task)\r\nbatch_size = 16\r\nattention_type = \"linear\"\r\n\r\nfrom transformers.models.mobilebert_mod import (\r\n    MobileBertForSequenceClassification,\r\n    MobileBertTokenizerFast,\r\n)\r\nfrom transformers.models.mobilebert_mod.configuration_mobilebert import (\r\n    MobileBertConfigMod,\r\n)\r\nfrom transformers import TrainingArguments, Trainer\r\n\r\nnum_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\r\ntokenizer = MobileBertTokenizerFast.from_pretrained(\r\n    \"\/media\/ad\/00b5422b-9d54-4449-8b5d-08eab5cdac8c\/training_trfm\/big_linear_layerdrop_shared\/checkpoint-23000\/\",\r\n    max_len=512,\r\n)\r\nmodel = MobileBertForSequenceClassification.from_pretrained(\r\n    \"\/media\/ad\/00b5422b-9d54-4449-8b5d-08eab5cdac8c\/training_trfm\/big_linear_layerdrop_shared\/checkpoint-23000\/\",\r\n    num_labels=num_labels,\r\n)\r\nprint(model.num_parameters())\r\n\r\ntask_to_keys = {\r\n    \"cola\": (\"sentence\", None),\r\n    \"mnli\": (\"premise\", \"hypothesis\"),\r\n    \"mnli-mm\": (\"premise\", \"hypothesis\"),\r\n    \"mrpc\": (\"sentence1\", \"sentence2\"),\r\n    \"qnli\": (\"question\", \"sentence\"),\r\n    \"qqp\": (\"question1\", \"question2\"),\r\n    \"rte\": (\"sentence1\", \"sentence2\"),\r\n    \"sst2\": (\"sentence\", None),\r\n    \"stsb\": (\"sentence1\", \"sentence2\"),\r\n    \"wnli\": (\"sentence1\", \"sentence2\"),\r\n}\r\n\r\nsentence1_key, sentence2_key = task_to_keys[task]\r\nif sentence2_key is None:\r\n    print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\r\nelse:\r\n    print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\r\n    print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")\r\n\r\n\r\ndef preprocess_function(examples):\r\n    if sentence2_key is None:\r\n        return tokenizer(examples[sentence1_key], truncation=True)\r\n    return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\r\n\r\n\r\nencoded_dataset = dataset.map(preprocess_function, batched=True)\r\nmetric_name = (\r\n    \"pearson\"\r\n    if task == \"stsb\"\r\n    else \"matthews_correlation\"\r\n    if task == \"cola\"\r\n    else \"accuracy\"\r\n)\r\n\r\nargs = TrainingArguments(\r\n    f\"test-glue\/{task}_{attention_type}\",\r\n    evaluation_strategy=\"steps\",\r\n    learning_rate=1e-5,\r\n    per_device_train_batch_size=batch_size,\r\n    per_device_eval_batch_size=batch_size,\r\n    logging_steps=200,\r\n    num_train_epochs=5,\r\n    gradient_accumulation_steps=1,\r\n    warmup_steps=10000,\r\n    fp16=True,\r\n    dataloader_num_workers=10,\r\n    weight_decay=0.1,\r\n    load_best_model_at_end=True,\r\n    metric_for_best_model=metric_name,\r\n)\r\n\r\n\r\ndef compute_metrics(eval_pred):\r\n    predictions, labels = eval_pred\r\n    if task != \"stsb\":\r\n        predictions = np.argmax(predictions, axis=1)\r\n    else:\r\n        predictions = predictions[:, 0]\r\n    return metric.compute(predictions=predictions, references=labels)\r\n\r\n\r\nvalidation_key = (\r\n    \"validation_mismatched\"\r\n    if task == \"mnli-mm\"\r\n    else \"validation_matched\"\r\n    if task == \"mnli\"\r\n    else \"validation\"\r\n)\r\n\r\ntrainer = Trainer(\r\n    model,\r\n    args,\r\n    train_dataset=encoded_dataset[\"train\"],\r\n    eval_dataset=encoded_dataset[validation_key],\r\n    tokenizer=tokenizer,\r\n    compute_metrics=compute_metrics,\r\n)\r\n\r\ntrainer.train()\r\n```\r\n\r\nNow, I have come back to pre-training. The changes that I think I have done are: not formatting the dataset to torch: ~~`big_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])`~~ so maybe some column is dropped and not freezed in memory and now I have not setted any validation dataset in the trainer. \r\n\r\nMy validation dataset before:\r\n```\r\nbook_corpus_eval = load_dataset(\r\n    \"bookcorpus\",\r\n    \"plain_text\",\r\n    cache_dir=\"\/home\/ad\/Desktop\/bookcorpus\",\r\n    split=\"train[98:99%]\",\r\n)\r\nbook_corpus_eval = book_corpus_eval.map(encode, batched=True)\r\nbook_corpus_eval.set_format(\r\n    type=\"torch\", columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"]\r\n)\r\n**book_corpus_eval = book_corpus_eval.select([i for i in range(1500)])**\r\n```\r\nMaybe _selecting_ or indexing the dataset before feeding it to the trainer, do something strange.\r\n\r\nMy trainer now:\r\n```\r\n\r\nbig_dataset = load_from_disk(\"\/home\/ad\/Desktop\/35percent_data.arrow\/\")\r\n\r\nfrom transformers import DataCollatorForWholeWordMask\r\n\r\ndata_collator = DataCollatorForWholeWordMask(\r\n    tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\r\n\r\nfrom transformers import Trainer, TrainingArguments\r\n\r\ntraining_args = TrainingArguments(\r\n    output_dir=\".\/big_linear_layerdrop_shared_silu_secondtry\",\r\n    overwrite_output_dir=True,\r\n    per_device_train_batch_size=60,\r\n    per_device_eval_batch_size=60,\r\n    save_steps=500,\r\n    save_total_limit=10,\r\n    logging_first_step=True,\r\n    logging_steps=100,\r\n#     evaluation_strategy='steps',\r\n#     eval_steps=250,\r\n    gradient_accumulation_steps=8,\r\n    fp16=True,\r\n    dataloader_num_workers=10,\r\n    warmup_steps=15000,\r\n    learning_rate=6e-4,\r\n    adam_epsilon=1e-6,\r\n    adam_beta2=0.98,\r\n    weight_decay=0.01,\r\n    max_grad_norm=1.0,\r\n    max_steps=500000, \r\n)\r\n\r\ntrainer = Trainer(\r\n    model=model,\r\n    args=training_args,\r\n    data_collator=data_collator,\r\n    train_dataset=big_dataset,\r\n#     eval_dataset=book_corpus_eval,\r\n    tokenizer=tokenizer)\r\n\r\nimport wandb\r\nwandb.login()\r\n\r\ntrainer.train()\r\n```\r\n\r\nAnd surprisingly, the ram now keeps going up and down. The training is up now for 12h without collapse the ram. I don't know what could cause the leakage. :mag: \r\n\r\nEdit: I didn't see the swap memory, that keeps increasing. So the problem persist. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"Thanks for sharing your results.\r\nSo you still had the issue for fine-tuning ?\r\nAnd the issue still appears with a bare-bone dataset from an arrow file...","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":27,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n Thanks for sharing your results.\r\nSo you still had the issue for fine-tuning ?\r\nAnd the issue still appears with a bare-bone dataset from an arrow file...","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.2593793571,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/633","title":"Load large text file for LM pre-training resulting in OOM","comments":"Yes, on both cases. Fine-tuning a pre-trained model and pre-training from scratch with a local arrow file already pre-processed.","body":"I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks. ","comment_length":19,"text":"Load large text file for LM pre-training resulting in OOM \n I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n@dataclass\r\nclass DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):\r\n    \"\"\"\r\n    Data collator used for language modeling based on DataCollatorForLazyLanguageModeling\r\n    - collates batches of tensors, honoring their tokenizer's pad_token\r\n    - preprocesses batches for masked language modeling\r\n    \"\"\"\r\n\r\n    block_size: int = 512\r\n\r\n    def __call__(self, examples: List[dict]) -> Dict[str, torch.Tensor]:\r\n        examples = [example['text'] for example in examples]\r\n        batch, attention_mask = self._tensorize_batch(examples)\r\n        if self.mlm:\r\n            inputs, labels = self.mask_tokens(batch)\r\n            return {\"input_ids\": inputs, \"labels\": labels}\r\n        else:\r\n            labels = batch.clone().detach()\r\n            if self.tokenizer.pad_token_id is not None:\r\n                labels[labels == self.tokenizer.pad_token_id] = -100\r\n            return {\"input_ids\": batch, \"labels\": labels}\r\n\r\n    def _tensorize_batch(self, examples: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:\r\n\r\n        if self.tokenizer._pad_token is None:\r\n            raise ValueError(\r\n                \"You are attempting to pad samples but the tokenizer you are using\"\r\n                f\" ({self.tokenizer.__class__.__name__}) does not have one.\"\r\n            )\r\n\r\n        tensor_examples = self.tokenizer.batch_encode_plus(\r\n            [ex for ex in examples if ex],\r\n            max_length=self.block_size,\r\n            return_tensors=\"pt\",\r\n            pad_to_max_length=True,\r\n            return_attention_mask=True,\r\n            truncation=True,\r\n        )\r\n\r\n        input_ids, attention_mask = tensor_examples[\"input_ids\"], tensor_examples[\"attention_mask\"]\r\n        return input_ids, attention_mask\r\n\r\ndataset = load_dataset('text', data_files='train.txt',cache_dir=\".\/\", , split='train')\r\ndata_collator = DataCollatorForDatasetsLanguageModeling(tokenizer=tokenizer, mlm=True, \r\n                      mlm_probability=0.15, block_size=tokenizer.max_len)\r\ntrainer = Trainer(model=model, args=args, data_collator=data_collator,\r\n                      train_dataset=train_dataset, prediction_loss_only=True, )\r\ntrainer.train(model_path=model_path)\r\n```\r\nThis train.txt is about 1.1GB and has 90k lines where each line is a sequence of 4k words. \r\nDuring training, the memory usage increased fast as the following graph and resulted in OOM before the finish of training.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/29704017\/93292112-5576b280-f817-11ea-8da2-b2db9bf35665.png)\r\n\r\nCould you please give me any suggestions on why this happened and how to fix it?\r\nThanks.  \n Yes, on both cases. Fine-tuning a pre-trained model and pre-training from scratch with a local arrow file already pre-processed.","embeddings":[-0.6339284182,-0.4775375128,0.0106934961,0.2986351252,0.360047698,-0.1518250406,0.5567328334,0.3738060296,0.010882902,0.0107194036,-0.1295929849,-0.1828003526,-0.2669845521,-0.1620899886,-0.0323722549,0.0232063718,-0.0998212025,0.1968754828,-0.277266562,-0.096666567,0.0570118688,-0.0776291639,-0.2127607018,-0.0297438148,-0.3879216313,-0.0617135428,0.3158175647,0.122121118,-0.164080888,-0.0958079994,-0.2297015041,0.1197901368,0.4393487871,0.4340215027,-0.0001147721,0.0066162376,0.189043954,-0.2363326699,-0.1672693491,0.0192164704,0.0775555968,-0.3022915423,-0.1064002961,-0.303617388,-0.0272462368,-0.1061251387,-0.0156819802,-0.2243732959,0.4987519383,0.5414040089,0.1587626338,0.2508126199,-0.2136609852,0.0723969489,0.2544373572,0.0488996431,0.1056831107,-0.2795141339,0.4687843025,-0.192318216,-0.4726884067,0.1478177905,-0.2247736454,-0.2016824782,-0.0950887725,0.0382044166,0.3660891652,0.0315693952,0.2641872764,0.3474211693,0.427207619,-0.0008118788,-0.2896353006,-0.3209854364,-0.1426243484,-0.0843160674,0.1892365068,0.2010775357,0.0598268472,0.1184589118,0.1071274877,-0.1253313571,-0.1519900858,0.0761608556,-0.2748079002,0.3397960365,-0.2012512684,-0.0380430035,0.1555751562,-0.0944791883,0.1961694509,-0.0997752547,0.0034040213,0.2567143738,-0.2454418987,-0.1123952121,-0.0716823488,-0.5194147229,0.1627843231,-0.2759447992,0.1981881857,0.297239095,-0.0876615867,-0.0676169693,0.0863937065,0.4202026427,-0.2524375618,0.2605278194,0.2272561342,0.1640114188,-0.1717065424,-0.0443987511,-0.3305119574,-0.1962455064,0.1015938595,-0.0773416013,-0.0110167162,-0.2546043396,-0.2404217273,0.0585469492,-0.1407701224,-0.0308455452,0.2748161852,0.3899729252,-0.3540612459,0.4294980168,0.1651093364,0.0523289666,-0.4855332375,-0.3365262449,-0.1744022071,0.1532574743,-0.2032443881,0.0389759205,0.1297923028,0.1396338344,0.0596543029,-0.0428832695,-0.0625896901,-0.4402190149,-0.0094448142,-0.0241036415,0.0207530651,-0.0425145514,0.0630237088,-0.0233655572,0.2347659767,-0.1249946058,-0.0253819562,0.3477917612,-0.2762119472,-0.2276407033,0.0509794205,0.2101766467,-0.0282483641,0.2091549635,0.0285065565,0.0768568069,0.5359784961,0.006653544,-0.0471478477,-0.3782060444,0.0945213884,0.0920173824,0.1619912833,0.1766304076,-0.0425883308,-0.0726022571,0.006389291,0.1035868526,0.255040735,0.3907330036,-0.2264698744,0.2906736135,0.0070044789,-0.0566558652,0.5319920182,-0.2620989978,-0.4484441876,0.2338527292,-0.3511554301,0.1081820279,0.2206698805,0.175902918,0.1138889194,-0.1089850739,0.2690151632,0.3656851351,0.0705158114,0.1209139228,-0.2366241813,-0.0922284573,-0.1715158075,0.4842026532,0.3627241552,-0.0255860128,-0.0730893835,0.3563797474,0.1833985001,-0.1992235482,0.1312804222,0.3693413436,-0.1744247079,-0.1261581182,0.0944813043,0.0655976534,-0.3000326455,-0.0709873512,-0.0908445567,0.0828670263,-0.1136056483,-0.0894262344,0.2825580239,0.0228704214,-0.0766655654,0.1131800264,0.0379414111,-0.1318219602,0.0248664208,-0.0704168379,0.1261966377,0.015421493,-0.0932066888,0.0873605087,-0.2184161246,-0.1239406168,0.0018026204,-0.1370613873,0.0769501179,0.1752732992,-0.0511878207,0.0384011976,-0.1730399132,0.1069574431,-0.1529778987,-0.2987073362,-0.5071573257,0.4358929098,0.1864788234,-0.338109225,0.3402600288,0.1040275991,-0.0295994803,-0.1062710211,-0.1198950335,0.5749334097,0.2730710804,0.0196176525,0.2990927994,-0.2454112023,0.2542806268,-0.2662391067,-0.1036156565,-0.0977647603,0.4209229648,0.0310557056,0.235062778,0.1482800692,-0.086945802,-0.232059598,0.4703815281,-0.1788095683,-0.0138725368,0.2665261328,-0.4247210622,-0.0115634901,-0.3480211198,-0.2494782507,0.0067336266,0.1732289195,-0.0944131836,-0.0554352179,0.0085378792,-0.2627930939,0.0078365039,0.1532371342,-0.0718523338,0.3198345006,0.1165149361,0.100595057,-0.1228921041,-0.1794070601,0.1028474271,0.178289935,-0.2751909494,0.1844432205,-0.0228225887,-0.0134467958,-0.2862635553,0.1904245317,-0.1268011034,0.0388152041,-0.0647433475,0.2586888969,-0.0779031143,0.1019119397,0.3001608551,0.3549227417,0.4629625678,-0.0780947357,0.3263479173,-0.082663402,-0.1796957552,0.0329435281,0.3345077932,-0.317992866,-0.0248905141,0.0053776167,0.0511768535,-0.098929964,-0.1660345495,0.0946889892,0.1968243569,-0.1661894023,-0.2573056817,-0.0721759722,0.1776906103,-0.0424321033,0.1197286993,-0.2324412018,0.0084075676,0.1450572908,-0.0062896051,-0.1843234897,0.0404453017,-0.119922094,-0.0470511988,-0.4116414189,-0.0535182059,0.107050769,0.136731714,0.359367758,0.1067891642,0.1944518089,0.2884452343,0.1585920751,0.0063675619,-0.0885991082,0.5141721964,-0.0175727438,-0.2971983254,-0.2908624113,-0.2397543192,0.0251130797,0.3155242205,-0.684260428,0.2337827832,-0.1138519347,-0.0940270722,-0.3955195546,0.0007256473,0.2522048652,-0.0761487111,0.0163396969,0.0954742506,0.2302770168,0.1546791047,-0.0387874991,0.385794431,-0.4130844176,0.3594082594,-0.1354581416,0.4855529368,-0.1221182346,-0.2300206572,0.0988747329,0.1504482478,-0.017115429,-0.0218022279,-0.0139687397,-0.259379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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/630","title":"Text dataset not working with large files","comments":"Basically ~600MB txt files(UTF-8) * 59. \r\ncontents like ```\uc548\ub155\ud558\uc138\uc694, \uc774\uac83\uc740 \uc608\uc81c\ub85c \ud55c\ubc88 \ub9d0\ud574\ubcf4\ub294 \ud14d\uc2a4\ud2b8\uc785\ub2c8\ub2e4. \uadf8\ub0e5 \uc774\ub807\ub2e4\uace0\uc694.<|endoftext|>\\n```\r\n\r\nAlso, it gets stuck for a loooong time at ```Testing the mapped function outputs```, for more than 12 hours(currently ongoing)","body":"```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry.","comment_length":36,"text":"Text dataset not working with large files \n ```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry. \n Basically ~600MB txt files(UTF-8) * 59. \r\ncontents like ```\uc548\ub155\ud558\uc138\uc694, \uc774\uac83\uc740 \uc608\uc81c\ub85c \ud55c\ubc88 \ub9d0\ud574\ubcf4\ub294 \ud14d\uc2a4\ud2b8\uc785\ub2c8\ub2e4. \uadf8\ub0e5 \uc774\ub807\ub2e4\uace0\uc694.<|endoftext|>\\n```\r\n\r\nAlso, it gets stuck for a loooong time at ```Testing the mapped function outputs```, for more than 12 hours(currently ongoing)","embeddings":[-0.4925667346,-0.2310241014,-0.1198643222,0.2836014926,0.4663560987,-0.0735016093,0.3053681254,0.5961018801,-0.1138256192,0.0461653806,-0.0624196492,-0.0304052103,-0.1033419743,0.310179472,-0.1062136963,-0.0389501564,-0.2278844863,0.1130641699,-0.0946916267,0.0499166101,-0.112550877,0.1110998988,-0.1209788397,-0.0956026316,-0.4804550409,-0.0829951093,0.1185314655,0.2535659075,-0.2920109332,-0.3053144217,-0.1657067835,0.0965852961,0.1122046039,0.6015893817,-0.0001037621,0.129462406,0.2131118327,-0.1573347151,-0.2265827805,-0.0314556658,0.1559481472,-0.4493688941,-0.1823892593,-0.2179644257,0.0261511691,-0.0627441034,-0.1082879007,-0.117559962,0.265335381,0.5598000884,0.3412032723,0.170758754,0.1321692318,-0.1129611656,0.2507540286,-0.0872494131,-0.0887443796,0.0848500654,0.3108180165,0.0265774522,-0.2564010024,0.3199440241,0.0101718027,0.0377005637,0.0003141626,-0.0240623355,-0.0827939883,-0.2940048873,0.3711950183,0.2321464419,0.5224136114,-0.3169358075,-0.1641277969,-0.3288432062,-0.1824413538,-0.3512556851,0.226632759,0.1845538467,-0.1113308817,0.1161273718,-0.1436178535,-0.1014853194,-0.2063972354,-0.0293161273,-0.2615340352,0.1022737771,-0.2105868608,-0.1004619375,0.2575800121,-0.2702479959,0.0108298594,-0.1056223363,-0.1307831109,-0.0068054325,-0.247213304,-0.0840224698,0.2468504906,-0.199078232,0.2889420688,-0.0489982776,-0.0504212566,0.224761039,-0.0796672255,-0.0302130375,0.0054934151,0.3368757069,0.0041132052,0.0638572946,0.4085853696,0.2356281877,-0.379357934,-0.2320603579,-0.149626568,-0.5145174861,-0.0293558314,-0.1177171171,0.01687208,-0.0452369191,-0.1210255995,0.1521080732,0.1642943472,0.1547948122,0.10596627,0.4816172421,0.1049144343,0.2937650084,-0.1649460793,0.1770391911,-0.144326508,-0.1722711623,-0.2492276877,-0.0456115939,-0.0579169057,-0.0926888585,0.2676637173,0.1338510513,0.2967094481,-0.1395806223,0.1908528805,-0.1090161651,0.1168141365,-0.5029688478,0.1476960927,0.1410785764,-0.0354773737,0.1686936468,0.2287830412,-0.1788699031,-0.1023910046,0.050235346,-0.1035604551,-0.2971284389,0.1772404164,0.3213210106,0.1347744316,-0.0030032352,-0.018904794,0.2770770788,0.375772059,-0.2176397294,0.0281005073,-0.177321583,-0.2569280863,-0.125802964,0.2650002837,0.4393820763,-0.425060302,0.2090140134,0.0690165609,0.1223087683,0.0890245587,0.3739468455,-0.0650732517,0.3143593669,-0.1145715341,0.2009439021,0.3094802201,-0.285585016,-0.5090754032,0.5406064391,-0.1859303266,-0.1181871668,0.099257268,-0.0444510505,0.1034918204,0.1358725727,0.3047401011,0.2793403268,0.0113194669,0.2667300403,-0.2604504526,-0.0832502395,-0.0501141697,0.1559398472,0.3121375144,-0.2080699205,0.1951689571,-0.017231321,0.19437626,-0.044446256,0.2128829956,0.5059319735,0.1271578521,-0.0078406455,-0.0123689231,-0.2239847332,0.0316570215,0.1751381308,0.1210090369,0.0843074545,-0.2666850388,-0.0822329894,-0.3092454672,-0.1341452003,-0.2703467607,-0.193386212,0.2836054265,0.0813239738,-0.0114756655,0.2551389933,-0.0167013835,0.0253130831,-0.1602133662,-0.0655840337,-0.0120967701,-0.0018739073,-0.091083996,-0.2782087028,0.1229490936,0.1102730632,-0.041142635,-0.0616680495,-0.1109172329,0.3928830624,-0.0020204084,-0.0810614601,-0.0360698178,-0.0300612058,0.1020015851,-0.0952062309,0.0719461367,0.0007549835,0.1388051212,-0.1053132713,-0.1801620126,0.3006142974,-0.1398149133,0.2031631023,0.3438572288,-0.2090742588,0.2556621134,-0.0663511157,0.2276659161,-0.1381171942,0.2406677008,0.0831321999,0.136321649,-0.0013489889,-0.2719839215,-0.0127429022,0.6895840764,0.171065256,0.0928926244,0.1747953147,-0.2453399599,-0.0179574843,-0.2034236491,0.1299691498,0.3976373672,0.2744440138,0.2679811418,0.0357754268,0.1870050132,-0.3740030229,0.1397108883,0.0032486171,0.0111852335,0.5398927927,0.2758919895,-0.1295805275,-0.4534206688,-0.1563689411,0.1057760417,0.3134025633,-0.0895873681,0.0198824629,-0.2155629992,-0.2920289338,-0.2959762812,0.2103271335,-0.2627325952,-0.2253175527,0.0694883317,0.0018545482,-0.0096535487,0.0968664065,0.0594205372,0.1454249322,0.3997489512,-0.198824957,-0.0615104102,-0.2715903819,-0.1730497926,0.1567441374,0.2421877533,-0.0123151606,0.106642887,-0.1534677446,-0.1015961543,0.0038625724,-0.2853763402,0.0006907425,-0.029190002,0.1006606892,-0.0571642891,0.2626269162,0.1662754267,0.0769828781,0.312800765,-0.1297491789,-0.080774948,0.2233646214,0.0906755924,-0.0652652159,-0.1705733836,-0.4048942327,-0.1530372351,-0.6100059748,0.357511282,0.0955663547,0.0380034447,0.4022583663,0.3181683421,0.2154984921,-0.0212013293,0.2124930322,0.1002640873,-0.1950187981,0.2925721705,-0.2248299271,-0.3832399845,-0.0998249501,0.1606033593,0.16261594,0.007262093,-0.5234432817,0.0302410219,-0.4368826151,0.1623679101,-0.0637961328,0.168691963,0.0814080685,-0.0684886277,-0.2522610426,-0.0842753425,0.0830210894,0.0467453748,-0.0724342912,0.0489535592,-0.1854182333,0.4121846557,0.2402956635,0.4626801014,0.163782239,0.0814501196,0.3938990235,-0.121706374,0.4271225929,-0.3532859087,-0.3265112638,0.1239757761,-0.0912457034,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/630","title":"Text dataset not working with large files","comments":"It gets stuck while doing `.map()` ? Are you using multiprocessing ?\r\nIf you could provide a code snippet it could be very useful","body":"```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry.","comment_length":24,"text":"Text dataset not working with large files \n ```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry. \n It gets stuck while doing `.map()` ? Are you using multiprocessing ?\r\nIf you could provide a code snippet it could be very useful","embeddings":[-0.4925667346,-0.2310241014,-0.1198643222,0.2836014926,0.4663560987,-0.0735016093,0.3053681254,0.5961018801,-0.1138256192,0.0461653806,-0.0624196492,-0.0304052103,-0.1033419743,0.310179472,-0.1062136963,-0.0389501564,-0.2278844863,0.1130641699,-0.0946916267,0.0499166101,-0.112550877,0.1110998988,-0.1209788397,-0.0956026316,-0.4804550409,-0.0829951093,0.1185314655,0.2535659075,-0.2920109332,-0.3053144217,-0.1657067835,0.0965852961,0.1122046039,0.6015893817,-0.0001037621,0.129462406,0.2131118327,-0.1573347151,-0.2265827805,-0.0314556658,0.1559481472,-0.4493688941,-0.1823892593,-0.2179644257,0.0261511691,-0.0627441034,-0.1082879007,-0.117559962,0.265335381,0.5598000884,0.3412032723,0.170758754,0.1321692318,-0.1129611656,0.2507540286,-0.0872494131,-0.0887443796,0.0848500654,0.3108180165,0.0265774522,-0.2564010024,0.3199440241,0.0101718027,0.0377005637,0.0003141626,-0.0240623355,-0.0827939883,-0.2940048873,0.3711950183,0.2321464419,0.5224136114,-0.3169358075,-0.1641277969,-0.3288432062,-0.1824413538,-0.3512556851,0.226632759,0.1845538467,-0.1113308817,0.1161273718,-0.1436178535,-0.1014853194,-0.2063972354,-0.0293161273,-0.2615340352,0.1022737771,-0.2105868608,-0.1004619375,0.2575800121,-0.2702479959,0.0108298594,-0.1056223363,-0.1307831109,-0.0068054325,-0.247213304,-0.0840224698,0.2468504906,-0.199078232,0.2889420688,-0.0489982776,-0.0504212566,0.224761039,-0.0796672255,-0.0302130375,0.0054934151,0.3368757069,0.0041132052,0.0638572946,0.4085853696,0.2356281877,-0.379357934,-0.2320603579,-0.149626568,-0.5145174861,-0.0293558314,-0.1177171171,0.01687208,-0.0452369191,-0.1210255995,0.1521080732,0.1642943472,0.1547948122,0.10596627,0.4816172421,0.1049144343,0.2937650084,-0.1649460793,0.1770391911,-0.144326508,-0.1722711623,-0.2492276877,-0.0456115939,-0.0579169057,-0.0926888585,0.2676637173,0.1338510513,0.2967094481,-0.1395806223,0.1908528805,-0.1090161651,0.1168141365,-0.5029688478,0.1476960927,0.1410785764,-0.0354773737,0.1686936468,0.2287830412,-0.1788699031,-0.1023910046,0.050235346,-0.1035604551,-0.2971284389,0.1772404164,0.3213210106,0.1347744316,-0.0030032352,-0.018904794,0.2770770788,0.375772059,-0.2176397294,0.0281005073,-0.177321583,-0.2569280863,-0.125802964,0.2650002837,0.4393820763,-0.425060302,0.2090140134,0.0690165609,0.1223087683,0.0890245587,0.3739468455,-0.0650732517,0.3143593669,-0.1145715341,0.2009439021,0.3094802201,-0.285585016,-0.5090754032,0.5406064391,-0.1859303266,-0.1181871668,0.099257268,-0.0444510505,0.1034918204,0.1358725727,0.3047401011,0.2793403268,0.0113194669,0.2667300403,-0.2604504526,-0.0832502395,-0.0501141697,0.1559398472,0.3121375144,-0.2080699205,0.1951689571,-0.017231321,0.19437626,-0.044446256,0.2128829956,0.5059319735,0.1271578521,-0.0078406455,-0.0123689231,-0.2239847332,0.0316570215,0.1751381308,0.1210090369,0.0843074545,-0.2666850388,-0.0822329894,-0.3092454672,-0.1341452003,-0.2703467607,-0.193386212,0.2836054265,0.0813239738,-0.0114756655,0.2551389933,-0.0167013835,0.0253130831,-0.1602133662,-0.0655840337,-0.0120967701,-0.0018739073,-0.091083996,-0.2782087028,0.1229490936,0.1102730632,-0.041142635,-0.0616680495,-0.1109172329,0.3928830624,-0.0020204084,-0.0810614601,-0.0360698178,-0.0300612058,0.1020015851,-0.0952062309,0.0719461367,0.0007549835,0.1388051212,-0.1053132713,-0.1801620126,0.3006142974,-0.1398149133,0.2031631023,0.3438572288,-0.2090742588,0.2556621134,-0.0663511157,0.2276659161,-0.1381171942,0.2406677008,0.0831321999,0.136321649,-0.0013489889,-0.2719839215,-0.0127429022,0.6895840764,0.171065256,0.0928926244,0.1747953147,-0.2453399599,-0.0179574843,-0.2034236491,0.1299691498,0.3976373672,0.2744440138,0.2679811418,0.0357754268,0.1870050132,-0.3740030229,0.1397108883,0.0032486171,0.0111852335,0.5398927927,0.2758919895,-0.1295805275,-0.4534206688,-0.1563689411,0.1057760417,0.3134025633,-0.0895873681,0.0198824629,-0.2155629992,-0.2920289338,-0.2959762812,0.2103271335,-0.2627325952,-0.2253175527,0.0694883317,0.0018545482,-0.0096535487,0.0968664065,0.0594205372,0.1454249322,0.3997489512,-0.198824957,-0.0615104102,-0.2715903819,-0.1730497926,0.1567441374,0.2421877533,-0.0123151606,0.106642887,-0.1534677446,-0.1015961543,0.0038625724,-0.2853763402,0.0006907425,-0.029190002,0.1006606892,-0.0571642891,0.2626269162,0.1662754267,0.0769828781,0.312800765,-0.1297491789,-0.080774948,0.2233646214,0.0906755924,-0.0652652159,-0.1705733836,-0.4048942327,-0.1530372351,-0.6100059748,0.357511282,0.0955663547,0.0380034447,0.4022583663,0.3181683421,0.2154984921,-0.0212013293,0.2124930322,0.1002640873,-0.1950187981,0.2925721705,-0.2248299271,-0.3832399845,-0.0998249501,0.1606033593,0.16261594,0.007262093,-0.5234432817,0.0302410219,-0.4368826151,0.1623679101,-0.0637961328,0.168691963,0.0814080685,-0.0684886277,-0.2522610426,-0.0842753425,0.0830210894,0.0467453748,-0.0724342912,0.0489535592,-0.1854182333,0.4121846557,0.2402956635,0.4626801014,0.163782239,0.0814501196,0.3938990235,-0.121706374,0.4271225929,-0.3532859087,-0.3265112638,0.1239757761,-0.0912457034,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/630","title":"Text dataset not working with large files","comments":"From transformers\/examples\/language-modeling\/run-language-modeling.py :\r\n```\r\ndef get_dataset(\r\n    args: DataTrainingArguments,\r\n    tokenizer: PreTrainedTokenizer,\r\n    evaluate: bool = False,\r\n    cache_dir: Optional[str] = None,\r\n):\r\n    file_path = args.eval_data_file if evaluate else args.train_data_file\r\n    if True:\r\n        dataset = load_dataset(\"text\", data_files=glob.glob(file_path), split='train', use_threads=True, \r\n        ignore_verifications=True, save_infos=True, block_size=104857600)\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), batched=True)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        return dataset\r\n    if args.line_by_line:\r\n        return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n    else:\r\n        return TextDataset(\r\n            tokenizer=tokenizer,\r\n            file_path=file_path,\r\n            block_size=args.block_size,\r\n            overwrite_cache=args.overwrite_cache,\r\n            cache_dir=cache_dir,\r\n        )\r\n```\r\n\r\nNo, I'm not using multiprocessing.","body":"```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry.","comment_length":71,"text":"Text dataset not working with large files \n ```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry. \n From transformers\/examples\/language-modeling\/run-language-modeling.py :\r\n```\r\ndef get_dataset(\r\n    args: DataTrainingArguments,\r\n    tokenizer: PreTrainedTokenizer,\r\n    evaluate: bool = False,\r\n    cache_dir: Optional[str] = None,\r\n):\r\n    file_path = args.eval_data_file if evaluate else args.train_data_file\r\n    if True:\r\n        dataset = load_dataset(\"text\", data_files=glob.glob(file_path), split='train', use_threads=True, \r\n        ignore_verifications=True, save_infos=True, block_size=104857600)\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                                truncation=True, max_length=args.block_size), batched=True)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        return dataset\r\n    if args.line_by_line:\r\n        return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n    else:\r\n        return TextDataset(\r\n            tokenizer=tokenizer,\r\n            file_path=file_path,\r\n            block_size=args.block_size,\r\n            overwrite_cache=args.overwrite_cache,\r\n            cache_dir=cache_dir,\r\n        )\r\n```\r\n\r\nNo, I'm not using multiprocessing.","embeddings":[-0.4925667346,-0.2310241014,-0.1198643222,0.2836014926,0.4663560987,-0.0735016093,0.3053681254,0.5961018801,-0.1138256192,0.0461653806,-0.0624196492,-0.0304052103,-0.1033419743,0.310179472,-0.1062136963,-0.0389501564,-0.2278844863,0.1130641699,-0.0946916267,0.0499166101,-0.112550877,0.1110998988,-0.1209788397,-0.0956026316,-0.4804550409,-0.0829951093,0.1185314655,0.2535659075,-0.2920109332,-0.3053144217,-0.1657067835,0.0965852961,0.1122046039,0.6015893817,-0.0001037621,0.129462406,0.2131118327,-0.1573347151,-0.2265827805,-0.0314556658,0.1559481472,-0.4493688941,-0.1823892593,-0.2179644257,0.0261511691,-0.0627441034,-0.1082879007,-0.117559962,0.265335381,0.5598000884,0.3412032723,0.170758754,0.1321692318,-0.1129611656,0.2507540286,-0.0872494131,-0.0887443796,0.0848500654,0.3108180165,0.0265774522,-0.2564010024,0.3199440241,0.0101718027,0.0377005637,0.0003141626,-0.0240623355,-0.0827939883,-0.2940048873,0.3711950183,0.2321464419,0.5224136114,-0.3169358075,-0.1641277969,-0.3288432062,-0.1824413538,-0.3512556851,0.226632759,0.1845538467,-0.1113308817,0.1161273718,-0.1436178535,-0.1014853194,-0.2063972354,-0.0293161273,-0.2615340352,0.1022737771,-0.2105868608,-0.1004619375,0.2575800121,-0.2702479959,0.0108298594,-0.1056223363,-0.1307831109,-0.0068054325,-0.247213304,-0.0840224698,0.2468504906,-0.199078232,0.2889420688,-0.0489982776,-0.0504212566,0.224761039,-0.0796672255,-0.0302130375,0.0054934151,0.3368757069,0.0041132052,0.0638572946,0.4085853696,0.2356281877,-0.379357934,-0.2320603579,-0.149626568,-0.5145174861,-0.0293558314,-0.1177171171,0.01687208,-0.0452369191,-0.1210255995,0.1521080732,0.1642943472,0.1547948122,0.10596627,0.4816172421,0.1049144343,0.2937650084,-0.1649460793,0.1770391911,-0.144326508,-0.1722711623,-0.2492276877,-0.0456115939,-0.0579169057,-0.0926888585,0.2676637173,0.1338510513,0.2967094481,-0.1395806223,0.1908528805,-0.1090161651,0.1168141365,-0.5029688478,0.1476960927,0.1410785764,-0.0354773737,0.1686936468,0.2287830412,-0.1788699031,-0.1023910046,0.050235346,-0.1035604551,-0.2971284389,0.1772404164,0.3213210106,0.1347744316,-0.0030032352,-0.018904794,0.2770770788,0.375772059,-0.2176397294,0.0281005073,-0.177321583,-0.2569280863,-0.125802964,0.2650002837,0.4393820763,-0.425060302,0.2090140134,0.0690165609,0.1223087683,0.0890245587,0.3739468455,-0.0650732517,0.3143593669,-0.1145715341,0.2009439021,0.3094802201,-0.285585016,-0.5090754032,0.5406064391,-0.1859303266,-0.1181871668,0.099257268,-0.0444510505,0.1034918204,0.1358725727,0.3047401011,0.2793403268,0.0113194669,0.2667300403,-0.2604504526,-0.0832502395,-0.0501141697,0.1559398472,0.3121375144,-0.2080699205,0.1951689571,-0.017231321,0.19437626,-0.044446256,0.2128829956,0.5059319735,0.1271578521,-0.0078406455,-0.0123689231,-0.2239847332,0.0316570215,0.1751381308,0.1210090369,0.0843074545,-0.2666850388,-0.0822329894,-0.3092454672,-0.1341452003,-0.2703467607,-0.193386212,0.2836054265,0.0813239738,-0.0114756655,0.2551389933,-0.0167013835,0.0253130831,-0.1602133662,-0.0655840337,-0.0120967701,-0.0018739073,-0.091083996,-0.2782087028,0.1229490936,0.1102730632,-0.041142635,-0.0616680495,-0.1109172329,0.3928830624,-0.0020204084,-0.0810614601,-0.0360698178,-0.0300612058,0.1020015851,-0.0952062309,0.0719461367,0.0007549835,0.1388051212,-0.1053132713,-0.1801620126,0.3006142974,-0.1398149133,0.2031631023,0.3438572288,-0.2090742588,0.2556621134,-0.0663511157,0.2276659161,-0.1381171942,0.2406677008,0.0831321999,0.136321649,-0.0013489889,-0.2719839215,-0.0127429022,0.6895840764,0.171065256,0.0928926244,0.1747953147,-0.2453399599,-0.0179574843,-0.2034236491,0.1299691498,0.3976373672,0.2744440138,0.2679811418,0.0357754268,0.1870050132,-0.3740030229,0.1397108883,0.0032486171,0.0111852335,0.5398927927,0.2758919895,-0.1295805275,-0.4534206688,-0.1563689411,0.1057760417,0.3134025633,-0.0895873681,0.0198824629,-0.2155629992,-0.2920289338,-0.2959762812,0.2103271335,-0.2627325952,-0.2253175527,0.0694883317,0.0018545482,-0.0096535487,0.0968664065,0.0594205372,0.1454249322,0.3997489512,-0.198824957,-0.0615104102,-0.2715903819,-0.1730497926,0.1567441374,0.2421877533,-0.0123151606,0.106642887,-0.1534677446,-0.1015961543,0.0038625724,-0.2853763402,0.0006907425,-0.029190002,0.1006606892,-0.0571642891,0.2626269162,0.1662754267,0.0769828781,0.312800765,-0.1297491789,-0.080774948,0.2233646214,0.0906755924,-0.0652652159,-0.1705733836,-0.4048942327,-0.1530372351,-0.6100059748,0.357511282,0.0955663547,0.0380034447,0.4022583663,0.3181683421,0.2154984921,-0.0212013293,0.2124930322,0.1002640873,-0.1950187981,0.2925721705,-0.2248299271,-0.3832399845,-0.0998249501,0.1606033593,0.16261594,0.007262093,-0.5234432817,0.0302410219,-0.4368826151,0.1623679101,-0.0637961328,0.168691963,0.0814080685,-0.0684886277,-0.2522610426,-0.0842753425,0.0830210894,0.0467453748,-0.0724342912,0.0489535592,-0.1854182333,0.4121846557,0.2402956635,0.4626801014,0.163782239,0.0814501196,0.3938990235,-0.121706374,0.4271225929,-0.3532859087,-0.3265112638,0.1239757761,-0.091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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/630","title":"Text dataset not working with large files","comments":"I am not able to reproduce on my side :\/\r\n\r\nCould you send the version of `datasets` and `pyarrow` you're using ?\r\nCould you try to update the lib and try again ?\r\nOr do you think you could try to reproduce it on google colab ?","body":"```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry.","comment_length":47,"text":"Text dataset not working with large files \n ```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry. \n I am not able to reproduce on my side :\/\r\n\r\nCould you send the version of `datasets` and `pyarrow` you're using ?\r\nCould you try to update the lib and try again ?\r\nOr do you think you could try to reproduce it on google colab ?","embeddings":[-0.4925667346,-0.2310241014,-0.1198643222,0.2836014926,0.4663560987,-0.0735016093,0.3053681254,0.5961018801,-0.1138256192,0.0461653806,-0.0624196492,-0.0304052103,-0.1033419743,0.310179472,-0.1062136963,-0.0389501564,-0.2278844863,0.1130641699,-0.0946916267,0.0499166101,-0.112550877,0.1110998988,-0.1209788397,-0.0956026316,-0.4804550409,-0.0829951093,0.1185314655,0.2535659075,-0.2920109332,-0.3053144217,-0.1657067835,0.0965852961,0.1122046039,0.6015893817,-0.0001037621,0.129462406,0.2131118327,-0.1573347151,-0.2265827805,-0.0314556658,0.1559481472,-0.4493688941,-0.1823892593,-0.2179644257,0.0261511691,-0.0627441034,-0.1082879007,-0.117559962,0.265335381,0.5598000884,0.3412032723,0.170758754,0.1321692318,-0.1129611656,0.2507540286,-0.0872494131,-0.0887443796,0.0848500654,0.3108180165,0.0265774522,-0.2564010024,0.3199440241,0.0101718027,0.0377005637,0.0003141626,-0.0240623355,-0.0827939883,-0.2940048873,0.3711950183,0.2321464419,0.5224136114,-0.3169358075,-0.1641277969,-0.3288432062,-0.1824413538,-0.3512556851,0.226632759,0.1845538467,-0.1113308817,0.1161273718,-0.1436178535,-0.1014853194,-0.2063972354,-0.0293161273,-0.2615340352,0.1022737771,-0.2105868608,-0.1004619375,0.2575800121,-0.2702479959,0.0108298594,-0.1056223363,-0.1307831109,-0.0068054325,-0.247213304,-0.0840224698,0.2468504906,-0.199078232,0.2889420688,-0.0489982776,-0.0504212566,0.224761039,-0.0796672255,-0.0302130375,0.0054934151,0.3368757069,0.0041132052,0.0638572946,0.4085853696,0.2356281877,-0.379357934,-0.2320603579,-0.149626568,-0.5145174861,-0.0293558314,-0.1177171171,0.01687208,-0.0452369191,-0.1210255995,0.1521080732,0.1642943472,0.1547948122,0.10596627,0.4816172421,0.1049144343,0.2937650084,-0.1649460793,0.1770391911,-0.144326508,-0.1722711623,-0.2492276877,-0.0456115939,-0.0579169057,-0.0926888585,0.2676637173,0.1338510513,0.2967094481,-0.1395806223,0.1908528805,-0.1090161651,0.1168141365,-0.5029688478,0.1476960927,0.1410785764,-0.0354773737,0.1686936468,0.2287830412,-0.1788699031,-0.1023910046,0.050235346,-0.1035604551,-0.2971284389,0.1772404164,0.3213210106,0.1347744316,-0.0030032352,-0.018904794,0.2770770788,0.375772059,-0.2176397294,0.0281005073,-0.177321583,-0.2569280863,-0.125802964,0.2650002837,0.4393820763,-0.425060302,0.2090140134,0.0690165609,0.1223087683,0.0890245587,0.3739468455,-0.0650732517,0.3143593669,-0.1145715341,0.2009439021,0.3094802201,-0.285585016,-0.5090754032,0.5406064391,-0.1859303266,-0.1181871668,0.099257268,-0.0444510505,0.1034918204,0.1358725727,0.3047401011,0.2793403268,0.0113194669,0.2667300403,-0.2604504526,-0.0832502395,-0.0501141697,0.1559398472,0.3121375144,-0.2080699205,0.1951689571,-0.017231321,0.19437626,-0.044446256,0.2128829956,0.5059319735,0.1271578521,-0.0078406455,-0.0123689231,-0.2239847332,0.0316570215,0.1751381308,0.1210090369,0.0843074545,-0.2666850388,-0.0822329894,-0.3092454672,-0.1341452003,-0.2703467607,-0.193386212,0.2836054265,0.0813239738,-0.0114756655,0.2551389933,-0.0167013835,0.0253130831,-0.1602133662,-0.0655840337,-0.0120967701,-0.0018739073,-0.091083996,-0.2782087028,0.1229490936,0.1102730632,-0.041142635,-0.0616680495,-0.1109172329,0.3928830624,-0.0020204084,-0.0810614601,-0.0360698178,-0.0300612058,0.1020015851,-0.0952062309,0.0719461367,0.0007549835,0.1388051212,-0.1053132713,-0.1801620126,0.3006142974,-0.1398149133,0.2031631023,0.3438572288,-0.2090742588,0.2556621134,-0.0663511157,0.2276659161,-0.1381171942,0.2406677008,0.0831321999,0.136321649,-0.0013489889,-0.2719839215,-0.0127429022,0.6895840764,0.171065256,0.0928926244,0.1747953147,-0.2453399599,-0.0179574843,-0.2034236491,0.1299691498,0.3976373672,0.2744440138,0.2679811418,0.0357754268,0.1870050132,-0.3740030229,0.1397108883,0.0032486171,0.0111852335,0.5398927927,0.2758919895,-0.1295805275,-0.4534206688,-0.1563689411,0.1057760417,0.3134025633,-0.0895873681,0.0198824629,-0.2155629992,-0.2920289338,-0.2959762812,0.2103271335,-0.2627325952,-0.2253175527,0.0694883317,0.0018545482,-0.0096535487,0.0968664065,0.0594205372,0.1454249322,0.3997489512,-0.198824957,-0.0615104102,-0.2715903819,-0.1730497926,0.1567441374,0.2421877533,-0.0123151606,0.106642887,-0.1534677446,-0.1015961543,0.0038625724,-0.2853763402,0.0006907425,-0.029190002,0.1006606892,-0.0571642891,0.2626269162,0.1662754267,0.0769828781,0.312800765,-0.1297491789,-0.080774948,0.2233646214,0.0906755924,-0.0652652159,-0.1705733836,-0.4048942327,-0.1530372351,-0.6100059748,0.357511282,0.0955663547,0.0380034447,0.4022583663,0.3181683421,0.2154984921,-0.0212013293,0.2124930322,0.1002640873,-0.1950187981,0.2925721705,-0.2248299271,-0.3832399845,-0.0998249501,0.1606033593,0.16261594,0.007262093,-0.5234432817,0.0302410219,-0.4368826151,0.1623679101,-0.0637961328,0.168691963,0.0814080685,-0.0684886277,-0.2522610426,-0.0842753425,0.0830210894,0.0467453748,-0.0724342912,0.0489535592,-0.1854182333,0.4121846557,0.2402956635,0.4626801014,0.163782239,0.0814501196,0.3938990235,-0.121706374,0.4271225929,-0.3532859087,-0.3265112638,0.1239757761,-0.0912457034,-0.0329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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/630","title":"Text dataset not working with large files","comments":"Huh, weird. It's fixed on my side too.\r\nBut now ```Caching processed dataset``` is taking forever - how can I disable it? Any flags?","body":"```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry.","comment_length":24,"text":"Text dataset not working with large files \n ```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry. \n Huh, weird. It's fixed on my side too.\r\nBut now ```Caching processed dataset``` is taking forever - how can I disable it? Any flags?","embeddings":[-0.4925667346,-0.2310241014,-0.1198643222,0.2836014926,0.4663560987,-0.0735016093,0.3053681254,0.5961018801,-0.1138256192,0.0461653806,-0.0624196492,-0.0304052103,-0.1033419743,0.310179472,-0.1062136963,-0.0389501564,-0.2278844863,0.1130641699,-0.0946916267,0.0499166101,-0.112550877,0.1110998988,-0.1209788397,-0.0956026316,-0.4804550409,-0.0829951093,0.1185314655,0.2535659075,-0.2920109332,-0.3053144217,-0.1657067835,0.0965852961,0.1122046039,0.6015893817,-0.0001037621,0.129462406,0.2131118327,-0.1573347151,-0.2265827805,-0.0314556658,0.1559481472,-0.4493688941,-0.1823892593,-0.2179644257,0.0261511691,-0.0627441034,-0.1082879007,-0.117559962,0.265335381,0.5598000884,0.3412032723,0.170758754,0.1321692318,-0.1129611656,0.2507540286,-0.0872494131,-0.0887443796,0.0848500654,0.3108180165,0.0265774522,-0.2564010024,0.3199440241,0.0101718027,0.0377005637,0.0003141626,-0.0240623355,-0.0827939883,-0.2940048873,0.3711950183,0.2321464419,0.5224136114,-0.3169358075,-0.1641277969,-0.3288432062,-0.1824413538,-0.3512556851,0.226632759,0.1845538467,-0.1113308817,0.1161273718,-0.1436178535,-0.1014853194,-0.2063972354,-0.0293161273,-0.2615340352,0.1022737771,-0.2105868608,-0.1004619375,0.2575800121,-0.2702479959,0.0108298594,-0.1056223363,-0.1307831109,-0.0068054325,-0.247213304,-0.0840224698,0.2468504906,-0.199078232,0.2889420688,-0.0489982776,-0.0504212566,0.224761039,-0.0796672255,-0.0302130375,0.0054934151,0.3368757069,0.0041132052,0.0638572946,0.4085853696,0.2356281877,-0.379357934,-0.2320603579,-0.149626568,-0.5145174861,-0.0293558314,-0.1177171171,0.01687208,-0.0452369191,-0.1210255995,0.1521080732,0.1642943472,0.1547948122,0.10596627,0.4816172421,0.1049144343,0.2937650084,-0.1649460793,0.1770391911,-0.144326508,-0.1722711623,-0.2492276877,-0.0456115939,-0.0579169057,-0.0926888585,0.2676637173,0.1338510513,0.2967094481,-0.1395806223,0.1908528805,-0.1090161651,0.1168141365,-0.5029688478,0.1476960927,0.1410785764,-0.0354773737,0.1686936468,0.2287830412,-0.1788699031,-0.1023910046,0.050235346,-0.1035604551,-0.2971284389,0.1772404164,0.3213210106,0.1347744316,-0.0030032352,-0.018904794,0.2770770788,0.375772059,-0.2176397294,0.0281005073,-0.177321583,-0.2569280863,-0.125802964,0.2650002837,0.4393820763,-0.425060302,0.2090140134,0.0690165609,0.1223087683,0.0890245587,0.3739468455,-0.0650732517,0.3143593669,-0.1145715341,0.2009439021,0.3094802201,-0.285585016,-0.5090754032,0.5406064391,-0.1859303266,-0.1181871668,0.099257268,-0.0444510505,0.1034918204,0.1358725727,0.3047401011,0.2793403268,0.0113194669,0.2667300403,-0.2604504526,-0.0832502395,-0.0501141697,0.1559398472,0.3121375144,-0.2080699205,0.1951689571,-0.017231321,0.19437626,-0.044446256,0.2128829956,0.5059319735,0.1271578521,-0.0078406455,-0.0123689231,-0.2239847332,0.0316570215,0.1751381308,0.1210090369,0.0843074545,-0.2666850388,-0.0822329894,-0.3092454672,-0.1341452003,-0.2703467607,-0.193386212,0.2836054265,0.0813239738,-0.0114756655,0.2551389933,-0.0167013835,0.0253130831,-0.1602133662,-0.0655840337,-0.0120967701,-0.0018739073,-0.091083996,-0.2782087028,0.1229490936,0.1102730632,-0.041142635,-0.0616680495,-0.1109172329,0.3928830624,-0.0020204084,-0.0810614601,-0.0360698178,-0.0300612058,0.1020015851,-0.0952062309,0.0719461367,0.0007549835,0.1388051212,-0.1053132713,-0.1801620126,0.3006142974,-0.1398149133,0.2031631023,0.3438572288,-0.2090742588,0.2556621134,-0.0663511157,0.2276659161,-0.1381171942,0.2406677008,0.0831321999,0.136321649,-0.0013489889,-0.2719839215,-0.0127429022,0.6895840764,0.171065256,0.0928926244,0.1747953147,-0.2453399599,-0.0179574843,-0.2034236491,0.1299691498,0.3976373672,0.2744440138,0.2679811418,0.0357754268,0.1870050132,-0.3740030229,0.1397108883,0.0032486171,0.0111852335,0.5398927927,0.2758919895,-0.1295805275,-0.4534206688,-0.1563689411,0.1057760417,0.3134025633,-0.0895873681,0.0198824629,-0.2155629992,-0.2920289338,-0.2959762812,0.2103271335,-0.2627325952,-0.2253175527,0.0694883317,0.0018545482,-0.0096535487,0.0968664065,0.0594205372,0.1454249322,0.3997489512,-0.198824957,-0.0615104102,-0.2715903819,-0.1730497926,0.1567441374,0.2421877533,-0.0123151606,0.106642887,-0.1534677446,-0.1015961543,0.0038625724,-0.2853763402,0.0006907425,-0.029190002,0.1006606892,-0.0571642891,0.2626269162,0.1662754267,0.0769828781,0.312800765,-0.1297491789,-0.080774948,0.2233646214,0.0906755924,-0.0652652159,-0.1705733836,-0.4048942327,-0.1530372351,-0.6100059748,0.357511282,0.0955663547,0.0380034447,0.4022583663,0.3181683421,0.2154984921,-0.0212013293,0.2124930322,0.1002640873,-0.1950187981,0.2925721705,-0.2248299271,-0.3832399845,-0.0998249501,0.1606033593,0.16261594,0.007262093,-0.5234432817,0.0302410219,-0.4368826151,0.1623679101,-0.0637961328,0.168691963,0.0814080685,-0.0684886277,-0.2522610426,-0.0842753425,0.0830210894,0.0467453748,-0.0724342912,0.0489535592,-0.1854182333,0.4121846557,0.2402956635,0.4626801014,0.163782239,0.0814501196,0.3938990235,-0.121706374,0.4271225929,-0.3532859087,-0.3265112638,0.1239757761,-0.0912457034,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/630","title":"Text dataset not working with large files","comments":"Right after `Caching processed dataset`, your function is applied to the dataset and there's a progress bar that shows how much time is left. How much time does it take for you ?\r\n\r\nAlso caching isn't supposed to slow down your processing. But if you still want to disable it you can do `.map(..., load_from_cache_file=False)`","body":"```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry.","comment_length":55,"text":"Text dataset not working with large files \n ```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry. \n Right after `Caching processed dataset`, your function is applied to the dataset and there's a progress bar that shows how much time is left. How much time does it take for you ?\r\n\r\nAlso caching isn't supposed to slow down your processing. But if you still want to disable it you can do `.map(..., load_from_cache_file=False)`","embeddings":[-0.4925667346,-0.2310241014,-0.1198643222,0.2836014926,0.4663560987,-0.0735016093,0.3053681254,0.5961018801,-0.1138256192,0.0461653806,-0.0624196492,-0.0304052103,-0.1033419743,0.310179472,-0.1062136963,-0.0389501564,-0.2278844863,0.1130641699,-0.0946916267,0.0499166101,-0.112550877,0.1110998988,-0.1209788397,-0.0956026316,-0.4804550409,-0.0829951093,0.1185314655,0.2535659075,-0.2920109332,-0.3053144217,-0.1657067835,0.0965852961,0.1122046039,0.6015893817,-0.0001037621,0.129462406,0.2131118327,-0.1573347151,-0.2265827805,-0.0314556658,0.1559481472,-0.4493688941,-0.1823892593,-0.2179644257,0.0261511691,-0.0627441034,-0.1082879007,-0.117559962,0.265335381,0.5598000884,0.3412032723,0.170758754,0.1321692318,-0.1129611656,0.2507540286,-0.0872494131,-0.0887443796,0.0848500654,0.3108180165,0.0265774522,-0.2564010024,0.3199440241,0.0101718027,0.0377005637,0.0003141626,-0.0240623355,-0.0827939883,-0.2940048873,0.3711950183,0.2321464419,0.5224136114,-0.3169358075,-0.1641277969,-0.3288432062,-0.1824413538,-0.3512556851,0.226632759,0.1845538467,-0.1113308817,0.1161273718,-0.1436178535,-0.1014853194,-0.2063972354,-0.0293161273,-0.2615340352,0.1022737771,-0.2105868608,-0.1004619375,0.2575800121,-0.2702479959,0.0108298594,-0.1056223363,-0.1307831109,-0.0068054325,-0.247213304,-0.0840224698,0.2468504906,-0.199078232,0.2889420688,-0.0489982776,-0.0504212566,0.224761039,-0.0796672255,-0.0302130375,0.0054934151,0.3368757069,0.0041132052,0.0638572946,0.4085853696,0.2356281877,-0.379357934,-0.2320603579,-0.149626568,-0.5145174861,-0.0293558314,-0.1177171171,0.01687208,-0.0452369191,-0.1210255995,0.1521080732,0.1642943472,0.1547948122,0.10596627,0.4816172421,0.1049144343,0.2937650084,-0.1649460793,0.1770391911,-0.144326508,-0.1722711623,-0.2492276877,-0.0456115939,-0.0579169057,-0.0926888585,0.2676637173,0.1338510513,0.2967094481,-0.1395806223,0.1908528805,-0.1090161651,0.1168141365,-0.5029688478,0.1476960927,0.1410785764,-0.0354773737,0.1686936468,0.2287830412,-0.1788699031,-0.1023910046,0.050235346,-0.1035604551,-0.2971284389,0.1772404164,0.3213210106,0.1347744316,-0.0030032352,-0.018904794,0.2770770788,0.375772059,-0.2176397294,0.0281005073,-0.177321583,-0.2569280863,-0.125802964,0.2650002837,0.4393820763,-0.425060302,0.2090140134,0.0690165609,0.1223087683,0.0890245587,0.3739468455,-0.0650732517,0.3143593669,-0.1145715341,0.2009439021,0.3094802201,-0.285585016,-0.5090754032,0.5406064391,-0.1859303266,-0.1181871668,0.099257268,-0.0444510505,0.1034918204,0.1358725727,0.3047401011,0.2793403268,0.0113194669,0.2667300403,-0.2604504526,-0.0832502395,-0.0501141697,0.1559398472,0.3121375144,-0.2080699205,0.1951689571,-0.017231321,0.19437626,-0.044446256,0.2128829956,0.5059319735,0.1271578521,-0.0078406455,-0.0123689231,-0.2239847332,0.0316570215,0.1751381308,0.1210090369,0.0843074545,-0.2666850388,-0.0822329894,-0.3092454672,-0.1341452003,-0.2703467607,-0.193386212,0.2836054265,0.0813239738,-0.0114756655,0.2551389933,-0.0167013835,0.0253130831,-0.1602133662,-0.0655840337,-0.0120967701,-0.0018739073,-0.091083996,-0.2782087028,0.1229490936,0.1102730632,-0.041142635,-0.0616680495,-0.1109172329,0.3928830624,-0.0020204084,-0.0810614601,-0.0360698178,-0.0300612058,0.1020015851,-0.0952062309,0.0719461367,0.0007549835,0.1388051212,-0.1053132713,-0.1801620126,0.3006142974,-0.1398149133,0.2031631023,0.3438572288,-0.2090742588,0.2556621134,-0.0663511157,0.2276659161,-0.1381171942,0.2406677008,0.0831321999,0.136321649,-0.0013489889,-0.2719839215,-0.0127429022,0.6895840764,0.171065256,0.0928926244,0.1747953147,-0.2453399599,-0.0179574843,-0.2034236491,0.1299691498,0.3976373672,0.2744440138,0.2679811418,0.0357754268,0.1870050132,-0.3740030229,0.1397108883,0.0032486171,0.0111852335,0.5398927927,0.2758919895,-0.1295805275,-0.4534206688,-0.1563689411,0.1057760417,0.3134025633,-0.0895873681,0.0198824629,-0.2155629992,-0.2920289338,-0.2959762812,0.2103271335,-0.2627325952,-0.2253175527,0.0694883317,0.0018545482,-0.0096535487,0.0968664065,0.0594205372,0.1454249322,0.3997489512,-0.198824957,-0.0615104102,-0.2715903819,-0.1730497926,0.1567441374,0.2421877533,-0.0123151606,0.106642887,-0.1534677446,-0.1015961543,0.0038625724,-0.2853763402,0.0006907425,-0.029190002,0.1006606892,-0.0571642891,0.2626269162,0.1662754267,0.0769828781,0.312800765,-0.1297491789,-0.080774948,0.2233646214,0.0906755924,-0.0652652159,-0.1705733836,-0.4048942327,-0.1530372351,-0.6100059748,0.357511282,0.0955663547,0.0380034447,0.4022583663,0.3181683421,0.2154984921,-0.0212013293,0.2124930322,0.1002640873,-0.1950187981,0.2925721705,-0.2248299271,-0.3832399845,-0.0998249501,0.1606033593,0.16261594,0.007262093,-0.5234432817,0.0302410219,-0.4368826151,0.1623679101,-0.0637961328,0.168691963,0.0814080685,-0.0684886277,-0.2522610426,-0.0842753425,0.0830210894,0.0467453748,-0.0724342912,0.0489535592,-0.1854182333,0.4121846557,0.2402956635,0.4626801014,0.163782239,0.0814501196,0.3938990235,-0.121706374,0.4271225929,-0.3532859087,-0.3265112638,0.12397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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/630","title":"Text dataset not working with large files","comments":"Ah, it\u2019s much faster now(Takes around 15~20min). \r\nBTW, any way to set default tensor output as plain tensors with distributed training? The ragged tensors are incompatible with tpustrategy :(","body":"```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry.","comment_length":29,"text":"Text dataset not working with large files \n ```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry. \n Ah, it\u2019s much faster now(Takes around 15~20min). \r\nBTW, any way to set default tensor output as plain tensors with distributed training? The ragged tensors are incompatible with tpustrategy 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/630","title":"Text dataset not working with large files","comments":"> Ah, it\u2019s much faster now(Takes around 15~20min).\r\n\r\nGlad to see that it's faster now. What did you change exactly ?\r\n\r\n> BTW, any way to set default tensor output as plain tensors with distributed training? The ragged tensors are incompatible with tpustrategy :(\r\n\r\nOh I didn't know about that. Feel free to open an issue to mention that.\r\nI guess what you can do for now is set the dataset format to numpy instead of tensorflow, and use a wrapper of the dataset that converts the numpy arrays to tf tensors.\r\n\r\n","body":"```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry.","comment_length":92,"text":"Text dataset not working with large files \n ```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry. \n > Ah, it\u2019s much faster now(Takes around 15~20min).\r\n\r\nGlad to see that it's faster now. What did you change exactly ?\r\n\r\n> BTW, any way to set default tensor output as plain tensors with distributed training? The ragged tensors are incompatible with tpustrategy :(\r\n\r\nOh I didn't know about that. Feel free to open an issue to mention that.\r\nI guess what you can do for now is set the dataset format to numpy instead of tensorflow, and use a wrapper of the dataset that converts the numpy arrays to tf tensors.\r\n\r\n","embeddings":[-0.4925667346,-0.2310241014,-0.1198643222,0.2836014926,0.4663560987,-0.0735016093,0.3053681254,0.5961018801,-0.1138256192,0.0461653806,-0.0624196492,-0.0304052103,-0.1033419743,0.310179472,-0.1062136963,-0.0389501564,-0.2278844863,0.1130641699,-0.0946916267,0.0499166101,-0.112550877,0.1110998988,-0.1209788397,-0.0956026316,-0.4804550409,-0.0829951093,0.1185314655,0.2535659075,-0.2920109332,-0.3053144217,-0.1657067835,0.0965852961,0.1122046039,0.6015893817,-0.0001037621,0.129462406,0.2131118327,-0.1573347151,-0.2265827805,-0.0314556658,0.1559481472,-0.4493688941,-0.1823892593,-0.2179644257,0.0261511691,-0.0627441034,-0.1082879007,-0.117559962,0.265335381,0.5598000884,0.3412032723,0.170758754,0.1321692318,-0.1129611656,0.2507540286,-0.0872494131,-0.0887443796,0.0848500654,0.3108180165,0.0265774522,-0.2564010024,0.3199440241,0.0101718027,0.0377005637,0.0003141626,-0.0240623355,-0.0827939883,-0.2940048873,0.3711950183,0.2321464419,0.5224136114,-0.3169358075,-0.1641277969,-0.3288432062,-0.1824413538,-0.3512556851,0.226632759,0.1845538467,-0.1113308817,0.1161273718,-0.1436178535,-0.1014853194,-0.2063972354,-0.0293161273,-0.2615340352,0.1022737771,-0.2105868608,-0.1004619375,0.2575800121,-0.2702479959,0.0108298594,-0.1056223363,-0.1307831109,-0.0068054325,-0.247213304,-0.0840224698,0.2468504906,-0.199078232,0.2889420688,-0.0489982776,-0.0504212566,0.224761039,-0.0796672255,-0.0302130375,0.0054934151,0.3368757069,0.0041132052,0.0638572946,0.4085853696,0.2356281877,-0.379357934,-0.2320603579,-0.149626568,-0.5145174861,-0.0293558314,-0.1177171171,0.01687208,-0.0452369191,-0.1210255995,0.1521080732,0.1642943472,0.1547948122,0.10596627,0.4816172421,0.1049144343,0.2937650084,-0.1649460793,0.1770391911,-0.144326508,-0.1722711623,-0.2492276877,-0.0456115939,-0.0579169057,-0.0926888585,0.2676637173,0.1338510513,0.2967094481,-0.1395806223,0.1908528805,-0.1090161651,0.1168141365,-0.5029688478,0.1476960927,0.1410785764,-0.0354773737,0.1686936468,0.2287830412,-0.1788699031,-0.1023910046,0.050235346,-0.1035604551,-0.2971284389,0.1772404164,0.3213210106,0.1347744316,-0.0030032352,-0.018904794,0.2770770788,0.375772059,-0.2176397294,0.0281005073,-0.177321583,-0.2569280863,-0.125802964,0.2650002837,0.4393820763,-0.425060302,0.2090140134,0.0690165609,0.1223087683,0.0890245587,0.3739468455,-0.0650732517,0.3143593669,-0.1145715341,0.2009439021,0.3094802201,-0.285585016,-0.5090754032,0.5406064391,-0.1859303266,-0.1181871668,0.099257268,-0.0444510505,0.1034918204,0.1358725727,0.3047401011,0.2793403268,0.0113194669,0.2667300403,-0.2604504526,-0.0832502395,-0.0501141697,0.1559398472,0.3121375144,-0.2080699205,0.1951689571,-0.017231321,0.19437626,-0.044446256,0.2128829956,0.5059319735,0.1271578521,-0.0078406455,-0.0123689231,-0.2239847332,0.0316570215,0.1751381308,0.1210090369,0.0843074545,-0.2666850388,-0.0822329894,-0.3092454672,-0.1341452003,-0.2703467607,-0.193386212,0.2836054265,0.0813239738,-0.0114756655,0.2551389933,-0.0167013835,0.0253130831,-0.1602133662,-0.0655840337,-0.0120967701,-0.0018739073,-0.091083996,-0.2782087028,0.1229490936,0.1102730632,-0.041142635,-0.0616680495,-0.1109172329,0.3928830624,-0.0020204084,-0.0810614601,-0.0360698178,-0.0300612058,0.1020015851,-0.0952062309,0.0719461367,0.0007549835,0.1388051212,-0.1053132713,-0.1801620126,0.3006142974,-0.1398149133,0.2031631023,0.3438572288,-0.2090742588,0.2556621134,-0.0663511157,0.2276659161,-0.1381171942,0.2406677008,0.0831321999,0.136321649,-0.0013489889,-0.2719839215,-0.0127429022,0.6895840764,0.171065256,0.0928926244,0.1747953147,-0.2453399599,-0.0179574843,-0.2034236491,0.1299691498,0.3976373672,0.2744440138,0.2679811418,0.0357754268,0.1870050132,-0.3740030229,0.1397108883,0.0032486171,0.0111852335,0.5398927927,0.2758919895,-0.1295805275,-0.4534206688,-0.1563689411,0.1057760417,0.3134025633,-0.0895873681,0.0198824629,-0.2155629992,-0.2920289338,-0.2959762812,0.2103271335,-0.2627325952,-0.2253175527,0.0694883317,0.0018545482,-0.0096535487,0.0968664065,0.0594205372,0.1454249322,0.3997489512,-0.198824957,-0.0615104102,-0.2715903819,-0.1730497926,0.1567441374,0.2421877533,-0.0123151606,0.106642887,-0.1534677446,-0.1015961543,0.0038625724,-0.2853763402,0.0006907425,-0.029190002,0.1006606892,-0.0571642891,0.2626269162,0.1662754267,0.0769828781,0.312800765,-0.1297491789,-0.080774948,0.2233646214,0.0906755924,-0.0652652159,-0.1705733836,-0.4048942327,-0.1530372351,-0.6100059748,0.357511282,0.0955663547,0.0380034447,0.4022583663,0.3181683421,0.2154984921,-0.0212013293,0.2124930322,0.1002640873,-0.1950187981,0.2925721705,-0.2248299271,-0.3832399845,-0.0998249501,0.1606033593,0.16261594,0.007262093,-0.5234432817,0.0302410219,-0.4368826151,0.1623679101,-0.0637961328,0.168691963,0.0814080685,-0.0684886277,-0.2522610426,-0.0842753425,0.0830210894,0.0467453748,-0.0724342912,0.0489535592,-0.1854182333,0.4121846557,0.2402956635,0.4626801014,0.163782239,0.0814501196,0.3938990235,-0.121706374,0.4271225929,-0.3532859087,-0.3265112638,0.1239757761,-0.091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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/630","title":"Text dataset not working with large files","comments":">>> Glad to see that it's faster now. What did you change exactly ?\r\nI don't know, it just worked...? Sorry I couldn't be more helpful.\r\n\r\nSetting with numpy array is a great idea! Thanks.","body":"```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry.","comment_length":35,"text":"Text dataset not working with large files \n ```\r\nTraceback (most recent call last):\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 333, in <module>\r\n    main()\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 262, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"examples\/language-modeling\/run_language_modeling.py\", line 144, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split='train+test')\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 469, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/ksjae\/.local\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/ksjae\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 104, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\n```\r\n\r\n**pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**\r\n\r\nIt gives the same message for both 200MB, 10GB .tx files but not for 700MB file.\r\nCan't upload due to size & copyright problem. sorry. \n >>> Glad to see that it's faster now. What did you change exactly ?\r\nI don't know, it just worked...? Sorry I couldn't be more helpful.\r\n\r\nSetting with numpy array is a great idea! Thanks.","embeddings":[-0.4925667346,-0.2310241014,-0.1198643222,0.2836014926,0.4663560987,-0.0735016093,0.3053681254,0.5961018801,-0.1138256192,0.0461653806,-0.0624196492,-0.0304052103,-0.1033419743,0.310179472,-0.1062136963,-0.0389501564,-0.2278844863,0.1130641699,-0.0946916267,0.0499166101,-0.112550877,0.1110998988,-0.1209788397,-0.0956026316,-0.4804550409,-0.0829951093,0.1185314655,0.2535659075,-0.2920109332,-0.3053144217,-0.1657067835,0.0965852961,0.1122046039,0.6015893817,-0.0001037621,0.129462406,0.2131118327,-0.1573347151,-0.2265827805,-0.0314556658,0.1559481472,-0.4493688941,-0.1823892593,-0.2179644257,0.0261511691,-0.0627441034,-0.1082879007,-0.117559962,0.265335381,0.5598000884,0.3412032723,0.170758754,0.1321692318,-0.1129611656,0.2507540286,-0.0872494131,-0.0887443796,0.0848500654,0.3108180165,0.0265774522,-0.2564010024,0.3199440241,0.0101718027,0.0377005637,0.0003141626,-0.0240623355,-0.0827939883,-0.2940048873,0.3711950183,0.2321464419,0.5224136114,-0.3169358075,-0.1641277969,-0.3288432062,-0.1824413538,-0.3512556851,0.226632759,0.1845538467,-0.1113308817,0.1161273718,-0.1436178535,-0.1014853194,-0.2063972354,-0.0293161273,-0.2615340352,0.1022737771,-0.2105868608,-0.1004619375,0.2575800121,-0.2702479959,0.0108298594,-0.1056223363,-0.1307831109,-0.0068054325,-0.247213304,-0.0840224698,0.2468504906,-0.199078232,0.2889420688,-0.0489982776,-0.0504212566,0.224761039,-0.0796672255,-0.0302130375,0.0054934151,0.3368757069,0.0041132052,0.0638572946,0.4085853696,0.2356281877,-0.379357934,-0.2320603579,-0.149626568,-0.5145174861,-0.0293558314,-0.1177171171,0.01687208,-0.0452369191,-0.1210255995,0.1521080732,0.1642943472,0.1547948122,0.10596627,0.4816172421,0.1049144343,0.2937650084,-0.1649460793,0.1770391911,-0.144326508,-0.1722711623,-0.2492276877,-0.0456115939,-0.0579169057,-0.0926888585,0.2676637173,0.1338510513,0.2967094481,-0.1395806223,0.1908528805,-0.1090161651,0.1168141365,-0.5029688478,0.1476960927,0.1410785764,-0.0354773737,0.1686936468,0.2287830412,-0.1788699031,-0.1023910046,0.050235346,-0.1035604551,-0.2971284389,0.1772404164,0.3213210106,0.1347744316,-0.0030032352,-0.018904794,0.2770770788,0.375772059,-0.2176397294,0.0281005073,-0.177321583,-0.2569280863,-0.125802964,0.2650002837,0.4393820763,-0.425060302,0.2090140134,0.0690165609,0.1223087683,0.0890245587,0.3739468455,-0.0650732517,0.3143593669,-0.1145715341,0.2009439021,0.3094802201,-0.285585016,-0.5090754032,0.5406064391,-0.1859303266,-0.1181871668,0.099257268,-0.0444510505,0.1034918204,0.1358725727,0.3047401011,0.2793403268,0.0113194669,0.2667300403,-0.2604504526,-0.0832502395,-0.0501141697,0.1559398472,0.3121375144,-0.2080699205,0.1951689571,-0.017231321,0.19437626,-0.044446256,0.2128829956,0.5059319735,0.1271578521,-0.0078406455,-0.0123689231,-0.2239847332,0.0316570215,0.1751381308,0.1210090369,0.0843074545,-0.2666850388,-0.0822329894,-0.3092454672,-0.1341452003,-0.2703467607,-0.193386212,0.2836054265,0.0813239738,-0.0114756655,0.2551389933,-0.0167013835,0.0253130831,-0.1602133662,-0.0655840337,-0.0120967701,-0.0018739073,-0.091083996,-0.2782087028,0.1229490936,0.1102730632,-0.041142635,-0.0616680495,-0.1109172329,0.3928830624,-0.0020204084,-0.0810614601,-0.0360698178,-0.0300612058,0.1020015851,-0.0952062309,0.0719461367,0.0007549835,0.1388051212,-0.1053132713,-0.1801620126,0.3006142974,-0.1398149133,0.2031631023,0.3438572288,-0.2090742588,0.2556621134,-0.0663511157,0.2276659161,-0.1381171942,0.2406677008,0.0831321999,0.136321649,-0.0013489889,-0.2719839215,-0.0127429022,0.6895840764,0.171065256,0.0928926244,0.1747953147,-0.2453399599,-0.0179574843,-0.2034236491,0.1299691498,0.3976373672,0.2744440138,0.2679811418,0.0357754268,0.1870050132,-0.3740030229,0.1397108883,0.0032486171,0.0111852335,0.5398927927,0.2758919895,-0.1295805275,-0.4534206688,-0.1563689411,0.1057760417,0.3134025633,-0.0895873681,0.0198824629,-0.2155629992,-0.2920289338,-0.2959762812,0.2103271335,-0.2627325952,-0.2253175527,0.0694883317,0.0018545482,-0.0096535487,0.0968664065,0.0594205372,0.1454249322,0.3997489512,-0.198824957,-0.0615104102,-0.2715903819,-0.1730497926,0.1567441374,0.2421877533,-0.0123151606,0.106642887,-0.1534677446,-0.1015961543,0.0038625724,-0.2853763402,0.0006907425,-0.029190002,0.1006606892,-0.0571642891,0.2626269162,0.1662754267,0.0769828781,0.312800765,-0.1297491789,-0.080774948,0.2233646214,0.0906755924,-0.0652652159,-0.1705733836,-0.4048942327,-0.1530372351,-0.6100059748,0.357511282,0.0955663547,0.0380034447,0.4022583663,0.3181683421,0.2154984921,-0.0212013293,0.2124930322,0.1002640873,-0.1950187981,0.2925721705,-0.2248299271,-0.3832399845,-0.0998249501,0.1606033593,0.16261594,0.007262093,-0.5234432817,0.0302410219,-0.4368826151,0.1623679101,-0.0637961328,0.168691963,0.0814080685,-0.0684886277,-0.2522610426,-0.0842753425,0.0830210894,0.0467453748,-0.0724342912,0.0489535592,-0.1854182333,0.4121846557,0.2402956635,0.4626801014,0.163782239,0.0814501196,0.3938990235,-0.121706374,0.4271225929,-0.3532859087,-0.3265112638,0.1239757761,-0.0912457034,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/625","title":"dtype of tensors should be preserved","comments":"Indeed we convert tensors to list to be able to write in arrow format. Because of this conversion we lose the dtype information. We should add the dtype detection when we do type inference. However it would require a bit of refactoring since currently the conversion happens before the type inference..\r\n\r\nAnd then for your information, when reading from arrow format we have to cast from arrow to numpy (which is fast since pyarrow has a numpy integration), and then to torch.\r\n\r\nHowever there's one thing that can help you: we make sure that the dtypes correspond to what is defined in `features`.\r\nTherefore what you can do is provide `features` in `.map(preprocess, feature=...)` to specify the output types.\r\n\r\nFor example in your case:\r\n```python\r\nfrom datasets import Features, Value, Sequence\r\n\r\nfeatures = Features({\r\n    \"input_ids\": Sequence(Value(\"int32\")),\r\n    \"sembedding\": Sequence(Value(\"float32\"))\r\n})\r\npreprocessed_dataset = dataset.map(preprocess, features=features)\r\n\r\npreprocessed_dataset.set_format(\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\nprint(preprocessed_dataset[0][\"sembedding\"].dtype)\r\n# \"torch.float32\"\r\n```\r\n\r\nLet me know if it helps","body":"After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed.","comment_length":156,"text":"dtype of tensors should be preserved \n After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed. \n Indeed we convert tensors to list to be able to write in arrow format. Because of this conversion we lose the dtype information. We should add the dtype detection when we do type inference. However it would require a bit of refactoring since currently the conversion happens before the type inference..\r\n\r\nAnd then for your information, when reading from arrow format we have to cast from arrow to numpy (which is fast since pyarrow has a numpy integration), and then to torch.\r\n\r\nHowever there's one thing that can help you: we make sure that the dtypes correspond to what is defined in `features`.\r\nTherefore what you can do is provide `features` in `.map(preprocess, feature=...)` to specify the output types.\r\n\r\nFor example in your case:\r\n```python\r\nfrom datasets import Features, Value, Sequence\r\n\r\nfeatures = Features({\r\n    \"input_ids\": Sequence(Value(\"int32\")),\r\n    \"sembedding\": Sequence(Value(\"float32\"))\r\n})\r\npreprocessed_dataset = dataset.map(preprocess, features=features)\r\n\r\npreprocessed_dataset.set_format(\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\nprint(preprocessed_dataset[0][\"sembedding\"].dtype)\r\n# \"torch.float32\"\r\n```\r\n\r\nLet me know if it helps","embeddings":[-0.1134333536,-0.2211149335,-0.0097106956,0.2073052824,0.5532286763,0.1730130315,0.5313699841,0.1225805879,0.150482595,-0.0665395483,-0.0843995288,0.2457148135,-0.1175516769,-0.175145492,0.1026156992,-0.2025278956,0.2282533199,-0.0652568042,-0.1441734284,-0.205046013,-0.2274058759,-0.0888313875,-0.0027089356,-0.2082730979,-0.1601844877,-0.1640590578,0.2403164208,-0.1392812729,-0.1851640195,-0.0010071443,0.1801282763,-0.1652227491,0.4167289436,0.5717647672,-0.0001163156,0.2217250615,-0.0266850162,-0.0754260793,-0.1886361241,-0.0001533552,0.0046074335,-0.1931504607,-0.0723768249,-0.1798607856,-0.1541208327,-0.1833256185,-0.0845149383,-0.7935453057,0.2889484465,0.410826385,0.1519325227,0.2570824623,0.0712021217,0.1525935829,-0.1351298988,0.2725163996,-0.103012912,0.2802624404,-0.0208770931,0.3604668975,-0.0614813603,0.4459063411,-0.3614792526,-0.0750737414,0.0291567855,0.11278864,0.0541251488,-0.4639117122,-0.066283904,0.1654170305,0.1973419636,-0.2320809811,-0.2843651474,-0.1672888398,-0.0933570042,-0.3671621978,0.0662379041,-0.0845816359,0.1454873085,0.1031548381,-0.0847080871,-0.1715696007,-0.0345691293,0.1320960969,-0.4753398299,0.2185822874,-0.0089362506,0.1474191993,-0.1035433561,-0.0327189155,0.0743184015,-0.1159510016,0.2209848762,-0.0332037657,-0.1700056195,-0.2236013412,0.1068102047,-0.4505304396,-0.1278472394,-0.481867671,0.2282644808,0.1562227011,-0.2183923125,-0.0274901818,0.2101505846,0.3317389488,-0.2976427972,0.3678765297,0.2994463146,-0.0447378792,0.2111329287,0.0985190496,0.1282290816,0.0464336239,-0.0115374727,0.1037944704,0.5391173363,-0.1061271057,-0.315695256,0.2423450053,-0.4640916288,0.0854395926,0.0290507488,0.0762219429,-0.1580673456,0.446918875,0.1909368932,0.0856407434,-0.1537808329,-0.0482112318,-0.1408704817,-0.2463302314,0.0493125133,-0.1757228523,0.0473481752,0.1110769287,-0.0556811914,0.290984422,0.1304951161,0.1616608948,-0.0623956695,-0.1468250006,0.5156334639,0.294393152,-0.3300797641,0.1609635502,0.2148128599,-0.3500457704,-0.1941231787,0.34138304,-0.2835562229,0.000267259,-0.255297929,0.0896733627,-0.0626333654,-0.1942966878,0.0383375585,0.516879797,0.5945786238,0.0745409578,0.3335529566,-0.525503695,-0.2444197536,-0.2121095061,0.0651086941,-0.0023088125,-0.4651449621,-0.0084925191,0.2366493046,0.1842959225,0.356190145,0.4137149155,0.0704921186,0.1077738181,0.0749110356,-0.0053551802,0.4220858216,0.1117162034,-0.2204668969,-0.0649491996,-0.0586552843,0.3635667264,-0.0807049423,0.0079559609,0.3194953501,-0.2035728991,0.2078322172,0.0254099835,-0.2768169343,0.0815219134,-0.2061402947,0.0387928039,0.4647621214,-0.0825524181,-0.0355014801,-0.0381801277,-0.3315752745,0.3626123369,0.2424901426,-0.1368885785,0.0368469059,0.0449779332,0.0548131727,-0.0328165814,0.0832959637,-0.082889691,-0.5234786868,-0.0470567532,0.0362101495,0.2670900524,-0.050319504,-0.136253953,0.2245899737,0.083187528,-0.010379171,0.0391422622,0.068150878,-0.1717522889,-0.4256085157,0.0723535568,-0.0966651067,-0.2199121267,-0.0393039584,0.1064351872,-0.4977482855,0.0808114558,0.0075054457,-0.1966703385,-0.2570157945,0.285153985,0.0231736973,-0.0891861841,-0.2323273271,0.0862708464,0.1931117773,-0.0923557654,-0.6234652996,0.5602859855,0.5223332047,0.0226684622,0.196981594,0.4178994596,0.0167538133,0.0668812171,-0.1525678933,0.1664802283,0.1083381176,0.0184073467,-0.2958197296,0.0697329938,0.2497304231,0.1761268973,-0.2609070837,-0.1829364747,-0.1405118257,-0.0761567354,-0.0279818904,0.1387428492,-0.4120871723,0.335906148,0.6934578419,-0.0322736017,0.2642434537,0.0308322366,-0.3138761222,-0.1533606052,0.2285325527,-0.1215764284,0.2470545769,0.0851404145,0.3608531654,-0.1999171078,-0.1625255197,-0.0748273805,0.2153988332,0.0469212495,0.0663601533,0.0049412171,0.0751011446,0.1541916281,-0.1057289094,0.27096048,-0.050399024,0.203207612,-0.4259132743,0.2171180546,-0.4016545713,0.0512693189,-0.2296758741,-0.0917817354,-0.0777890086,-0.2456137538,-0.0293583423,0.0008401733,-0.2700945437,0.3935126066,0.2614880204,0.1166923568,0.0873444155,-0.2350945622,-0.1146133244,-0.1724233925,-0.2385483682,0.040034391,0.208354637,-0.3795832694,0.1849566102,0.3574534059,-0.2676813304,-0.2696791887,-0.7421143651,0.0485985689,-0.2355481237,0.0960817412,0.038121704,0.1293122768,-0.0246390663,0.0510537662,0.0254741795,0.0944768935,-0.0181039479,0.1180345491,-0.1916359514,0.0291311461,-0.2872902155,-0.2801524401,-0.0367826261,-0.1219953895,0.1812550128,-0.0798306614,-0.0676227063,0.0514861755,0.0912298411,0.0310504753,-0.0581302159,0.2310893536,-0.4207531214,0.0914393589,0.3349058032,-0.1597370058,-0.3068858087,-0.1432787478,0.0234636441,-0.018258201,0.0745135397,-0.2800920606,-0.0570334829,-0.2157623917,0.2658351958,-0.014034423,-0.1009145305,0.5142831206,0.232959345,0.0712706,-0.0603918321,0.0153217725,0.0630327985,0.2177911401,0.2721605003,-0.0094194403,0.3699365258,0.1042176038,0.3581045866,0.2943382859,-0.694893539,-0.1260363609,-0.0573534109,0.2153844386,-0.2174506038,-0.2911497355,0.0320028886,-0.2955263555,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/625","title":"dtype of tensors should be preserved","comments":"If the arrow format is basically lists, why is the intermediate step to numpy necessary? I am a bit confused about that part.\r\n\r\nThanks for your suggestion. as I have currently implemented this, I cast to torch.Tensor in my collate_fn to save disk space (so I do not have to save padded tensors to max_len but can pad up to max batch len in collate_fn) at the cost of a bit slower processing. So for me this is not relevant anymore, but I am sure it is for others!","body":"After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed.","comment_length":89,"text":"dtype of tensors should be preserved \n After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed. \n If the arrow format is basically lists, why is the intermediate step to numpy necessary? I am a bit confused about that part.\r\n\r\nThanks for your suggestion. as I have currently implemented this, I cast to torch.Tensor in my collate_fn to save disk space (so I do not have to save padded tensors to max_len but can pad up to max batch len in collate_fn) at the cost of a bit slower processing. So for me this is not relevant anymore, but I am sure it is for others!","embeddings":[-0.1134333536,-0.2211149335,-0.0097106956,0.2073052824,0.5532286763,0.1730130315,0.5313699841,0.1225805879,0.150482595,-0.0665395483,-0.0843995288,0.2457148135,-0.1175516769,-0.175145492,0.1026156992,-0.2025278956,0.2282533199,-0.0652568042,-0.1441734284,-0.205046013,-0.2274058759,-0.0888313875,-0.0027089356,-0.2082730979,-0.1601844877,-0.1640590578,0.2403164208,-0.1392812729,-0.1851640195,-0.0010071443,0.1801282763,-0.1652227491,0.4167289436,0.5717647672,-0.0001163156,0.2217250615,-0.0266850162,-0.0754260793,-0.1886361241,-0.0001533552,0.0046074335,-0.1931504607,-0.0723768249,-0.1798607856,-0.1541208327,-0.1833256185,-0.0845149383,-0.7935453057,0.2889484465,0.410826385,0.1519325227,0.2570824623,0.0712021217,0.1525935829,-0.1351298988,0.2725163996,-0.103012912,0.2802624404,-0.0208770931,0.3604668975,-0.0614813603,0.4459063411,-0.3614792526,-0.0750737414,0.0291567855,0.11278864,0.0541251488,-0.4639117122,-0.066283904,0.1654170305,0.1973419636,-0.2320809811,-0.2843651474,-0.1672888398,-0.0933570042,-0.3671621978,0.0662379041,-0.0845816359,0.1454873085,0.1031548381,-0.0847080871,-0.1715696007,-0.0345691293,0.1320960969,-0.4753398299,0.2185822874,-0.0089362506,0.1474191993,-0.1035433561,-0.0327189155,0.0743184015,-0.1159510016,0.2209848762,-0.0332037657,-0.1700056195,-0.2236013412,0.1068102047,-0.4505304396,-0.1278472394,-0.481867671,0.2282644808,0.1562227011,-0.2183923125,-0.0274901818,0.2101505846,0.3317389488,-0.2976427972,0.3678765297,0.2994463146,-0.0447378792,0.2111329287,0.0985190496,0.1282290816,0.0464336239,-0.0115374727,0.1037944704,0.5391173363,-0.1061271057,-0.315695256,0.2423450053,-0.4640916288,0.0854395926,0.0290507488,0.0762219429,-0.1580673456,0.446918875,0.1909368932,0.0856407434,-0.1537808329,-0.0482112318,-0.1408704817,-0.2463302314,0.0493125133,-0.1757228523,0.0473481752,0.1110769287,-0.0556811914,0.290984422,0.1304951161,0.1616608948,-0.0623956695,-0.1468250006,0.5156334639,0.294393152,-0.3300797641,0.1609635502,0.2148128599,-0.3500457704,-0.1941231787,0.34138304,-0.2835562229,0.000267259,-0.255297929,0.0896733627,-0.0626333654,-0.1942966878,0.0383375585,0.516879797,0.5945786238,0.0745409578,0.3335529566,-0.525503695,-0.2444197536,-0.2121095061,0.0651086941,-0.0023088125,-0.4651449621,-0.0084925191,0.2366493046,0.1842959225,0.356190145,0.4137149155,0.0704921186,0.1077738181,0.0749110356,-0.0053551802,0.4220858216,0.1117162034,-0.2204668969,-0.0649491996,-0.0586552843,0.3635667264,-0.0807049423,0.0079559609,0.3194953501,-0.2035728991,0.2078322172,0.0254099835,-0.2768169343,0.0815219134,-0.2061402947,0.0387928039,0.4647621214,-0.0825524181,-0.0355014801,-0.0381801277,-0.3315752745,0.3626123369,0.2424901426,-0.1368885785,0.0368469059,0.0449779332,0.0548131727,-0.0328165814,0.0832959637,-0.082889691,-0.5234786868,-0.0470567532,0.0362101495,0.2670900524,-0.050319504,-0.136253953,0.2245899737,0.083187528,-0.010379171,0.0391422622,0.068150878,-0.1717522889,-0.4256085157,0.0723535568,-0.0966651067,-0.2199121267,-0.0393039584,0.1064351872,-0.4977482855,0.0808114558,0.0075054457,-0.1966703385,-0.2570157945,0.285153985,0.0231736973,-0.0891861841,-0.2323273271,0.0862708464,0.1931117773,-0.0923557654,-0.6234652996,0.5602859855,0.5223332047,0.0226684622,0.196981594,0.4178994596,0.0167538133,0.0668812171,-0.1525678933,0.1664802283,0.1083381176,0.0184073467,-0.2958197296,0.0697329938,0.2497304231,0.1761268973,-0.2609070837,-0.1829364747,-0.1405118257,-0.0761567354,-0.0279818904,0.1387428492,-0.4120871723,0.335906148,0.6934578419,-0.0322736017,0.2642434537,0.0308322366,-0.3138761222,-0.1533606052,0.2285325527,-0.1215764284,0.2470545769,0.0851404145,0.3608531654,-0.1999171078,-0.1625255197,-0.0748273805,0.2153988332,0.0469212495,0.0663601533,0.0049412171,0.0751011446,0.1541916281,-0.1057289094,0.27096048,-0.050399024,0.203207612,-0.4259132743,0.2171180546,-0.4016545713,0.0512693189,-0.2296758741,-0.0917817354,-0.0777890086,-0.2456137538,-0.0293583423,0.0008401733,-0.2700945437,0.3935126066,0.2614880204,0.1166923568,0.0873444155,-0.2350945622,-0.1146133244,-0.1724233925,-0.2385483682,0.040034391,0.208354637,-0.3795832694,0.1849566102,0.3574534059,-0.2676813304,-0.2696791887,-0.7421143651,0.0485985689,-0.2355481237,0.0960817412,0.038121704,0.1293122768,-0.0246390663,0.0510537662,0.0254741795,0.0944768935,-0.0181039479,0.1180345491,-0.1916359514,0.0291311461,-0.2872902155,-0.2801524401,-0.0367826261,-0.1219953895,0.1812550128,-0.0798306614,-0.0676227063,0.0514861755,0.0912298411,0.0310504753,-0.0581302159,0.2310893536,-0.4207531214,0.0914393589,0.3349058032,-0.1597370058,-0.3068858087,-0.1432787478,0.0234636441,-0.018258201,0.0745135397,-0.2800920606,-0.0570334829,-0.2157623917,0.2658351958,-0.014034423,-0.1009145305,0.5142831206,0.232959345,0.0712706,-0.0603918321,0.0153217725,0.0630327985,0.2177911401,0.2721605003,-0.0094194403,0.3699365258,0.1042176038,0.3581045866,0.2943382859,-0.694893539,-0.1260363609,-0.0573534109,0.2153844386,-0.2174506038,-0.2911497355,0.0320028886,-0.2955263555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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/625","title":"dtype of tensors should be preserved","comments":"I'm glad you managed to figure something out :)\r\n\r\nCasting from arrow to numpy can be 100x faster than casting from arrow to list.\r\nThis is because arrow has an integration with numpy that allows it to instantiate numpy arrays with zero-copy from arrow.\r\nOn the other hand to create python lists it is slow since it has to recreate the list object by iterating through each element in python.","body":"After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed.","comment_length":70,"text":"dtype of tensors should be preserved \n After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed. \n I'm glad you managed to figure something out :)\r\n\r\nCasting from arrow to numpy can be 100x faster than casting from arrow to list.\r\nThis is because arrow has an integration with numpy that allows it to instantiate numpy arrays with zero-copy from arrow.\r\nOn the other hand to create python lists it is slow since it has to recreate the list object by iterating through each element in python.","embeddings":[-0.1134333536,-0.2211149335,-0.0097106956,0.2073052824,0.5532286763,0.1730130315,0.5313699841,0.1225805879,0.150482595,-0.0665395483,-0.0843995288,0.2457148135,-0.1175516769,-0.175145492,0.1026156992,-0.2025278956,0.2282533199,-0.0652568042,-0.1441734284,-0.205046013,-0.2274058759,-0.0888313875,-0.0027089356,-0.2082730979,-0.1601844877,-0.1640590578,0.2403164208,-0.1392812729,-0.1851640195,-0.0010071443,0.1801282763,-0.1652227491,0.4167289436,0.5717647672,-0.0001163156,0.2217250615,-0.0266850162,-0.0754260793,-0.1886361241,-0.0001533552,0.0046074335,-0.1931504607,-0.0723768249,-0.1798607856,-0.1541208327,-0.1833256185,-0.0845149383,-0.7935453057,0.2889484465,0.410826385,0.1519325227,0.2570824623,0.0712021217,0.1525935829,-0.1351298988,0.2725163996,-0.103012912,0.2802624404,-0.0208770931,0.3604668975,-0.0614813603,0.4459063411,-0.3614792526,-0.0750737414,0.0291567855,0.11278864,0.0541251488,-0.4639117122,-0.066283904,0.1654170305,0.1973419636,-0.2320809811,-0.2843651474,-0.1672888398,-0.0933570042,-0.3671621978,0.0662379041,-0.0845816359,0.1454873085,0.1031548381,-0.0847080871,-0.1715696007,-0.0345691293,0.1320960969,-0.4753398299,0.2185822874,-0.0089362506,0.1474191993,-0.1035433561,-0.0327189155,0.0743184015,-0.1159510016,0.2209848762,-0.0332037657,-0.1700056195,-0.2236013412,0.1068102047,-0.4505304396,-0.1278472394,-0.481867671,0.2282644808,0.1562227011,-0.2183923125,-0.0274901818,0.2101505846,0.3317389488,-0.2976427972,0.3678765297,0.2994463146,-0.0447378792,0.2111329287,0.0985190496,0.1282290816,0.0464336239,-0.0115374727,0.1037944704,0.5391173363,-0.1061271057,-0.315695256,0.2423450053,-0.4640916288,0.0854395926,0.0290507488,0.0762219429,-0.1580673456,0.446918875,0.1909368932,0.0856407434,-0.1537808329,-0.0482112318,-0.1408704817,-0.2463302314,0.0493125133,-0.1757228523,0.0473481752,0.1110769287,-0.0556811914,0.290984422,0.1304951161,0.1616608948,-0.0623956695,-0.1468250006,0.5156334639,0.294393152,-0.3300797641,0.1609635502,0.2148128599,-0.3500457704,-0.1941231787,0.34138304,-0.2835562229,0.000267259,-0.255297929,0.0896733627,-0.0626333654,-0.1942966878,0.0383375585,0.516879797,0.5945786238,0.0745409578,0.3335529566,-0.525503695,-0.2444197536,-0.2121095061,0.0651086941,-0.0023088125,-0.4651449621,-0.0084925191,0.2366493046,0.1842959225,0.356190145,0.4137149155,0.0704921186,0.1077738181,0.0749110356,-0.0053551802,0.4220858216,0.1117162034,-0.2204668969,-0.0649491996,-0.0586552843,0.3635667264,-0.0807049423,0.0079559609,0.3194953501,-0.2035728991,0.2078322172,0.0254099835,-0.2768169343,0.0815219134,-0.2061402947,0.0387928039,0.4647621214,-0.0825524181,-0.0355014801,-0.0381801277,-0.3315752745,0.3626123369,0.2424901426,-0.1368885785,0.0368469059,0.0449779332,0.0548131727,-0.0328165814,0.0832959637,-0.082889691,-0.5234786868,-0.0470567532,0.0362101495,0.2670900524,-0.050319504,-0.136253953,0.2245899737,0.083187528,-0.010379171,0.0391422622,0.068150878,-0.1717522889,-0.4256085157,0.0723535568,-0.0966651067,-0.2199121267,-0.0393039584,0.1064351872,-0.4977482855,0.0808114558,0.0075054457,-0.1966703385,-0.2570157945,0.285153985,0.0231736973,-0.0891861841,-0.2323273271,0.0862708464,0.1931117773,-0.0923557654,-0.6234652996,0.5602859855,0.5223332047,0.0226684622,0.196981594,0.4178994596,0.0167538133,0.0668812171,-0.1525678933,0.1664802283,0.1083381176,0.0184073467,-0.2958197296,0.0697329938,0.2497304231,0.1761268973,-0.2609070837,-0.1829364747,-0.1405118257,-0.0761567354,-0.0279818904,0.1387428492,-0.4120871723,0.335906148,0.6934578419,-0.0322736017,0.2642434537,0.0308322366,-0.3138761222,-0.1533606052,0.2285325527,-0.1215764284,0.2470545769,0.0851404145,0.3608531654,-0.1999171078,-0.1625255197,-0.0748273805,0.2153988332,0.0469212495,0.0663601533,0.0049412171,0.0751011446,0.1541916281,-0.1057289094,0.27096048,-0.050399024,0.203207612,-0.4259132743,0.2171180546,-0.4016545713,0.0512693189,-0.2296758741,-0.0917817354,-0.0777890086,-0.2456137538,-0.0293583423,0.0008401733,-0.2700945437,0.3935126066,0.2614880204,0.1166923568,0.0873444155,-0.2350945622,-0.1146133244,-0.1724233925,-0.2385483682,0.040034391,0.208354637,-0.3795832694,0.1849566102,0.3574534059,-0.2676813304,-0.2696791887,-0.7421143651,0.0485985689,-0.2355481237,0.0960817412,0.038121704,0.1293122768,-0.0246390663,0.0510537662,0.0254741795,0.0944768935,-0.0181039479,0.1180345491,-0.1916359514,0.0291311461,-0.2872902155,-0.2801524401,-0.0367826261,-0.1219953895,0.1812550128,-0.0798306614,-0.0676227063,0.0514861755,0.0912298411,0.0310504753,-0.0581302159,0.2310893536,-0.4207531214,0.0914393589,0.3349058032,-0.1597370058,-0.3068858087,-0.1432787478,0.0234636441,-0.018258201,0.0745135397,-0.2800920606,-0.0570334829,-0.2157623917,0.2658351958,-0.014034423,-0.1009145305,0.5142831206,0.232959345,0.0712706,-0.0603918321,0.0153217725,0.0630327985,0.2177911401,0.2721605003,-0.0094194403,0.3699365258,0.1042176038,0.3581045866,0.2943382859,-0.694893539,-0.1260363609,-0.0573534109,0.2153844386,-0.2174506038,-0.2911497355,0.0320028886,-0.2955263555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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/625","title":"dtype of tensors should be preserved","comments":"I encountered a simliar issue: `datasets` converted my float numpy array to `torch.float64` tensors, while many pytorch operations require `torch.float32` inputs and it's very troublesome. \r\n\r\nI tried @lhoestq 's solution, but since it's mixed with the preprocess function, it's not very intuitive. \r\n\r\nI just want to share another possible simpler solution: directly cast the dtype of the processed dataset.\r\n\r\nNow I want to change the type of `labels` in `train_dataset` from float64 to float32, I can do this.\r\n\r\n```\r\nfrom datasets import Value, Sequence, Features\r\nfeats = train_dataset.features.copy()\r\nfeats['labels'].feature = Value(dtype='float32')\r\nfeats = Features(feats)\r\ntrain_dataset.cast_(feats)\r\n```\r\n","body":"After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed.","comment_length":96,"text":"dtype of tensors should be preserved \n After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed. \n I encountered a simliar issue: `datasets` converted my float numpy array to `torch.float64` tensors, while many pytorch operations require `torch.float32` inputs and it's very troublesome. \r\n\r\nI tried @lhoestq 's solution, but since it's mixed with the preprocess function, it's not very intuitive. \r\n\r\nI just want to share another possible simpler solution: directly cast the dtype of the processed dataset.\r\n\r\nNow I want to change the type of `labels` in `train_dataset` from float64 to float32, I can do this.\r\n\r\n```\r\nfrom datasets import Value, Sequence, Features\r\nfeats = train_dataset.features.copy()\r\nfeats['labels'].feature = Value(dtype='float32')\r\nfeats = Features(feats)\r\ntrain_dataset.cast_(feats)\r\n```\r\n","embeddings":[-0.1134333536,-0.2211149335,-0.0097106956,0.2073052824,0.5532286763,0.1730130315,0.5313699841,0.1225805879,0.150482595,-0.0665395483,-0.0843995288,0.2457148135,-0.1175516769,-0.175145492,0.1026156992,-0.2025278956,0.2282533199,-0.0652568042,-0.1441734284,-0.205046013,-0.2274058759,-0.0888313875,-0.0027089356,-0.2082730979,-0.1601844877,-0.1640590578,0.2403164208,-0.1392812729,-0.1851640195,-0.0010071443,0.1801282763,-0.1652227491,0.4167289436,0.5717647672,-0.0001163156,0.2217250615,-0.0266850162,-0.0754260793,-0.1886361241,-0.0001533552,0.0046074335,-0.1931504607,-0.0723768249,-0.1798607856,-0.1541208327,-0.1833256185,-0.0845149383,-0.7935453057,0.2889484465,0.410826385,0.1519325227,0.2570824623,0.0712021217,0.1525935829,-0.1351298988,0.2725163996,-0.103012912,0.2802624404,-0.0208770931,0.3604668975,-0.0614813603,0.4459063411,-0.3614792526,-0.0750737414,0.0291567855,0.11278864,0.0541251488,-0.4639117122,-0.066283904,0.1654170305,0.1973419636,-0.2320809811,-0.2843651474,-0.1672888398,-0.0933570042,-0.3671621978,0.0662379041,-0.0845816359,0.1454873085,0.1031548381,-0.0847080871,-0.1715696007,-0.0345691293,0.1320960969,-0.4753398299,0.2185822874,-0.0089362506,0.1474191993,-0.1035433561,-0.0327189155,0.0743184015,-0.1159510016,0.2209848762,-0.0332037657,-0.1700056195,-0.2236013412,0.1068102047,-0.4505304396,-0.1278472394,-0.481867671,0.2282644808,0.1562227011,-0.2183923125,-0.0274901818,0.2101505846,0.3317389488,-0.2976427972,0.3678765297,0.2994463146,-0.0447378792,0.2111329287,0.0985190496,0.1282290816,0.0464336239,-0.0115374727,0.1037944704,0.5391173363,-0.1061271057,-0.315695256,0.2423450053,-0.4640916288,0.0854395926,0.0290507488,0.0762219429,-0.1580673456,0.446918875,0.1909368932,0.0856407434,-0.1537808329,-0.0482112318,-0.1408704817,-0.2463302314,0.0493125133,-0.1757228523,0.0473481752,0.1110769287,-0.0556811914,0.290984422,0.1304951161,0.1616608948,-0.0623956695,-0.1468250006,0.5156334639,0.294393152,-0.3300797641,0.1609635502,0.2148128599,-0.3500457704,-0.1941231787,0.34138304,-0.2835562229,0.000267259,-0.255297929,0.0896733627,-0.0626333654,-0.1942966878,0.0383375585,0.516879797,0.5945786238,0.0745409578,0.3335529566,-0.525503695,-0.2444197536,-0.2121095061,0.0651086941,-0.0023088125,-0.4651449621,-0.0084925191,0.2366493046,0.1842959225,0.356190145,0.4137149155,0.0704921186,0.1077738181,0.0749110356,-0.0053551802,0.4220858216,0.1117162034,-0.2204668969,-0.0649491996,-0.0586552843,0.3635667264,-0.0807049423,0.0079559609,0.3194953501,-0.2035728991,0.2078322172,0.0254099835,-0.2768169343,0.0815219134,-0.2061402947,0.0387928039,0.4647621214,-0.0825524181,-0.0355014801,-0.0381801277,-0.3315752745,0.3626123369,0.2424901426,-0.1368885785,0.0368469059,0.0449779332,0.0548131727,-0.0328165814,0.0832959637,-0.082889691,-0.5234786868,-0.0470567532,0.0362101495,0.2670900524,-0.050319504,-0.136253953,0.2245899737,0.083187528,-0.010379171,0.0391422622,0.068150878,-0.1717522889,-0.4256085157,0.0723535568,-0.0966651067,-0.2199121267,-0.0393039584,0.1064351872,-0.4977482855,0.0808114558,0.0075054457,-0.1966703385,-0.2570157945,0.285153985,0.0231736973,-0.0891861841,-0.2323273271,0.0862708464,0.1931117773,-0.0923557654,-0.6234652996,0.5602859855,0.5223332047,0.0226684622,0.196981594,0.4178994596,0.0167538133,0.0668812171,-0.1525678933,0.1664802283,0.1083381176,0.0184073467,-0.2958197296,0.0697329938,0.2497304231,0.1761268973,-0.2609070837,-0.1829364747,-0.1405118257,-0.0761567354,-0.0279818904,0.1387428492,-0.4120871723,0.335906148,0.6934578419,-0.0322736017,0.2642434537,0.0308322366,-0.3138761222,-0.1533606052,0.2285325527,-0.1215764284,0.2470545769,0.0851404145,0.3608531654,-0.1999171078,-0.1625255197,-0.0748273805,0.2153988332,0.0469212495,0.0663601533,0.0049412171,0.0751011446,0.1541916281,-0.1057289094,0.27096048,-0.050399024,0.203207612,-0.4259132743,0.2171180546,-0.4016545713,0.0512693189,-0.2296758741,-0.0917817354,-0.0777890086,-0.2456137538,-0.0293583423,0.0008401733,-0.2700945437,0.3935126066,0.2614880204,0.1166923568,0.0873444155,-0.2350945622,-0.1146133244,-0.1724233925,-0.2385483682,0.040034391,0.208354637,-0.3795832694,0.1849566102,0.3574534059,-0.2676813304,-0.2696791887,-0.7421143651,0.0485985689,-0.2355481237,0.0960817412,0.038121704,0.1293122768,-0.0246390663,0.0510537662,0.0254741795,0.0944768935,-0.0181039479,0.1180345491,-0.1916359514,0.0291311461,-0.2872902155,-0.2801524401,-0.0367826261,-0.1219953895,0.1812550128,-0.0798306614,-0.0676227063,0.0514861755,0.0912298411,0.0310504753,-0.0581302159,0.2310893536,-0.4207531214,0.0914393589,0.3349058032,-0.1597370058,-0.3068858087,-0.1432787478,0.0234636441,-0.018258201,0.0745135397,-0.2800920606,-0.0570334829,-0.2157623917,0.2658351958,-0.014034423,-0.1009145305,0.5142831206,0.232959345,0.0712706,-0.0603918321,0.0153217725,0.0630327985,0.2177911401,0.2721605003,-0.0094194403,0.3699365258,0.1042176038,0.3581045866,0.2943382859,-0.694893539,-0.1260363609,-0.0573534109,0.2153844386,-0.21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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/625","title":"dtype of tensors should be preserved","comments":"Reopening since @bhavitvyamalik started looking into it !\r\n\r\nAlso I'm posting here a function that could be helpful to support preserving the dtype of tensors.\r\n\r\nIt's used to build a pyarrow array out of a numpy array and:\r\n- it doesn't convert the numpy array to a python list\r\n- it keeps the precision of the numpy array for the pyarrow array\r\n- it works with multidimensional arrays (while `pa.array` can only take a 1D array as input)\r\n- it builds the pyarrow ListArray from offsets created on-the-fly and values that come from the flattened numpy array\r\n\r\n```python\r\nfrom functools import reduce\r\nfrom operator import mul\r\n\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\ndef pa_ndarray(a):\r\n    \"\"\"Build a PyArrow ListArray from a multidimensional NumPy array\"\"\"\r\n    values = pa.array(a.flatten()) \r\n    for i in range(a.ndim - 1): \r\n        n_offsets = reduce(mul, a.shape[:a.ndim - i - 1], 1) \r\n        step_offsets = a.shape[a.ndim - i - 1] \r\n        offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32()) \r\n        values = pa.ListArray.from_arrays(offsets, values) \r\n    return values \r\n\r\nnarr = np.arange(42).reshape(7, 2, 3).astype(np.uint8)\r\nparr = pa_ndarray(narr)\r\nassert isinstance(parr, pa.Array)\r\nassert parr.type == pa.list_(pa.list_(pa.uint8()))\r\nassert narr.tolist() == parr.to_pylist()\r\n```\r\n\r\nThe only costly operation is the offsets computations. Since it doesn't iterate on the numpy array values this function is pretty fast.","body":"After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed.","comment_length":206,"text":"dtype of tensors should be preserved \n After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed. \n Reopening since @bhavitvyamalik started looking into it !\r\n\r\nAlso I'm posting here a function that could be helpful to support preserving the dtype of tensors.\r\n\r\nIt's used to build a pyarrow array out of a numpy array and:\r\n- it doesn't convert the numpy array to a python list\r\n- it keeps the precision of the numpy array for the pyarrow array\r\n- it works with multidimensional arrays (while `pa.array` can only take a 1D array as input)\r\n- it builds the pyarrow ListArray from offsets created on-the-fly and values that come from the flattened numpy array\r\n\r\n```python\r\nfrom functools import reduce\r\nfrom operator import mul\r\n\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\ndef pa_ndarray(a):\r\n    \"\"\"Build a PyArrow ListArray from a multidimensional NumPy array\"\"\"\r\n    values = pa.array(a.flatten()) \r\n    for i in range(a.ndim - 1): \r\n        n_offsets = reduce(mul, a.shape[:a.ndim - i - 1], 1) \r\n        step_offsets = a.shape[a.ndim - i - 1] \r\n        offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32()) \r\n        values = pa.ListArray.from_arrays(offsets, values) \r\n    return values \r\n\r\nnarr = np.arange(42).reshape(7, 2, 3).astype(np.uint8)\r\nparr = pa_ndarray(narr)\r\nassert isinstance(parr, pa.Array)\r\nassert parr.type == pa.list_(pa.list_(pa.uint8()))\r\nassert narr.tolist() == parr.to_pylist()\r\n```\r\n\r\nThe only costly operation is the offsets computations. Since it doesn't iterate on the numpy array values this function is pretty fast.","embeddings":[-0.1134333536,-0.2211149335,-0.0097106956,0.2073052824,0.5532286763,0.1730130315,0.5313699841,0.1225805879,0.150482595,-0.0665395483,-0.0843995288,0.2457148135,-0.1175516769,-0.175145492,0.1026156992,-0.2025278956,0.2282533199,-0.0652568042,-0.1441734284,-0.205046013,-0.2274058759,-0.0888313875,-0.0027089356,-0.2082730979,-0.1601844877,-0.1640590578,0.2403164208,-0.1392812729,-0.1851640195,-0.0010071443,0.1801282763,-0.1652227491,0.4167289436,0.5717647672,-0.0001163156,0.2217250615,-0.0266850162,-0.0754260793,-0.1886361241,-0.0001533552,0.0046074335,-0.1931504607,-0.0723768249,-0.1798607856,-0.1541208327,-0.1833256185,-0.0845149383,-0.7935453057,0.2889484465,0.410826385,0.1519325227,0.2570824623,0.0712021217,0.1525935829,-0.1351298988,0.2725163996,-0.103012912,0.2802624404,-0.0208770931,0.3604668975,-0.0614813603,0.4459063411,-0.3614792526,-0.0750737414,0.0291567855,0.11278864,0.0541251488,-0.4639117122,-0.066283904,0.1654170305,0.1973419636,-0.2320809811,-0.2843651474,-0.1672888398,-0.0933570042,-0.3671621978,0.0662379041,-0.0845816359,0.1454873085,0.1031548381,-0.0847080871,-0.1715696007,-0.0345691293,0.1320960969,-0.4753398299,0.2185822874,-0.0089362506,0.1474191993,-0.1035433561,-0.0327189155,0.0743184015,-0.1159510016,0.2209848762,-0.0332037657,-0.1700056195,-0.2236013412,0.1068102047,-0.4505304396,-0.1278472394,-0.481867671,0.2282644808,0.1562227011,-0.2183923125,-0.0274901818,0.2101505846,0.3317389488,-0.2976427972,0.3678765297,0.2994463146,-0.0447378792,0.2111329287,0.0985190496,0.1282290816,0.0464336239,-0.0115374727,0.1037944704,0.5391173363,-0.1061271057,-0.315695256,0.2423450053,-0.4640916288,0.0854395926,0.0290507488,0.0762219429,-0.1580673456,0.446918875,0.1909368932,0.0856407434,-0.1537808329,-0.0482112318,-0.1408704817,-0.2463302314,0.0493125133,-0.1757228523,0.0473481752,0.1110769287,-0.0556811914,0.290984422,0.1304951161,0.1616608948,-0.0623956695,-0.1468250006,0.5156334639,0.294393152,-0.3300797641,0.1609635502,0.2148128599,-0.3500457704,-0.1941231787,0.34138304,-0.2835562229,0.000267259,-0.255297929,0.0896733627,-0.0626333654,-0.1942966878,0.0383375585,0.516879797,0.5945786238,0.0745409578,0.3335529566,-0.525503695,-0.2444197536,-0.2121095061,0.0651086941,-0.0023088125,-0.4651449621,-0.0084925191,0.2366493046,0.1842959225,0.356190145,0.4137149155,0.0704921186,0.1077738181,0.0749110356,-0.0053551802,0.4220858216,0.1117162034,-0.2204668969,-0.0649491996,-0.0586552843,0.3635667264,-0.0807049423,0.0079559609,0.3194953501,-0.2035728991,0.2078322172,0.0254099835,-0.2768169343,0.0815219134,-0.2061402947,0.0387928039,0.4647621214,-0.0825524181,-0.0355014801,-0.0381801277,-0.3315752745,0.3626123369,0.2424901426,-0.1368885785,0.0368469059,0.0449779332,0.0548131727,-0.0328165814,0.0832959637,-0.082889691,-0.5234786868,-0.0470567532,0.0362101495,0.2670900524,-0.050319504,-0.136253953,0.2245899737,0.083187528,-0.010379171,0.0391422622,0.068150878,-0.1717522889,-0.4256085157,0.0723535568,-0.0966651067,-0.2199121267,-0.0393039584,0.1064351872,-0.4977482855,0.0808114558,0.0075054457,-0.1966703385,-0.2570157945,0.285153985,0.0231736973,-0.0891861841,-0.2323273271,0.0862708464,0.1931117773,-0.0923557654,-0.6234652996,0.5602859855,0.5223332047,0.0226684622,0.196981594,0.4178994596,0.0167538133,0.0668812171,-0.1525678933,0.1664802283,0.1083381176,0.0184073467,-0.2958197296,0.0697329938,0.2497304231,0.1761268973,-0.2609070837,-0.1829364747,-0.1405118257,-0.0761567354,-0.0279818904,0.1387428492,-0.4120871723,0.335906148,0.6934578419,-0.0322736017,0.2642434537,0.0308322366,-0.3138761222,-0.1533606052,0.2285325527,-0.1215764284,0.2470545769,0.0851404145,0.3608531654,-0.1999171078,-0.1625255197,-0.0748273805,0.2153988332,0.0469212495,0.0663601533,0.0049412171,0.0751011446,0.1541916281,-0.1057289094,0.27096048,-0.050399024,0.203207612,-0.4259132743,0.2171180546,-0.4016545713,0.0512693189,-0.2296758741,-0.0917817354,-0.0777890086,-0.2456137538,-0.0293583423,0.0008401733,-0.2700945437,0.3935126066,0.2614880204,0.1166923568,0.0873444155,-0.2350945622,-0.1146133244,-0.1724233925,-0.2385483682,0.040034391,0.208354637,-0.3795832694,0.1849566102,0.3574534059,-0.2676813304,-0.2696791887,-0.7421143651,0.0485985689,-0.2355481237,0.0960817412,0.038121704,0.1293122768,-0.0246390663,0.0510537662,0.0254741795,0.0944768935,-0.0181039479,0.1180345491,-0.1916359514,0.0291311461,-0.2872902155,-0.2801524401,-0.0367826261,-0.1219953895,0.1812550128,-0.0798306614,-0.0676227063,0.0514861755,0.0912298411,0.0310504753,-0.0581302159,0.2310893536,-0.4207531214,0.0914393589,0.3349058032,-0.1597370058,-0.3068858087,-0.1432787478,0.0234636441,-0.018258201,0.0745135397,-0.2800920606,-0.0570334829,-0.2157623917,0.2658351958,-0.014034423,-0.1009145305,0.5142831206,0.232959345,0.0712706,-0.0603918321,0.0153217725,0.0630327985,0.2177911401,0.2721605003,-0.0094194403,0.3699365258,0.1042176038,0.3581045866,0.2943382859,-0.694893539,-0.1260363609,-0.0573534109,0.2153844386,-0.2174506038,-0.2911497355,0.0320028886,-0.2955263555,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/625","title":"dtype of tensors should be preserved","comments":"@lhoestq Have you thought about this further?\r\n\r\nWe have a use case where we're attempting to load data containing numpy arrays using the `datasets` library.\r\n\r\nWhen using one of the \"standard\" methods (`[Value(...)]` or `Sequence()`) we see ~200 samples processed per second during the call to `_prepare_split`. This slowdown is caused by the vast number of calls to `encode_nested_example` (each sequence is converted to a list, and each element in the sequence...). \r\n\r\nUsing the `Feature` `ArrayND` improves this somewhat to ~500\/s as it now uses numpy's `tolist()` rather than iterating over each value in the array and converting them individually.\r\n\r\nHowever, it's still pretty slow and in theory it should be possible to avoid the `numpy -> python -> arrow` dance altogether. To demonstrate this, if you keep the `Feature` set to an `ArrayND` but instead return a `pa_ndarray(...)` in `_generate_examples` it skips the conversion (`return obj, False`) and hits ~11_000\/s. Two orders of magnitude speed up! The problem is this then fails later on when the `ArrowWriter` tries to write the examples to disk :-( \r\n\r\nIt would be nice to have first-class support for user-defined PyArrow objects. Is this a possibility? We have _large_ datasets where even an order of magnitude difference is important so settling on the middle ~500\/s is less than ideal! \r\n\r\nIs there a workaround for this or another method that should be used instead that gets  near-to or equal performance to returning PyArrow arrays?","body":"After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed.","comment_length":239,"text":"dtype of tensors should be preserved \n After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed. \n @lhoestq Have you thought about this further?\r\n\r\nWe have a use case where we're attempting to load data containing numpy arrays using the `datasets` library.\r\n\r\nWhen using one of the \"standard\" methods (`[Value(...)]` or `Sequence()`) we see ~200 samples processed per second during the call to `_prepare_split`. This slowdown is caused by the vast number of calls to `encode_nested_example` (each sequence is converted to a list, and each element in the sequence...). \r\n\r\nUsing the `Feature` `ArrayND` improves this somewhat to ~500\/s as it now uses numpy's `tolist()` rather than iterating over each value in the array and converting them individually.\r\n\r\nHowever, it's still pretty slow and in theory it should be possible to avoid the `numpy -> python -> arrow` dance altogether. To demonstrate this, if you keep the `Feature` set to an `ArrayND` but instead return a `pa_ndarray(...)` in `_generate_examples` it skips the conversion (`return obj, False`) and hits ~11_000\/s. Two orders of magnitude speed up! The problem is this then fails later on when the `ArrowWriter` tries to write the examples to disk :-( \r\n\r\nIt would be nice to have first-class support for user-defined PyArrow objects. Is this a possibility? We have _large_ datasets where even an order of magnitude difference is important so settling on the middle ~500\/s is less than ideal! \r\n\r\nIs there a workaround for this or another method that should be used instead that gets  near-to or equal performance to returning PyArrow arrays?","embeddings":[-0.1134333536,-0.2211149335,-0.0097106956,0.2073052824,0.5532286763,0.1730130315,0.5313699841,0.1225805879,0.150482595,-0.0665395483,-0.0843995288,0.2457148135,-0.1175516769,-0.175145492,0.1026156992,-0.2025278956,0.2282533199,-0.0652568042,-0.1441734284,-0.205046013,-0.2274058759,-0.0888313875,-0.0027089356,-0.2082730979,-0.1601844877,-0.1640590578,0.2403164208,-0.1392812729,-0.1851640195,-0.0010071443,0.1801282763,-0.1652227491,0.4167289436,0.5717647672,-0.0001163156,0.2217250615,-0.0266850162,-0.0754260793,-0.1886361241,-0.0001533552,0.0046074335,-0.1931504607,-0.0723768249,-0.1798607856,-0.1541208327,-0.1833256185,-0.0845149383,-0.7935453057,0.2889484465,0.410826385,0.1519325227,0.2570824623,0.0712021217,0.1525935829,-0.1351298988,0.2725163996,-0.103012912,0.2802624404,-0.0208770931,0.3604668975,-0.0614813603,0.4459063411,-0.3614792526,-0.0750737414,0.0291567855,0.11278864,0.0541251488,-0.4639117122,-0.066283904,0.1654170305,0.1973419636,-0.2320809811,-0.2843651474,-0.1672888398,-0.0933570042,-0.3671621978,0.0662379041,-0.0845816359,0.1454873085,0.1031548381,-0.0847080871,-0.1715696007,-0.0345691293,0.1320960969,-0.4753398299,0.2185822874,-0.0089362506,0.1474191993,-0.1035433561,-0.0327189155,0.0743184015,-0.1159510016,0.2209848762,-0.0332037657,-0.1700056195,-0.2236013412,0.1068102047,-0.4505304396,-0.1278472394,-0.481867671,0.2282644808,0.1562227011,-0.2183923125,-0.0274901818,0.2101505846,0.3317389488,-0.2976427972,0.3678765297,0.2994463146,-0.0447378792,0.2111329287,0.0985190496,0.1282290816,0.0464336239,-0.0115374727,0.1037944704,0.5391173363,-0.1061271057,-0.315695256,0.2423450053,-0.4640916288,0.0854395926,0.0290507488,0.0762219429,-0.1580673456,0.446918875,0.1909368932,0.0856407434,-0.1537808329,-0.0482112318,-0.1408704817,-0.2463302314,0.0493125133,-0.1757228523,0.0473481752,0.1110769287,-0.0556811914,0.290984422,0.1304951161,0.1616608948,-0.0623956695,-0.1468250006,0.5156334639,0.294393152,-0.3300797641,0.1609635502,0.2148128599,-0.3500457704,-0.1941231787,0.34138304,-0.2835562229,0.000267259,-0.255297929,0.0896733627,-0.0626333654,-0.1942966878,0.0383375585,0.516879797,0.5945786238,0.0745409578,0.3335529566,-0.525503695,-0.2444197536,-0.2121095061,0.0651086941,-0.0023088125,-0.4651449621,-0.0084925191,0.2366493046,0.1842959225,0.356190145,0.4137149155,0.0704921186,0.1077738181,0.0749110356,-0.0053551802,0.4220858216,0.1117162034,-0.2204668969,-0.0649491996,-0.0586552843,0.3635667264,-0.0807049423,0.0079559609,0.3194953501,-0.2035728991,0.2078322172,0.0254099835,-0.2768169343,0.0815219134,-0.2061402947,0.0387928039,0.4647621214,-0.0825524181,-0.0355014801,-0.0381801277,-0.3315752745,0.3626123369,0.2424901426,-0.1368885785,0.0368469059,0.0449779332,0.0548131727,-0.0328165814,0.0832959637,-0.082889691,-0.5234786868,-0.0470567532,0.0362101495,0.2670900524,-0.050319504,-0.136253953,0.2245899737,0.083187528,-0.010379171,0.0391422622,0.068150878,-0.1717522889,-0.4256085157,0.0723535568,-0.0966651067,-0.2199121267,-0.0393039584,0.1064351872,-0.4977482855,0.0808114558,0.0075054457,-0.1966703385,-0.2570157945,0.285153985,0.0231736973,-0.0891861841,-0.2323273271,0.0862708464,0.1931117773,-0.0923557654,-0.6234652996,0.5602859855,0.5223332047,0.0226684622,0.196981594,0.4178994596,0.0167538133,0.0668812171,-0.1525678933,0.1664802283,0.1083381176,0.0184073467,-0.2958197296,0.0697329938,0.2497304231,0.1761268973,-0.2609070837,-0.1829364747,-0.1405118257,-0.0761567354,-0.0279818904,0.1387428492,-0.4120871723,0.335906148,0.6934578419,-0.0322736017,0.2642434537,0.0308322366,-0.3138761222,-0.1533606052,0.2285325527,-0.1215764284,0.2470545769,0.0851404145,0.3608531654,-0.1999171078,-0.1625255197,-0.0748273805,0.2153988332,0.0469212495,0.0663601533,0.0049412171,0.0751011446,0.1541916281,-0.1057289094,0.27096048,-0.050399024,0.203207612,-0.4259132743,0.2171180546,-0.4016545713,0.0512693189,-0.2296758741,-0.0917817354,-0.0777890086,-0.2456137538,-0.0293583423,0.0008401733,-0.2700945437,0.3935126066,0.2614880204,0.1166923568,0.0873444155,-0.2350945622,-0.1146133244,-0.1724233925,-0.2385483682,0.040034391,0.208354637,-0.3795832694,0.1849566102,0.3574534059,-0.2676813304,-0.2696791887,-0.7421143651,0.0485985689,-0.2355481237,0.0960817412,0.038121704,0.1293122768,-0.0246390663,0.0510537662,0.0254741795,0.0944768935,-0.0181039479,0.1180345491,-0.1916359514,0.0291311461,-0.2872902155,-0.2801524401,-0.0367826261,-0.1219953895,0.1812550128,-0.0798306614,-0.0676227063,0.0514861755,0.0912298411,0.0310504753,-0.0581302159,0.2310893536,-0.4207531214,0.0914393589,0.3349058032,-0.1597370058,-0.3068858087,-0.1432787478,0.0234636441,-0.018258201,0.0745135397,-0.2800920606,-0.0570334829,-0.2157623917,0.2658351958,-0.014034423,-0.1009145305,0.5142831206,0.232959345,0.0712706,-0.0603918321,0.0153217725,0.0630327985,0.2177911401,0.2721605003,-0.0094194403,0.3699365258,0.1042176038,0.3581045866,0.2943382859,-0.694893539,-0.1260363609,-0.0573534109,0.2153844386,-0.2174506038,-0.2911497355,0.0320028886,-0.2955263555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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/625","title":"dtype of tensors should be preserved","comments":"Hi !\r\n\r\nIt would be awesome to achieve this speed for numpy arrays !\r\nFor now we have to use `encode_nested_example` to convert numpy arrays to python lists since pyarrow doesn't support multidimensional numpy arrays (only 1D).\r\n\r\nMaybe let's start a new PR from your PR @bhavitvyamalik (idk why we didn't answer your PR at that time, sorry about that).\r\nBasically the idea is to allow `TypedSequence` to support numpy arrays as you did, and remove the numpy->python casting in `_cast_to_python_objects`.\r\n\r\nThis is really important since we are starting to have a focus on other modalities than text as well (audio, images).\r\n\r\nThough until then @samgd, there is another feature that may interest you and that may give you the speed you want:\r\n\r\nIn a dataset script you can subclass either a GeneratorBasedBuilder (with the `_generate_examples ` method) or an ArrowBasedBuilder if you want. the ArrowBasedBuilder allows to yield arrow data by implementing the `_generate_tables` method (it's the same as `_generate_examples` except you must yield arrow tables). Since the data are already in arrow format, it doesn't call `encode_nested_example`. Let me know if that helps.","body":"After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed.","comment_length":185,"text":"dtype of tensors should be preserved \n After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https:\/\/discuss.pytorch.org\/t\/is-it-required-that-input-and-hidden-for-gru-have-the-same-dtype-float32\/96221)). \r\n\r\nAs a user I did not expect this bug. I have a `map` function that I call on the Dataset that looks like this:\r\n\r\n```python\r\ndef preprocess(sentences: List[str]):\r\n    token_ids = [[vocab.to_index(t) for t in s.split()] for s in sentences]\r\n\r\n    sembeddings = stransformer.encode(sentences)\r\n    print(sembeddings.dtype)\r\n    return {\"input_ids\": token_ids, \"sembedding\": sembeddings}\r\n```\r\n\r\nGiven a list of `sentences` (`List[str]`), it converts those into token_ids on the one hand (list of lists of ints; `List[List[int]]`) and into sentence embeddings on the other (Tensor of dtype `torch.float32`). That means that I actually set the column \"sembedding\" to a tensor that I as a user expect to be a float32.\r\n\r\nIt appears though that behind the scenes, this tensor is converted into a **list**. I did not find this documented anywhere but I might have missed it. From a user's perspective this is incredibly important though, because it means you cannot do any data_type or tensor casting yourself in a mapping function! Furthermore, this can lead to issues, as was my case. \r\n\r\nMy model expected float32 precision, which I thought `sembedding` was because that is what `stransformer.encode` outputs. But behind the scenes this tensor is first cast to a list, and when we then set its format, as below, this column is cast not to float32 but to double precision float64.\r\n\r\n```python\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n```\r\n\r\nThis happens because apparently there is an intermediate step of casting to a **numpy** array (?) **whose dtype creation\/deduction is different from torch dtypes** (see the snippet below).  As you can see, this means that the dtype is not preserved: if I got it right, the dataset goes from torch.float32 -> list -> float64 (numpy) -> torch.float64. \r\n\r\n```python\r\nimport torch\r\nimport numpy as np\r\n\r\nl = [-0.03010837361216545, -0.035979013890028, -0.016949838027358055]\r\ntorch_tensor = torch.tensor(l)\r\nnp_array = np.array(l)\r\nnp_to_torch = torch.from_numpy(np_array)\r\n\r\nprint(torch_tensor.dtype)\r\n# torch.float32\r\nprint(np_array.dtype)\r\n# float64\r\nprint(np_to_torch.dtype)\r\n# torch.float64\r\n```\r\n\r\nThis might lead to unwanted behaviour. I understand that the whole library is probably built around casting from numpy to other frameworks, so this might be difficult to solve. Perhaps `set_format` should include a `dtypes` option where for each input column the user can specify the wanted precision.\r\n\r\nThe alternative is that the user needs to cast manually after loading data from the dataset but that does not seem user-friendly, makes the dataset less portable, and might use more space in memory as well as on disk than is actually needed. \n Hi !\r\n\r\nIt would be awesome to achieve this speed for numpy arrays !\r\nFor now we have to use `encode_nested_example` to convert numpy arrays to python lists since pyarrow doesn't support multidimensional numpy arrays (only 1D).\r\n\r\nMaybe let's start a new PR from your PR @bhavitvyamalik (idk why we didn't answer your PR at that time, sorry about that).\r\nBasically the idea is to allow `TypedSequence` to support numpy arrays as you did, and remove the numpy->python casting in `_cast_to_python_objects`.\r\n\r\nThis is really important since we are starting to have a focus on other modalities than text as well (audio, images).\r\n\r\nThough until then @samgd, there is another feature that may interest you and that may give you the speed you want:\r\n\r\nIn a dataset script you can subclass either a GeneratorBasedBuilder (with the `_generate_examples ` method) or an ArrowBasedBuilder if you want. the ArrowBasedBuilder allows to yield arrow data by implementing the `_generate_tables` method (it's the same as `_generate_examples` except you must yield arrow tables). Since the data are already in arrow format, it doesn't call `encode_nested_example`. Let me know if that helps.","embeddings":[-0.1134333536,-0.2211149335,-0.0097106956,0.2073052824,0.5532286763,0.1730130315,0.5313699841,0.1225805879,0.150482595,-0.0665395483,-0.0843995288,0.2457148135,-0.1175516769,-0.175145492,0.1026156992,-0.2025278956,0.2282533199,-0.0652568042,-0.1441734284,-0.205046013,-0.2274058759,-0.0888313875,-0.0027089356,-0.2082730979,-0.1601844877,-0.1640590578,0.2403164208,-0.1392812729,-0.1851640195,-0.0010071443,0.1801282763,-0.1652227491,0.4167289436,0.5717647672,-0.0001163156,0.2217250615,-0.0266850162,-0.0754260793,-0.1886361241,-0.0001533552,0.0046074335,-0.1931504607,-0.0723768249,-0.1798607856,-0.1541208327,-0.1833256185,-0.0845149383,-0.7935453057,0.2889484465,0.410826385,0.1519325227,0.2570824623,0.0712021217,0.1525935829,-0.1351298988,0.2725163996,-0.103012912,0.2802624404,-0.0208770931,0.3604668975,-0.0614813603,0.4459063411,-0.3614792526,-0.0750737414,0.0291567855,0.11278864,0.0541251488,-0.4639117122,-0.066283904,0.1654170305,0.1973419636,-0.2320809811,-0.2843651474,-0.1672888398,-0.0933570042,-0.3671621978,0.0662379041,-0.0845816359,0.1454873085,0.1031548381,-0.0847080871,-0.1715696007,-0.0345691293,0.1320960969,-0.4753398299,0.2185822874,-0.0089362506,0.1474191993,-0.1035433561,-0.0327189155,0.0743184015,-0.1159510016,0.2209848762,-0.0332037657,-0.1700056195,-0.2236013412,0.1068102047,-0.4505304396,-0.1278472394,-0.481867671,0.2282644808,0.1562227011,-0.2183923125,-0.0274901818,0.2101505846,0.3317389488,-0.2976427972,0.3678765297,0.2994463146,-0.0447378792,0.2111329287,0.0985190496,0.1282290816,0.0464336239,-0.0115374727,0.1037944704,0.5391173363,-0.1061271057,-0.315695256,0.2423450053,-0.4640916288,0.0854395926,0.0290507488,0.0762219429,-0.1580673456,0.446918875,0.1909368932,0.0856407434,-0.1537808329,-0.0482112318,-0.1408704817,-0.2463302314,0.0493125133,-0.1757228523,0.0473481752,0.1110769287,-0.0556811914,0.290984422,0.1304951161,0.1616608948,-0.0623956695,-0.1468250006,0.5156334639,0.294393152,-0.3300797641,0.1609635502,0.2148128599,-0.3500457704,-0.1941231787,0.34138304,-0.2835562229,0.000267259,-0.255297929,0.0896733627,-0.0626333654,-0.1942966878,0.0383375585,0.516879797,0.5945786238,0.0745409578,0.3335529566,-0.525503695,-0.2444197536,-0.2121095061,0.0651086941,-0.0023088125,-0.4651449621,-0.0084925191,0.2366493046,0.1842959225,0.356190145,0.4137149155,0.0704921186,0.1077738181,0.0749110356,-0.0053551802,0.4220858216,0.1117162034,-0.2204668969,-0.0649491996,-0.0586552843,0.3635667264,-0.0807049423,0.0079559609,0.3194953501,-0.2035728991,0.2078322172,0.0254099835,-0.2768169343,0.0815219134,-0.2061402947,0.0387928039,0.4647621214,-0.0825524181,-0.0355014801,-0.0381801277,-0.3315752745,0.3626123369,0.2424901426,-0.1368885785,0.0368469059,0.0449779332,0.0548131727,-0.0328165814,0.0832959637,-0.082889691,-0.5234786868,-0.0470567532,0.0362101495,0.2670900524,-0.050319504,-0.136253953,0.2245899737,0.083187528,-0.010379171,0.0391422622,0.068150878,-0.1717522889,-0.4256085157,0.0723535568,-0.0966651067,-0.2199121267,-0.0393039584,0.1064351872,-0.4977482855,0.0808114558,0.0075054457,-0.1966703385,-0.2570157945,0.285153985,0.0231736973,-0.0891861841,-0.2323273271,0.0862708464,0.1931117773,-0.0923557654,-0.6234652996,0.5602859855,0.5223332047,0.0226684622,0.196981594,0.4178994596,0.0167538133,0.0668812171,-0.1525678933,0.1664802283,0.1083381176,0.0184073467,-0.2958197296,0.0697329938,0.2497304231,0.1761268973,-0.2609070837,-0.1829364747,-0.1405118257,-0.0761567354,-0.0279818904,0.1387428492,-0.4120871723,0.335906148,0.6934578419,-0.0322736017,0.2642434537,0.0308322366,-0.3138761222,-0.1533606052,0.2285325527,-0.1215764284,0.2470545769,0.0851404145,0.3608531654,-0.1999171078,-0.1625255197,-0.0748273805,0.2153988332,0.0469212495,0.0663601533,0.0049412171,0.0751011446,0.1541916281,-0.1057289094,0.27096048,-0.050399024,0.203207612,-0.4259132743,0.2171180546,-0.4016545713,0.0512693189,-0.2296758741,-0.0917817354,-0.0777890086,-0.2456137538,-0.0293583423,0.0008401733,-0.2700945437,0.3935126066,0.2614880204,0.1166923568,0.0873444155,-0.2350945622,-0.1146133244,-0.1724233925,-0.2385483682,0.040034391,0.208354637,-0.3795832694,0.1849566102,0.3574534059,-0.2676813304,-0.2696791887,-0.7421143651,0.0485985689,-0.2355481237,0.0960817412,0.038121704,0.1293122768,-0.0246390663,0.0510537662,0.0254741795,0.0944768935,-0.0181039479,0.1180345491,-0.1916359514,0.0291311461,-0.2872902155,-0.2801524401,-0.0367826261,-0.1219953895,0.1812550128,-0.0798306614,-0.0676227063,0.0514861755,0.0912298411,0.0310504753,-0.0581302159,0.2310893536,-0.4207531214,0.0914393589,0.3349058032,-0.1597370058,-0.3068858087,-0.1432787478,0.0234636441,-0.018258201,0.0745135397,-0.2800920606,-0.0570334829,-0.2157623917,0.2658351958,-0.014034423,-0.1009145305,0.5142831206,0.232959345,0.0712706,-0.0603918321,0.0153217725,0.0630327985,0.2177911401,0.2721605003,-0.0094194403,0.3699365258,0.1042176038,0.3581045866,0.2943382859,-0.694893539,-0.1260363609,-0.0573534109,0.2153844386,-0.2174506038,-0.2911497355,0.0320028886,-0.2955263555,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/623","title":"Custom feature types in `load_dataset` from CSV","comments":"Currently `csv` doesn't support the `features` attribute (unlike `json`).\r\nWhat you can do for now is cast the features using the in-place transform `cast_`\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'])\r\ndataset.cast_(emotion_features)\r\n```\r\n","body":"I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction.","comment_length":38,"text":"Custom feature types in `load_dataset` from CSV \n I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction. \n Currently `csv` doesn't support the `features` attribute (unlike `json`).\r\nWhat you can do for now is cast the features using the in-place transform `cast_`\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'])\r\ndataset.cast_(emotion_features)\r\n```\r\n","embeddings":[0.0802028403,-0.2782896161,-0.0531788729,0.3509230018,0.3172290027,-0.194310233,0.570133388,0.1113843918,0.4461252391,0.0253307857,0.094744429,0.3161773682,-0.0919099823,0.3901156783,-0.0581883378,0.0267204568,-0.1612745821,0.3348051906,-0.0091216536,-0.3497938216,-0.2713248432,0.1799540073,-0.0930471644,-0.0134863621,-0.3072171211,0.3395891786,0.2221690416,0.1434383839,0.0583117977,-0.3765774071,0.4237109721,0.1486945748,0.3030693531,0.0801393613,-0.0001151109,0.0768831447,0.0293591339,-0.1740051955,-0.0040852865,-0.3799194694,-0.0387842841,-0.202906549,0.424708277,-0.3651530445,-0.2385735363,-0.4564031661,-0.2810543478,-0.12927185,0.2869229019,0.3292911053,0.1512023062,-0.0502676293,-0.2750737369,0.2184944302,0.3587991297,0.6755337119,-0.2523884773,0.1870203614,-0.0406348482,-0.1123654619,0.0939737037,-0.102286227,-0.1438639611,0.3534234762,0.492610544,0.1710615605,-0.064391844,-0.1303502023,-0.1733280122,0.281424731,0.3474106789,0.0562936403,-0.0464353785,-0.3231960535,-0.1238626838,-0.2973287702,0.3824171126,0.0848603621,-0.1818491668,0.1604426801,-0.0789407417,0.4355748296,-0.0581504479,0.325290978,0.0186878722,-0.0715494603,-0.2392114401,0.1055342928,0.0308269262,-0.2173112333,0.0974367186,-0.3491620123,0.2218384594,0.1172008961,-0.0551008694,0.0995841473,0.0039355028,-0.0604649708,-0.1210799068,0.0352325253,0.2292758375,0.1932827532,-0.1932464093,0.0317142829,0.2777897716,0.2441826016,0.2002113014,-0.2317828387,0.1527378112,0.1047160104,-0.4232792258,0.0183332302,0.0277891699,-0.3080181777,0.3964600265,0.0869553015,0.5022855401,-0.2398283333,0.0576624535,-0.0980121568,0.0428094268,-0.0321919918,0.1175666079,0.1835571378,-0.0262655746,0.5809873939,-0.0656143278,0.1488876939,-0.3211797774,0.1283438653,-0.058127325,-0.2226473987,-0.037468221,-0.0493204929,0.4030344188,0.0941047966,0.2478337586,0.199026525,-0.0237019584,-0.3186688125,-0.12700589,-0.0959565863,0.1245454475,0.044335667,-0.441672951,0.3375264406,0.2412355691,-0.2255857587,-0.2444112748,0.095285885,-0.2509175837,-0.2328004986,0.1970367134,0.1555833071,-0.178460896,-0.0209625904,-0.1068924516,0.0988962203,0.1280106902,0.2582726479,0.0263719391,-0.4864165485,-0.3809947371,-0.3249777257,-0.0254871491,0.395431906,-0.4154390991,-0.2039041966,0.0394427255,-0.0463082902,0.1385587603,0.253090173,-0.3661015928,0.1451614648,-0.2393052876,0.2845743299,0.5265450478,0.0433984175,-0.2177205533,0.4294431508,0.1565902233,0.3267548084,0.1860678196,0.0742609575,0.0775937811,0.1260919273,0.1594997197,0.4206010401,0.1657205969,0.0673819482,-0.1837765872,-0.1553835273,0.1656042039,-0.0030714199,-0.3288172483,0.3090256751,0.2257003337,-0.5133963823,0.2171112299,-0.1282682717,-0.2264051139,-0.0468896255,0.4030794501,0.5202861428,0.0057925163,-0.0192435756,-0.4824976325,0.2246332169,-0.1493484527,-0.0522524752,-0.0067959139,-0.3252949715,-0.4934936166,-0.0567670278,-0.2471699268,0.1995169818,0.0646642148,0.3136295676,-0.3358725905,0.1588522345,-0.0870924741,0.0709819943,-0.2088876218,-0.2521634698,-0.0801553875,-0.0217766315,0.1720784605,-0.090050593,0.0157923438,0.0775028616,0.2781896889,0.0767477527,-0.3883281648,0.1277148426,0.2795608342,0.1613467485,-0.11299555,0.2116078585,-0.0030284876,-0.0095141912,-0.0278555136,0.173227042,0.1935628951,-0.0937048942,-0.0290854536,0.6565463543,0.1539311856,0.2489783466,-0.2678474784,-0.1128533781,0.2808355391,-0.0006696465,-0.1841066182,-0.0670059398,-0.2146361172,-0.0485210009,0.0014958512,0.4216481745,-0.3299655318,-0.1157782078,0.4788905382,0.0954588801,0.1291622519,-0.080403775,-0.0172782745,-0.0290924869,0.0167227238,-0.237050727,0.4729242623,-0.0610821843,-0.0803651959,-0.0082820412,0.0192120504,-0.224336803,0.0599999838,-0.1394983679,-0.1910201013,0.2915715277,0.0343268067,-0.1579163671,-0.3376892507,0.2412526906,-0.1466078162,-0.2058871537,-0.634283483,-0.0990623534,-0.5823052526,0.1683030874,-0.4637621641,-0.0822290853,-0.069795303,0.0163359847,-0.2120191008,0.1033786386,-0.0353961922,0.090043202,-0.1784474552,0.3625629246,0.0643079206,-0.8123736382,0.2222122103,0.1188194975,-0.449981004,-0.0581739321,0.1749914587,0.2886117697,0.0355603248,0.0289639011,-0.2870355844,0.0080763036,-0.0132933594,-0.1399178654,0.1707450449,0.5402772427,0.1732708216,0.1756921262,0.1394032091,-0.1260671914,0.3983735442,-0.0131169017,0.0591224469,-0.1250230521,-0.0263378602,0.1114321202,-0.2428705394,-0.6793348789,-0.150197506,-0.2003138959,0.2844246328,0.2139621377,0.0511261635,0.1971265525,0.3683053255,-0.2396501005,0.1546966583,-0.0263569597,-0.2073858082,-0.0725123882,0.4438348711,-0.1748046428,-0.2186147124,0.0093017723,-0.3093702197,-0.312223345,0.1340720356,-0.311917454,0.0612174384,-0.3492916822,0.5237395167,0.0541125759,0.0240740851,0.1082262248,-0.1130078137,0.0396237858,-0.1758637279,-0.3381882906,0.4521704912,0.3127176166,0.0991492271,0.4245265722,-0.0252545048,-0.4129971862,0.4985539317,-0.2855618596,-0.1919134259,0.5953598022,-0.1800899804,0.178718701,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/623","title":"Custom feature types in `load_dataset` from CSV","comments":"Hi @lhoestq we've tried out your suggestion but are now running into the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-163-81ffd5ac18c9> in <module>\r\n----> 1 dataset.cast_(emotion_features)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/dataset_dict.py in cast_(self, features)\r\n    125         self._check_values_type()\r\n    126         for dataset in self.values():\r\n--> 127             dataset.cast_(features=features)\r\n    128 \r\n    129     def remove_columns_(self, column_names: Union[str, List[str]]):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    161             # Call actual function\r\n    162 \r\n--> 163             out = func(self, *args, **kwargs)\r\n    164 \r\n    165             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in cast_(self, features)\r\n    602         self._info.features = features\r\n    603         schema = pa.schema(features.type)\r\n--> 604         self._data = self._data.cast(schema)\r\n    605 \r\n    606     @fingerprint(inplace=True)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.cast()\r\n\r\nValueError: Target schema's field names are not matching the table's field names: ['text', 'label'], ['label', 'text']\r\n```\r\n\r\nLooking at the types in `emotion_features` we see that `label` and `text` appear to be swapped in the Arrow table:\r\n\r\n```\r\nemotion_features.type\r\nStructType(struct<label: int64, text: string>)\r\n```\r\n\r\nDid we define the `emotion_features` incorrectly? We just followed the instructions from the [docs](https:\/\/huggingface.co\/docs\/datasets\/features.html?highlight=features#dataset-features), but perhaps we misunderstood something \ud83d\ude2c \r\n\r\n","body":"I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction.","comment_length":168,"text":"Custom feature types in `load_dataset` from CSV \n I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction. \n Hi @lhoestq we've tried out your suggestion but are now running into the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-163-81ffd5ac18c9> in <module>\r\n----> 1 dataset.cast_(emotion_features)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/dataset_dict.py in cast_(self, features)\r\n    125         self._check_values_type()\r\n    126         for dataset in self.values():\r\n--> 127             dataset.cast_(features=features)\r\n    128 \r\n    129     def remove_columns_(self, column_names: Union[str, List[str]]):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/fingerprint.py in wrapper(*args, **kwargs)\r\n    161             # Call actual function\r\n    162 \r\n--> 163             out = func(self, *args, **kwargs)\r\n    164 \r\n    165             # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/datasets\/arrow_dataset.py in cast_(self, features)\r\n    602         self._info.features = features\r\n    603         schema = pa.schema(features.type)\r\n--> 604         self._data = self._data.cast(schema)\r\n    605 \r\n    606     @fingerprint(inplace=True)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.cast()\r\n\r\nValueError: Target schema's field names are not matching the table's field names: ['text', 'label'], ['label', 'text']\r\n```\r\n\r\nLooking at the types in `emotion_features` we see that `label` and `text` appear to be swapped in the Arrow table:\r\n\r\n```\r\nemotion_features.type\r\nStructType(struct<label: int64, text: string>)\r\n```\r\n\r\nDid we define the `emotion_features` incorrectly? We just followed the instructions from the [docs](https:\/\/huggingface.co\/docs\/datasets\/features.html?highlight=features#dataset-features), but perhaps we misunderstood something \ud83d\ude2c 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/623","title":"Custom feature types in `load_dataset` from CSV","comments":"In general, I don't think there is any hard reason we don't allow to use `features` in the csv script, right @lhoestq?\r\n\r\nShould I add it?","body":"I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction.","comment_length":26,"text":"Custom feature types in `load_dataset` from CSV \n I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction. \n In general, I don't think there is any hard reason we don't allow to use `features` in the csv script, right @lhoestq?\r\n\r\nShould I add it?","embeddings":[0.0802028403,-0.2782896161,-0.0531788729,0.3509230018,0.3172290027,-0.194310233,0.570133388,0.1113843918,0.4461252391,0.0253307857,0.094744429,0.3161773682,-0.0919099823,0.3901156783,-0.0581883378,0.0267204568,-0.1612745821,0.3348051906,-0.0091216536,-0.3497938216,-0.2713248432,0.1799540073,-0.0930471644,-0.0134863621,-0.3072171211,0.3395891786,0.2221690416,0.1434383839,0.0583117977,-0.3765774071,0.4237109721,0.1486945748,0.3030693531,0.0801393613,-0.0001151109,0.0768831447,0.0293591339,-0.1740051955,-0.0040852865,-0.3799194694,-0.0387842841,-0.202906549,0.424708277,-0.3651530445,-0.2385735363,-0.4564031661,-0.2810543478,-0.12927185,0.2869229019,0.3292911053,0.1512023062,-0.0502676293,-0.2750737369,0.2184944302,0.3587991297,0.6755337119,-0.2523884773,0.1870203614,-0.0406348482,-0.1123654619,0.0939737037,-0.102286227,-0.1438639611,0.3534234762,0.492610544,0.1710615605,-0.064391844,-0.1303502023,-0.1733280122,0.281424731,0.3474106789,0.0562936403,-0.0464353785,-0.3231960535,-0.1238626838,-0.2973287702,0.3824171126,0.0848603621,-0.1818491668,0.1604426801,-0.0789407417,0.4355748296,-0.0581504479,0.325290978,0.0186878722,-0.0715494603,-0.2392114401,0.1055342928,0.0308269262,-0.2173112333,0.0974367186,-0.3491620123,0.2218384594,0.1172008961,-0.0551008694,0.0995841473,0.0039355028,-0.0604649708,-0.1210799068,0.0352325253,0.2292758375,0.1932827532,-0.1932464093,0.0317142829,0.2777897716,0.2441826016,0.2002113014,-0.2317828387,0.1527378112,0.1047160104,-0.4232792258,0.0183332302,0.0277891699,-0.3080181777,0.3964600265,0.0869553015,0.5022855401,-0.2398283333,0.0576624535,-0.0980121568,0.0428094268,-0.0321919918,0.1175666079,0.1835571378,-0.0262655746,0.5809873939,-0.0656143278,0.1488876939,-0.3211797774,0.1283438653,-0.058127325,-0.2226473987,-0.037468221,-0.0493204929,0.4030344188,0.0941047966,0.2478337586,0.199026525,-0.0237019584,-0.3186688125,-0.12700589,-0.0959565863,0.1245454475,0.044335667,-0.441672951,0.3375264406,0.2412355691,-0.2255857587,-0.2444112748,0.095285885,-0.2509175837,-0.2328004986,0.1970367134,0.1555833071,-0.178460896,-0.0209625904,-0.1068924516,0.0988962203,0.1280106902,0.2582726479,0.0263719391,-0.4864165485,-0.3809947371,-0.3249777257,-0.0254871491,0.395431906,-0.4154390991,-0.2039041966,0.0394427255,-0.0463082902,0.1385587603,0.253090173,-0.3661015928,0.1451614648,-0.2393052876,0.2845743299,0.5265450478,0.0433984175,-0.2177205533,0.4294431508,0.1565902233,0.3267548084,0.1860678196,0.0742609575,0.0775937811,0.1260919273,0.1594997197,0.4206010401,0.1657205969,0.0673819482,-0.1837765872,-0.1553835273,0.1656042039,-0.0030714199,-0.3288172483,0.3090256751,0.2257003337,-0.5133963823,0.2171112299,-0.1282682717,-0.2264051139,-0.0468896255,0.4030794501,0.5202861428,0.0057925163,-0.0192435756,-0.4824976325,0.2246332169,-0.1493484527,-0.0522524752,-0.0067959139,-0.3252949715,-0.4934936166,-0.0567670278,-0.2471699268,0.1995169818,0.0646642148,0.3136295676,-0.3358725905,0.1588522345,-0.0870924741,0.0709819943,-0.2088876218,-0.2521634698,-0.0801553875,-0.0217766315,0.1720784605,-0.090050593,0.0157923438,0.0775028616,0.2781896889,0.0767477527,-0.3883281648,0.1277148426,0.2795608342,0.1613467485,-0.11299555,0.2116078585,-0.0030284876,-0.0095141912,-0.0278555136,0.173227042,0.1935628951,-0.0937048942,-0.0290854536,0.6565463543,0.1539311856,0.2489783466,-0.2678474784,-0.1128533781,0.2808355391,-0.0006696465,-0.1841066182,-0.0670059398,-0.2146361172,-0.0485210009,0.0014958512,0.4216481745,-0.3299655318,-0.1157782078,0.4788905382,0.0954588801,0.1291622519,-0.080403775,-0.0172782745,-0.0290924869,0.0167227238,-0.237050727,0.4729242623,-0.0610821843,-0.0803651959,-0.0082820412,0.0192120504,-0.224336803,0.0599999838,-0.1394983679,-0.1910201013,0.2915715277,0.0343268067,-0.1579163671,-0.3376892507,0.2412526906,-0.1466078162,-0.2058871537,-0.634283483,-0.0990623534,-0.5823052526,0.1683030874,-0.4637621641,-0.0822290853,-0.069795303,0.0163359847,-0.2120191008,0.1033786386,-0.0353961922,0.090043202,-0.1784474552,0.3625629246,0.0643079206,-0.8123736382,0.2222122103,0.1188194975,-0.449981004,-0.0581739321,0.1749914587,0.2886117697,0.0355603248,0.0289639011,-0.2870355844,0.0080763036,-0.0132933594,-0.1399178654,0.1707450449,0.5402772427,0.1732708216,0.1756921262,0.1394032091,-0.1260671914,0.3983735442,-0.0131169017,0.0591224469,-0.1250230521,-0.0263378602,0.1114321202,-0.2428705394,-0.6793348789,-0.150197506,-0.2003138959,0.2844246328,0.2139621377,0.0511261635,0.1971265525,0.3683053255,-0.2396501005,0.1546966583,-0.0263569597,-0.2073858082,-0.0725123882,0.4438348711,-0.1748046428,-0.2186147124,0.0093017723,-0.3093702197,-0.312223345,0.1340720356,-0.311917454,0.0612174384,-0.3492916822,0.5237395167,0.0541125759,0.0240740851,0.1082262248,-0.1130078137,0.0396237858,-0.1758637279,-0.3381882906,0.4521704912,0.3127176166,0.0991492271,0.4245265722,-0.0252545048,-0.4129971862,0.4985539317,-0.2855618596,-0.1919134259,0.5953598022,-0.1800899804,0.178718701,-0.0728689805,-0.2670245767,-0.112602815,-0.0425651111,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/623","title":"Custom feature types in `load_dataset` from CSV","comments":"> In general, I don't think there is any hard reason we don't allow to use `features` in the csv script, right @lhoestq?\r\n> \r\n> Should I add it?\r\n\r\nSure let's add it. Setting the convert options should do the job\r\n\r\n> Hi @lhoestq we've tried out your suggestion but are now running into the following error:\r\n> \r\n> ```\r\n> ---------------------------------------------------------------------------\r\n> ValueError                                Traceback (most recent call last)\r\n> <ipython-input-163-81ffd5ac18c9> in <module>\r\n> ----> 1 dataset.cast_(emotion_features)\r\n>\r\n>  \/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.cast()\r\n> \r\n> ValueError: Target schema's field names are not matching the table's field names: ['text', 'label'], ['label', 'text']\r\n> ```\r\n>\r\n> Did we define the `emotion_features` incorrectly? We just followed the instructions from the [docs](https:\/\/huggingface.co\/docs\/datasets\/features.html?highlight=features#dataset-features), but perhaps we misunderstood something \ud83d\ude2c\r\n\r\nThanks for reporting, that's a bug :) I'm fixing it right now","body":"I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction.","comment_length":136,"text":"Custom feature types in `load_dataset` from CSV \n I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction. \n > In general, I don't think there is any hard reason we don't allow to use `features` in the csv script, right @lhoestq?\r\n> \r\n> Should I add it?\r\n\r\nSure let's add it. Setting the convert options should do the job\r\n\r\n> Hi @lhoestq we've tried out your suggestion but are now running into the following error:\r\n> \r\n> ```\r\n> ---------------------------------------------------------------------------\r\n> ValueError                                Traceback (most recent call last)\r\n> <ipython-input-163-81ffd5ac18c9> in <module>\r\n> ----> 1 dataset.cast_(emotion_features)\r\n>\r\n>  \/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.cast()\r\n> \r\n> ValueError: Target schema's field names are not matching the table's field names: ['text', 'label'], ['label', 'text']\r\n> ```\r\n>\r\n> Did we define the `emotion_features` incorrectly? We just followed the instructions from the [docs](https:\/\/huggingface.co\/docs\/datasets\/features.html?highlight=features#dataset-features), but perhaps we misunderstood something \ud83d\ude2c\r\n\r\nThanks for reporting, that's a bug :) I'm fixing it right 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/623","title":"Custom feature types in `load_dataset` from CSV","comments":"PR is open for the `ValueError: Target schema's field names are not matching the table's field names` error.\r\n\r\nI'm adding the features parameter to csv","body":"I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction.","comment_length":25,"text":"Custom feature types in `load_dataset` from CSV \n I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`. \r\n\r\nI am working with the local files from the emotion dataset. To get the data you can use the following code:\r\n\r\n```Python\r\nfrom pathlib import Path\r\nimport wget\r\n\r\nEMOTION_PATH = Path(\".\/data\/emotion\")\r\nDOWNLOAD_URLS = [\r\n    \"https:\/\/www.dropbox.com\/s\/1pzkadrvffbqw6o\/train.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/2mzialpsgf9k5l3\/val.txt?dl=1\",\r\n    \"https:\/\/www.dropbox.com\/s\/ikkqxfdbdec3fuj\/test.txt?dl=1\",\r\n]\r\n\r\nif not Path.is_dir(EMOTION_PATH):\r\n     Path.mkdir(EMOTION_PATH)\r\nfor url in DOWNLOAD_URLS:\r\n     wget.download(url, str(EMOTION_PATH))\r\n```\r\n\r\nThe first five lines of the train set are:\r\n```\r\ni didnt feel humiliated;sadness\r\ni can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake;sadness\r\nim grabbing a minute to post i feel greedy wrong;anger\r\ni am ever feeling nostalgic about the fireplace i will know that it is still on the property;love\r\ni am feeling grouchy;anger\r\n```\r\n\r\nHere the code to reproduce the issue:\r\n```Python\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\nclass_names = [\"sadness\", \"joy\", \"love\", \"anger\", \"fear\", \"surprise\"]\r\nemotion_features = Features({'text': Value('string'), 'label': ClassLabel(names=class_names)})\r\nfile_dict = {'train': EMOTION_PATH\/'train.txt'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'], features=emotion_features)\r\n```\r\n\r\n**Observed behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': Value(dtype='string', id=None)}\r\n```\r\n**Expected behaviour:**\r\n```Python\r\ndataset['train'].features\r\n```\r\n```Python\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None)}\r\n```\r\n\r\n**Things I've tried:**\r\n- deleting the cache\r\n- trying other types such as `int64`\r\n\r\nAm I missing anything? Thanks for any pointer in the right direction. \n PR is open for the `ValueError: Target schema's field names are not matching the table's field names` error.\r\n\r\nI'm adding the features parameter to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"@thomwolf Sure. I'll try downgrading to 3.7 now even though Arrow say they support >=3.5.\r\n\r\nLinux (Ubuntu 18.04) - Python 3.8\r\n======================\r\nPackage   -   Version\r\n---------------------\r\ncertifi               2020.6.20\r\nchardet               3.0.4\r\nclick                 7.1.2\r\ndatasets              1.0.1\r\ndill                  0.3.2\r\nfasttext              0.9.2\r\nfilelock              3.0.12\r\nfuture                0.18.2\r\nidna                  2.10\r\njoblib                0.16.0\r\nnltk                  3.5\r\nnumpy                 1.19.1\r\npackaging             20.4\r\npandas                1.1.2\r\npip                   20.0.2\r\nprotobuf              3.13.0\r\npyarrow               1.0.1\r\npybind11              2.5.0\r\npyparsing             2.4.7\r\npython-dateutil       2.8.1\r\npytz                  2020.1\r\nregex                 2020.7.14\r\nrequests              2.24.0\r\nsacremoses            0.0.43\r\nscikit-learn          0.23.2\r\nscipy                 1.5.2\r\nsentence-transformers 0.3.6\r\nsentencepiece         0.1.91\r\nsetuptools            46.1.3\r\nsix                   1.15.0\r\nstanza                1.1.1\r\nthreadpoolctl         2.1.0\r\ntokenizers            0.8.1rc2\r\ntorch                 1.6.0+cu101\r\ntqdm                  4.48.2\r\ntransformers          3.1.0\r\nurllib3               1.25.10\r\nwheel                 0.34.2\r\nxxhash                2.0.0\r\n\r\nWindows 10 - Python 3.8\r\n================\r\nPackage       -        Version\r\n----------------------------\r\ncertifi               2020.6.20\r\nchardet               3.0.4\r\nclick                 7.1.2\r\ndatasets              1.0.1\r\ndill                  0.3.2\r\nfasttext              0.9.2\r\nfilelock              3.0.12\r\nfuture                0.18.2\r\nidna                  2.10\r\njoblib                0.16.0\r\nnlp                   0.4.0\r\nnltk                  3.5\r\nnumpy                 1.19.1\r\npackaging             20.4\r\npandas                1.1.1\r\npip                   20.0.2\r\nprotobuf              3.13.0\r\npyarrow               1.0.1\r\npybind11              2.5.0\r\npyparsing             2.4.7\r\npython-dateutil       2.8.1\r\npytz                  2020.1\r\nregex                 2020.7.14\r\nrequests              2.24.0\r\nsacremoses            0.0.43\r\nscikit-learn          0.23.2\r\nscipy                 1.5.2\r\nsentence-transformers 0.3.5.1\r\nsentencepiece         0.1.91\r\nsetuptools            46.1.3\r\nsix                   1.15.0\r\nstanza                1.1.1\r\nthreadpoolctl         2.1.0\r\ntokenizers            0.8.1rc1\r\ntorch                 1.6.0+cu101\r\ntqdm                  4.48.2\r\ntransformers          3.0.2\r\nurllib3               1.25.10\r\nwheel                 0.34.2\r\nxxhash                2.0.0","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":194,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n @thomwolf Sure. I'll try downgrading to 3.7 now even though Arrow say they support >=3.5.\r\n\r\nLinux (Ubuntu 18.04) - Python 3.8\r\n======================\r\nPackage   -   Version\r\n---------------------\r\ncertifi               2020.6.20\r\nchardet               3.0.4\r\nclick                 7.1.2\r\ndatasets              1.0.1\r\ndill                  0.3.2\r\nfasttext              0.9.2\r\nfilelock              3.0.12\r\nfuture                0.18.2\r\nidna                  2.10\r\njoblib                0.16.0\r\nnltk                  3.5\r\nnumpy                 1.19.1\r\npackaging             20.4\r\npandas                1.1.2\r\npip                   20.0.2\r\nprotobuf              3.13.0\r\npyarrow               1.0.1\r\npybind11              2.5.0\r\npyparsing             2.4.7\r\npython-dateutil       2.8.1\r\npytz                  2020.1\r\nregex                 2020.7.14\r\nrequests              2.24.0\r\nsacremoses            0.0.43\r\nscikit-learn          0.23.2\r\nscipy                 1.5.2\r\nsentence-transformers 0.3.6\r\nsentencepiece         0.1.91\r\nsetuptools            46.1.3\r\nsix                   1.15.0\r\nstanza                1.1.1\r\nthreadpoolctl         2.1.0\r\ntokenizers            0.8.1rc2\r\ntorch                 1.6.0+cu101\r\ntqdm                  4.48.2\r\ntransformers          3.1.0\r\nurllib3               1.25.10\r\nwheel                 0.34.2\r\nxxhash                2.0.0\r\n\r\nWindows 10 - Python 3.8\r\n================\r\nPackage       -        Version\r\n----------------------------\r\ncertifi               2020.6.20\r\nchardet               3.0.4\r\nclick                 7.1.2\r\ndatasets              1.0.1\r\ndill                  0.3.2\r\nfasttext              0.9.2\r\nfilelock              3.0.12\r\nfuture                0.18.2\r\nidna                  2.10\r\njoblib                0.16.0\r\nnlp                   0.4.0\r\nnltk                  3.5\r\nnumpy                 1.19.1\r\npackaging             20.4\r\npandas                1.1.1\r\npip                   20.0.2\r\nprotobuf              3.13.0\r\npyarrow               1.0.1\r\npybind11              2.5.0\r\npyparsing             2.4.7\r\npython-dateutil       2.8.1\r\npytz                  2020.1\r\nregex                 2020.7.14\r\nrequests              2.24.0\r\nsacremoses            0.0.43\r\nscikit-learn          0.23.2\r\nscipy                 1.5.2\r\nsentence-transformers 0.3.5.1\r\nsentencepiece         0.1.91\r\nsetuptools            46.1.3\r\nsix                   1.15.0\r\nstanza                1.1.1\r\nthreadpoolctl         2.1.0\r\ntokenizers            0.8.1rc1\r\ntorch                 1.6.0+cu101\r\ntqdm                  4.48.2\r\ntransformers          3.0.2\r\nurllib3               1.25.10\r\nwheel                 0.34.2\r\nxxhash                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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"Downgrading to 3.7 does not help. Here is a dummy text file:\r\n\r\n```text\r\nVerzekering weigert vaker te betalen\r\nBedrijven van verzekeringen erkennen steeds minder arbeidsongevallen .\r\nIn 2012 weigerden de bedrijven te betalen voor 21.055 ongevallen op het werk .\r\nDat is 11,8 % van alle ongevallen op het werk .\r\nNog nooit weigerden verzekeraars zoveel zaken .\r\nIn 2012 hadden 135.118 mensen een ongeval op het werk .\r\nDat zijn elke werkdag 530 mensen .\r\nBij die ongevallen stierven 67 mensen .\r\nBijna 12.000 hebben een handicap na het ongeval .\r\nGeen echt arbeidsongeval Bedrijven moeten een verzekering hebben voor hun werknemers .\r\n```\r\n\r\nA temporary work around for the \"text\" type, is\r\n\r\n```python\r\ndataset = Dataset.from_dict({\"text\": Path(dataset_f).read_text().splitlines()})\r\n```","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":120,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n Downgrading to 3.7 does not help. Here is a dummy text file:\r\n\r\n```text\r\nVerzekering weigert vaker te betalen\r\nBedrijven van verzekeringen erkennen steeds minder arbeidsongevallen .\r\nIn 2012 weigerden de bedrijven te betalen voor 21.055 ongevallen op het werk .\r\nDat is 11,8 % van alle ongevallen op het werk .\r\nNog nooit weigerden verzekeraars zoveel zaken .\r\nIn 2012 hadden 135.118 mensen een ongeval op het werk .\r\nDat zijn elke werkdag 530 mensen .\r\nBij die ongevallen stierven 67 mensen .\r\nBijna 12.000 hebben een handicap na het ongeval .\r\nGeen echt arbeidsongeval Bedrijven moeten een verzekering hebben voor hun werknemers .\r\n```\r\n\r\nA temporary work around for the \"text\" type, is\r\n\r\n```python\r\ndataset = Dataset.from_dict({\"text\": Path(dataset_f).read_text().splitlines()})\r\n```","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.164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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"@banunitte Please do not post screenshots in the future but copy-paste your code and the errors. That allows others to copy-and-paste your code and test it. You may also want to provide the Python version that you are using.","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":39,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n @banunitte Please do not post screenshots in the future but copy-paste your code and the errors. That allows others to copy-and-paste your code and test it. You may also want to provide the Python version that you are using.","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21773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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"I have the same  problem on Linux of the  script crashing with a CSV error.  This may be caused by 'CRLF', when changed 'CRLF' to 'LF', the problem solved.","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":29,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n I have the same  problem on Linux of the  script crashing with a CSV error.  This may be caused by 'CRLF', when changed 'CRLF' to 'LF', the problem solved.","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.2177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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"I pushed a fix for `pyarrow.lib.ArrowInvalid: CSV parse error`. Let me know if you still have this issue.\r\n\r\nNot sure about the windows one yet","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":25,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n I pushed a fix for `pyarrow.lib.ArrowInvalid: CSV parse error`. Let me know if you still have this issue.\r\n\r\nNot sure about the windows one yet","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21773469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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n```python\r\ndataset = load_dataset('text', script_version='master', data_files=XXX)\r\n```\r\nWe do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":107,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n```python\r\ndataset = load_dataset('text', script_version='master', data_files=XXX)\r\n```\r\nWe do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.217734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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"![image](https:\/\/user-images.githubusercontent.com\/36957508\/93300760-fa9a8680-f829-11ea-9105-7a6f67ad8373.png)\r\nwin10, py3.6\r\n\r\n\r\n```\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\n\r\nfeatures = Features({'text': Value('string'), 'ctext': Value('string')})\r\nfile_dict = {'train': PATH\/'summary.csv'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n```","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":31,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n ![image](https:\/\/user-images.githubusercontent.com\/36957508\/93300760-fa9a8680-f829-11ea-9105-7a6f67ad8373.png)\r\nwin10, py3.6\r\n\r\n\r\n```\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\n\r\nfeatures = Features({'text': Value('string'), 'ctext': Value('string')})\r\nfile_dict = {'train': PATH\/'summary.csv'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n```","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"```python\r\nTraceback` (most recent call last):\r\n  File \"main.py\", line 281, in <module>\r\n    main()\r\n  File \"main.py\", line 190, in main\r\n    train_data, test_data = data_factory(\r\n  File \"main.py\", line 129, in data_factory\r\n    train_data = load_dataset('text', \r\n  File \"\/home\/me\/Downloads\/datasets\/src\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/me\/Downloads\/datasets\/src\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/me\/Downloads\/datasets\/src\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/me\/Downloads\/datasets\/src\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/me\/.local\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/me\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014\/text.py\", line 103, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 617, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 123, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nUnfortunately i am still getting this issue on Linux. I installed datasets from source and specified script_version to master.\r\n\r\n","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":135,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n ```python\r\nTraceback` (most recent call last):\r\n  File \"main.py\", line 281, in <module>\r\n    main()\r\n  File \"main.py\", line 190, in main\r\n    train_data, test_data = data_factory(\r\n  File \"main.py\", line 129, in data_factory\r\n    train_data = load_dataset('text', \r\n  File \"\/home\/me\/Downloads\/datasets\/src\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/me\/Downloads\/datasets\/src\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/me\/Downloads\/datasets\/src\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/me\/Downloads\/datasets\/src\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/me\/.local\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/me\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014\/text.py\", line 103, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 617, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 123, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nUnfortunately i am still getting this issue on Linux. I installed datasets from source and specified script_version to master.\r\n\r\n","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"> ![image](https:\/\/user-images.githubusercontent.com\/36957508\/93300760-fa9a8680-f829-11ea-9105-7a6f67ad8373.png)\r\n> win10, py3.6\r\n> \r\n> ```\r\n> from datasets import Features, Value, ClassLabel, load_dataset\r\n> \r\n> \r\n> features = Features({'text': Value('string'), 'ctext': Value('string')})\r\n> file_dict = {'train': PATH\/'summary.csv'}\r\n> \r\n> dataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n> ```\r\n\r\nSince #644 it should now work on windows @ScottishFold007 \r\n\r\n> Trying the following snippet, I get different problems on Linux and Windows.\r\n> \r\n> ```python\r\n> dataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n> # or \r\n> dataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n> ```\r\n>\r\n> Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n> \r\n> ```\r\n> Checking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\n> Found main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\n> Found specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\n> Found script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\n> Couldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\n> Found metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\n> Using custom data configuration default\r\n> ```\r\n\r\nSame for you @BramVanroy .\r\n\r\nNot sure about the one on linux though","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":184,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n > ![image](https:\/\/user-images.githubusercontent.com\/36957508\/93300760-fa9a8680-f829-11ea-9105-7a6f67ad8373.png)\r\n> win10, py3.6\r\n> \r\n> ```\r\n> from datasets import Features, Value, ClassLabel, load_dataset\r\n> \r\n> \r\n> features = Features({'text': Value('string'), 'ctext': Value('string')})\r\n> file_dict = {'train': PATH\/'summary.csv'}\r\n> \r\n> dataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n> ```\r\n\r\nSince #644 it should now work on windows @ScottishFold007 \r\n\r\n> Trying the following snippet, I get different problems on Linux and Windows.\r\n> \r\n> ```python\r\n> dataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n> # or \r\n> dataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n> ```\r\n>\r\n> Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n> \r\n> ```\r\n> Checking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\n> Found main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\n> Found specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\n> Found script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\n> Couldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\n> Found metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\n> Using custom data configuration default\r\n> ```\r\n\r\nSame for you @BramVanroy .\r\n\r\nNot sure about the one on linux though","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21773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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"> To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n> \r\n> ```python\r\n> dataset = load_dataset('text', script_version='master', data_files=XXX)\r\n> ```\r\n> \r\n> We do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).\r\n\r\nLinux here:\r\n\r\nI was using the 0.4.0 nlp library load_dataset to load a text dataset of 9-10Gb without collapsing the RAM memory. However, today I got the csv error message mentioned in this issue. After installing the new (datasets) library from source and specifying the script_verson = 'master' I'm still having this same error message. Furthermore, I cannot use the dictionary \"trick\" to load the dataset since the system kills the process due to a RAM out of memory problem. Is there any other solution to this error? Thank you in advance. ","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":206,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n > To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n> \r\n> ```python\r\n> dataset = load_dataset('text', script_version='master', data_files=XXX)\r\n> ```\r\n> \r\n> We do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).\r\n\r\nLinux here:\r\n\r\nI was using the 0.4.0 nlp library load_dataset to load a text dataset of 9-10Gb without collapsing the RAM memory. However, today I got the csv error message mentioned in this issue. After installing the new (datasets) library from source and specifying the script_verson = 'master' I'm still having this same error message. Furthermore, I cannot use the dictionary \"trick\" to load the dataset since the system kills the process due to a RAM out of memory problem. Is there any other solution to this error? Thank you in advance. ","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.2177346945,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"Hi @raruidol \r\nTo fix the RAM issue you'll need to shard your text files into smaller files (see https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-691672919 for example)\r\n\r\nI'm not sure why you're having the csv error on linux.\r\nDo you think you could to to reproduce it on google colab for example ?\r\nOr send me a dummy .txt file that reproduces the issue ?","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":59,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n Hi @raruidol \r\nTo fix the RAM issue you'll need to shard your text files into smaller files (see https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-691672919 for example)\r\n\r\nI'm not sure why you're having the csv error on linux.\r\nDo you think you could to to reproduce it on google colab for example ?\r\nOr send me a dummy .txt file that reproduces the issue ?","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.2177346945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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"@lhoestq \r\n\r\nThe crash message shows up when loading the dataset:\r\n```\r\nprint('Loading corpus...') \r\nfiles = glob.glob('corpora\/shards\/*') \r\n-> dataset = load_dataset('text', script_version='master', data_files=files) \r\nprint('Corpus loaded.')\r\n```\r\nAnd this is the exact message:\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_language_modeling.py\", line 27, in <module>\r\n    dataset = load_dataset('text', script_version='master', data_files=files)\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 471, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 548, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 892, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/jupyter-raruidol\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014\/text.py\", line 107, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nAnd these are the pip packages I have atm and their versions:\r\n\r\n```\r\nPackage         Version   Location                                                     \r\n--------------- --------- -------------------------------------------------------------\r\ncertifi         2020.6.20 \r\nchardet         3.0.4     \r\nclick           7.1.2     \r\ndatasets        1.0.2     \r\ndill            0.3.2     \r\nfilelock        3.0.12    \r\nfuture          0.18.2    \r\nidna            2.10      \r\njoblib          0.16.0    \r\nnumpy           1.19.1    \r\npackaging       20.4      \r\npandas          1.1.1     \r\npip             19.0.3    \r\npyarrow         1.0.1     \r\npyparsing       2.4.7     \r\npython-dateutil 2.8.1     \r\npytz            2020.1    \r\nregex           2020.7.14 \r\nrequests        2.24.0    \r\nsacremoses      0.0.43    \r\nsentencepiece   0.1.91    \r\nsetuptools      40.8.0    \r\nsix             1.15.0    \r\ntokenizers      0.8.1rc2  \r\ntorch           1.6.0     \r\ntqdm            4.48.2    \r\ntransformers    3.0.2     \/home\/jupyter-raruidol\/DebatAnalyser\/env\/src\/transformers\/src\r\n```\r\n\r\n\r\n","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":207,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n @lhoestq \r\n\r\nThe crash message shows up when loading the dataset:\r\n```\r\nprint('Loading corpus...') \r\nfiles = glob.glob('corpora\/shards\/*') \r\n-> dataset = load_dataset('text', script_version='master', data_files=files) \r\nprint('Corpus loaded.')\r\n```\r\nAnd this is the exact message:\r\n```\r\nTraceback (most recent call last):\r\n  File \"run_language_modeling.py\", line 27, in <module>\r\n    dataset = load_dataset('text', script_version='master', data_files=files)\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 611, in load_dataset\r\n    ignore_verifications=ignore_verifications,\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 471, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 548, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 892, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/jupyter-raruidol\/DebatAnalyser\/env\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/jupyter-raruidol\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014\/text.py\", line 107, in _generate_tables\r\n    convert_options=self.config.convert_options,\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nAnd these are the pip packages I have atm and their versions:\r\n\r\n```\r\nPackage         Version   Location                                                     \r\n--------------- --------- -------------------------------------------------------------\r\ncertifi         2020.6.20 \r\nchardet         3.0.4     \r\nclick           7.1.2     \r\ndatasets        1.0.2     \r\ndill            0.3.2     \r\nfilelock        3.0.12    \r\nfuture          0.18.2    \r\nidna            2.10      \r\njoblib          0.16.0    \r\nnumpy           1.19.1    \r\npackaging       20.4      \r\npandas          1.1.1     \r\npip             19.0.3    \r\npyarrow         1.0.1     \r\npyparsing       2.4.7     \r\npython-dateutil 2.8.1     \r\npytz            2020.1    \r\nregex           2020.7.14 \r\nrequests        2.24.0    \r\nsacremoses      0.0.43    \r\nsentencepiece   0.1.91    \r\nsetuptools      40.8.0    \r\nsix             1.15.0    \r\ntokenizers      0.8.1rc2  \r\ntorch           1.6.0     \r\ntqdm            4.48.2    \r\ntransformers    3.0.2     \/home\/jupyter-raruidol\/DebatAnalyser\/env\/src\/transformers\/src\r\n```\r\n\r\n\r\n","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.247382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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"I tested on google colab which is also linux using this code:\r\n\r\n- first download an arbitrary text file\r\n```bash\r\nwget https:\/\/raw.githubusercontent.com\/abisee\/cnn-dailymail\/master\/url_lists\/all_train.txt\r\n```\r\n- then run\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"text\", data_files=\"all_train.txt\", script_version='master')\r\n```\r\nAnd I don't get this issue.\r\n\r\n\\> Could you test on your side if these lines work @raruidol ?\r\n\r\nalso cc @Skyy93 as it seems you have the same issue\r\n\r\nIf it works:\r\nIt could mean that the issue could come from unexpected patterns in the files you want to use.\r\nIn that case we should find a way to handle them.\r\n\r\nAnd if it doesn't work:\r\nIt could mean that it comes from the way pyarrow reads text files on linux.\r\nIn that case we should report it to pyarrow and find a workaround in the meantime\r\n\r\nEither way it should help to find where this bug comes from and fix it :)\r\n\r\nThank you in advance !","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":156,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n I tested on google colab which is also linux using this code:\r\n\r\n- first download an arbitrary text file\r\n```bash\r\nwget https:\/\/raw.githubusercontent.com\/abisee\/cnn-dailymail\/master\/url_lists\/all_train.txt\r\n```\r\n- then run\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"text\", data_files=\"all_train.txt\", script_version='master')\r\n```\r\nAnd I don't get this issue.\r\n\r\n\\> Could you test on your side if these lines work @raruidol ?\r\n\r\nalso cc @Skyy93 as it seems you have the same issue\r\n\r\nIf it works:\r\nIt could mean that the issue could come from unexpected patterns in the files you want to use.\r\nIn that case we should find a way to handle them.\r\n\r\nAnd if it doesn't work:\r\nIt could mean that it comes from the way pyarrow reads text files on linux.\r\nIn that case we should report it to pyarrow and find a workaround in the meantime\r\n\r\nEither way it should help to find where this bug comes from and fix it :)\r\n\r\nThank you in advance !","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.2177346945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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"Update: also tested the above code in a docker container from [jupyter\/minimal-notebook](https:\/\/hub.docker.com\/r\/jupyter\/minimal-notebook\/) (based on ubuntu) and still not able to reproduce","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":21,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n Update: also tested the above code in a docker container from [jupyter\/minimal-notebook](https:\/\/hub.docker.com\/r\/jupyter\/minimal-notebook\/) (based on ubuntu) and still not able to reproduce","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"It looks like with your text input file works without any problem. I have been doing some experiments this morning with my input files and I'm almost certain that the crash is caused by some unexpected pattern in the files. However, I've not been able to spot the main cause of it. What I find strange is that this same corpus was being loaded by the nlp 0.4.0 library without any problem... Where can I find the code where you structure the input text data in order to use it with pyarrow?","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":92,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n It looks like with your text input file works without any problem. I have been doing some experiments this morning with my input files and I'm almost certain that the crash is caused by some unexpected pattern in the files. However, I've not been able to spot the main cause of it. What I find strange is that this same corpus was being loaded by the nlp 0.4.0 library without any problem... Where can I find the code where you structure the input text data in order to use it with pyarrow?","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.217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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"Under the hood it does\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\n# Use csv reader from Pyarrow with one column for text files\r\n\r\n# To force the one-column setting, we set an arbitrary character\r\n# that is not in text files as delimiter, such as \\b or \\v.\r\n# The bell character, \\b, was used to make beeps back in the days\r\nparse_options = pa.csv.ParseOptions( \r\n    delimiter=\"\\b\", \r\n    quote_char=False, \r\n    double_quote=False, \r\n    escape_char=False, \r\n    newlines_in_values=False, \r\n    ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"all_train.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\nNote that we changed the parse options with datasets 1.0\r\nIn particular the delimiter used to be `\\r` but this delimiter doesn't work on windows.","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":107,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n Under the hood it does\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\n# Use csv reader from Pyarrow with one column for text files\r\n\r\n# To force the one-column setting, we set an arbitrary character\r\n# that is not in text files as delimiter, such as \\b or \\v.\r\n# The bell character, \\b, was used to make beeps back in the days\r\nparse_options = pa.csv.ParseOptions( \r\n    delimiter=\"\\b\", \r\n    quote_char=False, \r\n    double_quote=False, \r\n    escape_char=False, \r\n    newlines_in_values=False, \r\n    ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"all_train.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\nNote that we changed the parse options with datasets 1.0\r\nIn particular the delimiter used to be `\\r` but this delimiter doesn't work on windows.","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.217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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"Could you try with `\\a` instead of `\\b` ? It looks like the bell character is \\a in python and not \\b","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":22,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n Could you try with `\\a` instead of `\\b` ? It looks like the bell character is \\a in python and not \\b","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21773469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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"I was just exploring if the crash was happening in every shard or not, and which shards were generating the error message. With \\b I got the following list of shards crashing:\r\n\r\n```\r\nErrors on files:  ['corpora\/shards\/shard_0069', 'corpora\/shards\/shard_0043', 'corpora\/shards\/shard_0014', 'corpora\/shards\/shard_0032', 'corpora\/shards\/shard_0088', 'corpora\/shards\/shard_0018', 'corpora\/shards\/shard_0073', 'corpora\/shards\/shard_0079', 'corpora\/shards\/shard_0038', 'corpora\/shards\/shard_0041', 'corpora\/shards\/shard_0007', 'corpora\/shards\/shard_0004', 'corpora\/shards\/shard_0102', 'corpora\/shards\/shard_0096', 'corpora\/shards\/shard_0030', 'corpora\/shards\/shard_0076', 'corpora\/shards\/shard_0067', 'corpora\/shards\/shard_0052', 'corpora\/shards\/shard_0026', 'corpora\/shards\/shard_0024', 'corpora\/shards\/shard_0064', 'corpora\/shards\/shard_0044', 'corpora\/shards\/shard_0013', 'corpora\/shards\/shard_0062', 'corpora\/shards\/shard_0057', 'corpora\/shards\/shard_0097', 'corpora\/shards\/shard_0094', 'corpora\/shards\/shard_0078', 'corpora\/shards\/shard_0075', 'corpora\/shards\/shard_0039', 'corpora\/shards\/shard_0077', 'corpora\/shards\/shard_0021', 'corpora\/shards\/shard_0040', 'corpora\/shards\/shard_0009', 'corpora\/shards\/shard_0023', 'corpora\/shards\/shard_0095', 'corpora\/shards\/shard_0107', 'corpora\/shards\/shard_0063', 'corpora\/shards\/shard_0086', 'corpora\/shards\/shard_0047', 'corpora\/shards\/shard_0089', 'corpora\/shards\/shard_0037', 'corpora\/shards\/shard_0101', 'corpora\/shards\/shard_0093', 'corpora\/shards\/shard_0082', 'corpora\/shards\/shard_0091', 'corpora\/shards\/shard_0065', 'corpora\/shards\/shard_0020', 'corpora\/shards\/shard_0070', 'corpora\/shards\/shard_0008', 'corpora\/shards\/shard_0058', 'corpora\/shards\/shard_0060', 'corpora\/shards\/shard_0022', 'corpora\/shards\/shard_0059', 'corpora\/shards\/shard_0100', 'corpora\/shards\/shard_0027', 'corpora\/shards\/shard_0072', 'corpora\/shards\/shard_0098', 'corpora\/shards\/shard_0019', 'corpora\/shards\/shard_0066', 'corpora\/shards\/shard_0042', 'corpora\/shards\/shard_0053']\r\n```\r\n\r\nI also tried with \\a and the list decreased but there were still several crashes:\r\n\r\n```\r\nErrors on files:  ['corpora\/shards\/shard_0069', 'corpora\/shards\/shard_0055', 'corpora\/shards\/shard_0043', 'corpora\/shards\/shard_0014', 'corpora\/shards\/shard_0073', 'corpora\/shards\/shard_0025', 'corpora\/shards\/shard_0068', 'corpora\/shards\/shard_0102', 'corpora\/shards\/shard_0096', 'corpora\/shards\/shard_0076', 'corpora\/shards\/shard_0067', 'corpora\/shards\/shard_0026', 'corpora\/shards\/shard_0024', 'corpora\/shards\/shard_0044', 'corpora\/shards\/shard_0087', 'corpora\/shards\/shard_0092', 'corpora\/shards\/shard_0074', 'corpora\/shards\/shard_0094', 'corpora\/shards\/shard_0078', 'corpora\/shards\/shard_0039', 'corpora\/shards\/shard_0077', 'corpora\/shards\/shard_0040', 'corpora\/shards\/shard_0009', 'corpora\/shards\/shard_0107', 'corpora\/shards\/shard_0063', 'corpora\/shards\/shard_0103', 'corpora\/shards\/shard_0047', 'corpora\/shards\/shard_0033', 'corpora\/shards\/shard_0089', 'corpora\/shards\/shard_0037', 'corpora\/shards\/shard_0082', 'corpora\/shards\/shard_0071', 'corpora\/shards\/shard_0091', 'corpora\/shards\/shard_0065', 'corpora\/shards\/shard_0070', 'corpora\/shards\/shard_0058', 'corpora\/shards\/shard_0081', 'corpora\/shards\/shard_0060', 'corpora\/shards\/shard_0002', 'corpora\/shards\/shard_0059', 'corpora\/shards\/shard_0027', 'corpora\/shards\/shard_0072', 'corpora\/shards\/shard_0098', 'corpora\/shards\/shard_0019', 'corpora\/shards\/shard_0045', 'corpora\/shards\/shard_0036', 'corpora\/shards\/shard_0066', 'corpora\/shards\/shard_0053']\r\n```\r\n\r\nWhich means that it is quite possible that the assumption of that some unexpected pattern in the files is causing the crashes is true. If I am able to reach any conclusion I will post It here asap.","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":205,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n I was just exploring if the crash was happening in every shard or not, and which shards were generating the error message. With \\b I got the following list of shards crashing:\r\n\r\n```\r\nErrors on files:  ['corpora\/shards\/shard_0069', 'corpora\/shards\/shard_0043', 'corpora\/shards\/shard_0014', 'corpora\/shards\/shard_0032', 'corpora\/shards\/shard_0088', 'corpora\/shards\/shard_0018', 'corpora\/shards\/shard_0073', 'corpora\/shards\/shard_0079', 'corpora\/shards\/shard_0038', 'corpora\/shards\/shard_0041', 'corpora\/shards\/shard_0007', 'corpora\/shards\/shard_0004', 'corpora\/shards\/shard_0102', 'corpora\/shards\/shard_0096', 'corpora\/shards\/shard_0030', 'corpora\/shards\/shard_0076', 'corpora\/shards\/shard_0067', 'corpora\/shards\/shard_0052', 'corpora\/shards\/shard_0026', 'corpora\/shards\/shard_0024', 'corpora\/shards\/shard_0064', 'corpora\/shards\/shard_0044', 'corpora\/shards\/shard_0013', 'corpora\/shards\/shard_0062', 'corpora\/shards\/shard_0057', 'corpora\/shards\/shard_0097', 'corpora\/shards\/shard_0094', 'corpora\/shards\/shard_0078', 'corpora\/shards\/shard_0075', 'corpora\/shards\/shard_0039', 'corpora\/shards\/shard_0077', 'corpora\/shards\/shard_0021', 'corpora\/shards\/shard_0040', 'corpora\/shards\/shard_0009', 'corpora\/shards\/shard_0023', 'corpora\/shards\/shard_0095', 'corpora\/shards\/shard_0107', 'corpora\/shards\/shard_0063', 'corpora\/shards\/shard_0086', 'corpora\/shards\/shard_0047', 'corpora\/shards\/shard_0089', 'corpora\/shards\/shard_0037', 'corpora\/shards\/shard_0101', 'corpora\/shards\/shard_0093', 'corpora\/shards\/shard_0082', 'corpora\/shards\/shard_0091', 'corpora\/shards\/shard_0065', 'corpora\/shards\/shard_0020', 'corpora\/shards\/shard_0070', 'corpora\/shards\/shard_0008', 'corpora\/shards\/shard_0058', 'corpora\/shards\/shard_0060', 'corpora\/shards\/shard_0022', 'corpora\/shards\/shard_0059', 'corpora\/shards\/shard_0100', 'corpora\/shards\/shard_0027', 'corpora\/shards\/shard_0072', 'corpora\/shards\/shard_0098', 'corpora\/shards\/shard_0019', 'corpora\/shards\/shard_0066', 'corpora\/shards\/shard_0042', 'corpora\/shards\/shard_0053']\r\n```\r\n\r\nI also tried with \\a and the list decreased but there were still several crashes:\r\n\r\n```\r\nErrors on files:  ['corpora\/shards\/shard_0069', 'corpora\/shards\/shard_0055', 'corpora\/shards\/shard_0043', 'corpora\/shards\/shard_0014', 'corpora\/shards\/shard_0073', 'corpora\/shards\/shard_0025', 'corpora\/shards\/shard_0068', 'corpora\/shards\/shard_0102', 'corpora\/shards\/shard_0096', 'corpora\/shards\/shard_0076', 'corpora\/shards\/shard_0067', 'corpora\/shards\/shard_0026', 'corpora\/shards\/shard_0024', 'corpora\/shards\/shard_0044', 'corpora\/shards\/shard_0087', 'corpora\/shards\/shard_0092', 'corpora\/shards\/shard_0074', 'corpora\/shards\/shard_0094', 'corpora\/shards\/shard_0078', 'corpora\/shards\/shard_0039', 'corpora\/shards\/shard_0077', 'corpora\/shards\/shard_0040', 'corpora\/shards\/shard_0009', 'corpora\/shards\/shard_0107', 'corpora\/shards\/shard_0063', 'corpora\/shards\/shard_0103', 'corpora\/shards\/shard_0047', 'corpora\/shards\/shard_0033', 'corpora\/shards\/shard_0089', 'corpora\/shards\/shard_0037', 'corpora\/shards\/shard_0082', 'corpora\/shards\/shard_0071', 'corpora\/shards\/shard_0091', 'corpora\/shards\/shard_0065', 'corpora\/shards\/shard_0070', 'corpora\/shards\/shard_0058', 'corpora\/shards\/shard_0081', 'corpora\/shards\/shard_0060', 'corpora\/shards\/shard_0002', 'corpora\/shards\/shard_0059', 'corpora\/shards\/shard_0027', 'corpora\/shards\/shard_0072', 'corpora\/shards\/shard_0098', 'corpora\/shards\/shard_0019', 'corpora\/shards\/shard_0045', 'corpora\/shards\/shard_0036', 'corpora\/shards\/shard_0066', 'corpora\/shards\/shard_0053']\r\n```\r\n\r\nWhich means that it is quite possible that the assumption of that some unexpected pattern in the files is causing the crashes is true. If I am able to reach any conclusion I will post It here asap.","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.217734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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"Hmmm I was expecting it to work with \\a, not sure why they appear in your text files though","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":19,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n Hmmm I was expecting it to work with \\a, not sure why they appear in your text files though","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21773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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"Hi @lhoestq, is there any input length restriction which was not before the update of the nlp library?","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":18,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n Hi @lhoestq, is there any input length restriction which was not before the update of the nlp library?","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.217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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"No we never set any input length restriction on our side (maybe arrow but I don't think so)","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":18,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n No we never set any input length restriction on our side (maybe arrow but I don't think so)","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21773469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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"@lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong? \r\n\r\nIf I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":118,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n @lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong? \r\n\r\nIf I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.2177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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones:\r\n\r\n\r\n_4.\u2003DE L\u2019ORGANITZACI\u00d3 ESTAMENTAL A L\u2019ORGANITZACI\u00d3 EN CLASSES A mesura que es desenvolupava un sistema econ\u00f2mic capitalista i naixia una classe burgesa cada vegada m\u00e9s preparada per a substituir els dirigents de les velles monarquies absolutistes, es q\u00fcestionava l\u2019abund\u00e0ncia de b\u00e9ns amortitzats, que com s\u2019ha dit estaven fora del mercat i no pagaven tributs, pels perjudicis que ocasionaven a les finances p\u00fabliques i a l\u2019economia en general. Aquest estat d\u2019opini\u00f3 revolucionari va desembocar en un conjunt de mesures pr\u00e0ctiques de car\u00e0cter liberal. D\u2019una banda, les que intentaven desposseir les mans mortes del domini de b\u00e9ns acumulats, proc\u00e9s que acostumem a denominar desamortitzaci\u00f3, i que no \u00e9s m\u00e9s que la nacionalitzaci\u00f3 i venda d\u2019aquests b\u00e9ns eclesi\u00e0stics o civils en subhasta p\u00fablica al millor postor. D\u2019altra banda, les que redimien o redu\u00efen els censos i delmes o aixecaven les prohibicions de venda, \u00e9s a dir, les vinculacions. La desamortitzaci\u00f3, que va afectar b\u00e9ns dels ordes religiosos, dels pobles i d\u2019algunes corporacions civils, no va ser un cam\u00ed f\u00e0cil, perqu\u00e8 costava i costa trobar alg\u00fa que sigui indiferent a la p\u00e8rdua de b\u00e9ns, drets i privilegis. I t\u00e9 una gran transcend\u00e8ncia, va privar els antics estaments de les Espanyes, clero i pobles \u2014la noblesa en queda al marge\u2014, de la for\u00e7a econ\u00f2mica que els donaven bona part de les seves terres i, en \u00faltima inst\u00e0ncia, va preparar el terreny per a la substituci\u00f3 de la vella societat estamental per la nova societat classista. En aquesta societat, en teoria, les agrupacions socials s\u00f3n obertes, no tenen cap estatut jur\u00eddic privilegiat i estan definides per la possessi\u00f3 o no d\u2019uns b\u00e9ns econ\u00f2mics que s\u00f3n lliurement alienables. A les Espanyes la transformaci\u00f3 va afectar poc l\u2019aristocr\u00e0cia latifundista, all\u00e0 on n\u2019hi havia. Aquesta situaci\u00f3 va afavorir, en part, la persist\u00e8ncia de la vella cultura de la societat estamental en determinats ambients, i aix\u00f2 ha influ\u00eft decisivament en la manca de democr\u00e0cia que caracteritza la majoria de r\u00e8gims pol\u00edtics que s\u2019han anat succeint. Una manera de pensar que sempre sura en un moment o altre, i que de fet no acaba de desapar\u00e8ixer del tot. 5.\u2003INICI DE LA DESAMORTITZACI\u00d3 A LES ESPANYES Durant el segle xviii, dins d\u2019aquesta visi\u00f3 lliberal, va agafar for\u00e7a en alguns cercles de les Espanyes el corrent d\u2019opini\u00f3 contrari a les mans mortes. Durant el regnat de Carles III, s\u2019arbitraren les primeres mesures desamortitzadores proposades per alguns ministres il\u00b7lustrats. Aquestes disposicions foren modestes i poc eficaces, no van aturar l\u2019acumulaci\u00f3 de terres per part dels estaments que constitu\u00efen les mans mortes i varen afectar principalment b\u00e9ns dels pobles. L\u2019Esgl\u00e9sia no va ser tocada, excepte en el cas de 110_\r\n\r\n_la revoluci\u00f3 liberal, perqu\u00e8, encara que havia perdut els seus drets jurisdiccionals, havia conservat la majoria de terres i fins i tot les havia incrementat amb d\u2019altres que procedien de la desamortitzaci\u00f3. En la nova situaci\u00f3, les mans mortes del bosc p\u00fablic eren l\u2019Estat, que no cerca mai l\u2019autofinan\u00e7ament de les despeses de gesti\u00f3; els diners que manquin ja els posar\u00e0 l\u2019Estat. 9.\u2003DEFENSA I INTENTS DE RECUPERACI\u00d3 DELS B\u00c9NS COMUNALS DESAMORTITZATS El proc\u00e9s de centralitzaci\u00f3 no era senzill, perqu\u00e8, d\u2019una banda, la nova organitzaci\u00f3 apartava de la gesti\u00f3 moltes corporacions locals i molts ve\u00efns que l\u2019havien portada des de l\u2019edat mitjana, i, de l\u2019altra, era dif\u00edcil de coordinar la nova silvicultura amb moltes pr\u00e0ctiques forestals i drets tradicionals, com la pastura, fer llenya o tallar un arbre aqu\u00ed i un altre all\u00e0 quan tenia el gruix suficient, les pr\u00e0ctiques que s\u2019havien fet sempre. Les primeres passes de la nova organitzaci\u00f3 centralitzada varen tenir moltes dificultats en aquells indrets en qu\u00e8 els terrenys municipals i comunals tenien un paper important en l\u2019economia local. La desobedi\u00e8ncia a determinades normes imposades varen prendre formes diferents. Algunes institucions, com, per exemple, la Diputaci\u00f3 de Lleida, varen retardar la tramitaci\u00f3 d\u2019alguns expedients i varen evitar la venda de b\u00e9ns municipals. Molts pobles permeteren deixar que els ve\u00efns continuessin amb les seves pr\u00e0ctiques tradicionals, d\u2019altres varen boicotejar les subhastes d\u2019aprofitaments. L\u2019Estat va reaccionar encomanant a la Gu\u00e0rdia Civil el compliment de les noves directrius. Imposar el nou r\u00e8gim va costar a l\u2019Administraci\u00f3 un grapat d\u2019anys, per\u00f2 de mica en mica, amb molta, molta guarderia i gens de negociaci\u00f3, ho va aconseguir. La nova gesti\u00f3 estatal dels b\u00e9ns municipals va deixar, com hem comentat, molta gent sense uns recursos necessaris per a la superviv\u00e8ncia, sobre tot en \u00e0rees on predominaven les grans propietats, i on els pagesos sense terra treballaven de jornalers temporers. Aix\u00f2 va afavorir que, a bona part de les Espanyes, les primeres lluites camperoles de la segona meitat del segle xix defensessin la recuperaci\u00f3 dels comunals desamortitzats; per a molts aquella expropiaci\u00f3 i venda dirigida pels governs mon\u00e0rquics era la causa de molta mis\u00e8ria. D\u2019altres, m\u00e9s radicalitzats, varen entendre que l\u2019eliminaci\u00f3 de la propietat col\u00b7lectiva i la gesti\u00f3 estatal dels boscos no desamortitzats suposava una usurpaci\u00f3 pura i dura. En les zones m\u00e9s afectades per la desamortitzaci\u00f3 aix\u00f2 va donar lloc a un imaginari centrat en la defensa del comunal. La Segona Rep\u00fablica va arribar en una conjuntura econ\u00f2mica de crisi, generada pel crac del 1929. Al camp, aquesta situaci\u00f3 va produir una forta caiguda dels preus dels productes agraris i un increment important de l\u2019atur. QUADERNS AGRARIS 42\u2002(juny 2017), p. 105-126_\r\n\r\nI think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":949,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones:\r\n\r\n\r\n_4.\u2003DE L\u2019ORGANITZACI\u00d3 ESTAMENTAL A L\u2019ORGANITZACI\u00d3 EN CLASSES A mesura que es desenvolupava un sistema econ\u00f2mic capitalista i naixia una classe burgesa cada vegada m\u00e9s preparada per a substituir els dirigents de les velles monarquies absolutistes, es q\u00fcestionava l\u2019abund\u00e0ncia de b\u00e9ns amortitzats, que com s\u2019ha dit estaven fora del mercat i no pagaven tributs, pels perjudicis que ocasionaven a les finances p\u00fabliques i a l\u2019economia en general. Aquest estat d\u2019opini\u00f3 revolucionari va desembocar en un conjunt de mesures pr\u00e0ctiques de car\u00e0cter liberal. D\u2019una banda, les que intentaven desposseir les mans mortes del domini de b\u00e9ns acumulats, proc\u00e9s que acostumem a denominar desamortitzaci\u00f3, i que no \u00e9s m\u00e9s que la nacionalitzaci\u00f3 i venda d\u2019aquests b\u00e9ns eclesi\u00e0stics o civils en subhasta p\u00fablica al millor postor. D\u2019altra banda, les que redimien o redu\u00efen els censos i delmes o aixecaven les prohibicions de venda, \u00e9s a dir, les vinculacions. La desamortitzaci\u00f3, que va afectar b\u00e9ns dels ordes religiosos, dels pobles i d\u2019algunes corporacions civils, no va ser un cam\u00ed f\u00e0cil, perqu\u00e8 costava i costa trobar alg\u00fa que sigui indiferent a la p\u00e8rdua de b\u00e9ns, drets i privilegis. I t\u00e9 una gran transcend\u00e8ncia, va privar els antics estaments de les Espanyes, clero i pobles \u2014la noblesa en queda al marge\u2014, de la for\u00e7a econ\u00f2mica que els donaven bona part de les seves terres i, en \u00faltima inst\u00e0ncia, va preparar el terreny per a la substituci\u00f3 de la vella societat estamental per la nova societat classista. En aquesta societat, en teoria, les agrupacions socials s\u00f3n obertes, no tenen cap estatut jur\u00eddic privilegiat i estan definides per la possessi\u00f3 o no d\u2019uns b\u00e9ns econ\u00f2mics que s\u00f3n lliurement alienables. A les Espanyes la transformaci\u00f3 va afectar poc l\u2019aristocr\u00e0cia latifundista, all\u00e0 on n\u2019hi havia. Aquesta situaci\u00f3 va afavorir, en part, la persist\u00e8ncia de la vella cultura de la societat estamental en determinats ambients, i aix\u00f2 ha influ\u00eft decisivament en la manca de democr\u00e0cia que caracteritza la majoria de r\u00e8gims pol\u00edtics que s\u2019han anat succeint. Una manera de pensar que sempre sura en un moment o altre, i que de fet no acaba de desapar\u00e8ixer del tot. 5.\u2003INICI DE LA DESAMORTITZACI\u00d3 A LES ESPANYES Durant el segle xviii, dins d\u2019aquesta visi\u00f3 lliberal, va agafar for\u00e7a en alguns cercles de les Espanyes el corrent d\u2019opini\u00f3 contrari a les mans mortes. Durant el regnat de Carles III, s\u2019arbitraren les primeres mesures desamortitzadores proposades per alguns ministres il\u00b7lustrats. Aquestes disposicions foren modestes i poc eficaces, no van aturar l\u2019acumulaci\u00f3 de terres per part dels estaments que constitu\u00efen les mans mortes i varen afectar principalment b\u00e9ns dels pobles. L\u2019Esgl\u00e9sia no va ser tocada, excepte en el cas de 110_\r\n\r\n_la revoluci\u00f3 liberal, perqu\u00e8, encara que havia perdut els seus drets jurisdiccionals, havia conservat la majoria de terres i fins i tot les havia incrementat amb d\u2019altres que procedien de la desamortitzaci\u00f3. En la nova situaci\u00f3, les mans mortes del bosc p\u00fablic eren l\u2019Estat, que no cerca mai l\u2019autofinan\u00e7ament de les despeses de gesti\u00f3; els diners que manquin ja els posar\u00e0 l\u2019Estat. 9.\u2003DEFENSA I INTENTS DE RECUPERACI\u00d3 DELS B\u00c9NS COMUNALS DESAMORTITZATS El proc\u00e9s de centralitzaci\u00f3 no era senzill, perqu\u00e8, d\u2019una banda, la nova organitzaci\u00f3 apartava de la gesti\u00f3 moltes corporacions locals i molts ve\u00efns que l\u2019havien portada des de l\u2019edat mitjana, i, de l\u2019altra, era dif\u00edcil de coordinar la nova silvicultura amb moltes pr\u00e0ctiques forestals i drets tradicionals, com la pastura, fer llenya o tallar un arbre aqu\u00ed i un altre all\u00e0 quan tenia el gruix suficient, les pr\u00e0ctiques que s\u2019havien fet sempre. Les primeres passes de la nova organitzaci\u00f3 centralitzada varen tenir moltes dificultats en aquells indrets en qu\u00e8 els terrenys municipals i comunals tenien un paper important en l\u2019economia local. La desobedi\u00e8ncia a determinades normes imposades varen prendre formes diferents. Algunes institucions, com, per exemple, la Diputaci\u00f3 de Lleida, varen retardar la tramitaci\u00f3 d\u2019alguns expedients i varen evitar la venda de b\u00e9ns municipals. Molts pobles permeteren deixar que els ve\u00efns continuessin amb les seves pr\u00e0ctiques tradicionals, d\u2019altres varen boicotejar les subhastes d\u2019aprofitaments. L\u2019Estat va reaccionar encomanant a la Gu\u00e0rdia Civil el compliment de les noves directrius. Imposar el nou r\u00e8gim va costar a l\u2019Administraci\u00f3 un grapat d\u2019anys, per\u00f2 de mica en mica, amb molta, molta guarderia i gens de negociaci\u00f3, ho va aconseguir. La nova gesti\u00f3 estatal dels b\u00e9ns municipals va deixar, com hem comentat, molta gent sense uns recursos necessaris per a la superviv\u00e8ncia, sobre tot en \u00e0rees on predominaven les grans propietats, i on els pagesos sense terra treballaven de jornalers temporers. Aix\u00f2 va afavorir que, a bona part de les Espanyes, les primeres lluites camperoles de la segona meitat del segle xix defensessin la recuperaci\u00f3 dels comunals desamortitzats; per a molts aquella expropiaci\u00f3 i venda dirigida pels governs mon\u00e0rquics era la causa de molta mis\u00e8ria. D\u2019altres, m\u00e9s radicalitzats, varen entendre que l\u2019eliminaci\u00f3 de la propietat col\u00b7lectiva i la gesti\u00f3 estatal dels boscos no desamortitzats suposava una usurpaci\u00f3 pura i dura. En les zones m\u00e9s afectades per la desamortitzaci\u00f3 aix\u00f2 va donar lloc a un imaginari centrat en la defensa del comunal. La Segona Rep\u00fablica va arribar en una conjuntura econ\u00f2mica de crisi, generada pel crac del 1929. Al camp, aquesta situaci\u00f3 va produir una forta caiguda dels preus dels productes agraris i un increment important de l\u2019atur. QUADERNS AGRARIS 42\u2002(juny 2017), p. 105-126_\r\n\r\nI think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"So we're using the csv reader to read text files because arrow doesn't have a text reader.\r\nTo workaround the fact that text files are just csv with one column, we want to set a delimiter that doesn't appear in text files.\r\nUntil now I thought that it would do the job but unfortunately it looks like even characters like \\a appear in text files.\r\n\r\nSo we have to option:\r\n- find another delimiter that does the job (maybe `\\x1b` esc or `\\x18` cancel)\r\n- don't use the csv reader from arrow but the text reader from pandas instead (or any other reader). The only important thing is that it must be fast (arrow's reader has a nice and fast multithreaded for csv that we're using now but hopefully we can find an alternative)\r\n\r\n\r\n\r\n> @lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong?\r\n\r\nAs long as the text file follows some encoding it wouldn't make sense to have characters such as the bell character. However I agree it can happen.\r\n\r\n> If I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?\r\n\r\nExactly. Arrow doesn't allow the newline character unfortunately.","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":289,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n So we're using the csv reader to read text files because arrow doesn't have a text reader.\r\nTo workaround the fact that text files are just csv with one column, we want to set a delimiter that doesn't appear in text files.\r\nUntil now I thought that it would do the job but unfortunately it looks like even characters like \\a appear in text files.\r\n\r\nSo we have to option:\r\n- find another delimiter that does the job (maybe `\\x1b` esc or `\\x18` cancel)\r\n- don't use the csv reader from arrow but the text reader from pandas instead (or any other reader). The only important thing is that it must be fast (arrow's reader has a nice and fast multithreaded for csv that we're using now but hopefully we can find an alternative)\r\n\r\n\r\n\r\n> @lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong?\r\n\r\nAs long as the text file follows some encoding it wouldn't make sense to have characters such as the bell character. However I agree it can happen.\r\n\r\n> If I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?\r\n\r\nExactly. Arrow doesn't allow the newline character unfortunately.","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"> Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones\r\n\r\nThanks for digging into it !\r\n\r\nCharacters like \\a or \\b are not shown when printing the text, so as it is I can't tell if it contains unexpected characters.\r\nMaybe could could open the file in python and check if `\"\\b\" in open(\"path\/to\/file\", \"r\").read()` ?\r\n\r\n> I think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.\r\n\r\nTo check that you could try to run \r\n\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\nopen(\"dummy.txt\", \"w\").write(((\"a\" * 10_000) + \"\\n\") * 4)  # 4 lines of 10 000 'a'\r\n\r\nparse_options = pa.csv.ParseOptions( \r\n    delimiter=\"\\b\", \r\n    quote_char=False, \r\n    double_quote=False, \r\n    escape_char=False, \r\n    newlines_in_values=False, \r\n    ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"dummy.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\non my side it runs without error though","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":178,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n > Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones\r\n\r\nThanks for digging into it !\r\n\r\nCharacters like \\a or \\b are not shown when printing the text, so as it is I can't tell if it contains unexpected characters.\r\nMaybe could could open the file in python and check if `\"\\b\" in open(\"path\/to\/file\", \"r\").read()` ?\r\n\r\n> I think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.\r\n\r\nTo check that you could try to run \r\n\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\nopen(\"dummy.txt\", \"w\").write(((\"a\" * 10_000) + \"\\n\") * 4)  # 4 lines of 10 000 'a'\r\n\r\nparse_options = pa.csv.ParseOptions( \r\n    delimiter=\"\\b\", \r\n    quote_char=False, \r\n    double_quote=False, \r\n    escape_char=False, \r\n    newlines_in_values=False, \r\n    ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"dummy.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\non my side it runs without error though","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21773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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":28,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.2177346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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/622","title":"load_dataset for text files not working","comments":"> That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?\r\n\r\nI don't think it would work since we only want one column, and \"\\b\" is set to be the delimiter between two columns, so it will raise the same issue again. Pyarrow would think that there is more than one column if the delimiter is found somewhere.\r\n\r\nAnyway, I I'll work on a new text reader if we don't find the right workaround about this delimiter issue.","body":"Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n","comment_length":96,"text":"load_dataset for text files not working \n Trying the following snippet, I get different problems on Linux and Windows.\r\n\r\n\r\n```python\r\ndataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n# or \r\ndataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n```\r\n\r\n(ps [This example](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.)\r\n\r\nThe problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file.\r\n\r\nLinux stack trace:\r\n```\r\nPyTorch version 1.6.0+cu101 available.\r\nChecking \/home\/bram\/.cache\/huggingface\/datasets\/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at \/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.json\r\nUsing custom data configuration default\r\nGenerating dataset text (\/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7)\r\nDownloading and preparing dataset text\/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/bram\/.cache\/huggingface\/datasets\/text\/default-0907112cc6cd2a38\/0.0.0\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7...\r\nDataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading took 0.0 min\r\nChecksum Computation took 0.0 min\r\nUnable to verify checksums.\r\nGenerating split train\r\nTraceback (most recent call last):\r\n  File \"\/home\/bram\/Python\/projects\/dutch-simplification\/utils.py\", line 45, in prepare_data\r\n    dataset = load_dataset(\"text\", data_files=dataset_f)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 608, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 468, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 546, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 888, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n  File \"\/home\/bram\/.local\/share\/virtualenvs\/dutch-simplification-NcpPZtDF\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1130, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/bram\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\/text.py\", line 100, in _generate_tables\r\n    pa_table = pac.read_csv(\r\n  File \"pyarrow\/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nWindows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n\r\n```\r\nChecking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\nCouldn't find dataset infos file at https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\\dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/datasets\/1.0.1\/datasets\/text\/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\nUsing custom data configuration default\r\n```\r\n \n > That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?\r\n\r\nI don't think it would work since we only want one column, and \"\\b\" is set to be the delimiter between two columns, so it will raise the same issue again. Pyarrow would think that there is more than one column if the delimiter is found somewhere.\r\n\r\nAnyway, I I'll work on a new text reader if we don't find the right workaround about this delimiter issue.","embeddings":[-0.2746651173,-0.4020575583,0.0175604951,0.3872526288,0.2696425617,-0.0386613607,0.3188881874,-0.0543565489,0.4263595045,-0.0580488145,0.0659720227,0.145524323,-0.1557630599,0.2742006779,0.0635561198,-0.035076078,0.1571835279,-0.0138417054,-0.2914433181,0.0402483419,-0.1144550294,0.3431209624,-0.2153358608,-0.1469300389,-0.4160707295,0.2740044296,-0.1050167531,0.4538784921,-0.0742620155,-0.2378563434,0.2710002363,0.1282079071,0.1639642566,0.6136498451,-0.0001269617,0.1100326851,0.2756481171,-0.1610112339,-0.2998276949,-0.5695467591,0.1585155129,-0.2294367254,0.2088484615,-0.0287806056,0.1301927119,-0.0045288531,0.1527263522,-0.3340618312,0.3551648259,0.3254779875,0.1009777859,0.3193374872,0.0620950386,-0.0146584613,0.0609620512,0.4765509367,-0.0917448476,0.3906496465,0.313786149,-0.1942874789,0.1169522405,-0.082400918,-0.173093155,-0.1696534455,0.3285271525,0.2871706486,-0.6134480238,-0.2057267129,0.0768413693,0.1859920323,0.4352324009,-0.4339176416,-0.221748367,-0.1591291726,-0.0756903589,-0.1254785657,0.4388749599,0.1783995628,-0.1985727847,0.0581210814,-0.2503564954,-0.0692397878,-0.1769828945,0.323066324,-0.0084304409,0.0508893542,-0.219182238,0.1040090844,0.3328258991,-0.0709382817,-0.2063759267,-0.2793429792,-0.0293538962,0.0611276552,-0.2661669552,0.1557293087,-0.2658762336,-0.0245267656,0.1117541268,0.1747620702,-0.0047685001,0.1417199671,0.0951443389,0.2306069136,0.2007873058,0.0978597626,0.4081541598,0.0843545943,0.2615751028,0.0263595376,-0.0160578508,-0.0952473879,-0.1963426471,-0.544190526,-0.069791168,-0.2300527245,0.4797008932,-0.2050437331,-0.1790357679,-0.0498552136,-0.0808124468,-0.0455941074,0.2161368579,0.6197093129,-0.0843391269,0.0338773318,0.1204905659,0.3182838857,-0.1168016568,0.1911230683,0.047579363,-0.0604269393,-0.0889259353,0.1795253456,0.5387113094,-0.3721969426,0.3424328566,0.1996889412,0.4671038091,-0.1905285269,-0.151450634,-0.1865481436,-0.0174010713,0.1886943281,0.0076277102,0.0419398174,0.2405289412,-0.1590055078,-0.1147044152,0.094007358,-0.2652543485,-0.0938472077,0.0322779305,0.0448435508,-0.09525875,-0.2330176234,-0.3073214889,0.0929248333,0.0373641104,-0.1054287404,0.0938850492,-0.1803099215,-0.2380830199,-0.1588562429,0.2716287971,0.6603162289,-0.2988535464,-0.1454449892,0.325542748,-0.1225560904,-0.1192727685,0.2550257146,-0.0943285748,0.0099470215,-0.2610239685,0.1993149221,0.1874883026,-0.4092430174,-0.1465579122,0.396353066,0.0584071279,0.1357305199,0.2080578804,0.0618107505,0.0415842496,0.0460110344,0.2806780338,0.0083062993,0.1047209352,-0.0915358141,-0.0034152512,-0.2037547082,0.0910752863,0.3670076728,-0.2113386095,0.0550341345,0.1075050905,-0.1117831096,0.1765403897,-0.1039660424,-0.034448199,0.5095410943,0.1362373233,0.3201455176,0.0793995485,-0.2116694301,-0.5979293585,0.1366195083,0.2275739461,-0.0535317548,-0.2638650537,-0.0957267582,-0.1770208925,0.0601343401,-0.2095396072,-0.0141126756,-0.0720959976,0.1533231437,0.2246878594,0.0292827152,-0.2047470063,0.4062135816,-0.1693811566,0.2454877943,-0.2644483447,0.1788318306,-0.0356653072,-0.2023017704,-0.0517953187,0.1018908471,-0.0046761343,-0.293756932,0.0954740718,0.4632967114,0.0546923168,0.08568497,-0.1847886443,-0.0953086168,0.2269652784,-0.0808336586,-0.0273770448,0.2919701338,0.2026348412,-0.1917808801,-0.2744134963,0.1912704259,-0.3355849385,0.1634294242,0.0298720635,-0.1083321646,-0.0484339148,0.1079697907,-0.3023562431,-0.0635969713,0.4508511424,-0.245456323,0.2605041265,0.001822158,-0.4244135916,-0.2174914628,0.4196653664,-0.0460522622,0.1226554886,0.1877402812,-0.2147010267,0.212012589,-0.0877679214,-0.0419770256,0.5764291883,0.1378007531,-0.2983497083,0.2298145294,-0.1166211367,-0.2212798446,0.289095819,-0.0472781882,-0.0982488245,0.0960121751,-0.1998828202,0.0083255861,-0.2600301802,-0.0678929389,-0.1015431955,0.0715258271,-0.412751019,0.222360611,-0.3261346519,-0.1620817333,-0.3533613086,0.1199700087,-0.2934476137,-0.0352531411,-0.2882407308,0.2256355137,0.1631828547,0.0368443988,-0.025598187,0.0039330921,0.1004287452,-0.5494058728,-0.1985674053,0.0081635946,-0.2308933288,-0.0934241638,0.3823769689,0.0441114455,0.2374777943,-0.3961957097,-0.1094290391,-0.0985937938,-0.1599082798,0.0557672121,-0.0614242442,0.1960316002,0.0531645492,0.2280198932,-0.123751916,-0.1310472041,0.4299977124,-0.0381334759,-0.1885631084,0.2453443706,0.418446213,-0.309718281,-0.2610920072,-0.3623771966,-0.1279267967,-0.2842009068,0.3581483066,0.1559180021,-0.0295690484,0.5024074316,0.4287550151,0.15696235,-0.1803515255,0.2165792435,0.0652968511,-0.1683056056,0.471650809,-0.1643847376,-0.6319022179,0.0775601417,0.4496120214,-0.2843745053,0.2175411284,-0.4422395527,-0.0620809942,-0.1192547455,0.092089884,0.0410850048,0.3046552837,0.1864622682,0.0804128125,0.0956785753,-0.0544941127,-0.3214206994,0.1158758476,-0.2110431045,0.03335388,0.2350959331,0.3713712096,-0.2012346834,0.4552719593,0.3391940594,-0.0664085746,0.2473822087,-0.5043381453,0.5094949603,-0.1640397608,-0.5347828269,0.0561735779,-0.21773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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"It seems that I ran into the same problem\r\n```\r\ndef tokenize(cols, example):\r\n  for in_col, out_col in cols.items():\r\n    example[out_col] = hf_tokenizer.convert_tokens_to_ids(hf_tokenizer.tokenize(example[in_col]))\r\n  return example\r\ncola = datasets.load_dataset('glue', 'cola')\r\ntokenized_cola = cola.map(partial(tokenize, {'sentence': 'text_idxs'}),\r\n                             num_proc=2,)\r\n```\r\nand it outpus (exceprts)\r\n```\r\nConcatenating 2 shards from multiprocessing\r\nSet __getitem__(key) output type to python objects for ['idx', 'label', 'sentence', 'text_idxs'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nDone writing 532 indices in 4256 bytes .\r\nDone writing 531 indices in 4248 bytes .\r\nProcess #0 will write at \/home\/yisiang\/.cache\/huggingface\/datasets\/glue\/cola\/1.0.0\/930e9d141872db65102cabb9fa8ac01c11ffc8a1b72c2e364d8cdda4610df542\/tokenized_test_00000_of_00002.arrow\r\nProcess #1 will write at \/home\/yisiang\/.cache\/huggingface\/datasets\/glue\/cola\/1.0.0\/930e9d141872db65102cabb9fa8ac01c11ffc8a1b72c2e364d8cdda4610df542\/tokenized_test_00001_of_00002.arrow\r\nSpawning 2 processes\r\n```\r\nand then the program never stop.","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":121,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n It seems that I ran into the same problem\r\n```\r\ndef tokenize(cols, example):\r\n  for in_col, out_col in cols.items():\r\n    example[out_col] = hf_tokenizer.convert_tokens_to_ids(hf_tokenizer.tokenize(example[in_col]))\r\n  return example\r\ncola = datasets.load_dataset('glue', 'cola')\r\ntokenized_cola = cola.map(partial(tokenize, {'sentence': 'text_idxs'}),\r\n                             num_proc=2,)\r\n```\r\nand it outpus (exceprts)\r\n```\r\nConcatenating 2 shards from multiprocessing\r\nSet __getitem__(key) output type to python objects for ['idx', 'label', 'sentence', 'text_idxs'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nDone writing 532 indices in 4256 bytes .\r\nDone writing 531 indices in 4248 bytes .\r\nProcess #0 will write at \/home\/yisiang\/.cache\/huggingface\/datasets\/glue\/cola\/1.0.0\/930e9d141872db65102cabb9fa8ac01c11ffc8a1b72c2e364d8cdda4610df542\/tokenized_test_00000_of_00002.arrow\r\nProcess #1 will write at \/home\/yisiang\/.cache\/huggingface\/datasets\/glue\/cola\/1.0.0\/930e9d141872db65102cabb9fa8ac01c11ffc8a1b72c2e364d8cdda4610df542\/tokenized_test_00001_of_00002.arrow\r\nSpawning 2 processes\r\n```\r\nand then the program never stop.","embeddings":[-0.4042771459,-0.0761643201,0.0068098302,0.146670714,0.1475021839,-0.1896045357,0.3224094212,0.3429279327,0.1341744512,0.1369507015,0.0367483571,0.3943180144,-0.4054729342,0.310338974,-0.3056828976,0.0618968457,0.1283408403,-0.0522314869,-0.2669127584,0.1578018814,-0.279797256,0.2109823078,-0.3103196323,0.2153621763,-0.5755159855,0.0590776615,-0.0540032201,0.2517116368,0.004229615,-0.5668848157,0.3097843826,0.3128777742,0.0841088668,0.4231201708,-0.0001253461,0.1279418021,0.3116935492,-0.1059477702,-0.0364288501,-0.3833858371,-0.0744350031,-0.2384934276,0.2321601957,0.1469333619,0.2514846027,-0.0637059957,-0.1770320237,-0.1964325458,0.1855876446,0.3598256409,0.0798642561,0.2393260002,0.2558632493,0.106185101,0.0308736991,0.1623829901,-0.0530019514,-0.0189514477,0.2539156675,-0.4286129177,-0.0830043256,0.2592658103,-0.2532216311,-0.2118041664,-0.1340045184,-0.2208434492,0.5308262706,-0.540256381,0.2115586698,0.0486420356,-0.2676083148,-0.0402106382,-0.4429701567,-0.176854074,-0.2546718717,-0.5399870872,0.1709608883,0.0307247601,-0.1698826551,0.1184880957,-0.439244777,-0.2035617232,0.0078960899,-0.0298823081,-0.1458258331,0.6477392912,0.2138981819,0.3055250049,0.1323184669,0.130570069,0.1838415712,-0.0163391028,-0.0709623694,0.207008481,-0.4304935038,0.0555331931,0.0561705679,-0.2127608955,-0.1796363294,-0.0278097931,-0.2530755997,0.1307653189,-0.1172353625,0.0927313417,0.4056189954,-0.0962312669,0.1677099168,0.3280444443,0.2209367603,-0.1989021003,-0.0220488459,0.0117406221,0.2665813565,-0.0622575358,-0.0979441181,0.2686050832,0.0859605074,-0.1967092603,-0.2017096877,0.1901551634,-0.1704506874,-0.1557840407,0.076307945,0.2368407995,0.0790321007,0.7875010967,0.1054009572,0.2169736475,-0.3290952146,-0.1062655374,-0.069049105,-0.0170265641,-0.3850457072,0.1155861765,0.2116104662,0.1460444331,-0.0303646792,0.0347133093,-0.3158589602,-0.2540472746,0.2134025246,-0.3203782737,0.1630386114,0.6764134169,-0.0766727179,0.0704787001,0.0608326644,-0.2051453292,0.0493657514,0.196880579,-0.528067708,-0.1739725918,-0.2313921601,0.0630153418,0.0921197608,0.2983644307,-0.1968151927,0.2527915239,0.3752551079,-0.272626251,-0.2939334512,-0.4000747204,-0.3631501496,-0.2814882994,-0.0012882305,0.2978385687,-0.4101808965,-0.0337190032,0.1389254481,-0.1741313934,0.3642458618,0.2580123246,-0.0363178253,0.0802293941,-0.1671099961,0.1751141399,0.1112992838,-0.2420786768,-0.0535121113,0.2751089334,0.0924496725,0.3352847695,-0.088490203,-0.3326949179,0.3726418614,-0.1397295892,0.2458258718,-0.0516839549,-0.1721580476,0.0256981496,-0.4304659367,-0.0167047903,-0.0738441423,-0.1338193566,0.4579972923,0.2050948143,-0.1281719357,-0.4877860248,0.3257185519,-0.1268086135,0.3116859496,0.0617736131,0.1279980689,0.1767352819,0.1328583807,-0.2497059554,-0.3686510623,0.1384370476,-0.2625176907,0.0193939805,-0.0266870987,-0.0783315599,0.0837063417,0.2017332166,-0.2120802253,-0.2150038928,0.0355656222,0.0074174814,-0.3615643382,-0.0625980422,-0.1797469854,0.625947237,0.091997318,0.1794136018,0.0131107029,0.2692207396,-0.1198470592,-0.4355103672,-0.2855910659,0.3075087667,0.0316634066,-0.0748290792,-0.200615108,0.4490844011,0.5101372004,-0.1541983187,-0.0260389913,0.1566484571,0.2412418425,-0.1208864748,-0.1833622456,0.037562713,0.0654287338,-0.1723884791,0.3006968796,0.483391881,0.201399982,0.3852221966,0.25669837,0.2287194878,0.3226880431,0.0051959977,0.0316118337,-0.1580841541,0.1246569902,-0.1312185675,0.1465567648,0.0016201094,-0.1420030296,-0.195558086,0.1355320066,0.0595095456,-0.1583060324,0.0607259907,0.0952029526,-0.1042194143,0.1780013293,-0.0132490825,0.3717621267,-0.0238193534,-0.2368368059,0.1435663253,-0.2938762903,0.0356035046,0.0739235356,-0.0384558737,0.3780089915,0.3359934688,0.0807103962,0.0478107147,-0.2073845714,-0.3678789437,0.0716596916,0.3440575302,-0.5090512037,0.2327922434,-0.2995890379,0.3964595795,0.1238909364,-0.1198131517,-0.2803930938,-0.5962172747,-0.1310129613,0.5344035625,-0.0878550559,0.1409512162,-0.1044902727,0.1036587059,-0.2177422494,0.4064002335,-0.1127116904,-0.286685884,-0.2238863558,-0.143870756,0.3367703259,-0.2551400959,0.2371955961,0.1972360015,-0.2832110822,0.1011269614,-0.3656711876,0.1703359783,-0.0298877079,0.0133362152,0.110932447,-0.0670838058,-0.0422333665,-0.2127739042,0.1584321856,-0.1304798871,-0.2130654156,0.3102205694,-0.1451127082,0.0382950902,-0.1842952371,-0.2188405544,-0.4120937288,-0.1025576442,-0.1735719442,-0.2267582864,0.3224339187,0.0706637204,-0.0400239602,-0.0154559342,-0.0948967189,-0.0137680816,-0.1022756919,-0.0214018058,-0.163213551,0.0092593357,-0.1443979144,-0.0533849858,0.0301886387,0.1384504139,0.4326987267,-0.3306240737,0.0789940655,0.1064507589,-0.1260214746,0.1771831661,-0.1903131157,0.3407360017,0.3817727864,0.049549032,-0.0437264889,-0.1391005218,0.1047363207,-0.0750269368,0.0507857837,0.0907464176,0.3363541961,0.1733752191,0.680211544,0.3181328773,0.0758746341,0.4411141574,-0.0873885751,0.1266505122,-0.2016421407,-0.4437966645,-0.1668882966,-0.33285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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"same problem.\r\n`encoded_dataset = core_data.map(lambda examples: tokenizer(examples[\"query\"], examples[\"document\"], padding=True, truncation='longest_first', return_tensors=\"pt\", max_length=384), num_proc=16, keep_in_memory=True)`\r\nit outputs:\r\n```\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787499 indices in 25568385696 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nSpawning 16 processes\r\n```","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":301,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n same problem.\r\n`encoded_dataset = core_data.map(lambda examples: tokenizer(examples[\"query\"], examples[\"document\"], padding=True, truncation='longest_first', return_tensors=\"pt\", max_length=384), num_proc=16, keep_in_memory=True)`\r\nit outputs:\r\n```\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787499 indices in 25568385696 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\nSpawning 16 processes\r\n```","embeddings":[-0.3812628984,-0.159982577,-0.0257185455,0.2450912893,0.1385162771,-0.1244136542,0.2277842462,0.3551832438,0.0971073061,0.1650547385,0.0904945731,0.4780934751,-0.437987715,0.3225879967,-0.3071635962,0.074354738,0.0885859951,-0.0340094566,-0.2489496171,0.2080955952,-0.240830496,0.2539783418,-0.3136201501,0.1324144155,-0.5448884964,0.0372945629,-0.0100327907,0.2946363986,0.0041617421,-0.5604776144,0.33509624,0.1862288415,0.0239839256,0.4058553278,-0.0001238069,0.1744511724,0.3322082162,-0.1320036054,-0.0576935597,-0.3747686148,-0.1845237017,-0.1774750054,0.2513703704,0.0584542006,0.1948399097,0.0846881494,-0.0990761369,-0.2675632834,0.2351568639,0.4671219587,0.0967172235,0.2684924901,0.2412029207,0.1626387089,-0.0700325146,0.1293651164,-0.0586703978,-0.0073627797,0.2534245551,-0.2842435539,-0.0843150318,0.2644785941,-0.2236341983,-0.2193562686,-0.1762116253,-0.1897657067,0.5956934094,-0.6405871511,0.2028373927,0.031608697,-0.2833524346,-0.0602123551,-0.3970498443,-0.1658379883,-0.2814686,-0.6259972453,0.2206950635,0.0547291599,-0.1740383953,0.1226082444,-0.4526482224,-0.2315917164,-0.0487773828,-0.058144398,-0.1401828974,0.5158827305,0.2073036283,0.307647109,0.1674176753,0.1691039801,0.140595302,-0.0439474881,-0.0630421638,0.1547047198,-0.3959687352,0.0444795601,0.0572552308,-0.1537994593,-0.2437874824,-0.0653126016,-0.08066836,0.1007320657,-0.1338203698,0.0974461958,0.3817778826,-0.0635505244,0.2099064738,0.3211019635,0.2443681359,-0.1982805729,-0.0408754162,0.0844115689,0.3090513945,-0.0766795278,-0.0829106718,0.3224020004,0.0780442804,-0.1507158279,-0.200878039,0.1684617549,-0.122961469,-0.1415818781,0.0955839083,0.1618539542,0.0173610169,0.7640119195,0.1510399133,0.1550600678,-0.3262269199,-0.1650597006,-0.0813882723,-0.0162959825,-0.383908242,0.1201186106,0.2784774303,-0.0458318479,-0.0025161454,-0.0125776334,-0.3755195141,-0.2901799381,0.1588222235,-0.2691520452,0.1290231645,0.6434388757,-0.0847705156,0.0378689505,-0.0311035067,-0.2311691344,0.0893548578,0.2703109682,-0.5454251766,-0.199884966,-0.1913015693,0.0830652118,-0.0090253511,0.2907006741,-0.1352845728,0.2626663744,0.4345033467,-0.3390246034,-0.3026486635,-0.3347218931,-0.4246219993,-0.3029883802,-0.0268523451,0.2384123504,-0.388689816,-0.0208522324,0.0716141015,-0.1603699178,0.3210649788,0.2287078202,-0.0067514163,0.022817051,-0.1003552526,0.1779856086,0.1289135963,-0.2334320247,-0.1171935499,0.229833886,0.0215960164,0.3336875737,-0.0718952119,-0.2325259596,0.3409836888,-0.1377684325,0.2706861496,-0.0474333502,-0.1684700102,0.0419768244,-0.445848763,-0.0475396179,-0.0104410807,-0.0907253698,0.3886668682,0.1698444337,-0.125853464,-0.4694304466,0.3113692999,-0.112603046,0.2983155847,0.1176054403,0.0700896457,-0.0038853672,0.1334573179,-0.2639538348,-0.4916706085,0.1975854933,-0.211062476,0.0598972403,-0.0548028573,0.0016005959,0.135636583,0.1703983545,-0.208983466,-0.2314667404,0.0527525395,-0.0362777449,-0.2970740497,-0.0225936715,-0.1978597641,0.6065127254,0.0651134476,0.1337962747,-0.1734601557,0.2274015099,-0.1264917105,-0.432724148,-0.3220412135,0.2733949125,-0.0145869879,-0.0576649196,-0.2054569572,0.412061125,0.4922932088,-0.0928073078,-0.1072134078,0.2081218958,0.2767289877,-0.2052212805,-0.1986125559,0.0897642225,0.0358788744,-0.1755609959,0.2432496101,0.4486861527,0.2031267583,0.3599848449,0.2320649624,0.2206025571,0.299762845,0.0055633401,-0.0274563059,-0.156145215,0.1594950259,-0.1439873427,0.1384494901,0.0725641176,-0.1243331432,-0.2617254853,0.1637731045,0.0659068897,-0.1640600413,0.0554417334,0.0439122729,-0.1209331304,0.1442405879,0.0353028253,0.3846464455,-0.0169444922,-0.1841903776,0.1875161231,-0.2923647761,-0.0041845525,0.1214142218,-0.060346216,0.4126350284,0.3325662017,0.0933479965,0.0121380128,-0.2061805278,-0.3470259011,0.1262339503,0.3581795096,-0.4770471156,0.2011184394,-0.2688544393,0.3921819627,0.1077217981,-0.0824889764,-0.251857698,-0.5163871646,-0.1252585202,0.5447285175,-0.0894417986,0.1139657944,-0.0750090778,0.0922435299,-0.1993822902,0.3349091411,-0.1236953363,-0.3494327962,-0.1855380386,-0.1118901297,0.3578799069,-0.3373328447,0.2036115825,0.1893173754,-0.2535944581,0.180045411,-0.3561492264,0.0978301764,0.0027099983,0.0007185338,0.0373477861,-0.0380866565,0.0021098915,-0.2566197813,0.1516730636,-0.0926751643,-0.2342160344,0.3143593967,-0.1155804098,0.0484064035,-0.2818495929,-0.3552564383,-0.4357712567,-0.1446245462,-0.1148808897,-0.2469650805,0.3538688421,0.2138126343,0.0279247425,0.0080096293,-0.0402045362,0.0558431558,-0.0504350811,-0.0470001921,-0.156967327,0.0019498187,-0.1449820697,-0.0489738211,0.0109178592,0.225982666,0.4416419864,-0.3533616662,0.1268649697,0.0998179391,-0.1105588973,0.2188432664,-0.2389684319,0.3640264273,0.3983311653,0.0564018935,-0.0557654351,-0.1833664924,0.1532932073,0.0631324276,0.0367054902,0.1003945768,0.3216459453,0.1986774653,0.6136776209,0.404703021,-0.0415371917,0.4049917161,-0.0742644444,0.0647051409,-0.1796070784,-0.3545342684,-0.1869882345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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Thanks for reporting.\r\n\r\n\r\nWhich tokenizers are you using ? What platform are you on ? Can you tell me which version of datasets and pyarrow you're using ? @timothyjlaurent @richarddwang @HuangLianzhe \r\n\r\nAlso if you're able to reproduce the issue on google colab that would be very helpful.\r\n\r\nI tried to run your code @richarddwang with the bert tokenizer and I wasn't able to reproduce","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":64,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Thanks for reporting.\r\n\r\n\r\nWhich tokenizers are you using ? What platform are you on ? Can you tell me which version of datasets and pyarrow you're using ? @timothyjlaurent @richarddwang @HuangLianzhe \r\n\r\nAlso if you're able to reproduce the issue on google colab that would be very helpful.\r\n\r\nI tried to run your code @richarddwang with the bert tokenizer and I wasn't able to reproduce","embeddings":[-0.4514070749,-0.0183560606,-0.0187173411,0.2539693415,0.1591782123,-0.1641509384,0.2910157144,0.3014787138,-0.0354786366,0.146110028,0.0482645482,0.4889172316,-0.4316676855,0.3358326554,-0.3009809554,0.0277109835,0.1179320961,-0.0190118216,-0.2634863555,0.2409516573,-0.2404624522,0.225755468,-0.401640892,0.1766656041,-0.6489459872,0.0602547824,-0.0938618779,0.2311900109,-0.0793512464,-0.586132586,0.3002739847,0.2776141763,0.070537962,0.4927061498,-0.0001242773,0.0863855034,0.3700333834,-0.068893984,-0.0755959973,-0.4253841937,-0.1398858577,-0.1962038279,0.326849699,0.1186022684,0.2297603786,-0.0144225005,-0.1105302274,-0.1358258724,0.295132339,0.4044710994,0.0998646319,0.2108288407,0.2679286003,0.1761285812,0.0266659819,0.1134238094,-0.029560836,0.0634082034,0.3047253788,-0.3775468469,-0.0981574059,0.1819476038,-0.2512390018,-0.2436913252,-0.1529686749,-0.1884289831,0.5462459326,-0.6417779922,0.1981923133,0.0507446788,-0.1269942969,-0.0952428058,-0.409552604,-0.1634819657,-0.2056479305,-0.5241117477,0.2612171471,0.0887136236,-0.1917433441,0.1744311899,-0.512126565,-0.1702480763,-0.0599607304,-0.0430708192,-0.1608616114,0.6147714853,0.1970791519,0.2824648619,0.1318354756,0.1094725952,0.3357935846,-0.0813709944,-0.1276591271,0.1960503757,-0.4639246464,0.0747697279,0.0073759221,-0.0823294818,-0.2194864303,0.020001635,-0.2418677807,0.1100883558,-0.0998958349,0.0787976682,0.384336561,-0.0076071243,0.2199829817,0.340882808,0.2288215309,-0.2596561313,0.0027711643,0.0371749103,0.2253895998,-0.1269138753,-0.0501925424,0.3050415814,0.1194529235,-0.2124556452,-0.2372740358,0.1194868237,-0.2387345582,-0.1723511666,0.094883658,0.1854165494,0.0307488758,0.7054215074,0.083328031,0.1961914897,-0.2631705105,-0.1573426425,-0.0654680729,-0.045228526,-0.4083966613,0.1598667949,0.2452349812,0.0504907779,-0.0003737888,0.050988391,-0.3588193655,-0.2416548431,0.1078233644,-0.2778948247,0.0396646708,0.6530490518,-0.0253033619,0.0413961373,-0.0589490347,-0.2250757962,0.0949930549,0.2815291286,-0.5119836926,-0.1841511428,-0.2612283528,0.0831530988,-0.0231683645,0.2442414463,-0.2944863737,0.2875611186,0.3829674125,-0.2585481405,-0.2988170981,-0.3133195937,-0.331846863,-0.3129889667,-0.0024773101,0.307767719,-0.4657447934,-0.0098974407,0.087454401,-0.1941268891,0.3770689368,0.1856148243,-0.0613604672,0.0669882447,-0.1267248392,0.215823561,0.140177384,-0.1279755533,-0.1124334782,0.2217224389,0.0198814105,0.2737292051,-0.0765961185,-0.2837847471,0.3305280805,-0.1273438632,0.240541622,-0.0251977723,-0.1801247448,0.055950325,-0.4307548404,-0.042464979,-0.0346674658,-0.0584328957,0.370148927,0.1658086181,-0.1280094981,-0.5494300723,0.3390696049,-0.0956562385,0.3101182878,0.0468285978,0.2010080665,0.0631357059,0.1250197142,-0.34169361,-0.4653599858,0.2084009796,-0.225909099,0.0564944372,-0.1141112745,-0.0186333749,0.0731931478,0.218865782,-0.2546346784,-0.1963988394,0.0548204556,-0.0569619983,-0.2888918519,-0.0090970388,-0.1978033781,0.6334373951,0.1330014765,0.1863329709,0.0088009676,0.3338902593,-0.1678679734,-0.4845399857,-0.2472648025,0.1851918697,0.0388390645,-0.0657138452,-0.2361524552,0.4898020327,0.4748798311,-0.1184359938,-0.0471902676,0.1707308143,0.238460511,-0.2020021379,-0.1777737737,0.0177077483,0.031491898,-0.1478212029,0.300881952,0.4878903329,0.1369510293,0.3793056309,0.1605117619,0.2333791256,0.2646214068,-0.0008948233,0.015010695,-0.1043363065,0.1605807543,-0.1033002213,0.16027686,0.0734191015,-0.0789115876,-0.2140072435,0.1174606234,-0.0077373013,-0.2083774805,0.0450219885,-0.0087575009,-0.1165949702,0.1818483919,0.0216803718,0.4007752836,0.0097523695,-0.2446662188,0.188806206,-0.2269085646,-0.0186138749,0.1428357065,-0.0703982338,0.4502498209,0.2981046438,0.1378419995,0.0256137382,-0.2035502344,-0.330483079,0.1311552525,0.411490649,-0.5120652318,0.2068114281,-0.3035531044,0.2663401067,0.0753615201,-0.1748274416,-0.3257455528,-0.5263910294,-0.1301469058,0.501065731,-0.0153540755,0.1526309401,-0.0430886745,0.075417839,-0.1909266114,0.3626380563,-0.1729192585,-0.3490327597,-0.2568057477,-0.096551016,0.4135924876,-0.226315558,0.2242790312,0.1385638565,-0.2426470965,0.1376253664,-0.3264600635,0.1218764111,-0.0456868373,0.0993344709,0.0476022996,-0.0871103406,-0.0611737072,-0.2198957205,0.1496847421,-0.1308501214,-0.2919811606,0.2979002297,-0.1592388898,0.0333513878,-0.2079053074,-0.2921704054,-0.4472483695,-0.0652050823,-0.1241436154,-0.2491881847,0.3339018524,0.1426371783,0.0502238907,-0.0268349778,-0.1156218126,-0.0214423239,-0.0339340977,-0.0353108235,-0.1688206941,0.0389618054,-0.1542485356,-0.0213817507,0.063800633,0.118798919,0.410574913,-0.3101975322,0.0174236409,0.0785765424,-0.021666104,0.2032386363,-0.2102433741,0.3423298001,0.4269163609,0.0653369948,-0.0130824624,-0.1810950488,0.0445570238,-0.0369701833,0.0857582092,0.0567320175,0.3177714348,0.1290831119,0.6365389824,0.3438287973,0.0245845001,0.3988358378,-0.1147427186,0.0726944283,-0.1767137796,-0.4179882407,-0.1399572194,-0.3385905027,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Hi, Sorry that I forgot to see what my version was.\r\nBut after updating datasets to master (editable install), and latest pyarrow. \r\nIt works now ~","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":26,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Hi, Sorry that I forgot to see what my version was.\r\nBut after updating datasets to master (editable install), and latest pyarrow. \r\nIt works now 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Sorry,  I just noticed this.\r\nI'm running this on MACOS the version of datasets I'm was 1.0.0 but I've also tried it on 1.0.2. `pyarrow==1.0.1`, Python 3.6\r\n\r\nConsider this code:\r\n```python\r\n\r\n    loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n    ds = load_dataset(\r\n        loader_path, name=\"prodigy-ds\", data_files=list(file_paths), cache_dir=cache_dir\r\n    )[\"train\"]\r\n    valid_relations = set(vocabulary.relation_types.keys())\r\n\r\n    ds = ds.filter(filter_good_rows, fn_kwargs=dict(valid_rel_labels=valid_relations))\r\n\r\n    ds = ds.map(map_bpe_encodings, batched=True, fn_kwargs=dict(tokenizer=vocabulary.tokenizer), num_proc=10)\r\n\r\n    # add all feature data\r\n    ner_ds: Dataset = ds.map(\r\n        add_bio_tags,\r\n        fn_kwargs=dict(ner_label_map=vocabulary.ner_labels, tokenizer=vocabulary.tokenizer),\r\n    )\r\n    rel_ds: Dataset = ner_ds.map(\r\n        relation_ds_factory,\r\n        batched=True,\r\n        writer_batch_size=100,\r\n        fn_kwargs=dict(tokenizer=vocabulary.tokenizer, vocabulary=vocabulary),\r\n    )\r\n```\r\nThe loader is essentially a jsonloader with some extra error handling. The data is a jsonlines format with text field and a list of span objects and relation objects. \r\n\r\nIn the `ner_ds` a field, `ner_labels` is added, this is used in the downstream `relation_ds_factory`. It all runs fine in a single process but I get a KeyError error if run with num_proc set\r\n:\r\n\r\n```\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 348, in relation_ds_factory\r\n    ner_labels = example[\"ner_labels\"]\r\nKeyError: 'ner_labels'\r\n``` \r\n\r\nThis is just one example of what goes wrong.  I've started just saving the dataset as arrow at the end because it takes a long time to map\/filter\/shuffle and the caching isn't working (tracked it down to byte differences in the pickled functions). \r\n\r\n^^ Interestingly if I heed the warning from Tokenizers and set the environment variable, `TOKENIZERS_PARALLELISM=true` the map just hangs:\r\n\r\n```\r\n[I 200921 21:43:18 filelock:318] Lock 5694118768 released on \/Users\/timothy.laurent\/.cache\/huggingface\/datasets\/_Users_timothy.laurent_.cache_huggingface_datasets_prodigy_dataset_builder_prodigy-ds-5f34378723c4e83f_0.0.0_e67d9b43d5cd82c50b1eae8f2097daf95b601a04dc03ddd504f2b234a5fa247a.lock\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.34ba\/s]\r\n#0:   0%|                                                                                                            | 0\/1 [00:00<?, ?ba\/s]\r\n#1:   0%|                                                                                                            | 0\/1 [00:00<?, ?ba\/s]\r\n#2:   0%|                                                                                                            | 0\/1 [00:00<?, ?ba\/s]\r\n#3:   0%|                                                                                                            | 0\/1 [00:00<?, ?ba\/s]\r\n#4:   0%|                                                                                                            | 0\/1 [00:00<?, ?ba\/s]\r\n#5:   0%|                                                                                                            | 0\/1 [00:00<?, ?ba\/s]\r\n#6:   0%|                                                                                                            | 0\/1 [00:00<?, ?ba\/s]\r\n#7:   0%|                                                                                                            | 0\/1 [00:00<?, ?ba\/s]\r\n#8:   0%|                                                                                                            | 0\/1 [00:00<?, ?ba\/s]\r\n```","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":289,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Sorry,  I just noticed this.\r\nI'm running this on MACOS the version of datasets I'm was 1.0.0 but I've also tried it on 1.0.2. `pyarrow==1.0.1`, Python 3.6\r\n\r\nConsider this code:\r\n```python\r\n\r\n    loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n    ds = load_dataset(\r\n        loader_path, name=\"prodigy-ds\", data_files=list(file_paths), cache_dir=cache_dir\r\n    )[\"train\"]\r\n    valid_relations = set(vocabulary.relation_types.keys())\r\n\r\n    ds = ds.filter(filter_good_rows, fn_kwargs=dict(valid_rel_labels=valid_relations))\r\n\r\n    ds = ds.map(map_bpe_encodings, batched=True, fn_kwargs=dict(tokenizer=vocabulary.tokenizer), num_proc=10)\r\n\r\n    # add all feature data\r\n    ner_ds: Dataset = ds.map(\r\n        add_bio_tags,\r\n        fn_kwargs=dict(ner_label_map=vocabulary.ner_labels, tokenizer=vocabulary.tokenizer),\r\n    )\r\n    rel_ds: Dataset = ner_ds.map(\r\n        relation_ds_factory,\r\n        batched=True,\r\n        writer_batch_size=100,\r\n        fn_kwargs=dict(tokenizer=vocabulary.tokenizer, vocabulary=vocabulary),\r\n    )\r\n```\r\nThe loader is essentially a jsonloader with some extra error handling. The data is a jsonlines format with text field and a list of span objects and relation objects. \r\n\r\nIn the `ner_ds` a field, `ner_labels` is added, this is used in the downstream `relation_ds_factory`. It all runs fine in a single process but I get a KeyError error if run with num_proc set\r\n:\r\n\r\n```\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 348, in relation_ds_factory\r\n    ner_labels = example[\"ner_labels\"]\r\nKeyError: 'ner_labels'\r\n``` \r\n\r\nThis is just one example of what goes wrong.  I've started just saving the dataset as arrow at the end because it takes a long time to map\/filter\/shuffle and the caching isn't working (tracked it down to byte differences in the pickled functions). \r\n\r\n^^ Interestingly if I heed the warning from Tokenizers and set the environment variable, `TOKENIZERS_PARALLELISM=true` the map just hangs:\r\n\r\n```\r\n[I 200921 21:43:18 filelock:318] Lock 5694118768 released on \/Users\/timothy.laurent\/.cache\/huggingface\/datasets\/_Users_timothy.laurent_.cache_huggingface_datasets_prodigy_dataset_builder_prodigy-ds-5f34378723c4e83f_0.0.0_e67d9b43d5cd82c50b1eae8f2097daf95b601a04dc03ddd504f2b234a5fa247a.lock\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00,  1.34ba\/s]\r\n#0:   0%|                                                                                        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?ba\/s]\r\n```","embeddings":[-0.3902367055,-0.0592111722,-0.0349889807,0.2178634107,0.1645161361,-0.1262937039,0.2920169532,0.2759281397,0.0126609746,0.1588909626,0.027785698,0.5151471496,-0.4663677812,0.1931906193,-0.3737515807,0.0636929423,0.1268112361,-0.0707599223,-0.2591288388,0.2371894121,-0.2843744755,0.2678748965,-0.3191679418,0.132583499,-0.4713807702,0.0843237191,-0.0683147013,0.3312543929,-0.0138635291,-0.5806868672,0.313513577,0.2603117228,0.0385840237,0.4804727435,-0.0001283317,0.1993655264,0.3485915363,-0.0834867507,-0.1267864257,-0.4279059172,-0.1911366284,-0.1817952991,0.3719674647,0.061871592,0.2269573808,0.065886721,-0.1034057289,-0.2707560956,0.2035187483,0.4774683416,0.0735512674,0.2868750393,0.2374197096,0.1393536776,0.1045707241,0.2273843884,-0.0284487754,0.1546577513,0.3186120391,-0.3993650973,-0.0570379756,0.2126248181,-0.2640066445,-0.190993011,-0.1678403616,-0.1613083333,0.6544430852,-0.5508720875,0.1051117852,0.0620830394,-0.279481411,-0.0378321335,-0.4952349067,-0.2726466954,-0.202734828,-0.4610621333,0.2794249058,0.1045864448,-0.1968158334,0.1120820493,-0.533808291,-0.1942967772,-0.0470323078,-0.0213961508,-0.146976158,0.4835234284,0.201926887,0.3012487888,0.2571425736,0.1812760085,0.2967933416,-0.0528956242,-0.0266065784,0.1361504942,-0.3973773122,0.1082416475,0.0853555724,-0.0201199614,-0.2698212862,-0.1237291172,-0.2139736712,0.0115729384,-0.1222900674,0.0902782008,0.401918143,-0.1053700149,0.1600659192,0.3277313113,0.2415083647,-0.1646747738,-0.0319317169,0.049474217,0.3143293262,-0.0866266862,-0.0327453688,0.253067106,0.1316755861,-0.1706767082,-0.2822547555,0.1109131128,-0.1060232967,-0.1977370679,0.0632683411,0.186062023,0.0114508094,0.7769070864,0.1179558113,0.2578349113,-0.3525600135,-0.0100732474,-0.0376886874,-0.0341650657,-0.3728174269,0.1222595721,0.1942777336,-0.0074799247,-0.0251636375,0.0497132204,-0.376331687,-0.2689580321,0.0794303417,-0.237399295,0.0149759576,0.6824306846,-0.1149511337,0.0577007979,0.0015735601,-0.0498101749,0.0627911463,0.3190524578,-0.4979275465,-0.1897996068,-0.2720981538,0.0515523367,-0.0527851842,0.2867564857,-0.1496298462,0.1937617064,0.4530760646,-0.3586957455,-0.2541362047,-0.3936060667,-0.3620464206,-0.3363011181,-0.0959209949,0.2916580439,-0.4016878307,-0.0132419793,-0.0017041432,-0.1993289739,0.3723531961,0.1838982105,-0.0309001766,0.0506740324,-0.1230260506,0.0603297278,0.1363580674,-0.1564815044,-0.1171422005,0.2111741006,0.0261724386,0.338583082,-0.0083542829,-0.2690427899,0.2257904112,-0.1163293719,0.1201394945,-0.0425291732,-0.1851770431,0.0972474888,-0.3684078157,-0.085811004,-0.0229808185,0.0099370144,0.3281287253,0.2012072057,-0.0785196722,-0.5475058556,0.359230727,-0.137417838,0.2546218038,0.1185212508,0.0758690834,0.103861019,0.0685261711,-0.3592191935,-0.4807923734,0.1885324717,-0.2075800449,0.0658969432,-0.1936080009,-0.0030003553,0.1345405281,0.2470803112,-0.2068517506,-0.1502073258,-0.009307676,0.0411521383,-0.269040525,-0.009131371,-0.2266809344,0.5915706754,0.1010039225,0.1743608713,-0.1415229887,0.2607125342,-0.1526126117,-0.4264979661,-0.2687478364,0.1833890527,-0.0396083258,-0.1014267504,-0.2160593718,0.4433250427,0.526933372,-0.0824615657,-0.1035685763,0.0848543718,0.2232703418,-0.1494114995,-0.1771420538,0.0278939512,0.0121736052,-0.1099253148,0.1677223295,0.4997619092,0.1795986891,0.4273737669,0.1819782108,0.2141730487,0.2338954657,-0.0310655665,0.07588806,-0.1896802485,0.2151067704,-0.0762616768,0.1706488878,0.0909808427,-0.1406539679,-0.1778077036,0.1783758551,-0.0294325668,-0.223291412,0.1606327742,0.1228898689,-0.0887502432,0.1456647664,0.0523058921,0.5058743954,-0.0183926448,-0.1601222306,0.1790274084,-0.2569560409,-0.0544666089,0.1409515887,-0.0223163348,0.4753065109,0.2240649611,0.1380575448,0.0309247989,-0.1429341882,-0.3365243375,0.1123440415,0.4086907804,-0.5074980855,0.2802318335,-0.2813071012,0.35570997,0.100207448,-0.1912258863,-0.3339085281,-0.574213028,-0.1533732563,0.5668952465,-0.0479360223,0.112604849,0.0036664954,0.0170777421,-0.1395082027,0.2880706787,-0.1432930082,-0.2916991711,-0.1898858547,-0.1433071494,0.351377964,-0.3711665273,0.1469879597,0.1352257282,-0.2889918387,0.1344395876,-0.2641728818,0.078927435,-0.0919097662,0.0494198874,0.1642391533,-0.0096581923,0.0157181714,-0.1921673864,0.1474490762,-0.1172628701,-0.2671200931,0.2706723511,-0.0943267047,0.1018959284,-0.2709847987,-0.241150558,-0.4557641149,-0.1072819084,-0.0832757503,-0.2724333107,0.4007447362,0.095407702,0.1183815822,-0.0040317304,-0.1268735975,0.0669996664,-0.0192441586,-0.1305200756,-0.1743671447,0.060360048,-0.1189939752,-0.0972301364,0.0485861599,0.1815907657,0.4506176412,-0.4062321782,0.019671455,0.0632361919,0.0106000947,0.1800069809,-0.0984769315,0.3727628589,0.4366258979,0.0714963973,-0.0349689238,-0.1571934372,0.0263095386,-0.010599629,0.1163603961,0.1012194455,0.3563603163,0.0692335144,0.6154002547,0.3028970361,0.024001047,0.4207400084,-0.1140316427,0.1770393252,-0.1278588474,-0.388689816,-0.2190794051,-0.25858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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"#659 should fix the `KeyError` issue. It was due to the formatting not getting updated the right way","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":18,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n #659 should fix the `KeyError` issue. It was due to the formatting not getting updated the right way","embeddings":[-0.3512131572,-0.1855427474,-0.0195908081,0.21030581,0.1381857842,-0.1670842022,0.279554069,0.3684962094,0.0815791711,0.1086883396,0.0889100879,0.3803418577,-0.4003902972,0.400125742,-0.3610644341,-0.0263170786,0.1120805815,-0.0113873798,-0.3007359803,0.2906777561,-0.2718750834,0.2635469139,-0.3654167056,0.1494772881,-0.5365363955,0.0468143262,-0.1020090133,0.2186754048,-0.0090193618,-0.6350335479,0.3107408583,0.2939074039,-0.0312150251,0.4254748225,-0.0001246411,0.1012204811,0.3174884915,-0.1645227522,-0.0690915287,-0.2924408019,-0.222808972,-0.2399374247,0.2814206481,0.1030045003,0.1462618262,-0.0645411089,-0.168445006,-0.1902516633,0.2530600131,0.3587377369,0.1109459624,0.198188141,0.3393394649,0.1626249552,-0.0395517275,0.1583103985,-0.0288628805,0.020966731,0.302185595,-0.2966939509,-0.0835430175,0.3755783737,-0.2106487155,-0.2462289929,-0.0806416124,-0.2204282433,0.6230471134,-0.5610292554,0.1792760789,-0.0036662992,-0.1783303022,0.0002152335,-0.4601679146,-0.239700675,-0.2292336524,-0.4920301735,0.2561888695,-0.009299662,-0.138959378,0.1675009578,-0.4596162438,-0.2790907919,-0.0793253854,-0.0925054401,-0.045141831,0.5593509674,0.1581875086,0.2434732765,0.0868087932,0.1045118049,0.1365901828,-0.0745362714,-0.1486057192,0.1676543504,-0.3396682441,0.0580588914,0.0380162559,-0.053946726,-0.2356516272,-0.0823000744,-0.195446074,0.1293760091,-0.0923483148,-0.0000644441,0.4277984798,-0.0651589036,0.3168876469,0.2542020977,0.2367102802,-0.2161653489,0.0550286956,0.0133819832,0.3371013701,-0.1049287543,-0.0162288528,0.3913570642,0.1192442626,-0.1721612811,-0.1057623848,0.1788408011,-0.0347326808,-0.2633396685,0.0211248323,0.1464975923,0.0519356765,0.7958670855,0.1379258931,0.1675080657,-0.303982079,-0.0909318626,-0.0441626161,-0.0180010125,-0.4197016954,0.0411583148,0.2246312052,-0.0470206589,-0.0232576262,0.0513088591,-0.3942948282,-0.2987011373,0.0350022763,-0.2101656497,0.1784780771,0.6543557048,-0.131706804,0.0979316607,-0.0364758149,-0.1969342381,0.0777080879,0.2401845455,-0.5455412269,-0.1287513226,-0.2255560607,0.0737909228,0.0727319196,0.3116742373,-0.1367508918,0.2338903546,0.438331902,-0.3816615045,-0.2752705216,-0.3559553027,-0.3625250161,-0.3146935701,-0.0502027422,0.3140787482,-0.4769178033,-0.0701623186,0.1530882567,-0.1712597907,0.3403357267,0.1905321032,0.023639353,0.0471595265,-0.1083849296,0.1005764231,0.1277480125,-0.2211735994,-0.1791697741,0.3235459626,0.013238363,0.365031004,0.0487262979,-0.2932927012,0.3140540421,-0.0850169659,0.282943368,-0.1017530113,-0.1259889454,0.0704030991,-0.4282627702,0.015011901,-0.0669372231,-0.167552039,0.357986331,0.1379951835,-0.2026560605,-0.475399226,0.33156389,-0.1146088541,0.3142383695,0.0391068459,0.0674777627,0.0599834397,0.1719403118,-0.2382501364,-0.4738487899,0.1375685781,-0.2226947695,0.0047201421,-0.1448158026,0.0010650369,0.0444621667,0.1925702542,-0.2922160327,-0.2492041886,0.041765593,0.0107115628,-0.3038726449,-0.0823195204,-0.1843638122,0.5929731131,0.2195536196,0.1409046352,-0.0882686749,0.1312265992,-0.2019506991,-0.4209198654,-0.2407811284,0.2778545618,-0.0094550001,-0.1175537631,-0.2070064843,0.5246531367,0.5095104575,-0.1137293354,-0.1429157555,0.1915371865,0.268491447,-0.0770466253,-0.1743265837,0.0702149495,0.0227089003,-0.1169540212,0.0810186639,0.5475662947,0.0962426066,0.4146905541,0.1494486034,0.2452741563,0.2949585617,0.0570024736,-0.0602537394,-0.2418469638,0.1514021009,-0.2580640316,0.1053335667,0.001240272,-0.1381929964,-0.1894830465,0.1466972381,0.105109401,-0.109724246,0.017060576,0.0435087867,-0.1117243171,0.0992233604,0.0218311194,0.3506157994,0.0209108498,-0.2784491479,0.1740038544,-0.1862997711,-0.0493131541,0.1387522221,-0.0766849741,0.3364537656,0.3832502365,0.056158632,0.010623144,-0.1192159429,-0.2493800223,0.0324690156,0.3667414188,-0.5073884726,0.2152490467,-0.2861204743,0.2549757063,0.0421197563,-0.109275654,-0.1926590949,-0.5729332566,-0.1137760282,0.4209921658,-0.0548844673,0.1483545303,-0.0123480288,0.0344811529,-0.1731377095,0.3812194765,-0.1331987977,-0.3283342719,-0.1470113397,-0.1047791094,0.3488055766,-0.3240474164,0.2100392729,0.0738013089,-0.1691029519,0.236829266,-0.3342378139,0.1082952544,-0.0472081602,-0.0064860615,0.1615286767,0.0518070683,0.0494501293,-0.2052884996,0.1594745666,-0.1578568518,-0.2601367831,0.3101465702,-0.149607718,0.1178078204,-0.2731796205,-0.3018988371,-0.3529520035,-0.0495851599,-0.0805207863,-0.2224941552,0.3450743854,0.1496513486,0.0659467652,-0.0863049626,-0.1298864484,0.0984945223,-0.0306528844,0.0026161505,-0.1935723275,0.0507384501,-0.1470983773,-0.0774887279,0.0760762319,0.0738083199,0.3409603536,-0.2849748135,0.0146599235,0.095136486,-0.125904575,0.2607448399,-0.1134286672,0.3486637771,0.4518114328,0.0555604286,-0.0486735776,-0.1659440994,0.0462608039,-0.0057883211,-0.0026092781,0.1092260778,0.3223183155,0.13276802,0.6104079485,0.3465014696,0.1186999306,0.3046036065,-0.1118744835,0.1353441477,-0.2403228581,-0.3785359859,-0.2232074589,-0.2772703767,0.057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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Also maybe @n1t0 knows why setting `TOKENIZERS_PARALLELISM=true` creates deadlock issues when calling `map` with multiprocessing ?","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":16,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Also maybe @n1t0 knows why setting `TOKENIZERS_PARALLELISM=true` creates deadlock issues when calling `map` with multiprocessing ?","embeddings":[-0.4357655942,-0.1091967374,-0.0417286009,0.2121776342,0.0695560127,-0.126509428,0.2546488643,0.3328145444,0.1938226819,0.1219439209,0.1160629243,0.4567498267,-0.4093019664,0.2884688675,-0.3752997816,0.0169866215,0.0992285386,-0.094248727,-0.3434426188,0.2482099235,-0.2782725096,0.2710521221,-0.3459435105,0.1593248844,-0.5353220701,0.0872885659,-0.0039736438,0.2528886497,-0.0188716929,-0.573179543,0.2949807346,0.2831947207,0.0004403761,0.4602254629,-0.0001217121,0.1132882759,0.3699000776,-0.0942805558,-0.0318265818,-0.3821792305,-0.2222522348,-0.2362090945,0.2945428491,0.103973031,0.2161948234,0.0128660435,-0.0772559792,-0.2985168695,0.1986693889,0.372841388,0.1237755194,0.2111014128,0.297875762,0.1220005155,-0.0423817784,0.1499406397,-0.0497977175,0.0485664085,0.3448382914,-0.3739497066,-0.1388048977,0.2796147764,-0.2362969965,-0.1811296493,-0.1206905842,-0.2400204986,0.6395820379,-0.5958400369,0.1814056486,0.0657546222,-0.2609025538,0.0006286997,-0.4477955699,-0.1799360365,-0.2648523152,-0.4586621821,0.2208007276,0.0218163226,-0.1726396382,0.1386403441,-0.5251956582,-0.2143575251,0.0352452695,-0.0544744171,-0.0366728529,0.5323663354,0.2305611372,0.3113503158,0.1949433684,0.1283584237,0.113835834,-0.0167996679,-0.0804867595,0.1571735591,-0.4042960703,0.1016928628,0.1372587383,-0.1121087521,-0.2406967133,-0.0560522564,-0.1629004329,0.0969965905,-0.0776492879,0.0668469444,0.4337721467,-0.1338667125,0.1751342565,0.2501194179,0.2519487739,-0.2092773765,0.0151370941,0.0317399129,0.3243446052,-0.1119964868,-0.0390037522,0.3069898784,0.0965447277,-0.1498585343,-0.1592805684,0.0878242329,-0.107860513,-0.2057652771,0.101189442,0.1439217776,0.0724177882,0.7728213072,0.1620066464,0.1372262686,-0.3340587616,-0.0898869187,-0.0551431254,-0.0666498616,-0.4002197087,0.1255273968,0.2563378513,0.0599527881,0.0080618942,0.0419398136,-0.3320827484,-0.21691598,0.1727746427,-0.2766205072,0.0906436667,0.631087482,-0.0971724987,0.0243760124,-0.0824455693,-0.0286204666,0.0511687733,0.2955014408,-0.5336505175,-0.1557149738,-0.1656827778,0.1078610867,0.0666458607,0.3060179949,-0.1915570945,0.254709214,0.3675672412,-0.3739640415,-0.2680085301,-0.3238871694,-0.4069840312,-0.3038038909,-0.0046002609,0.2971160412,-0.3287827969,-0.0234114919,0.0687612444,-0.2095829844,0.2784111798,0.2272229344,-0.0418685414,0.0260143224,-0.1166272312,0.1641643196,0.0441024639,-0.2357017696,-0.0901374593,0.2893678546,-0.008890464,0.329949826,-0.0470988005,-0.2168494463,0.3346088231,-0.1311822236,0.2851608992,-0.0148701631,-0.1969221085,0.1048761457,-0.4357783198,-0.0461987182,-0.0751762018,-0.082395494,0.3545601666,0.1582431942,-0.1317896545,-0.4724713862,0.2870244682,-0.071243085,0.3042713404,0.0659307986,0.0621412434,0.0573612377,0.1319958866,-0.2830133736,-0.5129542947,0.199440226,-0.2078189552,-0.0241884403,-0.0505439565,0.015270507,0.1671999097,0.1793141961,-0.1974955499,-0.1488039494,0.0632102564,-0.0676070079,-0.2822651863,-0.0553281121,-0.1842774153,0.6492671371,0.1441810876,0.0867221281,-0.0818904936,0.221170336,-0.1520411223,-0.4408725798,-0.2989481688,0.2226276249,-0.045403786,-0.0716135427,-0.2133348137,0.4630851448,0.4911286831,-0.0391312689,-0.037637122,0.1667612046,0.2644596696,-0.1828808486,-0.1570649445,0.1237259135,0.0492204279,-0.1837459803,0.2333987504,0.4180732071,0.0836041048,0.3880741,0.2444252819,0.238742426,0.2779892385,0.0294546597,-0.0278633907,-0.1282213926,0.2124024332,-0.1187339947,0.1564511061,0.0764603615,-0.0925443396,-0.2441881597,0.1627016068,0.0704164654,-0.1369761974,0.0474271998,0.0893530995,-0.1253041923,0.1578324735,0.0026259758,0.4480130076,0.0147289056,-0.1836158633,0.1479921937,-0.2042004019,-0.0055106869,0.088725321,-0.0780102462,0.4078099132,0.2825418711,0.0658046678,-0.0059386119,-0.152056396,-0.3619617522,0.1194626912,0.3093265593,-0.4322062135,0.1622132808,-0.2236522287,0.3789779544,0.140934065,-0.1527124643,-0.2679890394,-0.5417202115,-0.1163036153,0.5250684619,-0.1006801128,0.132585749,-0.0776599199,0.0356060602,-0.2600830793,0.3981772959,-0.1304369122,-0.3576631248,-0.1955149621,-0.0964715779,0.3104203343,-0.2298806906,0.2243646383,0.1590366215,-0.2553647459,0.1742384136,-0.246335566,0.0499618873,-0.0595836341,0.0070893858,0.1035203412,-0.0094830273,0.007403533,-0.1684115678,0.1527904123,-0.1796357632,-0.2490491569,0.2781174183,-0.1366073042,0.0774834082,-0.2758553922,-0.3428668976,-0.4242911637,-0.1626048833,-0.0231468678,-0.2679255307,0.3306127191,0.0996191949,0.0179547556,0.0415381156,-0.0787686855,0.0687170997,-0.0472171456,-0.062253952,-0.2065602243,0.0454690121,-0.1702650785,-0.0546774678,0.1007058099,0.0798726752,0.4131040275,-0.2472681254,0.076609537,0.0603867844,-0.0629385114,0.2250357568,-0.1975023001,0.3669516146,0.3852897882,0.0340522006,-0.0286275465,-0.1490013599,0.1410466731,0.0047071753,-0.0262567904,0.1507259905,0.309121877,0.1691416353,0.6082265973,0.3968889117,0.0640839487,0.3936433792,-0.0730804801,0.0929013193,-0.2089506835,-0.3948884904,-0.2156747133,-0.2659694552,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"@lhoestq  \r\n\r\nThanks for taking a look. I pulled the master but I still see the key error.\r\n\r\n```\r\nTo disable this warning, you can either:\r\n        - Avoid using `tokenizers` before the fork if possible\r\n        - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 21.56ba\/s]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 17.71ba\/s]\r\n#2: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 20.45ba\/s]\r\n#3: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 26.05ba\/s]\r\n#4: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 26.83ba\/s]\r\n#5: 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100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 27.40ba\/s]\r\n#7: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 25.91ba\/s]\r\n#8: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 22.46ba\/s]\r\n#9: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 20.15ba\/s]\r\n#10: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 26.81ba\/s]\r\n#11: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 27.45ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 322\/322 [00:00<00:00, 1462.85ex\/s]\r\nTraceback (most recent call last):                                                                                      | 0\/1 [00:00<?, ?ba\/s]\r\n  File \"text2struct\/run_model.py\", line 372, in <module>\r\n    main()\r\n  File \"text2struct\/run_model.py\", line 368, in main                                                                    | 0\/1 [00:00<?, ?ba\/s]\r\n    run_model(auto_envvar_prefix=\"GFB_CIES\")  # pragma: no cover\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)                                                                                   | 0\/1 [00:00<?, ?ba\/s]\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 1236, in invoke\r\n    return Command.invoke(self, ctx)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/decorators.py\", line 21, in new_func\r\n    return f(get_current_context(), *args, **kwargs)\r\n  File \"text2struct\/run_model.py\", line 136, in run_model\r\n    ctx.invoke(ctx.command.commands[config_dict[\"mode\"]])\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/decorators.py\", line 21, in new_func\r\n    return f(get_current_context(), *args, **kwargs)\r\n  File \"text2struct\/run_model.py\", line 187, in train\r\n    run_train_model(_parse_subcommand(ctx))\r\n  File \"text2struct\/run_model.py\", line 241, in run_train_model\r\n    checkpoint_steps=config.train.checkpoint_steps,\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/train.py\", line 153, in alternate_training\r\n    max_len=config.model.dim.max_len,\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 466, in load_prodigy_tf_datasets\r\n    folder, file_patterns, vocabulary, cache_dir=cache_dir, test_pct=test_pct\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 447, in load_prodigy_arrow_datasets\r\n    fn_kwargs=dict(tokenizer=vocabulary.tokenizer, vocabulary=vocabulary),\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1224, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 348, in relation_ds_factory\r\n    ner_labels = example[\"ner_labels\"]\r\nKeyError: 'ner_labels'\r\n\r\n```","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":299,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n @lhoestq  \r\n\r\nThanks for taking a look. I pulled the master but I still see the key error.\r\n\r\n```\r\nTo disable this warning, you can either:\r\n        - Avoid using `tokenizers` before the fork if possible\r\n        - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 21.56ba\/s]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 17.71ba\/s]\r\n#2: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 20.45ba\/s]\r\n#3: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 26.05ba\/s]\r\n#4: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 26.83ba\/s]\r\n#5: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 27.00ba\/s]\r\n#6: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 27.40ba\/s]\r\n#7: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 25.91ba\/s]\r\n#8: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 22.46ba\/s]\r\n#9: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 20.15ba\/s]\r\n#10: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 26.81ba\/s]\r\n#11: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 27.45ba\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 322\/322 [00:00<00:00, 1462.85ex\/s]\r\nTraceback (most recent call last):                                                                                      | 0\/1 [00:00<?, ?ba\/s]\r\n  File \"text2struct\/run_model.py\", line 372, in <module>\r\n    main()\r\n  File \"text2struct\/run_model.py\", line 368, in main                                                                    | 0\/1 [00:00<?, ?ba\/s]\r\n    run_model(auto_envvar_prefix=\"GFB_CIES\")  # pragma: no cover\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 829, in __call__\r\n    return self.main(*args, **kwargs)                                                                                   | 0\/1 [00:00<?, ?ba\/s]\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 782, in main\r\n    rv = self.invoke(ctx)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 1236, in invoke\r\n    return Command.invoke(self, ctx)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 1066, in invoke\r\n    return ctx.invoke(self.callback, **ctx.params)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/decorators.py\", line 21, in new_func\r\n    return f(get_current_context(), *args, **kwargs)\r\n  File \"text2struct\/run_model.py\", line 136, in run_model\r\n    ctx.invoke(ctx.command.commands[config_dict[\"mode\"]])\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/core.py\", line 610, in invoke\r\n    return callback(*args, **kwargs)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/click\/decorators.py\", line 21, in new_func\r\n    return f(get_current_context(), *args, **kwargs)\r\n  File \"text2struct\/run_model.py\", line 187, in train\r\n    run_train_model(_parse_subcommand(ctx))\r\n  File \"text2struct\/run_model.py\", line 241, in run_train_model\r\n    checkpoint_steps=config.train.checkpoint_steps,\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/train.py\", line 153, in alternate_training\r\n    max_len=config.model.dim.max_len,\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 466, in load_prodigy_tf_datasets\r\n    folder, file_patterns, vocabulary, cache_dir=cache_dir, test_pct=test_pct\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 447, in load_prodigy_arrow_datasets\r\n    fn_kwargs=dict(tokenizer=vocabulary.tokenizer, vocabulary=vocabulary),\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1224, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 348, in relation_ds_factory\r\n    ner_labels = example[\"ner_labels\"]\r\nKeyError: 'ner_labels'\r\n\r\n```","embeddings":[-0.4438695014,-0.1675047278,-0.040071819,0.1716096848,0.0889460295,-0.1188245341,0.207846269,0.3421575725,0.1180476472,0.157603845,0.1306535751,0.4856553674,-0.4362103641,0.2786436677,-0.3663627207,0.0951046795,0.111420311,-0.0841650814,-0.2514494061,0.2458094209,-0.2524542809,0.293427676,-0.2909142971,0.1944313943,-0.5435567498,0.0569215901,-0.0106121879,0.3160980046,0.005523209,-0.6119182706,0.2811330557,0.30997172,-0.0368735269,0.3930642903,-0.0001247698,0.1379997134,0.3410488963,-0.0913274884,-0.0481128283,-0.3334736228,-0.1873978972,-0.2403929383,0.267213732,0.0872503072,0.1997458488,-0.0002410833,-0.0975959301,-0.2515730858,0.2583797872,0.3503323197,0.0834934041,0.2617163062,0.2949203849,0.1206849888,-0.0550052226,0.1494259536,-0.0134723131,0.0250229966,0.3076893687,-0.3135743141,-0.1309461594,0.2591975331,-0.2412051111,-0.2611987889,-0.1408810616,-0.2327822149,0.6852400899,-0.6341823936,0.2206975073,0.0248721112,-0.282739073,-0.0443475544,-0.4056611061,-0.2426628619,-0.2770734131,-0.4417158663,0.2661078572,-0.021212779,-0.2025263309,0.1933264285,-0.4429095984,-0.266000241,0.0245455261,-0.1426336616,-0.049413491,0.570002079,0.2672023475,0.2987840176,0.2195273638,0.1560344845,0.1490322053,-0.020523157,-0.1217988506,0.1732535809,-0.421153605,0.0671509504,0.085211575,-0.1909997761,-0.2070730031,0.0080284579,-0.2620820999,0.0897589177,-0.1146723181,0.0613033324,0.3845333755,-0.1019945443,0.1810257286,0.3499873877,0.2183627039,-0.2259050012,0.0349917933,0.0682251081,0.2776801586,-0.0831107944,-0.057865832,0.3552602232,0.1289922595,-0.1625501066,-0.136644721,0.185089916,-0.050749585,-0.1519199163,0.0860182345,0.2178291678,0.0702168196,0.7802461386,0.1076600105,0.1804842949,-0.3048225641,-0.0502079763,-0.0702662542,-0.0836341903,-0.3501508832,0.0839562565,0.2303638756,0.0645156056,0.0073764003,0.0501154736,-0.3548419774,-0.2700493038,0.0857494026,-0.2150368989,0.0925924852,0.7242619991,-0.1044124588,0.0710303262,-0.0193017423,-0.1493759304,0.0569707677,0.3055911064,-0.5235205889,-0.2392239273,-0.1953841895,0.0776035339,-0.0194743387,0.2954956591,-0.1016284972,0.2702437937,0.4557403326,-0.3296603858,-0.268432796,-0.32086429,-0.365288198,-0.3097073436,0.0002421204,0.2654046714,-0.4719291627,-0.0701095015,0.0881139189,-0.2383557707,0.3207790554,0.2269821465,-0.0228191037,-0.0254130438,-0.1377120614,0.1407947391,0.0430819169,-0.2105707228,-0.1295746416,0.2465180904,-0.0217696242,0.3109727502,-0.0010192796,-0.2680616081,0.3701477945,-0.1722933501,0.2614529729,-0.122810021,-0.159778893,0.0373745412,-0.4497857392,-0.0502865836,-0.0986655951,-0.1323395222,0.4002127051,0.1608778834,-0.1596159935,-0.4839442968,0.3021200597,-0.069291614,0.345036149,0.0765082836,0.1038474068,0.0943718925,0.1545059681,-0.1950791925,-0.4406284988,0.2530566752,-0.2359959036,0.0414475277,-0.0916195959,-0.0021407029,0.0889056846,0.2467318475,-0.2203757167,-0.1574905962,0.0297026597,-0.0926260054,-0.2858552039,0.0036854541,-0.1975900531,0.5724753141,0.075917922,0.1623217762,-0.0595488586,0.1222857535,-0.2260802537,-0.3706210256,-0.2846194506,0.2243590057,0.01142901,-0.0456318222,-0.1860871613,0.4331724942,0.4528889358,-0.0840670317,-0.0524535701,0.1870767325,0.2882177234,-0.210293293,-0.1363984495,0.0566668138,-0.000644474,-0.200467363,0.2589430809,0.4819489419,0.1949970573,0.4277967215,0.1515733898,0.247333765,0.313282907,0.0197373424,-0.0024224338,-0.1308805048,0.1747965068,-0.1996351928,0.1646611989,-0.0104093347,-0.0819548815,-0.1719728261,0.0619493946,0.0059801014,-0.177711457,0.0690228119,0.106748715,-0.1143574268,0.1525004804,0.0578253046,0.3588520885,0.0116988104,-0.1743934751,0.1712271422,-0.2872713208,-0.0165318716,0.1565526873,-0.0600656755,0.3869134486,0.3310392201,0.029359499,-0.0537556335,-0.1345203519,-0.2860231996,0.1273595095,0.3807802796,-0.4825971723,0.2592690885,-0.2457223833,0.3851275146,0.169353053,-0.1360460371,-0.2819573283,-0.6221180558,-0.0652160794,0.4566330016,-0.1154796779,0.1542272866,-0.0666648746,0.0565893091,-0.2285957336,0.3749101162,-0.2166072577,-0.3992305696,-0.16723001,-0.1114408523,0.3502193689,-0.3183429539,0.2055116743,0.1797622591,-0.2845513225,0.193957448,-0.3226080239,0.1029553413,-0.123383224,0.0229367036,0.1417060643,-0.029571183,0.0921471864,-0.2455625087,0.1196155474,-0.2157841325,-0.270142138,0.253403157,-0.1736672521,0.0497094691,-0.2838630378,-0.2666267753,-0.4348909557,-0.1038086563,-0.061585471,-0.2117896378,0.3445335627,0.1505988836,0.0339488834,-0.0018496364,-0.0442048237,0.0065714754,-0.0929128677,-0.0397026315,-0.2193152755,0.0723461807,-0.0794137493,0.008724628,0.0408147462,0.1786851734,0.2683210671,-0.3155303299,0.1561607718,0.0617141463,-0.0350545458,0.1973545253,-0.2283177674,0.2923780382,0.438633889,0.0282353722,-0.0359945595,-0.1160214245,0.1322571039,-0.058225695,0.0320601165,0.0781751871,0.3204672635,0.1851849258,0.6763290763,0.3327035308,0.0967166722,0.3227106035,-0.0756793469,0.0984892473,-0.1708204597,-0.3568721116,-0.2422763854,-0.29255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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"The parallelism is automatically disabled on `tokenizers` when the process gets forked, while we already used the parallelism capabilities of a tokenizer. We have to do it in order to avoid having the process hang, because we cannot safely fork a multithreaded process (cf https:\/\/github.com\/huggingface\/tokenizers\/issues\/187).\r\nSo if possible, the tokenizers shouldn't be used before the fork, so that each process can then make use of the parallelism. Otherwise using `TOKENIZERS_PARALLELISM=false` is the way to go.","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":75,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n The parallelism is automatically disabled on `tokenizers` when the process gets forked, while we already used the parallelism capabilities of a tokenizer. We have to do it in order to avoid having the process hang, because we cannot safely fork a multithreaded process (cf https:\/\/github.com\/huggingface\/tokenizers\/issues\/187).\r\nSo if possible, the tokenizers shouldn't be used before the fork, so that each process can then make use of the parallelism. Otherwise using `TOKENIZERS_PARALLELISM=false` is the way to go.","embeddings":[-0.469833523,-0.1674960703,-0.0140371053,0.1800090671,0.0854438543,-0.1905385852,0.2459647357,0.2756293416,0.1016369238,0.1726740003,0.0916070193,0.4495114982,-0.3661407232,0.2278046459,-0.3540114462,0.0023671666,0.1458534151,-0.1064164191,-0.1076287925,0.2664557099,-0.1931275576,0.324302882,-0.2955477238,0.1895089447,-0.5507256985,0.0559619181,-0.0354776047,0.2832084894,-0.0593082495,-0.6136327982,0.2357300073,0.3920495212,0.0007346575,0.4456866086,-0.0001240691,0.1468001455,0.3075380921,-0.0330980793,0.0126842633,-0.3793185055,0.00191086,-0.3051974773,0.2444296628,0.1721025556,0.2013462186,-0.1140399799,-0.1220822409,-0.1859960407,0.3015083671,0.297714591,0.0780777559,0.3740778565,0.2976615131,0.1366759241,-0.1064472646,0.1475401223,-0.0475674085,0.0469951853,0.3224133849,-0.2692290545,-0.1280239373,0.2051905096,-0.2646250427,-0.2231662869,-0.1501526535,-0.2274798453,0.533118248,-0.5728849173,0.2040055245,0.097276777,-0.2752830684,-0.0516938679,-0.4340721667,-0.2814349234,-0.3205261827,-0.5091427565,0.2326749265,-0.0793353468,-0.1704740673,0.177923426,-0.4278577566,-0.2340727746,0.0443510264,-0.101955086,-0.1272596419,0.5708979368,0.1884500533,0.3039306998,0.1742693335,0.1578971744,0.0501511022,-0.0234679058,-0.1019761935,0.1722763628,-0.4143748581,0.0462940373,0.1032643095,-0.3558402658,-0.1825723648,0.1217038929,-0.2746934891,0.1585308909,-0.1526545435,0.0980686024,0.3795083463,-0.0054288921,0.1715634614,0.2787450552,0.2465554625,-0.2089062184,0.0966488421,0.0370953567,0.2416516393,-0.0345837399,-0.1681513041,0.2515701056,0.1012417451,-0.149758637,-0.1228373423,0.1711271703,-0.0874946639,-0.1317547411,0.1335784197,0.2265292406,0.0358568653,0.7409479022,0.0959845632,0.1662244052,-0.3377198577,-0.0669960976,-0.0701267347,-0.0903126001,-0.3008594811,0.125000298,0.1836821288,0.133335948,0.0110817431,0.0407856591,-0.3716782629,-0.2365080118,0.1697461456,-0.246915251,0.0945088789,0.6521362662,-0.0997272208,0.0555373952,-0.0186683238,-0.0468873233,0.0317685418,0.1966137141,-0.4765743911,-0.18053855,-0.243328616,0.0614766963,-0.0035067094,0.2686824799,-0.268176496,0.3582110107,0.377532959,-0.339284271,-0.2765756249,-0.3204176724,-0.3733099103,-0.2689908147,0.0655429363,0.2235532403,-0.3428300619,-0.1210215762,0.137930885,-0.2942818701,0.342664808,0.3083378673,-0.0074371686,0.071666941,-0.1562374234,0.2892130315,0.0653889999,-0.2158596814,-0.0655001327,0.1830647439,-0.0553093627,0.3571151793,-0.0528864972,-0.293948859,0.4720177948,-0.0873196125,0.2116505504,-0.0297005158,-0.1419220567,0.0854478702,-0.4349107742,-0.0488808565,-0.0801716149,-0.1022236124,0.3707356751,0.1900942028,-0.1735134721,-0.4965021312,0.2677107155,-0.1114346385,0.3222943842,0.0715091303,0.1434360743,0.2017229795,0.1305195838,-0.2422705293,-0.377504468,0.2210634649,-0.2406433672,0.0430389866,-0.014628767,-0.0765243322,0.1771068722,0.1960383058,-0.2122108042,-0.1775559336,0.029469993,-0.0402090661,-0.2606339455,-0.0142164379,-0.220460847,0.5387184024,0.0587076545,0.2526376247,-0.0555030666,0.1792503744,-0.1390038282,-0.4171824753,-0.3341962695,0.2444838732,0.0404223092,-0.1081418023,-0.181263268,0.4281125665,0.4532399774,-0.0259470884,-0.078176409,0.1995645314,0.303053081,-0.1107944921,-0.1663929075,0.0268019866,-0.0005163518,-0.1456858218,0.3252193332,0.4752385914,0.2583381832,0.3575917184,0.2223508656,0.1967348456,0.3676941693,0.0150888059,0.0022732753,-0.076925613,0.2094924748,-0.0871469751,0.1809675097,-0.0041484931,-0.1150159165,-0.1338171363,0.0661967769,0.0200306438,-0.152868256,0.0386654325,0.0771412104,-0.1419483721,0.1957781911,-0.0908225253,0.324619323,0.0216593146,-0.1594498307,0.1472177804,-0.2333879024,0.0095722321,0.0928074569,0.0083769727,0.2946350873,0.3339911103,0.0658817887,0.0199006703,-0.29267326,-0.3580291867,0.1330217123,0.3469077647,-0.4571527541,0.2395317554,-0.2332216501,0.4305514097,0.1446413994,-0.1823011488,-0.3057281673,-0.5543479919,-0.0787178427,0.6065199971,-0.1380046755,0.1733262986,0.000842168,0.2153340429,-0.242401734,0.4286283255,-0.2241805494,-0.328969121,-0.2124576867,-0.1325620264,0.3449445069,-0.2445281148,0.2342283428,0.1797371209,-0.317370832,0.0695009604,-0.4021351933,0.1804139763,-0.0857736245,0.0844072625,0.1158489883,-0.0306755491,0.0407308005,-0.2014771551,0.1514792293,-0.2354545593,-0.2923106849,0.2084235847,-0.1431322545,-0.0121390671,-0.2302771956,-0.2263071537,-0.4417113066,-0.1428988278,0.0151706589,-0.2623472512,0.2979028225,0.0964594707,-0.0297172647,0.030512508,-0.1678612381,0.0696147978,-0.1205818728,-0.0331982225,-0.264649719,0.0089349076,-0.1214702502,-0.0549475551,0.0504694581,0.1772573441,0.3300919533,-0.2351266891,0.0866732523,0.0608826503,-0.0232305191,0.1820591241,-0.2267749608,0.3626005054,0.3413053751,0.0460003205,0.0077072466,-0.1542690843,0.0969479382,-0.1244616807,0.0159213953,0.1115942448,0.4085763097,0.2045243084,0.7586420178,0.3068211079,0.0815780014,0.4106377363,-0.1149264872,0.1306596696,-0.1736628562,-0.4086289704,-0.1645549536,-0.2788659334,-0.018116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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"> Thanks for taking a look. I pulled the master but I still see the key error.\r\n\r\nI am no longer able to get the error since #659 was merged. Not sure why you still have it @timothyjlaurent \r\nMaybe it is a cache issue ? Could you try to use `load_from_cache_file=False` in your `.map()` calls ?","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":56,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n > Thanks for taking a look. I pulled the master but I still see the key error.\r\n\r\nI am no longer able to get the error since #659 was merged. Not sure why you still have it @timothyjlaurent \r\nMaybe it is a cache issue ? Could you try to use `load_from_cache_file=False` in your `.map()` calls 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"> The parallelism is automatically disabled on `tokenizers` when the process gets forked, while we already used the parallelism capabilities of a tokenizer. We have to do it in order to avoid having the process hang, because we cannot safely fork a multithreaded process (cf [huggingface\/tokenizers#187](https:\/\/github.com\/huggingface\/tokenizers\/issues\/187)).\r\n> So if possible, the tokenizers shouldn't be used before the fork, so that each process can then make use of the parallelism. Otherwise using `TOKENIZERS_PARALLELISM=false` is the way to go.\r\n\r\nOk thanks :)\r\n\r\nIs there something we should do on the `datasets` side to avoid that that the program hangs ?\r\n\r\nAlso when doing `.map` with a tokenizer, the tokenizer is called once on the first examples of the dataset to check the function output before spawning the processes. Is that compatible with how tokenizers are supposed to be used with multiprocessing ?","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":140,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n > The parallelism is automatically disabled on `tokenizers` when the process gets forked, while we already used the parallelism capabilities of a tokenizer. We have to do it in order to avoid having the process hang, because we cannot safely fork a multithreaded process (cf [huggingface\/tokenizers#187](https:\/\/github.com\/huggingface\/tokenizers\/issues\/187)).\r\n> So if possible, the tokenizers shouldn't be used before the fork, so that each process can then make use of the parallelism. Otherwise using `TOKENIZERS_PARALLELISM=false` is the way to go.\r\n\r\nOk thanks :)\r\n\r\nIs there something we should do on the `datasets` side to avoid that that the program hangs ?\r\n\r\nAlso when doing `.map` with a tokenizer, the tokenizer is called once on the first examples of the dataset to check the function output before spawning the processes. Is that compatible with how tokenizers are supposed to be used with multiprocessing 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Hmmm I pulled the latest commit, `b93c5517f70a480533a44e0c42638392fd53d90`, and I'm still seeing both the hanging and the key error. ","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":18,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Hmmm I pulled the latest commit, `b93c5517f70a480533a44e0c42638392fd53d90`, and I'm still seeing both the hanging and the key error. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Hi @timothyjlaurent \r\n\r\nThe hanging fix just got merged, that why you still had it.\r\n\r\nFor the key error it's possible that the code you ran reused cached datasets from where the KeyError bug was still there.\r\nCould you try to clear your cache or make sure that it doesn't reuse cached data with `.map(..., load_from_cache=False)` ?\r\nLet me know if it it helps","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":63,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Hi @timothyjlaurent \r\n\r\nThe hanging fix just got merged, that why you still had it.\r\n\r\nFor the key error it's possible that the code you ran reused cached datasets from where the KeyError bug was still there.\r\nCould you try to clear your cache or make sure that it doesn't reuse cached data with `.map(..., load_from_cache=False)` ?\r\nLet me know if it it 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Hi @lhoestq , \r\n\r\nThanks for letting me know about the update.\r\n\r\nSo I don't think it's the caching - because hashing mechanism isn't stable for me -- but that's a different issue. In any case I `rm -rf ~\/.cache\/huggingface` to make a clean slate.\r\n\r\nI synced with master and I see the key error has gone away, I tried with and without the `TOKENIZERS_PARALLELISM` variable set and see the log line for setting the value false before the map.\r\n\r\nNow I'm seeing an issue with `.train_test_split()` on datasets that are the product of a multiprocess map.\r\n\r\nHere is the stack trace\r\n\r\n```\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 451, in load_prodigy_arrow_datasets\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/src\/datasets\/src\/datasets\/arrow_dataset.py\", line 168, in wrapper\r\n    dataset.set_format(**new_format)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/src\/datasets\/src\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/src\/datasets\/src\/datasets\/arrow_dataset.py\", line 794, in set_format\r\n    list(filter(lambda col: col not in self._data.column_names, columns)), self._data.column_names\r\nValueError: Columns ['train', 'test'] not in the dataset. Current columns in the dataset: ['_input_hash', '_task_hash', '_view_id', 'answer', 'encoding__ids', 'encoding__offsets', 'encoding__overflowing', 'encoding__tokens', 'encoding__words', 'ner_ids', 'ner_labels', 'relations', 'spans', 'text', 'tokens']\r\n```\r\n\r\n\r\n","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":174,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Hi @lhoestq , \r\n\r\nThanks for letting me know about the update.\r\n\r\nSo I don't think it's the caching - because hashing mechanism isn't stable for me -- but that's a different issue. In any case I `rm -rf ~\/.cache\/huggingface` to make a clean slate.\r\n\r\nI synced with master and I see the key error has gone away, I tried with and without the `TOKENIZERS_PARALLELISM` variable set and see the log line for setting the value false before the map.\r\n\r\nNow I'm seeing an issue with `.train_test_split()` on datasets that are the product of a multiprocess map.\r\n\r\nHere is the stack trace\r\n\r\n```\r\n  File \"\/Users\/timothy.laurent\/src\/inv-text2struct\/text2struct\/model\/dataset.py\", line 451, in load_prodigy_arrow_datasets\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/src\/datasets\/src\/datasets\/arrow_dataset.py\", line 168, in wrapper\r\n    dataset.set_format(**new_format)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/src\/datasets\/src\/datasets\/fingerprint.py\", line 163, in wrapper\r\n    out = func(self, *args, **kwargs)\r\n  File \"\/Users\/timothy.laurent\/.virtualenvs\/inv-text2struct\/src\/datasets\/src\/datasets\/arrow_dataset.py\", line 794, in set_format\r\n    list(filter(lambda col: col not in self._data.column_names, columns)), self._data.column_names\r\nValueError: Columns ['train', 'test'] not in the dataset. Current columns in the dataset: ['_input_hash', '_task_hash', '_view_id', 'answer', 'encoding__ids', 'encoding__offsets', 'encoding__overflowing', 'encoding__tokens', 'encoding__words', 'ner_ids', 'ner_labels', 'relations', 'spans', 'text', 'tokens']\r\n```\r\n\r\n\r\n","embeddings":[-0.3455711901,-0.124476321,-0.0099463714,0.2436582297,0.132649079,-0.1085882708,0.2091186345,0.3842711151,0.1275978088,0.0554183125,0.0650868118,0.2885191441,-0.4160088003,0.3402267098,-0.2788427174,0.064945899,0.1176876798,-0.1377786398,-0.3737223148,0.2305669636,-0.2718407214,0.3353238404,-0.2338251621,0.1210066006,-0.5046049953,0.0449887589,-0.1111245677,0.3717213869,0.068482928,-0.5890556574,0.3367897868,0.2819594145,-0.0480297953,0.3480781317,-0.0001237825,0.1063452214,0.3847542405,-0.1089715138,-0.0622288585,-0.3263475299,-0.1737760901,-0.1924261302,0.2838899493,0.0872735232,0.1946082562,0.0350470878,-0.1431172341,-0.2287731469,0.3298036158,0.3584726453,0.1089558676,0.2274534702,0.2610171735,0.1091630161,0.0079336753,0.0144447973,0.0120129287,0.0105952481,0.2656100094,-0.3017512858,-0.1495733559,0.3722892404,-0.255100131,-0.2173593491,-0.0058807489,-0.2193851024,0.6108903885,-0.5381208062,0.2770029306,0.0419181697,-0.2300306261,0.0360986963,-0.4760312736,-0.2049938142,-0.2889748514,-0.5068151355,0.3482626975,-0.0966814458,-0.1204990447,0.1607678086,-0.5080752373,-0.2501933575,0.0702679902,-0.1309080273,-0.0440390669,0.4516026974,0.2043748349,0.2419659644,0.2371707857,0.1206098124,0.0828298554,0.0368181914,-0.1755397916,0.1539328247,-0.4410711527,0.0727010891,0.1501148492,-0.0112307305,-0.2254723161,0.0307366364,-0.1942606717,0.0992030576,-0.0916412547,-0.0203569066,0.3888459802,0.0562038198,0.1859543175,0.1347609758,0.2832926512,-0.1339153051,0.0400829352,0.0852165148,0.3456960917,-0.0811674669,-0.0124403508,0.4133488834,0.112997517,-0.2294027358,-0.0599842444,0.1502421945,-0.0300123934,-0.2148933411,0.0187901184,0.1588859707,-0.0029365425,0.7559267879,0.1216320768,0.1799938977,-0.2985371649,-0.1033544913,-0.0879793242,-0.0783394799,-0.4462857842,0.1121379957,0.2708312273,-0.1769164801,0.0029232351,-0.0293803643,-0.3343157172,-0.2672925591,-0.0057176384,-0.180957824,0.1032915786,0.6875529289,-0.1682294309,0.0362343602,-0.0051768241,-0.0747529343,0.0804438144,0.2214311063,-0.5367627144,-0.2646110058,-0.2048505247,0.0789481029,0.0069822115,0.3434107304,-0.0932201669,0.2554689646,0.4977132976,-0.3562063873,-0.2382134497,-0.257095933,-0.4370432198,-0.3161766231,0.0009105206,0.3135167956,-0.3315528929,-0.1214467958,0.0876626521,-0.1581772268,0.179208383,0.2633360922,0.0320779644,-0.0481657609,-0.1246599108,0.0071643279,0.0008243211,-0.2042131126,-0.2987396121,0.1712997258,0.0336488411,0.3593222499,0.0736718997,-0.2474052161,0.3209016025,-0.184307009,0.3235274553,-0.1109328866,-0.1499824524,0.0182688311,-0.4165968001,-0.0977840871,-0.1438877136,-0.1557713598,0.4019341171,0.2475280762,-0.0925158262,-0.4669560492,0.2758161724,-0.1199947596,0.3561442792,0.1221896559,0.0755639821,0.070230417,0.1730285585,-0.1853505522,-0.6047538519,0.3041417301,-0.2501339614,0.0431420989,-0.1477176547,-0.0157312006,0.1335422844,0.0891357735,-0.14906919,-0.2978046238,0.0218345337,-0.0204796121,-0.2045932263,-0.0690909475,-0.2441110313,0.7258079648,0.1483658403,0.1382077187,-0.1974518001,0.1050306037,-0.1945034415,-0.3886584938,-0.2837040126,0.2788521647,0.0128649343,-0.1279384792,-0.1980294138,0.3907418847,0.4001692235,-0.0490544103,-0.0479035042,0.299172312,0.2560943067,-0.1387450397,-0.0958262756,0.0244669486,-0.0276676193,-0.1636607051,0.2050244659,0.4712374806,0.0942035839,0.4115881324,0.1363151371,0.2084512264,0.3067332208,0.0051696063,0.0097786048,-0.1651752889,0.1741481572,-0.1718353182,0.2099791318,0.0183888171,0.0095019555,-0.1484384835,0.1744221747,0.0208028071,-0.1467273831,0.080833979,0.0518874079,-0.1830231547,0.1379190236,0.0632489324,0.3419297934,-0.0183102023,-0.1588670611,0.2417920679,-0.1436592042,0.0302835852,0.1401726007,-0.1044881418,0.2664687335,0.2962377071,0.0669785216,0.0796569288,-0.183586508,-0.1547777653,0.087174125,0.3825924993,-0.4576377869,0.1936836094,-0.2836962044,0.2762703001,0.2124966979,-0.0819847286,-0.3219939768,-0.5981811881,-0.0910403207,0.5325677395,-0.0922538713,0.1659387499,0.0012374313,0.1561725736,-0.1698634177,0.3994178772,-0.2692892849,-0.4219844043,-0.1239215508,-0.1292490661,0.3829295039,-0.4229315817,0.2115245908,-0.0087398402,-0.259565413,0.1289770454,-0.3988409936,0.0933481008,-0.1177257746,-0.0403881818,0.1544983983,-0.0220289361,0.0301756337,-0.161573723,0.1917395145,-0.3205642402,-0.3525020182,0.2628386617,-0.1099278182,0.1426189095,-0.3057217002,-0.2488423884,-0.3357530534,-0.0755689591,-0.0051657162,-0.265730828,0.3179179132,0.2084101737,-0.0123669673,-0.0700820461,-0.1287317872,0.0919010267,-0.1273197532,-0.1091576889,-0.2312927544,0.0972522646,-0.1596218795,0.033574909,0.0853756219,0.1164713725,0.3502861261,-0.3690614402,-0.0689097047,0.092549026,-0.0223239325,0.2291084081,-0.1680183113,0.3719292879,0.4319148362,0.0209557656,-0.0569459982,-0.2369533777,0.080542013,0.0453715585,0.0599122755,0.1212506443,0.3780147135,0.1420286298,0.7747809291,0.2866213918,0.0910940319,0.3499988019,-0.0283163488,0.2063764483,-0.2522653937,-0.39351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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Thanks for reporting.\r\nI'm going to fix that and add a test case so that it doesn't happen again :) \r\nI'll let you know when it's done\r\n\r\nIn the meantime if you could make a google colab that reproduces the issue it would be helpful ! @timothyjlaurent ","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":47,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Thanks for reporting.\r\nI'm going to fix that and add a test case so that it doesn't happen again :) \r\nI'll let you know when it's done\r\n\r\nIn the meantime if you could make a google colab that reproduces the issue it would be helpful ! @timothyjlaurent 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Thanks @timothyjlaurent ! I just merged a fix on master. I also checked your notebook and it looks like it's working now.\r\nI added some tests to make sure it works as expected now :)","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":35,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Thanks @timothyjlaurent ! I just merged a fix on master. I also checked your notebook and it looks like it's working now.\r\nI added some tests to make sure it works as expected now :)","embeddings":[-0.4193556011,-0.1071404666,-0.0392603949,0.2147689611,0.1458776742,-0.1598080397,0.2604388893,0.3937074542,0.0989538953,0.1513863951,0.0195596758,0.4504702091,-0.3637407124,0.4398677647,-0.319118619,0.0345809273,0.0604736246,-0.0270151775,-0.3903119564,0.2088315785,-0.2636983693,0.2989494503,-0.3305636048,0.1506016999,-0.5216234326,0.1386964023,-0.1130683497,0.2653171122,-0.1030749157,-0.4939137399,0.2864601016,0.2612058818,0.0879322216,0.4689686894,-0.000114397,0.1324707568,0.3333232403,-0.1248081103,-0.0798088238,-0.4356539845,-0.1579086185,-0.2550667822,0.2872832417,0.1007006541,0.0749271736,0.0389388353,-0.090324752,-0.1414096206,0.2836947739,0.4652611017,0.1934158057,0.2085710764,0.2621919215,0.0558455847,0.0510044582,0.0678695366,-0.0874518901,0.0078280475,0.3059768975,-0.4177977145,-0.1298090667,0.3312835991,-0.1521880329,-0.2131036073,-0.0349628963,-0.192424655,0.5803083181,-0.5957256556,0.2125930339,0.0949263498,-0.1266435832,-0.0087137632,-0.3816846311,-0.1348068714,-0.2131182551,-0.4743331075,0.2450659275,0.0600356758,-0.2198235989,0.1639676094,-0.4446834028,-0.1741554737,-0.0448266342,-0.130474031,-0.1257179081,0.5451292396,0.1489355117,0.2062472105,0.2068864256,0.1561340839,0.2369428277,0.0344263203,-0.056958802,0.1895682663,-0.4668980837,0.0611613691,0.0921528935,-0.0954745263,-0.2770744562,0.0083476817,-0.088118881,0.1364300549,-0.0016214101,0.0187218487,0.3237471581,-0.029831551,0.1760365963,0.2339707762,0.2529138923,-0.1339113861,0.0106226373,-0.032067012,0.2401254177,-0.1618898809,-0.1238920242,0.2203139216,0.1702696532,-0.2124930322,-0.212342456,0.1235531121,-0.0652692989,-0.1341298968,0.0883261785,0.2103799134,-0.0055050431,0.6364831924,0.2363899052,0.1668306589,-0.3084965646,-0.074295789,-0.1081771702,-0.0752082616,-0.4380477369,0.0819578096,0.3093464375,0.0654760227,0.0247549154,0.0464761332,-0.3424578011,-0.1757465005,0.1699210703,-0.2142257243,0.0935268775,0.6487029791,-0.0624318235,-0.0729875714,-0.0194960609,-0.1211733371,0.1072037518,0.2887684703,-0.4147422314,-0.1756033152,-0.1448325366,0.1784071028,0.0624776594,0.1968678981,-0.1354858726,0.2495191991,0.3412889242,-0.2765164375,-0.295017302,-0.2627283931,-0.3219247758,-0.264962256,0.0088395793,0.1723242551,-0.4067887068,-0.0042888788,0.0150060412,-0.1794583201,0.2717075646,0.1691123396,-0.0282548945,0.0070827706,-0.0883457139,0.0955991149,0.0768802315,-0.1691041142,-0.1678660512,0.2952840924,0.0339029543,0.2496963441,-0.0105180228,-0.2364145517,0.3208428025,-0.0797638372,0.244471252,0.0074716737,-0.1422511488,0.0394449793,-0.4914548099,-0.0142364688,-0.079917632,-0.002936464,0.4351004958,0.1397288442,-0.19181934,-0.5129804015,0.3801808655,-0.1166738868,0.3654141128,0.0820588022,0.1434988976,0.1009431332,0.1733538955,-0.2495142519,-0.3573037684,0.2332869023,-0.2872871161,0.1226692721,-0.0040788511,0.0095777353,0.0459842421,0.0689831972,-0.2158465236,-0.2534330785,0.1461217999,-0.0497329086,-0.2649739385,-0.0695729107,-0.2448036522,0.6643331647,0.1081313491,0.1286108643,-0.1205222532,0.2059649974,-0.2012664825,-0.4136802554,-0.2300351411,0.1771996021,-0.0337785371,-0.0788786635,-0.196377039,0.4693377316,0.4146348238,-0.2028420717,-0.0868468285,0.1255912632,0.1912124008,-0.1506284326,-0.1443390846,0.1170540452,0.0710575134,-0.1876674891,0.1343067437,0.5086156726,0.0340318941,0.3584803045,0.2193984985,0.2494549453,0.3088573515,-0.0572914593,-0.0569906831,-0.1041017026,0.1401890814,-0.1657108814,0.1885280013,0.0321423002,-0.1542482376,-0.2205114812,0.0744976252,0.0366966315,-0.2033707052,0.0992500782,0.0222155657,-0.0738715306,0.1572184414,0.0868034586,0.435272038,0.0814119726,-0.1514406502,0.2476586252,-0.2685739696,-0.0336246639,0.1898126304,-0.1886319965,0.3337744474,0.3131489158,0.0016618175,-0.064507246,-0.2526164353,-0.2312238514,0.1290824711,0.3988618553,-0.4570883811,0.1193113253,-0.2042877078,0.2391608059,0.0630442053,-0.1325523555,-0.2879113555,-0.4914489686,-0.0683125779,0.5388637781,-0.0008215093,0.1363489777,0.0390867554,0.1202900931,-0.1639818847,0.3817952573,-0.1218742654,-0.339301914,-0.2346854359,0.0099810669,0.3615128994,-0.1558511108,0.2152867764,0.0898219794,-0.2461383492,0.1259487867,-0.3414521813,0.0274587981,-0.1188350245,0.0474604592,0.0826992169,-0.0263408255,-0.0158582143,-0.1403867304,0.146448195,-0.1827648282,-0.2847910225,0.3372772336,-0.1016341746,0.0589413941,-0.2651586235,-0.3587749898,-0.3944595754,-0.1789947748,-0.0360180959,-0.2031382173,0.2997399271,0.1594230682,-0.0117753418,0.0170342978,-0.0087831635,0.0573236682,-0.108346872,0.0103760883,-0.1428316981,-0.0703980029,-0.2448671162,0.0341876782,0.1254526079,0.1972046494,0.3103440404,-0.3343865573,0.0510897487,-0.0141258808,-0.0532010905,0.2020154595,-0.137279138,0.4295086265,0.3030903339,-0.018551955,-0.0289912019,-0.1327067763,0.0661371946,-0.0121595031,0.1117382348,0.026613513,0.2794807255,0.1757091582,0.5257003903,0.3726461232,0.1225586161,0.3527100384,-0.0770341083,0.11402601,-0.2462852001,-0.4222671986,-0.1674271375,-0.344594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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"Great, @lhoestq . I'm trying to verify in the colab:\r\nchanged\r\n```\r\n!pip install datasets\r\n```\r\nto \r\n\r\n```\r\n!pip install git+https:\/\/github.com\/huggingface\/datasets@master\r\n```\r\n\r\nBut I'm still seeing the error - I wonder why?","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":32,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n Great, @lhoestq . I'm trying to verify in the colab:\r\nchanged\r\n```\r\n!pip install datasets\r\n```\r\nto \r\n\r\n```\r\n!pip install git+https:\/\/github.com\/huggingface\/datasets@master\r\n```\r\n\r\nBut I'm still seeing the error - I wonder why?","embeddings":[-0.4416673183,-0.1638828814,-0.033368811,0.2726554275,0.1398596466,-0.1488540173,0.2888736427,0.2428048104,0.1233460158,0.2103493214,0.0198594723,0.5049683452,-0.3794004917,0.366486311,-0.3114085793,0.0422243029,0.082374312,-0.0164952278,-0.3751588166,0.2027810365,-0.1774687022,0.2914339304,-0.3229137957,0.1249253452,-0.5289024115,0.0481469445,-0.0232043732,0.2264141887,-0.1429692358,-0.5102818608,0.4412212372,0.2774354517,0.0068003652,0.4950698614,-0.0001267765,0.1621522009,0.4178577662,-0.12817958,-0.1998692304,-0.4242440164,-0.2842195034,-0.2430701256,0.3733290136,0.0529889204,0.1932425946,0.1888237149,-0.0460889228,-0.2160014063,0.2681325972,0.3944459558,0.0823459849,0.2147485912,0.2474290878,0.104615882,0.0297514275,0.1779660583,-0.0468287989,0.0709813312,0.3152913153,-0.2751640379,-0.0240561645,0.2880619466,-0.2587496936,-0.0838409141,-0.2140988559,-0.1705383509,0.4943110645,-0.6263384819,0.1955993026,0.081557855,-0.14066495,-0.074899599,-0.4533854425,-0.2244773209,-0.2576598823,-0.5030200481,0.2255763113,0.0383145958,-0.2167304158,0.118720226,-0.5351097584,-0.2030429691,0.0226368159,-0.0981145278,-0.1145919189,0.5615178943,0.0796506554,0.3092218637,0.2317216545,0.2045359164,0.2542188764,-0.0397200175,-0.0713268816,0.1538144201,-0.3840873837,0.0325841345,0.0669131801,0.1032212079,-0.199235037,-0.0264021195,-0.2357920557,0.0283500869,-0.1383860558,0.0962060317,0.4001834691,0.0043299948,0.138394773,0.287150383,0.2604061067,-0.1034703553,0.0161071625,0.0157799069,0.3302517235,-0.0691931769,-0.1319175959,0.3151136041,0.225154534,-0.2654107213,-0.243190214,0.0499477126,-0.0663372204,-0.2566161156,0.0794131011,0.1817845702,-0.0491336361,0.6901754141,0.0495948121,0.146594733,-0.3301025331,-0.0150012281,-0.0359648019,-0.0239534974,-0.3363146484,0.071367763,0.2595929205,-0.0516902432,0.0886395276,0.032740254,-0.3011175692,-0.2104311436,0.1050219163,-0.3483010828,0.054035604,0.7567947507,-0.0330230221,0.1069527194,0.0345672928,-0.1978487968,0.0832137614,0.2143735737,-0.5603205562,-0.2310544103,-0.253677398,0.0324034058,-0.0642949715,0.2737276554,-0.3409034312,0.2217778116,0.4327417016,-0.3949703872,-0.2841382921,-0.3484717011,-0.3798283935,-0.2361614555,-0.039976377,0.3014243543,-0.4129752517,-0.0434269309,-0.0027410423,-0.2266343832,0.412774235,0.2082967609,-0.0585957058,0.0484883599,-0.175472483,-0.0414133742,0.1345539391,-0.2448756397,-0.1387614906,0.2229841501,0.0678844824,0.3529622257,0.0010165133,-0.2929701209,0.2707001567,-0.1585672945,0.2899981141,-0.1811337024,-0.2222993821,0.0575292185,-0.427988708,-0.0444761291,-0.1362227798,-0.0758409053,0.3197173476,0.1904804111,-0.0917359963,-0.4803735018,0.291433841,-0.0974822417,0.2749807239,0.0469423085,0.1833526492,0.0583575293,0.0860807151,-0.2779419422,-0.5560165644,0.1758852005,-0.1128361821,0.0734608397,-0.181999594,-0.0033157822,-0.0047651408,0.2145400345,-0.2078028768,-0.187245205,0.0109829074,0.0678195879,-0.2397839576,-0.0476466753,-0.1803411692,0.7802555561,0.0948861465,0.1407016963,-0.1968691349,0.218893528,-0.1770649403,-0.3737702966,-0.2619193196,0.2147626728,-0.0545192882,-0.1538217217,-0.2200833708,0.3961577713,0.4616061449,-0.1174414009,0.005523534,0.10436479,0.2650132775,-0.1628609449,-0.194704473,-0.0392282978,0.0907205492,-0.1257073581,0.1508945972,0.3900067508,0.0292396881,0.3952741027,0.1306958497,0.1663772613,0.3614394069,0.0658566505,-0.0800634846,-0.125314787,0.1883155107,-0.091673322,0.190022409,0.0967751741,-0.1025893465,-0.1702999324,0.1979158521,0.0816696659,-0.2172452062,0.0747730136,0.199974522,-0.1187727079,0.2165575325,0.1139418259,0.4971821606,-0.0219785459,-0.1903031468,0.1335120201,-0.279881984,-0.0160766132,0.125905782,-0.0240719486,0.3989955187,0.2772562802,0.1201241314,0.0499581583,-0.1512105465,-0.3175322711,0.0898070037,0.4272669256,-0.4888765812,0.2710611224,-0.2688693404,0.2669654191,0.052004803,-0.2310130149,-0.2184808254,-0.5585610867,-0.138685137,0.4793069363,-0.0087354407,0.1128482893,0.0227441136,0.0532073863,-0.1948923469,0.3044198751,-0.2504921257,-0.3013234735,-0.1368227303,-0.1110471934,0.3174388409,-0.2189763933,0.1903361976,0.0410231017,-0.2808040679,0.161413312,-0.3627012968,0.1494130939,-0.1711054295,0.212898016,0.1890973896,0.0244966391,-0.0382207111,-0.1943714023,0.1044634655,-0.1192779019,-0.2969190478,0.1680939794,-0.194265008,0.0368101113,-0.2712195218,-0.3120834231,-0.4605070055,-0.0989578143,-0.0250403397,-0.1841688752,0.2989388108,0.164873749,0.0333095007,-0.0042152,-0.033041168,0.0822724625,-0.1137121916,-0.2012418807,-0.2248912901,0.0750421882,-0.0986689627,-0.0048671509,0.0550270677,0.1905474663,0.3111845851,-0.3943756819,-0.1212726831,0.0237769987,-0.0814216658,0.2374360263,-0.0792715922,0.340854615,0.3825591803,0.101941593,-0.0926316977,-0.2722113132,0.0375120528,0.0219186805,0.101978533,0.066500023,0.3042426407,0.1053648293,0.6797077656,0.4086302817,0.0721286535,0.3929714561,-0.108514674,0.2568466067,-0.2528787851,-0.501732409,-0.1630624533,-0.3097382784,-0.0505203977,-0.13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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"It works on my side @timothyjlaurent on google colab.\r\nDid you try to uninstall datasets first, before updating it to master's version ?","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":23,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n It works on my side @timothyjlaurent on google colab.\r\nDid you try to uninstall datasets first, before updating it to master's version 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/620","title":"map\/filter multiprocessing raises errors and corrupts datasets","comments":"I didn't -- it was a new sessions --- buuut - look like it's working today -- woot! I'll close this issue. Thanks @lhoestq ","body":"After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n```","comment_length":24,"text":"map\/filter multiprocessing raises errors and corrupts datasets \n After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.\r\n\r\n```python\r\n    ...\r\n    ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    ner_ds_dict[\"validation\"] = ner_ds_dict[\"test\"]\r\n    rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n    rel_ds_dict[\"validation\"] = rel_ds_dict[\"test\"]\r\n    return ner_ds_dict, rel_ds_dict\r\n```\r\n\r\nThe first train_test_split, `ner_ds`\/`ner_ds_dict`, returns a `train` and `test` split that are iterable.\r\nThe second, `rel_ds`\/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`.\r\n\r\nOk I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads.\r\n\r\nI also see errors with other map and filter functions when `num_proc` is set.\r\n\r\n```\r\nDone writing 67 indices in 536 bytes .\r\nDone writing 67 indices in 536 bytes .\r\nFatal Python error: PyCOND_WAIT(gil_cond) failed\r\n``` \n I didn't -- it was a new sessions --- buuut - look like it's working today -- woot! I'll close this issue. Thanks @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/619","title":"Mistakes in MLQA features names","comments":"Indeed you're right ! Thanks for reporting that\r\n\r\nCould you open a PR to fix the features names ?","body":"I think the following features in MLQA shouldn't be named the way they are:\r\n1. `questions` (should be `question`)\r\n2. `ids` (should be `id`)\r\n3. `start` (should be `answer_start`)\r\n\r\nThe reasons I'm suggesting these features be renamed are:\r\n* To make them consistent with other QA datasets like SQuAD, XQuAD, TyDiQA etc. and hence make it easier to concatenate multiple QA datasets.\r\n* The features names are not the same as the ones provided in the original MLQA datasets (it uses the names I suggested).\r\n\r\nI know these columns can be renamed using  using `Dataset.rename_column_`, `questions` and `ids` can be easily renamed but `start` on the other hand is annoying to rename since it's nested inside the feature `answers`.\r\n","comment_length":19,"text":"Mistakes in MLQA features names \n I think the following features in MLQA shouldn't be named the way they are:\r\n1. `questions` (should be `question`)\r\n2. `ids` (should be `id`)\r\n3. `start` (should be `answer_start`)\r\n\r\nThe reasons I'm suggesting these features be renamed are:\r\n* To make them consistent with other QA datasets like SQuAD, XQuAD, TyDiQA etc. and hence make it easier to concatenate multiple QA datasets.\r\n* The features names are not the same as the ones provided in the original MLQA datasets (it uses the names I suggested).\r\n\r\nI know these columns can be renamed using  using `Dataset.rename_column_`, `questions` and `ids` can be easily renamed but `start` on the other hand is annoying to rename since it's nested inside the feature `answers`.\r\n \n Indeed you're right ! Thanks for reporting that\r\n\r\nCould you open a PR to fix the features names ?","embeddings":[0.2796131968,-0.0672781244,-0.0831842795,0.1901165694,0.2244755626,0.3354707658,0.5049102902,0.1621009558,-0.2027406991,-0.0173162166,0.1145086139,0.189746365,0.358310312,0.4374582171,-0.0695425272,-0.2454162985,0.2463854551,-0.0470877476,0.2421891391,-0.0504073612,-0.2164766192,0.2113196552,-0.0955329239,0.6324017048,0.0162046198,0.0617662631,-0.0821974203,0.0513545536,0.1023243293,-0.2556810081,-0.3580749631,0.0417952128,-0.1288834363,0.0629301444,-0.0001121551,-0.5027588606,0.3252595067,-0.0481603742,0.0559792109,0.195929721,-0.4208875597,-0.4244924188,0.0467999913,-0.3844260871,0.2206073552,-0.2236407399,-0.2441880554,-0.1287856698,-0.1785168946,0.1382639706,0.2021540403,-0.0150762191,0.2414555103,0.0380664207,0.156750083,-0.0216935109,-0.2375083417,0.0635238811,0.1898536682,-0.3023804724,0.3947696686,0.4994888604,-0.2198397964,0.1615970284,0.2153594345,0.1484520584,0.4425870478,-0.4972687364,0.2903123796,0.2118736655,0.3128450811,-0.1872746497,-0.2283304334,-0.2488459945,0.1180141941,-0.4643637538,0.1259203851,0.1799848676,0.0420384631,-0.2551813722,0.0971525013,-0.2348621786,0.24766168,-0.0904301181,-0.1196242496,-0.1861042827,0.0913874209,0.103745617,-0.2814544141,-0.3552182913,0.3920393288,0.1443683803,-0.1899144053,-0.0709320381,-0.44919613,-0.1471944898,-0.0656530187,0.1372911781,-0.183368057,-0.0760083646,-0.2866718471,-0.1546825767,0.2513923645,-0.0729935318,-0.1349556446,0.2213105261,0.2043882757,-0.1287403554,0.2029762715,-0.0088791093,-0.1400403678,-0.0656036437,0.0924017653,-0.0184069425,-0.4182289541,0.2236414552,0.1739640385,-0.2739310861,-0.318123132,0.3076445162,-0.4477353394,-0.1837328821,-0.1012836769,0.1104021817,0.058475215,0.1314394772,-0.0421452299,-0.0141479019,-0.1282830089,-0.236476317,-0.016901277,-0.0350200832,-0.2116493136,0.288821429,-0.1696249694,0.312384665,0.2091035396,-0.3090595007,0.0306379795,0.0261928122,0.1595200598,0.244290188,-0.1613461375,0.1271407902,-0.1375322193,0.0108513646,0.077898398,0.0282534733,-0.0115864566,0.154052034,-0.4452196658,-0.0020242357,-0.3583761454,0.2011039853,0.2075180262,0.0912626609,0.5391229987,0.3284410536,0.2340393215,-0.1146792397,0.101470165,0.0638985857,0.4210463464,-0.0761173815,-0.037354596,0.3379053175,-0.2224426419,-0.2421078682,-0.2108462602,0.2244683057,0.0717405602,0.2689327896,0.1512112468,-0.2363493741,-0.0420139469,0.1127992198,-0.0634274334,-0.1214621142,0.0940467492,-0.00918076,0.0562501661,-0.4241338372,0.1855673194,-0.0839033425,-0.2598454058,-0.1348484755,0.0801539049,-0.0857665017,-0.1918994933,-0.0042833462,-0.2674156129,-0.3934065998,0.2177091241,-0.0340570621,-0.1972797364,0.2775914073,-0.1092591435,-0.372240603,0.0812074021,0.0164585207,-0.2207569331,-0.2087622881,0.2250334471,-0.0180336181,0.1622039825,0.1617988348,-0.4457290173,-0.2008742988,-0.4516760409,0.0267479271,0.1582198143,-0.0885870978,-0.1440156549,-0.0856709853,0.1257941425,0.0197070986,0.1156930104,0.1000402942,-0.3079137206,-0.0546972193,-0.2999891341,-0.0695870221,0.0166508127,0.0884377584,-0.2995299101,0.0845690593,0.006773775,0.0245156139,0.0497617871,0.3868393004,0.2614257336,0.2171546817,-0.1582842767,0.2827342153,-0.1130648404,0.1268773079,0.2862364054,0.3946857154,0.1731519848,0.0114114676,0.1446131617,0.1174112111,-0.0964709371,0.2288684994,-0.1158267558,0.2167550623,0.08290869,0.2774330378,0.0137833301,0.3125977218,-0.0607790686,-0.3890331089,-0.1617789865,-0.2932703793,0.2806172669,0.1219407544,-0.2302554846,0.0286681671,-0.4353847206,0.3081432581,0.4657300711,0.0086177075,-0.0998735279,-0.1286444962,-0.0286652073,0.019542262,0.3403352201,-0.133849442,0.2067207247,-0.0138759902,-0.3927894235,0.0305581223,0.2801202238,-0.170256272,0.1206268668,0.3795162439,-0.4961919188,-0.0308025293,0.399510622,-0.1206594482,-0.1075789556,0.2712964416,0.3922182322,-0.1185784265,-0.3020518422,0.1671589911,-0.0590958185,-0.4547241032,0.0114507945,-0.4382162094,-0.2242616266,-0.3971214294,0.2313409299,-0.1345754862,-0.1287674904,0.4221910834,0.3064456582,0.3904313445,-0.1393619031,0.5181222558,-0.2536642849,-0.0292948205,-0.0275446456,0.0332499146,-0.0914422944,0.2400507331,0.1717616767,-0.0654342473,-0.0571082905,-0.4989719093,-0.4638471901,0.136767298,-0.1452696472,0.5637741685,-0.0681429952,-0.248379752,-0.1504255831,-0.2278665006,0.181530714,0.4881224334,0.0109627135,-0.0555702411,-0.0401342958,-0.0752229914,-0.1130447835,-0.4911833405,-0.1803040504,-0.1180189773,0.1634392589,-0.1793171316,0.1086950079,-0.0388177298,-0.6115885377,-0.2516984046,0.0535071939,-0.1561785787,-0.3134723902,0.0295977537,0.1696384102,-0.2660049796,-0.0662297085,-0.0142088495,0.114947632,0.0425457731,-0.4492481351,-0.2512800992,-0.440128237,-0.0633307248,0.1214749515,0.3817400336,-0.048870448,0.0095455674,0.2082291692,-0.0724832341,0.1266065687,-0.4921844006,0.0524862967,0.5331341624,0.6120025516,-0.0265210047,0.4932017624,-0.3559103608,0.3424315751,0.0715392604,0.3521508574,0.0787941664,0.0839557871,-0.278308332,-0.0198826902,-0.2667330801,0.4488635063,-0.0902373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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/617","title":"Compare different Rouge implementations  ","comments":"Updates - the differences between the following three\r\n(1) https:\/\/github.com\/bheinzerling\/pyrouge (previously popular. The one I trust the most)\r\n(2) https:\/\/github.com\/google-research\/google-research\/tree\/master\/rouge\r\n(3) https:\/\/github.com\/pltrdy\/files2rouge (used in fairseq)\r\ncan be explained by two things, stemming and handling multiple sentences.\r\n\r\nStemming: \r\n(1), (2): default is no stemming. (3): default is with stemming ==> No stemming is the correct default as you did [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py#L84)\r\n\r\nMultiple sentences:\r\n(1) `rougeL` splits text using `\\n`\r\n(2) `rougeL` ignores `\\n`. \r\n(2) `rougeLsum` splits text using `\\n`\r\n(3) `rougeL` splits text using `.`\r\n\r\nFor (2), `rougeL` and `rougeLsum` are identical if the sequence doesn't contain `\\n`. With `\\n`, it is `rougeLsum` that matches (1) not `rougeL`. \r\n\r\nOverall, and as far as I understand, for your implementation here https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py#L65 to match the default, you only need to change `rougeL` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py#L86) to `rougeLsum` to correctly compute metrics for text with newlines.\r\n\r\nTagging @sshleifer who might be interested.","body":"I used RougeL implementation provided in `datasets` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https:\/\/arxiv.org\/pdf\/1909.03186.pdf) for example.\r\nCan you make sure the google-research implementation you are using matches the official perl implementation? \r\nThere are a couple of python wrappers around the perl implementation, [this](https:\/\/pypi.org\/project\/pyrouge\/) has been commonly used, and [this](https:\/\/github.com\/pltrdy\/files2rouge) is used in fairseq). \r\nThere's also a python reimplementation [here](https:\/\/github.com\/pltrdy\/rouge) but its RougeL numbers are way off. \r\n","comment_length":145,"text":"Compare different Rouge implementations   \n I used RougeL implementation provided in `datasets` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https:\/\/arxiv.org\/pdf\/1909.03186.pdf) for example.\r\nCan you make sure the google-research implementation you are using matches the official perl implementation? \r\nThere are a couple of python wrappers around the perl implementation, [this](https:\/\/pypi.org\/project\/pyrouge\/) has been commonly used, and [this](https:\/\/github.com\/pltrdy\/files2rouge) is used in fairseq). \r\nThere's also a python reimplementation [here](https:\/\/github.com\/pltrdy\/rouge) but its RougeL numbers are way off. \r\n \n Updates - the differences between the following three\r\n(1) https:\/\/github.com\/bheinzerling\/pyrouge (previously popular. The one I trust the most)\r\n(2) https:\/\/github.com\/google-research\/google-research\/tree\/master\/rouge\r\n(3) https:\/\/github.com\/pltrdy\/files2rouge (used in fairseq)\r\ncan be explained by two things, stemming and handling multiple sentences.\r\n\r\nStemming: \r\n(1), (2): default is no stemming. (3): default is with stemming ==> No stemming is the correct default as you did [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py#L84)\r\n\r\nMultiple sentences:\r\n(1) `rougeL` splits text using `\\n`\r\n(2) `rougeL` ignores `\\n`. \r\n(2) `rougeLsum` splits text using `\\n`\r\n(3) `rougeL` splits text using `.`\r\n\r\nFor (2), `rougeL` and `rougeLsum` are identical if the sequence doesn't contain `\\n`. With `\\n`, it is `rougeLsum` that matches (1) not `rougeL`. \r\n\r\nOverall, and as far as I understand, for your implementation here https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py#L65 to match the default, you only need to change `rougeL` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py#L86) to `rougeLsum` to correctly compute metrics for text with newlines.\r\n\r\nTagging @sshleifer who might be interested.","embeddings":[-0.0971114635,-0.1454416215,-0.1018338054,0.2446954995,-0.2790500522,-0.6873996258,0.0966215879,0.0406908654,-0.2773724794,0.2449082136,-0.1052665338,0.1245480254,0.156569168,0.0299380291,0.1508821547,-0.2753460407,0.0824456811,0.0195159987,-0.0070209946,-0.1224717423,-0.0989000425,0.710848093,-0.1582221389,0.0294798966,-0.1360562593,0.0632249787,-0.065176338,0.1977922171,-0.1465395987,-0.1077265888,-0.4238384366,0.2580074668,-0.0102514327,0.5024155378,-0.0001064565,-0.2057293504,0.1464959681,-0.0821161121,0.0163352154,-0.0362428129,-0.1412746161,-0.1513666213,-0.0882195458,-0.1276598573,-0.1156207323,-0.1115842536,-0.1406907737,0.1013708264,0.3020978868,0.461953491,0.2020174712,-0.2780742645,0.1017406732,-0.1463398635,0.5364743471,-0.0650649816,0.178373754,0.4277877212,0.1088055074,-0.0589308254,-0.2776232958,0.3784532249,0.0933002383,-0.2227546126,-0.0347062945,0.1359006912,-0.0522903986,-0.1840516031,0.0191417355,0.5499538779,-0.0278989412,-0.1120534241,-0.5472528338,-0.0820501149,-0.1682856977,-0.3088389933,-0.0079201665,0.2469446361,0.0827694684,-0.0889340937,-0.0894185379,0.0640295371,-0.106120877,-0.1750179976,-0.4134925008,0.0716322884,-0.148103863,-0.0499196239,0.2254970819,-0.138600409,-0.4518758357,0.0884429961,-0.2105749547,-0.224803865,-0.2263540477,0.030409513,0.2012510896,0.2470627427,0.1548953056,0.3268068135,0.1311066896,0.0373832807,0.100532271,0.0954623744,-0.2064218968,0.02000219,0.1247547567,0.0741545409,0.4574242532,0.1187108085,-0.0139872888,0.1224831119,0.2310451865,-0.4499968588,-0.2169406265,0.0168354549,-0.1520794034,-0.2141431421,-0.447525233,0.0682152659,-0.4359628558,-0.0626249835,0.052083049,0.2803235352,-0.0924392194,0.1899290681,0.0190020502,0.0347934514,-0.3867938221,-0.4065914452,-0.2543853819,0.1753966212,-0.1582213193,-0.0041399603,0.1424891353,0.0595285818,0.1201603115,0.0901783407,0.1998115033,-0.1900429577,0.1238479465,-0.3567660451,-0.1528307498,-0.1231164187,-0.1686126739,-0.1810043752,-0.072272785,-0.0784543008,-0.108654812,-0.0798542425,-0.3861472011,-0.0888710842,0.2821951807,0.2549304664,-0.1683550626,-0.0753798783,-0.0070643811,0.3652495742,0.0471455082,-0.2536486685,0.2029476911,-0.3402794003,-0.3170870245,-0.1564066559,0.1576577723,0.1496517211,-0.329675138,-0.1588445455,0.4732919931,0.0124880495,0.2169860601,0.4738480747,0.0712074786,0.1423042268,0.0155601818,0.1054048166,0.1755568385,-0.3649576604,-0.2462287545,0.2770293951,0.1828013361,-0.1516365409,0.1605482548,-0.1878699064,0.289907068,-0.0099040708,0.4108474553,0.2934286594,0.2333953381,0.1149007306,-0.0389558189,0.0885279849,0.1400673985,0.0026700818,-0.1767756641,-0.2594023645,-0.0072355797,-0.5122947097,0.2567877769,-0.219949618,-0.0949148536,0.437561512,0.0547254719,0.2224579304,0.3824210763,-0.0100100078,0.0522029512,-0.1480383873,-0.9107893109,0.1051268354,-0.1624167264,-0.1286664754,-0.1426205635,-0.2644803226,0.2685120106,-0.3495296836,0.2174311876,0.1194116175,0.2412713766,0.1883353144,0.0180596076,0.4312154651,-0.0966070518,0.136684075,-0.1902314574,0.0697116554,-0.0828463957,-0.1460764259,0.3277026415,0.4177888632,0.1070450917,0.1123865917,0.1384594589,0.4820681214,0.4255564809,0.2667169273,0.0809953362,-0.0636455938,0.2385511249,-0.1907710582,0.1061717644,0.4484041333,0.1561098844,0.0829993039,-0.5478582978,0.4688169658,-0.2570522428,0.0163856652,0.1720330417,-0.1505869776,0.1022734419,0.0802513286,-0.3891167045,-0.0443931371,0.3786428869,0.2883983552,0.5072104931,0.0236743093,-0.2626601458,0.0863702819,0.4717354178,-0.217466265,0.238318488,0.0318508707,-0.1205308437,-0.2995311916,0.0457245074,0.0068179835,-0.0083268434,0.2829852998,0.1845151484,-0.0496972837,-0.0539589524,-0.4153895676,0.2375267744,-0.1250955462,-0.2692331374,0.2895718813,0.3184883296,-0.0660476908,-0.4066005349,-0.4403741658,-0.1574134976,-0.0045677782,-0.1147072092,0.2354545444,-0.0973481089,-0.0767505765,0.0552370027,-0.3195218742,-0.3229712248,-0.2448723018,0.2438192666,0.0047329529,-0.1418255121,-0.1242140979,0.3198378384,0.1610330343,0.1131194308,-0.0448290743,-0.0473403968,-0.099969171,-0.1550525725,0.1665180326,-0.3360775709,0.0314207785,0.0443984643,-0.3856732547,-0.1498594284,-0.0498602055,-0.4121781886,0.3080253899,-0.1008325145,0.0904304236,0.3280991316,-0.3643452525,0.0567746982,0.3632389009,0.1514820158,-0.2763025761,-0.2511568964,0.2864522338,-0.1681543291,0.1224940717,-0.2411346585,-0.405803442,-0.0488448031,-0.2470820397,0.3765846491,0.1368347257,0.1807420105,0.2938701808,-0.0960831866,0.0734526515,0.2460882366,0.3446050882,-0.2459904402,-0.0772866383,0.2710967064,-0.4211298227,-0.4233298004,-0.1781837642,0.0295938309,-0.2226561457,0.3266169131,-0.0901057944,-0.2683164477,-0.2974977493,-0.0205990821,0.0652812421,0.1605580896,0.4207520485,-0.1087337881,-0.1601273865,-0.2147703618,0.3006651103,0.1324690878,0.0576618239,0.1184684932,-0.111734882,0.3356950581,0.1246398091,0.1817935556,0.6110421419,0.4109737873,-0.1472970694,-0.1327874362,0.1051609591,-0.2374237776,0.4501044154,0.28834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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/617","title":"Compare different Rouge implementations  ","comments":"This is a real issue, sorry for missing the mention @ibeltagy\r\n\r\nWe implemented a more involved [solution](https:\/\/github.com\/huggingface\/transformers\/blob\/99cb924bfb6c4092bed9232bea3c242e27c6911f\/examples\/seq2seq\/utils.py#L481) that enforces that sentences are split with `\\n` so that rougeLsum scores match papers even if models don't generate newlines. \r\n\r\nUnfortunately, the best\/laziest way I found to do this introduced an `nltk` dependency (For sentence splitting, all sentences don't end in `.`!!!), but this might be avoidable with some effort.\r\n\r\n#### Sidebar: Wouldn't Deterministic Be Better?\r\n\r\n`rouge_scorer.scoring.BootstrapAggregator` is well named but is not deterministic which I would like to change for my mental health, unless there is some really good reason to sample 500 observations before computing f-scores.\r\n\r\nI have a fix on a branch, but I wanted to get some context before introducting a 4th way to compute rouge. Scores are generally within .03 Rouge2 of boostrap after multiplying by 100, e.g 22.05 vs 22.08 Rouge2.\r\n\r\n","body":"I used RougeL implementation provided in `datasets` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https:\/\/arxiv.org\/pdf\/1909.03186.pdf) for example.\r\nCan you make sure the google-research implementation you are using matches the official perl implementation? \r\nThere are a couple of python wrappers around the perl implementation, [this](https:\/\/pypi.org\/project\/pyrouge\/) has been commonly used, and [this](https:\/\/github.com\/pltrdy\/files2rouge) is used in fairseq). \r\nThere's also a python reimplementation [here](https:\/\/github.com\/pltrdy\/rouge) but its RougeL numbers are way off. \r\n","comment_length":144,"text":"Compare different Rouge implementations   \n I used RougeL implementation provided in `datasets` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https:\/\/arxiv.org\/pdf\/1909.03186.pdf) for example.\r\nCan you make sure the google-research implementation you are using matches the official perl implementation? \r\nThere are a couple of python wrappers around the perl implementation, [this](https:\/\/pypi.org\/project\/pyrouge\/) has been commonly used, and [this](https:\/\/github.com\/pltrdy\/files2rouge) is used in fairseq). \r\nThere's also a python reimplementation [here](https:\/\/github.com\/pltrdy\/rouge) but its RougeL numbers are way off. \r\n \n This is a real issue, sorry for missing the mention @ibeltagy\r\n\r\nWe implemented a more involved [solution](https:\/\/github.com\/huggingface\/transformers\/blob\/99cb924bfb6c4092bed9232bea3c242e27c6911f\/examples\/seq2seq\/utils.py#L481) that enforces that sentences are split with `\\n` so that rougeLsum scores match papers even if models don't generate newlines. \r\n\r\nUnfortunately, the best\/laziest way I found to do this introduced an `nltk` dependency (For sentence splitting, all sentences don't end in `.`!!!), but this might be avoidable with some effort.\r\n\r\n#### Sidebar: Wouldn't Deterministic Be Better?\r\n\r\n`rouge_scorer.scoring.BootstrapAggregator` is well named but is not deterministic which I would like to change for my mental health, unless there is some really good reason to sample 500 observations before computing f-scores.\r\n\r\nI have a fix on a branch, but I wanted to get some context before introducting a 4th way to compute rouge. Scores are generally within .03 Rouge2 of boostrap after multiplying by 100, e.g 22.05 vs 22.08 Rouge2.\r\n\r\n","embeddings":[-0.1379877776,0.0537790135,-0.065096423,0.2345050275,-0.0384498984,-0.6865086555,-0.1374980062,-0.0325634032,-0.2779031098,0.395267278,-0.0672829151,0.1716644168,0.1589004546,0.363702029,0.2211120129,-0.0784863532,0.1345872134,-0.0168794338,-0.1050260067,-0.1088072285,0.0128314812,0.6053342223,-0.0310589336,0.1092312336,-0.1490680575,0.1322920471,0.2132202089,0.0989613906,-0.1409740895,-0.10045214,-0.1728941202,0.1978754103,-0.109423846,0.4348340333,-0.0001064469,-0.1936359555,0.1715347022,-0.1881962866,0.0778008103,-0.1544618458,0.0580871999,-0.0233251452,-0.1111885011,-0.038434051,-0.2476809025,-0.0726091638,-0.2509014606,0.0748237446,0.1554621905,0.3872460723,0.2037396878,-0.0639967397,0.1957696378,-0.2286606133,0.5280544758,-0.0566640608,0.0506690852,0.5248132944,-0.12089324,-0.107549496,-0.3474807739,0.3372290134,0.1901493073,-0.3185483217,0.1235745698,0.0154710636,-0.0340329148,-0.2020000219,-0.1000618935,0.6137260199,-0.1752004027,0.0161953811,-0.5487056375,-0.0667547062,0.0393242054,-0.1948316991,0.0913067833,-0.0565147027,0.036862243,-0.1985515356,-0.0645336062,0.3034345508,-0.0474751815,-0.1840778142,-0.1046651229,-0.0378048271,-0.1254391521,-0.0757325441,0.4095121622,-0.1220963448,-0.5111020207,0.3069493175,-0.1545218378,-0.0891522393,-0.5262218714,-0.0610734038,0.1598901302,0.4157986641,0.2494131476,0.4495767057,0.3137447238,0.053171549,0.0750584751,0.0652157143,-0.3272762299,0.0348073766,-0.0133848796,-0.0332113691,0.3852156997,0.131689325,0.0124615915,0.1276688725,0.3607757986,-0.5237594247,-0.2875634432,0.1228606254,-0.4410630465,-0.3779605329,-0.4348496199,0.1786829531,-0.58181566,-0.0216637384,0.0735606402,0.1862042248,-0.1235137582,0.2579784691,0.01279213,-0.0408552103,-0.3306750655,-0.2836543024,-0.2149548233,0.0749734938,-0.1546937078,0.1803346425,0.0942670256,-0.0190296117,0.1723968834,0.0128592616,0.1753636003,-0.2587369978,0.0640308708,-0.2548103034,-0.1448667794,-0.0185460914,-0.1496222019,-0.2562123835,-0.0164534934,-0.1931044012,-0.130374521,-0.1442642361,-0.1164204851,-0.0432634428,0.4312921166,0.2439413667,-0.1952553988,-0.166735217,-0.0696855113,0.2667293549,-0.2485964149,-0.1255652905,0.1539250165,-0.2475672364,-0.2683533728,-0.0707558915,0.3554469347,0.1554047316,-0.1645865887,-0.1988318264,0.5341036916,-0.0445965305,0.0099409474,0.3509596288,0.144003734,0.3776940107,-0.0017380156,-0.1347105801,0.2774407268,-0.3410018384,-0.276017189,-0.0057499236,0.1186543182,-0.0679779425,0.1401611567,-0.0949994102,0.0232433118,-0.0039749565,0.1682724208,0.2526464164,0.0599440262,0.0217985604,-0.1456855536,0.0653613731,0.2068350762,0.1683458388,-0.0439079329,-0.2805389762,-0.0583609864,-0.608435154,0.330196321,-0.1606402695,-0.3503609002,0.3265835047,0.0191348307,0.165494442,0.2853588462,-0.0028769886,0.1243408024,-0.0277984142,-0.8134545088,0.1548524648,0.125845179,-0.1853113025,-0.326454699,-0.1159099936,0.3186032772,-0.4248637259,0.1786702722,-0.0310022291,0.4205991328,0.1584344208,0.0026366166,0.3995909989,-0.1581787318,0.1119308248,-0.3919112682,-0.0032044484,-0.0094384365,-0.1483703554,0.2668654621,0.4590812922,0.1804880053,0.1636718512,-0.0739021748,0.4581432343,0.3668297529,0.0855815038,0.1537888348,0.1852805465,0.0301368963,0.0182526968,-0.1596736908,0.5856784582,0.1365930885,0.1660449803,-0.343705982,0.5487450957,-0.4739865661,0.0545277968,0.180081591,-0.1455039084,0.0748512819,0.1776161045,-0.4026848078,0.0675391853,0.4525692463,0.1796044707,0.5824773312,0.0392693095,-0.2232962996,-0.0138708372,0.296569556,-0.2222269028,0.1048379168,0.0292703211,-0.0729205385,-0.1611636728,0.0832695216,-0.04691967,-0.0286678821,0.2086730599,0.0139425118,-0.0416535251,-0.1332062185,-0.4041922092,0.1018150374,-0.1700228602,0.0515731499,0.2663269043,0.2414890677,0.1121663079,-0.3807005286,-0.3593528271,-0.360661149,-0.0638499707,-0.1678470969,0.1870641261,-0.1131305397,0.0091959471,-0.1322094053,-0.2192118615,-0.4324485958,-0.298661083,0.1582986712,0.0585571602,-0.1180024147,-0.0339629017,0.3236483634,0.2754863203,0.0101452358,0.0402028747,-0.022171773,-0.0944166854,-0.2344562411,0.1769817322,-0.2460259348,-0.1812212616,0.0618322268,-0.3107549846,-0.0086973663,0.0190707985,-0.4526582956,0.2786203623,-0.1029074192,0.2484103441,0.2135023028,-0.4276442826,0.0032276656,0.4406077564,0.1218789071,-0.3287892044,-0.3009264469,0.1931613237,-0.0724636465,0.1929080039,-0.087208651,-0.2683808804,0.0207664911,-0.0987430736,0.5206075907,0.114422828,0.1009172648,0.3030361831,-0.091273807,0.0563136749,0.0778944567,0.1816982329,-0.2014032602,-0.0219408758,0.3603029847,-0.3418867886,-0.4680043757,-0.1450920701,0.0597500168,-0.2673578858,0.1555731744,-0.1215312704,-0.3317548335,-0.0737196356,0.0234594159,-0.0890874565,0.0716887563,0.4092116654,-0.2179155201,-0.0893819481,-0.1051583812,0.0778067634,0.1946632862,-0.1805390269,0.1870252341,-0.1966016889,0.3595887125,0.0451525673,0.0217612889,0.5290668011,0.3862200379,-0.2149225473,-0.0533157438,0.0830888152,-0.2630346715,0.4513287842,0.35526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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/617","title":"Compare different Rouge implementations  ","comments":"> This is a real issue, sorry for missing the mention @ibeltagy\r\n> \r\n> We implemented a more involved [solution](https:\/\/github.com\/huggingface\/transformers\/blob\/99cb924bfb6c4092bed9232bea3c242e27c6911f\/examples\/seq2seq\/utils.py#L481) that enforces that sentences are split with `\\n` so that rougeLsum scores match papers even if models don't generate newlines.\r\n> \r\n> Unfortunately, the best\/laziest way I found to do this introduced an `nltk` dependency (For sentence splitting, all sentences don't end in `.`!!!), but this might be avoidable with some effort.\r\n\r\nThanks for the details, I didn't know about that. Maybe we should consider adding this processing step or at least mention it somewhere in the library or the documentation\r\n\r\n> #### Sidebar: Wouldn't Deterministic Be Better?\r\n> `rouge_scorer.scoring.BootstrapAggregator` is well named but is not deterministic which I would like to change for my mental health, unless there is some really good reason to sample 500 observations before computing f-scores.\r\n> \r\n> I have a fix on a branch, but I wanted to get some context before introducting a 4th way to compute rouge. Scores are generally within .03 Rouge2 of boostrap after multiplying by 100, e.g 22.05 vs 22.08 Rouge2.\r\n\r\nI think the default `n_samples` of the aggregator is 1000. We could increase it or at least allow users to change it if they want more precise results.","body":"I used RougeL implementation provided in `datasets` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https:\/\/arxiv.org\/pdf\/1909.03186.pdf) for example.\r\nCan you make sure the google-research implementation you are using matches the official perl implementation? \r\nThere are a couple of python wrappers around the perl implementation, [this](https:\/\/pypi.org\/project\/pyrouge\/) has been commonly used, and [this](https:\/\/github.com\/pltrdy\/files2rouge) is used in fairseq). \r\nThere's also a python reimplementation [here](https:\/\/github.com\/pltrdy\/rouge) but its RougeL numbers are way off. \r\n","comment_length":210,"text":"Compare different Rouge implementations   \n I used RougeL implementation provided in `datasets` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https:\/\/arxiv.org\/pdf\/1909.03186.pdf) for example.\r\nCan you make sure the google-research implementation you are using matches the official perl implementation? \r\nThere are a couple of python wrappers around the perl implementation, [this](https:\/\/pypi.org\/project\/pyrouge\/) has been commonly used, and [this](https:\/\/github.com\/pltrdy\/files2rouge) is used in fairseq). \r\nThere's also a python reimplementation [here](https:\/\/github.com\/pltrdy\/rouge) but its RougeL numbers are way off. \r\n \n > This is a real issue, sorry for missing the mention @ibeltagy\r\n> \r\n> We implemented a more involved [solution](https:\/\/github.com\/huggingface\/transformers\/blob\/99cb924bfb6c4092bed9232bea3c242e27c6911f\/examples\/seq2seq\/utils.py#L481) that enforces that sentences are split with `\\n` so that rougeLsum scores match papers even if models don't generate newlines.\r\n> \r\n> Unfortunately, the best\/laziest way I found to do this introduced an `nltk` dependency (For sentence splitting, all sentences don't end in `.`!!!), but this might be avoidable with some effort.\r\n\r\nThanks for the details, I didn't know about that. Maybe we should consider adding this processing step or at least mention it somewhere in the library or the documentation\r\n\r\n> #### Sidebar: Wouldn't Deterministic Be Better?\r\n> `rouge_scorer.scoring.BootstrapAggregator` is well named but is not deterministic which I would like to change for my mental health, unless there is some really good reason to sample 500 observations before computing f-scores.\r\n> \r\n> I have a fix on a branch, but I wanted to get some context before introducting a 4th way to compute rouge. Scores are generally within .03 Rouge2 of boostrap after multiplying by 100, e.g 22.05 vs 22.08 Rouge2.\r\n\r\nI think the default `n_samples` of the aggregator is 1000. We could increase it or at least allow users to change it if they want more precise results.","embeddings":[-0.1199247912,0.0578211658,-0.0259994827,0.247537449,-0.0194163173,-0.7114323378,-0.0715660974,-0.0210268293,-0.2410765439,0.3681191504,-0.0042177765,0.1752641201,0.1229400113,0.3598643839,0.2975478172,-0.1921318769,0.0986103565,-0.0162566714,-0.0499816351,-0.0920571387,-0.0059650606,0.6030076146,-0.0572601967,0.1056353524,-0.1943653226,0.1431973875,0.1966951936,0.0866381153,-0.1192162037,-0.1853498966,-0.2451326549,0.2077442408,-0.0349605605,0.4523561895,-0.0001091167,-0.2276299447,0.2159281522,-0.2167396098,0.1104495153,-0.1605566889,0.0378186703,-0.1098205,-0.0797066987,-0.081466049,-0.2882637382,0.00725001,-0.1965866834,0.096768409,0.1674869955,0.356051892,0.1705368161,-0.0147535512,0.1806708574,-0.2480346411,0.5668400526,-0.0728992522,0.0628357455,0.6113482714,-0.0754710585,-0.0680768639,-0.3386306465,0.3610008359,0.1657429338,-0.3372178674,0.0628842488,0.1117008552,-0.0137623856,-0.1807054728,-0.0311415475,0.6794782281,-0.1486427635,0.0029165316,-0.4949690402,-0.0983795896,0.0643410534,-0.3069671988,0.1008419693,0.0712011755,0.0650982261,-0.1525472999,-0.1313446015,0.2446857393,-0.1071738079,-0.1755681932,-0.1494294852,-0.0151282949,-0.0904636607,-0.0794895366,0.3360760808,-0.0673165768,-0.5943489075,0.3753002584,-0.1992860883,-0.1545648128,-0.4956997633,-0.0426405221,0.1276846379,0.433683604,0.3025707603,0.4458981454,0.3535336852,0.0588151217,0.1667743474,0.0529164411,-0.2282678634,0.0254596267,-0.0179456957,-0.0743466988,0.4144559503,0.1811351031,0.0121966442,0.1405445039,0.4187540114,-0.5451748967,-0.1845691502,0.1008505449,-0.3514792323,-0.3432897627,-0.3676083088,0.1872015446,-0.5367393494,-0.0158157442,-0.0174540486,0.1968393773,-0.1532119215,0.2220282555,0.0514642969,0.0718086883,-0.3348800242,-0.3064831197,-0.1950988621,0.0791321099,-0.1510117799,0.2155078202,0.0868384466,-0.010045832,0.2042098194,0.0597339235,0.1382122189,-0.366733253,0.0484401695,-0.2148968875,-0.1565047055,-0.0596122667,-0.1266817898,-0.2758532465,-0.0598979481,-0.2508656383,-0.1477456987,-0.1589676142,-0.1754635721,-0.0020290541,0.4212627709,0.2016867548,-0.2421361953,-0.1874312162,-0.0104133161,0.293857336,-0.1305388212,-0.0845900849,0.1509460062,-0.2787753642,-0.324457109,-0.0561419316,0.3232987821,0.2026695162,-0.1975643933,-0.247759819,0.6061241031,0.040327698,0.0336668044,0.3809573352,0.1653750241,0.3888631761,0.0549484938,-0.0502892025,0.1929959655,-0.270024091,-0.2422163486,-0.0212492794,0.0832388774,-0.0768052712,0.1449465603,-0.1102794558,0.1698947251,0.0691119581,0.1431253254,0.2125446796,0.105232574,0.0390273184,-0.1694833636,0.0231248569,0.2083104551,0.182622835,-0.0441775657,-0.247535497,-0.0602207705,-0.5506232381,0.3593122065,-0.2336244881,-0.2559227049,0.3169041574,0.0317749605,0.1331079006,0.2627291381,0.0109732095,0.1177085638,-0.0579709969,-0.8542650342,0.2441546172,0.0617075711,-0.1692024767,-0.3705717921,-0.1805855781,0.300323844,-0.4371094406,0.1600407958,-0.0143354591,0.3412575424,0.2341626287,0.0369942561,0.4147161543,-0.1173598021,0.1140119284,-0.4648653269,0.00194202,-0.0095589487,-0.2148297876,0.3159090281,0.4534549713,0.1284361184,0.1765092313,-0.0494395345,0.4062876105,0.339990586,0.1041297242,0.1333085597,0.0938198641,0.0694995672,-0.0890278891,-0.1304594874,0.5995194912,0.124730587,0.1686528623,-0.3227340877,0.5980030298,-0.3787491322,0.046395205,0.1247782335,-0.1300297976,0.1045121178,0.105768241,-0.3970209956,0.0756273046,0.448471576,0.1915378124,0.5518527031,0.0433866121,-0.3257263303,-0.0421123505,0.3595901132,-0.1903334707,0.1797349006,0.0371973775,-0.0867273584,-0.1773941517,-0.021960875,-0.0783407465,0.0025477135,0.2195889205,0.0197215639,-0.0007429314,-0.1408705413,-0.4390758276,0.0764092132,-0.1429211348,-0.0605827421,0.3043664396,0.2423512638,0.0617750138,-0.4098843932,-0.2927568853,-0.3892460167,-0.042088937,-0.1957097948,0.1807440072,-0.0944136754,0.1418761909,-0.1394220442,-0.1282729506,-0.5086534023,-0.2359361649,0.2334479392,0.076352343,-0.1304826438,-0.0245747045,0.3039119542,0.3134801984,0.0245656967,0.0222486332,0.0049761296,-0.1759691834,-0.2359918803,0.146082893,-0.2856435776,-0.1068563312,0.0836335942,-0.3754407465,-0.0247780848,0.0332225077,-0.4409699142,0.2545731366,-0.0748175904,0.2755791545,0.2000907958,-0.3662665188,-0.0426011495,0.4201520383,0.1592188478,-0.3122100532,-0.2852800786,0.2487560064,-0.0951729193,0.2048257738,-0.1195692271,-0.412758857,0.0095930491,-0.1093281582,0.4913555086,0.1069434434,0.0888677239,0.2736880481,-0.0542880371,0.0500341319,-0.0098215146,0.2073385566,-0.1839255691,-0.0335972644,0.3422642648,-0.3354378641,-0.5135300159,-0.1427979469,0.0637115762,-0.201381281,0.2057778686,-0.1288751066,-0.2995453477,-0.0593241528,-0.0508388728,-0.055118192,0.082038857,0.43496418,-0.2504155934,-0.0757332966,-0.1027090251,0.0838338882,0.3216798306,-0.1883363128,0.2084263116,-0.158868283,0.3865533173,0.037282519,0.0425752699,0.6114041209,0.3491770029,-0.1800512373,-0.0786046907,0.0960942432,-0.2694198489,0.4766199291,0.2780717611,-0.015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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/617","title":"Compare different Rouge implementations  ","comments":"Hi, thanks for the solution. \r\n\r\nI am not sure if this is a bug, but on line [510](https:\/\/github.com\/huggingface\/transformers\/blob\/99cb924bfb6c4092bed9232bea3c242e27c6911f\/examples\/seq2seq\/utils.py#L510), are pred, tgt supposed to be swapped?","body":"I used RougeL implementation provided in `datasets` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https:\/\/arxiv.org\/pdf\/1909.03186.pdf) for example.\r\nCan you make sure the google-research implementation you are using matches the official perl implementation? \r\nThere are a couple of python wrappers around the perl implementation, [this](https:\/\/pypi.org\/project\/pyrouge\/) has been commonly used, and [this](https:\/\/github.com\/pltrdy\/files2rouge) is used in fairseq). \r\nThere's also a python reimplementation [here](https:\/\/github.com\/pltrdy\/rouge) but its RougeL numbers are way off. \r\n","comment_length":25,"text":"Compare different Rouge implementations   \n I used RougeL implementation provided in `datasets` [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/metrics\/rouge\/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https:\/\/arxiv.org\/pdf\/1909.03186.pdf) for example.\r\nCan you make sure the google-research implementation you are using matches the official perl implementation? \r\nThere are a couple of python wrappers around the perl implementation, [this](https:\/\/pypi.org\/project\/pyrouge\/) has been commonly used, and [this](https:\/\/github.com\/pltrdy\/files2rouge) is used in fairseq). \r\nThere's also a python reimplementation [here](https:\/\/github.com\/pltrdy\/rouge) but its RougeL numbers are way off. \r\n \n Hi, thanks for the solution. \r\n\r\nI am not sure if this is a bug, but on line [510](https:\/\/github.com\/huggingface\/transformers\/blob\/99cb924bfb6c4092bed9232bea3c242e27c6911f\/examples\/seq2seq\/utils.py#L510), are pred, tgt supposed to be swapped?","embeddings":[-0.0462399237,-0.3402347565,-0.0140987523,0.379104495,-0.0787526146,-0.6177638173,0.0081204483,0.0337361768,-0.3628861308,0.3499982357,-0.2657788098,-0.1302823424,0.1970662624,0.2637876868,0.260407567,-0.2404887378,0.0870682597,-0.015829904,-0.3073508739,-0.1472104639,-0.125898093,0.7282140255,-0.1187530011,0.1422054619,-0.1178556606,0.3190756738,0.0754643902,0.1428857297,-0.07691098,0.0503387451,-0.0773925483,0.1364061683,0.0535053238,0.6273003817,-0.0001120195,-0.0545938276,0.0474308021,-0.1357920468,0.069316633,-0.0228229351,-0.1206796393,-0.1080061793,-0.1051929966,-0.1453908086,-0.2766059041,-0.1439552605,-0.2889887393,0.2026909292,0.2424778342,0.4388734996,0.1830443293,0.037156608,0.1999942809,-0.1163281426,0.5332654715,-0.0113954572,0.0095240204,0.4414730668,0.0845701098,0.0121362219,-0.1068575233,0.2801780999,0.2016871572,-0.2245485634,0.0831993297,0.1899203956,0.0646672696,-0.0947259068,0.0609512553,0.4944716394,-0.033806514,-0.2444932461,-0.4565926492,0.0317237042,-0.2688064575,-0.334459424,0.0806375667,0.0214642473,0.3556173146,-0.0614489019,-0.2099113315,0.0630044863,-0.0412148051,-0.2200860828,-0.4216421545,-0.0584213063,-0.1846101433,0.0430524424,0.1635533422,-0.1405409724,-0.5604932904,0.2374844104,-0.1599112004,-0.1976955086,-0.3531385362,-0.1151283458,0.1373972148,0.3342143595,0.0371527448,0.4631068408,0.1254409701,0.1216638759,0.0856851414,0.0586970858,-0.3227404058,0.2932855487,-0.1890389472,0.172027275,0.355443418,0.0926138535,0.0749712437,0.0584116131,0.4156941772,-0.6989457011,-0.2002404928,0.0524525307,-0.167194441,-0.4326241612,-0.4299024642,-0.0271917507,-0.1767520905,-0.0308983214,0.1167363971,0.1503123492,-0.1495216936,0.0424442813,0.1605858803,-0.0055337274,-0.5098376274,-0.1614808142,-0.2780581713,0.1651213616,-0.1665259153,0.1229581088,0.0699660331,0.1001441777,0.2009737194,0.096145995,0.1871931851,-0.0009884766,0.1759388894,-0.0594167598,-0.1663112491,0.137147814,-0.1835516989,-0.0147337569,0.0321947448,0.0244991519,-0.1782720834,-0.2502812147,-0.4921041727,-0.1060738936,0.2954384089,0.1907461584,-0.2942804694,-0.0692167655,-0.1578979194,0.2298372984,-0.0357162841,-0.5121923685,0.0543526635,-0.3018949032,-0.3901182115,-0.1606630385,0.3361691236,0.1987190396,-0.1800176501,-0.1948147118,0.5356314778,-0.0672032684,0.110706456,0.4747158885,0.0492928214,0.0055499384,-0.174530983,-0.2681955695,0.1999701262,-0.4523166418,-0.3830577135,0.0476567633,0.0821941793,-0.2138216645,0.1366918385,-0.1346656978,-0.0554777682,-0.1711653918,0.145175308,0.0700774342,0.0844067633,0.0982008427,-0.2546145916,0.0316351429,0.2501725256,0.0316305794,-0.0061235502,-0.0144204451,-0.0123525774,-0.4560586512,0.1696414351,-0.0568991266,-0.1102579534,0.3962090313,-0.025027737,0.0397525132,0.3803070188,0.1711030155,-0.1741192788,-0.0921760648,-0.9340215921,0.1069794446,0.1560128778,-0.017484121,-0.4241196513,-0.2808053493,0.3445939422,-0.4135504663,0.1371365488,-0.0728048682,0.3419049084,0.1799040586,0.2065040767,0.3033022285,0.2343675196,0.0536500812,-0.3724871576,0.1102484018,-0.0694722682,-0.0858074278,0.2577384412,0.3375180662,0.2463002056,-0.022180615,-0.0713821277,0.4225411415,0.2885462344,0.3854725659,0.3367461562,0.4276495278,0.2519129813,-0.2109387517,-0.1562893838,0.5646339059,0.1081745997,0.0383515358,-0.4680090547,0.5065370202,-0.2661363482,-0.012103837,0.1127237529,-0.0463253818,0.0814128891,0.091087237,-0.3921841085,0.1565761864,0.291778326,0.3464295566,0.6304529309,0.1406931132,-0.0967639983,0.1004109383,0.3997428119,-0.1017156243,-0.0067254175,0.1135695502,-0.1569254696,-0.2928291857,0.1107685864,-0.0893801227,0.0598920658,0.2097007781,0.1487043947,0.0371913947,-0.1481204927,-0.3523471653,0.0504463166,-0.1629639864,-0.215439558,0.1390164644,0.1744087487,0.0353199206,-0.24279733,-0.2507183254,-0.2253893763,0.1806869805,-0.2774429619,0.2457858622,0.0038503595,0.2574873269,-0.074709937,-0.3186459541,-0.3954653144,-0.2954204082,0.2648135722,0.0942249298,-0.1397356391,0.08062388,0.2114585042,0.1704198271,0.1263626665,-0.0609066598,-0.2691183984,0.0670473799,-0.3442390859,0.1047258601,-0.2446593642,-0.0788622051,0.08067099,-0.429623872,0.0975872129,-0.1162439436,-0.5498434305,0.1776524335,0.0356237218,0.3770739138,0.1881416589,-0.3002074361,-0.0184461065,0.4710696638,0.2002328038,-0.3231211007,-0.1657702625,0.3012695312,-0.2839270234,0.2294908613,-0.2235215604,-0.3297278583,0.0143189803,-0.0862902552,0.2834471166,0.1472921818,0.0925280601,0.2298971713,0.064915359,-0.1279930472,0.3870963156,0.1898573488,-0.3099277914,-0.2520888746,0.3893834054,-0.350076139,-0.5373863578,-0.2022144198,0.0193111096,-0.0161610916,0.2700971365,-0.1697930992,-0.3686186373,-0.3688879013,-0.1145493239,0.0201562624,0.1319062859,0.4350543618,-0.1713933051,-0.1104349494,-0.2714865506,0.052683685,0.1724307984,-0.0486953072,0.219272241,-0.0858299881,0.2024034709,-0.0671014264,0.3914042413,0.4602386057,0.1244401336,-0.0473767966,-0.1539421827,0.2863889933,-0.1996272355,0.2588322759,0.1851464808,0.2490403801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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/616","title":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors","comments":"I think the only way to avoid this warning would be to do a copy of the numpy array before providing it.\r\n\r\nThis would slow down a bit the iteration over the dataset but maybe it would be safer. We could disable the copy with a flag on the `set_format` command.\r\n\r\nIn most typical cases of training a NLP model, PyTorch shouldn't modify the input so it's ok to have a non-writable array but I can understand the warning is a bit scary so maybe we could choose the side of non-warning\/slower by default and have an option to speedup.\r\n\r\nWhat do you think @lhoestq ? ","body":"I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.","comment_length":106,"text":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors \n I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. \n I think the only way to avoid this warning would be to do a copy of the numpy array before providing it.\r\n\r\nThis would slow down a bit the iteration over the dataset but maybe it would be safer. We could disable the copy with a flag on the `set_format` command.\r\n\r\nIn most typical cases of training a NLP model, PyTorch shouldn't modify the input so it's ok to have a non-writable array but I can understand the warning is a bit scary so maybe we could choose the side of non-warning\/slower by default and have an option to speedup.\r\n\r\nWhat do you think @lhoestq ? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/616","title":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors","comments":"@thomwolf Would it be possible to have the array look writeable, but raise an error if it is actually written to?\r\n\r\nI would like to keep my code free of warning, but I also wouldn't like to slow down the program because of unnecessary copy operations.   ","body":"I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.","comment_length":46,"text":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors \n I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. \n @thomwolf Would it be possible to have the array look writeable, but raise an error if it is actually written to?\r\n\r\nI would like to keep my code free of warning, but I also wouldn't like to slow down the program because of unnecessary copy operations.   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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/616","title":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors","comments":"Well because I don't know the internal of numpy as well as you I guess hahahah, do you want to try to open a PR proposing a solution?","body":"I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.","comment_length":28,"text":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors \n I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. \n Well because I don't know the internal of numpy as well as you I guess hahahah, do you want to try to open a PR proposing a solution?","embeddings":[0.2382437438,-0.3691007793,0.0651260018,0.1298748255,0.4380830228,0.0914000422,0.6286588907,0.2343869954,0.2976013124,0.0604875013,-0.1968635917,0.4237814248,-0.3091077805,-0.2186422497,-0.256572783,-0.2071904987,-0.019928148,0.1609406769,0.0264154281,-0.0268473886,-0.0556126274,0.0349227302,-0.1734228432,0.3379645646,-0.2255875468,-0.4098304808,0.2081689388,-0.0633970499,-0.0799697861,-0.3318474889,0.1405587047,-0.2741777301,-0.0424051508,0.1276783198,-0.0001266726,0.1157198995,0.0211008638,-0.0969710872,-0.0743481889,-0.1103790775,0.7791628242,-0.3006570041,0.2598286569,-0.5041794181,-0.0403003171,-0.2053057849,-0.0674014911,-0.2887423038,0.6341741681,0.2032751888,0.0752525479,0.5057702661,0.1049243435,0.3881487846,-0.066052407,0.3452899456,-0.2550107539,-0.0293222163,0.0295194145,0.1392835528,-0.0000625259,0.2642535865,-0.5914075375,0.0171453189,0.152644217,-0.0334382728,-0.349470824,-0.1997036189,-0.0568565764,0.4435260892,0.3706563115,-0.2254174352,-0.046209719,-0.3330504894,-0.1216260865,-0.1587676555,0.1649687737,0.047861699,-0.1130082533,0.1626306921,0.0142950015,-0.0069782236,-0.1369986981,0.2631300986,0.0543763451,0.1673310399,-0.1042311937,0.3020676374,-0.070318155,-0.0098416703,0.0525531098,0.0074251285,0.1901581138,0.196217075,-0.1164147928,-0.3119843602,-0.2599380016,-0.7474357486,-0.0870942324,-0.2492239624,0.3341346085,0.2540561259,-0.1607192904,0.0971489623,0.1469385177,0.0135778785,0.1208697632,0.303039968,-0.0211054645,-0.2641310394,0.4715952277,0.0567326099,0.1360289454,-0.2395026833,0.0944854543,0.0855747908,0.0345738009,0.0435557812,-0.4489809275,0.1235723495,-0.2449784279,0.0512567237,-0.0606115498,0.043743331,-0.0110608209,0.008455541,0.0809402019,0.3331671059,-0.2172211111,0.2093106508,0.0050662151,0.2291956097,-0.1825448275,-0.1943188906,-0.0641829967,0.2339443862,0.0189591032,0.261413455,0.0302922912,0.0009383697,0.1234879196,-0.2359409928,0.6530778408,0.2942767441,-0.1781089008,0.2948439717,0.3621653318,-0.0307599809,-0.3972631097,0.2834928632,-0.3104486465,-0.2276656926,-0.5769506097,0.0020175837,-0.1900073141,0.1614836454,0.1787664145,-0.2559919059,0.6716828942,-0.1626638323,0.4028995037,-0.437556386,-0.0927184001,-0.3298016787,-0.0530223027,0.3662085533,-0.3259876072,-0.064038679,0.258772701,-0.0162683986,0.4336455464,0.4094212055,-0.1056687534,0.1454505175,-0.1220321953,0.2255036682,0.3607753515,-0.1224518269,-0.0684338585,0.188791126,-0.1807031184,0.3171916902,0.1701442301,0.0854641721,-0.1042878255,0.1792410761,0.2188509107,0.0108668376,0.3695318699,-0.0156879947,-0.3240776658,-0.2027052045,0.4832918048,-0.1357165277,-0.0672293007,-0.1775234044,-0.1403667331,0.0221992601,0.3671857715,-0.1119876876,0.0290920548,-0.0411325768,-0.167381376,0.2396441549,-0.140446946,0.1050849631,-0.1710649133,-0.0152083375,0.0745915994,0.1085796431,-0.2709115148,-0.1766555607,-0.105172202,0.0071568051,-0.3517931402,-0.0465408675,-0.1111049056,-0.1885502487,-0.1136639938,0.1317765117,-0.1785905063,-0.1946374923,-0.5005659461,0.1517338455,-0.4120471478,-0.1020534709,-0.1012726128,-0.212443158,-0.3982954323,0.3267838955,0.232444495,-0.035569869,-0.0395304337,0.0819807947,0.2321104854,-0.1997635812,-0.917568922,0.1638941765,0.1763911694,-0.090930514,0.2472078055,0.1546784341,-0.1466421038,-0.096070841,-0.0257543679,0.6306298971,-0.0646008551,0.0608098134,-0.5003480315,0.261929512,-0.1911876351,0.1908106506,-0.1049912944,-0.4325117767,-0.0143134892,0.1304034591,-0.2320648581,-0.0843060389,-0.4490361214,-0.1313851327,0.0598223768,0.0488689318,0.2781392932,-0.080569014,0.1933736503,-0.2550162077,0.2152756453,-0.5420799255,0.1550077051,-0.1119838879,-0.0120686553,-0.0852294937,-0.1558951139,-0.2969710827,-0.0197968502,0.0393947251,-0.0165408477,0.1850436181,-0.0722502619,0.1588746011,-0.3456683457,0.1977573484,-0.1231665015,0.0587118678,-0.498372376,0.2802625299,-0.1423191577,0.0969332159,-0.2249715179,-0.2606532276,0.0266311578,-0.0663827807,-0.0077088084,0.0847671181,-0.2427747101,0.1668353975,-0.0719061345,0.0822579786,0.1976013184,-0.1022716314,-0.0642502904,0.090562731,-0.0684159249,0.021913901,-0.0892241076,-0.1524734795,0.2516383827,-0.0162396878,-0.2449783832,-0.100350745,-0.2109436393,0.1382278502,-0.1868399531,-0.1263592839,0.3529059291,0.1456685066,-0.2808859944,-0.2442344129,-0.0112630334,-0.0327507965,0.0331098884,0.1553820074,0.0217528194,0.13360475,-0.0648512319,-0.1858778149,-0.423663497,-0.0582789257,0.1405140013,-0.1072303206,0.4788049161,0.2815490067,0.25558272,0.154078871,-0.0803942084,0.2228560597,-0.2713088393,-0.004655215,0.314692229,-0.1752711385,-0.1823733896,-0.2065906823,0.1696434915,0.0142171262,0.0761997998,-0.3303349316,-0.1466784775,-0.0949350446,0.120766893,-0.2022130787,-0.1867319793,0.241856724,0.228496924,0.0300903972,0.1885070801,0.2500557899,0.0332654417,-0.2170521766,-0.0861481056,-0.043779742,0.1183455214,0.2379999757,0.4617770314,-0.0420077182,-0.2846428156,0.1601472199,0.0010739247,0.5781360269,-0.1647348851,-0.3022521436,0.0216061492,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/616","title":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors","comments":"@thomwolf @AndreasMadsen I think this is a terrible idea, n\/o, and I am very much against it. Modifying internals of an array in such a hacky way is bound to run into other (user) issues down the line. To users it would not be clear at all what is going on e.g. when they check for write access (which will return True) but then they get a warning that the array is not writeable. That's extremely confusing. \r\n\r\nIf your only goal is to get rid of warnings in your code, then you can just use a [simplefilter](https:\/\/docs.python.org\/3.8\/library\/warnings.html#temporarily-suppressing-warnings) for UserWarnings in your own code. Changing the code-base in such an intuitive way does not seem like a good way to go and sets a bad precedent, imo. \r\n\r\n(Feel free to disagree, of course.)\r\n\r\nIMO a warning can stay (as they can be filtered by users anyway), but it can be clarified why the warning takes place.","body":"I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.","comment_length":155,"text":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors \n I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. \n @thomwolf @AndreasMadsen I think this is a terrible idea, n\/o, and I am very much against it. Modifying internals of an array in such a hacky way is bound to run into other (user) issues down the line. To users it would not be clear at all what is going on e.g. when they check for write access (which will return True) but then they get a warning that the array is not writeable. That's extremely confusing. \r\n\r\nIf your only goal is to get rid of warnings in your code, then you can just use a [simplefilter](https:\/\/docs.python.org\/3.8\/library\/warnings.html#temporarily-suppressing-warnings) for UserWarnings in your own code. Changing the code-base in such an intuitive way does not seem like a good way to go and sets a bad precedent, imo. \r\n\r\n(Feel free to disagree, of course.)\r\n\r\nIMO a warning can stay (as they can be filtered by users anyway), but it can be clarified why the warning takes place.","embeddings":[0.2382437438,-0.3691007793,0.0651260018,0.1298748255,0.4380830228,0.0914000422,0.6286588907,0.2343869954,0.2976013124,0.0604875013,-0.1968635917,0.4237814248,-0.3091077805,-0.2186422497,-0.256572783,-0.2071904987,-0.019928148,0.1609406769,0.0264154281,-0.0268473886,-0.0556126274,0.0349227302,-0.1734228432,0.3379645646,-0.2255875468,-0.4098304808,0.2081689388,-0.0633970499,-0.0799697861,-0.3318474889,0.1405587047,-0.2741777301,-0.0424051508,0.1276783198,-0.0001266726,0.1157198995,0.0211008638,-0.0969710872,-0.0743481889,-0.1103790775,0.7791628242,-0.3006570041,0.2598286569,-0.5041794181,-0.0403003171,-0.2053057849,-0.0674014911,-0.2887423038,0.6341741681,0.2032751888,0.0752525479,0.5057702661,0.1049243435,0.3881487846,-0.066052407,0.3452899456,-0.2550107539,-0.0293222163,0.0295194145,0.1392835528,-0.0000625259,0.2642535865,-0.5914075375,0.0171453189,0.152644217,-0.0334382728,-0.349470824,-0.1997036189,-0.0568565764,0.4435260892,0.3706563115,-0.2254174352,-0.046209719,-0.3330504894,-0.1216260865,-0.1587676555,0.1649687737,0.047861699,-0.1130082533,0.1626306921,0.0142950015,-0.0069782236,-0.1369986981,0.2631300986,0.0543763451,0.1673310399,-0.1042311937,0.3020676374,-0.070318155,-0.0098416703,0.0525531098,0.0074251285,0.1901581138,0.196217075,-0.1164147928,-0.3119843602,-0.2599380016,-0.7474357486,-0.0870942324,-0.2492239624,0.3341346085,0.2540561259,-0.1607192904,0.0971489623,0.1469385177,0.0135778785,0.1208697632,0.303039968,-0.0211054645,-0.2641310394,0.4715952277,0.0567326099,0.1360289454,-0.2395026833,0.0944854543,0.0855747908,0.0345738009,0.0435557812,-0.4489809275,0.1235723495,-0.2449784279,0.0512567237,-0.0606115498,0.043743331,-0.0110608209,0.008455541,0.0809402019,0.3331671059,-0.2172211111,0.2093106508,0.0050662151,0.2291956097,-0.1825448275,-0.1943188906,-0.0641829967,0.2339443862,0.0189591032,0.261413455,0.0302922912,0.0009383697,0.1234879196,-0.2359409928,0.6530778408,0.2942767441,-0.1781089008,0.2948439717,0.3621653318,-0.0307599809,-0.3972631097,0.2834928632,-0.3104486465,-0.2276656926,-0.5769506097,0.0020175837,-0.1900073141,0.1614836454,0.1787664145,-0.2559919059,0.6716828942,-0.1626638323,0.4028995037,-0.437556386,-0.0927184001,-0.3298016787,-0.0530223027,0.3662085533,-0.3259876072,-0.064038679,0.258772701,-0.0162683986,0.4336455464,0.4094212055,-0.1056687534,0.1454505175,-0.1220321953,0.2255036682,0.3607753515,-0.1224518269,-0.0684338585,0.188791126,-0.1807031184,0.3171916902,0.1701442301,0.0854641721,-0.1042878255,0.1792410761,0.2188509107,0.0108668376,0.3695318699,-0.0156879947,-0.3240776658,-0.2027052045,0.4832918048,-0.1357165277,-0.0672293007,-0.1775234044,-0.1403667331,0.0221992601,0.3671857715,-0.1119876876,0.0290920548,-0.0411325768,-0.167381376,0.2396441549,-0.140446946,0.1050849631,-0.1710649133,-0.0152083375,0.0745915994,0.1085796431,-0.2709115148,-0.1766555607,-0.105172202,0.0071568051,-0.3517931402,-0.0465408675,-0.1111049056,-0.1885502487,-0.1136639938,0.1317765117,-0.1785905063,-0.1946374923,-0.5005659461,0.1517338455,-0.4120471478,-0.1020534709,-0.1012726128,-0.212443158,-0.3982954323,0.3267838955,0.232444495,-0.035569869,-0.0395304337,0.0819807947,0.2321104854,-0.1997635812,-0.917568922,0.1638941765,0.1763911694,-0.090930514,0.2472078055,0.1546784341,-0.1466421038,-0.096070841,-0.0257543679,0.6306298971,-0.0646008551,0.0608098134,-0.5003480315,0.261929512,-0.1911876351,0.1908106506,-0.1049912944,-0.4325117767,-0.0143134892,0.1304034591,-0.2320648581,-0.0843060389,-0.4490361214,-0.1313851327,0.0598223768,0.0488689318,0.2781392932,-0.080569014,0.1933736503,-0.2550162077,0.2152756453,-0.5420799255,0.1550077051,-0.1119838879,-0.0120686553,-0.0852294937,-0.1558951139,-0.2969710827,-0.0197968502,0.0393947251,-0.0165408477,0.1850436181,-0.0722502619,0.1588746011,-0.3456683457,0.1977573484,-0.1231665015,0.0587118678,-0.498372376,0.2802625299,-0.1423191577,0.0969332159,-0.2249715179,-0.2606532276,0.0266311578,-0.0663827807,-0.0077088084,0.0847671181,-0.2427747101,0.1668353975,-0.0719061345,0.0822579786,0.1976013184,-0.1022716314,-0.0642502904,0.090562731,-0.0684159249,0.021913901,-0.0892241076,-0.1524734795,0.2516383827,-0.0162396878,-0.2449783832,-0.100350745,-0.2109436393,0.1382278502,-0.1868399531,-0.1263592839,0.3529059291,0.1456685066,-0.2808859944,-0.2442344129,-0.0112630334,-0.0327507965,0.0331098884,0.1553820074,0.0217528194,0.13360475,-0.0648512319,-0.1858778149,-0.423663497,-0.0582789257,0.1405140013,-0.1072303206,0.4788049161,0.2815490067,0.25558272,0.154078871,-0.0803942084,0.2228560597,-0.2713088393,-0.004655215,0.314692229,-0.1752711385,-0.1823733896,-0.2065906823,0.1696434915,0.0142171262,0.0761997998,-0.3303349316,-0.1466784775,-0.0949350446,0.120766893,-0.2022130787,-0.1867319793,0.241856724,0.228496924,0.0300903972,0.1885070801,0.2500557899,0.0332654417,-0.2170521766,-0.0861481056,-0.043779742,0.1183455214,0.2379999757,0.4617770314,-0.0420077182,-0.2846428156,0.1601472199,0.0010739247,0.5781360269,-0.1647348851,-0.3022521436,0.0216061492,0.076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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/616","title":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors","comments":"> To users it would not be clear at all what is going on e.g. when they check for write access (which will return True) but then they get a warning that the array is not writeable. That's extremely confusing.\r\n\r\nConfusion can be resolved with a helpful error message. In this case, that error message can be controlled by huggingface\/datasets. The right argument here is that if code depends on `.flags.writable` being truthful (not just for warnings), then it will cause unavoidable errors. Although, I can't imagine such a use-case.\r\n\r\n> If your only goal is to get rid of warnings in your code, then you can just use a simplefilter for UserWarnings in your own code. Changing the code-base in such an intuitive way does not seem like a good way to go and sets a bad precedent, imo.\r\n\r\nI don't want to ignore all `UserWarnings`, nor all warnings regarding non-writable arrays. Ignoring warnings leads to hard to debug issues.\r\n\r\n> IMO a warning can stay (as they can be filtered by users anyway), but it can be clarified why the warning takes place.\r\n\r\nPlain use cases should really not generate warnings. It teaches developers to ignore warnings which is a terrible practice.\r\n\r\n---\r\n\r\nThe best solution would be to allow non-writable arrays in `DataLoader`, but that is a PyTorch issue.","body":"I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.","comment_length":222,"text":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors \n I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. \n > To users it would not be clear at all what is going on e.g. when they check for write access (which will return True) but then they get a warning that the array is not writeable. That's extremely confusing.\r\n\r\nConfusion can be resolved with a helpful error message. In this case, that error message can be controlled by huggingface\/datasets. The right argument here is that if code depends on `.flags.writable` being truthful (not just for warnings), then it will cause unavoidable errors. Although, I can't imagine such a use-case.\r\n\r\n> If your only goal is to get rid of warnings in your code, then you can just use a simplefilter for UserWarnings in your own code. Changing the code-base in such an intuitive way does not seem like a good way to go and sets a bad precedent, imo.\r\n\r\nI don't want to ignore all `UserWarnings`, nor all warnings regarding non-writable arrays. Ignoring warnings leads to hard to debug issues.\r\n\r\n> IMO a warning can stay (as they can be filtered by users anyway), but it can be clarified why the warning takes place.\r\n\r\nPlain use cases should really not generate warnings. It teaches developers to ignore warnings which is a terrible practice.\r\n\r\n---\r\n\r\nThe best solution would be to allow non-writable arrays in `DataLoader`, but that is a PyTorch issue.","embeddings":[0.2382437438,-0.3691007793,0.0651260018,0.1298748255,0.4380830228,0.0914000422,0.6286588907,0.2343869954,0.2976013124,0.0604875013,-0.1968635917,0.4237814248,-0.3091077805,-0.2186422497,-0.256572783,-0.2071904987,-0.019928148,0.1609406769,0.0264154281,-0.0268473886,-0.0556126274,0.0349227302,-0.1734228432,0.3379645646,-0.2255875468,-0.4098304808,0.2081689388,-0.0633970499,-0.0799697861,-0.3318474889,0.1405587047,-0.2741777301,-0.0424051508,0.1276783198,-0.0001266726,0.1157198995,0.0211008638,-0.0969710872,-0.0743481889,-0.1103790775,0.7791628242,-0.3006570041,0.2598286569,-0.5041794181,-0.0403003171,-0.2053057849,-0.0674014911,-0.2887423038,0.6341741681,0.2032751888,0.0752525479,0.5057702661,0.1049243435,0.3881487846,-0.066052407,0.3452899456,-0.2550107539,-0.0293222163,0.0295194145,0.1392835528,-0.0000625259,0.2642535865,-0.5914075375,0.0171453189,0.152644217,-0.0334382728,-0.349470824,-0.1997036189,-0.0568565764,0.4435260892,0.3706563115,-0.2254174352,-0.046209719,-0.3330504894,-0.1216260865,-0.1587676555,0.1649687737,0.047861699,-0.1130082533,0.1626306921,0.0142950015,-0.0069782236,-0.1369986981,0.2631300986,0.0543763451,0.1673310399,-0.1042311937,0.3020676374,-0.070318155,-0.0098416703,0.0525531098,0.0074251285,0.1901581138,0.196217075,-0.1164147928,-0.3119843602,-0.2599380016,-0.7474357486,-0.0870942324,-0.2492239624,0.3341346085,0.2540561259,-0.1607192904,0.0971489623,0.1469385177,0.0135778785,0.1208697632,0.303039968,-0.0211054645,-0.2641310394,0.4715952277,0.0567326099,0.1360289454,-0.2395026833,0.0944854543,0.0855747908,0.0345738009,0.0435557812,-0.4489809275,0.1235723495,-0.2449784279,0.0512567237,-0.0606115498,0.043743331,-0.0110608209,0.008455541,0.0809402019,0.3331671059,-0.2172211111,0.2093106508,0.0050662151,0.2291956097,-0.1825448275,-0.1943188906,-0.0641829967,0.2339443862,0.0189591032,0.261413455,0.0302922912,0.0009383697,0.1234879196,-0.2359409928,0.6530778408,0.2942767441,-0.1781089008,0.2948439717,0.3621653318,-0.0307599809,-0.3972631097,0.2834928632,-0.3104486465,-0.2276656926,-0.5769506097,0.0020175837,-0.1900073141,0.1614836454,0.1787664145,-0.2559919059,0.6716828942,-0.1626638323,0.4028995037,-0.437556386,-0.0927184001,-0.3298016787,-0.0530223027,0.3662085533,-0.3259876072,-0.064038679,0.258772701,-0.0162683986,0.4336455464,0.4094212055,-0.1056687534,0.1454505175,-0.1220321953,0.2255036682,0.3607753515,-0.1224518269,-0.0684338585,0.188791126,-0.1807031184,0.3171916902,0.1701442301,0.0854641721,-0.1042878255,0.1792410761,0.2188509107,0.0108668376,0.3695318699,-0.0156879947,-0.3240776658,-0.2027052045,0.4832918048,-0.1357165277,-0.0672293007,-0.1775234044,-0.1403667331,0.0221992601,0.3671857715,-0.1119876876,0.0290920548,-0.0411325768,-0.167381376,0.2396441549,-0.140446946,0.1050849631,-0.1710649133,-0.0152083375,0.0745915994,0.1085796431,-0.2709115148,-0.1766555607,-0.105172202,0.0071568051,-0.3517931402,-0.0465408675,-0.1111049056,-0.1885502487,-0.1136639938,0.1317765117,-0.1785905063,-0.1946374923,-0.5005659461,0.1517338455,-0.4120471478,-0.1020534709,-0.1012726128,-0.212443158,-0.3982954323,0.3267838955,0.232444495,-0.035569869,-0.0395304337,0.0819807947,0.2321104854,-0.1997635812,-0.917568922,0.1638941765,0.1763911694,-0.090930514,0.2472078055,0.1546784341,-0.1466421038,-0.096070841,-0.0257543679,0.6306298971,-0.0646008551,0.0608098134,-0.5003480315,0.261929512,-0.1911876351,0.1908106506,-0.1049912944,-0.4325117767,-0.0143134892,0.1304034591,-0.2320648581,-0.0843060389,-0.4490361214,-0.1313851327,0.0598223768,0.0488689318,0.2781392932,-0.080569014,0.1933736503,-0.2550162077,0.2152756453,-0.5420799255,0.1550077051,-0.1119838879,-0.0120686553,-0.0852294937,-0.1558951139,-0.2969710827,-0.0197968502,0.0393947251,-0.0165408477,0.1850436181,-0.0722502619,0.1588746011,-0.3456683457,0.1977573484,-0.1231665015,0.0587118678,-0.498372376,0.2802625299,-0.1423191577,0.0969332159,-0.2249715179,-0.2606532276,0.0266311578,-0.0663827807,-0.0077088084,0.0847671181,-0.2427747101,0.1668353975,-0.0719061345,0.0822579786,0.1976013184,-0.1022716314,-0.0642502904,0.090562731,-0.0684159249,0.021913901,-0.0892241076,-0.1524734795,0.2516383827,-0.0162396878,-0.2449783832,-0.100350745,-0.2109436393,0.1382278502,-0.1868399531,-0.1263592839,0.3529059291,0.1456685066,-0.2808859944,-0.2442344129,-0.0112630334,-0.0327507965,0.0331098884,0.1553820074,0.0217528194,0.13360475,-0.0648512319,-0.1858778149,-0.423663497,-0.0582789257,0.1405140013,-0.1072303206,0.4788049161,0.2815490067,0.25558272,0.154078871,-0.0803942084,0.2228560597,-0.2713088393,-0.004655215,0.314692229,-0.1752711385,-0.1823733896,-0.2065906823,0.1696434915,0.0142171262,0.0761997998,-0.3303349316,-0.1466784775,-0.0949350446,0.120766893,-0.2022130787,-0.1867319793,0.241856724,0.228496924,0.0300903972,0.1885070801,0.2500557899,0.0332654417,-0.2170521766,-0.0861481056,-0.043779742,0.1183455214,0.2379999757,0.4617770314,-0.0420077182,-0.2846428156,0.1601472199,0.0010739247,0.5781360269,-0.1647348851,-0.3022521436,0.0216061492,0.076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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/616","title":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors","comments":"> The right argument here is that if code depends on `.flags.writable` being truthful (not just for warnings), then it will cause unavoidable errors. Although, I can't imagine such a use-case.\r\n\r\nThat's exactly the argument in my first sentence. Too often someone \"cannot think of a use-case\", but you can not foresee the use-cases of a whole research community.\r\n \r\n> I don't want to ignore all `UserWarnings`, nor all warnings regarding non-writable arrays. Ignoring warnings leads to hard to debug issues.\r\n\r\nThat's fair.\r\n\r\n> Plain use cases should really not generate warnings. It teaches developers to ignore warnings which is a terrible practice.\r\n\r\nBut this is not a plain use-case (because Pytorch does not support these read-only tensors). Manually setting the flag to writable will solve the issue on the surface but is basically just a hack to compensate for something that is not allowed in another library. \r\n\r\nWhat about an \"ignore_warnings\" flag in `set_format` that when True wraps the offending code in a block to ignore userwarnings at that specific step in [_convert_outputs](https:\/\/github.com\/huggingface\/datasets\/blob\/880c2c76a8223a00c303eab2909371e857113063\/src\/datasets\/arrow_dataset.py#L821)? Something like:\r\n\r\n```python\r\ndef _convert_outputs(..., ignore_warnings=True):\r\n    ...\r\n    with warnings.catch_warnings():\r\n        if ignore_warnings:\r\n            warnings.simplefilter(\"ignore\", UserWarning)\r\n        return torch.tensor(...)\r\n# continues without warning filter after context manager...\r\n```","body":"I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.","comment_length":198,"text":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors \n I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. \n > The right argument here is that if code depends on `.flags.writable` being truthful (not just for warnings), then it will cause unavoidable errors. Although, I can't imagine such a use-case.\r\n\r\nThat's exactly the argument in my first sentence. Too often someone \"cannot think of a use-case\", but you can not foresee the use-cases of a whole research community.\r\n \r\n> I don't want to ignore all `UserWarnings`, nor all warnings regarding non-writable arrays. Ignoring warnings leads to hard to debug issues.\r\n\r\nThat's fair.\r\n\r\n> Plain use cases should really not generate warnings. It teaches developers to ignore warnings which is a terrible practice.\r\n\r\nBut this is not a plain use-case (because Pytorch does not support these read-only tensors). Manually setting the flag to writable will solve the issue on the surface but is basically just a hack to compensate for something that is not allowed in another library. \r\n\r\nWhat about an \"ignore_warnings\" flag in `set_format` that when True wraps the offending code in a block to ignore userwarnings at that specific step in [_convert_outputs](https:\/\/github.com\/huggingface\/datasets\/blob\/880c2c76a8223a00c303eab2909371e857113063\/src\/datasets\/arrow_dataset.py#L821)? Something like:\r\n\r\n```python\r\ndef _convert_outputs(..., ignore_warnings=True):\r\n    ...\r\n    with warnings.catch_warnings():\r\n        if ignore_warnings:\r\n            warnings.simplefilter(\"ignore\", UserWarning)\r\n        return torch.tensor(...)\r\n# continues without warning filter after context manager...\r\n```","embeddings":[0.2382437438,-0.3691007793,0.0651260018,0.1298748255,0.4380830228,0.0914000422,0.6286588907,0.2343869954,0.2976013124,0.0604875013,-0.1968635917,0.4237814248,-0.3091077805,-0.2186422497,-0.256572783,-0.2071904987,-0.019928148,0.1609406769,0.0264154281,-0.0268473886,-0.0556126274,0.0349227302,-0.1734228432,0.3379645646,-0.2255875468,-0.4098304808,0.2081689388,-0.0633970499,-0.0799697861,-0.3318474889,0.1405587047,-0.2741777301,-0.0424051508,0.1276783198,-0.0001266726,0.1157198995,0.0211008638,-0.0969710872,-0.0743481889,-0.1103790775,0.7791628242,-0.3006570041,0.2598286569,-0.5041794181,-0.0403003171,-0.2053057849,-0.0674014911,-0.2887423038,0.6341741681,0.2032751888,0.0752525479,0.5057702661,0.1049243435,0.3881487846,-0.066052407,0.3452899456,-0.2550107539,-0.0293222163,0.0295194145,0.1392835528,-0.0000625259,0.2642535865,-0.5914075375,0.0171453189,0.152644217,-0.0334382728,-0.349470824,-0.1997036189,-0.0568565764,0.4435260892,0.3706563115,-0.2254174352,-0.046209719,-0.3330504894,-0.1216260865,-0.1587676555,0.1649687737,0.047861699,-0.1130082533,0.1626306921,0.0142950015,-0.0069782236,-0.1369986981,0.2631300986,0.0543763451,0.1673310399,-0.1042311937,0.3020676374,-0.070318155,-0.0098416703,0.0525531098,0.0074251285,0.1901581138,0.196217075,-0.1164147928,-0.3119843602,-0.2599380016,-0.7474357486,-0.0870942324,-0.2492239624,0.3341346085,0.2540561259,-0.1607192904,0.0971489623,0.1469385177,0.0135778785,0.1208697632,0.303039968,-0.0211054645,-0.2641310394,0.4715952277,0.0567326099,0.1360289454,-0.2395026833,0.0944854543,0.0855747908,0.0345738009,0.0435557812,-0.4489809275,0.1235723495,-0.2449784279,0.0512567237,-0.0606115498,0.043743331,-0.0110608209,0.008455541,0.0809402019,0.3331671059,-0.2172211111,0.2093106508,0.0050662151,0.2291956097,-0.1825448275,-0.1943188906,-0.0641829967,0.2339443862,0.0189591032,0.261413455,0.0302922912,0.0009383697,0.1234879196,-0.2359409928,0.6530778408,0.2942767441,-0.1781089008,0.2948439717,0.3621653318,-0.0307599809,-0.3972631097,0.2834928632,-0.3104486465,-0.2276656926,-0.5769506097,0.0020175837,-0.1900073141,0.1614836454,0.1787664145,-0.2559919059,0.6716828942,-0.1626638323,0.4028995037,-0.437556386,-0.0927184001,-0.3298016787,-0.0530223027,0.3662085533,-0.3259876072,-0.064038679,0.258772701,-0.0162683986,0.4336455464,0.4094212055,-0.1056687534,0.1454505175,-0.1220321953,0.2255036682,0.3607753515,-0.1224518269,-0.0684338585,0.188791126,-0.1807031184,0.3171916902,0.1701442301,0.0854641721,-0.1042878255,0.1792410761,0.2188509107,0.0108668376,0.3695318699,-0.0156879947,-0.3240776658,-0.2027052045,0.4832918048,-0.1357165277,-0.0672293007,-0.1775234044,-0.1403667331,0.0221992601,0.3671857715,-0.1119876876,0.0290920548,-0.0411325768,-0.167381376,0.2396441549,-0.140446946,0.1050849631,-0.1710649133,-0.0152083375,0.0745915994,0.1085796431,-0.2709115148,-0.1766555607,-0.105172202,0.0071568051,-0.3517931402,-0.0465408675,-0.1111049056,-0.1885502487,-0.1136639938,0.1317765117,-0.1785905063,-0.1946374923,-0.5005659461,0.1517338455,-0.4120471478,-0.1020534709,-0.1012726128,-0.212443158,-0.3982954323,0.3267838955,0.232444495,-0.035569869,-0.0395304337,0.0819807947,0.2321104854,-0.1997635812,-0.917568922,0.1638941765,0.1763911694,-0.090930514,0.2472078055,0.1546784341,-0.1466421038,-0.096070841,-0.0257543679,0.6306298971,-0.0646008551,0.0608098134,-0.5003480315,0.261929512,-0.1911876351,0.1908106506,-0.1049912944,-0.4325117767,-0.0143134892,0.1304034591,-0.2320648581,-0.0843060389,-0.4490361214,-0.1313851327,0.0598223768,0.0488689318,0.2781392932,-0.080569014,0.1933736503,-0.2550162077,0.2152756453,-0.5420799255,0.1550077051,-0.1119838879,-0.0120686553,-0.0852294937,-0.1558951139,-0.2969710827,-0.0197968502,0.0393947251,-0.0165408477,0.1850436181,-0.0722502619,0.1588746011,-0.3456683457,0.1977573484,-0.1231665015,0.0587118678,-0.498372376,0.2802625299,-0.1423191577,0.0969332159,-0.2249715179,-0.2606532276,0.0266311578,-0.0663827807,-0.0077088084,0.0847671181,-0.2427747101,0.1668353975,-0.0719061345,0.0822579786,0.1976013184,-0.1022716314,-0.0642502904,0.090562731,-0.0684159249,0.021913901,-0.0892241076,-0.1524734795,0.2516383827,-0.0162396878,-0.2449783832,-0.100350745,-0.2109436393,0.1382278502,-0.1868399531,-0.1263592839,0.3529059291,0.1456685066,-0.2808859944,-0.2442344129,-0.0112630334,-0.0327507965,0.0331098884,0.1553820074,0.0217528194,0.13360475,-0.0648512319,-0.1858778149,-0.423663497,-0.0582789257,0.1405140013,-0.1072303206,0.4788049161,0.2815490067,0.25558272,0.154078871,-0.0803942084,0.2228560597,-0.2713088393,-0.004655215,0.314692229,-0.1752711385,-0.1823733896,-0.2065906823,0.1696434915,0.0142171262,0.0761997998,-0.3303349316,-0.1466784775,-0.0949350446,0.120766893,-0.2022130787,-0.1867319793,0.241856724,0.228496924,0.0300903972,0.1885070801,0.2500557899,0.0332654417,-0.2170521766,-0.0861481056,-0.043779742,0.1183455214,0.2379999757,0.4617770314,-0.0420077182,-0.2846428156,0.1601472199,0.0010739247,0.5781360269,-0.1647348851,-0.3022521436,0.02160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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/616","title":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors","comments":"> But this is not a plain use-case (because Pytorch does not support these read-only tensors).\r\n\r\nBy \"plain\", I mean the recommended way to use `datasets` with PyTorch according to the `datasets` documentation.","body":"I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.","comment_length":33,"text":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors \n I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. \n > But this is not a plain use-case (because Pytorch does not support these read-only tensors).\r\n\r\nBy \"plain\", I mean the recommended way to use `datasets` with PyTorch according to the `datasets` documentation.","embeddings":[0.2382437438,-0.3691007793,0.0651260018,0.1298748255,0.4380830228,0.0914000422,0.6286588907,0.2343869954,0.2976013124,0.0604875013,-0.1968635917,0.4237814248,-0.3091077805,-0.2186422497,-0.256572783,-0.2071904987,-0.019928148,0.1609406769,0.0264154281,-0.0268473886,-0.0556126274,0.0349227302,-0.1734228432,0.3379645646,-0.2255875468,-0.4098304808,0.2081689388,-0.0633970499,-0.0799697861,-0.3318474889,0.1405587047,-0.2741777301,-0.0424051508,0.1276783198,-0.0001266726,0.1157198995,0.0211008638,-0.0969710872,-0.0743481889,-0.1103790775,0.7791628242,-0.3006570041,0.2598286569,-0.5041794181,-0.0403003171,-0.2053057849,-0.0674014911,-0.2887423038,0.6341741681,0.2032751888,0.0752525479,0.5057702661,0.1049243435,0.3881487846,-0.066052407,0.3452899456,-0.2550107539,-0.0293222163,0.0295194145,0.1392835528,-0.0000625259,0.2642535865,-0.5914075375,0.0171453189,0.152644217,-0.0334382728,-0.349470824,-0.1997036189,-0.0568565764,0.4435260892,0.3706563115,-0.2254174352,-0.046209719,-0.3330504894,-0.1216260865,-0.1587676555,0.1649687737,0.047861699,-0.1130082533,0.1626306921,0.0142950015,-0.0069782236,-0.1369986981,0.2631300986,0.0543763451,0.1673310399,-0.1042311937,0.3020676374,-0.070318155,-0.0098416703,0.0525531098,0.0074251285,0.1901581138,0.196217075,-0.1164147928,-0.3119843602,-0.2599380016,-0.7474357486,-0.0870942324,-0.2492239624,0.3341346085,0.2540561259,-0.1607192904,0.0971489623,0.1469385177,0.0135778785,0.1208697632,0.303039968,-0.0211054645,-0.2641310394,0.4715952277,0.0567326099,0.1360289454,-0.2395026833,0.0944854543,0.0855747908,0.0345738009,0.0435557812,-0.4489809275,0.1235723495,-0.2449784279,0.0512567237,-0.0606115498,0.043743331,-0.0110608209,0.008455541,0.0809402019,0.3331671059,-0.2172211111,0.2093106508,0.0050662151,0.2291956097,-0.1825448275,-0.1943188906,-0.0641829967,0.2339443862,0.0189591032,0.261413455,0.0302922912,0.0009383697,0.1234879196,-0.2359409928,0.6530778408,0.2942767441,-0.1781089008,0.2948439717,0.3621653318,-0.0307599809,-0.3972631097,0.2834928632,-0.3104486465,-0.2276656926,-0.5769506097,0.0020175837,-0.1900073141,0.1614836454,0.1787664145,-0.2559919059,0.6716828942,-0.1626638323,0.4028995037,-0.437556386,-0.0927184001,-0.3298016787,-0.0530223027,0.3662085533,-0.3259876072,-0.064038679,0.258772701,-0.0162683986,0.4336455464,0.4094212055,-0.1056687534,0.1454505175,-0.1220321953,0.2255036682,0.3607753515,-0.1224518269,-0.0684338585,0.188791126,-0.1807031184,0.3171916902,0.1701442301,0.0854641721,-0.1042878255,0.1792410761,0.2188509107,0.0108668376,0.3695318699,-0.0156879947,-0.3240776658,-0.2027052045,0.4832918048,-0.1357165277,-0.0672293007,-0.1775234044,-0.1403667331,0.0221992601,0.3671857715,-0.1119876876,0.0290920548,-0.0411325768,-0.167381376,0.2396441549,-0.140446946,0.1050849631,-0.1710649133,-0.0152083375,0.0745915994,0.1085796431,-0.2709115148,-0.1766555607,-0.105172202,0.0071568051,-0.3517931402,-0.0465408675,-0.1111049056,-0.1885502487,-0.1136639938,0.1317765117,-0.1785905063,-0.1946374923,-0.5005659461,0.1517338455,-0.4120471478,-0.1020534709,-0.1012726128,-0.212443158,-0.3982954323,0.3267838955,0.232444495,-0.035569869,-0.0395304337,0.0819807947,0.2321104854,-0.1997635812,-0.917568922,0.1638941765,0.1763911694,-0.090930514,0.2472078055,0.1546784341,-0.1466421038,-0.096070841,-0.0257543679,0.6306298971,-0.0646008551,0.0608098134,-0.5003480315,0.261929512,-0.1911876351,0.1908106506,-0.1049912944,-0.4325117767,-0.0143134892,0.1304034591,-0.2320648581,-0.0843060389,-0.4490361214,-0.1313851327,0.0598223768,0.0488689318,0.2781392932,-0.080569014,0.1933736503,-0.2550162077,0.2152756453,-0.5420799255,0.1550077051,-0.1119838879,-0.0120686553,-0.0852294937,-0.1558951139,-0.2969710827,-0.0197968502,0.0393947251,-0.0165408477,0.1850436181,-0.0722502619,0.1588746011,-0.3456683457,0.1977573484,-0.1231665015,0.0587118678,-0.498372376,0.2802625299,-0.1423191577,0.0969332159,-0.2249715179,-0.2606532276,0.0266311578,-0.0663827807,-0.0077088084,0.0847671181,-0.2427747101,0.1668353975,-0.0719061345,0.0822579786,0.1976013184,-0.1022716314,-0.0642502904,0.090562731,-0.0684159249,0.021913901,-0.0892241076,-0.1524734795,0.2516383827,-0.0162396878,-0.2449783832,-0.100350745,-0.2109436393,0.1382278502,-0.1868399531,-0.1263592839,0.3529059291,0.1456685066,-0.2808859944,-0.2442344129,-0.0112630334,-0.0327507965,0.0331098884,0.1553820074,0.0217528194,0.13360475,-0.0648512319,-0.1858778149,-0.423663497,-0.0582789257,0.1405140013,-0.1072303206,0.4788049161,0.2815490067,0.25558272,0.154078871,-0.0803942084,0.2228560597,-0.2713088393,-0.004655215,0.314692229,-0.1752711385,-0.1823733896,-0.2065906823,0.1696434915,0.0142171262,0.0761997998,-0.3303349316,-0.1466784775,-0.0949350446,0.120766893,-0.2022130787,-0.1867319793,0.241856724,0.228496924,0.0300903972,0.1885070801,0.2500557899,0.0332654417,-0.2170521766,-0.0861481056,-0.043779742,0.1183455214,0.2379999757,0.4617770314,-0.0420077182,-0.2846428156,0.1601472199,0.0010739247,0.5781360269,-0.1647348851,-0.3022521436,0.02160614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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/616","title":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors","comments":"This error is what I see when I run the first lines of the Pytorch Quickstart.  It should also say that it should be ignored and\/or how to fix it.   BTW, this is a Pytorch error message -- not a Huggingface error message.   My code runs anyway.","body":"I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.","comment_length":47,"text":"UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors \n I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace:\r\n\r\n> Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n> C:\\Users\\bramv\\.virtualenvs\\dutch-simplification-nbNdqK9u\\lib\\site-packages\\datasets\\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\r\n>   return torch.tensor(x, **format_kwargs)\r\n\r\nThe first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment.\r\n\r\nSnippet:\r\n```\r\n    dataset = Dataset.from_dict(torch.load(\"data\/dummy.pt.pt\"))\r\n    print(dataset)\r\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\n    keys_to_retain = {\"input_ids\", \"sembedding\"}\r\n    dataset = dataset.map(lambda example: tokenizer(example[\"text\"], padding='max_length'), batched=True)\r\n    dataset.remove_columns_(set(dataset.column_names) - keys_to_retain)\r\n\r\n    dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)\r\n\r\n    print(next(iter(dataloader)))\r\n```\r\n\r\nPS: the input type for `remove_columns_` should probably be an Iterable rather than just a List. \n This error is what I see when I run the first lines of the Pytorch Quickstart.  It should also say that it should be ignored and\/or how to fix it.   BTW, this is a Pytorch error message -- not a Huggingface error message.   My code runs anyway.","embeddings":[0.2382437438,-0.3691007793,0.0651260018,0.1298748255,0.4380830228,0.0914000422,0.6286588907,0.2343869954,0.2976013124,0.0604875013,-0.1968635917,0.4237814248,-0.3091077805,-0.2186422497,-0.256572783,-0.2071904987,-0.019928148,0.1609406769,0.0264154281,-0.0268473886,-0.0556126274,0.0349227302,-0.1734228432,0.3379645646,-0.2255875468,-0.4098304808,0.2081689388,-0.0633970499,-0.0799697861,-0.3318474889,0.1405587047,-0.2741777301,-0.0424051508,0.1276783198,-0.0001266726,0.1157198995,0.0211008638,-0.0969710872,-0.0743481889,-0.1103790775,0.7791628242,-0.3006570041,0.2598286569,-0.5041794181,-0.0403003171,-0.2053057849,-0.0674014911,-0.2887423038,0.6341741681,0.2032751888,0.0752525479,0.5057702661,0.1049243435,0.3881487846,-0.066052407,0.3452899456,-0.2550107539,-0.0293222163,0.0295194145,0.1392835528,-0.0000625259,0.2642535865,-0.5914075375,0.0171453189,0.152644217,-0.0334382728,-0.349470824,-0.1997036189,-0.0568565764,0.4435260892,0.3706563115,-0.2254174352,-0.046209719,-0.3330504894,-0.1216260865,-0.1587676555,0.1649687737,0.047861699,-0.1130082533,0.1626306921,0.0142950015,-0.0069782236,-0.1369986981,0.2631300986,0.0543763451,0.1673310399,-0.1042311937,0.3020676374,-0.070318155,-0.0098416703,0.0525531098,0.0074251285,0.1901581138,0.196217075,-0.1164147928,-0.3119843602,-0.2599380016,-0.7474357486,-0.0870942324,-0.2492239624,0.3341346085,0.2540561259,-0.1607192904,0.0971489623,0.1469385177,0.0135778785,0.1208697632,0.303039968,-0.0211054645,-0.2641310394,0.4715952277,0.0567326099,0.1360289454,-0.2395026833,0.0944854543,0.0855747908,0.0345738009,0.0435557812,-0.4489809275,0.1235723495,-0.2449784279,0.0512567237,-0.0606115498,0.043743331,-0.0110608209,0.008455541,0.0809402019,0.3331671059,-0.2172211111,0.2093106508,0.0050662151,0.2291956097,-0.1825448275,-0.1943188906,-0.0641829967,0.2339443862,0.0189591032,0.261413455,0.0302922912,0.0009383697,0.1234879196,-0.2359409928,0.6530778408,0.2942767441,-0.1781089008,0.2948439717,0.3621653318,-0.0307599809,-0.3972631097,0.2834928632,-0.3104486465,-0.2276656926,-0.5769506097,0.0020175837,-0.1900073141,0.1614836454,0.1787664145,-0.2559919059,0.6716828942,-0.1626638323,0.4028995037,-0.437556386,-0.0927184001,-0.3298016787,-0.0530223027,0.3662085533,-0.3259876072,-0.064038679,0.258772701,-0.0162683986,0.4336455464,0.4094212055,-0.1056687534,0.1454505175,-0.1220321953,0.2255036682,0.3607753515,-0.1224518269,-0.0684338585,0.188791126,-0.1807031184,0.3171916902,0.1701442301,0.0854641721,-0.1042878255,0.1792410761,0.2188509107,0.0108668376,0.3695318699,-0.0156879947,-0.3240776658,-0.2027052045,0.4832918048,-0.1357165277,-0.0672293007,-0.1775234044,-0.1403667331,0.0221992601,0.3671857715,-0.1119876876,0.0290920548,-0.0411325768,-0.167381376,0.2396441549,-0.140446946,0.1050849631,-0.1710649133,-0.0152083375,0.0745915994,0.1085796431,-0.2709115148,-0.1766555607,-0.105172202,0.0071568051,-0.3517931402,-0.0465408675,-0.1111049056,-0.1885502487,-0.1136639938,0.1317765117,-0.1785905063,-0.1946374923,-0.5005659461,0.1517338455,-0.4120471478,-0.1020534709,-0.1012726128,-0.212443158,-0.3982954323,0.3267838955,0.232444495,-0.035569869,-0.0395304337,0.0819807947,0.2321104854,-0.1997635812,-0.917568922,0.1638941765,0.1763911694,-0.090930514,0.2472078055,0.1546784341,-0.1466421038,-0.096070841,-0.0257543679,0.6306298971,-0.0646008551,0.0608098134,-0.5003480315,0.261929512,-0.1911876351,0.1908106506,-0.1049912944,-0.4325117767,-0.0143134892,0.1304034591,-0.2320648581,-0.0843060389,-0.4490361214,-0.1313851327,0.0598223768,0.0488689318,0.2781392932,-0.080569014,0.1933736503,-0.2550162077,0.2152756453,-0.5420799255,0.1550077051,-0.1119838879,-0.0120686553,-0.0852294937,-0.1558951139,-0.2969710827,-0.0197968502,0.0393947251,-0.0165408477,0.1850436181,-0.0722502619,0.1588746011,-0.3456683457,0.1977573484,-0.1231665015,0.0587118678,-0.498372376,0.2802625299,-0.1423191577,0.0969332159,-0.2249715179,-0.2606532276,0.0266311578,-0.0663827807,-0.0077088084,0.0847671181,-0.2427747101,0.1668353975,-0.0719061345,0.0822579786,0.1976013184,-0.1022716314,-0.0642502904,0.090562731,-0.0684159249,0.021913901,-0.0892241076,-0.1524734795,0.2516383827,-0.0162396878,-0.2449783832,-0.100350745,-0.2109436393,0.1382278502,-0.1868399531,-0.1263592839,0.3529059291,0.1456685066,-0.2808859944,-0.2442344129,-0.0112630334,-0.0327507965,0.0331098884,0.1553820074,0.0217528194,0.13360475,-0.0648512319,-0.1858778149,-0.423663497,-0.0582789257,0.1405140013,-0.1072303206,0.4788049161,0.2815490067,0.25558272,0.154078871,-0.0803942084,0.2228560597,-0.2713088393,-0.004655215,0.314692229,-0.1752711385,-0.1823733896,-0.2065906823,0.1696434915,0.0142171262,0.0761997998,-0.3303349316,-0.1466784775,-0.0949350446,0.120766893,-0.2022130787,-0.1867319793,0.241856724,0.228496924,0.0300903972,0.1885070801,0.2500557899,0.0332654417,-0.2170521766,-0.0861481056,-0.043779742,0.1183455214,0.2379999757,0.4617770314,-0.0420077182,-0.2846428156,0.1601472199,0.0010739247,0.5781360269,-0.1647348851,-0.3022521436,0.0216061492,0.07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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/615","title":"Offset overflow when slicing a big dataset with an array of indices in Pyarrow >= 1.0.0","comments":"Related: https:\/\/issues.apache.org\/jira\/browse\/ARROW-9773\r\n\r\nIt's definitely a size thing. I took a smaller dataset with 87000 rows and did:\r\n```\r\nfor i in range(10,1000,20):\r\n    table = pa.concat_tables([dset._data]*i)\r\n    table.take([0])\r\n```\r\nand it broke at around i=300.\r\n\r\nAlso when `_indices` is not None, this breaks indexing by slice. E.g. `dset.shuffle()[:1]` breaks.\r\n\r\nLuckily so far I haven't seen `_indices.column(0).take` break, which means it doesn't break `select` or anything like that which is where the speed really matters, it's just `_getitem`. So I'm currently working around it by just doing the arrow v0 method in `_getitem`:\r\n```\r\n#if PYARROW_V0:\r\ndata_subset = pa.concat_tables(\r\n    self._data.slice(indices_array[i].as_py(), 1) for i in range(len(indices_array))\r\n)\r\n#else:\r\n    #data_subset = self._data.take(indices_array)\r\n```","body":"How to reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nwiki = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\")\r\nwiki[[0]]\r\n\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-13-381aedc9811b> in <module>\r\n----> 1 wikipedia[[0]]\r\n\r\n~\/Desktop\/hf\/nlp\/src\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1069             format_columns=self._format_columns,\r\n   1070             output_all_columns=self._output_all_columns,\r\n-> 1071             format_kwargs=self._format_kwargs,\r\n   1072         )\r\n   1073 \r\n\r\n~\/Desktop\/hf\/nlp\/src\/datasets\/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)\r\n   1037                 )\r\n   1038             else:\r\n-> 1039                 data_subset = self._data.take(indices_array)\r\n   1040 \r\n   1041             if format_type is not None:\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.take()\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/compute.py in take(data, indices, boundscheck)\r\n    266     \"\"\"\r\n    267     options = TakeOptions(boundscheck)\r\n--> 268     return call_function('take', [data, indices], options)\r\n    269 \r\n    270 \r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/_compute.pyx in pyarrow._compute.call_function()\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/_compute.pyx in pyarrow._compute.Function.call()\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: offset overflow while concatenating arrays\r\n```\r\n\r\nIt seems to work fine with small datasets or with pyarrow 0.17.1","comment_length":108,"text":"Offset overflow when slicing a big dataset with an array of indices in Pyarrow >= 1.0.0 \n How to reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nwiki = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\")\r\nwiki[[0]]\r\n\r\n---------------------------------------------------------------------------\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-13-381aedc9811b> in <module>\r\n----> 1 wikipedia[[0]]\r\n\r\n~\/Desktop\/hf\/nlp\/src\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1069             format_columns=self._format_columns,\r\n   1070             output_all_columns=self._output_all_columns,\r\n-> 1071             format_kwargs=self._format_kwargs,\r\n   1072         )\r\n   1073 \r\n\r\n~\/Desktop\/hf\/nlp\/src\/datasets\/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)\r\n   1037                 )\r\n   1038             else:\r\n-> 1039                 data_subset = self._data.take(indices_array)\r\n   1040 \r\n   1041             if format_type is not None:\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.take()\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/compute.py in take(data, indices, boundscheck)\r\n    266     \"\"\"\r\n    267     options = TakeOptions(boundscheck)\r\n--> 268     return call_function('take', [data, indices], options)\r\n    269 \r\n    270 \r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/_compute.pyx in pyarrow._compute.call_function()\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/_compute.pyx in pyarrow._compute.Function.call()\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: offset overflow while concatenating arrays\r\n```\r\n\r\nIt seems to work fine with small datasets or with pyarrow 0.17.1 \n Related: https:\/\/issues.apache.org\/jira\/browse\/ARROW-9773\r\n\r\nIt's definitely a size thing. I took a smaller dataset with 87000 rows and did:\r\n```\r\nfor i in range(10,1000,20):\r\n    table = pa.concat_tables([dset._data]*i)\r\n    table.take([0])\r\n```\r\nand it broke at around i=300.\r\n\r\nAlso when `_indices` is not None, this breaks indexing by slice. E.g. `dset.shuffle()[:1]` breaks.\r\n\r\nLuckily so far I haven't seen `_indices.column(0).take` break, which means it doesn't break `select` or anything like that which is where the speed really matters, it's just `_getitem`. So I'm currently working around it by just doing the arrow v0 method in `_getitem`:\r\n```\r\n#if PYARROW_V0:\r\ndata_subset = pa.concat_tables(\r\n    self._data.slice(indices_array[i].as_py(), 1) for i in range(len(indices_array))\r\n)\r\n#else:\r\n    #data_subset = self._data.take(indices_array)\r\n```","embeddings":[-0.3078599274,-0.1624817699,-0.0384140052,0.1917552799,0.1244793832,-0.0056269262,0.2420143485,0.1383444518,-0.3982105851,0.361923933,0.3079869449,0.4601465166,0.0280525908,0.0383535996,0.0241384003,-0.0889734328,-0.0426646657,0.0609558672,-0.1887903959,-0.0936412439,-0.0416765921,-0.3072259426,-0.2502094805,-0.0529374555,-0.1218299344,-0.2309742272,0.1539252698,0.1009820625,-0.1768930852,-0.2024064958,0.3342114687,-0.2671542764,0.0081014773,-0.0058169933,-0.0001175907,0.105093196,0.3507690728,0.0269848593,-0.0879454762,0.36186257,-0.1608607024,-0.3452382684,0.0494743697,-0.4793946147,0.2600331306,-0.1769219935,-0.2077457309,0.0676089823,0.0854020566,0.2236685753,0.1557555348,-0.0401188843,0.1585735977,0.2635318935,0.3263535202,0.0234108511,-0.0072753155,-0.2422338426,-0.167885378,0.0705335438,-0.2242981493,-0.0016125748,-0.0947054923,-0.0284246001,0.0539905503,0.0067401864,-0.023341896,0.0207042638,-0.1426187307,0.0567588471,0.2461706698,-0.2803447843,-0.1456433535,-0.2259111553,-0.0329124369,-0.3547538817,0.1401233077,0.3031680286,-0.0155505668,0.0339879505,-0.2461449653,0.2101866305,-0.2522290647,0.2505633831,-0.1889606565,0.2698267996,0.1512380987,0.1663444042,0.1357590854,-0.1141221076,0.4339353442,-0.1705885679,-0.0047746915,0.1696425527,-0.2869728804,0.1074186414,0.1948883533,-0.1316955835,0.500872314,-0.3496707678,0.0059454655,0.044077985,0.2339591086,0.0869400725,0.2841058373,0.1922837794,-0.5885927081,0.1779753715,-0.0207448937,0.0505632646,0.0146464435,0.0611452945,0.2007251531,-0.1749119461,0.3466876447,-0.080063574,0.0683387741,-0.1202837303,-0.2903328538,0.1888431013,-0.4950132966,0.3146744967,0.2096391916,-0.0979561433,0.0071371985,-0.0668994933,-0.2257014215,0.0162179675,-0.2137298733,0.0277413074,-0.1721847653,-0.0789436623,-0.097892873,-0.1193299741,0.0237431806,0.1679458022,-0.0813873485,0.3148367107,0.3989399672,-0.1049106643,-0.1097599268,-0.1568699181,0.4402474463,0.2657408416,-0.037249025,0.0719270855,-0.1062029973,0.0841025859,-0.0842686966,0.5176148415,-0.2612574995,-0.3617993593,-0.1042305902,0.1721643656,-0.1236087084,0.1671472192,-0.2392052561,0.1035816893,0.2674862444,-0.0605446771,0.186840266,-0.1679096073,-0.0465435795,-0.1870518327,0.2324185967,0.0651653484,-0.5403259993,0.3515728414,0.046457205,0.2777175009,0.3522078097,0.5056605935,-0.3103647828,-0.0813853443,-0.2419678718,0.054232087,0.1743374467,0.0021221105,-0.8825092912,-0.0902406722,-0.1311735809,-0.0398749858,0.2627778053,-0.1125264093,0.2651942372,0.0228159875,-0.0259930156,0.4810886681,-0.2747821212,0.2685475051,-0.4711420536,-0.1202219874,0.0109113958,0.0561424904,0.0007090906,-0.4467764199,-0.0522028096,-0.2801512182,0.2582900226,0.0480177514,0.2383665144,0.2446468771,0.0595670231,-0.2582804263,0.1781593412,-0.2147070318,-0.0670889765,-0.0253214389,-0.0054617655,-0.0934444442,-0.3762587905,-0.2153983414,-0.1107436791,0.3079881072,-0.1378659904,0.2697454691,0.1490946114,-0.3101868927,0.3051522672,0.1536781639,0.144846037,-0.1100105867,-0.1788464487,-0.0944767296,-0.2770965993,0.3967710435,-0.1217573658,-0.3430443704,-0.0394438095,0.3696358204,0.1240580752,-0.1210040003,-0.08161515,0.247591719,0.1220882535,-0.1240401193,-0.1544880718,0.201361239,0.1901596636,-0.6690385938,0.1178058013,-0.0190334246,-0.0259280931,0.073074013,-0.214742884,0.3537632227,-0.3796572685,0.1163655147,-0.0941120759,-0.0308949854,0.1070786119,-0.0249294546,0.1990388334,-0.0057956716,-0.1353929043,0.2408878505,-0.0255299453,-0.05827583,0.085350886,0.2082442045,0.4890706241,-0.1404571235,-0.0235346928,0.0426869988,-0.2933749259,-0.0679957122,0.1969684809,-0.2779559195,-0.0518844575,0.4408052564,0.0995412171,-0.234200567,-0.0678505599,-0.1258074045,0.236642167,0.3833583295,0.0870453045,0.1480712146,0.4960047007,-0.1378153265,-0.3675361574,0.0245736167,0.2212060094,0.4458927512,-0.1369953752,0.3641352952,-0.1575205475,-0.2291455865,-0.1539961249,-0.5479347706,-0.0495434105,-0.5558484793,-0.1171553135,0.2539521158,-0.2222500741,0.3176632822,0.028670188,-0.0788918287,0.4207021296,-0.2142774016,-0.0134648597,-0.2432038486,-0.1864147335,0.0226918533,0.3362286985,0.0649512634,0.0128789227,0.4299613833,0.0976502746,-0.0092566181,-0.3197989464,0.0494458601,0.1751476824,0.2215363234,0.0291967578,-0.2809969187,-0.3562772572,-0.2338242084,-0.0001732999,0.1419413835,-0.1411358565,0.4679219723,-0.1558834314,0.0448507592,0.008400552,-0.0066716825,-0.0028689625,-0.3170768321,-0.106845133,-0.0941461399,0.146625787,0.1291240901,0.2173149884,0.0217559095,0.1971845925,-0.3625352681,-0.2118215263,0.0719039589,0.3074546754,-0.0073131048,-0.1995082647,-0.1844004095,-0.4688217044,-0.120662719,0.3121152222,-0.43527022,0.1149598956,-0.165476054,0.5477545261,-0.4002652466,0.0760845542,0.1913095415,0.1314117759,-0.0754614696,-0.1794867814,-0.0041376762,0.0360306315,-0.1388119757,-0.0993702859,-0.0103422347,0.35558483,0.1919984221,0.6874073744,0.182875067,-0.0826884285,0.1499190331,-0.103769809,0.0607788451,-0.0972274169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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/611","title":"ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648","comments":"```\r\n<class 'pandas.core.frame.DataFrame'>\r\nInt64Index: 17136104 entries, 0 to 17136103\r\nData columns (total 6 columns):\r\n #   Column        Dtype  \r\n---  ------        -----  \r\n 0   item_id       int64  \r\n 1   item_titl     object \r\n 2   start_price   float64\r\n 3   shipping_fee  float64\r\n 4   picture_url   object \r\n 5   embeddings    object \r\ndtypes: float64(2), int64(1), object(3)\r\nmemory usage: 915.2+ MB\r\n```","body":"Hi, I'm trying to load a dataset from Dataframe, but I get the error:\r\n```bash\r\n---------------------------------------------------------------------------\r\nArrowCapacityError                        Traceback (most recent call last)\r\n<ipython-input-7-146b6b495963> in <module>\r\n----> 1 dataset = Dataset.from_pandas(emb)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py in from_pandas(cls, df, features, info, split)\r\n    223         info.features = features\r\n    224         pa_table: pa.Table = pa.Table.from_pandas(\r\n--> 225             df=df, schema=pa.schema(features.type) if features is not None else None\r\n    226         )\r\n    227         return cls(pa_table, info=info, split=split)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pandas()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)\r\n    591         for i, maybe_fut in enumerate(arrays):\r\n    592             if isinstance(maybe_fut, futures.Future):\r\n--> 593                 arrays[i] = maybe_fut.result()\r\n    594 \r\n    595     types = [x.type for x in arrays]\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    426                 raise CancelledError()\r\n    427             elif self._state == FINISHED:\r\n--> 428                 return self.__get_result()\r\n    429 \r\n    430             self._condition.wait(timeout)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in convert_column(col, field)\r\n    557 \r\n    558         try:\r\n--> 559             result = pa.array(col, type=type_, from_pandas=True, safe=safe)\r\n    560         except (pa.ArrowInvalid,\r\n    561                 pa.ArrowNotImplementedError,\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._ndarray_to_array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\nMy code is :\r\n```python\r\nfrom nlp import Dataset\r\ndataset = Dataset.from_pandas(emb)\r\n```","comment_length":47,"text":"ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648 \n Hi, I'm trying to load a dataset from Dataframe, but I get the error:\r\n```bash\r\n---------------------------------------------------------------------------\r\nArrowCapacityError                        Traceback (most recent call last)\r\n<ipython-input-7-146b6b495963> in <module>\r\n----> 1 dataset = Dataset.from_pandas(emb)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py in from_pandas(cls, df, features, info, split)\r\n    223         info.features = features\r\n    224         pa_table: pa.Table = pa.Table.from_pandas(\r\n--> 225             df=df, schema=pa.schema(features.type) if features is not None else None\r\n    226         )\r\n    227         return cls(pa_table, info=info, split=split)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pandas()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)\r\n    591         for i, maybe_fut in enumerate(arrays):\r\n    592             if isinstance(maybe_fut, futures.Future):\r\n--> 593                 arrays[i] = maybe_fut.result()\r\n    594 \r\n    595     types = [x.type for x in arrays]\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    426                 raise CancelledError()\r\n    427             elif self._state == FINISHED:\r\n--> 428                 return self.__get_result()\r\n    429 \r\n    430             self._condition.wait(timeout)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in convert_column(col, field)\r\n    557 \r\n    558         try:\r\n--> 559             result = pa.array(col, type=type_, from_pandas=True, safe=safe)\r\n    560         except (pa.ArrowInvalid,\r\n    561                 pa.ArrowNotImplementedError,\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._ndarray_to_array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\nMy code is :\r\n```python\r\nfrom nlp import Dataset\r\ndataset = Dataset.from_pandas(emb)\r\n``` \n ```\r\n<class 'pandas.core.frame.DataFrame'>\r\nInt64Index: 17136104 entries, 0 to 17136103\r\nData columns (total 6 columns):\r\n #   Column        Dtype  \r\n---  ------        -----  \r\n 0   item_id       int64  \r\n 1   item_titl     object \r\n 2   start_price   float64\r\n 3   shipping_fee  float64\r\n 4   picture_url   object \r\n 5   embeddings    object \r\ndtypes: float64(2), int64(1), object(3)\r\nmemory usage: 915.2+ MB\r\n```","embeddings":[-0.2813221216,-0.0744086578,-0.2195497006,0.4159464538,0.2823321819,-0.0299135447,0.4081522524,0.2113159895,0.3901511431,0.047444731,0.0003714884,0.3082345426,-0.0869823992,0.0685475916,0.000835677,-0.3419586718,-0.0933910608,0.2279389799,-0.275508076,0.0938671231,-0.2262557447,0.0042208233,-0.2458188832,0.2069006562,-0.099163264,-0.1639172286,0.0946853608,-0.0185281597,-0.0302411988,-0.4899121225,0.3556722403,-0.2945443392,0.0409597605,0.1827444583,-0.0001102196,0.0405716449,0.3709658682,0.0665661767,-0.2005666047,-0.1553708464,-0.3287505507,-0.4413967729,0.4300814569,-0.0768523142,0.2361876369,-0.2503988445,-0.2843058705,-0.3228805363,0.3386787176,0.0333642215,0.2106780857,0.2027079761,0.4377660751,0.121098958,0.0229463894,-0.0303455219,-0.1591518968,0.4214888215,0.1034795642,-0.2736412883,-0.0080701439,0.0098081287,-0.142464757,0.3750428259,0.148845315,-0.0841920599,-0.1104783043,-0.3776778579,-0.2088728249,0.2992431521,0.6146755219,-0.2124876976,-0.3339965045,-0.0728103667,-0.0512901135,-0.3944200575,0.0467973463,0.2284300476,-0.0249728207,0.055753652,0.0425886177,0.0693220422,-0.2296036631,0.0915905237,-0.0219255704,0.1695368588,-0.1137782633,0.2228228748,0.3527334929,-0.1476604491,0.1830546558,0.1812524498,0.0453062244,0.1507354528,-0.4711746573,-0.0076138237,-0.2046742588,-0.4235938787,0.0187764075,0.3424940407,0.2327027917,0.0266376585,0.0264474768,0.2103405595,0.1510000825,0.0093807904,-0.1516859233,0.0805711374,-0.2187978178,-0.0026093989,0.1261629015,-0.0250724573,-0.0848933235,-0.1750027239,0.1665083319,-0.1399342269,0.3136582673,-0.1227992103,-0.4663731754,0.2898985445,-0.494212687,-0.1571940184,0.106676884,0.1941330135,0.1898524016,0.3169753551,-0.0033455505,0.1713694483,-0.0771825686,0.1586159617,-0.1784182787,0.2248283327,0.0160623174,0.1040193215,-0.0073826094,0.0587322824,0.1619348079,0.1394265741,0.3225094676,-0.1406376809,0.2424962372,-0.4746845663,-0.0491963215,0.4604407549,-0.0665069968,0.137398988,0.4816637933,0.0661066324,-0.0525114462,0.287420392,-0.0509188175,-0.1669092923,-0.307931006,0.2034970522,0.0864146128,0.1019393206,-0.5072076321,-0.1107438877,0.2790833116,0.0325417966,0.2052158564,-0.1718201488,-0.0322390348,-0.237004444,-0.0113939112,0.2493876666,-0.6265670657,0.1540756524,-0.0097450269,-0.004250614,0.2222199887,0.3802011907,-0.371242255,0.0840109363,-0.1422454864,0.1392559856,-0.0864757076,0.0490292422,-0.4882147908,0.1294535846,0.0305430219,-0.0098824194,0.136037454,0.1243114844,0.0164619777,0.0188371874,-0.0198179577,0.2659706473,0.1596460044,-0.1190212071,-0.0785414279,-0.0919746459,0.1880332828,0.0425336324,-0.1309635341,-0.1111637652,-0.037798252,-0.058344353,0.0506681018,-0.0628661066,-0.1312288791,0.3077427745,0.102279827,0.0275660791,-0.1993941218,-0.1144554392,-0.2583920658,-0.0028342633,0.0060153445,-0.2196477205,-0.3686653972,0.0322350599,-0.3015362918,0.4142707586,-0.003701322,0.1467631906,0.1555080861,0.0351495408,-0.1392640173,-0.3405443728,-0.1125302464,0.3829867542,-0.2520798147,0.117400378,-0.2488704324,0.6389963627,-0.1225095615,-0.2358955741,0.102029413,-0.0589136705,0.0078237727,-0.0617898889,-0.0541578941,0.3003418446,0.2589096129,-0.0539924949,-0.1232170388,-0.1712924838,0.1711663902,-0.4476631284,-0.0194845684,0.3259800076,0.2496611625,0.0040201214,0.2005787194,0.2582695484,-0.230536446,0.1718457788,0.0606855899,0.0135376034,0.2570950389,0.2372244,-0.0010136174,-0.1002517715,-0.0585765764,0.1710237116,0.2922351956,0.0836870968,-0.3078921139,-0.0896601155,0.0380948409,0.0779544115,0.1145797297,0.1024587899,-0.1011391133,-0.1450705826,0.2871288657,-0.15945144,0.36046803,0.2623209059,-0.3645984828,-0.3160845041,-0.1206738278,0.0692813694,0.3395498991,0.2408055216,0.4563713074,-0.0912684724,0.0961696357,-0.0320504308,-0.2791270316,-0.5238831639,0.0624611005,0.4196495712,-0.3285613656,0.2295600623,-0.1541727185,-0.1055464894,0.2673347592,-0.6564484835,0.0172794033,-0.1459008306,-0.3326733708,0.3113354743,0.0069784429,0.098078616,-0.3459626734,-0.1520460844,0.3765098751,0.0735324323,0.0101614492,-0.0400554463,0.1425979286,0.1107221246,0.2702673376,0.0741861239,0.2685500979,0.3350822628,0.0054364279,-0.1947278678,-0.0174658187,-0.0798835009,-0.1669342369,0.0517802648,0.0623224191,0.3742077351,-0.5855019093,-0.5999208093,0.1264113486,0.0736583471,-0.1970529705,0.1744908839,0.0701362565,0.1771164238,0.1904895902,-0.1944365352,-0.314404428,-0.3713174164,0.1869551688,0.223639369,0.3277160227,0.3159987032,0.2656185925,0.1239757687,0.2631948292,-0.1057830453,-0.1982412785,0.1272568703,0.1754867584,0.0087331478,-0.2702606916,-0.1913998574,-0.267470181,0.2551690936,0.0270455424,-0.3893484771,-0.2998656631,-0.361228317,-0.08038757,-0.3379433453,-0.0242435355,0.270458132,0.1402743757,-0.2321830541,0.0587796047,-0.1001932099,-0.0318213888,-0.1266248375,0.1443426162,-0.1426805705,0.5946801901,-0.1982656568,0.5423558354,0.1506075263,-0.0378133692,0.4440318942,-0.0917442888,0.1699853688,-0.3620955348,-0.4971083701,-0.0669029206,-0.1251891553,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/611","title":"ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648","comments":"Thanks and some more on the `embeddings` and `picture_url` would be nice as well (type and max lengths of the elements)","body":"Hi, I'm trying to load a dataset from Dataframe, but I get the error:\r\n```bash\r\n---------------------------------------------------------------------------\r\nArrowCapacityError                        Traceback (most recent call last)\r\n<ipython-input-7-146b6b495963> in <module>\r\n----> 1 dataset = Dataset.from_pandas(emb)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py in from_pandas(cls, df, features, info, split)\r\n    223         info.features = features\r\n    224         pa_table: pa.Table = pa.Table.from_pandas(\r\n--> 225             df=df, schema=pa.schema(features.type) if features is not None else None\r\n    226         )\r\n    227         return cls(pa_table, info=info, split=split)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pandas()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)\r\n    591         for i, maybe_fut in enumerate(arrays):\r\n    592             if isinstance(maybe_fut, futures.Future):\r\n--> 593                 arrays[i] = maybe_fut.result()\r\n    594 \r\n    595     types = [x.type for x in arrays]\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    426                 raise CancelledError()\r\n    427             elif self._state == FINISHED:\r\n--> 428                 return self.__get_result()\r\n    429 \r\n    430             self._condition.wait(timeout)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in convert_column(col, field)\r\n    557 \r\n    558         try:\r\n--> 559             result = pa.array(col, type=type_, from_pandas=True, safe=safe)\r\n    560         except (pa.ArrowInvalid,\r\n    561                 pa.ArrowNotImplementedError,\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._ndarray_to_array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\nMy code is :\r\n```python\r\nfrom nlp import Dataset\r\ndataset = Dataset.from_pandas(emb)\r\n```","comment_length":21,"text":"ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648 \n Hi, I'm trying to load a dataset from Dataframe, but I get the error:\r\n```bash\r\n---------------------------------------------------------------------------\r\nArrowCapacityError                        Traceback (most recent call last)\r\n<ipython-input-7-146b6b495963> in <module>\r\n----> 1 dataset = Dataset.from_pandas(emb)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py in from_pandas(cls, df, features, info, split)\r\n    223         info.features = features\r\n    224         pa_table: pa.Table = pa.Table.from_pandas(\r\n--> 225             df=df, schema=pa.schema(features.type) if features is not None else None\r\n    226         )\r\n    227         return cls(pa_table, info=info, split=split)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pandas()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)\r\n    591         for i, maybe_fut in enumerate(arrays):\r\n    592             if isinstance(maybe_fut, futures.Future):\r\n--> 593                 arrays[i] = maybe_fut.result()\r\n    594 \r\n    595     types = [x.type for x in arrays]\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    426                 raise CancelledError()\r\n    427             elif self._state == FINISHED:\r\n--> 428                 return self.__get_result()\r\n    429 \r\n    430             self._condition.wait(timeout)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in convert_column(col, field)\r\n    557 \r\n    558         try:\r\n--> 559             result = pa.array(col, type=type_, from_pandas=True, safe=safe)\r\n    560         except (pa.ArrowInvalid,\r\n    561                 pa.ArrowNotImplementedError,\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._ndarray_to_array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\nMy code is :\r\n```python\r\nfrom nlp import Dataset\r\ndataset = Dataset.from_pandas(emb)\r\n``` \n Thanks and some more on the `embeddings` and `picture_url` would be nice as well (type and max lengths of the elements)","embeddings":[-0.2813221216,-0.0744086578,-0.2195497006,0.4159464538,0.2823321819,-0.0299135447,0.4081522524,0.2113159895,0.3901511431,0.047444731,0.0003714884,0.3082345426,-0.0869823992,0.0685475916,0.000835677,-0.3419586718,-0.0933910608,0.2279389799,-0.275508076,0.0938671231,-0.2262557447,0.0042208233,-0.2458188832,0.2069006562,-0.099163264,-0.1639172286,0.0946853608,-0.0185281597,-0.0302411988,-0.4899121225,0.3556722403,-0.2945443392,0.0409597605,0.1827444583,-0.0001102196,0.0405716449,0.3709658682,0.0665661767,-0.2005666047,-0.1553708464,-0.3287505507,-0.4413967729,0.4300814569,-0.0768523142,0.2361876369,-0.2503988445,-0.2843058705,-0.3228805363,0.3386787176,0.0333642215,0.2106780857,0.2027079761,0.4377660751,0.121098958,0.0229463894,-0.0303455219,-0.1591518968,0.4214888215,0.1034795642,-0.2736412883,-0.0080701439,0.0098081287,-0.142464757,0.3750428259,0.148845315,-0.0841920599,-0.1104783043,-0.3776778579,-0.2088728249,0.2992431521,0.6146755219,-0.2124876976,-0.3339965045,-0.0728103667,-0.0512901135,-0.3944200575,0.0467973463,0.2284300476,-0.0249728207,0.055753652,0.0425886177,0.0693220422,-0.2296036631,0.0915905237,-0.0219255704,0.1695368588,-0.1137782633,0.2228228748,0.3527334929,-0.1476604491,0.1830546558,0.1812524498,0.0453062244,0.1507354528,-0.4711746573,-0.0076138237,-0.2046742588,-0.4235938787,0.0187764075,0.3424940407,0.2327027917,0.0266376585,0.0264474768,0.2103405595,0.1510000825,0.0093807904,-0.1516859233,0.0805711374,-0.2187978178,-0.0026093989,0.1261629015,-0.0250724573,-0.0848933235,-0.1750027239,0.1665083319,-0.1399342269,0.3136582673,-0.1227992103,-0.4663731754,0.2898985445,-0.494212687,-0.1571940184,0.106676884,0.1941330135,0.1898524016,0.3169753551,-0.0033455505,0.1713694483,-0.0771825686,0.1586159617,-0.1784182787,0.2248283327,0.0160623174,0.1040193215,-0.0073826094,0.0587322824,0.1619348079,0.1394265741,0.3225094676,-0.1406376809,0.2424962372,-0.4746845663,-0.0491963215,0.4604407549,-0.0665069968,0.137398988,0.4816637933,0.0661066324,-0.0525114462,0.287420392,-0.0509188175,-0.1669092923,-0.307931006,0.2034970522,0.0864146128,0.1019393206,-0.5072076321,-0.1107438877,0.2790833116,0.0325417966,0.2052158564,-0.1718201488,-0.0322390348,-0.237004444,-0.0113939112,0.2493876666,-0.6265670657,0.1540756524,-0.0097450269,-0.004250614,0.2222199887,0.3802011907,-0.371242255,0.0840109363,-0.1422454864,0.1392559856,-0.0864757076,0.0490292422,-0.4882147908,0.1294535846,0.0305430219,-0.0098824194,0.136037454,0.1243114844,0.0164619777,0.0188371874,-0.0198179577,0.2659706473,0.1596460044,-0.1190212071,-0.0785414279,-0.0919746459,0.1880332828,0.0425336324,-0.1309635341,-0.1111637652,-0.037798252,-0.058344353,0.0506681018,-0.0628661066,-0.1312288791,0.3077427745,0.102279827,0.0275660791,-0.1993941218,-0.1144554392,-0.2583920658,-0.0028342633,0.0060153445,-0.2196477205,-0.3686653972,0.0322350599,-0.3015362918,0.4142707586,-0.003701322,0.1467631906,0.1555080861,0.0351495408,-0.1392640173,-0.3405443728,-0.1125302464,0.3829867542,-0.2520798147,0.117400378,-0.2488704324,0.6389963627,-0.1225095615,-0.2358955741,0.102029413,-0.0589136705,0.0078237727,-0.0617898889,-0.0541578941,0.3003418446,0.2589096129,-0.0539924949,-0.1232170388,-0.1712924838,0.1711663902,-0.4476631284,-0.0194845684,0.3259800076,0.2496611625,0.0040201214,0.2005787194,0.2582695484,-0.230536446,0.1718457788,0.0606855899,0.0135376034,0.2570950389,0.2372244,-0.0010136174,-0.1002517715,-0.0585765764,0.1710237116,0.2922351956,0.0836870968,-0.3078921139,-0.0896601155,0.0380948409,0.0779544115,0.1145797297,0.1024587899,-0.1011391133,-0.1450705826,0.2871288657,-0.15945144,0.36046803,0.2623209059,-0.3645984828,-0.3160845041,-0.1206738278,0.0692813694,0.3395498991,0.2408055216,0.4563713074,-0.0912684724,0.0961696357,-0.0320504308,-0.2791270316,-0.5238831639,0.0624611005,0.4196495712,-0.3285613656,0.2295600623,-0.1541727185,-0.1055464894,0.2673347592,-0.6564484835,0.0172794033,-0.1459008306,-0.3326733708,0.3113354743,0.0069784429,0.098078616,-0.3459626734,-0.1520460844,0.3765098751,0.0735324323,0.0101614492,-0.0400554463,0.1425979286,0.1107221246,0.2702673376,0.0741861239,0.2685500979,0.3350822628,0.0054364279,-0.1947278678,-0.0174658187,-0.0798835009,-0.1669342369,0.0517802648,0.0623224191,0.3742077351,-0.5855019093,-0.5999208093,0.1264113486,0.0736583471,-0.1970529705,0.1744908839,0.0701362565,0.1771164238,0.1904895902,-0.1944365352,-0.314404428,-0.3713174164,0.1869551688,0.223639369,0.3277160227,0.3159987032,0.2656185925,0.1239757687,0.2631948292,-0.1057830453,-0.1982412785,0.1272568703,0.1754867584,0.0087331478,-0.2702606916,-0.1913998574,-0.267470181,0.2551690936,0.0270455424,-0.3893484771,-0.2998656631,-0.361228317,-0.08038757,-0.3379433453,-0.0242435355,0.270458132,0.1402743757,-0.2321830541,0.0587796047,-0.1001932099,-0.0318213888,-0.1266248375,0.1443426162,-0.1426805705,0.5946801901,-0.1982656568,0.5423558354,0.1506075263,-0.0378133692,0.4440318942,-0.0917442888,0.1699853688,-0.3620955348,-0.4971083701,-0.0669029206,-0.1251891553,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/611","title":"ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648","comments":"It looks like a Pyarrow limitation.\r\nI was able to reproduce the error with \r\n\r\n```python\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\n n = 1713614\r\ndf = pd.DataFrame.from_dict({\"a\": list(np.zeros((n, 128))), \"b\": range(n)})\r\npa.Table.from_pandas(df)\r\n```\r\n\r\nI also tried with 50% of the dataframe and it actually works.\r\nI created an issue on Apache Arrow's JIRA [here](https:\/\/issues.apache.org\/jira\/browse\/ARROW-9976)\r\n\r\nOne way to fix that would be to chunk the dataframe and concatenate arrow tables.","body":"Hi, I'm trying to load a dataset from Dataframe, but I get the error:\r\n```bash\r\n---------------------------------------------------------------------------\r\nArrowCapacityError                        Traceback (most recent call last)\r\n<ipython-input-7-146b6b495963> in <module>\r\n----> 1 dataset = Dataset.from_pandas(emb)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py in from_pandas(cls, df, features, info, split)\r\n    223         info.features = features\r\n    224         pa_table: pa.Table = pa.Table.from_pandas(\r\n--> 225             df=df, schema=pa.schema(features.type) if features is not None else None\r\n    226         )\r\n    227         return cls(pa_table, info=info, split=split)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pandas()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)\r\n    591         for i, maybe_fut in enumerate(arrays):\r\n    592             if isinstance(maybe_fut, futures.Future):\r\n--> 593                 arrays[i] = maybe_fut.result()\r\n    594 \r\n    595     types = [x.type for x in arrays]\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    426                 raise CancelledError()\r\n    427             elif self._state == FINISHED:\r\n--> 428                 return self.__get_result()\r\n    429 \r\n    430             self._condition.wait(timeout)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in convert_column(col, field)\r\n    557 \r\n    558         try:\r\n--> 559             result = pa.array(col, type=type_, from_pandas=True, safe=safe)\r\n    560         except (pa.ArrowInvalid,\r\n    561                 pa.ArrowNotImplementedError,\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._ndarray_to_array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\nMy code is :\r\n```python\r\nfrom nlp import Dataset\r\ndataset = Dataset.from_pandas(emb)\r\n```","comment_length":75,"text":"ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648 \n Hi, I'm trying to load a dataset from Dataframe, but I get the error:\r\n```bash\r\n---------------------------------------------------------------------------\r\nArrowCapacityError                        Traceback (most recent call last)\r\n<ipython-input-7-146b6b495963> in <module>\r\n----> 1 dataset = Dataset.from_pandas(emb)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py in from_pandas(cls, df, features, info, split)\r\n    223         info.features = features\r\n    224         pa_table: pa.Table = pa.Table.from_pandas(\r\n--> 225             df=df, schema=pa.schema(features.type) if features is not None else None\r\n    226         )\r\n    227         return cls(pa_table, info=info, split=split)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pandas()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)\r\n    591         for i, maybe_fut in enumerate(arrays):\r\n    592             if isinstance(maybe_fut, futures.Future):\r\n--> 593                 arrays[i] = maybe_fut.result()\r\n    594 \r\n    595     types = [x.type for x in arrays]\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    426                 raise CancelledError()\r\n    427             elif self._state == FINISHED:\r\n--> 428                 return self.__get_result()\r\n    429 \r\n    430             self._condition.wait(timeout)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in convert_column(col, field)\r\n    557 \r\n    558         try:\r\n--> 559             result = pa.array(col, type=type_, from_pandas=True, safe=safe)\r\n    560         except (pa.ArrowInvalid,\r\n    561                 pa.ArrowNotImplementedError,\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._ndarray_to_array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\nMy code is :\r\n```python\r\nfrom nlp import Dataset\r\ndataset = Dataset.from_pandas(emb)\r\n``` \n It looks like a Pyarrow limitation.\r\nI was able to reproduce the error with \r\n\r\n```python\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\n n = 1713614\r\ndf = pd.DataFrame.from_dict({\"a\": list(np.zeros((n, 128))), \"b\": range(n)})\r\npa.Table.from_pandas(df)\r\n```\r\n\r\nI also tried with 50% of the dataframe and it actually works.\r\nI created an issue on Apache Arrow's JIRA [here](https:\/\/issues.apache.org\/jira\/browse\/ARROW-9976)\r\n\r\nOne way to fix that would be to chunk the dataframe and concatenate arrow 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/611","title":"ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648","comments":"It looks like it's going to be fixed in pyarrow 2.0.0 :)\r\n\r\nIn the meantime I suggest to chunk big dataframes to create several small datasets, and then concatenate them using [concatenate_datasets](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=concatenate#datasets.concatenate_datasets)","body":"Hi, I'm trying to load a dataset from Dataframe, but I get the error:\r\n```bash\r\n---------------------------------------------------------------------------\r\nArrowCapacityError                        Traceback (most recent call last)\r\n<ipython-input-7-146b6b495963> in <module>\r\n----> 1 dataset = Dataset.from_pandas(emb)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py in from_pandas(cls, df, features, info, split)\r\n    223         info.features = features\r\n    224         pa_table: pa.Table = pa.Table.from_pandas(\r\n--> 225             df=df, schema=pa.schema(features.type) if features is not None else None\r\n    226         )\r\n    227         return cls(pa_table, info=info, split=split)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pandas()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)\r\n    591         for i, maybe_fut in enumerate(arrays):\r\n    592             if isinstance(maybe_fut, futures.Future):\r\n--> 593                 arrays[i] = maybe_fut.result()\r\n    594 \r\n    595     types = [x.type for x in arrays]\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    426                 raise CancelledError()\r\n    427             elif self._state == FINISHED:\r\n--> 428                 return self.__get_result()\r\n    429 \r\n    430             self._condition.wait(timeout)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in convert_column(col, field)\r\n    557 \r\n    558         try:\r\n--> 559             result = pa.array(col, type=type_, from_pandas=True, safe=safe)\r\n    560         except (pa.ArrowInvalid,\r\n    561                 pa.ArrowNotImplementedError,\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._ndarray_to_array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\nMy code is :\r\n```python\r\nfrom nlp import Dataset\r\ndataset = Dataset.from_pandas(emb)\r\n```","comment_length":32,"text":"ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648 \n Hi, I'm trying to load a dataset from Dataframe, but I get the error:\r\n```bash\r\n---------------------------------------------------------------------------\r\nArrowCapacityError                        Traceback (most recent call last)\r\n<ipython-input-7-146b6b495963> in <module>\r\n----> 1 dataset = Dataset.from_pandas(emb)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py in from_pandas(cls, df, features, info, split)\r\n    223         info.features = features\r\n    224         pa_table: pa.Table = pa.Table.from_pandas(\r\n--> 225             df=df, schema=pa.schema(features.type) if features is not None else None\r\n    226         )\r\n    227         return cls(pa_table, info=info, split=split)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/table.pxi in pyarrow.lib.Table.from_pandas()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe)\r\n    591         for i, maybe_fut in enumerate(arrays):\r\n    592             if isinstance(maybe_fut, futures.Future):\r\n--> 593                 arrays[i] = maybe_fut.result()\r\n    594 \r\n    595     types = [x.type for x in arrays]\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    426                 raise CancelledError()\r\n    427             elif self._state == FINISHED:\r\n--> 428                 return self.__get_result()\r\n    429 \r\n    430             self._condition.wait(timeout)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/concurrent\/futures\/thread.py in run(self)\r\n     55 \r\n     56         try:\r\n---> 57             result = self.fn(*self.args, **self.kwargs)\r\n     58         except BaseException as exc:\r\n     59             self.future.set_exception(exc)\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py in convert_column(col, field)\r\n    557 \r\n    558         try:\r\n--> 559             result = pa.array(col, type=type_, from_pandas=True, safe=safe)\r\n    560         except (pa.ArrowInvalid,\r\n    561                 pa.ArrowNotImplementedError,\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._ndarray_to_array()\r\n\r\n~\/miniconda3\/envs\/dev\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648\r\n```\r\nMy code is :\r\n```python\r\nfrom nlp import Dataset\r\ndataset = Dataset.from_pandas(emb)\r\n``` \n It looks like it's going to be fixed in pyarrow 2.0.0 :)\r\n\r\nIn the meantime I suggest to chunk big dataframes to create several small datasets, and then concatenate them using [concatenate_datasets](https:\/\/huggingface.co\/docs\/datasets\/package_reference\/main_classes.html?highlight=concatenate#datasets.concatenate_datasets)","embeddings":[-0.2813221216,-0.0744086578,-0.2195497006,0.4159464538,0.2823321819,-0.0299135447,0.4081522524,0.2113159895,0.3901511431,0.047444731,0.0003714884,0.3082345426,-0.0869823992,0.0685475916,0.000835677,-0.3419586718,-0.0933910608,0.2279389799,-0.275508076,0.0938671231,-0.2262557447,0.0042208233,-0.2458188832,0.2069006562,-0.099163264,-0.1639172286,0.0946853608,-0.0185281597,-0.0302411988,-0.4899121225,0.3556722403,-0.2945443392,0.0409597605,0.1827444583,-0.0001102196,0.0405716449,0.3709658682,0.0665661767,-0.2005666047,-0.1553708464,-0.3287505507,-0.4413967729,0.4300814569,-0.0768523142,0.2361876369,-0.2503988445,-0.2843058705,-0.3228805363,0.3386787176,0.0333642215,0.2106780857,0.2027079761,0.4377660751,0.121098958,0.0229463894,-0.0303455219,-0.1591518968,0.4214888215,0.1034795642,-0.2736412883,-0.0080701439,0.0098081287,-0.142464757,0.3750428259,0.148845315,-0.0841920599,-0.1104783043,-0.3776778579,-0.2088728249,0.2992431521,0.6146755219,-0.2124876976,-0.3339965045,-0.0728103667,-0.0512901135,-0.3944200575,0.0467973463,0.2284300476,-0.0249728207,0.055753652,0.0425886177,0.0693220422,-0.2296036631,0.0915905237,-0.0219255704,0.1695368588,-0.1137782633,0.2228228748,0.3527334929,-0.1476604491,0.1830546558,0.1812524498,0.0453062244,0.1507354528,-0.4711746573,-0.0076138237,-0.2046742588,-0.4235938787,0.0187764075,0.3424940407,0.2327027917,0.0266376585,0.0264474768,0.2103405595,0.1510000825,0.0093807904,-0.1516859233,0.0805711374,-0.2187978178,-0.0026093989,0.1261629015,-0.0250724573,-0.0848933235,-0.1750027239,0.1665083319,-0.1399342269,0.3136582673,-0.1227992103,-0.4663731754,0.2898985445,-0.494212687,-0.1571940184,0.106676884,0.1941330135,0.1898524016,0.3169753551,-0.0033455505,0.1713694483,-0.0771825686,0.1586159617,-0.1784182787,0.2248283327,0.0160623174,0.1040193215,-0.0073826094,0.0587322824,0.1619348079,0.1394265741,0.3225094676,-0.1406376809,0.2424962372,-0.4746845663,-0.0491963215,0.4604407549,-0.0665069968,0.137398988,0.4816637933,0.0661066324,-0.0525114462,0.287420392,-0.0509188175,-0.1669092923,-0.307931006,0.2034970522,0.0864146128,0.1019393206,-0.5072076321,-0.1107438877,0.2790833116,0.0325417966,0.2052158564,-0.1718201488,-0.0322390348,-0.237004444,-0.0113939112,0.2493876666,-0.6265670657,0.1540756524,-0.0097450269,-0.004250614,0.2222199887,0.3802011907,-0.371242255,0.0840109363,-0.1422454864,0.1392559856,-0.0864757076,0.0490292422,-0.4882147908,0.1294535846,0.0305430219,-0.0098824194,0.136037454,0.1243114844,0.0164619777,0.0188371874,-0.0198179577,0.2659706473,0.1596460044,-0.1190212071,-0.0785414279,-0.0919746459,0.1880332828,0.0425336324,-0.1309635341,-0.1111637652,-0.037798252,-0.058344353,0.0506681018,-0.0628661066,-0.1312288791,0.3077427745,0.102279827,0.0275660791,-0.1993941218,-0.1144554392,-0.2583920658,-0.0028342633,0.0060153445,-0.2196477205,-0.3686653972,0.0322350599,-0.3015362918,0.4142707586,-0.003701322,0.1467631906,0.1555080861,0.0351495408,-0.1392640173,-0.3405443728,-0.1125302464,0.3829867542,-0.2520798147,0.117400378,-0.2488704324,0.6389963627,-0.1225095615,-0.2358955741,0.102029413,-0.0589136705,0.0078237727,-0.0617898889,-0.0541578941,0.3003418446,0.2589096129,-0.0539924949,-0.1232170388,-0.1712924838,0.1711663902,-0.4476631284,-0.0194845684,0.3259800076,0.2496611625,0.0040201214,0.2005787194,0.2582695484,-0.230536446,0.1718457788,0.0606855899,0.0135376034,0.2570950389,0.2372244,-0.0010136174,-0.1002517715,-0.0585765764,0.1710237116,0.2922351956,0.0836870968,-0.3078921139,-0.0896601155,0.0380948409,0.0779544115,0.1145797297,0.1024587899,-0.1011391133,-0.1450705826,0.2871288657,-0.15945144,0.36046803,0.2623209059,-0.3645984828,-0.3160845041,-0.1206738278,0.0692813694,0.3395498991,0.2408055216,0.4563713074,-0.0912684724,0.0961696357,-0.0320504308,-0.2791270316,-0.5238831639,0.0624611005,0.4196495712,-0.3285613656,0.2295600623,-0.1541727185,-0.1055464894,0.2673347592,-0.6564484835,0.0172794033,-0.1459008306,-0.3326733708,0.3113354743,0.0069784429,0.098078616,-0.3459626734,-0.1520460844,0.3765098751,0.0735324323,0.0101614492,-0.0400554463,0.1425979286,0.1107221246,0.2702673376,0.0741861239,0.2685500979,0.3350822628,0.0054364279,-0.1947278678,-0.0174658187,-0.0798835009,-0.1669342369,0.0517802648,0.0623224191,0.3742077351,-0.5855019093,-0.5999208093,0.1264113486,0.0736583471,-0.1970529705,0.1744908839,0.0701362565,0.1771164238,0.1904895902,-0.1944365352,-0.314404428,-0.3713174164,0.1869551688,0.223639369,0.3277160227,0.3159987032,0.2656185925,0.1239757687,0.2631948292,-0.1057830453,-0.1982412785,0.1272568703,0.1754867584,0.0087331478,-0.2702606916,-0.1913998574,-0.267470181,0.2551690936,0.0270455424,-0.3893484771,-0.2998656631,-0.361228317,-0.08038757,-0.3379433453,-0.0242435355,0.270458132,0.1402743757,-0.2321830541,0.0587796047,-0.1001932099,-0.0318213888,-0.1266248375,0.1443426162,-0.1426805705,0.5946801901,-0.1982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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Could you try\r\n```python\r\nload_dataset('text', data_files='test.txt',cache_dir=\".\/\", split=\"train\")\r\n```\r\n?\r\n\r\n`load_dataset` returns a dictionary by default, like {\"train\": your_dataset}","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":18,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Could you try\r\n```python\r\nload_dataset('text', data_files='test.txt',cache_dir=\".\/\", split=\"train\")\r\n```\r\n?\r\n\r\n`load_dataset` returns a dictionary by default, like {\"train\": 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Hi @lhoestq\r\nThanks for your suggestion.\r\n\r\nI tried \r\n```\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\", split=\"train\")\r\nprint(dataset)\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut it still doesn't work and got error:\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-7-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1069             format_columns=self._format_columns,\r\n   1070             output_all_columns=self._output_all_columns,\r\n-> 1071             format_kwargs=self._format_kwargs,\r\n   1072         )\r\n   1073 \r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)\r\n   1056                 format_columns=format_columns,\r\n   1057                 output_all_columns=output_all_columns,\r\n-> 1058                 format_kwargs=format_kwargs,\r\n   1059             )\r\n   1060         return outputs\r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/arrow_dataset.py in _convert_outputs(self, outputs, format_type, format_columns, output_all_columns, format_kwargs)\r\n    872                     continue\r\n    873                 if format_columns is None or k in format_columns:\r\n--> 874                     v = map_nested(command, v, **map_nested_kwargs)\r\n    875                 output_dict[k] = v\r\n    876         return output_dict\r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/utils\/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n    214     # Singleton\r\n    215     if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 216         return function(data_struct)\r\n    217 \r\n    218     disable_tqdm = bool(logger.getEffectiveLevel() > INFO)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/arrow_dataset.py in command(x)\r\n    833                     if x.dtype == np.object:  # pytorch tensors cannot be instantied from an array of objects\r\n    834                         return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n--> 835                 return torch.tensor(x, **format_kwargs)\r\n    836 \r\n    837         elif format_type == \"tensorflow\":\r\n\r\nTypeError: new(): invalid data type 'str'\r\n```\r\n\r\nI found type can be ['numpy', 'torch', 'tensorflow', 'pandas'] only, how can I deal with the string type?","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":312,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Hi @lhoestq\r\nThanks for your suggestion.\r\n\r\nI tried \r\n```\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\", split=\"train\")\r\nprint(dataset)\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut it still doesn't work and got error:\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-7-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/arrow_dataset.py in __getitem__(self, key)\r\n   1069             format_columns=self._format_columns,\r\n   1070             output_all_columns=self._output_all_columns,\r\n-> 1071             format_kwargs=self._format_kwargs,\r\n   1072         )\r\n   1073 \r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)\r\n   1056                 format_columns=format_columns,\r\n   1057                 output_all_columns=output_all_columns,\r\n-> 1058                 format_kwargs=format_kwargs,\r\n   1059             )\r\n   1060         return outputs\r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/arrow_dataset.py in _convert_outputs(self, outputs, format_type, format_columns, output_all_columns, format_kwargs)\r\n    872                     continue\r\n    873                 if format_columns is None or k in format_columns:\r\n--> 874                     v = map_nested(command, v, **map_nested_kwargs)\r\n    875                 output_dict[k] = v\r\n    876         return output_dict\r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/utils\/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n    214     # Singleton\r\n    215     if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 216         return function(data_struct)\r\n    217 \r\n    218     disable_tqdm = bool(logger.getEffectiveLevel() > INFO)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/datasets-0.4.0-py3.7.egg\/datasets\/arrow_dataset.py in command(x)\r\n    833                     if x.dtype == np.object:  # pytorch tensors cannot be instantied from an array of objects\r\n    834                         return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n--> 835                 return torch.tensor(x, **format_kwargs)\r\n    836 \r\n    837         elif format_type == \"tensorflow\":\r\n\r\nTypeError: new(): invalid data type 'str'\r\n```\r\n\r\nI found type can be ['numpy', 'torch', 'tensorflow', 'pandas'] only, how can I deal with the string type?","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.3349307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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"You need to tokenize the string inputs to convert them in integers before you can feed them to a pytorch dataloader.\r\n\r\nYou can read the quicktour of the datasets or the transformers libraries to know more about that:\r\n- transformers: https:\/\/huggingface.co\/transformers\/quicktour.html\r\n- dataset: https:\/\/huggingface.co\/docs\/datasets\/quicktour.html","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":44,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n You need to tokenize the string inputs to convert them in integers before you can feed them to a pytorch dataloader.\r\n\r\nYou can read the quicktour of the datasets or the transformers libraries to know more about that:\r\n- transformers: https:\/\/huggingface.co\/transformers\/quicktour.html\r\n- dataset: https:\/\/huggingface.co\/docs\/datasets\/quicktour.html","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\nBut finally got it working. This is what I did after looking into the documentation.\r\n\r\n1. split the whole dataset file into smaller files\r\n```bash\r\nmkdir .\/shards\r\nsplit -a 4 -l 256000 -d full_raw_corpus.txt .\/shards\/shard_\r\n````\r\n2. Pass paths of small data files to `load_dataset`\r\n```python\r\nfiles = glob.glob('shards\/*')\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files=files, split='train')\r\n```\r\n(On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n\r\n3. Tokenization\r\n```python\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\ndataset = dataset.map(encode, batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n```\r\n Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n```python\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\nnext(iter(dataloader))\r\n```\r\nHope this helps\r\n","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":125,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\nBut finally got it working. This is what I did after looking into the documentation.\r\n\r\n1. split the whole dataset file into smaller files\r\n```bash\r\nmkdir .\/shards\r\nsplit -a 4 -l 256000 -d full_raw_corpus.txt .\/shards\/shard_\r\n````\r\n2. Pass paths of small data files to `load_dataset`\r\n```python\r\nfiles = glob.glob('shards\/*')\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files=files, split='train')\r\n```\r\n(On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n\r\n3. Tokenization\r\n```python\r\ndef encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')\r\ndataset = dataset.map(encode, batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n```\r\n Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n```python\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\nnext(iter(dataloader))\r\n```\r\nHope this helps\r\n","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.334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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Thanks, @thomwolf  and @sipah00 ,\r\n\r\nI tried to implement your suggestions in my scripts. \r\nNow, I am facing some connection time-out error. I am using my local file, I have no idea why the module request s3 database.\r\n\r\nThe log is:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/requests\/adapters.py\", line 449, in send\r\n    raise err\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py\", line 74, in create_connection\r\n    timeout=timeout\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 720, in urlopen\r\n    sock.connect(sa)\r\nTimeoutError: [Errno 110] Connection timed out\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 672, in urlopen\r\n    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py\", line 436, in increment\r\n    chunked=chunked,\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 376, in _make_request\r\n    raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/text\/text.py (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection obj\r\nect at 0x7fff401e0e48>: Failed to establish a new connection: [Errno 110] Connection timed out',))\r\n\r\nTraceback (most recent call last):\r\n  File \"\/scratch\/roberta_emohash\/run_language_modeling.py\", line 1019, in <module>\r\n    main()\r\n  File \"\/scratch\/roberta_emohash\/run_language_modeling.py\", line 962, in main\r\n    train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n  File \"\/scratch\/roberta_emohash\/run_language_modeling.py\", line 177, in load_and_cache_examples\r\n    return HG_Datasets(tokenizer, file_path, args)\r\n  File \"\/scratch\/roberta_emohash\/run_language_modeling.py\", line 117, in HG_Datasets\r\n    dataset = load_dataset('text', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n  File \"\/arc\/project\/evn_py36\/datasets\/datasets\/src\/datasets\/load.py\", line 590, in load_dataset\r\n    self._validate_conn(conn)\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 994, in _validate_conn\r\n    conn.connect()\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 300, in connect\r\n    conn = self._new_conn()\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 169, in _new_conn\r\n    self, \"Failed to establish a new connection: %s\" % e\r\nurllib3.exceptions.NewConnectionError: <urllib3.connection.VerifiedHTTPSConnection object at 0x7fff401e0da0>: Failed to establish a new connection: [Errno 110] Connection timed out\r\n\r\n``` \r\n\r\nDo you have any experience on this issue?","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":248,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Thanks, @thomwolf  and @sipah00 ,\r\n\r\nI tried to implement your suggestions in my scripts. \r\nNow, I am facing some connection time-out error. I am using my local file, I have no idea why the module request s3 database.\r\n\r\nThe log is:\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/requests\/adapters.py\", line 449, in send\r\n    raise err\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/util\/connection.py\", line 74, in create_connection\r\n    timeout=timeout\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 720, in urlopen\r\n    sock.connect(sa)\r\nTimeoutError: [Errno 110] Connection timed out\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 672, in urlopen\r\n    method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/util\/retry.py\", line 436, in increment\r\n    chunked=chunked,\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 376, in _make_request\r\n    raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: \/datasets.huggingface.co\/datasets\/datasets\/text\/text.py (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection obj\r\nect at 0x7fff401e0e48>: Failed to establish a new connection: [Errno 110] Connection timed out',))\r\n\r\nTraceback (most recent call last):\r\n  File \"\/scratch\/roberta_emohash\/run_language_modeling.py\", line 1019, in <module>\r\n    main()\r\n  File \"\/scratch\/roberta_emohash\/run_language_modeling.py\", line 962, in main\r\n    train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n  File \"\/scratch\/roberta_emohash\/run_language_modeling.py\", line 177, in load_and_cache_examples\r\n    return HG_Datasets(tokenizer, file_path, args)\r\n  File \"\/scratch\/roberta_emohash\/run_language_modeling.py\", line 117, in HG_Datasets\r\n    dataset = load_dataset('text', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n  File \"\/arc\/project\/evn_py36\/datasets\/datasets\/src\/datasets\/load.py\", line 590, in load_dataset\r\n    self._validate_conn(conn)\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connectionpool.py\", line 994, in _validate_conn\r\n    conn.connect()\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 300, in connect\r\n    conn = self._new_conn()\r\n  File \"\/home\/.local\/lib\/python3.6\/site-packages\/urllib3\/connection.py\", line 169, in _new_conn\r\n    self, \"Failed to establish a new connection: %s\" % e\r\nurllib3.exceptions.NewConnectionError: <urllib3.connection.VerifiedHTTPSConnection object at 0x7fff401e0da0>: Failed to establish a new connection: [Errno 110] Connection timed out\r\n\r\n``` \r\n\r\nDo you have any experience on this issue?","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.334930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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"I noticed this is because I use a cloud server where does not provide for connections from our standard compute nodes to outside resources. \r\n\r\nFor the `datasets` package, it seems that if the loading script is not already cached in the library it will attempt to connect to an AWS resource to download the dataset loading script. \r\n\r\nI am wondering why the package works in this way. Do you have any suggestions to solve this issue? ","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":76,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n I noticed this is because I use a cloud server where does not provide for connections from our standard compute nodes to outside resources. \r\n\r\nFor the `datasets` package, it seems that if the loading script is not already cached in the library it will attempt to connect to an AWS resource to download the dataset loading script. \r\n\r\nI am wondering why the package works in this way. Do you have any suggestions to solve this issue? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"I solved the above issue by downloading text.py manually and passing the path to the `load_dataset` function. \r\n\r\nNow, I have a new issue with the Read-only file system.\r\n\r\nThe error is: \r\n```\r\nI0916 22:14:38.453380 140737353971520 filelock.py:274] Lock 140734268996072 acquired on \/scratch\/chiyuzh\/roberta\/text.py.lock\r\nFound main folder for dataset \/scratch\/chiyuzh\/roberta\/text.py at \/home\/chiyuzh\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nCreating specific version folder for dataset \/scratch\/chiyuzh\/roberta\/text.py at \/home\/chiyuzh\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014\r\nI0916 22:14:38.530371 140737353971520 filelock.py:318] Lock 140734268996072 released on \/scratch\/chiyuzh\/roberta\/text.py.lock\r\nTraceback (most recent call last):\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling_hg.py\", line 1019, in <module>\r\n    main()\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling_hg.py\", line 962, in main\r\n    train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling_hg.py\", line 177, in load_and_cache_examples\r\n    return HG_Datasets(tokenizer, file_path, args)\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling_hg.py\", line 117, in HG_Datasets\r\n    dataset = load_dataset('\/scratch\/chiyuzh\/roberta\/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n  File \"\/arc\/project\/chiyuzh\/evn_py36\/datasets\/src\/datasets\/load.py\", line 590, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/arc\/project\/chiyuzh\/evn_py36\/datasets\/src\/datasets\/load.py\", line 385, in prepare_module\r\n    os.makedirs(hash_folder_path)\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/home\/chiyuzh\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014'\r\n\r\n```\r\n\r\nI installed datasets at \/project\/chiyuzh\/evn_py36\/datasets\/src where is a writable directory.\r\nI also tried change the environment variables to the writable directory:\r\n`export HF_MODULES_PATH=\/project\/chiyuzh\/evn_py36\/datasets\/cache_dir\/`\r\n`export HF_DATASETS_CACHE=\/project\/chiyuzh\/evn_py36\/datasets\/cache_dir\/`\r\n \r\nIn my scripts, I also changed to:\r\n`dataset = load_dataset('\/scratch\/chiyuzh\/roberta\/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")`\r\n`data_cache_dir = $TMPDIR\/data\/` that also a writable directory.\r\n \r\nBut it still try to make directory at \/home\/chiyuzh\/.cache\/huggingface\/modules\/.\r\nDo you have any idea about this issue? @thomwolf \r\n","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":214,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n I solved the above issue by downloading text.py manually and passing the path to the `load_dataset` function. \r\n\r\nNow, I have a new issue with the Read-only file system.\r\n\r\nThe error is: \r\n```\r\nI0916 22:14:38.453380 140737353971520 filelock.py:274] Lock 140734268996072 acquired on \/scratch\/chiyuzh\/roberta\/text.py.lock\r\nFound main folder for dataset \/scratch\/chiyuzh\/roberta\/text.py at \/home\/chiyuzh\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\r\nCreating specific version folder for dataset \/scratch\/chiyuzh\/roberta\/text.py at \/home\/chiyuzh\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014\r\nI0916 22:14:38.530371 140737353971520 filelock.py:318] Lock 140734268996072 released on \/scratch\/chiyuzh\/roberta\/text.py.lock\r\nTraceback (most recent call last):\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling_hg.py\", line 1019, in <module>\r\n    main()\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling_hg.py\", line 962, in main\r\n    train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling_hg.py\", line 177, in load_and_cache_examples\r\n    return HG_Datasets(tokenizer, file_path, args)\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling_hg.py\", line 117, in HG_Datasets\r\n    dataset = load_dataset('\/scratch\/chiyuzh\/roberta\/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n  File \"\/arc\/project\/chiyuzh\/evn_py36\/datasets\/src\/datasets\/load.py\", line 590, in load_dataset\r\n    path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n  File \"\/arc\/project\/chiyuzh\/evn_py36\/datasets\/src\/datasets\/load.py\", line 385, in prepare_module\r\n    os.makedirs(hash_folder_path)\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/home\/chiyuzh\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/text\/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014'\r\n\r\n```\r\n\r\nI installed datasets at \/project\/chiyuzh\/evn_py36\/datasets\/src where is a writable directory.\r\nI also tried change the environment variables to the writable directory:\r\n`export HF_MODULES_PATH=\/project\/chiyuzh\/evn_py36\/datasets\/cache_dir\/`\r\n`export HF_DATASETS_CACHE=\/project\/chiyuzh\/evn_py36\/datasets\/cache_dir\/`\r\n \r\nIn my scripts, I also changed to:\r\n`dataset = load_dataset('\/scratch\/chiyuzh\/roberta\/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")`\r\n`data_cache_dir = $TMPDIR\/data\/` that also a writable directory.\r\n \r\nBut it still try to make directory at \/home\/chiyuzh\/.cache\/huggingface\/modules\/.\r\nDo you have any idea about this issue? @thomwolf \r\n","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.33493071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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\n> But finally got it working. This is what I did after looking into the documentation.\r\n> \r\n> 1. split the whole dataset file into smaller files\r\n> \r\n> ```shell\r\n> mkdir .\/shards\r\n> split -a 4 -l 256000 -d full_raw_corpus.txt .\/shards\/shard_\r\n> ```\r\n> \r\n> 1. Pass paths of small data files to `load_dataset`\r\n> \r\n> ```python\r\n> files = glob.glob('shards\/*')\r\n> from datasets import load_dataset\r\n> dataset = load_dataset('text', data_files=files, split='train')\r\n> ```\r\n> \r\n> (On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n> \r\n> 1. Tokenization\r\n> \r\n> ```python\r\n> def encode(examples):\r\n>   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> dataset = dataset.map(encode, batched=True)\r\n> dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n> ```\r\n> \r\n> Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n> \r\n> ```python\r\n> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n> next(iter(dataloader))\r\n> ```\r\n> \r\n> Hope this helps\r\n\r\nWhen I run 'dataset = dataset.map(encode, batched=True)',\r\nI encountered a problem like this:\r\n\r\n> Testing the mapped function outputs\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 300, in map\r\n    for k, dataset in self.items()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 300, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1224, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"<stdin>\", line 3, in encode\r\nTypeError: __init__() takes 1 positional argument but 2 were given","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":254,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\n> But finally got it working. This is what I did after looking into the documentation.\r\n> \r\n> 1. split the whole dataset file into smaller files\r\n> \r\n> ```shell\r\n> mkdir .\/shards\r\n> split -a 4 -l 256000 -d full_raw_corpus.txt .\/shards\/shard_\r\n> ```\r\n> \r\n> 1. Pass paths of small data files to `load_dataset`\r\n> \r\n> ```python\r\n> files = glob.glob('shards\/*')\r\n> from datasets import load_dataset\r\n> dataset = load_dataset('text', data_files=files, split='train')\r\n> ```\r\n> \r\n> (On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n> \r\n> 1. Tokenization\r\n> \r\n> ```python\r\n> def encode(examples):\r\n>   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> dataset = dataset.map(encode, batched=True)\r\n> dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n> ```\r\n> \r\n> Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n> \r\n> ```python\r\n> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n> next(iter(dataloader))\r\n> ```\r\n> \r\n> Hope this helps\r\n\r\nWhen I run 'dataset = dataset.map(encode, batched=True)',\r\nI encountered a problem like this:\r\n\r\n> Testing the mapped function outputs\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 300, in map\r\n    for k, dataset in self.items()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 300, in <dictcomp>\r\n    for k, dataset in self.items()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1224, in map\r\n    update_data = does_function_return_dict(test_inputs, test_indices)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n    function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n  File \"<stdin>\", line 3, in encode\r\nTypeError: __init__() takes 1 positional argument but 2 were given","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.3349307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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> > Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\n> > But finally got it working. This is what I did after looking into the documentation.\r\n> > \r\n> > 1. split the whole dataset file into smaller files\r\n> > \r\n> > ```shell\r\n> > mkdir .\/shards\r\n> > split -a 4 -l 256000 -d full_raw_corpus.txt .\/shards\/shard_\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > 1. Pass paths of small data files to `load_dataset`\r\n> > \r\n> > ```python\r\n> > files = glob.glob('shards\/*')\r\n> > from datasets import load_dataset\r\n> > dataset = load_dataset('text', data_files=files, split='train')\r\n> > ```\r\n> > \r\n> > \r\n> > (On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n> > \r\n> > 1. Tokenization\r\n> > \r\n> > ```python\r\n> > def encode(examples):\r\n> >   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > dataset = dataset.map(encode, batched=True)\r\n> > dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n> > ```\r\n> > \r\n> > \r\n> > Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n> > ```python\r\n> > dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n> > next(iter(dataloader))\r\n> > ```\r\n> > \r\n> > \r\n> > Hope this helps\r\n> \r\n> When I run 'dataset = dataset.map(encode, batched=True)',\r\n> I encountered a problem like this:\r\n> \r\n> > Testing the mapped function outputs\r\n> > Traceback (most recent call last):\r\n> > File \"\", line 1, in \r\n> > File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 300, in map\r\n> > for k, dataset in self.items()\r\n> > File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 300, in \r\n> > for k, dataset in self.items()\r\n> > File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1224, in map\r\n> > update_data = does_function_return_dict(test_inputs, test_indices)\r\n> > File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n> > function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n> > File \"\", line 3, in encode\r\n> > TypeError: **init**() takes 1 positional argument but 2 were given\r\n\r\nWhat is your encoder function?","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":331,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > > Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\n> > But finally got it working. This is what I did after looking into the documentation.\r\n> > \r\n> > 1. split the whole dataset file into smaller files\r\n> > \r\n> > ```shell\r\n> > mkdir .\/shards\r\n> > split -a 4 -l 256000 -d full_raw_corpus.txt .\/shards\/shard_\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > 1. Pass paths of small data files to `load_dataset`\r\n> > \r\n> > ```python\r\n> > files = glob.glob('shards\/*')\r\n> > from datasets import load_dataset\r\n> > dataset = load_dataset('text', data_files=files, split='train')\r\n> > ```\r\n> > \r\n> > \r\n> > (On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n> > \r\n> > 1. Tokenization\r\n> > \r\n> > ```python\r\n> > def encode(examples):\r\n> >   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > dataset = dataset.map(encode, batched=True)\r\n> > dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n> > ```\r\n> > \r\n> > \r\n> > Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n> > ```python\r\n> > dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n> > next(iter(dataloader))\r\n> > ```\r\n> > \r\n> > \r\n> > Hope this helps\r\n> \r\n> When I run 'dataset = dataset.map(encode, batched=True)',\r\n> I encountered a problem like this:\r\n> \r\n> > Testing the mapped function outputs\r\n> > Traceback (most recent call last):\r\n> > File \"\", line 1, in \r\n> > File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 300, in map\r\n> > for k, dataset in self.items()\r\n> > File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/dataset_dict.py\", line 300, in \r\n> > for k, dataset in self.items()\r\n> > File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1224, in map\r\n> > update_data = does_function_return_dict(test_inputs, test_indices)\r\n> > File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n> > function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n> > File \"\", line 3, in encode\r\n> > TypeError: **init**() takes 1 positional argument but 2 were given\r\n\r\nWhat is your encoder function?","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.334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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> ```python\r\n> def encode(examples):\r\n>   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> ```\r\n\r\nIt is the same as suggested:\r\n\r\n> def encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":25,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > ```python\r\n> def encode(examples):\r\n>   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> ```\r\n\r\nIt is the same as suggested:\r\n\r\n> def encode(examples):\r\n  return tokenizer(examples['text'], truncation=True, padding='max_length')","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.28487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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> > ```python\r\n> > def encode(examples):\r\n> >   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > ```\r\n> \r\n> It is the same as suggested:\r\n> \r\n> > def encode(examples):\r\n> > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\nDo you use this function in a `class` object?  \r\n\r\ninit() takes 1 positional argument but 2 were given. I guess the additional argument is self?","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":60,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > > ```python\r\n> > def encode(examples):\r\n> >   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > ```\r\n> \r\n> It is the same as suggested:\r\n> \r\n> > def encode(examples):\r\n> > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\nDo you use this function in a `class` object?  \r\n\r\ninit() takes 1 positional argument but 2 were given. I guess the additional argument is 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> > > ```python\r\n> > > def encode(examples):\r\n> > >   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > > ```\r\n> > \r\n> > \r\n> > It is the same as suggested:\r\n> > > def encode(examples):\r\n> > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> \r\n> Do you use this function in a `class` object?\r\n> \r\n> init() takes 1 positional argument but 2 were given. I guess the additional argument is self?\r\n\r\nThanks for your reply.\r\nCould you provide some simple example here?\r\nCurrently, I do not use this function in a class object. \r\nI think you are right and I was wondering how to construct this class.\r\nI try to modify it based on transformers' LineByLineTextDataset. Am I correct?\r\n\r\n> class LineByLineTextDataset(Dataset):\r\n    \"\"\"\r\n    This will be superseded by a framework-agnostic approach\r\n    soon.\r\n    \"\"\"\r\n\r\n    def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):\r\n        assert os.path.isfile(file_path), f\"Input file path {file_path} not found\"\r\n        # Here, we do not cache the features, operating under the assumption\r\n        # that we will soon use fast multithreaded tokenizers from the\r\n        # `tokenizers` repo everywhere =)\r\n        #logger.info(\"Creating features from dataset file at %s\", file_path)\r\n        #with open(file_path, encoding=\"utf-8\") as f:\r\n        #    lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\r\n        #batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)\r\n\r\n\timport glob\r\n\tfiles = glob.glob('\/home\/mtzhang111\/fairseq\/cs_doc\/shards\/shard_003*')\r\n\tfrom datasets import load_dataset\r\n\tdataset = load_dataset('text', data_files=files)\r\n        batch_encoding= dataset.map(encode, batched=True)\r\n        self.examples = batch_encoding[\"input_ids\"]\r\n\t\r\n\r\n    def encode(examples):\r\n        return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\n    def __len__(self):\r\n        return len(self.examples)\r\n\r\n    def __getitem__(self, i) -> torch.Tensor:\r\n        return torch.tensor(self.examples[i], dtype=torch.long)\r\n","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":250,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > > > ```python\r\n> > > def encode(examples):\r\n> > >   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > > ```\r\n> > \r\n> > \r\n> > It is the same as suggested:\r\n> > > def encode(examples):\r\n> > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> \r\n> Do you use this function in a `class` object?\r\n> \r\n> init() takes 1 positional argument but 2 were given. I guess the additional argument is self?\r\n\r\nThanks for your reply.\r\nCould you provide some simple example here?\r\nCurrently, I do not use this function in a class object. \r\nI think you are right and I was wondering how to construct this class.\r\nI try to modify it based on transformers' LineByLineTextDataset. Am I correct?\r\n\r\n> class LineByLineTextDataset(Dataset):\r\n    \"\"\"\r\n    This will be superseded by a framework-agnostic approach\r\n    soon.\r\n    \"\"\"\r\n\r\n    def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):\r\n        assert os.path.isfile(file_path), f\"Input file path {file_path} not found\"\r\n        # Here, we do not cache the features, operating under the assumption\r\n        # that we will soon use fast multithreaded tokenizers from the\r\n        # `tokenizers` repo everywhere =)\r\n        #logger.info(\"Creating features from dataset file at %s\", file_path)\r\n        #with open(file_path, encoding=\"utf-8\") as f:\r\n        #    lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\r\n        #batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)\r\n\r\n\timport glob\r\n\tfiles = glob.glob('\/home\/mtzhang111\/fairseq\/cs_doc\/shards\/shard_003*')\r\n\tfrom datasets import load_dataset\r\n\tdataset = load_dataset('text', data_files=files)\r\n        batch_encoding= dataset.map(encode, batched=True)\r\n        self.examples = batch_encoding[\"input_ids\"]\r\n\t\r\n\r\n    def encode(examples):\r\n        return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\n    def __len__(self):\r\n        return len(self.examples)\r\n\r\n    def __getitem__(self, i) -> torch.Tensor:\r\n        return torch.tensor(self.examples[i], dtype=torch.long)\r\n","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.28487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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> > > > ```python\r\n> > > > def encode(examples):\r\n> > > >   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > > > ```\r\n> > > \r\n> > > \r\n> > > It is the same as suggested:\r\n> > > > def encode(examples):\r\n> > > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > \r\n> > \r\n> > Do you use this function in a `class` object?\r\n> > init() takes 1 positional argument but 2 were given. I guess the additional argument is self?\r\n> \r\n> Thanks for your reply.\r\n> Could you provide some simple example here?\r\n> Currently, I do not use this function in a class object.\r\n> I think you are right and I was wondering how to construct this class.\r\n> I try to modify it based on transformers' LineByLineTextDataset. Am I correct?\r\n> \r\n> > class LineByLineTextDataset(Dataset):\r\n> > \"\"\"\r\n> > This will be superseded by a framework-agnostic approach\r\n> > soon.\r\n> > \"\"\"\r\n> \r\n> ```\r\n> def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):\r\n>     assert os.path.isfile(file_path), f\"Input file path {file_path} not found\"\r\n>     # Here, we do not cache the features, operating under the assumption\r\n>     # that we will soon use fast multithreaded tokenizers from the\r\n>     # `tokenizers` repo everywhere =)\r\n>     #logger.info(\"Creating features from dataset file at %s\", file_path)\r\n>     #with open(file_path, encoding=\"utf-8\") as f:\r\n>     #    lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\r\n>     #batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)\r\n> \r\n> import glob\r\n> files = glob.glob('\/home\/mtzhang111\/fairseq\/cs_doc\/shards\/shard_003*')\r\n> from datasets import load_dataset\r\n> dataset = load_dataset('text', data_files=files)\r\n>     batch_encoding= dataset.map(encode, batched=True)\r\n>     self.examples = batch_encoding[\"input_ids\"]\r\n> \r\n> \r\n> def encode(examples):\r\n>     return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> \r\n> def __len__(self):\r\n>     return len(self.examples)\r\n> \r\n> def __getitem__(self, i) -> torch.Tensor:\r\n>     return torch.tensor(self.examples[i], dtype=torch.long)\r\n> ```\r\n\r\nI am also struggling with this adaptation. \r\nI am not sure whether I am right.\r\n\r\nI think you don't need to construct `class LazyLineByLineTextDataset(Dataset)` at all. \r\ntorch.utils.data.Dataset is a generator. \r\n\r\nNow, we use `dataset = dataset.map(encode, batched=True)` as a generator. So we just pass dataset to torch.utils.data.DataLoader. ","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":357,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > > > > ```python\r\n> > > > def encode(examples):\r\n> > > >   return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > > > ```\r\n> > > \r\n> > > \r\n> > > It is the same as suggested:\r\n> > > > def encode(examples):\r\n> > > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > \r\n> > \r\n> > Do you use this function in a `class` object?\r\n> > init() takes 1 positional argument but 2 were given. I guess the additional argument is self?\r\n> \r\n> Thanks for your reply.\r\n> Could you provide some simple example here?\r\n> Currently, I do not use this function in a class object.\r\n> I think you are right and I was wondering how to construct this class.\r\n> I try to modify it based on transformers' LineByLineTextDataset. Am I correct?\r\n> \r\n> > class LineByLineTextDataset(Dataset):\r\n> > \"\"\"\r\n> > This will be superseded by a framework-agnostic approach\r\n> > soon.\r\n> > \"\"\"\r\n> \r\n> ```\r\n> def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):\r\n>     assert os.path.isfile(file_path), f\"Input file path {file_path} not found\"\r\n>     # Here, we do not cache the features, operating under the assumption\r\n>     # that we will soon use fast multithreaded tokenizers from the\r\n>     # `tokenizers` repo everywhere =)\r\n>     #logger.info(\"Creating features from dataset file at %s\", file_path)\r\n>     #with open(file_path, encoding=\"utf-8\") as f:\r\n>     #    lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\r\n>     #batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)\r\n> \r\n> import glob\r\n> files = glob.glob('\/home\/mtzhang111\/fairseq\/cs_doc\/shards\/shard_003*')\r\n> from datasets import load_dataset\r\n> dataset = load_dataset('text', data_files=files)\r\n>     batch_encoding= dataset.map(encode, batched=True)\r\n>     self.examples = batch_encoding[\"input_ids\"]\r\n> \r\n> \r\n> def encode(examples):\r\n>     return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> \r\n> def __len__(self):\r\n>     return len(self.examples)\r\n> \r\n> def __getitem__(self, i) -> torch.Tensor:\r\n>     return torch.tensor(self.examples[i], dtype=torch.long)\r\n> ```\r\n\r\nI am also struggling with this adaptation. \r\nI am not sure whether I am right.\r\n\r\nI think you don't need to construct `class LazyLineByLineTextDataset(Dataset)` at all. \r\ntorch.utils.data.Dataset is a generator. \r\n\r\nNow, we use `dataset = dataset.map(encode, batched=True)` as a generator. So we just pass dataset to torch.utils.data.DataLoader. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"@chiyuzhang94 Thanks for your reply. After some changes, currently, I managed to make the data loading process running.\r\nI published it in case you might want to take a look. Thanks for your help!\r\nhttps:\/\/github.com\/shizhediao\/Transformers_TPU","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":35,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n @chiyuzhang94 Thanks for your reply. After some changes, currently, I managed to make the data loading process running.\r\nI published it in case you might want to take a look. Thanks for your help!\r\nhttps:\/\/github.com\/shizhediao\/Transformers_TPU","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.169751882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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Hi @shizhediao ,\r\n\r\nThanks! It looks great!\r\n\r\nBut my problem still is the cache directory is a read-only file system. \r\n[As I mentioned](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-693912285), I tried to change the cache directory but it didn't work. \r\n\r\nDo you have any suggestions?\r\n\r\n","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":39,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Hi @shizhediao ,\r\n\r\nThanks! It looks great!\r\n\r\nBut my problem still is the cache directory is a read-only file system. \r\n[As I mentioned](https:\/\/github.com\/huggingface\/datasets\/issues\/610#issuecomment-693912285), I tried to change the cache directory but it didn't work. \r\n\r\nDo you have any suggestions?\r\n\r\n","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.284876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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> I installed datasets at \/project\/chiyuzh\/evn_py36\/datasets\/src where is a writable directory.\r\n> I also tried change the environment variables to the writable directory:\r\n> `export HF_MODULES_PATH=\/project\/chiyuzh\/evn_py36\/datasets\/cache_dir\/`\r\n\r\nI think it is `HF_MODULES_CACHE` and not `HF_MODULES_PATH` @chiyuzhang94 .\r\nCould you try again and let me know if it fixes your issue ?\r\n","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":50,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > I installed datasets at \/project\/chiyuzh\/evn_py36\/datasets\/src where is a writable directory.\r\n> I also tried change the environment variables to the writable directory:\r\n> `export HF_MODULES_PATH=\/project\/chiyuzh\/evn_py36\/datasets\/cache_dir\/`\r\n\r\nI think it is `HF_MODULES_CACHE` and not `HF_MODULES_PATH` @chiyuzhang94 .\r\nCould you try again and let me know if it fixes your issue ?\r\n","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.3349307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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"We should probably add a section in the doc on the caching system with the env variables in particular.","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":19,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n We should probably add a section in the doc on the caching system with the env variables in 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Hi @thomwolf , @lhoestq ,\r\n\r\nThanks for your suggestions. With the latest version of this package, I can load text data without Internet. \r\n\r\nBut I found the speed of dataset loading is very slow. \r\n\r\nMy scrips like this: \r\n```\r\n    def token_encode(examples):\r\n        tokenizer_out = tokenizer(examples['text'], truncation=True,  padding=\"max_length\", add_special_tokens=True, max_length=args.block_size)\r\n        return tokenizer_out\r\n    \r\n    path = Path(file_path)\r\n    files = sorted(path.glob('*'))\r\n    dataset = load_dataset('.\/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n    dataset = dataset.map(token_encode, batched=True)\r\n\r\n    dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n```\r\n\r\nI have 1,123,870,657 lines in my input directory. \r\nI can find the processing speed as following. It is very slow.  \r\n```\r\n| 13\/1123871 [00:02<62:37:39,  4.98ba\/s]^M  0%|   \r\n| 14\/1123871 [00:03<61:27:31,  5.08ba\/s]^M  0%|          \r\n| 15\/1123871 [00:03<66:34:19,  4.69ba\/s]^M  0%|         \r\n| 16\/1123871 [00:03<68:25:01,  4.56ba\/s]^M  0%|          \r\n| 17\/1123871 [00:03<72:00:03,  4.34ba\/s]^M  0%|       \r\n```\r\nDo you have any suggestions to accelerate this loading process?","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":129,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Hi @thomwolf , @lhoestq ,\r\n\r\nThanks for your suggestions. With the latest version of this package, I can load text data without Internet. \r\n\r\nBut I found the speed of dataset loading is very slow. \r\n\r\nMy scrips like this: \r\n```\r\n    def token_encode(examples):\r\n        tokenizer_out = tokenizer(examples['text'], truncation=True,  padding=\"max_length\", add_special_tokens=True, max_length=args.block_size)\r\n        return tokenizer_out\r\n    \r\n    path = Path(file_path)\r\n    files = sorted(path.glob('*'))\r\n    dataset = load_dataset('.\/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n    dataset = dataset.map(token_encode, batched=True)\r\n\r\n    dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n```\r\n\r\nI have 1,123,870,657 lines in my input directory. \r\nI can find the processing speed as following. It is very slow.  \r\n```\r\n| 13\/1123871 [00:02<62:37:39,  4.98ba\/s]^M  0%|   \r\n| 14\/1123871 [00:03<61:27:31,  5.08ba\/s]^M  0%|          \r\n| 15\/1123871 [00:03<66:34:19,  4.69ba\/s]^M  0%|         \r\n| 16\/1123871 [00:03<68:25:01,  4.56ba\/s]^M  0%|          \r\n| 17\/1123871 [00:03<72:00:03,  4.34ba\/s]^M  0%|       \r\n```\r\nDo you have any suggestions to accelerate this loading process?","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.3349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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"You can use multiprocessing by specifying `num_proc=` in `.map()`\r\n\r\nAlso it looks like you have `1123871` batches of 1000 elements (default batch size), i.e. 1,123,871,000 lines in total.\r\nAm I right ?","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":32,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n You can use multiprocessing by specifying `num_proc=` in `.map()`\r\n\r\nAlso it looks like you have `1123871` batches of 1000 elements (default batch size), i.e. 1,123,871,000 lines in total.\r\nAm I right ?","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.3349307179,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> You can use multiprocessing by specifying `num_proc=` in `.map()`\r\n> \r\n> Also it looks like you have `1123871` batches of 1000 elements (default batch size), i.e. 1,123,871,000 lines in total.\r\n> Am I right ?\r\n\r\nHi @lhoestq ,\r\n\r\nThanks. I will try it.\r\n\r\nYou are right. I have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines.\r\n\r\nI have another question. Because I am using a cloud server where only allows running a job up to 7 days. Hence, I need to resume my model every week. If the script needs to load and process the dataset every time. It is very low efficient based on the current processing speed. Is it possible that I process the dataset once and use the process cache to in the future work? \r\n","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":141,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > You can use multiprocessing by specifying `num_proc=` in `.map()`\r\n> \r\n> Also it looks like you have `1123871` batches of 1000 elements (default batch size), i.e. 1,123,871,000 lines in total.\r\n> Am I right ?\r\n\r\nHi @lhoestq ,\r\n\r\nThanks. I will try it.\r\n\r\nYou are right. I have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines.\r\n\r\nI have another question. Because I am using a cloud server where only allows running a job up to 7 days. Hence, I need to resume my model every week. If the script needs to load and process the dataset every time. It is very low efficient based on the current processing speed. Is it possible that I process the dataset once and use the process cache to in the future work? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Hi @lhoestq ,\r\n\r\nI tried to use multi-processor, but I got errors as follow: \r\nBecause I am using python distributed training, it seems some conflicts with the distributed job. \r\n\r\nDo you have any suggestions?\r\n```\r\nI0925 10:19:35.603023 140737353971520 filelock.py:318] Lock 140737229443368 released on \/tmp\/pbs.1120510.pbsha.ib.sockeye\/cache\/_tmp_pbs.1120510.pbsha.ib.sockeye_cache_text_default-7fb934ed6fac5d01_0.0.0_512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7\r\nfcc649178b014.lock\r\nTraceback (most recent call last):\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling.py\", line 1024, in <module>\r\n    main()\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling.py\", line 967, in main\r\n    train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling.py\", line 180, in load_and_cache_examples\r\n    return HG_Datasets(tokenizer, file_path, args)\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling.py\", line 119, in HG_Datasets\r\n    dataset = dataset.map(token_encode, batched=True, batch_size = 10000, num_proc = 16)\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1287, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1287, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/multiprocessing\/pool.py\", line 644, in get\r\n    raise self._value\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/multiprocessing\/pool.py\", line 424, in _handle_tasks\r\n    put(task)\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/multiprocessing\/connection.py\", line 206, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/multiprocessing\/reduction.py\", line 51, in dumps\r\n    cls(buf, protocol).dump(obj)\r\nAttributeError: Can't pickle local object 'HG_Datasets.<locals>.token_encode'\r\n```","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":157,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Hi @lhoestq ,\r\n\r\nI tried to use multi-processor, but I got errors as follow: \r\nBecause I am using python distributed training, it seems some conflicts with the distributed job. \r\n\r\nDo you have any suggestions?\r\n```\r\nI0925 10:19:35.603023 140737353971520 filelock.py:318] Lock 140737229443368 released on \/tmp\/pbs.1120510.pbsha.ib.sockeye\/cache\/_tmp_pbs.1120510.pbsha.ib.sockeye_cache_text_default-7fb934ed6fac5d01_0.0.0_512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7\r\nfcc649178b014.lock\r\nTraceback (most recent call last):\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling.py\", line 1024, in <module>\r\n    main()\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling.py\", line 967, in main\r\n    train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling.py\", line 180, in load_and_cache_examples\r\n    return HG_Datasets(tokenizer, file_path, args)\r\n  File \"\/scratch\/chiyuzh\/roberta\/run_language_modeling.py\", line 119, in HG_Datasets\r\n    dataset = dataset.map(token_encode, batched=True, batch_size = 10000, num_proc = 16)\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1287, in map\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/site-packages\/datasets\/arrow_dataset.py\", line 1287, in <listcomp>\r\n    transformed_shards = [r.get() for r in results]\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/multiprocessing\/pool.py\", line 644, in get\r\n    raise self._value\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/multiprocessing\/pool.py\", line 424, in _handle_tasks\r\n    put(task)\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/multiprocessing\/connection.py\", line 206, in send\r\n    self._send_bytes(_ForkingPickler.dumps(obj))\r\n  File \"\/project\/chiyuzh\/evn_py36\/lib\/python3.6\/multiprocessing\/reduction.py\", line 51, in dumps\r\n    cls(buf, protocol).dump(obj)\r\nAttributeError: Can't pickle local object 'HG_Datasets.<locals>.token_encode'\r\n```","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"For multiprocessing, the function given to `map` must be picklable.\r\nMaybe you could try to define `token_encode` outside `HG_Datasets` ?\r\n\r\nAlso maybe #656 could make functions defined locally picklable for multiprocessing, once it's merged.","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":34,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n For multiprocessing, the function given to `map` must be picklable.\r\nMaybe you could try to define `token_encode` outside `HG_Datasets` ?\r\n\r\nAlso maybe #656 could make functions defined locally picklable for multiprocessing, once it's merged.","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.33493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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> I have another question. Because I am using a cloud server where only allows running a job up to 7 days. Hence, I need to resume my model every week. If the script needs to load and process the dataset every time. It is very low efficient based on the current processing speed. Is it possible that I process the dataset once and use the process cache to in the future work?\r\n\r\nFeel free to save your processed dataset using `dataset.save_to_disk(\"path\/to\/save\/directory\")`.\r\n\r\nThen you'll be able to reload it again using\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset = load_from_disk(\"path\/to\/save\/directory\")\r\n```","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":100,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > I have another question. Because I am using a cloud server where only allows running a job up to 7 days. Hence, I need to resume my model every week. If the script needs to load and process the dataset every time. It is very low efficient based on the current processing speed. Is it possible that I process the dataset once and use the process cache to in the future work?\r\n\r\nFeel free to save your processed dataset using `dataset.save_to_disk(\"path\/to\/save\/directory\")`.\r\n\r\nThen you'll be able to reload it again using\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset = load_from_disk(\"path\/to\/save\/directory\")\r\n```","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.14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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Hi @lhoestq ,\r\n\r\nThanks for your suggestion. \r\nI tried to process the dataset and save it to disk. \r\nI have 1.12B samples in the raw dataset. I used 16 processors.\r\nI run this process job for 7 days. But it didn't finish. I don't why the processing is such slow. \r\n\r\nThe log shows that some processors (\\#12, \\#14, \\#15) are very slow. The different processor has a different speed. These slow processors look like a bottleneck. \r\n\r\nCould you please give me any suggestion to improve the processing speed? \r\n\r\nThanks. \r\nChiyu\r\n\r\nHere is my code:\r\n```\r\ndef token_encode(examples):\r\n        tokenizer_out = tokenizer(examples['text'], truncation=True,  padding=\"max_length\", add_special_tokens=True, max_length=args.block_size)\r\n        return tokenizer_out\r\n\r\npath = Path(file_path)\r\nfiles = sorted(path.glob('*'))\r\ndataset = load_dataset('.\/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\ndataset = dataset.map(token_encode, batched=True, batch_size = 16384, num_proc = 16)\r\ndataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\ndataset.save_to_disk(output_dir)\r\n```\r\nHere is the log. \r\n```\r\n^M#6:   1%|\u258f         | 59\/4288 [55:10<66:11:58, 56.35s\/ba]\r\n^M#1:   8%|\u258a         | 356\/4288 [55:39<10:40:02,  9.77s\/ba]\r\n^M#2:   5%|\u258d         | 210\/4288 [55:33<17:47:19, 15.70s\/ba]\r\n^M#0:  19%|\u2588\u2589        | 836\/4288 [55:53<4:08:56,  4.33s\/ba]\r\n^M#0:  20%|\u2588\u2589        | 837\/4288 [55:57<4:01:52,  4.21s\/ba]\r\n^M#1:   8%|\u258a         | 357\/4288 [55:48<10:38:09,  9.74s\/ba]\r\n^M#0:  20%|\u2588\u2589        | 838\/4288 [56:01<4:02:56,  4.23s\/ba]\r\n^M#3:   4%|\u258e         | 155\/4288 [55:43<24:41:20, 21.51s\/ba]\r\n^M#0:  20%|\u2588\u2589        | 839\/4288 [56:05<4:04:48,  4.26s\/ba]\r\n^M#12:   1%|          | 29\/4288 [54:50<133:20:53, 112.72s\/ba]\r\n^M#2:   5%|\u258d         | 211\/4288 [55:48<17:40:33, 15.61s\/ba]\r\n^M#14:   0%|          | 2\/4288 [04:24<157:17:50, 132.12s\/ba]\r\n^M#15:   0%|          | 1\/4288 [02:24<172:11:37, 144.60s\/ba]\r\n```","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":219,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Hi @lhoestq ,\r\n\r\nThanks for your suggestion. \r\nI tried to process the dataset and save it to disk. \r\nI have 1.12B samples in the raw dataset. I used 16 processors.\r\nI run this process job for 7 days. But it didn't finish. I don't why the processing is such slow. \r\n\r\nThe log shows that some processors (\\#12, \\#14, \\#15) are very slow. The different processor has a different speed. These slow processors look like a bottleneck. \r\n\r\nCould you please give me any suggestion to improve the processing speed? \r\n\r\nThanks. \r\nChiyu\r\n\r\nHere is my code:\r\n```\r\ndef token_encode(examples):\r\n        tokenizer_out = tokenizer(examples['text'], truncation=True,  padding=\"max_length\", add_special_tokens=True, max_length=args.block_size)\r\n        return tokenizer_out\r\n\r\npath = Path(file_path)\r\nfiles = sorted(path.glob('*'))\r\ndataset = load_dataset('.\/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\ndataset = dataset.map(token_encode, batched=True, batch_size = 16384, num_proc = 16)\r\ndataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\ndataset.save_to_disk(output_dir)\r\n```\r\nHere is the log. \r\n```\r\n^M#6:   1%|\u258f         | 59\/4288 [55:10<66:11:58, 56.35s\/ba]\r\n^M#1:   8%|\u258a         | 356\/4288 [55:39<10:40:02,  9.77s\/ba]\r\n^M#2:   5%|\u258d         | 210\/4288 [55:33<17:47:19, 15.70s\/ba]\r\n^M#0:  19%|\u2588\u2589        | 836\/4288 [55:53<4:08:56,  4.33s\/ba]\r\n^M#0:  20%|\u2588\u2589        | 837\/4288 [55:57<4:01:52,  4.21s\/ba]\r\n^M#1:   8%|\u258a         | 357\/4288 [55:48<10:38:09,  9.74s\/ba]\r\n^M#0:  20%|\u2588\u2589        | 838\/4288 [56:01<4:02:56,  4.23s\/ba]\r\n^M#3:   4%|\u258e         | 155\/4288 [55:43<24:41:20, 21.51s\/ba]\r\n^M#0:  20%|\u2588\u2589        | 839\/4288 [56:05<4:04:48,  4.26s\/ba]\r\n^M#12:   1%|          | 29\/4288 [54:50<133:20:53, 112.72s\/ba]\r\n^M#2:   5%|\u258d         | 211\/4288 [55:48<17:40:33, 15.61s\/ba]\r\n^M#14:   0%|          | 2\/4288 [04:24<157:17:50, 132.12s\/ba]\r\n^M#15:   0%|          | 1\/4288 [02:24<172:11:37, 144.60s\/ba]\r\n```","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Hi !\r\n\r\nAs far as I can tell, there could be several reasons for your processes to have different speeds:\r\n- some parts of your dataset have short passages while some have longer passages, that take more time to be processed\r\n- OR there are other processes running that prevent some of them to run at full speed\r\n- OR the value of `num_proc` is higher than the number of actual processes that you can run in parallel at full speed.\r\n\r\nSo I'd suggest you to check that you have nothing else running in parallel to your processing job, and also maybe take a look at the slow parts of the datasets.\r\nWhen doing multiprocessing, the dataset is sharded in `num_proc` contiguous parts that are processed individually in each process. If you want to take a look at the dataset processed in the 12th shard of 16 for example, you can do:\r\n\r\n```python\r\nmy_shard = dataset.shard(num_shards=16, index=12, contiguous=True)\r\n```\r\n\r\nHope this helps, let me know if you find what is causing this slow down.","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":174,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Hi !\r\n\r\nAs far as I can tell, there could be several reasons for your processes to have different speeds:\r\n- some parts of your dataset have short passages while some have longer passages, that take more time to be processed\r\n- OR there are other processes running that prevent some of them to run at full speed\r\n- OR the value of `num_proc` is higher than the number of actual processes that you can run in parallel at full speed.\r\n\r\nSo I'd suggest you to check that you have nothing else running in parallel to your processing job, and also maybe take a look at the slow parts of the datasets.\r\nWhen doing multiprocessing, the dataset is sharded in `num_proc` contiguous parts that are processed individually in each process. If you want to take a look at the dataset processed in the 12th shard of 16 for example, you can do:\r\n\r\n```python\r\nmy_shard = dataset.shard(num_shards=16, index=12, contiguous=True)\r\n```\r\n\r\nHope this helps, let me know if you find what is causing this slow down.","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.3349307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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> Do you use a fast or a slow tokenizer from the `transformers` library @chiyuzhang94?\r\n\r\nHi @thomwolf ,\r\n I use this: \r\n```\r\nfrom transformers import\r\nAutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)\r\n```\r\n\r\nI guess this is a slow one, let me explore the fast tokenizer. ","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":41,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > Do you use a fast or a slow tokenizer from the `transformers` library @chiyuzhang94?\r\n\r\nHi @thomwolf ,\r\n I use this: \r\n```\r\nfrom transformers import\r\nAutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)\r\n```\r\n\r\nI guess this is a slow one, let me explore the fast tokenizer. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"> Hi !\r\n> \r\n> As far as I can tell, there could be several reasons for your processes to have different speeds:\r\n> \r\n> * some parts of your dataset have short passages while some have longer passages, that take more time to be processed\r\n> * OR there are other processes running that prevent some of them to run at full speed\r\n> * OR the value of `num_proc` is higher than the number of actual processes that you can run in parallel at full speed.\r\n> \r\n> So I'd suggest you to check that you have nothing else running in parallel to your processing job, and also maybe take a look at the slow parts of the datasets.\r\n> When doing multiprocessing, the dataset is sharded in `num_proc` contiguous parts that are processed individually in each process. If you want to take a look at the dataset processed in the 12th shard of 16 for example, you can do:\r\n> \r\n> ```python\r\n> my_shard = dataset.shard(num_shards=16, index=12, contiguous=True)\r\n> ```\r\n> \r\n> Hope this helps, let me know if you find what is causing this slow down.\r\n\r\nHi @lhoestq ,\r\n\r\nThanks for your suggestions. \r\nI don't think my problem is due to any one of these seasons. \r\n\r\n1.  I have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines. The last file is smaller a little bit. But they are similar. I randomly shuffled all the 1,123,870,657 lines. Hence, the sequences should also be similar across all the files. \r\n\r\n2. I run this script on the entire node. I requested all the resources on the nodes (40 CPUs, 384GB memory). Hence, these were not any other processes. \r\n\r\n 3. As I say, the node has 40 CPUs, but I set num_proc = 16. This should not be a problem.","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":312,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n > Hi !\r\n> \r\n> As far as I can tell, there could be several reasons for your processes to have different speeds:\r\n> \r\n> * some parts of your dataset have short passages while some have longer passages, that take more time to be processed\r\n> * OR there are other processes running that prevent some of them to run at full speed\r\n> * OR the value of `num_proc` is higher than the number of actual processes that you can run in parallel at full speed.\r\n> \r\n> So I'd suggest you to check that you have nothing else running in parallel to your processing job, and also maybe take a look at the slow parts of the datasets.\r\n> When doing multiprocessing, the dataset is sharded in `num_proc` contiguous parts that are processed individually in each process. If you want to take a look at the dataset processed in the 12th shard of 16 for example, you can do:\r\n> \r\n> ```python\r\n> my_shard = dataset.shard(num_shards=16, index=12, contiguous=True)\r\n> ```\r\n> \r\n> Hope this helps, let me know if you find what is causing this slow down.\r\n\r\nHi @lhoestq ,\r\n\r\nThanks for your suggestions. \r\nI don't think my problem is due to any one of these seasons. \r\n\r\n1.  I have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines. The last file is smaller a little bit. But they are similar. I randomly shuffled all the 1,123,870,657 lines. Hence, the sequences should also be similar across all the files. \r\n\r\n2. I run this script on the entire node. I requested all the resources on the nodes (40 CPUs, 384GB memory). Hence, these were not any other processes. \r\n\r\n 3. As I say, the node has 40 CPUs, but I set num_proc = 16. This should not be a problem.","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.3349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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/610","title":"Load text file for RoBERTa pre-training. ","comments":"Hi @thomwolf \r\nI am using `RobertaTokenizerFast` now. \r\n\r\nBut the speed is still imbalanced, some processors are still slow. \r\nHere is the part of the log. #0 is always much fast than lower rank processors. \r\n\r\n```\r\n#15:   3%|\u258e         | 115\/3513 [3:18:36<98:01:33, 103.85s\/ba]\r\n#2:  24%|\u2588\u2588\u258d       | 847\/3513 [3:20:43<11:06:49, 15.01s\/ba]\r\n#1:  37%|\u2588\u2588\u2588\u258b      | 1287\/3513 [3:20:52<6:19:02, 10.22s\/ba]\r\n#0:  72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  | 2546\/3513 [3:20:52<1:51:03,  6.89s\/ba]\r\n#3:  18%|\u2588\u258a        | 617\/3513 [3:20:36<15:50:30, 19.69s\/ba]\r\n#0:  73%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e  | 2547\/3513 [3:20:59<1:50:25,  6.86s\/ba]\r\n#1:  37%|\u2588\u2588\u2588\u258b      | 1288\/3513 [3:21:02<6:21:13, 10.28s\/ba]\r\n#7:   7%|\u258b         | 252\/3513 [3:20:09<44:09:03, 48.74s\/ba]\r\n#12:   4%|\u258d         | 144\/3513 [3:19:19<78:00:54, 83.36s\/ba]\r\n#4:  14%|\u2588\u258d        | 494\/3513 [3:20:37<20:46:06, 24.77s\/ba]\r\n#0:  73%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e  | 2548\/3513 [3:21:06<1:49:26,  6.80s\/ba]\r\n#2:  24%|\u2588\u2588\u258d       | 848\/3513 [3:20:58<11:06:17, 15.00s\/ba]\r\n```\r\nHere is my script related to the datasets processing, \r\n\r\n```\r\ntokenizer = RobertaTokenizerFast.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)\r\n\r\ndef token_encode(examples):\r\n    tokenizer_out = tokenizer(examples['text'], truncation=True,  padding=\"max_length\", add_special_tokens=True, max_length=128)\r\n    return tokenizer_out\r\n\r\ndef HG_Datasets(tokenizer, file_path, args):\r\n    \r\n    path = Path(file_path)\r\n    files = sorted(path.glob('*'))\r\n    dataset = load_dataset('.\/text.py', data_files=files, cache_dir = \"\".\/, split=\"train\")\r\n    dataset = dataset.map(token_encode, batched=True, batch_size = 20000, num_proc = 16)\r\n\r\n    dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n    return dataset\r\n\r\n```\r\nI have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines.\r\n\r\nCould you please give any suggestion? Thanks very much!!","body":"I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1","comment_length":198,"text":"Load text file for RoBERTa pre-training.  \n I migrate my question from https:\/\/github.com\/huggingface\/transformers\/pull\/4009#issuecomment-690039444\r\n\r\nI tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. \r\nAccording to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence.\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\".\/\")\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut dataload cannot yield sample and error is:\r\n```\r\n---------------------------------------------------------------------------\r\nKeyError                                  Traceback (most recent call last)\r\n<ipython-input-12-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in __next__(self)\r\n    361 \r\n    362     def __next__(self):\r\n--> 363         data = self._next_data()\r\n    364         self._num_yielded += 1\r\n    365         if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/dataloader.py in _next_data(self)\r\n    401     def _next_data(self):\r\n    402         index = self._next_index()  # may raise StopIteration\r\n--> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n    404         if self._pin_memory:\r\n    405             data = _utils.pin_memory.pin_memory(data)\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in fetch(self, possibly_batched_index)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\n\/Library\/Python\/3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py in <listcomp>(.0)\r\n     42     def fetch(self, possibly_batched_index):\r\n     43         if self.auto_collation:\r\n---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]\r\n     45         else:\r\n     46             data = self.dataset[possibly_batched_index]\r\n\r\nKeyError: 0\r\n```\r\n\r\n`dataset.set_format(type='torch',columns=[\"text\"])` returns a log says:\r\n```\r\nSet __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```\r\n\r\nI noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`.\r\nEach sample can be accessed by `dataset[\"train\"][\"text\"]` instead of `dataset[\"text\"]`. \r\n\r\nCould you please give me any suggestions on how to modify this code to load the text file?\r\n\r\nVersions:\r\nPython version 3.7.3\r\nPyTorch version 1.6.0 \r\nTensorFlow version 2.3.0 \r\ndatasets version: 1.0.1 \n Hi @thomwolf \r\nI am using `RobertaTokenizerFast` now. \r\n\r\nBut the speed is still imbalanced, some processors are still slow. \r\nHere is the part of the log. #0 is always much fast than lower rank processors. \r\n\r\n```\r\n#15:   3%|\u258e         | 115\/3513 [3:18:36<98:01:33, 103.85s\/ba]\r\n#2:  24%|\u2588\u2588\u258d       | 847\/3513 [3:20:43<11:06:49, 15.01s\/ba]\r\n#1:  37%|\u2588\u2588\u2588\u258b      | 1287\/3513 [3:20:52<6:19:02, 10.22s\/ba]\r\n#0:  72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  | 2546\/3513 [3:20:52<1:51:03,  6.89s\/ba]\r\n#3:  18%|\u2588\u258a        | 617\/3513 [3:20:36<15:50:30, 19.69s\/ba]\r\n#0:  73%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e  | 2547\/3513 [3:20:59<1:50:25,  6.86s\/ba]\r\n#1:  37%|\u2588\u2588\u2588\u258b      | 1288\/3513 [3:21:02<6:21:13, 10.28s\/ba]\r\n#7:   7%|\u258b         | 252\/3513 [3:20:09<44:09:03, 48.74s\/ba]\r\n#12:   4%|\u258d         | 144\/3513 [3:19:19<78:00:54, 83.36s\/ba]\r\n#4:  14%|\u2588\u258d        | 494\/3513 [3:20:37<20:46:06, 24.77s\/ba]\r\n#0:  73%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e  | 2548\/3513 [3:21:06<1:49:26,  6.80s\/ba]\r\n#2:  24%|\u2588\u2588\u258d       | 848\/3513 [3:20:58<11:06:17, 15.00s\/ba]\r\n```\r\nHere is my script related to the datasets processing, \r\n\r\n```\r\ntokenizer = RobertaTokenizerFast.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)\r\n\r\ndef token_encode(examples):\r\n    tokenizer_out = tokenizer(examples['text'], truncation=True,  padding=\"max_length\", add_special_tokens=True, max_length=128)\r\n    return tokenizer_out\r\n\r\ndef HG_Datasets(tokenizer, file_path, args):\r\n    \r\n    path = Path(file_path)\r\n    files = sorted(path.glob('*'))\r\n    dataset = load_dataset('.\/text.py', data_files=files, cache_dir = \"\".\/, split=\"train\")\r\n    dataset = dataset.map(token_encode, batched=True, batch_size = 20000, num_proc = 16)\r\n\r\n    dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n    return dataset\r\n\r\n```\r\nI have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines.\r\n\r\nCould you please give any suggestion? Thanks very much!!","embeddings":[-0.2351640612,-0.2028229982,-0.0119608929,0.408447057,0.3831166625,-0.1324438751,0.5139924288,0.4229528308,-0.1767195165,0.0487692058,-0.2432833612,0.1370509565,-0.144555673,-0.389241904,-0.0388365388,-0.3020621836,-0.1219130456,0.1924059838,-0.4711352587,-0.0367562771,0.1829230189,0.0783707425,-0.137515828,0.1973749697,-0.6427964568,0.1069567874,0.2162100375,0.2214321196,-0.0749238133,-0.3002363741,0.2062293738,0.0434491038,0.5629116297,0.7953851223,-0.0001255157,0.1221125871,0.248756811,-0.2410901189,-0.312382251,-0.1114152744,0.4535882175,-0.2304576486,0.1303864568,-0.3562261164,0.0759145692,-0.1436130852,0.0986075252,-0.0940504894,0.5390230417,0.3707350791,0.0733966455,0.4406839907,-0.0109579563,-0.0613048933,0.3599255383,0.1098705828,-0.0299898889,0.0779794678,0.3455821574,-0.2738672793,-0.4754483998,0.1371811926,-0.1577266455,0.0799182877,-0.056934163,0.127282083,0.1293712556,-0.0320416205,0.0433062501,0.3101655841,0.5464712381,-0.1133196056,-0.3787391484,-0.3641907275,-0.1538963616,-0.1453783661,0.3427860439,0.0990873426,-0.2295717299,0.1603964567,-0.1188358143,0.0387966447,-0.4248600304,0.0132333254,-0.16641289,0.3269487023,-0.1371064335,0.0592869185,0.3322789371,-0.0902749598,0.2790078521,0.0059216856,0.1468862295,0.3295742571,-0.3641236424,-0.0306546167,-0.1202296764,-0.397066474,0.2847504616,0.059628021,0.2434100658,0.1548983753,-0.1783426106,-0.0348099098,0.2422806919,0.1821570992,-0.180077076,0.2752799094,0.2457235157,0.3165408075,-0.1399267316,-0.140717715,-0.4243340492,-0.2287802696,0.0969789028,-0.1354229897,0.1783904135,-0.1036362052,0.0023347768,0.0298269074,-0.0968155116,-0.1359277219,0.1534668803,0.396576494,-0.1335892081,0.3401686251,0.156048581,0.0891145095,-0.2729740441,-0.2191158235,-0.0154704899,-0.2309262753,-0.2201966196,0.1377543509,0.2425263524,-0.0458625555,0.2399176359,-0.3079228699,0.2493965626,-0.158296898,-0.1100429296,-0.3570912778,0.0135691743,0.2636014819,-0.1435478032,0.2023341358,0.1805712581,-0.0803246349,-0.0784907416,0.3265265822,-0.1811527014,-0.4468552768,0.1354891807,0.0617074184,-0.1038749516,-0.0352106765,-0.0966458619,0.0569574088,0.4921211302,-0.319745928,-0.0241774004,-0.1626605541,-0.1030522734,0.0550209507,0.2268247902,0.5365388989,-0.1438212693,0.020377133,-0.0253133215,-0.0516009592,0.1682346612,0.6376840472,-0.2747702599,0.5127100945,-0.136062175,0.1061270759,0.2240159214,-0.0344659239,-0.3717764914,0.3262194693,-0.0751044452,0.0406257249,0.2201387882,-0.0307628587,0.052055411,-0.0443849154,0.2690665424,0.2103522122,0.1113788635,0.1413730085,-0.1208721325,-0.1993711889,-0.0637961626,0.5253330469,0.2333912998,0.0096206898,-0.2704213262,0.0675348863,0.2778604031,-0.2674022317,0.1534748375,0.1924356371,-0.2048864365,0.2004879117,0.1048062071,-0.1451075971,-0.1432375461,0.0395660326,0.0148979388,-0.0170760285,-0.1478383094,0.1222516596,-0.2059447616,0.073311761,-0.1966186017,0.0283276383,-0.0339356661,-0.0356443822,-0.096428737,-0.1215626895,-0.2722823322,0.2359145582,-0.1684353948,0.1812613457,-0.2048251629,0.1474659592,-0.0961617008,-0.2596963048,-0.0846058205,0.1086725593,-0.1160761043,-0.0557811707,-0.1087903157,0.1681584567,-0.0896209404,-0.1042771265,-0.1853775233,0.0577392504,0.1356106848,-0.2802486122,0.1246793121,0.2198598534,0.1722252071,0.012317379,-0.1304285973,0.239719674,-0.0239515845,0.1762232035,0.1257120371,-0.1457850486,0.2596863806,-0.1022094563,-0.1274696141,0.1805504411,0.4158622921,0.2873030007,0.2258876562,0.0142894555,-0.1222361177,-0.5006818771,0.274925977,-0.1510628462,0.0672706962,0.2920909226,-0.377589047,0.0398272388,-0.2509226203,-0.2540422976,0.3311235607,0.0367435068,-0.0564246066,-0.0796815231,0.0952707604,-0.1589733064,0.1148993522,0.2903901637,0.0871184096,0.4159710407,0.0195508339,-0.01286125,-0.2850967646,-0.2793557644,-0.0324503444,0.0772638097,-0.2523705363,0.3918679953,0.181742996,0.1658732146,-0.3981604576,-0.0665213987,-0.0594438277,0.1600899696,-0.0328797214,0.2245890498,-0.1531229019,0.1375098228,0.2761565447,0.2386553884,0.531009078,-0.3450524807,-0.0228330046,-0.2657009661,-0.1711072922,-0.0281735435,0.1564720124,-0.3800223768,-0.0227340013,-0.1593811363,0.0414373428,-0.0410522707,-0.2128789574,0.1461291462,0.095018141,-0.0657052845,-0.0873159245,0.3188772202,0.2235686481,-0.2584017813,0.165287137,-0.3514517248,-0.0836746767,0.1345466673,-0.0258615669,-0.0039209398,0.1339139193,-0.4912370741,-0.0777524337,-0.4011791348,0.206659466,0.2536621094,0.1190750003,0.2927311957,0.3777467906,0.2591692209,-0.1536995769,0.2036137134,0.0069752932,-0.2739513814,0.6016744375,0.037279278,-0.3855601549,-0.273537755,-0.1876644045,-0.0212981012,0.1292121708,-0.501755774,0.2092529982,-0.1055911705,-0.0392211415,-0.2687577605,0.2106827796,0.3517278433,-0.0582844391,0.0220018271,0.0811398253,-0.1444359869,0.0589835458,0.0612709299,0.2923367918,-0.2327822298,0.0906195641,-0.1289009154,0.7936124206,0.0342532881,-0.0950503871,0.1772329956,0.0900984257,0.1697518826,-0.1435037255,-0.1156521142,-0.2848767042,-0.334930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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/600","title":"Pickling error when loading dataset","comments":"I wasn't able to reproduce on google colab (python 3.6.9 as well) with \r\n\r\npickle==4.0\r\ndill=0.3.2\r\ntransformers==3.1.0\r\ndatasets=1.0.1 (also tried nlp 0.4.0)\r\n\r\nIf I try\r\n\r\n```python\r\nfrom datasets import load_dataset  # or from nlp\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=512), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nIt runs without error","body":"Hi,\r\n\r\nI modified line 136 in the original [run_language_modeling.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py) as:\r\n\r\n```\r\n# line 136: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with transformers (3.1.0) and nlp (0.4.0), I get the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"src\/run_language_modeling.py\", line 319, in <module>\r\n    main()\r\n  File \"src\/run_language_modeling.py\", line 248, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"src\/run_language_modeling.py\", line 139, in get_dataset\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True)\r\n  File \"\/data\/nlp\/src\/nlp\/arrow_dataset.py\", line 1136, in map\r\n    new_fingerprint=new_fingerprint,\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 158, in wrapper\r\n    self._fingerprint, transform, kwargs_for_fingerprint\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 105, in update_fingerprint\r\n    hasher.update(transform_args[key])\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/data\/nlp\/src\/nlp\/utils\/py_utils.py\", line 362, in dumps\r\n    dump(obj, file)\r\n  File \"\/data\/nlp\/src\/nlp\/utils\/py_utils.py\", line 339, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n```","comment_length":61,"text":"Pickling error when loading dataset \n Hi,\r\n\r\nI modified line 136 in the original [run_language_modeling.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py) as:\r\n\r\n```\r\n# line 136: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with transformers (3.1.0) and nlp (0.4.0), I get the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"src\/run_language_modeling.py\", line 319, in <module>\r\n    main()\r\n  File \"src\/run_language_modeling.py\", line 248, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"src\/run_language_modeling.py\", line 139, in get_dataset\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True)\r\n  File \"\/data\/nlp\/src\/nlp\/arrow_dataset.py\", line 1136, in map\r\n    new_fingerprint=new_fingerprint,\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 158, in wrapper\r\n    self._fingerprint, transform, kwargs_for_fingerprint\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 105, in update_fingerprint\r\n    hasher.update(transform_args[key])\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/data\/nlp\/src\/nlp\/utils\/py_utils.py\", line 362, in dumps\r\n    dump(obj, file)\r\n  File \"\/data\/nlp\/src\/nlp\/utils\/py_utils.py\", line 339, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n``` \n I wasn't able to reproduce on google colab (python 3.6.9 as well) with \r\n\r\npickle==4.0\r\ndill=0.3.2\r\ntransformers==3.1.0\r\ndatasets=1.0.1 (also tried nlp 0.4.0)\r\n\r\nIf I try\r\n\r\n```python\r\nfrom datasets import load_dataset  # or from nlp\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=512), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nIt runs without error","embeddings":[-0.2029621452,-0.1918641627,0.1510746926,0.2650444508,0.1417573392,-0.1736165881,0.2733694315,0.3465311229,0.0597782321,-0.0787867382,0.0942319855,0.3698832691,-0.2363304645,0.0360416137,0.0861706808,-0.4138222039,-0.0133403046,0.1228954196,-0.172864154,0.0111276107,-0.1992149353,0.319937855,-0.2243003845,0.1471229792,-0.505433321,-0.0055762944,0.075616017,0.1886179298,-0.0382539779,-0.3906199336,0.357863754,-0.0553878881,0.2542710304,0.4166387916,-0.0001185715,0.1009830087,0.4450067282,-0.1613719016,-0.2186082751,-0.5004016757,0.3419595361,-0.1968306452,0.1952064484,-0.119422853,-0.138964057,0.0235723462,0.0966473296,0.0984587669,0.3546349704,0.2370629162,0.2198283076,0.7399469614,0.0196053255,-0.004001346,-0.1397824585,0.3455076218,-0.0702364743,0.1682084203,0.0740607306,0.0049477555,-0.250533253,0.2688044012,0.0097407419,-0.1106548905,0.0340497196,0.0430935025,-0.0089143366,-0.0071578994,0.2315981686,0.1808640808,0.3163917959,-0.1332210153,-0.4018171132,-0.4381490052,-0.0321017392,-0.2538236082,0.3569175601,0.0049056802,-0.1719962656,-0.0149831194,-0.1687421799,-0.2313759178,0.0281378422,0.3235939145,0.1266870797,0.6207981706,-0.0286829732,0.2285003811,0.3370732367,-0.188007012,0.1313721091,-0.0277588796,0.0349624828,0.4023726285,-0.238850221,-0.1197175607,0.0664065331,-0.1022803336,0.040619906,0.0130149191,-0.3214137554,0.0603422858,-0.2768704593,-0.0223208498,0.2950415611,0.2889094651,0.0581622198,0.5016816258,-0.0262932908,-0.1388157159,0.2159735262,0.0168195665,-0.124156177,-0.281819135,-0.1952973008,0.0977016166,0.0427054726,-0.2523407638,-0.0277266987,-0.243488133,-0.0807851404,0.0121760387,0.1265031397,0.482233882,0.0803427994,0.1158533469,0.0164868422,0.1796439886,-0.4184333384,-0.2533637583,-0.1892677546,0.022043325,-0.3372351229,-0.1492154151,0.2223524898,0.1535892189,0.3354985416,-0.1102682278,-0.0604240373,0.1364385486,-0.0179690253,-0.1492116302,-0.0685894415,0.2175128609,-0.2264653891,0.1727866828,0.2571268976,-0.1251242161,-0.2969128489,0.1636125892,-0.0111897727,-0.2530519068,-0.1209304109,0.0851275399,-0.1824117154,-0.2599703074,-0.3591947556,0.2333444357,0.3632029891,-0.263068974,-0.1501649469,-0.3836107254,-0.1859427094,-0.1210468039,0.1095169187,0.6124694347,-0.1049165651,-0.4261669219,-0.1083565578,-0.1307385713,0.386395663,0.4650295377,-0.4532147944,0.153607592,-0.2186063975,0.5694765449,0.383651495,-0.1736365557,-0.2988277078,0.2628951669,-0.2244315892,0.2072925121,-0.0707563609,-0.1237978861,-0.1637935787,0.0578438863,-0.1647842675,0.3448475599,0.1585469395,0.2666162848,-0.2812247872,-0.2925919592,0.4360112846,0.2920269966,0.216148749,0.1050296426,-0.1254457384,0.3208654523,0.2130340934,-0.2217202485,0.191267103,0.0267760381,0.0222752038,-0.1574062705,-0.032853514,0.0872845724,-0.6515653729,0.1748918146,-0.2471687347,0.3482395709,-0.3800010085,0.1222224906,-0.0670937449,-0.1169426218,-0.3418495059,-0.2125000358,0.0444627553,0.183524698,-0.0876024067,-0.0400484204,-0.0541984327,0.0558037721,0.0123563483,0.1081894338,-0.4298703969,0.1635243893,-0.1746212542,-0.3540358245,-0.0586851798,0.2108436823,0.2123282403,-0.2704420388,-0.2660779059,0.2112212032,0.0572887398,0.2215671539,0.0190088879,-0.00503748,0.1972181201,-0.192833066,0.0177532136,0.3558892608,0.0182690863,0.0179571509,0.0842206031,0.3661319017,0.0188044794,0.0989182368,0.1062778458,0.0629072785,0.1784609258,-0.0767172575,0.163967669,-0.1630000323,0.300399065,0.0798098519,0.4254424274,0.0218044408,-0.336406678,-0.2099469602,0.4499590993,0.0788158178,0.1665172726,0.3088004887,-0.0811552033,-0.1771025658,-0.0583941601,0.0550881997,0.2792567611,0.0804542229,-0.0513546728,0.0372813307,-0.0395417921,-0.1005213484,0.1776282787,0.101741232,-0.0386786871,0.3335563242,-0.0069130347,0.0972452164,-0.4322930276,-0.2849202156,0.1184080318,0.3154613972,-0.2130566686,0.1573490947,-0.1570317745,-0.0666516423,-0.4013566077,-0.2381341457,-0.4762080014,-0.1907107383,-0.2355116904,0.1614567786,-0.1278426498,0.2946344018,-0.0560883917,0.0913452059,0.0177859832,-0.0834005475,-0.1864178628,-0.2087877989,-0.4619050622,-0.0359911546,0.2449781895,0.021026792,0.1043828949,0.123727344,-0.0020178452,0.1463040262,-0.3587393761,0.2713183761,0.0121457307,0.187657088,0.1870482713,0.0780553222,-0.1918313205,-0.4377750158,0.2089122981,-0.062545836,-0.2025256455,0.0551075153,0.0965651721,-0.0389919728,-0.1555091143,-0.3513694406,-0.3187605441,-0.4107227623,0.1541299969,-0.1068721488,0.192685917,0.6526251435,-0.0405006818,0.3030754626,-0.23926799,0.0254845191,-0.1506410986,0.1499269158,0.0620459914,-0.1284027398,-0.3454410732,-0.3472175002,-0.0850819349,0.2574238479,-0.0885448754,-0.2285953015,0.3524973392,-0.1272809058,-0.0153178964,-0.0792646483,-0.0165644158,0.437337935,0.1797020435,0.0240603052,-0.0108518275,-0.0252295118,0.2277865112,-0.0137914531,0.3982838094,-0.0115693389,0.121883519,0.0862016305,0.8433319926,0.1893257499,-0.3235135376,0.3949736059,0.3149036169,-0.0733484626,-0.256616503,-0.3480521142,-0.0821666047,-0.0401236229,0.17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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/600","title":"Pickling error when loading dataset","comments":"Closing since it looks like it's working on >= 3.6.9\r\nFeel free to re-open if you have other questions :)","body":"Hi,\r\n\r\nI modified line 136 in the original [run_language_modeling.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py) as:\r\n\r\n```\r\n# line 136: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with transformers (3.1.0) and nlp (0.4.0), I get the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"src\/run_language_modeling.py\", line 319, in <module>\r\n    main()\r\n  File \"src\/run_language_modeling.py\", line 248, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"src\/run_language_modeling.py\", line 139, in get_dataset\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True)\r\n  File \"\/data\/nlp\/src\/nlp\/arrow_dataset.py\", line 1136, in map\r\n    new_fingerprint=new_fingerprint,\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 158, in wrapper\r\n    self._fingerprint, transform, kwargs_for_fingerprint\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 105, in update_fingerprint\r\n    hasher.update(transform_args[key])\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/data\/nlp\/src\/nlp\/utils\/py_utils.py\", line 362, in dumps\r\n    dump(obj, file)\r\n  File \"\/data\/nlp\/src\/nlp\/utils\/py_utils.py\", line 339, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n```","comment_length":20,"text":"Pickling error when loading dataset \n Hi,\r\n\r\nI modified line 136 in the original [run_language_modeling.py](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py) as:\r\n\r\n```\r\n# line 136: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with transformers (3.1.0) and nlp (0.4.0), I get the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"src\/run_language_modeling.py\", line 319, in <module>\r\n    main()\r\n  File \"src\/run_language_modeling.py\", line 248, in main\r\n    get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n  File \"src\/run_language_modeling.py\", line 139, in get_dataset\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True)\r\n  File \"\/data\/nlp\/src\/nlp\/arrow_dataset.py\", line 1136, in map\r\n    new_fingerprint=new_fingerprint,\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 158, in wrapper\r\n    self._fingerprint, transform, kwargs_for_fingerprint\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 105, in update_fingerprint\r\n    hasher.update(transform_args[key])\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/data\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/data\/nlp\/src\/nlp\/utils\/py_utils.py\", line 362, in dumps\r\n    dump(obj, file)\r\n  File \"\/data\/nlp\/src\/nlp\/utils\/py_utils.py\", line 339, in dump\r\n    Pickler(file, recurse=True).dump(obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 446, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 409, in dump\r\n    self.save(obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1438, in save_function\r\n    obj.__dict__, fkwdefaults), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1170, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 736, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 521, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 605, in save_reduce\r\n    save(cls)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 1365, in save_type\r\n    obj.__bases__, _dict), obj=obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 610, in save_reduce\r\n    save(args)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 751, in save_tuple\r\n    save(element)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 476, in save\r\n    f(self, obj) # Call unbound method with explicit self\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/site-packages\/dill\/_dill.py\", line 933, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 821, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 847, in _batch_setitems\r\n    save(v)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 507, in save\r\n    self.save_global(obj, rv)\r\n  File \"\/root\/miniconda3\/envs\/py3.6\/lib\/python3.6\/pickle.py\", line 927, in save_global\r\n    (obj, module_name, name))\r\n_pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union\r\n``` \n Closing since it looks like it's working on >= 3.6.9\r\nFeel free to re-open if you have other questions :)","embeddings":[-0.2029621452,-0.1918641627,0.1510746926,0.2650444508,0.1417573392,-0.1736165881,0.2733694315,0.3465311229,0.0597782321,-0.0787867382,0.0942319855,0.3698832691,-0.2363304645,0.0360416137,0.0861706808,-0.4138222039,-0.0133403046,0.1228954196,-0.172864154,0.0111276107,-0.1992149353,0.319937855,-0.2243003845,0.1471229792,-0.505433321,-0.0055762944,0.075616017,0.1886179298,-0.0382539779,-0.3906199336,0.357863754,-0.0553878881,0.2542710304,0.4166387916,-0.0001185715,0.1009830087,0.4450067282,-0.1613719016,-0.2186082751,-0.5004016757,0.3419595361,-0.1968306452,0.1952064484,-0.119422853,-0.138964057,0.0235723462,0.0966473296,0.0984587669,0.3546349704,0.2370629162,0.2198283076,0.7399469614,0.0196053255,-0.004001346,-0.1397824585,0.3455076218,-0.0702364743,0.1682084203,0.0740607306,0.0049477555,-0.250533253,0.2688044012,0.0097407419,-0.1106548905,0.0340497196,0.0430935025,-0.0089143366,-0.0071578994,0.2315981686,0.1808640808,0.3163917959,-0.1332210153,-0.4018171132,-0.4381490052,-0.0321017392,-0.2538236082,0.3569175601,0.0049056802,-0.1719962656,-0.0149831194,-0.1687421799,-0.2313759178,0.0281378422,0.3235939145,0.1266870797,0.6207981706,-0.0286829732,0.2285003811,0.3370732367,-0.188007012,0.1313721091,-0.0277588796,0.0349624828,0.4023726285,-0.238850221,-0.1197175607,0.0664065331,-0.1022803336,0.040619906,0.0130149191,-0.3214137554,0.0603422858,-0.2768704593,-0.0223208498,0.2950415611,0.2889094651,0.0581622198,0.5016816258,-0.0262932908,-0.1388157159,0.2159735262,0.0168195665,-0.124156177,-0.281819135,-0.1952973008,0.0977016166,0.0427054726,-0.2523407638,-0.0277266987,-0.243488133,-0.0807851404,0.0121760387,0.1265031397,0.482233882,0.0803427994,0.1158533469,0.0164868422,0.1796439886,-0.4184333384,-0.2533637583,-0.1892677546,0.022043325,-0.3372351229,-0.1492154151,0.2223524898,0.1535892189,0.3354985416,-0.1102682278,-0.0604240373,0.1364385486,-0.0179690253,-0.1492116302,-0.0685894415,0.2175128609,-0.2264653891,0.1727866828,0.2571268976,-0.1251242161,-0.2969128489,0.1636125892,-0.0111897727,-0.2530519068,-0.1209304109,0.0851275399,-0.1824117154,-0.2599703074,-0.3591947556,0.2333444357,0.3632029891,-0.263068974,-0.1501649469,-0.3836107254,-0.1859427094,-0.1210468039,0.1095169187,0.6124694347,-0.1049165651,-0.4261669219,-0.1083565578,-0.1307385713,0.386395663,0.4650295377,-0.4532147944,0.153607592,-0.2186063975,0.5694765449,0.383651495,-0.1736365557,-0.2988277078,0.2628951669,-0.2244315892,0.2072925121,-0.0707563609,-0.1237978861,-0.1637935787,0.0578438863,-0.1647842675,0.3448475599,0.1585469395,0.2666162848,-0.2812247872,-0.2925919592,0.4360112846,0.2920269966,0.216148749,0.1050296426,-0.1254457384,0.3208654523,0.2130340934,-0.2217202485,0.191267103,0.0267760381,0.0222752038,-0.1574062705,-0.032853514,0.0872845724,-0.6515653729,0.1748918146,-0.2471687347,0.3482395709,-0.3800010085,0.1222224906,-0.0670937449,-0.1169426218,-0.3418495059,-0.2125000358,0.0444627553,0.183524698,-0.0876024067,-0.0400484204,-0.0541984327,0.0558037721,0.0123563483,0.1081894338,-0.4298703969,0.1635243893,-0.1746212542,-0.3540358245,-0.0586851798,0.2108436823,0.2123282403,-0.2704420388,-0.2660779059,0.2112212032,0.0572887398,0.2215671539,0.0190088879,-0.00503748,0.1972181201,-0.192833066,0.0177532136,0.3558892608,0.0182690863,0.0179571509,0.0842206031,0.3661319017,0.0188044794,0.0989182368,0.1062778458,0.0629072785,0.1784609258,-0.0767172575,0.163967669,-0.1630000323,0.300399065,0.0798098519,0.4254424274,0.0218044408,-0.336406678,-0.2099469602,0.4499590993,0.0788158178,0.1665172726,0.3088004887,-0.0811552033,-0.1771025658,-0.0583941601,0.0550881997,0.2792567611,0.0804542229,-0.0513546728,0.0372813307,-0.0395417921,-0.1005213484,0.1776282787,0.101741232,-0.0386786871,0.3335563242,-0.0069130347,0.0972452164,-0.4322930276,-0.2849202156,0.1184080318,0.3154613972,-0.2130566686,0.1573490947,-0.1570317745,-0.0666516423,-0.4013566077,-0.2381341457,-0.4762080014,-0.1907107383,-0.2355116904,0.1614567786,-0.1278426498,0.2946344018,-0.0560883917,0.0913452059,0.0177859832,-0.0834005475,-0.1864178628,-0.2087877989,-0.4619050622,-0.0359911546,0.2449781895,0.021026792,0.1043828949,0.123727344,-0.0020178452,0.1463040262,-0.3587393761,0.2713183761,0.0121457307,0.187657088,0.1870482713,0.0780553222,-0.1918313205,-0.4377750158,0.2089122981,-0.062545836,-0.2025256455,0.0551075153,0.0965651721,-0.0389919728,-0.1555091143,-0.3513694406,-0.3187605441,-0.4107227623,0.1541299969,-0.1068721488,0.192685917,0.6526251435,-0.0405006818,0.3030754626,-0.23926799,0.0254845191,-0.1506410986,0.1499269158,0.0620459914,-0.1284027398,-0.3454410732,-0.3472175002,-0.0850819349,0.2574238479,-0.0885448754,-0.2285953015,0.3524973392,-0.1272809058,-0.0153178964,-0.0792646483,-0.0165644158,0.437337935,0.1797020435,0.0240603052,-0.0108518275,-0.0252295118,0.2277865112,-0.0137914531,0.3982838094,-0.0115693389,0.121883519,0.0862016305,0.8433319926,0.1893257499,-0.3235135376,0.3949736059,0.3149036169,-0.0733484626,-0.256616503,-0.3480521142,-0.0821666047,-0.0401236229,0.17376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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/598","title":"The current version of the package on github has an error when loading dataset","comments":"Thanks for reporting !\r\nWhich version of transformers are you using ?\r\nIt looks like it doesn't have the PreTrainedTokenizerBase class","body":"Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):\r\n\r\nTo recreate the error: \r\nFirst, installing nlp directly from source:\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp.git\r\ncd nlp\r\npip install -e .\r\n```\r\nThen run:\r\n```\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train') \r\n```\r\nwill give error:\r\n\r\n```\r\n>>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')\r\nChecking \/home\/zeyuy\/.cache\/huggingface\/datasets\/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.py\r\nFound dataset infos file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/dataset_infos.json to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.json\r\nLoading Dataset Infos from \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nOverwrite dataset info from restored data version.\r\nLoading Dataset info from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nReusing dataset wikitext (\/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d)\r\nConstructing Dataset for split train, from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/load.py\", line 600, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 611, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 631, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 704, in _as_dataset\r\n    return Dataset(**dataset_kwargs)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/arrow_dataset.py\", line 188, in __init__\r\n    self._fingerprint = generate_fingerprint(self)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 91, in generate_fingerprint\r\n    hasher.update(key)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 361, in dumps\r\n    with _no_cache_fields(obj):\r\n  File \"\/home\/zeyuy\/miniconda3\/lib\/python3.8\/contextlib.py\", line 113, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 348, in _no_cache_fields\r\n    if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, \"cache\") and isinstance(obj.cache, dict):\r\nAttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'\r\n\r\n```\r\n\r\n\r\n","comment_length":21,"text":"The current version of the package on github has an error when loading dataset \n Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):\r\n\r\nTo recreate the error: \r\nFirst, installing nlp directly from source:\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp.git\r\ncd nlp\r\npip install -e .\r\n```\r\nThen run:\r\n```\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train') \r\n```\r\nwill give error:\r\n\r\n```\r\n>>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')\r\nChecking \/home\/zeyuy\/.cache\/huggingface\/datasets\/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.py\r\nFound dataset infos file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/dataset_infos.json to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.json\r\nLoading Dataset Infos from \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nOverwrite dataset info from restored data version.\r\nLoading Dataset info from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nReusing dataset wikitext (\/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d)\r\nConstructing Dataset for split train, from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/load.py\", line 600, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 611, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 631, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 704, in _as_dataset\r\n    return Dataset(**dataset_kwargs)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/arrow_dataset.py\", line 188, in __init__\r\n    self._fingerprint = generate_fingerprint(self)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 91, in generate_fingerprint\r\n    hasher.update(key)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 361, in dumps\r\n    with _no_cache_fields(obj):\r\n  File \"\/home\/zeyuy\/miniconda3\/lib\/python3.8\/contextlib.py\", line 113, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 348, in _no_cache_fields\r\n    if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, \"cache\") and isinstance(obj.cache, dict):\r\nAttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'\r\n\r\n```\r\n\r\n\r\n \n Thanks for reporting !\r\nWhich version of transformers are you using ?\r\nIt looks like it doesn't have the PreTrainedTokenizerBase 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/598","title":"The current version of the package on github has an error when loading dataset","comments":"I was using transformer 2.9. And I switch to the latest transformer package. Everything works just fine!!\r\n\r\nThanks for helping! I should look more carefully next time. Didn't realize loading the data part requires using tokenizer.\r\n","body":"Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):\r\n\r\nTo recreate the error: \r\nFirst, installing nlp directly from source:\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp.git\r\ncd nlp\r\npip install -e .\r\n```\r\nThen run:\r\n```\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train') \r\n```\r\nwill give error:\r\n\r\n```\r\n>>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')\r\nChecking \/home\/zeyuy\/.cache\/huggingface\/datasets\/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.py\r\nFound dataset infos file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/dataset_infos.json to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.json\r\nLoading Dataset Infos from \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nOverwrite dataset info from restored data version.\r\nLoading Dataset info from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nReusing dataset wikitext (\/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d)\r\nConstructing Dataset for split train, from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/load.py\", line 600, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 611, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 631, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 704, in _as_dataset\r\n    return Dataset(**dataset_kwargs)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/arrow_dataset.py\", line 188, in __init__\r\n    self._fingerprint = generate_fingerprint(self)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 91, in generate_fingerprint\r\n    hasher.update(key)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 361, in dumps\r\n    with _no_cache_fields(obj):\r\n  File \"\/home\/zeyuy\/miniconda3\/lib\/python3.8\/contextlib.py\", line 113, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 348, in _no_cache_fields\r\n    if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, \"cache\") and isinstance(obj.cache, dict):\r\nAttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'\r\n\r\n```\r\n\r\n\r\n","comment_length":36,"text":"The current version of the package on github has an error when loading dataset \n Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):\r\n\r\nTo recreate the error: \r\nFirst, installing nlp directly from source:\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp.git\r\ncd nlp\r\npip install -e .\r\n```\r\nThen run:\r\n```\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train') \r\n```\r\nwill give error:\r\n\r\n```\r\n>>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')\r\nChecking \/home\/zeyuy\/.cache\/huggingface\/datasets\/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.py\r\nFound dataset infos file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/dataset_infos.json to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.json\r\nLoading Dataset Infos from \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nOverwrite dataset info from restored data version.\r\nLoading Dataset info from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nReusing dataset wikitext (\/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d)\r\nConstructing Dataset for split train, from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/load.py\", line 600, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 611, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 631, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 704, in _as_dataset\r\n    return Dataset(**dataset_kwargs)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/arrow_dataset.py\", line 188, in __init__\r\n    self._fingerprint = generate_fingerprint(self)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 91, in generate_fingerprint\r\n    hasher.update(key)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 361, in dumps\r\n    with _no_cache_fields(obj):\r\n  File \"\/home\/zeyuy\/miniconda3\/lib\/python3.8\/contextlib.py\", line 113, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 348, in _no_cache_fields\r\n    if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, \"cache\") and isinstance(obj.cache, dict):\r\nAttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'\r\n\r\n```\r\n\r\n\r\n \n I was using transformer 2.9. And I switch to the latest transformer package. Everything works just fine!!\r\n\r\nThanks for helping! I should look more carefully next time. Didn't realize loading the data part requires using tokenizer.\r\n","embeddings":[-0.2237439454,-0.2012975067,-0.0425649211,0.0305607617,0.0355139226,-0.0190251414,-0.1124798357,0.2452018559,-0.001779821,-0.2171765715,0.2775869966,0.3542352021,-0.2464630753,0.2480493188,0.3240589201,-0.2881516814,0.0670975968,0.3065455556,-0.2375215143,0.052771423,-0.0257973578,0.4857282341,-0.2306242734,0.3760013878,-0.0834271014,0.0187894478,0.0690767914,0.1313544363,-0.290976733,-0.5385344028,0.6074130535,-0.0616439879,0.050687056,0.1241366863,-0.0001099683,0.0639232248,0.4524998665,0.0468346737,-0.2115150392,-0.5344376564,-0.0258459039,-0.4982024729,0.2533906996,-0.0385189801,-0.0332290567,-0.1539615691,0.1237362921,0.214582786,0.5887579322,0.2153976262,0.2569969594,0.3765678108,0.3491860032,-0.0690731034,-0.0353674218,-0.2311026156,-0.0577439554,0.1733411849,0.0412600711,-0.0806533322,0.2716600895,0.1918052435,0.0599418171,-0.0913049728,0.4098450243,-0.161454156,-0.1533882469,-0.1004508808,-0.106811516,0.2216578424,0.2371093333,-0.3243218064,-0.4073660076,-0.3286474347,0.187576741,-0.2094341666,0.4702663124,0.0190892965,-0.186120376,0.1364380717,-0.2939095795,-0.3428574502,-0.1923741549,0.2609534264,0.1397571862,0.3448449671,-0.1708793938,0.1244879737,0.2748580575,-0.1763910651,-0.1969624013,0.1317265481,-0.1033229306,0.3190252185,-0.1158773601,-0.0795881972,-0.1095688045,0.1889611632,0.0918167755,0.0788195804,-0.1971868873,-0.1108211726,-0.0314432606,0.1409585923,0.2720894814,0.2717589438,0.17719993,-0.1189834997,-0.0032822043,0.1774206609,0.3424126506,0.0457538143,-0.0453365743,-0.330901742,-0.4002842307,-0.1914319694,0.1130491421,-0.1737478673,-0.3322196305,-0.081326887,0.0919059366,0.0365613513,0.1109031439,0.0576236881,-0.1283797175,0.292615056,0.0234916713,0.1923785359,-0.3092001081,0.0621640123,-0.1687899083,0.1422373801,-0.2686138749,-0.1336476505,0.1294742972,-0.2735540867,0.5659924746,-0.0004660598,-0.0992649272,0.0367269032,-0.2505792677,0.2368855476,-0.3236500919,0.1910729706,-0.0812207609,0.2065933347,-0.0171954483,-0.079016231,-0.2939078808,-0.1111744195,-0.3565986454,-0.2549518347,-0.1689952463,0.2119121999,-0.2016157508,-0.2702847123,-0.28041628,0.0198180582,-0.0658054501,-0.3608138859,-0.0046903952,-0.2430276424,-0.1055391654,-0.0930548757,0.3117880523,0.2317885309,0.0926560387,-0.3515031338,-0.2359995544,-0.3532510996,0.085713774,0.232038483,-0.2740488648,0.2068709433,-0.1880133897,0.1380047649,0.6235006452,-0.5698119998,-0.5247965455,0.2389446348,-0.1837093979,0.1494831592,-0.1065205559,-0.0558203906,-0.2375170439,-0.130237326,0.081613116,0.2938145995,0.0514477938,-0.1161220297,-0.1398784667,-0.4660894871,0.2725190222,0.0642341301,-0.0643576309,-0.0080756107,-0.0887142718,0.4918843508,0.1930050403,0.1258874834,-0.0510848165,0.197620526,0.0127383536,0.0237861667,-0.3512737453,-0.2618550062,-0.4162878692,0.1538453549,-0.2101598233,-0.0290679373,-0.1326701939,-0.0577359386,-0.3078288436,-0.0738359988,-0.1671573669,-0.2715986669,0.160721153,0.0048288829,0.295542568,0.1357762218,-0.170800522,0.3214167953,0.0418822467,0.1781069189,-0.4866747558,0.0572806597,-0.1335321963,-0.04602357,0.1729026735,0.4731178582,0.1276734173,-0.2077899277,0.0151710575,0.4866738915,-0.3806300759,0.2787311375,-0.0023203127,-0.1690996885,0.1775311083,-0.3081972003,-0.1312314123,0.0708644241,-0.0935908034,0.0851684138,-0.0395264849,0.0182773862,-0.0922875032,0.0961840451,0.2280829251,0.1834034473,0.3326526284,-0.0641416088,-0.0241613369,-0.1820687354,0.2553633749,-0.0649808645,0.1488990933,-0.1461163461,-0.0508810841,-0.0003214711,0.492379874,0.1942920536,0.0343220159,0.2010842264,0.0021548562,-0.0955146253,0.0481096655,0.2638821304,0.2911488116,0.2004128695,-0.0828938261,0.1889248341,-0.2043614239,-0.14940539,0.2577418983,0.1075863168,-0.0307695027,0.1811572015,-0.133785665,0.0114306705,-0.2375520319,0.094807364,-0.0222688876,0.2971349061,-0.0851938948,-0.0681524053,-0.4806866348,-0.3239578605,-0.3728132248,-0.2951772809,-0.4344964921,-0.1589564532,-0.0362031534,0.1149727404,0.0485616215,0.1649898738,-0.0553438179,-0.0526930653,-0.2266286761,-0.0447862186,-0.0267593414,-0.048416011,-0.3857755959,0.0981594473,0.1967359185,0.2448918372,0.4569402337,-0.2492747158,-0.1252939254,-0.0644187778,-0.2730083168,0.1938122362,-0.2926279902,0.2592026591,0.3556085229,-0.0516292565,-0.2703355253,-0.3359704614,0.3509817421,-0.4124706089,-0.1979120374,-0.0850107968,0.0744214356,-0.0316736996,-0.201679945,-0.3083908558,-0.2766191661,-0.3505234122,0.311194241,0.0806484818,0.0984401703,0.4184328318,-0.0125570213,0.2687783539,-0.1211048067,0.0469820276,-0.1270651519,-0.2123743892,0.0569491833,-0.3140262067,-0.3949981332,0.0235566385,0.228068009,0.3154696226,0.08416567,-0.4360543191,-0.2520715296,0.0119622005,0.0624023601,0.0597277135,0.3059398532,0.3308093846,-0.0383228585,-0.0712782368,-0.161549747,-0.0299329776,0.0604027025,-0.1182855293,0.3523581624,0.1799397916,0.4678028822,-0.0238170847,0.6063979864,0.4972340763,0.0376835503,0.3265112936,-0.0553836562,0.3800790012,-0.1730224341,-0.2814178467,0.13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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/598","title":"The current version of the package on github has an error when loading dataset","comments":"Yes it shouldn\u2019t fail with older version of transformers since this is only a special feature to make caching more efficient when using transformers for tokenization.\r\nWe\u2019ll update this.","body":"Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):\r\n\r\nTo recreate the error: \r\nFirst, installing nlp directly from source:\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp.git\r\ncd nlp\r\npip install -e .\r\n```\r\nThen run:\r\n```\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train') \r\n```\r\nwill give error:\r\n\r\n```\r\n>>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')\r\nChecking \/home\/zeyuy\/.cache\/huggingface\/datasets\/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.py\r\nFound dataset infos file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/dataset_infos.json to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.json\r\nLoading Dataset Infos from \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nOverwrite dataset info from restored data version.\r\nLoading Dataset info from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nReusing dataset wikitext (\/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d)\r\nConstructing Dataset for split train, from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/load.py\", line 600, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 611, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 631, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 704, in _as_dataset\r\n    return Dataset(**dataset_kwargs)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/arrow_dataset.py\", line 188, in __init__\r\n    self._fingerprint = generate_fingerprint(self)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 91, in generate_fingerprint\r\n    hasher.update(key)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 361, in dumps\r\n    with _no_cache_fields(obj):\r\n  File \"\/home\/zeyuy\/miniconda3\/lib\/python3.8\/contextlib.py\", line 113, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 348, in _no_cache_fields\r\n    if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, \"cache\") and isinstance(obj.cache, dict):\r\nAttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'\r\n\r\n```\r\n\r\n\r\n","comment_length":29,"text":"The current version of the package on github has an error when loading dataset \n Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):\r\n\r\nTo recreate the error: \r\nFirst, installing nlp directly from source:\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp.git\r\ncd nlp\r\npip install -e .\r\n```\r\nThen run:\r\n```\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train') \r\n```\r\nwill give error:\r\n\r\n```\r\n>>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train')\r\nChecking \/home\/zeyuy\/.cache\/huggingface\/datasets\/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports.\r\nFound main folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\r\nFound specific version folder for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nFound script file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.py\r\nFound dataset infos file from https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/dataset_infos.json to \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/dataset_infos.json\r\nFound metadata file for dataset https:\/\/raw.githubusercontent.com\/huggingface\/nlp\/0.4.0\/datasets\/wikitext\/wikitext.py at \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\/wikitext.json\r\nLoading Dataset Infos from \/home\/zeyuy\/.cache\/huggingface\/modules\/nlp_modules\/datasets\/wikitext\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nOverwrite dataset info from restored data version.\r\nLoading Dataset info from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nReusing dataset wikitext (\/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d)\r\nConstructing Dataset for split train, from \/home\/zeyuy\/.cache\/huggingface\/datasets\/wikitext\/wikitext-2-v1\/1.0.0\/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/load.py\", line 600, in load_dataset\r\n    ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 611, in as_dataset\r\n    datasets = utils.map_nested(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 216, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 631, in _build_single_dataset\r\n    ds = self._as_dataset(\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/builder.py\", line 704, in _as_dataset\r\n    return Dataset(**dataset_kwargs)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/arrow_dataset.py\", line 188, in __init__\r\n    self._fingerprint = generate_fingerprint(self)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 91, in generate_fingerprint\r\n    hasher.update(key)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 57, in update\r\n    self.m.update(self.hash(value).encode(\"utf-8\"))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 53, in hash\r\n    return cls.hash_default(value)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/fingerprint.py\", line 46, in hash_default\r\n    return cls.hash_bytes(dumps(value))\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 361, in dumps\r\n    with _no_cache_fields(obj):\r\n  File \"\/home\/zeyuy\/miniconda3\/lib\/python3.8\/contextlib.py\", line 113, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/zeyuy\/transformers\/examples\/language-modeling\/nlp\/src\/nlp\/utils\/py_utils.py\", line 348, in _no_cache_fields\r\n    if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, \"cache\") and isinstance(obj.cache, dict):\r\nAttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase'\r\n\r\n```\r\n\r\n\r\n \n Yes it shouldn\u2019t fail with older version of transformers since this is only a special feature to make caching more efficient when using transformers for tokenization.\r\nWe\u2019ll update this.","embeddings":[-0.2237439454,-0.2012975067,-0.0425649211,0.0305607617,0.0355139226,-0.0190251414,-0.1124798357,0.2452018559,-0.001779821,-0.2171765715,0.2775869966,0.3542352021,-0.2464630753,0.2480493188,0.3240589201,-0.2881516814,0.0670975968,0.3065455556,-0.2375215143,0.052771423,-0.0257973578,0.4857282341,-0.2306242734,0.3760013878,-0.0834271014,0.0187894478,0.0690767914,0.1313544363,-0.290976733,-0.5385344028,0.6074130535,-0.0616439879,0.050687056,0.1241366863,-0.0001099683,0.0639232248,0.4524998665,0.0468346737,-0.2115150392,-0.5344376564,-0.0258459039,-0.4982024729,0.2533906996,-0.0385189801,-0.0332290567,-0.1539615691,0.1237362921,0.214582786,0.5887579322,0.2153976262,0.2569969594,0.3765678108,0.3491860032,-0.0690731034,-0.0353674218,-0.2311026156,-0.0577439554,0.1733411849,0.0412600711,-0.0806533322,0.2716600895,0.1918052435,0.0599418171,-0.0913049728,0.4098450243,-0.161454156,-0.1533882469,-0.1004508808,-0.106811516,0.2216578424,0.2371093333,-0.3243218064,-0.4073660076,-0.3286474347,0.187576741,-0.2094341666,0.4702663124,0.0190892965,-0.186120376,0.1364380717,-0.2939095795,-0.3428574502,-0.1923741549,0.2609534264,0.1397571862,0.3448449671,-0.1708793938,0.1244879737,0.2748580575,-0.1763910651,-0.1969624013,0.1317265481,-0.1033229306,0.3190252185,-0.1158773601,-0.0795881972,-0.1095688045,0.1889611632,0.0918167755,0.0788195804,-0.1971868873,-0.1108211726,-0.0314432606,0.1409585923,0.2720894814,0.2717589438,0.17719993,-0.1189834997,-0.0032822043,0.1774206609,0.3424126506,0.0457538143,-0.0453365743,-0.330901742,-0.4002842307,-0.1914319694,0.1130491421,-0.1737478673,-0.3322196305,-0.081326887,0.0919059366,0.0365613513,0.1109031439,0.0576236881,-0.1283797175,0.292615056,0.0234916713,0.1923785359,-0.3092001081,0.0621640123,-0.1687899083,0.1422373801,-0.2686138749,-0.1336476505,0.1294742972,-0.2735540867,0.5659924746,-0.0004660598,-0.0992649272,0.0367269032,-0.2505792677,0.2368855476,-0.3236500919,0.1910729706,-0.0812207609,0.2065933347,-0.0171954483,-0.079016231,-0.2939078808,-0.1111744195,-0.3565986454,-0.2549518347,-0.1689952463,0.2119121999,-0.2016157508,-0.2702847123,-0.28041628,0.0198180582,-0.0658054501,-0.3608138859,-0.0046903952,-0.2430276424,-0.1055391654,-0.0930548757,0.3117880523,0.2317885309,0.0926560387,-0.3515031338,-0.2359995544,-0.3532510996,0.085713774,0.232038483,-0.2740488648,0.2068709433,-0.1880133897,0.1380047649,0.6235006452,-0.5698119998,-0.5247965455,0.2389446348,-0.1837093979,0.1494831592,-0.1065205559,-0.0558203906,-0.2375170439,-0.130237326,0.081613116,0.2938145995,0.0514477938,-0.1161220297,-0.1398784667,-0.4660894871,0.2725190222,0.0642341301,-0.0643576309,-0.0080756107,-0.0887142718,0.4918843508,0.1930050403,0.1258874834,-0.0510848165,0.197620526,0.0127383536,0.0237861667,-0.3512737453,-0.2618550062,-0.4162878692,0.1538453549,-0.2101598233,-0.0290679373,-0.1326701939,-0.0577359386,-0.3078288436,-0.0738359988,-0.1671573669,-0.2715986669,0.160721153,0.0048288829,0.295542568,0.1357762218,-0.170800522,0.3214167953,0.0418822467,0.1781069189,-0.4866747558,0.0572806597,-0.1335321963,-0.04602357,0.1729026735,0.4731178582,0.1276734173,-0.2077899277,0.0151710575,0.4866738915,-0.3806300759,0.2787311375,-0.0023203127,-0.1690996885,0.1775311083,-0.3081972003,-0.1312314123,0.0708644241,-0.0935908034,0.0851684138,-0.0395264849,0.0182773862,-0.0922875032,0.0961840451,0.2280829251,0.1834034473,0.3326526284,-0.0641416088,-0.0241613369,-0.1820687354,0.2553633749,-0.0649808645,0.1488990933,-0.1461163461,-0.0508810841,-0.0003214711,0.492379874,0.1942920536,0.0343220159,0.2010842264,0.0021548562,-0.0955146253,0.0481096655,0.2638821304,0.2911488116,0.2004128695,-0.0828938261,0.1889248341,-0.2043614239,-0.14940539,0.2577418983,0.1075863168,-0.0307695027,0.1811572015,-0.133785665,0.0114306705,-0.2375520319,0.094807364,-0.0222688876,0.2971349061,-0.0851938948,-0.0681524053,-0.4806866348,-0.3239578605,-0.3728132248,-0.2951772809,-0.4344964921,-0.1589564532,-0.0362031534,0.1149727404,0.0485616215,0.1649898738,-0.0553438179,-0.0526930653,-0.2266286761,-0.0447862186,-0.0267593414,-0.048416011,-0.3857755959,0.0981594473,0.1967359185,0.2448918372,0.4569402337,-0.2492747158,-0.1252939254,-0.0644187778,-0.2730083168,0.1938122362,-0.2926279902,0.2592026591,0.3556085229,-0.0516292565,-0.2703355253,-0.3359704614,0.3509817421,-0.4124706089,-0.1979120374,-0.0850107968,0.0744214356,-0.0316736996,-0.201679945,-0.3083908558,-0.2766191661,-0.3505234122,0.311194241,0.0806484818,0.0984401703,0.4184328318,-0.0125570213,0.2687783539,-0.1211048067,0.0469820276,-0.1270651519,-0.2123743892,0.0569491833,-0.3140262067,-0.3949981332,0.0235566385,0.228068009,0.3154696226,0.08416567,-0.4360543191,-0.2520715296,0.0119622005,0.0624023601,0.0597277135,0.3059398532,0.3308093846,-0.0383228585,-0.0712782368,-0.161549747,-0.0299329776,0.0604027025,-0.1182855293,0.3523581624,0.1799397916,0.4678028822,-0.0238170847,0.6063979864,0.4972340763,0.0376835503,0.3265112936,-0.0553836562,0.3800790012,-0.1730224341,-0.2814178467,0.1348730773,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/597","title":"Indices incorrect with multiprocessing","comments":"I fixed a bug that could cause this issue earlier today. Could you pull the latest version and try again ?","body":"When `num_proc` > 1, the indices argument passed to the map function is incorrect:\r\n\r\n```python\r\nd = load_dataset('imdb', split='test[:1%]')\r\n\r\ndef fn(x, inds):\r\n    print(inds)\r\n    return x\r\n\r\nd.select(range(10)).map(fn, with_indices=True, batched=True)\r\n# [0, 1]\r\n# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n\r\nd.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)\r\n# [0, 1]\r\n# [0, 1]\r\n# [0, 1, 2, 3, 4]\r\n# [0, 1, 2, 3, 4]\r\n```\r\n\r\nAs you can see, the subset passed to each thread is indexed from 0 to N which doesn't reflect their positions in `d`.","comment_length":21,"text":"Indices incorrect with multiprocessing \n When `num_proc` > 1, the indices argument passed to the map function is incorrect:\r\n\r\n```python\r\nd = load_dataset('imdb', split='test[:1%]')\r\n\r\ndef fn(x, inds):\r\n    print(inds)\r\n    return x\r\n\r\nd.select(range(10)).map(fn, with_indices=True, batched=True)\r\n# [0, 1]\r\n# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n\r\nd.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)\r\n# [0, 1]\r\n# [0, 1]\r\n# [0, 1, 2, 3, 4]\r\n# [0, 1, 2, 3, 4]\r\n```\r\n\r\nAs you can see, the subset passed to each thread is indexed from 0 to N which doesn't reflect their positions in `d`. \n I fixed a bug that could cause this issue earlier today. Could you pull the latest version and try again ?","embeddings":[-0.4318974614,-0.3361007869,-0.1858976632,0.2841148973,-0.2439694554,-0.0441068672,0.4962969422,0.0556306951,0.2259730697,0.3695326447,-0.0362011306,0.3468486965,0.1309847087,0.132168293,-0.2639608979,0.125157252,-0.0425510369,0.0501092598,-0.1806087345,-0.1643419564,-0.3834481537,0.2268317491,-0.4945385754,-0.1573975533,-0.4757076502,-0.1956421882,-0.0759058446,0.1099578887,0.3210601807,-0.160506621,0.009957877,0.022106763,-0.0914489552,0.7085773945,-0.0001031148,-0.1593160182,0.0751710683,-0.1524419636,0.1996055543,-0.0002129417,-0.0909554511,0.1350019425,0.2246314734,-0.2731075287,0.0537289158,0.0532294475,-0.0010817386,-0.5365207195,0.188287884,0.114147231,0.2736756802,0.2315301895,-0.1976775974,0.1363347322,-0.0576324798,0.0317330249,0.0174953546,0.1988241076,0.5325119495,-0.0010723029,-0.0761025771,0.2661803365,-0.2675154209,0.1340358108,-0.055068668,0.2426737398,0.1910540611,-0.5292800069,0.0899396464,0.0624273382,0.2397211492,-0.014680095,-0.3417310417,-0.1670342833,-0.0660365671,-0.2750051916,-0.1892737597,0.1301205754,0.0398401432,-0.1010042951,-0.3078900874,0.2415540963,0.1040982306,-0.0423946232,0.1155258343,0.2264727652,0.1334076822,0.0960728675,0.1119297966,0.0600199662,0.0332671404,0.0170928892,0.2272539884,0.0101899169,-0.2228100002,0.06591966,0.1626019627,-0.1847358346,-0.2487383336,-0.032761436,-0.0716658756,0.1557407379,0.0264391396,0.1709657758,0.2026150674,-0.1219339147,-0.0715444162,0.0419611968,0.3141613305,-0.1366681457,-0.2969742715,-0.0143852308,0.2374504507,-0.2750277221,0.1658841968,0.2358232588,-0.1792279631,0.0275973007,-0.3413961232,0.0850766897,-0.059376616,-0.1049611866,0.0169430971,0.154386282,-0.0021363408,0.2345193923,0.1239192858,0.4017362893,-0.4142128229,0.1561328024,-0.3718011379,-0.108310394,-0.0729487687,0.1471422315,0.1215673387,0.0886527449,0.2377531976,0.2813608944,0.2871539891,-0.2365341783,0.0768127069,-0.2243103236,0.2108921558,0.1928569078,0.2893156409,0.257409215,0.1592210978,-0.08742062,-0.1630048156,0.2510741651,-0.2757888734,-0.0819021389,-0.1048828438,0.2451403886,0.3177548349,0.0950774923,-0.3571501374,0.0302917883,0.3217176795,0.0474879816,-0.0547763146,-0.2276129425,-0.264395386,-0.2511063814,0.1363691092,0.2460228652,-0.1555967331,0.131892994,0.0226910915,0.1686579585,0.2406096011,0.3759451509,0.0269461777,-0.0760053471,-0.1439495683,0.4667575359,-0.1113358811,-0.4037052393,-0.1221790835,0.321708113,-0.166230917,-0.0430526957,0.1146674678,0.1156925857,0.3915439844,0.0218701866,0.5178156495,0.1550714672,-0.0904079825,0.3614135385,-0.2356896102,0.0310664084,-0.0882619843,0.2377884388,0.1266495585,-0.1160868704,-0.1165002361,-0.7145783901,0.5741788149,0.0097651007,0.0703204125,0.2118837386,-0.0618280582,0.3727458715,-0.0360418856,-0.3215188682,0.0664668232,0.1910869628,0.1904696077,0.1615994722,0.0930091366,-0.2018201053,-0.0757172033,0.0433045439,-0.0996581092,-0.0145305907,0.2032821476,0.0761818215,-0.273105979,-0.0606174059,-0.2873538435,0.1573041826,0.2398821563,-0.1844333261,-0.3069197536,0.2067868561,-0.2794632614,-0.4271344244,0.0233322047,0.2178389728,0.3047004044,-0.0238305163,0.1050538942,0.3498241305,0.1846109182,-0.0830978602,0.0375301018,0.1117563024,0.1906618774,-0.2784384787,0.1273007095,0.0858666003,-0.0252231322,-0.187318936,0.0788250342,0.348264873,-0.5426028371,0.2951740623,-0.0744437799,-0.0522126071,0.1970887482,0.0442084596,0.158654213,-0.2298734188,-0.0192227215,0.1169385538,-0.004867496,-0.0748983994,-0.4469334781,0.3034744263,0.1718006283,0.2147850543,0.0117487311,0.0673645809,-0.143504411,0.0583524108,-0.0029330356,-0.1886029243,0.1613481045,0.3425671756,0.0278087314,-0.1712353379,0.0145638864,-0.1994079202,0.0625373498,-0.008083256,0.2212474048,0.241286993,-0.0798238367,-0.1361546963,-0.1645224839,-0.1607881337,-0.0919127911,-0.0036566989,-0.3200867474,-0.0344343521,-0.2061431855,0.1468798816,-0.0806964263,-0.3674132228,0.1646693349,-0.2895928919,0.0637264922,0.4193501174,-0.0656610951,0.1587560922,-0.0810854957,-0.0964359269,-0.1005364954,0.097990416,0.0630263761,-0.1917273402,-0.1016526222,0.1198314577,0.2083981633,0.2010071278,0.1114135906,-0.0428648926,-0.2291794568,0.0643692464,0.1898302436,-0.2369539738,-0.1981433481,0.236365512,-0.2259238809,0.2451674193,-0.0284197386,-0.4693149924,0.0851324871,-0.144576177,-0.0585238338,0.0074144285,0.0539941266,-0.2731432617,-0.1177036464,-0.2989680469,-0.386208266,-0.1980379522,0.2820741534,0.0527222864,0.2734357715,-0.0726147443,0.0076205987,-0.3040592968,0.4685657322,0.1552221924,-0.3286643326,-0.0194332562,0.1838425547,-0.2203141898,-0.1887140721,-0.0422976166,-0.0904345363,-0.0526839793,-0.1176268011,-0.2137936652,0.0841793418,-0.1675969064,0.1001660526,0.0828324854,0.3064706922,0.3374299109,0.1993242651,-0.2635637522,-0.0856798887,-0.3538393974,0.0745084733,0.1289100051,-0.1440953761,0.0223594662,0.3712635934,-0.2230851352,0.2971282005,0.4055191576,0.0535829403,0.3756560087,-0.1485211551,-0.1126590595,-0.1953375787,-0.3681219816,-0.0093424153,-0.1500668228,-0.23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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/597","title":"Indices incorrect with multiprocessing","comments":"Still the case on master.\r\nI guess we should have an offset in the multi-procs indeed (hopefully it's enough).\r\n\r\nAlso, side note is that we should add some logging before the \"test\" to say we are testing the function otherwise its confusing for the user to see two outputs I think. Proposal (see the \"Testing the mapped function outputs:\" lines):\r\n```\r\n>>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)\r\nDone writing 10 indices in 80 bytes .\r\nDone writing 5 indices in 41 bytes .\r\nDone writing 5 indices in 41 bytes .\r\nSpawning 2 processes\r\nTesting the mapped function outputs:\r\ninds: [0, 1]\r\ninds: [0, 1]\r\nTesting finished, running the mapped function on the dataset:\r\n#0:   0%|                                                                                                                                                                    | 0\/1 [00:00<?, ?ba\/s]\r\ninds: [0, 1, 2, 3, 4]                                                                                                                                                                             inds: [0, 1, 2, 3, 4]                                                                                                                                                         | 0\/1 [00:00<?, ?ba\/s]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 1321.04ba\/s]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 1841.22ba\/s]\r\nConcatenating 2 shards from multiprocessing\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None)}, num_rows: 10)\r\n```","body":"When `num_proc` > 1, the indices argument passed to the map function is incorrect:\r\n\r\n```python\r\nd = load_dataset('imdb', split='test[:1%]')\r\n\r\ndef fn(x, inds):\r\n    print(inds)\r\n    return x\r\n\r\nd.select(range(10)).map(fn, with_indices=True, batched=True)\r\n# [0, 1]\r\n# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n\r\nd.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)\r\n# [0, 1]\r\n# [0, 1]\r\n# [0, 1, 2, 3, 4]\r\n# [0, 1, 2, 3, 4]\r\n```\r\n\r\nAs you can see, the subset passed to each thread is indexed from 0 to N which doesn't reflect their positions in `d`.","comment_length":163,"text":"Indices incorrect with multiprocessing \n When `num_proc` > 1, the indices argument passed to the map function is incorrect:\r\n\r\n```python\r\nd = load_dataset('imdb', split='test[:1%]')\r\n\r\ndef fn(x, inds):\r\n    print(inds)\r\n    return x\r\n\r\nd.select(range(10)).map(fn, with_indices=True, batched=True)\r\n# [0, 1]\r\n# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n\r\nd.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)\r\n# [0, 1]\r\n# [0, 1]\r\n# [0, 1, 2, 3, 4]\r\n# [0, 1, 2, 3, 4]\r\n```\r\n\r\nAs you can see, the subset passed to each thread is indexed from 0 to N which doesn't reflect their positions in `d`. \n Still the case on master.\r\nI guess we should have an offset in the multi-procs indeed (hopefully it's enough).\r\n\r\nAlso, side note is that we should add some logging before the \"test\" to say we are testing the function otherwise its confusing for the user to see two outputs I think. Proposal (see the \"Testing the mapped function outputs:\" lines):\r\n```\r\n>>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)\r\nDone writing 10 indices in 80 bytes .\r\nDone writing 5 indices in 41 bytes .\r\nDone writing 5 indices in 41 bytes .\r\nSpawning 2 processes\r\nTesting the mapped function outputs:\r\ninds: [0, 1]\r\ninds: [0, 1]\r\nTesting finished, running the mapped function on the dataset:\r\n#0:   0%|                                                                                                                                                                    | 0\/1 [00:00<?, ?ba\/s]\r\ninds: [0, 1, 2, 3, 4]                                                                                                                                                                             inds: [0, 1, 2, 3, 4]                                                                                                                                                         | 0\/1 [00:00<?, ?ba\/s]\r\n#0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 1321.04ba\/s]\r\n#1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 1841.22ba\/s]\r\nConcatenating 2 shards from multiprocessing\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None)}, num_rows: 10)\r\n```","embeddings":[-0.3910766244,-0.4112408459,-0.1836059242,0.2711610198,-0.3301049173,-0.1160226539,0.5005037189,0.116604574,0.1060393304,0.4203519523,0.0210538805,0.3124271631,0.0579331666,0.1175346673,-0.2667937875,0.2599059641,-0.0655998066,0.0601689331,-0.3335288465,-0.1833892167,-0.3181264102,0.2125172466,-0.4628998339,-0.1326190233,-0.3110152781,-0.2076466978,-0.066436477,0.1109300777,0.2402825207,-0.1356410086,0.062812075,-0.0520633645,-0.2454714328,0.8725656867,-0.0001055827,-0.1686014384,0.1327808201,-0.1767240614,0.2557112873,-0.0440281928,-0.1471693963,0.1887268722,0.0456941016,-0.3676322997,-0.0554586388,0.0771236047,-0.0901453346,-0.562695384,0.0713642761,0.1461571753,0.2426275015,0.0921505615,-0.2653035223,0.1799373776,-0.0514845178,0.0000009364,0.0786735266,0.1380770206,0.3338859379,0.0206266865,-0.1120761037,0.2574878633,-0.1998972595,0.1658307612,0.0479150973,0.1884689927,0.4429793954,-0.4548024833,0.0140016992,0.1714254171,0.0765016004,-0.0286791045,-0.2724144459,-0.2252686471,-0.2483912706,-0.2456665933,-0.0806914344,0.0676270798,-0.0155447358,-0.1526164263,-0.4177740812,0.2804908156,0.062269792,0.0412678458,-0.0223894585,0.2801387608,0.1688672751,0.1247981191,0.2155305892,-0.0031879039,-0.0951534882,-0.0528317653,0.215305537,0.0714306608,-0.1718955487,-0.0526902415,0.2076423764,-0.2216391414,-0.2764878869,0.0082333321,-0.1167063937,0.1178073958,-0.0458209738,0.1933470666,0.2213090211,0.0341462828,-0.0692190379,0.1834474355,0.2160073817,-0.0012567108,-0.3653990626,0.1079142094,0.3758712709,-0.2926915884,0.2313117534,0.3381733894,-0.3253424466,-0.1112418547,-0.2383358479,0.1973326653,-0.10213238,-0.153431043,0.0871516168,0.1127257422,-0.0100677069,0.2547240257,0.0523730107,0.2664981484,-0.4644142091,0.2895242274,-0.2862216234,-0.076608412,-0.2670091987,0.125899449,0.0665823221,0.0638546273,0.1573699415,0.2249409854,0.4051101804,-0.137728408,0.0269698892,-0.2504670918,0.4778960049,0.137529403,0.1411994845,0.1892673373,0.0875435919,-0.0034042746,-0.2521336377,0.2119447291,-0.1446776837,-0.0629120246,0.0058162473,0.2167235017,0.151655063,0.3152199984,-0.2772162557,0.1823466867,0.3284090459,-0.1004108489,0.0519218817,-0.2757975757,-0.3509438038,-0.2752364874,0.203074947,0.1950675398,-0.0062630852,0.1461381614,0.1258711964,0.2690039575,0.2144625932,0.3467648923,0.0196750183,-0.1099930182,-0.1407660097,0.3324015141,-0.1847784668,-0.3128069639,-0.1643877178,0.3284085691,-0.1849719733,0.0688828528,0.1857278645,-0.0957635194,0.3820743859,-0.0666145459,0.4744650126,0.1066794395,-0.1397284269,0.3166435063,-0.3250187039,0.0013281275,-0.039994549,0.1066809818,0.145159483,-0.2366889417,-0.0859722048,-0.6444997787,0.5220373869,-0.0885261521,0.0595652163,0.1397806406,-0.0939240977,0.2035678774,-0.0551493652,-0.3799473941,0.128973946,0.2552985251,0.0092973942,0.2037033588,0.0678791627,-0.1114592627,0.0459924079,-0.0456701331,-0.0376253836,-0.0912924558,0.1730395555,0.0572230294,-0.2121395767,-0.0726199448,-0.1362882853,0.1295445859,0.2848334014,-0.2879860997,-0.3537170589,0.0829535946,-0.2009760588,-0.2908987999,-0.0033921464,0.3161678016,0.3665759861,-0.1081898138,0.0095778676,0.2655245364,0.2048649639,-0.0258755796,0.0107743731,0.2131205052,0.2367673814,-0.324680984,0.1559289396,0.2504885495,-0.05550991,-0.1659860462,-0.0835673511,0.4199184775,-0.4968352616,0.2824420333,-0.1163877472,-0.1537791491,0.0523724295,0.0249147415,-0.0073133968,-0.2020616382,0.060329359,0.1109678075,-0.1008910239,-0.0723247528,-0.2344715148,0.2854291797,0.1713829488,0.0873312801,-0.0693238974,-0.0081384573,-0.0807934105,-0.0266584493,0.1020047441,-0.2638395727,0.1642602533,0.3359586895,0.1982012987,-0.1459436268,-0.034151081,-0.0403983556,0.1023056805,-0.0844201148,0.2667040229,0.2048464417,-0.0976207331,-0.1244319081,-0.0333177745,-0.1147602573,-0.0911622345,-0.0124763437,-0.3146415949,-0.0698381141,-0.1532041878,0.2369310111,-0.0572197996,-0.3602379858,0.1795164943,-0.3023767471,-0.0132756997,0.4150726497,-0.0780604929,0.1847166419,-0.0846399739,-0.1025816277,-0.1331312209,-0.0831397325,0.0670240298,-0.1013770923,-0.059189938,0.1163101718,0.1773077995,0.21440956,0.1332989633,0.094060801,-0.1922879964,0.1254293919,0.0153614385,-0.2188111693,-0.2710222602,0.1883387864,-0.170957908,0.0261903554,-0.02336278,-0.3288132846,0.1242882162,-0.2972332835,-0.0278342664,-0.1320944875,-0.014627656,-0.2380347699,-0.1538012475,-0.1921212077,-0.2937818468,-0.139625147,0.3042711318,0.1084468514,0.3475036621,-0.1840423346,0.0001092674,-0.2826226652,0.3912380636,0.0776480287,-0.4431112409,-0.141406402,0.1247887164,-0.2343481034,-0.1426165998,-0.1725436747,-0.1278419197,-0.1158872694,-0.0639464557,-0.226484254,0.0080261091,-0.1173777804,0.1169526353,0.0998287424,0.3103832901,0.3031769693,0.2281727195,-0.2444687486,-0.0598863736,-0.4934159219,0.0906015858,0.281075865,-0.1371043324,0.0189824738,0.2403505594,-0.1708362997,0.3510899842,0.5338469744,-0.0504777357,0.1747781336,-0.2027628273,-0.0765639693,-0.0659675151,-0.2839648426,0.160335362,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/595","title":"`Dataset`\/`DatasetDict` has no attribute 'save_to_disk'","comments":"`pip install git+https:\/\/github.com\/huggingface\/nlp.git` should have done the job.\r\n\r\nDid you uninstall `nlp` before installing from github ?","body":"Hi,\r\n\r\nAs the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method.  While the file [`arrow_dataset.py`](https:\/\/github.com\/huggingface\/nlp\/blob\/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1\/src\/nlp\/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.py` which is saved after `pip install nlp -U` in my `conda` environment DOES NOT contain the `save_to_disk` method. I even tried `pip install git+https:\/\/github.com\/huggingface\/nlp.git ` and still no luck. Do I need to install the library in another way?","comment_length":17,"text":"`Dataset`\/`DatasetDict` has no attribute 'save_to_disk' \n Hi,\r\n\r\nAs the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method.  While the file [`arrow_dataset.py`](https:\/\/github.com\/huggingface\/nlp\/blob\/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1\/src\/nlp\/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.py` which is saved after `pip install nlp -U` in my `conda` environment DOES NOT contain the `save_to_disk` method. I even tried `pip install git+https:\/\/github.com\/huggingface\/nlp.git ` and still no luck. Do I need to install the library in another way? \n `pip install git+https:\/\/github.com\/huggingface\/nlp.git` should have done the job.\r\n\r\nDid you uninstall `nlp` before installing from github ?","embeddings":[-0.0576853193,0.3064997196,-0.024444893,0.095245108,0.1892760843,0.2053219229,-0.0553886071,0.1952711195,-0.1948140264,-0.0407715924,0.1610014737,0.6358869076,-0.2991715968,-0.0846181512,0.4080449939,0.0153641645,0.2040481418,0.4670255482,0.0720534474,-0.2329986393,-0.1809488535,0.3781033754,0.0341222435,0.0287979152,-0.1356095374,-0.1028221548,-0.2556759119,-0.1338198632,-0.0067095151,-0.4790412784,0.5973632932,0.0164337642,-0.1387768835,0.1500869244,-0.0001247489,-0.1039686576,0.1948121935,-0.090165101,-0.5097925663,-0.1813150495,-0.095452413,-0.4069551528,0.4231102169,-0.3911996186,0.0021168832,-0.0709338486,0.3261789083,-0.0896711871,0.2776719332,0.239450857,0.1396989077,0.0691265687,0.2781968415,-0.0320287347,-0.0457666405,0.3639844358,-0.2870591581,0.168753773,-0.0774382949,-0.0247770045,0.272967279,0.0327313095,-0.0990402102,-0.1614608765,0.3065212071,0.15005292,-0.2726331949,-0.1285836548,-0.1275789887,-0.123738572,0.4632946551,-0.4910497963,-0.3580473959,-0.1172125936,0.1763523668,-0.3220726252,0.1518509239,0.2760243118,-0.2431336492,0.0617489628,-0.1482972503,-0.4829071462,-0.2292247266,0.3118004501,0.1502443701,-0.011266958,-0.2004208416,0.0965088382,0.2891165316,0.0886989757,0.0511457473,-0.044186309,0.1211484522,0.0314178355,0.1531008631,-0.1660218686,-0.0288153552,-0.0116273686,-0.0508583374,0.3493904769,0.1532950848,-0.2702512443,-0.0151150012,0.2592653632,0.0581006818,0.2494726926,0.5383609533,0.3887923658,0.4372162223,-0.1594181806,0.2229388058,-0.0370935649,0.1210599616,0.0154879522,0.1832456887,-0.2957262099,0.2924045026,-0.3235220909,-0.284522891,0.0290026981,0.1464640796,-0.075319469,-0.0601791367,0.280354321,0.1070963517,0.2418390512,0.1395728588,0.359318465,0.0801763237,-0.0368359983,-0.1544838101,0.0541916825,0.0157968849,0.1448024362,0.5917967558,-0.0685569197,0.3948677182,-0.0441050641,-0.3212051392,0.0737070963,0.0234104004,-0.1391793936,0.0231919531,0.2737842798,0.0101857483,0.2499607652,0.0163397007,-0.2614886761,-0.3220079839,0.1296747923,-0.0567860641,-0.0743324012,-0.043953985,0.0210302379,0.114560917,-0.3268068433,-0.2669492066,0.1397685409,0.3172675073,-0.3323731124,0.0321769118,-0.1229317635,0.0017140181,-0.4182407558,0.372102648,0.0798885375,-0.191876173,-0.178402856,-0.0792943984,-0.0416536443,0.2084515393,0.3483615816,-0.0795303956,0.1954371482,-0.1444522589,0.0965556651,0.8378008604,-0.5005511642,-0.5021432042,-0.0146165071,0.2384306043,-0.139271602,-0.1509952843,0.2623471618,0.0058822003,-0.1259303689,0.144475624,0.2695761919,0.1040392816,0.01575079,-0.3102188408,-0.0621160753,0.2738034427,0.0696571618,-0.1182746589,0.0085191336,-0.0438954644,0.0914581865,0.2146680951,0.0336318016,0.1904893368,0.2619516253,0.2920945287,-0.0155354124,-0.295684725,-0.1719345152,-0.3159604967,-0.0912840366,0.0618651658,-0.2314924449,-0.1292692125,-0.0992313102,-0.04523791,-0.0977917463,-0.1335943788,-0.2724175155,0.009216818,0.0959903449,0.2064702362,0.3455800116,-0.4039347768,0.2961389124,-0.0212742984,0.1568083465,-0.2580255866,0.2367762774,-0.2979541421,-0.3281771839,-0.092259787,0.4159758091,0.0844305381,-0.0270584468,-0.0332022049,0.3201881945,-0.3143803477,-0.1482999623,0.1487742662,-0.0822434127,0.200581342,-0.4163779318,-0.0879674181,0.0980151892,0.1353491843,0.0386444479,-0.3379626274,0.1252183765,0.152457878,0.1615891457,0.2309930027,0.2231138945,0.2986209691,-0.0681873709,-0.092169404,-0.0746713132,0.2888090014,-0.1447555721,0.4032860994,-0.2652861178,-0.3689486682,0.1862704903,0.573530674,0.1868016422,0.5875403881,0.2163973749,-0.1049691439,-0.0852610841,-0.04492075,0.469073683,0.6300858259,0.1020102575,0.0310124718,-0.1670400202,-0.4748409986,-0.2145324498,0.2900676429,-0.0415595211,0.1297402531,0.1205121353,0.1305897683,0.0523800328,-0.3836540878,-0.0486110412,-0.1777878404,0.0709195286,-0.068803139,-0.042856276,-0.396068275,-0.1929874122,-0.1187582165,-0.3759918511,-0.2696293294,-0.363648802,-0.048149325,0.1673814505,0.0596651398,-0.0209342111,-0.3365979791,0.3685643673,-0.3781700432,-0.098981075,-0.3116406202,-0.1323969811,-0.2740909159,-0.0800352097,0.1156490371,0.1323539019,0.4838153422,-0.1280393749,0.0651177838,-0.1661777496,-0.12330845,0.0426873192,-0.1347209811,0.2439864129,0.0614738241,0.1860231459,-0.3289710581,-0.2357398421,0.3705380559,-0.1777910739,-0.192727685,0.0620921217,0.0706929937,-0.3187223375,-0.2763979435,-0.2736289799,-0.4865009487,-0.4724040627,0.4291748703,0.0022045847,0.2098549753,0.1633350402,-0.1868857145,0.3642731011,-0.0969282761,0.3241606653,-0.0344818309,-0.1116034612,0.4596477151,-0.4573006928,-0.5065928698,0.0659337789,0.1178522334,0.3186712265,-0.0784476399,-0.476791501,-0.0632539764,-0.3380720317,0.1348123997,-0.0090778386,0.1956044436,0.3882959485,0.0982562602,0.0456519388,-0.1662946641,-0.4008443654,0.3753771782,0.1130128503,0.3920342922,-0.1350152493,0.1833522469,-0.0676996037,0.290586412,0.2983123362,0.0744908974,0.4115737081,0.3516529202,0.4719513357,-0.1254168302,-0.5036275983,0.041128505,0.0504853241,0.1892404407,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/595","title":"`Dataset`\/`DatasetDict` has no attribute 'save_to_disk'","comments":"> Did you uninstall `nlp` before installing from github ?\r\n\r\nI did not. I created a new environment and installed `nlp` directly from `github` and it worked!\r\n\r\nThanks.\r\n","body":"Hi,\r\n\r\nAs the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method.  While the file [`arrow_dataset.py`](https:\/\/github.com\/huggingface\/nlp\/blob\/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1\/src\/nlp\/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.py` which is saved after `pip install nlp -U` in my `conda` environment DOES NOT contain the `save_to_disk` method. I even tried `pip install git+https:\/\/github.com\/huggingface\/nlp.git ` and still no luck. Do I need to install the library in another way?","comment_length":28,"text":"`Dataset`\/`DatasetDict` has no attribute 'save_to_disk' \n Hi,\r\n\r\nAs the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method.  While the file [`arrow_dataset.py`](https:\/\/github.com\/huggingface\/nlp\/blob\/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1\/src\/nlp\/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.py` which is saved after `pip install nlp -U` in my `conda` environment DOES NOT contain the `save_to_disk` method. I even tried `pip install git+https:\/\/github.com\/huggingface\/nlp.git ` and still no luck. Do I need to install the library in another way? \n > Did you uninstall `nlp` before installing from github ?\r\n\r\nI did not. I created a new environment and installed `nlp` directly from `github` and it worked!\r\n\r\nThanks.\r\n","embeddings":[-0.0511759333,0.3495239913,-0.0176616888,0.1092848703,0.1668482125,0.1748827398,-0.0380238071,0.1976730078,-0.1885312051,-0.043285016,0.1474733651,0.6693127155,-0.3041138947,-0.0753769055,0.4125314057,0.016558148,0.2039823532,0.4532092214,0.0589582846,-0.2329033464,-0.1727200449,0.3684439659,0.0128143821,0.0299812425,-0.1168765053,-0.0699494556,-0.2774166167,-0.1472542584,0.0213420279,-0.4878751338,0.6232535839,0.025658058,-0.0995816737,0.1401851177,-0.0001252176,-0.0947313309,0.2191047817,-0.0743310079,-0.4842466116,-0.186062932,-0.1118605658,-0.4106766582,0.4256405532,-0.4007542729,-0.0077748084,-0.1093160957,0.3234489858,-0.0821146891,0.2835487127,0.2425789386,0.1348460019,0.0490633287,0.2647358179,-0.0296148248,-0.0352693498,0.3740502894,-0.2868793011,0.1654687673,-0.0698279291,-0.0520019419,0.2786721885,0.0281591397,-0.1160386205,-0.148350805,0.2933444381,0.1480827779,-0.2648943067,-0.1451873034,-0.1403990388,-0.1053526253,0.4972685874,-0.4533620775,-0.3359110355,-0.0857649148,0.1459582001,-0.33955881,0.1564502567,0.2751291096,-0.2350642234,0.0538978092,-0.1645874381,-0.4610552192,-0.2480334491,0.3096344173,0.1412266195,0.0225567967,-0.1946240664,0.1092327237,0.2540315688,0.0773193613,0.0809601694,-0.039671652,0.1217784435,0.0294953454,0.1341829747,-0.1684680432,-0.0206556562,-0.025548039,-0.0454425476,0.3416440189,0.1140734181,-0.2621801198,-0.0336799212,0.2551231682,0.0709185377,0.2513377964,0.5684151649,0.3965986669,0.45133394,-0.180692777,0.1973213255,-0.0392242447,0.1051931754,-0.0114816437,0.1841273308,-0.2710450292,0.2821151018,-0.3572694361,-0.2509005964,0.0311010964,0.1265180856,-0.0671607479,-0.0512965843,0.2768733203,0.1140203625,0.2726812065,0.1592994779,0.3604344428,0.0709906071,-0.0292262845,-0.1366823316,0.0375475399,-0.000931723,0.152721554,0.5961227417,-0.0289045926,0.4048804641,-0.0496452525,-0.329703033,0.0721500665,0.0249023866,-0.1425147653,0.0376485698,0.2746228576,-0.0059602386,0.2406769544,0.0008031203,-0.2513996065,-0.3149660528,0.1724728793,-0.0685228184,-0.0784183443,-0.047056064,0.0120060993,0.1201016381,-0.3300995529,-0.2568689883,0.1341209412,0.3234741092,-0.319558531,0.026189249,-0.1375035048,-0.0047139255,-0.4191738963,0.3578749895,0.0753017217,-0.2224448323,-0.1512532532,-0.0840386152,-0.0440831594,0.2268172503,0.3499010503,-0.1063032374,0.1825851202,-0.1158606783,0.0924044997,0.8516139388,-0.489674896,-0.4979109168,-0.0161963794,0.2707594633,-0.1674655676,-0.1509110034,0.2612154186,0.0241094604,-0.1258374453,0.1311420649,0.2887346447,0.1377986521,0.0192628074,-0.3267795742,-0.0591942333,0.3038318753,0.0636950061,-0.1093660668,0.0202697143,-0.0482570603,0.0815662667,0.221841231,0.0228147805,0.1908590049,0.2665262222,0.3088770509,-0.0222313702,-0.2593230009,-0.1783483773,-0.2849615812,-0.0821536556,0.0372208245,-0.211175397,-0.0952329338,-0.0784115791,-0.0413034707,-0.0949671715,-0.1408421993,-0.2969379127,-0.0042201378,0.0681393966,0.206966117,0.3199113607,-0.4070872962,0.2959427536,-0.01905833,0.1253743768,-0.228302747,0.253534615,-0.3011040092,-0.3270973563,-0.094833374,0.3961188793,0.0923491791,-0.0210860353,-0.0583561398,0.3021885753,-0.298795104,-0.1604407281,0.1566125453,-0.1066597253,0.21144633,-0.4306330681,-0.0665082633,0.0895551071,0.1336699575,0.0361181013,-0.3419333696,0.1272631884,0.1734529436,0.1750142872,0.2303904295,0.2305631787,0.3229705989,-0.0843192935,-0.0659552217,-0.069305107,0.2749518156,-0.1217547134,0.4015532732,-0.2916473746,-0.3590727448,0.2103360742,0.555231154,0.2151245475,0.5608869791,0.2083569914,-0.1154359728,-0.1199423149,-0.0386915579,0.4832510352,0.6548285484,0.0990174189,0.0429351702,-0.1476528794,-0.4620834589,-0.2071966678,0.3073485792,-0.0605245456,0.134557873,0.1047619283,0.1706264466,0.0544817634,-0.3766708076,-0.0536191016,-0.1458656192,0.0812437162,-0.0552989841,-0.0335780419,-0.4216027856,-0.2106356025,-0.100279443,-0.3712030649,-0.2571751475,-0.3521026373,-0.0483965129,0.1493587047,0.0526550226,-0.0050586625,-0.3272660375,0.3506123722,-0.3900284767,-0.0734015554,-0.3050143421,-0.1633831412,-0.2843654752,-0.0878532529,0.1165301204,0.1370327622,0.465826571,-0.1015403792,0.0480973721,-0.1767470092,-0.1433378458,0.0440041907,-0.1066029817,0.2553695142,0.0543655939,0.1757211238,-0.3232786059,-0.2217730731,0.3675338626,-0.1886543483,-0.1849095672,0.0674366131,0.0623550378,-0.319128722,-0.2779297233,-0.2873338163,-0.5013769865,-0.4498511255,0.374222219,0.0061319438,0.2335651666,0.162081033,-0.2169314623,0.3564762175,-0.080360204,0.2904996276,-0.0246064477,-0.1150031388,0.4654689729,-0.4859730899,-0.5153086782,0.0681334138,0.1344354749,0.3015755713,-0.0698592365,-0.4759966135,-0.0519197769,-0.3486807346,0.1344190687,0.0311377272,0.1858066618,0.4061759114,0.1082538962,0.042835217,-0.1794448644,-0.404648453,0.3869394362,0.1595667154,0.4086622298,-0.1234142706,0.1844185442,-0.0775087178,0.3114279509,0.2893519998,0.0900779441,0.4319112599,0.3623682857,0.4333250225,-0.1328260303,-0.4879168272,0.03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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/590","title":"The process cannot access the file because it is being used by another process (windows)","comments":"Hi, which version of `nlp` are you using?\r\n\r\nBy the way we'll be releasing today a significant update fixing many issues (but also comprising a few breaking changes).\r\nYou can see more informations here #545 and try it by installing from source from the master branch.","body":"Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n```","comment_length":46,"text":"The process cannot access the file because it is being used by another process (windows) \n Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n``` \n Hi, which version of `nlp` are you using?\r\n\r\nBy the way we'll be releasing today a significant update fixing many issues (but also comprising a few breaking changes).\r\nYou can see more informations here #545 and try it by installing from source from the master branch.","embeddings":[-0.1765670329,0.1705409884,-0.0338350125,0.1972525716,0.276063025,0.046986036,0.2461643964,0.3142565191,0.1317553222,0.1372045875,-0.0612205826,0.529302001,-0.3115153611,-0.0680440515,0.0767634884,0.014277596,-0.0274664685,0.1223226339,0.0646729916,0.1522342712,-0.4235683978,0.089819245,-0.321685195,0.3932027221,-0.2467512786,-0.2833744586,-0.0073881098,0.4002689421,-0.1476258785,-0.3065513074,-0.1500901431,-0.1016815007,0.1635933965,0.339730531,-0.0001092507,0.1117590815,0.1657463014,0.0723258033,-0.0584406592,-0.186177507,-0.0081050647,-0.4370200336,-0.0492740385,-0.0949192494,0.2509933114,-0.0299171451,0.2664938271,-0.2296128422,0.3667313457,0.4969288409,0.2682473063,0.2371559888,0.1719648689,-0.0822591037,0.4717095494,-0.091223307,-0.0806378946,0.5170618892,0.2832086682,-0.3384489715,-0.0735353529,0.1981331557,-0.1567203254,-0.2741836011,-0.0703398734,-0.1646665037,0.3293884099,-0.5745841861,0.3498039246,-0.0944107249,0.1831559241,-0.0103389062,-0.1127805263,-0.1288834214,-0.0901363567,-0.1023518667,0.2645036876,0.4485737979,-0.2464579493,0.1231353655,-0.1331927925,-0.1977988333,-0.2744648159,0.0432025567,0.4913312495,0.1742404103,-0.0321537815,0.2832345068,0.3321835101,0.0047879368,0.1627507508,0.1906650066,0.082251437,0.2991720438,-0.2648767829,0.2358040959,-0.1145857275,0.3475693464,-0.2456605732,0.211557731,-0.0407701842,-0.2607629299,0.2219271809,0.3335291743,-0.096292004,0.365791291,-0.1115012839,0.0800432563,0.3007517457,-0.0706384107,-0.1508823186,0.0309130736,-0.5127479434,-0.7366874814,-0.3002051413,0.2035432458,0.3316034377,0.019516971,-0.2784527838,-0.2108523101,-0.1304586828,0.1237716749,0.2527556419,0.3760255575,-0.0118372235,0.1665927768,0.2711794078,0.2126277685,-0.1514804661,0.1475888342,0.0255004298,0.2315755039,-0.8735941052,-0.0115483757,0.1191699207,-0.0363761596,0.0999692082,-0.0447639152,-0.2247330546,-0.2866726518,0.2228051871,-0.1009206548,-0.0311283488,0.1471240222,0.0334274806,0.0902855694,0.1703851372,-0.2340905219,0.000517272,0.2663551271,-0.470720917,-0.3566271663,-0.1959459037,0.1416383684,-0.0774970502,-0.0170253646,0.5023498535,-0.0425135083,0.2484270781,-0.3122112453,-0.0222024266,-0.0065074591,-0.3388591707,-0.3757831156,-0.1752881259,0.1712938845,-0.4227336049,0.0585829876,-0.0057373825,-0.1998024583,0.2495300621,0.4242102802,-0.2043909878,0.3054891527,-0.0658724383,0.0704578534,0.8549776673,-0.1990090311,-0.6082813144,0.3079836667,-0.5397832394,-0.3694066107,0.1779018641,0.24567765,0.2107000053,-0.0261017065,0.3786222041,0.0901168063,0.2324507833,-0.0657918081,-0.2402687073,-0.1774432659,0.285887301,0.0218282063,-0.1026386321,0.2008702904,0.2385696173,0.2402634323,0.3049709499,-0.1066568941,0.330407232,0.218122527,0.2690805793,0.3724405766,-0.1290147156,-0.0760848969,0.1096108705,0.1873374283,-0.2275865525,0.0113488091,0.0424690694,-0.1055788919,-0.1242938191,0.1277856976,-0.4303869307,-0.3422091603,0.1733956784,0.1383132041,-0.0282927584,-0.0741612837,-0.00461961,0.0543021001,-0.066528976,-0.0914480835,-0.3181314468,0.1229491159,-0.4717287719,-0.1494160891,-0.1340329945,0.3342444599,0.1710793376,0.1060616747,-0.1678994298,0.1558741927,0.0076921401,0.0440265015,0.1992292851,-0.0338902585,-0.0364170112,-0.1528815776,0.2365458906,0.0526504964,0.0528609902,0.1382517368,0.1897073239,0.1646210551,0.0175435692,0.2983325422,0.0722362548,0.4031214118,0.0345559493,-0.0202592239,0.0355991833,0.1939075738,0.339620322,0.0514374934,-0.0347824581,0.0088161211,-0.0511058494,-0.0156220803,0.5277141333,0.1092472225,-0.0153419496,0.0442291871,-0.1107063815,-0.0175625384,0.0518276393,0.3424906135,0.4299674332,0.0601907298,0.0533262081,0.1188571304,-0.0037312207,-0.0990176797,0.0872748718,0.1116725355,0.3432447016,0.1183098555,0.154489547,0.2098272741,-0.1425979286,-0.3123348355,0.1767480224,0.4526069462,-0.2081012577,-0.0024063524,-0.2591174841,-0.3028211594,-0.3574784994,0.080826886,-0.2272000015,-0.0903564543,0.0413530469,0.2309697717,0.0392375439,0.2571948767,-0.1960360855,0.0636123046,-0.1070971116,-0.0212115739,0.1065067649,-0.2841461897,-0.3487016857,0.0732532516,0.4635375142,-0.0886287913,0.3015563488,0.0722924098,0.1173654124,-0.1981844008,-0.1850028336,-0.1156247705,-0.0971706361,0.0256881621,-0.0124279102,0.1032664031,0.023947414,-0.409684211,0.3573969603,-0.3606680036,-0.2367313504,0.0684053376,-0.1406067759,-0.322173506,-0.2491959482,-0.3021603227,-0.3738672733,-0.5032862425,0.4350882471,-0.176232174,0.2365990728,0.2102874368,-0.0092038447,0.4069841504,0.0855106711,0.1652594805,-0.0485855862,0.1821389943,-0.0066839829,-0.2967914641,-0.3691343963,0.1906411201,0.185684368,0.0145070814,0.104168646,-0.2383521497,-0.1529042572,-0.3123936057,-0.0752274767,-0.0741516799,-0.0384576321,0.4369110167,0.1654579043,-0.1200055033,-0.0298598167,-0.236050874,0.1802698821,0.0876838937,0.1502386034,0.1737029105,0.509760499,0.2883393764,0.2158128172,0.3241437376,0.1112618819,0.1727733314,-0.2193650454,0.1895129979,0.0585503243,-0.3553052843,0.3438940048,-0.095478937,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/590","title":"The process cannot access the file because it is being used by another process (windows)","comments":"Ok, it's probably fixed on master. Otherwise if you can give me a fully self-contained exemple to reproduce the error, I can try to investigate.","body":"Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n```","comment_length":25,"text":"The process cannot access the file because it is being used by another process (windows) \n Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n``` \n Ok, it's probably fixed on master. Otherwise if you can give me a fully self-contained exemple to reproduce the error, I can try to investigate.","embeddings":[-0.2114655524,0.0665597841,-0.0459682867,0.2789514065,0.3627562821,0.1312585175,0.3703974187,0.2482227832,0.2258239537,0.219642058,-0.1714614034,0.2673845291,-0.1824055612,-0.1288236976,-0.1086891592,0.0654447675,-0.0072369794,0.0088248989,0.0774251744,0.226959154,-0.4474639297,0.0641376451,-0.3092188239,0.4365319908,-0.3265941441,-0.2484493554,0.0702646598,0.4150611758,-0.0526370592,-0.285081327,-0.1506447941,-0.0810049251,0.1276627034,0.5536198616,-0.0001143734,0.2240380496,0.0947395191,0.0214060824,0.0279271845,-0.1374551207,-0.0875019953,-0.2366851121,-0.1152373105,-0.0333837569,0.1649273336,0.031001538,0.2015654296,-0.3285351992,0.2990384102,0.4722038209,0.2349824011,0.2076999396,0.1914312392,-0.2376170754,0.5633089542,-0.1176098436,-0.0370919183,0.6386207342,0.4471789896,-0.242445454,-0.1647862792,0.1773202717,-0.2169586867,-0.1768907905,-0.0892447755,-0.264503479,0.4385124743,-0.6413016319,0.4716545939,-0.0395026579,0.237539947,-0.0037552058,-0.1493100226,-0.0266971048,-0.1550510824,0.1002503559,0.2798582017,0.4880426824,-0.2075369507,0.2474027276,-0.1368513256,-0.0809995905,-0.2716522515,-0.0632070899,0.3949955106,0.0777700618,-0.0472148731,0.262033999,0.3292509019,0.0647502691,0.2178885192,0.0957395509,-0.0180257894,0.2244765759,-0.3569532931,0.3265299797,-0.1940017343,0.349080652,-0.2772836089,0.1277076155,-0.1150758639,-0.2551479936,0.2213638872,0.4073951244,-0.0367275998,0.298748374,-0.2618215382,0.1614613235,0.3607685864,-0.0533831976,-0.2936809361,0.0442496389,-0.534307003,-0.832441926,-0.1913453341,0.3128830791,0.344866544,0.0278751533,-0.3136811256,-0.141304031,-0.1441437602,0.139582485,0.2897669077,0.3550514877,-0.0385161415,0.1184187606,0.1915986389,0.1892247647,-0.0693101808,0.096791856,0.0672732964,0.1931974739,-0.8466009498,-0.0646453723,-0.0300680194,-0.151851818,0.0300290622,0.1117195636,-0.0966770649,-0.4035180807,0.2738670111,-0.1954499185,-0.0170250703,0.2679279745,0.0631162301,0.0826185793,0.3132300973,-0.1889096797,0.0930427983,0.3087938428,-0.5649157166,-0.4224681556,-0.1513073295,0.1013720408,-0.0662613809,0.1933198124,0.5052144527,-0.1071624979,0.310756892,-0.3346762657,0.0081839086,-0.045209568,-0.3426704705,-0.4344490767,-0.116665341,0.2212156504,-0.4432276487,0.1203273088,0.1230368912,-0.3053646088,0.1948789209,0.3802189827,-0.1538111418,0.3382058144,-0.1127017066,-0.1150262505,0.5909101367,-0.1668607295,-0.6615204215,0.2475111336,-0.5012634993,-0.2549396455,0.3151992261,0.1936203986,0.3255032897,-0.0650660768,0.4324889481,-0.0485434271,0.1648044884,-0.0730378628,-0.1286816448,-0.1494332403,0.1266527623,0.0218284857,-0.0161906332,0.2625211179,0.3569929898,0.0693524405,0.2791787386,-0.1492733955,0.3342377245,0.2405438125,0.3433425725,0.4428647459,-0.0851452649,-0.1356274933,0.1946950108,0.2756517529,-0.0956630334,-0.1632959098,0.0504540987,-0.1325701326,-0.0460985787,0.2153440267,-0.5356934667,-0.2269077897,0.1207040995,0.1248268113,-0.0949283689,-0.0859355479,0.0596954674,-0.004747049,-0.053257335,-0.0512145683,-0.1109489426,0.0484345406,-0.416023314,-0.1190474778,-0.2173052579,0.1694720834,0.2049477249,0.1270708144,-0.1877919436,0.1887207031,0.1872302443,0.131691888,0.1440542042,0.0508290343,-0.060366109,0.0016104854,0.197579816,0.084423244,0.0869315714,0.0391190425,0.2750764787,0.0635229126,-0.0350247808,0.3883447051,0.0055476683,0.3557156026,0.0042983671,0.0541647822,0.0657000467,0.2314148098,0.2577147782,0.0834213123,-0.0048109335,0.0656048879,0.0971830562,-0.0874451622,0.4135709405,0.0413592532,-0.0040190318,0.0519632809,-0.1366141438,0.0770386085,0.0497035757,0.3433394432,0.3794067204,-0.0573430583,0.0114713805,0.0805661678,0.1906318367,-0.0809611976,0.1559361219,0.1926278919,0.3218732178,0.2225374579,0.0483527742,0.1897525638,0.0217799656,-0.2853983939,0.1454841495,0.3537414372,-0.2817568183,0.0333628543,-0.1760063171,0.03251791,-0.2888410687,0.0986624807,-0.11784596,-0.0183148552,-0.0627996475,0.3851552904,-0.0559987165,0.2293783128,-0.1767348349,-0.0484613702,-0.0998528749,-0.0012479118,0.2512572408,-0.3172087371,-0.1889255196,0.0308867637,0.4870844781,-0.3395869136,0.2789709866,0.0210346933,0.1365533024,-0.3264134228,-0.1483816355,-0.0809008777,-0.0233134478,0.0468306616,0.0199408177,0.0718649849,0.1895557642,-0.3815601766,0.4461351931,-0.2553527057,-0.213452816,0.1011984199,-0.2133298516,-0.3726608157,-0.1734943986,-0.1339586526,-0.3441823423,-0.4939570129,0.4266889989,-0.3828506172,0.1781271994,-0.0357736275,0.055256635,0.2962765396,0.1091315448,0.1583042145,-0.0177807063,0.0172436275,-0.101785332,-0.249112308,-0.2508819997,0.2443485707,0.2196863741,-0.0482334644,0.2001391202,-0.1813926697,-0.1735420227,-0.3676159978,-0.0372530073,-0.1183127314,-0.0274094045,0.3658221662,0.2106409967,-0.1014410928,-0.0020055287,-0.1801065207,0.1635891944,-0.0637449473,-0.0035238403,0.2608179152,0.5538358688,0.2435319424,0.310675621,0.2992294729,0.2581651807,0.1126635522,-0.2037102431,0.1955663264,0.0714910403,-0.3873255849,0.3228870332,-0.2160300165,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/590","title":"The process cannot access the file because it is being used by another process (windows)","comments":"I get the same behavior, on Windows, when `map`ping a function to a loaded dataset. \r\nThe error doesn't occur if I re-run the cell a second time though! \r\nI'm on version 1.0.1.","body":"Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n```","comment_length":32,"text":"The process cannot access the file because it is being used by another process (windows) \n Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n``` \n I get the same behavior, on Windows, when `map`ping a function to a loaded dataset. \r\nThe error doesn't occur if I re-run the cell a second time though! \r\nI'm on version 1.0.1.","embeddings":[-0.14136222,0.0115144076,-0.0121005736,0.0894324929,0.3479508162,0.0587963611,0.4676170945,0.1985688657,0.2368670702,0.2460598201,-0.2052183598,0.2554389834,-0.0708039179,-0.2149474472,0.0211809669,0.1801772118,0.004898577,0.0978128985,0.1116587073,0.2412271351,-0.5601937175,0.0767828226,-0.3426276147,0.4758161306,-0.2404768169,-0.2781504095,0.0295200013,0.3297816217,-0.0508581176,-0.2468936443,-0.0585948527,-0.0388170071,0.0610766411,0.5021083355,-0.0001234325,0.2076142132,0.0500871316,0.0183689892,-0.0317803435,-0.1070131138,-0.0684424564,-0.1512527317,-0.0919808596,-0.06773635,0.1647817194,0.1099002808,0.1434138715,-0.4373567104,0.3229967058,0.4147151113,0.1281574517,0.2726634741,0.1071127132,-0.1961139441,0.6379075646,-0.0293761101,-0.1111278534,0.6946662068,0.5680832863,-0.409153372,-0.1346031725,0.2250454128,-0.2191309035,-0.0264014807,-0.1551886946,-0.1384028345,0.4357500374,-0.4799388349,0.4535281658,-0.0303509794,0.1233582273,-0.0895661861,-0.0924161375,-0.1571206003,-0.1803879887,-0.0489244312,0.3667474687,0.3262472153,-0.1456913948,0.1990912855,-0.2490430772,-0.0421518348,-0.2591157854,-0.0333371423,0.3093458712,-0.0745700523,-0.0314113349,0.3234914541,0.3730352223,0.1414939016,0.2741088867,0.0841259658,-0.01832016,0.2918751538,-0.2928037643,0.3861653209,-0.0639367774,0.3130858243,-0.1918380558,-0.0625624359,-0.0166269969,-0.3786750436,0.1871845424,0.3874210417,0.0990446582,0.1986857504,-0.2627533078,0.1062393636,0.3773981333,-0.2037296742,-0.3466935158,-0.0822355822,-0.3972129524,-0.6542537212,-0.0959610492,0.2318628579,0.3864630461,0.0201445948,-0.444794476,-0.130255267,-0.2066166252,0.1450045407,0.2346306294,0.3613013625,0.1014283523,0.0251276139,0.2232877314,0.1528564543,-0.1587819606,0.0670550242,0.1297106445,0.0575867407,-0.8975262046,-0.0086455056,0.0262226686,-0.0616105609,-0.0616382845,0.0595624484,-0.0952669755,-0.4462268353,0.2453478575,-0.2969215512,0.1318940818,0.3331855834,-0.035110198,0.0989937037,0.3047444522,-0.2101074755,0.0419480726,0.3383021653,-0.542812109,-0.3435719013,-0.0544381738,-0.0100679323,-0.1112358421,0.2478314638,0.4594936669,-0.0881026462,0.2433748096,-0.5887208581,-0.0351895951,-0.1100902855,-0.4130099714,-0.4058870971,-0.1700188965,0.3269718289,-0.34586972,0.1046348438,0.0815209448,-0.2160977721,0.1933882684,0.3409868479,-0.2419815511,0.4212700129,-0.2040447146,-0.0338634886,0.5636239648,-0.1880694479,-0.752253592,0.283264488,-0.4704799354,-0.1629840285,0.1556879282,0.2498376071,0.3320500553,-0.0803198889,0.4212530851,-0.0354684442,0.1602882892,-0.0513002351,-0.0560669564,-0.0933916569,0.209737137,-0.1015670672,-0.0983515009,0.3848287463,0.5134427547,0.0934621617,0.2899844646,-0.1401347667,0.2673074305,0.0757894069,0.2811129987,0.4180851281,-0.114678748,-0.1326086074,0.0769589394,0.2450481951,-0.0843716487,-0.2340932935,-0.0087467162,-0.2115938514,-0.0939519778,0.2580208778,-0.4916742444,-0.1750731766,0.0038237234,0.1637924314,-0.0698042959,-0.0209143776,0.0334407724,0.0008902663,-0.1489134133,-0.0832627788,-0.0076971375,0.0859409049,-0.2953228354,-0.1756304055,-0.2129376531,0.1347346753,0.1840849817,0.1390432566,-0.1797684282,0.2243450433,0.3209833503,0.2022684813,0.0562611297,0.0483096763,-0.0491747931,0.1920040101,0.1274475157,-0.001757763,0.1362547576,-0.0406481251,0.1508340389,0.1049964502,0.0537456982,0.375671804,-0.0394800678,0.26376912,-0.0308439601,0.0898995325,-0.0284792911,0.2370778024,0.1620222181,0.060071934,-0.031735599,0.0925721526,0.021815503,-0.1094775796,0.4865920842,-0.0019061774,0.1156216115,-0.0405843444,-0.1285411566,0.0856793076,0.1505112201,0.3509045541,0.4571465254,-0.1010679305,-0.0190003589,0.0774334818,0.0760467872,-0.0636864901,0.0982598513,0.1598389894,0.3917525113,0.1816726476,0.0771106333,0.1335733384,-0.1835931242,-0.2560079396,0.2024307549,0.3051225543,-0.3449656963,0.0733584017,-0.1770001203,0.0318625942,-0.3168502152,0.0179167707,-0.0927894935,-0.0392646044,-0.1229689866,0.5164975524,-0.0034537071,0.2352937311,-0.2392572761,0.0669594407,-0.1224464104,-0.0871035233,0.220988825,-0.2567580342,-0.2006324232,-0.0195788965,0.4293872118,-0.3859557807,0.3175410032,-0.0251149498,0.2133794725,-0.2417886108,-0.1630088389,-0.0314556696,0.0066923751,0.075885877,-0.0006341499,0.162471056,0.0390705802,-0.2285880446,0.380076617,-0.3706241846,-0.2118948102,0.1213602722,-0.1508995146,-0.3524764776,-0.1128026918,-0.2050598562,-0.2362649441,-0.3290404975,0.5150355101,-0.5015131235,0.1188741103,0.0526286513,0.1022956967,0.3818595707,0.0674314052,0.1462170333,-0.0838080272,0.0460403748,-0.0927362815,-0.2266786546,-0.1957887262,0.1894262135,0.3228176236,-0.151099056,0.4219771922,-0.2286889553,-0.2187833786,-0.2935786545,-0.0576859079,-0.0539284572,-0.0149472523,0.3952738643,0.2193965763,-0.0485511832,-0.0531065166,-0.2103042901,0.0815929845,-0.0166885015,-0.0519202724,0.3699101806,0.5677775145,0.2121956348,0.2237595469,0.3477529883,0.2361708581,0.196256429,-0.2543728948,0.3022272289,-0.0147841973,-0.3478395045,0.3301239908,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/590","title":"The process cannot access the file because it is being used by another process (windows)","comments":"@saareliad I got the same issue that troubled me quite a while. Unfortunately, there are no good answers to this issue online, I tried it on Linux and that's absolutely fine. After hacking the source code, I solved this problem as follows.\r\n\r\nIn the source code file: arrow_dataset.py -> _map_single(...)\r\n\r\nchange\r\n```python\r\nif update_data and tmp_file is not None:\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n```\r\nto\r\n```python\r\ntmp_file.close()\r\nif update_data and tmp_file is not None:\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n```\r\n\r\nThen it works without needing multiple times runs to avoid the permission error.\r\nI know this solution is unusual since it changes the source code. Hopefully, the lib's contributors can have better solutions in the future.\r\n","body":"Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n```","comment_length":111,"text":"The process cannot access the file because it is being used by another process (windows) \n Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n``` \n @saareliad I got the same issue that troubled me quite a while. Unfortunately, there are no good answers to this issue online, I tried it on Linux and that's absolutely fine. After hacking the source code, I solved this problem as follows.\r\n\r\nIn the source code file: arrow_dataset.py -> _map_single(...)\r\n\r\nchange\r\n```python\r\nif update_data and tmp_file is not None:\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n```\r\nto\r\n```python\r\ntmp_file.close()\r\nif update_data and tmp_file is not None:\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n```\r\n\r\nThen it works without needing multiple times runs to avoid the permission error.\r\nI know this solution is unusual since it changes the source code. Hopefully, the lib's contributors can have better solutions in the future.\r\n","embeddings":[-0.1046754122,0.1074618921,-0.0233315229,0.2269619256,0.3214992881,0.0007905434,0.4211469889,0.1908867508,0.1951685995,0.1922733337,-0.1767421812,0.3607426584,-0.174327001,-0.2670363486,-0.1233475432,0.0355832614,0.0496971495,0.013052769,0.2309346646,0.2061697394,-0.3807088435,-0.0331440866,-0.3299262524,0.3217106164,-0.2837905884,-0.3542702496,0.0100989966,0.4027675092,0.0295142867,-0.288077116,-0.1288164705,0.0231063496,0.1196947098,0.5545585752,-0.0001154608,0.1748197377,-0.046863202,0.0502059348,0.0135354763,-0.0613390207,0.061984878,-0.2356380969,-0.0451326482,-0.1107083708,0.1963998228,0.0137783512,0.2351698577,-0.4139168859,0.4707049131,0.4032635391,0.1879202724,0.2586315572,0.1164235771,-0.0457814038,0.4986590743,-0.0801039413,-0.0995624736,0.5888605118,0.4468511343,-0.2438413948,-0.1711247116,0.3385624886,-0.2344774902,-0.1508841813,-0.1414978206,-0.2691135406,0.2938320339,-0.6156047583,0.4682242274,-0.0681593418,0.0907823145,-0.0633870959,-0.2490687221,-0.1350107938,-0.0936150178,-0.1098759249,0.2257647365,0.4395219982,-0.1495494992,0.2055870593,-0.0682476908,-0.1247708127,-0.3324677944,-0.1159746796,0.4712693393,0.1288682073,-0.0207260437,0.2954647243,0.3008286655,0.1232295334,0.2916143537,0.17420654,0.0111910403,0.2981752753,-0.3884970248,0.3316394687,-0.1883032471,0.2630648017,-0.3031633198,0.2108683884,-0.013326996,-0.1471738964,0.245511815,0.3030039966,-0.0604699589,0.2595026195,-0.2948144376,0.2529595792,0.4194628894,-0.1222944558,-0.2796795964,0.0336038806,-0.5552170277,-0.7762519717,-0.0632790774,0.3375050724,0.4028723538,0.1093554124,-0.3044160903,-0.132616356,-0.2338767499,0.208268255,0.2471144944,0.4144230485,0.0425139293,0.1491455883,0.2400327325,0.1745164543,-0.1405508071,0.1698295623,0.0849562585,0.149600625,-0.7198398113,-0.0348419026,0.0289795231,-0.0626349375,0.0019792835,0.0834667459,-0.1175395846,-0.411811769,0.3406249881,-0.2460239381,0.1409452856,0.3160174489,0.0178084187,0.1441170573,0.2787432373,-0.1227599531,0.071984686,0.2836942375,-0.4965551198,-0.3666153848,-0.0984075516,0.0370425209,-0.0430696607,0.1910544634,0.4627479017,-0.1594442725,0.3161435723,-0.3744051754,0.1293583661,-0.0766193196,-0.3452595472,-0.4303708673,-0.1973848492,0.3492318988,-0.2921376824,0.1789638847,0.1128091514,-0.1752108783,0.1565735042,0.3864723444,-0.2189261317,0.2811246216,-0.1052904055,-0.0941804722,0.6837505102,-0.2806610465,-0.6752010584,0.2573552728,-0.5013668537,-0.1445819587,0.4097953439,0.2358091772,0.2997538149,-0.0794482082,0.3253081143,-0.0315070562,0.1878620088,0.0564687699,-0.1306353956,-0.1833696067,0.1399638653,-0.0709243044,-0.0423557572,0.1124436036,0.4389398098,0.0218531154,0.3322861195,-0.1725773066,0.3336981535,0.2270243317,0.3746827245,0.3700474203,-0.1073651388,-0.070631884,0.0883536935,0.2813763916,-0.0693755671,-0.2177545428,-0.0564315692,-0.218089655,0.0088667516,0.1772125065,-0.5243186951,-0.2778482139,0.1006738693,0.1897175312,-0.0861744285,-0.1850094199,0.0722659305,0.0684186518,-0.074706614,-0.0877977982,-0.1331679076,-0.0027411866,-0.3007888794,-0.141679287,-0.2466587722,0.1272350699,0.230346024,0.1158881038,-0.1657595187,0.2577354014,0.1691140532,0.2231011242,0.0879704133,0.0498900078,-0.0814919546,0.1020352989,0.158797577,0.0954468995,0.0937408209,0.0492445976,0.1746814102,0.0925376788,0.0108560948,0.3938214779,0.0800941512,0.3215031922,0.0077006505,0.0573983341,-0.0739320442,0.2918778956,0.1616856754,0.1740435958,0.1012408882,0.0455260538,0.0264242776,0.0338476188,0.4503731132,0.0911366045,-0.0042150235,-0.0510589108,-0.1219831035,0.0876495838,0.0985331684,0.4043946862,0.3523017168,-0.0472353771,0.0283693671,-0.0014660567,0.1685306877,-0.0364185572,0.1677808166,0.1691948622,0.313115865,0.1606737673,0.0856153145,0.2174445987,-0.0743992031,-0.3199755251,0.2708792388,0.2420294732,-0.2740249038,0.1205577478,-0.1823228151,0.1474021226,-0.3863972127,0.0602678768,-0.0597069375,0.0446175709,-0.0311288331,0.4068607986,-0.1384714395,0.3061871827,-0.2012708634,-0.0706980675,-0.0700489953,-0.2389224321,0.2810952067,-0.1928637624,-0.1357761621,-0.0142605733,0.5200597048,-0.3461202979,0.3348174691,0.1310818046,0.1168422252,-0.4002668262,-0.1492413282,-0.1489002556,-0.0322173573,-0.0071632434,-0.05770652,0.082321614,0.0533612333,-0.3870067596,0.2943769097,-0.3648621738,-0.1971298605,0.0926634222,-0.1414785832,-0.2842856646,-0.2090806812,-0.1819683909,-0.2725278437,-0.4515674412,0.5456200242,-0.3829762936,0.2032080144,0.0438957587,0.1365900189,0.3035441935,0.1075681672,0.1809549034,-0.0206499323,0.0749729201,-0.0540520772,-0.2144558281,-0.2169629186,0.2142417282,0.2077107579,-0.0423310585,0.3718558252,-0.1384136528,-0.2154281288,-0.3418720961,0.0235143956,-0.1657322794,0.0905483663,0.4791874588,0.2240640819,-0.1020764261,-0.0121041443,-0.0576759577,0.1700657755,-0.0863168687,-0.0665369779,0.2340048552,0.4373008609,0.2596574426,0.4526251554,0.2906372249,0.2908763885,0.0928928256,-0.1935479641,0.2867951393,0.1278290898,-0.316806525,0.2851980031,-0.2700603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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/590","title":"The process cannot access the file because it is being used by another process (windows)","comments":"@wangcongcong123 thanks  for sharing.\n(BTW I also solved it locally on windows by putting the problematic line under try except and not using cache... On windows I just needed 1% of the dataset anyway)","body":"Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n```","comment_length":34,"text":"The process cannot access the file because it is being used by another process (windows) \n Hi, I consistently get the following error when developing in my PC (windows 10):\r\n\r\n```\r\n    train_dataset = train_dataset.map(convert_to_features, batched=True)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\site-packages\\nlp\\arrow_dataset.py\", line 970, in map\r\n    shutil.move(tmp_file.name, cache_file_name)\r\n  File \"C:\\Users\\saareliad\\AppData\\Local\\Continuum\\miniconda3\\envs\\py38\\lib\\shutil.py\", line 803, in move\r\n    os.unlink(src)\r\nPermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\\\Users\\\\saareliad\\\\.cache\\\\huggingface\\\\datasets\\\\squad\\\\plain_text\\\\1.0.0\\\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\\\tmpsau1bep1'\r\n\r\n``` \n @wangcongcong123 thanks  for sharing.\n(BTW I also solved it locally on windows by putting the problematic line under try except and not using cache... On windows I just needed 1% of the dataset anyway)","embeddings":[-0.1660128385,-0.0378365368,-0.0557806827,0.2940466702,0.3540613651,0.1522651315,0.3368764222,0.1749328077,0.2806835771,0.2250535935,-0.1342353523,0.2158219069,-0.1404852122,-0.1683883667,-0.1173466891,0.0108611938,0.0012655875,0.044212468,0.2459802777,0.2743896544,-0.4984654486,0.0607167557,-0.3295137882,0.423772186,-0.2919598818,-0.3280044198,0.1058607846,0.3697049916,-0.073203221,-0.1974390894,-0.1002669632,-0.0513668768,0.1314433068,0.4546838403,-0.0001180091,0.1998518556,0.0130158756,0.0264493786,0.0207782388,-0.0899392739,-0.0563950725,-0.2078053057,-0.1073655263,-0.066042006,0.1141617745,0.0234638974,0.2015790343,-0.4571410716,0.3327825367,0.4084486961,0.1968295425,0.2311187088,0.1027954519,-0.1659160107,0.535613656,-0.1112563685,-0.0627501085,0.633163631,0.5471028686,-0.2220690846,-0.1172490567,0.2611917853,-0.2444720715,-0.1755072474,-0.1392346472,-0.2325413078,0.3693843186,-0.6541210413,0.4994104505,-0.0672556311,0.2061076164,0.0079516917,-0.1065307781,-0.1031479985,-0.1989775598,-0.0291665532,0.2501770556,0.4200428426,-0.1939842254,0.3057061434,-0.1787290424,-0.1260132641,-0.3501567245,-0.0605799817,0.4386653304,0.0405456498,-0.0260064825,0.3118020296,0.3198761046,0.134772256,0.255161196,0.1011163965,0.0105901724,0.2738932073,-0.3511509597,0.263247937,-0.2067010701,0.4223089218,-0.2271409184,0.1090166867,-0.109516643,-0.2546097338,0.2531674206,0.3653867245,-0.0747126415,0.314797163,-0.252848357,0.1199978143,0.3119694293,-0.1192552969,-0.2826243043,-0.0470495634,-0.5266233087,-0.8045742512,-0.1368512362,0.3325532973,0.354899019,0.0067585111,-0.4248265624,-0.1458636522,-0.1495648474,0.1357555538,0.2901575565,0.3832624853,0.0116868084,0.1563884169,0.2046037763,0.2144590616,-0.1127433702,0.0718330964,0.0921355113,0.2212531865,-0.8064484596,-0.0484174006,-0.0237104427,-0.0383469947,0.0575435348,0.0691565275,-0.1150588319,-0.393950969,0.3609554172,-0.2968108654,0.0476291552,0.2490938604,0.0270233378,0.1089756787,0.3133922219,-0.1210688651,0.0360730924,0.2741183043,-0.594530344,-0.4111284018,-0.0860513076,0.0659642816,-0.0545929894,0.1561235189,0.4824938774,-0.0974189118,0.2904032469,-0.3547206223,0.0470065475,0.0024051284,-0.3527668715,-0.4089997113,-0.1699237972,0.2275535166,-0.4454240799,0.1663402319,0.1123914272,-0.2967844009,0.2837726474,0.4242579341,-0.2254429162,0.3697239459,-0.1372352839,-0.1389300972,0.6785027981,-0.1987152994,-0.7276574373,0.3511730134,-0.49825266,-0.2206837982,0.3715967238,0.2742252946,0.3183171153,-0.0356604569,0.4256315827,-0.011666554,0.1258437932,-0.1144285873,-0.1041864529,-0.2354744673,0.2226243168,-0.0457357429,-0.0473345406,0.3152661324,0.3644979894,0.0500470363,0.2891131938,-0.1426452249,0.3079093993,0.2466266304,0.3382252157,0.4254822135,-0.1479532272,-0.1629213691,0.1703911126,0.2562148273,-0.1407578737,-0.2786255777,-0.0121229701,-0.2053186148,-0.1437116414,0.2086337209,-0.5470626354,-0.265106827,0.076324977,0.0634872615,-0.0284603648,-0.1560453475,0.0889445841,0.0087253191,-0.0728094056,-0.097151041,-0.12181364,0.0821682215,-0.3950124085,-0.0846489146,-0.2371075153,0.1842457354,0.2675737739,0.1738244593,-0.1304078102,0.1893813461,0.1834195554,0.1690430045,0.231851697,0.0930386782,-0.026241364,0.0120444121,0.2170071155,-0.0181245822,0.0920294449,0.0306708012,0.3035518229,0.0972554833,-0.0145244868,0.3563963473,0.0089260023,0.2917680442,-0.029410949,0.0325310305,0.1224771515,0.2409925312,0.2025087327,0.1058615595,-0.0261074025,0.0663528293,0.039524354,-0.0594210848,0.4154398739,0.094932422,-0.0369529687,-0.0006532009,-0.1412418038,0.0572685376,0.0906977803,0.2913498878,0.3895952404,-0.0294962283,-0.0036336074,0.0386686549,0.1876131147,-0.068315886,0.1674478948,0.204569295,0.3450443447,0.1418389678,0.0323923305,0.1896013767,0.0150764957,-0.3164551854,0.175363332,0.3392398655,-0.2660369277,0.1201162115,-0.1579214931,-0.041079253,-0.3458783329,0.122357212,-0.07665243,0.0178315733,-0.0643588826,0.4527756274,0.0092966976,0.1905372441,-0.2618510425,-0.0504557639,-0.1107772216,-0.1444194615,0.2394903451,-0.2877012193,-0.1475091577,0.0261554532,0.4310839474,-0.3701074719,0.3875534236,0.0265530068,0.1025777906,-0.3318633735,-0.0847977027,-0.1104743555,0.0069137928,0.0146128265,-0.0169821903,0.1785103381,0.116805993,-0.4084207714,0.3876773417,-0.2849356532,-0.1894249469,0.038214393,-0.2024391145,-0.329521209,-0.0973660871,-0.1877679527,-0.3236171603,-0.3973131478,0.4519200325,-0.4148595631,0.1817364395,-0.0535670221,0.0407032557,0.2566218674,0.1344563961,0.1836619079,-0.0362395085,-0.0234081801,-0.1205165833,-0.1703297347,-0.1716614068,0.2624780238,0.2322476208,-0.1153168082,0.3312906325,-0.1592457145,-0.2347825021,-0.3380510211,0.0444339402,-0.0777586699,-0.0934742317,0.4242709279,0.1935469061,-0.0781683996,0.0072097885,-0.1666396409,0.1691445708,-0.0822771415,0.0318536609,0.2952131331,0.5357869864,0.2394001484,0.4260253608,0.3088305593,0.2428982258,0.1612043679,-0.2039854228,0.178150177,0.08118321,-0.3624286652,0.3248820603,-0.1848437935,-0.535646736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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/580","title":"nlp re-creates already-there caches when using a script, but not within a shell","comments":"Couln't reproduce on my side :\/ \r\nlet me know if you manage to reproduce on another env (colab for example)","body":"`nlp` keeps creating new caches for the same file when launching `filter` from a script, and behaves correctly from within the shell.\r\n\r\nExample: try running\r\n\r\n```\r\nimport nlp\r\n\r\nhans_easy_data = nlp.load_dataset('hans', split=\"validation\").filter(lambda x: x['label'] == 0)\r\nhans_hard_data = nlp.load_dataset('hans', split=\"validation\").filter(lambda x: x['label'] == 1)\r\n```\r\n\r\ntwice. If launched from a `file.py` script, the cache will be re-created the second time. If launched as 3 shell\/`ipython` commands, `nlp` will correctly re-use the cache.\r\nAs observed with @lhoestq.","comment_length":20,"text":"nlp re-creates already-there caches when using a script, but not within a shell \n `nlp` keeps creating new caches for the same file when launching `filter` from a script, and behaves correctly from within the shell.\r\n\r\nExample: try running\r\n\r\n```\r\nimport nlp\r\n\r\nhans_easy_data = nlp.load_dataset('hans', split=\"validation\").filter(lambda x: x['label'] == 0)\r\nhans_hard_data = nlp.load_dataset('hans', split=\"validation\").filter(lambda x: x['label'] == 1)\r\n```\r\n\r\ntwice. If launched from a `file.py` script, the cache will be re-created the second time. If launched as 3 shell\/`ipython` commands, `nlp` will correctly re-use the cache.\r\nAs observed with @lhoestq. \n Couln't reproduce on my side :\/ \r\nlet me know if you manage to reproduce on another env (colab for example)","embeddings":[0.0140547734,0.1271244735,0.0085562682,0.035959471,-0.0078010503,-0.263797164,0.1551121324,0.1553373635,0.3577845991,-0.1724361777,-0.1574480087,0.2879821062,-0.0159159172,-0.1409790665,0.3365516365,0.1459569633,-0.0274409931,0.1052429006,0.1590004116,-0.0974338725,-0.0484839082,0.1789569855,-0.1397798955,-0.168349281,-0.0173317902,0.0266732313,-0.0224758815,0.155035004,0.1056409478,-0.4688453674,0.2854130864,-0.0673121512,-0.095046483,0.1259214133,-0.0001123684,-0.1467732191,0.2711631358,-0.082623899,-0.2608337402,-0.1274135113,0.1139277816,-0.3116106689,0.3685198724,-0.1763194054,-0.0037387295,0.1525944471,0.137779966,-0.167603299,0.5938943028,0.3666618466,0.2137849182,0.2000362128,-0.2147941738,0.197613284,0.2465297282,-0.008641189,-0.1001662463,-0.1787401438,-0.0123234885,-0.3991587162,-0.1550955474,0.4522329569,-0.2373663634,0.0255578868,0.1741417646,0.1520873308,0.169741556,-0.3322789967,0.0382096209,-0.1236505732,0.0047663492,-0.3702811897,-0.1622067541,-0.3918675482,-0.1547731161,-0.5328938961,0.2281316966,0.2824671268,-0.0714175254,0.0352046788,-0.1941294372,0.1388888806,0.172004953,0.0363139659,0.2497341186,0.4306173027,0.0625300854,0.0532180816,0.1106561273,-0.0548592508,0.2229954898,-0.3569903672,-0.0537067614,0.3228704035,-0.2641911507,0.0885866731,0.3014730215,0.4047347307,-0.0454725511,0.2235714048,0.173475638,-0.04801891,-0.0876520574,0.225335598,0.0472554676,0.3800198138,0.3655150831,0.0478012338,0.0904157385,-0.010813619,-0.3588793874,-0.0273606144,0.3107827008,0.1221773773,0.4281068146,-0.0614237674,-0.0855408087,-0.0838730782,-0.2211291343,0.1604165435,-0.1021044776,-0.1083700731,0.2620150745,0.3968604207,0.0097243367,0.1738788635,0.0508838631,-0.0545453094,-0.4351886511,-0.1983556896,-0.174329102,0.1057347655,-0.2974101603,0.187769264,0.4734048247,0.2011061907,0.449016273,0.1165864989,-0.2418131381,-0.2520127296,0.3037593365,-0.1120486856,0.4608233273,0.2307967097,0.196698159,0.0357480943,0.2566693127,-0.2837709188,-0.1765892208,0.1323595643,-0.1480898261,0.0681454539,0.446412921,0.1733050793,-0.0703175217,0.0103104915,0.3509064615,0.0753671378,0.3642429113,-0.5678489208,-0.0792800933,-0.4126440585,-0.363712877,-0.251506865,-0.0087802894,0.4585021138,-0.1346430629,-0.1648874581,-0.1391745508,0.1942669749,0.2277397811,0.3964073062,-0.1332827061,0.0929469988,-0.1476305723,0.0661578849,0.6278782487,-0.4900433421,-0.3707662821,0.1228375509,0.0793688223,0.3197263777,0.1245157644,0.2428554446,-0.0670710653,-0.2258004695,0.2498877048,0.3515277207,0.0406160578,0.082237117,-0.2879509926,0.3968786597,0.325894624,-0.2016420513,-0.1354915053,0.2156065553,-0.1007497683,0.0996777192,0.0922810361,0.013635885,0.1314320713,0.040105816,-0.0570404343,-0.0098581789,0.1718413234,-0.0933756903,-0.0365489274,0.033453308,-0.2740916014,-0.2214099765,0.1858896166,-0.1368455738,0.0164683778,-0.1372162998,-0.1522569656,-0.4593999088,0.1264474988,0.3427806497,0.346229583,-0.1067051068,-0.219979316,0.5316773057,0.1187795922,-0.0748461634,-0.2673670948,-0.0871285871,-0.151425764,-0.2447949052,-0.2198594064,0.1518070996,0.2090099454,0.098716788,-0.155787915,0.192658186,0.2483176738,-0.1676100791,-0.2118837237,0.0143151721,0.0387563407,0.3125903904,0.0876144692,0.1030734256,0.1594628841,-0.1032630876,-0.1650417894,0.3819607794,0.1783604622,0.072118029,0.1815746725,0.0295195691,0.3982230723,-0.2489920259,-0.025376942,-0.2632983923,0.4047840834,-0.1850556433,0.089964658,0.045469474,-0.2181368172,0.3232727051,0.4830141664,0.2757501006,0.0691799819,0.0878766105,-0.0159705598,-0.3959516585,-0.0724546015,0.4940246344,0.4645235837,0.2245407701,-0.0549589097,0.1367132813,-0.1678971052,-0.2303491235,0.1029078588,-0.1981987059,-0.0195112955,0.1492987871,0.3859745264,0.0612337776,-0.3336632848,0.0843749121,-0.0284442119,-0.1334321648,-0.1216012686,0.3592764437,-0.370267123,-0.4869056344,-0.1300432235,0.2459070086,-0.1161158234,-0.0988213941,0.0744908452,0.3453322351,0.0831707343,0.1217840612,-0.0779095143,0.2753459811,-0.1485340148,-0.1314699948,-0.0340485126,-0.2705312073,-0.3422706425,-0.0438280068,0.1777164489,0.0886241719,0.337908417,0.0205061566,-0.0477558561,-0.1575678438,-0.3425697088,0.0794402957,0.0691463202,0.2530013323,-0.0915529504,-0.3343288302,-0.3508038819,-0.0153951058,0.1838683039,-0.3609211743,-0.1059565619,0.0877094343,0.0262161009,-0.1499068588,-0.3578155637,-0.339805454,-0.2054053992,-0.1251423955,-0.0889979973,0.2696052194,0.2463214695,0.5280355811,-0.4274564385,-0.185481146,-0.3738567531,0.2974689901,-0.2761282921,0.1026844755,0.0667226389,-0.2120818049,-0.2694002092,-0.1537080854,-0.2264178246,-0.0149639407,0.1810590029,-0.4297280908,-0.4037624896,-0.1235879362,-0.1860659719,0.0473243669,0.1130345613,0.4327495098,-0.0400290005,-0.112348929,-0.4418010712,-0.3570384383,0.1656191945,-0.0183242783,0.2703091502,-0.1238624826,0.3400833905,-0.0012423571,0.0214991551,0.2903298438,0.0951482877,0.3572483659,0.123428911,0.3901816308,-0.2690267563,-0.2034891993,-0.090188168,0.0914639235,-0.0907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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"Some wikipedia languages have already been processed by us and are hosted on our google storage. This is the case for \"fr\" and \"en\" for example.\r\n\r\nFor other smaller languages (in terms of bytes), they are directly downloaded and parsed from the wikipedia dump site.\r\nParsing can take some time for languages with hundreds of MB of xml.\r\n\r\nLet me know if you encounter an error or if you feel that is is taking too long for you.\r\nWe could process those that really take too much time","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":88,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n Some wikipedia languages have already been processed by us and are hosted on our google storage. This is the case for \"fr\" and \"en\" for example.\r\n\r\nFor other smaller languages (in terms of bytes), they are directly downloaded and parsed from the wikipedia dump site.\r\nParsing can take some time for languages with hundreds of MB of xml.\r\n\r\nLet me know if you encounter an error or if you feel that is is taking too long for you.\r\nWe could process those that really take too much 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"Ok, thanks for clarifying, that makes sense. I will time those examples later today and post back here.\r\n\r\nAlso, it seems that not all dumps should use the same date. For instance, I was checking the Spanish dump doing the following:\r\n```\r\ndata = nlp.load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner', split='train')\r\n```\r\n\r\nI got the error below because this URL does not exist: https:\/\/dumps.wikimedia.org\/eswiki\/20200501\/dumpstatus.json. So I checked the actual available dates here https:\/\/dumps.wikimedia.org\/eswiki\/ and there is no 20200501. If one tries for a date available in the link, then the nlp library does not allow such a request because is not in the list of expected datasets.\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.es\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 965, in _download_and_prepare\r\n    super(BeamBasedBuilder, self)._download_and_prepare(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 422, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/download_manager.py\", line 155, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 163, in map_nested\r\n    return {\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 164, in <dictcomp>\r\n    k: map_nested(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 191, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/download_manager.py\", line 156, in <lambda>\r\n    lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/file_utils.py\", line 191, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/dumps.wikimedia.org\/eswiki\/20200501\/dumpstatus.json\r\n```","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":252,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n Ok, thanks for clarifying, that makes sense. I will time those examples later today and post back here.\r\n\r\nAlso, it seems that not all dumps should use the same date. For instance, I was checking the Spanish dump doing the following:\r\n```\r\ndata = nlp.load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner', split='train')\r\n```\r\n\r\nI got the error below because this URL does not exist: https:\/\/dumps.wikimedia.org\/eswiki\/20200501\/dumpstatus.json. So I checked the actual available dates here https:\/\/dumps.wikimedia.org\/eswiki\/ and there is no 20200501. If one tries for a date available in the link, then the nlp library does not allow such a request because is not in the list of expected datasets.\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.es\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 965, in _download_and_prepare\r\n    super(BeamBasedBuilder, self)._download_and_prepare(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 422, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/download_manager.py\", line 155, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 163, in map_nested\r\n    return {\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 164, in <dictcomp>\r\n    k: map_nested(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 191, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/download_manager.py\", line 156, in <lambda>\r\n    lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/file_utils.py\", line 191, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/gaguilar\/.conda\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/dumps.wikimedia.org\/eswiki\/20200501\/dumpstatus.json\r\n```","embeddings":[0.3074045777,-0.113163203,-0.1143275797,0.2666960657,0.0679331198,0.2159503251,0.1886423379,0.1882861257,0.6027131081,-0.2003641725,0.032980103,0.0883672684,0.1432431787,-0.2862560451,0.0379251018,-0.1857149005,0.0711626112,-0.0526695661,0.0999960229,-0.3232582808,-0.3721318245,0.2316435426,-0.3498652279,0.0395479575,-0.2949983478,0.3205533326,-0.1095639318,-0.1376003921,0.0048030014,-0.3297347724,0.1528253704,0.2533638775,0.3197317719,0.2253631353,-0.0001195317,-0.1390187144,0.7226005197,-0.140297249,-0.5811544657,-0.1576965749,-0.3304528296,-0.474478662,0.1427856833,-0.2372422367,0.1085572839,-0.1610044986,0.29019472,-0.372911334,0.2277098149,0.1040740609,0.1720932275,-0.0161261316,0.0532660335,0.0327017643,0.310792774,0.1675486565,0.1112300977,0.0960423201,0.0196501911,-0.2150571346,0.1037457734,0.0984232426,-0.1336673051,-0.1492940038,0.1253485233,-0.0472491719,-0.1610984057,-0.616353929,0.1744622141,0.2040951699,0.6709717512,0.0023055098,-0.4189354181,-0.1440745294,-0.022451736,0.0209414754,0.1702196002,0.3295253515,-0.0454614237,0.0398623869,-0.0027117806,-0.2038349658,-0.0404362828,0.7206988931,-0.0264509171,0.4018862247,0.1202324033,0.1144707501,-0.0478507318,-0.1456205547,-0.1257717609,-0.3335688412,0.4209320843,0.4332474768,-0.1695616692,0.2906306982,-0.0857509226,0.2263407409,-0.0529668033,-0.1909829229,-0.1185327768,0.2109177113,-0.0961131901,0.1913405508,0.3146859109,-0.1448129117,0.2309992313,-0.0167630315,0.3436278105,0.0785777792,-0.1617887169,-0.023676727,-0.2893207371,-0.2270640582,-0.2285067141,-0.0829101056,0.0350426435,-0.2333207279,0.2474828213,0.1096672639,-0.238432169,-0.2751595676,-0.1687636971,0.3228849769,-0.1710451692,0.2898728251,0.2068962008,0.3334832788,-0.4393334091,-0.098703973,0.0050612511,-0.0328752212,-0.2345456481,0.2383075505,0.2733896673,0.1644213349,0.3222702444,0.0639748201,-0.2206522822,-0.1693531275,-0.0159264226,-0.1536651552,-0.0792210624,0.0881713629,0.2245720029,0.5646492839,0.0242779851,-0.2164819539,-0.1830213815,0.1098443568,-0.1811427623,0.2454306185,-0.0224614739,0.0989767835,-0.3706935048,-0.053505253,-0.2598139346,0.1856046021,0.1031376943,-0.1530772299,-0.0132446084,-0.2585122287,-0.1633183956,-0.0153553355,0.2881461084,0.5672477484,-0.1877700835,-0.1831033081,-0.2109172642,0.1560409516,0.3122498691,0.2041439116,-0.1045734137,0.3262509406,-0.1384691745,0.0980197638,0.3381272256,-0.0435735025,0.0776512176,0.0450996086,0.2526730895,0.2135817856,-0.0926935524,-0.0261055101,0.1963848472,0.0178859457,0.1415536255,0.4359198213,0.3257624507,-0.0453435518,-0.374733448,-0.0472105034,0.3917998672,0.2488121539,0.1009588838,-0.1692661792,0.1102248505,0.5450981259,0.1534947455,0.0131801693,0.1428936273,0.4438541532,-0.3199238479,0.2711729407,0.2049239427,0.0090840152,-0.5281934142,0.2005945742,0.0112970527,0.1782017499,0.0175968874,0.052591607,-0.3461596668,-0.113277562,-0.201107353,0.2239731997,0.0632472038,0.2544429302,-0.0102858078,0.5279006958,0.0372149125,-0.3473691642,-0.000489692,-0.1019139811,-0.59104985,0.4307764769,-0.0491725318,-0.0058684056,0.0674812496,0.2370138764,0.3196126521,-0.1044643372,-0.0402181149,-0.223232165,0.3271289468,0.3333978951,0.187366575,-0.096820496,0.308155328,-0.3724738657,0.1896894872,0.3602362871,0.1580131799,-0.2589322925,0.0363305509,0.3114947677,-0.0588045307,0.3653423488,0.0718835965,0.0265556257,0.3908677995,-0.0897051767,-0.1538592875,-0.2190165222,0.475998491,0.1982602775,0.0277488716,0.3858515024,-0.1408335567,-0.1522122175,0.5553575158,-0.0403646119,-0.050155852,0.2276264429,-0.1491426229,-0.1826263368,0.0057154684,-0.2520778179,-0.1125383675,0.1228652298,0.0823420882,-0.3532349765,-0.0523493327,-0.249039501,0.0300016236,0.0911749154,0.0588251203,0.1508001536,0.1172384769,-0.0549026728,-0.3138919473,0.0181929115,0.0604091324,0.1874241233,-0.1832011491,0.0711913705,-0.4476852417,-0.3566983044,-0.2708434165,-0.3444032967,-0.3639487028,-0.5259601474,-0.0327019654,0.0293999556,-0.0621467941,0.2045760006,-0.1936113983,0.0379354395,-0.2525600791,-0.0846444443,-0.4149157703,-0.4787170887,-0.3776288033,-0.0720942989,0.5312838554,0.2609661222,0.0445938744,-0.112190865,-0.2606809735,-0.216250062,-0.3026230335,-0.1276061535,-0.1894437373,0.1767017096,-0.2689674199,0.4213791192,-0.0727718398,-0.2691791654,0.3827326298,-0.0300239883,0.0335943997,0.1892677993,0.1611525416,0.1392670125,-0.0332661197,-0.4139075875,-0.3171475828,-0.2624527812,-0.1804500818,0.0568670258,-0.009888825,0.1248526648,-0.0625321344,0.044401776,-0.0467396528,-0.028973205,-0.1634233147,0.2337409705,0.5412666202,-0.1276019514,-0.4130738974,0.1248721778,-0.102524437,0.0197148491,0.0213591382,-0.4710836112,0.1845600605,-0.1556138545,-0.0356668569,0.2318301201,-0.0825889111,-0.0668094903,-0.1799345613,0.0941499844,-0.0223419052,-0.106119521,-0.1568667442,-0.0057118949,0.2745938897,0.1639134735,0.3346804082,-0.3372355103,0.746999383,0.2176746428,0.0936311558,0.3313852549,0.2281756401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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"Thanks ! This will be very helpful.\r\n\r\nAbout the date issue, I think it's possible to use another date with\r\n\r\n```python\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n```\r\n\r\nHowever we've not processed wikipedia dumps for other dates than 20200501 (yet ?)\r\n\r\nOne more thing that is specific to 20200501.es: it was available once but the `mwparserfromhell` was not able to parse it for some reason, so we didn't manage to get a processed version of 20200501.es (see #321 )","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":77,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n Thanks ! This will be very helpful.\r\n\r\nAbout the date issue, I think it's possible to use another date with\r\n\r\n```python\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n```\r\n\r\nHowever we've not processed wikipedia dumps for other dates than 20200501 (yet ?)\r\n\r\nOne more thing that is specific to 20200501.es: it was available once but the `mwparserfromhell` was not able to parse it for some reason, so we didn't manage to get a processed version of 20200501.es (see #321 )","embeddings":[0.1844169199,-0.1612697095,-0.14086321,0.3425940871,0.0797085017,0.2668132186,0.1986873895,0.1461935341,0.7380709052,-0.2608357668,0.117104128,0.1079088822,0.0482873581,-0.1868229806,0.0673659146,-0.2168420255,0.1130851358,-0.1392923295,0.0121253384,-0.2986245155,-0.375538826,0.2062976807,-0.3456492424,-0.0187360477,-0.2716134489,0.2526730895,-0.0286885835,-0.1416581571,0.0758292824,-0.3684397042,0.1234105751,0.2239328474,0.3555548787,0.1723899692,-0.0001185649,-0.1505396366,0.660580039,-0.1414551139,-0.5648554564,-0.101155445,-0.1321353316,-0.4575914145,0.2321635932,-0.1510665417,0.015325794,-0.2116499543,0.2019940317,-0.495457083,0.1956826001,0.1200146303,0.1927446127,-0.093378976,0.0861300603,0.0984007418,0.2837163806,0.0862573832,0.1070939824,0.091626972,0.0906863883,-0.2530971169,0.0317131244,0.1068276763,-0.1099942774,-0.1209026426,0.1942079663,-0.0663056523,-0.0168333966,-0.6403782964,0.1275741011,0.1657272279,0.7213552594,0.0507114045,-0.3950336277,-0.223338291,-0.0087345932,0.0409741402,0.167893976,0.2475132942,-0.0853280202,-0.0411406644,0.0339016505,-0.2376619428,-0.0329024345,0.6365787983,-0.0692424849,0.4427610636,0.1174650714,0.1288728416,0.0202501975,-0.253596276,-0.1812103689,-0.2587616444,0.4181684554,0.4655200839,-0.174560383,0.3636753559,-0.0123904804,0.3851032257,-0.038643036,-0.15390791,-0.2357905805,0.2513465881,-0.1333470494,0.1615580171,0.2684187889,-0.1073283479,0.2236352265,-0.1620290279,0.3467641175,0.0117434561,-0.1397831887,0.0552117676,-0.2117568851,-0.2181311399,-0.1720184833,-0.0451148674,-0.0281924997,-0.2950689793,0.2572841644,0.1664818823,-0.2195028663,-0.3274429142,-0.1749036163,0.2692579329,-0.1951353103,0.2675597966,0.1724327058,0.2840561867,-0.4226954281,-0.1039365456,0.0161602814,-0.0995109975,-0.257117182,0.2204846144,0.3558721244,0.1486310512,0.2602377832,0.0523075499,-0.0767712891,-0.1491639316,-0.0647039264,-0.163499251,-0.0249815471,0.0091658114,0.1766334772,0.5606009364,0.0988832936,-0.2323977351,-0.1752841026,0.1039439142,-0.0761017725,0.3082154691,0.0369334295,0.1233540773,-0.3571022153,-0.104668878,-0.298697412,0.2197835743,0.1202819943,-0.200995326,-0.0416015312,-0.3253329098,-0.1707216948,-0.0548555851,0.3113587797,0.5726601481,-0.2665934861,-0.1806461811,-0.1147188693,0.1087629795,0.207697615,0.2036616057,-0.0525589585,0.257996738,-0.1294812411,-0.0633769482,0.2858005166,-0.0579142123,0.10676568,-0.0260217004,0.2796825767,0.3171775043,-0.1271193177,0.0283081699,0.1985153556,0.0353313871,0.0832561329,0.4870526791,0.2065672874,-0.0626081824,-0.3746045232,-0.0362061933,0.5943416953,0.3290396333,0.1499209255,-0.1518611312,0.0305371415,0.3674328327,0.1763261408,0.0274736993,0.1374637932,0.3568587005,-0.3556275666,0.3041117489,0.2620894611,-0.0356626809,-0.5659899116,0.2383675426,0.1369163841,0.1958183497,0.0540325716,0.0960886702,-0.2962388992,-0.1357355118,-0.2376848906,0.2135418653,0.0795716047,0.1882343739,-0.0467985235,0.5260180235,0.0267842729,-0.0761934221,0.0167776328,-0.1406954825,-0.5490181446,0.3648965657,-0.0192004647,-0.078961879,0.0225067642,0.2183268368,0.329241395,-0.1560719758,-0.0868927315,-0.2810145617,0.3929997683,0.2565650642,0.1176106855,-0.064565137,0.2742711604,-0.3607213199,0.2317126989,0.3237192631,0.1243609935,-0.2508164942,-0.0573165268,0.3349547386,-0.1451161206,0.4635535181,0.035760574,0.0688562021,0.3251148462,0.0063651716,-0.1108129844,-0.1346651912,0.5287485123,0.1029547378,0.0637287572,0.2845212817,-0.1806773543,-0.0947574303,0.5067564249,0.0490307026,-0.0480579324,0.2475455403,-0.2007910758,-0.1672690511,-0.0358605571,-0.2484871894,-0.182742551,0.132170409,0.0703616589,-0.3738316596,0.0458124578,-0.1439269185,-0.0153811239,0.1464487612,0.1807280481,0.0824580863,0.1403475404,-0.000978288,-0.2181272358,0.0029938754,0.0622046776,0.1825273335,-0.1979463845,0.0681489632,-0.3990391195,-0.3739188015,-0.2093369812,-0.2234026939,-0.4862132072,-0.4510011077,-0.1158518568,0.0748049617,0.0489552431,0.1520549506,-0.0781797916,0.035830643,-0.244780466,-0.091454424,-0.4282477498,-0.5143586397,-0.4257921278,-0.025491789,0.540037334,0.1605172604,0.0958300158,-0.0363360979,-0.265871197,-0.1976131648,-0.2801333666,-0.1823983938,-0.2643185556,0.2399842441,-0.3431635499,0.3949019313,-0.0874270499,-0.0930264741,0.3740706444,-0.0331536047,0.0601067021,0.1564411521,0.1854311973,0.2013899237,-0.0076232972,-0.4389497936,-0.369666338,-0.2770028412,-0.0789736286,0.1606952548,-0.058080636,0.0915826932,-0.1584427953,-0.0235236343,0.020878952,0.0305209514,-0.1258708239,0.2937546968,0.4754168093,-0.0671438798,-0.4101143777,0.0925857797,0.0022497803,0.0188389048,0.037371181,-0.4818988144,0.110514991,-0.1167071015,0.0338356383,0.1245819107,-0.124604784,-0.0315149836,-0.1618935913,0.118665278,0.0365787409,-0.1993729621,-0.1834090501,-0.0256385431,0.1785925329,0.0766231343,0.3736392856,-0.3757041097,0.6833336353,0.191984728,0.0517833717,0.2668404877,0.2044528127,0.0208218321,-0.1411595494,-0.2687708139,0.0289678834,-0.0435255691,-0.0692294836,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"Cool! Thanks for the trick regarding different dates!\r\n\r\nI checked the download\/processing time for retrieving the Arabic Wikipedia dump, and it took about 3.2 hours. I think that this may be a bit impractical when it comes to working with multiple languages (although I understand that storing those datasets in your Google storage may not be very appealing either). \r\n\r\nFor the record, here's what I did:\r\n```python\r\nimport nlp\r\nimport time\r\n\r\ndef timeit(filename):\r\n    elapsed = time.time()\r\n    data = nlp.load_dataset('wikipedia', filename, beam_runner='DirectRunner', split='train')\r\n    elapsed = time.time() - elapsed\r\n    print(f\"Loading the '{filename}' data took {elapsed:,.1f} seconds...\")\r\n    return data\r\n\r\ndata = timeit('20200501.ar')\r\n```\r\n\r\nHere's the output:\r\n```\r\nDownloading: 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100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 28.7k\/28.7k [00:00<00:00, 954kB\/s]\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguil20\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 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1\/1 [03:16<00:00, 196.39s\/sources]\r\nDataset wikipedia downloaded and prepared to \/home\/gaguil20\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50. Subsequent calls will reuse this data.\r\nLoading the '20200501.ar' data took 11,582.7 seconds...\r\n````","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":202,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n Cool! Thanks for the trick regarding different dates!\r\n\r\nI checked the download\/processing time for retrieving the Arabic Wikipedia dump, and it took about 3.2 hours. I think that this may be a bit impractical when it comes to working with multiple languages (although I understand that storing those datasets in your Google storage may not be very appealing either). \r\n\r\nFor the record, here's what I did:\r\n```python\r\nimport nlp\r\nimport time\r\n\r\ndef timeit(filename):\r\n    elapsed = time.time()\r\n    data = nlp.load_dataset('wikipedia', filename, beam_runner='DirectRunner', split='train')\r\n    elapsed = time.time() - elapsed\r\n    print(f\"Loading the '{filename}' data took {elapsed:,.1f} seconds...\")\r\n    return data\r\n\r\ndata = timeit('20200501.ar')\r\n```\r\n\r\nHere's the output:\r\n```\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13.0k\/13.0k [00:00<00:00, 8.34MB\/s]\r\nDownloading: 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wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguil20\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 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1\/1 [03:16<00:00, 196.39s\/sources]\r\nDataset wikipedia downloaded and prepared to \/home\/gaguil20\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50. Subsequent calls will reuse this data.\r\nLoading the '20200501.ar' data took 11,582.7 seconds...\r\n````","embeddings":[0.2451880127,-0.1499561667,-0.1770797819,0.4052183628,0.0632281676,0.2607442439,0.2458155751,0.1112545356,0.6888960004,-0.2664470673,-0.0213993397,0.0063388622,0.0856510848,-0.2514183521,0.0070183449,-0.1802873462,0.0203865115,-0.1383438259,0.1124566123,-0.2367403209,-0.2798018456,0.2774860263,-0.2395900041,-0.0487777628,-0.1759125739,0.2128080875,-0.0351585783,-0.2206657529,0.1427014172,-0.3392617404,0.1575160921,0.2800291777,0.2215889841,0.1722692549,-0.0001170091,-0.0706660226,0.6258941889,-0.0953066498,-0.4801300168,-0.1560158134,-0.108958818,-0.4129000306,0.1846741289,-0.1244983152,0.0033279601,-0.2184667885,0.2557478845,-0.5376159549,0.1487024873,0.1018920243,0.1965543479,-0.1393259913,-0.0304985978,0.1324132234,0.2393972725,0.0642504022,0.151079759,0.1061608791,0.1205729097,-0.2250445187,-0.0113991546,0.148513779,-0.1639057994,-0.0380540639,0.1549713314,-0.0637798086,-0.1280984879,-0.6488245726,0.1541220397,0.1783592552,0.6348540187,0.0936589912,-0.3493359387,-0.2358499914,0.0220760126,0.0087388158,0.1717161685,0.3690199256,-0.0816911906,-0.0795899257,-0.0399339795,-0.1963656247,-0.0322664864,0.5931959152,-0.0880079716,0.3404202163,0.1230519637,0.1242888495,0.0851862431,-0.1302557439,-0.2010220289,-0.3093886375,0.4458721876,0.3907281458,-0.2508179545,0.4231896996,0.0125241587,0.2542313933,-0.0182336401,-0.1473664641,-0.1184965745,0.2253141105,-0.1155808792,0.1374964118,0.1883317232,-0.124688454,0.1700281799,-0.1154406294,0.3546131849,0.0449151173,-0.1789989918,0.0283960514,-0.1855123341,-0.1656094342,-0.2223558724,-0.0903871953,-0.0302750859,-0.2254109085,0.1415824294,0.1084441096,-0.3119995892,-0.3404396474,-0.1642335355,0.2511446178,-0.1807250381,0.2364733964,0.1775927693,0.3219090402,-0.4573749602,-0.0067457012,0.0449120812,-0.0690974742,-0.2206031978,0.2642526329,0.4220621884,0.0948154628,0.2164129764,0.0365128182,-0.0398966447,-0.0909143984,0.0710607097,-0.2667188942,-0.0585894696,-0.0462982021,0.2065355182,0.5741945505,0.0112998402,-0.2553770244,-0.2352945656,0.070635505,-0.0643666163,0.3766923547,0.0205886737,0.1219099164,-0.2673357427,-0.1256442368,-0.2556728423,0.3742669821,0.1674688905,-0.1810794771,-0.0655050129,-0.2110247314,-0.2137752175,-0.0543820895,0.2509810328,0.5896446109,-0.2892338634,-0.1646264493,-0.1988548636,0.2035034448,0.1886523813,0.2982170284,-0.0958646461,0.2337156236,-0.1778505743,0.0835026726,0.2486936152,-0.0395822115,-0.0014971978,-0.0257272013,0.2922070622,0.3353715837,-0.007831594,0.0580954328,0.2001075298,0.0750987157,0.1405767798,0.621952951,0.2040380388,-0.0073912088,-0.3208083808,-0.050289724,0.584579587,0.3552081585,0.1517485082,-0.2365605384,0.0374267027,0.2481883466,0.1562710702,-0.0131509518,0.0936758518,0.3756870329,-0.4390864372,0.2866098881,0.1611397713,0.0143171204,-0.6067688465,0.2630679607,0.0607769564,0.1707911491,-0.031874612,0.1079960689,-0.2593958378,-0.2113682032,-0.2820684314,0.1819368005,0.0974049121,0.2778010368,-0.0736682266,0.5513207316,0.1415987462,-0.0580300502,0.1464496255,-0.1990238428,-0.6403774619,0.4351526201,-0.02461222,-0.0105862133,0.075666368,0.1637992114,0.2957056165,-0.1776614934,-0.0669073537,-0.3276809156,0.2493428886,0.3236111999,0.225586772,-0.0355046913,0.3311399817,-0.3649567068,0.2097979635,0.2917325497,0.1693488955,-0.2859678268,-0.0535754189,0.312384963,-0.0520770177,0.5205019712,0.0842649043,0.0086803883,0.3458456397,-0.0037226875,-0.1675105989,-0.0433898382,0.5325737596,0.0772654563,0.0950237438,0.2842740715,-0.2170914263,-0.0199629348,0.5617513657,0.1191320047,-0.0381754115,0.1991221607,-0.1175560355,-0.1439589709,-0.0568963364,-0.2177955955,-0.1218238845,0.1635314077,0.111062631,-0.4647266865,-0.0015269194,-0.1823050082,-0.0400638804,0.0966213271,0.1555220783,0.1344235539,0.1967048496,-0.0348842032,-0.2852691412,-0.1075898558,0.143174246,0.1040027365,-0.0965625718,0.1613909155,-0.403183341,-0.3990436196,-0.2320873141,-0.3176559508,-0.4566809237,-0.4382843375,-0.1172616631,0.0020064681,0.0365824588,0.1779958308,-0.0860112309,-0.0040276083,-0.214853406,-0.1572204083,-0.3402951956,-0.4157637656,-0.3499432504,-0.0113514634,0.6082346439,0.1408375353,0.1521293223,-0.0444762409,-0.3557099104,-0.1870456636,-0.2607334256,-0.2199682742,-0.2619196177,0.1499442905,-0.3553150892,0.335590601,-0.1427180022,-0.1477808505,0.3654433787,0.0682374239,0.0449981689,0.1869262755,0.2114941031,0.2520879209,0.0519438162,-0.4182102084,-0.2767595053,-0.297721535,-0.0779388696,0.0720985383,-0.0323851146,0.0740771145,-0.1342922002,0.0277838763,0.0462004617,0.0540389381,-0.1266372651,0.3575998545,0.498567462,-0.0664858893,-0.3655041754,0.0541130602,0.0312132072,0.0838306397,0.0972238481,-0.4861781597,0.1801841259,-0.2643375993,0.0509781353,0.1153757274,-0.1262599528,-0.0208539218,-0.2068902999,0.0848828331,0.0921390206,-0.2170365453,-0.1743117273,-0.0443453491,0.1687156707,0.0408560038,0.3214994371,-0.3043214083,0.7088569403,0.1946599185,0.0952943116,0.2897074819,0.2598703802,0.0279643163,-0.1108570918,-0.361111998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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"> About the date issue, I think it's possible to use another date with\r\n> ```python\r\n> load_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n> ```\r\n\r\nI tried your suggestion about the date and the function does not accept the language and date keywords. I tried both on `nlp` v0.4 and the new `datasets` library (v1.0.2):\r\n```\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"20200601\", beam_runner='DirectRunner', split='train')\r\n```\r\nFor now, my quick workaround to keep things moving was to simply change the date inside the library at this line: [https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wikipedia\/wikipedia.py#L403](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wikipedia\/wikipedia.py#L403)\r\n\r\nNote that the date and languages are valid: [https:\/\/dumps.wikimedia.org\/eswiki\/20200601\/dumpstatus.json](https:\/\/dumps.wikimedia.org\/eswiki\/20200601\/dumpstatus.json)\r\n\r\nAny suggestion is welcome :) @lhoestq \r\n\r\n\r\n## **[UPDATE]**\r\n\r\nThe workaround I mentioned fetched the data, but then I faced another issue (even the log says to report this as bug):\r\n```\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\n```\r\n\r\nHere's the full stack (which says that there is a key error caused by this key: `KeyError: '000nbsp'`):\r\n\r\n```Downloading and preparing dataset wikipedia\/20200601.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gustavoag\/.cache\/huggingface\/datasets\/wikipedia\/20200601.es\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 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423M\/423M [01:36<00:00, 4.38MB\/s]\r\nWARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['--lang', 'es', '--date', '20200601', '--tokenizer', 'bert-base-multilingual-cased', '--cache', 'train', 'valid', '--max_dataset_length', '200000', '10000']\r\n\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nTraceback (most recent call last):\r\n  File \"apache_beam\/runners\/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 500, in _clean_content\r\n    text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n    section_text.append(section.strip_code().strip())\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/wikicode.py\", line 643, in strip_code\r\n    stripped = node.__strip__(**kwargs)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 63, in __strip__\r\n    return self.normalize()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 178, in normalize\r\n    return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/runpy.py\", line 194, in _run_module_as_main\r\n    return _run_code(code, main_globals, None,\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/runpy.py\", line 87, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/raid\/data\/gustavoag\/projects\/char2subword\/research\/preprocessing\/split_wiki.py\", line 96, in <module>\r\n    main()\r\n  File \"\/raid\/data\/gustavoag\/projects\/char2subword\/research\/preprocessing\/split_wiki.py\", line 65, in main\r\n    data = nlp.load_dataset('wikipedia', f'{args.date}.{args.lang}', beam_runner='DirectRunner', split='train')\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/direct\/direct_runner.py\", line 119, in run_pipeline\r\n    return runner.run_pipeline(pipeline, options)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 172, in run_pipeline\r\n    self._latest_run_result = self.run_via_runner_api(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 183, in run_via_runner_api\r\n    return self.run_stages(stage_context, stages)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 338, in run_stages\r\n    stage_results = self._run_stage(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 512, in _run_stage\r\n    last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 556, in _run_bundle\r\n    result, splits = bundle_manager.process_bundle(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 940, in process_bundle\r\n    for result, split_result in executor.map(execute, zip(part_inputs,  # pylint: disable=zip-builtin-not-iterating\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/concurrent\/futures\/_base.py\", line 611, in result_iterator\r\n    yield fs.pop().result()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/concurrent\/futures\/_base.py\", line 439, in result\r\n    return self.__get_result()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/concurrent\/futures\/_base.py\", line 388, in __get_result\r\n    raise self._exception\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/utils\/thread_pool_executor.py\", line 44, in run\r\n    self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 932, in execute\r\n    return bundle_manager.process_bundle(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 837, in process_bundle\r\n    result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/worker_handlers.py\", line 352, in push\r\n    response = self.worker.do_instruction(request)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/worker\/sdk_worker.py\", line 479, in do_instruction\r\n    return getattr(self, request_type)(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/worker\/sdk_worker.py\", line 515, in process_bundle\r\n    bundle_processor.process_bundle(instruction_id))\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/worker\/bundle_processor.py\", line 977, in process_bundle\r\n    input_op_by_transform_id[element.transform_id].process_encoded(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/worker\/bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 330, in apache_beam.runners.worker.operations.Operation.output\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 332, in apache_beam.runners.worker.operations.Operation.output\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"apache_beam\/runners\/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"apache_beam\/runners\/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1045, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/future\/utils\/__init__.py\", line 446, in raise_with_traceback\r\n    raise exc.with_traceback(traceback)\r\n  File \"apache_beam\/runners\/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 500, in _clean_content\r\n    text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n    section_text.append(section.strip_code().strip())\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/wikicode.py\", line 643, in strip_code\r\n    stripped = node.__strip__(**kwargs)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 63, in __strip__\r\n    return self.normalize()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 178, in normalize\r\n    return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train\/Clean content']\"```","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":841,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n > About the date issue, I think it's possible to use another date with\r\n> ```python\r\n> load_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n> ```\r\n\r\nI tried your suggestion about the date and the function does not accept the language and date keywords. I tried both on `nlp` v0.4 and the new `datasets` library (v1.0.2):\r\n```\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"20200601\", beam_runner='DirectRunner', split='train')\r\n```\r\nFor now, my quick workaround to keep things moving was to simply change the date inside the library at this line: [https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wikipedia\/wikipedia.py#L403](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/datasets\/wikipedia\/wikipedia.py#L403)\r\n\r\nNote that the date and languages are valid: [https:\/\/dumps.wikimedia.org\/eswiki\/20200601\/dumpstatus.json](https:\/\/dumps.wikimedia.org\/eswiki\/20200601\/dumpstatus.json)\r\n\r\nAny suggestion is welcome :) @lhoestq \r\n\r\n\r\n## **[UPDATE]**\r\n\r\nThe workaround I mentioned fetched the data, but then I faced another issue (even the log says to report this as bug):\r\n```\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\n```\r\n\r\nHere's the full stack (which says that there is a key error caused by this key: `KeyError: '000nbsp'`):\r\n\r\n```Downloading and preparing dataset wikipedia\/20200601.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gustavoag\/.cache\/huggingface\/datasets\/wikipedia\/20200601.es\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 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423M\/423M [01:36<00:00, 4.38MB\/s]\r\nWARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['--lang', 'es', '--date', '20200601', '--tokenizer', 'bert-base-multilingual-cased', '--cache', 'train', 'valid', '--max_dataset_length', '200000', '10000']\r\n\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nTraceback (most recent call last):\r\n  File \"apache_beam\/runners\/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 500, in _clean_content\r\n    text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n    section_text.append(section.strip_code().strip())\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/wikicode.py\", line 643, in strip_code\r\n    stripped = node.__strip__(**kwargs)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 63, in __strip__\r\n    return self.normalize()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 178, in normalize\r\n    return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/runpy.py\", line 194, in _run_module_as_main\r\n    return _run_code(code, main_globals, None,\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/runpy.py\", line 87, in _run_code\r\n    exec(code, run_globals)\r\n  File \"\/raid\/data\/gustavoag\/projects\/char2subword\/research\/preprocessing\/split_wiki.py\", line 96, in <module>\r\n    main()\r\n  File \"\/raid\/data\/gustavoag\/projects\/char2subword\/research\/preprocessing\/split_wiki.py\", line 65, in main\r\n    data = nlp.load_dataset('wikipedia', f'{args.date}.{args.lang}', beam_runner='DirectRunner', split='train')\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/direct\/direct_runner.py\", line 119, in run_pipeline\r\n    return runner.run_pipeline(pipeline, options)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 172, in run_pipeline\r\n    self._latest_run_result = self.run_via_runner_api(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 183, in run_via_runner_api\r\n    return self.run_stages(stage_context, stages)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 338, in run_stages\r\n    stage_results = self._run_stage(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 512, in _run_stage\r\n    last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 556, in _run_bundle\r\n    result, splits = bundle_manager.process_bundle(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 940, in process_bundle\r\n    for result, split_result in executor.map(execute, zip(part_inputs,  # pylint: disable=zip-builtin-not-iterating\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/concurrent\/futures\/_base.py\", line 611, in result_iterator\r\n    yield fs.pop().result()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/concurrent\/futures\/_base.py\", line 439, in result\r\n    return self.__get_result()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/concurrent\/futures\/_base.py\", line 388, in __get_result\r\n    raise self._exception\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/utils\/thread_pool_executor.py\", line 44, in run\r\n    self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 932, in execute\r\n    return bundle_manager.process_bundle(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 837, in process_bundle\r\n    result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/worker_handlers.py\", line 352, in push\r\n    response = self.worker.do_instruction(request)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/worker\/sdk_worker.py\", line 479, in do_instruction\r\n    return getattr(self, request_type)(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/worker\/sdk_worker.py\", line 515, in process_bundle\r\n    bundle_processor.process_bundle(instruction_id))\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/worker\/bundle_processor.py\", line 977, in process_bundle\r\n    input_op_by_transform_id[element.transform_id].process_encoded(\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/apache_beam\/runners\/worker\/bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 330, in apache_beam.runners.worker.operations.Operation.output\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 332, in apache_beam.runners.worker.operations.Operation.output\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"apache_beam\/runners\/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"apache_beam\/runners\/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1045, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/future\/utils\/__init__.py\", line 446, in raise_with_traceback\r\n    raise exc.with_traceback(traceback)\r\n  File \"apache_beam\/runners\/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 500, in _clean_content\r\n    text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/nlp\/datasets\/wikipedia\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50\/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n    section_text.append(section.strip_code().strip())\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/wikicode.py\", line 643, in strip_code\r\n    stripped = node.__strip__(**kwargs)\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 63, in __strip__\r\n    return self.normalize()\r\n  File \"\/home\/gustavoag\/anaconda3\/envs\/pytorch\/lib\/python3.8\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 178, in normalize\r\n    return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train\/Clean content']\"```","embeddings":[0.2467407435,-0.0418637209,-0.121031262,0.312300235,0.1007470265,0.200199604,0.1836347133,0.1085749045,0.6142448187,-0.2803642154,0.1189876348,0.2007731944,0.034528438,-0.1363366842,0.0880576223,-0.2634973526,0.0546107702,-0.1040422618,0.0303132087,-0.2449040413,-0.3982990086,0.2610419989,-0.3530089557,0.0145684741,-0.1812228709,0.2081045955,-0.0124996239,-0.1226630807,0.0534755848,-0.4016560614,0.1271000654,0.1967625618,0.3313166201,0.197395429,-0.0001186253,-0.1977957487,0.6336007118,-0.162105158,-0.5641887784,-0.1122205406,-0.1143344641,-0.436532259,0.2821566463,-0.1752434075,-0.0461251885,-0.3043342829,0.161864087,-0.5166298747,0.1445365399,0.22253187,0.1954718828,-0.0924769938,0.0923195779,0.1296254545,0.2619315684,0.0538863093,0.0945693552,0.1151472703,0.0538561195,-0.3287509978,0.0647287369,0.0624158122,-0.1051380038,-0.1172371209,0.3672706485,-0.1092405543,-0.1217817441,-0.5950627327,0.0641387179,0.0947354436,0.7301491499,-0.0377021991,-0.3581014872,-0.1889031976,-0.006987181,0.0573049374,0.172826916,0.1559323519,-0.0244679321,-0.1372971684,0.0292161051,-0.2469111979,-0.0619474538,0.743431747,-0.0167284198,0.3858577013,0.0737346858,0.1732941717,0.0626394004,-0.3136678934,-0.1730396897,-0.219293803,0.3790635467,0.4839769602,-0.1226430684,0.3475767076,-0.0123350741,0.3825616837,-0.0521783829,-0.158069253,-0.2352990359,0.1662348509,-0.1662418544,0.142632857,0.2290004045,-0.0134099592,0.3087078631,-0.135526821,0.2983295918,-0.064053908,-0.0458319858,0.0196876992,-0.1417012215,-0.199209854,-0.1657811403,-0.0544730239,-0.0478284135,-0.3128400147,0.2918701172,0.1117087528,-0.2736557126,-0.3444135487,-0.1295112967,0.3133083284,-0.1838987172,0.2302260101,0.1439910084,0.2676378489,-0.3891966045,-0.0696639568,-0.0016796045,0.0119818524,-0.2927108705,0.1612307727,0.3973585963,0.0833454207,0.2268027514,0.074819766,-0.014652444,-0.1550946087,-0.0783686116,-0.2006682754,0.0307389423,-0.0432604589,0.1019617915,0.5389502048,0.0627788231,-0.2294377238,-0.1606117338,0.0834421068,-0.109654516,0.3419541717,-0.0056775445,0.1212532222,-0.3625359833,-0.1406649351,-0.2977912128,0.2064987868,0.0987751856,-0.2669972479,-0.022539977,-0.3690509796,-0.1852134615,-0.1132443026,0.2871932089,0.4969678819,-0.2388131768,-0.2270080596,-0.0786340609,0.1175903901,0.1469183862,0.2245139778,-0.0805962011,0.1937002093,-0.1273546219,-0.0071627893,0.4132717252,-0.1051344052,0.0572991483,0.0223826915,0.2743159533,0.2108414769,-0.1830105931,0.0946493596,0.1899325997,0.0379426181,0.064643465,0.5457744002,0.2533354759,-0.0215168614,-0.3435814083,-0.0450481027,0.6537558436,0.3368974626,0.1342661381,-0.1086801291,0.05735882,0.3899138272,0.1507699043,0.0243369956,0.1013401523,0.3543474972,-0.3717959821,0.2649192512,0.309679687,-0.0367005803,-0.5847884417,0.2162316889,0.1441769451,0.2161539346,0.0928700194,0.1093354151,-0.3285709321,-0.1730718166,-0.2318118662,0.2040987909,0.0549687706,0.2256593853,-0.0271209273,0.5444970727,0.0311963856,0.0278387927,0.0366641656,-0.1735834479,-0.5712451935,0.3580466509,-0.0180360805,-0.0789082348,0.0505060814,0.2768629491,0.3914181292,-0.1247505546,-0.1328833103,-0.2583323121,0.3054012656,0.285580188,0.0374736823,-0.0770552754,0.2321472019,-0.3242640495,0.2337021232,0.3573760986,0.1669023335,-0.2340485007,-0.0023754444,0.33484447,-0.1550549567,0.4224194884,0.0630625188,0.0345317647,0.2765224874,-0.0269019473,-0.1470502466,-0.1411589533,0.5319138765,0.0347096138,0.0096832868,0.2768490314,-0.1995253563,-0.1459798813,0.5744214058,0.1044289395,-0.0474535115,0.2356465757,-0.1933622509,-0.2013408244,-0.0289734472,-0.1284898221,-0.1779083312,0.1603291482,0.0171624683,-0.3485314846,-0.0272583812,-0.1230650768,-0.0213758126,0.1418350786,0.1582038552,0.0385224223,0.2043384463,0.0613393486,-0.2022944093,-0.032809563,0.0632717684,0.1949344426,-0.2581511736,0.1352342963,-0.4075537622,-0.5762093663,-0.1227530763,-0.1689467728,-0.495135963,-0.4757381082,-0.1143935025,0.0920536369,0.1348278672,0.1173320115,0.0116768619,0.0618192367,-0.2605223656,-0.1536409855,-0.3928337991,-0.4991735518,-0.4106238484,-0.0192187112,0.511539638,0.2060919553,0.1550516188,0.0181787405,-0.2731821239,-0.2046999186,-0.3014617562,-0.1553871632,-0.2485721558,0.2819806039,-0.3579009175,0.3568585217,-0.1203139722,-0.0770154297,0.3586685956,-0.0063796281,0.0426307246,0.1374920458,0.1480117887,0.2510440052,-0.1259438097,-0.4387935698,-0.3378150463,-0.2986637652,-0.0107980268,0.1471051723,-0.0097866282,0.1274691075,-0.1898834705,-0.0101257004,-0.0537630767,0.0646510273,-0.1916065961,0.3349404335,0.4417556822,-0.1034759954,-0.4246730208,0.0266974531,0.0199538432,0.055486802,0.0543134846,-0.4534945488,0.048951108,-0.1315477341,0.016514061,0.1188752204,-0.1290848553,-0.0327551737,-0.1807679683,0.1202562675,0.0126705309,-0.2608505785,-0.0970647559,0.0698099807,0.1594927311,0.1414654106,0.3957380056,-0.3709970713,0.7079386711,0.1465926766,-0.0535260104,0.3381591439,0.1850141287,0.0966084599,-0.1214873642,-0.3148031533,0.0809997991,0.056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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"Hey @gaguilar ,\r\n\r\nI just found the [\"char2subword\" paper](https:\/\/arxiv.org\/pdf\/2010.12730.pdf) and I'm really interested in trying it out on own vocabs\/datasets like for historical texts (I've already [trained some lms](https:\/\/github.com\/stefan-it\/europeana-bert) on newspaper articles with OCR errors).\r\n\r\nDo you plan to release the code for your paper or is it possible to get the implementation \ud83e\udd14 Many thanks :hugs: ","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":57,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n Hey @gaguilar ,\r\n\r\nI just found the [\"char2subword\" paper](https:\/\/arxiv.org\/pdf\/2010.12730.pdf) and I'm really interested in trying it out on own vocabs\/datasets like for historical texts (I've already [trained some lms](https:\/\/github.com\/stefan-it\/europeana-bert) on newspaper articles with OCR errors).\r\n\r\nDo you plan to release the code for your paper or is it possible to get the implementation \ud83e\udd14 Many thanks :hugs: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"Hi @stefan-it! Thanks for your interest in our work! We do plan to release the code, but we will make it available once the paper has been published at a conference. Sorry for the inconvenience!\r\n\r\nHi @lhoestq, do you have any insights for this issue by any chance? Thanks!","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":49,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n Hi @stefan-it! Thanks for your interest in our work! We do plan to release the code, but we will make it available once the paper has been published at a conference. Sorry for the inconvenience!\r\n\r\nHi @lhoestq, do you have any insights for this issue by any chance? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"This is an issue on the `mwparserfromhell` side. You could try to update `mwparserfromhell` and see if it fixes the issue. If it doesn't we'll have to create an issue on their repo for them to fix it.\r\nBut first let's see if the latest version of `mwparserfromhell` does the job.","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":51,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n This is an issue on the `mwparserfromhell` side. You could try to update `mwparserfromhell` and see if it fixes the issue. If it doesn't we'll have to create an issue on their repo for them to fix it.\r\nBut first let's see if the latest version of `mwparserfromhell` does the job.","embeddings":[0.081004858,-0.1919707954,-0.1455876678,0.357018739,0.1417111307,0.2244725823,0.1284718215,0.1698514074,0.7591428757,-0.1323028803,0.1258179694,0.0651477799,0.0723554045,-0.225640744,0.1120552346,-0.1160828695,0.0668614954,-0.0926693827,-0.119749248,-0.3306937516,-0.378772378,0.2551675141,-0.3586254418,0.0707237497,-0.2304441184,0.2964781225,0.0954792574,-0.0933470801,0.0668037683,-0.354336828,0.1113957912,0.1501275301,0.3143440187,0.0501736812,-0.0001221014,-0.0832126215,0.7176688313,-0.1283557862,-0.417483598,-0.1124236509,-0.1387168765,-0.4225863516,0.1965474933,-0.0338458717,0.1579807252,-0.0946854874,0.2541137636,-0.4594916105,0.1522707194,0.1381663382,0.1686459035,-0.0437433794,0.037888892,0.1722898483,0.2732972801,0.0850297958,0.1379061788,0.0558566637,0.1328401268,-0.2986986339,-0.0256934054,0.1416166276,-0.1255062521,-0.074288018,0.1094742343,-0.0571211763,0.0959105417,-0.6544166803,0.1758662611,0.1662172973,0.7025114894,0.1253193319,-0.2536801696,-0.1987040043,0.0069871801,0.1365217417,0.2370205373,0.4124928117,-0.1284551024,0.0225707535,-0.0646742135,-0.1853040457,0.0156961363,0.6050239801,-0.0556667522,0.5664605498,0.1225819662,0.194029972,0.096586369,-0.2046299726,-0.2477408946,-0.2428136617,0.3923789859,0.3300985992,-0.133600533,0.4396103919,-0.0657655746,0.3765960932,-0.017255893,-0.1677450538,-0.3217781186,0.3225260973,-0.0982338339,0.1468278468,0.2242142856,-0.1507669389,0.3737666309,-0.3395674527,0.3315449059,0.0707732588,-0.3030894101,0.0902165771,-0.2847909927,-0.2116484493,-0.2382559925,-0.0894366726,-0.0229720268,-0.2458191216,0.2052039802,0.1451718658,-0.1429190934,-0.4003151953,-0.1844200492,0.3283189535,-0.0951183513,0.3528809547,0.2199086994,0.279997915,-0.471241802,-0.1743672341,0.0938560963,-0.1050391197,-0.3094645143,0.2047819495,0.311884284,0.2557216883,0.2917598188,-0.0396315493,0.092054002,-0.2491992712,-0.117688641,-0.0920443237,-0.0362549052,0.0806826577,0.2162978649,0.5053890347,0.1520207375,-0.2430995256,-0.173117891,0.1904238462,-0.1428010464,0.3200359046,0.0128071336,0.0700483397,-0.2752066255,-0.0271426681,-0.3407329917,0.2371091247,0.0794993863,-0.1422304362,-0.0680596605,-0.4404708445,-0.284509629,-0.0630098954,0.2751733661,0.6824002862,-0.2337113619,-0.0756816491,-0.1166577637,0.0289645363,0.2386087924,0.1880309433,-0.1038100198,0.2789823413,-0.1744201332,0.0498729125,0.3225488365,-0.0450440086,0.0905496404,-0.0659107715,0.2943434715,0.2915300727,-0.0669319257,-0.076311402,0.0243998226,0.0208586454,0.050952971,0.4110230505,0.1989032179,0.0261734091,-0.234259367,-0.1510098726,0.5229259133,0.3386918604,0.0948804393,-0.1500118226,0.0498552509,0.340354532,0.2524162531,-0.0655561164,0.1375987232,0.3369295001,-0.3582295179,0.2677420974,0.2269896865,-0.1253042817,-0.554710269,0.230648309,0.0456744879,0.2032351941,0.0385237113,0.2390528321,-0.2087599188,-0.0982533023,-0.2538189292,0.3036789298,0.0383167453,0.2109606117,-0.1044602767,0.3884797394,0.0119993826,-0.0699289888,-0.0308246613,-0.0951843336,-0.4704369903,0.3934468925,0.0150187491,-0.135568589,0.0250264332,0.2128004134,0.3387266099,-0.1139988452,-0.1212918684,-0.2969061434,0.4140083492,0.2411861569,0.1252679527,-0.0791362524,0.2526995838,-0.4128444791,0.2862435281,0.1946456432,0.1356674284,-0.2306513935,-0.0101967659,0.2143626511,-0.0276604202,0.5676321983,0.0566371791,0.0776869804,0.3403141797,0.0193824731,-0.0210541133,-0.1676110923,0.546212852,0.0987184644,0.0150502147,0.2127047777,-0.0319072343,-0.0564370602,0.5448497534,0.0253492389,0.0208523683,0.2844620049,-0.2476340681,-0.0999411419,-0.0892350823,-0.2863973677,-0.0896072984,0.1175357625,0.0390074588,-0.2837412655,0.0367046148,-0.1309304982,0.0369652845,0.214072004,0.2218250334,0.0949605778,0.1513848454,-0.1968063265,-0.2821566164,0.0594320521,0.0114388391,0.1479180604,-0.1853646785,0.075736925,-0.4506164193,-0.1568676084,-0.3748697042,-0.2476449758,-0.5724465251,-0.378277868,-0.1889107078,0.0723680109,0.0509037264,0.0925706699,-0.0886129066,0.0059156343,-0.3178310692,0.0326358005,-0.3865585327,-0.4102072418,-0.4526662529,-0.1088003442,0.5771566033,0.151923418,0.0683279783,-0.0507632196,-0.2250634879,-0.1655669659,-0.3020949364,-0.1577259004,-0.2327002287,0.2592408657,-0.2609623969,0.3641046584,-0.0438750237,-0.1165322736,0.3952418864,-0.1275030226,0.0709459558,0.1280759424,0.1700384915,0.1128410324,0.0035246571,-0.4678861499,-0.358343482,-0.1989178509,-0.1360440254,0.2233173698,-0.0345684141,-0.1036565453,-0.1498719156,0.0007474066,-0.0653173178,-0.0321483277,-0.1435091347,0.3579942286,0.4484443069,-0.0049899849,-0.3743659556,0.1497588754,0.0967432335,0.0119670462,-0.0661526024,-0.5379254818,0.0361202471,-0.1953772306,-0.0451239944,0.0857066661,-0.0456166975,-0.0016662735,-0.1290929317,0.1255060732,-0.0732008666,-0.3776082993,-0.2028995603,-0.0218626857,0.1950786859,-0.0038253164,0.3918199539,-0.3685402274,0.6188606024,0.1327216029,0.0883559212,0.1921495497,0.195550248,-0.0770062804,-0.1292403191,-0.2143438458,0.1045699269,-0.1352505237,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"I think the work around  as suggested in the issue [#886] is not working for several languages, such as `id`. For example, I tried all the dates to download dataset for `id` langauge from the following link: (https:\/\/github.com\/huggingface\/datasets\/pull\/886) [https:\/\/dumps.wikimedia.org\/idwiki\/](https:\/\/dumps.wikimedia.org\/idwiki\/ )\r\n\r\n>  >>> dataset = load_dataset('wikipedia', language='id', date=\"20210501\",  beam_runner='DirectRunner')\r\nWARNING:datasets.builder:Using custom data configuration 20210501.id-date=20210501,language=id\r\nDownloading and preparing dataset wikipedia\/20210501.id (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/.cache\/huggingface\/datasets\/wikipedia\/20210501.id-date=20210501,language=id\/0.0.0\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 745, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1139, in _download_and_prepare\r\n    super(BeamBasedBuilder, self)._download_and_prepare(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 630, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 420, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 287, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 195, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 203, in map_nested\r\n    mapped = [\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 218, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 281, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/dumps.wikimedia.org\/idwiki\/20210501\/dumpstatus.json\r\n\r\nMoreover the downloading speed for `non-en` language is very very slow. And interestingly the download stopped after approx a couple minutes due to the read time-out. I tried numerous times and the results is same. Is there any feasible way to download non-en language using huggingface?\r\n\r\n> File \"\/Users\/miislamg\/opt\/anaconda3\/envs\/proj-semlm\/lib\/python3.9\/site-packages\/requests\/models.py\", line 760, in generate\r\n    raise ConnectionError(e)\r\nrequests.exceptions.ConnectionError: HTTPSConnectionPool(host='dumps.wikimedia.org', port=443): Read timed out.\r\nDownloading:   7%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e                                                                                                                   | 10.2M\/153M [03:35<50:07, 47.4kB\/s]","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":274,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n I think the work around  as suggested in the issue [#886] is not working for several languages, such as `id`. For example, I tried all the dates to download dataset for `id` langauge from the following link: (https:\/\/github.com\/huggingface\/datasets\/pull\/886) [https:\/\/dumps.wikimedia.org\/idwiki\/](https:\/\/dumps.wikimedia.org\/idwiki\/ )\r\n\r\n>  >>> dataset = load_dataset('wikipedia', language='id', date=\"20210501\",  beam_runner='DirectRunner')\r\nWARNING:datasets.builder:Using custom data configuration 20210501.id-date=20210501,language=id\r\nDownloading and preparing dataset wikipedia\/20210501.id (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/Users\/.cache\/huggingface\/datasets\/wikipedia\/20210501.id-date=20210501,language=id\/0.0.0\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 745, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 574, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 1139, in _download_and_prepare\r\n    super(BeamBasedBuilder, self)._download_and_prepare(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 630, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/Users\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 420, in _split_generators\r\n    downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 287, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 195, in download\r\n    downloaded_path_or_paths = map_nested(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 203, in map_nested\r\n    mapped = [\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 204, in <listcomp>\r\n    _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/py_utils.py\", line 142, in _single_map_nested\r\n    return function(data_struct)\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/download_manager.py\", line 218, in _download\r\n    return cached_path(url_or_filename, download_config=download_config)\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 281, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/Users\/opt\/anaconda3\/envs\/proj\/lib\/python3.9\/site-packages\/datasets\/utils\/file_utils.py\", line 623, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/dumps.wikimedia.org\/idwiki\/20210501\/dumpstatus.json\r\n\r\nMoreover the downloading speed for `non-en` language is very very slow. And interestingly the download stopped after approx a couple minutes due to the read time-out. I tried numerous times and the results is same. Is there any feasible way to download non-en language using huggingface?\r\n\r\n> File \"\/Users\/miislamg\/opt\/anaconda3\/envs\/proj-semlm\/lib\/python3.9\/site-packages\/requests\/models.py\", line 760, in generate\r\n    raise ConnectionError(e)\r\nrequests.exceptions.ConnectionError: HTTPSConnectionPool(host='dumps.wikimedia.org', port=443): Read timed out.\r\nDownloading:   7%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e                                                                                                                   | 10.2M\/153M [03:35<50:07, 47.4kB\/s]","embeddings":[0.1800149828,-0.1523410529,-0.0989488661,0.4668173194,0.1006341428,0.2423329055,0.1486937255,0.1840970814,0.7133534551,-0.2562913597,-0.0293328445,0.0879321471,0.1379880607,-0.0704495609,0.0778176859,-0.2036105841,0.083321698,-0.1366672218,0.0640653819,-0.2263311893,-0.3286511898,0.3770771027,-0.3152113259,-0.033993151,-0.3056198657,0.2438495308,0.000596139,-0.1118378639,0.1123140231,-0.4247998893,0.2647848129,0.3021344244,0.312507391,0.175404191,-0.0001204848,-0.0556482673,0.704850018,-0.1893924773,-0.52306813,-0.2352164388,-0.0554581322,-0.4046289921,0.2281317711,-0.1170848757,-0.0360485911,-0.2035437673,0.1830866933,-0.390080601,0.1351653188,0.0376099162,0.1968803406,0.0330657512,0.1856203824,0.0348662511,0.2729909122,0.1124169081,0.1574663818,0.1152702719,-0.0042906459,-0.1470837593,0.0060088797,0.1632699221,-0.0448037684,-0.1061679423,0.2156065851,-0.0627294928,-0.1983726472,-0.6513531804,0.2012463361,0.2551749945,0.6670199037,-0.0362070054,-0.4080487788,-0.2918708622,0.008493525,0.0092041604,0.2186987996,0.2262768894,-0.1332885921,-0.0248336159,-0.0666920543,-0.2193876654,-0.0538428053,0.6153132915,-0.0989302322,0.3721018136,0.0412183702,0.152586624,0.1164748147,-0.2843738794,-0.3300948739,-0.1842395365,0.308218807,0.4228442907,-0.2161461562,0.3507112861,-0.0911564007,0.3997006714,0.0851336569,-0.1119415164,-0.2023396641,0.2832679749,-0.0947817862,0.1178443879,0.2353040576,-0.0526314341,0.2513489723,-0.084284395,0.3670358658,0.1552246362,-0.1794159114,0.0032228411,-0.2408719063,-0.2111485898,-0.2467986941,-0.1010616198,-0.056251429,-0.270691663,0.2040662915,0.1117742062,-0.2330821306,-0.288991034,-0.0754254609,0.2926601171,-0.1363919079,0.1686062813,0.1217710078,0.3821804821,-0.4291238785,-0.0510994084,0.0246106163,-0.0835722834,-0.1841303259,0.1816866994,0.4236653447,-0.0112116886,0.3521850109,-0.0009735774,-0.0124729387,-0.1143772751,-0.13916789,-0.1302518398,-0.0192519668,0.0520948581,0.1551325321,0.5195742249,0.0761604831,-0.2763779759,-0.2286849022,0.0425432995,-0.14053303,0.2529540062,0.0347801484,0.0855048075,-0.4118609428,-0.157736361,-0.2202835381,0.3061152697,0.1613567173,-0.1992474198,-0.0212133266,-0.2634349465,-0.1758718491,-0.0303868521,0.3723707497,0.7429201007,-0.2238100767,-0.1922197342,-0.0357124098,-0.0061537009,0.1992998868,0.246270895,-0.0146878511,0.2034340352,-0.1720418781,0.0657365918,0.2481043041,-0.1104744747,-0.0709453002,0.0185716581,0.2829237878,0.3354863524,-0.0245479904,0.0082481876,0.1751103103,0.0824777931,0.0755898803,0.4516778588,0.2592275441,-0.0364522003,-0.2878525555,-0.0955098271,0.4473000765,0.3634183109,0.1111965775,-0.1489389688,0.0520696752,0.3142115772,0.2028692216,-0.0173192602,0.1274423599,0.3553062975,-0.412081331,0.2675910592,0.1979930252,-0.0266612824,-0.6482115388,0.3039937019,0.0955562815,0.1926107109,-0.0523342416,0.0743491352,-0.3088146746,-0.176993385,-0.277630806,0.152644828,0.0550505407,0.1985759437,0.0841121599,0.4623348415,-0.0035472361,-0.0708874241,0.0051468941,-0.0730241761,-0.625194788,0.3269507587,0.0571064837,-0.0073050014,0.0225015488,0.2638174891,0.3264119327,-0.1936039031,-0.0540632159,-0.2034285814,0.291021198,0.341198355,0.1482165605,-0.0214505158,0.3269128799,-0.3324068785,0.2147027105,0.2711626291,0.109329395,-0.2314001322,-0.0771375597,0.3427499235,-0.176651448,0.5679537058,0.0784945339,0.0279703494,0.349321425,0.05606848,-0.1829757988,-0.2069450021,0.4769160151,0.0130715976,0.1207293496,0.2523759305,-0.2548974752,-0.0791640878,0.4247357845,0.0567899123,-0.0763744786,0.3021983802,-0.1847210675,-0.0454660542,0.0024955848,-0.1456357092,-0.1451688111,0.1742578447,0.1809597313,-0.3056588471,0.0288486108,-0.2651314437,0.0159962103,0.1129490957,0.093893528,0.1430916786,0.1447440237,-0.0515068546,-0.3176003098,0.0371384881,0.05871718,0.1647081524,-0.2192389965,0.0716491863,-0.4835647047,-0.4850093424,-0.2407976985,-0.2680956721,-0.5067273974,-0.4104590714,-0.1513090879,0.0539763756,0.0409823917,0.1727270186,-0.0090345284,-0.0408731215,-0.2526899874,-0.2420945019,-0.4165675938,-0.4249743819,-0.3751249611,-0.0479990691,0.5167554021,0.1541214287,0.1876805127,-0.1379991919,-0.2960053086,-0.2453897446,-0.3184579611,-0.1335677058,-0.2716222405,0.2574396431,-0.2281763256,0.363048166,-0.1546144634,-0.0545480475,0.439535886,0.0442846529,0.0154841458,0.1544584036,0.1573001891,0.208096832,-0.0512999631,-0.3745378554,-0.3137008548,-0.3135613203,0.0784837976,0.1661369205,-0.0123981154,0.1932970285,-0.1125321835,0.0462635607,-0.0381767265,0.0103718117,-0.1559996009,0.1697083861,0.4793511927,-0.1100016087,-0.393274188,0.0673182309,0.0674255788,0.1897398531,0.0910398215,-0.5468358994,0.1703419536,-0.1504279077,0.1138713583,0.2040820271,0.0073746983,-0.0466771312,-0.2249495387,0.1329428554,0.0290109813,-0.2154764235,-0.099813439,-0.1156409532,0.1483947337,0.1870363951,0.3458723724,-0.3495883346,0.7946072221,0.3428243697,0.0824462622,0.3724907339,0.1699153334,0.1285421401,-0.1782819033,-0.3313499689,0.0166985262,-0.0605239123,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"Hi ! The link https:\/\/dumps.wikimedia.org\/idwiki\/20210501\/dumpstatus.json seems to be working fine for me.\r\n\r\nRegarding the time outs, it must come either from an issue on the wikimedia host side, or from your internet connection.\r\nFeel free to try again several times.","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":40,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n Hi ! The link https:\/\/dumps.wikimedia.org\/idwiki\/20210501\/dumpstatus.json seems to be working fine for me.\r\n\r\nRegarding the time outs, it must come either from an issue on the wikimedia host side, or from your internet connection.\r\nFeel free to try again several times.","embeddings":[0.1920681745,-0.2602579296,-0.1416171342,0.3762246966,0.1157500148,0.2121071965,0.1645559072,0.1551883221,0.7062768936,-0.2246863097,0.11798051,0.0496410765,0.2343845218,-0.2096705884,-0.0094013372,-0.2210128754,0.0925963148,-0.195391655,0.0648471639,-0.2729018927,-0.2977039218,0.2671948969,-0.397896409,0.027171284,-0.1857133359,0.1696379483,0.0080057522,-0.1815737188,0.0773589015,-0.3585591912,0.1663145572,0.2452066392,0.3375380635,0.1511198282,-0.0001189297,-0.1920975596,0.6491462588,-0.1044014916,-0.4709958136,-0.0881614313,-0.1650997996,-0.3635079265,0.2519365549,-0.1299187392,0.0853196234,-0.1647308618,0.2627398074,-0.4774175286,0.1545994282,0.0544664823,0.1764225662,-0.0442770943,0.0150865419,0.1050192267,0.1392659098,0.0897769555,0.1414282769,0.0952253267,0.1480285823,-0.332827121,0.0266098175,0.1536878496,-0.1374328434,-0.1619034559,0.2353250384,-0.1162593514,-0.2261555344,-0.671456337,0.1671416461,0.2753604352,0.6968308091,0.1771871299,-0.3633895814,-0.2033320814,0.0303305909,0.1108860597,0.2197708786,0.3188805282,-0.1332755089,-0.0756762326,-0.0223537441,-0.1838998497,-0.0571159758,0.6162976027,-0.0327543244,0.4338471293,0.1757819206,0.2188185155,0.0114605315,-0.2223478407,-0.2297370881,-0.2822299302,0.2777375877,0.2912098467,-0.229287982,0.3861026764,-0.0092938757,0.3389609158,-0.0212205537,-0.1730937809,-0.1703128666,0.2720246017,-0.0565527044,0.1500321776,0.2194157392,-0.1049375907,0.2648623884,-0.05036043,0.3448389471,0.0464350432,-0.2778557837,-0.0219871625,-0.2163523585,-0.1501250714,-0.1527246833,-0.1844341904,-0.1040594503,-0.3015193343,0.2841693759,0.117397055,-0.1959318817,-0.2869795561,-0.1895691007,0.2224102914,-0.1789711416,0.3431453407,0.2166811675,0.318279326,-0.4698387384,-0.0308829788,0.0582000241,-0.0295045059,-0.2047911286,0.2661013007,0.2671023309,0.0934590027,0.3063895106,-0.0349103101,0.0334962569,-0.1906967908,0.0378600135,-0.19088763,0.0086274091,-0.0087980423,0.1492285728,0.6243290901,0.1069346964,-0.240696758,-0.1143351868,0.0173425358,-0.1332241297,0.3218250871,-0.0038698744,0.1082379073,-0.2380398959,-0.0695936158,-0.2211727202,0.2241665572,0.1904148757,-0.2009399086,-0.0108837662,-0.3347891271,-0.1034783125,-0.0116115492,0.2275258154,0.5787898898,-0.2940459847,0.0078724902,-0.0782736838,0.2460217774,0.2944015265,0.2770064175,-0.1376061738,0.224449873,-0.1103304029,0.1050942391,0.2300578058,-0.0963964611,0.063338019,-0.0674745888,0.2769455016,0.2506719828,-0.101845704,0.07155402,0.3002308309,0.0578249432,0.1811822504,0.5470150113,0.1700084656,-0.0198371634,-0.3321273029,0.0105104512,0.4842863977,0.2271985263,0.0975403637,-0.2832230031,-0.0301945675,0.3478222191,0.1292705387,0.0006163185,0.1187987328,0.3935267627,-0.4654506445,0.2700213194,0.3104889095,-0.0189492647,-0.4321421385,0.220550999,0.1768899113,0.2310065031,-0.0370158181,0.1332352906,-0.2768265903,-0.0912002176,-0.2021674514,0.2402665019,0.078774482,0.1118167341,0.0146720903,0.5503216386,0.0871952623,-0.234334752,-0.0230131373,-0.0929099247,-0.5333667994,0.4093146026,0.0253338981,-0.0747516975,0.0875979662,0.1141729727,0.3228402138,-0.156650275,-0.1230947673,-0.2445740551,0.24396725,0.3421175778,0.176921621,-0.0512005314,0.2956596017,-0.4270628095,0.2064131796,0.230202809,0.2040228695,-0.294174403,-0.0031867428,0.2778055966,-0.1314051747,0.579092443,0.0430002473,0.0254932567,0.3273768723,0.0562182739,-0.1040597111,-0.1395439655,0.4879278839,0.0925179869,-0.1234057173,0.2163667828,-0.1414022297,0.0244224835,0.5701662898,0.0576848425,-0.1411892027,0.174388811,-0.1842634827,-0.2130209208,0.0451659337,-0.1988901794,-0.1464824677,0.1640647352,0.119608447,-0.4188327193,0.0040637571,-0.1981055439,0.0572085343,0.0680594742,0.1505713761,0.1231385544,0.1096363813,-0.0984696671,-0.2919638753,-0.0995791927,0.0575046167,0.1817233711,-0.125602901,0.1521684825,-0.3066744208,-0.335704565,-0.142748028,-0.2836947143,-0.4758937657,-0.5971008539,-0.1310851127,0.0254992601,0.0646237209,0.1711786985,-0.0614617653,-0.0896035582,-0.1060794964,-0.1029613987,-0.4092002809,-0.4708696604,-0.3191872537,-0.0478072725,0.4986441135,0.2129051983,0.0927867144,-0.0574292466,-0.3174837828,-0.1957260519,-0.2670004964,-0.1926907897,-0.2869168222,0.1864595413,-0.302075088,0.4951706231,-0.1321394891,-0.1355850101,0.4025033116,-0.0661303475,0.1005680189,0.1314277947,0.1966309994,0.1588290632,-0.0201147087,-0.3963410854,-0.2530008256,-0.2210238278,-0.1711599678,0.0928545967,-0.0977484211,-0.0573782437,-0.0299816933,-0.0126924701,0.0298288558,-0.0714874566,-0.0779576674,0.2201895714,0.5501049161,-0.0635473356,-0.4144967198,0.1688749641,0.0828099772,0.1014464945,0.039773535,-0.5010535121,0.1527956128,-0.2344356179,0.0599981397,0.1821861565,-0.083700113,-0.0569189191,-0.2782068849,0.0634085312,0.0754753873,-0.1482956409,-0.1136238128,-0.0790371597,0.1974343807,0.0912215263,0.3415472507,-0.3307110071,0.6799039841,0.1604367793,0.0958411172,0.1546342075,0.2151065469,-0.0109759877,-0.1354507953,-0.2767878771,0.0811419487,-0.0431491956,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"I was trying to download dataset for `es` language, however I am getting the following error:\r\n```\r\ndataset = load_dataset('wikipedia', language='es', date=\"20210320\", beam_runner='DirectRunner') \r\n```\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20210320.es (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/scratch\/user_name\/datasets\/wikipedia\/20210320.es-date=20210320,language=es\/0.0.0\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n  File \"apache_beam\/runners\/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"\/scratch\/user_name\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 492, in _clean_content\r\n    text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n  File \"\/scratch\/user_name\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n    section_text.append(section.strip_code().strip())\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/wikicode.py\", line 639, in strip_code\r\n    stripped = node.__strip__(**kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 60, in __strip__\r\n    return self.normalize()\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 150, in normalize\r\n    return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"download_dataset_all.py\", line 8, in <module>\r\n    dataset = load_dataset('wikipedia', language=language, date=\"20210320\", beam_runner='DirectRunner') \r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1152, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/pipeline.py\", line 564, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/direct\/direct_runner.py\", line 131, in run_pipeline\r\n    return runner.run_pipeline(pipeline, options)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 190, in run_pipeline\r\n    pipeline.to_runner_api(default_environment=self._default_environment))\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 200, in run_via_runner_api\r\n    return self.run_stages(stage_context, stages)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 366, in run_stages\r\n    bundle_context_manager,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 562, in _run_stage\r\n    bundle_manager)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 602, in _run_bundle\r\n    data_input, data_output, input_timers, expected_timer_output)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 903, in process_bundle\r\n    result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/worker_handlers.py\", line 378, in push\r\n    response = self.worker.do_instruction(request)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/worker\/sdk_worker.py\", line 610, in do_instruction\r\n    getattr(request, request_type), request.instruction_id)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/worker\/sdk_worker.py\", line 647, in process_bundle\r\n    bundle_processor.process_bundle(instruction_id))\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/worker\/bundle_processor.py\", line 1001, in process_bundle\r\n    element.data)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/worker\/bundle_processor.py\", line 229, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 356, in apache_beam.runners.worker.operations.Operation.output\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 358, in apache_beam.runners.worker.operations.Operation.output\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"apache_beam\/runners\/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"apache_beam\/runners\/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1315, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/future\/utils\/__init__.py\", line 446, in raise_with_traceback\r\n    raise exc.with_traceback(traceback)\r\n  File \"apache_beam\/runners\/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"\/scratch\/user_name\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 492, in _clean_content\r\n    text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n  File \"\/scratch\/user_name\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n    section_text.append(section.strip_code().strip())\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/wikicode.py\", line 639, in strip_code\r\n    stripped = node.__strip__(**kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 60, in __strip__\r\n    return self.normalize()\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 150, in normalize\r\n    return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train\/Clean content']\"\r\n```","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":481,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n I was trying to download dataset for `es` language, however I am getting the following error:\r\n```\r\ndataset = load_dataset('wikipedia', language='es', date=\"20210320\", beam_runner='DirectRunner') \r\n```\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20210320.es (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/scratch\/user_name\/datasets\/wikipedia\/20210320.es-date=20210320,language=es\/0.0.0\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n  File \"apache_beam\/runners\/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"\/scratch\/user_name\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 492, in _clean_content\r\n    text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n  File \"\/scratch\/user_name\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n    section_text.append(section.strip_code().strip())\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/wikicode.py\", line 639, in strip_code\r\n    stripped = node.__strip__(**kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 60, in __strip__\r\n    return self.normalize()\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 150, in normalize\r\n    return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n  File \"download_dataset_all.py\", line 8, in <module>\r\n    dataset = load_dataset('wikipedia', language=language, date=\"20210320\", beam_runner='DirectRunner') \r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/load.py\", line 748, in load_dataset\r\n    use_auth_token=use_auth_token,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 575, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/datasets\/builder.py\", line 1152, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/pipeline.py\", line 564, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/direct\/direct_runner.py\", line 131, in run_pipeline\r\n    return runner.run_pipeline(pipeline, options)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 190, in run_pipeline\r\n    pipeline.to_runner_api(default_environment=self._default_environment))\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 200, in run_via_runner_api\r\n    return self.run_stages(stage_context, stages)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 366, in run_stages\r\n    bundle_context_manager,\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 562, in _run_stage\r\n    bundle_manager)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 602, in _run_bundle\r\n    data_input, data_output, input_timers, expected_timer_output)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/fn_runner.py\", line 903, in process_bundle\r\n    result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/portability\/fn_api_runner\/worker_handlers.py\", line 378, in push\r\n    response = self.worker.do_instruction(request)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/worker\/sdk_worker.py\", line 610, in do_instruction\r\n    getattr(request, request_type), request.instruction_id)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/worker\/sdk_worker.py\", line 647, in process_bundle\r\n    bundle_processor.process_bundle(instruction_id))\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/worker\/bundle_processor.py\", line 1001, in process_bundle\r\n    element.data)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/apache_beam\/runners\/worker\/bundle_processor.py\", line 229, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 356, in apache_beam.runners.worker.operations.Operation.output\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 358, in apache_beam.runners.worker.operations.Operation.output\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"apache_beam\/runners\/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"apache_beam\/runners\/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/worker\/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n  File \"apache_beam\/runners\/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 1315, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/future\/utils\/__init__.py\", line 446, in raise_with_traceback\r\n    raise exc.with_traceback(traceback)\r\n  File \"apache_beam\/runners\/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n  File \"apache_beam\/runners\/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n  File \"apache_beam\/runners\/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n  File \"\/scratch\/user_name\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 492, in _clean_content\r\n    text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n  File \"\/scratch\/user_name\/modules\/datasets_modules\/datasets\/wikipedia\/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1\/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n    section_text.append(section.strip_code().strip())\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/wikicode.py\", line 639, in strip_code\r\n    stripped = node.__strip__(**kwargs)\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 60, in __strip__\r\n    return self.normalize()\r\n  File \"\/opt\/conda\/lib\/python3.7\/site-packages\/mwparserfromhell\/nodes\/html_entity.py\", line 150, in normalize\r\n    return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train\/Clean content']\"\r\n```","embeddings":[0.1763544828,-0.1677631289,-0.1718394458,0.4323384762,0.1707598567,0.2831906378,0.1634615809,0.2103996426,0.7203700542,-0.2296734005,0.0827716962,0.0324949548,0.0824812278,-0.1710957587,0.1047614142,-0.1762412637,0.040906731,-0.1311677247,0.0619521402,-0.3332505226,-0.3264745176,0.2989555299,-0.2558051348,0.0192325842,-0.2063501179,0.2564130425,0.0552222505,-0.1168860421,0.0501078293,-0.3417122662,0.1863566786,0.2099172026,0.3298726082,0.1184753329,-0.0001155041,-0.0839764923,0.6460733414,-0.1828085482,-0.4777913392,-0.2485990077,-0.1580174863,-0.4117628932,0.1903592348,-0.1931241453,0.085821338,-0.2423582971,0.2660349309,-0.5352807045,0.1104979366,0.1375032067,0.2382594645,-0.1910074055,0.0760797933,0.071904026,0.3408456445,0.0599353276,0.1818883121,0.1272431165,0.0240778606,-0.1747232676,0.0576144196,0.1426581889,-0.1027082428,-0.0544828437,0.1305572391,-0.0746287107,-0.093501769,-0.6982363462,0.1663738638,0.2483552247,0.7527777553,0.0537605397,-0.3433137238,-0.1573726535,0.0807394534,0.0541956611,0.1600305289,0.2955708802,-0.1713262051,-0.08595182,-0.0020556361,-0.2566790283,-0.0361364037,0.6079903245,-0.0604825802,0.3638177514,0.1059381887,0.1484155953,0.0372281857,-0.2130876333,-0.2027879804,-0.208680585,0.3671191633,0.3883555532,-0.2663078606,0.4091638923,-0.0272477381,0.2662196159,0.0512345843,-0.1118468046,-0.171297729,0.2166763991,-0.1110823005,0.1603291929,0.2141034156,-0.1045850217,0.2297871709,-0.0994358137,0.282810539,0.0523053743,-0.10580156,0.0235038307,-0.2767423987,-0.2106363624,-0.1899599135,-0.0080819987,-0.0186589397,-0.3135660589,0.1164932996,0.1050030962,-0.2387083322,-0.2927421331,-0.1104368269,0.2609954178,-0.1404688805,0.2598820925,0.1735314131,0.3614093363,-0.4104596376,-0.0088007478,-0.0065336893,-0.0349614099,-0.2180782855,0.1077685952,0.3512766659,0.1387448013,0.3635692,-0.044670552,-0.0229277033,-0.1345911473,-0.0369594246,-0.2337093353,-0.0712124258,-0.0027836114,0.1963965744,0.5106027722,0.0689720139,-0.2143213302,-0.1292956918,0.1026896834,-0.136296019,0.333781451,-0.0152218118,0.1500983089,-0.3008658588,-0.1998071223,-0.2625985444,0.2575986981,0.1306094229,-0.1939145327,-0.0293582138,-0.3026609421,-0.1415430009,-0.0959850922,0.320959121,0.5890487432,-0.2810034454,-0.1207376346,-0.1368455738,0.0293474011,0.2669731379,0.2324832678,-0.0744377524,0.2620208263,-0.1818814278,0.0912397951,0.3925758302,-0.0914892703,-0.033330705,0.0226022806,0.2543193102,0.2409895658,-0.0208703689,0.0407411717,0.185290426,0.1152122691,0.0883842483,0.5103183985,0.1995154321,-0.0357514285,-0.2663413584,-0.0222022217,0.5889864564,0.3162772059,0.1695824713,-0.1865864843,0.0164659489,0.3192102909,0.2271896303,-0.0213697348,0.1035802662,0.3533834219,-0.3805092871,0.3051334023,0.1942701191,-0.0205473118,-0.5447943807,0.2211422026,0.0312379785,0.1221075654,-0.0230360944,0.1085037813,-0.3356630504,-0.1956944764,-0.210438922,0.2059174925,0.1153013036,0.2285695523,-0.0890166312,0.4337516129,0.0359382555,-0.074444063,0.0314512327,-0.0982374176,-0.5547204018,0.4431158006,0.0008211986,-0.0520538166,0.0469282679,0.1857425421,0.2999597788,-0.1909607202,-0.1065998077,-0.2020519823,0.3487930894,0.1899758577,0.1270586699,-0.1278118193,0.253605634,-0.3870398104,0.1879015118,0.3023718596,0.1857383102,-0.1931846142,-0.0406670645,0.1949964166,-0.12047977,0.4849776924,0.045072291,0.0869900361,0.3448004425,0.0223699026,-0.0912765786,-0.1143773645,0.5354329348,0.0885521397,0.0355971567,0.2150045931,-0.1734351814,-0.1120484024,0.5037209988,0.112896882,0.0008994567,0.2261939347,-0.1032246575,-0.0716235861,-0.01897466,-0.170152083,-0.1271852851,0.1564020216,0.0838888958,-0.3795368075,0.0664776638,-0.1950324625,0.001023291,0.1105647981,0.2348871529,0.1291821301,0.0971046537,-0.0754145384,-0.2708680332,-0.1090857089,0.0891002491,0.2237171382,-0.1484306157,0.0937630162,-0.4318659008,-0.4758825302,-0.2640722096,-0.2109764814,-0.4392780662,-0.4192179143,-0.1311877221,-0.0238214266,0.0547445118,0.1354451627,-0.078701742,-0.1223691925,-0.2312014997,-0.1154662892,-0.3434458673,-0.4542329907,-0.3919792175,0.0169904344,0.4950798154,0.141469121,0.1429104656,-0.0854893997,-0.2279533744,-0.1821111888,-0.253644973,-0.2276076972,-0.3089286685,0.1341107935,-0.2234796435,0.4357998669,-0.1184876263,-0.1121197715,0.3430631161,0.0052084159,0.0267195683,0.175797388,0.2352900058,0.1736939251,0.002161144,-0.5106552839,-0.3253031075,-0.3045636714,-0.1049587354,0.1949892938,-0.0025412478,0.1089148521,-0.1016926765,0.0883664936,0.0876663551,-0.0099650202,-0.0963250697,0.2964367568,0.4350730479,-0.0457885973,-0.3770374656,0.1137984246,0.0461983941,0.1506627649,0.1252917349,-0.5083814859,0.2113194168,-0.1774634719,0.0534333959,0.0646804944,-0.0603528544,0.0183729641,-0.1751566082,0.0705562979,0.0486055724,-0.1803525239,-0.1996345222,-0.0730896443,0.2446131706,0.0437166244,0.3491378129,-0.3166738451,0.6470992565,0.1936473697,0.0915279239,0.3157174289,0.1844841838,0.0257805251,-0.1023911536,-0.3294164538,0.0942497775,-0.01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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/577","title":"Some languages in wikipedia dataset are not loading","comments":"Hi ! This looks related to this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1994\r\nBasically the parser that is used (mwparserfromhell) has some issues for some pages in `es`.\r\nWe already reported some issues for `es` on their repo at https:\/\/github.com\/earwig\/mwparserfromhell\/issues\/247 but it looks like there are still a few issues. Might be a good idea to open a new issue on the mwparserfromhell repo","body":"Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!","comment_length":60,"text":"Some languages in wikipedia dataset are not loading \n Hi,\r\n\r\nI am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them:\r\n\r\n```\r\nimport nlp\r\n\r\nlangs = ['ar'. 'af', 'an']\r\n\r\nfor lang in langs:\r\n    data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') \r\n    print(lang, len(data))\r\n```\r\n\r\nHere's what I see for 'ar' (it gets stuck there):\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/gaguilar\/.cache\/huggingface\/datasets\/wikipedia\/20200501.ar\/1.0.0\/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\n```\r\n\r\nNote that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks! \n Hi ! This looks related to this issue: https:\/\/github.com\/huggingface\/datasets\/issues\/1994\r\nBasically the parser that is used (mwparserfromhell) has some issues for some pages in `es`.\r\nWe already reported some issues for `es` on their repo at https:\/\/github.com\/earwig\/mwparserfromhell\/issues\/247 but it looks like there are still a few issues. Might be a good idea to open a new issue on the mwparserfromhell 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/575","title":"Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading.","comments":"Update:\r\n\r\nThe imdb download completed after a long time (about 45 mins). Ofcourse once download loading was instantaneous. Also, the loaded object was of type `arrow_dataset`. \r\n\r\nThe urls for glue still doesn't work though.","body":"Hi,\r\n\r\nI'm following the [quick tour](https:\/\/huggingface.co\/nlp\/quicktour.html) and tried to load the glue dataset:\r\n```\r\n>>> from nlp import load_dataset\r\n>>> dataset = load_dataset('glue', 'mrpc', split='train')\r\n```\r\n\r\nHowever, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):\r\n```\r\n\r\n\/net\/vaosl01\/opt\/NFS\/su0\/miniconda3\/envs\/hf\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)\r\n    354                 \" to False.\"\r\n    355             )\r\n--> 356         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    357 \r\n    358     # From now on, connected is True.\r\n\r\nConnectionError: Couldn't reach https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc\r\n```\r\n\r\nI tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.\r\n\r\nSince this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:\r\n```\r\nds = load_dataset('imdb', split='train')\r\n```\r\nThis downloads the data, but it just blocks after that:\r\n```\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.56k\/4.56k [00:00<00:00, 1.38MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.07k\/2.07k [00:00<00:00, 1.15MB\/s]\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to \/net\/vaosl01\/opt\/NFS\/su0\/huggingface\/datasets\/imdb\/plain_text\/1.0.0\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84.1M\/84.1M [00:07<00:00, 11.1MB\/s]\r\n```\r\n\r\nI checked the folder `$HF_HOME\/datasets\/downloads\/extracted\/<id>\/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:\r\n1. Why is it still blocking? Is it still downloading?\r\n2. I specified split as train, so why is the test folder being populated?\r\n3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?\r\n\r\nThanks.\r\n","comment_length":34,"text":"Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading. \n Hi,\r\n\r\nI'm following the [quick tour](https:\/\/huggingface.co\/nlp\/quicktour.html) and tried to load the glue dataset:\r\n```\r\n>>> from nlp import load_dataset\r\n>>> dataset = load_dataset('glue', 'mrpc', split='train')\r\n```\r\n\r\nHowever, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):\r\n```\r\n\r\n\/net\/vaosl01\/opt\/NFS\/su0\/miniconda3\/envs\/hf\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)\r\n    354                 \" to False.\"\r\n    355             )\r\n--> 356         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    357 \r\n    358     # From now on, connected is True.\r\n\r\nConnectionError: Couldn't reach https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc\r\n```\r\n\r\nI tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.\r\n\r\nSince this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:\r\n```\r\nds = load_dataset('imdb', split='train')\r\n```\r\nThis downloads the data, but it just blocks after that:\r\n```\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.56k\/4.56k [00:00<00:00, 1.38MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.07k\/2.07k [00:00<00:00, 1.15MB\/s]\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to \/net\/vaosl01\/opt\/NFS\/su0\/huggingface\/datasets\/imdb\/plain_text\/1.0.0\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84.1M\/84.1M [00:07<00:00, 11.1MB\/s]\r\n```\r\n\r\nI checked the folder `$HF_HOME\/datasets\/downloads\/extracted\/<id>\/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:\r\n1. Why is it still blocking? Is it still downloading?\r\n2. I specified split as train, so why is the test folder being populated?\r\n3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?\r\n\r\nThanks.\r\n \n Update:\r\n\r\nThe imdb download completed after a long time (about 45 mins). Ofcourse once download loading was instantaneous. Also, the loaded object was of type `arrow_dataset`. \r\n\r\nThe urls for glue still doesn't work 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/575","title":"Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading.","comments":"I am also seeing a similar error when running the following:\r\n\r\n```\r\nimport nlp\r\ndataset = load_dataset('cola')\r\n```\r\nError:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/js11133\/.conda\/envs\/jiant\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 509, in load_dataset\r\n    module_path = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/home\/js11133\/.conda\/envs\/jiant\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 248, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/js11133\/.conda\/envs\/jiant\/lib\/python3.8\/site-packages\/nlp\/utils\/file_utils.py\", line 191, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/js11133\/.conda\/envs\/jiant\/lib\/python3.8\/site-packages\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cola\/cola.py\r\n```","body":"Hi,\r\n\r\nI'm following the [quick tour](https:\/\/huggingface.co\/nlp\/quicktour.html) and tried to load the glue dataset:\r\n```\r\n>>> from nlp import load_dataset\r\n>>> dataset = load_dataset('glue', 'mrpc', split='train')\r\n```\r\n\r\nHowever, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):\r\n```\r\n\r\n\/net\/vaosl01\/opt\/NFS\/su0\/miniconda3\/envs\/hf\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)\r\n    354                 \" to False.\"\r\n    355             )\r\n--> 356         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    357 \r\n    358     # From now on, connected is True.\r\n\r\nConnectionError: Couldn't reach https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc\r\n```\r\n\r\nI tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.\r\n\r\nSince this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:\r\n```\r\nds = load_dataset('imdb', split='train')\r\n```\r\nThis downloads the data, but it just blocks after that:\r\n```\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.56k\/4.56k [00:00<00:00, 1.38MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.07k\/2.07k [00:00<00:00, 1.15MB\/s]\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to \/net\/vaosl01\/opt\/NFS\/su0\/huggingface\/datasets\/imdb\/plain_text\/1.0.0\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84.1M\/84.1M [00:07<00:00, 11.1MB\/s]\r\n```\r\n\r\nI checked the folder `$HF_HOME\/datasets\/downloads\/extracted\/<id>\/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:\r\n1. Why is it still blocking? Is it still downloading?\r\n2. I specified split as train, so why is the test folder being populated?\r\n3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?\r\n\r\nThanks.\r\n","comment_length":76,"text":"Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading. \n Hi,\r\n\r\nI'm following the [quick tour](https:\/\/huggingface.co\/nlp\/quicktour.html) and tried to load the glue dataset:\r\n```\r\n>>> from nlp import load_dataset\r\n>>> dataset = load_dataset('glue', 'mrpc', split='train')\r\n```\r\n\r\nHowever, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):\r\n```\r\n\r\n\/net\/vaosl01\/opt\/NFS\/su0\/miniconda3\/envs\/hf\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)\r\n    354                 \" to False.\"\r\n    355             )\r\n--> 356         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    357 \r\n    358     # From now on, connected is True.\r\n\r\nConnectionError: Couldn't reach https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc\r\n```\r\n\r\nI tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.\r\n\r\nSince this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:\r\n```\r\nds = load_dataset('imdb', split='train')\r\n```\r\nThis downloads the data, but it just blocks after that:\r\n```\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.56k\/4.56k [00:00<00:00, 1.38MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.07k\/2.07k [00:00<00:00, 1.15MB\/s]\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to \/net\/vaosl01\/opt\/NFS\/su0\/huggingface\/datasets\/imdb\/plain_text\/1.0.0\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84.1M\/84.1M [00:07<00:00, 11.1MB\/s]\r\n```\r\n\r\nI checked the folder `$HF_HOME\/datasets\/downloads\/extracted\/<id>\/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:\r\n1. Why is it still blocking? Is it still downloading?\r\n2. I specified split as train, so why is the test folder being populated?\r\n3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?\r\n\r\nThanks.\r\n \n I am also seeing a similar error when running the following:\r\n\r\n```\r\nimport nlp\r\ndataset = load_dataset('cola')\r\n```\r\nError:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/js11133\/.conda\/envs\/jiant\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 509, in load_dataset\r\n    module_path = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/home\/js11133\/.conda\/envs\/jiant\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 248, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/home\/js11133\/.conda\/envs\/jiant\/lib\/python3.8\/site-packages\/nlp\/utils\/file_utils.py\", line 191, in cached_path\r\n    output_path = get_from_cache(\r\n  File \"\/home\/js11133\/.conda\/envs\/jiant\/lib\/python3.8\/site-packages\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cola\/cola.py\r\n```","embeddings":[-0.112636596,-0.0148180015,-0.0514469258,0.2499045283,0.2150651067,-0.1080487967,-0.0023050923,0.1171098575,0.144177258,-0.1348839551,-0.425668329,-0.0689154193,0.1447221488,0.1289693564,0.274225086,-0.0121180899,-0.1593028903,-0.0744765624,-0.0916164145,0.017701054,-0.2575627863,0.3606531918,-0.2036087364,0.1764502376,-0.1922911257,-0.0935443118,-0.1606316715,0.1398071498,-0.1229681745,-0.3245210052,0.0947918966,0.3048549592,0.0481997393,0.3857811987,-0.0001219456,-0.0564453825,0.5723124146,-0.0555414632,-0.3910080492,-0.4353305697,-0.2779269516,-0.0589925274,0.355984658,-0.0667517558,0.130753994,0.1634606421,0.0878987014,-0.2419316471,0.2720347047,0.26318115,0.1377917379,0.0733033642,0.1359027624,0.0714743361,0.3174403608,-0.2632397711,0.0046695629,0.3708156645,0.3326888382,-0.291418612,0.0472086743,0.0577220842,-0.205646798,0.0583759286,0.1841480434,0.1635258496,-0.25784266,-0.5462059379,0.0292282049,0.3213647902,0.2748629451,0.0140512539,-0.4840654433,-0.3165412545,-0.0286721792,0.1173614413,0.2789925039,0.1334600002,-0.2538383007,0.0309729725,-0.5450456142,-0.1321876645,0.0033698108,0.5532464981,0.1101105139,-0.0098115858,0.0639427006,0.1689395458,0.3128620982,-0.0395253114,0.1148340628,-0.0232361071,0.2994754314,0.1872622669,-0.0450580493,-0.0265388358,0.0512051396,0.4236578643,0.1454071999,0.1372276694,0.1992809027,0.0405518636,-0.1392972767,0.1321616024,0.15483661,-0.0140559683,-0.0127521874,0.0798756033,0.6293030977,0.3603568673,-0.1680348217,0.0711006969,-0.1136864573,-0.0785518587,-0.3989927769,-0.0077087539,-0.1199624836,-0.284093976,-0.1622226536,-0.0771386921,-0.1368774027,0.0150720552,0.0852001831,0.3928332329,-0.3108938336,0.2066407949,-0.087643005,0.3793320358,-0.1213530898,0.0319333375,-0.0930832773,0.1856428087,-0.1889153719,0.2517310679,0.4859232306,0.0784364417,0.3209574521,-0.3183521628,-0.1498351097,-0.2291524559,0.0177800152,-0.3440533578,0.0556664392,0.0532136112,0.3014571071,0.3389762938,-0.0610685609,-0.1870903969,-0.1006215811,-0.2109535187,-0.2240741253,-0.1662695557,0.3674656749,0.1121519953,-0.4041662812,-0.1559494436,-0.1344938278,-0.004829342,-0.0961971432,-0.0948827863,-0.1139309928,-0.0595751256,-0.1584257782,-0.200951606,0.3062122166,0.6965598464,-0.1322715133,-0.2766490877,-0.1275884658,0.041212976,-0.0782895386,0.502037406,-0.0070996187,0.1006353199,-0.4256356955,0.2437322885,0.5672342777,-0.2894794941,-0.3902561069,0.5320960879,-0.0154582411,0.0364813991,0.1849975735,0.0225609727,0.131248787,-0.0723032951,0.4729860723,0.5410932899,-0.0449328944,-0.0970429629,-0.2779184282,-0.1605929285,0.1289730817,0.1257047504,0.223503828,0.0212896317,-0.0032729721,-0.079706423,0.233262375,0.2292817086,0.0552493818,0.2484004796,-0.1013187617,0.1788349301,-0.104797557,-0.1381653398,-0.3142169118,0.2160550207,-0.0629844964,0.1218500361,-0.3327453732,0.2367781252,-0.1883798987,-0.1930935532,0.0038398847,-0.0187660865,0.0700894296,0.2044757456,0.131302923,0.0753203556,-0.2522730231,0.5734852552,0.0272469651,0.1724773198,-0.316007942,0.6796264052,-0.1542854309,-0.191763401,0.1090584621,0.3169533312,0.2635817826,-0.2635529935,0.0134699494,0.3022545278,-0.1289988309,0.1917850226,0.2702765465,0.0180154573,0.3637427986,-0.2720222771,0.1126302034,0.0491894484,0.1340863556,-0.1252143681,0.1797020435,0.2376711965,-0.2186419368,0.6319778562,0.4755757749,-0.0091407485,0.1447475404,-0.1831095815,-0.2134258151,-0.2173276395,0.6007691026,0.2402251363,0.2317631692,-0.1197275817,-0.148830682,0.1870426685,0.3490749896,0.0156644378,0.05569648,0.2178222686,-0.1637909412,-0.1998849362,-0.1010185257,0.3804479241,0.2209589183,0.1598923951,0.1135150269,0.021280108,-0.2521316409,-0.3306078017,0.2030390054,0.1683373004,-0.0889684558,0.0819643363,0.2019196898,0.1494818777,-0.0947707817,-0.4382834136,0.0053283786,-0.0233714301,-0.3714630306,-0.0647940934,-0.445727706,-0.6604163051,0.0467646122,-0.1720999628,-0.2792548835,-0.646451056,-0.1037925184,0.2034947723,0.0090118954,0.0356713496,-0.2063030154,-0.0062600239,-0.2111667097,-0.0129495598,-0.1801105589,-0.3260889649,-0.062903434,0.0395989567,0.3534008265,0.0574423596,0.1485339999,-0.2441104352,-0.1611087322,-0.1612910032,-0.1101251915,0.19283548,-0.1821724176,0.3608709574,0.2109528184,0.4903079271,0.2029083073,-0.2494547963,0.3121628463,-0.1433433294,0.0774954781,-0.0519493446,0.0077687465,-0.0471082143,0.1043553874,-0.0099998713,-0.471308589,-0.4650456309,0.2359144241,-0.0598598197,0.2176345289,0.1203419119,-0.041052755,0.2929945886,-0.0266634505,0.1280004233,-0.041178517,-0.3354517817,0.5650700331,-0.2066883743,-0.2000689805,-0.0150570692,0.0398590788,-0.2016781569,0.0807290226,-0.706532836,-0.2792837024,-0.4299277067,0.0273614805,0.2272491306,-0.0665315464,-0.0501204729,-0.2217652798,-0.0760565326,-0.0140878279,-0.4060663879,-0.0029456718,0.3619810045,0.3475250006,0.1065041944,0.5122767091,-0.0753890201,0.272541374,-0.0739138052,0.0747887865,0.6189810038,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/575","title":"Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading.","comments":"@jeswan `\"cola\"` is not a valid dataset identifier (you can check the up-to-date list on https:\/\/huggingface.co\/datasets) but you can find cola inside glue.","body":"Hi,\r\n\r\nI'm following the [quick tour](https:\/\/huggingface.co\/nlp\/quicktour.html) and tried to load the glue dataset:\r\n```\r\n>>> from nlp import load_dataset\r\n>>> dataset = load_dataset('glue', 'mrpc', split='train')\r\n```\r\n\r\nHowever, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):\r\n```\r\n\r\n\/net\/vaosl01\/opt\/NFS\/su0\/miniconda3\/envs\/hf\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)\r\n    354                 \" to False.\"\r\n    355             )\r\n--> 356         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    357 \r\n    358     # From now on, connected is True.\r\n\r\nConnectionError: Couldn't reach https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc\r\n```\r\n\r\nI tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.\r\n\r\nSince this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:\r\n```\r\nds = load_dataset('imdb', split='train')\r\n```\r\nThis downloads the data, but it just blocks after that:\r\n```\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.56k\/4.56k [00:00<00:00, 1.38MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.07k\/2.07k [00:00<00:00, 1.15MB\/s]\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to \/net\/vaosl01\/opt\/NFS\/su0\/huggingface\/datasets\/imdb\/plain_text\/1.0.0\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84.1M\/84.1M [00:07<00:00, 11.1MB\/s]\r\n```\r\n\r\nI checked the folder `$HF_HOME\/datasets\/downloads\/extracted\/<id>\/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:\r\n1. Why is it still blocking? Is it still downloading?\r\n2. I specified split as train, so why is the test folder being populated?\r\n3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?\r\n\r\nThanks.\r\n","comment_length":23,"text":"Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading. \n Hi,\r\n\r\nI'm following the [quick tour](https:\/\/huggingface.co\/nlp\/quicktour.html) and tried to load the glue dataset:\r\n```\r\n>>> from nlp import load_dataset\r\n>>> dataset = load_dataset('glue', 'mrpc', split='train')\r\n```\r\n\r\nHowever, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):\r\n```\r\n\r\n\/net\/vaosl01\/opt\/NFS\/su0\/miniconda3\/envs\/hf\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)\r\n    354                 \" to False.\"\r\n    355             )\r\n--> 356         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    357 \r\n    358     # From now on, connected is True.\r\n\r\nConnectionError: Couldn't reach https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc\r\n```\r\n\r\nI tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.\r\n\r\nSince this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:\r\n```\r\nds = load_dataset('imdb', split='train')\r\n```\r\nThis downloads the data, but it just blocks after that:\r\n```\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.56k\/4.56k [00:00<00:00, 1.38MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.07k\/2.07k [00:00<00:00, 1.15MB\/s]\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to \/net\/vaosl01\/opt\/NFS\/su0\/huggingface\/datasets\/imdb\/plain_text\/1.0.0\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84.1M\/84.1M [00:07<00:00, 11.1MB\/s]\r\n```\r\n\r\nI checked the folder `$HF_HOME\/datasets\/downloads\/extracted\/<id>\/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:\r\n1. Why is it still blocking? Is it still downloading?\r\n2. I specified split as train, so why is the test folder being populated?\r\n3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?\r\n\r\nThanks.\r\n \n @jeswan `\"cola\"` is not a valid dataset identifier (you can check the up-to-date list on https:\/\/huggingface.co\/datasets) but you can find cola inside glue.","embeddings":[-0.112636596,-0.0148180015,-0.0514469258,0.2499045283,0.2150651067,-0.1080487967,-0.0023050923,0.1171098575,0.144177258,-0.1348839551,-0.425668329,-0.0689154193,0.1447221488,0.1289693564,0.274225086,-0.0121180899,-0.1593028903,-0.0744765624,-0.0916164145,0.017701054,-0.2575627863,0.3606531918,-0.2036087364,0.1764502376,-0.1922911257,-0.0935443118,-0.1606316715,0.1398071498,-0.1229681745,-0.3245210052,0.0947918966,0.3048549592,0.0481997393,0.3857811987,-0.0001219456,-0.0564453825,0.5723124146,-0.0555414632,-0.3910080492,-0.4353305697,-0.2779269516,-0.0589925274,0.355984658,-0.0667517558,0.130753994,0.1634606421,0.0878987014,-0.2419316471,0.2720347047,0.26318115,0.1377917379,0.0733033642,0.1359027624,0.0714743361,0.3174403608,-0.2632397711,0.0046695629,0.3708156645,0.3326888382,-0.291418612,0.0472086743,0.0577220842,-0.205646798,0.0583759286,0.1841480434,0.1635258496,-0.25784266,-0.5462059379,0.0292282049,0.3213647902,0.2748629451,0.0140512539,-0.4840654433,-0.3165412545,-0.0286721792,0.1173614413,0.2789925039,0.1334600002,-0.2538383007,0.0309729725,-0.5450456142,-0.1321876645,0.0033698108,0.5532464981,0.1101105139,-0.0098115858,0.0639427006,0.1689395458,0.3128620982,-0.0395253114,0.1148340628,-0.0232361071,0.2994754314,0.1872622669,-0.0450580493,-0.0265388358,0.0512051396,0.4236578643,0.1454071999,0.1372276694,0.1992809027,0.0405518636,-0.1392972767,0.1321616024,0.15483661,-0.0140559683,-0.0127521874,0.0798756033,0.6293030977,0.3603568673,-0.1680348217,0.0711006969,-0.1136864573,-0.0785518587,-0.3989927769,-0.0077087539,-0.1199624836,-0.284093976,-0.1622226536,-0.0771386921,-0.1368774027,0.0150720552,0.0852001831,0.3928332329,-0.3108938336,0.2066407949,-0.087643005,0.3793320358,-0.1213530898,0.0319333375,-0.0930832773,0.1856428087,-0.1889153719,0.2517310679,0.4859232306,0.0784364417,0.3209574521,-0.3183521628,-0.1498351097,-0.2291524559,0.0177800152,-0.3440533578,0.0556664392,0.0532136112,0.3014571071,0.3389762938,-0.0610685609,-0.1870903969,-0.1006215811,-0.2109535187,-0.2240741253,-0.1662695557,0.3674656749,0.1121519953,-0.4041662812,-0.1559494436,-0.1344938278,-0.004829342,-0.0961971432,-0.0948827863,-0.1139309928,-0.0595751256,-0.1584257782,-0.200951606,0.3062122166,0.6965598464,-0.1322715133,-0.2766490877,-0.1275884658,0.041212976,-0.0782895386,0.502037406,-0.0070996187,0.1006353199,-0.4256356955,0.2437322885,0.5672342777,-0.2894794941,-0.3902561069,0.5320960879,-0.0154582411,0.0364813991,0.1849975735,0.0225609727,0.131248787,-0.0723032951,0.4729860723,0.5410932899,-0.0449328944,-0.0970429629,-0.2779184282,-0.1605929285,0.1289730817,0.1257047504,0.223503828,0.0212896317,-0.0032729721,-0.079706423,0.233262375,0.2292817086,0.0552493818,0.2484004796,-0.1013187617,0.1788349301,-0.104797557,-0.1381653398,-0.3142169118,0.2160550207,-0.0629844964,0.1218500361,-0.3327453732,0.2367781252,-0.1883798987,-0.1930935532,0.0038398847,-0.0187660865,0.0700894296,0.2044757456,0.131302923,0.0753203556,-0.2522730231,0.5734852552,0.0272469651,0.1724773198,-0.316007942,0.6796264052,-0.1542854309,-0.191763401,0.1090584621,0.3169533312,0.2635817826,-0.2635529935,0.0134699494,0.3022545278,-0.1289988309,0.1917850226,0.2702765465,0.0180154573,0.3637427986,-0.2720222771,0.1126302034,0.0491894484,0.1340863556,-0.1252143681,0.1797020435,0.2376711965,-0.2186419368,0.6319778562,0.4755757749,-0.0091407485,0.1447475404,-0.1831095815,-0.2134258151,-0.2173276395,0.6007691026,0.2402251363,0.2317631692,-0.1197275817,-0.148830682,0.1870426685,0.3490749896,0.0156644378,0.05569648,0.2178222686,-0.1637909412,-0.1998849362,-0.1010185257,0.3804479241,0.2209589183,0.1598923951,0.1135150269,0.021280108,-0.2521316409,-0.3306078017,0.2030390054,0.1683373004,-0.0889684558,0.0819643363,0.2019196898,0.1494818777,-0.0947707817,-0.4382834136,0.0053283786,-0.0233714301,-0.3714630306,-0.0647940934,-0.445727706,-0.6604163051,0.0467646122,-0.1720999628,-0.2792548835,-0.646451056,-0.1037925184,0.2034947723,0.0090118954,0.0356713496,-0.2063030154,-0.0062600239,-0.2111667097,-0.0129495598,-0.1801105589,-0.3260889649,-0.062903434,0.0395989567,0.3534008265,0.0574423596,0.1485339999,-0.2441104352,-0.1611087322,-0.1612910032,-0.1101251915,0.19283548,-0.1821724176,0.3608709574,0.2109528184,0.4903079271,0.2029083073,-0.2494547963,0.3121628463,-0.1433433294,0.0774954781,-0.0519493446,0.0077687465,-0.0471082143,0.1043553874,-0.0099998713,-0.471308589,-0.4650456309,0.2359144241,-0.0598598197,0.2176345289,0.1203419119,-0.041052755,0.2929945886,-0.0266634505,0.1280004233,-0.041178517,-0.3354517817,0.5650700331,-0.2066883743,-0.2000689805,-0.0150570692,0.0398590788,-0.2016781569,0.0807290226,-0.706532836,-0.2792837024,-0.4299277067,0.0273614805,0.2272491306,-0.0665315464,-0.0501204729,-0.2217652798,-0.0760565326,-0.0140878279,-0.4060663879,-0.0029456718,0.3619810045,0.3475250006,0.1065041944,0.5122767091,-0.0753890201,0.272541374,-0.0739138052,0.0747887865,0.6189810038,0.0821451545,0.3144127727,-0.1913370788,-0.269040674,0.1089694574,-0.3142771423,0.052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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/575","title":"Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading.","comments":"Hi. Closing this one since #626 updated the glue urls.\r\n\r\n> 1. Why is it still blocking? Is it still downloading?\r\n\r\nAfter downloading it generates the arrow file by iterating through the examples.\r\nThe number of examples processed by second is shown during the processing (not sure why it was not the case for you)\r\n\r\n> 2. I specified split as train, so why is the test folder being populated?\r\n\r\nIt downloads every split\r\n\r\n\r\n\r\n","body":"Hi,\r\n\r\nI'm following the [quick tour](https:\/\/huggingface.co\/nlp\/quicktour.html) and tried to load the glue dataset:\r\n```\r\n>>> from nlp import load_dataset\r\n>>> dataset = load_dataset('glue', 'mrpc', split='train')\r\n```\r\n\r\nHowever, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):\r\n```\r\n\r\n\/net\/vaosl01\/opt\/NFS\/su0\/miniconda3\/envs\/hf\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)\r\n    354                 \" to False.\"\r\n    355             )\r\n--> 356         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    357 \r\n    358     # From now on, connected is True.\r\n\r\nConnectionError: Couldn't reach https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc\r\n```\r\n\r\nI tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.\r\n\r\nSince this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:\r\n```\r\nds = load_dataset('imdb', split='train')\r\n```\r\nThis downloads the data, but it just blocks after that:\r\n```\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.56k\/4.56k [00:00<00:00, 1.38MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.07k\/2.07k [00:00<00:00, 1.15MB\/s]\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to \/net\/vaosl01\/opt\/NFS\/su0\/huggingface\/datasets\/imdb\/plain_text\/1.0.0\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84.1M\/84.1M [00:07<00:00, 11.1MB\/s]\r\n```\r\n\r\nI checked the folder `$HF_HOME\/datasets\/downloads\/extracted\/<id>\/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:\r\n1. Why is it still blocking? Is it still downloading?\r\n2. I specified split as train, so why is the test folder being populated?\r\n3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?\r\n\r\nThanks.\r\n","comment_length":74,"text":"Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading. \n Hi,\r\n\r\nI'm following the [quick tour](https:\/\/huggingface.co\/nlp\/quicktour.html) and tried to load the glue dataset:\r\n```\r\n>>> from nlp import load_dataset\r\n>>> dataset = load_dataset('glue', 'mrpc', split='train')\r\n```\r\n\r\nHowever, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines):\r\n```\r\n\r\n\/net\/vaosl01\/opt\/NFS\/su0\/miniconda3\/envs\/hf\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only)\r\n    354                 \" to False.\"\r\n    355             )\r\n--> 356         raise ConnectionError(\"Couldn't reach {}\".format(url))\r\n    357 \r\n    358     # From now on, connected is True.\r\n\r\nConnectionError: Couldn't reach https:\/\/firebasestorage.googleapis.com\/v0\/b\/mtl-sentence-representations.appspot.com\/o\/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc\r\n```\r\n\r\nI tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2.\r\n\r\nSince this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset:\r\n```\r\nds = load_dataset('imdb', split='train')\r\n```\r\nThis downloads the data, but it just blocks after that:\r\n```\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.56k\/4.56k [00:00<00:00, 1.38MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2.07k\/2.07k [00:00<00:00, 1.15MB\/s]\r\nDownloading and preparing dataset imdb\/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to \/net\/vaosl01\/opt\/NFS\/su0\/huggingface\/datasets\/imdb\/plain_text\/1.0.0\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 84.1M\/84.1M [00:07<00:00, 11.1MB\/s]\r\n```\r\n\r\nI checked the folder `$HF_HOME\/datasets\/downloads\/extracted\/<id>\/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are:\r\n1. Why is it still blocking? Is it still downloading?\r\n2. I specified split as train, so why is the test folder being populated?\r\n3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here?\r\n\r\nThanks.\r\n \n Hi. Closing this one since #626 updated the glue urls.\r\n\r\n> 1. Why is it still blocking? Is it still downloading?\r\n\r\nAfter downloading it generates the arrow file by iterating through the examples.\r\nThe number of examples processed by second is shown during the processing (not sure why it was not the case for you)\r\n\r\n> 2. I specified split as train, so why is the test folder being populated?\r\n\r\nIt downloads every 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/568","title":"`metric.compute` throws `ArrowInvalid` error","comments":"Could you try to update to `datasets>=1.0.0` (we changed the name of the library) and try again ?\r\nIf is was related to the distributed setup settings it must be fixed.\r\nIf it was related to empty metric inputs it's going to be fixed in #654 ","body":"I get the following error with `rouge.compute`. It happens only with distributed training, and it occurs randomly I can't easily reproduce it. This is using `nlp==0.4.0`\r\n\r\n```\r\n  File \"\/home\/beltagy\/trainer.py\", line 92, in validation_step\r\n    rouge_scores = rouge.compute(predictions=generated_str, references=gold_str, rouge_types=['rouge2', 'rouge1', 'rougeL'])\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 224, in compute\r\n    self.finalize(timeout=timeout)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 213, in finalize\r\n    self.data = Dataset(**reader.read_files(node_files))\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 217, in read_files\r\n    dataset_kwargs = self._read_files(files=files, info=self._info, original_instructions=original_instructions)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 162, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 276, in _get_dataset_from_filename\r\n    f = pa.ipc.open_stream(mmap)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/pyarrow\/ipc.py\", line 173, in open_stream\r\n    return RecordBatchStreamReader(source)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/pyarrow\/ipc.py\", line 64, in __init__\r\n    self._open(source)\r\n  File \"pyarrow\/ipc.pxi\", line 469, in pyarrow.lib._RecordBatchStreamReader._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Tried reading schema message, was null or length 0\r\n```","comment_length":46,"text":"`metric.compute` throws `ArrowInvalid` error \n I get the following error with `rouge.compute`. It happens only with distributed training, and it occurs randomly I can't easily reproduce it. This is using `nlp==0.4.0`\r\n\r\n```\r\n  File \"\/home\/beltagy\/trainer.py\", line 92, in validation_step\r\n    rouge_scores = rouge.compute(predictions=generated_str, references=gold_str, rouge_types=['rouge2', 'rouge1', 'rougeL'])\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 224, in compute\r\n    self.finalize(timeout=timeout)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 213, in finalize\r\n    self.data = Dataset(**reader.read_files(node_files))\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 217, in read_files\r\n    dataset_kwargs = self._read_files(files=files, info=self._info, original_instructions=original_instructions)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 162, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 276, in _get_dataset_from_filename\r\n    f = pa.ipc.open_stream(mmap)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/pyarrow\/ipc.py\", line 173, in open_stream\r\n    return RecordBatchStreamReader(source)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/pyarrow\/ipc.py\", line 64, in __init__\r\n    self._open(source)\r\n  File \"pyarrow\/ipc.pxi\", line 469, in pyarrow.lib._RecordBatchStreamReader._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Tried reading schema message, was null or length 0\r\n``` \n Could you try to update to `datasets>=1.0.0` (we changed the name of the library) and try again ?\r\nIf is was related to the distributed setup settings it must be fixed.\r\nIf it was related to empty metric inputs it's going to be fixed in #654 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/568","title":"`metric.compute` throws `ArrowInvalid` error","comments":"Closing this one as it was fixed in #654 \r\nFeel free to re-open if you have other questions","body":"I get the following error with `rouge.compute`. It happens only with distributed training, and it occurs randomly I can't easily reproduce it. This is using `nlp==0.4.0`\r\n\r\n```\r\n  File \"\/home\/beltagy\/trainer.py\", line 92, in validation_step\r\n    rouge_scores = rouge.compute(predictions=generated_str, references=gold_str, rouge_types=['rouge2', 'rouge1', 'rougeL'])\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 224, in compute\r\n    self.finalize(timeout=timeout)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 213, in finalize\r\n    self.data = Dataset(**reader.read_files(node_files))\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 217, in read_files\r\n    dataset_kwargs = self._read_files(files=files, info=self._info, original_instructions=original_instructions)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 162, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 276, in _get_dataset_from_filename\r\n    f = pa.ipc.open_stream(mmap)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/pyarrow\/ipc.py\", line 173, in open_stream\r\n    return RecordBatchStreamReader(source)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/pyarrow\/ipc.py\", line 64, in __init__\r\n    self._open(source)\r\n  File \"pyarrow\/ipc.pxi\", line 469, in pyarrow.lib._RecordBatchStreamReader._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Tried reading schema message, was null or length 0\r\n```","comment_length":18,"text":"`metric.compute` throws `ArrowInvalid` error \n I get the following error with `rouge.compute`. It happens only with distributed training, and it occurs randomly I can't easily reproduce it. This is using `nlp==0.4.0`\r\n\r\n```\r\n  File \"\/home\/beltagy\/trainer.py\", line 92, in validation_step\r\n    rouge_scores = rouge.compute(predictions=generated_str, references=gold_str, rouge_types=['rouge2', 'rouge1', 'rougeL'])\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 224, in compute\r\n    self.finalize(timeout=timeout)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 213, in finalize\r\n    self.data = Dataset(**reader.read_files(node_files))\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 217, in read_files\r\n    dataset_kwargs = self._read_files(files=files, info=self._info, original_instructions=original_instructions)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 162, in _read_files\r\n    pa_table: pa.Table = self._get_dataset_from_filename(f_dict)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py\", line 276, in _get_dataset_from_filename\r\n    f = pa.ipc.open_stream(mmap)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/pyarrow\/ipc.py\", line 173, in open_stream\r\n    return RecordBatchStreamReader(source)\r\n  File \"\/home\/beltagy\/miniconda3\/envs\/allennlp\/lib\/python3.7\/site-packages\/pyarrow\/ipc.py\", line 64, in __init__\r\n    self._open(source)\r\n  File \"pyarrow\/ipc.pxi\", line 469, in pyarrow.lib._RecordBatchStreamReader._open\r\n  File \"pyarrow\/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n  File \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Tried reading schema message, was null or length 0\r\n``` \n Closing this one as it was fixed in #654 \r\nFeel free to re-open if you have other questions","embeddings":[-0.4075993598,-0.2417839468,0.0564272702,0.2862923145,0.3149873018,-0.1689801663,-0.159183085,0.2923860252,-0.1545246542,0.4045127332,0.0525604412,0.5000802875,-0.1109343246,-0.3641472757,-0.094238542,-0.1911460012,-0.1322497874,0.0535943359,0.0623223409,-0.2779391706,-0.3765873313,0.0985771418,-0.2131596059,0.3267936409,-0.1932066679,-0.3521064818,0.1751866639,0.1816710234,-0.3589941561,-0.5263907909,0.2709598839,-0.2937383056,0.0037990306,0.1183886901,-0.0001191873,0.1296570301,0.3363063931,-0.0975792706,-0.0041522533,-0.1153502688,-0.0072667021,-0.0410663113,0.3097956181,-0.1023338959,0.1306812316,-0.1691236049,-0.2776666284,-0.2377279848,0.2970281839,0.3777075708,0.0835815147,0.2802878916,-0.0036599415,-0.2270237654,-0.1957388967,-0.2623844147,-0.0339903161,0.7621635795,0.0858863145,-0.212425679,0.0804970711,0.2712044716,0.3043489158,0.1928760856,0.434317559,-0.2398328185,0.2804588079,-0.002974591,-0.1896120757,0.1256083846,-0.0713665262,-0.1000525355,-0.2646160424,0.2437917441,0.2630293965,-0.5339521766,-0.0313146561,0.2237594873,-0.0697997287,0.0458759703,-0.240044713,0.0889871642,-0.1897262484,0.0984046757,0.163955614,0.130643785,0.015154738,0.2536816597,0.0861735046,0.0687298328,-0.3007571697,0.3731757998,-0.2901461422,-0.0234646499,-0.4769316614,-0.1539025456,-0.0954827592,0.0382494144,0.0169564392,0.1846893579,0.6731569171,-0.0968763605,0.2253452539,0.3182927072,0.1315817535,0.2608215213,-0.2110044658,0.5256132483,0.0850065351,-0.0524344891,-0.0642045215,0.0298821982,0.2078827918,-0.6180186272,0.1823914796,0.3520441949,0.0247808602,0.1011312529,-0.5585077405,-0.187474221,-0.4208103716,0.1441128701,0.1162040085,0.070241943,-0.0098986542,0.1733299792,0.2674604952,0.2471818775,-0.1467236131,-0.3565670252,-0.2359515429,0.1969593465,-0.2147193253,0.049003385,-0.0045695757,-0.0573178567,0.1841375381,0.090225853,0.0255612973,-0.1761726886,0.5448206067,-0.2204472274,-0.0769289583,0.0257570073,-0.3781884015,-0.2873996794,0.229777202,0.1334006041,0.037450254,-0.1065518111,-0.2053185105,-0.3397361338,0.2455221862,0.1057567075,0.3015190959,-0.0896550715,0.2021171451,0.1027184129,0.1186710373,-0.1679324359,0.1719890237,-0.2009641826,-0.1101601794,0.0065554334,0.0725460723,-0.0001086169,-0.1014669389,0.1107227728,0.1415624768,0.1545994133,0.0349537246,0.4689890444,-0.2500044107,0.2202896327,0.0234921817,-0.3029279113,0.5286731124,-0.9485337734,-0.2145374864,0.0507860072,-0.1609332114,-0.5299543142,-0.1245944723,-0.1013837904,-0.1401650608,0.1376924664,0.3832190335,0.0421500206,-0.1681721956,-0.2119553238,-0.2906453907,0.0716145709,0.246895358,0.1549103707,0.0768484026,0.0673309341,0.0960901529,-0.2710840702,0.0719325691,0.031280566,-0.0309009049,0.2132062167,-0.1415386796,-0.143858254,0.4165867269,-0.0793790221,0.2842078209,0.1112132147,-0.3692346513,-0.1870997995,-0.2376635224,0.0109891947,-0.3217407465,0.0674759075,-0.0672575831,-0.0084727677,0.0498084016,0.0204183981,0.2208724916,0.1332473457,-0.3149588406,-0.221074149,-0.4542842507,-0.030646855,-0.1260975599,0.0629270151,0.0274376441,-0.2461945862,0.0837185308,0.1811012775,0.160761103,-0.0457629487,-0.2479192317,0.4671181738,0.0894628614,0.0979057625,0.4763180912,-0.1720580012,0.1261303127,-0.2955779731,-0.3338622749,0.2377325296,0.3096678555,0.0850776434,0.1167073324,0.2165964097,-0.3274505138,-0.0149451001,-0.0602276884,0.2954143882,0.219982028,0.1660233587,-0.1664334536,0.0484345555,0.1911974251,-0.1303867102,0.39263165,-0.2581726313,-0.3667012453,-0.2043807954,0.1814842671,0.1247731745,0.0273581091,-0.2630428374,0.2203611284,0.2895263433,0.057939209,0.1685934812,0.4083440602,-0.08389882,0.0149456374,0.2547494471,-0.0987793058,-0.1508776844,0.0551872589,0.0896368101,0.1410100907,0.3735880852,0.2065856308,0.0663291514,-0.3672625124,-0.1021530479,0.2359729409,0.2093068361,-0.0221330989,0.4142645001,-0.0017026064,0.277654469,-0.1761566401,-0.1490000039,-0.1172153726,-0.1813320518,0.0745567977,0.0275700875,-0.0813113302,0.2263077348,0.2115996629,0.34382689,0.3297126591,0.0593655221,0.069641985,-0.1464833021,-0.1094480455,-0.12097238,-0.1135568023,0.0799365714,0.1879225671,-0.079188183,0.2124749422,-0.1500804126,-0.0069223507,-0.0119671673,0.0123387277,-0.0033971411,0.3124577403,-0.419359833,-0.6186656356,0.1612169147,0.3857408762,-0.1651768237,-0.1729445606,0.1802288145,0.0099464897,-0.0189911984,-0.1486451179,-0.3686364293,-0.408726573,-0.3623014987,0.3295700252,0.0106309811,-0.1517916471,0.1586492658,0.0680436641,0.1036152095,0.2316931784,-0.0726489872,0.020813426,-0.0302379169,0.2664110959,-0.2160875201,-0.2734997869,-0.0737783611,-0.1136969328,0.3361850679,0.4906616807,-0.1465961337,-0.2178466171,0.1189218536,0.1014851928,-0.5226741433,0.1076532006,0.5523734093,-0.1283311099,-0.0396117494,-0.1679284573,0.3205580413,0.4238615036,-0.0645174012,0.0601091981,-0.1110722274,0.1943630576,0.105065316,0.750133872,0.2554729879,-0.0024984078,0.1955074072,0.04087184,0.1205502972,0.1490536183,-0.0575668849,0.2131001651,0.1321832836,-0.1164219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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/565","title":"No module named 'nlp.logging'","comments":"Thanks for reporting.\r\n\r\nApparently this is a versioning issue: the lib downloaded the `bleurt` script from the master branch where we did this change recently. We'll fix that in a new release this week or early next week. Cc @thomwolf \r\n\r\nUntil that, I'd suggest you to download the right bleurt folder from github ([this one](https:\/\/github.com\/huggingface\/nlp\/tree\/0.4.0\/metrics\/bleurt)) and do\r\n\r\n```python\r\nfrom nlp import load_metric\r\n\r\nbleurt = load_metric(\"path\/to\/bleurt\/folder\")\r\n```\r\n\r\nTo download it you can either clone the repo or download the `bleurt.py` file and place it in a folder named `bleurt` ","body":"Hi, I am using nlp version 0.4.0. Trying to use bleurt as an eval metric, however, the bleurt script imports nlp.logging which creates the following error. What am I missing?\r\n\r\n```\r\n>>> import nlp\r\n2020-09-02 13:47:09.210310: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n>>> bleurt = nlp.load_metric(\"bleurt\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 443, in load_metric\r\n    metric_cls = import_main_class(module_path, dataset=False)\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 61, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/metrics\/bleurt\/43448cf2959ea81d3ae0e71c5c8ee31dc15eed9932f197f5f50673cbcecff2b5\/bleurt.py\", line 20, in <module>\r\n    from nlp.logging import get_logger\r\nModuleNotFoundError: No module named 'nlp.logging'\r\n```\r\n\r\nJust to show once again that I can't import the logging module:\r\n\r\n```\r\n>>> import nlp\r\n2020-09-02 13:48:38.190621: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n>>> nlp.__version__\r\n'0.4.0'\r\n>>> from nlp.logging import get_logger\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nModuleNotFoundError: No module named 'nlp.logging'\r\n```","comment_length":88,"text":"No module named 'nlp.logging' \n Hi, I am using nlp version 0.4.0. Trying to use bleurt as an eval metric, however, the bleurt script imports nlp.logging which creates the following error. What am I missing?\r\n\r\n```\r\n>>> import nlp\r\n2020-09-02 13:47:09.210310: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n>>> bleurt = nlp.load_metric(\"bleurt\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 443, in load_metric\r\n    metric_cls = import_main_class(module_path, dataset=False)\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 61, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/metrics\/bleurt\/43448cf2959ea81d3ae0e71c5c8ee31dc15eed9932f197f5f50673cbcecff2b5\/bleurt.py\", line 20, in <module>\r\n    from nlp.logging import get_logger\r\nModuleNotFoundError: No module named 'nlp.logging'\r\n```\r\n\r\nJust to show once again that I can't import the logging module:\r\n\r\n```\r\n>>> import nlp\r\n2020-09-02 13:48:38.190621: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n>>> nlp.__version__\r\n'0.4.0'\r\n>>> from nlp.logging import get_logger\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nModuleNotFoundError: No module named 'nlp.logging'\r\n``` \n Thanks for reporting.\r\n\r\nApparently this is a versioning issue: the lib downloaded the `bleurt` script from the master branch where we did this change recently. We'll fix that in a new release this week or early next week. Cc @thomwolf \r\n\r\nUntil that, I'd suggest you to download the right bleurt folder from github ([this one](https:\/\/github.com\/huggingface\/nlp\/tree\/0.4.0\/metrics\/bleurt)) and do\r\n\r\n```python\r\nfrom nlp import load_metric\r\n\r\nbleurt = load_metric(\"path\/to\/bleurt\/folder\")\r\n```\r\n\r\nTo download it you can either clone the repo or download the `bleurt.py` file and place it in a folder named `bleurt` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/565","title":"No module named 'nlp.logging'","comments":"Actually we can fix this on our side, this script didn't had to be updated. I'll do it in a few minutes","body":"Hi, I am using nlp version 0.4.0. Trying to use bleurt as an eval metric, however, the bleurt script imports nlp.logging which creates the following error. What am I missing?\r\n\r\n```\r\n>>> import nlp\r\n2020-09-02 13:47:09.210310: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n>>> bleurt = nlp.load_metric(\"bleurt\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 443, in load_metric\r\n    metric_cls = import_main_class(module_path, dataset=False)\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 61, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/metrics\/bleurt\/43448cf2959ea81d3ae0e71c5c8ee31dc15eed9932f197f5f50673cbcecff2b5\/bleurt.py\", line 20, in <module>\r\n    from nlp.logging import get_logger\r\nModuleNotFoundError: No module named 'nlp.logging'\r\n```\r\n\r\nJust to show once again that I can't import the logging module:\r\n\r\n```\r\n>>> import nlp\r\n2020-09-02 13:48:38.190621: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n>>> nlp.__version__\r\n'0.4.0'\r\n>>> from nlp.logging import get_logger\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nModuleNotFoundError: No module named 'nlp.logging'\r\n```","comment_length":22,"text":"No module named 'nlp.logging' \n Hi, I am using nlp version 0.4.0. Trying to use bleurt as an eval metric, however, the bleurt script imports nlp.logging which creates the following error. What am I missing?\r\n\r\n```\r\n>>> import nlp\r\n2020-09-02 13:47:09.210310: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n>>> bleurt = nlp.load_metric(\"bleurt\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 443, in load_metric\r\n    metric_cls = import_main_class(module_path, dataset=False)\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 61, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n  File \"\/home\/melody\/anaconda3\/envs\/transformers\/lib\/python3.6\/site-packages\/nlp\/metrics\/bleurt\/43448cf2959ea81d3ae0e71c5c8ee31dc15eed9932f197f5f50673cbcecff2b5\/bleurt.py\", line 20, in <module>\r\n    from nlp.logging import get_logger\r\nModuleNotFoundError: No module named 'nlp.logging'\r\n```\r\n\r\nJust to show once again that I can't import the logging module:\r\n\r\n```\r\n>>> import nlp\r\n2020-09-02 13:48:38.190621: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n>>> nlp.__version__\r\n'0.4.0'\r\n>>> from nlp.logging import get_logger\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\nModuleNotFoundError: No module named 'nlp.logging'\r\n``` \n Actually we can fix this on our side, this script didn't had to be updated. I'll do it in a few minutes","embeddings":[0.0177445877,-0.3961698413,-0.0208087899,-0.2511645257,0.2170929313,-0.0493598208,0.2021470666,0.3259567022,0.0938878134,-0.0865905285,0.157289505,0.3769112527,-0.678425312,0.1394217014,0.2663362026,-0.1221125945,-0.1415552497,0.1301270425,0.4967287779,-0.0840892345,-0.1551878899,0.289421618,-0.125125289,0.1515601426,-0.237595126,0.1572917402,0.1200002357,-0.061919082,-0.0776275024,-0.3836641014,0.070940122,-0.3669742346,0.3694146574,0.0429327115,-0.0001146313,-0.2010705024,0.3691684306,-0.0789471492,-0.307600379,-0.4952634275,0.0295944158,-0.5176201463,0.2748705149,-0.1576724201,0.149302572,-0.2864575684,0.2366618663,-0.3097525537,0.1938056946,0.4161177576,0.2068959326,-0.0221976209,-0.0768320784,0.0798196793,0.0021625236,-0.0918753371,-0.0400303714,0.3646017313,0.1682526171,-0.2020527273,-0.2094080299,0.0075306385,0.0123415431,-0.0307616368,0.4454035163,0.0410990827,0.4342164695,0.0744407326,0.1132750139,0.0331307426,0.3260391355,-0.2414316237,0.2124530524,0.2793803215,0.2856973112,-0.6526633501,0.0322943069,0.3493673801,-0.1642590314,-0.1975704432,-0.3211485744,-0.1605102569,-0.0767731816,0.2774338126,-0.0267945323,0.2369682789,-0.1171348989,-0.0183080826,0.0479584634,0.0457221493,0.051273074,0.3074576557,0.0579455532,0.2783448696,-0.0295917876,0.0939961076,0.3766542375,0.0809352994,-0.3641400933,0.037301816,-0.0792932957,-0.2028973997,0.0826363415,0.1005701274,0.128095448,0.482413888,0.5802249908,0.0861540437,0.1211153269,0.1061822399,0.0224334728,0.1806164533,0.0178397074,-0.2114959955,-0.0291198101,-0.0329339616,-0.0712683052,-0.0087648984,-0.2257304788,-0.0556228422,-0.2265833467,-0.0307782069,0.1380367726,0.3203542829,-0.2769455314,-0.3906171024,0.5350926518,0.1231341138,-0.2232646644,-0.032176964,-0.1262366623,0.5046454072,-0.4369259179,-0.1370981336,0.143480733,-0.0797784775,0.4327978492,-0.1558860689,-0.1006639972,-0.2113872468,-0.0947681069,0.2198672891,-0.0771770924,-0.0099433828,-0.0664815083,-0.3026097715,0.0548935197,-0.1666546017,-0.1033997461,-0.0218892228,-0.1572464854,-0.3142599761,-0.2244267315,0.1509290785,-0.3393890858,-0.2686007321,0.2011347711,0.0782153308,-0.0370585285,-0.3826788962,-0.1565061957,-0.1386644989,0.0589941666,-0.0779406652,0.1836316586,-0.0987101644,0.07261803,-0.3945173919,-0.3643250465,0.2603411674,0.3422808349,-0.1949792653,-0.2013522387,0.2731038332,-0.1028451994,-0.1197198927,0.625600636,-0.2656618953,0.2711018324,-0.2094727308,-0.0848327279,-0.3129795194,-0.0258243866,-0.0342767574,-0.1933761388,-0.0780976713,0.3543556333,0.2127129138,-0.0401689,-0.294375211,-0.1962094605,-0.1480362713,0.1277350634,0.1170276999,0.3017060757,0.042928759,0.1312465668,0.5985461473,-0.0901134312,-0.0685134977,-0.0750644207,0.1411289424,0.180771023,-0.2709906995,0.1386940926,-0.1707386076,0.119585596,-0.1768057793,-0.3409104049,0.0959557295,0.0197773781,0.2479205132,-0.1413602382,-0.1239601895,0.117582202,-0.3142851293,0.1030290574,0.0294074677,-0.1288951039,0.3554201126,-0.382553339,-0.0465960838,-0.2218794823,-0.1060575098,-0.1710862815,-0.1487460881,-0.0094743446,-0.2253688574,0.1783795655,0.6324031949,-0.0850064158,0.2346969992,-0.0037051507,-0.1035135016,-0.0135534313,0.2673344016,0.0565778017,-0.0577418916,-0.0482448675,-0.0602159128,0.1752789021,0.0162852742,-0.096297048,-0.0791670084,0.4494024813,0.0644367188,0.2025585771,0.0629727095,0.1511247158,0.2264125198,-0.0125715872,-0.2510657012,0.0872598439,-0.1092128158,0.8314300776,-0.1710357815,0.2515512705,-0.2746801674,0.0517181419,-0.200482592,0.6626394391,0.2042042166,0.0820650309,0.180214867,-0.2810432315,-0.0692387745,0.1406123638,0.0014656645,0.3870842755,0.2310897112,-0.0509548113,0.2067657709,-0.1034539565,-0.1705688387,0.1831772625,-0.0443811901,-0.2436577231,0.0247675907,0.1182230189,-0.130331859,-0.1481409669,-0.1530951858,-0.123313278,0.1996001899,-0.0350606553,0.1603679359,-0.3734546304,-0.1339826435,-0.2300897241,-0.1811053157,-0.229476884,-0.2983353436,0.0284396969,0.0945010856,0.0437802263,0.085607782,0.4398249984,0.2297273874,-0.2515345216,-0.1649123132,-0.0883319676,-0.4656353891,-0.468177855,0.0709280223,0.1150501594,0.2313400209,0.2546295226,-0.1552510709,0.1433707327,-0.0437558442,-0.5664678216,-0.0181738697,0.0860506445,0.070925042,0.3076506257,-0.06650085,-0.1981247663,-0.3041720986,0.1178149879,-0.2248552144,0.0074069975,0.2838937044,-0.1443242431,-0.0391858928,-0.3543541729,-0.3782945871,-0.2264765948,-0.5012181401,0.0852806047,0.0689325258,-0.0958226547,0.438611716,0.0011936188,0.5463566184,-0.0049784738,0.2645167112,0.0071177036,0.3665676117,0.1443119496,-0.2059247047,-0.3020400703,0.1247395277,-0.123570092,0.3829789162,0.0842039064,-0.3999665678,-0.3124961853,0.3001262546,-0.4379544556,0.0871436968,0.1368221492,0.2238348871,0.017371418,-0.0351178348,-0.1492095739,-0.1678654104,0.1437755525,0.137040928,0.7387797832,-0.0642652363,0.2167179286,0.0948870853,0.3520082235,0.1043622345,-0.3068554103,0.0734939575,0.044195313,0.2859508991,0.1100366488,-0.2359181792,0.1601347625,0.0702000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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/560","title":"Using custom DownloadConfig results in an error","comments":"From my limited understanding, part of the issue seems related to the `prepare_module`  and `download_and_prepare` functions each handling the case where no config is passed. For example, `prepare_module` does mutate the object passed and forces the flags `extract_compressed_file` and `force_extract` to `True`.\r\n\r\nSee:\r\n* https:\/\/github.com\/huggingface\/nlp\/blob\/5fb61e1012bda724a9b6b847307d90a1380abfa5\/src\/nlp\/load.py#L227\r\n* https:\/\/github.com\/huggingface\/nlp\/blob\/5fb61e1012bda724a9b6b847307d90a1380abfa5\/src\/nlp\/builder.py#L388\r\n\r\nMaybe a cleaner solution would be to always instantiate a default `DownloadConfig` object at the top-level, have it as non-optional for the lower-level functions and treat it as immutable. ","body":"## Version \/ Environment\r\n\r\nUbuntu 18.04\r\nPython 3.6.8\r\nnlp 0.4.0\r\n\r\n## Description\r\n\r\nLoading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.\r\n\r\n## How to reproduce\r\n\r\n### Example without DownloadConfig --> works\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-without-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    imdb = nlp.load_dataset(path=\"imdb\")\r\n```\r\n\r\n### Example with DownloadConfig --> doesn't work\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-with-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\nfrom nlp.utils import DownloadConfig\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    download_config = DownloadConfig()\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n```\r\n\r\nError traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/...\/example_with_dl_config.py\", line 13, in <module>\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 463, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/datasets\/imdb\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743\/imdb.py\", line 86, in _split_generators\r\n    arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 158, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 108, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/info_utils.py\", line 79, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/data\/hf-test-with-dl-config-01\/datasets\/extracted\/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'\r\n```\r\n\r\n","comment_length":76,"text":"Using custom DownloadConfig results in an error \n ## Version \/ Environment\r\n\r\nUbuntu 18.04\r\nPython 3.6.8\r\nnlp 0.4.0\r\n\r\n## Description\r\n\r\nLoading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.\r\n\r\n## How to reproduce\r\n\r\n### Example without DownloadConfig --> works\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-without-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    imdb = nlp.load_dataset(path=\"imdb\")\r\n```\r\n\r\n### Example with DownloadConfig --> doesn't work\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-with-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\nfrom nlp.utils import DownloadConfig\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    download_config = DownloadConfig()\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n```\r\n\r\nError traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/...\/example_with_dl_config.py\", line 13, in <module>\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 463, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/datasets\/imdb\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743\/imdb.py\", line 86, in _split_generators\r\n    arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 158, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 108, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/info_utils.py\", line 79, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/data\/hf-test-with-dl-config-01\/datasets\/extracted\/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'\r\n```\r\n\r\n \n From my limited understanding, part of the issue seems related to the `prepare_module`  and `download_and_prepare` functions each handling the case where no config is passed. For example, `prepare_module` does mutate the object passed and forces the flags `extract_compressed_file` and `force_extract` to `True`.\r\n\r\nSee:\r\n* https:\/\/github.com\/huggingface\/nlp\/blob\/5fb61e1012bda724a9b6b847307d90a1380abfa5\/src\/nlp\/load.py#L227\r\n* https:\/\/github.com\/huggingface\/nlp\/blob\/5fb61e1012bda724a9b6b847307d90a1380abfa5\/src\/nlp\/builder.py#L388\r\n\r\nMaybe a cleaner solution would be to always instantiate a default `DownloadConfig` object at the top-level, have it as non-optional for the lower-level functions and treat it as immutable. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/560","title":"Using custom DownloadConfig results in an error","comments":"Thanks for the report, I'll take a look.\r\n\r\nWhat is your specific use-case for providing a DownloadConfig object?\r\n","body":"## Version \/ Environment\r\n\r\nUbuntu 18.04\r\nPython 3.6.8\r\nnlp 0.4.0\r\n\r\n## Description\r\n\r\nLoading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.\r\n\r\n## How to reproduce\r\n\r\n### Example without DownloadConfig --> works\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-without-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    imdb = nlp.load_dataset(path=\"imdb\")\r\n```\r\n\r\n### Example with DownloadConfig --> doesn't work\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-with-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\nfrom nlp.utils import DownloadConfig\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    download_config = DownloadConfig()\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n```\r\n\r\nError traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/...\/example_with_dl_config.py\", line 13, in <module>\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 463, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/datasets\/imdb\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743\/imdb.py\", line 86, in _split_generators\r\n    arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 158, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 108, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/info_utils.py\", line 79, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/data\/hf-test-with-dl-config-01\/datasets\/extracted\/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'\r\n```\r\n\r\n","comment_length":18,"text":"Using custom DownloadConfig results in an error \n ## Version \/ Environment\r\n\r\nUbuntu 18.04\r\nPython 3.6.8\r\nnlp 0.4.0\r\n\r\n## Description\r\n\r\nLoading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.\r\n\r\n## How to reproduce\r\n\r\n### Example without DownloadConfig --> works\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-without-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    imdb = nlp.load_dataset(path=\"imdb\")\r\n```\r\n\r\n### Example with DownloadConfig --> doesn't work\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-with-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\nfrom nlp.utils import DownloadConfig\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    download_config = DownloadConfig()\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n```\r\n\r\nError traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/...\/example_with_dl_config.py\", line 13, in <module>\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 463, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/datasets\/imdb\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743\/imdb.py\", line 86, in _split_generators\r\n    arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 158, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 108, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/info_utils.py\", line 79, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/data\/hf-test-with-dl-config-01\/datasets\/extracted\/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'\r\n```\r\n\r\n \n Thanks for the report, I'll take a look.\r\n\r\nWhat is your specific use-case for providing a DownloadConfig object?\r\n","embeddings":[-0.1150195375,0.1608139873,0.1123033017,0.0204855502,-0.0762844905,-0.0592143387,0.2878563702,0.0386445262,0.3867911994,-0.1992649138,-0.1571436226,0.3636115789,-0.2807902694,-0.0396447405,0.1373905092,0.1345225722,-0.335785538,0.191671595,0.2066785991,-0.0992470011,-0.4401432276,0.4904036224,-0.1363261491,-0.1055808887,-0.1104631573,0.2965768874,-0.0853590146,0.6002389789,-0.0052087544,-0.3369760513,0.5871984363,0.0767923817,0.2937197685,0.0780296773,-0.0001159093,0.0705925748,0.2336821258,-0.3101004958,-0.3799623847,-0.2622923553,-0.0511955321,-0.1243966147,0.2814418674,-0.1946797967,-0.0140370019,-0.0242791511,0.0815578252,0.0334612019,0.0941410288,0.2972890437,0.1419871897,-0.0229717921,-0.2864381075,-0.1697261631,0.2262740731,0.260076493,0.0480878502,0.5292792916,0.3167858422,-0.2207205594,0.0189583451,-0.3049541116,-0.1514441222,0.0622548983,0.5781335831,0.0206799582,0.2644710541,-0.0439114347,-0.1765135378,0.3859302104,0.2035041451,-0.2281819582,-0.0819195062,-0.3655665219,0.144602403,-0.4327120185,0.1780243218,0.1168847531,-0.273757726,0.0803424343,-0.2586563826,-0.1680175215,-0.0763951689,0.5087516308,0.2171331793,0.3228867352,-0.0183474217,0.1714263111,-0.0767275244,-0.0576790571,0.252420336,-0.3967368007,0.0253677983,0.1348518133,-0.0958460644,-0.0770932958,0.1277973652,0.0256882552,-0.0327643231,0.1246374547,0.0682523176,-0.1068671271,0.0644042119,0.0538509563,0.0521950275,0.286323905,-0.076600723,0.1167676002,-0.2036602199,0.1669653356,-0.0508592837,-0.0176646784,0.0861193165,-0.075323768,-0.3973380923,-0.1976338178,0.168750599,0.0014936652,-0.0629578158,-0.339681685,-0.0813496038,0.1825856417,0.338355571,0.2477036715,-0.0689882264,-0.2663639188,0.1952618361,0.2603036761,-0.0893417671,0.1586500257,-0.088268958,-0.1036935523,-0.1506859362,-0.0623486452,0.4825169444,0.2577634156,0.6017351151,0.0311783329,0.0021425504,0.0281839706,-0.0155198788,0.1216142625,0.0396912619,0.1164999828,0.004311326,0.0528843515,0.0625880286,-0.2535326481,-0.2557506263,-0.0347330607,-0.1973161101,-0.5240719318,-0.32106933,0.1228109673,-0.3273592293,-0.3346843123,0.2478318959,-0.0070183282,0.060555689,-0.3129452467,-0.0337526128,0.029548144,-0.4143957198,-0.2400684655,-0.089732632,0.3797775209,0.0012102607,-0.2380746603,-0.4831041098,-0.1488144845,0.2068390101,-0.0621503629,-0.4448852539,0.2460324764,-0.3705919683,0.1259263307,0.7066502571,-0.593824029,-0.475862354,0.7060877681,-0.1530996859,0.1541841626,0.1148285046,0.3047769666,0.3644037843,-0.1938655227,0.2743984759,0.449900806,0.1645402014,-0.0376653224,-0.1342462301,-0.2366191745,-0.0452952273,0.069085665,-0.2126618922,0.3807032704,0.0322402194,0.228694275,0.2805713415,0.0719816536,-0.0035698258,0.0699649006,-0.0190699659,0.2078530788,-0.216038838,0.2683116198,-0.5918141603,0.3081945777,-0.2088387758,0.0111979246,0.1865449697,0.0156147871,-0.3768417537,-0.2749233842,-0.1442956775,-0.391390115,0.0948154703,0.3689242601,0.1382951736,-0.1243658364,-0.1281298399,0.3340533972,-0.2191484272,-0.0176077504,-0.1604177207,-0.0221684948,0.1522677392,-0.1637913138,0.0569932424,0.105084911,0.2022019923,0.1713188738,-0.0114865042,0.4094794691,-0.1540582776,0.0669990554,-0.08875563,-0.2120270282,0.0864794329,-0.0025839424,0.2240317017,0.0819461644,0.1684664637,-0.227870211,-0.0677169561,0.1494398415,0.0992672071,0.3279950321,0.0210084673,0.1423203647,0.1164696813,-0.2840330005,-0.0270047877,-0.34892869,0.0761514902,0.1613445729,0.3522972167,0.0295899063,-0.2389683723,-0.0452459715,0.2232688069,0.1981347203,0.0204542782,0.1191779003,-0.0028204762,-0.0736611784,0.029122578,0.6464995742,0.816970706,0.110755451,-0.0627681091,0.4516937435,-0.2712000906,-0.2540049255,0.279292047,-0.1479594558,0.1289023012,0.1667049378,0.0079044867,0.0080792001,-0.1439014524,-0.4434364438,-0.0547379367,-0.0084888637,-0.4380910993,-0.0696294531,-0.2960466444,-0.4512906969,-0.1279855371,-0.2876825333,-0.0485401414,-0.2141084671,-0.159273237,0.1131819934,0.1047847271,-0.0083428686,-0.2631918788,0.019749023,-0.2949643731,-0.8628898859,0.0819824636,-0.0314406157,-0.3450305164,-0.0835407823,0.0386174619,0.1652457118,0.073131986,-0.1557795554,-0.2440009415,0.1602346897,-0.1029197425,0.1122968048,0.1613503247,0.3075020313,0.3883806169,-0.1511534303,0.2565552294,-0.4360593557,0.1637900174,0.2162101418,-0.0127829853,0.0424747802,0.0326359421,-0.0875902697,-0.368976295,-0.2115421742,-0.1649634391,-0.450252533,-0.0885402486,0.28491202,0.1961458027,0.4581585228,0.1651855111,0.3243179917,-0.1645777971,0.0822794884,-0.0674815327,-0.3117092252,0.2791596651,-0.4733329117,-0.0789996907,0.085931167,-0.2227194011,0.2154388875,0.1335568577,-0.3884005845,0.199653998,-0.1469651461,0.0002483857,-0.0470809862,-0.0934197456,0.2494487315,-0.082257472,0.0479591452,-0.2609181702,-0.3588173091,0.3365956545,0.3105373681,0.5375539064,0.275968343,0.2986249626,-0.1452536583,0.4846876264,0.1508263201,-0.1560512483,0.6103883386,-0.1947124898,0.4601784647,-0.0382337458,-0.2387869209,-0.0032227396,0.3633525074,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/560","title":"Using custom DownloadConfig results in an error","comments":"Thanks. Our use case involves running a training job behind a corporate firewall with no access to any external resources (S3, GCP or other web resources).\r\n\r\nI was thinking about a 2-steps process:\r\n1) Download the resources \/ artifacts using some secure corporate channel, ie run `nlp.load_dataset()` without a specific `DownloadConfig`. After that, collect the files from the `$HF_HOME` folder\r\n2) Copy the `$HF_HOME` folder in the firewalled environment. Run `nlp.load_dataset()` with a custom config `DownloadConfig(local_files_only=True)`\r\n\r\nHowever this ends up a bit clunky in practice, even when solving the `DownloadConfig` issue above. For example, the `filename` hash computed in `get_from_cache()` differs in the `local_files_only=False` vs `local_files_only=True` case (local case defaults `etag` to `None`, which results in a different hash). So effectively step 2) above doesn't work because the hash computed differs from the hash in the cache folder. Some hacks \/ workaround are possible but this solution becomes very convoluted.\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/c214aa5a4430c1df1bcd0619fd94d6abdf9d2da7\/src\/nlp\/utils\/file_utils.py#L417\r\n\r\nWould you recommend a different path?\r\n","body":"## Version \/ Environment\r\n\r\nUbuntu 18.04\r\nPython 3.6.8\r\nnlp 0.4.0\r\n\r\n## Description\r\n\r\nLoading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.\r\n\r\n## How to reproduce\r\n\r\n### Example without DownloadConfig --> works\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-without-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    imdb = nlp.load_dataset(path=\"imdb\")\r\n```\r\n\r\n### Example with DownloadConfig --> doesn't work\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-with-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\nfrom nlp.utils import DownloadConfig\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    download_config = DownloadConfig()\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n```\r\n\r\nError traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/...\/example_with_dl_config.py\", line 13, in <module>\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 463, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/datasets\/imdb\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743\/imdb.py\", line 86, in _split_generators\r\n    arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 158, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 108, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/info_utils.py\", line 79, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/data\/hf-test-with-dl-config-01\/datasets\/extracted\/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'\r\n```\r\n\r\n","comment_length":157,"text":"Using custom DownloadConfig results in an error \n ## Version \/ Environment\r\n\r\nUbuntu 18.04\r\nPython 3.6.8\r\nnlp 0.4.0\r\n\r\n## Description\r\n\r\nLoading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.\r\n\r\n## How to reproduce\r\n\r\n### Example without DownloadConfig --> works\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-without-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    imdb = nlp.load_dataset(path=\"imdb\")\r\n```\r\n\r\n### Example with DownloadConfig --> doesn't work\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-with-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\nfrom nlp.utils import DownloadConfig\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    download_config = DownloadConfig()\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n```\r\n\r\nError traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/...\/example_with_dl_config.py\", line 13, in <module>\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 463, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/datasets\/imdb\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743\/imdb.py\", line 86, in _split_generators\r\n    arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 158, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 108, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/info_utils.py\", line 79, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/data\/hf-test-with-dl-config-01\/datasets\/extracted\/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'\r\n```\r\n\r\n \n Thanks. Our use case involves running a training job behind a corporate firewall with no access to any external resources (S3, GCP or other web resources).\r\n\r\nI was thinking about a 2-steps process:\r\n1) Download the resources \/ artifacts using some secure corporate channel, ie run `nlp.load_dataset()` without a specific `DownloadConfig`. After that, collect the files from the `$HF_HOME` folder\r\n2) Copy the `$HF_HOME` folder in the firewalled environment. Run `nlp.load_dataset()` with a custom config `DownloadConfig(local_files_only=True)`\r\n\r\nHowever this ends up a bit clunky in practice, even when solving the `DownloadConfig` issue above. For example, the `filename` hash computed in `get_from_cache()` differs in the `local_files_only=False` vs `local_files_only=True` case (local case defaults `etag` to `None`, which results in a different hash). So effectively step 2) above doesn't work because the hash computed differs from the hash in the cache folder. Some hacks \/ workaround are possible but this solution becomes very convoluted.\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/c214aa5a4430c1df1bcd0619fd94d6abdf9d2da7\/src\/nlp\/utils\/file_utils.py#L417\r\n\r\nWould you recommend a different path?\r\n","embeddings":[-0.1150195375,0.1608139873,0.1123033017,0.0204855502,-0.0762844905,-0.0592143387,0.2878563702,0.0386445262,0.3867911994,-0.1992649138,-0.1571436226,0.3636115789,-0.2807902694,-0.0396447405,0.1373905092,0.1345225722,-0.335785538,0.191671595,0.2066785991,-0.0992470011,-0.4401432276,0.4904036224,-0.1363261491,-0.1055808887,-0.1104631573,0.2965768874,-0.0853590146,0.6002389789,-0.0052087544,-0.3369760513,0.5871984363,0.0767923817,0.2937197685,0.0780296773,-0.0001159093,0.0705925748,0.2336821258,-0.3101004958,-0.3799623847,-0.2622923553,-0.0511955321,-0.1243966147,0.2814418674,-0.1946797967,-0.0140370019,-0.0242791511,0.0815578252,0.0334612019,0.0941410288,0.2972890437,0.1419871897,-0.0229717921,-0.2864381075,-0.1697261631,0.2262740731,0.260076493,0.0480878502,0.5292792916,0.3167858422,-0.2207205594,0.0189583451,-0.3049541116,-0.1514441222,0.0622548983,0.5781335831,0.0206799582,0.2644710541,-0.0439114347,-0.1765135378,0.3859302104,0.2035041451,-0.2281819582,-0.0819195062,-0.3655665219,0.144602403,-0.4327120185,0.1780243218,0.1168847531,-0.273757726,0.0803424343,-0.2586563826,-0.1680175215,-0.0763951689,0.5087516308,0.2171331793,0.3228867352,-0.0183474217,0.1714263111,-0.0767275244,-0.0576790571,0.252420336,-0.3967368007,0.0253677983,0.1348518133,-0.0958460644,-0.0770932958,0.1277973652,0.0256882552,-0.0327643231,0.1246374547,0.0682523176,-0.1068671271,0.0644042119,0.0538509563,0.0521950275,0.286323905,-0.076600723,0.1167676002,-0.2036602199,0.1669653356,-0.0508592837,-0.0176646784,0.0861193165,-0.075323768,-0.3973380923,-0.1976338178,0.168750599,0.0014936652,-0.0629578158,-0.339681685,-0.0813496038,0.1825856417,0.338355571,0.2477036715,-0.0689882264,-0.2663639188,0.1952618361,0.2603036761,-0.0893417671,0.1586500257,-0.088268958,-0.1036935523,-0.1506859362,-0.0623486452,0.4825169444,0.2577634156,0.6017351151,0.0311783329,0.0021425504,0.0281839706,-0.0155198788,0.1216142625,0.0396912619,0.1164999828,0.004311326,0.0528843515,0.0625880286,-0.2535326481,-0.2557506263,-0.0347330607,-0.1973161101,-0.5240719318,-0.32106933,0.1228109673,-0.3273592293,-0.3346843123,0.2478318959,-0.0070183282,0.060555689,-0.3129452467,-0.0337526128,0.029548144,-0.4143957198,-0.2400684655,-0.089732632,0.3797775209,0.0012102607,-0.2380746603,-0.4831041098,-0.1488144845,0.2068390101,-0.0621503629,-0.4448852539,0.2460324764,-0.3705919683,0.1259263307,0.7066502571,-0.593824029,-0.475862354,0.7060877681,-0.1530996859,0.1541841626,0.1148285046,0.3047769666,0.3644037843,-0.1938655227,0.2743984759,0.449900806,0.1645402014,-0.0376653224,-0.1342462301,-0.2366191745,-0.0452952273,0.069085665,-0.2126618922,0.3807032704,0.0322402194,0.228694275,0.2805713415,0.0719816536,-0.0035698258,0.0699649006,-0.0190699659,0.2078530788,-0.216038838,0.2683116198,-0.5918141603,0.3081945777,-0.2088387758,0.0111979246,0.1865449697,0.0156147871,-0.3768417537,-0.2749233842,-0.1442956775,-0.391390115,0.0948154703,0.3689242601,0.1382951736,-0.1243658364,-0.1281298399,0.3340533972,-0.2191484272,-0.0176077504,-0.1604177207,-0.0221684948,0.1522677392,-0.1637913138,0.0569932424,0.105084911,0.2022019923,0.1713188738,-0.0114865042,0.4094794691,-0.1540582776,0.0669990554,-0.08875563,-0.2120270282,0.0864794329,-0.0025839424,0.2240317017,0.0819461644,0.1684664637,-0.227870211,-0.0677169561,0.1494398415,0.0992672071,0.3279950321,0.0210084673,0.1423203647,0.1164696813,-0.2840330005,-0.0270047877,-0.34892869,0.0761514902,0.1613445729,0.3522972167,0.0295899063,-0.2389683723,-0.0452459715,0.2232688069,0.1981347203,0.0204542782,0.1191779003,-0.0028204762,-0.0736611784,0.029122578,0.6464995742,0.816970706,0.110755451,-0.0627681091,0.4516937435,-0.2712000906,-0.2540049255,0.279292047,-0.1479594558,0.1289023012,0.1667049378,0.0079044867,0.0080792001,-0.1439014524,-0.4434364438,-0.0547379367,-0.0084888637,-0.4380910993,-0.0696294531,-0.2960466444,-0.4512906969,-0.1279855371,-0.2876825333,-0.0485401414,-0.2141084671,-0.159273237,0.1131819934,0.1047847271,-0.0083428686,-0.2631918788,0.019749023,-0.2949643731,-0.8628898859,0.0819824636,-0.0314406157,-0.3450305164,-0.0835407823,0.0386174619,0.1652457118,0.073131986,-0.1557795554,-0.2440009415,0.1602346897,-0.1029197425,0.1122968048,0.1613503247,0.3075020313,0.3883806169,-0.1511534303,0.2565552294,-0.4360593557,0.1637900174,0.2162101418,-0.0127829853,0.0424747802,0.0326359421,-0.0875902697,-0.368976295,-0.2115421742,-0.1649634391,-0.450252533,-0.0885402486,0.28491202,0.1961458027,0.4581585228,0.1651855111,0.3243179917,-0.1645777971,0.0822794884,-0.0674815327,-0.3117092252,0.2791596651,-0.4733329117,-0.0789996907,0.085931167,-0.2227194011,0.2154388875,0.1335568577,-0.3884005845,0.199653998,-0.1469651461,0.0002483857,-0.0470809862,-0.0934197456,0.2494487315,-0.082257472,0.0479591452,-0.2609181702,-0.3588173091,0.3365956545,0.3105373681,0.5375539064,0.275968343,0.2986249626,-0.1452536583,0.4846876264,0.1508263201,-0.1560512483,0.6103883386,-0.1947124898,0.4601784647,-0.0382337458,-0.2387869209,-0.0032227396,0.3633525074,-0.01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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/560","title":"Using custom DownloadConfig results in an error","comments":"I see.\r\n\r\nProbably the easiest way for you would be that we add simple serialization\/deserialization methods to the Dataset and DatasetDict objects once the data files have been downloaded and all the dataset is processed.\r\n\r\nWhat do you think @lhoestq ?","body":"## Version \/ Environment\r\n\r\nUbuntu 18.04\r\nPython 3.6.8\r\nnlp 0.4.0\r\n\r\n## Description\r\n\r\nLoading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.\r\n\r\n## How to reproduce\r\n\r\n### Example without DownloadConfig --> works\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-without-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    imdb = nlp.load_dataset(path=\"imdb\")\r\n```\r\n\r\n### Example with DownloadConfig --> doesn't work\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-with-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\nfrom nlp.utils import DownloadConfig\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    download_config = DownloadConfig()\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n```\r\n\r\nError traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/...\/example_with_dl_config.py\", line 13, in <module>\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 463, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/datasets\/imdb\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743\/imdb.py\", line 86, in _split_generators\r\n    arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 158, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 108, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/info_utils.py\", line 79, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/data\/hf-test-with-dl-config-01\/datasets\/extracted\/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'\r\n```\r\n\r\n","comment_length":41,"text":"Using custom DownloadConfig results in an error \n ## Version \/ Environment\r\n\r\nUbuntu 18.04\r\nPython 3.6.8\r\nnlp 0.4.0\r\n\r\n## Description\r\n\r\nLoading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error.\r\n\r\n## How to reproduce\r\n\r\n### Example without DownloadConfig --> works\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-without-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    imdb = nlp.load_dataset(path=\"imdb\")\r\n```\r\n\r\n### Example with DownloadConfig --> doesn't work\r\n\r\n```python\r\nimport os\r\n\r\nos.environ[\"HF_HOME\"] = \"\/data\/hf-test-with-dl-config-01\/\"\r\n\r\nimport logging\r\nimport nlp\r\nfrom nlp.utils import DownloadConfig\r\n\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\nif __name__ == \"__main__\":\r\n    download_config = DownloadConfig()\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n```\r\n\r\nError traceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/...\/example_with_dl_config.py\", line 13, in <module>\r\n    imdb = nlp.load_dataset(path=\"imdb\", download_config=download_config)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 463, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/builder.py\", line 518, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/datasets\/imdb\/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743\/imdb.py\", line 86, in _split_generators\r\n    arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 220, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 158, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 108, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n  File \"\/...\/python3.6\/python3.6\/site-packages\/nlp\/utils\/info_utils.py\", line 79, in get_size_checksum_dict\r\n    with open(path, \"rb\") as f:\r\nIsADirectoryError: [Errno 21] Is a directory: '\/data\/hf-test-with-dl-config-01\/datasets\/extracted\/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5'\r\n```\r\n\r\n \n I see.\r\n\r\nProbably the easiest way for you would be that we add simple serialization\/deserialization methods to the Dataset and DatasetDict objects once the data files have been downloaded and all the dataset is processed.\r\n\r\nWhat do you think @lhoestq ?","embeddings":[-0.1150195375,0.1608139873,0.1123033017,0.0204855502,-0.0762844905,-0.0592143387,0.2878563702,0.0386445262,0.3867911994,-0.1992649138,-0.1571436226,0.3636115789,-0.2807902694,-0.0396447405,0.1373905092,0.1345225722,-0.335785538,0.191671595,0.2066785991,-0.0992470011,-0.4401432276,0.4904036224,-0.1363261491,-0.1055808887,-0.1104631573,0.2965768874,-0.0853590146,0.6002389789,-0.0052087544,-0.3369760513,0.5871984363,0.0767923817,0.2937197685,0.0780296773,-0.0001159093,0.0705925748,0.2336821258,-0.3101004958,-0.3799623847,-0.2622923553,-0.0511955321,-0.1243966147,0.2814418674,-0.1946797967,-0.0140370019,-0.0242791511,0.0815578252,0.0334612019,0.0941410288,0.2972890437,0.1419871897,-0.0229717921,-0.2864381075,-0.1697261631,0.2262740731,0.260076493,0.0480878502,0.5292792916,0.3167858422,-0.2207205594,0.0189583451,-0.3049541116,-0.1514441222,0.0622548983,0.5781335831,0.0206799582,0.2644710541,-0.0439114347,-0.1765135378,0.3859302104,0.2035041451,-0.2281819582,-0.0819195062,-0.3655665219,0.144602403,-0.4327120185,0.1780243218,0.1168847531,-0.273757726,0.0803424343,-0.2586563826,-0.1680175215,-0.0763951689,0.5087516308,0.2171331793,0.3228867352,-0.0183474217,0.1714263111,-0.0767275244,-0.0576790571,0.252420336,-0.3967368007,0.0253677983,0.1348518133,-0.0958460644,-0.0770932958,0.1277973652,0.0256882552,-0.0327643231,0.1246374547,0.0682523176,-0.1068671271,0.0644042119,0.0538509563,0.0521950275,0.286323905,-0.076600723,0.1167676002,-0.2036602199,0.1669653356,-0.0508592837,-0.0176646784,0.0861193165,-0.075323768,-0.3973380923,-0.1976338178,0.168750599,0.0014936652,-0.0629578158,-0.339681685,-0.0813496038,0.1825856417,0.338355571,0.2477036715,-0.0689882264,-0.2663639188,0.1952618361,0.2603036761,-0.0893417671,0.1586500257,-0.088268958,-0.1036935523,-0.1506859362,-0.0623486452,0.4825169444,0.2577634156,0.6017351151,0.0311783329,0.0021425504,0.0281839706,-0.0155198788,0.1216142625,0.0396912619,0.1164999828,0.004311326,0.0528843515,0.0625880286,-0.2535326481,-0.2557506263,-0.0347330607,-0.1973161101,-0.5240719318,-0.32106933,0.1228109673,-0.3273592293,-0.3346843123,0.2478318959,-0.0070183282,0.060555689,-0.3129452467,-0.0337526128,0.029548144,-0.4143957198,-0.2400684655,-0.089732632,0.3797775209,0.0012102607,-0.2380746603,-0.4831041098,-0.1488144845,0.2068390101,-0.0621503629,-0.4448852539,0.2460324764,-0.3705919683,0.1259263307,0.7066502571,-0.593824029,-0.475862354,0.7060877681,-0.1530996859,0.1541841626,0.1148285046,0.3047769666,0.3644037843,-0.1938655227,0.2743984759,0.449900806,0.1645402014,-0.0376653224,-0.1342462301,-0.2366191745,-0.0452952273,0.069085665,-0.2126618922,0.3807032704,0.0322402194,0.228694275,0.2805713415,0.0719816536,-0.0035698258,0.0699649006,-0.0190699659,0.2078530788,-0.216038838,0.2683116198,-0.5918141603,0.3081945777,-0.2088387758,0.0111979246,0.1865449697,0.0156147871,-0.3768417537,-0.2749233842,-0.1442956775,-0.391390115,0.0948154703,0.3689242601,0.1382951736,-0.1243658364,-0.1281298399,0.3340533972,-0.2191484272,-0.0176077504,-0.1604177207,-0.0221684948,0.1522677392,-0.1637913138,0.0569932424,0.105084911,0.2022019923,0.1713188738,-0.0114865042,0.4094794691,-0.1540582776,0.0669990554,-0.08875563,-0.2120270282,0.0864794329,-0.0025839424,0.2240317017,0.0819461644,0.1684664637,-0.227870211,-0.0677169561,0.1494398415,0.0992672071,0.3279950321,0.0210084673,0.1423203647,0.1164696813,-0.2840330005,-0.0270047877,-0.34892869,0.0761514902,0.1613445729,0.3522972167,0.0295899063,-0.2389683723,-0.0452459715,0.2232688069,0.1981347203,0.0204542782,0.1191779003,-0.0028204762,-0.0736611784,0.029122578,0.6464995742,0.816970706,0.110755451,-0.0627681091,0.4516937435,-0.2712000906,-0.2540049255,0.279292047,-0.1479594558,0.1289023012,0.1667049378,0.0079044867,0.0080792001,-0.1439014524,-0.4434364438,-0.0547379367,-0.0084888637,-0.4380910993,-0.0696294531,-0.2960466444,-0.4512906969,-0.1279855371,-0.2876825333,-0.0485401414,-0.2141084671,-0.159273237,0.1131819934,0.1047847271,-0.0083428686,-0.2631918788,0.019749023,-0.2949643731,-0.8628898859,0.0819824636,-0.0314406157,-0.3450305164,-0.0835407823,0.0386174619,0.1652457118,0.073131986,-0.1557795554,-0.2440009415,0.1602346897,-0.1029197425,0.1122968048,0.1613503247,0.3075020313,0.3883806169,-0.1511534303,0.2565552294,-0.4360593557,0.1637900174,0.2162101418,-0.0127829853,0.0424747802,0.0326359421,-0.0875902697,-0.368976295,-0.2115421742,-0.1649634391,-0.450252533,-0.0885402486,0.28491202,0.1961458027,0.4581585228,0.1651855111,0.3243179917,-0.1645777971,0.0822794884,-0.0674815327,-0.3117092252,0.2791596651,-0.4733329117,-0.0789996907,0.085931167,-0.2227194011,0.2154388875,0.1335568577,-0.3884005845,0.199653998,-0.1469651461,0.0002483857,-0.0470809862,-0.0934197456,0.2494487315,-0.082257472,0.0479591452,-0.2609181702,-0.3588173091,0.3365956545,0.3105373681,0.5375539064,0.275968343,0.2986249626,-0.1452536583,0.4846876264,0.1508263201,-0.1560512483,0.6103883386,-0.1947124898,0.4601784647,-0.0382337458,-0.2387869209,-0.0032227396,0.3633525074,-0.0172826368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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/554","title":"nlp downloads to its module path","comments":"Indeed this is a known issue arising from the fact that we try to be compatible with cloupickle.\r\n\r\nDoes this also happen if you are installing in a virtual environment?","body":"I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`?","comment_length":30,"text":"nlp downloads to its module path \n I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`? \n Indeed this is a known issue arising from the fact that we try to be compatible with cloupickle.\r\n\r\nDoes this also happen if you are installing in a virtual environment?","embeddings":[0.0326843746,0.2539580166,0.1499526352,0.0345963463,0.214757815,-0.1813214272,-0.0204587802,-0.0045512239,0.1024628431,-0.0508642942,0.1380285323,0.8880224228,-0.442250967,0.0815139189,0.3362323046,0.3320013881,-0.1796586066,-0.0002901663,-0.4403924942,-0.0244457889,-0.212073341,0.345549643,0.0295308251,0.1748972386,0.0630624965,-0.2714599073,0.1764766276,0.2207587212,-0.2017823905,-0.2983646989,0.2262380868,0.0677955747,0.0233549811,0.2312595993,-0.0001246572,-0.1266600043,0.1466834694,-0.2211489081,-0.5942364335,-0.4229247868,0.0836751238,-0.1504780948,0.3097313643,-0.2473908216,0.4054462016,0.6272526979,0.2100753635,-0.0133366985,0.0938679725,0.180040136,0.129822284,0.3722446263,-0.146121189,0.354054302,0.1991157532,0.1898444295,-0.2509313822,0.5630124211,-0.0468491726,-0.1564094275,0.1237194836,-0.0159906466,-0.1453780383,-0.2201298177,0.4053889215,0.0232429337,0.542170763,-0.1580370963,-0.1846934557,0.002779186,0.1580805629,-0.6352776885,-0.2875439227,-0.3459047377,0.0451218262,-0.1250391304,0.520231545,0.3565153182,-0.2196206152,0.1207837164,0.062723279,-0.3631262481,-0.1046481654,0.3527787626,0.0758769885,0.4675630331,-0.0018829404,0.0300988741,0.3376191258,0.2649231553,0.3683661222,0.1396361142,0.0817191228,0.2940796912,0.2760203183,-0.0600535199,-0.0974647105,0.3331544697,-0.0598653778,0.0230617691,0.1331875026,-0.1023270339,-0.0971473083,0.2347497195,0.240801245,-0.2937859595,-0.0199316982,0.1705410331,0.2132526338,0.1521571428,0.2788050175,0.0625127256,-0.0701544359,-0.0998438001,-0.1074586064,-0.1125402004,0.0609444045,-0.1470344216,-0.0614198893,-0.1838048697,-0.1485045552,0.2996825278,0.2301567495,0.0253079124,-0.1921526641,0.1033578962,0.2294237614,0.0773207694,-0.3032056093,0.4586708248,0.0294333175,0.1733400971,-0.3338633478,0.0488014333,0.1534634978,0.1618083417,0.3029888868,-0.2545301914,-0.0548669472,-0.1869945824,0.0137971649,0.2578770518,-0.231493175,0.3605966866,0.0254138689,0.3670630753,-0.2235779911,-0.2853199542,-0.459127903,0.1197424158,-0.2980464101,-0.3935777247,0.1862976104,0.0347367786,-0.2341649532,-0.0807254016,0.1144634932,-0.6620383859,0.2572256327,-0.2190913707,0.1985624731,-0.2899947166,-0.2044749856,-0.2870658338,-0.0835918561,0.4170087576,0.1406819522,-0.2434347421,-0.2343941927,-0.0062076612,0.4222149253,0.1802350581,-0.1540425867,-0.0480853803,-0.2764587998,-0.2796525359,0.8423376083,-0.3605712056,-0.3386266232,0.2357316464,-0.5933491588,-0.0297031067,0.0297485106,0.1759302914,-0.4824045002,-0.1026944295,0.0016464071,0.7592406273,0.2715176642,0.0893569589,-0.3122752309,-0.2933425307,0.2515257597,-0.1365662664,-0.0736148879,0.0352933146,-0.0354187638,0.5409154892,0.5577620864,0.2064168006,0.0009637355,0.3216338158,0.5599628091,0.1643122435,-0.2873654068,-0.1587284207,0.0221837405,-0.0262412354,-0.8242051005,0.1558991969,-0.5611915588,-0.2444065213,-0.104234457,0.1596709043,0.1721691191,0.0421344712,-0.0329916552,0.2325987965,0.0532537661,0.1800094694,-0.1128176898,0.4923970401,-0.1988110244,0.1270056963,-0.1246593744,0.1227730662,-0.1702817678,-0.1980679631,-0.0759830251,0.4849922657,0.1350174248,-0.1688594818,0.0287453476,0.5297238827,-0.1243889034,0.1040727273,-0.3746854961,0.0564400889,0.3235250711,0.0784604698,0.0241341349,0.2581119537,-0.0472500548,0.0969391465,-0.1821463704,0.1139857545,-0.3834557831,0.0970005989,0.1707639843,0.2843174636,0.033099696,0.053851448,-0.1857933253,-0.0878794566,0.2567599416,0.2180497795,0.4287627041,0.3316879869,0.0636417195,-0.0161924902,0.3589437008,0.1764912158,0.1916058064,0.0315664075,0.041478321,-0.038178131,0.0644056499,0.2960349917,0.1246425807,0.0143525209,0.115152061,0.2016739547,-0.3002697825,-0.1990064383,0.1411170363,0.002523032,0.2384322286,0.1320196837,0.1465292722,-0.1913343221,-0.1618216187,0.0934488699,0.0271345172,0.1175148338,-0.3330697417,0.0698605552,-0.5621652007,-0.3618030846,-0.6628373265,0.1497510076,-0.5517084002,-0.2448141128,-0.1711098552,0.3884853125,-0.0105235167,-0.0930337682,-0.0906846225,-0.087428771,-0.4265940189,-0.5033159852,0.0046081385,0.0666370764,-0.3229703009,-0.1529540569,0.2286383063,0.1363472044,0.1716546863,-0.0452005267,-0.4361425042,0.0560572073,-0.2377965301,0.0539311469,0.3451829553,0.1509552449,0.2952367961,-0.1043479443,0.0696856603,-0.2573923171,0.0952086374,-0.1837004572,0.1074125841,0.0065734577,0.1170465201,0.0201885458,-0.3216100335,-0.1229667962,-0.4191868007,-0.4624139071,0.3291345835,0.2890335917,0.3142392635,0.8311154842,-0.1648727506,-0.0336302593,-0.1469506174,0.1657740474,0.2017695904,0.035377536,0.2355373502,-0.226958558,-0.1271612644,-0.1235052645,0.018370498,0.0342529044,0.3961649537,-0.2507331967,-0.0285671856,-0.0195147749,0.0932909027,0.1410595924,0.0818161294,0.5214660764,0.2803615928,0.1878141314,-0.0578506142,-0.2552235723,0.2268039137,0.5429323316,0.4109835029,0.3115747571,0.1435751021,0.2280550152,0.312443763,-0.0587832779,0.215649724,0.279889673,0.014291686,0.5034257174,0.0840158239,-0.0997243598,-0.2104303986,0.0449197255,-0.10262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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/554","title":"nlp downloads to its module path","comments":"> Indeed this is a know issue with the fact that we try to be compatible with cloupickle.\r\n> \r\n> Does this also happen if you are installing in a virtual environment?\r\n\r\nThen it would work, because the package is in a writable path.","body":"I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`?","comment_length":44,"text":"nlp downloads to its module path \n I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`? \n > Indeed this is a know issue with the fact that we try to be compatible with cloupickle.\r\n> \r\n> Does this also happen if you are installing in a virtual environment?\r\n\r\nThen it would work, because the package is in a writable path.","embeddings":[0.0366535224,0.2192299068,0.1506813467,0.0508494563,0.2112177163,-0.1820767671,-0.0289928615,-0.0183257349,0.1189896837,-0.0385014191,0.1243856326,0.8661038876,-0.4588842094,0.1108861566,0.3196983039,0.3289014399,-0.1757936031,0.0106611783,-0.3965616226,-0.0296090506,-0.2183236629,0.3573447168,0.0127435839,0.1647962034,0.0456613861,-0.2805224657,0.1733621806,0.2096358538,-0.2175506502,-0.2872889638,0.2205725312,0.0743843615,0.019858567,0.2375182509,-0.0001259466,-0.140705511,0.1535024792,-0.2212117612,-0.5928941965,-0.4169439375,0.0931646377,-0.1434482336,0.3012827635,-0.2460523099,0.375099957,0.6685689688,0.1899013072,-0.0168130044,0.0882839486,0.1640619189,0.1206227168,0.3852365315,-0.1497676969,0.355763793,0.1676640064,0.1658295542,-0.2532820106,0.5958601236,-0.0119575001,-0.1434496194,0.1398182958,-0.0086906506,-0.1408176571,-0.2264499068,0.3995679915,0.0035117115,0.5514386296,-0.1675674915,-0.1826414019,0.0073816292,0.1411041468,-0.6322299242,-0.2780581415,-0.3192709088,0.0291315671,-0.1716597229,0.5286079645,0.3607387841,-0.1927578896,0.1301563531,0.0535914674,-0.33201316,-0.0955119804,0.363376677,0.0818298087,0.4446329474,-0.0106202653,0.0516017005,0.3404615521,0.2785791755,0.3875044584,0.1370323002,0.065361701,0.2994748056,0.2602428198,-0.0839847103,-0.0904842168,0.3794725537,-0.0536357053,0.0383315831,0.1583432108,-0.1037540361,-0.0878680497,0.2539205551,0.2582300901,-0.2878369093,-0.0004792726,0.1772267222,0.2212037593,0.1572444588,0.2616731524,0.063905254,-0.0639304668,-0.1136361361,-0.1016861498,-0.0943214074,0.0696439072,-0.1461735815,-0.0453581512,-0.1766080111,-0.1582485139,0.2957185507,0.2490972877,0.0295461416,-0.2259839177,0.0910730213,0.2279285491,0.0887091979,-0.2838964164,0.4456208944,0.0281991865,0.1882098019,-0.3258990943,0.0462065153,0.1481841803,0.1662860364,0.3029676676,-0.2558451593,-0.0446325429,-0.2242345065,0.018395761,0.2445895374,-0.2678960264,0.3420213163,0.0084259668,0.3626168072,-0.2094111145,-0.3191005588,-0.4718196988,0.1166787669,-0.3187381029,-0.3920495808,0.1970468313,0.021062497,-0.2289592326,-0.0609708056,0.0684654936,-0.6516927481,0.2548456192,-0.2134694755,0.2012834549,-0.295047462,-0.2088278681,-0.2876152992,-0.0944900587,0.4052164853,0.0979430377,-0.2253578752,-0.2361149788,-0.010029288,0.4378164709,0.182920441,-0.1644482464,-0.0694433227,-0.282744199,-0.2839719057,0.830616653,-0.3531689644,-0.3510722816,0.2237607986,-0.5753187537,-0.0287069101,0.0173475128,0.1781503856,-0.4840010703,-0.1129441932,-0.030246133,0.7501533628,0.2822034061,0.0799725801,-0.3088632822,-0.2825063765,0.2679076791,-0.1572669894,-0.0863526389,0.0594158471,-0.0044740187,0.5208021998,0.5502972603,0.2040090412,0.003794922,0.3402992189,0.5700147152,0.1585389078,-0.288918972,-0.1508489698,0.0248760767,-0.0060572233,-0.8670706153,0.1508536041,-0.5519198179,-0.2643413246,-0.1136568189,0.1670417935,0.1619913578,0.0302645303,-0.0453914404,0.2329876423,0.0791513547,0.1599001884,-0.108599849,0.4938775897,-0.2198509723,0.1199445203,-0.0938762575,0.1448790133,-0.1620064378,-0.2122442126,-0.0867494196,0.4839967489,0.1669929922,-0.1863855422,0.014285584,0.5541186333,-0.0953236669,0.1087051928,-0.3931443393,0.0577358343,0.3354277015,0.0622458309,0.0238070022,0.2601512969,-0.0441208966,0.0759531558,-0.156748414,0.111864455,-0.3385421932,0.0956282839,0.1660507619,0.2892729938,0.0081913136,0.0418537594,-0.1756075025,-0.0711489022,0.2505498528,0.2442206889,0.4438206255,0.3397476673,0.0398138948,-0.0256215241,0.3678896725,0.1768845469,0.1766762137,0.029185148,0.0396356061,-0.0324893668,0.0751423538,0.2753876448,0.1064353511,-0.0058809309,0.0884655491,0.2145641297,-0.2644822896,-0.1744257212,0.1345820725,0.0081139337,0.2572434545,0.1063861623,0.1480322033,-0.1938437819,-0.1374715269,0.0872050896,0.0261675101,0.1036969721,-0.3324563205,0.0644233748,-0.557071507,-0.3485966921,-0.6488167644,0.1673888862,-0.5379951,-0.2224393785,-0.1973872185,0.4104060829,0.0084693497,-0.0865177736,-0.0707248375,-0.0870466828,-0.4106627405,-0.4929046035,-0.0081484821,0.0776349828,-0.3279862106,-0.1643287987,0.2320948243,0.1121879742,0.1739886105,-0.0162180196,-0.4475990832,0.0440152287,-0.254773438,0.0593611263,0.3497127593,0.1541694999,0.3018980026,-0.093814455,0.0188857596,-0.2698601782,0.1019585282,-0.1533108056,0.1081808209,0.0174670145,0.1186528802,0.0067654438,-0.3135100007,-0.1002925336,-0.4666461647,-0.445424825,0.3380752206,0.2953378856,0.3194355369,0.824881196,-0.1670692712,-0.0477720015,-0.1108454391,0.1610222608,0.2100711912,0.0273991562,0.221004039,-0.2460757196,-0.138740018,-0.1334884018,0.0053342599,0.0291796364,0.3621094823,-0.2343441099,-0.0264554434,-0.0387189537,0.1095168814,0.1662641764,0.0689311698,0.534698844,0.2771008611,0.2051319778,-0.071721673,-0.2466054112,0.2251665592,0.5450434089,0.4341472983,0.3294350207,0.14637582,0.2293754816,0.2922331989,-0.0532068275,0.2080352902,0.30007568,0.0098279063,0.4775596261,0.0632710755,-0.1031333208,-0.1840748787,0.0382701196,-0.1106667742,0.04867131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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/554","title":"nlp downloads to its module path","comments":"> If it's fine for you then this is the recommended way to solve this issue.\r\n\r\nI don't want to use a virtual environment, because Nix is fully reproducible, and virtual environments are not. And I am the maintainer of the `transformers` in nixpkgs, so sooner or later I will have to package `nlp`, since it is becoming a dependency of `transformers` ;).","body":"I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`?","comment_length":63,"text":"nlp downloads to its module path \n I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`? \n > If it's fine for you then this is the recommended way to solve this issue.\r\n\r\nI don't want to use a virtual environment, because Nix is fully reproducible, and virtual environments are not. And I am the maintainer of the `transformers` in nixpkgs, so sooner or later I will have to package `nlp`, since it is becoming a dependency of `transformers` ;).","embeddings":[0.046116367,0.234247297,0.1267920136,0.0296455473,0.2665544748,-0.1070262492,-0.0495609231,0.0400402658,0.0180516001,-0.0675463974,0.1628862768,0.9074655771,-0.3907279372,-0.0176893678,0.4088222384,0.325348407,-0.1871856004,-0.0095229726,-0.5038885474,-0.0106999353,-0.1629397571,0.3172166646,0.0928088799,0.2191578299,0.1168668419,-0.2506914735,0.1828442365,0.2161523998,-0.1610955298,-0.3452356458,0.2223353237,0.0172989201,0.0685411766,0.2536882162,-0.0001222753,-0.1021162271,0.0917157605,-0.2052916735,-0.5588748455,-0.4225788116,-0.0087841656,-0.0983675718,0.2872693837,-0.2772430182,0.4104694128,0.4269267917,0.2175244987,-0.0110216606,0.1032749563,0.2261535525,0.1559865922,0.2630299628,-0.0832814127,0.3464424908,0.2658575475,0.2363731116,-0.1880696565,0.4692555666,-0.195657894,-0.1145167723,0.1284811795,0.0002660673,-0.188188374,-0.2027669996,0.4447340071,0.0490654148,0.5470705628,-0.1268076301,-0.1714593172,-0.0210750327,0.2907862067,-0.645606935,-0.2828428447,-0.3490971625,0.0826938525,0.0141357509,0.4553363621,0.3532887399,-0.2175490409,0.1618506908,0.1237481236,-0.4460406899,-0.1745378077,0.3221803606,0.1006399542,0.5085071921,0.0016213604,0.00436628,0.3273635209,0.2121271938,0.310200572,0.1156195328,0.0912705809,0.2791387737,0.2880519331,-0.0127185676,-0.1458795518,0.1641432941,-0.0892470777,-0.1161925867,0.006875712,-0.1094334498,-0.1109065935,0.1684023142,0.1201752499,-0.2382375151,-0.0224934928,0.1595935076,0.1828632057,0.1355985999,0.2601260841,0.0825105309,-0.0887886435,-0.033735089,-0.1104378924,-0.1315760463,0.0319398306,-0.123421289,-0.087278612,-0.2416331768,-0.1768742949,0.3003259599,0.1871353984,-0.000803103,-0.0788418576,0.1259497851,0.23518911,0.0070649926,-0.272452414,0.4379551709,0.0300905649,0.1560270637,-0.2845471203,0.078573294,0.131322071,0.079952769,0.3415975273,-0.2330340147,-0.1420311928,-0.1482820064,-0.0054023978,0.2611575723,-0.1751811057,0.3640359342,0.0621220618,0.4477259517,-0.2313425243,-0.1666173935,-0.4070670605,0.1336168498,-0.284452945,-0.402718544,0.1284139901,0.0695235208,-0.2449092269,-0.1395531148,0.1998022348,-0.6429062486,0.2645700574,-0.179700911,0.2135895789,-0.182408303,-0.1748671234,-0.3008734882,-0.0477913022,0.4714634418,0.229443565,-0.2523702681,-0.2462406605,0.0334514081,0.3789203465,0.1632091105,-0.1067515537,0.1069479734,-0.2686387002,-0.2919618189,0.9047156572,-0.3906837404,-0.3091709912,0.2915870249,-0.6707193851,-0.0479654893,0.1383062303,0.1629870683,-0.471446991,-0.0274625439,0.072186172,0.7123209834,0.2875203788,0.1052506864,-0.2627530098,-0.3227465451,0.222829774,-0.1149718761,-0.038980417,-0.0065271291,-0.1487011313,0.597994864,0.5738622546,0.1660397798,-0.0016501561,0.3604936898,0.5375530124,0.1958610117,-0.27916044,-0.2807016671,0.0373023115,-0.0844608471,-0.6614479423,0.107897684,-0.5330183506,-0.1834053397,-0.1122304201,0.1228865534,0.1824475229,0.0702087656,-0.0002824996,0.2549110651,0.0022279522,0.2083616406,-0.0960031152,0.4704718292,-0.1031578407,0.1595535725,-0.1962206364,0.0275265183,-0.1773788333,-0.1482795328,-0.0782647207,0.4618581235,0.12504071,-0.1315963715,0.0474949852,0.4746610522,-0.2096363902,0.0889927298,-0.2957380414,0.0512470976,0.2695703208,0.0370287374,0.0307874102,0.2559651434,-0.0543590561,0.2016519308,-0.2219351679,0.0880995989,-0.4998498559,0.0931912512,0.1127002537,0.2424832135,0.063227728,0.0523401685,-0.1859665513,-0.1516279876,0.2849550843,0.100478597,0.2885392606,0.3445744216,0.161164254,-0.0273502786,0.3952870071,0.1889089197,0.1825788766,0.0537530147,0.0252611041,-0.014876904,0.0243096557,0.3026709259,0.1458862126,0.0798739642,0.1653839201,0.1257158965,-0.2704928219,-0.1986791939,0.2159199417,-0.0208988097,0.1586565226,0.1924609542,0.1183894873,-0.2064065486,-0.1669658422,0.1002587825,0.0627789795,0.168289572,-0.3316838145,0.0543968603,-0.595418334,-0.3879919648,-0.7191829681,0.0387097411,-0.5594838858,-0.2267316431,-0.1493154168,0.2969737053,0.0152958473,-0.0988679603,-0.0740788728,-0.2233131528,-0.4157322645,-0.6021327972,0.0733015463,0.0929680392,-0.287340343,-0.1341560185,0.1705031097,0.1962793618,0.1610697657,-0.1474251747,-0.3979492188,0.0391923487,-0.1772091687,0.0086356094,0.3191351891,0.1386234015,0.2791619599,-0.1118232235,0.2117307484,-0.199268803,0.0509133562,-0.2132970989,0.1117410809,-0.0033845371,0.1127620935,0.0831770897,-0.344376564,-0.1749547869,-0.253012538,-0.5123717785,0.2787902653,0.216633141,0.3217908144,0.7720021605,-0.102707155,0.0091882246,-0.2122785002,0.1872679591,0.2074654847,0.0008651846,0.2596406043,-0.1210708171,-0.0713396743,-0.0554444529,0.0765059814,0.0564994514,0.4624179602,-0.2822106481,-0.033298634,0.0441931263,0.0560681969,0.0114322631,0.1246184781,0.4576794505,0.2989203334,0.1324489564,-0.0288429037,-0.2807248831,0.2095328569,0.5572180748,0.3679825366,0.3192533255,0.2056295127,0.1678475142,0.3601109385,-0.0422435254,0.2414954007,0.2436206937,0.0004374397,0.529665947,0.1801270843,-0.1364840865,-0.3310603499,0.1352423877,-0.0557458214,0.0593938008,0.120919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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/554","title":"nlp downloads to its module path","comments":"Ok interesting. We could have another check to see if it's possible to download and import the datasets script at another location than the module path. I think this would probably involve tweaking the python system path dynamically.\r\n\r\nI don't know anything about Nix so if you want to give this a try your self we can guide you or you can give us more information on your general project and how this works.\r\n\r\nRegarding `nlp` and `transformers`, we are not sure `nlp` will become a required dependency for `transformers`.  It will probably be used a lot in the examples but I think it probably won't be a required dependency for the main package since we try to keep it as light as possible in terms of deps.\r\n\r\nHappy to help you make all these things work better for your use-case ","body":"I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`?","comment_length":141,"text":"nlp downloads to its module path \n I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`? \n Ok interesting. We could have another check to see if it's possible to download and import the datasets script at another location than the module path. I think this would probably involve tweaking the python system path dynamically.\r\n\r\nI don't know anything about Nix so if you want to give this a try your self we can guide you or you can give us more information on your general project and how this works.\r\n\r\nRegarding `nlp` and `transformers`, we are not sure `nlp` will become a required dependency for `transformers`.  It will probably be used a lot in the examples but I think it probably won't be a required dependency for the main package since we try to keep it as light as possible in terms of deps.\r\n\r\nHappy to help you make all these things work better for your use-case 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/554","title":"nlp downloads to its module path","comments":"@danieldk modules are now installed in a different location (by default in the cache directory of the lib, in `~\/.cache\/huggingface\/modules`). You can also change that using the environment variable `HF_MODULES_PATH`\r\n\r\nFeel free to play with this change from the master branch for now, and let us know if it sounds good for you :)\r\nWe plan to do a release in the next coming days","body":"I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`?","comment_length":65,"text":"nlp downloads to its module path \n I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`? \n @danieldk modules are now installed in a different location (by default in the cache directory of the lib, in `~\/.cache\/huggingface\/modules`). You can also change that using the environment variable `HF_MODULES_PATH`\r\n\r\nFeel free to play with this change from the master branch for now, and let us know if it sounds good for you :)\r\nWe plan to do a release in the next coming days","embeddings":[0.0594276041,0.2854037285,0.1192257926,0.0074428776,0.2506986558,-0.1265826225,-0.0366810672,0.0295479912,0.0179713909,-0.119238168,0.145190075,0.9340407252,-0.348520726,-0.0550438054,0.448151648,0.3217197955,-0.1729900539,-0.0191877522,-0.5424788594,-0.0087753208,-0.1322143078,0.343559891,0.0899610296,0.2194026858,0.1086243019,-0.2739531398,0.1533594579,0.1835228354,-0.1502880007,-0.361440748,0.2200702876,0.0330717936,0.0396199599,0.2460679263,-0.0001222595,-0.0723031759,0.0618373081,-0.1991177052,-0.5908346772,-0.4036874473,0.0073660119,-0.1110637337,0.331789583,-0.2588049471,0.4212759435,0.4604949653,0.2072204202,-0.0632975549,0.0779818371,0.1990575045,0.1536840349,0.2625417411,-0.1157127097,0.3399074078,0.2558580935,0.206086278,-0.2066236734,0.4231725633,-0.1807647943,-0.1173191592,0.1137165353,0.0151541848,-0.1605414748,-0.2232352942,0.4218443334,0.0965171233,0.5065210462,-0.1041269973,-0.178357169,-0.057623636,0.2488173693,-0.6838291883,-0.3021194637,-0.3956843615,0.0583829917,-0.011045713,0.4367433786,0.2950358689,-0.1969232112,0.1277929246,0.1174732447,-0.4387536049,-0.1516216099,0.2879826128,0.0643961206,0.5356558561,-0.0010861605,-0.0201419462,0.3124943674,0.2233442515,0.3028106093,0.1413413584,0.1304742694,0.2637385428,0.306614846,-0.0090490058,-0.126691401,0.1287931651,-0.1076702848,-0.0864102021,0.0486149788,-0.1003772989,-0.100146845,0.1405427754,0.128412351,-0.2314269096,-0.0331277698,0.1172136813,0.1847460717,0.1608623266,0.237539351,0.0487547666,-0.0829556063,-0.0250671338,-0.0955912769,-0.1017725915,0.0238328222,-0.1320399195,-0.1010759845,-0.2369559705,-0.1393790841,0.2759144604,0.1524428576,0.0107965935,-0.0849052668,0.1406838745,0.2397924215,0.0124138677,-0.3015604913,0.4846943617,0.0200684797,0.1443138719,-0.3159932196,0.0632190555,0.1285133362,0.0914971083,0.3637309074,-0.2417959124,-0.1056750491,-0.1247909218,-0.0109360656,0.284550339,-0.1785971522,0.3613257706,0.0448554307,0.4277518094,-0.2470026016,-0.1423707306,-0.4103898406,0.1094860882,-0.2655018866,-0.351354003,0.1702574342,0.0741485283,-0.2385264635,-0.1675650924,0.228550598,-0.6515527368,0.2796333432,-0.1777371764,0.1845386773,-0.2027804404,-0.1684630066,-0.2977358103,-0.055017367,0.4658385813,0.2440615594,-0.2764915824,-0.2273944169,0.0714991987,0.3334225714,0.157590881,-0.0960775763,0.1057573482,-0.2538484037,-0.2903698981,0.9286288619,-0.4024112523,-0.2970966101,0.2756410539,-0.6943036914,-0.0525972098,0.1200872064,0.1547679156,-0.4531956017,-0.0136170126,0.0777316689,0.7065265775,0.2650775313,0.128693521,-0.2549385428,-0.3223257065,0.2341711223,-0.1441075802,-0.0270406026,-0.0166181941,-0.129373461,0.6533600092,0.5752850175,0.1956337392,0.0006461617,0.3474607468,0.5321191549,0.2032576501,-0.2729887068,-0.2872490287,0.003228341,-0.0935835093,-0.6751148701,0.1077949554,-0.5177379847,-0.1886842549,-0.1189373508,0.1137020066,0.1747476161,0.0378866047,0.0076451357,0.2549017966,0.022808522,0.2261755615,-0.132650122,0.457488209,-0.0661611483,0.1869132668,-0.1980352551,0.0420323275,-0.2060025334,-0.1463294029,-0.0704797655,0.5003626943,0.1262582242,-0.1380066425,0.0808468759,0.4534180462,-0.2065954953,0.0868909508,-0.2848199606,0.0214223545,0.3208904862,0.0965320095,0.1042212471,0.2300522178,-0.0680665001,0.1967657506,-0.202443406,0.1004064009,-0.4833717346,0.0858843699,0.1622382998,0.2138819098,0.0588472784,0.040826492,-0.1509973109,-0.1954513937,0.2688579559,0.1386809647,0.2791650593,0.366545409,0.1968338192,0.0444832966,0.3903595805,0.2215695381,0.1931091547,0.0381867364,0.0259094518,-0.0182071514,0.0148508698,0.2840825021,0.1803556532,0.0792310834,0.1897486299,0.1379857808,-0.2874861956,-0.2119708955,0.2023925185,-0.0441415682,0.1804206222,0.2209619582,0.1378938556,-0.1611262709,-0.1813827306,0.1015159264,0.0782349482,0.1337401569,-0.2980790734,0.0790636539,-0.6222834587,-0.3927905262,-0.7182297707,0.0430182479,-0.5489847064,-0.247586295,-0.1140798628,0.2927651107,0.0120862331,-0.1029171571,-0.0963459015,-0.1998202503,-0.4088550806,-0.5224940181,0.0758768991,0.0744953901,-0.3028307557,-0.1217320487,0.1361252815,0.1972023994,0.1763104349,-0.1033913568,-0.3834490478,0.0270648189,-0.2044581473,0.0011427145,0.3371047974,0.1227916777,0.264877975,-0.1386473328,0.2038497329,-0.1997368932,0.0460605621,-0.2331735641,0.0697179586,-0.008256549,0.1032613739,0.0749440789,-0.3153726161,-0.2225984186,-0.2827696502,-0.4827471375,0.2971625626,0.2415864319,0.3336547613,0.7670289874,-0.144687593,-0.0249776915,-0.2470210344,0.1891744137,0.1809889525,0.0252696518,0.2319223434,-0.1236328185,-0.0485379882,-0.0837118626,0.0633222982,0.059877146,0.4791371524,-0.2922404408,-0.0563170575,0.0676808879,0.0439293869,0.0678461567,0.1174633205,0.472961992,0.2859099805,0.1283735335,-0.0220060572,-0.3026004434,0.187647447,0.5915962458,0.3701991141,0.2775197029,0.1701226085,0.2223974168,0.3195584714,-0.0285745673,0.2068920135,0.2589379549,0.0239454638,0.5233561993,0.1765196323,-0.1383137852,-0.3046542108,0.1189453676,-0.052725073,0.04060366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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/554","title":"nlp downloads to its module path","comments":"> Feel free to play with this change from the master branch for now, and let us know if it sounds good for you :)\r\n> We plan to do a release in the next coming days\r\n\r\nThanks for making this change! I just packaged the latest commit on master and it works like a charm now! :partying_face: ","body":"I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`?","comment_length":58,"text":"nlp downloads to its module path \n I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems:\r\n\r\n```>>> import nlp\r\n>>> squad_dataset = nlp.load_dataset('squad')\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 530, in load_dataset\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n  File \"\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/load.py\", line 329, in prepare_module\r\n    os.makedirs(main_folder_path, exist_ok=True)\r\n  File \"\/nix\/store\/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5\/lib\/python3.8\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '\/nix\/store\/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env\/lib\/python3.8\/site-packages\/nlp\/datasets\/squad'\r\n```\r\n\r\nDo you have any suggested workaround for this issue?\r\n\r\nPerhaps overriding the default value for `force_local_path` of `prepare_module`? \n > Feel free to play with this change from the master branch for now, and let us know if it sounds good for you :)\r\n> We plan to do a release in the next coming days\r\n\r\nThanks for making this change! I just packaged the latest commit on master and it works like a charm now! :partying_face: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"When you load a text file for the first time with `nlp`, the file is converted into Apache Arrow format. Arrow allows to use memory-mapping, which means that you can load an arbitrary large dataset.\r\n\r\nNote that as soon as the conversion has been done once, the next time you'll load the dataset it will be much faster.\r\n\r\nHowever for a 1TB dataset, the conversion can indeed take time. You could try to load parts of it in parallel, and then use `nlp.concatenate_datasets` to get your full dataset.","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":88,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n When you load a text file for the first time with `nlp`, the file is converted into Apache Arrow format. Arrow allows to use memory-mapping, which means that you can load an arbitrary large dataset.\r\n\r\nNote that as soon as the conversion has been done once, the next time you'll load the dataset it will be much faster.\r\n\r\nHowever for a 1TB dataset, the conversion can indeed take time. You could try to load parts of it in parallel, and then use `nlp.concatenate_datasets` to get your full dataset.","embeddings":[-0.2580243051,-0.1429562569,-0.0743959993,0.201565221,-0.1192451119,0.0688815638,0.0702806711,0.448238492,0.1473057866,-0.2636481225,0.148645401,0.2279233038,-0.106906116,0.061183054,0.1534855515,0.0742182583,-0.0200049654,0.2010743171,-0.1128234789,-0.1518828869,0.0267371889,0.0252636019,-0.1835510284,-0.2006816268,-0.1286405623,-0.0606779456,-0.0646085069,0.0253585782,-0.0933855772,-0.2799025178,-0.0734302998,-0.0075133867,0.0779141262,0.163817063,-0.0001190489,-0.1963580251,0.3564788699,0.0899229571,-0.2416128963,-0.1499068588,0.2623203397,-0.6448490024,0.2374050021,-0.1103647798,0.0207275543,-0.1262657791,0.1428301334,-0.1979647428,0.1357072145,0.0908239335,0.1139508039,-0.0867789313,-0.2106594443,0.3208089769,0.1221834123,0.1906038821,-0.0291808713,0.2637990713,0.2932355404,-0.2001265585,-0.3337846398,0.0332033969,-0.1195470244,0.1559601575,0.3333066404,0.0464646779,-0.0898462012,-0.076048851,0.1180850491,0.284802258,0.5425196886,-0.0258227885,-0.0519602261,-0.4517751336,-0.0019575551,-0.1291745007,0.1924464405,0.3310850263,-0.2503350079,-0.2429402322,-0.2714089155,-0.3056858182,-0.126796633,0.2447672635,0.0714997202,0.059363354,-0.0026139019,0.2128312439,0.2367528975,0.0035537938,0.0124043925,-0.197156623,0.2276108563,0.4581683576,-0.3844109774,-0.0025614724,0.0357512124,0.1673823744,0.1374229044,0.1301303357,0.1820942909,0.0464357771,-0.2015420198,0.0214452725,0.2738535106,0.342161119,-0.2031341344,-0.1676602513,0.3533450067,-0.2663768828,-0.1084761694,0.2691608071,-0.1542274356,-0.1744618267,0.0330999903,-0.4120718241,-0.1778144836,-0.3051295578,-0.0733598024,-0.0831516236,-0.0601924062,-0.2440093905,-0.0297536664,0.1259436011,-0.3379230201,0.2886785269,0.1437388062,-0.1046234518,-0.3226682842,-0.0085545257,-0.0555738546,-0.0189568829,-0.1516158581,0.3027732968,0.5116083622,0.0325876735,0.1436165571,0.0451546311,-0.2622653842,-0.0435528383,-0.177493304,-0.2493155152,-0.0659802258,-0.0313736387,-0.0227674898,0.1939994693,0.0823346153,0.3259328306,-0.4238597155,0.1759598851,-0.3380049467,-0.0592473708,0.0427806303,0.0781180412,-0.3854233623,-0.3015832305,-0.3347423077,0.4295698702,-0.060089048,-0.1445232779,-0.2657331228,-0.0977050513,-0.2564182878,-0.002068582,0.0752363428,0.1738006324,-0.1466029882,-0.0385921225,-0.0385037363,0.4130982757,0.2468641102,0.5918000937,-0.1988864988,0.2207202166,-0.1733455658,0.0261158813,0.5016420484,0.124737151,-0.3987202644,0.5826674104,-0.2134343833,0.0198439807,0.1535961777,0.3465153575,0.010509843,-0.0216892827,0.2502728105,0.700524509,0.2258731574,0.1835709065,-0.5376799703,-0.1484410465,0.2145620733,0.3103547394,-0.1488063633,0.0026408879,0.0340144858,0.0508759953,0.4399492741,0.1979080886,-0.0152008375,0.3547917604,-0.3042240441,-0.0217803176,0.0680044815,0.2160368264,-0.0974014997,0.1468753815,0.0538267344,0.087903969,0.3113023341,0.1676249653,-0.2224716693,-0.2288695425,0.0802318975,0.2670134306,-0.0229946245,0.0762374923,0.0345356502,0.1919613034,-0.1195787191,0.3874516785,-0.0643284991,-0.1230421886,-0.438457191,-0.0962342992,0.2748400569,-0.0464416407,0.4039375484,0.2337038219,-0.1707218587,0.0219945908,-0.1359869242,-0.0038257232,-0.3297897875,0.2558680177,0.0886448249,0.0330987796,0.0563279726,-0.1082670093,0.395526588,0.2351761758,0.3025710583,-0.3483243585,-0.2962740958,0.4585195184,-0.1163601726,0.3341569901,0.1840611398,-0.3987696469,0.0885084271,-0.0222317837,0.0819798484,0.3170708418,0.8408857584,0.0845447481,0.6601690054,0.2273520827,-0.1195096523,-0.0499757677,0.4336675107,-0.1112653166,-0.2259491682,0.5013071895,-0.1831347346,-0.406235069,0.0281976368,-0.1438335925,0.391063869,0.2931973934,0.1178413928,0.0659755021,-0.1019050628,-0.1797468215,0.2066102773,-0.1422030032,0.3015500903,0.0961891562,0.3848954737,0.0827190503,-0.4828436673,-0.2860980928,-0.0393602513,0.3457221687,-0.0580390021,0.108785294,-0.0568862036,-0.3092046976,-0.2312429249,0.0379159488,-0.3179530203,-0.2344306707,-0.010179257,0.0390949994,0.4102935493,-0.176364392,-0.0082466369,0.1628456563,-0.0902125314,-0.4379183054,-0.2815780044,-0.1494336426,-0.4388256371,-0.0467550047,0.3423187733,0.2407114059,0.3009675145,0.1920312345,-0.1717655063,0.2372120619,-0.0423781425,-0.1797998101,-0.0757500753,-0.0906863809,-0.2423758209,0.1320564747,0.1255113333,0.0224761236,0.0044578537,-0.2911377549,-0.0708213076,0.210793227,-0.0324204527,-0.0122498367,0.0540511534,-0.265748471,-0.1826431751,-0.2270224094,0.4667129517,0.1411596537,0.1653353274,0.2913052738,-0.1021565497,0.2856541276,0.1465475112,0.2479116172,-0.0135069024,-0.1057498008,0.3281100094,0.2865343988,-0.2873679399,-0.3175397813,0.0452958569,0.1026076004,0.1219858006,-0.7564432025,0.4603051841,-0.4373180866,-0.0935813338,-0.1840261519,0.1150645688,0.1560970396,-0.2291910648,-0.0480020866,0.2379166037,-0.0687173679,-0.0098802671,-0.0480650626,0.2736463845,-0.0779336616,0.3746143579,-0.0265049282,0.1672289968,0.1788381189,-0.0635445639,0.1850762814,-0.0453564562,0.0589768477,-0.2291685045,-0.1360009164,0.12137402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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"Humm, we can give a look at these large scale datasets indeed.\r\n\r\nDo you mind sharing a few stats on your dataset so I can try to test on a similar one?\r\n\r\nIn particular some orders of magnitudes for the number of files, number of lines per files, line lengths.","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":50,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n Humm, we can give a look at these large scale datasets indeed.\r\n\r\nDo you mind sharing a few stats on your dataset so I can try to test on a similar one?\r\n\r\nIn particular some orders of magnitudes for the number of files, number of lines per files, line lengths.","embeddings":[-0.2486386895,-0.2108440548,-0.130509004,0.2715967298,-0.0312591419,0.0532569923,0.1728605479,0.3125979006,0.3433538675,-0.2542510331,0.1040127277,0.2262744755,-0.1480088234,0.2491922528,0.1742842495,0.0440121144,-0.0694732442,0.1338013709,-0.0940501094,-0.2089585066,0.0764913261,-0.0042570992,-0.2356909961,-0.2695990503,-0.1871654689,-0.0503750071,-0.0101462258,0.0359051377,-0.1234337389,-0.2105946243,0.0055685746,0.093107231,0.0121268416,0.3132479787,-0.0001156001,-0.2746174634,0.3784097731,0.0973197743,-0.1762195677,-0.0817299411,0.208563149,-0.6034761071,0.1212058067,-0.1240255088,0.0174892824,-0.0432460383,0.076217033,-0.1090654433,-0.0044657835,0.1309253275,0.1321333945,-0.0904468521,-0.2628418207,0.2112321109,0.0767723173,0.2716992795,-0.0006690567,0.357282728,0.4154173136,-0.1923882812,-0.278337121,0.0211374331,-0.1434020549,0.0957906246,0.2736075819,0.0057202582,-0.1591182649,-0.0868941173,0.1164136529,0.4194946885,0.5542607903,-0.1248918623,-0.0682062954,-0.4857752025,0.0235811286,-0.1223733574,0.1659093499,0.2761653662,-0.1462365985,-0.2896139622,-0.4326581061,-0.2402228564,-0.07956025,0.1657622755,0.0045831068,0.0249823406,-0.013176525,0.1476900578,0.1474984437,-0.0551853701,-0.0441146642,-0.2092203051,0.2359211296,0.4067614675,-0.4368546903,-0.1065014154,0.0749983415,0.1771083176,0.1731025875,0.067776598,0.1592899114,0.0109073902,-0.1891322881,0.0263830461,0.2703831792,0.3900518119,-0.1767294258,-0.1684203744,0.3515302837,-0.1697862595,-0.2296455353,0.2596701682,-0.1128102168,-0.1672840863,0.0136890551,-0.4427077472,-0.2214354128,-0.352612406,-0.0619462952,-0.0342046469,0.0437178351,-0.2788405418,0.1019065306,0.2122916579,-0.3088967204,0.290129602,0.0159919169,-0.0993956551,-0.2755247653,0.0564937964,-0.1495098025,0.04312874,-0.1205473617,0.2141251713,0.5559558868,-0.0202070978,0.1875703633,0.1515731514,-0.2232275158,-0.1161600724,-0.2009130865,-0.342775315,-0.0972758606,-0.0436135381,-0.0318155065,0.1685258895,0.0070512062,0.3376305401,-0.3838861883,0.1070853099,-0.3090755343,-0.0513507724,0.0715455115,0.1486166865,-0.4272139668,-0.3566762507,-0.2842693329,0.3984337747,-0.102804184,-0.1434931308,-0.2055092156,-0.1970776767,-0.2360456586,-0.0038905607,0.0610405579,0.2080202997,-0.1028612778,-0.0373454466,-0.0420119278,0.327589184,0.3008471429,0.5783417821,-0.1911014467,0.2444197983,-0.1414471567,0.1041969508,0.4420722723,0.0621602535,-0.4157561064,0.5438098907,-0.1831786335,0.0356256217,0.224320814,0.3146437705,0.0846073031,-0.0453319326,0.3129044175,0.6097602844,0.2616622448,0.2236879766,-0.5192388892,-0.0816205963,0.2502227128,0.4377360046,-0.0662628189,-0.110010758,0.0123595959,0.021278359,0.4793024957,0.1545644701,-0.1717208624,0.3233118355,-0.2008062452,-0.0290947109,0.0442672558,0.1464744657,-0.0480411388,0.2284378856,0.1344222724,0.0384671986,0.3972015977,0.1483384818,-0.1765311062,-0.1649584025,0.0871813297,0.1871472597,0.0015195954,-0.0436734445,0.073898837,0.2142150402,-0.1339357942,0.3165629506,-0.2223711163,-0.1686550677,-0.3253329098,-0.0225288868,0.3222882748,-0.0935231373,0.3534987271,0.2709269524,-0.1474179626,0.0001545947,-0.1234338731,-0.102943942,-0.1997460574,0.2632876933,0.1220244989,0.1304640919,0.0262938794,-0.0328233205,0.4114790559,0.1348858625,0.2995357513,-0.3384072185,-0.3134112954,0.412679255,-0.1818640232,0.2255385816,0.1676179767,-0.512196362,0.1029440761,-0.1461997926,0.1794183105,0.3618108332,0.9970802665,0.0423891991,0.6520142555,0.3005900681,-0.157351464,-0.0620913506,0.4747378826,-0.0766688213,-0.1524769813,0.4595927298,-0.2282647789,-0.3162681162,0.0290650055,-0.0418973565,0.3655613363,0.2885782719,0.05707426,0.0865556076,-0.0182121042,-0.2234887928,0.206138432,-0.1537454426,0.1727264971,0.2333604395,0.3821172416,0.0576562136,-0.4399826229,-0.2300445288,-0.0233045202,0.3337501585,-0.0937670544,-0.012236774,0.0247635879,-0.2615176737,-0.1847613454,0.0775963962,-0.3184903264,-0.2108866721,-0.0157956146,-0.0252601802,0.4601272345,-0.191271171,-0.0180885643,0.1107639596,-0.1913944632,-0.499602437,-0.2530306876,-0.1412377506,-0.3140839338,0.0136330742,0.2918311059,0.2350362688,0.3330124915,0.1568288356,-0.1925464123,0.2865509689,-0.0626537725,-0.1530089378,-0.0221395642,-0.0233355109,-0.1805532277,0.1107464135,0.2681524754,0.0269478168,0.0560398772,-0.3479304612,0.0260532834,0.1491565704,-0.044270277,-0.0498942733,0.0474365987,-0.2053926289,-0.2814620137,-0.217321381,0.5193728805,0.1146609113,0.1249854565,0.2194504887,-0.1814265251,0.2895323932,0.0622400455,0.2473640293,-0.0041090539,-0.2301887572,0.2904568911,0.3158654869,-0.2225532532,-0.2283245325,0.025532674,0.0695408583,0.0832812041,-0.7475146651,0.4059768319,-0.4893417358,-0.0323973782,-0.0399835818,0.0885111019,0.1408664435,-0.2680480182,-0.0466729552,0.2261449993,-0.1216790006,-0.0299028456,-0.0066040559,0.2154010236,-0.217801258,0.3159351349,-0.1042943895,0.0511522889,0.1029468924,-0.1928379238,0.1229181513,-0.0626878962,0.0938585922,-0.1282745004,-0.2376913577,0.2467371225,-0.083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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"@lhoestq Yes, I understand that the first time requires more time. The concatenate_datasets seems to be a workaround, but I believe a multi-processing method should be integrated into load_dataset to make it easier and more efficient for users.\r\n\r\n@thomwolf Sure, here are the statistics:\r\nNumber of lines: 4.2 Billion\r\nNumber of files: 6K\r\nNumber of tokens: 800 Billion\r\nThe number of lines is distributed equally across these 6k files.\r\nThe line length varies between 100 tokens to 40k tokens.\r\n","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":79,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n @lhoestq Yes, I understand that the first time requires more time. The concatenate_datasets seems to be a workaround, but I believe a multi-processing method should be integrated into load_dataset to make it easier and more efficient for users.\r\n\r\n@thomwolf Sure, here are the statistics:\r\nNumber of lines: 4.2 Billion\r\nNumber of files: 6K\r\nNumber of tokens: 800 Billion\r\nThe number of lines is distributed equally across these 6k files.\r\nThe line length varies between 100 tokens to 40k tokens.\r\n","embeddings":[-0.2211722136,-0.069399111,-0.0759154335,0.1695175469,-0.0901319832,0.1008208171,0.2206684351,0.35938555,0.2234858721,-0.1874328107,0.1198083907,0.2741488814,-0.0474218689,0.258961916,0.1557987183,0.1031196713,-0.0261339173,0.2411929667,0.0420610458,-0.0941148102,0.0570230298,-0.0064296168,-0.2886626422,-0.3357908726,-0.2980341613,0.0214096457,-0.1646300852,0.0981199443,-0.0391979553,-0.2644697428,-0.0098630665,0.2474714071,0.0183744244,0.3060204387,-0.0001189172,-0.2587305009,0.3416219056,0.0662196055,-0.2302351892,-0.1102391034,0.2941592336,-0.623270154,0.1834743619,-0.1261273175,0.0808529034,-0.0354745463,0.1360356659,-0.2005755305,0.0374361277,-0.0441890098,0.0706425011,-0.0996934995,-0.2695122659,0.2410738319,-0.0642084703,0.1584541947,0.1085236445,0.2832097113,0.3556242585,-0.2277128398,-0.2626972497,0.0034155196,-0.1359223872,0.1137017906,0.2706817985,0.0631936714,-0.1407952905,-0.1139549166,0.0160542745,0.3350263238,0.5140841603,-0.0896612927,-0.076942943,-0.6029696465,0.0666935295,-0.223353073,0.1713902354,0.2411392033,-0.1910333633,-0.2759585381,-0.3890081048,-0.3037951291,-0.066812776,0.1294828355,0.106554538,0.107335411,0.0603799447,0.183560282,0.2886107266,0.05188445,0.1139529124,-0.2195518464,0.2720116973,0.4078280926,-0.524374783,0.0028063587,0.0755731687,0.1262906641,0.1804340035,0.134829089,0.1348883361,0.0588204302,-0.129231438,0.0144624915,0.279881835,0.3531160355,-0.1694983989,-0.1542180032,0.3105047345,-0.2360600233,-0.1550752372,0.21929425,-0.1841177791,-0.2138880342,-0.0357904285,-0.387501359,-0.2708041072,-0.2539885342,-0.1084271148,-0.1540873796,-0.0759232044,-0.2951635122,0.1072275192,0.1636065245,-0.3497135043,0.4387899637,0.0694812536,-0.0635824502,-0.3524895608,0.0796268955,-0.1247897372,-0.0223259125,-0.1649293303,0.2461105138,0.5220390558,-0.0665994883,0.0762934014,0.1878412962,-0.1300724894,-0.1539223194,-0.1207379475,-0.2807799578,-0.0596730858,-0.0509844087,0.1413926035,0.2089808136,-0.0016483591,0.2444801331,-0.3578422368,0.1410300136,-0.4562772512,-0.1309579909,0.0828592405,0.1161715761,-0.3526193202,-0.3164808452,-0.3232347667,0.4442940354,-0.0430524684,-0.1600922644,-0.3826977909,-0.0633413047,-0.283069551,-0.0609633923,0.156142205,0.2751457691,-0.1106501445,-0.0312856287,-0.1239522472,0.3189246058,0.3326700628,0.5696031451,-0.2447539121,0.3569869697,-0.2544994354,0.0757549554,0.4739772677,0.1069935411,-0.3331349492,0.5554558039,-0.2506341636,0.0948168859,0.2838944197,0.3763917685,0.0054065464,-0.0773129091,0.3804531395,0.6409078836,0.0993211567,0.2818810344,-0.4149831831,-0.1698682606,0.3565430641,0.320394814,-0.1597365737,-0.0079932474,-0.0604932308,0.0265476257,0.5553174615,0.0830677673,-0.0824094042,0.3210537434,-0.2720299065,0.0587018356,-0.0290385094,0.2288731188,-0.1514300406,0.2201864719,0.171594739,0.0485988632,0.3051662147,0.0657510608,-0.1193264052,-0.2602834404,0.0140938004,0.2311348766,-0.0359791517,0.1465760916,0.0275787972,0.0845088735,-0.1976344138,0.4954994917,-0.1594910771,-0.1767944098,-0.4130460322,-0.0086111827,0.2972275615,-0.0934247896,0.3136305809,0.2108672261,-0.0928665251,-0.0361031741,-0.0675518885,0.0511842184,-0.1797370166,0.2204971313,0.0708774626,0.0440140367,0.0122543173,0.0982807577,0.2722420096,0.0909829587,0.2742013931,-0.4083100855,-0.247096017,0.4286514223,-0.1855553091,0.3100507557,0.2059784085,-0.4950374961,0.096775569,-0.0787006319,0.1401259154,0.3078310788,0.9347122312,0.0532533452,0.5731901526,0.2683214247,-0.2199278772,0.0297283325,0.389318198,0.0419210158,-0.2104264945,0.4030595422,-0.0934763327,-0.3390423357,-0.0685087368,-0.0761765987,0.4614566565,0.2961539626,0.0580204912,0.0992351845,-0.0641596764,-0.2159323394,0.1537287086,-0.0942450613,0.2555584311,0.2294766456,0.3906415403,0.0528665856,-0.4248518348,-0.2571054697,0.1408696473,0.2379993051,-0.1475533992,0.0597534142,-0.1099086925,-0.2331698537,-0.2371135205,-0.0225572698,-0.3098423183,-0.2584304214,-0.0043134238,0.1100212708,0.3845556974,-0.1346055865,0.0247181803,0.1140392944,-0.2102863193,-0.5112171769,-0.2711211145,-0.0879051611,-0.3018433452,-0.0550501943,0.3290278912,0.2225958556,0.3865032196,0.151008293,-0.2263593972,0.2597491741,-0.1077080071,-0.2157651335,-0.0335097052,-0.0349800214,-0.1383125186,0.0841221288,0.2307423055,-0.1095758229,0.0561306104,-0.2220048904,0.0077633588,0.11203219,-0.0254599061,-0.0381971747,-0.0416819192,-0.2548500299,-0.2413040251,-0.2299583703,0.4161978364,0.0681597814,0.1740722209,0.135834232,-0.1965063959,0.1943809986,0.0443454981,0.2354702204,-0.0281360261,-0.2152674645,0.2673341334,0.265014559,-0.2199538797,-0.2671394646,0.0153983338,0.0858292282,0.1488799751,-0.5809306502,0.3345518708,-0.4182727337,-0.0692416579,-0.2077288777,0.1764719486,0.1968632042,-0.2409963459,-0.0182339493,0.1923283488,-0.1248925477,0.003407666,-0.0392328985,0.2761059701,-0.1794133782,0.3650551438,-0.0341829583,0.0906497538,0.1667857319,-0.0816928521,0.1674102247,-0.1193677858,0.1143756285,-0.2352804095,-0.2032779902,0.162955001,-0.18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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"@agemagician you can give a try at a multithreaded version if you want (currently on the #548).\r\n\r\nTo test it, you just need to copy the new `text` processing script which is [here](https:\/\/github.com\/huggingface\/nlp\/blob\/07d92a82b7594498ff702f3cca55c074e2052257\/datasets\/text\/text.py) somewhere on your drive and give it's local path instead of `text` to `load_dataset`. E.g. in your example:\r\n```python\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('.\/datasets\/text.py',   # path to where you've dowloaded the multi-threaded text loading script\r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":76,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n @agemagician you can give a try at a multithreaded version if you want (currently on the #548).\r\n\r\nTo test it, you just need to copy the new `text` processing script which is [here](https:\/\/github.com\/huggingface\/nlp\/blob\/07d92a82b7594498ff702f3cca55c074e2052257\/datasets\/text\/text.py) somewhere on your drive and give it's local path instead of `text` to `load_dataset`. E.g. in your example:\r\n```python\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('.\/datasets\/text.py',   # path to where you've dowloaded the multi-threaded text loading script\r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```","embeddings":[-0.2351232022,-0.1589976102,-0.1092258841,0.1558210254,-0.0522058196,0.0935270935,0.2101726234,0.3627994359,0.2417389005,-0.1184581593,0.0937155634,0.2659687698,-0.1745844036,0.2608832419,0.2182195783,0.1295302063,-0.0509276241,0.1763873547,0.0324817151,-0.0707732961,0.0224847477,0.0642834753,-0.2663993835,-0.2938196063,-0.1675427556,-0.0311636087,-0.060530778,0.1297042221,-0.0731489435,-0.2301614434,-0.033592727,0.1957894713,0.0279497448,0.2765226066,-0.0001104622,-0.2245029658,0.3785304427,0.0646175668,-0.1945024282,-0.1081684455,0.3321503997,-0.5667905807,0.1606936753,-0.156064555,0.0011674038,0.0118144704,0.0906925872,-0.0626586527,-0.0046143015,0.0926827937,0.1748110205,-0.0288208555,-0.2254574299,0.2356196195,0.0194670707,0.1631803662,-0.0351904556,0.3379810154,0.4009987712,-0.1895385832,-0.300290823,0.1376228929,-0.1286110282,0.1814782917,0.280821979,0.0355102047,-0.1779794246,-0.123710148,0.088163048,0.3048611581,0.4366061985,-0.0966573954,-0.0096868891,-0.4623732269,0.0237911232,-0.2291672081,0.2377023995,0.2097762823,-0.2134128809,-0.3197594881,-0.4350693822,-0.2194339782,-0.050846979,0.1706949174,0.1299187988,0.1563623399,-0.0357263796,0.0971484333,0.3201568425,-0.0055307108,-0.0444112532,-0.070935674,0.2302666754,0.4172829688,-0.4461006224,-0.0729122311,0.1141119599,0.2020266205,0.0926702619,0.0474264696,0.1258238852,0.0924155712,-0.1682846695,0.0449279882,0.2149930745,0.3550407887,-0.2170166969,-0.2151004672,0.3845054209,-0.0908288732,-0.1448483914,0.2591442466,-0.0805850029,-0.1868590266,-0.0264053736,-0.3573792577,-0.1944363117,-0.3192492723,-0.0887169391,-0.1874363124,0.0210011899,-0.2330123484,0.1086945757,0.2244624197,-0.3101556897,0.273878634,0.0386037901,-0.0659090355,-0.3392502069,0.0695986524,-0.1426309943,0.081539318,-0.18821235,0.1823238134,0.6098544002,-0.0081199119,0.1737595499,0.1366225481,-0.2034400851,-0.1409626305,-0.1883977056,-0.2797947526,-0.1239791363,-0.0474042222,0.0543628745,0.1805571914,-0.0143887866,0.2696852386,-0.3400957882,0.1253848523,-0.3222605586,-0.1604731381,0.0425276905,0.1878402382,-0.40412274,-0.4088690877,-0.2737722993,0.4251868427,-0.0619101822,-0.1103430912,-0.258369267,-0.0493566953,-0.3161037266,0.0233812016,0.1262766868,0.2047499269,-0.049246192,-0.0198602993,-0.1246586367,0.3684651852,0.2619371414,0.5896151662,-0.1822747439,0.2504915297,-0.1912335902,0.0575083308,0.4228338301,0.0254763179,-0.3447416127,0.4530771971,-0.2493126094,0.0352213383,0.1712365597,0.335291326,0.0411991738,-0.0678871721,0.2889809906,0.6735248566,0.1797504127,0.2687721252,-0.4803863168,-0.1913736761,0.2381407171,0.4988001585,-0.100481905,-0.1164523736,-0.0320142917,0.0593519621,0.4168198407,0.1106979772,-0.0889548659,0.3573492467,-0.2799513638,0.0601352081,0.0224748477,0.0664092824,-0.1698401123,0.2152332067,0.1656542122,0.1306633204,0.3433740437,0.1143689454,-0.1496990025,-0.3231546879,0.0520347878,0.1409707814,0.0762310252,0.0999655798,0.060535837,0.2033236474,-0.1631735861,0.3446237147,-0.149008736,-0.2035680562,-0.3692210615,-0.0732146874,0.1707623452,-0.1606675237,0.3976022005,0.3120849133,-0.1375889331,-0.0085790148,-0.124214448,0.014577386,-0.317317903,0.3243600726,0.0493608341,0.1176916808,0.0104946699,0.0003519012,0.3571496904,0.1619955301,0.233458221,-0.3475341797,-0.3124429584,0.3943000138,-0.2278010994,0.2335012257,0.1397653371,-0.4423324466,0.0909141377,-0.1508065313,0.1609528214,0.2870859206,0.864359796,0.10864795,0.4909005761,0.2559530437,-0.2232835889,0.0353816114,0.5316964984,-0.0115907546,-0.1453190893,0.4955512881,-0.1591791064,-0.3933447301,-0.076403372,-0.0008987524,0.3892951012,0.3365332484,0.0801728591,0.0598104633,-0.1004606485,-0.2265397459,0.21119757,-0.1728012115,0.2205049843,0.1607719362,0.3817356825,0.07273601,-0.5193353891,-0.2708147168,-0.0174346864,0.3317105174,-0.0837924108,-0.0142663177,-0.0677763447,-0.332364887,-0.2030872852,0.0228839312,-0.3021137416,-0.2011318803,0.0522101261,0.1582282186,0.3566643298,-0.1291055828,0.03498872,0.1396400332,-0.2011191398,-0.4455116689,-0.2782613337,-0.1495083421,-0.3876416087,0.0376855396,0.2981480062,0.2329722196,0.3325830102,0.1779067218,-0.2270571589,0.2563956976,-0.0381544344,-0.2229510248,-0.1001670808,-0.027787013,-0.1903454363,0.0827777013,0.1761137694,-0.1057824567,0.0916429833,-0.3298029006,-0.0209475365,0.1437876225,0.048124779,0.0051461952,-0.0873239487,-0.2857705951,-0.2702842951,-0.3121495545,0.5135660768,0.0740733221,0.1946208477,0.2714239359,-0.2012180984,0.2871980965,0.0897094682,0.2429691106,-0.0052619549,-0.1578310728,0.3171799481,0.2386637479,-0.2387669981,-0.2388914376,0.0595538206,0.0963346809,-0.0034868771,-0.6804850101,0.2912909985,-0.4513459206,-0.0845335871,-0.0776610747,0.1385241151,0.2351496965,-0.1911541522,-0.1271463782,0.2458197623,-0.1807982773,0.0519634373,0.0566413142,0.178040117,-0.1369871944,0.3521686196,-0.0042590508,0.026034398,0.0612689145,-0.1209980249,0.2219278365,-0.038306646,0.0854202658,-0.1794352978,-0.2658483684,0.2797511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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"I have already generated the dataset, but now I tried to reload it and it is still very slow.\r\n\r\nI also have installed your commit and it is slow, even after the dataset was already generated.\r\n`pip install git+https:\/\/github.com\/huggingface\/nlp.git@07d92a82b7594498ff702f3cca55c074e2052257`\r\n\r\nIt uses only a single thread.\r\n\r\nDid I miss something ?","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":50,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n I have already generated the dataset, but now I tried to reload it and it is still very slow.\r\n\r\nI also have installed your commit and it is slow, even after the dataset was already generated.\r\n`pip install git+https:\/\/github.com\/huggingface\/nlp.git@07d92a82b7594498ff702f3cca55c074e2052257`\r\n\r\nIt uses only a single thread.\r\n\r\nDid I miss something ?","embeddings":[-0.199171856,-0.1527013481,-0.0914235115,0.1850308776,-0.0580269136,0.0069911219,0.0542506613,0.3648399115,0.226775974,-0.1864666641,0.1637936831,0.271445334,-0.0985700637,0.165866375,0.1985837221,0.1851770878,-0.0191418119,0.2842988074,0.1013761908,-0.188750878,0.0956259891,0.0487127341,-0.184758231,-0.2295959294,-0.1763940454,0.0025341206,-0.027706068,0.1009524688,-0.1621125638,-0.2173818052,0.0553882159,0.1236786172,-0.0306907948,0.3531843424,-0.0001217054,-0.2242824882,0.3527976871,0.1074252874,-0.1829125136,-0.0307793859,0.2109853476,-0.6483900547,0.1963076144,-0.1187645718,-0.0270373039,0.0886713043,0.086174272,-0.0851289555,0.0995737836,0.1071528643,0.0966105983,-0.1017713249,-0.2455634326,0.2750653625,0.018805882,0.3272772431,-0.0368346199,0.3568100035,0.3645662665,-0.1647836119,-0.1937814802,0.054430034,-0.1813870668,0.1767542511,0.3036622405,0.0144899813,-0.0835126117,-0.1341192871,0.1963970661,0.317099452,0.5264444351,-0.118086569,-0.0595808811,-0.4169434011,0.0415285006,-0.172948122,0.144746393,0.260304451,-0.2250424176,-0.2841115594,-0.4277854264,-0.41022259,-0.0714328289,0.2077246755,0.1785079688,0.0898235813,-0.0192157719,0.1839961261,0.2658657432,0.1081896275,-0.1406799108,-0.0842886716,0.1884384006,0.4555005133,-0.3959882855,-0.0676118731,0.148156926,0.1502484828,0.1318481565,0.082677573,-0.0320088454,0.0502000451,-0.1457344443,-0.0421881117,0.2815548778,0.388105005,-0.2023485452,-0.1266419739,0.4440859854,-0.2445262969,-0.1741784364,0.2413170338,-0.1810796708,-0.1101007089,0.0550281592,-0.3759531975,-0.1100725979,-0.3834215105,-0.1334659457,-0.0778839961,0.0038247406,-0.298048526,0.0217744652,0.222545132,-0.3536616862,0.2660466731,0.038554579,-0.1337033659,-0.4125122726,0.0827439129,-0.1261990517,0.0016385531,-0.1777823716,0.2467430234,0.5104455948,-0.1568821967,0.1790259182,0.1848551035,-0.2046812028,-0.2262265831,-0.2678073347,-0.2743785679,-0.2211675644,0.0030917912,-0.0159184523,0.2825623453,0.0864949673,0.2100139707,-0.3664640188,0.1766580045,-0.4176154733,-0.1564798653,-0.0340588056,0.0695470273,-0.4505048394,-0.297231406,-0.2967454493,0.3501704037,-0.021043634,-0.0672935098,-0.2913905978,-0.0671825707,-0.2015482783,-0.0603885539,0.1400727779,0.2322684675,-0.0928435847,-0.0590415448,-0.1035439819,0.2933353186,0.3807816505,0.6156035662,-0.1922990084,0.3422890902,-0.1217292547,-0.0452178679,0.5736959577,-0.0108839273,-0.4533556998,0.5298498869,-0.2530936897,0.0212491974,0.1293158382,0.3815018237,0.0154520711,-0.0342910774,0.2500491738,0.5158036351,0.1687353998,0.2006347179,-0.4838428795,-0.1462139189,0.2273876816,0.3783594668,-0.103270568,0.015090947,0.0358741879,0.0942553431,0.4295170307,0.1692921966,-0.1244556606,0.3971879184,-0.1047697663,0.0351576433,0.0449835397,0.1743526608,-0.1608433127,0.1670681685,0.1669221222,0.1247466952,0.4018214047,0.1500276774,-0.2643034756,-0.1956247538,0.0506964028,0.1468177736,-0.0498642996,0.0479860418,0.0912837088,0.179148227,-0.185681209,0.3405303955,-0.1098366678,-0.216119349,-0.3428982496,-0.0301032979,0.2130297869,-0.1742124557,0.4311957359,0.3232764304,-0.1331785768,-0.0647700354,-0.0922236368,-0.0369056426,-0.2966367006,0.248290807,0.1047014147,0.143235147,-0.003089936,-0.0303133354,0.3299259543,0.023629114,0.2452347577,-0.4048092365,-0.1249653175,0.3379196227,-0.2271563709,0.2508988082,0.0562171228,-0.5008863807,0.1578982323,-0.1253249943,0.1313193738,0.3620710969,0.9713236094,0.0677562281,0.5750047565,0.2706082761,-0.1945349574,0.0007566935,0.5275571942,-0.0308702756,-0.1748841703,0.5408584476,-0.1377526224,-0.3145546317,-0.0478421077,-0.1603901237,0.4037880898,0.2320174873,-0.0110661304,0.0500108562,-0.0768371075,-0.2358856797,0.1037636623,-0.122410059,0.3007444739,0.1260949522,0.3335475028,0.0369228572,-0.4046323597,-0.158119455,-0.0177756529,0.5295583606,-0.0765737295,0.0730884224,-0.024825152,-0.3180570304,-0.218727693,-0.0239542779,-0.2930161059,-0.1844211668,-0.0439070277,0.0747429952,0.5305743217,-0.1234557554,0.1065886468,0.1333356947,-0.2151924074,-0.4275615513,-0.3017472029,-0.1941060722,-0.3608124852,-0.0455354378,0.3237594068,0.2041276395,0.3716340661,0.1838134825,-0.2097916156,0.1934048831,-0.078715533,-0.2579253018,-0.1157392934,0.0656484738,-0.2042783201,0.254460752,0.1804481596,0.0209541935,0.1526819021,-0.3680243492,-0.0320829935,0.1338160634,-0.1131739467,-0.0788569599,-0.0251730029,-0.1890720576,-0.2600283623,-0.1960692853,0.5295091867,0.1719647348,0.202089414,0.214591831,-0.1746442616,0.2627972364,0.1632849723,0.1996219307,-0.0905221254,-0.2076040655,0.2065794617,0.3381420672,-0.2271890342,-0.1761685014,0.1211566851,0.1823153347,-0.0823850557,-0.7940250635,0.2953012586,-0.4839649498,-0.0214370377,-0.0950324461,0.1529513299,0.2004410326,-0.2212822735,-0.0338631682,0.2205282003,-0.2697232366,-0.0121434135,0.0616766922,0.1756334752,-0.162468031,0.4543955922,-0.0773452744,0.1778048575,0.2052347809,-0.1453509629,0.1505452693,-0.0238698591,0.0915137529,-0.1570835263,-0.2992313504,0.260060966,-0.1520539969,-0.101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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"As mentioned in #548 , each time you call `load_dataset` with `data_files=`, they are hashed to get the cache directory name. Hashing can be too slow with 1TB of data. I feel like we should have a faster way of getting a hash that identifies the input data files","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":49,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n As mentioned in #548 , each time you call `load_dataset` with `data_files=`, they are hashed to get the cache directory name. Hashing can be too slow with 1TB of data. I feel like we should have a faster way of getting a hash that identifies the input data 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"I believe this is really a very important feature, otherwise, we will still have the issue of too slow loading problems even if the data cache generation is fast.","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":29,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n I believe this is really a very important feature, otherwise, we will still have the issue of too slow loading problems even if the data cache generation is fast.","embeddings":[-0.1708837897,-0.1193052456,-0.108886078,0.2128687948,-0.0477886051,0.0981999189,0.1322444677,0.4315571785,0.3260004818,-0.1955100894,0.1550403088,0.2180582285,-0.1044813842,0.1303240508,0.2459831089,0.0549582057,0.0207399093,0.271002084,0.054923296,-0.1215847731,0.0354797617,-0.0033766872,-0.2422816604,-0.2905796766,-0.2007866055,0.0008391837,-0.0093753505,-0.0193931069,-0.1012573987,-0.2493915856,-0.016847536,0.0840887427,0.0404026546,0.1795384139,-0.000121539,-0.288066864,0.479102999,0.1190052107,-0.299382031,-0.0663854405,0.3308892548,-0.6440515518,0.1856696159,-0.1526859552,-0.0242588725,0.0225023907,0.1224434227,-0.1704470813,-0.0260150451,0.0512879379,0.0980148762,-0.076127857,-0.2565419078,0.2432713807,0.1302573234,0.2178975642,-0.0313274562,0.2933136821,0.4627696574,-0.2035045624,-0.2461470813,0.0658878833,-0.1829822361,0.0979476199,0.2545043826,0.0872411802,-0.1545416564,-0.0985595062,0.0784101933,0.3017698526,0.6385815144,-0.1194592118,-0.0844112113,-0.5116711855,0.1060038283,-0.2073562443,0.1937394291,0.2269611061,-0.2527628541,-0.23120749,-0.3389865756,-0.3271773458,-0.0391761065,0.2343063504,0.1697995514,0.0866003186,-0.0181806758,0.1299438924,0.189647615,0.1051436961,0.0536866523,-0.1221146211,0.256398201,0.380194515,-0.3880558908,-0.0825358033,0.0302411728,0.2858523428,0.1697465181,0.1421570927,0.1205964163,-0.0010591052,-0.1231785417,0.0225766636,0.2663192153,0.3897802532,-0.1854939759,-0.1530641019,0.4266661406,-0.1951057911,-0.1227803379,0.213754043,-0.1022178084,-0.0929636657,0.1184921041,-0.3602445126,-0.2023613751,-0.3070412278,-0.0508143753,-0.0440401994,0.0419510193,-0.2672597468,0.0199080184,0.1974839717,-0.3789928555,0.2880629301,0.0326742083,-0.082256645,-0.3433534205,0.0434473604,-0.132945925,0.019297367,-0.1289641857,0.237637639,0.5631651282,-0.0453249775,0.1705602854,0.0663459599,-0.1947800815,-0.0836630762,-0.1964970231,-0.3833451867,-0.0816078112,-0.0069685495,-0.0666886419,0.2241399139,0.0425942913,0.350884527,-0.439255774,0.1806218624,-0.3687042594,-0.1353670806,0.0462310985,0.1133784577,-0.4325674474,-0.3168877959,-0.2426804304,0.3940674961,-0.009996214,-0.2400588095,-0.17383039,-0.0357681774,-0.2878268957,-0.0154794501,0.093803063,0.2874269485,-0.0541599654,-0.0180748347,-0.0591328964,0.2659279406,0.3198640347,0.495621711,-0.1973638684,0.2581637502,-0.2065196186,-0.0021846569,0.5175064206,0.0865026414,-0.4842506945,0.5351499915,-0.2245032638,0.0454430096,0.2786035538,0.4554198682,0.0155416271,-0.0804585889,0.2262421399,0.6157296896,0.1381959319,0.2069051415,-0.4678855836,-0.1348220408,0.3957818747,0.414177537,-0.0689775348,0.0771701708,0.0468459278,0.1267206818,0.4656170011,0.1988309622,-0.0619081482,0.2839721739,-0.2368707359,0.0469793715,0.0830770507,0.1733320951,-0.2105592787,0.1638224274,0.155417949,0.0450230278,0.3792930841,0.1307999492,-0.1590068787,-0.211835295,0.1023089439,0.2358945608,-0.0275605209,-0.0554423295,0.0593646839,0.2304652631,-0.2147925347,0.440354079,-0.1765075177,-0.2186828852,-0.3693207204,-0.0035073732,0.2707007527,-0.049532406,0.2977488339,0.3167923987,-0.1896380633,0.0506399535,-0.131039381,-0.0754616484,-0.1972570419,0.1816370636,-0.0243195985,0.0862565935,0.0342186131,0.0019279764,0.3978556693,0.1988737136,0.2990909219,-0.4056387842,-0.2126014531,0.3579401076,-0.1928501427,0.2532295287,0.1134444475,-0.4938903451,0.1087489203,-0.1259760857,0.1698671281,0.2656657398,0.903899312,0.034859851,0.6662992239,0.2545955479,-0.1685732901,0.0134953149,0.4577048123,-0.0228779409,-0.0987972021,0.4839093983,-0.0615176596,-0.3843647838,0.0158465989,-0.111004211,0.4429910779,0.2537376285,0.0886205807,0.0565742515,-0.0263063088,-0.2527052462,0.1614331752,-0.1258850545,0.2274756581,0.2149233669,0.3197237849,0.1339851767,-0.4487345219,-0.2647607327,-0.0168763604,0.3032929599,-0.0794268474,-0.0173537135,-0.0245067421,-0.3835326433,-0.148156032,0.0173822474,-0.2176360339,-0.2085160613,0.0413723812,0.0394828171,0.4439233541,-0.1601712853,-0.006861045,0.1897861809,-0.2549465001,-0.5058605075,-0.3831742406,-0.1712242216,-0.3200800419,-0.0412952639,0.2373730987,0.1959146112,0.3190254271,0.1532439739,-0.1738554388,0.170615226,-0.1240507141,-0.1911413074,-0.0086410912,-0.005972161,-0.185142979,0.1018374562,0.2547336221,-0.0169275142,-0.0134271495,-0.3910280764,-0.059179917,0.1095030159,-0.0506862924,-0.0129666999,-0.0616958104,-0.2215944827,-0.3127224147,-0.2671322525,0.4247215986,0.0532290004,0.1396271139,0.157479763,-0.2318089604,0.2191645354,0.0809980482,0.2858385444,-0.040409822,-0.2382849306,0.2692323029,0.2617773414,-0.1944067031,-0.2885739207,0.0042175879,0.1090979725,0.0836814493,-0.7654343843,0.3025305867,-0.4086547196,-0.0389932729,-0.1383836418,0.169416219,0.2052452117,-0.2655721009,-0.0068380916,0.2257292122,-0.1746085882,-0.0823598206,-0.0131540671,0.2187665105,-0.1661929637,0.3839714229,-0.0843010768,0.0593582876,0.065373525,-0.0612383522,0.2289560139,-0.0226926059,0.1704556495,-0.2278119624,-0.2003519684,0.2423760146,-0.1477457881,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"Hmm ok then maybe it's the hashing step indeed.\r\n\r\nLet's see if we can improve this as well.\r\n\r\n(you will very likely have to regenerate your dataset if we change this part of the lib though since I expect modifications on this part of the lib to results in new hashes)","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":51,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n Hmm ok then maybe it's the hashing step indeed.\r\n\r\nLet's see if we can improve this as well.\r\n\r\n(you will very likely have to regenerate your dataset if we change this part of the lib though since I expect modifications on this part of the lib to results in new hashes)","embeddings":[-0.1601843834,-0.1073325351,-0.1088037193,0.2356132567,0.0101085901,0.0855073333,0.0967391133,0.4174629152,0.2409227192,-0.2387262434,0.1788458377,0.2212671041,-0.1573331654,0.2012864649,0.2440309823,0.0954292566,-0.0814827681,0.2566314042,-0.0297538601,-0.150974825,0.0977721512,-0.0070644319,-0.2352129817,-0.3177333474,-0.1379797757,-0.0537621565,-0.0029595352,0.1181511506,-0.1523030698,-0.2356422842,-0.0744617581,0.0946115702,-0.0337065309,0.1301782578,-0.000117367,-0.254348129,0.4368434548,0.103002578,-0.178303346,-0.0722184181,0.2452519536,-0.6293510199,0.1563462615,-0.113575235,0.0100137265,0.0221238304,0.0306232534,-0.0951652974,0.0151720811,0.0258699339,0.124812901,-0.157610178,-0.2547904849,0.3403293192,0.1736136824,0.1329304278,-0.0348422751,0.411365211,0.3614937663,-0.1394686401,-0.2442426682,0.0927665606,-0.1439820975,0.0500793457,0.299335897,0.0624082498,-0.0894317627,-0.0703959763,0.0655167624,0.3119708598,0.5350320935,-0.1155160889,-0.0738632008,-0.3971727192,0.0449689142,-0.1798521131,0.2664926052,0.1910128295,-0.1697357595,-0.291092962,-0.3915231824,-0.3189979792,-0.005201444,0.2116544843,0.2382818013,0.0413417444,0.0166872703,0.1217721477,0.3245376348,-0.0493633337,-0.0690267235,-0.0392776802,0.1879114807,0.3829360306,-0.3432565629,-0.0498863161,0.0962742269,0.2656586766,0.0248609781,0.1510502845,0.111714758,0.1768588722,-0.1563506573,0.006316388,0.2069072425,0.283395797,-0.0738158971,-0.2024073899,0.3430435956,-0.1749476045,-0.1971488148,0.2683002949,-0.1556073427,-0.218659997,-0.0255660247,-0.342505157,-0.2920804918,-0.3515900671,0.0043010889,-0.0825531855,-0.058711037,-0.2975071967,0.1371726394,0.2337220013,-0.4289889038,0.1917135715,-0.0391213372,-0.2029282302,-0.3366799057,0.0984217227,-0.1477579325,0.0849541873,-0.2443003058,0.2111695558,0.5522867441,-0.1529755741,0.2215201706,0.128443256,-0.1704710126,-0.0670168996,-0.2042763978,-0.2548729777,-0.059762869,-0.0222387612,-0.0546885282,0.2033077329,-0.0341567919,0.3675209284,-0.42348966,0.1777499765,-0.3505127728,-0.1370326877,0.0521859601,0.1265480518,-0.4527393878,-0.2849046886,-0.1414549798,0.3504903615,-0.0039889622,-0.2377236933,-0.2531360686,-0.0554263704,-0.1817226559,-0.0687226728,0.1630561203,0.2110370696,-0.1411571801,-0.0157532096,-0.0991911963,0.3512475789,0.3494640589,0.5109770298,-0.2431333363,0.2242118716,-0.1391341537,0.0753151849,0.5104481578,0.1495012492,-0.4814804792,0.5334195495,-0.1864397824,0.066399537,0.1667491645,0.4503003657,-0.015408475,-0.0686402321,0.1876243353,0.657384336,0.1865057796,0.2055634707,-0.5292838216,-0.1271956712,0.3831863701,0.3882476091,-0.1265484691,-0.0448073931,0.0644438118,0.2115140855,0.4432809651,0.1463390738,-0.0802837387,0.3024356365,-0.1130207554,-0.008691892,0.0369792543,0.1256117523,-0.0781993195,0.1612611562,0.1250467002,0.1854242384,0.2704234719,0.15310058,-0.137463361,-0.3148970306,0.0726239979,0.19421345,-0.0297198072,-0.0767292157,-0.0417796895,0.1080722511,-0.1495256722,0.2906084657,-0.2226118296,-0.1651922613,-0.4035986662,-0.0779212937,0.2123479843,-0.1582205445,0.3385802209,0.3289043903,-0.1488137245,-0.0629239455,-0.1064999029,-0.0856109187,-0.309491843,0.2200084329,0.0904118344,0.1272014976,0.0426448509,-0.0571574755,0.4239549041,0.1180785373,0.1862309575,-0.4027871192,-0.2185846269,0.4604168832,-0.2816222608,0.195881173,0.0863028616,-0.4638467431,0.065761894,-0.1463585347,0.1708156168,0.2818573415,0.8769376874,0.0479910634,0.6095963717,0.3669523001,-0.0704372153,0.0417420529,0.529009223,0.0008987521,-0.2038211375,0.521261692,-0.0849338695,-0.4068973958,-0.0294889249,-0.0923200771,0.3518699706,0.2258281559,0.0709662363,0.1058856919,-0.05677066,-0.2607472539,0.1747760922,-0.2040489167,0.2004164308,0.1718323082,0.2445509732,0.1183077246,-0.4640571475,-0.1943505853,0.0368423387,0.4481584728,-0.1012153774,-0.0404050685,-0.1280096173,-0.2470637858,-0.2019764185,-0.0580903403,-0.3640301228,-0.239800483,-0.0023724409,0.0400862098,0.383977294,-0.1228239685,0.0325509384,0.0749538541,-0.1560163945,-0.5239127278,-0.2754573524,-0.1642967016,-0.3267016113,-0.0128085306,0.3502834737,0.2617174089,0.2895052731,0.1520948857,-0.2226365209,0.2321971059,-0.198388651,-0.2286720276,-0.0677984878,0.0007592857,-0.1222772971,0.1360549033,0.1543964744,-0.048221536,0.0794742629,-0.3189553916,-0.0575904027,0.1524783075,-0.0264733545,0.0079263281,-0.0531881191,-0.3366261423,-0.1873835921,-0.2386597246,0.5235024095,0.0800590664,0.2400283366,0.2175990045,-0.1314043701,0.2389299721,0.0015136065,0.263714999,0.0267378297,-0.318177402,0.3042290807,0.3015608788,-0.1725576669,-0.1673830748,0.0746447816,0.114982307,0.1365856081,-0.7411957979,0.2916590571,-0.3721135557,-0.0532522425,-0.040381439,0.1372641474,0.161243856,-0.1977793127,-0.0433081537,0.2443802357,-0.2286248803,0.0600735471,-0.0032308525,0.2971607745,-0.1220101938,0.4379106462,-0.0273348112,0.1570204496,0.1014796421,-0.1560010314,0.1715011448,0.0441330485,0.0634028614,-0.1760016829,-0.2444697022,0.2238000631,-0.0720512792,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"Also, @agemagician you have to follow the step I indicate in my previous message [here](https:\/\/github.com\/huggingface\/nlp\/issues\/546#issuecomment-684648927) to use the new text loading script.\r\n\r\nJust doing `pip install git+https:\/\/github.com\/huggingface\/nlp.git@07d92a82b7594498ff702f3cca55c074e2052257` like you did won't use the new script (they are not inside the library but hosted on our hub).","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":46,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n Also, @agemagician you have to follow the step I indicate in my previous message [here](https:\/\/github.com\/huggingface\/nlp\/issues\/546#issuecomment-684648927) to use the new text loading script.\r\n\r\nJust doing `pip install git+https:\/\/github.com\/huggingface\/nlp.git@07d92a82b7594498ff702f3cca55c074e2052257` like you did won't use the new script (they are not inside the library but hosted on our hub).","embeddings":[-0.1538135111,-0.2194951475,-0.0950479582,0.1538355052,-0.0239236262,0.0457142889,0.1359891295,0.4179357886,0.3139533103,-0.176857695,0.1352542192,0.2417795211,-0.1323707402,0.2570710182,0.2852427363,0.0912736505,-0.0661661029,0.2590822577,0.0090136901,-0.1174314693,0.1052107662,0.0947560593,-0.2691370547,-0.2267983854,-0.1341264844,0.0427628644,-0.017962059,0.0809901729,-0.1078435481,-0.2789711058,0.0466638505,0.1192347333,-0.013505592,0.2386166304,-0.0001188216,-0.2400173396,0.4164008498,0.0876647085,-0.2083815336,-0.1413666755,0.3876317739,-0.589399755,0.221742332,-0.0976361707,-0.0658547804,0.0419179946,0.0641734824,-0.0316150971,0.0141374767,0.0813605562,0.1186160296,-0.069691956,-0.1954965889,0.2457896918,0.0393077806,0.2178897113,0.0103265932,0.355119437,0.4591064453,-0.2080248147,-0.2608448863,0.0574853346,-0.1576683521,0.1157764345,0.2842022181,0.053822007,-0.1553236246,-0.0795961469,0.1041076407,0.316495508,0.5134063959,-0.1090567484,0.012628559,-0.4890187085,0.014008943,-0.2325054556,0.2363929898,0.2235964835,-0.2313871235,-0.2666046917,-0.4041782022,-0.3050924242,-0.0624962598,0.2165593654,0.1770122647,0.0690088496,-0.0753798783,0.1108511388,0.2798572779,0.0148910899,-0.1217617914,-0.0262428243,0.2419862747,0.438036114,-0.3837111592,-0.1077051163,0.1144530848,0.2436068058,0.1071792543,0.1331069916,0.0522230975,0.0372730419,-0.1837209165,-0.0216732118,0.2654947042,0.4267695844,-0.1390134692,-0.1967364997,0.3785618842,-0.0578008182,-0.1065250263,0.2549192309,-0.1018854827,-0.1470218301,-0.0624277145,-0.4120223224,-0.1541306674,-0.4001076818,-0.0897248015,-0.1155261397,0.1008113995,-0.2847557068,0.0834816098,0.2124907076,-0.3558643162,0.289180696,0.110858269,-0.0932926536,-0.3905107379,0.1106145307,-0.0973509327,0.0845343545,-0.15649046,0.2319569737,0.5620113611,-0.1151951328,0.1986724883,0.1585441977,-0.1802897751,-0.1654041857,-0.306299895,-0.2818942964,-0.1220208928,-0.0347553603,-0.0456170775,0.2278311402,0.0319258347,0.2941394448,-0.3541756868,0.122178942,-0.360491097,-0.1590794921,0.0083839772,0.1173997149,-0.4411828816,-0.4041283429,-0.2906878591,0.403341502,-0.0657472387,-0.1836446822,-0.2509905696,-0.0534896962,-0.3046061695,0.0265602935,0.145907864,0.2639088929,-0.0665433556,-0.0997292176,-0.0743318498,0.3339369297,0.30014503,0.5857614279,-0.1756265014,0.2770580649,-0.177849561,0.0134650553,0.4331519902,0.0287085902,-0.3839407861,0.4948644638,-0.2551279664,0.0936986282,0.1792030334,0.3430300057,-0.021170212,-0.1265482903,0.2647036016,0.5780928135,0.210837543,0.2118260711,-0.4929418862,-0.1691846251,0.2549579442,0.4343913198,-0.1443443298,-0.0401955657,-0.07403256,0.1021754965,0.4322358668,0.1786887497,-0.1159980893,0.3622051775,-0.1837661564,0.1298588663,0.0351084508,0.1390739828,-0.1245252043,0.157169804,0.1711615473,0.1409959495,0.342733413,0.1587989479,-0.2499694973,-0.2745953798,0.1144898161,0.1902351528,-0.0246140845,-0.043202661,0.109130539,0.2025135458,-0.2353196442,0.3263877928,-0.1443891823,-0.1601638943,-0.4143637121,-0.0626252517,0.2259680033,-0.1562429965,0.36173895,0.3640159965,-0.119945243,-0.03927017,-0.1009056866,-0.0439311415,-0.3276138604,0.2787549794,0.0462335013,0.0630746409,0.0254493598,-0.0205107369,0.3717831969,0.1491747648,0.2595142424,-0.360008806,-0.2161252797,0.3663825393,-0.2371299416,0.2518211007,0.1492191404,-0.4946300685,0.0410462096,-0.1400924325,0.1856495738,0.26307863,0.9809722304,0.0491882898,0.5438917279,0.2374862581,-0.1860270947,0.0135343093,0.4733050168,-0.0671336725,-0.1612819731,0.5493712425,-0.0972786024,-0.3391580284,-0.0454707332,-0.1855878681,0.3958618641,0.2632412612,0.027101215,0.134681195,-0.0331405513,-0.2451714426,0.1936379522,-0.1551497281,0.1723720878,0.1338720918,0.3242029548,0.0977158695,-0.4654926956,-0.1708809137,-0.0920765027,0.3634902835,-0.1205447838,-0.0147363553,-0.0754011124,-0.3507580161,-0.2301577181,-0.0362827294,-0.3193768561,-0.1914129555,0.0126554789,0.1060666591,0.418626368,-0.1617042124,0.0398750491,0.1301965564,-0.2356345356,-0.4469167888,-0.3646012843,-0.1023060977,-0.311170429,-0.0221492946,0.2604712546,0.2491402775,0.3346447647,0.1370214373,-0.2228450477,0.2680353224,-0.1485192925,-0.2124429941,-0.1045691669,-0.009521598,-0.1503900439,0.2072898299,0.2030453831,0.017159624,0.1038346887,-0.4042710662,-0.0231148154,0.1369468719,-0.025765216,0.0110459067,-0.0730471164,-0.264147073,-0.2755765617,-0.2684983313,0.547409296,0.1586174965,0.1890622526,0.236809358,-0.2227825373,0.2654804289,0.049927853,0.2723809779,-0.0391039401,-0.1843655556,0.2653585672,0.2940625548,-0.2037299126,-0.2006460279,0.1070316434,0.1367928982,-0.0888872966,-0.7485929728,0.2411628515,-0.4384424686,-0.0879486427,-0.057007391,0.1646114588,0.2118792385,-0.2826988995,-0.0507269502,0.2520308495,-0.227019459,-0.0051456485,-0.0192309879,0.1749121249,-0.1399839669,0.4712072015,-0.071143508,0.0566901155,0.1988454908,-0.0808589086,0.2197439671,-0.0378409512,0.1163278893,-0.2227664441,-0.2527053356,0.2082594782,-0.0971232653,-0.0405654721,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"No problem, I will regenerate it. This will make us see if we solved both issues and now both the data generation step, as well as the hashing step, is fast.","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":31,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n No problem, I will regenerate it. This will make us see if we solved both issues and now both the data generation step, as well as the hashing step, is fast.","embeddings":[-0.151921019,-0.1488330811,-0.1157126203,0.2396368831,0.0376232602,0.0494575314,0.1378421187,0.4157027304,0.2257434875,-0.2202196121,0.1811740845,0.2084628195,-0.1486444473,0.2225388736,0.2400526702,0.1334556341,-0.0106428927,0.2384750545,-0.0394382626,-0.1812114716,0.0848225802,-0.0164611079,-0.1985274255,-0.2775098979,-0.2173950672,-0.0564304069,-0.0055379877,0.0889179185,-0.1562844366,-0.246810019,-0.0427505188,0.065219067,-0.0512204841,0.1408244818,-0.0001163276,-0.2455975711,0.4251794219,0.1135242581,-0.1921709031,-0.0729340538,0.2216125429,-0.623847127,0.0891681835,-0.0979589522,-0.0205341838,-0.0389111713,0.0668587983,-0.0750634819,0.0484487824,0.0367645472,0.1141063571,-0.1823955625,-0.2229845375,0.2734904587,0.0973575711,0.2033148557,-0.0078115729,0.4100628495,0.3281129003,-0.1822448522,-0.2425540239,0.0311107002,-0.152671814,0.0686310306,0.2688613236,0.0768638253,-0.0380756818,-0.1262125522,0.117283076,0.2772989273,0.5142145157,-0.074316822,-0.0818330944,-0.3553854525,0.0407789089,-0.1829119474,0.3108657598,0.2620792687,-0.2431778312,-0.2519623935,-0.3767651021,-0.310742408,-0.035888847,0.1691657752,0.1969483644,-0.0173001047,0.0154272225,0.111554116,0.2200426757,-0.0238085818,-0.0098397965,-0.0847477019,0.2152493298,0.3841085434,-0.3390983343,-0.0486883745,0.0806409791,0.2237048447,0.0989988446,0.1023349762,0.0983228534,0.0823981762,-0.0574505329,-0.000183278,0.1947009563,0.2265612036,-0.1181398332,-0.2156523764,0.3537078202,-0.241489321,-0.2157759666,0.2924395204,-0.1502350569,-0.164395541,0.0223640148,-0.3545993567,-0.2640551031,-0.3176326454,0.0175317023,-0.0621748343,-0.0178758595,-0.3224639297,0.0862378106,0.2377759814,-0.4192526937,0.1554706991,-0.0714039877,-0.1302153915,-0.2981193662,0.1277148724,-0.1737806499,0.0912984237,-0.2290061861,0.2068091035,0.5647193193,-0.1109810919,0.1604576707,0.2225558013,-0.2360689342,-0.1250260323,-0.2276332825,-0.3216445148,-0.0550676398,-0.0739355162,-0.0266831201,0.2162742764,-0.0251332857,0.378280431,-0.3872926533,0.1844607294,-0.3624752164,-0.1348638237,0.0935598612,0.1424205601,-0.4249199927,-0.2871745527,-0.1541262567,0.3690338433,-0.0186956637,-0.1709997356,-0.3164302707,-0.0107481014,-0.1685535312,-0.0905547142,0.1508934796,0.1897432357,-0.0913726017,0.0287128892,-0.0428013168,0.3228217661,0.3777242303,0.5567439795,-0.2224965096,0.2703514695,-0.1818801761,0.0393540747,0.5186567307,0.1584905088,-0.4370102286,0.5346621871,-0.2084816396,0.0218877029,0.2098893076,0.356403321,0.0203293487,-0.02317366,0.3230544329,0.6156225204,0.1618902087,0.2482868731,-0.5206919909,-0.1039238572,0.3324407637,0.3731040657,-0.1081128865,-0.000086043,0.030773703,0.1343970597,0.4153932929,0.1156594455,-0.0639218614,0.3338144124,-0.1479671746,-0.0461843796,0.0276160985,0.1599299312,-0.1134731323,0.160213992,0.160799697,0.1595616192,0.2463900149,0.1536947638,-0.1623039842,-0.2808181047,0.1097992957,0.2023738772,-0.0130556077,-0.0812413245,0.0570364483,0.1287442595,-0.1415074021,0.3597144485,-0.2277530432,-0.1650277674,-0.3817080259,-0.0339083448,0.2300840914,-0.1821877211,0.3948881328,0.3099307716,-0.131473437,-0.0184682682,-0.1039199531,-0.1602776349,-0.2824917138,0.2352555543,0.0922763199,0.1354871094,0.0085610785,-0.0969768763,0.4391179979,0.0276454967,0.223895818,-0.4000404477,-0.1980136633,0.4823370874,-0.2769957483,0.1535592973,0.0893522128,-0.4861752391,0.165191859,-0.1509474516,0.2142757028,0.2887521982,0.8977122903,0.0871162489,0.6330669522,0.3522446454,-0.0897753462,0.0587165989,0.5588106513,0.0003701936,-0.2278028131,0.4814253747,-0.1175043285,-0.4113434851,-0.0957972556,-0.1145308167,0.3423905373,0.2279917747,0.0552585199,0.0877815932,-0.0244908687,-0.2676412761,0.2117271125,-0.1949855238,0.219550252,0.1812288314,0.3146229386,0.0641861632,-0.4541725516,-0.1606372297,0.0818958655,0.4434013665,-0.0880121142,-0.0837822482,-0.065564476,-0.2384914905,-0.1835364699,0.042312637,-0.3184978068,-0.2116201371,-0.0081897108,0.0785562247,0.3982796967,-0.1201592758,0.0863153487,0.0470030606,-0.2222497761,-0.4293803871,-0.3244926929,-0.178023234,-0.2633098066,-0.0021639171,0.3373177648,0.2433393449,0.357989639,0.1530640423,-0.2724926472,0.2025842816,-0.1350709647,-0.2082981318,-0.0527464487,0.0306908339,-0.1651145816,0.1683881879,0.1188065708,-0.0561857074,0.118415527,-0.3244405687,-0.1042575091,0.1782276928,-0.0425552502,0.0216902774,-0.0231979545,-0.2697674632,-0.2457210869,-0.2183193415,0.4573231637,0.0718535185,0.2080427259,0.1711173505,-0.1983994991,0.1929040849,0.0591406003,0.2377710491,-0.0346497893,-0.3612533212,0.3110781908,0.2732957304,-0.2055792212,-0.2129628658,-0.000386686,0.1451835185,0.1592358202,-0.6935850978,0.3073457181,-0.4094820917,0.0009773812,-0.0250767004,0.1169303656,0.1280314624,-0.1906652898,-0.0838195235,0.2492346168,-0.1603615284,0.0423882455,-0.0349028073,0.2069481313,-0.0972284749,0.493847996,-0.0372571051,0.1877983958,0.1699877828,-0.1237929985,0.1305236965,0.007146819,0.0334258601,-0.1676997095,-0.2454053611,0.2036920339,-0.09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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"Ok so now the text files won't be hashed.\r\n\r\nI also updated #548 to include this change.\r\nLet us know if it helps @agemagician :)","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":25,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n Ok so now the text files won't be hashed.\r\n\r\nI also updated #548 to include this change.\r\nLet us know if it helps @agemagician 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"Right now, for caching 18Gb data, it is taking 1 hour 10 minute. Is that proper expected time? @lhoestq @agemagician \r\nIn this rate (assuming large file will caching at the same rate) caching full mC4 (27TB) requires a month (~26 days). \r\n","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":41,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n Right now, for caching 18Gb data, it is taking 1 hour 10 minute. Is that proper expected time? @lhoestq @agemagician \r\nIn this rate (assuming large file will caching at the same rate) caching full mC4 (27TB) requires a month (~26 days). \r\n","embeddings":[-0.200733006,-0.1194672212,-0.0965193734,0.2104257792,-0.0640927851,0.0504144765,0.0429921411,0.3229283094,0.2590814531,-0.3136385381,0.1074275151,0.122472629,-0.1483511478,0.1026053578,0.153373614,0.1431346387,0.0386225954,0.2054864168,0.090605475,-0.1987737119,0.0433908962,0.0771731064,-0.2624670267,-0.1930685192,-0.2417532802,-0.1084380224,-0.0226167999,-0.0270905867,-0.1552197635,-0.1500114799,0.0986482725,0.0171784908,0.0466096736,0.1934605688,-0.0001193562,-0.3864537477,0.4085769951,0.0821109787,-0.2169730365,-0.0488423817,0.2622045875,-0.6517884731,0.0913200155,-0.021774875,-0.0950624421,0.0556465164,0.1286059916,-0.2020494044,-0.0243016873,0.1082283407,0.0858620852,-0.2000370771,-0.228410989,0.4107760191,0.0794733763,0.1412685961,0.0115198866,0.3255575001,0.412974149,-0.2097355574,-0.3291651011,0.0680739731,-0.1126225665,0.1592311561,0.4092826843,0.1332376301,-0.2023112625,-0.0341588035,0.1211514547,0.2779500186,0.7070121765,-0.0576822609,-0.1283989549,-0.4418166876,0.0077954382,-0.1808744818,0.2208213955,0.2703562975,-0.273393482,-0.298222065,-0.4193870127,-0.2725031674,-0.0414756313,0.2371795624,0.0172832552,0.0723488703,-0.0130918445,0.1376423687,0.1580783427,-0.1057178229,0.0614535548,-0.1429954171,0.2336741835,0.5173973441,-0.4786495864,-0.0692080483,-0.0073541654,0.3960412443,0.0742185488,0.1138058975,0.0588269196,0.0726194903,-0.1519881785,-0.0192447286,0.2282356322,0.4407414794,-0.2982258201,-0.1490938514,0.3761630058,-0.2770836949,-0.1800489724,0.1736204028,-0.1076708287,-0.1137272865,0.1643472165,-0.3344151974,-0.2945969403,-0.402015239,-0.0311468355,-0.0526213422,0.076312229,-0.2988095582,0.0405323133,0.1994400471,-0.4971088469,0.2647592127,-0.0366579443,-0.0735016838,-0.2859938741,-0.061771147,-0.133428216,0.0120746382,-0.1672134846,0.2724776268,0.5780673027,0.0076897349,0.1310218573,0.1038891897,-0.1082729027,-0.0818942115,-0.1536854953,-0.363186121,-0.0283256955,-0.0558292456,-0.1139925644,0.2333811522,0.0349249765,0.3742826581,-0.39288041,0.0671157837,-0.4104271531,-0.1496416777,-0.0005813356,0.1185256913,-0.3708865047,-0.3321453929,-0.2643685341,0.3167611361,0.1011095792,-0.2539609969,-0.258992821,-0.0228507444,-0.1946536899,-0.0057431413,0.0267914236,0.2153952867,0.0629139468,0.0178223476,0.0442384556,0.4903242886,0.3730920255,0.5845885277,-0.2350431532,0.2435449511,-0.2204051614,-0.0326061994,0.4120652676,0.0159889236,-0.5023543239,0.589489162,-0.1331486106,-0.0331861377,0.274082303,0.4637946486,-0.0312670991,-0.0687534809,0.3373536468,0.5763086081,0.1720018238,0.1928188205,-0.5475795865,-0.1380731761,0.31828475,0.3411977589,-0.0857134312,0.0116136232,-0.0330709368,0.0870062783,0.3800094724,0.1741742045,-0.0897451118,0.3613525927,-0.1662582159,-0.033411663,0.1460031867,0.221631974,-0.2519662082,0.1865082234,0.1051103845,0.0026957495,0.3253484964,0.1137662157,-0.1588791013,-0.2889762819,0.046256233,0.1964196861,-0.0293240938,-0.0183886439,0.0766948164,0.0757097974,-0.1762064844,0.5043461323,-0.0838826001,-0.1174323931,-0.3374384344,-0.1842922866,0.2107199579,-0.0462434515,0.3550534546,0.2066314816,-0.0629895329,-0.0303342436,-0.1194723397,-0.055493962,-0.1596002281,0.3007642031,-0.0138207413,0.0393144451,0.0710395202,0.0139443157,0.4483268857,0.1492967159,0.2782325447,-0.4162150919,-0.3049135506,0.4234603345,-0.1331143081,0.2115752101,0.1134757325,-0.5431999564,0.126843363,-0.0398896933,0.2259110212,0.2712495923,0.8856946826,-0.0080371238,0.712394774,0.2768935561,-0.0650995597,0.0742003843,0.372122854,-0.0340723135,-0.2205072492,0.4832865298,-0.0364698619,-0.4884057045,0.0842966288,-0.049530305,0.3691362441,0.2177101821,0.1029092148,0.0067333798,-0.0288362857,-0.2116122246,0.1606641263,-0.1850011945,0.1312213242,0.2033476084,0.3223537505,0.0798201412,-0.3573750257,-0.2198203057,0.0454232208,0.3658950329,-0.1164344996,0.049883604,0.1151324287,-0.3985390663,-0.0942465439,0.0901598781,-0.2924839556,-0.2239394486,-0.0832067803,0.035034392,0.4655153155,-0.2060136497,-0.1607322097,0.1042949259,-0.1965368688,-0.5194991827,-0.2785893977,-0.08650738,-0.288713187,-0.0231850743,0.1987841278,0.140193671,0.2097018361,0.0672864914,-0.0883016661,0.2245807201,-0.0945912451,-0.1761835068,0.0190100428,-0.1079838648,-0.1967414916,0.0400002301,0.1967970133,0.0283547509,-0.0101142237,-0.3253506124,-0.0805563405,0.1055776775,-0.1232483983,-0.0762369633,0.0106875272,-0.1328060329,-0.4000940323,-0.1466261297,0.389282465,0.1229510829,0.0843102783,0.2759679556,-0.312407732,0.1452917457,0.1478309631,0.2481773645,-0.1417517215,-0.2602272332,0.3157471418,0.3487635851,-0.1661350578,-0.2574094832,0.0064555332,0.1575722694,0.1635592133,-0.6677362323,0.2638923228,-0.4032862484,0.0279339459,-0.2015591562,0.0783838406,0.1221968681,-0.2725511491,-0.0055725281,0.2260057479,-0.1326278895,0.0655222982,0.0243981909,0.206052959,-0.1661557257,0.3205508888,-0.0077235266,0.1359032542,0.2086704522,-0.1254386306,0.1860540509,0.0431185365,0.152219668,-0.0652974099,-0.124233447,0.3424771428,-0.1216169298,-0.26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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/546","title":"Very slow data loading on large dataset","comments":"Hi ! Currently it is that slow because we haven't implemented parallelism for the dataset generation yet.\r\nThough we will definitely work on this :)\r\n\r\nFor now I'd recommend loading the dataset shard by shard in parallel, and then concatenate them:\r\n```python\r\n# in one process, load first 100 files for english\r\nshard1 = load_dataset(\"allenai\/c4\", data_files=\"multilingual\/c4-en.tfrecord-000**.json.gz\")\r\n# in another process load next 100 files for english\r\nshard2 = load_dataset(\"allenai\/c4\", data_files=\"multilingual\/c4-en.tfrecord-001**.json.gz\")\r\n\r\n# finally\r\nconcatenate_datasets([shard1, shard2, ...])","body":"I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ?","comment_length":75,"text":"Very slow data loading on large dataset \n I made a simple python script to check the NLP library speed, which loads 1.1 TB of textual data.\r\nIt has been 8 hours and still, it is on the loading steps.\r\nIt does work when the text dataset size is small about  1 GB, but it doesn't scale.\r\nIt also uses a single thread during the data loading step.\r\n\r\n```\r\ntrain_files = glob.glob(\"xxx\/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('text', \r\n                           data_files=train_files,\r\n                           name=\"customDataset\",\r\n                           version=\"1.0.0\",\r\n                           cache_dir=\"xxx\/nlp\")\r\n```\r\n\r\nIs there something that I am missing ? \n Hi ! Currently it is that slow because we haven't implemented parallelism for the dataset generation yet.\r\nThough we will definitely work on this :)\r\n\r\nFor now I'd recommend loading the dataset shard by shard in parallel, and then concatenate them:\r\n```python\r\n# in one process, load first 100 files for english\r\nshard1 = load_dataset(\"allenai\/c4\", data_files=\"multilingual\/c4-en.tfrecord-000**.json.gz\")\r\n# in another process load next 100 files for english\r\nshard2 = load_dataset(\"allenai\/c4\", data_files=\"multilingual\/c4-en.tfrecord-001**.json.gz\")\r\n\r\n# finally\r\nconcatenate_datasets([shard1, shard2, ...])","embeddings":[-0.2651066482,-0.1227212548,-0.0954301134,0.1771882921,-0.0975767151,0.0928760469,0.2035612762,0.3488460481,0.129884243,-0.1564114243,0.0786781982,0.1867337674,-0.0765858293,0.2162315249,0.1551423222,0.0474084578,-0.0029005464,0.1883883774,0.0590906888,-0.1620482951,0.0220342167,0.0098425383,-0.2354194075,-0.304611057,-0.22105214,-0.0360609666,-0.0901220068,0.0777104348,-0.1272873878,-0.2238777876,-0.0070148734,0.1883373559,-0.0179000087,0.1729165614,-0.0001180431,-0.2391902208,0.4562422037,0.117009677,-0.183422178,0.0252683796,0.1349596083,-0.57306844,0.0849778205,-0.142400682,0.0609756708,-0.0057399091,0.108848311,-0.1043438613,0.0499631055,-0.0062501868,0.0890280232,-0.0928788856,-0.1765647084,0.2924057245,0.0420400053,0.1932971627,0.054611586,0.221273616,0.3197039962,-0.1329624355,-0.2427630872,0.077480711,-0.1690820158,0.1026197597,0.188833639,0.0607142225,-0.1862697452,-0.1047671661,0.1152875051,0.329882443,0.4704979658,-0.1399514973,-0.0951587632,-0.5176128745,0.0293675903,-0.2166452706,0.1681041718,0.2908698916,-0.1982522756,-0.2695603967,-0.3870593905,-0.3003825843,-0.0178450309,0.2391457558,0.2002466768,0.0406559333,0.0590052791,0.1479998231,0.2362357527,-0.1366303265,-0.0246297494,-0.2613228559,0.2447009534,0.3835780621,-0.5616384745,0.0154473362,0.0507549942,0.1999635249,0.2014201134,0.0072584036,0.0627310574,0.132329464,-0.0855657607,0.0572810769,0.2735306621,0.3885991573,-0.163598612,-0.1788794398,0.2742896974,-0.2101902068,-0.1117656305,0.3118145168,-0.0875827372,-0.1981582195,-0.1003367379,-0.3438499272,-0.2322290987,-0.2596674562,-0.0079285046,-0.1282296479,0.0921359807,-0.2715500593,0.0525488071,0.2202550322,-0.3109106719,0.2935532629,-0.0329252854,-0.115815267,-0.3328211308,0.0226893239,-0.124701567,0.0759205073,-0.1554695666,0.2498854399,0.4790711701,0.0329782441,0.1000826582,0.1986066401,-0.0260103885,-0.0448678844,-0.1610790491,-0.3360444307,-0.0767321214,-0.0212757196,0.0462550372,0.1719718575,-0.0421847925,0.1761456728,-0.4205129147,0.1158582866,-0.3446106613,-0.125396058,0.0481880531,0.1197257116,-0.3990412652,-0.2591846287,-0.4781784117,0.4528963268,0.0333452225,-0.1689012647,-0.392819643,-0.0860379785,-0.3060979247,-0.0429669879,0.2103073448,0.2869827747,-0.1261309236,0.0096638566,-0.2165444344,0.3413548172,0.3081223071,0.652565062,-0.1758549362,0.1876748949,-0.2254611701,0.1686031669,0.4423519969,0.0973003581,-0.4201310277,0.5512775779,-0.2468331456,0.056033399,0.2850047648,0.3448021114,0.0918557569,-0.0465427861,0.3067872822,0.6966630816,0.1070005894,0.2504422963,-0.4676657021,-0.1644146144,0.2723557949,0.4647450149,-0.1365659982,-0.0112979086,0.0634822622,0.0646906868,0.5011214614,0.0447213538,0.0024787537,0.3559911847,-0.2613925636,-0.011332796,0.0403598063,0.2178166658,-0.2197909206,0.2016975433,0.1182098016,0.1334245801,0.2723483443,0.0722213537,-0.0408316292,-0.2521376312,-0.0222148579,0.2886550128,-0.0159195065,0.0540586561,0.0661182031,0.164725557,-0.1127626374,0.3963683248,-0.2652451694,-0.2208227962,-0.5490863323,0.0070824847,0.2594197989,-0.1363931894,0.4261371493,0.2710792422,-0.1181881055,-0.04094759,-0.0645098761,-0.0907052904,-0.2788843513,0.3200149536,0.1063794568,0.10308256,0.0312815271,0.0160749257,0.3334369957,0.0420882925,0.2083441019,-0.4022840858,-0.1836066991,0.470248878,-0.223067686,0.3024149835,0.1661379933,-0.438262552,0.1621463597,-0.1020191461,0.1687289476,0.2713018954,0.9766665101,0.153699711,0.5289859772,0.3381226063,-0.1339673996,0.1230302751,0.5674777031,-0.0375349969,-0.190242216,0.451348424,-0.0597376972,-0.3497223258,0.0561388098,0.0438969471,0.3988237679,0.2919707298,0.0791422501,0.0680446103,-0.0000825256,-0.2077599168,0.0891854018,-0.1085086241,0.2200201452,0.2025998533,0.3239005208,-0.02722257,-0.433537662,-0.2874743044,0.100684002,0.3373127282,-0.1097357795,-0.0120419823,-0.0954930931,-0.1531209052,-0.2987460494,0.0454459265,-0.3204612434,-0.210707292,-0.0355288908,0.0314711072,0.3852093518,-0.0600163527,0.1792493016,-0.0144234188,-0.2547540367,-0.4910281301,-0.3066861033,-0.1357051432,-0.2762719691,-0.0362701677,0.3847944438,0.3216302693,0.2536756396,0.0349461101,-0.2565544546,0.2018606514,-0.0777953267,-0.2300608456,-0.1171058267,0.0143916793,-0.1628672034,0.1155283526,0.1237586811,-0.1726987362,0.0796606764,-0.1728402823,-0.0619618669,0.2096222937,-0.0153855616,-0.0913216695,0.0742265582,-0.2904971242,-0.3166500926,-0.203402698,0.4752539098,0.0386103913,0.1877702922,0.0740286559,-0.1446872801,0.1473507732,0.0175518524,0.2518851757,0.0013841874,-0.2948675156,0.2883594036,0.2600439489,-0.1920140088,-0.2501113415,-0.0196689665,0.1233320907,0.1294755489,-0.5931720734,0.3715705574,-0.4714067876,-0.0056471457,-0.0283728465,0.1213883981,0.1574657112,-0.1993599832,-0.032224115,0.2935230136,-0.1522729695,-0.0288516991,-0.061150901,0.2073863596,-0.1139524803,0.4198216796,-0.0190012641,0.1870367676,0.2107936144,-0.1574719846,0.1460188627,-0.0024024793,0.0071511944,-0.218319416,-0.2701705396,0.2473880351,-0.1439512372,-0.0804958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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/539","title":"[Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data","comments":"Hi @gaguilar \r\n\r\nIf you want to take care of this, it very simple, you just need to regenerate the `dataset_infos.json` file as indicated [in the doc](https:\/\/huggingface.co\/nlp\/share_dataset.html#adding-metadata) by [installing from source](https:\/\/huggingface.co\/nlp\/installation.html#installing-from-source) and running the following command from the root of the repo:\r\n```bash\r\npython nlp-cli test .\/datasets\/lince --save_infos --all_configs\r\n```\r\nAnd then you can open a pull-request with the updated json file.\r\n\r\nOtherwise we'll do it sometime this week.","body":"Hi,\r\n\r\nThere is a `NonMatchingChecksumError` error for the `lid_msaea` (language identification for Modern Standard Arabic - Egyptian Arabic) dataset from the LinCE benchmark due to a minor update on that dataset. \r\n\r\nHow can I update the checksum of the library to solve this issue? The error is below and it also appears in the [nlp viewer](https:\/\/huggingface.co\/nlp\/viewer\/?dataset=lince&config=lid_msaea):\r\n\r\n```python\r\nimport nlp\r\nnlp.load_dataset('lince', 'lid_msaea')\r\n```\r\n\r\nOutput:\r\n```\r\nNonMatchingChecksumError: ['https:\/\/ritual.uh.edu\/lince\/libaccess\/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9\/lid_msaea.zip']\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 196, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 150, in get\r\n    builder_instance.download_and_prepare()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    download_config.force_download = download_mode == FORCE_REDOWNLOAD\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 469, in _download_and_prepare\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 36, in verify_checksums\r\n    raise NonMatchingChecksumError(str(bad_urls))\r\n```\r\n\r\nThank you in advance!\r\n\r\n@lhoestq ","comment_length":68,"text":"[Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data \n Hi,\r\n\r\nThere is a `NonMatchingChecksumError` error for the `lid_msaea` (language identification for Modern Standard Arabic - Egyptian Arabic) dataset from the LinCE benchmark due to a minor update on that dataset. \r\n\r\nHow can I update the checksum of the library to solve this issue? The error is below and it also appears in the [nlp viewer](https:\/\/huggingface.co\/nlp\/viewer\/?dataset=lince&config=lid_msaea):\r\n\r\n```python\r\nimport nlp\r\nnlp.load_dataset('lince', 'lid_msaea')\r\n```\r\n\r\nOutput:\r\n```\r\nNonMatchingChecksumError: ['https:\/\/ritual.uh.edu\/lince\/libaccess\/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9\/lid_msaea.zip']\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 196, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 150, in get\r\n    builder_instance.download_and_prepare()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    download_config.force_download = download_mode == FORCE_REDOWNLOAD\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 469, in _download_and_prepare\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 36, in verify_checksums\r\n    raise NonMatchingChecksumError(str(bad_urls))\r\n```\r\n\r\nThank you in advance!\r\n\r\n@lhoestq  \n Hi @gaguilar \r\n\r\nIf you want to take care of this, it very simple, you just need to regenerate the `dataset_infos.json` file as indicated [in the doc](https:\/\/huggingface.co\/nlp\/share_dataset.html#adding-metadata) by [installing from source](https:\/\/huggingface.co\/nlp\/installation.html#installing-from-source) and running the following command from the root of the repo:\r\n```bash\r\npython nlp-cli test .\/datasets\/lince --save_infos --all_configs\r\n```\r\nAnd then you can open a pull-request with the updated json file.\r\n\r\nOtherwise we'll do it sometime this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/539","title":"[Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data","comments":"Hi @thomwolf \r\n\r\nThanks for the details! I just created a PR with the updated `dataset_infos.json` file (#550).","body":"Hi,\r\n\r\nThere is a `NonMatchingChecksumError` error for the `lid_msaea` (language identification for Modern Standard Arabic - Egyptian Arabic) dataset from the LinCE benchmark due to a minor update on that dataset. \r\n\r\nHow can I update the checksum of the library to solve this issue? The error is below and it also appears in the [nlp viewer](https:\/\/huggingface.co\/nlp\/viewer\/?dataset=lince&config=lid_msaea):\r\n\r\n```python\r\nimport nlp\r\nnlp.load_dataset('lince', 'lid_msaea')\r\n```\r\n\r\nOutput:\r\n```\r\nNonMatchingChecksumError: ['https:\/\/ritual.uh.edu\/lince\/libaccess\/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9\/lid_msaea.zip']\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 196, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 150, in get\r\n    builder_instance.download_and_prepare()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    download_config.force_download = download_mode == FORCE_REDOWNLOAD\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 469, in _download_and_prepare\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 36, in verify_checksums\r\n    raise NonMatchingChecksumError(str(bad_urls))\r\n```\r\n\r\nThank you in advance!\r\n\r\n@lhoestq ","comment_length":17,"text":"[Dataset] `NonMatchingChecksumError` due to an update in the LinCE benchmark data \n Hi,\r\n\r\nThere is a `NonMatchingChecksumError` error for the `lid_msaea` (language identification for Modern Standard Arabic - Egyptian Arabic) dataset from the LinCE benchmark due to a minor update on that dataset. \r\n\r\nHow can I update the checksum of the library to solve this issue? The error is below and it also appears in the [nlp viewer](https:\/\/huggingface.co\/nlp\/viewer\/?dataset=lince&config=lid_msaea):\r\n\r\n```python\r\nimport nlp\r\nnlp.load_dataset('lince', 'lid_msaea')\r\n```\r\n\r\nOutput:\r\n```\r\nNonMatchingChecksumError: ['https:\/\/ritual.uh.edu\/lince\/libaccess\/eyJ1c2VybmFtZSI6ICJodWdnaW5nZmFjZSBubHAiLCAidXNlcl9pZCI6IDExMSwgImVtYWlsIjogImR1bW15QGVtYWlsLmNvbSJ9\/lid_msaea.zip']\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 196, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 150, in get\r\n    builder_instance.download_and_prepare()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    download_config.force_download = download_mode == FORCE_REDOWNLOAD\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 469, in _download_and_prepare\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 36, in verify_checksums\r\n    raise NonMatchingChecksumError(str(bad_urls))\r\n```\r\n\r\nThank you in advance!\r\n\r\n@lhoestq  \n Hi @thomwolf \r\n\r\nThanks for the details! I just created a PR with the updated `dataset_infos.json` file (#550).","embeddings":[-0.0616527833,0.4437022507,-0.0581613593,0.0664172098,-0.307210058,0.1153274924,-0.2732498646,0.5395020843,-0.0016354375,-0.1196584553,0.0857631639,0.2251154929,0.0789773464,-0.1222939044,0.0061919261,0.1934710592,0.0321503244,0.0933852121,0.0987019464,0.0393903553,-0.2053777874,0.2148771733,-0.057483878,-0.2985480428,0.0893565938,-0.004181175,0.0692205578,0.0182900559,0.1299763471,-0.4194515646,0.3206056654,0.2793268561,0.0003896341,0.1324239075,-0.0001272806,-0.0694950148,0.3210234344,-0.0919738933,-0.2670393586,-0.0846346244,0.0034744749,-0.4629070163,-0.0613037869,-0.2885442674,0.1637886763,0.2399886698,0.1029127687,-0.4139173031,-0.095096156,0.1851278096,0.1083362922,0.5190732479,0.0809718668,0.3380495608,0.256370753,-0.42114079,0.0306582432,0.3837837577,0.3393696845,-0.1366900653,-0.4413134158,0.3681635559,-0.3522348702,0.0423129387,-0.0161721241,-0.3124465644,0.0194569509,-0.0667776987,0.302544415,0.1907632947,0.3051871955,-0.4455226064,-0.2008479834,0.0067092571,-0.2116422057,-0.1155177131,0.4846940637,0.1011212692,-0.0301548149,-0.1416909546,-0.0993822813,-0.0432990901,0.1072188467,0.2136925161,0.3474704325,0.4857030511,0.243153125,0.1118917316,0.1624684483,0.0055500744,0.503978312,-0.0932113156,-0.1411801726,0.1578629613,-0.3675528467,-0.1388427168,0.0753963515,0.3629109859,0.1303834319,0.4638726413,0.5369125009,0.247657612,-0.1883206666,0.1930721104,-0.1384759098,0.2871352434,0.4059982002,-0.1246177554,0.1441257894,0.4248977005,0.0256022699,0.065224953,0.1155529246,-0.2771501243,0.3135475516,0.4061032236,0.0826807097,-0.4132894278,-0.227190122,0.307826966,-0.1956929564,-0.3644892573,0.211998567,0.1644034833,-0.3546676934,0.2263011485,0.1475031525,0.0085846949,-0.1239643097,-0.5568845868,-0.0674783885,0.2780528069,-0.1873663217,0.1560589522,0.2345919013,-0.3751237094,0.4669339359,-0.3525468111,0.0226076655,-0.0944171771,0.2752314806,-0.1536362767,0.0063340105,0.2864765227,-0.0740386918,-0.1444516182,0.1842181534,0.1451916844,-0.3300545812,0.2802166045,0.1353553981,-0.1413926184,-0.1740648896,-0.0231370293,-0.4117465615,-0.2353129536,0.272849828,-0.0858844668,0.390968442,-0.6479693055,0.0522488616,-0.0388518497,-0.2448469102,-0.1984824985,-0.1250327229,0.0340124369,-0.1752650887,0.0889368281,-0.2065021992,0.1218558773,0.301831007,0.1827165931,0.059068989,-0.2488514483,-0.1102877334,0.1323599666,0.2490991056,-0.3191206157,-0.6921525002,-0.0797567293,0.1015781909,0.3253774941,0.2345360368,0.4361954927,-0.1628785282,-0.2904224396,-0.0313170739,0.1885817349,-0.0350644253,-0.0714681223,-0.3921247721,-0.1106406301,0.5248131156,0.0953375474,0.0534198955,-0.0261638723,0.1034081951,-0.0051165503,0.2173566222,0.0385111012,0.0118800262,0.0543003716,0.0686132163,0.0348475017,0.0741711408,-0.0369870961,-0.4227679074,0.1104315296,-0.2665937245,0.2791252434,0.0841261968,-0.1472602785,-0.0798559189,-0.2010103911,0.008968005,0.0517491885,0.0013107359,0.3164534271,0.1445411444,-0.0932148546,0.0056738132,0.036909584,-0.2276382744,-0.0102180643,-0.5147677064,0.2930194736,-0.0692704618,-0.2226178199,0.2267709523,0.2620928288,0.1244557053,-0.067651242,-0.3514721692,0.379909277,-0.049288936,-0.139429003,0.0233200267,0.4658017457,-0.1902141124,-0.1928856522,0.2801755071,0.7407286763,-0.0178896319,-0.0470744595,0.188155219,0.4245472848,-0.0047320961,0.1563244313,-0.0887772217,0.1785681695,0.1582686752,-0.2391062677,-0.256729424,-0.0941786394,0.4943873882,-0.148961395,0.178952679,0.0457770936,-0.1842954308,-0.0852906406,0.3507559299,0.0191176645,0.0700724646,0.2472043037,0.1055887192,-0.3084563613,0.1519667804,0.34983477,0.2286102325,-0.0166477971,0.006565324,0.2571461201,-0.0707734674,0.0059679616,0.0090975491,0.0203417037,0.2311230451,0.4381026924,0.2708524466,0.0421677716,-0.4537835121,-0.2937007248,-0.0823328644,0.3356816173,-0.0985868648,0.0412338115,-0.2519593835,-0.3669678867,-0.1616517901,-0.3328652084,-0.5280544162,-0.3471707702,0.0056135268,0.3331266046,-0.0336648524,0.3375812769,-0.2607325315,-0.041509334,-0.2104506344,-0.055516988,-0.0397895426,-0.3035803735,-0.3481827378,-0.100549072,0.6255894899,0.1226193532,0.3256288469,-0.2633631825,-0.0386355855,-0.4359060526,-0.4488388598,-0.2283978611,-0.0557631403,0.0680425093,0.3516705632,0.0911212862,-0.0205849521,-0.3282206953,0.1320411414,-0.0937089622,-0.1445062608,0.0734271407,-0.1742323339,0.1782113463,0.1171157733,-0.1628405005,-0.0572150797,-0.2265317887,-0.0431701057,-0.1390309632,0.3625038266,0.0243449211,-0.3539898098,-0.0659313425,0.0604897924,0.4428186417,-0.1127821505,-0.5371189117,0.183861807,0.1153431833,-0.1968841404,0.0928090662,-0.1693492532,0.6429797411,-0.092325367,-0.509729445,-0.0690603107,-0.1017558351,-0.0644859299,-0.0095773246,-0.0312164482,0.4292153716,-0.0215043649,-0.0031932807,-0.2097126395,-0.4482651353,0.1087318808,0.0067476174,0.437325418,0.073648639,0.3563734889,0.170651257,0.0788869634,0.1601974666,-0.162990272,0.4158077538,0.4866502881,0.1476314217,0.0428027995,-0.0988665894,-0.1026581004,-0.0358230583,-0.04710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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/537","title":"[Dataset] RACE dataset Checksums error","comments":"`NonMatchingChecksumError` means that the checksum of the downloaded file is not the expected one.\r\nEither the file you downloaded was corrupted along the way, or the host updated the file.\r\nCould you try to clear your cache and run `load_dataset` again ? If the error is still there, it means that there was an update in the data, and we may have to update the expected checksum value.","body":"Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n```","comment_length":68,"text":"[Dataset] RACE dataset Checksums error \n Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n``` \n `NonMatchingChecksumError` means that the checksum of the downloaded file is not the expected one.\r\nEither the file you downloaded was corrupted along the way, or the host updated the file.\r\nCould you try to clear your cache and run `load_dataset` again ? If the error is still there, it means that there was an update in the data, and we may have to update the expected checksum value.","embeddings":[-0.3484401107,0.3749217391,-0.0223544165,0.2424075007,0.2333744168,0.0591794364,0.2440315485,0.4674772024,0.2553084195,-0.1403686702,-0.0667250529,-0.0904459357,-0.3057557046,0.1228690147,0.0535939559,-0.0088576656,-0.0977109894,0.0319078714,-0.1895279884,0.0566937141,-0.1161630303,-0.0643239096,-0.1031743437,0.0158479381,-0.0917348042,-0.1198823452,0.2069767118,0.1899706721,-0.2297097296,-0.457950443,0.2975064218,0.1518295407,0.2854932845,0.4001206756,-0.0001180434,0.1198679805,0.4619445801,-0.0878783837,-0.4845319688,-0.3111259639,-0.2444009781,-0.0946979746,0.0279894862,-0.1625524312,-0.1754536331,0.3403500319,0.1200167835,-0.3093126714,0.2425986379,0.415902704,0.1814554483,0.4850332439,-0.0983780921,0.1419311464,0.2775052786,0.0405619107,-0.0977338254,0.4429739118,0.0883837417,-0.2895773947,-0.0681834817,0.1827246845,-0.1868143529,0.2293165624,0.2373239398,-0.031059809,0.050049074,-0.1472805589,0.0172663014,0.3044555187,0.228475675,-0.4627510011,-0.262774229,-0.198295325,-0.0720933005,-0.663425982,0.309194535,0.2011436969,-0.239018321,-0.1425317824,-0.5278380513,0.0906221122,-0.0025051723,0.116995424,0.1789869219,0.4105425179,0.2487375885,0.1001004428,0.1208602414,0.0906594023,0.1509335041,-0.2384599894,-0.1124067903,0.2280863971,-0.5045996308,-0.167716071,0.1644721627,0.1875035167,0.0313857719,0.3799568415,0.1418694258,0.084726125,0.0732125565,0.0911256745,0.2244704515,0.1387256682,0.1841384023,0.2849820256,0.384634763,0.1717725396,0.2062670141,0.1483997554,-0.0711589828,-0.1690804064,0.0967379138,0.0995636731,-0.0724148899,-0.3175649941,-0.550168097,0.2802394927,0.0591629855,-0.10878966,0.3846683204,0.2568905056,-0.2069443613,-0.3697853684,0.0991448984,0.2307722121,-0.2812793255,-0.0285786558,-0.1864865869,0.2175771296,-0.3917308152,-0.0531366691,0.3407814205,-0.0546818748,0.3931039572,-0.1745660603,0.0894263536,0.2351038456,0.1652139723,-0.2833355367,-0.372736454,0.4511757195,0.0721225217,-0.0609272681,0.2574218214,-0.1946486831,-0.3575665653,-0.0453447327,-0.0227217954,-0.1541518718,-0.2694600523,0.1336429864,-0.5319498181,-0.1265995502,-0.1047882885,-0.0980740264,0.4659026563,-0.2218079269,0.0720761493,-0.2355969846,-0.4272306859,-0.2113928646,0.2053787857,0.2566948831,-0.2136314958,-0.0465420075,-0.2215101272,-0.0849393308,0.3301586509,0.2536371946,-0.1534425318,0.2487071902,-0.237203747,0.1597365588,0.4282442331,-0.0966892466,-0.377505362,0.0635767803,0.0420482419,0.0293667056,-0.0294797532,0.1122540906,-0.0142485276,-0.067274861,0.1905495077,0.3296660483,-0.1435996741,-0.1567834467,-0.2640849948,0.0402008817,0.4529900849,0.3493306935,0.2753326297,-0.1353898197,0.0652282834,0.1668525487,0.1153825596,0.0573682748,0.0887684748,0.1663028598,0.1947500855,-0.0009249717,-0.3076634705,-0.1934029013,-0.428485781,0.2985518277,-0.4023680389,0.3442194164,-0.0052058366,0.1163645461,-0.2743442655,-0.0884458572,-0.0779779553,-0.0349813402,0.0552481115,-0.0362682566,0.118636325,-0.1575817913,-0.1216600239,0.4596948028,-0.3044737279,0.0306052417,-0.203237772,0.5589396954,-0.2494065017,-0.3483188748,0.1816463619,0.1678283066,-0.0748553053,-0.0887406096,-0.1464426816,0.4584696591,0.1846816242,-0.2115025371,0.0676155612,0.2728219628,0.2086811811,0.0457450002,0.1133401245,0.3485707939,-0.0103805559,-0.1062241271,-0.225029394,0.5668245554,-0.1356490254,0.1098725349,-0.0194805656,0.1030010805,0.1647879183,-0.1018060818,-0.1466487348,-0.0287135188,0.5365939736,0.1954443306,0.2387123257,0.0966354981,-0.3509839475,-0.1509943455,0.5099392533,0.0974254385,0.037500862,0.3271868825,0.1645047069,-0.2205292135,0.1525416374,0.0362701751,0.5619978905,0.0448566377,-0.0008600512,0.3147718906,-0.3628351986,-0.0569822639,0.0665312409,-0.158066377,0.3175257444,0.323420912,0.0579751097,0.0612480752,-0.3958386183,-0.2288500965,0.1042920575,0.4989686012,-0.2964100242,0.066174075,-0.3261881471,-0.1147774085,-0.0888150036,-0.1403396726,-0.5619576573,-0.2250494063,0.0893958807,0.3187947571,0.090573065,0.350672394,-0.4854868352,-0.0084516723,0.1274918616,-0.3142074049,-0.0129661616,-0.1558169574,-0.2733362615,0.0355255343,0.7237205505,0.0963545367,0.3842619359,0.0550471693,-0.2218773365,-0.0491370149,-0.3143673539,-0.0223673265,-0.2295466214,0.338960737,0.3319755793,0.1100180224,-0.1604092568,-0.2559396625,0.2994101048,-0.0435467698,-0.1957678199,0.1944876164,-0.0658890158,-0.0019956648,-0.1117209345,-0.0121067651,-0.1920267642,-0.2215815634,-0.1455163062,0.1978730559,0.3210581243,0.3196024299,0.0293593053,0.1503638178,0.0955186337,-0.0287066139,-0.179673478,-0.3023663461,0.354970634,0.0689084977,-0.4053297043,-0.214728415,-0.1006930247,0.2305930257,0.1018887013,-0.5546712875,0.1395835578,-0.0337636657,0.0982766151,0.1617600173,-0.0188597664,0.4739741981,-0.0388860777,0.056005761,-0.1978461742,-0.2456507236,0.1355912387,0.1616822779,0.6563411951,-0.0545418188,0.4591037631,-0.1254960299,0.5254045129,0.2798558772,-0.1763413548,0.3539974391,0.2002420872,0.2736523151,-0.1534941196,-0.2914330065,0.2855210304,-0.0495390184,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/537","title":"[Dataset] RACE dataset Checksums error","comments":"I just cleared the cache an run it again. The error persists ):\r\n\r\n```\r\n nlp (master) $ rm -rf  \/Users\/abarbosa\/.cache\/huggingface\/\r\n nlp (master) $ python\r\nPython 3.8.5 (default, Aug  5 2020, 03:39:04)\r\n[Clang 10.0.0 ] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import nlp\r\n>>> dataset = nlp.load_dataset(\"race\")\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.39k\/4.39k [00:00<00:00, 661kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1.81k\/1.81k [00:00<00:00, 644kB\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset race\/default (download: 84.52 MiB, generated: 132.61 MiB, post-processed: Unknown size, total: 217.13 MiB) to \/Users\/abarbosa\/.cache\/huggingface\/datasets\/race\/default\/0.1.0\/5461327f1a83549ca0d845a3159c806d2baf4f8d0d8f7d657157ce7cdf3899c2...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25.4M\/25.4M [01:03<00:00, 401kB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/abarbosa\/Documents\/nlp\/src\/nlp\/load.py\", line 550, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/abarbosa\/Documents\/nlp\/src\/nlp\/builder.py\", line 471, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/abarbosa\/Documents\/nlp\/src\/nlp\/builder.py\", line 530, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/abarbosa\/Documents\/nlp\/src\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n>>>\r\n```","body":"Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n```","comment_length":147,"text":"[Dataset] RACE dataset Checksums error \n Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n``` \n I just cleared the cache an run it again. The error persists ):\r\n\r\n```\r\n nlp (master) $ rm -rf  \/Users\/abarbosa\/.cache\/huggingface\/\r\n nlp (master) $ python\r\nPython 3.8.5 (default, Aug  5 2020, 03:39:04)\r\n[Clang 10.0.0 ] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import nlp\r\n>>> dataset = nlp.load_dataset(\"race\")\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.39k\/4.39k [00:00<00:00, 661kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1.81k\/1.81k [00:00<00:00, 644kB\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset race\/default (download: 84.52 MiB, generated: 132.61 MiB, post-processed: Unknown size, total: 217.13 MiB) to \/Users\/abarbosa\/.cache\/huggingface\/datasets\/race\/default\/0.1.0\/5461327f1a83549ca0d845a3159c806d2baf4f8d0d8f7d657157ce7cdf3899c2...\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25.4M\/25.4M [01:03<00:00, 401kB\/s]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/abarbosa\/Documents\/nlp\/src\/nlp\/load.py\", line 550, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/Users\/abarbosa\/Documents\/nlp\/src\/nlp\/builder.py\", line 471, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/Users\/abarbosa\/Documents\/nlp\/src\/nlp\/builder.py\", line 530, in _download_and_prepare\r\n    verify_checksums(\r\n  File \"\/Users\/abarbosa\/Documents\/nlp\/src\/nlp\/utils\/info_utils.py\", line 38, in verify_checksums\r\n    raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n>>>\r\n```","embeddings":[-0.3484401107,0.3749217391,-0.0223544165,0.2424075007,0.2333744168,0.0591794364,0.2440315485,0.4674772024,0.2553084195,-0.1403686702,-0.0667250529,-0.0904459357,-0.3057557046,0.1228690147,0.0535939559,-0.0088576656,-0.0977109894,0.0319078714,-0.1895279884,0.0566937141,-0.1161630303,-0.0643239096,-0.1031743437,0.0158479381,-0.0917348042,-0.1198823452,0.2069767118,0.1899706721,-0.2297097296,-0.457950443,0.2975064218,0.1518295407,0.2854932845,0.4001206756,-0.0001180434,0.1198679805,0.4619445801,-0.0878783837,-0.4845319688,-0.3111259639,-0.2444009781,-0.0946979746,0.0279894862,-0.1625524312,-0.1754536331,0.3403500319,0.1200167835,-0.3093126714,0.2425986379,0.415902704,0.1814554483,0.4850332439,-0.0983780921,0.1419311464,0.2775052786,0.0405619107,-0.0977338254,0.4429739118,0.0883837417,-0.2895773947,-0.0681834817,0.1827246845,-0.1868143529,0.2293165624,0.2373239398,-0.031059809,0.050049074,-0.1472805589,0.0172663014,0.3044555187,0.228475675,-0.4627510011,-0.262774229,-0.198295325,-0.0720933005,-0.663425982,0.309194535,0.2011436969,-0.239018321,-0.1425317824,-0.5278380513,0.0906221122,-0.0025051723,0.116995424,0.1789869219,0.4105425179,0.2487375885,0.1001004428,0.1208602414,0.0906594023,0.1509335041,-0.2384599894,-0.1124067903,0.2280863971,-0.5045996308,-0.167716071,0.1644721627,0.1875035167,0.0313857719,0.3799568415,0.1418694258,0.084726125,0.0732125565,0.0911256745,0.2244704515,0.1387256682,0.1841384023,0.2849820256,0.384634763,0.1717725396,0.2062670141,0.1483997554,-0.0711589828,-0.1690804064,0.0967379138,0.0995636731,-0.0724148899,-0.3175649941,-0.550168097,0.2802394927,0.0591629855,-0.10878966,0.3846683204,0.2568905056,-0.2069443613,-0.3697853684,0.0991448984,0.2307722121,-0.2812793255,-0.0285786558,-0.1864865869,0.2175771296,-0.3917308152,-0.0531366691,0.3407814205,-0.0546818748,0.3931039572,-0.1745660603,0.0894263536,0.2351038456,0.1652139723,-0.2833355367,-0.372736454,0.4511757195,0.0721225217,-0.0609272681,0.2574218214,-0.1946486831,-0.3575665653,-0.0453447327,-0.0227217954,-0.1541518718,-0.2694600523,0.1336429864,-0.5319498181,-0.1265995502,-0.1047882885,-0.0980740264,0.4659026563,-0.2218079269,0.0720761493,-0.2355969846,-0.4272306859,-0.2113928646,0.2053787857,0.2566948831,-0.2136314958,-0.0465420075,-0.2215101272,-0.0849393308,0.3301586509,0.2536371946,-0.1534425318,0.2487071902,-0.237203747,0.1597365588,0.4282442331,-0.0966892466,-0.377505362,0.0635767803,0.0420482419,0.0293667056,-0.0294797532,0.1122540906,-0.0142485276,-0.067274861,0.1905495077,0.3296660483,-0.1435996741,-0.1567834467,-0.2640849948,0.0402008817,0.4529900849,0.3493306935,0.2753326297,-0.1353898197,0.0652282834,0.1668525487,0.1153825596,0.0573682748,0.0887684748,0.1663028598,0.1947500855,-0.0009249717,-0.3076634705,-0.1934029013,-0.428485781,0.2985518277,-0.4023680389,0.3442194164,-0.0052058366,0.1163645461,-0.2743442655,-0.0884458572,-0.0779779553,-0.0349813402,0.0552481115,-0.0362682566,0.118636325,-0.1575817913,-0.1216600239,0.4596948028,-0.3044737279,0.0306052417,-0.203237772,0.5589396954,-0.2494065017,-0.3483188748,0.1816463619,0.1678283066,-0.0748553053,-0.0887406096,-0.1464426816,0.4584696591,0.1846816242,-0.2115025371,0.0676155612,0.2728219628,0.2086811811,0.0457450002,0.1133401245,0.3485707939,-0.0103805559,-0.1062241271,-0.225029394,0.5668245554,-0.1356490254,0.1098725349,-0.0194805656,0.1030010805,0.1647879183,-0.1018060818,-0.1466487348,-0.0287135188,0.5365939736,0.1954443306,0.2387123257,0.0966354981,-0.3509839475,-0.1509943455,0.5099392533,0.0974254385,0.037500862,0.3271868825,0.1645047069,-0.2205292135,0.1525416374,0.0362701751,0.5619978905,0.0448566377,-0.0008600512,0.3147718906,-0.3628351986,-0.0569822639,0.0665312409,-0.158066377,0.3175257444,0.323420912,0.0579751097,0.0612480752,-0.3958386183,-0.2288500965,0.1042920575,0.4989686012,-0.2964100242,0.066174075,-0.3261881471,-0.1147774085,-0.0888150036,-0.1403396726,-0.5619576573,-0.2250494063,0.0893958807,0.3187947571,0.090573065,0.350672394,-0.4854868352,-0.0084516723,0.1274918616,-0.3142074049,-0.0129661616,-0.1558169574,-0.2733362615,0.0355255343,0.7237205505,0.0963545367,0.3842619359,0.0550471693,-0.2218773365,-0.0491370149,-0.3143673539,-0.0223673265,-0.2295466214,0.338960737,0.3319755793,0.1100180224,-0.1604092568,-0.2559396625,0.2994101048,-0.0435467698,-0.1957678199,0.1944876164,-0.0658890158,-0.0019956648,-0.1117209345,-0.0121067651,-0.1920267642,-0.2215815634,-0.1455163062,0.1978730559,0.3210581243,0.3196024299,0.0293593053,0.1503638178,0.0955186337,-0.0287066139,-0.179673478,-0.3023663461,0.354970634,0.0689084977,-0.4053297043,-0.214728415,-0.1006930247,0.2305930257,0.1018887013,-0.5546712875,0.1395835578,-0.0337636657,0.0982766151,0.1617600173,-0.0188597664,0.4739741981,-0.0388860777,0.056005761,-0.1978461742,-0.2456507236,0.1355912387,0.1616822779,0.6563411951,-0.0545418188,0.4591037631,-0.1254960299,0.5254045129,0.2798558772,-0.1763413548,0.3539974391,0.20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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/537","title":"[Dataset] RACE dataset Checksums error","comments":"Dealing with the same issue please update the checksum on nlp library end. The data seems to have changed on their end.","body":"Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n```","comment_length":22,"text":"[Dataset] RACE dataset Checksums error \n Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n``` \n Dealing with the same issue please update the checksum on nlp library end. The data seems to have changed on their end.","embeddings":[-0.3484401107,0.3749217391,-0.0223544165,0.2424075007,0.2333744168,0.0591794364,0.2440315485,0.4674772024,0.2553084195,-0.1403686702,-0.0667250529,-0.0904459357,-0.3057557046,0.1228690147,0.0535939559,-0.0088576656,-0.0977109894,0.0319078714,-0.1895279884,0.0566937141,-0.1161630303,-0.0643239096,-0.1031743437,0.0158479381,-0.0917348042,-0.1198823452,0.2069767118,0.1899706721,-0.2297097296,-0.457950443,0.2975064218,0.1518295407,0.2854932845,0.4001206756,-0.0001180434,0.1198679805,0.4619445801,-0.0878783837,-0.4845319688,-0.3111259639,-0.2444009781,-0.0946979746,0.0279894862,-0.1625524312,-0.1754536331,0.3403500319,0.1200167835,-0.3093126714,0.2425986379,0.415902704,0.1814554483,0.4850332439,-0.0983780921,0.1419311464,0.2775052786,0.0405619107,-0.0977338254,0.4429739118,0.0883837417,-0.2895773947,-0.0681834817,0.1827246845,-0.1868143529,0.2293165624,0.2373239398,-0.031059809,0.050049074,-0.1472805589,0.0172663014,0.3044555187,0.228475675,-0.4627510011,-0.262774229,-0.198295325,-0.0720933005,-0.663425982,0.309194535,0.2011436969,-0.239018321,-0.1425317824,-0.5278380513,0.0906221122,-0.0025051723,0.116995424,0.1789869219,0.4105425179,0.2487375885,0.1001004428,0.1208602414,0.0906594023,0.1509335041,-0.2384599894,-0.1124067903,0.2280863971,-0.5045996308,-0.167716071,0.1644721627,0.1875035167,0.0313857719,0.3799568415,0.1418694258,0.084726125,0.0732125565,0.0911256745,0.2244704515,0.1387256682,0.1841384023,0.2849820256,0.384634763,0.1717725396,0.2062670141,0.1483997554,-0.0711589828,-0.1690804064,0.0967379138,0.0995636731,-0.0724148899,-0.3175649941,-0.550168097,0.2802394927,0.0591629855,-0.10878966,0.3846683204,0.2568905056,-0.2069443613,-0.3697853684,0.0991448984,0.2307722121,-0.2812793255,-0.0285786558,-0.1864865869,0.2175771296,-0.3917308152,-0.0531366691,0.3407814205,-0.0546818748,0.3931039572,-0.1745660603,0.0894263536,0.2351038456,0.1652139723,-0.2833355367,-0.372736454,0.4511757195,0.0721225217,-0.0609272681,0.2574218214,-0.1946486831,-0.3575665653,-0.0453447327,-0.0227217954,-0.1541518718,-0.2694600523,0.1336429864,-0.5319498181,-0.1265995502,-0.1047882885,-0.0980740264,0.4659026563,-0.2218079269,0.0720761493,-0.2355969846,-0.4272306859,-0.2113928646,0.2053787857,0.2566948831,-0.2136314958,-0.0465420075,-0.2215101272,-0.0849393308,0.3301586509,0.2536371946,-0.1534425318,0.2487071902,-0.237203747,0.1597365588,0.4282442331,-0.0966892466,-0.377505362,0.0635767803,0.0420482419,0.0293667056,-0.0294797532,0.1122540906,-0.0142485276,-0.067274861,0.1905495077,0.3296660483,-0.1435996741,-0.1567834467,-0.2640849948,0.0402008817,0.4529900849,0.3493306935,0.2753326297,-0.1353898197,0.0652282834,0.1668525487,0.1153825596,0.0573682748,0.0887684748,0.1663028598,0.1947500855,-0.0009249717,-0.3076634705,-0.1934029013,-0.428485781,0.2985518277,-0.4023680389,0.3442194164,-0.0052058366,0.1163645461,-0.2743442655,-0.0884458572,-0.0779779553,-0.0349813402,0.0552481115,-0.0362682566,0.118636325,-0.1575817913,-0.1216600239,0.4596948028,-0.3044737279,0.0306052417,-0.203237772,0.5589396954,-0.2494065017,-0.3483188748,0.1816463619,0.1678283066,-0.0748553053,-0.0887406096,-0.1464426816,0.4584696591,0.1846816242,-0.2115025371,0.0676155612,0.2728219628,0.2086811811,0.0457450002,0.1133401245,0.3485707939,-0.0103805559,-0.1062241271,-0.225029394,0.5668245554,-0.1356490254,0.1098725349,-0.0194805656,0.1030010805,0.1647879183,-0.1018060818,-0.1466487348,-0.0287135188,0.5365939736,0.1954443306,0.2387123257,0.0966354981,-0.3509839475,-0.1509943455,0.5099392533,0.0974254385,0.037500862,0.3271868825,0.1645047069,-0.2205292135,0.1525416374,0.0362701751,0.5619978905,0.0448566377,-0.0008600512,0.3147718906,-0.3628351986,-0.0569822639,0.0665312409,-0.158066377,0.3175257444,0.323420912,0.0579751097,0.0612480752,-0.3958386183,-0.2288500965,0.1042920575,0.4989686012,-0.2964100242,0.066174075,-0.3261881471,-0.1147774085,-0.0888150036,-0.1403396726,-0.5619576573,-0.2250494063,0.0893958807,0.3187947571,0.090573065,0.350672394,-0.4854868352,-0.0084516723,0.1274918616,-0.3142074049,-0.0129661616,-0.1558169574,-0.2733362615,0.0355255343,0.7237205505,0.0963545367,0.3842619359,0.0550471693,-0.2218773365,-0.0491370149,-0.3143673539,-0.0223673265,-0.2295466214,0.338960737,0.3319755793,0.1100180224,-0.1604092568,-0.2559396625,0.2994101048,-0.0435467698,-0.1957678199,0.1944876164,-0.0658890158,-0.0019956648,-0.1117209345,-0.0121067651,-0.1920267642,-0.2215815634,-0.1455163062,0.1978730559,0.3210581243,0.3196024299,0.0293593053,0.1503638178,0.0955186337,-0.0287066139,-0.179673478,-0.3023663461,0.354970634,0.0689084977,-0.4053297043,-0.214728415,-0.1006930247,0.2305930257,0.1018887013,-0.5546712875,0.1395835578,-0.0337636657,0.0982766151,0.1617600173,-0.0188597664,0.4739741981,-0.0388860777,0.056005761,-0.1978461742,-0.2456507236,0.1355912387,0.1616822779,0.6563411951,-0.0545418188,0.4591037631,-0.1254960299,0.5254045129,0.2798558772,-0.1763413548,0.3539974391,0.2002420872,0.2736523151,-0.1534941196,-0.2914330065,0.2855210304,-0.0495390184,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/537","title":"[Dataset] RACE dataset Checksums error","comments":"We have a discussion on this datasets here: https:\/\/github.com\/huggingface\/nlp\/pull\/540\r\n\r\nFeel free to participate if you have some opinion on the scope of data which should be included in this dataset.","body":"Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n```","comment_length":30,"text":"[Dataset] RACE dataset Checksums error \n Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n``` \n We have a discussion on this datasets here: https:\/\/github.com\/huggingface\/nlp\/pull\/540\r\n\r\nFeel free to participate if you have some opinion on the scope of data which should be included in this dataset.","embeddings":[-0.3484401107,0.3749217391,-0.0223544165,0.2424075007,0.2333744168,0.0591794364,0.2440315485,0.4674772024,0.2553084195,-0.1403686702,-0.0667250529,-0.0904459357,-0.3057557046,0.1228690147,0.0535939559,-0.0088576656,-0.0977109894,0.0319078714,-0.1895279884,0.0566937141,-0.1161630303,-0.0643239096,-0.1031743437,0.0158479381,-0.0917348042,-0.1198823452,0.2069767118,0.1899706721,-0.2297097296,-0.457950443,0.2975064218,0.1518295407,0.2854932845,0.4001206756,-0.0001180434,0.1198679805,0.4619445801,-0.0878783837,-0.4845319688,-0.3111259639,-0.2444009781,-0.0946979746,0.0279894862,-0.1625524312,-0.1754536331,0.3403500319,0.1200167835,-0.3093126714,0.2425986379,0.415902704,0.1814554483,0.4850332439,-0.0983780921,0.1419311464,0.2775052786,0.0405619107,-0.0977338254,0.4429739118,0.0883837417,-0.2895773947,-0.0681834817,0.1827246845,-0.1868143529,0.2293165624,0.2373239398,-0.031059809,0.050049074,-0.1472805589,0.0172663014,0.3044555187,0.228475675,-0.4627510011,-0.262774229,-0.198295325,-0.0720933005,-0.663425982,0.309194535,0.2011436969,-0.239018321,-0.1425317824,-0.5278380513,0.0906221122,-0.0025051723,0.116995424,0.1789869219,0.4105425179,0.2487375885,0.1001004428,0.1208602414,0.0906594023,0.1509335041,-0.2384599894,-0.1124067903,0.2280863971,-0.5045996308,-0.167716071,0.1644721627,0.1875035167,0.0313857719,0.3799568415,0.1418694258,0.084726125,0.0732125565,0.0911256745,0.2244704515,0.1387256682,0.1841384023,0.2849820256,0.384634763,0.1717725396,0.2062670141,0.1483997554,-0.0711589828,-0.1690804064,0.0967379138,0.0995636731,-0.0724148899,-0.3175649941,-0.550168097,0.2802394927,0.0591629855,-0.10878966,0.3846683204,0.2568905056,-0.2069443613,-0.3697853684,0.0991448984,0.2307722121,-0.2812793255,-0.0285786558,-0.1864865869,0.2175771296,-0.3917308152,-0.0531366691,0.3407814205,-0.0546818748,0.3931039572,-0.1745660603,0.0894263536,0.2351038456,0.1652139723,-0.2833355367,-0.372736454,0.4511757195,0.0721225217,-0.0609272681,0.2574218214,-0.1946486831,-0.3575665653,-0.0453447327,-0.0227217954,-0.1541518718,-0.2694600523,0.1336429864,-0.5319498181,-0.1265995502,-0.1047882885,-0.0980740264,0.4659026563,-0.2218079269,0.0720761493,-0.2355969846,-0.4272306859,-0.2113928646,0.2053787857,0.2566948831,-0.2136314958,-0.0465420075,-0.2215101272,-0.0849393308,0.3301586509,0.2536371946,-0.1534425318,0.2487071902,-0.237203747,0.1597365588,0.4282442331,-0.0966892466,-0.377505362,0.0635767803,0.0420482419,0.0293667056,-0.0294797532,0.1122540906,-0.0142485276,-0.067274861,0.1905495077,0.3296660483,-0.1435996741,-0.1567834467,-0.2640849948,0.0402008817,0.4529900849,0.3493306935,0.2753326297,-0.1353898197,0.0652282834,0.1668525487,0.1153825596,0.0573682748,0.0887684748,0.1663028598,0.1947500855,-0.0009249717,-0.3076634705,-0.1934029013,-0.428485781,0.2985518277,-0.4023680389,0.3442194164,-0.0052058366,0.1163645461,-0.2743442655,-0.0884458572,-0.0779779553,-0.0349813402,0.0552481115,-0.0362682566,0.118636325,-0.1575817913,-0.1216600239,0.4596948028,-0.3044737279,0.0306052417,-0.203237772,0.5589396954,-0.2494065017,-0.3483188748,0.1816463619,0.1678283066,-0.0748553053,-0.0887406096,-0.1464426816,0.4584696591,0.1846816242,-0.2115025371,0.0676155612,0.2728219628,0.2086811811,0.0457450002,0.1133401245,0.3485707939,-0.0103805559,-0.1062241271,-0.225029394,0.5668245554,-0.1356490254,0.1098725349,-0.0194805656,0.1030010805,0.1647879183,-0.1018060818,-0.1466487348,-0.0287135188,0.5365939736,0.1954443306,0.2387123257,0.0966354981,-0.3509839475,-0.1509943455,0.5099392533,0.0974254385,0.037500862,0.3271868825,0.1645047069,-0.2205292135,0.1525416374,0.0362701751,0.5619978905,0.0448566377,-0.0008600512,0.3147718906,-0.3628351986,-0.0569822639,0.0665312409,-0.158066377,0.3175257444,0.323420912,0.0579751097,0.0612480752,-0.3958386183,-0.2288500965,0.1042920575,0.4989686012,-0.2964100242,0.066174075,-0.3261881471,-0.1147774085,-0.0888150036,-0.1403396726,-0.5619576573,-0.2250494063,0.0893958807,0.3187947571,0.090573065,0.350672394,-0.4854868352,-0.0084516723,0.1274918616,-0.3142074049,-0.0129661616,-0.1558169574,-0.2733362615,0.0355255343,0.7237205505,0.0963545367,0.3842619359,0.0550471693,-0.2218773365,-0.0491370149,-0.3143673539,-0.0223673265,-0.2295466214,0.338960737,0.3319755793,0.1100180224,-0.1604092568,-0.2559396625,0.2994101048,-0.0435467698,-0.1957678199,0.1944876164,-0.0658890158,-0.0019956648,-0.1117209345,-0.0121067651,-0.1920267642,-0.2215815634,-0.1455163062,0.1978730559,0.3210581243,0.3196024299,0.0293593053,0.1503638178,0.0955186337,-0.0287066139,-0.179673478,-0.3023663461,0.354970634,0.0689084977,-0.4053297043,-0.214728415,-0.1006930247,0.2305930257,0.1018887013,-0.5546712875,0.1395835578,-0.0337636657,0.0982766151,0.1617600173,-0.0188597664,0.4739741981,-0.0388860777,0.056005761,-0.1978461742,-0.2456507236,0.1355912387,0.1616822779,0.6563411951,-0.0545418188,0.4591037631,-0.1254960299,0.5254045129,0.2798558772,-0.1763413548,0.3539974391,0.2002420872,0.2736523151,-0.1534941196,-0.2914330065,0.2855210304,-0.0495390184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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/537","title":"[Dataset] RACE dataset Checksums error","comments":"At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n\r\n","body":"Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n```","comment_length":61,"text":"[Dataset] RACE dataset Checksums error \n Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n``` \n At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n\r\n","embeddings":[-0.3484401107,0.3749217391,-0.0223544165,0.2424075007,0.2333744168,0.0591794364,0.2440315485,0.4674772024,0.2553084195,-0.1403686702,-0.0667250529,-0.0904459357,-0.3057557046,0.1228690147,0.0535939559,-0.0088576656,-0.0977109894,0.0319078714,-0.1895279884,0.0566937141,-0.1161630303,-0.0643239096,-0.1031743437,0.0158479381,-0.0917348042,-0.1198823452,0.2069767118,0.1899706721,-0.2297097296,-0.457950443,0.2975064218,0.1518295407,0.2854932845,0.4001206756,-0.0001180434,0.1198679805,0.4619445801,-0.0878783837,-0.4845319688,-0.3111259639,-0.2444009781,-0.0946979746,0.0279894862,-0.1625524312,-0.1754536331,0.3403500319,0.1200167835,-0.3093126714,0.2425986379,0.415902704,0.1814554483,0.4850332439,-0.0983780921,0.1419311464,0.2775052786,0.0405619107,-0.0977338254,0.4429739118,0.0883837417,-0.2895773947,-0.0681834817,0.1827246845,-0.1868143529,0.2293165624,0.2373239398,-0.031059809,0.050049074,-0.1472805589,0.0172663014,0.3044555187,0.228475675,-0.4627510011,-0.262774229,-0.198295325,-0.0720933005,-0.663425982,0.309194535,0.2011436969,-0.239018321,-0.1425317824,-0.5278380513,0.0906221122,-0.0025051723,0.116995424,0.1789869219,0.4105425179,0.2487375885,0.1001004428,0.1208602414,0.0906594023,0.1509335041,-0.2384599894,-0.1124067903,0.2280863971,-0.5045996308,-0.167716071,0.1644721627,0.1875035167,0.0313857719,0.3799568415,0.1418694258,0.084726125,0.0732125565,0.0911256745,0.2244704515,0.1387256682,0.1841384023,0.2849820256,0.384634763,0.1717725396,0.2062670141,0.1483997554,-0.0711589828,-0.1690804064,0.0967379138,0.0995636731,-0.0724148899,-0.3175649941,-0.550168097,0.2802394927,0.0591629855,-0.10878966,0.3846683204,0.2568905056,-0.2069443613,-0.3697853684,0.0991448984,0.2307722121,-0.2812793255,-0.0285786558,-0.1864865869,0.2175771296,-0.3917308152,-0.0531366691,0.3407814205,-0.0546818748,0.3931039572,-0.1745660603,0.0894263536,0.2351038456,0.1652139723,-0.2833355367,-0.372736454,0.4511757195,0.0721225217,-0.0609272681,0.2574218214,-0.1946486831,-0.3575665653,-0.0453447327,-0.0227217954,-0.1541518718,-0.2694600523,0.1336429864,-0.5319498181,-0.1265995502,-0.1047882885,-0.0980740264,0.4659026563,-0.2218079269,0.0720761493,-0.2355969846,-0.4272306859,-0.2113928646,0.2053787857,0.2566948831,-0.2136314958,-0.0465420075,-0.2215101272,-0.0849393308,0.3301586509,0.2536371946,-0.1534425318,0.2487071902,-0.237203747,0.1597365588,0.4282442331,-0.0966892466,-0.377505362,0.0635767803,0.0420482419,0.0293667056,-0.0294797532,0.1122540906,-0.0142485276,-0.067274861,0.1905495077,0.3296660483,-0.1435996741,-0.1567834467,-0.2640849948,0.0402008817,0.4529900849,0.3493306935,0.2753326297,-0.1353898197,0.0652282834,0.1668525487,0.1153825596,0.0573682748,0.0887684748,0.1663028598,0.1947500855,-0.0009249717,-0.3076634705,-0.1934029013,-0.428485781,0.2985518277,-0.4023680389,0.3442194164,-0.0052058366,0.1163645461,-0.2743442655,-0.0884458572,-0.0779779553,-0.0349813402,0.0552481115,-0.0362682566,0.118636325,-0.1575817913,-0.1216600239,0.4596948028,-0.3044737279,0.0306052417,-0.203237772,0.5589396954,-0.2494065017,-0.3483188748,0.1816463619,0.1678283066,-0.0748553053,-0.0887406096,-0.1464426816,0.4584696591,0.1846816242,-0.2115025371,0.0676155612,0.2728219628,0.2086811811,0.0457450002,0.1133401245,0.3485707939,-0.0103805559,-0.1062241271,-0.225029394,0.5668245554,-0.1356490254,0.1098725349,-0.0194805656,0.1030010805,0.1647879183,-0.1018060818,-0.1466487348,-0.0287135188,0.5365939736,0.1954443306,0.2387123257,0.0966354981,-0.3509839475,-0.1509943455,0.5099392533,0.0974254385,0.037500862,0.3271868825,0.1645047069,-0.2205292135,0.1525416374,0.0362701751,0.5619978905,0.0448566377,-0.0008600512,0.3147718906,-0.3628351986,-0.0569822639,0.0665312409,-0.158066377,0.3175257444,0.323420912,0.0579751097,0.0612480752,-0.3958386183,-0.2288500965,0.1042920575,0.4989686012,-0.2964100242,0.066174075,-0.3261881471,-0.1147774085,-0.0888150036,-0.1403396726,-0.5619576573,-0.2250494063,0.0893958807,0.3187947571,0.090573065,0.350672394,-0.4854868352,-0.0084516723,0.1274918616,-0.3142074049,-0.0129661616,-0.1558169574,-0.2733362615,0.0355255343,0.7237205505,0.0963545367,0.3842619359,0.0550471693,-0.2218773365,-0.0491370149,-0.3143673539,-0.0223673265,-0.2295466214,0.338960737,0.3319755793,0.1100180224,-0.1604092568,-0.2559396625,0.2994101048,-0.0435467698,-0.1957678199,0.1944876164,-0.0658890158,-0.0019956648,-0.1117209345,-0.0121067651,-0.1920267642,-0.2215815634,-0.1455163062,0.1978730559,0.3210581243,0.3196024299,0.0293593053,0.1503638178,0.0955186337,-0.0287066139,-0.179673478,-0.3023663461,0.354970634,0.0689084977,-0.4053297043,-0.214728415,-0.1006930247,0.2305930257,0.1018887013,-0.5546712875,0.1395835578,-0.0337636657,0.0982766151,0.1617600173,-0.0188597664,0.4739741981,-0.0388860777,0.056005761,-0.1978461742,-0.2456507236,0.1355912387,0.1616822779,0.6563411951,-0.0545418188,0.4591037631,-0.1254960299,0.5254045129,0.2798558772,-0.1763413548,0.3539974391,0.2002420872,0.2736523151,-0.1534941196,-0.2914330065,0.2855210304,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/537","title":"[Dataset] RACE dataset Checksums error","comments":"> At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n\r\nCould you upload this please?","body":"Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n```","comment_length":67,"text":"[Dataset] RACE dataset Checksums error \n Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n``` \n > At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n\r\nCould you upload this please?","embeddings":[-0.3484401107,0.3749217391,-0.0223544165,0.2424075007,0.2333744168,0.0591794364,0.2440315485,0.4674772024,0.2553084195,-0.1403686702,-0.0667250529,-0.0904459357,-0.3057557046,0.1228690147,0.0535939559,-0.0088576656,-0.0977109894,0.0319078714,-0.1895279884,0.0566937141,-0.1161630303,-0.0643239096,-0.1031743437,0.0158479381,-0.0917348042,-0.1198823452,0.2069767118,0.1899706721,-0.2297097296,-0.457950443,0.2975064218,0.1518295407,0.2854932845,0.4001206756,-0.0001180434,0.1198679805,0.4619445801,-0.0878783837,-0.4845319688,-0.3111259639,-0.2444009781,-0.0946979746,0.0279894862,-0.1625524312,-0.1754536331,0.3403500319,0.1200167835,-0.3093126714,0.2425986379,0.415902704,0.1814554483,0.4850332439,-0.0983780921,0.1419311464,0.2775052786,0.0405619107,-0.0977338254,0.4429739118,0.0883837417,-0.2895773947,-0.0681834817,0.1827246845,-0.1868143529,0.2293165624,0.2373239398,-0.031059809,0.050049074,-0.1472805589,0.0172663014,0.3044555187,0.228475675,-0.4627510011,-0.262774229,-0.198295325,-0.0720933005,-0.663425982,0.309194535,0.2011436969,-0.239018321,-0.1425317824,-0.5278380513,0.0906221122,-0.0025051723,0.116995424,0.1789869219,0.4105425179,0.2487375885,0.1001004428,0.1208602414,0.0906594023,0.1509335041,-0.2384599894,-0.1124067903,0.2280863971,-0.5045996308,-0.167716071,0.1644721627,0.1875035167,0.0313857719,0.3799568415,0.1418694258,0.084726125,0.0732125565,0.0911256745,0.2244704515,0.1387256682,0.1841384023,0.2849820256,0.384634763,0.1717725396,0.2062670141,0.1483997554,-0.0711589828,-0.1690804064,0.0967379138,0.0995636731,-0.0724148899,-0.3175649941,-0.550168097,0.2802394927,0.0591629855,-0.10878966,0.3846683204,0.2568905056,-0.2069443613,-0.3697853684,0.0991448984,0.2307722121,-0.2812793255,-0.0285786558,-0.1864865869,0.2175771296,-0.3917308152,-0.0531366691,0.3407814205,-0.0546818748,0.3931039572,-0.1745660603,0.0894263536,0.2351038456,0.1652139723,-0.2833355367,-0.372736454,0.4511757195,0.0721225217,-0.0609272681,0.2574218214,-0.1946486831,-0.3575665653,-0.0453447327,-0.0227217954,-0.1541518718,-0.2694600523,0.1336429864,-0.5319498181,-0.1265995502,-0.1047882885,-0.0980740264,0.4659026563,-0.2218079269,0.0720761493,-0.2355969846,-0.4272306859,-0.2113928646,0.2053787857,0.2566948831,-0.2136314958,-0.0465420075,-0.2215101272,-0.0849393308,0.3301586509,0.2536371946,-0.1534425318,0.2487071902,-0.237203747,0.1597365588,0.4282442331,-0.0966892466,-0.377505362,0.0635767803,0.0420482419,0.0293667056,-0.0294797532,0.1122540906,-0.0142485276,-0.067274861,0.1905495077,0.3296660483,-0.1435996741,-0.1567834467,-0.2640849948,0.0402008817,0.4529900849,0.3493306935,0.2753326297,-0.1353898197,0.0652282834,0.1668525487,0.1153825596,0.0573682748,0.0887684748,0.1663028598,0.1947500855,-0.0009249717,-0.3076634705,-0.1934029013,-0.428485781,0.2985518277,-0.4023680389,0.3442194164,-0.0052058366,0.1163645461,-0.2743442655,-0.0884458572,-0.0779779553,-0.0349813402,0.0552481115,-0.0362682566,0.118636325,-0.1575817913,-0.1216600239,0.4596948028,-0.3044737279,0.0306052417,-0.203237772,0.5589396954,-0.2494065017,-0.3483188748,0.1816463619,0.1678283066,-0.0748553053,-0.0887406096,-0.1464426816,0.4584696591,0.1846816242,-0.2115025371,0.0676155612,0.2728219628,0.2086811811,0.0457450002,0.1133401245,0.3485707939,-0.0103805559,-0.1062241271,-0.225029394,0.5668245554,-0.1356490254,0.1098725349,-0.0194805656,0.1030010805,0.1647879183,-0.1018060818,-0.1466487348,-0.0287135188,0.5365939736,0.1954443306,0.2387123257,0.0966354981,-0.3509839475,-0.1509943455,0.5099392533,0.0974254385,0.037500862,0.3271868825,0.1645047069,-0.2205292135,0.1525416374,0.0362701751,0.5619978905,0.0448566377,-0.0008600512,0.3147718906,-0.3628351986,-0.0569822639,0.0665312409,-0.158066377,0.3175257444,0.323420912,0.0579751097,0.0612480752,-0.3958386183,-0.2288500965,0.1042920575,0.4989686012,-0.2964100242,0.066174075,-0.3261881471,-0.1147774085,-0.0888150036,-0.1403396726,-0.5619576573,-0.2250494063,0.0893958807,0.3187947571,0.090573065,0.350672394,-0.4854868352,-0.0084516723,0.1274918616,-0.3142074049,-0.0129661616,-0.1558169574,-0.2733362615,0.0355255343,0.7237205505,0.0963545367,0.3842619359,0.0550471693,-0.2218773365,-0.0491370149,-0.3143673539,-0.0223673265,-0.2295466214,0.338960737,0.3319755793,0.1100180224,-0.1604092568,-0.2559396625,0.2994101048,-0.0435467698,-0.1957678199,0.1944876164,-0.0658890158,-0.0019956648,-0.1117209345,-0.0121067651,-0.1920267642,-0.2215815634,-0.1455163062,0.1978730559,0.3210581243,0.3196024299,0.0293593053,0.1503638178,0.0955186337,-0.0287066139,-0.179673478,-0.3023663461,0.354970634,0.0689084977,-0.4053297043,-0.214728415,-0.1006930247,0.2305930257,0.1018887013,-0.5546712875,0.1395835578,-0.0337636657,0.0982766151,0.1617600173,-0.0188597664,0.4739741981,-0.0388860777,0.056005761,-0.1978461742,-0.2456507236,0.1355912387,0.1616822779,0.6563411951,-0.0545418188,0.4591037631,-0.1254960299,0.5254045129,0.2798558772,-0.1763413548,0.3539974391,0.2002420872,0.2736523151,-0.1534941196,-0.2914330065,0.2855210304,-0.0495390184,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/537","title":"[Dataset] RACE dataset Checksums error","comments":"> > At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n> \r\n> Could you upload this please?\r\n\r\nNot sure if I can upload it according to their license (\"You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purpose, any portion of the contexts and any portion of derived data.\").","body":"Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n```","comment_length":108,"text":"[Dataset] RACE dataset Checksums error \n Hi there, I just would like to use this awesome lib to perform a dataset fine-tuning on RACE dataset. I have performed the following steps:\r\n\r\n```\r\ndataset = nlp.load_dataset(\"race\")\r\nlen(dataset[\"train\"]), len(dataset[\"validation\"])\r\n```\r\n\r\nBut then I got the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-15-8bf7603ce0ed> in <module>\r\n----> 1 dataset = nlp.load_dataset(\"race\")\r\n      2 len(dataset[\"train\"]), len(dataset[\"validation\"])\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    546 \r\n    547     # Download and prepare data\r\n--> 548     builder_instance.download_and_prepare(\r\n    549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    460                         logger.info(\"Dataset not on Hf google storage. Downloading and preparing it from source\")\r\n    461                 if not downloaded_from_gcs:\r\n--> 462                     self._download_and_prepare(\r\n    463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n--> 521             verify_checksums(\r\n    522                 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n    523             )\r\n\r\n~\/miniconda3\/envs\/masters\/lib\/python3.8\/site-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n     36     if len(bad_urls) > 0:\r\n     37         error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 38         raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n     39     logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n     40 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http:\/\/www.cs.cmu.edu\/~glai1\/data\/race\/RACE.tar.gz']\r\n``` \n > > At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n> \r\n> Could you upload this please?\r\n\r\nNot sure if I can upload it according to their license (\"You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purpose, any portion of the contexts and any portion of derived data.\").","embeddings":[-0.3484401107,0.3749217391,-0.0223544165,0.2424075007,0.2333744168,0.0591794364,0.2440315485,0.4674772024,0.2553084195,-0.1403686702,-0.0667250529,-0.0904459357,-0.3057557046,0.1228690147,0.0535939559,-0.0088576656,-0.0977109894,0.0319078714,-0.1895279884,0.0566937141,-0.1161630303,-0.0643239096,-0.1031743437,0.0158479381,-0.0917348042,-0.1198823452,0.2069767118,0.1899706721,-0.2297097296,-0.457950443,0.2975064218,0.1518295407,0.2854932845,0.4001206756,-0.0001180434,0.1198679805,0.4619445801,-0.0878783837,-0.4845319688,-0.3111259639,-0.2444009781,-0.0946979746,0.0279894862,-0.1625524312,-0.1754536331,0.3403500319,0.1200167835,-0.3093126714,0.2425986379,0.415902704,0.1814554483,0.4850332439,-0.0983780921,0.1419311464,0.2775052786,0.0405619107,-0.0977338254,0.4429739118,0.0883837417,-0.2895773947,-0.0681834817,0.1827246845,-0.1868143529,0.2293165624,0.2373239398,-0.031059809,0.050049074,-0.1472805589,0.0172663014,0.3044555187,0.228475675,-0.4627510011,-0.262774229,-0.198295325,-0.0720933005,-0.663425982,0.309194535,0.2011436969,-0.239018321,-0.1425317824,-0.5278380513,0.0906221122,-0.0025051723,0.116995424,0.1789869219,0.4105425179,0.2487375885,0.1001004428,0.1208602414,0.0906594023,0.1509335041,-0.2384599894,-0.1124067903,0.2280863971,-0.5045996308,-0.167716071,0.1644721627,0.1875035167,0.0313857719,0.3799568415,0.1418694258,0.084726125,0.0732125565,0.0911256745,0.2244704515,0.1387256682,0.1841384023,0.2849820256,0.384634763,0.1717725396,0.2062670141,0.1483997554,-0.0711589828,-0.1690804064,0.0967379138,0.0995636731,-0.0724148899,-0.3175649941,-0.550168097,0.2802394927,0.0591629855,-0.10878966,0.3846683204,0.2568905056,-0.2069443613,-0.3697853684,0.0991448984,0.2307722121,-0.2812793255,-0.0285786558,-0.1864865869,0.2175771296,-0.3917308152,-0.0531366691,0.3407814205,-0.0546818748,0.3931039572,-0.1745660603,0.0894263536,0.2351038456,0.1652139723,-0.2833355367,-0.372736454,0.4511757195,0.0721225217,-0.0609272681,0.2574218214,-0.1946486831,-0.3575665653,-0.0453447327,-0.0227217954,-0.1541518718,-0.2694600523,0.1336429864,-0.5319498181,-0.1265995502,-0.1047882885,-0.0980740264,0.4659026563,-0.2218079269,0.0720761493,-0.2355969846,-0.4272306859,-0.2113928646,0.2053787857,0.2566948831,-0.2136314958,-0.0465420075,-0.2215101272,-0.0849393308,0.3301586509,0.2536371946,-0.1534425318,0.2487071902,-0.237203747,0.1597365588,0.4282442331,-0.0966892466,-0.377505362,0.0635767803,0.0420482419,0.0293667056,-0.0294797532,0.1122540906,-0.0142485276,-0.067274861,0.1905495077,0.3296660483,-0.1435996741,-0.1567834467,-0.2640849948,0.0402008817,0.4529900849,0.3493306935,0.2753326297,-0.1353898197,0.0652282834,0.1668525487,0.1153825596,0.0573682748,0.0887684748,0.1663028598,0.1947500855,-0.0009249717,-0.3076634705,-0.1934029013,-0.428485781,0.2985518277,-0.4023680389,0.3442194164,-0.0052058366,0.1163645461,-0.2743442655,-0.0884458572,-0.0779779553,-0.0349813402,0.0552481115,-0.0362682566,0.118636325,-0.1575817913,-0.1216600239,0.4596948028,-0.3044737279,0.0306052417,-0.203237772,0.5589396954,-0.2494065017,-0.3483188748,0.1816463619,0.1678283066,-0.0748553053,-0.0887406096,-0.1464426816,0.4584696591,0.1846816242,-0.2115025371,0.0676155612,0.2728219628,0.2086811811,0.0457450002,0.1133401245,0.3485707939,-0.0103805559,-0.1062241271,-0.225029394,0.5668245554,-0.1356490254,0.1098725349,-0.0194805656,0.1030010805,0.1647879183,-0.1018060818,-0.1466487348,-0.0287135188,0.5365939736,0.1954443306,0.2387123257,0.0966354981,-0.3509839475,-0.1509943455,0.5099392533,0.0974254385,0.037500862,0.3271868825,0.1645047069,-0.2205292135,0.1525416374,0.0362701751,0.5619978905,0.0448566377,-0.0008600512,0.3147718906,-0.3628351986,-0.0569822639,0.0665312409,-0.158066377,0.3175257444,0.323420912,0.0579751097,0.0612480752,-0.3958386183,-0.2288500965,0.1042920575,0.4989686012,-0.2964100242,0.066174075,-0.3261881471,-0.1147774085,-0.0888150036,-0.1403396726,-0.5619576573,-0.2250494063,0.0893958807,0.3187947571,0.090573065,0.350672394,-0.4854868352,-0.0084516723,0.1274918616,-0.3142074049,-0.0129661616,-0.1558169574,-0.2733362615,0.0355255343,0.7237205505,0.0963545367,0.3842619359,0.0550471693,-0.2218773365,-0.0491370149,-0.3143673539,-0.0223673265,-0.2295466214,0.338960737,0.3319755793,0.1100180224,-0.1604092568,-0.2559396625,0.2994101048,-0.0435467698,-0.1957678199,0.1944876164,-0.0658890158,-0.0019956648,-0.1117209345,-0.0121067651,-0.1920267642,-0.2215815634,-0.1455163062,0.1978730559,0.3210581243,0.3196024299,0.0293593053,0.1503638178,0.0955186337,-0.0287066139,-0.179673478,-0.3023663461,0.354970634,0.0689084977,-0.4053297043,-0.214728415,-0.1006930247,0.2305930257,0.1018887013,-0.5546712875,0.1395835578,-0.0337636657,0.0982766151,0.1617600173,-0.0188597664,0.4739741981,-0.0388860777,0.056005761,-0.1978461742,-0.2456507236,0.1355912387,0.1616822779,0.6563411951,-0.0545418188,0.4591037631,-0.1254960299,0.5254045129,0.2798558772,-0.1763413548,0.3539974391,0.2002420872,0.2736523151,-0.1534941196,-0.2914330065,0.2855210304,-0.049539018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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/534","title":"`list_datasets()` is broken.","comments":"Thanks for reporting !\r\nThis has been fixed in #475 and the fix will be available in the next release","body":"version = '0.4.0'\r\n\r\n`list_datasets()` is broken. It results in the following error : \r\n\r\n```\r\nIn [3]: nlp.list_datasets()\r\nOut[3]: ---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/core\/formatters.py in __call__(self, obj)\r\n    700                 type_pprinters=self.type_printers,\r\n    701                 deferred_pprinters=self.deferred_printers)\r\n--> 702             printer.pretty(obj)\r\n    703             printer.flush()\r\n    704             return stream.getvalue()\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in pretty(self, obj)\r\n    375                 if cls in self.type_pprinters:\r\n    376                     # printer registered in self.type_pprinters\r\n--> 377                     return self.type_pprinters[cls](obj, self, cycle)\r\n    378                 else:\r\n    379                     # deferred printer\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in inner(obj, p, cycle)\r\n    553                 p.text(',')\r\n    554                 p.breakable()\r\n--> 555             p.pretty(x)\r\n    556         if len(obj) == 1 and type(obj) is tuple:\r\n    557             # Special case for 1-item tuples.\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in pretty(self, obj)\r\n    392                         if cls is not object \\\r\n    393                                 and callable(cls.__dict__.get('__repr__')):\r\n--> 394                             return _repr_pprint(obj, self, cycle)\r\n    395\r\n    396             return _default_pprint(obj, self, cycle)\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in _repr_pprint(obj, p, cycle)\r\n    698     \"\"\"A pprint that just redirects to the normal repr function.\"\"\"\r\n    699     # Find newlines and replace them with p.break_()\r\n--> 700     output = repr(obj)\r\n    701     lines = output.splitlines()\r\n    702     with p.group():\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/nlp\/hf_api.py in __repr__(self)\r\n    110\r\n    111     def __repr__(self):\r\n--> 112         single_line_description = self.description.replace(\"\\n\", \"\")\r\n    113         return f\"nlp.ObjectInfo(id='{self.id}', description='{single_line_description}', files={self.siblings})\"\r\n    114\r\n\r\nAttributeError: 'NoneType' object has no attribute 'replace'\r\n```","comment_length":20,"text":"`list_datasets()` is broken. \n version = '0.4.0'\r\n\r\n`list_datasets()` is broken. It results in the following error : \r\n\r\n```\r\nIn [3]: nlp.list_datasets()\r\nOut[3]: ---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/core\/formatters.py in __call__(self, obj)\r\n    700                 type_pprinters=self.type_printers,\r\n    701                 deferred_pprinters=self.deferred_printers)\r\n--> 702             printer.pretty(obj)\r\n    703             printer.flush()\r\n    704             return stream.getvalue()\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in pretty(self, obj)\r\n    375                 if cls in self.type_pprinters:\r\n    376                     # printer registered in self.type_pprinters\r\n--> 377                     return self.type_pprinters[cls](obj, self, cycle)\r\n    378                 else:\r\n    379                     # deferred printer\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in inner(obj, p, cycle)\r\n    553                 p.text(',')\r\n    554                 p.breakable()\r\n--> 555             p.pretty(x)\r\n    556         if len(obj) == 1 and type(obj) is tuple:\r\n    557             # Special case for 1-item tuples.\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in pretty(self, obj)\r\n    392                         if cls is not object \\\r\n    393                                 and callable(cls.__dict__.get('__repr__')):\r\n--> 394                             return _repr_pprint(obj, self, cycle)\r\n    395\r\n    396             return _default_pprint(obj, self, cycle)\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in _repr_pprint(obj, p, cycle)\r\n    698     \"\"\"A pprint that just redirects to the normal repr function.\"\"\"\r\n    699     # Find newlines and replace them with p.break_()\r\n--> 700     output = repr(obj)\r\n    701     lines = output.splitlines()\r\n    702     with p.group():\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/nlp\/hf_api.py in __repr__(self)\r\n    110\r\n    111     def __repr__(self):\r\n--> 112         single_line_description = self.description.replace(\"\\n\", \"\")\r\n    113         return f\"nlp.ObjectInfo(id='{self.id}', description='{single_line_description}', files={self.siblings})\"\r\n    114\r\n\r\nAttributeError: 'NoneType' object has no attribute 'replace'\r\n``` \n Thanks for reporting !\r\nThis has been fixed in #475 and the fix will be available in the next release","embeddings":[-0.2925504744,0.1287720501,-0.1103174761,0.2530123293,0.1547410935,0.0697497725,0.3828648925,0.4221462011,-0.0766394809,-0.049442023,-0.1751877069,0.4431259036,-0.2499777824,-0.0311814267,-0.0670694038,-0.4052232206,-0.0786968246,0.3645731211,0.0530656092,0.1719869971,-0.177527532,0.3013062477,-0.2915241718,0.1099653244,0.0032130617,-0.3575332165,-0.0393835679,-0.0953138992,-0.2738591433,-0.6101930737,0.2050484121,0.0413203202,0.145760715,0.191788584,-0.0001070091,-0.1000377238,0.3466987014,0.1209742874,-0.4599771798,-0.1070150882,-0.3484742939,-0.1636984348,0.15418455,-0.1577059478,0.2177172452,-0.298948288,0.0492456704,-0.0324081853,0.1428057849,0.4736910462,0.2920954823,0.1383364052,0.3362931013,-0.0987469628,0.1389920712,0.1220614463,-0.2137827873,0.1378371567,0.2005366683,-0.1644205153,0.1876337528,0.3156543076,-0.1838986576,0.0428843275,0.080845803,-0.1026863679,-0.0092912083,-0.2145709842,0.1760112792,0.0995607078,0.4164287746,-0.2796163559,-0.3078490496,0.0164624508,0.058969751,-0.7774239182,0.1081925631,0.0224134699,0.0358126685,0.019904213,-0.3528318703,-0.0658771247,-0.2318228632,0.4673488736,-0.0962115526,0.2898914516,-0.1140294299,0.0171308834,-0.0981983617,-0.0868966356,0.0178055316,0.1038976535,-0.2775534987,-0.006325121,-0.1692347378,-0.1642162651,0.2114341259,0.0902850628,-0.0042494638,0.4375266731,-0.0416292585,-0.1178216562,0.0063491771,0.1518926173,0.1883146763,0.1584148854,0.2687661946,0.3543733954,0.1175390035,0.2619755566,0.1657694131,-0.0674194694,0.1508336514,-0.2813037336,0.1861238033,0.0103147179,0.34960109,-0.110645175,-0.2098476887,0.2666292191,-0.1843719929,-0.0758719295,0.0960812718,0.2046851963,-0.0793879256,0.2663103342,0.1156045943,-0.0302527286,-0.1726293713,-0.0930825919,0.0089965528,0.0878812671,-0.0294366628,-0.0644476563,0.0349587202,-0.0605724901,0.2429809868,-0.1208946779,0.1779672801,0.0197550375,0.1838933676,-0.4000001848,0.1542435884,0.2231269926,-0.1593793035,0.1788142025,0.2923768163,-0.4119538963,-0.1074468568,-0.1714627445,-0.076747492,-0.2104943842,-0.4990203083,0.2266946137,0.1809665859,-0.0194095653,-0.096170783,-0.1299234927,0.3491951823,-0.2621610463,0.0378679335,-0.5697956681,-0.099115558,-0.1339209676,0.1721034348,0.300033778,-0.3002201021,0.1677255332,-0.2391221225,-0.0513175763,0.0095718605,0.2239179015,-0.1983066946,0.2528689802,-0.1809735298,-0.0589219183,0.2944368422,-0.1372349709,-0.2301748246,0.2795769572,0.2491778433,-0.0680247694,-0.2227915227,-0.0755549893,0.2689787447,0.0481586978,0.239718467,0.1333486587,0.0076559032,-0.1522696316,-0.1668646932,-0.0934467167,0.381395787,-0.1442018896,0.1337888688,0.1369581521,-0.0157831535,0.1134748012,0.1468215734,-0.1264295727,-0.0556389503,0.1905207187,0.2522134185,0.1714027375,0.0542701334,-0.3509204388,-0.4462583363,-0.0797010213,-0.2040190697,0.0652356073,-0.2956978679,0.1840091497,-0.229904905,0.1598493457,-0.1872381717,-0.2770086527,0.199253276,0.2211045772,0.0691296235,-0.0560180396,-0.1147577688,-0.0540781133,-0.0689443648,0.081534408,-0.4202454388,0.3430917263,-0.2603977621,-0.2390125692,0.0644561425,0.0965775475,0.1995361745,-0.1361991763,-0.2320535332,0.4403236806,0.1650057137,-0.2004151046,-0.1622930318,0.077113837,0.1574232131,-0.1858457625,0.0220011715,0.3202284276,0.098340556,0.057860136,-0.0076144696,0.2805489302,-0.1693376452,0.1688168645,0.2262055576,0.3731301129,0.2607486248,0.1023362055,-0.0886058956,-0.2534463406,0.4879236221,0.0339732207,0.2914642394,-0.2156838924,-0.5336611271,0.0760332271,0.3073705435,0.148070097,0.1422265172,-0.0100683542,-0.1057600006,0.0769602954,0.2739711702,0.1093880683,0.3183995187,0.0714439526,-0.2827411294,-0.06643226,-0.268722266,-0.1903395504,0.2828251719,0.0425259769,0.1601824164,0.1422438473,0.2664467096,0.0376957208,-0.38587901,-0.4234346151,-0.0569498241,0.3714588881,-0.1407547593,0.2643475235,-0.3491425514,-0.4249199927,-0.2960482836,-0.2388049364,-0.2406001985,-0.3623484075,-0.0960663483,0.1309019029,0.0306267813,0.23544842,-0.1495063454,-0.0630283803,0.1627140343,0.1025079563,-0.0452507511,-0.1686990857,-0.2626588941,0.1136495322,0.1955718696,-0.0830842555,0.376789093,-0.0422195643,-0.1625643373,-0.2199974954,-0.4036245048,0.1120402217,-0.0680637285,0.198832646,0.5051586032,0.0949800089,-0.0435776599,-0.1564034969,0.4378064275,-0.056366723,0.1829486191,0.1709958315,-0.0545085445,-0.3030057251,-0.1642799526,-0.4381222725,-0.1721548587,-0.1750888675,0.1431864202,0.3657177091,0.367105037,0.5672655702,0.0486722551,0.1627244651,-0.1498387605,0.1697799414,-0.0384772494,0.0926329643,0.1827802211,-0.2975689173,-0.4033008814,-0.205091387,0.1107111797,0.0534857623,-0.0109257596,-0.4335919619,-0.1561717689,-0.2316404432,0.2016340494,-0.0561200008,0.0157572627,0.2922447622,0.1445402205,-0.0896595716,-0.0230043251,-0.0041051153,-0.0687814355,-0.1858177781,0.1621867567,0.02480096,0.2922298312,-0.0115637807,0.2237012982,0.3424477279,-0.21517995,0.127971068,-0.1084780023,0.4514208436,-0.1578459591,-0.4963950813,0.2596535981,-0.0834074542,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/534","title":"`list_datasets()` is broken.","comments":"What you can do instead to get the list of the datasets is call\r\n\r\n```python\r\nprint([dataset.id for dataset in nlp.list_datasets()])\r\n```","body":"version = '0.4.0'\r\n\r\n`list_datasets()` is broken. It results in the following error : \r\n\r\n```\r\nIn [3]: nlp.list_datasets()\r\nOut[3]: ---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/core\/formatters.py in __call__(self, obj)\r\n    700                 type_pprinters=self.type_printers,\r\n    701                 deferred_pprinters=self.deferred_printers)\r\n--> 702             printer.pretty(obj)\r\n    703             printer.flush()\r\n    704             return stream.getvalue()\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in pretty(self, obj)\r\n    375                 if cls in self.type_pprinters:\r\n    376                     # printer registered in self.type_pprinters\r\n--> 377                     return self.type_pprinters[cls](obj, self, cycle)\r\n    378                 else:\r\n    379                     # deferred printer\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in inner(obj, p, cycle)\r\n    553                 p.text(',')\r\n    554                 p.breakable()\r\n--> 555             p.pretty(x)\r\n    556         if len(obj) == 1 and type(obj) is tuple:\r\n    557             # Special case for 1-item tuples.\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in pretty(self, obj)\r\n    392                         if cls is not object \\\r\n    393                                 and callable(cls.__dict__.get('__repr__')):\r\n--> 394                             return _repr_pprint(obj, self, cycle)\r\n    395\r\n    396             return _default_pprint(obj, self, cycle)\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in _repr_pprint(obj, p, cycle)\r\n    698     \"\"\"A pprint that just redirects to the normal repr function.\"\"\"\r\n    699     # Find newlines and replace them with p.break_()\r\n--> 700     output = repr(obj)\r\n    701     lines = output.splitlines()\r\n    702     with p.group():\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/nlp\/hf_api.py in __repr__(self)\r\n    110\r\n    111     def __repr__(self):\r\n--> 112         single_line_description = self.description.replace(\"\\n\", \"\")\r\n    113         return f\"nlp.ObjectInfo(id='{self.id}', description='{single_line_description}', files={self.siblings})\"\r\n    114\r\n\r\nAttributeError: 'NoneType' object has no attribute 'replace'\r\n```","comment_length":21,"text":"`list_datasets()` is broken. \n version = '0.4.0'\r\n\r\n`list_datasets()` is broken. It results in the following error : \r\n\r\n```\r\nIn [3]: nlp.list_datasets()\r\nOut[3]: ---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/core\/formatters.py in __call__(self, obj)\r\n    700                 type_pprinters=self.type_printers,\r\n    701                 deferred_pprinters=self.deferred_printers)\r\n--> 702             printer.pretty(obj)\r\n    703             printer.flush()\r\n    704             return stream.getvalue()\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in pretty(self, obj)\r\n    375                 if cls in self.type_pprinters:\r\n    376                     # printer registered in self.type_pprinters\r\n--> 377                     return self.type_pprinters[cls](obj, self, cycle)\r\n    378                 else:\r\n    379                     # deferred printer\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in inner(obj, p, cycle)\r\n    553                 p.text(',')\r\n    554                 p.breakable()\r\n--> 555             p.pretty(x)\r\n    556         if len(obj) == 1 and type(obj) is tuple:\r\n    557             # Special case for 1-item tuples.\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in pretty(self, obj)\r\n    392                         if cls is not object \\\r\n    393                                 and callable(cls.__dict__.get('__repr__')):\r\n--> 394                             return _repr_pprint(obj, self, cycle)\r\n    395\r\n    396             return _default_pprint(obj, self, cycle)\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/IPython\/lib\/pretty.py in _repr_pprint(obj, p, cycle)\r\n    698     \"\"\"A pprint that just redirects to the normal repr function.\"\"\"\r\n    699     # Find newlines and replace them with p.break_()\r\n--> 700     output = repr(obj)\r\n    701     lines = output.splitlines()\r\n    702     with p.group():\r\n\r\n~\/.virtualenvs\/san-lgUCsFg_\/lib\/python3.8\/site-packages\/nlp\/hf_api.py in __repr__(self)\r\n    110\r\n    111     def __repr__(self):\r\n--> 112         single_line_description = self.description.replace(\"\\n\", \"\")\r\n    113         return f\"nlp.ObjectInfo(id='{self.id}', description='{single_line_description}', files={self.siblings})\"\r\n    114\r\n\r\nAttributeError: 'NoneType' object has no attribute 'replace'\r\n``` \n What you can do instead to get the list of the datasets is call\r\n\r\n```python\r\nprint([dataset.id for dataset in nlp.list_datasets()])\r\n```","embeddings":[-0.2925504744,0.1287720501,-0.1103174761,0.2530123293,0.1547410935,0.0697497725,0.3828648925,0.4221462011,-0.0766394809,-0.049442023,-0.1751877069,0.4431259036,-0.2499777824,-0.0311814267,-0.0670694038,-0.4052232206,-0.0786968246,0.3645731211,0.0530656092,0.1719869971,-0.177527532,0.3013062477,-0.2915241718,0.1099653244,0.0032130617,-0.3575332165,-0.0393835679,-0.0953138992,-0.2738591433,-0.6101930737,0.2050484121,0.0413203202,0.145760715,0.191788584,-0.0001070091,-0.1000377238,0.3466987014,0.1209742874,-0.4599771798,-0.1070150882,-0.3484742939,-0.1636984348,0.15418455,-0.1577059478,0.2177172452,-0.298948288,0.0492456704,-0.0324081853,0.1428057849,0.4736910462,0.2920954823,0.1383364052,0.3362931013,-0.0987469628,0.1389920712,0.1220614463,-0.2137827873,0.1378371567,0.2005366683,-0.1644205153,0.1876337528,0.3156543076,-0.1838986576,0.0428843275,0.080845803,-0.1026863679,-0.0092912083,-0.2145709842,0.1760112792,0.0995607078,0.4164287746,-0.2796163559,-0.3078490496,0.0164624508,0.058969751,-0.7774239182,0.1081925631,0.0224134699,0.0358126685,0.019904213,-0.3528318703,-0.0658771247,-0.2318228632,0.4673488736,-0.0962115526,0.2898914516,-0.1140294299,0.0171308834,-0.0981983617,-0.0868966356,0.0178055316,0.1038976535,-0.2775534987,-0.006325121,-0.1692347378,-0.1642162651,0.2114341259,0.0902850628,-0.0042494638,0.4375266731,-0.0416292585,-0.1178216562,0.0063491771,0.1518926173,0.1883146763,0.1584148854,0.2687661946,0.3543733954,0.1175390035,0.2619755566,0.1657694131,-0.0674194694,0.1508336514,-0.2813037336,0.1861238033,0.0103147179,0.34960109,-0.110645175,-0.2098476887,0.2666292191,-0.1843719929,-0.0758719295,0.0960812718,0.2046851963,-0.0793879256,0.2663103342,0.1156045943,-0.0302527286,-0.1726293713,-0.0930825919,0.0089965528,0.0878812671,-0.0294366628,-0.0644476563,0.0349587202,-0.0605724901,0.2429809868,-0.1208946779,0.1779672801,0.0197550375,0.1838933676,-0.4000001848,0.1542435884,0.2231269926,-0.1593793035,0.1788142025,0.2923768163,-0.4119538963,-0.1074468568,-0.1714627445,-0.076747492,-0.2104943842,-0.4990203083,0.2266946137,0.1809665859,-0.0194095653,-0.096170783,-0.1299234927,0.3491951823,-0.2621610463,0.0378679335,-0.5697956681,-0.099115558,-0.1339209676,0.1721034348,0.300033778,-0.3002201021,0.1677255332,-0.2391221225,-0.0513175763,0.0095718605,0.2239179015,-0.1983066946,0.2528689802,-0.1809735298,-0.0589219183,0.2944368422,-0.1372349709,-0.2301748246,0.2795769572,0.2491778433,-0.0680247694,-0.2227915227,-0.0755549893,0.2689787447,0.0481586978,0.239718467,0.1333486587,0.0076559032,-0.1522696316,-0.1668646932,-0.0934467167,0.381395787,-0.1442018896,0.1337888688,0.1369581521,-0.0157831535,0.1134748012,0.1468215734,-0.1264295727,-0.0556389503,0.1905207187,0.2522134185,0.1714027375,0.0542701334,-0.3509204388,-0.4462583363,-0.0797010213,-0.2040190697,0.0652356073,-0.2956978679,0.1840091497,-0.229904905,0.1598493457,-0.1872381717,-0.2770086527,0.199253276,0.2211045772,0.0691296235,-0.0560180396,-0.1147577688,-0.0540781133,-0.0689443648,0.081534408,-0.4202454388,0.3430917263,-0.2603977621,-0.2390125692,0.0644561425,0.0965775475,0.1995361745,-0.1361991763,-0.2320535332,0.4403236806,0.1650057137,-0.2004151046,-0.1622930318,0.077113837,0.1574232131,-0.1858457625,0.0220011715,0.3202284276,0.098340556,0.057860136,-0.0076144696,0.2805489302,-0.1693376452,0.1688168645,0.2262055576,0.3731301129,0.2607486248,0.1023362055,-0.0886058956,-0.2534463406,0.4879236221,0.0339732207,0.2914642394,-0.2156838924,-0.5336611271,0.0760332271,0.3073705435,0.148070097,0.1422265172,-0.0100683542,-0.1057600006,0.0769602954,0.2739711702,0.1093880683,0.3183995187,0.0714439526,-0.2827411294,-0.06643226,-0.268722266,-0.1903395504,0.2828251719,0.0425259769,0.1601824164,0.1422438473,0.2664467096,0.0376957208,-0.38587901,-0.4234346151,-0.0569498241,0.3714588881,-0.1407547593,0.2643475235,-0.3491425514,-0.4249199927,-0.2960482836,-0.2388049364,-0.2406001985,-0.3623484075,-0.0960663483,0.1309019029,0.0306267813,0.23544842,-0.1495063454,-0.0630283803,0.1627140343,0.1025079563,-0.0452507511,-0.1686990857,-0.2626588941,0.1136495322,0.1955718696,-0.0830842555,0.376789093,-0.0422195643,-0.1625643373,-0.2199974954,-0.4036245048,0.1120402217,-0.0680637285,0.198832646,0.5051586032,0.0949800089,-0.0435776599,-0.1564034969,0.4378064275,-0.056366723,0.1829486191,0.1709958315,-0.0545085445,-0.3030057251,-0.1642799526,-0.4381222725,-0.1721548587,-0.1750888675,0.1431864202,0.3657177091,0.367105037,0.5672655702,0.0486722551,0.1627244651,-0.1498387605,0.1697799414,-0.0384772494,0.0926329643,0.1827802211,-0.2975689173,-0.4033008814,-0.205091387,0.1107111797,0.0534857623,-0.0109257596,-0.4335919619,-0.1561717689,-0.2316404432,0.2016340494,-0.0561200008,0.0157572627,0.2922447622,0.1445402205,-0.0896595716,-0.0230043251,-0.0041051153,-0.0687814355,-0.1858177781,0.1621867567,0.02480096,0.2922298312,-0.0115637807,0.2237012982,0.3424477279,-0.21517995,0.127971068,-0.1084780023,0.4514208436,-0.1578459591,-0.4963950813,0.25965359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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"Could you try to run `dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")` once before calling the script ?\r\n\r\nIt looks like several processes try to create the dataset in arrow format at the same time. If the dataset is already created it should be fine","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":43,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n Could you try to run `dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")` once before calling the script ?\r\n\r\nIt looks like several processes try to create the dataset in arrow format at the same time. If the dataset is already created it should be fine","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-0.05941396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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"Thanks! I tested on 328MB text data on `n1-standard-8 (8 vCPUs, 30 GB memory)`. The main script ran without any issue, but it seems to require a huge space in the drive.\r\n\r\nAs suggested, I ran the following script before running the pre-training command with `xla_spawn.py`.\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n\r\nfile_path=\"your_file_name\"\r\nload_dataset(\"text\", data_files=file_path, split=\"train\")\r\n```\r\nThis will create `text-train.arrow` under the default cache directory. Then, I run the script with `xla_spawn.py`. It will load data from the cached file. My understanding is that there's no other way but to do this two-step process with the current version (0.4) of `nlp`.\r\n\r\nDuring another caching process that happens in the main script:\r\n\r\n```\r\n08\/26\/2020 09:19:51 - INFO - nlp.utils.info_utils -   All the checksums matched successfully for post processing resources\r\n08\/26\/2020 09:19:53 - INFO - nlp.arrow_dataset -   Caching processed dataset at \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\/cache-f90f341e5308a7469\r\n8d872bcc88f9c0e.arrow\r\n```\r\n\r\n`nlp` generates a temporary file per core, each of which is three times larger than the original text data. If each process is actually writing on the disk, you will need a huge amount of space in your drive. (Maybe I'm missing something.)\r\n\r\n```\r\n-rw-r--r-- 1 ***** *****  674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp0k43sazw\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp7sxs9mj5\r\n-rw------- 1 ***** ***** 939M Aug 26 09:31 tmpbbiqw2vp\r\n-rw------- 1 ***** ***** 937M Aug 26 09:31 tmpjxb5ptyu\r\n-rw------- 1 ***** ***** 933M Aug 26 09:31 tmpk3hkdh0e\r\n-rw------- 1 ***** ***** 944M Aug 26 09:31 tmpnoalwftz\r\n-rw------- 1 ***** ***** 931M Aug 26 09:31 tmpuxdr_dz3\r\n-rw------- 1 ***** ***** 945M Aug 26 09:31 tmpxjyuy6dk\r\n```\r\nAfter the caching process, they seem to be merged into one file.\r\n\r\n```\r\n-rw------- 1  ***** ***** 989M Aug 26 09:32 cache-f90f341e5308a74698d872bcc88f9c0e.arrow\r\n-rw-r--r-- 1  ***** *****  674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1  ***** *****    0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1  ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n```","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":336,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n Thanks! I tested on 328MB text data on `n1-standard-8 (8 vCPUs, 30 GB memory)`. The main script ran without any issue, but it seems to require a huge space in the drive.\r\n\r\nAs suggested, I ran the following script before running the pre-training command with `xla_spawn.py`.\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n\r\nfile_path=\"your_file_name\"\r\nload_dataset(\"text\", data_files=file_path, split=\"train\")\r\n```\r\nThis will create `text-train.arrow` under the default cache directory. Then, I run the script with `xla_spawn.py`. It will load data from the cached file. My understanding is that there's no other way but to do this two-step process with the current version (0.4) of `nlp`.\r\n\r\nDuring another caching process that happens in the main script:\r\n\r\n```\r\n08\/26\/2020 09:19:51 - INFO - nlp.utils.info_utils -   All the checksums matched successfully for post processing resources\r\n08\/26\/2020 09:19:53 - INFO - nlp.arrow_dataset -   Caching processed dataset at \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\/cache-f90f341e5308a7469\r\n8d872bcc88f9c0e.arrow\r\n```\r\n\r\n`nlp` generates a temporary file per core, each of which is three times larger than the original text data. If each process is actually writing on the disk, you will need a huge amount of space in your drive. (Maybe I'm missing something.)\r\n\r\n```\r\n-rw-r--r-- 1 ***** *****  674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp0k43sazw\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp7sxs9mj5\r\n-rw------- 1 ***** ***** 939M Aug 26 09:31 tmpbbiqw2vp\r\n-rw------- 1 ***** ***** 937M Aug 26 09:31 tmpjxb5ptyu\r\n-rw------- 1 ***** ***** 933M Aug 26 09:31 tmpk3hkdh0e\r\n-rw------- 1 ***** ***** 944M Aug 26 09:31 tmpnoalwftz\r\n-rw------- 1 ***** ***** 931M Aug 26 09:31 tmpuxdr_dz3\r\n-rw------- 1 ***** ***** 945M Aug 26 09:31 tmpxjyuy6dk\r\n```\r\nAfter the caching process, they seem to be merged into one file.\r\n\r\n```\r\n-rw------- 1  ***** ***** 989M Aug 26 09:32 cache-f90f341e5308a74698d872bcc88f9c0e.arrow\r\n-rw-r--r-- 1  ***** *****  674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1  ***** *****    0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1  ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n```","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.286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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"Again it looks like every process tries to tokenize the full dataset at the same time.\r\nIf you do the tokenization before calling `xla_spawn.py` once, then each process will then use the tokenized cached file `cache-f90f341e5308a74698d872bcc88f9c0e.arrow` and not recompute it.\r\n\r\nNot sure if there's a better way to do that cc @julien-c @thomwolf ","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":53,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n Again it looks like every process tries to tokenize the full dataset at the same time.\r\nIf you do the tokenization before calling `xla_spawn.py` once, then each process will then use the tokenized cached file `cache-f90f341e5308a74698d872bcc88f9c0e.arrow` and not recompute it.\r\n\r\nNot sure if there's a better way to do that cc @julien-c @thomwolf ","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-0.0594139695,0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0.3100019097,-0.559237659,-0.0874062255,0.1202496514,-0.0655824319,0.0252231155,0.1328889132,-0.642411828,0.0706516281,0.1075027138,0.1937766373,-0.3184123635,0.0537423715,0.0611682348,-0.2190911621,0.1069517285,0.3757636845,0.0110927885,-0.4208606184,0.1000858918,-0.1757013351]}
{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"I wrote a separate script just for preparing a cached file, including tokenization. Each process did use the tokenized cached file.\r\n\r\nCurrently I'm testing the pipeline on 24GB text data. It took about 1.5 hour to create a cached file on `n1-highmem-16 (16 vCPUs, 104 GB memory)`. I assume loading this cached file in the main script with `xla_spawn.py` won't be an issue (even if there are 8 processes).\r\n\r\n```\r\ntotal 98G\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 13:38 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 12:24 ..\r\n-rw------- 1 ***** *****  74G Aug 26 13:38 cache-a7aa04134ba7b1aff5d9710f14a4e334.arrow\r\n-rw-r--r-- 1 ***** *****  681 Aug 26 12:24 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 12:24 LICENSE\r\n-rw-r--r-- 1 ***** *****  25G Aug 26 12:24 text-train.arrow\r\n```","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":127,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n I wrote a separate script just for preparing a cached file, including tokenization. Each process did use the tokenized cached file.\r\n\r\nCurrently I'm testing the pipeline on 24GB text data. It took about 1.5 hour to create a cached file on `n1-highmem-16 (16 vCPUs, 104 GB memory)`. I assume loading this cached file in the main script with `xla_spawn.py` won't be an issue (even if there are 8 processes).\r\n\r\n```\r\ntotal 98G\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 13:38 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 12:24 ..\r\n-rw------- 1 ***** *****  74G Aug 26 13:38 cache-a7aa04134ba7b1aff5d9710f14a4e334.arrow\r\n-rw-r--r-- 1 ***** *****  681 Aug 26 12:24 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 12:24 LICENSE\r\n-rw-r--r-- 1 ***** *****  25G Aug 26 12:24 text-train.arrow\r\n```","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.286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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"Sorry, I thought it was working, but actually the second call doesn't use the cached file that was generated separately, and it will generate another cache-****.arrorw file with a different name. If I run the training script again (with `xla_spawn.py`), it will use the second cached file, which was generated by the training script itself in the previous run.\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:35 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:29 ..\r\n-rw------- 1 ***** *****  99M Aug 26 15:35 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** *****  99M Aug 26 15:29 cache-69633651476e943b93c89ace715f9487.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 26 15:33 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 15:33 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 26 15:29 text-train.arrow\r\n```","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":124,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n Sorry, I thought it was working, but actually the second call doesn't use the cached file that was generated separately, and it will generate another cache-****.arrorw file with a different name. If I run the training script again (with `xla_spawn.py`), it will use the second cached file, which was generated by the training script itself in the previous run.\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:35 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:29 ..\r\n-rw------- 1 ***** *****  99M Aug 26 15:35 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** *****  99M Aug 26 15:29 cache-69633651476e943b93c89ace715f9487.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 26 15:33 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 15:33 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 26 15:29 text-train.arrow\r\n```","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.286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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"So if I understand correctly it means that the cached file generated by your separated script is different by the one used by the training script ?","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":27,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n So if I understand correctly it means that the cached file generated by your separated script is different by the one used by the training script ?","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-0.0594139695,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"Yes.\r\n\r\n1. `cache-69633651476e943b93c89ace715f9487.arrow` was generated with a separate script. \r\n2. I ran the entire script with `xla_spawn.py`.\r\n3. `cache-69633651476e943b93c89ace715f9487.arrow` is not used.\r\n4. `cache-0d77dfce704493dbe63f071eed6a5431.arrow` is created.\r\n5. training starts...\r\n\r\nNow, if I kill the process at step 5, and do the step 2 again, it will use `cache-0d77dfce704493dbe63f071eed6a5431.arrow` (cached file created at step 4) without any issue.\r\n\r\nI used the following to generate the first cached file.\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":85,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n Yes.\r\n\r\n1. `cache-69633651476e943b93c89ace715f9487.arrow` was generated with a separate script. \r\n2. I ran the entire script with `xla_spawn.py`.\r\n3. `cache-69633651476e943b93c89ace715f9487.arrow` is not used.\r\n4. `cache-0d77dfce704493dbe63f071eed6a5431.arrow` is created.\r\n5. training starts...\r\n\r\nNow, if I kill the process at step 5, and do the step 2 again, it will use `cache-0d77dfce704493dbe63f071eed6a5431.arrow` (cached file created at step 4) without any issue.\r\n\r\nI used the following to generate the first cached file.\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"1.  Here's the log from the first step.\r\n```\r\nDownloading and preparing dataset text\/default-e84dd29acc4ad9ef (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDataset text downloaded and prepared to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d. Subsequent calls will reuse this data.\r\n```\r\nThere's a file named `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow`, so it did create a cached file.\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:59 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** *****  99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 26 15:58 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 15:58 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 26 15:58 text-train.arrow\r\n```\r\n2. Ideally, `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow` should be used in `run_language_modeling.py` (modified version using `nlp`) with `xla_spawn.py`. But it looks like it's creating a new cached file.\r\n\r\n```\r\n08\/26\/2020 16:13:03 - INFO - filelock -   Lock 139635836351096 released on \/home\/*****\/.cache\/huggingface\/datasets\/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.202fa4f84f552bff1f5400ae012663839c61efb3de068c6c8722d34ac0ea6192\r\n.py.lock\r\n08\/26\/2020 16:13:03 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Overwrite dataset info from restored data version.\r\n08\/26\/2020 16:13:03 - INFO - nlp.info -   Loading Dataset info from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Reusing dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Constructing Dataset for split train, from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:13:03 - INFO - nlp.utils.info_utils -   All the checksums matched successfully for post processing resources\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Overwrite dataset info from restored data version.\r\n08\/26\/2020 16:13:03 - INFO - nlp.info -   Loading Dataset info from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Reusing dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Constructing Dataset for split train, from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:13:03 - INFO - nlp.utils.info_utils -   All the checksums matched successfully for post processing resources\r\n08\/26\/2020 16:13:05 - INFO - nlp.arrow_dataset -   Caching processed dataset at \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\/cache-0d77dfce704493dbe\r\n63f071eed6a5431.arrow\r\n^M  0%|          | 0\/100 [00:00<?, ?it\/s]08\/26\/2020 16:13:05 - INFO - nlp.arrow_dataset -   Caching processed dataset at \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6\r\nfe661fe4d070d380d\/cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n```\r\n\r\nThere are two cached files in the directory:\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 16:14 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** *****  99M Aug 26 16:14 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** *****  99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 26 16:13 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 16:13 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 26 15:58 text-train.arrow\r\n```\r\n\r\nIf I kill the process, and run it again, it will use the second cached file.\r\n\r\n```\r\n08\/26\/2020 16:19:52 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/26\/2020 16:19:52 - INFO - nlp.builder -   Overwrite dataset info from restored data version.\r\n08\/26\/2020 16:19:52 - INFO - nlp.info -   Loading Dataset info from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:19:52 - INFO - nlp.builder -   Reusing dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/26\/2020 16:19:52 - INFO - nlp.builder -   Constructing Dataset for split train, from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:19:52 - INFO - nlp.utils.info_utils -   All the checksums matched successfully for post processing resources\r\n08\/26\/2020 16:19:53 - INFO - nlp.arrow_dataset -   Loading cached processed dataset at \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\/cache-0d77dfce70\r\n4493dbe63f071eed6a5431.arrow\r\n08\/26\/2020 16:19:53 - INFO - nlp.arrow_dataset -   Set __getitem__(key) output type to torch for ['input_ids'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n```","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":539,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n 1.  Here's the log from the first step.\r\n```\r\nDownloading and preparing dataset text\/default-e84dd29acc4ad9ef (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDataset text downloaded and prepared to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d. Subsequent calls will reuse this data.\r\n```\r\nThere's a file named `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow`, so it did create a cached file.\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:59 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** *****  99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 26 15:58 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 15:58 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 26 15:58 text-train.arrow\r\n```\r\n2. Ideally, `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow` should be used in `run_language_modeling.py` (modified version using `nlp`) with `xla_spawn.py`. But it looks like it's creating a new cached file.\r\n\r\n```\r\n08\/26\/2020 16:13:03 - INFO - filelock -   Lock 139635836351096 released on \/home\/*****\/.cache\/huggingface\/datasets\/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.202fa4f84f552bff1f5400ae012663839c61efb3de068c6c8722d34ac0ea6192\r\n.py.lock\r\n08\/26\/2020 16:13:03 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Overwrite dataset info from restored data version.\r\n08\/26\/2020 16:13:03 - INFO - nlp.info -   Loading Dataset info from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Reusing dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Constructing Dataset for split train, from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:13:03 - INFO - nlp.utils.info_utils -   All the checksums matched successfully for post processing resources\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Overwrite dataset info from restored data version.\r\n08\/26\/2020 16:13:03 - INFO - nlp.info -   Loading Dataset info from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Reusing dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/26\/2020 16:13:03 - INFO - nlp.builder -   Constructing Dataset for split train, from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:13:03 - INFO - nlp.utils.info_utils -   All the checksums matched successfully for post processing resources\r\n08\/26\/2020 16:13:05 - INFO - nlp.arrow_dataset -   Caching processed dataset at \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\/cache-0d77dfce704493dbe\r\n63f071eed6a5431.arrow\r\n^M  0%|          | 0\/100 [00:00<?, ?it\/s]08\/26\/2020 16:13:05 - INFO - nlp.arrow_dataset -   Caching processed dataset at \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6\r\nfe661fe4d070d380d\/cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n```\r\n\r\nThere are two cached files in the directory:\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 16:14 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** *****  99M Aug 26 16:14 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** *****  99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 26 16:13 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 26 16:13 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 26 15:58 text-train.arrow\r\n```\r\n\r\nIf I kill the process, and run it again, it will use the second cached file.\r\n\r\n```\r\n08\/26\/2020 16:19:52 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/26\/2020 16:19:52 - INFO - nlp.builder -   Overwrite dataset info from restored data version.\r\n08\/26\/2020 16:19:52 - INFO - nlp.info -   Loading Dataset info from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:19:52 - INFO - nlp.builder -   Reusing dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/26\/2020 16:19:52 - INFO - nlp.builder -   Constructing Dataset for split train, from \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08\/26\/2020 16:19:52 - INFO - nlp.utils.info_utils -   All the checksums matched successfully for post processing resources\r\n08\/26\/2020 16:19:53 - INFO - nlp.arrow_dataset -   Loading cached processed dataset at \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\/cache-0d77dfce70\r\n4493dbe63f071eed6a5431.arrow\r\n08\/26\/2020 16:19:53 - INFO - nlp.arrow_dataset -   Set __getitem__(key) output type to torch for ['input_ids'] columns  (when key is int or slice) and don't output other (un-formatted) columns.\r\n```","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"Thanks for all the details.\r\nThe two cached files are supposed to be the same. I suspect that the caching has a problem with the tokenizer.\r\nWhich tokenizer did you use ?","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":32,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n Thanks for all the details.\r\nThe two cached files are supposed to be the same. I suspect that the caching has a problem with the tokenizer.\r\nWhich tokenizer did you use ?","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-0.0594139695,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"I trained a byte-level BPE tokenizer on my data with `tokenziers` library following this [example](https:\/\/github.com\/huggingface\/tokenizers\/blob\/master\/bindings\/python\/examples\/train_bytelevel_bpe.py).\r\n\r\nAnd I put these model files in a directory named `\"model_name\"`. I also put config.json, which is the original RoBERTa config file.\r\n\r\n```bash\r\n%ls  model_name\r\nconfig.json     merges.txt      vocab.json\r\n```\r\n\r\n[This](https:\/\/github.com\/huggingface\/transformers\/blob\/4bd7be9a4268221d2a0000c7e8033aaeb365c03b\/examples\/language-modeling\/run_language_modeling.py#L196) is the line where `run_language_modeling.py` loads the tokenier.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\n\r\nI use `\"model_name\"` for `model_args.tokenizer_name`. I don't specify `model_args.cache_dir`. It is 'None' by default.","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":73,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n I trained a byte-level BPE tokenizer on my data with `tokenziers` library following this [example](https:\/\/github.com\/huggingface\/tokenizers\/blob\/master\/bindings\/python\/examples\/train_bytelevel_bpe.py).\r\n\r\nAnd I put these model files in a directory named `\"model_name\"`. I also put config.json, which is the original RoBERTa config file.\r\n\r\n```bash\r\n%ls  model_name\r\nconfig.json     merges.txt      vocab.json\r\n```\r\n\r\n[This](https:\/\/github.com\/huggingface\/transformers\/blob\/4bd7be9a4268221d2a0000c7e8033aaeb365c03b\/examples\/language-modeling\/run_language_modeling.py#L196) is the line where `run_language_modeling.py` loads the tokenier.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\n\r\nI use `\"model_name\"` for `model_args.tokenizer_name`. I don't specify `model_args.cache_dir`. It is 'None' by default.","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-0.0594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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"In my separated script for caching, I'm using `use_fast=True` when initializing a tokenizer.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(args.config_name, use_fast=True)\r\n```\r\nI wasn't using that option in the main script. That could be the reason...","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":33,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n In my separated script for caching, I'm using `use_fast=True` when initializing a tokenizer.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(args.config_name, use_fast=True)\r\n```\r\nI wasn't using that option in the main script. That could be the reason...","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-0.059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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"Yea it could definitely explain why you have two different cache files.\r\nLet me know if using the same tokenizers on both sides fixes the issue","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":26,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n Yea it could definitely explain why you have two different cache files.\r\nLet me know if using the same tokenizers on both sides fixes the issue","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-0.0594139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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"It still creates a new file even if I remove `use_fast=True`... \r\n\r\nHere's the script used to create a cached file.\r\n```python \r\n#!\/usr\/bin\/env python3\r\n\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\n\r\nfrom nlp import load_dataset\r\n\r\n\r\ndef main():\r\n    parser = argparse.ArgumentParser(description='description')\r\n    parser.add_argument('--config_name', type=str, help='Pretrained config name or path if not the same as model_name')\r\n    parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n    parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n    args = parser.parse_args()\r\n\r\n    tokenizer = AutoTokenizer.from_pretrained(args.config_name)\r\n\r\n    dataset = load_dataset(\"text\", data_files=args.data_file, split=\"train\")\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                truncation=True, max_length=args.block_size), batched=True)\r\n    dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\nHere's how the data is loaded in the modified `run_language_modeling.py`. [[original function](https:\/\/github.com\/huggingface\/transformers\/blob\/971d1802d009d9996b36a34a34477cee849ef39f\/examples\/language-modeling\/run_language_modeling.py#L128-L135)]\r\n\r\n```python\r\ndef get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):\r\n    file_path = args.eval_data_file if evaluate else args.train_data_file\r\n    split = \"validation\" if evaluate else \"train\"\r\n    if args.line_by_line:\r\n        # return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n        dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                              truncation=True, max_length=args.block_size), batched=True)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        return dataset\r\n\r\n    else:\r\n        return TextDataset(\r\n            tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache\r\n        )\r\n```\r\n\r\nProbably I don't need this part in the main script,\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                              truncation=True, max_length=args.block_size), batched=True)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nand simply do this?\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\nreturn dataset\r\n```","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":207,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n It still creates a new file even if I remove `use_fast=True`... \r\n\r\nHere's the script used to create a cached file.\r\n```python \r\n#!\/usr\/bin\/env python3\r\n\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\n\r\nfrom nlp import load_dataset\r\n\r\n\r\ndef main():\r\n    parser = argparse.ArgumentParser(description='description')\r\n    parser.add_argument('--config_name', type=str, help='Pretrained config name or path if not the same as model_name')\r\n    parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n    parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n    args = parser.parse_args()\r\n\r\n    tokenizer = AutoTokenizer.from_pretrained(args.config_name)\r\n\r\n    dataset = load_dataset(\"text\", data_files=args.data_file, split=\"train\")\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                truncation=True, max_length=args.block_size), batched=True)\r\n    dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\nHere's how the data is loaded in the modified `run_language_modeling.py`. [[original function](https:\/\/github.com\/huggingface\/transformers\/blob\/971d1802d009d9996b36a34a34477cee849ef39f\/examples\/language-modeling\/run_language_modeling.py#L128-L135)]\r\n\r\n```python\r\ndef get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):\r\n    file_path = args.eval_data_file if evaluate else args.train_data_file\r\n    split = \"validation\" if evaluate else \"train\"\r\n    if args.line_by_line:\r\n        # return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n        dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n        dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                              truncation=True, max_length=args.block_size), batched=True)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n        return dataset\r\n\r\n    else:\r\n        return TextDataset(\r\n            tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache\r\n        )\r\n```\r\n\r\nProbably I don't need this part in the main script,\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                              truncation=True, max_length=args.block_size), batched=True)\r\n        dataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nand simply do this?\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\nreturn dataset\r\n```","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"You need this part in the main script or it will use the dataset that is not tokenized\r\n\r\n","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":18,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n You need this part in the main script or it will use the dataset that is not tokenized\r\n\r\n","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.286698192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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"I can see that the tokenizer in `run_language_modeling.py` is not instantiated the same way as in your separated script.\r\nIndeed we can see L196:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\nCould you try to make it so they are instantiated the exact same way please ?","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":46,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n I can see that the tokenizer in `run_language_modeling.py` is not instantiated the same way as in your separated script.\r\nIndeed we can see L196:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\nCould you try to make it so they are instantiated the exact same way please ?","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-0.0594139695,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"I updated my separated script, but it's creating a cached file again. If I don't use the `model_args.cache_dir`, both will get `None`, so they should be the same.\r\n\r\n```python\r\n#!\/usr\/bin\/env python3\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n    parser = argparse.ArgumentParser(description='description')\r\n    parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n    parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n    parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n    parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n\r\n    model_args = parser.parse_args()\r\n\r\n    tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n\r\n    dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                truncation=True, max_length=model_args.block_size), batched=True)\r\n    dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\nIs there a way to specify the cache file to load, and skip the re-computation?","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":143,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n I updated my separated script, but it's creating a cached file again. If I don't use the `model_args.cache_dir`, both will get `None`, so they should be the same.\r\n\r\n```python\r\n#!\/usr\/bin\/env python3\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n    parser = argparse.ArgumentParser(description='description')\r\n    parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n    parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n    parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n    parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n\r\n    model_args = parser.parse_args()\r\n\r\n    tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n\r\n    dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                truncation=True, max_length=model_args.block_size), batched=True)\r\n    dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\nIs there a way to specify the cache file to load, and skip the re-computation?","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"Could you also check that the `args.block_size` used in the lambda function is the same as well ?","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":18,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n Could you also check that the `args.block_size` used in the lambda function is the same as well ?","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0.2866981924,-0.0594139695,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"Here's a minimal working example to reproduce this issue.\r\n\r\nAssumption:\r\n- You have access to TPU.\r\n- You have installed `transformers` and `nlp`.\r\n- You have tokenizer files (`config.json`, `merges.txt`, `vocab.json`) under the directory named `model_name`.\r\n- You have `xla_spawn.py` (Download from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py).\r\n- You have saved the following script as `prepare_cached_dataset.py`.\r\n\r\n```python\r\n#!\/usr\/bin\/env python3\r\nimport argparse\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n    parser = argparse.ArgumentParser(description='description')\r\n    parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n    parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n    parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n    parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n    parser.add_argument('--tpu_num_cores', type=int, default=1, help='Number of TPU cores to use (1 or 8). For xla_apwan.py')\r\n    model_args = parser.parse_args()\r\n    \r\n    tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=True)\r\n    \r\n    dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                truncation=True, max_length=model_args.block_size), batched=True)\r\n    dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\ndef _mp_fn(index):\r\n    # For xla_spawn (TPUs)\r\n    main()\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n- Run the following command. Replace `your_training_data` with some text file.\r\n\r\n```bash\r\nexport TRAIN_DATA=your_training_data\r\n\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** *****  99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 28 13:08 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 28 13:08 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 28 13:08 text-train.arrow\r\n```\r\n\r\n- Run the same script again. (The output should be just `Using custom data configuration default`.)\r\n```\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** *****  99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 28 13:20 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 28 13:20 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 28 13:08 text-train.arrow\r\n```\r\n- The cached file (`cache-bfc7cb0702426d19242db5e8c079f04b.arrow`) is reused.\r\n- Now, run this script with `xla_spawn.py`. Ideally, it should reuse the cached file, however, you will see each process is creating a cache file again.\r\n\r\n```bash\r\npython xla_spawn.py --num_cores 8 \\\r\nprepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n\r\n- Check the cached directory. There are two arrrow files.\r\n```bash\r\nls -lha \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 230M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:25 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** *****  99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw------- 1 ***** *****  99M Aug 28 13:25 cache-e0e2313e49c8a110aafcc8133154c19a.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 28 13:24 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 28 13:24 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 28 13:08 text-train.arrow\r\n```\r\n","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":482,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n Here's a minimal working example to reproduce this issue.\r\n\r\nAssumption:\r\n- You have access to TPU.\r\n- You have installed `transformers` and `nlp`.\r\n- You have tokenizer files (`config.json`, `merges.txt`, `vocab.json`) under the directory named `model_name`.\r\n- You have `xla_spawn.py` (Download from https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py).\r\n- You have saved the following script as `prepare_cached_dataset.py`.\r\n\r\n```python\r\n#!\/usr\/bin\/env python3\r\nimport argparse\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n    parser = argparse.ArgumentParser(description='description')\r\n    parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n    parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n    parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n    parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n    parser.add_argument('--tpu_num_cores', type=int, default=1, help='Number of TPU cores to use (1 or 8). For xla_apwan.py')\r\n    model_args = parser.parse_args()\r\n    \r\n    tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=True)\r\n    \r\n    dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n    dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                truncation=True, max_length=model_args.block_size), batched=True)\r\n    dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\ndef _mp_fn(index):\r\n    # For xla_spawn (TPUs)\r\n    main()\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\n- Run the following command. Replace `your_training_data` with some text file.\r\n\r\n```bash\r\nexport TRAIN_DATA=your_training_data\r\n\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** *****  99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 28 13:08 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 28 13:08 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 28 13:08 text-train.arrow\r\n```\r\n\r\n- Run the same script again. (The output should be just `Using custom data configuration default`.)\r\n```\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** *****  99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 28 13:20 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 28 13:20 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 28 13:08 text-train.arrow\r\n```\r\n- The cached file (`cache-bfc7cb0702426d19242db5e8c079f04b.arrow`) is reused.\r\n- Now, run this script with `xla_spawn.py`. Ideally, it should reuse the cached file, however, you will see each process is creating a cache file again.\r\n\r\n```bash\r\npython xla_spawn.py --num_cores 8 \\\r\nprepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n\r\n- Check the cached directory. There are two arrrow files.\r\n```bash\r\nls -lha \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-e84dd29acc4ad9ef\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 230M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:25 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** *****  99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw------- 1 ***** *****  99M Aug 28 13:25 cache-e0e2313e49c8a110aafcc8133154c19a.arrow\r\n-rw-r--r-- 1 ***** *****  670 Aug 28 13:24 dataset_info.json\r\n-rw-r--r-- 1 ***** *****    0 Aug 28 13:24 LICENSE\r\n-rw-r--r-- 1 ***** *****  33M Aug 28 13:08 text-train.arrow\r\n```\r\n","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807,-0.034518227,0.3092693985,-0.253087014,0.0555535518,0.0235939417,0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-0.5772278309,-0.360363245,0.3100019097,-0.559237659,-0.0874062255,0.1202496514,-0.0655824319,0.0252231155,0.1328889132,-0.642411828,0.0706516281,0.1075027138,0.1937766373,-0.3184123635,0.0537423715,0.0611682348,-0.2190911621,0.1069517285,0.3757636845,0.0110927885,-0.4208606184,0.1000858918,-0.1757013351]}
{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/532","title":"File exists error when used with TPU","comments":"I ended up specifying the `cache_file_name` argument when I call `map` function.\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size),\r\n                      batched=True,\r\n                      cache_file_name=cache_file_name)\r\n```\r\n\r\nNote:\r\n- `text` dataset in `nlp` does not strip `\"\\n\"`.  If you want the same output as in [`LineByLineTextDataset`](https:\/\/github.com\/huggingface\/transformers\/blob\/afc4ece462ad83a090af620ff4da099a0272e171\/src\/transformers\/data\/datasets\/language_modeling.py#L88-L111), you would need to create your own dataset class where you replace `line` to `line.strip()` [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/text\/text.py#L35).\r\n","body":"Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n","comment_length":59,"text":"File exists error when used with TPU \n Hi,\r\n\r\nI'm getting a \"File exists\" error when I use [text dataset](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text) for pre-training a RoBERTa model using `transformers` (3.0.2) and `nlp`(0.4.0) on a VM with TPU (v3-8).\r\n\r\nI modified [line 131 in the original `run_language_modeling.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/language-modeling\/run_language_modeling.py#L131) as follows:\r\n\r\n```python\r\n# line 131: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n                                        truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\nreturn dataset\r\n```\r\n\r\nWhen I run this with [`xla_spawn.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/master\/examples\/xla_spawn.py), I get the following error (it produces one message per core in TPU, which I believe is fine).\r\n\r\nIt seems the current version doesn't take into account distributed training processes as in [this example](https:\/\/github.com\/huggingface\/transformers\/blob\/a573777901e662ec2e565be312ffaeedef6effec\/src\/transformers\/data\/datasets\/language_modeling.py#L35-L38)?\r\n\r\n```\r\n08\/25\/2020 13:59:41 - WARNING - nlp.builder -   Using custom data configuration default\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08\/25\/2020 13:59:43 - INFO - nlp.builder -   Generating dataset text (\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:6: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:4: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:1: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:7: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:3: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nDownloading and preparing dataset text\/default-b0932b2bdbb63283 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nException in device=TPU:2: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nException in device=TPU:0: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n      main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\n      File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nTraceback (most recent call last):\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\nTraceback (most recent call last):\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/torch_xla\/distributed\/xla_multiprocessing.py\", line 231, in _start_fn\r\n    fn(gindex, *args)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 300, in _mp_fn\r\n    main()\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 240, in main\r\n    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None\r\n  File \"\/home\/*****\/huggingface_roberta\/run_language_modeling.py\", line 134, in get_dataset\r\n    dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 546, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 450, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/contextlib.py\", line 81, in __enter__\r\n    return next(self.gen)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/site-packages\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/anaconda3\/envs\/torch-xla-1.6\/lib\/python3.6\/os.py\", line 220, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/*****\/.cache\/huggingface\/datasets\/text\/default-b0932b2bdbb63283\/0.0.0\/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d.incomplete'\r\n```\r\n\r\n \n I ended up specifying the `cache_file_name` argument when I call `map` function.\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size),\r\n                      batched=True,\r\n                      cache_file_name=cache_file_name)\r\n```\r\n\r\nNote:\r\n- `text` dataset in `nlp` does not strip `\"\\n\"`.  If you want the same output as in [`LineByLineTextDataset`](https:\/\/github.com\/huggingface\/transformers\/blob\/afc4ece462ad83a090af620ff4da099a0272e171\/src\/transformers\/data\/datasets\/language_modeling.py#L88-L111), you would need to create your own dataset class where you replace `line` to `line.strip()` [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/text\/text.py#L35).\r\n","embeddings":[-0.0798375383,-0.1442335248,0.1469174325,0.1047480032,0.2392238975,-0.183026433,0.5247991085,0.2587524354,-0.2769027948,0.1412049085,0.1099315658,-0.2591763437,-0.0396562777,-0.2304338664,0.2745446861,-0.1979209632,0.1962260306,0.3209769726,-0.1909585297,-0.0990928262,-0.0947270691,0.2928031087,-0.0418691412,0.1614839286,-0.6791219711,0.1413165182,-0.1830788553,0.2864961624,0.1081077009,-0.3061461747,0.1703537852,0.0626990348,0.2858562171,0.5413048863,-0.0001269419,0.3936147988,0.1948049366,-0.2527728081,-0.0595969483,-0.1641455293,0.3079850674,-0.0324931145,0.0716107562,-0.1129939109,-0.1293354481,0.1014449,0.3417716622,0.0041012326,0.4713276625,0.2538045943,-0.0111972932,0.7012689114,0.0628258288,-0.1866247505,-0.0218900703,0.2796743512,-0.1007430106,0.1228495017,0.3792977631,-0.2468184531,0.0520924367,0.0928169936,0.220889613,-0.209236607,-0.0801462457,0.2455779612,0.4375521541,-0.0800734386,0.0164037794,0.2247600257,0.2114127874,-0.2419933081,-0.4570935369,-0.4064103663,-0.0087269936,-0.3136442304,0.5325062275,0.1598000973,-0.2045152038,0.27584216,-0.0676135495,-0.0035868529,-0.0381930172,-0.0560378879,0.1150068715,0.2927227318,0.1143157631,-0.0198008679,-0.0402777568,0.0766948983,-0.2098604888,-0.089777112,0.1380930543,0.3785318732,-0.2391121984,-0.253087908,-0.1679145396,-0.5764261484,-0.1399088353,0.0170649737,-0.0117709413,-0.3582884967,-0.27250126,0.0793670937,0.5551717281,0.175221771,-0.1873039603,0.0450787358,0.0471253842,0.1027877629,-0.131499216,-0.0840475485,-0.3446531892,-0.2317399234,-0.2921254933,-0.0994022787,0.0419315882,0.0865661278,-0.0301301107,-0.1701417565,-0.3134407699,0.0887426212,0.1542658508,0.0939973891,0.1844564378,0.3348243237,0.4495030046,0.0126924552,-0.5675520301,-0.326512903,-0.1946421266,-0.1032909453,-0.3978535235,-0.0439434163,0.2442614734,-0.1168974414,0.2883491516,-0.260386914,0.2765443623,-0.0780453756,-0.1356095821,-0.0873277634,0.0491121672,0.3619853258,-0.0605050921,-0.103941977,0.0495957844,-0.3493099809,-0.1817699224,0.3404088914,-0.1699376851,-0.3592523634,0.0314719565,0.0184519328,-0.1031052247,0.0612264127,0.1891774684,0.094912298,0.5039544702,-0.5416415334,-0.0628860444,0.0121024968,-0.3074621856,0.1219959557,0.1785236001,0.5345861316,-0.008132427,-0.16372329,-0.1448525041,-0.3543258905,0.0238805059,0.4770064354,-0.1161619872,0.4217163324,-0.2756722569,0.1975223124,0.2805541754,-0.3485553563,-0.0487581454,0.2919351757,-0.2538606524,-0.1722111553,0.1669990718,-0.279571861,-0.2911915183,-0.3079813421,0.0637209266,0.051063668,-0.000889027,0.0401973054,-0.2602468133,-0.1193800643,-0.4364037216,0.1627896279,0.0766466632,0.1036475599,-0.092042774,0.6391347051,0.2819698453,0.0595624037,0.2897178531,-0.0073050591,0.1470348239,0.1178078055,-0.0899871364,0.1348540038,0.0085599395,0.1045292765,-0.031775713,0.3992015123,-0.0099070687,-0.0652452186,0.2647108138,0.1742208302,0.0090645598,-0.3494711816,-0.0604715943,0.0241230484,-0.1790014207,0.1490033269,-0.3081002831,0.0328932963,-0.1788248569,0.0819587409,-0.2277283072,0.2888807952,-0.1237493753,-0.2570460141,-0.1084304973,0.3896348476,-0.4102819562,0.0260165893,-0.0665694252,0.3695499599,-0.1494660527,0.2167463154,0.0701968446,0.2750688195,0.4112743139,0.1022071391,-0.1609389633,0.2704153061,0.0733857602,-0.0277392995,-0.1982143223,0.2000836134,0.0843485445,-0.0878728479,0.4576553702,-0.1464757472,0.1857974678,-0.0860165507,-0.0115564875,-0.1726840585,0.158526808,0.3137045801,0.0797688439,0.2871515453,-0.2362147272,-0.3226622641,0.48514238,-0.1081836447,0.0846824422,0.1235769615,0.0567587055,0.0045667798,-0.0996449441,0.1038167328,0.7618492246,0.0303733144,0.0746840164,0.2042744458,0.002426604,-0.4016212523,0.3145742118,0.0199703872,0.1478983015,0.3603564501,0.0456466228,0.1358236223,-0.2201711088,-0.2086426169,0.0125130415,0.1076703444,-0.3380920291,-0.0305709019,-0.1294817477,-0.2260117084,-0.613512218,0.3738873303,-0.2147596031,0.0300704781,0.1859934032,-0.1521373242,-0.3798165321,0.2276350409,0.2427494526,0.4120456576,-0.0400349014,-0.1004720181,0.2312813997,-0.2532883286,-0.413808614,-0.0716314539,0.1834110618,-0.1839823872,-0.1943470687,-0.1855919212,0.051695589,0.1973858327,-0.283944726,0.2684098184,0.1496329755,0.0128650814,0.0611016154,-0.0220944546,-0.0020250932,-0.338844955,0.3798138499,-0.3485740423,-0.3173532784,-0.0116605479,0.236385107,-0.1102096513,-0.3155271411,-0.3423826993,-0.0144651989,-0.2746013105,0.1298458278,-0.0374870226,0.2161976397,0.4914858937,0.3568271995,0.1663624346,-0.1525254846,-0.1984069198,0.153575018,-0.2659078538,0.0295194909,-0.2206698954,-0.2314794362,0.0058998899,-0.2214410901,0.3120176196,0.0577066243,-0.2935745716,-0.2030821145,0.1279263198,-0.1166286319,0.1098140776,0.329225421,0.5302072763,0.0346470438,-0.0924844742,-0.0594452322,-0.0121635487,0.4074875116,-0.0231232122,0.3068994284,0.0181849729,0.2183344066,0.1459243,0.702552259,0.2315587401,-0.0831463337,0.119037807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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/525","title":"wmt download speed example","comments":"Thanks for creating the issue :)\r\nThe download link for wmt-en-de raw looks like a mirror. We should use that instead of the current url.\r\nIs this mirror official ?\r\n\r\nAlso it looks like for `ro-en` it tried to download other languages. If we manage to only download the one that is asked it'd be cool\r\n\r\nAlso cc @patrickvonplaten ","body":"Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n","comment_length":59,"text":"wmt download speed example \n Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n \n Thanks for creating the issue :)\r\nThe download link for wmt-en-de raw looks like a mirror. We should use that instead of the current url.\r\nIs this mirror official ?\r\n\r\nAlso it looks like for `ro-en` it tried to download other languages. If we manage to only download the one that is asked it'd be cool\r\n\r\nAlso cc @patrickvonplaten 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/525","title":"wmt download speed example","comments":"Shall we host the files ourselves or it is fine to use this mirror in your opinion ?","body":"Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n","comment_length":18,"text":"wmt download speed example \n Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n \n Shall we host the files ourselves or it is fine to use this mirror in your opinion ?","embeddings":[-0.0970186219,-0.2302699536,0.0760004371,0.2327990234,-0.1930162907,-0.1516977102,0.4099264741,0.0660621449,0.2584859431,0.0476587974,-0.0551418997,0.2684978545,-0.1681045443,0.0384293236,0.2093773037,0.0515972152,0.0763443038,0.0341624543,-0.6077142358,-0.2628243268,0.1051906794,0.1649488509,-0.0395846143,-0.0544994175,-0.096891135,0.2049501091,-0.065912649,0.019605305,-0.3745268583,-0.1257733554,0.1418088824,0.2571884394,0.0026763303,0.3433766365,-0.0001119774,0.0217483398,0.3440234065,-0.0119890571,-0.3946137428,-0.1340293139,-0.5043621063,-0.5133453012,-0.0980625674,-0.0678515211,0.0444890261,-0.1336168796,-0.0031023214,0.0976151079,0.2608041465,-0.1199490204,0.1531336159,0.3764331639,-0.553996861,-0.0628617033,0.5288295746,0.0120592564,-0.1928016245,0.7287992835,0.4486648738,0.2702367902,0.0053199776,0.3857813776,-0.2627511621,0.2417167574,0.2182659209,0.143761754,0.3119071722,0.0835657045,-0.2040938735,0.4565157294,0.3446314335,0.1195577309,-0.5175538063,-0.3979912102,-0.0086078746,-0.0808486566,0.1689206958,0.3056764007,-0.1893408448,0.301427573,-0.6494131684,-0.2435513884,0.0007555283,0.2693094611,-0.0311490763,0.5902032256,-0.0753811598,0.1342080235,-0.1156806871,0.0863806382,0.1333897263,-0.3744549751,0.0500809476,-0.1554635614,0.1346354783,-0.2157566249,0.0398157686,-0.2851393819,-0.1335162371,0.2030915916,0.0032352936,0.0864800587,0.126071319,0.0952772573,0.1827128828,0.3722531199,-0.3179148734,-0.1064494848,0.1842977256,0.0189920552,0.2428282052,0.1555784047,-0.1604377776,-0.0409256518,-0.5879229307,-0.086891681,-0.1165796444,-0.1082954705,-0.1772287935,-0.1111123934,0.1398072243,-0.1824859977,-0.0964270607,0.1045272201,-0.1154746786,0.3642384112,-0.0578075163,0.0434145629,-0.1267211884,-0.1380361319,-0.0077105891,-0.0665884987,-0.093504481,0.0864177197,0.3612859547,0.1556963474,0.3075782657,0.0156511683,0.0400045998,0.1399882734,0.1401408166,0.1032531708,-0.0399017371,0.1006247252,-0.1518379748,0.3922111094,-0.2451858968,0.3385667503,-0.3105312288,0.0323683619,-0.339302808,-0.3392120898,0.0903845355,0.1400927901,-0.2683833838,-0.1725406349,-0.1806355566,0.1373317391,-0.391908586,0.0645885915,-0.0769555941,-0.0070266626,-0.2558456659,-0.0696706101,-0.032681942,0.3766858876,-0.1034617871,0.0147951078,-0.0211647227,0.2590466142,0.4362417161,0.5246804953,-0.2769944072,0.1391410083,-0.1622255743,0.0690056831,0.0558938012,-0.2430541068,-0.2646181583,0.6330524683,-0.5914660692,-0.0062272605,0.3196915388,0.037497934,0.2989468873,-0.2110330909,0.2449943423,0.5267232656,0.1126668304,0.0809040293,-0.4941202104,-0.4558441937,0.2412892133,0.1632135361,-0.2086906284,-0.1027116254,-0.0507310182,0.2361124754,0.2248370647,-0.1360492557,-0.0092619238,-0.1300798059,-0.0910939276,-0.3411992788,-0.1972526312,0.2972263098,-0.184872061,0.2816551626,0.0172871873,0.250207305,0.3418705165,0.0803093389,-0.0942329466,-0.2476306707,0.0873019844,-0.0315813683,0.0378059782,0.1351808608,-0.0566881597,-0.0686647668,0.0052511431,0.1243499741,-0.0264909919,0.1590866745,0.3992736638,-0.0466005318,0.0200522244,0.0172211025,0.2545689344,-0.0975966305,0.037316788,-0.1404786259,-0.0191654563,0.1281843334,-0.2272661924,0.8664548993,0.3883675933,-0.0091440883,0.4069119096,-0.3548854291,0.5632680655,0.4918876886,0.0938520357,-0.0801300406,-0.3097838461,0.530461669,-0.0195397921,0.1419156045,0.2477449626,-0.0110155512,-0.1525164396,0.0978008136,-0.2506643236,-0.0653886348,0.4053527117,0.1671307236,0.0045260834,0.2160785645,0.077575177,0.1888605058,0.481002897,-0.1825459152,-0.0097448779,0.0617801361,-0.1871864051,-0.3809093535,0.3489971459,0.3133142889,0.4159398377,0.2065536082,0.4286356866,0.1747942865,0.0932023525,-0.2560546994,0.4070366025,0.0821523443,-0.3458167911,0.1380351633,0.0604515634,0.0187940393,-0.2305562645,0.178094849,0.0391163118,-0.1586551517,-0.1597523093,0.0542651601,-0.2796344161,-0.137821123,-0.386287123,-0.1363766193,-0.2332428545,-0.2318859845,0.0297000054,-0.0364917666,-0.2091930062,-0.1093644574,0.3059449196,0.1775646955,-0.511921525,0.0521571189,-0.1740999818,-0.0387714207,-0.3981323242,-0.0045327442,0.2409882098,0.0839544907,0.4800804853,-0.2831428647,-0.0959866494,-0.1280893981,-0.2729632258,0.1172757372,0.0654704869,0.1425894499,0.0450697206,0.0209899656,-0.148674652,0.0222847611,-0.058536347,-0.2918953896,0.1592782736,-0.0876687467,-0.2113157958,-0.050128635,-0.2477100044,-0.5144851804,-0.2921965718,-0.1058580354,0.163472414,-0.197786957,0.2133414596,0.1712711751,-0.1417769492,0.1947842985,-0.1191123575,0.1587194353,-0.0597623587,-0.5474858284,0.2177067995,-0.0215540417,-0.2499404699,0.1782260835,0.2267056108,-0.0758668929,0.133033365,-0.4908933043,0.2066096216,0.0889606774,-0.0867545307,0.5087793469,0.2504207492,0.1344650239,-0.0427322648,-0.012821747,-0.0286974013,-0.2431729883,-0.0593144186,0.4615968168,0.0892490149,-0.0717798695,-0.0344243273,0.2145276666,0.7158355117,0.1123102084,0.2709057629,0.0734363049,0.1690087914,-0.0442204662,0.1032898054,-0.0450689606,0.3228220046,-0.0915730968,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/525","title":"wmt download speed example","comments":"Should we add an argument in `load_dataset` to override some URL with a custom URL (e.g. mirror) or a local path?\r\n\r\nThis could also be used to provide local files instead of the original files as requested by some users (e.g. when you made a dataset with the same format than SQuAD and what to use it instead of the official dataset files).","body":"Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n","comment_length":63,"text":"wmt download speed example \n Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n \n Should we add an argument in `load_dataset` to override some URL with a custom URL (e.g. mirror) or a local path?\r\n\r\nThis could also be used to provide local files instead of the original files as requested by some users (e.g. when you made a dataset with the same format than SQuAD and what to use it instead of the official dataset 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/525","title":"wmt download speed example","comments":"@lhoestq I think we should host it ourselves. I'll put the subset of wmt (without preprocessed files) that we need on s3 and post a link over the weekend.","body":"Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n","comment_length":29,"text":"wmt download speed example \n Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n \n @lhoestq I think we should host it ourselves. I'll put the subset of wmt (without preprocessed files) that we need on s3 and post a link over the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/525","title":"wmt download speed example","comments":"Is there a solution yet? The download speed is still too slow. 60-70kbps download for wmt16 and around 100kbps for wmt19. @sshleifer ","body":"Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n","comment_length":22,"text":"wmt download speed example \n Continuing from the slack 1.0 roadmap thread w @lhoestq , I realized the slow downloads is only a thing sometimes. Here are a few examples, I suspect there are multiple issues. All commands were run from the same gcp us-central-1f machine.\r\n\r\n```\r\nimport nlp\r\nnlp.load_dataset('wmt16', 'de-en')\r\n```\r\nDownloads at 49.1 KB\/S\r\n\r\nWhereas \r\n```\r\npip install gdown # download from google drive\r\n!gdown https:\/\/drive.google.com\/uc?id=1iO7um-HWoNoRKDtw27YUSgyeubn9uXqj\r\n```\r\nDownloads at 127 MB\/s. (The file is a copy of wmt-en-de raw).\r\n\r\n\r\n```\r\nnlp.load_dataset('wmt16', 'ro-en')\r\n```\r\ngoes at 27 MB\/s, much faster. \r\n\r\nif we wget the same data from s3 is the same download speed, but \u00bc the file size:\r\n```\r\nwget https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/translation\/wmt_en_ro_packed_200_rand.tgz\r\n```\r\n\r\nFinally,\r\n```\r\nnlp.load_dataset('wmt19', 'zh-en')\r\n```\r\nStarts fast, but broken. (duplicate of #493 )\r\n\r\n\r\n \n Is there a solution yet? The download speed is still too slow. 60-70kbps download for wmt16 and around 100kbps for wmt19. @sshleifer 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/519","title":"[BUG] Metrics throwing new error on master since 0.4.0","comments":"Update - maybe this is only failing on bleu because I was not tokenizing inputs to the metric","body":"The following error occurs when passing in references of type `List[List[str]]` to metrics like bleu.\r\nWasn't happening on 0.4.0 but happening now on master.\r\n\r\n```\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 226, in compute\r\n    self.add_batch(predictions=predictions, references=references)\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 242, in add_batch\r\n    batch = self.info.features.encode_batch(batch)\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 527, in encode_batch\r\n    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 527, in <listcomp>\r\n    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 456, in encode_nested_example\r\n    raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n```","comment_length":18,"text":"[BUG] Metrics throwing new error on master since 0.4.0 \n The following error occurs when passing in references of type `List[List[str]]` to metrics like bleu.\r\nWasn't happening on 0.4.0 but happening now on master.\r\n\r\n```\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 226, in compute\r\n    self.add_batch(predictions=predictions, references=references)\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 242, in add_batch\r\n    batch = self.info.features.encode_batch(batch)\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 527, in encode_batch\r\n    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 527, in <listcomp>\r\n    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 456, in encode_nested_example\r\n    raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n``` \n Update - maybe this is only failing on bleu because I was not tokenizing inputs to the metric","embeddings":[-0.1062732562,-0.007290211,0.0222176891,0.2218198925,0.3199834824,-0.0804266036,0.1759800762,0.3901971579,0.0343043692,0.1756983399,-0.0845413059,0.2548736334,-0.4288854301,0.0719972551,-0.028758537,-0.0816463232,-0.0013923431,0.0024744708,-0.0758355707,-0.0748338327,-0.4520992935,0.1437273473,-0.1970937401,0.0110387532,0.0288925469,0.0917715877,-0.1796934754,0.0378206633,-0.2270138264,-0.6951411366,0.0842948109,-0.1058674529,0.0286651198,0.5098218918,-0.0001164395,0.004818005,0.5615224242,-0.0969978422,-0.2757411599,-0.6296263933,-0.1194718406,-0.3785984516,0.2123195082,-0.0551587865,-0.1251197904,-0.4211822152,-0.1600915343,-0.0245703179,-0.2853537798,0.5097929239,0.1826967448,0.3170953095,-0.1970277429,-0.3702695668,0.1295918822,-0.4435131848,-0.0542089976,0.2791592181,0.0688031539,0.0893617868,-0.3565911353,0.2778955102,-0.16722022,0.097716637,0.4126311839,-0.0599433407,0.7112179995,-0.0169462506,-0.0244267806,0.0683050305,0.26930058,-0.0594066605,-0.326531589,0.1645624489,-0.0124039454,-0.5957030058,0.1381164193,-0.0336661972,-0.1020849347,-0.2063252032,-0.3794083595,-0.1562570184,-0.0071801376,0.2209166288,-0.2712134719,0.3870169818,-0.1360195428,-0.0998500958,0.5003242493,-0.0388078876,0.1800909787,-0.0616373532,-0.3171218038,-0.0552313365,-0.270742178,-0.1092254743,0.094979845,0.2227651924,-0.0316815488,-0.0119900247,0.0349352881,-0.0800457671,0.0522040986,0.1384292692,0.3684639335,0.4071957469,0.2165304273,-0.1421268731,0.2275400907,0.2707014084,0.0833954141,-0.0756214187,0.2968890667,-0.2845106125,-0.0679679215,0.3694419861,0.0975967422,-0.2575008869,-0.3548736274,0.1660371125,-0.2789395154,-0.0550797619,0.4045330286,0.4178312719,-0.3578873277,0.2668999434,0.2166235298,0.3325395584,-0.032659106,-0.0041469946,-0.1672389358,0.0001239339,-0.300743252,-0.1598049402,-0.1282812655,-0.3283575773,0.3711374402,0.1049494967,0.4938421547,-0.1525180042,-0.1196286753,-0.0951244906,0.0839676037,0.1391263902,0.0361173414,-0.1184324995,0.2583685815,-0.4067990482,0.0937797576,0.1375787258,-0.3253022134,-0.1255240887,-0.0379998982,0.1357336044,-0.1714640707,0.1331057101,-0.2497183383,0.1519280821,0.1851925105,0.0763436854,-0.2300545126,-0.2365286946,-0.2112006247,-0.27439785,0.0838536769,-0.067543067,-0.0719906315,-0.0643367991,-0.0047448212,0.2580306828,0.0987973884,0.1628796309,0.0601906888,0.1947447211,-0.1199280471,-0.4076298475,0.2281286269,-0.4222308695,-0.0846987516,0.0330415629,-0.1003820747,-0.1777200848,0.0184921157,-0.0879658461,0.1359378695,-0.2058136314,0.5759731531,-0.2368007451,-0.0427050069,-0.1251954138,-0.3218889534,-0.225340724,0.0200865585,-0.1174828857,0.4351535738,0.3503578007,0.047109846,0.4298745394,0.0101366341,-0.3278263211,-0.013037323,0.2420766652,0.3168124855,-0.288885057,0.2373013645,-0.0589065328,-0.0143643785,-0.0380503275,-0.1371418089,0.2937998474,0.0554157272,0.0134860519,-0.1908234805,0.0431649238,0.0552901067,0.2678282857,0.1259252578,-0.0428628549,-0.2989124358,0.2762337625,0.0067476421,-0.3360529542,-0.2650056481,0.0583856106,0.2053063214,0.0922112316,-0.125864923,-0.2059825063,0.1046516597,0.396045059,0.3106920719,0.0560849868,-0.1756464541,0.117861703,0.2674864233,-0.2520056665,-0.078571476,0.4476377368,0.4362250865,-0.013738309,0.0325252376,0.4991463721,0.0985601917,-0.1266162395,0.2538799942,0.4040437639,0.0748697892,0.176336363,0.0878888667,0.2562079132,0.0964245051,0.0888751,0.0462802276,-0.2134583145,0.3034289181,-0.2395786494,-0.0124711236,-0.3341283202,-0.0273536295,-0.0680245906,0.4389280677,0.1797917038,0.1811548024,0.0823640153,-0.046151381,-0.0485799685,-0.1481972486,-0.0649200678,0.3559918702,0.0830437019,0.1323611587,0.3042525649,0.0315914974,-0.2434706837,0.2299399525,0.1900543123,-0.2009668797,-0.0143520478,0.188591212,-0.1266460866,0.1589463651,0.1349481344,-0.0589856319,0.352466315,-0.386965394,-0.0252722129,-0.3195974529,0.2235095352,-0.0465928316,-0.2895771563,-0.2419312447,-0.383918792,0.0785665885,0.5633406043,-0.172150597,0.2836770415,0.2250238657,0.2812371552,0.0664489865,0.4272817075,0.2186665684,-0.3022622764,-0.1205074936,0.0455397107,0.2007766664,-0.3718587458,0.1317168921,0.0621055104,0.0519474186,-0.3125457168,-0.4706139863,0.0956862569,0.0487275198,0.5173864961,0.30605492,-0.1674262732,-0.1922731698,0.0310937576,0.2899303734,-0.0361479819,-0.0908919647,0.2110298723,-0.2417314053,-0.0862906054,-0.3249903023,-0.0833851174,-0.0758275166,-0.4211907089,-0.0845593214,0.1301460564,0.1021899655,0.1177106649,0.0031333398,0.3794730008,0.3393113315,-0.2537638843,-0.131380111,0.0278131291,-0.1283527166,-0.2453269958,-0.5491186976,-0.0731105581,0.2241434157,0.2258107215,-0.0659760088,-0.3508491516,-0.7318812013,0.3000254333,-0.0032866569,0.1589718759,0.1296351552,-0.0681936294,0.1262823641,-0.1781275868,-0.2250598371,-0.0352242924,0.1025236174,0.0331365652,0.2410041243,0.1788245291,-0.0349804163,0.3686442971,0.7059157491,0.2269937545,-0.2379549146,0.3036684394,0.1353710145,0.4072172642,-0.0264269281,-0.308899045,-0.173540771,0.102548070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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/519","title":"[BUG] Metrics throwing new error on master since 0.4.0","comments":"Closing - seems to be just forgetting to tokenize. And found the helpful discussion in #137 ","body":"The following error occurs when passing in references of type `List[List[str]]` to metrics like bleu.\r\nWasn't happening on 0.4.0 but happening now on master.\r\n\r\n```\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 226, in compute\r\n    self.add_batch(predictions=predictions, references=references)\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 242, in add_batch\r\n    batch = self.info.features.encode_batch(batch)\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 527, in encode_batch\r\n    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 527, in <listcomp>\r\n    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 456, in encode_nested_example\r\n    raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n```","comment_length":16,"text":"[BUG] Metrics throwing new error on master since 0.4.0 \n The following error occurs when passing in references of type `List[List[str]]` to metrics like bleu.\r\nWasn't happening on 0.4.0 but happening now on master.\r\n\r\n```\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 226, in compute\r\n    self.add_batch(predictions=predictions, references=references)\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 242, in add_batch\r\n    batch = self.info.features.encode_batch(batch)\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 527, in encode_batch\r\n    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 527, in <listcomp>\r\n    encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column]\r\n  File \"\/usr\/local\/lib\/python3.7\/site-packages\/nlp\/features.py\", line 456, in encode_nested_example\r\n    raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n``` \n Closing - seems to be just forgetting to tokenize. And found the helpful discussion in #137 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/517","title":"add MLDoc dataset","comments":"This request is still an open issue waiting to be addressed by any community member, @GuillemGSubies.","body":"Hi,\r\n\r\nI am recommending that someone add MLDoc, a multilingual news topic classification dataset.\r\n\r\n- Here's a link to the Github: https:\/\/github.com\/facebookresearch\/MLDoc\r\n- and the paper: http:\/\/www.lrec-conf.org\/proceedings\/lrec2018\/pdf\/658.pdf\r\n\r\nLooks like the dataset contains news stories in multiple languages that can be classified into four hierarchical groups: CCAT (Corporate\/Industrial), ECAT (Economics), GCAT (Government\/Social) and MCAT (Markets). There are 13 languages: Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish","comment_length":16,"text":"add MLDoc dataset \n Hi,\r\n\r\nI am recommending that someone add MLDoc, a multilingual news topic classification dataset.\r\n\r\n- Here's a link to the Github: https:\/\/github.com\/facebookresearch\/MLDoc\r\n- and the paper: http:\/\/www.lrec-conf.org\/proceedings\/lrec2018\/pdf\/658.pdf\r\n\r\nLooks like the dataset contains news stories in multiple languages that can be classified into four hierarchical groups: CCAT (Corporate\/Industrial), ECAT (Economics), GCAT (Government\/Social) and MCAT (Markets). There are 13 languages: Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish \n This request is still an open issue waiting to be addressed by any community member, @GuillemGSubies.","embeddings":[-0.3504601717,0.0476948284,-0.179826811,0.1780563295,-0.0401054062,0.0907025114,0.2102559358,0.0325836353,-0.2079457641,-0.0521601848,-0.0914575011,0.2889948189,-0.3860351443,0.2393020391,0.00817692,-0.275159061,0.0485755689,-0.1197368801,-0.0523765422,-0.1522308141,-0.2527983487,0.2394448966,-0.0691751987,-0.3075847328,-0.1811061352,-0.0353651531,-0.3153686821,0.0952790231,-0.0668420419,-0.2999739647,-0.0394034944,0.0626558885,0.0979469121,0.2279056162,-0.0001061797,-0.2723769248,-0.0213431939,-0.0787593275,-0.0399520546,-0.342012465,-0.192526713,-0.5444474816,-0.1018266603,-0.0767772272,-0.3466023803,-0.049074471,0.0083702337,-0.3613840938,0.0605379716,0.5007581711,0.2565388381,0.0859355852,0.102427505,-0.0976428464,0.2727009058,0.1102413088,-0.224194482,0.4792916179,0.3978053033,-0.0129565382,-0.1251174808,0.3853281736,0.0855682343,-0.1745478362,-0.235900104,-0.1055123433,0.3037681878,-0.4347405136,0.2402412146,0.4121541083,0.4717878997,-0.0578594469,-0.0302899815,0.0187110994,0.2011547238,-0.1232533678,-0.1241777465,0.42480281,-0.1183524132,0.2057818323,0.2912897766,-0.2708719373,-0.1782761812,0.2673748434,0.0062592584,0.5904614925,-0.0948563814,-0.0398360714,0.0286296066,0.0630985871,0.089431785,-0.0559796952,0.0216120202,0.0299717579,-0.0069941855,-0.1323748678,0.1900782585,0.0338908285,-0.0118479012,-0.5495747328,-0.2824370265,0.1264005452,0.0002330515,0.2171889991,0.1047805846,-0.1002691984,-0.0895598084,-0.1077378467,0.0900662839,-0.6283724904,-0.041423589,0.134491533,-0.0186324716,0.2106272131,-0.2073094696,0.2827202082,0.0501628965,0.0374758169,-0.1497849524,0.02024775,-0.0945357606,-0.1005709767,-0.0626533106,-0.0025815703,0.2395416796,0.3365658522,-0.2852636874,0.4120237529,-0.0436523892,-0.2059766799,-0.012076308,0.2986410558,-0.4672502875,0.2174862772,0.2671689987,0.3142031133,-0.1296132654,0.0681928843,-0.1805099845,-0.0437338948,-0.0008034438,-0.0104817133,0.0942154899,-0.2559864819,-0.1792082936,-0.1735662818,0.1549897939,0.0668152347,-0.3196915686,0.1501157582,-0.2282118052,-0.1416861713,-0.4731708467,0.1578960866,0.2616533041,-0.2436589897,0.0642135367,0.863278985,0.0825070366,0.0251896027,0.1984636635,0.1562655717,-0.3449949324,-0.0244769491,-0.3151106834,0.3379444182,-0.2713032961,0.0985440686,-0.3621907532,0.1460315883,0.4985351861,-0.1491980255,-0.2632514834,-0.0646698102,-0.0307394043,0.1373834014,0.0982732922,-0.1820759624,-0.1346080452,-0.063598603,-0.274834305,-0.1773222387,0.3722852767,0.3560859561,-0.0993723348,0.0530079119,-0.2976567447,0.197338596,-0.1643756181,0.1864760369,-0.2438937128,0.0223280117,0.3751322925,0.2322484851,0.1834988594,-0.1514345556,0.3337680697,-0.1110692397,0.1882600337,-0.0859177709,0.0978523567,0.0397928469,0.3091090024,-0.2474258095,0.0880564377,-0.1876769513,-0.4251469076,-0.195760712,0.0680512935,0.5277488828,0.0006937525,-0.249694109,-0.1992037147,-0.1998839229,-0.0813732296,-0.2588812709,0.2315973639,0.2079891711,-0.1649474651,0.3441869617,0.044437509,-0.1457084119,-0.1748246998,-0.1963101923,0.0520573445,0.3023568094,-0.1225667968,0.1410601437,0.2456605583,0.3988873661,0.0367090739,-0.0794375688,-0.0863954872,-0.0437920429,-0.1423499584,0.1276963502,0.460562408,0.3530477285,0.2352763414,-0.5769765377,0.052005183,0.0045328485,-0.0322566405,-0.0442243777,-0.1316456348,0.5089737177,0.2918946445,-0.0439195074,-0.1299797744,0.0080994666,0.2802174091,0.1063909233,0.1076645255,-0.2521215975,0.1961372048,0.2229685634,0.223180145,0.0003108688,-0.2700794339,0.3575141728,0.4288192689,0.2195136547,0.4198083878,0.1980836242,-0.1720623076,-0.1587088257,0.1949118972,0.3316400349,0.3206742406,0.2881241739,0.2182106227,-0.1101256385,0.1997352391,-0.2270747423,0.0574108139,-0.2346522808,0.0194468722,0.1496636868,0.2977226675,-0.0787324831,-0.3853931725,-0.0284576844,0.168272227,0.1194262505,0.2478954494,0.0757955685,0.0107711926,-0.3028209805,-0.1325542033,-0.0371492356,-0.3319294155,-0.0881230086,0.2266663462,-0.1962447464,0.0135379657,0.1910297722,0.073874101,0.6659824252,-0.2408099025,0.3134451509,0.0946907178,-0.3747076094,-0.1084236056,0.1872622818,0.3665884137,0.0572728589,0.4213939607,-0.0802005529,0.0244263876,-0.1391052902,-0.3946224153,0.1376084685,-0.1326623261,0.0045509716,-0.2764537632,-0.2783454955,-0.2011666447,-0.2879025936,0.0679926872,0.2422076911,0.0488183051,0.096886687,-0.2544697225,-0.0422844253,-0.0170372613,-0.5840312243,-0.5971542597,-0.3213184774,0.1237818822,-0.4896151721,0.0652241856,-0.1523710787,-0.1793899387,-0.0761272013,-0.1064795256,0.0808062255,-0.1429606378,-0.1013497114,0.1082436442,-0.4424519837,-0.2948853374,-0.228261739,-0.1236577481,0.3014537692,-0.113021642,-0.3047885299,-0.0105106989,0.0000253713,0.2592225373,0.2193441689,-0.1279331297,0.3854506314,0.179912746,-0.2204765528,0.1150018796,0.1998904645,-0.0299933851,0.1024783179,0.1932315528,-0.1513638794,0.144139275,0.2045750618,0.6741436124,-0.2040857524,-0.2150021046,0.3979616165,0.0647444651,0.2752732038,-0.1986930966,-0.4880429208,0.06174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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/514","title":"dataset.shuffle(keep_in_memory=True) is never allowed","comments":"This seems to be fixed in #513 for the filter function, replacing `cache_file_name` with `indices_cache_file_name` in the assert. Although not for the `map()` function @thomwolf ","body":"As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.","comment_length":25,"text":"dataset.shuffle(keep_in_memory=True) is never allowed \n As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`. \n This seems to be fixed in #513 for the filter function, replacing `cache_file_name` with `indices_cache_file_name` in the assert. Although not for the `map()` function @thomwolf 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/514","title":"dataset.shuffle(keep_in_memory=True) is never allowed","comments":"Maybe I'm a bit tired but I fail to see the issue here.\r\n\r\nSince `cache_file_name` is `None` by default, if you set `keep_in_memory` to `True`, the assert should pass, no?","body":"As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.","comment_length":30,"text":"dataset.shuffle(keep_in_memory=True) is never allowed \n As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`. \n Maybe I'm a bit tired but I fail to see the issue here.\r\n\r\nSince `cache_file_name` is `None` by default, if you set `keep_in_memory` to `True`, the assert should pass, no?","embeddings":[-0.0392353348,0.0279586129,-0.0443971194,0.2410495281,0.1197215095,-0.0215087403,-0.2454705983,0.2770644426,-0.0534341335,0.2102242708,0.2128321975,0.374445051,-0.2902280986,-0.566421926,-0.0628235266,0.2306990027,0.0152336266,0.0731291249,0.1307362616,-0.1656005234,-0.409574151,-0.131580174,-0.524050951,-0.1571469009,-0.428753376,-0.1573809236,-0.0444145277,0.117156662,-0.2388194799,-0.5017880797,0.0678522363,0.2654946446,-0.4624954164,0.4916145802,-0.0001249819,-0.3131682873,0.2609167993,-0.0434021205,-0.1833150089,-0.0600501485,-0.4701530933,0.3102053702,-0.1049670875,-0.1583370715,0.32724455,-0.17662099,0.0549891926,-0.4825304449,0.17302531,0.2994125187,0.0882786587,-0.0590316318,-0.0274076667,0.0266639832,0.3734092712,0.0743080527,-0.0033938757,0.2121803761,0.0776693746,-0.116405569,0.1800208092,0.1766789705,-0.3082759678,0.228798002,0.1727747917,-0.2034926265,-0.2115484774,-0.1800227016,0.3078238368,0.2102493942,0.1988276988,-0.3202574551,-0.1345006824,0.0092537589,-0.0782134682,-0.202175796,0.3165804446,0.286834687,-0.0235549174,-0.052101884,-0.1797533333,-0.2429484725,0.106021978,-0.0864027515,0.0385790765,0.2909682989,0.032297194,0.2232920974,0.510096848,0.1254252195,0.0449493974,-0.1143685654,-0.2260143906,0.33785671,-0.4495311081,-0.0888596252,0.1479341537,0.1570460945,0.3375706971,0.0762877688,0.1617085636,-0.0072407457,0.0201300737,0.1431003213,0.1532425284,0.4152951539,0.0578295439,0.1727067232,0.1973043829,-0.0983939022,-0.1155526266,0.1021902636,0.0477520376,-0.2676830292,0.0527646616,-0.0756467879,0.2449690402,-0.2947060764,-0.1255384982,0.1726624072,-0.3767850399,-0.3416075706,0.0408682935,0.5163072348,0.258212328,-0.1329903305,-0.0419685505,0.0868414119,0.0162561126,0.0719358474,-0.3517907858,0.175907433,-0.2351176739,0.0944050401,0.2889090776,-0.055646874,0.2828926444,-0.1205164939,-0.0619889721,0.1201131865,0.2415632904,-0.3670422733,0.5084925294,0.29857409,-0.3389808834,0.0358220562,0.0677113086,0.1633681059,-0.1894767731,0.4758026898,-0.0540290587,-0.3840192854,0.4273476005,0.0068682996,-0.2204317451,0.0482815169,0.0162271895,-0.0989364684,0.3985984921,-0.4084577858,0.4869121611,-0.1724495292,0.0578270145,-0.3036775291,0.069000423,0.1707658321,-0.2531927526,0.0957381651,-0.2667095661,0.0407610163,0.0872286707,0.3894156516,0.0169782303,-0.2980735004,-0.3070945144,0.1243021786,0.1682263315,-0.2866532803,-0.450091064,-0.2724568248,-0.0869327709,0.0789938644,0.2291227877,-0.0798648521,-0.1744347662,-0.1958563924,0.4720093608,-0.0606687814,0.0313487612,-0.013874433,-0.272058934,-0.0306105837,0.3783480227,-0.2080346793,-0.2740474045,-0.1138512269,0.0983329192,-0.1043224782,0.3244960308,0.0963459611,-0.1518213451,-0.1861663908,0.4907175899,-0.3960990906,-0.1179611385,-0.0278684553,-0.0874459073,0.2058205307,0.157132417,-0.1732318848,-0.1034319848,-0.1785058528,-0.0762262717,-0.0115153044,-0.1123682559,-0.3707544506,-0.0385702662,0.1194324344,0.1440198272,0.1694183052,-0.1760571897,0.1061042249,-0.5986754298,-0.2244273424,0.0427490026,0.1040752828,0.0717406422,-0.0567062646,-0.0475695245,0.1452860981,-0.0732137039,-0.0363402218,-0.2680030167,0.4468207061,0.2502593696,0.0327756293,0.0547514074,0.1621982157,-0.1792761832,-0.1653306186,0.2657489181,-0.2976213098,0.1159587651,-0.1690282226,-0.0656774491,0.1331111491,-0.3098518252,0.1112017483,0.1109440625,-0.1791750938,0.2296435237,0.2283749282,-0.1756385118,-0.1526007354,0.3091276586,0.2405741811,0.4722813368,0.2488897443,-0.1919386387,-0.1872116178,0.657630682,0.204226166,0.1307446808,0.0229937173,0.0130414646,-0.2385837287,0.2374875844,0.3303774297,0.6330350041,0.0619604848,0.1140250936,-0.0172825735,0.0254269727,-0.0293959361,0.3783991933,0.0832827911,-0.0130594857,0.0471797958,0.2299642861,-0.2011452615,-0.5206315517,-0.1508069783,0.316210717,0.2098184675,-0.1740718484,-0.1102673262,-0.31410411,-0.0405462272,-0.1490080208,-0.1887919307,-0.2578429878,-0.131034404,-0.0023850203,0.3185665607,-0.0844664276,0.3169512451,-0.3148087859,0.6230222583,-0.0225864425,-0.227621749,-0.2458033562,-0.1571443379,0.1797525138,0.0086066034,0.1364538074,0.1759322435,0.3079749048,0.2266804725,-0.2091944665,0.0181867294,-0.2771199942,0.1260360926,-0.3050534129,0.027729759,0.1027247533,0.0826681107,0.070201315,-0.3488433361,-0.0780072883,-0.2902079225,-0.2417974323,0.1993187666,0.2402353138,-0.256210804,-0.3174752593,-0.3330124021,-0.1823951304,-0.3930699825,0.4581944644,-0.0623161644,0.1128846854,0.3934788704,-0.0587169975,0.1582943648,-0.4475107491,0.12444406,-0.4678843319,-0.3296846151,0.1020309106,-0.0584399737,0.0074493526,0.0205647647,-0.0924885571,0.2599042952,0.2751397192,-0.2820496559,-0.0737171397,-0.3988437057,-0.1644539833,-0.056347128,0.0964503959,0.4778026342,0.0493492745,-0.0785903186,-0.1939099729,-0.216729179,0.2699857354,0.5140612721,0.0867329761,0.1301528215,-0.1105623916,0.3138841987,0.5761359334,0.0391610898,0.2408781201,0.1803646088,0.1420124471,0.4015878439,-0.2043007761,-0.1414286047,0.4393417537,0.2179206908,-0.409155637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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/514","title":"dataset.shuffle(keep_in_memory=True) is never allowed","comments":"I failed to realise that this only applies to `shuffle()`. Whenever `keep_in_memory` is set to True, this is passed on to the `select()` function. However, if `cache_file_name` is None, it will be defined in the `shuffle()` function before it is passed on to `select()`. \r\n\r\nThus, `select()` is called with `keep_in_memory=True` and a not None value for `cache_file_name`. \r\nThis is essentially fixed in #513 \r\n\r\nEasily reproducible:\r\n```python\r\n>>> import nlp\r\n>>> data = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> data.shuffle(keep_in_memory=True)\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 1398, in shuffle\r\n    verbose=verbose,\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 1178, in select\r\n    ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\nAssertionError: Please use either `keep_in_memory` or `cache_file_name` but not both.\r\n>>>data.select([0], keep_in_memory=True)\r\n# No error\r\n```","body":"As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.","comment_length":131,"text":"dataset.shuffle(keep_in_memory=True) is never allowed \n As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`. \n I failed to realise that this only applies to `shuffle()`. Whenever `keep_in_memory` is set to True, this is passed on to the `select()` function. However, if `cache_file_name` is None, it will be defined in the `shuffle()` function before it is passed on to `select()`. \r\n\r\nThus, `select()` is called with `keep_in_memory=True` and a not None value for `cache_file_name`. \r\nThis is essentially fixed in #513 \r\n\r\nEasily reproducible:\r\n```python\r\n>>> import nlp\r\n>>> data = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> data.shuffle(keep_in_memory=True)\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 1398, in shuffle\r\n    verbose=verbose,\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 1178, in select\r\n    ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\nAssertionError: Please use either `keep_in_memory` or `cache_file_name` but not both.\r\n>>>data.select([0], keep_in_memory=True)\r\n# No error\r\n```","embeddings":[0.0582521483,0.1715381891,0.0236405991,0.1729352325,0.1541408747,-0.1635663211,-0.3068185747,0.3015698195,-0.1271577775,0.203600049,0.1368852705,0.5172116756,-0.3025810719,-0.431401819,0.0418817587,0.1355720311,0.0163559653,-0.0274757352,0.0766994432,-0.1642751247,-0.4050556719,-0.0679784268,-0.5050225258,-0.2724936903,-0.353969872,-0.0422089845,-0.0651145205,0.0619966909,-0.2063216567,-0.5102363825,0.046151191,0.363330096,-0.370182693,0.3270519972,-0.0001268262,-0.2589108348,0.3602529764,-0.0730153546,-0.336335063,-0.0587591082,-0.3150103986,0.2423155159,-0.0876233578,-0.1639830023,0.2200228721,-0.1693580896,0.05125577,-0.4195265472,0.1856562495,0.3109852374,0.0398746654,-0.0168228857,-0.0427034087,0.0539652221,0.400021553,0.050295461,-0.0710290521,0.1810719371,0.025824314,-0.1065656245,0.1176697016,0.1653529853,-0.2495699078,0.0350748859,0.1981938332,-0.1733987331,-0.1578304023,-0.1440363079,0.2231003642,0.226267755,0.1969543546,-0.3949074447,-0.144900009,-0.1585543305,0.0652295798,-0.3188783824,0.3673403263,0.2271049023,-0.0369679816,-0.141162172,-0.281786561,-0.1317581236,0.0459681116,0.0505823828,0.1565725356,0.4199050069,0.0662545115,0.2579324543,0.5411183834,0.0532425605,0.1429492086,-0.1205495819,-0.1223397031,0.3546843827,-0.4297558963,-0.1071819961,0.1272524744,0.2284680307,0.3260549009,0.1716279387,0.2923562229,-0.001856618,0.0666188002,0.1755289882,0.1559852511,0.4208346903,0.0823542625,0.0897043049,0.2520473301,-0.0248581469,-0.0233360268,0.1855761856,0.0089342995,-0.251447916,-0.0599062853,0.028395813,0.1699498892,-0.2244923413,-0.1456382424,0.1323855221,-0.4279551804,-0.2963871658,0.0615714565,0.4393815398,0.2439153194,-0.036311131,-0.0334248208,0.0826520845,0.0316745453,-0.0285779368,-0.2911154032,0.1890472174,-0.3774471879,0.1855505109,0.414896071,0.0236336868,0.4221939743,-0.1622270644,-0.0405370481,0.1626266539,0.0964482576,-0.3839091659,0.4567762613,0.2786706388,-0.2528736889,0.0173069797,-0.0764115006,0.1386786997,-0.2524915934,0.4537697434,-0.0491760075,-0.2829820812,0.3743939996,-0.0348970592,-0.2979338169,-0.034002915,-0.1033734307,0.0014509729,0.3998070955,-0.3425259888,0.5083597898,-0.2778913975,-0.0489257202,-0.3168386817,0.1293801218,0.2588361502,-0.1447185874,0.0277004186,-0.2785175145,0.0615408309,0.1749753505,0.3470169306,-0.0739551783,-0.0982237309,-0.3033793271,0.0451323539,0.2472506315,-0.233133778,-0.5170556307,-0.4015239775,-0.0577961132,0.1500942856,0.1228232384,-0.0361853391,-0.0466819741,-0.2397120595,0.569152832,0.1230766848,0.0507953987,0.0702164099,-0.2244808674,-0.144208923,0.3917486966,-0.1554684937,-0.2716077566,-0.1668291688,0.1186869442,0.0079841008,0.3367796242,0.0780103803,-0.0938457996,-0.0691634417,0.3975318074,-0.2684621811,-0.1448493302,-0.0017916842,-0.262835145,0.1890727282,0.1500018835,-0.0776878074,-0.0046193781,-0.1408882439,0.0069597056,-0.1317948103,-0.0786308944,-0.4655655921,-0.0289580505,0.2572417259,0.2690214515,0.1674773395,-0.1987805367,0.1474260539,-0.6084166169,-0.1418688595,-0.0942370966,0.1213890314,0.1407781988,-0.2250754982,-0.0015512937,0.2883162498,0.0494448617,0.0084298318,-0.2568846345,0.4210997522,0.2343772948,0.0555305518,-0.0846201852,0.2513121367,-0.0905506313,-0.1039312556,0.25464046,-0.227212593,0.1112728789,-0.1098298207,-0.1313947737,0.1791956276,-0.3276397586,0.0956532955,0.1494388431,-0.2250342667,0.1931090653,0.2141469121,-0.3467211127,-0.0755976886,0.3512639701,0.2432071269,0.3952656984,0.2722056508,-0.2244189084,-0.0795437843,0.7486112714,0.1755332649,0.1091258749,-0.0206410158,0.0418238193,-0.2721835971,0.2493070662,0.3991541564,0.6660166979,0.0948135853,0.0460690781,-0.0085152518,-0.1063189507,-0.1277070343,0.3653984368,0.1909220964,0.116327934,0.0705738366,0.345372498,-0.1046396941,-0.583958149,-0.2935898304,0.2861483097,0.1273398697,-0.1608408391,-0.1700657904,-0.3559508622,-0.0956454054,-0.197192952,-0.1961465329,-0.1914370507,-0.1775632054,-0.0734645426,0.4637481868,-0.1138138771,0.3132784963,-0.3044902086,0.5873445272,-0.1263481826,-0.3305932283,-0.2006736249,-0.1024089828,0.0209167693,-0.0236809943,0.0142460261,0.0744796768,0.2375768572,0.2396815717,-0.1397291273,0.0067295702,-0.4025407434,0.1071235389,-0.2851748466,0.0803081468,-0.0229946487,-0.0686065257,0.0633816049,-0.3273164034,-0.0840858668,-0.3485420346,-0.1023633555,0.129064545,0.210666284,-0.1978334188,-0.3947789073,-0.3335371315,-0.1437161863,-0.4087192118,0.408521384,-0.129422769,0.1063499674,0.4895442724,-0.070001848,0.2074606419,-0.5142788291,0.0601189658,-0.3819688857,-0.3399350643,0.2264350206,-0.1062783524,-0.0376657844,-0.0557117052,-0.1253292561,0.3742881715,0.3618136644,-0.1126945838,0.0698801205,-0.3953334987,-0.1428849101,-0.0798912719,0.0358972922,0.5086120367,0.011170717,0.0101618664,-0.1552386284,-0.3371166587,0.2735789418,0.5556554794,0.1936832964,0.1405683011,-0.1279440522,0.3053508699,0.6116023064,0.0180628728,0.2218025476,0.2513357699,0.198574245,0.3103548288,-0.1589478552,-0.0326582529,0.5106629133,0.21746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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/514","title":"dataset.shuffle(keep_in_memory=True) is never allowed","comments":"Oh yes ok got it thanks. Should be fixed if we are happy with #513 indeed.","body":"As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.","comment_length":16,"text":"dataset.shuffle(keep_in_memory=True) is never allowed \n As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`. \n Oh yes ok got it thanks. Should be fixed if we are happy with #513 indeed.","embeddings":[-0.1078727394,0.0314778425,-0.0592158996,0.1798339337,0.2271602452,-0.0569601916,-0.0781591311,0.3618608415,-0.017298406,0.310203135,0.0617669411,0.5769485235,-0.2770041823,-0.4012131095,-0.0302102193,0.0821130574,0.0426669531,0.0644076467,-0.0605757385,-0.1543073803,-0.4035514593,-0.1343421936,-0.4251638949,-0.1685622483,-0.2936737835,-0.1476836503,-0.0694249272,0.1123735085,-0.2651752532,-0.4221323133,-0.0507327616,0.2752957046,-0.4287704825,0.4618907571,-0.0001105258,-0.2425429821,0.2252237648,-0.0732617751,-0.2165711671,-0.0847063959,-0.3749551475,0.102944456,-0.0625902563,-0.1702314466,0.1931007504,-0.1649805158,0.050829947,-0.4214198887,0.2389518917,0.212985903,0.2309739739,0.0795905069,-0.0047133146,-0.0425943919,0.5332520604,0.0685249642,-0.1711269021,0.1763259321,0.1607525796,-0.2377842218,0.0212521441,0.2817619741,-0.1588008255,0.2197417319,0.2054112852,-0.078985557,-0.1350956708,-0.1896862388,0.309653163,0.179354459,0.2681084871,-0.3994344473,-0.2405280471,-0.1129989251,0.0084619131,-0.3494419754,0.3002124131,0.2281771451,-0.0264050737,-0.1879418641,-0.1457694769,-0.1069997698,0.163274169,-0.0938220322,0.2004605085,0.2594092786,0.0756183565,0.072026521,0.492928952,-0.0876783207,0.2041461468,-0.0494837165,-0.2196620554,0.311832726,-0.4002455771,-0.0920264721,0.1614938527,0.074483484,0.2304908633,0.0756535754,0.2575995326,0.0067211818,0.0132537782,0.2516729832,0.2001265883,0.3784680367,-0.0654057786,0.2134313285,0.1905719191,-0.0800729692,-0.1731542647,0.0880875438,0.0415702164,-0.3028277457,0.074925825,-0.0800301507,0.1167870611,-0.2463715225,-0.1012335047,0.1318372935,-0.1357847601,-0.2121957242,0.0453829467,0.5516231656,0.1866026372,-0.0767670795,0.0147760455,0.0687277243,-0.058700826,0.0963963494,-0.362719357,0.1153650358,-0.3626089096,0.0938812569,0.2376080155,0.1213927269,0.3731115162,-0.1051931605,-0.0658724904,0.1102133766,0.273516506,-0.2634017169,0.3925337791,0.2290444672,-0.2552441955,0.0241906587,0.0206683706,0.1493790001,-0.2203023881,0.4370922148,-0.1299475282,-0.2776747048,0.2739734352,0.1745291054,-0.0480163023,-0.0379783921,-0.003666705,-0.0730223134,0.446079582,-0.3608433902,0.3171112537,-0.2089282572,-0.0279092379,-0.3870055079,0.0059968843,0.1643785387,-0.2229548395,0.0757542104,-0.2734275162,-0.0148449326,0.1948697418,0.4441378117,-0.0910409391,-0.3584691286,-0.2399041206,0.173092097,0.2468248755,-0.1380795836,-0.5333728194,-0.2369209528,-0.1584784836,0.0361692458,0.0879132673,0.0584418848,-0.0627003312,-0.1163539216,0.4518163204,0.0210287981,0.0183452349,0.1343852878,-0.3099245727,-0.1400605291,0.3697386086,-0.15208067,-0.2401830852,-0.1730976254,0.1195153147,-0.1781291813,0.3807413876,0.0413312055,-0.0308136549,-0.0349831395,0.484125644,-0.2198288441,-0.084740378,-0.0747508183,-0.1592671126,0.1509011388,0.0692167953,-0.128204301,-0.1019469574,-0.2161985338,-0.050065089,-0.0720342547,-0.1936576068,-0.3217894733,0.1400498748,0.055104278,0.0714577511,0.055319421,-0.1687679589,0.092811428,-0.4236603677,-0.1620372534,-0.0887979195,0.1601252258,-0.1244970262,-0.2009057105,0.0920647532,0.2002391964,-0.0514342897,0.0513803363,-0.3045920432,0.4380547404,0.2062827349,0.0823625103,0.0751083344,0.0988028497,-0.1049174219,-0.1015241146,0.264321208,-0.1814331412,0.113137573,-0.1391750276,-0.0818582401,0.2396378219,-0.2278598845,0.0760872215,0.1914031506,-0.2140289843,0.2335218787,0.1947780401,-0.0763356611,-0.18927975,0.3093521893,0.2143834978,0.3748636544,0.2533474267,-0.2395371348,-0.0730855763,0.752476573,0.2206949443,0.1499158293,0.0220452789,0.0211722478,-0.3006110787,0.1650642008,0.4131223559,0.4986058176,0.1892418116,0.1480913907,-0.002885808,0.0206921361,-0.0762027353,0.3187276125,0.0252004452,0.1475012004,0.0877850279,0.276910454,-0.2225174606,-0.5638150573,-0.1646709442,0.3080355525,0.2424911112,-0.0820208788,-0.2388604134,-0.3599188328,-0.1361257285,-0.1408329159,-0.106000632,-0.212834239,-0.1299735904,0.092754364,0.3725082874,-0.2125671655,0.3739692271,-0.2482064068,0.4168656766,-0.0222027451,-0.1171981692,-0.2320803404,-0.2177790105,0.1436156332,0.1165102497,0.1697309017,0.0945722312,0.3899741173,0.2874011993,-0.1426670104,-0.0584663749,-0.215809986,0.0347240977,-0.1650770605,-0.0293394774,0.0274550952,0.0614132807,-0.1137502044,-0.3544992208,0.0075111478,-0.3451034725,-0.2569625676,0.1152637005,0.2479770482,-0.1941602677,-0.3728398681,-0.4107463658,-0.2554987073,-0.4678596258,0.4274529815,-0.0421122909,0.1697332114,0.4122061133,0.0268561561,0.0871611238,-0.3679885268,0.0467262566,-0.4801112711,-0.3200534582,0.1664246619,-0.2421019971,-0.1274799556,-0.0509109646,-0.0797985941,0.2987231314,0.2556759417,-0.1701513231,-0.014400091,-0.4049577415,-0.0595423244,0.0206536874,0.1705158949,0.5260155797,0.1508603543,-0.1882628798,-0.1759503633,-0.1672910303,0.2345252633,0.3991559744,0.197608456,0.030670708,-0.0013973066,0.2786433995,0.6611911058,0.0348750874,0.2650378644,0.2444595844,0.1208591983,0.3795444071,-0.3707634807,-0.0451818556,0.3426940143,0.1007099003,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/514","title":"dataset.shuffle(keep_in_memory=True) is never allowed","comments":"My bad. This is actually not fixed in #513. Sorry about that...\r\nThe new `indices_cache_file_name` is set to a non-None value in the new `shuffle()` as well. \r\n\r\nThe buffer and caching mechanisms used in the `select()` function are too intricate for me to understand why the check is there at all. I've removed it in my local build and it seems to be working fine for my project, without really considering other implications of the change. \r\n\r\n","body":"As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.","comment_length":76,"text":"dataset.shuffle(keep_in_memory=True) is never allowed \n As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`. \n My bad. This is actually not fixed in #513. Sorry about that...\r\nThe new `indices_cache_file_name` is set to a non-None value in the new `shuffle()` as well. \r\n\r\nThe buffer and caching mechanisms used in the `select()` function are too intricate for me to understand why the check is there at all. I've removed it in my local build and it seems to be working fine for my project, without really considering other implications of the change. \r\n\r\n","embeddings":[-0.1627905816,0.1008973792,-0.0472362339,0.141304642,0.1969874352,-0.0894875675,-0.0724679828,0.3746205568,0.0284968764,0.3297641873,0.054085426,0.5689108968,-0.2949666977,-0.4070610702,-0.063083522,0.1825318038,0.0914553255,0.0464378409,-0.06404268,-0.1149820238,-0.4023231566,-0.1355522275,-0.3807732165,-0.2072246373,-0.2809173167,-0.1180846766,-0.0617817342,0.0914643034,-0.2582835555,-0.4361536205,-0.0063434797,0.347877264,-0.409319073,0.4160298705,-0.0001099763,-0.1791142374,0.2073139101,-0.0738750994,-0.2709137797,-0.0989495292,-0.3752152026,0.1104811728,-0.0958124101,-0.0919574276,0.1756296158,-0.1854459643,0.0588805191,-0.5130761266,0.1786990166,0.2128080279,0.2111129314,0.0499816909,-0.0335805938,-0.0293106381,0.5861102939,0.0566133894,-0.1890409291,0.1663571447,0.1492808759,-0.1905910522,-0.0007994702,0.2823547721,-0.2549176812,0.2575418353,0.2611257136,-0.0905021727,-0.1236087754,-0.1846337467,0.3069356084,0.2646509707,0.2544342279,-0.3675505519,-0.2848781049,-0.1775666922,-0.0083235037,-0.3538299799,0.3373945653,0.1521790475,0.0035764987,-0.1769806594,-0.1892120242,-0.0270581804,0.2319644541,-0.0966159105,0.2491226494,0.2606118023,0.0594690368,0.0409907475,0.5399266481,-0.0905899554,0.2658842802,-0.1224403903,-0.2388854772,0.2838874161,-0.3870707452,-0.1137686595,0.1962217242,0.0459888168,0.2212799788,0.0704398379,0.2729631364,0.0708098859,0.0240048617,0.202840656,0.1022178531,0.4053637981,-0.0894306824,0.1724646837,0.1995564401,-0.0315999389,-0.140944764,0.0801540986,0.005148387,-0.2488575727,0.0470433049,-0.0482765324,0.099941209,-0.2141382694,-0.1254854649,0.1203748956,-0.1201372221,-0.2233006209,0.090140745,0.5079164505,0.1808762103,-0.0202656128,-0.0539890192,0.0603303425,-0.0707249343,0.1533785611,-0.3479059041,0.0679386929,-0.3014752865,0.1446823776,0.2900347412,0.0929114297,0.3374700546,-0.0530469567,-0.0744542181,0.1677108854,0.2409879118,-0.2988532186,0.4511573911,0.2090821862,-0.327780515,0.0116757499,0.0305169038,0.0661879629,-0.271343112,0.4143098891,-0.1308401823,-0.2582525611,0.2951088846,0.168598488,-0.078163974,-0.0627123937,-0.0282901675,0.0004497216,0.4258086979,-0.3920407295,0.3442200422,-0.2152245194,-0.0734057575,-0.3512366414,0.0223537236,0.1592847109,-0.2199881375,0.0661382601,-0.264898777,0.0545590147,0.1390299946,0.4122771025,-0.0707731023,-0.398648262,-0.1971273273,0.0905979946,0.2141714394,-0.1521358639,-0.5648226738,-0.2408738732,-0.1316463053,0.0403598472,0.1658996195,0.0475503877,-0.0199212302,-0.1683561504,0.4874163866,0.0159762781,0.0379024521,0.1605656594,-0.286796838,-0.2108647674,0.2988801003,-0.1238557547,-0.25221017,-0.1908814013,0.1199977994,-0.1736690849,0.3175570071,0.0462701507,-0.0111070732,-0.033735577,0.4648877978,-0.2766029239,-0.0674634948,-0.1012833118,-0.1785651147,0.1913275123,0.068677716,-0.2096097767,-0.1075913832,-0.1848170906,0.0236457866,-0.1183180138,-0.2057343572,-0.3201948106,0.114191398,0.1147771478,0.158302471,0.0917552337,-0.1539441496,0.0645098165,-0.3795572221,-0.1858312935,-0.0621419549,0.1152379289,-0.0434533209,-0.242235139,0.0461175591,0.2248344421,-0.0923197567,0.0642551631,-0.3035538495,0.4044660032,0.2118751705,0.0654413402,0.0497317687,0.2067206651,-0.0972361639,-0.0455045328,0.3066761494,-0.2456496656,0.1084035635,-0.0401805677,-0.0883613303,0.2758997381,-0.2985675931,0.1285168231,0.1937158853,-0.3063238561,0.2506430745,0.1813519746,-0.0775220171,-0.237853542,0.2764934599,0.2271322012,0.4078924954,0.2792389095,-0.168536976,-0.0030661023,0.8022277355,0.2215924859,0.1226186454,0.0282464344,-0.0099236406,-0.3311356604,0.1510760486,0.4368934929,0.5458803773,0.2066428065,0.139176324,-0.0523492135,0.0427910052,-0.1248243377,0.3032709658,-0.0051012756,0.0998852178,0.1477361619,0.2927755713,-0.1784620583,-0.5660678148,-0.117468372,0.3162509203,0.1801416129,-0.0468137003,-0.1696550548,-0.3915604055,-0.1134415269,-0.1453727484,-0.1362470835,-0.2019047141,-0.1397088468,0.1181998402,0.4280184209,-0.2240033746,0.4030801654,-0.2191064954,0.3761196434,-0.0820556134,-0.2009871602,-0.2323857844,-0.2473513186,0.113504082,0.1071829796,0.1801324487,0.0349476971,0.3226132095,0.2647101879,-0.1561774015,-0.1072735488,-0.2211646885,0.0503194369,-0.1555036157,-0.0216474384,-0.0479001515,0.0101211295,-0.0882760808,-0.3869800568,0.008692015,-0.3831766248,-0.2412016094,0.178995043,0.1787716001,-0.1435182989,-0.4018124938,-0.333520323,-0.2333360314,-0.4529966116,0.3741290569,-0.0639918149,0.0997937471,0.4208821952,0.0501933917,0.1192922294,-0.370349288,0.0566270426,-0.5147151351,-0.3384055495,0.1441303939,-0.1881360114,-0.0859256759,-0.1079170406,-0.1023709923,0.2616953552,0.3022656441,-0.1528601944,0.0201328509,-0.4135584831,-0.0220302772,0.0647840053,0.0957225263,0.5864396691,0.1653224379,-0.2120554745,-0.192295596,-0.138011992,0.178935796,0.3718593121,0.179812029,0.0572609268,0.0182418022,0.2684489489,0.7134554386,-0.0110729849,0.2548037171,0.2837665975,0.1679668427,0.4330673218,-0.4372291565,-0.0121131735,0.3658877015,0.0932755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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/514","title":"dataset.shuffle(keep_in_memory=True) is never allowed","comments":"Ok I'll investigate and add a series of tests on the `keep_in_memory=True` settings which is under-tested atm","body":"As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.","comment_length":17,"text":"dataset.shuffle(keep_in_memory=True) is never allowed \n As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)`\r\n\r\nThe commit added the lines\r\n```python\r\n# lines 994-996 in src\/nlp\/arrow_dataset.py\r\n       assert (\r\n            not keep_in_memory or cache_file_name is None\r\n        ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\n```\r\n\r\nThis affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. \r\n\r\nI'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`. \n Ok I'll investigate and add a series of tests on the `keep_in_memory=True` settings which is under-tested atm","embeddings":[-0.1645395905,0.0470636711,-0.0645132363,0.1816313863,0.2262281924,-0.0658221468,-0.0984032601,0.3320508301,-0.0075618103,0.3399212062,0.0444224738,0.5816398859,-0.2912329435,-0.4286259413,-0.076349996,0.1383523792,0.043693874,0.0938878059,-0.0431256704,-0.1475760788,-0.348734051,-0.1134749576,-0.4357581139,-0.1966107786,-0.3342380524,-0.1588747054,-0.0248811655,0.1103741303,-0.2372039557,-0.3972377479,-0.0687004775,0.2789515257,-0.4423449636,0.4937436581,-0.0001106297,-0.2429226488,0.1797085553,-0.0578649975,-0.2435786724,-0.1217864305,-0.3575274646,0.1902090311,-0.0383543484,-0.1658546627,0.1627754569,-0.1657254994,0.062461216,-0.4391321242,0.2739268541,0.1919246465,0.2325711399,0.0483618416,-0.0108647291,-0.0708407983,0.5056999326,0.0983686373,-0.1865060776,0.2023808509,0.0994287729,-0.1826705188,-0.0003844972,0.2622644007,-0.1502898932,0.236128211,0.2072391212,-0.0669974536,-0.1666733474,-0.1825502366,0.3554884493,0.1823657453,0.2292565554,-0.4242457747,-0.223812148,-0.1083013117,0.006393515,-0.2964934111,0.2984439135,0.1853243411,-0.0228906292,-0.1757450849,-0.1930435896,-0.0560921468,0.1507157087,-0.0823626518,0.2069921494,0.2619947195,0.0504164807,0.0567099378,0.5261256099,-0.0970137864,0.1877243519,-0.0374600217,-0.2361270636,0.302949965,-0.4230674207,-0.0624217503,0.1713781357,0.087275736,0.220587641,0.0613539331,0.3008513153,0.0446182489,0.0128952106,0.2106889188,0.1390131712,0.3808430731,-0.072035715,0.1835352629,0.1963306814,-0.0748435408,-0.2012492716,0.049445685,0.0088462615,-0.274849087,0.08455275,-0.134238869,0.1005304381,-0.2576947808,-0.1026129425,0.1227495745,-0.1149030998,-0.1920341849,0.0423770733,0.603325665,0.1603025496,-0.1170068979,0.0255678259,0.0903490186,-0.0460837707,0.1373942345,-0.3694564402,0.0849426389,-0.3214630485,0.1383457333,0.2982785106,0.150287658,0.395069629,-0.0642243326,-0.0251956563,0.1381615847,0.2827377617,-0.2803497314,0.3846621811,0.2303172797,-0.2404924482,0.0470300131,-0.0196590833,0.1189517751,-0.1882035434,0.4020310044,-0.0872791409,-0.2613456845,0.3605814278,0.1778236181,-0.1003840268,-0.0343159847,-0.0103852917,-0.0915901288,0.412330538,-0.3571671546,0.2896513641,-0.1929020882,-0.0031734719,-0.3660291433,0.0472139865,0.1411512494,-0.1684119701,0.0470237285,-0.2436847091,0.0236433167,0.1621584594,0.4457203746,-0.0874056965,-0.3389571011,-0.2185922712,0.1965607107,0.22976771,-0.1487362832,-0.5433413982,-0.2355117053,-0.1417669803,0.0443009064,0.1115820706,0.0318915658,-0.0390054062,-0.1002301201,0.4659562409,0.0601362363,0.0381388813,0.1441458017,-0.3020870388,-0.1312883049,0.328831017,-0.1144824699,-0.2394287735,-0.1633696705,0.0918087438,-0.1207452714,0.3615780175,0.065369688,-0.0403922722,-0.0620871745,0.4602644444,-0.2890093029,-0.0927340314,-0.0882570371,-0.1043707728,0.1592429578,0.0940951928,-0.1410191357,-0.1503851563,-0.2046491057,-0.0333179273,-0.1032901332,-0.242335245,-0.3282055259,0.154852137,0.1008489504,0.1014075354,0.0617164709,-0.1063408852,0.086782366,-0.4196273088,-0.1925985664,-0.0756413564,0.1260682344,-0.0884511992,-0.2383362204,0.0605838597,0.2049890608,-0.0931285471,0.009616022,-0.3148371875,0.4407109022,0.1878857315,0.0636855885,0.0666090772,0.096437104,-0.1263602227,-0.1158674881,0.2639688253,-0.214144513,0.1109960377,-0.1265970021,-0.1125413924,0.2180500329,-0.2423674315,0.0968880281,0.2393686324,-0.2473141849,0.263948679,0.1885925233,-0.0416331217,-0.2077914625,0.2843483984,0.1691623032,0.4292958975,0.2045132369,-0.2278526723,-0.0814930797,0.7257265449,0.2631525695,0.1692700535,-0.0289141275,-0.0314215757,-0.2949008048,0.1342020482,0.3861592114,0.5165145397,0.2118197978,0.1496459842,-0.0296350438,0.0169992521,-0.0878011957,0.2908545434,-0.0056398436,0.1365726143,0.1303857714,0.2946321964,-0.2110142112,-0.5754868984,-0.1763487756,0.3066597581,0.2283549607,-0.0538047813,-0.2455100417,-0.3411333561,-0.0883902833,-0.0771433786,-0.11255797,-0.191927284,-0.1004242226,0.1275380552,0.3741232157,-0.1871351898,0.3885907233,-0.2175397128,0.3834332526,-0.0255779549,-0.1685042977,-0.2406041175,-0.1994387656,0.124864921,0.1316697896,0.1653927565,0.1222232431,0.3936085999,0.3040719926,-0.1854711622,-0.0635928363,-0.1819154322,0.0548923053,-0.1391015351,-0.0073849428,-0.0143501479,0.0617336519,-0.1076501533,-0.3901997209,0.0434611812,-0.3487757742,-0.2571064234,0.1572529078,0.222551465,-0.2505584061,-0.401353687,-0.4012826383,-0.2319550514,-0.4734946787,0.4063262045,-0.0250745174,0.121636413,0.4188084602,0.0301111676,0.1068951562,-0.3666160107,0.1007186696,-0.4784907699,-0.2979616225,0.1623972803,-0.2508962154,-0.1209593266,-0.044663284,-0.0964062363,0.2673754692,0.3345435262,-0.197452426,0.001811086,-0.4214383662,-0.0430031754,0.0390927419,0.1247504428,0.5532547832,0.1390643418,-0.1910766661,-0.1802101433,-0.1579673886,0.2839006484,0.410150975,0.180718109,-0.0001552659,-0.0088671837,0.2447098494,0.6376883388,-0.0267477818,0.2307050824,0.2228118628,0.0709281787,0.3748376369,-0.3868434727,-0.0213324372,0.3736646473,0.1293521821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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/511","title":"dataset.shuffle() and select() resets format. Intended?","comments":"Hi @vegarab yes feel free to open a discussion here.\r\n\r\nThis design choice was not very much thought about.\r\n\r\nSince `dataset.select()` (like all the method without a trailing underscore) is non-destructive and returns a new dataset it has most of its properties initialized from scratch (except the table and infos).\r\n\r\nThinking about it I don't see a strong reason against transmitting the format from the parent dataset to its newly created child. It's probably what's expected by the user in most cases. What do you think @lhoestq?\r\n\r\nBy the way, I've been working today on a refactoring of all the samples re-ordering\/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`). The idea is to speed them up by a lot (like, really a lot) by working as much as possible with an indices mapping table instead of doing a deep copy of the full dataset as we've been doing currently. You can give it a look and try it here: https:\/\/github.com\/huggingface\/nlp\/pull\/513\r\nFeedbacks are very much welcome","body":"Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight?\r\n\r\nWhen working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save(\"dataset.pt\")`. Later loading the dataset object using `torch.load(\"dataset.pt\")`, which conserves the defined format before saving. \r\nI do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset. \r\n\r\nThe obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`.\r\n\r\n_I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_\r\n\r\n####  How to reproduce:\r\n\r\n```python\r\nimport nlp\r\nfrom transformers import T5Tokenizer\r\n\r\ntokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\r\ndef create_features(batch):\r\n    context_encoding = tokenizer.batch_encode_plus(batch[\"context\"])\r\n    return {\"input_ids\": context_encoding[\"input_ids\"]}\r\n\r\ndataset = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\ndataset = dataset.map(create_features, batched=True)\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\"])\r\ndataset[0]\r\n# {'input_ids': tensor([ 1804,  3525,  1602,  ...   0,     0])}\r\n\r\ndataset = dataset.shuffle()\r\ndataset[0]\r\n# {'id': '3Q9(...)20', 'context': \"Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]}\r\n\r\n```","comment_length":164,"text":"dataset.shuffle() and select() resets format. Intended? \n Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight?\r\n\r\nWhen working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save(\"dataset.pt\")`. Later loading the dataset object using `torch.load(\"dataset.pt\")`, which conserves the defined format before saving. \r\nI do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset. \r\n\r\nThe obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`.\r\n\r\n_I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_\r\n\r\n####  How to reproduce:\r\n\r\n```python\r\nimport nlp\r\nfrom transformers import T5Tokenizer\r\n\r\ntokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\r\ndef create_features(batch):\r\n    context_encoding = tokenizer.batch_encode_plus(batch[\"context\"])\r\n    return {\"input_ids\": context_encoding[\"input_ids\"]}\r\n\r\ndataset = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\ndataset = dataset.map(create_features, batched=True)\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\"])\r\ndataset[0]\r\n# {'input_ids': tensor([ 1804,  3525,  1602,  ...   0,     0])}\r\n\r\ndataset = dataset.shuffle()\r\ndataset[0]\r\n# {'id': '3Q9(...)20', 'context': \"Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]}\r\n\r\n``` \n Hi @vegarab yes feel free to open a discussion here.\r\n\r\nThis design choice was not very much thought about.\r\n\r\nSince `dataset.select()` (like all the method without a trailing underscore) is non-destructive and returns a new dataset it has most of its properties initialized from scratch (except the table and infos).\r\n\r\nThinking about it I don't see a strong reason against transmitting the format from the parent dataset to its newly created child. It's probably what's expected by the user in most cases. What do you think @lhoestq?\r\n\r\nBy the way, I've been working today on a refactoring of all the samples re-ordering\/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`). The idea is to speed them up by a lot (like, really a lot) by working as much as possible with an indices mapping table instead of doing a deep copy of the full dataset as we've been doing currently. You can give it a look and try it here: https:\/\/github.com\/huggingface\/nlp\/pull\/513\r\nFeedbacks are very much welcome","embeddings":[-0.1382125914,-0.2247202247,0.0239643287,0.2476742417,0.1281202286,-0.1362012476,0.2032122612,0.0824569091,-0.6032932997,-0.0022735526,-0.2842715979,0.322750628,-0.1951235682,0.1738311946,0.1398987323,-0.1447517425,0.1145583838,0.2089668661,-0.0435798876,-0.115610823,-0.1563792527,-0.2866491675,-0.2859784365,-0.0606167801,-0.4345372915,0.0260992981,0.0050066882,0.1479002982,-0.0802040696,0.0760351792,0.0442324206,0.2561855018,-0.1987256706,0.5789396763,-0.0001289723,-0.1655297279,0.0554003008,-0.2835808396,-0.292496562,0.0555677451,-0.0595302619,0.2450539023,0.0335684307,-0.1791678071,0.0966271758,-0.3774504662,-0.0920630097,-0.1147696525,0.189046368,0.1550750881,0.0798439384,0.3368454278,-0.2491133064,0.2438392788,0.3745867014,0.557667613,-0.1313131005,0.2056202143,0.101867266,0.3548139632,0.4095387161,0.1268223077,-0.2900162339,-0.2419890761,0.2838993073,0.0142173441,-0.2483233958,-0.2783831358,0.1692030579,0.0764759481,0.7366990447,-0.5577983856,-0.1140442863,-0.430565536,0.1685490608,-0.3902668655,0.071951814,0.154959783,0.0929495022,-0.0118967332,-0.4119156003,-0.0944790542,0.1351483017,0.0751213357,0.2118790597,0.2510275543,-0.1262820661,0.2214883268,-0.2606827915,0.3279381096,0.2481326014,-0.3805216253,-0.0002255971,-0.0308669601,-0.1849082708,-0.0945181474,-0.0433808714,0.045279514,-0.0704916492,0.3731215,0.5257031322,0.2177648246,-0.1684404016,0.1170973629,0.375684768,0.1648922712,0.1534628421,0.062367551,0.0424035825,-0.3042173982,0.0391020514,0.0110796252,0.2551579475,-0.0433039553,0.3766871095,-0.0346268825,-0.0403555222,-0.1273494214,-0.0218834579,0.0662864149,-0.2807991803,-0.3609564006,-0.1409435421,0.111996375,0.0275993235,0.334613651,-0.2615431249,0.2003477812,-0.3364670575,0.0672368109,-0.1536249816,-0.1003374457,-0.2305383235,0.1230605617,0.1779459715,-0.341763556,0.2291339785,0.085795626,-0.055318255,0.0410853773,0.1837367415,-0.3757719398,0.4564963281,0.23024863,-0.1737115979,0.1937036365,0.0566022396,0.2335437834,-0.2558582723,0.497835964,-0.241155833,-0.2049873471,0.2311902791,0.0342101119,-0.1543439478,-0.0228702538,-0.208795324,0.1444380581,0.4477293193,-0.3578275442,0.2071652412,-0.5868683457,-0.2272828221,-0.4626278877,0.0286326837,0.2016877979,-0.3084818423,-0.0998898596,0.5335569382,-0.1290865541,0.2292512059,0.2242171019,-0.3500424922,0.3968575001,-0.1544257998,-0.063399151,0.5925039053,0.0699077174,-0.2872909904,-0.3488504887,-0.0791895986,0.3577805459,-0.1115627959,0.1525214911,-0.1749596149,-0.0800708011,0.2018082291,0.11498449,-0.0569889396,0.1131259128,-0.2052199543,0.0311108716,0.4739532173,0.1160250679,-0.085915342,-0.0094829211,-0.0761847273,0.158943966,0.1887992769,-0.0295025203,-0.2351531982,0.0499477834,-0.1133157834,0.1867135465,-0.0154641299,-0.1854670942,-0.2254036516,-0.1526828557,0.1799524128,-0.3040947616,0.3576213419,0.0391091369,0.010645817,-0.332868278,-0.4570793509,-0.3931130469,-0.0499445647,0.4065027535,0.0490477979,0.1186681837,-0.3195305169,0.0614476018,-0.2063674182,-0.0083991289,-0.4115723372,0.1248530522,0.1170983836,-0.2123429328,-0.3896256983,0.1242080256,-0.1243458912,-0.0857831985,-0.2129511684,0.3616207242,0.1442990601,-0.2647690475,-0.3784194589,0.4010251462,0.1274124384,0.2065180242,0.1345853209,-0.1895992756,0.3265334368,-0.1915585399,-0.2690972388,0.3493041098,-0.6575779319,-0.1802589595,-0.1800181866,-0.6270247698,-0.0393623821,0.2436851114,-0.38536641,0.045983322,0.0531655848,0.3270132244,0.1938665211,0.3799907863,-0.5700509548,-0.0476526283,0.7285046577,-0.2212718576,0.0553403497,0.0533638261,-0.1717451513,-0.3505660892,-0.1845951527,-0.0203250516,0.782093823,-0.0153637482,-0.0367374867,0.0553940795,0.0814553723,-0.3519118428,0.0955269486,0.2491502464,0.00695509,0.4411150217,0.3575190604,0.1117630824,-0.488229394,0.1302794069,0.1637688428,-0.0803554654,-0.3349176943,0.0944054872,-0.155654043,0.0596321747,-0.3772047162,-0.0254960172,0.0446690954,-0.0599994063,-0.081163533,0.0489457361,0.1002306193,-0.0627609044,0.2464330047,0.224451378,-0.0175593719,-0.3098870516,0.3108996451,-0.0712586492,-0.1993415505,-0.1147271618,0.0123944832,-0.0296001565,0.3543699086,-0.0558917895,-0.2055740356,-0.1391870677,-0.1954615563,0.2927092016,-0.0928981304,-0.0006683394,0.0193675347,-0.0350417793,0.0142719653,-0.1980372071,0.1020730063,0.1147153229,0.1975731552,0.0408127047,0.1796193719,0.1345437765,-0.1993232816,-0.291289866,-0.0313384794,0.088165395,0.2162626535,-0.1674561948,-0.0139829572,0.465908438,-0.0845903978,-0.1470181197,-0.6666821837,0.1076658294,-0.3695809245,-0.5208802819,0.129328534,-0.0555758886,-0.2137968689,-0.2652085423,0.1450389624,-0.0572913438,0.5463920832,-0.2382081747,-0.1953873187,-0.1374084204,-0.0648620725,-0.0691306964,0.4167664349,0.3020017147,0.0571569018,0.1945846677,0.038517151,-0.043403767,0.1414676905,0.3747738302,0.0798519477,0.0951740593,0.1910053194,-0.0178647935,0.5995121002,-0.1991274357,-0.1613339633,0.1348063946,0.1795824766,0.3882859349,-0.4722309709,-0.104178369,0.4822199345,-0.0516313165,-0.09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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/511","title":"dataset.shuffle() and select() resets format. Intended?","comments":"I think it's ok to keep the format.\r\nIf we want to have this behavior for `.map` too we just have to make sure it doesn't keep a column that's been removed.","body":"Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight?\r\n\r\nWhen working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save(\"dataset.pt\")`. Later loading the dataset object using `torch.load(\"dataset.pt\")`, which conserves the defined format before saving. \r\nI do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset. \r\n\r\nThe obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`.\r\n\r\n_I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_\r\n\r\n####  How to reproduce:\r\n\r\n```python\r\nimport nlp\r\nfrom transformers import T5Tokenizer\r\n\r\ntokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\r\ndef create_features(batch):\r\n    context_encoding = tokenizer.batch_encode_plus(batch[\"context\"])\r\n    return {\"input_ids\": context_encoding[\"input_ids\"]}\r\n\r\ndataset = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\ndataset = dataset.map(create_features, batched=True)\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\"])\r\ndataset[0]\r\n# {'input_ids': tensor([ 1804,  3525,  1602,  ...   0,     0])}\r\n\r\ndataset = dataset.shuffle()\r\ndataset[0]\r\n# {'id': '3Q9(...)20', 'context': \"Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]}\r\n\r\n```","comment_length":32,"text":"dataset.shuffle() and select() resets format. Intended? \n Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight?\r\n\r\nWhen working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save(\"dataset.pt\")`. Later loading the dataset object using `torch.load(\"dataset.pt\")`, which conserves the defined format before saving. \r\nI do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset. \r\n\r\nThe obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`.\r\n\r\n_I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_\r\n\r\n####  How to reproduce:\r\n\r\n```python\r\nimport nlp\r\nfrom transformers import T5Tokenizer\r\n\r\ntokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\r\ndef create_features(batch):\r\n    context_encoding = tokenizer.batch_encode_plus(batch[\"context\"])\r\n    return {\"input_ids\": context_encoding[\"input_ids\"]}\r\n\r\ndataset = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\ndataset = dataset.map(create_features, batched=True)\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\"])\r\ndataset[0]\r\n# {'input_ids': tensor([ 1804,  3525,  1602,  ...   0,     0])}\r\n\r\ndataset = dataset.shuffle()\r\ndataset[0]\r\n# {'id': '3Q9(...)20', 'context': \"Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]}\r\n\r\n``` \n I think it's ok to keep the format.\r\nIf we want to have this behavior for `.map` too we just have to make sure it doesn't keep a column that's been removed.","embeddings":[-0.1382125914,-0.2247202247,0.0239643287,0.2476742417,0.1281202286,-0.1362012476,0.2032122612,0.0824569091,-0.6032932997,-0.0022735526,-0.2842715979,0.322750628,-0.1951235682,0.1738311946,0.1398987323,-0.1447517425,0.1145583838,0.2089668661,-0.0435798876,-0.115610823,-0.1563792527,-0.2866491675,-0.2859784365,-0.0606167801,-0.4345372915,0.0260992981,0.0050066882,0.1479002982,-0.0802040696,0.0760351792,0.0442324206,0.2561855018,-0.1987256706,0.5789396763,-0.0001289723,-0.1655297279,0.0554003008,-0.2835808396,-0.292496562,0.0555677451,-0.0595302619,0.2450539023,0.0335684307,-0.1791678071,0.0966271758,-0.3774504662,-0.0920630097,-0.1147696525,0.189046368,0.1550750881,0.0798439384,0.3368454278,-0.2491133064,0.2438392788,0.3745867014,0.557667613,-0.1313131005,0.2056202143,0.101867266,0.3548139632,0.4095387161,0.1268223077,-0.2900162339,-0.2419890761,0.2838993073,0.0142173441,-0.2483233958,-0.2783831358,0.1692030579,0.0764759481,0.7366990447,-0.5577983856,-0.1140442863,-0.430565536,0.1685490608,-0.3902668655,0.071951814,0.154959783,0.0929495022,-0.0118967332,-0.4119156003,-0.0944790542,0.1351483017,0.0751213357,0.2118790597,0.2510275543,-0.1262820661,0.2214883268,-0.2606827915,0.3279381096,0.2481326014,-0.3805216253,-0.0002255971,-0.0308669601,-0.1849082708,-0.0945181474,-0.0433808714,0.045279514,-0.0704916492,0.3731215,0.5257031322,0.2177648246,-0.1684404016,0.1170973629,0.375684768,0.1648922712,0.1534628421,0.062367551,0.0424035825,-0.3042173982,0.0391020514,0.0110796252,0.2551579475,-0.0433039553,0.3766871095,-0.0346268825,-0.0403555222,-0.1273494214,-0.0218834579,0.0662864149,-0.2807991803,-0.3609564006,-0.1409435421,0.111996375,0.0275993235,0.334613651,-0.2615431249,0.2003477812,-0.3364670575,0.0672368109,-0.1536249816,-0.1003374457,-0.2305383235,0.1230605617,0.1779459715,-0.341763556,0.2291339785,0.085795626,-0.055318255,0.0410853773,0.1837367415,-0.3757719398,0.4564963281,0.23024863,-0.1737115979,0.1937036365,0.0566022396,0.2335437834,-0.2558582723,0.497835964,-0.241155833,-0.2049873471,0.2311902791,0.0342101119,-0.1543439478,-0.0228702538,-0.208795324,0.1444380581,0.4477293193,-0.3578275442,0.2071652412,-0.5868683457,-0.2272828221,-0.4626278877,0.0286326837,0.2016877979,-0.3084818423,-0.0998898596,0.5335569382,-0.1290865541,0.2292512059,0.2242171019,-0.3500424922,0.3968575001,-0.1544257998,-0.063399151,0.5925039053,0.0699077174,-0.2872909904,-0.3488504887,-0.0791895986,0.3577805459,-0.1115627959,0.1525214911,-0.1749596149,-0.0800708011,0.2018082291,0.11498449,-0.0569889396,0.1131259128,-0.2052199543,0.0311108716,0.4739532173,0.1160250679,-0.085915342,-0.0094829211,-0.0761847273,0.158943966,0.1887992769,-0.0295025203,-0.2351531982,0.0499477834,-0.1133157834,0.1867135465,-0.0154641299,-0.1854670942,-0.2254036516,-0.1526828557,0.1799524128,-0.3040947616,0.3576213419,0.0391091369,0.010645817,-0.332868278,-0.4570793509,-0.3931130469,-0.0499445647,0.4065027535,0.0490477979,0.1186681837,-0.3195305169,0.0614476018,-0.2063674182,-0.0083991289,-0.4115723372,0.1248530522,0.1170983836,-0.2123429328,-0.3896256983,0.1242080256,-0.1243458912,-0.0857831985,-0.2129511684,0.3616207242,0.1442990601,-0.2647690475,-0.3784194589,0.4010251462,0.1274124384,0.2065180242,0.1345853209,-0.1895992756,0.3265334368,-0.1915585399,-0.2690972388,0.3493041098,-0.6575779319,-0.1802589595,-0.1800181866,-0.6270247698,-0.0393623821,0.2436851114,-0.38536641,0.045983322,0.0531655848,0.3270132244,0.1938665211,0.3799907863,-0.5700509548,-0.0476526283,0.7285046577,-0.2212718576,0.0553403497,0.0533638261,-0.1717451513,-0.3505660892,-0.1845951527,-0.0203250516,0.782093823,-0.0153637482,-0.0367374867,0.0553940795,0.0814553723,-0.3519118428,0.0955269486,0.2491502464,0.00695509,0.4411150217,0.3575190604,0.1117630824,-0.488229394,0.1302794069,0.1637688428,-0.0803554654,-0.3349176943,0.0944054872,-0.155654043,0.0596321747,-0.3772047162,-0.0254960172,0.0446690954,-0.0599994063,-0.081163533,0.0489457361,0.1002306193,-0.0627609044,0.2464330047,0.224451378,-0.0175593719,-0.3098870516,0.3108996451,-0.0712586492,-0.1993415505,-0.1147271618,0.0123944832,-0.0296001565,0.3543699086,-0.0558917895,-0.2055740356,-0.1391870677,-0.1954615563,0.2927092016,-0.0928981304,-0.0006683394,0.0193675347,-0.0350417793,0.0142719653,-0.1980372071,0.1020730063,0.1147153229,0.1975731552,0.0408127047,0.1796193719,0.1345437765,-0.1993232816,-0.291289866,-0.0313384794,0.088165395,0.2162626535,-0.1674561948,-0.0139829572,0.465908438,-0.0845903978,-0.1470181197,-0.6666821837,0.1076658294,-0.3695809245,-0.5208802819,0.129328534,-0.0555758886,-0.2137968689,-0.2652085423,0.1450389624,-0.0572913438,0.5463920832,-0.2382081747,-0.1953873187,-0.1374084204,-0.0648620725,-0.0691306964,0.4167664349,0.3020017147,0.0571569018,0.1945846677,0.038517151,-0.043403767,0.1414676905,0.3747738302,0.0798519477,0.0951740593,0.1910053194,-0.0178647935,0.5995121002,-0.1991274357,-0.1613339633,0.1348063946,0.1795824766,0.3882859349,-0.4722309709,-0.104178369,0.4822199345,-0.0516313165,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/511","title":"dataset.shuffle() and select() resets format. Intended?","comments":"Since datasets 1.0.0 the format is not reset anymore.\r\nClosing this one, but feel free to re-open if you have other questions","body":"Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight?\r\n\r\nWhen working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save(\"dataset.pt\")`. Later loading the dataset object using `torch.load(\"dataset.pt\")`, which conserves the defined format before saving. \r\nI do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset. \r\n\r\nThe obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`.\r\n\r\n_I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_\r\n\r\n####  How to reproduce:\r\n\r\n```python\r\nimport nlp\r\nfrom transformers import T5Tokenizer\r\n\r\ntokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\r\ndef create_features(batch):\r\n    context_encoding = tokenizer.batch_encode_plus(batch[\"context\"])\r\n    return {\"input_ids\": context_encoding[\"input_ids\"]}\r\n\r\ndataset = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\ndataset = dataset.map(create_features, batched=True)\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\"])\r\ndataset[0]\r\n# {'input_ids': tensor([ 1804,  3525,  1602,  ...   0,     0])}\r\n\r\ndataset = dataset.shuffle()\r\ndataset[0]\r\n# {'id': '3Q9(...)20', 'context': \"Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]}\r\n\r\n```","comment_length":22,"text":"dataset.shuffle() and select() resets format. Intended? \n Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight?\r\n\r\nWhen working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save(\"dataset.pt\")`. Later loading the dataset object using `torch.load(\"dataset.pt\")`, which conserves the defined format before saving. \r\nI do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset. \r\n\r\nThe obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`.\r\n\r\n_I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_\r\n\r\n####  How to reproduce:\r\n\r\n```python\r\nimport nlp\r\nfrom transformers import T5Tokenizer\r\n\r\ntokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\r\ndef create_features(batch):\r\n    context_encoding = tokenizer.batch_encode_plus(batch[\"context\"])\r\n    return {\"input_ids\": context_encoding[\"input_ids\"]}\r\n\r\ndataset = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\ndataset = dataset.map(create_features, batched=True)\r\ndataset.set_format(type=\"torch\", columns=[\"input_ids\"])\r\ndataset[0]\r\n# {'input_ids': tensor([ 1804,  3525,  1602,  ...   0,     0])}\r\n\r\ndataset = dataset.shuffle()\r\ndataset[0]\r\n# {'id': '3Q9(...)20', 'context': \"Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]}\r\n\r\n``` \n Since datasets 1.0.0 the format is not reset anymore.\r\nClosing this one, but feel free to re-open if you have other questions","embeddings":[-0.1382125914,-0.2247202247,0.0239643287,0.2476742417,0.1281202286,-0.1362012476,0.2032122612,0.0824569091,-0.6032932997,-0.0022735526,-0.2842715979,0.322750628,-0.1951235682,0.1738311946,0.1398987323,-0.1447517425,0.1145583838,0.2089668661,-0.0435798876,-0.115610823,-0.1563792527,-0.2866491675,-0.2859784365,-0.0606167801,-0.4345372915,0.0260992981,0.0050066882,0.1479002982,-0.0802040696,0.0760351792,0.0442324206,0.2561855018,-0.1987256706,0.5789396763,-0.0001289723,-0.1655297279,0.0554003008,-0.2835808396,-0.292496562,0.0555677451,-0.0595302619,0.2450539023,0.0335684307,-0.1791678071,0.0966271758,-0.3774504662,-0.0920630097,-0.1147696525,0.189046368,0.1550750881,0.0798439384,0.3368454278,-0.2491133064,0.2438392788,0.3745867014,0.557667613,-0.1313131005,0.2056202143,0.101867266,0.3548139632,0.4095387161,0.1268223077,-0.2900162339,-0.2419890761,0.2838993073,0.0142173441,-0.2483233958,-0.2783831358,0.1692030579,0.0764759481,0.7366990447,-0.5577983856,-0.1140442863,-0.430565536,0.1685490608,-0.3902668655,0.071951814,0.154959783,0.0929495022,-0.0118967332,-0.4119156003,-0.0944790542,0.1351483017,0.0751213357,0.2118790597,0.2510275543,-0.1262820661,0.2214883268,-0.2606827915,0.3279381096,0.2481326014,-0.3805216253,-0.0002255971,-0.0308669601,-0.1849082708,-0.0945181474,-0.0433808714,0.045279514,-0.0704916492,0.3731215,0.5257031322,0.2177648246,-0.1684404016,0.1170973629,0.375684768,0.1648922712,0.1534628421,0.062367551,0.0424035825,-0.3042173982,0.0391020514,0.0110796252,0.2551579475,-0.0433039553,0.3766871095,-0.0346268825,-0.0403555222,-0.1273494214,-0.0218834579,0.0662864149,-0.2807991803,-0.3609564006,-0.1409435421,0.111996375,0.0275993235,0.334613651,-0.2615431249,0.2003477812,-0.3364670575,0.0672368109,-0.1536249816,-0.1003374457,-0.2305383235,0.1230605617,0.1779459715,-0.341763556,0.2291339785,0.085795626,-0.055318255,0.0410853773,0.1837367415,-0.3757719398,0.4564963281,0.23024863,-0.1737115979,0.1937036365,0.0566022396,0.2335437834,-0.2558582723,0.497835964,-0.241155833,-0.2049873471,0.2311902791,0.0342101119,-0.1543439478,-0.0228702538,-0.208795324,0.1444380581,0.4477293193,-0.3578275442,0.2071652412,-0.5868683457,-0.2272828221,-0.4626278877,0.0286326837,0.2016877979,-0.3084818423,-0.0998898596,0.5335569382,-0.1290865541,0.2292512059,0.2242171019,-0.3500424922,0.3968575001,-0.1544257998,-0.063399151,0.5925039053,0.0699077174,-0.2872909904,-0.3488504887,-0.0791895986,0.3577805459,-0.1115627959,0.1525214911,-0.1749596149,-0.0800708011,0.2018082291,0.11498449,-0.0569889396,0.1131259128,-0.2052199543,0.0311108716,0.4739532173,0.1160250679,-0.085915342,-0.0094829211,-0.0761847273,0.158943966,0.1887992769,-0.0295025203,-0.2351531982,0.0499477834,-0.1133157834,0.1867135465,-0.0154641299,-0.1854670942,-0.2254036516,-0.1526828557,0.1799524128,-0.3040947616,0.3576213419,0.0391091369,0.010645817,-0.332868278,-0.4570793509,-0.3931130469,-0.0499445647,0.4065027535,0.0490477979,0.1186681837,-0.3195305169,0.0614476018,-0.2063674182,-0.0083991289,-0.4115723372,0.1248530522,0.1170983836,-0.2123429328,-0.3896256983,0.1242080256,-0.1243458912,-0.0857831985,-0.2129511684,0.3616207242,0.1442990601,-0.2647690475,-0.3784194589,0.4010251462,0.1274124384,0.2065180242,0.1345853209,-0.1895992756,0.3265334368,-0.1915585399,-0.2690972388,0.3493041098,-0.6575779319,-0.1802589595,-0.1800181866,-0.6270247698,-0.0393623821,0.2436851114,-0.38536641,0.045983322,0.0531655848,0.3270132244,0.1938665211,0.3799907863,-0.5700509548,-0.0476526283,0.7285046577,-0.2212718576,0.0553403497,0.0533638261,-0.1717451513,-0.3505660892,-0.1845951527,-0.0203250516,0.782093823,-0.0153637482,-0.0367374867,0.0553940795,0.0814553723,-0.3519118428,0.0955269486,0.2491502464,0.00695509,0.4411150217,0.3575190604,0.1117630824,-0.488229394,0.1302794069,0.1637688428,-0.0803554654,-0.3349176943,0.0944054872,-0.155654043,0.0596321747,-0.3772047162,-0.0254960172,0.0446690954,-0.0599994063,-0.081163533,0.0489457361,0.1002306193,-0.0627609044,0.2464330047,0.224451378,-0.0175593719,-0.3098870516,0.3108996451,-0.0712586492,-0.1993415505,-0.1147271618,0.0123944832,-0.0296001565,0.3543699086,-0.0558917895,-0.2055740356,-0.1391870677,-0.1954615563,0.2927092016,-0.0928981304,-0.0006683394,0.0193675347,-0.0350417793,0.0142719653,-0.1980372071,0.1020730063,0.1147153229,0.1975731552,0.0408127047,0.1796193719,0.1345437765,-0.1993232816,-0.291289866,-0.0313384794,0.088165395,0.2162626535,-0.1674561948,-0.0139829572,0.465908438,-0.0845903978,-0.1470181197,-0.6666821837,0.1076658294,-0.3695809245,-0.5208802819,0.129328534,-0.0555758886,-0.2137968689,-0.2652085423,0.1450389624,-0.0572913438,0.5463920832,-0.2382081747,-0.1953873187,-0.1374084204,-0.0648620725,-0.0691306964,0.4167664349,0.3020017147,0.0571569018,0.1945846677,0.038517151,-0.043403767,0.1414676905,0.3747738302,0.0798519477,0.0951740593,0.1910053194,-0.0178647935,0.5995121002,-0.1991274357,-0.1613339633,0.1348063946,0.1795824766,0.3882859349,-0.4722309709,-0.104178369,0.4822199345,-0.0516313165,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/509","title":"Converting TensorFlow dataset example","comments":"Do you want to convert a dataset script to the tfds format ?\r\nIf so, we currently have a comversion script nlp\/commands\/convert.py but it is a conversion script that goes from tfds to nlp.\r\nI think it shouldn't be too hard to do the changes in reverse (at some manual adjustments).\r\nIf you manage to make it work in reverse, feel free to open a PR to share it with the community :)","body":"Hi,\r\nI want to use TensorFlow datasets with this repo, I noticed you made some conversion script,\r\ncan you give a simple example of using it?\r\n\r\nThanks\r\n","comment_length":73,"text":"Converting TensorFlow dataset example \n Hi,\r\nI want to use TensorFlow datasets with this repo, I noticed you made some conversion script,\r\ncan you give a simple example of using it?\r\n\r\nThanks\r\n \n Do you want to convert a dataset script to the tfds format ?\r\nIf so, we currently have a comversion script nlp\/commands\/convert.py but it is a conversion script that goes from tfds to nlp.\r\nI think it shouldn't be too hard to do the changes in reverse (at some manual adjustments).\r\nIf you manage to make it work in reverse, feel free to open a PR to share it with the community :)","embeddings":[-0.1354902536,-0.0565532297,-0.1098584607,-0.1969328523,0.165716216,0.0915980563,0.3340383768,0.4940679669,-0.1472647488,0.1399837881,0.0302083679,0.3426104784,0.0195342824,0.3617252707,0.3380131125,-0.3144352734,0.0504774712,0.2795871794,-0.3302764893,-0.1137338728,-0.0358067453,0.10837356,-0.111526899,0.0685933232,0.1368517131,-0.0301686917,-0.0850050896,0.2507574558,0.1226198226,0.0422674604,0.1680342853,0.1560598016,0.3117433488,0.728592217,-0.0001057781,-0.0028073546,0.0753042623,-0.0946940407,-0.0748347789,-0.3386337757,-0.1552296132,-0.2183431983,0.0090980828,-0.3499976695,-0.2264173031,0.0812482461,0.1151307598,-0.1407833397,0.5385851264,0.5260947347,0.2700379193,0.1933607906,-0.0691369101,0.1015990376,0.0665854886,0.1731276512,-0.2012015432,0.044669345,0.3796443939,-0.1914269626,0.2758765817,0.23360838,-0.0874221846,-0.1543228477,0.3159394562,0.0481564514,0.131105572,-0.4717644453,0.008744332,0.5226515532,0.5459263921,-0.4398654997,-0.0945537388,-0.1390538067,0.0545596778,-0.3348767757,-0.4834914804,0.1121080294,-0.1392924339,0.2684394717,-0.5411630869,-0.2216578275,-0.402703464,0.1094899997,-0.0812656283,0.0930473581,0.0270113312,0.0200878419,-0.0979301855,-0.2529070973,0.0744435787,-0.0001649336,0.2594025731,0.1288956255,-0.129897818,-0.3237948418,-0.0438239872,-0.4010471404,0.0947273746,0.0584126823,-0.0523551367,0.2425812632,-0.3122434318,0.1642353833,-0.1862477213,0.2841514647,0.1887120306,0.2639867365,0.1826592833,-0.2470732033,0.0311288536,0.1899619997,-0.2797750235,-0.1054207385,-0.1855313182,-0.0917769894,0.3033269048,-0.1337188035,-0.1332099736,-0.2010025382,-0.0339619778,-0.0861754417,-0.1275319308,0.2554565668,-0.0343337655,0.097173132,0.2132084966,0.2463805079,0.0076521351,0.0132319778,-0.0899327248,0.1835042089,-0.2858375013,-0.1959553808,0.0537286289,0.1041620076,0.0944256186,-0.1755783111,0.0349992812,0.305578649,0.3406420052,0.369146347,0.1823938936,0.0859625936,0.154000029,-0.0796315372,0.237705946,-0.1891966909,-0.3463330269,0.3555153012,-0.3057293296,-0.1236113682,-0.1417661756,0.2352067381,-0.2167689949,-0.4702011645,-0.2125683427,0.4416574836,0.0098987501,0.0000363395,0.1393336207,-0.2634074092,-0.2979614139,-0.2006848603,0.0635969266,0.2533058822,-0.6270143986,0.0087208096,0.0280472562,-0.3241333961,0.2353955954,0.0472910367,-0.0420394689,0.1241582856,0.1520136893,-0.0321764946,0.8314973712,-0.4936859012,0.0705592558,0.375086844,-0.182338655,-0.1454542726,-0.1348911971,0.0472492017,0.1545653939,-0.1671396941,-0.2246347368,0.4862645864,-0.3266904354,0.2609779537,-0.0088492017,-0.187792778,0.3314785957,0.1990187913,0.0573097691,0.2233078778,-0.0590549372,0.0614016205,0.3003413379,-0.3258717954,0.0479201935,-0.0598340705,0.1043673828,-0.0128765935,0.1901646405,-0.2196418494,-0.3102653027,0.051224947,0.0617602915,0.1311339587,0.242563054,-0.1107348651,-0.2938144803,-0.1632141918,0.109819375,0.0496342853,0.1978273392,-0.2180907726,0.081186451,-0.127427429,-0.4316331446,-0.0382887237,-0.0973472819,0.0361173637,-0.1963023245,0.3646839559,0.148756057,-0.1214118525,-0.0107458867,0.2152694613,-0.0853616744,-0.5249539614,-0.0451358519,0.3602922559,-0.241701141,0.3648475707,0.2504191399,0.3131833673,0.2691744566,-0.1689711362,0.2763992548,0.1496813744,-0.0801793337,-0.0926614776,-0.3937797844,0.4031621516,0.0032226385,0.246151194,0.0937030539,0.1271241903,0.3030202687,-0.3468945324,-0.236931026,0.0371233709,0.0832642764,-0.0511141159,-0.292799592,-0.1821140796,-0.442122519,0.4492573142,0.4657908082,-0.4190252125,-0.0151990587,0.3167186379,-0.0855055675,-0.0136619536,0.1080683619,0.2586348057,0.021684086,0.1125841439,0.0199201442,0.2573532462,-0.1341613233,-0.0874178782,0.1846426725,-0.0575618483,0.4302837551,-0.2699303031,0.018453259,0.0372076929,0.0475731604,-0.2376663536,0.0429766402,0.3551800251,-0.1561983079,0.1930073798,-0.4067861438,-0.4654603302,-0.2318651378,0.0395088196,0.2529284656,-0.1253548861,0.0527370051,0.0213936809,-0.0492600612,0.1367943436,0.2264103889,0.2378944606,0.1030076072,0.0996955484,-0.2383313179,-0.2714831233,-0.37849015,0.2252317965,0.3144752979,0.1246517226,0.3925198019,-0.3350910544,-0.0369047485,-0.4471805692,-0.3675509691,0.1797019392,-0.3059438467,-0.1407954842,0.1060349494,0.1329557449,-0.0322531462,0.056858439,-0.1369748861,-0.0169070587,0.0253216904,0.205211997,-0.2348159254,-0.2529453039,-0.338911891,-0.2970342934,-0.205621779,-0.1967361569,0.1507043839,0.3304749429,0.3571401536,0.1313921362,0.2177330703,0.4648527801,0.2672098577,-0.1147541106,0.1104004532,-0.1542226374,0.2004641145,-0.39842242,-0.4548293352,0.2888484895,0.0177486539,0.1205377132,0.1832213849,-0.238778919,-0.2569132745,-0.1529293507,0.0101189315,0.0296911895,0.1653308123,0.2616771162,-0.0988902897,-0.1023724079,-0.1688304096,-0.0462651253,0.1744609475,-0.0326427966,0.1593564004,0.0931049585,0.5094342828,0.0120586604,0.57361871,0.0804745555,-0.4635077715,-0.1816733629,-0.3495344222,0.109951362,0.2126186639,0.0766795054,0.2035273761,0.1046320051,0.1543194056,0.20837397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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/508","title":"TypeError: Receiver() takes no arguments","comments":"Which version of Apache Beam do you have (can you copy your full environment info here)?","body":"I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump.","comment_length":16,"text":"TypeError: Receiver() takes no arguments \n I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump. \n Which version of Apache Beam do you have (can you copy your full environment info here)?","embeddings":[-0.0946546644,0.3181282282,0.0874954313,0.362924248,0.2740803361,-0.0989540517,0.2871833742,0.2369655818,-0.0286748931,0.0688424557,0.2388483435,0.3297018707,-0.0908722356,-0.3363240659,0.1751256734,-0.5368747115,-0.2141874284,0.1947945654,-0.232953012,-0.1850881428,-0.1442272663,0.3994988501,-0.3325727284,-0.0724646524,-0.1412276328,0.1027451456,-0.0761774182,0.1753544658,-0.118464604,-0.3360362947,0.1966992915,-0.0673624724,0.0733791813,0.2750073373,-0.0001245085,0.3304243982,0.6380137801,-0.1410859525,-0.5410779715,-0.0631846488,-0.0811768249,-0.2349257469,0.1993419528,-0.2498530447,-0.2083238214,-0.2352619022,0.1023960337,-0.0581367277,0.3563393056,0.281555891,0.0985948294,0.1995158792,0.1826244593,-0.1368430406,0.5043628216,0.043215435,0.0561958142,0.0361742526,-0.2274192423,0.2372251302,-0.2068565637,0.0459311418,-0.2663237154,-0.0578701384,0.3306712508,-0.2204922587,0.0874133259,0.1752309054,-0.0942779183,-0.0104458947,0.8057671189,0.0052634547,0.0640940294,-0.201214239,0.0806409568,-0.1908854842,0.2383200824,0.0715610385,-0.4543537199,-0.1370184273,0.1143337935,-0.1281690449,-0.341394484,0.3352718651,-0.0335033908,0.4075168669,-0.0230080616,0.328975141,0.1422492564,0.1264762729,0.2072722167,-0.2324920595,-0.1268543601,0.1452392787,-0.3808562756,0.174837634,0.1763764918,0.2966895998,-0.0637696385,0.1829950064,0.0932282135,-0.1865078211,0.0735035315,0.1276494712,0.3398443162,0.1209282503,0.1319723129,0.0127151208,0.0047444995,-0.0747593865,0.0249654111,0.0320717469,-0.0236140732,-0.36054492,-0.2406122535,0.2245736271,0.2357540876,-0.0485627688,0.0452682115,-0.2116847932,-0.3131856322,-0.2082335055,0.1694273204,0.4560175538,0.0537248924,0.4907977581,0.3138802648,0.1613340378,-0.1267466247,-0.5722319484,-0.1803703606,0.3171550035,-0.161258176,-0.1637429446,0.3268589377,0.0613696501,0.2865219116,-0.2762343287,0.1872421801,0.1734672487,-0.0168005116,-0.1587234586,-0.2288744003,0.1743646711,0.1935789287,0.2789788842,0.2141343802,-0.2745317817,0.0189148672,0.2086554021,-0.2223849446,-0.1689475477,0.2648113072,0.042568069,0.107154578,-0.1384298354,-0.507543385,0.1115983129,-0.1357539892,-0.2659593821,-0.2610871792,-0.2019345164,-0.0996060669,-0.2297554612,0.3767790496,0.5294706821,-0.0989685282,-0.0291802213,-0.0063148174,0.1354220808,0.0829522833,0.0357792452,-0.311412245,0.492174834,-0.1760831773,-0.1047421619,0.8686538339,-0.5204976797,-0.0506761558,0.3639996946,0.1172987446,-0.071886532,0.3070545495,0.0618702248,0.1170750111,-0.0958950073,0.0317528211,0.4021973014,-0.0465446152,0.0234558284,-0.1533483863,0.0390015058,0.2969612777,0.3607648909,0.0934307128,0.2212417275,-0.2734820545,0.4947689474,0.2071569562,0.0037385139,0.0605673529,0.5123981833,-0.3901424408,-0.0827326104,0.0390271321,0.113077119,0.0266657285,-0.0488630608,-0.1335092783,0.1562322825,0.1626265943,0.1614460051,-0.4275047183,0.0230152868,-0.0830781758,0.0427630432,0.0567392223,0.2840376198,-0.1937215477,0.2580310404,0.0285491738,0.3317639828,0.0175104998,-0.0234073736,-0.3775363863,0.2045926601,-0.1200193763,-0.2554772794,0.282967478,-0.0228369217,0.4561701119,0.0420877747,-0.1734325886,0.1393243074,0.1799443066,-0.0209084228,0.0413391441,-0.2081551701,0.0283872373,-0.134917751,-0.0474718511,0.5747932792,0.2473167032,-0.193128854,0.0073986109,-0.0563839599,-0.0950333998,0.2234849483,-0.0520732105,0.1890359968,0.0732780173,0.2233697176,-0.0849315748,0.2273704857,0.204699859,0.1990070641,0.2141200006,-0.0926080421,-0.0686815456,-0.2286660373,0.2874112725,0.2148696929,0.0324278735,-0.0144205429,-0.2963917255,0.002503603,0.2251099199,-0.0638526604,0.2678775489,0.1726977378,-0.12468867,-0.0075488235,0.0018160535,-0.1072777137,0.4460032582,0.0564808063,0.261120379,-0.016631458,-0.1084316075,-0.0197839774,-0.2426254004,-0.2545631528,0.0278431252,0.2242687643,-0.4247313738,0.0356792621,-0.2087251991,-0.6600081921,-0.2359620631,-0.0494453721,-0.1605994701,-0.5055602193,-0.1612770408,0.3273543119,0.0146426978,0.1275832504,-0.120585151,0.2305703163,0.3319926858,-0.2339310497,-0.4185202122,-0.1734266132,-0.2996529937,-0.0336430594,0.1122232676,0.132373184,0.1385983676,0.113913238,-0.0306637753,-0.0660983101,-0.0057053682,0.2152480781,-0.1090568751,0.123509571,0.2650186121,0.3777044117,-0.2389560044,-0.0974703133,0.3813408017,0.0026626792,0.1714810729,0.3198922575,0.0355916694,-0.075479649,-0.1061445251,-0.2500555813,0.0514185093,-0.6115214825,-0.1775638014,0.237305969,0.000052158,0.1542863101,0.2415067703,0.1805426329,0.1287678778,0.3176608682,0.0169246066,-0.2387425154,0.3329898715,-0.1693131328,-0.3372408748,-0.1376382411,0.1169654056,0.2338880152,0.219890222,-0.4601656795,-0.3833651543,0.29610461,-0.034795884,0.0065297475,0.1694480926,0.3062468469,0.017128082,0.0924849436,-0.2547699511,-0.198965624,0.0468889549,-0.2222736031,0.2826959491,0.2882086039,0.3215004206,-0.0384758487,0.9122626185,0.237421751,0.143084988,0.5309516788,0.1021699384,0.2991394699,-0.0367710814,-0.2422951907,0.230656445,0.1812363267,0.1366699338,0.20913481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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/508","title":"TypeError: Receiver() takes no arguments","comments":"apache-beam==2.23.0\r\nnlp==0.4.0\r\n\r\nFor me this was resolved by running the same python script on Linux (or really WSL). ","body":"I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump.","comment_length":18,"text":"TypeError: Receiver() takes no arguments \n I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump. \n apache-beam==2.23.0\r\nnlp==0.4.0\r\n\r\nFor me this was resolved by running the same python script on Linux (or really WSL). 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/508","title":"TypeError: Receiver() takes no arguments","comments":"Do you manage to run a dummy beam pipeline with python on windows ? \r\nYou can test a dummy pipeline with [this code](https:\/\/github.com\/apache\/beam\/blob\/master\/sdks\/python\/apache_beam\/examples\/wordcount_minimal.py)\r\n\r\nIf you get the same error, it means that the issue comes from apache beam.\r\nOtherwise we'll investigate what went wrong here","body":"I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump.","comment_length":45,"text":"TypeError: Receiver() takes no arguments \n I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump. \n Do you manage to run a dummy beam pipeline with python on windows ? \r\nYou can test a dummy pipeline with [this code](https:\/\/github.com\/apache\/beam\/blob\/master\/sdks\/python\/apache_beam\/examples\/wordcount_minimal.py)\r\n\r\nIf you get the same error, it means that the issue comes from apache beam.\r\nOtherwise we'll investigate what went wrong 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/508","title":"TypeError: Receiver() takes no arguments","comments":"Still, same error, so I guess it is on apache beam then. \r\nThanks for the investigation.","body":"I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump.","comment_length":16,"text":"TypeError: Receiver() takes no arguments \n I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump. \n Still, same error, so I guess it is on apache beam then. \r\nThanks for the investigation.","embeddings":[-0.0946546644,0.3181282282,0.0874954313,0.362924248,0.2740803361,-0.0989540517,0.2871833742,0.2369655818,-0.0286748931,0.0688424557,0.2388483435,0.3297018707,-0.0908722356,-0.3363240659,0.1751256734,-0.5368747115,-0.2141874284,0.1947945654,-0.232953012,-0.1850881428,-0.1442272663,0.3994988501,-0.3325727284,-0.0724646524,-0.1412276328,0.1027451456,-0.0761774182,0.1753544658,-0.118464604,-0.3360362947,0.1966992915,-0.0673624724,0.0733791813,0.2750073373,-0.0001245085,0.3304243982,0.6380137801,-0.1410859525,-0.5410779715,-0.0631846488,-0.0811768249,-0.2349257469,0.1993419528,-0.2498530447,-0.2083238214,-0.2352619022,0.1023960337,-0.0581367277,0.3563393056,0.281555891,0.0985948294,0.1995158792,0.1826244593,-0.1368430406,0.5043628216,0.043215435,0.0561958142,0.0361742526,-0.2274192423,0.2372251302,-0.2068565637,0.0459311418,-0.2663237154,-0.0578701384,0.3306712508,-0.2204922587,0.0874133259,0.1752309054,-0.0942779183,-0.0104458947,0.8057671189,0.0052634547,0.0640940294,-0.201214239,0.0806409568,-0.1908854842,0.2383200824,0.0715610385,-0.4543537199,-0.1370184273,0.1143337935,-0.1281690449,-0.341394484,0.3352718651,-0.0335033908,0.4075168669,-0.0230080616,0.328975141,0.1422492564,0.1264762729,0.2072722167,-0.2324920595,-0.1268543601,0.1452392787,-0.3808562756,0.174837634,0.1763764918,0.2966895998,-0.0637696385,0.1829950064,0.0932282135,-0.1865078211,0.0735035315,0.1276494712,0.3398443162,0.1209282503,0.1319723129,0.0127151208,0.0047444995,-0.0747593865,0.0249654111,0.0320717469,-0.0236140732,-0.36054492,-0.2406122535,0.2245736271,0.2357540876,-0.0485627688,0.0452682115,-0.2116847932,-0.3131856322,-0.2082335055,0.1694273204,0.4560175538,0.0537248924,0.4907977581,0.3138802648,0.1613340378,-0.1267466247,-0.5722319484,-0.1803703606,0.3171550035,-0.161258176,-0.1637429446,0.3268589377,0.0613696501,0.2865219116,-0.2762343287,0.1872421801,0.1734672487,-0.0168005116,-0.1587234586,-0.2288744003,0.1743646711,0.1935789287,0.2789788842,0.2141343802,-0.2745317817,0.0189148672,0.2086554021,-0.2223849446,-0.1689475477,0.2648113072,0.042568069,0.107154578,-0.1384298354,-0.507543385,0.1115983129,-0.1357539892,-0.2659593821,-0.2610871792,-0.2019345164,-0.0996060669,-0.2297554612,0.3767790496,0.5294706821,-0.0989685282,-0.0291802213,-0.0063148174,0.1354220808,0.0829522833,0.0357792452,-0.311412245,0.492174834,-0.1760831773,-0.1047421619,0.8686538339,-0.5204976797,-0.0506761558,0.3639996946,0.1172987446,-0.071886532,0.3070545495,0.0618702248,0.1170750111,-0.0958950073,0.0317528211,0.4021973014,-0.0465446152,0.0234558284,-0.1533483863,0.0390015058,0.2969612777,0.3607648909,0.0934307128,0.2212417275,-0.2734820545,0.4947689474,0.2071569562,0.0037385139,0.0605673529,0.5123981833,-0.3901424408,-0.0827326104,0.0390271321,0.113077119,0.0266657285,-0.0488630608,-0.1335092783,0.1562322825,0.1626265943,0.1614460051,-0.4275047183,0.0230152868,-0.0830781758,0.0427630432,0.0567392223,0.2840376198,-0.1937215477,0.2580310404,0.0285491738,0.3317639828,0.0175104998,-0.0234073736,-0.3775363863,0.2045926601,-0.1200193763,-0.2554772794,0.282967478,-0.0228369217,0.4561701119,0.0420877747,-0.1734325886,0.1393243074,0.1799443066,-0.0209084228,0.0413391441,-0.2081551701,0.0283872373,-0.134917751,-0.0474718511,0.5747932792,0.2473167032,-0.193128854,0.0073986109,-0.0563839599,-0.0950333998,0.2234849483,-0.0520732105,0.1890359968,0.0732780173,0.2233697176,-0.0849315748,0.2273704857,0.204699859,0.1990070641,0.2141200006,-0.0926080421,-0.0686815456,-0.2286660373,0.2874112725,0.2148696929,0.0324278735,-0.0144205429,-0.2963917255,0.002503603,0.2251099199,-0.0638526604,0.2678775489,0.1726977378,-0.12468867,-0.0075488235,0.0018160535,-0.1072777137,0.4460032582,0.0564808063,0.261120379,-0.016631458,-0.1084316075,-0.0197839774,-0.2426254004,-0.2545631528,0.0278431252,0.2242687643,-0.4247313738,0.0356792621,-0.2087251991,-0.6600081921,-0.2359620631,-0.0494453721,-0.1605994701,-0.5055602193,-0.1612770408,0.3273543119,0.0146426978,0.1275832504,-0.120585151,0.2305703163,0.3319926858,-0.2339310497,-0.4185202122,-0.1734266132,-0.2996529937,-0.0336430594,0.1122232676,0.132373184,0.1385983676,0.113913238,-0.0306637753,-0.0660983101,-0.0057053682,0.2152480781,-0.1090568751,0.123509571,0.2650186121,0.3777044117,-0.2389560044,-0.0974703133,0.3813408017,0.0026626792,0.1714810729,0.3198922575,0.0355916694,-0.075479649,-0.1061445251,-0.2500555813,0.0514185093,-0.6115214825,-0.1775638014,0.237305969,0.000052158,0.1542863101,0.2415067703,0.1805426329,0.1287678778,0.3176608682,0.0169246066,-0.2387425154,0.3329898715,-0.1693131328,-0.3372408748,-0.1376382411,0.1169654056,0.2338880152,0.219890222,-0.4601656795,-0.3833651543,0.29610461,-0.034795884,0.0065297475,0.1694480926,0.3062468469,0.017128082,0.0924849436,-0.2547699511,-0.198965624,0.0468889549,-0.2222736031,0.2826959491,0.2882086039,0.3215004206,-0.0384758487,0.9122626185,0.237421751,0.143084988,0.5309516788,0.1021699384,0.2991394699,-0.0367710814,-0.2422951907,0.230656445,0.1812363267,0.1366699338,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/508","title":"TypeError: Receiver() takes no arguments","comments":"Thanks for trying\r\nLet us know if you find clues of what caused this issue, or if you find a fix","body":"I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump.","comment_length":21,"text":"TypeError: Receiver() takes no arguments \n I am trying to load a wikipedia data set\r\n\r\n```\r\nimport nlp\r\nfrom nlp import load_dataset\r\n\r\ndataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=data_path, beam_runner='DirectRunner')\r\n#dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner')\r\n```\r\n\r\nThis fails in the apache beam runner. \r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"D:\/ML\/wikiembedding\/gpt2_sv.py\", line 36, in <module>\r\n    dataset = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=my_cache_dir, beam_runner='DirectRunner')\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\load.py\", line 548, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 462, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nlp\\builder.py\", line 969, in _download_and_prepare\r\n    pipeline_results = pipeline.run()\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\pipeline.py\", line 534, in run\r\n    return self.runner.run_pipeline(self, self._options)\r\n....\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\bundle_processor.py\", line 218, in process_encoded\r\n    self.output(decoded_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\apache_beam\\runners\\worker\\operations.py\", line 332, in output\r\n    cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)\r\n  File \"C:\\Users\\seto\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\Cython\\Shadow.py\", line 167, in cast\r\n    return type(*args)\r\nTypeError: Receiver() takes no arguments\r\n\r\n```\r\n\r\nThis is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump. \n Thanks for trying\r\nLet us know if you find clues of what caused this issue, or if you find a fix","embeddings":[-0.0946546644,0.3181282282,0.0874954313,0.362924248,0.2740803361,-0.0989540517,0.2871833742,0.2369655818,-0.0286748931,0.0688424557,0.2388483435,0.3297018707,-0.0908722356,-0.3363240659,0.1751256734,-0.5368747115,-0.2141874284,0.1947945654,-0.232953012,-0.1850881428,-0.1442272663,0.3994988501,-0.3325727284,-0.0724646524,-0.1412276328,0.1027451456,-0.0761774182,0.1753544658,-0.118464604,-0.3360362947,0.1966992915,-0.0673624724,0.0733791813,0.2750073373,-0.0001245085,0.3304243982,0.6380137801,-0.1410859525,-0.5410779715,-0.0631846488,-0.0811768249,-0.2349257469,0.1993419528,-0.2498530447,-0.2083238214,-0.2352619022,0.1023960337,-0.0581367277,0.3563393056,0.281555891,0.0985948294,0.1995158792,0.1826244593,-0.1368430406,0.5043628216,0.043215435,0.0561958142,0.0361742526,-0.2274192423,0.2372251302,-0.2068565637,0.0459311418,-0.2663237154,-0.0578701384,0.3306712508,-0.2204922587,0.0874133259,0.1752309054,-0.0942779183,-0.0104458947,0.8057671189,0.0052634547,0.0640940294,-0.201214239,0.0806409568,-0.1908854842,0.2383200824,0.0715610385,-0.4543537199,-0.1370184273,0.1143337935,-0.1281690449,-0.341394484,0.3352718651,-0.0335033908,0.4075168669,-0.0230080616,0.328975141,0.1422492564,0.1264762729,0.2072722167,-0.2324920595,-0.1268543601,0.1452392787,-0.3808562756,0.174837634,0.1763764918,0.2966895998,-0.0637696385,0.1829950064,0.0932282135,-0.1865078211,0.0735035315,0.1276494712,0.3398443162,0.1209282503,0.1319723129,0.0127151208,0.0047444995,-0.0747593865,0.0249654111,0.0320717469,-0.0236140732,-0.36054492,-0.2406122535,0.2245736271,0.2357540876,-0.0485627688,0.0452682115,-0.2116847932,-0.3131856322,-0.2082335055,0.1694273204,0.4560175538,0.0537248924,0.4907977581,0.3138802648,0.1613340378,-0.1267466247,-0.5722319484,-0.1803703606,0.3171550035,-0.161258176,-0.1637429446,0.3268589377,0.0613696501,0.2865219116,-0.2762343287,0.1872421801,0.1734672487,-0.0168005116,-0.1587234586,-0.2288744003,0.1743646711,0.1935789287,0.2789788842,0.2141343802,-0.2745317817,0.0189148672,0.2086554021,-0.2223849446,-0.1689475477,0.2648113072,0.042568069,0.107154578,-0.1384298354,-0.507543385,0.1115983129,-0.1357539892,-0.2659593821,-0.2610871792,-0.2019345164,-0.0996060669,-0.2297554612,0.3767790496,0.5294706821,-0.0989685282,-0.0291802213,-0.0063148174,0.1354220808,0.0829522833,0.0357792452,-0.311412245,0.492174834,-0.1760831773,-0.1047421619,0.8686538339,-0.5204976797,-0.0506761558,0.3639996946,0.1172987446,-0.071886532,0.3070545495,0.0618702248,0.1170750111,-0.0958950073,0.0317528211,0.4021973014,-0.0465446152,0.0234558284,-0.1533483863,0.0390015058,0.2969612777,0.3607648909,0.0934307128,0.2212417275,-0.2734820545,0.4947689474,0.2071569562,0.0037385139,0.0605673529,0.5123981833,-0.3901424408,-0.0827326104,0.0390271321,0.113077119,0.0266657285,-0.0488630608,-0.1335092783,0.1562322825,0.1626265943,0.1614460051,-0.4275047183,0.0230152868,-0.0830781758,0.0427630432,0.0567392223,0.2840376198,-0.1937215477,0.2580310404,0.0285491738,0.3317639828,0.0175104998,-0.0234073736,-0.3775363863,0.2045926601,-0.1200193763,-0.2554772794,0.282967478,-0.0228369217,0.4561701119,0.0420877747,-0.1734325886,0.1393243074,0.1799443066,-0.0209084228,0.0413391441,-0.2081551701,0.0283872373,-0.134917751,-0.0474718511,0.5747932792,0.2473167032,-0.193128854,0.0073986109,-0.0563839599,-0.0950333998,0.2234849483,-0.0520732105,0.1890359968,0.0732780173,0.2233697176,-0.0849315748,0.2273704857,0.204699859,0.1990070641,0.2141200006,-0.0926080421,-0.0686815456,-0.2286660373,0.2874112725,0.2148696929,0.0324278735,-0.0144205429,-0.2963917255,0.002503603,0.2251099199,-0.0638526604,0.2678775489,0.1726977378,-0.12468867,-0.0075488235,0.0018160535,-0.1072777137,0.4460032582,0.0564808063,0.261120379,-0.016631458,-0.1084316075,-0.0197839774,-0.2426254004,-0.2545631528,0.0278431252,0.2242687643,-0.4247313738,0.0356792621,-0.2087251991,-0.6600081921,-0.2359620631,-0.0494453721,-0.1605994701,-0.5055602193,-0.1612770408,0.3273543119,0.0146426978,0.1275832504,-0.120585151,0.2305703163,0.3319926858,-0.2339310497,-0.4185202122,-0.1734266132,-0.2996529937,-0.0336430594,0.1122232676,0.132373184,0.1385983676,0.113913238,-0.0306637753,-0.0660983101,-0.0057053682,0.2152480781,-0.1090568751,0.123509571,0.2650186121,0.3777044117,-0.2389560044,-0.0974703133,0.3813408017,0.0026626792,0.1714810729,0.3198922575,0.0355916694,-0.075479649,-0.1061445251,-0.2500555813,0.0514185093,-0.6115214825,-0.1775638014,0.237305969,0.000052158,0.1542863101,0.2415067703,0.1805426329,0.1287678778,0.3176608682,0.0169246066,-0.2387425154,0.3329898715,-0.1693131328,-0.3372408748,-0.1376382411,0.1169654056,0.2338880152,0.219890222,-0.4601656795,-0.3833651543,0.29610461,-0.034795884,0.0065297475,0.1694480926,0.3062468469,0.017128082,0.0924849436,-0.2547699511,-0.198965624,0.0468889549,-0.2222736031,0.2826959491,0.2882086039,0.3215004206,-0.0384758487,0.9122626185,0.237421751,0.143084988,0.5309516788,0.1021699384,0.2991394699,-0.0367710814,-0.2422951907,0.230656445,0.1812363267,0.1366699338,0.2091348171,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/507","title":"Errors when I use ","comments":"Looks like an issue with 3.0.2 transformers version. Works fine when I use \"master\" version of transformers.","body":"I tried the following example code from https:\/\/huggingface.co\/deepset\/roberta-base-squad2 and got errors \r\nI am using **transformers 3.0.2** code .\r\n\r\n\r\nfrom transformers.pipelines import pipeline\r\nfrom transformers.modeling_auto import AutoModelForQuestionAnswering\r\nfrom transformers.tokenization_auto import AutoTokenizer\r\n\r\nmodel_name = \"deepset\/roberta-base-squad2\"\r\n\r\nnlp = pipeline('question-answering', model=model_name, tokenizer=model_name)\r\nQA_input = {\r\n    'question': 'Why is model conversion important?',\r\n    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'\r\n}\r\nres = nlp(QA_input)\r\n\r\nThe errors are :\r\n\r\nres = nlp(QA_input)\r\n  File \".local\/lib\/python3.6\/site-packages\/transformers\/pipelines.py\", line 1316, in __call__\r\n    for s, e, score in zip(starts, ends, scores)\r\n  File \".local\/lib\/python3.6\/site-packages\/transformers\/pipelines.py\", line 1316, in <listcomp>\r\n    for s, e, score in zip(starts, ends, scores)\r\nKeyError: 0\r\n\r\n","comment_length":17,"text":"Errors when I use  \n I tried the following example code from https:\/\/huggingface.co\/deepset\/roberta-base-squad2 and got errors \r\nI am using **transformers 3.0.2** code .\r\n\r\n\r\nfrom transformers.pipelines import pipeline\r\nfrom transformers.modeling_auto import AutoModelForQuestionAnswering\r\nfrom transformers.tokenization_auto import AutoTokenizer\r\n\r\nmodel_name = \"deepset\/roberta-base-squad2\"\r\n\r\nnlp = pipeline('question-answering', model=model_name, tokenizer=model_name)\r\nQA_input = {\r\n    'question': 'Why is model conversion important?',\r\n    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'\r\n}\r\nres = nlp(QA_input)\r\n\r\nThe errors are :\r\n\r\nres = nlp(QA_input)\r\n  File \".local\/lib\/python3.6\/site-packages\/transformers\/pipelines.py\", line 1316, in __call__\r\n    for s, e, score in zip(starts, ends, scores)\r\n  File \".local\/lib\/python3.6\/site-packages\/transformers\/pipelines.py\", line 1316, in <listcomp>\r\n    for s, e, score in zip(starts, ends, scores)\r\nKeyError: 0\r\n\r\n \n Looks like an issue with 3.0.2 transformers version. Works fine when I use \"master\" version of transformers.","embeddings":[0.1425897479,-0.3862664104,0.101900138,0.2882798612,-0.0408770628,-0.0037297707,0.360788852,0.286631912,-0.2326703668,-0.0361642577,0.1111190468,0.1749107987,-0.3020108342,-0.1347761005,-0.1869156063,-0.2722382843,0.2231000662,0.1648703665,-0.2249961048,-0.075563103,-0.0981416553,0.4294788837,-0.5823258162,0.3994284868,-0.6540243626,0.0634840205,-0.0976940095,-0.0882224217,0.2179895788,-0.4048303068,0.1664741337,-0.1379939169,0.4408763647,0.4950669706,-0.0001128557,0.0662931651,0.5166268349,0.0034337423,-0.1831671298,-0.4616500437,-0.0958666354,-0.021679448,0.2401293516,-0.1488670856,0.1099354774,-0.0095904814,0.1123630106,0.1161488667,0.6158462167,0.2604494393,0.2036687583,0.5679879785,0.1606728882,-0.1586021334,0.0386505686,0.0651195422,-0.1622345448,-0.2532220483,-0.0711850077,-0.1541238725,0.0513456874,0.3433673978,-0.1545834094,-0.319506824,0.1965100169,-0.0248659048,0.3771715462,-0.6403257251,0.1768134832,0.344715029,-0.0220762305,0.0297095384,-0.4338785112,-0.370990634,-0.0477737077,0.0846328735,0.0715651065,-0.0575927012,-0.1082190797,-0.0320369713,-0.3570268154,0.1110915169,-0.2577053308,-0.0387131535,-0.0056881788,0.7290472388,-0.0043151458,0.115419209,0.0263560321,-0.3988368511,0.1119359508,-0.0886761546,-0.1108078659,0.2810792029,-0.4623845816,-0.4678985178,-0.1054500863,-0.0141506167,-0.1258595437,0.132976383,-0.047635071,-0.0400705934,0.1146947145,0.050596036,0.2708026767,0.3776749372,0.3136990964,0.3700650334,0.3415612578,-0.0040864917,0.1595462114,-0.0196935255,-0.4887844622,-0.0226209201,0.0947434604,0.1682706028,0.2845754027,-0.0403796956,0.0695829839,-0.0889555886,-0.2725495696,0.0308969915,0.2877452075,0.3125798106,0.1607083231,0.4081320167,-0.0442818627,0.3618612885,-0.1540920287,-0.4788371027,-0.0482453443,-0.0248205196,-0.1305386573,0.0259037018,0.183268398,0.3635356128,0.2959641218,-0.3133236766,0.1574114859,0.1929110885,0.0696845949,-0.2652225494,-0.1392059326,0.222193867,0.0477906168,0.1235276908,0.2732222676,-0.1546162963,-0.0269144867,0.2888833284,-0.152669847,-0.0730928034,0.0382680893,0.128095746,-0.1564274281,-0.2049593031,-0.1068510935,0.1424414068,0.2601503134,-0.0196878687,-0.1511559039,-0.2680458724,-0.0712297335,-0.0273503531,0.0152345067,0.4272629619,0.1217842326,-0.2629700005,-0.0074936883,0.1439886689,-0.105313167,0.1660331339,-0.0594261736,0.239754051,-0.0067093009,0.3142331541,0.3387302756,-0.2347958386,0.0610828958,0.1511665434,-0.0109123942,-0.2349623442,0.055894576,-0.1820522696,0.1183111966,-0.2271659523,-0.0724461749,-0.1401990652,0.0083090318,0.1611102819,-0.0977460369,-0.2160190493,-0.0398633629,0.2065846771,0.1662975699,0.1019943804,-0.379703939,0.5313245058,-0.1683852822,-0.190780282,0.1780082732,-0.0916158035,0.4059320092,-0.241859749,0.0768683404,-0.1072881296,-0.2731477022,0.2203312218,-0.498021841,0.1964557916,-0.0521731004,0.2371753007,-0.1382563263,0.0803377777,-0.1817128807,-0.0770964175,0.170181185,-0.0776247233,0.1599178612,0.1136876568,-0.2996758223,-0.0866934285,0.4864613116,0.0755586326,-0.3800067008,0.2390677333,-0.256290853,-0.3221542835,-0.172824353,0.1800199896,0.4133439064,0.1605977416,-0.0603638999,0.2198885977,-0.2134710997,-0.0920353681,-0.1008523032,0.3315393627,0.2418498993,-0.3857061863,0.153454572,0.4965134859,0.1735297292,0.1489476264,0.1390587538,0.4117442369,0.049088195,0.1475323439,0.0893392488,0.1436101794,0.027317645,-0.1975849867,-0.1786641628,-0.0489501692,0.0124958521,0.0653677955,-0.0209296271,-0.3088859022,0.0854294524,-0.190109387,0.4991106093,-0.0220074784,0.1619540155,0.1475793719,-0.4284462333,0.040990755,-0.1600481272,0.2272354066,0.3109731674,0.1093931422,-0.1593066603,0.1008156389,-0.0423444621,-0.0339870341,0.330276072,0.0721159279,0.2826166451,0.1288802326,0.1914828718,0.1401966363,0.1415277123,-0.2043960243,-0.0989474133,0.203945294,-0.3245393932,0.1887784898,0.0387539268,0.0697252005,-0.1371349692,-0.243154645,-0.2385838777,-0.3978781402,0.0922368541,0.0347949155,0.0489942655,0.2307594121,0.0914829001,0.2400474399,0.2203381807,0.4928330481,-0.0906588361,-0.0119173201,-0.4879196286,-0.0100958925,0.0699659437,-0.5400472283,-0.0604566336,0.0447766706,-0.0678852722,0.2947699726,-0.3794869781,0.3721455038,-0.1519844085,0.0831870884,0.1768323928,-0.1078044176,-0.0183152072,-0.3880954683,0.429258436,-0.3619121611,-0.0171322078,0.3871999681,0.0423268676,-0.1268575191,-0.2220814228,-0.0811966434,0.0566206016,-0.1438731849,-0.0924244896,-0.134232834,0.1725471616,0.4180764258,0.02203325,0.4735864699,-0.2557699978,-0.1696811765,0.1678778827,0.1819523722,0.5343099236,-0.0216974895,-0.3780197799,0.1458331943,-0.2738334537,-0.0258694589,-0.5093508363,-0.0553693734,-0.25315997,-0.1245511249,-0.1565585136,0.1502877772,0.0557691082,0.2515535653,0.0170890093,0.00738212,0.0466912054,-0.1840882599,0.1286096573,0.2061227858,0.3723753393,-0.0267038718,0.4878305793,0.10480652,0.629336834,0.3555672169,-0.0857290626,0.1341524422,0.0791091472,-0.2296394855,0.0292939637,-0.2251011133,-0.3233571649,0.0312585235,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/501","title":"Caching doesn't work for map (non-deterministic)","comments":"Thanks for reporting !\r\n\r\nTo store the cache file, we compute a hash of the function given in `.map`, using our own hashing function.\r\nThe hash doesn't seem to stay the same over sessions for the tokenizer.\r\nApparently this is because of the regex at `tokenizer.pat` is not well supported by our hashing function.\r\n\r\nI'm working on a fix","body":"The caching functionality doesn't work reliably when tokenizing a dataset. Here's a small example to reproduce it. \r\n\r\n```python\r\nimport nlp\r\nimport transformers\r\n\r\ndef main():\r\n    ds = nlp.load_dataset(\"reddit\", split=\"train[:500]\")\r\n\r\n    tokenizer = transformers.AutoTokenizer.from_pretrained(\"gpt2\")\r\n\r\n    def convert_to_features(example_batch):\r\n        input_str = example_batch[\"body\"]\r\n        encodings = tokenizer(input_str, add_special_tokens=True, truncation=True)\r\n        return encodings\r\n\r\n    ds = ds.map(convert_to_features, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\nRoughly 3\/10 times, this example recomputes the tokenization.\r\n\r\nIs this expected behaviour?","comment_length":59,"text":"Caching doesn't work for map (non-deterministic) \n The caching functionality doesn't work reliably when tokenizing a dataset. Here's a small example to reproduce it. \r\n\r\n```python\r\nimport nlp\r\nimport transformers\r\n\r\ndef main():\r\n    ds = nlp.load_dataset(\"reddit\", split=\"train[:500]\")\r\n\r\n    tokenizer = transformers.AutoTokenizer.from_pretrained(\"gpt2\")\r\n\r\n    def convert_to_features(example_batch):\r\n        input_str = example_batch[\"body\"]\r\n        encodings = tokenizer(input_str, add_special_tokens=True, truncation=True)\r\n        return encodings\r\n\r\n    ds = ds.map(convert_to_features, batched=True)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n```\r\n\r\nRoughly 3\/10 times, this example recomputes the tokenization.\r\n\r\nIs this expected behaviour? \n Thanks for reporting !\r\n\r\nTo store the cache file, we compute a hash of the function given in `.map`, using our own hashing function.\r\nThe hash doesn't seem to stay the same over sessions for the tokenizer.\r\nApparently this is because of the regex at `tokenizer.pat` is not well supported by our hashing function.\r\n\r\nI'm working on a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/492","title":"nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema","comments":"In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.\r\nCould you try to update `nlp` ?\r\n\r\nAlso, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.","body":"Here's the code I'm trying to run:\r\n\r\n```python\r\ndset_wikipedia = nlp.load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=args.cache_dir)\r\ndset_wikipedia.drop(columns=[\"title\"])\r\ndset_wikipedia.features.pop(\"title\")\r\ndset_books = nlp.load_dataset(\"bookcorpus\", split=\"train\", cache_dir=args.cache_dir)\r\ndset = nlp.concatenate_datasets([dset_wikipedia, dset_books])\r\n```\r\n\r\nThis fails because they have different schemas, despite having identical features.\r\n\r\n```python\r\nassert dset_wikipedia.features == dset_books.features # True\r\nassert dset_wikipedia._data.schema == dset_books._data.schema # False\r\n```\r\n\r\nThe Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.\r\n\r\n```python\r\ndset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)\r\n```\r\n","comment_length":35,"text":"nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema \n Here's the code I'm trying to run:\r\n\r\n```python\r\ndset_wikipedia = nlp.load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=args.cache_dir)\r\ndset_wikipedia.drop(columns=[\"title\"])\r\ndset_wikipedia.features.pop(\"title\")\r\ndset_books = nlp.load_dataset(\"bookcorpus\", split=\"train\", cache_dir=args.cache_dir)\r\ndset = nlp.concatenate_datasets([dset_wikipedia, dset_books])\r\n```\r\n\r\nThis fails because they have different schemas, despite having identical features.\r\n\r\n```python\r\nassert dset_wikipedia.features == dset_books.features # True\r\nassert dset_wikipedia._data.schema == dset_books._data.schema # False\r\n```\r\n\r\nThe Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.\r\n\r\n```python\r\ndset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)\r\n```\r\n \n In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.\r\nCould you try to update `nlp` ?\r\n\r\nAlso, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.","embeddings":[-0.0643963218,0.11732544,0.0268085655,0.1252689362,0.1567067057,-0.0773083195,0.3404867053,0.1692478806,-0.3219616711,-0.3533984423,0.0765748993,0.3673611879,-0.0683538541,0.3275818527,0.0926476717,-0.0278011616,0.20226641,0.2302506864,0.0969581977,-0.0026237783,-0.0930334851,0.1811446249,-0.3965626359,0.0402221829,-0.5902644396,0.2065486014,-0.0333003029,-0.0195062682,-0.1387539655,-0.5088027716,0.344329983,0.20197393,-0.1002951264,0.3447961807,-0.0001246,-0.1214664951,0.5814360976,-0.1061937883,-0.1357245892,-0.1315975189,-0.1061730608,-0.334274441,0.1451298594,-0.184059307,0.0441182479,-0.1426272988,-0.0075730518,0.3041886091,-0.5539480448,0.2694932222,0.0474009812,0.5145810246,0.4430456161,0.2505997121,0.3381246626,0.0039449814,-0.1633378565,0.0774524137,-0.2217185795,-0.2308957428,0.2946070433,-0.004089464,-0.3032649755,-0.2862060964,0.189220652,0.0053237276,0.036097832,-0.1486219764,-0.1327947378,0.4225979745,0.4490500093,-0.2722763419,-0.3485791087,-0.2052143365,0.0249234177,-0.2226937264,0.3266037405,0.4107818007,0.0495450981,0.0445891209,-0.4404838681,0.1101490781,-0.1068293229,0.2732964456,-0.3308849335,0.3821586967,0.423401922,0.1096609756,0.0128080724,-0.1292209476,0.2586248517,-0.3746066988,-0.0194233023,0.1626722962,-0.2908125818,-0.1841994226,-0.22538656,0.0221132934,0.430544436,-0.0290179849,-0.1455563158,0.0746988207,-0.1401955932,0.1076272652,0.1945328861,0.2544096708,0.2239787877,-0.1042137668,0.0038693603,0.076795876,-0.2166543156,0.2659810185,0.2274273485,-0.2757505774,0.0893795118,-0.0405187644,0.5651307702,-0.0694724619,-0.3042345047,0.0565479025,-0.7364634871,-0.1235802546,0.102914609,-0.0402498618,-0.0763520151,0.4491232932,0.0586440302,0.3635566235,-0.197696045,-0.3798347414,-0.0722624809,0.0145610329,0.016205024,0.06115219,0.1437865645,0.0790431872,0.1670461595,-0.0208609458,-0.4102073908,0.205223456,-0.2113888413,-0.1676452011,-0.1489060223,0.0289777331,-0.2191667557,-0.3602612317,0.0156736393,-0.3502401114,-0.2368439138,0.153339684,-0.0034028466,-0.3047691882,-0.2618936896,0.032093484,-0.3846156001,-0.1074053645,-0.0526561029,0.3227862716,0.2190195769,-0.0189926382,0.0711328015,-0.1152549013,-0.0018663049,-0.2445031852,0.1468636692,0.1550917625,-0.0509334356,-0.0164897721,0.1121340021,-0.1654553711,-0.0499183312,0.2630227506,-0.0839521363,0.0946981981,-0.1670563072,0.4398614168,0.3568460941,-0.0073680687,-0.0031482261,0.0458445586,0.2689749002,0.0852905959,0.1107114032,0.0083126724,-0.3739365041,0.0349341445,-0.0445479415,0.3406242728,-0.0643275231,-0.1324491948,-0.200401634,-0.329082936,0.6721149087,-0.1642895341,-0.0742964223,0.1388433725,-0.0933730677,0.2371647358,0.3771465123,-0.2393366247,-0.1351750642,-0.0320394002,0.2976107299,0.2248855978,-0.3573701382,-0.3245314062,-0.6215187907,0.1153126806,-0.2744531631,0.5649373531,-0.0713170171,-0.3467866778,-0.0226434506,0.1168041304,0.1842118055,0.0491243191,0.0502412207,0.1320669651,0.003475416,0.2040457278,-0.017224595,-0.0341619737,-0.0021852811,0.0743370801,-0.1543972343,0.5079219341,-0.050801307,-0.2447683811,0.0116013149,0.5782472491,0.2310594618,0.0018774219,0.0209860038,0.2861755192,-0.0210095346,0.0169910416,-0.1841734499,0.3597716987,0.0103445137,-0.108213231,-0.1197339594,0.2958289087,0.0824739859,-0.0362321101,0.106977649,0.5403357744,0.0890617818,-0.1274826825,0.2386863232,-0.0310108252,0.0641216114,-0.1456842124,-0.0917195156,-0.0687631965,0.1033114865,-0.029711565,-0.3782554865,0.4686181545,-0.5818831325,-0.1038340107,0.4880428314,-0.1803923398,0.2012025714,-0.1890496463,-0.1980809718,0.0216359086,0.3691531718,-0.36863482,0.1642698497,0.1738227159,0.0248235594,0.0485812575,-0.3041326702,0.0645630807,0.3759593666,-0.0363258719,0.1276257038,-0.0101232845,0.2944433391,-0.2063151449,-0.3622223139,0.1404734701,0.1248603091,-0.0001578323,-0.3340364397,-0.00883877,-0.3641410172,-0.0675907582,-0.4399199188,-0.2286157608,-0.5033985972,-0.4318349063,-0.148087725,0.7034394741,0.0043244301,0.2784733474,-0.6784133911,-0.2246820331,0.0438677296,-0.2637732327,-0.0135059636,0.0479043722,-0.1932651848,-0.0891905725,0.2925310135,0.1887551546,-0.2067164034,0.0206944756,-0.1609638333,-0.2025876343,-0.7249019146,0.0783891156,-0.3375943601,0.0019903327,0.2957773507,-0.0846194029,0.1590904295,-0.3467276096,0.1459008306,0.1718693674,-0.4136032164,0.4664402902,0.0333243459,-0.2877027988,-0.008931377,-0.2185085714,-0.0588836856,-0.2434386015,0.23347947,-0.0135711432,0.0751471817,0.4313396513,0.0159301758,0.0507930443,-0.1473100036,-0.0440619811,0.0670361146,0.4366896451,0.4299979508,0.2493715286,-0.1570455581,0.1447858959,-0.2302629799,-0.1630174965,0.2447808534,0.0009895115,-0.1102875695,-0.1580325514,0.5572417378,0.2054716796,-0.5082899332,0.3505128622,0.418728143,0.0752615407,0.0139181046,-0.232442379,-0.0090982756,0.4431093037,0.2931427658,0.0866401121,-0.1674508154,0.1715910435,0.052488599,-0.0354896784,-0.2688117921,0.5545909405,-0.1980309933,-0.0067355544,0.1651455164,-0.0011852155,-0.1469272524,0.0155081609,0.279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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/492","title":"nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema","comments":"I'm using the master branch. The assertion failure comes from the underlying `pa.concat_tables()`, which is in the pyarrow package. That method does check schemas.\r\n\r\nSince `features.type` does not contain information about nullable vs non-nullable features, the `cast_()` method won't resolve the schema mismatch. There is information in a schema which is not stored in features.","body":"Here's the code I'm trying to run:\r\n\r\n```python\r\ndset_wikipedia = nlp.load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=args.cache_dir)\r\ndset_wikipedia.drop(columns=[\"title\"])\r\ndset_wikipedia.features.pop(\"title\")\r\ndset_books = nlp.load_dataset(\"bookcorpus\", split=\"train\", cache_dir=args.cache_dir)\r\ndset = nlp.concatenate_datasets([dset_wikipedia, dset_books])\r\n```\r\n\r\nThis fails because they have different schemas, despite having identical features.\r\n\r\n```python\r\nassert dset_wikipedia.features == dset_books.features # True\r\nassert dset_wikipedia._data.schema == dset_books._data.schema # False\r\n```\r\n\r\nThe Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.\r\n\r\n```python\r\ndset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)\r\n```\r\n","comment_length":55,"text":"nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema \n Here's the code I'm trying to run:\r\n\r\n```python\r\ndset_wikipedia = nlp.load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=args.cache_dir)\r\ndset_wikipedia.drop(columns=[\"title\"])\r\ndset_wikipedia.features.pop(\"title\")\r\ndset_books = nlp.load_dataset(\"bookcorpus\", split=\"train\", cache_dir=args.cache_dir)\r\ndset = nlp.concatenate_datasets([dset_wikipedia, dset_books])\r\n```\r\n\r\nThis fails because they have different schemas, despite having identical features.\r\n\r\n```python\r\nassert dset_wikipedia.features == dset_books.features # True\r\nassert dset_wikipedia._data.schema == dset_books._data.schema # False\r\n```\r\n\r\nThe Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.\r\n\r\n```python\r\ndset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)\r\n```\r\n \n I'm using the master branch. The assertion failure comes from the underlying `pa.concat_tables()`, which is in the pyarrow package. That method does check schemas.\r\n\r\nSince `features.type` does not contain information about nullable vs non-nullable features, the `cast_()` method won't resolve the schema mismatch. There is information in a schema which is not stored in 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/492","title":"nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema","comments":"I'm doing a refactor of type inference in #363 . Both text fields should match after that","body":"Here's the code I'm trying to run:\r\n\r\n```python\r\ndset_wikipedia = nlp.load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=args.cache_dir)\r\ndset_wikipedia.drop(columns=[\"title\"])\r\ndset_wikipedia.features.pop(\"title\")\r\ndset_books = nlp.load_dataset(\"bookcorpus\", split=\"train\", cache_dir=args.cache_dir)\r\ndset = nlp.concatenate_datasets([dset_wikipedia, dset_books])\r\n```\r\n\r\nThis fails because they have different schemas, despite having identical features.\r\n\r\n```python\r\nassert dset_wikipedia.features == dset_books.features # True\r\nassert dset_wikipedia._data.schema == dset_books._data.schema # False\r\n```\r\n\r\nThe Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.\r\n\r\n```python\r\ndset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)\r\n```\r\n","comment_length":17,"text":"nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema \n Here's the code I'm trying to run:\r\n\r\n```python\r\ndset_wikipedia = nlp.load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=args.cache_dir)\r\ndset_wikipedia.drop(columns=[\"title\"])\r\ndset_wikipedia.features.pop(\"title\")\r\ndset_books = nlp.load_dataset(\"bookcorpus\", split=\"train\", cache_dir=args.cache_dir)\r\ndset = nlp.concatenate_datasets([dset_wikipedia, dset_books])\r\n```\r\n\r\nThis fails because they have different schemas, despite having identical features.\r\n\r\n```python\r\nassert dset_wikipedia.features == dset_books.features # True\r\nassert dset_wikipedia._data.schema == dset_books._data.schema # False\r\n```\r\n\r\nThe Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.\r\n\r\n```python\r\ndset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)\r\n```\r\n \n I'm doing a refactor of type inference in #363 . Both text fields should match after that","embeddings":[-0.0161189064,0.0418332927,0.0497547574,0.1791476756,0.2209598422,-0.1551372409,0.3485466242,0.1771947145,-0.2337825149,-0.2496900111,0.0528562553,0.2427443117,0.0418508314,0.3009526134,-0.0142641636,-0.0392464958,0.1369884163,0.1927271336,0.057350155,-0.0133017655,0.0279206391,0.2128109336,-0.432697773,0.0798531547,-0.6052960753,0.2832012773,0.1217158511,-0.081533961,-0.1724741757,-0.3538147807,0.3396440744,0.0633926839,-0.1273943186,0.2941175103,-0.0001230619,-0.0787973627,0.4711290002,-0.1221959591,0.0056540486,-0.1541832536,0.0033247711,-0.2184462398,0.1188606918,-0.1594703943,-0.0122466227,0.0787842572,0.0574800968,0.2655121088,-0.485758543,0.2143756896,0.0313540623,0.5556202531,0.3611120582,0.2949572802,0.4820652604,0.1081078947,-0.1640469879,-0.0403706841,-0.1488456428,-0.2206766009,0.3500571847,0.0233771205,-0.3031662405,-0.2488754392,0.2104114145,0.1371807605,0.0132312728,-0.1658138186,-0.2161864638,0.3846069574,0.378490299,-0.2721752524,-0.2982418239,-0.2871522605,-0.063659139,-0.1074372679,0.3144471645,0.2532115877,-0.0958044007,0.0034741391,-0.2865937948,0.1025461406,-0.0919411853,0.1352296621,-0.3512834609,0.3031047285,0.4236579239,0.0838388056,-0.1090577915,-0.058107052,0.2037526518,-0.368291378,0.1821449399,0.1974302232,-0.3036396205,-0.2578523457,-0.1963276267,-0.1283039302,0.3435287476,-0.0971284658,-0.1011330709,0.0921988636,-0.1799586564,0.2135977298,0.2164671272,0.194020465,0.0921862796,-0.046675887,0.0324375629,0.0155875199,-0.2754154503,0.1777701378,0.2533297539,-0.3579941392,0.2222444415,-0.0222091675,0.5951607227,-0.2146412283,-0.3581434488,0.1046301797,-0.6537966728,-0.029283965,0.1376782507,-0.124590382,-0.1058189422,0.4346594214,-0.0013267018,0.3968073726,-0.2712625265,-0.363132298,-0.0425493792,-0.0084453467,0.0379900299,-0.011499295,0.1196713448,0.2067755312,0.1114208326,0.0167975277,-0.3512586355,0.2169020176,-0.1675958633,-0.2053929716,-0.0912243351,0.0087310197,-0.2506656051,-0.3312960863,0.1384344697,-0.3670900762,-0.2213854194,0.1448272616,0.0643103421,-0.1541718245,-0.1422813088,0.0524084941,-0.4513302147,-0.1260323673,-0.162942782,0.335595727,0.2153673619,0.0086010993,0.0566011667,-0.1638466418,-0.010453105,-0.2158825845,0.2360284179,0.1352060288,-0.0815292671,-0.0126469173,0.1752243489,-0.2601315379,-0.0406284444,0.2410740405,-0.0236211754,0.06748835,-0.1356779188,0.5942892432,0.3529723883,0.0254981369,0.1012028828,0.0385647714,0.2206334919,0.0668069571,0.0872135535,0.0233997442,-0.3188511729,0.0589516349,-0.0854753703,0.3682381809,-0.1921682507,-0.0841463506,-0.2003959566,-0.2794536054,0.5485409498,-0.1183056757,-0.0097655393,0.0945071802,-0.1517408937,0.2460001558,0.265645057,-0.2121187449,-0.1468266547,-0.0330821797,0.3460656106,0.3285520673,-0.425127387,-0.4670061767,-0.5462675095,0.1526118815,-0.351154387,0.5649885535,-0.0926044285,-0.3628206551,-0.0444890596,0.0957760215,0.2239724696,0.1940458715,0.0716375783,0.0665750653,-0.0451185443,0.1773070395,-0.0164547116,-0.146097675,0.029327767,0.1820035428,-0.1878122091,0.5039507151,-0.0505503863,-0.2504658997,-0.0486585088,0.5068321228,0.2212622017,0.0639989302,0.0746512488,0.1706355363,0.0844889209,0.0274592768,-0.2565710843,0.2689789534,-0.0357396677,-0.092710413,-0.1811239123,0.2658528686,0.1443213224,-0.0504332259,0.0820591524,0.478107959,0.1191560924,-0.168379426,0.2042897195,-0.1395613402,-0.0092684506,-0.118708849,-0.0515521392,-0.0625140518,0.0655582249,0.0588733144,-0.5917344689,0.5582193136,-0.6158355474,-0.136264056,0.4373213649,-0.1705741733,0.1929085851,-0.2389010042,-0.1187842935,0.0776206404,0.2546368241,-0.4221937656,0.1173963323,0.1194532886,0.048579853,-0.0174208209,-0.3542957008,0.1041503176,0.2996139228,0.0059601068,0.1607751846,-0.0010493792,0.2029417902,-0.0292646028,-0.3321914077,0.1610555351,0.1127512977,-0.0855798572,-0.3652036488,-0.051685445,-0.2765150666,-0.0831770301,-0.4930624068,-0.1210074648,-0.3591436148,-0.4036301076,-0.0751148537,0.7243289351,-0.0182254557,0.1753558666,-0.6069014072,-0.2430244237,-0.0019760968,-0.342610836,-0.0480912104,0.0056586768,-0.1899309903,-0.0440212749,0.349336803,0.2407254428,-0.2683977783,-0.0032677299,-0.2104715109,-0.2128612995,-0.8254592419,0.0923086256,-0.3423710763,0.0749224648,0.2931859493,-0.0475140214,0.2073671073,-0.2992775142,0.1691264212,0.1805768609,-0.4905545413,0.4479922354,0.0758826062,-0.289455086,-0.0016760846,-0.171876967,0.0003845845,-0.1767751426,0.0933616087,-0.0921217948,-0.0221567992,0.3520934582,-0.0063570072,-0.0300375801,-0.1265924871,0.0043456126,0.1129131913,0.4491636157,0.4396447837,0.3248732686,-0.0922381207,0.1975491196,-0.2271224707,-0.2233732939,0.1804771572,0.0070676259,-0.1562499404,-0.2261992991,0.5512505174,0.2355549634,-0.4834789038,0.3058188558,0.4202595651,0.077481702,0.0824681446,-0.2298171669,-0.0179771837,0.3961814344,0.3203352988,-0.0228164364,-0.1455749869,0.1353580505,0.0627212152,-0.1255173981,-0.2235631943,0.5573557615,-0.1932648718,-0.1299394816,0.1700903475,0.0713229105,-0.1176922396,-0.0582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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/492","title":"nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema","comments":"It should be good now. I was able to run\r\n\r\n```python\r\n>>> from nlp import concatenate_datasets, load_dataset\r\n>>>\r\n>>> bookcorpus = load_dataset(\"bookcorpus\", split=\"train\")\r\n>>> wiki = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\")\r\n>>> wiki.remove_columns_(\"title\")  # only keep the text\r\n>>>\r\n>>> assert bookcorpus.features.type == wiki.features.type\r\n>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])\r\n```","body":"Here's the code I'm trying to run:\r\n\r\n```python\r\ndset_wikipedia = nlp.load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=args.cache_dir)\r\ndset_wikipedia.drop(columns=[\"title\"])\r\ndset_wikipedia.features.pop(\"title\")\r\ndset_books = nlp.load_dataset(\"bookcorpus\", split=\"train\", cache_dir=args.cache_dir)\r\ndset = nlp.concatenate_datasets([dset_wikipedia, dset_books])\r\n```\r\n\r\nThis fails because they have different schemas, despite having identical features.\r\n\r\n```python\r\nassert dset_wikipedia.features == dset_books.features # True\r\nassert dset_wikipedia._data.schema == dset_books._data.schema # False\r\n```\r\n\r\nThe Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.\r\n\r\n```python\r\ndset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)\r\n```\r\n","comment_length":48,"text":"nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema \n Here's the code I'm trying to run:\r\n\r\n```python\r\ndset_wikipedia = nlp.load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\", cache_dir=args.cache_dir)\r\ndset_wikipedia.drop(columns=[\"title\"])\r\ndset_wikipedia.features.pop(\"title\")\r\ndset_books = nlp.load_dataset(\"bookcorpus\", split=\"train\", cache_dir=args.cache_dir)\r\ndset = nlp.concatenate_datasets([dset_wikipedia, dset_books])\r\n```\r\n\r\nThis fails because they have different schemas, despite having identical features.\r\n\r\n```python\r\nassert dset_wikipedia.features == dset_books.features # True\r\nassert dset_wikipedia._data.schema == dset_books._data.schema # False\r\n```\r\n\r\nThe Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves.\r\n\r\n```python\r\ndset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema)\r\n```\r\n \n It should be good now. I was able to run\r\n\r\n```python\r\n>>> from nlp import concatenate_datasets, load_dataset\r\n>>>\r\n>>> bookcorpus = load_dataset(\"bookcorpus\", split=\"train\")\r\n>>> wiki = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\")\r\n>>> wiki.remove_columns_(\"title\")  # only keep the text\r\n>>>\r\n>>> assert bookcorpus.features.type == wiki.features.type\r\n>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])\r\n```","embeddings":[0.0195755307,0.1014376283,0.0100496262,0.1525079161,0.2617855966,-0.0262445733,0.3992982209,0.1835874319,-0.2757073045,-0.3310970366,0.0578915067,0.2993277311,-0.0961161107,0.3490905464,-0.0248573888,-0.0886692926,0.2123325616,0.2151870579,0.0904195979,-0.0006232074,-0.021691367,0.1471001059,-0.4190040827,0.0407256186,-0.5618267059,0.1993517131,0.0091327727,-0.0635869876,-0.1321011037,-0.3558357954,0.359895885,0.1632291377,-0.0964083001,0.3494224846,-0.000123727,-0.0465317518,0.4617714584,-0.0944593549,-0.1142689288,-0.1229414791,-0.0468562543,-0.2261640728,0.1428212374,-0.2239898592,0.0660102889,0.0396810472,-0.0237114392,0.2180909365,-0.4827174544,0.3005023897,0.0497984998,0.6041247249,0.3216882646,0.2887707055,0.4325879812,0.0260213073,-0.2378447503,0.0571174398,-0.2866386473,-0.240039289,0.3645997345,-0.0030348324,-0.4224071801,-0.2776353657,0.17048572,0.0321026519,0.0139935929,-0.1811416149,-0.1326822788,0.3763589561,0.3835633993,-0.3270166814,-0.2761546075,-0.2740529478,-0.0383335501,-0.1471807957,0.2313626409,0.3422130942,0.0426287167,0.0377751254,-0.3583473861,0.1332840919,-0.0611011237,0.1729693711,-0.357649982,0.2891921997,0.4066442549,0.126505509,-0.0574196093,-0.0956979245,0.239703387,-0.3863389492,0.1325589269,0.1334773153,-0.3427969813,-0.1534195989,-0.2158363461,-0.1752316058,0.4142799973,-0.1013137847,-0.0684355274,0.0484885499,-0.1178586558,0.158159852,0.1803003252,0.3018612862,0.1722828746,-0.0425041802,-0.0473931432,0.0539680012,-0.2548178434,0.268329978,0.278373152,-0.2501658499,0.1644538939,-0.0081232116,0.5877899528,-0.1211650744,-0.3953981698,0.1054600105,-0.6579975486,-0.0653714165,0.1158040315,-0.0750866905,-0.016488662,0.4329295158,-0.0250338651,0.4224492311,-0.2510067225,-0.2912806273,-0.1103081182,0.0483359583,-0.006532588,-0.028801525,0.1892850101,0.0969447643,0.1741938889,-0.0417579561,-0.3898367584,0.2016262263,-0.1408275366,-0.2009076327,-0.0840657949,0.0512061007,-0.1916183382,-0.2432677597,0.1291822046,-0.4263624549,-0.2597584426,0.1686255932,0.0584447272,-0.228746593,-0.1498566717,0.0685939789,-0.4282655418,-0.1048147976,-0.0640074462,0.2332176566,0.230550766,-0.0454131365,0.0735665709,-0.1591985375,0.021661913,-0.262665391,0.1199827045,0.2031269819,-0.0939802006,0.0117671741,0.1568477154,-0.2104033381,0.005221765,0.2258634269,-0.0269044489,0.115931727,-0.186822623,0.5118384361,0.3278872371,-0.0432593897,0.0168937389,0.0380694084,0.2024354339,0.0965592265,0.1285848469,0.044415094,-0.2982770503,-0.0274744742,-0.0779013932,0.3087241948,-0.0985608324,-0.0781478584,-0.1820751131,-0.2565665543,0.6591675878,-0.0943205431,-0.056405399,0.1404180378,-0.1382977962,0.3328261375,0.3180729449,-0.1969291568,-0.1213603988,0.0179033335,0.1878581941,0.220866397,-0.4277662635,-0.3764052689,-0.6329755187,0.1295931786,-0.2952991128,0.5714241862,-0.0909824297,-0.3621647954,-0.1214407682,0.1148455888,0.1560097188,0.0661059469,0.0689284652,0.1525991112,0.019554507,0.1733784527,-0.063270323,-0.0691425502,-0.0001670228,0.1299988627,-0.3026860654,0.479421109,-0.0680714473,-0.2394234985,0.0380316302,0.5497893691,0.2630757987,0.0291558802,0.0447435454,0.2267813236,-0.0474290662,-0.0320529081,-0.184887141,0.3059113622,-0.006551655,-0.0952601209,-0.1367530078,0.2938750982,0.1112039983,-0.1022120267,0.0039689308,0.5206984282,-0.0201631188,-0.0484421477,0.187858358,-0.0610092357,0.0482265204,-0.1584980637,-0.0894819722,-0.1095933318,0.1126954183,0.0823305026,-0.4957492948,0.5330727696,-0.5667440891,-0.1811655313,0.4938714802,-0.1976945847,0.1824948192,-0.2020718157,-0.1289619952,0.0910811648,0.3797803521,-0.4210346043,0.2070302069,0.1284208745,0.1107249856,0.0249741543,-0.3196204901,0.0812596232,0.3331664205,-0.0291224178,0.111473456,-0.0383761674,0.2469995618,-0.1389680803,-0.3223757446,0.098164089,0.1357905269,-0.0476688854,-0.3277385831,0.0029740955,-0.3342837989,-0.1165993363,-0.3874281347,-0.2271279544,-0.4085310102,-0.4292827845,-0.1264097542,0.6499423385,-0.0563761368,0.2817405462,-0.654840529,-0.294658035,0.07852678,-0.4065558612,-0.0109403497,0.0041679931,-0.125518918,-0.067432791,0.3089804053,0.2916698754,-0.1843713522,-0.0413820632,-0.2161363065,-0.1620108336,-0.7112454772,0.1253072172,-0.4536364079,0.0261319242,0.297493577,-0.0095083332,0.1389731616,-0.3155109584,0.186070472,0.1755691916,-0.3917913139,0.4901833236,0.1041634083,-0.2944886684,-0.0321601257,-0.1916290522,-0.0293731745,-0.2649886012,0.1147539094,-0.0891405493,0.0408561379,0.3860730827,0.021111479,0.0487828329,-0.0745107234,0.0348818749,0.1042637676,0.4250565767,0.4264548421,0.2812412679,-0.1241302937,0.1868460029,-0.2605314851,-0.2054006904,0.2208205462,-0.0510112084,-0.1454127729,-0.2333434671,0.5366603732,0.2374630123,-0.3884631693,0.2863419354,0.4431610107,0.0826120079,0.0101798903,-0.3189225197,-0.0405623019,0.384085685,0.2121475488,0.0399056971,-0.1200202778,0.1387341172,0.0387932435,-0.0864963159,-0.2054846883,0.530977726,-0.2393932194,-0.0613697208,0.1415787041,-0.03202191,-0.1077440456,-0.0494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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/488","title":"issues with downloading datasets for wmt16 and wmt19","comments":"I found `UNv1.0.en-ru.tar.gz` here: https:\/\/conferences.unite.un.org\/uncorpus\/en\/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https:\/\/stuncorpusprod.blob.core.windows.net\/corpusfiles\/UNv1.0.en-ru.tar.gz.00\r\nwget -c https:\/\/stuncorpusprod.blob.core.windows.net\/corpusfiles\/UNv1.0.en-ru.tar.gz.01\r\nwget -c https:\/\/stuncorpusprod.blob.core.windows.net\/corpusfiles\/UNv1.0.en-ru.tar.gz.02\r\ncat UNv1.0.en-ru.tar.gz.0* > UNv1.0.en-ru.tar.gz\r\n```\r\nit has other languages as well, in case https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/ is gone","body":"I  have encountered multiple issues while trying to:\r\n```\r\nimport nlp\r\ndataset = nlp.load_dataset('wmt16', 'ru-en')\r\nmetric = nlp.load_metric('wmt16')\r\n```\r\n1. I had to do `pip install -e \".[dev]\" ` on master, currently released nlp didn't work (sorry, didn't save the error) - I went back to the released version and now it worked. So it must have been some outdated dependencies that  `pip install -e \".[dev]\" ` fixed.\r\n\r\n2. it was downloading at 60kbs - almost 5 hours to get the dataset. It was downloading all pairs and not just the one I asked for. \r\n\r\nI tried the same code with `wmt19` in parallel and it took a few secs to download and it only fetched data for the requested pair. (but it failed too, see below)\r\n\r\n3. my machine has crushed and when I retried I got:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/download.py\", line 9, in <module>\r\n    dataset = nlp.load_dataset('wmt16', 'ru-en')\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/builder.py\", line 449, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/stas\/anaconda3\/envs\/main\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/stas\/anaconda3\/envs\/main\/lib\/python3.7\/os.py\", line 221, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ru-en\/1.0.0\/4d8269cdd971ed26984a9c0e4a158e0c7afc8135fac8fb8ee43ceecf38fd422d.incomplete'\r\n```\r\nit can't handle resumes. but neither allows a new start. Had to delete it manually.\r\n\r\n4. and finally when it downloaded the dataset, it then failed to fetch the metrics:\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/download.py\", line 15, in <module>\r\n    metric = nlp.load_metric('wmt16')\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 442, in load_metric\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=False)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 258, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/utils\/file_utils.py\", line 198, in cached_path\r\n    local_files_only=download_config.local_files_only,\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/metrics\/wmt16\/wmt16.py\r\n```\r\n\r\n5. If I run the same code with `wmt19`, it fails too:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-ru.tar.gz\r\n```","comment_length":37,"text":"issues with downloading datasets for wmt16 and wmt19 \n I  have encountered multiple issues while trying to:\r\n```\r\nimport nlp\r\ndataset = nlp.load_dataset('wmt16', 'ru-en')\r\nmetric = nlp.load_metric('wmt16')\r\n```\r\n1. I had to do `pip install -e \".[dev]\" ` on master, currently released nlp didn't work (sorry, didn't save the error) - I went back to the released version and now it worked. So it must have been some outdated dependencies that  `pip install -e \".[dev]\" ` fixed.\r\n\r\n2. it was downloading at 60kbs - almost 5 hours to get the dataset. It was downloading all pairs and not just the one I asked for. \r\n\r\nI tried the same code with `wmt19` in parallel and it took a few secs to download and it only fetched data for the requested pair. (but it failed too, see below)\r\n\r\n3. my machine has crushed and when I retried I got:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/download.py\", line 9, in <module>\r\n    dataset = nlp.load_dataset('wmt16', 'ru-en')\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/builder.py\", line 449, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/stas\/anaconda3\/envs\/main\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/stas\/anaconda3\/envs\/main\/lib\/python3.7\/os.py\", line 221, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ru-en\/1.0.0\/4d8269cdd971ed26984a9c0e4a158e0c7afc8135fac8fb8ee43ceecf38fd422d.incomplete'\r\n```\r\nit can't handle resumes. but neither allows a new start. Had to delete it manually.\r\n\r\n4. and finally when it downloaded the dataset, it then failed to fetch the metrics:\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/download.py\", line 15, in <module>\r\n    metric = nlp.load_metric('wmt16')\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 442, in load_metric\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=False)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 258, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/utils\/file_utils.py\", line 198, in cached_path\r\n    local_files_only=download_config.local_files_only,\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/metrics\/wmt16\/wmt16.py\r\n```\r\n\r\n5. If I run the same code with `wmt19`, it fails too:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-ru.tar.gz\r\n``` \n I found `UNv1.0.en-ru.tar.gz` here: https:\/\/conferences.unite.un.org\/uncorpus\/en\/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https:\/\/stuncorpusprod.blob.core.windows.net\/corpusfiles\/UNv1.0.en-ru.tar.gz.00\r\nwget -c https:\/\/stuncorpusprod.blob.core.windows.net\/corpusfiles\/UNv1.0.en-ru.tar.gz.01\r\nwget -c https:\/\/stuncorpusprod.blob.core.windows.net\/corpusfiles\/UNv1.0.en-ru.tar.gz.02\r\ncat UNv1.0.en-ru.tar.gz.0* > UNv1.0.en-ru.tar.gz\r\n```\r\nit has other languages as well, in case https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/ is 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/488","title":"issues with downloading datasets for wmt16 and wmt19","comments":"Further, `nlp.load_dataset('wmt19', 'ru-en')` has only the `train` and `val` datasets. `test` is missing.\r\n\r\nFixed locally for summarization needs, by running:\r\n```\r\npip install sacrebleu\r\nsacrebleu -t wmt19 -l ru-en --echo src > test.source\r\nsacrebleu -t wmt19 -l ru-en --echo ref > test.target\r\n```\r\nh\/t @sshleifer ","body":"I  have encountered multiple issues while trying to:\r\n```\r\nimport nlp\r\ndataset = nlp.load_dataset('wmt16', 'ru-en')\r\nmetric = nlp.load_metric('wmt16')\r\n```\r\n1. I had to do `pip install -e \".[dev]\" ` on master, currently released nlp didn't work (sorry, didn't save the error) - I went back to the released version and now it worked. So it must have been some outdated dependencies that  `pip install -e \".[dev]\" ` fixed.\r\n\r\n2. it was downloading at 60kbs - almost 5 hours to get the dataset. It was downloading all pairs and not just the one I asked for. \r\n\r\nI tried the same code with `wmt19` in parallel and it took a few secs to download and it only fetched data for the requested pair. (but it failed too, see below)\r\n\r\n3. my machine has crushed and when I retried I got:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/download.py\", line 9, in <module>\r\n    dataset = nlp.load_dataset('wmt16', 'ru-en')\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/builder.py\", line 449, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/stas\/anaconda3\/envs\/main\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/stas\/anaconda3\/envs\/main\/lib\/python3.7\/os.py\", line 221, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ru-en\/1.0.0\/4d8269cdd971ed26984a9c0e4a158e0c7afc8135fac8fb8ee43ceecf38fd422d.incomplete'\r\n```\r\nit can't handle resumes. but neither allows a new start. Had to delete it manually.\r\n\r\n4. and finally when it downloaded the dataset, it then failed to fetch the metrics:\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/download.py\", line 15, in <module>\r\n    metric = nlp.load_metric('wmt16')\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 442, in load_metric\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=False)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 258, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/utils\/file_utils.py\", line 198, in cached_path\r\n    local_files_only=download_config.local_files_only,\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/metrics\/wmt16\/wmt16.py\r\n```\r\n\r\n5. If I run the same code with `wmt19`, it fails too:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-ru.tar.gz\r\n```","comment_length":45,"text":"issues with downloading datasets for wmt16 and wmt19 \n I  have encountered multiple issues while trying to:\r\n```\r\nimport nlp\r\ndataset = nlp.load_dataset('wmt16', 'ru-en')\r\nmetric = nlp.load_metric('wmt16')\r\n```\r\n1. I had to do `pip install -e \".[dev]\" ` on master, currently released nlp didn't work (sorry, didn't save the error) - I went back to the released version and now it worked. So it must have been some outdated dependencies that  `pip install -e \".[dev]\" ` fixed.\r\n\r\n2. it was downloading at 60kbs - almost 5 hours to get the dataset. It was downloading all pairs and not just the one I asked for. \r\n\r\nI tried the same code with `wmt19` in parallel and it took a few secs to download and it only fetched data for the requested pair. (but it failed too, see below)\r\n\r\n3. my machine has crushed and when I retried I got:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/download.py\", line 9, in <module>\r\n    dataset = nlp.load_dataset('wmt16', 'ru-en')\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 549, in load_dataset\r\n    download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/builder.py\", line 449, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/stas\/anaconda3\/envs\/main\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/builder.py\", line 422, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/stas\/anaconda3\/envs\/main\/lib\/python3.7\/os.py\", line 221, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/stas\/.cache\/huggingface\/datasets\/wmt16\/ru-en\/1.0.0\/4d8269cdd971ed26984a9c0e4a158e0c7afc8135fac8fb8ee43ceecf38fd422d.incomplete'\r\n```\r\nit can't handle resumes. but neither allows a new start. Had to delete it manually.\r\n\r\n4. and finally when it downloaded the dataset, it then failed to fetch the metrics:\r\n```\r\nTraceback (most recent call last):\r\n  File \".\/download.py\", line 15, in <module>\r\n    metric = nlp.load_metric('wmt16')\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 442, in load_metric\r\n    module_path, hash = prepare_module(path, download_config=download_config, dataset=False)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/load.py\", line 258, in prepare_module\r\n    local_path = cached_path(file_path, download_config=download_config)\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/utils\/file_utils.py\", line 198, in cached_path\r\n    local_files_only=download_config.local_files_only,\r\n  File \"\/mnt\/nvme1\/code\/huggingface\/nlp-master\/src\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/metrics\/wmt16\/wmt16.py\r\n```\r\n\r\n5. If I run the same code with `wmt19`, it fails too:\r\n\r\n```\r\nConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-ru.tar.gz\r\n``` \n Further, `nlp.load_dataset('wmt19', 'ru-en')` has only the `train` and `val` datasets. `test` is missing.\r\n\r\nFixed locally for summarization needs, by running:\r\n```\r\npip install sacrebleu\r\nsacrebleu -t wmt19 -l ru-en --echo src > test.source\r\nsacrebleu -t wmt19 -l ru-en --echo ref > test.target\r\n```\r\nh\/t @sshleifer 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/486","title":"Bookcorpus data contains pretokenized text","comments":"Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do it. Could you provide more details ?","body":"It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575","comment_length":69,"text":"Bookcorpus data contains pretokenized text \n It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575 \n Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do it. Could you provide more details ?","embeddings":[0.1711838841,0.225408867,-0.0682412684,0.348397702,-0.1143134087,-0.2559943497,-0.1209403202,0.2354474664,-0.4317489266,0.1244580597,-0.0878593251,0.226625219,0.3232100308,-0.1945853382,-0.0046418118,0.1363860369,0.2212239802,0.3154225349,0.1631603688,-0.2263966203,-0.1609849334,0.2912673056,0.1281992197,-0.0333504714,-0.2382831573,-0.3374491632,-0.2922897935,0.2494613975,-0.1388570219,-0.4692249596,0.0219636299,0.313759625,-0.0443142876,0.3362240791,-0.0001108157,-0.2952581942,-0.0561965853,-0.052477628,-0.2681582272,-0.0142242312,0.2646716833,-0.0555802546,0.3515130877,-0.4346842468,-0.3570377231,0.0960462764,-0.1705732793,-0.0543890223,0.3019437492,0.1815719455,0.2007733434,0.2229604572,-0.1164248139,0.2234891802,0.2198396772,-0.0061349124,-0.1732557118,0.1943600178,0.5178596377,0.2606931925,-0.0824627802,0.3769720197,-0.1626883,-0.1177665815,0.0906067342,0.034189146,0.0038020799,-0.1884816736,0.1414439082,0.1694197804,0.2140863389,-0.1607313454,-0.1241174042,-0.1997764558,0.0149029605,-0.1498395205,0.3236131668,-0.0014166802,0.1347049624,0.0760397166,-0.2752534449,-0.1492274702,-0.0142614702,0.3484376967,-0.0572859459,0.3520627916,-0.0388945304,-0.0381912291,-0.1972888112,-0.1449823827,-0.2161726952,0.1024223641,-0.3670475185,-0.0338461995,-0.0150918476,-0.1852909923,-0.1053255051,0.3172924221,-0.0513036028,0.1927736253,0.1294379979,0.1439675987,-0.1002326831,-0.0902262926,0.0809145719,0.1594159901,0.1486373544,0.2822788954,0.0600122698,0.003229551,-0.3305776417,-0.1897467226,0.0025670317,-0.0278814211,-0.0324557908,0.0732346922,-0.0350145772,-0.450147599,0.2254944891,0.1886160374,-0.0430544391,0.2807793021,-0.0074396078,0.1009571999,-0.096537061,0.0122182937,0.2783244848,0.1033927649,-0.1605129987,-0.1804709136,-0.1714458615,-0.0501717664,-0.303201288,0.0928248093,0.1805255115,0.0485841073,0.1871951669,0.089273721,-0.0540964268,0.0202349387,-0.1291193217,-0.1583362073,0.2625409663,0.0551409759,-0.0162990857,0.2405933589,-0.0453124866,-0.0048087128,-0.1340314597,0.0975835696,-0.3343697488,0.2123455107,-0.3812570572,0.1735198796,-0.3609201014,-0.2368099689,-0.0987068862,-0.0088431677,0.3678664267,-0.3480273187,0.3336296976,0.0685062632,-0.078144066,-0.0212075319,-0.1076178923,0.2046420425,-0.1081343442,-0.1003820226,-0.1654623598,0.2421965301,0.4265679717,0.1452926397,-0.0860859007,0.0135072637,0.1440583169,0.8647382855,0.1361807883,-0.1356698275,-0.4758000672,0.3001531661,0.018385727,0.0494007803,-0.0647801682,-0.1640854627,0.3293263614,0.0272320416,-0.175546214,0.120677717,0.4675885141,0.1378512383,-0.636767447,-0.1789665371,0.3183293939,0.0800496638,-0.047018636,-0.0316244401,0.169001326,0.0419173017,0.0185092632,-0.2328215837,0.2899684906,0.4480457306,0.2772974968,0.3473074138,0.1860031784,-0.0661409572,0.147070393,-0.0265920796,-0.6945602894,0.5669020414,-0.2257548124,-0.3093989491,0.0004434552,-0.3298888206,-0.0985443443,0.0407630838,0.2374934107,0.2225187719,-0.0592692494,0.3315542042,0.1590389311,-0.0570656545,0.1234892458,0.201455459,-0.2365746498,0.1906151325,-0.1024696156,-0.3099570572,-0.087907359,0.4058858454,0.4630976617,-0.0385711454,-0.2972506285,0.6062339544,-0.3367236853,0.2219973803,-0.207753554,0.1725815535,0.3421447873,0.0432178825,0.1004510596,0.640265584,0.2788161635,-0.006633033,0.1500734538,0.5099726319,0.3225123286,-0.0841197819,0.0058407453,-0.0700925589,0.1578987539,-0.380923003,0.2004105002,-0.1249167621,0.2203355134,-0.0428412259,0.1233304143,0.3034807146,-0.2406615913,0.2080424428,0.4111535251,-0.0495025255,0.1402168572,0.1452310681,-0.2254825681,-0.3757791221,0.096760124,0.0503878631,0.1027316079,0.1209968254,-0.0806207135,-0.0165553987,0.303905189,-0.3542489111,0.2387602031,0.1112501547,-0.2931630611,0.0804677457,0.2978738844,0.3431167006,-0.3636696935,0.0125109674,-0.0580732189,0.1891938001,-0.1028236076,0.2186203748,-0.3165397346,-0.7242130637,0.0452753268,0.011635324,-0.1649993807,-0.2254279256,0.3410965502,-0.0040558805,-0.146143809,0.0730768517,0.2235157937,0.3386718035,-0.3230760694,-0.1607567221,-0.2665176392,-0.3520283997,-0.7646338344,0.1563434005,0.1334807277,0.0910889506,-0.0997747481,-0.2050942183,0.0495026,-0.2066179961,-0.2803930342,0.0593728051,-0.1930378079,-0.1592743099,0.0604399331,-0.0513258129,-0.3704760373,-0.30636096,-0.0696584433,-0.2150731236,-0.1219453514,0.2372481823,-0.2543063462,-0.4921061397,-0.4656765461,-0.4422762394,-0.0322483145,-0.1254855841,0.2829438448,0.1891656518,0.0109011913,0.3011928797,-0.3569279015,0.0509781875,0.1335722059,0.3939691484,-0.2407732159,-0.4349057078,0.3319368958,-0.0415879153,-0.3880459368,-0.0198517106,0.1343091875,0.012537105,-0.0809675083,-0.4142768681,0.2543452382,-0.2746359408,0.0899759531,0.009953904,-0.0172472857,0.3932931423,0.0561426654,-0.2075251937,-0.0990403816,-0.0020561488,0.2808200717,0.305360198,0.3586889505,-0.3069497645,-0.1700131148,0.0090943836,-0.0627477542,-0.1406327039,-0.1806910783,0.4801080823,-0.1187062189,0.2573770583,-0.1555959433,-0.0260785371,0.15320611,-0.1746580452,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/486","title":"Bookcorpus data contains pretokenized text","comments":"I'm afraid that I don't know how to obtain the original BookCorpus data. I believe this version came from an anonymous Google Drive link posted in another issue.\r\n\r\nGoing through the raw text in this version, it's apparent that NLTK's TreebankWordTokenizer was applied on it (I gave some examples in my original post), followed by:\r\n`' '.join(tokens)`\r\nYou can retrieve the tokenization by splitting on whitespace. You can then \"detokenize\" it with TreebankWordDetokenizer class of NLTK (though, as I suggested, use the fixed version in my repo). This will bring the text closer to its original form, but some steps of TreebankWordTokenizer are destructive, so it wouldn't be one-to-one. Something along the lines of the following should work:\r\n```\r\ntreebank_detokenizer = nltk.tokenize.treebank.TreebankWordDetokenizer()\r\ndb = nlp.load_dataset('bookcorpus', split=nlp.Split.TRAIN)\r\ndb = db.map(lambda x: treebank_detokenizer.detokenize(x['text'].split()))\r\n```\r\n\r\nRegarding other issues beyond the above, I'm afraid that I can't help with that.","body":"It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575","comment_length":146,"text":"Bookcorpus data contains pretokenized text \n It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575 \n I'm afraid that I don't know how to obtain the original BookCorpus data. I believe this version came from an anonymous Google Drive link posted in another issue.\r\n\r\nGoing through the raw text in this version, it's apparent that NLTK's TreebankWordTokenizer was applied on it (I gave some examples in my original post), followed by:\r\n`' '.join(tokens)`\r\nYou can retrieve the tokenization by splitting on whitespace. You can then \"detokenize\" it with TreebankWordDetokenizer class of NLTK (though, as I suggested, use the fixed version in my repo). This will bring the text closer to its original form, but some steps of TreebankWordTokenizer are destructive, so it wouldn't be one-to-one. Something along the lines of the following should work:\r\n```\r\ntreebank_detokenizer = nltk.tokenize.treebank.TreebankWordDetokenizer()\r\ndb = nlp.load_dataset('bookcorpus', split=nlp.Split.TRAIN)\r\ndb = db.map(lambda x: treebank_detokenizer.detokenize(x['text'].split()))\r\n```\r\n\r\nRegarding other issues beyond the above, I'm afraid that I can't help with 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/486","title":"Bookcorpus data contains pretokenized text","comments":"Ok I get it, that would be very cool indeed\r\n\r\nWhat kinds of patterns the detokenizer can't retrieve ?","body":"It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575","comment_length":19,"text":"Bookcorpus data contains pretokenized text \n It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575 \n Ok I get it, that would be very cool indeed\r\n\r\nWhat kinds of patterns the detokenizer can't retrieve ?","embeddings":[0.0604030266,0.2112906575,0.0049694735,0.4674902856,-0.1865660697,-0.3642662466,0.0308460351,0.2487041056,-0.4858066738,0.1431304365,-0.1751267612,0.4655150771,0.1896488965,-0.1176308468,-0.1269879937,0.2603909969,0.0707708225,0.2080192715,0.0444150902,-0.1715232879,-0.0605966896,0.3185583949,0.0735049993,-0.0343129635,-0.3340622485,-0.2613661587,-0.1723051667,0.2871147692,-0.1565576792,-0.3885434866,0.0394663811,0.2147516757,-0.0296005215,0.3092271984,-0.0001112136,-0.1212928072,0.0267073531,-0.1100272685,-0.1816276312,-0.0909245014,0.1232385263,-0.0185541585,0.3582623005,-0.5265985727,-0.2822774649,0.2261689901,-0.1437604278,0.1404644549,0.2870262265,0.4154458344,0.187061131,0.2642733753,-0.1532092392,0.1688100398,0.3264146149,-0.0386349112,-0.2777245939,0.0013342452,0.386443764,0.3084563315,-0.1662812531,0.2464140952,-0.1460218281,0.0169743355,0.1081547067,-0.0495128781,0.1588053852,-0.2349087,0.2553260326,0.3478803337,0.2573385835,-0.038626384,0.0085572451,-0.2316288054,-0.13998124,0.2030919194,0.2722402513,0.127852276,0.0821161717,0.1513773948,-0.1062148735,-0.0710781962,-0.0499277711,0.1270304024,-0.1186060011,0.3517419696,0.0026368664,-0.0285530593,-0.0115611814,0.0066976305,-0.1764365286,0.0958928987,-0.3348130584,0.0043225819,-0.0044847033,-0.2323170602,0.0973935872,0.2527470291,-0.045265615,0.4082658589,0.3445390165,0.2949661016,-0.1966409832,0.0793915167,0.0403271466,0.1573100388,0.0555436425,0.2430897802,0.1665278375,0.0107505927,-0.3805959225,-0.0743013397,0.0317922011,0.0189552568,0.0036563515,0.061177168,0.0374833718,-0.483212769,0.1632658392,0.1853301674,-0.2136845589,0.1784506589,0.0564471483,0.0898110941,-0.0515366346,0.0880343542,0.0918418765,-0.0330606177,-0.0517974459,-0.199009493,-0.1495576948,-0.1054217443,-0.3613607883,0.0929126143,0.2237260342,0.034839619,0.1418288052,-0.0173506569,-0.172521919,0.0216433182,0.020218771,-0.2706736922,0.4571626782,-0.0256435797,-0.1910112649,0.0324009992,0.0236856248,-0.1179529876,-0.303106606,-0.0246074237,-0.3003565371,0.1305866539,-0.2167600542,0.1804510504,-0.3390056193,-0.1201605648,0.0040062373,0.2269220352,0.1144161671,-0.1300052851,0.2640655041,-0.0077673234,-0.1130756289,-0.0804778636,-0.1571694463,0.2868357599,-0.291061759,-0.1324337721,-0.072908476,-0.0038544538,0.2922712564,0.1737480909,-0.0803821608,0.0385204628,0.0571063794,0.8963900208,-0.0338544361,-0.0474755131,-0.5306514502,0.2030206472,-0.0835102797,0.1309683174,0.0783679411,-0.100826405,0.2333675176,-0.0387079976,-0.1269482821,0.1347050071,0.5446136594,-0.0159926135,-0.5618402362,-0.2940213084,0.2203255743,0.052337084,0.0197996944,0.133662045,0.2531479299,-0.0214614328,0.0830839276,-0.230712682,0.1561270654,0.3136553168,0.3593850434,0.406624198,0.2494668514,-0.1262724549,0.1709288657,0.085590668,-0.7975932956,0.4854716361,-0.110042654,-0.2766066194,0.0316400863,-0.2976920903,-0.1390509605,-0.0308803059,0.2581126988,0.2348937541,0.0465355329,0.434281379,0.158879444,0.0390868373,0.1868077815,0.0412401073,-0.4006344676,0.1447036713,-0.1005987972,-0.2781329155,-0.0628803223,0.3709408343,0.2360568345,0.0312169883,-0.0716287792,0.289059639,-0.3318715096,0.1124550849,-0.2324206829,0.1594908535,0.1141446009,0.0137153827,-0.0172763169,0.6242741346,0.2798282206,-0.1008123234,0.3238973916,0.4158972204,0.377671212,0.0362377726,-0.0003744426,-0.1702014506,0.1261226833,-0.3321750164,0.0453589559,-0.2139383256,0.2807053626,0.1363817453,0.2800716162,0.1733032465,-0.2103452682,0.2834628522,0.3500041068,-0.0286059752,0.1028477326,0.2167858332,-0.240759626,-0.2014989555,0.0918725133,0.1266491264,0.1089738011,0.0998078138,0.0215668827,0.0722229928,0.0722113624,-0.3865079284,0.1687373817,0.2188985646,-0.2239869684,0.129643321,0.3718090057,0.1892755926,-0.4481157362,-0.2308838069,-0.1116982102,0.0805018842,0.0273709688,0.2684747577,-0.5430806279,-0.6649031043,0.0325669535,0.0656293556,-0.0501057059,-0.2324708253,0.2761966586,0.1472476274,-0.1875771284,-0.2033764124,0.2906611562,0.3883592784,-0.2766059041,-0.0873173848,-0.4001852274,-0.470796138,-0.6657466888,0.1560483277,0.1820809692,-0.0244103279,-0.0165222082,-0.1729425639,0.0603350289,-0.1272507757,-0.2706115842,0.066847831,-0.0042660078,-0.0731112137,0.0747511908,-0.1444064975,-0.4029604197,-0.2654490471,-0.0277516767,-0.1547914296,-0.2307020277,0.2833867371,-0.2646175623,-0.312178582,-0.3437876999,-0.4522251487,-0.1565519422,-0.1983984411,0.5167419314,0.3702398837,0.0481463149,0.4064434469,-0.348472327,0.1662015766,-0.0078285737,0.4200567305,-0.2951849997,-0.2476711422,0.449470073,-0.0966084152,-0.4395467639,-0.0071631572,0.0947204605,0.1041146815,-0.0838209838,-0.4395299852,0.2352150977,-0.4147758782,0.2214820385,0.0919786841,-0.0054008611,0.4141243994,0.1176143065,-0.1854997575,-0.0184925795,0.0200538039,0.38923648,0.2683612108,0.1220119298,-0.1243890002,-0.1852469444,0.2529534101,-0.0080480669,-0.154407531,-0.0834846944,0.535487175,-0.0166322477,0.1233316883,-0.2894328535,0.080849506,0.0365821421,-0.363463521,-0.0181334727,0.695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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/486","title":"Bookcorpus data contains pretokenized text","comments":"The TreebankTokenizer makes some assumptions about whitespace, parentheses, quotation marks, etc. For instance, while tokenizing the following text:\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nwill result in:\r\n```\r\nDwayne `` The Rock '' Johnson\r\n```\r\nwhere the left and right quotation marks are turned into distinct symbols. Upon reconstruction, we can attach the left part to its token on the right, and respectively for the right part. However, the following texts would be tokenized exactly the same:\r\n```\r\nDwayne \" The Rock \" Johnson\r\nDwayne \" The Rock\" Johnson\r\nDwayne     \" The Rock\" Johnson\r\n...\r\n```\r\nIn the above examples, the detokenizer would correct these inputs into the canonical text\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nHowever, there are cases where there the solution cannot easily be inferred (at least without a true LM - this tokenizer is just a bunch of regexes). For instance, in cases where you have a fragment that contains the end of quote, but not its beginning, plus an accidental space:\r\n```\r\n... and it sounds fantastic, \" he said.\r\n```\r\nIn the above case, the tokenizer would assume that the quotes refer to the next token, and so upon detokenization it will result in the following mistake:\r\n```\r\n... and it sounds fantastic, \"he said.\r\n```\r\n\r\nWhile these are all odd edge cases (the basic assumptions do make sense), in noisy data they can occur, which is why I mentioned that the detokenizer cannot restore the original perfectly.\r\n","body":"It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575","comment_length":244,"text":"Bookcorpus data contains pretokenized text \n It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575 \n The TreebankTokenizer makes some assumptions about whitespace, parentheses, quotation marks, etc. For instance, while tokenizing the following text:\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nwill result in:\r\n```\r\nDwayne `` The Rock '' Johnson\r\n```\r\nwhere the left and right quotation marks are turned into distinct symbols. Upon reconstruction, we can attach the left part to its token on the right, and respectively for the right part. However, the following texts would be tokenized exactly the same:\r\n```\r\nDwayne \" The Rock \" Johnson\r\nDwayne \" The Rock\" Johnson\r\nDwayne     \" The Rock\" Johnson\r\n...\r\n```\r\nIn the above examples, the detokenizer would correct these inputs into the canonical text\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nHowever, there are cases where there the solution cannot easily be inferred (at least without a true LM - this tokenizer is just a bunch of regexes). For instance, in cases where you have a fragment that contains the end of quote, but not its beginning, plus an accidental space:\r\n```\r\n... and it sounds fantastic, \" he said.\r\n```\r\nIn the above case, the tokenizer would assume that the quotes refer to the next token, and so upon detokenization it will result in the following mistake:\r\n```\r\n... and it sounds fantastic, \"he said.\r\n```\r\n\r\nWhile these are all odd edge cases (the basic assumptions do make sense), in noisy data they can occur, which is why I mentioned that the detokenizer cannot restore the original perfectly.\r\n","embeddings":[-0.0130970906,0.2090142518,-0.0026562868,0.3173747659,-0.1956932545,-0.2996238172,-0.0173011087,0.2006196082,-0.4918516278,0.0816216096,-0.172097221,0.2842743099,0.1636684388,-0.2349403501,-0.1071667597,0.2111709714,0.1792732924,0.2823951542,0.220247522,-0.153670311,-0.1823280305,0.2915745974,0.1904963702,-0.0035161814,-0.2633253932,-0.2643784583,-0.1785983741,0.3562456369,-0.1762152612,-0.3386277854,-0.1300115585,0.2864885032,-0.0567627177,0.3584989309,-0.0001062125,-0.2521669269,-0.0237554628,-0.0865369737,-0.3363088369,-0.0799458772,0.2673516572,-0.064941071,0.2785297632,-0.3779522181,-0.3191954195,0.0946913511,-0.1585107893,0.0207608454,0.2960724831,0.2338730991,0.2192092985,0.1864215434,-0.119373396,0.2506962121,0.3235376179,-0.1017344892,-0.1702123731,0.0435539894,0.3703986406,0.3260974288,-0.1662236303,0.3166057467,-0.1516396105,0.0288449693,0.0620671697,-0.1197543442,0.0588301048,-0.1836254001,0.1840665042,0.2314481139,0.2577361763,-0.1910746992,-0.1388308704,-0.2062944174,-0.0055133468,-0.0866998211,0.2901036143,0.1626656651,0.1006433815,0.1169619262,-0.2036015093,-0.1254278868,0.1330465078,0.2178181261,-0.0939432234,0.3291178942,-0.0937990099,-0.0315528624,-0.0638477504,-0.0639029443,-0.2752233744,0.0771327317,-0.3466393352,-0.0471422412,-0.0716824234,-0.2270145118,0.0099338954,0.3180498183,-0.0523345694,0.291467458,0.1656623334,0.20682019,-0.1103693247,0.0022941055,0.142885834,0.1520640403,0.0951022878,0.2186256051,0.1616232544,0.0547114499,-0.2744164765,-0.0449897759,0.0262068305,0.0409516282,0.0673123077,0.1175812185,-0.0945215449,-0.4092077911,0.1104818434,0.1627935916,-0.175558269,0.351349324,0.0304975193,0.1711644083,-0.1282082945,0.1095000282,0.2333882898,0.0160870925,-0.1464368999,-0.1457025409,-0.1949326694,-0.0282436144,-0.3139037788,0.1749850214,0.2165554911,-0.024981942,0.1767987311,0.0159949977,-0.0900460109,-0.0779916942,-0.0937322676,-0.1390446872,0.2256879061,-0.0904374868,0.0073019317,0.2255372107,-0.067043297,-0.0109519763,-0.272883147,0.0048361868,-0.3288038075,0.1000960693,-0.2406043559,0.2492175847,-0.3356083035,-0.1833829433,-0.1336333454,0.0967791751,0.249566853,-0.205788523,0.2461419255,0.0952634141,-0.0845146403,0.0397761501,-0.1700638384,0.1673566103,-0.1764993668,-0.166233927,-0.0244440436,0.2212650031,0.2749224603,0.1089541391,-0.1420920938,-0.0086042462,0.0813517869,0.7183502316,-0.0174182076,-0.0456807055,-0.5037994981,0.2438368201,-0.0503257662,0.0734820142,-0.0498397648,-0.1478775591,0.2721961737,-0.025697317,-0.1284986287,0.1694664806,0.5301482081,0.1767308861,-0.5494855046,-0.2097418457,0.1637216359,0.0617381781,-0.1836184412,-0.0272415951,0.2119203657,0.1857924163,0.0499813482,-0.2376085967,0.2587558031,0.3918693662,0.3059104085,0.3005813062,0.2135974318,-0.1132968068,0.1385641843,-0.0084675467,-0.5750662684,0.5548720956,-0.1922527254,-0.325068891,0.0255882666,-0.3308907747,-0.1080776826,-0.0202899445,0.2663990557,0.2701419294,-0.0265747681,0.2296952158,0.1098188907,-0.0887290388,0.0138865039,0.144550696,-0.2879339457,0.0096704438,-0.1162086129,-0.2436328828,-0.091394648,0.3883041143,0.3055767119,0.0095238565,-0.1216301098,0.5886529684,-0.2743594944,0.2116333544,-0.3422011733,0.1967356205,0.2266284823,0.0910278186,0.1045902595,0.6204184294,0.3134614527,-0.0619515926,0.1241833344,0.4847055972,0.5381141305,-0.0348015986,0.135763973,-0.2097647935,0.1407641023,-0.277854681,0.0349247418,-0.1607512832,0.1870877594,0.2222881168,0.2258031666,0.2511379421,-0.1797742993,0.3216660321,0.3824762404,-0.1144577339,0.1248017922,0.1802485585,-0.3248032629,-0.3172587156,0.0615759529,0.0733916759,0.0257054139,0.1317723989,0.076852113,0.0449810438,0.1044769511,-0.3250133991,0.2166339904,0.0959457681,-0.3224861324,0.1516514719,0.3414753675,0.1739849746,-0.4222617149,-0.0884152651,-0.0366610624,0.0930631384,-0.1447469741,0.2682285607,-0.425195843,-0.6615489125,-0.0893082097,-0.0604751445,-0.1013161093,-0.247446835,0.3624102473,0.2117522061,-0.1529857814,-0.0137812076,0.2746281624,0.3462324739,-0.3052076101,-0.1405730844,-0.1333603561,-0.3698670864,-0.606369853,0.172246784,0.0836452246,0.0029904635,-0.1088610515,-0.2052446008,0.0304429177,-0.3144642413,-0.2817363441,0.125975132,-0.1332973391,-0.0618762933,0.0989273265,-0.2075009495,-0.2802496552,-0.3656508625,-0.0915759131,-0.1999159604,-0.2207590342,0.2158221453,-0.2644087672,-0.3948630691,-0.3731863797,-0.4336093664,0.0073938593,-0.1918857098,0.3253252804,0.3003253937,0.015803067,0.3178790808,-0.3654292524,0.0570112951,0.2947160602,0.4680432975,-0.2738643885,-0.3697005212,0.379750818,-0.0870073736,-0.3737559021,0.0451309457,0.106417805,0.1011918783,0.0062330649,-0.3513376117,0.2975459695,-0.2787443697,0.118222557,0.1003810763,0.0857115015,0.3026743233,0.1589624733,-0.242217645,-0.0651043579,0.018234862,0.2500403821,0.3025486469,0.3333814442,-0.215423733,-0.1298262626,0.0965181664,0.0774964914,-0.1745658219,-0.2244542837,0.4646266401,-0.056080807,0.1519815624,-0.1868851781,0.0825462192,0.1256967634,-0.297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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/486","title":"Bookcorpus data contains pretokenized text","comments":"To confirm, since this is preprocessed, this was not the exact version of the Book Corpus used to actually train the models described here (particularly Distilbert)?  https:\/\/huggingface.co\/datasets\/bookcorpus\r\n\r\nOr does this preprocessing exactly match that of the papers?","body":"It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575","comment_length":37,"text":"Bookcorpus data contains pretokenized text \n It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575 \n To confirm, since this is preprocessed, this was not the exact version of the Book Corpus used to actually train the models described here (particularly Distilbert)?  https:\/\/huggingface.co\/datasets\/bookcorpus\r\n\r\nOr does this preprocessing exactly match that of the papers?","embeddings":[0.0296658985,0.1984409541,-0.0193816107,0.3028745055,-0.080079928,-0.2216101289,0.1067400351,0.1523843706,-0.3669324219,-0.007889973,-0.2142251432,0.3167914748,0.2326788157,-0.1111376584,0.0192023013,0.0697399378,0.2531403005,0.2138079405,0.0349262245,-0.2171892971,-0.1444738209,0.2366247177,0.0563932396,0.1152127907,-0.0726907924,-0.3140857518,-0.2279933542,0.3609324396,-0.2332173884,-0.3183438182,0.1385847479,0.3533370197,0.0491578691,0.4791388214,-0.0001063632,-0.0733194351,-0.1108100265,-0.0216152892,-0.1269199103,0.1278881729,0.2702474296,0.0159017872,0.3585571349,-0.4776166677,-0.3692617118,0.1781267524,-0.2240692377,0.1918198764,0.2211266309,0.3104521036,0.2462922782,0.0918430984,0.022231115,0.2674802542,0.1596523374,-0.133894369,-0.2298511714,0.0877113342,0.5446926951,0.2163472325,-0.1553095877,0.3055977225,-0.1081151217,-0.044440303,0.0253587738,-0.0049127494,0.18997702,-0.3225924373,0.1490077525,0.2697640657,0.277718395,-0.1194734648,-0.1294752061,-0.0601966754,-0.0922961086,0.1277790517,0.2424373329,0.0890644714,0.0441507921,0.2079998851,-0.1743232161,-0.1435809284,-0.0047822334,0.0986920595,-0.1490667015,0.3915131688,-0.0701674148,-0.0638668165,-0.0509070642,-0.0633310005,-0.3180634975,-0.0380534641,-0.2157049626,-0.0351227075,-0.0073074475,-0.219176352,-0.0236476567,0.4003375173,-0.1009498611,0.2117394358,0.1576360166,0.1187427044,-0.0770310834,-0.1793920994,0.1342777908,0.2484534085,0.1051763892,0.2507029176,0.0362859033,0.3239161968,-0.2562924623,0.0412143655,-0.1305710822,-0.0480502546,-0.1039604619,-0.086110495,-0.0972716585,-0.491089493,0.0703444704,0.2772708535,0.0333309099,0.3170373738,0.0583801717,0.0865695998,-0.2092938572,0.0372137092,0.2322643846,0.0183618534,-0.1913560778,-0.2068758011,-0.2237014323,0.0077761565,-0.3948843181,0.0062467833,0.1177478805,-0.0474415161,0.2660805285,-0.0383913964,-0.2094124854,-0.1035205051,-0.0290001296,-0.1996834725,0.2668502927,0.0191406272,-0.1366973072,0.2283186018,0.0493180603,-0.1478088349,-0.1507509798,0.0246911049,-0.3962158263,0.0323202685,-0.1493204981,0.232612744,-0.2601950765,-0.2491978258,-0.0344613791,0.281306982,0.4073366225,-0.1509707421,0.3375005126,-0.0243922994,-0.1385217905,0.0281361956,0.0209932309,0.4135779142,-0.22823973,-0.1458941996,-0.0486666076,0.0728444606,0.3752904534,0.0883204937,-0.2509766221,0.061753951,0.2696670294,0.6624370813,0.1866119504,-0.0779509097,-0.4325422943,0.2136226296,-0.0138359247,0.2117446512,-0.1904442906,-0.1700180471,0.180130586,-0.0564443059,-0.0000731084,0.0359709337,0.4290822744,0.1707169861,-0.6041297913,-0.3096036911,0.3253692985,0.2513689399,-0.1255486906,-0.0189415403,0.0184531193,0.0625682697,0.1516380012,-0.1676374972,0.1570030451,0.4770158529,0.180464223,0.3032194078,0.2585037351,-0.0054152706,0.2384948283,-0.0026990685,-0.4634099603,0.6628252268,0.0335819907,-0.2584096789,-0.0643427968,-0.2789319754,-0.1723700017,-0.2421185374,0.27399382,0.0556422584,0.0018351172,0.28350842,0.0382650867,-0.1210398674,-0.2297665924,0.1404663622,-0.5065470338,-0.0698980018,-0.2286457568,-0.0394805558,-0.2563893199,0.3594435155,0.178655073,0.0626103655,-0.1487026066,0.457759589,-0.3280336559,0.0037435619,-0.0326191001,0.2570511699,0.1916282028,-0.0837774202,0.0840337351,0.463596046,0.2697112262,-0.0392536707,0.2076291144,0.3762252033,0.4181046188,0.0358300246,0.0566478185,-0.1161638349,0.0447982661,-0.2119199336,0.0115555702,-0.1377198994,0.2113891095,0.1241783649,0.1486055702,0.2420843989,-0.2317489237,0.3815658689,0.259077698,-0.1092300713,0.0798091665,0.1837252975,-0.3506711423,-0.3496268988,-0.0154661844,0.1806977242,0.1021821201,0.0754515529,0.007575315,0.0766030326,-0.0453089289,-0.3259964287,0.285486877,0.151080206,-0.2582577467,0.0437834263,0.234524712,0.2017498165,-0.4386012852,-0.1384997815,-0.0466303416,0.1867870837,-0.1489841193,0.3591692746,-0.2862590551,-0.4356797636,-0.1512416303,0.048651915,-0.0093042972,-0.1176725402,0.2985786498,0.1213746741,-0.0993097052,0.0566712692,0.149135232,0.5113168359,-0.2363129556,-0.0650571138,-0.1264048517,-0.2729800642,-0.7907334566,0.1810279191,0.1005466953,-0.0117973955,-0.1401486248,-0.3721141219,-0.1473334134,-0.1202430502,-0.4255813956,0.0287551545,-0.1445327997,0.0502932929,0.1586819589,-0.1826369017,-0.2583869994,-0.1930638701,-0.0241111033,-0.111395292,-0.1810622811,0.1034460813,-0.2522014081,-0.4218706787,-0.3365124762,-0.5817195177,-0.0893881619,0.0237446446,0.2863693237,0.232751295,0.007716727,0.4235409796,-0.1916831881,-0.014036607,0.1494739056,0.452454865,-0.2753588855,-0.2926751077,0.3957184851,-0.0382814221,-0.4434627891,-0.1504343897,0.0747855902,0.0841897503,-0.0416624509,-0.3317264318,0.1113496125,-0.3541252315,-0.0893000141,0.0395525433,0.081608437,0.2867455482,0.0311784595,-0.2125947922,-0.1179542243,-0.0140804518,0.5295315385,0.2311033905,0.2775559425,-0.3133275211,-0.060712073,-0.0815422609,0.0009640065,-0.0211240016,-0.2409221232,0.3181604147,0.0015801466,0.0636589825,-0.1606695652,-0.032405939,0.0874614045,-0.2342914194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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/486","title":"Bookcorpus data contains pretokenized text","comments":"I believe these are just artifacts of this particular source. It might be better to crawl it again, or use another preprocessed source, as found here: https:\/\/github.com\/soskek\/bookcorpus ","body":"It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575","comment_length":27,"text":"Bookcorpus data contains pretokenized text \n It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575 \n I believe these are just artifacts of this particular source. It might be better to crawl it again, or use another preprocessed source, as found here: https:\/\/github.com\/soskek\/bookcorpus 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/486","title":"Bookcorpus data contains pretokenized text","comments":"Yes actually the BookCorpus on hugginface is based on [this](https:\/\/github.com\/soskek\/bookcorpus\/issues\/24#issuecomment-643933352). And I kind of regret naming it as \"BookCorpus\" instead of something like \"BookCorpusLike\".\r\n\r\nBut there is a good news ! @shawwn has replicated BookCorpus in his way, and also provided a link to download the plain text files. see [here](https:\/\/github.com\/soskek\/bookcorpus\/issues\/27). There is chance we can have a \"OpenBookCorpus\" !","body":"It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575","comment_length":60,"text":"Bookcorpus data contains pretokenized text \n It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, \"didn't\" becomes \"did\" + \"n't\", and double quotes are changed to `` and '' for start and end quotes, respectively.\r\n\r\nOn my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https:\/\/github.com\/nltk\/nltk\/pull\/2575 \n Yes actually the BookCorpus on hugginface is based on [this](https:\/\/github.com\/soskek\/bookcorpus\/issues\/24#issuecomment-643933352). And I kind of regret naming it as \"BookCorpus\" instead of something like \"BookCorpusLike\".\r\n\r\nBut there is a good news ! @shawwn has replicated BookCorpus in his way, and also provided a link to download the plain text files. see [here](https:\/\/github.com\/soskek\/bookcorpus\/issues\/27). There is chance we can have a \"OpenBookCorpus\" !","embeddings":[0.093844004,-0.1207267866,0.0057472717,0.3477259874,-0.0450499319,-0.2574845254,0.0231197923,0.1923967004,-0.3466641903,0.1204395294,-0.2318461388,0.0498438291,0.2508267164,0.2811335027,0.0633646697,0.1325377971,0.287984848,0.1430951655,-0.0784864053,-0.1598722935,-0.2173705101,0.4336135089,0.116646789,0.1677406728,-0.0388899483,-0.1268423349,-0.2003344297,0.4001177847,-0.207802996,-0.2791159749,0.152703926,0.2251888514,-0.1080720127,0.4011824429,-0.0001066821,-0.1593561471,-0.1229970977,-0.0155404378,-0.3229634464,0.0267891679,0.3888083696,-0.1011466682,0.1833069026,-0.3243453205,-0.3408608139,0.1802245229,-0.1350855082,0.0620260052,0.1249145865,0.103968285,0.2136241943,0.3086166382,0.0371994488,0.2225579172,0.0696240216,-0.0375635587,-0.352791667,0.2101964653,0.491291523,0.2205249071,-0.0633485019,0.2900327742,-0.1276962608,-0.1118726209,0.0393990688,-0.0221189708,0.2333828956,-0.115067035,0.2039379179,0.3186214566,-0.0194089804,-0.2599667609,-0.1064576581,-0.1943802088,-0.0761001483,-0.0726003647,0.3364339769,0.2846116722,0.2253692299,0.096883148,-0.3741988838,-0.2277469039,0.0811936334,0.1313805431,-0.0405469351,0.0361633077,-0.1846025884,-0.0237984136,0.0744446889,0.0134654371,-0.4897994399,0.0269408822,-0.3042084873,0.0350655206,-0.0974979848,-0.149834767,-0.0538868718,0.7010756135,0.0746983662,0.0693677664,0.0530724972,0.1078742296,-0.3359383345,-0.0265541635,0.0820744038,0.1007655188,0.1369506568,0.0156086097,0.0678852722,0.1843396723,-0.0453089848,0.0362810791,0.0438416339,0.1020604074,-0.1639277041,-0.0209339894,-0.0724795759,-0.3996303976,0.126874879,0.1415815949,-0.1334972382,0.364220053,0.0879585668,0.0076306001,-0.2582374215,-0.0210713036,0.296479553,-0.0102810683,-0.2136100382,-0.2217784077,-0.16154778,-0.0958715677,-0.3191138506,0.2264874578,0.146472156,-0.3952271342,0.1858628094,0.0444760211,-0.3081521988,-0.0185635202,-0.0056978543,-0.0902869701,0.1193651035,-0.1410991549,-0.0466800146,0.1980329752,0.0333113484,0.0604279637,-0.2304292917,-0.0702006742,-0.2735208571,0.012078396,-0.3084296882,0.2019418627,-0.3340657055,-0.1973949969,-0.2077858448,0.0061911861,0.1389762759,-0.2477938682,0.3023016453,0.1350104809,-0.1767831594,0.0876918957,-0.140010938,0.2940986454,-0.0604807362,-0.2447988987,0.2254447043,0.031728033,0.1518551409,0.1032098159,-0.1967705488,-0.0480698682,0.0697814971,0.6613706946,-0.1448672712,-0.1817839146,-0.4148927331,0.1682779044,-0.112037316,0.1598674804,-0.1114613265,-0.1683021486,-0.0464653559,0.0063901581,-0.2677539289,0.1824931204,0.5105293393,0.2303016931,-0.5244213343,-0.3914472461,0.2984268665,0.1271823496,-0.2067021132,-0.0531212427,-0.0020292336,0.0140398871,0.1554815918,-0.2145636827,0.1634925157,0.3532806039,0.2083569169,0.3966579437,0.2448130995,-0.0061980812,0.1457246989,-0.0089488942,-0.6330733299,0.5566461682,0.1423982382,-0.373445034,-0.1040412486,-0.347904712,-0.091561757,-0.1056637168,0.2224725783,0.1875253022,0.0559776649,0.2985978723,0.1524914354,0.2028595805,0.0043207542,0.1445838511,-0.6349841952,-0.034098044,-0.1526804864,-0.0318962373,-0.1222349033,0.3136814237,0.1959505826,0.0005569774,-0.0889044479,0.5558328629,-0.5106656551,0.2036436498,-0.191286698,0.2681001127,0.2760772407,0.1986155957,0.0812509358,0.4749922156,0.3740749657,0.0067572808,0.1219974533,0.3797285259,0.4839081764,-0.0598702766,0.1076591164,-0.15554142,0.035182979,-0.2817848325,0.0004006945,0.0369275361,0.2631747127,-0.1114642695,0.2040321678,0.1861056387,-0.3016117215,0.257679075,0.4086394906,-0.1320691109,0.0894851312,0.1542828232,-0.3398966193,-0.1776928604,0.0346417055,-0.126543656,0.0189012662,0.0660448223,-0.0793131217,0.1490279734,0.1661371887,-0.23138313,0.3121629059,0.1072962955,-0.2484957576,-0.033841297,0.2163281888,0.1573525667,-0.4162901044,-0.069895938,-0.1470281631,-0.0663297027,-0.1102841347,0.2880488634,-0.2646134496,-0.5740508437,-0.1406423002,-0.001930643,-0.1594645232,-0.1174939051,0.3127172291,0.3862805068,-0.0017168057,-0.1491294056,0.1377091557,0.4958688617,-0.3587916195,-0.0933328494,-0.1212109774,-0.2203113735,-0.6400431991,0.1287747324,0.0795243084,-0.1360808611,0.0171533003,-0.2981890738,0.0103077944,-0.3268692791,-0.3456943035,0.1376442462,-0.2445939332,-0.1988650262,0.0822715163,-0.1902072579,-0.4083915949,-0.1923135668,0.0181793198,-0.1432582587,-0.2758737803,0.1623868495,-0.1835000366,-0.3752998114,-0.3884890378,-0.3572832644,-0.0505762696,-0.1736682206,0.5705422163,0.191896379,-0.0065136272,0.5147127509,-0.3683890104,0.1496383548,0.2725290954,0.3947570026,-0.3479954302,-0.4685353041,0.3225314319,-0.0649405494,-0.4029202163,-0.0109294038,0.0989057571,0.0454924107,-0.092030108,-0.4073903263,0.0923260376,-0.2119834721,-0.015826799,0.1361187845,0.1005617604,0.4459448159,0.1266246885,-0.2117586136,-0.0010535609,-0.2075610161,0.3247995973,0.3419704139,0.3122250736,-0.1932136416,-0.3087399304,0.0377808772,-0.1208563894,-0.0683812499,-0.0469391122,0.4255401492,-0.0183441024,0.2324013263,-0.2110125571,-0.0943398401,-0.0144865038,-0.3801892102,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/482","title":"Bugs : dataset.map() is frozen on ELI5","comments":"This comes from an overflow in pyarrow's array.\r\nIt is stuck inside the loop that reduces the batch size to avoid the overflow.\r\nI'll take a look","body":"Hi Huggingface Team!\r\n\r\nThank you guys once again for this amazing repo.\r\n\r\nI have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https:\/\/github.com\/patil-suraj\/exploring-T5\/blob\/master\/T5_on_TPU.ipynb) \r\n\r\nHowever, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 \/ 0.17.0 \/ 1.0.0 also have the same frozen process.\r\n\r\nReproducible code can be found on [this colab notebook ](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.\r\n\r\n----------------------------------------\r\n**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)\r\n\r\nI also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ?","comment_length":27,"text":"Bugs : dataset.map() is frozen on ELI5 \n Hi Huggingface Team!\r\n\r\nThank you guys once again for this amazing repo.\r\n\r\nI have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https:\/\/github.com\/patil-suraj\/exploring-T5\/blob\/master\/T5_on_TPU.ipynb) \r\n\r\nHowever, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 \/ 0.17.0 \/ 1.0.0 also have the same frozen process.\r\n\r\nReproducible code can be found on [this colab notebook ](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.\r\n\r\n----------------------------------------\r\n**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)\r\n\r\nI also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ? \n This comes from an overflow in pyarrow's array.\r\nIt is stuck inside the loop that reduces the batch size to avoid the overflow.\r\nI'll take a 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/482","title":"Bugs : dataset.map() is frozen on ELI5","comments":"I created a PR to fix the issue.\r\nIt was due to an overflow check that handled badly an empty list.\r\n\r\nYou can try the changes by using \r\n```\r\n!pip install git+https:\/\/github.com\/huggingface\/nlp.git@fix-bad-type-in-overflow-check\r\n```\r\n\r\nAlso I noticed that the first 1000 examples have an empty list in the `title_urls` field. The feature type inference in `.map` will consider it `null` because of that, and it will crash when it encounter the next example with a `title_urls` that is not empty.\r\n\r\nTherefore to fix that, what you can do for now is increase the writer batch size so that the feature inference will take into account at least one example with a non-empty `title_urls`:\r\n\r\n```python\r\n# default batch size is 1_000 and it's not enough for feature type inference because of empty lists\r\nvalid_dataset = valid_dataset.map(make_input_target, writer_batch_size=3_000) \r\n```\r\n\r\nI was able to run the frozen cell with these changes.","body":"Hi Huggingface Team!\r\n\r\nThank you guys once again for this amazing repo.\r\n\r\nI have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https:\/\/github.com\/patil-suraj\/exploring-T5\/blob\/master\/T5_on_TPU.ipynb) \r\n\r\nHowever, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 \/ 0.17.0 \/ 1.0.0 also have the same frozen process.\r\n\r\nReproducible code can be found on [this colab notebook ](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.\r\n\r\n----------------------------------------\r\n**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)\r\n\r\nI also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ?","comment_length":147,"text":"Bugs : dataset.map() is frozen on ELI5 \n Hi Huggingface Team!\r\n\r\nThank you guys once again for this amazing repo.\r\n\r\nI have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https:\/\/github.com\/patil-suraj\/exploring-T5\/blob\/master\/T5_on_TPU.ipynb) \r\n\r\nHowever, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 \/ 0.17.0 \/ 1.0.0 also have the same frozen process.\r\n\r\nReproducible code can be found on [this colab notebook ](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.\r\n\r\n----------------------------------------\r\n**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)\r\n\r\nI also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ? \n I created a PR to fix the issue.\r\nIt was due to an overflow check that handled badly an empty list.\r\n\r\nYou can try the changes by using \r\n```\r\n!pip install git+https:\/\/github.com\/huggingface\/nlp.git@fix-bad-type-in-overflow-check\r\n```\r\n\r\nAlso I noticed that the first 1000 examples have an empty list in the `title_urls` field. The feature type inference in `.map` will consider it `null` because of that, and it will crash when it encounter the next example with a `title_urls` that is not empty.\r\n\r\nTherefore to fix that, what you can do for now is increase the writer batch size so that the feature inference will take into account at least one example with a non-empty `title_urls`:\r\n\r\n```python\r\n# default batch size is 1_000 and it's not enough for feature type inference because of empty lists\r\nvalid_dataset = valid_dataset.map(make_input_target, writer_batch_size=3_000) \r\n```\r\n\r\nI was able to run the frozen cell with these changes.","embeddings":[0.1289257258,-0.3091129959,-0.06825611,0.1572482884,0.2135083973,-0.1734551787,0.3533759713,0.1509891152,-0.1582410634,0.1141942814,-0.2389454395,0.4564895332,0.108321473,0.1284253299,-0.0192696843,0.1580439359,-0.1359721124,0.0216666386,-0.138399452,-0.121157378,0.128272742,0.3092678487,-0.2475365996,0.0375251397,-0.1744519174,0.2080098689,-0.1016796157,0.0072080214,-0.1468411386,-0.3405945599,0.1364496052,0.0454997532,-0.1865528375,0.1471100897,-0.0001257587,0.0547152981,0.0463493541,-0.0335171595,-0.0842954516,-0.0720032007,0.3044876158,-0.1794094592,0.3073302507,-0.1446628273,0.127319634,0.3064395189,0.3526678681,0.2476817071,0.6475158334,-0.1010444462,0.0140077453,0.341314137,0.0202773102,0.5116378069,0.243788138,-0.0982789919,-0.3499835432,0.0335915461,0.3750883937,-0.1832286865,0.1367816925,-0.0608269759,-0.0739217028,-0.0898051485,0.19255732,0.4039262831,-0.0741314888,-0.6528962255,-0.0021793693,0.2572253644,0.2794762552,-0.1696970314,-0.1200202331,-0.3368326128,0.0148051614,-0.4079931676,0.4944007397,-0.0794702917,-0.1530280858,0.0317088887,-0.1824898273,-0.0652211085,-0.0135252317,0.2793029249,0.0590097718,0.1251696646,-0.0725423694,0.0867164731,0.0587756559,0.0040247319,-0.379796654,-0.181068778,-0.0119346613,0.3528054059,-0.5798324943,-0.2715927958,-0.0327341259,0.0594736859,0.1253583431,0.0762948468,0.1430134922,0.1827675253,0.0188215673,0.041459322,0.4148799777,0.0017276356,0.2859444022,0.0397364311,0.2681476772,-0.0332963541,-0.2699636221,0.1644416004,-0.0023881095,-0.143230781,-0.1291372329,-0.2096811086,0.1443166882,-0.3338890374,0.2238596082,0.1400536895,-0.5295664072,-0.0235963576,0.0451130867,0.348033309,-0.1645060629,-0.0118470388,-0.0154613648,0.028652804,-0.4626110792,-0.0920969024,0.0960532278,-0.400587678,-0.1462285817,0.0067687454,0.2733539939,-0.3486697674,0.2713479698,-0.1475230157,0.2598335445,0.1013623103,0.1670903414,-0.0870442986,0.2051347792,0.0524544194,0.1002826616,0.1181941032,-0.133488521,-0.1894541234,-0.0926409513,0.2097845674,0.1490687281,0.015464914,0.0886512548,-0.0408379659,-0.2455912977,-0.0535609424,-0.0409294963,0.2703981996,0.3567351997,-0.3351740539,0.0605804026,-0.1100196838,-0.16547966,0.0336645097,0.3799240887,0.2518183589,0.0483144373,-0.241093874,0.0075256964,-0.0359109528,-0.2282191813,0.4138222039,0.0667631179,-0.0232566744,0.0048591988,0.1988445371,0.4201336801,-0.2138728797,-0.5843521357,0.1227919087,-0.290155828,-0.0611700825,-0.3185210228,0.0572850741,0.1865839958,-0.1132518649,0.108619906,0.3744200468,-0.152241379,0.0520576946,-0.4770565927,-0.2232019603,-0.0265080687,0.0327686444,-0.1929167211,-0.2336608022,-0.1907966137,0.2977527082,-0.0531564094,0.0488934442,0.1820814759,0.3569732606,0.1773028821,0.1362027526,-0.1015440002,-0.1310838908,-0.5059298277,0.0301138759,-0.082680501,0.436884135,-0.0742785484,0.0415672958,0.216215238,-0.0862872154,-0.2092489004,-0.0907132924,-0.0478548557,-0.1511384547,0.0789053962,0.2883453667,-0.0765572265,-0.146270141,-0.0684965029,0.2294391543,-0.5454393029,0.2303234339,-0.1101198122,-0.3616029024,-0.2829392552,0.0522339493,-0.3480919898,-0.1953464448,-0.1199693307,0.0457643829,-0.4057110548,-0.1182210371,-0.0787373632,0.39961344,0.3000273108,-0.2792613208,0.0287999511,-0.1769136488,0.0589298122,-0.0994167253,-0.1693899333,0.3000504673,-0.1911527514,0.1421568394,0.0937402993,0.0221831705,-0.0751492158,0.0131150745,-0.1991497427,0.0291856732,0.2271129638,0.3052901626,0.3777759671,0.0039162342,-0.2030637562,0.2866193652,0.8647702932,0.2128255963,0.0915230587,0.1407784224,0.0420252047,-0.1311241686,-0.3347011805,-0.2349749207,0.2353041619,-0.0368026346,0.0103973439,0.0471230038,-0.0723187029,0.0022590877,0.2268696427,0.2180667669,-0.0744219571,0.1118378416,0.4056487083,0.1075129434,-0.1748203337,0.0449038595,0.0736119226,0.1916543245,-0.2182614058,0.0064427429,-0.0735023543,-0.1446316391,0.1790862083,0.0557615347,-0.4943912029,-0.3087976277,0.1794397235,0.086846754,-0.0174654014,0.2599861324,0.3050743043,-0.0461216941,0.0266661495,-0.1626860201,-0.0823711902,-0.371319592,-0.3768123388,-0.0614233613,0.4895643294,0.0645242706,-0.108421877,0.0187370367,-0.5041586757,-0.0094818696,-0.6358896494,0.3481926322,-0.3971453011,0.4128521681,-0.0781828463,0.2758353055,-0.4864445031,-0.0928508863,0.347694844,-0.3571275473,-0.3248539567,0.0642937124,0.084577702,0.0274519268,-0.2390641421,-0.5075418949,-0.0715254396,-0.200479269,0.6052663922,0.0293335542,0.0404657349,0.2019646466,-0.0366263017,0.4214391708,-0.1367387027,-0.1317187399,-0.028817758,-0.1558273286,0.3360876739,-0.1849846542,-0.3257573545,-0.1265583932,0.1665827483,-0.1240958273,-0.1564632952,-0.3962708116,-0.408788681,-0.3515097499,0.419516623,-0.0856776983,0.0416094027,0.3636344075,0.2145564407,0.0893893614,0.0769142732,-0.3594811559,0.4156855643,0.5649589896,0.0811531171,-0.0618204251,0.5869220495,0.1258977056,0.76325351,0.164718166,-0.0777330026,0.2583013773,0.0622731037,0.0986941308,-0.3876313865,-0.0207066331,0.1824162602,0.0286726989,0.10558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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/482","title":"Bugs : dataset.map() is frozen on ELI5","comments":"@lhoestq mapping the function `make_input_target` was passed by your fixing.\r\n\r\nHowever, there is another error in the final step of `valid_dataset.map(convert_to_features, batched=True)`\r\n\r\n`ArrowInvalid: Could not convert Thepiratebay.vg with type str: converting to null type`\r\n(The [same colab notebook above with new error message](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing#scrollTo=5sRrJ3_C8rLt))\r\n\r\nDo you have some ideas? (I am really sorry I could not debug it by myself since I never used `pyarrow` before) \r\nNote that `train_dataset.map(convert_to_features, batched=True)` can be run successfully even though train_dataset is 27x bigger than `valid_dataset` so I believe the problem lies in some field of `valid_dataset` again .","body":"Hi Huggingface Team!\r\n\r\nThank you guys once again for this amazing repo.\r\n\r\nI have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https:\/\/github.com\/patil-suraj\/exploring-T5\/blob\/master\/T5_on_TPU.ipynb) \r\n\r\nHowever, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 \/ 0.17.0 \/ 1.0.0 also have the same frozen process.\r\n\r\nReproducible code can be found on [this colab notebook ](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.\r\n\r\n----------------------------------------\r\n**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)\r\n\r\nI also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ?","comment_length":94,"text":"Bugs : dataset.map() is frozen on ELI5 \n Hi Huggingface Team!\r\n\r\nThank you guys once again for this amazing repo.\r\n\r\nI have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https:\/\/github.com\/patil-suraj\/exploring-T5\/blob\/master\/T5_on_TPU.ipynb) \r\n\r\nHowever, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 \/ 0.17.0 \/ 1.0.0 also have the same frozen process.\r\n\r\nReproducible code can be found on [this colab notebook ](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.\r\n\r\n----------------------------------------\r\n**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)\r\n\r\nI also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ? \n @lhoestq mapping the function `make_input_target` was passed by your fixing.\r\n\r\nHowever, there is another error in the final step of `valid_dataset.map(convert_to_features, batched=True)`\r\n\r\n`ArrowInvalid: Could not convert Thepiratebay.vg with type str: converting to null type`\r\n(The [same colab notebook above with new error message](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing#scrollTo=5sRrJ3_C8rLt))\r\n\r\nDo you have some ideas? (I am really sorry I could not debug it by myself since I never used `pyarrow` before) \r\nNote that `train_dataset.map(convert_to_features, batched=True)` can be run successfully even though train_dataset is 27x bigger than `valid_dataset` so I believe the problem lies in some field of `valid_dataset` again 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/482","title":"Bugs : dataset.map() is frozen on ELI5","comments":"I got this issue too and fixed it by specifying `writer_batch_size=3_000` in `.map`.\r\nThis is because Arrow didn't expect `Thepiratebay.vg` in `title_urls `, as all previous examples have empty lists in `title_urls `","body":"Hi Huggingface Team!\r\n\r\nThank you guys once again for this amazing repo.\r\n\r\nI have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https:\/\/github.com\/patil-suraj\/exploring-T5\/blob\/master\/T5_on_TPU.ipynb) \r\n\r\nHowever, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 \/ 0.17.0 \/ 1.0.0 also have the same frozen process.\r\n\r\nReproducible code can be found on [this colab notebook ](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.\r\n\r\n----------------------------------------\r\n**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)\r\n\r\nI also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ?","comment_length":33,"text":"Bugs : dataset.map() is frozen on ELI5 \n Hi Huggingface Team!\r\n\r\nThank you guys once again for this amazing repo.\r\n\r\nI have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https:\/\/github.com\/patil-suraj\/exploring-T5\/blob\/master\/T5_on_TPU.ipynb) \r\n\r\nHowever, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 \/ 0.17.0 \/ 1.0.0 also have the same frozen process.\r\n\r\nReproducible code can be found on [this colab notebook ](https:\/\/colab.research.google.com\/drive\/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.\r\n\r\n----------------------------------------\r\n**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)\r\n\r\nI also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ? \n I got this issue too and fixed it by specifying `writer_batch_size=3_000` in `.map`.\r\nThis is because Arrow didn't expect `Thepiratebay.vg` in `title_urls `, as all previous examples have empty lists in `title_urls 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/478","title":"Export TFRecord to GCP bucket","comments":"Nevermind, I restarted my python session and it worked fine...\r\n\r\n---\r\n\r\nI had an authentification error, and I authenticated from another terminal. After that, no more error but it was not working. Restarting the sessions makes it work :)","body":"Previously, I was writing TFRecords manually to GCP bucket with : `with tf.io.TFRecordWriter('gs:\/\/my_bucket\/x.tfrecord')`\r\n\r\nSince `0.4.0` is out with the `export()` function, I tried it. But it seems TFRecords cannot be directly written to GCP bucket.\r\n\r\n`dataset.export('local.tfrecord')` works fine,  \r\nbut `dataset.export('gs:\/\/my_bucket\/x.tfrecord')` does not work. \r\n\r\nThere is no error message, I just can't find the file on my bucket...\r\n\r\n---\r\n\r\nLooking at the code, `nlp` is using `tf.data.experimental.TFRecordWriter`, while I was using `tf.io.TFRecordWriter`.  \r\n\r\n**What's the difference between those 2 ? How can I write TFRecords files directly to GCP bucket ?**\r\n\r\n@jarednielsen @lhoestq ","comment_length":39,"text":"Export TFRecord to GCP bucket \n Previously, I was writing TFRecords manually to GCP bucket with : `with tf.io.TFRecordWriter('gs:\/\/my_bucket\/x.tfrecord')`\r\n\r\nSince `0.4.0` is out with the `export()` function, I tried it. But it seems TFRecords cannot be directly written to GCP bucket.\r\n\r\n`dataset.export('local.tfrecord')` works fine,  \r\nbut `dataset.export('gs:\/\/my_bucket\/x.tfrecord')` does not work. \r\n\r\nThere is no error message, I just can't find the file on my bucket...\r\n\r\n---\r\n\r\nLooking at the code, `nlp` is using `tf.data.experimental.TFRecordWriter`, while I was using `tf.io.TFRecordWriter`.  \r\n\r\n**What's the difference between those 2 ? How can I write TFRecords files directly to GCP bucket ?**\r\n\r\n@jarednielsen @lhoestq  \n Nevermind, I restarted my python session and it worked fine...\r\n\r\n---\r\n\r\nI had an authentification error, and I authenticated from another terminal. After that, no more error but it was not working. Restarting the sessions makes it work :)","embeddings":[0.1043086722,-0.0196554437,0.0418730006,0.1138932854,0.0320830382,-0.1475580037,0.1983275712,0.1378264129,-0.4294385016,0.1086212397,-0.2599080205,0.236711055,-0.2770297229,0.1902204603,0.4431501031,0.0458829962,-0.1624467522,-0.0884751529,-0.061361704,-0.2464275807,-0.2078003883,0.3399341702,-0.1633239537,0.1298568994,-0.1636379361,-0.3291425407,-0.139205575,0.0100800823,0.2158965915,-0.0817750022,0.1531289816,-0.1572728902,0.2936231494,0.3548467457,-0.0001241777,0.0557242855,0.0362486765,-0.2642901838,-0.100471437,-0.1585778296,-0.3611402512,0.0675945058,0.0743403882,-0.2352459431,0.0370032154,0.1353317052,0.5893600583,0.2575761676,-0.1871163547,0.4628814161,0.1116086021,0.4620780349,0.1533130854,0.171333313,-0.1146622375,-0.240670979,-0.3874956667,0.056643907,-0.1590555906,0.1580326706,0.143365398,-0.1098379567,0.0033528022,-0.2057417184,0.316311717,0.1110550016,-0.1072750613,-0.3120269477,-0.0031496282,0.1946424842,0.6180038452,-0.5000046492,-0.5036103725,0.0647539794,-0.0492016487,-0.2039890885,0.3115359247,0.4222564101,0.3510525525,0.1510547101,-0.2972370982,-0.1687379032,-0.305837661,0.1791097075,-0.0949265212,0.6592948437,-0.063075982,0.0978821591,-0.2444780767,0.0606238991,0.1662686914,-0.0485495552,-0.1336772144,0.3695758283,-0.1432873607,-0.3499161899,0.2528569102,0.4646779299,-0.551558435,0.3166106343,0.3559399545,-0.194340691,-0.0089944676,0.0084281713,-0.008708938,0.0439142585,0.3559091091,0.4459598958,0.0651987717,-0.3567683697,0.0706164166,-0.1742616594,-0.0649130419,-0.2846621871,-0.1426874548,0.0446223691,0.0379393287,-0.1374833286,0.0949023291,-0.1490453333,-0.0987590104,0.1095617786,-0.3324379027,0.0881567821,0.078425169,-0.2568153739,0.2772000432,-0.0266955011,-0.6436520815,0.0527903698,-0.0495500788,0.1958491653,0.1571635455,-0.0411067493,-0.0443773828,0.0338041112,0.0224749073,-0.2291047275,0.050985489,0.2616701722,0.3601639271,-0.0454379916,-0.1735641062,0.2035631239,0.039008785,0.2462253273,0.0227716509,-0.1490443349,-0.2866272926,0.1152702644,-0.2529262006,-0.3067851067,-0.0610647313,0.0415606424,-0.1484234333,-0.3995211124,-0.2166468948,0.001940498,0.12320555,-0.3684224784,0.2590978742,-0.3070381284,-0.0856938884,-0.3298635483,-0.0941886902,0.1332684308,-0.4248131216,-0.0791197941,-0.0175602064,0.0383081697,0.2924700975,0.2817420959,0.1365849376,0.183299154,0.0643903017,0.3293117881,0.3842251599,-0.5604013801,0.03586917,0.3170734644,-0.3729988337,-0.1465917081,-0.2245298326,-0.0009617406,0.3030170798,-0.2277836204,-0.036367517,0.3661177754,0.0217803996,0.173636511,-0.2771191001,-0.1050426215,0.0246056728,-0.277939409,-0.0159468167,-0.131043762,0.4008926451,0.1757673174,0.1823630035,-0.0711171404,0.2193006724,0.4072651267,0.3658869863,0.0230924152,-0.0836257339,0.0767629668,-0.531768918,0.0073541328,-0.2281797528,0.183049798,0.3466967344,0.0587602891,0.1974603981,-0.2921520174,0.1712455302,-0.1641822904,-0.0951042697,0.1215174198,0.1402537674,0.3574543297,-0.0441086814,0.2640912533,-0.1312345117,-0.1229351237,-0.2632279694,0.3617303073,0.1286933124,-0.3282949626,0.0945415348,-0.1186136305,0.0846155584,-0.2326744199,-0.2019333243,0.3773418665,-0.2473888844,0.5208188891,0.0495079793,0.6487165093,0.1313399225,-0.2497121394,-0.1034749895,0.3585964739,-0.0531645492,0.2756613791,-0.1737519354,0.5877634287,0.1556873918,-0.1525974125,0.386910677,0.0487791412,-0.2978127301,-0.1340873986,-0.0127204712,0.1818125993,0.1251281351,-0.1221780106,0.0884689987,-0.1293487549,-0.3697161674,0.3899160624,0.6853585243,-0.1292418689,0.0035886897,-0.1097488627,0.0905126184,-0.0675529614,0.512488842,0.0625751838,0.2000595033,0.0357431956,0.1346185952,-0.1173051447,-0.2365724593,-0.1088180393,0.3869111836,-0.1922742873,-0.2225830555,-0.0102991899,-0.0086948993,0.256480515,-0.1329466552,-0.0602575056,0.0856533647,0.3868994415,-0.4979802072,0.070247151,-0.4411150217,-0.1988404244,-0.107116513,0.3426580131,-0.3205321133,-0.327308774,0.0365762599,0.0401102044,-0.1647715867,-0.0049517136,-0.115125224,0.0103284158,0.0407161601,-0.1432955116,-0.1063830405,0.0423582457,-0.2163069546,-0.0144215478,0.0300439857,0.1656529754,0.288261354,-0.0380332991,-0.1832355559,-0.0969579294,-0.3768307269,0.0041226777,-0.1586945951,0.1078701615,0.0106572863,0.0450568758,-0.5808387399,-0.1346590817,-0.1810050756,-0.4385964274,-0.2040456384,0.0246810187,-0.0896000415,-0.0534392856,-0.25321576,-0.1335134059,-0.237013936,-0.3757142723,0.2796498239,0.2792012393,0.3979219794,0.4036666453,-0.464941889,0.2192778587,0.1281318218,-0.2627945244,0.271951437,-0.1234670654,0.4003706574,-0.5539553165,-0.3774174154,0.1360996515,0.1978217512,-0.0095765833,0.0663508475,-0.2033832073,0.0864781067,-0.3496908844,-0.0653625652,0.1325469315,0.2524751425,0.4491384029,-0.2184735686,0.161797449,-0.1543953419,-0.1096281409,0.0963791758,0.9362360239,0.023415098,-0.0219114739,0.115382202,0.058070343,0.3341713846,0.0435016714,0.0677085593,0.0357965454,-0.3759589493,0.19030267,-0.2492443174,-0.1469091624,0.6196631193,0.4616970718,-0.1018358991,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/477","title":"Overview.ipynb throws exceptions with nlp 0.4.0","comments":"Thanks for reporting this issue\r\n\r\nThere was a bug where numpy arrays would get returned instead of tensorflow tensors.\r\nThis is fixed on master.\r\n\r\nI tried to re-run the colab and encountered this error instead:\r\n\r\n```\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'to_tensor'\r\n```\r\n\r\nThis is because the dataset returns a Tensor and not a RaggedTensor.\r\nBut I think we should always return a RaggedTensor unless the length of the sequence is fixed (it that case they can be stack into a Tensor).","body":"with nlp 0.4.0, the TensorFlow example in Overview.ipynb throws the following exceptions:\r\n\r\n\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-48907f2ad433> in <module>\r\n----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}\r\n      2 labels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\n      3 labels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n\r\n<ipython-input-5-48907f2ad433> in <dictcomp>(.0)\r\n----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}\r\n      2 labels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\n      3 labels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n\r\nAttributeError: 'numpy.ndarray' object has no attribute 'to_tensor'","comment_length":83,"text":"Overview.ipynb throws exceptions with nlp 0.4.0 \n with nlp 0.4.0, the TensorFlow example in Overview.ipynb throws the following exceptions:\r\n\r\n\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-48907f2ad433> in <module>\r\n----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}\r\n      2 labels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\n      3 labels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n\r\n<ipython-input-5-48907f2ad433> in <dictcomp>(.0)\r\n----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}\r\n      2 labels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\n      3 labels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n\r\nAttributeError: 'numpy.ndarray' object has no attribute 'to_tensor' \n Thanks for reporting this issue\r\n\r\nThere was a bug where numpy arrays would get returned instead of tensorflow tensors.\r\nThis is fixed on master.\r\n\r\nI tried to re-run the colab and encountered this error instead:\r\n\r\n```\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'to_tensor'\r\n```\r\n\r\nThis is because the dataset returns a Tensor and not a RaggedTensor.\r\nBut I think we should always return a RaggedTensor unless the length of the sequence is fixed (it that case they can be stack into a Tensor).","embeddings":[-0.0391482376,0.082511723,-0.0073065483,-0.0530798919,0.0318035781,-0.0482144579,0.8157610297,0.2792399526,-0.0684225112,0.1789912581,0.0316702574,0.2038193345,-0.297031194,-0.0530733988,0.0210223142,-0.4359816313,-0.1079105809,0.286254257,0.0336867906,-0.0560540147,-0.1683885306,0.2695775628,-0.4814034402,0.0699896812,-0.3009614944,-0.2224038243,-0.003463051,-0.1184036061,0.1805594563,-0.3218075335,0.2983432412,-0.0853027105,-0.0729661137,0.2157041878,-0.0001094074,0.1312537491,0.2247508913,0.0399746299,-0.1744725704,-0.3294422925,-0.1835713238,-0.0175784472,-0.0488839783,-0.3077403009,0.1411332339,0.0695705861,0.2543091178,0.2293205559,0.187586844,0.4259967804,0.2343905419,0.3789539039,0.0684154481,0.0336801223,0.0507045202,-0.0231374409,-0.0912747681,0.1140883863,-0.0621383898,-0.1997990906,-0.2145836651,0.2809059322,-0.0087522771,0.0915340558,-0.158366695,0.0241951719,0.0742403716,-0.2711643577,-0.0711959004,0.4140101969,-0.0402942188,-0.0426515564,-0.392829448,-0.3703237176,0.0652718395,-0.5704241991,-0.060038127,0.1048349366,-0.1118555367,-0.016561389,-0.1545907557,0.095754236,-0.5827885866,0.3474161923,-0.1908776015,0.1448999345,0.0082724849,0.081057407,-0.0094359042,-0.0986803472,-0.0254501868,-0.0089679696,0.1772603244,0.0017488351,-0.2649015784,-0.220381543,0.1818951219,-0.206580326,-0.1672124565,-0.0775770694,0.0233217161,0.0268009454,0.0007514657,0.3499961197,-0.122096628,0.2610676587,0.1183250472,0.1576666385,0.3323271275,-0.0056893472,-0.1858868748,0.0312410183,0.0496797599,-0.1437185258,-0.1408727169,0.017316794,0.3466324508,0.1334186941,-0.5548805594,0.0387058556,-0.3659700453,0.1409137547,0.192010656,0.2450779825,-0.0872803405,0.2586591542,0.2062356174,0.1746394187,-0.4506019652,-0.0207848474,-0.2161477357,0.3215353191,-0.054345686,-0.2500003576,0.2410529703,0.32728827,0.3226420283,-0.0850195363,0.0863246098,0.0834727958,0.3203797638,-0.2739877701,0.1113804728,0.2461777329,-0.0800185576,-0.1207126752,0.046420671,-0.1517851949,-0.0383200198,-0.0575329512,-0.2080945373,-0.3066107631,-0.0356256999,0.2675876915,0.1201595962,-0.0608261302,-0.2594278157,0.1365095973,0.0316395611,-0.1947879344,-0.0350830965,-0.3620331287,-0.1259934753,-0.0612786673,0.0401064605,0.190676704,0.1341350079,-0.1566854864,0.1043589339,0.1802005768,0.2523798943,0.2696486115,-0.2044544071,0.2307111025,-0.036194697,0.3748850822,0.3027423024,-0.2861011326,-0.1879547834,0.1153768301,-0.0510694645,0.0571820475,-0.0915255472,0.019735707,0.4229823351,-0.038066525,0.3864845932,0.2939309478,-0.2305163592,0.098540768,-0.0844678134,0.1140950769,0.292221874,-0.0645060986,0.1169267669,0.0630712658,-0.1999797374,0.5250784159,0.0386198647,-0.1206296608,-0.0273567997,0.0281608887,0.0251637865,-0.3499792218,-0.1735033095,-0.1349679232,-0.3476230204,0.0723298341,0.0827099904,0.2156667709,0.3347974122,-0.0584712513,0.0152782639,0.1186521649,-0.077429764,-0.2848463356,0.2511830032,0.1563904881,-0.1726750731,0.0747464448,-0.1370531023,0.0778900459,-0.3810476959,0.1017818525,-0.1399620175,0.0689370707,-0.0984523445,-0.1941973716,-0.2610077858,0.3692515492,-0.0739928558,-0.1785955727,-0.0611250252,0.2158330977,0.1814824939,0.2297656089,-0.2306142896,0.1413954794,0.3241619766,-0.0578077212,-0.1601210982,0.2085707486,0.0083810706,-0.1782839298,0.1594430953,0.4819951057,0.2851334512,0.1752204299,0.4278370738,0.1367619187,0.2068368047,0.0023742397,-0.1972270608,0.08879821,0.3635754287,-0.0712224767,0.1973559558,-0.2939554155,-0.7215968966,0.1118019372,0.549682498,0.1343672872,0.0659737885,0.1962781847,-0.0909676552,0.1029396802,0.3837305307,-0.2072698623,0.1110235453,0.0864473283,0.2527721822,0.0789593309,-0.3717411757,-0.2521877289,0.0808835775,0.1414926648,-0.1022182554,-0.0228342731,0.2730284631,0.2171438932,-0.2615646124,-0.3487101197,-0.1701575369,0.2663602531,-0.3354310095,0.3268645406,-0.1205521524,-0.2741838694,-0.0820815712,-0.5632296801,-0.1426299363,-0.3243647516,-0.0196133722,0.626950562,-0.3355834782,0.2322810441,0.1679948866,0.0385323465,0.0734659433,0.2469550222,0.1575049609,0.0089548556,-0.3124403656,0.1394797713,0.1999257207,-0.1503335834,0.2847865522,-0.0808443576,-0.3065349758,0.2594654858,-0.5440360904,0.1175272763,-0.1258309186,-0.0007881688,0.2060860097,0.2676906288,-0.2906229794,-0.5785192251,0.0555495806,-0.1116683707,-0.1575769037,0.3648533523,-0.0376024731,-0.0444794111,-0.3675981462,-0.5435934067,-0.1716129035,-0.4446734786,0.361694634,-0.0806766227,0.307082206,0.7853786349,-0.0730392784,0.5275722146,0.2695943415,0.1862934232,0.2173419595,-0.0682444572,-0.00816104,-0.2857410312,-0.3695432544,-0.106303297,-0.2888970375,0.1831954867,0.1218610108,-0.329808116,-0.0585052744,-0.0348194987,0.134925127,0.1361687481,-0.1742237657,0.3653981388,-0.0563014857,-0.0302218366,-0.0342406295,0.0573622622,0.3721608818,0.2451589406,-0.0622616112,-0.0394694619,0.2059445679,0.1754864305,0.3692196012,0.0424718671,-0.2425438613,0.2184517384,0.0004712994,0.0349336825,-0.1755885184,-0.276870966,0.2389113009,-0.0171982739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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/477","title":"Overview.ipynb throws exceptions with nlp 0.4.0","comments":"Hi, I got another error (on Colab):\r\n\r\n```python\r\n# You can read a few attributes of the datasets before loading them (they are python dataclasses)\r\nfrom dataclasses import asdict\r\n\r\nfor key, value in asdict(datasets[6]).items():\r\n    print('\ud83d\udc49 ' + key + ': ' + str(value))\r\n\r\n---------------------------------------------------------------------------\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\r\n<ipython-input-6-b8ace6c227a2> in <module>()\r\n      2 from dataclasses import asdict\r\n      3 \r\n----> 4 for key, value in asdict(datasets[6]).items():\r\n      5     print('\ud83d\udc49 ' + key + ': ' + str(value))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/dataclasses.py in asdict(obj, dict_factory)\r\n   1008     \"\"\"\r\n   1009     if not _is_dataclass_instance(obj):\r\n-> 1010         raise TypeError(\"asdict() should be called on dataclass instances\")\r\n   1011     return _asdict_inner(obj, dict_factory)\r\n   1012 \r\n\r\nTypeError: asdict() should be called on dataclass instances\r\n```","body":"with nlp 0.4.0, the TensorFlow example in Overview.ipynb throws the following exceptions:\r\n\r\n\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-48907f2ad433> in <module>\r\n----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}\r\n      2 labels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\n      3 labels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n\r\n<ipython-input-5-48907f2ad433> in <dictcomp>(.0)\r\n----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}\r\n      2 labels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\n      3 labels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n\r\nAttributeError: 'numpy.ndarray' object has no attribute 'to_tensor'","comment_length":110,"text":"Overview.ipynb throws exceptions with nlp 0.4.0 \n with nlp 0.4.0, the TensorFlow example in Overview.ipynb throws the following exceptions:\r\n\r\n\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-5-48907f2ad433> in <module>\r\n----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}\r\n      2 labels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\n      3 labels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n\r\n<ipython-input-5-48907f2ad433> in <dictcomp>(.0)\r\n----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}\r\n      2 labels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\n      3 labels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n      4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n\r\nAttributeError: 'numpy.ndarray' object has no attribute 'to_tensor' \n Hi, I got another error (on Colab):\r\n\r\n```python\r\n# You can read a few attributes of the datasets before loading them (they are python dataclasses)\r\nfrom dataclasses import asdict\r\n\r\nfor key, value in asdict(datasets[6]).items():\r\n    print('\ud83d\udc49 ' + key + ': ' + str(value))\r\n\r\n---------------------------------------------------------------------------\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\r\n<ipython-input-6-b8ace6c227a2> in <module>()\r\n      2 from dataclasses import asdict\r\n      3 \r\n----> 4 for key, value in asdict(datasets[6]).items():\r\n      5     print('\ud83d\udc49 ' + key + ': ' + str(value))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/dataclasses.py in asdict(obj, dict_factory)\r\n   1008     \"\"\"\r\n   1009     if not _is_dataclass_instance(obj):\r\n-> 1010         raise TypeError(\"asdict() should be called on dataclass instances\")\r\n   1011     return _asdict_inner(obj, dict_factory)\r\n   1012 \r\n\r\nTypeError: asdict() should be called on dataclass instances\r\n```","embeddings":[-0.0391482376,0.082511723,-0.0073065483,-0.0530798919,0.0318035781,-0.0482144579,0.8157610297,0.2792399526,-0.0684225112,0.1789912581,0.0316702574,0.2038193345,-0.297031194,-0.0530733988,0.0210223142,-0.4359816313,-0.1079105809,0.286254257,0.0336867906,-0.0560540147,-0.1683885306,0.2695775628,-0.4814034402,0.0699896812,-0.3009614944,-0.2224038243,-0.003463051,-0.1184036061,0.1805594563,-0.3218075335,0.2983432412,-0.0853027105,-0.0729661137,0.2157041878,-0.0001094074,0.1312537491,0.2247508913,0.0399746299,-0.1744725704,-0.3294422925,-0.1835713238,-0.0175784472,-0.0488839783,-0.3077403009,0.1411332339,0.0695705861,0.2543091178,0.2293205559,0.187586844,0.4259967804,0.2343905419,0.3789539039,0.0684154481,0.0336801223,0.0507045202,-0.0231374409,-0.0912747681,0.1140883863,-0.0621383898,-0.1997990906,-0.2145836651,0.2809059322,-0.0087522771,0.0915340558,-0.158366695,0.0241951719,0.0742403716,-0.2711643577,-0.0711959004,0.4140101969,-0.0402942188,-0.0426515564,-0.392829448,-0.3703237176,0.0652718395,-0.5704241991,-0.060038127,0.1048349366,-0.1118555367,-0.016561389,-0.1545907557,0.095754236,-0.5827885866,0.3474161923,-0.1908776015,0.1448999345,0.0082724849,0.081057407,-0.0094359042,-0.0986803472,-0.0254501868,-0.0089679696,0.1772603244,0.0017488351,-0.2649015784,-0.220381543,0.1818951219,-0.206580326,-0.1672124565,-0.0775770694,0.0233217161,0.0268009454,0.0007514657,0.3499961197,-0.122096628,0.2610676587,0.1183250472,0.1576666385,0.3323271275,-0.0056893472,-0.1858868748,0.0312410183,0.0496797599,-0.1437185258,-0.1408727169,0.017316794,0.3466324508,0.1334186941,-0.5548805594,0.0387058556,-0.3659700453,0.1409137547,0.192010656,0.2450779825,-0.0872803405,0.2586591542,0.2062356174,0.1746394187,-0.4506019652,-0.0207848474,-0.2161477357,0.3215353191,-0.054345686,-0.2500003576,0.2410529703,0.32728827,0.3226420283,-0.0850195363,0.0863246098,0.0834727958,0.3203797638,-0.2739877701,0.1113804728,0.2461777329,-0.0800185576,-0.1207126752,0.046420671,-0.1517851949,-0.0383200198,-0.0575329512,-0.2080945373,-0.3066107631,-0.0356256999,0.2675876915,0.1201595962,-0.0608261302,-0.2594278157,0.1365095973,0.0316395611,-0.1947879344,-0.0350830965,-0.3620331287,-0.1259934753,-0.0612786673,0.0401064605,0.190676704,0.1341350079,-0.1566854864,0.1043589339,0.1802005768,0.2523798943,0.2696486115,-0.2044544071,0.2307111025,-0.036194697,0.3748850822,0.3027423024,-0.2861011326,-0.1879547834,0.1153768301,-0.0510694645,0.0571820475,-0.0915255472,0.019735707,0.4229823351,-0.038066525,0.3864845932,0.2939309478,-0.2305163592,0.098540768,-0.0844678134,0.1140950769,0.292221874,-0.0645060986,0.1169267669,0.0630712658,-0.1999797374,0.5250784159,0.0386198647,-0.1206296608,-0.0273567997,0.0281608887,0.0251637865,-0.3499792218,-0.1735033095,-0.1349679232,-0.3476230204,0.0723298341,0.0827099904,0.2156667709,0.3347974122,-0.0584712513,0.0152782639,0.1186521649,-0.077429764,-0.2848463356,0.2511830032,0.1563904881,-0.1726750731,0.0747464448,-0.1370531023,0.0778900459,-0.3810476959,0.1017818525,-0.1399620175,0.0689370707,-0.0984523445,-0.1941973716,-0.2610077858,0.3692515492,-0.0739928558,-0.1785955727,-0.0611250252,0.2158330977,0.1814824939,0.2297656089,-0.2306142896,0.1413954794,0.3241619766,-0.0578077212,-0.1601210982,0.2085707486,0.0083810706,-0.1782839298,0.1594430953,0.4819951057,0.2851334512,0.1752204299,0.4278370738,0.1367619187,0.2068368047,0.0023742397,-0.1972270608,0.08879821,0.3635754287,-0.0712224767,0.1973559558,-0.2939554155,-0.7215968966,0.1118019372,0.549682498,0.1343672872,0.0659737885,0.1962781847,-0.0909676552,0.1029396802,0.3837305307,-0.2072698623,0.1110235453,0.0864473283,0.2527721822,0.0789593309,-0.3717411757,-0.2521877289,0.0808835775,0.1414926648,-0.1022182554,-0.0228342731,0.2730284631,0.2171438932,-0.2615646124,-0.3487101197,-0.1701575369,0.2663602531,-0.3354310095,0.3268645406,-0.1205521524,-0.2741838694,-0.0820815712,-0.5632296801,-0.1426299363,-0.3243647516,-0.0196133722,0.626950562,-0.3355834782,0.2322810441,0.1679948866,0.0385323465,0.0734659433,0.2469550222,0.1575049609,0.0089548556,-0.3124403656,0.1394797713,0.1999257207,-0.1503335834,0.2847865522,-0.0808443576,-0.3065349758,0.2594654858,-0.5440360904,0.1175272763,-0.1258309186,-0.0007881688,0.2060860097,0.2676906288,-0.2906229794,-0.5785192251,0.0555495806,-0.1116683707,-0.1575769037,0.3648533523,-0.0376024731,-0.0444794111,-0.3675981462,-0.5435934067,-0.1716129035,-0.4446734786,0.361694634,-0.0806766227,0.307082206,0.7853786349,-0.0730392784,0.5275722146,0.2695943415,0.1862934232,0.2173419595,-0.0682444572,-0.00816104,-0.2857410312,-0.3695432544,-0.106303297,-0.2888970375,0.1831954867,0.1218610108,-0.329808116,-0.0585052744,-0.0348194987,0.134925127,0.1361687481,-0.1742237657,0.3653981388,-0.0563014857,-0.0302218366,-0.0342406295,0.0573622622,0.3721608818,0.2451589406,-0.0622616112,-0.0394694619,0.2059445679,0.1754864305,0.3692196012,0.0424718671,-0.2425438613,0.2184517384,0.0004712994,0.0349336825,-0.1755885184,-0.276870966,0.2389113009,-0.01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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/474","title":"test_load_real_dataset when config has BUILDER_CONFIGS that matter","comments":"The `data_dir` parameter has been removed. Now the error is `ValueError: Config name is missing`\r\n\r\nAs mentioned in #470 I think we can have one test with the first config of BUILDER_CONFIGS, and another test that runs all of the configs in BUILDER_CONFIGS","body":"It a dataset has custom `BUILDER_CONFIGS` with non-keyword arguments (or keyword arguments with non default values), the config is not loaded during the test and causes an error.\r\nI think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/tests\/test_dataset_common.py#L200)). This causes [this line](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/builder.py#L201) to always be false because `config_kwargs` is not `None`. [This line](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/builder.py#L222) will be run instead, which doesn't use `BUILDER_CONFIGS`.\r\n\r\nFor an example, you can try running the test for lince:\r\n` RUN_SLOW=1 pytest tests\/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lince`\r\nwhich yields\r\n> E           TypeError: __init__() missing 3 required positional arguments: 'colnames', 'classes', and 'label_column'","comment_length":43,"text":"test_load_real_dataset when config has BUILDER_CONFIGS that matter \n It a dataset has custom `BUILDER_CONFIGS` with non-keyword arguments (or keyword arguments with non default values), the config is not loaded during the test and causes an error.\r\nI think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/tests\/test_dataset_common.py#L200)). This causes [this line](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/builder.py#L201) to always be false because `config_kwargs` is not `None`. [This line](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/builder.py#L222) will be run instead, which doesn't use `BUILDER_CONFIGS`.\r\n\r\nFor an example, you can try running the test for lince:\r\n` RUN_SLOW=1 pytest tests\/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lince`\r\nwhich yields\r\n> E           TypeError: __init__() missing 3 required positional arguments: 'colnames', 'classes', and 'label_column' \n The `data_dir` parameter has been removed. Now the error is `ValueError: Config name is missing`\r\n\r\nAs mentioned in #470 I think we can have one test with the first config of BUILDER_CONFIGS, and another test that runs all of the configs in 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/474","title":"test_load_real_dataset when config has BUILDER_CONFIGS that matter","comments":"This was fixed in #527 \r\n\r\nClosing this one, but feel free to re-open if you have other questions","body":"It a dataset has custom `BUILDER_CONFIGS` with non-keyword arguments (or keyword arguments with non default values), the config is not loaded during the test and causes an error.\r\nI think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/tests\/test_dataset_common.py#L200)). This causes [this line](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/builder.py#L201) to always be false because `config_kwargs` is not `None`. [This line](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/builder.py#L222) will be run instead, which doesn't use `BUILDER_CONFIGS`.\r\n\r\nFor an example, you can try running the test for lince:\r\n` RUN_SLOW=1 pytest tests\/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lince`\r\nwhich yields\r\n> E           TypeError: __init__() missing 3 required positional arguments: 'colnames', 'classes', and 'label_column'","comment_length":18,"text":"test_load_real_dataset when config has BUILDER_CONFIGS that matter \n It a dataset has custom `BUILDER_CONFIGS` with non-keyword arguments (or keyword arguments with non default values), the config is not loaded during the test and causes an error.\r\nI think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/tests\/test_dataset_common.py#L200)). This causes [this line](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/builder.py#L201) to always be false because `config_kwargs` is not `None`. [This line](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/builder.py#L222) will be run instead, which doesn't use `BUILDER_CONFIGS`.\r\n\r\nFor an example, you can try running the test for lince:\r\n` RUN_SLOW=1 pytest tests\/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lince`\r\nwhich yields\r\n> E           TypeError: __init__() missing 3 required positional arguments: 'colnames', 'classes', and 'label_column' \n This was fixed in #527 \r\n\r\nClosing this one, but feel free to re-open if you have other questions","embeddings":[-0.3385565877,-0.0536466315,0.0424275808,0.1108628884,0.1634410322,0.0783360898,0.1228836551,0.3306728005,-0.0973314494,-0.0040300302,0.0537734479,0.4402484596,-0.1571183205,0.0414613187,0.1643651128,0.0443918817,-0.2682584524,0.2769702077,0.0980256796,-0.0319090597,-0.1879016161,0.1579577476,-0.0973070934,0.0949115977,-0.2986759841,0.0340311453,-0.1379248351,0.2986700535,0.1990152597,-0.5106943846,0.4461774826,0.0938677639,-0.0859578028,0.1431889981,-0.0001083159,0.0801491439,0.3250587583,-0.0839207619,-0.637114048,-0.0625694469,0.1405450553,-0.2929130793,0.2855110765,-0.3282714188,-0.3212667704,-0.2411285639,-0.1270383745,0.0736482516,0.0725026578,0.3807226121,0.2747091055,0.3636160195,-0.2297531366,-0.1485881507,-0.1620922387,-0.0258874465,-0.0437101349,0.3394280672,0.1688621491,0.0183412638,0.0527107343,-0.1217424348,-0.0641052276,0.0916210115,0.0495265983,-0.1177207232,-0.0773677304,-0.0686179549,-0.0201732405,0.2787656188,0.2818546593,-0.4329428077,-0.0211793836,-0.2513436973,0.1684996784,-0.1498256773,0.3519796133,-0.081268847,-0.4076192379,0.0426622964,-0.4676843584,-0.1900425106,0.0658134222,0.1104304567,0.0950841829,-0.0414986461,-0.0108464593,0.2007044852,0.0036649953,0.1042253673,-0.1386699528,-0.176100865,-0.1310182065,0.127907902,-0.1391436011,-0.3100568354,0.2468237728,0.2167610675,0.2853586078,0.0043460596,-0.0273894947,0.0024011645,0.2310737222,0.2241033465,-0.1374670118,0.3698545098,0.3622436821,-0.0116292955,0.1981758475,0.010260568,-0.1906317323,0.0484408103,0.1153854132,-0.3490093052,-0.0083672144,-0.0628785267,0.2651869655,-0.4105986357,-0.3491712511,0.0293950774,-0.055791311,0.165577814,0.3375837207,0.4199337661,-0.0324665643,-0.1970600188,0.0237554982,0.3201874793,-0.1912802011,-0.1085969433,-0.3206711113,-0.0218131747,-0.1324210614,0.0523711406,0.4688149393,-0.2727923095,0.6226427555,0.1460936219,0.0216841716,0.0165438391,0.0746406168,-0.0024734484,-0.0568611361,0.3785873055,-0.1605929583,-0.0764332563,0.2329165339,-0.1536602527,-0.3517065644,0.2044932842,-0.0884962678,-0.5394568443,0.2401414067,0.2285760939,-0.3029271066,-0.0736225322,0.0094516249,0.0187685583,0.0577064641,-0.1259180307,-0.1104718149,-0.3919752538,0.0619846098,-0.1528332233,0.2685030401,0.2812782228,-0.1850993037,-0.1322146803,0.1188690141,-0.143509388,0.1952687651,-0.0970532298,-0.2381485105,0.1500054151,-0.1737826616,0.1072408408,0.5219545364,-0.2305840254,-0.1180210561,0.2901421785,-0.0084151104,0.0573636927,-0.1372818202,0.0773709044,-0.1516706347,-0.037383344,0.0596372448,0.3409302235,0.0408540182,0.0341271013,-0.4409683347,-0.1354342848,0.1570859104,0.1454819739,-0.346373409,-0.0475006253,-0.146941334,-0.0601771884,0.199085027,-0.0826975927,-0.2510528862,0.0619646609,0.0419992507,-0.0967449993,0.042194359,0.0955780298,-0.556489706,0.4191554189,0.0833571851,0.2934090197,0.158155486,-0.0835636482,-0.2718794048,0.0456508473,-0.2793190479,-0.3855575025,0.1636399031,0.2197535187,0.1555460095,-0.1151784882,-0.1425247043,-0.0953576714,-0.3342646956,0.0108676804,-0.0560782999,0.0678906813,-0.0168093368,-0.0625691488,-0.074856855,0.2662242055,0.1372029185,-0.2954993546,-0.2036425918,0.4747344553,0.0138577111,-0.0961078107,0.107180208,-0.0413774624,-0.0719467476,-0.0976847112,0.0078976294,0.1678729057,-0.0173534788,-0.1123137698,-0.046283327,0.3852535784,0.1947698593,0.17417337,-0.081750229,0.0258382596,0.341634661,-0.1674983799,-0.0014194042,-0.1094363108,0.495426178,0.1282527,0.4595437348,0.0573717766,-0.3472210169,0.0108899977,0.279953748,0.0147187952,-0.0306053627,0.3178577125,-0.1561895907,-0.0745468959,0.1391989291,-0.0617589951,0.5611637235,0.1805538386,-0.0291944649,0.1363343,-0.6024135947,-0.2829419971,0.3375745118,-0.1507919133,0.2357178032,0.0174277071,0.1795159876,-0.1059577316,-0.5612171292,-0.2691088319,0.0600530989,0.3444687724,-0.31918329,0.104244478,-0.1219017431,0.0684502944,-0.1120392606,-0.2230800092,-0.1525081098,-0.3797399998,0.2065999806,0.019258704,-0.0720106736,0.2248980701,-0.064521417,0.2702748775,-0.1633848846,-0.5609422326,-0.3093764484,-0.2441608906,-0.5122612119,0.0824075118,0.1711145639,-0.0119708125,0.3132204115,-0.0825492069,-0.3603592515,0.1028654799,-0.329537183,0.1290487647,-0.0900543705,0.7290681601,0.2706765831,0.1940115541,0.3838451207,-0.1142290682,0.5275809169,0.0100331483,-0.1275102943,0.281848222,0.1550060958,-0.1615920663,-0.3950868845,-0.4603402317,-0.0449561626,-0.251778543,0.0392451547,0.1456494033,0.0847620443,0.4176315665,0.2565802038,0.2545304894,-0.1059367731,0.3779492676,-0.1628271192,-0.2203240842,-0.0969339535,-0.0557349101,-0.3735413253,-0.3066041768,-0.099149026,0.3467929065,-0.1125307754,-0.4803193212,-0.2475796938,-0.2052383423,0.4086900651,-0.0434773453,-0.2014959753,0.2554351985,0.2472823262,-0.1151552126,-0.1670496315,0.0405262187,0.2664709091,0.0364575721,0.1248781085,-0.0117564164,0.3133134544,-0.185517028,0.2949364185,0.2942859232,-0.3394878507,0.2867256701,-0.2529634535,0.327739805,-0.2344418019,-0.322924614,0.2463134229,0.087120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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/469","title":"invalid data type 'str' at _convert_outputs in arrow_dataset.py","comments":"Hi ! Did you try to set the output format to pytorch ? (or tensorflow if you're using tensorflow)\r\nIt can be done with `dataset.set_format(\"torch\", columns=columns)`  (or \"tensorflow\").\r\n\r\nNote that for pytorch, string columns can't be converted to `torch.Tensor`, so you have to specify in `columns=` the list of columns you want to keep (`input_ids` for example)","body":"I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n","comment_length":57,"text":"invalid data type 'str' at _convert_outputs in arrow_dataset.py \n I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n \n Hi ! Did you try to set the output format to pytorch ? (or tensorflow if you're using tensorflow)\r\nIt can be done with `dataset.set_format(\"torch\", columns=columns)`  (or \"tensorflow\").\r\n\r\nNote that for pytorch, string columns can't be converted to `torch.Tensor`, so you have to specify in `columns=` the list of columns you want to keep (`input_ids` for example)","embeddings":[-0.2228239626,0.0242501777,0.1073713675,0.2303070426,0.5109525919,-0.1072595492,0.5323296189,0.0775902644,-0.3410620093,-0.1247765422,0.036657881,0.3864071369,-0.3696954846,0.0422176793,0.0443617031,-0.2024641186,-0.0880346373,0.0099904612,-0.0232526027,-0.0536389053,0.0482440926,0.1829803139,-0.1568261683,0.4144774675,-0.6451317668,-0.1835785806,0.1746639758,-0.0963928327,-0.0992212296,-0.4487785995,0.2202744484,-0.1020444036,0.4976367652,0.3472318351,-0.0001241712,0.1380359232,0.1092858762,0.1016949043,-0.2908720374,-0.3016375303,0.1341145188,-0.0593848564,0.4149942696,-0.2015384734,-0.0571896322,-0.2600911558,-0.0606259108,0.2617689073,0.3837757409,0.5016983747,0.1173220798,0.1685167998,0.2464857996,0.0738129616,0.2595641315,0.3155063391,0.0754768178,0.2071238756,0.0168337896,0.0266056098,-0.0856947526,0.0748076811,-0.0701284856,-0.1997739971,0.2166583389,0.227282986,0.17457892,-0.1476221532,-0.2287852317,0.1198840439,0.4332152605,-0.3214256167,-0.1559445858,0.0079212757,-0.0111622717,-0.2727991641,-0.0061669713,0.2483308762,-0.4277769327,0.1667014062,-0.1622606069,-0.0254668966,-0.3792498708,0.199556306,-0.0925177336,0.5032640696,0.1183697209,0.1568167061,0.1842403114,-0.0144359078,0.371710062,0.0876382813,0.1397552341,0.0621479377,-0.3116942644,-0.030463824,-0.4463334084,-0.648362577,-0.2685149014,-0.2125236392,0.1655772626,-0.0711523443,0.2047266066,0.202950567,0.233322382,0.1388006508,-0.2101232409,0.5648387074,0.1000927314,-0.1552505791,-0.1335455626,-0.0250890013,-0.3282387555,-0.0922897011,0.5121243596,0.1628403068,0.1257427186,-0.0050322958,-0.0550226681,-0.1488328129,-0.2688644528,-0.0882172287,0.2847151458,0.1850690395,0.0958356038,0.3832220435,0.2154195458,0.111135751,0.0493784584,-0.1352442801,0.0353224054,0.0855564028,-0.2179709226,-0.1198172048,0.1821132153,0.3281980753,-0.2699041963,0.1215015873,-0.1425367743,-0.2085365504,0.001571518,-0.293589741,-0.0053074467,0.0134973433,-0.2574837208,0.3097872436,0.0935748518,-0.3035934269,0.0196346212,0.4677716196,-0.4638129771,-0.4341678917,-0.4358201325,0.0899604112,0.3708474934,-0.1129878536,-0.0923099965,0.059735287,0.4520861804,-0.1888256073,0.1029469818,-0.5797597766,0.3521030843,-0.0152924247,-0.0832994059,0.0905463472,-0.4035288692,-0.1816472858,0.1696708053,-0.1533035785,0.3111130297,0.4423996806,-0.3827602863,0.2947919369,0.0795271397,0.0166932028,0.8023683429,-0.3571899831,-0.2179583609,0.132254988,-0.1704488099,-0.3259194195,-0.0770724937,0.2736980319,0.0906305313,0.0717987269,-0.0094030369,0.3914262056,-0.0933154151,0.2531608641,-0.3342471123,-0.0749925673,0.3700163364,0.3320881724,0.2507386208,-0.0335372835,0.0665313303,0.3422166705,0.2596169412,-0.1454966366,0.2384007722,0.1618343294,0.0183554627,-0.1759249866,-0.1052252948,-0.1606465131,-0.3050886989,-0.2020210922,-0.3353252709,0.484374553,-0.2394975275,0.08094953,-0.1629112065,-0.0729379505,-0.1123220474,0.2390538901,0.1080784798,0.0814386383,-0.1436548978,-0.0689880103,-0.1000490189,-0.0618887804,-0.0523724556,0.1881001443,0.025137445,0.2732270956,-0.1691009253,-0.1986376792,-0.2601747513,0.0564328432,0.2558159232,0.0960626528,-0.2582112551,0.2561086416,-0.1447918415,-0.2581233084,-0.308811754,0.3103091717,0.155409649,-0.5250985622,0.0369421802,0.1614515334,0.1663212776,-0.0896117315,0.1419673562,0.1363632828,0.0686425567,0.1380071789,-0.0040352675,0.3693377972,0.2155335993,-0.0409564897,0.2696602643,0.0495535694,-0.1201176718,-0.1636262536,0.1193024367,0.0317241512,-0.2905445397,-0.0196277313,0.4356467724,-0.1525559574,0.241422236,0.4414304197,-0.1187741309,-0.0253542997,-0.1871619076,0.21855928,0.140799433,-0.0268120375,-0.0851378366,-0.0053834487,-0.253885746,-0.0652514324,0.3296323121,-0.1268236339,0.4315139949,0.1532039493,0.2892072797,-0.0184702352,-0.0514069125,-0.2919022739,0.2905476987,0.2543340623,-0.2341018468,0.4157405496,-0.1025249436,-0.2135272771,-0.6377745271,-0.3706792295,-0.0837398767,-0.1330630034,-0.0448598526,-0.0630305111,0.1643538177,0.0614361018,0.0771597028,-0.0667615905,0.2787643373,-0.1845620722,0.1062294468,-0.3318827152,-0.3990285695,-0.019308228,0.238119483,0.0182728078,-0.1148811653,0.0174304564,-0.0464675352,-0.0756031647,-0.1490138918,0.1128379405,-0.0141116986,-0.1526148766,0.1377332509,-0.0253324211,-0.2624071538,-0.227836132,0.3238838017,-0.3288731277,0.0045541548,0.4567353427,-0.1896839291,-0.0553343557,-0.0512933806,-0.3140721619,-0.5814594626,-0.0539097823,0.0172906443,0.0343012847,0.3296330273,0.3480425179,0.0307737906,0.1842597723,0.0340055116,-0.0597997718,0.0854178891,0.1192031726,0.3146250844,-0.0384516716,-0.288662076,-0.3471459448,-0.1107914522,0.0709793866,0.0762151107,-0.319314748,0.1262018532,-0.1260373443,0.0512030981,-0.2906250954,0.0220351126,0.4480994344,0.1102448702,-0.0087918788,0.2124655098,0.0139716482,0.3060884774,0.0416650251,0.2144845873,-0.034694355,0.1787259728,-0.04564083,0.6495577097,-0.1418004334,-0.3859343827,0.1767015159,-0.1262178123,-0.1793362498,-0.0094065778,-0.3002758324,-0.0031256527,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/469","title":"invalid data type 'str' at _convert_outputs in arrow_dataset.py","comments":"Hello . Yes, I did set the output format as below for the two columns \r\n\r\n  `train_dataset.set_format('torch',columns=['Text','Label'])`\r\n ","body":"I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n","comment_length":16,"text":"invalid data type 'str' at _convert_outputs in arrow_dataset.py \n I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n \n Hello . Yes, I did set the output format as below for the two columns \r\n\r\n  `train_dataset.set_format('torch',columns=['Text','Label'])`\r\n ","embeddings":[-0.2228239626,0.0242501777,0.1073713675,0.2303070426,0.5109525919,-0.1072595492,0.5323296189,0.0775902644,-0.3410620093,-0.1247765422,0.036657881,0.3864071369,-0.3696954846,0.0422176793,0.0443617031,-0.2024641186,-0.0880346373,0.0099904612,-0.0232526027,-0.0536389053,0.0482440926,0.1829803139,-0.1568261683,0.4144774675,-0.6451317668,-0.1835785806,0.1746639758,-0.0963928327,-0.0992212296,-0.4487785995,0.2202744484,-0.1020444036,0.4976367652,0.3472318351,-0.0001241712,0.1380359232,0.1092858762,0.1016949043,-0.2908720374,-0.3016375303,0.1341145188,-0.0593848564,0.4149942696,-0.2015384734,-0.0571896322,-0.2600911558,-0.0606259108,0.2617689073,0.3837757409,0.5016983747,0.1173220798,0.1685167998,0.2464857996,0.0738129616,0.2595641315,0.3155063391,0.0754768178,0.2071238756,0.0168337896,0.0266056098,-0.0856947526,0.0748076811,-0.0701284856,-0.1997739971,0.2166583389,0.227282986,0.17457892,-0.1476221532,-0.2287852317,0.1198840439,0.4332152605,-0.3214256167,-0.1559445858,0.0079212757,-0.0111622717,-0.2727991641,-0.0061669713,0.2483308762,-0.4277769327,0.1667014062,-0.1622606069,-0.0254668966,-0.3792498708,0.199556306,-0.0925177336,0.5032640696,0.1183697209,0.1568167061,0.1842403114,-0.0144359078,0.371710062,0.0876382813,0.1397552341,0.0621479377,-0.3116942644,-0.030463824,-0.4463334084,-0.648362577,-0.2685149014,-0.2125236392,0.1655772626,-0.0711523443,0.2047266066,0.202950567,0.233322382,0.1388006508,-0.2101232409,0.5648387074,0.1000927314,-0.1552505791,-0.1335455626,-0.0250890013,-0.3282387555,-0.0922897011,0.5121243596,0.1628403068,0.1257427186,-0.0050322958,-0.0550226681,-0.1488328129,-0.2688644528,-0.0882172287,0.2847151458,0.1850690395,0.0958356038,0.3832220435,0.2154195458,0.111135751,0.0493784584,-0.1352442801,0.0353224054,0.0855564028,-0.2179709226,-0.1198172048,0.1821132153,0.3281980753,-0.2699041963,0.1215015873,-0.1425367743,-0.2085365504,0.001571518,-0.293589741,-0.0053074467,0.0134973433,-0.2574837208,0.3097872436,0.0935748518,-0.3035934269,0.0196346212,0.4677716196,-0.4638129771,-0.4341678917,-0.4358201325,0.0899604112,0.3708474934,-0.1129878536,-0.0923099965,0.059735287,0.4520861804,-0.1888256073,0.1029469818,-0.5797597766,0.3521030843,-0.0152924247,-0.0832994059,0.0905463472,-0.4035288692,-0.1816472858,0.1696708053,-0.1533035785,0.3111130297,0.4423996806,-0.3827602863,0.2947919369,0.0795271397,0.0166932028,0.8023683429,-0.3571899831,-0.2179583609,0.132254988,-0.1704488099,-0.3259194195,-0.0770724937,0.2736980319,0.0906305313,0.0717987269,-0.0094030369,0.3914262056,-0.0933154151,0.2531608641,-0.3342471123,-0.0749925673,0.3700163364,0.3320881724,0.2507386208,-0.0335372835,0.0665313303,0.3422166705,0.2596169412,-0.1454966366,0.2384007722,0.1618343294,0.0183554627,-0.1759249866,-0.1052252948,-0.1606465131,-0.3050886989,-0.2020210922,-0.3353252709,0.484374553,-0.2394975275,0.08094953,-0.1629112065,-0.0729379505,-0.1123220474,0.2390538901,0.1080784798,0.0814386383,-0.1436548978,-0.0689880103,-0.1000490189,-0.0618887804,-0.0523724556,0.1881001443,0.025137445,0.2732270956,-0.1691009253,-0.1986376792,-0.2601747513,0.0564328432,0.2558159232,0.0960626528,-0.2582112551,0.2561086416,-0.1447918415,-0.2581233084,-0.308811754,0.3103091717,0.155409649,-0.5250985622,0.0369421802,0.1614515334,0.1663212776,-0.0896117315,0.1419673562,0.1363632828,0.0686425567,0.1380071789,-0.0040352675,0.3693377972,0.2155335993,-0.0409564897,0.2696602643,0.0495535694,-0.1201176718,-0.1636262536,0.1193024367,0.0317241512,-0.2905445397,-0.0196277313,0.4356467724,-0.1525559574,0.241422236,0.4414304197,-0.1187741309,-0.0253542997,-0.1871619076,0.21855928,0.140799433,-0.0268120375,-0.0851378366,-0.0053834487,-0.253885746,-0.0652514324,0.3296323121,-0.1268236339,0.4315139949,0.1532039493,0.2892072797,-0.0184702352,-0.0514069125,-0.2919022739,0.2905476987,0.2543340623,-0.2341018468,0.4157405496,-0.1025249436,-0.2135272771,-0.6377745271,-0.3706792295,-0.0837398767,-0.1330630034,-0.0448598526,-0.0630305111,0.1643538177,0.0614361018,0.0771597028,-0.0667615905,0.2787643373,-0.1845620722,0.1062294468,-0.3318827152,-0.3990285695,-0.019308228,0.238119483,0.0182728078,-0.1148811653,0.0174304564,-0.0464675352,-0.0756031647,-0.1490138918,0.1128379405,-0.0141116986,-0.1526148766,0.1377332509,-0.0253324211,-0.2624071538,-0.227836132,0.3238838017,-0.3288731277,0.0045541548,0.4567353427,-0.1896839291,-0.0553343557,-0.0512933806,-0.3140721619,-0.5814594626,-0.0539097823,0.0172906443,0.0343012847,0.3296330273,0.3480425179,0.0307737906,0.1842597723,0.0340055116,-0.0597997718,0.0854178891,0.1192031726,0.3146250844,-0.0384516716,-0.288662076,-0.3471459448,-0.1107914522,0.0709793866,0.0762151107,-0.319314748,0.1262018532,-0.1260373443,0.0512030981,-0.2906250954,0.0220351126,0.4480994344,0.1102448702,-0.0087918788,0.2124655098,0.0139716482,0.3060884774,0.0416650251,0.2144845873,-0.034694355,0.1787259728,-0.04564083,0.6495577097,-0.1418004334,-0.3859343827,0.1767015159,-0.1262178123,-0.1793362498,-0.0094065778,-0.3002758324,-0.0031256527,-0.258731484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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/469","title":"invalid data type 'str' at _convert_outputs in arrow_dataset.py","comments":"I think you're having this issue because you try to format strings as pytorch tensors, which is not possible.\r\nIndeed by having \"Text\" in `columns=['Text','Label']`, you try to convert the text values to pytorch tensors.\r\n\r\nInstead I recommend you to first tokenize your dataset using a tokenizer from transformers. For example\r\n\r\n```python\r\nfrom transformers import BertTokenizer\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\ntrain_dataset.map(lambda x: tokenizer(x[\"Text\"]), batched=True)\r\ntrain_dataset.set_format(\"torch\", column=[\"input_ids\"])\r\n```\r\n\r\nAnother way to fix your issue would be to not set the format to pytorch, and leave the dataset as it is by default. In that case, the strings are returned normally when you get examples from your dataloader. It means that you would have to tokenize the examples in the training loop (or using a data collator) though.\r\n\r\nLet me know if you have other questions","body":"I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n","comment_length":133,"text":"invalid data type 'str' at _convert_outputs in arrow_dataset.py \n I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n \n I think you're having this issue because you try to format strings as pytorch tensors, which is not possible.\r\nIndeed by having \"Text\" in `columns=['Text','Label']`, you try to convert the text values to pytorch tensors.\r\n\r\nInstead I recommend you to first tokenize your dataset using a tokenizer from transformers. For example\r\n\r\n```python\r\nfrom transformers import BertTokenizer\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\ntrain_dataset.map(lambda x: tokenizer(x[\"Text\"]), batched=True)\r\ntrain_dataset.set_format(\"torch\", column=[\"input_ids\"])\r\n```\r\n\r\nAnother way to fix your issue would be to not set the format to pytorch, and leave the dataset as it is by default. In that case, the strings are returned normally when you get examples from your dataloader. It means that you would have to tokenize the examples in the training loop (or using a data collator) though.\r\n\r\nLet me know if you have other questions","embeddings":[-0.2228239626,0.0242501777,0.1073713675,0.2303070426,0.5109525919,-0.1072595492,0.5323296189,0.0775902644,-0.3410620093,-0.1247765422,0.036657881,0.3864071369,-0.3696954846,0.0422176793,0.0443617031,-0.2024641186,-0.0880346373,0.0099904612,-0.0232526027,-0.0536389053,0.0482440926,0.1829803139,-0.1568261683,0.4144774675,-0.6451317668,-0.1835785806,0.1746639758,-0.0963928327,-0.0992212296,-0.4487785995,0.2202744484,-0.1020444036,0.4976367652,0.3472318351,-0.0001241712,0.1380359232,0.1092858762,0.1016949043,-0.2908720374,-0.3016375303,0.1341145188,-0.0593848564,0.4149942696,-0.2015384734,-0.0571896322,-0.2600911558,-0.0606259108,0.2617689073,0.3837757409,0.5016983747,0.1173220798,0.1685167998,0.2464857996,0.0738129616,0.2595641315,0.3155063391,0.0754768178,0.2071238756,0.0168337896,0.0266056098,-0.0856947526,0.0748076811,-0.0701284856,-0.1997739971,0.2166583389,0.227282986,0.17457892,-0.1476221532,-0.2287852317,0.1198840439,0.4332152605,-0.3214256167,-0.1559445858,0.0079212757,-0.0111622717,-0.2727991641,-0.0061669713,0.2483308762,-0.4277769327,0.1667014062,-0.1622606069,-0.0254668966,-0.3792498708,0.199556306,-0.0925177336,0.5032640696,0.1183697209,0.1568167061,0.1842403114,-0.0144359078,0.371710062,0.0876382813,0.1397552341,0.0621479377,-0.3116942644,-0.030463824,-0.4463334084,-0.648362577,-0.2685149014,-0.2125236392,0.1655772626,-0.0711523443,0.2047266066,0.202950567,0.233322382,0.1388006508,-0.2101232409,0.5648387074,0.1000927314,-0.1552505791,-0.1335455626,-0.0250890013,-0.3282387555,-0.0922897011,0.5121243596,0.1628403068,0.1257427186,-0.0050322958,-0.0550226681,-0.1488328129,-0.2688644528,-0.0882172287,0.2847151458,0.1850690395,0.0958356038,0.3832220435,0.2154195458,0.111135751,0.0493784584,-0.1352442801,0.0353224054,0.0855564028,-0.2179709226,-0.1198172048,0.1821132153,0.3281980753,-0.2699041963,0.1215015873,-0.1425367743,-0.2085365504,0.001571518,-0.293589741,-0.0053074467,0.0134973433,-0.2574837208,0.3097872436,0.0935748518,-0.3035934269,0.0196346212,0.4677716196,-0.4638129771,-0.4341678917,-0.4358201325,0.0899604112,0.3708474934,-0.1129878536,-0.0923099965,0.059735287,0.4520861804,-0.1888256073,0.1029469818,-0.5797597766,0.3521030843,-0.0152924247,-0.0832994059,0.0905463472,-0.4035288692,-0.1816472858,0.1696708053,-0.1533035785,0.3111130297,0.4423996806,-0.3827602863,0.2947919369,0.0795271397,0.0166932028,0.8023683429,-0.3571899831,-0.2179583609,0.132254988,-0.1704488099,-0.3259194195,-0.0770724937,0.2736980319,0.0906305313,0.0717987269,-0.0094030369,0.3914262056,-0.0933154151,0.2531608641,-0.3342471123,-0.0749925673,0.3700163364,0.3320881724,0.2507386208,-0.0335372835,0.0665313303,0.3422166705,0.2596169412,-0.1454966366,0.2384007722,0.1618343294,0.0183554627,-0.1759249866,-0.1052252948,-0.1606465131,-0.3050886989,-0.2020210922,-0.3353252709,0.484374553,-0.2394975275,0.08094953,-0.1629112065,-0.0729379505,-0.1123220474,0.2390538901,0.1080784798,0.0814386383,-0.1436548978,-0.0689880103,-0.1000490189,-0.0618887804,-0.0523724556,0.1881001443,0.025137445,0.2732270956,-0.1691009253,-0.1986376792,-0.2601747513,0.0564328432,0.2558159232,0.0960626528,-0.2582112551,0.2561086416,-0.1447918415,-0.2581233084,-0.308811754,0.3103091717,0.155409649,-0.5250985622,0.0369421802,0.1614515334,0.1663212776,-0.0896117315,0.1419673562,0.1363632828,0.0686425567,0.1380071789,-0.0040352675,0.3693377972,0.2155335993,-0.0409564897,0.2696602643,0.0495535694,-0.1201176718,-0.1636262536,0.1193024367,0.0317241512,-0.2905445397,-0.0196277313,0.4356467724,-0.1525559574,0.241422236,0.4414304197,-0.1187741309,-0.0253542997,-0.1871619076,0.21855928,0.140799433,-0.0268120375,-0.0851378366,-0.0053834487,-0.253885746,-0.0652514324,0.3296323121,-0.1268236339,0.4315139949,0.1532039493,0.2892072797,-0.0184702352,-0.0514069125,-0.2919022739,0.2905476987,0.2543340623,-0.2341018468,0.4157405496,-0.1025249436,-0.2135272771,-0.6377745271,-0.3706792295,-0.0837398767,-0.1330630034,-0.0448598526,-0.0630305111,0.1643538177,0.0614361018,0.0771597028,-0.0667615905,0.2787643373,-0.1845620722,0.1062294468,-0.3318827152,-0.3990285695,-0.019308228,0.238119483,0.0182728078,-0.1148811653,0.0174304564,-0.0464675352,-0.0756031647,-0.1490138918,0.1128379405,-0.0141116986,-0.1526148766,0.1377332509,-0.0253324211,-0.2624071538,-0.227836132,0.3238838017,-0.3288731277,0.0045541548,0.4567353427,-0.1896839291,-0.0553343557,-0.0512933806,-0.3140721619,-0.5814594626,-0.0539097823,0.0172906443,0.0343012847,0.3296330273,0.3480425179,0.0307737906,0.1842597723,0.0340055116,-0.0597997718,0.0854178891,0.1192031726,0.3146250844,-0.0384516716,-0.288662076,-0.3471459448,-0.1107914522,0.0709793866,0.0762151107,-0.319314748,0.1262018532,-0.1260373443,0.0512030981,-0.2906250954,0.0220351126,0.4480994344,0.1102448702,-0.0087918788,0.2124655098,0.0139716482,0.3060884774,0.0416650251,0.2144845873,-0.034694355,0.1787259728,-0.04564083,0.6495577097,-0.1418004334,-0.3859343827,0.1767015159,-0.1262178123,-0.1793362498,-0.0094065778,-0.3002758324,-0.0031256527,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/469","title":"invalid data type 'str' at _convert_outputs in arrow_dataset.py","comments":"Hi, actually the thing is I am getting the same error and even after tokenizing them I am passing them through batch_encode_plus.\r\nI dont know what seems to be the problem is. I even converted it into 'pt' while passing them through batch_encode_plus but when I am evaluating my model , i am getting this error\r\n\r\n\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-145-ca218223c9fc> in <module>()\r\n----> 1 val_loss, predictions, true_val = evaluate(dataloader_validation)\r\n      2 val_f1 = f1_score_func(predictions, true_val)\r\n      3 tqdm.write(f'Validation loss: {val_loss}')\r\n      4 tqdm.write(f'F1 Score (Weighted): {val_f1}')\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataset.py in <genexpr>(.0)\r\n    160 \r\n    161     def __getitem__(self, index):\r\n--> 162         return tuple(tensor[index] for tensor in self.tensors)\r\n    163 \r\n    164     def __len__(self):\r\n\r\nTypeError: new(): invalid data type 'str' ","body":"I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n","comment_length":115,"text":"invalid data type 'str' at _convert_outputs in arrow_dataset.py \n I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n \n Hi, actually the thing is I am getting the same error and even after tokenizing them I am passing them through batch_encode_plus.\r\nI dont know what seems to be the problem is. I even converted it into 'pt' while passing them through batch_encode_plus but when I am evaluating my model , i am getting this error\r\n\r\n\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-145-ca218223c9fc> in <module>()\r\n----> 1 val_loss, predictions, true_val = evaluate(dataloader_validation)\r\n      2 val_f1 = f1_score_func(predictions, true_val)\r\n      3 tqdm.write(f'Validation loss: {val_loss}')\r\n      4 tqdm.write(f'F1 Score (Weighted): {val_f1}')\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataset.py in <genexpr>(.0)\r\n    160 \r\n    161     def __getitem__(self, index):\r\n--> 162         return tuple(tensor[index] for tensor in self.tensors)\r\n    163 \r\n    164     def __len__(self):\r\n\r\nTypeError: new(): invalid data type 'str' ","embeddings":[-0.2228239626,0.0242501777,0.1073713675,0.2303070426,0.5109525919,-0.1072595492,0.5323296189,0.0775902644,-0.3410620093,-0.1247765422,0.036657881,0.3864071369,-0.3696954846,0.0422176793,0.0443617031,-0.2024641186,-0.0880346373,0.0099904612,-0.0232526027,-0.0536389053,0.0482440926,0.1829803139,-0.1568261683,0.4144774675,-0.6451317668,-0.1835785806,0.1746639758,-0.0963928327,-0.0992212296,-0.4487785995,0.2202744484,-0.1020444036,0.4976367652,0.3472318351,-0.0001241712,0.1380359232,0.1092858762,0.1016949043,-0.2908720374,-0.3016375303,0.1341145188,-0.0593848564,0.4149942696,-0.2015384734,-0.0571896322,-0.2600911558,-0.0606259108,0.2617689073,0.3837757409,0.5016983747,0.1173220798,0.1685167998,0.2464857996,0.0738129616,0.2595641315,0.3155063391,0.0754768178,0.2071238756,0.0168337896,0.0266056098,-0.0856947526,0.0748076811,-0.0701284856,-0.1997739971,0.2166583389,0.227282986,0.17457892,-0.1476221532,-0.2287852317,0.1198840439,0.4332152605,-0.3214256167,-0.1559445858,0.0079212757,-0.0111622717,-0.2727991641,-0.0061669713,0.2483308762,-0.4277769327,0.1667014062,-0.1622606069,-0.0254668966,-0.3792498708,0.199556306,-0.0925177336,0.5032640696,0.1183697209,0.1568167061,0.1842403114,-0.0144359078,0.371710062,0.0876382813,0.1397552341,0.0621479377,-0.3116942644,-0.030463824,-0.4463334084,-0.648362577,-0.2685149014,-0.2125236392,0.1655772626,-0.0711523443,0.2047266066,0.202950567,0.233322382,0.1388006508,-0.2101232409,0.5648387074,0.1000927314,-0.1552505791,-0.1335455626,-0.0250890013,-0.3282387555,-0.0922897011,0.5121243596,0.1628403068,0.1257427186,-0.0050322958,-0.0550226681,-0.1488328129,-0.2688644528,-0.0882172287,0.2847151458,0.1850690395,0.0958356038,0.3832220435,0.2154195458,0.111135751,0.0493784584,-0.1352442801,0.0353224054,0.0855564028,-0.2179709226,-0.1198172048,0.1821132153,0.3281980753,-0.2699041963,0.1215015873,-0.1425367743,-0.2085365504,0.001571518,-0.293589741,-0.0053074467,0.0134973433,-0.2574837208,0.3097872436,0.0935748518,-0.3035934269,0.0196346212,0.4677716196,-0.4638129771,-0.4341678917,-0.4358201325,0.0899604112,0.3708474934,-0.1129878536,-0.0923099965,0.059735287,0.4520861804,-0.1888256073,0.1029469818,-0.5797597766,0.3521030843,-0.0152924247,-0.0832994059,0.0905463472,-0.4035288692,-0.1816472858,0.1696708053,-0.1533035785,0.3111130297,0.4423996806,-0.3827602863,0.2947919369,0.0795271397,0.0166932028,0.8023683429,-0.3571899831,-0.2179583609,0.132254988,-0.1704488099,-0.3259194195,-0.0770724937,0.2736980319,0.0906305313,0.0717987269,-0.0094030369,0.3914262056,-0.0933154151,0.2531608641,-0.3342471123,-0.0749925673,0.3700163364,0.3320881724,0.2507386208,-0.0335372835,0.0665313303,0.3422166705,0.2596169412,-0.1454966366,0.2384007722,0.1618343294,0.0183554627,-0.1759249866,-0.1052252948,-0.1606465131,-0.3050886989,-0.2020210922,-0.3353252709,0.484374553,-0.2394975275,0.08094953,-0.1629112065,-0.0729379505,-0.1123220474,0.2390538901,0.1080784798,0.0814386383,-0.1436548978,-0.0689880103,-0.1000490189,-0.0618887804,-0.0523724556,0.1881001443,0.025137445,0.2732270956,-0.1691009253,-0.1986376792,-0.2601747513,0.0564328432,0.2558159232,0.0960626528,-0.2582112551,0.2561086416,-0.1447918415,-0.2581233084,-0.308811754,0.3103091717,0.155409649,-0.5250985622,0.0369421802,0.1614515334,0.1663212776,-0.0896117315,0.1419673562,0.1363632828,0.0686425567,0.1380071789,-0.0040352675,0.3693377972,0.2155335993,-0.0409564897,0.2696602643,0.0495535694,-0.1201176718,-0.1636262536,0.1193024367,0.0317241512,-0.2905445397,-0.0196277313,0.4356467724,-0.1525559574,0.241422236,0.4414304197,-0.1187741309,-0.0253542997,-0.1871619076,0.21855928,0.140799433,-0.0268120375,-0.0851378366,-0.0053834487,-0.253885746,-0.0652514324,0.3296323121,-0.1268236339,0.4315139949,0.1532039493,0.2892072797,-0.0184702352,-0.0514069125,-0.2919022739,0.2905476987,0.2543340623,-0.2341018468,0.4157405496,-0.1025249436,-0.2135272771,-0.6377745271,-0.3706792295,-0.0837398767,-0.1330630034,-0.0448598526,-0.0630305111,0.1643538177,0.0614361018,0.0771597028,-0.0667615905,0.2787643373,-0.1845620722,0.1062294468,-0.3318827152,-0.3990285695,-0.019308228,0.238119483,0.0182728078,-0.1148811653,0.0174304564,-0.0464675352,-0.0756031647,-0.1490138918,0.1128379405,-0.0141116986,-0.1526148766,0.1377332509,-0.0253324211,-0.2624071538,-0.227836132,0.3238838017,-0.3288731277,0.0045541548,0.4567353427,-0.1896839291,-0.0553343557,-0.0512933806,-0.3140721619,-0.5814594626,-0.0539097823,0.0172906443,0.0343012847,0.3296330273,0.3480425179,0.0307737906,0.1842597723,0.0340055116,-0.0597997718,0.0854178891,0.1192031726,0.3146250844,-0.0384516716,-0.288662076,-0.3471459448,-0.1107914522,0.0709793866,0.0762151107,-0.319314748,0.1262018532,-0.1260373443,0.0512030981,-0.2906250954,0.0220351126,0.4480994344,0.1102448702,-0.0087918788,0.2124655098,0.0139716482,0.3060884774,0.0416650251,0.2144845873,-0.034694355,0.1787259728,-0.04564083,0.6495577097,-0.1418004334,-0.3859343827,0.1767015159,-0.1262178123,-0.1793362498,-0.0094065778,-0.3002758324,-0.0031256527,-0.258731484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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/469","title":"invalid data type 'str' at _convert_outputs in arrow_dataset.py","comments":"> Hi, actually the thing is I am getting the same error and even after tokenizing them I am passing them through batch_encode_plus.\r\n> I dont know what seems to be the problem is. I even converted it into 'pt' while passing them through batch_encode_plus but when I am evaluating my model , i am getting this error\r\n> \r\n> TypeError Traceback (most recent call last)\r\n> in ()\r\n> ----> 1 val_loss, predictions, true_val = evaluate(dataloader_validation)\r\n> 2 val_f1 = f1_score_func(predictions, true_val)\r\n> 3 tqdm.write(f'Validation loss: {val_loss}')\r\n> 4 tqdm.write(f'F1 Score (Weighted): {val_f1}')\r\n> \r\n> 6 frames\r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataset.py in (.0)\r\n> 160\r\n> 161 def **getitem**(self, index):\r\n> --> 162 return tuple(tensor[index] for tensor in self.tensors)\r\n> 163\r\n> 164 def **len**(self):\r\n> \r\n> TypeError: new(): invalid data type 'str'\r\n\r\nI got the same error and fix it .\r\nyou can check your input where there may be string contained.\r\nsuch as\r\n```\r\na = [1,2,3,4,'<unk>']\r\ntorch.tensor(a)\r\n```","body":"I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n","comment_length":160,"text":"invalid data type 'str' at _convert_outputs in arrow_dataset.py \n I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n \n > Hi, actually the thing is I am getting the same error and even after tokenizing them I am passing them through batch_encode_plus.\r\n> I dont know what seems to be the problem is. I even converted it into 'pt' while passing them through batch_encode_plus but when I am evaluating my model , i am getting this error\r\n> \r\n> TypeError Traceback (most recent call last)\r\n> in ()\r\n> ----> 1 val_loss, predictions, true_val = evaluate(dataloader_validation)\r\n> 2 val_f1 = f1_score_func(predictions, true_val)\r\n> 3 tqdm.write(f'Validation loss: {val_loss}')\r\n> 4 tqdm.write(f'F1 Score (Weighted): {val_f1}')\r\n> \r\n> 6 frames\r\n> \/usr\/local\/lib\/python3.6\/dist-packages\/torch\/utils\/data\/dataset.py in (.0)\r\n> 160\r\n> 161 def **getitem**(self, index):\r\n> --> 162 return tuple(tensor[index] for tensor in self.tensors)\r\n> 163\r\n> 164 def **len**(self):\r\n> \r\n> TypeError: new(): invalid data type 'str'\r\n\r\nI got the same error and fix it .\r\nyou can check your input where there may be string contained.\r\nsuch as\r\n```\r\na = [1,2,3,4,'<unk>']\r\ntorch.tensor(a)\r\n```","embeddings":[-0.2228239626,0.0242501777,0.1073713675,0.2303070426,0.5109525919,-0.1072595492,0.5323296189,0.0775902644,-0.3410620093,-0.1247765422,0.036657881,0.3864071369,-0.3696954846,0.0422176793,0.0443617031,-0.2024641186,-0.0880346373,0.0099904612,-0.0232526027,-0.0536389053,0.0482440926,0.1829803139,-0.1568261683,0.4144774675,-0.6451317668,-0.1835785806,0.1746639758,-0.0963928327,-0.0992212296,-0.4487785995,0.2202744484,-0.1020444036,0.4976367652,0.3472318351,-0.0001241712,0.1380359232,0.1092858762,0.1016949043,-0.2908720374,-0.3016375303,0.1341145188,-0.0593848564,0.4149942696,-0.2015384734,-0.0571896322,-0.2600911558,-0.0606259108,0.2617689073,0.3837757409,0.5016983747,0.1173220798,0.1685167998,0.2464857996,0.0738129616,0.2595641315,0.3155063391,0.0754768178,0.2071238756,0.0168337896,0.0266056098,-0.0856947526,0.0748076811,-0.0701284856,-0.1997739971,0.2166583389,0.227282986,0.17457892,-0.1476221532,-0.2287852317,0.1198840439,0.4332152605,-0.3214256167,-0.1559445858,0.0079212757,-0.0111622717,-0.2727991641,-0.0061669713,0.2483308762,-0.4277769327,0.1667014062,-0.1622606069,-0.0254668966,-0.3792498708,0.199556306,-0.0925177336,0.5032640696,0.1183697209,0.1568167061,0.1842403114,-0.0144359078,0.371710062,0.0876382813,0.1397552341,0.0621479377,-0.3116942644,-0.030463824,-0.4463334084,-0.648362577,-0.2685149014,-0.2125236392,0.1655772626,-0.0711523443,0.2047266066,0.202950567,0.233322382,0.1388006508,-0.2101232409,0.5648387074,0.1000927314,-0.1552505791,-0.1335455626,-0.0250890013,-0.3282387555,-0.0922897011,0.5121243596,0.1628403068,0.1257427186,-0.0050322958,-0.0550226681,-0.1488328129,-0.2688644528,-0.0882172287,0.2847151458,0.1850690395,0.0958356038,0.3832220435,0.2154195458,0.111135751,0.0493784584,-0.1352442801,0.0353224054,0.0855564028,-0.2179709226,-0.1198172048,0.1821132153,0.3281980753,-0.2699041963,0.1215015873,-0.1425367743,-0.2085365504,0.001571518,-0.293589741,-0.0053074467,0.0134973433,-0.2574837208,0.3097872436,0.0935748518,-0.3035934269,0.0196346212,0.4677716196,-0.4638129771,-0.4341678917,-0.4358201325,0.0899604112,0.3708474934,-0.1129878536,-0.0923099965,0.059735287,0.4520861804,-0.1888256073,0.1029469818,-0.5797597766,0.3521030843,-0.0152924247,-0.0832994059,0.0905463472,-0.4035288692,-0.1816472858,0.1696708053,-0.1533035785,0.3111130297,0.4423996806,-0.3827602863,0.2947919369,0.0795271397,0.0166932028,0.8023683429,-0.3571899831,-0.2179583609,0.132254988,-0.1704488099,-0.3259194195,-0.0770724937,0.2736980319,0.0906305313,0.0717987269,-0.0094030369,0.3914262056,-0.0933154151,0.2531608641,-0.3342471123,-0.0749925673,0.3700163364,0.3320881724,0.2507386208,-0.0335372835,0.0665313303,0.3422166705,0.2596169412,-0.1454966366,0.2384007722,0.1618343294,0.0183554627,-0.1759249866,-0.1052252948,-0.1606465131,-0.3050886989,-0.2020210922,-0.3353252709,0.484374553,-0.2394975275,0.08094953,-0.1629112065,-0.0729379505,-0.1123220474,0.2390538901,0.1080784798,0.0814386383,-0.1436548978,-0.0689880103,-0.1000490189,-0.0618887804,-0.0523724556,0.1881001443,0.025137445,0.2732270956,-0.1691009253,-0.1986376792,-0.2601747513,0.0564328432,0.2558159232,0.0960626528,-0.2582112551,0.2561086416,-0.1447918415,-0.2581233084,-0.308811754,0.3103091717,0.155409649,-0.5250985622,0.0369421802,0.1614515334,0.1663212776,-0.0896117315,0.1419673562,0.1363632828,0.0686425567,0.1380071789,-0.0040352675,0.3693377972,0.2155335993,-0.0409564897,0.2696602643,0.0495535694,-0.1201176718,-0.1636262536,0.1193024367,0.0317241512,-0.2905445397,-0.0196277313,0.4356467724,-0.1525559574,0.241422236,0.4414304197,-0.1187741309,-0.0253542997,-0.1871619076,0.21855928,0.140799433,-0.0268120375,-0.0851378366,-0.0053834487,-0.253885746,-0.0652514324,0.3296323121,-0.1268236339,0.4315139949,0.1532039493,0.2892072797,-0.0184702352,-0.0514069125,-0.2919022739,0.2905476987,0.2543340623,-0.2341018468,0.4157405496,-0.1025249436,-0.2135272771,-0.6377745271,-0.3706792295,-0.0837398767,-0.1330630034,-0.0448598526,-0.0630305111,0.1643538177,0.0614361018,0.0771597028,-0.0667615905,0.2787643373,-0.1845620722,0.1062294468,-0.3318827152,-0.3990285695,-0.019308228,0.238119483,0.0182728078,-0.1148811653,0.0174304564,-0.0464675352,-0.0756031647,-0.1490138918,0.1128379405,-0.0141116986,-0.1526148766,0.1377332509,-0.0253324211,-0.2624071538,-0.227836132,0.3238838017,-0.3288731277,0.0045541548,0.4567353427,-0.1896839291,-0.0553343557,-0.0512933806,-0.3140721619,-0.5814594626,-0.0539097823,0.0172906443,0.0343012847,0.3296330273,0.3480425179,0.0307737906,0.1842597723,0.0340055116,-0.0597997718,0.0854178891,0.1192031726,0.3146250844,-0.0384516716,-0.288662076,-0.3471459448,-0.1107914522,0.0709793866,0.0762151107,-0.319314748,0.1262018532,-0.1260373443,0.0512030981,-0.2906250954,0.0220351126,0.4480994344,0.1102448702,-0.0087918788,0.2124655098,0.0139716482,0.3060884774,0.0416650251,0.2144845873,-0.034694355,0.1787259728,-0.04564083,0.6495577097,-0.1418004334,-0.3859343827,0.1767015159,-0.1262178123,-0.1793362498,-0.00940657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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/469","title":"invalid data type 'str' at _convert_outputs in arrow_dataset.py","comments":"I didn't know tokenizers could return strings in the token ids. Which tokenizer are you using to get this @Doragd ?","body":"I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n","comment_length":21,"text":"invalid data type 'str' at _convert_outputs in arrow_dataset.py \n I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n \n I didn't know tokenizers could return strings in the token ids. Which tokenizer are you using to get this @Doragd ?","embeddings":[-0.2228239626,0.0242501777,0.1073713675,0.2303070426,0.5109525919,-0.1072595492,0.5323296189,0.0775902644,-0.3410620093,-0.1247765422,0.036657881,0.3864071369,-0.3696954846,0.0422176793,0.0443617031,-0.2024641186,-0.0880346373,0.0099904612,-0.0232526027,-0.0536389053,0.0482440926,0.1829803139,-0.1568261683,0.4144774675,-0.6451317668,-0.1835785806,0.1746639758,-0.0963928327,-0.0992212296,-0.4487785995,0.2202744484,-0.1020444036,0.4976367652,0.3472318351,-0.0001241712,0.1380359232,0.1092858762,0.1016949043,-0.2908720374,-0.3016375303,0.1341145188,-0.0593848564,0.4149942696,-0.2015384734,-0.0571896322,-0.2600911558,-0.0606259108,0.2617689073,0.3837757409,0.5016983747,0.1173220798,0.1685167998,0.2464857996,0.0738129616,0.2595641315,0.3155063391,0.0754768178,0.2071238756,0.0168337896,0.0266056098,-0.0856947526,0.0748076811,-0.0701284856,-0.1997739971,0.2166583389,0.227282986,0.17457892,-0.1476221532,-0.2287852317,0.1198840439,0.4332152605,-0.3214256167,-0.1559445858,0.0079212757,-0.0111622717,-0.2727991641,-0.0061669713,0.2483308762,-0.4277769327,0.1667014062,-0.1622606069,-0.0254668966,-0.3792498708,0.199556306,-0.0925177336,0.5032640696,0.1183697209,0.1568167061,0.1842403114,-0.0144359078,0.371710062,0.0876382813,0.1397552341,0.0621479377,-0.3116942644,-0.030463824,-0.4463334084,-0.648362577,-0.2685149014,-0.2125236392,0.1655772626,-0.0711523443,0.2047266066,0.202950567,0.233322382,0.1388006508,-0.2101232409,0.5648387074,0.1000927314,-0.1552505791,-0.1335455626,-0.0250890013,-0.3282387555,-0.0922897011,0.5121243596,0.1628403068,0.1257427186,-0.0050322958,-0.0550226681,-0.1488328129,-0.2688644528,-0.0882172287,0.2847151458,0.1850690395,0.0958356038,0.3832220435,0.2154195458,0.111135751,0.0493784584,-0.1352442801,0.0353224054,0.0855564028,-0.2179709226,-0.1198172048,0.1821132153,0.3281980753,-0.2699041963,0.1215015873,-0.1425367743,-0.2085365504,0.001571518,-0.293589741,-0.0053074467,0.0134973433,-0.2574837208,0.3097872436,0.0935748518,-0.3035934269,0.0196346212,0.4677716196,-0.4638129771,-0.4341678917,-0.4358201325,0.0899604112,0.3708474934,-0.1129878536,-0.0923099965,0.059735287,0.4520861804,-0.1888256073,0.1029469818,-0.5797597766,0.3521030843,-0.0152924247,-0.0832994059,0.0905463472,-0.4035288692,-0.1816472858,0.1696708053,-0.1533035785,0.3111130297,0.4423996806,-0.3827602863,0.2947919369,0.0795271397,0.0166932028,0.8023683429,-0.3571899831,-0.2179583609,0.132254988,-0.1704488099,-0.3259194195,-0.0770724937,0.2736980319,0.0906305313,0.0717987269,-0.0094030369,0.3914262056,-0.0933154151,0.2531608641,-0.3342471123,-0.0749925673,0.3700163364,0.3320881724,0.2507386208,-0.0335372835,0.0665313303,0.3422166705,0.2596169412,-0.1454966366,0.2384007722,0.1618343294,0.0183554627,-0.1759249866,-0.1052252948,-0.1606465131,-0.3050886989,-0.2020210922,-0.3353252709,0.484374553,-0.2394975275,0.08094953,-0.1629112065,-0.0729379505,-0.1123220474,0.2390538901,0.1080784798,0.0814386383,-0.1436548978,-0.0689880103,-0.1000490189,-0.0618887804,-0.0523724556,0.1881001443,0.025137445,0.2732270956,-0.1691009253,-0.1986376792,-0.2601747513,0.0564328432,0.2558159232,0.0960626528,-0.2582112551,0.2561086416,-0.1447918415,-0.2581233084,-0.308811754,0.3103091717,0.155409649,-0.5250985622,0.0369421802,0.1614515334,0.1663212776,-0.0896117315,0.1419673562,0.1363632828,0.0686425567,0.1380071789,-0.0040352675,0.3693377972,0.2155335993,-0.0409564897,0.2696602643,0.0495535694,-0.1201176718,-0.1636262536,0.1193024367,0.0317241512,-0.2905445397,-0.0196277313,0.4356467724,-0.1525559574,0.241422236,0.4414304197,-0.1187741309,-0.0253542997,-0.1871619076,0.21855928,0.140799433,-0.0268120375,-0.0851378366,-0.0053834487,-0.253885746,-0.0652514324,0.3296323121,-0.1268236339,0.4315139949,0.1532039493,0.2892072797,-0.0184702352,-0.0514069125,-0.2919022739,0.2905476987,0.2543340623,-0.2341018468,0.4157405496,-0.1025249436,-0.2135272771,-0.6377745271,-0.3706792295,-0.0837398767,-0.1330630034,-0.0448598526,-0.0630305111,0.1643538177,0.0614361018,0.0771597028,-0.0667615905,0.2787643373,-0.1845620722,0.1062294468,-0.3318827152,-0.3990285695,-0.019308228,0.238119483,0.0182728078,-0.1148811653,0.0174304564,-0.0464675352,-0.0756031647,-0.1490138918,0.1128379405,-0.0141116986,-0.1526148766,0.1377332509,-0.0253324211,-0.2624071538,-0.227836132,0.3238838017,-0.3288731277,0.0045541548,0.4567353427,-0.1896839291,-0.0553343557,-0.0512933806,-0.3140721619,-0.5814594626,-0.0539097823,0.0172906443,0.0343012847,0.3296330273,0.3480425179,0.0307737906,0.1842597723,0.0340055116,-0.0597997718,0.0854178891,0.1192031726,0.3146250844,-0.0384516716,-0.288662076,-0.3471459448,-0.1107914522,0.0709793866,0.0762151107,-0.319314748,0.1262018532,-0.1260373443,0.0512030981,-0.2906250954,0.0220351126,0.4480994344,0.1102448702,-0.0087918788,0.2124655098,0.0139716482,0.3060884774,0.0416650251,0.2144845873,-0.034694355,0.1787259728,-0.04564083,0.6495577097,-0.1418004334,-0.3859343827,0.1767015159,-0.1262178123,-0.1793362498,-0.0094065778,-0.3002758324,-0.0031256527,-0.25873148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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/469","title":"invalid data type 'str' at _convert_outputs in arrow_dataset.py","comments":"> I didn't know tokenizers could return strings in the token ids. Which tokenizer are you using to get this @Doragd ?\r\n\r\ni'm sorry that i met this issue in another place (not in huggingface repo). ","body":"I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n","comment_length":36,"text":"invalid data type 'str' at _convert_outputs in arrow_dataset.py \n I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n \n > I didn't know tokenizers could return strings in the token ids. Which tokenizer are you using to get this @Doragd ?\r\n\r\ni'm sorry that i met this issue in another place (not in huggingface repo). 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/469","title":"invalid data type 'str' at _convert_outputs in arrow_dataset.py","comments":"@akhilkapil do you have strings in your dataset ? When you set the dataset format to \"pytorch\" you should exclude columns with strings as pytorch can't make tensors out of strings","body":"I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n","comment_length":31,"text":"invalid data type 'str' at _convert_outputs in arrow_dataset.py \n I trying to build multi label text classifier model using Transformers lib. \r\n\r\nI'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error \r\n\r\nFile \"C:\\***\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n\r\nI'm using pyarrow 1.0.0.  And I have simple custom data set with Text and Integer Label.  \r\nEx: Data\r\n Text ,     Label  #Column Header\r\n I'm facing an Network issue, 1\r\n I forgot my password, 2\r\n\r\nError StackTrace:\r\n\r\nFile \"C:\\**\\transformers\\trainer.py\", line 492, in train\r\n    for step, inputs in enumerate(epoch_iterator):\r\n  File \"C:\\**\\tqdm\\std.py\", line 1104, in __iter__\r\n    for obj in iterable:\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"C:\\**\\torch\\utils\\data\\dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 414, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 403, in _getitem\r\n    outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns\r\n  File \"C:\\**\\nlp\\arrow_dataset.py\", line 343, in _convert_outputs\r\n    v = command(v)\r\nTypeError: new(): invalid data type 'str'\r\n \r\n \n @akhilkapil do you have strings in your dataset ? When you set the dataset format to \"pytorch\" you should exclude columns with strings as pytorch can't make tensors out of 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/468","title":"UnicodeDecodeError while loading PAN-X task of XTREME dataset","comments":"Indeed. Solution 1 is the simplest.\r\n\r\nThis is actually a recurring problem.\r\nI think we should scan all the datasets with regexpr to fix the use of `open()` without encodings.\r\nAnd probably add a test in the CI to forbid using this in the future.","body":"Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nI'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset: \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnicodeDecodeError                        Traceback (most recent call last)\r\n<ipython-input-5-1d61f439b843> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    528     ignore_verifications = ignore_verifications or save_infos\r\n    529     # Download\/copy dataset processing script\r\n--> 530     module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n    531 \r\n    532     # Get dataset builder class from the processing script\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)\r\n    265 \r\n    266     # Download external imports if needed\r\n--> 267     imports = get_imports(local_path)\r\n    268     local_imports = []\r\n    269     library_imports = []\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in get_imports(file_path)\r\n    156     lines = []\r\n    157     with open(file_path, mode=\"r\") as f:\r\n--> 158         lines.extend(f.readlines())\r\n    159 \r\n    160     logger.info(\"Checking %s for additional imports.\", file_path)\r\n\r\n\/usr\/lib\/python3.6\/encodings\/ascii.py in decode(self, input, final)\r\n     24 class IncrementalDecoder(codecs.IncrementalDecoder):\r\n     25     def decode(self, input, final=False):\r\n---> 26         return codecs.ascii_decode(input, self.errors)[0]\r\n     27 \r\n     28 class StreamWriter(Codec,codecs.StreamWriter):\r\n\r\nUnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)\r\n```\r\n\r\n## Steps to reproduce\r\nInstall from nlp's master branch\r\n```python\r\npip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n```\r\nthen run\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n```\r\n\r\n## OS \/ platform details\r\n\r\n- `nlp` version: latest from master\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: True\r\n- Using distributed or parallel set-up in script?: False\r\n\r\n## Proposed solution\r\nEither change [line 762](https:\/\/github.com\/huggingface\/nlp\/blob\/7ada00b1d62f94eee22a7df38c6b01e3f27194b7\/datasets\/xtreme\/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:\r\n\r\n```\r\n# old\r\nwith open(filepath) as f\r\n# new\r\nwith open(filepath, encoding='utf-8') as f\r\n```\r\n\r\nor raise a warning that suggests setting the locale explicitly, e.g.\r\n```python\r\nimport locale\r\nlocale.setlocale(locale.LC_ALL, 'C.UTF-8')\r\n```\r\nI have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix!","comment_length":45,"text":"UnicodeDecodeError while loading PAN-X task of XTREME dataset \n Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nI'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset: \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnicodeDecodeError                        Traceback (most recent call last)\r\n<ipython-input-5-1d61f439b843> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    528     ignore_verifications = ignore_verifications or save_infos\r\n    529     # Download\/copy dataset processing script\r\n--> 530     module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n    531 \r\n    532     # Get dataset builder class from the processing script\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)\r\n    265 \r\n    266     # Download external imports if needed\r\n--> 267     imports = get_imports(local_path)\r\n    268     local_imports = []\r\n    269     library_imports = []\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in get_imports(file_path)\r\n    156     lines = []\r\n    157     with open(file_path, mode=\"r\") as f:\r\n--> 158         lines.extend(f.readlines())\r\n    159 \r\n    160     logger.info(\"Checking %s for additional imports.\", file_path)\r\n\r\n\/usr\/lib\/python3.6\/encodings\/ascii.py in decode(self, input, final)\r\n     24 class IncrementalDecoder(codecs.IncrementalDecoder):\r\n     25     def decode(self, input, final=False):\r\n---> 26         return codecs.ascii_decode(input, self.errors)[0]\r\n     27 \r\n     28 class StreamWriter(Codec,codecs.StreamWriter):\r\n\r\nUnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)\r\n```\r\n\r\n## Steps to reproduce\r\nInstall from nlp's master branch\r\n```python\r\npip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n```\r\nthen run\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n```\r\n\r\n## OS \/ platform details\r\n\r\n- `nlp` version: latest from master\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: True\r\n- Using distributed or parallel set-up in script?: False\r\n\r\n## Proposed solution\r\nEither change [line 762](https:\/\/github.com\/huggingface\/nlp\/blob\/7ada00b1d62f94eee22a7df38c6b01e3f27194b7\/datasets\/xtreme\/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:\r\n\r\n```\r\n# old\r\nwith open(filepath) as f\r\n# new\r\nwith open(filepath, encoding='utf-8') as f\r\n```\r\n\r\nor raise a warning that suggests setting the locale explicitly, e.g.\r\n```python\r\nimport locale\r\nlocale.setlocale(locale.LC_ALL, 'C.UTF-8')\r\n```\r\nI have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix! \n Indeed. Solution 1 is the simplest.\r\n\r\nThis is actually a recurring problem.\r\nI think we should scan all the datasets with regexpr to fix the use of `open()` without encodings.\r\nAnd probably add a test in the CI to forbid using this in the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/468","title":"UnicodeDecodeError while loading PAN-X task of XTREME dataset","comments":"I've created a simple function that seems to do the trick:\r\n\r\n```python\r\ndef apply_encoding_on_file_open(filepath: str):\r\n    \"\"\"Apply UTF-8 encoding for all instances where a non-binary file is opened.\"\"\"\r\n    \r\n    with open(filepath, 'r', encoding='utf-8') as input_file:\r\n        regexp = re.compile(r\"\"\"\r\n                            (?!.*\\b(?:encoding|rb|wb|wb+|ab|ab+)\\b)\r\n                            (open)\r\n                            \\((.*)\\)\r\n                            \"\"\")\r\n        input_text = input_file.read()\r\n        match = regexp.search(input_text)\r\n        \r\n        if match:\r\n            print('Found match!', match.group())\r\n            # append utf-8 encoding to matching groups in-place\r\n            output = regexp.sub(lambda m: m.group()[:-1]+', encoding=\"utf-8\")', input_text)\r\n            with open(filepath, 'w', encoding='utf-8') as output_file:\r\n                output_file.write(output)\r\n        else:\r\n            print(\"No match found!\")\r\n```\r\n\r\nThe regexp does a negative lookahead to avoid matching on cases where the encoding is already specified or when binary files are involved.\r\n\r\nFrom an implementation perspective:\r\n\r\n* Would it make sense to include this function in `nlp-cli` so that we can run something like\r\n```\r\nnlp-cli fix_encoding path\/to\/folder\r\n```\r\nand the command recursively fixes all files in the target?\r\n* What is the desired behaviour in the CI test? Here we could either have a simple script that we run as a `job` in the CI and raises an error if a missing encoding is detected. Alternatively we could incorporate this behaviour into the CLI and run that in the CI.\r\n\r\nPlease let me know what you prefer among the alternatives.\r\n","body":"Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nI'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset: \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnicodeDecodeError                        Traceback (most recent call last)\r\n<ipython-input-5-1d61f439b843> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    528     ignore_verifications = ignore_verifications or save_infos\r\n    529     # Download\/copy dataset processing script\r\n--> 530     module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n    531 \r\n    532     # Get dataset builder class from the processing script\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)\r\n    265 \r\n    266     # Download external imports if needed\r\n--> 267     imports = get_imports(local_path)\r\n    268     local_imports = []\r\n    269     library_imports = []\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in get_imports(file_path)\r\n    156     lines = []\r\n    157     with open(file_path, mode=\"r\") as f:\r\n--> 158         lines.extend(f.readlines())\r\n    159 \r\n    160     logger.info(\"Checking %s for additional imports.\", file_path)\r\n\r\n\/usr\/lib\/python3.6\/encodings\/ascii.py in decode(self, input, final)\r\n     24 class IncrementalDecoder(codecs.IncrementalDecoder):\r\n     25     def decode(self, input, final=False):\r\n---> 26         return codecs.ascii_decode(input, self.errors)[0]\r\n     27 \r\n     28 class StreamWriter(Codec,codecs.StreamWriter):\r\n\r\nUnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)\r\n```\r\n\r\n## Steps to reproduce\r\nInstall from nlp's master branch\r\n```python\r\npip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n```\r\nthen run\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n```\r\n\r\n## OS \/ platform details\r\n\r\n- `nlp` version: latest from master\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: True\r\n- Using distributed or parallel set-up in script?: False\r\n\r\n## Proposed solution\r\nEither change [line 762](https:\/\/github.com\/huggingface\/nlp\/blob\/7ada00b1d62f94eee22a7df38c6b01e3f27194b7\/datasets\/xtreme\/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:\r\n\r\n```\r\n# old\r\nwith open(filepath) as f\r\n# new\r\nwith open(filepath, encoding='utf-8') as f\r\n```\r\n\r\nor raise a warning that suggests setting the locale explicitly, e.g.\r\n```python\r\nimport locale\r\nlocale.setlocale(locale.LC_ALL, 'C.UTF-8')\r\n```\r\nI have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix!","comment_length":200,"text":"UnicodeDecodeError while loading PAN-X task of XTREME dataset \n Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nI'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset: \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnicodeDecodeError                        Traceback (most recent call last)\r\n<ipython-input-5-1d61f439b843> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    528     ignore_verifications = ignore_verifications or save_infos\r\n    529     # Download\/copy dataset processing script\r\n--> 530     module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n    531 \r\n    532     # Get dataset builder class from the processing script\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)\r\n    265 \r\n    266     # Download external imports if needed\r\n--> 267     imports = get_imports(local_path)\r\n    268     local_imports = []\r\n    269     library_imports = []\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in get_imports(file_path)\r\n    156     lines = []\r\n    157     with open(file_path, mode=\"r\") as f:\r\n--> 158         lines.extend(f.readlines())\r\n    159 \r\n    160     logger.info(\"Checking %s for additional imports.\", file_path)\r\n\r\n\/usr\/lib\/python3.6\/encodings\/ascii.py in decode(self, input, final)\r\n     24 class IncrementalDecoder(codecs.IncrementalDecoder):\r\n     25     def decode(self, input, final=False):\r\n---> 26         return codecs.ascii_decode(input, self.errors)[0]\r\n     27 \r\n     28 class StreamWriter(Codec,codecs.StreamWriter):\r\n\r\nUnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)\r\n```\r\n\r\n## Steps to reproduce\r\nInstall from nlp's master branch\r\n```python\r\npip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n```\r\nthen run\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n```\r\n\r\n## OS \/ platform details\r\n\r\n- `nlp` version: latest from master\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: True\r\n- Using distributed or parallel set-up in script?: False\r\n\r\n## Proposed solution\r\nEither change [line 762](https:\/\/github.com\/huggingface\/nlp\/blob\/7ada00b1d62f94eee22a7df38c6b01e3f27194b7\/datasets\/xtreme\/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:\r\n\r\n```\r\n# old\r\nwith open(filepath) as f\r\n# new\r\nwith open(filepath, encoding='utf-8') as f\r\n```\r\n\r\nor raise a warning that suggests setting the locale explicitly, e.g.\r\n```python\r\nimport locale\r\nlocale.setlocale(locale.LC_ALL, 'C.UTF-8')\r\n```\r\nI have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix! \n I've created a simple function that seems to do the trick:\r\n\r\n```python\r\ndef apply_encoding_on_file_open(filepath: str):\r\n    \"\"\"Apply UTF-8 encoding for all instances where a non-binary file is opened.\"\"\"\r\n    \r\n    with open(filepath, 'r', encoding='utf-8') as input_file:\r\n        regexp = re.compile(r\"\"\"\r\n                            (?!.*\\b(?:encoding|rb|wb|wb+|ab|ab+)\\b)\r\n                            (open)\r\n                            \\((.*)\\)\r\n                            \"\"\")\r\n        input_text = input_file.read()\r\n        match = regexp.search(input_text)\r\n        \r\n        if match:\r\n            print('Found match!', match.group())\r\n            # append utf-8 encoding to matching groups in-place\r\n            output = regexp.sub(lambda m: m.group()[:-1]+', encoding=\"utf-8\")', input_text)\r\n            with open(filepath, 'w', encoding='utf-8') as output_file:\r\n                output_file.write(output)\r\n        else:\r\n            print(\"No match found!\")\r\n```\r\n\r\nThe regexp does a negative lookahead to avoid matching on cases where the encoding is already specified or when binary files are involved.\r\n\r\nFrom an implementation perspective:\r\n\r\n* Would it make sense to include this function in `nlp-cli` so that we can run something like\r\n```\r\nnlp-cli fix_encoding path\/to\/folder\r\n```\r\nand the command recursively fixes all files in the target?\r\n* What is the desired behaviour in the CI test? Here we could either have a simple script that we run as a `job` in the CI and raises an error if a missing encoding is detected. Alternatively we could incorporate this behaviour into the CLI and run that in the CI.\r\n\r\nPlease let me know what you prefer among the alternatives.\r\n","embeddings":[-0.3326011896,-0.0930106342,-0.1336479485,0.2554301322,0.3521757722,-0.0277493317,0.2249831408,0.3496463001,-0.0286989305,0.038228903,-0.0904103443,0.1502787173,-0.0761749223,-0.1970372051,0.1891056299,-0.2467030734,-0.0738269314,0.1824576855,0.1078069955,0.0069923094,-0.3558878899,0.1144126877,-0.1869958341,0.2637093067,-0.3771478236,0.0810821429,0.2978654206,0.3204651177,-0.193280682,-0.6116119623,0.4130367041,-0.0536639951,0.4325025082,0.3163448274,-0.0001086041,0.0825855061,0.3929110467,-0.1319698691,-0.3346782327,-0.1939976513,-0.5915244818,-0.1812518835,-0.0098235868,-0.2595872581,0.1902403831,0.0562078469,-0.0642362684,-0.3830473125,0.1811341196,0.2071898282,0.24163872,0.3705638647,0.0213383194,0.0912284628,-0.045574829,0.1074261367,-0.1542558074,0.249418959,0.4346242249,0.0627749935,-0.019149052,0.2926935852,-0.0486163385,0.248120591,-0.1023555249,0.1306457818,0.1399910003,-0.4660047889,0.1134835854,0.0036376556,0.2718741894,-0.3664452136,-0.2865583897,-0.2252654582,-0.1094700545,-0.5443437099,0.2644951046,0.0905644149,-0.159659639,-0.0211321656,0.113159202,-0.0423871912,-0.1735193729,0.0391214527,0.0543593504,0.3878184855,-0.0600738861,-0.0513223708,-0.0941757336,-0.1786022931,0.4082849622,-0.1458789855,-0.1436942965,0.324629128,-0.2863637805,0.072422564,-0.0550461523,-0.3371772468,0.0668165982,-0.0463103987,-0.078823179,0.2744791806,-0.0495554432,0.053195674,0.3093065917,0.1556752175,0.2095072418,0.2245565802,0.1834983528,0.1982241273,0.0829440206,-0.0012048965,-0.1758536994,-0.1771315336,0.3654833138,0.1934714913,0.5531736016,-0.3191607296,-0.1221157163,0.0084841102,0.1728852391,-0.1787237525,0.0444031768,0.0144082429,0.2223311216,0.5070413947,-0.2224069536,0.2185738385,-0.4622749686,-0.1283478737,-0.1358893216,0.1796104163,-0.1460784674,-0.0783130527,0.0963894874,0.1252889782,0.163143605,-0.1422412395,0.3579080105,-0.0693807527,0.0396495759,-0.2039281875,-0.2201946378,0.0137812095,0.1307834387,0.1039626971,0.0551503152,-0.1753356308,-0.0369680114,0.2171099186,-0.1871833205,-0.2070101798,-0.0715540051,0.2629823387,0.0267733559,0.025153663,-0.4134668708,-0.0887362659,0.3430744112,0.062088266,0.1139909923,-0.2957776785,0.0404423103,-0.0858764797,0.2037117779,0.3430534005,-0.1321642697,0.0464633144,0.1779045761,-0.1550109833,0.2246282697,0.296744436,-0.1922280043,0.0937530622,-0.0989214331,0.5539204478,0.5879890919,-0.4102394879,-0.4520799518,0.1696793735,-0.2588975728,0.1952017099,-0.1164151728,0.1070558354,0.326749444,-0.117432341,0.306620568,0.6193355918,-0.1105521396,0.1110311449,-0.215863362,0.0245816521,0.0014836154,0.2785247266,0.2359109074,-0.1938206851,0.1145048365,0.2641795576,0.243651405,-0.3050079346,0.1434532404,0.2776193917,0.0995111689,0.0506758131,-0.1596455127,-0.3252392113,-0.2702022195,0.2318239212,-0.1383464932,0.1085467264,-0.3714389801,0.0401465409,-0.2963201106,0.0447943099,-0.5638593435,-0.1213942021,0.1630701125,0.2012814134,0.0795095712,0.0613085926,-0.3085088432,0.2793070674,-0.1050261855,-0.0565539338,-0.5994650126,0.2591193616,-0.0674927309,-0.3134038746,-0.0747863129,0.3225338161,0.0828683749,-0.13501966,-0.3367395401,0.2787976265,-0.0855251029,0.0143846814,0.0664433986,-0.1704661995,0.2425206453,-0.4210937619,0.1616301388,0.0719261542,0.12785393,0.0151306158,0.1468042135,0.3288503289,0.0096400762,0.0002207728,-0.0022295045,-0.0426436365,0.4274256229,-0.0020315761,0.0864704177,-0.257260561,0.2339752018,-0.0238232464,0.1626571417,-0.0563809276,-0.1947318316,-0.0884142816,0.5688619018,-0.1365721375,0.1961637586,0.1271996349,-0.0223116353,0.0912192017,-0.0803106651,0.0161401406,0.2614569962,0.0181957334,0.0555701181,0.0068808333,0.1075893268,0.02909513,0.1412385255,-0.084366791,-0.0864135996,-0.0710775107,-0.0867036805,-0.0300667249,-0.3335590065,-0.1923588961,-0.1055369303,0.3316015005,-0.1660034209,-0.0535194837,-0.1744429022,-0.1915307194,0.037373513,-0.1773097664,-0.1640250832,-0.2362444401,-0.2318590879,0.0255354829,0.1254676878,0.322096318,-0.4353750944,-0.35697487,0.1276556998,-0.3397228718,0.0316216648,-0.2119199336,-0.2546372116,0.1059964895,0.3112894893,0.1780466586,0.0755409226,-0.389688462,0.1155692562,-0.2473884374,-0.0755902529,0.0593283139,-0.3163183928,0.1726506948,-0.0393578149,0.0839216486,-0.0562964082,-0.181186527,0.2916715443,-0.2333991379,-0.1716851741,0.206258446,0.0019390341,-0.19655931,0.0024222732,-0.1196481287,-0.3280853927,-0.3669332564,0.0012898719,-0.0777989402,0.1852790117,0.1442967504,-0.2602292299,-0.1182346195,0.1143380255,-0.1334733516,-0.1536588073,-0.4095715284,0.4874741733,-0.372284323,-0.2143343836,-0.0219009388,-0.1756924838,0.4781981111,0.1308036745,-0.22841084,-0.0028280204,-0.3269365728,0.2939243317,0.0070022531,0.0189219732,0.1398832947,0.0004282062,0.0058593466,0.0415178165,-0.0540438183,-0.1960995942,0.2766460776,0.1708433926,0.1009547412,0.383543551,-0.1219677925,0.1801291257,-0.0492355041,0.3242917955,0.5478320718,0.0955017433,0.0651276484,-0.0233803149,-0.4221803546,-0.0489595979,-0.055107202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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/468","title":"UnicodeDecodeError while loading PAN-X task of XTREME dataset","comments":"I realised I was overthinking the problem, so decided to just run the regexp over the codebase and make the PR. In other words, we can ignore my comments about using the CLI \ud83d\ude38 ","body":"Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nI'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset: \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnicodeDecodeError                        Traceback (most recent call last)\r\n<ipython-input-5-1d61f439b843> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    528     ignore_verifications = ignore_verifications or save_infos\r\n    529     # Download\/copy dataset processing script\r\n--> 530     module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n    531 \r\n    532     # Get dataset builder class from the processing script\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)\r\n    265 \r\n    266     # Download external imports if needed\r\n--> 267     imports = get_imports(local_path)\r\n    268     local_imports = []\r\n    269     library_imports = []\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in get_imports(file_path)\r\n    156     lines = []\r\n    157     with open(file_path, mode=\"r\") as f:\r\n--> 158         lines.extend(f.readlines())\r\n    159 \r\n    160     logger.info(\"Checking %s for additional imports.\", file_path)\r\n\r\n\/usr\/lib\/python3.6\/encodings\/ascii.py in decode(self, input, final)\r\n     24 class IncrementalDecoder(codecs.IncrementalDecoder):\r\n     25     def decode(self, input, final=False):\r\n---> 26         return codecs.ascii_decode(input, self.errors)[0]\r\n     27 \r\n     28 class StreamWriter(Codec,codecs.StreamWriter):\r\n\r\nUnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)\r\n```\r\n\r\n## Steps to reproduce\r\nInstall from nlp's master branch\r\n```python\r\npip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n```\r\nthen run\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n```\r\n\r\n## OS \/ platform details\r\n\r\n- `nlp` version: latest from master\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: True\r\n- Using distributed or parallel set-up in script?: False\r\n\r\n## Proposed solution\r\nEither change [line 762](https:\/\/github.com\/huggingface\/nlp\/blob\/7ada00b1d62f94eee22a7df38c6b01e3f27194b7\/datasets\/xtreme\/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:\r\n\r\n```\r\n# old\r\nwith open(filepath) as f\r\n# new\r\nwith open(filepath, encoding='utf-8') as f\r\n```\r\n\r\nor raise a warning that suggests setting the locale explicitly, e.g.\r\n```python\r\nimport locale\r\nlocale.setlocale(locale.LC_ALL, 'C.UTF-8')\r\n```\r\nI have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix!","comment_length":34,"text":"UnicodeDecodeError while loading PAN-X task of XTREME dataset \n Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nI'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset: \r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnicodeDecodeError                        Traceback (most recent call last)\r\n<ipython-input-5-1d61f439b843> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    528     ignore_verifications = ignore_verifications or save_infos\r\n    529     # Download\/copy dataset processing script\r\n--> 530     module_path, hash = prepare_module(path, download_config=download_config, dataset=True)\r\n    531 \r\n    532     # Get dataset builder class from the processing script\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs)\r\n    265 \r\n    266     # Download external imports if needed\r\n--> 267     imports = get_imports(local_path)\r\n    268     local_imports = []\r\n    269     library_imports = []\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in get_imports(file_path)\r\n    156     lines = []\r\n    157     with open(file_path, mode=\"r\") as f:\r\n--> 158         lines.extend(f.readlines())\r\n    159 \r\n    160     logger.info(\"Checking %s for additional imports.\", file_path)\r\n\r\n\/usr\/lib\/python3.6\/encodings\/ascii.py in decode(self, input, final)\r\n     24 class IncrementalDecoder(codecs.IncrementalDecoder):\r\n     25     def decode(self, input, final=False):\r\n---> 26         return codecs.ascii_decode(input, self.errors)[0]\r\n     27 \r\n     28 class StreamWriter(Codec,codecs.StreamWriter):\r\n\r\nUnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128)\r\n```\r\n\r\n## Steps to reproduce\r\nInstall from nlp's master branch\r\n```python\r\npip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n```\r\nthen run\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\n```\r\n\r\n## OS \/ platform details\r\n\r\n- `nlp` version: latest from master\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: True\r\n- Using distributed or parallel set-up in script?: False\r\n\r\n## Proposed solution\r\nEither change [line 762](https:\/\/github.com\/huggingface\/nlp\/blob\/7ada00b1d62f94eee22a7df38c6b01e3f27194b7\/datasets\/xtreme\/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding:\r\n\r\n```\r\n# old\r\nwith open(filepath) as f\r\n# new\r\nwith open(filepath, encoding='utf-8') as f\r\n```\r\n\r\nor raise a warning that suggests setting the locale explicitly, e.g.\r\n```python\r\nimport locale\r\nlocale.setlocale(locale.LC_ALL, 'C.UTF-8')\r\n```\r\nI have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix! \n I realised I was overthinking the problem, so decided to just run the regexp over the codebase and make the PR. In other words, we can ignore my comments about using the CLI \ud83d\ude38 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/444","title":"Keep loading old file even I specify a new file in load_dataset","comments":"This is the only fix I could come up with without touching the repo's code.\r\n```python\r\nfrom nlp.builder import FORCE_REDOWNLOAD\r\ndataset = load_dataset('csv', data_file='.\/a.csv', download_mode=FORCE_REDOWNLOAD, version='0.0.1')\r\n```\r\nYou'll have to change the version each time you want to load a different csv file.\r\nIf you're willing to add a ```print```, you can go to ```nlp.load``` and add ```print(builder_instance.cache_dir)``` right before the ```return ds``` in the ```load_dataset``` method. It'll print the cache folder, and you'll just have to erase it (and then you won't need the change here above).","body":"I used load a file called 'a.csv' by \r\n```\r\ndataset = load_dataset('csv', data_file='.\/a.csv')\r\n```\r\nAnd after a while, I tried to load another csv called 'b.csv'\r\n```\r\ndataset = load_dataset('csv', data_file='.\/b.csv')\r\n```\r\nHowever, the new dataset seems to remain the old 'a.csv' and not loading new csv file.\r\n\r\nEven worse, after I load a.csv, the load_dataset function keeps loading the 'a.csv' afterward. \r\n\r\nIs this a cache problem?\r\n","comment_length":88,"text":"Keep loading old file even I specify a new file in load_dataset \n I used load a file called 'a.csv' by \r\n```\r\ndataset = load_dataset('csv', data_file='.\/a.csv')\r\n```\r\nAnd after a while, I tried to load another csv called 'b.csv'\r\n```\r\ndataset = load_dataset('csv', data_file='.\/b.csv')\r\n```\r\nHowever, the new dataset seems to remain the old 'a.csv' and not loading new csv file.\r\n\r\nEven worse, after I load a.csv, the load_dataset function keeps loading the 'a.csv' afterward. \r\n\r\nIs this a cache problem?\r\n \n This is the only fix I could come up with without touching the repo's code.\r\n```python\r\nfrom nlp.builder import FORCE_REDOWNLOAD\r\ndataset = load_dataset('csv', data_file='.\/a.csv', download_mode=FORCE_REDOWNLOAD, version='0.0.1')\r\n```\r\nYou'll have to change the version each time you want to load a different csv file.\r\nIf you're willing to add a ```print```, you can go to ```nlp.load``` and add ```print(builder_instance.cache_dir)``` right before the ```return ds``` in the ```load_dataset``` method. It'll print the cache folder, and you'll just have to erase it (and then you won't need the change here above).","embeddings":[-0.0888758972,0.2823802829,-0.0012339302,0.1619006693,-0.1625473797,0.0777331367,0.1432165802,0.2737467587,0.3626849055,-0.113328442,0.1163817868,0.189830035,0.1802767515,-0.0821299329,0.1950766742,0.3331559002,-0.0630543754,0.5072548389,0.1366073638,-0.0146824615,-0.3488639891,0.1107328013,-0.0943152234,-0.07250195,-0.1926436722,0.1469480544,-0.0438434035,0.13965334,-0.2056970298,-0.4888148606,0.3809181452,0.3573814631,0.2581088245,0.4660643637,-0.0001152982,-0.0119408052,0.2476485223,0.0682446957,-0.5148969293,-0.3869971335,0.0314668678,-0.5372769833,0.3891557753,-0.1841786206,-0.1016150042,-0.0859777629,0.1840104908,-0.0501311496,0.5789194703,0.2138358355,0.195167318,-0.1492288709,0.0171368178,-0.0130087351,0.1113954931,-0.2091115862,0.1625352651,0.353564024,0.1116896793,-0.2694231272,-0.2668689489,0.0342900194,-0.2663342357,0.2355277091,0.2771705687,0.0081724627,0.2694102824,-0.0516900495,0.0297865998,-0.0389136113,0.66036129,-0.0337102041,-0.1231487319,-0.2626151741,0.0286018085,-0.5128679276,0.1346926093,0.1874417514,-0.0809473768,-0.0461172797,-0.2042680085,-0.4057559073,-0.0188115314,0.2534582317,0.1112423763,0.123455584,-0.0647201687,0.228922829,0.2850901783,0.1747080088,0.2771388292,-0.1166692227,-0.2123604566,0.2447838634,-0.4885699153,-0.0201186202,0.1123057678,0.1282074004,-0.1136159897,0.1372920871,0.2320876122,0.0109714754,-0.1496887505,0.1623385996,0.3279623091,0.3042567372,0.2717228532,-0.1469151378,0.1787361205,0.158106178,-0.3821918964,-0.094241932,-0.0421042815,-0.2711846828,0.2378876805,-0.1810322255,0.3370941877,-0.1668266654,-0.0508520752,0.0400000699,0.1158589423,-0.2263805419,0.0172547791,0.4845026135,-0.1868886054,0.1356288791,0.1583126336,0.0463617519,-0.5059089065,-0.1612841636,-0.0898496956,-0.1798771769,-0.3385742307,0.0726501048,0.4552691579,-0.2072428316,0.3022795916,0.3358885646,-0.2005399913,-0.3571424782,0.074267894,0.1200318113,-0.0250850767,0.0515104644,0.0902633667,0.3180943727,0.024142446,-0.5446966887,-0.173861593,0.1558584124,-0.5722796321,-0.3847288191,0.0376878008,0.177387774,-0.150834173,-0.1242483631,-0.0813765153,-0.0549606383,0.152668938,-0.4405647218,-0.2240802795,-0.1603152603,-0.230242759,-0.2056117654,-0.0603124611,0.5919473171,-0.107986778,-0.0069066458,-0.1732042134,0.1078955978,-0.1307269037,0.1100469753,-0.1966307312,-0.0026769985,-0.2292711586,-0.5279220343,0.6706701517,-0.0909134001,-0.482563138,0.2108750939,-0.1627443135,0.1470751166,0.1583846807,0.2092542499,-0.166003868,-0.1586555839,0.0442557707,0.2686133683,0.1136607304,0.0111965248,-0.1344201863,-0.2561934888,0.2314466238,0.0314552635,-0.1690050215,0.2235095501,-0.024848463,0.13542521,0.273614049,0.0281254537,0.037286479,-0.0211752299,-0.0188249201,0.0938779712,-0.0387785695,0.2074213624,-0.6352767944,0.2108055055,-0.1857466847,-0.3761024773,0.1938500106,-0.0648839474,-0.2942895591,-0.2255121917,-0.4506825805,-0.2055748105,0.018885212,0.3500680923,0.0924992934,-0.1076359674,-0.1527515501,0.3722836077,-0.07438232,-0.0678525418,-0.5102145672,0.1139070317,0.0197452512,-0.0636630729,0.113454327,0.2977822125,0.3451760709,-0.0230163988,-0.1121129692,0.2423195541,-0.0802451372,0.3043351471,0.0062468676,-0.3474616408,0.0004544011,0.2994796336,0.1332806349,0.1017207205,-0.0083979983,-0.0168770626,-0.0799977407,0.3322361112,-0.0560341217,0.3085152805,0.0633942559,-0.1673322618,0.2275238931,-0.3928000331,0.2067195475,-0.1777364165,0.2628163099,0.2479507774,0.1953124255,0.2646502554,-0.0829107538,0.0650189519,0.4671657681,-0.0469818301,-0.2923628986,0.1642831862,-0.0841177851,-0.1841216385,-0.1236988753,0.3632374108,0.4298818707,0.1477506459,-0.1818337142,0.0822671801,-0.0667831078,-0.2304429114,0.1101651788,-0.2013234943,0.2853393853,0.2399768084,0.2579445541,-0.0999965593,-0.3501327932,0.3381066918,0.2240700126,0.110327065,-0.0796889514,0.2068432868,-0.5125057697,-0.1935468465,-0.5543268919,0.0061852699,-0.2469133139,-0.0969223008,-0.0754553229,0.6249861717,0.2246181369,0.1039894149,0.0526425838,0.3406598568,-0.1474728882,-0.3065598607,0.0291031431,-0.3341251016,-0.3812094033,-0.0456452146,0.2931637168,-0.0742159411,0.2642597258,0.0402296893,-0.1407391429,-0.2185639888,-0.1337854117,-0.0987230465,0.1271894276,0.2915703356,-0.1833294779,0.1628341079,0.138350606,-0.2592160702,0.1522710025,-0.4241508245,-0.0251136199,0.172117725,-0.0859804153,0.1537035853,-0.0118244858,-0.422603339,-0.2225028872,-0.2736027241,0.1390750706,0.1597259045,0.151983887,0.3598029017,-0.108785063,-0.1524826139,-0.1071985587,0.2338398695,-0.3367555141,-0.2792285085,0.2858326435,-0.1888992041,-0.2545824349,0.250490427,-0.0153042125,0.2039731592,-0.1013497114,-0.6121990085,-0.07070259,-0.009309873,-0.0277735572,0.0958586261,0.0553385541,0.1928489059,-0.1464696229,-0.0504265502,-0.039213635,-0.1802838445,-0.0088396231,0.2555666864,0.4061007798,0.2051307261,0.1547252089,-0.2963939011,0.5076721311,0.399600178,-0.1259077191,0.410513401,-0.0076688351,0.4529300928,-0.3399046659,-0.1743122488,-0.1087409109,-0.1506913602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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/443","title":"Cannot unpickle saved .pt dataset with torch.save()\/load()","comments":"This seems to be fixed in a non-released version. \r\n\r\nInstalling nlp from source\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp\r\ncd nlp\r\npip install .\r\n```\r\nsolves the issue. ","body":"Saving a formatted torch dataset to file using `torch.save()`. Loading the same file fails during unpickling:\r\n\r\n```python\r\n>>> import torch\r\n>>> import nlp\r\n\r\n>>> squad = nlp.load_dataset(\"squad.py\", split=\"train\")\r\n>>> squad\r\nDataset(features: {'source_text': Value(dtype='string', id=None), 'target_text': Value(dtype='string', id=None)}, num_rows: 87599)\r\n>>> squad = squad.map(create_features, batched=True)\r\n>>> squad.set_format(type=\"torch\", columns=[\"source_ids\", \"target_ids\", \"attention_mask\"])\r\n>>> torch.save(squad, \"squad.pt\")\r\n\r\n>>> squad_pt = torch.load(\"squad.pt\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/torch\/serialization.py\", line 593, in load\r\n    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/torch\/serialization.py\", line 773, in _legacy_load\r\n    result = unpickler.load()\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/splits.py\", line 493, in __setitem__\r\n    raise ValueError(\"Cannot add elem. Use .add() instead.\")\r\nValueError: Cannot add elem. Use .add() instead.\r\n```\r\nwhere `create_features` is a function that tokenizes the data using `batch_encode_plus` and returns a Dict with `input_ids`, `target_ids` and `attention_mask`. \r\n```python\r\ndef create_features(batch):\r\n    source_text_encoding = tokenizer.batch_encode_plus(\r\n        batch[\"source_text\"],\r\n        max_length=max_source_length,\r\n        pad_to_max_length=True,\r\n        truncation=True)\r\n\r\n    target_text_encoding = tokenizer.batch_encode_plus(\r\n        batch[\"target_text\"],\r\n        max_length=max_target_length,\r\n        pad_to_max_length=True,\r\n        truncation=True)\r\n\r\n    features = {\r\n        \"source_ids\": source_text_encoding[\"input_ids\"],\r\n        \"target_ids\": target_text_encoding[\"input_ids\"],\r\n        \"attention_mask\": source_text_encoding[\"attention_mask\"]\r\n    }\r\n\r\n    return features\r\n```\r\n\r\nI found a similar issue in [issue 5267 in the huggingface\/transformers repo](https:\/\/github.com\/huggingface\/transformers\/issues\/5267) which was solved by downgrading to `nlp==0.2.0`. That did not solve this problem, however. ","comment_length":26,"text":"Cannot unpickle saved .pt dataset with torch.save()\/load() \n Saving a formatted torch dataset to file using `torch.save()`. Loading the same file fails during unpickling:\r\n\r\n```python\r\n>>> import torch\r\n>>> import nlp\r\n\r\n>>> squad = nlp.load_dataset(\"squad.py\", split=\"train\")\r\n>>> squad\r\nDataset(features: {'source_text': Value(dtype='string', id=None), 'target_text': Value(dtype='string', id=None)}, num_rows: 87599)\r\n>>> squad = squad.map(create_features, batched=True)\r\n>>> squad.set_format(type=\"torch\", columns=[\"source_ids\", \"target_ids\", \"attention_mask\"])\r\n>>> torch.save(squad, \"squad.pt\")\r\n\r\n>>> squad_pt = torch.load(\"squad.pt\")\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/torch\/serialization.py\", line 593, in load\r\n    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/torch\/serialization.py\", line 773, in _legacy_load\r\n    result = unpickler.load()\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/splits.py\", line 493, in __setitem__\r\n    raise ValueError(\"Cannot add elem. Use .add() instead.\")\r\nValueError: Cannot add elem. Use .add() instead.\r\n```\r\nwhere `create_features` is a function that tokenizes the data using `batch_encode_plus` and returns a Dict with `input_ids`, `target_ids` and `attention_mask`. \r\n```python\r\ndef create_features(batch):\r\n    source_text_encoding = tokenizer.batch_encode_plus(\r\n        batch[\"source_text\"],\r\n        max_length=max_source_length,\r\n        pad_to_max_length=True,\r\n        truncation=True)\r\n\r\n    target_text_encoding = tokenizer.batch_encode_plus(\r\n        batch[\"target_text\"],\r\n        max_length=max_target_length,\r\n        pad_to_max_length=True,\r\n        truncation=True)\r\n\r\n    features = {\r\n        \"source_ids\": source_text_encoding[\"input_ids\"],\r\n        \"target_ids\": target_text_encoding[\"input_ids\"],\r\n        \"attention_mask\": source_text_encoding[\"attention_mask\"]\r\n    }\r\n\r\n    return features\r\n```\r\n\r\nI found a similar issue in [issue 5267 in the huggingface\/transformers repo](https:\/\/github.com\/huggingface\/transformers\/issues\/5267) which was solved by downgrading to `nlp==0.2.0`. That did not solve this problem, however.  \n This seems to be fixed in a non-released version. \r\n\r\nInstalling nlp from source\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp\r\ncd nlp\r\npip install .\r\n```\r\nsolves the issue. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/439","title":"Issues: Adding a FAISS or Elastic Search index to a Dataset","comments":"`DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.\r\n\r\nRight now you can experiment with it by installing `transformers` from the master branch.\r\nYou can also check the docs of DPR [here](https:\/\/huggingface.co\/transformers\/master\/model_doc\/dpr.html).\r\n\r\nMoreover all the indexing features will also be available in the next release of `nlp`.","body":"It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https:\/\/huggingface.co\/nlp\/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab.  Is  there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ?","comment_length":50,"text":"Issues: Adding a FAISS or Elastic Search index to a Dataset \n It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https:\/\/huggingface.co\/nlp\/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab.  Is  there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ? \n `DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.\r\n\r\nRight now you can experiment with it by installing `transformers` from the master branch.\r\nYou can also check the docs of DPR [here](https:\/\/huggingface.co\/transformers\/master\/model_doc\/dpr.html).\r\n\r\nMoreover all the indexing features will also be available in the next release of `nlp`.","embeddings":[0.0365026444,-0.1409246475,-0.0793723911,-0.1369627267,-0.1559900939,-0.3242330253,-0.1591320634,0.182186991,-0.3461941779,0.015834054,-0.0663877428,0.3206751347,-0.1125187874,-0.0442969948,0.0447908007,-0.2458745092,0.2475958169,0.41393134,0.1482364088,-0.1551828384,-0.0532758646,0.2191117406,-0.2554685771,0.0467051938,-0.4506125152,0.1217017248,-0.4123180807,-0.2625599802,-0.153648138,-0.5197570324,0.4782420397,0.1243595406,0.267839849,0.0743239671,-0.0001179493,-0.0224405769,0.5727151632,0.0642087758,-0.109931156,0.1077400967,0.0450584255,-0.0126223117,0.5089781284,-0.0682091266,-0.0491528623,-0.3134188652,0.2057967186,0.506159544,0.3378701508,0.0675188676,0.1206033751,-0.0178223103,0.3498007655,-0.0210936479,0.4944708645,0.1088032499,-0.1879488379,-0.060293179,0.4111689627,0.0164823085,-0.0369286165,0.0635463297,0.1623755395,-0.2319170237,0.1169097349,0.2194074392,0.1412903219,-0.3700337708,-0.0103806946,0.1250045151,0.2487776577,-0.1683013588,-0.4690059423,-0.1358831078,0.0131439865,-0.3554195464,0.3728207052,-0.2848743498,-0.1211849079,0.1636741906,-0.0299982075,-0.2509265244,-0.1611312628,0.3247069716,-0.1813227534,0.5299859047,0.0605144016,-0.2540729642,0.2310189754,-0.2415739745,0.1807152778,0.1000634655,0.0172251817,0.3674331605,-0.1381929964,-0.2774769664,0.2277394235,0.1133941337,-0.1607001722,0.0322502255,-0.40910694,0.1459733844,0.1459764689,0.0879990384,-0.1691190153,0.2571031451,0.108583197,-0.0899450853,0.2458999306,-0.4051271975,0.1125740632,0.1378308535,0.0466918498,0.162292853,-0.3634460568,-0.3564530611,-0.1548929811,-0.1499220878,0.0551367179,-0.2625108063,-0.2247933596,-0.3351933956,-0.1180677488,0.297842443,0.1253295094,-0.3970762193,0.0724128932,0.1841661036,-0.1341165602,0.0026780257,-0.0640867129,-0.1569979489,-0.1755444109,0.264549017,0.2724645436,-0.3028456867,0.1628210098,-0.1974896938,-0.2274571806,0.2945098579,-0.2795906067,0.12166325,-0.0080272518,0.2258044034,-0.1684639603,-0.1023586318,-0.4655607045,-0.1620360315,-0.2403615564,0.0633199513,-0.0646843016,-0.2811254859,-0.3323854208,0.1153732687,-0.2723724544,-0.2752392888,0.3653652966,0.6273083091,-0.1054331884,-0.1463750899,0.037082769,0.1117174402,0.2649072707,-0.0908317789,0.1279012114,-0.0669749528,-0.2392733842,-0.3873827755,-0.1192891672,0.1379834414,0.0270555951,0.2212779969,-0.2794346213,0.2451289445,0.0291392673,0.5474945307,0.6518902183,-0.2099924684,-0.1275744587,0.0607614219,0.1220447719,-0.2443282157,-0.0022637483,-0.1838946193,0.135699302,-0.0307523031,-0.0200368818,0.2575927973,0.0576005317,-0.302549839,-0.2730573714,-0.3634371459,0.3274671137,0.3770821989,0.1571439058,-0.0795033798,0.2327446043,-0.3032660186,-0.0990575701,-0.0700983182,-0.0342253484,0.1696012467,0.1677809805,0.0799389184,0.326069504,-0.2400519401,-0.1048390195,-0.0388669893,-0.6121016145,0.5534069538,-0.1013173312,0.0261404924,0.0357758291,0.0137112308,0.0058050686,-0.3206295073,0.0161508936,-0.1123400554,0.2814399302,0.1460608691,-0.302093327,-0.0639567226,0.3813228309,0.0859741345,-0.2923620939,0.4071855247,-0.2209590226,-0.5338458419,0.0497613251,0.260435015,0.0968717858,-0.146481961,-0.0170062333,0.1078794524,-0.3001843989,-0.1210719272,0.254668802,0.185167402,0.2065512985,-0.4720928073,0.0112438379,0.0818925053,-0.0036514818,-0.0161530916,-0.0921355337,0.2416178137,0.484639585,0.0955204144,0.2377486825,-0.1293536872,0.2788258791,-0.1929997653,0.0459284931,0.0898495391,0.1539402604,-0.0809658393,0.2004221827,0.048523102,-0.2047701925,0.1411314607,0.2866167724,-0.2145594358,0.0336788446,0.5055497289,-0.2039590478,-0.2953153849,0.0027814482,-0.4623137116,0.2144079655,0.3736828566,-0.0918909013,-0.1253003329,-0.2086354643,-0.2770307362,0.2586474717,0.0922297165,-0.0739523172,-0.0727834627,0.3840747476,0.1688706279,-0.243040517,-0.4180779755,-0.076842986,-0.0994065329,-0.027301304,-0.0034203569,-0.2512782514,-0.4178160429,0.069103308,-0.1639865041,-0.4351183772,-0.1990760267,0.4360974431,-0.2216433734,-0.0673548952,0.4732997715,0.1288897693,0.2370726615,-0.1431431621,0.007104923,-0.4106118679,-0.2103329301,-0.5618729591,0.0192084201,-0.1001333073,0.2254370749,0.0012152165,0.0646459237,0.2162142843,0.2121963054,-0.7023997903,0.3988601863,-0.0864363834,0.2627839744,-0.1488780379,-0.5324491262,-0.226772964,-0.4946579635,0.0077943332,-0.1952170283,-0.1020655558,0.0882287621,-0.0602550656,-0.2269314528,-0.1090350673,-0.3814074993,-0.5280771852,-0.1570465714,0.204012841,-0.067910254,0.1073895395,0.318112731,-0.0506506078,0.1100220159,-0.212894097,0.004577552,-0.0235255323,0.3745417893,0.6374518871,-0.0600388274,-0.318636924,-0.2763063908,-0.3181281686,-0.0593611188,0.1465306282,-0.1140685603,0.1144101694,-0.2133482397,0.0933070555,0.2897822559,0.2270315737,0.5762727857,0.1408128142,-0.11085926,0.0571631454,-0.2429828346,0.1431851685,0.1934714764,0.3914706111,-0.1920492202,-0.0581867434,-0.2578208148,0.752178967,-0.1656473577,-0.3004691899,0.3863733709,0.0796524957,0.1235759407,0.0249821786,-0.1162035987,0.2011026591,0.0311027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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/439","title":"Issues: Adding a FAISS or Elastic Search index to a Dataset","comments":"@lhoestq  I tried installing transformer from the master branch. Python imports for DPR again didnt' work.  Anyways, Looking forward to trying it in the next release of nlp ","body":"It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https:\/\/huggingface.co\/nlp\/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab.  Is  there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ?","comment_length":28,"text":"Issues: Adding a FAISS or Elastic Search index to a Dataset \n It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https:\/\/huggingface.co\/nlp\/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab.  Is  there any dependency on the latest PyArrow 1.0.0 ? Is it yet to be made generally available ? \n @lhoestq  I tried installing transformer from the master branch. Python imports for DPR again didnt' work.  Anyways, Looking forward to trying it in the next release of nlp 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/438","title":"New Datasets: IWSLT15+, ITTB","comments":"Thanks Sam, we now have a very detailed tutorial and template on how to add a new dataset to the library. It typically take 1-2 hours to add one. Do you want to give it a try ?\r\nThe tutorial on writing a new dataset loading script is here: https:\/\/huggingface.co\/nlp\/add_dataset.html\r\nAnd the part on how to share a new dataset is here: https:\/\/huggingface.co\/nlp\/share_dataset.html","body":"**Links:**\r\n[iwslt](https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/_modules\/torchnlp\/datasets\/iwslt.html)\r\nDon't know if that link is up to date.\r\n\r\n[ittb](http:\/\/www.cfilt.iitb.ac.in\/iitb_parallel\/)\r\n**Motivation**: replicate mbart finetuning results (table below)\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/88490093-0c1c8c00-cf67-11ea-960d-8dcaad2aa8eb.png)\r\n\r\n\r\nFor future readers, we already have the following language pairs in the wmt namespaces:\r\n\r\n```\r\nwmt14: ['cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en']\r\nwmt15: ['cs-en', 'de-en', 'fi-en', 'fr-en', 'ru-en']\r\nwmt16: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en']\r\nwmt17: ['cs-en', 'de-en', 'fi-en', 'lv-en', 'ru-en', 'tr-en', 'zh-en']\r\nwmt18: ['cs-en', 'de-en', 'et-en', 'fi-en', 'kk-en', 'ru-en', 'tr-en', 'zh-en']\r\nwmt19: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']\r\n```","comment_length":63,"text":"New Datasets: IWSLT15+, ITTB \n **Links:**\r\n[iwslt](https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/_modules\/torchnlp\/datasets\/iwslt.html)\r\nDon't know if that link is up to date.\r\n\r\n[ittb](http:\/\/www.cfilt.iitb.ac.in\/iitb_parallel\/)\r\n**Motivation**: replicate mbart finetuning results (table below)\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/88490093-0c1c8c00-cf67-11ea-960d-8dcaad2aa8eb.png)\r\n\r\n\r\nFor future readers, we already have the following language pairs in the wmt namespaces:\r\n\r\n```\r\nwmt14: ['cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en']\r\nwmt15: ['cs-en', 'de-en', 'fi-en', 'fr-en', 'ru-en']\r\nwmt16: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en']\r\nwmt17: ['cs-en', 'de-en', 'fi-en', 'lv-en', 'ru-en', 'tr-en', 'zh-en']\r\nwmt18: ['cs-en', 'de-en', 'et-en', 'fi-en', 'kk-en', 'ru-en', 'tr-en', 'zh-en']\r\nwmt19: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']\r\n``` \n Thanks Sam, we now have a very detailed tutorial and template on how to add a new dataset to the library. It typically take 1-2 hours to add one. Do you want to give it a try ?\r\nThe tutorial on writing a new dataset loading script is here: https:\/\/huggingface.co\/nlp\/add_dataset.html\r\nAnd the part on how to share a new dataset is here: https:\/\/huggingface.co\/nlp\/share_dataset.html","embeddings":[-0.3334828615,-0.3722026646,-0.0924769789,0.0178870913,-0.182699725,0.1093524843,0.3688517809,0.2643735707,-0.125605315,0.3148258328,-0.4613344967,-0.0776399374,-0.064803049,0.1805540472,0.1016528159,0.1559136212,0.0136737432,-0.0235788152,-0.1810164452,-0.0543645583,-0.2652773261,0.2664156854,-0.1272369921,-0.1815755814,-0.3280387521,0.0483214222,-0.3616868556,0.148147583,-0.1781795323,-0.3179267943,-0.0962832645,0.2018675804,0.0043573729,0.2368448973,-0.0001070355,-0.1905525923,-0.0762819201,-0.0908374637,-0.3510018885,0.1006621495,-0.2646728456,-0.2651143074,-0.1263943166,-0.2893665731,-0.3872865438,-0.0671802238,-0.1151145101,-0.3258391023,0.0424567536,0.2000764608,0.2592927814,0.2519880533,-0.363966167,-0.2319181859,0.0204405412,-0.172586754,-0.1983187199,-0.0694193318,0.5608642101,0.2720471323,-0.2547256052,0.222103551,-0.0003467133,0.1921161562,-0.0048931451,0.121178329,0.3246239126,-0.006517875,-0.1593531072,0.4086940289,0.3740608096,-0.1164143011,-0.2830058634,-0.0593642667,-0.1900770962,-0.2303505838,-0.210508734,0.0816255063,-0.1196680292,0.124930203,-0.0415161848,0.0868017897,-0.2472940832,0.1161827669,-0.0923119634,0.9784516096,-0.0090050101,-0.0894223601,-0.0342593007,-0.2545594573,-0.045578938,-0.1130447984,0.0165110826,-0.0601291843,-0.0861638039,-0.2744801641,0.2924725115,0.0434693918,-0.0622249246,0.0590407737,0.1029751599,0.071152173,0.3325048089,-0.0934899375,-0.2517021,0.0551238433,-0.1344221234,-0.4063022733,0.092986919,-0.1309958398,0.049163416,0.1383208483,0.1221535429,-0.1984278262,-0.5016508698,0.0056258766,-0.1008068398,0.0083084553,-0.1851601899,-0.0633359104,0.1519845128,-0.217383787,-0.0983585417,0.1566028446,0.0331035592,0.0121343229,0.141125977,-0.0101370718,-0.2406610996,-0.2322077155,-0.1520594209,0.1009305716,-0.2026448548,0.0283981077,0.3800966144,0.5238032937,-0.041899018,-0.018971961,0.2195511907,0.40004915,-0.069653362,0.1028330252,0.7088277936,0.1578485519,-0.0999300256,0.0830124691,-0.0932005867,0.1064212769,-0.2886227369,-0.0697039813,0.2261247188,-0.1155597046,-0.0921943486,0.2713848352,-0.1784690917,-0.2367864996,0.5732960701,0.4183843136,-0.0948930457,-0.0091865566,-0.05276094,0.0809449703,-0.3100984395,-0.1258616447,0.0837474838,0.2288593352,-0.5910980701,-0.1100829914,-0.1314503253,0.2189666182,0.2230558246,0.1125309691,-0.0430241302,0.1650476307,-0.2222633064,0.3110174239,-0.0164760873,0.0106547289,0.1134816855,-0.0392748713,-0.1310180575,-0.1334378123,0.4240181148,0.191364333,0.2322646677,-0.1136516184,-0.1647869945,0.2035278976,-0.2517412901,0.0421081819,-0.2451383621,-0.2300683558,0.5268051028,0.1110182777,0.1924537271,-0.3725347519,0.0999875665,0.1659070253,0.4689549506,-0.2773914337,0.1345429271,-0.1531642228,0.3069565296,-0.0678336099,0.1636535823,-0.2083519697,-0.0154855736,0.1424131989,-0.1754942983,0.6150257587,0.2148750275,0.3419876397,0.0176722854,-0.0650309548,-0.2481763512,-0.2017679214,0.2806344032,0.2754361033,-0.1205326095,-0.283652246,-0.1200032383,-0.0141040767,-0.079423286,-0.1126092747,-0.3751195669,0.0413340479,-0.1244366914,-0.0230781771,0.11000631,0.4384022355,0.0560680926,-0.1176028624,-0.0888798907,0.0916532204,-0.2073235661,0.280642122,0.259760052,0.5066600442,0.1799169332,-0.3302954435,0.2640108168,-0.0370446555,0.0020613095,-0.1470851004,0.2458806783,0.4308546185,-0.1414052993,0.2891124785,0.0322199203,-0.1204729751,0.1530533135,-0.140721187,-0.1774052829,-0.2646365762,0.2876604795,-0.0283265524,0.030748507,-0.037250746,0.3311652243,0.398981601,0.4106965065,0.1012398601,0.0374423377,-0.1233828664,-0.0856721327,-0.1140096337,0.1963426024,-0.1481398344,0.1982419342,0.3941271305,0.2930689156,-0.0063033886,-0.0081451247,-0.2674535811,0.368232429,0.0831596777,-0.0806127489,0.3035694659,-0.1025728881,-0.0797005966,-0.3303620815,0.0464310311,0.0004443525,-0.0467909686,0.0490889437,0.0173673406,-0.234914422,-0.1608103514,-0.2334724814,-0.3551646471,-0.0800617859,-0.3991764784,0.2940744758,-0.0091903163,-0.2574594021,0.1491001695,0.0934518054,0.5956983566,-0.1647156179,0.3882248104,-0.2032001019,-0.2412425578,-0.2354210913,0.3034731448,0.1065895557,0.0333327726,0.5829252005,-0.287176162,0.1692098826,-0.0815070644,-0.4728700519,0.1743195802,0.0151837757,0.0840854123,0.2246305645,0.0063932128,0.1907295883,-0.2680400908,0.1473170817,-0.1281913519,-0.0734211504,-0.0660985336,-0.2781065702,-0.3567057848,-0.0101308562,-0.8072680831,-0.4668091536,-0.2272167355,-0.001244503,-0.274687618,0.077118367,0.2422076464,-0.1127375364,0.1935001612,-0.1483844221,0.1213304102,-0.2372917533,-0.0725687891,0.2338352799,-0.3333695829,-0.1917576641,0.0768791437,-0.0045270985,0.3215009868,-0.1392311901,-0.0256989654,0.4002657533,0.0484406389,0.2887951732,0.3326581419,0.4452306926,0.2124417275,0.2143184096,-0.278519243,-0.3447280824,-0.0780801773,0.0277503394,0.1879918724,0.2707400024,0.1232825741,-0.0448735654,0.1365181059,0.4299829304,0.1162668988,-0.1589788347,0.2109692693,0.0902185962,-0.017117288,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/438","title":"New Datasets: IWSLT15+, ITTB","comments":"Hi @sshleifer, I'm trying to add IWSLT using the link you provided but the download urls are not working. Only `[en, de]` pair is working. For others language pairs it throws a `404` error.\r\n\r\n","body":"**Links:**\r\n[iwslt](https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/_modules\/torchnlp\/datasets\/iwslt.html)\r\nDon't know if that link is up to date.\r\n\r\n[ittb](http:\/\/www.cfilt.iitb.ac.in\/iitb_parallel\/)\r\n**Motivation**: replicate mbart finetuning results (table below)\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/88490093-0c1c8c00-cf67-11ea-960d-8dcaad2aa8eb.png)\r\n\r\n\r\nFor future readers, we already have the following language pairs in the wmt namespaces:\r\n\r\n```\r\nwmt14: ['cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en']\r\nwmt15: ['cs-en', 'de-en', 'fi-en', 'fr-en', 'ru-en']\r\nwmt16: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en']\r\nwmt17: ['cs-en', 'de-en', 'fi-en', 'lv-en', 'ru-en', 'tr-en', 'zh-en']\r\nwmt18: ['cs-en', 'de-en', 'et-en', 'fi-en', 'kk-en', 'ru-en', 'tr-en', 'zh-en']\r\nwmt19: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']\r\n```","comment_length":34,"text":"New Datasets: IWSLT15+, ITTB \n **Links:**\r\n[iwslt](https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/_modules\/torchnlp\/datasets\/iwslt.html)\r\nDon't know if that link is up to date.\r\n\r\n[ittb](http:\/\/www.cfilt.iitb.ac.in\/iitb_parallel\/)\r\n**Motivation**: replicate mbart finetuning results (table below)\r\n![image](https:\/\/user-images.githubusercontent.com\/6045025\/88490093-0c1c8c00-cf67-11ea-960d-8dcaad2aa8eb.png)\r\n\r\n\r\nFor future readers, we already have the following language pairs in the wmt namespaces:\r\n\r\n```\r\nwmt14: ['cs-en', 'de-en', 'fr-en', 'hi-en', 'ru-en']\r\nwmt15: ['cs-en', 'de-en', 'fi-en', 'fr-en', 'ru-en']\r\nwmt16: ['cs-en', 'de-en', 'fi-en', 'ro-en', 'ru-en', 'tr-en']\r\nwmt17: ['cs-en', 'de-en', 'fi-en', 'lv-en', 'ru-en', 'tr-en', 'zh-en']\r\nwmt18: ['cs-en', 'de-en', 'et-en', 'fi-en', 'kk-en', 'ru-en', 'tr-en', 'zh-en']\r\nwmt19: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']\r\n``` \n Hi @sshleifer, I'm trying to add IWSLT using the link you provided but the download urls are not working. Only `[en, de]` pair is working. For others language pairs it throws a `404` error.\r\n\r\n","embeddings":[-0.3510955274,-0.4201570749,-0.0554755107,0.0014696205,-0.2052135766,0.1677963436,0.3096143603,0.2312010378,-0.1078790054,0.326869607,-0.474678725,-0.0717082173,-0.0530310422,0.0761437491,0.112648055,0.203145802,-0.04159775,0.0062062764,-0.205002591,-0.0367566273,-0.2811895311,0.2966844738,-0.1565782279,-0.1628477275,-0.3049374223,0.1075593084,-0.323240906,0.1289975792,-0.0864088759,-0.2628643215,-0.0710038841,0.1811725646,-0.0486795306,0.0939843208,-0.0001127129,-0.1136948019,0.011659598,-0.1016759723,-0.3240738809,0.0458127372,-0.366542697,-0.2639515102,-0.2142889798,-0.3062394559,-0.4367866516,0.0272238739,-0.1831997633,-0.3564963043,-0.0052962503,0.1945134848,0.2235888541,0.3457286954,-0.314399749,-0.282266587,0.0567384325,-0.2643663585,-0.1838622093,-0.0266631953,0.476251781,0.3321308494,-0.0531216003,0.2584946156,0.0270025842,0.1334303468,-0.0349096283,0.1428125501,0.3649306893,0.0173942931,-0.0727352574,0.3325250447,0.4082353711,-0.0555605181,-0.2953056693,0.0243605524,-0.1866715103,-0.1836407036,-0.16567716,-0.0150444638,-0.0690909922,0.1800312996,-0.0824468732,0.0707493275,-0.2317941189,0.2495971769,-0.0581960455,0.9896836281,0.0268575624,-0.027306173,0.0627432838,-0.2822177708,-0.1278498471,-0.1018943191,0.0349727683,-0.053546384,-0.0472452939,-0.1655717939,0.3346602917,0.1215329766,-0.1151128337,0.0373837128,0.0262791626,0.018120233,0.3821308017,-0.1026368216,-0.3399943113,0.1468216777,-0.1371391863,-0.3576861024,0.067668654,-0.0888855606,0.0849977955,0.1274543256,0.0989745855,-0.2352352142,-0.5490626693,-0.0274979714,-0.1431760043,-0.0249166712,-0.3145843446,-0.0480165891,0.0230618082,-0.2022242695,-0.0835162848,0.2277546972,0.0744655356,-0.0106371902,0.295471251,0.0556020476,-0.2634866834,-0.1860876977,-0.156822443,0.2214243561,-0.1861123443,0.0067126662,0.3576880693,0.5407779813,-0.0904791281,-0.0771081075,0.2304045409,0.3313212097,-0.2134877145,0.0881097764,0.688242197,0.1761722863,-0.0530854613,0.1961775124,-0.127132833,0.1290379614,-0.2528000772,0.035009563,0.1868709922,-0.1165912598,-0.0831105784,0.2308927774,-0.1885018349,-0.2557547688,0.6013925672,0.2973755598,0.0287985578,-0.0596831441,-0.0814999938,0.0976899937,-0.3347741663,-0.0301047657,0.1800950915,0.2333528847,-0.6572201252,-0.166527912,-0.2068193406,0.2950187325,0.3693426549,0.1796917021,-0.045350723,0.2924351096,-0.258497715,0.4198895097,0.01014918,-0.004906219,-0.0069708456,-0.1160336435,-0.1494865566,-0.0744260177,0.4314526916,0.2410665005,0.1457803398,-0.0348599926,-0.1443373263,0.176410988,-0.1311493665,-0.0318324305,-0.2274167985,-0.1784517914,0.5984969139,0.1068547294,0.1780119687,-0.307426095,0.1291626096,0.2362651676,0.4651766121,-0.3246689737,0.2200807035,-0.1310893893,0.3670401573,-0.0452757701,0.1571962982,-0.1688234806,0.0765762031,0.1846998334,-0.1489457935,0.6234954,0.1539902389,0.3873569369,-0.0878154188,-0.0449774489,-0.3361417353,-0.1787450463,0.208551541,0.3206881285,-0.1827824414,-0.3383514583,-0.0287235677,-0.0390562378,-0.0480930693,-0.2065082639,-0.4026569426,0.1354035735,-0.2129510343,-0.0521700233,0.1030203104,0.5125248432,0.1033758,-0.0699645653,-0.1244540438,0.0962446481,-0.3203604519,0.303958267,0.2060095519,0.3942100406,0.1310862303,-0.3835148811,0.1998650879,0.0237835627,0.0238178968,-0.1549521089,0.3204905093,0.3441520035,-0.0772997662,0.3010514975,-0.0337892212,-0.1454748809,0.1417863369,-0.1534954756,-0.1454747617,-0.3616620302,0.3138562441,-0.0337949321,0.0471854769,-0.1138969809,0.3828154504,0.4016873837,0.3852693737,0.1135991439,0.1079316214,-0.0844484568,-0.0436761789,-0.0492627881,0.228262201,-0.2101130486,0.2338314205,0.3430885971,0.2765452266,0.0183295012,-0.0082963184,-0.2246361673,0.3973411024,0.0774704963,-0.1221395805,0.2896922827,-0.1076157615,-0.0456981845,-0.2772288918,0.1048429385,0.0116622923,-0.0693208501,-0.1093093231,-0.0416320115,-0.3411887586,-0.2628680468,-0.238880977,-0.3346974552,-0.0919712856,-0.403072983,0.2417892963,-0.0480080359,-0.2613851428,0.2518025041,0.1039010063,0.541272819,-0.1396017075,0.2570070028,-0.2098888457,-0.1634466499,-0.3350367844,0.2615450919,0.0714806691,-0.0096898237,0.6337125301,-0.2877264917,0.2285160273,-0.0371143296,-0.4671827555,0.113104485,0.0834075585,0.0288717747,0.2216667682,0.0138424914,0.2017314732,-0.3470591903,0.221379295,-0.0603058264,-0.0653206185,-0.1738288552,-0.2651665211,-0.4449757338,-0.0321839564,-0.7677303553,-0.4418911934,-0.265173316,-0.086501509,-0.3642118871,0.0767292082,0.2934867144,-0.1501422673,0.219566226,-0.0729876533,0.1199759915,-0.2860831618,0.0994729996,0.3871655166,-0.2667453885,-0.2149743736,0.1425022781,0.010607346,0.3104967177,-0.1521057487,0.0015578999,0.4561625421,0.08439935,0.2617935538,0.3264421523,0.5436169505,0.1978022754,0.1510165781,-0.2413122505,-0.3730677664,-0.184239924,0.1567745954,0.161958158,0.4169943631,0.1445748657,0.064528212,0.1511705369,0.4219455421,0.11032819,-0.1442943364,0.2931169569,0.0483067818,-0.0265367664,-0.0388945378,-0.5767920017,0.4208676219,-0.02561646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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/436","title":"Google Colab - load_dataset - PyArrow exception","comments":"+1! this is the reason our tests are failing at [TextAttack](https:\/\/github.com\/QData\/TextAttack) \r\n\r\n(Though it's worth noting if we fixed the version number of pyarrow to 0.16.0 that would fix our problem too. But in this case we'll just wait for you all to update)","body":"With latest PyArrow 1.0.0 installed, I get the following exception   . Restarting colab has the same issue\r\n\r\nImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.\r\n\r\nThe error goes only when I install version 0.16.0 \r\ni.e.  !pip install pyarrow==0.16.0","comment_length":43,"text":"Google Colab - load_dataset - PyArrow exception \n With latest PyArrow 1.0.0 installed, I get the following exception   . Restarting colab has the same issue\r\n\r\nImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.\r\n\r\nThe error goes only when I install version 0.16.0 \r\ni.e.  !pip install pyarrow==0.16.0 \n +1! this is the reason our tests are failing at [TextAttack](https:\/\/github.com\/QData\/TextAttack) \r\n\r\n(Though it's worth noting if we fixed the version number of pyarrow to 0.16.0 that would fix our problem too. But in this case we'll just wait for you all to update)","embeddings":[-0.3629812002,0.2522223592,0.0292827375,0.0668664575,-0.0656062812,-0.3179534078,0.368732363,0.1455648243,-0.137786448,0.1537568867,-0.0934416428,0.4229706526,-0.1183706224,0.0239222087,0.1916114539,-0.0460920483,0.0454057381,0.3213040233,-0.1009332687,0.2128739059,-0.1327811182,0.3483653367,-0.5873017311,0.2542974353,-0.2375742793,-0.0698926151,-0.0117018726,-0.0221687034,-0.1265799105,-0.7411338687,0.3176329434,-0.0789566487,0.0764134601,0.3993419707,-0.0001173493,0.0144254118,0.4822439849,0.0728600919,-0.2069341391,-0.3872157335,0.038675338,-0.2277514338,0.3138642013,0.0373717546,0.0358879901,0.1512118876,0.2212150395,0.4453008771,0.3842617869,0.4977120161,0.1408520192,0.1463243812,0.2370873541,0.0650931373,0.5099895,-0.2810863853,-0.1513428986,0.495528698,0.3179702461,-0.1587514877,-0.0368670933,0.103491798,-0.1534598768,0.0865598246,0.0030837287,0.1041215062,-0.2000458539,-0.125892058,-0.0360218585,0.2756792307,0.3768588006,-0.450868994,-0.2491981983,-0.01317563,0.1185371056,-0.6331674457,0.2870849073,0.116099149,-0.2995830178,0.2648494542,-0.1115242243,0.0107542686,-0.1450327188,0.1135522872,-0.0236420818,0.6451193094,0.012620586,0.1286133379,0.1633942574,-0.0405531228,0.4524587691,0.1952841729,-0.0274376236,0.1291082948,-0.0782346576,0.0992477909,-0.1888786703,0.0984959528,0.1803100705,-0.1165357903,0.0249171834,0.0484945662,-0.0032617196,0.354835093,-0.0418050028,0.19820939,0.1764465868,-0.075462766,0.4000109434,0.2195628434,-0.156437695,0.1064228714,0.0805545896,-0.3143377602,0.011677769,-0.092380479,0.2431232333,-0.334220916,-0.4480620027,-0.2376210988,-0.5925034285,-0.058828339,-0.0057860017,0.3084498942,-0.1026286483,-0.2543334961,0.0440540761,0.3627564907,-0.3065457642,-0.103482984,-0.0444494896,0.1358826905,-0.1913894862,0.0998171195,0.3135398924,-0.1987226009,0.2418410033,0.0600405857,-0.06561023,0.1117260531,-0.1427793056,-0.1940472722,-0.0809840113,0.2763290703,0.1291200817,-0.1630798131,-0.177103579,-0.4345960021,-0.1928695738,0.341162473,-0.1402751505,-0.221914947,0.0338350162,0.123443462,-0.3155338764,-0.2954392433,-0.2915062904,0.242105931,-0.1154171899,-0.3325147629,0.0051547447,-0.338324517,-0.2549366951,-0.0052289949,0.1019866019,0.317040205,-0.338414371,-0.0378375091,-0.0716530606,-0.1312973946,0.1759172082,0.1195302606,-0.1356283724,0.0528887697,-0.0810474902,0.3707363605,0.1033161879,-0.0800416991,-0.3151873052,-0.1095573083,-0.0785582811,-0.1270705312,-0.0561670698,-0.2193423212,-0.2982594669,-0.0450606719,0.2091311812,0.2197275311,0.0764507055,0.0485545918,-0.3125258684,-0.2832559049,0.277035594,0.1366285682,0.0436743051,-0.2376753539,-0.0044845329,-0.3707582653,0.0189564694,-0.1722556204,-0.2514601946,-0.0375154167,0.3037078083,-0.267026782,-0.0709287301,-0.0049370858,-0.251334697,0.0999912769,-0.198675558,0.3306252658,-0.2112036496,-0.101684548,-0.3360035121,0.2426897883,0.1568356603,-0.0757179931,0.0537428781,0.0139934346,0.1294900775,0.4455618858,-0.0342876799,0.1879517436,-0.0059918989,0.1484079063,0.2585426569,0.1822604388,-0.3008645475,-0.5154079199,0.2087287307,0.1776776761,0.0894742981,0.0034555825,-0.0724015683,0.1765432656,-0.009275347,0.0763942003,-0.2671247125,-0.2846678197,0.1067418158,-0.4681561887,-0.0044742413,0.0258461498,-0.0397189781,0.0368184112,0.3439415097,0.1939021349,0.086098358,-0.0027802992,0.1075394079,-0.0836933181,0.1214539632,-0.1025411636,0.1862477362,0.1555067599,0.1888844669,0.1804465204,0.3516589105,-0.081736885,-0.1929059774,0.0209464189,0.5004723668,-0.0104153566,0.3968636394,0.2594711483,-0.1391992867,-0.2249691039,0.1984295547,-0.2625118792,0.3533537388,0.1530496925,-0.0697853416,0.3267526031,-0.2691057026,-0.3553437293,0.1939434111,0.0401574075,0.2229404449,-0.0164994374,0.38502267,-0.000071901,-0.4463835061,-0.2158685774,-0.0241146572,0.2902513146,-0.195781976,0.2333467603,-0.2788990736,-0.4786580503,-0.3216550648,-0.3746164441,-0.2644890845,-0.1139181554,0.1861757487,0.0875777602,0.0209138468,0.2171658278,0.3065685928,0.253418535,-0.2405567765,-0.2795983553,-0.2933273613,-0.2630257607,-0.6036442518,0.0401455499,0.3904749453,0.1700224131,0.162003234,-0.168516472,-0.0609380081,0.4120291173,-0.5072560906,0.0628771558,-0.1883171499,0.3759168983,0.0254065637,-0.1615848392,-0.495667547,-0.4668341577,0.0776250064,-0.3616183698,-0.271908015,0.0947322994,-0.0823626518,-0.0533253923,-0.2126678675,-0.4288894832,-0.4334754944,-0.176218003,0.2912440598,0.1114313304,0.0875864848,0.4321451485,0.1826329231,0.3838966787,-0.0788138732,-0.0156474859,0.0568739362,0.157979548,0.1709013134,-0.1130349115,-0.2863180339,0.0840016007,-0.0555577464,0.1250379384,-0.1318465769,-0.5317515731,-0.2815971673,-0.0561024584,0.1225522831,0.2538729012,0.023475131,0.3396735489,0.0629492849,-0.0319777355,0.0732188746,-0.4039273262,0.033862669,-0.0514901988,-0.0284922495,0.0802505016,0.1063992977,-0.0677924603,0.7185683846,-0.130193755,-0.109185569,0.3972301483,-0.103979826,0.1648678333,-0.2360560596,-0.0234847609,0.1025646329,-0.0177805927,-0.1606339812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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/436","title":"Google Colab - load_dataset - PyArrow exception","comments":"Came to raise this issue, great to see other already have and it's being fixed so soon!\r\n\r\nAs an aside, since no one wrote this already, it seems like the version check only looks at the second part of the version number making sure it is >16, but pyarrow newest version is 1.0.0 so the second past is 0!","body":"With latest PyArrow 1.0.0 installed, I get the following exception   . Restarting colab has the same issue\r\n\r\nImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.\r\n\r\nThe error goes only when I install version 0.16.0 \r\ni.e.  !pip install pyarrow==0.16.0","comment_length":59,"text":"Google Colab - load_dataset - PyArrow exception \n With latest PyArrow 1.0.0 installed, I get the following exception   . Restarting colab has the same issue\r\n\r\nImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.\r\n\r\nThe error goes only when I install version 0.16.0 \r\ni.e.  !pip install pyarrow==0.16.0 \n Came to raise this issue, great to see other already have and it's being fixed so soon!\r\n\r\nAs an aside, since no one wrote this already, it seems like the version check only looks at the second part of the version number making sure it is >16, but pyarrow newest version is 1.0.0 so the second past is 0!","embeddings":[-0.3563594818,0.269282788,0.0403541103,0.0255090278,-0.1082258523,-0.2124165297,0.1698410064,0.2209273875,-0.14622657,0.1146730036,0.0956548974,0.5587813854,-0.2984774113,0.0380698033,0.0623061098,-0.0809185207,0.1304244846,0.3858693838,0.0610733591,0.1696226895,-0.2576373518,0.3070941865,-0.4419914484,0.2494819164,-0.2984107435,0.0139223505,0.0193617586,0.0773457959,-0.3677995801,-0.6848931909,0.4637121558,-0.1372096092,0.2255123407,0.3252450824,-0.000117499,-0.0126847932,0.4980806112,0.1090355963,-0.1948447227,-0.4525268376,0.0962289944,-0.4826610386,0.3875705004,0.0097497031,0.0457869023,-0.0264520142,0.3028815985,0.6757784486,0.2657588124,0.2176844776,0.1667813957,0.2213878483,0.4098852873,0.0257353429,0.4948376417,-0.2362098843,-0.1034501716,0.762858808,0.2007291913,-0.3635478616,0.0591918826,0.1609631181,-0.2842781842,0.0079968292,0.0868148655,-0.0093613975,-0.0282807872,-0.2803566754,-0.070423916,0.2334549874,0.2786686122,-0.3727610111,-0.2139852345,-0.0369421542,0.1685265005,-0.5201522708,0.2031876445,0.2061320841,-0.2479265481,0.2166700363,0.0423115343,-0.0363257118,-0.1677532941,0.1888074577,-0.1594609469,0.7550790906,0.0910478309,0.1258877069,0.2515863478,-0.0595551692,0.5646770597,0.2610051334,-0.0171777867,0.1957466453,-0.0181440879,0.0440122299,0.0082142614,0.5163198113,0.0293623358,-0.3448951542,0.0096516013,-0.0534603298,0.1265423,0.4474957287,0.0249658301,-0.063434042,0.0032090126,-0.125906378,0.3919048011,0.0218870789,0.1578956246,0.1030367613,0.0269265324,-0.3716893792,-0.0051645674,-0.0394150317,0.3415209949,-0.2093355209,-0.3028344214,-0.256716758,-0.5051163435,-0.0087984167,0.0819451138,0.1959880888,-0.1884271652,-0.0456597507,-0.013504128,0.1332431287,-0.2568027377,-0.1132037044,-0.0069235414,0.1749416292,-0.2337508351,0.0082895365,0.2488785535,-0.1568795741,0.2385914922,0.1839190722,-0.1583384871,0.1740048379,-0.0879792348,-0.1543875486,-0.1690748334,0.3463720977,0.0948102027,-0.0689558387,-0.154970929,-0.4418059886,-0.2765375674,0.3989022672,-0.2178521305,-0.3779511452,-0.2776358426,0.1390897632,-0.41422382,-0.1977523565,-0.2286471874,0.1975177526,-0.0626465976,-0.5185403824,-0.1106970683,-0.4185709655,-0.2588008642,-0.2167459875,-0.0355000496,0.2313151956,-0.4213174284,-0.0013216604,-0.205294922,-0.1577323377,0.2029190063,0.1241731644,-0.2396297157,0.0035150398,-0.0710430518,0.200630635,0.3293647766,-0.1011555046,-0.3482894599,0.0037729219,-0.124355115,-0.0796842873,-0.0503579341,-0.0146262608,-0.2075435221,-0.1026687846,0.111505717,0.2191842645,-0.0628921464,0.0518534742,-0.3201494515,-0.282230407,0.2409398407,0.1795413792,0.029073203,-0.1322246045,0.1251459122,-0.051167652,0.150691092,-0.1088017076,-0.1956821382,-0.0068311817,0.3765248358,-0.1861535609,-0.1213011965,-0.0252113603,-0.424626112,0.1397025734,-0.2081705332,0.2105352879,-0.181357637,-0.1387983263,-0.1585410833,0.2024338245,0.0917202979,0.0864932016,0.0574882589,-0.0538417473,0.1482982486,0.2236875743,0.0374069326,0.0488238707,0.0423657559,0.1162125692,-0.0075122435,0.2853103876,-0.3939154744,-0.5148022175,0.110420078,0.1565466523,0.1446731836,0.0030891907,0.0473880097,0.0985333472,0.0302920323,0.1519058049,-0.4095864296,-0.1629502177,0.1721079648,-0.436049968,0.0903435722,-0.0005215072,-0.0892995968,0.1381736994,0.3890042007,0.2938117683,0.1787322462,-0.0771885663,-0.0478576161,0.0068728905,0.0108244224,-0.0219762344,0.0616079159,0.0079064276,0.1270696223,0.3067643642,0.116550684,0.0130092064,-0.3182851076,0.1458008438,0.4736977518,0.0787649006,0.1612203717,0.1202903986,-0.0150473509,-0.2019604892,0.2824196815,-0.2004913539,0.236426726,0.2488380522,-0.0791958645,0.2862409353,-0.3474994898,-0.2463964373,0.066219762,0.0795558318,0.2478338629,0.0545605719,0.280064851,0.0440858603,-0.3395344317,-0.0405010656,-0.0013776888,0.3130808175,-0.1538745612,0.2280437797,-0.3657150269,-0.5152053833,-0.2934384942,-0.3485786617,-0.4190300107,-0.0455743335,0.0744508505,0.3852240443,0.0903392434,0.226042062,0.3749234974,0.0267463271,-0.2834899724,-0.2804327011,-0.1622307599,-0.3187162578,-0.5653555393,0.0051587559,0.2741716206,0.1464026272,0.1834054291,-0.1747495085,-0.0374336317,0.1914411783,-0.6024135351,0.0411879085,-0.2708110511,0.3899646997,0.1654329151,-0.0756173134,-0.4261088669,-0.3029899299,0.0499657393,-0.2672175169,-0.2311026752,-0.0775561035,-0.3112016916,0.1056579724,-0.2025433332,-0.0183191933,-0.5836764574,-0.2559036613,0.0728108585,0.0824737847,0.1966979802,0.3112359941,0.2778764069,0.204835847,0.0999180824,-0.1780395508,0.0744474828,0.0728753358,0.2003842145,-0.1788429618,-0.2410305887,0.2172353715,-0.1202084944,0.021112103,-0.0540615655,-0.4464507699,-0.4577817917,0.0401118957,0.3177597523,0.176334843,-0.0380546562,0.4169327021,0.0920343176,0.0213226434,0.0110069215,-0.4104071558,-0.2225304395,0.0307433084,0.195093289,0.1226700991,0.2151464373,-0.0781218708,0.6302813888,-0.2324964255,-0.1335863471,0.4108282328,-0.1130619198,0.2641091943,-0.0445308462,-0.0441889837,0.0038075019,0.0769339874,-0.0762546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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/436","title":"Google Colab - load_dataset - PyArrow exception","comments":"> Indeed, we\u2019ll make a new PyPi release next week to solve this. Cc @lhoestq\r\n\r\nYes definitely","body":"With latest PyArrow 1.0.0 installed, I get the following exception   . Restarting colab has the same issue\r\n\r\nImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.\r\n\r\nThe error goes only when I install version 0.16.0 \r\ni.e.  !pip install pyarrow==0.16.0","comment_length":17,"text":"Google Colab - load_dataset - PyArrow exception \n With latest PyArrow 1.0.0 installed, I get the following exception   . Restarting colab has the same issue\r\n\r\nImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.\r\n\r\nThe error goes only when I install version 0.16.0 \r\ni.e.  !pip install pyarrow==0.16.0 \n > Indeed, we\u2019ll make a new PyPi release next week to solve this. Cc @lhoestq\r\n\r\nYes definitely","embeddings":[-0.3427734375,0.2993348837,0.0278658252,0.0008248843,-0.0533736497,-0.2322781533,0.2582436204,0.1790954322,-0.2328974456,0.1098069698,-0.0035005987,0.6347985268,-0.259023875,0.1106953695,0.1111482233,-0.0966253504,0.1224771291,0.3842332661,0.0320785642,0.1150280535,-0.2321145833,0.3013066947,-0.4014840126,0.2736143768,-0.3001113236,-0.0178907402,-0.0336268581,0.0969627202,-0.2867270708,-0.663888514,0.4799574316,-0.0461135171,0.2491188794,0.2177296728,-0.0001120963,0.0018571639,0.4446177185,0.0588436872,-0.1980582774,-0.4029640257,0.0341191813,-0.4738245308,0.4145335853,-0.0984018818,0.009839911,-0.0233778544,0.2621846795,0.6624282002,0.3113102913,0.202608943,0.2112783194,0.2363433391,0.3234765232,0.0074101943,0.4227241278,-0.1575131118,-0.0963935852,0.7648112178,0.1429357082,-0.3419243395,0.0089764548,0.1031950638,-0.2788573802,0.0289603621,0.021922132,0.0070117442,-0.1472040117,-0.307143867,-0.063910909,0.2735510468,0.2462306172,-0.369807303,-0.1704452038,-0.0098759793,0.1577402055,-0.5250217319,0.1430397928,0.2469917387,-0.3028105199,0.2058746964,0.1286191195,-0.1188331693,-0.1860572994,0.1715101898,-0.1171362326,0.7375593781,0.1335549504,0.115255326,0.2805883288,-0.0567426905,0.540766418,0.3088673055,-0.0792701244,0.1202857196,-0.0427252576,0.0211688671,0.0225457773,0.3409097195,0.0249087419,-0.2473079115,0.0255866759,0.0225669984,0.1256219298,0.4503282309,0.0599614382,-0.0484906733,-0.0453351624,-0.0046340348,0.4267911315,-0.0066436813,0.1369570494,0.0808409229,-0.0122739682,-0.2847607136,-0.0170374289,-0.0676540509,0.2775869071,-0.1611774117,-0.3963501453,-0.2821988463,-0.435975343,0.0238579642,0.0372934192,0.1950220466,-0.1200190112,-0.1023528725,0.0121809551,0.1517194808,-0.2696354389,-0.0800236538,-0.0334360711,0.1952785403,-0.2237413228,0.0621654652,0.2317143083,-0.1511159539,0.2136739492,0.1638038158,-0.1062263176,0.2310115993,-0.0294301063,-0.1341701299,-0.1386497468,0.2887904346,0.1331017762,-0.0839099661,-0.1724686921,-0.3421243429,-0.26027897,0.3834969401,-0.281029433,-0.34865731,-0.3057534695,0.1715805233,-0.3623221517,-0.2298499197,-0.2415509671,0.1925079226,-0.1000900492,-0.5023195744,-0.103967838,-0.352139771,-0.0717574656,-0.201839149,-0.0292974189,0.203484118,-0.3529725671,-0.0353136845,-0.1182195693,-0.1545044184,0.2850580513,0.2002337426,-0.2665144205,0.0042171464,-0.113277331,0.2782341242,0.2827667296,-0.078201808,-0.3504206538,-0.0246291589,-0.0884435028,-0.1190676168,-0.056546621,-0.0112745231,-0.0793270618,-0.0798390061,0.1883729994,0.228994295,-0.0206914451,0.0899544433,-0.3444251418,-0.3358956873,0.2805465162,0.1920298785,0.0722375363,-0.1789543182,0.0699494854,-0.0459714234,0.1574608237,-0.1699227095,-0.1814156622,0.0086773662,0.418684721,-0.1727122962,-0.1212951094,0.0149611123,-0.363623023,0.0653887987,-0.2499880642,0.3446653783,-0.168976739,-0.1383545101,-0.0968618467,0.1540222764,-0.0032698438,0.044844158,0.1141757965,-0.0692190751,0.0930160359,0.230772078,0.0139852418,0.0700308532,0.0974470749,0.0902948454,0.0755294412,0.3200631142,-0.3432150185,-0.5013923645,0.1558911055,0.1919502318,0.0954931602,-0.015271429,0.0595762432,0.0701608732,-0.0414922535,0.1012136936,-0.3247621655,-0.1853398979,0.2462165505,-0.4993384182,0.0803937092,0.0131683946,-0.0752409548,0.1265430897,0.4028690457,0.3070367277,0.1803319901,-0.0566328913,-0.0034645754,0.060174834,0.15966627,-0.0279672854,0.0723464265,0.0341917388,0.1038167626,0.3129440248,0.1196284741,-0.0223808829,-0.3551903069,0.1838106662,0.4711066186,0.0623613261,0.2583274245,0.2070450187,-0.0968046412,-0.1911536008,0.2963224947,-0.2238921374,0.2534521222,0.2617909908,-0.0768608302,0.2503487766,-0.3105472326,-0.3091457188,0.0943920091,0.0421279073,0.1733682454,0.0248517804,0.3159976006,0.0110517154,-0.3823555112,-0.0541484468,0.0020076446,0.2897807658,-0.1607694328,0.2292197645,-0.3812175989,-0.5844123363,-0.2033057958,-0.3761793077,-0.3669314086,-0.0743166432,0.0838027894,0.310374558,0.0543873236,0.2557633817,0.4026352465,0.0697187334,-0.2327948362,-0.2434329242,-0.2093127072,-0.4130916893,-0.5413900018,0.0675750971,0.2533719242,0.1499053538,0.2001347244,-0.1714408398,0.0169509649,0.1043809354,-0.589812994,0.1002970487,-0.2247340679,0.3380070925,0.1060066372,-0.0794158205,-0.3944554627,-0.3828268647,0.1079265326,-0.2567720413,-0.1813481152,0.0085919993,-0.3333748877,0.0928751379,-0.2160847336,-0.1032108814,-0.6534888744,-0.2656674087,0.1510529071,0.1169588268,0.1299255788,0.3539073765,0.2291953713,0.1824577004,0.0490744039,-0.1751547903,-0.0114472955,0.0290607493,0.2000274956,-0.2042187601,-0.2945664227,0.1472581327,-0.1673745513,0.0931809545,-0.0429615006,-0.4215467572,-0.4417282939,0.085579209,0.3840212226,0.1543041021,-0.0067445892,0.3419800699,0.0956139043,-0.0341994241,0.0839712992,-0.3151020706,-0.0966725573,0.1171037853,0.1417523026,0.0434586443,0.1518180668,-0.1093009561,0.6300576925,-0.2139459401,-0.1077207401,0.4509525597,-0.1072049215,0.3438112736,-0.0944144279,-0.0752923638,0.048007112,0.0440239683,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/435","title":"ImportWarning for pyarrow 1.0.0","comments":"This was fixed in #434 \r\nWe'll do a release later this week to include this fix.\r\nThanks for reporting","body":"The following PR raised ImportWarning at `pyarrow ==1.0.0` https:\/\/github.com\/huggingface\/nlp\/pull\/265\/files","comment_length":19,"text":"ImportWarning for pyarrow 1.0.0 \n The following PR raised ImportWarning at `pyarrow ==1.0.0` https:\/\/github.com\/huggingface\/nlp\/pull\/265\/files \n This was fixed in #434 \r\nWe'll do a release later this week to include this fix.\r\nThanks for reporting","embeddings":[-0.0830754414,-0.1102307364,-0.0867729485,-0.139640525,0.2431191951,-0.2674666047,0.3366219699,0.3281983733,-0.1044746488,0.1609233618,-0.0459907986,0.2184556425,-0.1111827567,0.0628343746,0.027580386,-0.2568036914,0.097771287,0.2359910011,0.0056410851,-0.0169456638,0.0004630512,0.2912732363,-0.1947281063,0.3622003198,-0.2395525277,0.0270071961,0.0885716602,0.1242647395,-0.2790331244,-0.5286542773,0.1933578402,-0.1631902903,-0.0732050613,-0.0040341383,-0.0001010639,0.1438983232,0.4179382026,0.1709832847,0.1610470265,-0.0188637301,0.4765137434,-0.3481638432,0.3162206411,0.0011413468,-0.2127271444,-0.3874779344,0.2012822777,0.6739887595,0.2996217012,0.0531941764,0.352093637,0.5651536584,0.3067946732,0.2126060575,0.3988427818,-0.1721241176,-0.0575685389,0.2334617823,0.0991275385,-0.4453291595,-0.2211328596,0.106356211,-0.0244036019,-0.0982079208,0.0148001928,-0.0480629094,0.0487468578,-0.1012411714,-0.2533308566,0.03156152,-0.1268713623,-0.2548316419,-0.1118742824,-0.0782755986,0.0606308952,-0.4917188287,0.2599356771,0.2389288992,-0.0639472902,0.2154643387,0.0959483609,0.0326453596,-0.1715609282,0.1390209645,0.0803394243,0.5061997771,0.0320007168,-0.0365809537,0.1415236145,-0.1213111877,-0.0105357459,0.1806747019,-0.065680109,-0.0150712943,-0.0555665717,-0.051771313,0.2417271435,0.2598069906,0.126937747,-0.1598719954,-0.053952124,0.1668393463,0.1309002042,0.2279284447,0.0629451945,0.0282733031,0.1008226648,-0.3034647107,0.3983899057,0.0791475028,0.1498911083,0.0987928882,0.15991956,-0.3288703859,0.0572980009,-0.0595705882,0.232298106,-0.1589361131,-0.1830835789,-0.1484579891,-0.2489197403,0.0096771494,-0.099664323,0.269536823,-0.0973090604,-0.0196081437,0.0986448377,0.262240082,-0.3504638672,-0.2332783192,-0.2548336983,0.0658039004,-0.3251521587,-0.0997655764,0.1824346632,-0.0633449033,0.3893645108,0.299908042,-0.0197889134,0.1312524229,-0.0293195471,0.2258119583,-0.00648562,0.2267420739,0.0049192179,-0.0279853512,-0.0467880853,-0.1139084473,-0.0660101324,0.0741590858,0.0568603761,-0.1301459819,-0.3839268684,0.3116227388,-0.2090824097,-0.104519397,0.0282637663,0.0513054468,0.1286733598,-0.0548700988,-0.0117081562,-0.1456808895,0.0393042564,-0.1020550281,-0.0698948428,0.0730507001,-0.1338871121,-0.1944649667,0.2042917013,-0.4161705375,0.2749917507,0.3692949414,-0.2970252335,-0.0789089277,-0.2009683996,0.3247884512,-0.0235642958,-0.0966951475,-0.1888201982,0.0235766321,-0.266833812,-0.0348267853,0.0754944086,-0.1217662469,-0.2040691674,0.0721507594,0.0707328022,0.3919305801,0.1285731643,0.2569903433,-0.4870685339,-0.2503657341,0.1534605175,0.0033284535,-0.0707069114,-0.2464265823,-0.0404124409,-0.1909304559,0.3194949925,-0.1372527778,-0.0349238999,0.0468536839,0.0672133341,0.0690261126,-0.0961697549,0.0366544388,-0.1779856831,0.021493515,-0.3616137803,0.3930594623,-0.1452844292,-0.3226929009,-0.07861007,0.0359093808,-0.1544843316,-0.1556718946,0.3084644675,-0.0589612313,-0.021510534,0.2313106507,0.0603005849,-0.1698938459,-0.0058377567,0.1254486144,0.0518530421,0.1411392987,-0.3814891279,-0.391199708,0.1973904222,-0.0112614939,0.1413132995,0.0031072774,0.1131301299,0.0895699263,-0.1998307854,0.3266761899,-0.318072319,-0.0501882657,0.134268716,-0.3729337454,0.0234289225,0.0651890188,-0.1765625924,-0.0148426471,0.2517152131,0.4727603793,0.2811139822,-0.1402591318,-0.0036962843,-0.1384207755,0.1402438134,-0.1233885586,0.0942482874,-0.0179691501,0.0567531511,0.1691510975,0.061151512,-0.1107048243,-0.3336109221,0.0328680091,0.3761782646,0.0379981548,0.1958342642,0.0069674528,-0.0281108972,-0.1388168633,0.2378657162,-0.4669159949,-0.0157597661,0.2753902078,0.0200040098,0.2059604824,-0.2243602127,-0.4125036895,0.048190143,0.0607327484,-0.0026752418,0.1401378214,0.4111925662,-0.2279912084,-0.7777081132,0.2353706658,-0.081293717,0.0095219081,-0.2907804549,0.0664653704,-0.29391554,-0.3391764164,-0.0755752698,-0.2050065994,-0.3589579761,-0.0981444642,0.3100822568,0.1755020916,-0.2631780505,0.3151647747,0.3475986719,-0.0642475411,-0.0756793693,-0.1202402487,-0.1154793203,-0.2051045746,-0.2663134038,0.1959459931,0.0624703765,0.0458264984,0.1699760705,-0.1139839962,-0.0664578676,0.0129902996,-0.5971788168,0.0656021759,-0.2121935189,0.1207150519,0.193306759,0.1167021841,-0.0155746909,-0.2416051924,0.229631871,-0.3196700215,-0.4370693564,-0.0598572269,-0.159314692,0.1028265432,-0.3726234436,-0.1548138708,-0.1904674768,-0.3148061037,0.1847970039,0.0559411347,0.0161652267,0.310484916,0.1640842408,0.0933733359,-0.317596823,0.0469761863,-0.0244123619,-0.1549781263,0.16934219,-0.2249408513,-0.4008027315,0.0334066935,0.0106638968,0.0975719392,-0.3147257268,-0.4104326069,-0.3181371987,-0.0403931998,0.5853024721,0.3685545027,-0.0770060644,0.289948076,0.1316108555,-0.2970892787,-0.052383028,-0.3068110049,0.0330360904,-0.1884214878,0.0231340695,-0.169214949,0.0882597715,-0.0709627643,0.6686916947,-0.3755657971,-0.1222009882,0.5347418785,-0.0492617823,0.3575075269,0.0169340614,-0.0162534211,0.0615126342,0.06712166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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/435","title":"ImportWarning for pyarrow 1.0.0","comments":"I dont know if the fix was made but the problem is still present : \r\nInstaled with pip : NLP 0.3.0 \/\/ pyarrow 1.0.0 \r\nOS : archlinux with kernel zen 5.8.5","body":"The following PR raised ImportWarning at `pyarrow ==1.0.0` https:\/\/github.com\/huggingface\/nlp\/pull\/265\/files","comment_length":31,"text":"ImportWarning for pyarrow 1.0.0 \n The following PR raised ImportWarning at `pyarrow ==1.0.0` https:\/\/github.com\/huggingface\/nlp\/pull\/265\/files \n I dont know if the fix was made but the problem is still present : \r\nInstaled with pip : NLP 0.3.0 \/\/ pyarrow 1.0.0 \r\nOS : archlinux with kernel zen 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/433","title":"How to reuse functionality of a (generic) dataset?","comments":"Hi @ArneBinder, we have a few \"generic\" datasets which are intended to load data files with a predefined format:\r\n- csv: https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/csv\r\n- json: https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/json\r\n- text: https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text\r\n\r\nYou can find more details about this way to load datasets here in the documentation: https:\/\/huggingface.co\/nlp\/loading_datasets.html#from-local-files\r\n\r\nMaybe your brat loading script could be shared in a similar fashion?","body":"I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https:\/\/brat.nlplab.org\/standoff.html), [dataset code](https:\/\/github.com\/ArneBinder\/nlp\/blob\/brat\/datasets\/brat\/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to reuse formats and loading functionality for datasets with a common format?\r\n\r\nIn my case, it took a bit of time to create the Brat dataset and I think others would appreciate to not have to think about that again. Also, I assume there are other formats (e.g. conll) that are widely used, so having this would really ease dataset onboarding and adoption of the library.","comment_length":56,"text":"How to reuse functionality of a (generic) dataset? \n I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https:\/\/brat.nlplab.org\/standoff.html), [dataset code](https:\/\/github.com\/ArneBinder\/nlp\/blob\/brat\/datasets\/brat\/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to reuse formats and loading functionality for datasets with a common format?\r\n\r\nIn my case, it took a bit of time to create the Brat dataset and I think others would appreciate to not have to think about that again. Also, I assume there are other formats (e.g. conll) that are widely used, so having this would really ease dataset onboarding and adoption of the library. \n Hi @ArneBinder, we have a few \"generic\" datasets which are intended to load data files with a predefined format:\r\n- csv: https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/csv\r\n- json: https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/json\r\n- text: https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text\r\n\r\nYou can find more details about this way to load datasets here in the documentation: https:\/\/huggingface.co\/nlp\/loading_datasets.html#from-local-files\r\n\r\nMaybe your brat loading script could be shared in a similar fashion?","embeddings":[-0.1659951061,-0.1904758215,0.1139429435,0.3808882535,0.2183723897,-0.0047787465,0.1885102093,0.112266995,-0.1110894829,-0.2132682204,-0.195565477,0.2885515392,-0.0397951044,0.5394513607,0.1926315129,-0.1346117556,-0.211735636,0.2186616659,-0.1162287667,-0.0262852851,0.0394088477,-0.1943162233,0.0686826631,-0.1159505472,-0.3702635169,0.1318924874,-0.2384282053,-0.0179268848,0.2592282891,-0.2346274108,0.5442937613,0.3445178866,0.45950225,0.2523490191,-0.0001251962,0.0330158919,-0.0391854458,-0.1448282897,-0.2924483716,-0.1255922019,-0.2134353817,-0.223388806,0.0898034647,-0.2437822074,-0.3222340643,-0.3877206147,0.1589093655,-0.5658196211,0.4574590027,-0.0978449285,0.0306623075,-0.0806691349,-0.2010449767,-0.1027776524,-0.1274460554,0.3995322883,-0.0048828083,0.4021582305,0.6236608028,0.3071166873,0.1142585874,0.0186164398,0.1892261654,0.092304334,0.2961039841,-0.1304038018,-0.0321157947,0.0373831466,0.0351924412,0.3731410205,0.9288996458,-0.3871008158,-0.057318639,-0.1039505824,-0.0022320303,-0.2916268706,-0.2740898132,0.1782106757,0.1706531495,0.1169880182,-0.2853214741,-0.3464595079,-0.2974860668,0.1213214993,0.2955724597,0.0247342754,-0.0860804692,0.2227665782,-0.202981934,-0.087689206,0.0028414812,-0.2409488559,-0.1546615213,0.0481973104,0.1250663996,-0.1266933084,-0.4318877161,0.1242781803,0.3226211965,0.4385862648,0.3070451021,0.2488357425,-0.3475722671,0.0543904081,0.4337318242,0.0727873668,0.3920842409,0.275516659,0.0898746774,0.2174168527,-0.4256957173,-0.1605033726,-0.4732805192,0.4096366763,0.2828017175,-0.0478396602,0.0258606561,-0.2413187325,-0.0057717771,-0.0553799048,-0.3146702945,-0.1710859239,0.220073089,0.0787808299,-0.1268196851,0.1943231225,-0.076090306,0.2540785372,-0.0605482236,-0.3372296393,0.0072380351,0.0385019518,-0.3324481845,0.1706179529,0.3277175426,-0.4035727084,0.2055005133,0.3074685931,-0.149845466,-0.1152982339,0.3684354126,0.1104314327,-0.2815550566,-0.5091332197,-0.1593072563,0.0936785638,0.2187217474,0.1501332074,-0.3380337358,0.0335500129,-0.1240274757,-0.3322888911,-0.1301243901,0.0158546586,-0.2962245345,0.0313484743,-0.1064650267,0.4295431674,-0.0012109492,-0.3559826612,-0.2109797001,-0.1043991074,-0.2914378643,-0.0680791661,0.1657022238,0.2590589225,-0.5212173462,-0.1286891103,-0.1151430011,0.000508684,0.414929688,0.2499226183,-0.4169629514,0.21721223,-0.1238055825,-0.1836066842,0.455332756,-0.0797947124,-0.0108388131,-0.1229192987,0.2009911388,0.1629832983,0.0826169178,0.0667771026,0.1893800944,-0.2476023138,-0.3442697525,0.5017463565,0.1047518551,-0.2457328141,-0.0767719299,-0.212257579,0.1183497384,0.0398364216,-0.2308100015,0.1301293969,0.0850554705,0.2706214488,0.0201237518,-0.2445560247,0.1147236824,-0.1340281516,0.0210207812,0.3941365778,0.1071013063,-0.2427259684,-0.501625061,0.1725225747,-0.3441224694,-0.2779965997,0.5025932193,-0.5051969886,-0.3756371439,-0.24389714,-0.1494172662,-0.0231227893,0.0117689408,0.5268944502,0.1075812802,-0.0793845057,-0.1046641544,0.1127329916,-0.1162446439,-0.0206026565,-0.0946501344,0.2827685475,0.1337838322,0.0327418298,0.0808496028,0.5369929671,0.0391520001,-0.0522328988,0.1695102006,0.2841656208,0.1377085,0.1723063439,0.0890184417,0.1135612205,0.1253343076,0.1243243963,0.4157356918,-0.0623390041,0.3338619769,-0.1436809897,-0.4933606386,0.2344053239,0.0853987038,0.0032412079,0.0942281261,-0.0990343839,0.1269897819,-0.1194883958,-0.163816914,-0.1160496622,-0.1618515253,0.166863054,-0.0490618013,0.3370729685,-0.3186077476,-0.0587684885,0.2334949374,-0.1676668525,0.2127047628,-0.1248301268,-0.1789677143,-0.1150687933,0.3755524158,-0.169630155,0.0814922675,0.051055152,0.2505188286,-0.009340059,-0.0731735155,0.0596740022,0.2454495728,-0.1996546388,0.1939686537,-0.2038097978,0.1229524687,0.072569862,0.0402918831,-0.0532920286,0.2882683873,0.0353524126,-0.1095572338,0.1736063063,-0.7263829708,-0.2955489755,-0.3641794622,0.0308190566,0.0220962502,0.0343479775,-0.2832489014,0.052305799,0.1293509156,0.1510513425,0.1038258076,0.5219677687,-0.3055048883,-0.0818183273,0.0544117503,-0.3273842633,-0.3867360055,0.0133745177,0.4593727887,0.3856511116,0.4869647026,-0.0203824341,0.0797765553,-0.4213525951,-0.2247262448,0.2008585185,0.2226727307,-0.0748157427,0.1745197177,0.0166827347,-0.3718517721,-0.2864752412,0.1982575506,0.1177977994,0.2160826027,-0.1023503989,-0.4804204106,0.16403763,-0.1767154932,-0.0417492054,-0.0790914074,-0.2007599622,0.2851938009,-0.0058270465,-0.0302391835,0.370518744,-0.2798815668,0.2028849274,-0.110438019,0.2872950435,-0.2397326082,-0.2474448681,0.2628939748,-0.1447509378,-0.149028644,-0.1419074386,-0.3319850862,0.1567607224,0.5356497169,-0.2468817383,-0.3932585418,-0.1005750448,-0.2145656049,0.2922896743,0.2270694226,0.0891076401,0.0088778539,0.1633804739,-0.0381374285,-0.1159494296,0.2437331229,-0.0833875611,0.1644108295,0.4508982301,0.0424259454,0.2239995003,0.3212416768,0.2181174904,-0.4716588557,0.4767279625,-0.1040036231,0.4264326394,0.0322025195,0.0494236611,0.24937509,-0.0998954177,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/433","title":"How to reuse functionality of a (generic) dataset?","comments":"> Maybe your brat loading script could be shared in a similar fashion?\r\n\r\n@thomwolf that was also my first idea and I think I will tackle that in the next days. I separated the code and created a real abstract class `AbstractBrat` to allow to inherit from that (I've just seen that the dataset_loader loads the first non abstract class), now `Brat` is very similar in its functionality to https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text but inherits from `AbstractBrat`.\r\n\r\nHowever, it is still not clear to me how to add a specific dataset (as explained in https:\/\/huggingface.co\/nlp\/add_dataset.html) to your repo that uses this format\/abstract class, i.e. re-using the `features` entry of the  `DatasetInfo` object and `_generate_examples()`. Again, by doing so, the only remaining entries\/functions to define would be `_DESCRIPTION`, `_CITATION`, `homepage` and `_URL` (which is all copy-paste stuff) and `_split_generators()`.\r\n \r\nIn a lack of better ideas, I tried sth like below, but of course it does not work outside `nlp` (`AbstractBrat` is currently defined in [datasets\/brat.py](https:\/\/github.com\/ArneBinder\/nlp\/blob\/5e81fb8710546ee7be3353a7f02a3045e9a8351e\/datasets\/brat\/brat.py)):\r\n```python\r\nfrom __future__ import absolute_import, division, print_function\r\n\r\nimport os\r\n\r\nimport nlp\r\n\r\nfrom datasets.brat.brat import AbstractBrat\r\n\r\n_CITATION = \"\"\"\r\n@inproceedings{lauscher2018b,\r\n  title = {An argument-annotated corpus of scientific publications},\r\n  booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},\r\n  publisher = {Association for Computational Linguistics},\r\n  author = {Lauscher, Anne and Glava\\v{s}, Goran and Ponzetto, Simone Paolo},\r\n  address = {Brussels, Belgium},\r\n  year = {2018},\r\n  pages = {40\u201346}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThis dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing \r\nfine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific \r\npublications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of \r\nscientific writing.\r\n\"\"\"\r\n\r\n_URL = \"http:\/\/data.dws.informatik.uni-mannheim.de\/sci-arg\/compiled_corpus.zip\"\r\n\r\n\r\nclass Sciarg(AbstractBrat):\r\n\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n\r\n    def _info(self):\r\n\r\n        brat_features = super()._info().features\r\n        return nlp.DatasetInfo(\r\n            # This is the description that will appear on the datasets page.\r\n            description=_DESCRIPTION,\r\n            # nlp.features.FeatureConnectors\r\n            features=brat_features,\r\n            # If there's a common (input, target) tuple from the features,\r\n            # specify them here. They'll be used if as_supervised=True in\r\n            # builder.as_dataset.\r\n            #supervised_keys=None,\r\n            # Homepage of the dataset for documentation\r\n            homepage=\"https:\/\/github.com\/anlausch\/ArguminSci\",\r\n            citation=_CITATION,\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n        # TODO: Downloads the data and defines the splits\r\n        # dl_manager is a nlp.download.DownloadManager that can be used to\r\n        # download and extract URLs\r\n        dl_dir = dl_manager.download_and_extract(_URL)\r\n        data_dir = os.path.join(dl_dir, \"compiled_corpus\")\r\n        print(f'data_dir: {data_dir}')\r\n        return [\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TRAIN,\r\n                # These kwargs will be passed to _generate_examples\r\n                gen_kwargs={\r\n                    \"directory\": data_dir,\r\n                },\r\n            ),\r\n        ]\r\n```  \r\n\r\nNevertheless, many thanks for tackling the dataset accessibility problem with this great library!","body":"I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https:\/\/brat.nlplab.org\/standoff.html), [dataset code](https:\/\/github.com\/ArneBinder\/nlp\/blob\/brat\/datasets\/brat\/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to reuse formats and loading functionality for datasets with a common format?\r\n\r\nIn my case, it took a bit of time to create the Brat dataset and I think others would appreciate to not have to think about that again. Also, I assume there are other formats (e.g. conll) that are widely used, so having this would really ease dataset onboarding and adoption of the library.","comment_length":416,"text":"How to reuse functionality of a (generic) dataset? \n I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https:\/\/brat.nlplab.org\/standoff.html), [dataset code](https:\/\/github.com\/ArneBinder\/nlp\/blob\/brat\/datasets\/brat\/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to reuse formats and loading functionality for datasets with a common format?\r\n\r\nIn my case, it took a bit of time to create the Brat dataset and I think others would appreciate to not have to think about that again. Also, I assume there are other formats (e.g. conll) that are widely used, so having this would really ease dataset onboarding and adoption of the library. \n > Maybe your brat loading script could be shared in a similar fashion?\r\n\r\n@thomwolf that was also my first idea and I think I will tackle that in the next days. I separated the code and created a real abstract class `AbstractBrat` to allow to inherit from that (I've just seen that the dataset_loader loads the first non abstract class), now `Brat` is very similar in its functionality to https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets\/text but inherits from `AbstractBrat`.\r\n\r\nHowever, it is still not clear to me how to add a specific dataset (as explained in https:\/\/huggingface.co\/nlp\/add_dataset.html) to your repo that uses this format\/abstract class, i.e. re-using the `features` entry of the  `DatasetInfo` object and `_generate_examples()`. Again, by doing so, the only remaining entries\/functions to define would be `_DESCRIPTION`, `_CITATION`, `homepage` and `_URL` (which is all copy-paste stuff) and `_split_generators()`.\r\n \r\nIn a lack of better ideas, I tried sth like below, but of course it does not work outside `nlp` (`AbstractBrat` is currently defined in [datasets\/brat.py](https:\/\/github.com\/ArneBinder\/nlp\/blob\/5e81fb8710546ee7be3353a7f02a3045e9a8351e\/datasets\/brat\/brat.py)):\r\n```python\r\nfrom __future__ import absolute_import, division, print_function\r\n\r\nimport os\r\n\r\nimport nlp\r\n\r\nfrom datasets.brat.brat import AbstractBrat\r\n\r\n_CITATION = \"\"\"\r\n@inproceedings{lauscher2018b,\r\n  title = {An argument-annotated corpus of scientific publications},\r\n  booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},\r\n  publisher = {Association for Computational Linguistics},\r\n  author = {Lauscher, Anne and Glava\\v{s}, Goran and Ponzetto, Simone Paolo},\r\n  address = {Brussels, Belgium},\r\n  year = {2018},\r\n  pages = {40\u201346}\r\n}\r\n\"\"\"\r\n\r\n_DESCRIPTION = \"\"\"\\\r\nThis dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing \r\nfine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific \r\npublications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of \r\nscientific writing.\r\n\"\"\"\r\n\r\n_URL = \"http:\/\/data.dws.informatik.uni-mannheim.de\/sci-arg\/compiled_corpus.zip\"\r\n\r\n\r\nclass Sciarg(AbstractBrat):\r\n\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n\r\n    def _info(self):\r\n\r\n        brat_features = super()._info().features\r\n        return nlp.DatasetInfo(\r\n            # This is the description that will appear on the datasets page.\r\n            description=_DESCRIPTION,\r\n            # nlp.features.FeatureConnectors\r\n            features=brat_features,\r\n            # If there's a common (input, target) tuple from the features,\r\n            # specify them here. They'll be used if as_supervised=True in\r\n            # builder.as_dataset.\r\n            #supervised_keys=None,\r\n            # Homepage of the dataset for documentation\r\n            homepage=\"https:\/\/github.com\/anlausch\/ArguminSci\",\r\n            citation=_CITATION,\r\n        )\r\n\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n        # TODO: Downloads the data and defines the splits\r\n        # dl_manager is a nlp.download.DownloadManager that can be used to\r\n        # download and extract URLs\r\n        dl_dir = dl_manager.download_and_extract(_URL)\r\n        data_dir = os.path.join(dl_dir, \"compiled_corpus\")\r\n        print(f'data_dir: {data_dir}')\r\n        return [\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TRAIN,\r\n                # These kwargs will be passed to _generate_examples\r\n                gen_kwargs={\r\n                    \"directory\": data_dir,\r\n                },\r\n            ),\r\n        ]\r\n```  \r\n\r\nNevertheless, many thanks for tackling the dataset accessibility problem with this great library!","embeddings":[-0.0527758598,-0.0183876511,0.1475319415,0.4042401016,0.2143378109,-0.0373157039,0.1315058917,0.1071476415,-0.2023022622,-0.2372688204,-0.1918668449,0.4055892527,-0.0635214299,0.5268573761,0.4249489903,-0.2587217391,-0.1975932866,0.1804039478,0.0004199797,-0.0935178176,-0.0021080349,-0.1374595165,0.1369740367,-0.0939151198,-0.2226285785,0.1669851691,-0.2874785364,-0.0323904529,0.1107129976,-0.2528283596,0.5313698053,0.3521875441,0.468341887,0.2782885134,-0.0001250765,0.0179283749,-0.0833464041,-0.1071614251,-0.2715194821,-0.166204229,-0.2018947899,-0.2514975965,0.0697826967,-0.2622407079,-0.333869338,-0.2891654074,0.0832434818,-0.420181632,0.4280590713,0.0142240403,0.012890866,-0.1331839859,-0.1333251446,-0.1284095943,0.028225448,0.4498718977,-0.1006873623,0.3456860483,0.5528991818,0.29368788,0.1292904764,0.028397087,0.1609407812,0.1065396816,0.3837427795,-0.0584125482,-0.0705620497,-0.0389486663,0.018995218,0.2638921142,0.8025806546,-0.4108220041,-0.0872110203,-0.1729798019,0.024767505,-0.4295556545,-0.2573292553,0.0599815175,0.1624982953,0.0851936564,-0.3099957705,-0.3607076705,-0.3834712803,0.2709828615,0.3352554142,0.0396562219,-0.0566550158,0.2215604335,-0.1515273005,-0.0298044477,-0.0301856287,-0.1971120536,-0.1586646438,0.1109791696,0.1712026298,-0.1918716282,-0.3047834337,0.0230149236,0.4192993343,0.4918847084,0.1451487094,0.20817779,-0.3522256017,-0.0213294961,0.4007333517,0.1259273291,0.2779958248,0.3652256131,0.0427371822,0.1957389116,-0.3644953966,-0.1703820825,-0.4490543008,0.5557587743,0.2351506203,-0.1451735795,0.0688069761,-0.2394081652,0.1407324374,-0.1055003926,-0.3880719841,-0.1388137192,0.2200806141,0.0473230407,0.0224381071,0.0921761915,0.0555789769,0.2978412211,-0.0114642493,-0.2726375759,-0.0049478202,0.0923360065,-0.4189287424,0.2664704025,0.3285477161,-0.310305357,0.1651708037,0.3441130519,-0.0810408816,-0.170816958,0.3620076478,0.1349384189,-0.3155711889,-0.4831865728,-0.233671546,0.2006834298,0.2484113574,0.2031096816,-0.2982755601,-0.0437861159,-0.0155711658,-0.395182848,-0.1822104603,0.0121255508,-0.371001482,-0.0800734833,-0.2152220756,0.4285044372,-0.0043485137,-0.33845824,-0.1976387054,-0.0232472923,-0.3284014165,-0.0783782676,0.1933834553,0.2733840644,-0.3810253441,-0.171832487,-0.2532711029,0.0059723281,0.435426414,0.1614467502,-0.4003018141,0.2875467539,-0.1048991606,-0.1292135268,0.4984529018,-0.0334244557,-0.0209876448,-0.0589336753,0.1858076751,0.1797676235,0.0416482836,0.0760742053,0.2536152005,-0.1743207127,-0.3745343983,0.5141628981,0.0862224251,-0.2672062814,-0.0373789333,-0.1952516288,0.1279145032,0.0897817835,-0.1702144742,0.1609873921,-0.03143242,0.2894899249,-0.017752042,-0.2148414999,0.1200026497,-0.1749357283,0.1473779976,0.3941198289,0.1085904539,-0.3249780834,-0.4225756228,0.1913678646,-0.2947507203,-0.0480473042,0.4472073019,-0.4088856578,-0.4264878631,-0.184352234,-0.1645276546,-0.1849027872,0.0076964754,0.5292384624,0.0993560106,-0.1695847213,-0.1643251032,0.1189443842,-0.1181549653,-0.0168597493,-0.1055908874,0.2538265586,0.0643545538,0.0435818844,0.0140847452,0.5441199541,0.1163565516,-0.075459525,0.1564706415,0.1206884012,0.037100032,0.0826740861,0.1455654502,0.1017806157,0.1178467348,0.1147307307,0.4078248143,0.0088951029,0.3467353284,-0.1688862741,-0.4644254446,0.134048894,0.2337526232,0.0326775424,0.061245203,-0.1139331609,0.1701093763,-0.1843965948,-0.0612344779,-0.1180025041,-0.0761754885,0.2510493696,-0.0876895115,0.3345283568,-0.2914963067,0.1417697072,0.141065836,-0.2346038222,0.1524762511,0.0045161257,-0.2224809974,-0.1769359112,0.3832339346,0.0758576915,0.1644231379,0.037848182,0.2431350946,0.0694824308,-0.1100002229,0.0444386192,0.2112405896,-0.1673858762,0.1849935353,-0.237921685,0.1198144183,0.1696004868,0.0800332278,-0.0412702784,0.3497784734,0.1414006054,-0.1712508053,0.2018589973,-0.6481920481,-0.3009235859,-0.3514772654,-0.0437039435,0.0139782606,0.0545269027,-0.1946784705,0.0467866361,0.1307185292,0.134296909,0.1131130457,0.4815168083,-0.3766599298,-0.1216218546,0.0801850632,-0.299516499,-0.3330663443,0.0063181017,0.4122456312,0.3769203722,0.554540813,0.0870391503,0.029055303,-0.4986234903,-0.2839494646,0.2014662921,0.0905627161,-0.03976468,0.1327048838,-0.0485130958,-0.3301447928,-0.3042160869,0.1590521336,0.0961806029,0.1809403598,-0.0600807853,-0.4992723167,0.1211428046,-0.1876276433,-0.1224418804,-0.1508996189,-0.19926247,0.283375591,0.0598632134,0.0701045096,0.3311741054,-0.3769756258,0.2394531071,-0.0450571552,0.2134193629,-0.3368493617,-0.1046841592,0.2901270688,-0.1467796415,-0.185217306,-0.0646872669,-0.3328315914,0.1002845466,0.5816040039,-0.1899225861,-0.4333093166,-0.0709262416,-0.193177253,0.335211575,0.2023753673,0.1774219871,-0.0302753989,0.1482476145,-0.0246563498,-0.2292751521,0.3347116113,-0.0698234886,0.2151486725,0.3179880083,0.136447832,0.1477787793,0.4958414137,0.1994640976,-0.3975553811,0.4418984354,-0.0755604357,0.2939326167,0.091992937,-0.0275791306,0.2489297539,-0.1203065887,0.09493748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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/426","title":"[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter","comments":"Yes, that would be nice. We could take a look at what tensorflow `tf.data` does under the hood for instance.","body":"It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process\/thread\/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together?","comment_length":20,"text":"[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter \n It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process\/thread\/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together? \n Yes, that would be nice. We could take a look at what tensorflow `tf.data` does under the hood for instance.","embeddings":[-0.2759443223,-0.2842460275,-0.2022300363,-0.1166619658,-0.2691851258,0.1010134444,0.2325940132,0.3550632596,-0.0165046155,0.0616694838,-0.1445138901,0.3324525654,-0.1353501827,0.4115578234,0.0130814621,-0.2661807835,0.0175814442,0.1032483652,-0.1281686127,0.1098897457,0.0762666091,-0.0655459911,-0.1153079122,-0.0926844701,-0.132297352,-0.1024550274,-0.097571224,-0.202066645,0.1825311631,-0.059196949,0.0819187984,0.4362562299,-0.1955319494,0.3209602237,-0.0001035186,-0.0330525786,0.0230353232,0.1411820501,0.0554775074,0.1950994432,-0.2407266498,-0.3496336043,0.0154762631,-0.2446846366,0.114108704,0.1449297965,0.2296795696,-0.2520439625,0.1510989964,-0.1482693851,0.1650044471,0.3244563043,-0.1013417765,0.3549084663,-0.1160900891,0.288120389,-0.1332563013,-0.2739548385,0.4300302565,-0.165862903,-0.1933044195,0.4834124148,-0.204895094,0.1841047704,0.1868391186,-0.0659237429,0.0773275569,-0.4097806513,-0.1818062216,0.4087259471,-0.0675883219,-0.2307182699,-0.4504499733,-0.278904289,-0.1264075339,-0.4070104361,-0.2271302044,0.0858728066,-0.2099242806,-0.0233104695,-0.4120799899,-0.3114105463,0.1997829229,-0.0812529847,0.1616560817,0.3613924086,0.2724672854,0.1311332434,0.1919373572,-0.1969308853,-0.0564663969,-0.2537738979,0.3110478818,0.0066752806,-0.6924254894,-0.1842935532,0.0167129505,-0.2867717147,-0.0392810479,-0.0706893578,0.1534596086,0.3635826707,0.0018450117,0.2756719887,0.0096383132,-0.3759275675,0.0630588233,-0.0125332382,0.1332935989,-0.4187017977,-0.2308539599,0.2932199836,0.126777783,-0.0834778473,-0.2717575133,0.0474008434,-0.0980958119,0.2626233101,0.0492146499,-0.4000425637,0.0890791491,-0.1915702969,0.1827962846,-0.0364476778,-0.0701015964,0.3581661284,-0.1625254452,-0.2483626902,-0.1925841868,-0.1561348289,-0.0763624981,-0.006903613,0.0626296625,0.1361531317,0.1571865976,0.1219406947,-0.1967089027,0.0479605384,0.1393325925,0.3047914207,0.3225729167,-0.1254656911,0.0392487682,0.1707835346,-0.1373326778,-0.1019913107,-0.1454034448,0.085971579,-0.3219798803,-0.0447810702,-0.3592560291,-0.2022037357,-0.0783007964,0.1522820741,0.1580559462,-0.083456777,-0.2392239273,0.727088213,0.1221747771,-0.013926208,-0.3829063475,-0.1173573509,-0.5056692362,-0.2212937474,0.3403012455,-0.007171046,-0.0925393403,-0.0244391449,-0.2103015184,-0.0364561193,0.2022960037,0.3431307077,-0.1650477052,0.2029755563,-0.0161112938,0.4196084142,0.5856342316,-0.1346532851,0.1360911876,0.3772717118,-0.1753327996,0.0519081727,0.2391431928,0.1840669513,0.1506536305,-0.1263084859,0.1601605415,0.4892570078,-0.3131014705,0.258397609,0.0568521433,-0.221428588,0.1675420702,0.163731873,0.0658375323,0.0036606842,-0.1034322381,-0.3204090595,0.3190239668,-0.3128894567,0.0569208525,0.0483365171,-0.1053899452,-0.0348551497,-0.1259045154,-0.3479111493,-0.3173058927,0.1093390733,0.0865781754,-0.0670085549,0.1533927917,-0.3714462221,0.4874791801,0.1221940294,0.1755738258,0.2396434397,0.180188939,0.2371768355,0.0878022239,-0.2353841513,-0.2851777971,0.1088109389,-0.1383740157,-0.1427969933,-0.1061125845,0.246758908,0.2857477069,-0.1014980897,-0.021430755,0.133170262,-0.1547442973,-0.1268596351,0.1578128189,-0.0227402691,-0.0729566664,0.3669102192,0.495400548,0.5253913403,0.1801846474,0.1083614379,-0.1301073432,-0.2087201476,-0.1430886388,-0.205413565,-0.1446287185,0.466601342,-0.0668567717,0.3619411588,0.1625888497,-0.0146639319,0.238034457,0.0549203418,-0.0770052522,0.2333343476,0.0734031051,-0.1057646275,-0.0397085734,0.1877189279,-0.4425652027,0.3263513744,0.5805601478,0.1085751727,-0.0362419263,0.1326859742,0.2328303456,-0.010291948,0.118101716,0.134821251,0.1647725701,0.1919315308,0.1233764365,-0.0926784351,-0.014028457,0.0075354669,0.1509769112,0.2267988175,0.297591567,0.0732766688,0.0710349008,-0.191386506,-0.1167141795,-0.3844871223,0.242283985,0.0336244367,0.022234071,-0.075333789,-0.2187243402,-0.0839655325,0.0200216454,0.1564753354,-0.108487092,-0.279620707,0.0375791527,0.2110372335,-0.1723946929,0.1639958471,0.1517231613,0.1216598004,-0.0795259029,-0.0444308482,-0.1731158495,-0.112800248,0.115402922,0.0780870467,0.3604660928,0.2093340456,0.4150375724,0.2297144085,0.101619646,-0.3085978627,-0.3013170362,-0.060573034,-0.2326071113,0.0706917867,-0.0197261609,0.0140115079,-0.2078688145,-0.424069345,0.1349754184,0.1233988032,-0.1107923985,-0.1494641453,0.0558032654,0.0630634502,-0.0742373988,-0.2053208798,-0.51438272,-0.4376353025,0.3669537008,-0.3244447112,0.349766463,-0.1991966963,0.0240951423,-0.0096491147,-0.174112469,0.0837804303,0.1551626623,-0.4178397059,0.1165356115,-0.0766956657,-0.1169132814,-0.3409132957,-0.0333880819,0.0715974271,0.4641480744,-0.2137114704,-0.0585741065,-0.2273505628,0.1493424028,0.1650560796,-0.0818075538,0.1073483229,-0.0158897005,-0.1121382341,0.0774197653,-0.1644622833,0.0516895056,-0.0614806153,-0.2959742248,0.2579024136,0.3612661958,0.1235006452,0.7081286907,0.1143643185,-0.2318828553,-0.0408289395,0.072738111,-0.2688436806,0.2038408667,-0.4948345423,-0.0708803236,-0.1688321829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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/426","title":"[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter","comments":"So `tf.data.Dataset.map()` returns a `ParallelMapDataset` if `num_parallel_calls is not None` [link](https:\/\/github.com\/tensorflow\/tensorflow\/blob\/2b96f3662bd776e277f86997659e61046b56c315\/tensorflow\/python\/data\/ops\/dataset_ops.py#L1623).\r\n\r\nThere, `num_parallel_calls` is turned into a tensor and and fed to `gen_dataset_ops.parallel_map_dataset` where it looks like tensorflow takes over.\r\n\r\nWe could start with something simple like a thread or process pool that `imap`s over some shards.\r\n ","body":"It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process\/thread\/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together?","comment_length":47,"text":"[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter \n It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process\/thread\/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together? \n So `tf.data.Dataset.map()` returns a `ParallelMapDataset` if `num_parallel_calls is not None` [link](https:\/\/github.com\/tensorflow\/tensorflow\/blob\/2b96f3662bd776e277f86997659e61046b56c315\/tensorflow\/python\/data\/ops\/dataset_ops.py#L1623).\r\n\r\nThere, `num_parallel_calls` is turned into a tensor and and fed to `gen_dataset_ops.parallel_map_dataset` where it looks like tensorflow takes over.\r\n\r\nWe could start with something simple like a thread or process pool that `imap`s over some shards.\r\n 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/426","title":"[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter","comments":"Multiprocessing was added in #552 . You can set the number of processes with `.map(..., num_proc=...)`. It also works for `filter`\r\n\r\nClosing this one, but feel free to reo-open if you have other questions","body":"It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process\/thread\/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together?","comment_length":34,"text":"[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter \n It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process\/thread\/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together? \n Multiprocessing was added in #552 . You can set the number of processes with `.map(..., num_proc=...)`. It also works for `filter`\r\n\r\nClosing this one, but feel free to reo-open if you have other 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/426","title":"[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter","comments":"@lhoestq Great feature implemented! Do you have plans to add it to official tutorials [Processing data in a Dataset](https:\/\/huggingface.co\/docs\/datasets\/processing.html?highlight=save#augmenting-the-dataset)? It took me sometime to find this parallel processing api.","body":"It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process\/thread\/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together?","comment_length":29,"text":"[FEATURE REQUEST] Multiprocessing with for dataset.map, dataset.filter \n It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process\/thread\/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together? \n @lhoestq Great feature implemented! Do you have plans to add it to official tutorials [Processing data in a Dataset](https:\/\/huggingface.co\/docs\/datasets\/processing.html?highlight=save#augmenting-the-dataset)? It took me sometime to find this parallel processing api.","embeddings":[-0.2608761489,-0.4052204192,-0.1671193838,0.0242641233,-0.347699672,0.1083791703,0.0958692953,0.1001096219,0.009775402,0.0779817924,-0.1297877133,0.2687852681,0.0288041253,0.5679566264,0.0352190323,-0.3084892929,0.0757969543,0.1260470152,-0.0477574356,0.197243616,0.0197631307,0.1598396897,-0.2788158059,-0.1252332777,-0.2452908158,-0.1634081453,-0.1911026686,-0.0196516346,-0.0594976693,-0.1503470093,0.0461669154,0.4871737361,-0.231605649,0.2361281961,-0.0001079078,-0.2029749304,0.1503316909,0.229871884,-0.0537671708,0.2007020563,-0.1934657097,-0.4777649045,-0.0147053944,-0.0763369575,0.0592207387,0.1195816919,0.1778105795,-0.2691320777,0.1904294044,-0.3726128042,0.1244619191,0.3582191467,-0.054527048,0.2704071105,-0.2098025978,0.4645996392,-0.0739304945,-0.1920111775,0.4698049128,-0.1441965103,-0.1906628162,0.5513617992,-0.2167661786,0.1467214376,0.2011539787,-0.0467660762,-0.1824139059,-0.4495758414,-0.1506305039,0.3538194895,-0.0240541082,-0.1382976919,-0.5037174225,-0.4508947432,-0.1339050084,-0.4086400867,-0.1706858724,0.1109529361,-0.1954035014,0.0309994668,-0.4884880483,-0.3645783663,0.2795680463,-0.1878647953,0.2982876897,0.2111876905,0.0041947966,0.0964184031,0.2584792674,-0.193264544,-0.2565283775,-0.2772119343,0.1194253042,0.0337730274,-0.6784651875,-0.0603897683,-0.0065606362,0.1300600022,0.1923829019,0.1290129721,0.1012567431,0.3147673905,-0.0229618046,0.2253417224,0.1074023172,-0.3512957096,0.1750337929,-0.1152893603,0.1311005801,-0.190440014,-0.0298142545,0.2025753856,0.2138280272,-0.0518173426,-0.4135623574,0.0292811263,0.0277363043,0.1004153416,0.0746325105,-0.3755646944,0.185649246,-0.2991416752,0.1410411149,0.0419949554,-0.0095862448,0.3397317529,-0.1418078989,-0.1332836449,-0.2340057939,-0.2382114083,-0.0971704721,-0.051624734,-0.0331980363,0.2781025767,0.1205001622,0.0442820638,-0.0506823249,0.1477277428,0.1439828128,0.2672173381,0.2075041085,-0.1242554262,0.0695268214,0.1833892465,-0.0726915076,-0.0383959524,-0.1169660687,0.222246632,-0.2571871281,-0.0959708989,-0.2205808759,-0.2075158358,-0.1869576871,0.0773487091,0.1048262939,-0.1092598736,-0.478769958,0.7779146433,0.0713087916,-0.0115013355,-0.2945772409,0.0227482058,-0.5353251696,-0.1817254275,0.3416647911,0.1284206361,-0.0485664867,-0.0365401208,-0.2228354365,-0.1078438088,0.240354076,0.4414246082,-0.2017016411,0.129213199,-0.1785436422,0.4836609364,0.5142174363,-0.1295250207,0.0830005407,0.374253124,-0.1477963775,0.0708050653,0.3142757714,0.1900290549,0.2082633078,0.0276135541,0.1444729567,0.5401500463,-0.198023513,0.2230969518,0.0059556887,-0.1897812635,0.2261707634,0.1623000354,-0.1524645388,0.0858475268,-0.0761108026,-0.5189324617,0.4866414666,-0.2974569798,0.1562602669,0.0589025021,-0.1313957125,0.0926597267,-0.0669747666,-0.3271737397,-0.3598528206,0.0688545182,-0.0417775176,-0.0491082408,0.0830473527,-0.3939555883,0.4155661464,0.098172076,0.0268570874,0.0086587686,0.1069580168,0.1812459826,0.1519801766,-0.2360940427,-0.3061080873,0.3358493447,-0.0799676403,-0.0123659791,-0.3526895046,0.2138335556,0.2508554459,-0.0553257056,0.0419051088,0.1212062687,-0.0514611639,-0.1264780462,0.1427656263,-0.0031845146,-0.0493048243,0.255017966,0.6550810337,0.2654030621,0.2627613246,0.0785564929,0.0230953749,-0.2753276527,0.0382473506,-0.2153047323,-0.1683977544,0.554084897,-0.0410236605,0.4023038745,0.0917556733,-0.0131629966,0.2349601239,0.073301129,-0.0701760054,0.2105982751,0.1526693106,-0.0811327845,0.0270831119,0.1431891918,-0.4629768431,0.3415274024,0.4402229786,0.005871688,-0.2078667134,0.0840375572,0.1872197688,-0.1110775024,0.1967876256,0.023821231,0.1881490797,0.1884752512,0.0503348112,-0.0454424955,0.1028473601,-0.0663725734,0.1176738515,0.3205150068,0.2168473303,-0.0265257265,0.1670744419,-0.2363259196,-0.282782346,-0.2901486754,0.1736860722,-0.1077105999,0.0196602978,-0.0615283288,-0.1852201074,-0.1912414283,-0.0461944677,0.0478870608,-0.3056801856,-0.304176867,-0.0812803283,0.3092374504,-0.0518721119,0.0761979371,0.2212892175,0.2130075097,-0.1976543516,-0.0081336992,-0.2818010747,0.012489561,0.1285527945,0.031392388,0.3022121191,0.3320901394,0.4555438161,0.1232950687,0.0910328701,-0.4455685318,-0.2040769607,-0.0179644022,-0.2767621279,0.1366851181,0.1226217151,0.1201799288,-0.1607810706,-0.2790879011,0.1972416043,0.1088804454,-0.212207824,-0.2680939436,0.0608802177,-0.0249814037,-0.0241125487,-0.1490212828,-0.4795736969,-0.3343916237,0.5942107439,-0.2876599729,0.2276882678,-0.3308070898,-0.1193307415,-0.0027465667,-0.350494504,0.0665277094,0.0190508571,-0.4718952477,0.020882234,0.0118575813,-0.1137490943,-0.249085173,-0.0198605601,-0.0645403117,0.2378332466,-0.0836322829,-0.1006002724,-0.358882457,0.2384422123,0.212108314,0.0981801376,0.2778333127,-0.0796130672,-0.0705577731,0.1338597387,-0.4800581038,0.0397935398,-0.0009143428,-0.1605335027,0.2632178068,0.3609154224,0.0008576288,0.7097066045,0.2304415405,-0.047403343,0.1350751817,0.1280041486,-0.1666100621,0.0568133518,-0.5510403514,-0.0351173393,-0.1622912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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/425","title":"Correct data structure for PAN-X task in XTREME dataset?","comments":"Hi @lhoestq \r\nI made the proposed changes to the `xtreme.py` script. I noticed that I also need to change the schema in the `dataset_infos.json` file.  More specifically the `\"features\"` part of the PAN-X.LANG dataset:\r\n\r\n```json\r\n\"features\":{\r\n   \"word\":{\r\n      \"dtype\":\"string\",\r\n      \"id\":null,\r\n      \"_type\":\"Value\"\r\n   },\r\n   \"ner_tag\":{\r\n      \"dtype\":\"string\",\r\n      \"id\":null,\r\n      \"_type\":\"Value\"\r\n   },\r\n   \"lang\":{\r\n      \"dtype\":\"string\",\r\n      \"id\":null,\r\n      \"_type\":\"Value\"\r\n   }\r\n}\r\n```\r\nTo fit the code above the fields `\"word\"`, `\"ner_tag\"`, and `\"lang\"` would become `\"words\"`, `ner_tags\"` and `\"langs\"`. In addition the `dtype` should be changed from `\"string\"` to `\"list\"`.\r\n\r\n I made this changes but when trying to test this locally with `dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')` I face the issue that the `dataset_info.json` file is always overwritten by a downloaded version with the old settings, which then throws an error because the schema does not match. This makes it hard to test the changes locally. Do you have any suggestions on how to deal with that?\r\n","body":"Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nThanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\ndataset_train = dataset['train']\r\n```\r\n\r\nHowever, I am not sure that `load_dataset()` is returning the correct data structure for NER. \r\n\r\nCurrently, every row in `dataset_train` is of the form\r\n```python\r\n{'word': str, 'ner_tag': str, 'lang': str}\r\n```\r\nbut I think we actually want something like\r\n```python\r\n{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}\r\n```\r\nso that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.\r\n\r\nIndeed, [this line](https:\/\/github.com\/google-research\/xtreme\/blob\/522434d1aece34131d997a97ce7e9242a51a688a\/third_party\/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.\r\n\r\n## Proposed solution\r\nReplace\r\n```python\r\nwith open(filepath) as f:\r\n    data = csv.reader(f, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\r\n    for id_, row in enumerate(data):\r\n        if row:\r\n            lang, word = row[0].split(\":\")[0], row[0].split(\":\")[1]\r\n            tag = row[1]\r\n            yield id_, {\"word\": word, \"ner_tag\": tag, \"lang\": lang}\r\n```\r\nfrom  [these lines](https:\/\/github.com\/huggingface\/nlp\/blob\/ce7d3a1d630b78fe27188d1706f3ea980e8eec43\/datasets\/xtreme\/xtreme.py#L881-L887) of the `_generate_examples()` function with something like\r\n\r\n```python\r\nguid_index = 1\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    words = []\r\n    ner_tags = []\r\n    langs = []\r\n    for line in f:\r\n        if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\r\n            if words:\r\n                yield guid_index, {\"words\": words, \"ner_tags\": ner_tags, \"langs\": langs}\r\n                guid_index += 1\r\n                words = []\r\n                ner_tags = []\r\n        else:\r\n            # pan-x data is tab separated\r\n            splits = line.split(\"\\t\")\r\n            # strip out en: prefix\r\n            langs.append(splits[0][:2])\r\n            words.append(splits[0][3:])\r\n            if len(splits) > 1:\r\n                labels.append(splits[-1].replace(\"\\n\", \"\"))\r\n            else:\r\n                # examples have no label in test set\r\n                labels.append(\"O\")\r\n```\r\nIf you agree, me or @lvwerra would be happy to implement this and create a PR.","comment_length":148,"text":"Correct data structure for PAN-X task in XTREME dataset? \n Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nThanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\ndataset_train = dataset['train']\r\n```\r\n\r\nHowever, I am not sure that `load_dataset()` is returning the correct data structure for NER. \r\n\r\nCurrently, every row in `dataset_train` is of the form\r\n```python\r\n{'word': str, 'ner_tag': str, 'lang': str}\r\n```\r\nbut I think we actually want something like\r\n```python\r\n{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}\r\n```\r\nso that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.\r\n\r\nIndeed, [this line](https:\/\/github.com\/google-research\/xtreme\/blob\/522434d1aece34131d997a97ce7e9242a51a688a\/third_party\/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.\r\n\r\n## Proposed solution\r\nReplace\r\n```python\r\nwith open(filepath) as f:\r\n    data = csv.reader(f, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\r\n    for id_, row in enumerate(data):\r\n        if row:\r\n            lang, word = row[0].split(\":\")[0], row[0].split(\":\")[1]\r\n            tag = row[1]\r\n            yield id_, {\"word\": word, \"ner_tag\": tag, \"lang\": lang}\r\n```\r\nfrom  [these lines](https:\/\/github.com\/huggingface\/nlp\/blob\/ce7d3a1d630b78fe27188d1706f3ea980e8eec43\/datasets\/xtreme\/xtreme.py#L881-L887) of the `_generate_examples()` function with something like\r\n\r\n```python\r\nguid_index = 1\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    words = []\r\n    ner_tags = []\r\n    langs = []\r\n    for line in f:\r\n        if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\r\n            if words:\r\n                yield guid_index, {\"words\": words, \"ner_tags\": ner_tags, \"langs\": langs}\r\n                guid_index += 1\r\n                words = []\r\n                ner_tags = []\r\n        else:\r\n            # pan-x data is tab separated\r\n            splits = line.split(\"\\t\")\r\n            # strip out en: prefix\r\n            langs.append(splits[0][:2])\r\n            words.append(splits[0][3:])\r\n            if len(splits) > 1:\r\n                labels.append(splits[-1].replace(\"\\n\", \"\"))\r\n            else:\r\n                # examples have no label in test set\r\n                labels.append(\"O\")\r\n```\r\nIf you agree, me or @lvwerra would be happy to implement this and create a PR. \n Hi @lhoestq \r\nI made the proposed changes to the `xtreme.py` script. I noticed that I also need to change the schema in the `dataset_infos.json` file.  More specifically the `\"features\"` part of the PAN-X.LANG dataset:\r\n\r\n```json\r\n\"features\":{\r\n   \"word\":{\r\n      \"dtype\":\"string\",\r\n      \"id\":null,\r\n      \"_type\":\"Value\"\r\n   },\r\n   \"ner_tag\":{\r\n      \"dtype\":\"string\",\r\n      \"id\":null,\r\n      \"_type\":\"Value\"\r\n   },\r\n   \"lang\":{\r\n      \"dtype\":\"string\",\r\n      \"id\":null,\r\n      \"_type\":\"Value\"\r\n   }\r\n}\r\n```\r\nTo fit the code above the fields `\"word\"`, `\"ner_tag\"`, and `\"lang\"` would become `\"words\"`, `ner_tags\"` and `\"langs\"`. In addition the `dtype` should be changed from `\"string\"` to `\"list\"`.\r\n\r\n I made this changes but when trying to test this locally with `dataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')` I face the issue that the `dataset_info.json` file is always overwritten by a downloaded version with the old settings, which then throws an error because the schema does not match. This makes it hard to test the changes locally. Do you have any suggestions on how to deal with that?\r\n","embeddings":[-0.0446428172,-0.1960519552,-0.0213917121,0.3716609478,0.0390401855,-0.2763307691,-0.0206372105,0.1579513103,-0.0717805475,-0.0662426949,-0.1613530368,0.2627067268,0.0014278574,0.1680819094,0.4264350533,-0.1467018872,0.0484421924,-0.0699055418,0.2786860168,-0.0526146963,-0.109093152,0.1506099999,-0.3253746927,0.0544689558,-0.3497874737,0.0837271884,-0.0678211153,0.3462400138,-0.2758393288,-0.493368417,0.5901547074,0.0610429049,0.3375472426,0.3253481388,-0.0001185823,0.007394406,0.0491500795,-0.2976795733,0.063000001,-0.5153079629,-0.21160613,-0.6882328987,-0.0707507059,-0.1863514483,-0.249424383,-0.0691500232,0.2359091789,0.1613155603,0.3286878169,0.404129982,0.0667554885,0.1186724603,-0.0112289721,0.1646236479,0.4069048762,0.3912606835,-0.2984111607,-0.0554718524,0.5836415291,0.1500933468,-0.2007237524,0.226136744,0.0665774941,-0.0957404003,0.2168319821,0.311748594,0.1106207892,-0.4583888352,-0.2030344903,0.2828918993,0.2304347605,-0.2553840578,-0.2035078704,-0.2779316902,-0.0572837256,-0.2900469899,-0.0280274134,0.3609626591,-0.0808850378,-0.0090993838,0.2543638647,-0.0029822402,-0.1114807874,0.2031368911,-0.0105202226,0.4096223116,-0.0134635018,-0.1075180918,-0.1396067441,-0.1162076294,0.7018569112,-0.0104258293,0.1576858014,0.1197108179,-0.6041291952,0.0803796276,-0.1363217384,-0.2818925083,0.1663054824,-0.0484371334,0.2098659426,0.1589148641,0.148524031,0.0145868212,0.3213757873,0.2003956288,0.2381373495,0.0310105663,-0.1307128817,0.0734806508,0.0858036652,-0.0963578075,-0.3183752894,0.0653057396,0.2368329167,-0.0228066426,0.2889071107,-0.1962373108,-0.3680241704,-0.0199918374,-0.1284303069,0.072580345,0.1414791495,0.061180193,0.0080608483,0.5874376297,-0.0742886215,0.1787090451,-0.2538342476,0.0291408673,-0.1472996622,0.1694096178,-0.6243599057,-0.1061867177,0.2582582831,0.091962114,0.1970394701,-0.0064032744,0.121820122,-0.3322490454,0.1758813262,-0.2791434526,0.2651101649,-0.0158670694,0.2457386106,-0.1295719892,-0.3093241453,-0.0095198667,-0.1647403091,0.2194372267,-0.7123686075,-0.0245730337,0.0450958833,0.0754593015,-0.214194268,0.1435741037,-0.504322648,0.0322593153,0.0630441457,0.2181688845,0.1069046035,-0.3306136727,-0.2142900974,-0.0720484778,0.0830698535,0.1326568276,-0.1291505694,0.0973811597,0.0630992949,0.0002566593,0.1041970924,0.2162794471,-0.0109667862,0.284181118,-0.1886345744,0.5723384023,0.4195818007,-0.8037276268,-0.3378547132,0.0875090212,-0.1656147987,0.0018321252,-0.0777145699,0.2999708056,0.2168296725,-0.1097091362,0.2860379517,0.4717787802,-0.0601560883,0.1129643992,-0.1055826619,0.2662775815,-0.1180936024,0.3215996325,0.0420975052,-0.2685292661,-0.1594627202,0.0751415715,0.1948614568,-0.1158283576,0.0870857239,0.4314538538,0.089273192,-0.0513841324,-0.0506237373,0.0383254662,-0.7624905109,0.1141011417,-0.4289596081,0.3793443143,0.0106568281,-0.0671950504,-0.1728422344,-0.0510757342,-0.296262145,-0.1906543225,0.107161127,-0.1209991425,-0.1237812713,-0.3034522235,-0.0762852654,0.3974147141,-0.2260980457,0.0737625808,-0.5780182481,0.340782702,0.1180048063,-0.0664847717,-0.0123105403,0.4472563863,0.1922498047,-0.0482966453,0.1684597582,0.2209219038,0.2148898542,-0.1571745276,-0.0612342209,-0.0020257682,0.1334930658,-0.5777854919,0.0659277737,0.2230994701,-0.001422982,-0.2143050879,-0.1243546456,0.5517559648,0.1847706735,0.2072845101,0.0289730113,0.0707501099,0.2122232467,-0.1713617146,-0.2344333678,-0.1039047167,0.1545492858,0.1233171746,0.0305474959,0.0995655283,-0.5023413897,0.0200132038,0.3077193201,0.0274304561,0.1898184419,0.2047269344,-0.1531089544,-0.0791647956,0.0374575406,0.3701202571,0.3590866625,0.0914273858,0.1098857149,0.1304076463,-0.0329413153,0.0724881813,-0.0358842276,0.0617540143,0.1510176659,0.2119652778,0.1785159111,-0.0841950774,-0.0858964771,-0.2191504538,0.0321776755,0.152163282,-0.3294998705,0.0780693293,0.0368220694,-0.450265348,-0.1473740041,0.0441721193,-0.1361576915,-0.3193361759,-0.0379218347,-0.0571559481,0.2387743443,0.084067151,-0.4746881425,-0.2150861621,-0.1986020505,-0.3406288028,-0.0583148114,0.0742932335,-0.1382389069,-0.0267540924,0.2119731903,0.5459074378,-0.031271968,-0.4353145659,0.3901607394,-0.3872416317,-0.2023112923,0.301656425,-0.2638221681,0.3121575713,0.217154935,-0.1040859297,-0.4046024084,0.195917204,0.1874918491,-0.321480006,-0.3141257763,0.3168615997,-0.1772629917,-0.0916748643,-0.0264440607,-0.40634498,-0.5237715244,-0.2054876089,0.0119940396,0.2230312675,0.3162436187,0.2151381969,-0.2596668601,-0.1451510787,-0.1321804971,-0.1955112368,0.0069593326,-0.5726137161,0.4568421841,-0.24419415,-0.2051525563,-0.0859806389,-0.2658783793,0.4713196158,0.3780293763,-0.0684937015,-0.0375404693,0.1413383931,0.0351820067,0.177708894,-0.0622194856,0.2964116931,-0.2141356468,0.0457051024,-0.0277902856,-0.0081237135,0.0785258338,0.3201446831,0.2304375917,-0.0265186951,-0.2571677864,0.110347338,0.0635766909,0.1552903205,0.0463344343,0.4367932975,0.005378745,0.1145690829,0.0847834796,0.0224085972,-0.164030239,0.0601466,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/425","title":"Correct data structure for PAN-X task in XTREME dataset?","comments":"Hi !\r\n\r\nYou have to point to your local script.\r\nFirst clone the repo and then:\r\n\r\n```python\r\ndataset = load_dataset(\".\/datasets\/xtreme\", \"PAN-X.en\")\r\n```\r\nThe \"xtreme\" directory contains \"xtreme.py\".\r\n\r\nYou also have to change the features definition in the `_info` method. You could use:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n    \"words\": [nlp.Value(\"string\")],\r\n    \"ner_tags\": [nlp.Value(\"string\")],\r\n    \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nHope this helps !\r\nLet me know if you have other questions.","body":"Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nThanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\ndataset_train = dataset['train']\r\n```\r\n\r\nHowever, I am not sure that `load_dataset()` is returning the correct data structure for NER. \r\n\r\nCurrently, every row in `dataset_train` is of the form\r\n```python\r\n{'word': str, 'ner_tag': str, 'lang': str}\r\n```\r\nbut I think we actually want something like\r\n```python\r\n{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}\r\n```\r\nso that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.\r\n\r\nIndeed, [this line](https:\/\/github.com\/google-research\/xtreme\/blob\/522434d1aece34131d997a97ce7e9242a51a688a\/third_party\/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.\r\n\r\n## Proposed solution\r\nReplace\r\n```python\r\nwith open(filepath) as f:\r\n    data = csv.reader(f, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\r\n    for id_, row in enumerate(data):\r\n        if row:\r\n            lang, word = row[0].split(\":\")[0], row[0].split(\":\")[1]\r\n            tag = row[1]\r\n            yield id_, {\"word\": word, \"ner_tag\": tag, \"lang\": lang}\r\n```\r\nfrom  [these lines](https:\/\/github.com\/huggingface\/nlp\/blob\/ce7d3a1d630b78fe27188d1706f3ea980e8eec43\/datasets\/xtreme\/xtreme.py#L881-L887) of the `_generate_examples()` function with something like\r\n\r\n```python\r\nguid_index = 1\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    words = []\r\n    ner_tags = []\r\n    langs = []\r\n    for line in f:\r\n        if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\r\n            if words:\r\n                yield guid_index, {\"words\": words, \"ner_tags\": ner_tags, \"langs\": langs}\r\n                guid_index += 1\r\n                words = []\r\n                ner_tags = []\r\n        else:\r\n            # pan-x data is tab separated\r\n            splits = line.split(\"\\t\")\r\n            # strip out en: prefix\r\n            langs.append(splits[0][:2])\r\n            words.append(splits[0][3:])\r\n            if len(splits) > 1:\r\n                labels.append(splits[-1].replace(\"\\n\", \"\"))\r\n            else:\r\n                # examples have no label in test set\r\n                labels.append(\"O\")\r\n```\r\nIf you agree, me or @lvwerra would be happy to implement this and create a PR.","comment_length":66,"text":"Correct data structure for PAN-X task in XTREME dataset? \n Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nThanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\ndataset_train = dataset['train']\r\n```\r\n\r\nHowever, I am not sure that `load_dataset()` is returning the correct data structure for NER. \r\n\r\nCurrently, every row in `dataset_train` is of the form\r\n```python\r\n{'word': str, 'ner_tag': str, 'lang': str}\r\n```\r\nbut I think we actually want something like\r\n```python\r\n{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}\r\n```\r\nso that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.\r\n\r\nIndeed, [this line](https:\/\/github.com\/google-research\/xtreme\/blob\/522434d1aece34131d997a97ce7e9242a51a688a\/third_party\/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.\r\n\r\n## Proposed solution\r\nReplace\r\n```python\r\nwith open(filepath) as f:\r\n    data = csv.reader(f, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\r\n    for id_, row in enumerate(data):\r\n        if row:\r\n            lang, word = row[0].split(\":\")[0], row[0].split(\":\")[1]\r\n            tag = row[1]\r\n            yield id_, {\"word\": word, \"ner_tag\": tag, \"lang\": lang}\r\n```\r\nfrom  [these lines](https:\/\/github.com\/huggingface\/nlp\/blob\/ce7d3a1d630b78fe27188d1706f3ea980e8eec43\/datasets\/xtreme\/xtreme.py#L881-L887) of the `_generate_examples()` function with something like\r\n\r\n```python\r\nguid_index = 1\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    words = []\r\n    ner_tags = []\r\n    langs = []\r\n    for line in f:\r\n        if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\r\n            if words:\r\n                yield guid_index, {\"words\": words, \"ner_tags\": ner_tags, \"langs\": langs}\r\n                guid_index += 1\r\n                words = []\r\n                ner_tags = []\r\n        else:\r\n            # pan-x data is tab separated\r\n            splits = line.split(\"\\t\")\r\n            # strip out en: prefix\r\n            langs.append(splits[0][:2])\r\n            words.append(splits[0][3:])\r\n            if len(splits) > 1:\r\n                labels.append(splits[-1].replace(\"\\n\", \"\"))\r\n            else:\r\n                # examples have no label in test set\r\n                labels.append(\"O\")\r\n```\r\nIf you agree, me or @lvwerra would be happy to implement this and create a PR. \n Hi !\r\n\r\nYou have to point to your local script.\r\nFirst clone the repo and then:\r\n\r\n```python\r\ndataset = load_dataset(\".\/datasets\/xtreme\", \"PAN-X.en\")\r\n```\r\nThe \"xtreme\" directory contains \"xtreme.py\".\r\n\r\nYou also have to change the features definition in the `_info` method. You could use:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n    \"words\": [nlp.Value(\"string\")],\r\n    \"ner_tags\": [nlp.Value(\"string\")],\r\n    \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nHope this helps !\r\nLet me know if you have other questions.","embeddings":[-0.0446428172,-0.1960519552,-0.0213917121,0.3716609478,0.0390401855,-0.2763307691,-0.0206372105,0.1579513103,-0.0717805475,-0.0662426949,-0.1613530368,0.2627067268,0.0014278574,0.1680819094,0.4264350533,-0.1467018872,0.0484421924,-0.0699055418,0.2786860168,-0.0526146963,-0.109093152,0.1506099999,-0.3253746927,0.0544689558,-0.3497874737,0.0837271884,-0.0678211153,0.3462400138,-0.2758393288,-0.493368417,0.5901547074,0.0610429049,0.3375472426,0.3253481388,-0.0001185823,0.007394406,0.0491500795,-0.2976795733,0.063000001,-0.5153079629,-0.21160613,-0.6882328987,-0.0707507059,-0.1863514483,-0.249424383,-0.0691500232,0.2359091789,0.1613155603,0.3286878169,0.404129982,0.0667554885,0.1186724603,-0.0112289721,0.1646236479,0.4069048762,0.3912606835,-0.2984111607,-0.0554718524,0.5836415291,0.1500933468,-0.2007237524,0.226136744,0.0665774941,-0.0957404003,0.2168319821,0.311748594,0.1106207892,-0.4583888352,-0.2030344903,0.2828918993,0.2304347605,-0.2553840578,-0.2035078704,-0.2779316902,-0.0572837256,-0.2900469899,-0.0280274134,0.3609626591,-0.0808850378,-0.0090993838,0.2543638647,-0.0029822402,-0.1114807874,0.2031368911,-0.0105202226,0.4096223116,-0.0134635018,-0.1075180918,-0.1396067441,-0.1162076294,0.7018569112,-0.0104258293,0.1576858014,0.1197108179,-0.6041291952,0.0803796276,-0.1363217384,-0.2818925083,0.1663054824,-0.0484371334,0.2098659426,0.1589148641,0.148524031,0.0145868212,0.3213757873,0.2003956288,0.2381373495,0.0310105663,-0.1307128817,0.0734806508,0.0858036652,-0.0963578075,-0.3183752894,0.0653057396,0.2368329167,-0.0228066426,0.2889071107,-0.1962373108,-0.3680241704,-0.0199918374,-0.1284303069,0.072580345,0.1414791495,0.061180193,0.0080608483,0.5874376297,-0.0742886215,0.1787090451,-0.2538342476,0.0291408673,-0.1472996622,0.1694096178,-0.6243599057,-0.1061867177,0.2582582831,0.091962114,0.1970394701,-0.0064032744,0.121820122,-0.3322490454,0.1758813262,-0.2791434526,0.2651101649,-0.0158670694,0.2457386106,-0.1295719892,-0.3093241453,-0.0095198667,-0.1647403091,0.2194372267,-0.7123686075,-0.0245730337,0.0450958833,0.0754593015,-0.214194268,0.1435741037,-0.504322648,0.0322593153,0.0630441457,0.2181688845,0.1069046035,-0.3306136727,-0.2142900974,-0.0720484778,0.0830698535,0.1326568276,-0.1291505694,0.0973811597,0.0630992949,0.0002566593,0.1041970924,0.2162794471,-0.0109667862,0.284181118,-0.1886345744,0.5723384023,0.4195818007,-0.8037276268,-0.3378547132,0.0875090212,-0.1656147987,0.0018321252,-0.0777145699,0.2999708056,0.2168296725,-0.1097091362,0.2860379517,0.4717787802,-0.0601560883,0.1129643992,-0.1055826619,0.2662775815,-0.1180936024,0.3215996325,0.0420975052,-0.2685292661,-0.1594627202,0.0751415715,0.1948614568,-0.1158283576,0.0870857239,0.4314538538,0.089273192,-0.0513841324,-0.0506237373,0.0383254662,-0.7624905109,0.1141011417,-0.4289596081,0.3793443143,0.0106568281,-0.0671950504,-0.1728422344,-0.0510757342,-0.296262145,-0.1906543225,0.107161127,-0.1209991425,-0.1237812713,-0.3034522235,-0.0762852654,0.3974147141,-0.2260980457,0.0737625808,-0.5780182481,0.340782702,0.1180048063,-0.0664847717,-0.0123105403,0.4472563863,0.1922498047,-0.0482966453,0.1684597582,0.2209219038,0.2148898542,-0.1571745276,-0.0612342209,-0.0020257682,0.1334930658,-0.5777854919,0.0659277737,0.2230994701,-0.001422982,-0.2143050879,-0.1243546456,0.5517559648,0.1847706735,0.2072845101,0.0289730113,0.0707501099,0.2122232467,-0.1713617146,-0.2344333678,-0.1039047167,0.1545492858,0.1233171746,0.0305474959,0.0995655283,-0.5023413897,0.0200132038,0.3077193201,0.0274304561,0.1898184419,0.2047269344,-0.1531089544,-0.0791647956,0.0374575406,0.3701202571,0.3590866625,0.0914273858,0.1098857149,0.1304076463,-0.0329413153,0.0724881813,-0.0358842276,0.0617540143,0.1510176659,0.2119652778,0.1785159111,-0.0841950774,-0.0858964771,-0.2191504538,0.0321776755,0.152163282,-0.3294998705,0.0780693293,0.0368220694,-0.450265348,-0.1473740041,0.0441721193,-0.1361576915,-0.3193361759,-0.0379218347,-0.0571559481,0.2387743443,0.084067151,-0.4746881425,-0.2150861621,-0.1986020505,-0.3406288028,-0.0583148114,0.0742932335,-0.1382389069,-0.0267540924,0.2119731903,0.5459074378,-0.031271968,-0.4353145659,0.3901607394,-0.3872416317,-0.2023112923,0.301656425,-0.2638221681,0.3121575713,0.217154935,-0.1040859297,-0.4046024084,0.195917204,0.1874918491,-0.321480006,-0.3141257763,0.3168615997,-0.1772629917,-0.0916748643,-0.0264440607,-0.40634498,-0.5237715244,-0.2054876089,0.0119940396,0.2230312675,0.3162436187,0.2151381969,-0.2596668601,-0.1451510787,-0.1321804971,-0.1955112368,0.0069593326,-0.5726137161,0.4568421841,-0.24419415,-0.2051525563,-0.0859806389,-0.2658783793,0.4713196158,0.3780293763,-0.0684937015,-0.0375404693,0.1413383931,0.0351820067,0.177708894,-0.0622194856,0.2964116931,-0.2141356468,0.0457051024,-0.0277902856,-0.0081237135,0.0785258338,0.3201446831,0.2304375917,-0.0265186951,-0.2571677864,0.110347338,0.0635766909,0.1552903205,0.0463344343,0.4367932975,0.005378745,0.1145690829,0.0847834796,0.0224085972,-0.164030239,0.0601466,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/425","title":"Correct data structure for PAN-X task in XTREME dataset?","comments":"Thanks, I am making progress. I got a new error `NonMatchingSplitsSizesError ` (see traceback below), which I suspect is due to the fact that number of rows in the dataset changed (one row per word --> one row per sentence) as well as the number of bytes due to the slightly updated data structure. \r\n\r\n```python\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=1756492, num_examples=80536, dataset_name='xtreme'), 'recorded': SplitInfo(name='validation', num_bytes=1837109, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='test', num_bytes=1752572, num_examples=80326, dataset_name='xtreme'), 'recorded': SplitInfo(name='test', num_bytes=1833214, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='train', num_bytes=3496832, num_examples=160394, dataset_name='xtreme'), 'recorded': SplitInfo(name='train', num_bytes=3658428, num_examples=20000, dataset_name='xtreme')}]\r\n```\r\nI can fix the error by replacing the values in the `datasets_infos.json` file, which I tested for English. However, to update this for all 40 datasets manually is slightly painful. Is there a better way to update the expected values for all datasets?","body":"Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nThanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\ndataset_train = dataset['train']\r\n```\r\n\r\nHowever, I am not sure that `load_dataset()` is returning the correct data structure for NER. \r\n\r\nCurrently, every row in `dataset_train` is of the form\r\n```python\r\n{'word': str, 'ner_tag': str, 'lang': str}\r\n```\r\nbut I think we actually want something like\r\n```python\r\n{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}\r\n```\r\nso that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.\r\n\r\nIndeed, [this line](https:\/\/github.com\/google-research\/xtreme\/blob\/522434d1aece34131d997a97ce7e9242a51a688a\/third_party\/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.\r\n\r\n## Proposed solution\r\nReplace\r\n```python\r\nwith open(filepath) as f:\r\n    data = csv.reader(f, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\r\n    for id_, row in enumerate(data):\r\n        if row:\r\n            lang, word = row[0].split(\":\")[0], row[0].split(\":\")[1]\r\n            tag = row[1]\r\n            yield id_, {\"word\": word, \"ner_tag\": tag, \"lang\": lang}\r\n```\r\nfrom  [these lines](https:\/\/github.com\/huggingface\/nlp\/blob\/ce7d3a1d630b78fe27188d1706f3ea980e8eec43\/datasets\/xtreme\/xtreme.py#L881-L887) of the `_generate_examples()` function with something like\r\n\r\n```python\r\nguid_index = 1\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    words = []\r\n    ner_tags = []\r\n    langs = []\r\n    for line in f:\r\n        if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\r\n            if words:\r\n                yield guid_index, {\"words\": words, \"ner_tags\": ner_tags, \"langs\": langs}\r\n                guid_index += 1\r\n                words = []\r\n                ner_tags = []\r\n        else:\r\n            # pan-x data is tab separated\r\n            splits = line.split(\"\\t\")\r\n            # strip out en: prefix\r\n            langs.append(splits[0][:2])\r\n            words.append(splits[0][3:])\r\n            if len(splits) > 1:\r\n                labels.append(splits[-1].replace(\"\\n\", \"\"))\r\n            else:\r\n                # examples have no label in test set\r\n                labels.append(\"O\")\r\n```\r\nIf you agree, me or @lvwerra would be happy to implement this and create a PR.","comment_length":130,"text":"Correct data structure for PAN-X task in XTREME dataset? \n Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nThanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\ndataset_train = dataset['train']\r\n```\r\n\r\nHowever, I am not sure that `load_dataset()` is returning the correct data structure for NER. \r\n\r\nCurrently, every row in `dataset_train` is of the form\r\n```python\r\n{'word': str, 'ner_tag': str, 'lang': str}\r\n```\r\nbut I think we actually want something like\r\n```python\r\n{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}\r\n```\r\nso that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.\r\n\r\nIndeed, [this line](https:\/\/github.com\/google-research\/xtreme\/blob\/522434d1aece34131d997a97ce7e9242a51a688a\/third_party\/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.\r\n\r\n## Proposed solution\r\nReplace\r\n```python\r\nwith open(filepath) as f:\r\n    data = csv.reader(f, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\r\n    for id_, row in enumerate(data):\r\n        if row:\r\n            lang, word = row[0].split(\":\")[0], row[0].split(\":\")[1]\r\n            tag = row[1]\r\n            yield id_, {\"word\": word, \"ner_tag\": tag, \"lang\": lang}\r\n```\r\nfrom  [these lines](https:\/\/github.com\/huggingface\/nlp\/blob\/ce7d3a1d630b78fe27188d1706f3ea980e8eec43\/datasets\/xtreme\/xtreme.py#L881-L887) of the `_generate_examples()` function with something like\r\n\r\n```python\r\nguid_index = 1\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    words = []\r\n    ner_tags = []\r\n    langs = []\r\n    for line in f:\r\n        if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\r\n            if words:\r\n                yield guid_index, {\"words\": words, \"ner_tags\": ner_tags, \"langs\": langs}\r\n                guid_index += 1\r\n                words = []\r\n                ner_tags = []\r\n        else:\r\n            # pan-x data is tab separated\r\n            splits = line.split(\"\\t\")\r\n            # strip out en: prefix\r\n            langs.append(splits[0][:2])\r\n            words.append(splits[0][3:])\r\n            if len(splits) > 1:\r\n                labels.append(splits[-1].replace(\"\\n\", \"\"))\r\n            else:\r\n                # examples have no label in test set\r\n                labels.append(\"O\")\r\n```\r\nIf you agree, me or @lvwerra would be happy to implement this and create a PR. \n Thanks, I am making progress. I got a new error `NonMatchingSplitsSizesError ` (see traceback below), which I suspect is due to the fact that number of rows in the dataset changed (one row per word --> one row per sentence) as well as the number of bytes due to the slightly updated data structure. \r\n\r\n```python\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=1756492, num_examples=80536, dataset_name='xtreme'), 'recorded': SplitInfo(name='validation', num_bytes=1837109, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='test', num_bytes=1752572, num_examples=80326, dataset_name='xtreme'), 'recorded': SplitInfo(name='test', num_bytes=1833214, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='train', num_bytes=3496832, num_examples=160394, dataset_name='xtreme'), 'recorded': SplitInfo(name='train', num_bytes=3658428, num_examples=20000, dataset_name='xtreme')}]\r\n```\r\nI can fix the error by replacing the values in the `datasets_infos.json` file, which I tested for English. However, to update this for all 40 datasets manually is slightly painful. Is there a better way to update the expected values for all datasets?","embeddings":[-0.0446428172,-0.1960519552,-0.0213917121,0.3716609478,0.0390401855,-0.2763307691,-0.0206372105,0.1579513103,-0.0717805475,-0.0662426949,-0.1613530368,0.2627067268,0.0014278574,0.1680819094,0.4264350533,-0.1467018872,0.0484421924,-0.0699055418,0.2786860168,-0.0526146963,-0.109093152,0.1506099999,-0.3253746927,0.0544689558,-0.3497874737,0.0837271884,-0.0678211153,0.3462400138,-0.2758393288,-0.493368417,0.5901547074,0.0610429049,0.3375472426,0.3253481388,-0.0001185823,0.007394406,0.0491500795,-0.2976795733,0.063000001,-0.5153079629,-0.21160613,-0.6882328987,-0.0707507059,-0.1863514483,-0.249424383,-0.0691500232,0.2359091789,0.1613155603,0.3286878169,0.404129982,0.0667554885,0.1186724603,-0.0112289721,0.1646236479,0.4069048762,0.3912606835,-0.2984111607,-0.0554718524,0.5836415291,0.1500933468,-0.2007237524,0.226136744,0.0665774941,-0.0957404003,0.2168319821,0.311748594,0.1106207892,-0.4583888352,-0.2030344903,0.2828918993,0.2304347605,-0.2553840578,-0.2035078704,-0.2779316902,-0.0572837256,-0.2900469899,-0.0280274134,0.3609626591,-0.0808850378,-0.0090993838,0.2543638647,-0.0029822402,-0.1114807874,0.2031368911,-0.0105202226,0.4096223116,-0.0134635018,-0.1075180918,-0.1396067441,-0.1162076294,0.7018569112,-0.0104258293,0.1576858014,0.1197108179,-0.6041291952,0.0803796276,-0.1363217384,-0.2818925083,0.1663054824,-0.0484371334,0.2098659426,0.1589148641,0.148524031,0.0145868212,0.3213757873,0.2003956288,0.2381373495,0.0310105663,-0.1307128817,0.0734806508,0.0858036652,-0.0963578075,-0.3183752894,0.0653057396,0.2368329167,-0.0228066426,0.2889071107,-0.1962373108,-0.3680241704,-0.0199918374,-0.1284303069,0.072580345,0.1414791495,0.061180193,0.0080608483,0.5874376297,-0.0742886215,0.1787090451,-0.2538342476,0.0291408673,-0.1472996622,0.1694096178,-0.6243599057,-0.1061867177,0.2582582831,0.091962114,0.1970394701,-0.0064032744,0.121820122,-0.3322490454,0.1758813262,-0.2791434526,0.2651101649,-0.0158670694,0.2457386106,-0.1295719892,-0.3093241453,-0.0095198667,-0.1647403091,0.2194372267,-0.7123686075,-0.0245730337,0.0450958833,0.0754593015,-0.214194268,0.1435741037,-0.504322648,0.0322593153,0.0630441457,0.2181688845,0.1069046035,-0.3306136727,-0.2142900974,-0.0720484778,0.0830698535,0.1326568276,-0.1291505694,0.0973811597,0.0630992949,0.0002566593,0.1041970924,0.2162794471,-0.0109667862,0.284181118,-0.1886345744,0.5723384023,0.4195818007,-0.8037276268,-0.3378547132,0.0875090212,-0.1656147987,0.0018321252,-0.0777145699,0.2999708056,0.2168296725,-0.1097091362,0.2860379517,0.4717787802,-0.0601560883,0.1129643992,-0.1055826619,0.2662775815,-0.1180936024,0.3215996325,0.0420975052,-0.2685292661,-0.1594627202,0.0751415715,0.1948614568,-0.1158283576,0.0870857239,0.4314538538,0.089273192,-0.0513841324,-0.0506237373,0.0383254662,-0.7624905109,0.1141011417,-0.4289596081,0.3793443143,0.0106568281,-0.0671950504,-0.1728422344,-0.0510757342,-0.296262145,-0.1906543225,0.107161127,-0.1209991425,-0.1237812713,-0.3034522235,-0.0762852654,0.3974147141,-0.2260980457,0.0737625808,-0.5780182481,0.340782702,0.1180048063,-0.0664847717,-0.0123105403,0.4472563863,0.1922498047,-0.0482966453,0.1684597582,0.2209219038,0.2148898542,-0.1571745276,-0.0612342209,-0.0020257682,0.1334930658,-0.5777854919,0.0659277737,0.2230994701,-0.001422982,-0.2143050879,-0.1243546456,0.5517559648,0.1847706735,0.2072845101,0.0289730113,0.0707501099,0.2122232467,-0.1713617146,-0.2344333678,-0.1039047167,0.1545492858,0.1233171746,0.0305474959,0.0995655283,-0.5023413897,0.0200132038,0.3077193201,0.0274304561,0.1898184419,0.2047269344,-0.1531089544,-0.0791647956,0.0374575406,0.3701202571,0.3590866625,0.0914273858,0.1098857149,0.1304076463,-0.0329413153,0.0724881813,-0.0358842276,0.0617540143,0.1510176659,0.2119652778,0.1785159111,-0.0841950774,-0.0858964771,-0.2191504538,0.0321776755,0.152163282,-0.3294998705,0.0780693293,0.0368220694,-0.450265348,-0.1473740041,0.0441721193,-0.1361576915,-0.3193361759,-0.0379218347,-0.0571559481,0.2387743443,0.084067151,-0.4746881425,-0.2150861621,-0.1986020505,-0.3406288028,-0.0583148114,0.0742932335,-0.1382389069,-0.0267540924,0.2119731903,0.5459074378,-0.031271968,-0.4353145659,0.3901607394,-0.3872416317,-0.2023112923,0.301656425,-0.2638221681,0.3121575713,0.217154935,-0.1040859297,-0.4046024084,0.195917204,0.1874918491,-0.321480006,-0.3141257763,0.3168615997,-0.1772629917,-0.0916748643,-0.0264440607,-0.40634498,-0.5237715244,-0.2054876089,0.0119940396,0.2230312675,0.3162436187,0.2151381969,-0.2596668601,-0.1451510787,-0.1321804971,-0.1955112368,0.0069593326,-0.5726137161,0.4568421841,-0.24419415,-0.2051525563,-0.0859806389,-0.2658783793,0.4713196158,0.3780293763,-0.0684937015,-0.0375404693,0.1413383931,0.0351820067,0.177708894,-0.0622194856,0.2964116931,-0.2141356468,0.0457051024,-0.0277902856,-0.0081237135,0.0785258338,0.3201446831,0.2304375917,-0.0265186951,-0.2571677864,0.110347338,0.0635766909,0.1552903205,0.0463344343,0.4367932975,0.005378745,0.1145690829,0.0847834796,0.0224085972,-0.164030239,0.0601466,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/425","title":"Correct data structure for PAN-X task in XTREME dataset?","comments":"One more thing about features. I mentioned\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n    \"words\": [nlp.Value(\"string\")],\r\n    \"ner_tags\": [nlp.Value(\"string\")],\r\n    \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nbut it's actually not consistent with the way we write datasets. Something like this is simpler to read and more consistent with the way we define datasets:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n    \"words\": nlp.Sequence(nlp.Value(\"string\")),\r\n    \"ner_tags\": nlp.Sequence(nlp.Value(\"string\")),\r\n    \"langs\": nlp.Sequence(nlp.Value(\"string\")),\r\n})\r\n```\r\n\r\nSorry about that","body":"Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nThanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\ndataset_train = dataset['train']\r\n```\r\n\r\nHowever, I am not sure that `load_dataset()` is returning the correct data structure for NER. \r\n\r\nCurrently, every row in `dataset_train` is of the form\r\n```python\r\n{'word': str, 'ner_tag': str, 'lang': str}\r\n```\r\nbut I think we actually want something like\r\n```python\r\n{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}\r\n```\r\nso that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.\r\n\r\nIndeed, [this line](https:\/\/github.com\/google-research\/xtreme\/blob\/522434d1aece34131d997a97ce7e9242a51a688a\/third_party\/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.\r\n\r\n## Proposed solution\r\nReplace\r\n```python\r\nwith open(filepath) as f:\r\n    data = csv.reader(f, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\r\n    for id_, row in enumerate(data):\r\n        if row:\r\n            lang, word = row[0].split(\":\")[0], row[0].split(\":\")[1]\r\n            tag = row[1]\r\n            yield id_, {\"word\": word, \"ner_tag\": tag, \"lang\": lang}\r\n```\r\nfrom  [these lines](https:\/\/github.com\/huggingface\/nlp\/blob\/ce7d3a1d630b78fe27188d1706f3ea980e8eec43\/datasets\/xtreme\/xtreme.py#L881-L887) of the `_generate_examples()` function with something like\r\n\r\n```python\r\nguid_index = 1\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    words = []\r\n    ner_tags = []\r\n    langs = []\r\n    for line in f:\r\n        if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\r\n            if words:\r\n                yield guid_index, {\"words\": words, \"ner_tags\": ner_tags, \"langs\": langs}\r\n                guid_index += 1\r\n                words = []\r\n                ner_tags = []\r\n        else:\r\n            # pan-x data is tab separated\r\n            splits = line.split(\"\\t\")\r\n            # strip out en: prefix\r\n            langs.append(splits[0][:2])\r\n            words.append(splits[0][3:])\r\n            if len(splits) > 1:\r\n                labels.append(splits[-1].replace(\"\\n\", \"\"))\r\n            else:\r\n                # examples have no label in test set\r\n                labels.append(\"O\")\r\n```\r\nIf you agree, me or @lvwerra would be happy to implement this and create a PR.","comment_length":61,"text":"Correct data structure for PAN-X task in XTREME dataset? \n Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nThanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is located in data\/\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.en\", data_dir='.\/data')\r\ndataset_train = dataset['train']\r\n```\r\n\r\nHowever, I am not sure that `load_dataset()` is returning the correct data structure for NER. \r\n\r\nCurrently, every row in `dataset_train` is of the form\r\n```python\r\n{'word': str, 'ner_tag': str, 'lang': str}\r\n```\r\nbut I think we actually want something like\r\n```python\r\n{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}\r\n```\r\nso that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.\r\n\r\nIndeed, [this line](https:\/\/github.com\/google-research\/xtreme\/blob\/522434d1aece34131d997a97ce7e9242a51a688a\/third_party\/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.\r\n\r\n## Proposed solution\r\nReplace\r\n```python\r\nwith open(filepath) as f:\r\n    data = csv.reader(f, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\r\n    for id_, row in enumerate(data):\r\n        if row:\r\n            lang, word = row[0].split(\":\")[0], row[0].split(\":\")[1]\r\n            tag = row[1]\r\n            yield id_, {\"word\": word, \"ner_tag\": tag, \"lang\": lang}\r\n```\r\nfrom  [these lines](https:\/\/github.com\/huggingface\/nlp\/blob\/ce7d3a1d630b78fe27188d1706f3ea980e8eec43\/datasets\/xtreme\/xtreme.py#L881-L887) of the `_generate_examples()` function with something like\r\n\r\n```python\r\nguid_index = 1\r\nwith open(filepath, encoding=\"utf-8\") as f:\r\n    words = []\r\n    ner_tags = []\r\n    langs = []\r\n    for line in f:\r\n        if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\r\n            if words:\r\n                yield guid_index, {\"words\": words, \"ner_tags\": ner_tags, \"langs\": langs}\r\n                guid_index += 1\r\n                words = []\r\n                ner_tags = []\r\n        else:\r\n            # pan-x data is tab separated\r\n            splits = line.split(\"\\t\")\r\n            # strip out en: prefix\r\n            langs.append(splits[0][:2])\r\n            words.append(splits[0][3:])\r\n            if len(splits) > 1:\r\n                labels.append(splits[-1].replace(\"\\n\", \"\"))\r\n            else:\r\n                # examples have no label in test set\r\n                labels.append(\"O\")\r\n```\r\nIf you agree, me or @lvwerra would be happy to implement this and create a PR. \n One more thing about features. I mentioned\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n    \"words\": [nlp.Value(\"string\")],\r\n    \"ner_tags\": [nlp.Value(\"string\")],\r\n    \"langs\": [nlp.Value(\"string\")],\r\n})\r\n```\r\n\r\nbut it's actually not consistent with the way we write datasets. Something like this is simpler to read and more consistent with the way we define datasets:\r\n\r\n```python\r\nfeatures = nlp.Features({\r\n    \"words\": nlp.Sequence(nlp.Value(\"string\")),\r\n    \"ner_tags\": nlp.Sequence(nlp.Value(\"string\")),\r\n    \"langs\": nlp.Sequence(nlp.Value(\"string\")),\r\n})\r\n```\r\n\r\nSorry about that","embeddings":[-0.0446428172,-0.1960519552,-0.0213917121,0.3716609478,0.0390401855,-0.2763307691,-0.0206372105,0.1579513103,-0.0717805475,-0.0662426949,-0.1613530368,0.2627067268,0.0014278574,0.1680819094,0.4264350533,-0.1467018872,0.0484421924,-0.0699055418,0.2786860168,-0.0526146963,-0.109093152,0.1506099999,-0.3253746927,0.0544689558,-0.3497874737,0.0837271884,-0.0678211153,0.3462400138,-0.2758393288,-0.493368417,0.5901547074,0.0610429049,0.3375472426,0.3253481388,-0.0001185823,0.007394406,0.0491500795,-0.2976795733,0.063000001,-0.5153079629,-0.21160613,-0.6882328987,-0.0707507059,-0.1863514483,-0.249424383,-0.0691500232,0.2359091789,0.1613155603,0.3286878169,0.404129982,0.0667554885,0.1186724603,-0.0112289721,0.1646236479,0.4069048762,0.3912606835,-0.2984111607,-0.0554718524,0.5836415291,0.1500933468,-0.2007237524,0.226136744,0.0665774941,-0.0957404003,0.2168319821,0.311748594,0.1106207892,-0.4583888352,-0.2030344903,0.2828918993,0.2304347605,-0.2553840578,-0.2035078704,-0.2779316902,-0.0572837256,-0.2900469899,-0.0280274134,0.3609626591,-0.0808850378,-0.0090993838,0.2543638647,-0.0029822402,-0.1114807874,0.2031368911,-0.0105202226,0.4096223116,-0.0134635018,-0.1075180918,-0.1396067441,-0.1162076294,0.7018569112,-0.0104258293,0.1576858014,0.1197108179,-0.6041291952,0.0803796276,-0.1363217384,-0.2818925083,0.1663054824,-0.0484371334,0.2098659426,0.1589148641,0.148524031,0.0145868212,0.3213757873,0.2003956288,0.2381373495,0.0310105663,-0.1307128817,0.0734806508,0.0858036652,-0.0963578075,-0.3183752894,0.0653057396,0.2368329167,-0.0228066426,0.2889071107,-0.1962373108,-0.3680241704,-0.0199918374,-0.1284303069,0.072580345,0.1414791495,0.061180193,0.0080608483,0.5874376297,-0.0742886215,0.1787090451,-0.2538342476,0.0291408673,-0.1472996622,0.1694096178,-0.6243599057,-0.1061867177,0.2582582831,0.091962114,0.1970394701,-0.0064032744,0.121820122,-0.3322490454,0.1758813262,-0.2791434526,0.2651101649,-0.0158670694,0.2457386106,-0.1295719892,-0.3093241453,-0.0095198667,-0.1647403091,0.2194372267,-0.7123686075,-0.0245730337,0.0450958833,0.0754593015,-0.214194268,0.1435741037,-0.504322648,0.0322593153,0.0630441457,0.2181688845,0.1069046035,-0.3306136727,-0.2142900974,-0.0720484778,0.0830698535,0.1326568276,-0.1291505694,0.0973811597,0.0630992949,0.0002566593,0.1041970924,0.2162794471,-0.0109667862,0.284181118,-0.1886345744,0.5723384023,0.4195818007,-0.8037276268,-0.3378547132,0.0875090212,-0.1656147987,0.0018321252,-0.0777145699,0.2999708056,0.2168296725,-0.1097091362,0.2860379517,0.4717787802,-0.0601560883,0.1129643992,-0.1055826619,0.2662775815,-0.1180936024,0.3215996325,0.0420975052,-0.2685292661,-0.1594627202,0.0751415715,0.1948614568,-0.1158283576,0.0870857239,0.4314538538,0.089273192,-0.0513841324,-0.0506237373,0.0383254662,-0.7624905109,0.1141011417,-0.4289596081,0.3793443143,0.0106568281,-0.0671950504,-0.1728422344,-0.0510757342,-0.296262145,-0.1906543225,0.107161127,-0.1209991425,-0.1237812713,-0.3034522235,-0.0762852654,0.3974147141,-0.2260980457,0.0737625808,-0.5780182481,0.340782702,0.1180048063,-0.0664847717,-0.0123105403,0.4472563863,0.1922498047,-0.0482966453,0.1684597582,0.2209219038,0.2148898542,-0.1571745276,-0.0612342209,-0.0020257682,0.1334930658,-0.5777854919,0.0659277737,0.2230994701,-0.001422982,-0.2143050879,-0.1243546456,0.5517559648,0.1847706735,0.2072845101,0.0289730113,0.0707501099,0.2122232467,-0.1713617146,-0.2344333678,-0.1039047167,0.1545492858,0.1233171746,0.0305474959,0.0995655283,-0.5023413897,0.0200132038,0.3077193201,0.0274304561,0.1898184419,0.2047269344,-0.1531089544,-0.0791647956,0.0374575406,0.3701202571,0.3590866625,0.0914273858,0.1098857149,0.1304076463,-0.0329413153,0.0724881813,-0.0358842276,0.0617540143,0.1510176659,0.2119652778,0.1785159111,-0.0841950774,-0.0858964771,-0.2191504538,0.0321776755,0.152163282,-0.3294998705,0.0780693293,0.0368220694,-0.450265348,-0.1473740041,0.0441721193,-0.1361576915,-0.3193361759,-0.0379218347,-0.0571559481,0.2387743443,0.084067151,-0.4746881425,-0.2150861621,-0.1986020505,-0.3406288028,-0.0583148114,0.0742932335,-0.1382389069,-0.0267540924,0.2119731903,0.5459074378,-0.031271968,-0.4353145659,0.3901607394,-0.3872416317,-0.2023112923,0.301656425,-0.2638221681,0.3121575713,0.217154935,-0.1040859297,-0.4046024084,0.195917204,0.1874918491,-0.321480006,-0.3141257763,0.3168615997,-0.1772629917,-0.0916748643,-0.0264440607,-0.40634498,-0.5237715244,-0.2054876089,0.0119940396,0.2230312675,0.3162436187,0.2151381969,-0.2596668601,-0.1451510787,-0.1321804971,-0.1955112368,0.0069593326,-0.5726137161,0.4568421841,-0.24419415,-0.2051525563,-0.0859806389,-0.2658783793,0.4713196158,0.3780293763,-0.0684937015,-0.0375404693,0.1413383931,0.0351820067,0.177708894,-0.0622194856,0.2964116931,-0.2141356468,0.0457051024,-0.0277902856,-0.0081237135,0.0785258338,0.3201446831,0.2304375917,-0.0265186951,-0.2571677864,0.110347338,0.0635766909,0.1552903205,0.0463344343,0.4367932975,0.005378745,0.1145690829,0.0847834796,0.0224085972,-0.164030239,0.0601466,0.233269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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/418","title":"Addition of google drive links to dl_manager","comments":"I think the problem is the way you wrote your urls. Try the following structure to see `https:\/\/drive.google.com\/uc?export=download&id=your_file_id` . \r\n\r\n@lhoestq  ","body":"Hello there, I followed the template to create a download script of my own, which works fine for me, although I had to shun the dl_manager because it was downloading nothing from the drive links and instead use gdown.\r\n\r\nThis is the script for me:\r\n\r\n```python\r\nclass EmoConfig(nlp.BuilderConfig):\r\n    \"\"\"BuilderConfig for SQUAD.\"\"\"\r\n\r\n    def __init__(self, **kwargs):\r\n        \"\"\"BuilderConfig for EmoContext.\r\n    Args:\r\n      **kwargs: keyword arguments forwarded to super.\r\n    \"\"\"\r\n        super(EmoConfig, self).__init__(**kwargs)\r\n\r\n_TEST_URL = \"https:\/\/drive.google.com\/file\/d\/1Hn5ytHSSoGOC4sjm3wYy0Dh0oY_oXBbb\/view?usp=sharing\"\r\n_TRAIN_URL = \"https:\/\/drive.google.com\/file\/d\/12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X\/view?usp=sharing\"\r\n\r\nclass EmoDataset(nlp.GeneratorBasedBuilder):\r\n    \"\"\" SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Version 1.0.0 \"\"\"\r\n\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n    force = False\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            features=nlp.Features(\r\n                {\r\n                    \"text\": nlp.Value(\"string\"),\r\n                    \"label\": nlp.features.ClassLabel(names=[\"others\", \"happy\", \"sad\", \"angry\"]),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/www.aclweb.org\/anthology\/S19-2005\/\",\r\n            citation=_CITATION,\r\n        )\r\n    \r\n    def _get_drive_url(self, url):\r\n        base_url = 'https:\/\/drive.google.com\/uc?id='\r\n        split_url = url.split('\/')\r\n        return base_url + split_url[5]\r\n    \r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n        if(not os.path.exists(\"emo-train.json\") or self.force):\r\n            gdown.download(self._get_drive_url(_TRAIN_URL), \"emo-train.json\", quiet = True)\r\n        if(not os.path.exists(\"emo-test.json\") or self.force):\r\n            gdown.download(self._get_drive_url(_TEST_URL), \"emo-test.json\", quiet = True)\r\n        return [\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TRAIN,\r\n                gen_kwargs={\r\n                    \"filepath\": \"emo-train.json\",\r\n                    \"split\": \"train\",\r\n                },\r\n            ),\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TEST,\r\n                gen_kwargs={\"filepath\": \"emo-test.json\", \"split\": \"test\"},\r\n            ),\r\n        ]\r\n\r\n    def _generate_examples(self, filepath, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n        with open(filepath, 'rb') as f:\r\n            data = json.load(f)\r\n            for id_, text, label in zip(data[\"text\"].keys(), data[\"text\"].values(), data[\"Label\"].values()):\r\n                yield id_, {\r\n                    \"text\": text,\r\n                    \"label\": label,\r\n                }\r\n```\r\n\r\nCan someone help me in adding gdrive links to be used with default dl_manager or adding gdown as another dl_manager, because I'd like to add this dataset to nlp's official database.","comment_length":20,"text":"Addition of google drive links to dl_manager \n Hello there, I followed the template to create a download script of my own, which works fine for me, although I had to shun the dl_manager because it was downloading nothing from the drive links and instead use gdown.\r\n\r\nThis is the script for me:\r\n\r\n```python\r\nclass EmoConfig(nlp.BuilderConfig):\r\n    \"\"\"BuilderConfig for SQUAD.\"\"\"\r\n\r\n    def __init__(self, **kwargs):\r\n        \"\"\"BuilderConfig for EmoContext.\r\n    Args:\r\n      **kwargs: keyword arguments forwarded to super.\r\n    \"\"\"\r\n        super(EmoConfig, self).__init__(**kwargs)\r\n\r\n_TEST_URL = \"https:\/\/drive.google.com\/file\/d\/1Hn5ytHSSoGOC4sjm3wYy0Dh0oY_oXBbb\/view?usp=sharing\"\r\n_TRAIN_URL = \"https:\/\/drive.google.com\/file\/d\/12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X\/view?usp=sharing\"\r\n\r\nclass EmoDataset(nlp.GeneratorBasedBuilder):\r\n    \"\"\" SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Version 1.0.0 \"\"\"\r\n\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n    force = False\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            features=nlp.Features(\r\n                {\r\n                    \"text\": nlp.Value(\"string\"),\r\n                    \"label\": nlp.features.ClassLabel(names=[\"others\", \"happy\", \"sad\", \"angry\"]),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/www.aclweb.org\/anthology\/S19-2005\/\",\r\n            citation=_CITATION,\r\n        )\r\n    \r\n    def _get_drive_url(self, url):\r\n        base_url = 'https:\/\/drive.google.com\/uc?id='\r\n        split_url = url.split('\/')\r\n        return base_url + split_url[5]\r\n    \r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n        if(not os.path.exists(\"emo-train.json\") or self.force):\r\n            gdown.download(self._get_drive_url(_TRAIN_URL), \"emo-train.json\", quiet = True)\r\n        if(not os.path.exists(\"emo-test.json\") or self.force):\r\n            gdown.download(self._get_drive_url(_TEST_URL), \"emo-test.json\", quiet = True)\r\n        return [\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TRAIN,\r\n                gen_kwargs={\r\n                    \"filepath\": \"emo-train.json\",\r\n                    \"split\": \"train\",\r\n                },\r\n            ),\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TEST,\r\n                gen_kwargs={\"filepath\": \"emo-test.json\", \"split\": \"test\"},\r\n            ),\r\n        ]\r\n\r\n    def _generate_examples(self, filepath, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n        with open(filepath, 'rb') as f:\r\n            data = json.load(f)\r\n            for id_, text, label in zip(data[\"text\"].keys(), data[\"text\"].values(), data[\"Label\"].values()):\r\n                yield id_, {\r\n                    \"text\": text,\r\n                    \"label\": label,\r\n                }\r\n```\r\n\r\nCan someone help me in adding gdrive links to be used with default dl_manager or adding gdown as another dl_manager, because I'd like to add this dataset to nlp's official database. \n I think the problem is the way you wrote your urls. Try the following structure to see `https:\/\/drive.google.com\/uc?export=download&id=your_file_id` . \r\n\r\n@lhoestq  ","embeddings":[-0.0986939371,0.0516564436,-0.1309203506,-0.0558965728,0.0639402568,-0.0953544378,0.3679338396,0.2208102643,0.2062921822,0.2249778062,-0.0750645027,0.3655760586,-0.2819617987,0.1416775286,0.1477239877,0.1173321381,-0.224351123,0.2025327682,0.2756347656,-0.1639436185,-0.2751327753,0.3278068602,-0.0318780653,-0.0081285173,0.0502305627,0.0505593009,-0.1391222626,0.198068127,-0.1188958138,-0.2999538481,-0.4197380543,0.1671042591,0.160240218,-0.0974567235,-0.0001042657,-0.1480781883,0.2225690186,-0.2084534466,-0.4087229073,-0.293666929,0.2981956303,-0.1724279672,0.1073081344,-0.2651377916,-0.0045758276,-0.0983251333,0.2041182965,0.1354039758,0.2655147016,0.3032342196,0.2717006207,0.0790492222,0.113470979,0.0298931692,0.3312549591,-0.0354893319,-0.0682809278,0.35271433,0.3344673514,-0.3054541349,-0.1618669927,0.2883867025,-0.005473515,-0.0109913573,0.2195129991,0.1113411263,0.5780613422,-0.3311682045,0.1066150144,0.0327493623,0.0236196127,-0.0413232967,-0.0850505233,-0.2842149436,0.08381401,-0.2874571979,0.2766533494,0.1495649666,-0.2334840745,-0.0543575138,-0.1294789314,-0.0255045816,-0.015468711,0.0540597402,-0.0889275596,0.4138535857,-0.0389394574,-0.2830852568,0.3861259818,-0.0381522439,-0.0971575156,-0.1532618254,-0.0394701101,0.0766005889,-0.0374772809,-0.2887773812,0.1567475349,0.0466397926,0.093797259,0.2489909828,0.0460647494,0.0328915268,0.2356236577,0.0752923787,0.1630280316,0.4459595382,-0.0003144037,0.0400670469,0.3538968563,0.4176515937,0.1609001309,0.1501174271,-0.1202337146,-0.1861605793,-0.1416518539,0.1408617496,-0.1575787514,0.0906012058,-0.4310445487,-0.1330971122,-0.3732341826,0.0073386524,-0.0343621001,0.4378283918,-0.0487489253,-0.1396611929,0.2441136092,0.2374013662,-0.0423724875,0.0900178328,-0.1254783124,0.255615443,-0.2168087512,-0.0520229526,0.3310387731,0.0770800337,0.421222806,-0.2571605742,-0.1213400289,-0.224132359,0.1388938576,0.0859965533,-0.0139247123,-0.064111203,0.4167032838,0.0155231385,-0.1533248723,-0.4158217013,-0.071225442,0.0388117135,0.0347075351,-0.037107002,-0.1740760654,0.3355267346,-0.0862290189,-0.0942458659,-0.0037766567,0.2271474302,-0.1502159536,0.0436013825,0.0858766139,0.3182860315,-0.0827771053,-0.000988554,0.1685232818,0.2986229062,-0.0949452594,-0.2877131999,-0.1839938015,0.1673677117,-0.1132550612,-0.0632707402,-0.044929985,0.2455672026,-0.3794006705,0.2419207394,0.304346174,-0.2896392047,-0.2050281167,0.2148452997,-0.3752191663,-0.1749029905,0.030269891,0.0142999822,0.4108042121,-0.0042553372,-0.1432295889,0.4827906489,0.0771704242,0.242865935,-0.3040136099,-0.466017276,-0.1829135418,0.2075032294,0.1516643167,-0.1184460148,0.1088404134,-0.0877448469,0.3002710938,-0.1562739909,0.1865597665,0.1028919965,0.082430616,0.0962019637,0.0902650356,0.1277572811,-0.4553069472,-0.0245841052,-0.1491093785,0.2400395274,-0.3181627095,-0.1654351503,-0.2285122573,-0.1823611856,-0.2087038606,-0.3032354414,0.2976228893,0.2181787938,0.0589148887,0.2489134073,0.0791672543,-0.078336969,0.1662929952,0.1352360249,0.0483056195,0.3308407664,-0.3822062016,-0.1154452637,0.1777767688,0.0533847101,0.1627697051,-0.0097971642,0.0177794714,0.2022307515,-0.2338354439,0.4560253918,0.0342990011,-0.018487528,0.2643815875,-0.001894454,0.3286045492,0.3785975277,0.1253685653,-0.042322088,-0.1335391253,0.3993208706,0.2978732884,0.1315351278,0.1984390169,0.1492633671,-0.0345823541,-0.3092212379,-0.0366149396,-0.175003618,0.1060321406,0.4917028844,0.3812161088,-0.1171898544,-0.1798555851,0.2268595248,0.3522838056,-0.0431040488,0.2884167135,0.0797794685,-0.3493873775,-0.1552882195,-0.1172491759,0.3525727689,0.1629645526,0.2543209791,0.1712719202,0.0493353643,-0.0080083162,-0.3064471781,0.399212122,0.0655613244,0.1062253714,0.3554275632,0.112254627,-0.1241622567,-0.3668965697,-0.0078185881,-0.0030904301,-0.051934097,-0.2809714377,-0.0359772854,-0.2021116912,-0.1673544347,-0.040416833,0.1189079285,-0.0453860685,-0.1994874179,0.241571486,0.2851106822,-0.2777339518,0.127188921,0.1675787568,0.3435669839,-0.0704192296,-0.051740665,-0.1093210727,0.0156291276,-0.331397295,0.1907478869,0.2792063951,0.0847769007,0.1668625176,0.035354279,0.0824102759,-0.4212260544,-0.1966773272,0.2405076027,-0.1467298567,0.0396607593,0.1082567051,-0.0911808386,-0.0478258096,-0.3472113609,0.0503748134,-0.2066100538,-0.2028837651,-0.3251749873,0.1594581753,0.0629494265,-0.488184154,-0.3987681568,-0.2369147837,-0.380084008,0.1677398533,0.1598065197,0.1822991818,0.1246550754,-0.0548261032,0.1800568253,0.2231410295,-0.1606066525,-0.147671774,0.2775504291,0.4198610485,-0.4095939994,-0.3190635145,0.0439115465,-0.3232084513,0.0133835878,-0.2990259528,-0.2501083314,-0.4383622408,0.1537317634,-0.177799508,-0.0845339298,-0.0026808882,0.2986175418,0.1873589605,-0.2003104836,0.026588954,-0.1110703126,0.2070545703,0.0882992595,0.3847719133,-0.1615374535,0.4067229629,0.1174867451,0.2566284835,-0.26918751,0.0335369334,0.3619191945,-0.0506728664,0.1701949239,0.1592020392,-0.0863516107,0.1363561302,0.0409470275,-0.0944347158,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/418","title":"Addition of google drive links to dl_manager","comments":"Oh sorry, I think `_get_drive_url` is doing that. \r\n\r\nHave you tried to use `dl_manager.download_and_extract(_get_drive_url(_TRAIN_URL)`? it should work with google drive links.\r\n","body":"Hello there, I followed the template to create a download script of my own, which works fine for me, although I had to shun the dl_manager because it was downloading nothing from the drive links and instead use gdown.\r\n\r\nThis is the script for me:\r\n\r\n```python\r\nclass EmoConfig(nlp.BuilderConfig):\r\n    \"\"\"BuilderConfig for SQUAD.\"\"\"\r\n\r\n    def __init__(self, **kwargs):\r\n        \"\"\"BuilderConfig for EmoContext.\r\n    Args:\r\n      **kwargs: keyword arguments forwarded to super.\r\n    \"\"\"\r\n        super(EmoConfig, self).__init__(**kwargs)\r\n\r\n_TEST_URL = \"https:\/\/drive.google.com\/file\/d\/1Hn5ytHSSoGOC4sjm3wYy0Dh0oY_oXBbb\/view?usp=sharing\"\r\n_TRAIN_URL = \"https:\/\/drive.google.com\/file\/d\/12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X\/view?usp=sharing\"\r\n\r\nclass EmoDataset(nlp.GeneratorBasedBuilder):\r\n    \"\"\" SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Version 1.0.0 \"\"\"\r\n\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n    force = False\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            features=nlp.Features(\r\n                {\r\n                    \"text\": nlp.Value(\"string\"),\r\n                    \"label\": nlp.features.ClassLabel(names=[\"others\", \"happy\", \"sad\", \"angry\"]),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/www.aclweb.org\/anthology\/S19-2005\/\",\r\n            citation=_CITATION,\r\n        )\r\n    \r\n    def _get_drive_url(self, url):\r\n        base_url = 'https:\/\/drive.google.com\/uc?id='\r\n        split_url = url.split('\/')\r\n        return base_url + split_url[5]\r\n    \r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n        if(not os.path.exists(\"emo-train.json\") or self.force):\r\n            gdown.download(self._get_drive_url(_TRAIN_URL), \"emo-train.json\", quiet = True)\r\n        if(not os.path.exists(\"emo-test.json\") or self.force):\r\n            gdown.download(self._get_drive_url(_TEST_URL), \"emo-test.json\", quiet = True)\r\n        return [\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TRAIN,\r\n                gen_kwargs={\r\n                    \"filepath\": \"emo-train.json\",\r\n                    \"split\": \"train\",\r\n                },\r\n            ),\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TEST,\r\n                gen_kwargs={\"filepath\": \"emo-test.json\", \"split\": \"test\"},\r\n            ),\r\n        ]\r\n\r\n    def _generate_examples(self, filepath, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n        with open(filepath, 'rb') as f:\r\n            data = json.load(f)\r\n            for id_, text, label in zip(data[\"text\"].keys(), data[\"text\"].values(), data[\"Label\"].values()):\r\n                yield id_, {\r\n                    \"text\": text,\r\n                    \"label\": label,\r\n                }\r\n```\r\n\r\nCan someone help me in adding gdrive links to be used with default dl_manager or adding gdown as another dl_manager, because I'd like to add this dataset to nlp's official database.","comment_length":21,"text":"Addition of google drive links to dl_manager \n Hello there, I followed the template to create a download script of my own, which works fine for me, although I had to shun the dl_manager because it was downloading nothing from the drive links and instead use gdown.\r\n\r\nThis is the script for me:\r\n\r\n```python\r\nclass EmoConfig(nlp.BuilderConfig):\r\n    \"\"\"BuilderConfig for SQUAD.\"\"\"\r\n\r\n    def __init__(self, **kwargs):\r\n        \"\"\"BuilderConfig for EmoContext.\r\n    Args:\r\n      **kwargs: keyword arguments forwarded to super.\r\n    \"\"\"\r\n        super(EmoConfig, self).__init__(**kwargs)\r\n\r\n_TEST_URL = \"https:\/\/drive.google.com\/file\/d\/1Hn5ytHSSoGOC4sjm3wYy0Dh0oY_oXBbb\/view?usp=sharing\"\r\n_TRAIN_URL = \"https:\/\/drive.google.com\/file\/d\/12Uz59TYg_NtxOy7SXraYeXPMRT7oaO7X\/view?usp=sharing\"\r\n\r\nclass EmoDataset(nlp.GeneratorBasedBuilder):\r\n    \"\"\" SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text. Version 1.0.0 \"\"\"\r\n\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n    force = False\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            features=nlp.Features(\r\n                {\r\n                    \"text\": nlp.Value(\"string\"),\r\n                    \"label\": nlp.features.ClassLabel(names=[\"others\", \"happy\", \"sad\", \"angry\"]),\r\n                }\r\n            ),\r\n            supervised_keys=None,\r\n            homepage=\"https:\/\/www.aclweb.org\/anthology\/S19-2005\/\",\r\n            citation=_CITATION,\r\n        )\r\n    \r\n    def _get_drive_url(self, url):\r\n        base_url = 'https:\/\/drive.google.com\/uc?id='\r\n        split_url = url.split('\/')\r\n        return base_url + split_url[5]\r\n    \r\n    def _split_generators(self, dl_manager):\r\n        \"\"\"Returns SplitGenerators.\"\"\"\r\n        if(not os.path.exists(\"emo-train.json\") or self.force):\r\n            gdown.download(self._get_drive_url(_TRAIN_URL), \"emo-train.json\", quiet = True)\r\n        if(not os.path.exists(\"emo-test.json\") or self.force):\r\n            gdown.download(self._get_drive_url(_TEST_URL), \"emo-test.json\", quiet = True)\r\n        return [\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TRAIN,\r\n                gen_kwargs={\r\n                    \"filepath\": \"emo-train.json\",\r\n                    \"split\": \"train\",\r\n                },\r\n            ),\r\n            nlp.SplitGenerator(\r\n                name=nlp.Split.TEST,\r\n                gen_kwargs={\"filepath\": \"emo-test.json\", \"split\": \"test\"},\r\n            ),\r\n        ]\r\n\r\n    def _generate_examples(self, filepath, split):\r\n        \"\"\" Yields examples. \"\"\"\r\n        with open(filepath, 'rb') as f:\r\n            data = json.load(f)\r\n            for id_, text, label in zip(data[\"text\"].keys(), data[\"text\"].values(), data[\"Label\"].values()):\r\n                yield id_, {\r\n                    \"text\": text,\r\n                    \"label\": label,\r\n                }\r\n```\r\n\r\nCan someone help me in adding gdrive links to be used with default dl_manager or adding gdown as another dl_manager, because I'd like to add this dataset to nlp's official database. \n Oh sorry, I think `_get_drive_url` is doing that. \r\n\r\nHave you tried to use `dl_manager.download_and_extract(_get_drive_url(_TRAIN_URL)`? it should work with google drive links.\r\n","embeddings":[-0.0986939371,0.0516564436,-0.1309203506,-0.0558965728,0.0639402568,-0.0953544378,0.3679338396,0.2208102643,0.2062921822,0.2249778062,-0.0750645027,0.3655760586,-0.2819617987,0.1416775286,0.1477239877,0.1173321381,-0.224351123,0.2025327682,0.2756347656,-0.1639436185,-0.2751327753,0.3278068602,-0.0318780653,-0.0081285173,0.0502305627,0.0505593009,-0.1391222626,0.198068127,-0.1188958138,-0.2999538481,-0.4197380543,0.1671042591,0.160240218,-0.0974567235,-0.0001042657,-0.1480781883,0.2225690186,-0.2084534466,-0.4087229073,-0.293666929,0.2981956303,-0.1724279672,0.1073081344,-0.2651377916,-0.0045758276,-0.0983251333,0.2041182965,0.1354039758,0.2655147016,0.3032342196,0.2717006207,0.0790492222,0.113470979,0.0298931692,0.3312549591,-0.0354893319,-0.0682809278,0.35271433,0.3344673514,-0.3054541349,-0.1618669927,0.2883867025,-0.005473515,-0.0109913573,0.2195129991,0.1113411263,0.5780613422,-0.3311682045,0.1066150144,0.0327493623,0.0236196127,-0.0413232967,-0.0850505233,-0.2842149436,0.08381401,-0.2874571979,0.2766533494,0.1495649666,-0.2334840745,-0.0543575138,-0.1294789314,-0.0255045816,-0.015468711,0.0540597402,-0.0889275596,0.4138535857,-0.0389394574,-0.2830852568,0.3861259818,-0.0381522439,-0.0971575156,-0.1532618254,-0.0394701101,0.0766005889,-0.0374772809,-0.2887773812,0.1567475349,0.0466397926,0.093797259,0.2489909828,0.0460647494,0.0328915268,0.2356236577,0.0752923787,0.1630280316,0.4459595382,-0.0003144037,0.0400670469,0.3538968563,0.4176515937,0.1609001309,0.1501174271,-0.1202337146,-0.1861605793,-0.1416518539,0.1408617496,-0.1575787514,0.0906012058,-0.4310445487,-0.1330971122,-0.3732341826,0.0073386524,-0.0343621001,0.4378283918,-0.0487489253,-0.1396611929,0.2441136092,0.2374013662,-0.0423724875,0.0900178328,-0.1254783124,0.255615443,-0.2168087512,-0.0520229526,0.3310387731,0.0770800337,0.421222806,-0.2571605742,-0.1213400289,-0.224132359,0.1388938576,0.0859965533,-0.0139247123,-0.064111203,0.4167032838,0.0155231385,-0.1533248723,-0.4158217013,-0.071225442,0.0388117135,0.0347075351,-0.037107002,-0.1740760654,0.3355267346,-0.0862290189,-0.0942458659,-0.0037766567,0.2271474302,-0.1502159536,0.0436013825,0.0858766139,0.3182860315,-0.0827771053,-0.000988554,0.1685232818,0.2986229062,-0.0949452594,-0.2877131999,-0.1839938015,0.1673677117,-0.1132550612,-0.0632707402,-0.044929985,0.2455672026,-0.3794006705,0.2419207394,0.304346174,-0.2896392047,-0.2050281167,0.2148452997,-0.3752191663,-0.1749029905,0.030269891,0.0142999822,0.4108042121,-0.0042553372,-0.1432295889,0.4827906489,0.0771704242,0.242865935,-0.3040136099,-0.466017276,-0.1829135418,0.2075032294,0.1516643167,-0.1184460148,0.1088404134,-0.0877448469,0.3002710938,-0.1562739909,0.1865597665,0.1028919965,0.082430616,0.0962019637,0.0902650356,0.1277572811,-0.4553069472,-0.0245841052,-0.1491093785,0.2400395274,-0.3181627095,-0.1654351503,-0.2285122573,-0.1823611856,-0.2087038606,-0.3032354414,0.2976228893,0.2181787938,0.0589148887,0.2489134073,0.0791672543,-0.078336969,0.1662929952,0.1352360249,0.0483056195,0.3308407664,-0.3822062016,-0.1154452637,0.1777767688,0.0533847101,0.1627697051,-0.0097971642,0.0177794714,0.2022307515,-0.2338354439,0.4560253918,0.0342990011,-0.018487528,0.2643815875,-0.001894454,0.3286045492,0.3785975277,0.1253685653,-0.042322088,-0.1335391253,0.3993208706,0.2978732884,0.1315351278,0.1984390169,0.1492633671,-0.0345823541,-0.3092212379,-0.0366149396,-0.175003618,0.1060321406,0.4917028844,0.3812161088,-0.1171898544,-0.1798555851,0.2268595248,0.3522838056,-0.0431040488,0.2884167135,0.0797794685,-0.3493873775,-0.1552882195,-0.1172491759,0.3525727689,0.1629645526,0.2543209791,0.1712719202,0.0493353643,-0.0080083162,-0.3064471781,0.399212122,0.0655613244,0.1062253714,0.3554275632,0.112254627,-0.1241622567,-0.3668965697,-0.0078185881,-0.0030904301,-0.051934097,-0.2809714377,-0.0359772854,-0.2021116912,-0.1673544347,-0.040416833,0.1189079285,-0.0453860685,-0.1994874179,0.241571486,0.2851106822,-0.2777339518,0.127188921,0.1675787568,0.3435669839,-0.0704192296,-0.051740665,-0.1093210727,0.0156291276,-0.331397295,0.1907478869,0.2792063951,0.0847769007,0.1668625176,0.035354279,0.0824102759,-0.4212260544,-0.1966773272,0.2405076027,-0.1467298567,0.0396607593,0.1082567051,-0.0911808386,-0.0478258096,-0.3472113609,0.0503748134,-0.2066100538,-0.2028837651,-0.3251749873,0.1594581753,0.0629494265,-0.488184154,-0.3987681568,-0.2369147837,-0.380084008,0.1677398533,0.1598065197,0.1822991818,0.1246550754,-0.0548261032,0.1800568253,0.2231410295,-0.1606066525,-0.147671774,0.2775504291,0.4198610485,-0.4095939994,-0.3190635145,0.0439115465,-0.3232084513,0.0133835878,-0.2990259528,-0.2501083314,-0.4383622408,0.1537317634,-0.177799508,-0.0845339298,-0.0026808882,0.2986175418,0.1873589605,-0.2003104836,0.026588954,-0.1110703126,0.2070545703,0.0882992595,0.3847719133,-0.1615374535,0.4067229629,0.1174867451,0.2566284835,-0.26918751,0.0335369334,0.3619191945,-0.0506728664,0.1701949239,0.1592020392,-0.0863516107,0.1363561302,0.0409470275,-0.094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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/414","title":"from_dict delete?","comments":"`from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\nRight now if you want to use `from_dict` you have to install the package from the master branch\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n```","body":"AttributeError: type object 'Dataset' has no attribute 'from_dict'","comment_length":53,"text":"from_dict delete? \n AttributeError: type object 'Dataset' has no attribute 'from_dict' \n `from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\nRight now if you want to use `from_dict` you have to install the package from the master branch\r\n```\r\npip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n```","embeddings":[-0.1168889999,-0.2096914053,-0.1167027056,0.0465560853,0.1620283723,-0.1888360828,0.094357267,0.3141218424,0.0377569273,0.1403821409,0.0539773069,0.5944421887,-0.1069423631,0.2920179665,-0.0426585861,-0.1649783999,-0.0688440055,0.3200041056,-0.3128439188,-0.0461874977,0.0110787461,0.3731954098,-0.1320789903,0.2549414635,0.1205590218,-0.1711758375,0.0303646121,0.1430951506,-0.1418486983,-0.331137538,0.2542820275,0.0454760641,-0.1363679469,0.1577937752,-0.000099203,-0.0431271009,0.3241799176,-0.0708885342,-0.2661366463,-0.3808972239,-0.1292472035,-0.1784549057,0.3302332461,-0.1057158113,-0.2622191906,0.148300916,0.0286048111,-0.0083772056,0.3838491142,0.3884246945,0.3685189188,0.1577926576,0.1676738858,-0.1256329417,0.1861926913,0.1869291961,-0.0660707951,-0.0968847573,0.0539165996,-0.1805331856,0.3063396215,0.2829711437,-0.1644661874,-0.0961181298,0.1874166429,-0.0243609287,0.0242319051,-0.1416930109,0.0300105345,0.1506774873,0.2641409039,-0.3806945384,-0.1140294224,-0.301546067,-0.0242865924,-0.3830109835,0.0237073582,-0.0531720333,-0.1575684696,0.1301054358,-0.0527608097,-0.4518622458,-0.1853362769,0.3525791764,0.1932297796,0.0689729527,-0.1127059087,0.0655093715,0.2613548338,-0.0540376678,-0.3767986,0.2988931835,-0.1951348484,0.1416613609,-0.0280430634,-0.2018370926,0.3356459141,-0.0524854325,-0.0726941824,0.1180981249,-0.0129491165,-0.0188899003,-0.0099218143,0.2746320367,0.4173329771,0.1929970533,0.3750910759,0.2258402407,0.1976595968,0.0005200541,-0.0670649111,-0.0705474317,0.1084683463,0.0079595437,-0.1542845666,-0.0837850049,0.2740116119,-0.1018361449,-0.2801439464,-0.2536678016,0.1547212452,0.1114788353,0.0496223792,0.3161873817,0.0285122562,0.2045546919,0.0234101508,0.1625921279,-0.3559416831,-0.0318716839,-0.2442645878,0.3273057044,-0.1398757249,-0.47908324,0.0662470534,0.1737167984,0.3600257933,-0.1125761643,0.0398115031,0.2609203756,0.0090481127,0.0365596674,0.0756462812,0.2162600309,-0.0976463109,-0.1582449973,0.2198809236,-0.535482049,-0.2740392387,0.1050391942,-0.2255008072,-0.2852151394,-0.450623244,0.3046717346,0.0804250538,-0.2931632996,-0.3293567002,0.1842859983,0.1359829754,-0.216448158,-0.1059303656,-0.1513110548,-0.0192243401,-0.1611665487,-0.0074461503,0.1157196164,-0.2353723943,-0.209140107,-0.2025369406,-0.3065539002,0.2619755566,0.3139546514,-0.2446835637,0.2455075383,-0.1226886734,0.1057924107,0.5160627961,-0.5724243522,-0.2312455475,0.2054415643,-0.0490397513,-0.1142272428,-0.1684024781,-0.017951373,0.1819056571,-0.0890898108,-0.0099350531,0.3474216759,0.1860726625,0.1431891173,-0.1495317519,-0.1222073957,-0.0283667054,-0.0966636017,-0.0759283155,0.0703809261,0.1684971303,0.1468969434,0.2025403529,-0.094522506,0.009312707,0.1962150484,0.5006925464,0.0556181557,-0.093551971,-0.4382086992,-0.0323105566,-0.139447704,-0.1978676319,0.3533257842,-0.318826437,-0.1176964566,-0.2525280714,0.0997808427,-0.2223487645,-0.1572247148,0.3052579463,0.0586955361,0.143273145,0.0759015009,-0.3448251784,0.1163838208,0.0605933741,0.0451134145,-0.2440586537,-0.0218843371,-0.3494740725,-0.2440223992,0.0595742241,0.4624376595,0.0109009612,-0.0529735908,0.0195062347,0.3652351201,-0.0927099735,0.0546632409,0.1078714877,-0.1840557158,0.0560944751,-0.2318473458,-0.0272628814,0.0637400672,0.0834897533,0.1336135119,-0.10646566,0.1729995161,0.2040770501,-0.0279307161,0.2542048097,0.1592370421,0.3644242585,-0.2514116168,0.1046667099,-0.3587047458,0.2191609293,-0.0493634529,-0.0715416819,-0.1774632037,-0.423636198,0.0452174321,0.6067508459,0.155034408,0.213289544,0.095278956,-0.0068472968,-0.1788431257,-0.05146119,0.0949727669,0.1725858301,0.2499797642,-0.1400128007,0.0440866053,-0.0233692378,-0.0711451247,0.3133590221,-0.1102237701,0.1291939616,0.1159423962,0.1464599818,-0.1220927611,-0.4732962847,-0.0990835652,-0.2719185948,0.31240803,-0.1202232242,0.2324871868,-0.3833058178,-0.511946559,0.029831389,-0.2020338774,-0.3217611313,-0.3416158855,0.2300844043,0.1038933098,-0.0862878338,0.1916826814,0.1746332943,0.0412034765,0.0341077857,-0.1091085002,-0.0448255539,-0.3886269331,-0.2937385142,0.2078532726,-0.0464740433,0.1856131107,0.34539029,-0.0927943438,-0.1346259564,-0.0653238818,-0.4512675405,0.1339701116,-0.1982107759,0.4171160161,0.4988261759,0.0458737314,-0.1586198509,-0.1184889674,0.2855276465,-0.5542433262,-0.2418099046,0.1625321209,-0.0430538021,0.0001885036,-0.2799878418,-0.2784693837,-0.2698887289,-0.4429297745,0.3062042296,0.2609966099,0.2338288426,0.3336763084,-0.0887839645,0.1830664426,-0.1976733357,0.3888370395,-0.1767815799,-0.1744702905,-0.0336391032,-0.2309451103,-0.4337511659,0.0063298405,0.1090219766,0.3247979581,-0.2439881861,-0.3170567751,-0.2650544345,0.0487910323,0.1092738509,0.0224366449,0.1758804917,0.2571715117,0.1897458881,-0.2251132876,-0.2148870528,-0.0785518885,0.2249791026,0.201737389,0.111191377,-0.0416105129,0.2193104327,0.1520111561,0.362911284,-0.0490334257,0.1733387113,0.328886658,-0.123388432,0.4986999035,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/414","title":"from_dict delete?","comments":"> `from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\n> Right now if you want to use `from_dict` you have to install the package from the master branch\r\n> \r\n> ```\r\n> pip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n> ```\r\nOK, thank you.\r\n","body":"AttributeError: type object 'Dataset' has no attribute 'from_dict'","comment_length":62,"text":"from_dict delete? \n AttributeError: type object 'Dataset' has no attribute 'from_dict' \n > `from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\n> Right now if you want to use `from_dict` you have to install the package from the master branch\r\n> \r\n> ```\r\n> pip install git+https:\/\/github.com\/huggingface\/nlp.git\r\n> ```\r\nOK, thank you.\r\n","embeddings":[-0.0747189745,-0.2516213059,-0.1155344173,0.0879213288,0.1837774664,-0.1787118465,0.0908716917,0.2998166084,0.0316059254,0.1319086105,0.051009573,0.5649474859,-0.1044338271,0.2966352105,-0.0471764207,-0.2244530171,-0.0426336601,0.2933095992,-0.3304531276,-0.0517251752,0.0404684059,0.3629773259,-0.1296854913,0.2741340995,0.1252758354,-0.1901750714,0.001523576,0.1526876837,-0.1340594888,-0.3176116943,0.2797694802,0.0268054567,-0.1014018655,0.1761731356,-0.0000985522,-0.0737006441,0.3171120882,-0.0745516494,-0.2624133527,-0.3476303816,-0.1504907906,-0.1589511782,0.2975644171,-0.1199103072,-0.2388195693,0.1387144476,0.0045639747,0.0186313447,0.3920916617,0.3837104142,0.3749192655,0.1249384284,0.1929481477,-0.121417217,0.1639735997,0.1866833717,-0.0807384998,-0.0862299129,0.0554489754,-0.1900994629,0.3049733937,0.2913117707,-0.1488623619,-0.1117041856,0.1986547112,-0.0462510139,0.0224084966,-0.159161672,0.0389379784,0.1428239197,0.3123072684,-0.3866829574,-0.1058308482,-0.2720894814,-0.0205371641,-0.3580051363,0.0111028478,-0.0354096703,-0.127446726,0.144500792,-0.0395705551,-0.4792864919,-0.2171789408,0.3515553772,0.2264175266,0.0382249355,-0.1236405745,0.0532368906,0.2304850519,-0.0637611374,-0.3869415224,0.2951688766,-0.1951296329,0.1586609036,-0.0446749255,-0.2178733796,0.3263928592,-0.0638526082,-0.0525567643,0.0825085267,0.0046483949,-0.0221263003,-0.0281217378,0.2544954121,0.4578906298,0.2475712299,0.3936284482,0.2212034017,0.1548516899,0.0250813756,-0.0860896185,-0.0489335395,0.0843025744,0.0313623026,-0.1318344325,-0.0685866177,0.2815732956,-0.1133547574,-0.2768691778,-0.206428647,0.1209661365,0.1106280833,0.0282629132,0.3377598524,0.0086521134,0.2231969088,0.0360624008,0.1516708881,-0.3684809208,-0.0670754686,-0.2348340005,0.3588404357,-0.1695245504,-0.4798001647,0.0434884764,0.175326407,0.3514825106,-0.1070454344,0.0640288368,0.2143966556,-0.005742793,0.0531018078,0.0964144394,0.1829386801,-0.0929335877,-0.156086877,0.210033372,-0.5214315653,-0.252196312,0.1298751682,-0.2238720655,-0.2987514734,-0.4562395811,0.2990592718,0.0899032652,-0.2795068324,-0.3280282915,0.1972243041,0.1388174295,-0.1668095142,-0.0827485323,-0.1410974413,-0.0044679153,-0.1480266899,-0.024340041,0.1140932292,-0.2422045469,-0.1710827649,-0.1728948206,-0.3147648573,0.2596482635,0.3290223181,-0.262842983,0.2250309587,-0.1182504892,0.1163873598,0.479701817,-0.5727295876,-0.1921099126,0.2145445198,-0.0597661547,-0.102557525,-0.1759753078,-0.0275619011,0.1942097992,-0.109426111,-0.0382939875,0.3194501102,0.1817690879,0.1489367485,-0.2012803257,-0.0979351029,-0.0093584759,-0.1098808944,-0.0755400434,0.0522700548,0.1559681892,0.1709434241,0.2374147177,-0.0858651698,0.0225113519,0.2546881735,0.4777030051,0.0747559741,-0.0862904862,-0.4368614554,-0.0294936486,-0.1539904773,-0.1910116524,0.3839293718,-0.2584607303,-0.1255776882,-0.2810029984,0.1069899797,-0.1987516731,-0.1712756753,0.3102854788,0.0364366509,0.128769666,0.0446912907,-0.3484444618,0.1158475727,0.029240394,0.0292404294,-0.2396176904,-0.0224088896,-0.366972357,-0.2318977118,0.101094231,0.4514656663,0.0169409215,-0.0570092387,0.029873278,0.3622139096,-0.107615076,0.0602518618,0.1294952929,-0.1758580655,0.0612258874,-0.2530985177,-0.0331598967,0.0573220812,0.0873133913,0.1328568459,-0.0833986476,0.1473474205,0.1943663806,-0.0475816242,0.2373598367,0.145862475,0.3561370671,-0.2580803931,0.0934287906,-0.3171673715,0.2263148725,-0.0559443198,-0.1203266159,-0.1707588434,-0.4570818841,0.072836414,0.6142229438,0.1181451082,0.2027907521,0.1100267321,-0.0104902601,-0.2033447474,-0.0465196967,0.1275081933,0.1598938704,0.2393365949,-0.1442097872,0.040248815,-0.0160735231,-0.0424356349,0.3344411254,-0.0953002125,0.1490649432,0.0981971845,0.1295661926,-0.1051477864,-0.4526612759,-0.1029855534,-0.2659780681,0.3086747527,-0.1300005466,0.2137014419,-0.3413686454,-0.5405299067,0.0080291582,-0.2147608399,-0.3553180695,-0.3191390634,0.2329045534,0.0794749558,-0.1158465967,0.1832075566,0.1557338089,0.0698950738,0.0368973091,-0.0881305784,-0.0506448783,-0.3942842782,-0.3033125699,0.2088561654,-0.030275939,0.21846506,0.3349041045,-0.092314288,-0.1171327457,-0.0730040818,-0.427192837,0.1169190109,-0.2132866383,0.4308361709,0.4775032103,0.1004523262,-0.1677221805,-0.1249687374,0.274867624,-0.5327470899,-0.2540537715,0.1580503881,-0.0154889198,-0.0048853084,-0.27590397,-0.2948980033,-0.2445005029,-0.4642463923,0.3006665707,0.2462503165,0.2239492089,0.3138276041,-0.0987153798,0.1614603102,-0.1947257966,0.4215764701,-0.1935763955,-0.1916532964,-0.0404682308,-0.2186050117,-0.4552693665,0.0204544384,0.1377618909,0.3500982821,-0.2896838188,-0.3144922554,-0.2251933664,0.058029402,0.0870286822,-0.0012439908,0.1882114261,0.2421658337,0.2206172049,-0.2163992375,-0.2033874989,-0.1030128598,0.2228536159,0.2019103914,0.1072848216,-0.0429824851,0.2035516948,0.142375052,0.3852213323,-0.0343018472,0.1985272169,0.3196974397,-0.0675486848,0.4661508203,-0.1550482363,-0.4070017934,-0.0091275973,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/413","title":"Is there a way to download only NQ dev?","comments":"Unfortunately it's not possible to download only the dev set of NQ.\r\n\r\nI think we could add a way to download only the test set by adding a custom configuration to the processing script though.","body":"Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)? \r\nAs we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data. \r\n\r\nI tried\r\n```\r\ndataset = nlp.load_dataset('natural_questions', split=\"validation\", beam_runner=\"DirectRunner\")\r\n```\r\nBut this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits \/ slicing options only available after downloading?\r\n\r\nThanks!","comment_length":35,"text":"Is there a way to download only NQ dev? \n Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)? \r\nAs we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data. \r\n\r\nI tried\r\n```\r\ndataset = nlp.load_dataset('natural_questions', split=\"validation\", beam_runner=\"DirectRunner\")\r\n```\r\nBut this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits \/ slicing options only available after downloading?\r\n\r\nThanks! \n Unfortunately it's not possible to download only the dev set of NQ.\r\n\r\nI think we could add a way to download only the test set by adding a custom configuration to the processing script though.","embeddings":[-0.4314169586,0.1772087067,-0.1129720211,0.1146225333,-0.1070222557,-0.034727104,-0.2576389313,0.7160260677,-0.0329578072,0.3145226538,-0.2831300795,0.0151575794,-0.2205640823,0.6092382073,0.4744931757,-0.0987759307,-0.1157774776,0.4419937134,-0.0799669698,0.0518985651,-0.1738967299,0.2375749052,-0.0511896051,0.1008467898,0.3044531047,-0.2877178788,0.1591368765,-0.0306114946,-0.3407664597,-0.2889768183,0.022300493,0.2152756155,0.174672693,-0.3234871924,-0.0001247323,-0.0638319403,-0.0178546533,-0.1316623539,-0.1318190396,0.1239356697,-0.1069383696,-0.133865431,0.0931724682,0.011332579,-0.0697069094,-0.2781607509,0.2135231346,-0.0861073732,0.3582045138,0.4454734623,0.0204185676,0.1405423582,-0.2529484332,0.1526878625,-0.0644539297,-0.282286644,-0.0892292708,0.0156455152,0.1498271525,0.135431245,0.1219885722,0.1407377571,0.2157315165,0.2066383064,0.0054100887,-0.0171899367,0.3492211103,-0.7237771153,-0.0792933777,0.7125211954,0.1285497844,0.1623681486,-0.1726223826,-0.1767225862,0.1247088239,-0.4916466177,-0.0423816964,0.5084100366,-0.565615952,-0.0027759231,-0.6391010284,-0.5820971131,-0.1727650017,0.1160465032,-0.0537559353,-0.0337662585,0.0801800266,0.0538342148,0.2881833017,0.1955110282,0.0965853035,0.1249824688,-0.1501685828,0.1346161813,-0.2485608608,-0.4248794615,-0.2950527072,-0.1588326544,0.2999621332,0.0453286581,-0.0710311085,0.0286634341,0.072369352,-0.1054556295,0.408921808,0.1076537222,0.284154892,0.231772691,0.0504684858,0.021156406,0.0802846327,0.1652096063,-0.0076865098,-0.0758912042,-0.6195682287,0.0124149127,-0.0180410407,-0.431645453,0.0045180675,-0.1753554791,-0.159151867,-0.1118600443,-0.1868516058,0.1990063787,0.0409796089,0.0766252801,-0.297126174,0.2975178957,-0.2782457471,-0.8463834524,-0.1588612795,0.0120070577,0.0453217141,0.1391306669,0.3189242482,0.1616570503,0.211309135,-0.2638457716,0.001660227,-0.0189123191,0.398670435,-0.0074116085,0.0302929301,0.3964280188,0.099882625,0.1514957845,-0.1389228553,0.3128338754,-0.4387754202,0.2237685323,-0.0319213159,-0.3537499309,0.2378725559,0.0444737375,-0.0815992504,0.0902851298,-0.0688426122,0.2779449522,-0.0511246175,-0.1465737671,-0.095301941,-0.184546411,-0.2540001869,-0.0510652103,0.3700125515,-0.0547664836,-0.317294836,-0.2180195451,-0.0503768437,-0.1873558611,0.2057102621,0.3892886043,-0.1953092515,0.2749291956,-0.1282560378,0.0419043265,0.6039460897,0.063419573,-0.1607677937,0.4452090263,-0.0958242044,-0.3545050323,-0.0886768773,0.2540343702,0.1982444972,-0.0106193749,-0.3353327513,0.7256408334,-0.1832430065,-0.2423432767,-0.0794680268,-0.5672937632,0.294388175,0.1072142869,0.2334671617,0.1547670364,-0.1318540126,-0.16444543,0.228008762,0.0481451899,-0.0218945872,0.0195631459,-0.0771089494,-0.4434758723,-0.1948779076,-0.1264057606,-0.4273959398,0.2192077935,-0.6686814427,-0.2727111876,0.433701098,-0.0741638169,-0.0856534392,-0.2160948962,0.2276449651,-0.3765787184,-0.0897453427,-0.0897621512,0.232812196,0.1345527172,-0.3531804979,-0.0775736645,-0.0844805837,0.1200894713,-0.1485579014,-0.1885522455,0.194761619,0.1635546535,0.3136901557,-0.0682017878,-0.1422620267,0.0293508675,0.1174350977,0.1840603054,-0.0653511137,0.0679311156,0.1901082098,-0.0035382891,0.0262003094,-0.0636295378,-0.0851446092,0.0705611259,-0.2034726292,0.0601695739,-0.1732619405,0.4890032113,-0.2501839697,0.238762185,-0.0243577268,0.0712083802,0.1150856763,-0.1953156888,0.0422033593,-0.1012481377,0.262136519,0.079492934,0.0874665529,-0.0781058222,-0.1392308772,0.0596786924,0.3397408128,-0.1995896846,-0.129084155,-0.0971145108,-0.0654710755,-0.1062469259,0.2073575854,0.3562300205,0.2953766584,0.1393584013,0.5149038434,0.0771054104,0.14949736,-0.251996845,0.1438812613,0.0286945105,-0.203351289,-0.061133597,-0.0969168097,-0.1329973936,-0.2285000235,0.5298206806,0.1884369105,-0.0437838472,-0.0721282363,-0.1056225598,-0.4070443809,-0.136988923,-0.0033696876,-0.0215591509,-0.1591154933,-0.1997402012,0.3900647759,-0.1750443578,-0.2246314883,0.1717157811,0.1362401545,0.4625746608,-0.1568747312,-0.2329501212,0.1639208943,0.0432777442,-0.0902830958,0.0886400566,0.085990347,0.3979047537,0.4500082433,-0.100373134,-0.1956561357,0.0405306928,0.0862389207,0.0650635436,0.2300628126,0.4814370871,-0.0224951878,0.4113826454,-0.0127600012,0.1447871774,-0.3928005695,-0.1107226685,-0.2695804536,-0.1097872332,0.2999337912,0.3856029212,-0.2025088966,-0.7505491376,-0.2044207156,-0.2655576468,0.1911294907,0.0078091859,0.2178954482,-0.1302042305,-0.2237562239,-0.0723755732,-0.419121325,-0.0299893711,-0.0546580702,-0.0272561871,0.2050245255,-0.1796647459,-0.1089262292,0.1190538779,-0.0054975273,0.3167388141,0.2234031409,-0.658018887,0.3405663371,-0.0411544591,0.173910901,-0.0416291095,-0.1339777559,0.4145452976,-0.4879589677,0.034420561,0.2119375765,0.2159923613,0.0893984661,0.0918622985,0.153855443,-0.0929381475,0.32253474,0.0503838137,0.9196417928,0.0844696537,0.2862994671,-0.0774068832,0.0287648663,0.2585729957,-0.1073842943,0.3119176328,0.2791598439,0.197838068,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/413","title":"Is there a way to download only NQ dev?","comments":"Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially also others. \r\nFor us, it will in this case make the difference of using the library or keeping the old downloads of the raw dev datasets.  \r\nHowever, I don't know if that fits into your plans with the library and can also understand if you don't want to support this.","body":"Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)? \r\nAs we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data. \r\n\r\nI tried\r\n```\r\ndataset = nlp.load_dataset('natural_questions', split=\"validation\", beam_runner=\"DirectRunner\")\r\n```\r\nBut this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits \/ slicing options only available after downloading?\r\n\r\nThanks!","comment_length":70,"text":"Is there a way to download only NQ dev? \n Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)? \r\nAs we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data. \r\n\r\nI tried\r\n```\r\ndataset = nlp.load_dataset('natural_questions', split=\"validation\", beam_runner=\"DirectRunner\")\r\n```\r\nBut this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits \/ slicing options only available after downloading?\r\n\r\nThanks! \n Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially also others. \r\nFor us, it will in this case make the difference of using the library or keeping the old downloads of the raw dev datasets.  \r\nHowever, I don't know if that fits into your plans with the library and can also understand if you don't want to support this.","embeddings":[-0.3837682307,0.2108084261,-0.0951718166,0.1044244468,-0.0920639411,-0.0907708108,-0.2257674634,0.6605112553,-0.0418677889,0.2795556784,-0.3325553834,0.0646842942,-0.2607530355,0.5805532932,0.4200989902,-0.1612845212,-0.1143595204,0.4430704117,-0.0725371018,0.0320941284,-0.1458450556,0.2355462015,-0.0852555707,0.0845535174,0.3158827424,-0.2681672573,0.1613009423,0.0003769562,-0.4130051434,-0.2256505787,0.1046490893,0.2318772078,0.2737070918,-0.315141201,-0.0001247978,-0.1767543703,-0.0492420606,-0.1076974943,-0.1048340127,0.0290610269,-0.0316629075,-0.1345097423,0.144841224,-0.0146086644,-0.0661740303,-0.2787023783,0.1802223027,-0.0877976641,0.2828414738,0.3137025237,0.0347506143,0.0575815029,-0.178581506,0.1744213551,0.0454849042,-0.1572285444,-0.1496081054,0.0710525289,0.2409635633,0.173973918,0.1371459216,0.1628846824,0.1711564213,0.1461558193,0.1041327715,-0.0084158126,0.2902481854,-0.6779151559,-0.1346675307,0.7349892259,0.2752510607,0.1195652485,-0.1453244388,-0.1751475334,0.1211699694,-0.4858598709,-0.1015630513,0.502063036,-0.5056434274,0.0058689145,-0.5925880671,-0.5954379439,-0.2289485931,0.1964994371,-0.0154539598,-0.0786429271,0.0763812885,0.080804728,0.343953222,0.1932834536,0.1554152519,0.1268605143,-0.1199788153,0.1305152327,-0.2635022998,-0.4542864263,-0.3151841164,-0.107878089,0.2948121428,0.1053656712,-0.1306050718,0.0311698355,0.0303931069,-0.1063595265,0.5197607279,0.0478787497,0.245787248,0.2374639064,0.1265533864,0.0372288898,0.0975349173,0.1258990169,-0.0222979411,-0.0219954774,-0.6431497335,-0.0611066706,-0.05613897,-0.4703986049,0.102476716,-0.2125980556,-0.1848107725,-0.0741305798,-0.0765821636,0.2119109333,0.0375511758,0.0674566627,-0.2454402,0.2834025621,-0.2661134005,-0.7744125724,-0.1252468824,-0.0079068132,-0.0679525137,0.061968524,0.3375356495,0.0844027996,0.1490256041,-0.1850019246,0.0073810844,0.0263682678,0.3335590363,-0.0166183654,-0.0097264368,0.3612703979,0.0033703598,0.1638413966,-0.1632596552,0.2585628331,-0.4758756161,0.2490354329,-0.0942887217,-0.3748087287,0.0877634361,0.0242971443,-0.0430559218,0.055895485,-0.0828236714,0.2786794305,-0.0765085891,-0.2345643044,-0.0444730856,-0.1747214794,-0.223338455,-0.0668757558,0.285582304,-0.0780791566,-0.2522703409,-0.1871508658,-0.0746145099,-0.1785927415,0.2108701766,0.3680124879,-0.2529716194,0.1583964825,-0.1155179739,0.0218368247,0.6580260396,0.0790001824,-0.2337393165,0.4908336997,-0.0526507497,-0.3138030469,-0.1015424505,0.3563975692,0.1839399785,-0.0589393936,-0.3391218185,0.7834393978,-0.137980625,-0.2503324449,-0.0784127042,-0.5916569233,0.3074536622,0.1783097833,0.2205729932,0.1342015266,-0.049559094,-0.1202784181,0.2245157361,0.0300307367,0.0112632439,-0.0088108769,0.0123789692,-0.3818090558,-0.1711748689,-0.1530351341,-0.4459687769,0.1846539676,-0.6536505222,-0.1945620179,0.4189762473,-0.0865709931,0.0073981299,-0.2341000736,0.2203123569,-0.4121864736,-0.1198642105,-0.1790969968,0.1835347563,0.1684643179,-0.3527587056,-0.1195261106,-0.1414876729,0.1672486067,-0.181732744,-0.121665515,0.1836248934,0.2101252377,0.2229205817,-0.0013606852,-0.1546615213,0.060196545,0.1264700443,0.1464357674,-0.1569622457,0.0748496726,0.1380552202,0.0602238365,0.1468370855,-0.0621340722,-0.010638955,0.0278234184,-0.1594226509,0.0359759629,-0.1841621548,0.467012316,-0.2941001356,0.2273011208,-0.0432110429,0.0427761637,0.0677795038,-0.1987802386,0.0702115521,-0.0314566493,0.2993671,0.0751121044,0.0893668532,-0.0684214234,-0.1516072303,0.0916004479,0.3092695177,-0.1839173287,-0.1773821414,-0.0365724377,-0.0742196068,-0.0707327425,0.2380770743,0.4090535045,0.3239270449,0.1457184553,0.5053703785,0.0464160293,0.1550115496,-0.2461326569,0.1222533956,-0.0016359024,-0.2593738437,-0.0784983635,-0.1056861132,-0.0683617219,-0.2644273341,0.4836115837,0.1211462021,-0.0516694672,-0.010891648,-0.0918668211,-0.4039867222,-0.1954006702,-0.0059841196,-0.0668179095,-0.2175918669,-0.1385278851,0.3773465157,-0.1401188821,-0.1859869957,0.1419488192,0.2079991102,0.4611381292,-0.2285566479,-0.3333145082,0.1294851452,0.064887926,-0.0823554248,0.0876156241,0.1210082322,0.3232124448,0.4368119538,-0.0148643134,-0.1111296266,-0.0220286027,-0.031248292,0.0310642365,0.2105919272,0.4511539042,0.0031831211,0.3574376702,-0.0207836907,0.1039652675,-0.3731699288,-0.2048400789,-0.2044688314,-0.1332655698,0.3195647001,0.3701706827,-0.2776895761,-0.709852159,-0.2263504416,-0.2926571965,0.2092171013,0.0054044379,0.1874392331,-0.0258354172,-0.2257738262,-0.0712637082,-0.3463261724,-0.0302542821,-0.0538278595,-0.0661431774,0.2306489795,-0.1736421436,-0.077406019,0.1423440576,0.0093302578,0.2786389291,0.2440709174,-0.7312856317,0.3878257275,-0.0149218971,0.179317981,0.012404128,-0.0316703245,0.3906842172,-0.4956606627,0.0461903438,0.2226561159,0.1801547706,0.0473610051,0.1463140994,0.2642141581,-0.0505623966,0.2984301746,0.0042140135,0.9026047587,-0.021901818,0.2770243287,-0.0279032495,0.1159623042,0.3132763207,-0.1179407462,0.2958511412,0.3645724654,0.1598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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/413","title":"Is there a way to download only NQ dev?","comments":"I don't think we could force this behavior generally since the dataset script authors are free to organize the file download as they want (sometimes the mapping between split and files can be very much nontrivial) but we can add an additional configuration for Natural Question indeed as @lhoestq indicate.","body":"Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)? \r\nAs we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data. \r\n\r\nI tried\r\n```\r\ndataset = nlp.load_dataset('natural_questions', split=\"validation\", beam_runner=\"DirectRunner\")\r\n```\r\nBut this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits \/ slicing options only available after downloading?\r\n\r\nThanks!","comment_length":50,"text":"Is there a way to download only NQ dev? \n Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)? \r\nAs we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data. \r\n\r\nI tried\r\n```\r\ndataset = nlp.load_dataset('natural_questions', split=\"validation\", beam_runner=\"DirectRunner\")\r\n```\r\nBut this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits \/ slicing options only available after downloading?\r\n\r\nThanks! \n I don't think we could force this behavior generally since the dataset script authors are free to organize the file download as they want (sometimes the mapping between split and files can be very much nontrivial) but we can add an additional configuration for Natural Question indeed as @lhoestq indicate.","embeddings":[-0.4887266457,0.2052318454,-0.0939359516,0.1260942966,-0.0418884084,-0.1087556854,-0.2285951823,0.6617497206,0.0076493579,0.2669901252,-0.2714673281,0.0445381142,-0.2082810551,0.5945587158,0.4444682896,-0.0646661669,-0.127142325,0.4709189236,-0.1361080408,-0.0069543622,-0.17444098,0.1564280093,-0.0427492335,0.0399172641,0.2861556411,-0.2435347885,0.1814636737,0.0041279709,-0.3567369282,-0.2835049629,0.0866329819,0.275703162,0.216697678,-0.3432943523,-0.0001268621,-0.1054657102,0.0004297808,-0.1328522861,-0.2147211879,0.0927825794,-0.0724057853,-0.1583822072,0.1609330475,0.0109025743,-0.0843144357,-0.3348846138,0.2662496567,-0.1632023156,0.377425164,0.4012565017,-0.0041164611,0.0548433065,-0.2272785604,0.1745741814,0.0175522603,-0.1025494188,-0.1552434415,0.0558356084,0.190091759,0.0763694495,0.0393826738,0.0963236168,0.2445637882,0.2181500047,0.1171687841,-0.0001179587,0.3439489305,-0.7146770358,-0.1467894167,0.7247014046,0.2575412095,0.0835120454,-0.122038886,-0.2418496609,0.1380847991,-0.4262854755,-0.0425271839,0.4883491695,-0.6017991304,0.0279397331,-0.5949159861,-0.5436315536,-0.1921218932,0.2189568728,-0.0272634011,-0.0606630482,0.0457835793,0.0554199666,0.2680762708,0.1433386952,0.1559547931,0.0705497861,-0.0610100403,0.1489268839,-0.1999875754,-0.4450471997,-0.2982934415,-0.0994832441,0.3308860064,0.1128259301,-0.0295024868,0.0462544113,0.0011221379,-0.087605603,0.496910125,0.0745897293,0.2353423387,0.2745423615,0.1146288663,0.1112658232,0.0898417383,0.1389095038,0.0243525449,-0.0688010678,-0.6210039258,0.0036772368,0.0770883262,-0.3594807088,-0.0306123234,-0.218272835,-0.0773649141,-0.1010038108,-0.1445732713,0.1883690059,0.0624618568,0.0796580985,-0.3489792049,0.3237820268,-0.2918675244,-0.7594263554,-0.141937539,-0.0309817586,-0.0322257578,0.130548954,0.4035463929,0.1777429879,0.1623430699,-0.1546227336,-0.0081777824,0.0228776559,0.3501376808,-0.0447858796,0.0521627255,0.3954898417,0.0708256289,0.1198439822,-0.0991976485,0.2762240171,-0.4767386317,0.2121436447,-0.0814641565,-0.303979218,0.2000146061,0.0312736481,-0.0611732416,0.0619083792,-0.0824498087,0.295879513,-0.0218027122,-0.261738658,-0.0780139342,-0.1849835515,-0.2660306096,-0.0194636844,0.281535089,0.0521525703,-0.2876964211,-0.1943801641,-0.0426557697,-0.195380345,0.2409430295,0.3353285789,-0.2221540213,0.2208238691,-0.1777199805,0.0727050379,0.6215856075,0.0408351049,-0.2153276354,0.5189719796,-0.125259012,-0.2580466866,0.0099432487,0.2915630341,0.2015536427,-0.0975813791,-0.3081535697,0.7577164173,-0.138188526,-0.2085577399,-0.0468732975,-0.5798709989,0.2178352922,0.1311499923,0.1677621305,0.1886194348,-0.0218464416,-0.177037105,0.230263114,0.0760411546,0.0210764278,-0.0027037351,-0.0927264541,-0.4067582488,-0.2314436287,-0.1624530256,-0.5866490602,0.1941296905,-0.5578173399,-0.2835443318,0.3848071396,-0.1371848881,-0.0203147884,-0.2599238455,0.1540474147,-0.3446823955,-0.1026777774,-0.0458075665,0.1526650041,0.1315482408,-0.3660292029,-0.0622352958,-0.0604103506,0.2207046449,-0.1313726455,-0.1769902259,0.2260778099,0.1880297214,0.2558657229,-0.0725524202,-0.1099506393,0.046778962,0.1513633579,0.1734099388,-0.0262590759,0.0936715454,0.208229512,-0.0001844334,0.0592573173,-0.019603692,-0.0299162604,0.0918958932,-0.2208607346,0.0049648001,-0.2566196918,0.4621081352,-0.2703951299,0.2382683903,-0.0310361367,0.0220022183,0.1374265552,-0.2340873629,0.0234775841,-0.1472925842,0.2413588911,0.1238056868,0.1553203613,-0.0022999132,-0.1333712041,0.0855032951,0.414788723,-0.2111367136,-0.1127043888,-0.0137233594,-0.055762101,-0.098478049,0.1594362855,0.4292439818,0.3710593879,0.1735153049,0.5662353039,0.048326917,0.1430452913,-0.3168494105,0.1112156808,-0.0117881577,-0.2532935441,0.0380241685,-0.1188154966,-0.1213262826,-0.2932133079,0.4736105204,0.1529059112,-0.0682681575,-0.0972216949,-0.1410442442,-0.4016961157,-0.15072909,-0.071524255,0.0242202077,-0.1566215456,-0.2005376518,0.3691398203,-0.1295188069,-0.2382731438,0.1708692461,0.1290085763,0.4911543131,-0.2071625888,-0.321387887,0.1032970324,0.087634027,-0.0775730237,0.0606639124,0.1547174305,0.3283340931,0.4007332921,-0.1120109856,-0.1641483605,0.0141161876,0.1065445766,0.0587569587,0.2443142682,0.4849342704,-0.0601027235,0.4556983113,0.0552116148,0.1288237125,-0.4062317014,-0.0473387539,-0.2428159565,-0.1148092747,0.2974045575,0.3979501128,-0.2779551744,-0.7279382944,-0.1925116926,-0.3107815087,0.19715482,0.0362021253,0.1660703123,-0.020988876,-0.1801543087,-0.1689496636,-0.4117167294,0.030272264,-0.0424679704,-0.0558419898,0.2495708019,-0.1656604111,-0.0818884,0.098591134,-0.0002208454,0.1828828752,0.3064815402,-0.7142486572,0.3078974783,-0.0837082341,0.1705634296,-0.0621972457,-0.0692710057,0.4284794033,-0.4704176784,0.0272308886,0.2099205852,0.2742372751,0.1153624803,0.0321403928,0.134550482,-0.0440304019,0.309697926,0.0385462567,0.8778749108,0.0845527649,0.2853832543,0.0053808554,0.0823159739,0.3701732159,-0.1114830747,0.3447764814,0.3482345343,0.1952168494,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/412","title":"Unable to load XTREME dataset from disk","comments":"Hi @lewtun, you have to provide the full path to the downloaded file for example `\/home\/lewtum\/..`","body":"Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nFollowing the [docs](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https:\/\/github.com\/google-research\/xtreme) benchmark.\r\n\r\nI have manually downloaded the `AmazonPhotos.zip` file from [here](https:\/\/www.amazon.com\/clouddrive\/share\/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset.\r\n\r\nAs far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path:\r\n\r\n```\r\n# path where load_dataset is looking for fr.tar.gz\r\n\/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/\r\n# path where it actually exists\r\n\/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/panx_dataset\/\r\n```\r\n\r\n## Steps to reproduce the problem\r\n\r\n1. Manually download the XTREME benchmark from [here](https:\/\/www.amazon.com\/clouddrive\/share\/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1)\r\n\r\n2. Run the following code snippet\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is in the root of the folder\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\", data_dir='.\/')\r\n```\r\n\r\n3. Here is the stack trace\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-26786bb5fa93> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.fr\", data_dir='.\/')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    464         split_dict = SplitDict(dataset_name=self.name)\r\n    465         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 466         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    467         # Checksums verification\r\n    468         if verify_infos:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/datasets\/xtreme\/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746\/xtreme.py in _split_generators(self, dl_manager)\r\n    725             panx_dl_dir = dl_manager.extract(panx_path)\r\n    726             lang = self.config.name.split(\".\")[1]\r\n--> 727             lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + \".tar.gz\"))\r\n    728             return [\r\n    729                 nlp.SplitGenerator(\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/download_manager.py in extract(self, path_or_paths)\r\n    196         \"\"\"\r\n    197         return map_nested(\r\n--> 198             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    199         )\r\n    200 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)\r\n    170                 return tuple(mapped)\r\n    171     # Singleton\r\n--> 172     return function(data_struct)\r\n    173 \r\n    174 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/download_manager.py in <lambda>(path)\r\n    196         \"\"\"\r\n    197         return map_nested(\r\n--> 198             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    199         )\r\n    200 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    203     elif urlparse(url_or_filename).scheme == \"\":\r\n    204         # File, but it doesn't exist.\r\n--> 205         raise FileNotFoundError(\"Local file {} doesn't exist\".format(url_or_filename))\r\n    206     else:\r\n    207         # Something unknown\r\n\r\nFileNotFoundError: Local file \/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/fr.tar.gz doesn't exist\r\n```\r\n\r\n## OS and hardware\r\n```\r\n- `nlp` version: 0.3.0\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n```","comment_length":16,"text":"Unable to load XTREME dataset from disk \n Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nFollowing the [docs](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https:\/\/github.com\/google-research\/xtreme) benchmark.\r\n\r\nI have manually downloaded the `AmazonPhotos.zip` file from [here](https:\/\/www.amazon.com\/clouddrive\/share\/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset.\r\n\r\nAs far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path:\r\n\r\n```\r\n# path where load_dataset is looking for fr.tar.gz\r\n\/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/\r\n# path where it actually exists\r\n\/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/panx_dataset\/\r\n```\r\n\r\n## Steps to reproduce the problem\r\n\r\n1. Manually download the XTREME benchmark from [here](https:\/\/www.amazon.com\/clouddrive\/share\/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1)\r\n\r\n2. Run the following code snippet\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is in the root of the folder\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\", data_dir='.\/')\r\n```\r\n\r\n3. Here is the stack trace\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-26786bb5fa93> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.fr\", data_dir='.\/')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    464         split_dict = SplitDict(dataset_name=self.name)\r\n    465         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 466         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    467         # Checksums verification\r\n    468         if verify_infos:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/datasets\/xtreme\/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746\/xtreme.py in _split_generators(self, dl_manager)\r\n    725             panx_dl_dir = dl_manager.extract(panx_path)\r\n    726             lang = self.config.name.split(\".\")[1]\r\n--> 727             lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + \".tar.gz\"))\r\n    728             return [\r\n    729                 nlp.SplitGenerator(\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/download_manager.py in extract(self, path_or_paths)\r\n    196         \"\"\"\r\n    197         return map_nested(\r\n--> 198             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    199         )\r\n    200 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)\r\n    170                 return tuple(mapped)\r\n    171     # Singleton\r\n--> 172     return function(data_struct)\r\n    173 \r\n    174 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/download_manager.py in <lambda>(path)\r\n    196         \"\"\"\r\n    197         return map_nested(\r\n--> 198             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    199         )\r\n    200 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    203     elif urlparse(url_or_filename).scheme == \"\":\r\n    204         # File, but it doesn't exist.\r\n--> 205         raise FileNotFoundError(\"Local file {} doesn't exist\".format(url_or_filename))\r\n    206     else:\r\n    207         # Something unknown\r\n\r\nFileNotFoundError: Local file \/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/fr.tar.gz doesn't exist\r\n```\r\n\r\n## OS and hardware\r\n```\r\n- `nlp` version: 0.3.0\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n``` \n Hi @lewtun, you have to provide the full path to the downloaded file for example `\/home\/lewtum\/..`","embeddings":[-0.2940948308,-0.3835189939,-0.0021737483,0.6607134342,0.1483453512,0.123879388,-0.1730880439,0.1085548103,0.1807117611,0.0440969653,-0.1427524537,-0.000344655,0.1383353919,-0.0291913021,0.1631223559,-0.1389353275,-0.0050913743,-0.109264195,-0.0455735289,0.0021922421,-0.3334750533,0.3826159835,-0.1232326701,-0.0158359203,-0.3343698978,0.0152691752,0.0525251888,0.6103966832,-0.2204438448,-0.0490750596,0.4548045695,-0.1380422413,0.4193027318,0.7166676521,-0.0001206809,0.036272157,0.1372581273,-0.1804513633,-0.1188215613,-0.525824964,-0.3079940677,-0.1304575354,-0.0578953996,-0.127268672,-0.072370559,0.0013968141,0.1307007074,-0.5407083035,0.4081201255,0.1079233736,0.1058065891,0.2785436809,0.0526131652,-0.0774010569,0.2205448151,0.1630187631,-0.3261559606,0.0098360153,0.3490776718,0.1568444073,0.0890085846,0.3036373258,0.0028810215,0.0416494124,0.4682084322,-0.0255379956,0.1632110029,-0.2858410776,0.0740050152,0.3027319312,0.5413934588,-0.1766107529,-0.4551265836,-0.1600851715,-0.0877793655,-0.2420943677,0.3571464717,0.28176108,-0.1429187506,-0.0044436865,-0.2909394205,-0.1955347657,-0.165093556,0.161051318,-0.0667801872,0.1623710394,-0.216212377,0.0020341689,0.3741413057,0.0450923182,0.2139801234,-0.0631333888,-0.1090405881,0.1156973541,-0.2318337858,0.237139225,-0.1404143274,-0.0313966423,0.237890929,0.197227791,0.02315435,0.1833641529,-0.0200086348,0.1590581387,0.2242522091,0.2844460011,0.2408591509,-0.0752442777,0.0127866007,0.1807836294,0.0062315604,-0.1993156075,-0.1559436321,-0.2016490996,-0.1380642653,-0.0938202664,0.117636323,-0.3426390588,-0.5313278437,-0.0701550394,-0.0944774523,0.0107492469,0.2356742918,0.2389828861,0.0993805081,0.0076575205,0.0823181123,0.3665469289,-0.318562001,-0.0045816679,-0.2031152993,0.2044391781,-0.3886571229,0.2901443839,0.1603095978,-0.0814564601,0.3730145693,-0.1143650338,0.3260141015,-0.3670960069,0.0424612015,-0.0647062659,-0.0382351913,0.0822852254,0.2362206876,0.1614243388,0.1086064428,-0.0067187431,-0.1187783182,0.1436628252,-0.4055755734,-0.1271426678,-0.0795442015,0.0333358757,-0.4040099978,0.01824327,-0.6510798931,-0.1683329791,-0.270290643,0.033224877,-0.0252170842,0.0418415554,-0.1289660037,-0.0762089863,0.2826340199,0.487043649,-0.1808573157,-0.035213463,-0.2090551108,-0.1099215448,0.0813191235,0.1574320048,-0.2371750027,0.0877061337,-0.4471827447,0.3432358503,0.2603805363,-0.6826903224,-0.2903496921,0.3264546096,-0.2940928042,0.2417879999,0.0807776526,0.0621065237,-0.2141914964,-0.0856752247,0.1738442183,0.1770916879,0.1379901171,-0.0998015851,-0.2131122798,-0.1681564748,-0.19047831,0.3321940601,-0.013252819,-0.089894034,0.1114539504,-0.0186509453,0.1093090326,-0.1374859065,-0.1267775446,0.5887778997,0.074264124,-0.0401530415,-0.188909471,0.0607483536,-0.6912902594,0.3157786131,-0.2630388141,-0.2366461307,-0.0897766799,0.1072436571,-0.3324464858,-0.090750128,-0.3421750963,-0.1773630977,-0.0320939831,0.1423979104,-0.0039518424,-0.1507627666,-0.237391457,0.3739419878,0.0338048264,0.1004236937,-0.5817263126,0.266469717,0.1891721934,-0.0366892628,-0.0080807889,0.0295071863,0.244940415,-0.301872015,0.0110715451,0.5150936246,0.0157895256,0.3034413755,0.3414364457,0.0678832307,0.2183351815,-0.4746906459,0.1706380397,0.1261678338,-0.0502253696,-0.0008387527,-0.3532588184,0.4718583226,-0.1958650202,-0.0360133201,-0.0729345977,-0.1564880461,0.1472139955,-0.1102163047,-0.1181403995,-0.102371864,0.3790089786,0.1971785873,0.2383924276,-0.1636102349,-0.1151506752,-0.1355713755,-0.078775309,0.0191710368,0.0012407303,0.0319226608,-0.2628386021,0.1123144925,-0.0349363983,0.4033972025,0.4151169658,0.1524639279,0.0233504567,-0.0273290258,-0.0414358824,-0.0764893144,0.0318663605,0.0110485563,0.1094328389,0.1649197638,-0.3268106282,-0.0543202497,-0.3668630719,-0.1084010601,0.1083225384,0.0949982926,-0.3544782996,0.0258565135,-0.1741150916,-0.3895713091,-0.1025009453,0.1597319096,-0.2119823694,-0.2974932194,-0.1224297583,0.5138393641,0.2439617366,-0.033237014,-0.3228708804,0.0551576391,-0.1608925462,-0.5349494219,-0.2296060771,0.2254982889,-0.0298479758,-0.0379258394,0.3940607607,0.1775508374,0.06346903,-0.3751940429,0.4378796816,-0.5723701119,-0.1013892964,0.1536201239,-0.1776455343,0.4700734317,-0.1234334558,0.1739168316,-0.4125457108,0.0737455785,0.2622705102,-0.1435983479,-0.0725756884,-0.0108244531,0.0582668334,-0.1717712879,0.1029991955,-0.1550430059,-0.0834874436,-0.2285580337,-0.0605197586,0.0269689281,0.0039468836,0.1430416107,0.0121737709,0.0526414923,0.0021120338,-0.2169024199,-0.2150437683,-0.9191765785,0.482129097,-0.1535041332,-0.2848579884,0.371545285,0.1730707735,0.2463556677,0.2914629281,-0.4455307424,-0.1748443544,0.1003422588,0.1766921878,0.079654336,0.0285435226,0.2198371738,-0.2039557248,0.0781378672,-0.1340860575,-0.3067451119,-0.0713055357,0.1857059449,0.6754620075,-0.0910091251,0.0155380322,0.0305088181,0.48123914,0.0822562873,0.2168375403,0.4140934944,-0.105378665,0.344689697,-0.084782213,-0.1683692187,-0.2016595155,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/412","title":"Unable to load XTREME dataset from disk","comments":"I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !","body":"Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nFollowing the [docs](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https:\/\/github.com\/google-research\/xtreme) benchmark.\r\n\r\nI have manually downloaded the `AmazonPhotos.zip` file from [here](https:\/\/www.amazon.com\/clouddrive\/share\/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset.\r\n\r\nAs far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path:\r\n\r\n```\r\n# path where load_dataset is looking for fr.tar.gz\r\n\/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/\r\n# path where it actually exists\r\n\/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/panx_dataset\/\r\n```\r\n\r\n## Steps to reproduce the problem\r\n\r\n1. Manually download the XTREME benchmark from [here](https:\/\/www.amazon.com\/clouddrive\/share\/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1)\r\n\r\n2. Run the following code snippet\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is in the root of the folder\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\", data_dir='.\/')\r\n```\r\n\r\n3. Here is the stack trace\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-26786bb5fa93> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.fr\", data_dir='.\/')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    464         split_dict = SplitDict(dataset_name=self.name)\r\n    465         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 466         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    467         # Checksums verification\r\n    468         if verify_infos:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/datasets\/xtreme\/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746\/xtreme.py in _split_generators(self, dl_manager)\r\n    725             panx_dl_dir = dl_manager.extract(panx_path)\r\n    726             lang = self.config.name.split(\".\")[1]\r\n--> 727             lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + \".tar.gz\"))\r\n    728             return [\r\n    729                 nlp.SplitGenerator(\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/download_manager.py in extract(self, path_or_paths)\r\n    196         \"\"\"\r\n    197         return map_nested(\r\n--> 198             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    199         )\r\n    200 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)\r\n    170                 return tuple(mapped)\r\n    171     # Singleton\r\n--> 172     return function(data_struct)\r\n    173 \r\n    174 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/download_manager.py in <lambda>(path)\r\n    196         \"\"\"\r\n    197         return map_nested(\r\n--> 198             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    199         )\r\n    200 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    203     elif urlparse(url_or_filename).scheme == \"\":\r\n    204         # File, but it doesn't exist.\r\n--> 205         raise FileNotFoundError(\"Local file {} doesn't exist\".format(url_or_filename))\r\n    206     else:\r\n    207         # Something unknown\r\n\r\nFileNotFoundError: Local file \/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/fr.tar.gz doesn't exist\r\n```\r\n\r\n## OS and hardware\r\n```\r\n- `nlp` version: 0.3.0\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n```","comment_length":17,"text":"Unable to load XTREME dataset from disk \n Hi \ud83e\udd17  team!\r\n\r\n## Description of the problem\r\nFollowing the [docs](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https:\/\/github.com\/google-research\/xtreme) benchmark.\r\n\r\nI have manually downloaded the `AmazonPhotos.zip` file from [here](https:\/\/www.amazon.com\/clouddrive\/share\/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset.\r\n\r\nAs far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path:\r\n\r\n```\r\n# path where load_dataset is looking for fr.tar.gz\r\n\/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/\r\n# path where it actually exists\r\n\/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/panx_dataset\/\r\n```\r\n\r\n## Steps to reproduce the problem\r\n\r\n1. Manually download the XTREME benchmark from [here](https:\/\/www.amazon.com\/clouddrive\/share\/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1)\r\n\r\n2. Run the following code snippet\r\n```python\r\nfrom nlp import load_dataset\r\n# AmazonPhotos.zip is in the root of the folder\r\ndataset = load_dataset(\"xtreme\", \"PAN-X.fr\", data_dir='.\/')\r\n```\r\n\r\n3. Here is the stack trace\r\n```\r\n---------------------------------------------------------------------------\r\nFileNotFoundError                         Traceback (most recent call last)\r\n<ipython-input-4-26786bb5fa93> in <module>\r\n----> 1 dataset = load_dataset(\"xtreme\", \"PAN-X.fr\", data_dir='.\/')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    464         split_dict = SplitDict(dataset_name=self.name)\r\n    465         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 466         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    467         # Checksums verification\r\n    468         if verify_infos:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/datasets\/xtreme\/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746\/xtreme.py in _split_generators(self, dl_manager)\r\n    725             panx_dl_dir = dl_manager.extract(panx_path)\r\n    726             lang = self.config.name.split(\".\")[1]\r\n--> 727             lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + \".tar.gz\"))\r\n    728             return [\r\n    729                 nlp.SplitGenerator(\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/download_manager.py in extract(self, path_or_paths)\r\n    196         \"\"\"\r\n    197         return map_nested(\r\n--> 198             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    199         )\r\n    200 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)\r\n    170                 return tuple(mapped)\r\n    171     # Singleton\r\n--> 172     return function(data_struct)\r\n    173 \r\n    174 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/download_manager.py in <lambda>(path)\r\n    196         \"\"\"\r\n    197         return map_nested(\r\n--> 198             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    199         )\r\n    200 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    203     elif urlparse(url_or_filename).scheme == \"\":\r\n    204         # File, but it doesn't exist.\r\n--> 205         raise FileNotFoundError(\"Local file {} doesn't exist\".format(url_or_filename))\r\n    206     else:\r\n    207         # Something unknown\r\n\r\nFileNotFoundError: Local file \/root\/.cache\/huggingface\/datasets\/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6\/fr.tar.gz doesn't exist\r\n```\r\n\r\n## OS and hardware\r\n```\r\n- `nlp` version: 0.3.0\r\n- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.6.9\r\n- PyTorch version (GPU?): 1.4.0 (True)\r\n- Tensorflow version (GPU?): 2.1.0 (True)\r\n- Using GPU in script?: <fill in>\r\n- Using distributed or parallel set-up in script?: <fill in>\r\n``` \n I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !","embeddings":[-0.2940948308,-0.3835189939,-0.0021737483,0.6607134342,0.1483453512,0.123879388,-0.1730880439,0.1085548103,0.1807117611,0.0440969653,-0.1427524537,-0.000344655,0.1383353919,-0.0291913021,0.1631223559,-0.1389353275,-0.0050913743,-0.109264195,-0.0455735289,0.0021922421,-0.3334750533,0.3826159835,-0.1232326701,-0.0158359203,-0.3343698978,0.0152691752,0.0525251888,0.6103966832,-0.2204438448,-0.0490750596,0.4548045695,-0.1380422413,0.4193027318,0.7166676521,-0.0001206809,0.036272157,0.1372581273,-0.1804513633,-0.1188215613,-0.525824964,-0.3079940677,-0.1304575354,-0.0578953996,-0.127268672,-0.072370559,0.0013968141,0.1307007074,-0.5407083035,0.4081201255,0.1079233736,0.1058065891,0.2785436809,0.0526131652,-0.0774010569,0.2205448151,0.1630187631,-0.3261559606,0.0098360153,0.3490776718,0.1568444073,0.0890085846,0.3036373258,0.0028810215,0.0416494124,0.4682084322,-0.0255379956,0.1632110029,-0.2858410776,0.0740050152,0.3027319312,0.5413934588,-0.1766107529,-0.4551265836,-0.1600851715,-0.0877793655,-0.2420943677,0.3571464717,0.28176108,-0.1429187506,-0.0044436865,-0.2909394205,-0.1955347657,-0.165093556,0.161051318,-0.0667801872,0.1623710394,-0.216212377,0.0020341689,0.3741413057,0.0450923182,0.2139801234,-0.0631333888,-0.1090405881,0.1156973541,-0.2318337858,0.237139225,-0.1404143274,-0.0313966423,0.237890929,0.197227791,0.02315435,0.1833641529,-0.0200086348,0.1590581387,0.2242522091,0.2844460011,0.2408591509,-0.0752442777,0.0127866007,0.1807836294,0.0062315604,-0.1993156075,-0.1559436321,-0.2016490996,-0.1380642653,-0.0938202664,0.117636323,-0.3426390588,-0.5313278437,-0.0701550394,-0.0944774523,0.0107492469,0.2356742918,0.2389828861,0.0993805081,0.0076575205,0.0823181123,0.3665469289,-0.318562001,-0.0045816679,-0.2031152993,0.2044391781,-0.3886571229,0.2901443839,0.1603095978,-0.0814564601,0.3730145693,-0.1143650338,0.3260141015,-0.3670960069,0.0424612015,-0.0647062659,-0.0382351913,0.0822852254,0.2362206876,0.1614243388,0.1086064428,-0.0067187431,-0.1187783182,0.1436628252,-0.4055755734,-0.1271426678,-0.0795442015,0.0333358757,-0.4040099978,0.01824327,-0.6510798931,-0.1683329791,-0.270290643,0.033224877,-0.0252170842,0.0418415554,-0.1289660037,-0.0762089863,0.2826340199,0.487043649,-0.1808573157,-0.035213463,-0.2090551108,-0.1099215448,0.0813191235,0.1574320048,-0.2371750027,0.0877061337,-0.4471827447,0.3432358503,0.2603805363,-0.6826903224,-0.2903496921,0.3264546096,-0.2940928042,0.2417879999,0.0807776526,0.0621065237,-0.2141914964,-0.0856752247,0.1738442183,0.1770916879,0.1379901171,-0.0998015851,-0.2131122798,-0.1681564748,-0.19047831,0.3321940601,-0.013252819,-0.089894034,0.1114539504,-0.0186509453,0.1093090326,-0.1374859065,-0.1267775446,0.5887778997,0.074264124,-0.0401530415,-0.188909471,0.0607483536,-0.6912902594,0.3157786131,-0.2630388141,-0.2366461307,-0.0897766799,0.1072436571,-0.3324464858,-0.090750128,-0.3421750963,-0.1773630977,-0.0320939831,0.1423979104,-0.0039518424,-0.1507627666,-0.237391457,0.3739419878,0.0338048264,0.1004236937,-0.5817263126,0.266469717,0.1891721934,-0.0366892628,-0.0080807889,0.0295071863,0.244940415,-0.301872015,0.0110715451,0.5150936246,0.0157895256,0.3034413755,0.3414364457,0.0678832307,0.2183351815,-0.4746906459,0.1706380397,0.1261678338,-0.0502253696,-0.0008387527,-0.3532588184,0.4718583226,-0.1958650202,-0.0360133201,-0.0729345977,-0.1564880461,0.1472139955,-0.1102163047,-0.1181403995,-0.102371864,0.3790089786,0.1971785873,0.2383924276,-0.1636102349,-0.1151506752,-0.1355713755,-0.078775309,0.0191710368,0.0012407303,0.0319226608,-0.2628386021,0.1123144925,-0.0349363983,0.4033972025,0.4151169658,0.1524639279,0.0233504567,-0.0273290258,-0.0414358824,-0.0764893144,0.0318663605,0.0110485563,0.1094328389,0.1649197638,-0.3268106282,-0.0543202497,-0.3668630719,-0.1084010601,0.1083225384,0.0949982926,-0.3544782996,0.0258565135,-0.1741150916,-0.3895713091,-0.1025009453,0.1597319096,-0.2119823694,-0.2974932194,-0.1224297583,0.5138393641,0.2439617366,-0.033237014,-0.3228708804,0.0551576391,-0.1608925462,-0.5349494219,-0.2296060771,0.2254982889,-0.0298479758,-0.0379258394,0.3940607607,0.1775508374,0.06346903,-0.3751940429,0.4378796816,-0.5723701119,-0.1013892964,0.1536201239,-0.1776455343,0.4700734317,-0.1234334558,0.1739168316,-0.4125457108,0.0737455785,0.2622705102,-0.1435983479,-0.0725756884,-0.0108244531,0.0582668334,-0.1717712879,0.1029991955,-0.1550430059,-0.0834874436,-0.2285580337,-0.0605197586,0.0269689281,0.0039468836,0.1430416107,0.0121737709,0.0526414923,0.0021120338,-0.2169024199,-0.2150437683,-0.9191765785,0.482129097,-0.1535041332,-0.2848579884,0.371545285,0.1730707735,0.2463556677,0.2914629281,-0.4455307424,-0.1748443544,0.1003422588,0.1766921878,0.079654336,0.0285435226,0.2198371738,-0.2039557248,0.0781378672,-0.1340860575,-0.3067451119,-0.0713055357,0.1857059449,0.6754620075,-0.0910091251,0.0155380322,0.0305088181,0.48123914,0.0822562873,0.2168375403,0.4140934944,-0.105378665,0.344689697,-0.084782213,-0.1683692187,-0.2016595155,-0.1253787726,0.034599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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/407","title":"MissingBeamOptions for Wikipedia 20200501.en","comments":"Fixed. Could you try again @mitchellgordon95 ?\r\nIt was due a file not being updated on S3.\r\n\r\nWe need to make sure all the datasets scripts get updated properly @julien-c ","body":"There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):\r\n\r\n```\r\nnlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n```\r\n\r\nAnd now, having pulled master, I get:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to \/home\/hltcoe\/mgordon\/.cache\/huggingface\/datasets\/wikipedia\/20200501.en\/1.0.0\/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...\r\nTraceback (most recent call last):\r\n  File \"scripts\/download.py\", line 11, in <module>\r\n    fire.Fire(download_pretrain)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 138, in Fire\r\n    component_trace = _Fire(component, args, parsed_flag_args, context, name)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 468, in _Fire\r\n    target=component.__name__)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 672, in _CallAndUpdateTrace\r\n    component = fn(*varargs, **kwargs)\r\n  File \"scripts\/download.py\", line 6, in download_pretrain\r\n    nlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/load.py\", line 534, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 460, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 870, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S\r\npark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).\r\nExample of usage:\r\n        `load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')`\r\n```","comment_length":30,"text":"MissingBeamOptions for Wikipedia 20200501.en \n There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):\r\n\r\n```\r\nnlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n```\r\n\r\nAnd now, having pulled master, I get:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to \/home\/hltcoe\/mgordon\/.cache\/huggingface\/datasets\/wikipedia\/20200501.en\/1.0.0\/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...\r\nTraceback (most recent call last):\r\n  File \"scripts\/download.py\", line 11, in <module>\r\n    fire.Fire(download_pretrain)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 138, in Fire\r\n    component_trace = _Fire(component, args, parsed_flag_args, context, name)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 468, in _Fire\r\n    target=component.__name__)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 672, in _CallAndUpdateTrace\r\n    component = fn(*varargs, **kwargs)\r\n  File \"scripts\/download.py\", line 6, in download_pretrain\r\n    nlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/load.py\", line 534, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 460, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 870, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S\r\npark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).\r\nExample of usage:\r\n        `load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')`\r\n``` \n Fixed. Could you try again @mitchellgordon95 ?\r\nIt was due a file not being updated on S3.\r\n\r\nWe need to make sure all the datasets scripts get updated properly @julien-c 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/407","title":"MissingBeamOptions for Wikipedia 20200501.en","comments":"I found the same issue with almost any language other than English. (For English, it works). Will someone need to update the file on S3 again?","body":"There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):\r\n\r\n```\r\nnlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n```\r\n\r\nAnd now, having pulled master, I get:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to \/home\/hltcoe\/mgordon\/.cache\/huggingface\/datasets\/wikipedia\/20200501.en\/1.0.0\/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...\r\nTraceback (most recent call last):\r\n  File \"scripts\/download.py\", line 11, in <module>\r\n    fire.Fire(download_pretrain)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 138, in Fire\r\n    component_trace = _Fire(component, args, parsed_flag_args, context, name)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 468, in _Fire\r\n    target=component.__name__)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 672, in _CallAndUpdateTrace\r\n    component = fn(*varargs, **kwargs)\r\n  File \"scripts\/download.py\", line 6, in download_pretrain\r\n    nlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/load.py\", line 534, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 460, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 870, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S\r\npark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).\r\nExample of usage:\r\n        `load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')`\r\n```","comment_length":26,"text":"MissingBeamOptions for Wikipedia 20200501.en \n There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):\r\n\r\n```\r\nnlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n```\r\n\r\nAnd now, having pulled master, I get:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to \/home\/hltcoe\/mgordon\/.cache\/huggingface\/datasets\/wikipedia\/20200501.en\/1.0.0\/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...\r\nTraceback (most recent call last):\r\n  File \"scripts\/download.py\", line 11, in <module>\r\n    fire.Fire(download_pretrain)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 138, in Fire\r\n    component_trace = _Fire(component, args, parsed_flag_args, context, name)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 468, in _Fire\r\n    target=component.__name__)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 672, in _CallAndUpdateTrace\r\n    component = fn(*varargs, **kwargs)\r\n  File \"scripts\/download.py\", line 6, in download_pretrain\r\n    nlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/load.py\", line 534, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 460, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 870, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S\r\npark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).\r\nExample of usage:\r\n        `load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')`\r\n``` \n I found the same issue with almost any language other than English. (For English, it works). Will someone need to update the file on S3 again?","embeddings":[0.0666168258,0.1266757995,-0.0205123089,-0.0542329103,0.1996194869,0.1526870281,0.1799744368,0.3656727076,-0.022208821,-0.081425719,0.1489358097,0.2753814459,-0.0917265415,-0.168423295,0.2434722483,-0.3058640659,0.1455037892,0.1239019409,-0.3501991034,-0.1286828965,-0.0025927718,0.4125896096,-0.2114285231,-0.1782554686,-0.5298822522,0.0631536841,-0.1315196306,0.1428049058,-0.0437919199,-0.2499871403,0.2779204845,0.0593028367,-0.0827241987,0.122238256,-0.0001143081,0.0776108727,0.62224859,-0.2239052504,-0.5686735511,0.0119778551,0.1300001889,-0.3336535096,0.384344399,-0.2550982535,-0.2759362757,-0.1181294397,0.1141192019,-0.0585302375,0.3938059509,-0.0109363226,0.2136250138,0.2360561192,0.297298044,-0.1291413456,0.6020246148,0.0674149916,-0.1662565321,0.0793358013,-0.1638745517,-0.0564770475,-0.2710405588,0.4797945023,0.1387171596,-0.1370008588,0.5526376367,-0.2816212773,0.0226213243,-0.2574208677,0.0174538549,0.1826507896,0.4940163493,-0.0229834914,-0.1063677594,-0.3746719062,0.0446043313,0.1787300706,0.2480826825,0.0172076635,-0.1532892138,-0.0672086403,0.0512106009,-0.2295793146,-0.0332925208,0.28875193,-0.0673940703,0.4202920794,-0.0289063603,0.0214702915,0.166854009,0.0496226139,-0.313372463,0.0365183763,0.0982150361,0.2350160927,-0.2165132016,0.0636979118,0.2868846357,0.2705270946,0.0620143712,0.1695944816,-0.2794000506,0.1912726462,0.0204293542,-0.0470910259,0.2198940963,0.2408223003,-0.0418657698,-0.353320688,0.251672864,0.3364218175,-0.0628019497,0.1384649873,0.1992915124,-0.1387363076,-0.2713562548,0.1334020048,0.0091168443,-0.1717023998,0.1072276235,0.0534083545,-0.2777944505,-0.1483329535,-0.2378234863,0.31988886,-0.0257740859,0.4086242616,0.2018820941,0.1185922623,-0.2835624218,-0.2737653553,-0.1957246959,0.0212342087,-0.4779030085,0.0258821566,0.3871365488,-0.2097051591,0.5165669322,0.0758559778,-0.0362861082,0.0592919849,-0.0301977824,0.2644242346,-0.0725837499,0.181024909,0.1212264374,0.2927809656,0.2036839426,0.0199421402,-0.0530857034,-0.329444617,0.0704844519,-0.2763873637,-0.2597137988,0.1677166075,-0.3119882941,0.1547896415,0.047507599,0.1872284114,-0.0253873691,-0.1822030544,-0.0967266262,-0.1229704767,-0.0582913347,-0.1482739151,0.5051084161,0.4033136666,0.0298880525,-0.3304650486,-0.0621768199,0.1041162759,-0.1011983678,0.0117212692,-0.1564818919,0.5247724652,-0.2857876718,-0.1501885206,0.224312827,0.0519686528,-0.2112267464,0.1486301273,-0.0126577159,0.2708986998,0.0975890458,-0.0528358966,-0.0672804639,0.0518001579,0.0919757187,0.2184601426,0.1131943539,0.0529397614,-0.3270510733,-0.0877883509,-0.0076871128,0.0795486197,0.3505393863,-0.0505552888,0.0635950118,0.5883682966,0.2980857491,-0.1387826651,0.2244143188,0.4500527084,-0.2889430523,0.1368469149,0.2453726381,-0.0598621815,-0.4651874602,0.1208006591,-0.1401829123,0.4367903173,-0.1831934154,-0.00221489,-0.3467137516,-0.1367142648,-0.1968161315,-0.5165299773,0.2065130919,0.2034472376,-0.018576948,0.3046480119,0.0254303049,0.1162029579,-0.040442083,0.0568883121,-0.7796523571,0.214526847,-0.3115582466,-0.1411182284,-0.0192293879,0.3081658483,0.1150461584,-0.1935398728,-0.1284949929,0.2234227657,-0.0789242238,0.1341899037,0.0148870628,-0.0550866164,0.2404268086,-0.3211013675,0.0984831378,0.3122585118,-0.0463057272,-0.1272407025,-0.3034783602,0.2799139619,0.0182238296,0.3226444721,0.033403419,0.0919322968,0.1548368037,0.0725830048,-0.2331330776,-0.202653572,0.3785173595,0.2176063359,0.1366422474,-0.0235250685,0.114962697,0.0045698718,0.1719020158,0.0660793856,0.0112598883,-0.0809159651,-0.4510022402,-0.2482421845,0.3509304225,-0.0857894942,0.0816844404,0.2645476758,0.0524951369,-0.0169572085,-0.1228682548,-0.2095779032,0.1974878311,0.1140880585,0.3243004978,0.0876459852,0.1405431777,0.1975445449,-0.3815246522,0.1906591803,-0.1774683148,0.0871339515,-0.2838542759,-0.0979878008,-0.3540599644,-0.4514617026,-0.1052412391,0.2488120049,-0.3974134922,-0.5874935985,-0.0569580458,0.1890996397,-0.1633748561,0.3057290614,-0.0293643046,-0.1303642839,0.0741929859,0.019791821,-0.3342637122,-0.3922384083,-0.200067386,0.0861517861,0.1043603644,-0.0021498958,0.2901448607,-0.0029742981,-0.0271919109,-0.4731692374,-0.575897634,0.3161930442,-0.2838526368,0.190267697,0.1779287755,0.0881575495,-0.2256871164,-0.058733575,0.4069474936,-0.1701157838,-0.1504672468,-0.0161600616,-0.1277897358,0.101005815,-0.1923410892,-0.2217373699,0.0953757316,-0.3692909479,0.0153438859,0.0921726599,-0.1812787056,0.4691084921,-0.1002497077,-0.0062876246,0.1005272493,0.1722671986,-0.2448800653,0.0835745335,0.3416597843,-0.3003979027,-0.4568088353,0.0295674503,-0.1735530943,0.0571707934,0.318931818,-0.4997883439,-0.0496766046,0.0941225365,0.0024681096,-0.1354268342,0.0942459553,0.5044302344,0.0114218183,-0.0168616381,-0.2326325625,-0.1519111693,-0.1495186388,-0.3148490787,0.4269850552,-0.192369014,0.32359007,-0.0036709276,1.0987776518,-0.0326621234,0.0656037927,0.4224383831,0.093303889,0.4521008432,-0.2759513259,-0.296728462,0.2323563248,-0.1486648619,0.301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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/407","title":"MissingBeamOptions for Wikipedia 20200501.en","comments":"This is because only some languages are already preprocessed (en, de, fr, it) and stored on our google storage.\r\nWe plan to have a systematic way to preprocess more wikipedia languages in the future.\r\n\r\nFor the other languages you have to process them on your side using apache beam. That's why the lib asks for a Beam runner.","body":"There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):\r\n\r\n```\r\nnlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n```\r\n\r\nAnd now, having pulled master, I get:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to \/home\/hltcoe\/mgordon\/.cache\/huggingface\/datasets\/wikipedia\/20200501.en\/1.0.0\/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...\r\nTraceback (most recent call last):\r\n  File \"scripts\/download.py\", line 11, in <module>\r\n    fire.Fire(download_pretrain)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 138, in Fire\r\n    component_trace = _Fire(component, args, parsed_flag_args, context, name)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 468, in _Fire\r\n    target=component.__name__)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 672, in _CallAndUpdateTrace\r\n    component = fn(*varargs, **kwargs)\r\n  File \"scripts\/download.py\", line 6, in download_pretrain\r\n    nlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/load.py\", line 534, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 460, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 870, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S\r\npark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).\r\nExample of usage:\r\n        `load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')`\r\n```","comment_length":58,"text":"MissingBeamOptions for Wikipedia 20200501.en \n There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):\r\n\r\n```\r\nnlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n```\r\n\r\nAnd now, having pulled master, I get:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to \/home\/hltcoe\/mgordon\/.cache\/huggingface\/datasets\/wikipedia\/20200501.en\/1.0.0\/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...\r\nTraceback (most recent call last):\r\n  File \"scripts\/download.py\", line 11, in <module>\r\n    fire.Fire(download_pretrain)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 138, in Fire\r\n    component_trace = _Fire(component, args, parsed_flag_args, context, name)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 468, in _Fire\r\n    target=component.__name__)\r\n  File \"\/home\/hltcoe\/mgordon\/.conda\/envs\/huggingface\/lib\/python3.6\/site-packages\/fire\/core.py\", line 672, in _CallAndUpdateTrace\r\n    component = fn(*varargs, **kwargs)\r\n  File \"scripts\/download.py\", line 6, in download_pretrain\r\n    nlp.load_dataset('wikipedia', \"20200501.en\", split='train')\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/load.py\", line 534, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 460, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/exp\/mgordon\/nlp\/src\/nlp\/builder.py\", line 870, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S\r\npark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).\r\nExample of usage:\r\n        `load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')`\r\n``` \n This is because only some languages are already preprocessed (en, de, fr, it) and stored on our google storage.\r\nWe plan to have a systematic way to preprocess more wikipedia languages in the future.\r\n\r\nFor the other languages you have to process them on your side using apache beam. That's why the lib asks for a Beam runner.","embeddings":[0.0666168258,0.1266757995,-0.0205123089,-0.0542329103,0.1996194869,0.1526870281,0.1799744368,0.3656727076,-0.022208821,-0.081425719,0.1489358097,0.2753814459,-0.0917265415,-0.168423295,0.2434722483,-0.3058640659,0.1455037892,0.1239019409,-0.3501991034,-0.1286828965,-0.0025927718,0.4125896096,-0.2114285231,-0.1782554686,-0.5298822522,0.0631536841,-0.1315196306,0.1428049058,-0.0437919199,-0.2499871403,0.2779204845,0.0593028367,-0.0827241987,0.122238256,-0.0001143081,0.0776108727,0.62224859,-0.2239052504,-0.5686735511,0.0119778551,0.1300001889,-0.3336535096,0.384344399,-0.2550982535,-0.2759362757,-0.1181294397,0.1141192019,-0.0585302375,0.3938059509,-0.0109363226,0.2136250138,0.2360561192,0.297298044,-0.1291413456,0.6020246148,0.0674149916,-0.1662565321,0.0793358013,-0.1638745517,-0.0564770475,-0.2710405588,0.4797945023,0.1387171596,-0.1370008588,0.5526376367,-0.2816212773,0.0226213243,-0.2574208677,0.0174538549,0.1826507896,0.4940163493,-0.0229834914,-0.1063677594,-0.3746719062,0.0446043313,0.1787300706,0.2480826825,0.0172076635,-0.1532892138,-0.0672086403,0.0512106009,-0.2295793146,-0.0332925208,0.28875193,-0.0673940703,0.4202920794,-0.0289063603,0.0214702915,0.166854009,0.0496226139,-0.313372463,0.0365183763,0.0982150361,0.2350160927,-0.2165132016,0.0636979118,0.2868846357,0.2705270946,0.0620143712,0.1695944816,-0.2794000506,0.1912726462,0.0204293542,-0.0470910259,0.2198940963,0.2408223003,-0.0418657698,-0.353320688,0.251672864,0.3364218175,-0.0628019497,0.1384649873,0.1992915124,-0.1387363076,-0.2713562548,0.1334020048,0.0091168443,-0.1717023998,0.1072276235,0.0534083545,-0.2777944505,-0.1483329535,-0.2378234863,0.31988886,-0.0257740859,0.4086242616,0.2018820941,0.1185922623,-0.2835624218,-0.2737653553,-0.1957246959,0.0212342087,-0.4779030085,0.0258821566,0.3871365488,-0.2097051591,0.5165669322,0.0758559778,-0.0362861082,0.0592919849,-0.0301977824,0.2644242346,-0.0725837499,0.181024909,0.1212264374,0.2927809656,0.2036839426,0.0199421402,-0.0530857034,-0.329444617,0.0704844519,-0.2763873637,-0.2597137988,0.1677166075,-0.3119882941,0.1547896415,0.047507599,0.1872284114,-0.0253873691,-0.1822030544,-0.0967266262,-0.1229704767,-0.0582913347,-0.1482739151,0.5051084161,0.4033136666,0.0298880525,-0.3304650486,-0.0621768199,0.1041162759,-0.1011983678,0.0117212692,-0.1564818919,0.5247724652,-0.2857876718,-0.1501885206,0.224312827,0.0519686528,-0.2112267464,0.1486301273,-0.0126577159,0.2708986998,0.0975890458,-0.0528358966,-0.0672804639,0.0518001579,0.0919757187,0.2184601426,0.1131943539,0.0529397614,-0.3270510733,-0.0877883509,-0.0076871128,0.0795486197,0.3505393863,-0.0505552888,0.0635950118,0.5883682966,0.2980857491,-0.1387826651,0.2244143188,0.4500527084,-0.2889430523,0.1368469149,0.2453726381,-0.0598621815,-0.4651874602,0.1208006591,-0.1401829123,0.4367903173,-0.1831934154,-0.00221489,-0.3467137516,-0.1367142648,-0.1968161315,-0.5165299773,0.2065130919,0.2034472376,-0.018576948,0.3046480119,0.0254303049,0.1162029579,-0.040442083,0.0568883121,-0.7796523571,0.214526847,-0.3115582466,-0.1411182284,-0.0192293879,0.3081658483,0.1150461584,-0.1935398728,-0.1284949929,0.2234227657,-0.0789242238,0.1341899037,0.0148870628,-0.0550866164,0.2404268086,-0.3211013675,0.0984831378,0.3122585118,-0.0463057272,-0.1272407025,-0.3034783602,0.2799139619,0.0182238296,0.3226444721,0.033403419,0.0919322968,0.1548368037,0.0725830048,-0.2331330776,-0.202653572,0.3785173595,0.2176063359,0.1366422474,-0.0235250685,0.114962697,0.0045698718,0.1719020158,0.0660793856,0.0112598883,-0.0809159651,-0.4510022402,-0.2482421845,0.3509304225,-0.0857894942,0.0816844404,0.2645476758,0.0524951369,-0.0169572085,-0.1228682548,-0.2095779032,0.1974878311,0.1140880585,0.3243004978,0.0876459852,0.1405431777,0.1975445449,-0.3815246522,0.1906591803,-0.1774683148,0.0871339515,-0.2838542759,-0.0979878008,-0.3540599644,-0.4514617026,-0.1052412391,0.2488120049,-0.3974134922,-0.5874935985,-0.0569580458,0.1890996397,-0.1633748561,0.3057290614,-0.0293643046,-0.1303642839,0.0741929859,0.019791821,-0.3342637122,-0.3922384083,-0.200067386,0.0861517861,0.1043603644,-0.0021498958,0.2901448607,-0.0029742981,-0.0271919109,-0.4731692374,-0.575897634,0.3161930442,-0.2838526368,0.190267697,0.1779287755,0.0881575495,-0.2256871164,-0.058733575,0.4069474936,-0.1701157838,-0.1504672468,-0.0161600616,-0.1277897358,0.101005815,-0.1923410892,-0.2217373699,0.0953757316,-0.3692909479,0.0153438859,0.0921726599,-0.1812787056,0.4691084921,-0.1002497077,-0.0062876246,0.1005272493,0.1722671986,-0.2448800653,0.0835745335,0.3416597843,-0.3003979027,-0.4568088353,0.0295674503,-0.1735530943,0.0571707934,0.318931818,-0.4997883439,-0.0496766046,0.0941225365,0.0024681096,-0.1354268342,0.0942459553,0.5044302344,0.0114218183,-0.0168616381,-0.2326325625,-0.1519111693,-0.1495186388,-0.3148490787,0.4269850552,-0.192369014,0.32359007,-0.0036709276,1.0987776518,-0.0326621234,0.0656037927,0.4224383831,0.093303889,0.4521008432,-0.2759513259,-0.296728462,0.2323563248,-0.1486648619,0.30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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/406","title":"Faster Shuffling?","comments":"I think the slowness here probably come from the fact that we are copying from and to python.\r\n\r\n@lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?","body":"Consider shuffling bookcorpus:\r\n\r\n```\r\ndataset = nlp.load_dataset('bookcorpus', split='train')\r\ndataset.shuffle()\r\n```\r\nAccording to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.\r\n\r\nBut I can also just write the lines to a text file:\r\n\r\n```\r\nbatch_size = 100000\r\nwith open('tmp.txt', 'w+') as out_f:\r\n    for i in tqdm(range(0, len(dataset), batch_size)):\r\n        batch = dataset[i:i+batch_size]['text']\r\n        print(\"\\n\".join(batch), file=out_f)\r\n```\r\n\r\nWhich completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,\r\n\r\n```\r\ndataset = nlp.load_dataset('text', data_files='tmp2.txt')\r\n```\r\n\r\nWhich completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping. \r\n\r\nIs shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.)","comment_length":51,"text":"Faster Shuffling? \n Consider shuffling bookcorpus:\r\n\r\n```\r\ndataset = nlp.load_dataset('bookcorpus', split='train')\r\ndataset.shuffle()\r\n```\r\nAccording to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.\r\n\r\nBut I can also just write the lines to a text file:\r\n\r\n```\r\nbatch_size = 100000\r\nwith open('tmp.txt', 'w+') as out_f:\r\n    for i in tqdm(range(0, len(dataset), batch_size)):\r\n        batch = dataset[i:i+batch_size]['text']\r\n        print(\"\\n\".join(batch), file=out_f)\r\n```\r\n\r\nWhich completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,\r\n\r\n```\r\ndataset = nlp.load_dataset('text', data_files='tmp2.txt')\r\n```\r\n\r\nWhich completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping. \r\n\r\nIs shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.) \n I think the slowness here probably come from the fact that we are copying from and to python.\r\n\r\n@lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/406","title":"Faster Shuffling?","comments":"> @lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?\r\n\r\nI just tried with `writer.write_table` with tables of 1000 elements and it's slower that the solution in #405 \r\n\r\nOn my side (select 10 000 examples):\r\n- Original implementation: 12s\r\n- Batched solution: 100ms\r\n- solution using arrow tables: 350ms\r\n\r\nI'll try with arrays and record batches to see if we can make it work.","body":"Consider shuffling bookcorpus:\r\n\r\n```\r\ndataset = nlp.load_dataset('bookcorpus', split='train')\r\ndataset.shuffle()\r\n```\r\nAccording to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.\r\n\r\nBut I can also just write the lines to a text file:\r\n\r\n```\r\nbatch_size = 100000\r\nwith open('tmp.txt', 'w+') as out_f:\r\n    for i in tqdm(range(0, len(dataset), batch_size)):\r\n        batch = dataset[i:i+batch_size]['text']\r\n        print(\"\\n\".join(batch), file=out_f)\r\n```\r\n\r\nWhich completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,\r\n\r\n```\r\ndataset = nlp.load_dataset('text', data_files='tmp2.txt')\r\n```\r\n\r\nWhich completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping. \r\n\r\nIs shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.)","comment_length":88,"text":"Faster Shuffling? \n Consider shuffling bookcorpus:\r\n\r\n```\r\ndataset = nlp.load_dataset('bookcorpus', split='train')\r\ndataset.shuffle()\r\n```\r\nAccording to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.\r\n\r\nBut I can also just write the lines to a text file:\r\n\r\n```\r\nbatch_size = 100000\r\nwith open('tmp.txt', 'w+') as out_f:\r\n    for i in tqdm(range(0, len(dataset), batch_size)):\r\n        batch = dataset[i:i+batch_size]['text']\r\n        print(\"\\n\".join(batch), file=out_f)\r\n```\r\n\r\nWhich completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,\r\n\r\n```\r\ndataset = nlp.load_dataset('text', data_files='tmp2.txt')\r\n```\r\n\r\nWhich completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping. \r\n\r\nIs shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.) \n > @lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?\r\n\r\nI just tried with `writer.write_table` with tables of 1000 elements and it's slower that the solution in #405 \r\n\r\nOn my side (select 10 000 examples):\r\n- Original implementation: 12s\r\n- Batched solution: 100ms\r\n- solution using arrow tables: 350ms\r\n\r\nI'll try with arrays and record batches to see if we can make it work.","embeddings":[-0.1645014137,0.1181401834,-0.0444608256,-0.1049747691,-0.0814409629,-0.1731703728,0.147723943,0.5459221005,-0.2346828729,0.0624632165,-0.000081832,0.5998945832,-0.1947995573,-0.2284020036,0.2151886672,-0.0612115674,0.0476633497,-0.0043822541,-0.2822352946,-0.0438992195,-0.0562193953,-0.4137574136,-0.1994884014,-0.199706912,-0.0257708244,-0.2938672304,-0.1878563613,0.0697672665,-0.1976449341,0.0757235065,-0.217338413,0.281186074,-0.0521544069,0.2584172785,-0.0001122204,-0.1701223403,0.0425045677,0.1037315354,-0.3138458133,0.1286937594,0.3800330162,-0.0521176793,-0.1779631376,-0.2150924802,0.1908933669,-0.3175557554,0.1569269449,-0.078463316,-0.1330254078,-0.0641253069,0.1537112445,0.2656764984,-0.158904925,0.2145538479,0.5277472734,0.2444077432,-0.3638020456,0.2725051939,0.3779156208,-0.1275762767,-0.2558321059,0.2600165606,-0.1540908068,-0.05056981,0.2140832543,0.1419372708,0.0656322241,0.3851957321,0.0977453068,0.3810899258,0.2966608405,-0.1960287392,-0.2890027761,-0.2553756237,0.1719614565,-0.036944937,0.0981168896,0.2776352167,-0.276034385,-0.2166483849,-0.1713003367,0.0930572525,0.2021472156,-0.2257492393,0.3682128191,0.3595198393,0.1895429045,-0.0979154855,0.6719359756,-0.1242221296,0.5510306358,-0.1262992322,-0.0047993273,0.4045140147,-0.3214240074,-0.318256259,-0.2337398827,-0.1205970347,-0.0468584299,0.1339881122,0.6908426881,0.2351148576,0.244086206,-0.0330901146,0.029474631,0.0050701234,-0.1881677061,0.0786928162,-0.1245698184,0.1490849257,0.254429251,0.1171050519,-0.0766196996,-0.3060936332,-0.1184603646,-0.2209762186,-0.5335650444,0.1360857934,0.0855467096,-0.2906821966,-0.0308560114,-0.0727870837,0.0191447586,0.217607528,-0.1134597436,0.2636504173,-0.0390845872,-0.1840486228,-0.0767866448,-0.0296166018,-0.0213162433,0.039533224,-0.1393529177,0.0402012654,0.117330268,0.4005283415,-0.2564073205,0.0705069825,0.1031442136,0.3123986125,0.023626605,-0.1334683448,0.0955936089,0.0091467304,-0.1462748051,-0.2022948563,-0.1624473184,0.0748468265,-0.2686469853,0.4047932327,0.008407644,-0.1314480156,0.1649600267,0.1682451367,0.2920650542,-0.2565487027,-0.5357545614,0.028208904,0.0150799761,-0.0602184683,0.0654673576,-0.0238683186,0.067362085,-0.1401836425,0.0244531073,-0.2616640925,-0.0000500487,0.150228098,-0.0067202686,0.551787138,0.4012793303,0.6005730629,-0.0661774054,0.0918936878,0.1528079659,-0.0147358095,0.65400213,0.2467100769,-0.2185637951,0.0435642451,-0.0482172333,-0.0262146741,0.3894706368,0.2075691968,0.1455650479,-0.1148245186,0.4333437085,0.236869961,-0.002023858,0.3048751354,-0.5437975526,-0.1804259866,0.2548130453,0.4310247898,-0.5073711276,-0.3955105245,0.0435986966,-0.3157792091,0.3072642088,-0.1929805428,-0.0006519615,0.048137743,-0.0866354853,0.0819223076,-0.180737868,0.0500819385,-0.2162789851,-0.0749926195,-0.0966446176,-0.0121546257,0.2877600491,-0.342728734,0.2107428014,-0.2337404788,0.2078987062,0.190705061,0.0213446133,-0.2241520733,0.3385126889,-0.1749186516,-0.1115132198,-0.0718107522,-0.2885776162,0.0425322279,-0.1818866283,-0.1339042038,0.0493438505,-0.2068353295,0.1409365982,0.3582656384,-0.3306203187,0.0571808144,0.1285651922,0.2276866734,-0.1215711236,-0.2004279792,0.1078855172,0.1838665158,-0.072261624,0.2219088227,0.1093335375,-0.2368921489,0.1598075479,-0.0240699071,-0.3439081609,0.5287792683,-0.4670099616,0.3060247004,0.1508340091,-0.2817956209,-0.1983257383,0.3005359471,-0.0364965759,0.4161673188,0.6197937131,0.0703198686,-0.0085375905,0.382840991,-0.2867331803,0.1110055223,0.4442731142,0.0354825109,-0.0874339268,0.0763645917,0.1151951253,-0.1434492767,-0.1449111551,0.0289835874,0.220110178,0.2666477263,0.0663305745,-0.0658477992,-0.0587906055,-0.2190838456,0.1854216009,-0.0540741794,0.1081660613,-0.116148755,0.1254767329,-0.0036939338,-0.1517492533,-0.1532523036,0.1124854162,0.1517265737,0.1317405552,0.0266395733,-0.0931461453,-0.1624273658,-0.1187378243,-0.0148237161,-0.047975637,-0.0085713789,0.2399174869,0.239157632,-0.2472816855,-0.2346794307,-0.036851123,0.3515058458,-0.1599138081,-0.0430689976,-0.1856266111,0.0427749306,-0.0941626951,0.0918409526,0.2291274518,0.2741205692,0.3628977239,0.098799035,-0.2361453772,0.0463990048,0.0200848281,-0.2806491256,0.0375690013,-0.2801628113,-0.0506352596,-0.1814999431,-0.4017320573,-0.2940499485,0.0484575406,-0.2136878073,-0.1209500507,0.3594197929,0.2213585973,-0.1924252957,-0.1647051722,-0.1412424743,-0.0271904748,-0.4196079373,0.3166359365,-0.1018242091,0.2229262888,-0.0784572884,-0.1216172352,-0.0703331009,-0.5059905052,-0.0254836939,0.1991568059,-0.1796475351,0.2476859689,0.0956990272,-0.1881326139,-0.4313289523,-0.0367180184,-0.0820129737,0.4609701633,-0.0559894554,0.2647229135,-0.3275972903,-0.0486322194,-0.0881516859,0.164072901,0.4675890207,-0.123099193,-0.1321263015,0.2906356752,0.1878260374,0.0932345688,0.2486408055,0.148440972,0.107899718,0.1583170742,0.2577732503,0.4758410454,-0.0132597741,0.3537451029,-0.1806486249,0.1811614782,-0.0629272386,-0.3044174314,0.0902216434,0.1761184484,-0.010492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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/406","title":"Faster Shuffling?","comments":"I tried using `.take` from pyarrow recordbatches but it doesn't improve the speed that much:\r\n```python\r\nimport nlp\r\nimport numpy as np\r\n\r\ndset = nlp.Dataset.from_file(\"dummy_test_select.arrow\")  # dummy dataset with 100000 examples like {\"a\": \"h\"*512}\r\nindices = np.random.randint(0, 100_000, 1000_000)\r\n```\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\",\r\n                     writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n    table = pa.concat_tables(dset._data.slice(int(i), 1) for i in indices[i : min(len(indices), i + batch_size)])\r\n    batch = table.to_pydict()\r\n    writer.write_batch(batch)\r\nwriter.finalize()\r\n# 9.12s\r\n```\r\n\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\", \r\n                     writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n    batch_indices = indices[i : min(len(indices), i + batch_size)]\r\n    # First, extract only the indices that we need with a mask\r\n    mask = [False] * len(dset)\r\n    for k in batch_indices:\r\n        mask[k] = True\r\n    t_batch = dset._data.filter(pa.array(mask))\r\n    # Second, build the list of indices for the filtered table, and taking care of duplicates\r\n    rev_positions = {}\r\n    duplicates = 0\r\n    for i, j in enumerate(sorted(batch_indices)):\r\n        if j in rev_positions:\r\n            duplicates += 1\r\n        else:\r\n            rev_positions[j] = i - duplicates\r\n    rev_map = [rev_positions[j] for j in batch_indices]\r\n    # Third, use `.take` from the combined recordbatch\r\n    t_combined = t_batch.combine_chunks()  # load in memory\r\n    recordbatch = t_combined.to_batches()[0]\r\n    table = pa.Table.from_arrays(\r\n        [recordbatch[c].take(pa.array(rev_map)) for c in range(len(dset._data.column_names))],\r\n        schema=writer.schema\r\n    )\r\n    writer.write_table(table)\r\nwriter.finalize()\r\n# 3.2s\r\n```\r\n","body":"Consider shuffling bookcorpus:\r\n\r\n```\r\ndataset = nlp.load_dataset('bookcorpus', split='train')\r\ndataset.shuffle()\r\n```\r\nAccording to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.\r\n\r\nBut I can also just write the lines to a text file:\r\n\r\n```\r\nbatch_size = 100000\r\nwith open('tmp.txt', 'w+') as out_f:\r\n    for i in tqdm(range(0, len(dataset), batch_size)):\r\n        batch = dataset[i:i+batch_size]['text']\r\n        print(\"\\n\".join(batch), file=out_f)\r\n```\r\n\r\nWhich completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,\r\n\r\n```\r\ndataset = nlp.load_dataset('text', data_files='tmp2.txt')\r\n```\r\n\r\nWhich completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping. \r\n\r\nIs shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.)","comment_length":210,"text":"Faster Shuffling? \n Consider shuffling bookcorpus:\r\n\r\n```\r\ndataset = nlp.load_dataset('bookcorpus', split='train')\r\ndataset.shuffle()\r\n```\r\nAccording to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.\r\n\r\nBut I can also just write the lines to a text file:\r\n\r\n```\r\nbatch_size = 100000\r\nwith open('tmp.txt', 'w+') as out_f:\r\n    for i in tqdm(range(0, len(dataset), batch_size)):\r\n        batch = dataset[i:i+batch_size]['text']\r\n        print(\"\\n\".join(batch), file=out_f)\r\n```\r\n\r\nWhich completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,\r\n\r\n```\r\ndataset = nlp.load_dataset('text', data_files='tmp2.txt')\r\n```\r\n\r\nWhich completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping. \r\n\r\nIs shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.) \n I tried using `.take` from pyarrow recordbatches but it doesn't improve the speed that much:\r\n```python\r\nimport nlp\r\nimport numpy as np\r\n\r\ndset = nlp.Dataset.from_file(\"dummy_test_select.arrow\")  # dummy dataset with 100000 examples like {\"a\": \"h\"*512}\r\nindices = np.random.randint(0, 100_000, 1000_000)\r\n```\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\",\r\n                     writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n    table = pa.concat_tables(dset._data.slice(int(i), 1) for i in indices[i : min(len(indices), i + batch_size)])\r\n    batch = table.to_pydict()\r\n    writer.write_batch(batch)\r\nwriter.finalize()\r\n# 9.12s\r\n```\r\n\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\", \r\n                     writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n    batch_indices = indices[i : min(len(indices), i + batch_size)]\r\n    # First, extract only the indices that we need with a mask\r\n    mask = [False] * len(dset)\r\n    for k in batch_indices:\r\n        mask[k] = True\r\n    t_batch = dset._data.filter(pa.array(mask))\r\n    # Second, build the list of indices for the filtered table, and taking care of duplicates\r\n    rev_positions = {}\r\n    duplicates = 0\r\n    for i, j in enumerate(sorted(batch_indices)):\r\n        if j in rev_positions:\r\n            duplicates += 1\r\n        else:\r\n            rev_positions[j] = i - duplicates\r\n    rev_map = [rev_positions[j] for j in batch_indices]\r\n    # Third, use `.take` from the combined recordbatch\r\n    t_combined = t_batch.combine_chunks()  # load in memory\r\n    recordbatch = t_combined.to_batches()[0]\r\n    table = pa.Table.from_arrays(\r\n        [recordbatch[c].take(pa.array(rev_map)) for c in range(len(dset._data.column_names))],\r\n        schema=writer.schema\r\n    )\r\n    writer.write_table(table)\r\nwriter.finalize()\r\n# 3.2s\r\n```\r\n","embeddings":[-0.2073036879,0.1424287409,-0.025603326,-0.1453503072,-0.1025906205,-0.1113140285,0.1211357713,0.5408861041,-0.2365823239,0.0190715939,-0.0381175689,0.5862330198,-0.1683035642,-0.276460439,0.2058225423,-0.0411366634,0.0331520326,-0.0362084135,-0.2147115171,-0.0471256189,-0.0500182919,-0.4499439895,-0.238439858,-0.2234770507,-0.0126475478,-0.225724414,-0.1654447317,0.0786909088,-0.165457949,0.0551608689,-0.1150205806,0.2012873143,-0.108007662,0.1667573005,-0.0001167829,-0.1386466324,0.099314861,0.1305755377,-0.3299337327,0.1783211082,0.451499939,-0.0504897796,-0.1290057003,-0.190895468,0.1902823597,-0.414375037,0.086571835,-0.1146503985,-0.1485535353,-0.0229181349,0.1140140891,0.3141115904,-0.1818512678,0.2556697726,0.4748253226,0.2703826129,-0.3750654459,0.3879562318,0.2586728334,-0.021415716,-0.1545352042,0.2556572556,-0.1949238777,0.0181237869,0.1430734247,0.1754679829,0.1023451537,0.4606055915,0.1073968634,0.4037255645,0.2905994356,-0.2549730241,-0.2978284657,-0.2941875756,0.2330021709,-0.0797465667,0.0570254363,0.3016262352,-0.2735319436,-0.2154012322,-0.0974206701,0.1640346795,0.2480155677,-0.2000269145,0.3221797049,0.3900923729,0.2021176666,-0.0607311912,0.7018341422,-0.105781056,0.4943529963,-0.1712976098,0.0219092872,0.411686033,-0.3191821575,-0.2636018097,-0.3010390103,-0.1255467832,-0.0711555183,0.1603339463,0.6939632297,0.2144636214,0.1904992461,-0.0473995619,0.0462994874,0.0036276807,-0.300027132,0.071423158,-0.1324055791,0.0648491308,0.3124971092,0.1253009737,-0.0777109712,-0.3089742362,-0.0426212512,-0.2280753404,-0.4801682532,0.1160597578,0.0091327066,-0.3454588354,-0.1375005543,-0.0533413328,0.0066610733,0.1520721167,-0.1462170929,0.2534679472,-0.0821314976,-0.1687271297,-0.1135871559,0.0287617557,-0.0324031971,0.0895730853,-0.1062909514,0.0718561411,0.0299970079,0.4717019498,-0.2529067397,0.0169554781,0.0952395871,0.3210919499,0.0433104672,-0.1136180982,0.083029367,0.1123675853,-0.0979958698,-0.149040252,-0.1326774359,0.0459325425,-0.339520812,0.3854333162,0.0259141456,-0.1179540604,0.0696305931,0.1244064644,0.2436410189,-0.288731873,-0.5230558515,-0.0648371875,-0.0311097726,-0.1096803695,0.0887992233,-0.0550133102,0.0886194408,-0.1288460195,0.0646334291,-0.2630294859,-0.0793608427,0.1027542576,-0.0587744936,0.5555560589,0.4522926211,0.6798374057,-0.071005933,0.0568028465,0.1080939099,0.0309065524,0.6843819618,0.1945423037,-0.253213495,-0.0193489902,-0.1120895743,0.0253338367,0.3429556489,0.2405741066,0.0689611956,-0.0779747441,0.3746800721,0.2942918837,0.0483492427,0.2648988962,-0.4871974587,-0.1362498254,0.2310036123,0.431266427,-0.6047837138,-0.3673205674,0.0409888513,-0.2627362311,0.3437721729,-0.1634198278,-0.0241476707,-0.002875691,-0.0728363693,0.0040968494,-0.1673831344,0.1009118035,-0.2770056427,-0.0851025879,-0.0123086534,-0.0934438035,0.2220733315,-0.3609888852,0.1847952455,-0.1915204227,0.1916127056,0.207287401,-0.010497516,-0.1980127543,0.4299571514,-0.1024159417,-0.0807139352,-0.031038601,-0.2975625396,0.0371089727,-0.1596858054,-0.1389845014,0.1140311584,-0.2169331312,0.135617584,0.3588954806,-0.2914271355,0.0721107796,0.0999829918,0.2370676994,-0.0914403051,-0.1885744333,0.0344020613,0.2401288152,-0.0574304909,0.2263465077,0.118693836,-0.2045537233,0.1560783982,-0.0530229695,-0.4021678865,0.5432929397,-0.4847533405,0.3439686,0.1877929568,-0.2420200109,-0.1256657988,0.2605871856,-0.0751947165,0.4508640468,0.59654212,0.0635082498,0.0748018399,0.4007713795,-0.2726922035,0.0491742678,0.4478168488,-0.0474845879,-0.0537610278,0.0778427869,-0.0244941916,-0.2125136256,-0.0856432244,0.0269686021,0.2142338306,0.2641347349,0.1126311794,-0.0479865111,-0.0872229114,-0.2304076701,0.1614469439,-0.0465985313,0.0837044716,-0.0975083485,0.1120569482,-0.0303853583,-0.2148164213,-0.1830169559,0.0966954678,0.1835551709,0.1354514509,0.0691546574,-0.1409353316,-0.1086602136,-0.1263204813,-0.0809588283,0.0280734841,-0.1020173505,0.1147613153,0.2489230931,-0.2036471665,-0.2030912042,-0.1168933585,0.335203737,-0.1054509804,-0.1940307021,-0.182344228,-0.0091365818,-0.1343149394,0.0485685654,0.197874859,0.3558698595,0.3271757662,0.1494715214,-0.220095247,0.0215608403,0.0938112438,-0.2209096402,0.0969474986,-0.3778560162,-0.0812894404,-0.1770183295,-0.4263556898,-0.2799151838,0.0594400652,-0.1173179075,-0.0908795819,0.3733411133,0.1376002133,-0.2064498812,-0.1201335639,-0.0493216701,-0.0749145225,-0.3606601655,0.3090262711,-0.1253742874,0.1422898024,-0.0030990706,-0.1096464843,-0.0249080025,-0.5705391169,-0.055480022,0.1581888199,-0.1193132997,0.2787475884,0.1174005941,-0.1336577386,-0.451035738,-0.0693096444,-0.0896496326,0.4983684719,-0.077488102,0.221291855,-0.3810477257,-0.0344085172,-0.1012929603,0.140446201,0.4684356451,-0.187535882,-0.1023728997,0.3078249693,0.1736066341,0.0701604113,0.2383754402,0.1195240542,0.1213327423,0.1363343149,0.2258581817,0.4733502865,-0.0470225215,0.2490298152,-0.2466053814,0.0461117513,-0.030770259,-0.3000233769,0.1443730891,0.1948063076,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/406","title":"Faster Shuffling?","comments":"Shuffling is now significantly faster thanks to #513 \r\nFeel free to play with it now :)\r\n\r\nClosing this one, but feel free to re-open if you have other questions","body":"Consider shuffling bookcorpus:\r\n\r\n```\r\ndataset = nlp.load_dataset('bookcorpus', split='train')\r\ndataset.shuffle()\r\n```\r\nAccording to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.\r\n\r\nBut I can also just write the lines to a text file:\r\n\r\n```\r\nbatch_size = 100000\r\nwith open('tmp.txt', 'w+') as out_f:\r\n    for i in tqdm(range(0, len(dataset), batch_size)):\r\n        batch = dataset[i:i+batch_size]['text']\r\n        print(\"\\n\".join(batch), file=out_f)\r\n```\r\n\r\nWhich completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,\r\n\r\n```\r\ndataset = nlp.load_dataset('text', data_files='tmp2.txt')\r\n```\r\n\r\nWhich completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping. \r\n\r\nIs shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.)","comment_length":29,"text":"Faster Shuffling? \n Consider shuffling bookcorpus:\r\n\r\n```\r\ndataset = nlp.load_dataset('bookcorpus', split='train')\r\ndataset.shuffle()\r\n```\r\nAccording to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`.\r\n\r\nBut I can also just write the lines to a text file:\r\n\r\n```\r\nbatch_size = 100000\r\nwith open('tmp.txt', 'w+') as out_f:\r\n    for i in tqdm(range(0, len(dataset), batch_size)):\r\n        batch = dataset[i:i+batch_size]['text']\r\n        print(\"\\n\".join(batch), file=out_f)\r\n```\r\n\r\nWhich completes in a couple minutes, followed by `shuf tmp.txt > tmp2.txt` which completes in under a minute. And finally,\r\n\r\n```\r\ndataset = nlp.load_dataset('text', data_files='tmp2.txt')\r\n```\r\n\r\nWhich completes in under 10 minutes. I read up on Apache Arrow this morning, and it seems like the columnar data format is not especially well-suited to shuffling rows, since moving items around requires a lot of book-keeping. \r\n\r\nIs shuffle inherently slow, or am I just using it wrong? And if it is slow, would it make sense to try converting the data to a row-based format on disk and then shuffling? (Instead of calling select with a random permutation, as is currently done.) \n Shuffling is now significantly faster thanks to #513 \r\nFeel free to play with it now :)\r\n\r\nClosing this one, but feel free to re-open if you have other questions","embeddings":[-0.1669747978,0.0758930519,-0.0756695494,-0.1069152504,-0.0451760292,-0.1096984372,0.1049450263,0.545971632,-0.2224266231,0.1107433885,-0.0171533898,0.5978963375,-0.1472128481,-0.2247790396,0.1525786072,-0.0917216018,0.0197470859,-0.0066342889,-0.2984012663,-0.0681981742,-0.1076799184,-0.3407295346,-0.1966788322,-0.2414292395,-0.0211049747,-0.2671126127,-0.1641610265,0.1271981448,-0.1086063236,0.035609737,-0.1816464514,0.2036722153,-0.1019436568,0.2518034875,-0.0001064402,-0.1878170967,-0.0013785269,0.1245126128,-0.3185117841,0.1700074822,0.4203979969,-0.0366483703,-0.1810671836,-0.2470211387,0.1335794181,-0.2439090461,0.1669650376,-0.0882611051,-0.0787344053,-0.0348335542,0.1946194321,0.3067126572,-0.1423862129,0.1317119598,0.5130022168,0.0983550847,-0.333455056,0.2333613783,0.2945879102,-0.0863215774,-0.1773552746,0.2387263626,-0.0959547982,-0.0672887415,0.1371427625,0.1144751385,0.0959913209,0.3252631128,0.0591608323,0.4156249166,0.2406332344,-0.2018209398,-0.2674714327,-0.2720342875,0.2022290379,-0.0449447893,0.1043059304,0.270442605,-0.2393940538,-0.1595278084,-0.0910153463,0.1850961596,0.2104175687,-0.1917834729,0.2724059224,0.4423377514,0.2230263501,-0.1347956955,0.6850678325,-0.1429264992,0.3533965349,-0.0897843167,0.0236880276,0.4135656357,-0.2834292352,-0.3294468224,-0.2499971837,-0.1825529486,-0.1107685864,0.183896631,0.7327424288,0.245347321,0.2195363343,-0.0025334849,0.0044257445,0.0172558874,-0.3033528924,0.0676690936,-0.083319135,0.1741636992,0.1895967871,0.1265306473,-0.0875518471,-0.3122540712,-0.1035777926,-0.167716518,-0.4825846553,0.1387092918,0.0155337201,-0.2844125628,-0.0667908415,-0.0099283764,0.0018037392,0.2151009291,-0.1538619548,0.2751540542,-0.03062962,-0.2389086932,-0.1474363059,-0.0921697393,-0.0841054097,0.0927617177,-0.1854152381,0.0111724278,0.1070576981,0.4471132457,-0.1813143045,0.0750126243,0.0932677612,0.3140338957,0.0747543871,-0.1450024843,0.052906286,0.0301001482,-0.1538800597,-0.1871067733,-0.163969323,0.1298393905,-0.2964134812,0.3371615708,0.0634578317,-0.1148025468,0.1358181983,0.241995126,0.2536460161,-0.2783489227,-0.3240192831,0.0042138305,-0.0300784502,-0.0588060208,0.0889037922,0.0079699345,0.0558057651,-0.1506493837,0.0332125574,-0.2832565904,-0.030102957,0.1387460679,-0.0630680472,0.4716358483,0.3784511387,0.5905828476,-0.0927200541,0.0524727404,0.1518076509,0.1000737399,0.6754481792,0.1847668141,-0.222253859,0.0069721234,-0.1234954968,0.0189208183,0.329983443,0.2190211713,0.1064449102,-0.0470619909,0.4243724942,0.3086519241,0.057607308,0.267239809,-0.5271468163,-0.1680125743,0.2397631556,0.4268015623,-0.4836195111,-0.3931616545,0.0215596184,-0.2457869351,0.3352416754,-0.2223267257,0.0048054908,0.0780970454,-0.0988785103,0.0684452802,-0.1722119153,-0.0461623035,-0.2284312844,-0.0307575669,-0.051114507,-0.0219243765,0.2823062837,-0.3760942221,0.138916105,-0.2351230532,0.2231093794,0.1523119509,0.1031891629,-0.1222540736,0.3160507083,-0.1441675276,-0.1249852479,-0.0329151638,-0.2858459651,0.0602336489,-0.1428825557,-0.1032397375,0.0414223596,-0.1743188947,0.1801927984,0.4312620759,-0.3207770288,0.0851427093,0.1532296389,0.2604211271,-0.1293295026,-0.1546777636,0.1511733979,0.177882418,-0.0839824229,0.2608782649,0.0671195239,-0.098170504,0.1191634312,0.0282842238,-0.3607949317,0.576117754,-0.4362382293,0.2577603459,0.1505965441,-0.2896023989,-0.1527445763,0.192486763,-0.0689205825,0.3874967694,0.6559903026,0.0933465883,0.0408643782,0.4048003852,-0.3637098074,0.1366246641,0.505181849,0.0175943319,-0.0612066314,0.0454889685,0.0998163521,-0.0828498527,-0.1516596079,0.0592621751,0.1986665726,0.3086874187,0.1105102152,-0.0429085679,-0.152237311,-0.2253775895,0.1467131227,-0.0810682774,0.1443347335,-0.0461751521,0.1731486171,0.0282911379,-0.246881023,-0.1958767325,0.1010336876,0.1578456461,0.1300912499,0.0456534065,-0.1185931787,-0.1928806603,-0.1469243616,-0.0087559484,-0.0410588533,-0.0655926466,0.220376268,0.2674484551,-0.2967941463,-0.2441508919,-0.0966950953,0.3949586451,-0.0981088355,-0.0882318169,-0.2022629529,0.0434115082,-0.1129604802,0.1577924937,0.2093465328,0.278398037,0.4065812826,0.1497819871,-0.2056581974,0.1125960425,-0.0035842732,-0.2856873572,0.0597447045,-0.2784018219,-0.0796291307,-0.1288377196,-0.3703228831,-0.3678686917,0.0662479699,-0.1745668799,-0.1471477002,0.2800329924,0.2361859679,-0.1627960354,-0.2388626486,-0.2241316885,-0.0004679693,-0.4718467295,0.331335783,-0.0771482959,0.2015341073,-0.0852287114,-0.1230721995,-0.0027067505,-0.4613552392,-0.0153979519,0.1507292241,-0.1685830355,0.2793680131,0.0150951482,-0.1759365797,-0.4089842737,-0.0797424465,-0.0427275747,0.4191710949,-0.1128403246,0.2114582658,-0.354473412,-0.0188404694,-0.1047074422,0.0991762578,0.4139265716,-0.1405698955,-0.2141785324,0.2670760453,0.1871360689,0.056598898,0.2416381836,0.1690844893,0.0107140765,0.1163991541,0.2601451874,0.4409126639,-0.0831339657,0.3029327691,-0.1711633205,0.1169440746,-0.0787562653,-0.2479393631,0.0979947448,0.159551829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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/388","title":"\ud83d\udc1b [Dataset] Cannot download wmt14, wmt15 and wmt17","comments":"similar slow download speed here for nlp.load_dataset('wmt14', 'fr-en')\r\n`\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 658M\/658M [1:00:42<00:00, 181kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 918M\/918M [1:39:38<00:00, 154kB\/s]\r\nDownloading:   2%|\u2589                                                       | 40.9M\/2.37G [04:48<5:03:06, 128kB\/s]\r\n`\r\nCould we just download a specific subdataset in 'wmt14', such as 'newstest14'? ","body":"1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code:\r\n```\r\nnlp.load_dataset('wmt14','de-en')\r\nnlp.load_dataset('wmt15','de-en')\r\nnlp.load_dataset('wmt17','de-en')\r\nnlp.load_dataset('wmt19','de-en')\r\n```\r\nThe code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18`\r\n\r\n2. When trying to download `wmt17 zh-en`, I got the following error:\r\n> ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-zh.tar.gz","comment_length":38,"text":"\ud83d\udc1b [Dataset] Cannot download wmt14, wmt15 and wmt17 \n 1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code:\r\n```\r\nnlp.load_dataset('wmt14','de-en')\r\nnlp.load_dataset('wmt15','de-en')\r\nnlp.load_dataset('wmt17','de-en')\r\nnlp.load_dataset('wmt19','de-en')\r\n```\r\nThe code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18`\r\n\r\n2. When trying to download `wmt17 zh-en`, I got the following error:\r\n> ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-zh.tar.gz \n similar slow download speed here for nlp.load_dataset('wmt14', 'fr-en')\r\n`\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 658M\/658M [1:00:42<00:00, 181kB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 918M\/918M [1:39:38<00:00, 154kB\/s]\r\nDownloading:   2%|\u2589                                                       | 40.9M\/2.37G [04:48<5:03:06, 128kB\/s]\r\n`\r\nCould we just download a specific subdataset in 'wmt14', such as 'newstest14'? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/388","title":"\ud83d\udc1b [Dataset] Cannot download wmt14, wmt15 and wmt17","comments":"> The code runs but the download speed is extremely slow, the same behaviour is not observed on wmt16 and wmt18\r\n\r\nThe original source for the files may provide slow download speeds.\r\nWe can probably host these files ourselves.\r\n\r\n> When trying to download wmt17 zh-en, I got the following error:\r\n> ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-zh.tar.gz\r\n\r\nLooks like the file`UNv1.0.en-zh.tar.gz` is missing, or the url changed. We need to fix that\r\n\r\n> Could we just download a specific subdataset in 'wmt14', such as 'newstest14'?\r\n\r\nRight now I don't think it's possible. Maybe @patrickvonplaten knows more about it\r\n","body":"1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code:\r\n```\r\nnlp.load_dataset('wmt14','de-en')\r\nnlp.load_dataset('wmt15','de-en')\r\nnlp.load_dataset('wmt17','de-en')\r\nnlp.load_dataset('wmt19','de-en')\r\n```\r\nThe code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18`\r\n\r\n2. When trying to download `wmt17 zh-en`, I got the following error:\r\n> ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-zh.tar.gz","comment_length":97,"text":"\ud83d\udc1b [Dataset] Cannot download wmt14, wmt15 and wmt17 \n 1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code:\r\n```\r\nnlp.load_dataset('wmt14','de-en')\r\nnlp.load_dataset('wmt15','de-en')\r\nnlp.load_dataset('wmt17','de-en')\r\nnlp.load_dataset('wmt19','de-en')\r\n```\r\nThe code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18`\r\n\r\n2. When trying to download `wmt17 zh-en`, I got the following error:\r\n> ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-zh.tar.gz \n > The code runs but the download speed is extremely slow, the same behaviour is not observed on wmt16 and wmt18\r\n\r\nThe original source for the files may provide slow download speeds.\r\nWe can probably host these files ourselves.\r\n\r\n> When trying to download wmt17 zh-en, I got the following error:\r\n> ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-zh.tar.gz\r\n\r\nLooks like the file`UNv1.0.en-zh.tar.gz` is missing, or the url changed. We need to fix that\r\n\r\n> Could we just download a specific subdataset in 'wmt14', such as 'newstest14'?\r\n\r\nRight now I don't think it's possible. Maybe @patrickvonplaten knows more about it\r\n","embeddings":[-0.268609345,-0.1404747516,-0.0071865544,0.5020000935,-0.0168406237,0.2251569629,-0.0530777313,0.2993670106,0.0911353305,-0.0368140861,-0.2252270728,-0.0799275488,0.1161911115,0.0058771251,0.034304779,-0.2081655562,-0.1193282083,0.0341802686,-0.3183064461,0.0080130184,-0.3116195202,0.1468855292,-0.0868213475,0.0733703375,0.2240645885,-0.0069648223,-0.2052784413,-0.0254717804,-0.1000093371,-0.3394281864,0.4278948009,0.2947577834,0.084403567,0.2282148302,-0.0001149252,0.0709944889,0.1664440781,-0.1815565974,-0.3427716792,-0.0916524976,-0.509786129,-0.4445599318,-0.1338346601,-0.0916885734,-0.0498927794,-0.4900558889,0.2561153769,-0.1568859816,0.3314899802,0.3418523967,0.1644345075,0.2418355197,-0.1462133229,-0.0085078701,0.2681643069,-0.241802901,-0.1191282272,0.1768724173,0.0858175084,-0.0647064373,0.2019879371,0.0539438315,0.0507229492,0.4143848717,0.0648013875,0.114726074,-0.0066977637,-0.2883557379,0.0657300279,0.4004980624,0.707028687,0.0280685835,-0.5350027084,0.094114162,-0.0312393866,-0.124926351,0.1994022578,0.2483088821,-0.2839077115,0.023875311,-0.2142187357,0.0485210232,-0.1946800798,0.5462664962,-0.0335705765,0.6662647128,0.2300116271,0.0512684174,0.0850208998,-0.0345224477,0.0830066949,-0.1274167895,0.0934071094,0.0012820574,-0.3166339695,-0.0942261145,-0.2909419537,-0.0173591822,0.040531341,0.0083770044,0.2951494753,0.2358808815,0.0183149688,-0.0712846443,0.1781585515,-0.1028664187,-0.1670800298,-0.2394867986,0.3152788579,0.1925608367,0.1304365695,0.1407724321,-0.0634528548,-0.1405701041,-0.4265267253,0.1794076413,0.0678066015,0.0819200277,-0.3032383025,-0.2359872609,-0.0952850729,-0.2585288584,-0.2333587557,0.2315392792,-0.2232052982,0.3033592701,-0.01407394,0.0879307836,-0.0782802776,-0.3754115403,-0.1115830392,-0.0619362295,-0.0947364196,0.1375948638,0.4606338143,0.2327832133,0.2125827819,-0.1855624467,-0.324811846,0.1220188588,0.0827296302,-0.0089063961,-0.2034354061,0.2094967663,0.0389030725,0.5146356821,-0.0152603425,0.2459966391,-0.2595425546,-0.0184405837,-0.0653082058,-0.0278567933,0.1655278355,0.0922636688,-0.1611418277,-0.1466825753,-0.3369729519,0.0296972711,-0.1942420602,-0.0796273872,-0.3151355982,-0.0935668498,-0.334267199,-0.1286198795,0.2433601469,0.4242477119,-0.3487033546,-0.0085138381,-0.3759478927,-0.0351442434,0.2923084199,0.3570439219,-0.0795979276,0.0716927201,-0.1589744389,-0.1769257188,0.5281008482,-0.1343882829,-0.4725863039,0.270416677,-0.3284674287,0.1723009497,0.246023193,0.1870347112,0.0267481897,-0.083505325,0.1047134697,0.6350846291,0.0012760299,0.0515114032,-0.0706807077,-0.3561582863,0.3798070848,0.1910879463,-0.0478560701,0.1234174594,-0.0262463279,0.2235040963,0.4299486279,-0.006066205,0.2387932241,-0.0190680958,-0.1557723433,-0.1321374476,0.0291817784,0.0744898766,-0.7336922884,0.3186699152,-0.1053289399,-0.165256232,0.0810761154,-0.0034324117,-0.2751204967,-0.3440809846,0.0790603012,0.097276628,0.1050883904,0.5249676108,-0.041287411,-0.0831199959,0.0002133563,0.1862969249,0.1373163313,0.0402249508,-0.0418928415,0.0532663576,-0.0445458777,0.171317786,0.5440144539,-0.2567563951,0.1717381179,-0.2130444944,-0.0371350423,0.1358809024,-0.1739643812,0.3611807823,0.1119710281,-0.1792988628,0.0926525816,-0.3793899119,0.0370959714,0.5412628055,0.0641174167,-0.1362142265,-0.072694622,0.1897853315,0.14487046,0.2603428662,0.292127192,0.1436413825,0.4012096822,-0.141046375,0.1060652286,-0.0886812508,0.1613156497,0.1243789494,-0.1367560625,-0.0958056077,-0.1503305286,0.4106875062,0.4148617685,-0.0372205786,0.0187883023,0.2357937843,-0.0668034405,-0.0749669373,-0.224123776,0.5326936841,0.4717298448,0.0562893786,0.3155216277,0.027367577,0.2627882063,-0.4930698276,0.2585000098,0.2068519592,-0.344170332,0.2035879642,0.0771230608,-0.1378411949,-0.1374786347,0.071620442,0.3822630644,0.2573137581,-0.1986294985,0.0536603667,-0.6793519258,-0.5351989865,-0.3434390128,-0.2511228621,-0.1482522488,-0.305246979,0.1282158345,0.2785882354,-0.2804341018,0.1202675849,0.0317885987,0.2259991914,-0.1738285571,0.1984794587,-0.2139069885,-0.1356356591,-0.2548263371,0.0976746306,0.358640343,0.337623179,0.2934270799,-0.3645837009,-0.1957132667,-0.3108948171,-0.0325513594,-0.1599157453,-0.0036146466,0.1372742951,-0.0390132852,0.593483448,0.1324960589,-0.3987885416,0.1563427597,-0.0093738837,-0.0448995493,-0.0634622052,-0.096499823,0.0993852839,-0.0341018587,-0.623754859,-0.590438664,-0.523463726,0.1860347688,-0.0353248976,0.4430011809,0.1396968961,-0.3087504506,-0.0415654331,0.1024816632,0.1844176352,-0.0566680357,-0.0280259289,0.5375439525,-0.1619543135,-0.3553983867,0.1727953702,0.2059616596,0.200956434,0.4632624984,-0.5046476126,0.0646782294,-0.1197670549,-0.2729349136,0.1870685965,-0.1981201172,0.1420211941,-0.1899036169,-0.1756812483,-0.0921619833,-0.110205248,-0.0474175699,0.1542581618,0.3609259427,0.1070156842,0.0580693409,0.0540272072,0.5281319618,0.2144051343,0.0288450476,0.382537514,0.0698562115,-0.0967571065,0.2006230056,-0.2211503983,0.055933401,-0.1493368596,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/388","title":"\ud83d\udc1b [Dataset] Cannot download wmt14, wmt15 and wmt17","comments":"Yeah, the download speed is sadly always extremely slow :-\/. \r\nI will try to check out the `wmt17 zh-en` bug :-) ","body":"1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code:\r\n```\r\nnlp.load_dataset('wmt14','de-en')\r\nnlp.load_dataset('wmt15','de-en')\r\nnlp.load_dataset('wmt17','de-en')\r\nnlp.load_dataset('wmt19','de-en')\r\n```\r\nThe code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18`\r\n\r\n2. When trying to download `wmt17 zh-en`, I got the following error:\r\n> ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-zh.tar.gz","comment_length":21,"text":"\ud83d\udc1b [Dataset] Cannot download wmt14, wmt15 and wmt17 \n 1. I try downloading `wmt14`, `wmt15`, `wmt17`, `wmt19` with the following code:\r\n```\r\nnlp.load_dataset('wmt14','de-en')\r\nnlp.load_dataset('wmt15','de-en')\r\nnlp.load_dataset('wmt17','de-en')\r\nnlp.load_dataset('wmt19','de-en')\r\n```\r\nThe code runs but the download speed is **extremely slow**, the same behaviour is not observed on `wmt16` and `wmt18`\r\n\r\n2. When trying to download `wmt17 zh-en`, I got the following error:\r\n> ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/tfdataset-data\/downloadataset\/uncorpus\/UNv1.0.en-zh.tar.gz \n Yeah, the download speed is sadly always extremely slow :-\/. \r\nI will try to check out the `wmt17 zh-en` bug :-) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/387","title":"Conversion through to_pandas output numpy arrays for lists instead of python objects","comments":"To convert from arrow type we have three options: to_numpy, to_pandas and to_pydict\/to_pylist.\r\n\r\n- to_numpy and to_pandas return numpy arrays instead of lists but are very fast.\r\n- to_pydict\/to_pylist can be 100x slower and become the bottleneck for reading data, but at least they return lists.\r\n\r\nMaybe we can have to_pydict\/to_pylist as the default and use to_numpy or to_pandas when the format (set by `set_format`) is 'numpy' or 'pandas'","body":"In a related question, the conversion through to_pandas output numpy arrays for the lists instead of python objects.\r\n\r\nHere is an example:\r\n```python\r\n>>> dataset._data.slice(key, 1).to_pandas().to_dict(\"list\")\r\n{'sentence1': ['Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [array([  101,  7277,  2180,  5303,  4806,  1117,  1711,   117,  2292,\r\n        1119,  1270,   107,  1103,  7737,   107,   117,  1104,  9938,\r\n        4267, 12223, 21811,  1117,  2554,   119,   102])], 'token_type_ids': [array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n       0, 0, 0])], 'attention_mask': [array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n       1, 1, 1])]}\r\n>>> type(dataset._data.slice(key, 1).to_pandas().to_dict(\"list\")['input_ids'][0])\r\n<class 'numpy.ndarray'>\r\n>>> dataset._data.slice(key, 1).to_pydict()\r\n{'sentence1': ['Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [[101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}\r\n```","comment_length":69,"text":"Conversion through to_pandas output numpy arrays for lists instead of python objects \n In a related question, the conversion through to_pandas output numpy arrays for the lists instead of python objects.\r\n\r\nHere is an example:\r\n```python\r\n>>> dataset._data.slice(key, 1).to_pandas().to_dict(\"list\")\r\n{'sentence1': ['Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [array([  101,  7277,  2180,  5303,  4806,  1117,  1711,   117,  2292,\r\n        1119,  1270,   107,  1103,  7737,   107,   117,  1104,  9938,\r\n        4267, 12223, 21811,  1117,  2554,   119,   102])], 'token_type_ids': [array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n       0, 0, 0])], 'attention_mask': [array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\r\n       1, 1, 1])]}\r\n>>> type(dataset._data.slice(key, 1).to_pandas().to_dict(\"list\")['input_ids'][0])\r\n<class 'numpy.ndarray'>\r\n>>> dataset._data.slice(key, 1).to_pydict()\r\n{'sentence1': ['Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [[101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}\r\n``` \n To convert from arrow type we have three options: to_numpy, to_pandas and to_pydict\/to_pylist.\r\n\r\n- to_numpy and to_pandas return numpy arrays instead of lists but are very fast.\r\n- to_pydict\/to_pylist can be 100x slower and become the bottleneck for reading data, but at least they return lists.\r\n\r\nMaybe we can have to_pydict\/to_pylist as the default and use to_numpy or to_pandas when the format (set by `set_format`) is 'numpy' or 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/378","title":"[dataset] Structure of MLQA seems unecessary nested","comments":"Same for the RACE dataset: https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/race\/race.py\r\n\r\nShould we scan all the datasets to remove this pattern of un-necessary nesting?","body":"The features of the MLQA dataset comprise several nested dictionaries with a single element inside (for `questions` and `ids`): https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/mlqa\/mlqa.py#L90-L97\r\n\r\nShould we keep this @mariamabarham @patrickvonplaten? Was this added for compatibility with tfds?\r\n\r\n```python\r\n            features=nlp.Features(\r\n                {\r\n                    \"context\": nlp.Value(\"string\"),\r\n                    \"questions\": nlp.features.Sequence({\"question\": nlp.Value(\"string\")}),\r\n                    \"answers\": nlp.features.Sequence(\r\n                        {\"text\": nlp.Value(\"string\"), \"answer_start\": nlp.Value(\"int32\"),}\r\n                    ),\r\n                    \"ids\": nlp.features.Sequence({\"idx\": nlp.Value(\"string\")})\r\n```","comment_length":19,"text":"[dataset] Structure of MLQA seems unecessary nested \n The features of the MLQA dataset comprise several nested dictionaries with a single element inside (for `questions` and `ids`): https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/mlqa\/mlqa.py#L90-L97\r\n\r\nShould we keep this @mariamabarham @patrickvonplaten? Was this added for compatibility with tfds?\r\n\r\n```python\r\n            features=nlp.Features(\r\n                {\r\n                    \"context\": nlp.Value(\"string\"),\r\n                    \"questions\": nlp.features.Sequence({\"question\": nlp.Value(\"string\")}),\r\n                    \"answers\": nlp.features.Sequence(\r\n                        {\"text\": nlp.Value(\"string\"), \"answer_start\": nlp.Value(\"int32\"),}\r\n                    ),\r\n                    \"ids\": nlp.features.Sequence({\"idx\": nlp.Value(\"string\")})\r\n``` \n Same for the RACE dataset: https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/race\/race.py\r\n\r\nShould we scan all the datasets to remove this pattern of un-necessary nesting?","embeddings":[-0.1161423251,-0.0593214743,-0.0939640701,0.3199597001,-0.0490284264,0.2495961785,0.087612316,0.2466984987,-0.1752906442,-0.1441369653,0.0130557083,0.197159186,0.058465369,0.6652584672,-0.037603993,-0.0648569986,0.2087631822,-0.0377437025,-0.056361977,0.1263812631,-0.2246391177,0.1446332335,-0.2206718177,0.0209032241,-0.0983920097,-0.1649568379,-0.3148606718,0.1034778133,0.052318912,-0.2634041607,0.1510075182,0.240365535,-0.1195746213,-0.0443268009,-0.0001109656,-0.0709206089,0.1456373781,-0.0216371175,-0.0548776016,0.0413485505,0.0320792012,-0.1099435091,0.0695012212,-0.2508447468,-0.0989763662,-0.2140037864,0.0711577013,-0.3372648358,0.2594273388,-0.0382847153,0.2054678649,-0.0469221212,-0.1306341141,0.3519122005,-0.1220628396,0.1027288139,0.0172574706,-0.0262117665,0.2594551742,-0.1247475371,-0.0430970453,0.415356338,-0.1193582788,-0.1493011266,0.2905679941,0.0684557483,0.1848945171,-0.596270442,-0.0432923436,0.4557463527,0.2367852926,-0.2617447674,-0.1849277169,-0.4822607934,-0.0259912424,-0.2272670269,0.1633026153,0.1675083935,0.0069016796,0.2347602099,0.1263682395,-0.1401169151,0.0433410294,0.0321894847,-0.2222628295,0.4312478602,0.1123895496,0.0557990298,-0.1916797012,-0.1940625161,0.3292733133,-0.2776329219,-0.1152416542,-0.0648504049,-0.2658900917,-0.2681444287,0.145891428,0.0540675484,0.0904277116,-0.0091617191,-0.1017989889,-0.1273285747,0.0415140092,-0.1156784967,0.4433220327,0.0349118933,0.0427645966,-0.3553722501,-0.0186075065,-0.11106354,0.0128494827,0.0441047587,0.1379095465,0.1818000823,-0.5502961874,0.0230321009,0.4006924629,0.1182039306,-0.359192878,-0.1037032604,-0.6785646677,-0.1690202504,0.0719344467,0.137252897,0.1910883784,0.2438910604,-0.0796461776,0.090106979,-0.1095337421,-0.1471690536,-0.1280268729,0.0261902623,-0.0045824051,-0.1100144163,-0.1038460433,0.2253595591,0.0583133064,0.0152876982,0.0062835799,0.2510578036,0.1159466356,-0.1665658504,0.2065866441,0.2225798517,-0.0297140665,0.0133785838,-0.0154654486,-0.0512965992,-0.2058114111,0.2050140053,-0.0776299611,-0.2186259031,0.0134882564,0.2043150067,-0.1696726829,-0.1015770286,0.1816952676,0.7562408447,0.3734673858,-0.0927801728,0.0911263749,0.009046712,-0.4748316705,-0.2003763467,-0.031352248,0.3942876458,-0.3361498117,0.0484468639,-0.3053285182,0.0333992802,-0.0127937766,0.3472161591,-0.0665526241,0.339394927,-0.2069299817,0.2875927687,-0.0089720543,-0.3737544715,-0.242327258,0.2417011708,0.0202056449,0.2550999224,0.2007426918,0.0926874056,-0.13739869,-0.1837859005,0.1889773756,0.3555290997,-0.1046040282,-0.0923256651,-0.0993929431,-0.1669281274,0.6348803639,-0.0364637971,-0.4140782654,-0.2391839176,-0.343290031,-0.0578938536,0.3980113268,-0.0094857067,-0.1960019618,-0.1509506404,0.1513853073,-0.1476749629,-0.0829645693,-0.1501871347,-0.5748469234,0.0441733375,-0.2565105557,0.1166458204,-0.1293932199,-0.4134356081,0.310305357,-0.0182228591,-0.3776371181,-0.1487938017,0.223158747,-0.2980284393,-0.1173890755,0.0568259954,-0.2501930296,0.0233592167,0.0178599674,0.0614118539,-0.3382733166,0.2788203061,-0.1605370939,0.308401227,0.3650185764,0.69320786,-0.0535367467,-0.1911392063,0.2040143311,0.1118071303,-0.0075117047,-0.1424809545,0.0454091579,0.2737965584,0.3954080641,-0.2320894599,0.0044339946,0.3511698544,0.0159224402,-0.2095693797,-0.2848925889,0.4127983451,0.0557215177,0.349927187,0.0185294058,0.2035997957,-0.0105989622,-0.1132051125,-0.0705682337,-0.3927462697,0.1088526025,0.1086640731,0.0308570322,0.426107645,-0.2589320838,0.8457382321,0.465623796,-0.0576035343,0.0363926813,0.0054506445,0.1375107765,-0.4387932718,0.2027046084,0.1513318121,0.282648325,0.1742970794,0.0417098217,0.2460756153,0.2267715335,-0.2295657694,0.3983789682,0.0990012512,-0.0684945509,0.1255247891,0.3096333146,0.1429218799,-0.4245000482,-0.2349423021,-0.0185276307,0.0328291021,-0.2017996162,0.2663801014,-0.3401319385,-0.8277870417,0.0951259658,-0.1595639139,-0.3452222645,-0.414337039,0.1041768119,-0.1909134388,-0.1400655657,0.2615066469,0.3718318343,0.3114873469,0.0528241023,0.1535454839,-0.0749019086,-0.0934202448,-0.1512100995,0.1434291303,0.1969043463,0.180300355,0.2717166543,0.2582640648,-0.0109019019,-0.1724998802,-0.6402465105,0.2058470547,-0.4038881958,0.3266704679,0.1748285741,-0.0207620133,-0.2076142728,-0.2606464028,-0.0391047262,0.6991737485,-0.2577943802,-0.0002876561,-0.0311224088,-0.1765300333,-0.1504601687,-0.3982402086,-0.4945943356,-0.2323882133,0.2652852535,-0.200856179,0.4566139579,0.2072576582,-0.3831195831,-0.2453195155,0.0288470536,-0.134417966,-0.1962102652,0.1302180588,0.1351571381,-0.0603557937,-0.1612295508,-0.3660825789,-0.2903484106,0.3013529181,-0.2624475658,-0.438018173,-0.2253845781,-0.1168083847,0.1511100382,0.226504758,-0.0880563706,0.2082458586,0.3800465167,0.0148182008,0.2296254635,-0.0620111972,0.1058310494,0.1495172232,0.3227373064,0.0125862993,0.437213093,0.1064922437,0.2702871263,0.6254138947,0.196948722,0.0795066059,0.1210876182,0.2560326755,-0.2426040024,-0.2051140815,0.4185655415,0.140414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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/376","title":"to_pandas conversion doesn't always work","comments":"Could you try to update pyarrow to >=0.17.0 ? It should fix the `to_pandas` bug\r\n\r\nAlso I'm not sure that structures like list<struct> are fully supported in the lib (none of the datasets use that).\r\nIt can cause issues when using dataset transforms like `filter` for example","body":"For some complex nested types, the conversion from Arrow to python dict through pandas doesn't seem to be possible.\r\n\r\nHere is an example using the official SQUAD v2 JSON file.\r\n\r\nThis example was found while investigating #373.\r\n\r\n```python\r\n>>> squad = load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/train-v2.0.json\"]}, download_mode=nlp.GenerateMode.FORCE_REDOWNLOAD, version=\"1.0.0\", field='data')\r\n>>> squad['train']\r\nDataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 442)\r\n>>> squad['train'][0]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/nlp\/arrow_dataset.py\", line 589, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/nlp\/arrow_dataset.py\", line 529, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict(\"list\"))\r\n  File \"pyarrow\/array.pxi\", line 559, in pyarrow.lib._PandasConvertible.to_pandas\r\n  File \"pyarrow\/table.pxi\", line 1367, in pyarrow.lib.Table._to_pandas\r\n  File \"\/Users\/thomwolf\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py\", line 766, in table_to_blockmanager\r\n    blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\r\n  File \"\/Users\/thomwolf\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py\", line 1101, in _table_to_blocks\r\n    list(extension_columns.keys()))\r\n  File \"pyarrow\/table.pxi\", line 881, in pyarrow.lib.table_to_blocks\r\n  File \"pyarrow\/error.pxi\", line 105, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n```\r\n\r\ncc @lhoestq would we have a way to detect this from the schema maybe?\r\n\r\nHere is the schema for this pretty complex JSON:\r\n```python\r\n>>> squad['train'].schema\r\ntitle: string\r\nparagraphs: list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>\r\n  child 0, item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n      child 0, qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>\r\n          child 0, item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>\r\n              child 0, question: string\r\n              child 1, id: string\r\n              child 2, answers: list<item: struct<text: string, answer_start: int64>>\r\n                  child 0, item: struct<text: string, answer_start: int64>\r\n                      child 0, text: string\r\n                      child 1, answer_start: int64\r\n              child 3, is_impossible: bool\r\n              child 4, plausible_answers: list<item: struct<text: string, answer_start: int64>>\r\n                  child 0, item: struct<text: string, answer_start: int64>\r\n                      child 0, text: string\r\n                      child 1, answer_start: int64\r\n      child 1, context: string\r\n```","comment_length":47,"text":"to_pandas conversion doesn't always work \n For some complex nested types, the conversion from Arrow to python dict through pandas doesn't seem to be possible.\r\n\r\nHere is an example using the official SQUAD v2 JSON file.\r\n\r\nThis example was found while investigating #373.\r\n\r\n```python\r\n>>> squad = load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/train-v2.0.json\"]}, download_mode=nlp.GenerateMode.FORCE_REDOWNLOAD, version=\"1.0.0\", field='data')\r\n>>> squad['train']\r\nDataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 442)\r\n>>> squad['train'][0]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/nlp\/arrow_dataset.py\", line 589, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/Users\/thomwolf\/Documents\/GitHub\/datasets\/src\/nlp\/arrow_dataset.py\", line 529, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict(\"list\"))\r\n  File \"pyarrow\/array.pxi\", line 559, in pyarrow.lib._PandasConvertible.to_pandas\r\n  File \"pyarrow\/table.pxi\", line 1367, in pyarrow.lib.Table._to_pandas\r\n  File \"\/Users\/thomwolf\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py\", line 766, in table_to_blockmanager\r\n    blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\r\n  File \"\/Users\/thomwolf\/miniconda2\/envs\/datasets\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py\", line 1101, in _table_to_blocks\r\n    list(extension_columns.keys()))\r\n  File \"pyarrow\/table.pxi\", line 881, in pyarrow.lib.table_to_blocks\r\n  File \"pyarrow\/error.pxi\", line 105, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n```\r\n\r\ncc @lhoestq would we have a way to detect this from the schema maybe?\r\n\r\nHere is the schema for this pretty complex JSON:\r\n```python\r\n>>> squad['train'].schema\r\ntitle: string\r\nparagraphs: list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>\r\n  child 0, item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n      child 0, qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>\r\n          child 0, item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>\r\n              child 0, question: string\r\n              child 1, id: string\r\n              child 2, answers: list<item: struct<text: string, answer_start: int64>>\r\n                  child 0, item: struct<text: string, answer_start: int64>\r\n                      child 0, text: string\r\n                      child 1, answer_start: int64\r\n              child 3, is_impossible: bool\r\n              child 4, plausible_answers: list<item: struct<text: string, answer_start: int64>>\r\n                  child 0, item: struct<text: string, answer_start: int64>\r\n                      child 0, text: string\r\n                      child 1, answer_start: int64\r\n      child 1, context: string\r\n``` \n Could you try to update pyarrow to >=0.17.0 ? It should fix the `to_pandas` bug\r\n\r\nAlso I'm not sure that structures like list<struct> are fully supported in the lib (none of the datasets use that).\r\nIt can cause issues when using dataset transforms like `filter` for 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/375","title":"TypeError when computing bertscore","comments":"I am not able to reproduce this issue on my side.\r\nCould you give us more details about the inputs you used ?\r\n\r\nI do get another error though:\r\n```\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/bert_score\/utils.py in bert_cos_score_idf(model, refs, hyps, tokenizer, idf_dict, verbose, batch_size, device, all_layers)\r\n    371         return sorted(list(set(l)), key=lambda x: len(x.split(\" \")))\r\n    372 \r\n--> 373     sentences = dedup_and_sort(refs + hyps)\r\n    374     embs = []\r\n    375     iter_range = range(0, len(sentences), batch_size)\r\n\r\nValueError: operands could not be broadcast together with shapes (0,) (2,)\r\n```\r\nThat's because it gets numpy arrays as input and not lists. See #387 ","body":"Hi, \r\n\r\nI installed nlp 0.3.0 via pip, and my python version is 3.7.\r\nWhen I tried to compute bertscore with the code:\r\n```\r\nimport nlp \r\nbertscore = nlp.load_metric('bertscore')  \r\n# load hyps and refs \r\n...\r\nprint (bertscore.compute(hyps, refs, lang='en'))\r\n```\r\n\r\nI got the following error.\r\n```\r\nTraceback (most recent call last):\r\n  File \"bert_score_evaluate.py\", line 16, in <module>\r\n    print (bertscore.compute(hyps, refs, lang='en'))\r\n  File \"\/home\/willywsm\/anaconda3\/envs\/torcher\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 200, in compute\r\n    output = self._compute(predictions=predictions, references=references, **metrics_kwargs)\r\n  File \"\/home\/willywsm\/anaconda3\/envs\/torcher\/lib\/python3.7\/site-packages\/nlp\/metrics\/bertscore\/fb176889831bf0ce995ed197edc94b2e9a83f647a869bb8c9477dbb2d04d0f08\/bertscore.py\", line 105, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() takes 3 positional arguments but 4 were given\r\n```\r\n\r\nIt seems like there is something wrong with get_hash() function?","comment_length":91,"text":"TypeError when computing bertscore \n Hi, \r\n\r\nI installed nlp 0.3.0 via pip, and my python version is 3.7.\r\nWhen I tried to compute bertscore with the code:\r\n```\r\nimport nlp \r\nbertscore = nlp.load_metric('bertscore')  \r\n# load hyps and refs \r\n...\r\nprint (bertscore.compute(hyps, refs, lang='en'))\r\n```\r\n\r\nI got the following error.\r\n```\r\nTraceback (most recent call last):\r\n  File \"bert_score_evaluate.py\", line 16, in <module>\r\n    print (bertscore.compute(hyps, refs, lang='en'))\r\n  File \"\/home\/willywsm\/anaconda3\/envs\/torcher\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 200, in compute\r\n    output = self._compute(predictions=predictions, references=references, **metrics_kwargs)\r\n  File \"\/home\/willywsm\/anaconda3\/envs\/torcher\/lib\/python3.7\/site-packages\/nlp\/metrics\/bertscore\/fb176889831bf0ce995ed197edc94b2e9a83f647a869bb8c9477dbb2d04d0f08\/bertscore.py\", line 105, in _compute\r\n    hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)\r\nTypeError: get_hash() takes 3 positional arguments but 4 were given\r\n```\r\n\r\nIt seems like there is something wrong with get_hash() function? \n I am not able to reproduce this issue on my side.\r\nCould you give us more details about the inputs you used ?\r\n\r\nI do get another error though:\r\n```\r\n~\/.virtualenvs\/hf-datasets\/lib\/python3.7\/site-packages\/bert_score\/utils.py in bert_cos_score_idf(model, refs, hyps, tokenizer, idf_dict, verbose, batch_size, device, all_layers)\r\n    371         return sorted(list(set(l)), key=lambda x: len(x.split(\" \")))\r\n    372 \r\n--> 373     sentences = dedup_and_sort(refs + hyps)\r\n    374     embs = []\r\n    375     iter_range = range(0, len(sentences), batch_size)\r\n\r\nValueError: operands could not be broadcast together with shapes (0,) (2,)\r\n```\r\nThat's because it gets numpy arrays as input and not lists. See #387 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/373","title":"Segmentation fault when loading local JSON dataset as of #372","comments":"I've seen this sort of thing before -- it might help to delete the directory -- I've also noticed that there is an error with the json Dataloader for any data I've tried to load. I've replaced it with this, which skips over the data feature population step:\r\n\r\n\r\n```python\r\nimport os\r\n\r\nimport pyarrow.json as paj\r\n\r\nimport nlp as hf_nlp\r\n\r\nfrom nlp import DatasetInfo, BuilderConfig, SplitGenerator, Split, utils\r\nfrom nlp.arrow_writer import ArrowWriter\r\n\r\n\r\nclass JSONDatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n    BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n    def _info(self):\r\n        return DatasetInfo()\r\n\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\" We handle string, list and dicts in datafiles\r\n        \"\"\"\r\n        if isinstance(self.config.data_files, (str, list, tuple)):\r\n            files = self.config.data_files\r\n            if isinstance(files, str):\r\n                files = [files]\r\n            return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n        splits = []\r\n        for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n            if split_name in self.config.data_files:\r\n                files = self.config.data_files[split_name]\r\n                if isinstance(files, str):\r\n                    files = [files]\r\n                splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n        return splits\r\n\r\n    def _prepare_split(self, split_generator):\r\n        fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n        fpath = os.path.join(self._cache_dir, fname)\r\n\r\n        writer = ArrowWriter(path=fpath)\r\n\r\n        generator = self._generate_tables(**split_generator.gen_kwargs)\r\n        for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n            writer.write_table(table)\r\n        num_examples, num_bytes = writer.finalize()\r\n\r\n        split_generator.split_info.num_examples = num_examples\r\n        split_generator.split_info.num_bytes = num_bytes\r\n\r\n    def _generate_tables(self, files):\r\n        for i, file in enumerate(files):\r\n            pa_table = paj.read_json(\r\n                file\r\n            )\r\n            yield i, pa_table\r\n\r\n```","body":"The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.)","comment_length":191,"text":"Segmentation fault when loading local JSON dataset as of #372 \n The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.) \n I've seen this sort of thing before -- it might help to delete the directory -- I've also noticed that there is an error with the json Dataloader for any data I've tried to load. I've replaced it with this, which skips over the data feature population step:\r\n\r\n\r\n```python\r\nimport os\r\n\r\nimport pyarrow.json as paj\r\n\r\nimport nlp as hf_nlp\r\n\r\nfrom nlp import DatasetInfo, BuilderConfig, SplitGenerator, Split, utils\r\nfrom nlp.arrow_writer import ArrowWriter\r\n\r\n\r\nclass JSONDatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n    BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n    def _info(self):\r\n        return DatasetInfo()\r\n\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\" We handle string, list and dicts in datafiles\r\n        \"\"\"\r\n        if isinstance(self.config.data_files, (str, list, tuple)):\r\n            files = self.config.data_files\r\n            if isinstance(files, str):\r\n                files = [files]\r\n            return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n        splits = []\r\n        for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n            if split_name in self.config.data_files:\r\n                files = self.config.data_files[split_name]\r\n                if isinstance(files, str):\r\n                    files = [files]\r\n                splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n        return splits\r\n\r\n    def _prepare_split(self, split_generator):\r\n        fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n        fpath = os.path.join(self._cache_dir, fname)\r\n\r\n        writer = ArrowWriter(path=fpath)\r\n\r\n        generator = self._generate_tables(**split_generator.gen_kwargs)\r\n        for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n            writer.write_table(table)\r\n        num_examples, num_bytes = writer.finalize()\r\n\r\n        split_generator.split_info.num_examples = num_examples\r\n        split_generator.split_info.num_bytes = num_bytes\r\n\r\n    def _generate_tables(self, files):\r\n        for i, file in enumerate(files):\r\n            pa_table = paj.read_json(\r\n                file\r\n            )\r\n            yield i, pa_table\r\n\r\n```","embeddings":[-0.0172924567,0.2893957794,0.0609415546,0.0578706525,0.2236097753,-0.0165483262,0.3200395405,0.6101837754,-0.4010095596,-0.2130427957,-0.2422538549,0.771407783,0.1327875555,-0.4265963435,0.1489231437,-0.0715281218,0.0462259129,0.1267296821,0.2239412665,0.0536700115,-0.1744497716,0.3416509628,-0.2299684733,0.1320418268,-0.1667520404,0.2660649717,0.0065363278,0.5419237018,-0.0745723546,-0.7302021384,0.2977763414,-0.0093303947,0.1517449468,0.3450249732,-0.0001186113,0.1845317483,0.3318485618,-0.1096370071,-0.3517018259,0.0521292798,0.2805030644,-0.2414649427,0.3380265236,-0.1453841031,0.0627313927,-0.3119795918,0.0318316966,0.0812750906,0.6866552234,0.1553962231,0.0921009928,0.1377252787,0.0738054588,0.0523657314,0.0207765196,0.0678778663,0.0166039914,0.7200931311,0.5202925205,-0.1260128319,-0.3388995528,-0.0720849857,0.121271871,-0.0382684954,0.20485726,-0.0635562018,-0.0138488207,-0.1832702905,0.0524652265,0.1170204207,0.2416470051,-0.2380599082,-0.2328487635,-0.2157492042,0.0747866482,-0.2181084901,0.3773207963,0.2772295773,-0.13986063,0.1523420066,-0.0569971204,0.0344605073,-0.1918932199,0.0293105822,-0.2142735124,0.5192520618,0.1215123013,0.0416844822,0.1051236168,-0.1629457474,-0.1519261301,0.3172393739,-0.2625278533,0.3998276293,-0.3908263743,-0.1686170995,0.1120524853,-0.3626522124,0.2163050026,0.0901769698,0.1800890863,0.167204693,0.2139120996,0.1203705147,0.4853105545,0.0546770915,0.1718356609,0.2025324851,0.1526307762,-0.0196298789,-0.0259742644,0.2397748679,-0.1398817003,-0.2250073105,-0.2135159373,0.0280673709,-0.0268218834,-0.10946697,0.0148830935,0.0695176572,-0.3147706687,-0.0120260334,0.0509856232,0.0848620832,0.1312868148,0.1926079243,-0.250028193,0.204761222,-0.101454854,-0.1726957709,-0.0175752416,-0.026819814,-0.3066568077,-0.0206658803,0.4054740667,-0.1225604787,0.1185211167,-0.0812497288,-0.1116154194,-0.2269219011,-0.4334718287,-0.0438977554,0.0076662428,0.1496739089,0.0316801295,-0.0704863369,-0.036723651,-0.2581102848,-0.1803209335,0.212189436,-0.351188004,-0.2851627171,-0.5924198031,0.1183918193,-0.3227140605,-0.0053090458,0.1444022059,0.2050788999,0.0566594712,-0.4543268085,-0.137789458,-0.0893592536,-0.0031521304,-0.2295428067,0.1466828585,0.2523785532,-0.8450297117,-0.2359818965,0.1668373644,-0.0015070704,0.0830982402,0.5275310278,-0.351418823,0.2732034326,-0.1530969292,0.5261092186,0.4013668597,-0.2779783309,-0.3369089961,0.2427911609,-0.3994488418,-0.0413578488,-0.2372673005,-0.0655715317,0.2967054844,0.0823332146,0.3412532806,0.44938609,-0.1155516654,0.1483249068,-0.3325651586,-0.0804989934,0.1726427674,0.3601403534,-0.067415975,-0.1608242691,-0.0298587419,0.1449324936,0.4040019214,-0.1719319224,-0.0207536705,0.1606730372,0.1372898668,-0.0173203666,0.1661421508,-0.1080801859,-0.775840342,0.102051191,-0.2894922793,0.0807941109,-0.0411318801,0.006053275,-0.2767830789,-0.1135980338,-0.2061314285,-0.0540232696,0.0321170986,-0.160494104,-0.0348596834,-0.0682966858,-0.4652936161,-0.1567875296,-0.154756695,0.378049463,-0.1153785959,0.4039317071,-0.1302649975,-0.4008334279,0.0927924365,0.3672265112,0.1282616705,-0.2672620118,-0.0068155113,0.3295680285,-0.0804471746,0.1338739544,-0.1708227694,-0.2979542315,0.2476089597,-0.0857613161,-0.1292840242,-0.0635809377,-0.1022319719,-0.0883648023,-0.3246678412,0.3362679482,0.0197260007,-0.0326367095,-0.0567735732,-0.2471363395,0.2981203198,-0.0848109946,-0.1274032891,-0.0282969773,0.0172862969,0.1671073884,-0.0124944821,0.1766584069,-0.1765216589,-0.0144745773,0.369494915,-0.0651190057,0.0355697162,0.1873179078,0.1008718833,-0.2918989658,-0.024752669,-0.0487226285,0.3634798825,-0.007696704,-0.1558919847,-0.0039152685,0.0336674862,-0.0184020884,-0.011545361,0.0482457764,0.4226091802,0.5049403906,0.3639849424,0.1684012264,-0.2733654976,-0.1548733562,0.1276226789,0.0563049763,-0.3743780255,0.0602172576,-0.2678267956,-0.144593209,-0.2930882573,-0.1426913887,-0.3419747949,-0.1941553801,0.1088883728,-0.0382642075,-0.086782597,0.2005051821,0.1665448993,0.4000805914,0.1002783403,-0.1863676459,0.0381456204,-0.1772579998,-0.2182420641,-0.0453671627,0.3783227801,-0.0644285679,-0.2067784965,-0.1894416362,-0.2709756494,0.1097929254,-0.4360963702,0.1680292189,-0.0091555053,0.1945903599,0.0222651493,0.2368633002,-0.1479672343,-0.3669347167,0.2814826965,-0.0462120995,-0.2511283457,0.0004014499,-0.1034957469,0.1337920129,-0.2878107727,-0.3897340596,-0.2453112602,-0.2271138728,0.4359025359,0.0752960369,0.1372660846,0.2004070431,-0.202068001,0.0147434054,-0.2370598018,-0.2110955119,0.0494783483,-0.1857471168,0.2052339017,-0.2988070846,-0.1417259425,-0.0219020918,0.0942525193,0.1670048088,-0.2243585736,-0.1604168266,0.1161189228,0.1713583618,0.2460084856,-0.3234409392,-0.2597648203,0.2524788082,-0.2297296822,0.0524282791,0.1972107291,-0.3602097034,-0.0937124863,0.518563509,0.1982491612,-0.2657060623,0.2363282442,0.0597211421,0.8267533183,-0.1374402046,-0.162537694,0.1258324534,0.0303803496,0.1219486594,-0.1489123851,-0.0352488011,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/373","title":"Segmentation fault when loading local JSON dataset as of #372","comments":"Yes, deleting the directory solves the error whenever I try to rerun.\r\n\r\nBy replacing the json-loader, you mean the cached file in my `site-packages` directory? e.g. `\/home\/XXX\/.cache\/lib\/python3.7\/site-packages\/nlp\/datasets\/json\/(...)\/json.py` \r\n\r\nWhen I was testing this out before the #372 PR was merged I had issues installing it properly locally. Since the `json.py` script was downloaded instead of actually using the one provided in the local install. Manually updating that file seemed to solve it, but it didn't seem like a proper solution. Especially when having to run this on a remote compute cluster with no access to that directory.","body":"The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.)","comment_length":96,"text":"Segmentation fault when loading local JSON dataset as of #372 \n The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.) \n Yes, deleting the directory solves the error whenever I try to rerun.\r\n\r\nBy replacing the json-loader, you mean the cached file in my `site-packages` directory? e.g. `\/home\/XXX\/.cache\/lib\/python3.7\/site-packages\/nlp\/datasets\/json\/(...)\/json.py` \r\n\r\nWhen I was testing this out before the #372 PR was merged I had issues installing it properly locally. Since the `json.py` script was downloaded instead of actually using the one provided in the local install. Manually updating that file seemed to solve it, but it didn't seem like a proper solution. Especially when having to run this on a remote compute cluster with no access to that directory.","embeddings":[-0.0172924567,0.2893957794,0.0609415546,0.0578706525,0.2236097753,-0.0165483262,0.3200395405,0.6101837754,-0.4010095596,-0.2130427957,-0.2422538549,0.771407783,0.1327875555,-0.4265963435,0.1489231437,-0.0715281218,0.0462259129,0.1267296821,0.2239412665,0.0536700115,-0.1744497716,0.3416509628,-0.2299684733,0.1320418268,-0.1667520404,0.2660649717,0.0065363278,0.5419237018,-0.0745723546,-0.7302021384,0.2977763414,-0.0093303947,0.1517449468,0.3450249732,-0.0001186113,0.1845317483,0.3318485618,-0.1096370071,-0.3517018259,0.0521292798,0.2805030644,-0.2414649427,0.3380265236,-0.1453841031,0.0627313927,-0.3119795918,0.0318316966,0.0812750906,0.6866552234,0.1553962231,0.0921009928,0.1377252787,0.0738054588,0.0523657314,0.0207765196,0.0678778663,0.0166039914,0.7200931311,0.5202925205,-0.1260128319,-0.3388995528,-0.0720849857,0.121271871,-0.0382684954,0.20485726,-0.0635562018,-0.0138488207,-0.1832702905,0.0524652265,0.1170204207,0.2416470051,-0.2380599082,-0.2328487635,-0.2157492042,0.0747866482,-0.2181084901,0.3773207963,0.2772295773,-0.13986063,0.1523420066,-0.0569971204,0.0344605073,-0.1918932199,0.0293105822,-0.2142735124,0.5192520618,0.1215123013,0.0416844822,0.1051236168,-0.1629457474,-0.1519261301,0.3172393739,-0.2625278533,0.3998276293,-0.3908263743,-0.1686170995,0.1120524853,-0.3626522124,0.2163050026,0.0901769698,0.1800890863,0.167204693,0.2139120996,0.1203705147,0.4853105545,0.0546770915,0.1718356609,0.2025324851,0.1526307762,-0.0196298789,-0.0259742644,0.2397748679,-0.1398817003,-0.2250073105,-0.2135159373,0.0280673709,-0.0268218834,-0.10946697,0.0148830935,0.0695176572,-0.3147706687,-0.0120260334,0.0509856232,0.0848620832,0.1312868148,0.1926079243,-0.250028193,0.204761222,-0.101454854,-0.1726957709,-0.0175752416,-0.026819814,-0.3066568077,-0.0206658803,0.4054740667,-0.1225604787,0.1185211167,-0.0812497288,-0.1116154194,-0.2269219011,-0.4334718287,-0.0438977554,0.0076662428,0.1496739089,0.0316801295,-0.0704863369,-0.036723651,-0.2581102848,-0.1803209335,0.212189436,-0.351188004,-0.2851627171,-0.5924198031,0.1183918193,-0.3227140605,-0.0053090458,0.1444022059,0.2050788999,0.0566594712,-0.4543268085,-0.137789458,-0.0893592536,-0.0031521304,-0.2295428067,0.1466828585,0.2523785532,-0.8450297117,-0.2359818965,0.1668373644,-0.0015070704,0.0830982402,0.5275310278,-0.351418823,0.2732034326,-0.1530969292,0.5261092186,0.4013668597,-0.2779783309,-0.3369089961,0.2427911609,-0.3994488418,-0.0413578488,-0.2372673005,-0.0655715317,0.2967054844,0.0823332146,0.3412532806,0.44938609,-0.1155516654,0.1483249068,-0.3325651586,-0.0804989934,0.1726427674,0.3601403534,-0.067415975,-0.1608242691,-0.0298587419,0.1449324936,0.4040019214,-0.1719319224,-0.0207536705,0.1606730372,0.1372898668,-0.0173203666,0.1661421508,-0.1080801859,-0.775840342,0.102051191,-0.2894922793,0.0807941109,-0.0411318801,0.006053275,-0.2767830789,-0.1135980338,-0.2061314285,-0.0540232696,0.0321170986,-0.160494104,-0.0348596834,-0.0682966858,-0.4652936161,-0.1567875296,-0.154756695,0.378049463,-0.1153785959,0.4039317071,-0.1302649975,-0.4008334279,0.0927924365,0.3672265112,0.1282616705,-0.2672620118,-0.0068155113,0.3295680285,-0.0804471746,0.1338739544,-0.1708227694,-0.2979542315,0.2476089597,-0.0857613161,-0.1292840242,-0.0635809377,-0.1022319719,-0.0883648023,-0.3246678412,0.3362679482,0.0197260007,-0.0326367095,-0.0567735732,-0.2471363395,0.2981203198,-0.0848109946,-0.1274032891,-0.0282969773,0.0172862969,0.1671073884,-0.0124944821,0.1766584069,-0.1765216589,-0.0144745773,0.369494915,-0.0651190057,0.0355697162,0.1873179078,0.1008718833,-0.2918989658,-0.024752669,-0.0487226285,0.3634798825,-0.007696704,-0.1558919847,-0.0039152685,0.0336674862,-0.0184020884,-0.011545361,0.0482457764,0.4226091802,0.5049403906,0.3639849424,0.1684012264,-0.2733654976,-0.1548733562,0.1276226789,0.0563049763,-0.3743780255,0.0602172576,-0.2678267956,-0.144593209,-0.2930882573,-0.1426913887,-0.3419747949,-0.1941553801,0.1088883728,-0.0382642075,-0.086782597,0.2005051821,0.1665448993,0.4000805914,0.1002783403,-0.1863676459,0.0381456204,-0.1772579998,-0.2182420641,-0.0453671627,0.3783227801,-0.0644285679,-0.2067784965,-0.1894416362,-0.2709756494,0.1097929254,-0.4360963702,0.1680292189,-0.0091555053,0.1945903599,0.0222651493,0.2368633002,-0.1479672343,-0.3669347167,0.2814826965,-0.0462120995,-0.2511283457,0.0004014499,-0.1034957469,0.1337920129,-0.2878107727,-0.3897340596,-0.2453112602,-0.2271138728,0.4359025359,0.0752960369,0.1372660846,0.2004070431,-0.202068001,0.0147434054,-0.2370598018,-0.2110955119,0.0494783483,-0.1857471168,0.2052339017,-0.2988070846,-0.1417259425,-0.0219020918,0.0942525193,0.1670048088,-0.2243585736,-0.1604168266,0.1161189228,0.1713583618,0.2460084856,-0.3234409392,-0.2597648203,0.2524788082,-0.2297296822,0.0524282791,0.1972107291,-0.3602097034,-0.0937124863,0.518563509,0.1982491612,-0.2657060623,0.2363282442,0.0597211421,0.8267533183,-0.1374402046,-0.162537694,0.1258324534,0.0303803496,0.1219486594,-0.1489123851,-0.0352488011,0.3921883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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/373","title":"Segmentation fault when loading local JSON dataset as of #372","comments":"I see, diving in the JSON file for SQuAD it's a pretty complex structure.\r\n\r\nThe best solution for you, if you have a dataset really similar to SQuAD would be to copy and modify the SQuAD data processing script. We will probably add soon an option to be able to specify file path to use instead of the automatic URL encoded in the script but in the meantime you can:\r\n- copy the [squad script](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/squad\/squad.py)  in a new script for your dataset\r\n- in the new script replace [these `urls_to_download `](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/squad\/squad.py#L99-L102) by `urls_to_download=self.config.data_files`\r\n- load the dataset with `dataset = load_dataset('path\/to\/your\/new\/script', data_files={nlp.Split.TRAIN: \".\/datasets\/train-v2.0.json\"})`\r\n\r\nThis way you can reuse all the processing logic of the SQuAD loading script.","body":"The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.)","comment_length":117,"text":"Segmentation fault when loading local JSON dataset as of #372 \n The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.) \n I see, diving in the JSON file for SQuAD it's a pretty complex structure.\r\n\r\nThe best solution for you, if you have a dataset really similar to SQuAD would be to copy and modify the SQuAD data processing script. We will probably add soon an option to be able to specify file path to use instead of the automatic URL encoded in the script but in the meantime you can:\r\n- copy the [squad script](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/squad\/squad.py)  in a new script for your dataset\r\n- in the new script replace [these `urls_to_download `](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/squad\/squad.py#L99-L102) by `urls_to_download=self.config.data_files`\r\n- load the dataset with `dataset = load_dataset('path\/to\/your\/new\/script', data_files={nlp.Split.TRAIN: \".\/datasets\/train-v2.0.json\"})`\r\n\r\nThis way you can reuse all the processing logic of the SQuAD loading 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/373","title":"Segmentation fault when loading local JSON dataset as of #372","comments":"This seems like a more sensible solution! Thanks, @thomwolf. It's been a little daunting to understand what these scripts actually do, due to the level of abstraction and central documentation.\r\n\r\nAm I correct in assuming that the `_generate_examples()` function is the actual procedure for how the data is loaded from file? Meaning that essentially with a file containing another format, that is the only function that requires re-implementation? I'm working with a lot of datasets that, due to licensing and privacy, cannot be published. As this library is so neatly integrated with the transformers library and gives easy access to public sets such as SQUAD and increased performance, it is very neat to be able to load my private sets as well. As of now, I have just been working on scripts for translating all my data into the SQUAD-format before using the json script, but I see that it might not be necessary after all. ","body":"The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.)","comment_length":156,"text":"Segmentation fault when loading local JSON dataset as of #372 \n The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.) \n This seems like a more sensible solution! Thanks, @thomwolf. It's been a little daunting to understand what these scripts actually do, due to the level of abstraction and central documentation.\r\n\r\nAm I correct in assuming that the `_generate_examples()` function is the actual procedure for how the data is loaded from file? Meaning that essentially with a file containing another format, that is the only function that requires re-implementation? I'm working with a lot of datasets that, due to licensing and privacy, cannot be published. As this library is so neatly integrated with the transformers library and gives easy access to public sets such as SQUAD and increased performance, it is very neat to be able to load my private sets as well. As of now, I have just been working on scripts for translating all my data into the SQUAD-format before using the json script, but I see that it might not be necessary after all. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/373","title":"Segmentation fault when loading local JSON dataset as of #372","comments":"Yes `_generate_examples()` is the main entry point. If you change the shape of the returned dictionary you also need to update the `features` in the `_info`.\r\n\r\nI'm currently writing the doc so it should be easier soon to use the library and know how to add your datasets.\r\n","body":"The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.)","comment_length":48,"text":"Segmentation fault when loading local JSON dataset as of #372 \n The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.) \n Yes `_generate_examples()` is the main entry point. If you change the shape of the returned dictionary you also need to update the `features` in the `_info`.\r\n\r\nI'm currently writing the doc so it should be easier soon to use the library and know how to add your datasets.\r\n","embeddings":[-0.0172924567,0.2893957794,0.0609415546,0.0578706525,0.2236097753,-0.0165483262,0.3200395405,0.6101837754,-0.4010095596,-0.2130427957,-0.2422538549,0.771407783,0.1327875555,-0.4265963435,0.1489231437,-0.0715281218,0.0462259129,0.1267296821,0.2239412665,0.0536700115,-0.1744497716,0.3416509628,-0.2299684733,0.1320418268,-0.1667520404,0.2660649717,0.0065363278,0.5419237018,-0.0745723546,-0.7302021384,0.2977763414,-0.0093303947,0.1517449468,0.3450249732,-0.0001186113,0.1845317483,0.3318485618,-0.1096370071,-0.3517018259,0.0521292798,0.2805030644,-0.2414649427,0.3380265236,-0.1453841031,0.0627313927,-0.3119795918,0.0318316966,0.0812750906,0.6866552234,0.1553962231,0.0921009928,0.1377252787,0.0738054588,0.0523657314,0.0207765196,0.0678778663,0.0166039914,0.7200931311,0.5202925205,-0.1260128319,-0.3388995528,-0.0720849857,0.121271871,-0.0382684954,0.20485726,-0.0635562018,-0.0138488207,-0.1832702905,0.0524652265,0.1170204207,0.2416470051,-0.2380599082,-0.2328487635,-0.2157492042,0.0747866482,-0.2181084901,0.3773207963,0.2772295773,-0.13986063,0.1523420066,-0.0569971204,0.0344605073,-0.1918932199,0.0293105822,-0.2142735124,0.5192520618,0.1215123013,0.0416844822,0.1051236168,-0.1629457474,-0.1519261301,0.3172393739,-0.2625278533,0.3998276293,-0.3908263743,-0.1686170995,0.1120524853,-0.3626522124,0.2163050026,0.0901769698,0.1800890863,0.167204693,0.2139120996,0.1203705147,0.4853105545,0.0546770915,0.1718356609,0.2025324851,0.1526307762,-0.0196298789,-0.0259742644,0.2397748679,-0.1398817003,-0.2250073105,-0.2135159373,0.0280673709,-0.0268218834,-0.10946697,0.0148830935,0.0695176572,-0.3147706687,-0.0120260334,0.0509856232,0.0848620832,0.1312868148,0.1926079243,-0.250028193,0.204761222,-0.101454854,-0.1726957709,-0.0175752416,-0.026819814,-0.3066568077,-0.0206658803,0.4054740667,-0.1225604787,0.1185211167,-0.0812497288,-0.1116154194,-0.2269219011,-0.4334718287,-0.0438977554,0.0076662428,0.1496739089,0.0316801295,-0.0704863369,-0.036723651,-0.2581102848,-0.1803209335,0.212189436,-0.351188004,-0.2851627171,-0.5924198031,0.1183918193,-0.3227140605,-0.0053090458,0.1444022059,0.2050788999,0.0566594712,-0.4543268085,-0.137789458,-0.0893592536,-0.0031521304,-0.2295428067,0.1466828585,0.2523785532,-0.8450297117,-0.2359818965,0.1668373644,-0.0015070704,0.0830982402,0.5275310278,-0.351418823,0.2732034326,-0.1530969292,0.5261092186,0.4013668597,-0.2779783309,-0.3369089961,0.2427911609,-0.3994488418,-0.0413578488,-0.2372673005,-0.0655715317,0.2967054844,0.0823332146,0.3412532806,0.44938609,-0.1155516654,0.1483249068,-0.3325651586,-0.0804989934,0.1726427674,0.3601403534,-0.067415975,-0.1608242691,-0.0298587419,0.1449324936,0.4040019214,-0.1719319224,-0.0207536705,0.1606730372,0.1372898668,-0.0173203666,0.1661421508,-0.1080801859,-0.775840342,0.102051191,-0.2894922793,0.0807941109,-0.0411318801,0.006053275,-0.2767830789,-0.1135980338,-0.2061314285,-0.0540232696,0.0321170986,-0.160494104,-0.0348596834,-0.0682966858,-0.4652936161,-0.1567875296,-0.154756695,0.378049463,-0.1153785959,0.4039317071,-0.1302649975,-0.4008334279,0.0927924365,0.3672265112,0.1282616705,-0.2672620118,-0.0068155113,0.3295680285,-0.0804471746,0.1338739544,-0.1708227694,-0.2979542315,0.2476089597,-0.0857613161,-0.1292840242,-0.0635809377,-0.1022319719,-0.0883648023,-0.3246678412,0.3362679482,0.0197260007,-0.0326367095,-0.0567735732,-0.2471363395,0.2981203198,-0.0848109946,-0.1274032891,-0.0282969773,0.0172862969,0.1671073884,-0.0124944821,0.1766584069,-0.1765216589,-0.0144745773,0.369494915,-0.0651190057,0.0355697162,0.1873179078,0.1008718833,-0.2918989658,-0.024752669,-0.0487226285,0.3634798825,-0.007696704,-0.1558919847,-0.0039152685,0.0336674862,-0.0184020884,-0.011545361,0.0482457764,0.4226091802,0.5049403906,0.3639849424,0.1684012264,-0.2733654976,-0.1548733562,0.1276226789,0.0563049763,-0.3743780255,0.0602172576,-0.2678267956,-0.144593209,-0.2930882573,-0.1426913887,-0.3419747949,-0.1941553801,0.1088883728,-0.0382642075,-0.086782597,0.2005051821,0.1665448993,0.4000805914,0.1002783403,-0.1863676459,0.0381456204,-0.1772579998,-0.2182420641,-0.0453671627,0.3783227801,-0.0644285679,-0.2067784965,-0.1894416362,-0.2709756494,0.1097929254,-0.4360963702,0.1680292189,-0.0091555053,0.1945903599,0.0222651493,0.2368633002,-0.1479672343,-0.3669347167,0.2814826965,-0.0462120995,-0.2511283457,0.0004014499,-0.1034957469,0.1337920129,-0.2878107727,-0.3897340596,-0.2453112602,-0.2271138728,0.4359025359,0.0752960369,0.1372660846,0.2004070431,-0.202068001,0.0147434054,-0.2370598018,-0.2110955119,0.0494783483,-0.1857471168,0.2052339017,-0.2988070846,-0.1417259425,-0.0219020918,0.0942525193,0.1670048088,-0.2243585736,-0.1604168266,0.1161189228,0.1713583618,0.2460084856,-0.3234409392,-0.2597648203,0.2524788082,-0.2297296822,0.0524282791,0.1972107291,-0.3602097034,-0.0937124863,0.518563509,0.1982491612,-0.2657060623,0.2363282442,0.0597211421,0.8267533183,-0.1374402046,-0.162537694,0.1258324534,0.0303803496,0.1219486594,-0.1489123851,-0.0352488011,0.3921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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/373","title":"Segmentation fault when loading local JSON dataset as of #372","comments":"Could you try to update pyarrow to >=0.17.0 @vegarab ?\r\nI don't have any segmentation fault with my version of pyarrow (0.17.1)\r\n\r\nI tested with\r\n```python\r\nimport nlp\r\ns = nlp.load_dataset(\"json\", data_files=\"train-v2.0.json\", field=\"data\", split=\"train\")\r\ns[0]\r\n# {'title': 'Normans', 'paragraphs': [{'qas': [{'question': 'In what country is Normandy located?', 'id':...\r\n```","body":"The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.)","comment_length":49,"text":"Segmentation fault when loading local JSON dataset as of #372 \n The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.) \n Could you try to update pyarrow to >=0.17.0 @vegarab ?\r\nI don't have any segmentation fault with my version of pyarrow (0.17.1)\r\n\r\nI tested with\r\n```python\r\nimport nlp\r\ns = nlp.load_dataset(\"json\", data_files=\"train-v2.0.json\", field=\"data\", split=\"train\")\r\ns[0]\r\n# {'title': 'Normans', 'paragraphs': [{'qas': [{'question': 'In what country is Normandy located?', 'id':...\r\n```","embeddings":[-0.0172924567,0.2893957794,0.0609415546,0.0578706525,0.2236097753,-0.0165483262,0.3200395405,0.6101837754,-0.4010095596,-0.2130427957,-0.2422538549,0.771407783,0.1327875555,-0.4265963435,0.1489231437,-0.0715281218,0.0462259129,0.1267296821,0.2239412665,0.0536700115,-0.1744497716,0.3416509628,-0.2299684733,0.1320418268,-0.1667520404,0.2660649717,0.0065363278,0.5419237018,-0.0745723546,-0.7302021384,0.2977763414,-0.0093303947,0.1517449468,0.3450249732,-0.0001186113,0.1845317483,0.3318485618,-0.1096370071,-0.3517018259,0.0521292798,0.2805030644,-0.2414649427,0.3380265236,-0.1453841031,0.0627313927,-0.3119795918,0.0318316966,0.0812750906,0.6866552234,0.1553962231,0.0921009928,0.1377252787,0.0738054588,0.0523657314,0.0207765196,0.0678778663,0.0166039914,0.7200931311,0.5202925205,-0.1260128319,-0.3388995528,-0.0720849857,0.121271871,-0.0382684954,0.20485726,-0.0635562018,-0.0138488207,-0.1832702905,0.0524652265,0.1170204207,0.2416470051,-0.2380599082,-0.2328487635,-0.2157492042,0.0747866482,-0.2181084901,0.3773207963,0.2772295773,-0.13986063,0.1523420066,-0.0569971204,0.0344605073,-0.1918932199,0.0293105822,-0.2142735124,0.5192520618,0.1215123013,0.0416844822,0.1051236168,-0.1629457474,-0.1519261301,0.3172393739,-0.2625278533,0.3998276293,-0.3908263743,-0.1686170995,0.1120524853,-0.3626522124,0.2163050026,0.0901769698,0.1800890863,0.167204693,0.2139120996,0.1203705147,0.4853105545,0.0546770915,0.1718356609,0.2025324851,0.1526307762,-0.0196298789,-0.0259742644,0.2397748679,-0.1398817003,-0.2250073105,-0.2135159373,0.0280673709,-0.0268218834,-0.10946697,0.0148830935,0.0695176572,-0.3147706687,-0.0120260334,0.0509856232,0.0848620832,0.1312868148,0.1926079243,-0.250028193,0.204761222,-0.101454854,-0.1726957709,-0.0175752416,-0.026819814,-0.3066568077,-0.0206658803,0.4054740667,-0.1225604787,0.1185211167,-0.0812497288,-0.1116154194,-0.2269219011,-0.4334718287,-0.0438977554,0.0076662428,0.1496739089,0.0316801295,-0.0704863369,-0.036723651,-0.2581102848,-0.1803209335,0.212189436,-0.351188004,-0.2851627171,-0.5924198031,0.1183918193,-0.3227140605,-0.0053090458,0.1444022059,0.2050788999,0.0566594712,-0.4543268085,-0.137789458,-0.0893592536,-0.0031521304,-0.2295428067,0.1466828585,0.2523785532,-0.8450297117,-0.2359818965,0.1668373644,-0.0015070704,0.0830982402,0.5275310278,-0.351418823,0.2732034326,-0.1530969292,0.5261092186,0.4013668597,-0.2779783309,-0.3369089961,0.2427911609,-0.3994488418,-0.0413578488,-0.2372673005,-0.0655715317,0.2967054844,0.0823332146,0.3412532806,0.44938609,-0.1155516654,0.1483249068,-0.3325651586,-0.0804989934,0.1726427674,0.3601403534,-0.067415975,-0.1608242691,-0.0298587419,0.1449324936,0.4040019214,-0.1719319224,-0.0207536705,0.1606730372,0.1372898668,-0.0173203666,0.1661421508,-0.1080801859,-0.775840342,0.102051191,-0.2894922793,0.0807941109,-0.0411318801,0.006053275,-0.2767830789,-0.1135980338,-0.2061314285,-0.0540232696,0.0321170986,-0.160494104,-0.0348596834,-0.0682966858,-0.4652936161,-0.1567875296,-0.154756695,0.378049463,-0.1153785959,0.4039317071,-0.1302649975,-0.4008334279,0.0927924365,0.3672265112,0.1282616705,-0.2672620118,-0.0068155113,0.3295680285,-0.0804471746,0.1338739544,-0.1708227694,-0.2979542315,0.2476089597,-0.0857613161,-0.1292840242,-0.0635809377,-0.1022319719,-0.0883648023,-0.3246678412,0.3362679482,0.0197260007,-0.0326367095,-0.0567735732,-0.2471363395,0.2981203198,-0.0848109946,-0.1274032891,-0.0282969773,0.0172862969,0.1671073884,-0.0124944821,0.1766584069,-0.1765216589,-0.0144745773,0.369494915,-0.0651190057,0.0355697162,0.1873179078,0.1008718833,-0.2918989658,-0.024752669,-0.0487226285,0.3634798825,-0.007696704,-0.1558919847,-0.0039152685,0.0336674862,-0.0184020884,-0.011545361,0.0482457764,0.4226091802,0.5049403906,0.3639849424,0.1684012264,-0.2733654976,-0.1548733562,0.1276226789,0.0563049763,-0.3743780255,0.0602172576,-0.2678267956,-0.144593209,-0.2930882573,-0.1426913887,-0.3419747949,-0.1941553801,0.1088883728,-0.0382642075,-0.086782597,0.2005051821,0.1665448993,0.4000805914,0.1002783403,-0.1863676459,0.0381456204,-0.1772579998,-0.2182420641,-0.0453671627,0.3783227801,-0.0644285679,-0.2067784965,-0.1894416362,-0.2709756494,0.1097929254,-0.4360963702,0.1680292189,-0.0091555053,0.1945903599,0.0222651493,0.2368633002,-0.1479672343,-0.3669347167,0.2814826965,-0.0462120995,-0.2511283457,0.0004014499,-0.1034957469,0.1337920129,-0.2878107727,-0.3897340596,-0.2453112602,-0.2271138728,0.4359025359,0.0752960369,0.1372660846,0.2004070431,-0.202068001,0.0147434054,-0.2370598018,-0.2110955119,0.0494783483,-0.1857471168,0.2052339017,-0.2988070846,-0.1417259425,-0.0219020918,0.0942525193,0.1670048088,-0.2243585736,-0.1604168266,0.1161189228,0.1713583618,0.2460084856,-0.3234409392,-0.2597648203,0.2524788082,-0.2297296822,0.0524282791,0.1972107291,-0.3602097034,-0.0937124863,0.518563509,0.1982491612,-0.2657060623,0.2363282442,0.0597211421,0.8267533183,-0.1374402046,-0.162537694,0.1258324534,0.0303803496,0.1219486594,-0.1489123851,-0.0352488011,0.39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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/373","title":"Segmentation fault when loading local JSON dataset as of #372","comments":"Also if you want to have your own dataset script, we now have a new documentation !\r\nSee here:\r\nhttps:\/\/huggingface.co\/nlp\/add_dataset.html","body":"The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.)","comment_length":20,"text":"Segmentation fault when loading local JSON dataset as of #372 \n The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.) \n Also if you want to have your own dataset script, we now have a new documentation !\r\nSee here:\r\nhttps:\/\/huggingface.co\/nlp\/add_dataset.html","embeddings":[-0.0172924567,0.2893957794,0.0609415546,0.0578706525,0.2236097753,-0.0165483262,0.3200395405,0.6101837754,-0.4010095596,-0.2130427957,-0.2422538549,0.771407783,0.1327875555,-0.4265963435,0.1489231437,-0.0715281218,0.0462259129,0.1267296821,0.2239412665,0.0536700115,-0.1744497716,0.3416509628,-0.2299684733,0.1320418268,-0.1667520404,0.2660649717,0.0065363278,0.5419237018,-0.0745723546,-0.7302021384,0.2977763414,-0.0093303947,0.1517449468,0.3450249732,-0.0001186113,0.1845317483,0.3318485618,-0.1096370071,-0.3517018259,0.0521292798,0.2805030644,-0.2414649427,0.3380265236,-0.1453841031,0.0627313927,-0.3119795918,0.0318316966,0.0812750906,0.6866552234,0.1553962231,0.0921009928,0.1377252787,0.0738054588,0.0523657314,0.0207765196,0.0678778663,0.0166039914,0.7200931311,0.5202925205,-0.1260128319,-0.3388995528,-0.0720849857,0.121271871,-0.0382684954,0.20485726,-0.0635562018,-0.0138488207,-0.1832702905,0.0524652265,0.1170204207,0.2416470051,-0.2380599082,-0.2328487635,-0.2157492042,0.0747866482,-0.2181084901,0.3773207963,0.2772295773,-0.13986063,0.1523420066,-0.0569971204,0.0344605073,-0.1918932199,0.0293105822,-0.2142735124,0.5192520618,0.1215123013,0.0416844822,0.1051236168,-0.1629457474,-0.1519261301,0.3172393739,-0.2625278533,0.3998276293,-0.3908263743,-0.1686170995,0.1120524853,-0.3626522124,0.2163050026,0.0901769698,0.1800890863,0.167204693,0.2139120996,0.1203705147,0.4853105545,0.0546770915,0.1718356609,0.2025324851,0.1526307762,-0.0196298789,-0.0259742644,0.2397748679,-0.1398817003,-0.2250073105,-0.2135159373,0.0280673709,-0.0268218834,-0.10946697,0.0148830935,0.0695176572,-0.3147706687,-0.0120260334,0.0509856232,0.0848620832,0.1312868148,0.1926079243,-0.250028193,0.204761222,-0.101454854,-0.1726957709,-0.0175752416,-0.026819814,-0.3066568077,-0.0206658803,0.4054740667,-0.1225604787,0.1185211167,-0.0812497288,-0.1116154194,-0.2269219011,-0.4334718287,-0.0438977554,0.0076662428,0.1496739089,0.0316801295,-0.0704863369,-0.036723651,-0.2581102848,-0.1803209335,0.212189436,-0.351188004,-0.2851627171,-0.5924198031,0.1183918193,-0.3227140605,-0.0053090458,0.1444022059,0.2050788999,0.0566594712,-0.4543268085,-0.137789458,-0.0893592536,-0.0031521304,-0.2295428067,0.1466828585,0.2523785532,-0.8450297117,-0.2359818965,0.1668373644,-0.0015070704,0.0830982402,0.5275310278,-0.351418823,0.2732034326,-0.1530969292,0.5261092186,0.4013668597,-0.2779783309,-0.3369089961,0.2427911609,-0.3994488418,-0.0413578488,-0.2372673005,-0.0655715317,0.2967054844,0.0823332146,0.3412532806,0.44938609,-0.1155516654,0.1483249068,-0.3325651586,-0.0804989934,0.1726427674,0.3601403534,-0.067415975,-0.1608242691,-0.0298587419,0.1449324936,0.4040019214,-0.1719319224,-0.0207536705,0.1606730372,0.1372898668,-0.0173203666,0.1661421508,-0.1080801859,-0.775840342,0.102051191,-0.2894922793,0.0807941109,-0.0411318801,0.006053275,-0.2767830789,-0.1135980338,-0.2061314285,-0.0540232696,0.0321170986,-0.160494104,-0.0348596834,-0.0682966858,-0.4652936161,-0.1567875296,-0.154756695,0.378049463,-0.1153785959,0.4039317071,-0.1302649975,-0.4008334279,0.0927924365,0.3672265112,0.1282616705,-0.2672620118,-0.0068155113,0.3295680285,-0.0804471746,0.1338739544,-0.1708227694,-0.2979542315,0.2476089597,-0.0857613161,-0.1292840242,-0.0635809377,-0.1022319719,-0.0883648023,-0.3246678412,0.3362679482,0.0197260007,-0.0326367095,-0.0567735732,-0.2471363395,0.2981203198,-0.0848109946,-0.1274032891,-0.0282969773,0.0172862969,0.1671073884,-0.0124944821,0.1766584069,-0.1765216589,-0.0144745773,0.369494915,-0.0651190057,0.0355697162,0.1873179078,0.1008718833,-0.2918989658,-0.024752669,-0.0487226285,0.3634798825,-0.007696704,-0.1558919847,-0.0039152685,0.0336674862,-0.0184020884,-0.011545361,0.0482457764,0.4226091802,0.5049403906,0.3639849424,0.1684012264,-0.2733654976,-0.1548733562,0.1276226789,0.0563049763,-0.3743780255,0.0602172576,-0.2678267956,-0.144593209,-0.2930882573,-0.1426913887,-0.3419747949,-0.1941553801,0.1088883728,-0.0382642075,-0.086782597,0.2005051821,0.1665448993,0.4000805914,0.1002783403,-0.1863676459,0.0381456204,-0.1772579998,-0.2182420641,-0.0453671627,0.3783227801,-0.0644285679,-0.2067784965,-0.1894416362,-0.2709756494,0.1097929254,-0.4360963702,0.1680292189,-0.0091555053,0.1945903599,0.0222651493,0.2368633002,-0.1479672343,-0.3669347167,0.2814826965,-0.0462120995,-0.2511283457,0.0004014499,-0.1034957469,0.1337920129,-0.2878107727,-0.3897340596,-0.2453112602,-0.2271138728,0.4359025359,0.0752960369,0.1372660846,0.2004070431,-0.202068001,0.0147434054,-0.2370598018,-0.2110955119,0.0494783483,-0.1857471168,0.2052339017,-0.2988070846,-0.1417259425,-0.0219020918,0.0942525193,0.1670048088,-0.2243585736,-0.1604168266,0.1161189228,0.1713583618,0.2460084856,-0.3234409392,-0.2597648203,0.2524788082,-0.2297296822,0.0524282791,0.1972107291,-0.3602097034,-0.0937124863,0.518563509,0.1982491612,-0.2657060623,0.2363282442,0.0597211421,0.8267533183,-0.1374402046,-0.162537694,0.1258324534,0.0303803496,0.12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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/373","title":"Segmentation fault when loading local JSON dataset as of #372","comments":"@lhoestq \r\nFor some reason, I am not able to reproduce the segmentation fault, on pyarrow==0.16.0. Using the exact same environment and file.\r\n\r\nAnyhow, I discovered that pyarrow>=0.17.0 is required to read in a JSON file where the pandas structs contain lists. Otherwise, pyarrow complains when attempting to cast the struct:\r\n```py\r\nimport nlp\r\n>>> s = nlp.load_dataset(\"json\", data_files=\"datasets\/train-v2.0.json\", field=\"data\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> s[0]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 558, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 498, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict(\"list\"))\r\n  File \"pyarrow\/array.pxi\", line 559, in pyarrow.lib._PandasConvertible.to_pandas\r\n  File \"pyarrow\/table.pxi\", line 1367, in pyarrow.lib.Table._to_pandas\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py\", line 766, in table_to_blockmanager\r\n    blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py\", line 1101, in _table_to_blocks\r\n    list(extension_columns.keys()))\r\n  File \"pyarrow\/table.pxi\", line 881, in pyarrow.lib.table_to_blocks\r\n  File \"pyarrow\/error.pxi\", line 105, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n>>> s\r\nDataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 35)\r\n```\r\n\r\nUpgrading to >=0.17.0 provides the same dataset structure, but accessing the records is possible without the same exception. \r\n\r\n","body":"The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.)","comment_length":219,"text":"Segmentation fault when loading local JSON dataset as of #372 \n The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault. \r\n\r\n```\r\ndataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n```\r\ncauses\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset json\/default (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0...\r\n0 tables [00:00, ? tables\/s]Segmentation fault (core dumped)\r\n```\r\nwhere `.\/datasets\/train-v2.0.json` is downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/.\r\nThis is consistent with other SQuAD-formatted JSON files.\r\n\r\nWhen attempting to load the dataset again, I get the following:\r\n```\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 6, in <module>\r\n    'json', data_files={nlp.Split.TRAIN: [\".\/datasets\/train-v2.0.json\"]}, field='data')\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 382, in download_and_prepare\r\n    with incomplete_dir(self._cache_dir) as tmp_data_dir:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/contextlib.py\", line 112, in __enter__\r\n    return next(self.gen)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 368, in incomplete_dir\r\n    os.makedirs(tmp_dir)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/os.py\", line 223, in makedirs\r\n    mkdir(name, mode)\r\nFileExistsError: [Errno 17] File exists: '\/home\/XXX\/.cache\/huggingface\/datasets\/json\/default\/0.0.0.incomplete'\r\n```\r\n\r\n(Not sure if you wanted this in the previous issue #369 or not as it was closed.) \n @lhoestq \r\nFor some reason, I am not able to reproduce the segmentation fault, on pyarrow==0.16.0. Using the exact same environment and file.\r\n\r\nAnyhow, I discovered that pyarrow>=0.17.0 is required to read in a JSON file where the pandas structs contain lists. Otherwise, pyarrow complains when attempting to cast the struct:\r\n```py\r\nimport nlp\r\n>>> s = nlp.load_dataset(\"json\", data_files=\"datasets\/train-v2.0.json\", field=\"data\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> s[0]\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 558, in __getitem__\r\n    format_kwargs=self._format_kwargs,\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 498, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict(\"list\"))\r\n  File \"pyarrow\/array.pxi\", line 559, in pyarrow.lib._PandasConvertible.to_pandas\r\n  File \"pyarrow\/table.pxi\", line 1367, in pyarrow.lib.Table._to_pandas\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py\", line 766, in table_to_blockmanager\r\n    blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\r\n  File \"\/home\/vegarab\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/pyarrow\/pandas_compat.py\", line 1101, in _table_to_blocks\r\n    list(extension_columns.keys()))\r\n  File \"pyarrow\/table.pxi\", line 881, in pyarrow.lib.table_to_blocks\r\n  File \"pyarrow\/error.pxi\", line 105, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n>>> s\r\nDataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 35)\r\n```\r\n\r\nUpgrading to >=0.17.0 provides the same dataset structure, but accessing the records is possible without the same exception. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/369","title":"can't load local dataset: pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries","comments":"I am able to reproduce this with the official SQuAD `train-v2.0.json` file downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/","body":"Trying to load a local SQuAD-formatted dataset (from a JSON file, about 60MB):\r\n```\r\ndataset = nlp.load_dataset(path='json', data_files={nlp.Split.TRAIN: [\".\/path\/to\/file.json\"]})\r\n```\r\ncauses\r\n```\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 9, in <module>\r\n    [\".\/path\/to\/file.json\"]})\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 483, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 719, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/datasets\/json\/88c1bc5c68489f7eda549ed05a5a738527c613b3e7a4ee3524d9d233353a949b\/json.py\", line 53, in _generate_tables\r\n    file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 191, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\n\r\nI haven't been able to find any reports of this specific pyarrow error here or elsewhere. ","comment_length":16,"text":"can't load local dataset: pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries \n Trying to load a local SQuAD-formatted dataset (from a JSON file, about 60MB):\r\n```\r\ndataset = nlp.load_dataset(path='json', data_files={nlp.Split.TRAIN: [\".\/path\/to\/file.json\"]})\r\n```\r\ncauses\r\n```\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 9, in <module>\r\n    [\".\/path\/to\/file.json\"]})\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 483, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 719, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/datasets\/json\/88c1bc5c68489f7eda549ed05a5a738527c613b3e7a4ee3524d9d233353a949b\/json.py\", line 53, in _generate_tables\r\n    file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 191, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\n\r\nI haven't been able to find any reports of this specific pyarrow error here or elsewhere.  \n I am able to reproduce this with the official SQuAD `train-v2.0.json` file downloaded directly from https:\/\/rajpurkar.github.io\/SQuAD-explorer\/","embeddings":[-0.2248245478,0.2079820931,0.0130987065,0.1377785653,0.278103739,-0.2597190738,0.3169232011,0.4687684178,-0.1691406667,-0.149010852,0.0023774887,0.4203414321,0.0690719336,-0.017227646,0.1122415587,-0.1478196979,-0.2049249411,0.2665922344,-0.0051441933,-0.0658807755,0.0004076251,0.2411607355,-0.1398990303,0.2639468908,-0.1967165917,-0.0133888656,0.1871291995,0.489185065,-0.2591634989,-0.5631475449,0.3153218329,-0.296150893,0.0954629779,0.1106008142,-0.0001166556,0.3696361482,0.1890549362,-0.0164393801,-0.3757407367,-0.3551534712,0.2414884269,-0.490455389,0.4686816335,-0.1142355204,-0.0764247999,-0.3508143425,-0.2223610729,-0.0738732889,0.4803349078,0.1195221841,0.1514048278,0.4681633711,-0.1832385957,0.1065228358,0.1193087772,0.1260323972,-0.1624573022,0.4610108435,0.3759771287,-0.3450180292,-0.3874123096,-0.1435601264,-0.1003543884,0.3243615329,0.1929460168,0.0093747759,-0.0391238257,0.1259256303,0.1254607588,0.1766230315,0.3256267607,-0.3496564627,-0.0632260889,-0.0745448321,0.0622528456,-0.0295382254,0.1810942143,0.3203273416,-0.3467211425,0.1248235554,-0.306050688,0.1964052171,-0.1067041978,0.3012481332,-0.1913444847,0.3296273649,0.0628052801,0.297368139,0.1061380431,0.1481057107,0.1631402075,0.2127095461,-0.0343891941,0.2124329805,-0.2666904628,0.2280091494,0.1013871729,-0.1723196059,0.2379253805,0.1013728306,0.2416795492,0.042447485,0.2140950859,0.2787180543,0.3740185499,-0.0143485079,0.1454510838,0.076501362,-0.017595483,0.0274803136,-0.1041604877,0.1215523779,-0.1991192549,-0.3780224919,0.0324992426,-0.1924571395,0.2749885619,-0.187133342,-0.2933093011,0.0940950513,-0.4935642183,-0.0608110391,0.1227081344,0.276789993,0.2118470371,0.2285734266,-0.1052113771,0.2164437771,-0.0928959176,-0.1712169647,-0.2024862021,0.0592293888,-0.319909662,-0.1551751643,0.4050982296,-0.2470349222,0.2493386269,-0.0130813839,0.1818499863,0.009779647,0.1716861129,0.0250100493,0.2165830731,0.1760776043,0.0181108508,0.0982851386,0.171315372,-0.2304755002,-0.3545801938,0.3259263337,0.0253611729,-0.320799768,-0.315030843,0.0986372679,-0.1578409076,0.0982589722,0.1755226851,0.0540475734,0.1067288667,-0.106240876,0.0962656811,-0.1180267259,-0.0711897016,-0.2982014418,0.3827488422,0.2899155319,-0.953222096,-0.1176798344,0.1975107193,-0.0724640563,0.0969277993,0.5727161169,-0.5708695054,0.1833847016,-0.0560126379,0.2390382886,0.5799345374,-0.3536818922,-0.5187262297,0.4949888289,-0.3832627237,-0.1650160998,-0.1425090581,-0.0413008891,-0.0210614819,0.282671243,0.094546847,0.683675468,-0.1377247125,0.1846747398,-0.3309652209,-0.1655487716,0.2316760421,0.4529773295,0.0024090782,-0.355371207,0.0153144868,0.1048190445,0.0743056834,-0.0041734935,0.2683136165,0.2235309184,-0.1914619803,-0.2516146898,0.0803214014,-0.0126006631,-0.7056607008,0.0893205032,-0.0640129596,-0.0399806537,-0.2193442732,0.0276732985,-0.389359206,0.2925418317,-0.1446148455,-0.0062229387,0.0418271422,-0.0105411699,0.0670851246,-0.0903656334,-0.3402930796,-0.149033159,0.0774711519,-0.0015936849,0.0514434464,0.4117664993,-0.0129660284,-0.278139919,0.0033560798,0.3550055921,-0.0173281319,-0.1272228658,-0.1079458073,0.2835788131,-0.1293059438,0.1203492433,-0.0040456974,-0.3351640403,0.2203945816,-0.3095673323,0.1677345783,-0.0205300692,-0.0447345078,-0.1287044138,-0.220154658,0.403267175,-0.190984115,0.1956115216,-0.0854582712,-0.1480924189,0.2147214115,-0.1240264177,-0.0586371161,0.0097111287,0.1784366816,0.2656929195,0.5207040906,0.2706751823,-0.2427762002,0.0891448408,0.3627480268,-0.0371227339,-0.04015835,0.0438290164,-0.2223280221,-0.1289470792,0.1081068367,-0.106606029,0.1868596524,0.1889305115,0.0756322891,-0.1392150819,-0.2522187829,-0.2151123434,-0.0319145508,-0.0436208732,0.1300845146,0.4038740695,0.448564589,-0.138234511,-0.4100547135,-0.0738447234,0.23459059,0.2450695783,-0.2443244755,0.0373928584,-0.3147729039,-0.0499378555,-0.1750921458,-0.4401914775,-0.264534831,-0.199967891,-0.0551090539,0.2310739011,0.0131704817,0.1373821497,-0.0116336057,0.2456823736,0.100873366,-0.4298436642,-0.0119966045,-0.5101564527,-0.3695273399,0.0328288302,0.4673339128,0.438559413,0.1786762923,0.1814610958,-0.0701135993,0.0090797385,-0.0991811231,0.2115611881,0.0304584187,0.1755426973,0.0036325869,0.3344269693,-0.4671179652,-0.3113957047,0.6026245952,-0.0913662165,-0.2721755505,0.1993504167,-0.3250698447,-0.1124728322,-0.2069631219,-0.1498916447,-0.2904261947,-0.3914505243,0.3365614712,-0.0109002227,0.0356000885,-0.1289472133,0.1338442415,0.2838368416,0.0949847326,-0.2116737813,-0.0383752808,-0.4520311952,0.2738104463,-0.115242824,-0.2694824636,-0.071028322,0.0409649499,0.2816606462,0.062776126,-0.3481173217,0.0024619957,0.0731174126,0.5344305038,-0.1907640994,-0.1876478046,0.3679308295,-0.0579305328,0.0226893835,0.0716024786,-0.2526082098,0.1822912842,0.0439497717,0.0337406024,-0.175504297,0.4175980687,0.1020742804,0.9232445955,-0.2643375397,-0.1505668312,0.3155281246,-0.0246923175,0.0868570358,-0.1268964261,-0.30847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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/369","title":"can't load local dataset: pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries","comments":"I am facing this issue in transformers library 3.0.2 while reading a csv using datasets.\r\nIs this fixed in latest version? \r\nI updated the latest version 4.0.1 but still getting this error. What could cause this error?","body":"Trying to load a local SQuAD-formatted dataset (from a JSON file, about 60MB):\r\n```\r\ndataset = nlp.load_dataset(path='json', data_files={nlp.Split.TRAIN: [\".\/path\/to\/file.json\"]})\r\n```\r\ncauses\r\n```\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 9, in <module>\r\n    [\".\/path\/to\/file.json\"]})\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 483, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 719, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/datasets\/json\/88c1bc5c68489f7eda549ed05a5a738527c613b3e7a4ee3524d9d233353a949b\/json.py\", line 53, in _generate_tables\r\n    file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 191, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\n\r\nI haven't been able to find any reports of this specific pyarrow error here or elsewhere. ","comment_length":37,"text":"can't load local dataset: pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries \n Trying to load a local SQuAD-formatted dataset (from a JSON file, about 60MB):\r\n```\r\ndataset = nlp.load_dataset(path='json', data_files={nlp.Split.TRAIN: [\".\/path\/to\/file.json\"]})\r\n```\r\ncauses\r\n```\r\nTraceback (most recent call last):\r\n  File \"dataloader.py\", line 9, in <module>\r\n    [\".\/path\/to\/file.json\"]})\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 524, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 483, in _download_and_prepare\r\n    self._prepare_split(split_generator, **prepare_split_kwargs)\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 719, in _prepare_split\r\n    for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/tqdm\/std.py\", line 1129, in __iter__\r\n    for obj in iterable:\r\n  File \"\/home\/XXX\/.conda\/envs\/torch\/lib\/python3.7\/site-packages\/nlp\/datasets\/json\/88c1bc5c68489f7eda549ed05a5a738527c613b3e7a4ee3524d9d233353a949b\/json.py\", line 53, in _generate_tables\r\n    file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options,\r\n  File \"pyarrow\/_json.pyx\", line 191, in pyarrow._json.read_json\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)\r\n```\r\n\r\nI haven't been able to find any reports of this specific pyarrow error here or elsewhere.  \n I am facing this issue in transformers library 3.0.2 while reading a csv using datasets.\r\nIs this fixed in latest version? \r\nI updated the latest version 4.0.1 but still getting this error. What could cause this error?","embeddings":[-0.2248245478,0.2079820931,0.0130987065,0.1377785653,0.278103739,-0.2597190738,0.3169232011,0.4687684178,-0.1691406667,-0.149010852,0.0023774887,0.4203414321,0.0690719336,-0.017227646,0.1122415587,-0.1478196979,-0.2049249411,0.2665922344,-0.0051441933,-0.0658807755,0.0004076251,0.2411607355,-0.1398990303,0.2639468908,-0.1967165917,-0.0133888656,0.1871291995,0.489185065,-0.2591634989,-0.5631475449,0.3153218329,-0.296150893,0.0954629779,0.1106008142,-0.0001166556,0.3696361482,0.1890549362,-0.0164393801,-0.3757407367,-0.3551534712,0.2414884269,-0.490455389,0.4686816335,-0.1142355204,-0.0764247999,-0.3508143425,-0.2223610729,-0.0738732889,0.4803349078,0.1195221841,0.1514048278,0.4681633711,-0.1832385957,0.1065228358,0.1193087772,0.1260323972,-0.1624573022,0.4610108435,0.3759771287,-0.3450180292,-0.3874123096,-0.1435601264,-0.1003543884,0.3243615329,0.1929460168,0.0093747759,-0.0391238257,0.1259256303,0.1254607588,0.1766230315,0.3256267607,-0.3496564627,-0.0632260889,-0.0745448321,0.0622528456,-0.0295382254,0.1810942143,0.3203273416,-0.3467211425,0.1248235554,-0.306050688,0.1964052171,-0.1067041978,0.3012481332,-0.1913444847,0.3296273649,0.0628052801,0.297368139,0.1061380431,0.1481057107,0.1631402075,0.2127095461,-0.0343891941,0.2124329805,-0.2666904628,0.2280091494,0.1013871729,-0.1723196059,0.2379253805,0.1013728306,0.2416795492,0.042447485,0.2140950859,0.2787180543,0.3740185499,-0.0143485079,0.1454510838,0.076501362,-0.017595483,0.0274803136,-0.1041604877,0.1215523779,-0.1991192549,-0.3780224919,0.0324992426,-0.1924571395,0.2749885619,-0.187133342,-0.2933093011,0.0940950513,-0.4935642183,-0.0608110391,0.1227081344,0.276789993,0.2118470371,0.2285734266,-0.1052113771,0.2164437771,-0.0928959176,-0.1712169647,-0.2024862021,0.0592293888,-0.319909662,-0.1551751643,0.4050982296,-0.2470349222,0.2493386269,-0.0130813839,0.1818499863,0.009779647,0.1716861129,0.0250100493,0.2165830731,0.1760776043,0.0181108508,0.0982851386,0.171315372,-0.2304755002,-0.3545801938,0.3259263337,0.0253611729,-0.320799768,-0.315030843,0.0986372679,-0.1578409076,0.0982589722,0.1755226851,0.0540475734,0.1067288667,-0.106240876,0.0962656811,-0.1180267259,-0.0711897016,-0.2982014418,0.3827488422,0.2899155319,-0.953222096,-0.1176798344,0.1975107193,-0.0724640563,0.0969277993,0.5727161169,-0.5708695054,0.1833847016,-0.0560126379,0.2390382886,0.5799345374,-0.3536818922,-0.5187262297,0.4949888289,-0.3832627237,-0.1650160998,-0.1425090581,-0.0413008891,-0.0210614819,0.282671243,0.094546847,0.683675468,-0.1377247125,0.1846747398,-0.3309652209,-0.1655487716,0.2316760421,0.4529773295,0.0024090782,-0.355371207,0.0153144868,0.1048190445,0.0743056834,-0.0041734935,0.2683136165,0.2235309184,-0.1914619803,-0.2516146898,0.0803214014,-0.0126006631,-0.7056607008,0.0893205032,-0.0640129596,-0.0399806537,-0.2193442732,0.0276732985,-0.389359206,0.2925418317,-0.1446148455,-0.0062229387,0.0418271422,-0.0105411699,0.0670851246,-0.0903656334,-0.3402930796,-0.149033159,0.0774711519,-0.0015936849,0.0514434464,0.4117664993,-0.0129660284,-0.278139919,0.0033560798,0.3550055921,-0.0173281319,-0.1272228658,-0.1079458073,0.2835788131,-0.1293059438,0.1203492433,-0.0040456974,-0.3351640403,0.2203945816,-0.3095673323,0.1677345783,-0.0205300692,-0.0447345078,-0.1287044138,-0.220154658,0.403267175,-0.190984115,0.1956115216,-0.0854582712,-0.1480924189,0.2147214115,-0.1240264177,-0.0586371161,0.0097111287,0.1784366816,0.2656929195,0.5207040906,0.2706751823,-0.2427762002,0.0891448408,0.3627480268,-0.0371227339,-0.04015835,0.0438290164,-0.2223280221,-0.1289470792,0.1081068367,-0.106606029,0.1868596524,0.1889305115,0.0756322891,-0.1392150819,-0.2522187829,-0.2151123434,-0.0319145508,-0.0436208732,0.1300845146,0.4038740695,0.448564589,-0.138234511,-0.4100547135,-0.0738447234,0.23459059,0.2450695783,-0.2443244755,0.0373928584,-0.3147729039,-0.0499378555,-0.1750921458,-0.4401914775,-0.264534831,-0.199967891,-0.0551090539,0.2310739011,0.0131704817,0.1373821497,-0.0116336057,0.2456823736,0.100873366,-0.4298436642,-0.0119966045,-0.5101564527,-0.3695273399,0.0328288302,0.4673339128,0.438559413,0.1786762923,0.1814610958,-0.0701135993,0.0090797385,-0.0991811231,0.2115611881,0.0304584187,0.1755426973,0.0036325869,0.3344269693,-0.4671179652,-0.3113957047,0.6026245952,-0.0913662165,-0.2721755505,0.1993504167,-0.3250698447,-0.1124728322,-0.2069631219,-0.1498916447,-0.2904261947,-0.3914505243,0.3365614712,-0.0109002227,0.0356000885,-0.1289472133,0.1338442415,0.2838368416,0.0949847326,-0.2116737813,-0.0383752808,-0.4520311952,0.2738104463,-0.115242824,-0.2694824636,-0.071028322,0.0409649499,0.2816606462,0.062776126,-0.3481173217,0.0024619957,0.0731174126,0.5344305038,-0.1907640994,-0.1876478046,0.3679308295,-0.0579305328,0.0226893835,0.0716024786,-0.2526082098,0.1822912842,0.0439497717,0.0337406024,-0.175504297,0.4175980687,0.1020742804,0.9232445955,-0.2643375397,-0.1505668312,0.3155281246,-0.0246923175,0.0868570358,-0.1268964261,-0.3084769845,0.1649303287,0.0774956495,-0.076903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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/368","title":"load_metric can't acquire lock anymore","comments":"I found that, in the same process (or the same interactive session), if I do\r\n\r\nimport nlp\r\n\r\nm1 = nlp.load_metric('glue', 'mrpc')\r\nm2 = nlp.load_metric('glue', 'sst2')\r\n\r\nI will get the same error `ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id'`.","body":"I can't load metric (glue) anymore after an error in a previous run. I even removed the whole cache folder `\/home\/XXX\/.cache\/huggingface\/`, and the issue persisted. What are the steps to fix this?\r\n\r\n    Traceback (most recent call last):\r\n      File \"\/home\/XXX\/miniconda3\/envs\/ML-DL-py-3.7\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 101, in __init__\r\n        self.filelock.acquire(timeout=1)\r\n      File \"\/home\/XXX\/miniconda3\/envs\/ML-DL-py-3.7\/lib\/python3.7\/site-packages\/filelock.py\", line 278, in acquire\r\n        raise Timeout(self._lock_file)\r\n    filelock.Timeout: The file lock '\/home\/XXX\/.cache\/huggingface\/metrics\/glue\/1.0.0\/1-glue-0.arrow.lock' could not be acquired.\r\n\r\n    During handling of the above exception, another exception occurred:\r\n\r\n    Traceback (most recent call last):\r\n      File \"examples_huggingface_nlp.py\", line 268, in <module>\r\n        main()\r\n      File \"examples_huggingface_nlp.py\", line 242, in main\r\n        dataset, metric = get_dataset_metric(glue_task)\r\n      File \"examples_huggingface_nlp.py\", line 77, in get_dataset_metric\r\n        metric = nlp.load_metric('glue', glue_config, experiment_id=1)\r\n      File \"\/home\/XXX\/miniconda3\/envs\/ML-DL-py-3.7\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 440, in load_metric\r\n        **metric_init_kwargs,\r\n      File \"\/home\/XXX\/miniconda3\/envs\/ML-DL-py-3.7\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 104, in __init__\r\n        \"Cannot acquire lock, caching file might be used by another process, \"\r\n    ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id' for this run.\r\n    I0709 15:54:41.008838 139854118430464 filelock.py:318] Lock 139852058030936 released on \/home\/XXX\/.cache\/huggingface\/metrics\/glue\/1.0.0\/1-glue-0.arrow.lock\r\n","comment_length":49,"text":"load_metric can't acquire lock anymore \n I can't load metric (glue) anymore after an error in a previous run. I even removed the whole cache folder `\/home\/XXX\/.cache\/huggingface\/`, and the issue persisted. What are the steps to fix this?\r\n\r\n    Traceback (most recent call last):\r\n      File \"\/home\/XXX\/miniconda3\/envs\/ML-DL-py-3.7\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 101, in __init__\r\n        self.filelock.acquire(timeout=1)\r\n      File \"\/home\/XXX\/miniconda3\/envs\/ML-DL-py-3.7\/lib\/python3.7\/site-packages\/filelock.py\", line 278, in acquire\r\n        raise Timeout(self._lock_file)\r\n    filelock.Timeout: The file lock '\/home\/XXX\/.cache\/huggingface\/metrics\/glue\/1.0.0\/1-glue-0.arrow.lock' could not be acquired.\r\n\r\n    During handling of the above exception, another exception occurred:\r\n\r\n    Traceback (most recent call last):\r\n      File \"examples_huggingface_nlp.py\", line 268, in <module>\r\n        main()\r\n      File \"examples_huggingface_nlp.py\", line 242, in main\r\n        dataset, metric = get_dataset_metric(glue_task)\r\n      File \"examples_huggingface_nlp.py\", line 77, in get_dataset_metric\r\n        metric = nlp.load_metric('glue', glue_config, experiment_id=1)\r\n      File \"\/home\/XXX\/miniconda3\/envs\/ML-DL-py-3.7\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 440, in load_metric\r\n        **metric_init_kwargs,\r\n      File \"\/home\/XXX\/miniconda3\/envs\/ML-DL-py-3.7\/lib\/python3.7\/site-packages\/nlp\/metric.py\", line 104, in __init__\r\n        \"Cannot acquire lock, caching file might be used by another process, \"\r\n    ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id' for this run.\r\n    I0709 15:54:41.008838 139854118430464 filelock.py:318] Lock 139852058030936 released on \/home\/XXX\/.cache\/huggingface\/metrics\/glue\/1.0.0\/1-glue-0.arrow.lock\r\n \n I found that, in the same process (or the same interactive session), if I do\r\n\r\nimport nlp\r\n\r\nm1 = nlp.load_metric('glue', 'mrpc')\r\nm2 = nlp.load_metric('glue', 'sst2')\r\n\r\nI will get the same error `ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id'`.","embeddings":[0.0387074426,-0.1158736274,0.0723508522,0.31246683,0.3204596639,0.0294491537,0.0900948569,0.0702063814,0.5023596287,-0.0785918608,-0.1850662529,0.030607095,-0.0480661876,-0.2614514828,-0.031660337,-0.1943873614,-0.2007998526,-0.0895176679,-0.0916291848,0.1228041351,-0.2072485536,0.3985546231,-0.1751362681,-0.1407353282,-0.3555804789,-0.1709385812,-0.0894152075,0.3625312448,-0.0941761583,-0.4639343917,0.1580357552,0.0359998159,0.0657720491,0.0958728641,-0.0001253988,0.0097096125,0.4328870475,0.0207843147,0.0158178359,-0.2742264271,-0.0472081974,-0.538641572,0.2313128412,-0.0381141342,0.0553530268,0.1542789787,0.0128486948,-0.3735764623,0.3929035664,0.2302975506,0.117739819,0.4218791127,0.24505198,-0.3009939492,-0.2283041924,-0.5178474188,0.1226883009,0.9323651195,0.1983928382,-0.4340361357,-0.1681259274,0.1428887099,-0.0306593888,0.1357378513,0.4364429712,-0.1017124504,0.3628014028,-0.1146173105,-0.0268294904,0.0082821026,0.033585906,-0.1131085306,-0.266302824,-0.1967049837,0.3940870166,-0.3095428348,0.3262719214,-0.2071050406,0.1107339635,0.0614548922,0.022858521,-0.3017476499,0.0311336033,0.1103480235,-0.0704878271,0.1314450353,-0.0304556023,0.1059605926,0.4824415147,0.1158646271,-0.1343362033,0.4646417797,-0.2977823317,0.252591908,-0.223045066,0.3128059208,0.0215294249,0.4838051498,0.0795658678,0.047243502,0.0058021699,0.0168824252,0.3405639827,0.1478574723,-0.0365649909,0.5148493648,0.406049639,-0.0803215504,0.1938289553,0.2729445696,0.007888251,-0.0202901494,-0.0450518727,-0.3363509476,0.1489802897,0.1632087529,0.0452220961,-0.088920109,0.0154884411,0.0259291045,0.0870660245,-0.1591012478,0.1520417929,0.1608738452,-0.0276133586,0.2953260541,0.1074499413,0.2743976414,-0.0106981592,-0.1547306031,-0.1928951293,0.3185302019,-0.3265420198,0.2753768265,0.2608951926,-0.2996115088,0.3550312221,0.0881137773,0.088500753,-0.4025236368,-0.0697804168,0.0475794412,-0.2875433266,0.2150298953,-0.1425008029,0.0949410796,0.1955335587,-0.2912884653,-0.0110831,-0.3652180433,-0.4574064314,-0.3263042271,-0.0511174686,0.0123522189,-0.4049867094,0.059775997,0.0596663393,0.1430255324,0.0267869532,0.0218423177,-0.0301715992,-0.1087390333,-0.1520867944,-0.2062555403,0.2813636065,0.5562860966,0.0976210237,-0.3056697249,-0.1878911257,-0.1079912558,-0.1382289678,0.234931007,0.0458945334,0.0970936641,-0.2242059708,0.0844082087,0.2618544698,-0.7882769108,-0.6193390489,-0.0095672943,-0.1618588418,-0.0446815379,0.0548863038,0.0888190717,0.1180931777,0.0993980765,0.3330300748,0.0924305469,0.0943365321,-0.4169382155,-0.2193696946,0.0052568289,0.0715348944,0.0779734626,0.2852445543,0.2650149465,0.1815573722,0.3520883322,0.0231217835,0.3938896656,-0.2276394665,0.4092956483,-0.0556347631,-0.2220461667,0.0867227688,-0.1873000264,-0.3539688587,0.4585732222,-0.5009264946,-0.0561315306,0.0323615,0.0708050653,-0.4591314793,0.0309203062,0.1142947748,-0.0322566815,0.0307951979,0.0454827324,0.0259703789,0.0601222664,-0.1053756922,0.5104598403,-0.0319066048,0.2045419663,-0.2858424187,-0.0922260359,0.0968826637,-0.0596505105,-0.0074587725,0.3105420172,0.494872421,-0.0591302067,0.046972122,0.5281563401,0.0510515682,0.260019958,-0.0119120143,-0.014790481,0.2664884925,0.0134368101,-0.0115792537,-0.5587484837,-0.0614249967,-0.061917197,0.3316039443,0.0798467323,0.1930182427,0.1027684882,-0.1634512842,0.1938889027,0.0462909266,-0.2189996392,-0.1978612393,-0.2039311379,0.5161855221,-0.1291542202,0.0738871396,0.0157022104,0.2341696322,-0.0599894449,0.2643677294,0.0904286429,0.1763944626,-0.1143529415,-0.1747926176,-0.0878666565,-0.2505739927,0.3127426505,0.3478238881,-0.0408706702,0.0977798402,0.1051948369,0.0304459818,-0.1265745908,0.0144020161,0.0762419403,-0.0217216946,0.3686549664,0.1944015324,0.0989873633,-0.0384875908,0.1585069001,0.1161005422,0.2183551937,-0.2039139122,0.0098381992,-0.3964014351,0.0102863135,0.2288608551,-0.0039137155,-0.4821487069,-0.4297889173,-0.0750375018,0.1887993068,0.3223307729,0.0609571673,0.1792288721,0.229039982,-0.1921593994,0.2414934486,-0.1553550065,-0.4484227598,-0.0379551537,-0.16159527,0.1437360942,-0.095669426,0.114574559,-0.1248923391,-0.1733731776,-0.1402734071,-0.2574286163,0.0239719469,0.0843010917,0.7145802975,0.2129424065,0.0088944845,-0.001066129,0.1230750382,0.3546610475,-0.1275537908,0.0524185337,0.2911889255,-0.3696325719,0.1463911831,-0.2685171366,0.1616153717,-0.5034708977,-0.4056679904,0.2910679579,-0.1105204225,-0.2216467559,0.1172694862,-0.2792262137,0.3122066855,-0.2726821899,0.1251493245,-0.2774215341,-0.1923308969,0.2103399932,0.0043342048,-0.0900195614,-0.2496180683,0.1020823419,0.1823785007,-0.1399124414,-0.392642647,-0.7514959574,-0.1141082719,0.0987052619,-0.2474500388,-0.1125537828,0.1439746022,-0.3512949944,0.0414206386,-0.1349384189,-0.0026854076,0.1042374671,0.0757852346,0.2012827694,0.0657725707,0.2217899412,0.1661201119,0.5310268402,-0.0442974865,-0.1021614,0.3465194404,0.0595608316,0.4767459035,0.1927142441,-0.3890644908,-0.1930055022,0.1417024732,0.2469887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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/365","title":"How to augment data ?","comments":"Using batched map is probably the easiest way at the moment.\r\nWhat kind of augmentation would you like to do ?","body":"Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n```","comment_length":21,"text":"How to augment data ? \n Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n``` \n Using batched map is probably the easiest way at the moment.\r\nWhat kind of augmentation would you like to do ?","embeddings":[-0.0076108566,-0.0786108673,-0.3029457033,-0.0933210775,0.0747116208,0.2788416147,-0.104579322,0.2814402282,0.1223137155,0.1161646768,-0.122473754,0.0561201237,-0.0021822758,0.1659331471,0.1615757942,-0.1614081562,0.1548456997,0.1445470452,-0.1470573843,0.1611988991,-0.1049567237,-0.4687489569,0.0024022388,-0.5504875779,-0.113978371,-0.1055827886,-0.2983653247,-0.1648691446,0.0454924963,-0.377905339,-0.2231483459,0.0820774585,0.1151698157,0.2421452105,-0.0000998411,-0.208939597,0.1646328717,0.0892125666,-0.3107390702,0.4721699059,0.1105210781,-0.8047289252,-0.0186209511,-0.0786827132,-0.0834561139,-0.4446455538,0.0461451188,-0.0636526868,0.3388094604,-0.1767680943,0.2676832378,-0.0098133795,0.374391377,0.0117657464,0.1348076314,0.3799756467,0.1615886688,0.0862629563,0.7172884345,0.143665731,0.336512953,0.1444842219,-0.1315525174,-0.0973731354,0.2357285768,0.0122268666,0.1705203503,-0.3990068138,-0.015450797,0.1021066234,0.3820779622,0.2447232902,-0.5512055755,-0.3171167672,0.2154936045,-0.0941848382,-0.3701936007,-0.1095228866,0.154990375,-0.0199620537,-0.2288735956,-0.2409577668,-0.0837807953,0.3638654649,0.0529173575,0.4135732949,0.0324129425,-0.0221883953,-0.1271591485,-0.279292196,-0.1728684753,0.1948121637,-0.15500696,0.4723395407,-0.1161077693,-0.1414955705,-0.2426722497,-0.2189955115,0.0774522275,0.10739398,0.1769826859,0.178008154,-0.1855886132,0.1188087761,0.1672571748,-0.300796181,-0.0579392277,0.1513618678,0.0100308871,-0.1358963102,0.2673924267,-0.0651103631,-0.104189299,-0.1596966088,0.0384277478,-0.1040328294,-0.1227104515,0.0058560395,-0.0125691555,0.0119503718,-0.2453552037,0.1206428185,-0.0216629002,0.0626219884,-0.2390582412,0.0745224953,-0.1906955987,0.2592640519,0.2707093954,-0.2050131112,-0.1140430719,0.1182821542,-0.0716915056,-0.1564167589,0.2377939522,0.117393069,0.2470694333,0.0270550232,0.0919327438,-0.036212869,0.0832180902,0.0104128635,0.3068697453,0.2046578526,0.1408862025,0.0554040857,-0.185100466,0.1971400678,-0.0127218086,0.0888806656,-0.3098223209,-0.0053880825,-0.3795255423,0.2868727744,0.0608546808,-0.1946115643,-0.1409720331,0.3655321002,0.1183648705,-0.2584454715,0.0642438978,0.0939885601,0.1048165783,-0.3767409027,0.4527572691,0.0382832251,-0.3822378814,0.0499950238,-0.1435533464,0.3040354252,0.1318435967,0.0393009409,-0.0303404424,0.1447838247,-0.1284517348,-0.1212250367,0.2840679884,-0.1609270573,-0.333278656,-0.0140593601,0.1716776937,0.1381157041,-0.1504609734,0.32857728,0.2457571626,-0.0475737303,0.1889441162,0.3833818734,-0.1481109262,-0.1654292792,-0.0901371315,-0.1093993634,0.3156548142,0.1865181774,-0.061891932,-0.1337626874,0.2854762673,-0.4684000611,0.1513703465,-0.1575251222,0.318633914,0.1901426464,0.1881601959,0.3953309953,-0.0614690818,-0.1812356263,-0.1726555377,-0.0147862732,-0.1361605525,-0.148172766,0.1438458115,-0.1893931925,-0.0441929176,-0.1230905354,0.1288403124,-0.3399832845,0.2959398031,-0.1768559813,0.0442280509,-0.4881070256,-0.003710927,-0.1384122372,0.2111998796,0.079161264,-0.200127244,0.0091773821,0.269851625,-0.3361878097,-0.2222453654,0.335079819,-0.1025168821,-0.1454635412,0.1096941531,0.2788130343,0.0695466027,0.210286662,0.2375261784,0.2521240711,0.0980793834,0.1130802631,0.2049984187,0.2804630697,0.0073606591,-0.1436427087,-0.0969061852,0.2299025208,0.0896204039,0.1053560823,-0.2545942366,0.2065225691,0.265427798,0.0409889631,-0.0918569341,-0.0353268236,-0.2036500126,-0.0124819223,0.2784784436,-0.2759414911,-0.1348335743,0.0308585111,-0.4751145542,-0.1810441017,-0.1317112744,-0.175999701,-0.4588342309,-0.1853166819,0.1180563569,-0.0558093674,-0.179898262,0.4024444222,0.1857137829,-0.0418794826,0.255289793,0.1530427337,0.0145458812,0.1135466024,-0.0431540236,-0.0487925969,0.3766633868,0.2131991088,0.1516609043,0.064551115,0.0053588343,0.1715642661,-0.0934923217,-0.2121171206,-0.0636444911,-0.189534232,0.0392831191,-0.6104679108,-0.2298272103,-0.3691900074,-0.0815134421,0.2527185977,-0.2411987484,0.0109083708,0.1769155115,0.1015365049,0.0514890067,-0.2087112665,0.0035752738,0.2888846099,0.002571587,0.2455696315,-0.0415379591,0.0583165772,0.5223010182,0.158653304,0.2197445631,-0.1253729761,-0.5068557262,-0.2053861767,0.0732154995,0.212038964,0.1939320415,-0.0102066258,-0.0845808759,-0.1673970968,-0.0973307937,-0.1113427505,0.1161694825,-0.1131237,-0.1461567432,-0.0387624428,-0.2443744093,0.1633575261,-0.4032546282,-0.1918665469,0.1039541438,0.2002733201,0.0443589427,-0.226942718,0.2483760267,-0.0357570499,0.1118209958,-0.0389118828,-0.1680570245,-0.2406021357,0.5701763034,-0.2656957507,0.0013287901,-0.0552209988,-0.3044517338,0.1406930983,0.1969478428,-0.040401794,-0.20166637,-0.0954496562,0.0220215674,-0.2020997852,0.1835167259,0.1146987304,-0.1391179115,-0.1791120172,0.058222495,-0.0121351136,-0.0202245116,0.3734327853,0.1653358638,-0.1720096022,-0.1517802924,-0.1649215519,0.0782194883,0.0789995417,-0.0428789146,0.1349996328,-0.1324146539,0.0905666873,0.0054748622,-0.0927537233,-0.0154377744,-0.1174418554,-0.225460961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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/365","title":"How to augment data ?","comments":"Some samples in the dataset are too long, I want to divide them in several samples.","body":"Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n```","comment_length":16,"text":"How to augment data ? \n Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n``` \n Some samples in the dataset are too long, I want to divide them in several samples.","embeddings":[-0.0921547189,-0.0665563941,-0.2976478934,-0.0226983447,0.0231515002,0.3546432853,-0.0144629246,0.2735868096,0.1672643125,0.1517361403,-0.1143039241,0.0416504815,-0.012731811,0.1264989078,0.1626523435,-0.2058319449,0.1696426421,0.0873438567,-0.1048345342,0.2275170088,-0.0604989119,-0.4782770574,-0.0764304549,-0.6003987789,-0.116329439,-0.0985122621,-0.3571471274,-0.3164554238,0.1215020195,-0.3110496104,-0.2560763955,0.0157930832,0.0884928107,0.280728966,-0.0001033333,-0.1614461541,0.1503436416,0.0704381019,-0.3406285942,0.4995984435,0.0792680457,-0.744867444,-0.0671173632,-0.0829781145,-0.1195215806,-0.4099473357,0.0361109711,-0.1217740104,0.3469309509,-0.143333286,0.2301471531,-0.0034851111,0.3508443832,-0.0468548723,0.1203243881,0.4339418113,0.2089641988,0.103696689,0.7045372128,0.1954564601,0.3622108996,0.1173312813,-0.1749290675,-0.0523294769,0.2249269783,-0.0106468778,0.2237958312,-0.4108218253,-0.0086791795,0.1689893305,0.3199174106,0.2096618265,-0.5844902992,-0.343937248,0.1641148627,-0.0818067342,-0.4392800629,0.0093651451,0.1522530168,-0.025808597,-0.2340262532,-0.2493508905,-0.0669920817,0.4183287024,0.0526309125,0.4480652213,0.0393823311,0.0323318653,-0.1009379029,-0.2489603311,-0.1554080993,0.1496042907,-0.1278482676,0.3905822039,-0.0650754347,-0.137221545,-0.3636052608,-0.2775261402,0.1508690566,0.0819587559,0.0989653319,0.1121674106,-0.1534661502,0.1329180747,0.2125650793,-0.342487067,-0.0359569117,0.2162266821,-0.0182291195,-0.1561191231,0.2932980955,-0.0941528976,-0.1405782849,-0.1551611722,0.0157136731,-0.0816375241,-0.1643199474,0.1094416156,-0.1225902736,-0.0545510948,-0.2011449635,0.0755982399,0.0647476166,0.1065370217,-0.2623960078,0.0036335993,-0.3086779118,0.2466654927,0.1757230908,-0.2556721866,-0.0760074407,0.1257371902,-0.1042709127,-0.1410946399,0.189350307,0.1175352484,0.2761706114,-0.0135685485,0.091220513,-0.1359864771,0.0667712688,-0.045589257,0.2609443665,0.13358365,0.1653188914,0.0235141478,-0.195586741,0.1977247447,-0.0709342733,0.0804622695,-0.3996521533,-0.018064918,-0.388820529,0.2422772646,0.1782967746,-0.1079659238,-0.1250014752,0.3729400039,0.2526080012,-0.3006931245,0.0890919492,0.0653070882,0.1029087678,-0.335172683,0.4814350307,0.0424609743,-0.3115996718,0.0160700977,-0.2066366225,0.2904301882,0.1537154317,0.0912341774,0.022525901,0.0954157561,-0.2393282652,-0.1379316151,0.3075343966,-0.1951460093,-0.3103208542,0.0397401154,0.1528491229,0.0272366367,-0.1793166995,0.3627087772,0.3158904016,-0.0820764452,0.2134750783,0.4815993309,-0.2080274224,-0.1461940557,-0.0089435065,-0.1502866894,0.3748991191,0.2271438837,-0.0541485064,-0.1308891028,0.2716344893,-0.4427704513,0.186020419,-0.1181798428,0.3794408739,0.1347667724,0.1710024625,0.3944880664,-0.0669768602,-0.2257087529,-0.2261144519,-0.0571709014,-0.0621210113,-0.2353710532,0.2126369625,-0.2676018178,0.0265949033,-0.1191705763,0.1559116095,-0.3202858865,0.2384455353,-0.1932311803,0.1870041043,-0.5061725974,0.0610345751,-0.2233324051,0.2332093269,0.1024845913,-0.2418869585,0.0311354287,0.2829587758,-0.2612969875,-0.2415936291,0.3624904156,-0.0874168128,-0.1497420222,0.1179000661,0.2847110629,0.1307742,0.1272636205,0.1616806686,0.1158380732,0.1229902804,0.0553345867,0.1622658372,0.2773104906,-0.0494484901,-0.1482307464,-0.0923692584,0.1870813966,0.0217762981,0.2239459604,-0.2352086902,0.244343549,0.2990865409,0.0249035638,-0.1108221039,-0.1024338529,-0.2274216712,-0.0303910505,0.3708465099,-0.2931017578,-0.0476089679,0.0122405682,-0.5297372937,-0.1937215775,-0.1063223928,-0.1373767853,-0.4725438654,-0.1186695844,0.1379899234,0.0225921012,-0.0970028266,0.3941380978,0.2377459854,-0.0841580778,0.2825246155,0.157409668,-0.0124919526,0.1354325414,-0.1045706049,0.0229452122,0.3718268573,0.1324880272,0.2272262275,0.0688881949,0.1317285001,0.1229524538,-0.0665533468,-0.1939289123,-0.1079429239,-0.2196011096,-0.0119364224,-0.6965720057,-0.1861798912,-0.3246959746,-0.1221535653,0.2187304348,-0.212937057,0.0391223133,0.2452377826,0.1445958167,0.083821021,-0.1548655033,0.090640299,0.2803698778,0.0687174201,0.203671217,0.0550056994,0.1548541039,0.5739241838,0.1061321124,0.1109563857,-0.2117847949,-0.3798002899,-0.221218884,0.0574127473,0.2341428548,0.2103155702,-0.0212551169,-0.0346369445,-0.1980115324,-0.0595406964,-0.1020124182,0.1978409588,-0.1250301749,-0.0831870735,-0.0428713299,-0.2294331342,0.1627705246,-0.4700725675,-0.2381106168,0.0521292984,0.1220853254,0.0903787166,-0.2794121504,0.3012722135,-0.0222951937,0.0641671419,-0.1506201327,-0.157328099,-0.179742679,0.4674947858,-0.3396631777,0.1202916577,-0.0520101637,-0.3217193186,0.1637375653,0.1866900027,-0.0275577847,-0.1436758637,-0.0969828665,0.0596022531,-0.1858639866,0.1629549712,0.0828569829,-0.2485521734,-0.0959439576,0.1055774838,-0.0112335328,-0.0025405842,0.3178333938,0.1828472167,-0.1401714236,-0.0772917643,-0.2146828622,0.1893817037,0.1611502469,-0.0674246475,0.0197200067,-0.2104617208,0.0398922637,0.0396238193,-0.096558854,-0.0287131257,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/365","title":"How to augment data ?","comments":"Using batched map is the way to go then.\r\nWe'll make it clearer in the docs that map could be used for augmentation.\r\n\r\nLet me know if you think there should be another way to do it. Or feel free to close the issue otherwise.","body":"Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n```","comment_length":45,"text":"How to augment data ? \n Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n``` \n Using batched map is the way to go then.\r\nWe'll make it clearer in the docs that map could be used for augmentation.\r\n\r\nLet me know if you think there should be another way to do it. Or feel free to close the issue otherwise.","embeddings":[-0.0169448406,-0.1455653608,-0.3001067638,-0.085400261,0.1244004071,0.1755867749,-0.0629433766,0.3091185093,0.1504426748,0.1681790799,-0.1306253672,0.187817201,0.0309323147,0.1995354891,0.1029892415,-0.0984935388,0.0637115464,0.1679257452,-0.2380012125,0.1587176472,-0.0724246055,-0.3081819117,-0.0647776127,-0.5189904571,-0.088035509,-0.0856838748,-0.2475226521,-0.0627598315,0.016347168,-0.3594219685,-0.2194650471,0.0810869411,0.0797761083,0.1623182744,-0.0000954823,-0.167870909,0.1755304486,0.0509370416,-0.2073566914,0.4534386992,0.0881541222,-0.6956601739,-0.0384642966,-0.1407383531,-0.1430088729,-0.4254902005,0.0193878524,-0.0791336894,0.393286854,-0.1298876554,0.3211502135,0.0678816885,0.3687788844,-0.0091608884,0.1637964994,0.2675371766,0.0934257507,0.0432249308,0.6970156431,0.1130867824,0.1968534589,0.275015533,-0.055956807,-0.0547927804,0.2242706716,-0.0399329886,0.1791331172,-0.4076254964,-0.0293971486,0.0814065933,0.361102283,0.2461704761,-0.4855210483,-0.2073439807,0.1587932259,-0.1088655069,-0.3031988144,-0.1451983899,0.077515237,-0.0084158154,-0.17961739,-0.2245142162,-0.1105176359,0.2781727016,0.0317730308,0.4702418745,-0.0542672537,-0.0470597856,-0.0925379768,-0.2123641819,-0.203853786,0.2598105669,-0.1692332923,0.3838725686,-0.1215264276,-0.1353017837,-0.1618470252,-0.2341883332,0.1250404119,0.0737611726,0.2169513404,0.2232525498,-0.126928404,0.1107358187,0.2387806475,-0.2189143598,-0.0347953998,0.1099753901,0.026437778,-0.0756686404,0.1884724945,-0.0773251802,-0.0992698893,-0.2176902741,-0.0355629064,-0.0619829111,-0.0680253729,-0.0185658894,-0.100947246,0.0252062753,-0.1755307615,0.1599729508,-0.0470886491,0.178590253,-0.1924699694,0.089439936,-0.1249763742,0.2691834867,0.1609803438,-0.1669647098,-0.179566592,0.14058204,-0.1030169353,-0.2629325688,0.1747775376,0.1583021581,0.3005460501,0.0060822605,0.2012646794,-0.0010647125,0.0921211541,0.0558235385,0.3494169414,0.2594133615,0.2155177891,0.0042818142,-0.2074762583,0.1923594624,0.0386597067,0.0949691683,-0.3523138762,0.0304879546,-0.3897178173,0.3262459338,0.1028606221,-0.1323000491,-0.0619796664,0.3417808712,0.1576268822,-0.2634567916,-0.051137533,0.0761926398,0.0479429476,-0.4188563824,0.4802416861,0.0124730654,-0.3166131675,0.0820771679,-0.0641261488,0.2990228236,0.156714946,0.0775551572,-0.1071477756,0.0946886539,-0.2257410139,-0.0112321898,0.3483181,-0.1518348157,-0.2772152722,0.0411917642,0.1846949011,0.070836246,-0.0940926746,0.2117481381,0.2242880613,-0.088944003,0.230582729,0.3381913304,-0.1150586158,-0.0823141411,-0.166919142,-0.103600435,0.2578800321,0.1245642677,-0.0462001152,-0.1519450694,0.2800815701,-0.3861431181,0.2009379119,-0.2013785094,0.3344907165,0.2194981426,0.263907522,0.2855584919,-0.0182075873,-0.1426215321,-0.1639259756,-0.0198118072,-0.1903655976,-0.0073357313,0.1801940948,-0.2361218929,-0.1505279839,-0.1746350527,0.0857909694,-0.3688027263,0.3643602431,-0.143016845,0.0284695011,-0.4876209199,-0.0113339555,-0.145951733,0.1322830915,0.0187699851,-0.1852544546,0.0301892981,0.1393851638,-0.2906778157,-0.1264595985,0.2679728866,-0.0552551448,-0.1496492177,0.0936707705,0.3210107982,0.0813130364,0.1584233195,0.1777062267,0.1764170676,0.1058721989,0.076997228,0.1516087651,0.2718897164,0.0201323647,-0.0853935108,-0.0976314321,0.28532511,0.209307164,0.0269206576,-0.1385434568,0.1989011019,0.2945254445,-0.0012597077,-0.1319825202,-0.0646301806,-0.2141540647,0.0002325894,0.2770747244,-0.312436074,-0.1753575802,0.1000304297,-0.3331007063,-0.0305125508,-0.0974095464,-0.2062395662,-0.3846090436,-0.1452928036,0.0785186142,-0.0137724206,-0.1569926143,0.4487093389,0.1871991456,-0.046175357,0.2047528327,0.1581123471,0.0465566106,0.0721344501,0.0315404572,0.0949202478,0.3714199662,0.0627289414,-0.0108150085,0.101358667,0.0127511006,0.1902557015,-0.1662208587,-0.2549054325,-0.0093709631,-0.1310875416,0.0247405376,-0.4721281826,-0.1677758098,-0.417199254,0.0260447096,0.2850016952,-0.3464559913,0.1147303507,0.1940826029,0.0446637273,0.0734466985,-0.013753878,-0.0212275814,0.2723361254,0.0149814077,0.2983080745,-0.04408326,0.0612418056,0.4653105736,0.1847611368,0.1090756357,-0.1462473273,-0.6015242934,-0.0726515353,0.0547881983,0.2365457863,0.2434007227,0.0315064788,-0.0687150732,-0.1536568403,-0.0579480641,-0.2477636337,-0.0023935768,-0.1426816136,-0.1327076107,-0.0629425645,-0.3509025574,-0.060577523,-0.2882076502,-0.2773609459,0.0394844338,0.1879211217,0.0891084373,-0.178179428,0.2530728281,-0.0417772494,0.1533772051,-0.0730848312,-0.3125744164,-0.2820256352,0.5944551826,-0.3446516395,-0.1548205763,-0.0636408851,-0.3578438461,0.2325512171,0.1982691437,-0.0591976233,-0.3129131794,-0.0276914071,-0.0003507619,-0.1766397357,0.1462289691,0.1281085014,-0.1459636241,-0.2287901491,-0.0527053252,0.0190038048,0.0899171531,0.3732918203,0.1933889538,-0.1765831411,-0.1203416884,-0.0765810534,0.0706177652,-0.0274457764,0.0174370408,0.2258876711,-0.1589358449,0.1872397214,0.0216044635,-0.1034894064,0.0051153959,-0.115891337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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/365","title":"How to augment data ?","comments":"It just feels awkward to use map to augment data. Also it means it's not possible to augment data in a non-batched way.\r\n\r\nBut to be honest I have no idea of a good API...","body":"Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n```","comment_length":35,"text":"How to augment data ? \n Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n``` \n It just feels awkward to use map to augment data. Also it means it's not possible to augment data in a non-batched way.\r\n\r\nBut to be honest I have no idea of a good API...","embeddings":[-0.0686048344,-0.030538993,-0.2990654707,-0.1101847813,0.0624446608,0.335452497,-0.1749581844,0.2741323113,0.1564610451,0.132221967,-0.044367671,0.0903131962,-0.017896561,0.2145764679,0.1571808308,-0.1104836017,0.1782806069,0.1195115671,-0.0964070708,0.2644732594,-0.068970643,-0.4659138918,-0.0256337281,-0.5230019093,-0.0577692166,-0.1176650152,-0.3457253575,-0.2689724863,0.0295523014,-0.3138853014,-0.1750326008,0.0457123145,0.1227496937,0.2257133126,-0.0001022616,-0.1665669084,0.2202338725,0.0800639912,-0.3130410612,0.3663995564,0.0928266197,-0.7476427555,0.0595020093,-0.0558866635,-0.120866172,-0.4913313389,0.1032007635,-0.1203033775,0.3686804175,-0.1997427195,0.2372495234,0.0310002714,0.3845404088,0.006108203,0.0283367317,0.4011588693,0.1855530292,0.0620645024,0.7107824683,0.1219980195,0.361277163,0.0551538989,-0.2042975426,-0.0643414855,0.242851451,0.0068094409,0.1272957474,-0.4292892516,0.0064514223,0.1234805584,0.4534501433,0.2251906097,-0.5979700089,-0.2979202271,0.1898910105,-0.0986823291,-0.4096084833,-0.0384184308,0.1686005443,0.0455293134,-0.2218215913,-0.2193209827,-0.0807439536,0.3997989297,0.1386635005,0.3918269277,0.0202561822,-0.0249934979,-0.1155003384,-0.2687825561,-0.1262351424,0.1236284077,-0.2063962519,0.452824831,-0.1286777258,-0.1373749077,-0.2147859484,-0.2292485088,0.1077811792,0.1561010182,0.1647679955,0.1895729601,-0.2065299004,0.1317075193,0.2149588764,-0.3526519239,0.0053517423,0.1251104176,-0.0039195661,-0.1864408851,0.3491332531,-0.0952046737,-0.0962555408,-0.0527517274,-0.0202529766,-0.1264339536,-0.1354442388,0.048149541,-0.0846445262,-0.0587637015,-0.2746983469,0.1306734085,-0.0200754125,0.0558104403,-0.2272862196,0.0899396613,-0.1803676784,0.2548744678,0.3009600639,-0.2191844583,-0.0647228807,0.1165493801,-0.0582758598,-0.0933070481,0.1988828778,0.1463552713,0.2114189863,0.0358114168,0.0836604089,-0.1050397009,0.0959555879,0.0261888225,0.2474202663,0.1416900605,0.2138693482,0.0465458594,-0.1783533096,0.1664629728,-0.0362404473,0.122170113,-0.4160352647,0.0128166247,-0.389531672,0.2586481869,0.1247188225,-0.2470235825,-0.2244285196,0.3998591006,0.0999816284,-0.294292748,0.0754973292,0.1829731762,0.1183260307,-0.3427895308,0.480463326,0.0219706092,-0.3363804221,0.0242480338,-0.1549204141,0.3433930576,0.1077004746,-0.0038099964,0.0000533018,0.198673889,-0.1652544141,-0.158856675,0.3236880004,-0.2099808455,-0.2859991491,-0.0238593593,0.199710682,0.0716654137,-0.1360217929,0.3386332691,0.2250989527,-0.0124285584,0.2704179883,0.4746152759,-0.1091526896,-0.1832757294,0.0216448363,-0.1854127795,0.2906926274,0.1925904304,-0.1357132345,-0.1341420859,0.3120118082,-0.510428369,0.1714842469,-0.2035457343,0.3540636897,0.1331249177,0.2125593871,0.395483762,-0.0858008265,-0.2248328626,-0.2453152537,-0.0238486901,-0.0882091299,-0.1936911196,0.1417811513,-0.2581710815,0.0926206112,-0.1290224344,0.1425096691,-0.3532492816,0.2598412633,-0.2204664499,0.0691283643,-0.5130602121,-0.0564298145,-0.1649242789,0.2361110747,0.066379644,-0.1898200512,-0.0410508253,0.3220966458,-0.3198963106,-0.2195447236,0.3375580609,-0.1587689668,-0.149061799,0.1133331582,0.2951254845,0.0626491159,0.2453920841,0.263964355,0.3063476682,0.0540742464,0.1400565803,0.0641869828,0.3234606385,-0.0238998681,-0.1454495341,-0.082139045,0.1868114322,0.1285758764,0.1723138243,-0.2582692802,0.2777769268,0.3268328905,0.030629823,-0.123232834,0.0002799981,-0.2246222049,-0.0325745121,0.3339380622,-0.2960822284,-0.0950020179,0.0237667747,-0.5188763738,-0.1817835569,-0.0920926556,-0.1316090822,-0.4617499113,-0.1724644899,0.0933030322,-0.0977834538,-0.1816693693,0.3911105096,0.1194313243,-0.0705558211,0.2989989221,0.1878963858,0.0435693376,0.1824368387,-0.0562934503,-0.0972770974,0.3971741796,0.222453028,0.2089003623,0.0221775714,0.0124482121,0.1462172121,-0.1039224342,-0.1517767012,-0.1404375583,-0.2071709782,0.0644374415,-0.6927348971,-0.1695429385,-0.4011876881,-0.0822533667,0.2475290596,-0.2263851762,0.0246047787,0.1577132791,0.1307263076,0.0276360642,-0.2404650599,-0.0117862448,0.237435326,0.0463207103,0.2038218826,0.00970532,0.0305844638,0.5850212574,0.1105990484,0.2222079486,-0.1531336606,-0.4871169627,-0.1613034755,0.122722283,0.2028142959,0.1914292276,-0.0455105864,-0.0616297498,-0.1874715239,-0.1129961312,-0.1646448076,0.1367574632,-0.156336844,-0.1317685843,-0.0678249002,-0.2224403918,0.1538297385,-0.4205823541,-0.232258141,0.0845218375,0.1622949392,0.0418925881,-0.2198492438,0.2272851616,0.0155684538,0.0743990764,-0.0559060834,-0.1184881479,-0.1790708005,0.5194659829,-0.263531357,0.0046393126,-0.0111848051,-0.3212347329,0.1550671607,0.1527661383,-0.0102336062,-0.2706620693,-0.0787213594,0.0689047128,-0.1759991795,0.159502551,0.082227923,-0.1964635849,-0.1351796985,0.0817197412,-0.0439850651,-0.0049758386,0.3328840137,0.1656613797,-0.0508646294,-0.1112074777,-0.1176544949,0.0994257778,0.1196711957,-0.1047912538,0.1222100109,-0.2279505879,0.1350808293,-0.0081044687,-0.070765458,-0.1330641508,-0.151509985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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/365","title":"How to augment data ?","comments":"Or for non-batched samples, how about returning a tuple ?\r\n\r\n```python\r\ndef aug(sample):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    return sample, sample\r\n\r\ndataset = dataset.map(aug)\r\n```\r\n\r\nIt feels really natural and easy, but :\r\n\r\n* it means the behavior with batched data is different\r\n* I don't know how doable it is backend-wise\r\n\r\n@lhoestq ","body":"Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n```","comment_length":60,"text":"How to augment data ? \n Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n``` \n Or for non-batched samples, how about returning a tuple ?\r\n\r\n```python\r\ndef aug(sample):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    return sample, sample\r\n\r\ndataset = dataset.map(aug)\r\n```\r\n\r\nIt feels really natural and easy, but :\r\n\r\n* it means the behavior with batched data is different\r\n* I don't know how doable it is backend-wise\r\n\r\n@lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/365","title":"How to augment data ?","comments":"As we're working with arrow's columnar format we prefer to play with batches that are dictionaries instead of tuples.\r\nIf we have tuple it implies to re-format the data each time we want to write to arrow, which can lower the speed of map for example.\r\n\r\nIt's also a matter of coherence, as we don't want users to be confused whether they have to return dictionaries for some functions and tuples for others when they're doing batches.","body":"Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n```","comment_length":77,"text":"How to augment data ? \n Is there any clean way to augment data ?\r\n\r\nFor now my work-around is to use batched map, like this :\r\n\r\n```python\r\ndef aug(samples):\r\n    # Simply copy the existing data to have x2 amount of data\r\n    for k, v in samples.items():\r\n        samples[k].extend(v)\r\n    return samples\r\n\r\ndataset = dataset.map(aug, batched=True)\r\n``` \n As we're working with arrow's columnar format we prefer to play with batches that are dictionaries instead of tuples.\r\nIf we have tuple it implies to re-format the data each time we want to write to arrow, which can lower the speed of map for example.\r\n\r\nIt's also a matter of coherence, as we don't want users to be confused whether they have to return dictionaries for some functions and tuples for others when they're doing batches.","embeddings":[-0.0449631922,0.0315054543,-0.2623604536,-0.0620328896,0.0712592602,0.1740825176,-0.0435555987,0.2856449783,0.2927056849,0.0688027442,-0.0452285558,0.2766702175,-0.0615255609,0.0991373286,0.0134356795,-0.2535323203,0.1473680437,0.1497779638,-0.3047791719,0.1889687628,-0.1046198532,-0.381380111,-0.0792351738,-0.4454675913,-0.1265061647,-0.2560842037,-0.1376860291,-0.2588270307,-0.0291532669,-0.4197904766,-0.1189498752,-0.1064741984,0.0321356878,0.2190885246,-0.0001070394,-0.0777587593,0.3182469606,0.0758765265,-0.3291758299,0.2881889045,0.0744246617,-0.5883304477,0.1895926893,-0.1483372748,0.0005680347,-0.566341579,-0.0038558543,-0.2213453054,0.4242578149,-0.1464163959,0.2143065035,-0.0009614629,0.3605853021,0.1151259467,0.2499141544,0.3350800276,-0.0361365899,0.052888982,0.6350506544,0.0298000295,0.1589223295,0.2770664394,-0.0203181915,-0.0861726552,0.1291488558,0.1219946519,0.1845043749,-0.2338151336,0.0329687484,0.1512702405,0.4620940983,0.1598397195,-0.5492655039,-0.1295826137,0.0587237403,-0.0840115994,-0.282930553,-0.168948248,0.1015856639,0.0041412404,-0.0054871086,-0.16555278,-0.0878733993,0.4407989681,0.1486133486,0.4273617268,0.0645060316,0.0699813664,-0.0995412394,-0.2775317729,-0.0916133299,0.1496660262,-0.1602429003,0.410500139,-0.0713777617,-0.012885455,-0.2568856776,-0.3334091008,0.0220548455,0.1018839553,0.3761999905,0.1902011037,-0.1630193144,0.0982387736,0.2746863663,-0.2005943656,0.0142447203,0.2476577461,-0.1037527099,-0.2508893311,0.2756493986,-0.0562036149,-0.083276473,-0.0784710497,0.070635438,-0.1746521145,-0.2213665694,0.0792391673,0.004904232,0.0197239909,-0.0175558124,0.0744814202,-0.083247833,0.1453243792,-0.0534715354,0.2024701536,-0.1878627539,0.2492563576,0.303598851,-0.0639066547,-0.0625420734,0.0752420053,-0.2145076841,-0.2098854333,0.0772959292,0.3709008098,0.1855743974,-0.0100021968,0.0702611804,-0.0770587474,0.1091511697,-0.1297928244,0.4284990132,0.0761953667,0.0568466485,0.0317525901,-0.1038799137,0.0815597102,-0.0219879746,0.1219736412,-0.3230205178,-0.0418441221,-0.4821155965,0.1992630512,0.1621777713,-0.1413287818,-0.2607134283,0.2704459131,0.1766418368,-0.3363068998,0.1516435593,0.077542305,0.2024887651,-0.499091953,0.3427675366,0.0446180478,-0.417014271,0.0328524783,-0.1063544825,0.4234547913,0.2125740647,0.1393312961,-0.1623224169,0.0343467146,-0.0578219667,-0.0136518758,0.5873201489,-0.250216186,-0.274402678,-0.0086100046,0.1392573714,0.1536213756,-0.0692370757,0.2453819811,0.156303212,0.0249975994,0.2388848662,0.407787025,-0.0065613319,-0.1438524425,0.0002215149,-0.0887074545,0.1144869179,0.1512439698,-0.2663123906,-0.3306487501,0.304225564,-0.641069293,0.2132044137,-0.2068176419,0.4185775518,0.0880037025,0.2264771312,0.368098855,0.0227470975,-0.0148942368,-0.3838593066,-0.0206501614,-0.0569170974,-0.2303418219,-0.0467187762,-0.2595000863,0.005536763,0.0464920029,0.1018628851,-0.1722168475,0.2130829245,-0.2088260949,0.1028579921,-0.443461448,-0.0221212357,-0.2194159776,0.0620040558,0.0842285678,-0.0593720265,-0.0488721803,0.2604750991,-0.3671447635,-0.2766019702,0.3287090063,-0.2676295042,-0.0874426514,0.0066956072,0.3033412993,0.215750277,0.1685023308,0.1334202737,0.1584985852,-0.0353387333,0.0663862601,0.1497315764,0.2639790773,0.0453885049,-0.1659338921,-0.1654551923,0.2021573782,0.0454257466,0.3024059236,-0.2025537342,0.2945614457,0.353086412,0.0562304556,-0.1930909306,-0.0953233168,-0.2077766806,-0.0979158953,0.3189956248,-0.2392760664,-0.0645780861,0.0109414458,-0.2084864527,0.0258278344,0.0223982967,-0.192753911,-0.5307372212,-0.1057418287,0.186987862,0.0848148316,-0.1719257683,0.3399724662,0.1259012371,-0.2647234797,0.2518343925,0.0908784866,0.0777939558,0.0999108925,0.1562826037,0.0097636748,0.3862086535,0.0888933167,0.0986336917,0.0392765552,0.0426651128,0.0370285958,-0.0083182584,-0.1605688184,0.0001100852,-0.1803583801,0.0516251922,-0.6276811361,-0.0828135237,-0.3152197003,-0.111690335,0.1336806864,-0.3548227251,0.0682942495,0.0428554304,0.0047916509,0.0162837543,-0.3871847689,-0.0655821785,0.0567806736,0.0432574376,0.1481971294,-0.0240253117,-0.0951555669,0.4585327506,0.3018179238,0.276140213,-0.0924377665,-0.4389209747,-0.0779127553,-0.0016337711,-0.0425758101,0.3088806272,-0.0789653137,-0.2523444295,-0.1831150502,-0.01051745,-0.1569566578,0.0649125353,-0.1248631701,-0.0938959643,-0.0743303224,-0.2183416337,-0.0330914892,-0.3234651089,-0.3384042978,0.2372867614,0.2095003873,0.0771850571,-0.3131403029,0.3482193053,0.0356433578,-0.0219961144,-0.127111882,-0.1488686949,0.0672438145,0.5090066195,-0.2755546868,-0.0019017439,-0.0643548071,-0.424572736,0.2866921425,0.3556601405,-0.0719246715,-0.0999897122,-0.1816033125,0.079202868,-0.2332166433,0.2640167773,0.2581160963,0.0931416005,-0.175895229,-0.019752793,0.0001382893,0.1556055844,0.337949723,0.1088867709,-0.0048563862,-0.0421648324,-0.1415073425,0.2414645404,0.0386009589,-0.0504027344,0.0848740339,-0.2094838023,0.0364745297,-0.0294405092,0.0693500862,-0.1476153284,-0.1958067268,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/361","title":"\ud83d\udc1b [Metrics] ROUGE is non-deterministic","comments":"> Hi, can you give a full self-contained example to reproduce this behavior?\r\n\r\nThere is a notebook in the post ;)","body":"If I run the ROUGE metric 2 times, with same predictions \/ references, the scores are slightly different.\r\n\r\nRefer to [this Colab notebook](https:\/\/colab.research.google.com\/drive\/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n\r\nExample of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n\r\n> ['0.3350', '0.1470', '0.2329']\r\n['0.3358', '0.1451', '0.2332']\r\n\r\n---\r\n\r\nWhy ROUGE is not deterministic ?","comment_length":21,"text":"\ud83d\udc1b [Metrics] ROUGE is non-deterministic \n If I run the ROUGE metric 2 times, with same predictions \/ references, the scores are slightly different.\r\n\r\nRefer to [this Colab notebook](https:\/\/colab.research.google.com\/drive\/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n\r\nExample of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n\r\n> ['0.3350', '0.1470', '0.2329']\r\n['0.3358', '0.1451', '0.2332']\r\n\r\n---\r\n\r\nWhy ROUGE is not deterministic ? \n > Hi, can you give a full self-contained example to reproduce this behavior?\r\n\r\nThere is a notebook in the post ;)","embeddings":[-0.0729421377,-0.4319974482,-0.0746613219,0.2472379804,0.1424586624,-0.2991015911,-0.0346640162,-0.3622231781,-0.0423693508,0.4704315066,0.0269525275,0.304450959,0.0302148387,0.0872765556,-0.2722607553,0.0752440989,0.0376889817,0.013303184,0.0488777757,-0.2914239764,0.0159806181,0.099529922,-0.2607537806,-0.1770699918,-0.1143433228,0.0565365963,-0.1556732804,-0.0681025684,-0.3433382809,-0.0870002061,0.0321296379,0.3313088417,-0.1052891612,0.493986696,-0.000117025,-0.4163560271,-0.0219289437,-0.1781865507,0.3075726628,-0.5136871338,-0.2497319132,0.3652491868,0.0798635185,-0.127443403,-0.1293844134,0.1144191846,-0.3657512963,0.01464028,0.4840198755,0.1275172383,0.1668632179,-0.1628429443,-0.3187301457,-0.3868788481,0.0139950886,0.059260942,-0.1181055233,0.4923393726,-0.0330632441,0.1233153045,-0.0663546845,0.0778925791,0.174616307,0.3484489322,0.4598203599,-0.1589015424,0.2443424612,-0.062895298,-0.0225948077,0.3563770354,0.2740356624,-0.1160635427,-0.1012605578,0.2822776139,0.2155551463,-0.3651972115,-0.0931866467,-0.073911868,0.1439158469,-0.1461345404,-0.0108550582,0.3827982545,-0.0761184171,-0.178085044,0.2129086852,-0.0751299411,-0.0184812434,0.0697376803,0.1373713762,0.3848738372,-0.1237272248,0.1314071715,-0.4110630751,-0.152568385,-0.5036755204,-0.0680446923,-0.0418667905,0.4301230311,0.0287697762,0.3245633841,0.5289825797,-0.1465084553,-0.1225256547,0.0420772284,-0.0674977675,0.3009240329,-0.1637224704,0.0034658327,0.0948855877,-0.0217084475,-0.161670506,0.0056091971,0.3751334846,-0.5000551343,0.2849849463,0.2503116727,-0.1803478599,-0.3356368542,-0.6347414851,-0.0664335564,-0.4162109792,-0.2036540061,-0.0150857586,0.2462238371,-0.271269381,0.0718201175,-0.2622617781,0.2142347842,-0.1250938922,-0.0586309284,-0.2955050468,0.0800914764,-0.5461654067,-0.0126219122,-0.3351665735,0.1911013871,0.1781329662,0.4183347821,0.0020132307,-0.3315204084,0.0598278269,-0.4487573206,0.072365433,-0.0718562976,-0.0791169256,0.0141067971,0.0821315646,-0.1971664429,-0.1339937598,-0.0057607917,-0.1412930787,0.2725314498,0.4450811148,0.0656629279,-0.0854326189,0.1210341156,-0.1398105472,0.1766982973,-0.317086935,-0.0083278306,0.2046132982,-0.1841770709,-0.3165988624,-0.3004755378,-0.001530298,0.2072269619,-0.0665686652,0.1484946162,0.6132826209,0.1436503977,0.2077416033,0.5073764324,0.0661428347,0.0879924595,-0.2220107317,-0.571229279,0.4453023374,-0.4446240366,0.0846231282,-0.0251812525,-0.0576002188,0.0062043541,0.1305315346,0.1785040796,-0.2394026369,-0.0235974994,0.1534713209,-0.2068719119,0.0482669435,-0.2135521472,-0.0194636304,0.0297448244,0.3167249858,-0.3030980229,0.1502765715,0.2283768803,-0.0114703728,-0.1718247235,0.3134987354,0.2069143802,-0.3694640398,0.0516028255,0.1938422918,0.3044385016,-0.0575993247,-0.2001896203,0.1571059376,0.1491965353,-0.5066497922,-0.1957545131,0.5925680995,-0.2029515654,-0.2326534986,-0.1298025697,0.0167076644,-0.2094645947,0.1213448197,0.3131524324,0.244681716,0.2755913138,0.0435582101,0.2283722609,-0.178737551,-0.1511544585,-0.1725236475,-0.1051983461,0.0018531756,-0.1402863562,0.0833922029,0.2186939716,0.3086818457,0.0139424242,-0.1252151877,0.3100949526,0.5252616405,-0.0263115745,0.1189501062,0.2512631714,0.1599371433,0.2055658996,-0.115612857,0.4238214493,0.1592493653,-0.1148899123,0.0598702095,0.3031254411,-0.4730485082,0.0715667158,-0.0767103732,-0.0230605267,0.0747991577,0.100366883,-0.1731087863,-0.0771300048,0.3988668025,0.0202211291,0.3070412874,0.0335895941,-0.4082301259,-0.1613553315,0.1065685079,0.2213362008,0.03647089,-0.1656696498,0.1028542891,0.0469974875,-0.0295289867,0.174823001,0.2265623212,0.0465116277,-0.0839708596,-0.0033859268,-0.3627881706,-0.3238306344,-0.2000466585,-0.1123357266,0.2540789247,0.356498301,0.2895348668,0.2467264384,-0.2504338324,-0.4256802499,-0.089273572,0.0223558098,0.0306770355,0.320001781,-0.3778673708,0.3741837442,-0.0508995801,-0.3276847005,-0.0824532956,-0.0896395445,-0.0475592837,0.330388993,0.3002762794,-0.1265237629,0.5133865476,0.1238656566,-0.0614105873,0.2800595164,-0.0947363824,-0.1490516961,0.0418749265,0.0614753775,-0.1346155852,0.0218229443,0.2588360906,-0.281209141,-0.0097511169,0.0195135772,-0.2017592639,-0.0430031568,0.1012569368,0.2257147729,0.0627168268,-0.4950441718,-0.2061870396,0.4547218382,0.3494848311,-0.1335418522,-0.1433508545,0.0927427411,-0.1561851501,0.1136120483,0.064965032,0.1115123779,0.0507349037,0.0077125579,0.4770751894,0.0406520516,-0.0323164463,0.3786587715,0.0874840021,-0.0692563355,0.1745936424,0.3019635379,-0.3619597554,-0.1703048944,0.1534349918,-0.3342146575,-0.3372672498,-0.0037971735,0.2548951507,0.0573144667,0.0719000846,-0.1600867659,-0.561626792,-0.0084271198,-0.1259204745,-0.2945589721,-0.2496599108,0.5192809105,-0.2608339489,-0.1672192514,-0.0561702922,0.1818184108,0.1151712909,0.0744687691,0.3241333365,-0.2724435329,0.14256455,-0.1437831819,0.0398101844,0.4241265953,-0.1063037962,0.0679801106,-0.0478787459,0.2415934354,-0.4286982417,0.0777276382,0.335377574,0.0047692019,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/361","title":"\ud83d\udc1b [Metrics] ROUGE is non-deterministic","comments":"> If I run the ROUGE metric 2 times, with same predictions \/ references, the scores are slightly different.\r\n> \r\n> Refer to [this Colab notebook](https:\/\/colab.research.google.com\/drive\/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n> \r\n> Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n> \r\n> > ['0.3350', '0.1470', '0.2329']\r\n> > ['0.3358', '0.1451', '0.2332']\r\n> \r\n> Why ROUGE is not deterministic ?\r\n\r\nThis is because of rouge's `BootstrapAggregator` that uses sampling to get confidence intervals (low, mid, high).\r\nYou can get deterministic scores per sentence pair by using\r\n```python\r\nscore = rouge.compute(rouge_types=[\"rouge1\", \"rouge2\", \"rougeL\"], use_agregator=False)\r\n```\r\nOr you can set numpy's random seed if you still want to use the aggregator.","body":"If I run the ROUGE metric 2 times, with same predictions \/ references, the scores are slightly different.\r\n\r\nRefer to [this Colab notebook](https:\/\/colab.research.google.com\/drive\/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n\r\nExample of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n\r\n> ['0.3350', '0.1470', '0.2329']\r\n['0.3358', '0.1451', '0.2332']\r\n\r\n---\r\n\r\nWhy ROUGE is not deterministic ?","comment_length":112,"text":"\ud83d\udc1b [Metrics] ROUGE is non-deterministic \n If I run the ROUGE metric 2 times, with same predictions \/ references, the scores are slightly different.\r\n\r\nRefer to [this Colab notebook](https:\/\/colab.research.google.com\/drive\/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n\r\nExample of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n\r\n> ['0.3350', '0.1470', '0.2329']\r\n['0.3358', '0.1451', '0.2332']\r\n\r\n---\r\n\r\nWhy ROUGE is not deterministic ? \n > If I run the ROUGE metric 2 times, with same predictions \/ references, the scores are slightly different.\r\n> \r\n> Refer to [this Colab notebook](https:\/\/colab.research.google.com\/drive\/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n> \r\n> Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n> \r\n> > ['0.3350', '0.1470', '0.2329']\r\n> > ['0.3358', '0.1451', '0.2332']\r\n> \r\n> Why ROUGE is not deterministic ?\r\n\r\nThis is because of rouge's `BootstrapAggregator` that uses sampling to get confidence intervals (low, mid, high).\r\nYou can get deterministic scores per sentence pair by using\r\n```python\r\nscore = rouge.compute(rouge_types=[\"rouge1\", \"rouge2\", \"rougeL\"], use_agregator=False)\r\n```\r\nOr you can set numpy's random seed if you still want to use the aggregator.","embeddings":[-0.0629726052,-0.1342073232,-0.0796853825,0.0105740586,0.1754827946,-0.3666586876,0.0312740766,-0.2816157341,-0.3356750607,0.4649936557,-0.1453202069,0.3407977819,-0.0238616895,0.1076028943,-0.289493829,-0.0058361944,-0.1367871761,0.0883074626,0.0505806021,-0.2794827819,-0.0264891218,0.2689063847,-0.1446817368,-0.112200141,-0.2301799059,0.0439043455,-0.1309628934,0.1760581583,-0.4420460463,-0.1366114914,0.0006488205,0.5135985017,-0.1335638762,0.3453351557,-0.0001090062,-0.3026773632,-0.0053829737,-0.2721399665,0.0684326664,-0.5490511656,-0.0850553513,0.1678696126,-0.0363746472,-0.1538967341,-0.230387792,0.1567343622,-0.132400021,-0.2148977518,0.6561849713,0.3610711992,0.2037741095,-0.0275056381,-0.1488342732,-0.211245507,0.1747769415,-0.0872877389,-0.0166684855,0.5573641658,0.0314750075,0.218893528,-0.2464755923,0.1858105808,0.152736187,0.0676839948,0.3688113093,-0.1152091995,0.1754817069,-0.3695171475,-0.1286011636,0.3732657731,0.2760186791,-0.0519507863,-0.4068128765,0.1725014895,0.194771871,-0.2736438513,-0.1944601536,-0.0175777674,-0.0913497135,-0.0652742907,-0.0967804939,0.4438829124,-0.1332540959,-0.1383667886,0.1420723051,0.0766993016,-0.0088885538,-0.0005147148,0.3281321228,0.1229601204,-0.2245833427,0.2116307616,-0.209591195,0.0695717409,-0.4204727709,-0.1357257813,0.1251077354,0.2849909067,0.1111524031,0.4854750931,0.3948625326,-0.1147538722,-0.069045119,0.0454537049,0.0252647009,0.1961714029,0.0500495322,0.0383876227,0.141609937,-0.0029745968,-0.0259299502,0.0993311703,0.3126792312,-0.315680027,0.0414018482,0.2174888849,-0.1523083597,-0.1720807254,-0.5653681159,-0.1114857048,-0.478197515,-0.1255738437,-0.1535402685,0.3803393543,-0.2059172988,0.1545051336,-0.1938217133,0.1907310188,-0.1400554031,-0.1622923762,-0.2599925399,0.1628428847,-0.2660717368,-0.0745896026,-0.1121097133,0.0864350796,0.1294143945,0.1545472443,0.1475837678,-0.2458141446,0.0382168405,-0.4583488107,-0.082585834,-0.1328946948,-0.0913568065,-0.0753936544,0.0561134219,-0.2839280069,-0.1321660876,-0.12134251,-0.1262935251,0.1846183389,0.3253518939,0.1625862718,0.0098106805,-0.0861550793,0.0743729025,0.2732861042,-0.3147985637,0.1554822028,0.1502420306,-0.025994584,-0.3226451874,-0.2363419086,0.0831082612,0.2124174386,-0.002733697,-0.1042993292,0.583800137,0.1370000392,0.1727111787,0.5475632548,0.1629826874,0.1839769036,-0.0027143301,-0.3590961993,0.4775166214,-0.4854388237,0.0683306158,0.0602491684,-0.0288142804,0.1495360583,0.2133648396,0.1030385718,-0.0766566545,-0.0438036285,0.2398133576,0.1870018542,0.1334643811,-0.2636555731,-0.0209828485,0.0908618793,0.2314932048,-0.1147617996,0.0626321137,0.046259813,-0.2069050819,-0.3015894294,0.3498267829,-0.0088291103,-0.3083656728,0.2478799373,0.0876596794,0.1918273717,0.0869458988,-0.1816856414,0.2812576592,0.1035984084,-0.8122193217,0.2431687415,0.2177292556,-0.2853504419,-0.451205045,-0.130316183,0.1445597708,-0.3410880268,0.1980885714,0.1725724339,0.2733030021,0.2643563747,-0.0477089435,0.3236653805,-0.1935337782,-0.0180978086,-0.2536085844,-0.198676005,-0.050671231,-0.0811391324,0.2211104631,0.44662112,0.0897334665,0.0987754241,-0.0810163692,0.4132423699,0.4271882772,-0.0247583576,-0.0330941342,-0.0327458382,0.0773893893,0.1873423755,-0.218270883,0.5565975904,0.225764662,-0.0682012364,-0.1123133674,0.3855130076,-0.4762592614,0.0049159378,0.0653227419,0.061057847,0.1700087488,0.1440412253,-0.321056217,-0.0902716517,0.3082022369,-0.0289418381,0.6414523721,0.2357598692,-0.2955445051,-0.1263951808,0.0865039602,0.1140249968,0.1410741061,-0.0957917199,0.0867771804,-0.0590308756,-0.0750466809,0.0270542186,-0.0322183296,0.211177811,-0.0056918808,-0.0626981854,-0.2837332189,-0.2822376192,-0.0775493383,-0.138440147,0.288238734,0.1391821504,0.3164981306,0.1880495399,-0.2739830017,-0.5686125755,-0.3180910647,-0.036398001,-0.1358696967,0.1271980405,-0.2475135028,-0.0630130917,0.0696974322,-0.2970941067,-0.0642220899,-0.2096978575,0.1557428986,0.0802705437,0.0693588182,-0.0306591969,0.5392485261,0.2734598219,0.011913632,0.2975368202,-0.1017268375,-0.2644327879,0.0604698844,0.2002763003,-0.1333313137,-0.0249790568,0.1201741472,-0.4248000383,-0.0719186813,0.1428778619,-0.1266681552,0.0757164732,-0.0981830135,0.2909098566,0.1341505945,-0.3271768987,-0.3285121918,0.3417824507,0.2961697876,-0.0150603671,-0.1789551675,0.1072572097,0.0355433114,0.0203547925,0.0356846638,-0.119449228,-0.2463433295,-0.1701472104,0.5418916345,0.242584601,-0.0166938938,0.4519591928,0.0108007621,-0.0595282167,0.102975361,0.4948757589,-0.2613433599,-0.2291278839,0.3377356231,-0.2354305536,-0.4837260246,-0.2980839908,0.088767685,-0.0603663549,0.0093466165,0.1168930605,-0.3563518822,-0.0826548263,-0.0415779985,-0.2018310726,-0.0710330829,0.4534035325,-0.172801733,-0.2120850384,-0.0440846831,0.3471273482,0.1045517921,0.1094752923,0.1872497201,-0.2009674907,0.0722362697,0.0548028834,0.1274549663,0.4970481992,0.0237850584,0.0555972345,-0.0792819187,0.2268170714,-0.2797750235,0.052875489,0.2462792993,0.07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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/361","title":"\ud83d\udc1b [Metrics] ROUGE is non-deterministic","comments":"Now if you re-run the notebook, the two printed results are the same @colanim\r\n```\r\n['0.3356', '0.1466', '0.2318']\r\n['0.3356', '0.1466', '0.2318']\r\n```\r\nHowever across sessions, the results may change (as numpy's random seed can be different). You can prevent that by setting your seed:\r\n```python\r\nrouge = nlp.load_metric('rouge', seed=42)\r\n```","body":"If I run the ROUGE metric 2 times, with same predictions \/ references, the scores are slightly different.\r\n\r\nRefer to [this Colab notebook](https:\/\/colab.research.google.com\/drive\/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n\r\nExample of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n\r\n> ['0.3350', '0.1470', '0.2329']\r\n['0.3358', '0.1451', '0.2332']\r\n\r\n---\r\n\r\nWhy ROUGE is not deterministic ?","comment_length":50,"text":"\ud83d\udc1b [Metrics] ROUGE is non-deterministic \n If I run the ROUGE metric 2 times, with same predictions \/ references, the scores are slightly different.\r\n\r\nRefer to [this Colab notebook](https:\/\/colab.research.google.com\/drive\/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n\r\nExample of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n\r\n> ['0.3350', '0.1470', '0.2329']\r\n['0.3358', '0.1451', '0.2332']\r\n\r\n---\r\n\r\nWhy ROUGE is not deterministic ? \n Now if you re-run the notebook, the two printed results are the same @colanim\r\n```\r\n['0.3356', '0.1466', '0.2318']\r\n['0.3356', '0.1466', '0.2318']\r\n```\r\nHowever across sessions, the results may change (as numpy's random seed can be different). You can prevent that by setting your seed:\r\n```python\r\nrouge = nlp.load_metric('rouge', seed=42)\r\n```","embeddings":[-0.1073278859,-0.2159305811,-0.0668013096,0.1431299895,0.182839334,-0.3188183606,-0.136663571,-0.1566527337,-0.0810187757,0.394208163,-0.0958697796,0.57323879,0.0757611692,-0.0353061259,-0.004969799,0.0457197651,-0.082976751,0.1978804171,-0.0148286335,-0.3589980304,-0.2548868954,0.1192239076,-0.1512854099,0.034798637,-0.1317696273,-0.0622886382,0.0263473354,0.1554979384,-0.4091148674,-0.2266361415,0.0821629763,0.2336032242,-0.1760028303,0.2849853337,-0.0001076949,-0.2813829482,0.1078296527,-0.116991885,0.0769580379,-0.4047334492,-0.0405928195,-0.0956251547,0.1767806113,-0.2945886552,-0.097563915,0.136918813,-0.0897162333,0.0338977352,0.4158981442,0.2269689739,0.2249114364,0.0455720685,-0.2391411811,-0.1621117443,0.0426181145,-0.0929484665,-0.0321005546,0.5454743505,-0.1822020411,-0.0067190747,-0.1354897916,0.1270417869,0.1383430809,0.2235369384,0.4007498324,0.0411152132,0.3929459453,-0.135845989,-0.2343785465,0.2525841892,-0.0129311113,-0.0997708142,-0.1324526966,0.0841452777,0.1587257683,-0.6156039834,-0.1732088774,0.044013951,-0.1350554824,-0.2012695372,-0.0508786775,0.3788500428,-0.0968494341,0.1117188707,-0.0043131849,0.1680879146,0.027076317,0.1550635397,0.1478287429,0.2116188109,-0.1319663823,0.1222030297,-0.2151378393,-0.0598680042,-0.3910151124,-0.0947208554,0.1377711445,0.351981163,0.0234441087,0.1232677326,0.5981099606,0.0189317055,-0.1684436947,0.1034671888,-0.1521719992,0.3907270133,-0.0975617915,0.020345198,0.2817369103,0.037295226,-0.040691454,0.0754893124,0.5044953227,-0.4349454939,0.205779776,0.2119951993,-0.1122406051,-0.2276438624,-0.5611451864,-0.0430729054,-0.2027511895,-0.019362092,-0.1004149541,0.331977874,-0.3697909117,0.1567452401,-0.0824483782,0.1941112578,-0.2658010125,-0.0755543262,-0.3253019154,0.0633458197,-0.5507808924,-0.0057863784,-0.1304560006,0.2926286161,0.2511183321,0.2495184094,0.0323722661,-0.12549375,0.07507395,-0.3315087259,0.0869627148,-0.0499799624,-0.1087681577,-0.0286275763,0.1192320287,-0.2189495564,-0.196575284,-0.0809318274,-0.05959085,0.2353450209,0.3346344829,0.1933609396,-0.1109464914,0.0923801437,-0.0715812221,0.2088449001,-0.3361066282,-0.0010506114,0.2094528973,-0.2351088524,-0.3856660128,-0.2787617743,0.0916537717,0.1444386244,0.1349799186,-0.0671066642,0.4620255828,0.0558448583,0.144136712,0.4726661146,0.1237938255,0.0034985109,-0.060925696,-0.4403909147,0.6245052218,-0.414652586,-0.041249264,-0.1303750575,-0.0703398138,0.0291820616,-0.0193940718,0.2022103518,-0.1669088006,0.0568864979,0.4013474584,0.1551738977,0.1170737743,-0.1946797669,-0.2683934867,0.0136913834,0.2815442681,-0.0744164065,0.1742078066,0.0707921907,-0.0301680341,-0.1171849221,0.371190846,0.1120216623,-0.4049769938,0.0647225007,0.0489668772,0.1148314252,-0.0608992241,-0.2330337018,0.136604473,0.097282283,-0.6623622179,0.105829224,0.402593255,-0.1487614512,-0.3113524616,-0.0709548816,0.0653429404,-0.1784998327,0.2433368862,0.1851489395,0.2427431047,0.3561433554,0.0626441985,0.3224667907,-0.2490847111,-0.0847934559,-0.381008178,-0.0408868678,-0.0289997775,-0.1580854654,0.1339620054,0.4530848563,0.3085568547,0.0720693171,-0.1417773813,0.3126994073,0.3389642537,-0.0263898242,-0.0527405776,0.0680244789,0.1982621104,0.2358064055,-0.0403654985,0.5818987489,0.1609146297,-0.0876307338,0.0840421468,0.4946383536,-0.4532775283,-0.013118078,-0.0674491301,-0.0155320885,0.1388317198,0.0436688177,-0.1320204288,-0.1433892548,0.4456670284,0.1765436679,0.1647564918,0.0733368993,-0.3982080817,-0.073458977,0.2575798333,0.0217039362,0.1302789897,-0.0767992288,0.0549408756,-0.1026630625,0.0229178928,0.0528387204,0.3336082101,0.1653774828,-0.0692492425,0.1218868271,-0.4862632453,-0.4011192918,-0.1301844567,-0.0406682976,0.1257009953,0.2686919868,0.39283517,0.1780779362,-0.3108200431,-0.1725024879,-0.1780834198,0.0028736275,-0.0867993236,0.262774229,-0.2829509079,0.0638921037,-0.1240324751,-0.2499917597,-0.2210532576,-0.3301238716,0.1189463511,0.2607831359,0.1801377535,-0.0213392973,0.4165262878,0.1643574536,-0.1106036603,0.177942425,-0.1152252406,-0.2789510787,-0.0980238542,0.1053920835,-0.2187730819,0.2054293007,0.2601990104,-0.3080379665,0.0203066058,0.1796343774,-0.2534666061,-0.0686162412,-0.0020722789,0.4098419547,0.1068234742,-0.335804671,-0.2656064034,0.3347665071,0.3430611193,-0.2291763425,-0.1546251178,0.1012243256,-0.0897234976,0.0792596042,-0.0167217255,-0.0430627316,-0.2317102551,-0.2127954215,0.4316241145,0.2908910215,0.0083824014,0.5002599955,-0.0831679404,-0.0073336782,0.137941733,0.2961850166,-0.467445761,-0.0331860483,0.2939333618,-0.3487680256,-0.4366020858,-0.2240982056,0.050590992,0.0724688843,0.0108167669,-0.2337208688,-0.57028234,0.0090026958,-0.0026481207,-0.19519642,-0.1662589312,0.4672875404,-0.2651472092,-0.2121557742,0.0052440828,0.0653482154,-0.0029160671,0.2327388972,0.3163963258,-0.3067874312,0.2171624005,-0.1260423213,0.0643994361,0.3522106111,-0.1832674742,0.1160768121,-0.0247520711,0.3117873371,-0.3168749213,0.0364080109,0.2948443294,0.0857056826,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/360","title":"[Feature request] Add dataset.ragged_map() function for many-to-many transformations","comments":"Actually `map(batched=True)` can already change the size of the dataset.\r\nIt can accept examples of length `N` and returns a batch of length `M` (can be null or greater than `N`).\r\n\r\nI'll make that explicit in the doc that I'm currently writing.","body":"`dataset.map()` enables one-to-one transformations. Input one example and output one example. This is helpful for tokenizing and cleaning individual lines.\r\n`dataset.filter()` enables one-to-(one-or-none) transformations. Input one example and output either zero\/one example. This is helpful for removing portions from the dataset.\r\nHowever, some dataset transformations are many-to-many. Consider constructing BERT training examples from a dataset of sentences, where you map `[\"a\", \"b\", \"c\"] -> [\"a[SEP]b\", \"a[SEP]c\", \"b[SEP]c\", \"c[SEP]b\", ...]`\r\n\r\nI propose a more general `ragged_map()` method that takes in a batch of examples of length `N` and return a batch of examples `M`. This is different from the `map(batched=True)` method, which takes examples of length `N` and returns a batch of length `N`, processing individual examples in parallel. I don't have a clear vision of how this would be implemented efficiently and lazily, but would love to hear the community's feedback on this.\r\n\r\nMy specific use case is creating an end-to-end ELECTRA data pipeline. I would like to take the raw WikiText data and generate training examples from this using the `ragged_map()` method, then export to TFRecords and train quickly. This would be a reproducible pipeline with no bash scripts. Currently I'm relying on scripts like https:\/\/github.com\/google-research\/electra\/blob\/master\/build_pretraining_dataset.py, which are less general.\r\n\r\n","comment_length":42,"text":"[Feature request] Add dataset.ragged_map() function for many-to-many transformations \n `dataset.map()` enables one-to-one transformations. Input one example and output one example. This is helpful for tokenizing and cleaning individual lines.\r\n`dataset.filter()` enables one-to-(one-or-none) transformations. Input one example and output either zero\/one example. This is helpful for removing portions from the dataset.\r\nHowever, some dataset transformations are many-to-many. Consider constructing BERT training examples from a dataset of sentences, where you map `[\"a\", \"b\", \"c\"] -> [\"a[SEP]b\", \"a[SEP]c\", \"b[SEP]c\", \"c[SEP]b\", ...]`\r\n\r\nI propose a more general `ragged_map()` method that takes in a batch of examples of length `N` and return a batch of examples `M`. This is different from the `map(batched=True)` method, which takes examples of length `N` and returns a batch of length `N`, processing individual examples in parallel. I don't have a clear vision of how this would be implemented efficiently and lazily, but would love to hear the community's feedback on this.\r\n\r\nMy specific use case is creating an end-to-end ELECTRA data pipeline. I would like to take the raw WikiText data and generate training examples from this using the `ragged_map()` method, then export to TFRecords and train quickly. This would be a reproducible pipeline with no bash scripts. Currently I'm relying on scripts like https:\/\/github.com\/google-research\/electra\/blob\/master\/build_pretraining_dataset.py, which are less general.\r\n\r\n \n Actually `map(batched=True)` can already change the size of the dataset.\r\nIt can accept examples of length `N` and returns a batch of length `M` (can be null or greater than `N`).\r\n\r\nI'll make that explicit in the doc that I'm currently writing.","embeddings":[-0.5117926598,-0.064943336,0.0467378795,-0.2127768099,-0.0525975712,-0.0972506702,0.2488025129,0.3504051864,-0.2492751777,0.0586383492,0.193172425,0.4199866951,-0.4208355546,-0.1185231209,0.3404656947,-0.2591928542,0.1204847023,-0.071868211,-0.0588385314,-0.0770459324,-0.3946816921,-0.0967775807,-0.251825273,-0.0019553835,-0.0170704108,-0.0298995767,-0.1776050031,-0.3150987327,0.1913707405,-0.026635265,0.1828670651,0.3647984862,-0.0834427327,0.198416695,-0.0001292811,-0.190408498,0.0921218768,-0.0385715701,-0.224856481,-0.1235299334,-0.0980342552,-0.1194367781,-0.1217392385,-0.1869545132,0.1676254869,0.0402919054,0.2579863667,-0.3276602328,0.4382550716,0.1333797723,-0.058807455,0.0316222496,-0.0949261263,0.0337903313,0.509775579,0.4657484889,-0.0328059047,-0.285799861,0.5987883806,-0.2376432717,-0.0481912158,0.3115279675,-0.0568308569,-0.0095256111,0.2963942289,-0.0102193793,-0.1644727737,-0.2860151827,-0.1781579256,0.6859874129,-0.1074637547,-0.3305649459,-0.5636526942,-0.3081480265,0.1056257188,-0.2365754098,-0.4840701222,0.031682048,-0.1258307993,-0.04669315,-0.8150773048,-0.1063044965,-0.3973284662,0.3978858888,-0.162531063,0.1373672634,0.2568991184,0.2220887244,0.1073892564,-0.2615977824,-0.1022015661,-0.1448865384,0.2572753727,0.4562511742,-0.0917671844,-0.3255052567,0.0890367627,-0.0486535728,0.550316155,0.0820575207,-0.00353126,0.1841469854,-0.2206216902,-0.0259346366,-0.028433146,-0.005363218,0.0318878628,0.5232826471,0.1720310748,-0.0608002618,-0.0620665215,0.3738218546,-0.0533123016,-0.1968531162,-0.1862699091,0.2287461907,-0.1539468169,0.1261386871,-0.0783340186,-0.2251610458,-0.2609084845,-0.0259206407,0.0960605145,0.1137732565,-0.0592957027,-0.0202015359,0.1481970996,0.118082881,0.0345744863,-0.0761324316,0.0366137736,-0.1324026436,-0.3662855625,0.1333995163,0.3161870241,0.1634283215,0.035779465,-0.0900668651,0.0493343472,0.3958720863,0.0317810066,-0.1015291959,0.4242266417,-0.1063067615,-0.3029841185,0.0902887806,0.0502145141,0.2103304714,-0.3275679946,0.1787117124,-0.5758726597,-0.5673735738,0.2367747873,0.0075080106,0.088310495,-0.0259559434,-0.1396830231,0.4832127392,0.3983263075,-0.3995802104,-0.0187349375,-0.4631467462,-0.3660453558,-0.0446320809,0.1977967173,0.3899159133,-0.0647996515,-0.1040581763,-0.1227666587,0.2528773546,0.2617071569,0.2549603283,-0.3634381294,0.3214164972,-0.0104309842,0.0024093937,0.5380627513,-0.1634080559,-0.0462382026,0.4684036672,-0.0806689039,0.1257233024,0.0206975732,-0.0552747622,0.630387485,-0.2714833617,0.5482940674,0.495585382,-0.2449007332,0.2318511903,0.1040629372,-0.2603802383,0.1129255816,0.1038386598,0.1581514329,-0.157986775,-0.0919636264,0.1619398892,0.2925562561,-0.3035011292,0.1494297534,-0.2504131198,-0.2802056968,0.0837863535,-0.1690109819,-0.2650311589,-0.4043860137,-0.0532115959,-0.1862901747,-0.1181837693,0.3396584988,-0.3409480751,0.3310184479,-0.0361087322,0.1190072149,0.0143996514,-0.0951026827,0.0769227296,0.0848621503,0.0141277006,-0.143578425,-0.3389590383,-0.0538143329,0.1589299142,0.0072393846,0.0650214627,0.3833913803,0.0107543766,-0.2258309573,0.2299621552,-0.0129864849,-0.2821635306,0.2307552844,0.2818350494,0.1516554207,-0.0679395497,-0.1761771142,0.3548897505,0.4281879961,0.1057404578,0.1138768941,0.2418976873,0.1026602164,-0.2012156546,-0.5654881597,0.3278254271,0.0159291048,0.5668686628,0.2172817886,0.0367184393,-0.2614708543,0.0843571872,-0.39247787,-0.1233014762,0.0642563999,-0.0924340263,0.0619214326,-0.1779865474,-0.1098280549,0.1385016441,0.3341235816,-0.0623130761,0.0160424057,0.4599877596,-0.0083584832,-0.1453412324,-0.1426701397,0.074156642,0.2954389751,0.1929785013,0.0457330942,-0.1567011923,0.0330740884,0.0837342963,0.215299055,0.1570747346,0.0650497451,0.125674516,0.1853105426,0.1826387197,0.0090827541,-0.5682793856,0.1882546246,-0.0079583153,-0.2539595962,0.131059587,-0.0517422855,-0.074928619,-0.3282676935,-0.380648464,0.2121582627,-0.2187453359,0.2884810269,-0.1282152534,-0.296951592,0.2772268057,0.4369253218,0.4191134572,0.0573175102,-0.2148327678,-0.0691943541,-0.2195558697,-0.0100067463,-0.1027998999,0.0953414962,-0.2313009351,0.31728369,0.0528557301,-0.0300484616,-0.403062433,-0.3596819043,-0.0422480777,-0.1454541534,-0.0949648321,0.4797690809,0.0653494895,-0.0845193639,-0.2374870777,0.0989018232,-0.3198269904,0.0672237352,-0.0130907139,0.0672867745,0.0276809167,-0.0035394682,-0.1749517918,-0.0653904378,-0.1445049196,0.285490036,-0.2513404787,0.1826523244,0.0410832167,0.2352617234,-0.0113291265,-0.0986149311,-0.1638326943,0.3204434216,-0.0722717419,0.1216054708,-0.2969004214,-0.0577400401,-0.3848017454,0.0147551904,-0.4168524742,0.8833577037,-0.2570570409,0.06533847,-0.010964782,0.114447847,-0.0286804121,0.1144721881,0.459435761,0.1734045744,0.0842865109,-0.0753118172,0.0018325129,0.0628118664,0.0764713213,-0.0132882753,0.1676447541,0.3107929826,0.1382342875,1.0362778902,0.4065803289,-0.1977274418,-0.1237772778,-0.0955225676,-0.1306502968,-0.0942156538,-0.0773937255,0.2283999175,-0.10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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/360","title":"[Feature request] Add dataset.ragged_map() function for many-to-many transformations","comments":"You're two steps ahead of me :) In my testing, it also works if `M` < `N`.\r\n\r\nA batched map of different length seems to work if you directly overwrite all of the original keys, but fails if any of the original keys are preserved.\r\n\r\nFor example,\r\n```python\r\n# Create a dummy dataset\r\ndset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")[\"test\"]\r\ndset = dset.map(lambda ex: {\"length\": len(ex[\"text\"]), \"foo\": 1})\r\n\r\n# Do an allreduce on each batch, overwriting both keys\r\ndset.map(lambda batch: {\"length\": [sum(batch[\"length\"])], \"foo\": [1]})\r\n# Dataset(schema: {'length': 'int64', 'foo': 'int64'}, num_rows: 5)\r\n\r\n# Now attempt an allreduce without touching the `foo` key\r\ndset.map(lambda batch: {\"length\": [sum(batch[\"length\"])]})\r\n# This fails with the error message below\r\n```\r\n\r\n```bash\r\n  File \"\/path\/to\/nlp\/src\/nlp\/arrow_dataset.py\", line 728, in map\r\n    arrow_schema = pa.Table.from_pydict(test_output).schema\r\n  File \"pyarrow\/io.pxi\", line 1532, in pyarrow.lib.Codec.detect\r\n  File \"pyarrow\/table.pxi\", line 1503, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 1 named foo expected length 1 but got length 2\r\n```\r\n\r\nAdding the `remove_columns=[\"length\", \"foo\"]` argument to `map()` solves the issue. Leaving the above error for future visitors. Perfect, thank you!","body":"`dataset.map()` enables one-to-one transformations. Input one example and output one example. This is helpful for tokenizing and cleaning individual lines.\r\n`dataset.filter()` enables one-to-(one-or-none) transformations. Input one example and output either zero\/one example. This is helpful for removing portions from the dataset.\r\nHowever, some dataset transformations are many-to-many. Consider constructing BERT training examples from a dataset of sentences, where you map `[\"a\", \"b\", \"c\"] -> [\"a[SEP]b\", \"a[SEP]c\", \"b[SEP]c\", \"c[SEP]b\", ...]`\r\n\r\nI propose a more general `ragged_map()` method that takes in a batch of examples of length `N` and return a batch of examples `M`. This is different from the `map(batched=True)` method, which takes examples of length `N` and returns a batch of length `N`, processing individual examples in parallel. I don't have a clear vision of how this would be implemented efficiently and lazily, but would love to hear the community's feedback on this.\r\n\r\nMy specific use case is creating an end-to-end ELECTRA data pipeline. I would like to take the raw WikiText data and generate training examples from this using the `ragged_map()` method, then export to TFRecords and train quickly. This would be a reproducible pipeline with no bash scripts. Currently I'm relying on scripts like https:\/\/github.com\/google-research\/electra\/blob\/master\/build_pretraining_dataset.py, which are less general.\r\n\r\n","comment_length":179,"text":"[Feature request] Add dataset.ragged_map() function for many-to-many transformations \n `dataset.map()` enables one-to-one transformations. Input one example and output one example. This is helpful for tokenizing and cleaning individual lines.\r\n`dataset.filter()` enables one-to-(one-or-none) transformations. Input one example and output either zero\/one example. This is helpful for removing portions from the dataset.\r\nHowever, some dataset transformations are many-to-many. Consider constructing BERT training examples from a dataset of sentences, where you map `[\"a\", \"b\", \"c\"] -> [\"a[SEP]b\", \"a[SEP]c\", \"b[SEP]c\", \"c[SEP]b\", ...]`\r\n\r\nI propose a more general `ragged_map()` method that takes in a batch of examples of length `N` and return a batch of examples `M`. This is different from the `map(batched=True)` method, which takes examples of length `N` and returns a batch of length `N`, processing individual examples in parallel. I don't have a clear vision of how this would be implemented efficiently and lazily, but would love to hear the community's feedback on this.\r\n\r\nMy specific use case is creating an end-to-end ELECTRA data pipeline. I would like to take the raw WikiText data and generate training examples from this using the `ragged_map()` method, then export to TFRecords and train quickly. This would be a reproducible pipeline with no bash scripts. Currently I'm relying on scripts like https:\/\/github.com\/google-research\/electra\/blob\/master\/build_pretraining_dataset.py, which are less general.\r\n\r\n \n You're two steps ahead of me :) In my testing, it also works if `M` < `N`.\r\n\r\nA batched map of different length seems to work if you directly overwrite all of the original keys, but fails if any of the original keys are preserved.\r\n\r\nFor example,\r\n```python\r\n# Create a dummy dataset\r\ndset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")[\"test\"]\r\ndset = dset.map(lambda ex: {\"length\": len(ex[\"text\"]), \"foo\": 1})\r\n\r\n# Do an allreduce on each batch, overwriting both keys\r\ndset.map(lambda batch: {\"length\": [sum(batch[\"length\"])], \"foo\": [1]})\r\n# Dataset(schema: {'length': 'int64', 'foo': 'int64'}, num_rows: 5)\r\n\r\n# Now attempt an allreduce without touching the `foo` key\r\ndset.map(lambda batch: {\"length\": [sum(batch[\"length\"])]})\r\n# This fails with the error message below\r\n```\r\n\r\n```bash\r\n  File \"\/path\/to\/nlp\/src\/nlp\/arrow_dataset.py\", line 728, in map\r\n    arrow_schema = pa.Table.from_pydict(test_output).schema\r\n  File \"pyarrow\/io.pxi\", line 1532, in pyarrow.lib.Codec.detect\r\n  File \"pyarrow\/table.pxi\", line 1503, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 1 named foo expected length 1 but got length 2\r\n```\r\n\r\nAdding the `remove_columns=[\"length\", \"foo\"]` argument to `map()` solves the issue. Leaving the above error for future visitors. Perfect, thank you!","embeddings":[-0.5140849352,-0.0638845786,0.0467357934,-0.2271822691,-0.0690640658,-0.0551058464,0.2775110304,0.3193071485,-0.2335919738,0.0517762713,0.1853372306,0.433014214,-0.3891707063,-0.156760633,0.3206688166,-0.2123526037,0.1380255073,-0.0705325082,-0.0471089743,-0.0676657781,-0.4131712615,-0.0813822672,-0.3076458871,-0.0193038564,0.0158393364,-0.0517234616,-0.1688470989,-0.3216543794,0.1985131055,-0.0572382286,0.1546299011,0.3466903567,-0.0777745917,0.2024631649,-0.0001282075,-0.183033362,0.0852343738,-0.0660537779,-0.2778266966,-0.1043357328,-0.1222465485,-0.0921766236,-0.1874970943,-0.1921699494,0.1732089221,0.0139108766,0.2294358611,-0.3430962861,0.4886731803,0.1127876192,-0.0509738103,0.0658145025,-0.0822368413,0.0404130407,0.4733759463,0.4220072329,-0.0335137136,-0.256085813,0.5853219628,-0.223612532,-0.0561208613,0.3167016804,-0.0640675277,-0.0125534339,0.2690361738,0.0069327224,-0.1276357174,-0.278216213,-0.1596962959,0.668779254,-0.1796213984,-0.3169804215,-0.5546558499,-0.3592832386,0.0945755467,-0.2510692477,-0.4691944718,0.0308720991,-0.1524645239,-0.0594953187,-0.848056078,-0.0725239664,-0.3502874374,0.3833610415,-0.1656908244,0.157326296,0.2363447398,0.2193863541,0.1254206747,-0.2635751665,-0.1169262007,-0.1893372387,0.2012728453,0.4865551293,-0.0621958971,-0.3287470937,0.0657368004,-0.0454892628,0.5274835825,0.0178330969,0.0014931947,0.1767750829,-0.1448729336,-0.0563268773,-0.02902597,0.0129228448,0.0860021412,0.5244885683,0.183690235,-0.0710849985,-0.0734400898,0.3761557639,0.001037673,-0.1939128339,-0.1375450641,0.2832209468,-0.1539544463,0.1606362313,-0.0712326467,-0.2117683589,-0.2780438662,-0.0161033422,0.0704892129,0.1073955446,-0.0445260406,-0.1205081493,0.1225223318,0.1043268889,0.0342733189,-0.0758775398,0.0335377976,-0.1791582555,-0.3669870794,0.112219952,0.3516450524,0.1253017336,0.0558127873,-0.0914677531,0.0226247273,0.3612656593,-0.0332889147,-0.0695660934,0.4820078313,-0.1084303185,-0.2738676667,0.1136994064,0.065422304,0.1628779471,-0.3124417365,0.1807008833,-0.5805314183,-0.4811038375,0.3143530786,0.024119895,0.0556711778,-0.0127374465,-0.2111569047,0.4394823909,0.3932487965,-0.3599758148,0.0097447149,-0.4304398894,-0.3189178705,-0.0402075797,0.1983999163,0.4012480378,-0.0779255331,-0.139183864,-0.0653007403,0.2619438171,0.2537104487,0.272292763,-0.3692961335,0.329579711,-0.0330144539,0.014164594,0.5044759512,-0.1357091367,-0.0761539638,0.4683811665,-0.1021965593,0.1658272296,-0.0335429013,-0.0839251727,0.6721920371,-0.3023916185,0.5716998577,0.4612060487,-0.2615791559,0.2165637463,0.0959388763,-0.2506913841,0.057170406,0.0597375743,0.17809847,-0.1654473394,-0.1107605547,0.1469749212,0.2859001458,-0.2964674532,0.1687088162,-0.2055943012,-0.3021275103,0.0792605057,-0.1763586402,-0.2184639126,-0.4042701423,-0.0012166777,-0.2266483456,-0.1566907614,0.2583780885,-0.3116954267,0.347312361,-0.02537046,0.0528371818,-0.0055875718,-0.0706905425,0.0335752405,0.122445479,0.0577409007,-0.1151438132,-0.3456280529,0.0082599223,0.1561136544,0.0033235655,0.050349161,0.3568118215,0.0058248593,-0.2318897247,0.2177124023,0.0298403669,-0.3160478473,0.2320103347,0.2861753106,0.1013383418,-0.0644035041,-0.2576484382,0.3316794634,0.4543642104,0.1182983294,0.0973233506,0.2350211143,0.1191035733,-0.214691028,-0.5587961078,0.3671116829,0.0026506779,0.5796784759,0.1737111509,0.0429742821,-0.2740180194,0.0838133618,-0.4269851744,-0.1542911083,0.0699479356,-0.1255001128,0.0179762356,-0.1776826978,-0.0578427464,0.1229690686,0.2958962619,-0.086525254,0.0253415611,0.3899826109,-0.0557599403,-0.1243669018,-0.162128076,0.0794301555,0.2992251515,0.1775403321,0.0780121312,-0.170812428,0.0283878185,0.0630374551,0.2288245857,0.1442825347,0.0305295512,0.156362623,0.1670929193,0.17812711,-0.0295912512,-0.507416904,0.2409530729,-0.0365322381,-0.2441274673,0.1146547943,-0.0737815127,-0.0787590295,-0.3204546273,-0.3741789162,0.2363279462,-0.2455091327,0.2917232513,-0.0937588289,-0.2794338763,0.3288775086,0.4729547799,0.373539567,0.0670037195,-0.2365320325,-0.0940204114,-0.2538489699,-0.0075246468,-0.0984296054,0.0669919476,-0.2963275015,0.3102849424,0.0491142683,-0.0590434112,-0.4437866807,-0.3047181368,-0.031240372,-0.1708153635,-0.0816821158,0.4561350942,0.0946931466,-0.1012774557,-0.2439613044,0.1165831238,-0.2921503186,0.033107996,0.0019354714,0.0883976817,-0.0165858939,-0.0104008699,-0.1024866402,-0.0310778525,-0.1095343307,0.2773324549,-0.3038092554,0.1510036737,0.1006516367,0.2372594923,-0.040225368,-0.1226488426,-0.217658788,0.2773085833,-0.0570498444,0.1644926518,-0.3148436844,-0.0804291517,-0.3558786511,0.0080240862,-0.4721953869,0.8751971126,-0.2329043746,0.0979556218,0.0061029769,0.1651857793,0.0104060406,0.1134646833,0.4450578392,0.1565902978,0.0744688809,-0.0869632214,0.0531915091,0.0861222893,0.0463439375,0.0251018163,0.1552979201,0.3127292395,0.1483407021,1.0749411583,0.4066047072,-0.2169663608,-0.1586939394,-0.1032632589,-0.1172735468,-0.1119469106,-0.1229794025,0.1862343848,-0.108673803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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/359","title":"ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures","comments":"Hi, it depends on what it is in your `dataset_builder.py` file. Can you share it?\r\n\r\nIf you are just loading `json` files, you can also directly use the `json` script (which will find the schema\/features from your JSON structure):\r\n\r\n```python\r\nfrom nlp import load_dataset\r\nds = load_dataset(\"json\", data_files=rel_datafiles)\r\n```","body":"I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-23-9aecfbee53bd> in <module>\r\n     55 from nlp import load_dataset\r\n     56 \r\n---> 57 ds = load_dataset(\"..\/text2struct\/model\/dataset_builder.py\", data_files=rel_datafiles)\r\n     58 \r\n     59 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\nIf I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well. ","comment_length":49,"text":"ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures \n I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-23-9aecfbee53bd> in <module>\r\n     55 from nlp import load_dataset\r\n     56 \r\n---> 57 ds = load_dataset(\"..\/text2struct\/model\/dataset_builder.py\", data_files=rel_datafiles)\r\n     58 \r\n     59 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\nIf I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well.  \n Hi, it depends on what it is in your `dataset_builder.py` file. Can you share it?\r\n\r\nIf you are just loading `json` files, you can also directly use the `json` script (which will find the schema\/features from your JSON structure):\r\n\r\n```python\r\nfrom nlp import load_dataset\r\nds = load_dataset(\"json\", data_files=rel_datafiles)\r\n```","embeddings":[-0.3260453045,0.3610112369,-0.0600554347,0.5486999154,0.0407031141,-0.1400738508,0.3431124687,0.4284070134,0.2143322676,-0.1139701083,-0.0436313152,0.3332704902,0.2364399433,0.0587036684,-0.2658620179,-0.1656162292,-0.0196319576,0.2937225401,-0.124623999,0.3482965231,-0.222588554,0.4523977935,-0.1000257656,0.1312664449,0.0139916008,-0.0840229094,0.0551255234,0.3630198836,-0.1661049724,-0.5221460462,0.261677146,-0.297565192,-0.1071128175,0.4265525639,-0.0001121765,0.283685714,0.2796024978,-0.1050383896,-0.367891252,-0.0563196056,0.0219089277,-0.1897962093,0.080716528,-0.1802360266,0.2029777616,-0.2733659148,-0.3524167538,-0.1409696192,0.5280256271,0.103697896,0.1615075171,-0.1449666619,0.3078524768,-0.0087297661,0.0976146981,-0.0128852651,-0.1341842711,0.3496532142,-0.2746606171,0.1606747657,-0.1773140728,0.0339708216,-0.1473898888,-0.0363861844,0.0670264214,0.0353453904,0.1572595835,0.0242215134,0.0062473495,-0.0083963685,0.1443327069,-0.1276887953,-0.0759726912,-0.2398124933,-0.2881832421,-0.309875071,0.2149091959,0.1042353511,-0.0598927923,0.1017578244,0.3165929914,-0.2579512298,-0.1195938438,0.0087799039,0.0931574926,0.3294774592,-0.0843710005,0.0652875751,0.2218031287,-0.0210187193,0.2137233913,-0.0134952012,-0.5275073051,0.069325693,-0.0279790387,0.0091241542,-0.1933581531,-0.626809001,-0.0088782348,0.1031659096,0.5481147766,-0.0142838014,0.1519878954,0.4092028141,0.2554835379,-0.1661252826,0.264388293,-0.0303027369,0.0417524204,0.2514635026,0.1111461669,-0.1434712708,-0.0249572955,-0.1038496122,-0.3425197601,-0.0681292191,0.1332846284,0.0181018002,-0.2350534201,0.0447584912,-0.4381090999,-0.103877835,0.2934699059,0.2456234843,0.2248572111,0.2331757993,0.1868967414,0.2919615805,0.0202706456,-0.2026331127,-0.1242306456,0.105250217,-0.1044747904,0.1108412072,0.2131985873,0.1113535017,0.2996945977,0.128821224,-0.3420161307,-0.3404206634,0.1220161244,-0.2721666992,0.0283037871,0.2411942035,0.0798239335,-0.1810231358,0.1847556829,-0.2773363292,-0.0575832799,0.0720744729,-0.3603481948,-0.354390502,-0.1715970337,0.1504694819,0.0036926903,0.1574962288,-0.4513111711,-0.0426098816,0.2559912503,-0.3391976357,0.1138054729,-0.1639285982,-0.1804626137,-0.1675998271,0.1240322888,0.0814560801,-0.1796029061,-0.0717220083,-0.2841574252,-0.0085382825,0.2089584321,0.1984399259,-0.1502616256,0.5191944242,-0.2032293975,0.5580466986,0.5535882711,-0.1875405759,-0.2054157257,0.5373868942,0.1265777946,-0.035294205,0.1371379495,-0.4054441452,0.2556640804,-0.1492992491,0.0239111912,0.4544432163,0.1228784025,-0.0366672389,-0.0533076935,0.0591707565,0.094570972,-0.0533153489,-0.4166417718,-0.2638634443,-0.1177741587,0.3790189922,0.2506234944,-0.0887018591,-0.0387289897,0.1767893434,0.0014025921,0.0067821331,-0.0009072539,-0.0842046812,-0.5144618154,0.025190603,-0.3117554784,-0.0911139995,-0.3747565448,0.1402051151,-0.1032433212,0.3250381052,-0.0609508604,-0.2853431404,0.2218149751,0.0576820746,-0.2218000144,-0.317121923,-0.0351766907,0.0971565247,-0.0542958602,0.3626121581,-0.099317044,0.4648391902,0.0869142637,-0.2966175377,0.0466390364,0.1641552597,0.0355114825,-0.2589477897,-0.180991292,0.4646167755,0.4503356814,-0.1478167474,-0.3648447692,-0.3587809801,0.0069056614,0.1058970094,0.0639766827,0.0289983246,0.1435213387,-0.3214043975,0.0080417814,0.3127244115,-0.0123363407,0.4519161284,0.0811970308,-0.0262396168,0.1977178007,-0.0217952374,0.1612604111,-0.3399371505,-0.1020602882,-0.0194319971,-0.0510117151,0.1063097194,0.0067979475,0.0667161867,0.3158483207,0.1552186608,0.1025185212,-0.0363587663,-0.0600491017,-0.1964571029,0.1785123348,0.08895538,0.4353640378,0.0364248864,-0.0735301599,0.042567309,0.1256317198,-0.091405414,0.354873836,0.0301692206,0.4449598491,0.3242442012,0.2033975869,-0.0742958412,-0.3939174116,-0.383282125,0.0276446324,0.118215248,-0.2580774128,0.2396309376,-0.2732917964,0.1311361641,-0.0723114461,-0.031521067,-0.1027374715,-0.3991473615,-0.2967226505,0.3699502349,-0.2913790047,0.0173462927,-0.1271987855,0.0827429891,0.1297951043,-0.1101027355,-0.3194954991,-0.1922740489,-0.1088828892,0.0139107835,0.3567505777,0.26393646,-0.1311806589,0.2827697992,-0.0451867245,-0.048971083,-0.3095544875,0.3275258541,-0.2431077212,-0.2856105268,0.2199111581,0.1815671623,0.1619457006,-0.6107788682,0.1853190362,0.0088885864,-0.2933406532,0.2972552478,0.223001197,-0.0029811368,-0.1525841057,-0.1341142058,-0.1398987919,-0.5762497187,0.4581654668,0.1357269585,0.2581652999,-0.0825648159,-0.0690140352,0.0106100226,-0.2777140737,-0.0837056562,0.0277054086,0.1249312013,0.100296624,-0.3619613945,0.0575923212,0.0255032443,0.0281640198,0.5084940195,-0.1329767108,-0.4221979976,0.4498475492,0.1196181849,-0.0777374431,-0.1762279719,-0.3853220046,0.0696588457,0.396043241,-0.0083335303,-0.1137979776,0.0838596746,0.2298501283,-0.0317384899,0.1729091257,-0.1163412631,0.5597530007,-0.0800676197,0.2650151253,0.0567664243,0.0075933975,0.2237437665,0.0908614248,-0.0370974168,-0.2848674953,-0.1019059196,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/359","title":"ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures","comments":"The behavior I'm seeing is from the `json` script. \r\nI hacked this together to overcome the error with the `JSON` dataloader\r\n\r\n```\r\nclass DatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n    BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n    def _info(self):\r\n        return DatasetInfo()\r\n\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\" We handle string, list and dicts in datafiles\r\n        \"\"\"\r\n        if isinstance(self.config.data_files, (str, list, tuple)):\r\n            files = self.config.data_files\r\n            if isinstance(files, str):\r\n                files = [files]\r\n            return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n        splits = []\r\n        for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n            if split_name in self.config.data_files:\r\n                files = self.config.data_files[split_name]\r\n                if isinstance(files, str):\r\n                    files = [files]\r\n                splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n        return splits\r\n\r\n    def _prepare_split(self, split_generator):\r\n        fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n        fpath = os.path.join(self._cache_dir, fname)\r\n\r\n        writer = ArrowWriter(path=fpath)\r\n\r\n        generator = self._generate_tables(**split_generator.gen_kwargs)\r\n        for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n            writer.write_table(table)\r\n        num_examples, num_bytes = writer.finalize()\r\n\r\n        split_generator.split_info.num_examples = num_examples\r\n        split_generator.split_info.num_bytes = num_bytes\r\n        # this is where the error is coming from\r\n        # def parse_schema(schema, schema_dict):\r\n        #     for field in schema:\r\n        #         if pa.types.is_struct(field.type):\r\n        #             schema_dict[field.name] = {}\r\n        #             parse_schema(field.type, schema_dict[field.name])\r\n        #         elif pa.types.is_list(field.type) and pa.types.is_struct(field.type.value_type):\r\n        #             schema_dict[field.name] = {}\r\n        #             parse_schema(field.type.value_type, schema_dict[field.name])\r\n        #         else:\r\n        #             schema_dict[field.name] = Value(str(field.type))\r\n        # \r\n        # parse_schema(writer.schema, features)\r\n        # self.info.features = Features(features)\r\n\r\n    def _generate_tables(self, files):\r\n        for i, file in enumerate(files):\r\n            pa_table = paj.read_json(\r\n                file\r\n            )\r\n            yield i, pa_table\r\n```\r\n\r\nSo I basically just don't populate the `self.info.features` though this doesn't seem to cause any problems in my downstream applications. \r\n\r\nThe other workaround I was doing was to just use pyarrow.json to build a table and then to create the Dataset with its constructor or from_table methods. `load_dataset` has nice split logic, so I'd prefer to use that.\r\n\r\n","body":"I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-23-9aecfbee53bd> in <module>\r\n     55 from nlp import load_dataset\r\n     56 \r\n---> 57 ds = load_dataset(\"..\/text2struct\/model\/dataset_builder.py\", data_files=rel_datafiles)\r\n     58 \r\n     59 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\nIf I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well. ","comment_length":254,"text":"ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures \n I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-23-9aecfbee53bd> in <module>\r\n     55 from nlp import load_dataset\r\n     56 \r\n---> 57 ds = load_dataset(\"..\/text2struct\/model\/dataset_builder.py\", data_files=rel_datafiles)\r\n     58 \r\n     59 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\nIf I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well.  \n The behavior I'm seeing is from the `json` script. \r\nI hacked this together to overcome the error with the `JSON` dataloader\r\n\r\n```\r\nclass DatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n    BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n    def _info(self):\r\n        return DatasetInfo()\r\n\r\n    def _split_generators(self, dl_manager):\r\n        \"\"\" We handle string, list and dicts in datafiles\r\n        \"\"\"\r\n        if isinstance(self.config.data_files, (str, list, tuple)):\r\n            files = self.config.data_files\r\n            if isinstance(files, str):\r\n                files = [files]\r\n            return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n        splits = []\r\n        for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n            if split_name in self.config.data_files:\r\n                files = self.config.data_files[split_name]\r\n                if isinstance(files, str):\r\n                    files = [files]\r\n                splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n        return splits\r\n\r\n    def _prepare_split(self, split_generator):\r\n        fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n        fpath = os.path.join(self._cache_dir, fname)\r\n\r\n        writer = ArrowWriter(path=fpath)\r\n\r\n        generator = self._generate_tables(**split_generator.gen_kwargs)\r\n        for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n            writer.write_table(table)\r\n        num_examples, num_bytes = writer.finalize()\r\n\r\n        split_generator.split_info.num_examples = num_examples\r\n        split_generator.split_info.num_bytes = num_bytes\r\n        # this is where the error is coming from\r\n        # def parse_schema(schema, schema_dict):\r\n        #     for field in schema:\r\n        #         if pa.types.is_struct(field.type):\r\n        #             schema_dict[field.name] = {}\r\n        #             parse_schema(field.type, schema_dict[field.name])\r\n        #         elif pa.types.is_list(field.type) and pa.types.is_struct(field.type.value_type):\r\n        #             schema_dict[field.name] = {}\r\n        #             parse_schema(field.type.value_type, schema_dict[field.name])\r\n        #         else:\r\n        #             schema_dict[field.name] = Value(str(field.type))\r\n        # \r\n        # parse_schema(writer.schema, features)\r\n        # self.info.features = Features(features)\r\n\r\n    def _generate_tables(self, files):\r\n        for i, file in enumerate(files):\r\n            pa_table = paj.read_json(\r\n                file\r\n            )\r\n            yield i, pa_table\r\n```\r\n\r\nSo I basically just don't populate the `self.info.features` though this doesn't seem to cause any problems in my downstream applications. \r\n\r\nThe other workaround I was doing was to just use pyarrow.json to build a table and then to create the Dataset with its constructor or from_table methods. `load_dataset` has nice split logic, so I'd prefer to use that.\r\n\r\n","embeddings":[-0.3260453045,0.3610112369,-0.0600554347,0.5486999154,0.0407031141,-0.1400738508,0.3431124687,0.4284070134,0.2143322676,-0.1139701083,-0.0436313152,0.3332704902,0.2364399433,0.0587036684,-0.2658620179,-0.1656162292,-0.0196319576,0.2937225401,-0.124623999,0.3482965231,-0.222588554,0.4523977935,-0.1000257656,0.1312664449,0.0139916008,-0.0840229094,0.0551255234,0.3630198836,-0.1661049724,-0.5221460462,0.261677146,-0.297565192,-0.1071128175,0.4265525639,-0.0001121765,0.283685714,0.2796024978,-0.1050383896,-0.367891252,-0.0563196056,0.0219089277,-0.1897962093,0.080716528,-0.1802360266,0.2029777616,-0.2733659148,-0.3524167538,-0.1409696192,0.5280256271,0.103697896,0.1615075171,-0.1449666619,0.3078524768,-0.0087297661,0.0976146981,-0.0128852651,-0.1341842711,0.3496532142,-0.2746606171,0.1606747657,-0.1773140728,0.0339708216,-0.1473898888,-0.0363861844,0.0670264214,0.0353453904,0.1572595835,0.0242215134,0.0062473495,-0.0083963685,0.1443327069,-0.1276887953,-0.0759726912,-0.2398124933,-0.2881832421,-0.309875071,0.2149091959,0.1042353511,-0.0598927923,0.1017578244,0.3165929914,-0.2579512298,-0.1195938438,0.0087799039,0.0931574926,0.3294774592,-0.0843710005,0.0652875751,0.2218031287,-0.0210187193,0.2137233913,-0.0134952012,-0.5275073051,0.069325693,-0.0279790387,0.0091241542,-0.1933581531,-0.626809001,-0.0088782348,0.1031659096,0.5481147766,-0.0142838014,0.1519878954,0.4092028141,0.2554835379,-0.1661252826,0.264388293,-0.0303027369,0.0417524204,0.2514635026,0.1111461669,-0.1434712708,-0.0249572955,-0.1038496122,-0.3425197601,-0.0681292191,0.1332846284,0.0181018002,-0.2350534201,0.0447584912,-0.4381090999,-0.103877835,0.2934699059,0.2456234843,0.2248572111,0.2331757993,0.1868967414,0.2919615805,0.0202706456,-0.2026331127,-0.1242306456,0.105250217,-0.1044747904,0.1108412072,0.2131985873,0.1113535017,0.2996945977,0.128821224,-0.3420161307,-0.3404206634,0.1220161244,-0.2721666992,0.0283037871,0.2411942035,0.0798239335,-0.1810231358,0.1847556829,-0.2773363292,-0.0575832799,0.0720744729,-0.3603481948,-0.354390502,-0.1715970337,0.1504694819,0.0036926903,0.1574962288,-0.4513111711,-0.0426098816,0.2559912503,-0.3391976357,0.1138054729,-0.1639285982,-0.1804626137,-0.1675998271,0.1240322888,0.0814560801,-0.1796029061,-0.0717220083,-0.2841574252,-0.0085382825,0.2089584321,0.1984399259,-0.1502616256,0.5191944242,-0.2032293975,0.5580466986,0.5535882711,-0.1875405759,-0.2054157257,0.5373868942,0.1265777946,-0.035294205,0.1371379495,-0.4054441452,0.2556640804,-0.1492992491,0.0239111912,0.4544432163,0.1228784025,-0.0366672389,-0.0533076935,0.0591707565,0.094570972,-0.0533153489,-0.4166417718,-0.2638634443,-0.1177741587,0.3790189922,0.2506234944,-0.0887018591,-0.0387289897,0.1767893434,0.0014025921,0.0067821331,-0.0009072539,-0.0842046812,-0.5144618154,0.025190603,-0.3117554784,-0.0911139995,-0.3747565448,0.1402051151,-0.1032433212,0.3250381052,-0.0609508604,-0.2853431404,0.2218149751,0.0576820746,-0.2218000144,-0.317121923,-0.0351766907,0.0971565247,-0.0542958602,0.3626121581,-0.099317044,0.4648391902,0.0869142637,-0.2966175377,0.0466390364,0.1641552597,0.0355114825,-0.2589477897,-0.180991292,0.4646167755,0.4503356814,-0.1478167474,-0.3648447692,-0.3587809801,0.0069056614,0.1058970094,0.0639766827,0.0289983246,0.1435213387,-0.3214043975,0.0080417814,0.3127244115,-0.0123363407,0.4519161284,0.0811970308,-0.0262396168,0.1977178007,-0.0217952374,0.1612604111,-0.3399371505,-0.1020602882,-0.0194319971,-0.0510117151,0.1063097194,0.0067979475,0.0667161867,0.3158483207,0.1552186608,0.1025185212,-0.0363587663,-0.0600491017,-0.1964571029,0.1785123348,0.08895538,0.4353640378,0.0364248864,-0.0735301599,0.042567309,0.1256317198,-0.091405414,0.354873836,0.0301692206,0.4449598491,0.3242442012,0.2033975869,-0.0742958412,-0.3939174116,-0.383282125,0.0276446324,0.118215248,-0.2580774128,0.2396309376,-0.2732917964,0.1311361641,-0.0723114461,-0.031521067,-0.1027374715,-0.3991473615,-0.2967226505,0.3699502349,-0.2913790047,0.0173462927,-0.1271987855,0.0827429891,0.1297951043,-0.1101027355,-0.3194954991,-0.1922740489,-0.1088828892,0.0139107835,0.3567505777,0.26393646,-0.1311806589,0.2827697992,-0.0451867245,-0.048971083,-0.3095544875,0.3275258541,-0.2431077212,-0.2856105268,0.2199111581,0.1815671623,0.1619457006,-0.6107788682,0.1853190362,0.0088885864,-0.2933406532,0.2972552478,0.223001197,-0.0029811368,-0.1525841057,-0.1341142058,-0.1398987919,-0.5762497187,0.4581654668,0.1357269585,0.2581652999,-0.0825648159,-0.0690140352,0.0106100226,-0.2777140737,-0.0837056562,0.0277054086,0.1249312013,0.100296624,-0.3619613945,0.0575923212,0.0255032443,0.0281640198,0.5084940195,-0.1329767108,-0.4221979976,0.4498475492,0.1196181849,-0.0777374431,-0.1762279719,-0.3853220046,0.0696588457,0.396043241,-0.0083335303,-0.1137979776,0.0838596746,0.2298501283,-0.0317384899,0.1729091257,-0.1163412631,0.5597530007,-0.0800676197,0.2650151253,0.0567664243,0.0075933975,0.2237437665,0.0908614248,-0.0370974168,-0.2848674953,-0.1019059196,-0.1746464074,-0.24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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/359","title":"ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures","comments":"Also noticed that if you for example in a loader script\r\n\r\n```\r\nfrom nlp import ArrowBasedBuilder\r\n\r\nclass MyBuilder(ArrowBasedBuilder):\r\n...\r\n\r\n```\r\nand use that in the subclass, it will be on the module's __dict__ and will be selected before the `MyBuilder` subclass, and it will raise `NotImplementedError` on its `_generate_examples` method... In the code it check for abstract classes but Builder and ArrowBasedBuilder aren't abstract classes, they're regular classes with `@abstract_methods`.","body":"I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-23-9aecfbee53bd> in <module>\r\n     55 from nlp import load_dataset\r\n     56 \r\n---> 57 ds = load_dataset(\"..\/text2struct\/model\/dataset_builder.py\", data_files=rel_datafiles)\r\n     58 \r\n     59 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\nIf I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well. ","comment_length":70,"text":"ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures \n I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-23-9aecfbee53bd> in <module>\r\n     55 from nlp import load_dataset\r\n     56 \r\n---> 57 ds = load_dataset(\"..\/text2struct\/model\/dataset_builder.py\", data_files=rel_datafiles)\r\n     58 \r\n     59 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\nIf I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well.  \n Also noticed that if you for example in a loader script\r\n\r\n```\r\nfrom nlp import ArrowBasedBuilder\r\n\r\nclass MyBuilder(ArrowBasedBuilder):\r\n...\r\n\r\n```\r\nand use that in the subclass, it will be on the module's __dict__ and will be selected before the `MyBuilder` subclass, and it will raise `NotImplementedError` on its `_generate_examples` method... In the code it check for abstract classes but Builder and ArrowBasedBuilder aren't abstract classes, they're regular classes with 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/359","title":"ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures","comments":"Indeed this is part of a more general limitation which is the fact that we should generate and update the `features` from the auto-inferred Arrow schema when they are not provided (also happen when a user change the schema using `map()`, the features should be auto-generated and guessed as much as possible to keep the `features` synced with the underlying Arrow table schema).\r\n\r\nWe will try to solve this soon.","body":"I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-23-9aecfbee53bd> in <module>\r\n     55 from nlp import load_dataset\r\n     56 \r\n---> 57 ds = load_dataset(\"..\/text2struct\/model\/dataset_builder.py\", data_files=rel_datafiles)\r\n     58 \r\n     59 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\nIf I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well. ","comment_length":70,"text":"ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures \n I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-23-9aecfbee53bd> in <module>\r\n     55 from nlp import load_dataset\r\n     56 \r\n---> 57 ds = load_dataset(\"..\/text2struct\/model\/dataset_builder.py\", data_files=rel_datafiles)\r\n     58 \r\n     59 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\r\n```\r\n\r\nIf I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well.  \n Indeed this is part of a more general limitation which is the fact that we should generate and update the `features` from the auto-inferred Arrow schema when they are not provided (also happen when a user change the schema using `map()`, the features should be auto-generated and guessed as much as possible to keep the `features` synced with the underlying Arrow table schema).\r\n\r\nWe will try to solve this soon.","embeddings":[-0.3260453045,0.3610112369,-0.0600554347,0.5486999154,0.0407031141,-0.1400738508,0.3431124687,0.4284070134,0.2143322676,-0.1139701083,-0.0436313152,0.3332704902,0.2364399433,0.0587036684,-0.2658620179,-0.1656162292,-0.0196319576,0.2937225401,-0.124623999,0.3482965231,-0.222588554,0.4523977935,-0.1000257656,0.1312664449,0.0139916008,-0.0840229094,0.0551255234,0.3630198836,-0.1661049724,-0.5221460462,0.261677146,-0.297565192,-0.1071128175,0.4265525639,-0.0001121765,0.283685714,0.2796024978,-0.1050383896,-0.367891252,-0.0563196056,0.0219089277,-0.1897962093,0.080716528,-0.1802360266,0.2029777616,-0.2733659148,-0.3524167538,-0.1409696192,0.5280256271,0.103697896,0.1615075171,-0.1449666619,0.3078524768,-0.0087297661,0.0976146981,-0.0128852651,-0.1341842711,0.3496532142,-0.2746606171,0.1606747657,-0.1773140728,0.0339708216,-0.1473898888,-0.0363861844,0.0670264214,0.0353453904,0.1572595835,0.0242215134,0.0062473495,-0.0083963685,0.1443327069,-0.1276887953,-0.0759726912,-0.2398124933,-0.2881832421,-0.309875071,0.2149091959,0.1042353511,-0.0598927923,0.1017578244,0.3165929914,-0.2579512298,-0.1195938438,0.0087799039,0.0931574926,0.3294774592,-0.0843710005,0.0652875751,0.2218031287,-0.0210187193,0.2137233913,-0.0134952012,-0.5275073051,0.069325693,-0.0279790387,0.0091241542,-0.1933581531,-0.626809001,-0.0088782348,0.1031659096,0.5481147766,-0.0142838014,0.1519878954,0.4092028141,0.2554835379,-0.1661252826,0.264388293,-0.0303027369,0.0417524204,0.2514635026,0.1111461669,-0.1434712708,-0.0249572955,-0.1038496122,-0.3425197601,-0.0681292191,0.1332846284,0.0181018002,-0.2350534201,0.0447584912,-0.4381090999,-0.103877835,0.2934699059,0.2456234843,0.2248572111,0.2331757993,0.1868967414,0.2919615805,0.0202706456,-0.2026331127,-0.1242306456,0.105250217,-0.1044747904,0.1108412072,0.2131985873,0.1113535017,0.2996945977,0.128821224,-0.3420161307,-0.3404206634,0.1220161244,-0.2721666992,0.0283037871,0.2411942035,0.0798239335,-0.1810231358,0.1847556829,-0.2773363292,-0.0575832799,0.0720744729,-0.3603481948,-0.354390502,-0.1715970337,0.1504694819,0.0036926903,0.1574962288,-0.4513111711,-0.0426098816,0.2559912503,-0.3391976357,0.1138054729,-0.1639285982,-0.1804626137,-0.1675998271,0.1240322888,0.0814560801,-0.1796029061,-0.0717220083,-0.2841574252,-0.0085382825,0.2089584321,0.1984399259,-0.1502616256,0.5191944242,-0.2032293975,0.5580466986,0.5535882711,-0.1875405759,-0.2054157257,0.5373868942,0.1265777946,-0.035294205,0.1371379495,-0.4054441452,0.2556640804,-0.1492992491,0.0239111912,0.4544432163,0.1228784025,-0.0366672389,-0.0533076935,0.0591707565,0.094570972,-0.0533153489,-0.4166417718,-0.2638634443,-0.1177741587,0.3790189922,0.2506234944,-0.0887018591,-0.0387289897,0.1767893434,0.0014025921,0.0067821331,-0.0009072539,-0.0842046812,-0.5144618154,0.025190603,-0.3117554784,-0.0911139995,-0.3747565448,0.1402051151,-0.1032433212,0.3250381052,-0.0609508604,-0.2853431404,0.2218149751,0.0576820746,-0.2218000144,-0.317121923,-0.0351766907,0.0971565247,-0.0542958602,0.3626121581,-0.099317044,0.4648391902,0.0869142637,-0.2966175377,0.0466390364,0.1641552597,0.0355114825,-0.2589477897,-0.180991292,0.4646167755,0.4503356814,-0.1478167474,-0.3648447692,-0.3587809801,0.0069056614,0.1058970094,0.0639766827,0.0289983246,0.1435213387,-0.3214043975,0.0080417814,0.3127244115,-0.0123363407,0.4519161284,0.0811970308,-0.0262396168,0.1977178007,-0.0217952374,0.1612604111,-0.3399371505,-0.1020602882,-0.0194319971,-0.0510117151,0.1063097194,0.0067979475,0.0667161867,0.3158483207,0.1552186608,0.1025185212,-0.0363587663,-0.0600491017,-0.1964571029,0.1785123348,0.08895538,0.4353640378,0.0364248864,-0.0735301599,0.042567309,0.1256317198,-0.091405414,0.354873836,0.0301692206,0.4449598491,0.3242442012,0.2033975869,-0.0742958412,-0.3939174116,-0.383282125,0.0276446324,0.118215248,-0.2580774128,0.2396309376,-0.2732917964,0.1311361641,-0.0723114461,-0.031521067,-0.1027374715,-0.3991473615,-0.2967226505,0.3699502349,-0.2913790047,0.0173462927,-0.1271987855,0.0827429891,0.1297951043,-0.1101027355,-0.3194954991,-0.1922740489,-0.1088828892,0.0139107835,0.3567505777,0.26393646,-0.1311806589,0.2827697992,-0.0451867245,-0.048971083,-0.3095544875,0.3275258541,-0.2431077212,-0.2856105268,0.2199111581,0.1815671623,0.1619457006,-0.6107788682,0.1853190362,0.0088885864,-0.2933406532,0.2972552478,0.223001197,-0.0029811368,-0.1525841057,-0.1341142058,-0.1398987919,-0.5762497187,0.4581654668,0.1357269585,0.2581652999,-0.0825648159,-0.0690140352,0.0106100226,-0.2777140737,-0.0837056562,0.0277054086,0.1249312013,0.100296624,-0.3619613945,0.0575923212,0.0255032443,0.0281640198,0.5084940195,-0.1329767108,-0.4221979976,0.4498475492,0.1196181849,-0.0777374431,-0.1762279719,-0.3853220046,0.0696588457,0.396043241,-0.0083335303,-0.1137979776,0.0838596746,0.2298501283,-0.0317384899,0.1729091257,-0.1163412631,0.5597530007,-0.0800676197,0.2650151253,0.0567664243,0.0075933975,0.2237437665,0.0908614248,-0.0370974168,-0.2848674953,-0.1019059196,-0.1746464074,-0.2492518425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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/355","title":"can't load SNLI dataset","comments":"I just added the processed files of `snli` on our google storage, so that when you do `load_dataset` it can download the processed files from there :)\r\n\r\nWe are thinking about having available those processed files for more datasets in the future, because sometimes files aren't available (like for `snli`), or the download speed is too slow, or sometimes the files take time to be processed.","body":"`nlp` seems to load `snli` from some URL based on nlp.stanford.edu. This subdomain is frequently down -- including right now, when I'd like to load `snli` in a Colab notebook, but can't.\r\n\r\nIs there a plan to move these datasets to huggingface servers for a more stable solution?\r\n\r\nBtw, here's the stack trace:\r\n\r\n```\r\nFile \"\/content\/nlp\/src\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/content\/nlp\/src\/nlp\/builder.py\", line 466, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/content\/nlp\/src\/nlp\/datasets\/snli\/e417f6f2e16254938d977a17ed32f3998f5b23e4fcab0f6eb1d28784f23ea60d\/snli.py\", line 76, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(_DATA_URL)\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/download_manager.py\", line 217, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/download_manager.py\", line 156, in download\r\n    lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/py_utils.py\", line 190, in map_nested\r\n    return function(data_struct)\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/download_manager.py\", line 156, in <lambda>\r\n    lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/file_utils.py\", line 198, in cached_path\r\n    local_files_only=download_config.local_files_only,\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/nlp.stanford.edu\/projects\/snli\/snli_1.0.zip\r\n```","comment_length":66,"text":"can't load SNLI dataset \n `nlp` seems to load `snli` from some URL based on nlp.stanford.edu. This subdomain is frequently down -- including right now, when I'd like to load `snli` in a Colab notebook, but can't.\r\n\r\nIs there a plan to move these datasets to huggingface servers for a more stable solution?\r\n\r\nBtw, here's the stack trace:\r\n\r\n```\r\nFile \"\/content\/nlp\/src\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/content\/nlp\/src\/nlp\/builder.py\", line 466, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/content\/nlp\/src\/nlp\/datasets\/snli\/e417f6f2e16254938d977a17ed32f3998f5b23e4fcab0f6eb1d28784f23ea60d\/snli.py\", line 76, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(_DATA_URL)\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/download_manager.py\", line 217, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/download_manager.py\", line 156, in download\r\n    lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/py_utils.py\", line 190, in map_nested\r\n    return function(data_struct)\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/download_manager.py\", line 156, in <lambda>\r\n    lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/file_utils.py\", line 198, in cached_path\r\n    local_files_only=download_config.local_files_only,\r\n  File \"\/content\/nlp\/src\/nlp\/utils\/file_utils.py\", line 356, in get_from_cache\r\n    raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https:\/\/nlp.stanford.edu\/projects\/snli\/snli_1.0.zip\r\n``` \n I just added the processed files of `snli` on our google storage, so that when you do `load_dataset` it can download the processed files from there :)\r\n\r\nWe are thinking about having available those processed files for more datasets in the future, because sometimes files aren't available (like for `snli`), or the download speed is too slow, or sometimes the files take time to be processed.","embeddings":[0.1304482073,-0.0483656712,0.059793774,0.4217781425,0.1687606275,-0.2157386839,0.2670191824,0.0161337201,-0.0244341921,-0.1066373214,-0.3873247504,-0.0008527985,0.1453942955,0.4104743004,0.3283267915,-0.0848518759,0.1194742993,-0.0357170701,0.0139390798,-0.1574850529,-0.348336339,0.3196726143,-0.375736624,0.0969095305,-0.0297649503,0.0196623597,-0.2143009752,0.2622815967,-0.1611686647,-0.1393920928,0.4381097257,0.164498955,0.3262892067,0.0996377394,-0.000117559,0.0295298286,0.1056620181,-0.0870951042,-0.5640883446,0.0936509073,-0.324842751,-0.2369283587,-0.0412270017,-0.0553937703,-0.2117430717,-0.0341277607,0.0878446028,-0.1338448077,0.2620665431,0.2927174568,0.1312162429,0.457020551,-0.5215488076,0.0837669373,0.0226054713,-0.1075958535,-0.1660803854,0.2811186016,0.0594205968,-0.2348731458,0.1584093273,0.1544827074,-0.3054082096,-0.391025722,0.1858433038,0.0251871701,-0.0524713881,-0.0781482533,0.1044324785,0.2801420391,0.069898501,0.1527647376,-0.2861181498,-0.3133838475,0.0973004028,-0.0441063717,0.1898174584,0.1714379638,0.1277593672,0.3028598428,-0.0501641259,-0.2644038498,-0.0007003463,0.2948257923,-0.1543981135,0.6570668221,-0.1417783648,0.1334464103,0.343195796,-0.0286403541,0.0854430571,-0.0099403653,0.0727775246,0.1083649546,-0.3882722855,0.0951081514,-0.1398562491,0.3578723669,0.021769397,0.326841563,0.2466684282,-0.0482897162,0.0075823609,0.1901656985,0.0771290883,0.1908450723,0.0269667581,-0.2320130914,0.5072234869,0.2394815236,0.2719387114,0.0962707847,-0.2195151895,-0.2679294348,-0.5367240906,-0.3278502524,0.0782074779,-0.2093335092,-0.1450171471,-0.1577027738,-0.0719775483,0.1155304983,0.1822850853,0.2834401429,-0.0627139285,0.5076025724,0.1111344695,0.1902718544,-0.2350015044,-0.2010626197,-0.1571724117,0.0150193674,-0.3410459161,0.3710554838,0.1181917563,-0.3645623624,0.1672880203,-0.0471499227,-0.1467894614,0.1659791023,-0.1471507847,0.3010153174,-0.03390342,0.1608228981,-0.0406754129,0.155169934,-0.028171353,-0.1401581466,-0.1304618865,-0.1663203984,-0.1513635218,-0.3299483657,-0.0857278407,0.0310140047,-0.3338066041,-0.2495877594,-0.4519361556,0.0037020426,0.0267700646,-0.1050219312,-0.0672885627,-0.0789620429,-0.238785848,-0.1213366464,0.3064864874,0.5235179067,0.2027140111,-0.3623385131,-0.2355785221,0.0376874134,0.1617934704,0.6364001036,-0.1188412756,-0.4001249969,-0.1824987233,0.1775024086,0.2002283484,-0.1794870049,-0.3256947696,0.2752823234,-0.3613670468,-0.1439986974,0.2223538607,0.1931883544,-0.1639113724,-0.0499501638,0.1892511249,0.6330448389,0.1260435879,-0.0856131613,-0.4507155418,-0.3640879691,0.0047560032,0.2229622453,0.1132772043,-0.0371184796,-0.1291671842,0.230927825,0.2957028747,-0.1445367038,0.0546408296,-0.0628841147,0.228770718,0.2753065228,-0.0180238392,0.1458810717,-0.3736081421,0.1601790786,-0.0156620312,0.4346553385,0.0774695277,0.0404837169,-0.1789983958,-0.0094729289,-0.1319319606,-0.1548575908,-0.0453127883,0.3207741976,0.2329060882,0.1456717849,-0.2976048589,0.2277678549,-0.2371035963,0.2496478856,-0.5744193792,0.5656714439,0.1177590191,0.2276495844,-0.011413021,0.319873482,0.0307091698,-0.1356557757,0.1645552218,0.0994904861,-0.3558295667,0.2359803766,0.4676783979,0.087553747,0.2638019621,-0.2606596351,0.4226095676,-0.0008728617,-0.056670703,0.0165115315,0.137795046,0.5027769804,-0.1210881695,0.4202392697,0.2470627725,0.2384273708,0.4183702469,-0.0620677955,-0.0541550741,-0.0759209841,0.20869039,0.243683219,0.4183528721,0.4291169643,-0.4238772094,0.1012035012,0.0573867261,-0.2955073416,0.0375080407,0.0305925068,-0.3085520267,-0.2286909372,0.3273907304,0.4721651673,0.2052293718,0.0524367541,-0.0489891656,-0.0664653108,-0.1507889926,-0.5673172474,0.3456598818,-0.0326571651,-0.0688784719,-0.0018092771,0.2520260513,-0.0300832093,-0.6293240786,-0.0304291733,-0.0839683637,-0.0329370573,-0.2263214886,0.197623089,-0.3592180908,-0.6340147257,-0.5378745794,0.0821900517,-0.3298075497,-0.5116081834,0.0921253562,0.1828790009,0.0226485208,-0.063473478,-0.0067485287,0.6645886302,-0.4301497042,-0.0604205877,-0.1470168829,-0.5190829635,-0.3770621419,0.0634477362,0.3587060571,0.200772509,0.1039768681,-0.26919505,-0.1576095521,-0.3166330755,-0.4544120729,0.0933533683,-0.0087030036,0.0978781506,-0.0349688865,0.3597619832,0.1173564121,-0.1316226274,0.2207645923,-0.1457942873,0.1616683453,0.3093687594,-0.1411330402,-0.0629481077,0.0991666466,-0.1920919567,-0.3986883461,-0.2858089209,0.158243984,-0.4708935916,-0.0865265802,0.4178456068,-0.1209960356,0.1282298565,-0.0140495216,-0.1653679162,-0.0069052121,-0.1221962199,0.3401512504,-0.0845435411,-0.1688057035,-0.0179712996,0.1193795726,0.2667138278,-0.0978106707,-0.5858650208,-0.0070258765,-0.0959324166,0.0875686035,0.3945233822,-0.062265709,-0.0200650394,-0.1766350716,0.0149214873,0.113628909,-0.1519600898,-0.0434901454,0.1634542197,0.3132613897,0.1599602699,0.2865488827,-0.0055425023,0.4817097783,0.1003508791,-0.1031534821,0.4657756984,0.1412285864,0.1298272312,0.0549871661,-0.4065544307,0.1902965307,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/353","title":"[Dataset requests] New datasets for Text Classification","comments":"- `nlp` has MR! It's called `rotten_tomatoes`\r\n- SST is part of GLUE, or is that just SST-2?\r\n- `nlp` also has `ag_news`, a popular news classification dataset\r\n\r\nI'd also like to see:\r\n- the Yahoo Answers topic classification dataset\r\n- the Kaggle Fake News classification dataset","body":"We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705","comment_length":47,"text":"[Dataset requests] New datasets for Text Classification \n We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705 \n - `nlp` has MR! It's called `rotten_tomatoes`\r\n- SST is part of GLUE, or is that just SST-2?\r\n- `nlp` also has `ag_news`, a popular news classification dataset\r\n\r\nI'd also like to see:\r\n- the Yahoo Answers topic classification dataset\r\n- the Kaggle Fake News classification 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/353","title":"[Dataset requests] New datasets for Text Classification","comments":"Thanks @jxmorris12 for pointing this out. \r\n\r\nIn glue we only have SST-2 maybe we can add separately SST-1.\r\n","body":"We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705","comment_length":18,"text":"[Dataset requests] New datasets for Text Classification \n We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705 \n Thanks @jxmorris12 for pointing this out. \r\n\r\nIn glue we only have SST-2 maybe we can add separately SST-1.\r\n","embeddings":[0.0815415308,0.0239060819,-0.1260785609,0.2537373304,0.2022433132,0.1909445673,0.2313451171,0.1288252771,-0.0196939688,-0.0524679348,-0.2371735871,0.0281864014,-0.0793611482,0.2284713984,0.1158258021,-0.3347227573,0.0327517204,-0.0278218016,-0.2127438188,-0.1855586022,-0.1148792878,0.3360117972,-0.0453988202,-0.2311266661,-0.4425388873,-0.0575625375,-0.2063391656,0.0668693855,-0.2878912389,-0.4556583166,0.0428925753,0.1286984086,0.2520892322,0.2492392361,-0.0001037809,-0.2161204368,0.0535999425,-0.091308713,-0.0742770955,-0.0727216601,-0.1487201601,-0.4849986732,-0.0241378415,-0.1006161571,-0.2236712724,0.2096851468,0.0609343462,-0.2331152707,0.0375484861,0.2961035371,0.2434536368,-0.0123440856,-0.0020559046,-0.1590737998,0.4260741174,0.3211229444,-0.3257283568,0.0370839611,0.6237726212,0.0663693473,-0.0005364151,0.3617648482,-0.2438280433,-0.0247037914,0.1756167412,0.2001561224,-0.2193982452,-0.4464477003,0.0715357512,0.3973942101,0.6543508768,-0.2877071798,-0.3910399973,-0.3127829134,0.1366227865,-0.0791728422,-0.0994484797,0.1954574585,0.0398405604,0.2265112102,-0.0492472313,-0.245719716,-0.1470465362,0.2701330185,0.3945715725,0.0568461344,-0.0370777883,-0.1775099933,-0.0003612236,0.179885,0.1766577512,-0.2836074233,0.1341858357,-0.0177958272,-0.2663389742,-0.177357927,-0.0541212335,-0.0193151273,-0.08409632,0.0844806358,-0.245130375,0.103039436,-0.0586198866,-0.0488160476,-0.017619174,-0.0382055715,0.2564458847,0.3307938576,0.0510782972,-0.3528569639,0.1201025695,0.0050097569,-0.1631537974,0.1350767314,-0.0659490302,0.0418742001,-0.2124118209,-0.1809259951,0.0381796286,-0.1414325237,-0.0032424561,-0.055586163,-0.2538170218,-0.0165537633,0.0661199987,0.1161912978,-0.1412743628,0.2230422795,-0.0193490442,-0.4236039817,-0.1252016127,0.0979864895,-0.1602702737,0.152887091,0.548071146,-0.2299818397,0.1155242324,-0.2185691297,-0.2966979146,0.1594123244,-0.1556195617,-0.0610738061,0.0852686614,0.2059712857,-0.1045297235,0.1281835884,0.1077968329,0.0212762039,-0.2046411932,0.1557881236,-0.038562499,-0.3110814989,-0.3726719618,0.2138151973,-0.1005914137,-0.4864225388,-0.1874146163,0.5722736716,-0.0236890614,-0.0310174394,0.2205074131,-0.0103252493,-0.1518809348,-0.1902374476,0.0116472179,0.4308137894,-0.4707907736,-0.2307440192,-0.3453292251,0.0235168077,0.0336860716,0.241004467,-0.0922274441,0.0245775469,0.0140255112,0.1602306068,0.2162276506,-0.068709366,-0.2146432847,-0.075349085,0.0846202299,-0.1966258287,0.1634407341,0.3142953217,0.2429367155,0.0132585289,-0.1556187272,0.1721234322,-0.2283507884,-0.0336175412,-0.1966410279,-0.1364067346,0.1465033889,0.3985637128,0.4805242121,-0.1305693984,0.068939738,-0.3062627017,-0.0355404355,0.1058141366,0.2744992673,0.1641733348,0.2053435147,0.2714706659,0.1024119854,-0.4187617302,-0.2145657688,0.0550114289,0.3147020042,0.4139363766,0.0726572648,-0.1219885647,-0.1317267716,-0.1913783103,0.0834059268,-0.0476080626,0.137248978,-0.1788128614,0.1703273058,0.1093635783,-0.2658056021,0.2994686365,-0.2152725458,0.4264630675,0.2125779539,0.3759650588,-0.0918564424,-0.1134921685,0.1790644974,0.3466808498,-0.0683494136,-0.0561639331,0.0313503332,0.140604347,-0.213462621,-0.0584961362,0.2669733465,0.3660677373,0.3261840641,-0.513733685,0.1703124791,-0.0794895589,0.0716542527,-0.1760181785,0.0449972637,0.189144358,0.0367133468,0.1446832567,0.005856161,0.1148122996,-0.1064943671,-0.0820765421,-0.0027086877,0.1102708802,0.304279685,-0.2344645709,0.3341112733,0.0095608598,-0.0775271282,0.2798493505,0.1818888038,-0.0192011576,0.2518855631,0.2147261202,-0.5472897291,-0.0057125818,0.0321520157,0.2584816217,0.3475606441,0.5090733171,-0.0490460396,-0.147824809,0.0817446262,-0.3104186952,0.0761174336,0.3464794755,-0.0318370871,-0.0022071903,0.2686341405,0.1926966906,-0.2491729409,0.0267807748,0.0885097533,-0.0470763668,-0.0210079327,-0.1603132933,-0.0493501872,-0.4877552688,0.012579483,0.0661214441,-0.1052722484,-0.2437970638,0.2872220278,-0.3143340945,0.0283844899,0.0965959057,-0.1657503843,0.4271807373,-0.2522673309,0.0750089884,-0.0557899326,-0.1706417203,-0.2362039387,0.2360295057,0.0335204862,0.0894505903,0.4961579144,-0.0915073827,0.3037363291,-0.2775154114,-0.5079554319,0.0609963723,-0.2328941077,-0.0041528451,-0.0171501767,-0.1594959348,0.1356656998,-0.2140608281,0.0266100951,-0.2283368558,-0.0827754438,-0.0041486304,-0.2452598065,-0.2524430156,-0.1303008348,-0.657120347,-0.5468477607,-0.1074697003,0.1612108797,-0.0874642953,-0.0005496375,0.1316719502,-0.1874059439,0.0237714704,-0.28291291,0.0646608174,-0.0692201927,-0.2697477043,0.1288060993,-0.1303965449,-0.2666116357,-0.0889950544,-0.0791966319,-0.1633375883,-0.1946291476,-0.456413269,-0.0327097625,-0.3555341065,0.1456098557,0.132913515,0.0244399905,0.4918686152,0.2438689172,-0.1384389251,0.0291558672,-0.2249152958,0.0785069689,0.0840614885,0.2012487799,-0.2755500376,0.1582756042,0.2967652678,0.4924660623,0.0400808565,-0.101574935,0.1274571568,0.1824348122,0.3775154054,-0.0146982595,-0.4498075545,0.26992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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/353","title":"[Dataset requests] New datasets for Text Classification","comments":"This is the homepage for the Amazon dataset: https:\/\/www.kaggle.com\/datafiniti\/consumer-reviews-of-amazon-products\r\n\r\nIs there an easy way to download kaggle datasets programmatically? If so, I can add this one!","body":"We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705","comment_length":26,"text":"[Dataset requests] New datasets for Text Classification \n We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705 \n This is the homepage for the Amazon dataset: https:\/\/www.kaggle.com\/datafiniti\/consumer-reviews-of-amazon-products\r\n\r\nIs there an easy way to download kaggle datasets programmatically? If so, I can add this one!","embeddings":[-0.1458348036,0.0658523887,-0.1481411606,0.190245375,0.3451894522,0.3538247645,0.1370032579,0.073779203,-0.1220624968,-0.0394350663,-0.2502006292,0.2587281764,-0.1631076485,0.4211382866,0.0899194926,-0.1647979021,-0.0227949545,-0.0886518732,0.1359842271,-0.1430172473,-0.1249985769,0.1028074697,-0.09122812,-0.2270246893,-0.1792382449,-0.3646232486,-0.1602799743,-0.1030361876,-0.3790989816,-0.3690577149,0.3359175324,0.1774003953,0.2373439074,0.1500301808,-0.0001078193,-0.3248600364,0.0003606891,-0.1197961271,-0.3088076413,-0.1295684427,-0.1039344147,-0.5073528886,-0.0890804902,-0.2598694265,0.0910557508,-0.0354643874,0.0495942235,-0.2475443631,-0.0602004416,0.2949465811,0.204582572,0.0185297076,0.0701869354,-0.1878490746,0.334087044,0.4829710722,-0.2553501427,0.0124023221,0.46330899,0.2070602924,0.1856566668,0.2027293295,-0.3064487576,-0.1065172404,0.2755382359,0.112584509,-0.3106137812,-0.4856223166,0.1785595268,0.4579693079,0.970321238,-0.4533036947,-0.5060064197,-0.3497488201,0.2375041395,-0.152540639,-0.3129490316,0.5556718707,-0.0784912631,0.0007059887,-0.1585119367,-0.4362519979,-0.1533342749,0.1665377468,0.2362736017,0.3287392855,0.070403479,-0.2436540127,0.0811757594,-0.0162672531,0.5947266221,-0.3934555352,0.130332619,0.0336790383,-0.2174791098,-0.2010198236,-0.1059347987,-0.0421087109,0.0622147433,0.0863996148,0.0007821672,0.1940434873,-0.1398465484,-0.1695522964,0.0122073432,0.0201016217,0.2767164409,0.3368701637,0.0530988425,-0.0473928787,0.2718046904,0.0226452984,-0.3008702993,0.1779805571,-0.0927955508,0.0095043881,-0.2158112824,-0.1667107344,-0.1336227059,-0.226462096,-0.1438032538,-0.0931730792,-0.2892290354,0.1214969605,-0.1194738746,0.0921548381,-0.087097995,0.2747785747,-0.1070149317,-0.3732473552,-0.0636869371,0.101596415,0.2123429179,0.0125101032,0.3886572123,-0.3078111112,0.2903837562,-0.2664626539,-0.0588190183,0.0681074262,0.0345649868,0.0559180491,-0.331359446,0.1262033135,0.089690797,0.0198177435,-0.0611537881,0.0645950586,-0.2371143401,0.2297895849,-0.0074012163,-0.2494551241,-0.1083722338,0.1898384243,-0.0466048308,-0.6931470037,-0.344630897,0.4325671792,-0.0215864442,-0.0997880623,0.1143115535,0.0151210381,-0.0687841699,-0.2183511853,0.0221006982,0.4878204167,-0.4603776932,-0.2505888939,-0.2430824935,0.0543338768,0.0991812646,0.0070484811,-0.0971060023,0.3354073763,0.0367300101,0.0289713331,0.4266469479,-0.1561398059,-0.2334921807,0.0047492487,-0.0274881925,-0.1572909802,0.1542441398,0.4053142369,0.0540761799,0.0954790413,-0.1891800463,0.2154071778,-0.147028327,-0.0636374205,-0.0311037321,-0.2104397118,0.1887797117,0.453360647,0.5115642548,-0.141790837,0.1126803458,-0.0789700672,0.204727754,-0.0264047273,0.1127469763,0.1084835976,0.147639811,0.2481903732,0.0519464202,-0.1844959855,-0.3535889089,0.1882290095,0.1195325404,0.1798555851,-0.1976394951,-0.2929627001,-0.1732512861,-0.0184195079,-0.0598591976,0.0343751498,0.0665083006,-0.1729312241,0.245930627,0.2049800158,-0.0696805269,0.2434435338,-0.2629662156,0.3557042181,-0.019129198,0.1765373647,0.0324240662,0.1675954014,0.1569022685,0.1811335087,0.0304620825,0.0196961537,0.0671713352,0.0794843137,-0.0288240444,0.2057452798,0.3982343674,0.2409583777,0.3269950449,-0.3789923787,0.3036179245,0.0010745006,0.1522791535,-0.135686934,-0.2688925266,0.3572216928,0.0751191676,0.0856947824,0.0338318124,0.1737848371,0.0815224648,0.1841052026,0.2052222788,0.1305986792,0.2918301523,-0.2030846328,0.3116686642,-0.2133226693,-0.2172529995,0.3556894362,0.1185146421,-0.1999642253,0.196610406,0.1007307768,-0.3290809393,0.0510095693,0.1607095897,0.1727669239,0.1420184821,0.4380430877,-0.0073880027,-0.0723125786,0.1096052527,-0.2723276615,0.0723109022,0.3957447708,0.114891991,0.0230934341,0.2149344236,0.1084581465,-0.2109977156,-0.0760432109,0.1719285697,-0.0094922613,-0.0051254821,-0.1017907709,-0.0998298675,-0.3285647333,-0.1100289747,0.152197957,0.0331786238,-0.3579837084,0.1127663478,-0.1880217344,0.0209956411,-0.0725448057,-0.3703675568,0.5109134316,-0.2676707506,-0.165388763,-0.0368975513,-0.309289664,-0.1858089268,0.1964372247,0.2492019534,-0.0065035434,0.4684090912,-0.1205239296,0.4063918889,-0.4003465176,-0.4549329877,-0.0711317286,-0.0402050354,-0.1495891511,-0.0947711021,0.0039905128,-0.0539056435,-0.18788369,-0.1421690881,-0.0577224419,-0.195838213,-0.0704630613,-0.0550234467,-0.3058831692,0.0188405868,-0.7203142047,-0.5714181066,-0.0859818906,0.1243749335,0.0709805116,0.0216838457,0.0199498422,-0.0857780129,0.0222123154,-0.1208952665,0.1738438904,0.1174019277,-0.0295295194,0.2599438131,-0.0651600882,-0.3358579576,0.0561509468,-0.0589567013,-0.1528324634,0.1433831751,-0.6533876657,0.2787922025,-0.1593821496,0.2507083118,0.0915816873,0.0094199954,0.3900659382,0.3259739876,-0.0046840375,0.0481488816,-0.2004248649,-0.0664402321,0.0186327696,0.1881430894,-0.2580963671,0.147907868,0.2330837548,0.7644212246,0.1004631072,-0.0396000445,0.004106604,0.1000653878,0.2880669832,0.1741345376,-0.3623054922,0.0765023604,-0.2538989484,0.19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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/353","title":"[Dataset requests] New datasets for Text Classification","comments":"Hi @jxmorris12 for now I think our `dl_manager` does not download from Kaggle.\r\n@thomwolf , @lhoestq","body":"We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705","comment_length":16,"text":"[Dataset requests] New datasets for Text Classification \n We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705 \n Hi @jxmorris12 for now I think our `dl_manager` does not download from Kaggle.\r\n@thomwolf , @lhoestq","embeddings":[0.0363270715,0.1208182648,-0.1671025902,0.2490486056,0.2784865797,0.2174416929,0.2918265462,0.058676593,-0.0694200024,0.0894200951,-0.0749871731,0.2429377139,-0.2216520309,0.1872980148,0.1075434536,-0.2674973309,-0.0024418817,-0.0645568967,-0.05856473,-0.1949672848,-0.1161485165,0.2323865891,-0.1021067202,-0.2988000214,-0.3894096315,-0.1188440919,-0.1741247177,0.203107357,-0.3696795106,-0.5515012741,0.0710659698,0.1108713523,0.2446143627,0.3044234514,-0.000100496,-0.2866234183,0.1290518939,-0.0962061509,-0.2718328536,-0.154832527,-0.1398425251,-0.5224165916,-0.0034975831,-0.1702831686,-0.0646550804,0.0960617885,-0.010226341,-0.1524552703,0.0233407225,0.3746043742,0.284389317,0.0045372597,-0.0091257114,-0.3068839014,0.398807466,0.3493154943,-0.2872608602,0.0362687595,0.6251108646,0.1298020631,-0.0595832877,0.3732942045,-0.2840688527,-0.0756148323,0.1847102493,0.1710983813,-0.0633509457,-0.4465399981,0.1536754221,0.3627845943,0.8537475467,-0.3193746805,-0.2965325415,-0.3729111254,0.1825645268,-0.2185397595,-0.0184796993,0.1393242925,-0.0681619048,0.0716142356,-0.0426279232,-0.244466722,-0.1498306096,0.2160182297,0.0402437672,0.3208631873,-0.0304148775,-0.3652468026,0.0909142271,0.1313055605,0.427668035,-0.2541504204,0.0599310137,-0.0015200948,-0.2455904633,-0.1704480052,-0.002382966,-0.1185175255,-0.0255971979,0.1150279269,-0.1519063264,0.1014916748,-0.1292260587,-0.1060267687,-0.0125242872,0.1141977087,0.1958247125,0.3053243756,0.1475717723,-0.0988136381,0.1847091764,0.045794826,-0.2336816341,0.13585715,-0.0357335284,0.0952232704,-0.2831825316,-0.0848114043,-0.0323271267,-0.0517270938,-0.049588751,-0.1634258926,-0.2022054493,0.1117959842,-0.0234020054,0.1728325188,-0.1526251286,0.2224625796,0.0433585718,-0.3606481254,-0.1383419335,-0.0274214,-0.0917269439,-0.0014240913,0.5291514993,-0.1442968696,0.2326451689,-0.2287693918,-0.1358251274,0.1641445458,-0.0226340704,0.0378942229,-0.0145485923,0.2572909892,-0.0061306641,0.1331600547,0.03365447,-0.0673391968,-0.2038238794,0.2830096483,0.0436704792,-0.3164167702,-0.2934389114,0.2884786725,0.0269857459,-0.5546091795,-0.1763255596,0.5332195163,-0.0657942817,-0.0989340022,0.2336559594,0.0619950481,0.0190879423,-0.1440949589,0.0625898838,0.3794981241,-0.4466327429,-0.1556040496,-0.3843356669,0.0678201392,0.1779544353,0.1370632648,-0.1412973702,0.103801243,0.0061652353,0.0018525793,0.3576285243,-0.2106905133,-0.2597446144,-0.0973088369,-0.032531511,-0.1417059153,0.2275728881,0.2446389496,0.2304923683,0.0032837908,-0.2420407385,0.1514585763,-0.2314142436,0.0088747144,-0.1517031193,-0.1266579181,0.1738824397,0.4673376083,0.5211064816,-0.2039029002,0.0964089707,-0.102705583,0.1300780773,0.06458202,0.181998238,0.1078508198,0.0502419733,0.2105703354,0.1069571823,-0.252481252,-0.3206230104,0.0419504344,0.2923546135,0.4220080674,0.0574039109,-0.1772577018,-0.2055796236,-0.0860821232,-0.0594474226,-0.1151983812,0.1802355498,-0.1373231858,0.0746803433,0.2228439152,-0.2554362416,0.1196095049,-0.2633062005,0.3440307677,0.1384392679,0.1995771229,-0.1859367192,0.0385998711,0.1123191118,0.3069336414,-0.0818982124,0.0136688957,0.0554979034,-0.0457963422,-0.1088721529,-0.0359763168,0.3803277016,0.3463966846,0.443066895,-0.3643418252,0.256474942,0.0166180059,0.1205729395,-0.1962995529,-0.1523725539,0.1953105032,0.1451340467,0.1179495901,0.0073442226,0.0682806447,0.0312686414,-0.0739850402,0.1388143301,0.0883899033,0.3093252778,-0.1635049582,0.2068687677,-0.0220921505,-0.1827761531,0.2687956095,0.2085866928,-0.114101164,0.28835392,0.1803227514,-0.4136097133,0.0028619606,0.0334143117,0.1817580909,0.1602043211,0.4741146266,0.0715278685,-0.1295448095,0.1909532696,-0.4396854043,0.0570931099,0.3365569711,0.0779422298,0.1949090809,0.288970381,0.1609755307,-0.252912432,-0.0739216432,0.1869309545,-0.0621049628,0.0964879394,-0.1228912994,-0.1473363042,-0.4734416306,0.009839247,-0.0316348821,-0.2634119987,-0.3572675884,0.2397592068,-0.1863238961,-0.0097318674,0.1090915799,-0.2038612962,0.4440691173,-0.3063391149,0.0034324632,-0.1450700462,-0.2510160804,-0.2677837014,0.2888260186,0.1671507657,-0.0761385188,0.4527226686,-0.139581725,0.3039369583,-0.2922314703,-0.5026999116,0.0818642601,-0.1006454229,-0.1514420956,-0.0796726793,-0.2038016468,0.0558108762,-0.132137239,-0.0117888311,-0.1040636003,-0.1461094916,-0.13238509,-0.1588013619,-0.1699044108,-0.0905093327,-0.6321281791,-0.4981149435,-0.1765124798,-0.0205679294,0.0031175094,0.0394998901,0.0527027212,-0.2293874621,-0.0312613882,-0.156179145,0.1062041372,-0.0379838757,-0.1608324349,0.1060853451,-0.1881787926,-0.2769782841,-0.0139446566,-0.0938116834,-0.0592466965,-0.2440544963,-0.5775509477,0.011682177,-0.1460658312,0.135050863,0.0823446438,0.014955977,0.4460029602,0.3186326623,-0.1670176983,0.0728072003,-0.1841626465,-0.0408815593,0.0871326849,0.3037729561,-0.3289527893,0.1120520905,0.1990931034,0.6252944469,-0.0762832314,0.0286285691,0.1277460605,0.1458060294,0.3261945248,0.0009282017,-0.3885497153,0.2392318547,-0.1540058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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/353","title":"[Dataset requests] New datasets for Text Classification","comments":"Great list. Any idea if Amazon Reviews has been added?\r\n\r\n- ~40 GB of text (sadly no emoji)\r\n- popular MLM pre-training dataset before bigger datasets like WebText https:\/\/arxiv.org\/abs\/1808.01371\r\n- turns out that binarizing the 1-5 star rating leads to great Pos\/Neg\/Neutral dataset, T5 paper claims to get very high accuracy (98%!) on this with small amount of finetuning https:\/\/arxiv.org\/abs\/2004.14546\r\n\r\nApologies if it's been included (great to see where) and if not, it's one of the better medium\/large NLP dataset for semi-supervised learning, albeit a bit out of date. \r\n\r\nThanks!! \r\n\r\ncc @sshleifer ","body":"We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705","comment_length":92,"text":"[Dataset requests] New datasets for Text Classification \n We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705 \n Great list. Any idea if Amazon Reviews has been added?\r\n\r\n- ~40 GB of text (sadly no emoji)\r\n- popular MLM pre-training dataset before bigger datasets like WebText https:\/\/arxiv.org\/abs\/1808.01371\r\n- turns out that binarizing the 1-5 star rating leads to great Pos\/Neg\/Neutral dataset, T5 paper claims to get very high accuracy (98%!) on this with small amount of finetuning https:\/\/arxiv.org\/abs\/2004.14546\r\n\r\nApologies if it's been included (great to see where) and if not, it's one of the better medium\/large NLP dataset for semi-supervised learning, albeit a bit out of date. \r\n\r\nThanks!! \r\n\r\ncc @sshleifer 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/353","title":"[Dataset requests] New datasets for Text Classification","comments":"On the Amazon Reviews dataset, the original UCSD website has noted these are now updated to include product reviews through 2018 -- actually quite recent compared to many other datasets. Almost certainly the largest NLP dataset out there with labels!\r\nhttps:\/\/jmcauley.ucsd.edu\/data\/amazon\/ \r\n\r\nAny chance someone has time to onboard this dataset in a HF way?\r\n\r\ncc @sshleifer ","body":"We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705","comment_length":56,"text":"[Dataset requests] New datasets for Text Classification \n We are missing a few datasets for Text Classification which is an important field.\r\n\r\nNamely, it would be really nice to add:\r\n- TREC-6 dataset (see here for instance: https:\/\/pytorchnlp.readthedocs.io\/en\/latest\/source\/torchnlp.datasets.html#torchnlp.datasets.trec_dataset)  **[done]**\r\n- Yelp-5\r\n- Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**\r\n- SST (Stanford Sentiment Treebank) **[include in glue]**\r\n- Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**\r\n- Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification\r\n- 20 Newsgroups. The 20 Newsgroups dataset  **[done]**\r\n- Sogou News dataset **[done]**\r\n- Reuters news. The Reuters-21578 dataset [165] **[done]**\r\n- DBpedia. The DBpedia dataset [170]\r\n- Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database\r\n- EUR-Lex. The EUR-Lex dataset\r\n- WOS. The Web Of Science (WOS) dataset **[done]**\r\n- PubMed. PubMed [173]\r\n- TREC-QA. TREC-QA\r\n- Quora. The Quora dataset [180]\r\n\r\nAll these datasets are cited in https:\/\/arxiv.org\/abs\/2004.03705 \n On the Amazon Reviews dataset, the original UCSD website has noted these are now updated to include product reviews through 2018 -- actually quite recent compared to many other datasets. Almost certainly the largest NLP dataset out there with labels!\r\nhttps:\/\/jmcauley.ucsd.edu\/data\/amazon\/ \r\n\r\nAny chance someone has time to onboard this dataset in a HF way?\r\n\r\ncc @sshleifer 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\nTry to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\nSee issues #242 and #307 ","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":38,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\nTry to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\nSee issues #242 and #307 ","embeddings":[-0.2610889971,-0.0766245872,-0.0532211661,0.1464464962,0.3858148754,0.0102546979,0.0254466422,0.1763569266,-0.1791240722,0.0838485435,0.1790162325,0.4352126122,0.0588609129,0.3545265794,0.2403868884,0.0416257381,0.0944179073,0.3228261769,0.0393954292,0.0472039506,-0.3697523773,0.1955367774,-0.103214398,0.0522328466,-0.2589356601,-0.0607879423,0.0105512701,0.2736924589,-0.2823358774,-0.5056849122,0.2017236203,-0.0907642022,0.2071509212,0.2249621302,-0.0001033256,-0.0700257793,0.2572823763,-0.2035377771,-0.0729974657,-0.3186079264,-0.1721763462,-0.3807736039,0.0590986423,-0.2513725162,0.0154255684,0.2983249426,-0.0178563762,-0.069501169,0.4933331013,0.3671520352,0.3225337267,0.1151859388,-0.120438993,0.1490378827,-0.0000738069,0.0481821895,-0.0301628429,0.4893274605,0.1714179367,-0.2337498516,0.0854414403,0.15753223,-0.2183983922,-0.1051619127,0.0754632354,0.1099671572,-0.1662448347,-0.3456841409,0.1217203811,0.0935375169,0.2599755526,-0.2827702761,-0.0374360569,0.1049030572,0.1600743979,-0.6936118007,0.2831137776,0.3683131933,-0.2686055005,0.134733662,0.013303794,0.1663535088,0.0272235144,-0.0592778325,0.0735416785,-0.0005281873,-0.1928128749,-0.0872904509,0.2068818808,0.0006389527,0.129370451,0.0012708894,-0.1845574528,0.2111000866,-0.2259348184,-0.0672173575,-0.1538407356,0.0041102082,-0.1760222912,-0.1566052586,0.5614205003,0.1108473465,0.047019057,0.3558768332,0.0998752788,0.1248829663,-0.2047813535,0.129919678,0.253900528,-0.0032394202,0.2112276554,0.0027607603,-0.1976271719,-0.3183389008,-0.2264608443,0.3368732929,0.1330850273,-0.167297408,-0.4322226942,0.1432407051,0.1057569832,0.1413569152,0.0213067234,0.2131028175,0.1955601424,0.2922651768,0.0155842183,0.2012343407,-0.349791944,-0.2578395903,-0.046728801,0.2218290865,-0.3768264055,-0.1314049214,0.2358993292,-0.1387218237,0.1109037548,0.2852515578,0.141297698,-0.183926031,-0.0799183846,-0.1549814343,0.0537384972,0.1382840127,0.0759721398,0.1385126859,0.1670151353,-0.3159835935,-0.0821788833,0.1015482843,-0.4818613827,-0.0225306526,-0.058851216,0.2757769227,-0.0652818531,-0.0694927648,-0.5188581347,0.1689957231,0.0898341611,-0.3107695878,0.0260849893,-0.2124463171,-0.1478360891,-0.1852401197,0.1550226808,0.5013543963,-0.558544457,0.1694433838,0.2123724222,0.0155501338,0.3184648752,0.1244617924,-0.0968644843,-0.1430572569,-0.1716031879,-0.0217708685,0.2493201792,-0.2651790679,-0.222624585,0.3089697361,-0.0901828632,-0.058612667,0.0460300893,-0.1065662131,0.1471290737,-0.1886292547,0.1144837588,0.3725188673,-0.0196821876,0.2809734643,-0.219192341,-0.1849874258,0.2239281535,-0.0044941646,-0.2701480985,-0.1170027182,0.2115180343,0.0669501349,0.1973384023,-0.1416933239,-0.2056812495,0.1531885713,0.2411946952,-0.2049695402,-0.1022406146,0.2412946075,0.0358477309,-0.0403329097,-0.256057173,0.163705647,-0.3069273829,-0.1502541304,-0.1704839915,0.0351581611,-0.3680139184,-0.2693879902,0.2428944856,-0.0371168219,-0.0683988929,-0.0123575265,-0.0955900922,0.2467948198,-0.013406449,-0.0126051661,-0.227121979,0.1789416075,-0.2131493539,-0.1868838519,0.3183470666,0.295512557,0.1668151915,-0.1177361012,-0.2245182097,0.2406990677,0.0372983851,0.363714993,-0.2209492177,-0.2943725288,0.4554791451,-0.4603476822,0.0675635487,0.4531856179,0.2957178056,-0.0873812363,0.22945638,0.3592467606,-0.1594866365,-0.0167384148,-0.0725952908,-0.0614305548,0.257991612,0.0848891437,0.1084826887,-0.1574765146,0.3592934906,0.6213696003,0.2019231766,-0.0257057976,-0.1232932657,0.0141802179,0.4981156886,-0.0518534034,-0.1169799641,0.0897622854,0.0535767749,-0.1114095151,0.0894828662,0.0712491646,0.227318868,-0.1056673229,-0.3125161827,0.2457751483,0.0206145719,-0.2307857871,0.1905181408,-0.1344037801,-0.0368113667,-0.0156292282,0.0608470812,0.1106140465,-0.2907760441,-0.128715992,0.1040016934,0.177634269,-0.3210408688,0.3218948245,-0.4479729831,-0.378326416,-0.3087700605,0.2326155007,-0.143322289,0.0219256971,-0.1595176905,0.0173547585,0.0631201416,0.1683906466,-0.2043867111,0.0400572196,-0.0683101192,-0.1754354537,0.1225209385,-0.2182811052,-0.2620295882,0.1559137553,0.428724438,-0.0599661134,0.0835664794,-0.3489724398,0.1415617019,0.2766743898,-0.146533072,0.1601233035,-0.2612069845,0.1343978047,-0.0371876061,0.0921340734,-0.061995592,-0.4000653028,0.1540004611,-0.0559684895,-0.0773286819,0.1851528734,0.0075843544,-0.3202289343,-0.2613763809,-0.1141118556,-0.4982915223,-0.3325833976,-0.1811806411,0.0014529042,0.1016743407,0.3449436724,0.136863023,0.0497554876,0.2142150104,-0.1142487302,-0.0944051892,-0.2219955176,0.3507091105,-0.3976939619,-0.5242287517,0.0896824673,-0.0443613939,0.4002031386,-0.2082488239,-0.185305953,-0.0273388159,-0.1131972522,0.1191595569,0.0449815802,0.1042199135,-0.0041818526,-0.0227979105,-0.0459144637,-0.1186295375,-0.1014546603,-0.1292091757,0.09624511,0.3029592037,0.0399042889,0.2478981167,-0.2661068738,0.09984795,0.2606476843,0.0474691428,0.5908392668,-0.1422195286,0.3743231297,-0.0816609934,-0.1248044372,0.1246356294,-0.2733381391,-0.0562205017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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"It should be in `xtreme.py:L755`:\r\n```python\r\n        if self.config.name == \"tydiqa\" or self.config.name.startswith(\"MLQA\") or self.config.name == \"SQuAD\":\r\n            with open(filepath) as f:\r\n                data = json.load(f)\r\n```\r\n\r\nCould you try to add the encoding parameter:\r\n```python\r\nopen(filepath, encoding='utf-8')\r\n```","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":36,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n It should be in `xtreme.py:L755`:\r\n```python\r\n        if self.config.name == \"tydiqa\" or self.config.name.startswith(\"MLQA\") or self.config.name == \"SQuAD\":\r\n            with open(filepath) as f:\r\n                data = json.load(f)\r\n```\r\n\r\nCould you try to add the encoding parameter:\r\n```python\r\nopen(filepath, encoding='utf-8')\r\n```","embeddings":[-0.269592315,-0.0288400799,-0.020871589,0.2028595954,0.4484202564,-0.1219135448,-0.1402623653,0.2741995752,-0.3068618476,0.1308181882,0.1182492003,0.5972364545,0.0107578505,0.3725733459,0.1493888646,0.0074850386,-0.0501957126,0.2807819247,0.1142940968,0.0160581674,-0.3124450147,0.1804717332,-0.1091933325,-0.0322549529,-0.3568943143,-0.0902507678,0.1276563406,0.278445214,-0.4236949384,-0.5119258165,0.1833940446,-0.0129814195,0.2230469882,0.2653470933,-0.0001058442,-0.0454376265,0.3183930814,-0.2982024848,-0.2216451466,-0.3611499667,-0.2516929507,-0.315095216,0.1989216208,-0.2637838125,-0.1006912366,0.2404016256,0.0199444219,-0.0952382758,0.5233158469,0.2909530699,0.2963303328,0.073832728,-0.0744115859,0.1486849338,-0.0282303728,0.1482578516,-0.1475578398,0.4451701045,0.2602996826,-0.0911355689,0.1426788718,0.2766979337,-0.1515211314,-0.0569933876,0.0407141112,0.0499193668,-0.151903525,-0.4266541302,0.1209061518,0.1055289283,0.1793570071,-0.3225930333,-0.0961947814,0.0870558769,0.1963281035,-0.5246263146,0.2534211278,0.3471793532,-0.2969875336,0.097451359,-0.0773089752,0.1050549448,0.0375204943,-0.0349980071,0.0779615492,0.210955441,-0.1810681522,-0.0667629689,0.1364755481,-0.0218385831,0.2110842615,0.0128953513,-0.2235882878,0.179078266,-0.1587533057,-0.1336388737,-0.1817007214,-0.0875805095,-0.087361142,-0.1382653415,0.5145932436,0.1206673607,-0.0586921424,0.3560672104,0.1107803285,0.1049097627,-0.0984211862,0.2221195102,0.1833802611,0.1279567331,0.09569408,0.0296438895,-0.2141443342,-0.1327173412,-0.3393443823,0.2891263664,0.1441908479,-0.1674303114,-0.3648921549,0.0851829275,-0.0286666937,0.1087728441,0.1422024965,0.2070315331,0.1510671526,0.2321667224,0.0610188432,0.2638306618,-0.3025706112,-0.2582405806,-0.0983013734,0.2584832609,-0.3669599593,-0.1151665226,0.1970708221,-0.0907112062,0.1982972622,0.1363954842,0.1843987405,-0.2621587217,0.0463314913,-0.1218954474,0.0610194989,0.0838399231,0.0885400698,0.035077408,0.1756173223,-0.3857584,-0.095351465,0.1032874957,-0.4930075705,0.0364094265,-0.1123503819,0.2696914375,-0.1408186704,-0.0545914955,-0.5896954536,0.1581823975,0.0557486117,-0.2681747675,0.0798110068,-0.1571032405,-0.0741815716,-0.1766550988,0.2183379233,0.4230662286,-0.4035671949,0.0333889574,0.2349449694,0.0550833866,0.3388772011,0.0612149462,-0.2030248791,-0.1410146654,-0.1047271565,-0.0357994325,0.3881499767,-0.4512214661,-0.2139129043,0.2725942433,-0.0509270504,-0.0216157157,-0.0301322732,-0.131116271,0.0554815792,-0.1026321203,0.1379778534,0.4275479913,0.0005008268,0.3331618607,-0.2610883713,-0.2735882103,0.1134488732,0.1089548767,-0.2255675197,-0.1487599313,0.1748792529,0.3316148221,0.2449594587,-0.1301338673,-0.245599404,0.1132457927,0.2360798419,-0.3353266418,-0.2025874555,0.230512172,0.0102625033,-0.0392397977,-0.2957889438,0.2397301346,-0.3415217996,-0.0709602088,-0.135705635,0.0705726892,-0.4842479229,-0.2870798409,0.2603209317,0.0040702247,-0.1470945925,-0.0489315167,-0.1445099413,0.3022420108,-0.047584746,0.0106714582,-0.2947007418,0.2131462991,-0.1876621097,-0.281137526,0.3004021347,0.2748621702,0.1912814528,-0.1671635062,-0.2761870623,0.172657907,0.0572981164,0.3439613581,-0.1737114638,-0.2655003071,0.4478878975,-0.4799709916,0.05142143,0.4397865236,0.2158050537,-0.1009260118,0.2564740777,0.3604639471,-0.0498562455,0.0057903202,-0.0060213162,-0.1606789827,0.1911538541,0.0216744412,0.0419140495,-0.141872257,0.2428831607,0.6975977421,0.2468798906,-0.0303255673,-0.1135328561,0.0522243008,0.5576232076,0.0134540731,-0.0485008955,0.0479931496,0.1208856702,-0.1739585549,0.123891376,0.0689123943,0.1667799205,-0.0529841036,-0.2737358212,0.2346289754,0.0185396355,-0.1578697264,0.1978085488,-0.0990896896,0.0686605126,0.0721914247,0.1992562711,0.0100442003,-0.3035995662,-0.2533633411,0.042458754,0.1445677429,-0.2297809273,0.3444653749,-0.4669023752,-0.402338773,-0.2357724309,0.1430747062,-0.1867650598,-0.0578529201,-0.120883368,-0.0568394773,0.15535146,0.2601627409,-0.1332838088,-0.0113901822,-0.1319015324,-0.206618309,0.0643789545,-0.2219242901,-0.2030799836,0.1332081258,0.4137871265,-0.0475224443,0.0261726025,-0.3408566713,0.0964660197,0.3336159289,-0.2299674153,0.3083393872,-0.2643299699,0.1596699506,-0.0279841069,0.1091463119,-0.0951455683,-0.3874913454,0.2449334264,-0.0245360136,-0.1276833713,0.0561904758,-0.1040130556,-0.2081224173,-0.2407755554,0.0273787212,-0.484936595,-0.3486636579,-0.2008547336,0.1295459569,0.1333154887,0.3928813636,0.2247519791,0.1689454764,0.2177811414,-0.0570540391,-0.1314014345,-0.381948173,0.2595172524,-0.3200514317,-0.335532397,0.0427410342,-0.0658447221,0.5374211073,-0.2214785963,-0.1797289848,-0.1533397585,-0.0088255834,0.1046936288,-0.0650105104,0.1340196282,0.0049341419,-0.0113704633,-0.0147210769,-0.1544592083,-0.139175117,-0.0744661093,0.1369945258,0.4062225819,-0.0833959356,0.2225062847,-0.226818487,0.0787741393,0.2252549678,0.0042761029,0.6793308854,-0.0974558964,0.3227324486,-0.1281467974,-0.0877529681,0.1625579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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"Hello @jerryIsHere :) Did it work ?\r\nIf so we may change the dataset script to force the utf-8 encoding","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":20,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n Hello @jerryIsHere :) Did it work ?\r\nIf so we may change the dataset script to force the utf-8 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"@lhoestq sorry for being that late, I found 4 copy of xtreme.py. I did the changes as what has been told to all of them.\r\nThe problem is not solved","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":30,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n @lhoestq sorry for being that late, I found 4 copy of xtreme.py. I did the changes as what has been told to all of them.\r\nThe problem is not solved","embeddings":[-0.2841983736,-0.1710122824,-0.0102975909,0.2463469803,0.4728092849,-0.0588771589,-0.1426550001,0.2119477093,-0.2602452338,0.1263464093,0.089154087,0.5027424693,-0.0454213433,0.429033041,0.2850128412,0.013322426,0.0035831209,0.2751812339,-0.0077735465,0.0021374247,-0.2719275057,0.1288376153,-0.1685154438,0.0082540326,-0.4515329897,0.0142073883,0.0879136324,0.2322887033,-0.3210153282,-0.6467185616,0.2900666296,-0.0919905528,0.2375106514,0.3235117793,-0.0001081838,-0.0482052565,0.3104091287,-0.2778413296,-0.0658099726,-0.2856352329,-0.2337793857,-0.3430391252,0.101397112,-0.2250879407,-0.0894931257,0.2258188576,-0.0545104966,-0.1139665097,0.4601177573,0.2725270689,0.2595206499,0.1287803799,-0.0217991527,0.0607361123,0.0604668036,0.1154146492,-0.090522632,0.4867531657,0.3114893436,-0.1195533574,0.1933287829,0.2543049455,-0.1568315923,-0.133475557,0.125910148,-0.0169644523,0.0053897742,-0.4357089698,0.0417733826,0.2204738408,0.1514525414,-0.3889326453,-0.0276959762,0.1341760457,0.1199851707,-0.5919212699,0.2534968257,0.3236535192,-0.2409827858,0.122527197,-0.0020337789,0.1651620567,0.0704384893,-0.1112985164,0.0444017649,0.1325530857,-0.2128743827,-0.0954208598,0.1789368838,0.0546290763,0.2867621779,0.0904347524,-0.2125891447,0.1940508038,-0.3190436959,-0.0655719638,-0.1915603131,-0.0587911159,-0.0675602034,-0.1182293817,0.4792559147,0.0995845497,0.0326140933,0.2687477767,0.1060628891,0.0777690187,-0.1224479824,0.2007592916,0.1557708085,0.0766420811,0.1295703799,0.0229571834,-0.2260320783,-0.1936638504,-0.2553225458,0.3502626717,0.1382745057,-0.2279368937,-0.435672462,0.0755720437,0.0437256806,0.1526127011,0.1308053136,0.2008614242,0.2930817604,0.3658074737,0.0010363258,0.3005890846,-0.319924891,-0.3942802846,-0.1051794812,0.3204549253,-0.4212428033,-0.1009720042,0.135539785,-0.1225451827,0.1391242296,0.2384171933,0.2263340205,-0.3725573719,0.0250783376,-0.1242956519,-0.0242590159,0.1046297476,0.0223297384,0.0954796895,0.1946078539,-0.2149739414,-0.095124267,0.0950684175,-0.5300650597,-0.0877708048,0.0242652074,0.2465445399,-0.0689616352,0.0110309822,-0.6024036407,0.0946132466,0.1463538259,-0.2219099253,0.1035979092,-0.2148195356,0.0085423989,-0.1859185398,0.2275683284,0.432982713,-0.4388881326,0.061635498,0.1866447628,0.0247416794,0.3251600862,-0.0308978539,-0.2035872191,-0.0952485949,-0.1479416788,-0.0822999328,0.336143285,-0.4542464614,-0.2818059623,0.2109288871,-0.0842543989,-0.006146031,0.0338435583,-0.0713115484,0.0243238676,-0.2501995564,0.0208876785,0.3124954104,0.0346341506,0.2238523364,-0.2625687122,-0.255695343,0.1888916343,0.022490548,-0.2124091983,-0.2279863209,0.1698408872,0.2444564998,0.2650094926,-0.0716834217,-0.2540249825,0.2266713232,0.1636253744,-0.2654485106,-0.1530476213,0.1478769779,0.0975921303,-0.0085667716,-0.4412302375,0.1540108025,-0.1883801818,-0.1166941449,-0.1519980431,0.0756862387,-0.5332611799,-0.2902416587,0.225946486,-0.0512886345,-0.1036307216,-0.0128428033,-0.1843506545,0.2484761029,-0.0960384905,-0.0238135438,-0.322747618,0.1692887843,-0.1479879618,-0.2545021176,0.3124353588,0.2807561159,0.2038686723,-0.188059926,-0.3029399812,0.2458579242,0.0449902639,0.3558740914,-0.3245793879,-0.2304282486,0.4206211567,-0.6723706722,0.146245122,0.4327425957,0.2300490141,-0.0863217786,0.3771478534,0.2802092135,0.0099499812,-0.0249159969,-0.0998435691,-0.1123463511,0.135006249,0.0347357765,0.149747178,-0.1532124579,0.3822408915,0.677051127,0.2885441482,-0.0659379065,-0.0031925647,0.0499922782,0.4282445014,-0.0313407183,0.009942499,0.0277372506,0.011520464,-0.1405825019,0.0482859537,0.0674979016,0.157757923,-0.0938323066,-0.3045616746,0.27883178,0.0654535219,-0.1355648935,0.1910290122,-0.1055879071,-0.0699119642,0.0142461183,0.0924220234,0.0405950993,-0.1860477328,-0.1129704267,0.0957059041,0.148297146,-0.3401213288,0.2751016915,-0.4626467824,-0.3754618168,-0.1783538014,0.2071150392,-0.1286837161,-0.0076481444,-0.1607840657,-0.1041119099,0.1516872495,0.284309268,-0.1858735085,-0.0620627627,-0.1134632751,-0.1641200632,0.1164784878,-0.1277104765,-0.2553988695,0.0823456049,0.4200480878,0.0251667332,0.0639339313,-0.3856783211,0.1357679069,0.3003232479,-0.1755381525,0.2783754766,-0.2725768387,0.156186372,0.0260643121,0.0388391837,-0.0439628772,-0.221233353,0.2425484657,-0.1499065608,-0.004079158,0.140884012,-0.0309933666,-0.2017132193,-0.1660741568,-0.0085946694,-0.514534831,-0.3367132545,-0.3198547065,0.0043239407,0.1315589249,0.2935812771,0.1360127479,0.1235325933,0.145314455,-0.1120187864,-0.0669114739,-0.4351952672,0.2787834704,-0.2855579555,-0.382735014,0.0567738861,-0.0542963892,0.5393147469,-0.1494328082,-0.1855553985,-0.0595885925,-0.0793232843,0.0459489711,-0.003210966,0.1624264121,-0.0027852738,-0.0708893687,0.0175639,-0.1337160766,0.0254941545,-0.0617818907,0.0362773314,0.3804641068,0.0331328586,0.1845371872,-0.2000236213,0.0022228998,0.2838160992,0.075142853,0.7266833186,-0.1042949632,0.3343746066,-0.1572365761,-0.0879113749,0.031177057,-0.1539879143,0.008196272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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"Could you provide a better error message so that we can make sure it comes from the opening of the `tydiqa`'s json files ?\r\n","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":24,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n Could you provide a better error message so that we can make sure it comes from the opening of the `tydiqa`'s json files ?\r\n","embeddings":[-0.2817687988,-0.0771224797,-0.0259544104,0.1864886135,0.431878835,-0.1063414365,-0.0490796752,0.2102413774,-0.2888760865,0.0789318606,0.169837907,0.5185136795,-0.0157341827,0.4039361477,0.2165159136,-0.0277956389,-0.0087143779,0.3307390511,-0.0386377312,0.0391564257,-0.3182015419,0.180805847,-0.062818177,0.0498370789,-0.3340267837,-0.1292742044,0.1371211559,0.3347364366,-0.3895091414,-0.5701818466,0.2702987194,-0.0080769332,0.170627743,0.3130139709,-0.0001040535,0.0268084146,0.3452884555,-0.3027345538,-0.104040131,-0.2532044947,-0.2504182756,-0.3094664514,0.1723178774,-0.1982563883,-0.0262208078,0.2419230789,-0.0710227042,-0.1036925316,0.4995187521,0.2284307033,0.2823840976,0.1334480047,0.0270902142,0.1191968992,0.0230809283,0.2401787043,-0.0793570653,0.4386374652,0.2769225538,-0.1497905105,0.2019462287,0.2887742221,-0.1177519113,-0.0590951592,0.0971384794,0.0272609908,-0.1220089495,-0.386395514,0.0478322245,0.1814053655,0.3376056552,-0.3324699104,-0.0737317801,0.1352531314,0.1053181142,-0.4322821498,0.3052654266,0.3276412189,-0.3073068261,0.0905286595,-0.0683731213,0.138113156,0.0039155181,-0.0165487751,0.036680311,0.0251126923,-0.1426296532,-0.0910675451,0.171558246,-0.1108821109,0.1577507257,0.0657360405,-0.2696868479,0.2028368711,-0.135870412,-0.1643541753,-0.1352392137,-0.1784209907,-0.0712781101,-0.1799986809,0.6284134984,0.0887388811,-0.0453395173,0.3436106443,0.2137916833,0.077939406,-0.1613688618,0.2298027128,0.1764535606,0.1067804992,0.135150075,0.0063298065,-0.1947845817,-0.2007736117,-0.3806664348,0.261467278,0.2055311203,-0.1674138606,-0.3905965984,0.1570671499,-0.0251287259,0.1251740307,0.1215743199,0.2305425704,0.2509425282,0.2337392271,0.1034999639,0.301407367,-0.2691760659,-0.2696082592,-0.0902691707,0.2240213007,-0.3366367221,-0.1306651384,0.1875256002,0.0248261392,0.1507218182,0.1681637764,0.1875312179,-0.1822350174,0.0831004009,-0.0185790062,0.0666431636,0.1819321811,0.01842333,0.0592943951,0.1957460344,-0.3177241683,-0.1496910155,0.0478967987,-0.4498771727,-0.1029236764,-0.0498548374,0.2764468193,-0.0709629059,0.0148943821,-0.5774091482,0.103324227,0.1036705449,-0.3065210879,0.1104103252,-0.1724876761,0.0652475953,-0.1789132655,0.1632585973,0.4239064157,-0.5196294785,0.061409194,0.2629779577,-0.008728506,0.3078123629,0.0395209603,-0.2188350558,0.001408648,-0.1103211194,-0.0673660189,0.3427746892,-0.3594678044,-0.2083869427,0.2648138702,-0.0599900931,-0.0064671785,-0.0183021985,-0.0459113009,-0.0296961311,-0.1674282551,0.1635740548,0.4073846638,0.0208942853,0.331974268,-0.1896002144,-0.2001384199,0.1352940947,0.1335037798,-0.3661079109,-0.1996991634,0.1727053821,0.3472834229,0.2326287776,-0.1098583341,-0.2995851934,0.1958723962,0.1872804165,-0.2911566794,-0.1688216776,0.2727039456,-0.0299199149,-0.0008161278,-0.46689412,0.1937803179,-0.3488879502,-0.125282824,-0.101116389,0.1086002886,-0.4706578851,-0.2607012987,0.2740674317,-0.0427687578,-0.0989649072,0.0541113913,-0.1565451622,0.1738281846,0.0019520853,-0.0128229782,-0.3405911624,0.1497582346,-0.136209771,-0.2563149035,0.3186994195,0.2369572818,0.1342215836,-0.1979389042,-0.2061989456,0.2130203396,0.0390104018,0.3725278676,-0.2939714491,-0.249104172,0.4123367071,-0.5082938671,0.0771567151,0.4808596075,0.2183993459,-0.1461538523,0.2769643962,0.2817266583,-0.0891663954,0.0151476813,-0.0292358566,-0.1517893523,0.1802475899,0.0448530503,0.0785501152,-0.1457226872,0.3540159166,0.6760111451,0.2317332625,-0.0618653074,-0.0612974912,0.063147597,0.4979129434,-0.0228534881,0.0224453695,0.0306606852,0.0818222836,-0.1498587877,0.0854414925,0.0991884395,0.1967508644,-0.0726002753,-0.2659802437,0.1621042192,-0.0040254625,-0.176297009,0.1893495917,-0.133513093,-0.0095683141,0.0479486361,0.1350293905,-0.0119345337,-0.2644740641,-0.2243680209,0.0472704321,0.1501175314,-0.2828509212,0.2840691507,-0.469281286,-0.3395822942,-0.1189216748,0.0577480309,-0.1735459566,-0.0477437265,-0.0287636388,-0.0875506178,0.02008464,0.2474021167,-0.1192872673,-0.0076294728,-0.0873216093,-0.1683951616,-0.0243215002,-0.2341928482,-0.2052877247,0.1376561821,0.4320602119,-0.0060566664,0.0468511246,-0.4066323936,0.1306010187,0.3167106807,-0.188171193,0.2786333859,-0.2370859981,0.083637476,0.0733186156,0.1783911884,0.0843783543,-0.3423646688,0.2534838915,0.0106194774,-0.1843847781,0.1638585776,-0.0660434067,-0.1703947484,-0.2623527944,0.0312266294,-0.4781665206,-0.3827649355,-0.1456872225,0.1230577156,0.1545903385,0.2949495018,0.2861062586,0.118511036,0.1604100317,-0.0210369192,-0.0711759627,-0.3474283814,0.2403917313,-0.3694214523,-0.3852407336,0.0362640545,-0.0017279785,0.5735808611,-0.2455367446,-0.2102106065,-0.012437663,-0.097946465,0.0303754266,-0.1026786491,0.0817887112,0.0242636185,0.0206769407,-0.0392615274,-0.1288281977,-0.0239885319,-0.0752912387,-0.028744407,0.3422817886,-0.0258510169,0.2903343439,-0.2431191504,0.0379476249,0.2327430695,-0.0770645812,0.656467855,-0.0829391703,0.3366741538,-0.2007137984,-0.0529074147,0.083165288,-0.1412670314,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"@lhoestq \r\nThe error message is same as before:\r\nException has occurred: UnicodeDecodeError\r\n'cp950' codec can't decode byte 0xe2 in position 111: illegal multibyte sequence\r\n  File \"D:\\python\\test\\test.py\", line 3, in <module>\r\n    dataset = load_dataset('xtreme', 'tydiqa')\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/87748794-7c216880-c829-11ea-94f0-7caeacb4d865.png)\r\n\r\nI said that I found 4 copy of xtreme.py and add the \u300c, encoding='utf-8'\u300d parameter to the open() function\r\nthese python script was found under this directory\r\nC:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python37\\Lib\\site-packages\\nlp\\datasets\\xtreme\r\n","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":63,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n @lhoestq \r\nThe error message is same as before:\r\nException has occurred: UnicodeDecodeError\r\n'cp950' codec can't decode byte 0xe2 in position 111: illegal multibyte sequence\r\n  File \"D:\\python\\test\\test.py\", line 3, in <module>\r\n    dataset = load_dataset('xtreme', 'tydiqa')\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/50871412\/87748794-7c216880-c829-11ea-94f0-7caeacb4d865.png)\r\n\r\nI said that I found 4 copy of xtreme.py and add the \u300c, encoding='utf-8'\u300d parameter to the open() function\r\nthese python script was found under this directory\r\nC:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python37\\Lib\\site-packages\\nlp\\datasets\\xtreme\r\n","embeddings":[-0.2993037999,-0.0795088634,-0.0254610032,0.2176792026,0.3856869936,-0.0703267455,-0.1006556004,0.1843703985,-0.2766493559,0.0560930595,0.1345290989,0.4247641265,-0.0865387693,0.2728081942,0.2161013633,-0.0068473215,-0.0502494387,0.2241975665,0.1564086825,0.0707991794,-0.4088910818,0.1733012795,-0.1744892299,0.1244190782,-0.4139832854,-0.059594024,0.0413277484,0.3134814799,-0.2633447051,-0.6551192999,0.1879901588,-0.0460015163,0.143569544,0.3817632496,-0.000106594,-0.0980057493,0.2307551205,-0.2170889676,-0.1518953592,-0.2767798305,-0.3016500175,-0.3093874454,0.1195951626,-0.3082416654,0.081188336,0.1373253018,0.0110626118,-0.3305060565,0.4484871328,0.3554725945,0.2603322268,0.1282583177,0.021756649,0.1405823976,0.02450905,0.0218689628,-0.0638803914,0.4434989989,0.2685891986,-0.0473258868,0.1204436198,0.1458221078,-0.1865746528,-0.048009187,0.0152620394,0.0447202437,0.0667636618,-0.4407677948,0.1138863415,0.118936263,0.080988206,-0.3776184916,-0.1859687865,0.0942310467,0.1264690012,-0.6105197072,0.2371102422,0.4000205994,-0.3269696534,0.1599569172,-0.067077361,0.0638428926,0.0541390106,-0.0907466486,-0.0465912186,0.1650673151,-0.1873859465,-0.0774285272,0.1572607011,0.0634271652,0.1613905579,0.0171967782,-0.1779077351,0.181212917,-0.2354558259,0.0056721289,-0.1401464492,-0.0122230006,-0.0569794104,-0.070378527,0.4700904489,0.1602924466,-0.0891896114,0.2671580017,0.0374502428,0.1032643393,-0.2167339176,0.100880295,0.1906232685,0.122556828,0.1977968961,0.0752735138,-0.2027528584,-0.2452730983,-0.1989915967,0.4515694082,0.3463511169,-0.1463848501,-0.4804702997,0.0671454966,0.015336344,0.0724580362,0.0974962115,0.1500560194,0.1725723296,0.3843930066,-0.0482332893,0.2677053809,-0.3312215507,-0.2914781868,-0.0979884863,0.2515812218,-0.3490155041,-0.0358149298,0.1118646935,-0.1107490659,0.1102609709,0.2399997562,0.1703832746,-0.3305484354,0.0894226581,-0.171946466,-0.0011075635,0.026131954,0.0958600864,0.155036062,0.1559718102,-0.3678236604,-0.0557275154,0.0636262074,-0.5577607155,-0.1297597438,-0.1729639024,0.2627521753,-0.0124775432,-0.0123801986,-0.5066611767,0.0706070215,0.1725929826,-0.2303977162,0.0631193146,-0.1705555022,-0.1377055347,-0.2525263429,0.175331369,0.4754906595,-0.2941102684,0.0538599826,0.1609711498,0.0338650122,0.3461773694,0.0864404142,-0.2136350572,-0.1357930154,-0.2194357067,-0.0354538262,0.3955557644,-0.4324065447,-0.2422579378,0.3587153852,-0.243787989,-0.008346376,0.0660074726,-0.131799683,0.0356077738,-0.2514543235,0.1312204003,0.4155409336,0.0266562011,0.2746024728,-0.1828944981,-0.3092409074,0.2006269693,0.0163544845,-0.2178280652,-0.220205456,0.2515701354,0.3486474752,0.2898349464,-0.1736857295,-0.1671789885,0.2128673196,0.027012622,-0.2167555988,-0.2222600579,0.0945966989,0.0223033037,-0.0189579371,-0.3267049193,0.0898943469,-0.3444603086,-0.1623487771,-0.1593017578,0.037658222,-0.6050456762,-0.2833513021,0.2350183278,0.2086819708,-0.0824370235,0.0819688886,-0.2075414658,0.2771047354,0.0008027555,-0.0396683104,-0.3657068312,0.0936851799,-0.1336218268,-0.1018197462,0.2282004058,0.3284243047,0.1678859293,-0.1420123279,-0.2412918061,0.3130163848,0.0796097592,0.3886031508,-0.2427394837,-0.1854430884,0.4853195548,-0.6272907257,0.1525293738,0.480600059,0.2121469826,-0.1064735949,0.3261395991,0.3260163665,-0.0221676007,-0.087656416,0.0208773259,-0.1277964264,0.1704484522,-0.0059429887,0.1041062623,-0.2580585182,0.3132897615,0.6806163788,0.3463375866,-0.0056937668,-0.0863397047,-0.0046069031,0.5590988994,-0.0820821971,0.0221946873,0.0778365806,0.12169002,-0.1580358744,-0.0382321887,0.0860441029,0.2751679122,-0.0825376585,-0.1489648372,0.2428869456,0.0354851931,-0.2573782802,0.152756542,-0.0567202866,0.0502872542,0.0647864863,0.1666754782,0.0959484652,-0.257853955,-0.3063188493,0.0870975703,0.1154112071,-0.3568193913,0.314188689,-0.5408015251,-0.3548946381,-0.271730274,0.1222766638,-0.1007573232,-0.0870124325,-0.1659294218,-0.0778104141,0.1684072912,0.1757585406,-0.2078566998,0.0004747527,-0.0593759939,-0.0908879787,0.1813361049,-0.1437168568,-0.2546927035,0.0867510512,0.4046459496,-0.0034862498,0.0353804938,-0.39510566,0.0685483217,0.1820477396,-0.1195266172,0.2201739848,-0.2149030715,0.1363780946,-0.0043688198,0.006755732,0.0071471189,-0.2906437218,0.1497115046,-0.023972474,-0.0502182283,0.1258330345,-0.0785772577,-0.1874869466,-0.2346599698,0.0157003179,-0.5259251595,-0.4137800336,-0.2053127885,0.0765401572,0.2234555483,0.2994021177,-0.0135563686,0.2073113024,0.2510852516,-0.0007844482,-0.121512264,-0.3657850921,0.3067182302,-0.4246502519,-0.4335766435,0.0322742648,-0.0820378885,0.4345403314,-0.0832580104,-0.1792014241,-0.0581072532,-0.1336797178,0.0765964463,-0.0586904474,0.1000950485,-0.0111700762,0.0214985237,-0.0183023475,-0.0689403266,0.080017589,-0.1598852575,0.0732782185,0.3267397285,0.0562297292,0.2255901694,-0.1048821285,-0.0407772698,0.3091386557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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"Hi there !\r\nI encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\nI added ```encoding='UTF-8'``` to both lines that have ```open``` in ```imdb.py```  (108 and 114) and it worked for me.\r\nThank you !","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":72,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n Hi there !\r\nI encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\nI added ```encoding='UTF-8'``` to both lines that have ```open``` in ```imdb.py```  (108 and 114) and it worked for me.\r\nThank you !","embeddings":[-0.3488940001,0.0580565482,-0.0053174524,0.2148150355,0.2911395133,0.0693918839,-0.0231634378,0.1682781577,-0.1171187684,-0.008803363,0.0351622328,0.3814254105,0.0592853576,0.2256459594,0.2042711079,0.137657851,0.0813008025,0.227961272,0.0762692764,-0.0321259983,-0.2550890446,0.2474260926,-0.1208336279,0.1178522632,-0.3460617363,-0.1771093756,0.1752639413,0.2684678733,-0.2989810705,-0.5578821898,0.3264290392,-0.0420311838,0.1637300253,0.3156361282,-0.000107127,-0.1720654815,0.4879135489,-0.2730462551,-0.0977526829,-0.2996372283,-0.0662683621,-0.1792032421,0.208705768,-0.1951277703,0.1642090976,0.2361993343,0.0254221167,-0.2193976045,0.2429872006,0.2548004389,0.2512708008,0.0758318156,-0.0124273971,0.0530121475,0.1059629247,0.0892353132,-0.0343464613,0.4207041264,0.4167104363,-0.0661733001,0.1576461345,0.1413359195,-0.2976844311,-0.1031862423,0.1930611432,0.1217105463,-0.1926509887,-0.5360171199,0.0280171651,0.237992093,0.4053965211,-0.4262819886,-0.2080039084,0.0869111568,0.2992160618,-0.4392408431,0.2078835219,0.4138452113,-0.3085672259,0.1394884735,-0.0505688712,0.1756735742,0.1524308771,-0.0138395345,0.0176998414,-0.0796573311,-0.0850763693,-0.0789386407,0.107614696,-0.0334568508,0.2237876803,0.0531084575,-0.1419592947,0.0521738902,-0.0420180485,-0.0374315307,-0.1653201431,0.0665675104,-0.0967051834,0.0523701906,0.3962653875,0.1933194697,-0.1017117128,0.3944354057,0.2274758965,0.0416718125,-0.2219593525,0.2559438944,0.3583293557,0.1660337746,0.0576864257,-0.0020175995,-0.1636369377,-0.1274368614,-0.1428083181,0.2887331247,0.1514272094,-0.1898154914,-0.4294265211,0.1617305577,0.1705228984,0.2196198106,0.1940185279,0.1838280559,0.1750237495,0.3112075627,0.0333496518,0.3036215901,-0.2544294596,-0.2565978169,-0.0809628963,0.1975775361,-0.2479849905,-0.1120387986,0.2367900759,-0.0987468883,0.1741169244,0.2018454969,0.2456615269,-0.1505301893,0.0676360577,-0.1074694172,0.2129864246,0.2414590269,0.1607815474,0.2428202629,0.1067941934,-0.2313483059,-0.1709671021,0.0372146033,-0.421536386,-0.1010330245,-0.0597743765,0.207286194,-0.081762813,-0.1171906739,-0.6567404866,0.0730897933,0.009207366,-0.3679394126,0.069724001,-0.1777156293,-0.0362018384,-0.0768480822,0.1931199431,0.5341475606,-0.4658977091,0.1469022632,0.0349595398,0.0121597387,0.224407956,0.1452992857,-0.138791889,-0.1420612782,-0.3158390522,0.1119498387,0.3089248538,-0.5038155913,-0.3365866244,0.3986084163,-0.0998629704,-0.0152446581,-0.0035208648,0.0348181501,0.1104175672,-0.2140943259,0.0104302736,0.3849824369,-0.0740710571,0.1820012629,-0.1900574714,-0.1728015393,0.1346137375,-0.0327699557,-0.171205312,-0.1084196121,0.1116732508,0.1699962765,0.230095759,0.0933081061,-0.1538109034,0.1082648337,0.2403595299,-0.008167116,-0.0733689293,0.1044959798,0.0796272904,0.0673356056,-0.2456248999,0.106788218,-0.4861810505,-0.2660392523,-0.1546813101,0.0328306705,-0.3553241491,-0.0886504427,0.2059870809,-0.0387346335,-0.2071613967,0.0492851771,-0.3249136806,0.1765441746,0.1097027436,0.0658427849,-0.2932023108,0.1220303252,-0.185179919,-0.2235825807,0.2431552261,0.1649621874,0.1026879102,-0.0626986623,-0.1499416083,0.2574966848,0.0730739236,0.3449715972,-0.2452047169,-0.2048249543,0.5607063174,-0.548206687,0.113046661,0.4191701412,0.2602438331,-0.1504663527,0.2045992911,0.2479991317,-0.1719045043,0.0086719245,0.0719211251,-0.0847103596,0.2972041368,0.1503051519,0.0177873746,-0.3461951315,0.2551012039,0.5953899026,0.2146585137,-0.0987160578,-0.1114511937,0.1296662688,0.4439419806,-0.0543929003,-0.0165188089,0.0682232007,0.0208389536,-0.0622440167,-0.0035003296,0.0270835478,0.0906314179,-0.0888238624,-0.2187934071,0.212004438,0.0657975301,-0.2460459769,0.1466578692,-0.0243357345,-0.1499833316,0.0709517151,0.0682789981,0.1316547096,-0.2474688441,-0.2776138484,0.0352979638,0.0612460449,-0.3537080884,0.2199955136,-0.4938854873,-0.3810700774,-0.1177900359,0.069818452,-0.0420004651,-0.1311085373,-0.0912832394,0.1379749924,0.1113818809,0.1218913347,-0.3576264679,-0.0745340884,-0.1458536834,-0.1930759996,0.03047801,-0.2158692032,-0.1704759747,0.083177276,0.4746145606,0.0928667188,0.0708577931,-0.4188654423,0.201561749,0.0766297132,-0.1373047084,0.2315514088,-0.2333486974,0.1168904975,0.0479770824,0.0582223013,-0.0004436653,-0.4387207329,0.1450605541,-0.1077361926,-0.0211324245,0.2000148147,-0.0105557768,-0.3811513782,-0.1138438806,0.0026009511,-0.4156636298,-0.2895945609,-0.1918073744,0.0485388711,0.0491561033,0.1971783787,0.1036536843,0.1325834692,0.2278992087,-0.0076735439,-0.0012111965,-0.3418912888,0.4118304849,-0.3309630156,-0.4681514204,0.2416665554,0.0385172293,0.370306462,-0.236439243,-0.2635747492,-0.0220803767,-0.1116258428,0.1798568368,0.1413113028,0.0253992938,-0.0990375355,-0.0507818423,-0.0319888592,-0.0363839045,-0.08936093,-0.1141126975,-0.0868043303,0.1754243821,0.0025579187,0.2680507004,-0.3292317688,-0.0546297096,0.2706374228,0.0768693388,0.7014579177,-0.1841569096,0.38472718,-0.2749556601,-0.1120501235,-0.1152989566,-0.1782622933,-0.045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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"> Hi there !\r\n> I encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\n> I added `encoding='UTF-8'` to both lines that have `open` in `imdb.py` (108 and 114) and it worked for me.\r\n> Thank you !\r\n\r\nHello !\r\nGlad you managed to fix this issue on your side.\r\nDo you mind opening a PR for IMDB ?","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":97,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n > Hi there !\r\n> I encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\n> I added `encoding='UTF-8'` to both lines that have `open` in `imdb.py` (108 and 114) and it worked for me.\r\n> Thank you !\r\n\r\nHello !\r\nGlad you managed to fix this issue on your side.\r\nDo you mind opening a PR for IMDB ?","embeddings":[-0.3819619417,0.0328492075,-0.0154810492,0.2028752118,0.3120900989,0.0689587519,0.0058169118,0.1825693846,-0.083670944,0.0293346681,0.014070197,0.4008607566,0.0577816293,0.2399227768,0.1625403166,0.14097929,0.0723040178,0.2220351994,0.1050566211,-0.0275190789,-0.2586425841,0.2288965434,-0.1308452934,0.1349900067,-0.3728035688,-0.1832351387,0.1941950768,0.2883248329,-0.2690778673,-0.5402297378,0.3226154447,-0.0409633145,0.1525747478,0.3042993248,-0.0001053745,-0.1787819266,0.5192816854,-0.2602533102,-0.1324340999,-0.2859140933,-0.0765627101,-0.1620351523,0.2123644501,-0.1953220963,0.1557098031,0.2527001202,0.0229268353,-0.2271054238,0.2151814699,0.2319758087,0.267660141,0.0608118139,-0.0186496228,0.045802366,0.0669800267,0.1003600582,-0.0261969399,0.429251641,0.4187467098,-0.0728279501,0.1366805434,0.1390876472,-0.2907060683,-0.117742613,0.2308941931,0.1356046498,-0.1527103037,-0.539737761,0.0303562395,0.2396232039,0.4043304026,-0.4341339767,-0.2198884487,0.0727621093,0.2851529717,-0.4451404214,0.2295992374,0.3813069165,-0.3105047345,0.1312863827,-0.0726721138,0.1851212233,0.1598808467,-0.0166890323,0.0241958275,-0.1213527247,-0.0719853118,-0.0958218426,0.0644970015,-0.0755638033,0.2218587697,0.0408137366,-0.1298449337,0.031149948,-0.0231444854,-0.0654445216,-0.1572759002,0.0888461322,-0.1031697839,0.0526708998,0.3892244995,0.1904302835,-0.1060628444,0.3926351666,0.2187597603,0.0764838234,-0.1863272786,0.2649510503,0.3690944612,0.1792969108,0.0352390744,-0.007588035,-0.1356833875,-0.1409618109,-0.1185562164,0.2891425192,0.1462384462,-0.1817835271,-0.4506699145,0.1488844603,0.1540304571,0.2377270162,0.1990226209,0.1871261448,0.1694632322,0.3224521875,0.0406058207,0.2857062519,-0.2505064309,-0.2612249553,-0.0903877988,0.1797525436,-0.2592477798,-0.1103608608,0.218302846,-0.101804696,0.1758921295,0.2183071077,0.2862075567,-0.1571925879,0.05349309,-0.0925322846,0.2198350728,0.2458468229,0.1864881665,0.2278587371,0.1167296171,-0.2549138665,-0.1482722759,0.0122956978,-0.4237723351,-0.1114712805,-0.0551036336,0.2240451723,-0.0579555444,-0.0987666398,-0.6468676925,0.06157168,0.0012063287,-0.36565727,0.075383015,-0.1864982992,-0.0492687449,-0.0849167258,0.1986242384,0.5331299901,-0.4609873891,0.1437164843,0.0010171983,0.0304283388,0.2228224427,0.1494708657,-0.1381026655,-0.1621319205,-0.3192888498,0.1065663695,0.2765079141,-0.5096930861,-0.2977873385,0.4063526988,-0.093693465,-0.0049822489,-0.0029611564,0.0064352555,0.1321465522,-0.2374905348,0.0350820944,0.3566595018,-0.0839654431,0.1826423705,-0.212310642,-0.1224609464,0.0972468033,-0.0655148029,-0.1674876511,-0.1106645837,0.13592498,0.1589934826,0.2215439826,0.1049713492,-0.1610754728,0.10810332,0.2616814077,0.0201453511,-0.0755081847,0.1233400404,0.0588506982,0.0779778659,-0.2107319385,0.0863468647,-0.4398860335,-0.2682012618,-0.1456341296,0.0335430428,-0.3326475322,-0.0839553401,0.2256686985,-0.0241779946,-0.2100736797,0.0258989856,-0.3468070328,0.1851453334,0.088781476,0.0475903675,-0.2662093937,0.1227076501,-0.166383177,-0.2624225616,0.2336537838,0.1494119316,0.1016341522,-0.0474017262,-0.1530420631,0.282733947,0.0698587149,0.3505370021,-0.2592075169,-0.1781454682,0.5491707921,-0.5299206972,0.1276390105,0.3686937094,0.246872142,-0.1486336738,0.201056689,0.2885649502,-0.1976458579,0.0161664598,0.0763596967,-0.0940714255,0.2952249348,0.1128005981,0.0039010101,-0.3647915423,0.2632720768,0.5909789801,0.2062430233,-0.1096372753,-0.1240621433,0.1479021758,0.449782759,-0.0390714072,-0.0305704251,0.0685048848,0.0158061367,-0.0551207773,-0.0131513281,-0.0006509282,0.078051284,-0.0851645991,-0.2253017575,0.2005329728,0.0598540753,-0.2409396917,0.1696550995,-0.0282123424,-0.1508947313,0.1010195836,0.0679655373,0.117464222,-0.2235124409,-0.2603949308,0.0180319659,0.0348623209,-0.3721189499,0.1928663701,-0.462643981,-0.3444407284,-0.0923713073,0.0627866834,-0.0048273318,-0.1374607533,-0.0746689439,0.1510362029,0.1117161438,0.1418263763,-0.328617245,-0.0649257377,-0.1468442678,-0.1589344442,0.0047206846,-0.2141066492,-0.159800753,0.098413527,0.4974024892,0.0695274174,0.0424909256,-0.4195205271,0.1661721468,0.0735995099,-0.1208945736,0.2249321342,-0.2262669951,0.1265570521,0.042630665,0.0500894226,0.0114448238,-0.4530969262,0.14853549,-0.123546958,-0.020363126,0.2205782533,-0.0266792905,-0.3890245259,-0.1062777266,-0.0042591956,-0.3799142241,-0.2897257507,-0.1875100136,0.0466420129,0.0419532172,0.2030296475,0.1531606913,0.1282235682,0.2437355965,-0.0116908848,-0.011725178,-0.3491702974,0.3963178694,-0.3242890537,-0.4361437857,0.2484275997,0.0181289911,0.3712930679,-0.2505528033,-0.2293863297,-0.0445735864,-0.11632967,0.2337530404,0.1334402561,0.0063670604,-0.1004881933,-0.0388952345,-0.0566139854,-0.0311206598,-0.0822284594,-0.1118074134,-0.0823013261,0.1642276645,-0.0008164028,0.3257593513,-0.344471097,-0.0641670078,0.2906715572,0.06649369,0.6802045703,-0.1825071126,0.3822169006,-0.2870160043,-0.106695205,-0.1105034053,-0.1770844311,-0.0840747654,0.14186893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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"> This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\n> Try to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\n> See issues #242 and #307\r\n\r\nSorry for not responding for about a month.\r\nI have just found that it is necessary to change \/ add the environment variable as what was told in #242.\r\nEverything works after I add the new environment variable and restart my PC.\r\n\r\nI think the encoding issue for windows isn't limited to the open() function call specific to few dataset, but actually in the entire library, depends on the machine \/ os you use.","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":115,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n > This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\n> Try to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\n> See issues #242 and #307\r\n\r\nSorry for not responding for about a month.\r\nI have just found that it is necessary to change \/ add the environment variable as what was told in #242.\r\nEverything works after I add the new environment variable and restart my PC.\r\n\r\nI think the encoding issue for windows isn't limited to the open() function call specific to few dataset, but actually in the entire library, depends on the machine \/ os you use.","embeddings":[-0.3218461871,0.0410403796,-0.0172628704,0.1921315044,0.384137392,-0.0418330804,0.0533850081,0.1839184314,-0.2438150942,0.07465633,0.1122436076,0.4671255648,0.1107988954,0.3263352215,0.2156340927,0.0793417469,0.121593371,0.2935247719,0.0367938206,0.0253933985,-0.4600175023,0.1801846474,-0.1028250009,0.1062120944,-0.2276374996,-0.1009323597,0.0553654954,0.2734963,-0.2023732513,-0.5459724069,0.1896327585,-0.0750284195,0.2605702877,0.2277506739,-0.0001059823,-0.0440538898,0.2804324925,-0.1803229153,-0.1569127738,-0.2889727354,-0.1975437403,-0.3678892255,0.1078184769,-0.2368742228,0.0635781735,0.2137286961,-0.002422865,-0.2136175185,0.4395005107,0.3770242333,0.2794305682,0.135423705,-0.1147260815,0.1099153012,0.0541248806,0.0573215336,-0.0186663661,0.5010844469,0.2131777406,-0.2339810729,0.0298318323,0.1721233726,-0.2423514128,-0.0580992885,0.0973245502,0.1594382823,-0.2101520449,-0.4088995457,0.1295930147,0.1351365596,0.3100127876,-0.32497558,-0.1276594549,0.0083150454,0.1066843569,-0.7205759287,0.3261366487,0.4268108606,-0.2456021011,0.1342461705,0.0609913804,0.2269624919,0.0461214818,-0.0214395002,0.0695758462,-0.0316437259,-0.2375248969,-0.0469956733,0.2229003608,-0.0380632356,0.0924569294,0.0012147638,-0.2106275111,0.0815219954,-0.2083330303,0.0376022011,-0.1750782877,0.0315689147,-0.1538994163,-0.0620154887,0.5168815851,0.1546454132,0.013629592,0.3213183582,0.1049254388,0.0797767639,-0.1220801398,0.1708958149,0.3792809248,-0.0342668928,0.1415133774,-0.0152551662,-0.2251727432,-0.347530961,-0.2094828337,0.3959975243,0.216550678,-0.1523399949,-0.4361654222,0.1212785318,0.0980656147,0.0702150315,0.0240607169,0.2243415564,0.2047697306,0.3466665447,0.0168047529,0.2039867938,-0.3592662215,-0.2429448366,-0.017309187,0.1819052845,-0.368889004,-0.069752641,0.2735366821,-0.1578790694,0.0549857467,0.2533173859,0.168840006,-0.2118099332,-0.0451447181,-0.2011538744,0.1154903769,0.1348486096,0.0499350391,0.2183388621,0.1298353076,-0.3230085373,-0.0904251114,0.0744729415,-0.5151638985,-0.0060920529,-0.0799663141,0.2387244105,-0.1374502778,-0.0513865463,-0.4799760282,0.1780171543,0.0198981147,-0.4306436777,0.0101035116,-0.2054034472,-0.1354725808,-0.2135009319,0.187598452,0.5726647377,-0.5493261814,0.2175498307,0.2308416069,0.0270515978,0.2820723355,0.1310224682,-0.1339088529,-0.1769125313,-0.1706348807,-0.0320758484,0.2616347671,-0.2815617621,-0.273617506,0.3522999585,-0.0072575039,-0.0457922742,0.0350057259,-0.1634368151,0.1933966726,-0.2519917786,0.0321125425,0.3793457448,0.0374853723,0.3035112619,-0.201108709,-0.1544061601,0.2732438147,0.0107407169,-0.2936501801,-0.2032541931,0.2821953595,0.1416630596,0.1744512916,-0.1511706412,-0.1337528527,0.1587605625,0.2327430993,-0.2096036524,-0.0254872553,0.1805101931,-0.0249594674,-0.0260686409,-0.2298215181,0.0581737272,-0.2890240252,-0.1495994925,-0.0929415002,0.0466047227,-0.324644804,-0.2399466932,0.2032574117,0.036951106,-0.1426317543,-0.0014226306,-0.1411515474,0.2768875062,0.0107317977,-0.0139016593,-0.1724307984,0.186179474,-0.0900348499,-0.2032659352,0.2726712525,0.3069351315,0.1852348894,-0.1314292997,-0.2873487175,0.291446209,0.0348336473,0.3578834236,-0.2649577558,-0.2401684523,0.4578382671,-0.4840072691,0.0709460452,0.4611472189,0.3018136621,-0.0979863778,0.1545099765,0.3615797758,-0.1767522395,0.0195953175,-0.0165709723,-0.1244196668,0.2131323516,0.030701099,0.0570870601,-0.1943311393,0.3480555415,0.5848902464,0.2559981644,0.0374703594,-0.1499472708,-0.0009214273,0.5480136275,-0.0926613361,-0.0730473921,0.1219992116,0.0281347297,-0.1021295711,0.0819737464,0.0354270525,0.3410062194,-0.118401669,-0.2693512142,0.2631337941,0.0719557628,-0.2493242174,0.1785944253,-0.0663466156,-0.0398111865,0.001624636,0.109225072,0.2062709332,-0.2464142591,-0.0896531641,0.1819762886,0.1307782084,-0.3706768751,0.3590084016,-0.5268562436,-0.4222705364,-0.2778965831,0.2370275557,-0.1506250948,0.0565890856,-0.2060832381,-0.0146384789,0.0048544742,0.1809751391,-0.1959636807,0.0591054447,-0.1677372307,-0.1239162162,0.1611836404,-0.2020172626,-0.3256495297,0.1091831252,0.4991661012,-0.067203477,0.0567062274,-0.3631934226,0.1340762377,0.1923754066,-0.1332850009,0.1386752278,-0.1958273351,0.217488721,-0.0461457185,0.0197295472,-0.0940226093,-0.4398115575,0.1757384837,-0.0620789118,0.0180765465,0.1688304543,-0.05194363,-0.2897469103,-0.2744269073,-0.1558913887,-0.5099146366,-0.3886059225,-0.1136434451,-0.1147130132,0.0890478119,0.3229112625,0.0256176572,0.0290212259,0.1873557121,-0.1173400953,-0.0495327041,-0.1487429291,0.3419561982,-0.4083913565,-0.5525043011,0.1008481905,-0.0198440161,0.319747597,-0.1563616544,-0.189694345,-0.006978326,-0.1507255435,0.1050705761,0.0035450684,0.1002129838,-0.0547260791,-0.047074724,-0.0254627932,-0.1998526454,-0.19223997,-0.0613547042,0.0895951837,0.2490141839,0.0475215241,0.2492836118,-0.2908984721,0.0163077638,0.3181402981,0.0356075317,0.5225813985,-0.136158064,0.4234559834,-0.0275136884,-0.0585542843,0.1832724214,-0.3034586012,-0.05938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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/347","title":"'cp950' codec error from load_dataset('xtreme', 'tydiqa')","comments":"Since #481 we shouldn't have other issues with encodings as they need to be set to \"utf-8\" be default.\r\n\r\nClosing this one, but feel free to re-open if you gave other questions","body":"![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n","comment_length":32,"text":"'cp950' codec error from load_dataset('xtreme', 'tydiqa') \n ![image](https:\/\/user-images.githubusercontent.com\/50871412\/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)\r\n\r\nI guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :\r\nhttps:\/\/www.python.org\/dev\/peps\/pep-0263\/\r\n\r\nI guess the error was triggered by the code \" module = importlib.import_module(module_path)\" at line 57 in the source code:  nlp\/src\/nlp\/load.py \/ (https:\/\/github.com\/huggingface\/nlp\/blob\/911d5596f9b500e39af8642fe3d1b891758999c7\/src\/nlp\/load.py#L51)\r\n\r\nAny ideas?\r\n\r\np.s. tried the same code on colab, that runs perfectly\r\n \n Since #481 we shouldn't have other issues with encodings as they need to be set to \"utf-8\" be default.\r\n\r\nClosing this one, but feel free to re-open if you gave other questions","embeddings":[-0.2920763791,-0.11546655,-0.0485051274,0.1429698169,0.3976854682,-0.1386851072,-0.0299375225,0.207310617,-0.3486039042,0.1107885614,0.1741642654,0.4716804028,0.0220731571,0.3367837369,0.2393419445,-0.022137329,0.034644302,0.2812201679,-0.0362177938,-0.0421609916,-0.270514071,0.1711585224,-0.0659664944,-0.0170620605,-0.3997241855,-0.012458141,0.0486944318,0.2974319458,-0.3648321629,-0.475156337,0.1951902062,0.0119117526,0.135913834,0.259455055,-0.0001011886,-0.0771028772,0.3384810984,-0.2374668419,-0.0726838037,-0.2214623988,-0.216215387,-0.3395412564,0.0997417122,-0.1581599265,-0.0460250676,0.2871535122,0.0547698215,-0.0264533162,0.4907978475,0.2225217819,0.3361176252,0.1082952097,-0.0871384889,0.0970920026,0.0383858159,0.1536255032,-0.0810595378,0.40453583,0.2657889128,-0.2489144504,0.130001545,0.3025204837,-0.1547402442,-0.1583583951,0.0455346406,0.0050726337,-0.07711339,-0.4147141278,0.0786356255,0.1539196968,0.1933080405,-0.3020585477,-0.0559194386,0.0919888839,0.1005073786,-0.5315515399,0.2414298356,0.2892900407,-0.2620902658,0.0932654068,-0.0564309619,0.1054813787,0.0520006791,-0.043190334,0.0325556062,0.1327358186,-0.1192581356,-0.124322392,0.1317437887,0.0007684184,0.1545886397,0.0858947411,-0.20291394,0.210920468,-0.1349487007,-0.1016724706,-0.119392328,-0.0037293723,-0.079894498,-0.1457085758,0.5618266463,0.1537452787,-0.0523674786,0.3236987889,0.1299596429,0.0667191371,-0.1919053942,0.1937870681,0.231200248,0.0484264903,0.1330812573,0.0626800954,-0.1734261662,-0.2025741041,-0.3286463916,0.2584735751,0.1405035704,-0.1802671403,-0.387039572,0.0865402147,0.0774649903,0.1144331023,0.0800361261,0.1966690421,0.2150232792,0.3200768828,-0.0310161225,0.2661576271,-0.3405717909,-0.3002048433,-0.1536055356,0.2318949401,-0.426307559,-0.1337781698,0.1555860192,-0.0103649162,0.1558938175,0.2073730826,0.1698080152,-0.124043718,0.0667798519,-0.0724978596,0.0879873931,0.1300014406,-0.0004067399,0.0970363915,0.1606966257,-0.2576760948,-0.0956009999,0.0696871206,-0.406974107,-0.0201010816,-0.052260939,0.3179693818,-0.0753247589,-0.046668414,-0.4660201669,0.1913840771,0.1194681749,-0.3060785532,0.1238185167,-0.1691370606,-0.0626114383,-0.1708720624,0.1583511978,0.4247146547,-0.4670240283,0.0496596918,0.2211828232,-0.0012428117,0.3695967495,0.0587585829,-0.1565062255,-0.1428921074,-0.1330500394,-0.0047206599,0.3198845088,-0.3632705808,-0.2036395967,0.2700951695,-0.0475943014,-0.0306829866,-0.0173880551,-0.0106357597,0.0530152656,-0.1864280701,0.0972895399,0.4720681906,0.0049763909,0.2972848713,-0.2908039093,-0.2138978094,0.1408780962,0.091919668,-0.2211559862,-0.205946371,0.1534100026,0.3134850562,0.2427745014,-0.0770800635,-0.2891998589,0.1630904824,0.2696890235,-0.2634699345,-0.1851076335,0.2320987582,0.0784944147,-0.0356946923,-0.3901937306,0.2268241793,-0.2439683229,-0.1308945715,-0.0936571956,0.0231815316,-0.4545505345,-0.262798816,0.2949182689,-0.0049352744,-0.1328277886,0.0112286191,-0.1847508252,0.2601161003,0.0009451048,-0.0335316882,-0.2378202379,0.1849799454,-0.1797088087,-0.2675313652,0.2869929969,0.2790139914,0.1291242242,-0.1447089165,-0.157281667,0.2860490382,0.01248312,0.41312778,-0.2609884441,-0.2436141074,0.5184406638,-0.5575979352,0.0700042397,0.4544993341,0.1927979589,-0.1603338122,0.243435055,0.2702005506,-0.0523837693,-0.054996945,0.0412183218,-0.1327365488,0.1504889429,0.0241405908,0.0469062366,-0.1603646725,0.3626900911,0.6080385447,0.2547626793,-0.0487532355,-0.1286948621,0.1122962534,0.4738336802,-0.010348795,-0.0013956904,0.0326432958,0.0848798305,-0.1663371325,0.1038350016,0.050344225,0.1257792264,-0.0344345793,-0.2082009315,0.2009344399,-0.0656058043,-0.2105448842,0.1600576788,-0.1011009738,-0.0115903672,0.0224148184,0.1100702882,0.0249632355,-0.3192924559,-0.1397740245,0.0235098451,0.0939735919,-0.2500429451,0.2658503652,-0.4543836415,-0.4244672954,-0.1767519563,0.0882067382,-0.2056944966,-0.0796077102,0.0103617562,-0.0777699351,0.0670166165,0.2921089828,-0.1647594422,-0.0042428644,-0.1235999987,-0.0989780948,-0.0117906826,-0.1825900376,-0.2122241855,0.1677803546,0.4413799942,-0.0398410149,0.1024440229,-0.3816798031,0.0646965057,0.2964733243,-0.3084105253,0.2883993983,-0.2581631541,0.1106206998,-0.0010713122,0.0948402807,-0.0560660399,-0.3148552179,0.2171664089,-0.1198956147,-0.0971306637,0.1169732958,-0.0459102392,-0.1961919665,-0.2785631418,0.0642169341,-0.441055119,-0.3425576687,-0.2023861259,0.1259260923,0.0929157734,0.415661633,0.2045500875,0.0987194106,0.1759499758,-0.0851396099,-0.1331690401,-0.3334023356,0.2693322003,-0.3517205119,-0.4344560206,-0.0000725311,-0.0121584991,0.5023617148,-0.1869822741,-0.2606030405,-0.1026283726,-0.0670835301,0.0555351451,-0.0144215785,0.1379838437,-0.0324569158,-0.0005478442,-0.0640170276,-0.1284192353,0.0312203113,-0.0455351882,0.0314098448,0.3433048427,-0.0890802518,0.1688875556,-0.2043135017,0.0703647956,0.169530645,0.0827557668,0.6934217811,-0.0739770532,0.4205706418,-0.1995118409,-0.0481585786,0.1415821463,-0.12069235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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/345","title":"Supporting documents in ELI5","comments":"Hi @saverymax ! For licensing reasons, the original team was unable to release pre-processed CommonCrawl documents. Instead, they provided a script to re-create them from a CommonCrawl dump, but it unfortunately requires access to a medium-large size cluster:\r\nhttps:\/\/github.com\/facebookresearch\/ELI5#downloading-support-documents-from-the-commoncrawl\r\n\r\nIn order to make the task accessible to people who may not have access to this kind of infrastructure, we suggest to use Wikipedia as a knowledge source rather than the full CommonCrawl. The following blog post shows how you can create Wikipedia support documents and get a performance that is on par with a system that uses CommonCrawl pages.\r\nhttps:\/\/yjernite.github.io\/lfqa.html#task_description\r\n\r\nHope that helps, using ElasticSearch to index Wiki40b and create the documents should take about 4 hours. Let us know if you have any trouble with the blog post though!","body":"I was attempting to use the ELI5 dataset, when I realized that huggingface does not provide the supporting documents (the source documents from the common crawl). Without the supporting documents, this makes the dataset about as useful for my project as a block of cheese, or some other more apt metaphor.  According to facebook, the entire document collection is quite large. However, it would still be helpful to at least include a subset of the supporting documents i.e., having some data is better than having a block of cheese, in my case at least.\r\n\r\nIf you choose not to include them, it would be helpful to have documentation mentioning this specifically. It is especially confusing because the hf nlp ELI5 dataset has the key `'document'` but there are no documents to be found :(","comment_length":130,"text":"Supporting documents in ELI5 \n I was attempting to use the ELI5 dataset, when I realized that huggingface does not provide the supporting documents (the source documents from the common crawl). Without the supporting documents, this makes the dataset about as useful for my project as a block of cheese, or some other more apt metaphor.  According to facebook, the entire document collection is quite large. However, it would still be helpful to at least include a subset of the supporting documents i.e., having some data is better than having a block of cheese, in my case at least.\r\n\r\nIf you choose not to include them, it would be helpful to have documentation mentioning this specifically. It is especially confusing because the hf nlp ELI5 dataset has the key `'document'` but there are no documents to be found :( \n Hi @saverymax ! For licensing reasons, the original team was unable to release pre-processed CommonCrawl documents. Instead, they provided a script to re-create them from a CommonCrawl dump, but it unfortunately requires access to a medium-large size cluster:\r\nhttps:\/\/github.com\/facebookresearch\/ELI5#downloading-support-documents-from-the-commoncrawl\r\n\r\nIn order to make the task accessible to people who may not have access to this kind of infrastructure, we suggest to use Wikipedia as a knowledge source rather than the full CommonCrawl. The following blog post shows how you can create Wikipedia support documents and get a performance that is on par with a system that uses CommonCrawl pages.\r\nhttps:\/\/yjernite.github.io\/lfqa.html#task_description\r\n\r\nHope that helps, using ElasticSearch to index Wiki40b and create the documents should take about 4 hours. Let us know if you have any trouble with the blog post though!","embeddings":[0.0806761757,-0.1414157599,-0.1308178753,-0.09870141,-0.3395778239,-0.0216143336,-0.2173523605,0.2103035599,0.050301034,-0.0259131584,0.1280906349,-0.2411950082,0.1008691564,0.1427407563,-0.2445807904,0.1461946964,0.0276544765,0.2660853565,0.2416867018,-0.1947776228,-0.0178429596,0.1976368874,0.1432036906,-0.1716149896,-0.3346624076,0.2507556081,-0.2730874717,0.1042412817,-0.237261638,-0.0662403479,0.1023939028,0.2378306091,-0.036256399,-0.0565391257,-0.0001082366,0.0151389698,0.0998261422,-0.1303936988,-0.0717165768,0.0352354124,0.3620178401,-0.4477023184,0.1384598911,-0.1620794684,-0.3059388995,-0.1202642322,0.2798631489,0.3556277752,0.3528578579,-0.004643877,0.1948395669,0.013586794,0.1496860087,0.0355122834,0.3426299393,0.3699345589,-0.2522276342,-0.262568593,0.2649269104,-0.2322939336,0.0697334111,0.3647731245,0.0875473768,-0.1129977629,0.3730433881,0.1021438241,0.0469903685,-0.613278985,0.2595945895,0.251003474,0.3036833107,0.2703653574,-0.1207334548,-0.3047217429,-0.2468187362,0.285892874,0.195596233,0.2177018672,-0.1502867788,0.15116404,0.0314800292,-0.1184746474,-0.018271666,0.3093078136,0.1452437937,0.1628545076,-0.0881980807,-0.1460662037,0.2186290026,-0.1449057013,-0.6590011716,0.1220768467,0.2056598514,0.134045437,0.048492562,-0.1340939105,0.1747570187,0.1377401203,0.5864340663,0.0596163012,-0.0866412669,0.127357617,-0.0404516757,0.0465862304,0.088900201,0.0602401681,0.0087468158,-0.2209196389,0.2579364777,0.1123382151,-0.0838561356,0.0046388875,0.0627129897,0.3078454733,-0.3241544962,-0.5395190716,0.120175004,-0.1676558703,-0.1507179439,0.1569003314,-0.1685593724,0.0254629962,-0.223440364,0.5335275531,0.0401802175,-0.0055817738,-0.0136975134,0.038695626,-0.1921836734,-0.1128755286,-0.0731188506,-0.1688325703,-0.1097167656,0.1998763829,0.1995472163,-0.3310860693,0.5541297197,-0.2196207196,0.2041632682,0.0977548063,0.1932164282,0.0301174037,0.253544122,0.3538282216,0.1545122564,0.0394503847,-0.0487941206,-0.1594578773,-0.2483039796,-0.247927621,0.069725357,-0.3629739881,-0.4516446888,0.2046125382,0.1505705267,0.2050661743,0.0231000446,0.5333048105,-0.3146410584,-0.10384579,0.1686959267,0.3648088276,-0.3038465977,-0.1020546108,0.4092980027,0.2519221604,0.4009886086,-0.3729685843,-0.0606633127,0.3335617185,-0.0977839157,0.3365208507,-0.0658703297,-0.2021239847,-0.033272773,0.3529548943,0.2694994807,-0.242778033,-0.5641846061,0.2591613829,-0.002454015,0.2508822978,0.0845526829,0.0684894547,0.0826148391,-0.2189555317,0.0249457024,0.2849489152,0.0519593544,0.0720635951,-0.3615594208,-0.4118731618,0.0270244945,0.143393442,-0.0667524934,-0.2504672706,0.0029691877,-0.0645665601,-0.0254445206,-0.0772941783,0.152787447,0.3830137551,0.1348627508,0.1990221888,0.1052660868,-0.0190326814,-0.3713848591,0.2328276932,-0.0967208967,0.4336290061,0.1736372411,-0.3850110471,-0.0803899765,-0.0744089335,-0.105005689,-0.4436501265,0.2301823944,-0.1027259529,0.2857616842,0.0918073878,-0.0282305367,-0.286475122,-0.2602204978,0.0710940734,-0.1651402712,0.3315623999,-0.3213315308,0.0342530198,0.0782489479,0.1614308506,-0.124255687,0.1105072349,0.1942817867,-0.0071293996,-0.3551361263,-0.0379281119,0.4002793133,0.7665842175,0.2776437402,-0.3818296492,0.1005618647,0.1995650083,-0.0353210419,-0.1732577682,-0.3145941496,0.0889273435,0.278570503,0.3200398982,0.1890916228,-0.0571337007,0.0382560976,0.0903183743,-0.1557933241,-0.1499132663,0.3771613836,-0.1340329051,0.0639237911,0.0019980865,-0.148224771,0.4138783514,0.1723026484,0.1056227162,0.1920792609,0.5266599655,-0.1382295191,-0.4718621373,-0.1715259701,-0.3971883059,0.0164452065,0.2203441113,0.1913430542,0.0278093237,0.1325486749,0.0655028746,0.1050011143,0.0083910571,-0.0028981415,0.0728535429,0.2263055742,0.2209694684,-0.2642669976,-0.1024458483,-0.3160074651,-0.079657793,0.1014831662,-0.0092377923,0.2213156968,-0.2507482767,0.2634441555,-0.0781887695,-0.414742142,-0.3153249025,0.2948235869,0.113560386,-0.1521184295,0.0933333561,-0.0359079204,0.1366750598,-0.1347661465,0.2000321746,-0.4448846579,-0.1261646748,-0.1927219033,0.0864221007,-0.0453944877,0.4896547794,0.1996760964,-0.191867426,-0.0891262367,-0.6268857718,-0.6305283904,0.2847003639,-0.1327684671,0.1055973545,0.2470073849,0.3481204212,-0.3925293088,-0.4493710101,0.2627792656,-0.4444777668,-0.1999738365,-0.2748990357,-0.1903129518,0.022515811,0.0493832305,-0.1188695207,-0.2579317689,-0.2666805983,0.3291163146,0.2762427926,-0.0609605238,-0.0458911248,0.0205528494,0.1251767427,0.2258081734,0.2626574636,-0.2936744094,-0.0474499576,0.2918271422,-0.3029043972,-0.2832897305,-0.037007954,-0.1125949994,-0.2324836552,-0.18669568,-0.3569921851,-0.3837314844,-0.1370197833,0.054883413,-0.0264961943,0.0882039368,0.1968396157,-0.0107379574,-0.2033339292,0.1613602638,-0.0292122066,-0.0383645259,0.0866158679,0.0075273709,-0.2022761106,0.2060182989,0.0629343763,0.7775626183,0.2560121715,0.0957140848,0.3554350734,0.0965010971,0.3023887575,-0.3307038844,-0.0481676236,0.0820795968,0.0261234082,0.1000516564,0.5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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/345","title":"Supporting documents in ELI5","comments":"Hi, thanks for the quick response. The blog post is quite an interesting working example, thanks for sharing it.\r\nTwo follow-up points\/questions about my original question:\r\n\r\n1. Yes, I read that the facebook team could not share the CommonCrawl b\/c of licensing reasons. They state \"No, we are not allowed to host processed Reddit or CommonCrawl data,\" which indicates they could also not share the Reddit data for licensing reasons. But it seems that HuggingFace is able to share the Reddit data, so why not a subset of CommonCrawl?\r\n\r\n2. Thanks for the suggestion about ElasticSearch and Wiki40b. This is good to know about performance. I definitely could do the indexing and querying myself. What I like about the ELI5 dataset though, at least what is suggested by the paper, is that to create the dataset they had already selected the top 100 web sources and made a single support document from those. Though it doesn't appear to be too sophisticated an approach, having a single support document pre-computed (without having to run the facebook code or a replacement with another dataset) is super useful for my work, especially since I'm not working on developing the latest and greatest retrieval model. Of course, I don't expect HF NLP datasets to be perfectly tailored to my use-case. I know there is overhead to any project, I'm just illustrating a use-case of ELI5 which is not possible with the data provided as-is. If it's for licensing reasons, that is perfectly acceptable a reason, and I appreciate your response.","body":"I was attempting to use the ELI5 dataset, when I realized that huggingface does not provide the supporting documents (the source documents from the common crawl). Without the supporting documents, this makes the dataset about as useful for my project as a block of cheese, or some other more apt metaphor.  According to facebook, the entire document collection is quite large. However, it would still be helpful to at least include a subset of the supporting documents i.e., having some data is better than having a block of cheese, in my case at least.\r\n\r\nIf you choose not to include them, it would be helpful to have documentation mentioning this specifically. It is especially confusing because the hf nlp ELI5 dataset has the key `'document'` but there are no documents to be found :(","comment_length":256,"text":"Supporting documents in ELI5 \n I was attempting to use the ELI5 dataset, when I realized that huggingface does not provide the supporting documents (the source documents from the common crawl). Without the supporting documents, this makes the dataset about as useful for my project as a block of cheese, or some other more apt metaphor.  According to facebook, the entire document collection is quite large. However, it would still be helpful to at least include a subset of the supporting documents i.e., having some data is better than having a block of cheese, in my case at least.\r\n\r\nIf you choose not to include them, it would be helpful to have documentation mentioning this specifically. It is especially confusing because the hf nlp ELI5 dataset has the key `'document'` but there are no documents to be found :( \n Hi, thanks for the quick response. The blog post is quite an interesting working example, thanks for sharing it.\r\nTwo follow-up points\/questions about my original question:\r\n\r\n1. Yes, I read that the facebook team could not share the CommonCrawl b\/c of licensing reasons. They state \"No, we are not allowed to host processed Reddit or CommonCrawl data,\" which indicates they could also not share the Reddit data for licensing reasons. But it seems that HuggingFace is able to share the Reddit data, so why not a subset of CommonCrawl?\r\n\r\n2. Thanks for the suggestion about ElasticSearch and Wiki40b. This is good to know about performance. I definitely could do the indexing and querying myself. What I like about the ELI5 dataset though, at least what is suggested by the paper, is that to create the dataset they had already selected the top 100 web sources and made a single support document from those. Though it doesn't appear to be too sophisticated an approach, having a single support document pre-computed (without having to run the facebook code or a replacement with another dataset) is super useful for my work, especially since I'm not working on developing the latest and greatest retrieval model. Of course, I don't expect HF NLP datasets to be perfectly tailored to my use-case. I know there is overhead to any project, I'm just illustrating a use-case of ELI5 which is not possible with the data provided as-is. If it's for licensing reasons, that is perfectly acceptable a reason, and I appreciate your response.","embeddings":[0.1618807167,-0.0452031456,-0.0387861505,-0.0477261692,-0.3124100566,0.0002395466,-0.1353973299,0.1302300096,-0.0219204035,-0.0501846857,0.0941168517,-0.2651159167,0.1130612791,0.1443804055,-0.1669064164,0.1842308939,0.0264950655,0.2427613288,0.3465193808,-0.3121981323,0.0172460712,0.2343271077,0.1443893909,-0.1357314438,-0.3666524291,0.28333202,-0.29987818,0.1768079549,-0.2238978297,-0.0061549218,0.0662747547,0.2734149992,0.0293229446,-0.2047086656,-0.000114399,0.0644626245,-0.0929929838,-0.1426993608,-0.1507161111,-0.1806523949,0.2431360483,-0.3142079413,-0.0273024756,-0.0974219441,-0.1486665159,-0.202641353,0.2346536964,0.2599248886,0.3535690904,-0.0374876298,0.1049089432,0.0820606202,0.0161260869,0.1020364538,0.1941461414,0.3405364156,-0.2443336695,-0.0740674809,0.3869939446,-0.202209726,0.0654283911,0.2211506218,0.0566235259,-0.1823208183,0.2999623716,0.0382442065,0.0203543436,-0.6953431368,0.2635998726,0.3578397334,0.4532786012,0.283123523,-0.2213172764,-0.4320504665,-0.1177456304,0.2731931806,-0.0302476678,0.3697188497,-0.0638160259,0.3528544009,0.0016578792,-0.2023566216,-0.0750342309,0.3632193506,0.2741084099,0.056665618,-0.1507907361,-0.0997989476,0.1249804646,0.0599622242,-0.4961788058,-0.0230370481,0.1168772578,-0.0398264416,0.1816927195,-0.1658809334,0.0937453657,0.3632128835,0.6906947494,0.2466049641,0.0316821747,-0.0119366283,-0.2202199697,0.0572159067,0.2629973888,-0.0168864857,-0.0620113946,-0.1783059835,0.1461059302,-0.0312419031,0.1357302517,-0.0091118775,-0.0422690026,0.5626630187,-0.5317935944,-0.5932882428,0.2085096389,-0.2363729179,-0.1962751895,0.1573073119,-0.2473305762,-0.0459486768,-0.1888718009,0.43571347,0.1879499108,0.0037178257,-0.3072887957,0.1680948287,0.0074788956,-0.3712285161,-0.0267664976,-0.1037182957,-0.1844659448,0.1345674396,0.1396351904,-0.3869445026,0.6252250075,-0.2365910709,0.347804606,0.1796341985,0.1624160111,-0.0185363404,0.3222464919,0.3538758755,0.1069955528,0.0527894758,0.0617658943,-0.0122318016,-0.2694570422,-0.2196465433,-0.0066613117,-0.456005007,-0.4140790105,0.0596289001,0.0276856106,0.2341445684,0.1031522974,0.513455689,-0.4354706407,-0.1399757564,0.2109805793,0.2206294537,-0.3961333632,-0.2374761701,0.3468028307,0.2940281332,0.3394902349,-0.4009534419,-0.0533855036,0.2656393647,-0.1998755634,0.3898680806,-0.2220280468,-0.2204244137,-0.0046833977,0.3456551135,0.3490616083,-0.1972573102,-0.7282791138,0.2450958639,0.1184997559,0.3259021938,0.1589938551,-0.0551896207,0.0623803996,-0.3640515804,-0.043246977,0.1965417415,-0.04358707,-0.1194300279,-0.2560020685,-0.4202414453,-0.0762731656,0.1884012222,-0.1058001444,-0.1561291069,0.0355004296,-0.0632957593,0.008365415,-0.0580607727,0.1475289613,0.3281921446,0.1072339192,0.159090206,0.0168096703,0.0722490102,-0.3731556535,0.3071223497,-0.0141973868,0.3480271399,0.3044205904,-0.3181044757,0.0587491319,-0.0931872576,-0.0007662947,-0.6210433841,0.1355814934,-0.0142499907,0.1797038913,0.0173147321,-0.2134844363,-0.0974055231,-0.1941886991,0.1273107678,-0.1348647773,0.3530066609,-0.2602706552,0.0966936871,0.0792050138,0.4140844643,-0.1390437633,0.0689707175,0.2654939592,-0.0479334146,-0.3764588535,-0.0900990143,0.2554413974,0.8737131953,0.3631456494,-0.3448274136,0.2278608829,0.066680409,-0.0577497259,-0.1940209866,-0.212673828,0.0391613171,0.1341947019,0.3453151286,0.3507530987,0.0651238263,-0.0663319826,0.0633670911,-0.2704552412,-0.214571476,0.4714906514,-0.102643691,0.2174859494,0.0538595393,-0.2446395755,0.4113785625,0.2338645756,0.165873453,-0.0037701109,0.4241563678,-0.2534435689,-0.3952467442,0.0571383052,-0.183164537,0.2414886951,0.2258030474,0.2277128696,0.0635995716,0.1560093015,0.0568045117,0.017448796,0.1503199488,-0.0517089963,0.0024007715,0.1140671596,0.3764139116,-0.2117667347,-0.2193944007,-0.3091649413,-0.278567493,0.0724299774,0.0705558062,0.1820521951,-0.3416146636,0.1765885949,-0.0956139043,-0.4134987891,-0.2661287189,0.2347912937,0.1461952627,-0.0589662455,-0.007164279,-0.2509242594,0.2849396169,-0.480771035,0.3026829064,-0.3664853275,-0.0576579049,-0.2014650553,0.0328033753,0.0083475029,0.3727059066,0.2562305927,-0.0921007767,-0.1207940727,-0.5289492607,-0.4521756768,0.3688605726,-0.2268683314,0.1133744866,0.1305222362,0.1967614889,-0.3303303123,-0.264618814,0.1088884845,-0.3639263213,-0.0934845358,-0.3351455033,-0.0973831713,-0.0986822918,-0.0030945179,-0.0411960892,-0.3535021544,-0.2808041573,0.3430304825,0.2254042476,-0.1302045584,0.0172077008,-0.0800358802,0.1123522669,0.1259935349,0.3323071599,-0.2970348895,-0.1613458693,0.1960982829,-0.2074230164,-0.2651272416,-0.0386919864,-0.0539244488,-0.0615293831,-0.100594312,-0.425477922,-0.3604826629,0.0556921661,-0.0251169037,-0.0703638494,0.0615719445,0.0977726057,0.0125412596,-0.0685769394,0.0065078842,0.00510677,-0.1429712772,0.1419217736,0.0686362609,-0.0926286355,0.1250013858,0.2023942322,0.7989224195,0.4217329025,0.1286455244,0.4140589237,0.2201206833,0.4891663492,-0.2331579179,-0.0684255064,0.1044610292,-0.01672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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/331","title":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError`","comments":"I couldn't reproduce on my side.\r\nIt looks like you were not able to generate all the examples, and you have the problem for each split train-test-validation.\r\nCould you try to enable logging, try again and send the logs ?\r\n```python\r\nimport logging\r\nlogging.basicConfig(level=logging.INFO)\r\n```","body":"```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n```","comment_length":45,"text":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError` \n ```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n``` \n I couldn't reproduce on my side.\r\nIt looks like you were not able to generate all the examples, and you have the problem for each split train-test-validation.\r\nCould you try to enable logging, try again and send the logs ?\r\n```python\r\nimport logging\r\nlogging.basicConfig(level=logging.INFO)\r\n```","embeddings":[-0.1410695314,-0.0569875538,-0.008213372,0.2086568773,0.0776493773,0.0907960758,0.1833027154,0.5251758695,0.0987313315,0.1542323083,0.0162980705,0.0769967064,-0.3141199946,-0.0437742993,-0.0335896686,0.0359017923,-0.1871679723,0.251511842,0.044887729,-0.0229966342,-0.2874323428,0.2817907035,-0.2087394744,-0.0366006866,-0.4835281372,-0.2941971123,0.0143964859,0.3698371649,0.037647862,-0.3093646169,-0.054115586,-0.1348574609,-0.1841386408,0.1029714793,-0.0001130321,0.1136059314,0.3911184371,-0.1433287412,-0.4867380261,0.0093758097,-0.210433796,-0.4906257391,0.0880899355,-0.354680568,-0.1552342176,0.0253197029,0.0945640802,-0.2195387632,0.4969272316,0.3904682994,0.2402192354,0.5636107922,0.0662313849,0.1633443981,-0.0887694806,-0.1809548885,-0.1458708197,-0.0048658135,-0.3178360164,-0.1510139853,-0.0242430959,0.4289468825,-0.0926206186,0.1253475994,-0.0872375593,0.1102501974,0.0889446437,-0.1658464372,0.093881309,0.1360183805,-0.0329074785,-0.0759069175,-0.2113918364,-0.4385608137,-0.0160641279,-0.3980173469,0.2251276523,0.2127174139,-0.1745027453,0.0653799996,-0.3212834597,0.0384899378,-0.0294721033,0.2034061104,0.0819332451,0.4111941159,-0.1002976,0.0258119442,0.0555451661,-0.0901165307,0.1567516327,-0.1769279093,0.1543263644,0.077836372,-0.1457687616,-0.045277901,0.1644648165,-0.1484151632,0.0430351757,-0.0675480068,0.1789103448,-0.0621050708,0.1744745076,0.0868742242,0.047065638,0.3125295639,-0.0163507275,0.268633306,0.1245981306,0.2852095664,0.0025192983,0.0181032717,0.1172021255,-0.0053176638,0.1188476905,0.2207996994,0.3734848201,-0.0592549704,-0.3276322186,0.1337225586,-0.2627150416,-0.2590237856,-0.0015641216,0.2562178373,-0.0583013371,0.3132656813,-0.0608430654,-0.0814521238,-0.3839427233,-0.1547316015,-0.3804471493,0.170080021,-0.1844006926,0.080809094,0.2795961797,-0.0796258152,0.457221657,-0.249753952,-0.0141821839,-0.14726381,0.2093333155,-0.039835576,0.0250497442,0.28318578,-0.130973205,0.1706077605,0.2590435743,0.1690691859,-0.2127455175,-0.0859967843,-0.0040091961,-0.605062902,0.1887341738,0.2487290353,0.059362527,-0.0686998665,0.6144146919,0.0758781433,0.2487533242,0.035048902,-0.1620905846,-0.0698391646,-0.0113207363,-0.1684927046,0.0756478012,0.4380223155,-0.2325228155,-0.2369064242,-0.0135412402,-0.1419267803,0.5154548883,0.4340136647,-0.1053208038,0.0439397804,-0.1265488267,0.1538269967,0.2003797293,-0.1545396298,-0.3955631256,0.1898384094,-0.0884127989,0.2039062083,0.2070534676,0.1967763603,-0.0294462275,0.1016341671,0.0539142787,0.5395390391,-0.2290878296,0.1055220217,-0.434442699,-0.4346444309,0.3582523465,0.0564582348,0.2241702229,-0.0408378653,-0.0627056584,0.4361121953,0.2115100026,-0.1110629141,0.0374838039,0.0333377346,-0.1516636014,-0.1143535376,0.0171922985,-0.0431710444,-0.4885517359,0.1375229061,-0.1571971476,0.3005177379,-0.050177224,-0.1014922038,-0.2627061307,-0.194733575,-0.1750023663,-0.2838497162,0.2178567648,0.2274239957,0.209719494,0.0044355784,-0.278423816,0.3022136688,-0.0314067528,0.1214026138,-0.5230283737,0.2627040744,-0.2279074043,-0.1810560226,0.2639639974,0.4298867285,0.2641758621,-0.1530768126,-0.2832179368,0.2833616138,-0.0861629993,0.231596604,-0.1480443329,-0.1591009498,0.1137663871,-0.0814554542,0.1697773635,0.4025975764,-0.03424301,-0.1033013612,-0.1191942096,0.4826929867,0.0484582894,0.0898778141,0.1499334574,-0.1141223013,0.0772690475,-0.1309580058,0.1130278036,-0.5237566233,0.3187466562,-0.169630602,0.2132307738,-0.0878212526,-0.2500077784,0.0530378111,0.6815172434,-0.0259576812,0.0975138694,0.0207601693,-0.1026206613,-0.2020810992,-0.1837860048,0.159725666,0.3386713266,0.2413775921,0.1866382957,0.2474578917,-0.1729585528,-0.286001265,0.27237764,0.095468618,0.2538036108,0.385949403,0.0060878471,-0.0024015717,-0.5314667225,-0.0248315446,0.013506908,0.3741949201,-0.2236362398,-0.201827243,-0.4316599369,-0.4015900493,-0.1702875495,-0.2987873554,-0.1874077171,-0.5853300691,0.0800147429,0.030056335,-0.1252746582,0.1624230742,-0.1581038833,0.231892392,0.2769706845,-0.0165657867,-0.192729637,-0.0813186616,-0.1340953261,0.0754104182,0.1756269485,0.038011238,0.3282631934,-0.1323553175,-0.1795409769,-0.0246300977,-0.4010378718,0.0776721239,0.0673702881,0.1397411227,0.304790169,0.1800093949,0.3312228322,-0.3353323936,0.1972529143,-0.2418714613,-0.2770200372,-0.0081244279,0.0024520406,0.1431996375,-0.2382315993,-0.4810171425,-0.4326365292,-0.5125101209,0.1009555608,0.0488469638,0.4066635966,0.2704299092,-0.2863166034,0.1968383491,-0.121017009,0.1822268218,-0.2584162951,-0.1776084006,0.250297606,-0.2715799809,-0.2704790533,-0.1265957206,-0.148699671,0.2048910111,-0.2855255306,-0.4239244759,-0.3295074403,-0.1458872259,-0.0527010113,0.1072003022,-0.0558419265,0.2296750695,-0.0121127972,-0.1654176414,-0.033512149,-0.3873255849,0.0534179732,0.1250744611,0.0702088997,-0.0927717984,0.6258087158,0.0622662865,0.5375280976,0.3718982339,0.0934443921,0.2314644009,0.1095010936,0.1484319568,-0.0122379875,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/331","title":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError`","comments":"here's the log\r\n```\r\n>>> import nlp\r\nimport logging\r\nlogging.basicConfig(level=logging.INFO)\r\nnlp.load_dataset('cnn_dailymail', '3.0.0')\r\n>>> import logging\r\n>>> logging.basicConfig(level=logging.INFO)\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nINFO:nlp.load:Checking \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py for additional imports.\r\nINFO:filelock:Lock 140443095301136 acquired on \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\nINFO:nlp.load:Found main folder for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\r\nINFO:nlp.load:Found specific version folder for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\nINFO:nlp.load:Found script file from https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py to \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/cnn_dailymail.py\r\nINFO:nlp.load:Updating dataset infos file from https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/dataset_infos.json to \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/dataset_infos.json\r\nINFO:nlp.load:Found metadata file for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/cnn_dailymail.json\r\nINFO:filelock:Lock 140443095301136 released on \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\nINFO:nlp.info:Loading Dataset Infos from \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\nINFO:nlp.builder:Generating dataset cnn_dailymail (\/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0)\r\nINFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\nINFO:nlp.utils.info_utils:All the checksums matched successfully.\r\nINFO:nlp.builder:Generating split train\r\nINFO:nlp.arrow_writer:Done writing 285161 examples in 1240618482 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-train.arrow.\r\nINFO:nlp.builder:Generating split validation\r\nINFO:nlp.arrow_writer:Done writing 13255 examples in 56637485 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-validation.arrow.\r\nINFO:nlp.builder:Generating split test\r\nINFO:nlp.arrow_writer:Done writing 11379 examples in 48931393 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-test.arrow.\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n```","body":"```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n```","comment_length":223,"text":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError` \n ```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n``` \n here's the log\r\n```\r\n>>> import nlp\r\nimport logging\r\nlogging.basicConfig(level=logging.INFO)\r\nnlp.load_dataset('cnn_dailymail', '3.0.0')\r\n>>> import logging\r\n>>> logging.basicConfig(level=logging.INFO)\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nINFO:nlp.load:Checking \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py for additional imports.\r\nINFO:filelock:Lock 140443095301136 acquired on \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\nINFO:nlp.load:Found main folder for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\r\nINFO:nlp.load:Found specific version folder for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\nINFO:nlp.load:Found script file from https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py to \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/cnn_dailymail.py\r\nINFO:nlp.load:Updating dataset infos file from https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/dataset_infos.json to \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/dataset_infos.json\r\nINFO:nlp.load:Found metadata file for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/cnn_dailymail.json\r\nINFO:filelock:Lock 140443095301136 released on \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\nINFO:nlp.info:Loading Dataset Infos from \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\nINFO:nlp.builder:Generating dataset cnn_dailymail (\/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0)\r\nINFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\nINFO:nlp.utils.info_utils:All the checksums matched successfully.\r\nINFO:nlp.builder:Generating split train\r\nINFO:nlp.arrow_writer:Done writing 285161 examples in 1240618482 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-train.arrow.\r\nINFO:nlp.builder:Generating split validation\r\nINFO:nlp.arrow_writer:Done writing 13255 examples in 56637485 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-validation.arrow.\r\nINFO:nlp.builder:Generating split test\r\nINFO:nlp.arrow_writer:Done writing 11379 examples in 48931393 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-test.arrow.\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n```","embeddings":[-0.1410695314,-0.0569875538,-0.008213372,0.2086568773,0.0776493773,0.0907960758,0.1833027154,0.5251758695,0.0987313315,0.1542323083,0.0162980705,0.0769967064,-0.3141199946,-0.0437742993,-0.0335896686,0.0359017923,-0.1871679723,0.251511842,0.044887729,-0.0229966342,-0.2874323428,0.2817907035,-0.2087394744,-0.0366006866,-0.4835281372,-0.2941971123,0.0143964859,0.3698371649,0.037647862,-0.3093646169,-0.054115586,-0.1348574609,-0.1841386408,0.1029714793,-0.0001130321,0.1136059314,0.3911184371,-0.1433287412,-0.4867380261,0.0093758097,-0.210433796,-0.4906257391,0.0880899355,-0.354680568,-0.1552342176,0.0253197029,0.0945640802,-0.2195387632,0.4969272316,0.3904682994,0.2402192354,0.5636107922,0.0662313849,0.1633443981,-0.0887694806,-0.1809548885,-0.1458708197,-0.0048658135,-0.3178360164,-0.1510139853,-0.0242430959,0.4289468825,-0.0926206186,0.1253475994,-0.0872375593,0.1102501974,0.0889446437,-0.1658464372,0.093881309,0.1360183805,-0.0329074785,-0.0759069175,-0.2113918364,-0.4385608137,-0.0160641279,-0.3980173469,0.2251276523,0.2127174139,-0.1745027453,0.0653799996,-0.3212834597,0.0384899378,-0.0294721033,0.2034061104,0.0819332451,0.4111941159,-0.1002976,0.0258119442,0.0555451661,-0.0901165307,0.1567516327,-0.1769279093,0.1543263644,0.077836372,-0.1457687616,-0.045277901,0.1644648165,-0.1484151632,0.0430351757,-0.0675480068,0.1789103448,-0.0621050708,0.1744745076,0.0868742242,0.047065638,0.3125295639,-0.0163507275,0.268633306,0.1245981306,0.2852095664,0.0025192983,0.0181032717,0.1172021255,-0.0053176638,0.1188476905,0.2207996994,0.3734848201,-0.0592549704,-0.3276322186,0.1337225586,-0.2627150416,-0.2590237856,-0.0015641216,0.2562178373,-0.0583013371,0.3132656813,-0.0608430654,-0.0814521238,-0.3839427233,-0.1547316015,-0.3804471493,0.170080021,-0.1844006926,0.080809094,0.2795961797,-0.0796258152,0.457221657,-0.249753952,-0.0141821839,-0.14726381,0.2093333155,-0.039835576,0.0250497442,0.28318578,-0.130973205,0.1706077605,0.2590435743,0.1690691859,-0.2127455175,-0.0859967843,-0.0040091961,-0.605062902,0.1887341738,0.2487290353,0.059362527,-0.0686998665,0.6144146919,0.0758781433,0.2487533242,0.035048902,-0.1620905846,-0.0698391646,-0.0113207363,-0.1684927046,0.0756478012,0.4380223155,-0.2325228155,-0.2369064242,-0.0135412402,-0.1419267803,0.5154548883,0.4340136647,-0.1053208038,0.0439397804,-0.1265488267,0.1538269967,0.2003797293,-0.1545396298,-0.3955631256,0.1898384094,-0.0884127989,0.2039062083,0.2070534676,0.1967763603,-0.0294462275,0.1016341671,0.0539142787,0.5395390391,-0.2290878296,0.1055220217,-0.434442699,-0.4346444309,0.3582523465,0.0564582348,0.2241702229,-0.0408378653,-0.0627056584,0.4361121953,0.2115100026,-0.1110629141,0.0374838039,0.0333377346,-0.1516636014,-0.1143535376,0.0171922985,-0.0431710444,-0.4885517359,0.1375229061,-0.1571971476,0.3005177379,-0.050177224,-0.1014922038,-0.2627061307,-0.194733575,-0.1750023663,-0.2838497162,0.2178567648,0.2274239957,0.209719494,0.0044355784,-0.278423816,0.3022136688,-0.0314067528,0.1214026138,-0.5230283737,0.2627040744,-0.2279074043,-0.1810560226,0.2639639974,0.4298867285,0.2641758621,-0.1530768126,-0.2832179368,0.2833616138,-0.0861629993,0.231596604,-0.1480443329,-0.1591009498,0.1137663871,-0.0814554542,0.1697773635,0.4025975764,-0.03424301,-0.1033013612,-0.1191942096,0.4826929867,0.0484582894,0.0898778141,0.1499334574,-0.1141223013,0.0772690475,-0.1309580058,0.1130278036,-0.5237566233,0.3187466562,-0.169630602,0.2132307738,-0.0878212526,-0.2500077784,0.0530378111,0.6815172434,-0.0259576812,0.0975138694,0.0207601693,-0.1026206613,-0.2020810992,-0.1837860048,0.159725666,0.3386713266,0.2413775921,0.1866382957,0.2474578917,-0.1729585528,-0.286001265,0.27237764,0.095468618,0.2538036108,0.385949403,0.0060878471,-0.0024015717,-0.5314667225,-0.0248315446,0.013506908,0.3741949201,-0.2236362398,-0.201827243,-0.4316599369,-0.4015900493,-0.1702875495,-0.2987873554,-0.1874077171,-0.5853300691,0.0800147429,0.030056335,-0.1252746582,0.1624230742,-0.1581038833,0.231892392,0.2769706845,-0.0165657867,-0.192729637,-0.0813186616,-0.1340953261,0.0754104182,0.1756269485,0.038011238,0.3282631934,-0.1323553175,-0.1795409769,-0.0246300977,-0.4010378718,0.0776721239,0.0673702881,0.1397411227,0.304790169,0.1800093949,0.3312228322,-0.3353323936,0.1972529143,-0.2418714613,-0.2770200372,-0.0081244279,0.0024520406,0.1431996375,-0.2382315993,-0.4810171425,-0.4326365292,-0.5125101209,0.1009555608,0.0488469638,0.4066635966,0.2704299092,-0.2863166034,0.1968383491,-0.121017009,0.1822268218,-0.2584162951,-0.1776084006,0.250297606,-0.2715799809,-0.2704790533,-0.1265957206,-0.148699671,0.2048910111,-0.2855255306,-0.4239244759,-0.3295074403,-0.1458872259,-0.0527010113,0.1072003022,-0.0558419265,0.2296750695,-0.0121127972,-0.1654176414,-0.033512149,-0.3873255849,0.0534179732,0.1250744611,0.0702088997,-0.0927717984,0.6258087158,0.0622662865,0.5375280976,0.3718982339,0.0934443921,0.2314644009,0.1095010936,0.1484319568,-0.0122379875,-0.3427927792,0.2506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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/331","title":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError`","comments":"> here's the log\r\n> \r\n> ```\r\n> >>> import nlp\r\n> import logging\r\n> logging.basicConfig(level=logging.INFO)\r\n> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\n> >>> import logging\r\n> >>> logging.basicConfig(level=logging.INFO)\r\n> >>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\n> INFO:nlp.load:Checking \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py for additional imports.\r\n> INFO:filelock:Lock 140443095301136 acquired on \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\n> INFO:nlp.load:Found main folder for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\r\n> INFO:nlp.load:Found specific version folder for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\n> INFO:nlp.load:Found script file from https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py to \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/cnn_dailymail.py\r\n> INFO:nlp.load:Updating dataset infos file from https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/dataset_infos.json to \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/dataset_infos.json\r\n> INFO:nlp.load:Found metadata file for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/cnn_dailymail.json\r\n> INFO:filelock:Lock 140443095301136 released on \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\n> INFO:nlp.info:Loading Dataset Infos from \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\n> INFO:nlp.builder:Generating dataset cnn_dailymail (\/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0)\r\n> INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source\r\n> Downloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n> INFO:nlp.utils.info_utils:All the checksums matched successfully.\r\n> INFO:nlp.builder:Generating split train\r\n> INFO:nlp.arrow_writer:Done writing 285161 examples in 1240618482 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-train.arrow.\r\n> INFO:nlp.builder:Generating split validation\r\n> INFO:nlp.arrow_writer:Done writing 13255 examples in 56637485 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-validation.arrow.\r\n> INFO:nlp.builder:Generating split test\r\n> INFO:nlp.arrow_writer:Done writing 11379 examples in 48931393 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-test.arrow.\r\n> Traceback (most recent call last):\r\n>   File \"<stdin>\", line 1, in <module>\r\n>   File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n>     builder_instance.download_and_prepare(\r\n>   File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n>     self._download_and_prepare(\r\n>   File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n>     verify_splits(self.info.splits, split_dict)\r\n>   File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n>     raise NonMatchingSplitsSizesError(str(bad_splits))\r\n> nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n> ```\r\n\r\nWith `nlp == 0.3.0` version, I'm not able to reproduce this error on my side.\r\nWhich version are you using for reproducing your bug?\r\n\r\n```\r\n>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\n\r\n8.90k\/8.90k [00:18<00:00, 486B\/s]\r\n\r\nDownloading: 100%\r\n9.37k\/9.37k [00:00<00:00, 234kB\/s]\r\n\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\nDownloading:\r\n159M\/? [00:09<00:00, 16.7MB\/s]\r\n\r\nDownloading:\r\n376M\/? [00:06<00:00, 62.6MB\/s]\r\n\r\nDownloading:\r\n2.11M\/? [00:06<00:00, 333kB\/s]\r\n\r\nDownloading:\r\n46.4M\/? [00:02<00:00, 18.4MB\/s]\r\n\r\nDownloading:\r\n2.43M\/? [00:00<00:00, 2.62MB\/s]\r\n\r\nDataset cnn_dailymail downloaded and prepared to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0. Subsequent calls will reuse this data.\r\n{'test': Dataset(schema: {'article': 'string', 'highlights': 'string'}, num_rows: 11490),\r\n 'train': Dataset(schema: {'article': 'string', 'highlights': 'string'}, num_rows: 287113),\r\n 'validation': Dataset(schema: {'article': 'string', 'highlights': 'string'}, num_rows: 13368)}\r\n\r\n>> ...\r\n\r\n```","body":"```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n```","comment_length":376,"text":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError` \n ```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n``` \n > here's the log\r\n> \r\n> ```\r\n> >>> import nlp\r\n> import logging\r\n> logging.basicConfig(level=logging.INFO)\r\n> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\n> >>> import logging\r\n> >>> logging.basicConfig(level=logging.INFO)\r\n> >>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\n> INFO:nlp.load:Checking \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py for additional imports.\r\n> INFO:filelock:Lock 140443095301136 acquired on \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\n> INFO:nlp.load:Found main folder for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\r\n> INFO:nlp.load:Found specific version folder for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\n> INFO:nlp.load:Found script file from https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py to \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/cnn_dailymail.py\r\n> INFO:nlp.load:Updating dataset infos file from https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/dataset_infos.json to \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/dataset_infos.json\r\n> INFO:nlp.load:Found metadata file for dataset https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/datasets\/cnn_dailymail\/cnn_dailymail.py at \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\/cnn_dailymail.json\r\n> INFO:filelock:Lock 140443095301136 released on \/u\/jm8wx\/.cache\/huggingface\/datasets\/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\n> INFO:nlp.info:Loading Dataset Infos from \/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/datasets\/cnn_dailymail\/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\n> INFO:nlp.builder:Generating dataset cnn_dailymail (\/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0)\r\n> INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source\r\n> Downloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n> INFO:nlp.utils.info_utils:All the checksums matched successfully.\r\n> INFO:nlp.builder:Generating split train\r\n> INFO:nlp.arrow_writer:Done writing 285161 examples in 1240618482 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-train.arrow.\r\n> INFO:nlp.builder:Generating split validation\r\n> INFO:nlp.arrow_writer:Done writing 13255 examples in 56637485 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-validation.arrow.\r\n> INFO:nlp.builder:Generating split test\r\n> INFO:nlp.arrow_writer:Done writing 11379 examples in 48931393 bytes \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0.incomplete\/cnn_dailymail-test.arrow.\r\n> Traceback (most recent call last):\r\n>   File \"<stdin>\", line 1, in <module>\r\n>   File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n>     builder_instance.download_and_prepare(\r\n>   File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n>     self._download_and_prepare(\r\n>   File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n>     verify_splits(self.info.splits, split_dict)\r\n>   File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n>     raise NonMatchingSplitsSizesError(str(bad_splits))\r\n> nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n> ```\r\n\r\nWith `nlp == 0.3.0` version, I'm not able to reproduce this error on my side.\r\nWhich version are you using for reproducing your bug?\r\n\r\n```\r\n>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\n\r\n8.90k\/8.90k [00:18<00:00, 486B\/s]\r\n\r\nDownloading: 100%\r\n9.37k\/9.37k [00:00<00:00, 234kB\/s]\r\n\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\nDownloading:\r\n159M\/? [00:09<00:00, 16.7MB\/s]\r\n\r\nDownloading:\r\n376M\/? [00:06<00:00, 62.6MB\/s]\r\n\r\nDownloading:\r\n2.11M\/? [00:06<00:00, 333kB\/s]\r\n\r\nDownloading:\r\n46.4M\/? [00:02<00:00, 18.4MB\/s]\r\n\r\nDownloading:\r\n2.43M\/? [00:00<00:00, 2.62MB\/s]\r\n\r\nDataset cnn_dailymail downloaded and prepared to \/root\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0. Subsequent calls will reuse this data.\r\n{'test': Dataset(schema: {'article': 'string', 'highlights': 'string'}, num_rows: 11490),\r\n 'train': Dataset(schema: {'article': 'string', 'highlights': 'string'}, num_rows: 287113),\r\n 'validation': Dataset(schema: {'article': 'string', 'highlights': 'string'}, num_rows: 13368)}\r\n\r\n>> ...\r\n\r\n```","embeddings":[-0.1410695314,-0.0569875538,-0.008213372,0.2086568773,0.0776493773,0.0907960758,0.1833027154,0.5251758695,0.0987313315,0.1542323083,0.0162980705,0.0769967064,-0.3141199946,-0.0437742993,-0.0335896686,0.0359017923,-0.1871679723,0.251511842,0.044887729,-0.0229966342,-0.2874323428,0.2817907035,-0.2087394744,-0.0366006866,-0.4835281372,-0.2941971123,0.0143964859,0.3698371649,0.037647862,-0.3093646169,-0.054115586,-0.1348574609,-0.1841386408,0.1029714793,-0.0001130321,0.1136059314,0.3911184371,-0.1433287412,-0.4867380261,0.0093758097,-0.210433796,-0.4906257391,0.0880899355,-0.354680568,-0.1552342176,0.0253197029,0.0945640802,-0.2195387632,0.4969272316,0.3904682994,0.2402192354,0.5636107922,0.0662313849,0.1633443981,-0.0887694806,-0.1809548885,-0.1458708197,-0.0048658135,-0.3178360164,-0.1510139853,-0.0242430959,0.4289468825,-0.0926206186,0.1253475994,-0.0872375593,0.1102501974,0.0889446437,-0.1658464372,0.093881309,0.1360183805,-0.0329074785,-0.0759069175,-0.2113918364,-0.4385608137,-0.0160641279,-0.3980173469,0.2251276523,0.2127174139,-0.1745027453,0.0653799996,-0.3212834597,0.0384899378,-0.0294721033,0.2034061104,0.0819332451,0.4111941159,-0.1002976,0.0258119442,0.0555451661,-0.0901165307,0.1567516327,-0.1769279093,0.1543263644,0.077836372,-0.1457687616,-0.045277901,0.1644648165,-0.1484151632,0.0430351757,-0.0675480068,0.1789103448,-0.0621050708,0.1744745076,0.0868742242,0.047065638,0.3125295639,-0.0163507275,0.268633306,0.1245981306,0.2852095664,0.0025192983,0.0181032717,0.1172021255,-0.0053176638,0.1188476905,0.2207996994,0.3734848201,-0.0592549704,-0.3276322186,0.1337225586,-0.2627150416,-0.2590237856,-0.0015641216,0.2562178373,-0.0583013371,0.3132656813,-0.0608430654,-0.0814521238,-0.3839427233,-0.1547316015,-0.3804471493,0.170080021,-0.1844006926,0.080809094,0.2795961797,-0.0796258152,0.457221657,-0.249753952,-0.0141821839,-0.14726381,0.2093333155,-0.039835576,0.0250497442,0.28318578,-0.130973205,0.1706077605,0.2590435743,0.1690691859,-0.2127455175,-0.0859967843,-0.0040091961,-0.605062902,0.1887341738,0.2487290353,0.059362527,-0.0686998665,0.6144146919,0.0758781433,0.2487533242,0.035048902,-0.1620905846,-0.0698391646,-0.0113207363,-0.1684927046,0.0756478012,0.4380223155,-0.2325228155,-0.2369064242,-0.0135412402,-0.1419267803,0.5154548883,0.4340136647,-0.1053208038,0.0439397804,-0.1265488267,0.1538269967,0.2003797293,-0.1545396298,-0.3955631256,0.1898384094,-0.0884127989,0.2039062083,0.2070534676,0.1967763603,-0.0294462275,0.1016341671,0.0539142787,0.5395390391,-0.2290878296,0.1055220217,-0.434442699,-0.4346444309,0.3582523465,0.0564582348,0.2241702229,-0.0408378653,-0.0627056584,0.4361121953,0.2115100026,-0.1110629141,0.0374838039,0.0333377346,-0.1516636014,-0.1143535376,0.0171922985,-0.0431710444,-0.4885517359,0.1375229061,-0.1571971476,0.3005177379,-0.050177224,-0.1014922038,-0.2627061307,-0.194733575,-0.1750023663,-0.2838497162,0.2178567648,0.2274239957,0.209719494,0.0044355784,-0.278423816,0.3022136688,-0.0314067528,0.1214026138,-0.5230283737,0.2627040744,-0.2279074043,-0.1810560226,0.2639639974,0.4298867285,0.2641758621,-0.1530768126,-0.2832179368,0.2833616138,-0.0861629993,0.231596604,-0.1480443329,-0.1591009498,0.1137663871,-0.0814554542,0.1697773635,0.4025975764,-0.03424301,-0.1033013612,-0.1191942096,0.4826929867,0.0484582894,0.0898778141,0.1499334574,-0.1141223013,0.0772690475,-0.1309580058,0.1130278036,-0.5237566233,0.3187466562,-0.169630602,0.2132307738,-0.0878212526,-0.2500077784,0.0530378111,0.6815172434,-0.0259576812,0.0975138694,0.0207601693,-0.1026206613,-0.2020810992,-0.1837860048,0.159725666,0.3386713266,0.2413775921,0.1866382957,0.2474578917,-0.1729585528,-0.286001265,0.27237764,0.095468618,0.2538036108,0.385949403,0.0060878471,-0.0024015717,-0.5314667225,-0.0248315446,0.013506908,0.3741949201,-0.2236362398,-0.201827243,-0.4316599369,-0.4015900493,-0.1702875495,-0.2987873554,-0.1874077171,-0.5853300691,0.0800147429,0.030056335,-0.1252746582,0.1624230742,-0.1581038833,0.231892392,0.2769706845,-0.0165657867,-0.192729637,-0.0813186616,-0.1340953261,0.0754104182,0.1756269485,0.038011238,0.3282631934,-0.1323553175,-0.1795409769,-0.0246300977,-0.4010378718,0.0776721239,0.0673702881,0.1397411227,0.304790169,0.1800093949,0.3312228322,-0.3353323936,0.1972529143,-0.2418714613,-0.2770200372,-0.0081244279,0.0024520406,0.1431996375,-0.2382315993,-0.4810171425,-0.4326365292,-0.5125101209,0.1009555608,0.0488469638,0.4066635966,0.2704299092,-0.2863166034,0.1968383491,-0.121017009,0.1822268218,-0.2584162951,-0.1776084006,0.250297606,-0.2715799809,-0.2704790533,-0.1265957206,-0.148699671,0.2048910111,-0.2855255306,-0.4239244759,-0.3295074403,-0.1458872259,-0.0527010113,0.1072003022,-0.0558419265,0.2296750695,-0.0121127972,-0.1654176414,-0.033512149,-0.3873255849,0.0534179732,0.1250744611,0.0702088997,-0.0927717984,0.6258087158,0.0622662865,0.5375280976,0.3718982339,0.0934443921,0.2314644009,0.1095010936,0.1484319568,-0.0122379875,-0.3427927792,0.2506149709,0.1518257856,0.05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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/331","title":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError`","comments":"In general if some examples are missing after processing (hence causing the `NonMatchingSplitsSizesError `), it is often due to either\r\n1) corrupted cached files\r\n2) decoding errors\r\n\r\nI just checked the dataset script for code that could lead to decoding errors but I couldn't find any. Before we try to dive more into the processing of the dataset, could you try to clear your cache ? Just to make sure that it isn't 1)","body":"```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n```","comment_length":74,"text":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError` \n ```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n``` \n In general if some examples are missing after processing (hence causing the `NonMatchingSplitsSizesError `), it is often due to either\r\n1) corrupted cached files\r\n2) decoding errors\r\n\r\nI just checked the dataset script for code that could lead to decoding errors but I couldn't find any. Before we try to dive more into the processing of the dataset, could you try to clear your cache ? Just to make sure that it isn't 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/331","title":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError`","comments":"Yes thanks for the support! I cleared out my cache folder and everything works fine now","body":"```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n```","comment_length":16,"text":"Loading CNN\/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError` \n ```\r\n>>> import nlp\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nDownloading and preparing dataset cnn_dailymail\/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to \/u\/jm8wx\/.cache\/huggingface\/datasets\/cnn_dailymail\/3.0.0\/3.0.0...\r\n\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/load.py\", line 520, in load_dataset\r\n    builder_instance.download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 431, in download_and_prepare\r\n    self._download_and_prepare(\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/p\/qdata\/jm8wx\/datasets\/nlp\/src\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n``` \n Yes thanks for the support! I cleared out my cache folder and everything works fine now","embeddings":[-0.1410695314,-0.0569875538,-0.008213372,0.2086568773,0.0776493773,0.0907960758,0.1833027154,0.5251758695,0.0987313315,0.1542323083,0.0162980705,0.0769967064,-0.3141199946,-0.0437742993,-0.0335896686,0.0359017923,-0.1871679723,0.251511842,0.044887729,-0.0229966342,-0.2874323428,0.2817907035,-0.2087394744,-0.0366006866,-0.4835281372,-0.2941971123,0.0143964859,0.3698371649,0.037647862,-0.3093646169,-0.054115586,-0.1348574609,-0.1841386408,0.1029714793,-0.0001130321,0.1136059314,0.3911184371,-0.1433287412,-0.4867380261,0.0093758097,-0.210433796,-0.4906257391,0.0880899355,-0.354680568,-0.1552342176,0.0253197029,0.0945640802,-0.2195387632,0.4969272316,0.3904682994,0.2402192354,0.5636107922,0.0662313849,0.1633443981,-0.0887694806,-0.1809548885,-0.1458708197,-0.0048658135,-0.3178360164,-0.1510139853,-0.0242430959,0.4289468825,-0.0926206186,0.1253475994,-0.0872375593,0.1102501974,0.0889446437,-0.1658464372,0.093881309,0.1360183805,-0.0329074785,-0.0759069175,-0.2113918364,-0.4385608137,-0.0160641279,-0.3980173469,0.2251276523,0.2127174139,-0.1745027453,0.0653799996,-0.3212834597,0.0384899378,-0.0294721033,0.2034061104,0.0819332451,0.4111941159,-0.1002976,0.0258119442,0.0555451661,-0.0901165307,0.1567516327,-0.1769279093,0.1543263644,0.077836372,-0.1457687616,-0.045277901,0.1644648165,-0.1484151632,0.0430351757,-0.0675480068,0.1789103448,-0.0621050708,0.1744745076,0.0868742242,0.047065638,0.3125295639,-0.0163507275,0.268633306,0.1245981306,0.2852095664,0.0025192983,0.0181032717,0.1172021255,-0.0053176638,0.1188476905,0.2207996994,0.3734848201,-0.0592549704,-0.3276322186,0.1337225586,-0.2627150416,-0.2590237856,-0.0015641216,0.2562178373,-0.0583013371,0.3132656813,-0.0608430654,-0.0814521238,-0.3839427233,-0.1547316015,-0.3804471493,0.170080021,-0.1844006926,0.080809094,0.2795961797,-0.0796258152,0.457221657,-0.249753952,-0.0141821839,-0.14726381,0.2093333155,-0.039835576,0.0250497442,0.28318578,-0.130973205,0.1706077605,0.2590435743,0.1690691859,-0.2127455175,-0.0859967843,-0.0040091961,-0.605062902,0.1887341738,0.2487290353,0.059362527,-0.0686998665,0.6144146919,0.0758781433,0.2487533242,0.035048902,-0.1620905846,-0.0698391646,-0.0113207363,-0.1684927046,0.0756478012,0.4380223155,-0.2325228155,-0.2369064242,-0.0135412402,-0.1419267803,0.5154548883,0.4340136647,-0.1053208038,0.0439397804,-0.1265488267,0.1538269967,0.2003797293,-0.1545396298,-0.3955631256,0.1898384094,-0.0884127989,0.2039062083,0.2070534676,0.1967763603,-0.0294462275,0.1016341671,0.0539142787,0.5395390391,-0.2290878296,0.1055220217,-0.434442699,-0.4346444309,0.3582523465,0.0564582348,0.2241702229,-0.0408378653,-0.0627056584,0.4361121953,0.2115100026,-0.1110629141,0.0374838039,0.0333377346,-0.1516636014,-0.1143535376,0.0171922985,-0.0431710444,-0.4885517359,0.1375229061,-0.1571971476,0.3005177379,-0.050177224,-0.1014922038,-0.2627061307,-0.194733575,-0.1750023663,-0.2838497162,0.2178567648,0.2274239957,0.209719494,0.0044355784,-0.278423816,0.3022136688,-0.0314067528,0.1214026138,-0.5230283737,0.2627040744,-0.2279074043,-0.1810560226,0.2639639974,0.4298867285,0.2641758621,-0.1530768126,-0.2832179368,0.2833616138,-0.0861629993,0.231596604,-0.1480443329,-0.1591009498,0.1137663871,-0.0814554542,0.1697773635,0.4025975764,-0.03424301,-0.1033013612,-0.1191942096,0.4826929867,0.0484582894,0.0898778141,0.1499334574,-0.1141223013,0.0772690475,-0.1309580058,0.1130278036,-0.5237566233,0.3187466562,-0.169630602,0.2132307738,-0.0878212526,-0.2500077784,0.0530378111,0.6815172434,-0.0259576812,0.0975138694,0.0207601693,-0.1026206613,-0.2020810992,-0.1837860048,0.159725666,0.3386713266,0.2413775921,0.1866382957,0.2474578917,-0.1729585528,-0.286001265,0.27237764,0.095468618,0.2538036108,0.385949403,0.0060878471,-0.0024015717,-0.5314667225,-0.0248315446,0.013506908,0.3741949201,-0.2236362398,-0.201827243,-0.4316599369,-0.4015900493,-0.1702875495,-0.2987873554,-0.1874077171,-0.5853300691,0.0800147429,0.030056335,-0.1252746582,0.1624230742,-0.1581038833,0.231892392,0.2769706845,-0.0165657867,-0.192729637,-0.0813186616,-0.1340953261,0.0754104182,0.1756269485,0.038011238,0.3282631934,-0.1323553175,-0.1795409769,-0.0246300977,-0.4010378718,0.0776721239,0.0673702881,0.1397411227,0.304790169,0.1800093949,0.3312228322,-0.3353323936,0.1972529143,-0.2418714613,-0.2770200372,-0.0081244279,0.0024520406,0.1431996375,-0.2382315993,-0.4810171425,-0.4326365292,-0.5125101209,0.1009555608,0.0488469638,0.4066635966,0.2704299092,-0.2863166034,0.1968383491,-0.121017009,0.1822268218,-0.2584162951,-0.1776084006,0.250297606,-0.2715799809,-0.2704790533,-0.1265957206,-0.148699671,0.2048910111,-0.2855255306,-0.4239244759,-0.3295074403,-0.1458872259,-0.0527010113,0.1072003022,-0.0558419265,0.2296750695,-0.0121127972,-0.1654176414,-0.033512149,-0.3873255849,0.0534179732,0.1250744611,0.0702088997,-0.0927717984,0.6258087158,0.0622662865,0.5375280976,0.3718982339,0.0934443921,0.2314644009,0.1095010936,0.1484319568,-0.0122379875,-0.3427927792,0.2506149709,0.1518257856,0.0592511334,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/329","title":"[Bug] FileLock dependency incompatible with filesystem","comments":"Environment is Ubuntu 18.04, Python 3.7.5, nlp==0.3.0, filelock=3.0.12.\r\n\r\nThe external volume is Amazon FSx for Lustre, and it by default creates files with limited permissions. My working theory is that FileLock creates a lockfile that isn't writable, and thus there's no way to acquire it by removing the .lock file. But Python is able to create new files and write to them outside of the FileLock package.\r\n\r\nWhen I attempt to use FileLock within a Docker container by writing to `\/root\/.cache\/hello.txt`, it succeeds. So there's some permissions issue. But it's not a Docker configuration issue; I've replicated it without Docker.\r\n```bash\r\necho \"hello world\" >> hello.txt\r\nls -l\r\n\r\n-rw-rw-r-- 1 ubuntu ubuntu 10 Jun 30 19:52 hello.txt\r\n```","body":"I'm downloading a dataset successfully with\r\n`load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")`\r\n\r\nBut when I attempt to cache it on an external volume, it hangs indefinitely:\r\n`load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", cache_dir=\"\/fsx\") # \/fsx is an external volume mount`\r\n\r\nThe filesystem when hanging looks like this:\r\n```bash\r\n\/fsx\r\n----downloads\r\n       ----94be...73.lock\r\n----wikitext\r\n       ----wikitext-2-raw\r\n             ----wikitext-2-raw-1.0.0.incomplete\r\n```\r\n\r\nIt appears that on this filesystem, the FileLock object is forever stuck in its \"acquire\" stage. I have verified that the issue lies specifically with the `filelock` dependency:\r\n```python\r\nopen(\"\/fsx\/hello.txt\").write(\"hello\") # succeeds\r\n\r\nfrom filelock import FileLock\r\nwith FileLock(\"\/fsx\/hello.lock\"):\r\n    open(\"\/fsx\/hello.txt\").write(\"hello\") # hangs indefinitely\r\n```\r\n\r\nHas anyone else run into this issue? I'd raise it directly on the FileLock repo, but that project appears abandoned with the last update over a year ago. Or if there's a solution that would remove the FileLock dependency from the project, I would appreciate that.","comment_length":118,"text":"[Bug] FileLock dependency incompatible with filesystem \n I'm downloading a dataset successfully with\r\n`load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")`\r\n\r\nBut when I attempt to cache it on an external volume, it hangs indefinitely:\r\n`load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", cache_dir=\"\/fsx\") # \/fsx is an external volume mount`\r\n\r\nThe filesystem when hanging looks like this:\r\n```bash\r\n\/fsx\r\n----downloads\r\n       ----94be...73.lock\r\n----wikitext\r\n       ----wikitext-2-raw\r\n             ----wikitext-2-raw-1.0.0.incomplete\r\n```\r\n\r\nIt appears that on this filesystem, the FileLock object is forever stuck in its \"acquire\" stage. I have verified that the issue lies specifically with the `filelock` dependency:\r\n```python\r\nopen(\"\/fsx\/hello.txt\").write(\"hello\") # succeeds\r\n\r\nfrom filelock import FileLock\r\nwith FileLock(\"\/fsx\/hello.lock\"):\r\n    open(\"\/fsx\/hello.txt\").write(\"hello\") # hangs indefinitely\r\n```\r\n\r\nHas anyone else run into this issue? I'd raise it directly on the FileLock repo, but that project appears abandoned with the last update over a year ago. Or if there's a solution that would remove the FileLock dependency from the project, I would appreciate that. \n Environment is Ubuntu 18.04, Python 3.7.5, nlp==0.3.0, filelock=3.0.12.\r\n\r\nThe external volume is Amazon FSx for Lustre, and it by default creates files with limited permissions. My working theory is that FileLock creates a lockfile that isn't writable, and thus there's no way to acquire it by removing the .lock file. But Python is able to create new files and write to them outside of the FileLock package.\r\n\r\nWhen I attempt to use FileLock within a Docker container by writing to `\/root\/.cache\/hello.txt`, it succeeds. So there's some permissions issue. But it's not a Docker configuration issue; I've replicated it without Docker.\r\n```bash\r\necho \"hello world\" >> hello.txt\r\nls -l\r\n\r\n-rw-rw-r-- 1 ubuntu ubuntu 10 Jun 30 19:52 hello.txt\r\n```","embeddings":[0.0329926312,0.0771608278,-0.0250304174,0.027964592,0.0889836028,0.1513945013,0.364697963,0.0651528686,0.7094057202,-0.0978959948,0.090967983,-0.0051194588,0.0214458238,-0.3159707189,-0.2095369399,0.2686368525,0.1863837689,0.0667651743,0.005568597,0.0047016451,-0.0693236962,0.3521534801,-0.1633292437,-0.2751578987,-0.7754433155,0.120644629,0.0365542248,0.0431686118,-0.2784680426,0.0024243908,0.4160512686,0.5284094214,-0.0816129148,0.496588856,-0.0001226855,-0.1948298067,0.2004580349,-0.0361196883,-0.1746515036,-0.2741996944,-0.2164897621,-0.3265877962,0.1097059995,-0.1128876805,0.1944538206,-0.2241576165,0.0919875652,-0.4995852709,0.3337172568,-0.0410731472,0.0680703148,0.2360705435,0.254280597,-0.0269481838,0.3872797191,-0.2296983749,-0.2215995938,0.5681561828,0.456705898,-0.2117682695,-0.140966773,-0.036995776,-0.0129712755,-0.0679331571,0.5948626995,-0.1976968348,0.3488734663,-0.2678504586,-0.1012830809,0.3191520572,0.6145647764,-0.0441019721,-0.5926792026,-0.2471349835,0.327287972,0.1455295384,0.5233204365,0.0666401461,-0.0742337778,0.3606702387,0.0522742458,-0.1590826958,0.0457444265,0.0224095583,-0.1197813973,0.2216324359,0.1453604996,0.0084855128,0.1708891541,-0.0806063488,0.3088315129,-0.1967713535,0.0066907033,0.0987603217,-0.454965502,0.1360242963,0.0213125143,0.1014321446,-0.140349254,-0.2220833302,-0.4776568115,0.1874590665,0.0509255603,0.3599926233,0.1120271906,0.1694357842,0.0524430089,0.0851673633,0.2234933525,-0.0380137265,-0.2711602747,0.0675251037,-0.0540335141,-0.1094276682,0.1089459434,-0.0756204799,0.026497189,-0.3769022226,0.171045959,0.1699299812,0.0443213135,0.3801297247,0.1577482671,0.0316457227,0.1108215377,0.4331046641,0.1974970698,0.0049862051,-0.1965510547,0.3437236845,-0.0323614851,-0.098464556,0.0690493658,0.0930997208,0.0324408524,0.2763173282,0.1785526872,0.156566456,0.3272791803,0.1014767811,0.2352108508,-0.1581271142,-0.127683118,0.393193692,-0.1377974898,0.0754465386,0.0243199468,-0.0310192034,-0.0276711918,0.0367911831,-0.1925075799,0.0691401735,-0.0996998847,0.0263191275,-0.3808284998,0.1100183353,0.1620588303,-0.2464067787,-0.1027880833,-0.4254748821,0.0668773428,-0.4721822739,-0.0609576814,-0.0015061117,-0.0517792217,0.3292973042,-0.2778441012,-0.0546500087,0.2590722144,-0.0380633622,-0.1223073527,0.2285180241,-0.0811449364,0.1059946492,-0.2078044564,0.0991857424,-0.0261375289,-0.4818366766,-0.7829118371,0.2422858626,-0.2895569205,0.2557770312,0.1629284471,0.3792766035,0.1021165773,-0.2274315655,0.0992249176,0.3591865301,0.1214847341,-0.217090264,-0.0638454482,-0.2845760286,-0.2017313987,0.0688606873,0.0111601846,0.2223489285,0.0180596188,0.6689196825,0.2116287053,0.0055397418,0.0050580725,0.5441519618,0.6210247278,0.0836993009,0.0081204129,-0.1322745979,-0.6575937271,0.5051038265,-0.4428118467,-0.0797979981,-0.2008097768,-0.0727460012,0.1174281761,-0.0122002792,-0.0691100806,0.1472261846,0.0574351884,-0.0790043771,0.0755307823,0.1750714034,0.0899828151,0.3439259827,0.0125084687,-0.0200022738,-0.0590691678,-0.0895981789,0.1630969197,-0.1867703646,0.053861659,-0.0638033301,0.1063678265,-0.2141563445,-0.1605974883,0.2369826883,0.3473391831,0.2953688502,-0.2741957605,0.3801431656,0.0418263227,0.4804558158,-0.1965429932,-0.0898995325,0.1089190617,-0.2300198227,0.0361249559,0.0947789326,-0.2191524804,0.0928045437,-0.2077879012,0.0561608113,0.0911774412,0.0453527644,-0.1688566357,-0.0269355029,0.2806842327,-0.0091582621,0.2706624269,-0.10584113,0.6271888614,0.0670904517,0.366843611,0.1496047974,0.0516429357,0.0494002216,0.0035033473,-0.1143359765,0.0166757964,0.3775188327,0.498829186,0.1017267331,0.288933903,-0.2164707333,0.2117234468,-0.2904098332,0.0543171652,-0.1254851967,-0.2110745907,0.3450728357,0.134191215,-0.1068298295,0.1315164119,-0.2868654728,0.1256860495,0.0205750726,0.1268946826,0.1132571846,-0.3515273929,0.5667666793,0.2086833566,0.1933236867,-0.2534320056,-0.4644887447,0.1059635729,0.7339765429,0.1522963494,0.0692449436,-0.4219465256,-0.0498631708,0.0390344374,-0.3337166607,-0.0399523787,-0.3362483978,-0.0026227303,-0.1162400544,0.553121686,0.0384058096,0.1448250413,0.1003429815,-0.3246200979,-0.3931279778,-0.18387191,0.1499125063,-0.0926098526,0.4356099367,0.0584887452,0.1634122133,-0.1018638685,0.0385645032,-0.0192833506,-0.3886204362,-0.0294425618,-0.0975789279,-0.0303108245,-0.1097448319,-0.2369118631,0.171840176,-0.1660530269,-0.3670009971,0.3042767346,-0.3411258161,-0.2106982768,0.2431464344,0.0185857695,0.287735641,-0.0033969062,-0.0847150981,0.0122465109,-0.459141165,0.2137269825,0.1240149066,-0.4351314008,-0.1326473653,0.1863021255,-0.4067225158,0.2642199993,-0.4266271293,-0.2256042361,-0.32558617,0.2897674143,-0.0849460885,-0.0755462423,0.4451619387,-0.2783597112,0.0496294312,-0.4187117219,0.3278357983,0.1877295375,0.0333668292,-0.0206506439,0.0646261796,-0.20487158,0.1377081126,0.513119638,-0.0706710443,0.0123607032,0.1415621191,0.3056558967,0.438752979,-0.3285657167,-0.1682540476,-0.1418815255,-0.0881157145,-0.14641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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/329","title":"[Bug] FileLock dependency incompatible with filesystem","comments":"Looks like the `flock` syscall does not work on Lustre filesystems by default: https:\/\/github.com\/benediktschmitt\/py-filelock\/issues\/67.\r\n\r\nI added the `-o flock` option when mounting the filesystem, as [described here](https:\/\/docs.aws.amazon.com\/fsx\/latest\/LustreGuide\/getting-started-step2.html), which fixed the issue.","body":"I'm downloading a dataset successfully with\r\n`load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")`\r\n\r\nBut when I attempt to cache it on an external volume, it hangs indefinitely:\r\n`load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", cache_dir=\"\/fsx\") # \/fsx is an external volume mount`\r\n\r\nThe filesystem when hanging looks like this:\r\n```bash\r\n\/fsx\r\n----downloads\r\n       ----94be...73.lock\r\n----wikitext\r\n       ----wikitext-2-raw\r\n             ----wikitext-2-raw-1.0.0.incomplete\r\n```\r\n\r\nIt appears that on this filesystem, the FileLock object is forever stuck in its \"acquire\" stage. I have verified that the issue lies specifically with the `filelock` dependency:\r\n```python\r\nopen(\"\/fsx\/hello.txt\").write(\"hello\") # succeeds\r\n\r\nfrom filelock import FileLock\r\nwith FileLock(\"\/fsx\/hello.lock\"):\r\n    open(\"\/fsx\/hello.txt\").write(\"hello\") # hangs indefinitely\r\n```\r\n\r\nHas anyone else run into this issue? I'd raise it directly on the FileLock repo, but that project appears abandoned with the last update over a year ago. Or if there's a solution that would remove the FileLock dependency from the project, I would appreciate that.","comment_length":31,"text":"[Bug] FileLock dependency incompatible with filesystem \n I'm downloading a dataset successfully with\r\n`load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")`\r\n\r\nBut when I attempt to cache it on an external volume, it hangs indefinitely:\r\n`load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", cache_dir=\"\/fsx\") # \/fsx is an external volume mount`\r\n\r\nThe filesystem when hanging looks like this:\r\n```bash\r\n\/fsx\r\n----downloads\r\n       ----94be...73.lock\r\n----wikitext\r\n       ----wikitext-2-raw\r\n             ----wikitext-2-raw-1.0.0.incomplete\r\n```\r\n\r\nIt appears that on this filesystem, the FileLock object is forever stuck in its \"acquire\" stage. I have verified that the issue lies specifically with the `filelock` dependency:\r\n```python\r\nopen(\"\/fsx\/hello.txt\").write(\"hello\") # succeeds\r\n\r\nfrom filelock import FileLock\r\nwith FileLock(\"\/fsx\/hello.lock\"):\r\n    open(\"\/fsx\/hello.txt\").write(\"hello\") # hangs indefinitely\r\n```\r\n\r\nHas anyone else run into this issue? I'd raise it directly on the FileLock repo, but that project appears abandoned with the last update over a year ago. Or if there's a solution that would remove the FileLock dependency from the project, I would appreciate that. \n Looks like the `flock` syscall does not work on Lustre filesystems by default: https:\/\/github.com\/benediktschmitt\/py-filelock\/issues\/67.\r\n\r\nI added the `-o flock` option when mounting the filesystem, as [described here](https:\/\/docs.aws.amazon.com\/fsx\/latest\/LustreGuide\/getting-started-step2.html), which fixed the issue.","embeddings":[-0.0220689438,-0.0703312829,-0.0241179243,0.0477825627,0.0648071468,0.0538179018,0.2719034553,-0.0904818401,0.7102741599,-0.0634486228,-0.0230357796,-0.0301015638,0.1403870285,-0.4040715694,-0.283573091,0.2419641763,0.2077274472,0.0305831209,-0.2452179492,-0.0770897567,-0.1613958776,0.4342415631,-0.0815225467,-0.2347530723,-0.5598556995,0.1654607058,-0.0396309122,0.1403411925,-0.3457119465,0.18981947,0.3417885005,0.6025447249,-0.0452574156,0.5372428298,-0.0001287701,-0.2627755105,0.2452052087,-0.0852422863,-0.2170967311,-0.1017389745,-0.0870472118,-0.1840791106,0.2249264866,-0.1094866619,0.160850212,0.1228189543,-0.0150860017,-0.5285058022,0.265088439,-0.1673846096,-0.0002370196,0.1131123155,0.167238161,-0.1510144174,0.3145914972,-0.2097328752,-0.1848717779,0.4976549149,0.4717492759,-0.0901188776,-0.2479738295,0.0887352005,0.025788812,0.0382361636,0.6046129465,-0.2951402366,0.4669860005,-0.2977612913,-0.3035379052,0.2270716578,0.5503914952,-0.0176632497,-0.5384922028,-0.1820754558,0.358096391,-0.0000865813,0.4926611483,0.0541597866,-0.1355169564,0.3503001034,0.015288325,-0.1867275983,0.0734807923,0.0515650138,-0.1668386459,0.0551695526,0.1540466845,0.0693725049,0.2360328436,-0.141763553,0.0047063935,-0.2013449967,-0.1389338672,0.0655190945,-0.5184341669,0.0440693386,-0.0237710048,0.0005390666,0.0014865845,-0.1775720417,-0.4711024463,0.1959103942,-0.1072873995,0.4131366014,0.1072750688,0.1250377297,0.1560991257,-0.2706227601,0.3129884303,-0.0366698503,-0.2005043477,0.1234974861,-0.0997190326,-0.2006221265,-0.0706194639,-0.0085441861,0.0244507249,-0.4772162735,0.1940928698,0.2955318987,0.1394579113,0.4006765485,0.1581330299,0.1941503435,0.1847222,0.4262399673,0.2075468004,0.0793263689,-0.1944486201,0.1226030812,0.0305183455,-0.1448899508,0.1050764844,0.1360038221,0.0371015035,0.2895999551,0.1767875552,0.1284878403,0.2166540474,0.1174142882,-0.0327689983,-0.0519724824,-0.1933226138,0.4518730342,-0.141878292,0.1657255739,0.0007304286,0.1726854295,-0.0379014574,0.0773888379,-0.3118938506,-0.0237897579,-0.1736692488,-0.0390766449,-0.4359885156,0.0211994778,-0.1416533291,-0.2198771387,-0.2442763001,-0.3579334319,0.0514369868,-0.504481256,-0.0349159278,0.0546138324,-0.0038452642,0.3324382603,-0.4034097791,-0.0246994253,0.2663726509,-0.1798488051,0.0174748898,0.3031401038,-0.0489033759,-0.0927232653,-0.1638783067,-0.009584276,0.1985514015,-0.4244553745,-0.6493101716,0.1907721907,-0.334951371,0.1472037286,0.1931843311,0.2882729173,-0.0813930184,-0.1296011358,0.0087062344,0.3986360133,0.1607896686,-0.198757872,-0.1371515691,-0.2944769859,-0.1860226989,0.1477249265,-0.0508104973,0.152619198,0.0321165994,0.5327267051,0.2337102592,-0.0062223668,-0.0406285077,0.6131659746,0.677834034,0.1461275667,0.1264191866,-0.1563685983,-0.5479946136,0.385871917,-0.3822480738,0.0200355537,-0.1693601161,-0.0390051603,0.1234154478,-0.0224512219,-0.0536840633,0.161467284,-0.018963689,-0.1154923663,0.0692482442,0.0914661661,0.1012218744,0.4054683447,-0.1135651767,0.0921659842,-0.1364718378,-0.0361966118,0.1138330176,-0.2384926677,0.0204266049,-0.0355385467,0.0971088484,-0.2887970805,-0.2271137089,0.4242851436,0.2979375124,0.1792170554,-0.0711193234,0.3551245034,0.0821090788,0.5420109034,-0.2560358644,-0.1445958018,0.0648711547,-0.1487627327,-0.1179759428,0.0460430309,-0.1618244499,0.1645622849,-0.2425416857,-0.0333045833,0.1862594187,0.1448422223,-0.0942100361,0.1040548384,0.3544766605,0.0335772596,-0.103646636,-0.1695373505,0.4346260428,0.0931999087,0.2976520658,0.0829860792,0.0781313181,0.107116282,-0.0289707314,-0.091502741,0.1539365351,0.2255529016,0.4854830503,0.0972242504,0.1201269925,-0.3374759555,0.1207467094,-0.2458670586,0.0951208025,-0.0483834073,-0.2100300491,0.2208928168,0.2606562376,-0.1177728176,0.0648326278,-0.4731079936,0.1089856178,0.0579156019,0.0545388721,-0.0150621124,-0.2951617241,0.4948147535,0.1755051762,0.3769619763,-0.2835226357,-0.4526466131,0.1798097193,0.8311291337,0.1327476948,0.0647038743,-0.4011010528,0.100080736,0.1955150813,-0.3513923585,-0.2648263276,-0.2396947294,0.0074710893,-0.1677084416,0.4072366357,0.1740886271,0.1044062749,0.1826917827,-0.1740866899,-0.4815850854,-0.1713532358,0.2042211145,-0.0078629497,0.4539900124,0.0329961255,0.1318009645,-0.1141724586,-0.0199321788,0.1900190413,-0.3241443634,-0.035949707,-0.0779606476,-0.0392701887,-0.1526338756,-0.280564189,0.2152979225,-0.1210353002,-0.3584309816,0.2134422213,-0.2896826267,-0.2595065534,0.0396323167,0.076623328,0.2536055148,-0.1284696013,-0.1014168411,0.0218411945,-0.4564404488,0.2862502635,0.0493835211,-0.5032615066,-0.0686936527,0.290350765,-0.4942887127,0.1319266856,-0.4954322875,-0.317903012,-0.2460888028,0.1960786581,0.035322126,-0.0818380564,0.4039615095,-0.2405238897,0.0513252616,-0.3743172586,0.2550759614,0.158996731,0.1650806814,0.0968525857,-0.0252178665,-0.1344376951,-0.0114859687,0.6712635159,-0.162784934,-0.1171938926,0.1715481579,0.2996037602,0.2783558369,-0.3603678346,-0.1101806983,-0.0983461738,-0.09169878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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/328","title":"Fork dataset","comments":"To be able to generate the Arrow dataset you need to either use our csv or json utilities `load_dataset(\"json\", data_files=my_json_files)` OR write your own custom dataset script (you can find some inspiration from the [squad](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/squad\/squad.py) script for example). Custom dataset scripts can be called locally with `nlp.load_dataset(path_to_my_script_directory)`.\r\n\r\nThis should help you get what you call \"Dataset1\".\r\n\r\nThen using some dataset transforms like `.map` for example you can get to \"DatasetNER\" and \"DatasetREL\".\r\n","body":"We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. \r\n\r\nWe're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.\r\n\r\nOur preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.\r\n\r\nIs there some good way to \"fork\" dataset-\r\n\r\nEG\r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 -> DatasetREL\r\n\r\nor \r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 + DatasetNER -> DatasetREL\r\n\r\n","comment_length":72,"text":"Fork dataset \n We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. \r\n\r\nWe're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.\r\n\r\nOur preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.\r\n\r\nIs there some good way to \"fork\" dataset-\r\n\r\nEG\r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 -> DatasetREL\r\n\r\nor \r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 + DatasetNER -> DatasetREL\r\n\r\n \n To be able to generate the Arrow dataset you need to either use our csv or json utilities `load_dataset(\"json\", data_files=my_json_files)` OR write your own custom dataset script (you can find some inspiration from the [squad](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/squad\/squad.py) script for example). Custom dataset scripts can be called locally with `nlp.load_dataset(path_to_my_script_directory)`.\r\n\r\nThis should help you get what you call \"Dataset1\".\r\n\r\nThen using some dataset transforms like `.map` for example you can get to \"DatasetNER\" and \"DatasetREL\".\r\n","embeddings":[-0.2403900176,-0.1519307494,-0.0046681575,0.1388017535,-0.1991832703,0.1661750227,0.0855344608,0.3236584067,0.1992565691,-0.1408088207,0.0522016436,0.676864922,-0.4008255899,0.1611249298,0.2155926079,-0.2229901701,0.1336966902,0.0569895543,0.1327417791,0.1123379022,0.0333925709,-0.1810865551,0.1336497664,0.0569470935,-0.0264412761,-0.228029862,-0.1203991249,0.1118857339,0.0269776415,0.0009552161,0.4616040885,0.1885261834,-0.0171257891,0.3208827376,-0.000117806,0.1305478066,0.0574894063,0.0988214687,-0.2189414799,-0.2057286054,-0.1271968186,-0.1774255037,0.1727922112,-0.1873126775,0.0866854414,-0.3770356774,0.1852132231,-0.2320502251,0.7697014809,0.1443224102,0.1043770611,0.0922689065,-0.124608703,0.2189230025,-0.2451384217,0.3413786292,0.0497781746,0.1736006588,-0.0169577524,-0.20677948,-0.0755041018,0.0774039701,-0.1792523563,0.0272165816,0.2267156541,-0.0575824305,-0.0144064045,-0.1606176794,0.0055113602,0.3372310698,-0.0058291848,-0.2154867202,-0.2079762518,-0.6004967093,0.1435125619,-0.1713543832,-0.1086236909,0.2095341831,-0.1596323252,0.2616223693,0.0679493621,-0.5672591925,-0.0091920318,0.0720446706,0.0448700786,0.2262707204,0.1848957539,0.2127395421,0.2443888336,0.0795683265,0.3197899163,-0.2550052404,-0.0211977717,0.0234170444,-0.1553244144,-0.2513640225,-0.1570304781,-0.5725252032,0.0786036775,0.297617346,0.3420595825,0.0134496707,-0.3720589876,0.1997283399,0.1516202688,-0.2357715368,-0.1162772998,0.3510949016,-0.0139152203,-0.2985035181,0.0886309892,0.1362757087,-0.4068128765,-0.0246962197,-0.275595963,-0.0017347616,0.303938508,0.24162817,-0.1230674088,-0.4214791059,-0.2392184436,-0.1486857384,0.1763511151,-0.0079226987,0.0141327465,0.5878418088,-0.0423438363,0.4042405486,-0.1854728758,-0.2160092145,0.0273767319,-0.0208279565,-0.1518221945,0.0427034162,0.1486830264,0.4183577597,0.1775204837,-0.0463534743,-0.2537013888,-0.0799321458,0.46836555,0.00269732,-0.0315183774,-0.0669803172,-0.0444803685,0.0338094793,0.0545952395,-0.0170926508,-0.2666883767,0.147213608,-0.1560296565,-0.1347330958,-0.0913876295,0.0619671568,-0.0013741046,-0.2966220379,-0.6265000105,0.6202912927,0.2018990964,-0.0099184895,0.0208921041,-0.0113555975,-0.3717825711,-0.2209550738,0.0867062584,0.1400279701,-0.3647662103,-0.3302684128,-0.2051743865,-0.0137742171,-0.1466054618,0.4300253093,-0.2711233497,0.1972029805,0.0315717794,-0.0230285693,0.6090839505,-0.2180972397,-0.036523357,0.1634851992,-0.1810098141,-0.102796562,0.1132742837,0.1215592399,0.0628547147,-0.1734144092,-0.0528556556,0.7855044603,-0.3208650947,0.0275785476,0.1742578149,-0.288834244,0.2234943807,0.2195893079,-0.0326541848,0.0045393747,-0.2716989517,-0.0584679432,0.2219848484,-0.1346555054,0.3524713516,-0.0911045223,-0.1485607177,-0.0693360269,-0.1277203113,-0.2041727602,-0.5392062664,-0.0095585017,0.0517538153,-0.0288864318,0.1372354478,-0.13561441,0.052892141,-0.0021244539,0.0300062131,0.0323185436,-0.0159958825,0.0022268153,0.0753961504,-0.3097703755,-0.3593778014,0.0514098927,0.0316320695,0.1768800467,-0.3762131035,0.5385991335,0.2853182554,0.0463969484,-0.0958447531,0.2762735486,-0.1842873245,0.019609997,0.1722978055,0.0894773006,-0.2095708102,0.3988023102,0.3215484619,0.2255489975,0.2627266347,-0.4603494704,0.350099653,0.0316814519,-0.0697623342,-0.1383180022,-0.2709311843,0.3280028403,0.1001761854,0.4635065794,-0.0369856022,0.2488966584,0.1319746375,-0.138108477,-0.2372635305,0.1167031154,0.011380407,-0.0981424898,0.2888348401,-0.0873024389,-0.4452435076,0.3614445031,0.3100599945,-0.0912935808,0.013084664,0.0618504137,-0.2570584416,-0.0112765171,-0.1076851338,0.5400252938,0.08790306,0.2115104347,0.2821084857,0.0279927403,-0.2887629271,0.0348274782,0.2299976349,0.1382597983,0.5379901528,-0.0090583256,0.1656315774,-0.2080513835,0.0035571624,-0.2566023171,0.3904086947,0.0426651984,-0.048581481,0.4440466166,-0.3302446306,-0.4054999352,-0.6255347729,-0.14469558,-0.0908595398,-0.2803320289,-0.0969087034,0.2085824758,-0.0711979792,0.2793180346,-0.0553824082,0.2379255444,-0.2599774897,-0.0371599421,0.0208123028,-0.515835762,-0.0122365383,0.0440675281,0.524874866,0.2014617622,0.228610158,0.4384919703,-0.2095719725,-0.1107245311,-0.1922592819,0.1964453459,-0.1147720665,-0.0068913992,0.1081255451,-0.1384060532,0.1701038778,-0.2603875101,-0.126014322,-0.0806494951,-0.1264219135,-0.1522290558,-0.1260615587,0.272267729,-0.0650102049,-0.1946340948,-0.3711027205,-0.2666157484,0.3674730062,0.019024875,0.2201631665,0.3498848379,0.1230061427,0.0886919498,0.0806673169,-0.0853569657,0.1626693457,-0.0287244804,-0.0575835221,-0.279826045,0.1778043211,-0.2847963572,-0.3303154409,0.356744945,0.1205294803,-0.2028897554,0.3326491416,-0.0591552928,-0.0081521729,-0.1078237444,-0.2849696875,0.073661685,0.1092720851,0.0739220679,-0.0420350507,0.192833662,0.1713174582,0.0816804022,0.1657755673,0.2389998138,0.2205332369,0.1532522291,0.8823812008,0.0961877257,-0.2332434058,-0.2094873488,0.1341323555,-0.3067849874,0.1412734985,-0.0705723837,-0.141685307,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/328","title":"Fork dataset","comments":"Thanks for the helpful advice, @lhoestq  -- I wasn't quite able to get the json recipe working - \r\n\r\n```\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/pyarrow\/ipc.py in __init__(self, source)\r\n     60 \r\n     61     def __init__(self, source):\r\n---> 62         self._open(source)\r\n     63 \r\n     64 \r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/pyarrow\/ipc.pxi in pyarrow.lib._RecordBatchStreamReader._open()\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\nArrowInvalid: Tried reading schema message, was null or length 0\r\n```\r\n\r\nBut I'm going to give the generator_dataset_builder a try.\r\n\r\n1 more quick question -- can .map be used to output different length mappings -- could I skip one, or yield 2, can you map_batch  ","body":"We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. \r\n\r\nWe're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.\r\n\r\nOur preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.\r\n\r\nIs there some good way to \"fork\" dataset-\r\n\r\nEG\r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 -> DatasetREL\r\n\r\nor \r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 + DatasetNER -> DatasetREL\r\n\r\n","comment_length":87,"text":"Fork dataset \n We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. \r\n\r\nWe're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.\r\n\r\nOur preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.\r\n\r\nIs there some good way to \"fork\" dataset-\r\n\r\nEG\r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 -> DatasetREL\r\n\r\nor \r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 + DatasetNER -> DatasetREL\r\n\r\n \n Thanks for the helpful advice, @lhoestq  -- I wasn't quite able to get the json recipe working - \r\n\r\n```\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/pyarrow\/ipc.py in __init__(self, source)\r\n     60 \r\n     61     def __init__(self, source):\r\n---> 62         self._open(source)\r\n     63 \r\n     64 \r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/pyarrow\/ipc.pxi in pyarrow.lib._RecordBatchStreamReader._open()\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\nArrowInvalid: Tried reading schema message, was null or length 0\r\n```\r\n\r\nBut I'm going to give the generator_dataset_builder a try.\r\n\r\n1 more quick question -- can .map be used to output different length mappings -- could I skip one, or yield 2, can you map_batch  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/328","title":"Fork dataset","comments":"Thanks this answers my question. I think the issue I was having using the json loader were due to using gzipped jsonl files.\r\n\r\nThe error I get now is :\r\n\r\n```\r\n\r\nUsing custom data configuration test\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-38-29082a31e5b2> in <module>\r\n      5 print(ner_datafiles)\r\n      6 \r\n----> 7 ds = nlp.load_dataset(\"json\", \"test\",  data_files=ner_datafiles[0])\r\n      8 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: int64> nor list<item: int64>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions.\r\n```\r\n\r\nIf I just create a pa- table manually like is done in the jsonloader -- it seems to work fine. Ths JSON I'm trying to load isn't overly complex -  1 integer field, the rest text fields with a nested list of objects with text fields .","body":"We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. \r\n\r\nWe're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.\r\n\r\nOur preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.\r\n\r\nIs there some good way to \"fork\" dataset-\r\n\r\nEG\r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 -> DatasetREL\r\n\r\nor \r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 + DatasetNER -> DatasetREL\r\n\r\n","comment_length":324,"text":"Fork dataset \n We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. \r\n\r\nWe're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.\r\n\r\nOur preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.\r\n\r\nIs there some good way to \"fork\" dataset-\r\n\r\nEG\r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 -> DatasetREL\r\n\r\nor \r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 + DatasetNER -> DatasetREL\r\n\r\n \n Thanks this answers my question. I think the issue I was having using the json loader were due to using gzipped jsonl files.\r\n\r\nThe error I get now is :\r\n\r\n```\r\n\r\nUsing custom data configuration test\r\n---------------------------------------------------------------------------\r\n\r\nValueError                                Traceback (most recent call last)\r\n\r\n<ipython-input-38-29082a31e5b2> in <module>\r\n      5 print(ner_datafiles)\r\n      6 \r\n----> 7 ds = nlp.load_dataset(\"json\", \"test\",  data_files=ner_datafiles[0])\r\n      8 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n--> 738         parse_schema(writer.schema, features)\r\n    739         self.info.features = Features(features)\r\n    740 \r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/builder.py in parse_schema(schema, schema_dict)\r\n    734                     parse_schema(field.type.value_type, schema_dict[field.name])\r\n    735                 else:\r\n--> 736                     schema_dict[field.name] = Value(str(field.type))\r\n    737 \r\n    738         parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in __post_init__(self)\r\n     55 \r\n     56     def __post_init__(self):\r\n---> 57         self.pa_type = string_to_arrow(self.dtype)\r\n     58 \r\n     59     def __call__(self):\r\n\r\n~\/.virtualenvs\/inv-text2struct\/lib\/python3.6\/site-packages\/nlp\/features.py in string_to_arrow(type_str)\r\n     32         if str(type_str + \"_\") not in pa.__dict__:\r\n     33             raise ValueError(\r\n---> 34                 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n     35                 f\"Please make sure to use a correct data type, see: \"\r\n     36                 f\"https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: int64> nor list<item: int64>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https:\/\/arrow.apache.org\/docs\/python\/api\/datatypes.html#factory-functions.\r\n```\r\n\r\nIf I just create a pa- table manually like is done in the jsonloader -- it seems to work fine. Ths JSON I'm trying to load isn't overly complex -  1 integer field, the rest text fields with a nested list of objects with text fields .","embeddings":[-0.2616365254,-0.2061418295,0.0375017449,0.3549158573,-0.1682421863,0.1174596846,0.1433506906,0.4742084146,0.3013393879,-0.0251564812,-0.0413274691,0.7794690728,-0.4380642772,-0.0006891052,-0.0343453512,-0.1329026371,0.078056626,0.0321564749,0.1479655653,0.1702057421,-0.0766780823,0.0815187022,0.1405754238,0.0701387823,-0.0335839391,-0.23559241,-0.0052827508,0.322276175,-0.0223012716,-0.0998795033,0.3949323893,0.025425557,0.0121661732,0.370148629,-0.0001212367,0.2414514273,0.1444253325,0.0434316434,-0.2983271182,-0.1597706378,-0.1191835627,-0.2082093805,0.2016608864,-0.1714549512,0.0930440053,-0.3221847415,0.010363779,-0.2175858319,0.8227084279,0.1959593892,0.085380286,0.094117865,-0.0375261307,0.1864823252,-0.1528877467,0.2486982346,0.0706446469,0.4197851419,-0.0719188601,-0.158483699,-0.1350350529,0.1528037339,-0.21148929,0.1532353312,0.1155335903,-0.0662333667,0.1061297208,-0.0129734129,-0.0425176546,0.2376263887,-0.0137331095,-0.1154446304,-0.2180214226,-0.5963210464,0.0687730238,-0.3153635263,0.1451151222,0.1890842319,-0.2590257525,0.2256511152,0.1236033216,-0.5307229757,-0.0557474494,0.1267889142,-0.0098405648,0.3766208887,0.1091585681,0.2603290081,0.2779296935,0.0567415208,0.1978452653,-0.1859574169,-0.1330088377,-0.0677112937,-0.1957906485,-0.272372067,-0.1936009526,-0.7956878543,-0.0301025473,0.2747699022,0.4105606079,-0.0649349988,-0.2221665382,0.2716937959,0.2230012715,-0.1622830629,-0.1264720708,0.3251129389,0.0797926933,-0.1740464419,0.111032851,0.1423989087,-0.3329451382,-0.1393825412,-0.2668761909,0.035817977,0.3838409483,0.1243890375,-0.1721629798,-0.3282208741,-0.4179406464,-0.1695621014,0.1243307143,0.0437096506,0.0265701655,0.4992043972,0.0284126885,0.3907045424,-0.1886146218,-0.2341752499,0.0649520531,-0.0155802146,-0.115491353,0.0258706603,0.2306987345,0.393681556,0.2521832585,0.0243458468,-0.2440880388,-0.084308289,0.3822432756,-0.0215821322,-0.0870607421,0.0885294601,-0.0290046353,0.0562349223,0.1478366107,-0.16957739,-0.328660965,0.2814548016,-0.2943262756,-0.1961506754,-0.0213513691,0.0327280276,-0.0374715142,-0.2179765999,-0.5268861651,0.3935555816,0.347150743,-0.1026957855,-0.0189551022,-0.1631554067,-0.3421741128,-0.2664163411,-0.0560863055,0.1445371211,-0.4778347909,-0.3250251114,-0.2092002928,0.0039791819,0.0233761054,0.5663744807,-0.2850210667,0.2381998748,-0.0070042107,0.0657345131,0.5314764977,-0.2201392949,-0.108461909,0.3468538821,-0.1492508948,0.0518519506,0.1439966857,0.039588891,0.0526149273,-0.1308285594,0.1937210113,0.6393967867,-0.2344771177,-0.0323568322,-0.0442832783,-0.2477199435,0.4179079533,0.2243718803,-0.0949275196,0.0199594758,-0.3340435326,-0.0949614346,0.1253610998,-0.1228609085,0.2665160596,0.0090401275,0.0167940482,-0.0821830109,-0.0557357036,-0.0020878376,-0.5716664195,0.0258828644,-0.1563588679,0.0483758412,0.0367052443,0.0840058327,0.0246533602,0.0242607631,-0.1075538546,-0.1872881055,-0.0309743583,0.132142216,0.0364425257,-0.3403074741,-0.3291967213,0.2118789703,-0.0324937887,0.2425681204,-0.4329964519,0.54313761,0.2083956897,-0.1171969622,-0.0873500779,0.0489445068,-0.0986335203,-0.0070063639,0.001949327,0.1942148209,-0.0757218078,0.2442175895,0.1699133962,0.065474458,0.1595394015,-0.3383493125,0.3486060202,0.2228185982,-0.0673015639,-0.1175182685,-0.2634740174,0.453484565,0.0424078889,0.4329811037,-0.1104202271,0.141562596,0.2285179049,-0.1195356101,-0.2843779027,0.0755167827,0.038110964,-0.198992908,0.2602618635,-0.0758707374,-0.5680077672,0.1407360286,0.2386331111,-0.0199066997,-0.0573452674,0.1785087138,-0.1155240834,-0.0487361103,-0.0507612117,0.5043224096,0.3902941644,0.2023362517,0.1330361664,0.0527726822,-0.333237946,-0.0390539505,0.2645007372,0.0731956884,0.572296381,0.1111420766,0.1884936392,-0.2298469096,-0.0260418691,-0.225959748,0.375713408,0.196081847,-0.2328561097,0.3923481405,-0.3859460056,-0.3946233392,-0.6372320652,-0.126499474,-0.2228471041,-0.4076824188,-0.0596623421,0.2941827476,-0.2008382082,0.3044977188,-0.0083969347,0.2533296943,-0.0969766378,-0.0633145422,-0.0650340095,-0.3226173222,-0.0971216485,0.0095733339,0.4663543105,0.0486909263,0.1779366434,0.4626578093,-0.3111791313,0.0040370659,-0.2330508381,0.2138879597,-0.1255089641,0.0886386111,0.1230219081,-0.0334311761,0.3305614293,-0.3943111002,0.0101411119,-0.0132068647,-0.2062790096,-0.0949026421,0.0286064222,0.3486715555,-0.1070190743,-0.309383601,-0.3159711063,-0.3946548104,0.2259314805,0.0516273938,0.2161054462,0.4295203686,0.1762686521,0.0463485681,0.1555684656,-0.0262172334,0.0147960875,-0.0455983505,0.0338850096,-0.3673450053,0.189736262,-0.2843198776,-0.2059624046,0.4252792597,0.1152253449,-0.3176317811,0.3440875411,-0.0434317552,-0.0167123545,-0.2224956304,-0.3622526824,0.0244202372,0.1780630201,0.0452707447,-0.0338765793,0.1468674242,0.226079613,0.1121597514,0.23243922,0.2633699179,0.3287668526,0.1926621348,0.8260392547,0.0851388574,-0.1176849231,-0.0616794378,0.0974354222,-0.272415936,0.0375084952,-0.0940982327,-0.2391368002,-0.0824233815,0.06999379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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/328","title":"Fork dataset","comments":"I'll close this -- It's still unclear how to go about troubleshooting the json example as I mentioned above. If I decide it's worth the trouble, I'll create another issue, or wait for a better support for using nlp for making custom data-loaders.","body":"We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. \r\n\r\nWe're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.\r\n\r\nOur preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.\r\n\r\nIs there some good way to \"fork\" dataset-\r\n\r\nEG\r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 -> DatasetREL\r\n\r\nor \r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 + DatasetNER -> DatasetREL\r\n\r\n","comment_length":43,"text":"Fork dataset \n We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset. \r\n\r\nWe're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.\r\n\r\nOur preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.\r\n\r\nIs there some good way to \"fork\" dataset-\r\n\r\nEG\r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 -> DatasetREL\r\n\r\nor \r\n\r\n1. text + json -> Dataset1\r\n1. Dataset1 -> DatasetNER\r\n1. Dataset1 + DatasetNER -> DatasetREL\r\n\r\n \n I'll close this -- It's still unclear how to go about troubleshooting the json example as I mentioned above. If I decide it's worth the trouble, I'll create another issue, or wait for a better support for using nlp for making custom data-loaders.","embeddings":[-0.225592494,-0.2139752954,0.0137120206,0.1874609441,-0.2855693996,0.1027285755,0.0859784484,0.3090703785,0.2629738152,-0.1347025335,0.0771383941,0.675709486,-0.3767235875,0.130015865,0.1040436774,-0.2021784484,0.1567045003,0.0305683278,0.2432409227,0.1246745661,-0.0030573662,-0.1404227614,0.1567752212,0.0151140513,-0.0611150563,-0.2410472333,-0.1119404659,0.1831781864,0.0436238237,0.0080808066,0.4242900312,0.1682476401,-0.0357528664,0.331484884,-0.0001182203,0.1242470443,0.1082486734,0.1458239555,-0.2584878206,-0.155663982,-0.1127671227,-0.1371055692,0.2131112218,-0.1448480487,0.1367443353,-0.3278983831,0.170842737,-0.1297238171,0.7699853778,0.1334997565,0.1088852584,0.0454335846,-0.1247143596,0.2250990868,-0.2478457242,0.2831077874,0.0789029673,0.2020106614,0.0115350084,-0.1621371806,-0.163832292,0.0203957241,-0.1525958925,0.1096807346,0.1250389367,-0.1221601069,0.0543632098,-0.0273976233,-0.0277142264,0.3535755873,-0.0114999125,-0.1214190722,-0.1666322649,-0.6308935881,0.0714958981,-0.1956183165,-0.0327244774,0.1070844233,-0.1702801138,0.2213978022,0.1279788017,-0.5801433325,0.0357806198,0.1103352532,-0.0623915866,0.3296544254,0.1729614139,0.197650671,0.2552981377,0.0594155826,0.3220237494,-0.2034488916,-0.0187667608,-0.0308435988,-0.1546315551,-0.2607319951,-0.1478810906,-0.626164794,-0.0011969999,0.2987721562,0.3444442451,0.0295497198,-0.3762353063,0.2349631339,0.1856475919,-0.2687038183,-0.1834815145,0.2808970213,0.0147724431,-0.2175779343,0.0500678793,0.1455634981,-0.3928111792,0.0109011577,-0.3534698486,-0.063665919,0.3451400697,0.1989928335,-0.1900560558,-0.402746737,-0.2324145585,-0.1443460584,0.1434203684,0.0017943544,0.0272262767,0.5649724007,-0.0731390789,0.3858673573,-0.213207379,-0.2311062813,0.0232887547,-0.0543792807,-0.1361839622,0.0107970713,0.107192874,0.5068042278,0.151108399,-0.0194066018,-0.3058052361,-0.0769732967,0.4386520684,-0.0546703786,-0.0773444548,-0.0458450578,-0.0622554049,0.0818981826,0.0954199135,-0.0816645473,-0.2606015503,0.21036686,-0.1766934097,-0.1315820366,0.0219493564,0.0671400279,0.0027995305,-0.3125676215,-0.5295146704,0.6395683289,0.1960008442,-0.0690217316,0.0101074744,-0.0039348067,-0.3481339216,-0.2317089736,-0.0104253301,0.0529447421,-0.3890049756,-0.3615546823,-0.2279181182,0.0117453504,-0.0676146746,0.4565347433,-0.2434231192,0.1787769645,0.0341737196,0.0166954305,0.5639643669,-0.1810501516,-0.0599438809,0.2342610359,-0.2342584878,0.0363017432,0.0825459138,0.1439548284,-0.038168285,-0.181454137,0.0570438057,0.7605223656,-0.2868873179,-0.0040725651,0.0645716265,-0.2386103421,0.3192780614,0.2190312594,-0.0627646372,0.0548263229,-0.3312347531,-0.0223843344,0.2203795016,-0.0492089242,0.3052375615,-0.1040297449,-0.1216966212,-0.0869738385,-0.1141555309,-0.1505255848,-0.4700725079,-0.0176507607,0.0689648986,0.0118586682,0.2829796374,-0.0816037804,0.0552300029,-0.0188461933,-0.0217032209,-0.0096874377,-0.0076297028,0.0964447707,0.0328528248,-0.2809767127,-0.37099576,0.1122867689,-0.0472183861,0.2059127092,-0.3807469606,0.4885462224,0.2925759852,0.0159944594,-0.0933833048,0.2137661129,-0.1594698429,0.0417959094,0.1802476794,0.0719100535,-0.2072035372,0.4196845889,0.1911338866,0.1224554256,0.1848059744,-0.3710247874,0.2803329825,0.1075646654,-0.1018229872,-0.1790726483,-0.2775411606,0.3033385277,0.1036186367,0.4381709397,-0.0083155483,0.1538201272,0.147460103,-0.1902009696,-0.244554624,0.1409129351,0.0918480381,-0.1703172177,0.2928293943,-0.0660921335,-0.5334093571,0.2663099468,0.2597095668,-0.0231016744,-0.0067645116,0.0656954944,-0.2278887928,-0.0272636358,-0.0800404102,0.5746644735,0.1669398397,0.2245680243,0.3080439568,0.0128028113,-0.3045589328,-0.0158982351,0.2137684673,0.1295600533,0.5631981492,0.0116662178,0.2122720331,-0.2407271266,-0.0450087413,-0.20996584,0.4044648409,0.0407788157,-0.0848146155,0.365542978,-0.3508611321,-0.4246234596,-0.6468885541,-0.185021773,-0.1552049071,-0.3520293832,-0.0254884977,0.3039807975,-0.1339466721,0.2258616388,-0.012356679,0.2932177782,-0.3522542119,0.0089806654,0.006000774,-0.4163363278,-0.0330904648,0.0651345029,0.5234524608,0.179954946,0.2201524079,0.4802725613,-0.2884744108,0.0228921548,-0.1987533867,0.2183101028,-0.0874336287,-0.0297724493,0.0596472658,-0.1122584566,0.2067912817,-0.2833401263,-0.101821214,-0.0160414036,-0.1360421628,-0.1377626657,-0.0377467163,0.3285039663,-0.0629631132,-0.165023908,-0.3227213323,-0.3119983077,0.350612402,0.036876291,0.1655424982,0.3113000691,0.0797174647,0.0510160699,0.1713953912,-0.0293928422,0.1030354202,-0.06838613,-0.1436235011,-0.277015835,0.2035324425,-0.2974310517,-0.2434927523,0.3983848989,0.128205657,-0.2371731848,0.402256906,-0.0576604716,-0.0482834689,-0.1453525871,-0.3861068785,0.0729031414,0.0853465497,0.0584493913,-0.0381551385,0.2228053659,0.1892830282,0.1013861746,0.1968120039,0.2205891758,0.1887685508,0.1956256628,0.8005090952,0.0264447257,-0.1882139146,-0.1373371035,0.1523606926,-0.3494243026,0.0859080628,-0.0366805531,-0.1492536515,-0.051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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/326","title":"Large dataset in Squad2-format","comments":"I'm pretty sure you can get some inspiration from the squad_v2 script. It looks like the dataset is quite big so it will take some time for the users to generate it, but it should be reasonable.\r\n\r\nAlso you are saying that you are still making the dataset grow in size right ?\r\nIt's probably good practice to let the users do their training\/evaluations with the exact same version of the dataset.\r\nWe allow for each dataset to specify a version (ex: 1.0.0) and increment this number every time there are new samples in the dataset for example. Does it look like a good solution for you ? Or would you rather have one final version with the full dataset ?","body":"At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ?","comment_length":121,"text":"Large dataset in Squad2-format \n At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ? \n I'm pretty sure you can get some inspiration from the squad_v2 script. It looks like the dataset is quite big so it will take some time for the users to generate it, but it should be reasonable.\r\n\r\nAlso you are saying that you are still making the dataset grow in size right ?\r\nIt's probably good practice to let the users do their training\/evaluations with the exact same version of the dataset.\r\nWe allow for each dataset to specify a version (ex: 1.0.0) and increment this number every time there are new samples in the dataset for example. Does it look like a good solution for you ? Or would you rather have one final version with the full dataset ?","embeddings":[-0.0997780412,-0.2414989769,-0.1118135303,0.1810322106,0.1970849931,-0.0152267907,0.0232527517,0.4885350168,-0.0912805051,0.1816319078,-0.2126611471,0.0559425987,-0.2452746332,0.3290416896,0.2647447884,-0.0670856759,0.0118722217,-0.0230084192,-0.1232943535,0.0525797717,0.0917730629,-0.1653291434,0.0919112861,-0.1205425188,-0.2654374838,-0.1660659611,-0.218966648,0.0960968584,-0.3439052105,-0.4318127632,-0.1703834683,0.2185877264,0.3581572771,0.6168690324,-0.0001192068,-0.2639674544,-0.1069821045,-0.3270840645,-0.3645772338,0.0406492725,-0.0091303037,-0.3253715634,-0.1189130396,-0.1130117327,-0.1868093908,-0.2391312569,0.1240132153,-0.2879826128,0.8988136649,-0.2257035673,0.0568251647,-0.0995194614,-0.1425751299,0.0200350657,0.1949758828,0.264967978,-0.0998327062,0.0275652818,0.7631838918,0.2729378045,-0.1379783005,0.1158566996,0.1028813273,-0.2239881754,0.0953689665,-0.2059375197,-0.0160013381,-0.3111697435,-0.0992633924,0.2254624814,0.4971261919,-0.231215775,-0.2989939153,-0.3494286239,0.0848995298,0.0883637816,-0.0953715444,0.3142319918,-0.0064442726,0.3016768992,-0.3257999122,-0.4009292126,-0.4019841254,-0.0720786899,0.2573390007,-0.1161980927,-0.0808769986,0.0244955681,-0.1471570432,-0.219811812,-0.0584718958,-0.1525382102,0.0715532452,0.2345580757,-0.0675154701,-0.3732780516,-0.1337952465,-0.1824221909,0.6460013986,0.1989931762,0.1953395307,-0.0929704607,0.1765871197,-0.086455822,0.3882081509,-0.0132769868,0.0069345436,-0.3535681069,0.1602530628,0.0566458814,0.015451747,-0.0166388899,-0.3482196331,0.1194508523,-0.0780948922,-0.1198165491,0.201299727,0.1279423088,0.1323255002,0.1488922238,-0.1224228665,0.0335170478,0.1445465982,0.2388262749,-0.1281583011,0.1534231901,-0.0863457993,0.0834686011,0.1609511524,-0.2093755305,0.0299157258,-0.0308845695,-0.105106391,0.330355376,0.2067345232,-0.0710580349,0.0653580055,0.0247822478,0.3602226377,-0.4267211854,0.1748328358,0.1002815887,0.0175224412,-0.1147703007,0.1647482067,0.0815888867,-0.2149352431,-0.1712434739,-0.2071367651,0.1294359118,-0.1964801997,-0.2423818558,-0.0544869825,0.0102973459,-0.3162892461,0.2142867744,0.0043270453,0.3083494604,0.0286607444,-0.026775999,-0.0905512422,0.2809029222,-0.2474737912,-0.2410406768,0.1092095897,0.6122671962,-0.2852084339,-0.0807601064,0.6636146307,0.0836110115,-0.5348961353,0.3374616504,-0.0009944156,0.1033220664,0.10663376,-0.2227986902,0.3268621564,-0.4240097106,-0.3810765743,0.4306105673,-0.2452858537,-0.1893838793,0.3636598587,-0.0155090056,0.728866756,-0.2947201133,-0.0227383524,0.3314570487,-0.0771267861,0.2354422808,-0.3025572896,-0.3982694745,-0.2546396852,0.2839325368,-0.2140710056,-0.1160830259,-0.1799302697,0.2502491474,0.2469008416,-0.1099305227,0.3165059686,-0.1018164009,0.3639549315,0.0839579701,-0.0795993358,-0.0867857933,-0.7564765811,-0.1431139708,-0.0840265155,-0.3280597031,0.186029613,-0.4696921706,-0.3212245703,-0.09506163,-0.071246326,-0.0954309925,-0.0386249088,-0.1620655358,0.2539693713,-0.079684414,-0.3652379811,0.1896637976,-0.0390120111,0.111178264,-0.1952002496,0.1073803604,0.113776505,-0.0149338366,0.2077325881,0.2578850091,0.0083844624,-0.3317626715,0.3545594215,0.1319757849,-0.1336446255,0.0812666565,0.1226986796,0.1317264885,0.3408952057,0.2437700927,0.3483716249,-0.0439622961,-0.0989810452,-0.1442310661,-0.5008667111,0.3706684113,-0.2921047509,0.1103616059,-0.1532380879,-0.1345479339,0.1723105758,0.069111295,-0.2613067031,0.0681070685,-0.1646813601,0.1101450771,0.344286263,0.0879935175,-0.0871090293,0.133363232,0.3919232488,-0.2169865966,0.0179702621,0.0516892448,0.1255054474,-0.0312472712,-0.0564965904,-0.2630293667,0.1382662058,0.1746782064,-0.128372401,0.0647285059,0.4244443774,-0.0671781078,-0.046236176,0.0543141514,0.2298889905,0.2028607279,0.3700594008,-0.1126025617,0.1116563529,0.0417751372,0.1205795705,-0.056896247,-0.1817296743,0.0054915077,0.1262895018,0.2495287359,-0.2198900133,0.235609889,-0.2114339471,0.2134434432,0.2909429967,0.1771951169,0.1042567864,0.0839726403,0.2970293164,0.8125568032,0.0710540861,-0.2512123883,0.1529860348,-0.2007253617,0.3018532395,-0.0371446386,0.3549658954,-0.4794067144,0.2100296468,-0.1089795157,0.0352245346,-0.2428153306,-0.1994035393,0.1299562752,-0.0244705826,0.1560817361,-0.1350118369,0.2721752524,-0.2808153331,-0.0307091903,0.1175482273,-0.0479569733,-0.1901573241,-0.2650650144,0.019728668,-0.0658896416,0.0830077156,-0.3270969987,-0.279815346,-0.2791698277,0.5616511106,0.1398451924,0.0851490423,-0.3313442171,0.17753461,-0.1528159827,-0.4699180126,-0.0373300873,-0.1394499689,-0.6506544948,0.1868302375,-0.2666991353,-0.2523920834,-0.197853446,0.094690986,-0.1084744781,-0.0140739512,-0.2855157256,-0.3582021594,0.1891213208,0.0941210687,-0.109618254,0.2015245706,0.3242135346,-0.2208324224,0.1413422376,0.0029685134,-0.1006147265,0.2027544826,0.2374801189,0.3559163511,-0.1488821059,0.3959993422,0.1915974766,0.8294717669,0.6231415272,0.1226647869,-0.192465961,0.1587837636,0.2736447155,0.0276941471,0.0053977608,0.4714065194,0.0324955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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/326","title":"Large dataset in Squad2-format","comments":"It would also be good if there is any possibility for versioning, I think this way is much better than the dynamic way.\nIf you mean that part to put the tiles into one is the generation it would take up to 15-20 minutes on home computer hardware.\nAre there any compression or optimization algorithms while generating the dataset ?\nOtherwise the hardware limit is around 32 GB ram at the moment.\nIf everything works well we will add some more gigabytes of data in future what would make it pretty memory costly.","body":"At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ?","comment_length":93,"text":"Large dataset in Squad2-format \n At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ? \n It would also be good if there is any possibility for versioning, I think this way is much better than the dynamic way.\nIf you mean that part to put the tiles into one is the generation it would take up to 15-20 minutes on home computer hardware.\nAre there any compression or optimization algorithms while generating the dataset ?\nOtherwise the hardware limit is around 32 GB ram at the moment.\nIf everything works well we will add some more gigabytes of data in future what would make it pretty memory costly.","embeddings":[-0.0783762857,-0.3369032741,-0.1021032855,0.3794317544,0.2913861275,-0.0967132673,-0.1217115596,0.5122983456,-0.0272251945,0.1946527064,-0.2856161296,0.0614134073,-0.2502669692,0.2593443692,0.3205595911,-0.0886087418,0.1549791992,0.0823775753,-0.2040606886,0.0569107495,0.0637137368,-0.1032855734,0.0612261407,-0.1653318405,-0.3009232581,-0.1412977427,-0.288174212,0.113891311,-0.3934970498,-0.3375560343,-0.2448669225,0.2171664685,0.1611695588,0.647347033,-0.0001227837,-0.1961065531,-0.1437792182,-0.3495814502,-0.2894806564,0.1330737621,-0.0990354195,-0.3333496749,-0.1614259183,-0.1127134338,-0.1404915005,-0.2584019601,0.1062348038,-0.3380098045,0.7726531625,-0.185261786,0.0068999734,-0.1393992007,-0.0483219214,0.0074566915,0.2478711307,0.2530166507,-0.1029361188,0.0350588039,0.624014616,0.3569599688,-0.0175891798,0.0792302564,0.0387504995,-0.1803491265,0.0834922567,-0.1305760592,-0.0905045271,-0.3459359109,-0.0884473249,0.2567327321,0.4648344815,-0.2146343887,-0.2226941884,-0.1702958196,-0.0250918809,0.1221155077,0.0624894463,0.412058264,-0.1602798402,0.3520800769,-0.3435668647,-0.4413897693,-0.3995464742,-0.0935547352,0.2771208286,-0.0322672129,-0.1175544858,-0.0084751481,-0.1265733689,-0.1373977065,-0.1024049148,-0.1397244185,0.0183658656,0.1883071661,-0.0581437014,-0.3634128869,-0.3048736751,-0.2218268216,0.7251416445,0.2197364718,0.3265017271,-0.0457135327,0.3206260204,-0.0938945189,0.2684270144,-0.0021524255,-0.2191832513,-0.4172424376,0.1518899649,-0.0420654975,0.005759927,-0.0518738441,-0.2956549227,0.1691857874,-0.1634948254,-0.2331241816,0.2861676514,0.1393417269,0.0892824307,-0.0214983411,-0.2318848521,0.0085954964,0.0988225043,0.2212495953,-0.1391449124,0.1223293319,-0.1243348643,-0.034625981,0.2165811956,-0.2578242123,0.0529310964,0.0112324674,-0.071440123,0.347055316,0.2074620873,-0.0121732913,0.0128071811,0.0960285291,0.3481047153,-0.363953799,0.1384862661,-0.0333423615,0.104071565,0.0320234001,0.1444004476,0.0015518777,-0.2644882202,-0.0145273749,-0.297685653,0.0540181771,-0.2035525143,-0.3303642571,-0.0882694274,-0.0250275768,-0.1353702098,0.3116150796,-0.1619094014,0.3599739969,0.0874314308,0.0490743369,-0.113785997,0.4489847124,-0.2182917148,-0.2930988073,0.1602414846,0.5045282245,-0.2007538974,0.0229594186,0.6979801655,0.0560174808,-0.2464072555,0.4481517673,-0.0043962421,0.1359654069,0.0660853833,-0.1463229507,0.2523387671,-0.4034131169,-0.393864423,0.5243748426,-0.1237470433,-0.2651093602,0.5103117228,0.0308409072,0.7380927801,-0.2041797936,0.006787654,0.4657239318,-0.0756600201,0.2678143084,-0.2925254703,-0.3666589856,-0.1064671427,0.1976788044,-0.3410522044,-0.1301359832,-0.1742994934,0.2694671154,0.085177891,-0.1051846445,0.3670702875,-0.1038185284,0.3626278341,0.0368106104,-0.0351086482,-0.0798206329,-0.706956923,-0.1754608452,-0.147510618,-0.1815338433,0.1756395847,-0.4423541129,-0.2127514631,-0.0871234462,-0.0436072201,-0.0111228097,-0.0349128731,-0.1196978018,0.2897192836,-0.2063937038,-0.4174753428,0.3462837338,-0.0044269282,0.0942240283,-0.0401619785,0.1176773384,0.0820627958,-0.0380719863,0.2208338231,0.1245052367,-0.0227843635,-0.3221158385,0.2823005915,0.1625126451,-0.1242315695,0.051511649,0.1698478609,0.0649467111,0.3528203368,0.184691295,0.4536722302,0.0004359923,-0.0661331266,-0.1742687076,-0.5200099945,0.248151958,-0.3177943826,0.1537080109,-0.0739712864,-0.1496352255,0.2833902538,0.02389233,-0.1768892854,0.0129621392,-0.0836063623,0.1472509205,0.3329172432,0.2726940811,-0.1475754976,0.1436816901,0.3839886189,-0.1751174331,0.0854555815,0.1256877333,0.1638944149,-0.28808406,-0.0547322556,-0.2870210111,0.0964189395,0.2132014334,-0.1603959799,0.0219216496,0.3833084106,-0.0723183453,-0.0162212215,0.035926085,0.2771426141,0.238175869,0.3160497248,-0.0604221039,0.0092027159,-0.0110462792,0.2079131007,-0.0585529953,-0.0915654078,-0.0322172232,0.0701841116,0.2753458023,-0.076545231,0.2308813334,-0.2097187191,0.2561206818,0.2956341505,0.052866485,0.1372467726,0.0791646242,0.4016389549,0.7862680554,0.0490032993,-0.1759125739,0.03916144,-0.1600472331,0.3815576136,-0.0766045973,0.3648779094,-0.234054327,0.2852953672,-0.0613864325,-0.018728612,-0.2080342174,-0.0753421038,0.2011942565,0.014133635,0.166196242,-0.1607528925,0.2517768145,-0.2142984569,-0.003629582,0.1522033215,0.100092046,-0.3120241463,-0.1916678995,-0.1176366061,-0.150331527,0.0887132958,-0.2238148153,-0.2860088348,-0.3231348395,0.5092948079,0.0681103617,0.1934662908,-0.4624144137,0.1838062853,-0.0821284205,-0.4521048963,0.0322987325,-0.1644722223,-0.6774635315,0.2262754291,-0.2423985749,-0.2435352802,-0.3196680546,0.1436239034,-0.0644794106,0.1405666769,-0.1824054569,-0.186115995,0.0617913976,0.0786859319,-0.0438218005,0.1198416352,0.4211664796,-0.1456255913,0.0989599824,-0.0075257407,-0.0118148373,0.1747241765,0.2913619876,0.213571161,0.0852979869,0.45124951,0.1385156065,0.7558050156,0.6317539215,0.2200107872,0.0183603205,0.1004882902,0.2332389951,-0.0699919164,0.0299024284,0.4696426392,0.0082808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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/326","title":"Large dataset in Squad2-format","comments":"15-20 minutes is fine !\r\nAlso there's no RAM limitations as we save to disk every 1000 elements while generating the dataset by default.\r\nAfter generation, the dataset is ready to use with (again) no RAM limitations as we do memory-mapping.","body":"At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ?","comment_length":41,"text":"Large dataset in Squad2-format \n At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ? \n 15-20 minutes is fine !\r\nAlso there's no RAM limitations as we save to disk every 1000 elements while generating the dataset by default.\r\nAfter generation, the dataset is ready to use with (again) no RAM limitations as we do memory-mapping.","embeddings":[-0.1409041733,-0.2805253863,-0.1005571783,0.2925080955,0.2343988717,-0.0667674467,-0.0066274032,0.4827185273,-0.0046411366,0.1012585014,-0.2878576517,0.0170630533,-0.240396753,0.2706007659,0.2778697908,-0.0782357678,0.0859452188,-0.0013715298,-0.1129796654,0.0685329288,0.1218710691,-0.1785973459,0.0677223429,-0.1662177593,-0.3052946329,-0.1456225216,-0.1592085809,0.1375635117,-0.3753189445,-0.373906672,-0.2109333277,0.2612871528,0.2146432996,0.5602203608,-0.0001202043,-0.1613312215,-0.0782385916,-0.3438850343,-0.3753677309,0.114575617,-0.1013673097,-0.3235392272,-0.1401280612,-0.1104314923,-0.2038571388,-0.1932175905,0.1400700957,-0.332315892,0.7989575267,-0.1259247661,0.0317238234,-0.0380418748,-0.0403262377,-0.0242629591,0.242518723,0.2816529274,-0.0963999555,0.0187547877,0.6647381783,0.3354052007,-0.1760163307,0.0907357857,-0.0098588532,-0.1400218606,0.1098083109,-0.1842206717,-0.1575744301,-0.2615042031,-0.0657859743,0.2858432531,0.50301373,-0.2781634629,-0.1896306127,-0.2847802937,-0.0658238083,0.1196045727,0.0054941443,0.3605892956,-0.0875722915,0.3904540241,-0.2851333916,-0.4337237477,-0.3542928696,-0.0511342846,0.2782336473,-0.0421493873,-0.1095748246,0.0358316749,-0.1736291051,-0.1611446738,-0.1055147126,-0.1820171177,-0.0435582623,0.195127368,-0.0500388965,-0.3236088157,-0.122834608,-0.2667889595,0.7373473048,0.2351019531,0.2762565017,-0.0252917502,0.2214840949,-0.055995997,0.3499588966,0.000797126,-0.1983939558,-0.3956947923,0.1951480955,0.0411391705,-0.0147488173,-0.1023097858,-0.2278895229,0.1696128249,-0.1113717034,-0.2389044464,0.2255721837,0.1624681652,0.0749958381,0.0988065898,-0.119023703,-0.0564194247,0.1590619981,0.2800046504,-0.1008720621,0.1065988094,-0.0931565464,0.0426950194,0.2466717809,-0.2234209776,0.0033720608,-0.0549612306,-0.1067692637,0.2609632611,0.2497688085,0.0787077397,0.0633990914,-0.0406716168,0.3254795074,-0.2624429464,0.1954028904,-0.0300896242,0.0556440949,0.0270401016,0.1214144602,0.0723473206,-0.2507173419,-0.037362583,-0.2716755271,0.0664346591,-0.1702026725,-0.3618006408,-0.0369636826,-0.000042417,-0.2681352198,0.2985506952,-0.127226606,0.3165004849,0.0211924613,0.0033346754,-0.0748790801,0.4382675886,-0.2252081931,-0.2618328929,0.0783004463,0.5588118434,-0.1850253046,-0.016268529,0.6108691096,-0.0059924601,-0.3543802798,0.3948062956,0.0075898021,0.1504805088,0.0346252993,-0.1278951168,0.2690083683,-0.4436065853,-0.4289497137,0.5240533948,-0.1946698129,-0.2246601731,0.4402690232,0.0410940126,0.6278269291,-0.242933929,0.0849857032,0.4986977279,-0.022191653,0.301345408,-0.2948740125,-0.3466869593,-0.1763312966,0.3227970302,-0.2370019406,-0.19503887,-0.2147206664,0.2757806182,0.1182711422,-0.0507150628,0.3458012938,-0.0659842789,0.3465012312,-0.0189724527,-0.109103851,-0.0796734467,-0.7441965938,-0.1172305197,-0.0487518944,-0.1769839525,0.1242545471,-0.3950254023,-0.2437802255,-0.0272528436,-0.0753839612,-0.0389587134,-0.0110309022,-0.1466056108,0.2431043833,-0.0624290034,-0.42396608,0.2198781222,0.0312689021,0.108875744,-0.0517491549,0.1104810834,0.1083669439,0.0403883792,0.2250187397,0.1406447887,-0.0452486873,-0.2843993604,0.3054430783,0.1263492703,-0.1156675667,0.0338573977,0.0939443111,0.124429673,0.3205665648,0.2131882906,0.3468077779,-0.0148217175,-0.0581191108,-0.2420777231,-0.5757752657,0.2814452648,-0.3202792704,0.1856947839,-0.1016290709,-0.1921323687,0.3548845351,0.02245868,-0.2167508304,0.0203052033,-0.0939081684,0.1647088826,0.4010157585,0.2314790338,-0.1396001726,0.1183304042,0.4107497931,-0.1698398441,0.085956201,0.1295030415,0.1112831309,-0.2252754122,-0.0007226478,-0.3508841693,0.1351929605,0.1990710944,-0.1331205815,-0.0045663142,0.4250622094,-0.1296106577,-0.0679164976,-0.0029469032,0.2279081345,0.2689410448,0.4185391068,-0.1249468029,0.0409382097,-0.0680257082,0.1883984506,-0.0257454589,-0.0917408541,-0.0187229235,0.1817604154,0.2700441778,0.0131419664,0.244939059,-0.1544848382,0.2557690442,0.3178787231,0.1129627526,0.0277429018,0.0555845946,0.3220249116,0.7086125612,0.1092418134,-0.3408541083,0.0270894449,-0.2112098336,0.4393980801,-0.0549041107,0.4286058545,-0.3180980086,0.2616630197,-0.0354951173,-0.0042358991,-0.1711000651,-0.0890160277,0.2154260129,0.0497117825,0.2052056789,-0.1179560125,0.3070597053,-0.2029962838,-0.0046422826,0.1793079972,0.1094417125,-0.2314153463,-0.1409710199,-0.0624786653,-0.139859125,0.0873501077,-0.1704577655,-0.2106005847,-0.3970167339,0.5515090823,0.0806931257,0.0940168872,-0.4090313613,0.1804582626,-0.1239489764,-0.415743351,0.0509497188,-0.1454322636,-0.7269209623,0.204637289,-0.1891167462,-0.2277164012,-0.3275865018,0.0779181123,-0.0304900706,0.0789903328,-0.3037673831,-0.2016584873,0.0868927017,0.143485263,-0.0471298397,0.0897522792,0.3968957663,-0.1114342362,0.0900504217,0.0583491474,-0.0112755001,0.1375245303,0.2120562494,0.2483747751,-0.0091642449,0.4458053708,0.1821142584,0.7829383612,0.4851695299,0.2196800262,0.0542259477,0.1727016866,0.275483638,-0.1015152708,0.019838972,0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/326","title":"Large dataset in Squad2-format","comments":"Wow, that sounds pretty cool.\nActually I have the problem of running out of memory while tokenization on our local machine.\nThat wouldn't happen again, would it ?","body":"At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ?","comment_length":28,"text":"Large dataset in Squad2-format \n At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ? \n Wow, that sounds pretty cool.\nActually I have the problem of running out of memory while tokenization on our local machine.\nThat wouldn't happen again, would it ?","embeddings":[-0.0048634037,-0.2359244674,-0.0683037639,0.2993221283,0.2564298809,-0.1159560755,0.0191383474,0.4660230875,-0.0981997773,0.1532815844,-0.2599274218,0.0292080212,-0.2432349324,0.2509172261,0.3011697829,-0.0480412319,0.1664877385,-0.0175047852,-0.0354582816,0.0578787178,0.1524417847,-0.1213407144,0.0502193794,-0.1121684983,-0.3433024585,-0.1410203576,-0.2227213234,0.1154344901,-0.382927388,-0.3995330334,-0.3136951029,0.2566596568,0.2417446673,0.5677559376,-0.000122708,-0.2018594295,-0.0883843675,-0.3403649628,-0.311219573,0.0545596182,-0.0006793588,-0.3438971639,-0.1172072738,-0.1090767756,-0.1672704965,-0.2621129453,0.1045962647,-0.287291795,0.8023911715,-0.185851872,0.0164926872,-0.016265979,-0.1020802408,0.0746310577,0.1943787187,0.3218402267,-0.0402478278,-0.0143877249,0.6634316444,0.2563692927,-0.1557704061,0.0979119316,-0.0329955295,-0.1636565626,0.0506472439,-0.1566651314,-0.1782923639,-0.1981963068,-0.0039009722,0.1783846915,0.3981475532,-0.3300261796,-0.1373391002,-0.2405589074,-0.1100009009,0.0567669086,0.0279380679,0.3217486441,-0.0983806923,0.3943783343,-0.3018188775,-0.4261778295,-0.3479032516,-0.0081264824,0.1863970608,0.0747401938,-0.1123981699,0.0162326712,-0.2107531875,-0.2084162682,-0.1750331819,-0.1212716326,-0.056275703,0.1857766509,-0.0294533614,-0.3806006014,-0.1769762039,-0.3344237208,0.7397287488,0.2407940477,0.2041114718,0.0006103968,0.2247766703,-0.0235044863,0.4215138257,0.0339765437,-0.1606846154,-0.2854788601,0.1769555509,-0.0767192245,0.007158454,-0.1267490238,-0.290168494,0.2594229877,-0.1199794859,-0.1985271275,0.2078863233,0.118146643,0.1250086576,0.1459026337,-0.1532063782,-0.0004797382,0.1926344037,0.2919611633,-0.0999253094,0.125199154,-0.1555048525,0.0132864499,0.1941238046,-0.2200011015,0.0151871825,-0.048892241,-0.1426734179,0.2775087953,0.1601999849,0.1904596239,0.0550614037,-0.0313021056,0.3454264402,-0.3316127956,0.1718173176,-0.0120868161,0.0918696374,-0.087102659,0.0966745988,0.1071960628,-0.1860133111,-0.0634552538,-0.280477494,0.0286109857,-0.1590733826,-0.2859141231,-0.0899710357,-0.0131995054,-0.2573435009,0.2753234506,-0.155698657,0.3370774388,0.0433541499,0.0276191849,-0.0760421306,0.4177356064,-0.1996265054,-0.2468789965,0.086254552,0.5264920592,-0.21275267,-0.0589169338,0.7256123424,0.0039113704,-0.2323844284,0.4522058666,0.0341198407,0.0996495932,0.0330233425,0.0171210337,0.1975421309,-0.5084146261,-0.3917007446,0.4872522354,-0.2710965574,-0.2888275087,0.4501453638,-0.0021801847,0.7244879007,-0.1779860854,0.1248246729,0.4527071416,-0.0112068933,0.2866778672,-0.3418312967,-0.3517705798,-0.1432753801,0.2145599872,-0.2476702631,-0.233724311,-0.1678469479,0.3443205953,0.1442345232,-0.0370383672,0.3658793271,-0.1258073151,0.4081765115,0.0815366507,-0.1075155735,-0.045349028,-0.6363104582,-0.1759152859,-0.0668965355,-0.2045734376,0.0875467882,-0.4391719401,-0.2339888364,-0.0397208706,-0.0037445631,-0.0227721911,-0.0105006043,-0.072168909,0.1975910366,0.0416513979,-0.4777804613,0.1027794331,0.0681987926,0.0919448584,0.0677362606,0.1153919026,0.0635745078,-0.027806107,0.1835619509,0.1694440395,0.013266569,-0.276183188,0.3075852394,0.136549443,-0.137611717,0.0771178082,0.0643713921,0.1885312498,0.3677638173,0.2471440583,0.3335402906,-0.0359484702,-0.0795969144,-0.1956493109,-0.4011143148,0.2823254168,-0.2059862465,0.1607313752,-0.1087658256,-0.1630889028,0.3603003025,0.0271318257,-0.1765018851,-0.007229628,-0.1080074459,0.1386487484,0.3167693913,0.2005201578,-0.1099159569,0.1504927576,0.419385761,-0.1662428975,0.1410766393,0.0860968456,0.0657812282,-0.2105067372,0.0731403157,-0.4206044078,0.1161367744,0.1916219145,-0.1124330088,0.0174667053,0.4644149244,-0.1069171578,-0.054423485,0.0615024157,0.1119858995,0.2068028748,0.3783842921,-0.1515436918,0.0632615536,-0.0613132752,0.1851516366,-0.018007718,-0.1255821288,0.0122132264,0.0384273268,0.2140253782,-0.0323152766,0.1993446648,-0.133793667,0.2352688462,0.3411096931,0.1273874789,-0.0041495147,0.0475620069,0.3337056339,0.7932181954,0.0033719291,-0.2434069216,0.0602368973,-0.2733080983,0.3194569349,-0.0889341235,0.4855739772,-0.3599860072,0.3138049543,0.0023291709,-0.0271195248,-0.1954461634,-0.1236829534,0.251401335,0.059504047,0.1228087917,-0.1811424792,0.2243694067,-0.3469936252,-0.0619622394,0.1353172213,0.0441457443,-0.2819411755,-0.1478100568,-0.0207597669,-0.1053292751,0.0789885521,-0.1557479352,-0.2638137043,-0.3474766016,0.56192559,0.0495658964,0.0918339193,-0.4269669056,0.123789601,-0.0336596966,-0.4764581919,-0.0442670435,-0.2064770162,-0.690692544,0.129042387,-0.2044298649,-0.1791326106,-0.2571054697,0.1340753138,-0.0105617484,0.0266334023,-0.2865175307,-0.2164182067,0.173415795,0.0656809136,-0.0573247857,0.1034618542,0.3878521025,-0.1101513952,0.0678931549,0.0896940306,0.0188467521,0.1214547008,0.1467282623,0.2475127131,-0.0135703562,0.4285876751,0.2638728321,0.7901008129,0.3915772438,0.1970973313,0.0834625512,0.1033207402,0.2767242789,-0.0046272958,-0.0110786567,0.4460401535,0.0492831655,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/326","title":"Large dataset in Squad2-format","comments":"You can do the tokenization step using `my_tokenized_dataset = my_dataset.map(my_tokenize_function)` that writes the tokenized texts on disk as well. And then `my_tokenized_dataset` will be a memory-mapped dataset too, so you should be fine :)","body":"At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ?","comment_length":34,"text":"Large dataset in Squad2-format \n At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ? \n You can do the tokenization step using `my_tokenized_dataset = my_dataset.map(my_tokenize_function)` that writes the tokenized texts on disk as well. And then `my_tokenized_dataset` will be a memory-mapped dataset too, so you should be fine :)","embeddings":[-0.0358469076,-0.247777611,-0.0586996078,0.246388644,0.2725090384,-0.0800485983,0.0325766578,0.4199478924,-0.1291513592,0.0406961478,-0.2698194087,0.1034108475,-0.2411333025,0.2175490558,0.2678762674,-0.0001746843,0.1660441458,-0.0381541513,0.0205726288,0.1060394868,0.1364686489,-0.0983715579,0.0692259222,-0.098045446,-0.3837281466,-0.2225388438,-0.2114028782,0.0674200729,-0.3816430867,-0.4354844391,-0.2983789444,0.2929075062,0.2630604506,0.5424135923,-0.0001224383,-0.2122880071,-0.0822095945,-0.355851531,-0.3333749473,0.0622420982,-0.0192783251,-0.3454458416,-0.1117130443,-0.1266127825,-0.1577755809,-0.2789115608,0.1271944791,-0.2555705011,0.8061004877,-0.1531662047,0.0214495026,-0.0215730332,-0.0481791385,0.0517999865,0.1918322295,0.3268030286,-0.0728117302,-0.0447113775,0.6106310487,0.2984131575,-0.184012413,0.1200443208,-0.0851198211,-0.1498504877,0.1096886098,-0.1629780978,-0.1704001427,-0.1921743155,-0.0237779431,0.2037169486,0.4222000241,-0.3385160565,-0.1862229705,-0.3305151165,-0.1230701283,0.0508738048,0.0350252986,0.3019004762,0.0060198815,0.3412303627,-0.3143237829,-0.4462244213,-0.3264684677,-0.0131069003,0.1752528548,0.1036070511,-0.1256473064,0.0252939034,-0.1996139437,-0.2140208185,-0.1438999474,-0.1332382113,-0.0655241832,0.188609004,-0.0463632196,-0.3719006479,-0.2080021352,-0.3558419347,0.7443717718,0.2502095401,0.1825263202,-0.0044432594,0.1786465645,-0.0180104822,0.3476016521,0.0791966021,-0.1344190389,-0.2228825241,0.2056197524,-0.0692040771,0.0441734232,-0.1626304239,-0.2966942191,0.3006076813,-0.1087574214,-0.2092300504,0.1497644633,0.1247977018,0.086250253,0.1220537871,-0.1460025162,0.0125121465,0.1612524539,0.3237168193,-0.0606556125,0.1013348028,-0.1591730565,0.0791071579,0.251031369,-0.1393309981,0.0203039441,-0.0825997218,-0.0755887032,0.2826264501,0.169211179,0.1916387081,0.1067391932,-0.0948662907,0.3511994183,-0.3164987862,0.1982142478,-0.0433665663,0.1172584742,-0.0536002629,0.1517839879,0.17542243,-0.2161428034,-0.0682597533,-0.2824198604,0.0142466454,-0.2111289352,-0.3164356351,-0.0480192788,-0.0132391006,-0.2957737744,0.2394763231,-0.1990526915,0.3587201536,0.0694444552,0.0011227143,-0.0613626428,0.3853585422,-0.2045324147,-0.2523868382,0.1157232821,0.4234727621,-0.165144816,-0.0755069181,0.6700637341,0.0387734957,-0.256937176,0.4542044699,0.0799492598,0.1556759775,0.0222006142,0.1333848983,0.1889381558,-0.5267193317,-0.4063301086,0.4948351085,-0.1921532303,-0.2608957291,0.3926098943,-0.027362138,0.7847687006,-0.224837631,0.0983915627,0.4408167899,0.0235162061,0.3045356274,-0.3460551202,-0.3247426152,-0.1307469457,0.2371600866,-0.2409185767,-0.2785597742,-0.1992860734,0.3613536954,0.0979598016,0.0059875655,0.3558785617,-0.0728290826,0.445025593,0.0458202623,-0.1008841172,-0.034379337,-0.6144986749,-0.1862875223,-0.0533258282,-0.1896263361,0.0219220128,-0.4489982426,-0.2040059865,-0.0548460037,-0.0650103986,-0.0996520445,-0.0107854232,-0.0867551491,0.1808195561,0.0646054968,-0.4297647774,0.0958206132,0.0679547489,0.121974729,0.1080547348,0.0707083717,0.1028700098,-0.0089806505,0.1983807087,0.1354849339,0.033813674,-0.2850415707,0.2865187526,0.1933010966,-0.122553274,0.0360604264,0.0323351398,0.1992418468,0.3765419424,0.2387423366,0.3395583332,-0.0528761707,-0.1034863591,-0.2263322324,-0.4660384059,0.3321805894,-0.172244817,0.1516910046,-0.1176439151,-0.1708733588,0.3835427463,0.0604184307,-0.1853945255,0.0116602741,-0.1172106266,0.1202117503,0.3583424985,0.1511693895,-0.0837305859,0.1534204334,0.4169473052,-0.188087061,0.1579304487,0.0922692046,0.0193203297,-0.2057335824,0.0339047126,-0.3745421469,0.1113106683,0.2045274973,-0.1165310144,-0.032033205,0.4354265034,-0.0989613235,-0.0211146828,0.0098216245,0.0550688617,0.2727505863,0.3929631412,-0.1295709163,0.0289757382,-0.0325896516,0.202135995,0.0086919954,-0.1281420588,-0.0460867733,0.0887818262,0.1770050675,0.0038029887,0.2265064269,-0.1486002207,0.1947254986,0.3439479768,0.1537312716,0.0585660562,0.1289347857,0.3415546715,0.7686249614,0.0309741292,-0.3049447238,0.0493489094,-0.2318971306,0.3245337307,-0.0766607448,0.43103984,-0.3617490232,0.2851543725,0.0097461278,-0.0011517979,-0.2504860163,-0.1496311426,0.2477441877,0.0177355893,0.1506178826,-0.1847651601,0.2407236993,-0.3253250718,-0.0264812615,0.1529536843,0.0557004921,-0.2686520517,-0.150921002,-0.0527387373,-0.1377538294,0.0886375308,-0.1250995845,-0.2744232714,-0.3307993412,0.516752243,0.0543538928,0.0817118734,-0.3696947992,0.0508889034,-0.0841617957,-0.4887417555,-0.0598633923,-0.189150244,-0.6689900756,0.2267435938,-0.2004956454,-0.1961240321,-0.2640039027,0.121244736,-0.0289270934,0.0722713619,-0.2839437127,-0.2136158347,0.1547720581,0.0793500543,-0.0785542503,0.0479094014,0.3909583986,-0.0872953907,0.0794652477,0.0557640977,-0.0035893854,0.1672790498,0.1231797263,0.2721035182,-0.0246197153,0.4078175128,0.2914268672,0.8061866164,0.3848915398,0.1959751844,0.0897487029,0.1119625866,0.2658505142,-0.0288793165,-0.007531255,0.4521793425,0.02775551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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/326","title":"Large dataset in Squad2-format","comments":"In your training loop, loading the tokenized texts is going to be fast and pretty much negligible compared to a forward pass. You shouldn't expect any slow down.","body":"At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ?","comment_length":28,"text":"Large dataset in Squad2-format \n At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.\r\nCaused the computing power we splitted it into multiple tiles, but they are all in the same format.\r\nRight now the most important facts about are this:\r\n- Contexts: 1.047.671\r\n- questions: 1.677.732\r\n- Answers: 6.742.406\r\n- unanswerable: 377.398\r\n\r\nIt is already cleaned\r\n\r\n<pre><code>\r\ntrain_data = [\r\n    {\r\n        'context': \"this is the context\",\r\n        'qas': [\r\n            {\r\n                'id': \"00002\",\r\n                'is_impossible': False,\r\n                'question': \"whats is this\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer\",\r\n                        'answer_start': 0\r\n                    }\r\n                ]\r\n            },\r\n            {\r\n                'id': \"00003\",\r\n                'is_impossible': False,\r\n                'question': \"question2\",\r\n                'answers': [\r\n                    {\r\n                        'text': \"answer2\",\r\n                        'answer_start': 1\r\n                    }\r\n                ]\r\n            }\r\n        ]\r\n    }\r\n]\r\n<\/code><\/pre>\r\n\r\nCause it is growing every day we are thinking about an structure like this:\r\nWe host an Json file, containing all the download links and the script can load it dynamically.\r\nAt the moment it is around ~20GB\r\n\r\nAny advice how to handle this, or an ready to use template ? \n In your training loop, loading the tokenized texts is going to be fast and pretty much negligible compared to a forward pass. You shouldn't expect any slow down.","embeddings":[-0.0998464674,-0.3400501907,-0.072254546,0.2740523219,0.233014524,-0.134118855,0.0534660295,0.4866881669,-0.0811557695,0.0389788896,-0.23714149,0.0705089793,-0.2566494942,0.309556216,0.2704547048,-0.0745398998,0.1412350386,-0.0320492834,0.0148968324,0.0299363341,0.1451997757,-0.1339221299,0.0269506257,-0.0343342684,-0.3312619925,-0.147840932,-0.1654999107,0.1486865729,-0.3085708022,-0.4827928841,-0.3069819808,0.2874170542,0.2753958702,0.4879121184,-0.0001232336,-0.1371826828,-0.0272386987,-0.3751021624,-0.3914620876,0.0622953884,0.0496604741,-0.2882883251,-0.0649245158,-0.0946050212,-0.1764810383,-0.2693074346,0.1124373823,-0.1765535325,0.9333483577,-0.0912950411,-0.001321081,-0.0185958948,-0.1706783324,0.0342927203,0.2240020037,0.1699875593,-0.0316277221,0.0094061894,0.653195858,0.3252064884,-0.2057896852,0.1627473533,0.00764257,-0.1622393876,0.0985202119,-0.15895392,-0.0159675162,-0.1528659612,-0.0900060683,0.2489254326,0.3597235978,-0.2345626652,-0.2427043319,-0.3881295919,-0.031607341,0.008495491,0.0847673863,0.2578563988,-0.0303193182,0.4028695822,-0.3450419307,-0.4935597479,-0.3452898264,-0.0025279061,0.1328754425,0.002325847,-0.0820589289,-0.0195719227,-0.1442527026,-0.1430077106,-0.0312887356,-0.1512053013,0.05735939,0.1760599464,0.0076766806,-0.3633486331,-0.1832547635,-0.3150159121,0.6128088236,0.2020144314,0.1659007668,0.0921715796,0.1930484474,-0.0760334879,0.3041756451,0.0039584069,-0.1741166562,-0.2357874066,0.1530188918,0.0032970435,-0.0408503562,-0.0910827443,-0.4117302001,0.2907006741,-0.2795992792,-0.2131229639,0.0398327783,0.1076254994,0.0285532866,0.1241719276,-0.1663440168,-0.0343140848,0.197809428,0.3367663622,-0.1896688044,0.2010002434,-0.0523402914,0.0550012775,0.193146199,-0.2182546258,0.0245846696,-0.0677965134,-0.1678190678,0.3149712086,0.1374841332,0.0973450169,0.1068066582,-0.0808860883,0.3495577574,-0.292362839,0.0944587961,0.0606519543,0.0520383343,-0.103023462,0.0815087631,0.0854252502,-0.2245240659,-0.0646581203,-0.2271315455,0.0724633411,-0.3223105967,-0.3137343824,0.0708979517,-0.0243879799,-0.2372407317,0.266965121,-0.1464351416,0.4468391836,-0.066742599,-0.0000228255,-0.0586486124,0.3950932324,-0.2568891943,-0.1637609303,0.1426975578,0.4170689881,-0.2085472345,-0.1686359644,0.767531395,0.1190056875,-0.2828905284,0.4321811199,-0.0407247879,0.1763848513,0.0575946942,0.0287949704,0.2927293181,-0.6150716543,-0.3437715471,0.5754372478,-0.2338035852,-0.2194793671,0.4251846075,-0.018134838,0.7501599193,-0.2715392709,0.1670282632,0.5265708566,0.0275566597,0.2613630593,-0.3428591192,-0.3132781684,-0.1130179688,0.352265507,-0.2507222891,-0.2015239447,-0.2499058098,0.3206180632,0.1277277023,-0.0399459191,0.2356550097,-0.0552047081,0.1949426532,0.107216157,-0.1261142045,0.068676129,-0.6354449987,-0.1800173968,-0.0353042781,-0.2504541576,0.0739325956,-0.4319304526,-0.2057418972,-0.0524757467,-0.0826043487,0.0094190594,-0.0734386444,-0.1099154279,0.1536721587,0.089274399,-0.4430952072,0.1576543003,-0.0317029841,0.1350343376,0.103367269,0.0933032632,0.1940133721,0.0167998485,0.2226579189,0.1754046977,0.019663956,-0.3061259091,0.3164180517,0.2310002595,-0.1803950965,0.107820563,0.0286296103,0.1224039197,0.3870017231,0.2914449871,0.2757450342,-0.020401625,-0.0890708119,-0.2014707327,-0.4354258478,0.4695908129,-0.1399256587,0.1029195413,-0.0469945371,-0.1922258437,0.2470768541,0.0279658064,-0.3278740346,0.0785472691,-0.041131489,0.1010354385,0.3779505789,0.1883982271,-0.2185820937,0.1035120264,0.5042096376,-0.2499515861,0.0631477311,0.1456290781,0.0739471838,-0.1624071002,-0.0654814914,-0.3691547215,0.1035001874,0.1509120315,-0.1132974997,0.0365013964,0.3595151901,-0.1414895505,-0.0264396388,0.0400507823,0.1576570719,0.2307530046,0.336263299,-0.1535274982,-0.012593152,-0.0430570208,0.1768823862,-0.020825278,-0.1473274678,0.0703120157,0.1081736684,0.2291824222,-0.1080216989,0.0923992395,-0.0407775417,0.2209887952,0.3710334003,0.1220967844,0.0494286269,0.1232839748,0.357422024,0.7777605653,0.0740785897,-0.2954411507,0.0345302224,-0.2509972751,0.2499700785,-0.109903276,0.4066915214,-0.3923880458,0.2088262588,-0.1002375409,-0.0704726055,-0.088101685,-0.1080953255,0.2357533723,-0.0128257424,0.040228229,-0.2133981436,0.2169346362,-0.3434363902,-0.0746857226,0.1513520479,-0.0094621992,-0.2644820213,-0.1470504105,-0.0022058873,-0.0863671303,0.0888572112,-0.2968888581,-0.2773435414,-0.2985850275,0.4928011894,0.0754599497,0.0640896633,-0.3779042959,0.0815671235,-0.0153703783,-0.4000914991,-0.06912449,-0.1580911875,-0.6986131072,0.2222498506,-0.165712297,-0.2289089561,-0.2681019604,0.0822325274,-0.0588756762,0.0815771818,-0.3671654463,-0.2016134858,0.2188031673,0.0321707651,-0.1601269394,0.0376335457,0.3266258538,-0.1108786836,0.09103778,0.0565655977,0.0788849667,0.1923375726,0.0437459983,0.2103662938,-0.045556847,0.5006765723,0.2444775254,0.8879320621,0.4185721874,0.1632732898,-0.0356384031,0.0654547885,0.164102599,-0.001624089,0.0714655146,0.4287313819,-0.0277694613,-0.0217478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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/324","title":"Error when calculating glue score","comments":"The glue metric for cola is a metric for classification. It expects label ids as integers as inputs.","body":"I was trying glue score along with other metrics here. But glue gives me this error;\r\n\r\n```\r\nimport nlp\r\nglue_metric = nlp.load_metric('glue',name=\"cola\")\r\n\r\nglue_score = glue_metric.compute(predictions, references)\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-8-b9210a524504> in <module>()\r\n----> 1 glue_score = glue_metric.compute(predictions, references)\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n    191         \"\"\"\r\n    192         if predictions is not None:\r\n--> 193             self.add_batch(predictions=predictions, references=references)\r\n    194         self.finalize(timeout=timeout)\r\n    195 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in add_batch(self, predictions, references, **kwargs)\r\n    207         if self.writer is None:\r\n    208             self._init_writer()\r\n--> 209         self.writer.write_batch(batch)\r\n    210 \r\n    211     def add(self, prediction=None, reference=None, **kwargs):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    155         if self.pa_writer is None:\r\n    156             self._build_writer(pa_table=pa.Table.from_pydict(batch_examples))\r\n--> 157         pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n    158         if writer_batch_size is None:\r\n    159             writer_batch_size = self.writer_batch_size\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/types.pxi in __iter__()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\nTypeError: an integer is required (got type str)\r\n```\r\nI'm not sure whether I'm doing this wrong or whether it's an issue. I would like to know a workaround. Thank you.","comment_length":18,"text":"Error when calculating glue score \n I was trying glue score along with other metrics here. But glue gives me this error;\r\n\r\n```\r\nimport nlp\r\nglue_metric = nlp.load_metric('glue',name=\"cola\")\r\n\r\nglue_score = glue_metric.compute(predictions, references)\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-8-b9210a524504> in <module>()\r\n----> 1 glue_score = glue_metric.compute(predictions, references)\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n    191         \"\"\"\r\n    192         if predictions is not None:\r\n--> 193             self.add_batch(predictions=predictions, references=references)\r\n    194         self.finalize(timeout=timeout)\r\n    195 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in add_batch(self, predictions, references, **kwargs)\r\n    207         if self.writer is None:\r\n    208             self._init_writer()\r\n--> 209         self.writer.write_batch(batch)\r\n    210 \r\n    211     def add(self, prediction=None, reference=None, **kwargs):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    155         if self.pa_writer is None:\r\n    156             self._build_writer(pa_table=pa.Table.from_pydict(batch_examples))\r\n--> 157         pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n    158         if writer_batch_size is None:\r\n    159             writer_batch_size = self.writer_batch_size\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/types.pxi in __iter__()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\nTypeError: an integer is required (got type str)\r\n```\r\nI'm not sure whether I'm doing this wrong or whether it's an issue. I would like to know a workaround. Thank you. \n The glue metric for cola is a metric for classification. It expects label ids as integers as 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/324","title":"Error when calculating glue score","comments":"I want to evaluate a sentence pair whether they are semantically equivalent, so I used MRPC and it gives the same error, does that mean we have to encode the sentences and parse as input?\r\n\r\nusing BertTokenizer;\r\n```\r\nencoded_reference=tokenizer.encode(reference, add_special_tokens=False)\r\nencoded_prediction=tokenizer.encode(prediction, add_special_tokens=False)\r\n```\r\n\r\n`glue_score = glue_metric.compute(encoded_prediction, encoded_reference)`\r\n```\r\n\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-9-4c3a3ce7b583> in <module>()\r\n----> 1 glue_score = glue_metric.compute(encoded_prediction, encoded_reference)\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n    198         predictions = self.data[\"predictions\"]\r\n    199         references = self.data[\"references\"]\r\n--> 200         output = self._compute(predictions=predictions, references=references, **metrics_kwargs)\r\n    201         return output\r\n    202 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metrics\/glue\/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302\/glue.py in _compute(self, predictions, references)\r\n    101             return pearson_and_spearman(predictions, references)\r\n    102         elif self.config_name in [\"mrpc\", \"qqp\"]:\r\n--> 103             return acc_and_f1(predictions, references)\r\n    104         elif self.config_name in [\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]:\r\n    105             return {\"accuracy\": simple_accuracy(predictions, references)}\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metrics\/glue\/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302\/glue.py in acc_and_f1(preds, labels)\r\n     60 def acc_and_f1(preds, labels):\r\n     61     acc = simple_accuracy(preds, labels)\r\n---> 62     f1 = f1_score(y_true=labels, y_pred=preds)\r\n     63     return {\r\n     64         \"accuracy\": acc,\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/sklearn\/metrics\/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\r\n   1097                        pos_label=pos_label, average=average,\r\n   1098                        sample_weight=sample_weight,\r\n-> 1099                        zero_division=zero_division)\r\n   1100 \r\n   1101 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/sklearn\/metrics\/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)\r\n   1224                                                  warn_for=('f-score',),\r\n   1225                                                  sample_weight=sample_weight,\r\n-> 1226                                                  zero_division=zero_division)\r\n   1227     return f\r\n   1228 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/sklearn\/metrics\/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\r\n   1482         raise ValueError(\"beta should be >=0 in the F-beta score\")\r\n   1483     labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n-> 1484                                     pos_label)\r\n   1485 \r\n   1486     # Calculate tp_sum, pred_sum, true_sum ###\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/sklearn\/metrics\/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\r\n   1314             raise ValueError(\"Target is %s but average='binary'. Please \"\r\n   1315                              \"choose another average setting, one of %r.\"\r\n-> 1316                              % (y_type, average_options))\r\n   1317     elif pos_label not in (None, 1):\r\n   1318         warnings.warn(\"Note that pos_label (set to %r) is ignored when \"\r\n\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n\r\n```","body":"I was trying glue score along with other metrics here. But glue gives me this error;\r\n\r\n```\r\nimport nlp\r\nglue_metric = nlp.load_metric('glue',name=\"cola\")\r\n\r\nglue_score = glue_metric.compute(predictions, references)\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-8-b9210a524504> in <module>()\r\n----> 1 glue_score = glue_metric.compute(predictions, references)\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n    191         \"\"\"\r\n    192         if predictions is not None:\r\n--> 193             self.add_batch(predictions=predictions, references=references)\r\n    194         self.finalize(timeout=timeout)\r\n    195 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in add_batch(self, predictions, references, **kwargs)\r\n    207         if self.writer is None:\r\n    208             self._init_writer()\r\n--> 209         self.writer.write_batch(batch)\r\n    210 \r\n    211     def add(self, prediction=None, reference=None, **kwargs):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    155         if self.pa_writer is None:\r\n    156             self._build_writer(pa_table=pa.Table.from_pydict(batch_examples))\r\n--> 157         pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n    158         if writer_batch_size is None:\r\n    159             writer_batch_size = self.writer_batch_size\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/types.pxi in __iter__()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\nTypeError: an integer is required (got type str)\r\n```\r\nI'm not sure whether I'm doing this wrong or whether it's an issue. I would like to know a workaround. Thank you.","comment_length":297,"text":"Error when calculating glue score \n I was trying glue score along with other metrics here. But glue gives me this error;\r\n\r\n```\r\nimport nlp\r\nglue_metric = nlp.load_metric('glue',name=\"cola\")\r\n\r\nglue_score = glue_metric.compute(predictions, references)\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-8-b9210a524504> in <module>()\r\n----> 1 glue_score = glue_metric.compute(predictions, references)\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n    191         \"\"\"\r\n    192         if predictions is not None:\r\n--> 193             self.add_batch(predictions=predictions, references=references)\r\n    194         self.finalize(timeout=timeout)\r\n    195 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in add_batch(self, predictions, references, **kwargs)\r\n    207         if self.writer is None:\r\n    208             self._init_writer()\r\n--> 209         self.writer.write_batch(batch)\r\n    210 \r\n    211     def add(self, prediction=None, reference=None, **kwargs):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    155         if self.pa_writer is None:\r\n    156             self._build_writer(pa_table=pa.Table.from_pydict(batch_examples))\r\n--> 157         pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n    158         if writer_batch_size is None:\r\n    159             writer_batch_size = self.writer_batch_size\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/types.pxi in __iter__()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\nTypeError: an integer is required (got type str)\r\n```\r\nI'm not sure whether I'm doing this wrong or whether it's an issue. I would like to know a workaround. Thank you. \n I want to evaluate a sentence pair whether they are semantically equivalent, so I used MRPC and it gives the same error, does that mean we have to encode the sentences and parse as input?\r\n\r\nusing BertTokenizer;\r\n```\r\nencoded_reference=tokenizer.encode(reference, add_special_tokens=False)\r\nencoded_prediction=tokenizer.encode(prediction, add_special_tokens=False)\r\n```\r\n\r\n`glue_score = glue_metric.compute(encoded_prediction, encoded_reference)`\r\n```\r\n\r\nValueError                                Traceback (most recent call last)\r\n<ipython-input-9-4c3a3ce7b583> in <module>()\r\n----> 1 glue_score = glue_metric.compute(encoded_prediction, encoded_reference)\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n    198         predictions = self.data[\"predictions\"]\r\n    199         references = self.data[\"references\"]\r\n--> 200         output = self._compute(predictions=predictions, references=references, **metrics_kwargs)\r\n    201         return output\r\n    202 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metrics\/glue\/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302\/glue.py in _compute(self, predictions, references)\r\n    101             return pearson_and_spearman(predictions, references)\r\n    102         elif self.config_name in [\"mrpc\", \"qqp\"]:\r\n--> 103             return acc_and_f1(predictions, references)\r\n    104         elif self.config_name in [\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]:\r\n    105             return {\"accuracy\": simple_accuracy(predictions, references)}\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metrics\/glue\/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302\/glue.py in acc_and_f1(preds, labels)\r\n     60 def acc_and_f1(preds, labels):\r\n     61     acc = simple_accuracy(preds, labels)\r\n---> 62     f1 = f1_score(y_true=labels, y_pred=preds)\r\n     63     return {\r\n     64         \"accuracy\": acc,\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/sklearn\/metrics\/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\r\n   1097                        pos_label=pos_label, average=average,\r\n   1098                        sample_weight=sample_weight,\r\n-> 1099                        zero_division=zero_division)\r\n   1100 \r\n   1101 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/sklearn\/metrics\/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)\r\n   1224                                                  warn_for=('f-score',),\r\n   1225                                                  sample_weight=sample_weight,\r\n-> 1226                                                  zero_division=zero_division)\r\n   1227     return f\r\n   1228 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/sklearn\/metrics\/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\r\n   1482         raise ValueError(\"beta should be >=0 in the F-beta score\")\r\n   1483     labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n-> 1484                                     pos_label)\r\n   1485 \r\n   1486     # Calculate tp_sum, pred_sum, true_sum ###\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/sklearn\/metrics\/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\r\n   1314             raise ValueError(\"Target is %s but average='binary'. Please \"\r\n   1315                              \"choose another average setting, one of %r.\"\r\n-> 1316                              % (y_type, average_options))\r\n   1317     elif pos_label not in (None, 1):\r\n   1318         warnings.warn(\"Note that pos_label (set to %r) is ignored when \"\r\n\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n\r\n```","embeddings":[-0.1694530249,-0.2395812869,-0.0304733198,0.1611590832,0.236740306,-0.0491650477,0.1073005199,0.3623578846,0.4330275059,0.0516285449,-0.3168233335,0.2763474584,-0.0798071995,-0.0241100863,0.1026181132,-0.2831492722,-0.1162880808,0.1604485661,-0.2202495039,0.3102566004,-0.324510783,0.2713671029,-0.1858564019,0.3197874725,-0.1767067164,-0.0903876349,0.0204841644,-0.196170941,-0.3263719678,-0.362935394,0.2481628209,-0.1168349832,0.02158558,0.1766418815,-0.0001102634,-0.0120561915,0.458953023,0.0023093964,0.1588456184,-0.1421633959,-0.3889809847,-0.083480984,0.0444146022,-0.2361500263,0.1346656531,0.2275212109,0.0206140038,-0.1826084852,0.2193325311,0.2709797919,0.2774609029,0.3871142864,0.3126004338,-0.1989937872,0.1724911779,-0.2082325965,0.0265877806,0.8870652914,-0.327216506,-0.4651894271,-0.0078018066,0.2069443315,-0.1057071611,0.2180079669,0.2240158021,0.0521962196,0.3716999292,-0.3202626705,-0.1326412559,-0.0713140443,-0.2181349844,-0.2285419106,-0.1499314606,0.0416573919,0.0471167676,-0.3763104677,0.0972394794,-0.0726784766,0.0581606068,-0.2293186486,-0.0549694002,0.1402760744,-0.1144136488,0.2752963901,0.2400909662,0.3214469552,-0.1981527656,0.296900332,0.4147271514,0.2154424191,-0.1390433908,0.0435455628,-0.2401257157,0.1653348356,-0.2926098704,-0.0423279516,-0.0104532884,0.1914083958,0.0239265561,-0.2354671657,0.0487309434,-0.2080052644,0.4857490361,0.3427954614,0.0602983236,0.3389247358,0.3534550667,0.1564653218,0.127425313,0.2417826504,0.002334035,-0.1694935113,0.2388441265,-0.3881970942,0.3655791283,0.2413906008,0.1085240468,0.0770600438,-0.4262593985,-0.1771688163,-0.0480151996,-0.0049847378,0.036921978,0.0718623921,-0.0187913645,0.0006606558,0.1049904004,0.1906935126,-0.1910691857,0.057039503,-0.1342848837,0.4053150415,-0.3368168175,0.1215166077,0.1513898671,-0.033291731,0.2043572962,-0.1073043272,0.1692515314,-0.1872263104,0.0212524477,-0.2450529933,-0.0659325495,0.1954428554,0.0480438098,0.0028141201,0.3261824846,-0.5611881614,-0.1635352671,-0.2155396938,-0.2283369601,-0.3000667095,-0.1469439864,0.2351692468,-0.1796001494,0.0300378092,-0.4003524482,-0.18054685,0.237726599,0.0449476801,0.1126893461,-0.2676103711,-0.2484904975,-0.2921720147,0.2016495019,0.1388746202,-0.08172521,-0.1147434115,-0.1918641925,-0.1030081287,0.1174745634,0.3144727945,0.1723901331,0.43979159,-0.2319522053,0.1515319943,0.5892598629,-0.6347135305,-0.2369804829,0.1007082835,0.0968757421,-0.2819084525,-0.0695398524,0.0950889289,0.2874577045,0.0750943869,0.4693049788,0.1775257438,-0.1200407296,-0.1910822093,-0.3003584146,0.0345603265,0.0852224529,0.0522735454,0.2045292258,-0.0504421517,0.1096888632,-0.0649215803,-0.1438204199,0.1973367929,0.0194501802,0.0301419217,0.331820786,-0.2376646549,0.0082451282,-0.5078004003,-0.0287532061,0.0765153319,-0.5539197922,0.198527962,0.0962853581,-0.0163019728,-0.5231425762,0.1050595492,-0.0699512661,-0.0393085517,0.2326908261,-0.2064474374,0.3046903312,0.0231801495,-0.0384821072,0.171043694,0.0765673891,0.0847157612,-0.0891413689,0.2145031542,-0.2762120366,-0.4148796499,0.138267681,0.3498590291,0.2727716267,0.0353043973,-0.1092623249,0.4997556508,-0.1424180567,-0.2285176069,0.0557274185,-0.0633073077,-0.0071699293,0.0306306798,-0.0186237693,-0.1132494584,0.1906821728,-0.029351959,0.3210164309,0.1958460063,0.0103191854,0.2974856794,-0.1180674285,0.327396512,-0.1225315109,-0.0584307276,-0.1500223279,-0.1341111809,0.4181139767,-0.297301203,-0.0799402669,-0.1827307642,0.3219168186,-0.2306055278,0.3350412548,-0.0496505089,0.1854770035,-0.0289649013,-0.2190921903,0.107579872,-0.1537142396,0.1282522231,0.5975934267,-0.0049649971,-0.1247712001,0.0250173025,-0.1926951706,-0.0234322157,0.1754012406,0.0674831867,0.0308192037,0.1081281975,0.1688194871,-0.0281777438,-0.0380484313,0.1020587161,-0.2313185483,0.2311968207,-0.322055459,-0.0178321414,-0.0451471135,-0.4319617748,-0.1677066386,-0.1099136993,-0.2902896106,-0.6119655967,-0.057234671,0.0513690375,0.0873179585,0.1892627627,0.322621733,0.0651795939,0.1116792187,0.0968883038,0.052267164,-0.2201310247,-0.0294674635,0.0525177605,0.0137362741,0.2037277669,0.4438380897,-0.1036847085,-0.0145902503,-0.1050944924,-0.3087322116,0.0173460748,-0.284208715,0.6033970118,0.4132930934,-0.1238139421,-0.0578880683,-0.3781408966,0.4658437669,-0.4250400364,0.0171624403,0.2245608568,-0.2028575689,-0.0457102694,-0.1568728834,0.0534092486,-0.2870553434,-0.3602614999,0.3244150281,0.1362109035,0.0870978311,0.0247128438,-0.2230973095,0.378121376,-0.1902915835,-0.1985457391,-0.1898382753,-0.0287204385,0.3248326778,-0.2061116695,-0.3413917124,-0.088632822,-0.0667745844,0.3206015527,-0.2410281748,-0.2447149307,-0.5733760595,-0.1851422489,0.0286152307,-0.1296885163,0.1928846389,0.0440016761,-0.1671079099,-0.064908646,-0.0949185565,-0.2339582145,0.1628335267,0.2192826271,0.092607379,-0.1301689148,0.3421754539,0.0853842348,0.3098371029,0.1035997644,-0.0924218148,0.2702838182,0.0431021787,0.2309778929,0.0065481076,-0.4910922647,0.187162011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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/324","title":"Error when calculating glue score","comments":"MRPC is also a binary classification task, so its metric is a binary classification metric.\r\n\r\nTo evaluate if pairs of sentences are semantically equivalent, maybe you could take a look at models that compute if one sentence entails the other or not (typically the kinds of model that could work well on the MRPC task).","body":"I was trying glue score along with other metrics here. But glue gives me this error;\r\n\r\n```\r\nimport nlp\r\nglue_metric = nlp.load_metric('glue',name=\"cola\")\r\n\r\nglue_score = glue_metric.compute(predictions, references)\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-8-b9210a524504> in <module>()\r\n----> 1 glue_score = glue_metric.compute(predictions, references)\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n    191         \"\"\"\r\n    192         if predictions is not None:\r\n--> 193             self.add_batch(predictions=predictions, references=references)\r\n    194         self.finalize(timeout=timeout)\r\n    195 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in add_batch(self, predictions, references, **kwargs)\r\n    207         if self.writer is None:\r\n    208             self._init_writer()\r\n--> 209         self.writer.write_batch(batch)\r\n    210 \r\n    211     def add(self, prediction=None, reference=None, **kwargs):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    155         if self.pa_writer is None:\r\n    156             self._build_writer(pa_table=pa.Table.from_pydict(batch_examples))\r\n--> 157         pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n    158         if writer_batch_size is None:\r\n    159             writer_batch_size = self.writer_batch_size\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/types.pxi in __iter__()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\nTypeError: an integer is required (got type str)\r\n```\r\nI'm not sure whether I'm doing this wrong or whether it's an issue. I would like to know a workaround. Thank you.","comment_length":55,"text":"Error when calculating glue score \n I was trying glue score along with other metrics here. But glue gives me this error;\r\n\r\n```\r\nimport nlp\r\nglue_metric = nlp.load_metric('glue',name=\"cola\")\r\n\r\nglue_score = glue_metric.compute(predictions, references)\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-8-b9210a524504> in <module>()\r\n----> 1 glue_score = glue_metric.compute(predictions, references)\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n    191         \"\"\"\r\n    192         if predictions is not None:\r\n--> 193             self.add_batch(predictions=predictions, references=references)\r\n    194         self.finalize(timeout=timeout)\r\n    195 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/metric.py in add_batch(self, predictions, references, **kwargs)\r\n    207         if self.writer is None:\r\n    208             self._init_writer()\r\n--> 209         self.writer.write_batch(batch)\r\n    210 \r\n    211     def add(self, prediction=None, reference=None, **kwargs):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n    155         if self.pa_writer is None:\r\n    156             self._build_writer(pa_table=pa.Table.from_pydict(batch_examples))\r\n--> 157         pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n    158         if writer_batch_size is None:\r\n    159             writer_batch_size = self.writer_batch_size\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/types.pxi in __iter__()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.asarray()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\nTypeError: an integer is required (got type str)\r\n```\r\nI'm not sure whether I'm doing this wrong or whether it's an issue. I would like to know a workaround. Thank you. \n MRPC is also a binary classification task, so its metric is a binary classification metric.\r\n\r\nTo evaluate if pairs of sentences are semantically equivalent, maybe you could take a look at models that compute if one sentence entails the other or not (typically the kinds of model that could work well on the MRPC task).","embeddings":[-0.1694530249,-0.2395812869,-0.0304733198,0.1611590832,0.236740306,-0.0491650477,0.1073005199,0.3623578846,0.4330275059,0.0516285449,-0.3168233335,0.2763474584,-0.0798071995,-0.0241100863,0.1026181132,-0.2831492722,-0.1162880808,0.1604485661,-0.2202495039,0.3102566004,-0.324510783,0.2713671029,-0.1858564019,0.3197874725,-0.1767067164,-0.0903876349,0.0204841644,-0.196170941,-0.3263719678,-0.362935394,0.2481628209,-0.1168349832,0.02158558,0.1766418815,-0.0001102634,-0.0120561915,0.458953023,0.0023093964,0.1588456184,-0.1421633959,-0.3889809847,-0.083480984,0.0444146022,-0.2361500263,0.1346656531,0.2275212109,0.0206140038,-0.1826084852,0.2193325311,0.2709797919,0.2774609029,0.3871142864,0.3126004338,-0.1989937872,0.1724911779,-0.2082325965,0.0265877806,0.8870652914,-0.327216506,-0.4651894271,-0.0078018066,0.2069443315,-0.1057071611,0.2180079669,0.2240158021,0.0521962196,0.3716999292,-0.3202626705,-0.1326412559,-0.0713140443,-0.2181349844,-0.2285419106,-0.1499314606,0.0416573919,0.0471167676,-0.3763104677,0.0972394794,-0.0726784766,0.0581606068,-0.2293186486,-0.0549694002,0.1402760744,-0.1144136488,0.2752963901,0.2400909662,0.3214469552,-0.1981527656,0.296900332,0.4147271514,0.2154424191,-0.1390433908,0.0435455628,-0.2401257157,0.1653348356,-0.2926098704,-0.0423279516,-0.0104532884,0.1914083958,0.0239265561,-0.2354671657,0.0487309434,-0.2080052644,0.4857490361,0.3427954614,0.0602983236,0.3389247358,0.3534550667,0.1564653218,0.127425313,0.2417826504,0.002334035,-0.1694935113,0.2388441265,-0.3881970942,0.3655791283,0.2413906008,0.1085240468,0.0770600438,-0.4262593985,-0.1771688163,-0.0480151996,-0.0049847378,0.036921978,0.0718623921,-0.0187913645,0.0006606558,0.1049904004,0.1906935126,-0.1910691857,0.057039503,-0.1342848837,0.4053150415,-0.3368168175,0.1215166077,0.1513898671,-0.033291731,0.2043572962,-0.1073043272,0.1692515314,-0.1872263104,0.0212524477,-0.2450529933,-0.0659325495,0.1954428554,0.0480438098,0.0028141201,0.3261824846,-0.5611881614,-0.1635352671,-0.2155396938,-0.2283369601,-0.3000667095,-0.1469439864,0.2351692468,-0.1796001494,0.0300378092,-0.4003524482,-0.18054685,0.237726599,0.0449476801,0.1126893461,-0.2676103711,-0.2484904975,-0.2921720147,0.2016495019,0.1388746202,-0.08172521,-0.1147434115,-0.1918641925,-0.1030081287,0.1174745634,0.3144727945,0.1723901331,0.43979159,-0.2319522053,0.1515319943,0.5892598629,-0.6347135305,-0.2369804829,0.1007082835,0.0968757421,-0.2819084525,-0.0695398524,0.0950889289,0.2874577045,0.0750943869,0.4693049788,0.1775257438,-0.1200407296,-0.1910822093,-0.3003584146,0.0345603265,0.0852224529,0.0522735454,0.2045292258,-0.0504421517,0.1096888632,-0.0649215803,-0.1438204199,0.1973367929,0.0194501802,0.0301419217,0.331820786,-0.2376646549,0.0082451282,-0.5078004003,-0.0287532061,0.0765153319,-0.5539197922,0.198527962,0.0962853581,-0.0163019728,-0.5231425762,0.1050595492,-0.0699512661,-0.0393085517,0.2326908261,-0.2064474374,0.3046903312,0.0231801495,-0.0384821072,0.171043694,0.0765673891,0.0847157612,-0.0891413689,0.2145031542,-0.2762120366,-0.4148796499,0.138267681,0.3498590291,0.2727716267,0.0353043973,-0.1092623249,0.4997556508,-0.1424180567,-0.2285176069,0.0557274185,-0.0633073077,-0.0071699293,0.0306306798,-0.0186237693,-0.1132494584,0.1906821728,-0.029351959,0.3210164309,0.1958460063,0.0103191854,0.2974856794,-0.1180674285,0.327396512,-0.1225315109,-0.0584307276,-0.1500223279,-0.1341111809,0.4181139767,-0.297301203,-0.0799402669,-0.1827307642,0.3219168186,-0.2306055278,0.3350412548,-0.0496505089,0.1854770035,-0.0289649013,-0.2190921903,0.107579872,-0.1537142396,0.1282522231,0.5975934267,-0.0049649971,-0.1247712001,0.0250173025,-0.1926951706,-0.0234322157,0.1754012406,0.0674831867,0.0308192037,0.1081281975,0.1688194871,-0.0281777438,-0.0380484313,0.1020587161,-0.2313185483,0.2311968207,-0.322055459,-0.0178321414,-0.0451471135,-0.4319617748,-0.1677066386,-0.1099136993,-0.2902896106,-0.6119655967,-0.057234671,0.0513690375,0.0873179585,0.1892627627,0.322621733,0.0651795939,0.1116792187,0.0968883038,0.052267164,-0.2201310247,-0.0294674635,0.0525177605,0.0137362741,0.2037277669,0.4438380897,-0.1036847085,-0.0145902503,-0.1050944924,-0.3087322116,0.0173460748,-0.284208715,0.6033970118,0.4132930934,-0.1238139421,-0.0578880683,-0.3781408966,0.4658437669,-0.4250400364,0.0171624403,0.2245608568,-0.2028575689,-0.0457102694,-0.1568728834,0.0534092486,-0.2870553434,-0.3602614999,0.3244150281,0.1362109035,0.0870978311,0.0247128438,-0.2230973095,0.378121376,-0.1902915835,-0.1985457391,-0.1898382753,-0.0287204385,0.3248326778,-0.2061116695,-0.3413917124,-0.088632822,-0.0667745844,0.3206015527,-0.2410281748,-0.2447149307,-0.5733760595,-0.1851422489,0.0286152307,-0.1296885163,0.1928846389,0.0440016761,-0.1671079099,-0.064908646,-0.0949185565,-0.2339582145,0.1628335267,0.2192826271,0.092607379,-0.1301689148,0.3421754539,0.0853842348,0.3098371029,0.1035997644,-0.0924218148,0.2702838182,0.0431021787,0.2309778929,0.0065481076,-0.4910922647,0.1871620119,-0.1212332994,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/321","title":"ERROR:root:mwparserfromhell","comments":"It looks like it comes from `mwparserfromhell`.\r\n\r\nWould it be possible to get the bad `section` that causes this issue ? The `section` string is from `datasets\/wikipedia.py:L548` ? You could just add a `try` statement and print the section if the line `section_text.append(section.strip_code().strip())` crashes.\r\n\r\nIt will help us know if we have to fix it on our side or if it is a `mwparserfromhell` issue.","body":"Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n","comment_length":65,"text":"ERROR:root:mwparserfromhell \n Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n \n It looks like it comes from `mwparserfromhell`.\r\n\r\nWould it be possible to get the bad `section` that causes this issue ? The `section` string is from `datasets\/wikipedia.py:L548` ? You could just add a `try` statement and print the section if the line `section_text.append(section.strip_code().strip())` crashes.\r\n\r\nIt will help us know if we have to fix it on our side or if it is a `mwparserfromhell` issue.","embeddings":[-0.2354938686,-0.1382549554,0.0552539006,0.5212737918,0.0680223629,-0.0856205523,-0.1348956376,0.3071224689,0.2067664564,0.2888492048,0.347761035,-0.0715623647,0.0598848015,-0.4790312052,0.0541907251,0.0624246337,0.2404568046,0.1717450917,0.0140154986,-0.1562183797,-0.0564819202,0.1827820987,-0.318266809,0.4079273045,-0.2893714607,0.1333573014,0.1821404994,0.0453482494,-0.0873938873,-0.5024523139,0.1602192223,-0.3359048069,0.1328683645,0.2546606958,-0.0001277348,-0.1548775285,0.530970335,-0.0702340454,-0.2122372091,-0.0369732305,-0.0304517858,-0.1057011634,-0.0895878971,-0.2332394421,0.2424845099,0.2433309108,0.1983077228,-0.071317412,0.3108440042,0.3353014588,0.0761991441,0.3869360983,0.1124016941,0.0967790335,0.6936830878,0.1377621144,0.125202626,0.0944369435,0.0384380855,-0.2644349337,0.0345922709,0.1256115586,-0.336327672,-0.250264585,-0.1778667122,-0.0576803312,0.1472638547,-0.2539338768,0.30596596,0.2866597474,0.3650677502,-0.0381625369,0.2111723274,-0.0308051053,-0.1637750566,0.2019702494,0.3874559402,0.4430146813,-0.5954430103,0.1332688928,0.479385227,-0.1277801991,-0.0204470921,0.2640166879,-0.2832796872,0.9716570377,0.122401759,0.2389769107,0.1774604172,-0.0569028258,-0.2832579315,-0.1096520424,-0.0692288727,0.1504844725,-0.1025137082,-0.0143687679,0.0064465175,-0.1386348158,0.0563424863,-0.5317035317,-0.4928170741,0.1415746957,-0.0694425851,0.163571313,0.5938709378,-0.2485040873,0.2245710641,0.0420949981,0.0299751759,-0.0062607736,0.0381632559,0.1198109835,-0.0576130264,0.0200764239,-0.350661397,0.1356573254,0.2376644164,-0.3176876903,-0.2114284188,0.1597348154,-0.1107552648,-0.1038236395,-0.0693252012,0.5388111472,-0.0688824281,0.3479135036,-0.0419887006,-0.0867054388,-0.1887230724,-0.4938837886,0.0754925832,0.1780680716,-0.3806151748,0.1249615997,-0.193040207,0.1050055996,0.044943396,0.2112226635,-0.0776245296,-0.5137030482,-0.2019978613,-0.16895625,0.0394029059,0.1415690631,0.2810630202,0.329131633,0.2446107566,-0.3599990904,-0.0871145725,0.240745604,-0.3768997192,-0.2545084357,-0.4904033542,-0.0008211152,-0.0353646055,0.2822312117,-0.59474051,0.0508756116,-0.0120046344,-0.0575802922,0.1264092624,-0.1706045866,-0.1604581326,-0.057974752,0.1905232072,0.450928092,-0.0708901063,-0.1168515608,-0.1774329096,-0.2140928507,0.2010282427,0.2269358635,-0.1839905083,0.5195731521,-0.4584503472,0.4583002627,0.3113233745,-0.2849512994,-0.0571353063,-0.0979459509,-0.1427361071,-0.0916979015,0.0400379486,-0.1657854319,0.1380462795,-0.187332809,-0.27854532,0.0720921829,0.0015590468,0.2039587945,-0.1975086182,-0.3238655031,0.257368505,0.1239002645,0.1697516441,-0.0927250609,0.1561171263,0.3895823061,0.5115286708,-0.0813133717,-0.0571516119,0.0208800733,0.2228118181,0.1539544761,-0.0132474313,-0.11784105,-0.075242497,0.0509776585,0.1157508269,0.2266798019,-0.253945142,-0.2227997333,-0.2215527445,0.2793840766,0.0249313395,0.1636644602,0.0264547113,0.1939318627,-0.2000493407,0.2564404309,-0.075311251,-0.2795470059,-0.3256709576,0.1935569346,-0.0402257368,0.2280983031,-0.0192772765,-0.1150707304,-0.2168326825,-0.1158802882,0.51837641,0.090927422,-0.1253851503,-0.0055526793,0.2218338996,0.1994571984,-0.3905209899,-0.1928244233,-0.0735775605,-0.4623733461,0.0073243184,0.2881063819,0.0147594763,-0.0881737769,0.3043561578,0.1623749733,0.363026619,0.1495341361,-0.1418321729,0.1046718732,0.111119777,-0.0588465407,0.31650123,-0.1134981662,0.3225903511,0.4323560297,-0.0882901549,-0.2456407249,0.1771972775,-0.0821040571,0.5798689723,-0.1613234133,0.3835196197,0.2482497096,-0.2514739633,-0.0573471934,0.2625975907,-0.3426445425,0.0388191789,-0.0855645016,-0.1710690111,0.260841608,0.1124908105,0.0950722992,0.4415653944,0.1859731227,0.0493018255,0.0334592499,-0.2050507665,-0.0729158074,-0.3128654063,-0.0842659175,-0.5056683421,0.2636985779,-0.4026723504,-0.0203204527,-0.3042700887,-0.0564648621,-0.4614537656,-0.0538029037,-0.3293029368,-0.1057729051,-0.1545158923,0.239153564,-0.1852569878,0.0493119583,-0.0950176641,0.2174655497,-0.0937689021,0.4357780516,-0.1978890449,-0.4379402101,-0.4635023177,-0.1898544729,0.5398993492,0.2548497617,0.0520797633,-0.1376512945,-0.260935545,-0.2251266539,-0.1003714874,0.4269749224,0.1238296553,0.012356597,-0.0263437498,0.2941504419,0.140590176,-0.3974824548,-0.021061305,-0.2681171894,-0.0495684333,0.0712934509,0.0634675398,-0.0828441158,0.1557223797,-0.0571126379,-0.384139657,-0.0979475603,-0.2573730052,-0.1597690284,0.1611708701,-0.4041493535,-0.1138582602,0.1818556041,-0.4152784944,-0.2532142997,-0.1731048375,0.0803441405,0.083342433,0.0995669663,-0.1332220733,0.2256107032,0.0259850845,0.0232237428,0.0667399168,-0.484084785,0.3821097314,0.1489899009,-0.069530502,-0.2207824439,-0.0596512295,0.2172506005,-0.1444769949,0.1558894068,0.064577952,-0.1554575861,-0.3619950116,-0.2255566865,0.4314027429,-0.1706152409,0.3485953212,0.0884206668,0.7554531097,-0.1121196747,0.1790819019,0.2185539603,-0.0767456293,-0.0468219779,0.2099902332,-0.1366433948,0.1960787326,0.0041359323,-0.33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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/321","title":"ERROR:root:mwparserfromhell","comments":"Hi, \r\n\r\nThank you for you answer.\r\nI have try to print the bad section using `try` and `except`, but it is a bit weird as the error seems to appear 3 times for instance, but the two first error does not print anything (as if the function did not go in the `except` part).\r\nFor the third one, I got that (I haven't display the entire text) :\r\n\r\n> error : ==== Parque nacional Cajas ====\r\n> {{AP|Parque nacional Cajas}}\r\n> [[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\n> El parque nacional Cajas est\u00e1 situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n>  [[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos m\u00e1s comunes al parque inician todos en Cuenca: Desde all\u00ed, la v\u00eda Cuenca-Mol\r\n> leturo atraviesa en Control de [[Surocucho]] en poco m\u00e1s de 30 minutos de viaje; m\u00e1s adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde est\u00e1n el Centro Administrativo y de Informaci\u00f3n del parque. Siguiendo de largo hacia [[Molleturo]], por esta v\u00eda se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n> Para acceder al parque desde la costa, la v\u00eda Molleturo-Cuenca es tambi\u00e9n la mejor opci\u00f3n.\r\n\r\nHow can I display the link instead of the text ? I suppose it will help you more ","body":"Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n","comment_length":234,"text":"ERROR:root:mwparserfromhell \n Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n \n Hi, \r\n\r\nThank you for you answer.\r\nI have try to print the bad section using `try` and `except`, but it is a bit weird as the error seems to appear 3 times for instance, but the two first error does not print anything (as if the function did not go in the `except` part).\r\nFor the third one, I got that (I haven't display the entire text) :\r\n\r\n> error : ==== Parque nacional Cajas ====\r\n> {{AP|Parque nacional Cajas}}\r\n> [[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\n> El parque nacional Cajas est\u00e1 situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n>  [[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos m\u00e1s comunes al parque inician todos en Cuenca: Desde all\u00ed, la v\u00eda Cuenca-Mol\r\n> leturo atraviesa en Control de [[Surocucho]] en poco m\u00e1s de 30 minutos de viaje; m\u00e1s adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde est\u00e1n el Centro Administrativo y de Informaci\u00f3n del parque. Siguiendo de largo hacia [[Molleturo]], por esta v\u00eda se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n> Para acceder al parque desde la costa, la v\u00eda Molleturo-Cuenca es tambi\u00e9n la mejor opci\u00f3n.\r\n\r\nHow can I display the link instead of the text ? I suppose it will help you more ","embeddings":[-0.2193864137,-0.2222698182,0.0420608148,0.3609670401,0.0011143076,-0.0577766001,-0.2417550832,0.209553659,0.3074944019,0.1932076514,0.3478331268,0.0101021947,0.0042322748,-0.4349834919,-0.0932994261,-0.023367146,0.2952888906,0.1979200393,0.04570226,-0.2040556818,-0.007366735,0.1882302612,-0.4374738634,0.3260341585,-0.1881965697,0.1131407544,0.1581795365,-0.1393144429,0.014603612,-0.3755434752,-0.0074203629,-0.314427346,0.0927146226,0.3675207794,-0.0001274272,-0.2371362448,0.4585935175,-0.0967858285,-0.1138205379,-0.1958162785,-0.0912482142,-0.1459701657,-0.1145082563,-0.295796603,0.2706686258,0.0221738387,0.2623672187,-0.2641826868,0.3727796972,0.311512351,0.0584836528,0.1152181178,0.2354289144,0.0652595684,0.4728451967,0.1147156358,0.1719979048,-0.0168300997,0.0028644218,-0.0566985495,0.1747065783,0.226606369,-0.3517799973,-0.2806597054,-0.1432513744,0.0191190019,0.3303057551,-0.3932858109,0.4076831639,0.1749070436,0.3341667354,-0.0630978495,0.3528113365,-0.0091338204,-0.2008414268,0.1968026757,0.4043156207,0.5426412225,-0.4197180569,0.0691999942,0.2718425393,-0.0963856652,-0.1063987538,0.3659130633,-0.1545752883,0.8306202292,0.0233020242,0.2787495255,0.0719276592,-0.0208499245,-0.0637707785,-0.2760691643,-0.0240615029,0.2852091193,-0.0458304361,0.0604048781,-0.1112740263,-0.1204128712,-0.0344832502,-0.5352146626,-0.3188851178,0.016314907,0.0520349368,0.2536082268,0.5370830297,-0.1810673624,0.1744203269,0.0504513346,-0.1436626613,-0.0362118743,0.0503579937,-0.0149717974,0.0126637584,-0.0031581181,-0.4311356246,0.1612440199,0.207602188,-0.1855865717,-0.043176949,0.3412980437,-0.2080373168,-0.0389119461,-0.1002752483,0.4228107929,-0.0640100911,0.3921245337,0.0432952233,-0.0033581243,-0.1112078279,-0.4731747806,0.0116122067,0.3763631284,-0.3916964531,0.1339963228,-0.2620747685,0.2182763219,0.1239406243,0.2374808937,-0.0575655848,-0.6647424698,-0.1636941582,-0.3196177781,0.0889815837,0.0721609294,0.2993113399,0.4473589957,0.2203225344,-0.3982048333,-0.0111890361,0.2441385239,-0.3707463741,-0.1091459915,-0.5119053125,0.0327002518,-0.0072276462,0.2863237858,-0.6130176783,0.071035035,-0.0608309433,-0.2034663558,0.1553456932,-0.136609152,-0.0946944132,-0.040131256,0.1314945221,0.284376502,-0.1842534989,0.1015154868,-0.1600361168,-0.1020812616,0.443862319,0.1019083261,-0.0799845234,0.6557809114,-0.4682842493,0.6272986531,0.2062439322,-0.3382780552,0.1129751652,0.0943775177,-0.0297438093,-0.1602768451,-0.0175427813,-0.2283287346,0.1636880189,-0.0794315636,-0.3810733557,-0.0160949845,0.0205538664,0.2330003977,-0.195982486,-0.228737548,0.146527797,0.0198918972,0.1913723201,-0.0826181471,0.1403740942,0.4307594001,0.4653141797,-0.1570250094,0.0332013443,0.0829630569,0.1245785058,0.2342986017,-0.1293135881,0.0222032443,-0.0531830825,-0.0452171713,0.0789411962,0.1511837393,-0.2835966945,-0.0415176526,-0.2271316946,0.2197405547,-0.0927394703,0.0808862746,0.0659536347,0.1650982946,-0.1487154365,0.3934485912,0.0759446323,-0.2305076122,-0.2285712212,0.1141640618,-0.0720312521,0.2543570101,-0.1999925524,-0.1869710833,-0.3368486166,-0.1040874645,0.5295463204,0.0206557009,-0.1265149117,0.1563125253,0.2563987076,0.1210784316,-0.4433177114,-0.1928886771,-0.0400879569,-0.5070744157,0.1419572085,0.2667557597,0.2609446943,-0.0980919302,0.1688242406,0.0551044792,0.3346451223,0.0481992736,-0.1643210053,0.0471285656,0.0951245204,0.0196772814,0.385991931,-0.2309042066,0.2975935638,0.5082976818,-0.1537844539,-0.3612570763,0.1945492774,-0.1508444548,0.5056219101,-0.0185458716,0.1731353551,0.1171935648,-0.1968074441,-0.0598715618,0.2999535799,-0.3947967589,0.0685457736,-0.0993594378,-0.1491765082,0.4354537427,0.2558443248,0.0767732114,0.4650770128,0.2169238925,-0.0594202429,0.1005321741,-0.2642533779,-0.0237028766,-0.2029424757,-0.2103270888,-0.3739588261,0.2363000214,-0.2853994966,-0.0107833426,-0.2933445275,-0.0377447046,-0.3747020662,0.134397462,-0.3128294051,-0.1369610578,-0.07216952,0.2654456794,-0.2574843764,0.1088799164,0.0163196735,0.0890122503,-0.0874495506,0.382876724,-0.3068619668,-0.2860129774,-0.4424818754,-0.1794234216,0.4841916263,0.2453587353,0.0898851156,-0.1131181568,-0.034018755,-0.2677024603,-0.1557061523,0.4435267746,-0.1022695825,-0.0443903804,-0.0383539796,0.2406124026,-0.008651997,-0.3758123517,-0.0006716651,-0.3829981685,-0.04584774,0.0885857791,0.0353933424,-0.1559861153,0.0406866111,-0.0269830637,-0.2393774092,-0.068910405,-0.3220782876,-0.1188825145,0.1221832037,-0.3866707683,0.082772769,0.2016867101,-0.3991760015,-0.2127141207,-0.1958945841,0.0294108149,0.078367725,0.0208120719,-0.1779778898,0.378598541,0.0338088907,0.171837464,-0.1126878709,-0.5451945662,0.4099715948,0.0749999136,-0.177185744,-0.3284282088,0.0607920513,0.2713207603,-0.1899035424,0.1288836598,0.0628008693,-0.1509239823,-0.3709451258,-0.2282283455,0.3510294259,-0.1403122842,0.3559291065,0.049637679,0.6082237363,-0.0265615489,0.2332248986,0.3531579077,-0.0431798659,0.04477337,0.0899039805,-0.0445801504,0.3161122799,0.1238505021,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/321","title":"ERROR:root:mwparserfromhell","comments":"The error appears several times as Apache Beam retries to process examples up to 4 times irc.\r\n\r\nI just tried to run this text into `mwparserfromhell` but it worked without the issue.\r\n\r\nI used this code (from the `wikipedia.py` script):\r\n```python\r\nimport mwparserfromhell as parser\r\nimport re\r\nimport six\r\n\r\nraw_content = r\"\"\"==== Parque nacional Cajas ====\r\n{{AP|Parque nacional Cajas}}\r\n[[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\nEl parque nacional Cajas est\u00e1 situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n[[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos m\u00e1s comunes al parque inician todos en Cuenca: Desde all\u00ed, la v\u00eda Cuenca-Mol\r\nleturo atraviesa en Control de [[Surocucho]] en poco m\u00e1s de 30 minutos de viaje; m\u00e1s adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde est\u00e1n el Centro Administrativo y de Informaci\u00f3n del parque. Siguiendo de largo hacia [[Molleturo]], por esta v\u00eda se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n\"\"\"\r\n\r\nwikicode = parser.parse(raw_content)\r\n\r\n# Filters for references, tables, and file\/image links.\r\nre_rm_wikilink = re.compile(\"^(?:File|Image|Media):\", flags=re.IGNORECASE | re.UNICODE)\r\n\r\ndef rm_wikilink(obj):\r\n    return bool(re_rm_wikilink.match(six.text_type(obj.title)))\r\n\r\ndef rm_tag(obj):\r\n    return six.text_type(obj.tag) in {\"ref\", \"table\"}\r\n\r\ndef rm_template(obj):\r\n    return obj.name.lower() in {\"reflist\", \"notelist\", \"notelist-ua\", \"notelist-lr\", \"notelist-ur\", \"notelist-lg\"}\r\n\r\ndef try_remove_obj(obj, section):\r\n    try:\r\n        section.remove(obj)\r\n    except ValueError:\r\n        # For unknown reasons, objects are sometimes not found.\r\n        pass\r\n\r\nsection_text = []\r\nfor section in wikicode.get_sections(flat=True, include_lead=True, include_headings=True):\r\n    for obj in section.ifilter_wikilinks(matches=rm_wikilink, recursive=True):\r\n        try_remove_obj(obj, section)\r\n    for obj in section.ifilter_templates(matches=rm_template, recursive=True):\r\n        try_remove_obj(obj, section)\r\n    for obj in section.ifilter_tags(matches=rm_tag, recursive=True):\r\n        try_remove_obj(obj, section)\r\n\r\n    section_text.append(section.strip_code().strip())\r\n```","body":"Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n","comment_length":264,"text":"ERROR:root:mwparserfromhell \n Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n \n The error appears several times as Apache Beam retries to process examples up to 4 times irc.\r\n\r\nI just tried to run this text into `mwparserfromhell` but it worked without the issue.\r\n\r\nI used this code (from the `wikipedia.py` script):\r\n```python\r\nimport mwparserfromhell as parser\r\nimport re\r\nimport six\r\n\r\nraw_content = r\"\"\"==== Parque nacional Cajas ====\r\n{{AP|Parque nacional Cajas}}\r\n[[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\nEl parque nacional Cajas est\u00e1 situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n[[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos m\u00e1s comunes al parque inician todos en Cuenca: Desde all\u00ed, la v\u00eda Cuenca-Mol\r\nleturo atraviesa en Control de [[Surocucho]] en poco m\u00e1s de 30 minutos de viaje; m\u00e1s adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde est\u00e1n el Centro Administrativo y de Informaci\u00f3n del parque. Siguiendo de largo hacia [[Molleturo]], por esta v\u00eda se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n\"\"\"\r\n\r\nwikicode = parser.parse(raw_content)\r\n\r\n# Filters for references, tables, and file\/image links.\r\nre_rm_wikilink = re.compile(\"^(?:File|Image|Media):\", flags=re.IGNORECASE | re.UNICODE)\r\n\r\ndef rm_wikilink(obj):\r\n    return bool(re_rm_wikilink.match(six.text_type(obj.title)))\r\n\r\ndef rm_tag(obj):\r\n    return six.text_type(obj.tag) in {\"ref\", \"table\"}\r\n\r\ndef rm_template(obj):\r\n    return obj.name.lower() in {\"reflist\", \"notelist\", \"notelist-ua\", \"notelist-lr\", \"notelist-ur\", \"notelist-lg\"}\r\n\r\ndef try_remove_obj(obj, section):\r\n    try:\r\n        section.remove(obj)\r\n    except ValueError:\r\n        # For unknown reasons, objects are sometimes not found.\r\n        pass\r\n\r\nsection_text = []\r\nfor section in wikicode.get_sections(flat=True, include_lead=True, include_headings=True):\r\n    for obj in section.ifilter_wikilinks(matches=rm_wikilink, recursive=True):\r\n        try_remove_obj(obj, section)\r\n    for obj in section.ifilter_templates(matches=rm_template, recursive=True):\r\n        try_remove_obj(obj, section)\r\n    for obj in section.ifilter_tags(matches=rm_tag, recursive=True):\r\n        try_remove_obj(obj, section)\r\n\r\n    section_text.append(section.strip_code().strip())\r\n```","embeddings":[-0.2439157814,-0.1970420778,0.0670820624,0.3134252429,0.0271570385,-0.0312242601,-0.1195121184,0.2109857798,0.3312293291,0.1418250799,0.3229496777,0.0170424618,0.0512088351,-0.4928643703,-0.1744854003,-0.0037909967,0.2605278194,0.126694575,-0.0522866212,-0.2089977413,0.0076541505,0.142170921,-0.4332667291,0.2717127204,-0.2520084083,0.1304321736,0.0929765031,-0.0105750877,0.0373966098,-0.469048351,-0.0403118506,-0.2240466177,0.1670672446,0.2844329476,-0.0001267064,-0.2108095735,0.4160577655,-0.0950901434,-0.18317312,-0.1716534048,0.0071890042,-0.1880869716,-0.044271756,-0.3014479578,0.2884559929,0.0185891744,0.2699138522,-0.286364913,0.3683305085,0.2292597592,0.0456214696,0.1972637475,0.2272142768,0.0291248728,0.450504601,0.0643212572,0.1650123149,-0.052069135,-0.0432651341,-0.0965783298,0.0542214997,0.2810028493,-0.3560197651,-0.1097043753,-0.1074362621,-0.03395373,0.3892698288,-0.2821047604,0.4064489007,0.0963136032,0.3552184999,0.0164924208,0.2594856918,0.0158013031,-0.0724432319,0.2757148743,0.3752604723,0.5088477135,-0.3879244924,0.0651457906,0.3225114644,-0.0166436248,-0.0790882036,0.2455794513,-0.0721082017,0.7677217126,0.1227155477,0.3747815788,-0.0259417295,-0.0633261204,0.0517114289,-0.3146010637,-0.0190164428,0.353582263,-0.1086524203,0.159908995,-0.156347692,-0.0853176191,-0.0002928512,-0.4934480786,-0.4367776811,0.0614360645,0.1088035107,0.2573606372,0.5199109316,-0.1411513686,0.1863741428,-0.0718507543,-0.0114212791,-0.0882609114,-0.0732454658,0.0659644008,-0.0933441818,-0.0662893206,-0.332628876,0.106274195,0.1409582198,-0.0423314087,-0.0592529289,0.2894517779,-0.3620603085,-0.0768241808,-0.2041373998,0.4364101887,-0.0336193815,0.5642702579,0.2208687216,0.0534734465,-0.1216491312,-0.6564360857,0.0444866419,0.1973567754,-0.3663577437,0.1661610454,-0.2031689584,0.2760706842,0.1157172173,0.1909150332,-0.070945099,-0.6753615141,-0.1433453411,-0.1477840245,-0.1164991707,0.0726512447,0.3854917884,0.4602942765,0.2932551503,-0.3723100126,0.005006732,0.3135888278,-0.3685143888,-0.0950808302,-0.3885682821,0.0424730852,0.0737980381,0.1866634488,-0.49588269,-0.0535191745,-0.0021891752,-0.1071103737,0.0766583607,-0.1638298184,-0.0927644372,-0.0717015788,-0.0266354829,0.3540619016,-0.0045451773,0.0871735662,-0.1042548344,0.0562980101,0.2985343039,0.1747146845,-0.1192095578,0.6493728757,-0.4247390032,0.6064457893,0.1375234872,-0.2236287892,0.0176330581,0.1443516314,0.0657686144,-0.2023114115,0.0165342167,-0.1092079058,0.1594649553,-0.1335683316,-0.4115052819,0.1158019677,0.0724975392,0.3051943481,-0.2148230076,-0.2910426259,0.1255895048,0.0655348375,0.2211541384,-0.1284651458,0.1120066941,0.4881643355,0.4339236617,-0.1908745766,0.101739496,0.0762710497,0.0587165207,0.1764653176,0.0147018321,0.0942199752,-0.0240320209,-0.0028472797,-0.0596542954,0.3432411849,-0.2033087164,-0.0566712767,-0.206263721,0.2809075713,-0.1454792321,-0.0117504541,0.0624536723,0.1869612336,-0.23657915,0.4068617225,0.0701657161,-0.1439375728,-0.1734018922,0.1468380094,0.0166568328,0.2651125193,-0.2953373194,-0.1468981802,-0.3140820563,-0.1859704703,0.4367952943,0.0147473076,-0.18538858,0.1461973488,0.2881453633,0.1721403003,-0.2892115712,-0.2493927181,0.0595733747,-0.4366012216,0.246480599,0.3074889183,0.3146463335,-0.1487218589,0.2846551239,0.0954824239,0.389208138,0.1007718593,-0.1450612098,0.1643443555,0.1466246843,-0.0050317668,0.3946208656,-0.0911520943,0.2289427221,0.5108888745,-0.273743093,-0.3508856893,0.2493451983,-0.1987016648,0.4050527811,-0.0175306182,0.2443616241,0.1115922183,-0.3060934544,-0.1200667471,0.2761844695,-0.4623517096,0.0935930386,-0.0911205113,-0.1070426106,0.3911009729,0.1414718926,0.0316717476,0.5108950138,0.1668330282,-0.0531923659,0.0966076776,-0.1132935286,-0.01941186,-0.2840810716,-0.2290095836,-0.2573992014,0.1794416904,-0.2868689001,0.0622661002,-0.3053764701,0.0137665346,-0.3138224781,0.1955426037,-0.4067344368,-0.216516301,-0.065108791,0.2800655663,-0.2110290676,0.2325728387,0.1181807816,0.0451997556,0.0876482502,0.3888649344,-0.2219836414,-0.3762750328,-0.5698435307,-0.2166003734,0.496360898,0.1641568094,0.102134265,0.0199719369,-0.0413026735,-0.3580471277,-0.1427371949,0.4228231907,-0.0582674593,-0.1273169518,-0.0762526169,0.2410059571,-0.1527664065,-0.3478518426,0.0598887801,-0.439645797,-0.0904707387,0.118610926,-0.0064835497,-0.1578289121,0.0286916588,-0.0873538032,-0.1545055509,-0.0583377182,-0.3719154,-0.2516958416,0.0327769816,-0.2780528963,0.0770787075,0.1496904194,-0.3611339629,-0.2150790244,-0.270290345,0.087501727,0.1113564372,0.0349699743,-0.2326234132,0.4163252413,-0.1056798473,0.1810459048,0.0059799314,-0.49488011,0.224997893,0.1587656289,-0.0572449118,-0.3003888428,0.1743222624,0.2831252217,-0.0949867666,0.110473983,0.0769067183,-0.0483278446,-0.2259436697,-0.2423432618,0.3762096763,-0.097295396,0.4344439209,0.1302692443,0.6834610105,0.0098076779,0.2261824459,0.3966525793,-0.0428141393,-0.0869307369,0.0329573266,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/321","title":"ERROR:root:mwparserfromhell","comments":"Not sure why we're having this issue. Maybe could you get also the file that's causing that ?","body":"Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n","comment_length":18,"text":"ERROR:root:mwparserfromhell \n Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n \n Not sure why we're having this issue. Maybe could you get also the file that's causing that ?","embeddings":[-0.2438737899,-0.1548451036,0.0903029814,0.4575005174,0.1244493946,-0.0543386936,-0.1172973961,0.2066474408,0.336443454,0.3008716106,0.2952870727,-0.0024946679,0.052222982,-0.3813283145,-0.0767171532,-0.0030087871,0.3680462241,0.1951178759,0.0440426283,-0.2419860959,-0.0590003878,0.2005376667,-0.3453764021,0.3041818738,-0.3107658625,0.0585562922,0.1418303251,-0.0247239042,-0.0764364898,-0.3120390773,0.0276921,-0.2750107944,0.1651804149,0.3228823543,-0.0001273461,-0.1857159138,0.5115447044,-0.1040547341,-0.1468497366,-0.2338778526,-0.0839670599,-0.1510858685,-0.0317203365,-0.3054434657,0.313524127,0.0639472082,0.2530777156,-0.2387647778,0.260045588,0.2302283645,0.0824697837,0.2199033499,0.2952759862,-0.0069826036,0.5136982203,0.1219803318,0.204753235,-0.0710389838,-0.0341430269,-0.0303169619,0.1266936362,0.0969860628,-0.4162338376,-0.2447789013,-0.1444558054,-0.0935338661,0.307528168,-0.4182212055,0.4121519327,0.1747305989,0.5269611478,-0.0202695616,0.350112766,0.1336372197,-0.0502374619,0.2415279597,0.4162761867,0.5217395425,-0.3993159235,0.0340274237,0.3900630772,-0.1469513923,-0.042469427,0.2651845813,-0.1742010415,0.8081724048,0.0326359831,0.3084394336,0.0507775359,-0.0183148943,-0.0851952806,-0.3319167495,-0.071087487,0.2830992341,-0.1364086866,0.032097768,-0.1755914986,-0.0749992207,0.0263354573,-0.5093848705,-0.4673197269,0.048002068,0.0184483379,0.2484384626,0.4564214647,-0.1146916524,0.1845860332,-0.0138370367,-0.0089980075,-0.144598335,-0.0538723581,0.0191114284,-0.2315288186,-0.0125745088,-0.4853001237,0.0780624151,0.095707573,-0.1704505533,-0.0402032472,0.2572525144,-0.2312712967,-0.0955126882,-0.1507345587,0.4952979088,-0.0619028546,0.391949147,-0.0485467128,0.0242672097,-0.137969628,-0.609387219,0.0128734466,0.1973122656,-0.3547971845,-0.0110471919,-0.1646290123,0.2207181454,0.133810088,0.3228284419,-0.0421385616,-0.4779711664,-0.0261187013,-0.2950364649,-0.1444501579,0.0541155748,0.4805473387,0.4583336413,0.199652046,-0.3672831357,-0.0564435907,0.4087544084,-0.4907042384,-0.1547195315,-0.5020549893,0.0364796184,0.0233863983,0.1601269841,-0.5785993934,0.0256016534,-0.0694199428,-0.1577776223,0.0533760712,-0.223376587,-0.1592518687,-0.1376728117,0.1006360278,0.3055128753,-0.1845821142,0.011036451,-0.0979872718,-0.1748080999,0.372736752,0.1509964615,-0.1719998866,0.659750104,-0.4492008686,0.5568723679,0.3406916559,-0.2888245583,-0.0907193422,0.0462819487,-0.064474389,-0.206685707,0.0266750138,-0.1909818053,0.2845521867,-0.0467438735,-0.3265430033,0.095997259,0.0915887803,0.2053874135,-0.2147358805,-0.3104133606,0.2907601893,0.0747240409,0.1716153026,-0.0663381964,0.171904996,0.465339154,0.4322321713,-0.2057147175,0.1602537483,0.115256682,0.1596601456,0.2181689441,-0.1131922901,0.1417740732,0.0020725175,-0.0545437187,0.0297569558,0.2059301436,-0.1255842447,-0.0830164254,-0.1817033142,0.2146631181,-0.0889083669,0.0787186027,0.0539172553,0.1325584203,-0.2125540823,0.3692745268,0.0476852544,-0.1967649013,-0.2540505528,0.110928975,0.1038986072,0.2972656786,-0.1605814397,-0.0571832471,-0.3508009911,-0.1678777635,0.5068054199,-0.013373916,-0.1443748772,0.0362109318,0.2480144054,0.0601928681,-0.272565335,-0.2072727382,-0.0175658297,-0.2871756554,0.2105044723,0.1699792594,0.2152316719,-0.1655080914,0.3415623009,0.088574186,0.3863001168,0.0712016597,-0.1960386485,0.1259265393,0.2389337122,0.0077397125,0.4671167731,-0.0711373538,0.2976313531,0.4176380336,-0.092295073,-0.2519229949,0.1286691278,-0.1277332604,0.5343972445,-0.069598116,0.1919314414,0.205151096,-0.3437318802,-0.0107756574,0.2965949774,-0.3911828697,0.1748312116,-0.0276965648,-0.1048231795,0.273758918,0.2530734837,0.0135616856,0.5135731697,0.0879016817,0.0418716595,0.1194233671,-0.261066705,-0.0293091163,-0.2766619623,-0.142339021,-0.2583797574,0.2075479627,-0.2546713054,-0.1088880748,-0.2646128833,-0.0210305918,-0.3601520658,0.1243790835,-0.3825925887,-0.0819281489,-0.161075145,0.254398793,-0.1200082675,0.1283909976,-0.0101331845,0.1112431213,-0.0355769284,0.2304631025,-0.2162801474,-0.2744900584,-0.5513046384,-0.152410239,0.5267803669,0.1114875078,0.0004289396,-0.0261537973,0.0205525216,-0.2660392225,-0.1641802639,0.4210189879,-0.0297951121,-0.0914885849,-0.085352622,0.1795302331,-0.1181223467,-0.3170990348,0.0051146424,-0.3451727033,-0.1308822036,0.0749335438,0.0684557408,-0.1905052811,0.06678655,-0.0430342034,-0.3454267383,-0.0301899146,-0.2688142955,-0.1228383631,0.1198506206,-0.5135689974,-0.0029473407,0.1931803226,-0.3985514343,-0.1948124766,-0.2629196644,0.0681876764,0.1352652162,-0.0106285997,-0.24457784,0.4565593898,0.0086395303,0.190732792,-0.0298137143,-0.6047429442,0.340696305,0.1188653782,-0.0786786899,-0.2802816331,0.0917687714,0.3030137122,-0.149182871,0.1661013663,0.034210708,-0.0800376087,-0.2589765191,-0.3078549802,0.3895877302,-0.0943142399,0.4309289753,-0.0003129902,0.5969522595,-0.0308421291,0.2419363856,0.3254552186,-0.0590361729,-0.025696395,-0.028413089,-0.082196109,0.3089119792,0.1155977473,-0.244757786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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/321","title":"ERROR:root:mwparserfromhell","comments":"thanks for your answer.\r\nHow can I know which file is causing the issue ? \r\nI am trying to load the spanish wikipedia data. ","body":"Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n","comment_length":24,"text":"ERROR:root:mwparserfromhell \n Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n \n thanks for your answer.\r\nHow can I know which file is causing the issue ? \r\nI am trying to load the spanish wikipedia data. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/321","title":"ERROR:root:mwparserfromhell","comments":"Because of the way Apache Beam works we indeed don't have access to the file name at this point in the code.\r\nWe'll have to use some tricks I think :p \r\n\r\nYou can append `filepath` to `title` in `wikipedia.py:L512` for example. [[EDIT: it's L494 my bad]]\r\nThen just do `try:...except:` on the  call of `_parse_and_clean_wikicode` L500 I guess.\r\n\r\nThanks for diving into this ! I tried it myself but I run out of memory on my laptop\r\nAs soon as we have the name of the file it should be easier to find what's wrong.","body":"Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n","comment_length":95,"text":"ERROR:root:mwparserfromhell \n Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n \n Because of the way Apache Beam works we indeed don't have access to the file name at this point in the code.\r\nWe'll have to use some tricks I think :p \r\n\r\nYou can append `filepath` to `title` in `wikipedia.py:L512` for example. [[EDIT: it's L494 my bad]]\r\nThen just do `try:...except:` on the  call of `_parse_and_clean_wikicode` L500 I guess.\r\n\r\nThanks for diving into this ! I tried it myself but I run out of memory on my laptop\r\nAs soon as we have the name of the file it should be easier to find what's wrong.","embeddings":[-0.1209630445,-0.070509471,0.0564221703,0.3891802132,0.1034142226,0.0170317814,-0.1214456111,0.3558146954,0.2514157593,0.264095217,0.2141057104,-0.0685191453,0.0406434871,-0.4498924315,-0.0869592652,-0.0778382346,0.3508561254,0.123478502,0.0555284396,-0.1732604504,-0.1695192754,0.1376484931,-0.281078428,0.4107494354,-0.1864092797,0.1112359762,0.0803811401,-0.0238146037,-0.0770189911,-0.3404312134,-0.1463585049,-0.168971613,0.2026515007,0.3334973752,-0.0001258567,-0.1855412722,0.5518958569,-0.1446356773,-0.2137812823,-0.1335806847,0.0046767704,-0.2667896748,-0.059188243,-0.3690649867,0.2201614976,-0.0541450605,0.3302631676,-0.2550405562,0.3609259725,0.2255510688,0.0779779851,0.1390962452,0.2775888741,0.0408106744,0.654358089,0.0034429049,0.1290146261,-0.224219203,-0.0136564439,-0.1525859684,-0.0030372231,0.2453565449,-0.2965688109,-0.199443087,-0.0675252303,-0.1138773635,0.2302039415,-0.2996582389,0.3847329617,0.1337920725,0.5107129216,-0.1019761339,0.1809576601,0.0640801936,-0.1153809279,0.2564850152,0.3498593569,0.4780451655,-0.4989578426,0.060281802,0.4844412208,-0.1789774746,-0.1038604304,0.298196286,-0.0074597811,0.7731332779,0.1381369978,0.3352176845,0.0184042919,-0.0671589151,-0.1388245672,-0.2876846492,-0.0027429822,0.3892367482,-0.0984645411,0.1176916435,-0.1336878538,-0.0798086748,0.0004215051,-0.4752697349,-0.3524791896,0.092343621,0.0655017346,0.257134527,0.3952152133,-0.088089034,0.2560520172,-0.1058404669,0.0649590343,-0.0035789935,0.031890519,0.0092387004,-0.1560273468,-0.1251688749,-0.5585864186,0.1572338194,0.1669795066,-0.0611287653,0.0867830813,0.2184611112,-0.198992908,-0.0944182649,-0.1439749002,0.5513176918,-0.0361210816,0.5017178655,0.1081734598,-0.0195120219,-0.2092121243,-0.6590039134,0.072628431,0.2387360185,-0.3837993145,0.0940520912,-0.146284461,0.2706366777,0.0817148611,0.1626767963,-0.1101477593,-0.4127649665,-0.0177707318,-0.1296966374,-0.1106903851,0.0623148791,0.4266298115,0.5009470582,0.2024616897,-0.4213831127,0.0324726477,0.3185638785,-0.4508625567,-0.1133395955,-0.3865263164,0.0219051,-0.0549518429,0.2963490486,-0.511759758,-0.0257374663,-0.0611916408,-0.1805220246,0.0579591729,-0.0974291936,-0.0502929054,-0.1684542298,0.1400205493,0.3438383639,-0.0868770704,0.0030445298,-0.1516425312,-0.1271295696,0.2069042474,0.0125187179,-0.0878881142,0.5803267956,-0.4632230401,0.5278597474,0.4331421256,-0.4172807932,-0.044425793,-0.0509065613,-0.0690591112,-0.1800686568,0.1532539129,-0.24177441,0.2028295994,-0.1651450992,-0.4169232547,0.1614945978,0.0947089121,0.1545333415,-0.2393751293,-0.2976189554,0.1369993687,0.0699921399,0.1159614846,-0.0554039627,0.2703327239,0.5132507086,0.5763164759,-0.2047070414,0.2086592317,0.1209062189,0.0107676694,0.177630797,-0.0026646431,0.1111140475,0.1003481075,-0.0080743851,-0.1155226752,0.19045192,-0.2601393759,-0.0636972785,-0.2423490286,0.1893315464,-0.2007885426,-0.0428602658,0.077681005,0.2335370481,-0.2802915871,0.3116144836,0.0665516779,-0.2075055391,-0.2028139532,0.1360837817,-0.0770246759,0.1176707223,-0.1301356256,-0.0456713773,-0.328032732,-0.1180562451,0.5273607373,0.0116142714,-0.1616954803,0.1149473786,0.2832993865,0.0874383971,-0.1325665861,-0.1691450626,-0.0989626125,-0.3816641569,0.1865923554,0.2674351633,0.2412260473,-0.1106106043,0.1445416361,0.1156604663,0.3446531594,0.1922703385,-0.0792750344,0.1298828423,0.1943041384,0.0106886914,0.2934870124,-0.0030797762,0.3046086431,0.499006778,-0.0706461146,-0.3424893916,0.32921803,-0.2922590375,0.4965381026,-0.113088049,0.1492168903,0.1261763275,-0.3448246121,0.0666225404,0.3503163457,-0.4112823904,-0.0033078764,0.0153660644,-0.1353278905,0.1966359615,0.3104834259,0.0533887148,0.3721376657,0.2042023391,0.0186498351,0.0301543158,-0.1795443147,-0.0855605751,-0.1612430811,-0.1782986075,-0.2557564378,0.1765659302,-0.2424640656,-0.0852350891,-0.1452090293,-0.0867879242,-0.4174173176,0.2448441684,-0.4018446505,-0.2548186779,-0.078374058,0.2720758915,-0.2720501721,0.1508205831,0.074082464,0.0892747641,0.0460765511,0.1715887934,-0.3309583068,-0.3067087829,-0.4702958167,-0.1624400914,0.5332528353,0.1399260014,0.0548889786,0.0383490734,-0.0015598963,-0.3136587739,-0.1001237929,0.3807926476,0.0008735982,-0.0985232592,-0.1401782483,0.2973882556,0.0664051101,-0.3065908253,0.0340856016,-0.3090626597,-0.0541217215,0.157516554,0.2250111103,-0.0126016084,0.1113775671,-0.0902564302,-0.3435205519,-0.1660826653,-0.1793526858,-0.0840207487,0.1074519679,-0.3885612786,-0.0375832282,0.167233333,-0.2739905119,-0.126218304,-0.1190674007,0.1150188521,0.1819080263,-0.0487881042,-0.2680825591,0.3679413497,-0.0389905758,0.069161512,0.02138846,-0.3876900673,0.2129806727,0.2526650131,-0.0327753536,-0.2018021047,0.1148760095,0.3068694174,-0.097650148,0.1494807303,-0.0055844239,0.0456261002,-0.2713431716,-0.3473313451,0.5005397201,0.0510833226,0.2984991968,0.139131248,0.7935022116,-0.0914077759,0.4170954823,0.3806064725,-0.050907854,-0.0616207384,-0.0068951314,-0.0638709962,0.2387561351,0.0170538314,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/321","title":"ERROR:root:mwparserfromhell","comments":"Thanks for your help.\r\n\r\nI tried to print the \"title\" of the document inside the` except (mwparserfromhell.parser.ParserError) as e`,the title displayed was : \"Campeonato Mundial de futsal de la AMF 2015\". (Wikipedia ES) Is it what you were looking for ?","body":"Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n","comment_length":41,"text":"ERROR:root:mwparserfromhell \n Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n \n Thanks for your help.\r\n\r\nI tried to print the \"title\" of the document inside the` except (mwparserfromhell.parser.ParserError) as e`,the title displayed was : \"Campeonato Mundial de futsal de la AMF 2015\". (Wikipedia ES) Is it what you were looking for ?","embeddings":[-0.0982404053,-0.1460119784,0.0579146668,0.4570070207,0.0399687998,0.0833492652,-0.2478527278,0.1913093477,0.3329879642,0.2941777706,0.2543114126,0.1080052555,0.0952981412,-0.3402068019,-0.1696409434,-0.0679538921,0.3555515409,0.1214177683,0.1624230444,-0.2468806505,-0.0501843765,0.1787006557,-0.3427317142,0.382601887,-0.1634172499,0.1669341028,0.1231548637,-0.1326595694,-0.1403289288,-0.2993070185,-0.0200498365,-0.2091211826,0.1560906768,0.3652326763,-0.0001279058,-0.3193122149,0.4868043661,-0.0847794339,-0.1486693174,-0.2189909369,0.0152916275,-0.1522385776,-0.0410055257,-0.4044004083,0.3248384893,0.1153984368,0.3341578841,-0.2809922099,0.1205001771,0.2197394818,0.0640458316,0.1066202298,0.3839101195,0.0759176463,0.480653286,0.0734132007,0.2054694891,-0.060079664,-0.016876379,-0.0532576554,0.149125278,0.2014700323,-0.3659819067,-0.1884918511,-0.163210243,0.0662546456,0.2595542669,-0.4452034533,0.4108956754,0.1394709051,0.61737746,-0.1191039309,0.2614586651,0.1654366851,-0.0580870099,0.2580067515,0.3013699353,0.5481126308,-0.3672949374,-0.0253248662,0.3877830803,-0.1134894118,-0.1369290501,0.3051530719,-0.2432048023,0.9164056182,0.1252373457,0.3172872663,0.035912808,0.0480789393,-0.0662555993,-0.251314193,-0.0493606776,0.334076643,0.0214808527,-0.0258580539,-0.1511856019,-0.0784204379,0.001178946,-0.625526607,-0.4130973518,0.0299955718,0.1209483594,0.3402904868,0.454151839,-0.2407009155,0.2941499054,-0.0054475013,-0.0771103427,-0.1497201025,0.0046592373,-0.0258476324,-0.1439839154,0.0026517075,-0.4691490531,-0.0029376033,0.2461206168,-0.2048180699,0.0865612179,0.3560785353,-0.2949361503,-0.1116065904,-0.2578809857,0.4341236651,-0.0717999339,0.3773729801,0.0606103987,-0.0367316529,-0.1395096928,-0.5028152466,-0.0180455018,0.2353696376,-0.3238372505,0.0698220283,-0.2390310317,0.226461634,0.1722396165,0.1966182142,-0.0110010495,-0.5448348522,-0.0428097472,-0.1836660951,-0.071903713,0.0472855903,0.406760335,0.5007110238,0.2680571079,-0.40572837,-0.0525238104,0.3967243731,-0.3704168499,-0.0222618766,-0.5510870814,0.0479742251,0.0428075977,0.1816760898,-0.5636150837,0.0236339998,-0.0637113526,-0.1677027494,0.2126279175,-0.0518712103,-0.1455517709,-0.0310135093,0.1124990955,0.2195572853,-0.1851063818,0.0615859032,-0.1012386531,-0.1575168818,0.2370682061,-0.0327689201,-0.0864664093,0.636880517,-0.3505786955,0.5782259107,0.2652637959,-0.4091205001,-0.0882848203,0.0071630459,-0.0275748428,-0.222207427,0.0340306722,-0.1393164545,0.1705590934,0.0085355034,-0.4780333042,-0.0440130718,0.0305705108,0.18126522,-0.1381178051,-0.2846617699,0.2403111309,0.0836685821,0.2543239892,-0.0825863183,0.1528435946,0.4838692844,0.6022037268,-0.1535146981,0.145203799,0.0552840605,0.0634704307,0.2141290158,-0.0845406055,0.0701060891,-0.0034425391,-0.0194442309,0.0826343819,0.1979782879,-0.146543026,-0.1380741596,-0.1379958242,0.2703558207,-0.1237415373,0.0237798672,0.0232579205,0.2133961022,-0.198654905,0.3907471597,0.0831923932,-0.2709238529,-0.2775025666,0.0981848761,-0.0184138268,0.1946448684,-0.1272756755,-0.0370278805,-0.3745705187,-0.1918487698,0.4063358903,0.1215744838,-0.1520330608,0.023461353,0.2480846196,0.12266922,-0.2635534406,-0.2410593182,-0.1296415031,-0.4255687892,0.1146242395,0.2754119933,0.2598394454,-0.1263753623,0.1595700383,0.0801101103,0.2849260271,0.0874281824,-0.1943712533,0.063453652,0.1280266345,-0.0008983696,0.382299006,-0.1133902892,0.2536658645,0.4015442729,-0.1322078109,-0.3764730692,0.1788337529,-0.2094368637,0.6232566833,-0.0297614131,0.1618248075,0.1505903751,-0.2678955793,0.0095711956,0.3111509681,-0.5253850818,0.1216418967,-0.0011219424,-0.1208436713,0.3589930236,0.3209595084,0.0280894954,0.4389223456,0.2746983767,-0.0538246557,0.0729867071,-0.2088221163,-0.0472809002,-0.2550527751,-0.0936595425,-0.249587521,0.1308402568,-0.2461634278,-0.0539874807,-0.1664287746,0.0453462154,-0.3202031255,0.044147931,-0.4074264467,-0.1982375681,-0.1249895766,0.4425379336,-0.1859439313,0.1449581236,0.0070478665,0.040186733,-0.0461187288,0.3184624314,-0.2622790933,-0.3390574753,-0.4685198069,-0.161923483,0.4164783061,0.1619075239,0.1300805956,-0.0816562623,0.0647757575,-0.3107574582,-0.1337570995,0.4158418477,-0.1041708291,-0.0374839753,-0.1843907237,0.1631098241,-0.2027106285,-0.3040036857,0.0082991933,-0.335069567,-0.0869709253,0.0554570556,0.2022849619,-0.1650488228,0.1563441306,-0.0912574306,-0.3194941878,-0.0305149443,-0.2652244866,-0.1757873148,0.1309884936,-0.5440905094,-0.1037883535,0.2221551985,-0.1957859546,-0.1582685262,-0.2416283339,0.0511857495,0.1386105865,-0.0271180645,-0.1973048896,0.4221188426,0.0898568109,0.1270066351,-0.1342784762,-0.531072557,0.3187642992,0.2872523665,-0.2071967572,-0.2658961117,0.0953802317,0.3380016387,-0.1261989325,0.1300807148,-0.0205747448,-0.0200337227,-0.3060702384,-0.2612015307,0.3659541011,-0.0696886629,0.3568731248,0.0052714818,0.5611320734,0.0077976589,0.3498138785,0.299805969,-0.0559647977,-0.1249294654,0.0478359722,-0.1055808663,0.2657906711,0.12482737,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/321","title":"ERROR:root:mwparserfromhell","comments":"Thanks a lot @Shiro-LK !\r\n\r\nI was able to reproduce the issue. It comes from [this table on wikipedia](https:\/\/es.wikipedia.org\/wiki\/Campeonato_Mundial_de_futsal_de_la_AMF_2015#Clasificados) that can't be parsed.\r\n\r\nThe file in which the problem occurs comes from the wikipedia dumps, and it can be downloaded [here](https:\/\/dumps.wikimedia.org\/eswiki\/20200501\/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2)\r\n\r\nParsing the file this way raises the parsing issue:\r\n\r\n```python\r\nimport mwparserfromhell as parser\r\nfrom tqdm.auto import tqdm\r\nimport bz2\r\nimport six\r\nimport logging\r\nimport codecs\r\nimport xml.etree.cElementTree as etree\r\n\r\nfilepath = \"path\/to\/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2\"\r\n\r\ndef _extract_content(filepath):\r\n    \"\"\"Extracts article content from a single WikiMedia XML file.\"\"\"\r\n    logging.info(\"generating examples from = %s\", filepath)\r\n    with open(filepath, \"rb\") as f:\r\n        f = bz2.BZ2File(filename=f)\r\n        if six.PY3:\r\n            # Workaround due to:\r\n            # https:\/\/github.com\/tensorflow\/tensorflow\/issues\/33563\r\n            utf_f = codecs.getreader(\"utf-8\")(f)\r\n        else:\r\n            utf_f = f\r\n        # To clear root, to free-up more memory than just `elem.clear()`.\r\n        context = etree.iterparse(utf_f, events=(\"end\",))\r\n        context = iter(context)\r\n        unused_event, root = next(context)\r\n        for unused_event, elem in tqdm(context, total=949087):\r\n            if not elem.tag.endswith(\"page\"):\r\n                continue\r\n            namespace = elem.tag[:-4]\r\n            title = elem.find(\".\/{0}title\".format(namespace)).text\r\n            ns = elem.find(\".\/{0}ns\".format(namespace)).text\r\n            id_ = elem.find(\".\/{0}id\".format(namespace)).text\r\n            # Filter pages that are not in the \"main\" namespace.\r\n            if ns != \"0\":\r\n                root.clear()\r\n                continue\r\n            raw_content = elem.find(\".\/{0}revision\/{0}text\".format(namespace)).text\r\n            root.clear()\r\n\r\n            if \"Campeonato Mundial de futsal de la AMF 2015\" in title:\r\n                yield (id_, title, raw_content)\r\n\r\nfor id_, title, raw_content in _extract_content(filepath):\r\n    wikicode = parser.parse(raw_content)\r\n```\r\n\r\nThe copied the raw content that can't be parsed [here](https:\/\/pastebin.com\/raw\/ZbmevLyH).\r\n\r\nThe minimal code to reproduce is:\r\n```python\r\nimport mwparserfromhell as parser\r\nimport requests\r\n\r\nraw_content = requests.get(\"https:\/\/pastebin.com\/raw\/ZbmevLyH\").content.decode(\"utf-8\")\r\nwikicode = parser.parse(raw_content)\r\n\r\n```\r\n\r\nI will create an issue on mwparserfromhell's repo to see if we can fix that\r\n","body":"Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n","comment_length":248,"text":"ERROR:root:mwparserfromhell \n Hi,\r\n\r\nI am trying to download some wikipedia data but I got this error for spanish \"es\" (but there are maybe some others languages which have the same error I haven't tried all of them ).\r\n\r\n`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`\r\n\r\nThe code I have use was : \r\n`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`\r\n\r\n \n Thanks a lot @Shiro-LK !\r\n\r\nI was able to reproduce the issue. It comes from [this table on wikipedia](https:\/\/es.wikipedia.org\/wiki\/Campeonato_Mundial_de_futsal_de_la_AMF_2015#Clasificados) that can't be parsed.\r\n\r\nThe file in which the problem occurs comes from the wikipedia dumps, and it can be downloaded [here](https:\/\/dumps.wikimedia.org\/eswiki\/20200501\/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2)\r\n\r\nParsing the file this way raises the parsing issue:\r\n\r\n```python\r\nimport mwparserfromhell as parser\r\nfrom tqdm.auto import tqdm\r\nimport bz2\r\nimport six\r\nimport logging\r\nimport codecs\r\nimport xml.etree.cElementTree as etree\r\n\r\nfilepath = \"path\/to\/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2\"\r\n\r\ndef _extract_content(filepath):\r\n    \"\"\"Extracts article content from a single WikiMedia XML file.\"\"\"\r\n    logging.info(\"generating examples from = %s\", filepath)\r\n    with open(filepath, \"rb\") as f:\r\n        f = bz2.BZ2File(filename=f)\r\n        if six.PY3:\r\n            # Workaround due to:\r\n            # https:\/\/github.com\/tensorflow\/tensorflow\/issues\/33563\r\n            utf_f = codecs.getreader(\"utf-8\")(f)\r\n        else:\r\n            utf_f = f\r\n        # To clear root, to free-up more memory than just `elem.clear()`.\r\n        context = etree.iterparse(utf_f, events=(\"end\",))\r\n        context = iter(context)\r\n        unused_event, root = next(context)\r\n        for unused_event, elem in tqdm(context, total=949087):\r\n            if not elem.tag.endswith(\"page\"):\r\n                continue\r\n            namespace = elem.tag[:-4]\r\n            title = elem.find(\".\/{0}title\".format(namespace)).text\r\n            ns = elem.find(\".\/{0}ns\".format(namespace)).text\r\n            id_ = elem.find(\".\/{0}id\".format(namespace)).text\r\n            # Filter pages that are not in the \"main\" namespace.\r\n            if ns != \"0\":\r\n                root.clear()\r\n                continue\r\n            raw_content = elem.find(\".\/{0}revision\/{0}text\".format(namespace)).text\r\n            root.clear()\r\n\r\n            if \"Campeonato Mundial de futsal de la AMF 2015\" in title:\r\n                yield (id_, title, raw_content)\r\n\r\nfor id_, title, raw_content in _extract_content(filepath):\r\n    wikicode = parser.parse(raw_content)\r\n```\r\n\r\nThe copied the raw content that can't be parsed [here](https:\/\/pastebin.com\/raw\/ZbmevLyH).\r\n\r\nThe minimal code to reproduce is:\r\n```python\r\nimport mwparserfromhell as parser\r\nimport requests\r\n\r\nraw_content = requests.get(\"https:\/\/pastebin.com\/raw\/ZbmevLyH\").content.decode(\"utf-8\")\r\nwikicode = parser.parse(raw_content)\r\n\r\n```\r\n\r\nI will create an issue on mwparserfromhell's repo to see if we can fix that\r\n","embeddings":[-0.1087391153,-0.2687698901,0.0588545799,0.3744995594,0.0680316836,0.0585111044,-0.0978485569,0.407232821,0.2762867808,0.2615219653,0.1648638099,0.1633142382,0.0697004646,-0.4300828576,-0.1707364321,-0.042684406,0.2212866992,0.118236579,0.059008494,-0.1014077961,-0.0859467611,0.1587132514,-0.3476288617,0.2824134529,-0.32690382,0.1148107052,0.1520309299,-0.065615125,-0.0889430493,-0.2003451288,-0.0028276851,-0.2012956291,0.2379267067,0.4786588848,-0.0001259802,-0.0412694849,0.3049947321,-0.093657434,0.0099669043,-0.2359312773,0.0336602069,-0.1631217003,-0.1159503236,-0.3689011633,0.2481287122,0.2071848214,0.1943381876,-0.1866433322,0.4095414579,0.2438102067,0.0544666685,0.163917467,0.2602112889,0.0912849233,0.514739275,-0.053879641,-0.0065108077,0.0079004159,0.0930049717,-0.1025947332,0.1022947431,0.2438964993,-0.420661658,-0.0717122629,-0.1377499551,0.1038781703,0.4466082454,-0.5326789618,0.4791566432,0.3168759346,0.3135971725,-0.022695519,0.1983242035,0.103534475,-0.1376882643,0.1046121716,0.3115144372,0.5089524984,-0.48955971,0.0953266397,0.4412794709,-0.0603463277,-0.2098179162,0.2732831836,-0.3379980326,0.8441174626,0.0538513251,0.1933108121,-0.0251846313,-0.0369251259,0.0193275176,-0.220910877,0.1413909644,0.378723979,-0.0517863333,-0.0091330903,-0.0343199112,-0.3973082006,-0.021723425,-0.6584088206,-0.3500780165,0.1816475093,0.0500032641,0.2907331586,0.3454244137,-0.337862879,0.1525789052,0.0352604389,0.0321704298,0.0292413831,-0.1271683574,0.0367852971,-0.2031404376,-0.0674105212,-0.5065873861,0.0622966178,0.2397127151,-0.0703944489,-0.0532746203,0.3891048431,-0.2007398307,0.0207773875,-0.1578020155,0.5316457748,-0.0691650882,0.4129965007,0.1543025076,0.0851949006,-0.1468566507,-0.5985732079,0.052663479,0.1228784323,-0.039723698,0.0134536037,-0.1145052537,0.1165548936,0.2161987126,0.2504267395,-0.0326060876,-0.5398552418,-0.0503432825,-0.3519892693,0.0567449853,0.1298061758,0.4478539824,0.34950158,0.215083316,-0.4113384783,-0.0219976194,0.4195792675,-0.4311141372,-0.1687888354,-0.4756811261,0.0521946289,0.0355714373,0.1392172575,-0.5501323938,0.1158676669,0.0992074832,-0.1055626422,-0.0358388014,-0.1966685802,-0.2580610216,-0.0632867962,0.1914139837,0.1181722507,-0.1422764808,0.126726836,-0.0599625632,0.0571596734,0.4125909805,0.1793214381,-0.163065955,0.7019707561,-0.2670831084,0.7228682041,0.1745021939,-0.2736831605,-0.0719350725,0.0337947607,0.0428299047,0.007124987,-0.1001690924,-0.2217167169,0.3694820106,-0.0816345289,-0.1842643321,0.1109004542,-0.003875616,0.2598793209,-0.2199014872,-0.2137375325,0.338465929,0.0155823752,0.2996574342,-0.2194487154,0.0218850672,0.4970418215,0.5048382282,-0.2736796141,0.1297584027,0.0826887786,0.1501490623,0.1439250559,-0.1093944609,-0.1013655439,-0.0906945467,0.0045490987,0.0658480749,0.3201473057,-0.2615011036,-0.158896327,-0.1252180934,0.2352294326,-0.2003859729,0.0534398891,0.0882508159,-0.0568753891,-0.292809099,0.3478443325,-0.0486512706,-0.4844489992,-0.3517448008,0.1733437777,0.1646933109,0.2952848077,-0.2190816253,-0.079315573,-0.3375533223,-0.0990324244,0.4453221858,-0.0610528551,-0.0529888757,-0.056188643,0.283511728,0.1120934263,-0.3946905434,-0.142821297,0.0544433855,-0.2703911364,0.0110277655,0.2399187833,0.2501266003,-0.1488434821,0.2306790799,0.2286777347,0.3325100541,0.0825077891,-0.1761720181,0.1144729629,0.2624149621,0.0093547711,0.3972715735,-0.1282763928,0.1555531919,0.4382648468,-0.5117701888,-0.3361923993,0.0879169554,-0.0545391068,0.5132347941,0.05086549,0.2247790992,0.0900133327,-0.1870054007,-0.0401293896,0.2298818529,-0.3210944533,0.1083917394,-0.1220101118,0.005767398,0.3593573868,0.1124442369,0.0358499289,0.5103645921,0.2113904655,0.0721914172,0.166925624,-0.1320455372,0.098707594,-0.1885120571,-0.035234198,-0.0812533349,0.2075201869,-0.3698166311,0.0277478304,-0.3147502542,0.0786038786,-0.1918708235,-0.0323620066,-0.3220308721,-0.2089153081,-0.0629463568,0.4096322358,-0.2718832791,0.0419884175,-0.0064639235,-0.1180246696,0.0215868615,0.2971606851,-0.2370031625,-0.3015561998,-0.3740295768,-0.1583939344,0.5779916644,0.0535107106,-0.0512510836,-0.0516674444,-0.2161799371,-0.35149014,-0.397675842,0.4286088943,-0.1134408191,0.0060054329,-0.1002593935,0.3180465102,-0.2111659199,-0.4990894496,0.1148440614,-0.2589308321,-0.0832984224,0.1671217531,0.0792736709,-0.2307243794,0.0519303642,-0.1746178865,-0.1400774121,-0.0965811983,-0.2825163305,-0.1297852546,0.1823790222,-0.3487784863,-0.0708561689,0.1796253771,-0.334259212,-0.1362028867,-0.2476677299,0.01838989,0.2872105837,-0.1329412907,-0.3237062097,0.4956243038,0.0269243158,0.3418346941,0.0566490144,-0.403057158,0.2267920822,0.2314048707,0.0250053797,-0.1748469323,0.0461068973,0.2127269655,-0.0847881883,0.1835100949,0.050592225,0.097263217,-0.3360319138,-0.1924429536,0.2831331491,-0.0903017372,0.5138000846,0.259572655,0.6778399348,-0.1312732249,0.2277941257,0.197369799,-0.1405770481,-0.1899258941,0.0949459448,-0.0761387199,0.1143446267,-0.1003623158,-0.1714862138,0.58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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/320","title":"Blog Authorship Corpus, Non Matching Splits Sizes Error, nlp viewer","comments":"This dataset seems to have a decoding problem that results in inconsistencies in the number of generated examples.\r\nSee #215.\r\nThat's why we end up with a `NonMatchingSplitsSizesError `.","body":"Selecting `blog_authorship_corpus` in the nlp viewer throws the following error: \r\n\r\n```\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 172, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 132, in get\r\n    builder_instance.download_and_prepare()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\n```\r\n@srush @lhoestq ","comment_length":29,"text":"Blog Authorship Corpus, Non Matching Splits Sizes Error, nlp viewer \n Selecting `blog_authorship_corpus` in the nlp viewer throws the following error: \r\n\r\n```\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]\r\nTraceback:\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 172, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/streamlit\/lib\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp-viewer\/run.py\", line 132, in get\r\n    builder_instance.download_and_prepare()\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 432, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 488, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\nFile \"\/home\/sasha\/.local\/share\/virtualenvs\/lib-ogGKnCK_\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\n```\r\n@srush @lhoestq  \n This dataset seems to have a decoding problem that results in inconsistencies in the number of generated examples.\r\nSee #215.\r\nThat's why we end up with a `NonMatchingSplitsSizesError 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/319","title":"Nested sequences with dicts","comments":"Oh yes, this is a backward compatibility feature with tensorflow_dataset in which a `Sequence` or `dict` is converted in a `dict` of `lists`, unfortunately it is not very intuitive, see here: https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/features.py#L409\r\n\r\nTo avoid this behavior, you can just define the list in the feature with a simple list or a tuple (which is also simpler to write).\r\nIn your case, the features could be as follow:\r\n``` python\r\n...\r\nfeatures=nlp.Features({\r\n    \"title\": nlp.Value(\"string\"),\r\n    \"vertexSet\": [[{\r\n        \"name\": nlp.Value(\"string\"),\r\n        \"sent_id\": nlp.Value(\"int32\"),\r\n        \"pos\": nlp.features.Sequence(nlp.Value(\"int32\")),\r\n        \"type\": nlp.Value(\"string\"),\r\n    }]],\r\n    ...\r\n    }),\r\n...\r\n```","body":"Am pretty much finished [adding a dataset](https:\/\/github.com\/ghomasHudson\/nlp\/blob\/DocRED\/datasets\/docred\/docred.py) for [DocRED](https:\/\/github.com\/thunlp\/DocRED), but am getting an error when trying to add a nested `nlp.features.sequence(nlp.features.sequence({key:value,...}))`. \r\n\r\nThe original data is in this format:\r\n```python\r\n{\r\n  'title': \"Title of wiki page\",\r\n  'vertexSet': [\r\n                  [\r\n                    { 'name': \"mention_name\", \r\n                      'sent_id': \"mention in which sentence\", \r\n                      'pos': [\"postion of mention in a sentence\"], \r\n                      'type': \"NER_type\"},\r\n                    {another mention}\r\n                  ], \r\n                  [another entity]\r\n                ]\r\n    ...\r\n}\r\n```\r\nSo to represent this I've attempted to write:\r\n```\r\n...\r\nfeatures=nlp.Features({\r\n    \"title\": nlp.Value(\"string\"),\r\n    \"vertexSet\": nlp.features.Sequence(nlp.features.Sequence({\r\n        \"name\": nlp.Value(\"string\"),\r\n        \"sent_id\": nlp.Value(\"int32\"),\r\n        \"pos\": nlp.features.Sequence(nlp.Value(\"int32\")),\r\n        \"type\": nlp.Value(\"string\"),\r\n    })),\r\n    ...\r\n    }),\r\n...\r\n```\r\nThis is giving me the error:\r\n```\r\npyarrow.lib.ArrowTypeError: Could not convert [{'pos': [[0,2], [2,4], [3,5]], \"type\": [\"ORG\", \"ORG\", \"ORG\"], \"name\": [\"Lark Force\", \"Lark Force\", \"Lark Force\", \"sent_id\": [0, 3, 4]}..... with type list: was not a dict, tuple, or recognized null value for conversion to struct type\r\n```\r\nDo we expect the pyarrow stuff to break when doing this deeper nesting? I've checked that it still works when you do `nlp.features.Sequence(nlp.features.Sequence(nlp.Value(\"string\"))` or `nlp.features.Sequence({key:value,...})` just not nested sequences with a dict.\r\n\r\nIf it's not possible, I can always convert it to a shallower structure. I'd rather not change the DocRED authors' structure if I don't have to though.","comment_length":88,"text":"Nested sequences with dicts \n Am pretty much finished [adding a dataset](https:\/\/github.com\/ghomasHudson\/nlp\/blob\/DocRED\/datasets\/docred\/docred.py) for [DocRED](https:\/\/github.com\/thunlp\/DocRED), but am getting an error when trying to add a nested `nlp.features.sequence(nlp.features.sequence({key:value,...}))`. \r\n\r\nThe original data is in this format:\r\n```python\r\n{\r\n  'title': \"Title of wiki page\",\r\n  'vertexSet': [\r\n                  [\r\n                    { 'name': \"mention_name\", \r\n                      'sent_id': \"mention in which sentence\", \r\n                      'pos': [\"postion of mention in a sentence\"], \r\n                      'type': \"NER_type\"},\r\n                    {another mention}\r\n                  ], \r\n                  [another entity]\r\n                ]\r\n    ...\r\n}\r\n```\r\nSo to represent this I've attempted to write:\r\n```\r\n...\r\nfeatures=nlp.Features({\r\n    \"title\": nlp.Value(\"string\"),\r\n    \"vertexSet\": nlp.features.Sequence(nlp.features.Sequence({\r\n        \"name\": nlp.Value(\"string\"),\r\n        \"sent_id\": nlp.Value(\"int32\"),\r\n        \"pos\": nlp.features.Sequence(nlp.Value(\"int32\")),\r\n        \"type\": nlp.Value(\"string\"),\r\n    })),\r\n    ...\r\n    }),\r\n...\r\n```\r\nThis is giving me the error:\r\n```\r\npyarrow.lib.ArrowTypeError: Could not convert [{'pos': [[0,2], [2,4], [3,5]], \"type\": [\"ORG\", \"ORG\", \"ORG\"], \"name\": [\"Lark Force\", \"Lark Force\", \"Lark Force\", \"sent_id\": [0, 3, 4]}..... with type list: was not a dict, tuple, or recognized null value for conversion to struct type\r\n```\r\nDo we expect the pyarrow stuff to break when doing this deeper nesting? I've checked that it still works when you do `nlp.features.Sequence(nlp.features.Sequence(nlp.Value(\"string\"))` or `nlp.features.Sequence({key:value,...})` just not nested sequences with a dict.\r\n\r\nIf it's not possible, I can always convert it to a shallower structure. I'd rather not change the DocRED authors' structure if I don't have to though. \n Oh yes, this is a backward compatibility feature with tensorflow_dataset in which a `Sequence` or `dict` is converted in a `dict` of `lists`, unfortunately it is not very intuitive, see here: https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/features.py#L409\r\n\r\nTo avoid this behavior, you can just define the list in the feature with a simple list or a tuple (which is also simpler to write).\r\nIn your case, the features could be as follow:\r\n``` python\r\n...\r\nfeatures=nlp.Features({\r\n    \"title\": nlp.Value(\"string\"),\r\n    \"vertexSet\": [[{\r\n        \"name\": nlp.Value(\"string\"),\r\n        \"sent_id\": nlp.Value(\"int32\"),\r\n        \"pos\": nlp.features.Sequence(nlp.Value(\"int32\")),\r\n        \"type\": nlp.Value(\"string\"),\r\n    }]],\r\n    ...\r\n    }),\r\n...\r\n```","embeddings":[0.1117630079,0.0667421669,-0.0714067966,-0.0210161116,-0.1091683283,-0.0410184972,0.198786512,0.1697288007,-0.0871766582,-0.4004120827,0.2879687548,0.3965295851,-0.0898342356,0.203804642,-0.0607240349,-0.2475784123,0.3430915177,0.1289698482,0.3750735223,0.2590895891,-0.2045025676,0.3637642562,-0.2815240622,0.1494687051,-0.2481655627,-0.3701804876,-0.3637534678,0.1604201645,-0.0595387518,-0.7306870818,0.327549696,0.2191215903,0.1450784504,0.1276401281,-0.0001186873,-0.0521878116,0.5560513139,-0.0750035197,-0.1546786278,-0.0694720596,0.007484843,-0.307399869,0.296666503,-0.2497453988,0.0215846244,-0.4088245034,-0.0574822165,-0.0613234304,0.3271240592,0.2150971889,0.1544761658,0.0688155666,0.3399304748,0.0271114036,0.4079800844,0.3201031387,-0.1407759339,0.1663142145,0.4056244791,-0.3216360211,0.0209346395,-0.097893022,-0.1353191733,-0.2777380347,0.3606657982,0.2268982381,0.0491360761,-0.1841319948,-0.5392251015,-0.0223106332,0.1228881702,-0.2456724495,-0.1384012252,-0.4200358987,0.0036535414,-0.4405346215,-0.0079782829,-0.1407092363,-0.2733757794,-0.0963975191,0.2459769398,-0.2561305463,-0.2779055834,0.3951284587,0.1447310299,0.2310712934,0.1184960008,0.0992585048,-0.1329668909,-0.4175079465,0.1348029524,-0.2447813153,0.0278784782,0.1005075127,0.0098711848,-0.1878099591,-0.0269921366,-0.1887381971,0.1825390458,0.0152063826,0.1940030903,-0.0737635717,-0.0238401555,0.0430311337,0.3456587493,0.1764035821,0.010860147,-0.1715756059,-0.066734232,0.311568588,0.0790737867,0.0870562047,-0.1188078448,0.0132933408,-0.2246382236,0.214470163,0.3883276284,0.0419625193,-0.1750754863,0.0624773428,-0.3113225698,0.0604519732,-0.0119610773,0.0920138583,0.0700005367,0.2033096254,0.1066470966,0.3950109184,0.0666037574,0.0462857634,-0.0807010829,0.007730695,-0.104136087,0.0657802448,-0.0892218798,0.1868112236,0.1713126451,-0.1115265265,0.1340780258,-0.0093421172,-0.2380873412,-0.0547784753,0.2477760762,-0.1615775973,-0.2585242987,0.1531671137,0.1702297181,-0.4171901941,-0.0409002751,0.1030413508,-0.1227635741,-0.2476044893,-0.3633392453,0.0623023622,-0.3166223764,-0.0854944289,-0.1410175264,0.3202441037,0.5429484844,-0.1632453352,-0.1814660281,-0.1992941946,-0.2664177418,-0.3964797556,-0.0749630183,0.1141208187,-0.2213226855,0.1271057725,-0.3402887285,0.1647937149,0.1423597783,0.1691092998,-0.0593792237,0.328269124,-0.2519994378,0.7448478937,0.2469037622,-0.1542544216,-0.0559407398,0.1489606947,-0.0351052023,0.1376333386,-0.0552322716,0.0722646639,0.1872952878,-0.3772203028,0.2196923494,-0.0028167854,0.0423983037,-0.3566228151,-0.1894056201,-0.0168206748,0.4003439248,-0.2266631722,-0.3518343568,-0.1862152517,-0.110184744,0.146551773,0.1216539294,-0.1506718546,0.2942144573,0.328137964,0.0982828662,0.0306445342,-0.0221919771,-0.1801386476,-0.6082834601,-0.0778388083,-0.4564249814,0.2655048966,-0.5156535506,-0.1381058097,-0.2986238003,0.2917177081,-0.2939352989,-0.0874444768,0.1657339483,0.0457946025,-0.0814892352,-0.1121656895,-0.0471532084,-0.2919530869,0.1465135962,-0.0049193334,-0.2575115561,0.4828610718,-0.0993677378,-0.3981579542,0.347478956,0.6011694074,0.2996060848,0.0108097121,0.2203307748,0.0839651003,0.0614008829,-0.270619154,-0.2162835747,-0.1576783806,0.1663332582,-0.5653815269,-0.0550803356,0.2663568854,-0.0157449953,-0.3664388955,-0.1052337065,0.3159486651,-0.0346250944,0.407774955,-0.1733401865,0.2705794871,-0.0348707028,0.1374605447,-0.1708569527,-0.333034128,-0.1048401818,0.420381546,-0.3121208847,-0.1443029046,-0.1692670435,0.479344815,0.3534285724,0.1225575507,0.0461264104,0.1554606557,-0.0794245601,-0.2559583485,0.1802677661,-0.2929849625,0.2055215836,0.2246131599,0.1453634948,0.0807232931,-0.2061256766,-0.0572400056,0.1440231949,-0.0237121787,0.3502274454,0.2081848532,0.3714060485,-0.1997290254,-0.0740707442,-0.410828352,-0.0180833675,0.1100207567,-0.2863145173,0.0280116592,-0.1382289827,-0.4561456442,-0.0179674197,-0.4906527698,-0.1596070677,-0.5329732299,-0.0305234063,0.1939590275,-0.1003117636,0.2125135064,0.1825372428,-0.2348060459,0.2344148457,-0.3206106722,-0.2240641713,-0.4881903827,-0.244237259,0.1311269253,0.151558429,0.0488087609,0.0672167316,0.2802769244,-0.0738631263,-0.1561627984,-0.5505623221,0.03042458,-0.4866124988,0.0957419798,0.4155690074,0.2472601235,0.1292892694,-0.4227377176,0.3008897007,0.5386326909,-0.3785912991,-0.0500573143,-0.0442277305,-0.0153104411,-0.0904574469,-0.0681429282,-0.074659206,-0.2739903033,0.3004772067,0.3360037506,0.1556708068,0.1452372372,-0.0839370787,0.0632244572,0.1560751498,-0.2299247682,0.073064968,0.4016881585,0.3970456123,-0.270750016,-0.3289731443,0.0066126189,-0.4844303131,0.0295713749,-0.1177048162,-0.1482520252,0.1077740118,0.1482848078,0.4257537127,0.3425695896,-0.3354375362,0.3598234951,0.4960214496,-0.0522433035,-0.0866467729,-0.0341401473,0.2497750968,0.1586211473,0.2001061589,0.2461517751,0.0882550776,0.1381200552,0.3682236671,0.450081557,-0.0455315635,0.41033113,-0.1635897905,-0.0244688429,-0.2582240701,0.0070215468,-0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/317","title":"Adding a dataset with multiple subtasks","comments":"For one dataset you can have different configurations that each have their own `nlp.Features`.\r\nWe imagine having one configuration per subtask for example.\r\nThey are loaded with `nlp.load_dataset(\"my_dataset\", \"my_config\")`.\r\n\r\nFor example the `glue` dataset has many configurations. It is a bit different from your case though because each configuration is a dataset by itself (sst2, mnli).\r\nAnother example is `wikipedia` that has one configuration per language.","body":"I intent to add the datasets of the MT Quality Estimation shared tasks to `nlp`. However, they have different subtasks -- such as word-level, sentence-level and document-level quality estimation, each of which having different language pairs, and some of the data reused in different subtasks.\r\n\r\nFor example, in [QE 2019,](http:\/\/www.statmt.org\/wmt19\/qe-task.html) we had the same English-Russian and English-German data for word-level and sentence-level QE. \r\n\r\nI suppose these datasets could have both their word and sentence-level labels inside `nlp.Features`; but what about other subtasks? Should they be considered a different dataset altogether?\r\n\r\nI read the discussion on #217 but the case of QE seems a lot simpler.","comment_length":66,"text":"Adding a dataset with multiple subtasks \n I intent to add the datasets of the MT Quality Estimation shared tasks to `nlp`. However, they have different subtasks -- such as word-level, sentence-level and document-level quality estimation, each of which having different language pairs, and some of the data reused in different subtasks.\r\n\r\nFor example, in [QE 2019,](http:\/\/www.statmt.org\/wmt19\/qe-task.html) we had the same English-Russian and English-German data for word-level and sentence-level QE. \r\n\r\nI suppose these datasets could have both their word and sentence-level labels inside `nlp.Features`; but what about other subtasks? Should they be considered a different dataset altogether?\r\n\r\nI read the discussion on #217 but the case of QE seems a lot simpler. \n For one dataset you can have different configurations that each have their own `nlp.Features`.\r\nWe imagine having one configuration per subtask for example.\r\nThey are loaded with `nlp.load_dataset(\"my_dataset\", \"my_config\")`.\r\n\r\nFor example the `glue` dataset has many configurations. It is a bit different from your case though because each configuration is a dataset by itself (sst2, mnli).\r\nAnother example is `wikipedia` that has one configuration per 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"Update: I think I've found a solution.\r\n\r\n```python\r\noutput_types = {\"input_ids\": tf.int64, \"token_type_ids\": tf.int64, \"attention_mask\": tf.int64}\r\ndef train_dataset_gen():\r\n    for i in range(len(train_dataset)):\r\n        yield train_dataset[i]\r\ntf_dataset = tf.data.Dataset.from_generator(train_dataset_gen, output_types=output_types)\r\n```\r\n\r\nloads WikiText-2 in 20 ms, and WikiText-103 in 20 ms. It appears to be lazily loading via indexing train_dataset.","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":48,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n Update: I think I've found a solution.\r\n\r\n```python\r\noutput_types = {\"input_ids\": tf.int64, \"token_type_ids\": tf.int64, \"attention_mask\": tf.int64}\r\ndef train_dataset_gen():\r\n    for i in range(len(train_dataset)):\r\n        yield train_dataset[i]\r\ntf_dataset = tf.data.Dataset.from_generator(train_dataset_gen, output_types=output_types)\r\n```\r\n\r\nloads WikiText-2 in 20 ms, and WikiText-103 in 20 ms. It appears to be lazily loading via indexing 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"Yes this is the current best solution. We should probably show it in the tutorial notebook.\r\n\r\nNote that this solution unfortunately doesn't allow to train on TPUs (yet). See #193 ","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":30,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n Yes this is the current best solution. We should probably show it in the tutorial notebook.\r\n\r\nNote that this solution unfortunately doesn't allow to train on TPUs (yet). See #193 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"This approach still seems quite slow. When using TFRecords with a similar training loop, I get ~3.0-3.5 it\/s on multi-node, multi-GPU training. I notice a pretty severe performance regression when scaling, with observed performance numbers. Since the allreduce step takes less than 100ms\/it and I've achieved 80% scaling efficiency up to 64 GPUs, it must be the data pipeline.\r\n\r\n| Nodes | GPUs | Iterations\/Second |\r\n| --- | --- | --- |\r\n| 1 | 2 | 2.01 |\r\n| 1 | 8 | 0.81 |\r\n| 2 | 16 | 0.37 |\r\n\r\nHere are performance metrics over 10k steps. The iteration speed appears to follow some sort of caching pattern. I would love to use `nlp` in my project, but a slowdown from 3.0 it\/s to 0.3 it\/s is too great to stomach.\r\n\r\n<img width=\"1361\" alt=\"Screen Shot 2020-07-02 at 8 29 22 AM\" src=\"https:\/\/user-images.githubusercontent.com\/4564897\/86378156-2f8d3900-bc3e-11ea-918b-c395c3df5377.png\">\r\n","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":146,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n This approach still seems quite slow. When using TFRecords with a similar training loop, I get ~3.0-3.5 it\/s on multi-node, multi-GPU training. I notice a pretty severe performance regression when scaling, with observed performance numbers. Since the allreduce step takes less than 100ms\/it and I've achieved 80% scaling efficiency up to 64 GPUs, it must be the data pipeline.\r\n\r\n| Nodes | GPUs | Iterations\/Second |\r\n| --- | --- | --- |\r\n| 1 | 2 | 2.01 |\r\n| 1 | 8 | 0.81 |\r\n| 2 | 16 | 0.37 |\r\n\r\nHere are performance metrics over 10k steps. The iteration speed appears to follow some sort of caching pattern. I would love to use `nlp` in my project, but a slowdown from 3.0 it\/s to 0.3 it\/s is too great to stomach.\r\n\r\n<img width=\"1361\" alt=\"Screen Shot 2020-07-02 at 8 29 22 AM\" src=\"https:\/\/user-images.githubusercontent.com\/4564897\/86378156-2f8d3900-bc3e-11ea-918b-c395c3df5377.png\">\r\n","embeddings":[-0.3098398447,-0.3549019396,0.040070232,-0.1314413697,0.043479424,0.1706920415,0.5454062223,0.3945962787,0.1853833199,0.0758509487,-0.0109290816,0.0905708447,-0.0458247811,0.1636851728,0.1464808881,-0.3626331091,0.0164577533,-0.2653392255,-0.0260757729,-0.1447885334,-0.2456075102,-0.2963108122,-0.3499272764,-0.2272665501,-0.3148984015,-0.2920510471,0.0221025404,-0.1786595732,0.2146256864,-0.0490131006,0.0490412712,0.0216406938,0.300095439,0.4448573887,-0.0001156601,-0.1326184422,0.0600120351,-0.0445338674,-0.0088806693,0.3237411678,-0.0612367652,-0.388153106,-0.0979186371,-0.4061881304,0.1105616391,0.1923644096,0.1939423829,-0.0472506657,0.1358859092,-0.0325134471,0.063217558,0.3727622628,-0.0247660466,0.1722879857,0.02888369,-0.0639728978,0.0118579278,0.0609920211,0.2795847654,-0.0697013512,-0.0007684377,0.3284053802,-0.1674046367,-0.0159423053,0.2925575972,0.2313953638,0.1201131791,-0.6053037047,0.031976074,0.3724275529,0.2989349961,0.003854932,-0.2751540542,-0.6016538739,-0.1216737404,-0.2600981593,-0.2543848455,0.2779847682,-0.2282436937,-0.1784859449,-0.2848558724,-0.0607504658,-0.111662358,0.1517273933,-0.17424573,0.1897965819,0.1561438292,0.2513833046,0.1800088286,-0.259157747,0.0723085105,-0.2944973111,0.391967684,0.2885423005,-0.5199069381,-0.161050126,0.1525548995,-0.2357104123,0.0620710067,-0.2695543766,-0.0892135128,0.0071072425,0.0950781703,0.046422746,0.1840151697,0.2864991128,-0.1267043054,0.0481852777,0.1040561199,-0.2625125349,-0.2421223223,0.3590080142,-0.108374469,-0.2902850807,0.0475210175,-0.1952093393,-0.358223021,0.1385153085,-0.0088153258,0.0312358961,0.0680118501,-0.0676135719,-0.031125294,0.1281121075,-0.2842437327,0.0787763745,-0.1488966644,-0.1395684034,-0.3805860281,0.1666765809,-0.1485890448,0.0174979772,-0.0054796408,-0.0163144097,0.1355892569,-0.139675349,0.1104079336,-0.1817804277,0.0023557388,0.2069333345,-0.1126759872,-0.2320376039,0.1354833096,0.2346876562,0.0579938069,0.2410531789,0.0312836021,0.1558558941,-0.2094338685,0.3726697862,-0.3099021912,-0.3395798504,0.1943150908,0.0668445751,0.0166570023,-0.3149917126,-0.5945531726,0.3981288075,0.321008116,0.0930782035,-0.0770611241,-0.2863120437,-0.1548867822,-0.2435482889,0.1875332594,0.2121235579,-0.4263499677,0.1576705426,0.4017448723,0.3291971684,0.3743069172,0.6147413254,-0.2548478842,0.4410940707,0.175022468,0.1125394627,0.1075415313,-0.25900051,-0.2935233712,0.4176078737,-0.2239006311,0.0911498666,0.0280616544,0.2754259109,0.290940702,-0.235840559,0.4677284658,0.5002925396,-0.1060514823,0.3253593147,-0.2121745497,-0.2624133229,0.1294199079,0.1681138873,0.1687644422,-0.1044366434,-0.2262644023,0.2172021866,0.2736003399,-0.1601257324,-0.1335326135,0.0144466078,0.0447470471,0.0450545736,-0.2553613484,0.0899209753,-0.5541221499,0.2491139024,0.104594931,-0.0720571429,0.2975907326,-0.3017668426,0.2677598596,-0.0850059688,0.0262452774,0.2439461052,-0.0638855994,-0.1151607782,0.2073930055,-0.0009455864,-0.2045055181,-0.0694354549,-0.45423159,0.0335031152,-0.3397281468,-0.1507048756,0.3422574997,0.1017849892,-0.1304057837,0.1610793471,-0.0711504072,-0.1975040138,0.0639175996,0.1212921143,-0.0829551965,-0.0109530622,-0.2208668292,0.3150741458,0.2363253683,0.3301177025,0.0516277552,-0.1459756196,0.0075899232,-0.3100179136,-0.2521992922,0.2785663307,-0.0317873061,0.3897755146,0.1379317939,-0.3446787596,-0.1478319615,0.1022378057,0.0575494617,0.1419411749,0.1398730576,0.1266010553,0.0635658801,0.1517291218,-0.4028158188,0.2708552778,0.5878329873,-0.1192695946,-0.069387868,0.2395924926,0.059559077,-0.1695690155,-0.0338157713,-0.217757225,0.1716604531,0.1619177461,0.1790524721,-0.0999094173,0.124293372,-0.1298576891,0.3223983645,0.2120993882,0.004855027,0.022191342,0.116418615,0.0722042248,0.0016253602,-0.0955381989,0.1305938661,0.3099609613,-0.1252200752,0.0044579892,0.1954187155,-0.1072649211,-0.1237375364,-0.0269091949,0.0157357194,-0.3178576529,-0.1369980425,0.248922646,-0.1166380346,0.0364544466,0.1061013117,0.3592701256,0.1271025687,-0.364212513,0.1010778546,-0.1170879453,-0.2541911304,-0.0160684511,0.1550412625,-0.0097691929,0.1884468198,0.0886323899,-0.1106292754,0.015857242,-0.1376692057,-0.0592114329,-0.4576979876,0.0734886229,-0.1006338224,0.335616827,-0.2249776274,-0.437174648,-0.0026174795,-0.0687950104,0.2533338964,-0.2674293518,0.0078697056,-0.0599730387,0.1682515144,-0.2091825753,-0.3979697526,-0.2773379683,0.1616996378,-0.0804175064,0.1120989919,0.3758215606,0.0861320272,0.0614354759,0.1984479427,-0.1117398441,0.0645064414,-0.3359968662,0.2933339775,-0.1133206114,-0.2322174907,-0.2972748578,-0.0569187999,-0.0541822575,0.3564843833,-0.5518575907,0.1781203598,-0.3643219471,0.1989420503,-0.005830613,0.5625964403,-0.0943654105,-0.4863793552,-0.0153563311,0.1136103272,0.1111946926,0.0132406522,0.2653706968,0.2140454203,0.2785107493,0.4317783415,0.133768037,1.1682617664,0.1721532643,-0.2398597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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"An interesting alternative to investigate here would be to use the tf.io library which has some support for Arrow to TF conversion: https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset\r\n\r\nThere are quite a few types supported, including lists so if the unsupported columns are dropped then we could maybe have a zero-copy mapping from Arrow to TensorFlow, including tokenized inputs and 1D tensors like the ones we mostly use in NLP: https:\/\/github.com\/tensorflow\/io\/blob\/322b3170c43ecac5c6af9e39dbd18fd747913e5a\/tensorflow_io\/arrow\/python\/ops\/arrow_dataset_ops.py#L44-L72\r\n\r\nHere is an introduction on Arrow to TF using tf.io: https:\/\/medium.com\/tensorflow\/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":77,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n An interesting alternative to investigate here would be to use the tf.io library which has some support for Arrow to TF conversion: https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset\r\n\r\nThere are quite a few types supported, including lists so if the unsupported columns are dropped then we could maybe have a zero-copy mapping from Arrow to TensorFlow, including tokenized inputs and 1D tensors like the ones we mostly use in NLP: https:\/\/github.com\/tensorflow\/io\/blob\/322b3170c43ecac5c6af9e39dbd18fd747913e5a\/tensorflow_io\/arrow\/python\/ops\/arrow_dataset_ops.py#L44-L72\r\n\r\nHere is an introduction on Arrow to TF using tf.io: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"Interesting. There's no support for strings, but it does enable int and floats so that would work for tokenized inputs. \r\n\r\nArrowStreamDataset requires loading from a \"record batch iterator\", which can be instantiated from in-memory arrays as described here: https:\/\/arrow.apache.org\/docs\/python\/ipc.html. \r\n\r\nBut the nlp.Dataset stores its data as a `pyarrow.lib.Table`, and the underlying features are `pyarrow.lib.ChunkedArray`. I can't find any documentation about lazily creating a record batch iterator from a ChunkedArray or a Table. Have you had any success?\r\n\r\nI can't find [any uses](https:\/\/grep.app\/search?q=ArrowDataset&filter[lang][0]=Python) of tfio.arrow.ArrowDataset on GitHub.","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":86,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n Interesting. There's no support for strings, but it does enable int and floats so that would work for tokenized inputs. \r\n\r\nArrowStreamDataset requires loading from a \"record batch iterator\", which can be instantiated from in-memory arrays as described here: https:\/\/arrow.apache.org\/docs\/python\/ipc.html. \r\n\r\nBut the nlp.Dataset stores its data as a `pyarrow.lib.Table`, and the underlying features are `pyarrow.lib.ChunkedArray`. I can't find any documentation about lazily creating a record batch iterator from a ChunkedArray or a Table. Have you had any success?\r\n\r\nI can't find [any uses](https:\/\/grep.app\/search?q=ArrowDataset&filter[lang][0]=Python) of tfio.arrow.ArrowDataset on GitHub.","embeddings":[-0.3098398447,-0.3549019396,0.040070232,-0.1314413697,0.043479424,0.1706920415,0.5454062223,0.3945962787,0.1853833199,0.0758509487,-0.0109290816,0.0905708447,-0.0458247811,0.1636851728,0.1464808881,-0.3626331091,0.0164577533,-0.2653392255,-0.0260757729,-0.1447885334,-0.2456075102,-0.2963108122,-0.3499272764,-0.2272665501,-0.3148984015,-0.2920510471,0.0221025404,-0.1786595732,0.2146256864,-0.0490131006,0.0490412712,0.0216406938,0.300095439,0.4448573887,-0.0001156601,-0.1326184422,0.0600120351,-0.0445338674,-0.0088806693,0.3237411678,-0.0612367652,-0.388153106,-0.0979186371,-0.4061881304,0.1105616391,0.1923644096,0.1939423829,-0.0472506657,0.1358859092,-0.0325134471,0.063217558,0.3727622628,-0.0247660466,0.1722879857,0.02888369,-0.0639728978,0.0118579278,0.0609920211,0.2795847654,-0.0697013512,-0.0007684377,0.3284053802,-0.1674046367,-0.0159423053,0.2925575972,0.2313953638,0.1201131791,-0.6053037047,0.031976074,0.3724275529,0.2989349961,0.003854932,-0.2751540542,-0.6016538739,-0.1216737404,-0.2600981593,-0.2543848455,0.2779847682,-0.2282436937,-0.1784859449,-0.2848558724,-0.0607504658,-0.111662358,0.1517273933,-0.17424573,0.1897965819,0.1561438292,0.2513833046,0.1800088286,-0.259157747,0.0723085105,-0.2944973111,0.391967684,0.2885423005,-0.5199069381,-0.161050126,0.1525548995,-0.2357104123,0.0620710067,-0.2695543766,-0.0892135128,0.0071072425,0.0950781703,0.046422746,0.1840151697,0.2864991128,-0.1267043054,0.0481852777,0.1040561199,-0.2625125349,-0.2421223223,0.3590080142,-0.108374469,-0.2902850807,0.0475210175,-0.1952093393,-0.358223021,0.1385153085,-0.0088153258,0.0312358961,0.0680118501,-0.0676135719,-0.031125294,0.1281121075,-0.2842437327,0.0787763745,-0.1488966644,-0.1395684034,-0.3805860281,0.1666765809,-0.1485890448,0.0174979772,-0.0054796408,-0.0163144097,0.1355892569,-0.139675349,0.1104079336,-0.1817804277,0.0023557388,0.2069333345,-0.1126759872,-0.2320376039,0.1354833096,0.2346876562,0.0579938069,0.2410531789,0.0312836021,0.1558558941,-0.2094338685,0.3726697862,-0.3099021912,-0.3395798504,0.1943150908,0.0668445751,0.0166570023,-0.3149917126,-0.5945531726,0.3981288075,0.321008116,0.0930782035,-0.0770611241,-0.2863120437,-0.1548867822,-0.2435482889,0.1875332594,0.2121235579,-0.4263499677,0.1576705426,0.4017448723,0.3291971684,0.3743069172,0.6147413254,-0.2548478842,0.4410940707,0.175022468,0.1125394627,0.1075415313,-0.25900051,-0.2935233712,0.4176078737,-0.2239006311,0.0911498666,0.0280616544,0.2754259109,0.290940702,-0.235840559,0.4677284658,0.5002925396,-0.1060514823,0.3253593147,-0.2121745497,-0.2624133229,0.1294199079,0.1681138873,0.1687644422,-0.1044366434,-0.2262644023,0.2172021866,0.2736003399,-0.1601257324,-0.1335326135,0.0144466078,0.0447470471,0.0450545736,-0.2553613484,0.0899209753,-0.5541221499,0.2491139024,0.104594931,-0.0720571429,0.2975907326,-0.3017668426,0.2677598596,-0.0850059688,0.0262452774,0.2439461052,-0.0638855994,-0.1151607782,0.2073930055,-0.0009455864,-0.2045055181,-0.0694354549,-0.45423159,0.0335031152,-0.3397281468,-0.1507048756,0.3422574997,0.1017849892,-0.1304057837,0.1610793471,-0.0711504072,-0.1975040138,0.0639175996,0.1212921143,-0.0829551965,-0.0109530622,-0.2208668292,0.3150741458,0.2363253683,0.3301177025,0.0516277552,-0.1459756196,0.0075899232,-0.3100179136,-0.2521992922,0.2785663307,-0.0317873061,0.3897755146,0.1379317939,-0.3446787596,-0.1478319615,0.1022378057,0.0575494617,0.1419411749,0.1398730576,0.1266010553,0.0635658801,0.1517291218,-0.4028158188,0.2708552778,0.5878329873,-0.1192695946,-0.069387868,0.2395924926,0.059559077,-0.1695690155,-0.0338157713,-0.217757225,0.1716604531,0.1619177461,0.1790524721,-0.0999094173,0.124293372,-0.1298576891,0.3223983645,0.2120993882,0.004855027,0.022191342,0.116418615,0.0722042248,0.0016253602,-0.0955381989,0.1305938661,0.3099609613,-0.1252200752,0.0044579892,0.1954187155,-0.1072649211,-0.1237375364,-0.0269091949,0.0157357194,-0.3178576529,-0.1369980425,0.248922646,-0.1166380346,0.0364544466,0.1061013117,0.3592701256,0.1271025687,-0.364212513,0.1010778546,-0.1170879453,-0.2541911304,-0.0160684511,0.1550412625,-0.0097691929,0.1884468198,0.0886323899,-0.1106292754,0.015857242,-0.1376692057,-0.0592114329,-0.4576979876,0.0734886229,-0.1006338224,0.335616827,-0.2249776274,-0.437174648,-0.0026174795,-0.0687950104,0.2533338964,-0.2674293518,0.0078697056,-0.0599730387,0.1682515144,-0.2091825753,-0.3979697526,-0.2773379683,0.1616996378,-0.0804175064,0.1120989919,0.3758215606,0.0861320272,0.0614354759,0.1984479427,-0.1117398441,0.0645064414,-0.3359968662,0.2933339775,-0.1133206114,-0.2322174907,-0.2972748578,-0.0569187999,-0.0541822575,0.3564843833,-0.5518575907,0.1781203598,-0.3643219471,0.1989420503,-0.005830613,0.5625964403,-0.0943654105,-0.4863793552,-0.0153563311,0.1136103272,0.1111946926,0.0132406522,0.2653706968,0.2140454203,0.2785107493,0.4317783415,0.133768037,1.1682617664,0.1721532643,-0.2398597002,-0.2142921984,-0.0006967018,-0.0267474819,-0.3446087837,-0.2672060728,0.3804666996,-0.0651110634,-0.12184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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"Also note that since #322 it is now possible to do\r\n```python\r\nids = [1, 10, 42, 100]\r\nbatch = dataset[ids]\r\n```\r\nFrom my experience it is quite fast but it can take lots of memory for large batches (haven't played that much with it).\r\nLet me know if you think there could be a better way to implement it. (current code is [here](https:\/\/github.com\/huggingface\/nlp\/blob\/78628649962671b4aaa31a6b24e7275533416845\/src\/nlp\/arrow_dataset.py#L463))","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":64,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n Also note that since #322 it is now possible to do\r\n```python\r\nids = [1, 10, 42, 100]\r\nbatch = dataset[ids]\r\n```\r\nFrom my experience it is quite fast but it can take lots of memory for large batches (haven't played that much with it).\r\nLet me know if you think there could be a better way to implement it. (current code is [here](https:\/\/github.com\/huggingface\/nlp\/blob\/78628649962671b4aaa31a6b24e7275533416845\/src\/nlp\/arrow_dataset.py#L463))","embeddings":[-0.3098398447,-0.3549019396,0.040070232,-0.1314413697,0.043479424,0.1706920415,0.5454062223,0.3945962787,0.1853833199,0.0758509487,-0.0109290816,0.0905708447,-0.0458247811,0.1636851728,0.1464808881,-0.3626331091,0.0164577533,-0.2653392255,-0.0260757729,-0.1447885334,-0.2456075102,-0.2963108122,-0.3499272764,-0.2272665501,-0.3148984015,-0.2920510471,0.0221025404,-0.1786595732,0.2146256864,-0.0490131006,0.0490412712,0.0216406938,0.300095439,0.4448573887,-0.0001156601,-0.1326184422,0.0600120351,-0.0445338674,-0.0088806693,0.3237411678,-0.0612367652,-0.388153106,-0.0979186371,-0.4061881304,0.1105616391,0.1923644096,0.1939423829,-0.0472506657,0.1358859092,-0.0325134471,0.063217558,0.3727622628,-0.0247660466,0.1722879857,0.02888369,-0.0639728978,0.0118579278,0.0609920211,0.2795847654,-0.0697013512,-0.0007684377,0.3284053802,-0.1674046367,-0.0159423053,0.2925575972,0.2313953638,0.1201131791,-0.6053037047,0.031976074,0.3724275529,0.2989349961,0.003854932,-0.2751540542,-0.6016538739,-0.1216737404,-0.2600981593,-0.2543848455,0.2779847682,-0.2282436937,-0.1784859449,-0.2848558724,-0.0607504658,-0.111662358,0.1517273933,-0.17424573,0.1897965819,0.1561438292,0.2513833046,0.1800088286,-0.259157747,0.0723085105,-0.2944973111,0.391967684,0.2885423005,-0.5199069381,-0.161050126,0.1525548995,-0.2357104123,0.0620710067,-0.2695543766,-0.0892135128,0.0071072425,0.0950781703,0.046422746,0.1840151697,0.2864991128,-0.1267043054,0.0481852777,0.1040561199,-0.2625125349,-0.2421223223,0.3590080142,-0.108374469,-0.2902850807,0.0475210175,-0.1952093393,-0.358223021,0.1385153085,-0.0088153258,0.0312358961,0.0680118501,-0.0676135719,-0.031125294,0.1281121075,-0.2842437327,0.0787763745,-0.1488966644,-0.1395684034,-0.3805860281,0.1666765809,-0.1485890448,0.0174979772,-0.0054796408,-0.0163144097,0.1355892569,-0.139675349,0.1104079336,-0.1817804277,0.0023557388,0.2069333345,-0.1126759872,-0.2320376039,0.1354833096,0.2346876562,0.0579938069,0.2410531789,0.0312836021,0.1558558941,-0.2094338685,0.3726697862,-0.3099021912,-0.3395798504,0.1943150908,0.0668445751,0.0166570023,-0.3149917126,-0.5945531726,0.3981288075,0.321008116,0.0930782035,-0.0770611241,-0.2863120437,-0.1548867822,-0.2435482889,0.1875332594,0.2121235579,-0.4263499677,0.1576705426,0.4017448723,0.3291971684,0.3743069172,0.6147413254,-0.2548478842,0.4410940707,0.175022468,0.1125394627,0.1075415313,-0.25900051,-0.2935233712,0.4176078737,-0.2239006311,0.0911498666,0.0280616544,0.2754259109,0.290940702,-0.235840559,0.4677284658,0.5002925396,-0.1060514823,0.3253593147,-0.2121745497,-0.2624133229,0.1294199079,0.1681138873,0.1687644422,-0.1044366434,-0.2262644023,0.2172021866,0.2736003399,-0.1601257324,-0.1335326135,0.0144466078,0.0447470471,0.0450545736,-0.2553613484,0.0899209753,-0.5541221499,0.2491139024,0.104594931,-0.0720571429,0.2975907326,-0.3017668426,0.2677598596,-0.0850059688,0.0262452774,0.2439461052,-0.0638855994,-0.1151607782,0.2073930055,-0.0009455864,-0.2045055181,-0.0694354549,-0.45423159,0.0335031152,-0.3397281468,-0.1507048756,0.3422574997,0.1017849892,-0.1304057837,0.1610793471,-0.0711504072,-0.1975040138,0.0639175996,0.1212921143,-0.0829551965,-0.0109530622,-0.2208668292,0.3150741458,0.2363253683,0.3301177025,0.0516277552,-0.1459756196,0.0075899232,-0.3100179136,-0.2521992922,0.2785663307,-0.0317873061,0.3897755146,0.1379317939,-0.3446787596,-0.1478319615,0.1022378057,0.0575494617,0.1419411749,0.1398730576,0.1266010553,0.0635658801,0.1517291218,-0.4028158188,0.2708552778,0.5878329873,-0.1192695946,-0.069387868,0.2395924926,0.059559077,-0.1695690155,-0.0338157713,-0.217757225,0.1716604531,0.1619177461,0.1790524721,-0.0999094173,0.124293372,-0.1298576891,0.3223983645,0.2120993882,0.004855027,0.022191342,0.116418615,0.0722042248,0.0016253602,-0.0955381989,0.1305938661,0.3099609613,-0.1252200752,0.0044579892,0.1954187155,-0.1072649211,-0.1237375364,-0.0269091949,0.0157357194,-0.3178576529,-0.1369980425,0.248922646,-0.1166380346,0.0364544466,0.1061013117,0.3592701256,0.1271025687,-0.364212513,0.1010778546,-0.1170879453,-0.2541911304,-0.0160684511,0.1550412625,-0.0097691929,0.1884468198,0.0886323899,-0.1106292754,0.015857242,-0.1376692057,-0.0592114329,-0.4576979876,0.0734886229,-0.1006338224,0.335616827,-0.2249776274,-0.437174648,-0.0026174795,-0.0687950104,0.2533338964,-0.2674293518,0.0078697056,-0.0599730387,0.1682515144,-0.2091825753,-0.3979697526,-0.2773379683,0.1616996378,-0.0804175064,0.1120989919,0.3758215606,0.0861320272,0.0614354759,0.1984479427,-0.1117398441,0.0645064414,-0.3359968662,0.2933339775,-0.1133206114,-0.2322174907,-0.2972748578,-0.0569187999,-0.0541822575,0.3564843833,-0.5518575907,0.1781203598,-0.3643219471,0.1989420503,-0.005830613,0.5625964403,-0.0943654105,-0.4863793552,-0.0153563311,0.1136103272,0.1111946926,0.0132406522,0.2653706968,0.2140454203,0.2785107493,0.4317783415,0.133768037,1.1682617664,0.172153264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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"Thanks @lhoestq! That format is much better to work with.\r\n\r\nI put together a benchmarking script. This doesn't measure the CPU-to-GPU efficiency, nor how it scales with multi-GPU multi-node training where many processes are making the same demands on the same dataset. But it does show some interesting results:\r\n\r\n```python\r\nimport nlp\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport time\r\n\r\ndset = nlp.load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"train\")\r\ndset = dset.filter(lambda ex: len(ex[\"text\"]) > 0)\r\nbsz = 1024\r\nn_batches = 100\r\n\r\ndef single_item_gen():\r\n    for i in range(len(dset)):\r\n        yield dset[i]\r\n\r\ndef sequential_batch_gen():\r\n    for i in range(0, len(dset), bsz):\r\n        yield dset[i:i+bsz]\r\n\r\ndef random_batch_gen():\r\n    for i in range(len(dset)):\r\n        indices = list(np.random.randint(len(dset), size=(bsz,)))\r\n        yield dset[indices]\r\n\r\noutput_types = {\"text\": tf.string}\r\nsingle_item = tf.data.Dataset.from_generator(single_item_gen, output_types=output_types).batch(bsz)\r\ninterleaved = tf.data.Dataset.range(10).interleave(\r\n    lambda idx: tf.data.Dataset.from_generator(single_item_gen, output_types=output_types),\r\n    cycle_length=10,\r\n)\r\nsequential_batch = tf.data.Dataset.from_generator(sequential_batch_gen, output_types=output_types)\r\nrandom_batch = tf.data.Dataset.from_generator(random_batch_gen, output_types=output_types)\r\n\r\ndef iterate(tf_dset):\r\n    start = time.perf_counter()\r\n    for i, batch in enumerate(tf_dset.take(n_batches)):\r\n        pass\r\n    elapsed = time.perf_counter() - start\r\n    print(f\"{tf_dset} took {elapsed:.3f} secs\")\r\n\r\niterate(single_item)\r\niterate(interleaved)\r\niterate(sequential_batch)\r\niterate(random_batch)\r\n```\r\n\r\nResults:\r\n```\r\n<BatchDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 23.005 secs\r\n<InterleaveDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.135 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.074 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.550 secs\r\n```\r\n\r\n- Batching a generator which fetches a single item is terrible.\r\n- Interleaving performs well on a single process, but doesn't scale well to multi-GPU training. I believe the bottleneck here is in Arrow dataset locking or something similar. The numbers from the table above are with interleaving.\r\n- The sequential access dominates the random access (7x faster). Is there any way to bring random access times closer to sequential access? Maybe re-indexing the dataset after shuffling each pass over the data.","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":285,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n Thanks @lhoestq! That format is much better to work with.\r\n\r\nI put together a benchmarking script. This doesn't measure the CPU-to-GPU efficiency, nor how it scales with multi-GPU multi-node training where many processes are making the same demands on the same dataset. But it does show some interesting results:\r\n\r\n```python\r\nimport nlp\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport time\r\n\r\ndset = nlp.load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"train\")\r\ndset = dset.filter(lambda ex: len(ex[\"text\"]) > 0)\r\nbsz = 1024\r\nn_batches = 100\r\n\r\ndef single_item_gen():\r\n    for i in range(len(dset)):\r\n        yield dset[i]\r\n\r\ndef sequential_batch_gen():\r\n    for i in range(0, len(dset), bsz):\r\n        yield dset[i:i+bsz]\r\n\r\ndef random_batch_gen():\r\n    for i in range(len(dset)):\r\n        indices = list(np.random.randint(len(dset), size=(bsz,)))\r\n        yield dset[indices]\r\n\r\noutput_types = {\"text\": tf.string}\r\nsingle_item = tf.data.Dataset.from_generator(single_item_gen, output_types=output_types).batch(bsz)\r\ninterleaved = tf.data.Dataset.range(10).interleave(\r\n    lambda idx: tf.data.Dataset.from_generator(single_item_gen, output_types=output_types),\r\n    cycle_length=10,\r\n)\r\nsequential_batch = tf.data.Dataset.from_generator(sequential_batch_gen, output_types=output_types)\r\nrandom_batch = tf.data.Dataset.from_generator(random_batch_gen, output_types=output_types)\r\n\r\ndef iterate(tf_dset):\r\n    start = time.perf_counter()\r\n    for i, batch in enumerate(tf_dset.take(n_batches)):\r\n        pass\r\n    elapsed = time.perf_counter() - start\r\n    print(f\"{tf_dset} took {elapsed:.3f} secs\")\r\n\r\niterate(single_item)\r\niterate(interleaved)\r\niterate(sequential_batch)\r\niterate(random_batch)\r\n```\r\n\r\nResults:\r\n```\r\n<BatchDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 23.005 secs\r\n<InterleaveDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.135 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.074 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.550 secs\r\n```\r\n\r\n- Batching a generator which fetches a single item is terrible.\r\n- Interleaving performs well on a single process, but doesn't scale well to multi-GPU training. I believe the bottleneck here is in Arrow dataset locking or something similar. The numbers from the table above are with interleaving.\r\n- The sequential access dominates the random access (7x faster). Is there any way to bring random access times closer to sequential access? Maybe re-indexing the dataset after shuffling each pass over the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"Hey @jarednielsen \r\n\r\nThanks for this very interesting analysis!! IMHO to read text data one should use `tf.data.TextLineDataset`. It would be interesting to compare what you have done with simply load with a `TextLineDataset` and see if there is a difference.\r\n\r\nA good example can be found here https:\/\/www.tensorflow.org\/tutorials\/load_data\/text","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":48,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n Hey @jarednielsen \r\n\r\nThanks for this very interesting analysis!! IMHO to read text data one should use `tf.data.TextLineDataset`. It would be interesting to compare what you have done with simply load with a `TextLineDataset` and see if there is a difference.\r\n\r\nA good example can be found here 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"Thanks! I'm not actually loading in raw text data, that was just the synthetic data I created for this benchmark. A more realistic use case would be a dataset of tokenized examples, which would be a dict of lists of integers. TensorFlow's TextLineDataset greedily loads the dataset into the graph itself, which can lead to out-of-memory errors - one of the main reason I'm so drawn to the `nlp` library is its zero-copy no-RAM approach to dataset loading and mapping. \r\n\r\nIt's quite helpful for running a preprocessing pipeline - a sample ELECTRA pipeline I've built is here: https:\/\/github.com\/jarednielsen\/deep-learning-models\/blob\/nlp\/models\/nlp\/common\/preprocess.py.","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":98,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n Thanks! I'm not actually loading in raw text data, that was just the synthetic data I created for this benchmark. A more realistic use case would be a dataset of tokenized examples, which would be a dict of lists of integers. TensorFlow's TextLineDataset greedily loads the dataset into the graph itself, which can lead to out-of-memory errors - one of the main reason I'm so drawn to the `nlp` library is its zero-copy no-RAM approach to dataset loading and mapping. \r\n\r\nIt's quite helpful for running a preprocessing pipeline - a sample ELECTRA pipeline I've built is here: https:\/\/github.com\/jarednielsen\/deep-learning-models\/blob\/nlp\/models\/nlp\/common\/preprocess.py.","embeddings":[-0.3098398447,-0.3549019396,0.040070232,-0.1314413697,0.043479424,0.1706920415,0.5454062223,0.3945962787,0.1853833199,0.0758509487,-0.0109290816,0.0905708447,-0.0458247811,0.1636851728,0.1464808881,-0.3626331091,0.0164577533,-0.2653392255,-0.0260757729,-0.1447885334,-0.2456075102,-0.2963108122,-0.3499272764,-0.2272665501,-0.3148984015,-0.2920510471,0.0221025404,-0.1786595732,0.2146256864,-0.0490131006,0.0490412712,0.0216406938,0.300095439,0.4448573887,-0.0001156601,-0.1326184422,0.0600120351,-0.0445338674,-0.0088806693,0.3237411678,-0.0612367652,-0.388153106,-0.0979186371,-0.4061881304,0.1105616391,0.1923644096,0.1939423829,-0.0472506657,0.1358859092,-0.0325134471,0.063217558,0.3727622628,-0.0247660466,0.1722879857,0.02888369,-0.0639728978,0.0118579278,0.0609920211,0.2795847654,-0.0697013512,-0.0007684377,0.3284053802,-0.1674046367,-0.0159423053,0.2925575972,0.2313953638,0.1201131791,-0.6053037047,0.031976074,0.3724275529,0.2989349961,0.003854932,-0.2751540542,-0.6016538739,-0.1216737404,-0.2600981593,-0.2543848455,0.2779847682,-0.2282436937,-0.1784859449,-0.2848558724,-0.0607504658,-0.111662358,0.1517273933,-0.17424573,0.1897965819,0.1561438292,0.2513833046,0.1800088286,-0.259157747,0.0723085105,-0.2944973111,0.391967684,0.2885423005,-0.5199069381,-0.161050126,0.1525548995,-0.2357104123,0.0620710067,-0.2695543766,-0.0892135128,0.0071072425,0.0950781703,0.046422746,0.1840151697,0.2864991128,-0.1267043054,0.0481852777,0.1040561199,-0.2625125349,-0.2421223223,0.3590080142,-0.108374469,-0.2902850807,0.0475210175,-0.1952093393,-0.358223021,0.1385153085,-0.0088153258,0.0312358961,0.0680118501,-0.0676135719,-0.031125294,0.1281121075,-0.2842437327,0.0787763745,-0.1488966644,-0.1395684034,-0.3805860281,0.1666765809,-0.1485890448,0.0174979772,-0.0054796408,-0.0163144097,0.1355892569,-0.139675349,0.1104079336,-0.1817804277,0.0023557388,0.2069333345,-0.1126759872,-0.2320376039,0.1354833096,0.2346876562,0.0579938069,0.2410531789,0.0312836021,0.1558558941,-0.2094338685,0.3726697862,-0.3099021912,-0.3395798504,0.1943150908,0.0668445751,0.0166570023,-0.3149917126,-0.5945531726,0.3981288075,0.321008116,0.0930782035,-0.0770611241,-0.2863120437,-0.1548867822,-0.2435482889,0.1875332594,0.2121235579,-0.4263499677,0.1576705426,0.4017448723,0.3291971684,0.3743069172,0.6147413254,-0.2548478842,0.4410940707,0.175022468,0.1125394627,0.1075415313,-0.25900051,-0.2935233712,0.4176078737,-0.2239006311,0.0911498666,0.0280616544,0.2754259109,0.290940702,-0.235840559,0.4677284658,0.5002925396,-0.1060514823,0.3253593147,-0.2121745497,-0.2624133229,0.1294199079,0.1681138873,0.1687644422,-0.1044366434,-0.2262644023,0.2172021866,0.2736003399,-0.1601257324,-0.1335326135,0.0144466078,0.0447470471,0.0450545736,-0.2553613484,0.0899209753,-0.5541221499,0.2491139024,0.104594931,-0.0720571429,0.2975907326,-0.3017668426,0.2677598596,-0.0850059688,0.0262452774,0.2439461052,-0.0638855994,-0.1151607782,0.2073930055,-0.0009455864,-0.2045055181,-0.0694354549,-0.45423159,0.0335031152,-0.3397281468,-0.1507048756,0.3422574997,0.1017849892,-0.1304057837,0.1610793471,-0.0711504072,-0.1975040138,0.0639175996,0.1212921143,-0.0829551965,-0.0109530622,-0.2208668292,0.3150741458,0.2363253683,0.3301177025,0.0516277552,-0.1459756196,0.0075899232,-0.3100179136,-0.2521992922,0.2785663307,-0.0317873061,0.3897755146,0.1379317939,-0.3446787596,-0.1478319615,0.1022378057,0.0575494617,0.1419411749,0.1398730576,0.1266010553,0.0635658801,0.1517291218,-0.4028158188,0.2708552778,0.5878329873,-0.1192695946,-0.069387868,0.2395924926,0.059559077,-0.1695690155,-0.0338157713,-0.217757225,0.1716604531,0.1619177461,0.1790524721,-0.0999094173,0.124293372,-0.1298576891,0.3223983645,0.2120993882,0.004855027,0.022191342,0.116418615,0.0722042248,0.0016253602,-0.0955381989,0.1305938661,0.3099609613,-0.1252200752,0.0044579892,0.1954187155,-0.1072649211,-0.1237375364,-0.0269091949,0.0157357194,-0.3178576529,-0.1369980425,0.248922646,-0.1166380346,0.0364544466,0.1061013117,0.3592701256,0.1271025687,-0.364212513,0.1010778546,-0.1170879453,-0.2541911304,-0.0160684511,0.1550412625,-0.0097691929,0.1884468198,0.0886323899,-0.1106292754,0.015857242,-0.1376692057,-0.0592114329,-0.4576979876,0.0734886229,-0.1006338224,0.335616827,-0.2249776274,-0.437174648,-0.0026174795,-0.0687950104,0.2533338964,-0.2674293518,0.0078697056,-0.0599730387,0.1682515144,-0.2091825753,-0.3979697526,-0.2773379683,0.1616996378,-0.0804175064,0.1120989919,0.3758215606,0.0861320272,0.0614354759,0.1984479427,-0.1117398441,0.0645064414,-0.3359968662,0.2933339775,-0.1133206114,-0.2322174907,-0.2972748578,-0.0569187999,-0.0541822575,0.3564843833,-0.5518575907,0.1781203598,-0.3643219471,0.1989420503,-0.005830613,0.5625964403,-0.0943654105,-0.4863793552,-0.0153563311,0.1136103272,0.1111946926,0.0132406522,0.2653706968,0.2140454203,0.2785107493,0.4317783415,0.133768037,1.1682617664,0.1721532643,-0.2398597002,-0.21429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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/315","title":"[Question] Best way to batch a large dataset?","comments":"Sorry, I think I badly expressed myself, my bad. What I suggested is to compare with the usual loading textual data in pure TF with `TextLineDataset` with `nlp`. I know it is not recommended with very large datasets to use it, but I was curious to see how it behaves compared to a processing with `nlp` on smaller datasets.\r\n\r\nBTW your script looks very interesting, thanks for sharing!!","body":"I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets?","comment_length":68,"text":"[Question] Best way to batch a large dataset? \n I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb), I see the following recommended for TensorFlow:\r\n\r\n```python\r\ntrain_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"].to_tensor(default_value=0, shape=[None, 1])}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"].to_tensor(default_value=0, shape=[None, 1])\r\n### Question about this last line ###\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nThis code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.\r\n\r\nSo I tried manual batching using `dataset.select()`:\r\n\r\n```python\r\nidxs = np.random.randint(len(dataset), size=bsz)\r\nbatch = dataset.select(idxs).map(lambda example: {\"input_ids\": tokenizer(example[\"text\"])})\r\ntf_batch = tf.constant(batch[\"ids\"], dtype=tf.int64)\r\n```\r\n\r\nThis appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.\r\n\r\nIs there a performant scalable way to lazily load batches of nlp Datasets? \n Sorry, I think I badly expressed myself, my bad. What I suggested is to compare with the usual loading textual data in pure TF with `TextLineDataset` with `nlp`. I know it is not recommended with very large datasets to use it, but I was curious to see how it behaves compared to a processing with `nlp` on smaller datasets.\r\n\r\nBTW your script looks very interesting, thanks for sharing!!","embeddings":[-0.3098398447,-0.3549019396,0.040070232,-0.1314413697,0.043479424,0.1706920415,0.5454062223,0.3945962787,0.1853833199,0.0758509487,-0.0109290816,0.0905708447,-0.0458247811,0.1636851728,0.1464808881,-0.3626331091,0.0164577533,-0.2653392255,-0.0260757729,-0.1447885334,-0.2456075102,-0.2963108122,-0.3499272764,-0.2272665501,-0.3148984015,-0.2920510471,0.0221025404,-0.1786595732,0.2146256864,-0.0490131006,0.0490412712,0.0216406938,0.300095439,0.4448573887,-0.0001156601,-0.1326184422,0.0600120351,-0.0445338674,-0.0088806693,0.3237411678,-0.0612367652,-0.388153106,-0.0979186371,-0.4061881304,0.1105616391,0.1923644096,0.1939423829,-0.0472506657,0.1358859092,-0.0325134471,0.063217558,0.3727622628,-0.0247660466,0.1722879857,0.02888369,-0.0639728978,0.0118579278,0.0609920211,0.2795847654,-0.0697013512,-0.0007684377,0.3284053802,-0.1674046367,-0.0159423053,0.2925575972,0.2313953638,0.1201131791,-0.6053037047,0.031976074,0.3724275529,0.2989349961,0.003854932,-0.2751540542,-0.6016538739,-0.1216737404,-0.2600981593,-0.2543848455,0.2779847682,-0.2282436937,-0.1784859449,-0.2848558724,-0.0607504658,-0.111662358,0.1517273933,-0.17424573,0.1897965819,0.1561438292,0.2513833046,0.1800088286,-0.259157747,0.0723085105,-0.2944973111,0.391967684,0.2885423005,-0.5199069381,-0.161050126,0.1525548995,-0.2357104123,0.0620710067,-0.2695543766,-0.0892135128,0.0071072425,0.0950781703,0.046422746,0.1840151697,0.2864991128,-0.1267043054,0.0481852777,0.1040561199,-0.2625125349,-0.2421223223,0.3590080142,-0.108374469,-0.2902850807,0.0475210175,-0.1952093393,-0.358223021,0.1385153085,-0.0088153258,0.0312358961,0.0680118501,-0.0676135719,-0.031125294,0.1281121075,-0.2842437327,0.0787763745,-0.1488966644,-0.1395684034,-0.3805860281,0.1666765809,-0.1485890448,0.0174979772,-0.0054796408,-0.0163144097,0.1355892569,-0.139675349,0.1104079336,-0.1817804277,0.0023557388,0.2069333345,-0.1126759872,-0.2320376039,0.1354833096,0.2346876562,0.0579938069,0.2410531789,0.0312836021,0.1558558941,-0.2094338685,0.3726697862,-0.3099021912,-0.3395798504,0.1943150908,0.0668445751,0.0166570023,-0.3149917126,-0.5945531726,0.3981288075,0.321008116,0.0930782035,-0.0770611241,-0.2863120437,-0.1548867822,-0.2435482889,0.1875332594,0.2121235579,-0.4263499677,0.1576705426,0.4017448723,0.3291971684,0.3743069172,0.6147413254,-0.2548478842,0.4410940707,0.175022468,0.1125394627,0.1075415313,-0.25900051,-0.2935233712,0.4176078737,-0.2239006311,0.0911498666,0.0280616544,0.2754259109,0.290940702,-0.235840559,0.4677284658,0.5002925396,-0.1060514823,0.3253593147,-0.2121745497,-0.2624133229,0.1294199079,0.1681138873,0.1687644422,-0.1044366434,-0.2262644023,0.2172021866,0.2736003399,-0.1601257324,-0.1335326135,0.0144466078,0.0447470471,0.0450545736,-0.2553613484,0.0899209753,-0.5541221499,0.2491139024,0.104594931,-0.0720571429,0.2975907326,-0.3017668426,0.2677598596,-0.0850059688,0.0262452774,0.2439461052,-0.0638855994,-0.1151607782,0.2073930055,-0.0009455864,-0.2045055181,-0.0694354549,-0.45423159,0.0335031152,-0.3397281468,-0.1507048756,0.3422574997,0.1017849892,-0.1304057837,0.1610793471,-0.0711504072,-0.1975040138,0.0639175996,0.1212921143,-0.0829551965,-0.0109530622,-0.2208668292,0.3150741458,0.2363253683,0.3301177025,0.0516277552,-0.1459756196,0.0075899232,-0.3100179136,-0.2521992922,0.2785663307,-0.0317873061,0.3897755146,0.1379317939,-0.3446787596,-0.1478319615,0.1022378057,0.0575494617,0.1419411749,0.1398730576,0.1266010553,0.0635658801,0.1517291218,-0.4028158188,0.2708552778,0.5878329873,-0.1192695946,-0.069387868,0.2395924926,0.059559077,-0.1695690155,-0.0338157713,-0.217757225,0.1716604531,0.1619177461,0.1790524721,-0.0999094173,0.124293372,-0.1298576891,0.3223983645,0.2120993882,0.004855027,0.022191342,0.116418615,0.0722042248,0.0016253602,-0.0955381989,0.1305938661,0.3099609613,-0.1252200752,0.0044579892,0.1954187155,-0.1072649211,-0.1237375364,-0.0269091949,0.0157357194,-0.3178576529,-0.1369980425,0.248922646,-0.1166380346,0.0364544466,0.1061013117,0.3592701256,0.1271025687,-0.364212513,0.1010778546,-0.1170879453,-0.2541911304,-0.0160684511,0.1550412625,-0.0097691929,0.1884468198,0.0886323899,-0.1106292754,0.015857242,-0.1376692057,-0.0592114329,-0.4576979876,0.0734886229,-0.1006338224,0.335616827,-0.2249776274,-0.437174648,-0.0026174795,-0.0687950104,0.2533338964,-0.2674293518,0.0078697056,-0.0599730387,0.1682515144,-0.2091825753,-0.3979697526,-0.2773379683,0.1616996378,-0.0804175064,0.1120989919,0.3758215606,0.0861320272,0.0614354759,0.1984479427,-0.1117398441,0.0645064414,-0.3359968662,0.2933339775,-0.1133206114,-0.2322174907,-0.2972748578,-0.0569187999,-0.0541822575,0.3564843833,-0.5518575907,0.1781203598,-0.3643219471,0.1989420503,-0.005830613,0.5625964403,-0.0943654105,-0.4863793552,-0.0153563311,0.1136103272,0.1111946926,0.0132406522,0.2653706968,0.2140454203,0.2785107493,0.4317783415,0.133768037,1.1682617664,0.1721532643,-0.2398597002,-0.2142921984,-0.0006967018,-0.0267474819,-0.3446087837,-0.2672060728,0.3804666996,-0.0651110634,-0.121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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/312","title":"[Feature request] Add `shard()` method to dataset","comments":"Hi Jared,\r\nInteresting, thanks for raising this question. You can also do that after loading with `dataset.select()` or `dataset.filter()` which let you keep only a specific subset of rows in a dataset.\r\nWhat is your use-case for sharding?","body":"Currently, to shard a dataset into 10 pieces on different ranks, you can run\r\n\r\n```python\r\nrank = 3 # for example\r\nsize = 10\r\ndataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f\"train[{rank*10}%:{(rank+1)*10}%]\")\r\n```\r\n\r\nHowever, this breaks down if you have a number of ranks that doesn't divide cleanly into 100, such as 64 ranks. Is there interest in adding a method shard() that looks like this?\r\n\r\n```python\r\nrank = 3\r\nsize = 64\r\ndataset = nlp.load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"train\").shard(rank=rank, size=size)\r\n```\r\n\r\nTensorFlow has a similar API: https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/data\/Dataset#shard. I'd be happy to contribute this code.","comment_length":38,"text":"[Feature request] Add `shard()` method to dataset \n Currently, to shard a dataset into 10 pieces on different ranks, you can run\r\n\r\n```python\r\nrank = 3 # for example\r\nsize = 10\r\ndataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f\"train[{rank*10}%:{(rank+1)*10}%]\")\r\n```\r\n\r\nHowever, this breaks down if you have a number of ranks that doesn't divide cleanly into 100, such as 64 ranks. Is there interest in adding a method shard() that looks like this?\r\n\r\n```python\r\nrank = 3\r\nsize = 64\r\ndataset = nlp.load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"train\").shard(rank=rank, size=size)\r\n```\r\n\r\nTensorFlow has a similar API: https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/data\/Dataset#shard. I'd be happy to contribute this code. \n Hi Jared,\r\nInteresting, thanks for raising this question. You can also do that after loading with `dataset.select()` or `dataset.filter()` which let you keep only a specific subset of rows in a dataset.\r\nWhat is your use-case for sharding?","embeddings":[-0.0902190059,0.0335450843,-0.1385268271,-0.0104933362,-0.1253202856,-0.0391625874,0.4583161175,0.4131177068,-0.1107414141,0.1866177917,0.1924446821,0.2701655626,-0.2045647353,0.3323414326,0.5994433761,-0.4415318668,-0.1018194929,0.0852711573,0.149879232,0.1868807077,-0.0623615421,-0.0934392959,-0.0521033406,-0.3756529093,-0.0738371238,0.0836312249,-0.106206283,-0.2192081213,0.2495964617,-0.2033970505,0.1981488764,0.0128354235,0.176435113,0.1089037955,-0.0001193276,-0.1619028896,0.1732525229,0.1418514401,-0.0407615453,-0.1341593862,-0.4363878965,-0.3930357397,-0.0248603262,-0.2685240805,-0.0763259456,0.4600835145,0.0376816317,0.0504966155,0.4158009291,0.0452725515,0.0574582145,0.0144584598,0.0706741959,0.2210827023,0.4204316735,0.1398421079,-0.2779010236,0.0068351403,-0.0258939732,0.2300103903,0.1723851413,0.1819727123,-0.0076822857,-0.1983823329,0.151426211,0.1420772225,-0.2785290182,-0.1684783101,-0.0783310905,0.4818460345,-0.2934041917,-0.0841815248,-0.3170888126,-0.4590073228,0.2600898445,-0.7193084359,-0.3265034556,0.2590245306,-0.2154867947,-0.1617243439,-0.1539446115,-0.2942079008,-0.2469418496,0.0869933292,0.1184398085,0.2054739743,0.1094351187,-0.0066468925,0.2596993744,-0.0784598142,-0.1302294582,0.0487918742,0.2060503513,0.1232482344,-0.6726326346,-0.349616617,0.151286304,0.3256623149,0.2887284458,0.1294467002,0.0897560567,0.3228828013,0.1871463805,0.0715471655,0.0216732733,-0.1212604046,0.2701044381,-0.0877677575,-0.0579494201,-0.2449847311,0.2663830519,0.0972311795,0.23987858,-0.2317632437,-0.0781483501,-0.1192791685,-0.1509444565,0.0851148069,0.0345103033,-0.2624329627,0.3014739454,-0.2090816349,0.2798621356,0.0331917107,0.2475590408,-0.2252887189,-0.3614956439,0.0553383306,-0.1140266731,-0.2651718855,-0.0561420918,0.0309462454,-0.1502551585,0.1724989712,0.0432409197,-0.280846566,-0.2295240164,-0.0929453671,0.2970185876,0.1192361861,0.0993343368,0.1004474238,0.119814001,0.181933105,-0.3314205408,-0.4015157521,-0.0938133523,-0.0558922775,-0.3344355822,0.0863510668,-0.484593153,-0.359936595,-0.1270346045,0.0499380194,0.0325772315,-0.1788466722,-0.6782975793,0.5066899061,0.1495945156,-0.1023567095,0.0311424267,-0.1672639102,-0.700308919,-0.2102397978,0.3099248111,-0.0475842394,-0.1396569312,-0.1577159166,-0.7277149558,0.1584021002,0.1372088045,0.0691284612,-0.0961400345,-0.0049770996,0.116054289,0.7563393116,0.9136978388,0.1492827684,-0.363624841,0.2881655395,-0.0713724792,-0.2263800502,0.101901032,0.2832311094,0.312705338,-0.0604075417,0.1139606759,0.3264332712,-0.2208423764,-0.1373609304,0.0063825226,-0.1372893304,0.2847053111,0.4371267557,0.1175920293,-0.0439295135,0.425958097,-0.2272982746,0.2227542251,-0.1066037789,-0.0137880147,-0.3090628088,0.1172641441,0.072384499,-0.0667463318,-0.5428250432,-0.0849225223,0.0770921186,0.0037511138,-0.0932952315,-0.0223909728,-0.3046743572,0.0948518887,-0.0015547101,0.116472654,0.1285893917,-0.0430854522,-0.258610785,0.0833002478,-0.1595627666,-0.3628672659,-0.4273320735,-0.4661524594,-0.0019465975,-0.3521492779,0.4206297994,0.3815576434,-0.2036995143,-0.0961357579,0.4029873013,0.0117044104,-0.2372408956,0.2422447354,0.2740434706,0.0853389353,-0.1135813519,0.0520370677,0.3304307759,0.1716755778,0.1707373261,-0.2814111412,-0.0287463311,-0.2243514061,-0.0752126351,-0.0451592542,0.4841749668,-0.4256809652,0.4008709788,-0.106112577,0.006309777,-0.0138321863,-0.0932262912,-0.1130578965,-0.1825072616,0.0705758408,0.200553298,-0.1365466565,-0.2851176262,0.0616345555,0.1975689977,0.5304974318,-0.2233927399,0.2951188385,0.3810320199,0.3891986609,0.108373493,0.1387320608,0.4842126966,0.4051701725,0.1774630696,0.0463757627,0.2085753977,0.0684135258,-0.2003530115,0.0488263071,0.2136185914,-0.2074343562,0.096232906,0.0300726052,0.0040939962,-0.1740270704,-0.1103289127,-0.105906114,0.0203829631,-0.0459676273,-0.1507743597,-0.2782159746,0.1470051408,-0.1693458706,0.198210597,-0.253113389,0.0215052031,0.0074579101,-0.1364809722,-0.1525467187,0.0969379246,0.151262939,0.4196157455,-0.1436355561,0.0009898827,0.0976945609,-0.0651281178,0.2122161686,-0.0127151748,0.1586858481,0.0745873824,0.5542141199,-0.0527701527,-0.1078748181,-0.1906172335,-0.4497658014,-0.0322911516,-0.2761199772,0.2822998464,0.1809542924,0.0685620159,-0.3565734923,-0.1503163427,-0.0005949574,0.0145139135,0.0318046696,0.1161490828,0.1840159297,0.3239157498,-0.0685231239,0.0758896768,-0.1115966812,-0.1751188487,0.3468532562,0.1412599087,0.1919829696,-0.2804489732,-0.0493816845,0.1952583939,-0.0353983268,-0.1059934273,0.1141613871,-0.3425091803,0.2413946092,-0.3241923749,-0.1880903989,-0.1552043557,-0.0892999992,-0.0342253931,0.6443938613,-0.1353792548,-0.0285966881,-0.0249955337,0.1881885082,-0.0791523978,0.014095949,0.4491424859,-0.1632229388,0.1547424346,0.214763552,0.1615513563,-0.026202539,0.0258960873,0.0613388903,0.3198076785,0.7087903619,0.3468720019,0.8575080633,0.0832482651,-0.0617544651,-0.1029329002,-0.1273305267,-0.1556652337,-0.0813511163,-0.1452895105,0.2121749222,0.05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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/312","title":"[Feature request] Add `shard()` method to dataset","comments":"Thanks for the pointer to those functions! It's still a little more verbose since you have to manually calculate which ids each rank would keep, but definitely works.\r\n\r\nMy use case is multi-node, multi-GPU training and avoiding global batches of duplicate elements. I'm using horovod. You can shuffle indices, or set random seeds, but explicitly sharding the dataset up front is the safest and clearest way I've found to do so.","body":"Currently, to shard a dataset into 10 pieces on different ranks, you can run\r\n\r\n```python\r\nrank = 3 # for example\r\nsize = 10\r\ndataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f\"train[{rank*10}%:{(rank+1)*10}%]\")\r\n```\r\n\r\nHowever, this breaks down if you have a number of ranks that doesn't divide cleanly into 100, such as 64 ranks. Is there interest in adding a method shard() that looks like this?\r\n\r\n```python\r\nrank = 3\r\nsize = 64\r\ndataset = nlp.load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"train\").shard(rank=rank, size=size)\r\n```\r\n\r\nTensorFlow has a similar API: https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/data\/Dataset#shard. I'd be happy to contribute this code.","comment_length":71,"text":"[Feature request] Add `shard()` method to dataset \n Currently, to shard a dataset into 10 pieces on different ranks, you can run\r\n\r\n```python\r\nrank = 3 # for example\r\nsize = 10\r\ndataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f\"train[{rank*10}%:{(rank+1)*10}%]\")\r\n```\r\n\r\nHowever, this breaks down if you have a number of ranks that doesn't divide cleanly into 100, such as 64 ranks. Is there interest in adding a method shard() that looks like this?\r\n\r\n```python\r\nrank = 3\r\nsize = 64\r\ndataset = nlp.load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"train\").shard(rank=rank, size=size)\r\n```\r\n\r\nTensorFlow has a similar API: https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/data\/Dataset#shard. I'd be happy to contribute this code. \n Thanks for the pointer to those functions! It's still a little more verbose since you have to manually calculate which ids each rank would keep, but definitely works.\r\n\r\nMy use case is multi-node, multi-GPU training and avoiding global batches of duplicate elements. I'm using horovod. You can shuffle indices, or set random seeds, but explicitly sharding the dataset up front is the safest and clearest way I've found to do so.","embeddings":[-0.0903999358,0.01625273,-0.1279894263,-0.0729840547,-0.0588933155,-0.0729780346,0.4991557598,0.4099660814,-0.0554774366,0.2099151611,0.2360797673,0.3211589158,-0.3143125176,0.4003581405,0.6131051183,-0.4336142242,-0.032296069,0.00938618,0.215573594,0.0877346918,-0.0656637102,-0.1401859075,-0.0400372222,-0.3743532002,-0.0052027269,0.1019332334,-0.1808533818,-0.2872148752,0.2049991786,-0.2163394541,0.166055724,0.0582099371,0.1823759824,0.1601049304,-0.0001183805,-0.191446349,0.1588507891,0.1180886403,0.0012730663,-0.139281705,-0.4328423738,-0.4010118246,-0.0340188183,-0.2599122524,-0.0119534042,0.4012717605,0.0698875487,-0.0125155281,0.392546773,0.0025384095,0.0516128317,-0.060711991,0.1544547975,0.1872902066,0.4036003351,0.2214667499,-0.2822946012,-0.0089455582,-0.0786861405,0.1254140884,0.1136673093,0.1217194945,-0.0275734589,-0.1082073972,0.1557449698,0.1765587926,-0.3047389686,-0.1531616449,-0.1950687766,0.4781175852,-0.3025984764,-0.1509925127,-0.4132490456,-0.3879085779,0.2934780717,-0.7473744154,-0.3074724674,0.2714187205,-0.2418925315,-0.27542454,-0.2380059063,-0.27680704,-0.2071870416,0.077737093,0.2221379429,0.263864696,0.1681626588,-0.0175340548,0.3311504424,-0.0402620845,-0.0945930332,0.0755236372,0.2097242475,0.1188467443,-0.614027977,-0.3843820095,0.1218651533,0.3380563557,0.3469469249,0.1159338206,0.1381366998,0.2720663846,0.1744915545,0.030086508,0.0391739421,-0.0918158963,0.220510453,-0.0723302066,-0.0907713547,-0.2289366275,0.2165081501,0.1132825091,0.2126709074,-0.2785083652,-0.0142555535,0.0081662051,-0.2290856242,0.1169550493,0.1137102172,-0.2728081346,0.2444725037,-0.2083791494,0.2825330198,-0.0320820659,0.2095986605,-0.2082325071,-0.3277778327,0.0349674299,-0.1531598717,-0.3048244119,-0.0477774031,0.0721688047,-0.1881659329,0.2256807834,-0.0001613488,-0.2330842167,-0.27420187,-0.0648637041,0.4346828461,0.1786167026,0.084065333,0.2126348764,0.1357833147,0.088941291,-0.257251054,-0.3714539409,-0.0325550213,-0.0415745378,-0.3541635871,0.0491739549,-0.587732017,-0.3894829452,-0.1214290634,0.0379108265,0.0685505718,-0.1741915941,-0.6619852781,0.4921070933,0.0940611511,-0.1506498903,0.0367395729,-0.1810275912,-0.6687296629,-0.2130110413,0.3053415716,0.0020801318,-0.0641986951,-0.1369177401,-0.7781395912,0.1745422035,0.1327816695,-0.0098212939,-0.0979701653,0.0036863945,0.06320519,0.6894866824,0.7901318073,0.0799240917,-0.259321481,0.249647662,-0.0498580597,-0.2648639977,0.0904735252,0.302419275,0.2453381717,-0.0833153278,0.0976925492,0.353602618,-0.2485919893,-0.0741748363,0.0000437247,-0.1481006593,0.2624673843,0.4019246697,0.1613715738,-0.099804841,0.4458760321,-0.2314695865,0.2351807207,-0.0646500513,-0.0449084193,-0.1955905259,0.1874001026,0.0401863866,-0.1198532954,-0.480161339,-0.0211492348,0.0615554862,0.0188921336,-0.0062778052,0.0000303681,-0.289360553,0.1609876752,-0.0148133347,0.015560302,0.0897698998,-0.024887925,-0.2294120044,0.0718062147,-0.1665593982,-0.3250158727,-0.4209618866,-0.4153961539,-0.0026576987,-0.3153293431,0.4252645075,0.3351682127,-0.1314558983,-0.1251170039,0.350163728,0.0106083928,-0.1990663111,0.2325002402,0.3144398332,0.1239589378,-0.1851849705,0.1384314001,0.318557173,0.1622317731,0.1150949895,-0.3427917063,0.021769261,-0.2812332511,-0.0201554876,-0.0249481257,0.4834856987,-0.4536584616,0.3446874917,-0.0948198065,0.0413978621,-0.0674914867,-0.0322815329,-0.082105495,-0.1548915505,0.0170999821,0.2470054626,-0.1021986157,-0.288100481,0.1100903898,0.1803701967,0.5856354237,-0.1634201407,0.2819884717,0.2988931835,0.4121267498,0.1175744161,0.1000778899,0.490033567,0.3577459455,0.1889505535,0.0540700667,0.1835535169,0.007465404,-0.1562810391,0.0177055821,0.1334724575,-0.1327423155,0.0875066817,0.0698189884,-0.0496655107,-0.1410765946,-0.1763355136,-0.0049803359,0.0110957921,-0.074994266,-0.2284101099,-0.2371111661,0.2976135015,-0.2259353101,0.1298104078,-0.3122288287,0.0116350716,0.0476827845,-0.1525698751,-0.1405093521,0.1476093829,0.2023098767,0.3973855972,-0.0970733762,-0.047663901,0.2124168575,-0.0644404441,0.1698105633,-0.0129557028,0.2171652317,0.0514901616,0.5093231797,0.0358145088,-0.0743879899,-0.2529281378,-0.4815770686,-0.0534883998,-0.2781827748,0.2317032069,0.156866774,-0.032728333,-0.408772558,-0.1373485923,0.0282857083,-0.1450952739,-0.071896106,0.1404522508,0.1567685306,0.2897759378,-0.0762949064,0.0976015851,-0.1189652979,-0.1235760674,0.3168518543,0.1304379255,0.2200096697,-0.2814023197,-0.0102225123,0.106131494,-0.0294495523,-0.1092444509,0.1849551201,-0.310992986,0.2382128984,-0.3582490087,-0.1694325507,-0.2485969216,-0.1250095218,-0.0642140955,0.7491729259,-0.0403823555,-0.066857405,0.0108843669,0.1439590752,-0.1054384038,0.0316976011,0.5483807325,-0.0572200306,0.1360258907,0.2081536502,0.2660071254,0.0347035527,0.0306900982,0.0958150104,0.2953639627,0.6451733708,0.3203065693,0.8731663227,0.0987205207,-0.0327910855,-0.0986943766,-0.1933798939,-0.1365426034,-0.0296432823,-0.129579857,0.1816159487,0.0352501906,0.0240287017,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/302","title":"Question - Sign Language Datasets","comments":"Even more complicating - \r\n\r\nAs I see it, datasets can have \"addons\".\r\nFor example, the WebNLG dataset is a dataset for data-to-text. However, a work of mine and other works enriched this dataset with text plans \/ underlying text structures. In that case, I see a need to load the dataset \"WebNLG\" with \"plans\" addon.\r\n\r\nSame for sign language - if there is a dataset of videos, one addon can be to run OpenPose, another to run ARKit4 pose estimation, and another to run PoseNet, or even just a video embedding addon. (which are expensive to run individually for everyone who wants to use these data)\r\n\r\nThis is something I dabbled with my own implementation to a [research datasets library](https:\/\/github.com\/AmitMY\/meta-scholar\/) and I love to get the discussion going on these topics.","body":"An emerging field in NLP is SLP - sign language processing.\r\n\r\nI was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.\r\nThe metrics for sign language to text translation are the same.\r\n\r\nSo, what do you think about (me, or others) adding datasets here?\r\n\r\n\r\nAn example dataset would be [RWTH-PHOENIX-Weather 2014 T](https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/)\r\nFor every item in the dataset, the data object includes:\r\n1. video_path - path to mp4 file\r\n2. pose_path - a path to `.pose` file with human pose landmarks\r\n3. openpose_path - a path to a `.json` file with human pose landmarks\r\n4. gloss - string\r\n5. text - string\r\n6. video_metadata - height, width, frames, framerate\r\n\r\n\r\n------\r\n\r\nTo make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so.","comment_length":131,"text":"Question - Sign Language Datasets \n An emerging field in NLP is SLP - sign language processing.\r\n\r\nI was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.\r\nThe metrics for sign language to text translation are the same.\r\n\r\nSo, what do you think about (me, or others) adding datasets here?\r\n\r\n\r\nAn example dataset would be [RWTH-PHOENIX-Weather 2014 T](https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/)\r\nFor every item in the dataset, the data object includes:\r\n1. video_path - path to mp4 file\r\n2. pose_path - a path to `.pose` file with human pose landmarks\r\n3. openpose_path - a path to a `.json` file with human pose landmarks\r\n4. gloss - string\r\n5. text - string\r\n6. video_metadata - height, width, frames, framerate\r\n\r\n\r\n------\r\n\r\nTo make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so. \n Even more complicating - \r\n\r\nAs I see it, datasets can have \"addons\".\r\nFor example, the WebNLG dataset is a dataset for data-to-text. However, a work of mine and other works enriched this dataset with text plans \/ underlying text structures. In that case, I see a need to load the dataset \"WebNLG\" with \"plans\" addon.\r\n\r\nSame for sign language - if there is a dataset of videos, one addon can be to run OpenPose, another to run ARKit4 pose estimation, and another to run PoseNet, or even just a video embedding addon. (which are expensive to run individually for everyone who wants to use these data)\r\n\r\nThis is something I dabbled with my own implementation to a [research datasets library](https:\/\/github.com\/AmitMY\/meta-scholar\/) and I love to get the discussion going on these topics.","embeddings":[-0.32776618,0.4525655508,-0.0732839257,0.0123400809,-0.0788401142,-0.0115335379,0.2201107293,0.2610009015,0.2013961822,-0.3687560558,0.0878804475,0.2947927713,-0.3939641416,0.2834338248,0.3449884951,-0.176551044,-0.0004947542,-0.0626648739,0.0087343119,-0.0980993658,-0.2763954997,-0.1909826398,0.0531514324,-0.007089871,-0.3837481141,-0.332061857,-0.1520041823,0.0552289262,-0.0798219368,-0.3670464754,-0.4654010832,0.1338605583,0.3811520338,-0.0573748946,-0.0001092122,-0.2131741047,0.0867840946,0.1366245598,-0.2981795073,-0.1120385602,-0.1225187331,-0.6363066435,-0.1847902238,-0.2197489291,0.3014643192,-0.3671759665,0.1177984476,-0.4387135506,0.0204097852,0.2650385797,0.2657268345,0.0734952241,-0.1763009578,0.2845447361,0.1073937044,0.2880243063,0.0539344735,-0.3114773035,0.3666415513,-0.0040134597,-0.1532109678,0.2822242379,0.0871672481,-0.5369473696,0.2831125557,-0.0832231119,-0.1681210697,-0.0665763915,-0.0609676987,0.0545954406,0.3695205152,-0.3887362778,-0.5512011051,-0.692758441,0.3190686107,-0.0417574681,-0.1850506514,0.3131968975,0.2486976981,-0.0441939384,-0.2032430619,-0.6055930257,0.0735249966,0.2032313198,0.0504943393,0.3771199882,0.1068511754,0.0853631496,-0.0598349199,-0.05807098,0.1020340174,-0.4142607152,0.0059124534,0.1247747168,-0.2750384808,-0.1684951633,0.0074741784,0.1095340252,0.2164651155,0.1288278699,-0.0004105956,0.0027560038,-0.336345166,0.0445142612,0.3745672107,-0.0391658172,-0.0283417664,0.144290179,0.1519366056,-0.2677285969,0.4116028547,0.0693721548,-0.0097140595,0.0064761494,-0.2346767187,-0.2014696002,0.119522281,-0.0326781608,-0.1603718996,0.0468571745,-0.1952341199,-0.2574142814,0.1204497814,-0.0789810494,0.0028640912,0.2978838086,0.0192716494,0.3454528153,0.1967356801,-0.168796137,0.0183068346,0.1815776974,-0.3700411618,0.2450752258,0.3831808269,0.0594607778,0.0507775694,0.0166308731,-0.1397555321,0.0706387311,0.3091606796,-0.0534301437,0.0902294517,-0.2173833251,0.0065714717,0.0854129121,-0.3741538823,-0.0530162305,-0.3044703603,0.11985939,0.0816684663,-0.0236122105,0.0384660922,0.2177266181,-0.3923450708,-0.3972732723,-0.3264209032,0.6124715805,0.0717181116,-0.0422144085,0.1649869084,-0.1042282134,-0.3048969507,-0.0357939191,-0.2105996013,0.4268853962,-0.3384500742,-0.1672324985,-0.1232564673,-0.0646909699,-0.2797655165,0.2127159387,-0.2385702431,0.1813210249,0.0024056698,0.2150002271,0.5056704879,-0.2569220066,-0.0756710097,0.3999677002,-0.0459146053,-0.0478044972,0.0169659127,0.3778139353,0.13293311,-0.1663077772,-0.2145010382,0.3691989481,-0.2047019601,-0.0959278271,-0.162826106,-0.1164556295,0.420647949,0.3798087239,0.1131544113,-0.0518590808,0.0357221775,0.3774410188,0.0435267687,-0.196443826,0.394294858,-0.1603751779,-0.017672481,-0.0753514692,-0.1399202645,-0.3019448817,-0.289950788,0.0062835142,0.0455887057,0.0574385636,0.1755376011,-0.0628907084,0.2070700079,0.0029975781,0.1741631925,0.1203707755,0.1408800334,0.1355981231,-0.2524044514,-0.1082754731,-0.4150164425,0.5105982423,-0.2547084391,-0.0392756015,-0.061357893,0.252538234,0.0246329252,0.1412556916,0.1342978776,0.6351205707,-0.2699644566,0.0135254422,0.2918762267,-0.1600831151,-0.2763584256,0.1621051282,0.3680669963,0.2745388746,0.3609109521,-0.1977920085,0.3337173164,-0.2925091088,0.2045737356,-0.2085653543,-0.0754081234,0.190060094,0.2169563025,0.1152892187,0.1386888027,-0.0741415396,0.0885092244,-0.0570485815,-0.2784792185,-0.0423538387,0.2771929204,-0.0012598449,0.4623275399,0.2014108598,-0.3186942637,0.1495314538,0.313357681,-0.1303913891,-0.013362282,0.2636477947,-0.0268867984,-0.1714869738,-0.0405314527,0.2153082192,0.414595753,0.3478784263,0.0927986503,0.046907533,-0.1673026681,-0.1603662968,0.0263926629,0.1448447108,-0.0611667261,0.2120802552,0.0907922462,0.1768361777,-0.1688581407,-0.2871466279,0.0197021011,-0.0383438766,-0.0456265509,0.1216837764,0.0811139867,-0.4516416788,-0.2804162204,0.0658235773,-0.2214261591,-0.3173054159,0.1547326893,-0.3717639148,-0.0085091023,0.0050645825,-0.3108474016,0.6126178503,-0.6380677223,-0.0599995442,-0.0429215617,-0.3297202587,-0.1528772861,0.1691616178,0.3665487766,0.152233243,0.5014584064,0.0929036438,0.2722419798,-0.2442156672,-0.5257211328,-0.1177096665,-0.3639345467,-0.1025497019,-0.118492797,-0.2681989372,0.3248793185,-0.1772309095,-0.1412790269,0.061333891,-0.0901023895,-0.3070274889,-0.0102554616,-0.3610468507,-0.0657807142,-0.2139178962,-0.2801144719,-0.3550033271,0.2222713381,0.1472243965,-0.1143486351,-0.082553491,-0.1171493009,0.2302026898,-0.0559170246,0.0973561779,-0.1091556102,0.221532777,0.0966774449,0.0737006962,-0.2337379009,0.0355563127,-0.059986189,0.085655205,0.2619890571,-0.5661298037,0.1449562758,-0.069379352,0.1110566705,-0.0226870645,-0.0608457588,0.0330215357,0.28169173,-0.0182203129,-0.1531530619,-0.4342613816,0.032331273,0.0822540969,0.2794435024,-0.0637432709,0.0371206515,-0.038826637,0.2141556442,0.2831184268,-0.3406554759,0.2703122795,0.3039261699,0.3658166528,0.1094877496,-0.1096167639,0.4175211191,0.00556831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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/302","title":"Question - Sign Language Datasets","comments":"This is a really cool idea !\r\nThe example for data objects you gave for the RWTH-PHOENIX-Weather 2014 T dataset can totally fit inside the library.\r\n\r\nFor your point about formats like `ilex`, `eaf`, or `srt`, it is possible to use any library in your dataset script.\r\nHowever most user probably won't need these libraries, as most datasets don't need them, and therefore it's unlikely that we will have them in the minimum requirements to use `nlp` (we want to keep it as light-weight as possible). If a user wants to load your dataset and doesn't have the libraries you need, an error is raised asking the user to install them.\r\n\r\nMore generally, we plan to have something like a `requirements.txt` per dataset. This could also be a place for addons as you said. What do you think ?","body":"An emerging field in NLP is SLP - sign language processing.\r\n\r\nI was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.\r\nThe metrics for sign language to text translation are the same.\r\n\r\nSo, what do you think about (me, or others) adding datasets here?\r\n\r\n\r\nAn example dataset would be [RWTH-PHOENIX-Weather 2014 T](https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/)\r\nFor every item in the dataset, the data object includes:\r\n1. video_path - path to mp4 file\r\n2. pose_path - a path to `.pose` file with human pose landmarks\r\n3. openpose_path - a path to a `.json` file with human pose landmarks\r\n4. gloss - string\r\n5. text - string\r\n6. video_metadata - height, width, frames, framerate\r\n\r\n\r\n------\r\n\r\nTo make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so.","comment_length":139,"text":"Question - Sign Language Datasets \n An emerging field in NLP is SLP - sign language processing.\r\n\r\nI was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.\r\nThe metrics for sign language to text translation are the same.\r\n\r\nSo, what do you think about (me, or others) adding datasets here?\r\n\r\n\r\nAn example dataset would be [RWTH-PHOENIX-Weather 2014 T](https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/)\r\nFor every item in the dataset, the data object includes:\r\n1. video_path - path to mp4 file\r\n2. pose_path - a path to `.pose` file with human pose landmarks\r\n3. openpose_path - a path to a `.json` file with human pose landmarks\r\n4. gloss - string\r\n5. text - string\r\n6. video_metadata - height, width, frames, framerate\r\n\r\n\r\n------\r\n\r\nTo make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so. \n This is a really cool idea !\r\nThe example for data objects you gave for the RWTH-PHOENIX-Weather 2014 T dataset can totally fit inside the library.\r\n\r\nFor your point about formats like `ilex`, `eaf`, or `srt`, it is possible to use any library in your dataset script.\r\nHowever most user probably won't need these libraries, as most datasets don't need them, and therefore it's unlikely that we will have them in the minimum requirements to use `nlp` (we want to keep it as light-weight as possible). If a user wants to load your dataset and doesn't have the libraries you need, an error is raised asking the user to install them.\r\n\r\nMore generally, we plan to have something like a `requirements.txt` per dataset. This could also be a place for addons as you said. What do you think 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/302","title":"Question - Sign Language Datasets","comments":"Thanks, Quentin, I think a `requirements.txt` per dataset will be a good thing.\r\nI will work on adding this dataset next week, and once we sort all of the kinks, I'll add more.","body":"An emerging field in NLP is SLP - sign language processing.\r\n\r\nI was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.\r\nThe metrics for sign language to text translation are the same.\r\n\r\nSo, what do you think about (me, or others) adding datasets here?\r\n\r\n\r\nAn example dataset would be [RWTH-PHOENIX-Weather 2014 T](https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/)\r\nFor every item in the dataset, the data object includes:\r\n1. video_path - path to mp4 file\r\n2. pose_path - a path to `.pose` file with human pose landmarks\r\n3. openpose_path - a path to a `.json` file with human pose landmarks\r\n4. gloss - string\r\n5. text - string\r\n6. video_metadata - height, width, frames, framerate\r\n\r\n\r\n------\r\n\r\nTo make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so.","comment_length":33,"text":"Question - Sign Language Datasets \n An emerging field in NLP is SLP - sign language processing.\r\n\r\nI was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.\r\nThe metrics for sign language to text translation are the same.\r\n\r\nSo, what do you think about (me, or others) adding datasets here?\r\n\r\n\r\nAn example dataset would be [RWTH-PHOENIX-Weather 2014 T](https:\/\/www-i6.informatik.rwth-aachen.de\/~koller\/RWTH-PHOENIX-2014-T\/)\r\nFor every item in the dataset, the data object includes:\r\n1. video_path - path to mp4 file\r\n2. pose_path - a path to `.pose` file with human pose landmarks\r\n3. openpose_path - a path to a `.json` file with human pose landmarks\r\n4. gloss - string\r\n5. text - string\r\n6. video_metadata - height, width, frames, framerate\r\n\r\n\r\n------\r\n\r\nTo make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so. \n Thanks, Quentin, I think a `requirements.txt` per dataset will be a good thing.\r\nI will work on adding this dataset next week, and once we sort all of the kinks, I'll add more.","embeddings":[-0.1974006295,0.4022528231,-0.079109177,-0.0050231428,-0.0767897144,0.0436102264,0.11855492,0.2503882945,0.2235915363,-0.3281885386,0.1770670414,0.2271167487,-0.4437556565,0.2644597292,0.3408164084,-0.1764732599,-0.084280245,0.0804555714,-0.023515759,-0.0963775143,-0.2208419442,-0.091707252,0.0955999345,-0.0577017106,-0.4422942698,-0.332775116,-0.178117156,0.0741666704,-0.204051435,-0.3707835078,-0.4634328485,0.1567583084,0.4244990051,-0.1583760083,-0.000114478,-0.2469081879,0.0123989526,-0.0152046494,-0.2423187345,-0.1023321301,-0.1577181667,-0.7473182678,-0.1494281739,-0.1643093228,0.2529296577,-0.443055898,0.1376864612,-0.2573286295,0.0975727811,0.3461552858,0.2374007404,0.1211299673,-0.1563312709,0.2964705527,0.1227010116,0.3389026225,-0.0006764327,-0.3003685772,0.3348721862,-0.0234885812,-0.2104610056,0.2818766236,0.1933553666,-0.5736477375,0.2710379958,-0.0685172454,-0.2079850733,-0.0676307306,-0.066908814,0.0754446387,0.3453674912,-0.3600135446,-0.4973927438,-0.7469828725,0.2285517901,-0.092820555,-0.1126513183,0.3469284475,0.1550317854,-0.010782606,-0.0998056531,-0.67773664,0.0045277481,0.109010458,0.1395772099,0.3664364815,0.1597377658,-0.0140163815,0.0213861279,-0.0601192638,0.0780716687,-0.3396180868,-0.0897082761,0.1468113363,-0.2279662341,-0.2546849251,0.0456875637,0.1795903295,0.1582259536,0.1055883467,-0.0377676859,-0.0872028396,-0.349296093,0.0652103499,0.3826233447,-0.0524613671,0.0003892317,0.0625457689,0.028248854,-0.3815040588,0.3718284667,0.0065557933,-0.037904989,0.0085483519,-0.1868052334,-0.1111243367,0.1067432761,-0.0339871123,0.0236690547,0.1212499067,-0.1666444838,-0.2111101747,0.0759290904,-0.0982967243,0.0746060163,0.3548745215,0.0401231274,0.2771657705,0.2093126178,-0.2290028632,0.0685553253,0.172265321,-0.3457767665,0.2788127363,0.4381219447,0.0669046342,0.1423583031,0.0603194907,-0.198967129,0.061864052,0.3503090143,-0.0628166124,0.1613736153,-0.1447441131,0.0385888293,0.0365707166,-0.3883935511,-0.0259430632,-0.2925308347,0.119131498,0.1366383731,-0.0804761723,-0.0388111621,0.1629549414,-0.4021618664,-0.3853216469,-0.3291303515,0.6202965379,0.1007386521,-0.0582490191,0.1596972644,-0.1684682071,-0.2560831606,0.014038221,-0.2791418135,0.428856492,-0.2821624279,-0.1059000939,-0.1297005266,-0.0637688488,-0.2600839436,0.2443723083,-0.2492683977,0.2110896707,-0.0272822473,0.2751368582,0.507065475,-0.2396633178,0.0208983328,0.4749806821,-0.0797762647,0.0007162541,0.1055531129,0.3611428142,0.1435505599,-0.1722275317,-0.3293012679,0.323480159,-0.2784798145,-0.0933738053,-0.2011948675,-0.1771761924,0.4429148436,0.2742109001,0.1469441056,-0.0275893416,-0.0663536265,0.406054318,0.1270267963,-0.1566640139,0.340169251,-0.0624594204,0.0352788046,0.0136017017,-0.0927390233,-0.3162546158,-0.1547408402,-0.0304822903,0.0125783207,0.1696051657,0.2088414431,-0.1213485077,0.2039934695,-0.1071820706,0.2525424361,0.0955638066,0.111690782,0.0382408984,-0.1850869954,-0.1132753566,-0.3947124779,0.4281033874,-0.2745808959,-0.0476967953,-0.0114997318,0.0725866556,0.0295501482,0.1697958857,0.1357233077,0.5882997513,-0.2614141405,0.0273805503,0.2882312536,-0.1669662595,-0.17252101,0.1844573468,0.3149498701,0.3089549541,0.3211069405,-0.1385314614,0.1999233961,-0.1884872913,0.1615176499,-0.184594214,-0.0175686348,0.195334211,0.1894375086,0.109466061,0.1455932856,-0.0465479717,0.1012184545,-0.0567432679,-0.3234140575,-0.0316369124,0.2800018489,-0.1444944888,0.3401680589,0.2117850333,-0.3706463575,0.1214513555,0.3513514996,-0.0525903329,0.0116539979,0.2522886395,0.0804406628,-0.1467967629,0.0063111018,0.2170946747,0.4361246824,0.3528653085,0.0151232611,0.0879413038,-0.1288971901,-0.2453832626,0.0566704609,0.1276622266,-0.1084780023,0.1986526251,0.0719914362,0.1995277703,-0.2864269614,-0.2640377283,-0.0211481228,-0.0154110361,0.0046084379,0.073966369,0.049952101,-0.5297708511,-0.365387857,0.1866244078,-0.2011543959,-0.2340789884,0.1968279183,-0.3085400164,0.0091536632,0.0580987334,-0.3039529324,0.6582548022,-0.6731199026,-0.0403692015,-0.0351419821,-0.3434549868,-0.1652197093,0.1436959654,0.3100822866,0.1160766482,0.4283594787,0.065305002,0.2960450053,-0.2109526098,-0.5485614538,-0.1787150651,-0.3139559925,-0.1921653599,-0.1019282117,-0.1495178491,0.3705406785,-0.1683762223,-0.2032722682,0.0717448294,-0.1242419183,-0.3720764816,-0.0146370251,-0.3222450614,-0.135581851,-0.172160387,-0.2186243534,-0.3289655149,0.2249587029,0.1005765647,0.0016788457,-0.0216464605,-0.1645269245,0.2052087784,-0.1831042916,0.0600906536,-0.0031858417,0.1764277965,0.1062486097,0.0799410865,-0.309022516,0.0749585256,-0.1006588638,0.1314843148,0.2565287352,-0.525586009,0.1268876642,-0.1069988981,0.0641059577,-0.0696773678,-0.1026674882,0.1247734725,0.2839758098,0.0247925073,-0.1574664414,-0.4571686387,0.0672450736,0.1272676438,0.343780607,-0.139229387,0.0594512597,-0.0022461715,0.2647852302,0.2434586287,-0.2642883658,0.3422489464,0.2653169334,0.4194591939,0.1689602584,-0.176170364,0.3868718743,0.063046068,0.13716146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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/301","title":"Setting cache_dir gives error on wikipedia download","comments":"Whoops didn't mean to close this one.\r\nI did some changes, could you try to run it from the master branch ?","body":"First of all thank you for a super handy library! I'd like to download large files to a specific drive so I set `cache_dir=my_path`. This works fine with e.g. imdb and squad. But on wikipedia I get an error:\r\n```\r\nnlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=my_path)\r\n```\r\n```\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-2-23551344d7bc> in <module>\r\n      1 import nlp\r\n----> 2 nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=path)\r\n\r\n~\/anaconda3\/envs\/fastai2\/lib\/python3.7\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/anaconda3\/envs\/fastai2\/lib\/python3.7\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    385                     with utils.temporary_assignment(self, \"_cache_dir\", tmp_data_dir):\r\n    386                         reader = ArrowReader(self._cache_dir, self.info)\r\n--> 387                         reader.download_from_hf_gcs(self._cache_dir, self._relative_data_dir(with_version=True))\r\n    388                         downloaded_info = DatasetInfo.from_directory(self._cache_dir)\r\n    389                         self.info.update(downloaded_info)\r\n\r\n~\/anaconda3\/envs\/fastai2\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py in download_from_hf_gcs(self, cache_dir, relative_data_dir)\r\n    231             remote_dataset_info = os.path.join(remote_cache_dir, \"dataset_info.json\")\r\n    232             downloaded_dataset_info = cached_path(remote_dataset_info)\r\n--> 233             os.rename(downloaded_dataset_info, os.path.join(cache_dir, \"dataset_info.json\"))\r\n    234             if self._info is not None:\r\n    235                 self._info.update(self._info.from_directory(cache_dir))\r\n\r\nOSError: [Errno 18] Invalid cross-device link: '\/home\/local\/NTU\/nn\/.cache\/huggingface\/datasets\/025fa4fd4f04aaafc9e939260fbc8f0bb190ce14c61310c8ae1ddd1dcb31f88c.9637f367b6711a79ca478be55fe6989b8aea4941b7ef7adc67b89ff403020947' -> '\/data\/nn\/nlp\/wikipedia\/20200501.de\/1.0.0.incomplete\/dataset_info.json'\r\n```","comment_length":22,"text":"Setting cache_dir gives error on wikipedia download \n First of all thank you for a super handy library! I'd like to download large files to a specific drive so I set `cache_dir=my_path`. This works fine with e.g. imdb and squad. But on wikipedia I get an error:\r\n```\r\nnlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=my_path)\r\n```\r\n```\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-2-23551344d7bc> in <module>\r\n      1 import nlp\r\n----> 2 nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=path)\r\n\r\n~\/anaconda3\/envs\/fastai2\/lib\/python3.7\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/anaconda3\/envs\/fastai2\/lib\/python3.7\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    385                     with utils.temporary_assignment(self, \"_cache_dir\", tmp_data_dir):\r\n    386                         reader = ArrowReader(self._cache_dir, self.info)\r\n--> 387                         reader.download_from_hf_gcs(self._cache_dir, self._relative_data_dir(with_version=True))\r\n    388                         downloaded_info = DatasetInfo.from_directory(self._cache_dir)\r\n    389                         self.info.update(downloaded_info)\r\n\r\n~\/anaconda3\/envs\/fastai2\/lib\/python3.7\/site-packages\/nlp\/arrow_reader.py in download_from_hf_gcs(self, cache_dir, relative_data_dir)\r\n    231             remote_dataset_info = os.path.join(remote_cache_dir, \"dataset_info.json\")\r\n    232             downloaded_dataset_info = cached_path(remote_dataset_info)\r\n--> 233             os.rename(downloaded_dataset_info, os.path.join(cache_dir, \"dataset_info.json\"))\r\n    234             if self._info is not None:\r\n    235                 self._info.update(self._info.from_directory(cache_dir))\r\n\r\nOSError: [Errno 18] Invalid cross-device link: '\/home\/local\/NTU\/nn\/.cache\/huggingface\/datasets\/025fa4fd4f04aaafc9e939260fbc8f0bb190ce14c61310c8ae1ddd1dcb31f88c.9637f367b6711a79ca478be55fe6989b8aea4941b7ef7adc67b89ff403020947' -> '\/data\/nn\/nlp\/wikipedia\/20200501.de\/1.0.0.incomplete\/dataset_info.json'\r\n``` \n Whoops didn't mean to close this one.\r\nI did some changes, could you try to run it from the master branch ?","embeddings":[-0.016413182,0.2276869714,-0.008926251,0.1092670187,0.122345984,0.199938044,0.2557855248,0.3317468464,0.4485464692,-0.1023546979,-0.1903628558,0.1178650558,0.0666657314,-0.5188595653,0.1406508833,-0.1102182716,0.0300002266,-0.0613132231,-0.0226511266,0.0516940393,-0.2747196257,0.2622623146,-0.0441821441,0.0540891103,-0.2433815002,-0.1050464287,-0.072632812,0.107462205,0.1136655733,-0.3596624136,0.4128053188,-0.0299529247,0.2771955132,0.4403319061,-0.000117424,-0.0152614946,0.3624263406,-0.2030952275,-0.5579511523,-0.2163559198,-0.2246102244,-0.2418591231,0.2105771154,-0.4761382043,0.3626435697,0.3921292424,0.3601877689,-0.2884105444,-0.1154452339,0.3803896308,0.1888131052,-0.2133142799,-0.0241495632,0.1325340867,0.4576618373,0.0064487583,-0.0460410677,0.4196564853,-0.5581924915,-0.182408005,-0.1978879273,0.3190628886,-0.3348386884,0.361063391,0.4996759593,0.1689672768,0.0551661104,-0.1576720774,0.1740521789,-0.0217134003,0.708702445,-0.3186961412,-0.1667527556,-0.1298629493,-0.1916632652,-0.3419360816,0.3887316585,0.3142914474,-0.2168809623,-0.1313343197,-0.0785200372,-0.1864174902,-0.2055503577,0.5084335208,0.0353473499,0.242180258,-0.0118902205,0.1077330783,0.3105280995,0.1559653878,-0.0152277891,-0.1883089542,0.0289224386,0.2680670321,-0.0917278454,0.0463293567,0.0164938383,0.1754599512,0.0469526201,-0.1358773112,-0.0364212319,-0.1584873199,-0.0180105586,0.2070783824,0.2671858966,0.4584434032,-0.2714021802,0.0725077316,0.345495373,0.2596298158,-0.1769945621,-0.0114095518,0.2153182775,-0.2061931342,0.0493750125,-0.0884282887,0.1682378054,-0.1910486072,-0.0727941915,-0.0134651763,-0.1205733865,-0.2002833188,0.0497518852,0.3913376331,-0.1108429283,0.0725893304,0.0393974856,0.3830533624,-0.2109767348,0.3658888936,-0.0863929614,0.1849439144,-0.0562552474,0.3640348613,0.3974510431,0.1643991321,0.2825456858,-0.2996708453,0.0105315642,-0.1080213264,0.0263676383,-0.355806917,0.0472795293,0.2118555009,0.1468363553,0.5156888962,0.2154467702,-0.1385783553,-0.3362908661,0.1985244751,-0.3842075467,-0.4099826813,0.0202297848,0.1066173166,-0.2228432149,-0.1802170724,-0.067409046,-0.0913380012,0.4154752791,-0.2722593844,0.2022363096,0.0881238207,-0.3097882867,-0.3220295906,0.0165564232,0.3973737955,0.0871979296,0.0317643508,-0.1866356879,0.471336633,0.2485871315,0.4971693456,-0.1658027619,0.3217129111,-0.2038130611,0.1477424651,0.5421157479,-0.408711195,-0.5469186902,0.2316524982,0.0064263502,-0.1150686443,0.1368109435,0.2783952951,0.1917880177,-0.0924685001,0.0588237122,0.5033121705,0.1440496892,0.2762487531,-0.4132539034,-0.1885228008,0.2845275402,-0.0427296385,-0.0707916096,0.0257842727,-0.0464463308,0.5719918013,0.133411184,-0.0220252387,0.0729474127,0.3717224896,0.0085802888,0.0894883573,0.0432725474,0.1852875799,-0.2929489315,0.0871187523,0.07798253,-0.0258016698,-0.1508039981,-0.0566889718,-0.206571281,-0.2405162305,-0.1476676166,-0.3601519167,0.1087893322,0.0835640505,0.4531356692,0.1844787002,0.0979959741,0.0464844815,0.1498785317,-0.0275073517,-0.1222449318,0.2475824654,-0.3175865114,-0.0543072,-0.2789750993,0.0616955161,0.0693085566,0.1027576625,-0.3141680062,0.2036349922,0.1215253547,-0.1842951477,0.0691569299,0.0348740034,0.0998132303,-0.1528588831,0.2042676359,0.4087335765,0.1507891715,0.0722956136,-0.4435012639,0.0004099495,-0.1792548448,0.1177941039,-0.0772794634,0.0483716987,0.3536260426,0.0317918509,0.3313835561,-0.0596574061,0.4391058087,0.2169996053,0.1532561779,-0.1297254562,0.0815455243,-0.129945308,0.1299858987,0.1706516743,0.2419709712,0.3518333435,-0.12397594,-0.3393210471,-0.0108792502,0.2468971163,0.4208874404,0.1096465737,0.2762743831,-0.1904606074,-0.0932282135,-0.1360231489,0.2923359871,-0.0277508367,0.0862574279,-0.0453641713,0.1577400416,-0.0386776738,-0.0647119582,-0.1363081187,0.0061430531,0.4186868668,-0.2352326363,0.2236887068,-0.0264421515,-0.5045306087,-0.1359294057,-0.1209725216,-0.2691159248,-0.3751654625,0.0155547122,0.5011117458,0.1319015622,0.0480404533,-0.2572091222,-0.0303094629,0.143494606,-0.4754090011,-0.0204601791,-0.3241183162,-0.1442911923,-0.0856804848,0.3514268994,-0.1224092692,0.0593446568,0.0404588059,-0.0966990665,-0.450017035,0.0011224836,-0.0597250126,0.0585074201,-0.1251749098,-0.0632037595,0.4381805658,-0.1269313544,-0.342823863,0.0628386065,-0.0527419336,0.1600717306,0.1398719102,0.102086544,0.2967457473,-0.1913740784,-0.0091445595,-0.1177046672,-0.3937448859,0.1796115637,0.1178223938,0.2193236202,0.5895373225,-0.0777764395,0.1191114113,0.1746857762,0.0949008465,-0.2395320535,-0.2428200841,0.5289345384,-0.1670523137,-0.1949594468,-0.0525024384,-0.0015181918,0.0463569909,0.1917174906,-0.7021474242,-0.1443182081,0.0001712659,0.3861824572,0.0606087372,0.0247652903,0.323287487,-0.2217648327,0.0259407721,0.0210828073,-0.2541020811,0.0653096586,0.2154113203,0.3576698303,-0.0258156843,0.2474928647,0.0925820619,0.7005705833,-0.0984533206,0.136724636,0.5910069942,0.2630674839,0.3123771846,-0.1081122383,-0.1415640563,0.0339756422,-0.2543646693,-0.4127692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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/297","title":"Error in Demo for Specific Datasets","comments":"Thanks for reporting these errors :)\r\n\r\nI can actually see two issues here.\r\n\r\nFirst, datasets like `natural_questions` require apache_beam to be processed. Right now the import is not at the right place so we have this error message. However, even the imports are fixed, the nlp viewer doesn't actually have the resources to process NQ right now so we'll have to wait until we have a version that we've already processed on our google storage (that's what we've done for wikipedia for example).\r\n\r\nSecond, datasets like `newsroom` require manual downloads as we're not allowed to redistribute the data ourselves (if I'm not wrong). An error message should be displayed saying that we're not allowed to show the dataset.\r\n\r\nI can fix the first issue with the imports but for the second one I think we'll have to see with @srush to show a message for datasets that require manual downloads (it can be checked whether a dataset requires manual downloads if `dataset_builder_instance.manual_download_instructions is not None`).\r\n\r\n","body":"Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/60150701\/85347842-ac861900-b4ae-11ea-98c4-a53a00934783.png)\r\n","comment_length":165,"text":"Error in Demo for Specific Datasets \n Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/60150701\/85347842-ac861900-b4ae-11ea-98c4-a53a00934783.png)\r\n \n Thanks for reporting these errors :)\r\n\r\nI can actually see two issues here.\r\n\r\nFirst, datasets like `natural_questions` require apache_beam to be processed. Right now the import is not at the right place so we have this error message. However, even the imports are fixed, the nlp viewer doesn't actually have the resources to process NQ right now so we'll have to wait until we have a version that we've already processed on our google storage (that's what we've done for wikipedia for example).\r\n\r\nSecond, datasets like `newsroom` require manual downloads as we're not allowed to redistribute the data ourselves (if I'm not wrong). An error message should be displayed saying that we're not allowed to show the dataset.\r\n\r\nI can fix the first issue with the imports but for the second one I think we'll have to see with @srush to show a message for datasets that require manual downloads (it can be checked whether a dataset requires manual downloads if `dataset_builder_instance.manual_download_instructions is not None`).\r\n\r\n","embeddings":[-0.1981071234,0.4033780098,-0.0345742553,0.0916168988,-0.0609801449,-0.040471375,0.2643227577,0.3957089484,-0.0206499416,0.1149799004,0.0365383849,0.1268178821,-0.1433795094,0.1707723886,0.192117855,-0.4499635398,-0.0038807646,0.1249553859,-0.0355561934,-0.069319725,-0.1204564869,0.1653505713,-0.4725416005,0.2649035454,-0.0176118817,-0.2618624568,-0.0384727344,0.0392732657,-0.3994920552,-0.4657357633,0.0809272528,0.1968013793,0.1599261612,-0.0005180874,-0.0001131029,0.0382400304,0.4839167297,-0.0483166315,-0.5484042764,-0.1696925312,-0.1286290139,-0.3020377755,0.1021560729,-0.0430534966,0.0262297224,-0.6082082391,0.1922516376,-0.0785224736,0.3578095734,0.5196115375,0.2201360911,0.5076093078,0.0161029883,-0.3282738924,0.4393441975,0.294282943,-0.1518373042,0.1352204829,0.0477910787,-0.0510030426,-0.0364651866,0.3086336255,0.0257014297,0.1527827829,-0.0602618046,-0.1421571821,-0.3206353188,-0.4986156225,-0.1197519824,0.4459383488,0.6633608341,0.0743716136,-0.1835986972,-0.1939502209,0.1495735347,0.0478801057,0.0631624535,0.4371371269,-0.2138037831,-0.0197302848,-0.3787341416,-0.2751147747,-0.2470450699,0.440015614,0.0848660022,0.0987464115,-0.0264036208,0.2691401839,-0.0508009307,-0.1801290363,0.1278465986,-0.0587071739,0.054145664,0.0016125643,-0.4455092251,-0.1112408042,-0.0311796758,0.040599037,0.0372829102,0.1945744157,0.1294780672,-0.011735267,0.0395428501,0.2793652713,0.2831104398,0.3266352713,0.3146273494,0.3872416914,0.4034557343,0.0530774109,0.1779122651,0.2051409781,-0.1392932832,-0.3384784162,-0.5065163374,0.0323855057,0.3175575137,-0.2519685328,-0.5763885975,0.2063839883,-0.2966667712,-0.0094444947,-0.0547546633,0.3192156851,0.0911048874,0.0232136268,0.1775573045,0.4780529439,-0.2147263139,-0.711591959,0.0356920063,0.3353013098,-0.2933809161,0.1421969831,0.2954668403,0.3753692508,0.1908024848,0.0171957389,0.1530253291,-0.0349088386,0.3172849715,-0.0082977293,-0.0913595706,0.2614539564,0.1109581515,0.1435957998,0.0197853297,-0.0449606888,-0.1087214723,0.1304396093,-0.0174404085,-0.4220673144,-0.0218519904,0.1434950233,-0.1796658039,0.0001322241,-0.1549212188,0.3701185286,-0.1066482663,-0.2454149276,-0.0933342949,-0.1988843232,0.0739992037,-0.0620109551,0.0609777942,0.4929059148,-0.3572953045,-0.014104777,-0.2872876227,-0.073183164,0.1695153862,0.3137457669,-0.3498011529,0.2884297073,-0.3124473095,0.0360883139,0.6690323353,-0.2652320564,-0.1361675262,0.3889521658,0.1521363407,-0.209536463,-0.1556265503,-0.0827601254,0.1384109259,-0.1652575582,-0.3314625621,0.5041031837,-0.0445608459,0.0315623507,-0.3019278347,-0.2324235886,0.4035744369,0.4479389489,0.1155449823,0.039159961,0.0323922746,0.0754637197,0.019848315,0.0247900635,0.061422646,0.1102890596,0.0182904135,-0.2559670806,-0.046059832,0.1362552643,-0.3674871325,0.0775811672,-0.5094981194,0.3944992721,0.1208044663,-0.0041109659,-0.4444130957,-0.0959618986,-0.2176892757,-0.2749752998,0.1779189706,0.0647481307,0.1631305218,0.2521955669,0.0554000326,0.0935483426,-0.1057463735,0.0657625347,-0.2442820966,0.2586836219,-0.1812973171,-0.0616905056,0.2920763493,-0.1391947269,0.3943233192,-0.0689119548,-0.1508922875,0.3955641091,-0.1765989959,0.2377078533,0.2862996459,-0.1709468663,0.2178450078,-0.5033503771,0.158449173,0.1945571303,-0.0532759465,0.2152076811,-0.1912836134,0.3722969592,0.0492303893,0.0986861661,0.0538784787,0.4147879481,0.1812629551,-0.0780160427,-0.0885619968,-0.1992299408,0.2647400796,0.4113313556,0.0834801346,-0.244946301,-0.4166576564,0.0249638725,0.1811139286,-0.0482652709,0.2014044374,0.0623043552,0.067928195,-0.1010516658,0.4000891447,0.4113735855,0.3827387989,0.0318042599,0.047577437,0.1044109687,-0.2516432405,-0.3253677487,0.1144971028,0.0414313301,0.1646135747,0.114533633,0.1352063566,0.0400645733,-0.364893198,0.0471885949,0.361200422,0.2409502119,-0.2400978506,-0.0440332964,-0.4294613004,-0.5175081491,-0.1684653759,0.1302052736,-0.256873101,-0.540941298,0.379493624,0.0211482923,-0.4098774493,0.2334162444,0.1367081851,0.4418497682,-0.2019059062,0.4963968992,-0.0761230364,-0.0833589584,-0.3440181017,0.0976666585,0.3494063318,0.3973334432,0.4992439747,-0.1747453958,-0.0280649159,-0.4055843949,-0.3387173712,0.2234098464,0.1596518457,0.5951380134,-0.094484508,0.3512040079,-0.0552666597,-0.1646458358,0.1619519144,-0.1427626908,-0.0991604999,0.1441089809,-0.048195865,-0.0626619682,-0.2645437717,-0.6977814436,-0.4090589285,-0.4599585533,-0.2028884143,-0.1979246885,0.2438067645,0.233697772,-0.1097362265,-0.0175722465,0.0205496885,-0.0083965752,-0.1718058437,-0.0689448118,0.2719592154,-0.6593863368,-0.4282494783,0.0517175868,0.1068603173,0.1274673343,0.2793439329,-0.4582374096,0.0910539702,-0.1234815419,-0.1770926565,0.1160819754,-0.285520792,0.2548550665,-0.1953578293,-0.0440754741,-0.1461909562,-0.0373638235,0.0814282671,0.1666160375,0.2665866911,-0.0415649414,0.1822211295,0.0857862011,0.6566123962,0.2117146999,0.2254574299,0.5008478761,0.1887433827,0.3019886017,0.0633157566,0.1152487695,0.2509574294,-0.1392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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/297","title":"Error in Demo for Specific Datasets","comments":"We don't plan to host the source files of newsroom ourselves for now.\r\nYou can still get the dataset if you follow the download instructions given by `dataset = load_dataset('newsroom')` though.\r\nThe viewer also shows the instructions now.\r\n\r\nClosing this one. If you have other questions, feel free to re-open :)","body":"Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/60150701\/85347842-ac861900-b4ae-11ea-98c4-a53a00934783.png)\r\n","comment_length":51,"text":"Error in Demo for Specific Datasets \n Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/60150701\/85347842-ac861900-b4ae-11ea-98c4-a53a00934783.png)\r\n \n We don't plan to host the source files of newsroom ourselves for now.\r\nYou can still get the dataset if you follow the download instructions given by `dataset = load_dataset('newsroom')` though.\r\nThe viewer also shows the instructions now.\r\n\r\nClosing this one. If you have other questions, feel free to re-open :)","embeddings":[-0.3872511983,0.3188091815,-0.1640232354,-0.0211658124,0.013869918,-0.0003127987,0.2441099882,0.4248589873,-0.1058463231,0.250238657,0.076460585,0.0662668496,-0.1547729522,0.198998034,0.1576560885,-0.3058364987,0.034139093,0.106728889,0.0080381259,0.0768803954,0.0208985768,0.1381000876,-0.3802264631,0.0807855427,0.0555668212,0.070566453,-0.0612187684,0.1005788371,-0.3853769004,-0.2824477255,0.1910808682,0.1865963787,0.1066664681,0.2580335736,-0.0000968782,0.1013725176,0.3720093668,-0.0740894452,-0.3853502274,-0.245289579,-0.1144347638,-0.1170114875,-0.1747405678,-0.0778490603,-0.3093830645,-0.3932517171,0.1433220655,-0.1347689331,0.3808894753,0.5134056211,0.3451370895,0.3197131455,0.0641769618,-0.3982389867,0.2445176691,0.3347768784,-0.2866628468,0.2551392317,0.1351358891,-0.0103753284,0.1773409843,0.269554466,0.131067425,0.1185721233,-0.1659903228,-0.0658473969,-0.1224851683,-0.3944908082,0.1921982467,0.3081759214,0.4608187079,0.2094991356,-0.040614184,0.0182142761,0.0734412521,0.0097016767,0.0295343827,0.3768169284,-0.2834703922,0.1123722047,-0.2613152564,-0.3204788268,-0.3009408116,0.3629044294,0.0215595607,0.2622927129,-0.2182945013,0.2114819884,-0.120443739,-0.1118349358,0.1694317311,0.05978873,-0.0276937988,-0.0231773928,-0.086862646,-0.0712509826,0.0011098736,-0.0449180752,0.2504673302,0.1148645133,-0.0322841145,-0.0790894106,0.1651591212,0.0767433271,0.2547540367,0.1350129694,0.3106298149,0.4236186445,0.2886647284,0.3024428189,-0.063519381,0.0966585651,-0.1747273952,-0.3139651716,-0.2929464281,0.045825161,0.4366629422,-0.3682164252,-0.371797353,0.1540751308,-0.0505330339,0.127332136,-0.1139572337,0.2986803651,0.1218006909,0.0631627142,0.0573716387,0.4153439999,-0.1190730631,-0.4704567492,-0.1539423019,0.1874102801,-0.191104874,0.0657421201,0.3246703446,0.1526779085,0.2951782942,0.0576418191,0.2079437971,-0.088049382,0.364474535,0.1267647743,0.1516768783,0.2503456771,0.1138969734,0.0518857576,0.0352837481,-0.0781417191,0.038864594,0.2460532188,-0.0280619748,-0.4773592949,-0.1569186747,0.3296296299,-0.0704427809,0.0011815069,-0.0395828821,0.2608208954,-0.0548480302,-0.1282824725,-0.1434522122,-0.0872637182,0.1408842802,0.0421402678,0.213459298,0.2267336845,-0.4083755612,0.2936526239,-0.1156534776,-0.0255732946,0.17785725,0.0088781919,-0.4150947332,0.2261175364,-0.302259326,-0.0209094603,0.4404123724,-0.3577588797,-0.2100269496,0.0945873335,-0.0135330679,-0.262396723,-0.230164513,-0.1978841275,-0.0164954923,0.0489151813,-0.1584012955,0.3208877146,0.0073946668,0.1409168094,-0.2430867702,-0.1350844502,0.2573469579,0.3529310524,0.014426575,-0.0176272206,-0.0714033917,0.0495391861,0.1344290674,-0.1043756902,-0.0211508982,0.1938442439,0.1335433871,-0.230402112,-0.0459466614,-0.043356806,-0.243736729,0.0554044768,-0.1315820813,0.3995983303,0.1226348802,0.0420893133,-0.4179421663,-0.037828166,-0.2738242149,-0.5199094415,0.3302708268,0.2020736635,0.0733386427,0.0772274882,-0.1083025038,-0.054993432,-0.0550580621,-0.0631411448,-0.2199083716,0.1707648486,-0.2846331596,-0.0056708436,0.096703425,-0.3065670431,0.283233881,-0.1726743877,-0.2059425861,0.4193875194,-0.0198369939,0.0575467013,0.4182517827,-0.1971727014,0.0525954291,-0.4988575876,-0.0080730114,0.1652863473,-0.1155634671,0.249667421,-0.1285469234,0.2141994238,0.1445358843,-0.0236836411,-0.0095277978,0.2160440236,0.3346383572,-0.0974224582,0.0177045129,-0.1007091701,0.1811771393,0.2839544415,0.0924012437,-0.3739606738,-0.3749496341,0.1001344174,0.0793851018,-0.031919498,0.0861245245,0.0888968855,-0.074501656,-0.2598748207,0.2304984331,0.3278220296,0.4170775115,0.0774272606,0.2484454662,0.1300415397,-0.1720896661,-0.2319050729,0.2817389071,-0.1592697948,0.1291060299,0.1429406404,0.1644272059,-0.0998535901,-0.4131441116,0.0557120442,0.1864568591,0.1960126013,-0.188638255,-0.0089930519,-0.2010096908,-0.1755679697,0.1071400717,0.0476272851,-0.0659389049,-0.3055223525,0.4321427941,0.0323332883,-0.3655504584,0.1023598313,-0.0179279149,0.361975044,-0.0456877425,0.4299207032,-0.0444134697,-0.0463662557,-0.3281881809,0.2481746972,0.1598792672,0.184389025,0.3590472937,-0.2736911774,-0.2064645886,-0.4784511328,-0.2711727321,0.2871100903,0.2367945313,0.7645660639,0.0121669192,0.4614914954,-0.112027809,-0.1231091172,0.2972155213,-0.1684675217,-0.0643439963,0.0680412129,-0.0657359734,-0.2628162205,-0.2710855603,-0.6054618359,-0.2764243782,-0.2533033788,-0.0740088224,-0.0343925059,0.0808572546,0.3402635157,0.0184953418,-0.0887746066,-0.0075627905,0.0359902084,-0.4659329355,-0.43918401,0.1614693701,-0.7218429446,-0.4966351688,0.0258448478,0.2140137106,0.2463016808,0.053470064,-0.3788907528,-0.0630356818,0.0697180703,-0.0572842136,0.0916902423,-0.1630955338,0.2149606198,-0.2943845689,-0.2422394454,-0.2595788836,0.0747232065,0.1373461038,0.0962980464,0.1859037131,-0.2009457797,-0.0745711029,0.0676011592,0.6230891347,0.085409604,0.0992675722,0.3722313046,-0.1327311397,0.3359413147,-0.1386457533,-0.0050208149,0.1669450253,-0.0674061403,-0.149818331,0.239537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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/296","title":"snli -1 labels","comments":"@jxmorris12  , we use `-1` to label examples for which `gold label`  is missing (`gold label = -` in the original dataset). ","body":"I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?\r\n```\r\nimport nlp\r\nfrom collections import Counter\r\ndata = nlp.load_dataset('snli')['train']\r\nprint(Counter(data['label']))\r\nCounter({0: 183416, 2: 183187, 1: 182764, -1: 785})\r\n```\r\n","comment_length":22,"text":"snli -1 labels \n I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?\r\n```\r\nimport nlp\r\nfrom collections import Counter\r\ndata = nlp.load_dataset('snli')['train']\r\nprint(Counter(data['label']))\r\nCounter({0: 183416, 2: 183187, 1: 182764, -1: 785})\r\n```\r\n \n @jxmorris12  , we use `-1` to label examples for which `gold label`  is missing (`gold label = -` in the original dataset). 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/296","title":"snli -1 labels","comments":"Thanks @mariamabarham! so the original dataset is missing some labels? That is weird. Is standard practice just to discard those examples training\/eval?","body":"I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?\r\n```\r\nimport nlp\r\nfrom collections import Counter\r\ndata = nlp.load_dataset('snli')['train']\r\nprint(Counter(data['label']))\r\nCounter({0: 183416, 2: 183187, 1: 182764, -1: 785})\r\n```\r\n","comment_length":22,"text":"snli -1 labels \n I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?\r\n```\r\nimport nlp\r\nfrom collections import Counter\r\ndata = nlp.load_dataset('snli')['train']\r\nprint(Counter(data['label']))\r\nCounter({0: 183416, 2: 183187, 1: 182764, -1: 785})\r\n```\r\n \n Thanks @mariamabarham! so the original dataset is missing some labels? That is weird. Is standard practice just to discard those examples training\/eval?","embeddings":[0.3789802194,-0.2363891453,0.0022033609,0.1978055984,0.1676168442,0.2278822511,0.6327139139,0.1720219404,0.048420202,0.1103978008,-0.1079240814,0.3746734858,-0.2219463885,0.1496949047,0.1942365319,0.1201866493,0.3345723152,0.3273703456,0.3262062371,-0.4975576699,-0.164640218,0.1366201341,-0.1091011316,0.2648751736,-0.4185903668,0.2373347431,-0.1538481414,-0.3429230154,0.104502432,-0.6462287903,0.2199270278,-0.1023126468,0.4217903912,-0.2809385657,-0.0001216671,-0.2675632536,0.0543847121,-0.0170518328,-0.5345324874,0.0139202308,-0.0729482174,-0.1938710213,0.290718168,-0.1057281047,-0.0797977522,-0.1985406131,0.2433385104,0.0082859797,0.074079141,0.2916976213,0.1208982617,-0.00731995,-0.4337947667,0.3430729508,0.3437729478,-0.2276860923,0.0630370229,0.0863161534,0.0201416314,-0.0772902891,0.4642212093,0.438005656,-0.1158606634,-0.2857222557,0.1743275672,0.1308352202,0.4274489284,-0.387041539,0.0074046087,0.2546112537,0.1439848244,-0.1291528344,-0.3260562122,-0.4347472489,0.2493036985,-0.311229974,-0.2360923588,0.2196952701,0.3017023504,0.1511388123,-0.2829215527,-0.0516654812,-0.1889192164,0.1414294839,0.1295459419,0.6374177337,0.1101341918,0.1782872379,0.1365814656,0.2269991934,-0.1342483014,-0.110567905,-0.0120628253,0.3769011497,-0.388661772,-0.2018814236,0.0016416555,0.0251683593,-0.0831509829,0.2363758236,0.0536901578,-0.0864013061,0.0708268061,0.0604363792,0.1855139136,0.1493222266,0.4604480565,0.3609256446,-0.0067780134,-0.2038100362,0.2374268025,-0.0178337768,-0.4318780899,0.0751332417,0.2864333391,-0.1190738007,-0.1524971426,-0.2075908333,-0.1685065478,-0.0080020847,-0.5624997616,0.0549807958,0.0852830261,-0.1243278682,0.0734069422,0.2966636419,-0.230012387,0.1054317728,-0.1578510106,-0.6040849686,-0.1277037412,0.1315526217,-0.1773129404,-0.2071239501,0.1995102465,-0.2129683048,0.2379116118,-0.0117013454,-0.3459094167,-0.0630118847,0.4673307836,0.0577621646,0.1729013324,0.3453713953,-0.1311792433,0.5312720537,-0.0620600693,-0.1381403953,-0.0748060942,0.0297695827,-0.2154980749,-0.3328003883,0.1816056669,0.1727150381,-0.1253916025,-0.153014496,0.0454456285,0.1467695981,0.0849798396,-0.2330768108,0.0582960956,-0.3490657806,0.042917598,-0.0943082795,0.1568502784,0.0098793786,-0.1807923168,-0.1234242022,0.0030136926,-0.1748470664,0.3881748617,0.4391433299,-0.0888142362,0.0585124977,-0.0513508096,0.0846712962,0.5011434555,-0.2736699283,-0.2654494941,0.1133242175,-0.1075648591,-0.2903260887,-0.1185356006,0.513369143,0.0103101078,-0.0220843237,-0.0262202695,-0.0576498248,-0.0469392613,-0.1908290982,-0.4078170657,0.0439910553,0.5144603252,0.1584966481,-0.103487052,0.1325870156,-0.46264413,0.1176945195,0.383008033,-0.1768920124,0.18461667,0.1184726954,0.2052664906,0.1556284726,0.0737579241,0.0711098909,-0.2450219393,0.2019115984,-0.0962226018,0.2569848299,0.5588251352,-0.1285279691,-0.0360272974,-0.18266581,-0.1530183256,-0.2860913575,0.0586607195,0.15145953,0.1641748101,-0.0385966934,-0.1827423871,0.1158315614,-0.2991262674,0.0803115442,-0.468260169,-0.0258936062,0.1107965484,0.0876264349,-0.1554864794,0.2683875859,0.216019839,0.0232611056,0.1758345813,0.097792007,-0.0976430029,-0.2183565795,0.1102543473,0.2468774468,0.0685358867,-0.2757854462,0.0964060649,0.4207382798,0.0108609125,-0.2066677362,-0.2059896439,0.2684469521,0.0079928413,0.2270089835,-0.1075849608,0.0882544816,0.0733909681,0.0765407085,-0.3425277174,-0.1568909883,0.1437413543,-0.3022581637,0.1347488016,0.2816454172,-0.3767746687,0.0181833934,0.0338232629,-0.1499401182,-0.0465881191,0.1338830441,-0.1493709832,-0.1522812396,0.3809719086,0.0114321765,0.163294971,0.2967981398,0.1102058738,0.0073573957,-0.4906772971,-0.1788840443,0.2380926907,0.1433742046,-0.2497667372,-0.1835832447,0.3590720594,0.1449327767,-0.3770843446,-0.4780540168,-0.2569273412,0.212987572,-0.2778189778,0.2787250578,-0.1515138298,-0.4487258792,-0.2847844958,-0.1748629361,-0.1372944117,-0.2256820053,0.2676307857,-0.2786779106,0.0791948736,0.0603089221,0.1153282523,0.2810527682,-0.3082813323,0.4000158012,-0.1465576887,-0.2746135592,-0.5887207389,-0.0012312586,-0.4473215044,0.0778465047,0.2033810318,-0.1895958632,-0.4071713984,0.0100831548,-0.2798158526,-0.0498667918,-0.0165852681,-0.0519570969,0.1574081331,-0.0276500024,-0.2775493264,0.3261582553,-0.0807925463,0.1119641885,-0.0124512371,0.043721687,-0.1787853986,0.1360493004,-0.4149359465,-0.2756350935,-0.0465781055,0.1876485646,0.2455367595,0.1591165066,0.1108182818,0.4958164692,-0.0989606306,0.0194263142,-0.1318602264,0.2323003262,-0.1400011927,-0.2537880838,0.1410228014,0.1477815956,-0.1040073186,-0.0014571632,0.0027141643,-0.0066846069,-0.1398134679,-0.3399149179,0.0674358383,0.2494532168,-0.1703867614,-0.0908595175,0.0514993705,0.0219412409,0.1329831183,0.2189226747,-0.0586002208,0.0718773082,0.3512069881,-0.1890821755,0.3821302056,-0.2982612848,0.1768516749,-0.1004799157,0.0370757096,0.4577076137,-0.0518539809,-0.0058301282,-0.047437299,-0.0291824881,0.0231919773,0.1906560063,0.3272813857,0.11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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/296","title":"snli -1 labels","comments":"Yes the original dataset is missing some labels maybe  @sleepinyourhat , @gangeli can correct me if I'm wrong \r\nFor my personal opinion at least if you want your model to learn to predict no answer (-1) you can leave it their but otherwise you can discard them. ","body":"I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?\r\n```\r\nimport nlp\r\nfrom collections import Counter\r\ndata = nlp.load_dataset('snli')['train']\r\nprint(Counter(data['label']))\r\nCounter({0: 183416, 2: 183187, 1: 182764, -1: 785})\r\n```\r\n","comment_length":47,"text":"snli -1 labels \n I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?\r\n```\r\nimport nlp\r\nfrom collections import Counter\r\ndata = nlp.load_dataset('snli')['train']\r\nprint(Counter(data['label']))\r\nCounter({0: 183416, 2: 183187, 1: 182764, -1: 785})\r\n```\r\n \n Yes the original dataset is missing some labels maybe  @sleepinyourhat , @gangeli can correct me if I'm wrong \r\nFor my personal opinion at least if you want your model to learn to predict no answer (-1) you can leave it their but otherwise you can discard them. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/294","title":"Cannot load arxiv dataset on MacOS?","comments":"I couldn't replicate this issue on my macbook :\/\r\nCould you try to play with different encodings in `with open(path, encoding=...) as f` in scientific_papers.py:L108 ?","body":"I am having trouble loading the `\"arxiv\"` config from the `\"scientific_papers\"` dataset on MacOS. When I try loading the dataset with:\r\n\r\n```python\r\narxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n```\r\n\r\nI get the following stack trace:\r\n\r\n```bash\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-2-8e00c55d5a59> in <module>\r\n----> 1 arxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    662 \r\n    663         generator = self._generate_examples(**split_generator.gen_kwargs)\r\n--> 664         for key, record in utils.tqdm(generator, unit=\" examples\", total=split_info.num_examples, leave=False):\r\n    665             example = self.info.features.encode_example(record)\r\n    666             writer.write(example)\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1106                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\r\n   1107 \r\n-> 1108         for obj in iterable:\r\n   1109             yield obj\r\n   1110             # Update and possibly print the progressbar.\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/datasets\/scientific_papers\/107a416c0e1958cb846f5934b5aae292f7884a5b27e86af3f3ef1a093e058bbc\/scientific_papers.py in _generate_examples(self, path)\r\n    114                 # \"section_names\": list[str], list of section names.\r\n    115                 # \"sections\": list[list[str]], list of sections (list of paragraphs)\r\n--> 116                 d = json.loads(line)\r\n    117                 summary = \"\\n\".join(d[\"abstract_text\"])\r\n    118                 # In original paper, <S> and <\/S> are not used in vocab during training\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    346             parse_int is None and parse_float is None and\r\n    347             parse_constant is None and object_pairs_hook is None and not kw):\r\n--> 348         return _default_decoder.decode(s)\r\n    349     if cls is None:\r\n    350         cls = JSONDecoder\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in decode(self, s, _w)\r\n    335 \r\n    336         \"\"\"\r\n--> 337         obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n    338         end = _w(s, end).end()\r\n    339         if end != len(s):\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in raw_decode(self, s, idx)\r\n    351         \"\"\"\r\n    352         try:\r\n--> 353             obj, end = self.scan_once(s, idx)\r\n    354         except StopIteration as err:\r\n    355             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 46983 (char 46982)\r\n\r\n163502 examples [02:10, 2710.68 examples\/s]   \r\n```\r\n\r\nI am not sure how to trace back to the specific JSON file that has the \"Unterminated string\". Also, I do not get this error on colab so I suspect it may be MacOS specific. Copy pasting the relevant lines from `transformers-cli env` below:\r\n\r\n- Platform: Darwin-19.5.0-x86_64-i386-64bit\r\n- Python version: 3.7.5\r\n- PyTorch version (GPU?): 1.5.0 (False)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n\r\nAny ideas?","comment_length":26,"text":"Cannot load arxiv dataset on MacOS? \n I am having trouble loading the `\"arxiv\"` config from the `\"scientific_papers\"` dataset on MacOS. When I try loading the dataset with:\r\n\r\n```python\r\narxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n```\r\n\r\nI get the following stack trace:\r\n\r\n```bash\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-2-8e00c55d5a59> in <module>\r\n----> 1 arxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    662 \r\n    663         generator = self._generate_examples(**split_generator.gen_kwargs)\r\n--> 664         for key, record in utils.tqdm(generator, unit=\" examples\", total=split_info.num_examples, leave=False):\r\n    665             example = self.info.features.encode_example(record)\r\n    666             writer.write(example)\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1106                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\r\n   1107 \r\n-> 1108         for obj in iterable:\r\n   1109             yield obj\r\n   1110             # Update and possibly print the progressbar.\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/datasets\/scientific_papers\/107a416c0e1958cb846f5934b5aae292f7884a5b27e86af3f3ef1a093e058bbc\/scientific_papers.py in _generate_examples(self, path)\r\n    114                 # \"section_names\": list[str], list of section names.\r\n    115                 # \"sections\": list[list[str]], list of sections (list of paragraphs)\r\n--> 116                 d = json.loads(line)\r\n    117                 summary = \"\\n\".join(d[\"abstract_text\"])\r\n    118                 # In original paper, <S> and <\/S> are not used in vocab during training\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    346             parse_int is None and parse_float is None and\r\n    347             parse_constant is None and object_pairs_hook is None and not kw):\r\n--> 348         return _default_decoder.decode(s)\r\n    349     if cls is None:\r\n    350         cls = JSONDecoder\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in decode(self, s, _w)\r\n    335 \r\n    336         \"\"\"\r\n--> 337         obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n    338         end = _w(s, end).end()\r\n    339         if end != len(s):\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in raw_decode(self, s, idx)\r\n    351         \"\"\"\r\n    352         try:\r\n--> 353             obj, end = self.scan_once(s, idx)\r\n    354         except StopIteration as err:\r\n    355             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 46983 (char 46982)\r\n\r\n163502 examples [02:10, 2710.68 examples\/s]   \r\n```\r\n\r\nI am not sure how to trace back to the specific JSON file that has the \"Unterminated string\". Also, I do not get this error on colab so I suspect it may be MacOS specific. Copy pasting the relevant lines from `transformers-cli env` below:\r\n\r\n- Platform: Darwin-19.5.0-x86_64-i386-64bit\r\n- Python version: 3.7.5\r\n- PyTorch version (GPU?): 1.5.0 (False)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n\r\nAny ideas? \n I couldn't replicate this issue on my macbook :\/\r\nCould you try to play with different encodings in `with open(path, encoding=...) as f` in scientific_papers.py:L108 ?","embeddings":[-0.0581401661,0.0877689496,-0.0419098586,0.1886685789,0.2178975642,0.0454704054,-0.0241877399,0.3616167009,0.0815683678,-0.1818822473,-0.0505227782,0.691057086,-0.1919905692,-0.2139135003,-0.1082298383,0.0094932541,-0.1276049614,0.0114534106,0.1146815494,-0.1110842973,-0.1230693087,0.1993306875,-0.1573242992,-0.0993064716,0.4090956151,0.0958301648,0.1364857703,0.3957486153,-0.0027542866,-0.3556491733,0.1349909604,0.0240957309,0.2171578258,0.5375875831,-0.0001107034,0.2718124986,0.3134252429,-0.0939889029,-0.2043009251,-0.5840528011,-0.4115153253,-0.306637615,0.1335057318,-0.2136874497,-0.0360715277,-0.2607661784,0.1242143437,-0.1539173871,0.3288322389,0.3756518662,0.207921654,0.0798494294,0.3830094635,0.1164422184,-0.0670725107,-0.02313504,0.0021167931,0.4649830163,0.0422845446,0.1263236105,0.1612235159,0.1448102146,-0.1489691883,0.1127980724,0.2092457116,-0.0296654217,-0.1075037271,-0.1436872929,0.0375226587,0.1304574609,0.2721568644,-0.0974486545,-0.2403058112,0.0564082675,0.2218078375,-0.1019054577,0.295181632,0.430082649,-0.1124939024,0.0761579052,-0.3062997162,-0.3497585952,-0.4034608901,0.4736581147,-0.0218985379,-0.1115582362,-0.200718835,0.2033873051,0.1595942974,-0.2415931225,0.0368958823,0.0939481407,0.1707556397,0.11694552,-0.4864809811,0.2469767183,0.0796729624,-0.3753705025,0.0753835738,-0.150146991,-0.1037795395,-0.0815444365,-0.2829775214,0.2728938162,0.1120163128,0.018602727,-0.1558181047,-0.0018167398,0.0582292303,-0.0125903161,-0.1686109453,0.0329580121,-0.3595866859,-0.1540408581,-0.3627384901,-0.3801209033,0.2619623542,-0.3207472861,0.0621837713,-0.0250361618,-0.0250417013,0.0168774351,0.0238443669,0.228412956,-0.0542670265,0.1421990842,0.1716236323,0.4296216667,-0.3466945887,0.1758017391,-0.0509758741,0.0754750222,-0.1097994223,-0.2968219519,0.2044046372,0.0846939757,0.1635968983,-0.0225838032,0.0854115933,-0.201628834,0.1252433211,0.1339762807,-0.1017934233,0.4808894694,0.3299891949,0.0564984679,0.2672579885,0.0386227481,-0.1250129193,0.2215334028,-0.4679540396,-0.1227610484,-0.2679323256,0.162654683,-0.1997473538,-0.1317337751,-0.1605494618,-0.1432931125,0.1379330009,0.2394692749,-0.3634818196,-0.2293667346,0.0001346738,-0.3333007991,0.2488360554,0.0392104574,-0.3133456707,0.1613029242,-0.1056164876,0.1475966722,0.2834636867,0.3652399778,-0.2056614608,0.1426015943,-0.0255504828,-0.1135711595,0.3589995801,-0.3276819885,-0.1195762455,0.0364844464,-0.1393856555,0.076270476,-0.0743433014,0.0231164042,-0.1838792413,0.2579210103,0.2040625364,0.2711192966,0.0826367363,0.0304179639,-0.1805005819,-0.2009442002,0.45898664,0.3523835838,-0.0857428014,0.0294668283,-0.0705721304,-0.072366327,0.1135509834,-0.099117592,-0.3065351248,0.2054903805,0.0127701126,0.0597909503,0.0696247518,0.1258382499,0.1664613187,0.0967324823,0.2396769971,0.2965689003,-0.2159079313,0.3827721477,-0.3872411549,0.0926724225,-0.0310393795,0.0711300671,0.065654926,0.0439680256,-0.0934773609,0.1950520128,-0.1820219457,0.2183669358,-0.3807091713,-0.0371009409,-0.8843935132,0.0560739338,-0.1170641184,0.1107353494,0.2100989223,0.1402559727,-0.1191787124,-0.2400333434,-0.1165203899,0.2139781266,0.2940783203,-0.0699314922,0.2279684991,0.042080678,0.0583746955,-0.3201346993,0.2476847917,0.0171775892,0.0454673953,0.0980466679,-0.2400624603,0.3954666555,0.0848648995,0.1853234768,0.1478035152,0.1319267154,0.4758432508,-0.0060153292,-0.0275846701,-0.0089764493,0.4516296685,0.1805775911,-0.340288192,-0.1420488507,-0.1855588555,-0.0007562374,0.0774995834,0.1592724323,-0.0840466544,0.3020502329,-0.1074541733,0.0607566088,-0.1180131286,0.0291220751,0.3401017785,0.1709996909,-0.3174942732,0.0375214443,-0.2074111104,-0.0640699863,0.1576645225,0.0042496887,0.0024907775,-0.1562239826,0.130223617,0.1190572008,-0.2286053598,-0.2915285528,-0.0149250245,0.4036516249,-0.3374558687,0.0624639392,-0.3534377515,-0.2601831853,-0.0542715788,-0.1631045043,-0.1600035727,-0.2046508193,-0.1103503406,0.1549523771,0.1971672177,0.1569382101,-0.3404233754,-0.0687447339,0.2280444354,-0.212758258,0.2178190351,-0.3201696575,-0.090573445,0.0915043354,0.2567333579,0.115356423,0.2168773413,-0.0711518824,-0.1991698444,0.1951156259,-0.3111149967,-0.0690635741,-0.2529280484,0.1622450054,-0.0127729326,0.2829394639,0.0663689077,-0.2645323873,0.2123935223,0.0352514498,-0.1587046087,0.302259326,0.0569703095,-0.4054757655,-0.1261679381,-0.6859310865,-0.2402991205,-0.2965233922,0.0207346324,0.2632628381,0.1707098782,0.4518840313,0.3053714931,0.021538198,0.0888195485,0.0629290193,-0.1581660807,-0.0866923556,0.2277038097,-0.1510714889,-0.4011754692,0.1772225201,0.2070526481,0.5639321804,0.1303535402,-0.4385875762,0.1577425152,-0.1061138362,-0.0755192265,-0.2182867229,0.1015386134,0.0663023368,-0.210977301,-0.0257533733,0.0079541886,-0.1914936155,-0.0301366076,0.1466688365,0.2630581856,0.2407557815,0.4278973341,-0.2681685686,0.5905352831,0.0142622264,-0.1609193683,0.069476828,-0.1589577198,0.4026772082,-0.0031177653,-0.5069510341,0.2158525139,0.284262687,0.0171935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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/294","title":"Cannot load arxiv dataset on MacOS?","comments":"I was able to track down the file causing the problem by adding the following to `scientific_papers.py` (starting at line 116):\r\n\r\n```python\r\n                from json import JSONDecodeError\r\n                try:\r\n                    d = json.loads(line)\r\n                    summary = \"\\n\".join(d[\"abstract_text\"])\r\n                except JSONDecodeError:\r\n                    print(path, line)\r\n```\r\n\r\n\r\n\r\nFor me it was at: `\/Users\/johngiorgi\/.cache\/huggingface\/datasets\/f87fd498c5003cbe253a2af422caa1e58f87a4fd74cb3e67350c635c8903b259\/arxiv-dataset\/train.txt` with `\"article_id\": \"1407.3051\"`.\r\n\r\nNot really 100% sure at the moment, but it looks like this specific substring from `\"article_text\"` may be causing the problem?\r\n\r\n```\r\n\"after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev\/@xmath1 . in fig .  [\", \"fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev\/@xmath1 . a fitting result with a lorentzian function for the @xmath86  ( dashed line ) and the three - body phas\r\n```\r\n\r\nperhaps because it appears to be truncated. I (think) I can recreate the problem by doing the following:\r\n\r\n```python\r\nimport json\r\n\r\n# A minimal example of the json file that causes the error\r\ninvalid_json = '{\"article_id\": \"1407.3051\", \"article_text\": [\"the missing - mass resolution was obtained to be 2.8 @xmath3 0.1  mev\/@xmath4  ( fwhm ) , which corresponds to the missing - mass resolution of 3.2  @xmath3  0.2  mev\/@xmath4  ( fwhm ) at the @xmath6 cusp region in the @xmath0 reaction .\", \"this resolution is at least by a factor of 2 better than the previous measurement with the same reaction ( 3.2@xmath595.5 mev\/@xmath4 in @xmath84 )  @xcite .\", \"after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev\/@xmath1 . in fig .  [\", \"fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev\/@xmath1 . a fitting result with a lorentzian function for the @xmath86  ( dashed line ) and the three - body phas'   \r\n# The line of code from `scientific_papers.py` which appears to cause the error\r\njson.loads(invalid_json)\r\n```\r\n\r\nThis is as far as I get before I am stumped.","body":"I am having trouble loading the `\"arxiv\"` config from the `\"scientific_papers\"` dataset on MacOS. When I try loading the dataset with:\r\n\r\n```python\r\narxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n```\r\n\r\nI get the following stack trace:\r\n\r\n```bash\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-2-8e00c55d5a59> in <module>\r\n----> 1 arxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    662 \r\n    663         generator = self._generate_examples(**split_generator.gen_kwargs)\r\n--> 664         for key, record in utils.tqdm(generator, unit=\" examples\", total=split_info.num_examples, leave=False):\r\n    665             example = self.info.features.encode_example(record)\r\n    666             writer.write(example)\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1106                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\r\n   1107 \r\n-> 1108         for obj in iterable:\r\n   1109             yield obj\r\n   1110             # Update and possibly print the progressbar.\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/datasets\/scientific_papers\/107a416c0e1958cb846f5934b5aae292f7884a5b27e86af3f3ef1a093e058bbc\/scientific_papers.py in _generate_examples(self, path)\r\n    114                 # \"section_names\": list[str], list of section names.\r\n    115                 # \"sections\": list[list[str]], list of sections (list of paragraphs)\r\n--> 116                 d = json.loads(line)\r\n    117                 summary = \"\\n\".join(d[\"abstract_text\"])\r\n    118                 # In original paper, <S> and <\/S> are not used in vocab during training\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    346             parse_int is None and parse_float is None and\r\n    347             parse_constant is None and object_pairs_hook is None and not kw):\r\n--> 348         return _default_decoder.decode(s)\r\n    349     if cls is None:\r\n    350         cls = JSONDecoder\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in decode(self, s, _w)\r\n    335 \r\n    336         \"\"\"\r\n--> 337         obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n    338         end = _w(s, end).end()\r\n    339         if end != len(s):\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in raw_decode(self, s, idx)\r\n    351         \"\"\"\r\n    352         try:\r\n--> 353             obj, end = self.scan_once(s, idx)\r\n    354         except StopIteration as err:\r\n    355             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 46983 (char 46982)\r\n\r\n163502 examples [02:10, 2710.68 examples\/s]   \r\n```\r\n\r\nI am not sure how to trace back to the specific JSON file that has the \"Unterminated string\". Also, I do not get this error on colab so I suspect it may be MacOS specific. Copy pasting the relevant lines from `transformers-cli env` below:\r\n\r\n- Platform: Darwin-19.5.0-x86_64-i386-64bit\r\n- Python version: 3.7.5\r\n- PyTorch version (GPU?): 1.5.0 (False)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n\r\nAny ideas?","comment_length":352,"text":"Cannot load arxiv dataset on MacOS? \n I am having trouble loading the `\"arxiv\"` config from the `\"scientific_papers\"` dataset on MacOS. When I try loading the dataset with:\r\n\r\n```python\r\narxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n```\r\n\r\nI get the following stack trace:\r\n\r\n```bash\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-2-8e00c55d5a59> in <module>\r\n----> 1 arxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    662 \r\n    663         generator = self._generate_examples(**split_generator.gen_kwargs)\r\n--> 664         for key, record in utils.tqdm(generator, unit=\" examples\", total=split_info.num_examples, leave=False):\r\n    665             example = self.info.features.encode_example(record)\r\n    666             writer.write(example)\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1106                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\r\n   1107 \r\n-> 1108         for obj in iterable:\r\n   1109             yield obj\r\n   1110             # Update and possibly print the progressbar.\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/datasets\/scientific_papers\/107a416c0e1958cb846f5934b5aae292f7884a5b27e86af3f3ef1a093e058bbc\/scientific_papers.py in _generate_examples(self, path)\r\n    114                 # \"section_names\": list[str], list of section names.\r\n    115                 # \"sections\": list[list[str]], list of sections (list of paragraphs)\r\n--> 116                 d = json.loads(line)\r\n    117                 summary = \"\\n\".join(d[\"abstract_text\"])\r\n    118                 # In original paper, <S> and <\/S> are not used in vocab during training\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    346             parse_int is None and parse_float is None and\r\n    347             parse_constant is None and object_pairs_hook is None and not kw):\r\n--> 348         return _default_decoder.decode(s)\r\n    349     if cls is None:\r\n    350         cls = JSONDecoder\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in decode(self, s, _w)\r\n    335 \r\n    336         \"\"\"\r\n--> 337         obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n    338         end = _w(s, end).end()\r\n    339         if end != len(s):\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in raw_decode(self, s, idx)\r\n    351         \"\"\"\r\n    352         try:\r\n--> 353             obj, end = self.scan_once(s, idx)\r\n    354         except StopIteration as err:\r\n    355             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 46983 (char 46982)\r\n\r\n163502 examples [02:10, 2710.68 examples\/s]   \r\n```\r\n\r\nI am not sure how to trace back to the specific JSON file that has the \"Unterminated string\". Also, I do not get this error on colab so I suspect it may be MacOS specific. Copy pasting the relevant lines from `transformers-cli env` below:\r\n\r\n- Platform: Darwin-19.5.0-x86_64-i386-64bit\r\n- Python version: 3.7.5\r\n- PyTorch version (GPU?): 1.5.0 (False)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n\r\nAny ideas? \n I was able to track down the file causing the problem by adding the following to `scientific_papers.py` (starting at line 116):\r\n\r\n```python\r\n                from json import JSONDecodeError\r\n                try:\r\n                    d = json.loads(line)\r\n                    summary = \"\\n\".join(d[\"abstract_text\"])\r\n                except JSONDecodeError:\r\n                    print(path, line)\r\n```\r\n\r\n\r\n\r\nFor me it was at: `\/Users\/johngiorgi\/.cache\/huggingface\/datasets\/f87fd498c5003cbe253a2af422caa1e58f87a4fd74cb3e67350c635c8903b259\/arxiv-dataset\/train.txt` with `\"article_id\": \"1407.3051\"`.\r\n\r\nNot really 100% sure at the moment, but it looks like this specific substring from `\"article_text\"` may be causing the problem?\r\n\r\n```\r\n\"after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev\/@xmath1 . in fig .  [\", \"fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev\/@xmath1 . a fitting result with a lorentzian function for the @xmath86  ( dashed line ) and the three - body phas\r\n```\r\n\r\nperhaps because it appears to be truncated. I (think) I can recreate the problem by doing the following:\r\n\r\n```python\r\nimport json\r\n\r\n# A minimal example of the json file that causes the error\r\ninvalid_json = '{\"article_id\": \"1407.3051\", \"article_text\": [\"the missing - mass resolution was obtained to be 2.8 @xmath3 0.1  mev\/@xmath4  ( fwhm ) , which corresponds to the missing - mass resolution of 3.2  @xmath3  0.2  mev\/@xmath4  ( fwhm ) at the @xmath6 cusp region in the @xmath0 reaction .\", \"this resolution is at least by a factor of 2 better than the previous measurement with the same reaction ( 3.2@xmath595.5 mev\/@xmath4 in @xmath84 )  @xcite .\", \"after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev\/@xmath1 . in fig .  [\", \"fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev\/@xmath1 . a fitting result with a lorentzian function for the @xmath86  ( dashed line ) and the three - body phas'   \r\n# The line of code from `scientific_papers.py` which appears to cause the error\r\njson.loads(invalid_json)\r\n```\r\n\r\nThis is as far as I get before I am stumped.","embeddings":[-0.0581401661,0.0877689496,-0.0419098586,0.1886685789,0.2178975642,0.0454704054,-0.0241877399,0.3616167009,0.0815683678,-0.1818822473,-0.0505227782,0.691057086,-0.1919905692,-0.2139135003,-0.1082298383,0.0094932541,-0.1276049614,0.0114534106,0.1146815494,-0.1110842973,-0.1230693087,0.1993306875,-0.1573242992,-0.0993064716,0.4090956151,0.0958301648,0.1364857703,0.3957486153,-0.0027542866,-0.3556491733,0.1349909604,0.0240957309,0.2171578258,0.5375875831,-0.0001107034,0.2718124986,0.3134252429,-0.0939889029,-0.2043009251,-0.5840528011,-0.4115153253,-0.306637615,0.1335057318,-0.2136874497,-0.0360715277,-0.2607661784,0.1242143437,-0.1539173871,0.3288322389,0.3756518662,0.207921654,0.0798494294,0.3830094635,0.1164422184,-0.0670725107,-0.02313504,0.0021167931,0.4649830163,0.0422845446,0.1263236105,0.1612235159,0.1448102146,-0.1489691883,0.1127980724,0.2092457116,-0.0296654217,-0.1075037271,-0.1436872929,0.0375226587,0.1304574609,0.2721568644,-0.0974486545,-0.2403058112,0.0564082675,0.2218078375,-0.1019054577,0.295181632,0.430082649,-0.1124939024,0.0761579052,-0.3062997162,-0.3497585952,-0.4034608901,0.4736581147,-0.0218985379,-0.1115582362,-0.200718835,0.2033873051,0.1595942974,-0.2415931225,0.0368958823,0.0939481407,0.1707556397,0.11694552,-0.4864809811,0.2469767183,0.0796729624,-0.3753705025,0.0753835738,-0.150146991,-0.1037795395,-0.0815444365,-0.2829775214,0.2728938162,0.1120163128,0.018602727,-0.1558181047,-0.0018167398,0.0582292303,-0.0125903161,-0.1686109453,0.0329580121,-0.3595866859,-0.1540408581,-0.3627384901,-0.3801209033,0.2619623542,-0.3207472861,0.0621837713,-0.0250361618,-0.0250417013,0.0168774351,0.0238443669,0.228412956,-0.0542670265,0.1421990842,0.1716236323,0.4296216667,-0.3466945887,0.1758017391,-0.0509758741,0.0754750222,-0.1097994223,-0.2968219519,0.2044046372,0.0846939757,0.1635968983,-0.0225838032,0.0854115933,-0.201628834,0.1252433211,0.1339762807,-0.1017934233,0.4808894694,0.3299891949,0.0564984679,0.2672579885,0.0386227481,-0.1250129193,0.2215334028,-0.4679540396,-0.1227610484,-0.2679323256,0.162654683,-0.1997473538,-0.1317337751,-0.1605494618,-0.1432931125,0.1379330009,0.2394692749,-0.3634818196,-0.2293667346,0.0001346738,-0.3333007991,0.2488360554,0.0392104574,-0.3133456707,0.1613029242,-0.1056164876,0.1475966722,0.2834636867,0.3652399778,-0.2056614608,0.1426015943,-0.0255504828,-0.1135711595,0.3589995801,-0.3276819885,-0.1195762455,0.0364844464,-0.1393856555,0.076270476,-0.0743433014,0.0231164042,-0.1838792413,0.2579210103,0.2040625364,0.2711192966,0.0826367363,0.0304179639,-0.1805005819,-0.2009442002,0.45898664,0.3523835838,-0.0857428014,0.0294668283,-0.0705721304,-0.072366327,0.1135509834,-0.099117592,-0.3065351248,0.2054903805,0.0127701126,0.0597909503,0.0696247518,0.1258382499,0.1664613187,0.0967324823,0.2396769971,0.2965689003,-0.2159079313,0.3827721477,-0.3872411549,0.0926724225,-0.0310393795,0.0711300671,0.065654926,0.0439680256,-0.0934773609,0.1950520128,-0.1820219457,0.2183669358,-0.3807091713,-0.0371009409,-0.8843935132,0.0560739338,-0.1170641184,0.1107353494,0.2100989223,0.1402559727,-0.1191787124,-0.2400333434,-0.1165203899,0.2139781266,0.2940783203,-0.0699314922,0.2279684991,0.042080678,0.0583746955,-0.3201346993,0.2476847917,0.0171775892,0.0454673953,0.0980466679,-0.2400624603,0.3954666555,0.0848648995,0.1853234768,0.1478035152,0.1319267154,0.4758432508,-0.0060153292,-0.0275846701,-0.0089764493,0.4516296685,0.1805775911,-0.340288192,-0.1420488507,-0.1855588555,-0.0007562374,0.0774995834,0.1592724323,-0.0840466544,0.3020502329,-0.1074541733,0.0607566088,-0.1180131286,0.0291220751,0.3401017785,0.1709996909,-0.3174942732,0.0375214443,-0.2074111104,-0.0640699863,0.1576645225,0.0042496887,0.0024907775,-0.1562239826,0.130223617,0.1190572008,-0.2286053598,-0.2915285528,-0.0149250245,0.4036516249,-0.3374558687,0.0624639392,-0.3534377515,-0.2601831853,-0.0542715788,-0.1631045043,-0.1600035727,-0.2046508193,-0.1103503406,0.1549523771,0.1971672177,0.1569382101,-0.3404233754,-0.0687447339,0.2280444354,-0.212758258,0.2178190351,-0.3201696575,-0.090573445,0.0915043354,0.2567333579,0.115356423,0.2168773413,-0.0711518824,-0.1991698444,0.1951156259,-0.3111149967,-0.0690635741,-0.2529280484,0.1622450054,-0.0127729326,0.2829394639,0.0663689077,-0.2645323873,0.2123935223,0.0352514498,-0.1587046087,0.302259326,0.0569703095,-0.4054757655,-0.1261679381,-0.6859310865,-0.2402991205,-0.2965233922,0.0207346324,0.2632628381,0.1707098782,0.4518840313,0.3053714931,0.021538198,0.0888195485,0.0629290193,-0.1581660807,-0.0866923556,0.2277038097,-0.1510714889,-0.4011754692,0.1772225201,0.2070526481,0.5639321804,0.1303535402,-0.4385875762,0.1577425152,-0.1061138362,-0.0755192265,-0.2182867229,0.1015386134,0.0663023368,-0.210977301,-0.0257533733,0.0079541886,-0.1914936155,-0.0301366076,0.1466688365,0.2630581856,0.2407557815,0.4278973341,-0.2681685686,0.5905352831,0.0142622264,-0.1609193683,0.069476828,-0.1589577198,0.4026772082,-0.0031177653,-0.5069510341,0.2158525139,0.284262687,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/294","title":"Cannot load arxiv dataset on MacOS?","comments":"I just checked inside `train.txt` and this line isn't truncated for me (line 163577).\r\nCould you try to clear your cache and re-download the dataset ?","body":"I am having trouble loading the `\"arxiv\"` config from the `\"scientific_papers\"` dataset on MacOS. When I try loading the dataset with:\r\n\r\n```python\r\narxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n```\r\n\r\nI get the following stack trace:\r\n\r\n```bash\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-2-8e00c55d5a59> in <module>\r\n----> 1 arxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    662 \r\n    663         generator = self._generate_examples(**split_generator.gen_kwargs)\r\n--> 664         for key, record in utils.tqdm(generator, unit=\" examples\", total=split_info.num_examples, leave=False):\r\n    665             example = self.info.features.encode_example(record)\r\n    666             writer.write(example)\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1106                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\r\n   1107 \r\n-> 1108         for obj in iterable:\r\n   1109             yield obj\r\n   1110             # Update and possibly print the progressbar.\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/datasets\/scientific_papers\/107a416c0e1958cb846f5934b5aae292f7884a5b27e86af3f3ef1a093e058bbc\/scientific_papers.py in _generate_examples(self, path)\r\n    114                 # \"section_names\": list[str], list of section names.\r\n    115                 # \"sections\": list[list[str]], list of sections (list of paragraphs)\r\n--> 116                 d = json.loads(line)\r\n    117                 summary = \"\\n\".join(d[\"abstract_text\"])\r\n    118                 # In original paper, <S> and <\/S> are not used in vocab during training\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    346             parse_int is None and parse_float is None and\r\n    347             parse_constant is None and object_pairs_hook is None and not kw):\r\n--> 348         return _default_decoder.decode(s)\r\n    349     if cls is None:\r\n    350         cls = JSONDecoder\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in decode(self, s, _w)\r\n    335 \r\n    336         \"\"\"\r\n--> 337         obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n    338         end = _w(s, end).end()\r\n    339         if end != len(s):\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in raw_decode(self, s, idx)\r\n    351         \"\"\"\r\n    352         try:\r\n--> 353             obj, end = self.scan_once(s, idx)\r\n    354         except StopIteration as err:\r\n    355             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 46983 (char 46982)\r\n\r\n163502 examples [02:10, 2710.68 examples\/s]   \r\n```\r\n\r\nI am not sure how to trace back to the specific JSON file that has the \"Unterminated string\". Also, I do not get this error on colab so I suspect it may be MacOS specific. Copy pasting the relevant lines from `transformers-cli env` below:\r\n\r\n- Platform: Darwin-19.5.0-x86_64-i386-64bit\r\n- Python version: 3.7.5\r\n- PyTorch version (GPU?): 1.5.0 (False)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n\r\nAny ideas?","comment_length":26,"text":"Cannot load arxiv dataset on MacOS? \n I am having trouble loading the `\"arxiv\"` config from the `\"scientific_papers\"` dataset on MacOS. When I try loading the dataset with:\r\n\r\n```python\r\narxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n```\r\n\r\nI get the following stack trace:\r\n\r\n```bash\r\nJSONDecodeError                           Traceback (most recent call last)\r\n<ipython-input-2-8e00c55d5a59> in <module>\r\n----> 1 arxiv = nlp.load_dataset(\"scientific_papers\", \"arxiv\")\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    522         download_mode=download_mode,\r\n    523         ignore_verifications=ignore_verifications,\r\n--> 524         save_infos=save_infos,\r\n    525     )\r\n    526 \r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    430                 verify_infos = not save_infos and not ignore_verifications\r\n    431                 self._download_and_prepare(\r\n--> 432                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    433                 )\r\n    434                 # Sync info\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    481             try:\r\n    482                 # Prepare split will record examples associated to the split\r\n--> 483                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    484             except OSError:\r\n    485                 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    662 \r\n    663         generator = self._generate_examples(**split_generator.gen_kwargs)\r\n--> 664         for key, record in utils.tqdm(generator, unit=\" examples\", total=split_info.num_examples, leave=False):\r\n    665             example = self.info.features.encode_example(record)\r\n    666             writer.write(example)\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/tqdm\/std.py in __iter__(self)\r\n   1106                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\r\n   1107 \r\n-> 1108         for obj in iterable:\r\n   1109             yield obj\r\n   1110             # Update and possibly print the progressbar.\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/site-packages\/nlp\/datasets\/scientific_papers\/107a416c0e1958cb846f5934b5aae292f7884a5b27e86af3f3ef1a093e058bbc\/scientific_papers.py in _generate_examples(self, path)\r\n    114                 # \"section_names\": list[str], list of section names.\r\n    115                 # \"sections\": list[list[str]], list of sections (list of paragraphs)\r\n--> 116                 d = json.loads(line)\r\n    117                 summary = \"\\n\".join(d[\"abstract_text\"])\r\n    118                 # In original paper, <S> and <\/S> are not used in vocab during training\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\r\n    346             parse_int is None and parse_float is None and\r\n    347             parse_constant is None and object_pairs_hook is None and not kw):\r\n--> 348         return _default_decoder.decode(s)\r\n    349     if cls is None:\r\n    350         cls = JSONDecoder\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in decode(self, s, _w)\r\n    335 \r\n    336         \"\"\"\r\n--> 337         obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n    338         end = _w(s, end).end()\r\n    339         if end != len(s):\r\n\r\n~\/miniconda3\/envs\/t2t\/lib\/python3.7\/json\/decoder.py in raw_decode(self, s, idx)\r\n    351         \"\"\"\r\n    352         try:\r\n--> 353             obj, end = self.scan_once(s, idx)\r\n    354         except StopIteration as err:\r\n    355             raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\n\r\nJSONDecodeError: Unterminated string starting at: line 1 column 46983 (char 46982)\r\n\r\n163502 examples [02:10, 2710.68 examples\/s]   \r\n```\r\n\r\nI am not sure how to trace back to the specific JSON file that has the \"Unterminated string\". Also, I do not get this error on colab so I suspect it may be MacOS specific. Copy pasting the relevant lines from `transformers-cli env` below:\r\n\r\n- Platform: Darwin-19.5.0-x86_64-i386-64bit\r\n- Python version: 3.7.5\r\n- PyTorch version (GPU?): 1.5.0 (False)\r\n- Tensorflow version (GPU?): 2.2.0 (False)\r\n\r\nAny ideas? \n I just checked inside `train.txt` and this line isn't truncated for me (line 163577).\r\nCould you try to clear your cache and re-download the dataset ?","embeddings":[-0.0581401661,0.0877689496,-0.0419098586,0.1886685789,0.2178975642,0.0454704054,-0.0241877399,0.3616167009,0.0815683678,-0.1818822473,-0.0505227782,0.691057086,-0.1919905692,-0.2139135003,-0.1082298383,0.0094932541,-0.1276049614,0.0114534106,0.1146815494,-0.1110842973,-0.1230693087,0.1993306875,-0.1573242992,-0.0993064716,0.4090956151,0.0958301648,0.1364857703,0.3957486153,-0.0027542866,-0.3556491733,0.1349909604,0.0240957309,0.2171578258,0.5375875831,-0.0001107034,0.2718124986,0.3134252429,-0.0939889029,-0.2043009251,-0.5840528011,-0.4115153253,-0.306637615,0.1335057318,-0.2136874497,-0.0360715277,-0.2607661784,0.1242143437,-0.1539173871,0.3288322389,0.3756518662,0.207921654,0.0798494294,0.3830094635,0.1164422184,-0.0670725107,-0.02313504,0.0021167931,0.4649830163,0.0422845446,0.1263236105,0.1612235159,0.1448102146,-0.1489691883,0.1127980724,0.2092457116,-0.0296654217,-0.1075037271,-0.1436872929,0.0375226587,0.1304574609,0.2721568644,-0.0974486545,-0.2403058112,0.0564082675,0.2218078375,-0.1019054577,0.295181632,0.430082649,-0.1124939024,0.0761579052,-0.3062997162,-0.3497585952,-0.4034608901,0.4736581147,-0.0218985379,-0.1115582362,-0.200718835,0.2033873051,0.1595942974,-0.2415931225,0.0368958823,0.0939481407,0.1707556397,0.11694552,-0.4864809811,0.2469767183,0.0796729624,-0.3753705025,0.0753835738,-0.150146991,-0.1037795395,-0.0815444365,-0.2829775214,0.2728938162,0.1120163128,0.018602727,-0.1558181047,-0.0018167398,0.0582292303,-0.0125903161,-0.1686109453,0.0329580121,-0.3595866859,-0.1540408581,-0.3627384901,-0.3801209033,0.2619623542,-0.3207472861,0.0621837713,-0.0250361618,-0.0250417013,0.0168774351,0.0238443669,0.228412956,-0.0542670265,0.1421990842,0.1716236323,0.4296216667,-0.3466945887,0.1758017391,-0.0509758741,0.0754750222,-0.1097994223,-0.2968219519,0.2044046372,0.0846939757,0.1635968983,-0.0225838032,0.0854115933,-0.201628834,0.1252433211,0.1339762807,-0.1017934233,0.4808894694,0.3299891949,0.0564984679,0.2672579885,0.0386227481,-0.1250129193,0.2215334028,-0.4679540396,-0.1227610484,-0.2679323256,0.162654683,-0.1997473538,-0.1317337751,-0.1605494618,-0.1432931125,0.1379330009,0.2394692749,-0.3634818196,-0.2293667346,0.0001346738,-0.3333007991,0.2488360554,0.0392104574,-0.3133456707,0.1613029242,-0.1056164876,0.1475966722,0.2834636867,0.3652399778,-0.2056614608,0.1426015943,-0.0255504828,-0.1135711595,0.3589995801,-0.3276819885,-0.1195762455,0.0364844464,-0.1393856555,0.076270476,-0.0743433014,0.0231164042,-0.1838792413,0.2579210103,0.2040625364,0.2711192966,0.0826367363,0.0304179639,-0.1805005819,-0.2009442002,0.45898664,0.3523835838,-0.0857428014,0.0294668283,-0.0705721304,-0.072366327,0.1135509834,-0.099117592,-0.3065351248,0.2054903805,0.0127701126,0.0597909503,0.0696247518,0.1258382499,0.1664613187,0.0967324823,0.2396769971,0.2965689003,-0.2159079313,0.3827721477,-0.3872411549,0.0926724225,-0.0310393795,0.0711300671,0.065654926,0.0439680256,-0.0934773609,0.1950520128,-0.1820219457,0.2183669358,-0.3807091713,-0.0371009409,-0.8843935132,0.0560739338,-0.1170641184,0.1107353494,0.2100989223,0.1402559727,-0.1191787124,-0.2400333434,-0.1165203899,0.2139781266,0.2940783203,-0.0699314922,0.2279684991,0.042080678,0.0583746955,-0.3201346993,0.2476847917,0.0171775892,0.0454673953,0.0980466679,-0.2400624603,0.3954666555,0.0848648995,0.1853234768,0.1478035152,0.1319267154,0.4758432508,-0.0060153292,-0.0275846701,-0.0089764493,0.4516296685,0.1805775911,-0.340288192,-0.1420488507,-0.1855588555,-0.0007562374,0.0774995834,0.1592724323,-0.0840466544,0.3020502329,-0.1074541733,0.0607566088,-0.1180131286,0.0291220751,0.3401017785,0.1709996909,-0.3174942732,0.0375214443,-0.2074111104,-0.0640699863,0.1576645225,0.0042496887,0.0024907775,-0.1562239826,0.130223617,0.1190572008,-0.2286053598,-0.2915285528,-0.0149250245,0.4036516249,-0.3374558687,0.0624639392,-0.3534377515,-0.2601831853,-0.0542715788,-0.1631045043,-0.1600035727,-0.2046508193,-0.1103503406,0.1549523771,0.1971672177,0.1569382101,-0.3404233754,-0.0687447339,0.2280444354,-0.212758258,0.2178190351,-0.3201696575,-0.090573445,0.0915043354,0.2567333579,0.115356423,0.2168773413,-0.0711518824,-0.1991698444,0.1951156259,-0.3111149967,-0.0690635741,-0.2529280484,0.1622450054,-0.0127729326,0.2829394639,0.0663689077,-0.2645323873,0.2123935223,0.0352514498,-0.1587046087,0.302259326,0.0569703095,-0.4054757655,-0.1261679381,-0.6859310865,-0.2402991205,-0.2965233922,0.0207346324,0.2632628381,0.1707098782,0.4518840313,0.3053714931,0.021538198,0.0888195485,0.0629290193,-0.1581660807,-0.0866923556,0.2277038097,-0.1510714889,-0.4011754692,0.1772225201,0.2070526481,0.5639321804,0.1303535402,-0.4385875762,0.1577425152,-0.1061138362,-0.0755192265,-0.2182867229,0.1015386134,0.0663023368,-0.210977301,-0.0257533733,0.0079541886,-0.1914936155,-0.0301366076,0.1466688365,0.2630581856,0.2407557815,0.4278973341,-0.2681685686,0.5905352831,0.0142622264,-0.1609193683,0.069476828,-0.1589577198,0.4026772082,-0.0031177653,-0.5069510341,0.2158525139,0.284262687,0.0171935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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/290","title":"ConnectionError - Eli5 dataset download","comments":"It should ne fixed now, thanks for reporting this one :)\r\nIt was an issue on our google storage.\r\n\r\nLet me now if you're still facing this issue.","body":"Hi, I have a problem with downloading Eli5 dataset. When typing `nlp.load_dataset('eli5')`, I get ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/eli5\/LFQA_reddit\/1.0.0\/explain_like_im_five-train_eli5.arrow\r\n\r\nI would appreciate if you could help me with this issue.","comment_length":28,"text":"ConnectionError - Eli5 dataset download \n Hi, I have a problem with downloading Eli5 dataset. When typing `nlp.load_dataset('eli5')`, I get ConnectionError: Couldn't reach https:\/\/storage.googleapis.com\/huggingface-nlp\/cache\/datasets\/eli5\/LFQA_reddit\/1.0.0\/explain_like_im_five-train_eli5.arrow\r\n\r\nI would appreciate if you could help me with this issue. \n It should ne fixed now, thanks for reporting this one :)\r\nIt was an issue on our google storage.\r\n\r\nLet me now if you're still facing this issue.","embeddings":[-0.2497588396,0.0893835127,-0.0778242871,0.3270986974,0.2081756443,0.0021226979,0.2745363414,0.1381624788,0.049143821,0.0405926295,-0.0316886939,0.0492954031,0.1268917173,0.332809031,0.0971176103,-0.1882539243,-0.0128969112,0.0856067389,-0.0095131677,0.107113421,-0.0457852446,0.1713877767,-0.1018920094,0.1726023257,-0.1952628344,0.1230360121,-0.0542774051,-0.0766947418,-0.3689592183,-0.2835099101,0.343839556,0.1248752102,0.1802075505,-0.0275570005,-0.0001161579,-0.0773379281,0.2845264673,-0.0068009025,-0.3015561402,-0.4885488451,-0.1385288537,-0.199436605,0.3694058955,-0.0722754598,0.2015680373,0.1652351767,0.4329591393,0.1027759016,0.4838895798,0.1664904654,0.1847645193,-0.033776179,0.3706132174,0.0979095697,0.069197841,-0.3523107469,0.0500903428,0.3374673724,-0.1407166868,-0.1857194006,0.1142202765,0.0921674445,0.1664485037,0.1101124659,0.1821706891,0.1309393942,-0.0719027966,-0.3520626426,0.0242044348,0.1167074814,0.997747004,-0.0397392809,-0.2393235117,0.0057089836,0.1492985785,-0.4184676111,0.5427045822,0.2712637782,-0.147865504,0.0503337421,-0.3415909708,-0.3982751071,-0.3517916799,0.3603954017,-0.0355244651,0.0635365844,-0.0816120878,0.1403473765,0.0928828716,-0.0811193436,-0.108157225,0.1471655369,-0.2366290092,0.2940263748,-0.3973180056,-0.0846187025,-0.2899481654,0.1905506849,0.2292871624,0.3845526278,0.0899111181,0.0555599593,-0.1263232529,0.1376539022,0.3958095312,0.2708767951,0.1903690696,0.0780157372,0.187390089,0.174881652,-0.0046946746,-0.0981151238,-0.1432070434,-0.0560345501,-0.1115585491,-0.2207060307,0.080759801,-0.3565367758,-0.3290453553,0.099036172,-0.4623711407,-0.1824753582,-0.0268413592,0.1823046505,-0.2728771865,0.0126221757,0.0528683886,0.3319451511,-0.1053778827,-0.2310124487,-0.1688058674,0.0135987885,-0.2198605984,-0.0283150654,0.1741315722,-0.1911731213,0.4688150883,-0.211623624,-0.1507113278,-0.1241675541,0.1094485894,-0.1262895465,-0.5147399306,0.0752008483,0.2205120325,0.0992490426,-0.0520787165,-0.273128897,0.0105490126,-0.1274969578,-0.2243053019,-0.1969204694,-0.4014467001,0.1562357694,-0.1802914143,-0.2092911154,-0.3340261281,0.1914106905,-0.0650037825,-0.2015036494,-0.1894827485,0.0536635891,-0.1326780617,-0.148902297,0.2816813886,0.2782120109,0.0476502664,-0.4040094614,-0.179675281,-0.1528379172,0.1077236012,0.4209942222,-0.0597602911,-0.1324637383,-0.1739879698,0.3515346646,0.6295803189,-0.1592783481,-0.9718024731,0.2665201426,-0.2599483132,-0.2456943542,-0.0510173216,0.1613211036,0.1380274147,0.1287878454,0.1731065363,0.4931621552,-0.1016826704,-0.0801599994,-0.1647747904,-0.3046265841,0.1237145513,0.0984527096,0.0070138671,0.08282125,0.2047087699,0.0117723318,0.121151872,0.2219158411,0.1049161106,0.1853275746,0.2719538212,-0.0845817104,-0.1859809011,0.0755384043,-0.5517844558,0.1320389956,-0.2430137545,0.2367604226,-0.3266122043,0.1815017909,-0.1378211081,-0.0250577684,-0.0871811658,-0.0196262524,0.0588310659,-0.2522036135,0.0864872783,0.36637941,-0.0120975785,0.4874075949,0.0745212212,0.2470305115,-0.3514170945,0.5134061575,-0.2010786831,-0.0856424868,0.1534211189,-0.1430327147,0.0028649687,0.0159374196,-0.2361585498,0.0854357108,-0.5183472037,0.1810060591,0.2617428303,0.4170763493,0.1843682975,-0.4226613939,0.2094871253,-0.2681599557,0.0940827876,0.0769026577,0.0457851514,0.0614014305,0.2973328531,0.0859338418,-0.0368909761,0.3502425849,0.3250182867,-0.032225877,0.0745291933,-0.1063506529,0.4227878451,0.2946760654,0.2827172875,-0.2102748156,-0.2436743975,0.2049201876,0.364874959,0.1089647487,0.0341823362,0.3822146356,-0.2421706766,-0.4425079525,-0.1141517386,-0.0854029432,0.3881253898,0.0548147075,-0.0007224341,0.1530988514,0.0520122349,-0.1004080027,0.1607725173,0.0738225654,0.0998866633,0.0479518995,0.1733155251,0.0522657521,-0.1489476562,-0.1117216125,0.0068897391,0.3233179748,-0.1116506308,0.1294988543,-0.0682972223,-0.2203239202,0.1653384119,-0.2381619662,-0.4337794483,-0.3147348762,0.0085767368,0.2693352401,0.1083348691,0.0735155046,-0.0276876055,0.0659514591,0.200631246,0.0095318379,-0.0412142724,-0.2337365597,-0.3130791783,0.071206592,0.3317569494,0.1017727405,0.1627256423,-0.175133571,-0.067290619,-0.4746840894,-0.2086940855,0.0796019286,-0.124481529,0.4008778334,0.0662376881,0.5214606524,-0.1920004785,-0.1050177738,0.1928012669,-0.1713085175,-0.1980884671,-0.028674027,-0.0131229199,0.1640209407,0.1673440933,-0.2523571551,-0.4018141925,-0.2180769891,0.4885856211,0.147012338,0.0510380492,-0.0898293257,0.1064495519,0.0771180466,0.1504940987,-0.1009458154,-0.2762487829,-0.4101827145,0.1920643151,-0.0836889148,-0.318220675,0.1295692176,0.1608963013,0.2064031959,0.0575537719,-0.5428879857,-0.1649180055,-0.0307975076,0.2012941539,0.0202498566,-0.1885973066,0.2706339955,-0.1079489961,0.0085336426,0.2917350829,-0.082199052,0.287941128,0.2989262044,0.4235113859,0.099251464,0.6737154722,-0.0067914613,0.4394054115,0.1497519314,-0.1561596543,0.5129275322,0.217713356,0.0470972024,-0.0618590079,-0.0526073612,0.048192367,-0.0784617364,0.036762788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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/288","title":"Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill'","comments":"It looks like the bug comes from `dill`. Which version of `dill` are you using ?","body":"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:470: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:471: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:472: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:473: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:476: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/importlib\/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\r\n  return f(*args, **kwds)\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/h5py\/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\r\n  from ._conv import register_converters as _register_converters\r\nTraceback (most recent call last):\r\n  File \"\/Users\/parasol_tree\/Resource\/019 - Github\/AcademicEnglishToolkit \/test.py\", line 7, in <module>\r\n    import nlp\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/__init__.py\", line 27, in <module>\r\n    from .arrow_dataset import Dataset\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/arrow_dataset.py\", line 31, in <module>\r\n    from nlp.utils.py_utils import dumps\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 25, in <module>\r\n    from .py_utils import flatten_nested, map_nested, size_str\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/py_utils.py\", line 244, in <module>\r\n    class Pickler(dill.Pickler):\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/py_utils.py\", line 247, in Pickler\r\n    dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())\r\nAttributeError: module 'dill' has no attribute '_dill'","comment_length":16,"text":"Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill' \n \/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:470: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:471: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:472: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:473: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:476: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/importlib\/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\r\n  return f(*args, **kwds)\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/h5py\/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\r\n  from ._conv import register_converters as _register_converters\r\nTraceback (most recent call last):\r\n  File \"\/Users\/parasol_tree\/Resource\/019 - Github\/AcademicEnglishToolkit \/test.py\", line 7, in <module>\r\n    import nlp\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/__init__.py\", line 27, in <module>\r\n    from .arrow_dataset import Dataset\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/arrow_dataset.py\", line 31, in <module>\r\n    from nlp.utils.py_utils import dumps\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 25, in <module>\r\n    from .py_utils import flatten_nested, map_nested, size_str\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/py_utils.py\", line 244, in <module>\r\n    class Pickler(dill.Pickler):\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/py_utils.py\", line 247, in Pickler\r\n    dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())\r\nAttributeError: module 'dill' has no attribute '_dill' \n It looks like the bug comes from `dill`. Which version of `dill` are you using ?","embeddings":[0.0769836977,-0.4020940661,-0.1799209863,0.0659818947,0.1492687613,-0.003346167,0.55732584,0.3920768797,-0.0114857536,0.2672935724,-0.0524433628,0.3347723782,-0.0505946018,-0.3120033741,0.0111635383,-0.3291765749,0.0835466534,0.2736496031,-0.37093997,-0.2723834813,-0.2335172594,0.2800564766,-0.0776633248,0.2484975308,-0.1481041014,-0.0349433534,0.1214392036,0.1059611663,0.0675767437,-0.2886580229,0.1965774745,-0.1745566279,0.2275901288,0.4768029451,-0.0001043869,0.1361589134,0.3375531137,-0.0346087962,-0.1342187971,-0.3505931497,0.4713913202,0.0593214221,0.3363775015,-0.3500793874,-0.0507910028,-0.2969646454,-0.0969579443,-0.30893448,0.4441095889,0.4418491125,0.3010698855,0.1026526392,0.2446943521,-0.169915244,-0.0208351463,-0.0297836401,-0.0677934811,0.3753479719,0.1383522898,0.1474164873,0.3823755085,0.342466712,0.160931766,0.0776078403,0.3393225968,0.0322579108,0.6600615978,-0.4541978836,-0.0775012821,0.2736726701,0.1812872142,-0.1184436977,-0.3209805787,0.1269780993,0.0060105366,-0.2475019395,0.0616756529,-0.06774728,-0.2496780753,-0.032574065,0.4458368123,-0.0442663617,-0.1771987081,0.1953953058,-0.0185109787,0.1636362374,-0.044930961,-0.0506545231,0.0271611586,-0.3340252638,0.3872074783,0.0928654596,0.4484997094,0.0960105956,-0.0759613588,-0.1219853684,0.0625471696,-0.6255782843,-0.2034097016,-0.2914340496,0.0739517361,-0.0122585492,0.1180225462,0.2710880339,-0.1634567976,0.0667565018,0.1067271829,0.160689339,0.335481286,0.0793941543,0.328011781,0.1698743701,-0.3021524251,-0.125138551,-0.1567854881,0.0640961453,0.3410718143,0.0939639807,-0.5376574993,-0.0486492701,-0.1924375296,0.0474512838,-0.0055814907,0.2568105459,-0.0379515067,0.1916810572,0.4150514603,0.11126706,-0.2454229593,0.0046106484,-0.2229356468,0.3555662632,-0.0703071654,-0.2020584792,0.0587319173,0.1554544717,0.3014602363,-0.0993096381,0.1310544163,-0.0825211927,0.1695728451,0.0146686807,-0.0396083631,0.1542768478,-0.2942762375,-0.0451418795,0.2622388005,-0.1855244786,-0.2611550987,0.091217421,-0.2335643321,-0.0095623313,-0.4541479945,0.3482017517,0.250795722,0.110424757,0.2338398695,0.0304904748,0.1052182466,0.0470620319,-0.101509586,-0.2446223497,-0.1023621485,-0.3306634724,0.2848880291,0.1544385999,-0.0588522553,-0.1574843526,-0.1423849314,-0.0397362337,0.1201601923,0.2202340066,-0.1351770014,-0.0135140996,0.1174672022,-0.1719239056,0.6146841645,-0.5745144486,0.1056552455,-0.0611012615,0.1371087134,-0.0482239611,-0.0108275972,-0.0786206573,0.1418243051,-0.0617279485,0.3198719323,0.3495156169,0.2515051663,-0.1104580089,-0.3646187782,0.012272289,0.1300430149,0.0910370499,0.0965716168,0.0635002851,-0.0523701161,-0.0480599813,0.0647452176,-0.1598062068,0.004075455,0.123443149,0.0875847489,-0.0867793262,0.1008282751,-0.1719257832,-0.3230362236,-0.0569832884,-0.3147102892,0.0343750939,-0.0152414292,-0.1913605928,-0.1983193457,0.0127634313,0.119129926,-0.2337505966,0.3220001459,0.0348381661,-0.0130126383,0.1630757153,-0.2321627587,-0.0329379737,-0.1512498111,0.181532681,-0.1353039891,0.1641567945,-0.4232264459,-0.2587807477,-0.1687233597,0.35354954,0.0873158574,-0.3229031265,-0.0398910791,0.3210483193,-0.0660059303,-0.0963132754,-0.3560896814,-0.0151594486,0.3564194143,-0.1473701298,0.044008974,0.3544332087,0.1194430888,-0.106795162,0.066043511,0.2442780733,0.2975997329,-0.0210516416,0.2813411057,0.1925182492,0.0381851532,0.1245420799,-0.0583589785,-0.1804328114,0.1559882909,0.3096714914,0.1182772219,-0.1786590964,-0.3969525397,-0.0277689844,0.2105372548,0.0029683402,0.2126078606,-0.0307502821,-0.0751878694,0.1257900149,0.1558475643,-0.0766748637,0.1838900596,0.0091597186,0.1322796494,0.0727801621,-0.2034448534,-0.1412041932,0.1152334958,-0.0268808324,0.3497841656,-0.0709351376,-0.0873165056,0.0785132051,-0.0844333768,-0.3782753944,0.05666437,0.1657810658,-0.0829095691,0.0378239527,-0.26275298,-0.0227957889,-0.0947818086,-0.2296084017,0.1324962825,-0.1437409669,-0.0149990348,0.1761470586,-0.1750652939,0.2052592635,0.1883973628,-0.0567656383,0.0960681811,0.3126211762,-0.2592220008,-0.0779284015,-0.1663522869,0.1553854793,0.0330693349,-0.0856425464,0.1994274557,-0.2755847275,-0.120114252,0.1499013901,-0.4520840049,0.0524351485,0.0140112396,0.1148459166,0.2227616012,0.2215283513,0.0248776749,-0.3447566926,0.2151990533,-0.3349262178,-0.0258942172,-0.0942451507,0.053977523,-0.0956519395,-0.4885933995,-0.6345106363,-0.3095667958,-0.538667798,0.0189340804,0.1400108635,0.3738848865,0.3208152652,0.2370781004,0.4575053453,0.3019067645,0.445266813,0.0590105243,0.0270972084,0.3070289791,-0.4646860957,-0.3787640929,-0.1151326671,-0.1516299397,0.3044160306,-0.1614550352,-0.0524057709,-0.3177403808,-0.2742889822,0.2044684142,-0.2654448748,-0.0834642574,0.0780671909,0.1683275998,-0.2234417647,-0.1472693682,0.0398055241,0.1982986629,-0.0899709836,0.0503881052,0.1322678328,0.4105213583,-0.1355243474,0.641283989,0.1535908431,-0.5416244864,0.1315539628,0.0941807106,0.1784284562,0.1610788852,-0.0770013109,0.0641207471,0.1061399058,0.3015512526,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/288","title":"Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill'","comments":"0.2.6 is three years old now, maybe try a more recent one, e.g. the current 0.3.2 if you can?","body":"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:470: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:471: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:472: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:473: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:476: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/importlib\/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\r\n  return f(*args, **kwds)\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/h5py\/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\r\n  from ._conv import register_converters as _register_converters\r\nTraceback (most recent call last):\r\n  File \"\/Users\/parasol_tree\/Resource\/019 - Github\/AcademicEnglishToolkit \/test.py\", line 7, in <module>\r\n    import nlp\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/__init__.py\", line 27, in <module>\r\n    from .arrow_dataset import Dataset\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/arrow_dataset.py\", line 31, in <module>\r\n    from nlp.utils.py_utils import dumps\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 25, in <module>\r\n    from .py_utils import flatten_nested, map_nested, size_str\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/py_utils.py\", line 244, in <module>\r\n    class Pickler(dill.Pickler):\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/py_utils.py\", line 247, in Pickler\r\n    dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())\r\nAttributeError: module 'dill' has no attribute '_dill'","comment_length":19,"text":"Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill' \n \/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:470: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:471: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:472: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:473: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/dtypes.py:476: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) \/ '(1,)type'.\r\n  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/importlib\/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\r\n  return f(*args, **kwds)\r\n\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/h5py\/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\r\n  from ._conv import register_converters as _register_converters\r\nTraceback (most recent call last):\r\n  File \"\/Users\/parasol_tree\/Resource\/019 - Github\/AcademicEnglishToolkit \/test.py\", line 7, in <module>\r\n    import nlp\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/__init__.py\", line 27, in <module>\r\n    from .arrow_dataset import Dataset\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/arrow_dataset.py\", line 31, in <module>\r\n    from nlp.utils.py_utils import dumps\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/__init__.py\", line 20, in <module>\r\n    from .download_manager import DownloadManager, GenerateMode\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py\", line 25, in <module>\r\n    from .py_utils import flatten_nested, map_nested, size_str\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/py_utils.py\", line 244, in <module>\r\n    class Pickler(dill.Pickler):\r\n  File \"\/Users\/parasol_tree\/anaconda3\/lib\/python3.6\/site-packages\/nlp\/utils\/py_utils.py\", line 247, in Pickler\r\n    dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())\r\nAttributeError: module 'dill' has no attribute '_dill' \n 0.2.6 is three years old now, maybe try a more recent one, e.g. the current 0.3.2 if you can?","embeddings":[0.0769836977,-0.4020940661,-0.1799209863,0.0659818947,0.1492687613,-0.003346167,0.55732584,0.3920768797,-0.0114857536,0.2672935724,-0.0524433628,0.3347723782,-0.0505946018,-0.3120033741,0.0111635383,-0.3291765749,0.0835466534,0.2736496031,-0.37093997,-0.2723834813,-0.2335172594,0.2800564766,-0.0776633248,0.2484975308,-0.1481041014,-0.0349433534,0.1214392036,0.1059611663,0.0675767437,-0.2886580229,0.1965774745,-0.1745566279,0.2275901288,0.4768029451,-0.0001043869,0.1361589134,0.3375531137,-0.0346087962,-0.1342187971,-0.3505931497,0.4713913202,0.0593214221,0.3363775015,-0.3500793874,-0.0507910028,-0.2969646454,-0.0969579443,-0.30893448,0.4441095889,0.4418491125,0.3010698855,0.1026526392,0.2446943521,-0.169915244,-0.0208351463,-0.0297836401,-0.0677934811,0.3753479719,0.1383522898,0.1474164873,0.3823755085,0.342466712,0.160931766,0.0776078403,0.3393225968,0.0322579108,0.6600615978,-0.4541978836,-0.0775012821,0.2736726701,0.1812872142,-0.1184436977,-0.3209805787,0.1269780993,0.0060105366,-0.2475019395,0.0616756529,-0.06774728,-0.2496780753,-0.032574065,0.4458368123,-0.0442663617,-0.1771987081,0.1953953058,-0.0185109787,0.1636362374,-0.044930961,-0.0506545231,0.0271611586,-0.3340252638,0.3872074783,0.0928654596,0.4484997094,0.0960105956,-0.0759613588,-0.1219853684,0.0625471696,-0.6255782843,-0.2034097016,-0.2914340496,0.0739517361,-0.0122585492,0.1180225462,0.2710880339,-0.1634567976,0.0667565018,0.1067271829,0.160689339,0.335481286,0.0793941543,0.328011781,0.1698743701,-0.3021524251,-0.125138551,-0.1567854881,0.0640961453,0.3410718143,0.0939639807,-0.5376574993,-0.0486492701,-0.1924375296,0.0474512838,-0.0055814907,0.2568105459,-0.0379515067,0.1916810572,0.4150514603,0.11126706,-0.2454229593,0.0046106484,-0.2229356468,0.3555662632,-0.0703071654,-0.2020584792,0.0587319173,0.1554544717,0.3014602363,-0.0993096381,0.1310544163,-0.0825211927,0.1695728451,0.0146686807,-0.0396083631,0.1542768478,-0.2942762375,-0.0451418795,0.2622388005,-0.1855244786,-0.2611550987,0.091217421,-0.2335643321,-0.0095623313,-0.4541479945,0.3482017517,0.250795722,0.110424757,0.2338398695,0.0304904748,0.1052182466,0.0470620319,-0.101509586,-0.2446223497,-0.1023621485,-0.3306634724,0.2848880291,0.1544385999,-0.0588522553,-0.1574843526,-0.1423849314,-0.0397362337,0.1201601923,0.2202340066,-0.1351770014,-0.0135140996,0.1174672022,-0.1719239056,0.6146841645,-0.5745144486,0.1056552455,-0.0611012615,0.1371087134,-0.0482239611,-0.0108275972,-0.0786206573,0.1418243051,-0.0617279485,0.3198719323,0.3495156169,0.2515051663,-0.1104580089,-0.3646187782,0.012272289,0.1300430149,0.0910370499,0.0965716168,0.0635002851,-0.0523701161,-0.0480599813,0.0647452176,-0.1598062068,0.004075455,0.123443149,0.0875847489,-0.0867793262,0.1008282751,-0.1719257832,-0.3230362236,-0.0569832884,-0.3147102892,0.0343750939,-0.0152414292,-0.1913605928,-0.1983193457,0.0127634313,0.119129926,-0.2337505966,0.3220001459,0.0348381661,-0.0130126383,0.1630757153,-0.2321627587,-0.0329379737,-0.1512498111,0.181532681,-0.1353039891,0.1641567945,-0.4232264459,-0.2587807477,-0.1687233597,0.35354954,0.0873158574,-0.3229031265,-0.0398910791,0.3210483193,-0.0660059303,-0.0963132754,-0.3560896814,-0.0151594486,0.3564194143,-0.1473701298,0.044008974,0.3544332087,0.1194430888,-0.106795162,0.066043511,0.2442780733,0.2975997329,-0.0210516416,0.2813411057,0.1925182492,0.0381851532,0.1245420799,-0.0583589785,-0.1804328114,0.1559882909,0.3096714914,0.1182772219,-0.1786590964,-0.3969525397,-0.0277689844,0.2105372548,0.0029683402,0.2126078606,-0.0307502821,-0.0751878694,0.1257900149,0.1558475643,-0.0766748637,0.1838900596,0.0091597186,0.1322796494,0.0727801621,-0.2034448534,-0.1412041932,0.1152334958,-0.0268808324,0.3497841656,-0.0709351376,-0.0873165056,0.0785132051,-0.0844333768,-0.3782753944,0.05666437,0.1657810658,-0.0829095691,0.0378239527,-0.26275298,-0.0227957889,-0.0947818086,-0.2296084017,0.1324962825,-0.1437409669,-0.0149990348,0.1761470586,-0.1750652939,0.2052592635,0.1883973628,-0.0567656383,0.0960681811,0.3126211762,-0.2592220008,-0.0779284015,-0.1663522869,0.1553854793,0.0330693349,-0.0856425464,0.1994274557,-0.2755847275,-0.120114252,0.1499013901,-0.4520840049,0.0524351485,0.0140112396,0.1148459166,0.2227616012,0.2215283513,0.0248776749,-0.3447566926,0.2151990533,-0.3349262178,-0.0258942172,-0.0942451507,0.053977523,-0.0956519395,-0.4885933995,-0.6345106363,-0.3095667958,-0.538667798,0.0189340804,0.1400108635,0.3738848865,0.3208152652,0.2370781004,0.4575053453,0.3019067645,0.445266813,0.0590105243,0.0270972084,0.3070289791,-0.4646860957,-0.3787640929,-0.1151326671,-0.1516299397,0.3044160306,-0.1614550352,-0.0524057709,-0.3177403808,-0.2742889822,0.2044684142,-0.2654448748,-0.0834642574,0.0780671909,0.1683275998,-0.2234417647,-0.1472693682,0.0398055241,0.1982986629,-0.0899709836,0.0503881052,0.1322678328,0.4105213583,-0.1355243474,0.641283989,0.1535908431,-0.5416244864,0.1315539628,0.0941807106,0.1784284562,0.1610788852,-0.0770013109,0.0641207471,0.1061399058,0.301551252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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/281","title":"Private\/sensitive data","comments":"Hi @MFreidank, you should already be able to load a dataset from local sources, indeed. (ping @lhoestq and @jplu)\r\n\r\nWe're also thinking about the ability to host private datasets on a hosted bucket with permission management, but that's further down the road.","body":"Hi all,\r\nThanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF\/Pytorch. \r\n\r\nUnfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information. \r\nIs there support\/a plan to support such data with NLP, e.g. by reading it from local sources?\r\n\r\nUse case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive\/private data without the need to rethink data processing pipelines. \r\n\r\nMany thanks for your responses ahead of time and kind regards,\r\nMFreidank","comment_length":42,"text":"Private\/sensitive data \n Hi all,\r\nThanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF\/Pytorch. \r\n\r\nUnfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information. \r\nIs there support\/a plan to support such data with NLP, e.g. by reading it from local sources?\r\n\r\nUse case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive\/private data without the need to rethink data processing pipelines. \r\n\r\nMany thanks for your responses ahead of time and kind regards,\r\nMFreidank \n Hi @MFreidank, you should already be able to load a dataset from local sources, indeed. (ping @lhoestq and @jplu)\r\n\r\nWe're also thinking about the ability to host private datasets on a hosted bucket with permission management, but that's further down the road.","embeddings":[-0.0707458109,0.4125887752,-0.1550576985,0.2336954176,-0.3745613098,-0.3104677498,0.1758047789,0.2741076052,-0.187402606,0.0843257084,-0.099034436,0.235512346,-0.1740504205,0.4147525132,0.156123817,-0.141062513,-0.1745984107,0.3279705346,0.0204366203,0.123477526,0.2168058455,-0.0035431683,-0.1398066282,0.1810401976,0.0957490802,-0.0827764943,0.0768581554,-0.0709032044,-0.0051912307,-0.2377834469,0.1994736344,0.2674770951,0.3569665551,0.1103215218,-0.0001181756,-0.0618794262,-0.0077543999,-0.0915450752,0.1014710963,-0.3869082034,0.0909538865,-0.3763212264,0.1161264405,-0.0942868665,-0.6016390324,-0.2237698138,0.223329857,-0.2056765258,0.2856585681,-0.1414274871,0.0898133889,0.4285238683,-0.0426420681,0.4334448576,0.2679482102,0.0425534435,-0.3776562512,-0.2314343005,0.4403627515,0.0660785809,-0.1837641597,0.0795590803,-0.1058342829,0.0370284282,0.2091089934,-0.0535619333,-0.2636175454,-0.4229537845,-0.0417968594,0.4476955831,0.6776380539,-0.0553220026,-0.3027353585,-0.1976625919,0.0497470535,-0.1227308437,0.0437942371,0.3050142825,-0.3012392819,0.2311728448,-0.2347911745,-0.5339365602,-0.5878560543,0.4005849063,0.3250871301,0.172593683,-0.1147480085,0.0097190253,-0.1834225655,-0.1467662305,0.3118985593,0.0840847716,0.3460735381,0.3613792658,0.2314935625,-0.3157440424,-0.123499766,-0.1307466477,0.1263952702,0.0600157343,0.0627787486,0.3807478547,-0.4343865812,0.1869550794,0.5641598105,-0.3314897716,-0.27328071,0.2127691805,0.6141747832,-0.2264549583,0.1624816805,0.1731202006,-0.0866314843,0.1338378638,-0.3697251081,-0.1365144104,0.0459270217,-0.3774811029,-0.0641482994,0.1029619426,-0.2001150548,0.0772923082,0.0345184803,0.1005116478,0.0606263764,0.0826020911,0.2194363475,-0.2596558928,0.1002445668,-0.4008681476,0.0736742765,-0.3296131194,-0.230513081,0.1907190084,0.358513236,0.052518107,0.1227400005,-0.1362953931,0.1661897004,0.0949334279,0.182130456,-0.0007126121,0.2064194828,0.2837126851,-0.1496316344,-0.2135072201,-0.2083340436,-0.0681238249,-0.2654196024,0.4042986333,-0.2182285637,-0.3589202166,-0.0729403868,-0.0733458102,-0.3603952825,0.0497680828,0.1830289215,0.1923413873,-0.0132968742,-0.357829839,0.1233032793,0.0540306941,-0.3091997802,-0.3107890487,0.2044462562,0.0079360837,-0.21543625,0.1870301515,0.0801904276,-0.0899683759,0.0907674581,0.3384080529,-0.3551779389,-0.1171650141,0.1238018721,0.1438644379,0.6366678476,-0.1484181285,-0.4632449448,0.773643136,0.0050810995,-0.349994719,-0.0010337892,0.5067375898,0.0797293782,-0.0977019444,-0.1144037992,0.7414237261,0.0494311638,0.0938225463,-0.1705517024,-0.452065438,0.4799664319,0.3717123866,-0.007751755,0.044731807,0.2441882193,0.3608098328,0.2845618129,-0.0610938668,0.0669619665,-0.0351169221,0.1061966494,0.2022458762,-0.0028933052,-0.2733085752,0.0834963098,-0.1555231512,-0.5438956022,0.149952814,0.033630468,-0.1679928154,0.228863582,-0.0996507481,0.1972913593,-0.0412272178,0.0136140008,-0.0050666025,-0.0585574917,-0.0237949733,-0.3576771021,-0.0481377579,-0.1494691968,-0.153427124,-0.0364810079,0.2351106852,0.0654012412,-0.0547394156,0.3041162193,0.5591760278,-0.0135732358,-0.1076304913,0.1822916567,0.1524810195,-0.185741961,0.2442169636,0.0726282597,0.5805903673,0.3432220519,-0.1757588387,0.2598481178,0.3666487038,-0.0341157392,0.1733643711,0.0984331816,0.7042987347,-0.2638027668,-0.1616846174,-0.0321972966,-0.149155587,-0.004895024,-0.173579365,-0.3520435691,-0.0304909572,0.010926743,-0.2666922212,0.1307037622,-0.0318236388,-0.5742003918,0.2258845866,-0.1088710055,-0.0877471045,0.305164665,-0.0261934493,-0.0274327938,-0.0649805889,0.1479395777,0.2978618741,0.3659072816,0.1113848761,-0.1728381664,-0.0731302276,-0.1175538823,-0.1416867375,-0.0020144924,-0.0593261346,0.1523485929,0.0491004735,0.1965176165,0.354804337,-0.0670658574,0.1788393408,-0.073028028,0.0943154618,-0.0095762732,0.1214590222,-0.2423714846,-0.38159886,0.072255075,-0.0107747633,-0.1310466826,0.2822643518,0.155437842,0.1999242604,-0.0822517946,-0.2093529701,-0.2692062855,0.4863938689,-0.2974672318,-0.157526046,-0.0836522803,-0.1240205914,-0.096003361,0.0475061834,0.3210487962,-0.0360234231,0.3541307151,0.4143390656,0.0107448259,-0.1943289042,-0.3171733916,0.0647922307,0.0316571966,0.2427788079,-0.0861499459,-0.0453930534,0.087456964,-0.2234255224,-0.0734251589,-0.419618398,-0.0061414493,-0.2804930806,-0.2080231458,-0.0215628836,-0.2728872299,-0.1179999635,-0.5042937398,-0.1746373326,0.3917945921,0.0175452866,0.4474543035,0.0974578485,-0.2773035169,0.0967274904,0.0109266415,-0.0492402613,-0.0719493032,-0.4233448207,0.2278181612,-0.5833615065,-0.4579364359,-0.116012685,-0.0033489377,-0.0401588045,0.206042245,-0.625767529,-0.037111897,0.1528884619,0.2928291261,-0.0337190218,-0.2740913033,0.2435399741,0.0247672219,0.070463635,-0.1655634791,-0.1309505552,-0.2497606277,0.0240281913,0.2235518694,0.3631480932,-0.3084203005,0.4264250398,0.3714079857,-0.0315979458,-0.1078972965,0.3445717096,0.2310940176,0.158288762,0.2725990117,0.0424835645,0.1564814299,-0.0483453274,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/281","title":"Private\/sensitive data","comments":"Hi @MFreidank, it is possible to load a dataset from your local storage, but only CSV\/TSV and JSON are supported. To load a dataset in JSON format:\r\n\r\n```\r\nnlp.load_dataset(path=\"json\", data_files={nlp.Split.TRAIN: [\"path\/to\/train.json\"], nlp.Split.TEST: [\"path\/to\/test.json\"]})\r\n```\r\n\r\nFor CSV\/TSV datasets, you have to replace `json` by `csv`.","body":"Hi all,\r\nThanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF\/Pytorch. \r\n\r\nUnfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information. \r\nIs there support\/a plan to support such data with NLP, e.g. by reading it from local sources?\r\n\r\nUse case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive\/private data without the need to rethink data processing pipelines. \r\n\r\nMany thanks for your responses ahead of time and kind regards,\r\nMFreidank","comment_length":44,"text":"Private\/sensitive data \n Hi all,\r\nThanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF\/Pytorch. \r\n\r\nUnfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information. \r\nIs there support\/a plan to support such data with NLP, e.g. by reading it from local sources?\r\n\r\nUse case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive\/private data without the need to rethink data processing pipelines. \r\n\r\nMany thanks for your responses ahead of time and kind regards,\r\nMFreidank \n Hi @MFreidank, it is possible to load a dataset from your local storage, but only CSV\/TSV and JSON are supported. To load a dataset in JSON format:\r\n\r\n```\r\nnlp.load_dataset(path=\"json\", data_files={nlp.Split.TRAIN: [\"path\/to\/train.json\"], nlp.Split.TEST: [\"path\/to\/test.json\"]})\r\n```\r\n\r\nFor CSV\/TSV datasets, you have to replace `json` by `csv`.","embeddings":[-0.0166605357,0.3993093073,-0.1244864762,0.2548536658,-0.2739622295,-0.2752925754,0.2181889862,0.308070004,-0.0690384805,0.0078153489,-0.1233371347,0.2415469289,-0.1098864228,0.431302011,0.2261041403,-0.1605279893,-0.2306254357,0.3290660679,0.0751647204,0.1278129816,0.1798416227,0.0108177625,-0.027710788,0.1978319138,0.1053762361,-0.0224126279,0.0391383097,-0.0110528544,0.0493090414,-0.2807991803,0.2194712162,0.1461137533,0.3711489141,0.1042129025,-0.0001194865,0.0547816083,0.0042786831,-0.1700389832,0.014008496,-0.5010938048,0.118179366,-0.4590620697,0.2127284855,-0.1323252469,-0.5507670641,-0.3828507364,0.195341289,-0.2550372779,0.4459568262,-0.0134742884,0.0972172618,0.3948141634,-0.0238350388,0.4301288128,0.1516950428,0.1558122784,-0.2978108227,-0.1546912193,0.3256403804,0.0576554276,-0.1445109546,-0.0260695852,-0.0631829277,0.108187668,0.1694032699,0.0011481006,-0.2196285576,-0.3277963698,-0.0628166795,0.399366051,0.5945553184,-0.0275267567,-0.2495580465,-0.2670380175,0.0319775194,-0.1998359114,0.0711245313,0.4118881226,-0.289634496,0.2282141596,-0.3382027447,-0.4331628382,-0.5370650291,0.4045125246,0.3479412496,0.0709438473,-0.0933347046,0.0860698596,-0.1627711803,-0.1994019747,0.197381556,-0.0023525564,0.3907437623,0.3041099906,0.1776646227,-0.3675431609,-0.1347600371,-0.3807192147,0.0989573076,-0.0090187527,0.0206542239,0.3384737968,-0.3744046092,0.212100625,0.5879611969,-0.2345270962,-0.2241452485,0.2144237012,0.4542486966,-0.2493967116,0.0432523601,0.0823550448,-0.052257441,0.1146748811,-0.4078924656,-0.1083300561,0.0567062013,-0.3220865428,-0.0730881244,0.0340624973,-0.2571914792,0.0476955101,-0.0437502377,0.1794607937,0.0698910952,0.104365401,0.28446576,-0.1818287075,0.1176628172,-0.3972922266,0.0611345358,-0.254008472,-0.1454838961,0.2200012803,0.4175849855,0.094150953,0.1839074492,-0.2577002645,0.1028045192,-0.0206567999,0.1476295441,-0.1018630341,0.2499348968,0.246468544,-0.0304197464,-0.2639297545,-0.0956272781,-0.1191156656,-0.3232686818,0.3597046435,-0.2853327096,-0.3630567491,-0.1132007241,-0.0605303459,-0.4056692123,0.099638015,0.1104567423,0.1905024499,0.0251190625,-0.3158795536,0.0254389681,0.0203312244,-0.3600462973,-0.2895978689,0.2112156898,-0.0422562957,-0.2529892325,0.0836327374,0.0734239519,-0.1013646945,0.0622593053,0.3870765865,-0.3909443915,-0.0891318396,0.0643113926,0.242074728,0.680860579,-0.0877781883,-0.3745702207,0.8095407486,-0.0852320418,-0.3504332304,-0.0121514369,0.4251954556,0.1100581288,0.0039028972,-0.0741009638,0.8450555801,0.048078645,0.1301187575,-0.2199296802,-0.5326390266,0.5446045399,0.3769712746,-0.0556492992,0.0064388844,0.2360025048,0.276570797,0.3048285246,-0.0953290686,0.1123543009,0.0592962503,0.0538814664,0.2307034582,-0.0348530076,-0.2128134817,-0.0293981824,-0.1252918839,-0.4451179504,0.0170486793,0.0235721376,-0.1477998644,0.1749249697,-0.09533225,0.1014212146,0.0133184325,0.0436021797,0.0738188699,0.0279040486,0.0492114946,-0.2668811083,-0.0932446718,-0.1285628378,-0.1814226657,-0.0982199833,0.3334493041,0.0696581826,-0.0688517541,0.3223309219,0.5017097592,0.0012493958,-0.1183680519,0.118309997,0.1488822252,-0.1287769079,0.3108648956,-0.025181774,0.5072873235,0.2461736947,-0.1875599325,0.2004159838,0.347764045,0.0099599473,0.1449291259,0.0077634887,0.7716162801,-0.2840053141,-0.1025070325,-0.0444631949,-0.1314356923,0.0763065144,-0.1933103651,-0.285468787,-0.0716620088,0.013611136,-0.2660618126,0.1443147063,-0.0389845259,-0.5130639672,0.1489171982,0.0806592926,-0.0316482037,0.272053957,0.0470779724,-0.0526903495,-0.1681950837,0.1684117466,0.2464767545,0.4391745925,0.0823374689,-0.1612999737,-0.0511493944,-0.1021538153,-0.1314149648,-0.0250146147,-0.1019916683,0.2686356604,0.1020199209,0.2694602907,0.2681826651,-0.0358641073,0.2545010448,-0.0103044305,0.0745376125,-0.0335338153,0.1670872718,-0.3016680479,-0.3807734549,-0.0498780422,-0.0134986546,-0.0696675926,0.245334506,0.0742738098,0.1491188705,-0.1015831977,-0.2358610183,-0.2040970922,0.3944403529,-0.3479895592,-0.2545349896,-0.0299717207,-0.1484679133,-0.1017738208,0.0162803475,0.3572624922,0.1227777824,0.3335718513,0.3912318945,-0.0778014809,-0.0446859039,-0.2103272825,0.050436724,0.080468215,0.2831274569,-0.1011571959,0.0038761778,0.0902633145,-0.2536836565,0.0319102742,-0.3446621597,-0.0655551404,-0.1926712692,-0.1188364625,-0.0175068323,-0.3231666088,-0.1083950102,-0.6310309768,-0.2055350989,0.5200822353,0.0638064742,0.4213783741,0.0322194248,-0.2250576317,0.1284990907,-0.0232753698,-0.0056158407,-0.0860952064,-0.3206884861,0.1609563529,-0.5506543517,-0.4848898351,-0.0580535531,-0.0164761432,-0.0689202771,0.141336903,-0.6018842459,-0.0163094085,0.1117276475,0.2389303148,-0.0290665906,-0.373802036,0.3266041279,-0.0315826051,0.1052386239,-0.1330154836,-0.1841297895,-0.1981923133,0.0980571136,0.1647399515,0.360386163,-0.1718626767,0.4904503524,0.4867066741,0.0318252631,-0.2435638607,0.3236861229,0.144201532,0.1402071118,0.2634196281,-0.0255519953,0.088593781,-0.1194417551,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/281","title":"Private\/sensitive data","comments":"Hi @julien-c @jplu,\r\nThanks for sharing this solution with me, it helps, this is what I was looking for. \r\nIf not already there and only missed by me, this could be a great addition in the docs.\r\n\r\nClosing my issue as resolved, thanks again.","body":"Hi all,\r\nThanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF\/Pytorch. \r\n\r\nUnfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information. \r\nIs there support\/a plan to support such data with NLP, e.g. by reading it from local sources?\r\n\r\nUse case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive\/private data without the need to rethink data processing pipelines. \r\n\r\nMany thanks for your responses ahead of time and kind regards,\r\nMFreidank","comment_length":44,"text":"Private\/sensitive data \n Hi all,\r\nThanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF\/Pytorch. \r\n\r\nUnfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information. \r\nIs there support\/a plan to support such data with NLP, e.g. by reading it from local sources?\r\n\r\nUse case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive\/private data without the need to rethink data processing pipelines. \r\n\r\nMany thanks for your responses ahead of time and kind regards,\r\nMFreidank \n Hi @julien-c @jplu,\r\nThanks for sharing this solution with me, it helps, this is what I was looking for. \r\nIf not already there and only missed by me, this could be a great addition in the docs.\r\n\r\nClosing my issue as resolved, thanks again.","embeddings":[0.0258489307,0.3822511435,-0.1474190801,0.1858093888,-0.3208057284,-0.2908139527,0.1759027839,0.31238994,-0.2786038816,0.079381302,-0.0625791848,0.2926683426,-0.0952913463,0.4064874947,0.1631554365,-0.2160253972,-0.2121718824,0.3283576965,0.0399660729,0.1607280374,0.3044917285,0.0305082686,-0.0668024793,0.2438914329,0.1339483112,-0.1141139269,0.1144153848,-0.1650368124,0.0475930087,-0.2738578618,0.1451488137,0.1825234443,0.2605031729,0.0681904629,-0.000118519,-0.0630127862,0.0080701588,-0.1219932362,0.1805238575,-0.3296790421,0.169830218,-0.4010601044,0.1010971516,-0.075986743,-0.6062310338,-0.1920445263,0.2262507528,-0.1614921689,0.233020559,-0.1536771208,0.1125909239,0.3779383898,0.1014638543,0.4482401311,0.2235453129,0.0362447686,-0.3435452282,-0.3351929486,0.4353991151,0.0831449628,-0.1078776717,0.1273633689,-0.0848964453,-0.0166317523,0.0692506805,-0.0495769009,-0.1109369695,-0.4897403717,0.0032607289,0.4364346266,0.6228167415,-0.0677119493,-0.2318775505,-0.1826434731,0.1105767116,-0.1296249628,0.0149735576,0.2753042877,-0.3189122379,0.1809320152,-0.1930097789,-0.4642404914,-0.5439631939,0.3049429357,0.3671753407,0.0410555787,-0.1337723732,0.0076692468,-0.1846585721,-0.2136173695,0.1772072017,0.0620255135,0.3948307037,0.2782180011,0.3060156405,-0.4116638005,-0.109462887,-0.2372367978,0.1159115061,-0.0435348153,0.0135567142,0.3441677988,-0.4206798375,0.1662000865,0.4476668537,-0.3055676222,-0.2571874857,0.2101721615,0.601036489,-0.2004008889,0.1096265018,0.1507365406,0.016363034,0.1081607938,-0.3722083867,-0.1131602004,0.0403443314,-0.3706246614,-0.1459184736,0.140654102,-0.2036221176,0.1335197985,0.0404557995,0.133901149,0.0462411642,0.0996247455,0.2197240144,-0.2955405116,0.1329736114,-0.4402605295,0.1208012402,-0.3411676884,-0.2346657515,0.2266023904,0.3104593456,0.1657722145,0.1307254583,-0.0847361684,0.102638863,0.051932428,0.1602874994,-0.0611958317,0.2653575838,0.2281562835,-0.1429543197,-0.205706805,-0.2207786441,-0.0444301516,-0.2550649941,0.4310934544,-0.2649871707,-0.3226992488,-0.0442522764,-0.0621587262,-0.3281317949,0.0434703268,0.3314890563,0.2257246971,0.0723519996,-0.3221593201,0.1531202495,0.053211268,-0.3242949247,-0.3337930441,0.1474688053,-0.112137951,-0.1244513318,0.1684965938,0.145300135,-0.0814065337,0.1459770203,0.3059071302,-0.3773403764,-0.2175031155,0.10776788,0.1623148769,0.7060899138,-0.0930079445,-0.4636054933,0.787204206,-0.0351001769,-0.3584575653,0.058801122,0.5080543756,0.0448329598,-0.1144134924,-0.1989552826,0.6941428185,0.0089125969,0.1344359517,-0.2006943971,-0.4680550098,0.5286426544,0.2506704628,-0.1092492491,0.0714813545,0.1706616879,0.2525339723,0.3444802463,-0.0604808405,0.0776817501,0.0565261282,0.1300673932,0.2442748398,0.0165426824,-0.2678135633,0.1944401711,-0.2378713042,-0.5962439775,0.1379382759,0.0044525987,-0.2270783484,0.2211285383,-0.1197347939,0.1918445379,-0.1130478084,0.0704182684,0.0728209391,-0.0637513101,0.0359921232,-0.1966178864,-0.130409345,-0.121753715,-0.1547304839,0.0039050996,0.2122249156,0.0312975273,-0.0897065103,0.3491774201,0.592879951,0.0300890785,-0.0807663575,0.1476536095,0.0750993863,-0.1631666124,0.2906976342,0.0195028614,0.6055518389,0.3023273647,-0.2451256514,0.1431187838,0.3871825635,-0.0319288336,0.2201086283,0.2057910115,0.6911458969,-0.1732664704,-0.2011304796,-0.0359998271,-0.0938760191,-0.0558814369,-0.2427799851,-0.3530630767,0.0092328265,-0.000906169,-0.2990610003,0.1023767292,-0.0631882399,-0.5435878634,0.2988537252,0.0494468585,-0.0285582934,0.3031966984,0.0058652665,0.0068820301,-0.0320099294,0.13250193,0.2278660834,0.29939273,0.1043857485,-0.1883507669,-0.0772017837,-0.2015258372,-0.113483794,0.0535620935,-0.0895324424,0.2411708236,0.0412974916,0.2565224171,0.4144940972,0.0141732339,0.2222730219,-0.0423172601,0.0757144839,0.0019608631,0.1507309973,-0.2659513354,-0.4235904217,-0.040043436,0.0292924866,-0.1178217083,0.2622919977,0.1448417008,0.1711197942,-0.0769885629,-0.173371166,-0.2095347345,0.3818931878,-0.3229215145,-0.1560789943,-0.060211733,-0.1195016727,-0.0817985162,0.0512832291,0.2745808661,-0.0639762059,0.3549925387,0.4507524073,-0.0075816363,-0.1911910176,-0.4339693189,0.01850456,0.0506454892,0.2169718891,-0.0752496123,-0.0754859149,0.1266775429,-0.2356222123,-0.0945631713,-0.4045507908,-0.0439575203,-0.2115865946,-0.1797845066,-0.0180365704,-0.306456387,-0.0857300609,-0.4518187046,-0.1782670766,0.4111146629,-0.1131807864,0.4838880301,0.1294627488,-0.3423334062,0.0717174858,-0.0214707889,0.1171506643,-0.0848596394,-0.2901393175,0.1469908953,-0.5867846608,-0.4462214112,-0.1477696896,-0.0219206382,0.0593528524,0.1654686183,-0.6037142277,-0.0400836915,0.1073319316,0.2574415505,-0.0711598396,-0.3257621825,0.3278180063,0.0800223053,0.0438431203,-0.203488633,-0.1508628726,-0.2604581416,-0.0460256562,0.2202904224,0.3321332335,-0.296866715,0.5189398527,0.3058698773,0.0639551133,-0.1734406054,0.2994426787,0.2157346308,0.0945931599,0.3629154563,0.0330703966,0.1078675166,-0.0555293895,-0.024301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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/279","title":"Dataset Preprocessing Cache with .map() function not working as expected","comments":"When you're processing a dataset with `.map`, it checks whether it has already done this computation using a hash based on the function and the input (using some fancy serialization with `dill`). If you found that it doesn't work as expected in some cases, let us know !\r\n\r\nGiven that, you can still force to re-process using `.map(my_func, load_from_cache_file=False)` if you want to.\r\n\r\nI am curious about the problem you have with splits. It makes me think about #160 that was an issue of version 0.1.0. What version of `nlp` are you running ? Could you give me more details ?","body":"I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system. \r\n\r\nIs there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file. \r\n\r\nCould you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess. \r\nI was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.\r\nThanks!","comment_length":101,"text":"Dataset Preprocessing Cache with .map() function not working as expected \n I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system. \r\n\r\nIs there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file. \r\n\r\nCould you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess. \r\nI was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.\r\nThanks! \n When you're processing a dataset with `.map`, it checks whether it has already done this computation using a hash based on the function and the input (using some fancy serialization with `dill`). If you found that it doesn't work as expected in some cases, let us know !\r\n\r\nGiven that, you can still force to re-process using `.map(my_func, load_from_cache_file=False)` if you want to.\r\n\r\nI am curious about the problem you have with splits. It makes me think about #160 that was an issue of version 0.1.0. What version of `nlp` are you running ? Could you give me more details 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/279","title":"Dataset Preprocessing Cache with .map() function not working as expected","comments":"Thanks, that's helpful! I was running 0.1.0, but since upgraded to 0.2.1. I can't reproduce the issue anymore as I've cleared the cache & everything now seems to be running fine since the upgrade. I've added some checks to my code, so if I do encounter it again I will reopen this issue.","body":"I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system. \r\n\r\nIs there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file. \r\n\r\nCould you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess. \r\nI was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.\r\nThanks!","comment_length":53,"text":"Dataset Preprocessing Cache with .map() function not working as expected \n I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system. \r\n\r\nIs there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file. \r\n\r\nCould you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess. \r\nI was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.\r\nThanks! \n Thanks, that's helpful! I was running 0.1.0, but since upgraded to 0.2.1. I can't reproduce the issue anymore as I've cleared the cache & everything now seems to be running fine since the upgrade. I've added some checks to my code, so if I do encounter it again I will reopen this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/279","title":"Dataset Preprocessing Cache with .map() function not working as expected","comments":"Just checking in, the cache sometimes still does not work when I make changes in my processing function in version `1.2.1`. The changes made to my data processing function only propagate to the dataset when I use `load_from_cache_file=False` or clear the cache. Is this a system-specific issue?","body":"I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system. \r\n\r\nIs there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file. \r\n\r\nCould you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess. \r\nI was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.\r\nThanks!","comment_length":47,"text":"Dataset Preprocessing Cache with .map() function not working as expected \n I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system. \r\n\r\nIs there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file. \r\n\r\nCould you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess. \r\nI was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.\r\nThanks! \n Just checking in, the cache sometimes still does not work when I make changes in my processing function in version `1.2.1`. The changes made to my data processing function only propagate to the dataset when I use `load_from_cache_file=False` or clear the cache. Is this a system-specific issue?","embeddings":[-0.0422306918,0.0891577378,-0.0817682147,0.0919544026,0.0423214361,0.0622354262,0.0255286153,0.2098047435,0.3237959445,0.0284722876,0.3000636697,0.2643457651,0.1776193827,-0.0800424367,-0.1128168851,0.1895933896,0.1895669103,0.2083784789,-0.0732567832,0.1837684959,-0.3083145916,0.0548591986,-0.2294518799,0.0391611382,-0.2585252821,-0.2065357119,0.1432308853,0.0060473899,0.0013979918,-0.2522467971,0.2973943949,0.33280918,-0.128296718,0.5881773829,-0.0001169303,0.0842275843,0.4561638832,-0.0505358204,-0.2349918932,-0.0099979267,-0.3796210885,-0.0702175722,0.1602348387,-0.0243576802,-0.0101384278,0.1162179038,0.0201478228,-0.9627584219,0.3745887876,-0.0856605768,0.148074165,0.0059180004,-0.369587034,0.1530929208,0.0492118634,0.2058280706,-0.1800124645,0.0365164429,0.4169890285,-0.1854203939,-0.139328897,0.1435709447,-0.1768898666,0.2246657759,0.224708274,-0.0970955789,-0.0745331347,-0.6531959772,0.4681518674,0.0929749683,0.502574563,-0.2382342964,-0.3564358354,-0.1886161566,-0.1998536289,-0.1284057647,0.4736365676,-0.0521153845,0.0480240099,0.2472449988,-0.8149891496,-0.2441741079,0.139962092,-0.0658177733,0.1589182317,-0.0606695451,-0.2202188224,0.1006012708,0.1753266901,-0.0335370228,0.2819596827,-0.4145128727,-0.0930637792,0.4279217422,-0.2198819518,0.0837464333,-0.12302766,0.2560547292,0.0892047137,-0.006210912,0.1486397833,0.4178132117,-0.0385146625,0.1505216211,0.2692398429,0.3919083476,0.1836466938,0.0295404531,0.2262314856,-0.3068936467,-0.181799382,0.0224110987,0.2195631564,-0.3019469082,0.1666239947,0.0519048311,-0.0434889719,-0.3644348383,0.147913903,-0.1613026857,-0.2701402009,-0.268709451,-0.0240079425,0.0419540741,0.0624130517,0.3686458468,-0.1567596197,-0.0583760887,-0.2892080545,-0.1535946876,-0.0568067878,-0.2732104361,-0.3307188153,0.3337659836,0.372641027,-0.1995537877,0.0967079625,0.2127576619,0.1194793731,-0.2556492984,0.402323097,-0.3718317747,0.6122225523,0.1673039049,-0.1996267587,0.313372463,0.0886345878,0.3282322884,-0.1233412698,0.319057107,-0.4271486998,-0.0732033551,0.5425621271,0.0426246487,-0.2365996838,0.272485286,-0.1576716304,0.1831423044,0.3819362223,-0.3411971927,-0.0193041209,0.0559044182,-0.3614040315,-0.2492461205,0.3011446893,0.4044575989,-0.313049078,-0.0062669669,-0.0966528431,0.0089608803,0.2724154294,0.0020196759,-0.1514182985,0.2569433451,-0.1890785396,0.0740880445,0.179725647,0.0462378711,-0.4824351668,0.4619077444,0.2666216493,0.3159234524,-0.3200908899,-0.0344842374,0.0754926205,-0.0292528663,-0.2274345905,0.0733203217,0.1126248091,0.0184201524,-0.1365385056,-0.3354551494,0.2387078404,-0.172780931,0.0098830936,0.5577960014,-0.1166372299,-0.453373909,-0.0055344477,-0.1171777472,0.2688828409,0.1867070496,-0.1241810918,-0.0835297853,0.112226367,-0.117894195,-0.2957796454,0.3868889809,-0.1607989222,-0.3888332546,0.0870992765,-0.0017753701,0.1489263773,-0.1508038789,-0.170879975,-0.0603685081,0.0341724642,-0.0398584269,0.2085989416,-0.2214135379,-0.1505843848,0.5996130109,0.2288369387,-0.111903742,-0.1441234946,-0.0906638652,0.1879694164,-0.1929447204,-0.3765650988,-0.0420685671,0.1240160316,-0.0717114881,-0.18283692,0.4112227261,0.364466995,0.2317550927,-0.0571934879,0.1484310776,0.0776228756,0.1053875834,0.0395125709,-0.0438413434,0.0481653288,-0.1259491593,-0.0457108915,0.2844944298,-0.0756516084,0.3332164288,0.025585454,-0.0784998611,0.1512382478,-0.0068959491,-0.032718908,-0.2883858979,-0.1371816546,-0.0856276006,0.5550134778,0.0193321556,0.0998920947,-0.0798730627,0.1644963175,0.1588204652,-0.0057599596,-0.1098353043,-0.0765024945,-0.222566992,0.0785853863,0.1421719342,0.3094837964,-0.003141151,0.3505507708,-0.0958959609,-0.0081953593,-0.0534472391,-0.1273302138,0.0642364025,0.3782822192,-0.0128259202,0.044713188,0.0554708354,-0.058836624,0.1394155622,0.0954171792,-0.2367599607,-0.1391814053,0.2114250213,-0.4382856786,-0.1031098217,0.1216110215,-0.0038585875,-0.2828514278,-0.1596031338,-0.0491864756,0.1624259353,0.077183269,0.2626119256,-0.0405067727,0.3092963398,-0.241481334,-0.3057519197,-0.2155146748,-0.1971511841,-0.1173083335,0.0255007688,0.0658701509,-0.1796990633,0.2166001499,-0.0736564919,-0.1284490526,-0.1553097069,0.0631600767,0.2894631922,0.1655323207,0.0497437865,-0.38831231,-0.0344401598,-0.2452546954,0.2555259466,0.0039207712,-0.434815973,-0.1521812528,-0.0571384616,0.0033029579,-0.0713680834,-0.3041388094,0.0055960943,0.0284589883,-0.0661439821,0.0078730276,-0.2158997953,0.0964919031,-0.0588539615,0.0630993769,-0.0804712921,0.0026187764,-0.1112724915,-0.3895037472,-0.5156039596,0.233699441,-0.2174013555,-0.1643688083,0.2138955593,0.2171540558,0.2134817839,0.8205273747,-0.1731255203,0.0194019359,-0.131186828,-0.052393306,0.0817247108,0.0411988609,0.4442794025,0.1278310716,0.046868775,-0.2690100372,-0.403957814,0.1687218249,0.097603336,0.1209678501,0.462066561,0.0261512864,-0.0262697395,0.9281291962,0.2807488441,-0.1106205508,0.2399621606,0.2195242196,0.3571163416,-0.2936383486,0.0774127245,-0.1929932088,-0.4637962878,-0.152919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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/279","title":"Dataset Preprocessing Cache with .map() function not working as expected","comments":"Hi @sarahwie \r\nThe data are reloaded from the cache if the hash of the function you provide is the same as a computation you've done before. The hash is computed by recursively looking at the python objects of the function you provide.\r\n\r\nIf you think there's an issue, can you share the function you used or a google colab please ?","body":"I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system. \r\n\r\nIs there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file. \r\n\r\nCould you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess. \r\nI was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.\r\nThanks!","comment_length":61,"text":"Dataset Preprocessing Cache with .map() function not working as expected \n I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system. \r\n\r\nIs there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file. \r\n\r\nCould you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess. \r\nI was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.\r\nThanks! \n Hi @sarahwie \r\nThe data are reloaded from the cache if the hash of the function you provide is the same as a computation you've done before. The hash is computed by recursively looking at the python objects of the function you provide.\r\n\r\nIf you think there's an issue, can you share the function you used or a google colab please 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/278","title":"MemoryError when loading German Wikipedia","comments":"Hi !\r\n\r\nAs you noticed, \"big\" datasets like Wikipedia require apache beam to be processed.\r\nHowever users usually don't have an apache beam runtime available (spark, dataflow, etc.) so our goal for this library is to also make available processed versions of these datasets, so that users can just download and use them right away.\r\n\r\nThis is the case for english and french wikipedia right now: we've processed them ourselves and now they are available from our google storage. However we've not processed the german one (yet).","body":"Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you!","comment_length":87,"text":"MemoryError when loading German Wikipedia \n Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you! \n Hi !\r\n\r\nAs you noticed, \"big\" datasets like Wikipedia require apache beam to be processed.\r\nHowever users usually don't have an apache beam runtime available (spark, dataflow, etc.) so our goal for this library is to also make available processed versions of these datasets, so that users can just download and use them right away.\r\n\r\nThis is the case for english and french wikipedia right now: we've processed them ourselves and now they are available from our google storage. However we've not processed the german one (yet).","embeddings":[-0.0479339696,0.1261895299,0.0182390139,0.6689180136,0.2445327044,0.1557507068,0.004935035,0.2083568722,0.3758642077,-0.0514769666,0.2673629224,-0.3312878907,-0.1734624952,-0.3341802955,0.2296689302,-0.4861274064,0.0930675492,0.0584275424,-0.0968580395,0.021348808,-0.2210392207,0.2842619419,-0.2860866785,-0.1296746433,-0.2414990813,0.0682763606,-0.0606940202,0.0540070944,-0.1613469124,-0.4543804526,0.1093896404,-0.2572765052,0.1181677878,0.1811023504,-0.0001217219,0.0400367081,0.4013991356,-0.1707563549,-0.4441632926,0.0392563045,-0.0454732403,-0.3407937586,0.0367344134,-0.2606716752,0.0609828457,-0.1065008789,0.3253657222,-0.082839556,0.3636316657,0.1792971939,0.1830790192,0.0583989583,0.2596977651,-0.0829991326,0.5757554173,0.0603940561,0.0517875366,0.1204346195,-0.3371914327,-0.3669273555,-0.266692996,0.2971111834,0.0168598853,0.1016100198,0.3428906202,-0.1183491275,0.014594771,-0.2151484787,0.2146662027,-0.0155843385,0.6984869242,-0.222539112,0.0544035435,-0.2987264991,-0.0819756612,0.1372263432,0.3699685335,0.3768196404,-0.53745538,-0.1752103865,-0.1816793531,-0.3489992321,-0.1634562314,0.3382711709,0.0580682494,0.2427105755,-0.046096243,0.165600881,0.3660611212,-0.0474638939,-0.2768139839,-0.1119219139,-0.0121806534,0.4462251961,-0.0702341348,0.0682196021,-0.0799727216,-0.0619321316,0.2351161987,-0.2792296708,-0.0940648913,-0.0614943281,0.2538709641,0.0773239955,0.2765713036,0.4570385516,-0.1349075586,-0.1987938285,0.1601509452,0.1806961447,0.0185495038,-0.1625983566,-0.0348524787,-0.163364619,-0.0773852915,-0.0258760657,-0.103963092,-0.0469388105,0.0200344175,0.1309570819,-0.096364513,-0.133965537,-0.0321695395,0.4189065993,-0.1397236735,0.3382306099,0.2956480384,0.0687169135,-0.384778291,-0.2950851619,-0.1493525356,0.1582213342,-0.5230259895,0.1237957925,0.2662939131,0.2771739364,0.3613065481,-0.1229094267,-0.2683769166,0.0498335995,0.2657951117,-0.1831974089,-0.0323753022,0.2156431675,0.14843674,0.2886892855,0.1391697973,-0.2281753868,0.000194584,0.0619199313,-0.1770699173,-0.1940729618,0.0692587718,0.0176814776,-0.1571019292,0.154286474,-0.4031710923,0.2367206365,0.2888612151,-0.2128570974,-0.0976049975,-0.0422633328,-0.23210226,-0.2615077198,0.2098641694,0.5494705439,-0.0914283767,-0.2689732015,-0.0759014413,-0.0987920538,0.4090284109,0.2375489473,-0.1052161306,0.2960037291,-0.2315713167,0.0987135246,0.3416324556,-0.1739220619,-0.331951797,0.1309767216,0.1102416217,0.1638558507,0.0562583506,0.0016745509,0.0118619101,0.1984750479,0.1997092515,0.3413085043,-0.0352610424,0.2907262743,-0.267072022,-0.2709881663,0.2600758076,0.0086501343,0.3516753912,0.0555153303,0.0945201367,0.8308191299,0.2724248767,-0.0689333975,0.3318047822,0.5638161898,-0.227718845,0.0573095493,-0.0112689119,-0.0633697733,-0.2486257851,0.1927720159,-0.3067270517,0.3094541728,-0.0214819368,0.1321331114,-0.1640296876,-0.0969142392,-0.0566746257,-0.3699617386,0.0888292938,0.1652462482,0.1101260856,0.2140970677,0.1379154623,0.2627291083,-0.0111610973,0.0226750709,-0.6933624744,0.2087740302,-0.1417605281,-0.1521971226,-0.0109318141,0.1096379384,0.1333905607,0.1438275576,-0.1323129535,0.1214753836,0.0990371704,-0.0469947085,0.1362223029,-0.0250016525,0.2658998072,-0.1880667061,0.4338091016,0.0586686023,0.1608509421,-0.1543041915,0.0469034724,-0.2138385028,0.072046563,0.2993028164,0.0846952274,0.0347199701,0.1672719419,0.1503384262,0.1518827975,-0.1656813025,0.4092136621,0.0806189775,0.1114226282,-0.0011588708,0.0266635604,-0.4050886929,0.4183508158,0.1737964302,0.1427270025,0.2619020641,-0.4456169605,-0.2904942334,0.2247490287,-0.057462208,0.2343137711,0.0811882243,0.1338569522,0.0289964098,0.1593956351,0.037909966,0.0936210901,0.058900822,0.5227373838,0.0115124211,0.0125517687,-0.1341618747,-0.2134275883,0.0808712468,0.0312990136,0.4169813991,-0.1902172863,-0.0390307494,-0.0026984455,-0.5402334332,-0.3053193986,0.2616876364,-0.4882035851,-0.2883309424,-0.2413621992,0.280143708,-0.0662873089,-0.0134526268,0.068596065,0.1989407092,0.1088028774,-0.1494421959,-0.1298904121,-0.2826367915,-0.3521508873,-0.0132163968,0.1780460626,0.0457917042,0.2574231327,0.0474953204,-0.129376784,-0.2252615541,-0.1390877813,0.193174839,0.0193976611,0.1323526949,0.0808379054,0.6441944838,-0.0916180611,-0.1589263827,0.3123699129,-0.1345023215,-0.1123504639,0.2058447748,0.0021174341,0.1894079149,0.0238474421,-0.0355216302,-0.1239717528,-0.4503405094,0.1686739624,0.1654811502,-0.0202529617,0.347874105,0.1282710731,0.0782882795,0.1775042415,0.1381355822,-0.190916121,-0.0510670096,0.2469130903,-0.3309720457,-0.3059126437,-0.0715213791,-0.1601582468,0.2242604643,0.4027511775,-0.6984960437,0.1061492711,-0.1276174188,-0.2190024704,0.0490920655,0.0372534879,0.3683996797,-0.1916541755,-0.0193818528,-0.0021286565,-0.0695347115,-0.0335374884,-0.2112904936,0.6732001901,0.1878428757,0.3525752127,0.2811793983,0.9196683168,0.2102591395,0.4203176796,0.4592570961,0.1845643818,0.2118268609,-0.1392084062,-0.2165400535,0.0542722419,-0.2958499491,-0.14695657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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/278","title":"MemoryError when loading German Wikipedia","comments":"Hi @lhoestq \r\n\r\nThank you for your quick reply. I thought this might be the case, that the processing was done for some languages and not for others. Is there any set timeline for when other languages (German, Italian) will be processed?\r\n\r\nGiven enough memory, is it possible to process the data ourselves by specifying the `beam_runner`?","body":"Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you!","comment_length":56,"text":"MemoryError when loading German Wikipedia \n Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you! \n Hi @lhoestq \r\n\r\nThank you for your quick reply. I thought this might be the case, that the processing was done for some languages and not for others. Is there any set timeline for when other languages (German, Italian) will be processed?\r\n\r\nGiven enough memory, is it possible to process the data ourselves by specifying the `beam_runner`?","embeddings":[-0.0479339696,0.1261895299,0.0182390139,0.6689180136,0.2445327044,0.1557507068,0.004935035,0.2083568722,0.3758642077,-0.0514769666,0.2673629224,-0.3312878907,-0.1734624952,-0.3341802955,0.2296689302,-0.4861274064,0.0930675492,0.0584275424,-0.0968580395,0.021348808,-0.2210392207,0.2842619419,-0.2860866785,-0.1296746433,-0.2414990813,0.0682763606,-0.0606940202,0.0540070944,-0.1613469124,-0.4543804526,0.1093896404,-0.2572765052,0.1181677878,0.1811023504,-0.0001217219,0.0400367081,0.4013991356,-0.1707563549,-0.4441632926,0.0392563045,-0.0454732403,-0.3407937586,0.0367344134,-0.2606716752,0.0609828457,-0.1065008789,0.3253657222,-0.082839556,0.3636316657,0.1792971939,0.1830790192,0.0583989583,0.2596977651,-0.0829991326,0.5757554173,0.0603940561,0.0517875366,0.1204346195,-0.3371914327,-0.3669273555,-0.266692996,0.2971111834,0.0168598853,0.1016100198,0.3428906202,-0.1183491275,0.014594771,-0.2151484787,0.2146662027,-0.0155843385,0.6984869242,-0.222539112,0.0544035435,-0.2987264991,-0.0819756612,0.1372263432,0.3699685335,0.3768196404,-0.53745538,-0.1752103865,-0.1816793531,-0.3489992321,-0.1634562314,0.3382711709,0.0580682494,0.2427105755,-0.046096243,0.165600881,0.3660611212,-0.0474638939,-0.2768139839,-0.1119219139,-0.0121806534,0.4462251961,-0.0702341348,0.0682196021,-0.0799727216,-0.0619321316,0.2351161987,-0.2792296708,-0.0940648913,-0.0614943281,0.2538709641,0.0773239955,0.2765713036,0.4570385516,-0.1349075586,-0.1987938285,0.1601509452,0.1806961447,0.0185495038,-0.1625983566,-0.0348524787,-0.163364619,-0.0773852915,-0.0258760657,-0.103963092,-0.0469388105,0.0200344175,0.1309570819,-0.096364513,-0.133965537,-0.0321695395,0.4189065993,-0.1397236735,0.3382306099,0.2956480384,0.0687169135,-0.384778291,-0.2950851619,-0.1493525356,0.1582213342,-0.5230259895,0.1237957925,0.2662939131,0.2771739364,0.3613065481,-0.1229094267,-0.2683769166,0.0498335995,0.2657951117,-0.1831974089,-0.0323753022,0.2156431675,0.14843674,0.2886892855,0.1391697973,-0.2281753868,0.000194584,0.0619199313,-0.1770699173,-0.1940729618,0.0692587718,0.0176814776,-0.1571019292,0.154286474,-0.4031710923,0.2367206365,0.2888612151,-0.2128570974,-0.0976049975,-0.0422633328,-0.23210226,-0.2615077198,0.2098641694,0.5494705439,-0.0914283767,-0.2689732015,-0.0759014413,-0.0987920538,0.4090284109,0.2375489473,-0.1052161306,0.2960037291,-0.2315713167,0.0987135246,0.3416324556,-0.1739220619,-0.331951797,0.1309767216,0.1102416217,0.1638558507,0.0562583506,0.0016745509,0.0118619101,0.1984750479,0.1997092515,0.3413085043,-0.0352610424,0.2907262743,-0.267072022,-0.2709881663,0.2600758076,0.0086501343,0.3516753912,0.0555153303,0.0945201367,0.8308191299,0.2724248767,-0.0689333975,0.3318047822,0.5638161898,-0.227718845,0.0573095493,-0.0112689119,-0.0633697733,-0.2486257851,0.1927720159,-0.3067270517,0.3094541728,-0.0214819368,0.1321331114,-0.1640296876,-0.0969142392,-0.0566746257,-0.3699617386,0.0888292938,0.1652462482,0.1101260856,0.2140970677,0.1379154623,0.2627291083,-0.0111610973,0.0226750709,-0.6933624744,0.2087740302,-0.1417605281,-0.1521971226,-0.0109318141,0.1096379384,0.1333905607,0.1438275576,-0.1323129535,0.1214753836,0.0990371704,-0.0469947085,0.1362223029,-0.0250016525,0.2658998072,-0.1880667061,0.4338091016,0.0586686023,0.1608509421,-0.1543041915,0.0469034724,-0.2138385028,0.072046563,0.2993028164,0.0846952274,0.0347199701,0.1672719419,0.1503384262,0.1518827975,-0.1656813025,0.4092136621,0.0806189775,0.1114226282,-0.0011588708,0.0266635604,-0.4050886929,0.4183508158,0.1737964302,0.1427270025,0.2619020641,-0.4456169605,-0.2904942334,0.2247490287,-0.057462208,0.2343137711,0.0811882243,0.1338569522,0.0289964098,0.1593956351,0.037909966,0.0936210901,0.058900822,0.5227373838,0.0115124211,0.0125517687,-0.1341618747,-0.2134275883,0.0808712468,0.0312990136,0.4169813991,-0.1902172863,-0.0390307494,-0.0026984455,-0.5402334332,-0.3053193986,0.2616876364,-0.4882035851,-0.2883309424,-0.2413621992,0.280143708,-0.0662873089,-0.0134526268,0.068596065,0.1989407092,0.1088028774,-0.1494421959,-0.1298904121,-0.2826367915,-0.3521508873,-0.0132163968,0.1780460626,0.0457917042,0.2574231327,0.0474953204,-0.129376784,-0.2252615541,-0.1390877813,0.193174839,0.0193976611,0.1323526949,0.0808379054,0.6441944838,-0.0916180611,-0.1589263827,0.3123699129,-0.1345023215,-0.1123504639,0.2058447748,0.0021174341,0.1894079149,0.0238474421,-0.0355216302,-0.1239717528,-0.4503405094,0.1686739624,0.1654811502,-0.0202529617,0.347874105,0.1282710731,0.0782882795,0.1775042415,0.1381355822,-0.190916121,-0.0510670096,0.2469130903,-0.3309720457,-0.3059126437,-0.0715213791,-0.1601582468,0.2242604643,0.4027511775,-0.6984960437,0.1061492711,-0.1276174188,-0.2190024704,0.0490920655,0.0372534879,0.3683996797,-0.1916541755,-0.0193818528,-0.0021286565,-0.0695347115,-0.0335374884,-0.2112904936,0.6732001901,0.1878428757,0.3525752127,0.2811793983,0.9196683168,0.2102591395,0.4203176796,0.4592570961,0.1845643818,0.2118268609,-0.1392084062,-0.2165400535,0.0542722419,-0.2958499491,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/278","title":"MemoryError when loading German Wikipedia","comments":"Adding them is definitely in our short term objectives. I'll be working on this early next week :)\r\n\r\nAlthough if you have an apache beam runtime feel free to specify the beam runner. You can find more info [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/docs\/beam_dataset.md) on how to make it work on Dataflow but you can adapt it for Spark or any other beam runtime (by changing the `runner`).\r\n\r\nHowever if you don't have a beam runtime and even if you have enough memory, I discourage you to use the `DirectRunner` on the german or italian wikipedia. According to Apache Beam documentation it was made for testing purposes and therefore it is memory-inefficient.","body":"Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you!","comment_length":107,"text":"MemoryError when loading German Wikipedia \n Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you! \n Adding them is definitely in our short term objectives. I'll be working on this early next week :)\r\n\r\nAlthough if you have an apache beam runtime feel free to specify the beam runner. You can find more info [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/docs\/beam_dataset.md) on how to make it work on Dataflow but you can adapt it for Spark or any other beam runtime (by changing the `runner`).\r\n\r\nHowever if you don't have a beam runtime and even if you have enough memory, I discourage you to use the `DirectRunner` on the german or italian wikipedia. According to Apache Beam documentation it was made for testing purposes and therefore it is memory-inefficient.","embeddings":[-0.0479339696,0.1261895299,0.0182390139,0.6689180136,0.2445327044,0.1557507068,0.004935035,0.2083568722,0.3758642077,-0.0514769666,0.2673629224,-0.3312878907,-0.1734624952,-0.3341802955,0.2296689302,-0.4861274064,0.0930675492,0.0584275424,-0.0968580395,0.021348808,-0.2210392207,0.2842619419,-0.2860866785,-0.1296746433,-0.2414990813,0.0682763606,-0.0606940202,0.0540070944,-0.1613469124,-0.4543804526,0.1093896404,-0.2572765052,0.1181677878,0.1811023504,-0.0001217219,0.0400367081,0.4013991356,-0.1707563549,-0.4441632926,0.0392563045,-0.0454732403,-0.3407937586,0.0367344134,-0.2606716752,0.0609828457,-0.1065008789,0.3253657222,-0.082839556,0.3636316657,0.1792971939,0.1830790192,0.0583989583,0.2596977651,-0.0829991326,0.5757554173,0.0603940561,0.0517875366,0.1204346195,-0.3371914327,-0.3669273555,-0.266692996,0.2971111834,0.0168598853,0.1016100198,0.3428906202,-0.1183491275,0.014594771,-0.2151484787,0.2146662027,-0.0155843385,0.6984869242,-0.222539112,0.0544035435,-0.2987264991,-0.0819756612,0.1372263432,0.3699685335,0.3768196404,-0.53745538,-0.1752103865,-0.1816793531,-0.3489992321,-0.1634562314,0.3382711709,0.0580682494,0.2427105755,-0.046096243,0.165600881,0.3660611212,-0.0474638939,-0.2768139839,-0.1119219139,-0.0121806534,0.4462251961,-0.0702341348,0.0682196021,-0.0799727216,-0.0619321316,0.2351161987,-0.2792296708,-0.0940648913,-0.0614943281,0.2538709641,0.0773239955,0.2765713036,0.4570385516,-0.1349075586,-0.1987938285,0.1601509452,0.1806961447,0.0185495038,-0.1625983566,-0.0348524787,-0.163364619,-0.0773852915,-0.0258760657,-0.103963092,-0.0469388105,0.0200344175,0.1309570819,-0.096364513,-0.133965537,-0.0321695395,0.4189065993,-0.1397236735,0.3382306099,0.2956480384,0.0687169135,-0.384778291,-0.2950851619,-0.1493525356,0.1582213342,-0.5230259895,0.1237957925,0.2662939131,0.2771739364,0.3613065481,-0.1229094267,-0.2683769166,0.0498335995,0.2657951117,-0.1831974089,-0.0323753022,0.2156431675,0.14843674,0.2886892855,0.1391697973,-0.2281753868,0.000194584,0.0619199313,-0.1770699173,-0.1940729618,0.0692587718,0.0176814776,-0.1571019292,0.154286474,-0.4031710923,0.2367206365,0.2888612151,-0.2128570974,-0.0976049975,-0.0422633328,-0.23210226,-0.2615077198,0.2098641694,0.5494705439,-0.0914283767,-0.2689732015,-0.0759014413,-0.0987920538,0.4090284109,0.2375489473,-0.1052161306,0.2960037291,-0.2315713167,0.0987135246,0.3416324556,-0.1739220619,-0.331951797,0.1309767216,0.1102416217,0.1638558507,0.0562583506,0.0016745509,0.0118619101,0.1984750479,0.1997092515,0.3413085043,-0.0352610424,0.2907262743,-0.267072022,-0.2709881663,0.2600758076,0.0086501343,0.3516753912,0.0555153303,0.0945201367,0.8308191299,0.2724248767,-0.0689333975,0.3318047822,0.5638161898,-0.227718845,0.0573095493,-0.0112689119,-0.0633697733,-0.2486257851,0.1927720159,-0.3067270517,0.3094541728,-0.0214819368,0.1321331114,-0.1640296876,-0.0969142392,-0.0566746257,-0.3699617386,0.0888292938,0.1652462482,0.1101260856,0.2140970677,0.1379154623,0.2627291083,-0.0111610973,0.0226750709,-0.6933624744,0.2087740302,-0.1417605281,-0.1521971226,-0.0109318141,0.1096379384,0.1333905607,0.1438275576,-0.1323129535,0.1214753836,0.0990371704,-0.0469947085,0.1362223029,-0.0250016525,0.2658998072,-0.1880667061,0.4338091016,0.0586686023,0.1608509421,-0.1543041915,0.0469034724,-0.2138385028,0.072046563,0.2993028164,0.0846952274,0.0347199701,0.1672719419,0.1503384262,0.1518827975,-0.1656813025,0.4092136621,0.0806189775,0.1114226282,-0.0011588708,0.0266635604,-0.4050886929,0.4183508158,0.1737964302,0.1427270025,0.2619020641,-0.4456169605,-0.2904942334,0.2247490287,-0.057462208,0.2343137711,0.0811882243,0.1338569522,0.0289964098,0.1593956351,0.037909966,0.0936210901,0.058900822,0.5227373838,0.0115124211,0.0125517687,-0.1341618747,-0.2134275883,0.0808712468,0.0312990136,0.4169813991,-0.1902172863,-0.0390307494,-0.0026984455,-0.5402334332,-0.3053193986,0.2616876364,-0.4882035851,-0.2883309424,-0.2413621992,0.280143708,-0.0662873089,-0.0134526268,0.068596065,0.1989407092,0.1088028774,-0.1494421959,-0.1298904121,-0.2826367915,-0.3521508873,-0.0132163968,0.1780460626,0.0457917042,0.2574231327,0.0474953204,-0.129376784,-0.2252615541,-0.1390877813,0.193174839,0.0193976611,0.1323526949,0.0808379054,0.6441944838,-0.0916180611,-0.1589263827,0.3123699129,-0.1345023215,-0.1123504639,0.2058447748,0.0021174341,0.1894079149,0.0238474421,-0.0355216302,-0.1239717528,-0.4503405094,0.1686739624,0.1654811502,-0.0202529617,0.347874105,0.1282710731,0.0782882795,0.1775042415,0.1381355822,-0.190916121,-0.0510670096,0.2469130903,-0.3309720457,-0.3059126437,-0.0715213791,-0.1601582468,0.2242604643,0.4027511775,-0.6984960437,0.1061492711,-0.1276174188,-0.2190024704,0.0490920655,0.0372534879,0.3683996797,-0.1916541755,-0.0193818528,-0.0021286565,-0.0695347115,-0.0335374884,-0.2112904936,0.6732001901,0.1878428757,0.3525752127,0.2811793983,0.9196683168,0.2102591395,0.4203176796,0.4592570961,0.1845643818,0.2118268609,-0.1392084062,-0.2165400535,0.0542722419,-0.295849949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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/278","title":"MemoryError when loading German Wikipedia","comments":"I added the German and the Italian Wikipedia to our google cloud storage:\r\nFirst update the `nlp` package to 0.3.0:\r\n```bash\r\npip install nlp --upgrade\r\n```\r\nand then\r\n```python\r\nfrom nlp import load_dataset\r\nwiki_de = load_dataset(\"wikipedia\", \"20200501.de\")\r\nwiki_it = load_dataset(\"wikipedia\", \"20200501.it\")\r\n```\r\nThe datasets are downloaded and directly ready to use (no processing).","body":"Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you!","comment_length":53,"text":"MemoryError when loading German Wikipedia \n Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you! \n I added the German and the Italian Wikipedia to our google cloud storage:\r\nFirst update the `nlp` package to 0.3.0:\r\n```bash\r\npip install nlp --upgrade\r\n```\r\nand then\r\n```python\r\nfrom nlp import load_dataset\r\nwiki_de = load_dataset(\"wikipedia\", \"20200501.de\")\r\nwiki_it = load_dataset(\"wikipedia\", \"20200501.it\")\r\n```\r\nThe datasets are downloaded and directly ready to use (no processing).","embeddings":[-0.0479339696,0.1261895299,0.0182390139,0.6689180136,0.2445327044,0.1557507068,0.004935035,0.2083568722,0.3758642077,-0.0514769666,0.2673629224,-0.3312878907,-0.1734624952,-0.3341802955,0.2296689302,-0.4861274064,0.0930675492,0.0584275424,-0.0968580395,0.021348808,-0.2210392207,0.2842619419,-0.2860866785,-0.1296746433,-0.2414990813,0.0682763606,-0.0606940202,0.0540070944,-0.1613469124,-0.4543804526,0.1093896404,-0.2572765052,0.1181677878,0.1811023504,-0.0001217219,0.0400367081,0.4013991356,-0.1707563549,-0.4441632926,0.0392563045,-0.0454732403,-0.3407937586,0.0367344134,-0.2606716752,0.0609828457,-0.1065008789,0.3253657222,-0.082839556,0.3636316657,0.1792971939,0.1830790192,0.0583989583,0.2596977651,-0.0829991326,0.5757554173,0.0603940561,0.0517875366,0.1204346195,-0.3371914327,-0.3669273555,-0.266692996,0.2971111834,0.0168598853,0.1016100198,0.3428906202,-0.1183491275,0.014594771,-0.2151484787,0.2146662027,-0.0155843385,0.6984869242,-0.222539112,0.0544035435,-0.2987264991,-0.0819756612,0.1372263432,0.3699685335,0.3768196404,-0.53745538,-0.1752103865,-0.1816793531,-0.3489992321,-0.1634562314,0.3382711709,0.0580682494,0.2427105755,-0.046096243,0.165600881,0.3660611212,-0.0474638939,-0.2768139839,-0.1119219139,-0.0121806534,0.4462251961,-0.0702341348,0.0682196021,-0.0799727216,-0.0619321316,0.2351161987,-0.2792296708,-0.0940648913,-0.0614943281,0.2538709641,0.0773239955,0.2765713036,0.4570385516,-0.1349075586,-0.1987938285,0.1601509452,0.1806961447,0.0185495038,-0.1625983566,-0.0348524787,-0.163364619,-0.0773852915,-0.0258760657,-0.103963092,-0.0469388105,0.0200344175,0.1309570819,-0.096364513,-0.133965537,-0.0321695395,0.4189065993,-0.1397236735,0.3382306099,0.2956480384,0.0687169135,-0.384778291,-0.2950851619,-0.1493525356,0.1582213342,-0.5230259895,0.1237957925,0.2662939131,0.2771739364,0.3613065481,-0.1229094267,-0.2683769166,0.0498335995,0.2657951117,-0.1831974089,-0.0323753022,0.2156431675,0.14843674,0.2886892855,0.1391697973,-0.2281753868,0.000194584,0.0619199313,-0.1770699173,-0.1940729618,0.0692587718,0.0176814776,-0.1571019292,0.154286474,-0.4031710923,0.2367206365,0.2888612151,-0.2128570974,-0.0976049975,-0.0422633328,-0.23210226,-0.2615077198,0.2098641694,0.5494705439,-0.0914283767,-0.2689732015,-0.0759014413,-0.0987920538,0.4090284109,0.2375489473,-0.1052161306,0.2960037291,-0.2315713167,0.0987135246,0.3416324556,-0.1739220619,-0.331951797,0.1309767216,0.1102416217,0.1638558507,0.0562583506,0.0016745509,0.0118619101,0.1984750479,0.1997092515,0.3413085043,-0.0352610424,0.2907262743,-0.267072022,-0.2709881663,0.2600758076,0.0086501343,0.3516753912,0.0555153303,0.0945201367,0.8308191299,0.2724248767,-0.0689333975,0.3318047822,0.5638161898,-0.227718845,0.0573095493,-0.0112689119,-0.0633697733,-0.2486257851,0.1927720159,-0.3067270517,0.3094541728,-0.0214819368,0.1321331114,-0.1640296876,-0.0969142392,-0.0566746257,-0.3699617386,0.0888292938,0.1652462482,0.1101260856,0.2140970677,0.1379154623,0.2627291083,-0.0111610973,0.0226750709,-0.6933624744,0.2087740302,-0.1417605281,-0.1521971226,-0.0109318141,0.1096379384,0.1333905607,0.1438275576,-0.1323129535,0.1214753836,0.0990371704,-0.0469947085,0.1362223029,-0.0250016525,0.2658998072,-0.1880667061,0.4338091016,0.0586686023,0.1608509421,-0.1543041915,0.0469034724,-0.2138385028,0.072046563,0.2993028164,0.0846952274,0.0347199701,0.1672719419,0.1503384262,0.1518827975,-0.1656813025,0.4092136621,0.0806189775,0.1114226282,-0.0011588708,0.0266635604,-0.4050886929,0.4183508158,0.1737964302,0.1427270025,0.2619020641,-0.4456169605,-0.2904942334,0.2247490287,-0.057462208,0.2343137711,0.0811882243,0.1338569522,0.0289964098,0.1593956351,0.037909966,0.0936210901,0.058900822,0.5227373838,0.0115124211,0.0125517687,-0.1341618747,-0.2134275883,0.0808712468,0.0312990136,0.4169813991,-0.1902172863,-0.0390307494,-0.0026984455,-0.5402334332,-0.3053193986,0.2616876364,-0.4882035851,-0.2883309424,-0.2413621992,0.280143708,-0.0662873089,-0.0134526268,0.068596065,0.1989407092,0.1088028774,-0.1494421959,-0.1298904121,-0.2826367915,-0.3521508873,-0.0132163968,0.1780460626,0.0457917042,0.2574231327,0.0474953204,-0.129376784,-0.2252615541,-0.1390877813,0.193174839,0.0193976611,0.1323526949,0.0808379054,0.6441944838,-0.0916180611,-0.1589263827,0.3123699129,-0.1345023215,-0.1123504639,0.2058447748,0.0021174341,0.1894079149,0.0238474421,-0.0355216302,-0.1239717528,-0.4503405094,0.1686739624,0.1654811502,-0.0202529617,0.347874105,0.1282710731,0.0782882795,0.1775042415,0.1381355822,-0.190916121,-0.0510670096,0.2469130903,-0.3309720457,-0.3059126437,-0.0715213791,-0.1601582468,0.2242604643,0.4027511775,-0.6984960437,0.1061492711,-0.1276174188,-0.2190024704,0.0490920655,0.0372534879,0.3683996797,-0.1916541755,-0.0193818528,-0.0021286565,-0.0695347115,-0.0335374884,-0.2112904936,0.6732001901,0.1878428757,0.3525752127,0.2811793983,0.9196683168,0.2102591395,0.4203176796,0.4592570961,0.1845643818,0.2118268609,-0.1392084062,-0.2165400535,0.0542722419,-0.2958499491,-0.14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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/278","title":"MemoryError when loading German Wikipedia","comments":"Hi @lhoestq \r\n\r\nWow, thanks so much, that's **really** incredible! I was considering looking at creating my own Beam Dataset, as per the doc you linked, but instead opted to process the data myself using `wikiextractor`. However, now that this is available, I'll definitely switch across and use it.\r\n\r\nThanks so much for the incredible work, this really helps out our team considerably!\r\n\r\nHave a great (and well-deserved ;) weekend ahead!\r\n\r\nP.S. I'm not sure if I should close the issue here - if so I'm happy to do so.","body":"Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you!","comment_length":89,"text":"MemoryError when loading German Wikipedia \n Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)\r\n\r\nI'm trying to download the German Wikipedia dataset as follows:\r\n\r\n```\r\nwiki = nlp.load_dataset(\"wikipedia\", \"20200501.de\", split=\"train\")\r\n```\r\n\r\nHowever, when I do so, I get the following error:\r\n\r\n```\r\nDownloading and preparing dataset wikipedia\/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/ubuntu\/.cache\/huggingface\/datasets\/wikipedia\/20200501.de\/1.0.0...\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 433, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/ubuntu\/anaconda3\/envs\/albert\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 824, in _download_and_prepare\r\n    \"\\n\\t`{}`\".format(usage_example)\r\nnlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https:\/\/beam.apache.org\/documentation\/runners\/capability-matrix\/\r\nIf you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). \r\nExample of usage: \r\n\t`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`\r\n```\r\n\r\nSo, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.\r\n\r\nThis isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?\r\n\r\nMy nlp version is 0.2.1.\r\n\r\nThank you! \n Hi @lhoestq \r\n\r\nWow, thanks so much, that's **really** incredible! I was considering looking at creating my own Beam Dataset, as per the doc you linked, but instead opted to process the data myself using `wikiextractor`. However, now that this is available, I'll definitely switch across and use it.\r\n\r\nThanks so much for the incredible work, this really helps out our team considerably!\r\n\r\nHave a great (and well-deserved ;) weekend ahead!\r\n\r\nP.S. I'm not sure if I should close the issue here - if so I'm happy to do so.","embeddings":[-0.0479339696,0.1261895299,0.0182390139,0.6689180136,0.2445327044,0.1557507068,0.004935035,0.2083568722,0.3758642077,-0.0514769666,0.2673629224,-0.3312878907,-0.1734624952,-0.3341802955,0.2296689302,-0.4861274064,0.0930675492,0.0584275424,-0.0968580395,0.021348808,-0.2210392207,0.2842619419,-0.2860866785,-0.1296746433,-0.2414990813,0.0682763606,-0.0606940202,0.0540070944,-0.1613469124,-0.4543804526,0.1093896404,-0.2572765052,0.1181677878,0.1811023504,-0.0001217219,0.0400367081,0.4013991356,-0.1707563549,-0.4441632926,0.0392563045,-0.0454732403,-0.3407937586,0.0367344134,-0.2606716752,0.0609828457,-0.1065008789,0.3253657222,-0.082839556,0.3636316657,0.1792971939,0.1830790192,0.0583989583,0.2596977651,-0.0829991326,0.5757554173,0.0603940561,0.0517875366,0.1204346195,-0.3371914327,-0.3669273555,-0.266692996,0.2971111834,0.0168598853,0.1016100198,0.3428906202,-0.1183491275,0.014594771,-0.2151484787,0.2146662027,-0.0155843385,0.6984869242,-0.222539112,0.0544035435,-0.2987264991,-0.0819756612,0.1372263432,0.3699685335,0.3768196404,-0.53745538,-0.1752103865,-0.1816793531,-0.3489992321,-0.1634562314,0.3382711709,0.0580682494,0.2427105755,-0.046096243,0.165600881,0.3660611212,-0.0474638939,-0.2768139839,-0.1119219139,-0.0121806534,0.4462251961,-0.0702341348,0.0682196021,-0.0799727216,-0.0619321316,0.2351161987,-0.2792296708,-0.0940648913,-0.0614943281,0.2538709641,0.0773239955,0.2765713036,0.4570385516,-0.1349075586,-0.1987938285,0.1601509452,0.1806961447,0.0185495038,-0.1625983566,-0.0348524787,-0.163364619,-0.0773852915,-0.0258760657,-0.103963092,-0.0469388105,0.0200344175,0.1309570819,-0.096364513,-0.133965537,-0.0321695395,0.4189065993,-0.1397236735,0.3382306099,0.2956480384,0.0687169135,-0.384778291,-0.2950851619,-0.1493525356,0.1582213342,-0.5230259895,0.1237957925,0.2662939131,0.2771739364,0.3613065481,-0.1229094267,-0.2683769166,0.0498335995,0.2657951117,-0.1831974089,-0.0323753022,0.2156431675,0.14843674,0.2886892855,0.1391697973,-0.2281753868,0.000194584,0.0619199313,-0.1770699173,-0.1940729618,0.0692587718,0.0176814776,-0.1571019292,0.154286474,-0.4031710923,0.2367206365,0.2888612151,-0.2128570974,-0.0976049975,-0.0422633328,-0.23210226,-0.2615077198,0.2098641694,0.5494705439,-0.0914283767,-0.2689732015,-0.0759014413,-0.0987920538,0.4090284109,0.2375489473,-0.1052161306,0.2960037291,-0.2315713167,0.0987135246,0.3416324556,-0.1739220619,-0.331951797,0.1309767216,0.1102416217,0.1638558507,0.0562583506,0.0016745509,0.0118619101,0.1984750479,0.1997092515,0.3413085043,-0.0352610424,0.2907262743,-0.267072022,-0.2709881663,0.2600758076,0.0086501343,0.3516753912,0.0555153303,0.0945201367,0.8308191299,0.2724248767,-0.0689333975,0.3318047822,0.5638161898,-0.227718845,0.0573095493,-0.0112689119,-0.0633697733,-0.2486257851,0.1927720159,-0.3067270517,0.3094541728,-0.0214819368,0.1321331114,-0.1640296876,-0.0969142392,-0.0566746257,-0.3699617386,0.0888292938,0.1652462482,0.1101260856,0.2140970677,0.1379154623,0.2627291083,-0.0111610973,0.0226750709,-0.6933624744,0.2087740302,-0.1417605281,-0.1521971226,-0.0109318141,0.1096379384,0.1333905607,0.1438275576,-0.1323129535,0.1214753836,0.0990371704,-0.0469947085,0.1362223029,-0.0250016525,0.2658998072,-0.1880667061,0.4338091016,0.0586686023,0.1608509421,-0.1543041915,0.0469034724,-0.2138385028,0.072046563,0.2993028164,0.0846952274,0.0347199701,0.1672719419,0.1503384262,0.1518827975,-0.1656813025,0.4092136621,0.0806189775,0.1114226282,-0.0011588708,0.0266635604,-0.4050886929,0.4183508158,0.1737964302,0.1427270025,0.2619020641,-0.4456169605,-0.2904942334,0.2247490287,-0.057462208,0.2343137711,0.0811882243,0.1338569522,0.0289964098,0.1593956351,0.037909966,0.0936210901,0.058900822,0.5227373838,0.0115124211,0.0125517687,-0.1341618747,-0.2134275883,0.0808712468,0.0312990136,0.4169813991,-0.1902172863,-0.0390307494,-0.0026984455,-0.5402334332,-0.3053193986,0.2616876364,-0.4882035851,-0.2883309424,-0.2413621992,0.280143708,-0.0662873089,-0.0134526268,0.068596065,0.1989407092,0.1088028774,-0.1494421959,-0.1298904121,-0.2826367915,-0.3521508873,-0.0132163968,0.1780460626,0.0457917042,0.2574231327,0.0474953204,-0.129376784,-0.2252615541,-0.1390877813,0.193174839,0.0193976611,0.1323526949,0.0808379054,0.6441944838,-0.0916180611,-0.1589263827,0.3123699129,-0.1345023215,-0.1123504639,0.2058447748,0.0021174341,0.1894079149,0.0238474421,-0.0355216302,-0.1239717528,-0.4503405094,0.1686739624,0.1654811502,-0.0202529617,0.347874105,0.1282710731,0.0782882795,0.1775042415,0.1381355822,-0.190916121,-0.0510670096,0.2469130903,-0.3309720457,-0.3059126437,-0.0715213791,-0.1601582468,0.2242604643,0.4027511775,-0.6984960437,0.1061492711,-0.1276174188,-0.2190024704,0.0490920655,0.0372534879,0.3683996797,-0.1916541755,-0.0193818528,-0.0021286565,-0.0695347115,-0.0335374884,-0.2112904936,0.6732001901,0.1878428757,0.3525752127,0.2811793983,0.9196683168,0.2102591395,0.4203176796,0.4592570961,0.1845643818,0.2118268609,-0.1392084062,-0.2165400535,0.0542722419,-0.2958499491,-0.1469565779,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/277","title":"Empty samples in glue\/qqp","comments":"We are only wrapping the original dataset.\r\n\r\nMaybe try to ask on the GLUE mailing list or reach out to the original authors?","body":"```\r\nqqp = nlp.load_dataset('glue', 'qqp')\r\nprint(qqp['train'][310121])\r\nprint(qqp['train'][362225])\r\n```\r\n```\r\n{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}\r\n{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}\r\n```\r\nNotice that question 2 is empty string. \r\nBTW, I have checked and these two are the only naughty ones in all splits of qqp.","comment_length":23,"text":"Empty samples in glue\/qqp \n ```\r\nqqp = nlp.load_dataset('glue', 'qqp')\r\nprint(qqp['train'][310121])\r\nprint(qqp['train'][362225])\r\n```\r\n```\r\n{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}\r\n{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}\r\n```\r\nNotice that question 2 is empty string. \r\nBTW, I have checked and these two are the only naughty ones in all splits of qqp. \n We are only wrapping the original dataset.\r\n\r\nMaybe try to ask on the GLUE mailing list or reach out to the original authors?","embeddings":[0.3944284618,-0.2275950313,-0.1333640814,0.1721872091,-0.0090440977,-0.1809310764,0.3399785757,0.4980908334,0.3016017675,0.2876112461,-0.1628892273,0.4013324678,-0.0513740256,0.0415244102,0.1211948544,-0.2321262211,-0.1426435709,0.4839450121,-0.2110554874,-0.0486818142,-0.2900344431,0.3497726321,-0.1607245803,0.0145700229,-0.2572481036,-0.0267112777,-0.4356914759,0.1250949204,-0.0838717371,-0.1141366884,-0.0953655615,0.088230148,-0.3356195092,0.0275134351,-0.0001076749,-0.1921067536,0.2097817063,-0.0667193532,0.0979350433,0.0208776332,-0.1886062175,-0.1601732671,-0.0197192859,-0.3324033618,-0.1081115827,0.0603447631,0.0312088747,0.0091472585,0.3878845572,0.2867927551,0.231938839,0.1878136843,-0.0533459149,0.0011456627,0.5737708211,-0.4523113966,0.1663807482,0.1940990835,0.1904550791,-0.0723294169,0.2012558132,0.2850463092,-0.0291922819,-0.1413354278,-0.3117975295,0.073603563,-0.0549966656,-0.5886821151,0.11005532,0.5429876447,-0.1368231475,0.0054109218,-0.1368394196,-0.2885940075,0.0308143701,-0.1221046373,-0.0853625312,0.289188534,-0.1083173379,0.1899968237,-0.091862075,-0.1836483926,0.0907038078,0.111167796,-0.1785192937,0.3162373602,-0.0265359003,0.0967656299,-0.0690471306,0.0069285724,-0.3025737703,-0.0438517183,-0.2633654177,-0.05087642,-0.0054775872,-0.1263584048,0.0265676584,-0.1544861495,0.014671864,0.0555241778,0.0235987026,0.0789896846,0.3578359783,0.1973656118,0.0603472888,0.1082852259,0.4745409489,0.2059003264,-0.2136047333,-0.1027712151,-0.0928279161,-0.1192266196,-0.0344808735,0.1058450416,-0.1463371068,-0.0181441456,0.1527648568,0.01987423,-0.1597275138,-0.0852263942,-0.2839925587,0.093300961,-0.2367840409,0.4214972854,0.0020284581,0.088876389,0.0664033443,0.1870643348,-0.1252355725,-0.4068473577,-0.3048885465,0.14692536,-0.0183423571,-0.2193646878,0.0506461672,0.0939232856,0.3305933774,0.0040266337,-0.3367695212,-0.0640058443,0.1815020293,-0.1282046139,0.2904049456,0.3732571304,0.1224220395,0.0452854969,0.0463478342,-0.1798280925,-0.0764913335,0.305179745,-0.2295915335,-0.0494144224,-0.2482259721,0.2842925489,-0.2753049433,0.2139467448,0.301047653,0.2676579356,-0.0028213784,0.0312818959,0.245218724,-0.0584058985,0.0224559084,-0.3709172904,0.1769923568,0.1493795067,-0.4684355259,-0.2842772603,-0.040240176,-0.0014727769,0.0031715131,0.2163157314,-0.0184906013,0.267180711,-0.3092720211,0.5628643632,0.3224934042,-0.3252393901,-0.2990133166,0.2551974952,-0.2539637685,-0.0020691596,0.0715375543,-0.0273460504,0.4085083306,0.2399876565,0.2770467699,0.2180753797,-0.007983976,-0.1678893268,-0.384680301,-0.1650359035,0.3145085275,-0.0528995022,-0.0659816638,0.1639251411,-0.112235412,-0.0719398186,0.2754436433,0.1192446798,0.0017785862,0.1193085462,0.3002591133,-0.2346257865,-0.0166703388,-0.0893278196,-0.2737095058,0.084728919,-0.5236303806,0.163312301,0.091062285,-0.1931527853,-0.1754205674,-0.0931807309,0.0721271187,-0.2961351573,0.2624043822,-0.0942217261,0.1309671551,-0.0039560669,-0.0591877177,0.2372475117,0.0441201329,0.1758411825,-0.2782508135,0.1913246214,0.110832192,0.0012173938,0.1393664628,0.603890717,0.1739385277,0.0278028324,0.3377902508,0.3833782673,-0.1247403473,-0.0124142682,-0.0620077699,-0.4666868746,-0.0670435056,-0.2006079704,-0.0397650041,0.1223443225,0.0825217515,-0.0669386536,-0.0259498823,0.582020998,0.2762618661,0.1307536066,0.026604116,0.1524035186,-0.3962775767,-0.4450353086,-0.1695312113,-0.307769835,0.1928272992,0.1617302597,0.3633176684,0.2564749718,-0.1709272712,0.3790645599,0.5739662051,-0.0692600757,0.3076536357,-0.2247022241,-0.2381239533,-0.1790424734,0.0701891482,0.3703540266,0.1997359395,0.3989004195,0.0155678988,-0.0836394578,0.0834258273,-0.3214094639,0.2073069364,0.1239553243,-0.229490906,0.1494203061,0.3624715507,-0.1701753438,-0.201259017,0.5741873384,0.0243121926,-0.0494152382,-0.1920899004,-0.1492207497,-0.3281409442,-0.2790329754,-0.0113154044,-0.2292650193,-0.2454906255,-0.4493693411,0.2525092959,-0.3933390677,-0.1659594923,0.0172137953,0.131049931,0.3514339328,-0.1337381005,0.0004678103,-0.012865006,-0.037069425,-0.3257400393,0.2296125144,-0.4175542295,0.3865434229,0.3919174373,-0.0575511046,-0.0735447109,0.1745669097,-0.280805856,0.107807599,-0.3091467023,0.4351453781,0.1558913887,0.0820875242,-0.0727378726,-0.0775733292,0.109332405,-0.0259627961,-0.1517684013,-0.0908388868,0.2362346351,0.232569918,-0.3710660338,-0.4433030486,-0.2700110972,-0.3494876027,0.4619119465,-0.2531074584,0.3448719084,0.0024914034,-0.1901856065,0.2387921065,-0.3217241764,-0.2853172421,-0.166195333,-0.1441185623,0.2234225124,-0.2578462064,-0.2959876359,0.0591706596,-0.2494992018,0.1469355822,-0.188769415,-0.3285140097,-0.3279092908,0.0730063245,0.0583032221,0.3050025702,-0.0555207282,0.1158126891,-0.2326511741,-0.1398031414,-0.1862350553,0.0044557997,0.4342602491,0.0017085961,0.0043306304,-0.0346563794,0.2183077931,-0.0416686498,0.2817901969,0.120159857,0.4252009988,0.1642449051,-0.0652260631,0.3786019981,0.0359279513,-0.0413284376,0.17578502,0.2188123316,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/277","title":"Empty samples in glue\/qqp","comments":"Tanks for the suggestion, I'll try to ask GLUE benchmark.\r\nI'll first close the issue,  post the following up here afterwards, and reopen the issue if needed. ","body":"```\r\nqqp = nlp.load_dataset('glue', 'qqp')\r\nprint(qqp['train'][310121])\r\nprint(qqp['train'][362225])\r\n```\r\n```\r\n{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}\r\n{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}\r\n```\r\nNotice that question 2 is empty string. \r\nBTW, I have checked and these two are the only naughty ones in all splits of qqp.","comment_length":27,"text":"Empty samples in glue\/qqp \n ```\r\nqqp = nlp.load_dataset('glue', 'qqp')\r\nprint(qqp['train'][310121])\r\nprint(qqp['train'][362225])\r\n```\r\n```\r\n{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}\r\n{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}\r\n```\r\nNotice that question 2 is empty string. \r\nBTW, I have checked and these two are the only naughty ones in all splits of qqp. \n Tanks for the suggestion, I'll try to ask GLUE benchmark.\r\nI'll first close the issue,  post the following up here afterwards, and reopen the issue if needed. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/275","title":"NonMatchingChecksumError when loading pubmed dataset","comments":"For some reason the files are not available for unauthenticated users right now (like the download service of this package). Instead of downloading the right files, it downloads the html of the error.\r\nAccording to the error it should be back again in 24h.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/42851186\/84751599-096c6580-afbd-11ea-97f3-ee4aef791711.png)\r\n","body":"I get this error when i run `nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')`.\r\nThe error is:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-2-7742dea167d0> in <module>()\r\n----> 1 df = nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')\r\n      2 df = pd.DataFrame(df)\r\n      3 gc.collect()\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    431                 verify_infos = not save_infos and not ignore_verifications\r\n    432                 self._download_and_prepare(\r\n--> 433                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    434                 )\r\n    435                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    468         # Checksums verification\r\n    469         if verify_infos:\r\n--> 470             verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())\r\n    471         for split_generator in split_generators:\r\n    472             if str(split_generator.split_info.name).lower() == \"all\":\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)\r\n     34     bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n     35     if len(bad_urls) > 0:\r\n---> 36         raise NonMatchingChecksumError(str(bad_urls))\r\n     37     logger.info(\"All the checksums matched successfully.\")\r\n     38 \r\n\r\nNonMatchingChecksumError: ['https:\/\/drive.google.com\/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https:\/\/drive.google.com\/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download']\r\n```\r\nI'm currently working on google colab.\r\n\r\nThat is quite strange because yesterday it was fine.\r\n","comment_length":45,"text":"NonMatchingChecksumError when loading pubmed dataset \n I get this error when i run `nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')`.\r\nThe error is:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError                  Traceback (most recent call last)\r\n<ipython-input-2-7742dea167d0> in <module>()\r\n----> 1 df = nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')\r\n      2 df = pd.DataFrame(df)\r\n      3 gc.collect()\r\n\r\n3 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    431                 verify_infos = not save_infos and not ignore_verifications\r\n    432                 self._download_and_prepare(\r\n--> 433                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    434                 )\r\n    435                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    468         # Checksums verification\r\n    469         if verify_infos:\r\n--> 470             verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())\r\n    471         for split_generator in split_generators:\r\n    472             if str(split_generator.split_info.name).lower() == \"all\":\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)\r\n     34     bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]\r\n     35     if len(bad_urls) > 0:\r\n---> 36         raise NonMatchingChecksumError(str(bad_urls))\r\n     37     logger.info(\"All the checksums matched successfully.\")\r\n     38 \r\n\r\nNonMatchingChecksumError: ['https:\/\/drive.google.com\/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https:\/\/drive.google.com\/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download']\r\n```\r\nI'm currently working on google colab.\r\n\r\nThat is quite strange because yesterday it was fine.\r\n \n For some reason the files are not available for unauthenticated users right now (like the download service of this package). Instead of downloading the right files, it downloads the html of the error.\r\nAccording to the error it should be back again in 24h.\r\n\r\n![image](https:\/\/user-images.githubusercontent.com\/42851186\/84751599-096c6580-afbd-11ea-97f3-ee4aef791711.png)\r\n","embeddings":[0.058758609,0.1301917434,0.0369352363,0.0248560738,0.2648719549,-0.0135630183,0.216802299,0.5417689085,0.2348525822,0.1036150381,-0.139252767,0.3025202751,-0.1345059574,-0.469260931,-0.1694715023,0.0336121656,0.0182243753,0.278247118,0.083115153,0.0176543035,-0.2718373835,0.3171768188,-0.2022533268,-0.0674488246,0.1895201951,-0.1108570844,0.1367979944,0.217150107,0.0484668277,-0.3944905996,0.2232331336,0.1793066412,0.1068236604,0.2187495083,-0.0001170882,0.1765800714,0.3026967645,0.0146549763,-0.2404377311,-0.3675601482,-0.306748867,-0.5333061218,0.2033980042,-0.3149736226,-0.0939296708,0.0818529204,0.0985179842,-0.0667017177,0.1352661699,0.2718558311,0.1855047345,0.4645368159,-0.0121475356,-0.0389801152,0.2210695595,-0.0710691288,-0.1167197675,0.4443893135,0.2470195442,-0.205421567,-0.4585720003,0.2826867402,-0.2289150953,0.4773514867,-0.0487231873,0.0054706582,0.4340510666,-0.2538340092,0.1287406385,0.3172093332,0.124449648,-0.1102622896,0.0408434309,-0.2443514317,-0.0612620674,-0.1005057618,0.3322421908,0.1642604917,-0.3393443227,-0.164248243,-0.2672503293,0.1548261195,-0.0184418168,0.2772804499,0.4511999488,0.0823994428,0.0259934198,0.0779543221,0.3885330856,-0.1400493532,0.4081314206,-0.1891864985,0.0559792668,0.2718269527,-0.3856696188,-0.1050526798,-0.0299018398,0.0830047354,0.2192598432,0.5085961819,0.1739616841,-0.0206740312,0.1601028144,0.1740664244,0.2345796525,-0.0325501524,-0.20497334,0.3127065301,0.2428222746,0.2654270232,-0.0817287639,0.1325980276,-0.1243766472,-0.2750798166,0.4795861244,-0.1543896943,0.1567379683,-0.3283813,-0.1853384376,0.2568361461,0.1256311685,-0.037092898,0.150780201,0.379488349,-0.0067011532,0.2513456941,-0.0467179157,-0.0900826231,-0.2285945415,-0.0017539149,-0.1619568467,0.078827545,-0.2954720855,0.0364404917,0.291310519,-0.1616549641,0.3400786221,-0.0261939988,0.1431849599,-0.1925871819,0.2771724463,-0.2258296162,-0.1703099459,0.400228858,0.0789528787,0.0937558636,0.259924233,-0.0361443907,-0.3035447896,0.171074912,-0.1880393922,-0.3761236668,-0.354393512,0.1679352224,-0.3283609748,-0.1124940366,0.1948510855,-0.1655438989,0.4185551405,-0.5822321773,0.0198790077,-0.3036311269,-0.2127438039,-0.1403591037,-0.0047661471,0.1324805021,0.3329315484,-0.0629779398,-0.0698211119,0.0543446913,0.5059974194,0.3451900184,-0.2003525347,-0.0730774403,-0.1836931109,-0.0042758714,0.6614866853,-0.1024645865,-0.5011329651,0.3050438166,0.0577829257,0.4503071904,-0.033370968,0.1673368067,-0.1188712195,-0.101896219,0.3278857768,0.0811803341,0.0006618578,0.2791234553,-0.3819270134,-0.1303378493,0.5268676877,0.2277288735,0.1866333634,-0.0063327644,0.0629571304,0.2206354439,0.1839743108,-0.0855364129,-0.0306455232,0.2708131969,0.0185403861,0.0692929104,0.0581090748,-0.2218934298,-0.3858188689,0.2098036557,-0.380053401,0.2502179742,0.0859001353,0.0226947721,-0.3482504189,-0.3304007053,-0.210401997,0.0228056498,0.0646701306,0.3360591531,0.1912897527,0.0297807064,-0.1321470737,0.2002444714,-0.3121551275,0.0384585224,-0.5431321859,0.2622187436,-0.2126975209,-0.1852943301,0.164986223,0.1927392185,0.0837604105,0.0281460565,-0.2496987879,0.3297187686,0.0849613547,-0.0043527321,0.0416009612,0.0834872201,-0.0649434477,-0.09139283,-0.003358939,0.450768888,0.0452840663,-0.0097539034,-0.2259782404,0.3569218516,-0.0916158184,0.1400891393,0.0180972219,0.111937426,0.292778492,-0.1206601635,-0.2199435085,-0.1293094158,0.5966858864,0.0419388562,-0.1533253789,0.1737088412,-0.4326075912,-0.295591563,0.3696337044,-0.0256391652,0.0271185786,0.1284158677,0.0668641329,-0.1737574339,-0.013439131,0.3423480392,0.502830863,0.1839086413,-0.1045734361,0.0342627466,-0.3598060608,-0.0776783898,-0.0758731961,0.1211585626,0.4027727842,0.5767911673,0.3505509794,0.0097552771,-0.5195763111,-0.2499975264,0.0892635733,0.5005633235,-0.3145840466,-0.0588865057,-0.2020569295,-0.1500694752,-0.2669269741,-0.2294199616,-0.2996838987,-0.2502132654,-0.0903199464,0.4568479955,0.013585669,0.3313672841,-0.7557685375,0.0948301107,-0.0509436615,-0.1814254373,-0.1037207767,-0.1316320896,-0.0341805406,-0.0372446664,0.4121696651,0.0776028782,0.4435406327,-0.0992219895,-0.1393858939,-0.2177254558,-0.2293803543,-0.0338992439,-0.0765170306,-0.0230811145,0.2885401845,0.0395289585,-0.1497325003,-0.3457106948,0.3683997691,-0.2874168754,-0.1831320226,0.2736922503,-0.0688725263,0.0477568209,0.0877032802,-0.2555535436,-0.1006214321,-0.4179308712,-0.0921681896,0.2130959332,0.1583324075,0.377029717,-0.1883330047,0.1201434508,0.0769679248,0.3528823853,-0.2467336208,-0.3859330118,0.3995440602,-0.0446745269,-0.2828446627,-0.0373046882,-0.2464389801,0.0727727264,0.0861012265,-0.4358641207,-0.3477205336,-0.2699185014,-0.2323732972,-0.0331604108,-0.1730334908,0.3908375204,0.0942935273,-0.1394832581,-0.0061373133,-0.1883255392,0.1998282671,0.0757934898,0.3481656909,-0.1597508639,0.2954999804,-0.136423707,0.572486639,0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/274","title":"PG-19","comments":"Got around to this today, and so far so good, I'm able to download and load pg19 locally. However, I think there may be an issue with the dummy data, and testing in general.\r\n\r\nThe problem lies in the fact that each book from pg19 actually resides as its own text file in a google cloud folder that denotes the split, where the book id is the name of the text file. https:\/\/console.cloud.google.com\/storage\/browser\/deepmind-gutenberg\/train\/ I don't believe there's anywhere else (even in the supplied metadata), where the mapping of id -> split can be found.\r\n\r\nTherefore I end up making a network call `tf.io.gfile.listdir` to get all the files within each of the split directories. https:\/\/github.com\/lucidrains\/nlp\/commit\/adbacbd85decc80db2347d0882e7dab4faa6fd03#diff-cece8f166a85dd927caf574ba303d39bR78\r\n\r\nDoes this network call need to be eventually stubbed out for testing?","body":"Hi, and thanks for all your open-sourced work, as always!\r\n\r\nI was wondering if you would be open to adding PG-19 to your collection of datasets. https:\/\/github.com\/deepmind\/pg19 It is often used for benchmarking long-range language modeling.","comment_length":127,"text":"PG-19 \n Hi, and thanks for all your open-sourced work, as always!\r\n\r\nI was wondering if you would be open to adding PG-19 to your collection of datasets. https:\/\/github.com\/deepmind\/pg19 It is often used for benchmarking long-range language modeling. \n Got around to this today, and so far so good, I'm able to download and load pg19 locally. However, I think there may be an issue with the dummy data, and testing in general.\r\n\r\nThe problem lies in the fact that each book from pg19 actually resides as its own text file in a google cloud folder that denotes the split, where the book id is the name of the text file. https:\/\/console.cloud.google.com\/storage\/browser\/deepmind-gutenberg\/train\/ I don't believe there's anywhere else (even in the supplied metadata), where the mapping of id -> split can be found.\r\n\r\nTherefore I end up making a network call `tf.io.gfile.listdir` to get all the files within each of the split directories. https:\/\/github.com\/lucidrains\/nlp\/commit\/adbacbd85decc80db2347d0882e7dab4faa6fd03#diff-cece8f166a85dd927caf574ba303d39bR78\r\n\r\nDoes this network call need to be eventually stubbed out for testing?","embeddings":[-0.2726430595,0.1023975164,-0.0761308819,0.0389732197,-0.3389706612,-0.1271181107,0.6120792031,0.1419056952,0.2338510901,0.0364079662,0.0945236534,-0.0275518056,-0.0765411258,0.8012996912,0.396630764,-0.0443375036,0.211777553,0.0587302968,0.0876412019,-0.0332939029,-0.313323468,0.3008314073,-0.0816275403,0.008407576,-0.5192525983,-0.4210985005,-0.2140348107,0.1362223029,-0.0564975664,-0.0908730552,0.0232849214,0.3214838207,-0.0683648884,0.7272160649,-0.0001202966,0.0550454371,0.0457218662,-0.1240919605,-0.2383431792,-0.2450154871,-0.0297817923,0.2153979242,-0.1817445755,-0.1727653742,-0.0562552996,0.0125058303,-0.0156625323,-0.0883732364,0.0534064882,0.1536814123,0.0577766858,0.1069048643,-0.1264804304,0.0249941312,0.3883236945,0.216906935,-0.3077532053,0.2421517074,0.4530335963,-0.1050634906,0.0934706926,0.3744819462,0.1002711728,-0.1324554533,0.2871436477,0.201012969,0.2314558476,-0.2365605384,-0.1251798123,0.3853826523,0.1542103142,-0.1849046797,-0.4427044094,-0.4211259186,-0.3595270813,-0.3846863806,0.1833167374,0.0560664907,-0.1714600921,0.1323085725,-0.0573672689,-0.5777279735,-0.0575946607,0.0093365954,-0.2454632521,0.6067339182,0.1900549978,-0.0070566242,0.3556369245,0.0511804558,-0.342284739,-0.2946127355,-0.0425567254,-0.3199821115,0.21934928,-0.2322885692,-0.0531322807,-0.1331907958,0.0561144575,0.174808085,0.2768690586,0.1526039988,-0.1641702354,0.1510071307,0.3916577101,0.2029719353,0.2193077058,0.3185265362,0.4196359813,0.1732527614,0.071432896,0.1075261906,-0.0424622297,-0.0206191055,-0.3254282773,-0.1253993809,0.0022389609,-0.2448345125,-0.0526662879,0.0094290199,-0.1056966186,-0.0337773263,0.1963540018,0.158449769,0.2853690386,-0.0132637536,-0.045060616,0.2140438706,-0.3329660594,-0.4193009138,-0.069764711,-0.0168797132,-0.160025999,0.3653959334,0.3247297406,0.0730307922,0.0274488293,-0.2186322808,0.0043942216,0.046492625,0.1876645386,-0.2817598879,0.3472299576,0.2262933552,-0.0165260583,-0.0629668012,-0.1108629107,-0.356213063,-0.3561598957,0.3167044818,0.0032073788,-0.1998966932,0.3542391062,0.0610938333,-0.2482744753,-0.0860883966,-0.4490329921,0.5363029838,-0.0586841106,-0.4931678176,0.1699602455,-0.1751819253,-0.5470999479,0.0817420334,0.0991437659,0.3235040009,-0.579390347,-0.2148498744,-0.1032194719,0.1039886922,0.0887611061,0.373023659,-0.1905645281,0.0898620114,-0.1698357463,0.9594690204,-0.0993583947,0.1975433379,-0.1675347984,0.2848426998,-0.3256671131,-0.0090381065,0.0218295734,0.0426345319,0.1131571978,-0.2048590928,0.0868954137,0.2947108746,0.0620340444,-0.0600580573,-0.2760382295,-0.1112532243,0.3911851048,0.0289650783,-0.0009515805,-0.4765183628,-0.1512331069,-0.2698189616,0.1740778685,-0.2358109951,-0.1551565081,0.0308812764,0.3874010146,0.0375196598,-0.0096713472,0.3352309167,-0.4020671248,0.2687511742,-0.012287871,0.1768016666,-0.1955323666,-0.2127089053,0.0497965924,0.1878638715,0.0376365334,-0.138183251,-0.0923651606,0.2016936839,0.0606909133,0.1877214462,-0.0458053388,-0.4293634295,-0.3604776561,-0.0348171927,-0.342639178,0.171873033,-0.0285636801,-0.0352945775,0.0439403094,0.0776204616,-0.0934830308,-0.2785622478,0.0286997221,0.2729766071,-0.001540517,0.3509218693,0.3272588253,0.6057779789,0.2587488294,-0.3310555816,0.0790191889,0.0507937931,-0.1362638921,-0.2429079413,-0.282789886,0.535728693,-0.2918588519,0.2532324493,0.0408431664,-0.2861541808,-0.1022042558,-0.0917572528,-0.2435637861,0.0972796902,0.3314112723,-0.4470750093,0.0934003145,0.0728303269,-0.2139514685,0.289252758,0.3497867584,-0.3756168485,0.2537577748,0.0915027782,0.0730308741,-0.3983814418,0.1558253467,-0.0336742252,0.3324222267,0.1943069398,0.3082286716,-0.1299704313,-0.0000342827,-0.3080719113,0.3471041024,0.0973096117,-0.2300127894,0.2968508601,-0.1393949986,-0.2148397416,-0.2560610175,0.076181002,0.1825117767,0.0446547084,-0.2624447644,0.2580497265,0.0524353907,-0.2476713806,-0.1175240278,-0.2869992256,-0.3177367449,-0.0545947403,0.2922195792,-0.174159795,-0.3121162653,0.1548203528,0.1175682917,0.521911025,-0.3469548821,-0.4283790588,-0.1055355668,-0.2466436923,-0.3328722417,0.0004457505,0.6615573168,0.1518442929,0.2738092542,-0.0665475428,-0.2734891176,0.0185178947,-0.3513025343,0.2038461715,-0.2112296969,0.3707543612,-0.1882704049,0.1462554038,0.1249106675,-0.1929099709,-0.0940069929,-0.729865253,-0.1402025223,-0.1654230952,0.0874549374,-0.0837273076,-0.3110301197,-0.5655396581,-0.2192698568,-0.0760293975,0.4412038326,0.0793059096,0.0764595345,0.3570248485,0.190431729,0.0222899653,0.3863989413,-0.2552841306,-0.1555346996,-0.0833690688,0.3025322556,0.0770509318,-0.0985095948,0.0111520272,-0.1882321835,0.0394399539,0.0151489293,-0.705918014,0.3068466187,0.2903669178,0.2597416937,0.0925674886,-0.3836504221,0.249778226,-0.1992362291,-0.0331020691,0.0072217989,-0.1347773224,0.351996094,0.1103857011,0.0250624456,0.0806694478,-0.1839890182,0.1030256078,0.6840956211,-0.2482760251,0.0036132797,0.0213546697,0.1816469878,-0.0131375417,-0.1698090732,-0.1296664476,-0.244847551,-0.2465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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/274","title":"PG-19","comments":"Ohh nevermind, I think I can use `download_custom` here with `listdir` as the custom function. Ok, I'll keep trying to make the dummy data work!","body":"Hi, and thanks for all your open-sourced work, as always!\r\n\r\nI was wondering if you would be open to adding PG-19 to your collection of datasets. https:\/\/github.com\/deepmind\/pg19 It is often used for benchmarking long-range language modeling.","comment_length":25,"text":"PG-19 \n Hi, and thanks for all your open-sourced work, as always!\r\n\r\nI was wondering if you would be open to adding PG-19 to your collection of datasets. https:\/\/github.com\/deepmind\/pg19 It is often used for benchmarking long-range language modeling. \n Ohh nevermind, I think I can use `download_custom` here with `listdir` as the custom function. Ok, I'll keep trying to make the dummy data work!","embeddings":[-0.4496191442,-0.0325611532,-0.2415347844,0.0113263791,0.0196675546,-0.2363707572,0.3253330886,0.2554726601,0.3786719143,0.1375896037,-0.1407309175,0.1234682724,-0.2844292819,0.6086748838,0.2480923682,-0.172456935,0.1320279837,0.1412584335,0.0229877122,-0.0359676145,-0.3278507292,0.1621127874,0.0156882498,-0.1066643074,0.0994011313,-0.051836092,-0.1455862224,0.1118728817,-0.2036280185,-0.2397161573,0.0195881221,0.248938486,0.0873469114,0.3737565577,-0.0000990424,-0.1411964595,0.1471233666,-0.1750644296,-0.1681011617,-0.177079469,0.0087815961,-0.1589248925,-0.3505608141,-0.071904704,-0.2097429186,-0.1847786009,-0.0747273117,-0.2173962146,-0.0259989146,0.17593427,0.3331770301,0.1625520885,-0.0765999183,-0.1970894337,0.3186580837,0.205319494,-0.3026254773,0.1041432694,0.5350350142,0.0575420745,0.226691708,0.1472870708,-0.1392449886,-0.0871092007,0.1038857847,0.0163980406,0.3358004391,-0.1456792206,-0.0325880758,0.4370433092,0.2743554115,-0.2226375192,-0.1406696737,-0.1740690917,-0.2891650498,-0.5619824529,-0.1779934615,0.0258650444,-0.1150368303,0.1747485399,0.1253533512,-0.2363759279,-0.1189032719,0.1020026132,-0.1371812522,0.5368193388,0.1239046082,-0.1856379509,0.2636006773,-0.2258602828,-0.2271918505,-0.3083097041,-0.0334562063,-0.2204840928,0.024958536,-0.3071707189,0.0811232328,-0.0263818279,0.2497797906,0.0611995757,0.403575778,0.3741003871,-0.0422348492,-0.0319192,0.0785553828,0.1675983369,-0.026160175,0.1110615581,0.1165724248,0.1275400668,0.0928875804,0.2510779202,-0.019871138,0.2266147286,-0.3101396561,0.1070979536,-0.2500466704,-0.1984442174,-0.173829183,0.1036912948,0.0853611529,-0.0045561823,0.1078005731,0.3054639995,-0.0083418963,0.0905207396,-0.2202184498,0.1306272298,-0.1280262172,-0.4333171844,-0.1433338076,0.1360117197,-0.1882008761,-0.0605785958,0.2425047904,0.2005563974,0.0558305383,0.065563038,0.2242944539,0.2239504457,0.4162147939,-0.3934520185,0.2677246332,-0.0238544345,-0.0135099487,-0.2324438393,-0.0271218661,-0.2948922217,-0.4005902708,0.1251236349,0.1765996367,-0.0966551304,-0.0518541113,0.2899715006,-0.2850761116,-0.2268649936,-0.1316840351,0.6907501817,-0.3521029651,-0.2408408076,-0.000249237,-0.0981628969,-0.3826604486,-0.1678152829,-0.0682638437,0.2230497748,-0.7102313042,-0.0014367804,-0.0881650895,0.0876414925,0.1221907213,0.1479886919,-0.1221750304,-0.0569751337,-0.1786092818,0.4285411835,-0.0073873335,0.0634549111,-0.1386584938,0.0479716659,-0.1762387156,-0.0238591805,0.0830367431,0.244820863,0.266395539,-0.1704757959,-0.2056723237,0.3559737504,0.1017345339,-0.0471819565,-0.2808577418,-0.2230838239,0.3721271455,0.2732970715,0.0245760772,-0.37380144,-0.0200691894,-0.1720552891,0.2108671367,-0.3505474329,-0.1919389963,0.1363509148,0.369763732,-0.0901834667,0.0271727443,-0.1142846793,-0.3181369603,0.224214986,-0.1626094431,0.2529934943,0.139137581,-0.0102434354,-0.1896639317,0.2022197247,0.1474494487,-0.092829816,0.2675631046,0.3086658716,0.0451175719,0.1465414762,0.0040356345,-0.1700763553,-0.082858555,-0.214568153,-0.0482161976,0.168878451,0.0446545221,-0.0821390003,0.0733653083,0.0036325641,0.0582691059,-0.0483385585,0.096712701,-0.0101715019,0.0568860881,0.1058743894,0.3047047257,0.4284171164,0.1185785756,-0.184403941,0.233685866,0.3589049578,0.0456297994,-0.1627823263,-0.1102421731,0.7311078906,0.0216261838,0.0830856636,-0.0175756346,-0.0984648168,0.1634133458,-0.0129959052,-0.0342781581,-0.068840988,0.6526178718,-0.0427350178,0.219409138,0.0551450774,-0.2367326766,0.3291032612,0.2710447907,-0.3213002086,0.390402019,0.1643798947,0.0217107311,-0.3258359432,0.2371312976,0.086586386,0.3153596818,0.3786853254,0.2012796998,-0.0551148392,-0.0395657755,-0.1276569217,0.513776958,-0.1240727007,-0.1921029389,0.1431730092,0.166623041,-0.2695827186,-0.2591991425,-0.0274482388,-0.0819627047,0.0517943278,0.0231285542,0.0882518291,-0.1653073132,-0.1684926599,0.1481920928,-0.2837992609,-0.3677750826,-0.1133735925,0.206483528,-0.1017498001,-0.3563189507,0.1745751947,0.05030163,0.4389922023,-0.3019811511,-0.3839160502,-0.0820413157,-0.1343021989,-0.1664964259,0.2428562641,0.2744905353,0.0489178412,0.3300122321,-0.0091046281,0.0731624141,-0.0430292748,-0.347976625,0.1718115658,-0.0593698807,0.1950065941,0.0625412911,0.1053026542,0.1494202912,-0.0789449438,0.027824726,-0.5253397226,-0.0980188921,-0.1136682183,-0.1326740384,0.0018229119,-0.2441450059,-0.1484719068,-0.4197816849,-0.4171620011,0.1512208283,0.2760438025,0.1007145494,0.119177334,0.2399676591,0.085902065,0.2548600137,-0.1275292486,-0.3787027001,-0.2274725735,0.3552961349,-0.1580530107,-0.1372421384,-0.3094339967,-0.3070884347,0.1115585193,0.0119988611,-0.4052853882,0.1284037977,0.1792709082,0.2972586453,0.0215697009,-0.2398805022,0.3363254964,0.0931689814,-0.1702373922,-0.1667775661,-0.1932845116,0.1437280476,-0.1239264756,0.0541550629,0.0257418193,-0.1417310685,0.1266516149,0.6686883569,-0.3144363463,-0.1296614856,0.3166135848,0.2104554623,0.116296798,-0.1307317317,-0.1326116174,-0.2857694328,-0.0050991527,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/263","title":"[Feature request] Support for external modality for language datasets","comments":"Thanks a lot, @aleSuglia for the very detailed and introductive feature request.\r\nIt seems like we could build something pretty useful here indeed.\r\n\r\nOne of the questions here is that Arrow doesn't have built-in support for generic \"tensors\" in records but there might be ways to do that in a clean way. We'll probably try to tackle this during the summer.","body":"# Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it!","comment_length":61,"text":"[Feature request] Support for external modality for language datasets \n # Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it! \n Thanks a lot, @aleSuglia for the very detailed and introductive feature request.\r\nIt seems like we could build something pretty useful here indeed.\r\n\r\nOne of the questions here is that Arrow doesn't have built-in support for generic \"tensors\" in records but there might be ways to do that in a clean way. We'll probably try to tackle this during the summer.","embeddings":[0.0000579982,-0.1425000429,-0.1048433781,-0.1137593612,-0.0208638422,-0.2757304013,0.3570465446,0.1619891822,-0.1701952517,-0.115410395,-0.1062394306,0.3741975129,-0.2327695489,0.0247858353,0.2276581675,-0.1631375402,0.1156109571,0.0206510182,0.1751564294,0.0377422199,-0.0802958757,-0.1862993091,-0.0996891856,0.0158086233,-0.1543438584,-0.0065530962,-0.2325278074,-0.0663946643,0.0174453668,-0.1567316949,-0.4168612659,0.1775110662,-0.0064961114,-0.0587129258,-0.000105389,0.0410651825,0.0508660674,-0.1544797868,-0.0636066273,-0.2922307551,-0.1718059927,-0.5856842995,-0.0051841163,-0.2138669938,0.1689768434,-0.2611020803,0.303366214,-0.0154541899,0.219957605,0.042448286,0.2338732183,0.1619654,0.0570976958,0.3144838214,0.1790788174,0.7218409181,-0.1756500155,-0.0262282193,0.3759553134,-0.1980069876,-0.1714316756,0.5319548249,0.0707272217,-0.1726881266,0.1047964245,-0.1554219872,-0.0742541477,-0.1862260252,-0.182682246,0.26961869,0.5540597439,-0.0946797803,-0.3956744969,-0.2771704197,0.101535894,0.2120435387,-0.1491226256,0.4695759714,-0.084704265,0.2618876398,-0.1411739141,-0.4172467589,-0.2269273251,0.4005026221,-0.0107411835,-0.1144736856,0.1139273718,0.1064259782,0.1271232069,-0.1394746155,0.2405346036,-0.0852413252,0.1974173635,0.1384702623,0.1199183315,-0.147744298,0.092940703,-0.0756618828,0.1010376588,-0.1379873306,0.0141931195,0.1652175933,-0.6861980557,0.3720004857,0.1384753585,-0.3171008229,-0.3466371894,0.1551730186,0.298522681,-0.0579590537,-0.0268653706,-0.0378671661,0.0169135183,0.0440881662,-0.3268453181,0.0000947688,0.1069344431,-0.0153924404,-0.0973811075,-0.292539537,-0.2431646138,0.0581131242,-0.0261005349,-0.0754403844,0.2316321433,0.295160681,-0.0103263939,0.2380757183,0.0051021767,-0.6885832548,0.0688787028,0.0822422132,-0.4507437944,0.112957038,0.0196862388,0.5769885778,0.1056848541,0.0224712547,0.0795072168,-0.0589267202,0.1154198125,-0.3274696171,0.0944871306,0.0146099059,-0.3158082962,-0.282207936,-0.0010033672,0.2286927402,-0.193028748,-0.0952018723,0.0137523692,-0.2000210136,-0.1908557564,0.121775277,-0.0717911124,0.0397867337,0.3123777509,0.9068838954,-0.3320865631,-0.4840673804,-0.0372311957,-0.0365264602,-0.324636817,-0.1530091465,-0.1083315238,0.2535992861,-0.4206463397,-0.1457893252,0.0214555152,-0.0782916471,-0.0730273351,0.2533953488,-0.3021765351,-0.0619365349,0.0733146667,-0.1439300179,0.422486335,-0.2967081666,-0.2142054886,0.1942324638,0.0423240773,0.2526317537,-0.0438900702,0.3999971449,-0.0188864972,-0.1381499916,0.1212410256,0.7113153338,-0.150968194,0.0519453287,-0.1938297302,-0.3600857258,0.2658928335,0.4229554236,0.0130498447,-0.3205803931,0.6397942305,-0.000615358,0.1015567109,-0.2774530053,0.1214610413,-0.193411395,0.2937177122,-0.0562877953,-0.1427305341,-0.2755998075,-0.2272177041,0.0019212904,-0.2704661489,0.2816795111,-0.1663463861,-0.0009555613,0.208978802,-0.1393232346,0.0956419855,0.0041683582,0.2037206292,0.1011403576,-0.4169175923,0.2500483096,-0.0902082399,0.0251920149,-0.0975428373,0.038838096,0.1815153807,0.2594423592,0.0138022294,0.1161562577,0.0984601974,0.5246058702,-0.2238256782,0.0367368646,0.0321959108,-0.0006297429,0.1992013603,0.480219692,-0.0132267922,0.3972020745,0.480912894,-0.6762456298,0.3488892317,-0.1610369831,0.2448391616,-0.1503302455,-0.042594105,0.2094171345,0.248840332,0.0853460506,-0.0793127269,0.0688090697,0.0853889957,0.0471753962,-0.3372054994,-0.1611867249,-0.0550510958,0.194349274,0.0049326029,0.03765646,-0.3816787601,0.1459192485,0.534516871,0.2316180319,0.179944545,0.1668641865,-0.2088233531,-0.3059071302,0.2450826764,-0.2178815007,0.0730601624,0.2838123739,-0.2241439521,0.0624792688,-0.212926656,0.0168988183,0.1520752311,0.06433817,0.4003802836,0.0581446253,0.379850626,0.0560440756,-0.0656488761,-0.4201531112,-0.0221136883,-0.2725446224,0.1746994704,-0.0094886972,-0.189365685,-0.6152865887,-0.1843091547,-0.0398294479,-0.445258677,-0.0595044978,0.0666307211,-0.2852560878,-0.1706150472,0.0751235336,0.0053999494,0.378551811,-0.3618397117,-0.0995696262,0.1389790475,-0.260455668,-0.1546472907,0.0843451992,0.4543462694,0.0610960312,0.2402535081,0.2454445511,-0.0476959907,-0.133087188,-0.4327585399,0.2702728808,0.0578765273,0.1886128038,-0.002010792,-0.5663678646,0.0909249336,-0.0657481998,0.2020861208,0.0889275745,-0.0042456607,-0.0664625615,-0.0416938066,0.1243292913,-0.2534283996,0.0937701836,-0.4923431575,-0.5119807124,0.3841014206,-0.1436410397,0.1656590104,0.5681470633,0.0995381847,-0.0953869373,-0.0172695182,0.2712105215,0.0300444029,0.3557389677,-0.0596465357,-0.000171215,-0.1874789894,-0.2881140709,-0.2174292356,0.1192458197,0.3493945003,-0.3474397063,-0.3242609501,0.0953799263,0.156168282,0.0391207188,0.0027884229,0.0905975923,0.0016481931,-0.0056026275,-0.0388501734,0.0010586765,-0.0685858503,0.3693791628,0.1696063131,0.3322416246,0.010456631,0.4449982047,0.3984428048,-0.0735823065,-0.1130097508,0.4104923606,0.341801554,0.2031654716,-0.1458218843,-0.2318649739,0.461813271,-0.099810183,-0.0304083619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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/263","title":"[Feature request] Support for external modality for language datasets","comments":"I was looking into Facebook MMF and apparently they decided to use LMDB to store additional features associated with every example: https:\/\/github.com\/facebookresearch\/mmf\/blob\/master\/mmf\/datasets\/databases\/features_database.py\r\n\r\n","body":"# Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it!","comment_length":22,"text":"[Feature request] Support for external modality for language datasets \n # Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it! \n I was looking into Facebook MMF and apparently they decided to use LMDB to store additional features associated with every example: https:\/\/github.com\/facebookresearch\/mmf\/blob\/master\/mmf\/datasets\/databases\/features_database.py\r\n\r\n","embeddings":[0.0000579982,-0.1425000429,-0.1048433781,-0.1137593612,-0.0208638422,-0.2757304013,0.3570465446,0.1619891822,-0.1701952517,-0.115410395,-0.1062394306,0.3741975129,-0.2327695489,0.0247858353,0.2276581675,-0.1631375402,0.1156109571,0.0206510182,0.1751564294,0.0377422199,-0.0802958757,-0.1862993091,-0.0996891856,0.0158086233,-0.1543438584,-0.0065530962,-0.2325278074,-0.0663946643,0.0174453668,-0.1567316949,-0.4168612659,0.1775110662,-0.0064961114,-0.0587129258,-0.000105389,0.0410651825,0.0508660674,-0.1544797868,-0.0636066273,-0.2922307551,-0.1718059927,-0.5856842995,-0.0051841163,-0.2138669938,0.1689768434,-0.2611020803,0.303366214,-0.0154541899,0.219957605,0.042448286,0.2338732183,0.1619654,0.0570976958,0.3144838214,0.1790788174,0.7218409181,-0.1756500155,-0.0262282193,0.3759553134,-0.1980069876,-0.1714316756,0.5319548249,0.0707272217,-0.1726881266,0.1047964245,-0.1554219872,-0.0742541477,-0.1862260252,-0.182682246,0.26961869,0.5540597439,-0.0946797803,-0.3956744969,-0.2771704197,0.101535894,0.2120435387,-0.1491226256,0.4695759714,-0.084704265,0.2618876398,-0.1411739141,-0.4172467589,-0.2269273251,0.4005026221,-0.0107411835,-0.1144736856,0.1139273718,0.1064259782,0.1271232069,-0.1394746155,0.2405346036,-0.0852413252,0.1974173635,0.1384702623,0.1199183315,-0.147744298,0.092940703,-0.0756618828,0.1010376588,-0.1379873306,0.0141931195,0.1652175933,-0.6861980557,0.3720004857,0.1384753585,-0.3171008229,-0.3466371894,0.1551730186,0.298522681,-0.0579590537,-0.0268653706,-0.0378671661,0.0169135183,0.0440881662,-0.3268453181,0.0000947688,0.1069344431,-0.0153924404,-0.0973811075,-0.292539537,-0.2431646138,0.0581131242,-0.0261005349,-0.0754403844,0.2316321433,0.295160681,-0.0103263939,0.2380757183,0.0051021767,-0.6885832548,0.0688787028,0.0822422132,-0.4507437944,0.112957038,0.0196862388,0.5769885778,0.1056848541,0.0224712547,0.0795072168,-0.0589267202,0.1154198125,-0.3274696171,0.0944871306,0.0146099059,-0.3158082962,-0.282207936,-0.0010033672,0.2286927402,-0.193028748,-0.0952018723,0.0137523692,-0.2000210136,-0.1908557564,0.121775277,-0.0717911124,0.0397867337,0.3123777509,0.9068838954,-0.3320865631,-0.4840673804,-0.0372311957,-0.0365264602,-0.324636817,-0.1530091465,-0.1083315238,0.2535992861,-0.4206463397,-0.1457893252,0.0214555152,-0.0782916471,-0.0730273351,0.2533953488,-0.3021765351,-0.0619365349,0.0733146667,-0.1439300179,0.422486335,-0.2967081666,-0.2142054886,0.1942324638,0.0423240773,0.2526317537,-0.0438900702,0.3999971449,-0.0188864972,-0.1381499916,0.1212410256,0.7113153338,-0.150968194,0.0519453287,-0.1938297302,-0.3600857258,0.2658928335,0.4229554236,0.0130498447,-0.3205803931,0.6397942305,-0.000615358,0.1015567109,-0.2774530053,0.1214610413,-0.193411395,0.2937177122,-0.0562877953,-0.1427305341,-0.2755998075,-0.2272177041,0.0019212904,-0.2704661489,0.2816795111,-0.1663463861,-0.0009555613,0.208978802,-0.1393232346,0.0956419855,0.0041683582,0.2037206292,0.1011403576,-0.4169175923,0.2500483096,-0.0902082399,0.0251920149,-0.0975428373,0.038838096,0.1815153807,0.2594423592,0.0138022294,0.1161562577,0.0984601974,0.5246058702,-0.2238256782,0.0367368646,0.0321959108,-0.0006297429,0.1992013603,0.480219692,-0.0132267922,0.3972020745,0.480912894,-0.6762456298,0.3488892317,-0.1610369831,0.2448391616,-0.1503302455,-0.042594105,0.2094171345,0.248840332,0.0853460506,-0.0793127269,0.0688090697,0.0853889957,0.0471753962,-0.3372054994,-0.1611867249,-0.0550510958,0.194349274,0.0049326029,0.03765646,-0.3816787601,0.1459192485,0.534516871,0.2316180319,0.179944545,0.1668641865,-0.2088233531,-0.3059071302,0.2450826764,-0.2178815007,0.0730601624,0.2838123739,-0.2241439521,0.0624792688,-0.212926656,0.0168988183,0.1520752311,0.06433817,0.4003802836,0.0581446253,0.379850626,0.0560440756,-0.0656488761,-0.4201531112,-0.0221136883,-0.2725446224,0.1746994704,-0.0094886972,-0.189365685,-0.6152865887,-0.1843091547,-0.0398294479,-0.445258677,-0.0595044978,0.0666307211,-0.2852560878,-0.1706150472,0.0751235336,0.0053999494,0.378551811,-0.3618397117,-0.0995696262,0.1389790475,-0.260455668,-0.1546472907,0.0843451992,0.4543462694,0.0610960312,0.2402535081,0.2454445511,-0.0476959907,-0.133087188,-0.4327585399,0.2702728808,0.0578765273,0.1886128038,-0.002010792,-0.5663678646,0.0909249336,-0.0657481998,0.2020861208,0.0889275745,-0.0042456607,-0.0664625615,-0.0416938066,0.1243292913,-0.2534283996,0.0937701836,-0.4923431575,-0.5119807124,0.3841014206,-0.1436410397,0.1656590104,0.5681470633,0.0995381847,-0.0953869373,-0.0172695182,0.2712105215,0.0300444029,0.3557389677,-0.0596465357,-0.000171215,-0.1874789894,-0.2881140709,-0.2174292356,0.1192458197,0.3493945003,-0.3474397063,-0.3242609501,0.0953799263,0.156168282,0.0391207188,0.0027884229,0.0905975923,0.0016481931,-0.0056026275,-0.0388501734,0.0010586765,-0.0685858503,0.3693791628,0.1696063131,0.3322416246,0.010456631,0.4449982047,0.3984428048,-0.0735823065,-0.113009750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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/263","title":"[Feature request] Support for external modality for language datasets","comments":"I saw the Mozilla common_voice dataset in model hub, which has mp3 audio recordings as part it. It's use predominantly maybe in ASR and TTS, but dataset is a Language + Voice Dataset similar to @aleSuglia's point about Language + Vision. \r\n\r\nhttps:\/\/huggingface.co\/datasets\/common_voice","body":"# Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it!","comment_length":42,"text":"[Feature request] Support for external modality for language datasets \n # Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it! \n I saw the Mozilla common_voice dataset in model hub, which has mp3 audio recordings as part it. It's use predominantly maybe in ASR and TTS, but dataset is a Language + Voice Dataset similar to @aleSuglia's point about Language + Vision. \r\n\r\nhttps:\/\/huggingface.co\/datasets\/common_voice","embeddings":[0.0000579982,-0.1425000429,-0.1048433781,-0.1137593612,-0.0208638422,-0.2757304013,0.3570465446,0.1619891822,-0.1701952517,-0.115410395,-0.1062394306,0.3741975129,-0.2327695489,0.0247858353,0.2276581675,-0.1631375402,0.1156109571,0.0206510182,0.1751564294,0.0377422199,-0.0802958757,-0.1862993091,-0.0996891856,0.0158086233,-0.1543438584,-0.0065530962,-0.2325278074,-0.0663946643,0.0174453668,-0.1567316949,-0.4168612659,0.1775110662,-0.0064961114,-0.0587129258,-0.000105389,0.0410651825,0.0508660674,-0.1544797868,-0.0636066273,-0.2922307551,-0.1718059927,-0.5856842995,-0.0051841163,-0.2138669938,0.1689768434,-0.2611020803,0.303366214,-0.0154541899,0.219957605,0.042448286,0.2338732183,0.1619654,0.0570976958,0.3144838214,0.1790788174,0.7218409181,-0.1756500155,-0.0262282193,0.3759553134,-0.1980069876,-0.1714316756,0.5319548249,0.0707272217,-0.1726881266,0.1047964245,-0.1554219872,-0.0742541477,-0.1862260252,-0.182682246,0.26961869,0.5540597439,-0.0946797803,-0.3956744969,-0.2771704197,0.101535894,0.2120435387,-0.1491226256,0.4695759714,-0.084704265,0.2618876398,-0.1411739141,-0.4172467589,-0.2269273251,0.4005026221,-0.0107411835,-0.1144736856,0.1139273718,0.1064259782,0.1271232069,-0.1394746155,0.2405346036,-0.0852413252,0.1974173635,0.1384702623,0.1199183315,-0.147744298,0.092940703,-0.0756618828,0.1010376588,-0.1379873306,0.0141931195,0.1652175933,-0.6861980557,0.3720004857,0.1384753585,-0.3171008229,-0.3466371894,0.1551730186,0.298522681,-0.0579590537,-0.0268653706,-0.0378671661,0.0169135183,0.0440881662,-0.3268453181,0.0000947688,0.1069344431,-0.0153924404,-0.0973811075,-0.292539537,-0.2431646138,0.0581131242,-0.0261005349,-0.0754403844,0.2316321433,0.295160681,-0.0103263939,0.2380757183,0.0051021767,-0.6885832548,0.0688787028,0.0822422132,-0.4507437944,0.112957038,0.0196862388,0.5769885778,0.1056848541,0.0224712547,0.0795072168,-0.0589267202,0.1154198125,-0.3274696171,0.0944871306,0.0146099059,-0.3158082962,-0.282207936,-0.0010033672,0.2286927402,-0.193028748,-0.0952018723,0.0137523692,-0.2000210136,-0.1908557564,0.121775277,-0.0717911124,0.0397867337,0.3123777509,0.9068838954,-0.3320865631,-0.4840673804,-0.0372311957,-0.0365264602,-0.324636817,-0.1530091465,-0.1083315238,0.2535992861,-0.4206463397,-0.1457893252,0.0214555152,-0.0782916471,-0.0730273351,0.2533953488,-0.3021765351,-0.0619365349,0.0733146667,-0.1439300179,0.422486335,-0.2967081666,-0.2142054886,0.1942324638,0.0423240773,0.2526317537,-0.0438900702,0.3999971449,-0.0188864972,-0.1381499916,0.1212410256,0.7113153338,-0.150968194,0.0519453287,-0.1938297302,-0.3600857258,0.2658928335,0.4229554236,0.0130498447,-0.3205803931,0.6397942305,-0.000615358,0.1015567109,-0.2774530053,0.1214610413,-0.193411395,0.2937177122,-0.0562877953,-0.1427305341,-0.2755998075,-0.2272177041,0.0019212904,-0.2704661489,0.2816795111,-0.1663463861,-0.0009555613,0.208978802,-0.1393232346,0.0956419855,0.0041683582,0.2037206292,0.1011403576,-0.4169175923,0.2500483096,-0.0902082399,0.0251920149,-0.0975428373,0.038838096,0.1815153807,0.2594423592,0.0138022294,0.1161562577,0.0984601974,0.5246058702,-0.2238256782,0.0367368646,0.0321959108,-0.0006297429,0.1992013603,0.480219692,-0.0132267922,0.3972020745,0.480912894,-0.6762456298,0.3488892317,-0.1610369831,0.2448391616,-0.1503302455,-0.042594105,0.2094171345,0.248840332,0.0853460506,-0.0793127269,0.0688090697,0.0853889957,0.0471753962,-0.3372054994,-0.1611867249,-0.0550510958,0.194349274,0.0049326029,0.03765646,-0.3816787601,0.1459192485,0.534516871,0.2316180319,0.179944545,0.1668641865,-0.2088233531,-0.3059071302,0.2450826764,-0.2178815007,0.0730601624,0.2838123739,-0.2241439521,0.0624792688,-0.212926656,0.0168988183,0.1520752311,0.06433817,0.4003802836,0.0581446253,0.379850626,0.0560440756,-0.0656488761,-0.4201531112,-0.0221136883,-0.2725446224,0.1746994704,-0.0094886972,-0.189365685,-0.6152865887,-0.1843091547,-0.0398294479,-0.445258677,-0.0595044978,0.0666307211,-0.2852560878,-0.1706150472,0.0751235336,0.0053999494,0.378551811,-0.3618397117,-0.0995696262,0.1389790475,-0.260455668,-0.1546472907,0.0843451992,0.4543462694,0.0610960312,0.2402535081,0.2454445511,-0.0476959907,-0.133087188,-0.4327585399,0.2702728808,0.0578765273,0.1886128038,-0.002010792,-0.5663678646,0.0909249336,-0.0657481998,0.2020861208,0.0889275745,-0.0042456607,-0.0664625615,-0.0416938066,0.1243292913,-0.2534283996,0.0937701836,-0.4923431575,-0.5119807124,0.3841014206,-0.1436410397,0.1656590104,0.5681470633,0.0995381847,-0.0953869373,-0.0172695182,0.2712105215,0.0300444029,0.3557389677,-0.0596465357,-0.000171215,-0.1874789894,-0.2881140709,-0.2174292356,0.1192458197,0.3493945003,-0.3474397063,-0.3242609501,0.0953799263,0.156168282,0.0391207188,0.0027884229,0.0905975923,0.0016481931,-0.0056026275,-0.0388501734,0.0010586765,-0.0685858503,0.3693791628,0.1696063131,0.3322416246,0.010456631,0.4449982047,0.3984428048,-0.0735823065,-0.1130097508,0.4104923606,0.341801554,0.2031654716,-0.1458218843,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/263","title":"[Feature request] Support for external modality for language datasets","comments":"Hey @thomwolf, are there any updates on this? I would love to contribute if possible!\r\n\r\nThanks, \r\nAlessandro ","body":"# Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it!","comment_length":17,"text":"[Feature request] Support for external modality for language datasets \n # Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it! \n Hey @thomwolf, are there any updates on this? I would love to contribute if possible!\r\n\r\nThanks, \r\nAlessandro 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/263","title":"[Feature request] Support for external modality for language datasets","comments":"Hi @aleSuglia :) In today's new release 1.17 of `datasets` we introduce a new feature type `Image` that allows to store images directly in a dataset, next to text features and labels for example. There is also an `Audio` feature type, for datasets containing audio data. For tensors there are `Array2D`, `Array3D`, etc. feature types\r\n\r\nNote that both Image and Audio feature types take care of decoding the images\/audio data if needed. The returned images are PIL images, and the audio signals are decoded as numpy arrays.\r\n\r\nAnd `datasets` also leverage end-to-end zero copy from the arrow data for all of them, for maximum speed :)","body":"# Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it!","comment_length":106,"text":"[Feature request] Support for external modality for language datasets \n # Background\r\n\r\nIn recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to \"learn to speak by listening to the radio\" [[E. Bender and A. Koller,2020](https:\/\/openreview.net\/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https:\/\/arxiv.org\/abs\/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https:\/\/github.com\/huggingface\/nlp\/pull\/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data. \r\n\r\n# Language + Vision\r\n\r\n## Use case\r\nTypically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https:\/\/cs.stanford.edu\/people\/dorarad\/gqa\/download.html#seconddown) dataset.\r\n\r\nCurrently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https:\/\/arxiv.org\/abs\/1611.08481), [Shekhar et.al, 2019](https:\/\/www.aclweb.org\/anthology\/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https:\/\/arxiv.org\/abs\/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https:\/\/arxiv.org\/abs\/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use one of the following formats:\r\n1. [HD5F](https:\/\/pypi.org\/project\/h5py\/)\r\n2. [NumPy](https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.savez.html)\r\n3. [LMDB](https:\/\/lmdb.readthedocs.io\/en\/release\/)\r\n\r\n## Implementation considerations\r\n\r\nI was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:\r\n\r\n1. Download dataset\r\n2. Download images associated with the dataset\r\n3. Write a script that generates the visual features for every image and store them in a specific file\r\n4. Create a DataLoader that maps the visual features to the corresponding language example\r\n\r\nIn my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https:\/\/github.com\/pytorch\/pytorch\/issues\/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.\r\n\r\nFor ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array. \r\n\r\nLooking forward to hearing your thoughts about it! \n Hi @aleSuglia :) In today's new release 1.17 of `datasets` we introduce a new feature type `Image` that allows to store images directly in a dataset, next to text features and labels for example. There is also an `Audio` feature type, for datasets containing audio data. For tensors there are `Array2D`, `Array3D`, etc. feature types\r\n\r\nNote that both Image and Audio feature types take care of decoding the images\/audio data if needed. The returned images are PIL images, and the audio signals are decoded as numpy arrays.\r\n\r\nAnd `datasets` also leverage end-to-end zero copy from the arrow data for all of them, for maximum speed :)","embeddings":[0.0000579982,-0.1425000429,-0.1048433781,-0.1137593612,-0.0208638422,-0.2757304013,0.3570465446,0.1619891822,-0.1701952517,-0.115410395,-0.1062394306,0.3741975129,-0.2327695489,0.0247858353,0.2276581675,-0.1631375402,0.1156109571,0.0206510182,0.1751564294,0.0377422199,-0.0802958757,-0.1862993091,-0.0996891856,0.0158086233,-0.1543438584,-0.0065530962,-0.2325278074,-0.0663946643,0.0174453668,-0.1567316949,-0.4168612659,0.1775110662,-0.0064961114,-0.0587129258,-0.000105389,0.0410651825,0.0508660674,-0.1544797868,-0.0636066273,-0.2922307551,-0.1718059927,-0.5856842995,-0.0051841163,-0.2138669938,0.1689768434,-0.2611020803,0.303366214,-0.0154541899,0.219957605,0.042448286,0.2338732183,0.1619654,0.0570976958,0.3144838214,0.1790788174,0.7218409181,-0.1756500155,-0.0262282193,0.3759553134,-0.1980069876,-0.1714316756,0.5319548249,0.0707272217,-0.1726881266,0.1047964245,-0.1554219872,-0.0742541477,-0.1862260252,-0.182682246,0.26961869,0.5540597439,-0.0946797803,-0.3956744969,-0.2771704197,0.101535894,0.2120435387,-0.1491226256,0.4695759714,-0.084704265,0.2618876398,-0.1411739141,-0.4172467589,-0.2269273251,0.4005026221,-0.0107411835,-0.1144736856,0.1139273718,0.1064259782,0.1271232069,-0.1394746155,0.2405346036,-0.0852413252,0.1974173635,0.1384702623,0.1199183315,-0.147744298,0.092940703,-0.0756618828,0.1010376588,-0.1379873306,0.0141931195,0.1652175933,-0.6861980557,0.3720004857,0.1384753585,-0.3171008229,-0.3466371894,0.1551730186,0.298522681,-0.0579590537,-0.0268653706,-0.0378671661,0.0169135183,0.0440881662,-0.3268453181,0.0000947688,0.1069344431,-0.0153924404,-0.0973811075,-0.292539537,-0.2431646138,0.0581131242,-0.0261005349,-0.0754403844,0.2316321433,0.295160681,-0.0103263939,0.2380757183,0.0051021767,-0.6885832548,0.0688787028,0.0822422132,-0.4507437944,0.112957038,0.0196862388,0.5769885778,0.1056848541,0.0224712547,0.0795072168,-0.0589267202,0.1154198125,-0.3274696171,0.0944871306,0.0146099059,-0.3158082962,-0.282207936,-0.0010033672,0.2286927402,-0.193028748,-0.0952018723,0.0137523692,-0.2000210136,-0.1908557564,0.121775277,-0.0717911124,0.0397867337,0.3123777509,0.9068838954,-0.3320865631,-0.4840673804,-0.0372311957,-0.0365264602,-0.324636817,-0.1530091465,-0.1083315238,0.2535992861,-0.4206463397,-0.1457893252,0.0214555152,-0.0782916471,-0.0730273351,0.2533953488,-0.3021765351,-0.0619365349,0.0733146667,-0.1439300179,0.422486335,-0.2967081666,-0.2142054886,0.1942324638,0.0423240773,0.2526317537,-0.0438900702,0.3999971449,-0.0188864972,-0.1381499916,0.1212410256,0.7113153338,-0.150968194,0.0519453287,-0.1938297302,-0.3600857258,0.2658928335,0.4229554236,0.0130498447,-0.3205803931,0.6397942305,-0.000615358,0.1015567109,-0.2774530053,0.1214610413,-0.193411395,0.2937177122,-0.0562877953,-0.1427305341,-0.2755998075,-0.2272177041,0.0019212904,-0.2704661489,0.2816795111,-0.1663463861,-0.0009555613,0.208978802,-0.1393232346,0.0956419855,0.0041683582,0.2037206292,0.1011403576,-0.4169175923,0.2500483096,-0.0902082399,0.0251920149,-0.0975428373,0.038838096,0.1815153807,0.2594423592,0.0138022294,0.1161562577,0.0984601974,0.5246058702,-0.2238256782,0.0367368646,0.0321959108,-0.0006297429,0.1992013603,0.480219692,-0.0132267922,0.3972020745,0.480912894,-0.6762456298,0.3488892317,-0.1610369831,0.2448391616,-0.1503302455,-0.042594105,0.2094171345,0.248840332,0.0853460506,-0.0793127269,0.0688090697,0.0853889957,0.0471753962,-0.3372054994,-0.1611867249,-0.0550510958,0.194349274,0.0049326029,0.03765646,-0.3816787601,0.1459192485,0.534516871,0.2316180319,0.179944545,0.1668641865,-0.2088233531,-0.3059071302,0.2450826764,-0.2178815007,0.0730601624,0.2838123739,-0.2241439521,0.0624792688,-0.212926656,0.0168988183,0.1520752311,0.06433817,0.4003802836,0.0581446253,0.379850626,0.0560440756,-0.0656488761,-0.4201531112,-0.0221136883,-0.2725446224,0.1746994704,-0.0094886972,-0.189365685,-0.6152865887,-0.1843091547,-0.0398294479,-0.445258677,-0.0595044978,0.0666307211,-0.2852560878,-0.1706150472,0.0751235336,0.0053999494,0.378551811,-0.3618397117,-0.0995696262,0.1389790475,-0.260455668,-0.1546472907,0.0843451992,0.4543462694,0.0610960312,0.2402535081,0.2454445511,-0.0476959907,-0.133087188,-0.4327585399,0.2702728808,0.0578765273,0.1886128038,-0.002010792,-0.5663678646,0.0909249336,-0.0657481998,0.2020861208,0.0889275745,-0.0042456607,-0.0664625615,-0.0416938066,0.1243292913,-0.2534283996,0.0937701836,-0.4923431575,-0.5119807124,0.3841014206,-0.1436410397,0.1656590104,0.5681470633,0.0995381847,-0.0953869373,-0.0172695182,0.2712105215,0.0300444029,0.3557389677,-0.0596465357,-0.000171215,-0.1874789894,-0.2881140709,-0.2174292356,0.1192458197,0.3493945003,-0.3474397063,-0.3242609501,0.0953799263,0.156168282,0.0391207188,0.0027884229,0.0905975923,0.0016481931,-0.0056026275,-0.0388501734,0.0010586765,-0.0685858503,0.3693791628,0.1696063131,0.3322416246,0.010456631,0.4449982047,0.3984428048,-0.0735823065,-0.1130097508,0.4104923606,0.341801554,0.2031654716,-0.1458218843,-0.2318649739,0.461813271,-0.099810183,-0.0304083619,0.28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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/261","title":"Downloading dataset error with pyarrow.lib.RecordBatch","comments":"When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n  [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.\r\nIf you don't restart, then it breaks like in your message.","body":"I am trying to download `sentiment140` and I have the following error\r\n\r\n```\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    418                 verify_infos = not save_infos and not ignore_verifications\r\n    419                 self._download_and_prepare(\r\n--> 420                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    421                 )\r\n    422                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    472             try:\r\n    473                 # Prepare split will record examples associated to the split\r\n--> 474                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    475             except OSError:\r\n    476                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    652         for key, record in utils.tqdm(generator, unit=\" examples\", total=split_info.num_examples, leave=False):\r\n    653             example = self.info.features.encode_example(record)\r\n--> 654             writer.write(example)\r\n    655         num_examples, num_bytes = writer.finalize()\r\n    656 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write(self, example, writer_batch_size)\r\n    143             self._build_writer(pa_table=pa.Table.from_pydict(example))\r\n    144         if writer_batch_size is not None and len(self.current_rows) >= writer_batch_size:\r\n--> 145             self.write_on_file()\r\n    146 \r\n    147     def write_batch(\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_on_file(self)\r\n    127             else:\r\n    128                 # All good\r\n--> 129                 self._write_array_on_file(pa_array)\r\n    130             self.current_rows = []\r\n    131 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in _write_array_on_file(self, pa_array)\r\n     96     def _write_array_on_file(self, pa_array):\r\n     97         \"\"\"Write a PyArrow Array\"\"\"\r\n---> 98         pa_batch = pa.RecordBatch.from_struct_array(pa_array)\r\n     99         self._num_bytes += pa_array.nbytes\r\n    100         self.pa_writer.write_batch(pa_batch)\r\n\r\nAttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'\r\n```\r\n\r\nI installed the last version and ran the following command:\r\n\r\n```python\r\nimport nlp\r\nsentiment140 = nlp.load_dataset('sentiment140', cache_dir='\/content')\r\n```","comment_length":77,"text":"Downloading dataset error with pyarrow.lib.RecordBatch \n I am trying to download `sentiment140` and I have the following error\r\n\r\n```\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    418                 verify_infos = not save_infos and not ignore_verifications\r\n    419                 self._download_and_prepare(\r\n--> 420                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    421                 )\r\n    422                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    472             try:\r\n    473                 # Prepare split will record examples associated to the split\r\n--> 474                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    475             except OSError:\r\n    476                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    652         for key, record in utils.tqdm(generator, unit=\" examples\", total=split_info.num_examples, leave=False):\r\n    653             example = self.info.features.encode_example(record)\r\n--> 654             writer.write(example)\r\n    655         num_examples, num_bytes = writer.finalize()\r\n    656 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write(self, example, writer_batch_size)\r\n    143             self._build_writer(pa_table=pa.Table.from_pydict(example))\r\n    144         if writer_batch_size is not None and len(self.current_rows) >= writer_batch_size:\r\n--> 145             self.write_on_file()\r\n    146 \r\n    147     def write_batch(\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_on_file(self)\r\n    127             else:\r\n    128                 # All good\r\n--> 129                 self._write_array_on_file(pa_array)\r\n    130             self.current_rows = []\r\n    131 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in _write_array_on_file(self, pa_array)\r\n     96     def _write_array_on_file(self, pa_array):\r\n     97         \"\"\"Write a PyArrow Array\"\"\"\r\n---> 98         pa_batch = pa.RecordBatch.from_struct_array(pa_array)\r\n     99         self._num_bytes += pa_array.nbytes\r\n    100         self.pa_writer.write_batch(pa_batch)\r\n\r\nAttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'\r\n```\r\n\r\nI installed the last version and ran the following command:\r\n\r\n```python\r\nimport nlp\r\nsentiment140 = nlp.load_dataset('sentiment140', cache_dir='\/content')\r\n``` \n When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n  [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.\r\nIf you don't restart, then it breaks like in your message.","embeddings":[-0.4892075658,0.3927338719,-0.0271265376,0.1367761344,0.1485611051,0.1173222139,0.2704391778,0.5356116295,0.0611658767,-0.1925094575,-0.1285087019,-0.0726970136,-0.0604599528,0.0435563102,0.1996953934,-0.3966560066,-0.0350689851,0.3276588619,0.1510497481,0.1637248844,-0.172482878,0.1492370367,-0.3593584895,0.1444754452,-0.3186692894,-0.3128395081,-0.1183318347,0.1084738076,-0.3839229643,-0.4500899911,0.3358423114,0.0578327812,0.3932600319,0.2650594115,-0.000117448,0.1116172746,0.1905764639,-0.1977074444,-0.4337591827,-0.4006903768,-0.0441719815,-0.230258882,0.0015386335,-0.2227816284,0.1908577085,-0.4440507293,0.1007414684,0.2061419487,0.2961962521,0.3937441409,0.1861062348,0.3695085943,0.3249904215,-0.1696384549,0.4855354428,0.1941499412,-0.0995773673,0.4899009764,0.0804633275,-0.1694681495,-0.0587596558,0.1225319132,-0.2555664182,0.3563007116,-0.0102964174,0.1473158002,0.2881261706,-0.1210029796,0.1599364132,0.1933370084,0.6528946161,-0.3635427058,-0.3038655221,0.0054645441,0.2109954208,-0.352116853,0.037499506,0.443918258,-0.5277152061,0.1056669205,-0.3487102091,-0.096262902,-0.0807079524,0.3184595406,-0.0237489827,-0.034094654,-0.0054722689,0.1258220822,0.0778660998,0.0793051571,0.4359017909,0.0207992177,-0.1023334712,0.1800541282,-0.2871754766,0.109818086,-0.1221947819,0.2700802386,0.2193382233,-0.0537812561,0.1338996291,-0.1652329117,0.2458812594,0.1196235791,0.1868953705,0.2888101339,-0.1429705471,-0.2260627449,0.0829186216,0.0914286673,0.184903115,-0.0814399645,-0.0812835768,-0.3262293637,0.2995933294,-0.0103330687,0.2676487863,-0.1157412603,-0.4714718759,-0.0896422118,-0.2914097011,-0.1754256636,0.0908308774,0.3734006584,-0.1583016962,-0.0375185944,0.0479477458,0.2270958871,-0.1774018407,-0.1854207963,-0.1569811702,0.0567496531,-0.2840721011,-0.1020767167,0.3571476638,0.1962182224,0.4431747198,0.0573271587,-0.0920140594,0.0906986073,0.1681345105,-0.2038195282,0.0099528041,0.4029393792,0.1610419899,-0.0081352834,0.05793266,-0.0021745807,-0.0843068138,0.226747632,0.0306001659,-0.3220888972,-0.199762851,0.1254594326,-0.2575152516,-0.0970946923,-0.293526113,-0.1229139864,0.0695314184,-0.2298915684,-0.165683642,-0.2550082207,-0.0134389959,-0.3429088891,0.2889151871,0.1961348653,-0.5239393115,-0.0606309958,-0.2506811321,-0.1861683428,0.3700190187,0.1299467832,-0.3432075083,0.563421011,-0.2346496284,0.3071200252,0.6222015023,-0.287848711,-0.7235483527,0.2745607197,-0.1867569089,-0.090546079,0.001762665,0.2890398502,0.286121875,-0.0463762544,0.0073553002,0.2426165342,-0.0895427242,0.0145119717,-0.209961623,-0.1354868859,0.2433937788,0.2387472242,-0.0977547318,0.1114543006,0.0455193296,0.2303031832,0.4403785169,0.0453526527,0.2413324118,0.0603364222,0.1927467883,-0.0562493317,-0.0980714411,-0.0956363752,-0.4647476673,0.057584241,-0.4637873173,-0.0198911894,-0.5254127383,-0.2377028763,-0.5607109666,0.070500955,-0.3272235394,-0.033638265,0.0952113345,0.0652716681,0.1929218471,-0.010928872,-0.0410111099,0.2827192545,0.1616543233,0.1452789456,0.1667373478,0.3377925754,-0.2518784404,-0.4513130486,0.2220562398,0.2295018733,0.2298679948,0.0118251415,-0.003103157,0.3668798506,-0.1924512088,-0.1402219832,-0.0555098988,-0.4057013094,0.1735838801,-0.2317463011,0.1499910355,-0.0521119349,0.14243415,0.0576541834,0.1917726099,0.0500125848,-0.1557822376,-0.0402871035,0.0167806037,0.2500864863,0.209070608,-0.0379022621,0.3518637717,0.1167308167,0.1862058342,-0.0336596109,0.3855115771,-0.1243394911,-0.0647965968,-0.059909761,0.4924831092,-0.1291579753,0.2608294189,0.0292297117,-0.1564101875,-0.1048164964,0.0698584393,-0.0398321673,0.477727145,0.2122194916,0.048465874,0.0114498651,-0.1163005158,-0.1007153019,0.1045714393,0.1227829307,0.1494815201,0.1262163073,0.122737743,-0.1358026266,-0.2230208069,-0.2798749208,0.2818372548,0.5004068017,-0.2372696996,-0.1715032756,-0.2075822502,-0.168447122,-0.2163604647,-0.3563213348,-0.0726947039,-0.320979923,0.0179656278,0.1279006004,0.084592469,0.1943110675,-0.0190217104,-0.1591059119,0.0522343367,-0.2081071585,-0.0398574248,-0.2335075438,-0.3417904973,0.0147319352,0.130126819,0.1167403236,0.363530755,-0.3642138243,0.0080448585,-0.3366523385,-0.0863478556,0.1599424332,-0.1055041999,0.1739455462,0.349922955,0.5634797812,-0.321973145,-0.1788387746,0.1370937079,-0.0134375338,-0.2322247028,0.1821423322,-0.0834413394,0.0898994505,-0.0191618372,-0.4091536999,-0.5689952374,-0.3764414191,0.1992646009,-0.1053888053,0.1356163621,0.1178278029,0.145246312,0.2287512273,-0.0664414391,0.1976379901,-0.0514190905,-0.2429492176,0.3138307333,-0.1990071535,-0.342282176,0.158926025,-0.0846386477,0.1505794525,0.3035141826,-0.5758484006,-0.0574271679,-0.0322524346,0.589515388,0.119772993,0.0387480259,0.3558401763,0.0227574892,-0.0657013655,0.009441861,-0.0545164831,0.0367188342,-0.2966586053,0.4122065902,-0.082840547,-0.0544182509,0.0060928394,0.8633522987,-0.0095525086,-0.0644375384,0.3446916342,-0.1457499564,0.0865022689,0.0207300466,-0.2893950045,-0.0176182128,-0.1351748407,0.245383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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/259","title":"documentation missing how to split a dataset","comments":"this seems to work for my specific problem:\r\n\r\n`self.train_ds, self.test_ds, self.val_ds = map(_prepare_ds, ('train', 'test[:25%]+test[50%:75%]', 'test[75%:]'))`","body":"I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n","comment_length":16,"text":"documentation missing how to split a dataset \n I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n \n this seems to work for my specific problem:\r\n\r\n`self.train_ds, self.test_ds, self.val_ds = map(_prepare_ds, ('train', 'test[:25%]+test[50%:75%]', 'test[75%:]'))`","embeddings":[-0.0186025947,0.0678199828,-0.0675335974,0.1767382026,-0.0039453311,0.1862957478,0.4544264674,0.3805294931,-0.0849740207,0.0096183019,0.157981649,0.2981640697,-0.1939596683,0.1171469241,0.1969036162,-0.3786970079,-0.1162521765,0.2196792066,0.2202848494,-0.0207722802,0.2683160305,-0.0878090486,-0.3013998568,0.1289382279,-0.3250898421,-0.4462779462,-0.162488386,-0.0560760982,-0.2404287905,-0.2245016843,0.2357390076,-0.2476490587,0.0229505431,0.2671960592,-0.0001169801,0.0858824998,0.2045546472,-0.1231242344,-0.0624568313,-0.2040258199,-0.3563699126,-0.0965228453,0.3687441051,-0.15157561,-0.0168993734,-0.2895177603,0.1156505421,-0.1246723235,0.5587252975,0.4705801606,0.0977748558,0.0605919696,0.1509509683,0.0747918859,-0.0116904005,0.3118744195,-0.1074403524,0.07410492,-0.1767975539,-0.1108882874,-0.1365486383,0.0705980286,0.1753382981,0.2046459615,0.0438751914,0.0106822299,-0.3863059282,-0.4449477196,-0.172561869,0.5125129819,0.5099626184,-0.3127263188,-0.3157458603,-0.343855232,0.0373566411,-0.2178646326,-0.1018407866,0.5787149072,-0.1215525493,0.1002738997,-0.1438938379,-0.1982794553,-0.3005862236,0.1699094027,0.0357606858,0.3928737938,-0.092836611,0.0900413319,0.2547083199,0.2649130523,0.2147781998,0.044595059,-0.0077961106,0.3059446216,-0.2638330758,-0.289809823,-0.1192446128,-0.4262012243,0.120447956,0.2507862151,0.13361305,-0.062240608,-0.197851792,0.1702111959,0.4436992109,0.234958902,0.1760654747,0.4938243032,0.0034347884,-0.0556002185,0.0040604332,-0.1536647081,-0.0026444367,-0.2552213371,-0.0048025316,0.1184806526,0.1445331126,0.0970536545,-0.2874823511,-0.3272711933,-0.3107089698,-0.1424574703,0.1399584413,0.1261718869,0.0506234169,-0.0749747679,-0.3134502769,0.2443236858,-0.3853572309,-0.1505621821,-0.1096616611,0.1972878575,-0.4032860398,0.1087486371,0.1017720103,0.0077641513,0.3967711926,-0.0983010009,0.0068024057,-0.3800592721,0.3235695064,-0.0060878438,0.2312981784,0.4229411781,0.1666384041,0.0244422127,0.0921589658,0.1239261255,-0.3356702328,-0.0331380926,-0.3759348691,-0.4590528309,-0.0085356832,0.0288570505,-0.011236514,0.2126413584,-0.2117210627,0.3877968788,0.0655360967,-0.0392750166,0.2040649354,0.0309319589,-0.1864057332,-0.3186094761,0.2620127499,-0.0355582573,-0.3765455484,-0.0840904489,-0.3531986773,-0.0105140554,-0.0375451483,0.1668779552,-0.3595998585,0.5219768882,-0.0765571743,0.4839746058,0.7543491125,-0.2417528182,-0.0982304886,0.2866485715,0.1120669767,-0.2819014192,-0.1141312718,-0.0469170697,0.2763130367,0.1434243768,-0.3279213309,0.4014891088,-0.3133448958,-0.1627570987,0.2922531962,-0.2228444666,0.237905249,-0.0628532991,-0.0785867423,-0.2706501186,0.2040354311,0.2698469758,0.4687471688,0.0151900779,0.1762865335,0.1804482639,0.0468781441,0.1226739883,-0.1034839973,-0.3599649668,-0.367416352,-0.0363817327,0.1209029034,-0.1167773381,0.0136490008,-0.2652081251,-0.1870301068,-0.1521000862,0.021210961,-0.2905018628,0.0500765219,-0.3772895634,0.1867760271,-0.102789849,-0.4393511117,0.0719605759,-0.1744220257,0.2400341481,-0.4450750351,0.1022045538,0.0919367373,-0.0668003112,0.0983601138,0.275967896,-0.0319311321,-0.113819167,0.0215031654,0.5539045334,0.5597566366,-0.0086867781,-0.2220508009,0.0216953121,0.4029690325,-0.4507803023,0.142492488,-0.0588942878,-0.0363886543,-0.0020125839,-0.4457806647,0.3606335819,-0.333240509,0.3146030605,0.1735803038,0.145176217,0.0354459733,-0.1915537566,-0.2770095468,-0.0292928629,-0.269133836,-0.3689307272,0.2141682059,-0.0282619838,-0.1507072747,0.0540156476,0.4456152022,-0.2017521709,0.1850414872,-0.1372690499,0.0644971356,-0.0147520527,0.164878726,0.4185732901,0.2900047898,-0.006122556,0.4006564617,-0.0790501311,-0.2734704912,-0.075665012,0.1154567376,0.2213155627,0.4420474768,-0.0329391249,-0.397331208,-0.086853683,-0.0056658448,0.0353841968,0.1722909659,0.0615746379,-0.2826004624,0.1121321395,-0.3698826134,-0.1115290821,-0.4540897906,-0.2572494745,-0.0854291394,-0.2304375172,-0.1461099237,-0.1568814069,-0.0309084263,-0.0247909334,-0.1584431678,-0.0758845955,-0.1544736028,0.1494784653,0.3911426365,-0.2639917731,-0.1655928046,0.0399479344,0.1162464842,0.595733285,0.2761847377,0.0691654757,-0.1688031852,0.1182705089,-0.2901183367,0.2202607691,-0.2356204689,0.008544609,0.2044394165,0.1658634245,0.0478423536,-0.2953163683,-0.2143628746,-0.1857382655,-0.1527206004,-0.0875526667,0.2334582359,0.0979503989,-0.1764697433,-0.7052025199,-0.2810895443,-0.1937929541,0.2698379159,-0.2516147494,0.2213246673,0.2111864388,0.1571695209,0.3712313473,0.107760489,-0.092757307,0.180675298,0.2621370554,0.0243270695,-0.4707919061,-0.2082182616,0.2600358427,-0.0280088428,0.2169561982,0.0294757113,-0.1711167097,0.1549678594,-0.1848180592,-0.0637829453,-0.0715663061,-0.021511456,0.2696675658,-0.1875339448,0.2374568582,-0.1606770456,0.1127752438,0.1508792937,-0.0787286758,0.1865571886,0.0295359045,0.3088374734,0.2784070075,0.7139962912,0.2629844844,-0.1093328744,-0.2752417028,-0.1456858069,0.0979027525,0.1112567931,-0.0715985149,0.00104605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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/259","title":"documentation missing how to split a dataset","comments":"Currently you can indeed split a dataset using `ds_test = nlp.load_dataset('imdb, split='test[:5000]')` (works also with percentages).\r\n\r\nHowever right now we don't have a way to shuffle a dataset but we are thinking about it in the discussion in #166. Feel free to share your thoughts about it.\r\n\r\nOne trick that you can do until we have a better solution is to shuffle and split the indices of your dataset:\r\n```python\r\nimport nlp\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nimdb = nlp.load_dataset('imbd', split='test')\r\ntest_indices, val_indices = train_test_split(range(len(imdb)))\r\n```\r\n\r\nand then to iterate each split:\r\n```python\r\nfor i in test_indices:\r\n    example = imdb[i]\r\n   ...\r\n```\r\n","body":"I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n","comment_length":101,"text":"documentation missing how to split a dataset \n I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n \n Currently you can indeed split a dataset using `ds_test = nlp.load_dataset('imdb, split='test[:5000]')` (works also with percentages).\r\n\r\nHowever right now we don't have a way to shuffle a dataset but we are thinking about it in the discussion in #166. Feel free to share your thoughts about it.\r\n\r\nOne trick that you can do until we have a better solution is to shuffle and split the indices of your dataset:\r\n```python\r\nimport nlp\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nimdb = nlp.load_dataset('imbd', split='test')\r\ntest_indices, val_indices = train_test_split(range(len(imdb)))\r\n```\r\n\r\nand then to iterate each split:\r\n```python\r\nfor i in test_indices:\r\n    example = imdb[i]\r\n   ...\r\n```\r\n","embeddings":[0.0600995272,0.059202835,-0.0615618974,0.1139700487,-0.0133908736,0.223149091,0.4341289997,0.3423463106,-0.0872614309,0.0904238746,0.112329334,0.3258261383,-0.2517769635,0.1168583408,0.1798712313,-0.3691290915,-0.066240415,0.1591636539,0.2443268448,0.0308177676,0.2214924395,-0.1523120105,-0.372136116,0.0719026774,-0.2917358577,-0.4570458829,-0.2463944703,-0.054137107,-0.2753708959,-0.236896053,0.1701683253,-0.0853734389,-0.0064511513,0.2993582189,-0.0001171174,-0.0006970303,0.221613571,-0.1150745973,-0.1520451605,-0.2749583423,-0.2659641504,0.0024026691,0.3048646152,-0.2019954473,0.0187097117,-0.2679358721,0.1041534394,-0.1138850152,0.5253756642,0.402502358,0.0973768905,-0.0187447518,0.0593397357,0.1293645203,0.0749443918,0.27727139,-0.0650620162,0.1716542989,-0.1823061854,-0.0956431925,-0.135114342,0.0488672815,0.1610796005,0.2262029648,0.1025451124,0.0239890199,-0.3529039025,-0.4566062391,-0.1725031585,0.5094365478,0.4698416889,-0.3314265609,-0.3592270613,-0.3861284554,0.1277313083,-0.2642101645,-0.1312361509,0.5653489828,-0.0973729268,-0.0550903566,-0.1545509845,-0.1297812462,-0.2560816407,0.1392175555,0.0853776261,0.5397906899,-0.032362543,0.0769093856,0.3139477372,0.204372853,0.4018697143,-0.0247702878,-0.0117319776,0.3584611714,-0.2561255097,-0.3121018112,-0.1303187609,-0.4234106243,0.1190472469,0.2578414977,0.1716760248,-0.0856156796,-0.178942889,0.1059741601,0.4475736618,0.2421585768,0.1864495426,0.4735388756,0.0036861252,0.0158502683,0.0550022162,-0.1519695222,-0.0304263625,-0.2850629389,-0.0540900081,0.1259509772,0.0423416011,0.1031108946,-0.3341119885,-0.3942473233,-0.3149623871,-0.1922617704,0.1769528389,0.1530546099,0.0330443643,-0.055171892,-0.3120959699,0.2518874407,-0.3018952608,-0.0045857793,-0.1249805763,0.1991766095,-0.4028161764,0.0909401029,0.137765035,0.0272540599,0.393316716,-0.0744716227,0.0851056948,-0.3233603835,0.2844261229,0.0135471094,0.2084345967,0.391625613,0.1960054189,0.0341712907,0.0379531942,0.1721700579,-0.3572608531,0.0106583713,-0.3762223423,-0.386336714,0.0590663776,0.0359242409,-0.0429656468,0.1375054419,-0.3059713244,0.3850699961,0.0028136522,-0.0842985436,0.1836826652,-0.0354707316,-0.1883960217,-0.3066426516,0.2635630667,-0.0423973612,-0.3014155328,-0.0850330666,-0.3118213713,0.0348725542,0.0092230486,0.2253821194,-0.3613464832,0.4354794025,-0.0787982419,0.4266362786,0.7096788883,-0.1668386012,-0.0830487385,0.1356394887,0.073025167,-0.2475161254,-0.0586425662,-0.0254105758,0.2792952359,0.0822786763,-0.2581385374,0.4794852138,-0.3317678571,-0.0684077665,0.2964524925,-0.2518995702,0.3039956987,0.0026511,-0.1129344031,-0.2537751794,0.1864834726,0.3416492045,0.5174593925,0.0002922888,0.1240524352,0.122132577,0.0347708501,0.2333393097,-0.0684840605,-0.3601818383,-0.2926032841,-0.0724027082,0.1696252078,-0.0901157707,0.0293577574,-0.2904085219,-0.2447777987,-0.2300977111,0.0267193448,-0.2659700215,0.0562235788,-0.3088188469,0.2587308586,-0.1421919763,-0.4538706243,-0.0228313711,-0.2623423636,0.1578739583,-0.4556034505,0.2260335088,0.0970976949,-0.1513530761,0.1235115901,0.3515447378,-0.0676233694,-0.1001838073,-0.0139017086,0.6143959761,0.5653508902,-0.0955953449,-0.1702238172,0.0294235516,0.4321298003,-0.3713900745,0.1643283516,-0.073419854,0.007383246,0.0067607639,-0.4206548035,0.4240078926,-0.5293163657,0.3825000226,0.2183520347,0.0554136634,0.0161225572,-0.0953370705,-0.3166333735,0.0221355837,-0.2015053928,-0.3675967157,0.1182452515,-0.0644110814,-0.206991449,0.0260133315,0.4118818343,-0.1996985674,0.1081074029,-0.1360199749,0.0132391714,-0.040350873,0.1119055226,0.4428805411,0.2646597326,0.0628821775,0.3699391186,-0.0981138274,-0.2832746208,-0.0941550434,0.1123309731,0.2549903393,0.4364367127,0.0110180536,-0.2843090296,-0.0864050016,-0.0240161959,-0.0805049613,0.1877817214,0.083736971,-0.2561196387,0.0638338253,-0.3536810577,-0.1584827751,-0.4681741893,-0.3202151954,-0.0568619035,-0.2556049824,-0.1227160916,-0.0339344889,-0.0542002209,0.0088231368,-0.1515870839,-0.0395451784,-0.124467805,0.1857990175,0.344530344,-0.3150709867,-0.1321641803,0.0329206064,0.1363002807,0.6758494377,0.3586135507,0.0377044044,-0.1696942449,0.1066739187,-0.2268465608,0.1913998276,-0.1884536445,-0.0037773808,0.1433348805,0.1277379245,-0.0425320528,-0.4658038318,-0.1937800497,-0.212925896,-0.1523442566,0.036781352,0.2246374488,0.1164393201,-0.1551230252,-0.6601370573,-0.343165189,-0.1810243577,0.1933230311,-0.207370609,0.2268755883,0.2438796163,0.1859435141,0.3274717629,0.0146223428,-0.117303066,0.1914955527,0.2556433678,0.0435611084,-0.4491837025,-0.1671505123,0.1876729876,-0.0665262043,0.0979472101,0.0923764408,-0.1088687405,0.1633361131,-0.2055927217,-0.054957144,-0.0582875721,0.0279396754,0.2760000229,-0.1686510444,0.2373516411,-0.1299887002,0.1291237622,0.1275572032,-0.0030874407,0.2730184495,0.0085010016,0.3296947479,0.2465957254,0.7530816197,0.2361808568,-0.0867044628,-0.2982821167,-0.1320105642,0.0575513244,0.0226036217,-0.0801631659,0.121966064,-0.0013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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/259","title":"documentation missing how to split a dataset","comments":"I added a small guide [here](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md) that explains how to split a dataset. It is very similar to the tensorflow datasets guide, as we kept the same logic.","body":"I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n","comment_length":28,"text":"documentation missing how to split a dataset \n I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n \n I added a small guide [here](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md) that explains how to split a dataset. It is very similar to the tensorflow datasets guide, as we kept the same logic.","embeddings":[-0.0108554037,0.1039159372,-0.0716410503,0.1723709404,-0.009352929,0.1751483977,0.4139045775,0.3510569036,-0.1455844194,0.0215435736,0.1812569052,0.2846695781,-0.1473984122,0.110882692,0.1814140528,-0.3872855008,-0.1115607172,0.229946211,0.2616378665,-0.042115178,0.2874653637,-0.0557160713,-0.2707217634,0.1046781465,-0.347628653,-0.4612329006,-0.1970775723,-0.0371643528,-0.2252805233,-0.2295696884,0.2283925712,-0.2733032405,0.038595479,0.2595723867,-0.0001157783,0.0629990995,0.2459522337,-0.1078856215,-0.1020556837,-0.2007867396,-0.3612455428,-0.0809873492,0.4162449241,-0.1300804913,0.008889907,-0.2497373223,0.1623073071,-0.1735195816,0.50085181,0.4594459832,0.0998941362,0.0351319499,0.1878993064,0.0838824585,-0.0086505422,0.3338405788,-0.1121975332,0.0764554814,-0.1113737598,-0.0766184032,-0.1672828346,0.0434849784,0.1838376224,0.2061329037,0.0807435662,-0.0273496639,-0.3851658106,-0.4645871222,-0.1427486241,0.5450023413,0.605633378,-0.3358956873,-0.2980398834,-0.3045441806,0.0450891331,-0.1760812998,-0.1278075129,0.6296437979,-0.1171363294,0.0876608789,-0.1201952696,-0.2391177118,-0.3045322597,0.1573000997,0.022686664,0.389533788,-0.1093668193,0.0562407859,0.1918927729,0.2463200688,0.2808936238,0.0417102873,-0.0121208252,0.2608874738,-0.179040283,-0.2431488782,-0.090922907,-0.3770047128,0.0993314087,0.2342866361,0.1321774572,-0.0647843778,-0.1741934717,0.1682287008,0.4464684725,0.1732770354,0.2441631854,0.4896550775,0.0379738994,-0.039864704,-0.00231804,-0.1706026644,-0.0124308001,-0.1979073584,-0.0247887224,0.0710361302,0.1537875086,0.0901100263,-0.2858226001,-0.3360194564,-0.2909919322,-0.16204907,0.124273859,0.1403428316,0.0750277936,-0.0726471096,-0.3455613256,0.2231651992,-0.3875484765,-0.1767753065,-0.1235925108,0.2355868071,-0.3899735212,0.1046811789,0.1119999737,0.0107036149,0.4004684985,-0.1086489856,0.0191355199,-0.3715496659,0.306962043,0.0410933457,0.2116015255,0.4237737358,0.2138502598,0.020229198,0.0617886782,0.1171503589,-0.3336219788,-0.0221009646,-0.3795628548,-0.4628950357,-0.0616194271,0.039680291,-0.0250289794,0.1905612051,-0.1604062021,0.3714154661,0.0512458533,-0.048003722,0.2013643533,0.0819937959,-0.1747274697,-0.3574976325,0.2417288423,0.0053395969,-0.4272280931,-0.0731006041,-0.3806426823,-0.0314782411,-0.0557448119,0.1454569399,-0.3786714673,0.5079536438,-0.0999322757,0.4572858214,0.7189478874,-0.278337419,-0.0706058815,0.3035817444,0.1537110507,-0.2755768299,-0.1114162579,-0.0518568456,0.2611777484,0.1561566591,-0.4373097122,0.3556575477,-0.307607621,-0.1306802183,0.3071198165,-0.2115510553,0.2188747823,-0.0768824071,-0.0840034261,-0.2523697317,0.2786921859,0.3431999087,0.4787201583,0.0270526204,0.1831009388,0.2342686355,0.0657580048,0.1468948424,-0.0490043424,-0.3427709639,-0.3662566841,-0.0524929129,0.1418801695,-0.118029058,-0.0259524621,-0.2883369029,-0.1490792334,-0.135079816,0.0492266454,-0.2921785712,0.0640786663,-0.3532393277,0.1958754659,-0.1191392839,-0.4262670577,0.0873605236,-0.2295692265,0.2660537958,-0.4764243364,0.1153511256,0.0713808537,-0.0815802366,0.1057398915,0.2592471838,-0.0510609597,-0.1367089897,0.0266248398,0.5864788294,0.5731501579,0.0404473953,-0.2118848413,0.0319817029,0.4351481497,-0.4945230782,0.1646827757,-0.0357021466,-0.0406303331,0.0197371207,-0.4918183088,0.3089127839,-0.3423790634,0.2918802798,0.2441430837,0.1246892214,0.0534768105,-0.1709048152,-0.2978188694,-0.0349749476,-0.2654487491,-0.3422133923,0.2162171155,-0.0248491727,-0.1540777087,0.1124038547,0.4354673624,-0.1889245361,0.2056400776,-0.1116753146,0.0765462443,-0.0406405702,0.2076422274,0.427867651,0.249644652,0.0174676441,0.4108245075,-0.0889343172,-0.2685613334,-0.0661095902,0.1127064452,0.2360969037,0.4292649329,-0.0678519383,-0.3976389468,-0.0774865597,-0.0187645387,0.0069756387,0.1440650672,0.0676456764,-0.2933172286,0.1057011485,-0.3916169405,-0.1474703848,-0.427064389,-0.2854633629,-0.0807077289,-0.2603794336,-0.1695373654,-0.1485543698,0.0122485897,-0.0461445116,-0.1674004644,-0.0769603178,-0.1710436791,0.1795431823,0.3728763759,-0.2571858466,-0.1507111043,0.0603423268,0.1510509253,0.6260809898,0.2506404221,0.0201234054,-0.1515856236,0.0628092065,-0.328525871,0.2419970185,-0.205597207,-0.0152375707,0.2106800973,0.186044544,0.0140250605,-0.3126119971,-0.2665431201,-0.1900269091,-0.1723769307,-0.0817518085,0.1955081671,0.0547106974,-0.1628088206,-0.7155647278,-0.2830574214,-0.2016948611,0.292462647,-0.2403344214,0.2397518754,0.1748123467,0.1396600455,0.355679065,0.1154142022,-0.0146244513,0.2010682076,0.2305342406,0.0061793267,-0.4905734658,-0.2406401783,0.3171946406,-0.0099633206,0.227502808,0.0062271859,-0.2001265883,0.180793941,-0.2070200741,-0.0661455244,-0.0552999005,-0.0082790134,0.2614949644,-0.1543386132,0.2438741773,-0.1787375808,0.1214395463,0.0943631604,-0.1324971169,0.1973887682,0.0055295047,0.2760886848,0.2678914964,0.6091409922,0.2550810874,-0.0876832902,-0.2653273642,-0.140034005,0.1400356889,0.1263163239,-0.0411760435,0.0360700637,-0.103470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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/259","title":"documentation missing how to split a dataset","comments":"Thanks a lot, the new explanation is very helpful!\r\n\r\nAbout using train_test_split from sklearn: I stumbled across the [same error message as this user ](https:\/\/github.com\/huggingface\/nlp\/issues\/147 )and thought it can't be used at the moment in this context. Will check it out again.\r\n\r\nOne of the problems is how to shuffle very large datasets, which don't fit into the memory. Well, one strategy could be shuffling data in sections. But in a case where the data is sorted by the labels you have to swap larger sections first. \r\n","body":"I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n","comment_length":87,"text":"documentation missing how to split a dataset \n I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n \n Thanks a lot, the new explanation is very helpful!\r\n\r\nAbout using train_test_split from sklearn: I stumbled across the [same error message as this user ](https:\/\/github.com\/huggingface\/nlp\/issues\/147 )and thought it can't be used at the moment in this context. Will check it out again.\r\n\r\nOne of the problems is how to shuffle very large datasets, which don't fit into the memory. Well, one strategy could be shuffling data in sections. But in a case where the data is sorted by the labels you have to swap larger sections first. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/259","title":"documentation missing how to split a dataset","comments":"We added a way to shuffle datasets (shuffle the indices and then reorder to make a new dataset).\r\nYou can do `shuffled_dset = dataset.shuffle(seed=my_seed)`. It shuffles the whole dataset.\r\nThere is also `dataset.train_test_split()` which if very handy (with the same signature as sklearn).\r\n\r\nClosing this issue as we added the docs for splits and tools to split datasets. Thanks again for your feedback !","body":"I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n","comment_length":64,"text":"documentation missing how to split a dataset \n I am trying to understand how to split a dataset ( as arrow_dataset). \r\nI know I can do something like this to access a split which is already in the original dataset : \r\n\r\n`ds_test = nlp.load_dataset('imdb, split='test') `\r\n\r\nBut how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?\r\nI guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description: \r\n\r\n> See the  [guide on splits](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/docs\/splits.md)  for more information.\r\n\r\nBut the guide seems to be missing.\r\n\r\nTo clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https:\/\/www.tensorflow.org\/datasets\/splits). But to come back to the example above: I cannot simply split the testset doing this: \r\n`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `\r\n`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`\r\n\r\nbecause the imdb test data is sorted by class (probably not a good idea anyway)\r\n \n We added a way to shuffle datasets (shuffle the indices and then reorder to make a new dataset).\r\nYou can do `shuffled_dset = dataset.shuffle(seed=my_seed)`. It shuffles the whole dataset.\r\nThere is also `dataset.train_test_split()` which if very handy (with the same signature as sklearn).\r\n\r\nClosing this issue as we added the docs for splits and tools to split datasets. Thanks again for your feedback !","embeddings":[0.0403223671,0.0286834165,-0.0598337613,0.1646729261,-0.0391383842,0.2028904557,0.4111513495,0.3333023489,-0.0838269815,0.0418192372,0.1581265479,0.3008451462,-0.1978239268,0.1513804793,0.1966464221,-0.3646208346,-0.0580054745,0.1984985769,0.2289592177,-0.0036904679,0.256201148,-0.1071294993,-0.304423362,0.120927833,-0.3231808543,-0.4618531764,-0.2295882851,-0.0311393272,-0.2734042108,-0.1920207292,0.2099711448,-0.1823351085,0.0320916846,0.3054240644,-0.000116105,-0.010881043,0.2465662807,-0.1087708473,-0.1147388145,-0.2236365676,-0.3785127699,-0.030364342,0.3681846559,-0.1375394017,0.0056159082,-0.2517320812,0.1225903183,-0.1782120317,0.5087322593,0.4407333136,0.1115595773,0.0035519912,0.1767529845,0.1035160571,0.042402748,0.3595411777,-0.1065701693,0.0957077816,-0.1545086503,-0.1049503535,-0.1268551499,0.0406020321,0.1666917205,0.1925523877,0.1087984517,-0.035980694,-0.3644660413,-0.4900597334,-0.1254992932,0.5456266999,0.6005104184,-0.3356675804,-0.3338814378,-0.2798808217,0.0761559084,-0.228790462,-0.1258219928,0.6406729817,-0.1184810773,0.0291664936,-0.1475769877,-0.2527887821,-0.2896288335,0.139794454,0.0476697795,0.432497412,-0.0843734443,0.0583464317,0.2639944255,0.2229848951,0.3153964579,-0.0034207574,-0.081353724,0.292568326,-0.2424946725,-0.292462647,-0.1715589464,-0.3974406421,0.132484138,0.2986210585,0.1361417919,-0.0838172808,-0.1949217319,0.1345125586,0.4678732455,0.2058346272,0.2396071851,0.4790492654,-0.0016800514,-0.0263343584,0.0180896092,-0.1862372309,-0.0208235234,-0.2293924093,-0.029344935,0.0750173256,0.1216978952,0.0660451204,-0.2564261556,-0.3403445184,-0.3157760501,-0.1848141253,0.1066501439,0.132505253,0.0828250721,-0.080006741,-0.3198460042,0.2792649269,-0.3513965607,-0.1381861866,-0.1297213584,0.2163873166,-0.4161950052,0.1454454958,0.1128338203,-0.0038959899,0.3922364712,-0.083182998,0.0201491434,-0.3755839169,0.2846872807,0.0171303116,0.1891864538,0.3779692054,0.1809567511,0.0440621153,0.0598460361,0.1318244934,-0.3288018405,-0.0048243543,-0.3971320987,-0.4248842299,-0.0321050212,0.0282722227,-0.0414998494,0.1902054697,-0.2672340572,0.351020664,-0.0028942707,-0.0546868294,0.2016918063,0.0277158506,-0.1759249419,-0.3253219426,0.2509885728,0.0148803042,-0.3857682347,-0.0391694866,-0.3769051731,-0.0401922204,-0.0184912179,0.1650433242,-0.3774425983,0.4833278358,-0.1127269715,0.4063957334,0.6908680201,-0.1989747882,-0.0340800621,0.2310912609,0.1239069849,-0.2687203884,-0.1132232249,-0.0545059368,0.3031643033,0.116834797,-0.4216461778,0.3843097985,-0.3077020049,-0.1268476248,0.2832005322,-0.254827857,0.2440154254,-0.0654645637,-0.0825421885,-0.254324168,0.2538763881,0.3207102418,0.4920059144,0.0280294158,0.1801446527,0.1909128726,0.061658036,0.2179094106,-0.0664332882,-0.3025019169,-0.3309570253,-0.0793043599,0.1560174525,-0.071212098,0.0058269701,-0.2646156847,-0.2066113055,-0.172613129,0.0351849459,-0.3387405574,0.0594701134,-0.3565586805,0.2210893333,-0.1259914637,-0.4023621976,0.0217912626,-0.2359521091,0.242100656,-0.4350490868,0.1747662723,0.0647162572,-0.0816771388,0.1286371648,0.2814127803,-0.0714057758,-0.1138372868,-0.0043459455,0.5958948135,0.5878075361,-0.0264427308,-0.1346909702,0.0432598852,0.4055946469,-0.5084712505,0.2129133195,-0.0599845313,0.0160070825,0.0081074769,-0.4583743513,0.3185171783,-0.4548642933,0.3451863825,0.2045084238,0.1280926019,0.0154547524,-0.1400159001,-0.2886721492,0.02641619,-0.2369019687,-0.3842772245,0.1804403812,-0.048252888,-0.16476129,0.062357232,0.4256391227,-0.2152217478,0.1237346753,-0.1150745898,0.0095078796,-0.0433240235,0.1387760043,0.4403604865,0.2106843144,0.0526768081,0.3590402603,-0.0993303731,-0.248431623,-0.0596648753,0.1357475668,0.2258344591,0.4377182126,-0.0557044819,-0.4013492167,-0.0979817435,0.0026900461,-0.0214847047,0.1622288078,0.0848039016,-0.2820232809,0.0794488713,-0.3813348711,-0.1499004364,-0.5209005475,-0.2775146663,-0.1069980189,-0.2646991014,-0.1675828397,-0.0983399972,0.0030451696,-0.026531009,-0.1790220439,-0.0568118766,-0.1716521382,0.2067324072,0.3574621975,-0.3144453168,-0.1490798295,0.0507454239,0.2032431066,0.6589824557,0.2827355862,0.053942617,-0.1018270403,0.0052011949,-0.2792691588,0.2223029882,-0.1885470599,-0.0103698988,0.1580085605,0.1867484599,0.0028782091,-0.3813948333,-0.2566469312,-0.2440122217,-0.1778089404,-0.0182332359,0.2075580806,0.0996807292,-0.1586859822,-0.729760766,-0.2971604168,-0.1867208928,0.2442190349,-0.2553653121,0.2198871225,0.201742053,0.1517691016,0.3269978762,0.0214531217,-0.065773651,0.2023561746,0.2355849892,-0.000492701,-0.4409202039,-0.1999350041,0.3105662465,-0.0308506079,0.1985801458,0.0315750167,-0.1794963181,0.2157322764,-0.1878322512,-0.0676265657,-0.0577653721,-0.0059105507,0.2933513224,-0.1467363834,0.2593975365,-0.1576316953,0.0894758999,0.1173988357,-0.1268907636,0.2416574806,0.0256769899,0.3007251918,0.2090368569,0.7162435055,0.2169198394,-0.0496118627,-0.3078827262,-0.1198214665,0.1227457449,0.0832005069,-0.0693561584,0.0601780042,-0.0565507971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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/258","title":"Why is dataset after tokenization far more larger than the orginal one ?","comments":"Hi ! This is because `.map` added the new column `input_ids` to the dataset, and so all the other columns were kept. Therefore the dataset size increased a lot.\r\n If you want to only keep the `input_ids` column, you can stash the other ones by specifying `remove_columns=[\"title\", \"text\"]` in the arguments of `.map`","body":"I tokenize wiki dataset by `map` and cache the results.\r\n```\r\ndef tokenize_tfm(example):\r\n    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))\r\n    return example\r\nwiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']\r\nwiki.map(tokenize_tfm, cache_file_name=cache_dir\/\"wikipedia\/20200501.en\/1.0.0\/tokenized_wiki.arrow\")\r\n```\r\nand when I see their size\r\n```\r\nls -l --block-size=M\r\n17460M  wikipedia-train.arrow\r\n47511M  tokenized_wiki.arrow\r\n```\r\nThe tokenized one is over 2x size of original one.\r\nIs there something I did wrong ?","comment_length":53,"text":"Why is dataset after tokenization far more larger than the orginal one ? \n I tokenize wiki dataset by `map` and cache the results.\r\n```\r\ndef tokenize_tfm(example):\r\n    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))\r\n    return example\r\nwiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']\r\nwiki.map(tokenize_tfm, cache_file_name=cache_dir\/\"wikipedia\/20200501.en\/1.0.0\/tokenized_wiki.arrow\")\r\n```\r\nand when I see their size\r\n```\r\nls -l --block-size=M\r\n17460M  wikipedia-train.arrow\r\n47511M  tokenized_wiki.arrow\r\n```\r\nThe tokenized one is over 2x size of original one.\r\nIs there something I did wrong ? \n Hi ! This is because `.map` added the new column `input_ids` to the dataset, and so all the other columns were kept. Therefore the dataset size increased a lot.\r\n If you want to only keep the `input_ids` column, you can stash the other ones by specifying `remove_columns=[\"title\", \"text\"]` in the arguments of 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/258","title":"Why is dataset after tokenization far more larger than the orginal one ?","comments":"Hi ! Thanks for your reply.\r\n\r\nBut since size of `input_ids` < size of `text`, I am wondering why\r\nsize of `input_ids` + `text` > 2x the size of `text`  \ud83e\udd14","body":"I tokenize wiki dataset by `map` and cache the results.\r\n```\r\ndef tokenize_tfm(example):\r\n    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))\r\n    return example\r\nwiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']\r\nwiki.map(tokenize_tfm, cache_file_name=cache_dir\/\"wikipedia\/20200501.en\/1.0.0\/tokenized_wiki.arrow\")\r\n```\r\nand when I see their size\r\n```\r\nls -l --block-size=M\r\n17460M  wikipedia-train.arrow\r\n47511M  tokenized_wiki.arrow\r\n```\r\nThe tokenized one is over 2x size of original one.\r\nIs there something I did wrong ?","comment_length":31,"text":"Why is dataset after tokenization far more larger than the orginal one ? \n I tokenize wiki dataset by `map` and cache the results.\r\n```\r\ndef tokenize_tfm(example):\r\n    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))\r\n    return example\r\nwiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']\r\nwiki.map(tokenize_tfm, cache_file_name=cache_dir\/\"wikipedia\/20200501.en\/1.0.0\/tokenized_wiki.arrow\")\r\n```\r\nand when I see their size\r\n```\r\nls -l --block-size=M\r\n17460M  wikipedia-train.arrow\r\n47511M  tokenized_wiki.arrow\r\n```\r\nThe tokenized one is over 2x size of original one.\r\nIs there something I did wrong ? \n Hi ! Thanks for your reply.\r\n\r\nBut since size of `input_ids` < size of `text`, I am wondering why\r\nsize of `input_ids` + `text` > 2x the size of `text`  \ud83e\udd14","embeddings":[-0.0532691069,-0.2086362988,0.0178327393,0.1911770701,0.0143836392,0.0087456573,0.2398011088,0.3740540445,-0.1149224043,-0.0138116116,-0.080173701,0.180669263,0.1408331692,-0.1129715592,0.1978251338,-0.1090740561,0.2893790305,-0.0118231466,0.1726615727,-0.1984837353,0.0641963705,0.0123498654,-0.204471454,0.0265595634,-0.4099167883,-0.0119645176,-0.143138513,-0.1234519556,-0.4040339291,-0.5144379139,-0.0526950508,0.0811511278,0.381595999,0.272680223,-0.000126695,-0.3327266574,0.0753403977,0.0116364425,-0.0123662101,0.267736882,0.1676332504,-0.5629211068,-0.1614192128,-0.253213048,0.2919107974,-0.5190222859,-0.2093334943,0.3381920159,0.0419585146,-0.1553609222,0.0847018063,-0.1341997832,0.033729028,0.4106487036,0.1461370587,0.094788827,-0.0445502438,-0.193176493,-0.125205189,-0.1861883402,-0.0148738222,0.1704450995,0.0846230239,-0.2187519372,0.3521589637,-0.0789667591,-0.1837906837,-0.0440309532,0.2250877321,0.2020062655,0.3780929446,-0.1213997677,0.0179003906,-0.2720344663,-0.1647694409,-0.0660214499,-0.1757876873,0.1807849854,0.2901870906,-0.0319655389,-0.3919959068,-0.0505461805,0.1163604036,0.2394372076,-0.1916068792,0.530661881,0.0373358503,0.1746242046,-0.2876830399,-0.171141848,-0.0772107616,-0.231599763,-0.1640636772,0.4628706574,-0.334744066,-0.3571234047,0.0454478711,-0.0553786941,0.5117006302,-0.3004132807,-0.3079538941,-0.0179550126,-0.0300103668,-0.1349734366,0.1017569602,0.5036150217,-0.320517689,0.4058420956,-0.1463568062,-0.0689106286,-0.5243211389,-0.1801228523,0.0581733361,0.0973942503,0.1247244701,-0.2626882493,-0.36893332,-0.0654681846,0.2246282846,0.2283162028,-0.5162183642,0.0501702949,-0.0462418012,0.353132993,-0.1877923906,0.398598671,-0.3270302415,0.2114166915,-0.136152789,0.0425623767,-0.1161684692,-0.1355384141,0.0962168351,0.1538248807,-0.2341097593,0.1981964558,0.3465766609,0.0936483592,0.0146889258,-0.0968535244,0.4784098566,-0.2083839923,0.3629956245,0.1219941303,0.1408423781,0.3056558073,-0.0901968256,-0.0957771912,-0.1201085597,0.1409538686,-0.5012376904,-0.169020012,0.0116774058,0.0399583317,0.0463613011,-0.0260105561,-0.3790266812,0.3396331072,0.445038259,-0.0485933945,0.2475073636,-0.2733230889,-0.3672395051,-0.1775032431,0.0153883873,-0.0809691027,-0.0650012121,-0.012406134,0.3479374945,0.2410304099,0.349231869,0.6582411528,-0.2303016782,0.5145463943,-0.2676454782,0.5004504919,0.4632590711,-0.4106018841,-0.7795951962,0.3592059314,0.2177218795,0.1909922659,-0.0905768275,-0.062267378,0.4403330982,0.1709681302,0.1503650248,0.2609789371,-0.0067491755,0.0891227871,-0.4096227586,-0.1871406883,0.3767555952,-0.5307529569,0.2135346979,-0.0000495143,0.0488071628,0.461697042,0.4492997229,0.0690544769,0.2840161324,0.2604551315,-0.0561473072,0.0318829864,0.2822740078,0.0226592347,0.2465954721,-0.0556628406,-0.2750283182,-0.0677846894,0.2460779548,-0.3916614354,-0.2138777971,0.0052284729,-0.1389308274,-0.2794297338,0.0234179758,-0.0932357311,0.1023214534,-0.0383189172,0.2316971719,0.1100384593,-0.1432964951,0.0935535952,0.1334498376,0.1645781398,0.0402019545,0.1588942707,-0.298945725,0.2747724354,0.150938496,0.0381687693,-0.2896446288,0.0561730787,0.1120401472,0.0079375952,0.1598926485,0.3029532433,0.2006729692,0.0209676512,0.3990192711,0.0519715957,0.0553002618,-0.1516284645,0.1953581423,0.2741399109,0.4093807042,0.0639325827,0.0830415934,-0.2988683581,0.1838340014,0.0164156202,0.1465729624,-0.210712105,-0.1486240029,0.2459668368,0.0925299302,0.0446782671,0.0847314447,0.3287924826,0.4958305061,-0.0970020592,-0.0951333717,0.092579633,-0.6602830291,-0.5403527021,0.148996532,-0.0497088321,0.2239417136,0.099999547,0.3472332358,0.0199308563,0.2298643291,0.0970973521,0.2053426355,-0.085895434,0.0185722504,0.2559397817,0.5013879538,0.1528055668,-0.2660228312,0.202513501,-0.0429480076,0.3088431358,-0.1124926284,0.0850555524,-0.1335549653,-0.1435682327,-0.0023192614,-0.039491009,-0.1966706067,-0.4011041224,-0.0901038498,-0.07314533,0.3624985516,-0.2025968432,-0.0469216406,0.0116731729,0.1543474793,0.1703260988,0.0842947587,0.0560884178,-0.3676507175,-0.0593170598,-0.0991245285,-0.0619967133,0.2375106215,0.1665263027,-0.0645672902,-0.3458328843,-0.8534325957,0.0825678632,-0.084121339,0.0708762929,-0.0375623852,-0.0424161553,-0.4549081028,0.1427483112,-0.0337599181,0.084429495,-0.2855614424,-0.1527382433,-0.3064069748,0.1108344123,0.0838663727,0.0327131152,-0.0781294554,-0.0054610954,0.0288356692,0.0639322698,0.0412345901,0.0331529342,-0.2343938798,-0.0055217049,-0.0295666941,-0.0494091026,-0.4937898219,-0.2349341959,0.2614579499,-0.186485216,-0.2871465981,-0.1379059851,-0.2127659768,0.1674518883,0.1235416904,-0.575129509,0.1784176081,-0.2078160346,0.191449523,-0.2898373604,0.0419713072,0.071193859,-0.1305159628,0.0412760638,-0.065564841,0.0627502576,-0.3446030915,0.1373836249,0.5308549404,-0.1019116789,0.441552937,0.3388991654,0.9411715865,0.0710766986,0.059741471,0.2623276114,-0.2129848003,0.1000787169,-0.2972133756,-0.1126083434,0.1811749935,-0.0857195929,0.19751968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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/258","title":"Why is dataset after tokenization far more larger than the orginal one ?","comments":"Hard to tell... This is probably related to the way apache arrow compresses lists of integers, that may be different from the compression of strings.","body":"I tokenize wiki dataset by `map` and cache the results.\r\n```\r\ndef tokenize_tfm(example):\r\n    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))\r\n    return example\r\nwiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']\r\nwiki.map(tokenize_tfm, cache_file_name=cache_dir\/\"wikipedia\/20200501.en\/1.0.0\/tokenized_wiki.arrow\")\r\n```\r\nand when I see their size\r\n```\r\nls -l --block-size=M\r\n17460M  wikipedia-train.arrow\r\n47511M  tokenized_wiki.arrow\r\n```\r\nThe tokenized one is over 2x size of original one.\r\nIs there something I did wrong ?","comment_length":25,"text":"Why is dataset after tokenization far more larger than the orginal one ? \n I tokenize wiki dataset by `map` and cache the results.\r\n```\r\ndef tokenize_tfm(example):\r\n    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))\r\n    return example\r\nwiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']\r\nwiki.map(tokenize_tfm, cache_file_name=cache_dir\/\"wikipedia\/20200501.en\/1.0.0\/tokenized_wiki.arrow\")\r\n```\r\nand when I see their size\r\n```\r\nls -l --block-size=M\r\n17460M  wikipedia-train.arrow\r\n47511M  tokenized_wiki.arrow\r\n```\r\nThe tokenized one is over 2x size of original one.\r\nIs there something I did wrong ? \n Hard to tell... This is probably related to the way apache arrow compresses lists of integers, that may be different from the compression of 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/258","title":"Why is dataset after tokenization far more larger than the orginal one ?","comments":"Thanks for your point. \ud83d\ude00, It might be answer.\r\nSince this is hard to know, I'll close this issue.\r\nBut if somebody knows more details, please comment below ~ \ud83d\ude01","body":"I tokenize wiki dataset by `map` and cache the results.\r\n```\r\ndef tokenize_tfm(example):\r\n    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))\r\n    return example\r\nwiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']\r\nwiki.map(tokenize_tfm, cache_file_name=cache_dir\/\"wikipedia\/20200501.en\/1.0.0\/tokenized_wiki.arrow\")\r\n```\r\nand when I see their size\r\n```\r\nls -l --block-size=M\r\n17460M  wikipedia-train.arrow\r\n47511M  tokenized_wiki.arrow\r\n```\r\nThe tokenized one is over 2x size of original one.\r\nIs there something I did wrong ?","comment_length":30,"text":"Why is dataset after tokenization far more larger than the orginal one ? \n I tokenize wiki dataset by `map` and cache the results.\r\n```\r\ndef tokenize_tfm(example):\r\n    example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))\r\n    return example\r\nwiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']\r\nwiki.map(tokenize_tfm, cache_file_name=cache_dir\/\"wikipedia\/20200501.en\/1.0.0\/tokenized_wiki.arrow\")\r\n```\r\nand when I see their size\r\n```\r\nls -l --block-size=M\r\n17460M  wikipedia-train.arrow\r\n47511M  tokenized_wiki.arrow\r\n```\r\nThe tokenized one is over 2x size of original one.\r\nIs there something I did wrong ? \n Thanks for your point. \ud83d\ude00, It might be answer.\r\nSince this is hard to know, I'll close this issue.\r\nBut if somebody knows more details, please comment below ~ 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/257","title":"Tokenizer pickling issue fix not landed in `nlp` yet?","comments":"Yes, the new release of tokenizers solves this and should be out soon.\r\nIn the meantime, you can install it with `pip install tokenizers==0.8.0-dev2`","body":"Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:\r\n\r\n```\r\ndataset = nlp.load_dataset('cos_e')\r\ntokenizer = GPT2TokenizerFast.from_pretrained('gpt2', cache_dir=cache_dir)\r\n\r\nfor split in dataset.keys():\r\n    dataset[split].map(lambda x: some_function(x, tokenizer))\r\n```\r\n```\r\n06\/09\/2020 10:09:19 - INFO - nlp.builder -   Constructing Dataset for split train[:10], from \/home\/sarahw\/.cache\/huggingface\/datasets\/cos_e\/default\/0.0.1\r\nTraceback (most recent call last):\r\n  File \"generation\/input_to_label_and_rationale.py\", line 390, in <module>\r\n    main()\r\n  File \"generation\/input_to_label_and_rationale.py\", line 263, in main\r\n    dataset[split] = dataset[split].map(lambda x: input_to_explanation_plus_label(x, tokenizer, max_length, datasource=data_args.task_name, wt5=(model_class=='t5'), expl_only=model_args.rationale_only), batched=False)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/arrow_dataset.py\", line 522, in map\r\n    cache_file_name = self._get_cache_file_path(function, cache_kwargs)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/arrow_dataset.py\", line 381, in _get_cache_file_path\r\n    function_bytes = dumps(function)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 257, in dumps\r\n    dump(obj, file)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 250, in dump\r\n    Pickler(file).dump(obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 445, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 485, in dump\r\n    self.save(obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1410, in save_function\r\n    pickler.save_reduce(_create_function, (obj.__code__,\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1147, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 884, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 601, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 715, in save_reduce\r\n    save(state)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 912, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 969, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 995, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 601, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 715, in save_reduce\r\n    save(state)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 912, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 969, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 995, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 576, in save\r\n    rv = reduce(self.proto)\r\nTypeError: cannot pickle 'Tokenizer' object\r\n```\r\nFix seems to be in the tokenizers [`0.8.0.dev1 pre-release`](https:\/\/github.com\/huggingface\/tokenizers\/issues\/87), which I can't install with any package managers. ","comment_length":24,"text":"Tokenizer pickling issue fix not landed in `nlp` yet? \n Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:\r\n\r\n```\r\ndataset = nlp.load_dataset('cos_e')\r\ntokenizer = GPT2TokenizerFast.from_pretrained('gpt2', cache_dir=cache_dir)\r\n\r\nfor split in dataset.keys():\r\n    dataset[split].map(lambda x: some_function(x, tokenizer))\r\n```\r\n```\r\n06\/09\/2020 10:09:19 - INFO - nlp.builder -   Constructing Dataset for split train[:10], from \/home\/sarahw\/.cache\/huggingface\/datasets\/cos_e\/default\/0.0.1\r\nTraceback (most recent call last):\r\n  File \"generation\/input_to_label_and_rationale.py\", line 390, in <module>\r\n    main()\r\n  File \"generation\/input_to_label_and_rationale.py\", line 263, in main\r\n    dataset[split] = dataset[split].map(lambda x: input_to_explanation_plus_label(x, tokenizer, max_length, datasource=data_args.task_name, wt5=(model_class=='t5'), expl_only=model_args.rationale_only), batched=False)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/arrow_dataset.py\", line 522, in map\r\n    cache_file_name = self._get_cache_file_path(function, cache_kwargs)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/arrow_dataset.py\", line 381, in _get_cache_file_path\r\n    function_bytes = dumps(function)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 257, in dumps\r\n    dump(obj, file)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 250, in dump\r\n    Pickler(file).dump(obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 445, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 485, in dump\r\n    self.save(obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1410, in save_function\r\n    pickler.save_reduce(_create_function, (obj.__code__,\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1147, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 884, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 601, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 715, in save_reduce\r\n    save(state)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 912, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 969, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 995, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 601, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 715, in save_reduce\r\n    save(state)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 912, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 969, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 995, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 576, in save\r\n    rv = reduce(self.proto)\r\nTypeError: cannot pickle 'Tokenizer' object\r\n```\r\nFix seems to be in the tokenizers [`0.8.0.dev1 pre-release`](https:\/\/github.com\/huggingface\/tokenizers\/issues\/87), which I can't install with any package managers.  \n Yes, the new release of tokenizers solves this and should be out soon.\r\nIn the meantime, you can install it with `pip install tokenizers==0.8.0-dev2`","embeddings":[0.0099123437,0.1761874706,0.0797633827,0.0332191736,-0.0187639873,-0.0983517841,-0.0439764857,0.3858543932,0.2328621596,-0.178395614,0.1533451229,0.7815694809,-0.1890581548,-0.2719969749,0.0566341542,-0.155905962,0.0068416926,0.1128314584,0.3384636045,0.0179074295,-0.2215670645,0.3663436174,-0.2035544813,0.2977550626,-0.4966283739,-0.2166752219,0.2067759931,-0.0472208932,-0.1231692284,-0.5728648305,0.1870387495,-0.0740047246,-0.1319389343,0.0616183169,-0.0001174416,-0.1495584548,0.3925046623,0.0270643979,-0.1482364833,-0.2368089408,0.1705570519,-0.0939855054,0.1326126754,-0.0232695937,-0.0295413285,-0.0769911557,0.1083666608,-0.1098065153,0.3249566257,-0.0344230719,0.1912767291,0.3061876297,0.1382566988,0.010819071,0.0969163477,-0.1552183479,-0.1684094965,-0.0005967876,0.090119198,-0.2653211653,-0.2927828133,0.4738675952,0.0086574946,-0.0582620651,0.06942375,0.0213256795,-0.1785932332,-0.0757203773,0.0937063918,-0.0271070879,0.0955460519,-0.185951829,-0.0445755795,-0.4417182207,-0.1290053278,-0.4550065696,0.1827386171,-0.1479194164,-0.2384156585,-0.044095207,-0.0090401918,-0.3884577453,0.1388706863,0.305347234,0.1289279908,0.6000414491,-0.0572790317,0.2780373394,0.2819808722,-0.0880403817,-0.2382824272,0.1567702591,-0.1878780276,0.4356526732,-0.3443645239,-0.1178101301,0.2205701023,0.0535372719,-0.0640307739,0.3975797296,-0.0016291763,0.2429233938,-0.1206074208,0.1644044071,0.0477179624,0.4591069818,0.3047444224,0.5499494672,0.0105261905,-0.3980909884,-0.0375846587,-0.0503841192,-0.0753701106,-0.2622773945,0.0318233259,0.0509526394,-0.117181316,-0.104768239,-0.1330924928,0.0608898513,-0.2686992288,0.0647368804,0.2208596468,0.1958482265,0.1352951229,0.1504516751,-0.1320771724,0.1724413484,-0.1877634674,-0.1661207825,-0.248142451,0.0797850415,-0.3150077462,0.181962356,0.097306259,0.2771179378,0.480322212,-0.1688139588,-0.0587520488,0.0628477111,0.1248926893,-0.0092322603,0.2322514802,0.0725742429,-0.1473613679,0.3290615082,0.3126963675,-0.141333133,-0.2816562951,-0.051630348,-0.2734317183,-0.3626489639,-0.1152614132,0.1254754364,-0.0622648075,-0.1384949982,-0.271987617,0.2420289963,0.4594833851,-0.1889908016,0.1101836562,-0.1990117133,-0.3264870942,-0.2962644696,0.0307122543,0.2880884409,0.0484273285,-0.3790308535,-0.1973203868,0.152703166,0.4366891086,0.5128081441,-0.3213967681,0.1518722922,-0.2618008852,0.814293623,0.5736558437,-0.2508249879,-0.4575694203,0.0358645134,-0.1898695827,0.0828077123,-0.2472429127,-0.0829209462,-0.0119433152,-0.1429459453,0.179187119,0.2489403337,-0.008139831,0.0076341168,-0.3845753372,-0.042985931,0.3610703051,-0.040211156,0.1359008253,-0.1101026833,-0.2231208682,0.0138509758,0.0019649428,-0.0150994463,0.3447351456,-0.0740484223,0.1137956753,-0.1738644242,0.0757068768,0.0411160365,-0.3737537563,0.1523695588,-0.2930880189,0.1511403322,-0.2010424584,0.021357473,-0.1287766695,0.0444968641,-0.3398889005,-0.233027786,0.1284675449,0.0046516308,0.1984073669,0.0344288088,-0.0145979878,-0.0610454157,0.0627615601,0.1549107283,-0.1303329468,-0.0565437041,-0.0467760414,-0.2752353847,-0.2991557121,0.1600325555,0.2399732471,-0.0254261903,-0.2603600323,0.294133842,0.0070313257,0.0486650318,-0.1207752451,-0.0605716966,0.0970292762,-0.1387098879,-0.1103155315,0.124253653,-0.0198693071,-0.2519939244,0.3183259368,0.3542825282,0.1884092987,0.0600378774,-0.2868296802,0.2985999286,0.0610981956,-0.0173833854,0.1314131021,-0.2714962363,0.066673331,-0.1250588149,0.2938562632,-0.1201208532,-0.0217081122,0.3552860916,0.3217470646,0.3191149235,0.0601604953,0.1106171981,-0.2048500776,-0.2746075094,0.0948237032,0.1242748946,0.1567907631,0.1859988868,0.0423683301,-0.0587046333,-0.1060367897,-0.0883650258,0.1340711117,0.0121885845,0.0686790571,0.159634769,0.0601588152,0.1171429902,-0.3626596928,0.0093214056,0.1092613563,0.3128890097,-0.1123162508,-0.0685740262,-0.3171990812,-0.0965782776,0.1861843467,-0.3486100435,-0.2434670776,-0.4538672864,0.1318599135,0.1396827549,-0.1204633787,0.4497210681,-0.1803721189,0.2464598715,0.0291105229,-0.0901661292,-0.3170962036,-0.5001772046,-0.3929176927,-0.0020681028,0.1086931378,-0.0656516925,0.1988438815,0.1700224876,0.0388037674,-0.0799686238,-0.3938218653,0.224536106,-0.1287586391,0.048669856,0.2407984734,-0.2067009807,-0.4435534477,-0.3115964532,0.1795250326,-0.3477775156,-0.3655576408,-0.1269761026,0.0443097316,-0.1161485836,-0.1567027569,-0.0487882085,-0.1834051311,-0.2831899822,0.2769730687,0.0246919971,0.1811643243,0.4258752167,-0.3476061225,0.0534039997,-0.3079631329,-0.1246998087,-0.5135576129,0.0901808962,0.3106078804,-0.320093751,-0.1387337446,-0.1729266346,-0.1581386179,0.3402467668,0.0410679355,-0.1939451247,0.1122732684,-0.0858655423,0.1610163599,-0.1077982262,-0.0623691827,0.4786886871,0.0970861316,-0.0032040772,-0.1829605103,-0.1646911353,0.4114802182,-0.0680685937,0.3304801583,0.0873666406,0.1052733064,0.2931070924,0.8183516264,0.2056207508,-0.1043714285,0.4881397486,0.233660996,0.0024523013,-0.2040912062,-0.2489936501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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/257","title":"Tokenizer pickling issue fix not landed in `nlp` yet?","comments":"If others run into this issue, a quick fix is to use python 3.6 instead of 3.7+. Serialization differences between the 3rd party `dataclasses` package for 3.6 and the built in `dataclasses` in 3.7+ cause the issue.\r\n\r\nProbably a dumb fix, but it works for me.","body":"Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:\r\n\r\n```\r\ndataset = nlp.load_dataset('cos_e')\r\ntokenizer = GPT2TokenizerFast.from_pretrained('gpt2', cache_dir=cache_dir)\r\n\r\nfor split in dataset.keys():\r\n    dataset[split].map(lambda x: some_function(x, tokenizer))\r\n```\r\n```\r\n06\/09\/2020 10:09:19 - INFO - nlp.builder -   Constructing Dataset for split train[:10], from \/home\/sarahw\/.cache\/huggingface\/datasets\/cos_e\/default\/0.0.1\r\nTraceback (most recent call last):\r\n  File \"generation\/input_to_label_and_rationale.py\", line 390, in <module>\r\n    main()\r\n  File \"generation\/input_to_label_and_rationale.py\", line 263, in main\r\n    dataset[split] = dataset[split].map(lambda x: input_to_explanation_plus_label(x, tokenizer, max_length, datasource=data_args.task_name, wt5=(model_class=='t5'), expl_only=model_args.rationale_only), batched=False)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/arrow_dataset.py\", line 522, in map\r\n    cache_file_name = self._get_cache_file_path(function, cache_kwargs)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/arrow_dataset.py\", line 381, in _get_cache_file_path\r\n    function_bytes = dumps(function)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 257, in dumps\r\n    dump(obj, file)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 250, in dump\r\n    Pickler(file).dump(obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 445, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 485, in dump\r\n    self.save(obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1410, in save_function\r\n    pickler.save_reduce(_create_function, (obj.__code__,\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1147, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 884, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 601, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 715, in save_reduce\r\n    save(state)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 912, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 969, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 995, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 601, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 715, in save_reduce\r\n    save(state)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 912, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 969, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 995, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 576, in save\r\n    rv = reduce(self.proto)\r\nTypeError: cannot pickle 'Tokenizer' object\r\n```\r\nFix seems to be in the tokenizers [`0.8.0.dev1 pre-release`](https:\/\/github.com\/huggingface\/tokenizers\/issues\/87), which I can't install with any package managers. ","comment_length":46,"text":"Tokenizer pickling issue fix not landed in `nlp` yet? \n Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:\r\n\r\n```\r\ndataset = nlp.load_dataset('cos_e')\r\ntokenizer = GPT2TokenizerFast.from_pretrained('gpt2', cache_dir=cache_dir)\r\n\r\nfor split in dataset.keys():\r\n    dataset[split].map(lambda x: some_function(x, tokenizer))\r\n```\r\n```\r\n06\/09\/2020 10:09:19 - INFO - nlp.builder -   Constructing Dataset for split train[:10], from \/home\/sarahw\/.cache\/huggingface\/datasets\/cos_e\/default\/0.0.1\r\nTraceback (most recent call last):\r\n  File \"generation\/input_to_label_and_rationale.py\", line 390, in <module>\r\n    main()\r\n  File \"generation\/input_to_label_and_rationale.py\", line 263, in main\r\n    dataset[split] = dataset[split].map(lambda x: input_to_explanation_plus_label(x, tokenizer, max_length, datasource=data_args.task_name, wt5=(model_class=='t5'), expl_only=model_args.rationale_only), batched=False)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/arrow_dataset.py\", line 522, in map\r\n    cache_file_name = self._get_cache_file_path(function, cache_kwargs)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/arrow_dataset.py\", line 381, in _get_cache_file_path\r\n    function_bytes = dumps(function)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 257, in dumps\r\n    dump(obj, file)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/nlp\/utils\/py_utils.py\", line 250, in dump\r\n    Pickler(file).dump(obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 445, in dump\r\n    StockPickler.dump(self, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 485, in dump\r\n    self.save(obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1410, in save_function\r\n    pickler.save_reduce(_create_function, (obj.__code__,\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 899, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 1147, in save_cell\r\n    pickler.save_reduce(_create_cell, (f,), obj=obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 690, in save_reduce\r\n    save(args)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 884, in save_tuple\r\n    save(element)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 601, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 715, in save_reduce\r\n    save(state)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 912, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 969, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 995, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 601, in save\r\n    self.save_reduce(obj=obj, *rv)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 715, in save_reduce\r\n    save(state)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 558, in save\r\n    f(self, obj)  # Call unbound method with explicit self\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/site-packages\/dill\/_dill.py\", line 912, in save_module_dict\r\n    StockPickler.save_dict(pickler, obj)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 969, in save_dict\r\n    self._batch_setitems(obj.items())\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 995, in _batch_setitems\r\n    save(v)\r\n  File \"\/home\/sarahw\/miniconda3\/envs\/project_huggingface\/lib\/python3.8\/pickle.py\", line 576, in save\r\n    rv = reduce(self.proto)\r\nTypeError: cannot pickle 'Tokenizer' object\r\n```\r\nFix seems to be in the tokenizers [`0.8.0.dev1 pre-release`](https:\/\/github.com\/huggingface\/tokenizers\/issues\/87), which I can't install with any package managers.  \n If others run into this issue, a quick fix is to use python 3.6 instead of 3.7+. Serialization differences between the 3rd party `dataclasses` package for 3.6 and the built in `dataclasses` in 3.7+ cause the issue.\r\n\r\nProbably a dumb fix, but it works for 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/256","title":"[Feature request] Add a feature to dataset","comments":"Do you have an example of what you would like to do? (you can just add a field in the output of the unction you give to map and this will add this field in the output table)","body":"Is there a straightforward way to add a field to the arrow_dataset, prior to performing map?","comment_length":38,"text":"[Feature request] Add a feature to dataset \n Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? \n Do you have an example of what you would like to do? (you can just add a field in the output of the unction you give to map and this will add this field in the output table)","embeddings":[-0.3230097592,-0.125670135,-0.1297954321,-0.3137857914,0.0523874797,0.1927578896,0.0315824375,0.0829818398,0.1852330863,0.0769858584,0.6435584426,0.7557856441,-0.0890218318,0.4095419049,0.2446237057,0.032614138,0.0282653682,0.2254813313,0.0143262055,0.0299326628,-0.3113274574,-0.3316054344,0.0440422781,-0.1416705102,-0.2610886395,-0.1055848822,0.0917241275,-0.2881222665,-0.2672578692,-0.4562530518,-0.0757000521,0.0525777973,-0.0076457122,-0.2505634129,-0.0001156606,-0.247966975,0.1716554314,0.0429585762,-0.1068635136,-0.099915199,-0.652341783,-0.46455881,0.2334650755,-0.1411959231,-0.0307949446,-0.0986400843,0.0351928398,-0.3240500391,-0.0623200461,-0.0979386419,0.2118946612,-0.1266070306,0.1847655624,-0.0503193736,0.2521041632,0.4834830463,-0.369220674,-0.2472529262,0.0324977897,0.0674415156,0.1742890328,0.3334390819,0.2909506559,-0.2479885072,0.4152329862,0.1051029712,0.0374160185,-0.1611279547,-0.0902455524,0.1184934154,0.7849738002,-0.2754909098,-0.2820773423,0.0049742302,0.120862186,-0.3142980933,-0.0033539662,-0.0835787952,0.1403281838,0.0212115422,0.2073623836,-0.548999846,-0.0674224645,0.1288884878,0.1251959801,0.3817350864,0.0620457865,0.115920037,-0.1173211113,-0.1588683873,0.0172910094,-0.0275660828,-0.2013939768,0.5192962289,-0.2226473689,-0.2803495526,0.2294760495,-0.3391990066,0.0392387472,0.1831898838,0.184876278,0.0174749363,-0.3253744543,0.174706772,0.1322717518,-0.0281038992,0.2036432475,0.1825891435,0.0067139035,-0.4320653975,-0.0406626016,-0.3651522994,0.2856080234,-0.0994315073,0.2918941677,0.0474699661,0.4299737513,-0.1470286846,0.2040187865,-0.1468968093,0.1710050553,-0.0374894105,0.0053038467,0.0882869512,0.2963477373,-0.1005245596,-0.0368408151,0.2756737471,0.3390971422,0.0404423624,-0.0502534881,-0.3233454525,0.0756810755,0.3453169763,0.0271619223,0.204601109,0.1169341281,-0.0236117169,-0.0765623599,0.0535558946,0.273286134,-0.0373285264,0.2673544884,0.2196780592,-0.1547069699,0.1350218654,-0.0075077419,0.4994907677,-0.2631914318,0.3902081847,-0.1304592341,-0.2942380011,-0.1127515808,0.1767225266,0.0951572433,-0.3213917315,-0.2972108722,0.3748586178,-0.0889949277,-0.1698729694,0.2799259126,0.3327130973,-0.248543337,-0.3037452102,-0.068134971,0.1408743709,-0.1992450058,0.0187904555,-0.421851635,-0.0745439306,-0.2238362879,-0.4167459309,-0.1549995542,0.3106938899,0.0001319322,0.3453457952,0.8380850554,0.0523078553,-0.1164599136,0.1494684517,-0.4384101331,-0.1004165038,-0.2182468027,0.5195293427,0.2225422859,0.1640651673,-0.0957169309,0.3228781521,-0.2440561503,-0.0722122341,0.0837466121,-0.0319254696,0.1864677519,0.079782024,-0.1155349016,0.0723225921,0.3547309637,-0.3399245143,-0.0757753849,0.2287126482,0.1548018754,0.0624214709,0.6634558439,-0.1903357953,-0.1494433284,-0.2541691959,-0.3794186711,0.0446260683,0.0183061734,-0.2327300757,-0.0779548734,-0.363858372,0.1358209848,0.3931482136,-0.0465489514,0.1942265481,0.0388862118,-0.291077286,-0.2459311485,-0.127122432,-0.2429455668,-0.286403507,-0.0538008511,0.0218144748,0.0602553673,-0.0607900172,0.1765721291,-0.0910034254,-0.3659125268,0.1023856401,0.0408974178,0.0884168819,-0.2122453004,0.2533627152,0.1685374975,0.2339468151,0.513903439,0.2232467234,0.0511075296,-0.250493288,0.0126367444,0.1277921498,-0.0999452472,-0.2233893126,-0.1853364855,0.2213394493,0.070104003,0.1439287215,-0.2015253305,0.17729792,0.2823123336,0.0923544243,-0.0955821276,-0.1467555314,-0.337774545,-0.0845686346,-0.0994954631,-0.1259720325,-0.0281348322,0.2752671242,0.3266276717,-0.0365251973,0.0784481689,0.2283449024,-0.1694997996,0.115801461,0.3157924116,-0.1905090809,0.3854655027,0.2556683719,-0.0222615898,-0.017681526,-0.0211289767,0.0807342902,0.2407308072,-0.137873143,0.2707097232,-0.016586408,0.3332600594,-0.0678861663,-0.0527783036,-0.0566621535,0.1189701855,0.0997710973,-0.0448881052,-0.2961109579,0.1191897765,0.0921307877,-0.0042592441,-0.0634180903,-0.1733612269,-0.3518213928,0.2331577986,-0.0514189973,-0.2088760436,0.1475667506,-0.334433347,0.2592228055,0.0295759346,-0.3412538767,-0.0522108823,-0.2054463923,0.2089969218,0.0529111624,-0.0920000598,-0.155643478,0.5642309189,0.5620846748,0.3921133876,-0.5298598409,-0.4843091071,-0.0141239166,0.0818410516,-0.1659708172,0.024325639,0.0106068142,-0.2986755967,-0.215908885,0.0515596904,0.0790272877,-0.2116862983,-0.143138364,-0.1234049052,-0.0902454406,-0.0976947024,-0.165838182,-0.0106627587,-0.3048554361,0.4907079935,0.0869383961,0.1383474022,-0.0187547524,0.1312554777,-0.0678128973,-0.1683114469,-0.3420356214,-0.1610829085,0.1426559687,0.3432585001,-0.4180550277,0.0558481812,0.2637420893,-0.1318227053,-0.0202905796,0.4314632416,-0.2649515867,0.0927449688,-0.0009440525,0.6025671363,-0.1807419211,-0.0231507495,0.5678798556,0.2796882689,-0.0451871902,-0.0530390739,-0.1038935333,0.1486031413,0.4860756397,0.204756096,-0.0486469157,0.3489842117,-0.2798686028,0.6352140307,-0.2009723634,-0.0147747695,0.2248992473,-0.210642755,0.1744220257,-0.1710287035,0.0627716854,-0.2686358392,0.02955525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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/256","title":"[Feature request] Add a feature to dataset","comments":"Given another source of data loaded in, I want to pre-add it to the dataset so that it aligns with the indices of the arrow dataset prior to performing map.\r\n\r\nE.g. \r\n```\r\nnew_info = list of length dataset['train']\r\n\r\ndataset['train'] = dataset['train'].map(lambda x: some_function(x, new_info[index of x]))\r\n\r\ndef some_function(x, new_info_x):\r\n    # adds new_info[index of x] as a field to x\r\n    x['new_info'] = new_info_x\r\n    return x\r\n```\r\nI was thinking to instead create a new field in the arrow dataset so that instance x contains all the necessary information when map function is applied (since I don't have index information to pass to map function).","body":"Is there a straightforward way to add a field to the arrow_dataset, prior to performing map?","comment_length":103,"text":"[Feature request] Add a feature to dataset \n Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? \n Given another source of data loaded in, I want to pre-add it to the dataset so that it aligns with the indices of the arrow dataset prior to performing map.\r\n\r\nE.g. \r\n```\r\nnew_info = list of length dataset['train']\r\n\r\ndataset['train'] = dataset['train'].map(lambda x: some_function(x, new_info[index of x]))\r\n\r\ndef some_function(x, new_info_x):\r\n    # adds new_info[index of x] as a field to x\r\n    x['new_info'] = new_info_x\r\n    return x\r\n```\r\nI was thinking to instead create a new field in the arrow dataset so that instance x contains all the necessary information when map function is applied (since I don't have index information to pass to map function).","embeddings":[-0.2954547703,0.0480495133,-0.068608582,-0.1894563437,0.0562324785,0.2561274171,0.0807101727,0.1100029051,0.1961615384,-0.0207735319,0.4314366281,0.5331404209,-0.1051034182,0.2124332637,0.133281514,0.0656450167,-0.0400395468,0.3422541916,-0.0246693976,0.0526039377,-0.2568831444,-0.3627748787,0.0121611562,-0.2262430042,-0.2012324333,-0.0531029813,0.0925348029,-0.2065407038,-0.0499845408,-0.4349793494,0.0697100982,0.0547874197,-0.0611637011,-0.0775067657,-0.0001218519,-0.0082797566,0.2286789864,-0.0181808807,-0.0220237598,-0.0886155665,-0.6228856444,-0.4504071474,0.1403827667,-0.1462102532,-0.1161957681,-0.2814851701,0.0643228516,-0.4899307191,-0.0304019302,0.1176671013,0.1175913066,-0.0339107066,0.3083866239,0.0327693485,0.3443826437,0.2178616673,-0.2083014548,-0.2057977468,-0.0556959473,-0.0051278574,0.2442174554,0.1893960685,0.1678595394,-0.3340607882,0.4431711733,0.1553868949,-0.0249546971,-0.2453878671,-0.3294202387,0.0895995572,0.6357524991,-0.4809344113,-0.4458319843,-0.2342638373,0.3120888174,-0.290114969,0.0607441217,-0.1333394945,0.210457027,-0.0164413694,0.2804879546,-0.389656961,0.0144422483,0.2269136161,0.0619366057,0.476678133,0.1661749482,0.096834287,-0.1452657282,-0.1335329562,0.1443166733,-0.330093056,-0.06665048,0.5027745962,-0.2417925447,-0.2139225453,-0.0244842526,-0.438134104,-0.0016758052,0.1643468589,0.1435663551,0.0206871275,-0.2361611426,0.1020519063,0.2505217791,0.1508660913,-0.0119642578,0.2477612644,-0.1225104108,-0.4533706009,-0.0715743825,-0.2039059997,0.3690459728,-0.1363199055,0.3272722363,-0.010967344,0.3330433071,0.1570524573,0.1418698132,0.0278924368,-0.0564527139,-0.0701018795,0.0854346454,-0.0442520306,0.1940442473,-0.0491920784,-0.1129735634,0.411008358,0.2865205407,0.217422843,-0.0495889969,-0.2126169056,0.151268959,0.2483240217,-0.0767468885,0.0651071891,0.2182892412,0.0567841195,0.1074002311,0.2571184635,0.362067759,-0.0483580343,0.1796389967,0.2562591732,-0.1308117658,0.1176817268,-0.0223747231,0.2681906223,-0.2887227237,0.4301692247,-0.1666408032,-0.3181441128,-0.0180657003,0.0798423514,0.0267928187,-0.373313576,-0.3221887946,0.5601476431,0.0531609878,-0.3258930147,0.3006869256,0.0765359029,-0.4147170186,-0.3155430555,0.0478864126,0.202069357,-0.0447325408,-0.0481433496,-0.3672377765,-0.0581971146,-0.289432168,-0.3591374457,-0.1361573339,0.1373808086,-0.0681172982,0.3618087471,0.7887734771,-0.1264313608,-0.3442746103,0.045232445,-0.3030264974,0.1571156383,-0.375082165,0.5562777519,-0.1387429684,0.1508688033,0.0626032874,0.5208093524,-0.2876549959,-0.0548995435,0.1433007419,0.0251336675,0.0928553715,0.1853526533,-0.1041533202,0.0862533078,0.2928877771,-0.361335516,-0.1255096048,0.229325816,0.078717418,0.2501796186,0.2867565453,-0.1407821476,-0.2265393436,-0.3820767105,-0.4558905959,0.1355385333,0.1613528728,-0.1518810391,-0.0022721998,-0.2403021157,0.0519773215,0.3842323422,-0.0291440953,0.1029371843,-0.0354005322,-0.1379572302,-0.1275209337,-0.1152086407,-0.1917721182,-0.0880454406,0.0234132558,0.1266871393,-0.081543833,-0.0156095549,0.2958852351,-0.1038988382,-0.4534268677,0.1745183468,-0.0810087323,0.1316818893,-0.2303139865,0.2882097661,0.281044513,0.307657212,0.3753387034,0.3596666753,0.1202720925,-0.2022271901,-0.0492429622,0.2572801113,-0.0293284841,-0.152689442,-0.1535563171,0.5156170726,0.1086487621,0.2238612622,-0.2337494642,0.041144494,0.1870003939,0.1000285894,-0.2504220605,-0.103762418,-0.3828133047,0.0285269767,0.0936289281,0.0024606087,-0.0357764363,0.2042023689,0.0699372068,-0.0240182914,0.1600016057,0.2019366175,-0.2276441306,0.1079131439,0.0774967149,-0.0624136589,0.3290600181,0.2227063626,0.0126179587,0.0426483452,0.1092888191,0.160209924,0.1915973574,-0.0738721341,0.3904670775,0.1353820264,0.3821550012,0.155317679,0.0920716226,0.0518261939,-0.0285529736,0.0855422318,-0.1734751314,-0.123380743,0.1931572258,0.1512268186,-0.1907939464,-0.0727324188,-0.1309424341,-0.2570100129,-0.0422084853,0.0283493903,-0.2032609433,0.0837598816,-0.366987437,0.1900515258,0.1666791588,-0.6432436705,0.0507394411,-0.0893904567,0.2384161651,-0.0036567324,-0.0164030138,-0.174557671,0.3104876876,0.7045741677,0.3526602685,-0.4002373219,-0.5986948013,-0.1394598931,0.0326243453,0.0610160977,-0.056439463,-0.1356823742,-0.2187397331,-0.2760376334,0.0529641323,0.0978603065,-0.1257185191,-0.2376390845,-0.0766170472,0.0012320286,-0.0057821316,-0.011828755,-0.2295980006,-0.2544108927,0.3818815649,0.2282903641,0.0037640331,0.0251771454,0.1335633695,-0.1741533428,0.0961103886,-0.3459210992,-0.0677424967,0.0082773566,0.4669155777,-0.291755408,0.2223026305,-0.0565286539,-0.4282010794,-0.1681381166,0.5416530371,-0.2858798802,0.0335521512,-0.0685808808,0.4442356825,-0.00547755,0.0415530168,0.4493017793,0.1307611614,0.1019128188,-0.0397400931,-0.1687309444,0.0195706673,0.3857576847,0.1371400952,0.026542509,0.2526220083,-0.4078219831,0.8244695067,-0.096462138,-0.0982213393,0.2135765702,-0.3015334606,0.2136496007,-0.1988614351,-0.0391930006,-0.3540034592,0.118720263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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/256","title":"[Feature request] Add a feature to dataset","comments":"This is what I have so far: \r\n\r\n```\r\nimport pyarrow as pa\r\nfrom nlp.arrow_dataset import Dataset\r\n\r\naug_dataset = dataset['train'][:]\r\naug_dataset['new_info'] = new_info\r\n\r\n#reformat as arrow-table\r\nschema = dataset['train'].schema\r\n\r\n# this line doesn't work:\r\nschema.append(pa.field('new_info', pa.int32()))\r\n\r\ntable = pa.Table.from_pydict(\r\n    aug_dataset,\r\n    schema=schema\r\n)\r\ndataset['train'] = Dataset(table) \r\n```","body":"Is there a straightforward way to add a field to the arrow_dataset, prior to performing map?","comment_length":45,"text":"[Feature request] Add a feature to dataset \n Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? \n This is what I have so far: \r\n\r\n```\r\nimport pyarrow as pa\r\nfrom nlp.arrow_dataset import Dataset\r\n\r\naug_dataset = dataset['train'][:]\r\naug_dataset['new_info'] = new_info\r\n\r\n#reformat as arrow-table\r\nschema = dataset['train'].schema\r\n\r\n# this line doesn't work:\r\nschema.append(pa.field('new_info', pa.int32()))\r\n\r\ntable = pa.Table.from_pydict(\r\n    aug_dataset,\r\n    schema=schema\r\n)\r\ndataset['train'] = Dataset(table) \r\n```","embeddings":[-0.1585631967,0.1218319461,-0.0199142918,-0.2379771769,0.1159518287,0.2459182143,0.0558399968,0.2358242124,-0.0567644648,-0.0269440133,0.5227229595,0.7792691588,-0.1148941293,0.2621577084,0.4461783767,0.0180788506,0.0926241353,0.3316138089,0.0632166937,0.053030584,-0.2023163885,-0.5101904869,-0.0154500231,-0.1047873199,-0.2550474703,0.0640518516,-0.0727066621,-0.2698785365,-0.1969700605,-0.6185671687,0.0552477315,-0.0052971891,0.0488106385,-0.2825003564,-0.0001257511,-0.0990261436,0.1238243207,0.0937622264,-0.1492837667,0.0070876474,-0.1820129901,-0.5508115888,0.3477323055,-0.25052917,-0.02778388,-0.3641993701,0.0755024999,0.0092849629,-0.0952078477,0.0579496585,0.0997853279,0.0939668193,0.2876735926,0.1071146429,0.3623693585,0.2798253298,-0.3339948356,-0.0428954586,-0.0220629424,-0.2151848674,0.1690187752,0.1127569675,0.1265625507,-0.2649116814,0.3952746689,0.2403582633,0.0384079106,-0.072328493,-0.2271326333,-0.1069110855,0.6156909466,-0.6202868819,-0.3876196146,-0.2214737386,0.3082877398,-0.4906707704,-0.0614589639,0.0448310971,0.1050458103,0.0437751263,0.3411901891,-0.4858388007,-0.1245170832,0.3369037807,0.1579572558,0.5274518728,0.3024938405,0.1550373435,-0.1368014067,0.0279536378,0.1394296288,0.0006353033,-0.0988960937,0.5474140048,-0.1916419119,-0.3288538456,0.1603990942,-0.3275590837,0.0637634099,0.225965783,0.072326228,-0.0258287359,-0.1592750549,0.1315601766,0.2263168544,0.0333113261,-0.0248911455,0.2305516154,-0.0531374067,-0.4014611244,0.0675160065,-0.1941779256,0.1835125685,-0.0240348242,0.5270875692,-0.0994340554,0.5241091251,0.0916185528,0.2641537189,-0.12292023,-0.0796188489,0.0289337803,0.0015675381,0.1388510913,0.1910752356,-0.0476425886,-0.1843174994,0.4463225305,0.2602829337,0.0229133125,-0.0585689172,-0.1158423573,0.1210389733,0.2123061568,0.053764984,0.1982225925,0.12853311,-0.0099368943,-0.0215493757,0.1386262029,0.1777260602,-0.0943022966,0.1793120801,0.3338983655,-0.0447518341,0.0700507686,-0.0151229054,0.2096881419,-0.2649386227,0.4940430522,-0.0427006558,-0.3354871869,-0.182712093,0.0792179629,-0.0760878697,-0.3811966777,-0.3896959424,0.2810362875,0.1529284716,-0.2052124441,0.3210585415,0.1022731066,-0.2074391693,-0.3183969557,0.0141020566,0.0050593172,0.020591436,-0.0731970966,-0.2081436962,-0.106873326,-0.254784435,-0.314312458,-0.2401307672,0.3415870965,0.0265480988,0.316865474,0.9826906323,-0.0947195664,-0.3102529645,-0.0044013709,-0.2885476947,-0.1376720071,-0.2810827494,0.5194311142,-0.000458518,0.1911732852,-0.1188182086,0.4386410117,-0.2460013777,0.0624340326,0.1339839846,-0.0622337013,0.3955076039,0.2214668244,-0.0669111311,0.0565835573,0.2403141856,-0.1898543835,0.078507632,0.1749491245,0.1417320371,-0.0234400928,0.5813657641,-0.1480278075,-0.1587654203,-0.3559047282,-0.5103592873,0.0481979623,0.0614799298,-0.0498385392,-0.2668022215,-0.3365260661,0.1379484087,0.410826236,-0.21857135,0.0828226581,-0.0337449946,-0.2657914162,0.0313534811,-0.0390634015,-0.3062026203,-0.2801218331,0.0827199146,0.2396755666,0.0705125704,0.1131925136,0.1532941163,-0.2490605712,-0.3930204511,0.2865010798,0.0241674427,0.1350463033,-0.112665385,0.2159660608,0.2123830169,0.190021053,0.2170919031,0.2195721418,0.0773657933,-0.2173906267,-0.0081111444,0.024139272,-0.0064256126,-0.1075872108,-0.0767975301,0.2526226938,0.1183936298,0.1508035362,-0.0551127419,0.1701215357,0.297231108,0.1012447327,-0.1686341017,-0.0361541808,-0.3948580325,0.082512103,0.0132988421,-0.0205064379,-0.1242800876,0.1715178043,0.3190536797,-0.1497215629,0.1284432113,0.193891719,-0.2011642456,0.0162294768,0.2585250139,-0.1799705178,0.2840501368,0.2247115523,-0.0071285721,0.1735942513,-0.1491429657,0.1777966022,0.1507430375,-0.0082725342,0.3791612387,0.0827663839,0.491884172,0.062659882,0.0409868211,-0.0164795388,0.1325978935,0.2742210925,-0.1277945936,-0.2125937641,0.1566524804,-0.0656216145,-0.1127957776,-0.2664131224,-0.1476638168,-0.3067302108,0.0748658925,0.0802909136,-0.0718798563,0.0658462271,-0.4538493156,-0.0331082083,0.0424247161,-0.6114932895,0.0338730961,-0.2173403203,0.1986373067,-0.0420190394,0.0002152516,-0.0223866981,0.4436041713,0.5154750347,0.3509569466,-0.4484282136,-0.5637549758,-0.0260125343,-0.0625211746,-0.0568652935,0.0072709452,-0.033459954,-0.1215717122,-0.2443925738,0.1578592807,0.0520993657,-0.1704946607,-0.0017590916,-0.1390076131,-0.0746620595,-0.1779552847,-0.1160608679,-0.253651917,-0.2001410425,0.4121743441,0.3110242486,0.1660983264,0.1023302376,0.0647426173,-0.0393917114,-0.1045548469,-0.2717067003,0.0465511717,0.2692864239,0.3771940768,-0.2905590534,-0.0700872988,0.1223501712,-0.3589475155,-0.0133673809,0.3452511132,-0.2681254447,-0.1045634449,-0.1315259039,0.6544305086,-0.1809949279,0.061110694,0.4917134643,0.1800765544,0.1259661913,0.0645482615,-0.0575284921,0.0581770651,0.4751567245,0.0536932535,-0.1974352598,0.3545249999,-0.1638989151,0.6824057102,-0.2674229443,-0.0324546099,0.2890657187,-0.4521875679,0.1243514866,-0.1754645258,-0.0518148616,-0.3754043877,-0.04024511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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/256","title":"[Feature request] Add a feature to dataset","comments":"Maybe you can use `with_indices`?\r\n\r\n```python\r\nnew_info = list of length dataset['train']\r\n\r\ndef some_function(indice, x):\r\n    # adds new_info[index of x] as a field to x\r\n    x['new_info'] = new_info_x[indice]\r\n    return x\r\n\r\ndataset['train'] = dataset['train'].map(some_function, with_indices=True)\r\n```","body":"Is there a straightforward way to add a field to the arrow_dataset, prior to performing map?","comment_length":35,"text":"[Feature request] Add a feature to dataset \n Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? \n Maybe you can use `with_indices`?\r\n\r\n```python\r\nnew_info = list of length dataset['train']\r\n\r\ndef some_function(indice, x):\r\n    # adds new_info[index of x] as a field to x\r\n    x['new_info'] = new_info_x[indice]\r\n    return x\r\n\r\ndataset['train'] = dataset['train'].map(some_function, with_indices=True)\r\n```","embeddings":[-0.1078236252,-0.0950366557,-0.1245010421,-0.1514483988,0.1871568114,0.3007179201,0.1838353723,0.2558493316,0.3210432529,0.1587405056,0.528386116,0.625244379,-0.2347141653,0.3278807402,0.1876332462,-0.05337587,0.0141954916,0.2249573022,0.0846671462,0.0613886155,-0.3617742062,-0.4533320963,-0.006954384,-0.1957277209,-0.1881195903,-0.0918976218,0.0972512886,-0.3355694413,-0.131321758,-0.5429760814,-0.0301965326,-0.1167532429,-0.0923570395,0.0524035841,-0.0001186418,-0.0422028378,0.2212127596,0.1339968294,-0.0558137521,-0.1594056785,-0.5856688023,-0.5728530884,0.1825901419,-0.3350245357,-0.0429508463,-0.2290669084,0.0593489781,-0.4260806143,-0.1803305894,0.0572605245,0.1637644172,-0.0679824352,0.233760044,-0.0360014699,0.4558517039,0.1536865234,-0.3162890673,-0.2017423809,0.0060712672,0.0041402406,0.2546449006,0.2826150358,0.1268410981,-0.3548519313,0.391894877,0.1401714236,0.155552879,-0.2356152833,-0.2917853594,0.0804627389,0.5871175528,-0.2631477118,-0.4045743942,-0.0798135847,0.2646789551,-0.4920731783,-0.0741465986,-0.1404269487,0.1963732094,-0.0694496855,0.2091938555,-0.4332793653,0.0142719373,0.2940422297,0.2138616443,0.545632422,0.1143340468,0.0920650661,-0.0423269831,-0.1644961834,0.074111186,-0.0618493184,-0.0081348037,0.4773364663,-0.2043196112,-0.2738350928,0.127216801,-0.4587990046,0.044262033,0.1155173257,-0.0419312678,-0.0565995537,-0.1757650077,0.1606503874,0.1501177251,0.1076475605,0.0052302694,0.2677212358,-0.1015377939,-0.35801211,-0.0443412885,-0.2704422772,0.2254671454,-0.0649589673,0.3445422053,0.047936812,0.275036633,0.0916398391,0.1036507785,0.0120589342,-0.1663980633,-0.1238511652,0.0839854181,0.1230355203,0.2344736904,0.0054374924,-0.1229794025,0.3372631669,0.3458981216,0.3112016022,-0.107442759,-0.2126322687,0.1547345072,0.1393012255,-0.0765942559,0.0856375098,0.1823187768,-0.0269806851,0.1582476944,0.1024156809,0.2272258848,-0.1828977764,0.2606956363,0.2110901177,-0.1837568283,0.2089575231,0.0518257543,0.1167875156,-0.3144630194,0.3690935671,-0.2824328244,-0.3489378691,-0.0826415867,0.1523860693,0.1272735447,-0.3446595073,-0.19757393,0.4396517873,0.037065085,-0.189479351,0.3492400646,0.2072193325,-0.4341925681,-0.31297189,-0.0276727639,0.1455973983,0.0120927002,-0.0448054299,-0.2212402523,0.0928503871,-0.2608798444,-0.2146037519,-0.1981479973,0.2057473361,-0.0680169538,0.3534968197,0.8596593738,-0.1093169302,-0.3311024308,0.1419070363,-0.3039422035,-0.0511692688,-0.1390286088,0.5475512743,0.1005674899,0.0178816319,0.0748453587,0.2675637603,-0.1341206729,-0.081064418,0.0801983178,-0.0713017732,0.3110402822,0.0757067204,-0.1374980658,0.0827305615,0.2140202373,-0.4347311258,0.0104316166,0.1827216893,0.0353515632,0.1471180767,0.4865828753,-0.0622773357,-0.139246285,-0.4023250043,-0.315068692,0.0850114971,0.2560750246,-0.1315557212,-0.0564922057,-0.3838046789,0.0411904715,0.3376157582,-0.0930948332,0.1920477748,0.0180984214,-0.2580021322,-0.2771020532,-0.1142548919,-0.3849006593,-0.2202653587,0.0025686605,0.0340221003,0.0429672673,-0.0383333191,0.2549908161,-0.069407545,-0.4769134223,0.1799545288,0.0135041503,0.0666203126,-0.203277275,0.1755382866,0.2284716219,0.0279199891,0.3076652288,0.320923239,0.1065977663,-0.219881326,-0.0806425959,0.2785798311,-0.0108550657,-0.1345078349,-0.1162842661,0.2652430832,-0.0090507288,0.2704108357,-0.1731457859,0.0539249368,0.1970151514,0.0934338197,-0.2376272976,-0.1993034482,-0.3792415261,0.0042476188,-0.1010407358,-0.1289854646,-0.0370637812,0.0835909918,0.3455303311,-0.1041750461,0.1496624351,0.1654145569,-0.1737333685,0.2314660996,0.0443180017,-0.2211854011,0.2762586772,0.253798306,-0.0167220104,0.0387470871,0.0860501677,0.1844401509,0.2718241513,-0.0072902129,0.2178813666,0.0403656326,0.3167016506,0.076929979,0.0469002984,-0.18509534,0.0664847791,0.1358944625,-0.1457649767,-0.181527555,0.1890415996,-0.0139392493,-0.0700866729,-0.1962982118,-0.1267551035,-0.2679468095,0.027739577,-0.0038654837,-0.1771871299,0.1943042427,-0.3962599337,0.0961064696,0.2864940166,-0.6227661371,0.0914276838,-0.0902328119,0.2919758558,0.0225172043,-0.023435317,-0.1501042843,0.3781115413,0.5751972198,0.3162941039,-0.3572919965,-0.4256483614,-0.1076565832,0.0035933314,0.0003379413,-0.0602452047,0.040925093,-0.3032838404,-0.2962276042,0.0606922954,0.1335870475,-0.1346251369,-0.2070714682,0.0026516218,0.0664110258,-0.053892225,-0.1274783313,-0.145945847,-0.3532822728,0.4266149402,0.1954131424,0.1405219287,0.0611188598,0.1954496801,-0.0433334298,0.0780377537,-0.2725583911,-0.119337596,0.0527477227,0.410685569,-0.1730504781,0.1069868356,0.0351223573,-0.4268287122,-0.2257262766,0.3809039593,-0.2895036936,-0.0058323168,-0.1255987734,0.5389932394,-0.1341796219,0.3405480385,0.4850676656,0.2227980793,0.0294600651,0.0210356098,-0.1961565912,0.0219083466,0.4201479554,0.1148672998,0.0238693971,0.3983840644,-0.261421144,0.6585185528,0.0316712223,-0.0323824994,0.2063956261,-0.3531016707,0.1559422314,-0.2816554308,-0.22521469,-0.3181138933,-0.003316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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/254","title":"[Feature request] Be able to remove a specific sample of the dataset","comments":"Oh yes you can now do that with the `dataset.filter()` method that was added in #214 ","body":"As mentioned in #117, it's currently not possible to remove a sample of the dataset.\r\n\r\nBut it is a important use case : After applying some preprocessing, some samples might be empty for example. We should be able to remove these samples from the dataset, or at least mark them as `removed` so when iterating the dataset, we don't iterate these samples.\r\n\r\nI think it should be a feature. What do you think ?\r\n\r\n---\r\n\r\nAny work-around in the meantime ?","comment_length":16,"text":"[Feature request] Be able to remove a specific sample of the dataset \n As mentioned in #117, it's currently not possible to remove a sample of the dataset.\r\n\r\nBut it is a important use case : After applying some preprocessing, some samples might be empty for example. We should be able to remove these samples from the dataset, or at least mark them as `removed` so when iterating the dataset, we don't iterate these samples.\r\n\r\nI think it should be a feature. What do you think ?\r\n\r\n---\r\n\r\nAny work-around in the meantime ? \n Oh yes you can now do that with the `dataset.filter()` method that was added in #214 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/252","title":"NonMatchingSplitsSizesError error when reading the IMDB dataset","comments":"I just tried on my side and I didn't encounter your problem.\r\nApparently the script doesn't generate all the examples on your side.\r\n\r\nCan you provide the version of `nlp` you're using ?\r\nCan you try to clear your cache and re-run the code ?","body":"Hi!\r\n\r\nI am trying to load the `imdb` dataset with this line:\r\n\r\n`dataset = nlp.load_dataset('imdb', data_dir='\/A\/PATH', cache_dir='\/A\/PATH')`\r\n\r\nbut I am getting the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 517, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 363, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 421, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=5929447, num_examples=4537, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n```\r\n\r\nAm I overlooking something? Thanks!","comment_length":45,"text":"NonMatchingSplitsSizesError error when reading the IMDB dataset \n Hi!\r\n\r\nI am trying to load the `imdb` dataset with this line:\r\n\r\n`dataset = nlp.load_dataset('imdb', data_dir='\/A\/PATH', cache_dir='\/A\/PATH')`\r\n\r\nbut I am getting the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 517, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 363, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 421, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=5929447, num_examples=4537, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n```\r\n\r\nAm I overlooking something? Thanks! \n I just tried on my side and I didn't encounter your problem.\r\nApparently the script doesn't generate all the examples on your side.\r\n\r\nCan you provide the version of `nlp` you're using ?\r\nCan you try to clear your cache and re-run the code ?","embeddings":[-0.5058609843,-0.1288104951,0.0182252526,0.3497672975,0.1497054547,0.2042558789,0.0464905091,0.4214467704,0.2376457453,0.0744059384,-0.1168221682,0.1823245883,-0.2613818645,-0.101923354,-0.15735282,0.0979988351,-0.1106186137,0.2100696564,0.0744425282,0.0653068647,-0.4028107226,0.2740860283,-0.3829877377,-0.0700217038,-0.3913931847,-0.1332287341,0.1459162831,0.1515464187,0.191357404,-0.3842667341,0.3481659591,0.0109436065,0.1533981115,0.1625825912,-0.0001161558,0.010485352,0.6431316733,-0.2872529328,-0.2771607041,-0.2786664367,-0.2890476584,-0.2957006395,0.2998387218,-0.3556797504,-0.0921420455,-0.0069964286,0.3332283497,-0.4666482806,0.1093039885,0.1632326394,0.17784594,0.1451750994,-0.1186726689,0.1979513317,0.0227693487,-0.1154360548,-0.1067392081,0.0767500103,-0.0245760996,-0.0722568482,-0.1163303331,0.2309108973,-0.4465887249,0.1689139605,0.3901188076,0.175498262,0.3332034945,-0.2765008509,0.075174287,0.2043976337,0.5286696553,-0.1759230644,-0.2950552106,-0.3949430287,0.0285661221,-0.2426873296,0.164115116,0.3492821455,-0.2348972708,-0.0012560944,-0.3458954692,0.0242989697,-0.2227833569,0.1689743102,0.3957193494,0.1043043733,0.1111679003,0.0797282085,0.2857824266,-0.0581012443,0.4545836747,-0.0850620642,0.1459582746,0.1444033682,-0.2710443139,-0.0759877861,-0.2171054035,0.2746360898,0.0082770418,0.3213576674,0.1967979074,0.0516806841,0.1436003894,0.3109807074,0.2449324131,0.0170768797,0.1431279927,0.3343563378,0.0498044267,0.1574208736,-0.1261125505,-0.0362699814,0.0853060409,-0.0314564854,0.0814510062,0.1571653485,0.2686862648,-0.2099481523,-0.5460675359,-0.0010156493,-0.2332894206,-0.1651888639,0.0959894434,0.2754747272,-0.1536782682,0.3319395185,0.042252142,0.1424370408,-0.3492205441,-0.1216831952,-0.2368981838,0.172750473,-0.2033310533,0.0771291628,0.31588009,0.1044000685,0.4351963103,-0.1986221075,-0.1153535098,-0.0420896672,0.4212338924,-0.1903631389,-0.0745942444,0.2226096541,0.1970767379,0.1623467505,0.0870599002,-0.0507297404,-0.3188578784,0.1813304275,0.0028089394,-0.3774318993,0.0150839789,0.1790364236,-0.1909037083,-0.0862755403,-0.034753602,0.1134413704,0.1302699894,-0.4634093642,-0.2054534554,-0.1073764414,-0.2702164054,-0.1232669726,-0.0165222269,0.5770250559,0.0338287167,-0.0637336597,-0.1441337615,0.008088435,0.5104398727,0.3860457838,-0.0508839116,-0.1670527458,-0.3651423454,0.3389675915,0.2271953523,-0.4194648564,-0.4263260365,0.4837440848,0.006436381,0.2578959167,0.3255821764,0.4054284692,0.1204781532,-0.0325896367,0.0521423072,0.4240181148,-0.1317705214,-0.0142845968,-0.2874594629,-0.2950439453,0.6005905271,0.1494239867,0.0687649548,-0.0436367318,0.0187510271,0.4665596485,0.3268418014,-0.001648157,-0.0108888811,0.1649563313,-0.2478443682,0.3226459622,-0.1736309826,-0.1761586815,-0.5091295242,0.074178949,-0.1129090339,0.090829961,-0.1763046086,-0.1781263351,-0.0882440358,-0.3431604803,-0.2358764261,0.0028419192,0.1064237207,0.2504612803,-0.0604404472,0.139773801,-0.1747403294,0.5486708283,0.0023630376,0.082942225,-0.8483415246,0.3239140511,-0.1733106077,-0.2676151097,0.3004835248,0.2964940369,0.2826043665,-0.1530664861,-0.1141646951,0.3894184828,0.0910348073,0.3174045682,-0.1669467986,-0.0319519527,0.2927332819,-0.363134414,0.1423194408,0.2724507153,0.1341876537,-0.1109847426,-0.0392149165,0.2899010479,-0.3300355971,0.2139120996,0.0790896118,0.0629346669,0.1603439301,-0.1450172812,-0.0745699927,-0.4325863719,0.4534665942,0.1690415144,-0.071861811,0.0622173026,-0.3637724817,-0.1239177287,0.4598456919,0.1139727235,0.0168150458,0.0469442867,-0.0513484478,-0.1667591631,0.1503908932,0.1913670003,0.2858821452,0.3570718765,-0.1138631478,0.0858264789,-0.094161734,-0.1824414432,0.1085890383,0.1541863382,0.2679120898,0.3302231431,-0.1167620197,-0.0563683361,-0.4007021487,-0.2456368655,0.0161414798,0.3377129138,-0.2668766081,-0.1578804553,-0.4097839892,-0.4801176488,-0.056841068,-0.0843887404,-0.1401246637,-0.4054656029,-0.1167454571,0.5066664815,0.0902764648,0.2009690404,-0.386033684,-0.056571804,-0.0384274311,-0.145033434,-0.1585079283,-0.3335529864,-0.0711717978,-0.0457949005,0.3911580443,0.2297028601,0.3629732132,-0.4176098406,-0.0948796198,0.0653366596,0.0540711619,-0.1577545255,-0.0800911337,0.0756564736,0.0688458905,0.0790497363,0.1566694677,-0.3845457137,0.2842551768,-0.2741718292,-0.0760483891,0.1950515956,0.1936430484,0.0419287495,0.0353900678,-0.3608492613,-0.3879698217,-0.4397134185,0.0191146694,0.2849380672,0.1020080447,0.165165633,-0.0089086527,0.0223700646,0.0546234101,0.1785691381,-0.1242079139,0.0252420697,0.3226629198,-0.0290788095,-0.1172380075,0.0563454255,0.0549977571,0.1520994306,-0.0223549381,-0.4382071197,-0.1349186003,-0.1488203108,0.1116709709,-0.090152517,-0.2011874467,0.3662034869,0.1344199479,-0.0220552888,-0.0281651579,-0.2943358719,0.0167569909,0.0694208816,0.2837187648,-0.0141324084,0.5791600943,-0.0643859282,0.3804620802,0.4987338781,0.0455647632,0.2685537636,-0.1942719817,0.2392872423,-0.0748686567,-0.3963817358,0.0430059806,-0.065911904,-0.090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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/252","title":"NonMatchingSplitsSizesError error when reading the IMDB dataset","comments":"Hi ! The cache is at ~\/.cache\/huggingface\r\nYou can just delete this folder if needed :)","body":"Hi!\r\n\r\nI am trying to load the `imdb` dataset with this line:\r\n\r\n`dataset = nlp.load_dataset('imdb', data_dir='\/A\/PATH', cache_dir='\/A\/PATH')`\r\n\r\nbut I am getting the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 517, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 363, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 421, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=5929447, num_examples=4537, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n```\r\n\r\nAm I overlooking something? Thanks!","comment_length":16,"text":"NonMatchingSplitsSizesError error when reading the IMDB dataset \n Hi!\r\n\r\nI am trying to load the `imdb` dataset with this line:\r\n\r\n`dataset = nlp.load_dataset('imdb', data_dir='\/A\/PATH', cache_dir='\/A\/PATH')`\r\n\r\nbut I am getting the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 517, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 363, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 421, in _download_and_prepare\r\n    verify_splits(self.info.splits, split_dict)\r\n  File \"\/mounts\/Users\/cisintern\/antmarakis\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/utils\/info_utils.py\", line 70, in verify_splits\r\n    raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=5929447, num_examples=4537, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n```\r\n\r\nAm I overlooking something? Thanks! \n Hi ! The cache is at ~\/.cache\/huggingface\r\nYou can just delete this folder if needed :)","embeddings":[-0.5058609843,-0.1288104951,0.0182252526,0.3497672975,0.1497054547,0.2042558789,0.0464905091,0.4214467704,0.2376457453,0.0744059384,-0.1168221682,0.1823245883,-0.2613818645,-0.101923354,-0.15735282,0.0979988351,-0.1106186137,0.2100696564,0.0744425282,0.0653068647,-0.4028107226,0.2740860283,-0.3829877377,-0.0700217038,-0.3913931847,-0.1332287341,0.1459162831,0.1515464187,0.191357404,-0.3842667341,0.3481659591,0.0109436065,0.1533981115,0.1625825912,-0.0001161558,0.010485352,0.6431316733,-0.2872529328,-0.2771607041,-0.2786664367,-0.2890476584,-0.2957006395,0.2998387218,-0.3556797504,-0.0921420455,-0.0069964286,0.3332283497,-0.4666482806,0.1093039885,0.1632326394,0.17784594,0.1451750994,-0.1186726689,0.1979513317,0.0227693487,-0.1154360548,-0.1067392081,0.0767500103,-0.0245760996,-0.0722568482,-0.1163303331,0.2309108973,-0.4465887249,0.1689139605,0.3901188076,0.175498262,0.3332034945,-0.2765008509,0.075174287,0.2043976337,0.5286696553,-0.1759230644,-0.2950552106,-0.3949430287,0.0285661221,-0.2426873296,0.164115116,0.3492821455,-0.2348972708,-0.0012560944,-0.3458954692,0.0242989697,-0.2227833569,0.1689743102,0.3957193494,0.1043043733,0.1111679003,0.0797282085,0.2857824266,-0.0581012443,0.4545836747,-0.0850620642,0.1459582746,0.1444033682,-0.2710443139,-0.0759877861,-0.2171054035,0.2746360898,0.0082770418,0.3213576674,0.1967979074,0.0516806841,0.1436003894,0.3109807074,0.2449324131,0.0170768797,0.1431279927,0.3343563378,0.0498044267,0.1574208736,-0.1261125505,-0.0362699814,0.0853060409,-0.0314564854,0.0814510062,0.1571653485,0.2686862648,-0.2099481523,-0.5460675359,-0.0010156493,-0.2332894206,-0.1651888639,0.0959894434,0.2754747272,-0.1536782682,0.3319395185,0.042252142,0.1424370408,-0.3492205441,-0.1216831952,-0.2368981838,0.172750473,-0.2033310533,0.0771291628,0.31588009,0.1044000685,0.4351963103,-0.1986221075,-0.1153535098,-0.0420896672,0.4212338924,-0.1903631389,-0.0745942444,0.2226096541,0.1970767379,0.1623467505,0.0870599002,-0.0507297404,-0.3188578784,0.1813304275,0.0028089394,-0.3774318993,0.0150839789,0.1790364236,-0.1909037083,-0.0862755403,-0.034753602,0.1134413704,0.1302699894,-0.4634093642,-0.2054534554,-0.1073764414,-0.2702164054,-0.1232669726,-0.0165222269,0.5770250559,0.0338287167,-0.0637336597,-0.1441337615,0.008088435,0.5104398727,0.3860457838,-0.0508839116,-0.1670527458,-0.3651423454,0.3389675915,0.2271953523,-0.4194648564,-0.4263260365,0.4837440848,0.006436381,0.2578959167,0.3255821764,0.4054284692,0.1204781532,-0.0325896367,0.0521423072,0.4240181148,-0.1317705214,-0.0142845968,-0.2874594629,-0.2950439453,0.6005905271,0.1494239867,0.0687649548,-0.0436367318,0.0187510271,0.4665596485,0.3268418014,-0.001648157,-0.0108888811,0.1649563313,-0.2478443682,0.3226459622,-0.1736309826,-0.1761586815,-0.5091295242,0.074178949,-0.1129090339,0.090829961,-0.1763046086,-0.1781263351,-0.0882440358,-0.3431604803,-0.2358764261,0.0028419192,0.1064237207,0.2504612803,-0.0604404472,0.139773801,-0.1747403294,0.5486708283,0.0023630376,0.082942225,-0.8483415246,0.3239140511,-0.1733106077,-0.2676151097,0.3004835248,0.2964940369,0.2826043665,-0.1530664861,-0.1141646951,0.3894184828,0.0910348073,0.3174045682,-0.1669467986,-0.0319519527,0.2927332819,-0.363134414,0.1423194408,0.2724507153,0.1341876537,-0.1109847426,-0.0392149165,0.2899010479,-0.3300355971,0.2139120996,0.0790896118,0.0629346669,0.1603439301,-0.1450172812,-0.0745699927,-0.4325863719,0.4534665942,0.1690415144,-0.071861811,0.0622173026,-0.3637724817,-0.1239177287,0.4598456919,0.1139727235,0.0168150458,0.0469442867,-0.0513484478,-0.1667591631,0.1503908932,0.1913670003,0.2858821452,0.3570718765,-0.1138631478,0.0858264789,-0.094161734,-0.1824414432,0.1085890383,0.1541863382,0.2679120898,0.3302231431,-0.1167620197,-0.0563683361,-0.4007021487,-0.2456368655,0.0161414798,0.3377129138,-0.2668766081,-0.1578804553,-0.4097839892,-0.4801176488,-0.056841068,-0.0843887404,-0.1401246637,-0.4054656029,-0.1167454571,0.5066664815,0.0902764648,0.2009690404,-0.386033684,-0.056571804,-0.0384274311,-0.145033434,-0.1585079283,-0.3335529864,-0.0711717978,-0.0457949005,0.3911580443,0.2297028601,0.3629732132,-0.4176098406,-0.0948796198,0.0653366596,0.0540711619,-0.1577545255,-0.0800911337,0.0756564736,0.0688458905,0.0790497363,0.1566694677,-0.3845457137,0.2842551768,-0.2741718292,-0.0760483891,0.1950515956,0.1936430484,0.0419287495,0.0353900678,-0.3608492613,-0.3879698217,-0.4397134185,0.0191146694,0.2849380672,0.1020080447,0.165165633,-0.0089086527,0.0223700646,0.0546234101,0.1785691381,-0.1242079139,0.0252420697,0.3226629198,-0.0290788095,-0.1172380075,0.0563454255,0.0549977571,0.1520994306,-0.0223549381,-0.4382071197,-0.1349186003,-0.1488203108,0.1116709709,-0.090152517,-0.2011874467,0.3662034869,0.1344199479,-0.0220552888,-0.0281651579,-0.2943358719,0.0167569909,0.0694208816,0.2837187648,-0.0141324084,0.5791600943,-0.0643859282,0.3804620802,0.4987338781,0.0455647632,0.2685537636,-0.1942719817,0.2392872423,-0.0748686567,-0.3963817358,0.0430059806,-0.065911904,-0.09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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/249","title":"[Dataset created] some critical small issues when I was creating a dataset","comments":"Alright I think I fixed all the problems you mentioned. Thanks again, that will be useful for many people.\r\nThere is still more work needed for point 7. but we plan to have some nice docs soon.","body":"Hi, I successfully created a dataset and has made a pr #248.\r\nBut I have encountered several problems when I was creating it, and those should be easy to fix.\r\n\r\n1. Not found dataset_info.json\r\nshould be fixed by #241 , eager to wait it be merged.\r\n\r\n2. Forced to install `apach_beam`\r\nIf we should install it, then it might be better to include it in the pakcage dependency or specified in `CONTRIBUTING.md`\r\n```\r\nTraceback (most recent call last):\r\n  File \"nlp-cli\", line 10, in <module>\r\n    from nlp.commands.run_beam import RunBeamCommand\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/commands\/run_beam.py\", line 6, in <module>\r\n    import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n```\r\n\r\n3.  `cached_dir` is `None`\r\n```\r\nFile \"\/home\/yisiang\/nlp\/src\/nlp\/datasets\/bookscorpus\/aea0bd5142d26df645a8fce23d6110bb95ecb81772bb2a1f29012e329191962c\/bookscorpus.py\", line 88, in _split_generators\r\n    downloaded_path_or_paths = dl_manager.download_custom(_GDRIVE_FILE_ID, download_file_from_google_drive)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/download_manager.py\", line 128, in download_custom\r\n    downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/download_manager.py\", line 126, in url_to_downloaded_path\r\n    return os.path.join(self._download_config.cache_dir, hash_url_to_filename(url))\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/nlppr\/lib\/python3.7\/posixpath.py\", line 80, in join\r\n    a = os.fspath(a)\r\n```\r\nThis is because this line\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/2e0a8639a79b1abc848cff5c669094d40bba0f63\/src\/nlp\/commands\/test.py#L30-L32\r\nAnd I add `--cache_dir=\"....\"` to `python nlp-cli test datasets\/<your-dataset-folder> --save_infos --all_configs`  in the doc, finally I could pass this error.\r\nBut it seems to ignore my arg and use `\/home\/yisiang\/.cache\/huggingface\/datasets\/bookscorpus\/plain_text\/1.0.0` as cahe_dir\r\n\r\n4. There is no `pytest`\r\nSo maybe in the doc we should specify a step to install pytest\r\n\r\n5. Not enough capacity in my `\/tmp`\r\nWhen run test for dummy data, I don't know why it ask me for 5.6g to download something, \r\n```\r\ndef download_and_prepare\r\n...\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n                raise IOError(\r\n                    \"Not enough disk space. Needed: {} (download: {}, generated: {})\".format(\r\n                        utils.size_str(self.info.size_in_bytes or 0),\r\n                        utils.size_str(self.info.download_size or 0),\r\n>                       utils.size_str(self.info.dataset_size or 0),\r\n                    )\r\n                )\r\nE               OSError: Not enough disk space. Needed: 5.62 GiB (download: 1.10 GiB, generated: 4.52 GiB)\r\n```\r\nI add a `processed_temp_dir=\"some\/dir\"; raw_temp_dir=\"another\/dir\"` to 71, and the test passed\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/a67a6c422dece904b65d18af65f0e024e839dbe8\/tests\/test_dataset_common.py#L70-L72\r\n\r\nI suggest we can create tmp dir under the `\/home\/user\/tmp` but not `\/tmp`, because take our lab server for example, everyone use `\/tmp` thus it has not much capacity. Or at least we can improve error message, so the user know is what directory has no space and how many has it lefted. Or we could do both.\r\n\r\n6. name of datasets\r\nI was surprised by the dataset name `books_corpus`, and didn't know it is from `class BooksCorpus(nlp.GeneratorBasedBuilder)` . I change it to `Bookscorpus` afterwards. I think this point shold be also on the doc.\r\n\r\n7. More thorough doc to how to create `dataset.py`\r\nI believe there will be.\r\n\r\n**Feel free to close this issue** if you think these are solved.","comment_length":37,"text":"[Dataset created] some critical small issues when I was creating a dataset \n Hi, I successfully created a dataset and has made a pr #248.\r\nBut I have encountered several problems when I was creating it, and those should be easy to fix.\r\n\r\n1. Not found dataset_info.json\r\nshould be fixed by #241 , eager to wait it be merged.\r\n\r\n2. Forced to install `apach_beam`\r\nIf we should install it, then it might be better to include it in the pakcage dependency or specified in `CONTRIBUTING.md`\r\n```\r\nTraceback (most recent call last):\r\n  File \"nlp-cli\", line 10, in <module>\r\n    from nlp.commands.run_beam import RunBeamCommand\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/commands\/run_beam.py\", line 6, in <module>\r\n    import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n```\r\n\r\n3.  `cached_dir` is `None`\r\n```\r\nFile \"\/home\/yisiang\/nlp\/src\/nlp\/datasets\/bookscorpus\/aea0bd5142d26df645a8fce23d6110bb95ecb81772bb2a1f29012e329191962c\/bookscorpus.py\", line 88, in _split_generators\r\n    downloaded_path_or_paths = dl_manager.download_custom(_GDRIVE_FILE_ID, download_file_from_google_drive)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/download_manager.py\", line 128, in download_custom\r\n    downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/download_manager.py\", line 126, in url_to_downloaded_path\r\n    return os.path.join(self._download_config.cache_dir, hash_url_to_filename(url))\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/nlppr\/lib\/python3.7\/posixpath.py\", line 80, in join\r\n    a = os.fspath(a)\r\n```\r\nThis is because this line\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/2e0a8639a79b1abc848cff5c669094d40bba0f63\/src\/nlp\/commands\/test.py#L30-L32\r\nAnd I add `--cache_dir=\"....\"` to `python nlp-cli test datasets\/<your-dataset-folder> --save_infos --all_configs`  in the doc, finally I could pass this error.\r\nBut it seems to ignore my arg and use `\/home\/yisiang\/.cache\/huggingface\/datasets\/bookscorpus\/plain_text\/1.0.0` as cahe_dir\r\n\r\n4. There is no `pytest`\r\nSo maybe in the doc we should specify a step to install pytest\r\n\r\n5. Not enough capacity in my `\/tmp`\r\nWhen run test for dummy data, I don't know why it ask me for 5.6g to download something, \r\n```\r\ndef download_and_prepare\r\n...\r\nif not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):\r\n                raise IOError(\r\n                    \"Not enough disk space. Needed: {} (download: {}, generated: {})\".format(\r\n                        utils.size_str(self.info.size_in_bytes or 0),\r\n                        utils.size_str(self.info.download_size or 0),\r\n>                       utils.size_str(self.info.dataset_size or 0),\r\n                    )\r\n                )\r\nE               OSError: Not enough disk space. Needed: 5.62 GiB (download: 1.10 GiB, generated: 4.52 GiB)\r\n```\r\nI add a `processed_temp_dir=\"some\/dir\"; raw_temp_dir=\"another\/dir\"` to 71, and the test passed\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/a67a6c422dece904b65d18af65f0e024e839dbe8\/tests\/test_dataset_common.py#L70-L72\r\n\r\nI suggest we can create tmp dir under the `\/home\/user\/tmp` but not `\/tmp`, because take our lab server for example, everyone use `\/tmp` thus it has not much capacity. Or at least we can improve error message, so the user know is what directory has no space and how many has it lefted. Or we could do both.\r\n\r\n6. name of datasets\r\nI was surprised by the dataset name `books_corpus`, and didn't know it is from `class BooksCorpus(nlp.GeneratorBasedBuilder)` . I change it to `Bookscorpus` afterwards. I think this point shold be also on the doc.\r\n\r\n7. More thorough doc to how to create `dataset.py`\r\nI believe there will be.\r\n\r\n**Feel free to close this issue** if you think these are solved. \n Alright I think I fixed all the problems you mentioned. Thanks again, that will be useful for many people.\r\nThere is still more work needed for point 7. but we plan to have some nice docs soon.","embeddings":[-0.2165798396,-0.0164848957,-0.1206973046,0.3510122001,0.2939466834,-0.0238549132,0.086690411,0.4066426158,-0.1998541057,0.0216472019,0.2210519165,0.2012127191,-0.2307064533,-0.0083013931,0.1113757342,-0.1998809725,0.2577252984,0.309070766,0.21963422,-0.1952063143,-0.4448091984,0.1668979228,-0.0202732198,0.0794764906,-0.1854971647,-0.3484351039,0.0012606009,0.3041497171,-0.27669245,-0.3347336054,0.0429069027,-0.0216965955,0.061426077,0.1791488975,-0.0001148037,-0.1664888561,0.3816398084,-0.1286742687,-0.4447022676,-0.0524816103,-0.2777306139,-0.5376020074,0.1146237329,-0.3695807159,0.1170074046,-0.4766104817,-0.2099819928,-0.2251408994,0.5323174,0.3592292964,0.2335041165,0.2630361617,0.2306550294,-0.0752441138,0.3409655988,0.0986781269,-0.2740069926,-0.0909317881,0.0840169713,0.191456601,0.0731679052,0.1320601255,0.1426730156,-0.1103613377,0.4137816429,-0.1641572863,-0.0283232555,-0.5333042741,-0.0329061784,-0.0450610407,0.4773083031,-0.3531982601,-0.3011023998,-0.3424141109,0.1612422019,-0.1395581961,0.238892436,0.4321993887,-0.1544614136,-0.1136258841,-0.1933754086,-0.1405055672,-0.2878140509,0.1813217252,0.4124358594,0.0853471979,0.1599856317,0.0504578277,-0.0027547576,-0.1099818051,0.1672928035,-0.3342158794,-0.0749942809,0.3552259803,-0.1991508752,-0.1965242326,-0.0443683229,0.0313737169,0.0599546842,0.2317240387,-0.2460917979,0.0044569359,-0.0118472353,0.0019146631,0.1471191496,0.1913504303,0.1334288269,0.0652819648,0.1037480012,-0.039223291,0.2899758518,0.0508449227,-0.0023807478,0.0406709425,-0.1865070015,0.0519573689,-0.0040365495,-0.0670203939,-0.0239564143,0.1387755275,-0.3734666407,-0.2500493526,0.042333439,0.2187863439,0.1310413629,0.2003273368,-0.0677166432,0.2659163773,-0.2425889075,-0.2212205827,-0.0890087709,0.1785895973,-0.3464155793,-0.008724126,0.4041240513,0.3678691387,0.3285503387,-0.0558720306,-0.1568581462,0.0804305598,0.3698014021,-0.1725635231,-0.164951548,0.3664527833,0.1163076907,0.3178191185,-0.2282937914,-0.0065453667,-0.1832158566,0.0733555034,0.0013044693,-0.2434278727,-0.0742869303,0.2257186919,-0.3786150217,0.3559951782,0.0095038917,0.085091956,0.1143025979,-0.2616052032,-0.1059470549,0.000416379,-0.0401857942,-0.3765689731,0.2222083956,0.3557888269,-0.2729791701,-0.117356427,-0.2392379493,-0.4322434068,-0.172145471,0.0143524855,-0.1099702269,0.4447313845,-0.1886315346,0.0590488017,0.5762619376,-0.1736322492,-0.3862493932,-0.0011030184,0.0378160216,-0.161188066,0.1443196833,0.0966827124,0.0397116542,-0.2814246118,-0.3508945405,0.3981647193,-0.0608353876,0.0863966048,-0.3207007647,-0.0957875624,0.1719620228,0.2355937362,-0.009895077,0.0999968201,0.2488787919,0.2356128842,-0.0183623042,-0.1480812132,0.2485683262,0.3226508498,0.3735436797,-0.3224754632,0.0843802467,0.0609972142,-0.4909184277,0.0059003527,-0.4745084047,0.0989301354,-0.1233595684,0.004432248,-0.2629866004,-0.0900884494,-0.0647096708,-0.3086581826,0.1512106508,-0.078992255,0.1919051856,-0.1069257483,-0.3420167267,0.5311259031,-0.1893766224,0.068848528,-0.5045070648,0.218655467,-0.1654846072,-0.121896334,0.3162867129,0.591373682,0.1859377474,-0.1297962517,-0.1571085155,0.376141876,-0.0072881156,0.1002138332,0.1314674765,0.108823508,0.3348307014,-0.1476595104,0.3715452254,-0.2893131375,0.108980827,-0.2142601311,-0.3619832397,0.2575721145,-0.0945694,0.1903479993,0.2109564394,-0.0040955762,0.2478683144,-0.0905265659,0.0391285047,-0.1222396418,0.2752211988,0.2415908575,0.3407287896,0.2105956227,0.1973709315,0.2309572399,0.2959473133,-0.0584616847,-0.1581475288,0.2084072828,-0.0477813818,-0.1669331044,0.2196256369,0.4565879405,0.5612210035,0.2158060372,-0.3815115392,0.1613033712,0.0972903296,-0.1790976673,0.1301805824,0.2057107389,0.267224133,0.0388933457,0.2177975923,0.0833001956,0.0917641595,-0.012623868,0.0895320624,0.3389057219,-0.1725993156,-0.156357497,-0.031914182,-0.3260663152,-0.1701599509,0.3311553299,-0.2817266881,-0.3784973323,-0.0004050345,-0.1697893739,-0.0229077768,0.2918809056,0.2162147611,0.2618064284,-0.1883648634,0.0976374298,-0.3757290244,-0.3888498545,-0.1720531881,0.1114448011,0.1218660325,0.3295152783,0.5941365957,-0.0154186925,0.12340004,-0.1669679731,-0.1062984541,0.0704004169,-0.0434974879,0.1907853782,0.0079046916,0.2060802579,-0.0516079739,-0.2771467268,0.1803607047,-0.0872100592,-0.2609621286,-0.0134241097,-0.147666499,-0.0718898848,-0.0679896995,-0.5194193125,-0.4250129759,-0.2148083299,0.1327863038,0.3100067079,0.1828389317,-0.0841341466,0.141230613,-0.0181512292,-0.0019709226,-0.0615515746,0.0066660936,0.0194676388,0.2024261504,-0.2755369544,-0.4629991949,0.0172609836,-0.2323603481,0.1478987336,0.1514241397,-0.3561945558,-0.0858034939,-0.1002788767,0.2035038769,0.0486067049,0.2767562568,0.3269117773,0.1419498324,0.0358995572,-0.1744469106,-0.3353277445,0.1088822708,-0.1908497959,0.59681952,-0.0053010136,0.1931440681,-0.3918654323,0.8372858167,0.4679619968,-0.1322748065,0.2019901276,0.0645503551,0.2530503273,-0.0333257355,-0.2155572325,0.5519694686,0.1865862757,0.1339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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/246","title":"What is the best way to cache a dataset? ","comments":"Everything is already cached by default in \ud83e\udd17nlp (in particular dataset\nloading and all the \u201cmap()\u201d operations) so I don\u2019t think you need to do any\nspecific caching in streamlit.\n\nTell us if you feel like it\u2019s not the case.\n\nOn Sat, 6 Jun 2020 at 13:02, Fabrizio Milo <notifications@github.com> wrote:\n\n> For example if I want to use streamlit with a nlp dataset:\n>\n> @st.cache\n> def load_data():\n>     return nlp.load_dataset('squad')\n>\n> This code raises the error \"uncachable object\"\n>\n> Right now I just fixed with a constant for my specific case:\n>\n>     @st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})\n>\n> But I was curious to know what is the best way in general\n>\n> \u2014\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/nlp\/issues\/246>, or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABYDIHKAKO7CWGX2QY55UXLRVIO3ZANCNFSM4NV333RQ>\n> .\n>\n","body":"For example if I want to use streamlit with a nlp dataset:\r\n\r\n```\r\n@st.cache\r\ndef load_data():\r\n    return nlp.load_dataset('squad')\r\n```\r\nThis code raises the error \"uncachable object\"\r\n\r\nRight now I just fixed with a constant for my specific case:\r\n```\r\n    @st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})\r\n```\r\nBut I was curious to know what is the best way in general\r\n\r\n","comment_length":151,"text":"What is the best way to cache a dataset?  \n For example if I want to use streamlit with a nlp dataset:\r\n\r\n```\r\n@st.cache\r\ndef load_data():\r\n    return nlp.load_dataset('squad')\r\n```\r\nThis code raises the error \"uncachable object\"\r\n\r\nRight now I just fixed with a constant for my specific case:\r\n```\r\n    @st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})\r\n```\r\nBut I was curious to know what is the best way in general\r\n\r\n \n Everything is already cached by default in \ud83e\udd17nlp (in particular dataset\nloading and all the \u201cmap()\u201d operations) so I don\u2019t think you need to do any\nspecific caching in streamlit.\n\nTell us if you feel like it\u2019s not the case.\n\nOn Sat, 6 Jun 2020 at 13:02, Fabrizio Milo <notifications@github.com> wrote:\n\n> For example if I want to use streamlit with a nlp dataset:\n>\n> @st.cache\n> def load_data():\n>     return nlp.load_dataset('squad')\n>\n> This code raises the error \"uncachable object\"\n>\n> Right now I just fixed with a constant for my specific case:\n>\n>     @st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})\n>\n> But I was curious to know what is the best way in general\n>\n> \u2014\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/nlp\/issues\/246>, or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABYDIHKAKO7CWGX2QY55UXLRVIO3ZANCNFSM4NV333RQ>\n> .\n>\n","embeddings":[-0.0791417584,-0.0054426533,0.0433646031,0.0309611745,0.3594623804,-0.1114412323,-0.0066469391,0.1171398461,0.0686439276,0.1308163553,0.1577533782,0.0367619656,-0.1826368272,0.1247068271,0.4915667176,-0.0963596031,0.0291531328,0.1217107698,-0.0822886825,0.0933153853,-0.1159205288,-0.2149104029,-0.0649334118,-0.1643327922,-0.29099527,-0.1210641488,-0.1207960397,0.0799476579,0.0311479159,-0.2001817971,0.3986590505,0.191817075,0.1369281113,-0.0135208312,-0.0001179134,-0.0179088991,0.3497399092,-0.2958795726,-0.3403178453,-0.2064831704,-0.3478736877,-0.3250800073,0.1356263608,-0.0289090984,-0.316403389,-0.1195975393,0.2626536191,-0.440908879,0.4450412095,0.1855327934,0.1102296188,-0.0131289838,0.1342876852,0.3962053359,0.0525613651,-0.1016618237,-0.1999524236,-0.1110631078,0.0836441219,0.0082153268,0.0463165529,0.2444910556,-0.1063841432,-0.0312433932,0.6360678673,-0.0506441221,-0.464953959,-0.2141303718,-0.0844174176,0.1957267821,0.6592319012,-0.2575154901,-0.5362505317,-0.3742972016,0.0216725916,-0.4770931304,0.1869377196,0.1748289317,-0.0589700304,0.2702479661,-0.4797565043,-0.3037990034,0.044083152,0.1916121244,0.2693197131,0.0252833385,-0.0207847878,0.1667786837,0.0161773544,-0.1872114241,0.1719463915,-0.0714344233,-0.1203716174,0.2191658467,-0.3066838086,0.1190677658,0.0155530497,0.2072263509,0.1255341023,0.1493085772,0.5070616007,0.2352126241,0.128145799,0.076477088,-0.0187271051,0.4619321227,0.0078621535,-0.0626628101,0.102245681,-0.1494984925,-0.2285330147,-0.1756053567,0.1721443832,0.2349360883,0.2839807272,0.1700600982,0.0865028948,-0.3802713752,0.1799581796,-0.108186543,-0.2296739519,-0.2214444578,0.1230745986,0.3695170879,-0.1252463013,0.295811832,-0.2560270131,0.0321612917,-0.1035373136,-0.3668451607,-0.1925476789,0.008249661,-0.0521777943,0.3953482211,0.1942145824,-0.487600714,0.1344993711,-0.0439002626,-0.0759318992,-0.0127570201,0.2849001884,-0.179495275,0.2617857456,0.1858826131,-0.0053232717,0.0123102395,-0.0514554717,-0.0501557589,-0.2150112987,0.2134146988,-0.1483264267,-0.7214868665,0.2737378776,0.0565578006,-0.4841581285,-0.1595443636,-0.3712064326,0.5935316086,0.2733146846,-0.0156454053,0.1179438531,0.0013978113,-0.1824920177,-0.3100670874,0.2377763093,0.2972569764,-0.0023034422,-0.1332862973,0.1639194489,0.0847359374,0.2712289989,0.2201752216,-0.3504532874,0.0753714368,0.0694509149,-0.1782054901,0.640879333,-0.1535963416,-0.5387370586,-0.0192201193,0.2734828889,0.2440693825,0.1072872877,0.315964669,0.0151742958,-0.0607365854,-0.0661827102,0.4188011289,-0.1644411832,-0.0135387508,-0.312063396,-0.428517282,0.5561183691,0.0591845736,0.0112291435,0.0635528639,0.0541882142,0.5534829497,-0.1423951238,-0.156803906,0.0572758839,0.4245156348,-0.0728099421,-0.1383220255,-0.0748131797,-0.1953760684,-0.7898908257,0.1433190703,-0.1094537377,-0.0945420861,-0.0670008063,-0.2357566804,0.4408468902,-0.1568446904,-0.0268691014,-0.1255964488,-0.0126039758,0.0297756214,0.5305255055,-0.1319667995,-0.4664229751,0.57159096,0.1120155901,0.1440637261,-0.3560891151,-0.1206869408,0.067279242,-0.3497923613,0.1039767414,0.2700039446,0.1601337045,-0.1713483781,-0.1626326293,0.1947911531,-0.2408110052,0.3363534212,-0.1240032315,0.3826124668,0.0867004916,-0.1623900086,0.0480391346,0.1188599616,-0.0355735011,0.0517503619,-0.0069937925,0.2490597665,0.072094366,0.0005589007,0.0950622708,-0.2466789037,0.0446445569,-0.0481995195,-0.0835933238,0.0030469694,0.4813488722,-0.1932514161,0.433524549,0.3845349848,-0.2530942261,0.359615922,0.6983598471,0.0457331762,0.2178187668,0.1418946087,0.0875151604,-0.5609429479,0.2564837635,-0.1957597286,0.1106264442,0.1782255024,0.3006596565,-0.0745081827,0.0362934545,-0.1051021442,0.1754598469,-0.1084127873,0.0257788412,-0.2941855788,0.3341449201,0.0356033593,-0.0822640806,0.1576630473,0.06330695,0.2589777708,-0.0923607871,-0.0568176024,-0.343698293,-0.3930536807,0.0127122421,-0.1565709412,-0.4962465465,-0.0980674699,-0.0486263894,0.1884430349,0.0583713017,0.0401819311,-0.3619216681,0.2649368346,-0.274990797,-0.3841170967,-0.3828631938,-0.1650548726,-0.3346540034,-0.0545994788,0.1334372163,-0.3610694408,0.2618533373,0.1698355824,0.0411344692,-0.1050665155,-0.2228546441,-0.0311315693,0.1255652457,0.2201830596,-0.1370445192,0.1204437912,-0.0660410747,-0.0860445127,0.2279858291,-0.3752197623,-0.1063523069,-0.0495339595,-0.0477896109,0.2464321107,-0.0740276575,-0.1765855104,-0.4447836876,-0.2995262444,0.2155094594,0.0241212938,0.019564528,0.0071496568,-0.2222803086,-0.0165380016,0.0308464728,0.0575084165,-0.2335250974,-0.5710589886,0.3007079959,0.1204299182,-0.330660969,-0.1724932194,-0.0910754725,-0.0497761331,0.3589242101,-0.4684201479,-0.1763007939,-0.163080886,0.0598162822,0.133515954,-0.3932551742,0.3917959034,-0.0955056325,0.0246955268,0.001585937,-0.3434217274,-0.0301468764,0.0686947107,0.467453301,0.3109143376,0.1002315879,0.3690013587,0.6891123056,0.1336451173,0.0258559529,0.4306862354,0.26535815,0.3063560426,-0.1425518394,-0.2282550633,0.3457773626,-0.1200147346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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/245","title":"SST-2 test labels are all -1","comments":"Yes, this is because the test sets for glue are hidden so the labels are\nnot publicly available. You can read the glue paper for more details.\n\nOn Sat, 6 Jun 2020 at 18:16, Jack Morris <notifications@github.com> wrote:\n\n> this also happened to me with nlp.load_datasets('glue', 'mnli')\n>\n> \u2014\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/nlp\/issues\/245#issuecomment-640083980>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABYDIHMVQD2EDX2HTZUXG5DRVJTWRANCNFSM4NVG3AKQ>\n> .\n>\n","body":"I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]\r\n```","comment_length":82,"text":"SST-2 test labels are all -1 \n I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, 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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]\r\n``` \n Yes, this is because the test sets for glue are hidden so the labels are\nnot publicly available. You can read the glue paper for more details.\n\nOn Sat, 6 Jun 2020 at 18:16, Jack Morris <notifications@github.com> wrote:\n\n> this also happened to me with nlp.load_datasets('glue', 'mnli')\n>\n> \u2014\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https:\/\/github.com\/huggingface\/nlp\/issues\/245#issuecomment-640083980>,\n> or unsubscribe\n> <https:\/\/github.com\/notifications\/unsubscribe-auth\/ABYDIHMVQD2EDX2HTZUXG5DRVJTWRANCNFSM4NVG3AKQ>\n> .\n>\n","embeddings":[0.3236779869,-0.3612535298,-0.0937504917,0.1778616607,-0.0011129238,0.1007492021,0.3361848891,0.449954927,0.575297296,0.0148675088,-0.1256535202,0.1346777976,-0.1473744661,0.2773214579,0.3333793879,-0.0530476905,0.1083805263,0.3197503388,-0.1676009595,-0.2568297088,-0.0326385088,0.382578373,-0.1446127892,0.1994652748,-0.2805328667,0.2485164255,0.0017220888,-0.0963404924,-0.0316199176,-0.5869413018,0.2044335455,0.2065011263,0.2146628797,0.041023083,-0.0001139119,0.0438769571,0.199644655,-0.1350758076,-0.2542124689,-0.1216584072,-0.1056764275,-0.202876538,0.0458618514,-0.1059137732,-0.3367511034,0.158604309,0.0468212739,0.0070835771,0.1640624404,0.1519432068,0.1746294796,-0.0413381346,0.0038401268,0.0002421326,0.098652482,-0.3579421639,0.0070241201,0.0418982767,-0.0449892581,-0.1923692524,0.2850547135,0.4425401986,0.1635231972,0.3538922369,0.0891574696,-0.1086178869,0.2901045084,-0.2414478511,0.0570447817,0.1071272865,0.1578318179,-0.157120496,-0.2407914549,-0.116439715,-0.0565617196,0.1006575152,-0.0059571643,0.0276229605,0.2176365852,0.2936665714,-0.0193677172,-0.0927764326,-0.1666014344,0.0875245631,0.0444382131,0.5951553583,0.0525575913,0.1323291361,0.3667913377,0.0886464491,-0.4231613576,0.289028585,-0.2783505917,0.1741924286,-0.5723791718,-0.1834564209,0.1960399151,-0.002869542,-0.2150663882,0.1016736701,0.025422398,0.0275838878,0.3623879552,-0.0517081767,-0.0184163898,0.3278754354,0.5321333408,0.3578608632,0.0279294159,-0.1615818143,0.0433757678,0.1671692431,-0.3254519105,-0.2536082268,0.055789616,0.2035899758,0.0716067106,-0.3534903228,-0.3213402331,-0.0689652935,-0.3975773156,0.1182760447,0.1045105457,0.0138104791,0.2748116255,0.0285410937,-0.1252366751,-0.005990752,-0.1177942976,-0.3868844509,-0.2563748062,0.1317728013,-0.1777149588,0.0184122026,0.4044367671,0.1601787508,0.1664631814,-0.2921445668,-0.3622271121,-0.0422817767,0.1334693283,-0.0434931256,0.1852776706,0.3485696316,-0.2840801477,0.3784767985,0.2428891361,-0.5122995973,-0.1410859227,-0.2726314664,-0.0292389225,-0.0726896748,0.0673725232,0.1427009851,-0.3055776358,-0.0654691905,-0.0006719767,0.3467418253,-0.0410009809,0.1258835644,0.0487167463,-0.2369191796,0.0019332132,0.2537924349,0.2284991294,0.2446567267,-0.4592027366,-0.097838752,0.3145676553,-0.0179248098,-0.1690036952,0.1724620908,0.2161767483,0.1493662596,-0.0602655187,0.2051674575,0.3542272151,-0.3027257919,-0.3342137635,0.1045898274,-0.2881499231,-0.4994359016,0.032340914,0.0609023944,-0.0006794172,0.1040444374,-0.0421709493,-0.3407530189,0.1207541898,0.0356192514,-0.5237576365,0.2544219494,0.5446698666,-0.016373327,-0.025428297,-0.1288862079,-0.3430946171,0.1978552639,0.1403770298,0.0544712953,0.0367121734,0.1544160545,0.1745421588,-0.1268416196,0.0858040377,-0.0954349041,-0.1772256047,0.1945025176,0.0130313449,0.393972367,0.5494517088,0.0648840144,-0.3999761641,-0.2042219192,0.0357141532,-0.2271699905,0.1765029281,0.4278755784,0.3945477307,-0.0470248908,0.0751001462,-0.0110270521,0.2815508842,0.0096159391,-0.2109095901,0.2346245795,0.0818820819,-0.2415735871,0.078686811,0.2926479578,0.2893486917,-0.0917757004,0.0322616473,0.22111696,-0.201456666,-0.0546025857,0.0886546671,0.2956277728,-0.0888027027,-0.4509783089,-0.2570939958,0.5002954602,-0.0395481475,-0.0208692644,-0.075032942,0.0755238459,-0.0529433861,0.1584235281,-0.1140918955,0.1547783911,-0.0377261303,-0.0591299087,-0.4671587944,-0.0529296584,0.224119246,-0.4385807812,-0.0076648807,0.1317540407,-0.0462878197,0.317730397,0.2281946093,0.1615154892,0.0979470983,-0.2021484226,-0.2077597827,-0.0914531872,0.2668095827,0.2907688022,-0.0192162395,0.1956549287,0.0562129952,-0.2310011387,-0.5293570757,-0.0398037657,0.0130055901,-0.0980209783,-0.2299815416,-0.1332415789,0.3371716142,-0.3350443542,-0.1945854574,0.2070712298,-0.0692047477,0.3786220849,-0.6263083816,-0.0622233488,-0.1187708005,-0.1242091954,-0.2760334909,-0.0520232283,0.0103210332,-0.2586920559,0.3558694422,0.0493571386,0.0848145783,0.0825465173,-0.0007399836,0.1618949324,-0.149728626,0.2043101043,-0.3001515269,-0.1242339164,-0.2505884767,-0.0001663062,-0.2251261473,0.3416076303,0.0314049311,-0.1615663469,-0.4707098305,0.2010546178,-0.3075324595,-0.0509093888,-0.311673373,0.1930027157,0.2237749994,0.1517865509,-0.2070730627,0.1381038427,0.104045935,-0.1074146777,-0.1088032797,0.1196152195,0.0314826034,0.0655656606,-0.4639659822,-0.4390846491,-0.0139324414,0.0409170836,0.40105474,0.0659215972,-0.1223512739,0.0496974736,-0.2470991015,-0.1264885962,-0.1755560488,0.1663680524,-0.1875957698,-0.2554449737,0.2479190975,0.1119256765,-0.3266984522,-0.2127224356,0.0801267102,0.0762675032,-0.7130568027,-0.2000487894,-0.1134926751,-0.1474514008,-0.0590478256,0.0865231156,-0.1103315428,0.1984110028,0.1098318845,-0.02925843,-0.1576776057,-0.3824420273,0.6548931599,0.3079114258,0.0966887325,-0.4028122723,0.0745698959,0.051984299,0.1751458198,0.1673040241,-0.1668654531,-0.0060525783,-0.1098987684,-0.0408829637,0.1531369984,-0.3298499584,0.4263611734,0.2392539829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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/245","title":"SST-2 test labels are all -1","comments":"It should be in the datasets card (the README.md and on the hub) in my opinion. What do you think @yjernite?","body":"I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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What do you think @yjernite?","embeddings":[0.3236779869,-0.3612535298,-0.0937504917,0.1778616607,-0.0011129238,0.1007492021,0.3361848891,0.449954927,0.575297296,0.0148675088,-0.1256535202,0.1346777976,-0.1473744661,0.2773214579,0.3333793879,-0.0530476905,0.1083805263,0.3197503388,-0.1676009595,-0.2568297088,-0.0326385088,0.382578373,-0.1446127892,0.1994652748,-0.2805328667,0.2485164255,0.0017220888,-0.0963404924,-0.0316199176,-0.5869413018,0.2044335455,0.2065011263,0.2146628797,0.041023083,-0.0001139119,0.0438769571,0.199644655,-0.1350758076,-0.2542124689,-0.1216584072,-0.1056764275,-0.202876538,0.0458618514,-0.1059137732,-0.3367511034,0.158604309,0.0468212739,0.0070835771,0.1640624404,0.1519432068,0.1746294796,-0.0413381346,0.0038401268,0.0002421326,0.098652482,-0.3579421639,0.0070241201,0.0418982767,-0.0449892581,-0.1923692524,0.2850547135,0.4425401986,0.1635231972,0.3538922369,0.0891574696,-0.1086178869,0.2901045084,-0.2414478511,0.0570447817,0.1071272865,0.1578318179,-0.157120496,-0.2407914549,-0.116439715,-0.0565617196,0.1006575152,-0.0059571643,0.0276229605,0.2176365852,0.2936665714,-0.0193677172,-0.0927764326,-0.1666014344,0.0875245631,0.0444382131,0.5951553583,0.0525575913,0.1323291361,0.3667913377,0.0886464491,-0.4231613576,0.289028585,-0.2783505917,0.1741924286,-0.5723791718,-0.1834564209,0.1960399151,-0.002869542,-0.2150663882,0.1016736701,0.025422398,0.0275838878,0.3623879552,-0.0517081767,-0.0184163898,0.3278754354,0.5321333408,0.3578608632,0.0279294159,-0.1615818143,0.0433757678,0.1671692431,-0.3254519105,-0.2536082268,0.055789616,0.2035899758,0.0716067106,-0.3534903228,-0.3213402331,-0.0689652935,-0.3975773156,0.1182760447,0.1045105457,0.0138104791,0.2748116255,0.0285410937,-0.1252366751,-0.005990752,-0.1177942976,-0.3868844509,-0.2563748062,0.1317728013,-0.1777149588,0.0184122026,0.4044367671,0.1601787508,0.1664631814,-0.2921445668,-0.3622271121,-0.0422817767,0.1334693283,-0.0434931256,0.1852776706,0.3485696316,-0.2840801477,0.3784767985,0.2428891361,-0.5122995973,-0.1410859227,-0.2726314664,-0.0292389225,-0.0726896748,0.0673725232,0.1427009851,-0.3055776358,-0.0654691905,-0.0006719767,0.3467418253,-0.0410009809,0.1258835644,0.0487167463,-0.2369191796,0.0019332132,0.2537924349,0.2284991294,0.2446567267,-0.4592027366,-0.097838752,0.3145676553,-0.0179248098,-0.1690036952,0.1724620908,0.2161767483,0.1493662596,-0.0602655187,0.2051674575,0.3542272151,-0.3027257919,-0.3342137635,0.1045898274,-0.2881499231,-0.4994359016,0.032340914,0.0609023944,-0.0006794172,0.1040444374,-0.0421709493,-0.3407530189,0.1207541898,0.0356192514,-0.5237576365,0.2544219494,0.5446698666,-0.016373327,-0.025428297,-0.1288862079,-0.3430946171,0.1978552639,0.1403770298,0.0544712953,0.0367121734,0.1544160545,0.1745421588,-0.1268416196,0.0858040377,-0.0954349041,-0.1772256047,0.1945025176,0.0130313449,0.393972367,0.5494517088,0.0648840144,-0.3999761641,-0.2042219192,0.0357141532,-0.2271699905,0.1765029281,0.4278755784,0.3945477307,-0.0470248908,0.0751001462,-0.0110270521,0.2815508842,0.0096159391,-0.2109095901,0.2346245795,0.0818820819,-0.2415735871,0.078686811,0.2926479578,0.2893486917,-0.0917757004,0.0322616473,0.22111696,-0.201456666,-0.0546025857,0.0886546671,0.2956277728,-0.0888027027,-0.4509783089,-0.2570939958,0.5002954602,-0.0395481475,-0.0208692644,-0.075032942,0.0755238459,-0.0529433861,0.1584235281,-0.1140918955,0.1547783911,-0.0377261303,-0.0591299087,-0.4671587944,-0.0529296584,0.224119246,-0.4385807812,-0.0076648807,0.1317540407,-0.0462878197,0.317730397,0.2281946093,0.1615154892,0.0979470983,-0.2021484226,-0.2077597827,-0.0914531872,0.2668095827,0.2907688022,-0.0192162395,0.1956549287,0.0562129952,-0.2310011387,-0.5293570757,-0.0398037657,0.0130055901,-0.0980209783,-0.2299815416,-0.1332415789,0.3371716142,-0.3350443542,-0.1945854574,0.2070712298,-0.0692047477,0.3786220849,-0.6263083816,-0.0622233488,-0.1187708005,-0.1242091954,-0.2760334909,-0.0520232283,0.0103210332,-0.2586920559,0.3558694422,0.0493571386,0.0848145783,0.0825465173,-0.0007399836,0.1618949324,-0.149728626,0.2043101043,-0.3001515269,-0.1242339164,-0.2505884767,-0.0001663062,-0.2251261473,0.3416076303,0.0314049311,-0.1615663469,-0.4707098305,0.2010546178,-0.3075324595,-0.0509093888,-0.311673373,0.1930027157,0.2237749994,0.1517865509,-0.2070730627,0.1381038427,0.104045935,-0.1074146777,-0.1088032797,0.1196152195,0.0314826034,0.0655656606,-0.4639659822,-0.4390846491,-0.0139324414,0.0409170836,0.40105474,0.0659215972,-0.1223512739,0.0496974736,-0.2470991015,-0.1264885962,-0.1755560488,0.1663680524,-0.1875957698,-0.2554449737,0.2479190975,0.1119256765,-0.3266984522,-0.2127224356,0.0801267102,0.0762675032,-0.7130568027,-0.2000487894,-0.1134926751,-0.1474514008,-0.0590478256,0.0865231156,-0.1103315428,0.1984110028,0.1098318845,-0.02925843,-0.1576776057,-0.3824420273,0.6548931599,0.3079114258,0.0966887325,-0.4028122723,0.0745698959,0.051984299,0.1751458198,0.1673040241,-0.1668654531,-0.0060525783,-0.1098987684,-0.0408829637,0.1531369984,-0.3298499584,0.4263611734,0.239253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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/245","title":"SST-2 test labels are all -1","comments":"I checked both before I got to looking at issues, so that would be fine as well.\r\n\r\nSome additional thoughts on this: Is there a specific reason why the \"test\" split even has a \"label\" column if it isn't tagged. Shouldn't there just not be any. Seems more transparent","body":"I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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Shouldn't there just not be any. Seems more 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/245","title":"SST-2 test labels are all -1","comments":"I'm a little confused with the data size.\r\n`sst2` dataset is referenced to `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank` and the link of the dataset in the paper is https:\/\/nlp.stanford.edu\/sentiment\/index.html which is often shown in GLUE\/SST2 reference.\r\nFrom the original data, the standard train\/dev\/test splits split is 6920\/872\/1821 for binary classification. \r\nWhy in GLUE\/SST2 the train\/dev\/test split is 67,349\/872\/1,821 ? \r\n\r\n","body":"I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]\r\n```","comment_length":64,"text":"SST-2 test labels are all -1 \n I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]\r\n``` \n I'm a little confused with the data size.\r\n`sst2` dataset is referenced to `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank` and the link of the dataset in the paper is https:\/\/nlp.stanford.edu\/sentiment\/index.html which is often shown in GLUE\/SST2 reference.\r\nFrom the original data, the standard train\/dev\/test splits split is 6920\/872\/1821 for binary classification. \r\nWhy in GLUE\/SST2 the train\/dev\/test split is 67,349\/872\/1,821 ? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/245","title":"SST-2 test labels are all -1","comments":"> I'm a little confused with the data size.\r\n> `sst2` dataset is referenced to `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank` and the link of the dataset in the paper is https:\/\/nlp.stanford.edu\/sentiment\/index.html which is often shown in GLUE\/SST2 reference.\r\n> From the original data, the standard train\/dev\/test splits split is 6920\/872\/1821 for binary classification.\r\n> Why in GLUE\/SST2 the train\/dev\/test split is 67,349\/872\/1,821 ?\r\n\r\nHave you figured out this problem? AFAIK, the original sst-2 dataset is totally different from the GLUE\/sst-2. Do you think so?","body":"I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]\r\n```","comment_length":89,"text":"SST-2 test labels are all -1 \n I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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https:\/\/nlp.stanford.edu\/sentiment\/index.html which is often shown in GLUE\/SST2 reference.\r\n> From the original data, the standard train\/dev\/test splits split is 6920\/872\/1821 for binary classification.\r\n> Why in GLUE\/SST2 the train\/dev\/test split is 67,349\/872\/1,821 ?\r\n\r\nHave you figured out this problem? AFAIK, the original sst-2 dataset is totally different from the GLUE\/sst-2. Do you think 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/245","title":"SST-2 test labels are all -1","comments":"@yc1999 Sorry, I didn't solve this conflict. In the end, I just use a local data file provided by the previous work I followed(for consistent comparison), not use `datasets` package.\r\n\r\nRelated information: https:\/\/github.com\/thunlp\/OpenAttack\/issues\/146#issuecomment-766323571","body":"I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]\r\n```","comment_length":33,"text":"SST-2 test labels are all -1 \n I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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this conflict. In the end, I just use a local data file provided by the previous work I followed(for consistent comparison), not use `datasets` package.\r\n\r\nRelated information: https:\/\/github.com\/thunlp\/OpenAttack\/issues\/146#issuecomment-766323571","embeddings":[0.3236779869,-0.3612535298,-0.0937504917,0.1778616607,-0.0011129238,0.1007492021,0.3361848891,0.449954927,0.575297296,0.0148675088,-0.1256535202,0.1346777976,-0.1473744661,0.2773214579,0.3333793879,-0.0530476905,0.1083805263,0.3197503388,-0.1676009595,-0.2568297088,-0.0326385088,0.382578373,-0.1446127892,0.1994652748,-0.2805328667,0.2485164255,0.0017220888,-0.0963404924,-0.0316199176,-0.5869413018,0.2044335455,0.2065011263,0.2146628797,0.041023083,-0.0001139119,0.0438769571,0.199644655,-0.1350758076,-0.2542124689,-0.1216584072,-0.1056764275,-0.202876538,0.0458618514,-0.1059137732,-0.3367511034,0.158604309,0.0468212739,0.0070835771,0.1640624404,0.1519432068,0.1746294796,-0.0413381346,0.0038401268,0.0002421326,0.098652482,-0.3579421639,0.0070241201,0.0418982767,-0.0449892581,-0.1923692524,0.2850547135,0.4425401986,0.1635231972,0.3538922369,0.0891574696,-0.1086178869,0.2901045084,-0.2414478511,0.0570447817,0.1071272865,0.1578318179,-0.157120496,-0.2407914549,-0.116439715,-0.0565617196,0.1006575152,-0.0059571643,0.0276229605,0.2176365852,0.2936665714,-0.0193677172,-0.0927764326,-0.1666014344,0.0875245631,0.0444382131,0.5951553583,0.0525575913,0.1323291361,0.3667913377,0.0886464491,-0.4231613576,0.289028585,-0.2783505917,0.1741924286,-0.5723791718,-0.1834564209,0.1960399151,-0.002869542,-0.2150663882,0.1016736701,0.025422398,0.0275838878,0.3623879552,-0.0517081767,-0.0184163898,0.3278754354,0.5321333408,0.3578608632,0.0279294159,-0.1615818143,0.0433757678,0.1671692431,-0.3254519105,-0.2536082268,0.055789616,0.2035899758,0.0716067106,-0.3534903228,-0.3213402331,-0.0689652935,-0.3975773156,0.1182760447,0.1045105457,0.0138104791,0.2748116255,0.0285410937,-0.1252366751,-0.005990752,-0.1177942976,-0.3868844509,-0.2563748062,0.1317728013,-0.1777149588,0.0184122026,0.4044367671,0.1601787508,0.1664631814,-0.2921445668,-0.3622271121,-0.0422817767,0.1334693283,-0.0434931256,0.1852776706,0.3485696316,-0.2840801477,0.3784767985,0.2428891361,-0.5122995973,-0.1410859227,-0.2726314664,-0.0292389225,-0.0726896748,0.0673725232,0.1427009851,-0.3055776358,-0.0654691905,-0.0006719767,0.3467418253,-0.0410009809,0.1258835644,0.0487167463,-0.2369191796,0.0019332132,0.2537924349,0.2284991294,0.2446567267,-0.4592027366,-0.097838752,0.3145676553,-0.0179248098,-0.1690036952,0.1724620908,0.2161767483,0.1493662596,-0.0602655187,0.2051674575,0.3542272151,-0.3027257919,-0.3342137635,0.1045898274,-0.2881499231,-0.4994359016,0.032340914,0.0609023944,-0.0006794172,0.1040444374,-0.0421709493,-0.3407530189,0.1207541898,0.0356192514,-0.5237576365,0.2544219494,0.5446698666,-0.016373327,-0.025428297,-0.1288862079,-0.3430946171,0.1978552639,0.1403770298,0.0544712953,0.0367121734,0.1544160545,0.1745421588,-0.1268416196,0.0858040377,-0.0954349041,-0.1772256047,0.1945025176,0.0130313449,0.393972367,0.5494517088,0.0648840144,-0.3999761641,-0.2042219192,0.0357141532,-0.2271699905,0.1765029281,0.4278755784,0.3945477307,-0.0470248908,0.0751001462,-0.0110270521,0.2815508842,0.0096159391,-0.2109095901,0.2346245795,0.0818820819,-0.2415735871,0.078686811,0.2926479578,0.2893486917,-0.0917757004,0.0322616473,0.22111696,-0.201456666,-0.0546025857,0.0886546671,0.2956277728,-0.0888027027,-0.4509783089,-0.2570939958,0.5002954602,-0.0395481475,-0.0208692644,-0.075032942,0.0755238459,-0.0529433861,0.1584235281,-0.1140918955,0.1547783911,-0.0377261303,-0.0591299087,-0.4671587944,-0.0529296584,0.224119246,-0.4385807812,-0.0076648807,0.1317540407,-0.0462878197,0.317730397,0.2281946093,0.1615154892,0.0979470983,-0.2021484226,-0.2077597827,-0.0914531872,0.2668095827,0.2907688022,-0.0192162395,0.1956549287,0.0562129952,-0.2310011387,-0.5293570757,-0.0398037657,0.0130055901,-0.0980209783,-0.2299815416,-0.1332415789,0.3371716142,-0.3350443542,-0.1945854574,0.2070712298,-0.0692047477,0.3786220849,-0.6263083816,-0.0622233488,-0.1187708005,-0.1242091954,-0.2760334909,-0.0520232283,0.0103210332,-0.2586920559,0.3558694422,0.0493571386,0.0848145783,0.0825465173,-0.0007399836,0.1618949324,-0.149728626,0.2043101043,-0.3001515269,-0.1242339164,-0.2505884767,-0.0001663062,-0.2251261473,0.3416076303,0.0314049311,-0.1615663469,-0.4707098305,0.2010546178,-0.3075324595,-0.0509093888,-0.311673373,0.1930027157,0.2237749994,0.1517865509,-0.2070730627,0.1381038427,0.104045935,-0.1074146777,-0.1088032797,0.1196152195,0.0314826034,0.0655656606,-0.4639659822,-0.4390846491,-0.0139324414,0.0409170836,0.40105474,0.0659215972,-0.1223512739,0.0496974736,-0.2470991015,-0.1264885962,-0.1755560488,0.1663680524,-0.1875957698,-0.2554449737,0.2479190975,0.1119256765,-0.3266984522,-0.2127224356,0.0801267102,0.0762675032,-0.7130568027,-0.2000487894,-0.1134926751,-0.1474514008,-0.0590478256,0.0865231156,-0.1103315428,0.1984110028,0.1098318845,-0.02925843,-0.1576776057,-0.3824420273,0.6548931599,0.3079114258,0.0966887325,-0.4028122723,0.0745698959,0.051984299,0.1751458198,0.1673040241,-0.1668654531,-0.0060525783,-0.1098987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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/245","title":"SST-2 test labels are all -1","comments":"@yc1999 I find that the original SST-2 dataset (6,920\/872\/1,821) can be loaded from https:\/\/huggingface.co\/datasets\/gpt3mix\/sst2 or built with SST data and the scripts in https:\/\/github.com\/prrao87\/fine-grained-sentiment\/tree\/master\/data\/sst.\r\nThe GLUE\/SST-2 dataset (67,349\/872\/1,821) should be a completely different version.\r\n","body":"I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]\r\n```","comment_length":34,"text":"SST-2 test labels are all -1 \n I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.\r\n```\r\n>>> import nlp\r\n>>> glue = nlp.load_dataset('glue', 'sst2')\r\n>>> glue\r\n{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}\r\n>>> list(l['label'] for l in glue['test'])\r\n[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]\r\n``` \n @yc1999 I find that the original SST-2 dataset (6,920\/872\/1,821) can be loaded from https:\/\/huggingface.co\/datasets\/gpt3mix\/sst2 or built with SST data and the scripts in https:\/\/github.com\/prrao87\/fine-grained-sentiment\/tree\/master\/data\/sst.\r\nThe GLUE\/SST-2 dataset (67,349\/872\/1,821) should be a completely different version.\r\n","embeddings":[0.3236779869,-0.3612535298,-0.0937504917,0.1778616607,-0.0011129238,0.1007492021,0.3361848891,0.449954927,0.575297296,0.0148675088,-0.1256535202,0.1346777976,-0.1473744661,0.2773214579,0.3333793879,-0.0530476905,0.1083805263,0.3197503388,-0.1676009595,-0.2568297088,-0.0326385088,0.382578373,-0.1446127892,0.1994652748,-0.2805328667,0.2485164255,0.0017220888,-0.0963404924,-0.0316199176,-0.5869413018,0.2044335455,0.2065011263,0.2146628797,0.041023083,-0.0001139119,0.0438769571,0.199644655,-0.1350758076,-0.2542124689,-0.1216584072,-0.1056764275,-0.202876538,0.0458618514,-0.1059137732,-0.3367511034,0.158604309,0.0468212739,0.0070835771,0.1640624404,0.1519432068,0.1746294796,-0.0413381346,0.0038401268,0.0002421326,0.098652482,-0.3579421639,0.0070241201,0.0418982767,-0.0449892581,-0.1923692524,0.2850547135,0.4425401986,0.1635231972,0.3538922369,0.0891574696,-0.1086178869,0.2901045084,-0.2414478511,0.0570447817,0.1071272865,0.1578318179,-0.157120496,-0.2407914549,-0.116439715,-0.0565617196,0.1006575152,-0.0059571643,0.0276229605,0.2176365852,0.2936665714,-0.0193677172,-0.0927764326,-0.1666014344,0.0875245631,0.0444382131,0.5951553583,0.0525575913,0.1323291361,0.3667913377,0.0886464491,-0.4231613576,0.289028585,-0.2783505917,0.1741924286,-0.5723791718,-0.1834564209,0.1960399151,-0.002869542,-0.2150663882,0.1016736701,0.025422398,0.0275838878,0.3623879552,-0.0517081767,-0.0184163898,0.3278754354,0.5321333408,0.3578608632,0.0279294159,-0.1615818143,0.0433757678,0.1671692431,-0.3254519105,-0.2536082268,0.055789616,0.2035899758,0.0716067106,-0.3534903228,-0.3213402331,-0.0689652935,-0.3975773156,0.1182760447,0.1045105457,0.0138104791,0.2748116255,0.0285410937,-0.1252366751,-0.005990752,-0.1177942976,-0.3868844509,-0.2563748062,0.1317728013,-0.1777149588,0.0184122026,0.4044367671,0.1601787508,0.1664631814,-0.2921445668,-0.3622271121,-0.0422817767,0.1334693283,-0.0434931256,0.1852776706,0.3485696316,-0.2840801477,0.3784767985,0.2428891361,-0.5122995973,-0.1410859227,-0.2726314664,-0.0292389225,-0.0726896748,0.0673725232,0.1427009851,-0.3055776358,-0.0654691905,-0.0006719767,0.3467418253,-0.0410009809,0.1258835644,0.0487167463,-0.2369191796,0.0019332132,0.2537924349,0.2284991294,0.2446567267,-0.4592027366,-0.097838752,0.3145676553,-0.0179248098,-0.1690036952,0.1724620908,0.2161767483,0.1493662596,-0.0602655187,0.2051674575,0.3542272151,-0.3027257919,-0.3342137635,0.1045898274,-0.2881499231,-0.4994359016,0.032340914,0.0609023944,-0.0006794172,0.1040444374,-0.0421709493,-0.3407530189,0.1207541898,0.0356192514,-0.5237576365,0.2544219494,0.5446698666,-0.016373327,-0.025428297,-0.1288862079,-0.3430946171,0.1978552639,0.1403770298,0.0544712953,0.0367121734,0.1544160545,0.1745421588,-0.1268416196,0.0858040377,-0.0954349041,-0.1772256047,0.1945025176,0.0130313449,0.393972367,0.5494517088,0.0648840144,-0.3999761641,-0.2042219192,0.0357141532,-0.2271699905,0.1765029281,0.4278755784,0.3945477307,-0.0470248908,0.0751001462,-0.0110270521,0.2815508842,0.0096159391,-0.2109095901,0.2346245795,0.0818820819,-0.2415735871,0.078686811,0.2926479578,0.2893486917,-0.0917757004,0.0322616473,0.22111696,-0.201456666,-0.0546025857,0.0886546671,0.2956277728,-0.0888027027,-0.4509783089,-0.2570939958,0.5002954602,-0.0395481475,-0.0208692644,-0.075032942,0.0755238459,-0.0529433861,0.1584235281,-0.1140918955,0.1547783911,-0.0377261303,-0.0591299087,-0.4671587944,-0.0529296584,0.224119246,-0.4385807812,-0.0076648807,0.1317540407,-0.0462878197,0.317730397,0.2281946093,0.1615154892,0.0979470983,-0.2021484226,-0.2077597827,-0.0914531872,0.2668095827,0.2907688022,-0.0192162395,0.1956549287,0.0562129952,-0.2310011387,-0.5293570757,-0.0398037657,0.0130055901,-0.0980209783,-0.2299815416,-0.1332415789,0.3371716142,-0.3350443542,-0.1945854574,0.2070712298,-0.0692047477,0.3786220849,-0.6263083816,-0.0622233488,-0.1187708005,-0.1242091954,-0.2760334909,-0.0520232283,0.0103210332,-0.2586920559,0.3558694422,0.0493571386,0.0848145783,0.0825465173,-0.0007399836,0.1618949324,-0.149728626,0.2043101043,-0.3001515269,-0.1242339164,-0.2505884767,-0.0001663062,-0.2251261473,0.3416076303,0.0314049311,-0.1615663469,-0.4707098305,0.2010546178,-0.3075324595,-0.0509093888,-0.311673373,0.1930027157,0.2237749994,0.1517865509,-0.2070730627,0.1381038427,0.104045935,-0.1074146777,-0.1088032797,0.1196152195,0.0314826034,0.0655656606,-0.4639659822,-0.4390846491,-0.0139324414,0.0409170836,0.40105474,0.0659215972,-0.1223512739,0.0496974736,-0.2470991015,-0.1264885962,-0.1755560488,0.1663680524,-0.1875957698,-0.2554449737,0.2479190975,0.1119256765,-0.3266984522,-0.2127224356,0.0801267102,0.0762675032,-0.7130568027,-0.2000487894,-0.1134926751,-0.1474514008,-0.0590478256,0.0865231156,-0.1103315428,0.1984110028,0.1098318845,-0.02925843,-0.1576776057,-0.3824420273,0.6548931599,0.3079114258,0.0966887325,-0.4028122723,0.0745698959,0.051984299,0.1751458198,0.1673040241,-0.1668654531,-0.0060525783,-0.1098987684,-0.0408829637,0.1531369984,-0.3298499584,0.4263611734,0.2392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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/242","title":"UnicodeDecodeError when downloading GLUE-MNLI","comments":"It should be good now, thanks for noticing and fixing it ! I would say that it was because you are on windows but not 100% sure","body":"When I run\r\n```python\r\ndataset = nlp.load_dataset('glue', 'mnli')\r\n```\r\nI get an encoding error (could it be because I'm using Windows?) :\r\n```python\r\n# Lots of error log lines later...\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\tqdm\\std.py in __iter__(self)\r\n   1128         try:\r\n-> 1129             for obj in iterable:\r\n   1130                 yield obj\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\datasets\\glue\\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\\glue.py in _generate_examples(self, data_file, split, mrpc_files)\r\n    529 \r\n--> 530                 for n, row in enumerate(reader):\r\n    531                     if is_cola_non_test:\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\csv.py in __next__(self)\r\n    110             self.fieldnames\r\n--> 111         row = next(self.reader)\r\n    112         self.line_num = self.reader.line_num\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\encodings\\cp1252.py in decode(self, input, final)\r\n     22     def decode(self, input, final=False):\r\n---> 23         return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\n     24 \r\n\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>\r\n```\r\nAnyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay.","comment_length":27,"text":"UnicodeDecodeError when downloading GLUE-MNLI \n When I run\r\n```python\r\ndataset = nlp.load_dataset('glue', 'mnli')\r\n```\r\nI get an encoding error (could it be because I'm using Windows?) :\r\n```python\r\n# Lots of error log lines later...\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\tqdm\\std.py in __iter__(self)\r\n   1128         try:\r\n-> 1129             for obj in iterable:\r\n   1130                 yield obj\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\datasets\\glue\\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\\glue.py in _generate_examples(self, data_file, split, mrpc_files)\r\n    529 \r\n--> 530                 for n, row in enumerate(reader):\r\n    531                     if is_cola_non_test:\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\csv.py in __next__(self)\r\n    110             self.fieldnames\r\n--> 111         row = next(self.reader)\r\n    112         self.line_num = self.reader.line_num\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\encodings\\cp1252.py in decode(self, input, final)\r\n     22     def decode(self, input, final=False):\r\n---> 23         return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\n     24 \r\n\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>\r\n```\r\nAnyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay. \n It should be good now, thanks for noticing and fixing it ! I would say that it was because you are on windows but not 100% 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/242","title":"UnicodeDecodeError when downloading GLUE-MNLI","comments":"On Windows Python supports Unicode almost everywhere, but one of the notable exceptions is open() where it uses the locale encoding schema. So platform independent python scripts would always set the encoding='utf-8' in calls to open explicitly. \r\nIn the meantime: since Python 3.7 Windows users can set the default encoding for everything including open() to Unicode by setting this environment variable: set PYTHONUTF8=1 (details can be found in [PEP 540](https:\/\/www.python.org\/dev\/peps\/pep-0540\/))\r\n\r\nFor me this fixed the problem described by the OP.","body":"When I run\r\n```python\r\ndataset = nlp.load_dataset('glue', 'mnli')\r\n```\r\nI get an encoding error (could it be because I'm using Windows?) :\r\n```python\r\n# Lots of error log lines later...\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\tqdm\\std.py in __iter__(self)\r\n   1128         try:\r\n-> 1129             for obj in iterable:\r\n   1130                 yield obj\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\datasets\\glue\\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\\glue.py in _generate_examples(self, data_file, split, mrpc_files)\r\n    529 \r\n--> 530                 for n, row in enumerate(reader):\r\n    531                     if is_cola_non_test:\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\csv.py in __next__(self)\r\n    110             self.fieldnames\r\n--> 111         row = next(self.reader)\r\n    112         self.line_num = self.reader.line_num\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\encodings\\cp1252.py in decode(self, input, final)\r\n     22     def decode(self, input, final=False):\r\n---> 23         return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\n     24 \r\n\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>\r\n```\r\nAnyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay.","comment_length":80,"text":"UnicodeDecodeError when downloading GLUE-MNLI \n When I run\r\n```python\r\ndataset = nlp.load_dataset('glue', 'mnli')\r\n```\r\nI get an encoding error (could it be because I'm using Windows?) :\r\n```python\r\n# Lots of error log lines later...\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\tqdm\\std.py in __iter__(self)\r\n   1128         try:\r\n-> 1129             for obj in iterable:\r\n   1130                 yield obj\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\datasets\\glue\\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\\glue.py in _generate_examples(self, data_file, split, mrpc_files)\r\n    529 \r\n--> 530                 for n, row in enumerate(reader):\r\n    531                     if is_cola_non_test:\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\csv.py in __next__(self)\r\n    110             self.fieldnames\r\n--> 111         row = next(self.reader)\r\n    112         self.line_num = self.reader.line_num\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\encodings\\cp1252.py in decode(self, input, final)\r\n     22     def decode(self, input, final=False):\r\n---> 23         return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\n     24 \r\n\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>\r\n```\r\nAnyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay. \n On Windows Python supports Unicode almost everywhere, but one of the notable exceptions is open() where it uses the locale encoding schema. So platform independent python scripts would always set the encoding='utf-8' in calls to open explicitly. \r\nIn the meantime: since Python 3.7 Windows users can set the default encoding for everything including open() to Unicode by setting this environment variable: set PYTHONUTF8=1 (details can be found in [PEP 540](https:\/\/www.python.org\/dev\/peps\/pep-0540\/))\r\n\r\nFor me this fixed the problem described by the OP.","embeddings":[0.0659664795,0.0692808926,-0.0075027859,0.1465992033,0.4890412092,-0.1362413168,-0.0057733776,0.0795608684,0.057453759,0.3163624704,0.1549845338,0.126315102,-0.0671637133,-0.1741780341,0.0777117684,-0.2971668839,0.1926772147,0.2051754296,-0.0492804386,-0.0339454375,-0.4367743731,0.3971391618,0.0737092793,0.2630495727,-0.0219343193,-0.0867550448,-0.152930513,0.2397748083,-0.3016114831,-0.57560426,-0.1237106919,-0.1266360134,0.3628610075,-0.2685573399,-0.0001157126,-0.012200146,0.3200551569,-0.2603190839,0.1376090944,0.1711819768,0.028887324,-0.294804424,-0.151617676,-0.177483201,0.3508855402,0.2826948166,0.1608464867,-0.4594718218,-0.1264068335,0.285561502,0.1487494558,0.3086079359,0.1480985582,0.4098854363,0.1059744209,-0.3754203618,-0.1038057655,0.3554778099,0.2898302078,-0.2861046493,-0.1215083003,0.1885137856,-0.2729696333,0.0802527964,-0.3073840737,0.3867275119,0.1543731093,-0.5894976854,-0.1191273183,0.0787643194,-0.1083671525,-0.2737489939,-0.3448261023,-0.1108141169,0.1624771506,-0.3434237242,0.3828711808,0.3813179433,-0.1983408779,0.3759185374,0.3234007657,0.0271237083,0.1297441721,0.1788926721,-0.1149421483,0.3030929863,-0.0484179333,0.0052201985,0.32939291,-0.1760675162,-0.2178891301,-0.0927065015,-0.028195003,-0.0783487111,-0.0077807754,-0.0275792498,-0.1730580926,0.0897668004,0.0650106892,0.0243771579,0.1349372417,0.0160542447,-0.0377602242,0.1903323233,-0.0071625346,0.1537086368,-0.1960662454,0.1105273664,0.5007560253,0.0054312591,0.3920165896,0.0856892392,-0.0916080102,-0.3336857855,-0.1481531411,0.3762517273,0.3734878898,0.0488297008,-0.3270634115,0.1245114505,-0.0932204127,0.0808945149,-0.268321991,-0.018830901,-0.1305695474,0.4093034267,-0.1884571761,0.3444765508,-0.412813127,-0.3085870445,0.0623199753,0.2654925287,-0.0576127358,-0.0216783267,0.1953626871,-0.0063774041,-0.0489238426,-0.0647775307,-0.0668369457,0.0914170519,-0.1626498997,-0.1058646441,0.1504982859,0.2132132053,0.1111661941,0.0766256154,0.1605852693,-0.7501699328,-0.3676734567,0.1320264041,-0.1136827841,-0.0327389948,-0.1624801457,0.0419723019,-0.2905904353,-0.268329531,0.0432097912,0.0893631652,-0.012850591,0.058380805,0.1423714906,-0.0658238605,-0.1256457716,-0.2885705233,0.1912132055,0.5120857358,-0.2714211643,0.11977835,0.2272893786,-0.2887923419,0.1852322519,0.3578078449,0.2416885197,-0.3323736787,-0.1923466474,0.4748018682,0.2483752966,-0.5571578145,-0.5454277396,0.4257736206,-0.1235029846,-0.0389658064,0.1891016364,-0.0144956205,0.1878460795,0.0291927364,0.1525562257,0.5387167931,-0.1974547654,0.1925505251,-0.2940203547,-0.4947428703,0.3141426444,0.1426318139,-0.1736194789,-0.0822926685,0.129146114,-0.4585199654,0.1723280698,-0.1112949252,0.2787634432,0.0282671954,0.3319406807,-0.211189881,0.0380765013,-0.0915697888,-0.0415454134,-0.01109784,-0.0237123705,0.2121428251,-0.69395715,-0.2628234029,0.0737558901,-0.1202406287,-0.2130515873,-0.0597773083,0.0705621764,0.1311570406,0.3312766552,0.3530176878,-0.2560486794,0.444412291,0.0291101765,0.0596346259,-0.1860978156,0.2995320857,-0.0332169123,-0.185224995,-0.0328014642,0.4689338803,0.0278450828,-0.1168617755,-0.0130393375,0.2994942367,0.0071417773,0.0874278173,0.099679254,0.0350923501,0.4462957084,-0.2842265069,0.139889732,0.4377090931,0.1405509412,0.1681509465,0.2921564579,0.4354314506,0.0450360291,0.140774563,0.2176133096,0.1677333117,0.2079701722,0.0885910019,-0.4142263234,-0.4824961424,0.1321980655,0.0846019536,0.2097947448,-0.0038142868,-0.3643803895,-0.1360848695,0.7756450176,-0.1477789134,0.1650189459,0.0773028359,-0.1030396968,0.343202889,-0.116912812,0.2165263444,0.4581326544,-0.0990952849,-0.0600243285,0.2631981075,0.0864502862,-0.0863333419,0.0601589531,-0.0021347655,-0.2038664222,0.0336265825,-0.1810392439,0.0602921583,-0.238687098,-0.0007922559,-0.0726954043,0.1344421059,-0.1566104144,0.018894786,-0.5958063006,-0.3180299103,-0.2905757427,-0.1569772214,-0.3728326559,-0.177833274,-0.08260452,-0.2205192745,-0.1535080373,-0.0098585598,-0.3806848228,-0.0583350398,-0.1645463705,-0.3551083207,0.287994653,-0.1519503444,-0.3765107691,0.0458634123,0.2971877456,-0.0745043233,0.0527410358,-0.3912444711,0.2198841721,-0.1161720082,-0.1598160565,0.1045536026,-0.0126124732,0.1350214183,0.0728747621,-0.2613962889,0.1342346221,-0.3289485276,0.2606318891,-0.1018458083,-0.0507454909,0.0579627044,0.1840224266,-0.3369160593,-0.0888148546,-0.0477336943,-0.6349591017,-0.2621896863,0.1085829437,-0.2164635956,-0.0296417847,-0.3215074539,-0.2497011125,-0.0321872681,-0.0824408755,-0.0988871232,-0.0484782159,-0.0408875011,0.4804697037,-0.2116928697,-0.3819882572,0.1253904253,-0.2614033222,-0.0949382335,-0.0084321955,-0.2596348822,-0.1206082031,-0.4150761962,0.0678995475,0.0816438198,0.2113174051,0.1888361722,-0.066246599,0.1889101863,0.0285259429,-0.2133290172,-0.2013958544,0.3152658939,0.1287043095,0.1994595081,0.1697904617,0.2229832262,-0.1316216737,0.1720539331,0.1581799388,0.5512159467,-0.2044785023,0.3849100173,0.2994396389,0.0794238672,0.0352438837,-0.1622576118,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/240","title":"Deterministic dataset loading","comments":"I think using `sorted(glob.glob())` would actually solve this problem. Can you think of other reasons why dataset loading might not be deterministic? @mariamabarham @yjernite @lhoestq @thomwolf . \r\n\r\nI can do a sweep through the dataset scripts and fix the glob.glob() if you guys are ok with it","body":"When calling:\r\n```python \r\nimport nlp\r\ndataset = nlp.load_dataset(\"trivia_qa\", split=\"validation[:1%]\")\r\n```\r\n\r\nthe resulting dataset is not deterministic over different google colabs. \r\nAfter talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/2e0a8639a79b1abc848cff5c669094d40bba0f63\/datasets\/trivia_qa\/trivia_qa.py#L180\r\n\r\nwhich seems to return an ordering of files that depends on the filesystem:\r\nhttps:\/\/stackoverflow.com\/questions\/6773584\/how-is-pythons-glob-glob-ordered\r\n\r\nI think we should go through all the dataset scripts and make sure to have deterministic behavior.\r\n\r\nA simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name. \r\n\r\nWhat do you think @lhoestq?","comment_length":47,"text":"Deterministic dataset loading \n When calling:\r\n```python \r\nimport nlp\r\ndataset = nlp.load_dataset(\"trivia_qa\", split=\"validation[:1%]\")\r\n```\r\n\r\nthe resulting dataset is not deterministic over different google colabs. \r\nAfter talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/2e0a8639a79b1abc848cff5c669094d40bba0f63\/datasets\/trivia_qa\/trivia_qa.py#L180\r\n\r\nwhich seems to return an ordering of files that depends on the filesystem:\r\nhttps:\/\/stackoverflow.com\/questions\/6773584\/how-is-pythons-glob-glob-ordered\r\n\r\nI think we should go through all the dataset scripts and make sure to have deterministic behavior.\r\n\r\nA simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name. \r\n\r\nWhat do you think @lhoestq? \n I think using `sorted(glob.glob())` would actually solve this problem. Can you think of other reasons why dataset loading might not be deterministic? @mariamabarham @yjernite @lhoestq @thomwolf . \r\n\r\nI can do a sweep through the dataset scripts and fix the glob.glob() if you guys are ok with it","embeddings":[0.1278988868,0.0680350214,0.018163044,0.1612559557,0.1494209617,-0.0938441157,0.2877918482,0.0737954974,0.2083065808,-0.0574632734,0.1042929068,0.3704168797,-0.1524901688,0.1114908755,-0.0127119003,-0.0483885668,-0.0260270219,0.1764348149,-0.0584851317,-0.1872009039,-0.3069525063,0.0784734413,-0.4576539397,-0.3690684438,-0.2645183802,0.0899192244,-0.0991521105,0.1341389865,-0.0830376893,-0.4587273598,0.1570782363,0.5054252148,0.1628576219,0.1823225468,-0.0001150454,-0.2656959295,0.4034866095,-0.1031261235,-0.4438330531,-0.3913377523,-0.3945771158,-0.0304798484,0.1379951537,-0.0177153181,-0.2774758041,0.0264262781,0.3882413805,-0.2673333287,0.2669376433,0.0418426171,0.1728890985,-0.1249023452,0.0030792588,0.1387374699,0.0104721747,0.0695920885,-0.0228298008,0.4524676204,0.1382265389,-0.2738265991,-0.0689454302,0.1072151735,0.031256482,0.1048302799,0.4822502732,0.1279492378,-0.2176283002,-0.400513202,-0.1074987575,0.2443935126,0.582634747,-0.1721649319,-0.2514915168,-0.352547586,0.2645475864,-0.1000305116,0.1244716272,0.380508095,-0.134509325,-0.0959320664,-0.0022379844,0.1671114117,-0.1371524483,0.0710875019,0.0856458023,0.5879830718,-0.013878718,0.0256638918,0.0793035403,0.2684374452,0.2062194347,-0.2505574524,-0.0696311295,0.1474831551,-0.316983819,-0.1194183305,0.1632873714,0.5273426175,0.0931168944,0.1871911436,0.226766631,0.0674799159,-0.1524080336,0.008962173,0.3967241645,0.5014610887,0.0129531249,0.0889035016,0.3393018246,0.0102449907,-0.1401090324,0.2734246552,-0.060456071,-0.318338275,0.1536530554,0.0968183875,0.1490698606,-0.2396645695,-0.4463087618,-0.2819098234,-0.2935085297,-0.0425756872,0.0255235918,0.2448837459,-0.3041346967,0.1373276114,-0.2099104524,0.1781618744,-0.1122649014,-0.066726014,-0.0442476049,-0.1189903989,-0.3672465682,0.1553732008,0.1779651642,0.0710023493,0.1543083936,0.2629646659,-0.2230555415,-0.0933227241,0.0318714604,-0.3338704109,0.0340087451,-0.0125172529,0.1098929197,-0.0834866986,-0.2122244835,-0.0992376581,-0.3635241091,0.109333314,-0.2231628746,-0.1021245122,-0.1164172366,0.1394500434,-0.3203218877,-0.141747877,-0.4392810762,0.3463121653,0.0661278293,-0.085641861,-0.1390953958,-0.2519544363,-0.3000761867,-0.3598109484,-0.1437502056,0.5643336773,-0.333956331,0.0883162245,-0.0775070265,0.2793798149,0.3261723518,0.3118193746,-0.0433119647,0.2612887621,-0.2711884975,-0.0348494351,0.0915929154,-0.076557532,-0.1479871869,0.1560693979,-0.2097709924,0.3643498123,-0.0059251441,0.288792491,0.0269558374,-0.2857646048,0.2122264951,0.5817522407,0.1125245392,0.1455496252,-0.2228961289,-0.1160703376,0.1606504917,0.1341605335,-0.1529251933,0.2557583153,-0.0797494054,-0.1022738442,0.2364648879,0.0587814227,-0.3761658072,0.0654713288,0.4298803508,0.0566270873,-0.0059010242,0.0919143707,-0.802533567,0.2653383017,-0.1356774271,0.2197048217,-0.1007074937,-0.1710778326,-0.0598150566,-0.1929310113,-0.3671398461,0.0432257727,0.1095663309,0.3891427517,0.2721548975,0.2649303973,0.1429004669,0.5052405596,0.0226428248,-0.1303787231,-0.4089696407,0.4076074958,-0.0580010265,-0.1394962817,0.3009549677,0.2322888821,0.0106304036,-0.2568787038,-0.1099532694,0.3478696346,0.1922554821,0.1490982622,0.1422685087,-0.0063865264,0.0790573433,0.0635892227,0.2284526229,0.2303832173,-0.0306497291,-0.142277196,-0.0237642191,0.4832135737,-0.1408339143,0.2432553321,0.106881395,-0.0918823034,0.0576850101,0.0561521091,-0.0098581268,-0.1848978996,0.2872168422,0.2241625935,0.3984701931,0.4891129434,-0.6162044406,0.2685339451,0.6741495728,0.1172868982,0.0545707718,0.0422465354,0.1281605065,-0.2923390865,0.2435434461,0.3109410107,0.5828956962,0.1728081405,-0.1215939075,-0.0487129018,-0.1323100924,-0.256632477,0.0082253413,-0.0497053936,0.231030643,0.068370007,0.3204985857,0.2718552351,-0.2242341489,-0.1144805327,-0.1183806583,0.0355489403,-0.1283758134,0.1732635051,-0.2431495041,-0.3185338974,-0.3550515175,-0.0998179838,-0.3305409849,-0.2406738102,0.0725947246,0.2184052914,0.0571436323,-0.1266395003,0.1862879097,0.2984232008,-0.0690757111,-0.4595802724,-0.1809671819,-0.0450526848,-0.3760229945,-0.0321737155,0.4337281585,0.2527377307,0.48213467,-0.134592101,-0.2056815326,0.1416743249,-0.2974398136,-0.1305258572,-0.3035556972,0.3335773945,-0.0823215246,-0.0986532941,-0.0242812317,-0.0864902288,0.0119557856,-0.0091673518,-0.0324624293,0.0095238388,0.0695247874,0.0021101534,-0.048866868,-0.341485709,-0.2831537426,-0.1440596282,0.0879117325,0.2736336887,0.1332778633,0.3045849502,-0.0792741999,-0.2478583753,0.1371490657,0.0449613631,-0.0806969404,-0.0878233761,0.3428190053,-0.1608350575,-0.2361774892,-0.3503187299,-0.1138889492,-0.070620127,0.1005068496,-0.3323993087,-0.2848387361,-0.3624153435,0.010045087,-0.07570225,-0.2865492105,0.3644975722,0.0775023624,0.0695832297,0.1888955235,-0.0041415044,0.1965331286,0.292099148,0.3772507012,0.1817639917,0.0183896068,-0.0339224599,0.4845993817,-0.0126197385,-0.41455549,0.2060723752,0.15878658,0.3422958553,-0.4762473106,-0.068302013,0.2320453376,0.1910715252,-0.105477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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/240","title":"Deterministic dataset loading","comments":"I'm pretty sure it would solve the problem too.\r\n\r\nThe only other dataset that is not deterministic right now is `blog_authorship_corpus` (see #215) but this is a problem related to string encodings.","body":"When calling:\r\n```python \r\nimport nlp\r\ndataset = nlp.load_dataset(\"trivia_qa\", split=\"validation[:1%]\")\r\n```\r\n\r\nthe resulting dataset is not deterministic over different google colabs. \r\nAfter talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/2e0a8639a79b1abc848cff5c669094d40bba0f63\/datasets\/trivia_qa\/trivia_qa.py#L180\r\n\r\nwhich seems to return an ordering of files that depends on the filesystem:\r\nhttps:\/\/stackoverflow.com\/questions\/6773584\/how-is-pythons-glob-glob-ordered\r\n\r\nI think we should go through all the dataset scripts and make sure to have deterministic behavior.\r\n\r\nA simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name. \r\n\r\nWhat do you think @lhoestq?","comment_length":32,"text":"Deterministic dataset loading \n When calling:\r\n```python \r\nimport nlp\r\ndataset = nlp.load_dataset(\"trivia_qa\", split=\"validation[:1%]\")\r\n```\r\n\r\nthe resulting dataset is not deterministic over different google colabs. \r\nAfter talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:\r\n\r\nhttps:\/\/github.com\/huggingface\/nlp\/blob\/2e0a8639a79b1abc848cff5c669094d40bba0f63\/datasets\/trivia_qa\/trivia_qa.py#L180\r\n\r\nwhich seems to return an ordering of files that depends on the filesystem:\r\nhttps:\/\/stackoverflow.com\/questions\/6773584\/how-is-pythons-glob-glob-ordered\r\n\r\nI think we should go through all the dataset scripts and make sure to have deterministic behavior.\r\n\r\nA simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name. \r\n\r\nWhat do you think @lhoestq? \n I'm pretty sure it would solve the problem too.\r\n\r\nThe only other dataset that is not deterministic right now is `blog_authorship_corpus` (see #215) but this is a problem related to string encodings.","embeddings":[0.1137944087,0.1788549572,0.0189524554,0.1695959121,0.1481847465,-0.0693371296,0.2104988247,0.1159460098,0.1663966626,0.0036022905,0.1294664294,0.3810071647,-0.1616073549,0.1054980531,-0.0421155952,-0.0011640498,-0.0110056577,0.1828359514,0.0234035216,-0.2169196308,-0.3032742143,0.0948169231,-0.4922880232,-0.4265719652,-0.2638105452,0.050531961,-0.0494492315,0.0728175268,-0.0821795687,-0.4266438484,0.0884041637,0.5214291215,0.1297443062,0.2086421102,-0.0001158346,-0.2780812979,0.3429811597,-0.1384103,-0.4034918249,-0.4584883749,-0.4235266447,0.0029786567,0.089284651,-0.0171199627,-0.2904814482,0.110562034,0.4717698693,-0.2651779354,0.2556129992,0.1604441106,0.1706977189,-0.1325588971,-0.0069109569,0.1613929272,0.0084528038,0.1136064753,-0.0788914859,0.365737319,0.0599491559,-0.237290293,-0.1026322246,0.1262462288,-0.0123312455,0.0853772238,0.4108412862,0.1294186264,-0.2025597692,-0.4190253019,-0.048245538,0.2851984501,0.5857836008,-0.1489483416,-0.2884296775,-0.3177915215,0.2706146836,-0.0686203614,0.164473787,0.4762815535,-0.1837822497,-0.0772719234,0.0095022209,0.2165177166,-0.1672361642,0.1256327331,0.0550950542,0.5633864403,0.020567894,-0.046430178,0.0403209664,0.1976746172,0.2665305734,-0.2073193192,-0.0149173141,0.1045660973,-0.2946223021,-0.1331092864,0.1327408403,0.4168833196,0.1423321217,0.1887691021,0.1884180754,0.1247835606,-0.1409153789,0.0277523864,0.3660642505,0.4443119764,0.0469317734,0.0882839635,0.4415364563,0.0005819829,-0.1401025355,0.2631989121,-0.0451807156,-0.2447943091,0.1352208853,0.0917385444,0.1828014702,-0.2448383868,-0.4639567435,-0.2088102549,-0.2868311405,-0.0472718664,-0.0736261457,0.2372121364,-0.277225554,0.1708800495,-0.2723610103,0.2396499515,-0.094397299,-0.0668712035,-0.0513294861,-0.14786461,-0.3596418202,0.1718271673,0.1715838909,0.0433012284,0.1575044394,0.2333868295,-0.2306009829,-0.1313460767,0.0631222874,-0.3846156895,0.0707031041,-0.0532546565,0.1267556697,-0.0679464936,-0.2007551938,-0.2258478999,-0.39488253,0.0732177719,-0.1875868291,-0.0415990837,-0.0811909139,0.1269268543,-0.3006807566,-0.1601521969,-0.3778267205,0.4154417515,0.1039694026,-0.0399899669,-0.1212582663,-0.2215484828,-0.3228255808,-0.3081771135,-0.2047908008,0.5929179192,-0.250954926,0.1347161233,-0.0144268936,0.3353873193,0.3447002172,0.342304498,0.0949027538,0.1837269217,-0.2054882348,0.0315852463,0.0978194252,-0.1302893907,-0.1156396642,0.1216481104,-0.1305008531,0.3174394965,-0.0036311306,0.2452240735,0.0708586574,-0.295288533,0.1327050626,0.5699854493,0.0917882249,0.1661237925,-0.2098805159,-0.1239983886,0.1535026878,0.0869185552,-0.073324956,0.2437751889,-0.0263798069,-0.0693856403,0.1995629221,0.0381739251,-0.354409337,0.1135728732,0.4417835474,0.0259771757,0.0174017753,0.032162834,-0.6761294007,0.2036518455,-0.0810295492,0.2807785273,-0.1386486143,-0.1894450784,-0.031260848,-0.2177087814,-0.351120919,0.0180445854,0.1251591444,0.4610756934,0.296536535,0.3313344717,0.1129760072,0.4452210963,0.027772449,-0.1577457041,-0.4701626301,0.3422989249,-0.0647957176,-0.1605475694,0.2936730087,0.2650282979,-0.0115144039,-0.2074688971,-0.0890064538,0.3012985289,0.2034747452,0.1466956586,0.196500361,-0.0047538569,0.0571860559,-0.0478245988,0.1256566197,0.3327848911,-0.0741519332,-0.0917756706,-0.0437165014,0.4265551269,-0.1051935852,0.245553717,0.0603084601,-0.0633689091,0.1409069449,0.0101236105,0.0338064693,-0.2565670907,0.3141155839,0.1884608716,0.4168983698,0.4318349659,-0.6472997069,0.2532051802,0.6687130928,0.1311240345,0.1337899566,0.0470857248,0.1058642715,-0.2604922056,0.1974286735,0.3241913319,0.4786657095,0.1711111516,-0.0800079778,-0.0753903538,-0.1365277171,-0.3183978498,0.010920926,-0.051234249,0.1863211393,0.1105131954,0.3885009587,0.2145434469,-0.3057562113,-0.0753770545,-0.0978050232,0.0150893796,-0.1057908759,0.1774474978,-0.3200418055,-0.2972366512,-0.3805834055,-0.077479586,-0.3543400466,-0.2322288901,0.1233587861,0.2657910883,0.0365579315,-0.0707651377,0.1533031464,0.2784759998,-0.0448838733,-0.447578162,-0.2064391673,-0.0843680203,-0.3556398451,-0.0400758237,0.4606668353,0.2419704497,0.4747899175,-0.1962220073,-0.2092666477,0.0662846267,-0.3068489134,-0.0887665451,-0.3029898405,0.366817683,-0.0931768641,-0.1227117851,-0.0300362613,-0.0684262738,-0.0041240379,0.0355628133,-0.0885950103,0.1138387918,0.028828647,-0.0352056511,-0.0114942016,-0.3158364892,-0.2380981296,-0.1639774442,0.1147304475,0.3120846748,0.1828108728,0.3057503402,-0.0969119221,-0.3069820404,0.1150780767,0.0978914499,-0.0968039334,-0.0391394645,0.4157397747,-0.1858071536,-0.2301391661,-0.3322431743,-0.1592838615,-0.1092950851,0.1176007167,-0.2903528214,-0.2713468671,-0.3909585476,0.0572606102,-0.0290543437,-0.3220250607,0.4101304412,0.0630945712,0.0524797514,0.1303123236,0.0009941681,0.2009332031,0.2924929261,0.3757012188,0.1786862612,-0.0003599727,0.017692795,0.4301039875,0.0923483148,-0.3738052845,0.2287277579,0.1366498768,0.316393286,-0.4368558526,-0.0289837066,0.2667537332,0.262501657,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/239","title":"[Creating new dataset] Not found dataset_info.json","comments":"@lhoestq - this seems to happen quite often (already the 2nd issue). Can we maybe delete this automatically?","body":"Hi, I am trying to create Toronto Book Corpus. #131 \r\n\r\nI ran\r\n`~\/nlp % python nlp-cli test datasets\/bookcorpus --save_infos --all_configs`\r\nbut this doesn't create `dataset_info.json` and try to use it\r\n```\r\nINFO:nlp.load:Checking datasets\/bookcorpus\/bookcorpus.py for additional imports.\r\nINFO:filelock:Lock 139795325778640 acquired on datasets\/bookcorpus\/bookcorpus.py.lock\r\nINFO:nlp.load:Found main folder for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\r\nINFO:nlp.load:Found specific version folder for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\r\nINFO:nlp.load:Found script file from datasets\/bookcorpus\/bookcorpus.py to \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\/bookcorpus.py\r\nINFO:nlp.load:Couldn't find dataset infos file at datasets\/bookcorpus\/dataset_infos.json\r\nINFO:nlp.load:Found metadata file for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\/bookcorpus.json\r\nINFO:filelock:Lock 139795325778640 released on datasets\/bookcorpus\/bookcorpus.py.lock\r\nINFO:nlp.builder:Overwrite dataset info from restored data version.\r\nINFO:nlp.info:Loading Dataset info from \/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\r\nTraceback (most recent call last):\r\n  File \"nlp-cli\", line 37, in <module>\r\n    service.run()\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/commands\/test.py\", line 78, in run\r\n    builders.append(builder_cls(name=config.name, data_dir=self._data_dir))\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 610, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 152, in __init__\r\n    self.info = DatasetInfo.from_directory(self._cache_dir)\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/info.py\", line 157, in from_directory\r\n    with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), \"r\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\/dataset_info.json'\r\n```\r\nbtw, `ls \/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\/` show me nothing is in the directory.\r\n\r\nI have also pushed the script to my fork [bookcorpus.py](https:\/\/github.com\/richardyy1188\/nlp\/blob\/bookcorpusdev\/datasets\/bookcorpus\/bookcorpus.py).\r\n","comment_length":18,"text":"[Creating new dataset] Not found dataset_info.json \n Hi, I am trying to create Toronto Book Corpus. #131 \r\n\r\nI ran\r\n`~\/nlp % python nlp-cli test datasets\/bookcorpus --save_infos --all_configs`\r\nbut this doesn't create `dataset_info.json` and try to use it\r\n```\r\nINFO:nlp.load:Checking datasets\/bookcorpus\/bookcorpus.py for additional imports.\r\nINFO:filelock:Lock 139795325778640 acquired on datasets\/bookcorpus\/bookcorpus.py.lock\r\nINFO:nlp.load:Found main folder for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\r\nINFO:nlp.load:Found specific version folder for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\r\nINFO:nlp.load:Found script file from datasets\/bookcorpus\/bookcorpus.py to \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\/bookcorpus.py\r\nINFO:nlp.load:Couldn't find dataset infos file at datasets\/bookcorpus\/dataset_infos.json\r\nINFO:nlp.load:Found metadata file for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\/bookcorpus.json\r\nINFO:filelock:Lock 139795325778640 released on datasets\/bookcorpus\/bookcorpus.py.lock\r\nINFO:nlp.builder:Overwrite dataset info from restored data version.\r\nINFO:nlp.info:Loading Dataset info from \/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\r\nTraceback (most recent call last):\r\n  File \"nlp-cli\", line 37, in <module>\r\n    service.run()\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/commands\/test.py\", line 78, in run\r\n    builders.append(builder_cls(name=config.name, data_dir=self._data_dir))\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 610, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 152, in __init__\r\n    self.info = DatasetInfo.from_directory(self._cache_dir)\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/info.py\", line 157, in from_directory\r\n    with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), \"r\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\/dataset_info.json'\r\n```\r\nbtw, `ls \/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\/` show me nothing is in the directory.\r\n\r\nI have also pushed the script to my fork [bookcorpus.py](https:\/\/github.com\/richardyy1188\/nlp\/blob\/bookcorpusdev\/datasets\/bookcorpus\/bookcorpus.py).\r\n \n @lhoestq - this seems to happen quite often (already the 2nd issue). Can we maybe delete this automatically?","embeddings":[-0.1165063456,0.1242502332,-0.0860032439,0.057063479,0.0176752172,0.1466908902,0.1669220626,0.3094637394,-0.2007142454,0.0218163859,0.0602891333,0.5790073276,-0.0935086533,0.0841157287,0.2967385054,-0.0632679313,0.0798937827,0.3547498286,0.4398046434,-0.1709556282,-0.3041882515,0.3338649273,-0.0877133608,0.01387031,-0.338992089,-0.1399389803,-0.1987462044,0.1517356932,-0.3689015806,-0.2440757155,0.4867274761,-0.0733882189,0.1036610082,0.5475575328,-0.0001140775,0.0102124736,0.3725022972,-0.1520183682,-0.463706404,-0.5580253005,-0.0097940266,-0.4027791023,0.1149742603,-0.3410841227,-0.1230617836,-0.3242848516,0.2014034241,-0.1395288706,-0.1161304712,0.4508829117,0.1798371077,0.0618324205,0.184210822,-0.0867190957,0.0001254161,0.2762156427,-0.2401407212,0.2098938376,0.5791471601,-0.03125735,0.2688177526,-0.0465215221,-0.0659272224,-0.2932347655,0.2591234148,0.159864217,0.0156177217,-0.4965935946,0.0261633452,0.0796523169,0.7736666799,-0.5069001317,-0.3475548625,0.0792120323,0.194891125,0.0815229118,0.0261326935,0.3870602548,-0.0036244129,-0.1468177885,-0.4909806252,-0.3836780488,-0.1394308358,0.3541182876,0.2187725157,0.2453514934,-0.0337953642,0.0422469042,-0.0160155166,-0.0535681583,-0.1302879304,-0.1859699339,-0.1286573559,0.2532887161,-0.053666506,-0.5121890903,0.1687165648,-0.0076868557,-0.0895367935,0.061128173,-0.0326223448,-0.274641186,-0.2865626812,0.1832764745,0.2046544701,-0.1106357574,0.279658407,0.0797307193,0.0774832591,0.055838082,0.0785688013,-0.0678011626,-0.0335660912,0.0156166032,-0.1277638078,-0.0994326547,0.3245657682,-0.0741777495,0.0718602091,0.0012805207,-0.1981693804,-0.133311823,0.1172707081,0.2936829925,-0.0719582215,-0.2800116837,-0.0251497515,0.2071141303,-0.4114207625,-0.0116609484,-0.1645233035,0.4944566786,-0.2056022882,-0.0262290053,0.3875793517,0.1016707048,0.3338846862,-0.0130871162,-0.368738085,-0.1556921601,0.3918465078,-0.2121744305,-0.1790122241,0.228953436,0.1160459667,0.1139768213,-0.0378630012,-0.3668879271,-0.3832911253,0.1916641742,-0.2350608408,-0.3481474817,-0.283872366,0.1933897138,0.0613683388,-0.2342845798,-0.3146072924,0.2751885056,0.3097748458,-0.3435207307,0.0633967742,0.0758629963,-0.1343005598,-0.2305130363,-0.0999691784,0.3998726904,-0.4836797416,-0.0721997991,-0.3215029836,-0.0094186161,0.1910135746,-0.0352825522,-0.2096750736,0.5172001123,-0.1338477433,0.1112853959,0.5153878927,-0.1893327534,-0.187461406,0.2695929706,-0.1940307021,-0.2928598523,0.0725557357,0.2507296503,-0.0414150171,-0.1687862128,0.1327093393,0.0917857811,-0.126192376,0.0494734347,0.0835390836,-0.0060451939,0.0510402881,0.1193809733,-0.2383529991,0.1086443737,0.1355073601,0.2590746582,0.097801879,-0.0312276371,0.1779914796,0.4847249985,0.3885298371,-0.0059467414,-0.0228138771,-0.0708329007,-0.3362148404,-0.0818720236,-0.2369996756,0.4043165743,0.0337655842,-0.002795812,-0.4716331661,-0.192667827,0.0334816724,-0.1176143065,0.0850239992,0.0162108652,0.3978447318,0.1003789753,-0.2538137734,0.0775243267,-0.1711559445,0.0665230826,-0.8675051332,0.3471957743,-0.2013229132,-0.2094378471,0.2470538467,0.2310829163,-0.0176213309,-0.1472965032,0.1502861083,0.2265686393,-0.1452424228,0.3361055255,0.2227082998,0.128842622,0.2885342836,-0.2038090229,0.1463573426,0.0493759997,0.1962344646,-0.0217218064,-0.0831139833,0.0535558686,0.1434141099,-0.0550300702,0.1617177576,-0.0439132974,0.1402127743,-0.1786086708,0.0769875199,-0.2421171665,0.1755624563,0.075510703,0.131608218,0.048625093,-0.0772270411,0.2307497859,0.3067060411,0.0531004742,0.0431879945,0.157994464,0.0398811735,-0.0914959162,-0.1635780036,0.2864362597,0.4337894619,0.1305443794,-0.1725736558,-0.0445974953,-0.1054399312,-0.2338734865,0.1762125492,-0.144586727,0.1815698743,0.0553759448,0.2064576298,-0.091882892,-0.1981725991,-0.4503073096,0.2582753003,0.2915617824,0.0125326188,0.2330195606,-0.0483596958,-0.2889663279,-0.1023572162,-0.2199970037,-0.027930662,-0.1097455919,0.0449513085,0.2148979455,0.2119145691,-0.1206713319,-0.1959022731,0.1891421676,-0.0882790238,-0.3575147688,-0.0885614678,-0.2564783394,-0.1869902909,0.1057349443,0.0348248258,0.1400998682,0.2495245636,-0.1843912452,0.0105685499,-0.1660453826,-0.2570820153,0.0180810299,-0.1595323384,0.19502756,0.1740631312,0.0597916804,-0.2608325481,-0.4254710078,0.0775657073,-0.0116267065,-0.4032616317,0.0009183167,-0.1733697355,-0.1220971048,-0.0407483503,-0.711997509,-0.3251382411,-0.213160634,-0.0155927436,0.1631813794,0.1977260411,0.4130190015,-0.0757454857,0.1082077697,0.1723190397,0.2316488922,-0.0692338049,-0.3634283841,0.3943191171,-0.5005172491,-0.2842501998,0.1889234185,-0.4618218243,0.2375343293,-0.1741824299,-0.5138138533,0.0168081373,-0.2980986834,0.0059204972,0.1343669146,0.2339942753,0.4963957071,-0.0279377773,-0.053906247,-0.1033315063,-0.4580044746,0.0228570011,0.4239569306,0.6483789682,-0.0955790132,0.1766877472,-0.4366553128,0.1395106316,0.3806612194,-0.0229976848,0.4596581459,-0.0642139837,0.2842175663,-0.2890721262,-0.3776216805,0.2021721154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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/239","title":"[Creating new dataset] Not found dataset_info.json","comments":"Hi, I rebase my local copy to `fix-empty-cache-dir`, and try to run again `python nlp-cli test datasets\/bookcorpus --save_infos --all_configs`.\r\n\r\nI got this,  \r\n```\r\nTraceback (most recent call last):\r\n  File \"nlp-cli\", line 10, in <module>\r\n    from nlp.commands.run_beam import RunBeamCommand\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/commands\/run_beam.py\", line 6, in <module>\r\n    import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n```\r\nAnd after I installed it. I got this\r\n```\r\nFile \"\/home\/yisiang\/nlp\/src\/nlp\/datasets\/bookcorpus\/aea0bd5142d26df645a8fce23d6110bb95ecb81772bb2a1f29012e329191962c\/bookcorpus.py\", line 88, in _split_generators\r\n    downloaded_path_or_paths = dl_manager.download_custom(_GDRIVE_FILE_ID, download_file_from_google_drive)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/download_manager.py\", line 128, in download_custom\r\n    downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/download_manager.py\", line 126, in url_to_downloaded_path\r\n    return os.path.join(self._download_config.cache_dir, hash_url_to_filename(url))\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/nlppr\/lib\/python3.7\/posixpath.py\", line 80, in join\r\n    a = os.fspath(a)\r\n```\r\nThe problem is when I print `self._download_config.cache_dir` using pdb, it is `None`.\r\n\r\nDid I miss something ?  Or can you provide a workaround first so I can keep testing my script ?","body":"Hi, I am trying to create Toronto Book Corpus. #131 \r\n\r\nI ran\r\n`~\/nlp % python nlp-cli test datasets\/bookcorpus --save_infos --all_configs`\r\nbut this doesn't create `dataset_info.json` and try to use it\r\n```\r\nINFO:nlp.load:Checking datasets\/bookcorpus\/bookcorpus.py for additional imports.\r\nINFO:filelock:Lock 139795325778640 acquired on datasets\/bookcorpus\/bookcorpus.py.lock\r\nINFO:nlp.load:Found main folder for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\r\nINFO:nlp.load:Found specific version folder for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\r\nINFO:nlp.load:Found script file from datasets\/bookcorpus\/bookcorpus.py to \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\/bookcorpus.py\r\nINFO:nlp.load:Couldn't find dataset infos file at datasets\/bookcorpus\/dataset_infos.json\r\nINFO:nlp.load:Found metadata file for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\/bookcorpus.json\r\nINFO:filelock:Lock 139795325778640 released on datasets\/bookcorpus\/bookcorpus.py.lock\r\nINFO:nlp.builder:Overwrite dataset info from restored data version.\r\nINFO:nlp.info:Loading Dataset info from \/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\r\nTraceback (most recent call last):\r\n  File \"nlp-cli\", line 37, in <module>\r\n    service.run()\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/commands\/test.py\", line 78, in run\r\n    builders.append(builder_cls(name=config.name, data_dir=self._data_dir))\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 610, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 152, in __init__\r\n    self.info = DatasetInfo.from_directory(self._cache_dir)\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/info.py\", line 157, in from_directory\r\n    with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), \"r\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\/dataset_info.json'\r\n```\r\nbtw, `ls \/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\/` show me nothing is in the directory.\r\n\r\nI have also pushed the script to my fork [bookcorpus.py](https:\/\/github.com\/richardyy1188\/nlp\/blob\/bookcorpusdev\/datasets\/bookcorpus\/bookcorpus.py).\r\n","comment_length":142,"text":"[Creating new dataset] Not found dataset_info.json \n Hi, I am trying to create Toronto Book Corpus. #131 \r\n\r\nI ran\r\n`~\/nlp % python nlp-cli test datasets\/bookcorpus --save_infos --all_configs`\r\nbut this doesn't create `dataset_info.json` and try to use it\r\n```\r\nINFO:nlp.load:Checking datasets\/bookcorpus\/bookcorpus.py for additional imports.\r\nINFO:filelock:Lock 139795325778640 acquired on datasets\/bookcorpus\/bookcorpus.py.lock\r\nINFO:nlp.load:Found main folder for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\r\nINFO:nlp.load:Found specific version folder for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\r\nINFO:nlp.load:Found script file from datasets\/bookcorpus\/bookcorpus.py to \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\/bookcorpus.py\r\nINFO:nlp.load:Couldn't find dataset infos file at datasets\/bookcorpus\/dataset_infos.json\r\nINFO:nlp.load:Found metadata file for dataset datasets\/bookcorpus\/bookcorpus.py at \/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/datasets\/bookcorpus\/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9\/bookcorpus.json\r\nINFO:filelock:Lock 139795325778640 released on datasets\/bookcorpus\/bookcorpus.py.lock\r\nINFO:nlp.builder:Overwrite dataset info from restored data version.\r\nINFO:nlp.info:Loading Dataset info from \/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\r\nTraceback (most recent call last):\r\n  File \"nlp-cli\", line 37, in <module>\r\n    service.run()\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/commands\/test.py\", line 78, in run\r\n    builders.append(builder_cls(name=config.name, data_dir=self._data_dir))\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 610, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 152, in __init__\r\n    self.info = DatasetInfo.from_directory(self._cache_dir)\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/ml\/lib\/python3.7\/site-packages\/nlp\/info.py\", line 157, in from_directory\r\n    with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), \"r\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\/dataset_info.json'\r\n```\r\nbtw, `ls \/home\/yisiang\/.cache\/huggingface\/datasets\/book_corpus\/plain_text\/1.0.0\/` show me nothing is in the directory.\r\n\r\nI have also pushed the script to my fork [bookcorpus.py](https:\/\/github.com\/richardyy1188\/nlp\/blob\/bookcorpusdev\/datasets\/bookcorpus\/bookcorpus.py).\r\n \n Hi, I rebase my local copy to `fix-empty-cache-dir`, and try to run again `python nlp-cli test datasets\/bookcorpus --save_infos --all_configs`.\r\n\r\nI got this,  \r\n```\r\nTraceback (most recent call last):\r\n  File \"nlp-cli\", line 10, in <module>\r\n    from nlp.commands.run_beam import RunBeamCommand\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/commands\/run_beam.py\", line 6, in <module>\r\n    import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n```\r\nAnd after I installed it. I got this\r\n```\r\nFile \"\/home\/yisiang\/nlp\/src\/nlp\/datasets\/bookcorpus\/aea0bd5142d26df645a8fce23d6110bb95ecb81772bb2a1f29012e329191962c\/bookcorpus.py\", line 88, in _split_generators\r\n    downloaded_path_or_paths = dl_manager.download_custom(_GDRIVE_FILE_ID, download_file_from_google_drive)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/download_manager.py\", line 128, in download_custom\r\n    downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/yisiang\/nlp\/src\/nlp\/utils\/download_manager.py\", line 126, in url_to_downloaded_path\r\n    return os.path.join(self._download_config.cache_dir, hash_url_to_filename(url))\r\n  File \"\/home\/yisiang\/miniconda3\/envs\/nlppr\/lib\/python3.7\/posixpath.py\", line 80, in join\r\n    a = os.fspath(a)\r\n```\r\nThe problem is when I print `self._download_config.cache_dir` using pdb, it is `None`.\r\n\r\nDid I miss something ?  Or can you provide a workaround first so I can keep testing my script ?","embeddings":[-0.1165063456,0.1242502332,-0.0860032439,0.057063479,0.0176752172,0.1466908902,0.1669220626,0.3094637394,-0.2007142454,0.0218163859,0.0602891333,0.5790073276,-0.0935086533,0.0841157287,0.2967385054,-0.0632679313,0.0798937827,0.3547498286,0.4398046434,-0.1709556282,-0.3041882515,0.3338649273,-0.0877133608,0.01387031,-0.338992089,-0.1399389803,-0.1987462044,0.1517356932,-0.3689015806,-0.2440757155,0.4867274761,-0.0733882189,0.1036610082,0.5475575328,-0.0001140775,0.0102124736,0.3725022972,-0.1520183682,-0.463706404,-0.5580253005,-0.0097940266,-0.4027791023,0.1149742603,-0.3410841227,-0.1230617836,-0.3242848516,0.2014034241,-0.1395288706,-0.1161304712,0.4508829117,0.1798371077,0.0618324205,0.184210822,-0.0867190957,0.0001254161,0.2762156427,-0.2401407212,0.2098938376,0.5791471601,-0.03125735,0.2688177526,-0.0465215221,-0.0659272224,-0.2932347655,0.2591234148,0.159864217,0.0156177217,-0.4965935946,0.0261633452,0.0796523169,0.7736666799,-0.5069001317,-0.3475548625,0.0792120323,0.194891125,0.0815229118,0.0261326935,0.3870602548,-0.0036244129,-0.1468177885,-0.4909806252,-0.3836780488,-0.1394308358,0.3541182876,0.2187725157,0.2453514934,-0.0337953642,0.0422469042,-0.0160155166,-0.0535681583,-0.1302879304,-0.1859699339,-0.1286573559,0.2532887161,-0.053666506,-0.5121890903,0.1687165648,-0.0076868557,-0.0895367935,0.061128173,-0.0326223448,-0.274641186,-0.2865626812,0.1832764745,0.2046544701,-0.1106357574,0.279658407,0.0797307193,0.0774832591,0.055838082,0.0785688013,-0.0678011626,-0.0335660912,0.0156166032,-0.1277638078,-0.0994326547,0.3245657682,-0.0741777495,0.0718602091,0.0012805207,-0.1981693804,-0.133311823,0.1172707081,0.2936829925,-0.0719582215,-0.2800116837,-0.0251497515,0.2071141303,-0.4114207625,-0.0116609484,-0.1645233035,0.4944566786,-0.2056022882,-0.0262290053,0.3875793517,0.1016707048,0.3338846862,-0.0130871162,-0.368738085,-0.1556921601,0.3918465078,-0.2121744305,-0.1790122241,0.228953436,0.1160459667,0.1139768213,-0.0378630012,-0.3668879271,-0.3832911253,0.1916641742,-0.2350608408,-0.3481474817,-0.283872366,0.1933897138,0.0613683388,-0.2342845798,-0.3146072924,0.2751885056,0.3097748458,-0.3435207307,0.0633967742,0.0758629963,-0.1343005598,-0.2305130363,-0.0999691784,0.3998726904,-0.4836797416,-0.0721997991,-0.3215029836,-0.0094186161,0.1910135746,-0.0352825522,-0.2096750736,0.5172001123,-0.1338477433,0.1112853959,0.5153878927,-0.1893327534,-0.187461406,0.2695929706,-0.1940307021,-0.2928598523,0.0725557357,0.2507296503,-0.0414150171,-0.1687862128,0.1327093393,0.0917857811,-0.126192376,0.0494734347,0.0835390836,-0.0060451939,0.0510402881,0.1193809733,-0.2383529991,0.1086443737,0.1355073601,0.2590746582,0.097801879,-0.0312276371,0.1779914796,0.4847249985,0.3885298371,-0.0059467414,-0.0228138771,-0.0708329007,-0.3362148404,-0.0818720236,-0.2369996756,0.4043165743,0.0337655842,-0.002795812,-0.4716331661,-0.192667827,0.0334816724,-0.1176143065,0.0850239992,0.0162108652,0.3978447318,0.1003789753,-0.2538137734,0.0775243267,-0.1711559445,0.0665230826,-0.8675051332,0.3471957743,-0.2013229132,-0.2094378471,0.2470538467,0.2310829163,-0.0176213309,-0.1472965032,0.1502861083,0.2265686393,-0.1452424228,0.3361055255,0.2227082998,0.128842622,0.2885342836,-0.2038090229,0.1463573426,0.0493759997,0.1962344646,-0.0217218064,-0.0831139833,0.0535558686,0.1434141099,-0.0550300702,0.1617177576,-0.0439132974,0.1402127743,-0.1786086708,0.0769875199,-0.2421171665,0.1755624563,0.075510703,0.131608218,0.048625093,-0.0772270411,0.2307497859,0.3067060411,0.0531004742,0.0431879945,0.157994464,0.0398811735,-0.0914959162,-0.1635780036,0.2864362597,0.4337894619,0.1305443794,-0.1725736558,-0.0445974953,-0.1054399312,-0.2338734865,0.1762125492,-0.144586727,0.1815698743,0.0553759448,0.2064576298,-0.091882892,-0.1981725991,-0.4503073096,0.2582753003,0.2915617824,0.0125326188,0.2330195606,-0.0483596958,-0.2889663279,-0.1023572162,-0.2199970037,-0.027930662,-0.1097455919,0.0449513085,0.2148979455,0.2119145691,-0.1206713319,-0.1959022731,0.1891421676,-0.0882790238,-0.3575147688,-0.0885614678,-0.2564783394,-0.1869902909,0.1057349443,0.0348248258,0.1400998682,0.2495245636,-0.1843912452,0.0105685499,-0.1660453826,-0.2570820153,0.0180810299,-0.1595323384,0.19502756,0.1740631312,0.0597916804,-0.2608325481,-0.4254710078,0.0775657073,-0.0116267065,-0.4032616317,0.0009183167,-0.1733697355,-0.1220971048,-0.0407483503,-0.711997509,-0.3251382411,-0.213160634,-0.0155927436,0.1631813794,0.1977260411,0.4130190015,-0.0757454857,0.1082077697,0.1723190397,0.2316488922,-0.0692338049,-0.3634283841,0.3943191171,-0.5005172491,-0.2842501998,0.1889234185,-0.4618218243,0.2375343293,-0.1741824299,-0.5138138533,0.0168081373,-0.2980986834,0.0059204972,0.1343669146,0.2339942753,0.4963957071,-0.0279377773,-0.053906247,-0.1033315063,-0.4580044746,0.0228570011,0.4239569306,0.6483789682,-0.0955790132,0.1766877472,-0.4366553128,0.1395106316,0.3806612194,-0.0229976848,0.4596581459,-0.0642139837,0.2842175663,-0.2890721262,-0.3776216805,0.2021721154,0.2846024036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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/238","title":"[Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0.","comments":"This print statement comes from the official implementation of bert_score (see [here](https:\/\/github.com\/Tiiiger\/bert_score\/blob\/master\/bert_score\/utils.py#L343)). The warning shows up only if the attention mask outputs no candidate.\r\nRight now we want to only use official code for metrics to have fair evaluations, so I'm not sure we can do anything about it. Maybe you can try to create an issue on their [repo](https:\/\/github.com\/Tiiiger\/bert_score) ?","body":"When running BERT-Score, I'm meeting this warning :\r\n\r\n> Warning: Empty candidate sentence; Setting recall to be 0.\r\n\r\nCode :\r\n\r\n```\r\nimport nlp\r\nmetric = nlp.load_metric(\"bertscore\")\r\nscores = metric.compute([\"swag\", \"swags\"], [\"swags\", \"totally something different\"], lang=\"en\", device=0)\r\n```\r\n\r\n---\r\n\r\n**What am I doing wrong \/ How can I hide this warning ?**","comment_length":61,"text":"[Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0. \n When running BERT-Score, I'm meeting this warning :\r\n\r\n> Warning: Empty candidate sentence; Setting recall to be 0.\r\n\r\nCode :\r\n\r\n```\r\nimport nlp\r\nmetric = nlp.load_metric(\"bertscore\")\r\nscores = metric.compute([\"swag\", \"swags\"], [\"swags\", \"totally something different\"], lang=\"en\", device=0)\r\n```\r\n\r\n---\r\n\r\n**What am I doing wrong \/ How can I hide this warning ?** \n This print statement comes from the official implementation of bert_score (see [here](https:\/\/github.com\/Tiiiger\/bert_score\/blob\/master\/bert_score\/utils.py#L343)). The warning shows up only if the attention mask outputs no candidate.\r\nRight now we want to only use official code for metrics to have fair evaluations, so I'm not sure we can do anything about it. Maybe you can try to create an issue on their [repo](https:\/\/github.com\/Tiiiger\/bert_score) ?","embeddings":[0.2172083408,-0.1352753937,0.1404270083,0.1618988514,0.1744060963,0.0671908632,-0.0303219259,0.264549464,0.4367605448,0.3824990094,0.4003357589,0.1243717819,-0.2570009232,-0.4931720793,-0.1998976916,-0.1357424855,0.0677198842,0.4522253573,0.5661281347,-0.251927197,-0.2824742794,-0.0953284204,-0.3659345508,0.2246672958,-0.269020021,0.0552604273,0.0221389793,-0.2626544237,-0.1808631271,-0.5885035992,0.1738504618,-0.1883931458,-0.2331287712,0.2377419025,-0.0001283982,-0.2515506148,0.4155663252,-0.0837017745,0.2523778975,-0.4310386777,-0.4188614786,-0.1371326596,0.2361352146,-0.2389804721,0.0187330488,0.2030011117,0.1517081559,-0.1645323634,0.0686172247,0.1861715019,0.0121348435,0.2936764956,-0.1058877259,-0.0611063577,0.0227679927,-0.1313389391,-0.0275754463,0.0413871147,-0.192634508,-0.2139684707,-0.0495755672,0.5230774283,-0.3952677846,0.1201559156,0.6345873475,0.0905861557,0.2315475643,-0.2104471177,0.0147829996,0.2737215161,-0.1209857985,-0.1175498515,-0.0278368797,-0.0735123008,0.3021912575,-0.3431499898,0.1852401495,0.0706935227,-0.0142913405,-0.2116330415,-0.3099201024,-0.2357536852,-0.2747386098,-0.0452069156,-0.0472432859,0.3264420331,0.0771593675,-0.064789623,0.285643369,-0.1446775496,-0.0932134464,-0.1892850995,-0.1289047003,0.1941079348,-0.0856098011,-0.2921285927,0.1814135462,-0.3983608484,0.2413861603,-0.4822866023,0.1300214827,-0.1615929604,0.0125321653,0.0205259118,0.2635143101,0.4210568666,0.2929509878,0.210457325,-0.2632245421,0.183308199,0.1879091561,-0.0303863995,0.1382540464,0.1980026811,0.427703172,0.2182935923,-0.0025749032,-0.3060588837,-0.378777951,0.3667081296,-0.6508225203,-0.1554356366,0.177965939,0.0961790308,-0.1908064187,0.1977982074,-0.120195359,0.1526350081,-0.2993704081,-0.2251215428,-0.2421463728,0.1063049808,-0.2950397432,-0.0109460047,0.1267605275,0.1687352061,0.2033834159,-0.0032161898,0.4691496491,0.1447551847,-0.0030009658,-0.4175774157,0.365311265,0.2870754004,0.0016980814,-0.0793112293,0.3670933247,-0.1423478574,-0.1115620732,0.4028783143,-0.1367293,-0.1410658509,0.1487500966,0.0409576446,-0.5017951131,-0.010038102,-0.1201108545,0.4336763024,-0.1826024354,0.0453266464,0.1114468575,-0.0820746645,-0.1678718925,-0.0720645711,0.3950432837,0.6641337872,-0.0311525594,-0.1508771628,0.0343001485,0.1536741853,0.2091035545,-0.0224782676,0.1260635406,0.2954151034,0.0210266784,0.2190592587,0.1616791487,-0.5407570004,-0.0825528353,-0.1191290393,-0.088856034,-0.0452513881,-0.039928481,-0.0125782918,-0.144595921,-0.077084519,0.1568501741,-0.4318296313,-0.0828227997,0.0029682973,-0.3594414294,-0.2030716389,0.2426879704,-0.2542194426,0.1952984184,0.1229350492,-0.3715901971,0.6814710498,-0.0656106472,-0.0377174579,0.0286733266,-0.2180281281,0.2738712728,-0.0218699072,-0.1736154705,0.0716456547,0.1327930689,-0.0182188973,-0.4321421683,0.129990235,0.2906796932,-0.2665609121,-0.2060015351,-0.1474108994,-0.2199855894,-0.1980659217,0.012501549,-0.2633131146,0.0091224909,0.1802123189,-0.2141342163,-0.2851103544,-0.1754494905,0.088712588,0.1457494944,-0.1454801261,0.1112036109,-0.1030413955,0.079609707,0.4481427372,0.4937630296,0.3303021491,0.0161165334,0.206701979,0.0104518421,-0.132597208,0.0350257233,0.0539596416,0.1724950522,-0.4314732254,0.1335953921,0.1833998114,-0.0259705875,0.0959281996,-0.134539783,0.0390607342,0.0892209709,0.2943095565,-0.2372471541,-0.1204894558,-0.3590610921,-0.0762530491,0.1567562073,-0.2641415,-0.0215879679,0.0554708019,-0.0585485138,-0.0392433479,-0.134301722,-0.2703173459,0.2294283807,0.227495417,0.166766867,0.0886536464,0.0664336383,-0.202432096,-0.0473284535,-0.4831431508,0.2371384501,0.170296818,-0.0470597669,0.0829712749,-0.272865653,-0.1448379457,0.0531097353,0.4124594033,-0.2057670057,-0.0480520688,-0.0068303221,0.0476070605,0.0982540548,0.056281358,-0.0962264687,0.2895222306,-0.333522439,0.0828367248,0.0470942408,-0.1901102662,-0.1628680676,0.0326192603,-0.3311604857,-0.077011466,0.2044581473,-0.1896677464,-0.0671472251,0.1545462906,-0.0388696752,0.2940200865,-0.0448274277,-0.029598739,-0.1341499537,-0.0887797847,-0.2249080986,-0.0249913167,-0.3071593046,0.2817917466,0.1091584787,-0.188946262,-0.131850481,0.1648333818,-0.1732589155,0.1726527959,-0.2086050063,-0.0028176755,0.0246709567,-0.0231984351,-0.5214915276,0.0940884873,0.1555965096,-0.1103254259,0.0398592912,0.0614583492,0.1027319655,0.3387366235,-0.2706692815,-0.3621058762,0.2669712007,0.0326420702,0.0121546034,-0.0966365784,0.1388606876,-0.0262364186,0.0883742943,0.3146154881,-0.2663766742,-0.2583737969,-0.176283434,-0.2877731919,0.2703225315,0.0103182225,-0.1003650278,0.3193663955,-0.1577481478,0.1230217591,-0.4959367812,-0.3708249331,-0.6145672202,0.1839373857,-0.4545835555,-0.1591233909,-0.0006665088,0.1189976782,-0.076492399,0.003376527,-0.0885705426,0.0732885152,0.2426484078,-0.4084762633,0.4219225943,-0.2583235502,-0.2140686661,-0.1020656526,0.4282715917,0.3861439824,0.2228370607,-0.029499514,0.1557049602,0.3310789168,-0.0301491953,-0.0243961662,0.1702740192,0.5506826043,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/237","title":"Can't download MultiNLI","comments":"Thanks! I thought I had to use the same code displayed in the live viewer:\r\n```python\r\n!pip install nlp\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('multi_nli', 'plain_text')\r\n```\r\nYour suggestion works, even if then I got a different issue (#242).  ","body":"When I try to download MultiNLI with \r\n```python\r\ndataset = load_dataset('multi_nli')\r\n```\r\n\r\nI get this long error:\r\n```python\r\n---------------------------------------------------------------------------\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-13-3b11f6be4cb9> in <module>\r\n      1 # Load a dataset and print the first examples in the training set\r\n      2 # nli_dataset = nlp.load_dataset('multi_nli')\r\n----> 3 dataset = load_dataset('multi_nli')\r\n      4 # nli_dataset = nlp.load_dataset('multi_nli', split='validation_matched[:10%]')\r\n      5 # print(nli_dataset['train'][0])\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    514 \r\n    515     # Download and prepare data\r\n--> 516     builder_instance.download_and_prepare(\r\n    517         download_config=download_config,\r\n    518         download_mode=download_mode,\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    417             with utils.temporary_assignment(self, \"_cache_dir\", tmp_data_dir):\r\n    418                 verify_infos = not save_infos and not ignore_verifications\r\n--> 419                 self._download_and_prepare(\r\n    420                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    421                 )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    455         split_dict = SplitDict(dataset_name=self.name)\r\n    456         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 457         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    458         # Checksums verification\r\n    459         if verify_infos:\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\datasets\\multi_nli\\60774175381b9f3f1e6ae1028229e3cdb270d50379f45b9f2c01008f50f09e6b\\multi_nli.py in _split_generators(self, dl_manager)\r\n     99     def _split_generators(self, dl_manager):\r\n    100 \r\n--> 101         downloaded_dir = dl_manager.download_and_extract(\r\n    102             \"http:\/\/storage.googleapis.com\/tfds-data\/downloads\/multi_nli\/multinli_1.0.zip\"\r\n    103         )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in download_and_extract(self, url_or_urls)\r\n    214             extracted_path(s): `str`, extracted paths of given URL(s).\r\n    215         \"\"\"\r\n--> 216         return self.extract(self.download(url_or_urls))\r\n    217 \r\n    218     def get_recorded_sizes_checksums(self):\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in extract(self, path_or_paths)\r\n    194                 path_or_paths.\r\n    195         \"\"\"\r\n--> 196         return map_nested(\r\n    197             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    198         )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)\r\n    168                 return tuple(mapped)\r\n    169     # Singleton\r\n--> 170     return function(data_struct)\r\n    171 \r\n    172 \r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in <lambda>(path)\r\n    195         \"\"\"\r\n    196         return map_nested(\r\n--> 197             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    198         )\r\n    199 \r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    231             if is_zipfile(output_path):\r\n    232                 with ZipFile(output_path, \"r\") as zip_file:\r\n--> 233                     zip_file.extractall(output_path_extracted)\r\n    234                     zip_file.close()\r\n    235             elif tarfile.is_tarfile(output_path):\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\zipfile.py in extractall(self, path, members, pwd)\r\n   1644 \r\n   1645         for zipinfo in members:\r\n-> 1646             self._extract_member(zipinfo, path, pwd)\r\n   1647 \r\n   1648     @classmethod\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\zipfile.py in _extract_member(self, member, targetpath, pwd)\r\n   1698 \r\n   1699         with self.open(member, pwd=pwd) as source, \\\r\n-> 1700              open(targetpath, \"wb\") as target:\r\n   1701             shutil.copyfileobj(source, target)\r\n   1702 \r\n\r\nOSError: [Errno 22] Invalid argument: 'C:\\\\Users\\\\Python\\\\.cache\\\\huggingface\\\\datasets\\\\3e12413b8ec69f22dfcfd54a79d1ba9e7aac2e18e334bbb6b81cca64fd16bffc\\\\multinli_1.0\\\\Icon\\r'\r\n```\r\n","comment_length":40,"text":"Can't download MultiNLI \n When I try to download MultiNLI with \r\n```python\r\ndataset = load_dataset('multi_nli')\r\n```\r\n\r\nI get this long error:\r\n```python\r\n---------------------------------------------------------------------------\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-13-3b11f6be4cb9> in <module>\r\n      1 # Load a dataset and print the first examples in the training set\r\n      2 # nli_dataset = nlp.load_dataset('multi_nli')\r\n----> 3 dataset = load_dataset('multi_nli')\r\n      4 # nli_dataset = nlp.load_dataset('multi_nli', split='validation_matched[:10%]')\r\n      5 # print(nli_dataset['train'][0])\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    514 \r\n    515     # Download and prepare data\r\n--> 516     builder_instance.download_and_prepare(\r\n    517         download_config=download_config,\r\n    518         download_mode=download_mode,\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    417             with utils.temporary_assignment(self, \"_cache_dir\", tmp_data_dir):\r\n    418                 verify_infos = not save_infos and not ignore_verifications\r\n--> 419                 self._download_and_prepare(\r\n    420                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    421                 )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    455         split_dict = SplitDict(dataset_name=self.name)\r\n    456         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 457         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    458         # Checksums verification\r\n    459         if verify_infos:\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\datasets\\multi_nli\\60774175381b9f3f1e6ae1028229e3cdb270d50379f45b9f2c01008f50f09e6b\\multi_nli.py in _split_generators(self, dl_manager)\r\n     99     def _split_generators(self, dl_manager):\r\n    100 \r\n--> 101         downloaded_dir = dl_manager.download_and_extract(\r\n    102             \"http:\/\/storage.googleapis.com\/tfds-data\/downloads\/multi_nli\/multinli_1.0.zip\"\r\n    103         )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in download_and_extract(self, url_or_urls)\r\n    214             extracted_path(s): `str`, extracted paths of given URL(s).\r\n    215         \"\"\"\r\n--> 216         return self.extract(self.download(url_or_urls))\r\n    217 \r\n    218     def get_recorded_sizes_checksums(self):\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in extract(self, path_or_paths)\r\n    194                 path_or_paths.\r\n    195         \"\"\"\r\n--> 196         return map_nested(\r\n    197             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    198         )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)\r\n    168                 return tuple(mapped)\r\n    169     # Singleton\r\n--> 170     return function(data_struct)\r\n    171 \r\n    172 \r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in <lambda>(path)\r\n    195         \"\"\"\r\n    196         return map_nested(\r\n--> 197             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    198         )\r\n    199 \r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    231             if is_zipfile(output_path):\r\n    232                 with ZipFile(output_path, \"r\") as zip_file:\r\n--> 233                     zip_file.extractall(output_path_extracted)\r\n    234                     zip_file.close()\r\n    235             elif tarfile.is_tarfile(output_path):\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\zipfile.py in extractall(self, path, members, pwd)\r\n   1644 \r\n   1645         for zipinfo in members:\r\n-> 1646             self._extract_member(zipinfo, path, pwd)\r\n   1647 \r\n   1648     @classmethod\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\zipfile.py in _extract_member(self, member, targetpath, pwd)\r\n   1698 \r\n   1699         with self.open(member, pwd=pwd) as source, \\\r\n-> 1700              open(targetpath, \"wb\") as target:\r\n   1701             shutil.copyfileobj(source, target)\r\n   1702 \r\n\r\nOSError: [Errno 22] Invalid argument: 'C:\\\\Users\\\\Python\\\\.cache\\\\huggingface\\\\datasets\\\\3e12413b8ec69f22dfcfd54a79d1ba9e7aac2e18e334bbb6b81cca64fd16bffc\\\\multinli_1.0\\\\Icon\\r'\r\n```\r\n \n Thanks! I thought I had to use the same code displayed in the live viewer:\r\n```python\r\n!pip install nlp\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('multi_nli', 'plain_text')\r\n```\r\nYour suggestion works, even if then I got a different issue (#242).  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/237","title":"Can't download MultiNLI","comments":"Glad it helps !\nThough I am not one of hf team, but maybe you should close this issue first.","body":"When I try to download MultiNLI with \r\n```python\r\ndataset = load_dataset('multi_nli')\r\n```\r\n\r\nI get this long error:\r\n```python\r\n---------------------------------------------------------------------------\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-13-3b11f6be4cb9> in <module>\r\n      1 # Load a dataset and print the first examples in the training set\r\n      2 # nli_dataset = nlp.load_dataset('multi_nli')\r\n----> 3 dataset = load_dataset('multi_nli')\r\n      4 # nli_dataset = nlp.load_dataset('multi_nli', split='validation_matched[:10%]')\r\n      5 # print(nli_dataset['train'][0])\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    514 \r\n    515     # Download and prepare data\r\n--> 516     builder_instance.download_and_prepare(\r\n    517         download_config=download_config,\r\n    518         download_mode=download_mode,\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    417             with utils.temporary_assignment(self, \"_cache_dir\", tmp_data_dir):\r\n    418                 verify_infos = not save_infos and not ignore_verifications\r\n--> 419                 self._download_and_prepare(\r\n    420                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    421                 )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    455         split_dict = SplitDict(dataset_name=self.name)\r\n    456         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 457         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    458         # Checksums verification\r\n    459         if verify_infos:\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\datasets\\multi_nli\\60774175381b9f3f1e6ae1028229e3cdb270d50379f45b9f2c01008f50f09e6b\\multi_nli.py in _split_generators(self, dl_manager)\r\n     99     def _split_generators(self, dl_manager):\r\n    100 \r\n--> 101         downloaded_dir = dl_manager.download_and_extract(\r\n    102             \"http:\/\/storage.googleapis.com\/tfds-data\/downloads\/multi_nli\/multinli_1.0.zip\"\r\n    103         )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in download_and_extract(self, url_or_urls)\r\n    214             extracted_path(s): `str`, extracted paths of given URL(s).\r\n    215         \"\"\"\r\n--> 216         return self.extract(self.download(url_or_urls))\r\n    217 \r\n    218     def get_recorded_sizes_checksums(self):\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in extract(self, path_or_paths)\r\n    194                 path_or_paths.\r\n    195         \"\"\"\r\n--> 196         return map_nested(\r\n    197             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    198         )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)\r\n    168                 return tuple(mapped)\r\n    169     # Singleton\r\n--> 170     return function(data_struct)\r\n    171 \r\n    172 \r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in <lambda>(path)\r\n    195         \"\"\"\r\n    196         return map_nested(\r\n--> 197             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    198         )\r\n    199 \r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    231             if is_zipfile(output_path):\r\n    232                 with ZipFile(output_path, \"r\") as zip_file:\r\n--> 233                     zip_file.extractall(output_path_extracted)\r\n    234                     zip_file.close()\r\n    235             elif tarfile.is_tarfile(output_path):\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\zipfile.py in extractall(self, path, members, pwd)\r\n   1644 \r\n   1645         for zipinfo in members:\r\n-> 1646             self._extract_member(zipinfo, path, pwd)\r\n   1647 \r\n   1648     @classmethod\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\zipfile.py in _extract_member(self, member, targetpath, pwd)\r\n   1698 \r\n   1699         with self.open(member, pwd=pwd) as source, \\\r\n-> 1700              open(targetpath, \"wb\") as target:\r\n   1701             shutil.copyfileobj(source, target)\r\n   1702 \r\n\r\nOSError: [Errno 22] Invalid argument: 'C:\\\\Users\\\\Python\\\\.cache\\\\huggingface\\\\datasets\\\\3e12413b8ec69f22dfcfd54a79d1ba9e7aac2e18e334bbb6b81cca64fd16bffc\\\\multinli_1.0\\\\Icon\\r'\r\n```\r\n","comment_length":20,"text":"Can't download MultiNLI \n When I try to download MultiNLI with \r\n```python\r\ndataset = load_dataset('multi_nli')\r\n```\r\n\r\nI get this long error:\r\n```python\r\n---------------------------------------------------------------------------\r\nOSError                                   Traceback (most recent call last)\r\n<ipython-input-13-3b11f6be4cb9> in <module>\r\n      1 # Load a dataset and print the first examples in the training set\r\n      2 # nli_dataset = nlp.load_dataset('multi_nli')\r\n----> 3 dataset = load_dataset('multi_nli')\r\n      4 # nli_dataset = nlp.load_dataset('multi_nli', split='validation_matched[:10%]')\r\n      5 # print(nli_dataset['train'][0])\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    514 \r\n    515     # Download and prepare data\r\n--> 516     builder_instance.download_and_prepare(\r\n    517         download_config=download_config,\r\n    518         download_mode=download_mode,\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    417             with utils.temporary_assignment(self, \"_cache_dir\", tmp_data_dir):\r\n    418                 verify_infos = not save_infos and not ignore_verifications\r\n--> 419                 self._download_and_prepare(\r\n    420                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    421                 )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    455         split_dict = SplitDict(dataset_name=self.name)\r\n    456         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 457         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    458         # Checksums verification\r\n    459         if verify_infos:\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\datasets\\multi_nli\\60774175381b9f3f1e6ae1028229e3cdb270d50379f45b9f2c01008f50f09e6b\\multi_nli.py in _split_generators(self, dl_manager)\r\n     99     def _split_generators(self, dl_manager):\r\n    100 \r\n--> 101         downloaded_dir = dl_manager.download_and_extract(\r\n    102             \"http:\/\/storage.googleapis.com\/tfds-data\/downloads\/multi_nli\/multinli_1.0.zip\"\r\n    103         )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in download_and_extract(self, url_or_urls)\r\n    214             extracted_path(s): `str`, extracted paths of given URL(s).\r\n    215         \"\"\"\r\n--> 216         return self.extract(self.download(url_or_urls))\r\n    217 \r\n    218     def get_recorded_sizes_checksums(self):\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in extract(self, path_or_paths)\r\n    194                 path_or_paths.\r\n    195         \"\"\"\r\n--> 196         return map_nested(\r\n    197             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    198         )\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)\r\n    168                 return tuple(mapped)\r\n    169     # Singleton\r\n--> 170     return function(data_struct)\r\n    171 \r\n    172 \r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\download_manager.py in <lambda>(path)\r\n    195         \"\"\"\r\n    196         return map_nested(\r\n--> 197             lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,\r\n    198         )\r\n    199 \r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\site-packages\\nlp\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n    231             if is_zipfile(output_path):\r\n    232                 with ZipFile(output_path, \"r\") as zip_file:\r\n--> 233                     zip_file.extractall(output_path_extracted)\r\n    234                     zip_file.close()\r\n    235             elif tarfile.is_tarfile(output_path):\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\zipfile.py in extractall(self, path, members, pwd)\r\n   1644 \r\n   1645         for zipinfo in members:\r\n-> 1646             self._extract_member(zipinfo, path, pwd)\r\n   1647 \r\n   1648     @classmethod\r\n\r\n~\\Miniconda3\\envs\\nlp\\lib\\zipfile.py in _extract_member(self, member, targetpath, pwd)\r\n   1698 \r\n   1699         with self.open(member, pwd=pwd) as source, \\\r\n-> 1700              open(targetpath, \"wb\") as target:\r\n   1701             shutil.copyfileobj(source, target)\r\n   1702 \r\n\r\nOSError: [Errno 22] Invalid argument: 'C:\\\\Users\\\\Python\\\\.cache\\\\huggingface\\\\datasets\\\\3e12413b8ec69f22dfcfd54a79d1ba9e7aac2e18e334bbb6b81cca64fd16bffc\\\\multinli_1.0\\\\Icon\\r'\r\n```\r\n \n Glad it helps !\nThough I am not one of hf team, but maybe you should close this issue first.","embeddings":[-0.0840254948,-0.0221919809,-0.0726869553,0.0423389748,0.1981838346,0.0664926246,0.457182169,0.0801374689,0.1033792794,0.0883751884,-0.1641247272,0.2453239113,-0.1955309659,-0.0816973373,-0.0615135431,-0.2790237069,-0.0329709835,-0.0675984398,0.028705474,0.1370823085,-0.2184431851,0.1371101737,-0.2122425735,0.2103482187,-0.1834195554,-0.1558727026,-0.2034406811,0.2328009605,-0.338126868,-0.360994488,0.1864654124,-0.1097058058,0.2480324358,0.0156800989,-0.0001071728,0.1085644364,0.1779890656,-0.0657087117,-0.406357944,-0.1557623595,-0.1127581894,-0.3171509802,0.1604038626,-0.3909189105,0.3036190569,0.3498140872,0.0443298407,0.0456054173,0.1174699664,0.4848155379,0.2677161992,0.4489547312,0.3633489907,-0.0720728859,-0.0470621996,-0.2057535052,0.0127581256,0.3778342307,-0.1025850102,-0.176113531,0.0909408852,0.0736363828,-0.1486242712,0.3225732148,-0.1937520951,-0.0052935621,0.3084739447,-0.2554347217,-0.0774453729,0.2574424744,0.0329429805,0.0830193385,-0.3229765594,0.0241578445,0.2695622742,-0.3627426624,0.0999657214,0.4350741804,-0.3351387978,-0.0922091752,-0.246600464,-0.1657491475,-0.1589851528,0.3905088007,-0.3112727106,0.5100572109,0.1047358438,0.1057526469,0.43421489,-0.2032002658,-0.2787941992,-0.053084895,0.0459684543,0.1960389763,-0.3286784589,0.0189728905,-0.0301640481,-0.2324981689,0.158440128,0.1301026046,-0.0234435536,-0.2342739105,-0.1908606142,0.2034369111,0.0483432971,0.0642103851,-0.2885984182,0.1249965057,0.10281533,0.2291225791,0.2289705127,0.0392173566,-0.1188764647,-0.0918307155,-0.116723232,0.0861231312,0.2541342378,0.0922482461,-0.4595086873,-0.1994170845,-0.5215702653,-0.019995559,0.2925751209,0.1465471834,-0.1126548126,0.3401513398,0.1732837707,0.3975913823,-0.2035049647,-0.3343687654,-0.1372718513,0.1671380103,-0.1231139451,-0.0419097692,0.435234338,-0.0595215447,0.3110079169,-0.0159450583,-0.0854558572,-0.0951857194,0.1114431769,-0.0950859785,-0.3110278845,0.2561810315,0.170438081,0.0722104535,0.1697930694,-0.1313999444,-0.0520505607,0.0058724899,-0.3391163945,-0.4312471151,-0.3035261929,0.2793096006,0.0968348831,-0.105003126,0.1401997209,-0.4007652998,0.2124014944,-0.0330316164,-0.0638782978,-0.2944987416,-0.0988622084,-0.2295627892,0.259360671,0.2700573802,0.1803033054,-0.0016141343,-0.349278003,-0.1656953394,0.2533250153,0.5466470718,-0.2035302222,0.1508474648,-0.1804694384,0.0348155797,0.5982265472,-0.4164569676,-0.0977124944,0.2224085629,-0.3346424699,0.0443879105,0.0076296059,0.179850027,0.0642710999,0.1057124212,0.3237139881,0.5148923397,-0.1797897667,0.079438813,-0.1894708723,-0.2525600791,0.1889620721,0.37766397,0.0521411523,0.0710817426,-0.1293640584,0.4729548395,0.4586094022,0.0054114168,0.0687954426,0.0822698623,0.145469591,-0.090170987,-0.3268409967,-0.0332494974,-0.3870306611,0.127906695,0.0395684429,0.103273347,-0.026721485,0.0882387906,-0.1036591455,0.0494505912,-0.1920175254,-0.1252931952,0.1495797187,0.2034477443,0.0919264555,0.0242259167,0.0234373137,0.516964972,-0.0308067165,0.1307230592,-0.4853770137,0.2479034066,-0.3664743602,-0.0430885442,0.0266262665,0.0217892621,-0.0637478679,0.0476308279,-0.0967497304,0.3046732545,-0.0396848843,-0.4234547317,-0.104751274,-0.1042764485,0.1154831797,-0.1144754738,0.1306092739,0.3364742696,0.0666871145,-0.0499959737,-0.3063918948,0.3337834477,0.0307656657,0.0900291875,-0.0031021968,0.3673850894,0.3018737435,-0.020218024,-0.0372559763,-0.1680344045,0.3337793052,0.0372740142,0.0485329591,-0.2252466381,-0.3169656098,-0.1664787829,0.3011681437,0.0414901115,0.0305451453,0.0421516337,0.1887793392,0.0492153913,-0.1162228882,0.0048489976,0.4669987857,0.0854075626,0.109497644,-0.0226920247,-0.1893053204,-0.2992962599,0.064426057,-0.0190867856,0.2057503611,-0.0591562502,0.130966872,-0.1099563316,-0.0401301496,-0.4957756102,0.2854011953,0.4715655744,-0.2018342465,0.0635068342,-0.3473848403,-0.5306869745,-0.2337204665,-0.2072554231,-0.3806086183,-0.3395351171,-0.1652444899,0.4490947723,0.0872477144,0.0202280376,-0.0163672008,-0.2310703546,0.2713745236,-0.0395486802,0.203712821,-0.071269013,-0.2772781551,0.1511174887,0.3364775479,-0.0214621387,0.2527517974,-0.1002357751,-0.173353374,-0.268103987,0.0158549752,-0.2235613465,0.0938835666,-0.264356792,0.2511912286,0.3671227098,0.046039965,-0.2325368524,0.2155634612,-0.0189510845,-0.1420431137,0.3279959261,0.0061510629,0.0973288193,-0.1499354541,-0.3068360388,-0.3677200377,-0.4596532583,0.2124171704,-0.0558876693,0.2125092894,0.374065578,0.0455109701,0.1976522058,0.0339022465,-0.1330120265,0.0363395475,0.0478519276,0.1572207958,-0.1895360351,-0.3011138439,-0.078162238,0.2041410804,0.2132148594,0.0037161643,-0.3780254722,-0.0257090516,0.0308423396,0.2746254802,-0.1909338534,-0.0438152179,0.2122442573,-0.3453694284,-0.0411612727,0.2166718096,0.0430256687,0.08033517,0.2186608016,0.4515864849,-0.1415331513,0.5075715184,0.0484841429,0.3951174617,0.2567915022,-0.0194629226,0.3725375533,-0.0210259911,-0.0696513951,0.0216038134,-0.4024845064,-0.0067745629,-0.1218655184,0.15624722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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/234","title":"Huggingface NLP, Uploading custom dataset","comments":"What do you mean 'custom' ? You may want to elaborate on it when ask a question.\r\n\r\nAnyway, there are two things you may interested\r\n`nlp.Dataset.from_file` and `load_dataset(..., cache_dir=)`","body":"Hello,\r\n\r\nDoes anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.\r\n\r\nThank you!","comment_length":29,"text":"Huggingface NLP, Uploading custom dataset \n Hello,\r\n\r\nDoes anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.\r\n\r\nThank you! \n What do you mean 'custom' ? You may want to elaborate on it when ask a question.\r\n\r\nAnyway, there are two things you may interested\r\n`nlp.Dataset.from_file` and `load_dataset(..., cache_dir=)`","embeddings":[0.2511941493,-0.1868027151,0.0526289828,0.0816768184,-0.0106313126,0.0912558138,0.3054780662,-0.0391972624,0.1293654442,-0.148810491,-0.2689487338,0.1939389706,-0.0605024397,0.220899567,0.5498043895,-0.006288135,-0.0692309439,0.2022132725,-0.0570144914,-0.1058279723,-0.1856539249,0.2207611203,-0.1370268613,0.1703862697,-0.1214475632,-0.0225457828,-0.0661958754,0.5867702365,-0.1426349282,-0.4699003994,0.3383302391,0.1335825622,0.2301869094,0.3451362848,-0.0001174077,-0.242519781,0.0866902247,-0.2588348687,-0.2761338353,-0.2854745984,0.1859979481,-0.5186660886,0.2983292341,-0.3381852508,-0.0134237055,0.0814301968,0.2943903804,-0.1260166019,0.5597587824,0.0612912029,0.101152733,0.202267617,-0.2472454458,0.079847008,-0.1332534701,0.1941750646,0.0277568046,0.1069401503,0.3506956398,-0.2971981168,-0.0304007865,-0.0905299783,0.1101559401,-0.0872577131,0.5247397423,0.1906174868,-0.2882422805,-0.1282220036,-0.1692812294,0.2119090408,0.406278044,-0.3414674997,-0.1741296947,-0.3976282179,0.1134680137,-0.1795766801,0.2533883452,0.1868111789,-0.2927107215,0.0189503897,-0.2960908115,-0.1277569532,-0.2604834139,0.1948349029,0.3770096898,0.0312416796,-0.1505837291,0.1121048406,0.2019895315,-0.2020061165,-0.2376164794,0.0816826448,0.1484282911,0.6347585917,-0.3498253524,-0.2218444198,-0.180939585,0.1708837748,0.2634724975,0.0313833244,0.3042993844,-0.0190500077,-0.1780896187,0.0734458193,0.3316245973,0.0621524788,0.4791782498,-0.1950465441,-0.0832278728,-0.0602650829,-0.0906262696,-0.0278990362,-0.3807085454,0.0610165149,-0.1343231052,-0.2206491977,0.0271761008,-0.183277458,0.1038198248,-0.3341500163,0.3256415129,-0.1706353873,0.1045821905,0.2052087039,-0.0413789079,-0.2097400427,-0.084017992,0.0338136777,-0.0752505213,-0.2528449595,-0.0328084417,-0.0397825316,-0.1726889312,0.0984549671,0.344299525,-0.2336006463,0.4295724332,-0.0807401687,0.0600132644,0.0233578179,-0.0694897771,0.1254695952,-0.2132776976,-0.2606166303,0.2250982225,-0.0894164816,0.1706477553,-0.3729172051,-0.1835775822,-0.0088387672,-0.2209742218,-0.045483999,-0.3162697554,0.0511501282,-0.213724032,-0.4107028246,-0.2110774219,0.4020387232,-0.1021610275,-0.2433484197,-0.0482000448,0.230752781,-0.1970522255,-0.0970444828,0.0752107352,0.2437968254,-0.4955697358,-0.3121131659,0.1605289876,-0.1799182892,-0.118223995,0.2004851997,-0.2654595077,0.0357513018,-0.1790160537,0.2451011688,0.6685561538,-0.3615259826,-0.2545460165,0.1291518509,-0.3375508487,0.0503921211,-0.0511076376,0.2312552333,0.2727699578,-0.0261819325,0.1195365563,0.7563425899,0.0304378755,-0.0457668565,-0.0146195935,0.012817245,0.1913543493,0.0880464911,-0.3754507601,0.1940221786,0.1528025419,0.1638249904,0.1648674905,-0.0200040583,-0.1410517842,-0.0115677733,-0.0874648392,0.373365283,-0.0119616948,0.0937986299,-0.7424104214,0.250795573,-0.3227907419,0.0265963618,0.3197612762,-0.0572920516,-0.5034213066,-0.1567646861,-0.1250696778,0.1394387335,0.003527012,0.1284808964,0.1161632463,-0.1500425935,-0.3917634785,0.4788067639,-0.2969135642,0.259866178,-0.4144138396,0.0279623326,0.2513425648,-0.0867765546,0.0578437597,0.3452283442,0.1667339057,-0.0923562199,0.085404709,0.3543488681,-0.1966007203,0.3160957992,0.385817796,-0.0798922554,-0.0119260559,0.0590403415,0.2474550307,-0.1868411601,0.1045483053,-0.2373169214,-0.3755524755,0.2033917308,-0.1266898066,0.1138185263,0.058688052,-0.2212678194,0.0994337723,-0.050987225,-0.2495695204,0.0228641126,0.1393253654,0.1846900582,0.5775030851,0.0293774754,-0.1229536533,-0.0937971547,0.5493193865,-0.0694452748,0.0978062898,0.2314471006,-0.0945181176,-0.239956513,0.1245625094,-0.1773858964,0.2780827284,0.2319319248,-0.0777869299,0.0474088043,-0.1571730524,0.0460164286,-0.0632321313,-0.0598318987,-0.0755402446,-0.2413527519,0.2460099757,-0.1520587355,-0.4141062796,0.1202446893,-0.0843704417,-0.0887901708,-0.1791388243,0.0327836908,0.0258512758,-0.3187442124,-0.2578939199,-0.260623008,-0.5657714605,0.1098884866,-0.3193959892,-0.1699989587,0.5434654355,-0.042633187,0.0894463807,0.679009974,-0.1581475735,-0.1685850024,0.1133372039,-0.0597933605,-0.2973839939,-0.03805843,0.1349502206,0.2562676072,0.4579537213,-0.0091979131,0.1660220772,0.1255880147,-0.2280965,0.0864352435,-0.1482443362,0.2335582227,0.1508990079,0.1980547756,-0.1274206787,-0.0572060756,0.0523496196,-0.1385670304,-0.179023996,-0.2404356152,0.0019247442,0.2437565923,-0.0372643843,-0.1320925504,-0.4951516092,-0.2063333541,0.5470715165,0.363799274,0.100857161,0.2450055033,-0.337979883,0.436318785,-0.2013397217,-0.0298940167,-0.2431279719,-0.5314003825,0.1110533327,-0.2212736011,-0.1870451123,0.1094565764,0.04110967,0.1178323105,-0.2680106759,-0.2026875168,-0.2860255241,0.2530061603,-0.0214734487,-0.0531165637,0.06862472,0.1988119781,-0.3225416541,0.1168540716,-0.0986807272,-0.3786769211,0.1931208372,0.3867774308,0.244505465,0.2283461541,0.1312942803,-0.0158447921,0.6685090065,0.0275276974,-0.1056762487,0.3860656321,-0.0057337312,0.221611321,0.0189451911,-0.4455029666,0.0968725905,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/234","title":"Huggingface NLP, Uploading custom dataset","comments":"To load a dataset you need to have a script that defines the format of the examples, the splits and the way to generate examples. As your dataset has the same format of squad, you can just copy the squad script (see the [datasets](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets) forlder) and just replace the url to load the data to your local or remote path.\r\n\r\nThen what you can do is `load_dataset(<path\/to\/your\/script>)`","body":"Hello,\r\n\r\nDoes anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.\r\n\r\nThank you!","comment_length":67,"text":"Huggingface NLP, Uploading custom dataset \n Hello,\r\n\r\nDoes anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.\r\n\r\nThank you! \n To load a dataset you need to have a script that defines the format of the examples, the splits and the way to generate examples. As your dataset has the same format of squad, you can just copy the squad script (see the [datasets](https:\/\/github.com\/huggingface\/nlp\/tree\/master\/datasets) forlder) and just replace the url to load the data to your local or remote path.\r\n\r\nThen what you can do is `load_dataset(<path\/to\/your\/script>)`","embeddings":[0.1632618755,-0.2137292325,0.0653117523,0.1629524976,-0.0624897406,0.0928252488,0.2512159646,-0.0430666991,0.1474726647,-0.1472516805,-0.2029948384,0.1871040314,-0.1082477719,0.3745346367,0.6062445045,-0.0716352537,-0.1152896136,0.1753334403,-0.0515353531,-0.0377068184,-0.1981060505,0.2560156584,-0.1257559955,0.2088316083,-0.157189548,0.0645788833,-0.1292643845,0.6166014671,-0.0432282425,-0.4653248489,0.4585714042,0.1644788831,0.3075277805,0.3923658431,-0.0001183142,-0.2341071218,0.1320631802,-0.2658723593,-0.2585261762,-0.435149461,0.115730226,-0.4135122597,0.357668817,-0.2580885291,-0.075783655,0.1012234688,0.256700784,-0.071713537,0.5230125785,0.0841127262,0.0794004872,0.1752882004,-0.1973444819,0.0069302241,-0.0799260363,0.2747301161,0.118542023,0.2547322214,0.4028348923,-0.3194004297,-0.0109980796,-0.1588699222,0.078856498,-0.0765652731,0.5086486936,0.1393877268,-0.3838796914,-0.1076978445,-0.1860266775,0.2605612576,0.441868633,-0.4110240042,-0.1638529003,-0.3494079411,0.1157299951,-0.0819408968,0.2004316747,0.2470340133,-0.2333192229,-0.0007751902,-0.3248098493,-0.0814079493,-0.2789474428,0.2652963996,0.4247459471,-0.0282355379,-0.1757750809,0.1322721392,0.1465883106,-0.1842674017,-0.229835391,0.0410306044,0.1278071702,0.5799487233,-0.2658235133,-0.226796478,-0.1616683006,0.1954281032,0.3089895546,0.0729882643,0.2669959366,0.0085437084,-0.2398358136,0.0831922814,0.3722091615,0.0566619597,0.4918217361,-0.1684591919,-0.1371180713,0.019395059,-0.1130543426,-0.0279668532,-0.4432641864,0.0435884334,-0.2443509847,-0.209770605,0.0535984859,-0.2177799493,0.0456063561,-0.3099789619,0.2870750725,-0.1471867561,0.1001880392,0.2248855978,-0.0065349527,-0.1765411496,-0.0419527143,0.1313504279,-0.0876829848,-0.2424858361,0.0154616591,-0.0259964149,-0.1502151787,0.1816100031,0.3011452556,-0.1626353562,0.4779955447,-0.1116511077,0.0032149018,-0.0790123716,-0.0785928816,0.2046216875,-0.2747231126,-0.228053987,0.2984566987,-0.1199111268,0.2767028511,-0.2447419614,-0.1950594187,-0.0161996968,-0.1980811805,-0.099808082,-0.2604421973,0.0298508797,-0.2414761037,-0.42902565,-0.3082430363,0.4173445404,-0.1880047917,-0.2443479747,-0.0652576238,0.2210941166,-0.2627894878,-0.1088764146,0.1435061842,0.3203823566,-0.5170246363,-0.3434064984,0.1513788998,-0.0854873806,-0.16925776,0.1477366835,-0.3176531196,0.1524892151,-0.2047575563,0.221737802,0.5584897995,-0.2829348147,-0.1232941374,0.2048125714,-0.3473904133,-0.0599625893,-0.0623305887,0.2136587203,0.2472444624,-0.0182476752,0.0306184273,0.699007988,0.0460638888,-0.0494595096,0.0443298556,-0.055604808,0.1161719635,0.1239847168,-0.4393498302,0.2005514354,0.1385040432,0.1559725255,0.1280867904,-0.0063274037,-0.1663253754,0.0146783758,-0.0424188823,0.3889630735,-0.0186274182,0.0057169311,-0.6861996651,0.2428305745,-0.3102069199,-0.1179864556,0.3537498415,-0.0747481659,-0.5600332022,-0.1199524626,-0.052174475,0.1423639953,-0.0310133155,0.0715241432,0.235191673,-0.1559379399,-0.419976294,0.3658932447,-0.2654148638,0.2219631225,-0.4422061145,0.0715068355,0.2992537022,-0.1190320328,0.1711467803,0.3448594809,0.1859295219,-0.1832340956,0.1324022412,0.4043011665,-0.0898139775,0.3038014174,0.3436336517,-0.1167560294,-0.0457207374,0.0180007908,0.2573494613,-0.0722771212,0.1622330695,-0.2356225401,-0.39416641,0.2469726801,-0.1662818342,0.1955576986,0.0855974704,-0.1404212266,0.0753035918,-0.1076562181,-0.2879719138,0.0308810845,0.1261328757,0.1642033309,0.5432072282,0.0151495188,-0.0742928758,-0.137536034,0.4589149952,-0.1621593237,0.0483373962,0.2757790089,-0.2151982337,-0.1649657488,0.0967513621,-0.1139991134,0.3100784719,0.2174781263,-0.1915289909,0.1209520027,-0.1546864957,0.0167939328,-0.0776061267,0.0096650738,-0.0724230111,-0.2934227288,0.0289833359,-0.1682446152,-0.3165303171,0.0073105465,-0.0837204754,-0.05273946,-0.279763788,0.0846176967,-0.0238074176,-0.3330242038,-0.3326044083,-0.3089468181,-0.409217298,0.1175934896,-0.3757782578,-0.0898815915,0.5219320655,-0.067493096,0.078946881,0.5729561448,-0.1967441142,-0.1401185095,0.149264589,-0.1234442294,-0.3371569812,-0.0581831336,0.2175436765,0.3872212768,0.4830297828,-0.0962698013,0.1698093116,0.1431282461,-0.1550988853,0.0761236474,-0.1170581281,0.1787731051,0.2281749099,0.2039536834,-0.1120222136,-0.0922498927,0.069925271,-0.1441305876,-0.0947047323,-0.2146958709,0.0065226643,0.1487960219,-0.0692987218,-0.2026622593,-0.392604053,-0.2046023011,0.5442581177,0.317915678,0.094566159,0.1486199498,-0.2004937977,0.5334941149,-0.2726586461,-0.0955983847,-0.1534222215,-0.5053127408,0.043326918,-0.1828352511,-0.2392140329,0.1731575429,-0.0023852864,0.0803936869,-0.2721155286,-0.2141652256,-0.3095809519,0.1658876389,-0.1243156865,0.0111166248,0.0636945069,0.1931689084,-0.3519292474,0.1423199177,-0.0800969973,-0.3716738522,0.1727839261,0.3058595657,0.1259484887,0.3053837717,0.2932281792,-0.0838696137,0.5675312281,0.1291265339,-0.1714803129,0.3443665206,-0.0402669162,0.1789775193,0.06799241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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/234","title":"Huggingface NLP, Uploading custom dataset","comments":"Also if you want to upload your script, you should be able to use the `nlp-cli`.\r\n\r\nUnfortunately the upload feature was not shipped in the latest version 0.2.0. so right now you can either clone the repo to use it or wait for the next release. We will add some docs to explain how to upload datasets.\r\n","body":"Hello,\r\n\r\nDoes anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.\r\n\r\nThank you!","comment_length":57,"text":"Huggingface NLP, Uploading custom dataset \n Hello,\r\n\r\nDoes anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.\r\n\r\nThank you! \n Also if you want to upload your script, you should be able to use the `nlp-cli`.\r\n\r\nUnfortunately the upload feature was not shipped in the latest version 0.2.0. so right now you can either clone the repo to use it or wait for the next release. We will add some docs to explain how to upload datasets.\r\n","embeddings":[0.1764683574,-0.1404461861,0.0849194527,-0.0182812419,-0.0717138723,0.075803332,0.3437079787,-0.0349298343,0.1954903901,-0.0800016373,-0.2074443698,0.2885677218,-0.0553389043,0.3174791336,0.6837016344,-0.0082312217,-0.1109836325,0.2270630151,-0.1107003391,-0.072724998,-0.1884524673,0.2482985109,-0.1515674591,0.1427882761,-0.0904977247,0.0327357911,-0.1368426234,0.5879563689,-0.0347582921,-0.4627845883,0.3801742792,0.1850762814,0.2705678046,0.2915583551,-0.0001199196,-0.3151123822,0.2196165025,-0.2283890694,-0.1992084533,-0.3886288106,0.2290245891,-0.4995836616,0.3296054304,-0.2545453608,0.0229102876,0.0480307788,0.3462797999,-0.0469349064,0.4474572241,0.1120043546,0.0682483017,0.2357687205,-0.2086908072,0.0165882111,-0.1261121035,0.2023485154,0.0364916101,0.0982222632,0.4745078087,-0.3767592609,-0.0204599723,-0.2292882949,0.1150449514,-0.2255510688,0.5860815048,0.1650473028,-0.2993729413,-0.1718950421,-0.1495643258,0.2630612552,0.4205902517,-0.3851597011,-0.1577212512,-0.3561770022,0.1602452248,-0.0932904631,0.2420265675,0.2168908119,-0.198820129,-0.0408759117,-0.3009008765,-0.083590284,-0.3048931062,0.2369063497,0.4349042773,0.0735771507,-0.0748693347,0.1235278696,0.2585088313,-0.2515004575,-0.1675399542,0.1206649542,0.0902183279,0.6502204537,-0.2848996818,-0.3007370234,-0.1803151667,0.1586339325,0.3465579748,0.0871054977,0.2340097725,-0.0379519314,-0.231588617,0.0632524639,0.4135237634,0.0722877532,0.5410382748,-0.2067291737,-0.1094599664,-0.0746562853,-0.0071321977,-0.0387752801,-0.3436836898,0.0666880682,-0.2101968825,-0.2071940452,-0.003997284,-0.2105837017,0.0884229988,-0.3016293347,0.3965158761,-0.2021564543,0.0337174647,0.2337683141,-0.0262790117,-0.1990767866,-0.0066285911,0.1639598608,-0.0240708608,-0.2833609283,0.0277406126,0.0290357713,-0.1441715509,0.1292189658,0.308316201,-0.119163394,0.4292005002,-0.1462306678,0.115179427,0.034417659,-0.1853409857,0.2004367262,-0.2792135775,-0.211660713,0.2990442514,-0.1502891481,0.1245240346,-0.3165315688,-0.1780049354,0.0566845685,-0.1173621565,-0.0222805347,-0.3355042636,-0.000865708,-0.1576640755,-0.5109561086,-0.2110397071,0.4058326483,-0.2044931352,-0.2549584508,-0.0943488777,0.1454541534,-0.233881861,-0.0956437364,0.0803910345,0.2395888716,-0.5089073181,-0.3144393265,0.1347630173,-0.0557294339,-0.0909917727,0.1236264408,-0.2257529497,0.0113541689,-0.1373366714,0.2648732662,0.6306740046,-0.3635216951,-0.170813024,0.1660255045,-0.284729749,-0.0126986876,-0.0783314705,0.2403729409,0.2574870884,-0.0433332622,-0.0072615067,0.7859998941,0.0204003416,-0.030374784,0.0199573878,-0.0455239676,0.1873829961,0.1366540343,-0.3432533741,0.2071154863,0.1615181714,0.1116489321,0.1957997233,0.0161686949,-0.1604333222,-0.0386950076,-0.0726060942,0.3785209954,0.0165354125,0.0375590101,-0.6924958229,0.2066673636,-0.418810606,-0.014900797,0.3599675596,-0.0527982041,-0.5091999173,-0.1182487607,-0.0001259361,0.1753367186,-0.0350242443,0.0883272365,0.1414326727,-0.1319975555,-0.4686155617,0.4200882912,-0.2656469345,0.2159391493,-0.383990407,0.0986372977,0.2503145933,-0.1872021258,0.1851067692,0.3410764337,0.1777301729,-0.1337010562,0.1010109782,0.3626877666,-0.1958237588,0.2949708998,0.4155726135,-0.0886107013,-0.052945476,0.0855376795,0.2579508126,-0.1100844741,0.0429702364,-0.2711121738,-0.392005235,0.2096698433,-0.1177296042,0.1833424121,0.0382431038,-0.1359160841,0.0667028725,-0.1294801086,-0.3018996716,0.0593983829,0.0913830251,0.1398960352,0.5469378829,-0.0844841823,-0.1183323562,-0.0494960733,0.5501151085,-0.1140015051,0.0779598504,0.2512154877,-0.1055456176,-0.1468648314,0.1260046959,-0.190888539,0.2596572936,0.2283757627,-0.2271471769,0.0737366378,-0.2061674297,0.0459526591,-0.0846225694,-0.1173196062,-0.0527106449,-0.2695628703,0.1740244776,-0.1882400364,-0.4044696391,0.0869704857,-0.1470432281,-0.0693099573,-0.2084822357,0.0153290853,0.0022148776,-0.3238930404,-0.2259030342,-0.2658852339,-0.4910959601,0.0526196323,-0.3498063982,-0.1062775552,0.5295960307,-0.0520726815,0.1573909074,0.683640182,-0.149899736,-0.066070497,0.0718422458,-0.0269821845,-0.2955059707,-0.059819933,0.1493528783,0.2859882712,0.4051009715,-0.0493484624,0.195365712,0.1278515905,-0.2362931222,0.074982129,-0.1610620469,0.1566876322,0.1923018098,0.1800318509,-0.0995386392,-0.0737074092,0.0470809266,-0.1955215633,-0.1439724863,-0.1943651289,0.0137509201,0.0518596806,-0.1114363894,-0.1601402909,-0.4644373953,-0.2090989947,0.5759682655,0.4488873482,0.1323640347,0.1834955066,-0.2322513461,0.4705428779,-0.2687545419,-0.0973618999,-0.1350892186,-0.4322135448,0.1380234659,-0.2309996188,-0.2651397288,0.2041730434,0.08503142,0.0729781091,-0.369435966,-0.1806652248,-0.2982805669,0.2305029929,-0.0645077378,0.0144791082,0.0836250484,0.2201632112,-0.3017017841,0.1690779179,-0.1146730855,-0.4005320668,0.1927462965,0.4177711308,0.2112343013,0.3016082048,0.1985858232,-0.121679306,0.4930608571,0.0962475762,-0.166952908,0.3587188721,-0.0510834903,0.2181683183,0.0389962494,-0.4225400984,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/234","title":"Huggingface NLP, Uploading custom dataset","comments":"Since the latest release 0.2.1 you can use \r\n```bash\r\nnlp-cli upload_dataset <path\/to\/dataset>\r\n```\r\nwhere `<path\/to\/dataset>` is a path to a folder containing your script (ex: `squad.py`).\r\nThis will upload the script under your namespace on our S3.\r\n\r\nOptionally the folder can also contain `dataset_infos.json` generated using\r\n```bash\r\nnlp-cli test <path\/to\/dataset> --all_configs --save_infos\r\n```\r\n\r\nThen you should be able to do\r\n```python\r\nnlp.load_dataset(\"my_namespace\/dataset_name\")\r\n```","body":"Hello,\r\n\r\nDoes anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.\r\n\r\nThank you!","comment_length":63,"text":"Huggingface NLP, Uploading custom dataset \n Hello,\r\n\r\nDoes anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.\r\n\r\nThank you! \n Since the latest release 0.2.1 you can use \r\n```bash\r\nnlp-cli upload_dataset <path\/to\/dataset>\r\n```\r\nwhere `<path\/to\/dataset>` is a path to a folder containing your script (ex: `squad.py`).\r\nThis will upload the script under your namespace on our S3.\r\n\r\nOptionally the folder can also contain `dataset_infos.json` generated using\r\n```bash\r\nnlp-cli test <path\/to\/dataset> --all_configs --save_infos\r\n```\r\n\r\nThen you should be able to do\r\n```python\r\nnlp.load_dataset(\"my_namespace\/dataset_name\")\r\n```","embeddings":[0.280041188,-0.2268893868,0.120161891,0.1158971786,-0.0624757409,-0.0111734392,0.2993092537,-0.0362044014,0.0847581029,-0.0956161693,-0.2538748085,0.3167442977,-0.0799233466,0.3306141794,0.5891800523,0.1280133426,0.0025305294,0.2043104917,-0.0012188745,-0.0587979071,-0.2672669888,0.3734842837,-0.1716811508,0.1110452861,-0.0410867184,0.1917255223,-0.1079308912,0.5931818485,-0.0679470599,-0.6077477336,0.4877357781,0.193832919,0.3708676398,0.409065038,-0.0001233944,-0.3133193552,0.2694946826,-0.2316975147,-0.3273893297,-0.3374843597,0.1249143034,-0.4609065354,0.3682494164,-0.2326495647,-0.0676464364,0.1287667751,0.235003069,-0.0852788314,0.4762735963,0.0823692679,0.0159708504,0.2765752077,-0.3785421252,0.0509530269,0.0250980575,0.2564996481,0.0095828464,0.0897440761,0.4341789484,-0.3698439598,0.0935192108,-0.0385057665,0.0429343171,-0.1325575262,0.6434711218,0.1918417066,-0.3280702233,-0.1321462542,-0.2390377969,0.2397617698,0.3419497013,-0.4234245121,-0.325639993,-0.4911620021,0.1315747201,-0.1563498974,0.2453739643,0.0697207898,-0.1712663323,-0.0648642853,-0.3384968042,-0.1390088201,-0.3118106127,0.2429273427,0.2955783606,0.2280941606,-0.2206053585,0.1583246142,0.235191226,-0.2058346719,-0.0886887908,0.1007154062,0.1064787805,0.564686954,-0.3737162948,-0.2899183929,-0.1165457219,0.10073971,0.2578741312,0.1344522089,0.1770187467,-0.0359660685,-0.1233959645,0.0913675278,0.390314877,-0.0014647252,0.5357534885,-0.1598761529,-0.0851837173,0.0251783356,0.0550952889,-0.0850275531,-0.4656982124,-0.0134292534,-0.2789924145,-0.2053917497,0.1622759849,-0.2037165463,0.0875260904,-0.2311067432,0.3427425325,-0.1016345471,0.1443826258,0.2277094871,-0.0353277661,-0.1291587502,-0.0205230527,0.1601542681,-0.0732647851,-0.1987454593,0.0590833277,-0.0491438583,-0.1831965744,0.0938047692,0.3327322006,-0.0904485211,0.4279941916,-0.1487330645,0.1311012954,-0.0102179246,-0.1246866733,0.24250108,-0.1801869869,-0.1258146018,0.2880768776,-0.0189978424,0.115399614,-0.3117888868,-0.2295581251,-0.0522932187,-0.1529917717,-0.0616694763,-0.3780478537,-0.0268930066,-0.3280336857,-0.4907149971,-0.2676517963,0.2743685246,-0.1532911658,-0.2338869572,-0.0303353649,0.1520068198,-0.2674449682,-0.1086116731,0.1839228123,0.2969834507,-0.4266986549,-0.302161783,0.0420350097,-0.0123940324,-0.1056095734,0.1387207955,-0.2472092658,-0.0542636998,-0.15707919,0.2555834949,0.574765563,-0.4837093353,-0.1388399899,0.2292327583,-0.2719882429,0.0131997671,0.0639390051,0.2463511378,0.1747874469,0.01045743,-0.0625673085,0.7491369247,0.0347712263,-0.0652870312,0.079337962,-0.0201671943,0.1566513777,0.1346314698,-0.3383793235,0.1531339139,0.0922186971,0.198026076,0.1743172258,-0.0980526507,-0.0739667267,0.034192659,0.1123767048,0.417150557,0.0026158318,0.0373882465,-0.695799768,0.2870245874,-0.2856880724,-0.0026063058,0.2908089459,-0.0849649012,-0.4383996725,-0.1123043969,-0.1052105948,0.1792892963,-0.0818910822,0.1696848273,0.2380019575,-0.1964533329,-0.4215344489,0.3596050739,-0.1228931174,0.2609522939,-0.4864855409,0.0444273315,0.2445634604,-0.1797391027,0.1138580963,0.3458587527,0.2149309367,-0.1512641758,0.1970122159,0.3514067531,-0.1065535247,0.3253590763,0.3652241528,0.0149927866,-0.0511014163,0.1266390532,0.2454586029,-0.1220536903,0.072183229,-0.1380444467,-0.3422394693,0.3357030451,-0.1649213731,0.2453159541,-0.0025176776,-0.1462320238,0.0532507189,-0.0891687125,-0.42794469,-0.0027036541,0.0352217779,0.1198277399,0.3981923163,-0.0135125397,-0.0769134611,-0.0973861739,0.6103428602,-0.1032547727,0.0376774333,0.2313651145,-0.1037518829,-0.1961955726,0.1441417187,-0.0011320115,0.3079428077,0.1700057536,-0.1886392981,0.0464922376,-0.0874040574,0.0712431967,-0.0334302746,0.0001723964,0.0166194774,-0.2834673226,0.2029100955,-0.1489429325,-0.317085743,0.0581376888,-0.0794910938,-0.1055355296,-0.257717669,0.1330084354,0.002569594,-0.3521052599,-0.2281140834,-0.3058689237,-0.4979859293,0.082530655,-0.3920715153,-0.0814008042,0.5837751031,-0.0671747774,0.1321170479,0.5852800608,-0.1560715139,-0.1986920238,0.0728469267,-0.0184183307,-0.2793127298,-0.1237122118,0.0595833659,0.1815273315,0.3396261632,-0.0761952922,0.1956444979,0.080611214,-0.2836532295,0.0786687732,-0.1990657598,0.1872478426,0.3521501422,0.1134731323,-0.1329292059,0.0083256094,0.1063299105,-0.1537938118,-0.1182949841,-0.0744151697,0.0029164657,0.07503362,-0.120759055,-0.0856740549,-0.3339976072,-0.1405495256,0.5175922513,0.3240983784,0.1171012595,0.1943234652,-0.2162727863,0.4645294845,-0.3083764017,-0.0089304494,-0.1356204152,-0.5117289424,0.032068789,-0.1415932626,-0.1379126757,0.17673783,0.1191289499,0.1214162558,-0.3518004119,-0.1929907054,-0.4288131297,0.219819665,0.0215193424,0.0349836424,0.1249322593,0.2229184061,-0.2214020044,0.1917724162,-0.1415399611,-0.4419384301,0.0736217573,0.418623656,0.2334130555,0.2650006413,0.2457137257,-0.0208604634,0.5214276314,0.1515869647,-0.1895602345,0.3977134228,-0.0435568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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/233","title":"Fail to download c4 english corpus","comments":"Hello ! Thanks for noticing this bug, let me fix that.\r\n\r\nAlso for information, as specified in the changelog of the latest release, C4 currently needs to have a runtime for apache beam to work on. Apache beam is used to process this very big dataset and it can work on dataflow, spark, flink, apex, etc. You can find more info on beam datasets [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/docs\/beam_dataset.md).\r\n\r\nOur goal in the future is to make available an already-processed version of C4 (as we do for wikipedia for example) so that users without apache beam runtimes can load it.","body":"i run following code to download c4 English corpus.\r\n\r\n```\r\ndataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'\r\n, data_dir='\/mypath')\r\n```\r\n\r\nand i met failure as follows\r\n\r\n```\r\nDownloading and preparing dataset c4\/en (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/adam\/.cache\/huggingface\/datasets\/c4\/en\/2.3.0...\r\nTraceback (most recent call last):\r\n  File \"download_corpus.py\", line 38, in <module>\r\n    , data_dir='\/home\/adam\/data\/corpus\/en\/c4')\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 420, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 816, in _download_and_prepare\r\n    dl_manager, verify_infos=False, pipeline=pipeline,\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 457, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/datasets\/c4\/f545de9f63300d8d02a6795e2eb34e140c47e62a803f572ac5599e170ee66ecc\/c4.py\", line 175, in _split_generators\r\n    dl_manager.download_checksums(_CHECKSUMS_URL)\r\nAttributeError: 'DownloadManager' object has no attribute 'download_checksums\r\n\r\n```\r\ncan i get any advice?","comment_length":96,"text":"Fail to download c4 english corpus \n i run following code to download c4 English corpus.\r\n\r\n```\r\ndataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'\r\n, data_dir='\/mypath')\r\n```\r\n\r\nand i met failure as follows\r\n\r\n```\r\nDownloading and preparing dataset c4\/en (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/adam\/.cache\/huggingface\/datasets\/c4\/en\/2.3.0...\r\nTraceback (most recent call last):\r\n  File \"download_corpus.py\", line 38, in <module>\r\n    , data_dir='\/home\/adam\/data\/corpus\/en\/c4')\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 420, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 816, in _download_and_prepare\r\n    dl_manager, verify_infos=False, pipeline=pipeline,\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 457, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/datasets\/c4\/f545de9f63300d8d02a6795e2eb34e140c47e62a803f572ac5599e170ee66ecc\/c4.py\", line 175, in _split_generators\r\n    dl_manager.download_checksums(_CHECKSUMS_URL)\r\nAttributeError: 'DownloadManager' object has no attribute 'download_checksums\r\n\r\n```\r\ncan i get any advice? \n Hello ! Thanks for noticing this bug, let me fix that.\r\n\r\nAlso for information, as specified in the changelog of the latest release, C4 currently needs to have a runtime for apache beam to work on. Apache beam is used to process this very big dataset and it can work on dataflow, spark, flink, apex, etc. You can find more info on beam datasets [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/docs\/beam_dataset.md).\r\n\r\nOur goal in the future is to make available an already-processed version of C4 (as we do for wikipedia for example) so that users without apache beam runtimes can load it.","embeddings":[0.0115546789,0.0910863578,-0.0556439348,0.2902231514,0.148630634,0.1652969122,-0.0632363707,0.3174011707,-0.1166139469,0.0394295342,-0.1543145627,0.0087515097,0.0267580524,0.084500052,0.1310365945,-0.5472636223,-0.2235968858,0.1527146846,-0.131220445,-0.1412544996,-0.0423424728,0.4258191586,-0.1137883365,-0.1747365743,-0.0600037314,-0.1374861747,-0.1974728554,-0.0550695211,-0.206342712,-0.544832468,0.4454170763,0.0049821967,0.1893910617,0.3470121026,-0.0001094098,-0.0178526044,0.410225302,-0.1208095476,-0.2488911003,-0.39947474,0.1793028861,-0.3214279711,-0.0999809876,-0.3226758242,-0.2282853127,-0.1305258572,0.1668989211,-0.2270071805,0.0431619808,0.3165642321,0.2735976577,-0.0255144741,0.2802649736,-0.1330512613,-0.1831474304,-0.2402020991,-0.0908977464,0.2393375039,0.1667280197,0.2533928156,-0.0951283351,0.1529464126,0.0502758324,0.0134145711,-0.1094024777,-0.2209978402,0.0489708111,-0.2720611691,0.3356462419,0.0553029366,0.7832083702,-0.2817870378,-0.1618426442,0.1612834483,0.283929795,-0.212313965,0.2203885913,0.4282406271,-0.1392534822,0.0007200533,-0.3249097168,-0.0999147892,-0.181789875,0.2037830502,0.0209814962,0.0087318849,-0.0804654807,-0.0100580025,0.2173687667,0.1014782935,0.0793812573,-0.0789981186,-0.2777691185,0.2961800694,-0.2832439542,0.020596629,-0.1462968141,0.5204554796,0.02538017,-0.3621297181,-0.1353999525,-0.1054494008,0.0756659955,0.0276800711,-0.0533361509,0.3394507468,0.1087685227,0.0285199098,0.079799287,0.130185023,0.0743425488,-0.0499666631,-0.1396474391,-0.2227846831,-0.2987947464,0.2747345567,-0.1234494448,-0.3272612691,-0.1521591097,0.0012162372,-0.2087304294,-0.0346010141,-0.0995942354,0.4414266348,-0.1579021513,0.1526206434,0.0880152136,0.1817054152,-0.1947121471,-0.3660129607,-0.1490098834,0.2008820623,-0.468621403,-0.3205323219,0.2491369843,-0.3165852427,0.4326317608,-0.0886029601,0.107325092,-0.1022875011,-0.1421988904,-0.2225233167,-0.2582617104,0.188044548,0.067876935,0.2651393414,0.0120807774,-0.0328292362,-0.0076496922,-0.2450187951,-0.129508391,-0.1909275204,-0.072525613,0.1784774959,-0.0103534712,-0.1338939667,-0.5229309797,0.0499758162,0.1910482049,0.0380627438,-0.002433571,-0.2078721821,-0.2314997613,-0.2085983604,0.117168434,0.5448202491,-0.0188138504,0.028379038,-0.0435673185,-0.0119555918,0.4996949434,0.3720760047,-0.0997387618,0.172167778,-0.2095858306,0.1426471472,0.1664265692,-0.5432876945,-0.2214052975,0.2418850362,-0.0492497012,0.0903555453,0.0329224057,-0.4252802134,0.2009977847,-0.130916357,0.1117296368,0.2759277225,0.0992452502,0.1265601665,-0.2411850691,-0.1267588139,0.2473011166,0.1046614796,0.1690177172,-0.1020453349,0.2604554296,0.1870794445,0.2613472939,-0.1208254769,0.3596661985,0.2470407784,-0.2625224292,-0.3072057366,0.1050221324,0.19859384,-0.3305431604,0.206600979,-0.2245477438,0.2911973596,0.0532628298,-0.0218927599,-0.3256198168,-0.2064074129,-0.2410363108,-0.2011760324,0.1774201095,-0.0429085456,0.3280696571,0.1672783643,0.1197206452,0.3532952666,-0.3063316047,-0.1096612364,-0.1067400351,0.1026701927,-0.2434271425,-0.0721904933,0.3241747916,0.2424546927,0.2653382123,-0.156836763,-0.1398927718,0.0729344636,-0.1470297724,0.4155639708,-0.1165600345,-0.1203745231,0.3694632649,-0.3810000718,0.4082131386,0.317717284,0.0645144135,-0.0745139942,0.3047270179,-0.2446958125,0.0516471826,0.0077799303,0.0869080424,-0.1271286458,0.331769824,-0.091515258,0.1778372526,-0.0359349363,0.5329710841,-0.0943983495,0.2545621097,-0.1527448297,-0.1970202029,0.027613027,0.4264924526,0.0807586312,-0.1145147458,0.171007663,-0.1288840175,-0.175828889,0.2758097649,0.1189039946,0.1102279648,0.1651006788,0.1731343567,0.1311655343,0.1335800737,-0.2187917084,0.2529250383,-0.2158294767,0.1718282849,0.3353054821,0.1661093533,-0.0029510632,-0.4320878088,-0.0286971219,0.2143987715,0.1778079122,-0.0685272738,-0.0997375175,-0.2467764765,-0.4081358314,-0.3134776056,-0.1185436398,0.0371320471,0.0172460489,-0.1968258172,0.114120625,-0.0407053009,-0.0076273112,-0.2748734355,-0.3112739325,-0.1994776577,-0.2850935757,-0.1725654304,0.0059298952,-0.4090853035,0.1231618375,0.1412646472,0.0594344474,0.3566991687,-0.33560583,-0.0827376172,-0.223473832,-0.0705547929,0.2513357699,-0.0651990846,-0.1545362324,0.0221690554,0.2006978989,-0.2227170467,-0.1932851523,0.0155675327,-0.1274445355,-0.1874835044,0.0146911833,-0.2334830463,-0.155720219,-0.1595274955,-0.6133767366,-0.4746182561,-0.4864419997,0.0599954873,-0.1413523406,0.1965738386,0.3935822845,-0.2014529854,0.1657384634,0.13731879,0.1436540931,-0.2028478831,-0.0262709111,0.1178618744,-0.3232525885,-0.454369545,0.117317833,0.0238614138,0.4150661826,0.1647234112,-0.3046405315,0.3316366673,-0.032270696,0.1291528493,0.0913803279,0.1418595463,0.4941840768,-0.0997108519,-0.0022173061,0.0292065702,0.1109181046,-0.0067351903,0.2292229086,0.4109459519,-0.2725373507,0.1508922577,0.1694056392,0.3010313809,0.3988194764,-0.2326139212,0.6556056738,0.3299936354,0.1660488546,-0.0785605311,-0.223159954,0.4240542054,-0.0242026802,0.0504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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/233","title":"Fail to download c4 english corpus","comments":"@lhoestq I am facing `IsADirectoryError` while downloading with this command.\r\nCan you pls look into it & help me.\r\nI'm using version 0.4.0 of `nlp`.\r\n\r\n```\r\ndataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n```\r\n\r\nHere's the complete stack trace.\r\n\r\n```\r\nDownloading and preparing dataset c4\/en (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/devops\/.cache\/huggingface\/datasets\/c4\/en\/2.3.0\/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7...\r\n\r\n---------------------------------------------------------------------------\r\nIsADirectoryError                         Traceback (most recent call last)\r\n<ipython-input-11-f622e6705e03> in <module>\r\n----> 1 dataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    547     # Download and prepare data\r\n    548     builder_instance.download_and_prepare(\r\n--> 549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n    551 \r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    461                 if not downloaded_from_gcs:\r\n    462                     self._download_and_prepare(\r\n--> 463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n    465                 # Sync info\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos)\r\n    964         pipeline = beam_utils.BeamPipeline(runner=beam_runner, options=beam_options,)\r\n    965         super(BeamBasedBuilder, self)._download_and_prepare(\r\n--> 966             dl_manager, verify_infos=False, pipeline=pipeline,\r\n    967         )  # TODO handle verify_infos in beam datasets\r\n    968         # Run pipeline\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    516         split_dict = SplitDict(dataset_name=self.name)\r\n    517         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 518         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/datasets\/c4\/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7\/c4.py in _split_generators(self, dl_manager, pipeline)\r\n    187         if self.config.realnewslike:\r\n    188             files_to_download[\"realnews_domains\"] = _REALNEWS_DOMAINS_URL\r\n--> 189         file_paths = dl_manager.download_and_extract(files_to_download)\r\n    190 \r\n    191         if self.config.webtextlike:\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py in download_and_extract(self, url_or_urls)\r\n    218             extracted_path(s): `str`, extracted paths of given URL(s).\r\n    219         \"\"\"\r\n--> 220         return self.extract(self.download(url_or_urls))\r\n    221 \r\n    222     def get_recorded_sizes_checksums(self):\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py in download(self, url_or_urls)\r\n    156             lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n    157         )\r\n--> 158         self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n    159         return downloaded_path_or_paths\r\n    160 \r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py in _record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths)\r\n    106         flattened_downloaded_path_or_paths = flatten_nested(downloaded_path_or_paths)\r\n    107         for url, path in zip(flattened_urls_or_urls, flattened_downloaded_path_or_paths):\r\n--> 108             self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n    109 \r\n    110     def download_custom(self, url_or_urls, custom_download):\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/utils\/info_utils.py in get_size_checksum_dict(path)\r\n     77     \"\"\"Compute the file size and the sha256 checksum of a file\"\"\"\r\n     78     m = sha256()\r\n---> 79     with open(path, \"rb\") as f:\r\n     80         for chunk in iter(lambda: f.read(1 << 20), b\"\"):\r\n     81             m.update(chunk)\r\n\r\nIsADirectoryError: [Errno 21] Is a directory: '\/'\r\n\r\n```\r\n\r\nCan anyone please try to see what I am doing wrong or is this a bug?","body":"i run following code to download c4 English corpus.\r\n\r\n```\r\ndataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'\r\n, data_dir='\/mypath')\r\n```\r\n\r\nand i met failure as follows\r\n\r\n```\r\nDownloading and preparing dataset c4\/en (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/adam\/.cache\/huggingface\/datasets\/c4\/en\/2.3.0...\r\nTraceback (most recent call last):\r\n  File \"download_corpus.py\", line 38, in <module>\r\n    , data_dir='\/home\/adam\/data\/corpus\/en\/c4')\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 420, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 816, in _download_and_prepare\r\n    dl_manager, verify_infos=False, pipeline=pipeline,\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 457, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/datasets\/c4\/f545de9f63300d8d02a6795e2eb34e140c47e62a803f572ac5599e170ee66ecc\/c4.py\", line 175, in _split_generators\r\n    dl_manager.download_checksums(_CHECKSUMS_URL)\r\nAttributeError: 'DownloadManager' object has no attribute 'download_checksums\r\n\r\n```\r\ncan i get any advice?","comment_length":341,"text":"Fail to download c4 english corpus \n i run following code to download c4 English corpus.\r\n\r\n```\r\ndataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'\r\n, data_dir='\/mypath')\r\n```\r\n\r\nand i met failure as follows\r\n\r\n```\r\nDownloading and preparing dataset c4\/en (download: Unknown size, generated: Unknown size, total: Unknown size) to \/home\/adam\/.cache\/huggingface\/datasets\/c4\/en\/2.3.0...\r\nTraceback (most recent call last):\r\n  File \"download_corpus.py\", line 38, in <module>\r\n    , data_dir='\/home\/adam\/data\/corpus\/en\/c4')\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 520, in load_dataset\r\n    save_infos=save_infos,\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 420, in download_and_prepare\r\n    dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 816, in _download_and_prepare\r\n    dl_manager, verify_infos=False, pipeline=pipeline,\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 457, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/adam\/anaconda3\/envs\/adam\/lib\/python3.7\/site-packages\/nlp\/datasets\/c4\/f545de9f63300d8d02a6795e2eb34e140c47e62a803f572ac5599e170ee66ecc\/c4.py\", line 175, in _split_generators\r\n    dl_manager.download_checksums(_CHECKSUMS_URL)\r\nAttributeError: 'DownloadManager' object has no attribute 'download_checksums\r\n\r\n```\r\ncan i get any advice? \n @lhoestq I am facing `IsADirectoryError` while downloading with this command.\r\nCan you pls look into it & help me.\r\nI'm using version 0.4.0 of `nlp`.\r\n\r\n```\r\ndataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n```\r\n\r\nHere's the complete stack trace.\r\n\r\n```\r\nDownloading and preparing dataset c4\/en (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to \/home\/devops\/.cache\/huggingface\/datasets\/c4\/en\/2.3.0\/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7...\r\n\r\n---------------------------------------------------------------------------\r\nIsADirectoryError                         Traceback (most recent call last)\r\n<ipython-input-11-f622e6705e03> in <module>\r\n----> 1 dataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    547     # Download and prepare data\r\n    548     builder_instance.download_and_prepare(\r\n--> 549         download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n    550     )\r\n    551 \r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n    461                 if not downloaded_from_gcs:\r\n    462                     self._download_and_prepare(\r\n--> 463                         dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    464                     )\r\n    465                 # Sync info\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos)\r\n    964         pipeline = beam_utils.BeamPipeline(runner=beam_runner, options=beam_options,)\r\n    965         super(BeamBasedBuilder, self)._download_and_prepare(\r\n--> 966             dl_manager, verify_infos=False, pipeline=pipeline,\r\n    967         )  # TODO handle verify_infos in beam datasets\r\n    968         # Run pipeline\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    516         split_dict = SplitDict(dataset_name=self.name)\r\n    517         split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 518         split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n    519         # Checksums verification\r\n    520         if verify_infos:\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/datasets\/c4\/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7\/c4.py in _split_generators(self, dl_manager, pipeline)\r\n    187         if self.config.realnewslike:\r\n    188             files_to_download[\"realnews_domains\"] = _REALNEWS_DOMAINS_URL\r\n--> 189         file_paths = dl_manager.download_and_extract(files_to_download)\r\n    190 \r\n    191         if self.config.webtextlike:\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py in download_and_extract(self, url_or_urls)\r\n    218             extracted_path(s): `str`, extracted paths of given URL(s).\r\n    219         \"\"\"\r\n--> 220         return self.extract(self.download(url_or_urls))\r\n    221 \r\n    222     def get_recorded_sizes_checksums(self):\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py in download(self, url_or_urls)\r\n    156             lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n    157         )\r\n--> 158         self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n    159         return downloaded_path_or_paths\r\n    160 \r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/utils\/download_manager.py in _record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths)\r\n    106         flattened_downloaded_path_or_paths = flatten_nested(downloaded_path_or_paths)\r\n    107         for url, path in zip(flattened_urls_or_urls, flattened_downloaded_path_or_paths):\r\n--> 108             self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n    109 \r\n    110     def download_custom(self, url_or_urls, custom_download):\r\n\r\n\/data\/anaconda\/envs\/hf\/lib\/python3.6\/site-packages\/nlp\/utils\/info_utils.py in get_size_checksum_dict(path)\r\n     77     \"\"\"Compute the file size and the sha256 checksum of a file\"\"\"\r\n     78     m = sha256()\r\n---> 79     with open(path, \"rb\") as f:\r\n     80         for chunk in iter(lambda: f.read(1 << 20), b\"\"):\r\n     81             m.update(chunk)\r\n\r\nIsADirectoryError: [Errno 21] Is a directory: '\/'\r\n\r\n```\r\n\r\nCan anyone please try to see what I am doing wrong or is this a bug?","embeddings":[0.0115546789,0.0910863578,-0.0556439348,0.2902231514,0.148630634,0.1652969122,-0.0632363707,0.3174011707,-0.1166139469,0.0394295342,-0.1543145627,0.0087515097,0.0267580524,0.084500052,0.1310365945,-0.5472636223,-0.2235968858,0.1527146846,-0.131220445,-0.1412544996,-0.0423424728,0.4258191586,-0.1137883365,-0.1747365743,-0.0600037314,-0.1374861747,-0.1974728554,-0.0550695211,-0.206342712,-0.544832468,0.4454170763,0.0049821967,0.1893910617,0.3470121026,-0.0001094098,-0.0178526044,0.410225302,-0.1208095476,-0.2488911003,-0.39947474,0.1793028861,-0.3214279711,-0.0999809876,-0.3226758242,-0.2282853127,-0.1305258572,0.1668989211,-0.2270071805,0.0431619808,0.3165642321,0.2735976577,-0.0255144741,0.2802649736,-0.1330512613,-0.1831474304,-0.2402020991,-0.0908977464,0.2393375039,0.1667280197,0.2533928156,-0.0951283351,0.1529464126,0.0502758324,0.0134145711,-0.1094024777,-0.2209978402,0.0489708111,-0.2720611691,0.3356462419,0.0553029366,0.7832083702,-0.2817870378,-0.1618426442,0.1612834483,0.283929795,-0.212313965,0.2203885913,0.4282406271,-0.1392534822,0.0007200533,-0.3249097168,-0.0999147892,-0.181789875,0.2037830502,0.0209814962,0.0087318849,-0.0804654807,-0.0100580025,0.2173687667,0.1014782935,0.0793812573,-0.0789981186,-0.2777691185,0.2961800694,-0.2832439542,0.020596629,-0.1462968141,0.5204554796,0.02538017,-0.3621297181,-0.1353999525,-0.1054494008,0.0756659955,0.0276800711,-0.0533361509,0.3394507468,0.1087685227,0.0285199098,0.079799287,0.130185023,0.0743425488,-0.0499666631,-0.1396474391,-0.2227846831,-0.2987947464,0.2747345567,-0.1234494448,-0.3272612691,-0.1521591097,0.0012162372,-0.2087304294,-0.0346010141,-0.0995942354,0.4414266348,-0.1579021513,0.1526206434,0.0880152136,0.1817054152,-0.1947121471,-0.3660129607,-0.1490098834,0.2008820623,-0.468621403,-0.3205323219,0.2491369843,-0.3165852427,0.4326317608,-0.0886029601,0.107325092,-0.1022875011,-0.1421988904,-0.2225233167,-0.2582617104,0.188044548,0.067876935,0.2651393414,0.0120807774,-0.0328292362,-0.0076496922,-0.2450187951,-0.129508391,-0.1909275204,-0.072525613,0.1784774959,-0.0103534712,-0.1338939667,-0.5229309797,0.0499758162,0.1910482049,0.0380627438,-0.002433571,-0.2078721821,-0.2314997613,-0.2085983604,0.117168434,0.5448202491,-0.0188138504,0.028379038,-0.0435673185,-0.0119555918,0.4996949434,0.3720760047,-0.0997387618,0.172167778,-0.2095858306,0.1426471472,0.1664265692,-0.5432876945,-0.2214052975,0.2418850362,-0.0492497012,0.0903555453,0.0329224057,-0.4252802134,0.2009977847,-0.130916357,0.1117296368,0.2759277225,0.0992452502,0.1265601665,-0.2411850691,-0.1267588139,0.2473011166,0.1046614796,0.1690177172,-0.1020453349,0.2604554296,0.1870794445,0.2613472939,-0.1208254769,0.3596661985,0.2470407784,-0.2625224292,-0.3072057366,0.1050221324,0.19859384,-0.3305431604,0.206600979,-0.2245477438,0.2911973596,0.0532628298,-0.0218927599,-0.3256198168,-0.2064074129,-0.2410363108,-0.2011760324,0.1774201095,-0.0429085456,0.3280696571,0.1672783643,0.1197206452,0.3532952666,-0.3063316047,-0.1096612364,-0.1067400351,0.1026701927,-0.2434271425,-0.0721904933,0.3241747916,0.2424546927,0.2653382123,-0.156836763,-0.1398927718,0.0729344636,-0.1470297724,0.4155639708,-0.1165600345,-0.1203745231,0.3694632649,-0.3810000718,0.4082131386,0.317717284,0.0645144135,-0.0745139942,0.3047270179,-0.2446958125,0.0516471826,0.0077799303,0.0869080424,-0.1271286458,0.331769824,-0.091515258,0.1778372526,-0.0359349363,0.5329710841,-0.0943983495,0.2545621097,-0.1527448297,-0.1970202029,0.027613027,0.4264924526,0.0807586312,-0.1145147458,0.171007663,-0.1288840175,-0.175828889,0.2758097649,0.1189039946,0.1102279648,0.1651006788,0.1731343567,0.1311655343,0.1335800737,-0.2187917084,0.2529250383,-0.2158294767,0.1718282849,0.3353054821,0.1661093533,-0.0029510632,-0.4320878088,-0.0286971219,0.2143987715,0.1778079122,-0.0685272738,-0.0997375175,-0.2467764765,-0.4081358314,-0.3134776056,-0.1185436398,0.0371320471,0.0172460489,-0.1968258172,0.114120625,-0.0407053009,-0.0076273112,-0.2748734355,-0.3112739325,-0.1994776577,-0.2850935757,-0.1725654304,0.0059298952,-0.4090853035,0.1231618375,0.1412646472,0.0594344474,0.3566991687,-0.33560583,-0.0827376172,-0.223473832,-0.0705547929,0.2513357699,-0.0651990846,-0.1545362324,0.0221690554,0.2006978989,-0.2227170467,-0.1932851523,0.0155675327,-0.1274445355,-0.1874835044,0.0146911833,-0.2334830463,-0.155720219,-0.1595274955,-0.6133767366,-0.4746182561,-0.4864419997,0.0599954873,-0.1413523406,0.1965738386,0.3935822845,-0.2014529854,0.1657384634,0.13731879,0.1436540931,-0.2028478831,-0.0262709111,0.1178618744,-0.3232525885,-0.454369545,0.117317833,0.0238614138,0.4150661826,0.1647234112,-0.3046405315,0.3316366673,-0.032270696,0.1291528493,0.0913803279,0.1418595463,0.4941840768,-0.0997108519,-0.0022173061,0.0292065702,0.1109181046,-0.0067351903,0.2292229086,0.4109459519,-0.2725373507,0.1508922577,0.1694056392,0.3010313809,0.3988194764,-0.2326139212,0.6556056738,0.3299936354,0.1660488546,-0.0785605311,-0.223159954,0.4240542054,-0.0242026802,0.050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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/228","title":"Not able to access the XNLI dataset","comments":"Thanks for reporting this bug !\r\nAs it seems to be just a cache problem, I closed your PR.\r\nI think we might just need to clear and reload the `xnli` cache @srush ? ","body":"When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.\r\n\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/sasha\/.cache\/huggingface\/datasets\/xnli\/plain_text\/1.0.0\/dataset_info.json'\r\nTraceback:\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp_viewer\/run.py\", line 86, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp_viewer\/run.py\", line 72, in get\r\n    builder_instance = builder_cls(name=conf)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 610, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 152, in __init__\r\n    self.info = DatasetInfo.from_directory(self._cache_dir)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/info.py\", line 157, in from_directory\r\n    with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), \"r\") as f:\r\n```\r\n\r\nIs it possible to see if the dataset_info.json is correctly placed?","comment_length":34,"text":"Not able to access the XNLI dataset \n When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.\r\n\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/sasha\/.cache\/huggingface\/datasets\/xnli\/plain_text\/1.0.0\/dataset_info.json'\r\nTraceback:\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp_viewer\/run.py\", line 86, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp_viewer\/run.py\", line 72, in get\r\n    builder_instance = builder_cls(name=conf)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 610, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 152, in __init__\r\n    self.info = DatasetInfo.from_directory(self._cache_dir)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/info.py\", line 157, in from_directory\r\n    with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), \"r\") as f:\r\n```\r\n\r\nIs it possible to see if the dataset_info.json is correctly placed? \n Thanks for reporting this bug !\r\nAs it seems to be just a cache problem, I closed your PR.\r\nI think we might just need to clear and reload the `xnli` cache @srush ? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/228","title":"Not able to access the XNLI dataset","comments":"Update: The dataset_info.json error is gone, but we have a new one instead:\r\n```\r\nConnectionError: Couldn't reach https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip\r\n```\r\nI am not able to reproduce on my side unfortunately. Any idea @srush ?","body":"When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.\r\n\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/sasha\/.cache\/huggingface\/datasets\/xnli\/plain_text\/1.0.0\/dataset_info.json'\r\nTraceback:\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp_viewer\/run.py\", line 86, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp_viewer\/run.py\", line 72, in get\r\n    builder_instance = builder_cls(name=conf)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 610, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 152, in __init__\r\n    self.info = DatasetInfo.from_directory(self._cache_dir)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/info.py\", line 157, in from_directory\r\n    with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), \"r\") as f:\r\n```\r\n\r\nIs it possible to see if the dataset_info.json is correctly placed?","comment_length":33,"text":"Not able to access the XNLI dataset \n When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.\r\n\r\n```\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/sasha\/.cache\/huggingface\/datasets\/xnli\/plain_text\/1.0.0\/dataset_info.json'\r\nTraceback:\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/ScriptRunner.py\", line 322, in _run_script\r\n    exec(code, module.__dict__)\r\nFile \"\/home\/sasha\/nlp_viewer\/run.py\", line 86, in <module>\r\n    dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 591, in wrapped_func\r\n    return get_or_create_cached_value()\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/streamlit\/caching.py\", line 575, in get_or_create_cached_value\r\n    return_value = func(*args, **kwargs)\r\nFile \"\/home\/sasha\/nlp_viewer\/run.py\", line 72, in get\r\n    builder_instance = builder_cls(name=conf)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 610, in __init__\r\n    super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/builder.py\", line 152, in __init__\r\n    self.info = DatasetInfo.from_directory(self._cache_dir)\r\nFile \"\/home\/sasha\/.local\/lib\/python3.7\/site-packages\/nlp\/info.py\", line 157, in from_directory\r\n    with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), \"r\") as f:\r\n```\r\n\r\nIs it possible to see if the dataset_info.json is correctly placed? \n Update: The dataset_info.json error is gone, but we have a new one instead:\r\n```\r\nConnectionError: Couldn't reach https:\/\/www.nyu.edu\/projects\/bowman\/xnli\/XNLI-1.0.zip\r\n```\r\nI am not able to reproduce on my side unfortunately. Any idea @srush ?","embeddings":[-0.1408677548,-0.1892592013,-0.033536762,0.4693672359,0.4054459035,0.1120007858,0.0560360961,0.3544986248,-0.1094894633,0.2927758694,-0.1404097974,0.1621204913,0.0502276979,-0.0280459467,0.0505416356,-0.0353243835,-0.1691273153,0.1510201693,0.132073015,0.0053231702,-0.1707711071,0.0455822125,-0.2690639794,0.2587417364,-0.286706537,-0.0852832273,0.0914464816,0.3859780431,-0.3081700802,-0.53493011,0.0450817719,-0.1443354785,0.2407738417,0.1957660019,-0.000111058,0.0493330322,0.2629134953,-0.0573490635,-0.5286895037,-0.1454180032,-0.3568858802,-0.1231440604,-0.0146996509,-0.3132304847,-0.1287814528,-0.2209604383,0.4022442102,-0.3689225614,0.2718051374,0.3152759969,0.2039080113,0.2000430375,0.1218272746,-0.054714378,0.049337551,0.1351935565,-0.1314477473,-0.0139704924,0.2392853945,0.1932622641,0.1636672765,0.4578833878,-0.1711953729,-0.1116460562,0.255207777,0.2438426167,-0.0302988421,-0.1604979932,0.3209942281,0.2331341654,0.4437571764,-0.4332896769,-0.4334993362,-0.1114859581,-0.0038188207,-0.2649722695,0.2488645911,0.1001946703,-0.3177075386,0.1554604769,-0.1199381873,-0.3665495813,-0.3681380153,0.1454014629,0.2627976835,0.3802297413,-0.0596888699,0.0670860037,-0.0611994453,-0.1958376616,0.0528130345,-0.0894943327,-0.1641328484,0.3026693761,-0.2269841582,0.1276466697,0.1352884024,0.0571524575,0.1198465228,0.3628030717,0.2032430172,-0.1840142161,-0.3755405843,0.06257765,-0.2645841837,0.4228107333,0.2957006991,0.1408858001,0.3827148676,-0.0089444388,0.2218957394,-0.3649003506,-0.2945749164,-0.1601772606,-0.114383772,0.2509687543,0.3615804911,-0.1595380753,-0.2476482093,-0.0595754758,-0.1263961792,0.0193485729,0.2029277384,0.5645589828,0.0536458157,0.1894836426,0.0351332389,0.3217778802,0.053703472,-0.5332477689,-0.1743085086,0.2165416032,-0.3087960184,0.1456948817,0.3105262816,-0.3313245475,0.3001628816,-0.1157548949,-0.3032845855,0.0032358915,0.1294727772,-0.1749177426,0.1020331085,0.0779607892,0.0931859761,0.1030509546,0.1904263943,-0.0799686313,0.0655035079,-0.0332771763,-0.0471923687,-0.4852254391,0.0255070888,0.1306854486,0.0716528967,-0.009506152,-0.0878667012,-0.0543031245,0.1962302923,-0.2217216939,-0.1117357835,0.112898834,-0.0255647469,-0.2713457048,0.2126425505,0.5540782213,-0.3136119843,0.2092669159,-0.1985261887,-0.1348921061,0.163162753,0.3628430367,-0.1167293563,0.0775886104,-0.3848822117,0.3266322613,0.6447759867,-0.5202516913,-0.507689178,0.2816505432,-0.2223585993,-0.0715679377,0.5067173243,0.2407118678,-0.0645311177,-0.113183476,0.0470005237,0.117008321,0.0467213541,-0.0885009691,-0.1114888489,-0.2450889647,0.3402366638,0.1130200699,-0.0306807663,0.1320907325,-0.029109424,0.1613193005,0.3716828525,-0.2910831869,0.1511864364,0.2840608954,0.3005669415,0.0372093096,-0.1425783336,-0.1196034774,-0.2946609557,0.2399859577,-0.0920302644,-0.0201166086,-0.3088808358,0.0002530748,-0.1130894944,-0.0409190543,-0.3876262605,-0.2783128321,0.1904964447,0.4527053237,0.227343753,-0.0196524505,-0.2899426222,0.3173918426,-0.4826749563,0.0027164356,-0.6354672313,0.1280632615,-0.0931880027,-0.1490348577,-0.1639639139,0.1639258713,0.0235497002,-0.0028483428,-0.092747651,0.212470293,-0.2380763143,0.0813123882,0.2638707161,0.1023620442,0.0382039137,-0.5709121823,0.3375821114,0.4717598259,0.2284216285,0.0894116163,-0.2693724036,0.1183070689,-0.0825481787,0.0903004631,0.0437282436,0.0172032174,0.4666255414,0.0148105649,0.0699819252,-0.212793991,0.3003255725,0.216489777,0.4621777833,-0.086125508,-0.5364867449,0.0861315429,0.2796010077,0.0605193675,-0.0986444056,-0.0109362863,-0.3510042727,-0.0416343696,0.0550630279,0.2289790064,0.2813903093,0.2278090268,-0.0653548166,0.0831116587,0.0983732715,-0.2621799409,0.1807291061,-0.2579271495,0.1012303159,0.2698712349,0.1998011768,0.0320530571,-0.0356441364,-0.5033630133,0.0721699521,0.4454903901,-0.270344466,-0.1728234887,-0.1369042546,-0.6854811907,0.0606506467,0.0763859451,-0.11054077,-0.3432247937,-0.0222284645,0.18776007,0.2145616412,-0.0719953775,-0.0950528979,0.0755412653,0.0686472133,-0.109409146,-0.419331491,-0.3149599433,-0.2077754438,0.046856489,0.2707952857,-0.0228072517,0.2469428778,-0.2802794874,0.0639284253,-0.4144373536,-0.0254895911,0.0234772526,0.0292966571,-0.0004744022,-0.0340683497,0.4818875194,0.4775179625,-0.0623232983,0.3475892544,0.0415477231,-0.1742302775,0.2578029335,-0.2152395099,-0.2344868481,-0.0418086126,-0.5249136686,-0.3563694656,-0.3282742202,0.0827530324,-0.1398809403,0.0803990811,0.3180901706,-0.1464771181,0.2062613368,-0.038883388,0.1958673,-0.0754591152,-0.2256141007,0.3382492363,-0.161397025,-0.2159953713,0.2360935062,0.0649059862,0.2100883275,-0.0412420146,-0.2306012511,0.1721257716,-0.2255693376,0.2844632864,-0.1305034161,-0.1519210339,0.3499826789,-0.0105040036,-0.001459451,0.0043800203,0.0118807135,0.1178084835,0.0100269215,0.571620822,0.2039424479,0.4134452343,0.1445199102,0.3723564744,0.4486645758,0.0267595891,0.3283901513,-0.2621395588,0.0669077039,-0.0658819005,-0.3156565428,0.1194543019,0.1747564822,-0.1733243912,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/225","title":"[ROUGE] Different scores with `files2rouge`","comments":"@Colanim unfortunately there are different implementations of the ROUGE metric floating around online which yield different results, and we had to chose one for the package :) We ended up including the one from the google-research repository, which does minimal post-processing before computing the P\/R\/F scores. If I recall correctly, files2rouge relies on the Perl, script, which among other things normalizes all numbers to a special token: in the case you presented, this should account for a good chunk of the difference.\r\n\r\nWe may end up adding in more versions of the metric, but probably not for a while (@lhoestq correct me if I'm wrong). However, feel free to take a stab at adding it in yourself and submitting a PR if you're interested!","body":"It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.\r\n\r\nHere is a self-contained notebook to reproduce both scores : https:\/\/colab.research.google.com\/drive\/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing\r\n\r\n---\r\n\r\n`nlp` : (Only mid F-scores)\r\n\r\n>rouge1 0.33508031962733364\r\nrouge2 0.14574333776191592\r\nrougeL 0.2321187823256159\r\n\r\n`files2rouge` :\r\n\r\n>Running ROUGE...\r\n===========================\r\n1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)\r\n1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)\r\n1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)\r\n===========================\r\n1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)\r\n1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)\r\n1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)\r\n===========================\r\n1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)\r\n1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)\r\n1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)\r\n\r\n---\r\n\r\nWhen using longer predictions\/gold, the difference is bigger.  \r\n**How can I reproduce same score as `files2rouge` ?**\r\n\r\n@lhoestq \r\n","comment_length":124,"text":"[ROUGE] Different scores with `files2rouge` \n It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.\r\n\r\nHere is a self-contained notebook to reproduce both scores : https:\/\/colab.research.google.com\/drive\/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing\r\n\r\n---\r\n\r\n`nlp` : (Only mid F-scores)\r\n\r\n>rouge1 0.33508031962733364\r\nrouge2 0.14574333776191592\r\nrougeL 0.2321187823256159\r\n\r\n`files2rouge` :\r\n\r\n>Running ROUGE...\r\n===========================\r\n1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)\r\n1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)\r\n1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)\r\n===========================\r\n1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)\r\n1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)\r\n1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)\r\n===========================\r\n1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)\r\n1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)\r\n1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)\r\n\r\n---\r\n\r\nWhen using longer predictions\/gold, the difference is bigger.  \r\n**How can I reproduce same score as `files2rouge` ?**\r\n\r\n@lhoestq \r\n \n @Colanim unfortunately there are different implementations of the ROUGE metric floating around online which yield different results, and we had to chose one for the package :) We ended up including the one from the google-research repository, which does minimal post-processing before computing the P\/R\/F scores. If I recall correctly, files2rouge relies on the Perl, script, which among other things normalizes all numbers to a special token: in the case you presented, this should account for a good chunk of the difference.\r\n\r\nWe may end up adding in more versions of the metric, but probably not for a while (@lhoestq correct me if I'm wrong). However, feel free to take a stab at adding it in yourself and submitting a PR if you're interested!","embeddings":[-0.1457264721,-0.1697432995,-0.1972297877,0.3397462964,-0.1194976568,-0.4242601097,-0.420399636,0.1476738602,-0.270043999,0.1725059152,-0.0828669071,0.1803876609,0.120330058,0.0080935061,-0.1003900841,-0.0979746804,0.1656547487,0.0388290472,0.0924810246,-0.3720678091,0.066559352,0.5696008801,-0.0313320532,0.21043396,0.0540217571,0.2702380717,0.2851136923,0.3035303652,-0.1485799253,0.0284368172,0.1280016154,0.0306413826,-0.023698464,0.2221485972,-0.0001088679,-0.2347189039,0.1026096642,-0.0031664839,-0.2757230699,-0.3026088178,-0.0345568508,0.0643639937,-0.1229285002,-0.0703938454,0.0858464167,-0.044892218,0.0487728454,0.2095425725,0.4293982685,0.1475735754,0.2262544334,-0.3024373949,-0.1853493452,0.0240821615,0.5763342977,0.1626760215,-0.1144831404,0.4472635388,0.0056994664,-0.3526385427,-0.2401907593,-0.0085632736,0.3430406153,-0.1725598574,0.1325590014,0.1931548268,0.148585543,-0.045940455,-0.079535611,0.2447285503,-0.2702822387,0.0322115794,-0.2271714061,-0.2778382003,-0.0763156638,-0.1997605413,0.1168917641,-0.0814458728,0.1642860919,0.0122475997,0.0441323034,0.2954271436,0.0496390946,0.007164266,-0.0540564582,-0.1078455895,0.0508458652,0.0984653682,0.2952796817,-0.0505435802,-0.5163154006,0.0107727246,-0.2593673468,0.0749608874,-0.302280724,-0.0378559306,0.3144967258,0.46597296,-0.200871408,0.499627769,0.2691969573,-0.0934173465,-0.132207945,-0.0394466408,-0.1378237605,0.1081323028,-0.0289463978,0.0206935201,0.0881181508,0.2246808112,-0.133582443,-0.0120077161,0.3982224762,-0.5637685657,-0.4777997434,0.1025914326,-0.2610344291,-0.432397604,-0.6891934872,-0.0673610121,-0.2554311156,-0.1192424744,0.0989084914,0.2272011787,-0.174884215,0.2922985256,0.0467879772,0.1833910197,-0.289503634,0.0196620282,-0.1839990616,0.0635797158,-0.3143928647,0.1345241964,0.1705666035,0.1127464101,0.3180577457,0.2560533583,0.2735133171,0.0386443697,0.3298657835,-0.2839507163,-0.022634726,-0.2111162692,-0.211391747,0.197453782,0.0271293335,0.0918488353,-0.0087018358,-0.0748942494,-0.1808407903,0.1442367584,0.4666180313,0.2065799385,0.1496509761,-0.1105541289,0.0406925417,0.116938062,-0.2401628494,-0.5936908722,0.2040610909,-0.136552155,-0.1270114928,-0.0581774674,0.1098197326,-0.0345822014,0.0931209251,-0.1174313724,0.3731989563,0.0635824502,0.2603128254,0.4534834325,-0.0408312865,0.0118638165,-0.1628564298,-0.0708854869,0.4665318727,-0.4385315478,-0.2222961336,0.3354998529,-0.1650701761,-0.065185748,0.239046663,0.0392757431,-0.3886532784,-0.166007787,0.1774507761,0.1975248605,0.0777985081,-0.0483966693,-0.2970068455,0.134958744,0.3166256249,0.0654775649,-0.2318288237,-0.2993499935,-0.1114049256,-0.0643908754,0.5141965151,0.1328068972,-0.2751375139,0.1049917638,-0.2721597254,0.2936930954,-0.0516953692,0.118573375,0.4230363369,-0.0233754385,-0.7951256037,-0.1602763534,0.4370327294,-0.05248693,-0.2122911811,-0.4714090824,0.2405331284,-0.2394646406,0.2232830822,0.0550486632,0.1873215437,0.0303547215,0.0240709484,0.0966067314,-0.2364263833,-0.1586188674,-0.1715354323,0.2036774307,-0.2210983336,-0.0074328831,-0.0105401725,0.3596087694,0.3116413951,0.0875802562,0.0373043455,0.263418287,0.0501557477,0.0980502665,0.3672500849,0.1448055357,-0.0007641261,0.1889616251,-0.1226600558,0.6879813671,0.0164625514,0.03225106,-0.3400395513,0.4259150624,-0.2170387655,0.2006913573,0.4093107283,-0.0717375576,-0.0500977673,0.0577801615,-0.0067999209,-0.0171536021,0.7467250228,0.195269227,0.2502554655,0.0029566153,-0.3220258057,0.2596178651,0.399140805,-0.0153429508,-0.0208532233,-0.1199541613,0.1937142164,-0.0933883861,-0.0367203467,0.2507390678,0.3810696304,0.1951338202,0.1557074785,-0.1311991662,-0.338236481,-0.3438020647,0.0351328626,-0.182103917,0.0874467045,0.4963860214,0.1016074717,0.2162867785,-0.3020778,-0.5422854424,-0.2536484003,-0.2223142087,0.0613259934,0.1616452485,-0.1439131945,-0.3678351939,-0.1536603719,-0.0919036344,-0.2918887734,-0.2732761204,0.2300974727,0.0208752602,-0.0724995062,-0.2189383954,0.0232325289,0.1487477869,-0.3703945577,0.1783805788,-0.0173747577,-0.0280433036,-0.2795380056,0.2228176445,-0.0567981303,-0.0193044487,-0.0314762257,-0.279186964,-0.4508359432,0.1696880311,-0.3762677908,-0.0109601086,0.0646441653,-0.0032368647,0.0596726015,-0.2506172359,-0.0705748871,0.1464014053,0.0922076777,-0.1741306931,-0.3507432342,-0.0896511376,0.0367848724,0.0142136998,-0.191406846,-0.2775212228,0.0793746784,-0.1433201283,0.6197952032,0.32701388,0.005700672,0.3858786821,-0.2369560748,0.0000112303,0.1717422307,0.5837681293,-0.2605839074,-0.0444282405,0.1986091286,-0.2997124791,-0.442188561,-0.2730611861,0.188300699,0.0323222205,-0.0084293075,-0.1786166281,0.0310044196,-0.1309438795,-0.0489977524,-0.2130453289,-0.0558184981,0.3378269076,-0.1836595982,-0.2421921641,-0.0644743517,0.2167336792,0.0299405474,0.286546886,0.4599354267,-0.4840275347,0.1646117717,-0.1400938034,0.1275601089,0.0440830775,0.5117965341,0.1533818245,0.1332314163,0.000875253,-0.1406763494,0.0981805027,0.4896937013,0.056188345,-0.144902870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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/225","title":"[ROUGE] Different scores with `files2rouge`","comments":"Thank you for your kind answer.\r\n\r\nAs a side question : Isn't it better to have a package that normalize more ?\r\n\r\nI understand to idea of having a package that does minimal post-processing for transparency.\r\n\r\nBut it means that people using different architecture (with different tokenizers for example) will have difference in ROUGE scores even if their predictions are actually similar.  \r\nThe goal of `nlp` is to have _one package to rule them all_, right ?\r\n\r\nI will look into it but I'm not sure I have the required skill for this ^^ ","body":"It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.\r\n\r\nHere is a self-contained notebook to reproduce both scores : https:\/\/colab.research.google.com\/drive\/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing\r\n\r\n---\r\n\r\n`nlp` : (Only mid F-scores)\r\n\r\n>rouge1 0.33508031962733364\r\nrouge2 0.14574333776191592\r\nrougeL 0.2321187823256159\r\n\r\n`files2rouge` :\r\n\r\n>Running ROUGE...\r\n===========================\r\n1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)\r\n1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)\r\n1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)\r\n===========================\r\n1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)\r\n1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)\r\n1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)\r\n===========================\r\n1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)\r\n1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)\r\n1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)\r\n\r\n---\r\n\r\nWhen using longer predictions\/gold, the difference is bigger.  \r\n**How can I reproduce same score as `files2rouge` ?**\r\n\r\n@lhoestq \r\n","comment_length":94,"text":"[ROUGE] Different scores with `files2rouge` \n It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.\r\n\r\nHere is a self-contained notebook to reproduce both scores : https:\/\/colab.research.google.com\/drive\/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing\r\n\r\n---\r\n\r\n`nlp` : (Only mid F-scores)\r\n\r\n>rouge1 0.33508031962733364\r\nrouge2 0.14574333776191592\r\nrougeL 0.2321187823256159\r\n\r\n`files2rouge` :\r\n\r\n>Running ROUGE...\r\n===========================\r\n1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)\r\n1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)\r\n1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)\r\n===========================\r\n1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)\r\n1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)\r\n1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)\r\n===========================\r\n1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)\r\n1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)\r\n1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)\r\n\r\n---\r\n\r\nWhen using longer predictions\/gold, the difference is bigger.  \r\n**How can I reproduce same score as `files2rouge` ?**\r\n\r\n@lhoestq \r\n \n Thank you for your kind answer.\r\n\r\nAs a side question : Isn't it better to have a package that normalize more ?\r\n\r\nI understand to idea of having a package that does minimal post-processing for transparency.\r\n\r\nBut it means that people using different architecture (with different tokenizers for example) will have difference in ROUGE scores even if their predictions are actually similar.  \r\nThe goal of `nlp` is to have _one package to rule them all_, right ?\r\n\r\nI will look into it but I'm not sure I have the required skill for this ^^ 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/225","title":"[ROUGE] Different scores with `files2rouge`","comments":"You're right, there's a pretty interesting trade-off here between robustness and sensitivity :) The flip side of your argument is that we also still want the metric to be sensitive to model mistakes. How we think about number normalization for example has evolved a fair bit since the Perl script was written: at the time, ROUGE was used mostly to evaluate short-medium text summarization systems, where there were only a few numbers in the input and it was assumed that the most popular methods in use at the time would get those right. However, as your example showcases, that assumption does not hold any more, and we do want to be able to penalize a model that generates a wrong numerical value.\r\n\r\nAlso, we think that abstracting away tokenization differences is the role of the model\/tokenizer: if you use the \ud83e\udd17Tokenizers library for example, it will handle that for you ;)\r\n\r\nFinally, there is a lot of active research on developing model-powered metrics that are both more sensitive and more robust than ROUGE. Check out for example BERTscore, which is implemented in this library!","body":"It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.\r\n\r\nHere is a self-contained notebook to reproduce both scores : https:\/\/colab.research.google.com\/drive\/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing\r\n\r\n---\r\n\r\n`nlp` : (Only mid F-scores)\r\n\r\n>rouge1 0.33508031962733364\r\nrouge2 0.14574333776191592\r\nrougeL 0.2321187823256159\r\n\r\n`files2rouge` :\r\n\r\n>Running ROUGE...\r\n===========================\r\n1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)\r\n1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)\r\n1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)\r\n===========================\r\n1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)\r\n1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)\r\n1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)\r\n===========================\r\n1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)\r\n1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)\r\n1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)\r\n\r\n---\r\n\r\nWhen using longer predictions\/gold, the difference is bigger.  \r\n**How can I reproduce same score as `files2rouge` ?**\r\n\r\n@lhoestq \r\n","comment_length":184,"text":"[ROUGE] Different scores with `files2rouge` \n It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.\r\n\r\nHere is a self-contained notebook to reproduce both scores : https:\/\/colab.research.google.com\/drive\/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing\r\n\r\n---\r\n\r\n`nlp` : (Only mid F-scores)\r\n\r\n>rouge1 0.33508031962733364\r\nrouge2 0.14574333776191592\r\nrougeL 0.2321187823256159\r\n\r\n`files2rouge` :\r\n\r\n>Running ROUGE...\r\n===========================\r\n1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)\r\n1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)\r\n1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)\r\n===========================\r\n1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)\r\n1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)\r\n1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)\r\n===========================\r\n1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)\r\n1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)\r\n1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)\r\n\r\n---\r\n\r\nWhen using longer predictions\/gold, the difference is bigger.  \r\n**How can I reproduce same score as `files2rouge` ?**\r\n\r\n@lhoestq \r\n \n You're right, there's a pretty interesting trade-off here between robustness and sensitivity :) The flip side of your argument is that we also still want the metric to be sensitive to model mistakes. How we think about number normalization for example has evolved a fair bit since the Perl script was written: at the time, ROUGE was used mostly to evaluate short-medium text summarization systems, where there were only a few numbers in the input and it was assumed that the most popular methods in use at the time would get those right. However, as your example showcases, that assumption does not hold any more, and we do want to be able to penalize a model that generates a wrong numerical value.\r\n\r\nAlso, we think that abstracting away tokenization differences is the role of the model\/tokenizer: if you use the \ud83e\udd17Tokenizers library for example, it will handle that for you ;)\r\n\r\nFinally, there is a lot of active research on developing model-powered metrics that are both more sensitive and more robust than ROUGE. Check out for example BERTscore, which is implemented in this library!","embeddings":[-0.1457264721,-0.1697432995,-0.1972297877,0.3397462964,-0.1194976568,-0.4242601097,-0.420399636,0.1476738602,-0.270043999,0.1725059152,-0.0828669071,0.1803876609,0.120330058,0.0080935061,-0.1003900841,-0.0979746804,0.1656547487,0.0388290472,0.0924810246,-0.3720678091,0.066559352,0.5696008801,-0.0313320532,0.21043396,0.0540217571,0.2702380717,0.2851136923,0.3035303652,-0.1485799253,0.0284368172,0.1280016154,0.0306413826,-0.023698464,0.2221485972,-0.0001088679,-0.2347189039,0.1026096642,-0.0031664839,-0.2757230699,-0.3026088178,-0.0345568508,0.0643639937,-0.1229285002,-0.0703938454,0.0858464167,-0.044892218,0.0487728454,0.2095425725,0.4293982685,0.1475735754,0.2262544334,-0.3024373949,-0.1853493452,0.0240821615,0.5763342977,0.1626760215,-0.1144831404,0.4472635388,0.0056994664,-0.3526385427,-0.2401907593,-0.0085632736,0.3430406153,-0.1725598574,0.1325590014,0.1931548268,0.148585543,-0.045940455,-0.079535611,0.2447285503,-0.2702822387,0.0322115794,-0.2271714061,-0.2778382003,-0.0763156638,-0.1997605413,0.1168917641,-0.0814458728,0.1642860919,0.0122475997,0.0441323034,0.2954271436,0.0496390946,0.007164266,-0.0540564582,-0.1078455895,0.0508458652,0.0984653682,0.2952796817,-0.0505435802,-0.5163154006,0.0107727246,-0.2593673468,0.0749608874,-0.302280724,-0.0378559306,0.3144967258,0.46597296,-0.200871408,0.499627769,0.2691969573,-0.0934173465,-0.132207945,-0.0394466408,-0.1378237605,0.1081323028,-0.0289463978,0.0206935201,0.0881181508,0.2246808112,-0.133582443,-0.0120077161,0.3982224762,-0.5637685657,-0.4777997434,0.1025914326,-0.2610344291,-0.432397604,-0.6891934872,-0.0673610121,-0.2554311156,-0.1192424744,0.0989084914,0.2272011787,-0.174884215,0.2922985256,0.0467879772,0.1833910197,-0.289503634,0.0196620282,-0.1839990616,0.0635797158,-0.3143928647,0.1345241964,0.1705666035,0.1127464101,0.3180577457,0.2560533583,0.2735133171,0.0386443697,0.3298657835,-0.2839507163,-0.022634726,-0.2111162692,-0.211391747,0.197453782,0.0271293335,0.0918488353,-0.0087018358,-0.0748942494,-0.1808407903,0.1442367584,0.4666180313,0.2065799385,0.1496509761,-0.1105541289,0.0406925417,0.116938062,-0.2401628494,-0.5936908722,0.2040610909,-0.136552155,-0.1270114928,-0.0581774674,0.1098197326,-0.0345822014,0.0931209251,-0.1174313724,0.3731989563,0.0635824502,0.2603128254,0.4534834325,-0.0408312865,0.0118638165,-0.1628564298,-0.0708854869,0.4665318727,-0.4385315478,-0.2222961336,0.3354998529,-0.1650701761,-0.065185748,0.239046663,0.0392757431,-0.3886532784,-0.166007787,0.1774507761,0.1975248605,0.0777985081,-0.0483966693,-0.2970068455,0.134958744,0.3166256249,0.0654775649,-0.2318288237,-0.2993499935,-0.1114049256,-0.0643908754,0.5141965151,0.1328068972,-0.2751375139,0.1049917638,-0.2721597254,0.2936930954,-0.0516953692,0.118573375,0.4230363369,-0.0233754385,-0.7951256037,-0.1602763534,0.4370327294,-0.05248693,-0.2122911811,-0.4714090824,0.2405331284,-0.2394646406,0.2232830822,0.0550486632,0.1873215437,0.0303547215,0.0240709484,0.0966067314,-0.2364263833,-0.1586188674,-0.1715354323,0.2036774307,-0.2210983336,-0.0074328831,-0.0105401725,0.3596087694,0.3116413951,0.0875802562,0.0373043455,0.263418287,0.0501557477,0.0980502665,0.3672500849,0.1448055357,-0.0007641261,0.1889616251,-0.1226600558,0.6879813671,0.0164625514,0.03225106,-0.3400395513,0.4259150624,-0.2170387655,0.2006913573,0.4093107283,-0.0717375576,-0.0500977673,0.0577801615,-0.0067999209,-0.0171536021,0.7467250228,0.195269227,0.2502554655,0.0029566153,-0.3220258057,0.2596178651,0.399140805,-0.0153429508,-0.0208532233,-0.1199541613,0.1937142164,-0.0933883861,-0.0367203467,0.2507390678,0.3810696304,0.1951338202,0.1557074785,-0.1311991662,-0.338236481,-0.3438020647,0.0351328626,-0.182103917,0.0874467045,0.4963860214,0.1016074717,0.2162867785,-0.3020778,-0.5422854424,-0.2536484003,-0.2223142087,0.0613259934,0.1616452485,-0.1439131945,-0.3678351939,-0.1536603719,-0.0919036344,-0.2918887734,-0.2732761204,0.2300974727,0.0208752602,-0.0724995062,-0.2189383954,0.0232325289,0.1487477869,-0.3703945577,0.1783805788,-0.0173747577,-0.0280433036,-0.2795380056,0.2228176445,-0.0567981303,-0.0193044487,-0.0314762257,-0.279186964,-0.4508359432,0.1696880311,-0.3762677908,-0.0109601086,0.0646441653,-0.0032368647,0.0596726015,-0.2506172359,-0.0705748871,0.1464014053,0.0922076777,-0.1741306931,-0.3507432342,-0.0896511376,0.0367848724,0.0142136998,-0.191406846,-0.2775212228,0.0793746784,-0.1433201283,0.6197952032,0.32701388,0.005700672,0.3858786821,-0.2369560748,0.0000112303,0.1717422307,0.5837681293,-0.2605839074,-0.0444282405,0.1986091286,-0.2997124791,-0.442188561,-0.2730611861,0.188300699,0.0323222205,-0.0084293075,-0.1786166281,0.0310044196,-0.1309438795,-0.0489977524,-0.2130453289,-0.0558184981,0.3378269076,-0.1836595982,-0.2421921641,-0.0644743517,0.2167336792,0.0299405474,0.286546886,0.4599354267,-0.4840275347,0.1646117717,-0.1400938034,0.1275601089,0.0440830775,0.5117965341,0.1533818245,0.1332314163,0.000875253,-0.1406763494,0.0981805027,0.4896937013,0.056188345,-0.1449028701,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/224","title":"[Feature Request\/Help] BLEURT model -> PyTorch","comments":"Hitting this error when using bleurt with PyTorch ...\r\n\r\n```\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n... and I'm assuming because it was built for TF specifically.  Is there a way to use this metric in PyTorch?","body":"Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).\r\n\r\nI had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried  https:\/\/colab.research.google.com\/drive\/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!\r\n\r\nThank you muchly!","comment_length":38,"text":"[Feature Request\/Help] BLEURT model -> PyTorch \n Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).\r\n\r\nI had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried  https:\/\/colab.research.google.com\/drive\/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!\r\n\r\nThank you muchly! \n Hitting this error when using bleurt with PyTorch ...\r\n\r\n```\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n... and I'm assuming because it was built for TF specifically.  Is there a way to use this metric in PyTorch?","embeddings":[0.0004879119,-0.1944001168,0.0977797955,0.1410354078,0.2860070765,-0.2194753885,0.2981606722,0.1757618785,0.1684712321,0.1584296376,-0.2239150256,0.2047086954,-0.4715835452,0.2436532378,-0.0394526012,-0.0937426761,-0.1899132431,-0.0336314701,0.1052042097,-0.015585267,-0.0906981975,0.2365888655,0.045650728,-0.0287116207,-0.3174606562,0.3504799306,0.0552200526,0.0559271649,-0.1453370899,-0.0327646509,0.3463766277,-0.1924692392,0.2557664514,0.7926077843,-0.0001235562,-0.2130512893,0.0671901554,-0.0112896813,-0.2755617499,-0.3058053255,0.4654067755,-0.2523365915,0.0014712351,-0.2047467679,-0.0270921495,-0.0622269325,-0.2024288625,-0.0805595145,0.0086525586,0.3162073791,0.0406865142,0.2029185295,0.0005698704,0.0706276149,0.0959588811,-0.1069801301,-0.1193717793,0.5899261832,0.1905050278,-0.0390190519,-0.1993449926,0.1651122421,0.1557437032,0.2218760997,0.7811316252,0.03963238,-0.1051483601,0.0078610759,0.0517428033,0.3505223095,-0.0570162758,-0.2330993563,-0.2421548218,0.1831979007,0.068287909,-0.281570822,-0.2522399426,0.0521995761,0.0469888858,-0.2774746716,-0.382629782,-0.354801625,-0.2562071383,0.1344293654,-0.2618772089,0.3999883831,-0.0648725256,-0.0589506961,0.1828382462,0.0548060238,0.209708631,-0.0122973248,0.3575534225,0.3185590506,0.0450001433,-0.2273323238,0.1566396207,-0.1462717354,0.0655388013,-0.2430343479,-0.2348572016,0.1291662008,-0.3112175465,0.0090820882,-0.0051812772,0.4701384902,0.0389155224,0.3749932647,0.3503911793,0.2842978239,-0.0297545362,-0.0786910653,-0.0677850991,-0.2594396472,0.2883807421,0.1613946557,-0.2171094269,-0.311069876,-0.1479407549,-0.0581485331,-0.1836255044,-0.2136000395,0.3854407668,0.2447400242,-0.3576726019,-0.1165842265,0.5851029754,0.0581300519,-0.2100519985,0.0859041661,-0.0929606929,-0.0970908031,-0.2978732288,0.0861729831,0.2061848044,-0.1500409245,0.209319979,-0.1078692079,0.6836862564,0.135818094,0.1862277985,0.3334340155,0.0832948312,-0.1664729863,-0.2499709129,-0.40275383,0.2584887743,0.0936398804,-0.1719942242,0.0645830929,-0.1143134162,-0.3340193331,-0.2537507415,0.0032774769,-0.2937202156,-0.1393714994,0.0467373393,0.3141675293,0.0852746069,-0.2999877334,0.0218755882,-0.2990497649,-0.2519973516,0.0970712528,0.2996884882,-0.3436619043,-0.3732590377,-0.4258345366,0.1134296656,0.3820712864,0.2269518673,0.1458987743,-0.1502639353,-0.1124973819,0.1804845333,-0.3285117447,0.3918120265,0.0386061333,-0.181296289,-0.2300805897,0.1016746685,-0.286238879,-0.2184966356,0.0639600009,-0.1890777797,-0.1219702139,0.3215975463,0.2692631185,-0.1522388756,-0.1192740947,-0.2392320782,-0.2757670581,0.2382387817,0.1066083983,0.3859713674,-0.0185017623,0.1580834538,0.5193030834,0.018723486,-0.4093893766,-0.0457703918,-0.1325852722,0.381831944,-0.2926695645,0.3908918798,-0.3206767738,-0.1213139519,0.2370038182,-0.2204726338,0.3617454469,0.199922502,0.1151999384,-0.1020439342,-0.0449570455,0.3409549594,0.0391774774,-0.1111276299,-0.1455000043,-0.2357196659,-0.1997219622,-0.2001805305,-0.3327550888,-0.291932404,-0.0543323532,0.4235497117,0.2041716129,-0.038499888,-0.0883931443,-0.0854253992,0.4208586514,0.13108477,0.2169723213,-0.1212121621,-0.2075590044,0.1365715414,-0.2814387977,0.2433534116,0.2878023982,0.345333606,-0.0836210623,0.1704854667,0.2630348206,-0.1229204163,-0.055307474,-0.0595499054,0.2311901003,0.298914969,0.2667693794,0.2532306015,0.0738804117,-0.2816995978,0.0544116236,0.0128507838,0.0123666888,0.4185680747,-0.2691677511,-0.1440791041,-0.1935264468,0.2079549879,0.0653934926,0.3745555878,-0.026033273,0.04296536,0.3506178856,-0.2904786766,-0.2164357752,-0.1224425733,-0.0230483133,0.1722002178,0.101308383,0.4216381907,0.1246316284,-0.1053852886,0.0762932077,-0.0540655442,0.1142887175,-0.199891746,-0.1042663828,0.0411479957,0.0894846171,0.1685812771,-0.0190609042,0.063017346,0.129863143,-0.2475427091,0.3689163625,0.0334745683,0.367395103,-0.1088698804,-0.3008708358,-0.2557474971,-0.1884493828,0.0084981322,0.2539114356,0.0916624069,-0.142018646,0.0540209822,0.4649915695,-0.0766244456,-0.1419015378,-0.0292699002,-0.3652302027,-0.3453891277,0.0093076173,0.0419486761,0.042096708,0.0964580029,0.0896503329,-0.0313360505,-0.2331273854,-0.7229447365,0.0043879054,-0.0067760991,0.2805350721,0.211117819,-0.0831347629,-0.3391121626,0.0040894351,0.0523563065,-0.5004475713,-0.0058011105,0.3323715925,-0.3564035594,0.0889470428,-0.0786410049,-0.1056697816,-0.0934436843,-0.0775417089,-0.0709223747,0.1984406114,0.0537065193,-0.0583876409,-0.0204657782,0.4024657011,0.5086855292,-0.0480325595,-0.1685403734,0.2404119968,0.3423588574,-0.3283778131,-0.3037554026,-0.0282775518,0.0658796206,0.029017875,0.0055691032,-0.3014440835,-0.4165248275,-0.1035077125,-0.0752799958,0.051425457,0.2057031095,0.0248824731,0.0247537456,0.0788896754,-0.2982492149,-0.2002647966,-0.0193369724,0.1483561546,0.7090571523,-0.1582219452,-0.1775605232,0.1485042125,0.8652088046,0.0129403165,-0.4598026872,0.1080646664,0.1744071394,0.0926937014,0.0157062057,-0.219392702,0.2692294717,-0.303392589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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/224","title":"[Feature Request\/Help] BLEURT model -> PyTorch","comments":"We currently provide a wrapper on the TensorFlow implementation: https:\/\/huggingface.co\/metrics\/bleurt\r\n\r\nWe have long term plans to better handle model-based metrics, but they probably won't be implemented right away\r\n\r\n@adamwlev it would still be cool to add the BLEURT checkpoints to the transformers repo if you're interested, but that would best be discussed there :) \r\n\r\nclosing for now","body":"Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).\r\n\r\nI had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried  https:\/\/colab.research.google.com\/drive\/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!\r\n\r\nThank you muchly!","comment_length":57,"text":"[Feature Request\/Help] BLEURT model -> PyTorch \n Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).\r\n\r\nI had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried  https:\/\/colab.research.google.com\/drive\/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!\r\n\r\nThank you muchly! \n We currently provide a wrapper on the TensorFlow implementation: https:\/\/huggingface.co\/metrics\/bleurt\r\n\r\nWe have long term plans to better handle model-based metrics, but they probably won't be implemented right away\r\n\r\n@adamwlev it would still be cool to add the BLEURT checkpoints to the transformers repo if you're interested, but that would best be discussed there :) \r\n\r\nclosing for now","embeddings":[0.0396036766,-0.294549346,0.0695946515,0.1643861979,0.2727992237,-0.2811159492,0.38066715,0.1402266473,0.1454873532,0.1657412946,-0.2105591744,0.2934762537,-0.4552504122,0.2241601944,0.0695941672,-0.1098843887,-0.0509779267,0.0371574424,0.1063593552,-0.1357742697,-0.1087616831,0.1649904698,0.0593500435,-0.1525469422,-0.2916157842,0.3176142275,0.0192493163,0.0257068686,-0.2094441354,-0.0577423349,0.2948090732,-0.0604142509,0.2058348507,0.7945739031,-0.0001224944,-0.2677385807,0.1325636506,-0.0253161397,-0.3406816423,-0.2211294025,0.3839514554,-0.3060537279,0.0823291987,-0.1754459292,0.0427883156,-0.0340652652,-0.2432406396,-0.0426557995,0.1874929368,0.2166575789,0.0422483832,0.2304356843,0.0012260919,0.1125814095,0.1391409039,-0.1055864319,-0.1043705791,0.5303354859,0.2758409083,-0.0624103881,-0.1922634393,0.2381688207,0.0778375641,0.101423353,0.6716713309,0.0390604064,-0.0555954054,-0.0681458116,0.005765269,0.3251337707,-0.114808701,-0.2151495516,-0.3067008257,0.0872814134,0.0710418969,-0.2673069239,-0.2179705352,0.106988363,0.1001451537,-0.2690118849,-0.4190244377,-0.364830941,-0.1958520859,0.0797381252,-0.2726512849,0.3813111484,0.009816546,-0.1550256461,0.1310456097,0.1450663954,0.3880723417,-0.0743771195,0.3471343517,0.2746399343,-0.0165070072,-0.3021687567,0.2685927153,-0.1360906065,-0.0050387089,-0.2247710675,-0.1665416211,0.2024484724,-0.2630818188,0.0775952637,-0.0429920703,0.401499778,-0.0002396844,0.372813046,0.3213628232,0.4031051695,0.104582943,-0.0196256172,-0.0739620849,-0.0863811821,0.2585074306,0.1930990219,-0.2239641994,-0.25219661,-0.0677620322,-0.0203725789,-0.2209860682,-0.2402275652,0.3333803415,0.1934585422,-0.4989129305,-0.0231807753,0.5008059144,0.0060213157,-0.1655693054,0.1050276309,-0.1418714076,-0.1260456741,-0.3094723821,0.0759087801,0.2528241277,-0.1601814777,0.2812950313,-0.076871708,0.4981269836,0.0475040898,0.3035526574,0.3707239926,0.0962487012,-0.1441248059,-0.2020063102,-0.3485707641,0.1855834424,0.0507264175,-0.1315112561,-0.0309726689,-0.114903979,-0.2794413269,-0.075020656,0.0509738848,-0.2576736212,-0.057642512,0.0253990646,0.3534557819,0.0978759676,-0.2471972257,0.0006365551,-0.2485276461,-0.167667836,0.0672941059,0.3956460655,-0.1806036234,-0.3170186579,-0.5069212317,0.1681725085,0.4395365715,0.1879903972,0.1661761254,-0.2475459576,-0.0329421535,0.2556152046,-0.3426558673,0.2737840414,0.1172076166,-0.3056722879,-0.2301910073,0.0894582793,-0.3044480085,-0.2189075649,0.0856976211,-0.1461465508,-0.0644501522,0.2756146193,0.2686757445,-0.1765097529,-0.012937896,-0.2883855999,-0.2562455535,0.246227473,0.1788948625,0.3769879639,-0.0379012376,0.1523504704,0.5387817621,0.0669796616,-0.3838602304,-0.0300253555,-0.1569999456,0.5144113898,-0.4600306153,0.3816400766,-0.2148496062,-0.117012158,0.1749205589,-0.3829161227,0.3466677368,0.1580247432,0.0597301014,-0.0823241696,0.0048180511,0.3204125762,-0.0448577777,-0.0798355788,-0.1922538877,-0.2519005835,-0.1517390609,-0.2314921319,-0.2959868014,-0.1850219518,-0.0406598635,0.2206051499,0.3695568442,-0.0213718712,-0.1467802376,-0.0849675387,0.3680124283,0.1280675977,0.1687961519,-0.0365346745,-0.2077385783,0.1696754694,-0.1845837981,0.2581163645,0.2752383053,0.407127738,0.0261994246,0.2872467041,0.2546685636,-0.100231804,-0.1049645543,-0.2070124,0.2988640964,0.3075419068,0.2385030687,0.2911813855,0.0936933905,-0.2713990808,0.0976055786,-0.0604119487,0.1711318344,0.3944917023,-0.1385252178,-0.0819859877,-0.1640891731,0.1472574621,0.1415482163,0.360725373,-0.052627977,0.0177914649,0.2372604907,-0.414567709,-0.2849433124,-0.0858028904,0.0554222129,0.1426686198,0.0641591698,0.4149323702,0.0514041185,-0.0188012589,0.044747971,-0.1211606413,0.1547817886,-0.2346344292,-0.0462681651,0.089615114,0.0987383351,0.090155676,0.0251399856,0.0745601952,0.1143124029,-0.1435763985,0.3518405259,0.1326592863,0.4257067144,-0.232666254,-0.364344418,-0.2572859228,-0.2122784108,0.0386481173,0.3008292019,0.0732662752,-0.085523665,0.2842047215,0.5591579676,-0.0492170826,-0.1014965698,-0.0635784343,-0.2979995012,-0.2619434893,0.0130494479,-0.0470721014,0.0210433081,-0.0121315392,0.1273240149,-0.0729090124,-0.1648213267,-0.7918711305,-0.0071387189,0.061953228,0.2949561179,0.1277306527,-0.1378466636,-0.327054441,-0.0012699482,0.0172598958,-0.4089310467,-0.0230823662,0.3334011436,-0.2403262109,0.0200988147,-0.0331980512,-0.1950121224,-0.1217609644,-0.0371470749,0.0350580998,0.1774721146,0.0372842364,-0.1275971979,0.1132897139,0.3214526772,0.4272084534,-0.1533857286,-0.0384360254,0.1742305607,0.4179562628,-0.2875500321,-0.3364490867,-0.1131410822,-0.0429405347,0.0972191468,0.0200368837,-0.3338596225,-0.3707358837,-0.1350100636,-0.1547125876,0.1676237881,0.2113265246,0.0806474239,0.1357162893,0.0812390894,-0.164341554,-0.1194946989,-0.0512098521,0.1652881056,0.6772111654,-0.2268800139,-0.1361592114,0.0998500213,0.865185678,0.147551924,-0.4560471177,0.0362508558,0.317102015,0.0258227959,-0.0309558529,-0.291069597,0.1881337017,-0.2465849519,-0.28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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/224","title":"[Feature Request\/Help] BLEURT model -> PyTorch","comments":"Hi there. We ran into the same problem this year (converting BLEURT to PyTorch) and thanks to @adamwlev found his colab notebook which didn't work but served as a good starting point. Finally, we **made it work** by doing just two simple conceptual fixes: \r\n\r\n1. Transposing 'kernel' layers instead of 'dense' ones when copying params from the original model;\r\n2. Taking pooler_output as a cls_state in forward function of the BleurtModel class.\r\n\r\nPlus few minor syntactical fixes for the outdated parts. The result is still not exactly the same, but is very close to the expected one (1.0483 vs 1.0474).\r\n\r\nFind the fixed version here (fixes are commented): https:\/\/colab.research.google.com\/drive\/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing  \r\n","body":"Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).\r\n\r\nI had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried  https:\/\/colab.research.google.com\/drive\/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!\r\n\r\nThank you muchly!","comment_length":109,"text":"[Feature Request\/Help] BLEURT model -> PyTorch \n Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).\r\n\r\nI had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried  https:\/\/colab.research.google.com\/drive\/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!\r\n\r\nThank you muchly! \n Hi there. We ran into the same problem this year (converting BLEURT to PyTorch) and thanks to @adamwlev found his colab notebook which didn't work but served as a good starting point. Finally, we **made it work** by doing just two simple conceptual fixes: \r\n\r\n1. Transposing 'kernel' layers instead of 'dense' ones when copying params from the original model;\r\n2. Taking pooler_output as a cls_state in forward function of the BleurtModel class.\r\n\r\nPlus few minor syntactical fixes for the outdated parts. The result is still not exactly the same, but is very close to the expected one (1.0483 vs 1.0474).\r\n\r\nFind the fixed version here (fixes are commented): https:\/\/colab.research.google.com\/drive\/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/223","title":"[Feature request] Add FLUE dataset ","comments":"@mariamabarham \r\nI put all the datasets on this drive: https:\/\/1drv.ms\/u\/s!Ao2Rcpiny7RFinDypq7w-LbXcsx9?e=iVsEDh\r\n\r\n\r\nSome information : \r\n\u2022 For FLUE, the quote used is\r\n\r\n> @misc{le2019flaubert,\r\n>     title={FlauBERT: Unsupervised Language Model Pre-training for French},\r\n>     author={Hang Le and Lo\u00efc Vial and Jibril Frej and Vincent Segonne and Maximin Coavoux and Benjamin Lecouteux and Alexandre Allauzen and Beno\u00eet Crabb\u00e9 and Laurent Besacier and Didier Schwab},\r\n>     year={2019},\r\n>     eprint={1912.05372},\r\n>     archivePrefix={arXiv},\r\n>     primaryClass={cs.CL}\r\n> }\r\n\r\n\u2022 The Github repo of FLUE is avaible here : https:\/\/github.com\/getalp\/Flaubert\/tree\/master\/flue\r\n\r\n\r\n\r\nInformation related to the different tasks of FLUE : \r\n\r\n**1. Classification**\r\nThree dataframes are available: \r\n- Book\r\n- DVD\r\n- Music\r\nFor each of these dataframes is available a set of training and test data, and a third one containing unlabelled data.\r\n\r\nCitation  : \r\n>@dataset{prettenhofer_peter_2010_3251672,\r\n  author       = {Prettenhofer, Peter and\r\n                  Stein, Benno},\r\n  title        = {{Webis Cross-Lingual Sentiment Dataset 2010 (Webis- \r\n                   CLS-10)}},\r\n  month        = jul,\r\n  year         = 2010,\r\n  publisher    = {Zenodo},\r\n  doi          = {10.5281\/zenodo.3251672},\r\n  url          = {https:\/\/doi.org\/10.5281\/zenodo.3251672}\r\n}\r\n\r\n\r\n**2. Paraphrasing** \r\nFrench part of the PAWS-X dataset (https:\/\/github.com\/google-research-datasets\/paws).\r\nThree dataframes are available: \r\n- train\r\n- dev\r\n- test \r\n\r\nCitation : \r\n> @InProceedings{pawsx2019emnlp,\r\n>   title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},\r\n>   author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},\r\n>   booktitle = {Proc. of EMNLP},\r\n>   year = {2019}\r\n> }\r\n\r\n\r\n\r\n**3. Natural Language Inference**\r\nFrench part of the XNLI dataset (https:\/\/github.com\/facebookresearch\/XNLI).\r\nThree dataframes are available: \r\n- train\r\n- dev\r\n- test \r\n\r\nFor the dev and test datasets, extra columns compared to the train dataset were available so I left them in the dataframe (I didn't know if these columns could be useful for other tasks or not). \r\nIn the context of the FLUE benchmark, only the columns gold_label, sentence1 and sentence2 are useful.\r\n\r\n\r\nCitation : \r\n\r\n> @InProceedings{conneau2018xnli,\r\n>   author = \"Conneau, Alexis\r\n>         and Rinott, Ruty\r\n>         and Lample, Guillaume\r\n>         and Williams, Adina\r\n>         and Bowman, Samuel R.\r\n>         and Schwenk, Holger\r\n>         and Stoyanov, Veselin\",\r\n>   title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\r\n>   booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\r\n>                in Natural Language Processing\",\r\n>   year = \"2018\",\r\n>   publisher = \"Association for Computational Linguistics\",\r\n>   location = \"Brussels, Belgium\",\r\n\r\n\r\n**4. Parsing**\r\nThe dataset used by the FLUE authors for this task is not freely available.\r\nUsers of your library will therefore not be able to access it.\r\nNevertheless, I think maybe it is useful to add a link to the site where to request this dataframe: http:\/\/ftb.linguist.univ-paris-diderot.fr\/telecharger.php?langue=en \r\n(personally it was sent to me less than 48 hours after I requested it).\r\n\r\n\r\n**5. Word Sense Disambiguation Tasks**\r\n5.1 Verb Sense Disambiguation\r\n\r\nTwo dataframes are available: train and test\r\nFor both dataframes, 4 columns are available: document, sentence, lemma and word.\r\nI created the document column thinking that there were several documents in the dataset but afterwards it turns out that there were not: several sentences but only one document. It's up to you to keep it or not when importing these two dataframes.\r\n\r\nThe sentence column is used to determine to which sentence the word in the word column belongs. It is in the form of a dictionary {'id': 'd000.s001', 'idx': '1'}. I thought for a while to keep only the idx because the id doesn't matter any more information. Nevertheless for the test dataset, the dictionary has an extra value indicating the source of the sentence. I don't know if it's useful or not, that's why I left the dictionary just in case. The user is free to do what he wants with it.\r\n\r\nCitation : \r\n\r\n> Segonne, V., Candito, M., and Crabb \u0301e, B. (2019). Usingwiktionary as a resource for wsd: the case of frenchverbs. InProceedings of the 13th International Confer-ence on Computational Semantics-Long Papers, pages259\u2013270\r\n\r\n5.2 Noun Sense Disambiguation\r\nTwo dataframes are available:  2 train and  1 test\r\n\r\nI confess I didn't fully understand the procedure for this task.\r\n\r\nCitation : \r\n\r\n> @dataset{loic_vial_2019_3549806,\r\n>   author       = {Lo\u00efc Vial},\r\n>   title        = {{French Word Sense Disambiguation with Princeton \r\n>                    WordNet Identifiers}},\r\n>   month        = nov,\r\n>   year         = 2019,\r\n>   publisher    = {Zenodo},\r\n>   version      = {1.0},\r\n>   doi          = {10.5281\/zenodo.3549806},\r\n>   url          = {https:\/\/doi.org\/10.5281\/zenodo.3549806}\r\n> }\r\n\r\nFinally, additional information about FLUE is available in the FlauBERT publication : \r\nhttps:\/\/arxiv.org\/abs\/1912.05372 (p. 4).\r\n\r\n\r\nHoping to have provided you with everything you need to add this benchmark  :) \r\n","body":"Hi,\r\n\r\nI think it would be interesting to add the FLUE dataset for francophones or anyone wishing to work on French.\r\n\r\nIn other requests, I read that you are already working on some datasets, and I was wondering if FLUE was planned.\r\n\r\nIf it is not the case, I can provide each of the cleaned FLUE datasets (in the form of a directly exploitable dataset rather than in the original xml formats which require additional processing, with the French part for cases where the dataset is based on a multilingual dataframe, etc.).","comment_length":723,"text":"[Feature request] Add FLUE dataset  \n Hi,\r\n\r\nI think it would be interesting to add the FLUE dataset for francophones or anyone wishing to work on French.\r\n\r\nIn other requests, I read that you are already working on some datasets, and I was wondering if FLUE was planned.\r\n\r\nIf it is not the case, I can provide each of the cleaned FLUE datasets (in the form of a directly exploitable dataset rather than in the original xml formats which require additional processing, with the French part for cases where the dataset is based on a multilingual dataframe, etc.). \n @mariamabarham \r\nI put all the datasets on this drive: https:\/\/1drv.ms\/u\/s!Ao2Rcpiny7RFinDypq7w-LbXcsx9?e=iVsEDh\r\n\r\n\r\nSome information : \r\n\u2022 For FLUE, the quote used is\r\n\r\n> @misc{le2019flaubert,\r\n>     title={FlauBERT: Unsupervised Language Model Pre-training for French},\r\n>     author={Hang Le and Lo\u00efc Vial and Jibril Frej and Vincent Segonne and Maximin Coavoux and Benjamin Lecouteux and Alexandre Allauzen and Beno\u00eet Crabb\u00e9 and Laurent Besacier and Didier Schwab},\r\n>     year={2019},\r\n>     eprint={1912.05372},\r\n>     archivePrefix={arXiv},\r\n>     primaryClass={cs.CL}\r\n> }\r\n\r\n\u2022 The Github repo of FLUE is avaible here : https:\/\/github.com\/getalp\/Flaubert\/tree\/master\/flue\r\n\r\n\r\n\r\nInformation related to the different tasks of FLUE : \r\n\r\n**1. Classification**\r\nThree dataframes are available: \r\n- Book\r\n- DVD\r\n- Music\r\nFor each of these dataframes is available a set of training and test data, and a third one containing unlabelled data.\r\n\r\nCitation  : \r\n>@dataset{prettenhofer_peter_2010_3251672,\r\n  author       = {Prettenhofer, Peter and\r\n                  Stein, Benno},\r\n  title        = {{Webis Cross-Lingual Sentiment Dataset 2010 (Webis- \r\n                   CLS-10)}},\r\n  month        = jul,\r\n  year         = 2010,\r\n  publisher    = {Zenodo},\r\n  doi          = {10.5281\/zenodo.3251672},\r\n  url          = {https:\/\/doi.org\/10.5281\/zenodo.3251672}\r\n}\r\n\r\n\r\n**2. Paraphrasing** \r\nFrench part of the PAWS-X dataset (https:\/\/github.com\/google-research-datasets\/paws).\r\nThree dataframes are available: \r\n- train\r\n- dev\r\n- test \r\n\r\nCitation : \r\n> @InProceedings{pawsx2019emnlp,\r\n>   title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},\r\n>   author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},\r\n>   booktitle = {Proc. of EMNLP},\r\n>   year = {2019}\r\n> }\r\n\r\n\r\n\r\n**3. Natural Language Inference**\r\nFrench part of the XNLI dataset (https:\/\/github.com\/facebookresearch\/XNLI).\r\nThree dataframes are available: \r\n- train\r\n- dev\r\n- test \r\n\r\nFor the dev and test datasets, extra columns compared to the train dataset were available so I left them in the dataframe (I didn't know if these columns could be useful for other tasks or not). \r\nIn the context of the FLUE benchmark, only the columns gold_label, sentence1 and sentence2 are useful.\r\n\r\n\r\nCitation : \r\n\r\n> @InProceedings{conneau2018xnli,\r\n>   author = \"Conneau, Alexis\r\n>         and Rinott, Ruty\r\n>         and Lample, Guillaume\r\n>         and Williams, Adina\r\n>         and Bowman, Samuel R.\r\n>         and Schwenk, Holger\r\n>         and Stoyanov, Veselin\",\r\n>   title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\r\n>   booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\r\n>                in Natural Language Processing\",\r\n>   year = \"2018\",\r\n>   publisher = \"Association for Computational Linguistics\",\r\n>   location = \"Brussels, Belgium\",\r\n\r\n\r\n**4. Parsing**\r\nThe dataset used by the FLUE authors for this task is not freely available.\r\nUsers of your library will therefore not be able to access it.\r\nNevertheless, I think maybe it is useful to add a link to the site where to request this dataframe: http:\/\/ftb.linguist.univ-paris-diderot.fr\/telecharger.php?langue=en \r\n(personally it was sent to me less than 48 hours after I requested it).\r\n\r\n\r\n**5. Word Sense Disambiguation Tasks**\r\n5.1 Verb Sense Disambiguation\r\n\r\nTwo dataframes are available: train and test\r\nFor both dataframes, 4 columns are available: document, sentence, lemma and word.\r\nI created the document column thinking that there were several documents in the dataset but afterwards it turns out that there were not: several sentences but only one document. It's up to you to keep it or not when importing these two dataframes.\r\n\r\nThe sentence column is used to determine to which sentence the word in the word column belongs. It is in the form of a dictionary {'id': 'd000.s001', 'idx': '1'}. I thought for a while to keep only the idx because the id doesn't matter any more information. Nevertheless for the test dataset, the dictionary has an extra value indicating the source of the sentence. I don't know if it's useful or not, that's why I left the dictionary just in case. The user is free to do what he wants with it.\r\n\r\nCitation : \r\n\r\n> Segonne, V., Candito, M., and Crabb \u0301e, B. (2019). Usingwiktionary as a resource for wsd: the case of frenchverbs. InProceedings of the 13th International Confer-ence on Computational Semantics-Long Papers, pages259\u2013270\r\n\r\n5.2 Noun Sense Disambiguation\r\nTwo dataframes are available:  2 train and  1 test\r\n\r\nI confess I didn't fully understand the procedure for this task.\r\n\r\nCitation : \r\n\r\n> @dataset{loic_vial_2019_3549806,\r\n>   author       = {Lo\u00efc Vial},\r\n>   title        = {{French Word Sense Disambiguation with Princeton \r\n>                    WordNet Identifiers}},\r\n>   month        = nov,\r\n>   year         = 2019,\r\n>   publisher    = {Zenodo},\r\n>   version      = {1.0},\r\n>   doi          = {10.5281\/zenodo.3549806},\r\n>   url          = {https:\/\/doi.org\/10.5281\/zenodo.3549806}\r\n> }\r\n\r\nFinally, additional information about FLUE is available in the FlauBERT publication : \r\nhttps:\/\/arxiv.org\/abs\/1912.05372 (p. 4).\r\n\r\n\r\nHoping to have provided you with everything you need to add this benchmark  :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/222","title":"Colab Notebook breaks when downloading the squad dataset","comments":"The notebook forces version 0.1.0. If I use the latest, things work, I'll run the whole notebook and create a PR.\r\n\r\nBut in the meantime, this issue gets fixed by changing:\r\n`!pip install nlp==0.1.0`\r\nto\r\n`!pip install nlp`","body":"When I run the notebook in Colab\r\nhttps:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb\r\nbreaks when running this cell:\r\n![image](https:\/\/user-images.githubusercontent.com\/338917\/83311709-ffd1b800-a1dd-11ea-8394-3a87df0d7f8b.png)\r\n","comment_length":38,"text":"Colab Notebook breaks when downloading the squad dataset \n When I run the notebook in Colab\r\nhttps:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb\r\nbreaks when running this cell:\r\n![image](https:\/\/user-images.githubusercontent.com\/338917\/83311709-ffd1b800-a1dd-11ea-8394-3a87df0d7f8b.png)\r\n \n The notebook forces version 0.1.0. If I use the latest, things work, I'll run the whole notebook and create a PR.\r\n\r\nBut in the meantime, this issue gets fixed by changing:\r\n`!pip install nlp==0.1.0`\r\nto\r\n`!pip install nlp`","embeddings":[-0.2037729025,0.1056738496,-0.0336354487,0.0640668422,-0.1007536948,-0.1691397429,0.1365311444,0.1091818288,-0.3450911641,0.1280623674,-0.164352268,0.5187450051,0.1809385419,0.0821388215,0.1669569165,0.0765133947,0.0644943118,0.4605704844,0.0468692891,0.156682536,-0.213108331,0.4624915421,-0.4079595804,0.1537030637,-0.2386357337,-0.1677411795,-0.206126675,0.2790915072,-0.5138830543,-0.2758500576,0.2979657948,0.1417234689,-0.0084389485,0.1811356694,-0.0001169652,-0.2367392331,0.1207963079,-0.0518519096,-0.3485308588,-0.3542282879,-0.0085788863,-0.4436558187,0.1595697105,-0.3419606388,0.1409291476,0.3921480477,0.3511864245,0.3706688583,0.3492597342,0.3874811232,0.1548643261,0.3137634397,0.2158098072,-0.0544759072,0.1636968553,-0.2806620598,-0.2976787686,0.3243994713,0.5822194219,-0.1685713977,0.003550905,0.1059031114,-0.0955071971,0.0559050888,0.1220199764,0.1638884097,-0.2535198331,-0.4291177094,0.2994447947,0.1212930828,0.0607747249,-0.2001691163,-0.0181519743,0.0014585996,0.2585046589,-0.3444949687,0.2189854831,0.3701309264,-0.3430844843,0.0796014294,0.0347072147,-0.0327624939,-0.217060253,0.2308544666,0.0566510111,0.3647040427,-0.0647751614,0.0102592483,0.2563159466,0.0780712813,0.2204387486,0.2324579954,-0.1993964911,-0.0125770569,-0.020614529,-0.1966523677,-0.1525165141,0.3759249151,0.235389784,-0.3535800874,0.1968852282,-0.0626712218,0.1999604702,0.1969324648,0.0232112259,-0.0120320274,0.3583292365,-0.0416987538,0.3990332186,0.4018417299,-0.0248469114,0.0016650113,0.0479009189,-0.1774966717,-0.2790907621,-0.1293845326,0.0646892264,-0.1897190213,-0.2175166458,-0.016830435,-0.1500985622,0.278216511,0.0331732593,0.194628939,-0.0681748316,-0.2387417108,0.1087198034,-0.0225516856,-0.3674689829,-0.1743022501,0.0099496134,-0.0019469556,-0.187640354,0.1096844599,0.2619293332,0.1836888492,0.4778348207,-0.1534589827,0.0996062905,-0.1178124025,0.052396033,-0.2653231621,-0.101341635,0.314409405,0.3155173361,-0.2200689912,-0.2717237175,-0.5757359862,0.0451195054,0.1975996643,-0.0320335366,-0.0128898425,-0.1250274926,0.1084180698,-0.2844673693,-0.070265539,-0.3243390918,0.0549854003,-0.2155044228,-0.2589317262,0.048960574,-0.1539312303,-0.2456499338,-0.0019286483,-0.0584790818,0.1869665235,-0.0323922038,-0.0988170281,0.1605466604,0.0084136315,-0.0025028242,0.2557320893,-0.1460038722,-0.0871363506,0.0606881231,0.1924744993,0.3825965226,-0.3142406344,-0.7157806754,0.1233162656,-0.0762078762,-0.2864676714,-0.09852314,0.1813633144,0.4203037322,-0.1855093539,0.0700470582,0.4979850352,-0.1739978939,0.1083303615,-0.2697708309,-0.1738209724,-0.128898561,0.0501525812,0.1142117381,-0.0171914566,-0.0391070768,0.7044432759,0.1668141633,-0.0989093333,-0.2014872432,0.0599216856,0.3261883259,-0.3010640442,-0.0882215127,-0.1463488638,-0.506205976,0.0100470018,-0.3460903466,0.2132534832,0.0968355238,-0.2111298591,-0.0591825619,-0.0682137907,-0.012183357,-0.1781014204,0.1000677645,0.0204873681,0.048870232,-0.0486058295,0.0901986808,0.3979105353,-0.1955251098,0.4248863161,-0.2051782757,0.1899199635,-0.3483103216,-0.3624228835,0.0692607537,0.3132476211,0.0034313435,-0.170596689,-0.1000657305,0.257260561,0.0810529739,-0.0746914893,0.1012284756,-0.3666822016,0.3960621059,0.0606683865,-0.0403566696,-0.0608279929,0.0121467514,-0.0353698842,-0.0288012698,0.2245773971,-0.1860223264,-0.0414232239,0.1730560809,0.2094190866,0.0392654352,-0.1198861673,-0.2044531107,0.1320448965,0.323166132,0.236813575,0.0411805324,0.0109152021,-0.3629996777,0.2105794847,0.4938595295,0.027466055,0.24809964,0.0198727529,0.0485267714,0.002644903,0.3425408006,0.0880601555,0.3751697838,0.1141346842,-0.110131368,-0.0038270995,-0.1853574663,-0.3078569472,0.1314207166,0.0830455497,0.1299071312,-0.169739753,0.303027153,0.0148459617,-0.3646787107,-0.1110709012,-0.0707152486,0.235033825,0.1090317667,0.3059636056,-0.1434033215,-0.6106244922,-0.2032148987,-0.1151826084,-0.186147213,-0.1863025725,-0.0547748841,0.3990697563,0.3937791884,0.1321959198,0.3591193855,0.2093052119,-0.1175979078,0.1829980314,0.1719687581,-0.0438949093,-0.429446429,0.0313699432,0.3862446845,0.297998786,0.1085413694,-0.2365486473,-0.2492028624,0.0154726664,-0.4298043847,0.2603361309,-0.3362569809,0.279479444,0.0997630954,0.247821793,-0.5951432586,-0.1031071842,0.1649894267,-0.1144916117,-0.122908406,-0.0929281935,-0.0368827581,-0.4055460393,-0.2671480477,-0.2051927149,-0.4359725416,-0.2693944275,0.3358285129,0.0157515723,0.2115788162,0.3739767075,0.0929482728,0.3100524545,0.0495364405,-0.2053632885,0.1208358854,0.0357938856,0.0885275751,-0.2505455911,-0.498541981,0.0997747332,0.0517346002,0.0376448929,-0.2449922562,-0.4161091149,-0.5225148201,0.1261278987,0.1882442236,-0.001207964,-0.0788631067,0.2069427967,-0.1973300278,0.1095579416,-0.0130770393,-0.2461119145,-0.1278255284,0.0830923319,0.3247517049,-0.1507366598,0.1058794037,0.1483967453,0.1934161335,0.3145639598,-0.0725045502,0.1143689454,0.1201350763,0.2739352882,-0.2105091661,-0.3405758739,0.3424800336,0.0101347398,-0.009618892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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/222","title":"Colab Notebook breaks when downloading the squad dataset","comments":"When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n  [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.\r\nIf you don't restart, then it breaks like in your first message ","body":"When I run the notebook in Colab\r\nhttps:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb\r\nbreaks when running this cell:\r\n![image](https:\/\/user-images.githubusercontent.com\/338917\/83311709-ffd1b800-a1dd-11ea-8394-3a87df0d7f8b.png)\r\n","comment_length":78,"text":"Colab Notebook breaks when downloading the squad dataset \n When I run the notebook in Colab\r\nhttps:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb\r\nbreaks when running this cell:\r\n![image](https:\/\/user-images.githubusercontent.com\/338917\/83311709-ffd1b800-a1dd-11ea-8394-3a87df0d7f8b.png)\r\n \n When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n  [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.\r\nIf you don't restart, then it breaks like in your first message 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/222","title":"Colab Notebook breaks when downloading the squad dataset","comments":"Thanks for reporting the second one ! We'll update the notebook to fix this one :)","body":"When I run the notebook in Colab\r\nhttps:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb\r\nbreaks when running this cell:\r\n![image](https:\/\/user-images.githubusercontent.com\/338917\/83311709-ffd1b800-a1dd-11ea-8394-3a87df0d7f8b.png)\r\n","comment_length":16,"text":"Colab Notebook breaks when downloading the squad dataset \n When I run the notebook in Colab\r\nhttps:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb\r\nbreaks when running this cell:\r\n![image](https:\/\/user-images.githubusercontent.com\/338917\/83311709-ffd1b800-a1dd-11ea-8394-3a87df0d7f8b.png)\r\n \n Thanks for reporting the second one ! We'll update the notebook to fix this one :)","embeddings":[-0.2081260532,0.0852969661,-0.0881832987,0.1644764543,-0.0231155716,-0.1033349931,0.3287060261,0.0826022476,-0.312695682,0.2114281356,-0.3994072378,0.3362334669,0.2900559604,0.1640942395,0.1548372507,0.1645147949,0.0983086675,0.3727589548,-0.066974692,0.1471362859,-0.228515476,0.451759398,-0.3685064018,0.0600332208,-0.2734017372,-0.2093191594,-0.2376538068,0.2732954919,-0.4236905873,-0.2180943191,0.2879371047,0.1839033067,-0.1117460802,0.2599040568,-0.0001143353,-0.2412296832,-0.0203225743,-0.0653363764,-0.3073404729,-0.1606719941,-0.0806202888,-0.2332572192,0.2200177163,-0.409855932,0.1319079399,0.4122153223,0.2227527201,0.28621158,0.293985337,0.2952934802,0.1804483533,0.4040353298,0.1271449178,-0.1776516438,0.325496614,-0.2722629905,-0.2431535721,0.410019666,0.6274053454,-0.0883135051,-0.0656272545,0.1342762262,-0.1280885339,0.1249476671,0.0890843719,0.2477122247,-0.2894340158,-0.4838736355,0.3436742723,0.1162116155,0.1510486752,-0.123151198,0.0589256324,0.0589955933,0.1713833958,-0.2932754159,0.2615860999,0.3350569904,-0.2227059156,0.1343462467,0.0062894667,0.089873597,-0.1818215251,0.1315548718,-0.027879525,0.1601843238,-0.1480301172,-0.0957625434,0.2256306261,0.011602832,0.2465777993,0.0990663767,-0.1621572673,-0.0948356315,-0.1089966148,-0.2423676401,-0.2443491966,0.249994114,0.2372867465,-0.241565004,0.3187463582,-0.0291611906,0.2450104654,0.1847766042,-0.0319995806,-0.1474552155,0.2595814466,-0.102215454,0.4240207374,0.416279465,-0.1882743984,-0.091848217,-0.0244186036,-0.1793902367,-0.2249310911,-0.0915122256,0.0407344513,-0.2405347228,-0.2889055908,0.0778213814,-0.136849463,0.2789804637,0.0691426918,0.2426396757,-0.001410056,-0.4101406932,0.0328567028,-0.0101671061,-0.2695299983,-0.2202639133,-0.0301732346,-0.0911093429,-0.1644539535,0.2610429227,0.2496694326,0.1362547725,0.5002061129,-0.0945061669,0.2376801968,-0.2036774755,0.0361762382,-0.4252381027,-0.0955571607,0.3575981557,0.3498216569,-0.2039547414,-0.2214946151,-0.5597006679,0.1084224805,0.0706817433,-0.035561271,-0.0227709413,-0.0372449458,0.1511408091,-0.2052285224,0.0243086144,-0.3127945662,0.0479006767,-0.2800324559,-0.1421732754,0.0136019904,-0.0340065658,-0.2153875828,-0.0319699533,-0.0166847929,0.2605358064,-0.1001942307,0.0080268718,0.2057422996,-0.046099551,-0.0403769165,0.3128234744,-0.1565088034,-0.0095242597,-0.010018927,0.2053337693,0.3080560565,-0.2581493556,-0.7119438648,0.0582446717,0.0052469377,-0.2625623643,-0.1313274056,0.2376966625,0.5601799488,-0.1987905353,0.114585489,0.4635337591,-0.1890334636,0.0986378938,-0.1500823498,-0.151231721,-0.1275015324,0.1236408129,0.2079047114,-0.0061938888,0.0766653419,0.5572876334,0.1662478298,-0.1265100688,-0.3530400693,0.0721036047,0.2784352601,-0.3214615881,-0.0980319455,-0.1085832566,-0.5646517873,0.0625723302,-0.2864468992,0.0835127607,0.1364913434,-0.2309456617,-0.0580955297,-0.0626566112,-0.0772447884,-0.0365413912,0.1337591112,-0.095026806,-0.0334592611,0.0200085845,0.1139889285,0.3453361392,-0.2902797163,0.5099155903,0.008631492,0.3248543739,-0.2755912244,-0.3000109792,0.1337193698,0.0963592008,0.0810678303,-0.1677869558,-0.145418182,0.2911042571,0.1966377646,0.0310584195,0.145625934,-0.2731467783,0.4598881602,0.0589739382,-0.0357784703,-0.1337578893,0.0844368339,-0.1913496405,-0.0462984331,0.2320576608,-0.2428800315,-0.0481875464,0.1195827127,0.1858395636,0.0412908457,-0.1335269511,-0.1565676481,0.0985781848,0.2388100326,0.2070394456,-0.0013626686,-0.0433761142,-0.3433444798,0.1956359893,0.4620435536,-0.0054820143,0.2048797756,-0.0629556626,-0.0692540556,0.0003487886,0.421723187,0.041216135,0.4217000902,0.0865193158,-0.0906889588,0.0007990873,-0.0349027254,-0.3570867777,0.1690445095,0.1428683847,0.2106125355,-0.1635544896,0.2298246324,-0.0365560018,-0.3995292485,-0.1075950339,-0.0882363096,0.1315501779,0.1349104345,0.3200077116,-0.1169909611,-0.5118410587,-0.0717158765,-0.1881686151,-0.0910916254,-0.2565027177,-0.0470915921,0.5061331391,0.3253305554,0.0137885762,0.3485425413,0.3416335583,-0.0021227498,0.3129969835,0.1184988618,-0.0434658229,-0.289424181,0.0792036131,0.4227304459,0.2414837331,0.212882787,-0.3029239476,-0.1514097899,-0.1465930343,-0.2868507206,0.2941179872,-0.1998257041,0.2345814556,-0.0236976035,0.1992693245,-0.536819458,-0.1262854487,0.1590674222,-0.0104005616,-0.0841393992,-0.0523559637,-0.0166616403,-0.4458810091,-0.2034320235,-0.1740429103,-0.3917513192,-0.2414952666,0.1878699064,-0.1172189564,0.1827541292,0.2395986766,0.0655275881,0.4017173946,-0.0359086171,-0.248591423,0.0184466317,-0.1290365458,0.2076357603,-0.323977232,-0.5115101337,0.1064165905,0.1879418343,0.0032006914,-0.2690907419,-0.4355993569,-0.3778634965,0.0499061234,0.1191340461,0.023841206,-0.2214809954,0.0059342468,-0.2621512711,0.0446545333,-0.0101997964,-0.2739887834,-0.1110514253,0.0766931325,0.3078039289,-0.1971263587,0.1102869809,0.0315279998,0.252944231,0.3045102954,-0.1189804822,0.1805392057,0.1001783684,0.2179147899,-0.2815532386,-0.4495449066,0.3379516304,-0.16195015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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/222","title":"Colab Notebook breaks when downloading the squad dataset","comments":"This trick from @thomwolf seems to be the most reliable solution to fix this colab notebook issue:\r\n\r\n```python\r\n# install nlp\r\n!pip install -qq nlp==0.2.0\r\n\r\n# Make sure that we have a recent version of pyarrow in the session before we continue - otherwise reboot Colab to activate it\r\nimport pyarrow\r\nif int(pyarrow.__version__.split('.')[1]) < 16:\r\n    import os\r\n    os.kill(os.getpid(), 9)\r\n```","body":"When I run the notebook in Colab\r\nhttps:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb\r\nbreaks when running this cell:\r\n![image](https:\/\/user-images.githubusercontent.com\/338917\/83311709-ffd1b800-a1dd-11ea-8394-3a87df0d7f8b.png)\r\n","comment_length":60,"text":"Colab Notebook breaks when downloading the squad dataset \n When I run the notebook in Colab\r\nhttps:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb\r\nbreaks when running this cell:\r\n![image](https:\/\/user-images.githubusercontent.com\/338917\/83311709-ffd1b800-a1dd-11ea-8394-3a87df0d7f8b.png)\r\n \n This trick from @thomwolf seems to be the most reliable solution to fix this colab notebook issue:\r\n\r\n```python\r\n# install nlp\r\n!pip install -qq nlp==0.2.0\r\n\r\n# Make sure that we have a recent version of pyarrow in the session before we continue - otherwise reboot Colab to activate it\r\nimport pyarrow\r\nif int(pyarrow.__version__.split('.')[1]) < 16:\r\n    import os\r\n    os.kill(os.getpid(), 9)\r\n```","embeddings":[-0.2108584791,0.3130596578,-0.0493807122,0.0397214517,-0.0872684419,-0.1855655313,0.263124615,0.1595547199,-0.4475891888,0.1087364554,-0.3607413769,0.5732998252,0.1958849877,0.0819763094,0.1732438505,0.1950283349,-0.0088848015,0.3963033259,-0.0574665889,0.1644324511,-0.2231678516,0.3445674479,-0.3933261037,0.1032137573,-0.2219548374,-0.1970246583,-0.1591427624,0.2976085246,-0.3919450045,-0.2167628407,0.276063025,0.1715926081,-0.0599931553,0.1792940795,-0.0001147339,-0.2016182393,-0.0302634668,-0.0780356601,-0.372962594,-0.2322010249,0.0352734253,-0.3360317647,0.3040677011,-0.4480285048,0.1321958601,0.4575712681,0.2886243761,0.3576161563,0.2969409525,0.2759976089,0.1642184556,0.3264267743,0.1345086545,0.0051154834,0.2324992865,-0.2531131506,-0.2693352103,0.4263532162,0.5949573517,-0.2634857297,-0.1326695681,0.1055862978,-0.1691093296,0.1500724107,0.1109077632,0.2283695638,-0.3441568017,-0.4259015918,0.2229602337,0.0606838465,0.0265284982,-0.2624763846,0.0548274443,0.0058779279,0.2763140202,-0.3067046404,0.1578664929,0.3533741832,-0.375043571,0.1199814379,0.0242759734,0.033814501,-0.1993505806,0.1858353913,0.058838632,0.3432296216,-0.0243609287,-0.0138749415,0.2171689123,0.0821247026,0.3446105123,0.2156177759,-0.0392461382,-0.0030000403,-0.13630265,-0.2019575089,-0.1412383467,0.2344243526,0.2335304469,-0.3234660923,0.2888835073,0.0063873846,0.291126281,0.1948486567,-0.0589854792,-0.1128916666,0.2124782652,-0.1368218511,0.3969634771,0.3778134286,-0.1054439396,0.0028238315,-0.0443405323,-0.1690574437,-0.1508910507,-0.1000082046,0.1224658787,-0.174937889,-0.28056705,0.0237823091,-0.2251797915,0.2988460064,0.042632129,0.2417269796,-0.0615691878,-0.376301378,0.0669527054,-0.0250224341,-0.3426691592,-0.2041161358,-0.0023027989,-0.0190374404,-0.1679764092,0.0972097889,0.300075382,0.1617040783,0.4099463522,-0.1188572943,0.1719561517,-0.1090329289,0.0323678665,-0.297097832,-0.0930035561,0.2763929367,0.3880445361,-0.2414577454,-0.2290214598,-0.5571026206,0.0456698202,0.1680841893,-0.0125241354,-0.008397527,-0.1567080021,0.1424696892,-0.2702636421,-0.0481065959,-0.3073248565,0.0109501826,-0.2560473382,-0.1812841147,0.0316573083,-0.0766721144,-0.1764997393,-0.0791532248,0.001023306,0.1980478913,-0.1631062776,-0.0346457921,0.1262241453,0.0313840248,0.0473505743,0.2351509184,-0.1826515347,0.0541924946,0.0288398024,0.2262758911,0.4111819565,-0.2042845339,-0.6995330453,-0.0071706502,-0.0884722844,-0.2417576164,-0.1328661144,0.2476087362,0.4465599358,-0.1435135156,0.210465923,0.5739889741,-0.1675453335,0.1404290944,-0.2227224708,-0.1723982543,-0.0670320243,0.1846537888,0.1432439983,-0.1096738055,0.0119318664,0.6371200085,0.2305385321,-0.1181589216,-0.2321148813,-0.0043965261,0.2663673759,-0.30782336,-0.1000126675,-0.0542001985,-0.5955289602,0.0301957056,-0.2725139856,0.1995994151,-0.0125481524,-0.1999818385,-0.0322625339,0.0027676311,-0.0214746576,-0.0415035374,0.1490339041,-0.1039347872,0.0377619043,0.0164105464,0.1371258944,0.3391304016,-0.2325789034,0.4468241036,-0.0039640521,0.2574146688,-0.2883975208,-0.3253237307,0.1558014005,0.2438882887,0.0305850264,-0.1248803809,-0.0695794597,0.216554746,0.1029064059,-0.0850576013,0.0670518056,-0.3381918669,0.3775502443,0.086209558,-0.0529328287,-0.079106465,0.0766893253,-0.0767324194,0.0185327604,0.2233506739,-0.312307477,-0.0703106746,0.10400635,0.2196217924,0.068207413,-0.1373142898,-0.127741307,0.2108820677,0.229276672,0.2444898784,0.0157216489,0.0384413302,-0.365306288,0.2714901567,0.4710392356,0.0090444358,0.2805314958,-0.0276142675,-0.067835696,-0.0176486485,0.3719348013,0.0231899228,0.3294185996,0.1166572496,-0.1165710315,0.0111387186,-0.1021077707,-0.2971960902,0.11165905,0.1278631538,0.317874074,-0.1010108069,0.3305140436,-0.0343724191,-0.3430919349,-0.2000886053,-0.0932968929,0.2421557605,0.1284599006,0.2591359317,-0.1354102492,-0.6098308563,-0.0550798923,-0.1672679186,-0.0903608426,-0.22800906,-0.0238990728,0.4561626017,0.2761535645,0.0735805854,0.2938821614,0.2140804976,0.0367488675,0.0862001106,0.1559814662,-0.1196362302,-0.3185096085,0.071880497,0.3952266872,0.2435241491,0.0868615359,-0.2415145785,-0.1873185188,-0.0984901264,-0.3265631795,0.2032726705,-0.235672757,0.3059905767,-0.0177470334,0.2388731092,-0.626424849,-0.2101235539,0.1028081775,-0.1280056238,-0.1129534319,-0.0388736688,-0.0058582094,-0.3610163629,-0.2418566942,-0.1475109756,-0.5018795133,-0.2425875813,0.2574924529,-0.0037881753,0.1998457462,0.2799191177,0.0712406114,0.3175303638,0.099508673,-0.2388119847,0.0621660985,0.0220669843,0.2359624356,-0.2466723472,-0.5061873794,0.0659509525,0.0568285398,0.0324610546,-0.2706146836,-0.4002767503,-0.4651815891,0.1409190595,0.2356439829,-0.0490813144,-0.1365013123,0.1378424913,-0.1899331659,0.0716856644,0.0150077883,-0.2303993702,-0.1759864241,0.1301798373,0.2832844257,-0.2543376982,0.0459595025,0.1570527852,0.2229388207,0.1117681637,-0.0831335261,0.1563901007,0.0955341384,0.1974157095,-0.1802189052,-0.4202899039,0.3344489038,-0.11916852,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"I like this feature! I think the first question we should decide on is how to convert all datasets into the same format. In T5, the authors decided to format every dataset into a text-to-text format. If the dataset had \"multiple\" inputs like MNLI, the inputs were concatenated. So in MNLI the input:\r\n\r\n> - **Hypothesis**: The St. Louis Cardinals have always won.\r\n> \r\n> - **Premise**: yeah well losing is i mean i\u2019m i\u2019m originally from Saint Louis and Saint Louis Cardinals when they were there were uh a mostly a losing team but \r\n\r\nwas flattened to a single input:\r\n\r\n> mnli hypothesis: The St. Louis Cardinals have always won. premise:\r\n> yeah well losing is i mean i\u2019m i\u2019m originally from Saint Louis and Saint Louis Cardinals\r\n> when they were there were uh a mostly a losing team but.\r\n\r\nThis flattening is actually a very simple operation in `nlp` already. You would just need to do the following:\r\n\r\n```python \r\ndef flatten_inputs(example):\r\n    return {\"input\": \"mnli hypothesis: \" + example['hypothesis'] + \" premise: \" + example['premise']}\r\n\r\nt5_ready_mnli_ds = mnli_ds.map(flatten_inputs, remove_columns=[<all columns except output>])\r\n```\r\n\r\nSo I guess converting the datasets into the same format can be left to the user for now. \r\nThen the question is how we can merge the datasets. I would probably be in favor of a simple \r\n\r\n```python \r\ndataset.add()\r\n```\r\n\r\nfunction that checks if the dataset is of the same format and if yes merges the two datasets. Finally, how should the sampling be implemented? **Examples-proportional mixing** corresponds to just merging the datasets and shuffling. For the other two sampling approaches we would need some higher-level features, maybe even a `dataset.sample()` function for merged datasets. \r\n\r\nWhat are your thoughts on this @thomwolf @lhoestq  @ghomasHudson @enzoampil ?","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":291,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n I like this feature! I think the first question we should decide on is how to convert all datasets into the same format. In T5, the authors decided to format every dataset into a text-to-text format. If the dataset had \"multiple\" inputs like MNLI, the inputs were concatenated. So in MNLI the input:\r\n\r\n> - **Hypothesis**: The St. Louis Cardinals have always won.\r\n> \r\n> - **Premise**: yeah well losing is i mean i\u2019m i\u2019m originally from Saint Louis and Saint Louis Cardinals when they were there were uh a mostly a losing team but \r\n\r\nwas flattened to a single input:\r\n\r\n> mnli hypothesis: The St. Louis Cardinals have always won. premise:\r\n> yeah well losing is i mean i\u2019m i\u2019m originally from Saint Louis and Saint Louis Cardinals\r\n> when they were there were uh a mostly a losing team but.\r\n\r\nThis flattening is actually a very simple operation in `nlp` already. You would just need to do the following:\r\n\r\n```python \r\ndef flatten_inputs(example):\r\n    return {\"input\": \"mnli hypothesis: \" + example['hypothesis'] + \" premise: \" + example['premise']}\r\n\r\nt5_ready_mnli_ds = mnli_ds.map(flatten_inputs, remove_columns=[<all columns except output>])\r\n```\r\n\r\nSo I guess converting the datasets into the same format can be left to the user for now. \r\nThen the question is how we can merge the datasets. I would probably be in favor of a simple \r\n\r\n```python \r\ndataset.add()\r\n```\r\n\r\nfunction that checks if the dataset is of the same format and if yes merges the two datasets. Finally, how should the sampling be implemented? **Examples-proportional mixing** corresponds to just merging the datasets and shuffling. For the other two sampling approaches we would need some higher-level features, maybe even a `dataset.sample()` function for merged datasets. \r\n\r\nWhat are your thoughts on this @thomwolf @lhoestq  @ghomasHudson @enzoampil ?","embeddings":[-0.0606288463,-0.4335705638,-0.0566293858,-0.0154857263,-0.1655908674,0.0452480465,0.1855864227,0.1840500981,0.2041598558,-0.1490784883,-0.2486168891,0.2844127417,-0.1800078154,0.471552074,0.3802833557,-0.4318719506,0.0726252794,-0.148357302,-0.327953428,0.346521765,0.0434317663,0.1234800965,-0.1807239652,0.0299022216,-0.3513098955,-0.1616335064,-0.385071367,0.0748649538,0.0141172633,0.0314689688,0.0698803589,0.3947065771,-0.0416435264,0.1824833006,-0.0001149848,-0.1062701494,0.1102551967,-0.1087463349,0.0937329605,-0.0340781435,0.1338173896,-0.3790647686,0.0580893494,-0.1654161513,0.2026768774,0.1457469612,0.1934349239,0.0513041392,0.3670559525,-0.2185335159,0.0840293467,0.2644307911,-0.1348502785,0.3218304515,-0.0651739612,0.0204702914,0.0448855683,0.2399405688,0.6195868254,-0.4785763025,-0.0277771205,0.2252210081,-0.0118415989,-0.0123158842,0.1250130832,-0.0638720319,-0.2911251783,-0.2389596552,-0.3664334118,0.244552955,-0.0434679091,-0.2190153152,-0.2268621624,-0.5487818718,-0.025222566,-0.0166294333,-0.3268792927,0.0636666864,-0.1695635319,-0.0531394035,-0.1652792394,-0.0777100474,-0.0644057468,0.1307319701,0.29616189,0.5442544818,0.0387099311,0.0535660908,0.2903420925,0.036766436,-0.1127419621,-0.2751785219,0.3076987863,0.0787266791,-0.3209531307,-0.3482193351,0.0695451275,-0.3125925362,0.321803242,0.0821104124,0.1377024502,0.0635517836,-0.130617559,0.2175747156,0.3832532763,0.0085670156,-0.075083077,-0.1686054468,0.0076402049,-0.1426707357,0.1661439538,0.1513280123,0.1184850931,-0.0039990922,-0.449752301,-0.0621160902,-0.0710137859,0.0521856472,-0.1257834733,-0.5080183744,-0.1141923368,-0.0967443734,-0.1139835641,-0.1760416627,-0.2671769857,0.1769250333,0.0036277203,0.2718095183,-0.1638914794,0.039084278,-0.0379803069,0.0168294962,-0.4351599813,-0.0823584646,0.2814767659,0.0876100659,0.0532539971,0.1616655886,-0.0415853783,0.0655003637,0.3322538435,0.0286847167,0.1381819993,-0.045800969,0.1786010563,-0.2670104504,-0.010550187,0.1570933014,-0.3184620142,-0.1224097684,-0.1524710655,-0.2089324147,0.0530343391,0.0109315291,-0.1749028862,-0.2891996503,-0.4556137323,0.7248809934,0.0154134696,-0.0831962973,-0.1941859424,0.1386067867,-0.1514987499,-0.0932872444,0.2236024737,0.1155076027,-0.0781733617,-0.2775520086,0.0191004183,-0.0802846476,0.0127374558,0.3045518994,-0.0646618158,0.2548953295,0.0614999644,0.2096288353,0.3009223938,-0.3996349275,-0.0024381455,0.3445948064,-0.055489637,0.3846751153,0.102682367,0.1290329546,0.0022143286,-0.1920385659,0.2654208541,0.9147472382,-0.3811212182,0.0671110302,-0.1982171685,-0.3537553251,0.5127952099,0.2841843069,0.0111314887,-0.3445620537,-0.1330880076,-0.0049945884,0.0784334987,0.0030070671,0.0373707712,-0.0265195891,-0.3109757006,0.0754680708,-0.1949543655,-0.1152403504,-0.4247468412,-0.1003589481,0.0457797125,0.3210237622,0.2605730593,-0.1533923596,0.2268137187,-0.3086336255,-0.1556716412,-0.2565404773,0.133099854,-0.0267518703,-0.0144738918,-0.2060856074,-0.0964523628,0.3236779273,0.0577925332,-0.075898692,-0.4959031343,0.2482441366,-0.0777617618,0.0200575497,0.0795487389,0.6802396774,-0.1879968047,-0.0758890882,0.1854728758,0.2653409243,-0.3162189126,0.2538578808,0.2200596333,0.3308732212,0.2140356302,-0.0697963163,0.1053741053,-0.063786611,-0.0416564196,-0.2234189659,-0.1915897131,0.4375747144,-0.1759518981,0.4636526704,-0.0321757197,0.0291524902,-0.2214388549,-0.0169028547,-0.2342641801,0.3847416639,0.3131430447,-0.2077127546,0.3308096528,-0.0469722785,-0.4412020147,0.2577673495,0.2447435558,0.0681833327,0.0957373753,-0.00955196,-0.0455345325,-0.3349420428,-0.0602991097,0.3420481086,0.5666439533,0.269957602,0.198374629,0.0683952942,-0.2664469481,-0.112411201,0.069297798,0.162815243,0.0128067406,0.2764882147,0.0359069817,0.0701989755,-0.0163582657,-0.0904838219,0.139838621,-0.1848670244,-0.159550786,0.0135105485,-0.2328624427,-0.6612980962,-0.2655380964,0.0595658682,-0.3062059283,-0.3195210397,0.1293625385,0.0172318295,-0.1474232078,0.1712545007,0.2321963161,0.6074471474,-0.3907059431,-0.1505924165,-0.0688083544,-0.3318816125,-0.1426523924,0.0778740942,0.5498899817,0.3654055893,0.4354554713,0.1750751734,0.0016457507,0.0403154679,-0.4197554588,0.0945982933,-0.3344560564,0.1600539535,0.0628429726,-0.1625745147,0.1456708461,-0.4231190979,-0.0899715796,0.0243899524,-0.0978647098,0.0175211001,-0.039675504,0.0278717205,-0.2234395891,-0.3024547994,-0.57163167,-0.4520238638,0.3431043327,-0.2798540294,0.144758299,0.0694180578,-0.225675419,0.1413625777,-0.1748025268,0.0703379437,-0.113563478,-0.1060817465,0.0421908833,-0.3436838984,-0.2414879054,-0.3852685094,-0.0269934312,0.1701690406,-0.0015300703,-0.0601092353,-0.2366360724,0.073477976,-0.2192817777,0.1113261655,0.2957314253,0.1376898587,-0.1666657329,0.1337774694,-0.0499742515,-0.1868763119,0.2078098208,0.3281923532,0.3863656819,0.1527036577,0.0017181401,0.2775653601,0.6705088615,0.3482151628,0.0438914299,0.0903099328,0.2588980198,0.0604608692,0.1286640763,-0.1806706488,0.2668640614,-0.2000270188,0.51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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"I agree that we should leave the flattening of the dataset to the user for now. Especially because although the T5 framing seems obvious, there are slight variations on how the T5 authors do it in comparison to other approaches such as gpt-3 and decaNLP.\r\n\r\nIn terms of sampling, Examples-proportional mixing does seem the simplest to implement so would probably be a good starting point.\r\n\r\nTemperature-scaled mixing would probably most useful, offering flexibility as it can simulate the other 2 methods by setting the temperature parameter. There is a [relevant part of the T5 repo](https:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/03c94165a7d52e4f7230e5944a0541d8c5710788\/t5\/data\/utils.py#L889-L1118) which should help with implementation.\r\n\r\nAccording to the T5 authors, equal-mixing performs worst. Among the other two methods, tuning the K value (the artificial dataset size limit) has a large impact.\r\n","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":126,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n I agree that we should leave the flattening of the dataset to the user for now. Especially because although the T5 framing seems obvious, there are slight variations on how the T5 authors do it in comparison to other approaches such as gpt-3 and decaNLP.\r\n\r\nIn terms of sampling, Examples-proportional mixing does seem the simplest to implement so would probably be a good starting point.\r\n\r\nTemperature-scaled mixing would probably most useful, offering flexibility as it can simulate the other 2 methods by setting the temperature parameter. There is a [relevant part of the T5 repo](https:\/\/github.com\/google-research\/text-to-text-transfer-transformer\/blob\/03c94165a7d52e4f7230e5944a0541d8c5710788\/t5\/data\/utils.py#L889-L1118) which should help with implementation.\r\n\r\nAccording to the T5 authors, equal-mixing performs worst. Among the other two methods, tuning the K value (the artificial dataset size limit) has a large impact.\r\n","embeddings":[-0.0724446997,-0.4672179222,-0.0401601158,-0.0550321825,-0.3045603633,0.0747443065,0.1744338721,0.0672955289,0.2653243542,-0.1193471253,-0.2597554028,0.3054882586,-0.1694985777,0.3780532479,0.3509474993,-0.4499824047,-0.0215500556,-0.146344617,-0.2921186686,0.3242069781,0.0543157682,0.1164425984,-0.1543542147,0.0661999434,-0.275216639,-0.1766957045,-0.3439988196,0.123409681,0.0848320276,0.0797640905,0.1358439922,0.3954449594,-0.0185932722,0.1279855967,-0.0001114127,-0.0947610736,0.0992532969,-0.0971221849,0.1526368111,0.0399923772,0.1779924929,-0.3429228663,0.0634030551,-0.1400868893,0.1315973103,0.2471916676,0.197243154,0.0887655392,0.4500890672,-0.3533523977,0.1048917621,0.3131383359,-0.2438293546,0.3010331988,-0.0856859535,-0.1528030336,0.0115065798,0.2888505161,0.6218152642,-0.5280056596,-0.0151433442,0.1048829183,-0.0462879315,0.0499921255,0.131043449,-0.066638656,-0.2823973,-0.253523469,-0.3627516925,0.2775124609,-0.1223972142,-0.0628897771,-0.1925864369,-0.565694809,-0.0037437642,0.0562744327,-0.3290131688,0.0878174901,-0.2160571516,0.0150353275,-0.124979414,-0.126868397,-0.007691354,0.1114815921,0.3081286848,0.5256880522,0.0743688941,0.1346539259,0.3524874151,0.1015161201,-0.1408928931,-0.2651450634,0.2907785773,0.1354706734,-0.3288773298,-0.377097249,0.1516861916,-0.2695581913,0.2802706063,0.0967135876,0.1468145251,0.052291058,-0.0826511756,0.264383316,0.3891301453,0.0126319667,-0.0967651606,-0.1197434366,0.0239591077,-0.105910629,0.1915580928,0.1394885629,0.1446611136,0.0090050101,-0.4349189997,-0.1123368219,-0.1909623295,0.0965473577,-0.180518657,-0.5517173409,-0.0857002214,-0.1018367335,-0.0204156153,-0.1243636906,-0.2886069715,0.1530839503,-0.0747468993,0.1504200399,-0.1804399937,0.0207882784,-0.0701470748,0.0663670599,-0.4629813135,-0.0349683575,0.2592130601,0.105593577,0.0714485869,0.1174032763,-0.0205039736,0.1679399163,0.426109314,0.0308056287,0.0287274756,-0.0310468953,0.1476192325,-0.3091061711,-0.0313938111,0.2360494584,-0.3518314362,-0.0972793326,-0.1445493251,-0.1775629669,0.1374153048,0.0454991013,-0.2158234864,-0.2692705393,-0.4415575564,0.7260335088,-0.0132843517,-0.0239318404,-0.1717699319,0.2594797313,-0.2092255652,-0.1180295646,0.1894045472,0.0955987945,-0.0213698354,-0.3190785348,-0.1060383692,-0.0609196313,-0.0156160584,0.3213067055,-0.1517840624,0.1992661208,0.1068785638,0.1746224463,0.2618967593,-0.4294566512,-0.0568967387,0.3527350724,-0.0614716336,0.3841181993,0.0508754663,0.1725042611,-0.0096142683,-0.2168364078,0.2445953488,0.9837132096,-0.4041246474,0.0024758638,-0.1859401464,-0.4244100451,0.563744843,0.3482621908,0.0648248196,-0.2935227156,-0.1886730939,0.03436758,0.0467180461,0.0923098326,0.0749095082,-0.0802322179,-0.2644890249,0.0103593497,-0.2241342962,-0.1698400527,-0.4216531515,-0.0087363916,-0.0369628407,0.3787116706,0.3727781773,-0.1403525323,0.2375966161,-0.3064366579,-0.1114936844,-0.3117160499,0.1445584893,-0.0680671409,0.0125660766,-0.2394195795,-0.0652765334,0.2053758502,0.0974027514,-0.1050228104,-0.4230077267,0.2914787829,-0.0407337584,0.0215077512,-0.0130759804,0.6146175861,-0.216123417,-0.1165854037,0.2079827189,0.2915941179,-0.3138335943,0.21099931,0.3093211353,0.3545559943,0.1958430856,-0.0614049137,0.0992705673,-0.012211035,-0.0899136811,-0.1710817516,-0.0809546337,0.4797086418,-0.2012840956,0.357545495,-0.0219102651,0.0348964967,-0.2077240497,-0.0569221973,-0.1906933188,0.3881003559,0.3106745183,-0.2279953659,0.3347797096,-0.0767852217,-0.4003093839,0.2434422076,0.2139726877,0.0134796128,0.090265505,-0.0230680667,0.0312409755,-0.3101988137,-0.0605027825,0.3375441432,0.5514169335,0.2894964516,0.1773703694,0.0167293996,-0.2175824344,-0.0726922899,0.0426138304,0.1653773636,-0.0631872639,0.1419843733,-0.041099038,0.098900713,0.0266580731,-0.1013484895,0.1228195503,-0.1445389092,-0.0426808633,-0.0375057459,-0.1547565758,-0.5833342671,-0.1911754608,0.0525763854,-0.3515312374,-0.2960594594,0.2087562084,0.0568543263,-0.1775723994,0.1679805666,0.1997511536,0.6398572326,-0.3923530579,-0.1751709133,-0.0524817295,-0.2999004722,-0.1152332574,0.137364313,0.4358258843,0.3023828566,0.4983601272,0.2307027131,-0.0328797661,0.1201639548,-0.386284858,0.1113508493,-0.3567822576,0.2921699286,0.0997610539,-0.1011456624,0.0970676467,-0.3661126196,-0.0739550591,-0.0943037197,-0.0616693757,-0.040393576,0.0080986591,0.0266808625,-0.2271050662,-0.2285712063,-0.5969745517,-0.4918062389,0.287645936,-0.2523241043,0.0960664153,0.0432314016,-0.2876449227,0.2085760832,-0.1413347572,0.0381555855,-0.1862201393,-0.0783927143,-0.0208623353,-0.3604214489,-0.1792836934,-0.4228295088,-0.0125843398,0.1930734217,-0.0777141228,-0.0673339814,-0.2891925573,0.0329765044,-0.1065610796,0.0893678963,0.2331583202,0.1441303045,-0.2032502443,0.0735996887,-0.027181806,-0.141878292,0.2734171748,0.3981159031,0.3655802906,0.1455877423,-0.0253874883,0.2197417766,0.7495043278,0.2815073729,0.0870178863,0.1091095433,0.2936366796,-0.0391648002,0.0949851051,-0.1791398525,0.2571568191,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"I agree with going with temperature-scaled mixing for its flexibility!\r\n\r\nFor the function that combines the datasets, I also find `dataset.add()` okay while also considering that users may want it to be easy to combine a list of say 10 data sources in one go.\r\n\r\n`dataset.sample()` should also be good. By the looks of it, we're planning to have as main parameters: `temperature`, and `K`.\r\n\r\nOn converting the datasets to the same format, I agree that we can leave these to the users for now. But, I do imagine it'd be an awesome feature for the future to have this automatically handled, based on a chosen *approach* to formatting :smile: \r\n\r\nE.g. T5, GPT-3, decaNLP, original raw formatting, or a contributed way of formatting in text-to-text. ","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":125,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n I agree with going with temperature-scaled mixing for its flexibility!\r\n\r\nFor the function that combines the datasets, I also find `dataset.add()` okay while also considering that users may want it to be easy to combine a list of say 10 data sources in one go.\r\n\r\n`dataset.sample()` should also be good. By the looks of it, we're planning to have as main parameters: `temperature`, and `K`.\r\n\r\nOn converting the datasets to the same format, I agree that we can leave these to the users for now. But, I do imagine it'd be an awesome feature for the future to have this automatically handled, based on a chosen *approach* to formatting :smile: \r\n\r\nE.g. T5, GPT-3, decaNLP, original raw formatting, or a contributed way of formatting in text-to-text. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"This is an interesting discussion indeed and it would be nice to make multi-task easier.\r\n\r\nProbably the best would be to have a new type of dataset especially designed for that in order to easily combine and sample from the multiple datasets.\r\n\r\nThis way we could probably handle the combination of datasets with differing schemas as well (unlike T5).","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":59,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n This is an interesting discussion indeed and it would be nice to make multi-task easier.\r\n\r\nProbably the best would be to have a new type of dataset especially designed for that in order to easily combine and sample from the multiple datasets.\r\n\r\nThis way we could probably handle the combination of datasets with differing schemas as well (unlike T5).","embeddings":[-0.0776049644,-0.4301363528,-0.0512811989,-0.0076487889,-0.2415291816,0.1148416847,0.1743364334,0.0923100263,0.2852181494,-0.1179621741,-0.2437363416,0.2735090852,-0.1946441382,0.4169626534,0.3981999755,-0.4560344517,0.0079969484,-0.2196667641,-0.2606224716,0.3799697757,0.0418277532,0.095559299,-0.1473453641,0.0627828836,-0.2902197242,-0.1371725649,-0.3435868025,0.0805074498,0.1044687256,0.0340153202,0.1990051121,0.3737783432,0.0089969868,0.1089183465,-0.0001125286,-0.0909401104,0.1605088562,-0.0712215528,0.1302847117,-0.0230519362,0.1236006096,-0.3596464097,0.0639435723,-0.1071627513,0.1815183014,0.186756596,0.2017910182,0.0839319006,0.3181020319,-0.3155004382,0.0999815166,0.290384084,-0.1883252263,0.2807796896,-0.0540621616,-0.0642039403,0.0306815002,0.228564024,0.6673142314,-0.5304598212,0.044611454,0.0683927238,-0.0271339901,0.0415637158,0.117888324,-0.0851886496,-0.3070947528,-0.2394675463,-0.3735044599,0.2802624702,-0.0719252825,-0.1064395383,-0.2332081348,-0.5333688259,0.0157342274,0.0769624412,-0.295244813,0.0596089661,-0.1627973914,-0.0191707928,-0.1291299015,-0.1218795106,-0.0014559383,0.1736190617,0.3086414337,0.5555925369,0.0679185465,0.166067943,0.3354541063,0.0697011054,-0.0984865054,-0.2893274128,0.283675164,0.0658083037,-0.4028246999,-0.3567550182,0.1301074028,-0.26543203,0.2804774344,0.1396420002,0.1625594497,0.0956441611,-0.1668536514,0.251871109,0.3397616148,-0.0224791672,-0.1228724495,-0.1360303015,0.0175045673,-0.1088773608,0.1665438712,0.136100173,0.1756422222,-0.0186628886,-0.4268658459,-0.1198429242,-0.1464762688,0.0982045382,-0.1879583001,-0.603138864,-0.0907536671,-0.1077904478,0.0310064703,-0.1576607078,-0.2877174616,0.0926540196,-0.1034632698,0.243978709,-0.1317925006,0.0085406294,-0.0485766232,0.065514192,-0.4239288867,-0.0484915897,0.2941403091,0.0993547812,0.0483110882,0.1649857163,-0.0342779905,0.1286848038,0.4205949903,0.0290484969,0.0141222924,-0.0367381349,0.1730397344,-0.3192974329,-0.0118671656,0.2226764262,-0.3717390895,-0.1183751225,-0.1698476076,-0.2173647434,0.0976075679,0.0334337987,-0.2183186114,-0.3184990585,-0.4913890362,0.712941885,-0.0447418466,-0.0464624129,-0.1646772921,0.266639322,-0.232261166,-0.0979923159,0.1632626653,0.0849712193,0.0131495027,-0.2949581742,-0.0931307301,-0.1038354039,-0.0648113415,0.3017258048,-0.1689353883,0.2122735828,0.0658584759,0.1369742304,0.249456495,-0.4536504745,-0.0488989614,0.3324487507,-0.0249690302,0.357055068,0.0768328607,0.2123102993,-0.0030692071,-0.2040670812,0.1949262917,1.0177958012,-0.3952714205,-0.0217342824,-0.1102285758,-0.3906148076,0.5425784588,0.3610700071,0.067350097,-0.3310129941,-0.1318062097,-0.0087063769,0.0908718035,0.0797558352,0.0483210608,-0.0628936738,-0.3434605598,0.0450412817,-0.2458027899,-0.1973199546,-0.4192028344,-0.0202788245,0.0169575177,0.2923036218,0.3340206146,-0.1000260711,0.2713528872,-0.3039948642,-0.1537616104,-0.3133296371,0.119758606,-0.0056195688,0.0379966423,-0.2483401,-0.0306746587,0.3191803098,0.0992679,-0.1351188719,-0.4337623417,0.3398182094,-0.047643017,0.0467606746,-0.0463985913,0.620665431,-0.1988634318,-0.0915856659,0.1701807678,0.2487031817,-0.3234956563,0.256033361,0.3241401017,0.3500341177,0.1919656843,-0.0883700922,0.0861881971,-0.0120608928,-0.0577380769,-0.1945427656,-0.0840635747,0.4658958018,-0.1854711324,0.4102387726,-0.0535622612,0.0147549119,-0.1941016763,-0.0550692603,-0.1838123798,0.4107014239,0.3079739511,-0.197261408,0.3365919888,-0.1108615696,-0.363048315,0.196744144,0.185142979,0.0567776822,0.0761518553,-0.0324886292,-0.0191662144,-0.2904635668,-0.1110854819,0.4045722485,0.632398963,0.2805095613,0.1170600653,-0.021244524,-0.2500744164,-0.1028238013,0.011356975,0.2060652524,-0.0168435108,0.150283888,0.0468926094,0.0736096278,0.043971926,-0.1459352523,0.193490237,-0.1794005781,-0.0495684855,0.0246106274,-0.1928532273,-0.5652402639,-0.2191136479,0.0437883101,-0.3279028237,-0.3013747036,0.1503418088,0.0533402786,-0.1769550741,0.1552890986,0.1746625155,0.5224668384,-0.4443944097,-0.2338437885,-0.0641313642,-0.2828474045,-0.0807362571,0.1160765886,0.4930141866,0.2858503759,0.4589028656,0.2643532157,-0.045532275,0.0037957064,-0.3939478099,0.0290108677,-0.3069182634,0.2791732252,0.0830265433,-0.2171403021,0.1505553871,-0.394996196,-0.0644386038,-0.0488226414,-0.0779298097,-0.0286925919,-0.0250476599,0.0897226259,-0.1780427694,-0.2269402891,-0.6084065437,-0.4707541168,0.2826776206,-0.2821086943,0.0727403238,0.0387645252,-0.277934283,0.1508194059,-0.1559341252,0.0280285962,-0.1545214653,-0.1129903123,-0.0186402239,-0.3668550253,-0.1596241295,-0.4123146832,-0.0737837255,0.2263587266,-0.0729172528,-0.0498354658,-0.2201999873,0.0606832951,-0.1032453701,0.0564823523,0.1742459983,0.1718525589,-0.2245576829,0.1106522009,-0.0412322246,-0.2030341327,0.2085092813,0.3692530394,0.3985555768,0.1724753976,-0.0453424975,0.1586776972,0.698872447,0.225701347,0.0746953636,0.1957734674,0.300358057,-0.0084648076,0.1412200034,-0.2332861125,0.2162008733,-0.1369470507,0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"@thomwolf Are you suggesting making a wrapper class which can take existing datasets as arguments and do all the required sampling\/combining, to present the same interface as a normal dataset?\r\n\r\nThat doesn't seem too complicated to implement.\r\n","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":37,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n @thomwolf Are you suggesting making a wrapper class which can take existing datasets as arguments and do all the required sampling\/combining, to present the same interface as a normal dataset?\r\n\r\nThat doesn't seem too complicated to implement.\r\n","embeddings":[-0.0898510367,-0.3969319463,-0.0316652842,-0.0373525135,-0.2264484614,0.0873821825,0.1969549954,0.066023387,0.3327300847,-0.13962093,-0.2261487246,0.3318240941,-0.2025055885,0.3645176291,0.4015562236,-0.501919508,-0.0090313414,-0.1732093245,-0.3404358923,0.3434834182,0.0159044527,0.0897598043,-0.1299690306,0.0539926477,-0.2659037709,-0.1480943859,-0.3480380177,0.1039842591,0.0902505815,0.0002097426,0.26544559,0.3834895492,-0.0041850111,0.1146860421,-0.000110999,-0.1040945202,0.1779927462,-0.061887417,0.1340163648,-0.0227332041,0.2059171796,-0.374109447,0.04129548,-0.1429956704,0.0951040164,0.186511755,0.1486477405,0.0613156334,0.4389664233,-0.3271816373,0.113716282,0.2715312541,-0.2127840817,0.2160600722,-0.103535369,-0.0817166641,0.0361859128,0.2948392332,0.6407872438,-0.5255509615,-0.0261882171,0.0954857841,-0.0273622703,-0.0269174017,0.1653091908,-0.0557940342,-0.3531101644,-0.2346815318,-0.3933763802,0.261813283,-0.1963312328,-0.1110707745,-0.2282507718,-0.5546969771,0.0432708822,0.0565065481,-0.3279976845,0.0498439819,-0.1630619168,0.0150655881,-0.1603159606,-0.1137707978,-0.0351350382,0.1509450972,0.3342216611,0.5391833186,0.0717265755,0.1810311377,0.3762328923,0.0664318129,-0.1559690833,-0.2558469474,0.2604014874,0.1465934813,-0.3001440167,-0.3815107048,0.1029030308,-0.2020753026,0.2648243606,0.1646739095,0.0934806019,0.126980558,-0.1174718142,0.2820120156,0.3259015679,-0.0005844994,-0.0584376119,-0.0893254504,0.0330901705,-0.0813911855,0.1632658243,0.1214111671,0.1739641428,0.0617650561,-0.4309351146,-0.1242405325,-0.2083215266,0.1426919699,-0.1268387288,-0.5166335106,-0.0674271211,-0.0839091614,0.0690300688,-0.0892471224,-0.3010070622,0.1144687757,-0.0229355283,0.2146689892,-0.0696992055,0.0141832316,-0.0549568981,0.1181560084,-0.4216566086,-0.0169754606,0.3122235835,0.0995567366,0.0971219018,0.1739335507,-0.0777562186,0.1229841337,0.4227028489,0.0299971607,0.0288171247,-0.0377979577,0.14484559,-0.3256048262,-0.006403306,0.1831966639,-0.3624858558,-0.1541394442,-0.0966254026,-0.165102303,0.1237905324,0.0594376586,-0.2251965404,-0.2789829969,-0.3781819642,0.7292924523,-0.0695030987,-0.0614325218,-0.1721529067,0.2976944447,-0.2514333725,-0.0713770092,0.1812423468,0.0710493848,-0.0328213945,-0.3899051249,-0.1003962681,-0.1081335098,-0.0720615834,0.2687501907,-0.2001522928,0.2165666968,0.0400445387,0.1675095111,0.2588683963,-0.4050790668,-0.1015974209,0.2870190144,-0.0375538357,0.3858802915,0.0768328905,0.2454753518,0.0891938135,-0.1763921827,0.252664268,1.0071948767,-0.3976961672,-0.0693167076,-0.1097234637,-0.3863373101,0.5040088296,0.3668292761,0.1083000153,-0.2368410081,-0.1491304785,-0.0592130013,0.0653444901,0.045004636,0.1062985361,-0.0819681883,-0.3427742124,0.0306715593,-0.216996491,-0.2022387087,-0.3672421277,-0.0022583967,-0.065089874,0.3331253231,0.3727762103,-0.1935801804,0.1813594997,-0.2651145458,-0.0785318315,-0.2893358469,0.1399663538,-0.072068423,0.0579696819,-0.2934385836,-0.0477033295,0.32401824,0.0663003772,-0.0903619602,-0.4725524187,0.3164875507,-0.0660732836,0.0410398617,-0.0641026646,0.6213018298,-0.1682782322,-0.1320482939,0.2150791287,0.2251460403,-0.3285654187,0.2118606567,0.3367442787,0.2405318171,0.1710718274,-0.0543709733,0.1039706469,0.0191598162,-0.0123938769,-0.2296965718,-0.1085937917,0.5694272518,-0.0680288002,0.4326314628,-0.0260058511,0.0427017175,-0.1831449568,-0.1110736579,-0.2428102493,0.3811077178,0.2990497053,-0.2120331079,0.352360338,-0.0513427258,-0.3553395569,0.1363967806,0.13285321,0.0007291199,0.0813225135,0.0181137118,-0.0639313385,-0.2471843958,-0.0975961983,0.4116670489,0.5995679498,0.2978718877,0.1413642615,0.0798594058,-0.2595912516,-0.075013943,0.0389111228,0.1910626441,-0.0794535726,0.154210344,0.0102188662,0.0571128428,-0.0155991139,-0.0966532603,0.1261167377,-0.2244980037,-0.0257465728,0.0404702388,-0.1504003704,-0.5861170292,-0.231887877,0.0051614125,-0.3123074174,-0.2645249665,0.1403571963,0.0393285602,-0.1784060895,0.1565387398,0.1852187216,0.6711630821,-0.4588549435,-0.2282123715,-0.0570804998,-0.2689540684,-0.1103215367,0.1257170886,0.4261064231,0.2776331007,0.5188305974,0.3089579046,-0.0348959155,0.0689550042,-0.3962318301,0.0578260273,-0.2660859823,0.3105842471,0.1134503111,-0.2200812101,0.1703180522,-0.3742897809,0.0022690678,-0.1026544869,-0.0340799056,-0.0740579218,-0.0439088307,0.0822699293,-0.2027610391,-0.1946837455,-0.6382994056,-0.4739052951,0.30521366,-0.2133403718,0.0697752684,0.0194587391,-0.2589373291,0.178520143,-0.1391531825,-0.013688419,-0.194584623,-0.1289618611,0.0167612638,-0.3375965953,-0.1641010046,-0.4051863253,-0.2008609921,0.2261935025,-0.090013206,-0.0637000054,-0.2524306774,0.0771816447,-0.1279675812,0.1112925783,0.2199829966,0.1511761546,-0.2682071924,0.0936457291,-0.0291541815,-0.2080545127,0.2571524084,0.2949794829,0.3784078658,0.1561033577,0.0382041782,0.1333256662,0.706382513,0.2446941286,0.0319510251,0.1457705498,0.3078972995,0.0172534864,0.1577182859,-0.2260904163,0.1919377297,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"I guess we're looking at the end user writing something like:\r\n``` python\r\nds = nlp.load_dataset('multitask-t5',datasets=[\"squad\",\"cnn_dm\",...], k=1000, t=2.0)\r\n```\r\nUsing the t5 method of combining here (or this could be a function passed in as an arg) \r\n\r\nPassing kwargs to each 'sub-dataset' might become tricky.","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":45,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n I guess we're looking at the end user writing something like:\r\n``` python\r\nds = nlp.load_dataset('multitask-t5',datasets=[\"squad\",\"cnn_dm\",...], k=1000, t=2.0)\r\n```\r\nUsing the t5 method of combining here (or this could be a function passed in as an arg) \r\n\r\nPassing kwargs to each 'sub-dataset' might become tricky.","embeddings":[-0.0292878691,-0.394043982,-0.0610294119,-0.0256624818,-0.2608686984,0.082581006,0.1810528636,0.0469555631,0.2868432105,-0.1594537348,-0.2330442071,0.3090499938,-0.2074714005,0.3853416443,0.3700452447,-0.446831882,-0.0071452288,-0.2071918547,-0.2622185349,0.3925702274,0.0369971208,0.1119897887,-0.1414716542,0.109804526,-0.3010460138,-0.136756286,-0.3321913779,0.1030589566,0.1164888591,0.0181283373,0.2122901678,0.3617036343,-0.0475504771,0.143895179,-0.0001149721,-0.0445622839,0.1405429542,-0.0745464861,0.1279869676,-0.0385321192,0.1727730036,-0.3879154325,0.0674856231,-0.1346675009,0.2163201421,0.1592068374,0.1809329093,0.0692611039,0.4056928158,-0.3285125494,0.0875926465,0.2849261165,-0.1900693029,0.3437137902,-0.106348373,-0.104068324,0.0376524441,0.2589140236,0.5864793658,-0.5919806361,0.0531647094,0.0858707279,0.0129780285,-0.0354294479,0.1583545208,-0.0285796337,-0.3110812306,-0.224444598,-0.4186833799,0.1887254119,-0.1044372246,-0.1551899761,-0.221528545,-0.5562813878,-0.0170732997,-0.0037981721,-0.3029250801,-0.0021318083,-0.1731025726,-0.0394975655,-0.1476764232,-0.1062141284,-0.0510747917,0.154971689,0.334846288,0.5709902048,0.1295996904,0.1710970849,0.319866538,0.0538763031,-0.1331725866,-0.2922166288,0.3081015646,0.1642882973,-0.3647081256,-0.3597905934,0.1917760223,-0.2589807212,0.2324257493,0.0934328586,0.127033025,0.0813640952,-0.1447874457,0.2692347169,0.3316326737,0.0368669368,-0.1052711457,-0.1273326576,0.0287625547,-0.1599454433,0.2352163494,0.0967027619,0.1859356016,-0.0221786052,-0.3954431713,-0.1633712053,-0.1346349418,0.1488838494,-0.1919805855,-0.5355588794,-0.1626533121,-0.1399561167,0.0474985354,-0.1304683983,-0.314229548,0.076599434,-0.0300824065,0.1895588785,-0.1426861584,0.1208322644,-0.0526403412,0.0712479576,-0.4480927885,-0.0460767262,0.2776448429,0.109525986,0.08483053,0.1338436902,0.0148406941,0.1254808456,0.401938498,0.0392921008,-0.0199368186,-0.033141423,0.1681917012,-0.3317155242,-0.0062631932,0.1757421792,-0.3530600965,-0.1490301788,-0.15961124,-0.1868329048,0.1211162359,0.0244976729,-0.2208403945,-0.3201468289,-0.4782351851,0.6768115163,-0.027634982,-0.0647312999,-0.16472359,0.2757855952,-0.2691693902,-0.1202769652,0.1744775176,0.0458281785,0.0011190666,-0.3560557067,-0.0667324588,-0.0792039707,-0.0776051804,0.2584712505,-0.1712939292,0.2623884678,0.0908837914,0.1857641786,0.3408804536,-0.4731499255,-0.0226834994,0.2970294952,-0.0556328595,0.4108914137,0.070467867,0.2279607058,-0.0812057629,-0.2089106292,0.2719022036,1.0307596922,-0.3764421046,-0.0222222898,-0.0957022458,-0.3904013932,0.5535189509,0.3494330049,0.0771400183,-0.3079709113,-0.1021800116,-0.0096340487,0.0667360649,0.0841099992,0.074733451,-0.0878115147,-0.3393520415,0.1139122844,-0.2102668732,-0.1518988907,-0.3530140519,0.0339234881,0.0241677295,0.3140191734,0.2573538721,-0.1301783174,0.2086983174,-0.2874013186,-0.1486529857,-0.2948302031,0.1073915586,-0.0712572262,0.0002874161,-0.2379792929,-0.0317758545,0.307772398,0.1399238557,-0.1362237781,-0.477207154,0.293318361,-0.0632221475,0.0578540787,-0.0563283488,0.648289144,-0.1621540636,-0.0672787055,0.192209065,0.3254882395,-0.3306884766,0.2363168746,0.3137443066,0.3130964339,0.1656142473,0.0061513577,0.1179905161,0.0116396798,-0.0012399972,-0.2144867331,-0.0947560817,0.5063752532,-0.1947730184,0.4248305261,-0.0239511244,-0.0024468424,-0.2053102404,-0.0152175212,-0.1752177924,0.4153912663,0.2905997634,-0.2353711426,0.2970917821,-0.1605678797,-0.3739417493,0.1721218228,0.1792156249,0.0188672002,0.0344038904,-0.0445342585,0.0077328938,-0.2754392922,-0.1060047299,0.3186826408,0.657732904,0.254458189,0.1376096606,0.0657043904,-0.2021635175,-0.0595594049,0.0485227667,0.2135917246,-0.0488939099,0.1590911001,0.0362472609,0.0808383077,0.0621163175,-0.1677286327,0.1392087489,-0.2044975758,-0.116217196,0.0541696772,-0.184016943,-0.6338432431,-0.1893022954,-0.0091773653,-0.3316513896,-0.2957566082,0.1372021437,0.0558245629,-0.1146438941,0.1387735605,0.1970451176,0.5815647244,-0.4308137894,-0.2768557668,-0.0366385765,-0.3137181103,-0.101734519,0.1042483672,0.4355737567,0.2959771752,0.5036947131,0.2990525961,-0.0688932687,0.0986968577,-0.4021719694,0.065417096,-0.3533987999,0.2585304081,0.1358294189,-0.2217337042,0.1458302438,-0.3897809684,-0.0530337431,-0.0256696921,-0.0749264508,-0.0513381362,0.0540681854,0.0693137795,-0.1985552162,-0.190367803,-0.6149933338,-0.4419181347,0.3062751889,-0.2279975116,0.082725279,0.0621675141,-0.2821539342,0.2345841825,-0.0894587934,0.0253634453,-0.1702772528,-0.0523394346,0.0101791928,-0.3848066926,-0.1447838843,-0.3393516243,-0.0398955233,0.2213984579,-0.0660574064,-0.0294074975,-0.2750928998,0.0828027353,-0.087789759,0.0721915066,0.262242943,0.1656526178,-0.2060345113,0.1317709833,-0.0811146721,-0.2719359398,0.2103470117,0.3302366436,0.3737383187,0.1844933927,0.0363181233,0.1870743483,0.7639582157,0.248142615,0.0630793497,0.1484031677,0.2611881196,-0.0126449708,0.1614734381,-0.1882690638,0.1840164065,-0.173315614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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"From thinking upon @thomwolf 's suggestion, I've started experimenting:\r\n```python\r\nclass MultitaskDataset(DatasetBuilder):\r\n    def __init__(self, *args, **kwargs):\r\n        super(MultitaskDataset, self).__init__(*args, **kwargs)\r\n        self._datasets = kwargs.get(\"datasets\")\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            features=nlp.Features({\r\n                    \"source\": nlp.Value(\"string\"),\r\n                    \"target\": nlp.Sequence(nlp.Value(\"string\"))\r\n                })\r\n        )\r\n\r\n    def _get_common_splits(self):\r\n        '''Finds the common splits present in all self._datasets'''\r\n        min_set = None\r\n        for dataset in self._datasets:\r\n            if min_set != None:\r\n                min_set.intersection(set(dataset.keys()))\r\n            else:\r\n                min_set = set(dataset.keys())\r\n        return min_set\r\n\r\n....\r\n\r\n# Maybe this?:\r\nsquad = nlp.load_dataset(\"squad\")\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\",\"3.0.0\")\r\nmultitask_dataset = nlp.load_dataset(\r\n    'multitask_dataset',\r\n    datasets=[squad,cnn_dailymail], \r\n    k=1000, \r\n    t=2.0\r\n)\r\n\r\n```\r\n\r\nDoes anyone know what methods of `MultitaskDataset` I would need to implement? Maybe `as_dataset` and `download_and_prepare`? Most of these should be just calling the methods of the sub-datasets. \r\n\r\nI'm assuming DatasetBuilder is better than the more specific `GeneratorBasedBuilder`, `BeamBasedBuilder`, etc....\r\n\r\nOne of the other problems is that the dataset size is unknown till you construct it (as you can pick the sub-datasets). Am hoping not to need to make changes to `nlp.load_dataset` just for this class.\r\n\r\nI'd appreciate it if anyone more familiar with nlp's internal workings could tell me if I'm on the right track!","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":177,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n From thinking upon @thomwolf 's suggestion, I've started experimenting:\r\n```python\r\nclass MultitaskDataset(DatasetBuilder):\r\n    def __init__(self, *args, **kwargs):\r\n        super(MultitaskDataset, self).__init__(*args, **kwargs)\r\n        self._datasets = kwargs.get(\"datasets\")\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(\r\n            description=_DESCRIPTION,\r\n            features=nlp.Features({\r\n                    \"source\": nlp.Value(\"string\"),\r\n                    \"target\": nlp.Sequence(nlp.Value(\"string\"))\r\n                })\r\n        )\r\n\r\n    def _get_common_splits(self):\r\n        '''Finds the common splits present in all self._datasets'''\r\n        min_set = None\r\n        for dataset in self._datasets:\r\n            if min_set != None:\r\n                min_set.intersection(set(dataset.keys()))\r\n            else:\r\n                min_set = set(dataset.keys())\r\n        return min_set\r\n\r\n....\r\n\r\n# Maybe this?:\r\nsquad = nlp.load_dataset(\"squad\")\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\",\"3.0.0\")\r\nmultitask_dataset = nlp.load_dataset(\r\n    'multitask_dataset',\r\n    datasets=[squad,cnn_dailymail], \r\n    k=1000, \r\n    t=2.0\r\n)\r\n\r\n```\r\n\r\nDoes anyone know what methods of `MultitaskDataset` I would need to implement? Maybe `as_dataset` and `download_and_prepare`? Most of these should be just calling the methods of the sub-datasets. \r\n\r\nI'm assuming DatasetBuilder is better than the more specific `GeneratorBasedBuilder`, `BeamBasedBuilder`, etc....\r\n\r\nOne of the other problems is that the dataset size is unknown till you construct it (as you can pick the sub-datasets). Am hoping not to need to make changes to `nlp.load_dataset` just for this class.\r\n\r\nI'd appreciate it if anyone more familiar with nlp's internal workings could tell me if I'm on the right track!","embeddings":[-0.0410289392,-0.3800130486,-0.0561361499,-0.0553337559,-0.226961717,0.0381454341,0.191163376,0.0285417549,0.3140310943,-0.1693242192,-0.2113295496,0.3226813376,-0.2117785513,0.3609280288,0.4108115137,-0.4081549644,0.0496115424,-0.2385492176,-0.3018552065,0.3816812336,0.0294328276,0.1105419919,-0.1815611124,0.1116595641,-0.2753484547,-0.1470191181,-0.3376018703,0.1057681218,0.1258859038,0.0001230787,0.2541147768,0.405015111,-0.0645343959,0.1640153676,-0.0001138703,-0.0289735682,0.2369827926,-0.0645472929,0.1149068475,-0.0750551447,0.2082991898,-0.4271639287,0.0591521002,-0.1675683409,0.1729619354,0.1567192823,0.1583682895,0.0400266796,0.3936338425,-0.2916638255,0.1032506153,0.2875022888,-0.1544683576,0.2761384845,-0.1251125187,-0.0476874821,0.036738351,0.2582830191,0.5602095127,-0.6138654351,0.0037755559,0.1158641875,0.0011100308,0.0048890761,0.1531963944,-0.0207958259,-0.3521731496,-0.2176881582,-0.4039084017,0.1862858385,-0.1451549232,-0.2017221302,-0.2375371158,-0.544827044,0.0046707969,0.0061735087,-0.2698778808,-0.0183589067,-0.2017983645,-0.0632458106,-0.1232680306,-0.0896949619,-0.0336662307,0.1552536786,0.3182489872,0.5842538476,0.0905896127,0.1673257947,0.3679713309,0.0413935184,-0.1508580148,-0.2804937363,0.327536732,0.1458220184,-0.3698496819,-0.3030605018,0.2041640729,-0.2367646396,0.2298245132,0.104572162,0.0904757529,0.1172205508,-0.1479838192,0.2619043887,0.3045757413,0.0461219847,-0.096149154,-0.0954477936,0.0346761346,-0.0859440193,0.1927355975,0.1121786162,0.1854983568,-0.0123846577,-0.3950420022,-0.1339060962,-0.1293433756,0.164744541,-0.2152846307,-0.4849033356,-0.1085741743,-0.1195227951,0.1177139729,-0.1045871302,-0.3091354072,0.0987556651,-0.0389532857,0.2612789869,-0.1374977529,0.1306159794,-0.071044147,0.0840983689,-0.4412369728,-0.0771046504,0.3059286177,0.1518349051,0.0807021558,0.1679016352,-0.0014322727,0.1236036271,0.3893814683,0.0155780558,0.0013976353,-0.0009416561,0.1396428198,-0.3161430657,0.0204230193,0.1589076817,-0.3474511504,-0.1902999729,-0.1193476021,-0.1685406864,0.140574187,0.032154683,-0.2005953342,-0.2385002077,-0.4981879592,0.7015710473,-0.0572152957,-0.0934591815,-0.1759139746,0.3037517369,-0.2464628518,-0.0800704435,0.2119654566,0.0576393493,0.0113504892,-0.3442129493,-0.1054180935,-0.0986813679,-0.0215478763,0.2500798404,-0.1620027721,0.2792710364,0.0527335331,0.2090146989,0.2798385322,-0.4821227789,-0.0626416206,0.2596439421,-0.0441140719,0.422999233,0.0580770448,0.1961356997,-0.0260135718,-0.1701483727,0.2901467979,0.9980714917,-0.3438392878,-0.0194317736,-0.1091452911,-0.3598763347,0.5201604962,0.325127393,0.1099428758,-0.2907424569,-0.1358843744,-0.0152131552,0.1217473596,0.0748822615,0.0830932632,-0.0717745125,-0.3480929434,0.0625114515,-0.2146702111,-0.1692674905,-0.3402894139,0.0233767964,0.0537420772,0.3371377587,0.2115899175,-0.1451852024,0.195847854,-0.2553427219,-0.1432838887,-0.3022666276,0.1462630332,-0.0699497685,0.0136631252,-0.2408796698,-0.0318978019,0.3690548241,0.1162230149,-0.1267782897,-0.4974001646,0.3005133867,-0.0742243752,0.0666137859,-0.0325902961,0.6585513353,-0.1506325752,-0.0851470754,0.1811940074,0.2812555134,-0.2942326665,0.201296702,0.303630203,0.1976070404,0.1517353952,0.0046875426,0.073297821,0.0522425622,0.0247967355,-0.2336047441,-0.0985799506,0.5100664496,-0.1613469124,0.452299118,-0.0280021038,0.0251580551,-0.1460136026,-0.0296826884,-0.2038654834,0.3801978528,0.2894101441,-0.1866005212,0.3278831244,-0.1623674035,-0.3643948436,0.1550501585,0.1312014014,0.0126263304,0.0546307974,-0.043492347,-0.0132482331,-0.279115051,-0.107753031,0.3511256278,0.680601418,0.2346359193,0.1918638498,0.0699950233,-0.2855549753,-0.0711565241,0.0013609271,0.2216838598,-0.0406082161,0.1673647016,0.0622549988,0.0424435064,0.0219647475,-0.1528270543,0.1556803733,-0.1852088571,-0.0936624333,0.1074038446,-0.2216831148,-0.5938890576,-0.1774952114,0.0022366841,-0.2878167629,-0.3085010648,0.103927806,0.0533200093,-0.1458798796,0.1620602161,0.2049497664,0.5456448197,-0.42458269,-0.2756488323,-0.0417231359,-0.3142536581,-0.1109102294,0.1089711413,0.4567675889,0.3351971805,0.4890900254,0.3100687563,-0.0816786662,0.0496938787,-0.4016466141,0.0475736223,-0.3254904449,0.2670509517,0.1173540875,-0.2622727752,0.1621409059,-0.3722044528,-0.0118275424,-0.087348938,-0.085972406,-0.0219025519,-0.0195316207,0.0591497868,-0.2100077868,-0.1561510116,-0.6618935466,-0.4537318647,0.3190036714,-0.2383414656,0.0822201148,0.0593159348,-0.2954932153,0.2223705649,-0.0649874806,0.0163610484,-0.1773646623,-0.0488452837,0.0293169599,-0.3534869552,-0.1780765206,-0.3723646402,-0.1382026076,0.2802268267,-0.0606195256,-0.0071239159,-0.2503638566,0.0998615101,-0.0790094286,0.1057046577,0.246562615,0.1785149276,-0.2391591221,0.1344874799,-0.0601592287,-0.2647292614,0.2468385696,0.2817914486,0.3843086064,0.1833924949,0.0599358752,0.1708058119,0.6879074574,0.2298003733,0.0656740442,0.1472759098,0.2549452186,0.019310737,0.1698751003,-0.2620752752,0.2082164139,-0.213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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"I think I would probably go for a `MultiDataset` wrapper around a list of `Dataset`.\r\n\r\nI'm not sure we need to give it `k` and `t` parameters at creation, it can maybe be something along the lines of:\r\n```python\r\nsquad = nlp.load_dataset(\"squad\")\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\",\"3.0.0\")\r\n\r\nmultitask_dataset = nlp.MultiDataset(squad, cnn_dm)\r\n\r\nbatch = multitask_dataset.sample(10, temperature=2.0, k=1000)\r\n```\r\n\r\nThe first proof-of-concept for multi-task datasets could definitely require that the provided datasets have the same name\/type for columns (if needed you easily rename\/cast a column prior to instantiating the `MultiDataset`).\r\n\r\nIt's good to think about it for some time though and don't overfit too much on the T5 examples (in particular for the ways\/kwargs for sampling among datasets).","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":114,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n I think I would probably go for a `MultiDataset` wrapper around a list of `Dataset`.\r\n\r\nI'm not sure we need to give it `k` and `t` parameters at creation, it can maybe be something along the lines of:\r\n```python\r\nsquad = nlp.load_dataset(\"squad\")\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\",\"3.0.0\")\r\n\r\nmultitask_dataset = nlp.MultiDataset(squad, cnn_dm)\r\n\r\nbatch = multitask_dataset.sample(10, temperature=2.0, k=1000)\r\n```\r\n\r\nThe first proof-of-concept for multi-task datasets could definitely require that the provided datasets have the same name\/type for columns (if needed you easily rename\/cast a column prior to instantiating the `MultiDataset`).\r\n\r\nIt's good to think about it for some time though and don't overfit too much on the T5 examples (in particular for the ways\/kwargs for sampling among datasets).","embeddings":[-0.0309596602,-0.4186734557,-0.0396580435,-0.0468707941,-0.2247027457,0.0937634408,0.1678373814,0.0375866704,0.2795366049,-0.1670990288,-0.2208789289,0.3342223167,-0.2148639411,0.3291554749,0.4196427464,-0.4405975938,0.0240546688,-0.2191532105,-0.2579804361,0.3798784018,0.0655685812,0.1019967496,-0.1636252403,0.0597926565,-0.2679932714,-0.1647084355,-0.3614471853,0.1224154383,0.1369815916,-0.0105154719,0.2192510962,0.3853777051,-0.0239703581,0.1168468222,-0.0001133946,-0.0766079575,0.1594891995,-0.1200244948,0.0910891593,0.0065836376,0.1546203941,-0.427708894,0.0729347542,-0.1584373564,0.1449337304,0.15658544,0.1450680494,-0.0069006812,0.4177408218,-0.2556488514,0.0976841375,0.3037799597,-0.1650875509,0.2707971334,-0.1420085579,-0.0669919997,0.0279299561,0.2208573371,0.614895761,-0.5899104476,0.0041678394,0.0785674825,-0.0143497717,0.0074051167,0.1554833502,-0.0298841391,-0.3357186317,-0.2630695403,-0.3932988048,0.2161172032,-0.130057469,-0.1769689173,-0.2505916059,-0.5384424329,0.0170675442,0.0354164727,-0.2903538942,0.0315727927,-0.2046495676,-0.0545909144,-0.1233536974,-0.1029213518,-0.0065230848,0.1564510912,0.3066310883,0.571914196,0.1277765781,0.171288386,0.352591455,0.0238961987,-0.1490521431,-0.2876000404,0.2940413952,0.1621154547,-0.4089529216,-0.3507789373,0.1309266686,-0.2493540794,0.2844475806,0.0881018192,0.118369028,0.0559124313,-0.1370865554,0.2888890803,0.3011126518,0.0355755202,-0.0766883716,-0.1267349124,0.0218941979,-0.1330881119,0.1889595985,0.1262667626,0.1817833036,-0.0086608129,-0.3749386966,-0.1357930005,-0.1604589373,0.173871994,-0.1611000299,-0.5166141391,-0.110078156,-0.0932472199,0.0721659958,-0.0924680829,-0.2943404615,0.0761008114,-0.0488542356,0.2247578651,-0.0882168263,0.0521588661,-0.0636374578,0.0874368623,-0.4271279275,-0.0311003216,0.3111233115,0.0953136384,0.0791382715,0.1683205217,-0.0090656439,0.107864365,0.4213511944,0.0207369979,-0.0052913204,-0.0257206541,0.2052460611,-0.2978515923,0.0131257866,0.1394484788,-0.3443307877,-0.1233699843,-0.1408030689,-0.1617729217,0.1051673219,0.0261682812,-0.2270695865,-0.2634857297,-0.4787130952,0.7096787691,-0.0163178314,-0.0407355018,-0.1800363213,0.2876241207,-0.2645999789,-0.1131608337,0.2292123139,0.1003069729,-0.0439029336,-0.2998158038,-0.0713219419,-0.0835302398,-0.0897591934,0.2788395584,-0.1998572201,0.2227457315,0.0680124685,0.1548843086,0.3027401865,-0.4629935324,-0.0365784392,0.269169122,-0.0357774608,0.3846664429,0.0656009838,0.2249258608,0.0193822924,-0.2001308948,0.2639879584,1.0421270132,-0.4086365104,0.0009421139,-0.0963041261,-0.3568603396,0.5225280523,0.3448227048,0.0528929122,-0.289357245,-0.1473952085,-0.0054200753,0.0723923892,0.0581510626,0.0951675251,-0.0369884484,-0.3416343629,0.0695472807,-0.2200360298,-0.1500298381,-0.4430118203,0.0194523167,0.0445789285,0.3434177339,0.2352165729,-0.1388451904,0.2276782244,-0.2906135023,-0.1399959624,-0.3124925792,0.1317205429,-0.0509352908,0.0310703609,-0.220759511,-0.0353601761,0.3621313572,0.1493374705,-0.1270153522,-0.480266124,0.3278311789,-0.0759468377,0.036641106,-0.0272858683,0.6227853298,-0.1506923288,-0.0730190575,0.1906048506,0.2668913901,-0.2859715223,0.2114320993,0.3612276018,0.2709768116,0.1712347567,-0.03409243,0.095298022,0.0238095596,-0.0051739481,-0.2202403247,-0.0857056677,0.5233914852,-0.1781087816,0.4187562466,-0.0450783186,0.0366004072,-0.1355301589,-0.018147558,-0.1988152266,0.4192889631,0.2726354301,-0.1745443195,0.3594210446,-0.1392042488,-0.3428289294,0.160084784,0.1558927,0.0385063365,0.0907846987,-0.003107762,-0.0485005416,-0.2614943981,-0.1089155227,0.3499152958,0.6467790008,0.2586946785,0.1375753731,0.0132202739,-0.2505892217,-0.0694816485,0.0318481065,0.1693363041,-0.0284860265,0.1438650191,0.061590679,0.0490800515,0.0293323211,-0.1715495288,0.1837420762,-0.1622173339,-0.0755687952,0.0723839849,-0.1664850414,-0.5669133067,-0.2283149213,0.0277331453,-0.3815065026,-0.2889538109,0.1047791615,0.0514129698,-0.1202398837,0.1866274923,0.2017738074,0.572204411,-0.3877091706,-0.2930240035,-0.0291458052,-0.3389903903,-0.1145647168,0.112439476,0.4557248652,0.2603594363,0.4842223227,0.3259260952,-0.0339717716,0.0463758931,-0.398424834,0.0176438,-0.3012909591,0.2676162124,0.073774904,-0.1858690083,0.1533944756,-0.406892091,-0.0056130914,-0.0210666899,-0.0708756596,-0.0528934598,0.0044161165,0.0954201221,-0.1834784299,-0.1902592927,-0.6412388086,-0.4301627874,0.26739344,-0.260163337,0.0865977556,0.0140416119,-0.2555756271,0.1796784401,-0.1078665927,-0.0026081605,-0.1634770185,-0.0389524139,0.0466123745,-0.3563415408,-0.1756981164,-0.3697786331,-0.1142726988,0.2460547686,-0.0694768429,-0.0248483159,-0.2876038253,0.0945280716,-0.1120574623,0.0530560985,0.2090707868,0.1939486712,-0.2207102031,0.1345973313,-0.0736984089,-0.2940365076,0.230275467,0.2727073729,0.3875804543,0.1529555172,0.0712210163,0.1755918711,0.7356215119,0.2542084754,0.0182265267,0.1526494622,0.2519257665,-0.0085175931,0.1782764047,-0.2468549311,0.1766627729,-0.1547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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"The problem with changing `k` and `t` per sampling is that you'd have to somehow remember which examples you'd already returned while re-weighting the remaining examples based on the new `k` and `t`values. It seems possible but complicated (I can't really see a reason why you'd want to change the weighting of datasets after you constructed the multidataset).\r\n\r\nWouldn't it be convenient if it implemented the dataset interface? Then if someone has code using a single nlp dataset, they can replace it with a multitask combination of more datasets without having to change other code. We would at least need to be able to pass it into a `DataLoader`.\r\n\r\n","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":109,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n The problem with changing `k` and `t` per sampling is that you'd have to somehow remember which examples you'd already returned while re-weighting the remaining examples based on the new `k` and `t`values. It seems possible but complicated (I can't really see a reason why you'd want to change the weighting of datasets after you constructed the multidataset).\r\n\r\nWouldn't it be convenient if it implemented the dataset interface? Then if someone has code using a single nlp dataset, they can replace it with a multitask combination of more datasets without having to change other code. We would at least need to be able to pass it into a `DataLoader`.\r\n\r\n","embeddings":[-0.0834357738,-0.3772905171,-0.0540124886,-0.0832781196,-0.2122467607,0.016944591,0.1334990859,0.0681641847,0.3124590814,-0.0252599195,-0.2377852947,0.3599816561,-0.1587046683,0.3497785032,0.3823591769,-0.4798976183,-0.0307444744,-0.1330458373,-0.3688006699,0.3823723197,0.0409010462,0.0993345529,-0.1226546094,0.0074622524,-0.2362718433,-0.1522186995,-0.3432965577,0.0929011106,0.1845614463,-0.0550318137,0.1963454932,0.4499551356,-0.0172803458,0.0523207374,-0.0001107237,-0.0873194411,0.1317604184,-0.1200200543,0.0498344786,0.0378307067,0.2165661305,-0.4117277861,-0.0697145388,-0.1880381107,0.1087680385,0.2151244581,0.183059305,0.0244122688,0.4155212045,-0.3210864365,0.1293699592,0.3268467784,-0.2219879478,0.2345312536,-0.0946439728,-0.1533048153,0.0941295028,0.3127886057,0.4916836321,-0.5477143526,-0.061179392,0.1415119916,0.0077215391,0.0596176833,0.1025726348,-0.0515166223,-0.3251841962,-0.2065102458,-0.3548207283,0.2940816879,-0.1811466068,-0.1491725594,-0.1987767816,-0.5856713653,0.0600595288,-0.0108269686,-0.2997102737,0.020461211,-0.149675414,-0.0127926776,-0.1295457035,-0.0983141139,0.0747852847,0.0990781561,0.3274924159,0.560352385,0.0938607454,0.183635056,0.3918946385,0.0773890615,-0.2122483999,-0.2460267693,0.2851282358,0.0963052288,-0.282671541,-0.3462053537,0.1630013734,-0.2639792562,0.2329585999,0.0578402653,0.167758882,0.1127669662,-0.137882337,0.2494637668,0.2821424901,0.0598511584,0.010651404,-0.1180342063,0.0149835404,-0.057337299,0.1585505754,0.0927682146,0.2161785364,-0.0449696481,-0.4050146937,-0.0826194361,-0.2754174471,0.1954955161,-0.2060203105,-0.5297424793,-0.0186265651,-0.114803344,0.0239045992,-0.1002877131,-0.3187436461,0.1601452827,-0.0565391108,0.1747000515,-0.1174471155,0.0728757977,-0.0856354162,0.0885189176,-0.4986366928,0.0251944605,0.2598644197,0.0800495073,0.162202552,0.1625870764,-0.0261631124,0.1506989747,0.3910187185,0.0596011877,0.0018361512,-0.0581313297,0.1590105444,-0.2949698567,-0.042152863,0.1823380589,-0.4246416986,-0.1548048556,-0.1469173878,-0.076041542,0.1608907133,0.0455429628,-0.244696945,-0.2981191278,-0.4465657175,0.7699325085,-0.0095635736,-0.1129112318,-0.1095577925,0.2079466134,-0.3065616786,-0.0800890848,0.2194667459,0.0320920646,-0.0190592427,-0.4033974111,-0.0422706418,-0.0371791124,-0.1039554253,0.2644662857,-0.2160887867,0.1813715249,0.0740556717,0.1739475131,0.2999924123,-0.4679256976,-0.0848970637,0.2344106138,-0.0360876322,0.3289677501,0.0849847868,0.1241306141,0.0519661009,-0.1715914905,0.1760984659,0.9709120393,-0.3548180759,0.0243716575,-0.1063789576,-0.3863515556,0.5253027081,0.2912895679,0.0707936659,-0.2695538402,-0.1268907189,0.004664849,0.0922796503,0.0185630433,0.0876701027,-0.0491235293,-0.330820024,0.0091974754,-0.2163795233,-0.1552149802,-0.3733976483,0.0538267419,-0.0618969314,0.3879180849,0.3986713886,-0.1182984784,0.2000267953,-0.3578913212,-0.0335234813,-0.2823462188,0.1489843577,-0.0139880786,0.0484355241,-0.220911622,-0.0495129675,0.2356532812,0.1350265741,-0.1555173248,-0.4074779451,0.3045088649,0.0201744139,-0.0015043471,-0.0542013943,0.5833072662,-0.1648832411,-0.1087331846,0.2087197006,0.3047441244,-0.3626170158,0.294107765,0.310453862,0.1920404434,0.158066541,0.0199261736,0.157711491,-0.0145369517,-0.0647680387,-0.1608451307,-0.0415621698,0.6416841745,-0.0483003706,0.3482078016,-0.0292414036,-0.0311008077,-0.1747988015,-0.1673587859,-0.2596576512,0.3309375048,0.2930160165,-0.2784144878,0.3624516129,-0.1274982095,-0.4090746045,0.1585979015,0.1983101666,0.0533816777,0.1220826581,0.0116764652,-0.0540280864,-0.3014111221,-0.1033004671,0.344779551,0.6520264149,0.2875113785,0.168321982,0.0216041952,-0.2816530168,-0.0925326347,0.0023980346,0.1340303868,-0.0298968572,0.182002008,0.1024383008,0.0314744525,-0.0856320933,-0.0643137246,0.1644387841,-0.2171367258,-0.03231683,0.0398653746,-0.178645879,-0.5128598213,-0.2909932137,0.0355958454,-0.3008249104,-0.3023713231,0.1804727167,0.0911055058,-0.2213292569,0.2116441429,0.2378734052,0.6204200983,-0.3974946439,-0.1298799217,-0.109520629,-0.3464152813,-0.1027458161,0.1299117357,0.3555285335,0.2745725214,0.4790076911,0.2562711835,-0.0584109835,0.1019947752,-0.4040745497,0.0262862705,-0.319781512,0.2502519786,0.1185360178,-0.1891649663,0.1525095254,-0.3981902003,0.0118647767,-0.1402017325,-0.0298847072,-0.0593073927,0.0118865576,0.1486317068,-0.202735275,-0.2027551234,-0.608305335,-0.4239632785,0.2456141859,-0.2116802186,0.0454282649,0.0518572964,-0.2142136395,0.1482136697,-0.1654004604,0.0154590076,-0.1877848804,-0.1026330888,-0.0060610902,-0.3906424344,-0.10505227,-0.5042768717,-0.1248725355,0.2693509758,-0.1049095318,-0.1020210385,-0.3205795288,0.05710008,-0.0957068577,0.0169347022,0.2777613997,0.1074365526,-0.2277356386,0.034652058,-0.069547154,-0.2141720951,0.2184725702,0.4109443128,0.387539953,0.1111993939,0.003796594,0.1425023228,0.7288064957,0.2042254061,-0.0062154955,0.2417244911,0.2230892926,0.0590062328,0.1169358939,-0.1488067657,0.2164951265,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"A very janky (but working) implementation of  `multitask_dataset.sample()` could be something like this:\r\n```python\r\nimport nlp\r\nimport torch\r\n\r\nclass MultiDataset():\r\n    def __init__(self, *args, temperature=2.0, k=1000, maximum=None, scale=1):\r\n        self.datasets = args\r\n        self._dataloaders = {}\r\n        for split in self._get_common_splits():\r\n            split_datasets = [ds[split] for ds in self.datasets]\r\n            mixing_rates = self._calc_mixing_rates(split_datasets,temperature, k, maximum, scale)\r\n            weights = []\r\n            for i in range(len(self.datasets)):\r\n                weights += [mixing_rates[i]]*len(self.datasets[i][split])\r\n            self._dataloaders[split] = torch.utils.data.DataLoader(torch.utils.data.ConcatDataset(split_datasets),\r\n                                                        sampler=torch.utils.data.sampler.WeightedRandomSampler(\r\n                                                            num_samples=len(weights),\r\n                                                            weights = weights,\r\n                                                            replacement=True),\r\n                                                        shuffle=False)\r\n\r\n    def _get_common_splits(self):\r\n        '''Finds the common splits present in all self.datasets'''\r\n        min_set = None\r\n        for dataset in self.datasets:\r\n            if min_set != None:\r\n                min_set.intersection(set(dataset.keys()))\r\n            else:\r\n                min_set = set(dataset.keys())\r\n        return min_set\r\n\r\n\r\n    def _calc_mixing_rates(self,datasets, temperature=2.0, k=1000, maximum=None, scale=1):\r\n       '''Work out the weighting of each dataset based on t and k'''\r\n        mixing_rates = []\r\n        for dataset in datasets:\r\n            rate = len(dataset)\r\n            rate *= scale\r\n            if maximum:\r\n                rate = min(rate, maximum)\r\n            if temperature != 1.0:\r\n                rate = rate ** (1.0\/temperature)\r\n            mixing_rates.append(rate)\r\n        return mixing_rates\r\n\r\n    def sample(self,n,split):\r\n        batch = []\r\n        for example in self._dataloaders[split]:\r\n            batch.append(example)\r\n            n -= 1\r\n            if n == 0:\r\n                return batch\r\n\r\n\r\ndef flatten(dataset,flatten_fn):\r\n    for k in dataset.keys():\r\n        if isinstance(dataset[k],nlp.Dataset):\r\n            dataset[k] = dataset[k].map(flatten_fn,remove_columns=dataset[k].column_names)\r\n\r\n# Squad\r\ndef flatten_squad(example):\r\n    return {\"source\": \"squad context: \" + example['context'] + \" question: \" + example['question'],\"target\":example[\"answers\"][\"text\"]}\r\nsquad = nlp.load_dataset(\"squad\")\r\nflatten(squad,flatten_squad)\r\n\r\n# CNN_DM\r\ndef flatten_cnn_dm(example):\r\n    return {\"source\": \"cnn_dm: \" + example['article'],\"target\":[example[\"highlights\"]]}\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\", \"3.0.0\")\r\nflatten(cnn_dm,flatten_cnn_dm)\r\n\r\nmultitask_dataset = MultiDataset(squad, cnn_dm)\r\nbatch = multitask_dataset.sample(100,\"train\")\r\n```\r\n\r\nThere's definitely a more sensible way than embedding `DataLoader`s inside. ","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":231,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n A very janky (but working) implementation of  `multitask_dataset.sample()` could be something like this:\r\n```python\r\nimport nlp\r\nimport torch\r\n\r\nclass MultiDataset():\r\n    def __init__(self, *args, temperature=2.0, k=1000, maximum=None, scale=1):\r\n        self.datasets = args\r\n        self._dataloaders = {}\r\n        for split in self._get_common_splits():\r\n            split_datasets = [ds[split] for ds in self.datasets]\r\n            mixing_rates = self._calc_mixing_rates(split_datasets,temperature, k, maximum, scale)\r\n            weights = []\r\n            for i in range(len(self.datasets)):\r\n                weights += [mixing_rates[i]]*len(self.datasets[i][split])\r\n            self._dataloaders[split] = torch.utils.data.DataLoader(torch.utils.data.ConcatDataset(split_datasets),\r\n                                                        sampler=torch.utils.data.sampler.WeightedRandomSampler(\r\n                                                            num_samples=len(weights),\r\n                                                            weights = weights,\r\n                                                            replacement=True),\r\n                                                        shuffle=False)\r\n\r\n    def _get_common_splits(self):\r\n        '''Finds the common splits present in all self.datasets'''\r\n        min_set = None\r\n        for dataset in self.datasets:\r\n            if min_set != None:\r\n                min_set.intersection(set(dataset.keys()))\r\n            else:\r\n                min_set = set(dataset.keys())\r\n        return min_set\r\n\r\n\r\n    def _calc_mixing_rates(self,datasets, temperature=2.0, k=1000, maximum=None, scale=1):\r\n       '''Work out the weighting of each dataset based on t and k'''\r\n        mixing_rates = []\r\n        for dataset in datasets:\r\n            rate = len(dataset)\r\n            rate *= scale\r\n            if maximum:\r\n                rate = min(rate, maximum)\r\n            if temperature != 1.0:\r\n                rate = rate ** (1.0\/temperature)\r\n            mixing_rates.append(rate)\r\n        return mixing_rates\r\n\r\n    def sample(self,n,split):\r\n        batch = []\r\n        for example in self._dataloaders[split]:\r\n            batch.append(example)\r\n            n -= 1\r\n            if n == 0:\r\n                return batch\r\n\r\n\r\ndef flatten(dataset,flatten_fn):\r\n    for k in dataset.keys():\r\n        if isinstance(dataset[k],nlp.Dataset):\r\n            dataset[k] = dataset[k].map(flatten_fn,remove_columns=dataset[k].column_names)\r\n\r\n# Squad\r\ndef flatten_squad(example):\r\n    return {\"source\": \"squad context: \" + example['context'] + \" question: \" + example['question'],\"target\":example[\"answers\"][\"text\"]}\r\nsquad = nlp.load_dataset(\"squad\")\r\nflatten(squad,flatten_squad)\r\n\r\n# CNN_DM\r\ndef flatten_cnn_dm(example):\r\n    return {\"source\": \"cnn_dm: \" + example['article'],\"target\":[example[\"highlights\"]]}\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\", \"3.0.0\")\r\nflatten(cnn_dm,flatten_cnn_dm)\r\n\r\nmultitask_dataset = MultiDataset(squad, cnn_dm)\r\nbatch = multitask_dataset.sample(100,\"train\")\r\n```\r\n\r\nThere's definitely a more sensible way than embedding `DataLoader`s inside. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"Good spot! Here are my thoughts:\r\n\r\n- Aside: Adding `MultitaskModel` to transformers might be a thing to raise - even though having task-specific heads has become unfashionable in recent times in favour of text-to-text type models.\r\n- Adding the task name as an extra field also seems useful for these kind of models which have task-specific heads\r\n- There is some validation of our approach that the user should be expected to `map` datasets into a common form.\r\n- The size-proportional sampling (also called \"Examples-proportional mixing\") used here doesn't perform too badly in the T5 paper (it's comparable to temperature-scaled mixing in many cases but less flexible. This is only reasonable with a `K` maximum size parameter to prevent very large datasets dominating). This might be good for a first prototype using:\r\n    ```python\r\n    def __iter__(self):\r\n        \"\"\"\r\n        For each batch, sample a task, and yield a batch from the respective\r\n        task Dataloader.\r\n\r\n        We use size-proportional sampling, but you could easily modify this\r\n        to sample from some-other distribution.\r\n        \"\"\"\r\n        task_choice_list = []\r\n        for i, task_name in enumerate(self.task_name_list):\r\n            task_choice_list += [i] * self.num_batches_dict[task_name]\r\n        task_choice_list = np.array(task_choice_list)\r\n        np.random.shuffle(task_choice_list)\r\n\r\n        dataloader_iter_dict = {\r\n            task_name: iter(dataloader) \r\n            for task_name, dataloader in self.dataloader_dict.items()\r\n        }\r\n        for task_choice in task_choice_list:\r\n            task_name = self.task_name_list[task_choice]\r\n            yield next(dataloader_iter_dict[task_name])    \r\n    ```\r\n    We'd just need to pull samples from the raw datasets and not from `DataLoader`s for each task. We can assume the user has done `dataset.shuffle()` if they want to.\r\n\r\n    Other sampling methods can later be implemented by changing how the `task_choice_list` is generated. This should allow more flexibility and not tie us to specific methods for sampling among datasets.\r\n","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":264,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n Good spot! Here are my thoughts:\r\n\r\n- Aside: Adding `MultitaskModel` to transformers might be a thing to raise - even though having task-specific heads has become unfashionable in recent times in favour of text-to-text type models.\r\n- Adding the task name as an extra field also seems useful for these kind of models which have task-specific heads\r\n- There is some validation of our approach that the user should be expected to `map` datasets into a common form.\r\n- The size-proportional sampling (also called \"Examples-proportional mixing\") used here doesn't perform too badly in the T5 paper (it's comparable to temperature-scaled mixing in many cases but less flexible. This is only reasonable with a `K` maximum size parameter to prevent very large datasets dominating). This might be good for a first prototype using:\r\n    ```python\r\n    def __iter__(self):\r\n        \"\"\"\r\n        For each batch, sample a task, and yield a batch from the respective\r\n        task Dataloader.\r\n\r\n        We use size-proportional sampling, but you could easily modify this\r\n        to sample from some-other distribution.\r\n        \"\"\"\r\n        task_choice_list = []\r\n        for i, task_name in enumerate(self.task_name_list):\r\n            task_choice_list += [i] * self.num_batches_dict[task_name]\r\n        task_choice_list = np.array(task_choice_list)\r\n        np.random.shuffle(task_choice_list)\r\n\r\n        dataloader_iter_dict = {\r\n            task_name: iter(dataloader) \r\n            for task_name, dataloader in self.dataloader_dict.items()\r\n        }\r\n        for task_choice in task_choice_list:\r\n            task_name = self.task_name_list[task_choice]\r\n            yield next(dataloader_iter_dict[task_name])    \r\n    ```\r\n    We'd just need to pull samples from the raw datasets and not from `DataLoader`s for each task. We can assume the user has done `dataset.shuffle()` if they want to.\r\n\r\n    Other sampling methods can later be implemented by changing how the `task_choice_list` is generated. This should allow more flexibility and not tie us to specific methods for sampling among datasets.\r\n","embeddings":[-0.0052698166,-0.4564427137,-0.0409008488,-0.033154469,-0.2421440929,0.0899885297,0.2262675017,0.0337931812,0.3455826938,-0.1832486987,-0.1746232957,0.2782377899,-0.2171302736,0.3147800267,0.3954238594,-0.4474587739,0.0529760905,-0.2687866986,-0.2423506677,0.376804173,0.0238288157,0.1376441717,-0.1650483012,0.1027793512,-0.3490164876,-0.1493272185,-0.2910457551,0.1054907218,0.1436435431,0.0494331829,0.244187519,0.3417589068,-0.0319728144,0.1957802624,-0.0001177549,-0.0263047367,0.2295914143,-0.0533907004,0.1380272061,-0.0514310077,0.1381418407,-0.3776621819,0.0677296743,-0.1242497787,0.1998292506,0.2635385692,0.1815100163,0.1306600869,0.3444681168,-0.3399113119,0.0587665401,0.2794277668,-0.2045923024,0.3041356802,-0.081949845,0.0475111082,0.0006843437,0.2406243682,0.6316853166,-0.6122648716,0.0095472531,0.1181457564,0.0206318013,-0.0755933002,0.2131651789,-0.0758276805,-0.308111757,-0.2063162178,-0.4016385972,0.1982733011,-0.1354376078,-0.126031056,-0.1750893742,-0.572663486,-0.0965319499,0.0781988278,-0.2844801545,0.0156463217,-0.1918295175,-0.048152294,-0.132640928,-0.1277317256,-0.0317760147,0.1317657679,0.3510734737,0.622261703,0.1479907334,0.155235827,0.3822132051,0.1009348929,-0.0878843218,-0.2404812276,0.3139014244,0.1148010492,-0.3939471841,-0.362313807,0.204201147,-0.2805657089,0.2293514609,0.0694336668,0.166472435,0.0844727755,-0.1230995134,0.2612062097,0.3129147887,0.0046515092,-0.1349536479,-0.097687386,0.0522312447,-0.0729626864,0.2354493439,0.1074520722,0.1639313996,-0.0166475121,-0.4331251085,-0.0816401616,-0.135004431,0.160520345,-0.1213202775,-0.5605207086,-0.0321442336,-0.1420903504,0.1316174716,-0.0990331993,-0.3058051169,0.1391584426,-0.0849037021,0.2681118548,-0.0944984704,0.1329028755,-0.0396047086,0.0485027283,-0.4495005608,-0.0649096295,0.3281109035,0.213588044,0.0528768487,0.1371210515,-0.0014478324,0.0744328275,0.3536281586,0.0548316017,-0.0100005325,0.0429559201,0.0779072419,-0.3095371127,-0.0271462183,0.2130467594,-0.3466988802,-0.1356944293,-0.1648989022,-0.2356415987,0.1343441159,-0.0148965195,-0.1898042858,-0.2510464787,-0.5263698697,0.6592676044,-0.0305614118,-0.0570743084,-0.1278265864,0.325268209,-0.2402964383,-0.0782807469,0.1297526062,0.0458408371,0.0346587524,-0.3742161393,-0.0962451994,-0.0856850073,-0.0403569527,0.3011822701,-0.1886329651,0.2752712369,0.0955034196,0.1311880052,0.2198126018,-0.4872383773,-0.030618431,0.293540895,-0.0262911692,0.378112644,0.0742825419,0.2482448965,-0.0497759134,-0.1843891442,0.2528439164,0.9806962609,-0.3445678055,0.0078132134,-0.1259717643,-0.3848104477,0.5938204527,0.3565863967,0.1247872785,-0.2823462188,-0.135115698,0.0828916058,0.0904686525,0.1159305573,0.0790559202,-0.0920598134,-0.3260626495,0.0239144992,-0.1623580605,-0.1164990962,-0.4359433651,0.0106736897,-0.0292150863,0.359777391,0.2142833769,-0.1092868671,0.271482259,-0.279142499,-0.1207623109,-0.302593559,0.07068979,-0.1252287775,-0.0336627178,-0.2842171788,-0.0142541584,0.2878228128,0.1741374135,-0.0913474709,-0.4625684023,0.3106147945,-0.1135091856,0.0794752613,-0.0783736259,0.671086669,-0.1330433488,-0.0517084524,0.1867444366,0.2545598149,-0.305372268,0.260253489,0.3174130917,0.3043528199,0.1962373555,-0.0506784692,0.0802746788,0.0710069239,-0.0063327975,-0.2542899549,-0.1688382477,0.4554283321,-0.1798306555,0.4642221034,-0.0735618845,-0.0063692285,-0.2080316544,-0.044366017,-0.2130458355,0.4461049139,0.2914744914,-0.1696991026,0.2145278305,-0.1094082892,-0.3521736264,0.1489396095,0.1390203834,0.0311614908,0.0351359546,0.0125844833,0.0066877385,-0.2945314348,-0.114424333,0.2900375724,0.5758755803,0.2188544869,0.1371133924,0.0179561619,-0.2088220268,-0.0903355405,0.0136938551,0.2752796113,0.016923096,0.1939088106,0.0156420991,0.0645550787,0.0424579158,-0.2073298991,0.1576102227,-0.2026110142,-0.0598201603,0.0319160037,-0.1862059683,-0.5582320094,-0.2554792762,-0.0109240115,-0.367121309,-0.2729380131,0.1540726423,0.0434343591,-0.1620724499,0.1376047432,0.2474056929,0.6375625134,-0.464548409,-0.2303404957,-0.1406392306,-0.3725031912,-0.0714472756,0.0738391206,0.4597982168,0.286105603,0.3831216097,0.3352016807,-0.0749679133,0.1093754768,-0.4088929594,0.0103904335,-0.281027019,0.2483323216,0.0805492625,-0.2144775093,0.174296543,-0.3981611431,-0.063097015,-0.0273258686,-0.0667391717,-0.0043625529,-0.0064716209,0.0804154351,-0.19725658,-0.1686619967,-0.6268009543,-0.4757328629,0.3247357011,-0.28774333,0.0625835881,0.0964683592,-0.2704408765,0.1228222027,-0.0775507092,-0.0060857539,-0.1258472651,-0.0420749076,0.0276470743,-0.3368201256,-0.1450543702,-0.3748605549,-0.0966837406,0.2254825532,-0.0754836425,-0.0141698429,-0.1489147395,0.1013894454,-0.1627754867,0.1472621113,0.1359443963,0.1742740273,-0.2007130235,0.1626321971,-0.0187071189,-0.3295623362,0.2292136699,0.3730612397,0.4572847784,0.1779815704,0.0543223433,0.164114818,0.7202286124,0.2120819688,0.1129700169,0.1207047924,0.3397210538,-0.0986955017,0.165661037,-0.2394076139,0.2550593317,-0.17682717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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"Another thought: Multitasking over benchmarks (represented as Meta-datasets in nlp) is probably a common use case. Would be nice to pass an entire benchmark to our `MultiDataset` wrapper rather than having to pass individual components.","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":35,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n Another thought: Multitasking over benchmarks (represented as Meta-datasets in nlp) is probably a common use case. Would be nice to pass an entire benchmark to our `MultiDataset` wrapper rather than having to pass individual components.","embeddings":[-0.0656119063,-0.3723547161,-0.0681932122,-0.0492688045,-0.2852725089,0.1337666065,0.2164802402,0.1206526011,0.3021679819,-0.142117992,-0.1687693298,0.3203654289,-0.1637993902,0.3486942053,0.4185857177,-0.4121801853,0.0316882245,-0.2107659131,-0.2145602852,0.424690038,0.0625791103,0.0833854899,-0.1656299382,0.1017269045,-0.296767503,-0.1877516061,-0.3303469718,0.1228973418,0.1802742928,-0.0067459373,0.2167422026,0.3386645317,-0.005248019,0.0762604773,-0.0001127535,-0.079764612,0.2180484086,-0.0533743948,0.1031291857,-0.0139625371,0.193061769,-0.3639018834,0.0466753058,-0.1068037078,0.16875875,0.1935687959,0.1722348034,-0.0764045641,0.3412645757,-0.25450176,0.1023415551,0.3156677186,-0.2399112135,0.2976697683,-0.1113831922,-0.0868199468,0.0119753554,0.2438913584,0.6718504429,-0.627389431,-0.0600717738,0.0572424307,-0.0323867463,0.0497779027,0.0926750675,-0.1301555783,-0.3021051586,-0.2108954191,-0.3377223611,0.2510986626,-0.1597300768,-0.1370611787,-0.2288980633,-0.5552752614,-0.0130585609,0.0512918793,-0.3080892265,0.0318329036,-0.1725958437,-0.0062471372,-0.1011449695,-0.1240547299,0.0575542711,0.0890993401,0.3357074857,0.5584859252,0.1207945719,0.1740791351,0.3698949516,0.0731535032,-0.0847848877,-0.2591187954,0.274199307,0.1001640707,-0.415807724,-0.3103589714,0.1395327002,-0.2330120653,0.3023065329,0.0970019847,0.1351532787,0.1109842435,-0.1236224025,0.2736952007,0.3402852118,0.0342561081,-0.0776993334,-0.1692268997,0.0107862754,-0.1416548043,0.1802741289,0.1135542169,0.2074791193,0.0258042999,-0.4392684698,-0.1369123459,-0.2284797579,0.1634885073,-0.2595624626,-0.5688914061,-0.1121168733,-0.0956882536,0.0620818324,-0.1388763189,-0.2614015639,0.1391816139,-0.0921640471,0.269533962,-0.0688549802,0.0062881103,-0.0510915071,0.0879677236,-0.4555426836,-0.0101290494,0.3323619366,0.1006498411,0.0862040222,0.1457322687,-0.0197839607,0.0622323789,0.3870140612,0.0119770197,-0.0044591278,-0.0121356696,0.1867395937,-0.314055413,0.0292185564,0.1831528246,-0.3368544579,-0.0813517347,-0.1455326527,-0.148672998,0.1425740868,0.0321329869,-0.225368157,-0.2860387266,-0.5482085943,0.6673361063,-0.0112446342,-0.0629958063,-0.1631250829,0.3023531139,-0.3023776412,-0.0930897295,0.1709520966,0.0131647279,0.0217474457,-0.2774837613,-0.1617570072,-0.070432432,-0.1019014791,0.3153512776,-0.192191571,0.3084415197,0.0420111939,0.1713878959,0.2346699983,-0.484659791,-0.0329162888,0.331353277,-0.0713307858,0.4018953145,0.0502471477,0.226153627,0.055644203,-0.1782498807,0.2328609526,0.9699891806,-0.3880668879,0.0095262546,-0.0859299898,-0.3920594156,0.543384254,0.3569907248,0.059594553,-0.2415657938,-0.1471162885,-0.01227789,0.0871227756,0.1098291799,0.0995172784,-0.0751654282,-0.420724839,0.0233743042,-0.22361058,-0.1986405104,-0.431843549,0.0181785617,0.0211398825,0.2296025604,0.3293683529,-0.1370198131,0.2478265911,-0.279499054,-0.0465501472,-0.375577569,0.1250411868,-0.0515294671,0.0089130327,-0.202386722,-0.0481616594,0.3567742109,0.1752245128,-0.1295220703,-0.3912127018,0.307117492,-0.0529440902,0.0381249189,-0.0405544713,0.6141855717,-0.2152328938,-0.057150811,0.1891903877,0.2451416552,-0.2727411985,0.2884795964,0.3536682427,0.2753916681,0.1926756799,-0.0359169692,0.0541778505,0.0602812544,-0.0329499058,-0.2539368272,-0.0517228842,0.4938859344,-0.1278951317,0.4670577645,-0.069475919,0.0535185635,-0.1416844428,-0.0365008079,-0.1959548295,0.4411692917,0.290050596,-0.2122812867,0.3862041235,-0.1093377545,-0.3189106286,0.1744893789,0.1380334646,0.0512453653,0.0646825656,-0.0090722945,-0.0365703031,-0.2727175057,-0.0716025606,0.3707677424,0.6577773094,0.2386050671,0.1774469763,0.0148176551,-0.2293145061,-0.0808017552,-0.0312070567,0.1939817816,0.0265200138,0.0988889784,0.0134747196,0.0223168992,0.048615586,-0.1400788873,0.1509891003,-0.1698394567,-0.0097850068,0.0413742438,-0.2205082625,-0.556699276,-0.2170759737,0.0291742794,-0.3283439279,-0.2532233,0.1027175263,0.1222499907,-0.1313920766,0.1512336433,0.2321764529,0.6160982847,-0.4460153282,-0.2688653469,-0.083535172,-0.30082196,-0.1132342294,0.129001677,0.4587604702,0.3014477491,0.3852969408,0.3329302371,-0.0090692043,-0.0216647424,-0.3423926234,-0.0389637984,-0.2504143715,0.2589896619,0.0364834853,-0.2416997552,0.144774124,-0.3871199787,-0.0762299225,-0.0768024102,-0.0706041306,-0.087010622,0.0312856399,0.1460472494,-0.1852710694,-0.1524782032,-0.5852788091,-0.4743441641,0.297499001,-0.2947556674,0.0538740195,-0.1080989987,-0.3133614063,0.1933502853,-0.1261135191,-0.0194320362,-0.1375253648,-0.0907317176,-0.0554949977,-0.3396884203,-0.1574500352,-0.3975775838,-0.0830503479,0.2783398032,-0.0493620038,-0.0424786881,-0.1559733897,0.1311392635,-0.1548351496,0.0600678958,0.092638351,0.1741753072,-0.2363420427,0.1292829514,-0.0282709207,-0.2620977163,0.2368300408,0.2830856144,0.3746196032,0.1705283523,0.0223739874,0.2380328178,0.7015311122,0.2394606918,0.0548744611,0.1664235741,0.2817083895,-0.023641102,0.1192622632,-0.2199314088,0.1519428492,-0.1884621233,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"Here's a fully working implementation based on the `__iter__` function of @zphang.\r\n\r\n- I've generated the task choice list in the constructor as it allows us to index into the MultiDataset just like a normal dataset. I'm changing `task_choice_list` into a list of `(dataset_idx, example_idx)` so each entry references a unique dataset example. The shuffling has to be done before this as we don't want to shuffle within each task (we assume this is done by the user if this is what they intend).\r\n-  I'm slightly concerned this list could become very large if many large datasets were used. Can't see a way round it at the moment though.\r\n- I've used `task.info.builder_name` as the dataset name. Not sure if this is correct.\r\n- I'd love to add some of the other `Dataset` methods (map, slicing by column, etc...). Would be great to implement the whole interface so a single dataset can be simply replaced by this.\r\n- This does everything on the individual example-level. If some application required batches all from a single task in turn we can't really do that.\r\n\r\n```python\r\nimport nlp\r\nimport numpy as np\r\n\r\nclass MultiDataset:\r\n    def __init__(self,tasks):\r\n        self.tasks = tasks\r\n\r\n        # Create random order of tasks\r\n        # Using size-proportional sampling\r\n        task_choice_list = []\r\n        for i, task in enumerate(self.tasks):\r\n            task_choice_list += [i] * len(task)\r\n        task_choice_list = np.array(task_choice_list)\r\n        np.random.shuffle(task_choice_list)\r\n\r\n        # Add index into each dataset\r\n        # - We don't want to shuffle within each task\r\n        counters = {}\r\n        self.task_choice_list = []\r\n        for i in range(len(task_choice_list)):\r\n            idx = counters.get(task_choice_list[i],0)\r\n            self.task_choice_list.append((task_choice_list[i],idx))\r\n            counters[task_choice_list[i]] = idx + 1\r\n\r\n\r\n    def __len__(self):\r\n        return np.sum([len(t) for t in self.tasks])\r\n\r\n    def __repr__(self):\r\n        task_str = \", \".join([str(t) for t in self.tasks])\r\n        return f\"MultiDataset(tasks: {task_str})\"\r\n\r\n    def __getitem__(self,key):\r\n        if isinstance(key, int):\r\n            task_idx, example_idx = self.task_choice_list[key]\r\n            task = self.tasks[task_idx]\r\n            example = task[example_idx]\r\n            example[\"task_name\"] = task.info.builder_name\r\n            return example\r\n        elif isinstance(key, slice):\r\n            raise NotImplementedError()\r\n\r\n    def __iter__(self):\r\n        for i in range(len(self)):\r\n            yield self[i]\r\n\r\n\r\ndef load_multitask(*datasets):\r\n    '''Create multitask datasets per split'''\r\n\r\n    def _get_common_splits(datasets):\r\n        '''Finds the common splits present in all self.datasets'''\r\n        min_set = None\r\n        for dataset in datasets:\r\n            if min_set != None:\r\n                min_set.intersection(set(dataset.keys()))\r\n            else:\r\n                min_set = set(dataset.keys())\r\n        return min_set\r\n\r\n    common_splits = _get_common_splits(datasets)\r\n    out = {}\r\n    for split in common_splits:\r\n        out[split] = MultiDataset([d[split] for d in datasets])\r\n    return out\r\n\r\n\r\n##########################################\r\n# Dataset Flattening\r\n\r\ndef flatten(dataset,flatten_fn):\r\n    for k in dataset.keys():\r\n        if isinstance(dataset[k],nlp.Dataset):\r\n            dataset[k] = dataset[k].map(flatten_fn,remove_columns=dataset[k].column_names)\r\n\r\n# Squad\r\ndef flatten_squad(example):\r\n    return {\"source\": \"squad context: \" + example['context'] + \" question: \" + example['question'],\r\n          \"target\":example[\"answers\"][\"text\"]}\r\nsquad = nlp.load_dataset(\"squad\")\r\nflatten(squad,flatten_squad)\r\n\r\n# CNN_DM\r\ndef flatten_cnn_dm(example):\r\n    return {\"source\": \"cnn_dm: \" + example['article'],\"target\":[example[\"highlights\"]]}\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\", \"3.0.0\")\r\nflatten(cnn_dm,flatten_cnn_dm)\r\n\r\n#############################################\r\n\r\nmtds = load_multitask(squad,cnn_dm)\r\n\r\nfor example in mtds[\"train\"]:\r\n    print(example[\"task_name\"],example[\"target\"])\r\n```\r\nLet me know if you have any thoughts. I've started using this in some of my projects and it seems to work. If people are happy with the general approach for a first version, I can make a pull request.","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":469,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n Here's a fully working implementation based on the `__iter__` function of @zphang.\r\n\r\n- I've generated the task choice list in the constructor as it allows us to index into the MultiDataset just like a normal dataset. I'm changing `task_choice_list` into a list of `(dataset_idx, example_idx)` so each entry references a unique dataset example. The shuffling has to be done before this as we don't want to shuffle within each task (we assume this is done by the user if this is what they intend).\r\n-  I'm slightly concerned this list could become very large if many large datasets were used. Can't see a way round it at the moment though.\r\n- I've used `task.info.builder_name` as the dataset name. Not sure if this is correct.\r\n- I'd love to add some of the other `Dataset` methods (map, slicing by column, etc...). Would be great to implement the whole interface so a single dataset can be simply replaced by this.\r\n- This does everything on the individual example-level. If some application required batches all from a single task in turn we can't really do that.\r\n\r\n```python\r\nimport nlp\r\nimport numpy as np\r\n\r\nclass MultiDataset:\r\n    def __init__(self,tasks):\r\n        self.tasks = tasks\r\n\r\n        # Create random order of tasks\r\n        # Using size-proportional sampling\r\n        task_choice_list = []\r\n        for i, task in enumerate(self.tasks):\r\n            task_choice_list += [i] * len(task)\r\n        task_choice_list = np.array(task_choice_list)\r\n        np.random.shuffle(task_choice_list)\r\n\r\n        # Add index into each dataset\r\n        # - We don't want to shuffle within each task\r\n        counters = {}\r\n        self.task_choice_list = []\r\n        for i in range(len(task_choice_list)):\r\n            idx = counters.get(task_choice_list[i],0)\r\n            self.task_choice_list.append((task_choice_list[i],idx))\r\n            counters[task_choice_list[i]] = idx + 1\r\n\r\n\r\n    def __len__(self):\r\n        return np.sum([len(t) for t in self.tasks])\r\n\r\n    def __repr__(self):\r\n        task_str = \", \".join([str(t) for t in self.tasks])\r\n        return f\"MultiDataset(tasks: {task_str})\"\r\n\r\n    def __getitem__(self,key):\r\n        if isinstance(key, int):\r\n            task_idx, example_idx = self.task_choice_list[key]\r\n            task = self.tasks[task_idx]\r\n            example = task[example_idx]\r\n            example[\"task_name\"] = task.info.builder_name\r\n            return example\r\n        elif isinstance(key, slice):\r\n            raise NotImplementedError()\r\n\r\n    def __iter__(self):\r\n        for i in range(len(self)):\r\n            yield self[i]\r\n\r\n\r\ndef load_multitask(*datasets):\r\n    '''Create multitask datasets per split'''\r\n\r\n    def _get_common_splits(datasets):\r\n        '''Finds the common splits present in all self.datasets'''\r\n        min_set = None\r\n        for dataset in datasets:\r\n            if min_set != None:\r\n                min_set.intersection(set(dataset.keys()))\r\n            else:\r\n                min_set = set(dataset.keys())\r\n        return min_set\r\n\r\n    common_splits = _get_common_splits(datasets)\r\n    out = {}\r\n    for split in common_splits:\r\n        out[split] = MultiDataset([d[split] for d in datasets])\r\n    return out\r\n\r\n\r\n##########################################\r\n# Dataset Flattening\r\n\r\ndef flatten(dataset,flatten_fn):\r\n    for k in dataset.keys():\r\n        if isinstance(dataset[k],nlp.Dataset):\r\n            dataset[k] = dataset[k].map(flatten_fn,remove_columns=dataset[k].column_names)\r\n\r\n# Squad\r\ndef flatten_squad(example):\r\n    return {\"source\": \"squad context: \" + example['context'] + \" question: \" + example['question'],\r\n          \"target\":example[\"answers\"][\"text\"]}\r\nsquad = nlp.load_dataset(\"squad\")\r\nflatten(squad,flatten_squad)\r\n\r\n# CNN_DM\r\ndef flatten_cnn_dm(example):\r\n    return {\"source\": \"cnn_dm: \" + example['article'],\"target\":[example[\"highlights\"]]}\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\", \"3.0.0\")\r\nflatten(cnn_dm,flatten_cnn_dm)\r\n\r\n#############################################\r\n\r\nmtds = load_multitask(squad,cnn_dm)\r\n\r\nfor example in mtds[\"train\"]:\r\n    print(example[\"task_name\"],example[\"target\"])\r\n```\r\nLet me know if you have any thoughts. I've started using this in some of my projects and it seems to work. If people are happy with the general approach for a first version, I can make a pull 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"Hey! Happy to jump into the discussion here. I'm still getting familiar with bits of this code, but the reasons I sampled over data loaders rather than datasets is 1) ensuring that each sampled batch corresponds to only 1 task (in case of different inputs formats\/downstream models) and 2) potentially having different batch sizes per task (e.g. some tasks have very long\/short inputs). How are you currently dealing with these in your PR?","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":73,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n Hey! Happy to jump into the discussion here. I'm still getting familiar with bits of this code, but the reasons I sampled over data loaders rather than datasets is 1) ensuring that each sampled batch corresponds to only 1 task (in case of different inputs formats\/downstream models) and 2) potentially having different batch sizes per task (e.g. some tasks have very long\/short inputs). How are you currently dealing with these in your PR?","embeddings":[-0.0896057039,-0.3705182076,-0.0441243164,-0.0641978309,-0.2286925614,0.0289512146,0.2676903605,0.0233601592,0.3950957656,-0.109334521,-0.1705919504,0.3203356266,-0.2015978396,0.3754858077,0.3984468877,-0.4378172457,-0.0025773386,-0.2555523515,-0.3273433149,0.3867934048,0.0030515916,0.1184610575,-0.1452497393,0.0565555431,-0.2847829163,-0.1290688217,-0.3320092857,0.1075519323,0.0970576555,-0.0115130125,0.2389508486,0.3638801575,-0.002958701,0.1261327714,-0.0001156875,-0.0413929038,0.2245578617,-0.0885122195,0.0648911446,-0.0456992388,0.1790006757,-0.3761777878,0.0587886237,-0.1425863653,0.2026034445,0.1782149673,0.1775251627,0.0471881181,0.3459135592,-0.3254346848,0.0824123248,0.320581913,-0.2082775831,0.2640051246,-0.1115657315,-0.0502124429,0.0747940615,0.3185981512,0.6831018329,-0.5693539977,-0.0752263293,0.1094844714,0.0188646726,-0.0638962463,0.1539567858,-0.0844456404,-0.3090027273,-0.2464439571,-0.436266005,0.2125426531,-0.1186305359,-0.1288326532,-0.2360724211,-0.6045432091,-0.0291276295,0.0723598972,-0.2734466493,-0.0096624047,-0.2152974904,-0.0498412289,-0.173860088,-0.1056186333,-0.0311045777,0.1421042234,0.353438884,0.5484316349,0.0771757662,0.2102186382,0.4161781371,0.0839211196,-0.1161305383,-0.2795420885,0.3173269629,0.1217986569,-0.3628462851,-0.3442519009,0.1783768237,-0.2727918327,0.2653506994,0.1431957632,0.1328389049,0.0566122085,-0.127791062,0.2421325296,0.3584896028,0.0815949067,-0.090619579,-0.1105466858,0.0110702831,-0.1044861078,0.2219539732,0.1037233397,0.1453360468,-0.0153993219,-0.4716430902,-0.084676832,-0.2308618426,0.1142077595,-0.1881165057,-0.5452076793,-0.0772759244,-0.1443603784,0.1103415936,-0.0899574906,-0.2681081891,0.1173449531,-0.0560462289,0.2535911202,-0.1384112686,0.0487506762,-0.0495746024,0.0613282546,-0.4584903717,-0.0933215618,0.2870365381,0.1720797718,0.1290307641,0.1202379391,-0.0191581734,0.144102037,0.3373575807,0.0285957996,0.0130509157,0.0109926695,0.186542213,-0.3049393296,0.0114053348,0.2314634621,-0.3490486443,-0.108992286,-0.2015261352,-0.1910812408,0.1673606187,0.0139306616,-0.2104642391,-0.2614148259,-0.5597922802,0.6944103241,-0.0327052698,-0.109348096,-0.1405427754,0.2590900362,-0.1600858718,-0.0846285,0.1551488638,0.0482387878,-0.0486482829,-0.3655715883,-0.0786841288,-0.1202846766,0.0507150069,0.2679366171,-0.2283245176,0.2701061368,0.0187885351,0.1974085569,0.2476556748,-0.476846993,-0.0329776853,0.3645942211,-0.0443459302,0.4032140672,0.1089770868,0.204736203,0.0427176319,-0.2009263635,0.2404806167,1.0242568254,-0.3771042824,0.0088222316,-0.1226192638,-0.3946356475,0.5419133902,0.3336593509,0.0873916745,-0.2646752596,-0.1131930202,0.0209318604,0.1400299817,0.068072021,0.091499716,-0.0633876696,-0.3728696406,0.045084402,-0.2132455558,-0.0941620916,-0.3640448451,0.0200417917,-0.070893757,0.3300634921,0.2768280208,-0.1019619852,0.2901110947,-0.2599795461,-0.1355253011,-0.2989259362,0.080994986,-0.0919807404,0.0196252745,-0.3017867804,-0.0435298048,0.3114897311,0.0931477696,-0.1114646718,-0.5201098323,0.2931295931,-0.0359981544,0.0615643486,-0.0562952273,0.5926681161,-0.1611738652,-0.1230566204,0.1540281773,0.3034602106,-0.2690724432,0.2271575332,0.2872880101,0.1880010813,0.1648530364,-0.0165219959,0.1333711892,0.0051811868,-0.0125397518,-0.222474739,-0.0711253434,0.4943287671,-0.1799316406,0.4873961508,-0.0446648411,0.0157403201,-0.1863206327,-0.0475344732,-0.2326929718,0.4075044692,0.3192885518,-0.179496929,0.3725852668,-0.0862381607,-0.3513277173,0.1785660088,0.0962764546,0.0001979609,0.0212551672,-0.01291491,-0.0091346251,-0.3338392675,-0.1100993603,0.335542202,0.6644569635,0.24936831,0.1772748083,-0.0286508407,-0.1969970316,-0.0943430513,0.0360674225,0.2123548388,-0.024821775,0.1570017487,-0.010826814,0.0367388166,-0.0019103896,-0.1451434344,0.1448417157,-0.2040403932,-0.0847463384,0.0830414295,-0.2019584924,-0.6090888381,-0.2475720197,-0.0173969977,-0.329018116,-0.288523823,0.1099270657,0.0824487135,-0.1586320847,0.1415619254,0.2259223461,0.649754703,-0.445845902,-0.2383549064,-0.0980748683,-0.2736441195,-0.1178834289,0.0897930041,0.4389152527,0.2987910509,0.4678762257,0.3357658088,-0.0729456469,0.0949567854,-0.3517068028,0.0355306976,-0.2930790782,0.309882462,0.1390076578,-0.1824035794,0.1960035115,-0.3770197928,-0.0679646358,-0.0490537733,-0.0927712917,-0.0687157884,-0.0261847433,0.0787532404,-0.1979088634,-0.1430408508,-0.6475348473,-0.4276148379,0.2624100745,-0.2563127875,0.0538112968,0.056188643,-0.2789657414,0.1672408581,-0.1140871122,-0.0301994085,-0.166753307,-0.1156590655,-0.0223159734,-0.3414051533,-0.1668613851,-0.4368031025,-0.0881424397,0.2031256109,-0.0194643922,-0.0194301363,-0.1868307441,0.1578907371,-0.1286054254,0.1338546425,0.2255497724,0.1400244534,-0.2490694523,0.1449827105,-0.0692463741,-0.2159969956,0.2266514599,0.310231477,0.4672707021,0.2042819411,0.021374898,0.1477927715,0.7359365225,0.2251019478,0.061652571,0.1580505669,0.2971984148,-0.0115221487,0.1084874123,-0.2090418637,0.2638306022,-0.1977590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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"The short answer is - I'm not! Everything is currently on a per-example basis. It would be fairly simple to add a `batch_size` argument which would ensure that every `batch_size` examples come from the same task. That should suit most use-cases (unless you wanted to ensure batches all came from the same task and apply something like `SortishSampler` on each task first)\r\n\r\nYour notebook was really inspiring by the way - thanks!","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":72,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n The short answer is - I'm not! Everything is currently on a per-example basis. It would be fairly simple to add a `batch_size` argument which would ensure that every `batch_size` examples come from the same task. That should suit most use-cases (unless you wanted to ensure batches all came from the same task and apply something like `SortishSampler` on each task first)\r\n\r\nYour notebook was really inspiring by the way - thanks!","embeddings":[-0.0956542194,-0.4303838611,-0.046293769,-0.0674511418,-0.2578944266,0.0099473,0.2022491246,0.0762412697,0.2903968692,-0.1109249666,-0.1859128326,0.2946228385,-0.2300930023,0.3789541125,0.4100244641,-0.4520003498,0.0244413298,-0.1772062331,-0.2945506573,0.3627444506,0.0154231098,0.1428891122,-0.1547029465,0.0810638219,-0.3128835857,-0.1692617238,-0.3484938145,0.1008630618,0.137015149,-0.0246207006,0.2090304345,0.3778528869,0.0340089686,0.101393275,-0.0001114291,-0.0972870216,0.2050793469,-0.0906254649,0.1029761508,-0.0590030178,0.1640173197,-0.3679223657,-0.0027350979,-0.126950115,0.159407258,0.2251133025,0.202381447,0.0856840611,0.3988807797,-0.3097245395,0.1181306392,0.3286939263,-0.18170169,0.2379733622,-0.0835704729,-0.0625105426,0.0609891564,0.234527722,0.607134819,-0.5887556672,-0.0640491694,0.1405183822,0.0385453366,0.0214208961,0.1444118172,-0.0387359113,-0.2962240577,-0.2327660322,-0.4362361431,0.2806535065,-0.1612733603,-0.1306264997,-0.2634522617,-0.5633007884,-0.0008527267,0.001461901,-0.3300979435,0.0447877906,-0.2143581957,-0.0334663987,-0.1524393559,-0.0602673888,-0.0406011157,0.1120113805,0.3631240129,0.5616534352,0.0817182064,0.1304565072,0.378122896,0.0584178232,-0.1990291923,-0.2562061548,0.2837378979,0.1137411967,-0.3466748297,-0.3419148326,0.175062716,-0.1930901557,0.3228087425,0.1010895297,0.0934974104,0.0911375284,-0.1003000438,0.2920592129,0.3476570249,0.1223694384,-0.0566057116,-0.124246493,0.0397594348,-0.0633675605,0.2047535181,0.1504277736,0.1986275166,-0.0070350585,-0.4239506125,-0.1135905012,-0.1826789975,0.1420101374,-0.2142997831,-0.5234180093,-0.029956447,-0.1304443479,0.0822643116,-0.0567394048,-0.2853320837,0.0973653644,-0.0880560875,0.1967229098,-0.12846829,0.074834168,-0.0825338438,0.0717627406,-0.4690391719,-0.0485996157,0.3015267253,0.1074483246,0.1313358247,0.1381658614,-0.0377435125,0.1328812093,0.3394283652,0.0303127151,-0.0003308657,-0.0361671411,0.1575369984,-0.3345584273,-0.0437694192,0.1560831964,-0.4061700106,-0.1570382714,-0.1264192313,-0.2152492553,0.133074224,0.0548871644,-0.234551549,-0.2543685734,-0.457261771,0.7054930925,-0.0462399013,-0.0334073231,-0.1386663914,0.2801197767,-0.2386475354,-0.0716435388,0.1591424644,0.0954545885,0.0009730769,-0.3780564368,-0.0324621834,-0.1053331867,-0.0680371821,0.3193644285,-0.1771673262,0.189245373,0.0511361398,0.2022205293,0.2415230572,-0.4966229796,-0.033442203,0.3052602112,-0.0365063101,0.3519688845,0.0778744444,0.1823500395,0.0981876105,-0.1924562603,0.2653273344,1.0035316944,-0.3828692436,0.0220557079,-0.1274592131,-0.4010963142,0.4855629504,0.3276019692,0.0234205406,-0.3136387467,-0.1300783306,-0.0256047193,0.0993120521,0.0698810965,0.0765289366,-0.0697749406,-0.3187339902,0.0139779644,-0.1563160568,-0.135272786,-0.4236288667,0.0341507196,-0.0694630593,0.3329489529,0.3250610232,-0.1376506835,0.2176266909,-0.2634049356,-0.1004827917,-0.3129209876,0.1418480575,-0.1044748574,0.0539262258,-0.2799595296,-0.0749296695,0.33102566,0.0866704583,-0.1224152371,-0.4675805569,0.2426160723,-0.1065036058,0.0175462756,0.0072998689,0.6171317101,-0.1495946944,-0.0700064003,0.2050337493,0.2820171416,-0.2821091712,0.2547475398,0.288651526,0.2265316844,0.1838992089,0.0129133407,0.1047694832,-0.0182390753,-0.0330988839,-0.1843897551,-0.1276169866,0.5337747931,-0.1134037822,0.4237475097,0.0102806175,-0.0016260303,-0.185748294,-0.087344192,-0.2569580674,0.3532716036,0.324221015,-0.2155417055,0.3575747609,-0.0927835107,-0.3873851001,0.1576682478,0.1813380569,0.0224516224,0.0883683413,0.0224850625,-0.0439936221,-0.2892819941,-0.0826865882,0.3609900475,0.6006555557,0.3052539527,0.1733634025,0.0270603634,-0.3042919636,-0.1120566651,0.0152275572,0.1831427813,-0.0301966835,0.1528551579,0.041586902,0.0559994616,-0.0489573814,-0.166114971,0.0925271213,-0.175807789,-0.0818899274,0.0395757742,-0.1671430916,-0.5780368447,-0.2460929602,0.0441225097,-0.3262543976,-0.2550864816,0.1588418782,0.0294343699,-0.1699263006,0.1729965061,0.2292761654,0.6286779642,-0.4388614297,-0.1850003749,-0.1076191068,-0.3222373426,-0.152625069,0.1209607422,0.4160206616,0.2905067801,0.4932436645,0.2830930352,-0.0260008276,0.1047434583,-0.3773645163,0.0544515029,-0.2754267156,0.2851442695,0.1103931069,-0.2209584415,0.1578573138,-0.3922051191,-0.0500610732,-0.1231088415,-0.0999128968,-0.0815797374,-0.0265806131,0.0420912169,-0.1918628216,-0.1675719917,-0.6334230304,-0.4681071341,0.3418900371,-0.2122168094,0.10471154,0.0577006973,-0.2606832683,0.1661696583,-0.1555620134,0.027958598,-0.161150977,-0.0806015879,0.0271599293,-0.3650360703,-0.1609771103,-0.4260776341,-0.131566301,0.2117258012,-0.0857354775,-0.0181610957,-0.25038746,0.0716508627,-0.1331433058,0.1169309691,0.2675018311,0.1917032152,-0.2375511378,0.0919945017,-0.0308797769,-0.2376433462,0.2405965626,0.3334428072,0.3925411403,0.1405200511,0.03054028,0.1958531737,0.735360384,0.2564295232,0.0238263607,0.1866267473,0.2621122897,0.0246364214,0.1601391584,-0.1698180139,0.2595056593,-0.175712198,0.449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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"@zphang is having different batch sizes per task actually helpful? Would be interesting to know as it's not something I've come across as a technique used by any MTL papers.","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":30,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n @zphang is having different batch sizes per task actually helpful? Would be interesting to know as it's not something I've come across as a technique used by any MTL 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"> @zphang is having different batch sizes per task actually helpful? Would be interesting to know as it's not something I've come across as a technique used by any MTL papers.\r\n\r\nI think having different batch sizes per task is particularly helpful in some scenarios where each task has different amount of data. For example, the problem I'm currently facing is one task has tens of thousands of samples while one task has a couple hundreds. I think in this case different batch size could help. But if using the same batch size is a lot simpler to implement, I guess it makes sense to go with that.","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":108,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n > @zphang is having different batch sizes per task actually helpful? Would be interesting to know as it's not something I've come across as a technique used by any MTL papers.\r\n\r\nI think having different batch sizes per task is particularly helpful in some scenarios where each task has different amount of data. For example, the problem I'm currently facing is one task has tens of thousands of samples while one task has a couple hundreds. I think in this case different batch size could help. But if using the same batch size is a lot simpler to implement, I guess it makes sense to go with that.","embeddings":[-0.1528656781,-0.485108465,-0.0882897079,0.043889109,-0.3023048937,0.0259212796,0.2686049938,0.0912450477,0.2722305059,-0.0741184279,-0.1674394459,0.1211875752,-0.2572728097,0.4499771893,0.3634528816,-0.4062088728,0.0306342803,-0.273011893,-0.2287893146,0.4066846669,0.0369204804,0.0514032245,-0.137498185,0.0486458354,-0.2384936064,-0.1524757743,-0.3196730018,0.0555802025,0.1975445747,0.0446504354,0.114674367,0.301550895,0.047314927,0.157008335,-0.000116407,-0.0821221247,0.229915306,-0.0201301072,0.079476513,-0.0190348867,0.1309003085,-0.5154117942,-0.0374999307,-0.1042581946,0.2734789848,0.2135601193,0.2659732997,0.0325354896,0.2544078827,-0.2947160304,0.0986241624,0.3221176267,-0.297036469,0.2996921837,-0.0103497924,-0.0147421807,0.0532822572,0.2629302144,0.6999950409,-0.4993149936,0.0012701716,0.1606194526,0.0273506884,0.0273662992,0.1041140184,-0.0575499535,-0.1475427598,-0.2259554565,-0.307974726,0.2468973398,-0.0819254518,-0.0660735071,-0.2369565815,-0.6513184905,-0.007323836,0.0499326214,-0.3508639634,0.1869454831,-0.1884657294,-0.096497342,-0.158461079,-0.0536108688,-0.0076526753,0.0664810017,0.3757089078,0.5052568316,0.0134295747,0.1736917645,0.3467091024,0.0312123857,-0.1065133959,-0.2447149903,0.2853565812,0.10458491,-0.3702590466,-0.2722153366,0.1617219448,-0.2643080652,0.2712616324,0.0236643199,0.1709341109,0.0216553751,-0.0688679814,0.1701315641,0.4097624719,0.1125170588,-0.0951912776,-0.1455382705,0.0410936289,-0.177620694,0.1831089258,0.2079978585,0.1485933512,-0.1357662082,-0.4656375647,-0.0821628198,-0.256293714,0.1493212283,-0.2005464286,-0.5421450138,-0.0468392186,-0.2318474203,-0.0090745185,-0.1137375161,-0.2909087241,0.1440445632,-0.1199963987,0.2271471173,-0.160934329,0.0743538141,-0.0578991622,0.0046857088,-0.4462988973,-0.1151438802,0.2692194879,0.1677381843,0.0834861994,0.0706990734,-0.0673724413,0.1179812849,0.2836702168,-0.0043958463,-0.0329799205,0.0396016166,0.2211727053,-0.2768903971,-0.1352516711,0.3050740957,-0.3997874558,-0.022525534,-0.2343412787,-0.1942861229,0.1870928556,0.0207450055,-0.128689602,-0.2231705487,-0.5973207355,0.6830005646,0.0675176308,-0.0465238169,-0.1566710174,0.1782171428,-0.1940242201,-0.1789400578,0.0115919681,0.0831236541,-0.0616525114,-0.346134752,0.0005161235,-0.1184058934,0.0154259559,0.3973837793,-0.1924615204,0.286556989,0.094451949,0.1200645417,0.2664086521,-0.4741751552,-0.049382288,0.4507997632,-0.01909432,0.3506062925,0.1672836989,0.2602223456,0.0020203993,-0.1747531444,0.2372568995,0.8971976042,-0.4060545564,0.0835805088,-0.139383927,-0.4228145182,0.5240346789,0.3101249635,0.1059497669,-0.3104820549,-0.0698605105,0.0462274626,0.1368227154,0.0981059596,0.1255739927,-0.0577630438,-0.4325707257,-0.0024134761,-0.1919256151,-0.0683367178,-0.4964828491,0.0032335885,-0.1554141492,0.2653370202,0.3736354709,-0.0585895851,0.3415445387,-0.3247838616,-0.0682675242,-0.3196668923,0.0744401738,-0.0956722498,0.0392858349,-0.3197910488,-0.0620633177,0.3237813413,0.1203211248,-0.1444507837,-0.4255874157,0.1923217475,-0.0377244428,0.0809303075,0.0166975502,0.5754878521,-0.291128695,-0.1207504794,0.109301351,0.2480295151,-0.2801344693,0.2186086327,0.2109756768,0.266854912,0.2549037039,0.1275349557,0.2021845281,-0.120138973,-0.0972060338,-0.2197655588,-0.0321399309,0.4492028356,-0.0958451554,0.4679341316,-0.0414217934,-0.1082644835,-0.228671968,-0.0420632996,-0.2095148116,0.3518670201,0.3388044238,-0.1527584195,0.3212234676,-0.0875376463,-0.3210728168,0.1529049575,0.0971042737,0.0753896609,0.0598750114,-0.0155222835,-0.0494279452,-0.2951928079,-0.1674006581,0.3495880067,0.7306095362,0.2229883075,0.1998189688,-0.0016515177,-0.1664979309,-0.1618093699,-0.0072607663,0.1925978661,-0.019020902,0.206328705,0.0260776617,0.0107030608,0.0475568175,-0.1425853074,0.1281280518,-0.1943386495,-0.0390218683,0.0012265577,-0.2181596458,-0.6630983949,-0.3449249268,0.0396685712,-0.2795931697,-0.2296168059,0.0963747874,0.0062006586,-0.202216655,0.0974417925,0.254486382,0.6557933092,-0.4205450416,-0.1220016405,-0.0118119549,-0.2465248108,-0.1822436005,0.0804419816,0.4787092507,0.1971721351,0.42263183,0.2554949522,-0.070756413,0.0809149221,-0.3518064022,0.0257015005,-0.2472163886,0.2424323112,0.116212517,-0.235052973,0.1969836354,-0.3383502662,-0.0317518748,-0.1152959615,-0.0735269263,-0.1828646362,-0.0227659773,0.0458274186,-0.2143455446,-0.1471020132,-0.6761795878,-0.4236723185,0.3288802505,-0.2589175701,0.0831294954,0.0686502457,-0.2425622791,0.1101025715,-0.1208234131,0.0201772153,-0.1074977741,-0.1358648539,-0.1051701903,-0.3834719956,-0.1640853286,-0.4417363703,-0.0516647995,0.2295840979,0.0273398571,-0.1045602262,-0.1372971982,0.0961832255,-0.229307726,0.0718224719,0.3079031706,0.0516071208,-0.3560309112,0.1333211809,-0.0120461481,-0.2183779329,0.1295349002,0.3778129518,0.4913676083,0.1492086649,0.0247739144,0.1245520413,0.7539132833,0.2017935365,0.0599366464,0.1063675955,0.2488217056,-0.0169444662,0.1358464509,-0.1161102206,0.3977303803,-0.1366483122,0.42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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"I think that instead of proportional to size sampling you should specify weights or probabilities for drawing a batch from each dataset. We should also ensure that the smaller datasets are repeated so that the encoder layer doesn't overtrain on the largest dataset.","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":43,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n I think that instead of proportional to size sampling you should specify weights or probabilities for drawing a batch from each dataset. We should also ensure that the smaller datasets are repeated so that the encoder layer doesn't overtrain on the largest 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"Are there any references for people doing different batch sizes per task in the literature? I've only seen constant batch sizes with differing numbers of batches for each task which seems sufficient to prevent the impact of large datasets (Read 3.5.3 of the [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) for example).\r\n\r\n","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":47,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n Are there any references for people doing different batch sizes per task in the literature? I've only seen constant batch sizes with differing numbers of batches for each task which seems sufficient to prevent the impact of large datasets (Read 3.5.3 of the [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) for example).\r\n\r\n","embeddings":[-0.0643153861,-0.4659213722,-0.0846638903,-0.0310634468,-0.2937629223,0.0142678423,0.2748717368,-0.001531178,0.327396214,-0.0881961659,-0.1483937055,0.1096212044,-0.1666349918,0.4398270845,0.3444593847,-0.3664786518,0.0387695245,-0.2969401777,-0.2708536386,0.3853909075,0.0380840339,0.0419314951,-0.133543089,0.0882256776,-0.2808085084,-0.2636702061,-0.3897076845,0.0344354585,0.1399751455,0.051349543,0.2340892702,0.3075521588,0.048616495,0.2791934013,-0.0001168718,-0.0837765485,0.2308102399,-0.0191363506,0.1168078035,-0.0018158883,0.1538009793,-0.4783226252,0.0054652067,-0.1108367369,0.2739711404,0.1568047404,0.2591815591,0.1064631566,0.2886648774,-0.3447953463,0.0977224261,0.2746801376,-0.2108391523,0.2263471484,-0.1184610054,-0.0141929314,0.0773544461,0.260610193,0.7055603266,-0.541046679,-0.0727652535,0.079444997,0.0422349125,0.0119696679,0.1090794876,-0.1714967191,-0.2628783286,-0.2975201309,-0.3295665979,0.2208566517,-0.0858785659,-0.0988260135,-0.2601613402,-0.6264220476,-0.0364246666,0.0315535776,-0.3546115458,0.1694985628,-0.2045316696,-0.1291310042,-0.2346027792,-0.100240767,-0.0264209136,0.0753989592,0.3474641144,0.5395688415,0.0419702642,0.1272194982,0.3926811814,-0.0382644981,-0.0889718533,-0.2737716436,0.2567122579,0.1343369037,-0.3992006779,-0.2938930392,0.1546251923,-0.2645098269,0.3152874708,0.0821964294,0.0817464069,0.0516123809,-0.1418322772,0.1976729929,0.4014687836,0.1253283024,-0.0997797996,-0.1122030392,0.0472247489,-0.1615311056,0.2122469842,0.182415992,0.1426582485,-0.1386050284,-0.5396947861,-0.1671905518,-0.2762538791,0.1414396912,-0.1807385981,-0.5744892955,-0.0204468071,-0.1508353353,0.020509243,-0.0943684354,-0.2721249759,0.0960933492,-0.1183325797,0.2690684199,-0.1600548774,0.0736635029,-0.0505211204,0.0471133962,-0.4707634449,-0.1046871394,0.2876490057,0.1782876998,0.0942670777,0.187098816,-0.0319584049,0.0859320387,0.3025982082,-0.0619176254,-0.0510382839,0.0393544436,0.2460583746,-0.2680261731,-0.0879347697,0.2486284375,-0.3791604936,-0.0590336211,-0.2317093462,-0.1593207866,0.1585091799,-0.0022804707,-0.1443956792,-0.2504307032,-0.5782610774,0.6736926436,0.0623671338,-0.0566855557,-0.1603138298,0.2204205543,-0.1436540931,-0.1869812459,0.0599551685,0.018209774,-0.0328351818,-0.3329956234,0.036641337,-0.0352408588,0.0035878927,0.4364959598,-0.1851605177,0.2895739377,0.081008926,0.1218289509,0.266451925,-0.4424839914,-0.011515771,0.4173333347,0.0503261499,0.3307783902,0.1234065443,0.2318913341,0.044218421,-0.2073778659,0.2358899266,0.9741356969,-0.3745271564,0.0966806635,-0.0580495447,-0.4658020437,0.441191256,0.2258762419,0.0897612348,-0.2925755084,-0.0739047229,-0.0540940836,0.0573772229,0.0896451846,0.0959465355,0.0477457196,-0.4209629595,0.0964864492,-0.1834495217,-0.0505201481,-0.4460891783,-0.0105639482,-0.0827766359,0.1956135482,0.3746560812,-0.0908392295,0.3362392485,-0.326307416,-0.0925636515,-0.3413313627,0.0661156401,-0.1217467189,0.0976743922,-0.3077352345,0.0007398505,0.3354276717,0.0945950076,-0.1044937447,-0.4976728559,0.1759320498,-0.0364608504,-0.0086301519,0.0581482835,0.5408452153,-0.1912011355,-0.0841429532,0.1043066084,0.2897190452,-0.2685348094,0.2316520214,0.311085552,0.260737747,0.2113591433,0.1183923185,0.2055728585,-0.1097736582,-0.0994369611,-0.224929899,-0.1034714803,0.4607518911,-0.1345931292,0.5010110736,-0.0296973847,-0.0716863722,-0.1886713952,-0.0173037425,-0.1613265574,0.45697245,0.3395256102,-0.1707567573,0.3268986344,-0.0983332917,-0.3560428023,0.0538365319,0.022897765,0.0538638495,0.0811352506,-0.0080035813,-0.0308276545,-0.2953766584,-0.1262144446,0.3653461039,0.6546179056,0.2216181606,0.2182417661,-0.0614946783,-0.2254962474,-0.1522153765,-0.0258746129,0.2021449655,0.0066400524,0.1604983211,0.026298061,-0.0057370472,0.0847122446,-0.105338335,0.1184067503,-0.2252216488,-0.0851924419,0.0054050749,-0.2221914977,-0.6311863661,-0.3055882752,0.0150794471,-0.3049708307,-0.2313881069,0.0390989184,0.0737466365,-0.1142402738,0.0818827003,0.2115861475,0.6373746991,-0.4346647263,-0.1099825054,-0.0314767323,-0.247411713,-0.1409847885,0.0675955936,0.4684339464,0.2097015977,0.4605123401,0.3219372332,-0.1092415825,0.1142005548,-0.3225851357,0.02633816,-0.2005291432,0.2423814386,0.0437043533,-0.1586246341,0.1765051335,-0.3366844356,-0.0731435791,-0.0610848852,-0.0528527126,-0.1735841036,-0.0702571347,0.0564881675,-0.1488361657,-0.1003974155,-0.672786653,-0.3964664638,0.3388643563,-0.256965071,0.0655088797,0.0254215319,-0.1942610592,0.0753900036,-0.1441620886,-0.0177366994,-0.0749676153,-0.1228859052,-0.0291451365,-0.4028765261,-0.1595147252,-0.3732082248,-0.0216745213,0.1983463764,0.0170577336,-0.0676832348,-0.0986860394,0.029117329,-0.1789455265,0.1095878631,0.3481385112,0.0832409114,-0.3999963403,0.15140073,-0.0556364655,-0.2490878701,0.1466619372,0.2859275043,0.4913568795,0.2199198753,0.0089417072,0.1551789939,0.7662396431,0.273021251,0.0236228127,0.0985928252,0.2874363959,0.0255047753,0.1275713444,-0.118877843,0.33744627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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/217","title":"Multi-task dataset mixing","comments":"Hi,\r\nregarding building T5 dataset , I think we can use datasets https:\/\/github.com\/huggingface\/datasets and then need something similar to tf.data.experimental.sample_from_datasets, do you know if similar functionality exist in pytorch? Which can sample multiple datasets with the given rates. thanks. ","body":"It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n","comment_length":39,"text":"Multi-task dataset mixing \n It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).\r\n\r\nThe [T5 paper](https:\/\/arxiv.org\/pdf\/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:\r\n- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size\r\n- **Equal mixing** - sample uniformly from each task\r\n- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1\/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.\r\n\r\nFollowing this discussion https:\/\/github.com\/huggingface\/transformers\/issues\/4340 in [transformers](https:\/\/github.com\/huggingface\/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.\r\n\r\nSome method for combining datasets could be implemented ,e.g.\r\n```\r\ndataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)\r\n```\r\n\r\nWe would need a few additions:\r\n- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?\r\n- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.\r\n\r\nIt would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. \r\n\r\nI'm willing to write bits\/most of this I just need some guidance on the interface and other library details so I can integrate it properly.\r\n\r\n \n Hi,\r\nregarding building T5 dataset , I think we can use datasets https:\/\/github.com\/huggingface\/datasets and then need something similar to tf.data.experimental.sample_from_datasets, do you know if similar functionality exist in pytorch? Which can sample multiple datasets with the given rates. thanks. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/216","title":"\u2753 How to get ROUGE-2 with the ROUGE metric ?","comments":"For the rouge2 metric you can do\r\n\r\n```python\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n    for lp, lg in zip(p, g):\r\n        rouge.add(lp, lg)\r\nscore = rouge.compute(rouge_types=[\"rouge2\"])\r\n```\r\n\r\nNote that I just did a PR to have both `.add` and `.add_batch` for metrics, that's why now this is `rouge.add(lp, lg)` and not `rouge.add([lp], [lg])`","body":"I'm trying to use ROUGE metric, but I don't know how to get the ROUGE-2 metric.\r\n\r\n---\r\n\r\nI compute scores with :\r\n\r\n```python\r\nimport nlp\r\n\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n    for lp, lg in zip(p, g):\r\n        rouge.add([lp], [lg])\r\nscore = rouge.compute()\r\n```\r\n\r\nthen : _(print only the F-score for readability)_\r\n\r\n```python\r\nfor k, s in score.items():\r\n    print(k, s.mid.fmeasure)\r\n```\r\n\r\nIt gives :\r\n\r\n>rouge1 0.7915168355671788\r\nrougeL 0.7915168355671788\r\n\r\n---\r\n\r\n**How can I get the ROUGE-2 score ?**\r\n\r\nAlso, it's seems weird that ROUGE-1 and ROUGE-L scores are the same. Did I made a mistake ?\r\n\r\n@lhoestq ","comment_length":56,"text":"\u2753 How to get ROUGE-2 with the ROUGE metric ? \n I'm trying to use ROUGE metric, but I don't know how to get the ROUGE-2 metric.\r\n\r\n---\r\n\r\nI compute scores with :\r\n\r\n```python\r\nimport nlp\r\n\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n    for lp, lg in zip(p, g):\r\n        rouge.add([lp], [lg])\r\nscore = rouge.compute()\r\n```\r\n\r\nthen : _(print only the F-score for readability)_\r\n\r\n```python\r\nfor k, s in score.items():\r\n    print(k, s.mid.fmeasure)\r\n```\r\n\r\nIt gives :\r\n\r\n>rouge1 0.7915168355671788\r\nrougeL 0.7915168355671788\r\n\r\n---\r\n\r\n**How can I get the ROUGE-2 score ?**\r\n\r\nAlso, it's seems weird that ROUGE-1 and ROUGE-L scores are the same. Did I made a mistake ?\r\n\r\n@lhoestq  \n For the rouge2 metric you can do\r\n\r\n```python\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n    for lp, lg in zip(p, g):\r\n        rouge.add(lp, lg)\r\nscore = rouge.compute(rouge_types=[\"rouge2\"])\r\n```\r\n\r\nNote that I just did a PR to have both `.add` and `.add_batch` for metrics, that's why now this is `rouge.add(lp, lg)` and not `rouge.add([lp], [lg])`","embeddings":[0.0825815573,-0.4401053488,-0.0926093534,0.3807037771,-0.0594926998,-0.1371209025,-0.3506770432,0.0906175971,0.0235020909,0.2352947742,-0.2559311986,0.1684113145,-0.0215512644,0.0022067709,0.0974013582,-0.3178662658,0.037422806,-0.0347548649,0.1625474393,-0.2055855244,-0.1583114564,0.3564315438,-0.0662797838,0.2676070035,0.0549102947,0.2283007354,0.1845670342,0.2020991892,-0.4124771357,-0.1735796481,0.1488842368,0.0051129377,0.2301985919,0.2997406125,-0.0001134505,-0.2268581986,0.1634779423,-0.0996424481,0.108243227,-0.5389806032,0.068422243,-0.2803028226,-0.1171009764,-0.0503603891,-0.077018328,-0.2652797699,-0.3086017072,-0.1595863998,0.3000632823,0.1558423191,0.136604473,-0.2055416405,-0.0622154027,-0.1061264127,0.2618375719,-0.1017468497,0.1145922467,0.5414428115,0.2648924589,0.0035370674,0.1269048452,0.1456472129,0.2574687004,0.0655306354,0.3002898097,0.2113054693,0.3186536133,-0.112101078,-0.1920291781,0.2805584967,0.1296021342,-0.1342128664,-0.3024221957,0.1002639085,0.0776538029,-0.5770205855,-0.2875574231,-0.1081864834,0.1591961682,-0.2118961662,-0.1913761348,-0.043241445,-0.1544982642,0.1063505411,0.0733333454,-0.0112977447,-0.0536589101,0.1988221407,0.184065178,-0.0033984715,-0.5394589901,0.2152126282,-0.3142087162,0.2942136228,-0.3065520525,-0.0951433256,0.1869513541,0.4066514373,0.1032526344,0.3489806354,0.1195347905,-0.258071661,-0.2762503028,0.0872939229,-0.0699587241,0.4045149088,0.1960490644,0.3381276429,-0.0284422114,0.0692702308,0.1954560876,-0.0628730208,0.2469357103,-0.4772729576,-0.0635710508,0.4770826995,-0.0077677444,-0.5834079385,-0.4588001668,-0.0287328344,-0.3379411995,-0.20796974,0.1575303078,0.1054935157,-0.2072849274,0.3233111203,-0.0341942124,0.2794035077,-0.3262607753,-0.1136728674,-0.1501775235,0.2062861472,-0.202261284,0.2235945165,0.1539401561,0.1776582748,0.2562233508,0.2119543105,0.2691634595,0.0598442294,0.2062682062,-0.2372273058,-0.1175263748,-0.1481491625,-0.034479402,0.0774111077,0.188406527,-0.2724808753,-0.1226747632,-0.0750541613,-0.3015033007,-0.0087420894,0.1592018306,0.1332612932,0.1258597076,0.0164227914,-0.2135912329,0.3548077345,-0.1503959894,-0.2694523036,0.2013503164,-0.0721282884,-0.2583304048,-0.1263648421,0.2293128222,-0.326433748,-0.1708662957,0.0443102866,0.2678412199,-0.0854357705,0.0589666888,0.3666197956,0.001542429,0.1980131865,-0.1521051228,-0.592318356,0.665184021,-0.7773889303,-0.0657990947,-0.0005517157,-0.1136176586,-0.1577291936,0.1390874684,0.0293319412,-0.296353519,-0.2013741434,0.3400664628,-0.0311039146,-0.1501097679,-0.3194093108,-0.0926349238,0.3367892504,0.4528051913,-0.1628918052,-0.0424935967,-0.1178130805,-0.0854873806,-0.0417402908,0.4088159502,0.069665581,-0.1271550059,0.0218936987,-0.1831500381,0.1643564403,0.2299869061,-0.2909383178,0.2540509999,0.1076658666,-0.9638232589,-0.2275743484,0.4533105195,-0.254757911,-0.6550403833,-0.2037677616,0.3090624213,-0.2672728002,0.0978610367,-0.1616641879,0.3661294878,0.0615179986,-0.1269472837,0.0966967791,0.0601762086,-0.004192079,-0.3270719051,-0.1326188743,0.1051758304,0.0788128972,0.2529537976,0.4529838562,0.4550014734,0.2661785185,0.1476079077,0.2340745926,0.2281712145,0.1169733554,0.3165963292,0.2823441923,0.1658865958,0.068170093,-0.0669259802,0.4591511488,0.3160399795,-0.1627162695,-0.195754379,0.4131931067,-0.4846227467,0.1242772117,0.0279041398,0.0121889245,-0.2059757113,0.1272017211,-0.4066193402,0.0046629691,0.2397213429,-0.1088741049,0.1383101642,-0.2654803693,0.0767369121,-0.0614245497,0.1953823417,0.0215802733,-0.0366844498,-0.1353598088,-0.0005478906,-0.0433564112,-0.0906143486,-0.2082996368,0.1854825616,0.1760426611,0.2232332528,0.1223328337,-0.3682170212,-0.1841252595,-0.0535663925,-0.0573278256,0.0160507746,0.0237289872,0.111025393,-0.0454005003,0.138350904,-0.2840763628,-0.2592444718,0.1057535112,-0.3508684337,0.100574173,-0.0994009376,-0.1100445762,-0.1776327342,-0.5282853842,-0.1343939006,-0.3477758169,-0.0261526201,0.0755158067,0.2976169884,-0.2034307271,0.2193649858,0.1803251803,-0.1081453189,0.3281554878,0.1060339138,-0.2106098682,-0.2679209113,0.1780754328,-0.2215532362,0.0079398891,0.2463553399,-0.1296310127,-0.0070060128,0.1543599516,-0.2549619675,-0.0985813141,-0.0539383031,0.2266653478,0.0624235868,-0.2883571386,-0.2040559649,0.3045315444,0.23586604,-0.0245086085,-0.2616884112,0.0340933166,-0.1985142529,0.2060337663,0.0521294214,-0.1808520854,0.0633005723,-0.0806743503,0.4839153886,0.2462226748,-0.0729641691,0.2791992724,-0.3118186295,0.2519313097,0.3141075969,0.3775237501,-0.3375565112,-0.2846821249,0.3150162697,-0.4162310362,-0.3131348789,-0.0967826098,0.0625382811,-0.0130871767,-0.0851631984,0.0659299865,-0.3470347226,0.0388141908,-0.0413189791,-0.1230345145,0.1854137927,0.2472903132,-0.2666775286,-0.0591413565,-0.1068544686,0.0815363601,-0.0134485196,0.1035355926,0.5398586988,-0.278263092,0.1178236455,0.1310110092,0.3444875777,0.3714714348,0.4343833029,0.054828763,0.1600300223,0.0291566886,0.0896737799,-0.1720827818,0.3043068349,0.077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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/216","title":"\u2753 How to get ROUGE-2 with the ROUGE metric ?","comments":"Well I just tested with the official script and both rouge1 and rougeL return exactly the same thing for the input you gave, so this is actually fine ^^\r\n\r\nI hope it helped :)","body":"I'm trying to use ROUGE metric, but I don't know how to get the ROUGE-2 metric.\r\n\r\n---\r\n\r\nI compute scores with :\r\n\r\n```python\r\nimport nlp\r\n\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n    for lp, lg in zip(p, g):\r\n        rouge.add([lp], [lg])\r\nscore = rouge.compute()\r\n```\r\n\r\nthen : _(print only the F-score for readability)_\r\n\r\n```python\r\nfor k, s in score.items():\r\n    print(k, s.mid.fmeasure)\r\n```\r\n\r\nIt gives :\r\n\r\n>rouge1 0.7915168355671788\r\nrougeL 0.7915168355671788\r\n\r\n---\r\n\r\n**How can I get the ROUGE-2 score ?**\r\n\r\nAlso, it's seems weird that ROUGE-1 and ROUGE-L scores are the same. Did I made a mistake ?\r\n\r\n@lhoestq ","comment_length":34,"text":"\u2753 How to get ROUGE-2 with the ROUGE metric ? \n I'm trying to use ROUGE metric, but I don't know how to get the ROUGE-2 metric.\r\n\r\n---\r\n\r\nI compute scores with :\r\n\r\n```python\r\nimport nlp\r\n\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n    for lp, lg in zip(p, g):\r\n        rouge.add([lp], [lg])\r\nscore = rouge.compute()\r\n```\r\n\r\nthen : _(print only the F-score for readability)_\r\n\r\n```python\r\nfor k, s in score.items():\r\n    print(k, s.mid.fmeasure)\r\n```\r\n\r\nIt gives :\r\n\r\n>rouge1 0.7915168355671788\r\nrougeL 0.7915168355671788\r\n\r\n---\r\n\r\n**How can I get the ROUGE-2 score ?**\r\n\r\nAlso, it's seems weird that ROUGE-1 and ROUGE-L scores are the same. Did I made a mistake ?\r\n\r\n@lhoestq  \n Well I just tested with the official script and both rouge1 and rougeL return exactly the same thing for the input you gave, so this is actually fine ^^\r\n\r\nI hope it helped :)","embeddings":[0.1239903495,-0.4979508221,-0.088984549,0.3463133872,-0.0841549039,-0.2206081003,-0.3729408681,0.0803578719,0.0360454321,0.2210490704,-0.2184779197,0.2334054857,-0.0111088082,-0.0014882199,0.0768619254,-0.2843629718,-0.01158862,-0.0326346718,0.0853345841,-0.2482199073,-0.1077575386,0.4054566026,-0.1544699073,0.2524574697,0.0654295012,0.3166961372,0.1700794101,0.2143239379,-0.4328656495,-0.1821267009,0.1652713269,0.0503630638,0.1496954411,0.2326778769,-0.0001142661,-0.2925375402,0.1849433631,-0.1414395273,0.1738614589,-0.6005346179,0.0583251007,-0.2691497505,-0.0811973214,-0.0645638555,-0.111022912,-0.1932000518,-0.2885150015,-0.1273674369,0.3743640482,0.2322625369,0.128094852,-0.2020400912,-0.0613144375,-0.0511284806,0.2469224036,-0.0883085206,0.0855068937,0.560139358,0.2660447061,-0.0374686196,0.0814225748,0.1509750187,0.231712684,0.0097445082,0.3903579712,0.1786261052,0.2437990755,-0.0938263983,-0.2231394649,0.3274884224,0.1833605468,-0.1542437077,-0.2336723953,0.1524391025,0.0272758584,-0.5276965499,-0.2502237856,-0.0963611454,0.2021198571,-0.1536117941,-0.2424559593,-0.0307536498,-0.2164042443,0.0850343779,0.0001797684,-0.0070787435,-0.0669669732,0.1687986851,0.2031937093,0.0118653523,-0.5337010026,0.2507768869,-0.3072173595,0.3494211733,-0.370957464,-0.1641173661,0.1327220947,0.4145557284,0.1002429798,0.4011620581,0.2246950269,-0.1404869854,-0.2672238052,0.1099193469,-0.0330820121,0.446931988,0.2299090624,0.3148649633,-0.0278021004,0.0251831245,0.148238197,-0.0226450935,0.2313371748,-0.4569521844,-0.0380098373,0.3978686333,-0.0274011027,-0.6806560159,-0.4113565981,0.0514224954,-0.321680963,-0.1402047426,0.1125793308,0.0980577096,-0.192164585,0.3302403986,-0.0078441193,0.2373747081,-0.3315191865,-0.1402775198,-0.0784643069,0.228780821,-0.2513328195,0.2152004093,0.1888292134,0.215188086,0.2448729575,0.2056077719,0.3032256365,0.0292105619,0.2829253376,-0.2480921745,-0.0271209087,-0.1494309306,-0.0716711283,0.0604845621,0.1127492487,-0.2844979167,-0.0919369161,-0.0552065,-0.2921645641,0.0097009111,0.1461049765,0.1334361136,0.0784384087,0.0127959736,-0.1120268926,0.3773163557,-0.2059684694,-0.26619941,0.2162994444,-0.1462043226,-0.282631129,-0.125677973,0.2128628939,-0.3043458462,-0.1561815441,0.0847705603,0.2528171241,-0.05326242,0.0146218482,0.406099081,0.0686085597,0.1522846371,-0.0909740776,-0.523879528,0.6502172947,-0.7507551908,-0.0961676016,0.0344627537,-0.080551222,-0.1089170799,0.0822352841,0.0219223499,-0.3146571219,-0.1447133422,0.281575948,0.0107191913,-0.0870100632,-0.3107838333,-0.1504411697,0.3970875442,0.4027325213,-0.0719477087,-0.0827456638,-0.1770658493,-0.1273582876,-0.0169230569,0.4076839685,0.0375565253,-0.1250233799,0.1138971671,-0.196759209,0.1464032382,0.272482723,-0.3125997484,0.3493408561,0.0773144439,-0.8894868493,-0.1724050641,0.4622159898,-0.2256922275,-0.6651101708,-0.215151459,0.3109326363,-0.3100345731,0.1021758616,-0.2257461399,0.3516809642,0.113977775,-0.1536433101,0.206938535,0.0619798601,0.0069304379,-0.3190974891,-0.1620740891,0.1061552316,0.0585050546,0.2559978664,0.4742427468,0.4499497712,0.2268804908,0.168827042,0.2143981606,0.2538842559,0.0349721834,0.258299619,0.2095764726,0.1959803849,-0.0184683632,-0.0774406195,0.5199013948,0.3037608266,-0.1541365236,-0.1943849921,0.4662745595,-0.4903933406,0.0574423149,0.0301229544,0.0035356553,-0.234408319,0.0489471927,-0.4269281328,0.0082103843,0.3378639221,-0.072214745,0.1508091837,-0.2143831402,0.0776554719,-0.0587222949,0.1669665128,-0.0231587589,-0.0700709522,-0.0991679057,0.048283577,-0.0524791405,-0.0871328712,-0.1464817375,0.1367138177,0.1424647272,0.1714953184,0.2192103863,-0.4117482305,-0.1840170622,-0.0079123853,-0.1532541811,0.0121837473,0.0154098012,0.0615092665,-0.0738395602,0.0518397205,-0.3049067259,-0.3022273779,0.0679023713,-0.3260828555,0.0888367966,-0.101811111,-0.1412836909,-0.1743475646,-0.4596986473,-0.1507118344,-0.3067997098,-0.0374746583,0.073218368,0.299755156,-0.2984479964,0.2118731886,0.2086891681,-0.0963942558,0.3506272435,0.0990061983,-0.2984908819,-0.2188930213,0.1485156566,-0.2297235727,0.0109957904,0.2069463879,-0.1500375867,-0.0032068694,0.1473244578,-0.2885562181,-0.0999546126,-0.0252586491,0.164885357,0.0557277091,-0.3303431571,-0.2132251412,0.3580795825,0.157057777,-0.0432526506,-0.2501104772,0.0918164849,-0.1292127669,0.1360247433,-0.005322271,-0.2049795836,0.0756529272,-0.0168042276,0.4890827537,0.2321534604,-0.0259796474,0.2851453722,-0.3540248871,0.212523669,0.2565449774,0.3914870322,-0.3267377317,-0.2725757062,0.3942364752,-0.435858041,-0.3721909821,-0.1029465869,0.1051651612,0.029439263,-0.0444234647,0.0571284555,-0.3295976818,0.0541082136,-0.0908435509,-0.1752023697,0.1330196112,0.2248006463,-0.2669880986,-0.0401029736,-0.1279033422,0.096050106,-0.002311877,0.1314290166,0.5086541176,-0.2481072396,0.0772690028,0.1404628903,0.2756562829,0.4029237926,0.4460240901,0.049148038,0.1697974652,0.0227131192,0.0836678892,-0.1309285164,0.282029748,0.0421498567,-0.1210120469,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/215","title":"NonMatchingSplitsSizesError when loading blog_authorship_corpus","comments":"I just ran it on colab and got this\r\n```\r\n[{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train',\r\nnum_bytes=611607465, num_examples=533285, dataset_name='blog_authorship_corpus')},\r\n{'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation',\r\nnum_bytes=35652716, num_examples=30804, dataset_name='blog_authorship_corpus')}]\r\n```\r\nwhich is different from the `dataset_infos.json` and also different from yours.\r\n\r\nIt looks like the script for generating examples is not consistent","body":"Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.","comment_length":53,"text":"NonMatchingSplitsSizesError when loading blog_authorship_corpus \n Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. \n I just ran it on colab and got this\r\n```\r\n[{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train',\r\nnum_bytes=611607465, num_examples=533285, dataset_name='blog_authorship_corpus')},\r\n{'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation',\r\nnum_bytes=35652716, num_examples=30804, dataset_name='blog_authorship_corpus')}]\r\n```\r\nwhich is different from the `dataset_infos.json` and also different from yours.\r\n\r\nIt looks like the script for generating examples is not consistent","embeddings":[-0.1318606585,0.1403096616,0.059409298,0.421038717,-0.1027606875,0.0639110357,-0.0993900523,0.2403311878,-0.1699515879,0.1674250662,-0.0025761358,0.2693625987,0.1059267074,-0.0292154495,-0.0612888411,0.116175577,-0.0757438838,0.2383864522,0.066430375,0.0098237591,-0.0296085756,0.4090332091,-0.2907909751,-0.0837625861,-0.2635079324,-0.2044773251,-0.0076957922,0.2664904296,-0.0280386489,-0.2925414443,0.2720763385,0.0110679027,0.1331819445,0.1919158548,-0.0001248043,0.0228433479,0.550614059,-0.2505318224,-0.2113020569,-0.2491973341,-0.4140854776,-0.2816134691,0.0394140258,-0.3059527576,-0.0753069445,0.1337391585,0.1635834575,-0.0529763326,0.2451409698,0.3204348981,0.1107066944,0.0796384364,-0.2686454356,0.1345798671,0.1266217679,0.0818086118,-0.0369906351,-0.0744712278,-0.1828916818,-0.1793120056,-0.0033597462,0.4617771208,-0.2776548862,0.1398821175,0.1918159872,0.1794870496,0.3482623994,-0.1386475712,0.1591957808,0.3170470893,0.4259499609,0.1577037722,-0.2048099488,-0.4242800474,-0.0219616164,0.1000380963,0.4799427688,0.3248815835,-0.2373103946,0.0331986733,-0.4330620468,-0.076351136,-0.0368182212,0.0093937339,0.1595861018,0.3218357861,0.0860608891,0.0775717348,0.0999623761,0.0130117293,0.3436033726,-0.2797834575,-0.2161586881,0.0420845933,-0.1020336077,-0.1977801323,-0.182736963,0.0624412261,0.156547457,0.0754775777,0.3709217012,-0.0862541124,0.1798445284,0.1177612022,0.3715391457,-0.0136769041,-0.0289674569,0.5226837397,0.1068688184,-0.009326756,0.1176358163,0.0450279489,0.2935001552,-0.1499844342,-0.1944580972,0.1255706251,0.1616524309,-0.6070366502,-0.4450771213,0.0165156908,-0.5715517998,-0.2078129947,-0.055248756,0.2461667955,-0.3623109162,0.2653523386,-0.1877928376,-0.0375909582,-0.3258183002,-0.0176446829,-0.1811062694,0.1677558273,-0.1367729008,0.257212013,0.1405435801,-0.1857023835,0.4198700786,-0.1785368174,-0.0307090133,-0.154903546,0.2606299818,-0.1932947189,0.0708691105,0.2253680974,0.0033081858,0.1662946194,-0.0920059085,-0.2952809036,-0.3580850065,0.1588877887,-0.2516792715,-0.5216350555,0.1034586579,0.1205809936,-0.4729642868,-0.0364872813,-0.0369016379,0.2329141051,0.4576260149,-0.3340524435,-0.0269594621,-0.1752264351,-0.2176076472,-0.0436786823,-0.2373847216,0.4392350614,0.1435437053,-0.2371553481,0.1398679465,0.0678040758,0.6044771671,0.3637804985,-0.002590589,0.0724760145,-0.1579887271,0.3344545662,0.1393306106,-0.3792095482,-0.220191434,0.4759375453,0.0770473778,0.2783876956,0.2617090642,0.1201213151,0.1928377748,-0.1457988918,0.1319236755,0.3967820108,-0.0160179604,0.0646982715,-0.4757282138,-0.3340060711,0.4510889947,0.1048206836,0.1060980335,0.0166583806,-0.0662410483,0.4533866048,0.2774912417,-0.1179685816,-0.0385755822,0.178915143,-0.2975054085,-0.003019769,-0.0089946594,0.0459393449,-0.4310577512,-0.0618973635,-0.1615540832,0.2573902011,0.0215470344,-0.3396947682,-0.1260259151,-0.3853749633,-0.0126097491,-0.0381515138,-0.0249999687,0.3279168308,0.389162451,0.0201133378,0.0473103113,0.516518712,-0.0756102204,0.1980000734,-0.7434920073,0.2497137785,-0.0970003754,-0.2209163755,0.4876715243,0.294072032,0.1883365959,-0.2082684189,-0.128199935,0.4203102589,-0.0991321951,0.4102058411,-0.3028308451,0.0094528515,0.2866004705,-0.2159036547,-0.0950905457,0.3127256334,-0.0143619888,-0.0296804067,-0.0852994472,0.4560612738,-0.355229497,0.1949430257,0.0601800233,-0.0744410902,0.1126468629,-0.2118273079,-0.2256216407,-0.4647096395,0.5550792217,-0.0112827644,-0.3389958441,0.0956973657,-0.2214524448,0.0983054042,0.4710589647,0.09217491,-0.1332444549,-0.1031344831,0.1075798199,-0.1303173751,0.2442013621,0.2132988572,0.4979693294,0.1266188174,-0.0254707485,0.0588753745,-0.1459996402,-0.3809733391,0.1322558671,-0.0173103679,0.143349424,0.2880009711,0.0936621428,-0.0403743759,-0.2727138102,-0.0497351997,0.1679785103,0.2461205721,-0.2921513617,-0.0409143791,-0.3865088224,-0.4747574925,-0.2583890259,-0.1584340036,-0.216693297,-0.3360840678,0.1613166928,0.2477845401,-0.132611528,0.0692499056,-0.1683563441,0.0661015287,-0.3122084141,-0.0732158124,-0.0626509041,-0.2862415612,-0.2431289405,-0.0438643284,0.1981613338,0.3729360998,0.2036013603,-0.42539379,-0.2250232846,0.1412787586,-0.2914526463,0.1450170428,-0.0265262555,-0.1244560331,0.0801766738,-0.217384696,0.2646995485,-0.2417492121,0.0449195132,-0.1044062376,-0.3310623467,-0.1003105938,0.1255846471,0.0045288811,-0.2103860229,-0.4195670485,-0.2590777576,-0.3447067142,-0.1063203663,0.2822982073,0.3047814667,0.1186570451,-0.1598611176,-0.1130956933,-0.0577273406,0.2196367383,-0.340500772,-0.0982074142,0.2392107099,-0.0872906819,-0.1138919741,0.0112999985,-0.0569706783,-0.0334286913,0.152232483,-0.5655209422,-0.2071268409,-0.1663601846,-0.1016507372,-0.2521025538,-0.2789226174,0.5203965306,-0.0568067804,0.0354509577,0.065879561,-0.4510561228,0.0322562382,0.167688325,0.5530168414,-0.1850311309,0.2017791122,-0.0593964048,0.4893557727,0.5270174146,0.0005125082,-0.1543316394,0.152440533,0.1464681327,0.0762568712,-0.0376843177,0.3673476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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/215","title":"NonMatchingSplitsSizesError when loading blog_authorship_corpus","comments":"The files provided by the authors are corrupted and the script seems to ignore the xml files that can't be decoded (it does `try:... except UnicodeDecodeError`). Maybe depending of the environment some files can be opened and some others don't but not sure why","body":"Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.","comment_length":44,"text":"NonMatchingSplitsSizesError when loading blog_authorship_corpus \n Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. \n The files provided by the authors are corrupted and the script seems to ignore the xml files that can't be decoded (it does `try:... except UnicodeDecodeError`). Maybe depending of the environment some files can be opened and some others don't but not sure 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/215","title":"NonMatchingSplitsSizesError when loading blog_authorship_corpus","comments":"Feel free to do `ignore_verifications=True` for now... The verifications only include a check on the checksums of the downloaded files, and a check on the number of examples in each splits.","body":"Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.","comment_length":31,"text":"NonMatchingSplitsSizesError when loading blog_authorship_corpus \n Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. \n Feel free to do `ignore_verifications=True` for now... The verifications only include a check on the checksums of the downloaded files, and a check on the number of examples in each splits.","embeddings":[-0.1591764539,0.0613832586,0.0613702349,0.3985078633,-0.09111882,0.0681395456,-0.1541567892,0.3743870556,-0.0539890267,0.1291232109,0.0949106291,0.1696771979,0.050890822,-0.0702365413,-0.0299417637,-0.0313884914,-0.0462678969,0.2041032165,0.0438870601,0.0010297163,-0.0196369626,0.4404026568,-0.3017500937,-0.0229673106,-0.2469935268,-0.1683312207,-0.0421307385,0.3281975985,0.0039551742,-0.3751760125,0.2439582944,-0.0751470774,0.1352920681,0.1908108741,-0.000121871,-0.0134052187,0.5785955787,-0.2156344354,-0.2062556744,-0.25492239,-0.4047058821,-0.3622810245,-0.083601512,-0.2355190068,-0.021686472,-0.0550388619,0.1720563173,-0.0859804302,0.2725291848,0.2854221761,0.1191038489,0.1523060501,-0.1194791868,0.0312914997,0.1314683855,0.1114799976,-0.0382259116,-0.0960215852,-0.0876140743,-0.2862399817,-0.1037817448,0.4399676025,-0.1971516311,0.1551972777,0.2258738726,0.11542999,0.400872916,-0.1145614833,0.0375812799,0.3904350996,0.5094953179,0.1603325605,-0.2086892426,-0.506644249,-0.0504567102,0.193820864,0.4156517982,0.364884764,-0.2391003668,0.0084717777,-0.4111099839,-0.0739310309,-0.0905095637,0.101740256,0.2668362558,0.1985677481,0.1183715761,0.0067396951,0.1296718568,-0.0410987772,0.1146330684,-0.244287923,-0.1399923265,0.1128277257,-0.1838782728,-0.193074137,-0.1448516548,-0.0174066816,0.2120881826,0.2155574709,0.3518286943,-0.0540539734,0.2256540507,0.1119684651,0.428317368,0.0479068607,-0.0107361032,0.5196453333,0.0832171366,0.0033718608,0.1151564568,0.0596730672,0.2659813464,-0.219095245,-0.1451855898,0.1199168414,0.1594631672,-0.5667434335,-0.4398617744,0.108157292,-0.5615888834,-0.2434341908,-0.0924841166,0.1756622344,-0.3614130318,0.3320044577,-0.1538780034,0.0477984622,-0.3616379201,-0.0594461039,-0.2066724449,0.170678705,-0.1974993199,0.205324918,0.2014609128,-0.1554444134,0.4026421309,-0.1078296602,-0.0586714819,-0.1968979836,0.401135087,-0.1081409603,0.0200106185,0.1572443396,-0.0291808303,0.1699854136,-0.0008439791,-0.2169329971,-0.3886170685,0.0795449987,-0.223024413,-0.5368488431,0.1046898589,0.1530231833,-0.4432890415,-0.0612912364,0.0962721482,0.2109623849,0.3643842638,-0.3324509263,-0.0974242464,-0.2202017158,-0.1294719428,-0.0545685366,-0.2055283487,0.4950897992,0.1402006447,-0.2660527229,0.130453065,-0.0182818323,0.5335456133,0.349309206,0.0343344957,0.0964401215,-0.1925892532,0.4875856042,0.1357104778,-0.3542971611,-0.1630119681,0.5454872251,0.0286830738,0.2807053328,0.2512045503,0.1191703603,0.1942970008,-0.0937625468,0.0107714497,0.4080866873,-0.0216021389,0.0351920836,-0.4514915943,-0.3096883297,0.4148988128,0.2066952139,0.1179958284,-0.0422624536,-0.0019413965,0.4964993894,0.3046291471,-0.1359486133,0.053966511,0.2919709682,-0.3540271819,0.1288687885,0.038535621,-0.1150446013,-0.3697339296,-0.0230219252,-0.2869818509,0.2818144858,0.1069893241,-0.2584026158,-0.1996174753,-0.4155744016,-0.0252256133,-0.0299624354,0.010713621,0.281521976,0.397457391,0.0511937663,0.0685104057,0.4284547865,-0.062188752,0.1739074141,-0.796795547,0.2320177257,-0.1187641472,-0.2111772895,0.5127651095,0.3413342535,0.2236994207,-0.1693107188,-0.0957317874,0.4365608394,-0.1037369147,0.4019582868,-0.1740678698,0.010552736,0.207886681,-0.295458734,0.0182179827,0.3747373223,-0.0284037571,-0.0686894059,-0.1335939765,0.4167664349,-0.3382882476,0.1654530913,0.0687626153,0.0150097655,0.0781649724,-0.238795951,-0.1913959235,-0.4554651678,0.6415628791,-0.095137015,-0.3222642243,0.0422930382,-0.2010635138,0.0330332108,0.4396526814,0.0218652375,-0.0908261016,-0.0264099967,0.0283896141,-0.160866186,0.0957219303,0.2750542164,0.5138388276,0.1237687096,0.0233142655,0.0150964083,-0.1677722335,-0.3314580917,0.1229685172,-0.0629390255,0.027277194,0.3670684099,-0.0033677937,-0.0319100954,-0.3108036518,-0.0123697128,0.1595615149,0.2488149554,-0.3282091022,-0.1341907233,-0.3583884537,-0.404402107,-0.3453231752,-0.1350653917,-0.2826974392,-0.3694410026,0.2020636797,0.1999648362,-0.1682595015,0.1388691515,-0.1668097675,0.0433350764,-0.3156359196,-0.0528267287,-0.0185160059,-0.2104287446,-0.2734283507,-0.0262423493,0.2240412682,0.4267170131,0.2425216883,-0.392234385,-0.1577690244,0.0467405058,-0.2580870688,0.0625039861,-0.0561855622,-0.151365906,0.176835835,-0.1386253834,0.3756527603,-0.2218138576,0.1016403735,-0.1899563521,-0.3374041617,0.0273164678,0.1791119277,0.035421472,-0.1923547089,-0.5341876745,-0.2685709596,-0.3635152578,-0.0623655543,0.2855772376,0.2418258786,0.0317275077,-0.1204401776,-0.1327033788,-0.1704918742,0.2503778636,-0.301707834,-0.0588897243,0.2404206246,-0.0978669599,-0.1675153971,-0.0314710289,0.0028389059,-0.0506655537,0.1228749007,-0.5468320251,-0.1095873564,-0.205603078,-0.0794581175,-0.3269419968,-0.2575340569,0.5263426304,-0.0734755918,-0.0241890699,0.0135372495,-0.3449537754,0.0683690906,0.1019062549,0.5138315558,-0.1611067802,0.1840385497,-0.0143205607,0.5651077032,0.5774591565,0.0766566023,-0.1154994592,0.1767917275,0.0924454182,0.1740482748,0.0098281307,0.3770256639,0.0225284211,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/215","title":"NonMatchingSplitsSizesError when loading blog_authorship_corpus","comments":"I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset(\"imdb\")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset. ","body":"Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.","comment_length":29,"text":"NonMatchingSplitsSizesError when loading blog_authorship_corpus \n Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. \n I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset(\"imdb\")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/215","title":"NonMatchingSplitsSizesError when loading blog_authorship_corpus","comments":"> I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset(\"imdb\")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset.\r\n\r\nWhen the checksums don't match, it may mean that the file you downloaded is corrupted. In this case you can try to load the dataset again `load_dataset(\"imdb\", download_mode=\"force_redownload\")`\r\n\r\nAlso I just checked on my side and it worked fine:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"imdb\")\r\nprint(len(dataset[\"train\"]))\r\n# 25000\r\n```\r\n\r\nLet me know if redownloading fixes your issue @EmilyAlsentzer .\r\nIf not, feel free to open a separate issue.","body":"Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.","comment_length":100,"text":"NonMatchingSplitsSizesError when loading blog_authorship_corpus \n Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. \n > I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset(\"imdb\")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset.\r\n\r\nWhen the checksums don't match, it may mean that the file you downloaded is corrupted. In this case you can try to load the dataset again `load_dataset(\"imdb\", download_mode=\"force_redownload\")`\r\n\r\nAlso I just checked on my side and it worked fine:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"imdb\")\r\nprint(len(dataset[\"train\"]))\r\n# 25000\r\n```\r\n\r\nLet me know if redownloading fixes your issue @EmilyAlsentzer .\r\nIf not, feel free to open a separate issue.","embeddings":[-0.1760099977,0.1923228204,0.0658075586,0.4156303108,0.012083455,0.193692103,-0.060682267,0.3878968954,-0.0591741167,0.1324820668,-0.0745147988,0.1716659218,0.0093409549,-0.1365589201,-0.0743669644,0.0001403463,-0.0610307083,0.148367539,0.0798779055,0.035928838,-0.0695639327,0.4301069975,-0.3462486863,-0.0888401791,-0.2378142327,-0.1127559021,0.0294194818,0.3264782727,0.0462553427,-0.362829715,0.2542954683,-0.0668971688,0.1275963783,0.301310122,-0.000123778,0.0187365673,0.6639293432,-0.2431373149,-0.1930371374,-0.3823130727,-0.2983905673,-0.2795032263,-0.0311899316,-0.2351135463,0.0000467804,-0.0455775484,0.1376455873,-0.1730502546,0.2249151021,0.3440396488,0.0974616855,0.1834928989,-0.0925603881,0.1112376601,0.2157849818,0.1207833514,-0.0489259101,-0.0144525822,-0.0177792516,-0.1584300846,-0.0385266319,0.386231631,-0.332886517,0.117399022,0.2570728064,0.149095431,0.3444834054,-0.1964065731,0.0314813405,0.3853250742,0.4552387893,0.0204213522,-0.3434735537,-0.438593775,-0.0033312282,0.0954965949,0.3857685626,0.3656494915,-0.2501454055,0.0412016883,-0.3448823988,0.0202553086,-0.123211056,0.1056362167,0.1657611728,0.0833722576,0.1527453512,-0.0133266831,0.0407765508,-0.0361177921,0.1446164101,-0.2656333447,-0.1059982851,0.0934185162,-0.1632417142,-0.1253025234,-0.1892376244,-0.0111017358,0.1671974361,0.1999484599,0.2925247252,0.0101394793,0.191058889,0.1882573068,0.469216913,-0.0550310351,-0.1077874675,0.5959754586,0.2141971886,0.0886910558,-0.0053858538,-0.0311124939,0.2485347092,-0.2011571229,0.0270569436,0.1297849119,0.2478101552,-0.5492246151,-0.5105435848,0.1785078794,-0.4724813402,-0.1294340193,-0.0333772078,0.2145411223,-0.3933617175,0.3246038258,-0.0649747923,0.1226364151,-0.3191176355,-0.0042859917,-0.1595644504,0.1274048835,-0.1345994771,0.1621820182,0.289172411,-0.1728476882,0.4029968977,-0.1103069633,0.0636114776,-0.2159257084,0.3070893884,-0.2068894506,0.065681994,0.2388720661,0.0471848585,0.2478989661,0.0007347426,-0.2099758983,-0.3573240042,0.1365561336,-0.2047909498,-0.4814672768,0.0713409409,0.1207398176,-0.3954432607,-0.0478433408,-0.0742641538,0.0361789316,0.3651829064,-0.4267303646,-0.0959469229,-0.2285137922,-0.0722583681,-0.0359340496,-0.1052930355,0.5175378919,0.1542671472,-0.1689531654,0.1386117041,0.0594591536,0.5408571959,0.3914321661,0.0583208688,0.0255098529,-0.1914317757,0.376696825,0.2035428286,-0.5307164788,-0.2367614061,0.5350233316,0.1370008439,0.3692914844,0.2253634185,0.1480951458,0.2658122778,-0.0620126352,0.016567653,0.423915863,-0.0463287123,0.0777939856,-0.3728453517,-0.2930844128,0.3921938837,0.1419514269,0.1503074318,0.043526344,-0.021335233,0.4459542632,0.323789537,-0.0458554961,0.0935382023,0.2675275505,-0.2806486189,0.2581963539,0.0494296029,-0.220071733,-0.3241138756,0.0276459549,-0.1992612481,0.2994350791,-0.0963057578,-0.2697671354,-0.1718753874,-0.4570285678,-0.0420803465,-0.0074901036,-0.0066508995,0.2086012214,0.2889246345,0.1307027191,-0.0683569536,0.4250882566,-0.0938529223,0.2411918938,-0.8024850488,0.2409891933,-0.1955877095,-0.3173593283,0.4423464239,0.2632887065,0.1987364143,-0.149531275,-0.1410844624,0.4424902797,-0.0311328247,0.4205708802,-0.3902110755,0.0709907487,0.3251236081,-0.3283196688,-0.0136464769,0.3792682588,0.0683734417,-0.091307804,-0.0997138619,0.3737076819,-0.3763327003,0.219367981,0.130830586,0.0296715721,0.1335918605,-0.2117361724,-0.272849232,-0.5291529894,0.5458315015,-0.0643115565,-0.312831223,0.0047126291,-0.2317238897,-0.063375555,0.4167566001,0.0390755311,-0.071549952,-0.0163541958,-0.0056102965,-0.097375907,0.0756489262,0.2083393335,0.4420288503,0.0971901342,0.0137554286,0.0784996748,-0.2087429613,-0.3179133534,0.1633073092,0.0187568143,0.0033221028,0.4489204884,0.0139329536,0.0849304721,-0.2789880037,-0.0302163623,0.1136982739,0.1889391243,-0.4271098375,-0.0949904174,-0.4230710566,-0.3812933564,-0.3252099454,-0.1917271614,-0.152148515,-0.3741566539,0.102218762,0.335670948,-0.0933515877,0.1520268768,-0.2747816145,-0.0771090239,-0.2856377661,-0.1671758443,-0.0400727093,-0.2553856969,-0.2222131193,-0.0603001043,0.2851201594,0.4162527919,0.2191535085,-0.4314364791,-0.1876335591,-0.0039614425,-0.2128283232,0.0471390933,-0.0921220183,-0.0109143779,0.1246257499,-0.0966826528,0.3530616462,-0.3084542751,0.1366530657,-0.1865907758,-0.2865263224,0.0870189667,0.1654970944,0.0059019439,-0.1674925834,-0.4755873978,-0.2604391873,-0.3520001173,-0.1506460309,0.2697541118,0.1826926023,0.0912531465,-0.1219488308,-0.1085171327,-0.0393231586,0.3502364457,-0.2149682194,-0.1261631995,0.2993681133,-0.1570667475,-0.1714879125,0.0066655269,-0.0190337375,-0.0280244853,0.1299344897,-0.5250765681,-0.1266783178,-0.2553530335,-0.0194481555,-0.2235793322,-0.2169738114,0.40171808,-0.0199700054,0.0072240285,-0.0070943772,-0.3523037732,0.0274134036,0.0531912521,0.3816002309,-0.134692505,0.3094494343,-0.034442164,0.4264933169,0.5960376859,0.0360532515,0.0276391506,0.071342811,0.1436524987,0.0747463033,-0.1054499149,0.2657156885,-0.0729660168,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/215","title":"NonMatchingSplitsSizesError when loading blog_authorship_corpus","comments":"I wasn't aware of the \"force_redownload\" option and manually removed the '\/home\/me\/.cache\/huggingface\/datasets\/' dir, this worked for me (dataset 'cnn_dailymail')","body":"Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.","comment_length":19,"text":"NonMatchingSplitsSizesError when loading blog_authorship_corpus \n Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. \n I wasn't aware of the \"force_redownload\" option and manually removed the '\/home\/me\/.cache\/huggingface\/datasets\/' dir, this worked for me (dataset 'cnn_dailymail')","embeddings":[-0.0595206991,0.1531680971,0.0704791918,0.3878914118,-0.0151472939,0.1419075578,-0.0923853889,0.3200816214,-0.0493569039,0.0081106173,-0.0866535455,0.1576867253,0.0365480743,0.0461476073,-0.0197161119,0.089791663,-0.045651257,0.2041377276,0.0454466417,0.0290790945,0.0009838509,0.5112382174,-0.2444841117,-0.0645389184,-0.3506295979,-0.1498450339,0.0544163249,0.3629713655,0.0106631266,-0.3910149038,0.3197171986,-0.0472841188,0.1141889095,0.2401960343,-0.0001225521,-0.0028425201,0.4699682295,-0.2191404551,-0.2050274163,-0.2703422606,-0.2168163657,-0.2991623282,-0.0463629216,-0.2182562351,-0.066741392,-0.0665390417,0.0799646303,-0.0519919395,0.3719491065,0.2974589765,0.1255471259,0.0959382281,-0.1272954643,0.1026805043,0.0848564133,0.0665248334,-0.0118846782,-0.0418558493,-0.172001794,-0.3219947815,-0.1281981915,0.5180576444,-0.1915793717,0.1262466908,0.1819391698,0.0800840184,0.2830646634,-0.1194166392,0.0679115728,0.3065373302,0.4064788222,0.1085797623,-0.2249051183,-0.5339212418,0.0071086911,0.0513608046,0.4527995586,0.3414332271,-0.1976626068,0.1005414873,-0.4158658087,-0.2263238728,-0.0545980595,0.0072788424,0.213900879,0.2067903578,0.0388180427,-0.0100916652,0.1374930739,-0.0378837809,0.224408865,-0.0922396928,-0.1446522772,0.0849092007,-0.2126174271,-0.1594928801,-0.1238902807,0.0378153287,0.1740897149,0.153521955,0.2372107655,-0.0782316625,0.1446377188,0.0419277325,0.3693934977,0.1749555022,-0.0018965586,0.4282423556,0.1405402869,0.0332893133,0.1102929413,0.003799333,0.2939818799,-0.1572295725,-0.0670643449,0.0451962948,0.2099593282,-0.5436299443,-0.4165382385,0.0798673034,-0.4917491674,-0.2023522109,-0.1229509637,0.1320477128,-0.3499180973,0.3523450494,-0.0788428336,0.0378077105,-0.3732774258,-0.0559959561,-0.2014294714,0.1090007573,-0.1936449111,0.2363349497,0.2714067996,-0.2821205556,0.4370520711,-0.0514768139,-0.0933633447,-0.2561869025,0.2646710575,-0.0307807121,-0.0232919883,0.1904060394,-0.1313756704,0.2399367094,0.0085804611,-0.2008440346,-0.2873153687,0.0163729154,-0.2768751383,-0.6388433576,0.0812386647,0.1171502993,-0.4046169817,-0.0596416369,0.0174986944,0.2203211784,0.3705640733,-0.260425508,-0.1059131399,-0.1687602401,-0.1013388857,-0.029852597,-0.1653283238,0.5042775869,0.1327273846,-0.3224145472,0.1607013941,-0.0313843079,0.4741957188,0.4351542592,-0.0065021119,0.1073416546,-0.1725720763,0.3553568721,0.1428028047,-0.3949939013,-0.25507918,0.4000764191,-0.0010343348,0.2915739715,0.1832810938,0.1310875714,0.092016153,-0.0645947754,0.0591232218,0.4729750752,0.0742925406,0.0992293283,-0.452811271,-0.4169977903,0.4330647886,0.1688203067,0.1097708866,0.0770275891,-0.0199286789,0.4880301654,0.3150661588,-0.1443881243,0.0633854643,0.3153182268,-0.3313460946,0.1586517394,0.0493910871,-0.0894606039,-0.5167136192,0.0077179689,-0.2730098367,0.3195577264,0.0504484996,-0.1981106102,-0.2350796461,-0.4627166688,-0.0789123029,-0.180663079,0.0007562484,0.2350212336,0.4090325832,0.0545505546,-0.0182933733,0.5882976055,-0.0964862183,0.2448498905,-0.884516418,0.2083256245,-0.1277618259,-0.1689957678,0.412289232,0.28587237,0.2703772783,-0.1416527331,-0.1319459081,0.4632707238,-0.2258070111,0.4788300693,-0.2279370576,-0.0022640536,0.2731258571,-0.2956173122,0.021120755,0.2180161327,-0.0465648845,-0.0552712381,-0.0661935881,0.3710585833,-0.3695765734,0.1385298371,0.0175738316,-0.0447714627,0.1000527516,-0.2872813642,-0.1437971741,-0.4895391762,0.578715384,-0.1502457857,-0.2525633574,0.0214846786,-0.1338328421,0.0163826402,0.3793990314,0.0649042577,-0.0964649171,-0.0319759324,-0.0160528589,-0.2002706826,0.0535912029,0.2453751862,0.5202929378,0.1364887804,0.0263755564,0.0923336148,-0.1641586721,-0.3306786716,0.1524433047,-0.0643642396,0.082352072,0.3692166805,0.175146237,-0.0021436773,-0.5159095526,0.1042157263,0.2119722068,0.2446636707,-0.3040995896,-0.1348413527,-0.3648944199,-0.3866154552,-0.2667773068,-0.1904563159,-0.3162629008,-0.3300524652,0.2057839334,0.285590589,-0.1177580953,0.1592735499,-0.0924357772,0.0607687272,-0.3277837634,0.0091106473,-0.109533146,-0.275867939,-0.2055223733,-0.0503627546,0.1038396284,0.3221497536,0.1894622594,-0.3560625911,-0.1991535574,-0.0704813153,-0.3379549086,0.0474636145,-0.0875671953,-0.0716575757,0.0805038214,-0.0683810934,0.3928327858,-0.1585329026,0.1405427605,-0.2609811425,-0.3093508184,-0.0416108705,0.1713787615,0.1956088841,-0.1676418483,-0.5419095755,-0.2437300086,-0.3634908497,-0.0151568409,0.2141262144,0.1851948798,0.1718039215,-0.1324822605,-0.2091613561,-0.1113305613,0.2509459555,-0.392528981,-0.1390981078,0.195878163,-0.0768251568,-0.1303210109,-0.0714152902,0.1017391905,-0.0858046189,0.0706128478,-0.6237045527,-0.2201807648,-0.2419821471,-0.0938954949,-0.3028301895,-0.1780101806,0.5007231236,-0.0454118252,-0.0041329497,0.0809499621,-0.4596955478,0.0179481264,0.1331700832,0.4873123765,-0.1790713072,0.2381062061,-0.019556094,0.5533105731,0.5295944214,0.0531762056,-0.0099898465,0.1688424796,0.2262649983,0.0400814116,-0.1329494268,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/215","title":"NonMatchingSplitsSizesError when loading blog_authorship_corpus","comments":"Yes I think this might not be documented well enough. Let\u2019s add it to the doc @lhoestq @SBrandeis.\r\nAnd everything on how to control the cache behavior better (removing, overriding, changing the path, etc)","body":"Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.","comment_length":34,"text":"NonMatchingSplitsSizesError when loading blog_authorship_corpus \n Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. \n Yes I think this might not be documented well enough. Let\u2019s add it to the doc @lhoestq @SBrandeis.\r\nAnd everything on how to control the cache behavior better (removing, overriding, changing the path, etc)","embeddings":[-0.0478634462,0.2781361639,0.0776575059,0.3915254474,-0.1035541743,0.0364038125,-0.082682997,0.2459606081,-0.083533369,0.0461862758,0.0891772434,0.174503088,0.0406080708,-0.1594099849,0.0179090556,0.0527998805,-0.0644671917,0.1248318329,0.1059798971,0.0428972952,-0.0105633764,0.3961234689,-0.1937782615,-0.040419735,-0.3053342998,-0.2336776257,0.0290222913,0.2334820032,0.0402284861,-0.4769920707,0.246647805,0.0566287637,0.1005317718,0.1670151651,-0.0001217629,-0.0632687733,0.5389484763,-0.1948429793,-0.2518877983,-0.0297897793,-0.3836036026,-0.2797572911,0.0038723538,-0.2842761874,0.0151785146,0.0235366579,0.1792152226,-0.1652392,0.229137063,0.3027590215,0.1213609725,0.109807238,-0.1881100684,0.1678940505,0.1732044816,-0.0276303552,-0.0647589192,-0.1565779895,-0.1117098108,-0.2819305658,-0.2403718829,0.4598938525,-0.2357244492,0.1500597298,0.2640921175,0.0883090869,0.3216771185,-0.0872647092,0.0130944196,0.3902913928,0.5862080455,-0.0468333252,-0.2849666774,-0.4985668957,-0.129673779,0.0286405049,0.5057656169,0.2795498967,-0.1570384949,0.0537012741,-0.4670750201,-0.1777775139,-0.0526111387,-0.00081012,0.3168709576,0.1980743259,0.1256275326,-0.0516351461,0.0307559595,-0.0101765851,0.2684497237,-0.1816899776,-0.1249931604,0.0854497477,-0.2321656793,-0.205163464,-0.0267253444,0.0911648199,0.2567503452,0.2174571157,0.4005258977,0.0058857715,0.2139704376,0.124216035,0.3358573914,0.2084189802,-0.0148705738,0.3932438493,0.1530789882,-0.0024870699,0.1373304725,0.0575754307,0.3515795469,-0.2048659772,-0.0110226339,0.2019051462,0.1597347856,-0.4877766073,-0.3507695198,0.1007281244,-0.3997793198,-0.260506779,-0.0586719476,0.2424853891,-0.3720569313,0.3226313591,-0.2690640688,-0.0335225835,-0.449954778,-0.1279056221,-0.2333000749,0.1798157245,-0.1468560398,0.338239193,0.239137724,-0.079446502,0.3943142891,-0.1676047742,-0.0994360372,-0.1319212317,0.3565537632,-0.1095732972,0.0893370956,0.2085787952,-0.0928035825,0.1552503854,-0.0484711193,-0.09950158,-0.485160321,0.0410177112,-0.3274896443,-0.671756506,0.0978627577,0.1147465035,-0.3720282912,-0.0490013696,0.1598810554,0.3045910299,0.5192116499,-0.3798107803,0.0015793662,-0.087711826,-0.1982912868,-0.1586367488,-0.1705338061,0.5231297612,0.0726187676,-0.3068563044,0.1242673993,0.0117179537,0.4826217294,0.3697668314,-0.0356649272,0.1533680558,-0.164187178,0.3558746576,0.1711984575,-0.4163642526,-0.2768096328,0.380389452,0.1063763201,0.2749008536,0.2496744096,0.1237706095,0.2442066967,-0.1929773241,0.0163563173,0.4298614264,-0.0630663037,0.093382135,-0.5093785524,-0.418787837,0.4363307953,0.1158372611,0.0694449022,0.0362953991,-0.0036744236,0.4742714763,0.1634601802,-0.085600771,0.098512955,0.2544678748,-0.3477673829,0.139681682,0.0839437395,-0.0734169707,-0.4933947623,-0.0299287718,-0.2795148492,0.1781546026,0.1481744349,-0.3201975226,-0.0982877761,-0.4774520695,-0.0937388912,-0.1365209669,0.0294803251,0.2034240365,0.5127832294,0.0822759047,0.019414166,0.5455961227,-0.006967701,0.1613585651,-0.8287149072,0.0638813823,-0.1368913502,-0.1838449389,0.3626117706,0.3595058322,0.2163828164,-0.0509442054,-0.0946413875,0.5340718627,-0.1414176077,0.4370479286,-0.2205443531,0.1521022022,0.2498196065,-0.2066895664,0.1167725325,0.2149262279,-0.1372483075,-0.002253104,-0.0948957726,0.3331183195,-0.374682188,0.086203523,0.0674552694,-0.1066150814,0.057475891,-0.255736351,-0.2727244198,-0.4599678516,0.5414722562,-0.0531041995,-0.2366636992,0.0136255063,-0.119912751,0.1013706997,0.4620293081,0.1043645069,-0.0153187793,-0.0629961863,0.0948043838,-0.2510462701,0.2087735236,0.2167150825,0.4686730206,0.1513731033,0.0737202018,0.0190006085,-0.1884738803,-0.336895138,0.1008825153,-0.0162118748,-0.0242401138,0.2759978473,0.0113301482,-0.0014322649,-0.3033410311,0.0221082624,0.191207841,0.1761494726,-0.3325128555,-0.0619676672,-0.3928717375,-0.3235004842,-0.2495739311,-0.1301067173,-0.2025113106,-0.3588606119,0.3024480045,0.1672642231,-0.2591952085,0.1308683753,-0.2516002059,0.1859038919,-0.276306957,-0.1052640676,-0.073060967,-0.2778658271,-0.2761032283,-0.0637727082,0.116792582,0.3153985739,0.3178684711,-0.415217787,-0.2130653858,0.0406742804,-0.3145892024,0.0671834499,0.059937343,-0.1061162055,0.1001225263,-0.2141468376,0.3740353584,-0.2210167497,-0.0558545925,-0.2211214155,-0.2521099746,-0.0674769878,0.2016217709,0.2455025017,-0.1995059699,-0.5588034987,-0.3258545101,-0.3663263321,-0.0570198968,0.1090074778,0.2995633483,0.0821922049,-0.2107693851,-0.2101608515,-0.1878654957,0.3125407994,-0.3795473874,-0.1632731259,0.3584213555,-0.1916775703,-0.0763096586,0.044109486,-0.0424670316,-0.071176447,0.1271946579,-0.5488827825,-0.1745836586,-0.1155960485,-0.0415999331,-0.2191823721,-0.2206530273,0.639005065,-0.0446474664,0.0478066728,0.0388732255,-0.3613528907,0.0701284856,0.0347477123,0.5672549605,-0.041620668,0.2360640019,0.0665677413,0.5585208535,0.4746896625,0.144145444,-0.0248094015,0.3020043075,0.1700406373,0.0852005705,0.0948510394,0.3581473529,-0.0735527873,-0.2010853291,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/215","title":"NonMatchingSplitsSizesError when loading blog_authorship_corpus","comments":"Already fixed:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"blog_authorship_corpus\")\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['text', 'date', 'gender', 'age', 'horoscope', 'job'],\r\n        num_rows: 689793\r\n    })\r\n    validation: Dataset({\r\n        features: ['text', 'date', 'gender', 'age', 'horoscope', 'job'],\r\n        num_rows: 37919\r\n    })\r\n})\r\n","body":"Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.","comment_length":44,"text":"NonMatchingSplitsSizesError when loading blog_authorship_corpus \n Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. \r\n\r\n```\r\nraise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', \r\nnum_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, \r\ndataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', \r\nnum_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), \r\n'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, \r\ndataset_name='blog_authorship_corpus')}]\r\n```\r\n\r\nUpon checking it seems like there is a disparity between the information in `datasets\/blog_authorship_corpus\/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. \n Already fixed:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"blog_authorship_corpus\")\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n    train: Dataset({\r\n        features: ['text', 'date', 'gender', 'age', 'horoscope', 'job'],\r\n        num_rows: 689793\r\n    })\r\n    validation: Dataset({\r\n        features: ['text', 'date', 'gender', 'age', 'horoscope', 'job'],\r\n        num_rows: 37919\r\n    })\r\n})\r\n","embeddings":[-0.0711071789,0.0366600752,0.04065625,0.3773265481,-0.0409733206,0.1363148689,-0.1240489408,0.3613279462,-0.0730661899,0.139670819,0.0171019062,0.1724952012,0.0348222107,-0.0652470738,-0.0861273557,0.0030060625,-0.0310244262,0.2065264583,0.0756496862,0.0239882004,-0.0526128784,0.3719523251,-0.2651765049,-0.071922414,-0.2073655725,-0.1239107624,-0.0102689331,0.2495513856,0.0412777402,-0.3996244073,0.2213791907,-0.1118549407,0.1721773893,0.2491206378,-0.0001249258,0.0326962918,0.511829555,-0.2296480685,-0.180483982,-0.2756153047,-0.3376261592,-0.4276964366,0.0168950967,-0.2696827054,-0.0643672422,-0.0759701207,0.0779451802,-0.112987034,0.277669698,0.3391931057,0.108400017,0.2249470204,-0.1934705824,0.1029748619,0.0743277222,0.0293578748,-0.0615083948,-0.0974561498,-0.1899436712,-0.2223801315,-0.0192021709,0.5000542402,-0.2779343128,0.1802406311,0.1965823174,0.1666819602,0.4029516876,-0.1281586885,0.0177654549,0.3015245795,0.4271243215,0.0764632374,-0.2973222733,-0.530202508,-0.0813803896,0.1460972279,0.3747635484,0.3606251776,-0.2342333049,0.0510240942,-0.3578081727,-0.0959917083,-0.0774408877,0.0825837851,0.2239559293,0.2442955971,0.1169911176,0.0159484092,0.0559062995,0.0123723503,0.2057384998,-0.3118657768,-0.0846870542,0.0952453241,-0.2150491029,-0.2024640292,-0.1346189976,-0.0912089124,0.1307764947,0.139858216,0.3494260013,-0.0952951238,0.2775011361,0.0660321936,0.4427476227,-0.0197889972,-0.1377231032,0.5545028448,0.0536927991,-0.0310327839,0.1535988599,0.0003340048,0.3343847692,-0.1820984781,-0.0528143346,0.1330490559,0.2744681537,-0.4923458993,-0.4501271844,0.1251451224,-0.5866674185,-0.1903084368,-0.1022366062,0.1611679345,-0.3953562677,0.3667610586,-0.187671423,0.0831981897,-0.334972471,0.0191690996,-0.2089672238,0.1853954494,-0.1051612571,0.2149007469,0.1611838341,-0.1813781708,0.3889810741,-0.0920421779,-0.0647489876,-0.1896371692,0.3035332561,-0.1890227944,0.0499620177,0.1915422827,-0.018234171,0.2436743975,0.0237190258,-0.1914799362,-0.3913136423,0.1243167594,-0.2425898761,-0.5291267037,0.1211742535,0.1480145901,-0.4233920574,-0.0826734379,0.0946053788,0.1543079317,0.4115430415,-0.2833365202,-0.0370274186,-0.1974685788,-0.1134148464,-0.0646389201,-0.1957641244,0.4935912788,0.1807061881,-0.2693584263,0.1669444591,0.0594332702,0.5444743633,0.4168525934,0.0357016884,0.0946139693,-0.17766276,0.355966866,0.187839359,-0.4151934683,-0.2399203628,0.5062507987,0.0147453249,0.3810857236,0.308857888,0.1639283746,0.1679847389,-0.0625511333,0.0953242108,0.4112588763,-0.033507172,0.0647728816,-0.4574200809,-0.3119203746,0.51858145,0.1685633063,0.1512960941,-0.0000894955,-0.0888109133,0.4161501825,0.3256084621,-0.1250260472,0.0288260728,0.2729932368,-0.2934310734,0.1688521802,0.0039303796,-0.1952166557,-0.3699212968,-0.0435619578,-0.2237849385,0.2626386583,0.0076298094,-0.2988544405,-0.1989631504,-0.4404383302,-0.0823375508,-0.0510393716,0.0069273422,0.2655256093,0.4304527938,0.0895081237,0.0335958749,0.4959015548,-0.0109866494,0.2284482569,-0.7593405247,0.2463506609,-0.1114588603,-0.2208027244,0.4527971745,0.3438969553,0.2338722199,-0.1370243728,-0.1336905956,0.3799188733,-0.1214729398,0.3614656329,-0.3569770455,0.0534895323,0.2235933989,-0.2682149112,-0.0054585561,0.4480225146,0.0375547335,-0.0402230024,-0.0746922791,0.4673366249,-0.3202619851,0.2021022439,0.0907128528,-0.0118939392,0.0247490462,-0.2131212205,-0.2415355742,-0.5174604058,0.567861259,-0.1021780521,-0.3153872788,0.0385450646,-0.190927878,-0.028225271,0.4396681488,0.060513027,-0.0907286629,-0.0609937422,0.0051531736,-0.0852829963,0.0844773725,0.216630578,0.5267036557,0.1216591746,-0.0469596274,0.1034684926,-0.1432747543,-0.2923764288,0.1202701107,0.0202835072,0.0351327769,0.3795378804,0.0757460073,0.0205469225,-0.2466588318,0.0290828198,0.1501139402,0.2274646163,-0.333561033,-0.0930938944,-0.3773392737,-0.3696152866,-0.3705157936,-0.2244632691,-0.2208389193,-0.3733644783,0.1391502172,0.2003883868,-0.1649987698,0.1180667579,-0.271011889,-0.1123094857,-0.3024365604,-0.1863212287,-0.0000405913,-0.2092605978,-0.1546286494,-0.0539903194,0.1140400767,0.3784252107,0.2770924568,-0.35989663,-0.1976271272,0.0077405954,-0.2402825803,0.0310289897,-0.0486895777,-0.1855034381,0.1168393195,-0.1636329293,0.4102542996,-0.1946711093,0.1455712765,-0.1180051416,-0.2683082819,0.0457181446,0.209194243,0.0770104155,-0.1461026073,-0.5317420959,-0.3520465195,-0.3627156019,-0.1308265477,0.2208127528,0.2409707606,0.0662402287,-0.1241693944,-0.1761159152,-0.1305198967,0.273270905,-0.269058615,0.0228425488,0.248506695,-0.0634472221,-0.1146764159,-0.0624054372,-0.0534180626,-0.1118669435,0.1366766542,-0.5014885664,-0.1364085227,-0.2978779674,-0.0490878336,-0.2984606326,-0.2005054802,0.5503311157,-0.038510479,-0.0023704448,0.0464076139,-0.3843616843,0.0260429233,0.1441857964,0.4148395658,-0.1687086821,0.3040979207,-0.0094639892,0.4915162623,0.5918393135,0.0471624061,-0.0789787769,0.1546161473,0.132616654,0.102011092,-0.0883353204,0.2796732783,-0.02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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/211","title":"[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type","comments":"Here the full error trace:\r\n\r\n```\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-1-7aaf3f011358> in <module>\r\n      1 import nlp\r\n      2 ds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\n----> 3 ds.map(lambda x: x, load_from_cache_file=False)\r\n\r\n~\/python_bin\/nlp\/arrow_dataset.py in map(self, function, with_indices, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, arrow_schema, disable_nullable)\r\n    549\r\n    550         if update_data:\r\n--> 551             writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n    552\r\n    553             # Create new Dataset from buffer or file\r\n\r\n~\/python_bin\/nlp\/arrow_writer.py in finalize(self, close_stream)\r\n    182     def finalize(self, close_stream=True):\r\n    183         if self.pa_writer is not None:\r\n--> 184             self.write_on_file()\r\n    185             self.pa_writer.close()\r\n    186         if close_stream:\r\n\r\n~\/python_bin\/nlp\/arrow_writer.py in write_on_file(self)\r\n    104         \"\"\"\r\n    105         if self.current_rows:\r\n--> 106             pa_array = pa.array(self.current_rows, type=self._type)\r\n    107             first_example = pa.array(self.current_rows[0:1], type=self._type)[0]\r\n    108             # Sanity check\r\n\r\n~\/hugging_face\/venv_3.7\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/hugging_face\/venv_3.7\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\n~\/hugging_face\/venv_3.7\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Could not convert TagMe with type str: converting to null type\r\n```","body":"Running the following code \r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, load_from_cache_file=False)\r\n```\r\n\r\ntriggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.\r\n\r\nOn the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:\r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, remove_columns=[\"entity_pages\"], load_from_cache_file=False)\r\n```\r\n\r\n. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?\r\n\r\n**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly.","comment_length":155,"text":"[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type \n Running the following code \r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, load_from_cache_file=False)\r\n```\r\n\r\ntriggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.\r\n\r\nOn the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:\r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, remove_columns=[\"entity_pages\"], load_from_cache_file=False)\r\n```\r\n\r\n. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?\r\n\r\n**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly. \n Here the full error trace:\r\n\r\n```\r\nArrowInvalid                              Traceback (most recent call last)\r\n<ipython-input-1-7aaf3f011358> in <module>\r\n      1 import nlp\r\n      2 ds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\n----> 3 ds.map(lambda x: x, load_from_cache_file=False)\r\n\r\n~\/python_bin\/nlp\/arrow_dataset.py in map(self, function, with_indices, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, arrow_schema, disable_nullable)\r\n    549\r\n    550         if update_data:\r\n--> 551             writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file\r\n    552\r\n    553             # Create new Dataset from buffer or file\r\n\r\n~\/python_bin\/nlp\/arrow_writer.py in finalize(self, close_stream)\r\n    182     def finalize(self, close_stream=True):\r\n    183         if self.pa_writer is not None:\r\n--> 184             self.write_on_file()\r\n    185             self.pa_writer.close()\r\n    186         if close_stream:\r\n\r\n~\/python_bin\/nlp\/arrow_writer.py in write_on_file(self)\r\n    104         \"\"\"\r\n    105         if self.current_rows:\r\n--> 106             pa_array = pa.array(self.current_rows, type=self._type)\r\n    107             first_example = pa.array(self.current_rows[0:1], type=self._type)[0]\r\n    108             # Sanity check\r\n\r\n~\/hugging_face\/venv_3.7\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib.array()\r\n\r\n~\/hugging_face\/venv_3.7\/lib\/python3.7\/site-packages\/pyarrow\/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\n~\/hugging_face\/venv_3.7\/lib\/python3.7\/site-packages\/pyarrow\/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Could not convert TagMe with type str: converting to null type\r\n```","embeddings":[0.0478234887,-0.0149945924,0.0999606401,0.4827658832,0.2526236176,0.118270196,0.1242586449,0.462997973,0.20459342,-0.1143017784,0.1954923421,0.5628960729,-0.1122900546,-0.214510262,-0.068543978,-0.18655321,0.1165156439,0.2422813624,-0.0039511644,-0.0194521397,-0.1214477792,0.124361366,-0.2644390762,0.1609538198,-0.450982064,-0.1343633235,0.1006035954,0.0705768988,-0.1665291041,-0.4529118836,0.13311553,-0.386316359,-0.0144939888,0.2676903605,-0.0001202561,-0.1023915559,0.3526799679,0.0484397747,-0.261015296,-0.2479179204,-0.4819888473,0.0986581743,0.3085628152,-0.0510609858,0.2251927555,0.0243791398,0.0238793492,-0.5974248052,0.1822833419,0.5890497565,0.1811947525,0.3117505908,-0.0586166196,0.0824080631,0.2270174176,0.0864315182,-0.3474987447,0.1586010456,-0.0418649204,-0.1637495905,-0.0972599834,0.4248969555,-0.1737870723,0.1287671775,0.6042653322,0.1013759524,0.1031220332,-0.3009963036,0.1894645393,0.0022626503,0.2738990486,-0.1859186739,-0.0806752667,0.0309147146,-0.1441309005,-0.3694722056,0.2096119672,0.1765302718,-0.3138512671,0.0176620092,0.0113149639,0.0840630755,-0.0632319376,-0.0736751035,-0.0597238019,0.067420505,0.0130822873,0.1142640635,-0.0543162897,-0.1860047132,-0.189728424,-0.2200310826,-0.2048834711,0.2414929867,-0.3378819525,0.048407536,-0.0579255559,-0.2089133859,-0.1648839414,-0.1189332604,0.2784720659,-0.0002529972,0.1446985453,0.1296414286,0.3333248198,0.2861360013,0.3204212785,0.2487848103,0.0986191705,-0.3073056638,-0.1854353249,-0.0136373509,-0.0114122313,-0.0097023286,0.5141096115,0.0163877103,0.3002519608,-0.3601064086,-0.3549937606,-0.0110491002,-0.7358067632,0.1599646956,-0.169023782,0.1066551134,-0.0082527203,0.4835471511,0.2476842701,0.2814028859,-0.3391996026,-0.1926279962,-0.1142075062,-0.0042975568,-0.2985197008,0.0156220309,0.3052985668,-0.0363648497,0.0539230071,0.0657088831,-0.4077064991,-0.2089091837,0.3739091754,-0.4226520061,0.1788685918,-0.0087085897,-0.3287378848,0.415250808,0.2411908805,-0.4089022577,-0.2129134387,0.1715905517,-0.13049604,0.0368987098,-0.1928086728,0.1247835532,0.1592132598,-0.2186264098,-0.1702641696,-0.0653255433,0.3925433457,-0.1083274633,0.1037179083,-0.2929559052,-0.0403226614,-0.2410746217,-0.0862942711,0.4788666368,-0.2231424302,0.0666108057,-0.0555775538,0.1835543215,0.3641396165,0.3781097531,-0.0203731935,0.1771148741,0.1071878076,0.2820343673,0.4739594162,-0.3007650971,-0.1000544131,0.0992059857,-0.0288218465,-0.0070865471,-0.0418070704,0.1214025915,0.0770688877,-0.1202662885,0.1674464494,0.1947706938,-0.0623199306,0.2353259474,-0.420689404,-0.1015318036,0.5093363523,-0.1742017865,-0.0756611675,0.0260948651,-0.0768755227,0.0502024256,-0.0676269755,-0.0130599225,0.4281769693,0.260076344,-0.0904399976,-0.0825848132,0.0832560062,-0.1882934272,-0.3873153925,-0.0520174839,-0.1053638533,0.133559376,-0.1724085957,-0.0403863043,-0.3455444574,0.0745703951,-0.0271313544,-0.1989130229,0.125344798,0.1082480922,0.1589182913,0.058027789,-0.2278685123,0.1251087636,0.0048239664,0.1273974925,-0.3979255557,-0.1481667906,-0.0802366436,-0.3554536402,-0.0991665199,0.1799599826,0.2376293093,0.167554155,-0.2698638439,0.1873165816,0.0186801143,0.2171432972,-0.254982084,0.0299019367,0.0187678691,-0.4021503031,0.2202013135,0.3269737065,0.361649245,-0.2435902208,-0.3964939117,-0.0214629062,0.0146005685,0.5858560205,-0.1325559169,0.108533524,-0.0767762065,-0.0362331606,0.09410505,-0.0797639191,0.0960107297,-0.1133068502,0.197952792,0.2924374342,-0.3097238243,0.2726490796,0.4216948152,0.3002494872,0.1342764795,0.1598110348,-0.3345211446,-0.0856646225,0.264416337,-0.0123851011,0.4610905945,-0.043286074,0.2145610303,-0.084927313,-0.1507028341,-0.1485962123,0.357840091,-0.0966183916,0.017265385,-0.0092660841,0.4907109439,0.0106255123,-0.1787142158,0.2008915693,0.0055136667,0.2383064181,-0.3552833498,0.3290377855,-0.3391131163,-0.1678956449,-0.3810411394,-0.2544403672,-0.0185093954,-0.5028193593,-0.1405358016,0.0792334303,0.0061113108,0.1203892753,-0.2541172802,-0.1418566704,0.0180851668,-0.1107123569,-0.3347356617,-0.3540616333,-0.3705020845,-0.0632876456,0.4646281004,0.3231955767,0.1873808503,0.053034436,-0.0629505813,-0.0690104067,-0.3603878021,0.0305633377,-0.1989224106,0.0571852922,0.1203932911,0.0761394128,-0.136030376,-0.1272963732,0.4256206751,0.0031831567,-0.2406023741,0.3291910887,-0.0207711682,0.0490220152,-0.2927949727,-0.4807073772,0.0135379611,-0.4682863951,0.1370299757,-0.1121180058,0.2093194723,0.1776323467,0.1237757653,0.3106841445,-0.0557440855,0.1851253062,0.2070276141,0.3653668463,0.6162765026,-0.1331360489,-0.2379468679,0.2084654719,-0.0769689456,0.3138104975,0.2074887305,-0.1788210571,0.3327707052,-0.4218785763,0.3207609653,-0.163307339,-0.1305053681,0.2217357457,0.2210210115,-0.0480525903,-0.0604383834,-0.0075168535,0.2183129489,-0.217765525,0.1517935246,0.1262533069,0.2142089605,-0.0534500591,0.3817186058,0.1439975351,-0.2284086347,0.283031106,0.0168088023,-0.047595825,-0.3022206724,-0.2784267068,-0.0881357342,-0.2115705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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/211","title":"[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type","comments":"Actually thinking a bit more about it, it's probably a data sample that is not correct in `trivia_qa`. But I'm a bit surprised though that we managed to write it in .arrow format and now cannot write it anymore after an \"identity\" mapping.","body":"Running the following code \r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, load_from_cache_file=False)\r\n```\r\n\r\ntriggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.\r\n\r\nOn the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:\r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, remove_columns=[\"entity_pages\"], load_from_cache_file=False)\r\n```\r\n\r\n. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?\r\n\r\n**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly.","comment_length":43,"text":"[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type \n Running the following code \r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, load_from_cache_file=False)\r\n```\r\n\r\ntriggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.\r\n\r\nOn the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:\r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, remove_columns=[\"entity_pages\"], load_from_cache_file=False)\r\n```\r\n\r\n. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?\r\n\r\n**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly. \n Actually thinking a bit more about it, it's probably a data sample that is not correct in `trivia_qa`. But I'm a bit surprised though that we managed to write it in .arrow format and now cannot write it anymore after an \"identity\" mapping.","embeddings":[0.1208235994,0.0813008696,0.088923201,0.4082977474,0.2814412713,0.0546012893,0.1137645766,0.3365483284,0.18600972,-0.186949715,0.2484580576,0.5634832382,0.0009860162,-0.0783177614,-0.0235590022,-0.1181143671,0.0730633289,0.2508925498,0.0506293997,-0.1244531348,-0.0924610645,0.1616433859,-0.1819394678,0.2225300521,-0.5130993128,-0.158728376,0.1441280693,0.080267176,-0.1196371838,-0.4990111291,0.0155159179,-0.3473426104,0.0308181904,0.2083607018,-0.0001191395,-0.165944159,0.3734931946,-0.0522149019,-0.1960327476,-0.2107961476,-0.5531388521,0.1664822996,0.3032821119,-0.0672414526,0.2399285287,-0.1021348611,0.0658057779,-0.5268741846,0.0961819738,0.4953772724,0.1949684322,0.1500273943,0.1189303175,0.0855403394,0.2670636475,0.1191722527,-0.2874541581,0.0432622246,-0.13040483,-0.2241957039,-0.0693887472,0.3475057781,-0.0491954423,-0.0045447596,0.5662901402,0.0445675813,0.1975944936,-0.1864271164,0.2564823329,0.0585066713,0.3549563587,-0.0617081113,-0.1086405069,0.0988542885,-0.1233241186,-0.3633818626,0.2884753346,0.2225210816,-0.2659069002,-0.0122918878,0.1163758263,0.0631242916,0.0097148437,-0.1246361732,-0.1565378457,0.1494541615,0.0186119024,0.11266765,-0.050113596,-0.197682336,-0.1804719716,-0.0793853924,-0.3031898737,0.2140202522,-0.2830839157,0.0116387224,-0.0516032428,-0.1638733,-0.2018789798,-0.0879100785,0.4137466848,-0.0001443101,-0.005353089,0.067548573,0.3144080937,0.4288606346,0.4697208703,0.0982727483,0.1366083026,-0.3048892617,-0.1826519817,-0.0344758928,-0.0252424069,-0.0097393803,0.4327159822,0.0240835566,0.0781966895,-0.3047506213,-0.2590415776,0.0268414505,-0.7115644813,0.2219687253,-0.216433391,0.1374626607,0.0648862645,0.4073728621,0.2952431142,0.2960962057,-0.2042858005,-0.2012081146,-0.1123460829,-0.0987347513,-0.3487420678,-0.1523423791,0.192709595,-0.0855007321,0.0856177956,0.0216545276,-0.5190793276,-0.2234737873,0.2967338264,-0.2855610549,0.2961819768,0.0604484342,-0.2745224535,0.4591630697,0.2122778594,-0.5164351463,-0.0973500162,0.0927968323,-0.2046000361,0.0706736818,-0.1819467545,0.1397913992,0.0373096131,-0.2744913399,-0.2020525485,-0.0635189414,0.3884651959,-0.1685949862,0.0939295813,-0.2046537399,-0.0126877688,-0.2841027379,-0.1489118785,0.3834084868,-0.1865783185,0.1084365919,-0.0751806051,0.1669818163,0.3044075966,0.3455912769,0.0228832755,0.1997970492,0.1055131555,0.3500428796,0.3927366734,-0.2295797467,-0.0097290073,-0.0022533732,-0.0176022891,-0.0076589468,-0.0582821034,-0.0028022679,0.042020902,-0.1174990311,0.0826956704,0.1232978255,0.0120985182,0.2591465414,-0.3936404586,-0.0621934682,0.4800761938,-0.3127318025,-0.2060414106,-0.0303945281,0.0164995361,0.1014273167,-0.0264296494,-0.0937464014,0.3080484867,0.1747931391,-0.0706622899,-0.1480063796,0.1571844518,-0.0412917696,-0.3996143341,-0.0596142188,-0.1100006402,0.1341490746,-0.0511530638,0.0029529678,-0.2747970819,0.0522805527,-0.0077958298,-0.2527814507,0.1424805522,0.0631686226,0.1646498889,0.1263308078,-0.2156060785,0.0616247617,0.0216923766,0.1292787641,-0.3672278523,-0.2625304163,0.0105466442,-0.3314329088,-0.1912827492,0.2312710881,0.2290376127,0.1343213916,-0.2543695867,0.1925375313,-0.0094262837,0.392041862,-0.2142837346,0.0745102167,-0.013634935,-0.5046822429,0.1931379139,0.4102119505,0.2995217443,-0.2957393825,-0.4034367502,-0.0535171553,-0.0316734761,0.5904880166,-0.093098402,0.2558086216,-0.0202942807,-0.06831415,0.0701616481,-0.1256335527,0.1901365817,-0.1690690517,0.1784565151,0.2348818928,-0.2307665944,0.3158989549,0.3804571629,0.327327013,0.2145264894,0.0387076735,-0.3840380907,-0.0601568036,0.2709558904,0.0669727698,0.3688614666,-0.0591402985,0.2242632508,-0.1338997483,-0.1544368416,-0.1763559282,0.3210907876,-0.2017697096,0.0710040629,0.0661776364,0.5125981569,0.017989248,-0.1894221157,0.3008747995,-0.0166824255,0.2317208946,-0.417604059,0.2389861047,-0.311637044,-0.0587846339,-0.3408745229,-0.2117813826,-0.0825011507,-0.5657789707,-0.0303330626,-0.0334508978,-0.0700718388,0.0360913277,-0.1194025055,-0.147991851,-0.1088990644,-0.0712956339,-0.3734095395,-0.4300117791,-0.2597958744,-0.0475394838,0.3729808033,0.2394888401,0.1047431976,-0.0126247574,-0.0753576159,0.0003270398,-0.4714281857,-0.004124477,-0.2229709774,0.067943275,0.1828233004,0.0210705195,-0.0303823911,-0.0144363465,0.3603616059,-0.0667396411,-0.2764347196,0.3501122594,0.0663592964,0.0544083752,-0.2934792936,-0.311521709,0.082166262,-0.4667845368,0.3006452918,-0.0964783654,0.2416380048,0.2114753276,-0.0179771408,0.2288765311,-0.0653321818,0.1617138833,0.1883900911,0.4239374697,0.540738225,-0.2293752283,-0.3174421489,0.2958124578,-0.0376458392,0.3881962299,0.1468117535,-0.0484379753,0.4057910144,-0.3843774498,0.1319413185,-0.1474188864,-0.0709135756,0.1809769124,0.2352913171,-0.0599621199,-0.1455631405,0.0331562944,0.2185854912,-0.0307559539,0.24850595,0.0911097676,0.0045341966,-0.1135431379,0.382022202,0.3040758669,-0.1570863426,0.2629907727,0.0060901893,-0.0635355636,-0.2300884128,-0.2707279921,-0.1288834065,-0.1735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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/211","title":"[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type","comments":"Actually, I know what the problem is...I'm quite sure it's a bug. Here we take some test inputs: https:\/\/github.com\/huggingface\/nlp\/blob\/0e0ef12c14d2175e0b0bd7d8aa814b09e2cd7e1f\/src\/nlp\/arrow_dataset.py#L472\r\n\r\nIt might be that in the test inputs, a `Sequence` type value is an emtpy list. So in my case I have `ds[0][\"entity_pages'][\"wiki_context\"] = []`. => this leads to an `arrow_schema` equal to `null`  for `[\"entity_pages'][\"wiki_context\"]` => see line: https:\/\/github.com\/huggingface\/nlp\/blob\/0e0ef12c14d2175e0b0bd7d8aa814b09e2cd7e1f\/src\/nlp\/arrow_dataset.py#L501  instead of list of string which it should for other examples. \r\n\r\nGuess it's an edge case, but it can happen.","body":"Running the following code \r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, load_from_cache_file=False)\r\n```\r\n\r\ntriggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.\r\n\r\nOn the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:\r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, remove_columns=[\"entity_pages\"], load_from_cache_file=False)\r\n```\r\n\r\n. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?\r\n\r\n**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly.","comment_length":79,"text":"[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type \n Running the following code \r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, load_from_cache_file=False)\r\n```\r\n\r\ntriggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.\r\n\r\nOn the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:\r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, remove_columns=[\"entity_pages\"], load_from_cache_file=False)\r\n```\r\n\r\n. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?\r\n\r\n**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly. \n Actually, I know what the problem is...I'm quite sure it's a bug. Here we take some test inputs: https:\/\/github.com\/huggingface\/nlp\/blob\/0e0ef12c14d2175e0b0bd7d8aa814b09e2cd7e1f\/src\/nlp\/arrow_dataset.py#L472\r\n\r\nIt might be that in the test inputs, a `Sequence` type value is an emtpy list. So in my case I have `ds[0][\"entity_pages'][\"wiki_context\"] = []`. => this leads to an `arrow_schema` equal to `null`  for `[\"entity_pages'][\"wiki_context\"]` => see line: https:\/\/github.com\/huggingface\/nlp\/blob\/0e0ef12c14d2175e0b0bd7d8aa814b09e2cd7e1f\/src\/nlp\/arrow_dataset.py#L501  instead of list of string which it should for other examples. \r\n\r\nGuess it's an edge case, but it can happen.","embeddings":[0.0935577601,-0.0521807335,0.0917302594,0.4922743142,0.2525187731,0.1492104828,0.1087132543,0.4076998234,0.1446877569,-0.0935794711,0.3454068303,0.584651649,-0.1262975335,-0.081480436,-0.039066568,-0.1896718293,0.1572439373,0.2686877549,0.0862914771,-0.0600314774,-0.1431038082,0.150540933,-0.2789250314,0.1321711391,-0.4200149477,-0.1031522155,0.0767619088,0.0571165048,-0.0884185731,-0.4528536499,0.0761469081,-0.3256983161,-0.0065747625,0.2591454387,-0.0001200363,-0.1207483485,0.4130158126,-0.0403311551,-0.2655786276,-0.2495539635,-0.4358327687,0.0318891145,0.3316243291,-0.0424906984,0.1805734038,-0.0637463704,0.0238026846,-0.5875159502,0.2061086893,0.5833625793,0.176483199,0.2174149603,-0.0684086382,0.1302618235,0.2942125499,0.0531598404,-0.338309139,-0.082702823,0.0226327311,-0.1217891127,-0.1331658512,0.3214308619,-0.0617509335,0.0585705712,0.6247366667,0.0932786316,0.1312825382,-0.3116702735,0.1474959999,0.1445359737,0.3962809741,-0.0886665434,-0.08991009,-0.0177704319,-0.1631635725,-0.3528529704,0.2785193622,0.1806472689,-0.2984367311,-0.0033442734,0.0748486668,0.1483760923,-0.0872329921,-0.0642626882,-0.1187096238,0.1531071216,0.0393165834,0.0668651089,-0.1040986702,-0.2040971518,-0.1083757952,-0.2334066629,-0.2524464428,0.31610322,-0.4382790625,0.0379723832,-0.0514421239,-0.137963295,-0.1476160586,0.0629595444,0.3841155767,0.0998169333,0.027437998,0.0716608018,0.3587138951,0.3659048676,0.3182042241,0.1350381672,0.0632362217,-0.2223580182,-0.0815998092,0.0175917875,0.0166998059,-0.039309673,0.4090114534,0.0091384687,0.1497365087,-0.3318137527,-0.2487682402,0.0587406121,-0.7114661932,0.230327636,-0.1983433962,0.0958948359,-0.0247162059,0.5226482153,0.2696353793,0.3240777254,-0.2275396883,-0.1015938893,-0.1469991058,-0.1375631243,-0.2435155362,-0.0453521013,0.2112690806,-0.0735243782,0.1657229066,0.0246097576,-0.4914286137,-0.2029517591,0.266869396,-0.3210095167,0.1762523651,0.0648161396,-0.3296069503,0.4532786608,0.1861167103,-0.3901233375,-0.181904912,0.103387557,-0.1929475814,0.0950197428,-0.1574056447,0.1287296116,0.0248793215,-0.1485266238,-0.3023641407,-0.0322797969,0.3422367275,-0.1120218337,0.1528177857,-0.2467699349,-0.1065720692,-0.2501494586,-0.0631762892,0.4090130329,-0.1929151863,0.0867756382,-0.0184203107,0.1405178308,0.2578379214,0.2855256498,0.0161442813,0.1215701923,0.063467972,0.3946039379,0.4315240085,-0.279081434,0.0075398842,0.1328537911,-0.0355771817,-0.0118561182,0.0022210458,0.1064522564,0.0330913626,-0.2236135006,0.1074813232,0.199086979,-0.0646120831,0.2510938942,-0.4027025104,-0.0663079694,0.4951446652,-0.2019708455,-0.1811583042,-0.0547601432,-0.0537452698,0.1514837444,-0.0141628962,-0.0288183056,0.3827757835,0.3288468122,-0.1439033747,-0.1607178599,0.100635983,-0.1073824242,-0.4678545594,0.011793917,-0.1890222728,0.2026111037,-0.0866255239,0.0280609094,-0.3410979807,0.1253488809,-0.1016520411,-0.2273107171,0.1494240463,0.0541739501,0.1610251516,0.1465349197,-0.1223564819,0.1121611074,0.0134227639,0.0821260437,-0.4639506638,-0.0835918933,0.0000041866,-0.2866661251,-0.0877610743,0.2652682364,0.2704835534,0.1123003811,-0.2972468436,0.2152297646,0.113789238,0.2788777649,-0.4021189809,0.0717955157,0.0632042587,-0.4297591448,0.1421477497,0.4353225231,0.3686198294,-0.3424832821,-0.388802886,-0.0415758975,0.0223490335,0.6803262234,-0.1308950037,0.1441080868,-0.0329954512,-0.0373431332,0.0707539394,-0.0549177192,0.1654370874,0.0179862641,0.123321794,0.3098256588,-0.2486727685,0.2911898494,0.3783714473,0.2824710608,0.1109832972,0.1221673265,-0.3852927983,-0.2181705385,0.3125986457,0.0250501968,0.3757635951,-0.0526905395,0.1864141971,-0.0541679859,-0.1827583611,-0.1757693738,0.3537929952,-0.0584243014,0.1938470602,0.0364122987,0.5011973381,-0.0158940665,-0.1313813627,0.2498072982,-0.0088671697,0.2077242434,-0.4109279811,0.3374972939,-0.4077059627,-0.1314309537,-0.3705583215,-0.1527723372,-0.0243969318,-0.6165552735,-0.0448949672,-0.0183785371,-0.0306285154,0.1224283129,-0.284360379,-0.1316617578,0.0132932328,-0.0776505396,-0.402428329,-0.4299962521,-0.4094293416,-0.0479497463,0.4301170409,0.3920782506,0.1036347523,0.0321872458,-0.1071349531,-0.0362480395,-0.4125105143,0.0343748108,-0.2577631474,0.0903121084,0.1510433257,0.0609035008,0.0196858011,-0.0868504643,0.3246415257,0.0006719413,-0.3420940638,0.3952510655,0.0531295091,0.1424460709,-0.305187732,-0.4616943896,0.0397003852,-0.4655478299,0.1820293069,0.0063488008,0.1815882623,0.2361270338,0.0544263609,0.2801814973,-0.0430229679,0.1691674441,0.1944939494,0.4796045125,0.5730380416,-0.1482831091,-0.3612468243,0.2676405013,-0.133779496,0.2956104875,0.1793285906,-0.1509426236,0.3191586733,-0.3194127977,0.1534694582,-0.1375831217,-0.1262972355,0.239814043,0.1834496409,-0.0422922596,-0.1074573323,0.0315641649,0.2597672045,-0.1865331382,0.1925479919,0.1195823997,0.2033810169,-0.046481736,0.3153975606,0.1880695075,-0.2586817443,0.3378894627,-0.0351511016,-0.0082450388,-0.2894046605,-0.1092589647,-0.0871891677,-0.14598034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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/211","title":"[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type","comments":"Good point, I think the schema should be infered at the writing stage where we have a `writer_batch_size` number of examples (typically 10k) so it's even less likely to run into this scenario.","body":"Running the following code \r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, load_from_cache_file=False)\r\n```\r\n\r\ntriggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.\r\n\r\nOn the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:\r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, remove_columns=[\"entity_pages\"], load_from_cache_file=False)\r\n```\r\n\r\n. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?\r\n\r\n**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly.","comment_length":33,"text":"[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type \n Running the following code \r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, load_from_cache_file=False)\r\n```\r\n\r\ntriggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.\r\n\r\nOn the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:\r\n\r\n```\r\nimport nlp\r\nds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\")  # this might take 2.3 min to download but it's cached afterwards...\r\nds.map(lambda x: x, remove_columns=[\"entity_pages\"], load_from_cache_file=False)\r\n```\r\n\r\n. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?\r\n\r\n**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly. \n Good point, I think the schema should be infered at the writing stage where we have a `writer_batch_size` number of examples (typically 10k) so it's even less likely to run into this scenario.","embeddings":[-0.0682159588,0.0609310456,0.1053825989,0.4737014472,0.2520805001,0.1043800265,0.0982681587,0.3812307119,0.2232889384,-0.0625068694,0.3270524144,0.4799827635,-0.0850764513,-0.0221855119,-0.0457396731,-0.13106893,0.1612938344,0.3236938417,0.0635415837,-0.1218426973,-0.143834874,0.0843204781,-0.2351943105,0.0762036294,-0.5430988073,-0.1695071906,0.0593361631,0.1031856909,-0.1546960771,-0.444822371,0.0146039668,-0.2505456209,0.1371708214,0.310092181,-0.0001177422,-0.1311674863,0.382363826,-0.0452552214,-0.2170929015,-0.1553127021,-0.4626958966,0.060809955,0.2633104622,-0.0684483796,0.1900988221,-0.0022964545,0.0641070083,-0.499919951,0.1078316048,0.5157188177,0.2082080543,0.2796896994,-0.0188001394,0.0332892723,0.3279869854,0.0210578609,-0.365647614,0.000564051,-0.0283158161,-0.1554972231,-0.1619089246,0.3987061381,-0.0881612748,0.0523482934,0.6084603667,0.0969526768,0.1183453053,-0.2912596464,0.1959602386,0.1253908724,0.4451818466,-0.1049862951,-0.1137465537,-0.0039369301,-0.1345479786,-0.3904245496,0.2390954942,0.1906224191,-0.2509997189,-0.0031803055,0.0595967285,0.1065190434,-0.0358187519,-0.1929807365,-0.066250287,0.2038974762,0.0615907647,0.042670615,-0.1046710238,-0.1581255347,-0.0125593115,-0.2313856333,-0.3124711215,0.250197202,-0.3363642097,0.0420314781,0.0092312638,-0.1078686789,-0.1433404088,-0.0746568143,0.3727562726,0.0236874502,0.1381733865,0.1114934534,0.3602565825,0.3803673387,0.3734174967,0.1389397085,0.1727739871,-0.2554156184,-0.2099593133,-0.0185158681,-0.0258867219,-0.0269207396,0.4833751321,-0.0181952342,0.126923278,-0.342744559,-0.286416173,0.0379694588,-0.6072553396,0.1798172444,-0.2164932191,0.180911392,0.014958024,0.4404576719,0.141150564,0.3197541833,-0.2866175771,-0.2129358798,-0.1690673083,-0.0671930015,-0.352997452,-0.0474066809,0.2316350043,0.0253452919,0.1709929854,0.0457521938,-0.5268825293,-0.1939303279,0.2752550542,-0.3364925086,0.1010017619,0.097999312,-0.18175596,0.3801154792,0.1523963213,-0.395026654,-0.2050777376,0.0757711753,-0.1422836185,-0.0146571267,-0.1426357627,0.1625868529,0.0194878001,-0.2284371108,-0.2854866982,-0.0441706069,0.3768354058,-0.0824771598,0.0331031308,-0.1942527443,-0.0768310428,-0.2546732128,-0.149711147,0.491435349,-0.1705068648,0.1637139916,0.0520674698,0.1588347703,0.3200914562,0.3464753926,-0.0512800626,0.2360598296,0.0744530186,0.3285940289,0.4783325791,-0.2767097354,0.054584939,0.1438684613,-0.0497444421,-0.0141193578,0.0557803698,0.0047464315,0.113134712,-0.1860814691,0.1133473292,0.1749228984,-0.1087215468,0.2992883325,-0.4459868968,-0.0496777222,0.3986061513,-0.2075344771,-0.081623584,0.0590871796,0.010010629,0.0553928502,0.0179253165,-0.0859188437,0.3454482555,0.298235774,-0.124192439,-0.1070694104,0.0870341286,-0.0994404778,-0.4447140992,0.0324312299,-0.1577639729,0.1230496839,0.050344836,-0.0975475609,-0.2944736779,0.0961500108,-0.038226407,-0.2505195737,0.1545303464,0.023196375,0.1731840819,0.0592500903,-0.2021054775,0.0661340356,-0.0157438554,0.107415773,-0.3512534201,-0.1788741052,-0.0442574099,-0.3509330451,-0.0398886614,0.2398837656,0.237018019,0.1002113223,-0.2585669458,0.2389192581,-0.001657697,0.3618729115,-0.2429510951,0.0875752792,-0.041957356,-0.2605344057,0.1956302673,0.2751905024,0.3589321077,-0.2975812852,-0.4221465588,-0.0065395865,0.0379454531,0.5811731815,-0.0447000004,0.1357868016,0.0336808674,-0.0795980692,0.1372735947,-0.1051984504,0.1780075729,-0.0735728443,0.0638778731,0.3371360302,-0.2674142718,0.3107471168,0.4285442829,0.2990176976,0.1116487235,0.0571209788,-0.2933969796,-0.136866942,0.3519334197,0.1324796528,0.4555586278,-0.0157048721,0.2098059505,-0.0727058724,-0.1649343669,-0.2009925693,0.3175549507,-0.1493178606,0.1967449933,0.0662128404,0.4594133794,0.0200231094,-0.2195431441,0.2116740495,0.036517866,0.2187836468,-0.3783154786,0.2838196456,-0.2859784365,-0.0772421584,-0.4313696325,-0.0424440168,-0.0464689098,-0.5650830269,-0.0383662693,0.007924024,-0.0930386931,0.036544878,-0.3283863366,-0.1192555726,-0.1217019036,-0.065365009,-0.2936858833,-0.4041466117,-0.3502987623,-0.0584838949,0.4213877022,0.3856321573,0.1997771859,0.0164284967,-0.0054529719,-0.0281507317,-0.4237630665,0.0470186062,-0.2218863964,0.043297112,0.1605933458,-0.0058562402,0.0517997704,-0.1029949561,0.3697011173,-0.0511306785,-0.2518925071,0.3521769047,0.0223147199,-0.0376341715,-0.2629387379,-0.4058380723,0.1166723669,-0.5667974949,0.10078796,-0.0469362214,0.2017458379,0.1716721356,0.0820748881,0.2255028635,-0.0652560666,0.1951522082,0.2803921103,0.3783949018,0.5403063297,-0.2259801775,-0.3230759203,0.2950859666,-0.1108957976,0.3608184755,0.2307130396,-0.1520201415,0.4052866697,-0.4103542268,0.1700476855,-0.0837432668,-0.117233336,0.3123679161,0.1904549301,-0.0683736354,-0.0968231037,0.0179946031,0.2136168331,-0.2014836669,0.1650692821,0.008489931,0.1998053938,-0.1033324972,0.3400717974,0.1244998872,-0.1656523943,0.3614376187,0.0115997614,0.0120794913,-0.2510290146,-0.1440345347,0.0199724529,-0.214868173,0.0616214536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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/207","title":"Remove test set from NLP viewer","comments":"Appears that [two thirds of those polled on Twitter](https:\/\/twitter.com\/srush_nlp\/status\/1265734497632477185) are in favor of _some_ mechanism for averting eyeballs from the test data.","body":"While the new [NLP viewer](https:\/\/huggingface.co\/nlp\/viewer\/) is a great tool, I think it would be best to outright remove the option of looking at the test sets. At the very least, a warning should be displayed to users before showing the test set. Newcomers to the field might not be aware of best practices, and small things like this can help increase awareness.","comment_length":22,"text":"Remove test set from NLP viewer \n While the new [NLP viewer](https:\/\/huggingface.co\/nlp\/viewer\/) is a great tool, I think it would be best to outright remove the option of looking at the test sets. At the very least, a warning should be displayed to users before showing the test set. Newcomers to the field might not be aware of best practices, and small things like this can help increase awareness. \n Appears that [two thirds of those polled on Twitter](https:\/\/twitter.com\/srush_nlp\/status\/1265734497632477185) are in favor of _some_ mechanism for averting eyeballs from the test 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/206","title":"[Question] Combine 2 datasets which have the same columns","comments":"We are thinking about ways to combine datasets for T5 in #217, feel free to share your thoughts about this.","body":"Hi,\r\n\r\nI am using ``nlp`` to load personal datasets. I created summarization datasets in multi-languages based on wikinews. I have one dataset for english and one for german (french is getting to be ready as well). I want to keep these datasets independent because they need different pre-processing (add different task-specific prefixes for T5 : *summarize:* for english and *zusammenfassen:* for german)\r\n\r\nMy issue is that I want to train T5 on the combined english and german datasets to see if it improves results. So I would like to combine 2 datasets (which have the same columns) to make one and train T5 on it. I was wondering if there is a proper way to do it? I assume that it can be done by combining all examples of each dataset but maybe you have a better solution.\r\n\r\nHoping this is clear enough,\r\n\r\nThanks a lot \ud83d\ude0a\r\nBest","comment_length":20,"text":"[Question] Combine 2 datasets which have the same columns \n Hi,\r\n\r\nI am using ``nlp`` to load personal datasets. I created summarization datasets in multi-languages based on wikinews. I have one dataset for english and one for german (french is getting to be ready as well). I want to keep these datasets independent because they need different pre-processing (add different task-specific prefixes for T5 : *summarize:* for english and *zusammenfassen:* for german)\r\n\r\nMy issue is that I want to train T5 on the combined english and german datasets to see if it improves results. So I would like to combine 2 datasets (which have the same columns) to make one and train T5 on it. I was wondering if there is a proper way to do it? I assume that it can be done by combining all examples of each dataset but maybe you have a better solution.\r\n\r\nHoping this is clear enough,\r\n\r\nThanks a lot \ud83d\ude0a\r\nBest \n We are thinking about ways to combine datasets for T5 in #217, feel free to share your thoughts about this.","embeddings":[-0.2445141971,0.0638994128,0.0241108686,0.3628230393,0.1096224189,0.4387528002,0.269988209,0.3603741229,0.0449791066,-0.1164474189,-0.3814806938,0.1237424836,0.0036282265,0.2719849944,0.271317482,-0.5642143488,0.0580447242,0.2187532783,-0.5200107098,0.1850373447,0.0075754696,0.005126602,-0.1654464453,0.0126771564,-0.2030433267,0.2349442244,-0.3422628641,-0.227939114,0.0463839993,0.1566460878,0.3019719422,0.1670797765,0.2146203518,0.3149155974,-0.0001199274,-0.0794955119,-0.0164892916,-0.0905234143,0.1418972611,-0.1987285167,0.0968255997,-0.3619499803,-0.0633546039,-0.156581834,0.0822774172,-0.2395310551,-0.2331836522,0.0375286452,0.2680856884,-0.1660301238,0.0541115366,-0.1883206815,-0.251149863,0.3409512341,-0.0712746456,-0.1605684906,0.0614655875,-0.0694343448,0.0818909332,-0.3195155561,0.577925384,0.2133333832,-0.0351103805,-0.1198773012,0.0823331848,0.1424835175,0.1433921158,-0.1095088795,0.0553349555,0.1900027841,0.7741910219,-0.059649311,0.008375858,-0.2915332317,0.4038740695,-0.1315465271,-0.1932114661,0.143045038,0.0170429479,0.0254980847,-0.2548200786,-0.1563202739,-0.016406402,0.3023144901,-0.0431645103,0.331581831,0.1102488413,0.2770792842,-0.0637215301,-0.1514382958,0.1660322398,-0.3364259899,0.0009921413,0.1547577828,-0.3073634803,-0.4046192765,-0.3330450058,-0.1842289269,0.2726255059,-0.1534878612,-0.1543884575,-0.0069713704,-0.1090398356,0.138723433,0.3026831448,0.1582723111,0.1386101842,0.2125378847,-0.1884720474,-0.3162833154,-0.3603146374,0.1616100669,0.0701176077,-0.0728297979,-0.1233948544,-0.2811374664,-0.0584523194,-0.3414462209,0.1455402374,-0.1935318559,-0.2755381167,-0.2310825884,-0.1025671437,-0.1516104788,-0.0181961898,0.3452565074,-0.1489931345,0.3091166914,-0.1060740799,-0.0139371408,-0.0250301678,0.0738149956,-0.1707864255,0.1282698512,0.0945632681,0.1703294069,-0.1308951527,0.4186178148,-0.1831040531,0.1015597805,0.3208992779,-0.1995742023,-0.0710753202,-0.2422419488,0.3129127324,0.163817659,0.1630841047,0.1007980108,-0.3006671667,0.1673175842,-0.1736656725,-0.0824754238,0.0157322194,0.0594997555,-0.0839434862,-0.3012195528,-0.1238102093,0.6368827224,0.2376348078,-0.3169041872,-0.1415876597,0.0955535248,-0.464184165,-0.0478923246,0.20856677,0.0251198672,-0.3501130342,-0.13585338,0.0010477458,-0.0063651754,-0.0501091294,0.2889217138,-0.2664492428,0.2065665275,0.09581846,0.0672085956,0.6602528095,0.0170827769,-0.2144925296,0.2215339541,-0.1507163346,0.132588014,-0.104750745,0.1174124926,-0.376698494,-0.0470037572,0.1844806373,0.8385096192,-0.2917749286,-0.1561029702,-0.0637751222,-0.3239151537,0.3379637003,0.1553742886,0.1021537781,-0.2498816997,-0.142595768,0.1694810838,0.1662994921,-0.3560666442,0.1682165414,0.1871119142,-0.0291251838,0.117339924,-0.227634564,-0.2857426107,-0.265973419,-0.1344855428,0.1124017239,0.0476165377,0.4460558593,-0.4509105086,0.0663456917,-0.6603160501,-0.0207441468,0.0910769999,-0.0005953129,0.3114060462,0.1058811396,-0.2892328501,-0.175703615,0.3350967765,-0.052014336,-0.1666298807,-0.1528071761,0.4420305789,0.0385310762,0.0737517253,0.0093516335,0.5092473626,0.048488263,0.034645427,0.1494198889,-0.1253807247,-0.5496708155,0.1898004413,0.1835702062,0.0285738744,-0.1553871185,-0.0680693835,0.3543197811,-0.3344199359,-0.005318779,-0.160242483,-0.0741474032,0.4110820889,-0.248981297,0.3699252307,-0.0203907881,-0.117495209,0.0169245657,-0.1534184664,0.0219020154,-0.1019337028,0.2140550166,-0.0077626077,0.0135509977,0.3495665789,-0.3878057897,0.246159941,0.2839236557,0.1036929712,-0.0467015766,-0.0142829837,-0.1404851228,-0.0473917983,0.0349292904,0.1737324595,0.5730592608,0.1965861171,0.2018268853,0.2332470417,-0.154273361,0.1257927865,0.0148142884,0.1872755885,0.0660852492,0.3079122007,0.0905814096,0.2213162035,0.1897095889,0.3987439275,0.2033168674,-0.0915317163,-0.1926696301,-0.0057482198,-0.2850170135,-0.6398733854,-0.5628804564,-0.128213644,-0.2211208194,-0.0530706234,-0.0937654004,-0.005285013,0.0087295771,0.1844754219,-0.1145416275,0.4212614596,-0.4750213623,-0.2778398693,0.2787618935,0.0416234657,-0.1818008274,0.070905216,0.407938838,0.2121359855,0.2545477152,-0.0574187525,-0.2327980399,-0.0601055101,-0.5316917896,0.0501952358,-0.3654118776,-0.1488939524,-0.2518881261,-0.1015012488,-0.2149767727,-0.3766777217,0.051559221,0.3118179142,0.052741304,-0.1287004203,0.0572642796,0.2869411707,-0.1050098315,-0.294531405,-0.4214810133,-0.059875574,0.0423832871,-0.1619119942,-0.0208190884,-0.3642593622,-0.0539556891,-0.0365090817,-0.2672071159,-0.011379499,-0.201623857,0.0008073317,0.1787796766,-0.1347468495,-0.1283462644,-0.0846113712,-0.1897967458,0.299744308,-0.0308160167,-0.1024918258,-0.151703611,-0.1399434954,-0.0023926657,-0.1428914666,0.3774222732,0.178900227,0.0176718123,0.1485676914,0.0718961582,-0.2294689864,0.0211154018,0.098553285,0.4574983418,-0.1967522502,0.2529270947,-0.1457327604,0.607717514,0.5213459134,0.166951254,0.0660566986,0.2555118799,0.1424033791,0.0633199066,-0.2097760737,0.2008449882,0.1946293563,0.0576697513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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/197","title":"Scientific Papers only downloading Pubmed","comments":"Hi so there are indeed two configurations in the datasets as you can see [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/scientific_papers\/scientific_papers.py#L81-L82).\r\n\r\nYou can load either one with:\r\n```python\r\ndataset = nlp.load_dataset('scientific_papers', 'pubmed')\r\ndataset = nlp.load_dataset('scientific_papers', 'arxiv')\r\n```\r\n\r\nThis issues is actually related to a similar user-experience issue with GLUE. When several configurations are available and the first configuration is loaded by default (see issue #152 and #130), it seems to be unexpected for users.\r\n\r\nI think we should maybe raise a (very explicit) error when there are several configurations available and the user doesn't specify one.\r\n\r\nWhat do you think @lhoestq @patrickvonplaten @mariamabarham ?","body":"Hi!\r\n\r\nI have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following:\r\n\r\n```\r\ndataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.')\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5.05k\/5.05k [00:00<00:00, 2.66MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.90k\/4.90k [00:00<00:00, 2.42MB\/s]\r\nDownloading and preparing dataset scientific_papers\/pubmed (download: 4.20 GiB, generated: 2.33 GiB, total: 6.53 GiB) to .\/scientific_papers\/pubmed\/1.1.1...\r\nDownloading: 3.62GB [00:40, 90.5MB\/s]\r\nDownloading: 880MB [00:08, 101MB\/s]\r\nDataset scientific_papers downloaded and prepared to .\/scientific_papers\/pubmed\/1.1.1. Subsequent calls will reuse this data.\r\n```\r\n\r\nonly a pubmed folder is created. There doesn't seem to be something for arxiv. Are these two datasets merged? Or have I misunderstood something?\r\n\r\nThanks!","comment_length":98,"text":"Scientific Papers only downloading Pubmed \n Hi!\r\n\r\nI have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following:\r\n\r\n```\r\ndataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.')\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5.05k\/5.05k [00:00<00:00, 2.66MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.90k\/4.90k [00:00<00:00, 2.42MB\/s]\r\nDownloading and preparing dataset scientific_papers\/pubmed (download: 4.20 GiB, generated: 2.33 GiB, total: 6.53 GiB) to .\/scientific_papers\/pubmed\/1.1.1...\r\nDownloading: 3.62GB [00:40, 90.5MB\/s]\r\nDownloading: 880MB [00:08, 101MB\/s]\r\nDataset scientific_papers downloaded and prepared to .\/scientific_papers\/pubmed\/1.1.1. Subsequent calls will reuse this data.\r\n```\r\n\r\nonly a pubmed folder is created. There doesn't seem to be something for arxiv. Are these two datasets merged? Or have I misunderstood something?\r\n\r\nThanks! \n Hi so there are indeed two configurations in the datasets as you can see [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/scientific_papers\/scientific_papers.py#L81-L82).\r\n\r\nYou can load either one with:\r\n```python\r\ndataset = nlp.load_dataset('scientific_papers', 'pubmed')\r\ndataset = nlp.load_dataset('scientific_papers', 'arxiv')\r\n```\r\n\r\nThis issues is actually related to a similar user-experience issue with GLUE. When several configurations are available and the first configuration is loaded by default (see issue #152 and #130), it seems to be unexpected for users.\r\n\r\nI think we should maybe raise a (very explicit) error when there are several configurations available and the user doesn't specify one.\r\n\r\nWhat do you think @lhoestq @patrickvonplaten @mariamabarham ?","embeddings":[0.3410282135,-0.0507830754,0.0498376898,0.2057841271,-0.0124936337,-0.0915830582,0.1314337552,0.2478140146,0.0751410052,-0.2193237543,-0.1656386554,0.1005809531,0.0638275295,-0.2900847793,0.2401754558,0.0783967748,0.0976624116,0.1165956408,0.1482492685,-0.1969807744,-0.1103292704,0.4662143588,-0.1177706718,-0.1241875067,0.0264826193,0.057828825,-0.2549771369,0.2292036861,-0.1758149266,-0.2693143189,0.2296958268,0.4607445002,0.0106472215,0.2442484796,-0.0001260212,-0.0266372915,0.4085169435,0.0374921151,-0.0753441975,-0.128483057,0.1525420547,-0.3127950728,0.397844553,-0.106541872,0.042281799,-0.3930912018,0.1760856211,-0.046389278,0.1798724681,0.0012882873,0.0871801004,0.1242344752,0.0638084337,0.0672760829,0.4933620095,0.0101154372,0.0661203563,0.4198920727,0.2697269619,-0.1633948535,0.1365495175,0.2445969731,-0.1149521098,0.1563950777,0.1796972901,0.4194620252,0.0596443713,-0.4671780169,-0.2021933943,0.3455394804,0.0148535902,0.0130189043,-0.0460986458,-0.4677795768,-0.0122009078,0.0002345637,0.2077282518,0.4563730955,-0.1608153731,-0.0648918375,0.0811817348,-0.2500706613,-0.0604624264,0.561724484,0.1643213481,0.048341386,-0.1973384768,0.3637367487,0.4003931284,0.1685729623,-0.2624977529,-0.287566334,0.1975403875,0.1875087172,-0.3029337227,0.0080817882,0.0892410204,-0.0668539926,0.2401620001,0.3428836167,0.0869829282,-0.043154031,0.1938953996,0.1217616498,0.5296929479,-0.0609327704,-0.0209741835,0.1862228066,0.3687579334,-0.0165914521,0.0886773691,0.0983025879,0.0916959196,-0.0254227165,-0.2339766324,-0.5981558561,0.2879770994,-0.2756765187,0.2487315238,-0.0236083772,-0.0111906305,-0.0559957027,-0.2106598765,0.0046254485,0.0189874526,0.2226313055,0.1190301552,0.2808913887,-0.2055922002,0.2087723464,-0.0782689825,0.0059810891,-0.3656755686,0.0397046246,0.2711539567,-0.2781743705,0.1672439575,0.0916653425,-0.1132247075,-0.18400985,0.1791826189,-0.1315311641,0.0667215213,0.3698316514,0.2778123021,0.508071959,-0.0019687526,-0.2545326948,-0.2754873335,-0.0611439943,-0.2797983587,-0.1683173478,-0.1868097186,0.0298579298,-0.3902364969,0.1666101366,0.000118563,0.2882416546,0.1552381516,-0.1705115885,-0.1155071557,-0.0045683435,-0.0851240903,-0.3703423738,0.1219870299,0.3437537849,0.1906010509,-0.1056915969,-0.1270908415,-0.1005398929,0.0820995644,0.3111583889,-0.2761954367,0.1420872211,-0.2416537255,0.2310815901,0.5904170871,-0.2710135281,-0.4526688159,0.3115585446,-0.0249654334,0.074895449,0.152230531,0.2749254405,-0.138735801,-0.0225615874,0.1659161896,0.366828233,0.0908415616,-0.146275267,-0.3049763143,-0.1942659616,0.3023201823,-0.0907404348,0.1159532443,0.0059388643,-0.0089752292,0.0697069168,0.306605041,0.2294956148,0.1860751212,0.246301949,-0.0749332532,-0.0090108374,0.0708625168,-0.2000005096,-0.3342154324,0.0563949533,-0.355522126,0.2431680262,0.2622113228,-0.0423657894,-0.232797116,-0.4931894541,-0.1797827929,-0.2683065534,0.0047272388,0.2189692855,0.1224113032,0.1607274115,-0.2678839266,0.1743549258,0.0098401709,0.0880597085,-0.8079344034,0.3179457486,-0.0178457256,-0.0704507381,0.0631345361,0.1433012336,0.0012621655,-0.1691232473,0.3244474828,0.4365813136,-0.1374934316,0.2282069325,0.2255856842,0.0575182065,0.2537788153,-0.1108493656,0.1513350457,-0.1257910579,0.1119107604,-0.2311308086,-0.3042535782,0.1168276817,-0.077087,0.3747087121,0.1469537616,-0.0114957523,0.0103485119,-0.2301260382,-0.474625349,-0.2160553038,0.2014878392,0.2280126214,-0.1119729355,0.1459307522,-0.2998267114,0.0780779719,0.070139572,0.0172316842,-0.0204678196,0.1736777574,-0.2714233398,-0.244095698,-0.1822142005,0.5162521005,0.8718118668,0.1978533715,0.3229739964,0.1088076681,-0.1763394624,-0.196975708,0.0869795531,0.0337333344,-0.1280592978,0.2140399367,0.1713266373,-0.0086421194,-0.4231769443,0.3805908263,0.1604651511,0.0212894902,-0.3763753772,-0.1903130859,-0.4012383521,-0.3012850881,-0.6075859666,-0.1843883842,-0.3624563217,-0.3466341197,-0.0647551119,0.1399495602,-0.0504873395,0.0246390924,-0.2065403014,0.1555749625,-0.2396001816,0.068418026,0.141157195,0.1248278171,-0.3013817668,-0.0807427615,0.0365163162,0.3887548745,0.5186214447,0.0546899512,-0.2073940039,-0.0898477808,-0.4579675496,0.2533678114,-0.2039087415,0.4136421978,0.28525576,0.0983376056,0.3634143174,-0.0833395272,0.2817313671,-0.1454360038,0.0304209553,0.0082155261,0.0834249482,-0.0202473532,-0.2850773931,-0.5693511367,-0.306091696,-0.2529954314,-0.110071376,-0.055023253,0.1214481741,0.2450508326,-0.0811236277,0.1239974424,-0.4648569524,-0.0455252044,-0.3640047908,-0.0893353149,0.2416918129,-0.2270824611,-0.4088026285,-0.0433627553,-0.0247738864,-0.0982614979,0.1594512016,-0.3808137774,-0.4189061522,-0.2150374353,-0.5069791079,0.1753206104,0.4170131087,0.3016415834,-0.3365058005,-0.1032984853,-0.0849574581,-0.1637304872,0.1886581033,0.2175232023,0.361068815,0.2080634087,-0.0973239541,-0.0154065378,0.5112137794,0.4535945356,0.1888438463,0.2588811815,-0.0305912402,0.6296029091,0.0801544785,-0.3526650667,0.0025885261,-0.023501467,0.2995642126,0.32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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/197","title":"Scientific Papers only downloading Pubmed","comments":"Now if you don't specify which part you want, it raises an error:\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['pubmed', 'arxiv']\r\nExample of usage:\r\n\t`load_dataset('scientific_papers', 'pubmed')`\r\n```","body":"Hi!\r\n\r\nI have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following:\r\n\r\n```\r\ndataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.')\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5.05k\/5.05k [00:00<00:00, 2.66MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.90k\/4.90k [00:00<00:00, 2.42MB\/s]\r\nDownloading and preparing dataset scientific_papers\/pubmed (download: 4.20 GiB, generated: 2.33 GiB, total: 6.53 GiB) to .\/scientific_papers\/pubmed\/1.1.1...\r\nDownloading: 3.62GB [00:40, 90.5MB\/s]\r\nDownloading: 880MB [00:08, 101MB\/s]\r\nDataset scientific_papers downloaded and prepared to .\/scientific_papers\/pubmed\/1.1.1. Subsequent calls will reuse this data.\r\n```\r\n\r\nonly a pubmed folder is created. There doesn't seem to be something for arxiv. Are these two datasets merged? Or have I misunderstood something?\r\n\r\nThanks!","comment_length":34,"text":"Scientific Papers only downloading Pubmed \n Hi!\r\n\r\nI have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following:\r\n\r\n```\r\ndataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.')\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5.05k\/5.05k [00:00<00:00, 2.66MB\/s]\r\nDownloading: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4.90k\/4.90k [00:00<00:00, 2.42MB\/s]\r\nDownloading and preparing dataset scientific_papers\/pubmed (download: 4.20 GiB, generated: 2.33 GiB, total: 6.53 GiB) to .\/scientific_papers\/pubmed\/1.1.1...\r\nDownloading: 3.62GB [00:40, 90.5MB\/s]\r\nDownloading: 880MB [00:08, 101MB\/s]\r\nDataset scientific_papers downloaded and prepared to .\/scientific_papers\/pubmed\/1.1.1. Subsequent calls will reuse this data.\r\n```\r\n\r\nonly a pubmed folder is created. There doesn't seem to be something for arxiv. Are these two datasets merged? Or have I misunderstood something?\r\n\r\nThanks! \n Now if you don't specify which part you want, it raises an error:\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['pubmed', 'arxiv']\r\nExample of usage:\r\n\t`load_dataset('scientific_papers', 'pubmed')`\r\n```","embeddings":[0.3337525129,0.1518255174,0.0403823219,0.1220906079,-0.011581541,0.0701765418,0.1087679043,0.3869033754,-0.1169545278,-0.2211576104,-0.1246671453,0.3359685242,0.0420467369,-0.3219137192,0.3353407681,0.0597895607,0.1705282032,0.1198785305,0.2469612211,-0.3221702874,-0.1509200633,0.3626807332,-0.2007835805,-0.0750292316,0.1717316508,0.1704218984,-0.1363465637,0.2355844378,-0.2803621292,-0.2833781838,0.3049050868,0.4118289053,0.1248244494,0.3285555542,-0.0001224009,-0.0510751307,0.3996664286,-0.1145327687,-0.1240762919,-0.2439041436,0.0256197173,-0.2772782743,0.361112684,-0.1576248407,0.1032313108,-0.501804173,0.2266834974,-0.1019288227,0.0027055452,0.2615558207,0.121453315,-0.082328327,0.1363512725,0.0036474764,0.3264043629,0.0186310876,0.0906237736,0.3079102635,0.1788234711,0.0836476088,0.1962370723,0.2178081572,-0.1276474744,0.0741181374,0.3337646723,0.3773284554,0.0491853617,-0.4533298314,-0.0953774229,0.2512944937,0.4695162475,-0.0825927407,0.0370471664,-0.2068697661,-0.0401060581,-0.0887057558,0.1081100479,0.3900105357,-0.2233262211,-0.0482567102,0.0210090317,-0.3863975704,-0.1963814646,0.5864767432,0.1692327112,0.1530348957,-0.0870963484,0.3638678789,0.2177418768,0.1375072896,0.0339366943,-0.3225851357,0.255410403,0.2819865346,-0.2922056913,-0.0079085166,-0.0350400247,-0.2091565132,0.1889663935,0.3261244297,0.0750263706,-0.0670474544,0.200577721,0.1209650263,0.4643370211,-0.1520892382,0.0867839679,0.3539930284,0.215610683,-0.1866046041,0.0340204164,0.0182454176,-0.0772188455,0.0717442855,-0.2296784818,-0.6142804027,0.1409284472,-0.4055893123,0.4478225708,-0.0252392478,0.2138482481,-0.1062910482,-0.2344533354,0.0817294493,0.105173789,0.2092121243,0.0853358954,0.1764572263,-0.1176484972,0.0951757878,-0.0868492872,0.0165046081,-0.3610500395,-0.0581570975,0.2363769412,-0.0132135935,0.2610935569,-0.0931467563,0.0739073083,-0.0901588872,0.2440550923,-0.0596809015,0.1449033171,0.4964265227,0.3115926981,0.5195352435,-0.0001566742,-0.1992076337,-0.2772220671,0.1592060477,-0.3895844519,-0.1244929358,-0.1183593273,0.0739863664,-0.3111489117,-0.0614527352,0.1078479663,0.2107418031,0.1814860553,-0.358273834,-0.1082867756,0.0256287791,0.0402183868,-0.3753201663,0.1704992652,0.1312366128,0.0658913404,-0.038904395,-0.2859943211,-0.0642904639,0.226696834,0.0782630667,-0.3078137934,0.1558772773,-0.1210069731,0.0539570004,0.864571929,-0.2199848443,-0.407153964,0.2794377208,0.0544365272,-0.1435594708,0.091761142,0.3109305203,-0.2846711874,0.0514984578,0.0913101882,0.2885004282,-0.0125902724,-0.24612014,-0.1448047906,-0.1127997264,0.3086381257,-0.0759325698,0.2943561971,0.1680896133,-0.0156032005,0.1497993022,0.4100165963,0.1875562817,0.2137866467,0.226598829,-0.1147905588,-0.1092510372,0.2086015493,-0.1760005653,-0.1642107219,0.0664224625,-0.4373755455,0.2642482817,0.3202396631,0.0615606867,-0.2903928161,-0.520103991,-0.0640273318,-0.3106152415,-0.0129940351,0.2909795046,-0.0014562281,0.0786508992,-0.3556837142,0.0474463776,-0.1270782799,0.0147784054,-0.7680270672,0.156828627,-0.00258573,-0.0141226333,0.0891618133,-0.0303555317,-0.0414165594,-0.0181819033,0.2261610627,0.4155773222,-0.0318803489,0.3567320406,0.3559613824,-0.1002450064,0.116301313,-0.2353426963,0.1572002321,-0.1083389297,0.2621348798,-0.2087305188,-0.3446258903,0.1056321934,-0.1530318856,0.3154357672,0.1534233242,-0.0027962315,0.0623033755,-0.1272726655,-0.4065901339,-0.1481285244,0.170978874,0.2773890793,-0.3150270879,0.0903303102,-0.2642230093,0.0373467393,0.1256252229,0.0065444163,-0.1745367348,0.1829848588,-0.3114659488,-0.2189500183,-0.1368601173,0.4669195712,0.9032630324,0.2567404509,0.3886661232,0.0915179029,-0.0587531216,-0.1681360751,0.1890705228,0.1316781193,0.0125590973,0.1673580706,-0.0691405162,-0.0487458222,-0.4017603397,0.2793741822,0.1685538441,0.0622381717,-0.302798152,-0.0377122723,-0.3783569634,-0.1974488497,-0.5116115212,-0.2571055293,-0.2551214099,-0.3293745518,-0.167897597,0.0100614792,0.0549546406,0.0197101347,-0.5319440365,0.0452209786,-0.1329424083,-0.1507658213,0.1229515299,-0.0229472239,-0.249567166,0.0073658298,-0.065862909,0.3898340762,0.4878613055,-0.0702571422,-0.0429270044,0.0528673567,-0.402985543,0.2302953154,-0.2439556867,0.2364079803,0.2415213734,0.1290678531,0.2934623361,-0.0029641523,0.1573615968,-0.1802592874,0.0249048714,-0.0403277613,0.1010967419,-0.1088800505,-0.2754941285,-0.8172607422,-0.3088196218,-0.1809062213,-0.238820076,0.189415589,0.1692732275,0.3574635386,0.0208667945,0.1280640513,-0.3271786273,0.0576983094,-0.368775785,-0.15246813,0.4007768631,-0.2736347914,-0.3794854283,0.1096101403,0.1409732252,0.0388059467,0.1496944129,-0.2708301842,-0.0501307733,-0.0078999493,-0.5291983485,0.1509534419,0.336230427,0.2992244065,-0.3369193673,-0.1137447059,-0.1398274899,-0.065526031,0.1196309403,0.242737487,0.5703095198,0.136684224,-0.0611464344,-0.1879300177,0.4702349305,0.3327802122,0.2193660885,0.1394729614,-0.0807742178,0.5153936148,0.0709885135,-0.2541883886,0.1776622981,0.0340987071,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/193","title":"[Tensorflow] Use something else than `from_tensor_slices()`","comments":"`from_generator` is not working on TPU, I met the following error :\r\n\r\n```\r\nFile \"\/usr\/local\/lib\/python3.6\/contextlib.py\", line 88, in __exit__\r\n    next(self.gen)\r\n  File \"\/home\/usr\/.venv\/bart\/lib\/python3.6\/site-packages\/tensorflow_core\/python\/eager\/context.py\", line 1900, in execution_mode\r\n    executor_new.wait()\r\n  File \"\/home\/usr\/.venv\/bart\/lib\/python3.6\/site-packages\/tensorflow_core\/python\/eager\/executor.py\", line 67, in wait\r\n    pywrap_tensorflow.TFE_ExecutorWaitForAllPendingNodes(self._handle)\r\ntensorflow.python.framework.errors_impl.NotFoundError: No registered 'PyFunc' OpKernel for 'CPU' devices compatible with node {{node PyFunc}}\r\n        .  Registered:  <no registered kernels>\r\n\r\n         [[PyFunc]]\r\n```\r\n\r\n---\r\n\r\n@lhoestq It seems you merged some changes that allow lazy-loading. **Can you give an example of how to use ?** Maybe the Colab notebook should be updated with this method as well.","body":"In the example notebook, the TF Dataset is built using `from_tensor_slices()` :\r\n\r\n```python\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x] for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"]}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"]\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nBut according to [official tensorflow documentation](https:\/\/www.tensorflow.org\/guide\/data#consuming_numpy_arrays), this will load the entire dataset to memory.\r\n\r\n**This defeats one purpose of this library, which is lazy loading.**\r\n\r\nIs there any other way to load the `nlp` dataset into TF dataset lazily ?\r\n\r\n---\r\n\r\nFor example, is it possible to use [Arrow dataset](https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset) ? If yes, is there any code example ?","comment_length":87,"text":"[Tensorflow] Use something else than `from_tensor_slices()` \n In the example notebook, the TF Dataset is built using `from_tensor_slices()` :\r\n\r\n```python\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x] for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"]}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"]\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nBut according to [official tensorflow documentation](https:\/\/www.tensorflow.org\/guide\/data#consuming_numpy_arrays), this will load the entire dataset to memory.\r\n\r\n**This defeats one purpose of this library, which is lazy loading.**\r\n\r\nIs there any other way to load the `nlp` dataset into TF dataset lazily ?\r\n\r\n---\r\n\r\nFor example, is it possible to use [Arrow dataset](https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset) ? If yes, is there any code example ? \n `from_generator` is not working on TPU, I met the following error :\r\n\r\n```\r\nFile \"\/usr\/local\/lib\/python3.6\/contextlib.py\", line 88, in __exit__\r\n    next(self.gen)\r\n  File \"\/home\/usr\/.venv\/bart\/lib\/python3.6\/site-packages\/tensorflow_core\/python\/eager\/context.py\", line 1900, in execution_mode\r\n    executor_new.wait()\r\n  File \"\/home\/usr\/.venv\/bart\/lib\/python3.6\/site-packages\/tensorflow_core\/python\/eager\/executor.py\", line 67, in wait\r\n    pywrap_tensorflow.TFE_ExecutorWaitForAllPendingNodes(self._handle)\r\ntensorflow.python.framework.errors_impl.NotFoundError: No registered 'PyFunc' OpKernel for 'CPU' devices compatible with node {{node PyFunc}}\r\n        .  Registered:  <no registered kernels>\r\n\r\n         [[PyFunc]]\r\n```\r\n\r\n---\r\n\r\n@lhoestq It seems you merged some changes that allow lazy-loading. **Can you give an example of how to use ?** Maybe the Colab notebook should be updated with this method as 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/193","title":"[Tensorflow] Use something else than `from_tensor_slices()`","comments":"Could you send me the code you used to run create the dataset using `.from_generator` ? What version of tensorflow are you using ?","body":"In the example notebook, the TF Dataset is built using `from_tensor_slices()` :\r\n\r\n```python\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x] for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"]}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"]\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nBut according to [official tensorflow documentation](https:\/\/www.tensorflow.org\/guide\/data#consuming_numpy_arrays), this will load the entire dataset to memory.\r\n\r\n**This defeats one purpose of this library, which is lazy loading.**\r\n\r\nIs there any other way to load the `nlp` dataset into TF dataset lazily ?\r\n\r\n---\r\n\r\nFor example, is it possible to use [Arrow dataset](https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset) ? If yes, is there any code example ?","comment_length":24,"text":"[Tensorflow] Use something else than `from_tensor_slices()` \n In the example notebook, the TF Dataset is built using `from_tensor_slices()` :\r\n\r\n```python\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x] for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"]}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"]\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nBut according to [official tensorflow documentation](https:\/\/www.tensorflow.org\/guide\/data#consuming_numpy_arrays), this will load the entire dataset to memory.\r\n\r\n**This defeats one purpose of this library, which is lazy loading.**\r\n\r\nIs there any other way to load the `nlp` dataset into TF dataset lazily ?\r\n\r\n---\r\n\r\nFor example, is it possible to use [Arrow dataset](https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset) ? If yes, is there any code example ? \n Could you send me the code you used to run create the dataset using `.from_generator` ? What version of tensorflow are you using ?","embeddings":[-0.1686090976,-0.0939882025,0.0118442392,0.1699765623,0.2723704576,0.0823565796,0.2256458849,0.3897307217,-0.0503767245,0.0051490618,-0.0811933503,0.6080824733,-0.1429212838,-0.0144129554,0.2508257926,-0.2165247649,-0.1014938354,0.2596186996,-0.1496244222,-0.0935945809,-0.0144119794,-0.0348175615,-0.1413571984,-0.0275248867,-0.0601284504,-0.0839252993,0.0791330338,-0.0956678763,0.3711793423,0.0390430689,0.2916139066,0.0025852246,-0.0244213697,0.1651110053,-0.0001032014,0.3215746284,0.0343726091,-0.1477330327,0.0488398597,-0.0791320577,0.1653178334,-0.2739711106,0.1919615418,-0.3028230667,-0.0543792732,0.0081698783,0.1938415617,-0.0288159121,0.3141269088,0.1588182747,0.2169723213,0.4294994175,-0.1177147701,0.3017023206,0.1172235608,-0.1770817935,-0.1900475323,-0.2290671021,-0.217553854,-0.0144306719,0.0329059288,0.2663014233,-0.2041040063,0.1305107474,0.4554982185,0.1430775374,-0.1110719219,-0.3703571856,-0.2649399936,0.3789654672,0.2244816422,-0.0839506388,-0.1515463144,-0.4547227919,-0.1709520817,-0.3064984381,-0.1505764425,0.118133828,-0.2600311637,0.1226125285,-0.101498723,-0.0880129784,-0.2320558578,0.1448360384,0.1663917601,-0.0429132916,0.0822631568,0.1081561074,0.1561127454,0.0490138382,0.2461539507,0.0588911548,0.3580822349,0.3697514832,-0.3404166102,0.0536975823,0.1779217869,-0.516630888,-0.0021290567,-0.2505258024,0.3231516778,0.1721430868,-0.2449933589,0.1835705638,-0.0725678429,0.012310721,-0.4178177416,0.1140060052,-0.1178707406,-0.4833922684,-0.0239285361,0.0217021257,-0.2394130379,-0.0485653132,0.1193628386,-0.4675555527,-0.0338964462,0.2352781147,-0.3758570254,-0.3709529936,-0.1966249645,0.0565313622,0.0138816126,0.2716265619,-0.1850642562,0.4295899868,0.1524079293,-0.1067645699,-0.3398886323,0.0041233669,-0.2882518768,0.1229175851,-0.1284395307,-0.1123177409,0.1438223869,-0.0785714909,0.2024256736,-0.0808299333,-0.1718904823,0.1340387315,0.2805534005,-0.1010799035,0.0838325694,0.2150489241,0.0513633229,-0.0131490817,0.0446443819,0.0967687666,-0.16510351,0.3000501394,-0.5144050121,-0.2434287816,-0.0340855084,0.2013803869,0.116215907,-0.149327457,-0.2542578578,0.3058813214,0.1306425184,-0.0581671111,-0.1796268672,-0.1691520065,-0.4182859361,-0.327901423,0.3185227513,0.0584320128,-0.4493255317,-0.0759404153,-0.0228949822,-0.000078702,0.2840332985,0.4118054509,-0.2025939673,0.4759196639,-0.0660431534,0.0623255298,0.7272694111,-0.0797071606,-0.2046185136,0.2472713292,-0.1875445694,0.1665145159,-0.0564950109,0.3881363273,0.0145793241,0.0066112438,0.3825980425,0.6268616319,-0.130277738,0.1262755394,-0.1689798385,-0.2685698867,0.3044632077,0.2909620702,0.0550540276,-0.0994176045,-0.2690316439,0.3530552685,0.1409158111,-0.0482356064,-0.1705192924,0.1743093431,-0.1014072672,0.0338936485,-0.1964698136,0.023537185,-0.6025986075,0.256714195,0.2188520581,0.1189933419,0.1482024044,-0.0970392376,0.2708938122,0.1100614667,0.032282345,0.1563063562,0.1124503687,0.1176761985,-0.0173104219,-0.0104484577,-0.2507628798,0.2139796764,-0.3762319088,-0.162748903,-0.3574134111,0.2085116506,0.3017752469,-0.0353901908,0.004790958,0.2599491179,-0.2557673454,-0.1147442386,0.0572551675,0.2753024995,-0.0281672217,0.2390883267,-0.6261082292,0.39822644,-0.0164093953,-0.0681696907,-0.0559390485,0.1453260183,0.0453526229,-0.1716874391,0.0163724478,0.2203736156,-0.1982612312,0.0816421136,0.2122797817,-0.2504985332,0.1703201383,0.0214308985,0.17780976,-0.0902487412,0.399679929,0.0517187938,0.1282099783,0.2929057777,-0.5027149916,0.2471539229,0.6455689073,0.0017061487,-0.0606105551,0.2191503942,-0.0046074232,-0.1864926517,-0.1146235764,-0.0755283237,0.2204622179,0.2333493531,0.0045612883,-0.0491562411,-0.1739037037,-0.2364361435,0.2298947722,0.1034812331,0.3950202763,-0.0964093208,0.0465534218,0.1559046358,-0.2110231072,0.0506055467,0.2233275622,0.2867364883,-0.2051321715,-0.0385508724,-0.2432170957,-0.2629223764,-0.0705011562,0.0183933731,0.1004700437,-0.1776507348,-0.0637674779,0.3268815875,-0.0052786567,-0.0022931152,-0.1070263535,0.2660950124,0.2539460063,-0.3675926924,-0.0481266864,-0.0965466201,-0.2597522438,0.1232628822,0.345780462,0.265076369,0.306099534,0.0424025282,-0.0293588545,0.1571471989,-0.0293092672,0.1693187058,-0.1896898746,0.1289821416,-0.0720650628,0.2025320381,-0.1092027426,-0.2199773192,-0.0401361063,-0.4229851067,0.0455172844,0.1925011128,0.0160646681,0.0531260408,-0.2264475822,-0.2200699598,-0.2942709327,-0.423545301,-0.0113177849,0.1150469258,0.2867074907,0.4668727219,-0.0324269757,0.2744036913,0.3595530391,0.1347762644,0.0210020859,-0.131843105,0.5142495036,-0.2914405167,-0.2923904955,-0.1259394288,-0.1577883065,0.2138791382,0.2645537257,-0.6998734474,0.1633132249,-0.1801667213,0.0906324387,-0.1719289273,-0.0230344385,0.2796549499,-0.1828378737,-0.0377399474,-0.0642140582,0.1282198876,0.1593093127,-0.0026167354,-0.1744403839,0.2911267579,0.4363160729,0.293207407,0.6823472977,-0.0495223328,-0.2472582161,0.245116502,-0.2034212351,0.1798978299,-0.1588044316,-0.1297353804,0.0691828653,0.04686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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/193","title":"[Tensorflow] Use something else than `from_tensor_slices()`","comments":"I'm using TF2.2\r\n\r\nHere is my code :\r\n```\r\nimport nlp\r\nfrom transformers import BartTokenizer\r\n\r\ntokenizer = BartTokenizer.from_pretrained('bart-large')\r\n\r\ndef encode(sample):\r\n    article_inputs = tokenizer.encode_plus(sample[\"article\"], max_length=tokenizer.model_max_length, pad_to_max_length=True)\r\n    summary_inputs = tokenizer.encode_plus(sample[\"highlights\"], max_length=tokenizer.model_max_length, pad_to_max_length=True)\r\n\r\n    article_inputs.update({\"lm_labels\": summary_inputs['input_ids']})\r\n    return article_inputs\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail', '3.0.0', split='test')\r\ncnn_dm = cnn_dm.map(encode)\r\n\r\ndef gen():\r\n    for sample in cnn_dm:\r\n        s = {}\r\n        s['input_ids'] = sample['input_ids']\r\n        s['attention_mask'] = sample['attention_mask']\r\n        s['lm_labels'] = sample['lm_labels']\r\n        yield s\r\n\r\ndataset = tf.data.Dataset.from_generator(gen, output_types={k: tf.int32 for k in ['input_ids', 'attention_mask', 'lm_labels']}, output_shapes={k: tf.TensorShape([tokenizer.model_max_length]) for k in ['input_ids', 'attention_mask', 'lm_labels']}\r\n```","body":"In the example notebook, the TF Dataset is built using `from_tensor_slices()` :\r\n\r\n```python\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x] for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"]}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"]\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nBut according to [official tensorflow documentation](https:\/\/www.tensorflow.org\/guide\/data#consuming_numpy_arrays), this will load the entire dataset to memory.\r\n\r\n**This defeats one purpose of this library, which is lazy loading.**\r\n\r\nIs there any other way to load the `nlp` dataset into TF dataset lazily ?\r\n\r\n---\r\n\r\nFor example, is it possible to use [Arrow dataset](https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset) ? If yes, is there any code example ?","comment_length":82,"text":"[Tensorflow] Use something else than `from_tensor_slices()` \n In the example notebook, the TF Dataset is built using `from_tensor_slices()` :\r\n\r\n```python\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x] for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"]}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"]\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nBut according to [official tensorflow documentation](https:\/\/www.tensorflow.org\/guide\/data#consuming_numpy_arrays), this will load the entire dataset to memory.\r\n\r\n**This defeats one purpose of this library, which is lazy loading.**\r\n\r\nIs there any other way to load the `nlp` dataset into TF dataset lazily ?\r\n\r\n---\r\n\r\nFor example, is it possible to use [Arrow dataset](https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset) ? If yes, is there any code example ? \n I'm using TF2.2\r\n\r\nHere is my code :\r\n```\r\nimport nlp\r\nfrom transformers import BartTokenizer\r\n\r\ntokenizer = BartTokenizer.from_pretrained('bart-large')\r\n\r\ndef encode(sample):\r\n    article_inputs = tokenizer.encode_plus(sample[\"article\"], max_length=tokenizer.model_max_length, pad_to_max_length=True)\r\n    summary_inputs = tokenizer.encode_plus(sample[\"highlights\"], max_length=tokenizer.model_max_length, pad_to_max_length=True)\r\n\r\n    article_inputs.update({\"lm_labels\": summary_inputs['input_ids']})\r\n    return article_inputs\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail', '3.0.0', split='test')\r\ncnn_dm = cnn_dm.map(encode)\r\n\r\ndef gen():\r\n    for sample in cnn_dm:\r\n        s = {}\r\n        s['input_ids'] = sample['input_ids']\r\n        s['attention_mask'] = sample['attention_mask']\r\n        s['lm_labels'] = sample['lm_labels']\r\n        yield s\r\n\r\ndataset = tf.data.Dataset.from_generator(gen, output_types={k: tf.int32 for k in ['input_ids', 'attention_mask', 'lm_labels']}, output_shapes={k: tf.TensorShape([tokenizer.model_max_length]) for k in ['input_ids', 'attention_mask', 'lm_labels']}\r\n```","embeddings":[-0.1217657998,-0.0259314273,0.0470915586,0.189820081,0.1980044991,0.1544274986,0.2483752966,0.45679757,-0.0371510051,-0.056614697,-0.1416853964,0.6093758941,-0.1823763102,-0.0313647278,0.2451841533,-0.1602426022,-0.0515337177,0.2101798803,-0.0837807283,-0.0511187688,0.0414349586,-0.1030471548,-0.1133751571,-0.0896192342,-0.0841150582,0.0378104895,0.0268044118,-0.1593336612,0.4063458145,-0.0158472583,0.3480536044,0.0354809612,-0.0619997717,0.1019106805,-0.0001139468,0.3830084801,0.0316342488,-0.1697195768,0.0051154783,-0.0486841574,0.1872231215,-0.2209469974,0.247618407,-0.2797369361,0.0229602605,-0.043305736,0.2282474935,0.0214918461,0.2741083801,0.0563438572,0.1135803461,0.4625842571,-0.2261624783,0.3665566444,0.1534703374,-0.1067058221,-0.138231352,-0.2763141394,-0.2109407485,0.0413133502,0.0462952703,0.214860484,-0.2967178822,0.1001085714,0.4883716702,0.1346100718,-0.1158743277,-0.2901355326,-0.2041688114,0.3537537158,0.1901264191,-0.0716039985,-0.1768607646,-0.5691713095,-0.2341983765,-0.2198168486,-0.2141052783,0.0487432033,-0.1875362694,0.1386623681,-0.216715768,-0.0914960578,-0.1921276599,0.1696150899,0.1730032712,0.0884684846,0.1593900025,0.1527348012,0.1636829078,0.0402699262,0.2768391371,-0.0111793596,0.2941534221,0.4132718444,-0.3397202492,-0.0049699522,0.1499769241,-0.5529497266,-0.0025993306,-0.2191033214,0.3147853017,0.2147977352,-0.2501312494,0.1788448095,-0.0064106318,-0.0640124604,-0.3965274692,0.1439047456,-0.1776492149,-0.5946298838,0.0709003583,0.0254464708,-0.2908785343,0.0922625288,0.0541443415,-0.5405897498,-0.0903404206,0.2274273634,-0.3082315326,-0.4565548897,-0.2495396137,0.0322222933,0.0282584541,0.2659028471,-0.1724849194,0.4949054122,0.0468011573,-0.1260062009,-0.2357968688,0.0714636967,-0.2054802477,0.085484013,-0.0532731339,-0.0908422247,0.1894188523,-0.0446499586,0.2361996174,-0.1806654483,-0.1580970287,0.1609508097,0.2584713101,-0.0925418213,0.0716399103,0.2176814377,-0.0144632915,0.0617462657,0.0473540463,0.1133971438,-0.2338122278,0.3567083776,-0.551022768,-0.2275333405,-0.0048400629,0.0849091113,0.1035248414,-0.2339808941,-0.3640972674,0.3549089432,0.1024191156,-0.1246886849,-0.1437174082,-0.1485555172,-0.4110409915,-0.263394773,0.2886543274,0.1181835011,-0.3723021746,-0.1996258497,0.0139911743,0.0392890982,0.2183583677,0.444042474,-0.2287716419,0.4910322726,-0.0685439557,0.1765822321,0.7466109991,-0.1683420092,-0.2163927257,0.2665832639,-0.1092089191,0.2027740777,0.0070782541,0.4875001609,0.0556126051,-0.0282452423,0.3278444111,0.607059896,-0.1100429371,0.0909122229,-0.0626382828,-0.3540176451,0.3833684623,0.2236017287,0.0494338833,-0.0890882313,-0.2451320291,0.4838311374,0.1579272896,0.0060733361,-0.1822026521,0.1302783191,-0.1859731525,0.0057758423,-0.2210057527,0.0439639203,-0.6262143254,0.2945075333,0.2898077071,0.0198014192,0.2248506397,-0.1320512146,0.3046332598,0.0869716927,0.0584740601,0.1558163464,-0.0363753103,0.1532447785,-0.0082864892,-0.0561432801,-0.3398274481,0.2135779709,-0.3556129336,-0.0859222114,-0.2873385251,0.2686126828,0.446282357,0.0063380175,-0.1396049559,0.2558359802,-0.2394817173,-0.09090738,0.0943144783,0.2214565724,-0.0374283791,0.3102308214,-0.6426632404,0.5175514817,-0.0072998405,-0.0519855805,-0.0380340815,0.1512567103,0.0408390574,-0.2298714817,-0.045041319,0.2038133591,-0.1811840981,0.2521127164,0.2275706381,-0.2973989844,0.0864404067,0.0949699059,0.1474915743,-0.1060408801,0.3818445802,0.0820869729,0.2589592338,0.2943733633,-0.4201218486,0.2011387348,0.5873517394,-0.033839304,-0.0882355794,0.2429160923,-0.0898037925,-0.2195173353,-0.0496823303,-0.1579284072,0.2387478501,0.1556952447,0.0475634746,-0.0183602758,-0.0851338357,-0.1895781159,0.2330112159,0.138178423,0.3442912102,-0.1048949435,0.079455547,0.2439493537,-0.2006927133,0.1206365302,0.2041326016,0.1614872366,-0.1821660399,0.0208744239,-0.2740591168,-0.3233437836,-0.0688158199,-0.0156345777,0.1198373586,-0.1766926646,-0.0839560255,0.3564248383,0.0389873683,-0.0269095302,-0.1203829944,0.3014902174,0.1829601973,-0.485211879,-0.1409324706,-0.0722896978,-0.1876286119,0.0123512261,0.3414314985,0.2476367354,0.2139724642,0.1156194732,-0.1064011455,0.0933765098,-0.1371449828,0.22248061,-0.1411897838,0.0744171813,-0.0913883373,0.085876815,-0.1317083389,-0.2374128699,-0.1384253502,-0.4001226127,0.0152844125,0.1781018674,0.0439833067,0.1407825798,-0.1730547547,-0.0541110225,-0.3113821447,-0.368055433,0.0251365807,0.164383471,0.2630543113,0.4266567826,-0.0977155864,0.2875241041,0.2841942906,0.0566007383,0.0843756571,-0.0591102839,0.5510883927,-0.2119562179,-0.2075658888,-0.1972450912,-0.1682775766,0.1238679662,0.2879946828,-0.7350276709,0.2582063377,-0.1476224959,0.0522895828,-0.136073038,-0.0432344712,0.3010390103,-0.2462277859,0.0579819977,-0.0192133784,0.0429808348,0.0740937069,0.0150421076,-0.2124611437,0.2835455835,0.4253311157,0.3563779891,0.8198500872,-0.0815245658,-0.195712477,0.2159968168,-0.2431849986,0.2042169571,-0.2124856114,-0.0797761902,0.07321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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/193","title":"[Tensorflow] Use something else than `from_tensor_slices()`","comments":"Apparently we'll have to wait for the next tensorflow release to use `.from_generator` and TPU. See https:\/\/github.com\/tensorflow\/tensorflow\/issues\/34346#issuecomment-598262489","body":"In the example notebook, the TF Dataset is built using `from_tensor_slices()` :\r\n\r\n```python\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x] for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"]}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"]\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nBut according to [official tensorflow documentation](https:\/\/www.tensorflow.org\/guide\/data#consuming_numpy_arrays), this will load the entire dataset to memory.\r\n\r\n**This defeats one purpose of this library, which is lazy loading.**\r\n\r\nIs there any other way to load the `nlp` dataset into TF dataset lazily ?\r\n\r\n---\r\n\r\nFor example, is it possible to use [Arrow dataset](https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset) ? If yes, is there any code example ?","comment_length":17,"text":"[Tensorflow] Use something else than `from_tensor_slices()` \n In the example notebook, the TF Dataset is built using `from_tensor_slices()` :\r\n\r\n```python\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\r\ntrain_tf_dataset.set_format(type='tensorflow', columns=columns)\r\nfeatures = {x: train_tf_dataset[x] for x in columns[:3]} \r\nlabels = {\"output_1\": train_tf_dataset[\"start_positions\"]}\r\nlabels[\"output_2\"] = train_tf_dataset[\"end_positions\"]\r\ntfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)\r\n```\r\n\r\nBut according to [official tensorflow documentation](https:\/\/www.tensorflow.org\/guide\/data#consuming_numpy_arrays), this will load the entire dataset to memory.\r\n\r\n**This defeats one purpose of this library, which is lazy loading.**\r\n\r\nIs there any other way to load the `nlp` dataset into TF dataset lazily ?\r\n\r\n---\r\n\r\nFor example, is it possible to use [Arrow dataset](https:\/\/www.tensorflow.org\/io\/api_docs\/python\/tfio\/arrow\/ArrowDataset) ? If yes, is there any code example ? \n Apparently we'll have to wait for the next tensorflow release to use `.from_generator` and TPU. See https:\/\/github.com\/tensorflow\/tensorflow\/issues\/34346#issuecomment-598262489","embeddings":[-0.1305493712,-0.0308594704,0.0363049358,0.1227419302,0.2281208932,0.0917753205,0.2730562389,0.4065895379,-0.1111178175,0.0027906678,-0.088807933,0.5513763428,-0.1521194577,0.0472110957,0.3196076751,-0.2627645731,-0.0665044412,0.2488584071,-0.1298071444,-0.014312909,-0.0405507199,-0.0532309711,-0.0955616459,-0.0164401829,0.0150601091,-0.033534877,0.0511503406,-0.1520288438,0.4169293344,0.0210770834,0.3553362489,0.0021882898,-0.0340940319,0.1122701243,-0.0001092273,0.3845244944,0.0321453772,-0.1496059895,0.0620988235,-0.0740662292,0.2081267238,-0.2273654491,0.1988500506,-0.2661621571,-0.0468088314,-0.0052516502,0.2368553579,-0.034285564,0.2574249506,0.1193688512,0.1560954005,0.5252822638,-0.1733796746,0.2786276639,0.1325304955,-0.1595598012,-0.2168976963,-0.2793309987,-0.1323648393,-0.0109547814,0.0657459348,0.1479676813,-0.2282933295,0.0962995291,0.4325979948,0.1546453238,-0.1112498567,-0.3790851235,-0.2610158622,0.4085841179,0.2015358955,-0.0729810297,-0.1770699024,-0.4851972461,-0.1757751554,-0.2934440076,-0.1783652008,0.0868638456,-0.2625950873,0.1819095612,-0.1011813283,-0.1204154864,-0.2189192027,0.1201040894,0.2120116055,-0.0355753787,0.1604187936,0.1168624461,0.2244132608,0.1082769483,0.3494542241,0.0806608647,0.35487023,0.3675652742,-0.284050256,-0.0114905862,0.1692720056,-0.5806717873,-0.0444342121,-0.2525962889,0.3571074605,0.1853387356,-0.2919266522,0.2165090591,-0.0133072734,-0.0735663027,-0.4224945009,0.1319459379,-0.1399093866,-0.5050939918,0.0470280461,0.0463061109,-0.2208214402,-0.0199277624,0.0605984516,-0.51125139,-0.147703439,0.2690734267,-0.2849743664,-0.4418404996,-0.2301685661,0.0283928644,-0.0143321892,0.1737463325,-0.1279168576,0.5364353657,0.1373176873,-0.1248362809,-0.2813409865,-0.0295778885,-0.2323073447,0.1130894944,-0.0614650324,-0.0960282385,0.1556884646,-0.1606730819,0.1797935963,-0.1226887256,-0.127771765,0.1816211492,0.2595539987,-0.0819504037,0.1001746058,0.2579149306,0.0148862759,-0.0645697564,0.0382283106,0.0749141052,-0.2161310017,0.3539175391,-0.5225843787,-0.2890223861,-0.0487967283,0.1349671781,0.0877802894,-0.2136898041,-0.3331672847,0.3261962831,0.120963119,-0.1192807257,-0.1852190197,-0.2075044811,-0.4238689244,-0.3011671007,0.3041065931,0.0406429134,-0.4613336623,-0.1152338833,0.0065076631,-0.0165925473,0.2493128031,0.4456069171,-0.2329480797,0.4801923335,-0.053436324,0.0012751004,0.7598665357,-0.0854216591,-0.16154176,0.2256312221,-0.1233380213,0.2028128058,-0.0049250848,0.4053750932,0.0003449573,-0.0291188397,0.3868638277,0.6318570971,-0.1584129184,0.0767637864,-0.1092906669,-0.3459326923,0.3001140058,0.2797210515,0.0952894017,-0.0565825552,-0.2650605738,0.3954328597,0.130624339,-0.0343222432,-0.1820747703,0.067004025,-0.1286901981,0.0004341092,-0.1707858741,0.0657679886,-0.6022973061,0.2533168495,0.1912777126,0.0960817561,0.1893976778,-0.1119040027,0.3626312912,0.1090806499,0.118262887,0.1590494215,0.0190945361,0.0754810348,-0.0283455104,-0.0117259379,-0.3443227112,0.1589715779,-0.3754411638,-0.126958698,-0.3489677012,0.2644969821,0.3732426167,0.017966941,-0.084560357,0.3013327718,-0.3664574921,-0.1209989563,0.1176432893,0.2152287513,-0.0305702519,0.2932139337,-0.667224288,0.442422688,0.0515987575,-0.0227559302,-0.1092137024,0.1241608113,0.0025712894,-0.1974492967,0.0108399848,0.2805424631,-0.2258610129,0.1471134871,0.2610126734,-0.2442211807,0.1527157277,0.0638118982,0.1342630833,-0.1127064526,0.3706358671,0.0992518589,0.1377707124,0.2951819897,-0.5292932987,0.2312241346,0.670640409,-0.0221984033,-0.1136625782,0.2168765515,0.0373343788,-0.2118321955,-0.08162269,-0.0811692923,0.2019304633,0.145782724,0.0416364148,0.0004896394,-0.164139986,-0.2583947182,0.2476225644,0.1311677694,0.3808506727,-0.1395992339,0.0300449897,0.2035273761,-0.1915541738,0.0832396448,0.2106474191,0.1754733324,-0.2056151778,0.0302167274,-0.2745707035,-0.3066859841,-0.0898331255,0.035019286,0.1440560818,-0.1836826354,-0.0499239415,0.3294695318,-0.0494266897,-0.0536168925,-0.1188829541,0.3149219155,0.1649483591,-0.4449187815,-0.0658236668,-0.1055019423,-0.1850717217,0.0902298316,0.3607676625,0.1774414331,0.242978856,0.0263939425,-0.087532863,0.1681303084,-0.0779821351,0.2226692438,-0.111642234,0.1100937501,-0.0496085063,0.0718158409,-0.1287714988,-0.2588922381,-0.1155318022,-0.4312819242,0.0538533144,0.1550911814,-0.0024251451,0.1485794932,-0.2426673174,-0.1758734882,-0.3055381775,-0.3943730593,0.0180975385,0.1643054336,0.3020911217,0.4260999858,-0.0299287923,0.2822209895,0.3855684698,0.0524073578,0.0802656263,-0.1383150667,0.4784578681,-0.2761949003,-0.2551722527,-0.1560890377,-0.1742354333,0.1930478215,0.3186536431,-0.7124727964,0.1455635726,-0.0832740888,0.086586833,-0.0899093747,-0.0410923846,0.2317247093,-0.2033178508,0.0205108784,0.0046083168,0.1650743186,0.1451765895,0.0248066206,-0.2041558921,0.3703911006,0.4237145483,0.3312722147,0.7575733662,-0.084291786,-0.2142929286,0.234271273,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/192","title":"[Question] Create Apache Arrow dataset from raw text file","comments":"We store every dataset in the Arrow format. This is convenient as it supports nested types and memory mapping. If you are curious feel free to check the [pyarrow documentation](https:\/\/arrow.apache.org\/docs\/python\/)\r\n\r\nYou can use this library to load your covid papers by creating a dataset script. You can find inspiration from the ones we've already written in `\/datasets`. Here is a link to the steps to [add a dataset](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/CONTRIBUTING.md#how-to-add-a-dataset)","body":"Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as \"Crime and punishment\" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?\r\nIs the worth of send it to you and add it to the NLP library?\r\nThanks, Manu\r\n","comment_length":68,"text":"[Question] Create Apache Arrow dataset from raw text file \n Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as \"Crime and punishment\" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?\r\nIs the worth of send it to you and add it to the NLP library?\r\nThanks, Manu\r\n \n We store every dataset in the Arrow format. This is convenient as it supports nested types and memory mapping. If you are curious feel free to check the [pyarrow documentation](https:\/\/arrow.apache.org\/docs\/python\/)\r\n\r\nYou can use this library to load your covid papers by creating a dataset script. You can find inspiration from the ones we've already written in `\/datasets`. Here is a link to the steps to [add a dataset](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/CONTRIBUTING.md#how-to-add-a-dataset)","embeddings":[-0.1738835275,-0.0641050488,-0.0457872935,0.0695887059,-0.3965011537,0.136375919,0.0676105917,0.4003833532,-0.0682438016,-0.3222419024,0.1313437074,0.1833892018,-0.082505092,0.0789601728,0.3914310634,-0.0407906063,-0.0588860214,0.2828747034,-0.0328622721,-0.0365570188,0.1609279215,-0.0212868117,-0.1044443101,0.0072559575,-0.1917187274,-0.3496663272,-0.1661534458,0.1569089741,-0.1471924633,-0.4131221771,0.1118626893,0.0859132558,0.5545091629,0.1522884071,-0.000120873,-0.4648412466,0.146173358,-0.0721157268,-0.1525297612,-0.2088950127,-0.2326005995,-0.0918239728,0.1721589863,-0.3390492201,0.0773122162,-0.4477847517,0.1712197363,-0.2712031901,0.6657809019,0.3493712246,0.0733593181,-0.1018955708,0.1893394291,0.2285748869,0.479952991,0.5429444909,-0.3052916229,0.1529542953,0.2607792616,-0.2076222152,-0.2825189829,0.1587996036,-0.0976548865,-0.2390976697,0.3305785358,0.1180302352,-0.2703990638,-0.3330769241,0.048938185,0.396437645,0.369625777,-0.4938062727,0.0485387929,-0.0501641035,0.1894060224,-0.4890819192,-0.148752749,0.6180791259,-0.5020700693,0.3597390354,0.3344428837,-0.3816461265,-0.433984071,0.1284186244,0.4575878978,-0.0715703219,-0.0471398421,-0.0758280829,-0.0502895489,0.1744943112,0.2112369835,-0.2455614209,-0.1487576514,0.3470050693,-0.1437056065,-0.1627587527,-0.487794131,-0.2078185678,0.2758220434,-0.0297537744,0.3700711131,-0.1301807463,-0.0390953608,-0.0916772112,0.0394635387,-0.0950186923,0.4510582089,0.2062097341,-0.0090025906,-0.2627836466,0.1622353196,-0.0809461623,-0.2055873126,0.0966669247,-0.1564095169,0.0332183614,0.0496243276,-0.1927904636,0.0111123761,0.0060190335,-0.4366478324,-0.0245696492,-0.2640629709,0.2335275561,0.1825598478,-0.0913557038,0.2279809117,0.3980584145,0.0025834572,-0.1910063475,0.1229753047,0.0735131875,-0.2823687494,0.2808929086,0.247234419,0.0487667546,-0.0020069233,-0.0952516273,0.0477823392,-0.3019622564,0.1904058754,-0.0115250554,0.1669414788,-0.1192823574,0.2679129839,0.0407756977,-0.1499408185,-0.1275525689,-0.1013562828,0.369210273,-0.3687529564,-0.1211427599,-0.1366639584,-0.0014926877,0.0550353937,-0.1415070891,-0.2777965963,0.2512921989,0.1334351301,0.1928410381,0.0556442216,0.2801023126,-0.0139733898,-0.2512498498,0.0342513993,0.4760324955,-0.2327194661,0.1828200668,-0.1303556114,0.4220273793,0.0418921448,0.1999807358,-0.2509055138,0.3373116553,0.0285330359,0.3784561157,0.7308462262,-0.0782934576,0.2264495641,0.4033631384,0.1815540344,-0.3522212803,0.1490732729,-0.0432250313,0.1029437035,-0.0991303548,-0.3000408709,0.4925096035,0.1188171133,0.0380928889,-0.0789848194,-0.2024594098,-0.1806162894,0.0138285318,-0.1424853951,-0.152122587,0.0905092135,-0.4588907659,0.4787380397,-0.2984763384,0.4021227658,0.290397644,0.0415256247,-0.0141991246,0.2463943064,-0.1117685288,-0.1824048758,-0.3121178448,-0.406529814,0.2906285822,-0.2542756498,-0.2743141353,-0.2539533079,-0.1194468737,0.0768854916,-0.0305947848,-0.031456843,-0.2436130941,-0.2193366289,0.0177638438,-0.207388252,0.2594847679,-0.2797718942,0.1349848956,-0.2302947938,0.2156806141,0.0819953457,-0.1958412677,0.2466090024,0.2380749583,-0.2799951732,0.1489574611,0.0656598657,0.1418323517,-0.0535547696,0.2025629133,0.4277290106,-0.2808156908,0.0847726315,-0.6659778357,0.3037104905,-0.0413484052,0.2257367522,-0.1121066958,-0.3082047403,0.4449282885,-0.062422622,0.1924302876,-0.1610332429,0.0182838421,-0.0741204917,-0.030854173,-0.0838211328,0.1798774302,0.0551718883,0.4346465766,0.156640172,-0.1513433754,0.0273626857,0.085149169,-0.127518326,-0.1711125374,0.2346558124,0.2375770062,-0.3029533029,-0.103598401,-0.1672834307,0.0483935922,0.0925913677,0.1848359704,-0.150638476,-0.040357966,0.080506593,-0.0968746543,0.3708855808,-0.030180322,0.2931441367,-0.1610877663,-0.0907879397,-0.0233558584,-0.1795140058,-0.2806978226,-0.0475527011,0.1384664476,-0.2195436507,0.0284088142,-0.0660056621,-0.3738856018,-0.3311776817,0.1296107471,-0.0414256863,0.2527340651,0.0128344148,-0.2428025901,0.1606639624,0.0032889307,-0.0153890615,0.5403869152,0.0907274038,0.0718730465,-0.1886594892,-0.5223944783,-0.1820635647,0.0564001463,0.2462329566,0.420417279,0.3654943705,-0.1879571527,0.120198153,-0.0372109525,-0.0155866034,0.0392338969,-0.1758018136,-0.2756327987,-0.1856742948,0.1290773302,-0.5696607828,-0.1051902771,-0.2161619216,0.0416453741,-0.1757654697,-0.2118123025,-0.0726570338,-0.0401740186,0.1962092221,-0.5246594548,-0.1546359658,-0.126838237,0.3574329615,0.3072159886,0.2926772535,-0.0244840514,0.2397527844,0.1166200936,-0.307590872,-0.0535371862,0.1910519451,-0.0654017478,0.3787032068,-0.3172714412,-0.5370230675,0.0119816223,-0.0533121489,-0.0727550015,0.3109488785,-0.3548155725,0.4196316004,0.0823071674,0.0295326319,-0.1032703221,-0.0128584262,0.2451626956,-0.0004229056,0.0613565557,-0.0583934337,0.0904885307,0.3568631411,-0.128423661,0.3276356161,0.3448913991,0.1456566602,-0.0466144495,0.5362749696,0.251748174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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/192","title":"[Question] Create Apache Arrow dataset from raw text file","comments":"Hello @mrm8488 and @lhoestq \r\n\r\nIs there a way to convert a dataset to Apache arrow format (locally\/personal use) & use it before sending it to hugging face?\r\n\r\nThanks :)","body":"Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as \"Crime and punishment\" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?\r\nIs the worth of send it to you and add it to the NLP library?\r\nThanks, Manu\r\n","comment_length":29,"text":"[Question] Create Apache Arrow dataset from raw text file \n Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as \"Crime and punishment\" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?\r\nIs the worth of send it to you and add it to the NLP library?\r\nThanks, Manu\r\n \n Hello @mrm8488 and @lhoestq \r\n\r\nIs there a way to convert a dataset to Apache arrow format (locally\/personal use) & use it before sending it to hugging face?\r\n\r\nThanks :)","embeddings":[-0.104102768,-0.0871481746,-0.0996507183,0.1544027179,-0.3756233454,0.1595188379,0.1173257455,0.4278326631,-0.0435959734,-0.2937572002,0.0730511174,0.1151047051,-0.0833791643,0.1475037634,0.2515309751,-0.1538529694,-0.0880172402,0.2165444642,-0.2933829427,0.0157115478,0.2740622461,-0.0247493405,-0.0877847821,0.0122401519,-0.1714434177,-0.236255154,-0.1281672567,-0.0033141167,-0.1450779289,-0.337683022,-0.0253848862,-0.0293533411,0.5116750002,0.2051157355,-0.0001217041,-0.3465603292,0.1274175495,-0.0041155894,-0.2411180586,-0.2082494944,-0.1519872993,-0.0090410477,0.1944746971,-0.3356013,-0.0600214079,-0.3797875345,0.0302869622,-0.468857348,0.839243114,0.3928316534,0.075771749,-0.1602265984,0.188713938,0.2179340422,0.3413510919,0.5511066914,-0.3402373195,-0.0774220377,0.1499584019,0.0333747119,-0.1650861204,0.1985009313,0.0439888611,-0.1532467604,0.3540650904,-0.0316225402,-0.2806330621,-0.2210430801,0.1231576055,0.326362133,0.4954088926,-0.4750736356,-0.0450375564,-0.0119728958,0.2050462216,-0.4249300361,-0.2257204354,0.5759353638,-0.4589058161,0.4854776561,0.1496035159,-0.4008947313,-0.4561852813,-0.0745547041,0.3925387859,-0.1183444932,-0.1044038236,-0.063812755,-0.1421257108,0.1629603058,0.2270020247,-0.3375430405,-0.1277933568,0.2690395713,-0.0427741259,-0.2151730806,-0.5973445773,-0.1973363012,0.1853763014,0.0773296952,0.3857090473,-0.1129084006,-0.0476093516,-0.2783759832,0.0771988481,-0.0727790147,0.4339829087,0.1329318434,-0.0070713861,-0.2632562816,0.1707571894,-0.1187153831,-0.0946807563,0.0173992645,-0.1367946863,0.2015711069,-0.0105000706,-0.2780988812,-0.0333565138,-0.0199090876,-0.38649562,-0.0769996718,-0.2138036489,0.0261282306,0.201365307,-0.146756202,0.2247111648,0.3776323199,0.0520545654,-0.2802136838,0.176209569,-0.0180223454,-0.2320825905,0.281655401,0.0870089158,0.165380612,-0.1353075802,-0.0685042292,-0.0582958013,-0.1452396214,0.2524908185,-0.036170084,0.2175890356,-0.0666913912,0.2069645077,0.0162982009,-0.1201662347,-0.0072476715,-0.0758111998,0.3549625874,-0.181900546,0.0353412069,-0.0445053093,0.0157345664,0.0706239417,-0.0845680982,-0.2357393354,0.1604427993,-0.0032508643,0.3336627185,0.0702999011,0.2868445218,0.0605237335,-0.142325744,-0.061350055,0.4451697171,-0.3385572433,0.1481717825,-0.0977572948,0.2030458152,-0.1150634736,0.3096966743,-0.2214799076,0.2855267525,-0.0217324067,0.2480599135,0.7609063983,-0.0031955305,0.1953616738,0.4988286793,0.032612469,-0.3793379366,0.0923480466,-0.0841630176,-0.0858473703,-0.0297111217,-0.2868655026,0.5281491876,0.1118569747,-0.0169506893,-0.1194049045,-0.17205365,-0.1165966019,-0.05393878,-0.2323151678,-0.0548298918,0.0921822786,-0.4823833406,0.4895274341,-0.3356698155,0.4532503784,0.237851724,0.0251209922,-0.0393288396,0.1749618948,-0.0319602005,-0.0986610278,-0.356051147,-0.3879675865,0.2141292244,-0.3269676268,-0.2382126153,-0.269683212,-0.1252905577,-0.0686217174,0.0822976828,-0.0197964236,-0.2352799475,-0.1557948887,0.0143629862,-0.2127346098,0.3180286288,-0.1101872921,0.1491360366,-0.2236446142,0.1163673326,0.1057078913,-0.004393863,0.2375984639,0.1367183775,-0.2308913916,0.0524221845,-0.0545833111,0.0032960109,-0.0711125582,0.1790067852,0.5197834373,-0.2920793891,0.0735270306,-0.6826456189,0.3104538023,0.0175315402,0.2184136361,-0.1010279655,-0.465402782,0.4807801247,-0.0809450373,0.1903368831,-0.1441085488,0.0432977416,-0.137209937,-0.1126281694,-0.1677837819,0.1439184099,-0.0678353235,0.3569740951,0.2148139775,-0.0920701772,0.0997203216,0.031152619,-0.1916439235,-0.1972690076,0.232971862,0.2144037038,-0.2332379818,-0.0765687302,-0.0611813888,0.0409181267,0.117594786,0.1491021514,0.0216915365,0.0765997991,0.2202986926,-0.1371531934,0.3341186047,-0.1321943402,0.3828599453,-0.1595306695,-0.1652635932,0.0211987104,-0.0997905806,-0.1932457834,-0.1010821089,0.1401480436,-0.0816581771,-0.0382212214,-0.1377267987,-0.4040078521,-0.3725430667,0.2617996633,-0.0313793011,0.2539508939,-0.0742642134,-0.4246178567,0.1473329961,-0.1559281051,-0.0753659233,0.5126544833,0.0790228024,0.0604605265,-0.283616811,-0.3982836306,-0.1630405188,0.0774199739,0.3069842458,0.4057583511,0.3491528034,-0.082093887,0.1948646009,-0.1915400624,0.0942462608,0.0271473583,-0.0349812098,-0.3089195788,-0.1563499421,0.1664443016,-0.554985404,0.1195530668,-0.1840698421,0.116887562,-0.2389353514,-0.2792014182,-0.0752747208,-0.0213916749,0.2210385203,-0.4112195671,-0.1371773481,-0.0841433927,0.4223146439,0.233963266,0.2500327229,-0.041657839,0.1320338845,0.1708656251,-0.23383075,-0.072331056,0.1905654371,-0.1538463831,0.2510851324,-0.3682453632,-0.5960060358,-0.0636397153,-0.0158421248,0.0210041441,0.3126493692,-0.421554476,0.5755354166,-0.0353645161,0.0539309606,-0.1453753859,-0.0059959465,0.2033376247,-0.0336186029,0.0440981165,-0.0300906785,0.0854771137,0.3154038489,-0.1693241596,0.3524861038,0.4192026258,0.1349170804,0.0818894729,0.6222640276,0.2455940098,0.1014534533,0.2365687639,-0.0248210039,0.0064176605,0.0260796119,0.0519021042,-0.1404498816,0.195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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/192","title":"[Question] Create Apache Arrow dataset from raw text file","comments":"> Is there a way to convert a dataset to Apache arrow format (locally\/personal use) & use it before sending it to hugging face?\r\n\r\nSure, to get a dataset in arrow format you can either:\r\n- [load from local files (txt, json, csv)](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#from-local-files)\r\n- OR [load from python data (dict, pandas)](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n- OR [create your own dataset script](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n","body":"Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as \"Crime and punishment\" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?\r\nIs the worth of send it to you and add it to the NLP library?\r\nThanks, Manu\r\n","comment_length":58,"text":"[Question] Create Apache Arrow dataset from raw text file \n Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as \"Crime and punishment\" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?\r\nIs the worth of send it to you and add it to the NLP library?\r\nThanks, Manu\r\n \n > Is there a way to convert a dataset to Apache arrow format (locally\/personal use) & use it before sending it to hugging face?\r\n\r\nSure, to get a dataset in arrow format you can either:\r\n- [load from local files (txt, json, csv)](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#from-local-files)\r\n- OR [load from python data (dict, pandas)](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n- OR [create your own dataset script](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n","embeddings":[-0.116234459,-0.1841169298,-0.0716124773,0.1751199812,-0.3280258477,0.139235884,0.11795903,0.3817427754,0.0022516297,-0.2727223337,0.0569148846,0.0983997434,-0.077237919,0.2192152888,0.3109797537,-0.0580277778,-0.1029317454,0.1872917861,-0.2899804413,0.0341969468,0.2486115843,0.0235500224,-0.0627381131,0.014807988,-0.1860498339,-0.2168583274,-0.1237601191,0.1029290184,-0.1195597574,-0.4056254625,0.0453822389,-0.0250188615,0.5199136138,0.1765504926,-0.0001225578,-0.3012937605,0.1491823643,-0.0089221923,-0.2559587955,-0.3521432877,-0.0911589414,-0.0514178649,0.3022276759,-0.2763149142,-0.1366523504,-0.3666140735,0.0447538495,-0.4632765949,0.8944173455,0.3823668361,0.066881977,-0.0697314069,0.1201731786,0.1746979356,0.2935884893,0.64989537,-0.298520267,0.0173336752,0.2269861549,-0.0276041813,-0.1870692223,0.2103879452,0.0335024111,-0.1601584703,0.4182694554,0.0329427719,-0.3197295964,-0.2667315304,0.1045991704,0.2918638289,0.4435722828,-0.5081265569,-0.0686361194,-0.15882954,0.195936814,-0.4047840536,-0.2023734301,0.5912524462,-0.4560746253,0.4850006402,0.1111562252,-0.3301271498,-0.4546756744,-0.0233673491,0.4130191803,-0.1192310303,-0.1424247622,-0.0342211239,-0.0878192484,0.1139591858,0.1446623802,-0.3402825296,-0.0819694698,0.2702946663,-0.0304175131,-0.1481847614,-0.5688718557,-0.11655467,0.2962391675,0.1948115677,0.3581244051,-0.1492294371,-0.0830274597,-0.2412634045,0.1170790344,-0.0359844863,0.4621579945,0.1229492575,-0.0228784699,-0.1033878848,0.2161062211,-0.1180476174,-0.0714673772,-0.0071603106,-0.1958083659,0.1252120286,0.0682130828,-0.3583876491,-0.063696757,-0.0635823607,-0.3160603642,-0.0648569912,-0.1641439497,0.121300675,0.1500302255,-0.148541376,0.2026017308,0.420989871,0.002455418,-0.2346609831,0.1313377917,-0.0109175872,-0.2892900407,0.34125489,0.151455,0.0410262048,-0.0336779617,-0.065133065,0.0418761894,-0.1866392046,0.2114453018,0.0035033743,0.2554564178,-0.0279187653,0.2255206704,0.0512548424,-0.0815235972,-0.0155559788,-0.1415526271,0.3194224834,-0.1971614808,-0.0013487145,-0.0195522457,-0.0125658587,-0.0712687001,-0.11709214,-0.2966439128,0.1667173505,-0.0553302169,0.3410833478,0.0515456945,0.3041132092,-0.004430512,-0.1568996459,0.0606452562,0.5751587749,-0.2798394859,0.0774808377,-0.0636781305,0.1921245456,-0.1320408732,0.3210464716,-0.2403280884,0.2045165598,-0.1077213213,0.2150873542,0.6738168597,-0.1115691736,0.1638736874,0.5188358426,0.0564497374,-0.2370747477,0.1192140207,-0.1164142415,-0.074343361,-0.0028069427,-0.3140346706,0.5571510792,0.0826089084,0.0182557777,-0.1299570054,-0.215236634,-0.0970101357,0.0046830564,-0.2340568751,-0.014258204,0.0603631474,-0.5500680804,0.4852304161,-0.3260752857,0.4003874362,0.2320333868,0.0025299124,0.0229876433,0.1399732828,-0.0311654154,-0.2683026791,-0.2566067278,-0.3144997358,0.2239765972,-0.4510672092,-0.2488138527,-0.3250751197,-0.1321393251,-0.1072458029,-0.0211220328,-0.0625171959,-0.2079957873,-0.1098496914,0.0530205593,-0.2107385993,0.3642225564,-0.0545293428,0.2473109215,-0.3691832721,0.1250728667,0.1314229667,-0.0102914404,0.233803913,0.176344797,-0.1896404177,0.03401158,0.0070233983,0.0841445401,0.0100615537,0.2041340321,0.4599420428,-0.2706058621,0.1242063195,-0.7130148411,0.2794928551,0.0074256659,0.1846458912,-0.0920473263,-0.4825463891,0.5747972131,-0.1449293643,0.2311107963,-0.1459647417,0.0274425037,-0.1334023923,-0.0777245685,-0.2256112099,0.0628116801,-0.0321381874,0.3259410262,0.3570249677,-0.1237271875,0.0172535423,0.0338917896,-0.2287790626,-0.220150128,0.1503489316,0.2633678019,-0.2448005676,-0.0723638907,-0.032712128,-0.0086683696,0.1141559407,0.1704614758,-0.025074387,0.1204680055,0.2024980336,-0.1406179965,0.3045222759,-0.100370951,0.4107743204,-0.1775361449,-0.1377764791,-0.0128715876,-0.1209045574,-0.2307565957,-0.1614343226,0.1069969758,-0.177055195,-0.0085621923,-0.1658426225,-0.4955016673,-0.4169091582,0.231995523,-0.0679309741,0.2515639365,-0.1629195064,-0.3335672021,0.1284248531,-0.1341255456,-0.1017299369,0.473461926,-0.0070495149,0.0162949879,-0.2771503329,-0.3815431595,-0.1648819149,0.0659755245,0.3686508238,0.4183353484,0.3976896703,-0.120051071,0.1377187222,-0.1911898404,0.0149206603,0.0485848896,-0.0908257589,-0.213777259,-0.1240315586,0.1324923187,-0.5243587494,0.0738317594,-0.1173050627,0.0836823061,-0.2369590402,-0.2871921957,-0.0981979892,0.0131122749,0.1493159533,-0.3908486664,-0.1651644558,-0.1018541083,0.5026175976,0.2510915101,0.1904059052,0.0437579565,0.1060587615,0.1319574416,-0.2570896745,-0.1064995229,0.1840862334,-0.2015497535,0.2977467477,-0.3921264112,-0.5845618248,-0.1108840927,-0.0068856301,0.0341881961,0.2856819928,-0.4618063271,0.4126827717,-0.071103029,0.0711725876,-0.1178861037,-0.0103956386,0.204182893,-0.0252516419,0.0849703252,-0.0204213206,-0.0172816776,0.2986920476,-0.2002993971,0.2878693938,0.4254688323,0.172322616,0.0580608957,0.5829063058,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/192","title":"[Question] Create Apache Arrow dataset from raw text file","comments":"> > Is there a way to convert a dataset to Apache arrow format (locally\/personal use) & use it before sending it to hugging face?\r\n> \r\n> Sure, to get a dataset in arrow format you can either:\r\n> \r\n>     * [load from local files (txt, json, csv)](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#from-local-files)\r\n> \r\n>     * OR [load from python data (dict, pandas)](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n> \r\n>     * OR [create your own dataset script](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n\r\nLinks were broken. \r\n\r\nUpdated links provided as below\r\n- [load from local files (txt, json, csv)](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#from-local-or-remote-files)\r\n- [load from python data (dict, pandas)](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#from-in-memory-data)\r\n- [create your own dataset script](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#using-a-custom-dataset-loading-script)\r\n","body":"Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as \"Crime and punishment\" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?\r\nIs the worth of send it to you and add it to the NLP library?\r\nThanks, Manu\r\n","comment_length":96,"text":"[Question] Create Apache Arrow dataset from raw text file \n Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as \"Crime and punishment\" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?\r\nIs the worth of send it to you and add it to the NLP library?\r\nThanks, Manu\r\n \n > > Is there a way to convert a dataset to Apache arrow format (locally\/personal use) & use it before sending it to hugging face?\r\n> \r\n> Sure, to get a dataset in arrow format you can either:\r\n> \r\n>     * [load from local files (txt, json, csv)](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#from-local-files)\r\n> \r\n>     * OR [load from python data (dict, pandas)](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n> \r\n>     * OR [create your own dataset script](https:\/\/huggingface.co\/nlp\/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n\r\nLinks were broken. \r\n\r\nUpdated links provided as below\r\n- [load from local files (txt, json, csv)](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#from-local-or-remote-files)\r\n- [load from python data (dict, pandas)](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#from-in-memory-data)\r\n- [create your own dataset script](https:\/\/huggingface.co\/docs\/datasets\/loading_datasets.html#using-a-custom-dataset-loading-script)\r\n","embeddings":[-0.1038161442,-0.2466497421,-0.0638294667,0.1784733832,-0.2581137121,0.1428021342,0.1377845854,0.3953749835,0.0053265598,-0.2353559583,0.0549273416,0.0873041749,-0.0744914785,0.2466412187,0.3077548146,-0.0621263348,-0.0998366401,0.1523499787,-0.2911973894,0.0361612625,0.2273101509,0.0633061454,-0.0354930982,0.0017388129,-0.1646287143,-0.1796199977,-0.133953169,0.1559446454,-0.1216913983,-0.4069451392,0.0338261798,-0.012788577,0.4881340861,0.2070336342,-0.0001221136,-0.277651459,0.1478410959,-0.0261555947,-0.3058749139,-0.3625970185,-0.0749124065,-0.0354043618,0.3130250573,-0.2594720423,-0.1411257684,-0.3375342488,0.0308725387,-0.5021820068,0.954010129,0.3575791717,0.0778182372,-0.0108533939,0.1365810633,0.1517772228,0.2576152086,0.6586973667,-0.2925234139,0.1150168851,0.1938347518,-0.024406828,-0.1751165241,0.2326538265,0.0534768887,-0.1904520392,0.4256590605,0.0297951326,-0.2974018157,-0.2786620557,0.1164889783,0.281460464,0.4427064359,-0.5092775822,-0.1094571948,-0.1849978864,0.1764959097,-0.4072861969,-0.2105818391,0.5788426995,-0.4214339256,0.4962906539,0.1022315845,-0.32901752,-0.4584326446,-0.0298315249,0.3706864417,-0.1374999583,-0.1774411052,-0.0397585891,-0.0696287453,0.0811895207,0.0856978297,-0.3075334132,-0.0814579129,0.2680883408,-0.0111105358,-0.1282114834,-0.5387436748,-0.0729424134,0.3124314249,0.2473572642,0.3776046932,-0.120039627,-0.091342859,-0.2472890764,0.1241989955,-0.0119312415,0.4723646641,0.1330219507,-0.0414006636,-0.0138786752,0.2262936085,-0.1195100248,-0.0631399676,-0.018222319,-0.2306061983,0.1123955995,0.0862268209,-0.3619479239,-0.0944665372,-0.0658566579,-0.2761764824,-0.063085027,-0.1510935128,0.1806655079,0.1541122198,-0.1351335347,0.2101379633,0.4079540074,-0.0136782164,-0.2369323522,0.115605101,0.0181850456,-0.3199980557,0.3689858019,0.1320215315,0.0227207914,-0.0280740932,-0.0658754781,0.1132722348,-0.1992305666,0.1252096295,0.0417235084,0.2980290353,-0.0248464718,0.1925857514,0.0537319928,-0.0512555391,-0.0465444215,-0.1434247643,0.2823869586,-0.2003550529,-0.0333875977,-0.0391922556,-0.0104782572,-0.1053520665,-0.0948779285,-0.3196413219,0.1640440226,-0.061796695,0.3104664385,0.0635946989,0.2971037626,-0.0475743152,-0.1613653749,0.1008470282,0.6478850245,-0.2875784338,0.0215028655,-0.0618485175,0.1838534325,-0.1300303042,0.318282634,-0.2305365652,0.1760488153,-0.1517537683,0.1949832588,0.6005390882,-0.1947419941,0.1463819444,0.5530235171,0.0307306834,-0.1841427088,0.1237969995,-0.159847796,-0.0484820344,-0.0215336829,-0.3050334454,0.4839855731,0.1062306911,0.021367332,-0.1362446398,-0.2183812112,-0.1014782935,0.0083584087,-0.2339886427,0.0108125303,0.0868162364,-0.5371927023,0.4845659435,-0.3358580172,0.4039578438,0.2589334249,0.0108703198,0.0447729155,0.1381760985,-0.0274826139,-0.3719767034,-0.2360167205,-0.3260758221,0.2218331099,-0.4936863184,-0.2473367006,-0.3659623861,-0.1332878172,-0.1272517741,-0.0535877906,-0.0594918802,-0.192742005,-0.0831747055,0.054635901,-0.2077915072,0.362438798,-0.0207298659,0.2902925909,-0.4242221415,0.140414834,0.1257181317,-0.0351882093,0.223937735,0.1718892157,-0.1581595838,0.0181084182,0.0160380099,0.1516156048,0.0233543552,0.2720309794,0.4134550393,-0.2655682266,0.1595946401,-0.7020245194,0.244035393,-0.0013473971,0.1541531086,-0.0727663562,-0.4927904308,0.585087359,-0.1594763696,0.2365358919,-0.1136936322,0.0458352305,-0.1465976983,-0.0502968952,-0.2769812644,-0.0154324761,-0.0266613849,0.3194800317,0.3679344654,-0.1649454981,-0.0093891416,0.0608625226,-0.2095643878,-0.2168572396,0.0859030485,0.25252527,-0.2286038399,-0.0456505194,-0.0332462564,0.0078679798,0.133438468,0.1866690516,-0.0120512294,0.1499159038,0.2242520899,-0.145981878,0.312587142,-0.08368247,0.3818781376,-0.1666721404,-0.1319358051,-0.0379216224,-0.137252748,-0.2523626685,-0.1887042224,0.0777628869,-0.2412629873,-0.01343357,-0.2188056558,-0.4939027727,-0.4372242093,0.1945793629,-0.1045598015,0.2105472833,-0.1974367201,-0.3020330071,0.0680553019,-0.1180939823,-0.107335858,0.4336645007,-0.0328286327,0.0033762974,-0.3064669967,-0.3647992015,-0.1459996998,0.0635384098,0.36008811,0.3919006288,0.3956675529,-0.1787957102,0.1090848371,-0.2133387774,-0.0196253341,0.085220024,-0.120549567,-0.1872910857,-0.0711187869,0.1386776417,-0.5521188974,0.037167266,-0.0700754002,0.0759134367,-0.2493603528,-0.2937630713,-0.0762437731,0.0377739146,0.1057036296,-0.3790570199,-0.1407455653,-0.1363106966,0.5363702774,0.2632987499,0.1816893667,0.0972161144,0.1146271899,0.1213914752,-0.286680907,-0.1028629914,0.1399902403,-0.2310560793,0.3158698678,-0.4142170548,-0.5795696974,-0.1270981729,0.0340336524,0.0733213723,0.2622746825,-0.444892019,0.3611671329,-0.0689748004,0.0749233514,-0.131532833,0.0017095152,0.2083196342,-0.0026777065,0.084752433,-0.0274860561,-0.0243537351,0.2768829465,-0.2394080013,0.2586248815,0.3798021376,0.2095822841,0.0688657761,0.5874854922,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/189","title":"[Question] BERT-style multiple choice formatting","comments":"Hi @sarahwie, can you details this a little more?\r\n\r\nI'm not sure I understand what you refer to and what you mean when you say \"Previously, this was done by passing a list of InputFeatures to the dataloader instead of a list of InputFeature\"","body":"Hello, I am wondering what the equivalent formatting of a dataset should be to allow for multiple-choice answering prediction, BERT-style. Previously, this was done by passing a list of `InputFeatures` to the dataloader instead of a list of `InputFeature`, where `InputFeatures` contained lists of length equal to the number of answer choices in the MCQ instead of single items. I'm a bit confused on what the output of my feature conversion function should be when using `dataset.map()` to ensure similar behavior.\r\n\r\nThanks!","comment_length":44,"text":"[Question] BERT-style multiple choice formatting \n Hello, I am wondering what the equivalent formatting of a dataset should be to allow for multiple-choice answering prediction, BERT-style. Previously, this was done by passing a list of `InputFeatures` to the dataloader instead of a list of `InputFeature`, where `InputFeatures` contained lists of length equal to the number of answer choices in the MCQ instead of single items. I'm a bit confused on what the output of my feature conversion function should be when using `dataset.map()` to ensure similar behavior.\r\n\r\nThanks! \n Hi @sarahwie, can you details this a little more?\r\n\r\nI'm not sure I understand what you refer to and what you mean when you say \"Previously, this was done by passing a list of InputFeatures to the dataloader instead of a list of InputFeature\"","embeddings":[0.1898331344,-0.4081771076,-0.0266713481,-0.0789325163,0.1755467206,-0.1144005209,0.2933261991,0.2346474379,-0.1131381989,-0.0067010592,-0.0750766918,0.5407450199,-0.2563856542,0.2198833674,0.0012709242,-0.4498939216,0.1627261937,0.1324364245,-0.1213597208,-0.1114255339,-0.2248265594,-0.1842457056,-0.2530254722,-0.1031176224,-0.1576482356,0.0686321408,-0.3666487634,-0.2160354257,-0.3754042387,-0.3336399198,-0.1936650425,0.2823522687,-0.2183113098,-0.036676608,-0.0001251408,-0.4015754759,-0.1773592681,-0.1747857928,-0.1102396101,-0.1179136857,-0.8842166066,-0.0070362641,0.384401083,0.018587172,0.0077913688,0.026045125,0.0269380696,-0.3077802956,0.4810141623,-0.0087722009,0.0720062032,-0.0372220948,0.0368573032,0.0641980544,0.130898267,0.5086032748,0.0186721031,-0.0431833044,0.6300452352,0.0035539258,-0.2057007253,0.3613417745,0.0969025716,0.1061301306,0.3440503478,-0.0262019616,-0.0527589843,-0.0893975794,-0.2308890373,0.3512215912,0.7839892507,-0.0999960825,-0.2693175673,-0.037072368,0.0213868059,0.0687108338,-0.1864119172,0.0146086719,-0.1401160657,-0.0611535646,-0.4859075844,-0.3253764212,-0.3561828732,0.0693899021,-0.2764470279,0.4265608191,0.0081101945,-0.0351795331,-0.0522108674,-0.2725637257,-0.021641355,-0.4392718971,0.3904174268,0.1420880258,0.1063589901,-0.3791531324,-0.1874579042,0.0773358643,0.3710345626,-0.294662565,-0.093178831,-0.0026960964,-0.5204651952,-0.0124727376,0.5711633563,0.0022485678,-0.0457250066,0.0721669048,-0.3457533121,-0.2913954854,-0.2567957044,0.0416471735,0.1252153665,0.2284183502,0.0716412142,0.1218287572,0.0568136945,-0.3215624094,0.0638229027,0.0779039189,-0.3623918593,-0.2042133212,0.0336744189,0.1545162797,-0.0849718377,0.5170226097,-0.1964963078,0.2077666819,0.0878812,-0.3568956256,0.1389586031,-0.1019800454,-0.5272906423,-0.2540571988,0.1949184388,0.287102133,-0.010821675,0.3692451119,0.3783965707,-0.2300265431,0.0706946179,0.0249621775,0.5642062426,-0.1838817745,-0.3527994156,0.3267552257,0.2906134427,0.3297919333,-0.0886824653,0.501160562,-0.2108788788,-0.0140871303,-0.1533492059,0.0175843053,0.2166631371,-0.211597234,0.0411195457,0.3512015343,0.0450342745,-0.335567832,0.1301427782,-0.5346509814,0.0498925187,-0.2956683636,0.0025754108,0.4049502313,-0.4627058506,0.0901223496,-0.1962527037,0.2359769344,0.2237932086,0.2625330985,-0.0753694549,-0.0596093945,-0.0112677189,0.3569813073,0.3155224323,-0.1638092399,-0.3183713555,0.2297862917,0.1608127654,0.1174034551,-0.0363770016,0.2344695628,0.1547364444,0.2690249979,0.051156465,-0.1387481242,-0.3022445738,0.3662523031,-0.0180012248,-0.042903509,0.1209712774,-0.0448402725,-0.0799431279,-0.1422651857,-0.0801847503,0.1147543415,0.2186723202,-0.2054872066,-0.0574702248,-0.4273211658,0.1527346522,0.0290086959,-0.1200327277,-0.1726429462,-0.6358430386,-0.0798971504,-0.0288463924,-0.1870038807,0.2269694358,-0.2735058367,-0.2580794692,0.0582880527,-0.3363609612,0.1047036499,-0.0136203682,0.0200140812,-0.2457858473,-0.2998512685,-0.2771873176,-0.0625879392,0.523280561,0.0186696853,0.0530535057,0.1163390502,0.0966972783,0.0029881662,0.1322368383,0.355776757,0.1922871321,0.2523916662,-0.0313612297,0.2370244563,0.4595884979,-0.0483255535,-0.0386576504,0.1157881245,0.4056983292,-0.2333194911,0.323117435,0.1775736511,0.0954629853,-0.0138599724,-0.8099329472,0.1893574744,-0.0785276443,0.1169799492,-0.1494040787,0.2087834477,-0.1929719895,0.2378272712,-0.0603553131,-0.4787614942,-0.1216315329,0.2656581402,0.1503820866,0.0750132725,-0.4771557748,0.2781422138,0.3794206381,-0.0178738348,0.2281641662,-0.0602244213,-0.017266579,-0.0987433717,0.419705689,-0.2194470763,0.2319580317,0.1562121958,-0.064319253,-0.1458725035,0.0085766567,-0.128225863,-0.0028874232,-0.0382087901,-0.1035607606,0.1028818786,0.1906561106,-0.105451636,0.0022769831,0.073290132,-0.0567633025,-0.0949905738,-0.1219476461,0.0056225667,-0.5228880048,-0.0163592119,-0.1550268829,-0.5842822194,-0.1949106604,-0.1818955988,0.2008174211,-0.196906507,-0.0877336711,0.0063602412,0.0872827098,0.3509713113,0.1391626,0.0905076191,0.0792290345,-0.0174420942,0.0698145255,0.0482323579,0.0040308577,0.0327776819,0.1947336942,0.1055906713,0.0115956618,-0.353723675,0.015375359,0.1942841113,-0.0494602025,0.0103383223,0.3358051181,0.0873691738,-0.4954452217,0.139918074,0.1405892521,0.3530600667,-0.0523260869,0.0308349282,0.3473846614,0.0177804008,0.0939540118,-0.3488003016,-0.1854479164,0.0460721403,0.0916791409,-0.0240837857,0.1084490493,-0.2396626472,0.1782774627,-0.1272112429,-0.3487858474,-0.2479266226,-0.2057134509,0.1175255775,0.0489020571,-0.1411753446,-0.0173885059,-0.217268154,0.1409626454,-0.138920784,0.1800364107,0.2704903781,-0.2778686583,-0.0454747006,0.1224455088,0.0982366428,0.4216068685,0.302885741,0.3918448091,0.0813359991,-0.0775967836,0.1031870842,0.1100871935,0.3211499751,0.0097876936,0.1452916414,0.04953501,-0.1308119446,0.7850805521,0.4763058126,0.1447903216,0.1125237569,0.0027535111,-0.1625672132,-0.1379141957,-0.1045337245,0.2698309422,-0.0767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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/189","title":"[Question] BERT-style multiple choice formatting","comments":"I think I've resolved it. For others' reference: to convert from using the [`MultipleChoiceDataset` class](https:\/\/github.com\/huggingface\/transformers\/blob\/a34a9896ac2a4a33ff9cd805c76eed914c8d8965\/examples\/multiple-choice\/utils_multiple_choice.py#L82)\/[`run_multiple_choice.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/a34a9896ac2a4a33ff9cd805c76eed914c8d8965\/examples\/multiple-choice\/run_multiple_choice.py) script in Huggingface Transformers, I've done the following for hellaswag:\r\n\r\n1. converted the `convert_examples_to_features()` function to only take one input and return a dictionary rather than a list:\r\n```\r\ndef convert_examples_to_features(example, tokenizer, max_length):\r\n\r\n    choices_inputs = defaultdict(list)\r\n    for ending_idx, ending in enumerate(example['endings']['ending']):\r\n        text_a = example['ctx']\r\n        text_b = ending\r\n\r\n        inputs = tokenizer.encode_plus(\r\n            text_a,\r\n            text_b,\r\n            add_special_tokens=True,\r\n            max_length=max_length,\r\n            pad_to_max_length=True,\r\n            return_overflowing_tokens=True,\r\n        )\r\n        if \"num_truncated_tokens\" in inputs and inputs[\"num_truncated_tokens\"] > 0:\r\n            logger.info(\r\n                \"Attention! you are cropping tokens (swag task is ok). \"\r\n                \"If you are training ARC and RACE and you are poping question + options,\"\r\n                \"you need to try to use a bigger max seq length!\"\r\n            )\r\n\r\n        for key in inputs:\r\n            choices_inputs[key].append(inputs[key])\r\n            \r\n        choices_inputs['label'] = int(example['label'])\r\n\r\n    return choices_inputs\r\n```\r\n2. apply this directly (instance-wise) to dataset, convert dataset to torch tensors. Dataset is then ready to be passed to `Trainer` instance.\r\n\r\n```\r\ndataset['train'] = dataset['train'].map(lambda x: convert_examples_to_features(x, tokenizer, max_length), batched=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'label']\r\ndataset['train'].set_format(type='torch', columns=columns)\r\n```","body":"Hello, I am wondering what the equivalent formatting of a dataset should be to allow for multiple-choice answering prediction, BERT-style. Previously, this was done by passing a list of `InputFeatures` to the dataloader instead of a list of `InputFeature`, where `InputFeatures` contained lists of length equal to the number of answer choices in the MCQ instead of single items. I'm a bit confused on what the output of my feature conversion function should be when using `dataset.map()` to ensure similar behavior.\r\n\r\nThanks!","comment_length":168,"text":"[Question] BERT-style multiple choice formatting \n Hello, I am wondering what the equivalent formatting of a dataset should be to allow for multiple-choice answering prediction, BERT-style. Previously, this was done by passing a list of `InputFeatures` to the dataloader instead of a list of `InputFeature`, where `InputFeatures` contained lists of length equal to the number of answer choices in the MCQ instead of single items. I'm a bit confused on what the output of my feature conversion function should be when using `dataset.map()` to ensure similar behavior.\r\n\r\nThanks! \n I think I've resolved it. For others' reference: to convert from using the [`MultipleChoiceDataset` class](https:\/\/github.com\/huggingface\/transformers\/blob\/a34a9896ac2a4a33ff9cd805c76eed914c8d8965\/examples\/multiple-choice\/utils_multiple_choice.py#L82)\/[`run_multiple_choice.py`](https:\/\/github.com\/huggingface\/transformers\/blob\/a34a9896ac2a4a33ff9cd805c76eed914c8d8965\/examples\/multiple-choice\/run_multiple_choice.py) script in Huggingface Transformers, I've done the following for hellaswag:\r\n\r\n1. converted the `convert_examples_to_features()` function to only take one input and return a dictionary rather than a list:\r\n```\r\ndef convert_examples_to_features(example, tokenizer, max_length):\r\n\r\n    choices_inputs = defaultdict(list)\r\n    for ending_idx, ending in enumerate(example['endings']['ending']):\r\n        text_a = example['ctx']\r\n        text_b = ending\r\n\r\n        inputs = tokenizer.encode_plus(\r\n            text_a,\r\n            text_b,\r\n            add_special_tokens=True,\r\n            max_length=max_length,\r\n            pad_to_max_length=True,\r\n            return_overflowing_tokens=True,\r\n        )\r\n        if \"num_truncated_tokens\" in inputs and inputs[\"num_truncated_tokens\"] > 0:\r\n            logger.info(\r\n                \"Attention! you are cropping tokens (swag task is ok). \"\r\n                \"If you are training ARC and RACE and you are poping question + options,\"\r\n                \"you need to try to use a bigger max seq length!\"\r\n            )\r\n\r\n        for key in inputs:\r\n            choices_inputs[key].append(inputs[key])\r\n            \r\n        choices_inputs['label'] = int(example['label'])\r\n\r\n    return choices_inputs\r\n```\r\n2. apply this directly (instance-wise) to dataset, convert dataset to torch tensors. Dataset is then ready to be passed to `Trainer` instance.\r\n\r\n```\r\ndataset['train'] = dataset['train'].map(lambda x: convert_examples_to_features(x, tokenizer, max_length), batched=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'label']\r\ndataset['train'].set_format(type='torch', columns=columns)\r\n```","embeddings":[0.1992308199,-0.5757998228,0.0108198822,-0.0699366182,0.2859191298,-0.1536672115,0.2446017712,0.2416757196,-0.0098712537,0.0180189833,-0.1549074948,0.4558004439,-0.2713595927,0.0753545314,0.1255868077,-0.3799526095,0.1526270956,0.0689935386,-0.201622799,-0.0675194636,-0.1130087897,0.0281150993,-0.1665605009,-0.0206472613,-0.351328373,0.0464573093,-0.3601536155,-0.0656297877,-0.302508533,-0.3981375098,-0.1327254027,0.1980736852,-0.256768018,0.1135846227,-0.0001305897,-0.3111949265,-0.2120848447,-0.2144840509,-0.0953421518,-0.0315434635,-0.7208180428,-0.0117110601,0.3938957155,0.1077938452,-0.0250609145,-0.0244077258,0.0035534585,-0.1321900189,0.6255206466,-0.0203529987,0.0092290873,0.0400460251,0.0817500129,0.0990012288,0.1613848209,0.5654866695,-0.0732178316,0.0807715878,0.5695483088,-0.0895334482,-0.2318441719,0.5064966083,0.0316469595,0.0750548765,0.2941819429,0.1139432862,-0.0156049412,-0.0805158168,-0.2227819115,0.4199120402,0.5713579655,-0.3070062101,-0.4449506998,-0.2811709642,0.077552855,-0.0533092804,-0.185295701,0.0442555472,-0.246355921,0.0007777097,-0.3870469928,-0.143476069,-0.2480071783,0.0358369835,-0.2692357004,0.5118787289,-0.050744392,0.0218754392,-0.0064533297,-0.2313463092,-0.1519096494,-0.3797215819,0.3062953651,0.2080069631,-0.0442397892,-0.3968067765,-0.1380621046,-0.0766860098,0.450856626,-0.2433416694,-0.0085721314,0.0231091548,-0.4934567213,-0.0227418467,0.6486942768,0.0418396853,-0.0119943768,-0.0025542285,-0.2303538024,-0.0869294554,-0.1043043584,0.0629381984,0.1495052874,0.1489631534,0.1232819036,0.1073075309,0.0246130433,-0.2623034418,-0.0393709689,0.0123500591,-0.3285461068,-0.138445273,0.1397070438,0.2474577129,-0.1276465207,0.5036812425,-0.1274145097,0.3086260855,-0.0092506576,-0.0996710584,0.0914359689,-0.158286944,-0.403457433,-0.1063968614,0.2117799073,0.3301987946,0.0904372707,0.4027234018,0.5290731192,-0.2610939145,0.021614287,0.0145388981,0.4841366112,0.022282023,-0.3328678906,0.4092332423,0.3574821353,0.2704078257,-0.1167816669,0.3718445003,-0.1770601422,-0.0374801345,-0.0164656676,-0.0591561496,0.1264020801,-0.1688946337,-0.1565997005,0.3634795547,0.1241389737,-0.1976222843,0.0890343264,-0.5058836341,-0.0126779247,-0.1831261963,0.1596045047,0.5324590206,-0.2871998549,-0.1431741863,-0.0231243819,0.1454491168,0.0770599991,0.36676386,-0.0832993761,-0.0956673846,-0.0285424031,0.4020661712,0.2019063383,-0.2790749669,-0.2886228263,0.1832921058,0.3203335404,0.3041915596,-0.1232166886,0.1302313954,0.0209683999,0.2288772762,0.1084147096,-0.1621450186,-0.1762799174,0.4218750596,-0.0592051782,-0.1318949908,0.0792518482,-0.0365051329,-0.1087161303,-0.2211264819,-0.2119843811,0.1571849883,0.3730957508,-0.1357677281,-0.1573298723,-0.1915747821,0.1454184055,-0.0841141045,-0.032169506,-0.1841538846,-0.6730769873,0.0318732187,-0.0588189177,-0.0139771802,-0.0235597268,-0.2371215969,-0.3311184049,0.1513948292,-0.3594202101,-0.0612323061,-0.0667840391,-0.0110397208,-0.0972960815,-0.2155428827,-0.2320966423,0.052623447,0.4880287647,0.1118904948,-0.0119324317,0.1936452538,0.1937323362,-0.1239278316,0.0630680695,0.3638551533,0.2385548502,0.1898649931,0.0156246871,0.2106879056,0.5333706141,-0.0716076717,-0.1271521747,0.236597091,0.4539563954,-0.1962525696,0.1511336416,0.2068120688,0.1926598549,-0.0478395596,-0.7790426016,0.2532613277,0.013714348,0.2599429786,-0.0932526216,0.1928777993,-0.0752389878,0.3315351009,-0.0595252439,-0.3936669827,-0.2014767826,0.1246120259,0.1480499357,-0.0295605622,-0.4988960326,0.3210488856,0.3738487959,-0.0521548726,0.2573857903,-0.0576330759,-0.1082976982,-0.1585582048,0.3733247221,-0.3535532951,0.2838003337,0.0691675022,-0.0836794227,-0.1215839759,-0.0424232483,-0.0347593985,0.0271577127,0.0674328208,-0.0719189718,0.1330576986,0.1848294139,-0.220438689,-0.0803161114,-0.005192922,-0.0877737924,-0.0895496309,-0.2064072192,0.0967238694,-0.5124549866,0.103811726,-0.2801758051,-0.5547010303,-0.3657353222,-0.2290549576,0.2649710178,-0.0899189562,-0.16554901,0.0586052202,0.1167238131,0.0993118212,0.1955944002,-0.0060084872,-0.0894494876,0.0460132658,0.1018408909,-0.0513067134,-0.0476841703,-0.0478527918,0.0376473926,0.1288282424,-0.1131999642,-0.4533536136,-0.0084238099,0.311193049,-0.1108270064,0.1549980193,0.3773562312,0.1029158831,-0.4873541296,0.0487930737,0.2122695744,0.2985990942,-0.0036083993,0.1128151342,0.3003280461,-0.0229476374,-0.0123437988,-0.1915768087,-0.1320614815,-0.003898083,0.1001858413,0.0269555002,0.1524860412,-0.1670546681,0.2679468095,-0.0359983742,-0.3355773389,-0.2415347695,-0.1400486678,0.0869605243,0.1467295587,-0.0843720436,-0.0522504188,-0.283863157,0.0482754037,-0.0188674629,0.1145023406,0.1831578165,-0.3538354635,-0.087027885,-0.0628895089,0.1746334583,0.4848902524,0.2857912183,0.2647238374,0.1194117144,-0.0561731532,-0.0061280429,0.163911581,0.3485115767,0.0466543473,0.004912517,-0.0949426815,0.0010067495,1.0838108063,0.4843838513,0.2481923103,-0.0036791754,-0.1181708276,-0.1263030767,-0.1567159295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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/188","title":"When will the remaining math_dataset modules be added as dataset objects","comments":"Hi @tylerroost, we don't have a timeline for this at the moment.\r\nIf you want to give it a look we would be happy to review a PR on it.\r\nAlso, the library is one week old so everything is quite barebones, in particular the doc.\r\nYou should expect some bumps on the road.\r\n\r\nTo get you started, you can check the datasets scripts in the `.\/datasets` folder on the repo and find the one on math_datasets that will need to be modified. Then you should check the original repository on the math_dataset to see where the other files to download are located and what is the expected format for the various parts of the dataset.\r\n\r\nTo get a general overview on how datasets scripts are written and used, you can read the nice tutorial on how to add a new dataset for TensorFlow Dataset [here](https:\/\/www.tensorflow.org\/datasets\/add_dataset), our API is not exactly identical but it can give you a high-level overview.","body":"Currently only the algebra_linear_1d is supported. Is there a timeline for making the other modules supported. If no timeline is established, how can I help?","comment_length":160,"text":"When will the remaining math_dataset modules be added as dataset objects \n Currently only the algebra_linear_1d is supported. Is there a timeline for making the other modules supported. If no timeline is established, how can I help? \n Hi @tylerroost, we don't have a timeline for this at the moment.\r\nIf you want to give it a look we would be happy to review a PR on it.\r\nAlso, the library is one week old so everything is quite barebones, in particular the doc.\r\nYou should expect some bumps on the road.\r\n\r\nTo get you started, you can check the datasets scripts in the `.\/datasets` folder on the repo and find the one on math_datasets that will need to be modified. Then you should check the original repository on the math_dataset to see where the other files to download are located and what is the expected format for the various parts of the dataset.\r\n\r\nTo get a general overview on how datasets scripts are written and used, you can read the nice tutorial on how to add a new dataset for TensorFlow Dataset [here](https:\/\/www.tensorflow.org\/datasets\/add_dataset), our API is not exactly identical but it can give you a high-level overview.","embeddings":[-0.2745408714,-0.0747141987,-0.2828445137,0.0816329569,0.1013158038,0.0851158649,0.1622705013,0.181571573,0.1549656242,0.028999202,0.0906958506,0.2542609274,-0.2430138886,0.091335535,0.255644381,-0.342015326,0.2013438195,-0.0719502717,-0.2481056452,-0.359226495,-0.108699061,0.0939241946,-0.2666563094,-0.211000964,-0.1336212009,-0.0600566156,-0.2486087978,-0.0442815199,-0.4253519475,-0.2308004946,0.0661661476,0.2980558574,0.2317584157,0.3571213186,-0.0001025364,-0.1791470647,0.1952542514,0.0659597665,-0.0967570692,-0.3587574065,-0.3629669845,-0.2668958306,0.1784650981,0.108467035,0.0992728546,0.0903836563,-0.0391332321,-0.4096406698,0.0279256608,0.3547129631,0.2386996895,0.3567646742,0.4147938788,-0.1960519999,0.2396775931,0.0669638962,-0.1821236759,0.0879681706,0.4211678803,0.1152278408,0.3046656549,0.1468877345,0.1961058527,0.1099760458,0.3800205886,0.0183225162,0.0137876738,-0.5149787068,-0.3262500465,0.2852599323,0.7866033316,-0.2344968766,-0.2374750227,-0.1045813859,-0.0337128527,-0.2809472382,0.0365477316,0.0314570703,0.2146729827,-0.163695693,0.0673857555,-0.40398857,-0.3353688121,0.4025502503,0.1397747397,0.4864596725,0.1236416698,0.1261004061,0.0988131762,-0.1491748095,0.3951944709,-0.0036637334,0.216307655,0.2663553953,-0.2195387036,-0.2165071964,-0.0990744382,-0.0851168185,0.0185330827,-0.1848434955,-0.1483968496,0.3597196937,-0.3094288707,0.2582101226,0.2134395242,-0.1501586139,-0.1043486893,-0.0047548832,0.4263304174,-0.0336952358,0.0713230073,0.1971784234,-0.1961677223,0.0076112011,-0.2757908106,0.0272748265,0.0295240488,-0.0128217051,0.1225248352,-0.3832071722,0.0956238732,-0.3619911373,-0.047780741,-0.0845338106,-0.3333867192,0.1714140326,-0.0095392363,0.0272273235,-0.1498740464,-0.0231564753,-0.0724998415,0.1515766829,-0.2152209878,0.0198928639,0.2334244549,-0.1018989086,0.0881501958,0.0125770569,0.2016483247,0.1553193033,0.1876082569,0.108616896,-0.1332701743,0.229112938,-0.1691801101,0.0380621664,-0.2693698406,0.1862420589,-0.0961535275,-0.0502220318,-0.149671793,-0.2168762386,-0.1686042398,0.2626042068,-0.1451126337,-0.3540813029,-0.2239722908,0.3776288629,-0.2807542384,0.2039030343,-0.1772480756,-0.1275356859,-0.0954099596,-0.3391961753,0.0054367357,0.0747958869,-0.4191736579,0.1555751264,-0.1323328465,-0.0583360679,-0.2366979718,-0.0541884974,-0.1949496567,0.0152082779,-0.095291689,-0.1207710579,0.4563644826,-0.5523675084,-0.0646584705,-0.169996798,0.2277211845,-0.2298590839,-0.1641166955,0.1930214912,0.0397006869,-0.1482824236,-0.2039136887,0.3211025894,-0.1425623,-0.2917489409,-0.2070939243,-0.1105629578,0.0519356839,0.1116952151,0.3645806611,0.0175068658,0.1615471691,0.2132958919,0.0144435093,0.1034300923,0.0315665305,0.2074112892,0.4144863188,0.2146508098,0.46539101,-0.4261690378,-0.4392787814,0.1380775422,0.1953701079,0.1881584078,0.1049885005,0.0862472281,-0.0043757618,0.1029463112,-0.1205544248,0.3178802729,0.14472574,-0.0405566916,0.2105709612,-0.1462246627,-0.5968189836,0.0926127657,-0.1041231528,0.4140124917,-0.4701019824,0.3047421575,0.0153421648,-0.1535006464,0.1693399101,0.2441736311,0.0057320679,-0.4595024884,0.0803301707,0.2964682579,0.1613706797,0.2357262075,-0.0338062085,0.4449404478,0.4259872139,-0.0609193929,0.311979115,-0.3168822825,-0.0265079327,-0.0569371842,0.0102055147,0.2147308439,-0.037214946,-0.0947376564,0.2684358656,0.0165636614,0.1671214402,0.2664136589,-0.0421637744,-0.2157949805,0.0300439075,-0.0084280111,-0.0213499125,-0.1336777806,-0.2969611585,0.228976801,0.6034273505,-0.1287028044,0.0356038362,0.0619414672,-0.2499331981,-0.0089356387,0.0916371346,0.2701995969,0.1916397214,0.1497255117,0.2187143415,-0.0189697258,-0.2912887931,-0.1258143336,0.0364939533,0.1996529698,0.1430567652,-0.0602197126,-0.0137000708,0.0058994349,-0.0994801819,-0.0192238316,-0.0458139889,0.4715058506,0.0794292912,-0.1826927215,-0.4099810123,-0.1873921901,-0.0715556815,-0.1772991121,-0.1551743746,-0.3065158427,0.0328201838,-0.0146003403,0.1989654899,0.0164232943,0.198269397,0.0992313027,-0.2671225071,0.3675664663,-0.2708781362,0.0022397877,-0.2175015211,0.1902993172,0.256509155,-0.086112164,0.4182340503,-0.0340089835,0.233294189,-0.1385352314,-0.640366137,0.1763880551,-0.1495903134,-0.0268919356,-0.0202498063,-0.08343108,0.179927662,0.1192989126,0.072791107,-0.291844964,-0.0514951162,-0.0440872423,-0.1970873624,-0.1112021282,-0.1318808645,-0.5581411123,-0.4593666792,-0.2616687715,0.1764215082,0.3262957931,0.0808147788,0.1624584645,0.0841361061,0.1643991172,-0.0631444678,0.0394746102,0.1127866805,-0.2584373057,0.4067581892,-0.307110697,-0.3843967021,0.4961076081,-0.1042147502,0.349216193,0.2127223015,-0.3054118156,-0.1986851692,-0.0253096335,-0.0159913544,0.1663932949,-0.0196063165,0.2342217118,-0.009280446,0.052656129,-0.2524116635,-0.1440183669,-0.0861576274,0.376968801,0.0294452496,0.143426463,0.3347580731,-0.0332237892,0.4305326343,0.0364487432,-0.2452243418,0.0504049435,0.16883564,0.1086771637,0.0113822129,-0.0502178073,0.0771260932,0.0313266069,0.29908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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/187","title":"[Question] How to load wikipedia ? Beam runner ?","comments":"I have seen that somebody is hard working on easierly loadable wikipedia. #129 \r\nMaybe I should wait a few days for that version ?","body":"When `nlp.load_dataset('wikipedia')`, I got\r\n* `WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.`\r\n* `AttributeError: 'NoneType' object has no attribute 'size'`\r\n\r\nCould somebody tell me what should I do ? \r\n\r\n# Env\r\nOn Colab,\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp\r\ncd nlp\r\npip install -q .\r\n```\r\n```\r\n%pip install -q apache_beam mwparserfromhell\r\n-> ERROR: pydrive 1.3.1 has requirement oauth2client>=4.0.0, but you'll have oauth2client 3.0.0 which is incompatible.\r\nERROR: google-api-python-client 1.7.12 has requirement httplib2<1dev,>=0.17.0, but you'll have httplib2 0.12.0 which is incompatible.\r\nERROR: chainer 6.5.0 has requirement typing-extensions<=3.6.6, but you'll have typing-extensions 3.7.4.2 which is incompatible.\r\n```\r\n```\r\npip install -q apache-beam[interactive]\r\nERROR: google-colab 1.0.0 has requirement ipython~=5.5.0, but you'll have ipython 5.10.0 which is incompatible.\r\n```\r\n\r\n# The whole message\r\n```\r\nWARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.\r\n\r\nDownloading and preparing dataset wikipedia\/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wikipedia\/20200501.aa\/1.0.0...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError                            Traceback (most recent call last)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()\r\n\r\n44 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/iobase.py in process(self, element, init_result)\r\n   1081       writer.write(e)\r\n-> 1082     return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]\r\n   1083 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/filebasedsink.py in close(self)\r\n    422   def close(self):\r\n--> 423     self.sink.close(self.temp_handle)\r\n    424     return self.temp_shard_path\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/parquetio.py in close(self, writer)\r\n    537     if len(self._buffer[0]) > 0:\r\n--> 538       self._flush_buffer()\r\n    539     if self._record_batches_byte_size > 0:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/parquetio.py in _flush_buffer(self)\r\n    569       for b in x.buffers():\r\n--> 570         size = size + b.size\r\n    571     self._record_batches_byte_size = self._record_batches_byte_size + size\r\n\r\nAttributeError: 'NoneType' object has no attribute 'size'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nAttributeError                            Traceback (most recent call last)\r\n\r\n<ipython-input-9-340aabccefff> in <module>()\r\n----> 1 dset = nlp.load_dataset('wikipedia')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    370                 verify_infos = not save_infos and not ignore_verifications\r\n    371                 self._download_and_prepare(\r\n--> 372                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    373                 )\r\n    374                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos)\r\n    770         with beam.Pipeline(runner=beam_runner, options=beam_options,) as pipeline:\r\n    771             super(BeamBasedBuilder, self)._download_and_prepare(\r\n--> 772                 dl_manager, pipeline=pipeline, verify_infos=False\r\n    773             )  # TODO{beam} verify infos\r\n    774 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/pipeline.py in __exit__(self, exc_type, exc_val, exc_tb)\r\n    501   def __exit__(self, exc_type, exc_val, exc_tb):\r\n    502     if not exc_type:\r\n--> 503       self.run().wait_until_finish()\r\n    504 \r\n    505   def visit(self, visitor):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/pipeline.py in run(self, test_runner_api)\r\n    481       return Pipeline.from_runner_api(\r\n    482           self.to_runner_api(use_fake_coders=True), self.runner,\r\n--> 483           self._options).run(False)\r\n    484 \r\n    485     if self._options.view_as(TypeOptions).runtime_type_check:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/pipeline.py in run(self, test_runner_api)\r\n    494       finally:\r\n    495         shutil.rmtree(tmpdir)\r\n--> 496     return self.runner.run_pipeline(self, self._options)\r\n    497 \r\n    498   def __enter__(self):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/direct\/direct_runner.py in run_pipeline(self, pipeline, options)\r\n    128       runner = BundleBasedDirectRunner()\r\n    129 \r\n--> 130     return runner.run_pipeline(pipeline, options)\r\n    131 \r\n    132 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in run_pipeline(self, pipeline, options)\r\n    553 \r\n    554     self._latest_run_result = self.run_via_runner_api(\r\n--> 555         pipeline.to_runner_api(default_environment=self._default_environment))\r\n    556     return self._latest_run_result\r\n    557 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in run_via_runner_api(self, pipeline_proto)\r\n    563     # TODO(pabloem, BEAM-7514): Create a watermark manager (that has access to\r\n    564     #   the teststream (if any), and all the stages).\r\n--> 565     return self.run_stages(stage_context, stages)\r\n    566 \r\n    567   @contextlib.contextmanager\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in run_stages(self, stage_context, stages)\r\n    704               stage,\r\n    705               pcoll_buffers,\r\n--> 706               stage_context.safe_coders)\r\n    707           metrics_by_stage[stage.name] = stage_results.process_bundle.metrics\r\n    708           monitoring_infos_by_stage[stage.name] = (\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in _run_stage(self, worker_handler_factory, pipeline_components, stage, pcoll_buffers, safe_coders)\r\n   1071         cache_token_generator=cache_token_generator)\r\n   1072 \r\n-> 1073     result, splits = bundle_manager.process_bundle(data_input, data_output)\r\n   1074 \r\n   1075     def input_for(transform_id, input_id):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)\r\n   2332 \r\n   2333     with UnboundedThreadPoolExecutor() as executor:\r\n-> 2334       for result, split_result in executor.map(execute, part_inputs):\r\n   2335 \r\n   2336         split_result_list += split_result\r\n\r\n\/usr\/lib\/python3.6\/concurrent\/futures\/_base.py in result_iterator()\r\n    584                     # Careful not to keep a reference to the popped future\r\n    585                     if timeout is None:\r\n--> 586                         yield fs.pop().result()\r\n    587                     else:\r\n    588                         yield fs.pop().result(end_time - time.monotonic())\r\n\r\n\/usr\/lib\/python3.6\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    430                 raise CancelledError()\r\n    431             elif self._state == FINISHED:\r\n--> 432                 return self.__get_result()\r\n    433             else:\r\n    434                 raise TimeoutError()\r\n\r\n\/usr\/lib\/python3.6\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/utils\/thread_pool_executor.py in run(self)\r\n     42       # If the future wasn't cancelled, then attempt to execute it.\r\n     43       try:\r\n---> 44         self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))\r\n     45       except BaseException as exc:\r\n     46         # Even though Python 2 futures library has #set_exection(),\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in execute(part_map)\r\n   2329           self._registered,\r\n   2330           cache_token_generator=self._cache_token_generator)\r\n-> 2331       return bundle_manager.process_bundle(part_map, expected_outputs)\r\n   2332 \r\n   2333     with UnboundedThreadPoolExecutor() as executor:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)\r\n   2243             process_bundle_descriptor_id=self._bundle_descriptor.id,\r\n   2244             cache_tokens=[next(self._cache_token_generator)]))\r\n-> 2245     result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n   2246 \r\n   2247     split_results = []  # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse]\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in push(self, request)\r\n   1557       self._uid_counter += 1\r\n   1558       request.instruction_id = 'control_%s' % self._uid_counter\r\n-> 1559     response = self.worker.do_instruction(request)\r\n   1560     return ControlFuture(request.instruction_id, response)\r\n   1561 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/sdk_worker.py in do_instruction(self, request)\r\n    413       # E.g. if register is set, this will call self.register(request.register))\r\n    414       return getattr(self, request_type)(\r\n--> 415           getattr(request, request_type), request.instruction_id)\r\n    416     else:\r\n    417       raise NotImplementedError\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/sdk_worker.py in process_bundle(self, request, instruction_id)\r\n    448         with self.maybe_profile(instruction_id):\r\n    449           delayed_applications, requests_finalization = (\r\n--> 450               bundle_processor.process_bundle(instruction_id))\r\n    451           monitoring_infos = bundle_processor.monitoring_infos()\r\n    452           monitoring_infos.extend(self.state_cache_metrics_fn())\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/bundle_processor.py in process_bundle(self, instruction_id)\r\n    837         for data in data_channel.input_elements(instruction_id,\r\n    838                                                 expected_transforms):\r\n--> 839           input_op_by_transform_id[data.transform_id].process_encoded(data.data)\r\n    840 \r\n    841       # Finish all operations.\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/bundle_processor.py in process_encoded(self, encoded_windowed_values)\r\n    214       decoded_value = self.windowed_coder_impl.decode_from_stream(\r\n    215           input_stream, True)\r\n--> 216       self.output(decoded_value)\r\n    217 \r\n    218   def try_split(self, fraction_of_remainder, total_buffer_size):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/future\/utils\/__init__.py in raise_with_traceback(exc, traceback)\r\n    417         if traceback == Ellipsis:\r\n    418             _, _, traceback = sys.exc_info()\r\n--> 419         raise exc.with_traceback(traceback)\r\n    420 \r\n    421 else:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/iobase.py in process(self, element, init_result)\r\n   1080     for e in bundle[1]:  # values\r\n   1081       writer.write(e)\r\n-> 1082     return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]\r\n   1083 \r\n   1084 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/filebasedsink.py in close(self)\r\n    421 \r\n    422   def close(self):\r\n--> 423     self.sink.close(self.temp_handle)\r\n    424     return self.temp_shard_path\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/parquetio.py in close(self, writer)\r\n    536   def close(self, writer):\r\n    537     if len(self._buffer[0]) > 0:\r\n--> 538       self._flush_buffer()\r\n    539     if self._record_batches_byte_size > 0:\r\n    540       self._write_batches(writer)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/parquetio.py in _flush_buffer(self)\r\n    568     for x in arrays:\r\n    569       for b in x.buffers():\r\n--> 570         size = size + b.size\r\n    571     self._record_batches_byte_size = self._record_batches_byte_size + size\r\n\r\nAttributeError: 'NoneType' object has no attribute 'size' [while running 'train\/Save to parquet\/Write\/WriteImpl\/WriteBundles']\r\n```","comment_length":24,"text":"[Question] How to load wikipedia ? Beam runner ? \n When `nlp.load_dataset('wikipedia')`, I got\r\n* `WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.`\r\n* `AttributeError: 'NoneType' object has no attribute 'size'`\r\n\r\nCould somebody tell me what should I do ? \r\n\r\n# Env\r\nOn Colab,\r\n```\r\ngit clone https:\/\/github.com\/huggingface\/nlp\r\ncd nlp\r\npip install -q .\r\n```\r\n```\r\n%pip install -q apache_beam mwparserfromhell\r\n-> ERROR: pydrive 1.3.1 has requirement oauth2client>=4.0.0, but you'll have oauth2client 3.0.0 which is incompatible.\r\nERROR: google-api-python-client 1.7.12 has requirement httplib2<1dev,>=0.17.0, but you'll have httplib2 0.12.0 which is incompatible.\r\nERROR: chainer 6.5.0 has requirement typing-extensions<=3.6.6, but you'll have typing-extensions 3.7.4.2 which is incompatible.\r\n```\r\n```\r\npip install -q apache-beam[interactive]\r\nERROR: google-colab 1.0.0 has requirement ipython~=5.5.0, but you'll have ipython 5.10.0 which is incompatible.\r\n```\r\n\r\n# The whole message\r\n```\r\nWARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.\r\n\r\nDownloading and preparing dataset wikipedia\/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/wikipedia\/20200501.aa\/1.0.0...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nAttributeError                            Traceback (most recent call last)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()\r\n\r\n44 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/iobase.py in process(self, element, init_result)\r\n   1081       writer.write(e)\r\n-> 1082     return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]\r\n   1083 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/filebasedsink.py in close(self)\r\n    422   def close(self):\r\n--> 423     self.sink.close(self.temp_handle)\r\n    424     return self.temp_shard_path\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/parquetio.py in close(self, writer)\r\n    537     if len(self._buffer[0]) > 0:\r\n--> 538       self._flush_buffer()\r\n    539     if self._record_batches_byte_size > 0:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/parquetio.py in _flush_buffer(self)\r\n    569       for b in x.buffers():\r\n--> 570         size = size + b.size\r\n    571     self._record_batches_byte_size = self._record_batches_byte_size + size\r\n\r\nAttributeError: 'NoneType' object has no attribute 'size'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nAttributeError                            Traceback (most recent call last)\r\n\r\n<ipython-input-9-340aabccefff> in <module>()\r\n----> 1 dset = nlp.load_dataset('wikipedia')\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    370                 verify_infos = not save_infos and not ignore_verifications\r\n    371                 self._download_and_prepare(\r\n--> 372                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    373                 )\r\n    374                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos)\r\n    770         with beam.Pipeline(runner=beam_runner, options=beam_options,) as pipeline:\r\n    771             super(BeamBasedBuilder, self)._download_and_prepare(\r\n--> 772                 dl_manager, pipeline=pipeline, verify_infos=False\r\n    773             )  # TODO{beam} verify infos\r\n    774 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/pipeline.py in __exit__(self, exc_type, exc_val, exc_tb)\r\n    501   def __exit__(self, exc_type, exc_val, exc_tb):\r\n    502     if not exc_type:\r\n--> 503       self.run().wait_until_finish()\r\n    504 \r\n    505   def visit(self, visitor):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/pipeline.py in run(self, test_runner_api)\r\n    481       return Pipeline.from_runner_api(\r\n    482           self.to_runner_api(use_fake_coders=True), self.runner,\r\n--> 483           self._options).run(False)\r\n    484 \r\n    485     if self._options.view_as(TypeOptions).runtime_type_check:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/pipeline.py in run(self, test_runner_api)\r\n    494       finally:\r\n    495         shutil.rmtree(tmpdir)\r\n--> 496     return self.runner.run_pipeline(self, self._options)\r\n    497 \r\n    498   def __enter__(self):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/direct\/direct_runner.py in run_pipeline(self, pipeline, options)\r\n    128       runner = BundleBasedDirectRunner()\r\n    129 \r\n--> 130     return runner.run_pipeline(pipeline, options)\r\n    131 \r\n    132 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in run_pipeline(self, pipeline, options)\r\n    553 \r\n    554     self._latest_run_result = self.run_via_runner_api(\r\n--> 555         pipeline.to_runner_api(default_environment=self._default_environment))\r\n    556     return self._latest_run_result\r\n    557 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in run_via_runner_api(self, pipeline_proto)\r\n    563     # TODO(pabloem, BEAM-7514): Create a watermark manager (that has access to\r\n    564     #   the teststream (if any), and all the stages).\r\n--> 565     return self.run_stages(stage_context, stages)\r\n    566 \r\n    567   @contextlib.contextmanager\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in run_stages(self, stage_context, stages)\r\n    704               stage,\r\n    705               pcoll_buffers,\r\n--> 706               stage_context.safe_coders)\r\n    707           metrics_by_stage[stage.name] = stage_results.process_bundle.metrics\r\n    708           monitoring_infos_by_stage[stage.name] = (\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in _run_stage(self, worker_handler_factory, pipeline_components, stage, pcoll_buffers, safe_coders)\r\n   1071         cache_token_generator=cache_token_generator)\r\n   1072 \r\n-> 1073     result, splits = bundle_manager.process_bundle(data_input, data_output)\r\n   1074 \r\n   1075     def input_for(transform_id, input_id):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)\r\n   2332 \r\n   2333     with UnboundedThreadPoolExecutor() as executor:\r\n-> 2334       for result, split_result in executor.map(execute, part_inputs):\r\n   2335 \r\n   2336         split_result_list += split_result\r\n\r\n\/usr\/lib\/python3.6\/concurrent\/futures\/_base.py in result_iterator()\r\n    584                     # Careful not to keep a reference to the popped future\r\n    585                     if timeout is None:\r\n--> 586                         yield fs.pop().result()\r\n    587                     else:\r\n    588                         yield fs.pop().result(end_time - time.monotonic())\r\n\r\n\/usr\/lib\/python3.6\/concurrent\/futures\/_base.py in result(self, timeout)\r\n    430                 raise CancelledError()\r\n    431             elif self._state == FINISHED:\r\n--> 432                 return self.__get_result()\r\n    433             else:\r\n    434                 raise TimeoutError()\r\n\r\n\/usr\/lib\/python3.6\/concurrent\/futures\/_base.py in __get_result(self)\r\n    382     def __get_result(self):\r\n    383         if self._exception:\r\n--> 384             raise self._exception\r\n    385         else:\r\n    386             return self._result\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/utils\/thread_pool_executor.py in run(self)\r\n     42       # If the future wasn't cancelled, then attempt to execute it.\r\n     43       try:\r\n---> 44         self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))\r\n     45       except BaseException as exc:\r\n     46         # Even though Python 2 futures library has #set_exection(),\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in execute(part_map)\r\n   2329           self._registered,\r\n   2330           cache_token_generator=self._cache_token_generator)\r\n-> 2331       return bundle_manager.process_bundle(part_map, expected_outputs)\r\n   2332 \r\n   2333     with UnboundedThreadPoolExecutor() as executor:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)\r\n   2243             process_bundle_descriptor_id=self._bundle_descriptor.id,\r\n   2244             cache_tokens=[next(self._cache_token_generator)]))\r\n-> 2245     result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n   2246 \r\n   2247     split_results = []  # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse]\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/portability\/fn_api_runner.py in push(self, request)\r\n   1557       self._uid_counter += 1\r\n   1558       request.instruction_id = 'control_%s' % self._uid_counter\r\n-> 1559     response = self.worker.do_instruction(request)\r\n   1560     return ControlFuture(request.instruction_id, response)\r\n   1561 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/sdk_worker.py in do_instruction(self, request)\r\n    413       # E.g. if register is set, this will call self.register(request.register))\r\n    414       return getattr(self, request_type)(\r\n--> 415           getattr(request, request_type), request.instruction_id)\r\n    416     else:\r\n    417       raise NotImplementedError\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/sdk_worker.py in process_bundle(self, request, instruction_id)\r\n    448         with self.maybe_profile(instruction_id):\r\n    449           delayed_applications, requests_finalization = (\r\n--> 450               bundle_processor.process_bundle(instruction_id))\r\n    451           monitoring_infos = bundle_processor.monitoring_infos()\r\n    452           monitoring_infos.extend(self.state_cache_metrics_fn())\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/bundle_processor.py in process_bundle(self, instruction_id)\r\n    837         for data in data_channel.input_elements(instruction_id,\r\n    838                                                 expected_transforms):\r\n--> 839           input_op_by_transform_id[data.transform_id].process_encoded(data.data)\r\n    840 \r\n    841       # Finish all operations.\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/bundle_processor.py in process_encoded(self, encoded_windowed_values)\r\n    214       decoded_value = self.windowed_coder_impl.decode_from_stream(\r\n    215           input_stream, True)\r\n--> 216       self.output(decoded_value)\r\n    217 \r\n    218   def try_split(self, fraction_of_remainder, total_buffer_size):\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/worker\/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/future\/utils\/__init__.py in raise_with_traceback(exc, traceback)\r\n    417         if traceback == Ellipsis:\r\n    418             _, _, traceback = sys.exc_info()\r\n--> 419         raise exc.with_traceback(traceback)\r\n    420 \r\n    421 else:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/runners\/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/iobase.py in process(self, element, init_result)\r\n   1080     for e in bundle[1]:  # values\r\n   1081       writer.write(e)\r\n-> 1082     return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]\r\n   1083 \r\n   1084 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/filebasedsink.py in close(self)\r\n    421 \r\n    422   def close(self):\r\n--> 423     self.sink.close(self.temp_handle)\r\n    424     return self.temp_shard_path\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/parquetio.py in close(self, writer)\r\n    536   def close(self, writer):\r\n    537     if len(self._buffer[0]) > 0:\r\n--> 538       self._flush_buffer()\r\n    539     if self._record_batches_byte_size > 0:\r\n    540       self._write_batches(writer)\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/apache_beam\/io\/parquetio.py in _flush_buffer(self)\r\n    568     for x in arrays:\r\n    569       for b in x.buffers():\r\n--> 570         size = size + b.size\r\n    571     self._record_batches_byte_size = self._record_batches_byte_size + size\r\n\r\nAttributeError: 'NoneType' object has no attribute 'size' [while running 'train\/Save to parquet\/Write\/WriteImpl\/WriteBundles']\r\n``` \n I have seen that somebody is hard working on easierly loadable wikipedia. #129 \r\nMaybe I should wait a few days for that version ?","embeddings":[-0.1516936719,0.0031176787,-0.0172203388,0.395411998,-0.0661803856,0.055734206,0.0433485359,0.3345056772,0.3488544822,0.116828151,0.3199312985,0.1732222289,-0.1478529274,-0.08189895,-0.0162129309,-0.4596623182,-0.0519881584,0.2463434488,0.0365840979,-0.055988159,-0.2965040207,0.2679029405,-0.2995700538,0.1040329561,-0.0625268295,-0.1476691663,0.1509711891,0.0239506122,-0.2917689383,-0.3756918311,0.0411315262,-0.2272832096,0.2764588892,0.1493850648,-0.0001126928,-0.033079192,0.6667644978,0.013357413,-0.4484111369,-0.3978943825,-0.1365267038,-0.4073244929,0.4514855444,-0.2783575654,0.0108114174,-0.183243528,0.3193400502,0.1798266172,0.4127219319,0.2974144518,0.2151003033,0.0132923853,0.3161814809,0.0289726257,0.4331317544,0.0234559532,0.0376815312,-0.082880117,-0.1319960505,0.0341566205,-0.1272097528,0.1512051821,-0.2554622293,0.085636802,0.3495388627,-0.1963473111,-0.0711795911,-0.5439116359,0.1323036849,0.1406851411,0.6836251616,-0.0462400094,0.1660724282,0.0973634869,0.1164916232,0.0578920729,0.2387089282,0.3242072165,-0.4263738096,-0.3079098165,-0.2179973125,-0.249674052,-0.2927875817,0.4647779167,0.0429484174,0.552410841,-0.0626783296,0.2276490927,-0.0418308824,-0.0239027701,-0.0688646659,-0.1292949915,0.0287907701,0.3013543785,0.0604953989,0.0935744867,0.0215015952,0.2295125425,0.1968696713,-0.0434167497,-0.1734355539,-0.1454145759,0.1463511586,0.1465433091,0.0333783031,0.2567222118,0.1367914528,-0.1662375182,0.1898491532,0.2154508084,-0.0659422502,0.087950483,-0.0317949057,-0.1787978262,-0.4661916196,0.0235311091,0.2609489262,-0.1763258427,-0.0362600125,0.0417041592,-0.425917685,-0.2416983396,-0.0293636434,0.3228060305,-0.2112709135,0.3525010645,0.3740545809,0.076737836,-0.3170039356,-0.3271934688,0.0201534927,0.29330194,-0.286726743,0.0849589035,0.2305870503,0.3047331572,0.3511444032,-0.0177782457,-0.077314876,0.00820887,0.1638403088,-0.0817262754,-0.2170658857,0.238672778,0.3142968714,0.2701243162,0.0584865883,-0.3325569332,-0.0798217133,0.2548536956,-0.33965832,-0.0750596598,-0.033942204,0.1641893834,-0.1057851315,0.0137594612,-0.3482524455,0.2475658506,0.0308313519,-0.2846438885,-0.1734679341,-0.1604102403,-0.0858308598,-0.3557700515,0.2626804113,0.3350428641,0.1315860003,-0.1066654027,-0.0884822458,0.1678850949,0.2250353396,0.0563847348,-0.1249937639,0.4726496041,-0.0444213226,-0.0787719712,0.742199719,-0.1341574937,-0.1724846959,0.2340096086,0.1318541765,0.0190685838,0.0614238754,-0.0625562072,0.1204571575,0.0860344321,-0.0515297279,0.5668154955,-0.0113387695,0.0343194455,-0.4098312557,-0.2514778972,0.1766582578,0.0684391409,0.3860545456,0.1507287621,0.0109391762,0.5279040337,0.0819583535,-0.0396940969,0.0264235213,0.3199964464,-0.2970614135,-0.0903743431,-0.0217857026,0.0699690059,-0.1751378328,-0.0254766699,-0.3948397934,0.1508505195,0.1584998071,0.106175594,-0.3540579379,-0.1429710537,-0.0524569601,-0.1548898518,0.1619729996,0.1855083555,0.0948660374,0.1506925076,0.0541367829,0.06682349,-0.1275253743,-0.04091952,-0.6120955944,0.1855074912,-0.273617208,-0.0768042579,0.1137746349,0.2102171928,0.2491545975,0.011265412,-0.1234422401,0.0068761823,-0.201151222,-0.0626368225,-0.0272328909,-0.0705084205,0.2022548467,-0.3002306819,0.3145110905,0.2933092713,0.1526011229,-0.132370472,0.0877334848,-0.0768909827,0.1184144169,0.340471983,0.074039489,0.103276886,0.0042449655,-0.0627106279,-0.0017371755,0.2277478278,0.3068399727,0.4291990101,0.0985184088,-0.1077882946,0.0011931499,-0.2093723416,0.3699370921,0.0447329767,0.0440538302,0.267177254,-0.4355993271,-0.0154325292,0.1121865287,-0.1628754288,0.1669237763,0.2249119431,-0.1555083841,-0.0398738533,-0.0813928023,-0.1273939759,0.2003766,0.1784821004,0.3696184754,0.1193412915,0.0897206366,0.0779439732,-0.0783375725,-0.1367147118,-0.1466954499,0.3170475364,-0.304097563,0.0782394558,-0.0234099664,-0.4767170846,-0.2430294603,0.0441244617,-0.4523335695,-0.3893648982,-0.0438593477,0.2663142383,0.1046174094,0.0952164233,0.3447324038,0.1438266933,-0.0165728834,-0.0947479755,-0.1618872583,-0.2388005406,-0.4626995325,0.0603272431,0.3643649817,0.2716529369,0.2625266314,-0.0358117782,0.0011127862,-0.0167188626,-0.2616984546,0.035663031,-0.0869233012,0.1974505633,-0.1058520079,0.4282002747,-0.067420736,-0.1935695857,0.2694040537,-0.035429474,-0.0946539193,0.0787811503,-0.1313625425,0.0198957752,-0.0187205374,-0.3938397765,-0.1960566044,-0.5722575784,0.0362823792,0.5017025471,0.1574628949,0.1577188671,0.2231185734,0.0792135075,0.3722141087,0.300367713,-0.0748495087,0.0828082561,0.3569961786,-0.2425844073,-0.453726083,0.006872945,-0.2535283864,0.2097843885,0.1102367938,-0.5408006907,-0.0630773604,-0.036176987,-0.0940902308,-0.0728066266,0.0922088176,0.4026173949,-0.0251933578,0.0642637908,0.0335506499,-0.0044596074,-0.1506370008,-0.2979058027,0.5397560596,0.3464972079,0.3857060075,-0.1238712668,0.9763130546,0.1193715855,-0.0108398749,0.2124872506,0.1578999907,0.0447257012,-0.2002599537,-0.2127510607,0.1421232671,-0.015238462,-0.0513092838,0.3709128797,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/186","title":"Weird-ish: Not creating unique caches for different phases","comments":"Looks like a duplicate of #120.\r\nThis is already fixed on master. We'll do a new release on pypi soon","body":"Sample code:\r\n\r\n```python\r\nimport nlp\r\ndataset = nlp.load_dataset('boolq')\r\n\r\ndef func1(x):\r\n    return x\r\n\r\ndef func2(x):\r\n    return None\r\n\r\ntrain_output = dataset[\"train\"].map(func1)\r\nvalid_output = dataset[\"validation\"].map(func1)\r\nprint()\r\nprint(len(train_output), len(valid_output))\r\n# Output: 9427 9427\r\n```\r\n\r\nThe map method in both cases seem to be pointing to the same cache, so the latter call based on the validation data will return the processed train data cache.\r\n\r\nWhat's weird is that the following doesn't seem to be an issue:\r\n\r\n```python\r\ntrain_output = dataset[\"train\"].map(func2)\r\nvalid_output = dataset[\"validation\"].map(func2)\r\nprint()\r\nprint(len(train_output), len(valid_output))\r\n# 9427 3270\r\n```","comment_length":20,"text":"Weird-ish: Not creating unique caches for different phases \n Sample code:\r\n\r\n```python\r\nimport nlp\r\ndataset = nlp.load_dataset('boolq')\r\n\r\ndef func1(x):\r\n    return x\r\n\r\ndef func2(x):\r\n    return None\r\n\r\ntrain_output = dataset[\"train\"].map(func1)\r\nvalid_output = dataset[\"validation\"].map(func1)\r\nprint()\r\nprint(len(train_output), len(valid_output))\r\n# Output: 9427 9427\r\n```\r\n\r\nThe map method in both cases seem to be pointing to the same cache, so the latter call based on the validation data will return the processed train data cache.\r\n\r\nWhat's weird is that the following doesn't seem to be an issue:\r\n\r\n```python\r\ntrain_output = dataset[\"train\"].map(func2)\r\nvalid_output = dataset[\"validation\"].map(func2)\r\nprint()\r\nprint(len(train_output), len(valid_output))\r\n# 9427 3270\r\n``` \n Looks like a duplicate of #120.\r\nThis is already fixed on master. We'll do a new release on pypi 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/183","title":"[Bug] labels of glue\/ax are all -1 ","comments":"This is the test set given by the Glue benchmark. The labels are not provided, and therefore set to -1.","body":"```\r\nax = nlp.load_dataset('glue', 'ax')\r\nfor i in range(30): print(ax['test'][i]['label'], end=', ')\r\n```\r\n```\r\n-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, \r\n```","comment_length":20,"text":"[Bug] labels of glue\/ax are all -1  \n ```\r\nax = nlp.load_dataset('glue', 'ax')\r\nfor i in range(30): print(ax['test'][i]['label'], end=', ')\r\n```\r\n```\r\n-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, \r\n``` \n This is the test set given by the Glue benchmark. The labels are not provided, and therefore set to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/181","title":"Cannot upload my own dataset","comments":"It's my misunderstanding. I cannot just upload a csv. I need to write a dataset loading script too.","body":"I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n```","comment_length":18,"text":"Cannot upload my own dataset \n I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n``` \n It's my misunderstanding. I cannot just upload a csv. I need to write a dataset loading script too.","embeddings":[0.1318956316,-0.1267824173,0.0566898957,-0.0285366997,0.1169987619,-0.1541467309,0.3969922364,-0.0621060431,-0.2148291469,0.2518130839,-0.0867060944,0.1588994563,-0.1717998087,0.2942122519,0.3824804425,0.1044842899,0.0907262638,0.313213408,0.039624013,-0.1279833913,-0.2627111375,0.0586452149,-0.0162037704,-0.0731670484,-0.0752847642,-0.1867671013,-0.1350204051,0.2678759992,-0.2258439064,-0.3100970387,0.4256777167,0.2096183598,0.3465848863,0.5284516215,-0.0001206829,-0.0640514717,0.1734646112,-0.2484510839,-0.5683873296,-0.3589856923,0.0835861117,-0.134131372,0.0810334161,-0.2865523398,0.2272181511,0.0529175773,0.0508734211,0.1323656142,0.5366080403,0.4095071852,0.1244776621,0.0669096932,-0.21695292,-0.1015119627,0.0081962785,0.1687242389,-0.0312566347,0.2934638262,0.319208473,-0.4081138968,0.2917584777,-0.2426822186,-0.0390427783,-0.0072850897,0.5370677114,0.0545539185,-0.0537988208,-0.4257257581,0.0046937442,0.1571441889,0.2818421721,0.0336492211,-0.328982234,-0.1129509732,0.1115847304,-0.5599861145,0.209152475,0.3053469658,-0.4256556034,-0.038526576,-0.2067479193,0.3366687596,-0.3919653594,0.4759362936,0.115687333,0.3350119591,-0.0372300595,0.1301802993,0.286183387,-0.0934038758,-0.0096256062,0.0732585043,0.0251915678,0.2944706678,-0.1664416045,-0.1814347655,-0.030566372,-0.2799897492,-0.1150280088,0.0909378901,0.4687089622,-0.1892622262,-0.2184372246,0.1630108356,0.1149361059,0.3294886053,-0.0234385896,0.052271124,0.1015268862,-0.0250006132,0.3100626469,-0.0896840543,-0.2538237274,-0.1026260257,-0.1475954801,-0.0238781832,0.2796851993,0.1287663728,0.0125933634,-0.1362918764,0.0550912954,0.0633083433,-0.1616130918,0.3230094314,-0.0883520097,-0.2684028149,0.0020205732,0.2783103585,0.1604540348,-0.2968745828,0.1016653851,0.3348389566,-0.1052022278,-0.1559351981,0.3859619498,0.0261231866,0.1852370948,-0.059755411,0.2265562862,-0.1464707106,0.0220260415,-0.0348720923,-0.1694041044,0.0149150034,0.2372968048,0.3928617537,-0.039099019,-0.5896613002,-0.1137735993,0.0959934145,-0.4078327417,-0.3410403728,-0.4582659602,0.0567067675,0.0479315221,-0.1880596131,-0.3508000672,-0.1340391636,0.100757651,-0.2789129019,-0.0907559171,0.0103238439,-0.0874788389,-0.1320454329,0.0613688715,0.0411118232,-0.5356760025,0.2628814578,-0.0881018788,0.5520697236,0.3589294255,0.2969783545,-0.3132716715,0.3023732305,-0.0438669398,0.1726223975,0.5356270671,-0.4726380408,-0.2246148288,0.0079028793,-0.0368109755,-0.1761097908,0.1135248244,0.1971012652,0.1221957281,-0.0631406084,0.0917988345,0.3859574795,0.0503729843,0.0187241938,-0.1974925548,-0.1673998982,0.2501933873,-0.0269129984,-0.1687581539,0.118255876,0.2636357844,0.0289663654,0.0302107874,-0.3133385479,0.119590722,0.2181630135,0.3369296789,-0.1820133775,-0.1540909559,0.2823067605,-0.4028119147,0.1659925282,0.1726571023,0.3474863768,-0.0552689508,-0.0441463403,-0.4338443279,-0.1111389622,-0.020369539,-0.0253519472,0.0457756147,0.1155959293,0.0229346696,-0.2324661314,-0.2718806565,0.106889233,-0.260445863,0.0493842512,-0.2114430219,-0.0395634696,-0.3254660666,-0.0634859204,0.2559999228,0.1197216883,0.2038004696,-0.012564647,-0.1673868001,0.1078442335,-0.4246124029,0.2669951618,-0.0308554564,0.1198563874,0.0077462881,-0.0065248855,-0.2597014606,-0.0786653385,0.2222268283,-0.2051438391,-0.384041369,0.1664907783,-0.0096187619,0.0653093308,0.1281350255,0.0084469281,0.2979522347,-0.1877758503,0.0634716079,0.1433500201,0.3599375188,0.1193041354,0.3806166947,-0.0215531122,-0.1204044446,-0.1549840271,0.1685704142,0.0108447364,0.1322262585,0.0583917722,0.0619405694,-0.0786255747,0.3246955276,0.5969873667,0.3846938312,0.0355436765,-0.0649995431,-0.0080587473,-0.1009131745,-0.2345053107,-0.0254966617,-0.0430704616,0.0423278436,0.1619130969,0.3972329795,0.229597196,-0.0900117978,0.0274449047,0.1321057826,0.4263569117,-0.2129421979,0.2630126178,-0.10497576,-0.4120586812,0.281242758,-0.2462048382,-0.067263253,-0.154713124,-0.1818923205,0.1328585297,0.1023472026,-0.1452899575,0.1561025977,0.3768164814,-0.1118422374,-0.2587055266,-0.2300248295,-0.0944366455,-0.2048037052,0.0228916407,0.282281965,-0.0796214938,0.3960422277,0.2977897227,0.0591257364,0.0036398415,-0.3079999387,-0.0184835792,-0.0448784009,0.1012091786,0.0983481482,0.3818153739,0.0118331155,-0.5280569196,0.1464271843,0.0774341896,-0.2765727043,0.2484791279,-0.0213040691,-0.3578555286,-0.4833171666,-0.0259705801,-0.2373687327,-0.3723592162,0.2339429259,0.3423468173,0.2010428011,0.6263756156,0.0330815539,0.1055231318,0.3673251569,0.15832524,0.0377818011,-0.1425475925,0.4890125096,-0.2197071463,-0.4154717326,0.1447934657,-0.1579416543,0.5447530746,-0.0712947398,-0.3519460559,-0.1823217869,0.0811840668,0.1464411169,-0.0377867222,-0.0392003171,0.3170751929,-0.2450888455,0.0634948164,-0.0740778372,-0.3456253111,0.1111996695,0.6317626238,0.2669855654,0.0019004043,0.4668571055,0.0014619923,0.7395948768,0.0747235268,-0.292644769,0.4109621644,-0.1004676968,0.2557592094,-0.1761987358,-0.3271772563,0.0276228432,0.1809422523,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/181","title":"Cannot upload my own dataset","comments":"I now try with the sample `datasets\/csv` folder. \r\n\r\n    nlp-cli upload csv\r\n\r\nThe error is still the same\r\n\r\n```\r\n2020-05-21 17:20:56.394659: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/csv\/csv.py to S3 under filename csv\/csv.py and namespace korakot\r\nAbout to upload file \/content\/csv\/dummy\/0.0.0\/dummy_data.zip to S3 under filename csv\/dummy\/0.0.0\/dummy_data.zip and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n```\r\n","body":"I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n```","comment_length":116,"text":"Cannot upload my own dataset \n I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n``` \n I now try with the sample `datasets\/csv` folder. \r\n\r\n    nlp-cli upload csv\r\n\r\nThe error is still the same\r\n\r\n```\r\n2020-05-21 17:20:56.394659: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/csv\/csv.py to S3 under filename csv\/csv.py and namespace korakot\r\nAbout to upload file \/content\/csv\/dummy\/0.0.0\/dummy_data.zip to S3 under filename csv\/dummy\/0.0.0\/dummy_data.zip and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n```\r\n","embeddings":[0.0526046231,-0.0281094722,0.0553304181,-0.0020762177,0.145578444,-0.1847578585,0.3376964033,-0.0477224775,-0.192668274,0.1797223389,-0.1303296536,0.2436908185,-0.21156165,0.2791502476,0.3693570495,0.0698641911,0.0132651599,0.2872202396,0.0126219867,-0.1083282828,-0.2543199956,0.091935806,-0.019309666,-0.0809588656,-0.1234256402,-0.1938010007,-0.08119528,0.2602367401,-0.223822698,-0.3616123497,0.4911182821,0.234791398,0.3647105396,0.579341054,-0.0001194561,-0.0481620245,0.1620747,-0.221251756,-0.6242922544,-0.4079536498,0.0205320753,-0.1510945857,0.070411697,-0.2569880784,0.1892614663,0.023185974,0.0217074882,0.0470516086,0.489642024,0.4066984653,0.137758106,0.1278687418,-0.2263692915,-0.1397725344,0.0078123156,0.173207283,-0.0587553121,0.2595106363,0.334825933,-0.4004638493,0.2734425962,-0.1960979998,0.0127515486,-0.0379062071,0.4887399375,-0.0217540264,-0.0607262999,-0.3897155821,-0.0111776432,0.1542044729,0.3593457043,-0.0174938589,-0.3905940056,-0.12496797,0.1351312995,-0.4883084893,0.2485231161,0.2740954161,-0.4021524489,-0.0272089615,-0.2619934678,0.3479141295,-0.4030778706,0.4445774555,0.1077126414,0.3708469868,-0.0370596536,0.0913995504,0.2884841561,-0.1540009379,0.0222615097,0.0381423905,0.0280135628,0.2681338489,-0.2156573981,-0.1543159336,0.0116408691,-0.2373854071,-0.0875118226,0.1148514226,0.4278518558,-0.1259322017,-0.2469718456,0.1405272335,0.1018434167,0.3375079334,0.0230610128,0.1245041862,0.1315496266,0.0036603063,0.2448296547,-0.0774665773,-0.294364959,-0.0886220858,-0.1120098606,-0.0521204583,0.2688553929,0.0746984109,-0.0075792344,-0.1084796488,0.078623794,0.0423113853,-0.0832311437,0.3526001275,-0.0594029352,-0.2365911007,-0.0201667901,0.2576301992,0.1414344907,-0.2609570622,0.0658907294,0.2955011129,-0.1243116632,-0.1489530802,0.4141170382,0.0235561319,0.1915086955,-0.0685793608,0.2129649371,-0.114051573,0.0568696,0.0295375474,-0.1648236364,0.0172457229,0.2235611975,0.3928875923,-0.0523391664,-0.5404902697,-0.1403445899,0.0234505367,-0.3477195799,-0.357789427,-0.4238156378,0.0750945732,0.020578349,-0.2402535379,-0.3374996483,-0.0972320661,0.0914815366,-0.2820447981,-0.0950792953,-0.045189213,-0.1562713236,-0.1163597256,0.1120778993,0.062964119,-0.4992431104,0.2119519413,-0.0793736652,0.5196451545,0.2760166526,0.3038301468,-0.3427072167,0.2330141068,-0.0663762987,0.1847803295,0.5762096643,-0.4968986511,-0.3117572069,0.0467118211,-0.0305642225,-0.1179906055,0.0722416341,0.1715793163,0.1136374399,-0.0842440352,0.1021822467,0.4385216832,0.067252405,0.0401820317,-0.2208324224,-0.1542976499,0.2543805838,0.0369843766,-0.1506792009,0.083122924,0.260474056,0.0628705621,0.0104562873,-0.3288799822,0.1415969878,0.2101691961,0.3580575585,-0.1686786115,-0.1278675348,0.2173071802,-0.4310545027,0.2184393257,0.1891910285,0.3485833108,-0.1336560696,-0.0364643224,-0.4364340007,-0.1022443995,-0.1047702953,-0.0485605337,0.0417283028,0.1295845807,0.0738745704,-0.1800095141,-0.222815603,0.1598291248,-0.2588529289,0.0683703944,-0.210019201,0.0358617045,-0.2738796175,-0.0956867859,0.2633078992,0.1670373529,0.2156515867,-0.0159932766,-0.2017098516,0.0932927281,-0.4534426332,0.2857999206,0.0175593048,0.1001565158,0.0362415425,-0.0552057512,-0.2138194591,-0.1098800674,0.1715619713,-0.1524938941,-0.3677259088,0.1745695621,-0.0044881394,0.0360841341,0.166636318,0.0101487059,0.2959846854,-0.1779489815,0.0921189636,0.0983786359,0.417483747,0.093171522,0.403357625,-0.0110187316,-0.1426615566,-0.0857242867,0.1682548672,-0.0006490924,0.1505849063,0.0749115199,0.0242283382,-0.0512584634,0.3066678345,0.6624006629,0.3770546615,0.0858841166,-0.026365513,-0.0519595072,-0.1250855476,-0.2106573433,-0.0400317982,-0.0779025778,0.0588910915,0.1829154342,0.4417646825,0.2363879383,-0.1361708194,-0.0484123416,0.1532731056,0.4534134269,-0.1956255138,0.2583765388,-0.1609421372,-0.4299409091,0.2424896061,-0.2555158734,-0.0626863539,-0.1816957295,-0.2170829773,0.146137014,0.1071710587,-0.0841627643,0.1007013097,0.3315699697,-0.1059278175,-0.2866389453,-0.2544071376,-0.1137493402,-0.1918030977,0.0277642626,0.3250083029,-0.0884016827,0.4124760032,0.3248804808,0.0886461809,-0.0461392142,-0.3268382251,-0.0045968406,-0.0374101885,0.1208912134,0.1213874146,0.356371671,0.0086652311,-0.5445611477,0.1948808581,0.0784640685,-0.2436255813,0.251660347,-0.0325384811,-0.3485889137,-0.4676508904,-0.0208269879,-0.2737125158,-0.36877051,0.239671275,0.2940942347,0.2099166214,0.5754036307,0.0177124944,0.1252833754,0.3206279576,0.1239150017,0.0683422983,-0.1860464215,0.5255692005,-0.2455919087,-0.4252485633,0.1025658846,-0.1634910107,0.5601887107,-0.0097183557,-0.3501398861,-0.1784346998,0.0594795719,0.2183053195,-0.0234066676,-0.0674496144,0.2789646685,-0.2049719989,0.0612629019,-0.0819354728,-0.3369690478,0.1344738603,0.6249414682,0.2558960021,-0.0422820225,0.4419958591,0.0380829684,0.702817142,0.1071994007,-0.3385474384,0.4494320452,-0.0385142528,0.1877281666,-0.1323684603,-0.3693924844,0.0650609732,0.2254458368,-0.06130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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/181","title":"Cannot upload my own dataset","comments":"We haven't tested the dataset upload feature yet cc @julien-c \r\nThis is on our short\/mid-term roadmap though","body":"I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n```","comment_length":17,"text":"Cannot upload my own dataset \n I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n``` \n We haven't tested the dataset upload feature yet cc @julien-c \r\nThis is on our short\/mid-term roadmap though","embeddings":[0.010165412,-0.0146975406,0.0639977977,-0.0472517423,0.1368083656,-0.1440622509,0.3703480661,-0.0352642201,-0.1715517193,0.1538790166,-0.1031072065,0.2589793801,-0.230888322,0.3168729246,0.3755461276,0.0948919877,0.019371815,0.2717739344,-0.0201163273,-0.0985039175,-0.2267878354,0.0701328591,-0.0022228684,-0.0957495868,-0.142616719,-0.2116264999,-0.0903275609,0.2581228912,-0.234555155,-0.341229558,0.4868592024,0.2359908074,0.3435915709,0.5804950595,-0.0001179577,-0.0519963317,0.1718314439,-0.2157051712,-0.6393771172,-0.3554878533,-0.0061307894,-0.134217307,0.0606778674,-0.2517402768,0.1928754896,0.0274626482,0.0349178202,0.0308800731,0.4822887778,0.4076221883,0.1489153057,0.1457220614,-0.1781223565,-0.1500244886,-0.0028327829,0.18243514,-0.0589718297,0.2539565563,0.3586966693,-0.4081481099,0.2644420862,-0.1995871067,0.0367807001,-0.0442407019,0.487485081,-0.0092500607,-0.0749194771,-0.4059659541,-0.0048401151,0.1771373749,0.3769998252,-0.0600269549,-0.3945055604,-0.145692274,0.1550535113,-0.4790179133,0.2537232935,0.2788683176,-0.424706161,-0.0225299057,-0.2711575925,0.3137456477,-0.4093555212,0.4221662879,0.1056479365,0.3526955247,-0.0337093771,0.0666819736,0.243746832,-0.1536932439,0.0117682517,0.0654737875,0.0214675888,0.2782631218,-0.2166639417,-0.1950291097,0.0027943654,-0.2405849695,-0.0581812523,0.129156217,0.4047325552,-0.1296833754,-0.2442011088,0.1506330818,0.133687526,0.3241246641,0.0014827683,0.1223634258,0.1223344952,-0.0058462271,0.2769157588,-0.051742468,-0.2742703557,-0.0508025326,-0.1130108014,-0.0688472167,0.2920281887,0.0677258074,-0.0055412739,-0.1293468624,0.0973584875,0.0165869407,-0.1025609896,0.3439234495,-0.0819455087,-0.2613025904,-0.0538049601,0.2326337695,0.1370870918,-0.2900106013,0.0570580028,0.2758283615,-0.1154935136,-0.1544326395,0.4356733561,0.0124119911,0.1956504881,-0.0544522628,0.2021948993,-0.0660793781,0.0722418875,0.0119863097,-0.1628797501,0.0086813662,0.2195927054,0.384788096,-0.0684984699,-0.5333330035,-0.1435816735,0.0261609294,-0.3532370031,-0.3473283052,-0.4594140351,0.0884744301,0.0037644345,-0.2817487121,-0.3244448006,-0.0715043247,0.0896539763,-0.2517111599,-0.0952162668,-0.0463004708,-0.1421903223,-0.122484535,0.1295616031,0.0880389139,-0.5273433328,0.184871614,-0.0860141665,0.4892792404,0.2712750137,0.2785508037,-0.3086805344,0.2438846678,-0.0801130012,0.1897517443,0.5997764468,-0.4807251692,-0.3346849978,0.0427862369,-0.0373171307,-0.0900270417,0.0372784212,0.1934347749,0.1080194935,-0.0963605344,0.0952949747,0.4479237199,0.0456785262,0.0166918822,-0.2161557674,-0.1669479311,0.2624107003,0.0407923721,-0.1555023193,0.0785308406,0.2734291255,0.0899971277,0.0241547208,-0.3324550092,0.1537160873,0.1996024102,0.3573826253,-0.1757684499,-0.1370925009,0.206030041,-0.4696049094,0.2170207202,0.1805566847,0.3490841985,-0.1210269406,-0.0183037631,-0.4437145293,-0.1005717665,-0.1240304336,-0.0586681627,0.0490877256,0.1163989827,0.0549807139,-0.1700619161,-0.2535095513,0.18731682,-0.2769084275,0.0612526424,-0.2197418362,0.0450597033,-0.2696869969,-0.1241740063,0.2587273717,0.1824533641,0.1657662839,0.0060952953,-0.1836488992,0.1066876352,-0.478485167,0.262398541,0.0336905457,0.1265535951,0.0412620455,-0.0676175654,-0.1832831055,-0.1436622143,0.1708056331,-0.1465517282,-0.4013840854,0.1432179064,-0.0279377457,0.0045213141,0.1643311828,0.0004223413,0.279594779,-0.179331392,0.1159435213,0.1068178341,0.3852045238,0.066510573,0.4020833373,-0.0413434729,-0.1261366606,-0.0323795192,0.2109304219,0.0192543678,0.1892586797,0.0736541077,0.0137931267,-0.0801438838,0.2915304005,0.6511793733,0.380454123,0.1110787094,-0.0320215598,-0.0562743917,-0.1082973033,-0.1881228238,-0.063018389,-0.1195570678,0.0715672448,0.1802178025,0.4236962199,0.2545208633,-0.1388218552,-0.0360268503,0.1298332661,0.4580771625,-0.1908658743,0.2338176519,-0.1396615803,-0.438040942,0.2469321489,-0.2298305929,-0.0904453471,-0.1691734046,-0.2033484131,0.1343929023,0.1450604498,-0.0874417275,0.1189224645,0.3378386497,-0.084738709,-0.2890054882,-0.2606640458,-0.0930047929,-0.2001828402,0.0532764979,0.3222643435,-0.1070774347,0.4432595372,0.3364593089,0.0863811001,-0.0549170114,-0.3232798576,0.0085717281,-0.0607416742,0.127227053,0.1032358482,0.343431294,-0.0030982832,-0.5138462782,0.1799697429,0.0642064512,-0.2702479959,0.2215505093,-0.0224287286,-0.3159378171,-0.4438218772,-0.0553473644,-0.2909525335,-0.3391727805,0.2574870884,0.3296154439,0.1998353153,0.5830568075,0.0271325316,0.1387715936,0.3214481771,0.1015031114,0.0490918346,-0.2018515617,0.5142780542,-0.2508527339,-0.4668107033,0.1272697002,-0.1354223341,0.5388168693,-0.0261844639,-0.3549962938,-0.2038773447,0.0651994944,0.2458015382,-0.0366456285,-0.0519297607,0.2837885618,-0.1914527267,0.0443885289,-0.0722914264,-0.3318912089,0.1452786326,0.6041272879,0.2712104023,-0.0585435331,0.4339739084,0.0434251577,0.7070642114,0.1327071488,-0.3311067522,0.4485009015,-0.0690921396,0.2065573931,-0.1401202977,-0.3747258186,0.1332441568,0.235465765,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/181","title":"Cannot upload my own dataset","comments":"Even if I fix the `TypeError: __init__() got an unexpected keyword argument 'cdn'` error, it looks like it still uploads to                                                                                                                               `https:\/\/s3.amazonaws.com\/models.huggingface.co\/bert\/<namespace>\/<dataset_name>` instead of `https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/<namespace>\/<dataset_name>`","body":"I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n```","comment_length":25,"text":"Cannot upload my own dataset \n I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n``` \n Even if I fix the `TypeError: __init__() got an unexpected keyword argument 'cdn'` error, it looks like it still uploads to                                                                                                                               `https:\/\/s3.amazonaws.com\/models.huggingface.co\/bert\/<namespace>\/<dataset_name>` instead of `https:\/\/s3.amazonaws.com\/datasets.huggingface.co\/nlp\/<namespace>\/<dataset_name>`","embeddings":[0.0766489804,-0.0518902279,0.0566434562,-0.0133813079,0.1621728241,-0.1869212389,0.3156289756,-0.0278216116,-0.1946031451,0.1959180981,-0.1271059215,0.2358573377,-0.2207303047,0.2926344573,0.3533962965,0.0726656169,0.005575384,0.2951037288,0.0090058008,-0.0938581005,-0.2198612839,0.0948086008,0.0193501543,-0.079871662,-0.1348169595,-0.1881403178,-0.0791992322,0.275642097,-0.1972334087,-0.3347654939,0.4486150742,0.2227426618,0.3325239122,0.600295186,-0.0001181157,-0.0586367175,0.1657020003,-0.2280745208,-0.6185942292,-0.3737724423,0.073014237,-0.1427434534,0.0919025093,-0.2383846194,0.1893360168,0.0374169759,0.0164695997,0.0653847903,0.5281576514,0.3778299391,0.1473527253,0.1031371132,-0.1705768555,-0.0949941054,0.0273437556,0.189317435,-0.0560426563,0.2944778502,0.3386437297,-0.3992356062,0.2927792072,-0.1987794042,-0.0034406974,-0.0603887588,0.482424438,-0.0279464349,0.0078290747,-0.4104376435,-0.029902583,0.1773936749,0.3018230498,-0.0301785674,-0.3591761887,-0.1138800085,0.1687852591,-0.4841401875,0.2560546398,0.2725909948,-0.4102144837,-0.0385259055,-0.2570946813,0.3279775381,-0.4180808961,0.4426954389,0.1196050569,0.3393745422,-0.0226807911,0.0766781867,0.2550292909,-0.1277886331,0.0078817355,0.0909430981,0.0217479058,0.2719116211,-0.1963246763,-0.1846594959,-0.0206889845,-0.28975752,-0.1094860509,0.0998324826,0.4507453442,-0.1309796423,-0.247373417,0.1494717002,0.0931351632,0.3291782141,-0.0070715589,0.0955357477,0.128275767,0.0212816391,0.2753561437,-0.0661067888,-0.2871695161,-0.0721225441,-0.1261585504,-0.0473260097,0.2747190893,0.0848323107,0.0117681734,-0.0948850736,0.0725315735,0.0604458526,-0.0801716447,0.341994524,-0.1117420197,-0.2417011708,0.0059717465,0.2359330803,0.1392108202,-0.2688980103,0.0752417669,0.3023251593,-0.111968182,-0.1445471346,0.4047563672,0.0083994977,0.1989607066,-0.0468069166,0.1874880344,-0.1364440322,0.0624775402,0.0066449237,-0.1763243079,0.0334348194,0.2295496315,0.3857797682,-0.0658191144,-0.5420824289,-0.1311396807,0.0309668574,-0.376873821,-0.3573203981,-0.4452536106,0.0883500725,0.0414214395,-0.2367779016,-0.3071362376,-0.1186672673,0.097911194,-0.293420583,-0.0951493606,-0.0466190241,-0.1209683344,-0.1328951567,0.1152042896,0.0470283739,-0.4755062461,0.2196235359,-0.0837467909,0.5124325752,0.3262296319,0.2987667918,-0.3122524619,0.2711575329,-0.0631727949,0.1732649654,0.5793398023,-0.4712457955,-0.2747651339,0.0279496312,-0.0340622887,-0.1028424278,0.0546566024,0.1710966527,0.0808372647,-0.0993412733,0.074894309,0.4404076636,0.0722699836,0.0462460928,-0.2394128293,-0.1674557775,0.2583916485,0.0339711159,-0.1678980887,0.0763512626,0.2412539423,0.089694351,0.0238092691,-0.3316558003,0.1337260306,0.2027384192,0.3554896116,-0.1569679976,-0.14370884,0.2355046421,-0.4078379571,0.1885665506,0.1236447915,0.3522777855,-0.1016198173,-0.0101924054,-0.4185565114,-0.0969462097,-0.0985112861,-0.0237516873,0.0604023002,0.1085125729,0.0505038947,-0.1806459129,-0.2265082896,0.1404366791,-0.3063790798,0.0572904982,-0.2142498791,0.0430720113,-0.2826433182,-0.1120526642,0.2531868219,0.1754763722,0.1947953105,-0.0150505332,-0.1971835494,0.0930893794,-0.4591653347,0.3227218091,-0.0023458232,0.1325975955,0.0305035859,-0.0227372944,-0.2367982566,-0.0917936787,0.1783348322,-0.1693652123,-0.3360210657,0.1382521838,-0.0299928598,0.0167360548,0.1587326378,0.0029888896,0.2892495096,-0.1894600242,0.1112693548,0.1314771324,0.401583761,0.097141467,0.3815457821,-0.0451287515,-0.1332823187,-0.0848595053,0.1830834597,0.0231890697,0.1644845456,0.0743936598,0.0209634881,-0.0921830609,0.3019861877,0.5998592377,0.3605666161,0.0762010813,-0.0564554706,-0.0607898794,-0.1149053946,-0.2258322686,-0.0362129882,-0.0858919993,0.0646131411,0.2199161798,0.439574331,0.2659415901,-0.1319428086,-0.0113981068,0.1058641523,0.4606073499,-0.1928886026,0.2437663823,-0.1272559911,-0.4152647257,0.2468311936,-0.2743420303,-0.0827637166,-0.1754637212,-0.1994408518,0.1508498639,0.0996925235,-0.1142217442,0.1735125631,0.3297783434,-0.1030074134,-0.2672968209,-0.2869341969,-0.0814498514,-0.2095011026,0.0438969024,0.3391340077,-0.1061070561,0.4255249202,0.3308122754,0.057941433,-0.0315249488,-0.3174512982,-0.0129783414,-0.0383924693,0.0903385654,0.0889073163,0.3747071624,0.0114755733,-0.5478414893,0.1954109967,0.0543338619,-0.2502206862,0.2478963584,-0.031556882,-0.3154171407,-0.4758595526,0.0024259677,-0.2676188648,-0.3631879389,0.2492437363,0.330157876,0.2313883603,0.5911985636,0.0197045896,0.1316882968,0.319717288,0.1506914645,0.0455147736,-0.1869827509,0.496663183,-0.2360724211,-0.4265482128,0.1076653004,-0.1438559294,0.5702787042,-0.0444080271,-0.3675763905,-0.1872586608,0.0848747119,0.1766058952,-0.0725205168,-0.0716762617,0.29713732,-0.192166999,0.0524090715,-0.0585342273,-0.2975660861,0.1311017275,0.6311603785,0.2633401752,-0.0588213019,0.4399241507,0.0179807954,0.6985471249,0.1050329879,-0.3126606047,0.4494108856,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/181","title":"Cannot upload my own dataset","comments":"@lhoestq The endpoints in https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/hf_api.py should be (depending on the type of file):\r\n```\r\nPOST   \/api\/datasets\/presign\r\nGET    \/api\/datasets\/listObjs\r\nDELETE \/api\/datasets\/deleteObj\r\nPOST   \/api\/metrics\/presign  \r\nGET    \/api\/metrics\/listObjs\r\nDELETE \/api\/metrics\/deleteObj\r\n```\r\n\r\nIn addition to this, @thomwolf cleaned up the objects with dataclasses but you should revert this and re-align to the hf_api that's in this branch of transformers: https:\/\/github.com\/huggingface\/transformers\/pull\/4632 (so that potential new JSON attributes in the API output don't break existing versions of any library)","body":"I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n```","comment_length":72,"text":"Cannot upload my own dataset \n I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n``` \n @lhoestq The endpoints in https:\/\/github.com\/huggingface\/nlp\/blob\/master\/src\/nlp\/hf_api.py should be (depending on the type of file):\r\n```\r\nPOST   \/api\/datasets\/presign\r\nGET    \/api\/datasets\/listObjs\r\nDELETE \/api\/datasets\/deleteObj\r\nPOST   \/api\/metrics\/presign  \r\nGET    \/api\/metrics\/listObjs\r\nDELETE \/api\/metrics\/deleteObj\r\n```\r\n\r\nIn addition to this, @thomwolf cleaned up the objects with dataclasses but you should revert this and re-align to the hf_api that's in this branch of transformers: https:\/\/github.com\/huggingface\/transformers\/pull\/4632 (so that potential new JSON attributes in the API output don't break existing versions of any library)","embeddings":[0.0944484174,-0.030214861,0.0476927236,-0.0215730593,0.1586551219,-0.1987861693,0.309314847,-0.0286144279,-0.2004603595,0.1795501411,-0.1415876746,0.2648700476,-0.2190395445,0.2876112759,0.3653645813,0.0843751803,-0.0124915065,0.2811934948,-0.0141321747,-0.0984324738,-0.211942777,0.1044818237,0.0320216455,-0.049357675,-0.1195409074,-0.1753448844,-0.0963914841,0.2744733393,-0.2059195936,-0.3228287399,0.4480949938,0.2225773484,0.3027473688,0.5770031214,-0.0001172611,-0.0428950414,0.1430104822,-0.2234495729,-0.6293331981,-0.3826371729,0.0799077004,-0.1367630661,0.0705319941,-0.2529910207,0.1908079833,0.0417050384,0.000706739,0.0639598146,0.5112629533,0.3838021159,0.1552750617,0.0833508521,-0.1944427937,-0.0838215053,0.0314243957,0.1894314736,-0.0444487631,0.2973888218,0.364697814,-0.3999806941,0.2879976034,-0.1958285272,-0.0049449289,-0.0952834189,0.4774852991,-0.0244300906,0.0022098885,-0.4087409079,-0.004733745,0.1479023844,0.2761023939,-0.044268284,-0.3474467397,-0.1056420058,0.1462824494,-0.4893601835,0.2515532076,0.3014510572,-0.4247399569,-0.0290612895,-0.2817880511,0.3245869875,-0.4137291014,0.4369983971,0.1153259203,0.354593873,-0.0213738903,0.074619472,0.230759427,-0.1369190216,-0.0222479887,0.0737877786,0.0122102657,0.2807020247,-0.1889118105,-0.1876102984,-0.0186335649,-0.2808482349,-0.0978708342,0.1118517295,0.4561248124,-0.1378393024,-0.2398003489,0.1559489369,0.0908848122,0.3234421909,-0.0065494422,0.0977970436,0.1205292344,0.0003337557,0.2836908996,-0.0605229773,-0.2795391977,-0.0779379979,-0.1403634995,-0.0355208144,0.2655667067,0.0827493966,-0.0020347519,-0.088930212,0.065670073,0.0615277812,-0.083274506,0.3429426253,-0.080831036,-0.2568761408,-0.0040633054,0.252545774,0.147270903,-0.2681153715,0.0705913752,0.3221955895,-0.1211353242,-0.149605453,0.4027736783,0.0167119913,0.2068513781,-0.0547541231,0.1655029655,-0.1426860988,0.0625669211,0.0156807788,-0.1528277248,0.0331363566,0.237373203,0.3591507375,-0.0791967884,-0.5343106985,-0.125402838,0.049368076,-0.3811899722,-0.3526668251,-0.4539186656,0.0959174708,0.0441683568,-0.2248820662,-0.3203067183,-0.101484552,0.0998919755,-0.2611241937,-0.071775645,-0.0267491229,-0.1189917624,-0.1263092607,0.1208137274,0.033902891,-0.4636068344,0.2092765123,-0.0461566262,0.523032248,0.2966082692,0.2885639369,-0.3173555136,0.2671290934,-0.0691568628,0.1890891343,0.5893782973,-0.4831946194,-0.2531136274,0.0503122136,-0.0370711274,-0.1145570129,0.0292967111,0.1607625484,0.0855262578,-0.0990604013,0.0783930346,0.4290344119,0.0783739761,0.045694191,-0.2462299019,-0.1808586866,0.2245772332,0.0309159346,-0.1713451445,0.0790283978,0.2619319856,0.0561614595,0.0280440822,-0.3400062919,0.1454513073,0.1951549798,0.3312384486,-0.1613267809,-0.1434575915,0.2471031547,-0.4086436927,0.1843587905,0.1157301217,0.3616276681,-0.1448758245,0.0029632335,-0.4288184047,-0.092421107,-0.0853365138,-0.0376782976,0.0695079416,0.0906381607,0.0685435906,-0.1673062444,-0.2222517282,0.1170056313,-0.3020484149,0.0560998693,-0.1878899485,0.061975915,-0.28403005,-0.1122310981,0.2280951589,0.1870625764,0.2036272883,-0.0150089432,-0.1789750606,0.090549551,-0.4720056057,0.3271060586,0.0459110849,0.1694826037,0.0231936872,-0.0260973182,-0.2118454725,-0.1186255291,0.1891427487,-0.1595870256,-0.3298256695,0.1566566974,-0.022237977,0.0081614153,0.1694997698,0.0123370783,0.2830250859,-0.1977061927,0.1146022975,0.1360907406,0.4101681411,0.0970576778,0.3762931824,-0.0375031307,-0.1397168487,-0.0750534981,0.1551811099,0.0004623414,0.171068266,0.072201483,0.0252788514,-0.094703041,0.2884230018,0.5951282382,0.3534690738,0.0794587955,-0.0524825007,-0.044750642,-0.1115141809,-0.2275600135,-0.0162841342,-0.0774828494,0.0485457778,0.2117167711,0.4386914372,0.2605527639,-0.1326118112,-0.0168820564,0.1147994399,0.454028517,-0.1906829625,0.2663320601,-0.0992186666,-0.4326516092,0.2592717707,-0.2881084979,-0.0862180293,-0.1827846467,-0.1833827049,0.1253715754,0.0859248787,-0.1114077941,0.152381748,0.3510534465,-0.0952787101,-0.2716924846,-0.2880837321,-0.1030787975,-0.2051630914,0.048939541,0.330488205,-0.1106445193,0.4104598165,0.3433226645,0.0662654787,-0.0462405868,-0.3267802,-0.0032144643,-0.0442584604,0.0937911645,0.0913999304,0.3395482898,0.0049578729,-0.5441505313,0.1870235205,0.0585749187,-0.2473661751,0.2597736716,-0.0378395431,-0.3145050108,-0.4980634153,0.0100823594,-0.2758832276,-0.3460385501,0.2394315749,0.3302131295,0.236339286,0.5939539671,0.0181891359,0.131915465,0.3163865507,0.1342408955,0.0335440598,-0.2057852,0.4849305451,-0.236068815,-0.4347625673,0.1069922596,-0.1374837905,0.5780151486,-0.0456713438,-0.3929759264,-0.1988608539,0.0978337601,0.206396237,-0.0661416799,-0.0829823688,0.2729762197,-0.1973797083,0.0393159352,-0.062912859,-0.298491627,0.1541345567,0.6249304414,0.2360728532,-0.0714840367,0.4156574607,0.0507278517,0.699492991,0.0851802602,-0.3150883317,0.4526679218,-0.0317254253,0.1771961451,-0.1375230402,-0.3402383029,0.0954925716,0.2374717593,-0.0405364446,0.15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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/181","title":"Cannot upload my own dataset","comments":"New commands are\r\n```\r\nnlp-cli upload_dataset <path\/to\/dataset>\r\nnlp-cli upload_metric <path\/to\/metric>\r\nnlp-cli s3_datasets {rm, ls}\r\nnlp-cli s3_metrics {rm, ls}\r\n```\r\nClosing this issue.","body":"I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n```","comment_length":22,"text":"Cannot upload my own dataset \n I look into `nlp-cli` and `user.py` to learn how to upload my own data.\r\n\r\nIt is supposed to work like this\r\n- Register to get username, password at huggingface.co\r\n- `nlp-cli login` and type username, passworld\r\n- I have a single file to upload at `.\/ttc\/ttc_freq_extra.csv`\r\n- `nlp-cli upload ttc\/ttc_freq_extra.csv`\r\n\r\nBut I got this error.\r\n\r\n```\r\n2020-05-21 16:33:52.722464: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file \/content\/ttc\/ttc_freq_extra.csv to S3 under filename ttc\/ttc_freq_extra.csv and namespace korakot\r\nProceed? [Y\/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n  File \"\/usr\/local\/bin\/nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/commands\/user.py\", line 234, in run\r\n    token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 141, in presign_and_upload\r\n    urls = self.presign(token, filename=filename, organization=organization)\r\n  File \"\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/hf_api.py\", line 132, in presign\r\n    return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n``` \n New commands are\r\n```\r\nnlp-cli upload_dataset <path\/to\/dataset>\r\nnlp-cli upload_metric <path\/to\/metric>\r\nnlp-cli s3_datasets {rm, ls}\r\nnlp-cli s3_metrics {rm, ls}\r\n```\r\nClosing this issue.","embeddings":[0.1084671691,-0.044078771,0.0504012294,-0.0276059136,0.1381330788,-0.1811706275,0.3266917765,-0.0091856392,-0.1950231344,0.180015564,-0.1299543679,0.2376451492,-0.230676949,0.291965574,0.3680000901,0.0748568103,0.0062184273,0.2890027463,0.0118492749,-0.1012402996,-0.2111017108,0.1081708297,0.035998825,-0.0665371567,-0.1229436174,-0.1740513891,-0.1002068445,0.2709647417,-0.2011140138,-0.3351971507,0.4402616322,0.2242947519,0.3215373158,0.5771903992,-0.0001176812,-0.0532847866,0.13914451,-0.2255368829,-0.6261975169,-0.3673120439,0.0849663466,-0.1735269576,0.0826737806,-0.2572462857,0.1832292378,0.0250916239,0.0085984897,0.0770226046,0.5234438777,0.3687664866,0.1549511999,0.0958202556,-0.1830172092,-0.0987186655,0.0291224457,0.1872793585,-0.0424793661,0.2965939641,0.368725121,-0.3961860538,0.3029372394,-0.1861630976,-0.0089923665,-0.0887328684,0.4844445884,-0.0220308937,0.0068499739,-0.4027802944,-0.0080832988,0.1580432355,0.272163868,-0.0382779129,-0.3466467857,-0.1168711558,0.1563580781,-0.4871679246,0.2421206385,0.2832774222,-0.4218559861,-0.0465164334,-0.2582421303,0.3114126921,-0.4107922316,0.4234362543,0.1253170967,0.3503036499,-0.0261408836,0.0610153116,0.259301126,-0.1453029364,-0.0212428421,0.0925706029,0.0176385213,0.2819576263,-0.1919323057,-0.1905701011,-0.0215728953,-0.2899370193,-0.1039742082,0.1091003865,0.4545297623,-0.1308467239,-0.2231484801,0.1516526043,0.0871840715,0.3312409818,0.0086245565,0.0796040446,0.1109220982,0.0127320886,0.2880072892,-0.06930051,-0.2757668793,-0.0784996375,-0.1371893734,-0.0488898866,0.2654902041,0.0865564197,0.0027184128,-0.0861177742,0.0716395155,0.0399115048,-0.0725512505,0.3346659243,-0.0997375473,-0.2767594457,0.0082464684,0.2441070974,0.1290178746,-0.2690321803,0.0709089339,0.3014498949,-0.1232390478,-0.1411055028,0.4131863117,0.0026039535,0.1909866333,-0.0497684591,0.1741697937,-0.1157201827,0.0725953132,0.0268774275,-0.1545820236,0.0261169504,0.2355850488,0.375002414,-0.0722648129,-0.5426658988,-0.1255455166,0.0468735807,-0.3762072921,-0.3481229544,-0.4494222701,0.0936678872,0.0353323221,-0.2343602479,-0.290365845,-0.0844530612,0.0992024839,-0.2737713158,-0.0880437568,-0.0178201012,-0.1080890968,-0.1402437836,0.114935793,0.0539799929,-0.4861147404,0.2089095861,-0.0626698956,0.5137918591,0.3013716042,0.2810108066,-0.3043970466,0.2599909008,-0.0623657331,0.1773784161,0.5754054189,-0.474339664,-0.2507374883,0.0342788249,-0.0365614444,-0.1030127406,0.0546859354,0.1682045311,0.0883628651,-0.1026604846,0.074139908,0.4095371664,0.0759576038,0.0300125126,-0.2245140672,-0.1747812778,0.2542684674,0.0123875318,-0.1504040956,0.0748391226,0.2523053885,0.0847450495,0.0307703875,-0.3352099657,0.1500530988,0.2014988661,0.3359648585,-0.1352574378,-0.1372293383,0.2380945235,-0.4048440456,0.1965625882,0.1271633655,0.3596282303,-0.1108400673,0.0062363427,-0.4200919569,-0.1138808578,-0.0957373306,-0.0317725502,0.0666666776,0.0963794664,0.0702915415,-0.1855908334,-0.2302042246,0.1288204789,-0.3153890073,0.0610579215,-0.1971267909,0.0591705479,-0.2863599956,-0.1116008535,0.2181829214,0.1753217429,0.2073204368,-0.0180181973,-0.1725531071,0.0894984901,-0.4653667212,0.3087368309,0.0124266222,0.14155294,0.0335689224,-0.0215841755,-0.1996200234,-0.117882289,0.1772723198,-0.1705975533,-0.3431076109,0.1499734372,-0.0331483968,0.0194792952,0.1668228805,-0.0024819076,0.2705669999,-0.1924484968,0.1081033424,0.1190124899,0.3931277692,0.1005677804,0.3735919595,-0.0623499416,-0.126172334,-0.080317907,0.1737629175,0.0145931114,0.1585074812,0.0684892833,0.0332597308,-0.0781026036,0.2860080004,0.6209813356,0.3515605628,0.0983222872,-0.0484041534,-0.0506114289,-0.1180213094,-0.2226050347,-0.032334052,-0.0865320414,0.0450485833,0.2136892676,0.4292725623,0.2628835738,-0.1386042386,0.002830114,0.1121918634,0.441070199,-0.1806763709,0.2472006083,-0.1107613221,-0.4309422076,0.2381785214,-0.2824086249,-0.0893063396,-0.1644198149,-0.1953134388,0.1184250563,0.1135315597,-0.1269594133,0.1662990153,0.3391794562,-0.1104057431,-0.2544942796,-0.2794032395,-0.0882852823,-0.2085284889,0.0529598072,0.328294456,-0.11787384,0.4269639254,0.3558065593,0.0669828057,-0.0482534915,-0.3466056585,-0.0127200242,-0.0637017488,0.0786134899,0.0849280879,0.3438898325,-0.0008332819,-0.5517418385,0.1905883104,0.0630328357,-0.252502948,0.2460184395,-0.0219418537,-0.3063010275,-0.4941290319,-0.0074582994,-0.2783525586,-0.3438791931,0.2504079938,0.3512364328,0.2351835072,0.6135793328,0.0208297428,0.1394834816,0.3170115054,0.1529634148,0.0331702419,-0.1874231398,0.4657980502,-0.2530597448,-0.4329993427,0.1104678884,-0.1329057962,0.5748018622,-0.0416008197,-0.3659122884,-0.1997584552,0.0914410055,0.1881645173,-0.0772584751,-0.0661343262,0.2856583595,-0.1821654141,0.0421079136,-0.0591190793,-0.3117053509,0.1501697004,0.6340502501,0.2549774647,-0.0765854865,0.421613127,0.0424232557,0.7029533982,0.0894530341,-0.3097903132,0.4549658895,-0.0282193925,0.1947965622,-0.1329689324,-0.3579265773,0.0959679559,0.2346650064,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/179","title":"[Feature request] separate split name and split instructions","comments":"If your dataset is a collection of sub-datasets, you should probably consider having one config per sub-dataset. For example for Glue, we have sst2, mnli etc.\r\nIf you want to have multiple train sets (for example one per stage). The easiest solution would be to name them `nlp.Split(\"train_stage1\")`, `nlp.Split(\"train_stage2\")`, etc. or something like that.","body":"Currently, the name of an nlp.NamedSplit is parsed in arrow_reader.py and used as the instruction.\r\n\r\nThis makes it impossible to have several training sets, which can occur when:\r\n- A dataset corresponds to a collection of sub-datasets\r\n- A dataset was built in stages, adding new examples at each stage\r\n\r\nWould it be possible to have two separate fields in the Split class, a name \/instruction and a unique ID that is used as the key in the builder's split_dict ?","comment_length":54,"text":"[Feature request] separate split name and split instructions \n Currently, the name of an nlp.NamedSplit is parsed in arrow_reader.py and used as the instruction.\r\n\r\nThis makes it impossible to have several training sets, which can occur when:\r\n- A dataset corresponds to a collection of sub-datasets\r\n- A dataset was built in stages, adding new examples at each stage\r\n\r\nWould it be possible to have two separate fields in the Split class, a name \/instruction and a unique ID that is used as the key in the builder's split_dict ? \n If your dataset is a collection of sub-datasets, you should probably consider having one config per sub-dataset. For example for Glue, we have sst2, mnli etc.\r\nIf you want to have multiple train sets (for example one per stage). The easiest solution would be to name them `nlp.Split(\"train_stage1\")`, `nlp.Split(\"train_stage2\")`, etc. or something like 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/179","title":"[Feature request] separate split name and split instructions","comments":"Thanks for the tip! I ended up setting up three different versions of the dataset with their own configs.\r\n\r\nfor the named splits, I was trying with `nlp.Split(\"train-stage1\")`, which fails. Changing to `nlp.Split(\"train_stage1\")` works :) I looked for examples of what works in the code comments, it may be worth adding some examples of valid\/invalid names in there?","body":"Currently, the name of an nlp.NamedSplit is parsed in arrow_reader.py and used as the instruction.\r\n\r\nThis makes it impossible to have several training sets, which can occur when:\r\n- A dataset corresponds to a collection of sub-datasets\r\n- A dataset was built in stages, adding new examples at each stage\r\n\r\nWould it be possible to have two separate fields in the Split class, a name \/instruction and a unique ID that is used as the key in the builder's split_dict ?","comment_length":58,"text":"[Feature request] separate split name and split instructions \n Currently, the name of an nlp.NamedSplit is parsed in arrow_reader.py and used as the instruction.\r\n\r\nThis makes it impossible to have several training sets, which can occur when:\r\n- A dataset corresponds to a collection of sub-datasets\r\n- A dataset was built in stages, adding new examples at each stage\r\n\r\nWould it be possible to have two separate fields in the Split class, a name \/instruction and a unique ID that is used as the key in the builder's split_dict ? \n Thanks for the tip! I ended up setting up three different versions of the dataset with their own configs.\r\n\r\nfor the named splits, I was trying with `nlp.Split(\"train-stage1\")`, which fails. Changing to `nlp.Split(\"train_stage1\")` works :) I looked for examples of what works in the code comments, it may be worth adding some examples of valid\/invalid names in there?","embeddings":[0.1045852527,-0.0982461423,0.0275993329,0.2224785239,0.1107441559,0.1121136993,0.5969958305,0.2325306684,-0.0358218625,0.1119877174,0.0187910739,0.4395620525,-0.2157790214,0.250805527,-0.0480731539,-0.3708141148,-0.0314742029,0.0995834023,0.3009313643,0.1106136888,0.0753546134,0.2413922995,-0.0459942184,0.2722317874,-0.2111954093,-0.1831194758,-0.2387676984,-0.1546239704,0.0284153596,-0.4011641443,0.1909267604,0.1710229218,-0.2977707386,0.1150344014,-0.0001220013,-0.1493762881,0.269780159,-0.0088353129,-0.1772001237,-0.231570214,-0.5108584166,-0.4750356078,0.441298455,-0.6071299314,0.0064698858,-0.1662882417,0.0098626055,0.2345714271,0.5989475846,-0.1420638859,0.0794020444,-0.3933258355,0.0042510843,0.0590030104,0.2985037565,0.2553159893,-0.3581681252,-0.3896652758,-0.1325118542,0.0193850994,0.0135845514,0.1901755184,0.0480410121,-0.1508929282,0.0564224124,0.1882013828,-0.2405911535,-0.1051746458,-0.031475611,0.3205355704,-0.1470056772,-0.133174777,-0.2248992175,-0.7789208293,0.12296433,-0.4444979727,0.0231793597,0.308399111,-0.0855548605,0.0007133393,0.159852773,-0.2577973306,-0.1586889476,0.0799732357,0.187296465,0.5755344629,0.0426866375,0.2130333036,0.130654946,0.0514660664,0.2459288538,0.0974831581,0.0370741785,0.1481966823,-0.2402214557,-0.3462637365,-0.3629279137,-0.0612269901,-0.2478050739,0.0727624744,0.1947965175,-0.0930430442,0.0379200615,0.109423928,0.3325090706,0.0307519361,0.3663776517,0.5928646326,0.0970462933,0.0739011765,-0.3302590847,-0.0182994399,-0.0558232032,0.1093028933,-0.2353015542,0.0842660591,-0.100039497,0.1635543555,-0.0458830334,-0.259771049,-0.0991402268,-0.2428592294,0.2244431674,0.0378052592,0.1180723682,0.0390675887,-0.3129239082,0.2033401132,-0.1228090003,-0.4388536513,-0.08893843,0.0996953845,-0.3002477884,0.2296502441,0.1069265753,0.1424677372,0.1824060529,0.0084284814,-0.1476583183,-0.2453731745,0.1563497186,0.0066439323,0.2317557186,0.042394314,0.0324652158,0.0562312268,-0.0377421491,-0.0723695308,-0.3021252155,0.0177272614,-0.07855957,-0.3099419177,0.1863626838,0.0404328778,0.1440342516,0.061936494,0.3620258272,0.6019592881,0.4055770338,-0.1647848189,0.10734272,0.037772011,-0.2596309185,-0.1934563071,-0.0436960123,-0.0139565021,-0.0107302275,-0.448692143,-0.3815647066,0.1615701914,-0.1280699223,0.072770834,-0.1476602703,0.0584482104,-0.0028749532,0.6331416965,0.5671984553,-0.2285178602,0.0892338976,0.3364673257,0.1159049943,-0.3352688253,0.4838373959,0.0501568131,0.107458055,-0.1066686958,-0.4239080846,0.1804618239,-0.5068801045,-0.2254240811,0.1737490892,-0.5565368533,0.1767485738,-0.0706296936,-0.0389987677,-0.3304858506,-0.0028007983,0.2975155115,0.4849632978,0.0340141691,0.2780067325,-0.1932996809,-0.067157656,0.2642988563,-0.1216097176,-0.3572362065,-0.3117902577,-0.0428921767,-0.1212896779,0.0310803726,-0.0378559791,-0.3895498216,-0.0125864493,-0.2790810168,-0.1604308188,-0.2774960101,0.031024849,-0.2263091356,-0.0619041249,-0.3348519504,-0.3435290158,0.1144687012,-0.0875819921,0.2657094598,-0.3278540969,-0.1681105047,0.2228049189,-0.0255954638,-0.18210648,0.6047663689,0.2861999869,-0.0080928607,0.0620746203,0.3274420798,0.3911905885,-0.0306486897,0.0057343468,-0.1291932613,0.1832372993,-0.123016797,-0.0524583384,-0.0439426266,-0.2799320817,-0.2084253132,-0.2021620423,0.5459465384,0.0275820028,0.6292759776,0.2911064029,0.049969472,-0.0128314495,-0.335465461,-0.1722602248,-0.5019676685,-0.211684823,-0.4157887995,0.1152676716,-0.1337288767,-0.2776992917,0.2355115712,0.430552274,0.177992776,0.0336464979,-0.234156087,0.1341372579,0.1435284764,-0.1983996779,0.6893722415,0.1891099066,0.1522488743,-0.0407744497,0.0803330094,0.0330183059,-0.1813274026,0.285543412,0.2572939694,0.0831954777,-0.001740361,-0.1671254486,-0.1373580396,0.0530168675,0.0606803,0.2253384292,-0.3368180692,-0.0538131148,-0.074822925,-0.1295629889,-0.3124649823,-0.5548498631,-0.0334240794,-0.1863946468,-0.2262301296,0.2090401202,0.0143301617,-0.2173095047,0.1576506197,-0.1592102647,0.1664498001,-0.4105944335,0.0815151557,0.0501448661,-0.4121741652,-0.0365711115,0.0229783617,0.0629541054,0.0134071205,0.2887684703,0.3602593839,-0.2948746383,0.1842560172,-0.3726987541,0.1845199615,-0.3997818232,-0.2675514817,0.2295847237,-0.303517282,0.0146265998,-0.4285719693,0.3069125414,-0.1674591601,-0.1307964623,-0.3343504071,0.3282942176,0.1910609454,-0.1777388155,-0.3134026527,-0.4024408162,-0.1457711458,0.3905413747,-0.0697919652,0.275059253,0.0724082813,-0.4712016284,-0.0289214309,-0.0028673685,-0.1211846471,0.0688783303,0.2207591087,-0.0744477063,-0.4078603387,0.1298802346,0.0845913589,-0.1246915534,0.3680259883,-0.247337088,-0.0626762062,0.2727819085,0.0512867309,-0.061664667,0.1613779515,0.0468612462,0.1606241167,0.2921684682,0.0672668666,-0.1720883101,-0.241377458,0.3609777391,0.1215413287,0.1340132207,-0.0718494728,0.0910204872,-0.0506043695,0.6642805338,0.2965461314,0.2599332035,-0.1523855478,0.0683135614,-0.3416393399,0.0813866407,-0.2282858938,0.1414750516,-0.05142067,-0.3744937181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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/168","title":"Loading 'wikitext' dataset fails","comments":"Hi, make sure you have a recent version of pyarrow.\r\n\r\nAre you using it in Google Colab? In this case, this error is probably the same as #128","body":"Loading the 'wikitext' dataset fails with Attribute error:\r\n\r\nCode to reproduce (From example notebook):\r\n\r\nimport nlp\r\nwikitext_dataset = nlp.load_dataset('wikitext')\r\n\r\n\r\nError:\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-17-d5d9df94b13c> in <module>()\r\n     11 \r\n     12 # Load a dataset and print the first examples in the training set\r\n---> 13 wikitext_dataset = nlp.load_dataset('wikitext')\r\n     14 print(wikitext_dataset['train'][0])\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    363                 verify_infos = not save_infos and not ignore_verifications\r\n    364                 self._download_and_prepare(\r\n--> 365                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    366                 )\r\n    367                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    416             try:\r\n    417                 # Prepare split will record examples associated to the split\r\n--> 418                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    419             except OSError:\r\n    420                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    594             example = self.info.features.encode_example(record)\r\n    595             writer.write(example)\r\n--> 596         num_examples, num_bytes = writer.finalize()\r\n    597 \r\n    598         assert num_examples == num_examples, f\"Expected to write {split_info.num_examples} but wrote {num_examples}\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in finalize(self, close_stream)\r\n    173     def finalize(self, close_stream=True):\r\n    174         if self.pa_writer is not None:\r\n--> 175             self.write_on_file()\r\n    176             self.pa_writer.close()\r\n    177         if close_stream:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_on_file(self)\r\n    124             else:\r\n    125                 # All good\r\n--> 126                 self._write_array_on_file(pa_array)\r\n    127             self.current_rows = []\r\n    128 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in _write_array_on_file(self, pa_array)\r\n     93     def _write_array_on_file(self, pa_array):\r\n     94         \"\"\"Write a PyArrow Array\"\"\"\r\n---> 95         pa_batch = pa.RecordBatch.from_struct_array(pa_array)\r\n     96         self._num_bytes += pa_array.nbytes\r\n     97         self.pa_writer.write_batch(pa_batch)\r\n\r\nAttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'","comment_length":28,"text":"Loading 'wikitext' dataset fails \n Loading the 'wikitext' dataset fails with Attribute error:\r\n\r\nCode to reproduce (From example notebook):\r\n\r\nimport nlp\r\nwikitext_dataset = nlp.load_dataset('wikitext')\r\n\r\n\r\nError:\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-17-d5d9df94b13c> in <module>()\r\n     11 \r\n     12 # Load a dataset and print the first examples in the training set\r\n---> 13 wikitext_dataset = nlp.load_dataset('wikitext')\r\n     14 print(wikitext_dataset['train'][0])\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    363                 verify_infos = not save_infos and not ignore_verifications\r\n    364                 self._download_and_prepare(\r\n--> 365                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    366                 )\r\n    367                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    416             try:\r\n    417                 # Prepare split will record examples associated to the split\r\n--> 418                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    419             except OSError:\r\n    420                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    594             example = self.info.features.encode_example(record)\r\n    595             writer.write(example)\r\n--> 596         num_examples, num_bytes = writer.finalize()\r\n    597 \r\n    598         assert num_examples == num_examples, f\"Expected to write {split_info.num_examples} but wrote {num_examples}\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in finalize(self, close_stream)\r\n    173     def finalize(self, close_stream=True):\r\n    174         if self.pa_writer is not None:\r\n--> 175             self.write_on_file()\r\n    176             self.pa_writer.close()\r\n    177         if close_stream:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_on_file(self)\r\n    124             else:\r\n    125                 # All good\r\n--> 126                 self._write_array_on_file(pa_array)\r\n    127             self.current_rows = []\r\n    128 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in _write_array_on_file(self, pa_array)\r\n     93     def _write_array_on_file(self, pa_array):\r\n     94         \"\"\"Write a PyArrow Array\"\"\"\r\n---> 95         pa_batch = pa.RecordBatch.from_struct_array(pa_array)\r\n     96         self._num_bytes += pa_array.nbytes\r\n     97         self.pa_writer.write_batch(pa_batch)\r\n\r\nAttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array' \n Hi, make sure you have a recent version of pyarrow.\r\n\r\nAre you using it in Google Colab? In this case, this error is probably the same as #128","embeddings":[-0.1193379238,0.0886276364,-0.0089776339,0.2101976722,0.3692684174,0.0260604266,0.4487809837,0.4131019711,0.4330818355,0.0125665311,0.1238447428,0.3954298794,-0.1505493075,0.0219624937,0.1209058017,-0.4289889932,-0.0337393396,0.3636732399,-0.0414666571,0.062973544,-0.1876838803,0.108516708,-0.2651918828,0.2550059557,-0.3057226241,-0.0570259951,0.2159208357,0.0820631012,-0.0789184123,-0.5590060353,0.2770831883,-0.2170494348,0.0644511506,0.1147467494,-0.0001099558,0.205306232,0.3326975107,0.0302131604,-0.5799459815,-0.1895776838,-0.4773317575,-0.1189394891,0.2974321842,-0.2497071624,-0.028513357,0.0538973026,0.0359112546,-0.0744121596,0.2967469394,0.5315061212,0.2378886342,0.5236920714,0.1545488685,0.0344940759,-0.0785685405,-0.1705711782,-0.0821156427,0.2239678055,0.0738770068,-0.3915446997,-0.0133740734,0.1269243062,0.1511732191,0.2637012899,0.5083716512,0.0143935969,0.240522787,-0.1506265402,0.0149465678,0.0764008239,0.3353828788,-0.1649338454,-0.2716008127,-0.2243597806,0.2476661652,-0.3347570896,0.2365306169,-0.0366766118,-0.1622047126,-0.0081965309,-0.2692284882,-0.0281475354,-0.2418207526,0.3526881039,-0.0582148433,0.4996492267,0.0435834154,0.1427477896,-0.0580658838,-0.075457558,0.1403762847,-0.0870907307,-0.1293002814,0.2578134239,-0.383156091,-0.0364675075,-0.0349461213,-0.1169757396,-0.0316241011,0.2635210156,0.219990015,-0.2248735428,0.3238382339,0.3154148161,0.2229899764,0.4204691052,0.0727646127,0.063592121,-0.0828047171,0.1360252202,-0.2337277681,-0.1554848254,-0.1555357128,-0.1765549034,0.0928974897,-0.1546022743,0.2260205299,-0.071116358,-0.4984739125,0.1147207841,-0.1434045881,0.046290502,0.2051148862,0.1794852465,-0.1276334524,0.2322352529,0.1441244632,0.2825672626,-0.2519766986,-0.0431360416,-0.1662892997,0.2492134273,-0.2343834639,-0.1822493821,0.2427540869,0.1934002936,0.3994777203,-0.1011680439,0.111264661,-0.2017549574,0.2273753434,-0.0455191471,-0.1288790852,0.2566530704,-0.2160614133,0.1548979729,0.2015910596,-0.2824236155,-0.0848238841,-0.10174492,-0.2726355791,-0.3080923855,-0.1262511909,0.2814154327,0.147381857,-0.2429646254,-0.1522630006,-0.0611871071,0.1474927962,-0.3687241077,-0.1181055456,-0.4612588286,-0.2206385881,-0.0230053104,0.3976424038,0.2049094886,0.1313870102,-0.1089720726,-0.1154211313,0.0778549612,-0.0317966752,0.1227136478,-0.4383572936,0.5421624184,-0.0610556342,0.118862763,0.6413468719,-0.3427866101,-0.4578541815,0.123568885,-0.0142798293,0.2093286514,-0.2253688425,-0.0167210288,-0.0132269189,-0.184551537,0.2123140246,0.6192135215,-0.0150432223,0.0824793652,-0.1268968582,-0.0155486753,0.5002205968,0.1098568216,0.0925252214,-0.0079286359,-0.0417051278,0.4966493547,-0.0013129428,-0.1259710789,-0.0148605099,-0.013630108,-0.0646863058,-0.104604587,-0.2178259641,-0.0738868713,-0.2776648998,0.3043035865,-0.117547363,-0.0585312918,-0.0471505858,0.1061868444,-0.3491126597,0.1684893072,-0.4232687652,-0.3773184419,0.1720929891,0.1551331282,-0.0108234957,0.2384415865,-0.2047417462,0.2194369435,-0.274028182,0.0555009618,-0.2189284861,0.1612891257,-0.0558832213,-0.0424223617,-0.0094791884,0.3087970316,0.0383313261,-0.0179966092,-0.3062886596,0.2860310078,-0.1254843473,0.0803563818,-0.0322646983,-0.1433701664,0.0688060746,-0.0715434849,-0.1751839221,0.2081920803,0.3319509625,-0.1801262945,-0.043142952,0.1128761917,0.1272121966,0.0814221054,0.0988641679,0.1639728397,0.1439921707,-0.0658260733,0.1012795344,0.0774310902,0.2337106764,-0.0949872434,0.2047105134,-0.1564514041,-0.167145744,-0.1188064739,0.3131195307,0.1700583696,0.167417407,0.0814866126,-0.275252223,-0.0948746875,0.0785581768,0.1460482925,0.4009645283,-0.0518327467,-0.1083415523,-0.0525707416,-0.4165976346,0.0711256638,0.1707080156,0.0366290882,0.2091285437,0.3018561602,0.0413160138,0.0873196498,-0.2952665389,-0.3570170999,0.2824332714,0.4405301809,-0.2284827828,0.2078800648,-0.0305316318,-0.0025111341,-0.0678482279,-0.3336339295,-0.188629657,-0.3429944813,-0.1213362366,0.4337745905,0.024119148,0.1810040623,-0.3977624178,-0.0220502764,0.1947504133,-0.1147857755,-0.0313790925,-0.3157985508,-0.269328475,0.0459421203,0.3818612099,0.0910761356,0.3510979414,-0.0667702854,-0.0925358012,-0.007033736,-0.0584152527,-0.0755677298,-0.1837216765,0.2440479547,0.009521856,0.2855836153,-0.1770296395,-0.5220327377,0.5017470121,-0.2557020187,-0.0577849969,0.3151076138,0.2711132169,-0.249190703,-0.0794775337,-0.3746882677,-0.1264498532,-0.280605495,-0.0594684482,0.0748294741,0.0620202571,0.3974690437,0.0708564147,0.2160111964,-0.0025732063,0.1134374887,-0.0482623316,-0.0900634304,0.1859846711,-0.310032934,-0.3173182309,-0.1213430315,0.0099359797,0.3347615004,0.1107365489,-0.3489591479,-0.2141319066,-0.1049579829,0.0853126571,-0.2213259488,-0.0400672704,0.1828597784,0.0723028034,-0.0657224879,-0.0866443887,0.035989143,0.0712399781,0.1523462385,0.1349620819,-0.0063045779,0.0892271996,-0.0848842263,0.6008986831,0.218693167,-0.2143773437,0.3002861738,-0.1032764912,0.005145404,-0.2086836398,-0.4398039281,0.2641642988,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/168","title":"Loading 'wikitext' dataset fails","comments":"Hi,\r\nThe squad bug seems to be fixed, but the loading of the 'wikitext' still suffers from this problem (on Colab with pyarrow=0.17.1).","body":"Loading the 'wikitext' dataset fails with Attribute error:\r\n\r\nCode to reproduce (From example notebook):\r\n\r\nimport nlp\r\nwikitext_dataset = nlp.load_dataset('wikitext')\r\n\r\n\r\nError:\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-17-d5d9df94b13c> in <module>()\r\n     11 \r\n     12 # Load a dataset and print the first examples in the training set\r\n---> 13 wikitext_dataset = nlp.load_dataset('wikitext')\r\n     14 print(wikitext_dataset['train'][0])\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    363                 verify_infos = not save_infos and not ignore_verifications\r\n    364                 self._download_and_prepare(\r\n--> 365                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    366                 )\r\n    367                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    416             try:\r\n    417                 # Prepare split will record examples associated to the split\r\n--> 418                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    419             except OSError:\r\n    420                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    594             example = self.info.features.encode_example(record)\r\n    595             writer.write(example)\r\n--> 596         num_examples, num_bytes = writer.finalize()\r\n    597 \r\n    598         assert num_examples == num_examples, f\"Expected to write {split_info.num_examples} but wrote {num_examples}\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in finalize(self, close_stream)\r\n    173     def finalize(self, close_stream=True):\r\n    174         if self.pa_writer is not None:\r\n--> 175             self.write_on_file()\r\n    176             self.pa_writer.close()\r\n    177         if close_stream:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_on_file(self)\r\n    124             else:\r\n    125                 # All good\r\n--> 126                 self._write_array_on_file(pa_array)\r\n    127             self.current_rows = []\r\n    128 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in _write_array_on_file(self, pa_array)\r\n     93     def _write_array_on_file(self, pa_array):\r\n     94         \"\"\"Write a PyArrow Array\"\"\"\r\n---> 95         pa_batch = pa.RecordBatch.from_struct_array(pa_array)\r\n     96         self._num_bytes += pa_array.nbytes\r\n     97         self.pa_writer.write_batch(pa_batch)\r\n\r\nAttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'","comment_length":23,"text":"Loading 'wikitext' dataset fails \n Loading the 'wikitext' dataset fails with Attribute error:\r\n\r\nCode to reproduce (From example notebook):\r\n\r\nimport nlp\r\nwikitext_dataset = nlp.load_dataset('wikitext')\r\n\r\n\r\nError:\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-17-d5d9df94b13c> in <module>()\r\n     11 \r\n     12 # Load a dataset and print the first examples in the training set\r\n---> 13 wikitext_dataset = nlp.load_dataset('wikitext')\r\n     14 print(wikitext_dataset['train'][0])\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    363                 verify_infos = not save_infos and not ignore_verifications\r\n    364                 self._download_and_prepare(\r\n--> 365                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    366                 )\r\n    367                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    416             try:\r\n    417                 # Prepare split will record examples associated to the split\r\n--> 418                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    419             except OSError:\r\n    420                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    594             example = self.info.features.encode_example(record)\r\n    595             writer.write(example)\r\n--> 596         num_examples, num_bytes = writer.finalize()\r\n    597 \r\n    598         assert num_examples == num_examples, f\"Expected to write {split_info.num_examples} but wrote {num_examples}\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in finalize(self, close_stream)\r\n    173     def finalize(self, close_stream=True):\r\n    174         if self.pa_writer is not None:\r\n--> 175             self.write_on_file()\r\n    176             self.pa_writer.close()\r\n    177         if close_stream:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_on_file(self)\r\n    124             else:\r\n    125                 # All good\r\n--> 126                 self._write_array_on_file(pa_array)\r\n    127             self.current_rows = []\r\n    128 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in _write_array_on_file(self, pa_array)\r\n     93     def _write_array_on_file(self, pa_array):\r\n     94         \"\"\"Write a PyArrow Array\"\"\"\r\n---> 95         pa_batch = pa.RecordBatch.from_struct_array(pa_array)\r\n     96         self._num_bytes += pa_array.nbytes\r\n     97         self.pa_writer.write_batch(pa_batch)\r\n\r\nAttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array' \n Hi,\r\nThe squad bug seems to be fixed, but the loading of the 'wikitext' still suffers from this problem (on Colab with pyarrow=0.17.1).","embeddings":[-0.1193379238,0.0886276364,-0.0089776339,0.2101976722,0.3692684174,0.0260604266,0.4487809837,0.4131019711,0.4330818355,0.0125665311,0.1238447428,0.3954298794,-0.1505493075,0.0219624937,0.1209058017,-0.4289889932,-0.0337393396,0.3636732399,-0.0414666571,0.062973544,-0.1876838803,0.108516708,-0.2651918828,0.2550059557,-0.3057226241,-0.0570259951,0.2159208357,0.0820631012,-0.0789184123,-0.5590060353,0.2770831883,-0.2170494348,0.0644511506,0.1147467494,-0.0001099558,0.205306232,0.3326975107,0.0302131604,-0.5799459815,-0.1895776838,-0.4773317575,-0.1189394891,0.2974321842,-0.2497071624,-0.028513357,0.0538973026,0.0359112546,-0.0744121596,0.2967469394,0.5315061212,0.2378886342,0.5236920714,0.1545488685,0.0344940759,-0.0785685405,-0.1705711782,-0.0821156427,0.2239678055,0.0738770068,-0.3915446997,-0.0133740734,0.1269243062,0.1511732191,0.2637012899,0.5083716512,0.0143935969,0.240522787,-0.1506265402,0.0149465678,0.0764008239,0.3353828788,-0.1649338454,-0.2716008127,-0.2243597806,0.2476661652,-0.3347570896,0.2365306169,-0.0366766118,-0.1622047126,-0.0081965309,-0.2692284882,-0.0281475354,-0.2418207526,0.3526881039,-0.0582148433,0.4996492267,0.0435834154,0.1427477896,-0.0580658838,-0.075457558,0.1403762847,-0.0870907307,-0.1293002814,0.2578134239,-0.383156091,-0.0364675075,-0.0349461213,-0.1169757396,-0.0316241011,0.2635210156,0.219990015,-0.2248735428,0.3238382339,0.3154148161,0.2229899764,0.4204691052,0.0727646127,0.063592121,-0.0828047171,0.1360252202,-0.2337277681,-0.1554848254,-0.1555357128,-0.1765549034,0.0928974897,-0.1546022743,0.2260205299,-0.071116358,-0.4984739125,0.1147207841,-0.1434045881,0.046290502,0.2051148862,0.1794852465,-0.1276334524,0.2322352529,0.1441244632,0.2825672626,-0.2519766986,-0.0431360416,-0.1662892997,0.2492134273,-0.2343834639,-0.1822493821,0.2427540869,0.1934002936,0.3994777203,-0.1011680439,0.111264661,-0.2017549574,0.2273753434,-0.0455191471,-0.1288790852,0.2566530704,-0.2160614133,0.1548979729,0.2015910596,-0.2824236155,-0.0848238841,-0.10174492,-0.2726355791,-0.3080923855,-0.1262511909,0.2814154327,0.147381857,-0.2429646254,-0.1522630006,-0.0611871071,0.1474927962,-0.3687241077,-0.1181055456,-0.4612588286,-0.2206385881,-0.0230053104,0.3976424038,0.2049094886,0.1313870102,-0.1089720726,-0.1154211313,0.0778549612,-0.0317966752,0.1227136478,-0.4383572936,0.5421624184,-0.0610556342,0.118862763,0.6413468719,-0.3427866101,-0.4578541815,0.123568885,-0.0142798293,0.2093286514,-0.2253688425,-0.0167210288,-0.0132269189,-0.184551537,0.2123140246,0.6192135215,-0.0150432223,0.0824793652,-0.1268968582,-0.0155486753,0.5002205968,0.1098568216,0.0925252214,-0.0079286359,-0.0417051278,0.4966493547,-0.0013129428,-0.1259710789,-0.0148605099,-0.013630108,-0.0646863058,-0.104604587,-0.2178259641,-0.0738868713,-0.2776648998,0.3043035865,-0.117547363,-0.0585312918,-0.0471505858,0.1061868444,-0.3491126597,0.1684893072,-0.4232687652,-0.3773184419,0.1720929891,0.1551331282,-0.0108234957,0.2384415865,-0.2047417462,0.2194369435,-0.274028182,0.0555009618,-0.2189284861,0.1612891257,-0.0558832213,-0.0424223617,-0.0094791884,0.3087970316,0.0383313261,-0.0179966092,-0.3062886596,0.2860310078,-0.1254843473,0.0803563818,-0.0322646983,-0.1433701664,0.0688060746,-0.0715434849,-0.1751839221,0.2081920803,0.3319509625,-0.1801262945,-0.043142952,0.1128761917,0.1272121966,0.0814221054,0.0988641679,0.1639728397,0.1439921707,-0.0658260733,0.1012795344,0.0774310902,0.2337106764,-0.0949872434,0.2047105134,-0.1564514041,-0.167145744,-0.1188064739,0.3131195307,0.1700583696,0.167417407,0.0814866126,-0.275252223,-0.0948746875,0.0785581768,0.1460482925,0.4009645283,-0.0518327467,-0.1083415523,-0.0525707416,-0.4165976346,0.0711256638,0.1707080156,0.0366290882,0.2091285437,0.3018561602,0.0413160138,0.0873196498,-0.2952665389,-0.3570170999,0.2824332714,0.4405301809,-0.2284827828,0.2078800648,-0.0305316318,-0.0025111341,-0.0678482279,-0.3336339295,-0.188629657,-0.3429944813,-0.1213362366,0.4337745905,0.024119148,0.1810040623,-0.3977624178,-0.0220502764,0.1947504133,-0.1147857755,-0.0313790925,-0.3157985508,-0.269328475,0.0459421203,0.3818612099,0.0910761356,0.3510979414,-0.0667702854,-0.0925358012,-0.007033736,-0.0584152527,-0.0755677298,-0.1837216765,0.2440479547,0.009521856,0.2855836153,-0.1770296395,-0.5220327377,0.5017470121,-0.2557020187,-0.0577849969,0.3151076138,0.2711132169,-0.249190703,-0.0794775337,-0.3746882677,-0.1264498532,-0.280605495,-0.0594684482,0.0748294741,0.0620202571,0.3974690437,0.0708564147,0.2160111964,-0.0025732063,0.1134374887,-0.0482623316,-0.0900634304,0.1859846711,-0.310032934,-0.3173182309,-0.1213430315,0.0099359797,0.3347615004,0.1107365489,-0.3489591479,-0.2141319066,-0.1049579829,0.0853126571,-0.2213259488,-0.0400672704,0.1828597784,0.0723028034,-0.0657224879,-0.0866443887,0.035989143,0.0712399781,0.1523462385,0.1349620819,-0.0063045779,0.0892271996,-0.0848842263,0.6008986831,0.218693167,-0.2143773437,0.3002861738,-0.1032764912,0.005145404,-0.2086836398,-0.4398039281,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/168","title":"Loading 'wikitext' dataset fails","comments":"When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n  [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.","body":"Loading the 'wikitext' dataset fails with Attribute error:\r\n\r\nCode to reproduce (From example notebook):\r\n\r\nimport nlp\r\nwikitext_dataset = nlp.load_dataset('wikitext')\r\n\r\n\r\nError:\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-17-d5d9df94b13c> in <module>()\r\n     11 \r\n     12 # Load a dataset and print the first examples in the training set\r\n---> 13 wikitext_dataset = nlp.load_dataset('wikitext')\r\n     14 print(wikitext_dataset['train'][0])\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    363                 verify_infos = not save_infos and not ignore_verifications\r\n    364                 self._download_and_prepare(\r\n--> 365                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    366                 )\r\n    367                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    416             try:\r\n    417                 # Prepare split will record examples associated to the split\r\n--> 418                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    419             except OSError:\r\n    420                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    594             example = self.info.features.encode_example(record)\r\n    595             writer.write(example)\r\n--> 596         num_examples, num_bytes = writer.finalize()\r\n    597 \r\n    598         assert num_examples == num_examples, f\"Expected to write {split_info.num_examples} but wrote {num_examples}\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in finalize(self, close_stream)\r\n    173     def finalize(self, close_stream=True):\r\n    174         if self.pa_writer is not None:\r\n--> 175             self.write_on_file()\r\n    176             self.pa_writer.close()\r\n    177         if close_stream:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_on_file(self)\r\n    124             else:\r\n    125                 # All good\r\n--> 126                 self._write_array_on_file(pa_array)\r\n    127             self.current_rows = []\r\n    128 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in _write_array_on_file(self, pa_array)\r\n     93     def _write_array_on_file(self, pa_array):\r\n     94         \"\"\"Write a PyArrow Array\"\"\"\r\n---> 95         pa_batch = pa.RecordBatch.from_struct_array(pa_array)\r\n     96         self._num_bytes += pa_array.nbytes\r\n     97         self.pa_writer.write_batch(pa_batch)\r\n\r\nAttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'","comment_length":66,"text":"Loading 'wikitext' dataset fails \n Loading the 'wikitext' dataset fails with Attribute error:\r\n\r\nCode to reproduce (From example notebook):\r\n\r\nimport nlp\r\nwikitext_dataset = nlp.load_dataset('wikitext')\r\n\r\n\r\nError:\r\n---------------------------------------------------------------------------\r\nAttributeError                            Traceback (most recent call last)\r\n<ipython-input-17-d5d9df94b13c> in <module>()\r\n     11 \r\n     12 # Load a dataset and print the first examples in the training set\r\n---> 13 wikitext_dataset = nlp.load_dataset('wikitext')\r\n     14 print(wikitext_dataset['train'][0])\r\n\r\n6 frames\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    518         download_mode=download_mode,\r\n    519         ignore_verifications=ignore_verifications,\r\n--> 520         save_infos=save_infos,\r\n    521     )\r\n    522 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    363                 verify_infos = not save_infos and not ignore_verifications\r\n    364                 self._download_and_prepare(\r\n--> 365                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    366                 )\r\n    367                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    416             try:\r\n    417                 # Prepare split will record examples associated to the split\r\n--> 418                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    419             except OSError:\r\n    420                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    594             example = self.info.features.encode_example(record)\r\n    595             writer.write(example)\r\n--> 596         num_examples, num_bytes = writer.finalize()\r\n    597 \r\n    598         assert num_examples == num_examples, f\"Expected to write {split_info.num_examples} but wrote {num_examples}\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in finalize(self, close_stream)\r\n    173     def finalize(self, close_stream=True):\r\n    174         if self.pa_writer is not None:\r\n--> 175             self.write_on_file()\r\n    176             self.pa_writer.close()\r\n    177         if close_stream:\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in write_on_file(self)\r\n    124             else:\r\n    125                 # All good\r\n--> 126                 self._write_array_on_file(pa_array)\r\n    127             self.current_rows = []\r\n    128 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/arrow_writer.py in _write_array_on_file(self, pa_array)\r\n     93     def _write_array_on_file(self, pa_array):\r\n     94         \"\"\"Write a PyArrow Array\"\"\"\r\n---> 95         pa_batch = pa.RecordBatch.from_struct_array(pa_array)\r\n     96         self._num_bytes += pa_array.nbytes\r\n     97         self.pa_writer.write_batch(pa_batch)\r\n\r\nAttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array' \n When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n  [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.","embeddings":[-0.1193379238,0.0886276364,-0.0089776339,0.2101976722,0.3692684174,0.0260604266,0.4487809837,0.4131019711,0.4330818355,0.0125665311,0.1238447428,0.3954298794,-0.1505493075,0.0219624937,0.1209058017,-0.4289889932,-0.0337393396,0.3636732399,-0.0414666571,0.062973544,-0.1876838803,0.108516708,-0.2651918828,0.2550059557,-0.3057226241,-0.0570259951,0.2159208357,0.0820631012,-0.0789184123,-0.5590060353,0.2770831883,-0.2170494348,0.0644511506,0.1147467494,-0.0001099558,0.205306232,0.3326975107,0.0302131604,-0.5799459815,-0.1895776838,-0.4773317575,-0.1189394891,0.2974321842,-0.2497071624,-0.028513357,0.0538973026,0.0359112546,-0.0744121596,0.2967469394,0.5315061212,0.2378886342,0.5236920714,0.1545488685,0.0344940759,-0.0785685405,-0.1705711782,-0.0821156427,0.2239678055,0.0738770068,-0.3915446997,-0.0133740734,0.1269243062,0.1511732191,0.2637012899,0.5083716512,0.0143935969,0.240522787,-0.1506265402,0.0149465678,0.0764008239,0.3353828788,-0.1649338454,-0.2716008127,-0.2243597806,0.2476661652,-0.3347570896,0.2365306169,-0.0366766118,-0.1622047126,-0.0081965309,-0.2692284882,-0.0281475354,-0.2418207526,0.3526881039,-0.0582148433,0.4996492267,0.0435834154,0.1427477896,-0.0580658838,-0.075457558,0.1403762847,-0.0870907307,-0.1293002814,0.2578134239,-0.383156091,-0.0364675075,-0.0349461213,-0.1169757396,-0.0316241011,0.2635210156,0.219990015,-0.2248735428,0.3238382339,0.3154148161,0.2229899764,0.4204691052,0.0727646127,0.063592121,-0.0828047171,0.1360252202,-0.2337277681,-0.1554848254,-0.1555357128,-0.1765549034,0.0928974897,-0.1546022743,0.2260205299,-0.071116358,-0.4984739125,0.1147207841,-0.1434045881,0.046290502,0.2051148862,0.1794852465,-0.1276334524,0.2322352529,0.1441244632,0.2825672626,-0.2519766986,-0.0431360416,-0.1662892997,0.2492134273,-0.2343834639,-0.1822493821,0.2427540869,0.1934002936,0.3994777203,-0.1011680439,0.111264661,-0.2017549574,0.2273753434,-0.0455191471,-0.1288790852,0.2566530704,-0.2160614133,0.1548979729,0.2015910596,-0.2824236155,-0.0848238841,-0.10174492,-0.2726355791,-0.3080923855,-0.1262511909,0.2814154327,0.147381857,-0.2429646254,-0.1522630006,-0.0611871071,0.1474927962,-0.3687241077,-0.1181055456,-0.4612588286,-0.2206385881,-0.0230053104,0.3976424038,0.2049094886,0.1313870102,-0.1089720726,-0.1154211313,0.0778549612,-0.0317966752,0.1227136478,-0.4383572936,0.5421624184,-0.0610556342,0.118862763,0.6413468719,-0.3427866101,-0.4578541815,0.123568885,-0.0142798293,0.2093286514,-0.2253688425,-0.0167210288,-0.0132269189,-0.184551537,0.2123140246,0.6192135215,-0.0150432223,0.0824793652,-0.1268968582,-0.0155486753,0.5002205968,0.1098568216,0.0925252214,-0.0079286359,-0.0417051278,0.4966493547,-0.0013129428,-0.1259710789,-0.0148605099,-0.013630108,-0.0646863058,-0.104604587,-0.2178259641,-0.0738868713,-0.2776648998,0.3043035865,-0.117547363,-0.0585312918,-0.0471505858,0.1061868444,-0.3491126597,0.1684893072,-0.4232687652,-0.3773184419,0.1720929891,0.1551331282,-0.0108234957,0.2384415865,-0.2047417462,0.2194369435,-0.274028182,0.0555009618,-0.2189284861,0.1612891257,-0.0558832213,-0.0424223617,-0.0094791884,0.3087970316,0.0383313261,-0.0179966092,-0.3062886596,0.2860310078,-0.1254843473,0.0803563818,-0.0322646983,-0.1433701664,0.0688060746,-0.0715434849,-0.1751839221,0.2081920803,0.3319509625,-0.1801262945,-0.043142952,0.1128761917,0.1272121966,0.0814221054,0.0988641679,0.1639728397,0.1439921707,-0.0658260733,0.1012795344,0.0774310902,0.2337106764,-0.0949872434,0.2047105134,-0.1564514041,-0.167145744,-0.1188064739,0.3131195307,0.1700583696,0.167417407,0.0814866126,-0.275252223,-0.0948746875,0.0785581768,0.1460482925,0.4009645283,-0.0518327467,-0.1083415523,-0.0525707416,-0.4165976346,0.0711256638,0.1707080156,0.0366290882,0.2091285437,0.3018561602,0.0413160138,0.0873196498,-0.2952665389,-0.3570170999,0.2824332714,0.4405301809,-0.2284827828,0.2078800648,-0.0305316318,-0.0025111341,-0.0678482279,-0.3336339295,-0.188629657,-0.3429944813,-0.1213362366,0.4337745905,0.024119148,0.1810040623,-0.3977624178,-0.0220502764,0.1947504133,-0.1147857755,-0.0313790925,-0.3157985508,-0.269328475,0.0459421203,0.3818612099,0.0910761356,0.3510979414,-0.0667702854,-0.0925358012,-0.007033736,-0.0584152527,-0.0755677298,-0.1837216765,0.2440479547,0.009521856,0.2855836153,-0.1770296395,-0.5220327377,0.5017470121,-0.2557020187,-0.0577849969,0.3151076138,0.2711132169,-0.249190703,-0.0794775337,-0.3746882677,-0.1264498532,-0.280605495,-0.0594684482,0.0748294741,0.0620202571,0.3974690437,0.0708564147,0.2160111964,-0.0025732063,0.1134374887,-0.0482623316,-0.0900634304,0.1859846711,-0.310032934,-0.3173182309,-0.1213430315,0.0099359797,0.3347615004,0.1107365489,-0.3489591479,-0.2141319066,-0.1049579829,0.0853126571,-0.2213259488,-0.0400672704,0.1828597784,0.0723028034,-0.0657224879,-0.0866443887,0.035989143,0.0712399781,0.1523462385,0.1349620819,-0.0063045779,0.0892271996,-0.0848842263,0.6008986831,0.218693167,-0.2143773437,0.3002861738,-0.1032764912,0.005145404,-0.2086836398,-0.4398039281,0.2641642988,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/166","title":"Add a method to shuffle a dataset","comments":"+1 for the naming convention\r\n\r\nAbout the `shuffle` method, from my understanding it should be done in `Dataloader` (better separation between dataset processing - usage)","body":"Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.\r\n\r\nAlso, we could maybe have a clear indication of which method modify in-place and which methods return\/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think?","comment_length":25,"text":"Add a method to shuffle a dataset \n Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.\r\n\r\nAlso, we could maybe have a clear indication of which method modify in-place and which methods return\/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think? \n +1 for the naming convention\r\n\r\nAbout the `shuffle` method, from my understanding it should be done in `Dataloader` (better separation between dataset processing - 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/166","title":"Add a method to shuffle a dataset","comments":"+1 for shuffle in `Dataloader`. \r\nSome `Dataloader` just store idxs of dataset and just shuffle those idxs, which might(?) be faster than do shuffle in dataset, especially when doing shuffle every epoch.\r\n\r\nAlso +1 for the naming convention.","body":"Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.\r\n\r\nAlso, we could maybe have a clear indication of which method modify in-place and which methods return\/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think?","comment_length":38,"text":"Add a method to shuffle a dataset \n Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.\r\n\r\nAlso, we could maybe have a clear indication of which method modify in-place and which methods return\/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think? \n +1 for shuffle in `Dataloader`. \r\nSome `Dataloader` just store idxs of dataset and just shuffle those idxs, which might(?) be faster than do shuffle in dataset, especially when doing shuffle every epoch.\r\n\r\nAlso +1 for the naming convention.","embeddings":[0.0704690665,0.0160601158,-0.081076622,-0.1330937445,0.1867506802,-0.0187649392,0.3612542748,0.2494378537,-0.1316797584,0.398124367,-0.1151702181,0.7459426522,-0.3297457397,-0.2149706483,-0.0445238054,-0.0798154026,0.2653796673,-0.1031481922,-0.2052428573,0.0562242642,-0.1247075349,-0.3481492698,0.0236733481,-0.1405295581,-0.0896880254,0.012186124,-0.1130371764,0.0714675263,-0.2733206451,-0.2186136097,-0.4450689554,0.7157347202,-0.1049824506,0.5668814778,-0.0001091151,-0.2588721216,0.3414728045,0.0329268649,-0.3560408652,0.1413132101,-0.133158654,-0.0783198178,0.0386806056,-0.2729430497,0.2220702767,-0.0343981311,0.1158991307,-0.1127861366,0.1109896749,-0.1473737508,0.1819331348,-0.0993652567,-0.3846822679,0.0020908492,0.578519702,0.4610337317,-0.2091200054,0.1824525148,0.574575305,0.3853920698,-0.3338279128,0.1915495694,-0.1309427023,-0.1019477099,0.5779697299,-0.3001551926,-0.5396052003,0.0647645891,-0.0393920168,0.2036895007,0.6476197243,-0.1902356595,-0.4480341077,-0.1510591656,0.1816706657,-0.0496767871,0.0307899434,-0.2421191484,-0.0195767432,-0.0853262246,-0.0373785309,-0.1129700616,-0.0025622498,-0.1363610774,0.421797514,0.5597810745,0.1549821496,-0.0286143627,0.2360943109,-0.0222396906,0.5372052789,-0.0696752667,0.0733335465,0.2372976393,-0.217320621,-0.1808136553,-0.0155333262,0.2117105424,0.1879729182,0.1057438105,0.3026645482,0.2721392512,0.013139585,0.1095649898,-0.1144884303,-0.038552694,0.0334646702,-0.1094348207,0.3762949109,-0.2285953909,0.1526614726,-0.0816256106,-0.2623736262,-0.0178392008,0.1021517888,0.1414059252,-0.4133082628,0.0568721928,0.1791498959,-0.4913693964,0.1562060118,-0.4261989594,0.1031714454,0.18962273,0.1870161742,0.1320405155,-0.1550371498,-0.0127899544,0.2115512788,-0.184740752,-0.1022361442,0.0491869822,-0.507145524,0.2194849998,0.1013816968,-0.2031907737,0.1159764007,0.0387395322,0.136518091,0.3439450264,0.1533025652,0.2006931305,0.1924792677,-0.2556225657,-0.4391724169,-0.1295641661,-0.1443853378,0.3152359426,-0.2803953588,0.2423091829,-0.29306826,-0.2366222292,-0.1138081625,0.1717359573,-0.0018636793,-0.0619996898,-0.2293261737,0.246396184,0.0740043819,-0.3056387901,0.4155263007,0.0785193592,0.1712349504,-0.1233353764,-0.0725498423,0.2618198693,-0.1112703532,-0.0184363145,-0.230252713,-0.1622869819,0.205648914,0.1142255291,-0.1920033842,0.0078952899,-0.1419530213,-0.1474459022,0.4907092154,0.1812635809,-0.1588147283,-0.0123546282,-0.1144363135,-0.1025750786,0.6056385636,0.3467120826,0.0564940237,-0.3341640234,0.2879296243,-0.0612017065,-0.3102137446,0.0981529653,-0.2247581184,-0.1184130087,0.3814633787,0.2611493468,-0.1228186637,-0.0091771241,0.0056606396,-0.3373622,0.1947128624,-0.1716935784,-0.0731263235,-0.2363464832,-0.0819968805,0.3089695275,-0.0150543284,-0.0649891198,-0.0631167591,-0.1157574281,0.29175964,0.0176495314,0.1858094186,-0.2812825739,0.0290214121,-0.2989604473,-0.0154391713,-0.174696058,0.0616281666,0.2616629303,-0.2221287042,-0.2475360483,-0.2646149695,-0.1795042455,-0.2702847719,-0.1623175293,-0.2577840388,-0.1003571302,-0.0421096273,0.0273020007,-0.2138474584,0.2204702348,-0.1302502453,-0.0731815547,-0.1548641026,0.248049289,0.1106282026,0.0012288903,0.3039054275,0.3766371608,-0.0230145231,0.0787241757,0.3076277077,-0.0839393139,0.0128669385,-0.1136038601,-0.2761874795,0.2542312741,-0.2328188866,0.0878826529,-0.2713023722,-0.3496966064,-0.0082614282,0.359682858,-0.3368625641,0.2372022718,0.1287111342,0.2788371742,0.407399863,0.2508471906,-0.3290274739,-0.1328150779,0.3740357757,-0.1232951954,-0.079807274,0.3697065413,0.1284963787,-0.2949621975,0.1104044765,0.0889155045,0.3699173927,0.3470844626,0.0725460127,-0.0338136256,0.3311807811,-0.1331802458,-0.0100540407,0.096919775,-0.1369654089,-0.0208794028,0.1016628444,0.0581178144,-0.1998404711,-0.5052341223,0.033565145,0.0917880833,0.0397617519,-0.0772798061,0.0556890666,-0.0526593328,-0.1414629519,-0.1758359224,-0.2801253498,-0.2714925408,0.2531290948,0.1074848771,-0.3382922411,0.3336867094,0.1831430197,0.5316727161,-0.1584078223,-0.0584187023,-0.3735506237,-0.4471942186,0.1538641453,0.0767324269,0.2285131365,-0.1827137917,0.6215420961,-0.0355267115,-0.0638798401,-0.3513756096,-0.3830470443,-0.062467549,-0.0042403601,-0.2293925732,0.3913440108,-0.2218208611,0.1168422997,-0.4907086492,-0.0055111027,-0.3185169697,-0.1076779142,0.0271484479,0.0252681263,0.0133220227,-0.1668285429,-0.2421270907,-0.17180264,-0.4843709469,0.0358714052,-0.5416013598,-0.0025590253,0.0130536994,-0.2118822634,-0.1035678983,-0.1109935269,0.1574204564,-0.1544938833,-0.8162309527,0.2280825973,-0.1825128943,0.0700531453,-0.2289283276,-0.115426816,0.0835939795,0.6077492833,0.2217947245,-0.0639686808,0.1345303655,0.1378970593,0.0393652804,0.1684257388,0.2622101903,0.3026329577,-0.1817001849,0.0207795333,-0.2253602445,-0.0899050459,-0.1223536357,0.2259428054,0.1704076976,0.3607722819,-0.015084832,0.7392673492,-0.677593708,-0.0537548438,-0.0282049254,0.1690758616,0.1355460584,-0.031218864,-0.1497747749,0.5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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/166","title":"Add a method to shuffle a dataset","comments":"As you might already know the issue of dataset shuffling came up in the nlp code [walkthrough](https:\/\/youtu.be\/G3pOvrKkFuk?t=3204) by Yannic Kilcher\r\n","body":"Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.\r\n\r\nAlso, we could maybe have a clear indication of which method modify in-place and which methods return\/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think?","comment_length":20,"text":"Add a method to shuffle a dataset \n Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method.\r\n\r\nAlso, we could maybe have a clear indication of which method modify in-place and which methods return\/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think? \n As you might already know the issue of dataset shuffling came up in the nlp code [walkthrough](https:\/\/youtu.be\/G3pOvrKkFuk?t=3204) by Yannic Kilcher\r\n","embeddings":[0.1168218851,0.0479950309,-0.0080725169,-0.2174368352,0.128882423,0.0286900382,0.1363591105,0.3606428206,-0.264738977,0.2979810238,0.0116364621,0.8511587381,-0.3536046445,-0.2320982218,0.2013928443,-0.0704999045,0.1584128886,-0.1035659686,-0.2414909154,0.0523635074,-0.1391384453,-0.3828111291,-0.0782272369,-0.2000364959,-0.1313398331,-0.0940043628,-0.0838561207,0.132300958,-0.1840190589,-0.2935966551,-0.4079650939,0.6355049014,-0.1021856815,0.1419230551,-0.0001081365,-0.2722443342,0.3262639046,0.1310779005,-0.4321397245,0.0446859859,-0.0818533301,-0.0996276066,0.0086366646,-0.1526380479,0.0655293167,0.1338957995,0.1654344797,-0.1464117467,0.2423582822,0.0503101274,0.2091447711,-0.0329092294,-0.1901006848,0.1584738642,0.5000153184,0.4068689048,-0.1012272462,0.0286281444,0.4395242333,0.2293315679,-0.265799284,0.2744675577,-0.0664383993,-0.1814917326,0.3078677058,-0.2879181802,-0.5689676404,0.0742852613,0.0025417558,0.1787862182,0.5037118793,-0.2853350341,-0.3903695643,-0.3526299596,0.330252111,-0.1884994954,0.0199903995,-0.0964175165,-0.0568163805,-0.0657435358,-0.2452473491,-0.1598949879,0.0368060954,-0.0217535887,0.5161419511,0.6055985093,0.2005672455,-0.0033858907,0.2449980676,0.0833965391,0.5854985714,-0.0093415622,0.0330339447,0.299644649,-0.1472086906,-0.2083730847,0.0550040305,0.2948370278,0.2364352643,0.1446129829,0.3270729184,0.2850995064,-0.0413031392,0.0373536497,0.0055521391,0.0381203219,-0.0531010404,0.0549554788,0.2810688913,-0.3015078008,0.231248647,0.0039709494,-0.0490369946,-0.0805971175,-0.0109761739,0.1324440092,-0.1945451051,-0.068498686,0.0361292921,-0.5815837979,-0.0496325232,-0.3245076537,0.1488962919,0.1077939048,0.277036041,0.1407219321,-0.1079227254,-0.048173897,0.1835096627,-0.2393871844,-0.1050535291,0.0258388333,-0.4939265847,0.170787856,0.1737404168,-0.0752229467,0.1390149891,0.1129442975,0.0670285374,0.2374949902,0.0788469091,0.2658469379,0.4057005644,-0.2036512047,-0.4380708635,-0.1334305704,-0.1084037796,0.2805595398,-0.3744524121,0.2528228462,-0.2730659544,-0.2303433716,-0.0922330618,0.1565239877,-0.0969693288,-0.2698227167,-0.1759152114,0.3530564308,0.0626457781,-0.4234461486,0.3546225131,0.0778842717,0.0622802265,-0.0040477095,0.0131624537,0.3176636398,0.0469545834,0.0204990134,-0.2403812259,0.0018366999,0.2378979623,0.2391389161,-0.1998987645,-0.0213025268,-0.1616416276,-0.0612693727,0.5229774714,0.1896463186,-0.2080697417,0.0131860506,-0.209504053,-0.0972790122,0.4091675282,0.3883785605,-0.1224163175,-0.3362308741,0.2075741887,0.1173946261,-0.410466373,0.1484215409,-0.3570763469,-0.0799920484,0.4687559903,0.3144295812,-0.2434637696,0.0008649587,0.0736546218,-0.1594797373,0.2940757275,-0.1337452233,-0.1370373666,-0.2356622666,-0.0692658871,0.1285503656,-0.1717895567,-0.1168628484,-0.1040293276,-0.2373951077,0.223628521,-0.0517794266,0.1164419204,-0.2859423459,0.0874580741,-0.3385697901,0.0740101263,-0.1682889313,0.0914287716,0.115264304,-0.1039150581,-0.1696283668,-0.2285808921,-0.0651362538,-0.1469430178,-0.18812038,-0.3230232,0.029987203,-0.1107577682,-0.0512310565,-0.0759257749,0.4667927623,-0.0837616101,-0.0623995699,-0.0960640013,0.2955295742,-0.0001282156,-0.2677128315,0.3739856482,0.2960723937,0.0336664915,0.0351459384,0.0965519771,0.0336218141,-0.0555981323,-0.0028627121,-0.3500953317,0.3163101971,-0.168394953,0.0289800316,-0.182195738,-0.2597624958,0.0674805865,0.22245875,-0.3342285156,0.1662608534,0.2199173868,0.2118288577,0.2813099325,0.2211573124,-0.4753617942,0.0671244785,0.4927187264,-0.0540447421,0.0184653047,0.3442239463,0.2549565434,-0.3329372406,0.1225965917,0.12657094,0.2766240239,0.4469533265,0.052660808,0.1430922449,0.095129028,-0.1651538163,-0.0443397202,-0.0692203566,-0.1712171882,-0.0281354692,0.2366150171,0.1459546834,-0.2876828909,-0.3273473978,-0.0469350293,0.1891978085,-0.1102173775,-0.0588323995,-0.1330818832,-0.306199789,-0.3187922537,-0.091669634,-0.3395288885,-0.3069963753,0.3917934895,0.0103865359,-0.3207394183,0.4042351246,0.1464307606,0.5348382592,-0.2747852206,-0.0112941647,-0.2897607386,-0.4303383827,-0.0809720308,0.0738233626,0.2886367142,-0.0477829576,0.7124966979,0.0789341927,-0.1327527761,-0.3044414222,-0.5318074822,-0.1408619136,0.031390328,-0.2137417048,0.3421264291,-0.3924229741,0.0901098996,-0.3882688582,-0.1876741946,-0.3428919315,-0.0802419633,0.1229611039,0.000893511,0.0224003438,-0.2999293804,-0.3448785543,-0.0723853931,-0.4497671723,0.0770968124,-0.6393800974,0.0720585957,0.0566139631,-0.2364704013,-0.1664166898,-0.372789681,0.0077646188,-0.1279707998,-0.5969657302,0.1661372185,-0.1103719324,-0.0612819679,-0.2556629777,-0.056040328,0.0138127496,0.6228568554,0.1948454231,-0.1397157311,0.1068795025,0.0764808729,-0.0811647028,0.1502829045,0.434381336,0.1620499194,-0.1234577894,0.1187695414,-0.182643339,0.0421155691,-0.0153984576,0.3286912739,0.034825176,0.2597435415,0.1325586289,0.5310879946,-0.5287257433,-0.0803312957,0.0568569489,0.1760204136,0.2583037019,-0.0953007266,0.0022558759,0.5387940407,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/163","title":"[Feature request] Add cos-e v1.0","comments":"Sounds good, @mariamabarham do you want to give a look?\r\nI think we should have two configurations so we can allow either version of the dataset to be loaded with the `1.0` version being the default maybe.\r\n\r\nCc some authors of the great cos-e: @nazneenrajani @bmccann","body":"I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf).","comment_length":46,"text":"[Feature request] Add cos-e v1.0 \n I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf). \n Sounds good, @mariamabarham do you want to give a look?\r\nI think we should have two configurations so we can allow either version of the dataset to be loaded with the `1.0` version being the default maybe.\r\n\r\nCc some authors of the great cos-e: @nazneenrajani 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/163","title":"[Feature request] Add cos-e v1.0","comments":"cos_e v1.0 is related to CQA v1.0 but only CQA v1.11 dataset is available on their website. Indeed their is lots of ids in cos_e v1, which are  not in CQA v1.11  or the other way around.\r\n@sarahwie, @thomwolf, @nazneenrajani,  @bmccann  do you know where I can  find CQA v1.0\r\n","body":"I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf).","comment_length":50,"text":"[Feature request] Add cos-e v1.0 \n I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf). \n cos_e v1.0 is related to CQA v1.0 but only CQA v1.11 dataset is available on their website. Indeed their is lots of ids in cos_e v1, which are  not in CQA v1.11  or the other way around.\r\n@sarahwie, @thomwolf, @nazneenrajani,  @bmccann  do you know where I can  find CQA v1.0\r\n","embeddings":[-0.0232327729,-0.1867629588,-0.1784631461,-0.3313111365,-0.5435762405,0.0657164901,-0.1783175617,0.1259405315,-0.3440611362,0.4689726233,0.2354179025,0.1240532696,0.0749001652,0.4212986231,0.0156426728,0.0894112587,0.117030628,0.4064915478,0.1564118713,-0.0762307197,-0.0469662733,0.3286637664,-0.2688592672,-0.012009602,0.1689498425,0.112477757,-0.3255741596,-0.2680034637,-0.1470949948,-0.1698807329,0.4301481247,0.2873969078,0.354203701,-0.0082968352,-0.0001180449,-0.2494498491,0.2678097486,-0.0638853982,-0.2248022854,-0.2884759903,-0.3987270594,-0.3756753504,-0.1216315106,-0.0159799121,0.1313795745,0.0580618493,0.2008041292,0.0156754162,-0.1259213537,0.1071852669,0.1894945502,0.1807599962,0.141135782,-0.3795875609,0.1705751866,0.3191810846,-0.2734742761,-0.2285316288,0.2699380815,0.0258557796,0.3077663779,0.4816246331,0.2874330282,-0.3409462869,-0.1720889509,-0.013270352,0.1167342588,-0.1250804812,0.2783508301,-0.0452661663,0.5306007266,0.1798347831,-0.3857192695,0.4131044745,0.0453327149,-0.4731240571,0.2673746645,-0.0138167813,0.0682043806,0.118957445,0.237447843,-0.305306524,-0.4384269118,0.1660591513,-0.1632631868,0.0052230353,-0.1122972071,-0.1868192852,0.0656315535,0.045105312,-0.0487550758,0.1840634644,-0.2509777844,0.2603937089,-0.2296653688,-0.3830533922,0.2817987502,0.0218084604,0.3297159374,-0.0346217491,0.1586210579,-0.2194430083,-0.2475143075,0.0007930144,0.1206260175,0.3228637278,0.3589960635,0.0529479943,0.1459193528,0.235377714,0.4406010211,-0.0404021442,0.0712785497,0.0129208686,-0.1671769172,0.0568953156,0.060212113,-0.6049147248,-0.1965153962,-0.0045784405,-0.2337331623,0.0628118888,-0.1438020915,0.0319252163,0.418045342,0.2180632949,0.2200229913,-0.1935508996,-0.0098073846,-0.6159117222,-0.2765554786,-0.0146776028,-0.0672637373,0.3063664138,0.2101648301,-0.3618155718,0.2205503136,-0.3328654766,0.224240616,0.0553091727,-0.1585887969,0.4364677966,-0.2266087979,0.1858067364,-0.0229867026,0.0607716367,-0.2817752361,0.4902441502,-0.2170691788,0.1842231899,-0.3824463189,-0.4308210611,-0.501421392,0.1403851956,0.0507101119,-0.1981070489,0.0367361046,0.2465415746,-0.2765118778,-0.1712324917,0.0227238573,0.2964550257,-0.1747252792,-0.1326307803,-0.0055325804,0.1790474206,-0.2631231844,-0.0621967837,-0.392976135,0.0770601183,-0.1144097224,0.0586968064,-0.0611227006,-0.3164398372,-0.1432292461,-0.2597507536,0.3386314511,-0.6128681302,-0.5238029957,0.1491823345,-0.0313184857,-0.2901610732,0.2189871818,0.0052803205,0.0072041349,-0.1621967703,-0.2095164955,-0.0362694375,-0.2873411179,-0.3331362903,0.0219733696,-0.4311449528,-0.0796960965,0.1074301749,0.0924729109,0.0935381427,0.2323921025,-0.6215128303,0.1444454491,-0.0195611008,0.0103544975,0.0693640336,0.6898933649,0.0778632462,0.2611390352,0.0516130961,-0.3183967769,0.0562004223,-0.2152469456,0.0695454702,0.2679488957,-0.3772905767,-0.1154734194,0.0034697491,0.1692007184,-0.2824362814,0.0478885844,0.0488528237,0.1080219224,0.0012774151,0.0135006458,0.3367107213,-0.1517360806,-0.1799661964,-0.103039369,0.1771976799,-0.1950794309,0.0085162651,0.0162855051,0.2220596969,0.1066612378,0.019013105,-0.0894381478,0.1999128014,-0.1644220203,0.2517156601,0.2599522173,0.4301055074,0.2277344763,-0.2260206789,0.0658660084,-0.0969092548,-0.1978674233,0.3507429659,-0.0107880915,0.2520744205,0.1498487294,-0.0752098784,-0.0074323537,0.2028425783,0.0315937921,0.0416572914,-0.2068519294,-0.1189873368,0.2147415876,0.1000060216,-0.3482510448,-0.3811196685,-0.2063105851,0.1608173549,0.1386514008,-0.1160973459,-0.0007920525,0.1139478981,-0.0206832141,-0.0809205472,0.3460274637,-0.1036329493,0.0956860334,0.2869534791,-0.2408586293,-0.1860807091,-0.009621487,0.1602338552,0.1520680189,0.2799004018,-0.2412097156,-0.1741050929,0.3792101443,0.1788065135,-0.1269617975,-0.2324094176,-0.0148720704,0.0560344458,0.0285747927,-0.0962511376,-0.1388391256,0.005258176,-0.0699222237,-0.0594110079,0.1831770986,-0.1977694035,0.277212888,0.1787653118,0.0725568607,0.2747685313,-0.0002004471,0.7763874531,-0.2226073742,0.303317517,-0.0758502036,0.1394991577,-0.4241236448,0.1356927454,-0.2917244136,-0.1730993241,0.4106993079,-0.098780632,0.3334311545,-0.0885260105,-0.8045449853,-0.0008278571,-0.1077457815,-0.1097392961,0.1192290485,-0.3133375645,-0.0917769819,0.0245353598,-0.0257826895,-0.2734339237,-0.4853473604,-0.1646532863,-0.3983456492,0.0878533795,-0.1717593968,-0.3822391033,-0.0438728295,-0.0767247751,-0.1242800876,-0.1243780553,0.2652041018,0.1253896803,-0.0085397772,-0.0731472969,0.1770051867,-0.0466657057,-0.1972223371,0.2090691924,0.3373953998,-0.3322137594,-0.3296049237,0.3415119648,-0.0217028633,0.2813383043,0.0114787035,-0.4279914498,-0.1520687789,0.066889599,0.1309575886,0.0516323112,-0.0365510024,0.7915483713,-0.116004616,-0.0761181116,-0.0275548529,-0.5734854341,0.1022515222,0.5704916716,0.2422737032,-0.1116487533,0.3174265921,0.1585417092,0.5127474666,0.3592986465,0.0104253078,0.022733897,0.1787563711,0.0413279384,-0.1243185475,-0.2332143337,0.030882345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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/163","title":"[Feature request] Add cos-e v1.0","comments":"@mariamabarham I'm also not sure where to find CQA 1.0. Perhaps it's not possible to include this version of the dataset. I'll close the issue if that's the case.","body":"I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf).","comment_length":29,"text":"[Feature request] Add cos-e v1.0 \n I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf). \n @mariamabarham I'm also not sure where to find CQA 1.0. Perhaps it's not possible to include this version of the dataset. I'll close the issue if that's the 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/163","title":"[Feature request] Add cos-e v1.0","comments":"You can now do\r\n```python\r\nfrom nlp import load_dataset\r\ncos_e = load_dataset(\"cos_e\", \"v1.0\")\r\n```\r\nThanks @mariamabarham !","body":"I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf).","comment_length":17,"text":"[Feature request] Add cos-e v1.0 \n I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf). \n You can now do\r\n```python\r\nfrom nlp import load_dataset\r\ncos_e = load_dataset(\"cos_e\", \"v1.0\")\r\n```\r\nThanks @mariamabarham !","embeddings":[-0.2524742186,-0.2647848129,-0.1142224446,-0.4864544868,-0.3794747293,-0.208027944,0.080287829,0.1892701834,0.117797479,0.3005792499,-0.1557758898,0.5226972699,-0.1476927847,0.4125771224,0.1462089717,0.1017268375,0.0327461325,0.3626562059,-0.1460937411,-0.2017550319,-0.2499855608,0.2243396193,-0.170479089,-0.0546534024,0.2192804813,0.0769513175,-0.29594329,0.079357408,-0.0616971627,-0.1953394562,0.3291566968,0.3292326927,0.1830692142,0.0646769628,-0.0001042149,-0.2351597548,0.2574845254,-0.128415674,-0.2869003117,-0.1815093756,-0.1894630939,-0.4737758636,0.2212621272,-0.2176605612,0.1091976464,0.1245936006,0.2031544,-0.1243735552,0.1270561814,0.2281515151,0.3232874572,0.4082860947,-0.0342665724,-0.1849743575,0.2077931166,0.2706400156,-0.2086310685,-0.0129490839,0.159164995,-0.2754572034,-0.1968272328,0.4057946503,0.1401126981,-0.0958480313,-0.1321593523,-0.0657319725,0.160822317,0.0215867739,0.0102871815,0.035435807,0.2350435704,-0.0497151688,-0.3959866166,0.1401738226,0.0289128553,-0.5668572783,0.0933171883,-0.0871446431,-0.1245133057,-0.1430565119,0.2074773461,-0.1623462439,-0.4726059437,0.2572447062,0.0048299152,0.2947573066,-0.0636480153,-0.2017008066,0.2266446352,0.0425014384,-0.0799821243,0.1515029669,-0.0328291841,0.3442159891,-0.532577157,-0.280259788,0.3405756354,0.0903657824,0.2678717971,0.0426026657,0.2928168476,0.141662091,-0.0038665642,0.1931778044,0.0302175097,0.2401251048,0.2926645279,-0.0412991047,0.4281789064,0.265352577,0.3137462735,0.0563644879,0.0704402551,-0.1089823171,-0.1450002193,-0.0684927106,0.0081538325,-0.4685888886,-0.2638157904,-0.053575594,-0.0558466762,0.1455134749,-0.0139182229,0.2732973695,0.1701271832,0.2448029518,0.4300428033,0.0092152497,-0.2248602808,-0.3951990008,-0.2916809022,0.0973813161,-0.3190465868,0.0840040892,0.3731903732,-0.2006007433,0.2742324769,-0.4334007502,0.2543345988,0.0711415559,-0.1991913319,0.2366589457,-0.1144714132,0.0614363663,0.2251132578,-0.0429788195,-0.238359943,0.2062325627,-0.208387658,-0.0808214471,-0.3151924908,-0.3829509318,-0.4068824351,0.2756214142,-0.0551683418,-0.3148973286,-0.1343500316,0.3234367371,-0.1739309579,-0.1865482032,-0.0361932665,0.1392180175,-0.2541751266,-0.1285971105,0.0910563022,0.1125260368,0.0546063036,-0.1368647069,-0.5214048624,0.0013883548,-0.0100025153,0.0492770411,0.021899486,-0.1245969906,-0.1308999807,-0.1309102178,0.5791584253,-0.3678940833,-0.0639109239,0.1252523661,0.0537508205,-0.288112998,0.0940648243,-0.171425581,-0.1428023726,-0.1066986248,-0.1012187973,0.3418894708,-0.0704961494,0.0995737687,-0.1165204719,-0.3035582006,-0.012261508,0.3013634682,0.2912403047,-0.0810912326,0.233009398,-0.1553655267,0.2071705461,-0.2782928348,0.0341411792,0.0545125641,0.5859817266,0.0462901928,0.0990734473,-0.0517431013,-0.3171615601,0.0166788325,-0.1142725125,0.2951973379,0.1128331125,-0.2925976515,-0.0322293863,0.0540716574,0.1180363968,-0.1114925593,0.2614135742,0.1112518013,0.0232048314,0.0792037472,0.074082613,0.3704974949,-0.1423626393,-0.1394184828,-0.2519687116,0.1947321892,-0.2114014775,-0.0909537897,0.0903237984,0.3076283634,-0.0141470851,-0.0164915174,-0.0900452584,0.3420787156,-0.197724387,0.3473968506,0.0661397278,0.1199117377,0.031651888,-0.1870265901,0.1046212167,0.0204780828,0.105019249,0.1983437836,-0.1874218136,0.4752044082,0.0901664048,-0.0594587885,0.093907468,0.0329540819,0.1509225816,-0.23204647,-0.3025615215,0.0127726402,0.387075305,0.1158940941,-0.3680178821,-0.3732737005,-0.3721951842,-0.0663403645,0.156610027,0.0056959055,0.217738837,0.1688701808,-0.0229636282,-0.244408235,0.2332585305,-0.0959547982,0.312954396,0.333904177,-0.088454403,0.0366148315,-0.3495225012,0.0381885469,0.1847692132,0.2104589939,-0.0037193808,0.181754753,0.4397560954,-0.033547584,-0.4896511137,-0.4122570157,-0.2240456194,0.048277352,-0.2493998855,-0.0127507159,-0.061692778,-0.14514485,-0.0550506599,0.0944197103,0.0958611444,-0.3651608229,0.2836418748,0.3310318589,-0.2519614398,0.1996840537,-0.0112702306,0.6269270778,-0.0407500565,0.1283237338,-0.2383580953,-0.0503174812,-0.2411791235,0.2618356347,-0.241127938,0.0310927499,0.4830776155,-0.1881505549,0.2633421421,0.0512588657,-0.6950424314,0.0192280635,-0.1217725649,-0.0672427341,0.1074874252,-0.3113571405,-0.1011445522,-0.0694991052,0.0436006822,-0.6364917755,-0.3448207676,-0.0135144899,-0.2336722165,0.166044116,-0.4094178975,-0.4348336458,-0.1046345457,-0.3718426228,-0.1279685348,0.0990161672,0.1822295785,0.3901272416,-0.0058501712,0.1749867499,0.0610397719,0.0588261858,-0.3134266734,0.179340139,0.4206720293,-0.4030599892,-0.5246385932,0.0087475618,-0.0924663991,0.3225848973,0.0916909128,-0.1163844243,-0.3931524158,0.171018675,0.0336882435,-0.1324877441,0.270876646,0.4044108391,-0.0321612321,-0.210416168,-0.1765942127,-0.2458321452,0.1508519351,0.4518745542,0.1313406825,-0.2489530593,0.2793438137,0.0862229019,0.1133267358,0.1045460254,-0.1675508916,0.2823275328,-0.0276979227,0.2028851956,-0.00161795,-0.000430525,0.0732810274,0.0729724988,0.2253548503,0.544148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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/163","title":"[Feature request] Add cos-e v1.0","comments":"@mariamabarham Just wanted to note that default behavior `cos_e = load_dataset(\"cos_e\")` now loads `v1.0`. Not sure if this is intentional (but the flag specification does work as intended). ","body":"I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf).","comment_length":28,"text":"[Feature request] Add cos-e v1.0 \n I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf). \n @mariamabarham Just wanted to note that default behavior `cos_e = load_dataset(\"cos_e\")` now loads `v1.0`. Not sure if this is intentional (but the flag specification does work as intended). 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/163","title":"[Feature request] Add cos-e v1.0","comments":"> @mariamabarham Just wanted to note that default behavior `cos_e = load_dataset(\"cos_e\")` now loads `v1.0`. Not sure if this is intentional (but the flag specification does work as intended).\r\n\r\nIn the new version of `nlp`, if you try `cos_e = load_dataset(\"cos_e\")` it throws this error:\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['v1.0', 'v1.11']\r\nExample of usage:\r\n\t`load_dataset('cos_e', 'v1.0')`\r\n```\r\nFor datasets with at least two configurations, we now force the user to pick one (no default)","body":"I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf).","comment_length":83,"text":"[Feature request] Add cos-e v1.0 \n I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https:\/\/www.aclweb.org\/anthology\/P19-1487\/), and v1.11 has noted [annotation](https:\/\/github.com\/salesforce\/cos-e\/issues\/2) [issues](https:\/\/arxiv.org\/pdf\/2004.14546.pdf). \n > @mariamabarham Just wanted to note that default behavior `cos_e = load_dataset(\"cos_e\")` now loads `v1.0`. Not sure if this is intentional (but the flag specification does work as intended).\r\n\r\nIn the new version of `nlp`, if you try `cos_e = load_dataset(\"cos_e\")` it throws this error:\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['v1.0', 'v1.11']\r\nExample of usage:\r\n\t`load_dataset('cos_e', 'v1.0')`\r\n```\r\nFor datasets with at least two configurations, we now force the user to pick one (no default)","embeddings":[-0.1603744179,-0.1905596256,-0.0409298912,-0.5251531005,-0.5301490426,-0.2091349959,0.0791700259,0.3027105331,-0.084867388,0.2993151248,0.0913367122,0.5883413553,-0.127389133,0.3007558584,0.1448273063,0.1922949404,0.0493811816,0.3573561311,0.0160913859,-0.1463334262,-0.2847516239,0.2668954134,-0.1590034366,0.022066094,0.1762136072,0.1823166907,-0.321796298,-0.02632479,-0.0610385649,-0.265681982,0.433855027,0.296884656,0.2419633865,-0.1544307172,-0.0001130699,-0.2487540096,0.5088639259,-0.0622031353,-0.4297463298,-0.3075879216,-0.3339643478,-0.5184953809,0.1690254658,-0.0987784192,0.1858433932,0.1506469548,0.3687634766,-0.0325421356,-0.0012714587,0.1081603914,0.2273946702,0.2823914587,-0.0840936154,-0.2982402146,0.1130262315,0.3750306368,-0.3086328804,-0.1634405702,-0.0422804318,-0.2713531256,-0.0524476096,0.3673297465,0.2016812265,-0.2252693772,-0.1958558112,-0.0478476547,0.1452473402,0.0442856513,0.0785973147,0.094222784,0.3499735892,0.0466120131,-0.2632308006,0.0423653424,0.0768405125,-0.5963779688,0.2815056145,-0.0695537999,-0.1712331176,-0.0754080042,0.2659721375,-0.2741574049,-0.4252688289,0.3804299533,-0.0608034842,0.2607444823,-0.0665872693,-0.0678771287,0.2350341678,0.0808953643,-0.1046843082,0.0276291668,-0.1441478878,0.2545187473,-0.4161166251,-0.1421226412,0.4012795389,0.0414198898,0.2769635022,0.1498364657,0.2790861428,0.0128509672,-0.0514417291,0.1853981018,0.0855170265,0.3146654069,0.6022409201,0.0287071243,0.2610608339,0.0919452533,0.5128621459,0.1002445891,0.0753753558,0.0205947272,-0.1638908088,-0.1278833449,0.1113580614,-0.38129282,-0.122656323,-0.0945472941,-0.0938417986,0.0630017295,0.0248092152,0.1908301115,0.2591687739,0.2565684021,0.3442850113,-0.0727608204,-0.1324234158,-0.3846977055,-0.2491017729,-0.0533332527,-0.3931257427,0.2702561021,0.3587175608,-0.23135297,0.2805841267,-0.4026278555,0.1932352036,0.0167675111,-0.2188313454,0.3100357056,-0.1656469554,0.0798900947,0.0468821861,0.0137233566,-0.3002426326,0.2614299953,-0.2551450133,0.095273219,-0.2514296174,-0.5217930675,-0.2898740172,0.2131017447,-0.2102160007,-0.3397215307,-0.1648019254,0.4888764322,-0.2243465483,-0.2618386745,-0.0197754744,0.0740577132,-0.3711227477,-0.1326876432,-0.0844178274,0.1536420733,0.0322751924,-0.2395110726,-0.6589326262,0.0237019323,0.0504046939,-0.1060368195,-0.1056155264,-0.2657812536,-0.1496505737,-0.059896972,0.7229759693,-0.4373762608,-0.1154996455,0.2689371109,-0.0676072016,-0.2052620798,0.1048451215,-0.1371217072,-0.2300135046,-0.1585985273,-0.1731006354,0.2563079596,-0.0513107441,-0.1048711464,-0.0849446282,-0.3723749518,-0.0840224028,0.2475238144,0.2598854303,0.0102373781,0.2654013932,-0.1716258377,0.2031891048,-0.0694108307,0.148471877,0.0382137522,0.4409762621,0.0429674685,0.0998889282,0.1025070101,-0.5050923824,0.0785629451,-0.0480642281,0.2747194171,0.3230306506,-0.2149771154,-0.0498168804,-0.0513810441,0.0627841502,-0.2429581732,0.1295619309,0.291669488,-0.0453818031,-0.027404394,-0.0881026238,0.4771677554,-0.2146998197,-0.1701603234,-0.225007683,0.0794707313,-0.1317952126,-0.038429413,0.0011605707,0.3066433072,-0.0057504578,0.031460952,-0.085896194,0.3410401046,-0.1707220674,0.2327118963,0.0020118935,0.0517823882,0.1553287953,-0.1710367948,0.0714992061,-0.0387482047,-0.0591474883,0.1741671711,-0.235874936,0.408785224,0.0555513836,-0.0049423906,0.0156648513,-0.025185002,0.0245814025,-0.2130450159,-0.5066873431,-0.1608449966,0.3907119632,0.0903762951,-0.41927737,-0.2182733417,-0.4219491482,-0.0539113581,0.2659189105,0.0109217949,0.0686053559,0.3461208642,0.1989822388,-0.2583040595,0.287675947,-0.0337567888,0.5213515162,0.3559094965,-0.1437416971,0.0553794093,-0.497071892,-0.0343345255,0.1937440932,0.2227172703,0.1649831682,0.0345131382,0.4799375534,0.0247187279,-0.4151760042,-0.4975746572,0.0300820656,-0.066778481,-0.170993045,-0.0767860264,-0.2356609255,-0.2485540956,-0.1343775839,0.1527370065,-0.0066434303,-0.2822704613,0.2530551255,0.2228182256,-0.0968491957,0.2704641521,-0.0881978497,0.7165158987,-0.1732389033,0.1325025558,-0.2111668289,-0.0197338276,-0.4386223555,0.1435934305,-0.3673205376,-0.1281872392,0.4306366146,0.0337064676,0.2416912466,0.1017245874,-0.6566215158,0.0737065673,-0.1674202383,-0.0849752128,0.0753148273,-0.3813074827,-0.1796533912,-0.0971656218,0.0917324945,-0.3468784392,-0.3567785919,-0.0608406849,-0.1778292805,0.1936436445,-0.386149019,-0.5043295026,-0.0028922216,-0.3854027689,-0.0603320077,-0.0328723416,0.2645630836,0.7287469506,-0.0292721316,0.0609108508,-0.0188146252,0.0539790057,-0.3222586513,0.4111197591,0.2417619824,-0.3663308024,-0.3256415725,-0.0940812826,-0.0090113189,0.275954932,0.137724191,-0.200719595,-0.3760524988,0.1728867441,0.0956325606,-0.1895506084,0.1695750803,0.4947325289,0.0356851928,-0.1434897184,-0.1199136004,-0.3978121579,0.2254878879,0.5513763428,0.3134689331,-0.0997380987,0.2379786521,0.1289057434,0.0546539351,0.1860589534,-0.031547334,0.2133136988,0.0751072168,0.3027555943,-0.0681923628,-0.0488389358,0.186204493,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"usually you can replace `download` in your dataset script with `download_and_prepare()` - could you share the code for your dataset here? :-) ","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":22,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n usually you can replace `download` in your dataset script with `download_and_prepare()` - could you share the code for your dataset here? :-) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"I have an initial version here: https:\/\/github.com\/EntilZha\/nlp\/tree\/master\/datasets\/qanta Thats pretty close to what I'll do as a PR, but still want to do some more sanity checks\/tests (just got tests passing).\r\n\r\nI figured out how to get all tests passing by adding a download command and some finagling with the data zip https:\/\/github.com\/EntilZha\/nlp\/blob\/master\/tests\/utils.py#L127\r\n\r\n","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":52,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n I have an initial version here: https:\/\/github.com\/EntilZha\/nlp\/tree\/master\/datasets\/qanta Thats pretty close to what I'll do as a PR, but still want to do some more sanity checks\/tests (just got tests passing).\r\n\r\nI figured out how to get all tests passing by adding a download command and some finagling with the data zip https:\/\/github.com\/EntilZha\/nlp\/blob\/master\/tests\/utils.py#L127\r\n\r\n","embeddings":[-0.1962168664,0.4000370204,-0.0314685591,-0.1644130796,0.0442840867,-0.133292973,0.2794850469,0.376173526,0.0094886748,0.0829261169,0.2064323872,0.1580682844,-0.3005688488,0.0680410042,0.1369243413,0.0012009284,-0.0564629063,0.1698959321,-0.097953327,0.2125060409,-0.1272202581,0.0748247653,0.0204572827,0.1080618128,-0.0279993936,0.0887562558,-0.2158944607,0.0343908481,-0.3809469342,-0.78580755,0.3818258643,0.0705721527,0.1851999313,0.3587058783,-0.0001226896,-0.0862562507,0.5880787969,-0.0560768247,-0.7038213611,-0.2316204906,0.2454068959,-0.4804557264,0.3018535376,-0.0215079132,0.1885171235,-0.131638214,0.2442083061,0.1043512151,0.1617511511,0.3224481046,0.1187366098,0.2776325345,0.0514680147,-0.1797240227,-0.1009447202,0.0860042721,-0.0892682076,0.5028808713,0.3648626804,0.0311197303,-0.1123634428,-0.1311188638,0.0922635347,0.4931352437,0.1250221133,-0.0222941581,0.5011738539,0.0191125106,-0.2128935456,0.0410488024,0.7716670036,-0.5064089894,-0.3754853308,-0.0786100179,0.0564953834,-0.1375145763,0.2607557178,-0.1600877792,-0.3946450949,0.0198199078,-0.1663755476,-0.280372113,-0.067122072,0.2935138643,-0.2830935717,0.2682918012,0.0777481496,0.1755786836,0.0895025879,-0.1008501872,0.2573432922,-0.0618371181,0.0244054813,0.2348817885,-0.0302161761,-0.2238747627,-0.2610195279,-0.2221421152,0.1922143996,0.087184079,-0.462625742,-0.1906755567,-0.0312074535,0.1495987922,0.14161475,0.4171294868,0.3384237289,0.238900423,0.2432880104,0.1627921462,0.2434393466,0.2634249628,-0.0598132797,-0.3704581857,-0.0324971564,0.0922335014,0.148252517,-0.3287043869,-0.1533401161,-0.2270715833,-0.3853175938,-0.0463141352,-0.0549251363,0.2376481295,-0.1088894606,-0.1465936154,0.0490113646,0.4120434821,-0.2063009888,-0.1511349827,-0.0294886027,0.0445264392,-0.1915404499,-0.0126141282,0.3086444139,0.0557580702,0.3780356348,-0.3671215475,-0.1176601425,-0.0343115553,0.1131467894,0.1852356195,-0.2689662874,0.2899587452,-0.1555463374,0.0520323552,0.0785048008,-0.2927011251,-0.2210675478,0.16356498,0.1203946844,-0.456910491,0.0378944427,0.0930617675,-0.6060150266,-0.064664647,0.0127269747,-0.1157290488,0.0913880691,-0.3326220512,0.026576288,-0.3913691044,-0.2052655965,-0.0672703683,-0.0997263268,0.2391047776,-0.266743809,-0.3372322023,-0.4481431246,-0.3259230256,0.1292015463,-0.2248050272,-0.0771240368,0.2149810642,-0.3794620037,-0.0085159829,0.549783051,-0.5216640234,-0.3586078286,0.4454957545,-0.4292176366,-0.2467299998,0.2341967672,-0.0489816442,-0.0027357105,-0.3621473014,-0.0894957483,0.0822743401,0.0496208407,-0.1781890541,-0.1884840727,-0.3439170718,0.1136098728,0.2200979888,-0.0398818441,0.088763535,0.0967642218,0.0540974848,0.3513938189,0.1143978909,0.1550448537,-0.1440186054,0.1771669984,-0.2378764302,-0.171195209,-0.0517720506,-0.5992661119,0.2523061931,-0.1184304655,0.2130238414,-0.046452675,-0.01118204,-0.5071431398,0.0033061588,0.0198404863,-0.2721042633,0.0020702777,0.2044220567,0.1296175867,-0.0479584932,-0.0106063243,0.0322238915,-0.2107295841,0.192533195,-0.0098091802,-0.0202862602,-0.0461895429,0.0624085069,0.2495948821,0.2555881739,0.0547183454,-0.3409820795,0.0079531223,0.2820033729,0.2083241642,0.1894648671,0.1891921461,-0.0701106638,0.1703492254,0.2639250159,0.1645806283,0.2629550397,-0.0151816439,-0.2123049349,-0.0861642584,0.244685933,0.168041423,0.0441920497,-0.1283625215,0.0657141581,0.3238113225,0.1138124764,-0.1348553449,-0.2280585468,0.1960445642,0.2852371633,0.6019870639,-0.0504562259,0.3259406686,-0.0479220226,-0.2781182826,-0.3706560135,0.0179368667,0.113899745,-0.0962130725,-0.0562753715,0.0941435024,0.3609081209,0.8490371704,-0.039258536,0.2709162831,0.0866793543,-0.1844285578,-0.2316581011,0.1509413719,0.1144368127,-0.0264656991,0.1556097716,0.0009208352,-0.4011897147,-0.089022167,-0.000131221,0.4331214428,0.39993155,-0.3471736908,-0.1098056212,-0.3021007478,-0.4445498586,-0.3985895216,-0.3630511463,-0.2080827355,-0.3813646138,-0.0614553764,0.221244067,-0.0000809908,0.2355280966,-0.1949504614,0.2832934558,-0.3094020486,-0.3343959749,0.1164741814,-0.2011949271,-0.4004581273,0.038076669,0.2813215256,0.0704870895,0.4426928461,-0.1078199968,-0.2973609269,-0.2132588625,-0.3188506663,0.1328554153,0.0609576516,0.5699258447,0.4761799574,-0.014884566,0.4204102755,0.0788300261,0.0420439132,-0.2335481942,-0.4335296154,-0.2341788411,0.0918297097,0.096914418,-0.1396645457,-0.397292465,-0.5266936421,-0.0855906904,0.1703432649,0.1270349622,0.0732077435,0.0939834714,-0.1114279777,0.2511834502,0.0868324786,-0.1874495,-0.1168527007,0.0710896701,-0.0090840636,-0.1429180652,-0.1871635616,0.066673696,-0.1160890758,0.0868185386,-0.1572062522,-0.4886566997,-0.4423266053,0.1542490423,0.2309943885,0.3925921023,-0.2067830712,0.2817326486,0.2018010765,0.1231473163,-0.1095167696,-0.3401258588,0.1602251381,0.0937714949,0.311019212,0.1370342374,0.3423713446,-0.117230013,0.7626640201,0.1187916175,-0.0941151008,-0.0333751999,-0.1344078928,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"I'm quite positive that you can just replace the `dl_manager.download()` statements here: https:\/\/github.com\/EntilZha\/nlp\/blob\/4d46443b65f1f756921db8275594e6af008a1de7\/datasets\/qanta\/qanta.py#L194 with `dl_manager.download_and_extract()` even though you don't extract anything. I would prefer to avoid adding more functions to the MockDataLoadManager and keep it as simple as possible (It's already to complex now IMO). \r\n\r\nCould you check if you can replace the `download()` function? ","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":55,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n I'm quite positive that you can just replace the `dl_manager.download()` statements here: https:\/\/github.com\/EntilZha\/nlp\/blob\/4d46443b65f1f756921db8275594e6af008a1de7\/datasets\/qanta\/qanta.py#L194 with `dl_manager.download_and_extract()` even though you don't extract anything. I would prefer to avoid adding more functions to the MockDataLoadManager and keep it as simple as possible (It's already to complex now IMO). \r\n\r\nCould you check if you can replace the `download()` function? ","embeddings":[-0.1008703038,0.502432704,-0.0397975706,-0.0897842571,0.0968036205,-0.122407198,0.2872873247,0.3135527372,-0.0543848462,0.1157420203,0.046182245,0.1649335027,-0.2744551301,-0.0059635905,0.1659567803,-0.0370165035,-0.0780216157,0.2551133931,-0.0911455303,0.2149973661,-0.2306919545,0.1244610325,-0.0709374547,0.0745792389,0.136530906,0.1996980011,-0.1658058912,0.0543188453,-0.3160357475,-0.8764674664,0.3479210436,0.1809952855,0.2423820496,0.238788411,-0.0001248056,-0.1538030356,0.5954886079,-0.1265063286,-0.6724078059,-0.1728314757,0.2416220158,-0.4436886013,0.2643420398,-0.0716193542,0.2790195942,-0.1683751345,0.1821474433,0.1102833897,0.0740535781,0.3863360286,0.1194621101,0.2407578826,0.0858564675,-0.1201937124,-0.0584492311,0.0240839645,-0.0008228987,0.6254271865,0.40664801,0.0214351378,0.021082202,-0.0100838691,-0.0617214106,0.4501312375,0.2495122999,-0.0478202999,0.4997144639,-0.0700687692,-0.1631965488,-0.0136147235,0.6971593499,-0.5866323709,-0.3620384037,-0.0547511168,0.1543341428,-0.2042603791,0.1349426657,-0.2809634805,-0.4394977689,0.0580523983,-0.1019300595,-0.5488113761,0.0178486109,0.2542369962,-0.3011981547,0.2867898345,0.113982752,0.1454486102,0.2424277663,-0.0527879111,0.2136763483,-0.0221389402,-0.0824553668,0.1698232442,0.0019520939,-0.3044983447,-0.3149802387,-0.2155281156,0.0969157442,0.1349225342,-0.3737144768,-0.1438041925,-0.0563400127,0.1341730803,0.0918924063,0.4165343642,0.3381826282,0.220636338,0.1082044318,0.2282307893,0.2354674041,0.1206872538,0.0386737771,-0.361107856,0.0198340695,0.19144997,0.134788096,-0.1944695115,-0.0890687183,-0.2402393818,-0.4562417865,-0.0577771999,-0.1779349297,0.2095633745,-0.0875371099,-0.0898265392,0.1073288992,0.4088947475,-0.1238566339,-0.2714741826,0.0032230511,0.1967519969,-0.0980255678,0.1111704409,0.2187103182,0.0249425564,0.3383955359,-0.3740005493,-0.0343965143,-0.0379927233,0.1008777991,-0.01392633,-0.1752636433,0.297956109,-0.1332934499,0.0354545861,0.0203104541,-0.3518683016,-0.2085856944,0.2470930368,0.2287328094,-0.457187593,-0.0947381333,0.0599102452,-0.500505805,-0.0223911442,-0.0530996174,-0.0767954215,-0.0751673281,-0.5091549754,0.1566996872,-0.3630222678,-0.289362967,-0.0502463989,-0.0594202727,0.3765130639,-0.2985684574,-0.2998124659,-0.5405735373,-0.3079764843,0.1497205496,-0.2449992448,-0.164001286,0.2662369907,-0.3465722799,0.043164555,0.667219758,-0.6826146841,-0.3817332983,0.3879492581,-0.2968750894,-0.2016317993,0.1372314394,-0.0817428753,0.0714176744,-0.3140597939,-0.2028535903,-0.0059267748,0.091598101,-0.2129977643,-0.1380927116,-0.4739530683,0.1392556578,0.2211888134,0.000785049,0.2456368357,0.0335898325,0.1470759213,0.3634047806,0.1003377736,0.1316768378,-0.1588163972,0.2499465644,-0.188180238,-0.1837327778,-0.0881156474,-0.5891324878,0.2165798545,-0.1606362164,0.2297133803,-0.0706375539,-0.0018133485,-0.4160644412,-0.0398944467,0.0938376635,-0.2353090942,-0.025887005,0.1969702244,0.1955227703,0.0044661476,-0.0157303475,0.0643759146,-0.1620636135,0.2334327549,0.0740081891,-0.0516507477,-0.0220283866,0.1428957134,0.2040299326,0.2436191887,0.0309016332,-0.2502624691,-0.0366772115,0.3134894073,0.14573057,0.2744227052,0.1060874239,-0.040288914,0.1848415285,0.2803111076,0.1604785472,0.2589093149,0.1125444993,-0.1240083873,0.0815058127,0.1140125692,0.3063770533,0.0369060151,-0.1014102846,0.0488382876,0.2526002526,0.0661955997,-0.1218185648,-0.3277988434,-0.0246300884,0.2636627555,0.6258199811,-0.0983051136,0.3067036271,-0.1081027836,-0.2765682042,-0.3729353547,0.027126193,0.1354558617,-0.2431386113,-0.0593481697,0.0059099658,0.4128134847,0.8891434669,-0.0876502767,0.2127116024,0.2576935589,0.0994640961,-0.1476352811,0.3102177083,0.2731757164,0.036704842,0.1560715735,0.0172955096,-0.2442479581,-0.032426957,-0.0920960009,0.3309157193,0.3591976166,-0.3003346622,-0.1063411608,-0.3063665032,-0.5244471431,-0.369181931,-0.3438985646,-0.2026222795,-0.3367348611,-0.1670247614,0.2561099529,-0.0168017894,0.2510785758,-0.1769596338,0.23437576,-0.3581812084,-0.2618915737,0.0140888477,-0.0698123351,-0.2865777612,0.0055031381,0.2328591645,0.1618770957,0.2496272922,-0.2545626462,-0.2123430222,-0.3464898169,-0.1836167276,0.1987740993,0.2477274835,0.4137883484,0.5279138684,-0.0702143088,0.3567334414,0.0594498403,-0.0660828054,-0.1920313388,-0.3716994226,-0.3034237325,-0.0005273036,0.2784799933,-0.0836251453,-0.3137311041,-0.6029018164,-0.1412212849,0.0342323072,0.0753482878,0.1252205074,0.0305907503,-0.2410265803,0.2264879048,-0.0675989687,-0.2334192097,-0.20004794,0.1599311978,-0.0091451891,-0.1631018668,-0.0454618149,0.0740075111,-0.1086775586,0.0565040857,-0.2311774343,-0.439026922,-0.4993732572,0.2032101899,-0.0114732869,0.1493686587,-0.1808473319,0.2266940176,0.2446268052,0.1441554874,0.0077745435,-0.4050812125,0.1296161264,0.1733418554,0.259601444,0.2025204897,0.4707221985,-0.1895778328,0.6809964776,0.0801900625,-0.0654067695,-0.0069155768,-0.1938064098,0.1298341453,0.091098316,-0.0736017227,0.2188321948,-0.190089196,-0.272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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"I might be doing something wrong, but swapping those two gives this error:\r\n```\r\n>       with open(path) as f:\r\nE       IsADirectoryError: [Errno 21] Is a directory: 'datasets\/qanta\/dummy\/mode=first,char_skip=25\/2018.4.18\/dummy_data-zip-extracted\/dummy_data'\r\n\r\nsrc\/nlp\/datasets\/qanta\/3d965403133687b819905ead4b69af7bcee365865279b2f797c79f809b4490c3\/qanta.py:280: IsADirectoryError\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n```\r\n\r\nSo it seems like the directory name is getting passed. Is this not functioning as expected, or is there some caching happening maybe? I deleted the dummy files and re-ran the import script with no changes. I'm digging a bit in with a debugger, but no clear reason yet","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":88,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n I might be doing something wrong, but swapping those two gives this error:\r\n```\r\n>       with open(path) as f:\r\nE       IsADirectoryError: [Errno 21] Is a directory: 'datasets\/qanta\/dummy\/mode=first,char_skip=25\/2018.4.18\/dummy_data-zip-extracted\/dummy_data'\r\n\r\nsrc\/nlp\/datasets\/qanta\/3d965403133687b819905ead4b69af7bcee365865279b2f797c79f809b4490c3\/qanta.py:280: IsADirectoryError\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n```\r\n\r\nSo it seems like the directory name is getting passed. Is this not functioning as expected, or is there some caching happening maybe? I deleted the dummy files and re-ran the import script with no changes. I'm digging a bit in with a debugger, but no clear reason 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"From what I can tell here: https:\/\/github.com\/huggingface\/nlp\/blob\/master\/tests\/utils.py#L115\r\n\r\n1. `data_url` is the correct http link\r\n2. `path_to_dummy_data` is a directory, which is causing the issue\r\n\r\nThat path comes from `download_dummy_data`, which I think assumes that the data comes from the zip file, but isn't aware of individual files. So it seems like it data manager needs to be aware if the url its getting is for a file or a zip\/directory, and pass this information along. This might happen in `download_dummy_data`, but probably better to happen in `download_and_extract`? Maybe a simple check to see if `os.path.basename` returns the dummy data zip filename, if not then join paths with the basename of the url?","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":112,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n From what I can tell here: https:\/\/github.com\/huggingface\/nlp\/blob\/master\/tests\/utils.py#L115\r\n\r\n1. `data_url` is the correct http link\r\n2. `path_to_dummy_data` is a directory, which is causing the issue\r\n\r\nThat path comes from `download_dummy_data`, which I think assumes that the data comes from the zip file, but isn't aware of individual files. So it seems like it data manager needs to be aware if the url its getting is for a file or a zip\/directory, and pass this information along. This might happen in `download_dummy_data`, but probably better to happen in `download_and_extract`? Maybe a simple check to see if `os.path.basename` returns the dummy data zip filename, if not then join paths with the basename of the url?","embeddings":[-0.0789025053,0.2855144143,0.0002484272,0.052754432,0.0506349877,-0.260754168,0.2143824548,0.2759754658,-0.054033909,0.0801737085,0.207228303,0.223573193,-0.2214686573,-0.0345815979,0.1244285852,0.0544766523,-0.1065842211,0.0847872198,-0.2250418067,0.155033648,-0.2493224442,0.1753319949,0.0034214412,0.1672303677,0.0154142119,0.1461061686,-0.2237707675,0.2018211782,-0.4194307923,-0.7930223346,0.3121376932,0.0302134454,0.2352778167,0.3840055466,-0.0001236156,-0.0918525681,0.589799881,-0.1559888422,-0.7334056497,-0.4215240777,0.2433728427,-0.5072749257,0.2773010731,-0.1551025063,0.185713321,-0.0634940639,0.2981808186,0.2096097767,0.1883738339,0.4003967941,0.0724132881,0.3003302813,0.0519982874,-0.1126803607,0.0085209534,0.1745276898,-0.0521616563,0.587381959,0.2025229931,0.0743161216,-0.1562705785,-0.0790689439,0.1053197011,0.5026990175,0.1522777379,0.0629803464,0.3990126252,0.0465288758,-0.2675873637,0.0866298825,0.6675570607,-0.5247456431,-0.3587066829,-0.0709844455,-0.0790122375,-0.0574314632,0.3807904422,-0.1414193809,-0.412658751,0.09209878,-0.1064498127,-0.1712896377,-0.0319949836,0.3967043757,-0.2983646691,0.4051716924,-0.0184657462,0.1622784734,0.1562570482,0.059117496,0.2566516697,-0.1750605255,0.1734549403,0.1806305349,0.1060590521,-0.2094244361,-0.2895837724,-0.2742270827,0.1452919543,0.1114526391,-0.3082630634,-0.1924095154,-0.0626133084,0.2252860814,0.2307136059,0.324116379,0.1796365976,0.269238323,0.2589327395,0.3053899407,0.2700368166,0.1619523466,0.0072083711,-0.3309308589,-0.121018663,0.1566589326,0.204818055,-0.212076515,-0.1972972006,-0.2485962361,-0.4048675597,-0.1265592724,-0.0687749311,0.311609596,-0.1900963336,-0.1030601785,0.092429176,0.3832482696,-0.1280384213,-0.0707097128,0.0286854729,0.04314382,-0.2482326329,0.0286369994,0.2963480651,0.2202127427,0.4854156077,-0.2904739082,-0.1611180604,-0.0584542193,0.1296471208,0.0434607528,-0.0472125523,0.2191486508,-0.0758894384,0.1827891171,0.0915671811,-0.3037326634,-0.299829036,0.1725249887,-0.0722899139,-0.4023593962,0.1072350442,0.0654562116,-0.5473223329,0.0217342135,0.0958953723,-0.1695723236,-0.0575396009,-0.3571426868,0.04796464,-0.2368825972,-0.2540795207,-0.1595515907,-0.0131576732,0.3331227899,-0.1687354594,-0.3116174638,-0.4004590511,-0.2978602946,0.2116440386,-0.1955409944,-0.1178338006,0.2728709877,-0.4967008233,0.0652605966,0.6163495183,-0.4722355604,-0.2997187078,0.5806676149,-0.4525257647,-0.0682009533,0.2638137341,-0.0369549468,-0.2212249935,-0.2977394462,-0.084492892,0.1349065602,0.0260605756,-0.1500234604,-0.1978701055,-0.3536165655,0.1459461004,0.2388371378,-0.0879810899,0.092689544,0.0530852154,-0.070395194,0.4528979957,0.106686607,0.0637685955,-0.0611375459,0.1207884699,-0.1968197525,-0.1071662679,0.0261884611,-0.5845915079,0.1251316816,-0.3148132861,0.1976306736,-0.1885021478,-0.0742158294,-0.5055440068,-0.018279003,0.0228491519,-0.2564983964,0.0097338939,0.3550091982,0.0908067226,-0.0491765253,0.0322510339,0.0155851385,-0.2026489973,0.2942970097,-0.0109322155,0.0641305745,-0.0877616107,0.1153794304,0.2468134761,0.25044626,0.1680199355,-0.4039673805,0.0306897704,0.3176622689,0.1910518259,0.1839980334,0.201267451,-0.0668875054,0.1683950275,0.1911004186,0.0835079551,0.4694286585,0.0475594029,-0.1295868456,0.0458018295,0.2079724371,0.1549796015,-0.0021298323,-0.0742816553,0.0792869031,0.3448398411,0.0221743993,-0.1013446897,-0.254516542,0.1040521711,0.3349339366,0.6334264874,-0.0067082136,0.2735137343,-0.1097401306,-0.1404369324,-0.3109175265,0.0469735488,0.1404445171,-0.1851261407,-0.1090435013,0.000903753,0.3755458593,0.8084435463,-0.008677111,0.3378500342,0.0986356437,-0.1634431928,-0.23178491,0.246971488,0.1726222634,-0.091688782,0.1258708537,-0.1599158794,-0.3018528819,-0.1450392008,0.0199347269,0.4233738482,0.3181325495,-0.4547547996,-0.0568398125,-0.2974067032,-0.5187103748,-0.5051652193,-0.3165683746,-0.2091601044,-0.3562681377,-0.056082841,0.2834823728,-0.1309288889,0.1668241769,-0.1150368974,0.2502073944,-0.3294556737,-0.3059851527,0.0541405305,-0.0925322548,-0.4306950271,0.0013973453,0.4053305984,0.0908558294,0.2507201433,-0.2317565978,-0.2034363896,-0.262078166,-0.2770365477,0.1347783506,0.1804633737,0.5565587878,0.4462135732,-0.0005814062,0.4154549241,0.0234471709,0.021318879,-0.1771230251,-0.3594786823,-0.1665748209,0.1146169975,0.1163782179,-0.1748237312,-0.435265094,-0.5285280347,-0.0585300401,0.0292403344,0.0863750428,0.1540934891,0.09188582,-0.1715522707,0.1888977438,0.042821262,-0.1449873298,-0.1551212221,-0.0749296099,0.0263330638,-0.1407961845,-0.1739190817,0.0086749513,-0.2095603198,0.0579381362,-0.1622326523,-0.4062244296,-0.5565926433,0.1409670264,0.1812706739,0.2534226179,-0.150748536,0.3773111403,0.182440564,0.0918047354,-0.1326707155,-0.3363351524,0.2459822148,0.1070011258,0.4016033113,0.2060058713,0.2957031429,-0.0263223983,0.7256447673,0.149187237,-0.0722948611,0.0998502597,-0.1860619634,0.1038331017,0.1562849879,0.0059008389,0.1845206171,-0.1581729054,-0.2670913637,0.3186191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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"I think the dataset script works correctly. Just the dummy data structure seems to be wrong. I will soon add more commands that should make the create of the dummy data easier.\r\n\r\nI'd recommend that you won't concentrate too much on the dummy data.\r\nIf you manage to load the dataset correctly via:\r\n\r\n```python \r\n# use local path to qanta\r\nnlp.load_dataset(\".\/datasets\/qanta\")\r\n```\r\n\r\nthen feel free to open a PR and we will look into the dummy data problem together :-) \r\n\r\nAlso please make sure that the Version is in the format 1.0.0 (three numbers separated by two points) - not a date. ","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":102,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n I think the dataset script works correctly. Just the dummy data structure seems to be wrong. I will soon add more commands that should make the create of the dummy data easier.\r\n\r\nI'd recommend that you won't concentrate too much on the dummy data.\r\nIf you manage to load the dataset correctly via:\r\n\r\n```python \r\n# use local path to qanta\r\nnlp.load_dataset(\".\/datasets\/qanta\")\r\n```\r\n\r\nthen feel free to open a PR and we will look into the dummy data problem together :-) \r\n\r\nAlso please make sure that the Version is in the format 1.0.0 (three numbers separated by two points) - not a date. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"The script loading seems to work fine so I'll work on getting a PR open after a few sanity checks on the data.\r\n\r\nOn version, we currently have it versioned with YYYY.MM.DD scheme so it would be nice to not change that, but will it cause issues?","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":47,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n The script loading seems to work fine so I'll work on getting a PR open after a few sanity checks on the data.\r\n\r\nOn version, we currently have it versioned with YYYY.MM.DD scheme so it would be nice to not change that, but will it cause issues?","embeddings":[-0.1767385751,0.2111754417,-0.0094802435,-0.1317930073,0.073443301,-0.1427535415,0.4176616967,0.2573601604,0.0499721952,0.1190561801,0.2233332545,0.069634974,-0.288746506,0.0840551481,0.0976942331,-0.0076815896,0.0639337674,0.1413368583,-0.1044421196,0.2076696455,-0.2553172708,-0.0057773921,-0.0539509095,0.1049623638,-0.0217238683,0.1230244637,-0.2095181495,0.1034018323,-0.436101228,-0.8527143598,0.3802421987,0.1069500297,0.2888356149,0.3949593008,-0.0001284344,-0.0574783869,0.5988144875,-0.1096532643,-0.6252249479,-0.1496018916,0.2785562873,-0.3912249804,0.3590856194,-0.0352212638,0.1500796974,-0.1874664128,0.2187322229,0.0418383554,0.1443508565,0.288277179,0.0764790773,0.2813302279,0.0681955814,-0.2176912427,-0.1155698746,0.0595381968,-0.0826354846,0.5680220127,0.4420571923,0.1645011157,-0.0479074344,-0.1347484142,0.0992561951,0.4558956921,0.1890973747,-0.0451924801,0.6421710253,0.017217312,-0.1417569667,-0.0005506419,0.9479646683,-0.5009502769,-0.3974162638,0.0502950549,-0.0116671054,-0.121365875,0.2301284969,-0.1927763224,-0.2900263965,0.0472511202,-0.1721949726,-0.3062464595,-0.1238460988,0.3003010154,-0.3025889993,0.3052990437,0.1033435538,0.2493603081,0.0188441053,-0.1182857454,0.2843585312,-0.0363516286,-0.0146416137,0.1863449365,0.0785246491,-0.2858282626,-0.3459248543,-0.2041811943,0.1131222174,-0.0412339792,-0.4233128428,-0.1887902319,-0.0349557586,0.176213488,0.2227994055,0.2793657184,0.3226895034,0.1964391321,0.2440313399,0.0767488107,0.3281917572,0.2670004368,-0.0798517466,-0.4030307531,0.0162077155,0.0597614497,0.2114082873,-0.3521575928,-0.0871662945,-0.2609820664,-0.3343007267,-0.0478125885,-0.1376745254,0.2543797791,-0.1169835031,-0.0950978771,0.0304058231,0.4238702059,-0.1529856771,-0.1591299325,-0.0107982727,0.0142800193,-0.1677898616,-0.0182297546,0.2101244032,0.0889372975,0.3157947659,-0.2774038315,-0.2017735839,0.0035693622,-0.015546673,0.0973913372,-0.1956209242,0.2981408238,-0.2475427687,-0.0211074036,0.0438069291,-0.2993895113,-0.2070538849,0.2779168189,0.0965090245,-0.4538351297,-0.0178777836,0.0486825742,-0.5866084099,-0.0063493485,-0.0185742844,-0.1163564175,0.1194530055,-0.3823113441,0.0136968838,-0.4451647103,-0.3795362413,-0.1074555367,-0.1562435031,0.2226886451,-0.3308186829,-0.2761642933,-0.3116811514,-0.3430005908,0.1597062796,-0.2424592674,-0.0967334658,0.0930971727,-0.3562758863,-0.2131888866,0.5165299773,-0.6141238809,-0.3168637156,0.479444325,-0.3933302164,-0.3192726672,0.2487607449,-0.0207883809,0.0985716134,-0.4370949566,-0.1215800643,-0.015840156,-0.0188480616,-0.1695326418,-0.2131860703,-0.343208313,0.1577182412,0.2769614756,-0.0671554729,0.1459979415,0.1615187675,0.135269165,0.3999652565,0.1997321099,0.1501396149,-0.1701677144,0.24233374,-0.2819080353,-0.034803208,-0.0649490952,-0.6823078394,0.2038476914,-0.0831387043,0.1682871282,0.0175625514,-0.0103570186,-0.509771347,-0.0043713702,0.0005460257,-0.1865508854,-0.0790807828,0.1180780455,0.1709702611,-0.060119167,-0.0290490147,0.0088177472,-0.2176761776,0.1949049234,0.0657253489,0.0218320377,-0.0467246063,0.0551206432,0.2055900395,0.2732928991,0.0578795038,-0.3460522294,-0.0076025217,0.2738026381,0.3753958642,0.2467864156,0.1457493305,0.0340562537,0.2007615715,0.2431774288,0.2701938748,0.2410458177,0.0138973389,-0.1885058433,0.0430782996,0.1935187876,0.165984109,-0.0397758484,-0.1623610109,-0.0281060059,0.273868978,0.1649010032,-0.1608117968,-0.3247186542,0.0163578633,0.321111232,0.6261168718,0.0213598311,0.2209395766,-0.1034023091,-0.2408190966,-0.368386507,-0.035853982,0.1324319243,-0.1466745883,-0.1312391907,0.0345725529,0.2909605801,0.8139572144,-0.0582411364,0.1461983919,0.0932113752,-0.086801596,-0.2109648883,0.1355714053,0.1335216016,0.0800888911,0.1352105886,0.0244504642,-0.3316611648,-0.0123217488,0.0506467633,0.4254483879,0.3760308027,-0.3850040436,-0.1450611502,-0.1898256391,-0.4163719714,-0.4648425579,-0.260025382,-0.2401769608,-0.3224987388,-0.1134426072,0.3259061873,0.0177899059,0.2050327212,-0.1692858189,0.2984496057,-0.2949590385,-0.2706933022,0.1276618838,-0.2065273076,-0.3765336573,-0.0059781531,0.2203705907,-0.0590680018,0.4229106009,-0.155665502,-0.3003937006,-0.1599166989,-0.3217740059,0.143232435,0.1649394631,0.5009924769,0.4258591533,-0.0190750472,0.4650248885,0.1807303876,0.0294762701,-0.1994131953,-0.4520103931,-0.2094367743,-0.0139306746,0.0920100883,-0.0975565389,-0.3627032936,-0.6268529296,-0.0494347513,0.0924643278,0.0464919582,0.086845383,0.07008598,-0.1743046492,0.1262971163,0.064890869,-0.1463151574,-0.139249593,0.1288548112,-0.0637990981,-0.1144398302,-0.2529369295,0.087097913,-0.0972301587,-0.0221045632,-0.0692762062,-0.4163517654,-0.4980790913,0.1860776693,0.1202047467,0.3934659064,-0.3054569364,0.351828903,0.2290000319,0.1961491853,-0.072484605,-0.3492940366,0.218824923,0.117685616,0.2108539343,0.3140124083,0.396840781,-0.1700614244,0.694856286,0.1315309554,-0.1743964106,-0.0818143189,-0.1846346408,0.0265189037,-0.071657151,0.0252696108,0.2892469764,-0.1218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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"> The script loading seems to work fine so I'll work on getting a PR open after a few sanity checks on the data.\r\n> \r\n> On version, we currently have it versioned with YYYY.MM.DD scheme so it would be nice to not change that, but will it cause issues?\r\n\r\nIt would cause issues for sure for the tests....not sure if it would also cause issues otherwise.\r\n\r\nI would prefer to keep the same version style as we have for other models. You could for example simply add version 1.0.0 and add a comment with the date you currently use for the versioning.\r\n\r\n What is your opinion regarding the version here @lhoestq @mariamabarham @thomwolf ? ","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":115,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n > The script loading seems to work fine so I'll work on getting a PR open after a few sanity checks on the data.\r\n> \r\n> On version, we currently have it versioned with YYYY.MM.DD scheme so it would be nice to not change that, but will it cause issues?\r\n\r\nIt would cause issues for sure for the tests....not sure if it would also cause issues otherwise.\r\n\r\nI would prefer to keep the same version style as we have for other models. You could for example simply add version 1.0.0 and add a comment with the date you currently use for the versioning.\r\n\r\n What is your opinion regarding the version here @lhoestq @mariamabarham @thomwolf ? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"Maybe use the YYYY.MM.DD as the config name ? That's what we are doing for wikipedia","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":16,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n Maybe use the YYYY.MM.DD as the config name ? That's what we are doing for 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"> Maybe use the YYYY.MM.DD as the config name ? That's what we are doing for wikipedia\r\n\r\nI'm not sure if this will work because the name should be unique and it seems that he has multiple config name in his data with the same version.\r\nAs @patrickvonplaten  suggested, I think  you can add a comment about the version in the data description.","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":63,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n > Maybe use the YYYY.MM.DD as the config name ? That's what we are doing for wikipedia\r\n\r\nI'm not sure if this will work because the name should be unique and it seems that he has multiple config name in his data with the same version.\r\nAs @patrickvonplaten  suggested, I think  you can add a comment about the version in the data description.","embeddings":[-0.0867200196,0.273745954,0.0112488763,-0.1407608241,0.06271144,-0.0481668338,0.3938752115,0.3804911971,0.089334771,0.2072925568,0.2584843934,0.1472849846,-0.2399412692,-0.015442811,0.0475122295,-0.0067649507,-0.0585154556,0.212169677,-0.0290324856,0.2738312185,-0.084899053,-0.0111241778,0.0752315372,0.0901787728,-0.0447208472,0.1121766046,-0.1227182001,-0.0627685487,-0.2637858689,-0.9248585701,0.3450054228,0.1243726015,0.1424257159,0.505398035,-0.000122348,-0.0531865545,0.6260417104,-0.1073716879,-0.6992914081,-0.1502024084,0.2308250368,-0.4619852304,0.2858994007,-0.1848255545,0.0345431305,-0.1320323795,0.2310450673,0.0503384918,0.0913198218,0.1461351812,0.1093361527,0.1425985098,0.1153796017,-0.1623488367,-0.1014972478,0.0278073363,-0.0967587978,0.5679475069,0.2544053793,0.1326054186,0.0163471717,-0.0107254805,0.0691266283,0.4541423321,0.130886063,-0.0550915748,0.5264000893,0.0546021387,-0.1274577975,0.1345887929,0.9771099687,-0.5941920877,-0.3401079178,0.0099459672,0.1404200494,-0.0033941194,0.2467773855,-0.1763136387,-0.3652087152,0.008559336,-0.0973320231,-0.2902490199,-0.1943590641,0.3475721776,-0.2534486353,0.424346745,0.1328440309,0.2170556635,-0.050475046,-0.2341308892,0.1444251835,-0.0475862585,0.0646230131,0.1204755977,0.0650520921,-0.3192999363,-0.4705287814,-0.328173697,0.112379916,-0.1774080396,-0.4003559053,-0.2411047369,0.0657076314,0.2026884854,0.1215652898,0.1992538273,0.3851887584,0.2224493027,0.2539424002,0.1447160691,0.1746027619,0.2078223377,0.0239006337,-0.4101084173,0.0025961222,0.030733617,0.2849099338,-0.2973404825,0.0172445662,-0.1251343787,-0.3631385267,-0.0263323933,-0.2165974975,0.3584257662,-0.2512477338,-0.2243993431,0.0818880796,0.3014504015,-0.1021782681,-0.0719931349,-0.0232946165,0.0245751254,-0.1246247068,0.1102535948,0.2417030931,0.0867036879,0.4142282903,-0.2295178771,-0.1562173814,-0.1250013113,-0.0037395095,0.2119347602,-0.236028567,0.3118693531,-0.1720710844,0.0903826058,0.0259262845,-0.4385855794,-0.3004496396,0.1951527148,0.0872920007,-0.3440077007,-0.1151545867,0.0968005285,-0.5595495701,-0.0811964795,0.1679898053,-0.1205598414,0.1339519322,-0.4694450796,0.036544919,-0.2592833936,-0.3031272888,-0.1586189866,-0.0653035566,0.2700704336,-0.3147053719,-0.3589433432,-0.3312329948,-0.207387194,0.0995778441,-0.3180844188,-0.0369836502,0.1321742237,-0.235939458,-0.074274011,0.5720569491,-0.5332984924,-0.3166052401,0.4164279103,-0.3450374603,-0.2490305007,0.2598500848,0.0241569262,0.1698991209,-0.2648550868,-0.1230879799,-0.0503988676,0.1220727861,-0.1038053483,-0.1910211146,-0.4137368798,0.0998167545,0.2717950046,-0.1085234359,0.1087996587,0.1280963123,0.190027833,0.4033350646,0.0872235,0.1264391541,-0.0844396129,0.1908574551,-0.0995471925,-0.155177623,-0.037927635,-0.6501656175,0.2827853262,-0.0225358084,0.2207427323,0.0293448586,-0.111420773,-0.594740808,-0.0711897612,-0.0147449775,-0.2298111916,0.0382472761,0.2451983094,0.040380951,0.0287419427,0.132302165,-0.0691986158,-0.1727848798,0.1583565623,-0.0575009398,-0.0359481126,-0.1088355333,0.1005447209,0.1413194686,0.1920615435,0.0882061794,-0.2097434551,-0.0082583241,0.1989328116,0.2664547861,0.2994746566,0.1711785495,-0.0397524312,0.1177480444,0.1686531454,0.2221972495,0.2807427347,0.0006243974,-0.2647777498,0.0124517307,0.1801750064,0.2809550166,-0.0212842952,-0.3174166083,-0.0282782242,0.319344759,0.0637189522,-0.1611047089,-0.2894610763,0.0456849746,0.3787927032,0.5581903458,-0.0528634191,0.1500062793,-0.2476996928,-0.2477092743,-0.3083853126,0.0146637214,0.0906416029,-0.1740710735,-0.0253096484,0.0588444695,0.2664115429,0.6372907758,0.0469010733,0.1413996965,0.0812915266,-0.2351587266,-0.1235398203,0.1703160852,0.1829021573,0.1264069676,0.1413461119,-0.0091574667,-0.2856316268,0.0274155401,-0.0330830887,0.4702055454,0.3248136342,-0.3375098705,-0.1011722386,-0.2301952243,-0.3934481442,-0.5377241969,-0.4357345104,-0.4151235223,-0.3019935489,-0.0380007587,0.2989151478,0.0214011818,0.3244083226,-0.2172331661,0.290810436,-0.2720581591,-0.4005092382,0.0599124655,-0.2860786915,-0.316165477,0.0083329827,0.2427163571,-0.0401453376,0.4774542153,-0.0952487439,-0.399439007,-0.2494901866,-0.3282950222,0.0896219239,0.0833929926,0.5332785845,0.4108942747,0.1148156673,0.5304839015,-0.0375305414,0.0177491456,-0.0744116679,-0.3561416566,-0.2321403772,-0.0141687626,0.2195192724,-0.0558517948,-0.3374412954,-0.5183429122,-0.1113724113,0.0803157538,0.0580617674,0.0783332139,0.2125877589,-0.0920212641,0.1044832841,0.0639614314,0.0126589676,-0.1317116618,0.1249209195,0.0439360775,-0.1699934155,-0.033919435,0.1276836991,-0.0689528733,0.048999235,-0.1415831447,-0.4356604815,-0.4000315368,0.1655731648,0.1715940833,0.3695299029,-0.2987922132,0.253908664,0.2139564604,0.1299117208,-0.1091062874,-0.4278604686,0.2279863507,0.1012911052,0.3282501698,0.2778740823,0.3687109649,-0.1983781159,0.8586021662,0.2069288045,-0.2106228769,-0.140954569,-0.2455222905,-0.0657978356,0.0359346904,0.0135255847,0.2188209742,-0.027356093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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"Actually maybe our versioning format (inherited from tfds) is too strong for what we use it for?\r\nWe could allow any string maybe?\r\n\r\nI see it more and more like an identifier for the user that we will back with a serious hashing\/versioning system.- so we could let the user quite free on it.","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":54,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n Actually maybe our versioning format (inherited from tfds) is too strong for what we use it for?\r\nWe could allow any string maybe?\r\n\r\nI see it more and more like an identifier for the user that we will back with a serious hashing\/versioning system.- so we could let the user quite free on it.","embeddings":[-0.1527510583,0.2105686069,0.0053558247,-0.1521225572,0.1695314944,-0.1229730174,0.3444485664,0.3772257268,0.0139800562,0.2296853364,0.1677791476,0.0152248899,-0.3342413306,0.0313071348,0.0228184462,-0.1002439708,-0.0594148934,0.1970065981,-0.07303565,0.3432954848,-0.060112685,-0.0731619373,0.0019713789,0.1934303194,0.0108176172,0.1458041072,-0.1153508872,-0.0187536702,-0.388517797,-0.8583580256,0.1343808174,0.0918508172,0.2320994288,0.4368851781,-0.0001261235,-0.0739656165,0.5866602063,-0.0240417346,-0.76744169,-0.1698298305,0.3446432054,-0.3756413162,0.2535519004,-0.0657789782,0.1137201115,-0.1838426143,0.1813541353,0.0753962398,0.1361462176,0.2455598861,0.0991945639,0.3106926978,0.028130766,-0.1111011356,-0.0595696643,0.12354514,-0.1438006163,0.5142219663,0.4895508885,0.2406701893,-0.078602612,-0.0708241239,0.062615484,0.4359807074,0.1371402293,-0.0326458737,0.5384781361,0.0442370549,-0.1900421828,0.1604462713,0.8945170641,-0.5086080432,-0.4655534029,-0.0676140115,0.0179396346,-0.1036736593,0.1076579317,-0.1938883811,-0.3774500787,0.208910659,-0.1669587791,-0.3321849406,-0.099039562,0.2889301181,-0.1762079895,0.2312367558,0.1270467192,0.2515535355,-0.0295838341,-0.0673802122,0.1549281627,-0.0520944595,0.0152693242,0.1340529621,0.0880432501,-0.3319088221,-0.4149749279,-0.2981059253,0.1995219588,0.0256361235,-0.5338360667,-0.1899030656,-0.0685549602,0.1762414128,0.0877644643,0.2744112909,0.3668691516,0.2205429822,0.3118174374,-0.0107747652,0.2858647406,0.2193337232,-0.0055318722,-0.2797216773,-0.0023883595,0.0753944889,0.1061919481,-0.2118771672,-0.0213846564,-0.2597497106,-0.4228295386,-0.0277204104,-0.0929705203,0.2329256982,-0.1784337461,-0.1466088146,-0.0199730396,0.3341645598,-0.10098546,-0.1949630827,-0.0001091545,-0.0203206725,-0.2484293729,-0.0608610325,0.2246825844,-0.1317874342,0.3598705232,-0.3554433286,-0.1441572607,-0.0381403603,-0.0225203186,0.0308944341,-0.1706526428,0.2882429957,-0.2357190102,0.0170724653,0.0558260903,-0.300290674,-0.2188138962,0.2182501704,-0.00322932,-0.436083585,-0.1364366561,0.0600697175,-0.5637034774,0.0217235312,0.0472865775,-0.1695634872,0.1888354123,-0.2967651188,0.1417250484,-0.2503827214,-0.360743463,-0.1515683681,-0.178382352,0.2280643433,-0.3107924759,-0.2898622751,-0.2297293544,-0.3846806288,0.0779804513,-0.1305169165,-0.0723214597,0.2086261064,-0.3361051977,-0.0359190889,0.6512774229,-0.5617482066,-0.2883246839,0.6501528621,-0.3996012509,-0.2595348358,0.2737458944,-0.0726999491,0.1499108374,-0.4444636405,-0.1325359792,0.0042216242,-0.0445069894,-0.1131415665,-0.0871563405,-0.4246346653,0.2098808438,0.2037368417,-0.0647907332,0.2234140486,0.1218448356,0.1067118198,0.447171241,0.0352130979,0.1692753583,-0.2102168351,0.2047894895,-0.1228681058,-0.1074659973,-0.1356255412,-0.5853859186,0.2420368791,-0.0473356619,0.1534970552,-0.0063745119,-0.0640541762,-0.4417180419,0.0285028666,0.1057558134,-0.2158669084,-0.0553909466,0.1458471715,0.0250658635,-0.0346959829,-0.0263200961,-0.0075410814,-0.2913226485,0.1625150293,0.2089127749,-0.1484843045,-0.0833220556,0.0995793715,0.1504308134,0.2522899508,-0.069793798,-0.3053160906,0.0206990167,0.3233892918,0.2586054206,0.1494383961,0.0798541307,0.0505342968,0.1862144172,0.2668224275,0.1463747621,0.2067105025,0.0248774067,-0.2785701752,0.023999054,0.1552948952,0.1547406465,-0.0246730689,-0.2123955339,-0.0428837165,0.2966695726,0.0858922228,-0.1595072448,-0.2886157036,0.1007187441,0.2132441103,0.6271631718,0.0130698625,0.234406665,-0.1607029736,-0.1911903769,-0.4197960496,0.0476297401,0.2504348159,-0.0863969773,-0.061566107,-0.0387018584,0.245597735,0.8113176823,0.0071627758,0.2236732692,0.059365768,-0.0622192919,-0.2031270564,0.2153962553,0.1874169111,-0.0199555196,0.1495851129,-0.0095342221,-0.2130720466,0.0111513054,-0.0299495626,0.3745067418,0.3891143501,-0.4598509073,-0.0712630153,-0.2434232235,-0.4637842774,-0.3513340056,-0.3214049935,-0.2830208838,-0.2886622846,-0.0760621578,0.3205254376,0.042951379,0.2218059152,-0.2701076567,0.2553033531,-0.2692339122,-0.2900582552,0.039597556,-0.1383657604,-0.3783037663,-0.0101342872,0.2034051865,-0.1298724264,0.4923704267,-0.0401080884,-0.2758656442,-0.2192289233,-0.3464267254,0.1785963625,-0.0080206348,0.5385330915,0.5283279419,0.011746807,0.4921999574,0.045098193,-0.1030585617,-0.282466799,-0.4460033178,-0.1996397674,0.0454242565,0.1237611547,-0.225199461,-0.2420060486,-0.5144339204,-0.1097066924,0.144529447,-0.034998782,0.1203451455,0.2705279887,-0.1861665696,0.1380648166,-0.0068364283,-0.0619552657,-0.1589343548,0.0721360371,-0.0934135169,-0.1512038261,-0.1503474265,0.0513874143,0.0091444002,-0.0261237267,-0.035869427,-0.3401893377,-0.4658867419,0.1552981436,0.1391379982,0.3482474387,-0.228883341,0.1996987313,0.2338796109,0.1612381637,-0.0610769652,-0.3452841043,0.1479627788,0.0475275852,0.244982183,0.2692882717,0.419529587,-0.0195856169,0.7755751014,0.1433252096,-0.0958903879,-0.0783183575,-0.2671538889,0.0062824115,-0.0542478226,-0.0055671064,0.3108572662,-0.100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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/161","title":"Discussion on version identifier & MockDataLoaderManager for test data","comments":"I'm good with either putting it in description, adding it to the config, or loosening version formatting. I mostly don't have a full conceptual grasp of what each identifier ends up meaning in the datasets code so hard to evaluate the best approach.\r\n\r\nFor background, the multiple formats is a consequence of:\r\n\r\n1. Each example is one multi-sentence trivia question\r\n2. For training, its better to treat each sentence as an example\r\n3. For evaluation, should test on: (1) first sentence, (2) full question, and (3) partial questions (does the model get the question right having seen the first half)\r\n\r\nWe use the date format for version since: (1) we expect some degree of updates since new questions come in every year and (2) the timestamp itself matches the Wikipedia dump that it is dependent on (so similar to the Wikipedia dataset).\r\n\r\nperhaps this is better discussed in https:\/\/github.com\/huggingface\/nlp\/pull\/169 or update title?","body":"Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.","comment_length":152,"text":"Discussion on version identifier & MockDataLoaderManager for test data \n Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp\/utils\/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests\/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done. \n I'm good with either putting it in description, adding it to the config, or loosening version formatting. I mostly don't have a full conceptual grasp of what each identifier ends up meaning in the datasets code so hard to evaluate the best approach.\r\n\r\nFor background, the multiple formats is a consequence of:\r\n\r\n1. Each example is one multi-sentence trivia question\r\n2. For training, its better to treat each sentence as an example\r\n3. For evaluation, should test on: (1) first sentence, (2) full question, and (3) partial questions (does the model get the question right having seen the first half)\r\n\r\nWe use the date format for version since: (1) we expect some degree of updates since new questions come in every year and (2) the timestamp itself matches the Wikipedia dump that it is dependent on (so similar to the Wikipedia dataset).\r\n\r\nperhaps this is better discussed in https:\/\/github.com\/huggingface\/nlp\/pull\/169 or update 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/160","title":"caching in map causes same result to be returned for train, validation and test","comments":"Hi @dpressel, \r\n\r\nthanks for posting your issue! Can you maybe add a complete code snippet that we can copy paste to reproduce the error? For example, I'm not sure where the variable `train_set` comes from in your code and it seems like you are loading multiple datasets at once?  ","body":"hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n","comment_length":49,"text":"caching in map causes same result to be returned for train, validation and test \n hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n \n Hi @dpressel, \r\n\r\nthanks for posting your issue! Can you maybe add a complete code snippet that we can copy paste to reproduce the error? For example, I'm not sure where the variable `train_set` comes from in your code and it seems like you are loading multiple datasets at once?  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/160","title":"caching in map causes same result to be returned for train, validation and test","comments":"Hi, the full example was listed in the PR above, but here is the exact link:\r\n\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/blob\/3c1aa3ca062cb23f303ca98ac40b6652b37ee971\/api-examples\/layers-classify-hf-datasets.py\r\n\r\nThe problem is coming from\r\n```\r\n            if cache_file_name is None:\r\n                # we create a unique hash from the function, current dataset file and the mapping args\r\n                cache_kwargs = {\r\n                    \"with_indices\": with_indices,\r\n                    \"batched\": batched,\r\n                    \"batch_size\": batch_size,\r\n                    \"remove_columns\": remove_columns,\r\n                    \"keep_in_memory\": keep_in_memory,\r\n                    \"load_from_cache_file\": load_from_cache_file,\r\n                    \"cache_file_name\": cache_file_name,\r\n                    \"writer_batch_size\": writer_batch_size,\r\n                    \"arrow_schema\": arrow_schema,\r\n                    \"disable_nullable\": disable_nullable,\r\n                }\r\n                cache_file_name = self._get_cache_file_path(function, cache_kwargs)\r\n```\r\nThe cached value is always the same, but I was able to change that by just renaming the function each time which seems to fix the issue.","body":"hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n","comment_length":99,"text":"caching in map causes same result to be returned for train, validation and test \n hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n \n Hi, the full example was listed in the PR above, but here is the exact link:\r\n\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/blob\/3c1aa3ca062cb23f303ca98ac40b6652b37ee971\/api-examples\/layers-classify-hf-datasets.py\r\n\r\nThe problem is coming from\r\n```\r\n            if cache_file_name is None:\r\n                # we create a unique hash from the function, current dataset file and the mapping args\r\n                cache_kwargs = {\r\n                    \"with_indices\": with_indices,\r\n                    \"batched\": batched,\r\n                    \"batch_size\": batch_size,\r\n                    \"remove_columns\": remove_columns,\r\n                    \"keep_in_memory\": keep_in_memory,\r\n                    \"load_from_cache_file\": load_from_cache_file,\r\n                    \"cache_file_name\": cache_file_name,\r\n                    \"writer_batch_size\": writer_batch_size,\r\n                    \"arrow_schema\": arrow_schema,\r\n                    \"disable_nullable\": disable_nullable,\r\n                }\r\n                cache_file_name = self._get_cache_file_path(function, cache_kwargs)\r\n```\r\nThe cached value is always the same, but I was able to change that by just renaming the function each time which seems to fix the issue.","embeddings":[-0.1263227314,-0.2207089961,-0.100632146,0.2780107558,0.2454756796,-0.2052229047,0.1372107863,0.5128592849,0.1882870793,-0.0623357445,0.1278197765,0.3891343772,0.0037762846,-0.1606175303,-0.0107436497,0.0242125317,0.0960802659,0.2350524068,-0.1455620974,-0.1648233086,0.081894055,0.2129840404,-0.0424102098,0.1165268347,-0.3903492093,0.0412896834,0.2123562694,-0.2308897674,0.0684574693,-0.2570649683,0.1678505987,-0.0932429209,0.0258333106,0.1663175523,-0.0001097382,0.0067983456,-0.0153878154,-0.1385697424,-0.0072745453,0.0401948094,0.1181339324,-0.2117293179,-0.1091826707,-0.3121318519,-0.3320115805,-0.0376609191,0.0178094879,-0.3556600213,0.3128580153,0.1474524736,0.2607103586,0.2249089628,-0.2538127303,0.2111362219,0.1451275945,-0.0293276701,-0.174197495,-0.042305138,-0.043978855,-0.3344650865,-0.3767647743,0.5254573822,-0.0765818357,0.1701412946,0.2146666646,0.2250556946,0.1726054549,0.03593757,0.1429362446,-0.1139624342,0.0110948198,-0.2269175202,-0.0271165781,-0.3733721972,-0.4186404347,-0.0785259828,0.137625128,0.1810450852,-0.1363935322,-0.06371627,-0.4319375753,0.2392898649,0.0652496368,-0.0127885444,0.1195269376,0.2951607704,0.0438062623,0.1952228397,0.1294467449,-0.069732815,-0.0225592721,-0.2145169973,-0.0589715466,0.2264011949,-0.4359855056,0.0720824599,0.4315197766,-0.1404137909,-0.0532427132,0.0306374077,0.3919309974,0.0792137086,0.1168181524,-0.0084918672,-0.104160659,0.5898677111,0.0443217419,0.171184063,-0.0149702216,-0.1982782632,-0.3560169637,0.0049879928,0.288017571,-0.286601156,0.4284112751,0.4023602307,-0.0422602966,-0.1215295419,-0.0301021244,0.0940235928,-0.3308537602,0.055669792,0.0587296784,0.3139583766,-0.2254502922,0.149265632,-0.1737674624,-0.1033549458,-0.3849695921,0.1273514628,-0.3378459215,-0.0333239511,-0.4962185323,0.0015165606,0.285384804,-0.0028597817,0.2853520513,-0.0384451598,-0.1372202933,-0.3019890487,0.1031737626,-0.3985118568,0.5702316761,-0.10722965,-0.197017163,0.2410212457,0.1303783357,0.0852641463,-0.2976881862,-0.1983774155,-0.1698076725,-0.2090685666,0.2598606944,0.2422455698,-0.0794276446,0.1413960606,0.0924820974,0.0776791126,0.3432580829,-0.232855022,0.1028404012,-0.2014892399,-0.2819799483,-0.2043585628,0.1625683904,0.2623100579,0.0667765215,-0.1373723596,0.4017969966,0.2371831983,0.1472906321,0.4138066471,-0.1965338886,0.3094943762,-0.4334916174,0.348619163,0.5064768195,-0.3267318308,-0.4963446558,0.0994484499,-0.1563160419,0.1002884582,0.1024493128,0.151373148,0.0290875416,0.0144196171,0.1285368353,0.0187024176,-0.0005147557,0.2084205002,-0.316927731,-0.0219340678,0.5500642657,-0.2524672449,0.0284720566,-0.131236881,-0.0550430529,0.0219331458,0.0067682369,-0.1121900007,0.1218594089,0.1146048158,-0.0568789616,-0.1581844538,-0.106438823,-0.0246925242,-0.2882628739,0.2383016348,-0.1498185694,-0.0999678373,0.0617249236,-0.0970491692,-0.1620027572,-0.129275158,-0.1809165776,-0.2730642855,0.2013016045,0.2305243611,0.3202398419,-0.0729073882,0.2252094597,0.3680819571,0.0483636484,-0.2160722017,-0.4300346375,0.0856799036,-0.1777346134,-0.2188281417,-0.0748463124,0.2230703831,0.2090260535,-0.0479263812,-0.1424278915,0.1482760459,-0.2220858783,0.4189184308,-0.1370279044,0.2694525421,-0.0696570575,0.0293060318,0.016667014,0.2540052533,0.0588200353,-0.0898012966,-0.0727058947,0.4742337465,0.1814022362,0.1701161861,-0.0480709597,-0.2162680477,0.0519842356,-0.1783178449,-0.1209156215,-0.1188160628,0.1364075691,-0.0805720538,0.148888275,0.152328819,-0.1069871038,0.3120084703,0.7600834966,0.1969186664,-0.0110169193,0.0157407038,-0.0572130904,-0.2177918702,0.3338562846,-0.058682885,0.31038782,0.1047316417,0.1271647215,-0.0462091751,-0.2196502686,-0.154435575,0.1246479601,-0.2143685222,-0.1919764429,0.3080338836,0.2820868194,-0.1583673358,-0.3537832797,0.0940154344,-0.0127645368,0.0261916555,-0.1580014974,0.2172367424,-0.166529417,-0.278084904,-0.2864152193,0.136156708,-0.1222038716,-0.0921654701,-0.0412631668,0.4254295826,-0.036654897,0.1951516867,-0.3149722517,0.0895192102,0.2266037613,-0.2663277388,-0.0716857836,-0.1902881712,-0.3949196339,0.042231977,-0.0952302068,-0.0929960757,0.3783293962,-0.0473160893,-0.067959927,-0.2881456017,-0.1725400686,0.1071778685,-0.0926118121,-0.1553680301,0.1672483534,-0.0313384198,-0.1011587083,-0.2987454534,0.2937971354,-0.3053128421,-0.299143523,-0.0519867688,0.0989064649,0.0301090628,-0.2803472877,-0.428624332,0.2735781968,-0.3172441423,0.0754678994,-0.1489290893,0.1862263232,0.3309345841,0.0089533916,-0.0145607088,-0.0640404522,0.1643940359,-0.4674099684,-0.1409540623,0.3225782216,0.0187610649,-0.2163045555,-0.3245913982,-0.2757247686,0.3477241099,0.1923889369,-0.5803956985,-0.2144230455,-0.048900146,0.2480322272,-0.0512586385,-0.0464955159,0.5745424032,0.0197255258,-0.179463625,-0.1672781259,-0.3318405151,0.359731406,0.4192632437,0.2494875044,-0.0111588463,0.1778150499,0.2009202093,0.6707463264,0.2019197792,-0.2590580583,0.331730634,0.1072319448,0.0749976784,-0.2034970075,-0.3130226135,0.1857604682,-0.1816030145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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/160","title":"caching in map causes same result to be returned for train, validation and test","comments":"Ok, I think @lhoestq has already found a solution :-) Maybe you can chime in @lhoestq ","body":"hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n","comment_length":16,"text":"caching in map causes same result to be returned for train, validation and test \n hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n \n Ok, I think @lhoestq has already found a solution :-) Maybe you can chime in @lhoestq 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/160","title":"caching in map causes same result to be returned for train, validation and test","comments":"> Ok, I think @lhoestq has already found a solution :-) Maybe you can chime in @lhoestq\r\n\r\nOh, awesome!  I see the PR, Ill check it out","body":"hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n","comment_length":27,"text":"caching in map causes same result to be returned for train, validation and test \n hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n \n > Ok, I think @lhoestq has already found a solution :-) Maybe you can chime in @lhoestq\r\n\r\nOh, awesome!  I see the PR, Ill check it out","embeddings":[-0.1263227314,-0.2207089961,-0.100632146,0.2780107558,0.2454756796,-0.2052229047,0.1372107863,0.5128592849,0.1882870793,-0.0623357445,0.1278197765,0.3891343772,0.0037762846,-0.1606175303,-0.0107436497,0.0242125317,0.0960802659,0.2350524068,-0.1455620974,-0.1648233086,0.081894055,0.2129840404,-0.0424102098,0.1165268347,-0.3903492093,0.0412896834,0.2123562694,-0.2308897674,0.0684574693,-0.2570649683,0.1678505987,-0.0932429209,0.0258333106,0.1663175523,-0.0001097382,0.0067983456,-0.0153878154,-0.1385697424,-0.0072745453,0.0401948094,0.1181339324,-0.2117293179,-0.1091826707,-0.3121318519,-0.3320115805,-0.0376609191,0.0178094879,-0.3556600213,0.3128580153,0.1474524736,0.2607103586,0.2249089628,-0.2538127303,0.2111362219,0.1451275945,-0.0293276701,-0.174197495,-0.042305138,-0.043978855,-0.3344650865,-0.3767647743,0.5254573822,-0.0765818357,0.1701412946,0.2146666646,0.2250556946,0.1726054549,0.03593757,0.1429362446,-0.1139624342,0.0110948198,-0.2269175202,-0.0271165781,-0.3733721972,-0.4186404347,-0.0785259828,0.137625128,0.1810450852,-0.1363935322,-0.06371627,-0.4319375753,0.2392898649,0.0652496368,-0.0127885444,0.1195269376,0.2951607704,0.0438062623,0.1952228397,0.1294467449,-0.069732815,-0.0225592721,-0.2145169973,-0.0589715466,0.2264011949,-0.4359855056,0.0720824599,0.4315197766,-0.1404137909,-0.0532427132,0.0306374077,0.3919309974,0.0792137086,0.1168181524,-0.0084918672,-0.104160659,0.5898677111,0.0443217419,0.171184063,-0.0149702216,-0.1982782632,-0.3560169637,0.0049879928,0.288017571,-0.286601156,0.4284112751,0.4023602307,-0.0422602966,-0.1215295419,-0.0301021244,0.0940235928,-0.3308537602,0.055669792,0.0587296784,0.3139583766,-0.2254502922,0.149265632,-0.1737674624,-0.1033549458,-0.3849695921,0.1273514628,-0.3378459215,-0.0333239511,-0.4962185323,0.0015165606,0.285384804,-0.0028597817,0.2853520513,-0.0384451598,-0.1372202933,-0.3019890487,0.1031737626,-0.3985118568,0.5702316761,-0.10722965,-0.197017163,0.2410212457,0.1303783357,0.0852641463,-0.2976881862,-0.1983774155,-0.1698076725,-0.2090685666,0.2598606944,0.2422455698,-0.0794276446,0.1413960606,0.0924820974,0.0776791126,0.3432580829,-0.232855022,0.1028404012,-0.2014892399,-0.2819799483,-0.2043585628,0.1625683904,0.2623100579,0.0667765215,-0.1373723596,0.4017969966,0.2371831983,0.1472906321,0.4138066471,-0.1965338886,0.3094943762,-0.4334916174,0.348619163,0.5064768195,-0.3267318308,-0.4963446558,0.0994484499,-0.1563160419,0.1002884582,0.1024493128,0.151373148,0.0290875416,0.0144196171,0.1285368353,0.0187024176,-0.0005147557,0.2084205002,-0.316927731,-0.0219340678,0.5500642657,-0.2524672449,0.0284720566,-0.131236881,-0.0550430529,0.0219331458,0.0067682369,-0.1121900007,0.1218594089,0.1146048158,-0.0568789616,-0.1581844538,-0.106438823,-0.0246925242,-0.2882628739,0.2383016348,-0.1498185694,-0.0999678373,0.0617249236,-0.0970491692,-0.1620027572,-0.129275158,-0.1809165776,-0.2730642855,0.2013016045,0.2305243611,0.3202398419,-0.0729073882,0.2252094597,0.3680819571,0.0483636484,-0.2160722017,-0.4300346375,0.0856799036,-0.1777346134,-0.2188281417,-0.0748463124,0.2230703831,0.2090260535,-0.0479263812,-0.1424278915,0.1482760459,-0.2220858783,0.4189184308,-0.1370279044,0.2694525421,-0.0696570575,0.0293060318,0.016667014,0.2540052533,0.0588200353,-0.0898012966,-0.0727058947,0.4742337465,0.1814022362,0.1701161861,-0.0480709597,-0.2162680477,0.0519842356,-0.1783178449,-0.1209156215,-0.1188160628,0.1364075691,-0.0805720538,0.148888275,0.152328819,-0.1069871038,0.3120084703,0.7600834966,0.1969186664,-0.0110169193,0.0157407038,-0.0572130904,-0.2177918702,0.3338562846,-0.058682885,0.31038782,0.1047316417,0.1271647215,-0.0462091751,-0.2196502686,-0.154435575,0.1246479601,-0.2143685222,-0.1919764429,0.3080338836,0.2820868194,-0.1583673358,-0.3537832797,0.0940154344,-0.0127645368,0.0261916555,-0.1580014974,0.2172367424,-0.166529417,-0.278084904,-0.2864152193,0.136156708,-0.1222038716,-0.0921654701,-0.0412631668,0.4254295826,-0.036654897,0.1951516867,-0.3149722517,0.0895192102,0.2266037613,-0.2663277388,-0.0716857836,-0.1902881712,-0.3949196339,0.042231977,-0.0952302068,-0.0929960757,0.3783293962,-0.0473160893,-0.067959927,-0.2881456017,-0.1725400686,0.1071778685,-0.0926118121,-0.1553680301,0.1672483534,-0.0313384198,-0.1011587083,-0.2987454534,0.2937971354,-0.3053128421,-0.299143523,-0.0519867688,0.0989064649,0.0301090628,-0.2803472877,-0.428624332,0.2735781968,-0.3172441423,0.0754678994,-0.1489290893,0.1862263232,0.3309345841,0.0089533916,-0.0145607088,-0.0640404522,0.1643940359,-0.4674099684,-0.1409540623,0.3225782216,0.0187610649,-0.2163045555,-0.3245913982,-0.2757247686,0.3477241099,0.1923889369,-0.5803956985,-0.2144230455,-0.048900146,0.2480322272,-0.0512586385,-0.0464955159,0.5745424032,0.0197255258,-0.179463625,-0.1672781259,-0.3318405151,0.359731406,0.4192632437,0.2494875044,-0.0111588463,0.1778150499,0.2009202093,0.6707463264,0.2019197792,-0.2590580583,0.331730634,0.1072319448,0.0749976784,-0.2034970075,-0.3130226135,0.1857604682,-0.1816030145,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/160","title":"caching in map causes same result to be returned for train, validation and test","comments":"The PR should prevent the cache from losing track of the of the dataset type (based on the location of its data). Not sure about your second problem though (cache off).","body":"hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n","comment_length":31,"text":"caching in map causes same result to be returned for train, validation and test \n hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n \n The PR should prevent the cache from losing track of the of the dataset type (based on the location of its data). Not sure about your second problem though (cache off).","embeddings":[-0.1263227314,-0.2207089961,-0.100632146,0.2780107558,0.2454756796,-0.2052229047,0.1372107863,0.5128592849,0.1882870793,-0.0623357445,0.1278197765,0.3891343772,0.0037762846,-0.1606175303,-0.0107436497,0.0242125317,0.0960802659,0.2350524068,-0.1455620974,-0.1648233086,0.081894055,0.2129840404,-0.0424102098,0.1165268347,-0.3903492093,0.0412896834,0.2123562694,-0.2308897674,0.0684574693,-0.2570649683,0.1678505987,-0.0932429209,0.0258333106,0.1663175523,-0.0001097382,0.0067983456,-0.0153878154,-0.1385697424,-0.0072745453,0.0401948094,0.1181339324,-0.2117293179,-0.1091826707,-0.3121318519,-0.3320115805,-0.0376609191,0.0178094879,-0.3556600213,0.3128580153,0.1474524736,0.2607103586,0.2249089628,-0.2538127303,0.2111362219,0.1451275945,-0.0293276701,-0.174197495,-0.042305138,-0.043978855,-0.3344650865,-0.3767647743,0.5254573822,-0.0765818357,0.1701412946,0.2146666646,0.2250556946,0.1726054549,0.03593757,0.1429362446,-0.1139624342,0.0110948198,-0.2269175202,-0.0271165781,-0.3733721972,-0.4186404347,-0.0785259828,0.137625128,0.1810450852,-0.1363935322,-0.06371627,-0.4319375753,0.2392898649,0.0652496368,-0.0127885444,0.1195269376,0.2951607704,0.0438062623,0.1952228397,0.1294467449,-0.069732815,-0.0225592721,-0.2145169973,-0.0589715466,0.2264011949,-0.4359855056,0.0720824599,0.4315197766,-0.1404137909,-0.0532427132,0.0306374077,0.3919309974,0.0792137086,0.1168181524,-0.0084918672,-0.104160659,0.5898677111,0.0443217419,0.171184063,-0.0149702216,-0.1982782632,-0.3560169637,0.0049879928,0.288017571,-0.286601156,0.4284112751,0.4023602307,-0.0422602966,-0.1215295419,-0.0301021244,0.0940235928,-0.3308537602,0.055669792,0.0587296784,0.3139583766,-0.2254502922,0.149265632,-0.1737674624,-0.1033549458,-0.3849695921,0.1273514628,-0.3378459215,-0.0333239511,-0.4962185323,0.0015165606,0.285384804,-0.0028597817,0.2853520513,-0.0384451598,-0.1372202933,-0.3019890487,0.1031737626,-0.3985118568,0.5702316761,-0.10722965,-0.197017163,0.2410212457,0.1303783357,0.0852641463,-0.2976881862,-0.1983774155,-0.1698076725,-0.2090685666,0.2598606944,0.2422455698,-0.0794276446,0.1413960606,0.0924820974,0.0776791126,0.3432580829,-0.232855022,0.1028404012,-0.2014892399,-0.2819799483,-0.2043585628,0.1625683904,0.2623100579,0.0667765215,-0.1373723596,0.4017969966,0.2371831983,0.1472906321,0.4138066471,-0.1965338886,0.3094943762,-0.4334916174,0.348619163,0.5064768195,-0.3267318308,-0.4963446558,0.0994484499,-0.1563160419,0.1002884582,0.1024493128,0.151373148,0.0290875416,0.0144196171,0.1285368353,0.0187024176,-0.0005147557,0.2084205002,-0.316927731,-0.0219340678,0.5500642657,-0.2524672449,0.0284720566,-0.131236881,-0.0550430529,0.0219331458,0.0067682369,-0.1121900007,0.1218594089,0.1146048158,-0.0568789616,-0.1581844538,-0.106438823,-0.0246925242,-0.2882628739,0.2383016348,-0.1498185694,-0.0999678373,0.0617249236,-0.0970491692,-0.1620027572,-0.129275158,-0.1809165776,-0.2730642855,0.2013016045,0.2305243611,0.3202398419,-0.0729073882,0.2252094597,0.3680819571,0.0483636484,-0.2160722017,-0.4300346375,0.0856799036,-0.1777346134,-0.2188281417,-0.0748463124,0.2230703831,0.2090260535,-0.0479263812,-0.1424278915,0.1482760459,-0.2220858783,0.4189184308,-0.1370279044,0.2694525421,-0.0696570575,0.0293060318,0.016667014,0.2540052533,0.0588200353,-0.0898012966,-0.0727058947,0.4742337465,0.1814022362,0.1701161861,-0.0480709597,-0.2162680477,0.0519842356,-0.1783178449,-0.1209156215,-0.1188160628,0.1364075691,-0.0805720538,0.148888275,0.152328819,-0.1069871038,0.3120084703,0.7600834966,0.1969186664,-0.0110169193,0.0157407038,-0.0572130904,-0.2177918702,0.3338562846,-0.058682885,0.31038782,0.1047316417,0.1271647215,-0.0462091751,-0.2196502686,-0.154435575,0.1246479601,-0.2143685222,-0.1919764429,0.3080338836,0.2820868194,-0.1583673358,-0.3537832797,0.0940154344,-0.0127645368,0.0261916555,-0.1580014974,0.2172367424,-0.166529417,-0.278084904,-0.2864152193,0.136156708,-0.1222038716,-0.0921654701,-0.0412631668,0.4254295826,-0.036654897,0.1951516867,-0.3149722517,0.0895192102,0.2266037613,-0.2663277388,-0.0716857836,-0.1902881712,-0.3949196339,0.042231977,-0.0952302068,-0.0929960757,0.3783293962,-0.0473160893,-0.067959927,-0.2881456017,-0.1725400686,0.1071778685,-0.0926118121,-0.1553680301,0.1672483534,-0.0313384198,-0.1011587083,-0.2987454534,0.2937971354,-0.3053128421,-0.299143523,-0.0519867688,0.0989064649,0.0301090628,-0.2803472877,-0.428624332,0.2735781968,-0.3172441423,0.0754678994,-0.1489290893,0.1862263232,0.3309345841,0.0089533916,-0.0145607088,-0.0640404522,0.1643940359,-0.4674099684,-0.1409540623,0.3225782216,0.0187610649,-0.2163045555,-0.3245913982,-0.2757247686,0.3477241099,0.1923889369,-0.5803956985,-0.2144230455,-0.048900146,0.2480322272,-0.0512586385,-0.0464955159,0.5745424032,0.0197255258,-0.179463625,-0.1672781259,-0.3318405151,0.359731406,0.4192632437,0.2494875044,-0.0111588463,0.1778150499,0.2009202093,0.6707463264,0.2019197792,-0.2590580583,0.331730634,0.1072319448,0.0749976784,-0.2034970075,-0.3130226135,0.1857604682,-0.1816030145,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/160","title":"caching in map causes same result to be returned for train, validation and test","comments":"Yes, with caching on, it seems to work without the function renaming hack, I mentioned this also in the PR. Thanks!","body":"hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n","comment_length":21,"text":"caching in map causes same result to be returned for train, validation and test \n hello,\r\n\r\nI am working on a program that uses the `nlp` library with the `SST2` dataset.\r\n\r\nThe rough outline of the program is:\r\n\r\n```\r\nimport nlp as nlp_datasets\r\n...\r\nparser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+')\r\n...\r\ndataset = nlp_datasets.load_dataset(*args.dataset)\r\n...\r\n# Create feature vocabs\r\nvocabs = create_vocabs(dataset.values(), vectorizers)\r\n...\r\n# Create a function to vectorize based on vectorizers and vocabs:\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n# factory method to create a `convert_to_features` function based on vocabs\r\nconvert_to_features = create_featurizer(vectorizers, vocabs)\r\ntrain_set = train_set.map(convert_to_features, batched=True)\r\ntrain_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz)\r\n\r\nvalid_set = valid_set.map(convert_to_features, batched=True)\r\nvalid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\nvalid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz)\r\n\r\ntest_set = test_set.map(convert_to_features, batched=True)\r\ntest_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths'])\r\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz)\r\n\r\nprint('TS', train_set.num_rows)\r\nprint('VS', valid_set.num_rows)\r\nprint('ES', test_set.num_rows)\r\n\r\n```\r\nIm not sure if Im using it incorrectly, but the results are not what I expect.  Namely, the `.map()`  seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets:\r\n\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\nTS 67349\r\nVS 67349\r\nES 67349\r\n```\r\n\r\nThe behavior changes if I turn off the caching but then the results fail:\r\n\r\n```\r\ntrain_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\nvalid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n...\r\ntest_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False)\r\n```\r\n\r\nNow I get the right set of features back...\r\n```\r\nTS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 68\/68 [00:00<00:00, 92.78it\/s]\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 75.47it\/s]\r\n  0%|          | 0\/2 [00:00<?, ?it\/s]TS 67349\r\nVS 872\r\nES 1821\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2\/2 [00:00<00:00, 77.19it\/s]\r\n```\r\nbut I think its losing track of the original training set:\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"\/home\/dpressel\/dev\/work\/baseline\/api-examples\/layers-classify-hf-datasets.py\", line 148, in <module>\r\n    for x in train_loader:\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 345, in __next__\r\n    data = self._next_data()\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/dataloader.py\", line 385, in _next_data\r\n    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in fetch\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/torch\/utils\/data\/_utils\/fetch.py\", line 44, in <listcomp>\r\n    data = [self.dataset[idx] for idx in possibly_batched_index]\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 338, in __getitem__\r\n    output_all_columns=self._output_all_columns,\r\n  File \"\/home\/dpressel\/anaconda3\/lib\/python3.7\/site-packages\/nlp\/arrow_dataset.py\", line 294, in _getitem\r\n    outputs = self._unnest(self._data.slice(key, 1).to_pydict())\r\n  File \"pyarrow\/table.pxi\", line 1211, in pyarrow.lib.Table.slice\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000)\r\n\r\nProcess finished with exit code 1\r\n```\r\n\r\nThe full-example program (minus the print stmts) is here:\r\nhttps:\/\/github.com\/dpressel\/mead-baseline\/pull\/620\/files\r\n\r\n \n Yes, with caching on, it seems to work without the function renaming hack, I mentioned this also in the PR. Thanks!","embeddings":[-0.1263227314,-0.2207089961,-0.100632146,0.2780107558,0.2454756796,-0.2052229047,0.1372107863,0.5128592849,0.1882870793,-0.0623357445,0.1278197765,0.3891343772,0.0037762846,-0.1606175303,-0.0107436497,0.0242125317,0.0960802659,0.2350524068,-0.1455620974,-0.1648233086,0.081894055,0.2129840404,-0.0424102098,0.1165268347,-0.3903492093,0.0412896834,0.2123562694,-0.2308897674,0.0684574693,-0.2570649683,0.1678505987,-0.0932429209,0.0258333106,0.1663175523,-0.0001097382,0.0067983456,-0.0153878154,-0.1385697424,-0.0072745453,0.0401948094,0.1181339324,-0.2117293179,-0.1091826707,-0.3121318519,-0.3320115805,-0.0376609191,0.0178094879,-0.3556600213,0.3128580153,0.1474524736,0.2607103586,0.2249089628,-0.2538127303,0.2111362219,0.1451275945,-0.0293276701,-0.174197495,-0.042305138,-0.043978855,-0.3344650865,-0.3767647743,0.5254573822,-0.0765818357,0.1701412946,0.2146666646,0.2250556946,0.1726054549,0.03593757,0.1429362446,-0.1139624342,0.0110948198,-0.2269175202,-0.0271165781,-0.3733721972,-0.4186404347,-0.0785259828,0.137625128,0.1810450852,-0.1363935322,-0.06371627,-0.4319375753,0.2392898649,0.0652496368,-0.0127885444,0.1195269376,0.2951607704,0.0438062623,0.1952228397,0.1294467449,-0.069732815,-0.0225592721,-0.2145169973,-0.0589715466,0.2264011949,-0.4359855056,0.0720824599,0.4315197766,-0.1404137909,-0.0532427132,0.0306374077,0.3919309974,0.0792137086,0.1168181524,-0.0084918672,-0.104160659,0.5898677111,0.0443217419,0.171184063,-0.0149702216,-0.1982782632,-0.3560169637,0.0049879928,0.288017571,-0.286601156,0.4284112751,0.4023602307,-0.0422602966,-0.1215295419,-0.0301021244,0.0940235928,-0.3308537602,0.055669792,0.0587296784,0.3139583766,-0.2254502922,0.149265632,-0.1737674624,-0.1033549458,-0.3849695921,0.1273514628,-0.3378459215,-0.0333239511,-0.4962185323,0.0015165606,0.285384804,-0.0028597817,0.2853520513,-0.0384451598,-0.1372202933,-0.3019890487,0.1031737626,-0.3985118568,0.5702316761,-0.10722965,-0.197017163,0.2410212457,0.1303783357,0.0852641463,-0.2976881862,-0.1983774155,-0.1698076725,-0.2090685666,0.2598606944,0.2422455698,-0.0794276446,0.1413960606,0.0924820974,0.0776791126,0.3432580829,-0.232855022,0.1028404012,-0.2014892399,-0.2819799483,-0.2043585628,0.1625683904,0.2623100579,0.0667765215,-0.1373723596,0.4017969966,0.2371831983,0.1472906321,0.4138066471,-0.1965338886,0.3094943762,-0.4334916174,0.348619163,0.5064768195,-0.3267318308,-0.4963446558,0.0994484499,-0.1563160419,0.1002884582,0.1024493128,0.151373148,0.0290875416,0.0144196171,0.1285368353,0.0187024176,-0.0005147557,0.2084205002,-0.316927731,-0.0219340678,0.5500642657,-0.2524672449,0.0284720566,-0.131236881,-0.0550430529,0.0219331458,0.0067682369,-0.1121900007,0.1218594089,0.1146048158,-0.0568789616,-0.1581844538,-0.106438823,-0.0246925242,-0.2882628739,0.2383016348,-0.1498185694,-0.0999678373,0.0617249236,-0.0970491692,-0.1620027572,-0.129275158,-0.1809165776,-0.2730642855,0.2013016045,0.2305243611,0.3202398419,-0.0729073882,0.2252094597,0.3680819571,0.0483636484,-0.2160722017,-0.4300346375,0.0856799036,-0.1777346134,-0.2188281417,-0.0748463124,0.2230703831,0.2090260535,-0.0479263812,-0.1424278915,0.1482760459,-0.2220858783,0.4189184308,-0.1370279044,0.2694525421,-0.0696570575,0.0293060318,0.016667014,0.2540052533,0.0588200353,-0.0898012966,-0.0727058947,0.4742337465,0.1814022362,0.1701161861,-0.0480709597,-0.2162680477,0.0519842356,-0.1783178449,-0.1209156215,-0.1188160628,0.1364075691,-0.0805720538,0.148888275,0.152328819,-0.1069871038,0.3120084703,0.7600834966,0.1969186664,-0.0110169193,0.0157407038,-0.0572130904,-0.2177918702,0.3338562846,-0.058682885,0.31038782,0.1047316417,0.1271647215,-0.0462091751,-0.2196502686,-0.154435575,0.1246479601,-0.2143685222,-0.1919764429,0.3080338836,0.2820868194,-0.1583673358,-0.3537832797,0.0940154344,-0.0127645368,0.0261916555,-0.1580014974,0.2172367424,-0.166529417,-0.278084904,-0.2864152193,0.136156708,-0.1222038716,-0.0921654701,-0.0412631668,0.4254295826,-0.036654897,0.1951516867,-0.3149722517,0.0895192102,0.2266037613,-0.2663277388,-0.0716857836,-0.1902881712,-0.3949196339,0.042231977,-0.0952302068,-0.0929960757,0.3783293962,-0.0473160893,-0.067959927,-0.2881456017,-0.1725400686,0.1071778685,-0.0926118121,-0.1553680301,0.1672483534,-0.0313384198,-0.1011587083,-0.2987454534,0.2937971354,-0.3053128421,-0.299143523,-0.0519867688,0.0989064649,0.0301090628,-0.2803472877,-0.428624332,0.2735781968,-0.3172441423,0.0754678994,-0.1489290893,0.1862263232,0.3309345841,0.0089533916,-0.0145607088,-0.0640404522,0.1643940359,-0.4674099684,-0.1409540623,0.3225782216,0.0187610649,-0.2163045555,-0.3245913982,-0.2757247686,0.3477241099,0.1923889369,-0.5803956985,-0.2144230455,-0.048900146,0.2480322272,-0.0512586385,-0.0464955159,0.5745424032,0.0197255258,-0.179463625,-0.1672781259,-0.3318405151,0.359731406,0.4192632437,0.2494875044,-0.0111588463,0.1778150499,0.2009202093,0.6707463264,0.2019197792,-0.2590580583,0.331730634,0.1072319448,0.0749976784,-0.2034970075,-0.3130226135,0.1857604682,-0.181603014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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/157","title":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\"","comments":"You can just run: \r\n`val = nlp.load_dataset('squad')` \r\n\r\nif you want to have just the validation script you can also do:\r\n\r\n`val = nlp.load_dataset('squad', split=\"validation\")`","body":"I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a","comment_length":24,"text":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\" \n I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a \n You can just run: \r\n`val = nlp.load_dataset('squad')` \r\n\r\nif you want to have just the validation script you can also do:\r\n\r\n`val = nlp.load_dataset('squad', split=\"validation\")`","embeddings":[-0.0479782261,0.0604858324,-0.0539134853,0.2444889098,0.248679772,0.1877578199,0.2922648191,0.3124673367,0.1899903119,-0.0237427168,0.0557831042,0.4075591564,-0.1347435117,-0.0079085575,0.2922923267,-0.2682780921,-0.1929560602,0.3292351067,0.1848603189,-0.06305556,-0.0684112608,0.1494150758,-0.2461704016,0.1525854617,-0.023350941,-0.1903261989,-0.0376415066,0.2420651466,-0.0751293227,-0.5705810785,0.2770506442,-0.1046772152,0.2867957354,-0.0100499829,-0.000113336,-0.0096305776,0.343242377,-0.2085264027,-0.285394311,-0.2410947084,-0.2574113607,-0.2226096839,0.4168426096,-0.2224058658,-0.1222060472,-0.0949645787,0.2801200449,0.0533588342,0.4846663475,0.4920531213,0.1911881864,0.2663962841,-0.2778134346,-0.2081823498,-0.2290327847,-0.0079413252,0.1164551675,-0.0115763806,0.0253511015,-0.1536256969,0.1678515226,0.0487463437,-0.1323892474,0.0573955439,0.2352476269,-0.1945355237,-0.1781451106,-0.2897628248,-0.0892733857,0.2384080291,0.3575641215,-0.0509690531,-0.1911022961,-0.0961414874,0.2534791529,-0.2780938447,0.1107778773,0.2565834224,-0.1418790221,-0.0119595416,-0.2243394703,-0.1495238245,-0.1682105362,0.400627166,0.2719267607,0.3005495965,0.0308479033,0.2198183835,0.4388645291,0.0139065729,0.0014640251,-0.1625842303,-0.0448136143,0.2608413398,-0.5341176987,-0.2073041797,-0.0824583173,0.186087817,0.0907499194,0.0411117375,0.2947389185,-0.0586644523,-0.0574181005,0.1990403235,0.508874774,0.1017294005,0.5623295903,0.2690935731,-0.1490220129,-0.1593780369,0.1682632715,0.0441463403,-0.1811117679,-0.1665756404,0.1207617223,0.1402468085,0.1163438037,-0.2006215453,-0.2761594355,-0.0517085679,-0.1991660744,-0.1768129617,0.160023123,0.5370280147,-0.0615797006,0.3261684477,0.0482984595,0.1132508442,-0.0572101362,-0.4197391868,-0.1476211399,0.3679234087,-0.072527118,-0.0302502383,0.4270748198,-0.0798367113,0.4679146409,-0.0434464402,-0.1424437314,-0.0100079579,0.2092293948,-0.1657940745,-0.0835500583,0.0574743077,0.379348278,-0.0478072017,0.2634988427,-0.2280272841,-0.1109257713,0.1095157266,-0.2067266852,-0.1016824841,0.0654660612,0.1888945103,-0.022871878,-0.131171301,-0.2601767182,0.1217983738,0.1881858557,-0.3063747585,-0.3416291177,-0.2337040454,-0.1677280217,-0.1121920049,-0.0668830872,0.1756419539,-0.2444734275,-0.0179958232,-0.0542966984,-0.0141140576,-0.1190045178,0.4451571107,-0.5217581987,0.4278976917,-0.1365601718,0.3605508506,0.9311334491,-0.3305851221,-0.0189148113,0.2314217836,0.0573704392,-0.2136706859,-0.0486658849,0.2341494858,0.1505651623,-0.0110798329,-0.0230927672,0.4446450472,-0.1218503714,0.069405362,-0.0394676141,0.0232371893,0.4373553097,0.159803167,-0.0309362561,0.1563685089,-0.034846168,0.495734483,0.3241556585,-0.2226721942,-0.1649337858,0.3829777539,-0.1546869427,-0.2373206764,-0.1041943505,-0.1764299124,-0.6095246673,-0.1106410772,-0.1527284682,-0.1248762459,0.2874699533,-0.0954664946,-0.3576019108,-0.0612154044,-0.0567842312,0.1104555428,0.0263876989,0.2085289955,0.1452484578,-0.2851122916,-0.3506065905,0.2338497043,-0.0766568854,-0.0593928844,-0.5049280524,0.1625627279,-0.1459736973,-0.1190996319,0.3205271661,-0.0516694896,0.2130184472,-0.0669354722,0.0374815054,0.3208777606,0.1133230999,-0.1674942374,-0.0458593778,-0.2503063977,0.1178534776,-0.1653389484,-0.082371451,0.2613770068,0.2618916631,-0.1377428621,-0.2714335918,0.3402355313,-0.191697821,0.2066088021,-0.0078753596,0.1686403155,0.3393786252,-0.092761755,-0.2258027941,-0.0144079085,0.2171371728,0.1687519699,0.1625562608,0.0007494665,-0.4013223052,-0.240463987,0.4795569777,0.1693531424,0.1784728318,-0.0569449067,0.2091763914,-0.025562143,0.1343107671,-0.1709836125,0.5497861505,0.1769136786,-0.2952726483,-0.0186025538,-0.2508745193,-0.1387842894,0.1455360502,-0.1979945451,0.2664521635,-0.001689852,-0.0935718343,-0.1395549476,-0.3698905706,-0.1032401547,0.2697143555,0.281714946,-0.3868989944,0.1220140383,-0.26945436,-0.3693347871,-0.1848937869,-0.0826629475,-0.2725851834,-0.2749228477,-0.0287234131,0.1694056988,0.187444672,0.219473809,-0.0533871651,0.1847233474,0.0460549854,0.122603789,-0.0121899676,-0.0678897053,-0.2740035057,0.0332430974,0.1798577458,0.2468972653,0.2460195273,-0.1143594384,-0.256623894,-0.0241387524,-0.0743716657,0.1052277461,-0.0097435946,0.1189465523,0.2973913252,0.3271968365,-0.0906634629,-0.1738584936,0.182153821,0.095028244,-0.0800516754,0.1346051395,0.0533426777,-0.1240585148,-0.2009021491,-0.5390253067,-0.1912579685,-0.2482042313,0.0243600085,0.2482373118,0.312301755,0.3128586411,0.0227755103,0.2032660842,-0.2936572134,0.0312259886,-0.2376646549,0.1938053221,0.1072007045,-0.0977869555,-0.458922714,0.03418741,0.0552466288,0.1852573752,-0.1203773245,-0.1107425466,-0.5271216631,0.0227927882,-0.1498503983,-0.3265810609,0.1050831974,0.3287192583,-0.2310402989,0.0733636245,-0.0461928286,-0.2906912267,0.2586800158,0.0462521799,0.365660876,-0.1040059254,0.4038248956,0.0919928402,0.2871785164,0.2608461082,-0.2382416129,0.2712729275,-0.2320260108,0.2584132552,0.2038302124,-0.49506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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/157","title":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\"","comments":"If you want to load a local dataset, make sure you include a `.\/` before the folder name. ","body":"I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a","comment_length":18,"text":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\" \n I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a \n If you want to load a local dataset, make sure you include a `.\/` before the folder name. ","embeddings":[-0.1817345172,0.1649202257,-0.037762545,0.3960107863,0.2116742432,0.0808849931,0.2252638936,0.2089404166,0.2736941576,-0.0764815882,0.0226199981,0.5386043787,-0.1813205481,-0.0346676223,0.266469568,-0.3476087451,-0.1070255116,0.3149689138,-0.065006569,-0.2064713985,-0.2161854804,0.2870637476,-0.2059969455,0.1824874878,0.032629434,-0.1372919679,-0.1574772447,0.3898998797,-0.0368854105,-0.4234907329,0.3749963343,-0.1197535917,0.3778780699,0.09478122,-0.0001058318,-0.0234758928,0.4701956809,-0.1295060962,-0.3101872802,-0.3924332559,-0.2680528164,-0.3620916307,0.4882286489,-0.2194731832,-0.0158795342,-0.2597392797,0.2412916571,0.0264009163,0.2533610463,0.3210203648,0.2670232356,0.1399294883,-0.2075907886,-0.2245950252,0.0458516367,0.1774975359,0.1134933233,0.1632965505,0.0754975826,-0.1619021595,0.1334751695,-0.0079500368,-0.1729715914,0.2086657733,0.280320853,-0.0951739773,-0.1461393088,-0.0608950183,-0.1281757504,0.2202229351,0.59426862,0.0227832869,-0.0991348475,-0.0659364983,0.1251932681,-0.1856793761,0.1517226398,0.282074064,-0.2423073351,-0.0171662848,-0.2248252332,-0.2694967091,-0.1921048313,0.4482887685,0.2587141395,0.2339924872,-0.1192826629,0.3122357726,0.3364270329,-0.0419210531,0.1714497656,-0.1993420869,-0.0360708274,0.2063961625,-0.3502089083,0.1492244601,0.0149744209,0.3842139244,0.05865217,0.0374896303,0.2414616644,-0.0637469664,-0.2141748369,0.1980293542,0.4698019028,0.0535757579,0.3585375249,0.2473382503,-0.1437019259,-0.2810351849,0.0694392845,0.0354901962,-0.2649764717,-0.0487420484,-0.1562064737,-0.1527792662,0.1816767603,-0.2239640802,-0.122301437,-0.2737606168,-0.1520317495,-0.1668013781,0.1815780699,0.4278885722,0.0906486213,0.298003763,0.0953643769,0.1498009562,-0.1026716456,-0.2856143117,-0.2298306674,0.1412283182,-0.1225494593,-0.1385333836,0.3747474551,-0.0187855698,0.4567199349,-0.0382334664,-0.2033585906,0.1767637879,0.1393191069,-0.0794531778,-0.2095606625,0.1328118742,0.311127305,0.0777217746,0.3022845984,-0.1965751052,-0.2245519012,0.1427998692,-0.3064493239,-0.1693765372,-0.146987319,0.2282441705,0.0955979079,-0.1747365445,-0.3551029563,0.1555950344,0.0108269304,-0.298584342,-0.3533901572,-0.1492172182,-0.1736250669,-0.1328094453,-0.0644598827,0.2461013347,-0.2640273571,-0.0133301113,-0.2493095249,-0.0340757743,0.1630187035,0.5130516291,-0.6319713593,0.327796489,-0.2078633755,0.391122818,0.755734086,-0.0893503949,-0.0843905434,0.4410306513,0.0372952782,-0.3427175581,-0.0661499798,0.3210615814,-0.073901467,0.0138341179,0.0989211723,0.5014772415,-0.0818543658,0.0567304194,-0.1080970913,-0.1147874147,0.3714604378,0.2119090855,-0.2093863338,0.2779374421,0.1506364942,0.4080718756,0.2564155757,-0.2179135233,-0.1621754169,0.3962439299,-0.1703134179,-0.1292039603,-0.1280872673,0.0495205075,-0.5174013376,-0.0258927997,-0.1493400037,-0.0429201834,0.2196694314,0.0843060687,-0.2865209877,0.0157589074,-0.0331116728,0.1085644737,0.0955378339,0.416079998,0.0223479699,-0.1381156743,-0.2010146528,0.1912926733,-0.1028567702,-0.188134402,-0.3548856974,0.2209466398,-0.1161208898,-0.1161725074,0.3425400257,-0.1195624396,0.2132452428,-0.0790648386,-0.0497983433,0.2547598481,-0.1155869365,0.0983075649,0.1258719265,-0.240930602,0.0365218557,-0.2851820886,0.1046782658,0.2166626602,0.3297916353,-0.1805115491,-0.3861504197,0.1478818059,-0.0720380694,0.2228576839,0.002266607,0.0943218246,0.4035722315,-0.0046275281,0.0257201679,0.1011727303,0.3559525013,0.3568498492,0.255599767,0.0850090161,-0.4724255502,-0.1945292503,0.2176447213,0.1026500836,0.1617790163,0.099341698,0.0584509447,-0.207224384,0.2317270637,-0.0166771412,0.6256718636,0.2888614833,-0.062880002,-0.0638780519,-0.2169774175,-0.2650143504,0.1154557988,-0.1677089036,0.2183244228,-0.0975164622,0.0157232191,-0.1876262873,-0.4450456798,-0.1722655594,0.1685965806,0.3621346951,-0.2655333281,0.1152258292,-0.3535382748,-0.5387765765,-0.0472406931,-0.2088954449,-0.2409414798,-0.2782775462,-0.1543750018,0.2228322178,0.0525749214,0.1316055655,-0.1063755602,0.2991155982,0.0982860923,0.0415978134,-0.1295877248,-0.0537600778,-0.4215118289,0.0692125857,0.3457301557,0.3664290309,0.2584702075,-0.1480545402,-0.0782568902,-0.1410404593,0.1053793803,0.1019720212,0.0545456074,0.2188705057,0.2128574997,0.2562524974,-0.1707851738,-0.2850758433,0.2987315059,-0.037657328,-0.0720784739,0.1106504947,-0.0113610905,-0.1662681848,-0.2586746514,-0.350299418,-0.2985755503,-0.3545612693,0.1117272228,0.4160958827,0.2045212537,0.2739645243,0.0068997019,0.3108428121,-0.1213606074,-0.0468598045,-0.1962010264,-0.1599239409,0.1169947758,-0.1250785738,-0.4658184946,-0.0345596671,0.1089994088,0.1442873031,-0.0446926579,-0.3045373261,-0.2324936241,-0.0408554673,-0.0980673134,-0.1314141303,0.1571474522,0.2371114045,-0.199622333,-0.0385133922,-0.0430273414,-0.2611531913,0.3550449312,0.0848537534,0.2365913391,0.0896642506,0.2459274977,-0.0686401725,0.2741695642,0.1861712635,-0.1141704693,0.4656280577,-0.1571580768,0.2868110538,-0.0404679328,-0.3110336959,0.0076055527,0.1537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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/157","title":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\"","comments":"This happens by just doing run all cells on colab ... I assumed the colab example is broken. ","body":"I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a","comment_length":18,"text":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\" \n I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a \n This happens by just doing run all cells on colab ... I assumed the colab example is broken. ","embeddings":[0.0188431982,0.0450551845,0.005720363,0.3553819954,0.0485084765,0.0158459563,0.3805162013,0.0017092479,0.0825143605,-0.0696413219,0.0609751008,0.6260883808,-0.1543067843,0.0177123547,0.2570040226,-0.1367096156,-0.0055067092,0.4703560174,-0.0491582714,-0.1032542363,-0.2574828565,0.298918128,-0.4601308405,0.0343436487,-0.1575132459,-0.1436825246,-0.2623033822,0.0875611082,-0.0323915444,-0.269321382,0.4908155203,-0.0376952179,0.159505859,0.2392023057,-0.0001199438,-0.1626406014,0.381934613,-0.186781764,-0.5980202556,-0.3188870847,-0.4396148622,-0.3556466997,0.5800086856,-0.2417218685,0.0015593286,0.1016805172,0.2442041337,0.2477970272,0.2513045967,0.2270814329,0.1859471053,0.0525158793,-0.2828712165,-0.2271952927,0.1020056307,0.0441174731,0.0202824473,0.2397984862,0.2175246328,-0.2253180593,0.2749579251,0.0992982015,-0.2582588494,0.1961469203,0.0591719523,-0.109739691,-0.190532133,-0.0806436539,-0.0734864399,0.2947736979,0.4633131027,0.1014977396,-0.0970329121,-0.1807356477,0.3349782526,-0.3535569608,0.250633657,0.1607245505,-0.3357418776,-0.1015135497,-0.2035338283,-0.1373180896,-0.0174727831,0.4194001853,0.1564692557,0.2983800769,-0.2599446476,0.2820228636,0.3466849923,0.0848815516,0.4672145844,-0.093956925,0.0315395258,0.1105768159,-0.3193621635,0.0937875733,-0.076409094,0.505810082,-0.0126253339,-0.2257357985,0.3272550106,-0.1687375307,-0.2140426338,0.2629674375,0.3502497971,-0.0067188279,0.3153295815,0.0902891457,-0.1434864402,-0.1687917709,-0.0142858811,0.0906808451,-0.3385536671,0.0852106139,-0.1945907325,-0.1255175024,0.1719267815,-0.2188440561,-0.2274805903,-0.2521557212,-0.3828362823,-0.0296623483,0.205755651,0.4171341062,-0.0021472008,0.1563944817,0.1263044477,0.0278350506,-0.168648079,-0.3810290396,-0.1644434929,0.1161909774,-0.216347456,-0.0061730864,0.3435818553,0.1051118821,0.4116132855,0.0553373024,-0.0924310535,0.1041881815,-0.0682782307,-0.330776304,-0.1765857339,0.1721393317,0.3596381247,0.0152125657,0.3292273283,-0.3052338958,-0.0320951939,0.3024981022,-0.2944398522,-0.0814929903,0.0301410239,0.0952656344,-0.0199779645,-0.0424025916,-0.5787671208,0.3203098178,0.3033498228,-0.5106570721,-0.3087828755,-0.3915750384,-0.2988180518,-0.125619635,-0.0953124538,0.2600243986,-0.1438380927,0.0360734016,-0.0859902725,0.2935400903,0.2463108003,0.3319583237,-0.5736114383,0.3138142526,-0.2048135698,0.2774712741,0.6275325418,0.1795435995,-0.1897026598,0.1105682924,0.1969002634,-0.141344741,-0.0844787359,0.2261341959,0.0577903315,-0.1141991615,0.2488618195,0.2980818748,-0.1208086163,0.0419587828,-0.1281765401,-0.0940658003,0.3527612388,0.0641502291,-0.1838858277,0.3009245098,-0.0646800622,0.3978241682,0.3637025356,-0.1571041346,-0.249550283,0.2259039879,-0.0780370981,-0.2327934653,-0.1085262969,0.1071589887,-0.4472696483,-0.1631046981,0.1275351197,0.0777038932,0.2421364337,0.004942107,-0.2343296111,0.046577353,0.1284436882,0.1167520732,-0.0086372411,0.3010903895,-0.1533358693,-0.176939562,-0.1945761591,0.3904251754,-0.1771713942,-0.1402009428,-0.2490786612,0.253318876,0.0073272223,-0.2020684928,0.2641592026,-0.1066093072,0.243747279,-0.0436390564,-0.1106656715,0.0648488924,0.0664474815,-0.1450033188,-0.1039982289,-0.2828156054,0.0237217899,-0.0298546068,-0.0533010475,0.0986916944,0.3930721283,-0.2130803764,-0.190332368,0.2627229095,-0.0860733241,0.2650961876,-0.0253574699,0.1188815162,0.2733763158,0.1228569672,-0.0279482231,0.1626714617,0.30422616,0.4033070505,0.0843398198,0.1841973513,-0.4527641237,0.0301091485,0.3008410335,0.1992160529,0.2322489023,0.0064755501,0.0678080991,-0.181238696,0.4324338734,-0.0643158108,0.6145316362,0.1886730939,-0.209035486,-0.1017479748,-0.1130301058,-0.2819030881,0.1876810342,-0.1347732395,0.4792538583,-0.3165477216,0.1207220033,-0.1672209501,-0.3869832754,-0.2485431433,0.2913652956,0.4233625829,-0.3012811542,0.2501380742,-0.3041700721,-0.5747203827,-0.0290657133,-0.2319538295,-0.0292114057,-0.2642417252,-0.1924958229,0.4679270387,0.2398442924,0.1629972905,-0.0738499388,0.4010507464,0.0614264533,0.2328597307,-0.0830552876,-0.0852954164,-0.4240858555,0.0136715453,0.1367811561,0.2953564227,0.0146755073,-0.2348014712,-0.244991526,0.1951456219,-0.0592318028,0.2362014651,-0.1503009498,0.1712605357,0.2429655641,0.1682321727,-0.562274456,-0.1670964211,0.0664915964,0.0026001146,-0.0596728399,0.0490776971,-0.0145267639,-0.2116800249,-0.1921958923,-0.0983457044,-0.2190615237,-0.1602042913,0.0941900536,0.2214883566,0.2686265707,0.3534071445,0.0601304024,0.3149447143,0.099223353,-0.0172488596,-0.2775678635,0.0167652499,0.0780190602,-0.0512637049,-0.2939233482,-0.0442048237,-0.00979516,0.1574009955,-0.0309384875,-0.3212945759,-0.4990661144,0.0285517722,-0.2699047029,-0.1555912346,0.1095616743,0.1630758941,-0.1443767846,0.111897856,0.0000050772,-0.3624994159,0.2137898505,0.0498596504,0.2663090825,0.0962129757,0.2913758755,-0.0136638312,0.1354088187,0.2348683625,-0.0575198159,0.3559513688,-0.1440768987,0.2338394672,0.0250850432,-0.4332297444,0.1090022624,0.0465004519,-0.199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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/157","title":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\"","comments":"Oh I see you might have a wrong version of pyarrow install on the colab -> could you try the following. Add these lines to the beginning of your notebook, restart the runtime and run it again:\r\n```\r\n!pip uninstall -y -qq pyarrow\r\n!pip uninstall -y -qq nlp\r\n!pip install -qq git+https:\/\/github.com\/huggingface\/nlp.git\r\n```","body":"I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a","comment_length":53,"text":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\" \n I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a \n Oh I see you might have a wrong version of pyarrow install on the colab -> could you try the following. Add these lines to the beginning of your notebook, restart the runtime and run it again:\r\n```\r\n!pip uninstall -y -qq pyarrow\r\n!pip uninstall -y -qq nlp\r\n!pip install -qq git+https:\/\/github.com\/huggingface\/nlp.git\r\n```","embeddings":[-0.1487929672,0.177358225,-0.0415252633,0.3428395987,0.1430013031,-0.1136175469,0.1551361531,0.1810095608,-0.1386966854,-0.0235533211,-0.1086137295,0.824855566,-0.1088479236,0.0025473528,0.2896052301,-0.273955971,-0.0902934968,0.4029020965,-0.076595448,-0.0639468357,-0.1341270208,0.3876859844,-0.2954268754,0.1872269958,-0.1233507991,-0.1301395297,-0.1366636753,0.1866870224,-0.2076076269,-0.4219763577,0.3289388418,-0.0730949938,0.2341246754,0.2311662287,-0.0001097652,-0.0474842824,0.2344948649,-0.0567841865,-0.2778341174,-0.3594297469,-0.09514191,-0.3835330904,0.6282963753,-0.3426682949,-0.061945729,-0.013691307,0.2064764798,0.5288489461,0.3657016456,0.3335526288,0.2460253388,0.2046816349,0.012928457,-0.1019961089,0.3749512136,-0.0657254681,-0.0736380145,0.4479817748,0.2363180369,-0.2795596421,0.1032444909,0.0722953826,-0.2124039084,0.1664639562,0.1779628992,0.0769344494,-0.0671167746,-0.123390466,-0.1497291774,0.115436919,0.3498918712,-0.199906677,0.0064630695,-0.0781896412,0.1789008677,-0.3727684319,0.1460716277,0.3057048917,-0.3531241417,0.0832639858,-0.0329890773,-0.181336239,-0.1379807144,0.3700060248,0.1592617184,0.3465178907,-0.0843496472,0.2189971656,0.3824252486,0.0736254156,0.4075033367,0.008381254,-0.036072854,0.0784764364,-0.1490985155,0.1150645837,0.0018030468,0.4292596579,-0.0158935711,-0.1278882474,0.2102989256,-0.0398970097,0.1295638382,0.2672181726,0.0784155056,0.1771277487,0.1082350761,0.0198865347,0.0342881978,-0.0640786588,0.0264675692,0.0537834838,-0.1929517835,-0.1257613897,-0.0986878201,-0.1596484482,0.2492925823,-0.1578544676,-0.3151191175,-0.3915263712,-0.3593124747,-0.0904713646,0.0977066159,0.4054092169,-0.1776591688,0.0079453774,0.1842356175,0.2125342637,-0.3329145312,-0.2086358815,-0.141910702,0.1688598245,-0.3038463891,-0.0869803727,0.3669921458,-0.086929746,0.4350621104,0.0915842801,-0.090644829,0.0990218073,-0.0513410829,-0.1203421578,-0.2850756645,0.1791954339,0.2982243299,-0.0311093982,0.1415540427,-0.3812169433,-0.1345567703,0.3453596234,-0.2035247684,-0.2004272789,-0.1206995025,0.1847997904,-0.022888355,-0.1863526404,-0.3829165697,0.1329852343,0.1375317723,-0.2288273126,-0.2935593426,-0.2705018818,0.0196134262,-0.2460684329,-0.0110319657,-0.009930037,-0.2936679721,-0.0903062448,-0.1332146525,0.04531762,0.2246524394,0.3951141834,-0.529274106,0.4179309309,-0.1316703111,0.3782483339,0.6474629045,-0.0438345782,-0.3523705602,0.077712886,0.0100973314,-0.131932348,-0.1612619311,0.2264738828,0.0355200097,0.0866481587,0.2476299405,0.2930698097,0.0733571351,0.1554504782,-0.340482682,-0.2222441286,0.2725386918,0.198863402,0.0550471209,0.0438532643,0.0352111273,0.1302620322,0.3609609306,-0.2110332102,-0.2131267786,0.1836090833,0.1716726571,-0.0342067294,-0.1786791086,0.0559241809,-0.3501668572,-0.0080574574,-0.1969958842,0.2563146055,-0.0577348173,-0.0100110248,-0.2123849392,0.1170311123,0.0096824169,0.0178452879,0.1186448783,0.2356435657,0.0754365325,-0.0449417457,-0.0608420111,0.2400655001,-0.0117313126,0.0717653781,-0.1706349701,0.3285128176,-0.2863952219,-0.4844674468,0.2462784499,0.0502860248,0.1879673451,-0.0319809169,0.0527980439,0.1622606963,-0.1116875559,-0.202608183,-0.1910264045,-0.1893150061,0.0705778226,-0.318967849,0.0696857125,0.0835235938,0.2004163563,0.0006734337,0.0999838635,0.2382402271,0.0537171625,0.0103423893,-0.0235097781,0.0162903983,0.1499005109,-0.1002154723,0.1077844501,0.3029182255,0.3645248115,0.319503665,0.1586574167,0.0025900907,-0.4805756211,-0.024724666,0.3976528347,0.1972628981,0.1791994125,0.0537569784,-0.0763690695,-0.045942273,0.3068538308,-0.3248250484,0.4616414607,0.3096235693,-0.2889766693,0.0340869911,-0.2853967547,-0.2893974185,0.137168631,-0.093845509,0.308326751,-0.0694536492,0.2494655699,-0.1373800337,-0.468562901,-0.1146634743,0.0716922432,0.3634001315,-0.2764385343,0.1655787677,-0.278968811,-0.5766425729,-0.0026118536,-0.3778031766,-0.2139499485,-0.1601440459,-0.0080164643,0.3329255581,0.251232326,0.2919051349,0.2842282951,0.1532880813,-0.0797626078,-0.0617443994,-0.1177826375,-0.2035913169,-0.4503104985,0.0792020112,0.2295377702,0.2759786546,0.1467198431,-0.2758328021,-0.1061238423,-0.0484323017,-0.2091184407,0.0643989444,-0.1999296844,0.4625612795,0.1053710431,0.1716955453,-0.4071997106,-0.3779241443,0.3300964832,-0.1246658117,-0.1140806824,0.0141204325,-0.066068992,-0.1999975592,-0.1609916091,-0.2568092942,-0.3521341681,-0.3320152164,0.2843401432,0.3300653398,0.183504656,0.2762922049,0.1325851828,0.3061435819,-0.0540185533,0.0262596291,-0.1248756647,0.0732353032,0.0929615498,-0.1124174595,-0.4067388177,0.0862567574,-0.0165202506,0.1904916912,-0.0490399972,-0.4750238955,-0.4325562119,-0.0386491977,0.3313347399,-0.1380579025,0.1436620653,0.3142635226,-0.077102609,0.0089175552,0.0811164603,-0.2799062431,0.1458631903,0.1194532737,0.2612983584,-0.0505969338,0.0772078484,-0.0448262095,0.5389634371,-0.0806145668,-0.0731455907,0.4156589806,-0.1554380357,0.1861876696,-0.000105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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/157","title":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\"","comments":"> Oh I see you might have a wrong version of pyarrow install on the colab -> could you try the following. Add these lines to the beginning of your notebook, restart the runtime and run it again:\r\n> \r\n> ```\r\n> !pip uninstall -y -qq pyarrow\r\n> !pip uninstall -y -qq nlp\r\n> !pip install -qq git+https:\/\/github.com\/huggingface\/nlp.git\r\n> ```\r\n\r\nTried, having the same error.","body":"I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a","comment_length":65,"text":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\" \n I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a \n > Oh I see you might have a wrong version of pyarrow install on the colab -> could you try the following. Add these lines to the beginning of your notebook, restart the runtime and run it again:\r\n> \r\n> ```\r\n> !pip uninstall -y -qq pyarrow\r\n> !pip uninstall -y -qq nlp\r\n> !pip install -qq git+https:\/\/github.com\/huggingface\/nlp.git\r\n> ```\r\n\r\nTried, having the same error.","embeddings":[-0.1167454496,0.1842945516,-0.0243378729,0.4034665227,0.1603583395,-0.0801019296,0.2126228809,0.1767457724,-0.1010505483,-0.0292401705,-0.0643654838,0.8222458959,-0.1264523864,-0.0137211913,0.3064845204,-0.3175931871,-0.0604344048,0.4053914547,-0.0765027627,-0.073327899,-0.1381295174,0.4036580622,-0.3136930764,0.175076738,-0.0893364996,-0.1419267952,-0.1608567387,0.1332477778,-0.1906817406,-0.4354628921,0.3544144034,-0.0906923413,0.2426740378,0.2432136685,-0.0001137994,-0.0411222465,0.241860643,-0.0652759075,-0.3070584536,-0.3983516097,-0.1334444284,-0.3729758859,0.6542971134,-0.3005972505,-0.0274334233,-0.0415602624,0.2386431545,0.5376312733,0.372884959,0.3187704384,0.2171253115,0.1901440918,-0.0212035589,-0.1347409785,0.3434083462,-0.0393818244,-0.0503408164,0.4133885205,0.2552083433,-0.264321357,0.1400381029,0.0521855764,-0.2430432737,0.1733251661,0.210436061,0.0216798708,-0.0455762856,-0.1478258073,-0.1497154981,0.1675877124,0.3823014796,-0.1461519599,-0.0075807711,-0.0868382379,0.1697665751,-0.3536817133,0.1502903253,0.2991668582,-0.3662483096,0.0429872498,-0.038737949,-0.2004193366,-0.1347132027,0.4069931209,0.1866998523,0.3191030025,-0.08083345,0.2694012523,0.4425932467,0.0977581143,0.3867422938,0.0186899193,-0.0305929445,0.0945214853,-0.2340413332,0.1411139369,-0.0283439737,0.4344560504,-0.0264845882,-0.1392008662,0.1912462711,-0.0719764531,0.0989132524,0.2541507781,0.1694355756,0.1823100895,0.1392900199,0.0455916077,-0.0352401771,-0.0994457081,0.0498521775,0.075848937,-0.2112872303,-0.1360410899,-0.0762299001,-0.1687743366,0.2506899238,-0.1541397721,-0.3055714071,-0.3601968884,-0.3899203837,-0.1199926063,0.1078698933,0.3979915977,-0.114564009,0.0822231323,0.1959000975,0.2271794677,-0.3056085706,-0.2729312181,-0.1145991012,0.1753754765,-0.288784802,-0.0823075995,0.3766977489,-0.1048520356,0.3892615438,0.1520421654,-0.1241002977,0.0676041543,-0.0555735901,-0.1365541667,-0.3021424711,0.1692290157,0.2672598362,-0.0021346167,0.2127933055,-0.3595523238,-0.150388822,0.3658851981,-0.2205357254,-0.2247951478,-0.1220563129,0.1412147284,0.028386917,-0.1768839955,-0.444557786,0.1517373621,0.1788907051,-0.2466423362,-0.2768021226,-0.3186874688,0.0077811056,-0.2150366306,-0.0710537508,0.008767209,-0.2900655568,-0.08582706,-0.1462416798,0.0773742869,0.2142516971,0.4388743043,-0.5672301054,0.4444164336,-0.1419268101,0.3385083973,0.678235054,-0.0183102805,-0.32352826,0.0883158296,0.0305543877,-0.1364798397,-0.1348195374,0.2449487597,0.0429064482,0.0743577257,0.2120756805,0.2548865378,0.037367519,0.1282247007,-0.3341502249,-0.2208392173,0.3260882199,0.1719200313,0.0449679792,0.0858539268,0.0007421771,0.1515171081,0.3673003316,-0.194008559,-0.2241534442,0.2160496712,0.1658680588,-0.0558227673,-0.1567902863,0.0522089601,-0.3774954975,-0.0272505376,-0.1940360069,0.2364625037,-0.0366313718,-0.0170107838,-0.2251413763,0.1077531055,0.0393063426,0.0416889675,0.0596334226,0.2118602544,0.0463873968,-0.0546706319,-0.0957266763,0.2498774827,-0.0153152961,0.045964919,-0.2043724954,0.2887839079,-0.2584067881,-0.4483318329,0.2522760332,-0.024650488,0.231608212,-0.0450183041,0.0484644398,0.1449431181,-0.109044157,-0.1970139444,-0.2191610485,-0.1847279966,0.0860859305,-0.3083516061,0.0279370472,0.1044317931,0.2074577808,-0.0422420092,0.0940594897,0.2297780812,0.0535009876,0.0939685702,-0.0705597028,0.0453821644,0.1401529163,-0.056574136,0.1223993227,0.3100036383,0.3866485655,0.3548178375,0.1566017568,0.0273696184,-0.4818149507,-0.0249916818,0.3454561234,0.2102758139,0.1618045419,0.0827300772,-0.0298936423,-0.0505774803,0.3143811822,-0.3307668865,0.4674462378,0.2590411007,-0.3121702969,0.0196408443,-0.2590818405,-0.276958257,0.1626333147,-0.1040933803,0.306971252,-0.1221771836,0.2225687504,-0.1370518208,-0.4423549771,-0.1141143963,0.1217770055,0.4049737155,-0.3246611655,0.1975838691,-0.2822492123,-0.5722625256,-0.0103235701,-0.3882720172,-0.2582880259,-0.1644416749,-0.0355504192,0.3743381202,0.258903563,0.2822013795,0.2758613229,0.220667094,-0.043692071,-0.0294897668,-0.1224362999,-0.1835069507,-0.4691478014,0.0328801945,0.232235983,0.2515956163,0.1045099199,-0.2957598269,-0.1997369677,-0.0619781204,-0.1777971983,0.0704857782,-0.2100956291,0.4583040476,0.1514371932,0.2439417243,-0.4031871855,-0.3655143976,0.3061071634,-0.0678867996,-0.0983480066,0.056174513,-0.101801835,-0.1774573326,-0.1754218638,-0.250941813,-0.299520731,-0.2887907624,0.3235304654,0.2983064353,0.234187752,0.2694034576,0.1197186485,0.2639577687,-0.0644705892,0.0239801705,-0.1363691986,0.0608765669,0.0808318183,-0.062325146,-0.3854481876,0.0866534561,0.0299976803,0.1967360675,-0.0461696647,-0.489589572,-0.4529497921,-0.0650658906,0.3251758516,-0.158857137,0.1583993137,0.3022809923,-0.0926661566,0.0465169959,0.1072315574,-0.2898403406,0.2133358717,0.1094611287,0.2854808271,-0.0139041962,0.1235152185,-0.0094498489,0.548443675,-0.001754429,-0.0684523657,0.3987729549,-0.1526180655,0.1563472599,0.0027434924,-0.3681469262,0.0373674221,-0.0233739745,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/157","title":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\"","comments":"Can you post a link here of your colab? I'll make a copy of it and see what's wrong","body":"I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a","comment_length":19,"text":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\" \n I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a \n Can you post a link here of your colab? I'll make a copy of it and see what's wrong","embeddings":[-0.0944177732,0.0577484891,-0.0563767403,0.4275850356,0.163964808,0.0742794946,0.2727659643,0.117012091,0.1710002273,-0.0799954087,0.0186920296,0.6328831911,-0.2422109395,0.0163328592,0.2107996494,-0.2766317427,-0.0907839686,0.3511370122,-0.0092701586,-0.0968043208,-0.1953075379,0.3334060013,-0.3628482819,0.0484167077,-0.0346082114,-0.2740375698,-0.1557290107,0.1878253669,-0.1242923886,-0.3305407763,0.4099054337,-0.0354296677,0.3256011903,0.1078403667,-0.0001117283,-0.1089981943,0.3479312658,-0.1518660933,-0.4430922568,-0.3908513784,-0.4066255093,-0.3311358094,0.5561751127,-0.2386394292,-0.0143266022,-0.0093313446,0.2354069352,0.2007259279,0.3323273659,0.2858671546,0.2596550584,0.0442861244,-0.2355460078,-0.2654969394,0.050662417,0.0879464373,0.0988134146,0.163086772,0.0925930962,-0.0973792523,0.1707882583,0.0481715389,-0.2419967353,0.1752585024,0.1400122643,-0.1347237825,-0.1181351542,-0.1624150425,-0.0806269869,0.1478787512,0.5082919598,0.1030740067,-0.0987901837,-0.1000477523,0.1896934956,-0.286546886,0.1539595574,0.1754411161,-0.2739141583,-0.1033988893,-0.1660094112,-0.2197421938,-0.1112829596,0.4456929862,0.2409761995,0.3562979996,-0.2257326692,0.298609972,0.3900255263,-0.0510845967,0.2951830328,-0.1574724019,-0.0734995455,0.131653443,-0.4317269921,0.074965544,-0.0500257723,0.4330421984,-0.0119189359,-0.0132096596,0.1902484596,-0.061675325,-0.2391259819,0.1778040081,0.4587350488,0.0673510954,0.3295468688,0.2016974092,-0.201498732,-0.1453443468,-0.0136575485,0.0482084043,-0.2767910063,-0.0094084013,-0.0984970555,-0.0740562975,0.1964335889,-0.2669284046,-0.2038516402,-0.3185021281,-0.2895675004,-0.1457579732,0.2442164272,0.4916263223,0.020174643,0.3007639349,0.1570000947,0.0578117892,-0.0931734443,-0.3615260124,-0.2038238198,0.1619384289,-0.1468799859,-0.0615600124,0.2930540442,0.0340116471,0.448358655,0.0203078482,-0.1338737011,0.0599753261,0.06086272,-0.2019215226,-0.2287667394,0.0642865524,0.3478239477,0.0982966796,0.3062968552,-0.2995078564,-0.1201545522,0.1795665324,-0.3466897309,-0.07242091,-0.0808773637,0.1977057308,0.0796237215,-0.141462639,-0.4857795537,0.2512125671,0.2131908238,-0.3777722716,-0.3341540992,-0.2774744034,-0.2586355507,-0.1477241069,-0.1644667387,0.192593798,-0.1633934379,0.0110202869,-0.1721020788,0.1691295505,0.2013225406,0.4515893161,-0.6045714617,0.3232270479,-0.1837305427,0.3216724992,0.6954843998,0.0425289907,-0.1176911518,0.2242796719,0.1377721727,-0.1988940984,-0.0700929463,0.3250871301,0.0055549396,-0.0742184818,0.194830209,0.2920792699,-0.070395194,0.0888627917,-0.1292112172,-0.085827291,0.3985470533,0.1036651358,-0.1579093635,0.2847554684,0.0225734469,0.387608707,0.318256706,-0.2133907676,-0.204695195,0.3692450523,-0.0771973133,-0.2113891542,-0.1649078429,0.0796319917,-0.5164647102,-0.148133561,0.0552004538,0.0568614788,0.2273904979,0.0358464494,-0.2773908675,0.0528135635,0.0634371564,0.1129316464,0.060874518,0.3140868247,-0.0475826748,-0.1477338076,-0.1583509892,0.3639943004,-0.1363474876,-0.1746114641,-0.3826625645,0.2217883319,-0.0929973945,-0.1971967965,0.3563100994,-0.1209462881,0.3216608763,0.0056677344,-0.076805912,0.1255416572,-0.0518943481,-0.0745484009,0.0108739641,-0.2348690331,0.0723031461,-0.2150939852,-0.0121060992,0.1338879019,0.3557282984,-0.1919794381,-0.1114845201,0.2194107622,-0.0630843714,0.2133897245,-0.0319266282,0.1152900681,0.3205166459,0.0824629217,0.0267626606,0.1700761914,0.3108051121,0.338758409,0.1589118838,0.096321106,-0.4620636702,-0.0288305581,0.3394735157,0.2506683767,0.1097115129,0.067457974,0.0738063157,-0.2183005214,0.3347753584,-0.0456084646,0.5907067657,0.2570639849,-0.1428965777,-0.0963693261,-0.1601371169,-0.2419663072,0.1867603809,-0.1417369246,0.3764169216,-0.2677262723,0.0374711044,-0.0967654884,-0.3154970706,-0.2467064857,0.2001125365,0.4145114422,-0.3021208644,0.1845778227,-0.3184514642,-0.6122184992,-0.0246766657,-0.2226867229,-0.2144084424,-0.2517209351,-0.2436459512,0.3359796405,0.1686677635,0.1485218108,0.0210639033,0.300331533,0.1470391154,0.2221864313,-0.0986351073,-0.1473153979,-0.3738417029,0.0583635867,0.2481480837,0.2452021241,0.1374836713,-0.1306252778,-0.1697440445,0.0066691185,0.0038388085,0.1205846593,-0.0324912146,0.216229409,0.2202266753,0.2286649942,-0.3838885128,-0.2931216359,0.1811462492,0.0501118302,-0.0635097176,-0.0123661328,-0.0663048998,-0.1467030644,-0.221685499,-0.2276797295,-0.2492044866,-0.2721238732,0.1024553478,0.3819594681,0.2514652908,0.4363299012,0.0330109,0.2322150022,0.018253345,0.0557875149,-0.3442589939,-0.1378060877,0.061183773,-0.0679440498,-0.3699589372,0.0090392902,0.0155556751,0.243000254,-0.0309312586,-0.3123928308,-0.4251820147,0.005747512,-0.1629866362,-0.1913186908,0.1102052927,0.1411829144,-0.162750721,0.049729567,-0.0122396694,-0.4197314084,0.32576859,0.0682948679,0.3534807265,0.0213272069,0.2272073478,-0.0540841185,0.2415468991,0.2091078907,-0.1829463542,0.3785313964,-0.102518931,0.1686152518,0.0769261047,-0.4180788398,-0.0118579948,0.0942209139,-0.1602270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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/157","title":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\"","comments":"This should be fixed in the current version of the notebook. You can try it again","body":"I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a","comment_length":16,"text":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\" \n I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a \n This should be fixed in the current version of the notebook. You can try it again","embeddings":[-0.14710401,0.0616018027,-0.0582842454,0.4253875017,0.1894991249,0.0785849765,0.2161241472,0.2134936303,0.1519843489,-0.1275146753,-0.0001826019,0.6510865688,-0.1451359391,-0.0014385447,0.3353012502,-0.3381648958,-0.0270487517,0.2980506122,-0.1423709244,-0.139957279,-0.251378715,0.4030191302,-0.403865695,0.1327827126,0.0217111856,-0.2106446177,-0.1193493381,0.1600602567,-0.0570843592,-0.4420138299,0.2749314308,-0.1162693277,0.3361519277,0.0596038662,-0.0001093193,-0.1074158251,0.4051618874,-0.1475655437,-0.324451685,-0.3263498247,-0.2186392695,-0.3992671371,0.4858598411,-0.236934334,-0.0837220028,-0.2342163175,0.2003939152,0.205556497,0.2355740815,0.3608186841,0.2482601255,0.2883740366,-0.0050674905,-0.2518624961,0.113341637,-0.0598288365,0.0556300804,0.0564066246,0.1227304563,-0.2810391188,0.1201603934,0.1123895049,-0.1244824529,0.140409559,0.32755813,-0.1153151914,-0.1606588662,-0.0343969502,-0.2053136081,0.139947474,0.6114565134,0.0074364212,-0.096150212,-0.0970607251,0.1883187741,-0.1917824,0.1913514286,0.1349308044,-0.2412607372,-0.0447604172,-0.0612316094,-0.2859053314,-0.1500891894,0.5021399856,0.2336249799,0.2242006361,-0.1589904577,0.2421950251,0.3899206519,-0.0083063142,0.2520094216,-0.0961503759,0.0210110079,0.1517125517,-0.4725143313,0.0512609258,-0.0563090779,0.2896998525,-0.0615376681,0.0276436303,0.3553225398,-0.0759071782,0.0143087953,0.1539303809,0.4142656624,0.0991450697,0.3659394681,0.142717585,-0.1154273152,-0.0909942761,0.1692388952,-0.0011916872,-0.1991012245,-0.1065868363,0.0003799207,-0.1910157353,0.2669310272,-0.1325427741,-0.2063051611,-0.1803048551,-0.2383127362,-0.2072892189,0.1692766249,0.386274755,-0.0172584821,0.2506605983,0.3198311925,0.1109934077,-0.1958430409,-0.4604044259,-0.2068077028,0.1390091777,-0.1498809159,-0.1078221053,0.4200163186,0.1649085879,0.4011068642,0.0820918605,-0.2094210684,0.0880855918,0.2217310965,-0.0882465318,-0.2672819197,0.0673334524,0.2882325351,0.0381790437,0.2759985328,-0.1466917247,-0.0678562969,0.2202020735,-0.1441067457,0.0143563533,-0.0321486965,0.1994994134,0.2235759497,-0.1856115609,-0.4228378534,0.261685729,0.184596464,-0.2667084038,-0.3265155852,-0.2504821122,-0.1455685645,-0.0913558602,-0.1373983771,0.0388476476,-0.134717375,-0.0570279807,-0.1768084913,-0.0377319232,-0.0007948495,0.4105513692,-0.5428647995,0.3664244711,-0.1221485138,0.3923411667,0.6489169598,-0.0662516207,-0.2524014711,0.2570919991,0.1391685456,-0.2042081952,-0.0069453134,0.4016405046,0.0115900282,0.0561003871,0.1736215502,0.4234896302,0.0155259436,-0.0658941641,-0.2105989456,0.0261427388,0.3903925419,0.2192244977,-0.0415549055,0.1964025646,-0.0098716207,0.4987840354,0.3632690609,-0.2133999169,-0.1948372573,0.411316663,-0.1184312031,-0.0656650141,-0.037386097,0.0369416513,-0.5206589103,-0.1199092865,-0.196219787,-0.0006293911,0.2955867946,0.0231932737,-0.1861868799,-0.0230503753,0.0493758731,0.0864595547,0.0804345906,0.2497896999,-0.0671436712,-0.0959643349,-0.1293256581,0.3399986029,-0.2296832353,-0.083242774,-0.4938007295,0.1520338804,-0.0635071248,-0.127120167,0.3829320073,-0.0805837885,0.2247433364,-0.0184061583,-0.0018849538,0.2358590513,0.0078952163,-0.1829628348,-0.1754606217,-0.2189997435,0.0580682904,-0.2521507442,0.003222249,0.3137159944,0.3588713408,-0.1463735104,-0.2388788909,0.2632762194,-0.058813192,0.077931717,0.036876671,0.1455331147,0.3402607143,-0.0000087834,-0.0452250615,0.1210005656,0.363027066,0.2222457528,0.2289000452,-0.0109761916,-0.5415526628,-0.166392535,0.236135602,0.1183262765,0.2350784242,0.0695296824,0.0505542159,-0.134726584,0.3139561117,-0.0789088756,0.564648807,0.258366704,-0.1720236689,-0.0106734736,-0.2668840289,-0.2177450806,0.2246934474,-0.1679311544,0.2259047627,-0.1199601144,-0.0455366187,-0.2276471853,-0.3853067458,-0.0195681211,0.2285125256,0.3585436642,-0.3488200903,0.0707585886,-0.2900004983,-0.5700064301,-0.0472625345,-0.0852352977,-0.2404441535,-0.2873762548,-0.1835914999,0.372112602,0.2075570822,0.1819979995,0.1255182475,0.3339088559,0.0761348605,0.3449693024,-0.032909546,-0.0340282954,-0.3831047416,0.0628434196,0.2666669488,0.2797673643,0.1618136168,-0.1823485941,-0.1599879563,-0.1668965816,-0.0793588087,0.0458260551,-0.019588232,0.2268973887,0.2402881831,0.1081629395,-0.2062222213,-0.1785273105,0.2137962878,-0.0022068836,-0.1256932914,0.1754659116,0.024077747,-0.2419172376,-0.1336048245,-0.3721008599,-0.2026903331,-0.2859755754,0.2898208201,0.2603527606,0.1501604766,0.2237322927,-0.0443140492,0.2782638967,-0.0599956773,0.059785068,-0.2615355551,0.1652655751,0.131751895,-0.1247980446,-0.474758625,0.0284088776,0.1332520843,0.2597393692,-0.0391957946,-0.3962197602,-0.2797995806,-0.0628721341,-0.0415767431,-0.2037835866,0.1857272685,0.2249608934,-0.2239194959,-0.0108788814,-0.0394017845,-0.2407147884,0.3065955043,-0.0597395748,0.3338377476,0.036213845,0.2909496725,0.0233216863,0.1673305929,0.2028820068,-0.0382280461,0.3601816893,-0.0221900027,0.2296243757,0.023822844,-0.3766422272,-0.0938368216,0.0425865166,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/157","title":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\"","comments":"I am getting this error when running this command\r\n```\r\nval = nlp.load_dataset('squad', split=\"validation\")\r\n```\r\n\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/root\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/dataset_info.json'\r\n\r\nCan anybody help?","body":"I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a","comment_length":27,"text":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\" \n I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a \n I am getting this error when running this command\r\n```\r\nval = nlp.load_dataset('squad', split=\"validation\")\r\n```\r\n\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/root\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/dataset_info.json'\r\n\r\nCan anybody help?","embeddings":[0.0945204496,-0.0742244646,-0.0324147865,0.4672505856,0.2305199057,0.2202830017,0.1371435225,0.2303495407,0.1524401158,-0.1321380436,0.0483731888,0.4689956903,-0.1310244799,-0.1885149926,0.2510724068,-0.2700096667,-0.1781660765,0.3005647063,0.2233232409,-0.0836042911,-0.0806101561,0.3751226962,-0.2587866485,0.1794959158,-0.0696576014,-0.1662869304,-0.1388200074,0.3408648074,-0.0691159666,-0.5248632431,0.3120110035,-0.2391539067,0.3402944803,0.1358475387,-0.0001175047,-0.0599622279,0.3421852589,-0.2002910823,-0.4428662658,-0.4260238707,-0.2834005356,-0.4043212235,0.4492724538,-0.2842895389,0.0768049881,-0.1121732667,0.3357010484,0.1443691105,0.5330747962,0.2761425376,0.1718373299,0.0707812831,-0.1612328887,-0.1983360797,-0.0910207927,0.1816204339,0.1172778234,-0.0071002003,0.0522170886,-0.0216119569,0.2575652599,0.1014679894,-0.2076477408,0.1450597048,0.2254332453,-0.0419415273,-0.1588707566,-0.2111566216,-0.0347859897,0.2176091373,0.459060967,-0.0522607416,-0.3170992434,-0.1796333343,0.2186481208,-0.1685426831,0.1927463114,0.3058081567,-0.2965562642,-0.0683341622,-0.1642871052,-0.3450191021,-0.1650012136,0.3975941539,0.2750882208,0.153581202,-0.2077714801,0.2252542078,0.3455044031,-0.0361194052,-0.1980067343,-0.1579736769,-0.0737132952,0.2626844943,-0.3977152109,-0.0694361851,-0.0915700495,0.1090952829,0.1139032021,0.0260958076,0.2392785549,-0.1550077498,-0.2616507709,0.2248243392,0.4656402171,0.1235243306,0.5095347166,0.1183806881,-0.1002926752,-0.1797017157,0.2383677065,-0.0380186066,-0.3207312226,-0.0792199075,-0.0185729247,-0.1307921857,0.2193070948,-0.3075301647,-0.2229974419,-0.1044669822,-0.277685076,-0.1764388531,0.1635672897,0.5018176436,0.0014942345,0.2334463447,0.1880491823,0.1749494523,0.0047678435,-0.347222209,-0.1687656045,0.2098423243,-0.0978954658,0.0369908288,0.3579716086,-0.0562426485,0.5068039894,0.0184472632,-0.2095424831,0.0261955187,0.1318286508,-0.118098937,-0.2231777161,0.0519675463,0.3733535707,0.1931856722,0.3314566612,-0.2338434309,-0.1246823817,0.1174580306,-0.4283104241,-0.103522025,-0.0473563373,0.1175819486,-0.0290024411,-0.1308630854,-0.4799637198,0.1306148916,0.1781004667,-0.4104081988,-0.2551843226,-0.1001109034,-0.1320746094,-0.159317404,-0.0487702191,0.3155113161,-0.1800805777,-0.2033411115,-0.0260751415,0.0535214245,-0.0411030874,0.5622394085,-0.5059040189,0.2713315785,-0.2215330005,0.3962130845,0.8447894454,-0.2791198194,-0.0745531693,0.3374209404,-0.0547371395,-0.1533975005,0.0275876578,0.3057096899,-0.0191108789,-0.0021677173,0.1813527495,0.3129216731,-0.0446244329,0.073014535,-0.094625555,-0.150456652,0.3942299187,0.1227044836,-0.2146468014,0.199151516,-0.0499236733,0.4528456032,0.2658727467,-0.1584924608,-0.1263805032,0.5303464532,-0.0729102418,-0.0173793975,-0.0843213871,0.0034207061,-0.6630390286,-0.0856323913,-0.079698585,-0.1029730737,0.0860074535,0.0132862804,-0.1351171136,-0.0978125557,-0.037882328,0.0486694463,-0.0134728076,0.2081670761,0.0850289389,-0.1480762511,-0.2450399697,0.2566251457,-0.2056079656,0.0003631883,-0.5732815266,0.2165286392,-0.0633266196,-0.1461006701,0.2288919985,-0.0382383727,0.2623355687,-0.0182976779,0.0105227828,0.1740931422,-0.0960973129,-0.0670991987,0.144589439,-0.1476926357,0.0615387186,-0.2101720721,-0.0106029259,0.2204058766,0.3082589805,-0.170662865,-0.3535010517,0.2086140811,-0.0569116659,0.3180491328,-0.0306315385,0.0880767256,0.2986536622,0.0288564973,-0.0738666952,0.1052178144,0.3209812343,0.2160905302,0.1885679662,0.0780155137,-0.3490473926,-0.198389262,0.3542863131,0.176728189,0.1097875759,0.1547648162,0.0201387741,-0.1638192981,0.1139688417,-0.1261243671,0.610334456,0.2790957689,-0.2174886912,-0.1357534379,-0.1227157116,-0.1258223504,0.1132868454,-0.1588297933,0.2757975757,0.004947497,0.0412526987,-0.0545984097,-0.317029804,-0.2727826238,0.1860586852,0.4085494578,-0.3574673235,0.0801150724,-0.2669263482,-0.4085353315,-0.0564550236,-0.1330900043,-0.3703535795,-0.2320026606,-0.1946538538,0.2208274752,0.3217781484,0.0679833293,-0.0536397845,0.3023664951,0.1571761072,0.010820169,-0.1574364752,-0.0606794804,-0.3003083169,-0.0399848297,0.1811008006,0.1816272438,0.1177246571,-0.1682049036,-0.189560473,-0.1653555036,-0.0380487554,0.0624029227,-0.0236980654,0.1431701332,0.3579214513,0.4521507919,-0.1662943363,-0.1952404678,0.2785131037,0.1151324362,-0.1622625589,0.0508082695,0.0326858833,-0.0698714033,-0.215606451,-0.3680701554,-0.1935552806,-0.2798214853,0.2910511792,0.309706986,0.2572421134,0.3628795743,0.0027240445,0.3270461857,-0.2055666298,0.2300758809,-0.3146944046,-0.146342501,0.1815907806,-0.0957210213,-0.3216098845,0.1054399759,0.1184149384,0.1621737182,-0.1154639795,-0.3580621779,-0.4874969721,0.0048743812,-0.0502634533,-0.3202095628,0.1484276205,0.3125919998,-0.274921149,0.0704339072,-0.0273816306,-0.480682075,0.3043287396,0.1231428012,0.4395596981,0.0070705772,0.465110451,0.0839045346,0.4180484712,0.2728458941,-0.1289700866,0.3143639565,-0.1395247728,0.2892531753,0.0977075323,-0.3613308072,-0.00551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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/157","title":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\"","comments":"It seems like your download was corrupted :-\/ Can you run the following command: \r\n\r\n```\r\nrm -r \/root\/.cache\/huggingface\/datasets\r\n```\r\n\r\nto delete the cache completely and rerun the download? ","body":"I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a","comment_length":28,"text":"nlp.load_dataset() gives \"TypeError: list_() takes exactly one argument (2 given)\" \n I'm trying to load datasets from nlp but there seems to have error saying \r\n\"TypeError: list_() takes exactly one argument (2 given)\"\r\n\r\ngist can be found here\r\nhttps:\/\/gist.github.com\/saahiluppal\/c4b878f330b10b9ab9762bc0776c0a6a \n It seems like your download was corrupted :-\/ Can you run the following command: \r\n\r\n```\r\nrm -r \/root\/.cache\/huggingface\/datasets\r\n```\r\n\r\nto delete the cache completely and rerun the download? ","embeddings":[-0.1208358333,-0.03516794,-0.0817960054,0.5541924834,0.302541554,0.1315348595,0.0039048679,0.1393863559,0.2804363668,-0.1001443416,-0.1951356232,0.3699634373,-0.1204647198,0.058258269,0.3326439559,-0.4019226432,-0.1763337702,0.2947899103,-0.1826591343,-0.0056841513,-0.0904862583,0.433855027,-0.1845223308,0.1346078217,0.0080535682,-0.187505886,-0.1384477317,0.4052764773,-0.2076117843,-0.49975124,0.3896077275,-0.0273205675,0.2414442897,0.1131715178,-0.0001096911,-0.0584160723,0.4368598461,-0.0966122523,-0.3577699065,-0.2647203505,-0.192922473,-0.350591898,0.3675930798,-0.2010151744,-0.0211331937,-0.0549998209,0.1864340752,0.1668653637,0.4770371318,0.2255970538,0.2994112968,0.3363180757,0.0674541742,-0.2147987038,0.0665507019,0.0725695267,0.0687307045,0.1771294177,0.0832876563,-0.0365010425,0.2348442078,0.1266146451,-0.0852469131,0.108896099,0.2413454205,-0.1036856472,-0.144518733,-0.0758871436,-0.083059907,0.183268562,0.4341345727,0.0184461698,-0.1963406056,-0.181243062,0.158436954,-0.3754579723,0.2296156585,0.1866718382,-0.1867710054,0.0540544204,-0.2525212467,-0.2835464478,-0.0359016508,0.333701849,0.1003297344,0.1187488586,-0.1964879036,0.1338862032,0.3819933534,-0.0455963202,-0.2346821278,-0.047804378,-0.1841405034,0.1175477728,-0.3242128193,0.127813831,-0.0064956178,0.3758376539,0.1633100808,0.1040660217,0.1301358342,0.0321311951,-0.2173241973,0.0430441052,0.4068357646,0.1638967842,0.2254475653,0.0350734815,-0.0719189197,0.0226660836,0.2335491329,0.0246645808,-0.1768237203,-0.0131368367,-0.1947178096,-0.1669892073,0.1918569654,-0.3178799748,-0.2259164155,-0.1674319208,-0.1062630415,-0.1299868673,0.1750203818,0.4389184713,-0.1331017017,0.2702865899,0.1546382159,0.2108220011,-0.0719088763,-0.3060039878,-0.2306702286,-0.022825975,-0.2534001172,-0.0309476517,0.3860832155,-0.1861059219,0.5473421216,0.0135182906,-0.1677367985,0.0134690646,-0.0357146971,0.0495146737,-0.3188311756,0.1386560649,0.2437673658,0.1978790015,0.2121309936,-0.1395173967,-0.0113407131,0.0430387855,-0.2953130007,-0.1464065462,-0.0801675171,0.2135620564,-0.1552684158,-0.1995833814,-0.4402094185,0.0507312864,0.1212089658,-0.2245112509,-0.2888998687,-0.1243139431,-0.1727029383,-0.1074631363,0.0130304741,0.3030004799,-0.1179322079,-0.2333218455,-0.0909878165,-0.1433808953,0.0395836607,0.5990224481,-0.4720256031,0.20328179,-0.2796126902,0.3399639726,0.5836390257,-0.0662525818,-0.3358995914,0.227344349,-0.0008168123,-0.01343079,-0.0993626267,0.1922669858,0.0965110585,0.0118185421,0.1164100394,0.3620453775,0.0287526902,0.1049309522,-0.1732213944,-0.1945458204,0.3117160201,0.0709671602,-0.093405135,0.3505002856,-0.0114897657,0.3573697209,0.4544574618,-0.087701261,-0.1222721264,0.5004968643,-0.1453568935,0.0492671989,-0.1668965369,-0.0882610455,-0.5441409945,0.0593109317,-0.1901802868,0.0315848924,0.0135777965,0.0000582695,-0.2903513312,-0.0430091172,-0.0103999386,-0.0132164368,0.1037643775,0.3401206732,0.0623906478,-0.1085375249,-0.1861585528,0.3082202673,-0.0766590238,0.0399836786,-0.5488080382,0.2430409193,-0.146076411,-0.184444204,0.3218106329,-0.0020530643,0.2478424311,-0.1411488652,-0.0050197276,0.2052924186,-0.2015739083,0.0224538557,0.0889196545,-0.2364064008,0.1708909124,-0.303313911,-0.0202919561,0.0921814814,0.2264540344,-0.1342033297,-0.1724210531,0.1043648496,-0.0441480801,0.1301497817,0.0137395272,0.1632827073,0.4534036517,-0.0649586022,-0.0343797356,0.0723703653,0.4089065492,0.1714710444,0.1922938973,0.0571836159,-0.4705581665,-0.0130068632,0.3720381856,0.0831544399,0.0520668589,0.1183371395,-0.0280825831,-0.1841575354,0.1566457301,-0.0922177061,0.4385109544,0.2807067037,0.021200642,0.0197020657,-0.1619890928,-0.2165618688,0.0882186219,-0.1398135424,0.1459080428,0.0268743746,0.0015689586,-0.1073058695,-0.5094411969,-0.0787741169,0.1528925449,0.4604257345,-0.344897747,0.0262953509,-0.4339517653,-0.5732690692,0.0247580074,-0.2075873166,-0.3741030991,-0.3285754025,-0.2151392549,0.3307510316,0.1884204149,0.2206446826,-0.0567403026,0.2805197239,0.0792720169,0.1388975084,-0.2551280558,-0.0306231957,-0.3055675328,0.0591915175,0.2956994176,0.1850128323,0.2659867406,-0.2459721565,-0.1127665192,-0.2350987643,0.0023600659,0.0553314984,-0.0132859424,0.2023050934,0.2612726092,0.3959534168,-0.1890043318,-0.1414331198,0.3529102206,-0.0753984004,-0.1788623333,0.0558834635,0.0728590265,-0.1878098398,-0.3019116521,-0.2071998566,-0.1801958829,-0.3855598569,0.2631172836,0.3475292027,0.15318349,0.4041582644,-0.1057731956,0.2807362676,-0.1813134402,0.1571633518,-0.3872302771,-0.178345561,0.1482327878,-0.1565333307,-0.482779175,0.0307455119,0.1828864962,0.2376771271,0.0537138619,-0.4516231418,-0.2450682372,-0.0631421953,-0.0250612721,-0.155939579,0.1497789621,0.2201609015,-0.2043899149,0.0002099902,-0.0150725851,-0.4173906147,0.2231901586,-0.0412197709,0.3961802423,-0.0561774448,0.3297177553,0.0454111546,0.3947474957,0.2492125481,-0.0296319164,0.4718222022,-0.0854204744,0.4036557376,0.0172223747,-0.3875497878,-0.0199890807,0.0398939177,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/156","title":"SyntaxError with WMT datasets","comments":"Jeez - don't know what happened there :D Should be fixed now! \r\n\r\nThanks a lot for reporting this @tomhosking !","body":"The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook","comment_length":20,"text":"SyntaxError with WMT datasets \n The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook \n Jeez - don't know what happened there :D Should be fixed now! \r\n\r\nThanks a lot for reporting this @tomhosking !","embeddings":[-0.32973665,0.0324432179,-0.0193531662,0.0127459355,0.2164737433,-0.0149221374,0.3609470129,0.5264438987,0.1301548183,-0.1121621057,0.0932864323,0.4350022972,-0.3767533898,0.1414317936,0.1508708149,-0.1754975915,-0.0268027131,0.1413965672,-0.3586361706,0.151956737,-0.2946045697,0.1583642811,-0.420961529,0.2173392326,-0.2542883456,-0.0902357399,-0.021146521,0.144950211,-0.1394597441,-0.462849319,0.140802905,-0.0065226816,0.2389953732,0.3692950606,-0.0001028357,-0.0074911187,0.1617367566,-0.0390187725,-0.3923019171,-0.3421501517,-0.4517919123,-0.1481353492,0.143838644,-0.2942019701,0.0407955088,-0.1019728407,0.012090249,-0.2335641831,0.5227859616,0.470978111,0.2622492909,0.4046530128,-0.0788426101,-0.1995568573,0.0400845148,0.1868067235,-0.0251018368,0.2721501887,-0.0462756231,-0.3078270257,0.0443689711,0.2844180167,-0.0971550569,0.2046322972,0.2288134843,0.0462167524,0.1737399995,0.0146226026,-0.1336928159,0.111689046,0.3766534328,-0.3340502977,-0.1922972947,-0.0254198257,-0.0237156339,-0.3796640635,0.1737448573,0.2510075867,-0.2430952042,0.2042771727,-0.2803643048,0.0721920952,-0.0495335385,0.280293107,-0.0419157632,0.7289190888,-0.1515552253,0.0830518156,-0.0326680392,-0.1897672564,0.1182416454,-0.0233938098,-0.0012605379,0.1437297612,-0.0696197599,-0.0688927695,0.2810450196,-0.0273937248,-0.2340159565,0.0267737973,0.1816953868,-0.1459489912,0.1988346726,0.1966277063,0.283844322,-0.064487271,0.0399041548,-0.0328306854,0.2843933702,0.2534207404,-0.027510548,0.0475533232,-0.1733619273,-0.4689917266,-0.0253391545,0.0136443032,0.5950089097,0.1803969294,-0.2701009512,-0.0289219059,-0.2787000239,-0.302798748,-0.0676975995,0.2093273848,0.121819444,-0.1600497216,0.3483577371,0.0752085149,-0.2440336943,-0.1647191793,-0.2930479646,0.340895921,-0.3188335896,-0.1684278697,0.2188079953,0.2253614962,0.0922131017,0.0471641496,-0.0809147358,-0.1988425553,0.1389292777,-0.0077622063,0.1081167087,0.1051954702,-0.1785956919,0.093339853,0.2775603235,-0.1004941463,-0.0483997054,0.2007462978,-0.0364864543,-0.0897136256,-0.1614140123,0.288959831,-0.1801721752,-0.0983834192,0.0543797128,0.0768853873,0.2552024722,-0.0877907947,0.0312968753,-0.3928907812,-0.2555431128,-0.164903596,0.1820681095,0.4742359221,-0.4417522848,-0.1943070441,-0.1998421103,-0.1128745079,0.196072191,0.1654726416,-0.1848081648,0.6032976508,-0.1668522954,0.1821197867,0.4813289344,-0.2698017657,0.1708316952,0.0655022413,-0.2529610395,0.076276429,-0.0907965153,-0.045418676,-0.0019625076,-0.0203296375,0.3685547709,0.2570970356,-0.0868278965,-0.0704206899,0.025802223,0.028126996,0.593431592,0.0462836511,-0.0008243334,-0.2142312676,0.0578708202,0.4939417243,0.2460762858,-0.057706587,-0.0333488211,-0.0137065565,0.0170148499,-0.1148447394,-0.3352916241,-0.2411742806,-0.22320728,-0.1093647555,0.0478304662,0.3630459607,-0.1524047107,0.2927405536,-0.2438348085,0.0848255977,-0.2252929807,-0.2423848957,0.2631049156,0.0465616249,-0.2522629201,-0.1086916551,-0.0686345994,-0.0800719112,0.0088367239,0.0938628986,-0.1322340667,0.1556389332,-0.2579882741,-0.3206021488,0.1305004209,0.256996721,0.1654368639,-0.0436956733,-0.2140459865,0.2780266702,0.1649134308,-0.0692972168,-0.2854145169,-0.0294151921,-0.0743030012,-0.160280183,0.1651479155,0.179478243,0.1970850676,-0.1973392218,-0.2075010985,0.1806471944,-0.023483254,0.0752795711,0.1489133984,0.1009815335,0.1457805783,0.0210331753,0.0285009481,-0.2971132398,0.1780157238,-0.0241094306,-0.1505931169,-0.0727762729,-0.1188315302,-0.1056578383,0.5293703079,0.2276985347,0.2241090834,-0.2177880704,-0.262104243,-0.081199497,0.0782550648,0.070517458,0.4799798429,0.1782226712,-0.0041789506,0.1772151589,-0.0156665258,-0.2165574729,0.216871202,0.1818329394,0.0331380144,-0.0351118222,0.0385485813,0.085117124,-0.1735186428,-0.2818623483,-0.0842829421,0.1642438173,-0.3306981623,0.0497132167,-0.4721483588,-0.3931072354,-0.4514593482,-0.3841335177,-0.1739959866,-0.4288794994,-0.1867683232,-0.0351117514,-0.2173171639,0.3002482057,-0.1404795647,-0.0482548997,0.1101874709,0.0663792789,0.098848708,-0.2670576572,-0.1530420929,0.1411233395,0.3205446899,0.1450353414,0.3701429069,-0.0794283301,0.0111533152,0.0473328829,-0.3408435583,0.1160992384,-0.1009821147,0.2626492679,0.4324879348,0.3414855003,0.0838382915,-0.2842497528,0.4270731211,-0.0637356862,0.0510102436,0.049875319,0.1940965652,-0.14796938,-0.3029948175,-0.4622400105,-0.3657283187,-0.4703692198,-0.12164215,0.1440427452,0.131994009,0.4903213084,-0.0182412937,0.3564141393,-0.0871031806,-0.0005423531,0.07355012,0.0614891574,0.2821263671,-0.312462002,-0.1920034438,0.0803076327,0.03580717,0.2169488519,-0.1713449955,-0.2317764312,0.1126878113,-0.105670698,0.1334697753,0.0481273234,0.101513952,0.1759227514,0.2410977781,-0.2015783489,-0.3006727397,-0.3467811942,0.0994992778,0.1069888249,0.125855729,-0.1017532796,0.1422888935,0.1000687107,0.3848873377,0.0874497145,-0.1828735769,0.3111949563,0.0411565527,0.3453080058,-0.0618227795,-0.3201507926,0.2449907064,-0.0587742515,0.1667978317,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/156","title":"SyntaxError with WMT datasets","comments":"Hi @patrickvonplaten!\r\n\r\nI'm now getting the below error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-28-3206959998b9> in <module>\r\n      1 import nlp\r\n      2 \r\n----> 3 dataset = nlp.load_dataset('wmt14')\r\n      4 print(dataset['train'][0])\r\n\r\n~\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    507     # Instantiate the dataset builder\r\n    508     builder_instance = builder_cls(\r\n--> 509         cache_dir=cache_dir, name=name, version=version, data_dir=data_dir, data_files=data_files, **config_kwargs,\r\n    510     )\r\n    511 \r\n\r\nTypeError: Can't instantiate abstract class Wmt with abstract methods _subsets\r\n```\r\n\r\n","body":"The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook","comment_length":76,"text":"SyntaxError with WMT datasets \n The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook \n Hi @patrickvonplaten!\r\n\r\nI'm now getting the below error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError                                 Traceback (most recent call last)\r\n<ipython-input-28-3206959998b9> in <module>\r\n      1 import nlp\r\n      2 \r\n----> 3 dataset = nlp.load_dataset('wmt14')\r\n      4 print(dataset['train'][0])\r\n\r\n~\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    507     # Instantiate the dataset builder\r\n    508     builder_instance = builder_cls(\r\n--> 509         cache_dir=cache_dir, name=name, version=version, data_dir=data_dir, data_files=data_files, **config_kwargs,\r\n    510     )\r\n    511 \r\n\r\nTypeError: Can't instantiate abstract class Wmt with abstract methods _subsets\r\n```\r\n\r\n","embeddings":[-0.32973665,0.0324432179,-0.0193531662,0.0127459355,0.2164737433,-0.0149221374,0.3609470129,0.5264438987,0.1301548183,-0.1121621057,0.0932864323,0.4350022972,-0.3767533898,0.1414317936,0.1508708149,-0.1754975915,-0.0268027131,0.1413965672,-0.3586361706,0.151956737,-0.2946045697,0.1583642811,-0.420961529,0.2173392326,-0.2542883456,-0.0902357399,-0.021146521,0.144950211,-0.1394597441,-0.462849319,0.140802905,-0.0065226816,0.2389953732,0.3692950606,-0.0001028357,-0.0074911187,0.1617367566,-0.0390187725,-0.3923019171,-0.3421501517,-0.4517919123,-0.1481353492,0.143838644,-0.2942019701,0.0407955088,-0.1019728407,0.012090249,-0.2335641831,0.5227859616,0.470978111,0.2622492909,0.4046530128,-0.0788426101,-0.1995568573,0.0400845148,0.1868067235,-0.0251018368,0.2721501887,-0.0462756231,-0.3078270257,0.0443689711,0.2844180167,-0.0971550569,0.2046322972,0.2288134843,0.0462167524,0.1737399995,0.0146226026,-0.1336928159,0.111689046,0.3766534328,-0.3340502977,-0.1922972947,-0.0254198257,-0.0237156339,-0.3796640635,0.1737448573,0.2510075867,-0.2430952042,0.2042771727,-0.2803643048,0.0721920952,-0.0495335385,0.280293107,-0.0419157632,0.7289190888,-0.1515552253,0.0830518156,-0.0326680392,-0.1897672564,0.1182416454,-0.0233938098,-0.0012605379,0.1437297612,-0.0696197599,-0.0688927695,0.2810450196,-0.0273937248,-0.2340159565,0.0267737973,0.1816953868,-0.1459489912,0.1988346726,0.1966277063,0.283844322,-0.064487271,0.0399041548,-0.0328306854,0.2843933702,0.2534207404,-0.027510548,0.0475533232,-0.1733619273,-0.4689917266,-0.0253391545,0.0136443032,0.5950089097,0.1803969294,-0.2701009512,-0.0289219059,-0.2787000239,-0.302798748,-0.0676975995,0.2093273848,0.121819444,-0.1600497216,0.3483577371,0.0752085149,-0.2440336943,-0.1647191793,-0.2930479646,0.340895921,-0.3188335896,-0.1684278697,0.2188079953,0.2253614962,0.0922131017,0.0471641496,-0.0809147358,-0.1988425553,0.1389292777,-0.0077622063,0.1081167087,0.1051954702,-0.1785956919,0.093339853,0.2775603235,-0.1004941463,-0.0483997054,0.2007462978,-0.0364864543,-0.0897136256,-0.1614140123,0.288959831,-0.1801721752,-0.0983834192,0.0543797128,0.0768853873,0.2552024722,-0.0877907947,0.0312968753,-0.3928907812,-0.2555431128,-0.164903596,0.1820681095,0.4742359221,-0.4417522848,-0.1943070441,-0.1998421103,-0.1128745079,0.196072191,0.1654726416,-0.1848081648,0.6032976508,-0.1668522954,0.1821197867,0.4813289344,-0.2698017657,0.1708316952,0.0655022413,-0.2529610395,0.076276429,-0.0907965153,-0.045418676,-0.0019625076,-0.0203296375,0.3685547709,0.2570970356,-0.0868278965,-0.0704206899,0.025802223,0.028126996,0.593431592,0.0462836511,-0.0008243334,-0.2142312676,0.0578708202,0.4939417243,0.2460762858,-0.057706587,-0.0333488211,-0.0137065565,0.0170148499,-0.1148447394,-0.3352916241,-0.2411742806,-0.22320728,-0.1093647555,0.0478304662,0.3630459607,-0.1524047107,0.2927405536,-0.2438348085,0.0848255977,-0.2252929807,-0.2423848957,0.2631049156,0.0465616249,-0.2522629201,-0.1086916551,-0.0686345994,-0.0800719112,0.0088367239,0.0938628986,-0.1322340667,0.1556389332,-0.2579882741,-0.3206021488,0.1305004209,0.256996721,0.1654368639,-0.0436956733,-0.2140459865,0.2780266702,0.1649134308,-0.0692972168,-0.2854145169,-0.0294151921,-0.0743030012,-0.160280183,0.1651479155,0.179478243,0.1970850676,-0.1973392218,-0.2075010985,0.1806471944,-0.023483254,0.0752795711,0.1489133984,0.1009815335,0.1457805783,0.0210331753,0.0285009481,-0.2971132398,0.1780157238,-0.0241094306,-0.1505931169,-0.0727762729,-0.1188315302,-0.1056578383,0.5293703079,0.2276985347,0.2241090834,-0.2177880704,-0.262104243,-0.081199497,0.0782550648,0.070517458,0.4799798429,0.1782226712,-0.0041789506,0.1772151589,-0.0156665258,-0.2165574729,0.216871202,0.1818329394,0.0331380144,-0.0351118222,0.0385485813,0.085117124,-0.1735186428,-0.2818623483,-0.0842829421,0.1642438173,-0.3306981623,0.0497132167,-0.4721483588,-0.3931072354,-0.4514593482,-0.3841335177,-0.1739959866,-0.4288794994,-0.1867683232,-0.0351117514,-0.2173171639,0.3002482057,-0.1404795647,-0.0482548997,0.1101874709,0.0663792789,0.098848708,-0.2670576572,-0.1530420929,0.1411233395,0.3205446899,0.1450353414,0.3701429069,-0.0794283301,0.0111533152,0.0473328829,-0.3408435583,0.1160992384,-0.1009821147,0.2626492679,0.4324879348,0.3414855003,0.0838382915,-0.2842497528,0.4270731211,-0.0637356862,0.0510102436,0.049875319,0.1940965652,-0.14796938,-0.3029948175,-0.4622400105,-0.3657283187,-0.4703692198,-0.12164215,0.1440427452,0.131994009,0.4903213084,-0.0182412937,0.3564141393,-0.0871031806,-0.0005423531,0.07355012,0.0614891574,0.2821263671,-0.312462002,-0.1920034438,0.0803076327,0.03580717,0.2169488519,-0.1713449955,-0.2317764312,0.1126878113,-0.105670698,0.1334697753,0.0481273234,0.101513952,0.1759227514,0.2410977781,-0.2015783489,-0.3006727397,-0.3467811942,0.0994992778,0.1069888249,0.125855729,-0.1017532796,0.1422888935,0.1000687107,0.3848873377,0.0874497145,-0.1828735769,0.3111949563,0.0411565527,0.3453080058,-0.0618227795,-0.3201507926,0.2449907064,-0.05877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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/156","title":"SyntaxError with WMT datasets","comments":"To correct this error I think you need the master branch of `nlp`. Can you try to install `nlp` with. `WMT` was not included at the beta release of the library. \r\n\r\nCan you try:\r\n`pip install git+https:\/\/github.com\/huggingface\/nlp.git`\r\n\r\nand check again? ","body":"The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook","comment_length":40,"text":"SyntaxError with WMT datasets \n The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook \n To correct this error I think you need the master branch of `nlp`. Can you try to install `nlp` with. `WMT` was not included at the beta release of the library. \r\n\r\nCan you try:\r\n`pip install git+https:\/\/github.com\/huggingface\/nlp.git`\r\n\r\nand check again? ","embeddings":[-0.32973665,0.0324432179,-0.0193531662,0.0127459355,0.2164737433,-0.0149221374,0.3609470129,0.5264438987,0.1301548183,-0.1121621057,0.0932864323,0.4350022972,-0.3767533898,0.1414317936,0.1508708149,-0.1754975915,-0.0268027131,0.1413965672,-0.3586361706,0.151956737,-0.2946045697,0.1583642811,-0.420961529,0.2173392326,-0.2542883456,-0.0902357399,-0.021146521,0.144950211,-0.1394597441,-0.462849319,0.140802905,-0.0065226816,0.2389953732,0.3692950606,-0.0001028357,-0.0074911187,0.1617367566,-0.0390187725,-0.3923019171,-0.3421501517,-0.4517919123,-0.1481353492,0.143838644,-0.2942019701,0.0407955088,-0.1019728407,0.012090249,-0.2335641831,0.5227859616,0.470978111,0.2622492909,0.4046530128,-0.0788426101,-0.1995568573,0.0400845148,0.1868067235,-0.0251018368,0.2721501887,-0.0462756231,-0.3078270257,0.0443689711,0.2844180167,-0.0971550569,0.2046322972,0.2288134843,0.0462167524,0.1737399995,0.0146226026,-0.1336928159,0.111689046,0.3766534328,-0.3340502977,-0.1922972947,-0.0254198257,-0.0237156339,-0.3796640635,0.1737448573,0.2510075867,-0.2430952042,0.2042771727,-0.2803643048,0.0721920952,-0.0495335385,0.280293107,-0.0419157632,0.7289190888,-0.1515552253,0.0830518156,-0.0326680392,-0.1897672564,0.1182416454,-0.0233938098,-0.0012605379,0.1437297612,-0.0696197599,-0.0688927695,0.2810450196,-0.0273937248,-0.2340159565,0.0267737973,0.1816953868,-0.1459489912,0.1988346726,0.1966277063,0.283844322,-0.064487271,0.0399041548,-0.0328306854,0.2843933702,0.2534207404,-0.027510548,0.0475533232,-0.1733619273,-0.4689917266,-0.0253391545,0.0136443032,0.5950089097,0.1803969294,-0.2701009512,-0.0289219059,-0.2787000239,-0.302798748,-0.0676975995,0.2093273848,0.121819444,-0.1600497216,0.3483577371,0.0752085149,-0.2440336943,-0.1647191793,-0.2930479646,0.340895921,-0.3188335896,-0.1684278697,0.2188079953,0.2253614962,0.0922131017,0.0471641496,-0.0809147358,-0.1988425553,0.1389292777,-0.0077622063,0.1081167087,0.1051954702,-0.1785956919,0.093339853,0.2775603235,-0.1004941463,-0.0483997054,0.2007462978,-0.0364864543,-0.0897136256,-0.1614140123,0.288959831,-0.1801721752,-0.0983834192,0.0543797128,0.0768853873,0.2552024722,-0.0877907947,0.0312968753,-0.3928907812,-0.2555431128,-0.164903596,0.1820681095,0.4742359221,-0.4417522848,-0.1943070441,-0.1998421103,-0.1128745079,0.196072191,0.1654726416,-0.1848081648,0.6032976508,-0.1668522954,0.1821197867,0.4813289344,-0.2698017657,0.1708316952,0.0655022413,-0.2529610395,0.076276429,-0.0907965153,-0.045418676,-0.0019625076,-0.0203296375,0.3685547709,0.2570970356,-0.0868278965,-0.0704206899,0.025802223,0.028126996,0.593431592,0.0462836511,-0.0008243334,-0.2142312676,0.0578708202,0.4939417243,0.2460762858,-0.057706587,-0.0333488211,-0.0137065565,0.0170148499,-0.1148447394,-0.3352916241,-0.2411742806,-0.22320728,-0.1093647555,0.0478304662,0.3630459607,-0.1524047107,0.2927405536,-0.2438348085,0.0848255977,-0.2252929807,-0.2423848957,0.2631049156,0.0465616249,-0.2522629201,-0.1086916551,-0.0686345994,-0.0800719112,0.0088367239,0.0938628986,-0.1322340667,0.1556389332,-0.2579882741,-0.3206021488,0.1305004209,0.256996721,0.1654368639,-0.0436956733,-0.2140459865,0.2780266702,0.1649134308,-0.0692972168,-0.2854145169,-0.0294151921,-0.0743030012,-0.160280183,0.1651479155,0.179478243,0.1970850676,-0.1973392218,-0.2075010985,0.1806471944,-0.023483254,0.0752795711,0.1489133984,0.1009815335,0.1457805783,0.0210331753,0.0285009481,-0.2971132398,0.1780157238,-0.0241094306,-0.1505931169,-0.0727762729,-0.1188315302,-0.1056578383,0.5293703079,0.2276985347,0.2241090834,-0.2177880704,-0.262104243,-0.081199497,0.0782550648,0.070517458,0.4799798429,0.1782226712,-0.0041789506,0.1772151589,-0.0156665258,-0.2165574729,0.216871202,0.1818329394,0.0331380144,-0.0351118222,0.0385485813,0.085117124,-0.1735186428,-0.2818623483,-0.0842829421,0.1642438173,-0.3306981623,0.0497132167,-0.4721483588,-0.3931072354,-0.4514593482,-0.3841335177,-0.1739959866,-0.4288794994,-0.1867683232,-0.0351117514,-0.2173171639,0.3002482057,-0.1404795647,-0.0482548997,0.1101874709,0.0663792789,0.098848708,-0.2670576572,-0.1530420929,0.1411233395,0.3205446899,0.1450353414,0.3701429069,-0.0794283301,0.0111533152,0.0473328829,-0.3408435583,0.1160992384,-0.1009821147,0.2626492679,0.4324879348,0.3414855003,0.0838382915,-0.2842497528,0.4270731211,-0.0637356862,0.0510102436,0.049875319,0.1940965652,-0.14796938,-0.3029948175,-0.4622400105,-0.3657283187,-0.4703692198,-0.12164215,0.1440427452,0.131994009,0.4903213084,-0.0182412937,0.3564141393,-0.0871031806,-0.0005423531,0.07355012,0.0614891574,0.2821263671,-0.312462002,-0.1920034438,0.0803076327,0.03580717,0.2169488519,-0.1713449955,-0.2317764312,0.1126878113,-0.105670698,0.1334697753,0.0481273234,0.101513952,0.1759227514,0.2410977781,-0.2015783489,-0.3006727397,-0.3467811942,0.0994992778,0.1069888249,0.125855729,-0.1017532796,0.1422888935,0.1000687107,0.3848873377,0.0874497145,-0.1828735769,0.3111949563,0.0411565527,0.3453080058,-0.0618227795,-0.3201507926,0.2449907064,-0.0587742515,0.1667978317,0.20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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/156","title":"SyntaxError with WMT datasets","comments":"That works, thanks :)\r\n\r\nThe WMT datasets are listed in by `list_datasets()` in the beta release on pypi - it would be good to only show datasets that are actually supported by that version?","body":"The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook","comment_length":34,"text":"SyntaxError with WMT datasets \n The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook \n That works, thanks :)\r\n\r\nThe WMT datasets are listed in by `list_datasets()` in the beta release on pypi - it would be good to only show datasets that are actually supported by that version?","embeddings":[-0.32973665,0.0324432179,-0.0193531662,0.0127459355,0.2164737433,-0.0149221374,0.3609470129,0.5264438987,0.1301548183,-0.1121621057,0.0932864323,0.4350022972,-0.3767533898,0.1414317936,0.1508708149,-0.1754975915,-0.0268027131,0.1413965672,-0.3586361706,0.151956737,-0.2946045697,0.1583642811,-0.420961529,0.2173392326,-0.2542883456,-0.0902357399,-0.021146521,0.144950211,-0.1394597441,-0.462849319,0.140802905,-0.0065226816,0.2389953732,0.3692950606,-0.0001028357,-0.0074911187,0.1617367566,-0.0390187725,-0.3923019171,-0.3421501517,-0.4517919123,-0.1481353492,0.143838644,-0.2942019701,0.0407955088,-0.1019728407,0.012090249,-0.2335641831,0.5227859616,0.470978111,0.2622492909,0.4046530128,-0.0788426101,-0.1995568573,0.0400845148,0.1868067235,-0.0251018368,0.2721501887,-0.0462756231,-0.3078270257,0.0443689711,0.2844180167,-0.0971550569,0.2046322972,0.2288134843,0.0462167524,0.1737399995,0.0146226026,-0.1336928159,0.111689046,0.3766534328,-0.3340502977,-0.1922972947,-0.0254198257,-0.0237156339,-0.3796640635,0.1737448573,0.2510075867,-0.2430952042,0.2042771727,-0.2803643048,0.0721920952,-0.0495335385,0.280293107,-0.0419157632,0.7289190888,-0.1515552253,0.0830518156,-0.0326680392,-0.1897672564,0.1182416454,-0.0233938098,-0.0012605379,0.1437297612,-0.0696197599,-0.0688927695,0.2810450196,-0.0273937248,-0.2340159565,0.0267737973,0.1816953868,-0.1459489912,0.1988346726,0.1966277063,0.283844322,-0.064487271,0.0399041548,-0.0328306854,0.2843933702,0.2534207404,-0.027510548,0.0475533232,-0.1733619273,-0.4689917266,-0.0253391545,0.0136443032,0.5950089097,0.1803969294,-0.2701009512,-0.0289219059,-0.2787000239,-0.302798748,-0.0676975995,0.2093273848,0.121819444,-0.1600497216,0.3483577371,0.0752085149,-0.2440336943,-0.1647191793,-0.2930479646,0.340895921,-0.3188335896,-0.1684278697,0.2188079953,0.2253614962,0.0922131017,0.0471641496,-0.0809147358,-0.1988425553,0.1389292777,-0.0077622063,0.1081167087,0.1051954702,-0.1785956919,0.093339853,0.2775603235,-0.1004941463,-0.0483997054,0.2007462978,-0.0364864543,-0.0897136256,-0.1614140123,0.288959831,-0.1801721752,-0.0983834192,0.0543797128,0.0768853873,0.2552024722,-0.0877907947,0.0312968753,-0.3928907812,-0.2555431128,-0.164903596,0.1820681095,0.4742359221,-0.4417522848,-0.1943070441,-0.1998421103,-0.1128745079,0.196072191,0.1654726416,-0.1848081648,0.6032976508,-0.1668522954,0.1821197867,0.4813289344,-0.2698017657,0.1708316952,0.0655022413,-0.2529610395,0.076276429,-0.0907965153,-0.045418676,-0.0019625076,-0.0203296375,0.3685547709,0.2570970356,-0.0868278965,-0.0704206899,0.025802223,0.028126996,0.593431592,0.0462836511,-0.0008243334,-0.2142312676,0.0578708202,0.4939417243,0.2460762858,-0.057706587,-0.0333488211,-0.0137065565,0.0170148499,-0.1148447394,-0.3352916241,-0.2411742806,-0.22320728,-0.1093647555,0.0478304662,0.3630459607,-0.1524047107,0.2927405536,-0.2438348085,0.0848255977,-0.2252929807,-0.2423848957,0.2631049156,0.0465616249,-0.2522629201,-0.1086916551,-0.0686345994,-0.0800719112,0.0088367239,0.0938628986,-0.1322340667,0.1556389332,-0.2579882741,-0.3206021488,0.1305004209,0.256996721,0.1654368639,-0.0436956733,-0.2140459865,0.2780266702,0.1649134308,-0.0692972168,-0.2854145169,-0.0294151921,-0.0743030012,-0.160280183,0.1651479155,0.179478243,0.1970850676,-0.1973392218,-0.2075010985,0.1806471944,-0.023483254,0.0752795711,0.1489133984,0.1009815335,0.1457805783,0.0210331753,0.0285009481,-0.2971132398,0.1780157238,-0.0241094306,-0.1505931169,-0.0727762729,-0.1188315302,-0.1056578383,0.5293703079,0.2276985347,0.2241090834,-0.2177880704,-0.262104243,-0.081199497,0.0782550648,0.070517458,0.4799798429,0.1782226712,-0.0041789506,0.1772151589,-0.0156665258,-0.2165574729,0.216871202,0.1818329394,0.0331380144,-0.0351118222,0.0385485813,0.085117124,-0.1735186428,-0.2818623483,-0.0842829421,0.1642438173,-0.3306981623,0.0497132167,-0.4721483588,-0.3931072354,-0.4514593482,-0.3841335177,-0.1739959866,-0.4288794994,-0.1867683232,-0.0351117514,-0.2173171639,0.3002482057,-0.1404795647,-0.0482548997,0.1101874709,0.0663792789,0.098848708,-0.2670576572,-0.1530420929,0.1411233395,0.3205446899,0.1450353414,0.3701429069,-0.0794283301,0.0111533152,0.0473328829,-0.3408435583,0.1160992384,-0.1009821147,0.2626492679,0.4324879348,0.3414855003,0.0838382915,-0.2842497528,0.4270731211,-0.0637356862,0.0510102436,0.049875319,0.1940965652,-0.14796938,-0.3029948175,-0.4622400105,-0.3657283187,-0.4703692198,-0.12164215,0.1440427452,0.131994009,0.4903213084,-0.0182412937,0.3564141393,-0.0871031806,-0.0005423531,0.07355012,0.0614891574,0.2821263671,-0.312462002,-0.1920034438,0.0803076327,0.03580717,0.2169488519,-0.1713449955,-0.2317764312,0.1126878113,-0.105670698,0.1334697753,0.0481273234,0.101513952,0.1759227514,0.2410977781,-0.2015783489,-0.3006727397,-0.3467811942,0.0994992778,0.1069888249,0.125855729,-0.1017532796,0.1422888935,0.1000687107,0.3848873377,0.0874497145,-0.1828735769,0.3111949563,0.0411565527,0.3453080058,-0.0618227795,-0.3201507926,0.2449907064,-0.0587742515,0.1667978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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/156","title":"SyntaxError with WMT datasets","comments":"Usually, the idea is that a dataset can be added without releasing a new version. The problem in the case of `WMT` was that some \"core\" code of the library had to be changed as well. \r\n\r\n@thomwolf @lhoestq @julien-c - How should we go about this. If we add a dataset that also requires \"core\" code changes, how do we handle the versioning? The moment a dataset is on AWS it will actually be listed with `list_datasets()` in all earlier versions...\r\n\r\nIs there a way to somehow insert the `pip version` to the HfApi() and get only the datasets that were available for this version (at the date of the release of the version) @julien-c ? ","body":"The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook","comment_length":116,"text":"SyntaxError with WMT datasets \n The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook \n Usually, the idea is that a dataset can be added without releasing a new version. The problem in the case of `WMT` was that some \"core\" code of the library had to be changed as well. \r\n\r\n@thomwolf @lhoestq @julien-c - How should we go about this. If we add a dataset that also requires \"core\" code changes, how do we handle the versioning? The moment a dataset is on AWS it will actually be listed with `list_datasets()` in all earlier versions...\r\n\r\nIs there a way to somehow insert the `pip version` to the HfApi() and get only the datasets that were available for this version (at the date of the release of the version) @julien-c ? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/156","title":"SyntaxError with WMT datasets","comments":"We plan to have something like a `requirements.txt` per dataset to prevent user from loading dataset with old version of `nlp` or any other libraries. Right now the solution is just to keep `nlp` up to date when you want to load a dataset that leverages the latests features of `nlp`.\r\n\r\nFor datasets that are on AWS but that use features that are not released yet we should be able to filter those from the `list_dataset` as soon as we have the `requirements.txt` feature on (filter datasets that need a future version of `nlp`).\r\n\r\nShall we rename this issue to be more explicit about the problem ?\r\nSomething like `Specify the minimum version of the nlp library required for each dataset` ?","body":"The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook","comment_length":122,"text":"SyntaxError with WMT datasets \n The following snippet produces a syntax error:\r\n\r\n```\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('wmt14')\r\nprint(dataset['train'][0])\r\n```\r\n\r\n```\r\nTraceback (most recent call last):\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/IPython\/core\/interactiveshell.py\", line 3326, in run_code\r\n    exec(code_obj, self.user_global_ns, self.user_ns)\r\n\r\n  File \"<ipython-input-8-3206959998b9>\", line 3, in <module>\r\n    dataset = nlp.load_dataset('wmt14')\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 505, in load_dataset\r\n    builder_cls = import_main_class(module_path, dataset=True)\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/load.py\", line 56, in import_main_class\r\n    module = importlib.import_module(module_path)\r\n\r\n  File \"\/usr\/lib\/python3.6\/importlib\/__init__.py\", line 126, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 955, in _find_and_load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 665, in _load_unlocked\r\n\r\n  File \"<frozen importlib._bootstrap_external>\", line 678, in exec_module\r\n\r\n  File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt14.py\", line 21, in <module>\r\n    from .wmt_utils import Wmt, WmtConfig\r\n\r\n  File \"\/home\/tom\/.local\/lib\/python3.6\/site-packages\/nlp\/datasets\/wmt14\/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2\/wmt_utils.py\", line 659\r\n    <<<<<<< HEAD\r\n     ^\r\nSyntaxError: invalid syntax\r\n```\r\n\r\nPython version:\r\n`3.6.9 (default, Apr 18 2020, 01:56:04)  [GCC 8.4.0]`\r\nRunning on Ubuntu 18.04, via a Jupyter notebook \n We plan to have something like a `requirements.txt` per dataset to prevent user from loading dataset with old version of `nlp` or any other libraries. Right now the solution is just to keep `nlp` up to date when you want to load a dataset that leverages the latests features of `nlp`.\r\n\r\nFor datasets that are on AWS but that use features that are not released yet we should be able to filter those from the `list_dataset` as soon as we have the `requirements.txt` feature on (filter datasets that need a future version of `nlp`).\r\n\r\nShall we rename this issue to be more explicit about the problem ?\r\nSomething like `Specify the minimum version of the nlp library required for each dataset` ?","embeddings":[-0.32973665,0.0324432179,-0.0193531662,0.0127459355,0.2164737433,-0.0149221374,0.3609470129,0.5264438987,0.1301548183,-0.1121621057,0.0932864323,0.4350022972,-0.3767533898,0.1414317936,0.1508708149,-0.1754975915,-0.0268027131,0.1413965672,-0.3586361706,0.151956737,-0.2946045697,0.1583642811,-0.420961529,0.2173392326,-0.2542883456,-0.0902357399,-0.021146521,0.144950211,-0.1394597441,-0.462849319,0.140802905,-0.0065226816,0.2389953732,0.3692950606,-0.0001028357,-0.0074911187,0.1617367566,-0.0390187725,-0.3923019171,-0.3421501517,-0.4517919123,-0.1481353492,0.143838644,-0.2942019701,0.0407955088,-0.1019728407,0.012090249,-0.2335641831,0.5227859616,0.470978111,0.2622492909,0.4046530128,-0.0788426101,-0.1995568573,0.0400845148,0.1868067235,-0.0251018368,0.2721501887,-0.0462756231,-0.3078270257,0.0443689711,0.2844180167,-0.0971550569,0.2046322972,0.2288134843,0.0462167524,0.1737399995,0.0146226026,-0.1336928159,0.111689046,0.3766534328,-0.3340502977,-0.1922972947,-0.0254198257,-0.0237156339,-0.3796640635,0.1737448573,0.2510075867,-0.2430952042,0.2042771727,-0.2803643048,0.0721920952,-0.0495335385,0.280293107,-0.0419157632,0.7289190888,-0.1515552253,0.0830518156,-0.0326680392,-0.1897672564,0.1182416454,-0.0233938098,-0.0012605379,0.1437297612,-0.0696197599,-0.0688927695,0.2810450196,-0.0273937248,-0.2340159565,0.0267737973,0.1816953868,-0.1459489912,0.1988346726,0.1966277063,0.283844322,-0.064487271,0.0399041548,-0.0328306854,0.2843933702,0.2534207404,-0.027510548,0.0475533232,-0.1733619273,-0.4689917266,-0.0253391545,0.0136443032,0.5950089097,0.1803969294,-0.2701009512,-0.0289219059,-0.2787000239,-0.302798748,-0.0676975995,0.2093273848,0.121819444,-0.1600497216,0.3483577371,0.0752085149,-0.2440336943,-0.1647191793,-0.2930479646,0.340895921,-0.3188335896,-0.1684278697,0.2188079953,0.2253614962,0.0922131017,0.0471641496,-0.0809147358,-0.1988425553,0.1389292777,-0.0077622063,0.1081167087,0.1051954702,-0.1785956919,0.093339853,0.2775603235,-0.1004941463,-0.0483997054,0.2007462978,-0.0364864543,-0.0897136256,-0.1614140123,0.288959831,-0.1801721752,-0.0983834192,0.0543797128,0.0768853873,0.2552024722,-0.0877907947,0.0312968753,-0.3928907812,-0.2555431128,-0.164903596,0.1820681095,0.4742359221,-0.4417522848,-0.1943070441,-0.1998421103,-0.1128745079,0.196072191,0.1654726416,-0.1848081648,0.6032976508,-0.1668522954,0.1821197867,0.4813289344,-0.2698017657,0.1708316952,0.0655022413,-0.2529610395,0.076276429,-0.0907965153,-0.045418676,-0.0019625076,-0.0203296375,0.3685547709,0.2570970356,-0.0868278965,-0.0704206899,0.025802223,0.028126996,0.593431592,0.0462836511,-0.0008243334,-0.2142312676,0.0578708202,0.4939417243,0.2460762858,-0.057706587,-0.0333488211,-0.0137065565,0.0170148499,-0.1148447394,-0.3352916241,-0.2411742806,-0.22320728,-0.1093647555,0.0478304662,0.3630459607,-0.1524047107,0.2927405536,-0.2438348085,0.0848255977,-0.2252929807,-0.2423848957,0.2631049156,0.0465616249,-0.2522629201,-0.1086916551,-0.0686345994,-0.0800719112,0.0088367239,0.0938628986,-0.1322340667,0.1556389332,-0.2579882741,-0.3206021488,0.1305004209,0.256996721,0.1654368639,-0.0436956733,-0.2140459865,0.2780266702,0.1649134308,-0.0692972168,-0.2854145169,-0.0294151921,-0.0743030012,-0.160280183,0.1651479155,0.179478243,0.1970850676,-0.1973392218,-0.2075010985,0.1806471944,-0.023483254,0.0752795711,0.1489133984,0.1009815335,0.1457805783,0.0210331753,0.0285009481,-0.2971132398,0.1780157238,-0.0241094306,-0.1505931169,-0.0727762729,-0.1188315302,-0.1056578383,0.5293703079,0.2276985347,0.2241090834,-0.2177880704,-0.262104243,-0.081199497,0.0782550648,0.070517458,0.4799798429,0.1782226712,-0.0041789506,0.1772151589,-0.0156665258,-0.2165574729,0.216871202,0.1818329394,0.0331380144,-0.0351118222,0.0385485813,0.085117124,-0.1735186428,-0.2818623483,-0.0842829421,0.1642438173,-0.3306981623,0.0497132167,-0.4721483588,-0.3931072354,-0.4514593482,-0.3841335177,-0.1739959866,-0.4288794994,-0.1867683232,-0.0351117514,-0.2173171639,0.3002482057,-0.1404795647,-0.0482548997,0.1101874709,0.0663792789,0.098848708,-0.2670576572,-0.1530420929,0.1411233395,0.3205446899,0.1450353414,0.3701429069,-0.0794283301,0.0111533152,0.0473328829,-0.3408435583,0.1160992384,-0.1009821147,0.2626492679,0.4324879348,0.3414855003,0.0838382915,-0.2842497528,0.4270731211,-0.0637356862,0.0510102436,0.049875319,0.1940965652,-0.14796938,-0.3029948175,-0.4622400105,-0.3657283187,-0.4703692198,-0.12164215,0.1440427452,0.131994009,0.4903213084,-0.0182412937,0.3564141393,-0.0871031806,-0.0005423531,0.07355012,0.0614891574,0.2821263671,-0.312462002,-0.1920034438,0.0803076327,0.03580717,0.2169488519,-0.1713449955,-0.2317764312,0.1126878113,-0.105670698,0.1334697753,0.0481273234,0.101513952,0.1759227514,0.2410977781,-0.2015783489,-0.3006727397,-0.3467811942,0.0994992778,0.1069888249,0.125855729,-0.1017532796,0.1422888935,0.1000687107,0.3848873377,0.0874497145,-0.1828735769,0.3111949563,0.0411565527,0.3453080058,-0.0618227795,-0.3201507926,0.2449907064,-0.0587742515,0.1667978317,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/153","title":"Meta-datasets (GLUE\/XTREME\/...) \u2013 Special care to attributions and citations","comments":"As @yoavgo suggested, there should be the possibility to call a function like  nlp.bib that outputs all bibtex ref from the datasets and models actually used and eventually nlp.bib.forreadme that would output the same info + versions numbers so they can be included in a readme.md file.","body":"Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation\/specific homepage\/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.\r\n\r\nLet's take GLUE as an example:\r\n\r\nThe configuration has the citation for each dataset included (e.g. [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/glue\/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded.","comment_length":47,"text":"Meta-datasets (GLUE\/XTREME\/...) \u2013 Special care to attributions and citations \n Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation\/specific homepage\/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.\r\n\r\nLet's take GLUE as an example:\r\n\r\nThe configuration has the citation for each dataset included (e.g. [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/glue\/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded. \n As @yoavgo suggested, there should be the possibility to call a function like  nlp.bib that outputs all bibtex ref from the datasets and models actually used and eventually nlp.bib.forreadme that would output the same info + versions numbers so they can be included in a readme.md file.","embeddings":[0.0246247817,0.118831113,-0.0451854467,0.0450591594,0.0441776179,-0.0376197174,0.1662002355,0.4080994725,-0.0163466334,-0.0382265486,-0.1563763916,0.4495483637,0.0651095584,0.1650387347,0.2494661063,0.1432768852,-0.0831086859,0.126027897,-0.0986576602,-0.1266354769,-0.0226358771,-0.000577053,0.2753751874,0.1213619411,-0.0726753324,0.0683133081,-0.0837138742,-0.1722621918,0.0561943837,-0.3044899702,0.0694034994,0.2606022656,-0.0170912296,-0.2680614293,-0.0000978849,0.0377667025,0.3440962732,0.0101948828,-0.3477296531,-0.429287374,-0.1936511248,-0.103100203,0.1493570805,-0.3307287097,0.003330522,-0.0333790928,0.2674756348,-0.1879940331,0.2923165858,-0.0892808661,0.2798752487,0.1032133102,-0.0715401173,0.163459152,0.3679850101,0.3241039813,0.0542786568,0.1083626524,0.4975113273,-0.0500445403,-0.1755764931,0.6305480599,0.0459234454,0.0036622845,0.2669553161,-0.0709657818,0.5887317657,-0.0492290631,-0.096299313,0.5263558626,0.2227196842,-0.6074836254,-0.4398880899,-0.3692813814,0.0451757535,0.2873602211,-0.1065417528,0.2674505413,-0.0276875682,0.1063177064,-0.2240421623,-0.0661293715,0.1180195734,-0.0483124331,0.4212715924,0.3543231487,-0.1338099837,0.119181104,-0.0516938493,-0.2293422073,-0.0088687297,-0.1549333185,0.0400073752,-0.0808936879,0.123238802,-0.3047404587,0.2001968026,0.0923784673,0.3088464141,0.1367182136,0.122009322,0.0266151652,-0.146077022,0.2947081029,0.5069969296,-0.1077478752,0.0833440572,-0.1293120384,0.412833482,-0.0750439763,-0.0995054394,0.1161198542,0.0377844684,0.2770670056,-0.4017126858,-0.0321898535,0.1351315826,-0.356089294,-0.1103997305,-0.209833771,-0.004396481,-0.1123190522,-0.1871229559,0.0738015249,-0.0747267827,0.0355087519,-0.2478306144,-0.0633666143,-0.0975732803,-0.2529425323,-0.1559454054,-0.0071639037,-0.4421660006,0.0629012138,0.1075693518,0.0717808902,0.43800354,-0.1486310512,-0.2177499384,-0.0932364985,0.0806349963,-0.1154959723,0.141805172,0.0463996716,-0.0120881284,0.1354492456,-0.0623426326,-0.1585858762,-0.5453760028,-0.281229496,-0.2335349023,-0.1142608896,-0.1217738688,0.2140998244,-0.2378810495,-0.0336710252,-0.1656148732,0.3436548412,0.0028632823,-0.2042075396,-0.1521623731,0.1259879768,-0.1789656878,-0.2321767509,0.167627275,0.4192044139,0.0847230032,-0.3906548917,0.0857027918,-0.1678960025,-0.224933967,0.0824193656,-0.2435826659,0.2264718413,0.0546728633,-0.0510314219,0.4179317355,-0.4451332986,-0.2007088959,0.1056299061,0.0069582798,-0.0058151111,0.1629912853,0.2626295388,0.2648093998,-0.4369670153,0.115082562,0.2264191359,-0.0909755379,0.1131956801,-0.0979316756,-0.3599979579,-0.1971422583,0.1153209433,-0.1953724027,-0.1405662,0.3687634468,0.2460271269,0.2487063557,-0.0708749965,0.0915198848,0.1125273183,0.0391069204,-0.1303308904,-0.0861907005,-0.1503329128,-0.1870399266,0.0249141753,-0.0964708105,-0.064290747,0.269320637,-0.4242702723,0.1844859421,-0.0245297439,0.0834137797,-0.3695567548,0.3480744064,0.1537958831,0.1634940058,0.1520516574,-0.0422113165,0.065561384,-0.3310511112,-0.0113126291,-0.5354913473,0.0942881033,-0.1187246069,0.0748171806,0.3878872097,0.4740018249,-0.0167377945,0.1244141161,0.0938351825,0.1163709238,-0.0099770743,0.5856169462,0.1358384788,0.544441402,0.3542374372,-0.2291153371,0.1989946216,-0.2761800289,-0.0334047265,-0.2197769135,-0.2923256159,0.0742766932,0.0806699544,0.1615872085,0.2401180714,-0.0031547656,0.0206981767,-0.127638787,-0.3540003598,-0.2646840811,-0.1925270706,0.2989507318,0.1064232588,0.2376115471,-0.0457903147,0.3892675638,0.588792026,0.2582814097,0.0722084194,-0.1035115495,-0.3918624818,-0.3607375324,0.1827531457,0.3668745756,0.0106802741,0.4147942066,0.2208257169,0.0703670457,0.0306203049,-0.0070171594,-0.0146290259,0.0639665723,0.0039685089,-0.1023051664,0.1829533577,0.2220139652,-0.0197271071,0.050205797,-0.0858023539,-0.2350770533,-0.3454783857,0.0444184206,-0.1313546598,-0.326444,-0.3867818415,-0.1903593838,-0.1276940554,-0.2918636501,0.0501473173,0.2012407184,-0.2010379136,0.2224684209,0.1106221527,0.2077444345,-0.3440878391,0.2071490139,0.0854750797,-0.3341499865,-0.0840324461,0.1393871307,0.2725419402,0.2580163479,0.3359660804,-0.06465175,0.0261683837,-0.4569425285,-0.515994966,0.1827281415,-0.2701183558,0.2415601015,0.156835109,-0.4601936936,0.122871004,-0.1160470769,-0.050750494,-0.1861074865,-0.0536630154,-0.0073613338,-0.0501456186,-0.1278600544,-0.1668647081,-0.2610401511,-0.2157847136,-0.3370561302,0.1664503664,0.0380573906,0.2831123173,0.0774715915,-0.2718665004,-0.0369202457,-0.4590601921,0.4280962944,-0.1247672215,-0.3134246171,0.1134670302,-0.2323602587,-0.28865394,-0.1188957989,0.0080536623,0.0566300452,-0.043539118,-0.406419903,-0.3878153265,0.1752029359,-0.4479329586,0.1624153554,0.3990525603,0.2329633683,0.0885439441,-0.1322378069,-0.0513090417,-0.1691470444,0.1176300421,0.0073705055,0.3056493402,-0.0071056825,-0.2780187428,0.260004878,0.4271394908,0.3567825258,-0.219072178,-0.0146385347,0.1176844388,0.1751934439,0.2322545946,-0.0309029538,0.2735729218,-0.2118646652,0.2610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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/153","title":"Meta-datasets (GLUE\/XTREME\/...) \u2013 Special care to attributions and citations","comments":"Actually, double checking with @mariamabarham, we already have this feature I think.\r\n\r\nIt's like this currently:\r\n```python\r\n>>> from nlp import load_dataset\r\n>>> \r\n>>> dataset = load_dataset('glue', 'cola', split='train')\r\n>>> print(dataset.info.citation)\r\n@article{warstadt2018neural,\r\n  title={Neural Network Acceptability Judgments},\r\n  author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},\r\n  journal={arXiv preprint arXiv:1805.12471},\r\n  year={2018}\r\n}\r\n@inproceedings{wang2019glue,\r\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\r\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\r\n  note={In the Proceedings of ICLR.},\r\n  year={2019}\r\n}\r\n\r\nNote that each GLUE dataset has its own citation. Please see the source to see\r\nthe correct citation for each contained dataset.\r\n```\r\n\r\nWhat do you think @dseddah?","body":"Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation\/specific homepage\/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.\r\n\r\nLet's take GLUE as an example:\r\n\r\nThe configuration has the citation for each dataset included (e.g. [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/glue\/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded.","comment_length":115,"text":"Meta-datasets (GLUE\/XTREME\/...) \u2013 Special care to attributions and citations \n Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation\/specific homepage\/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.\r\n\r\nLet's take GLUE as an example:\r\n\r\nThe configuration has the citation for each dataset included (e.g. [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/glue\/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded. \n Actually, double checking with @mariamabarham, we already have this feature I think.\r\n\r\nIt's like this currently:\r\n```python\r\n>>> from nlp import load_dataset\r\n>>> \r\n>>> dataset = load_dataset('glue', 'cola', split='train')\r\n>>> print(dataset.info.citation)\r\n@article{warstadt2018neural,\r\n  title={Neural Network Acceptability Judgments},\r\n  author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},\r\n  journal={arXiv preprint arXiv:1805.12471},\r\n  year={2018}\r\n}\r\n@inproceedings{wang2019glue,\r\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\r\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\r\n  note={In the Proceedings of ICLR.},\r\n  year={2019}\r\n}\r\n\r\nNote that each GLUE dataset has its own citation. Please see the source to see\r\nthe correct citation for each contained dataset.\r\n```\r\n\r\nWhat do you think @dseddah?","embeddings":[0.1459961236,0.1260700375,-0.0172191672,0.089572154,0.0626265556,-0.0980593339,0.1901406646,0.3274239004,-0.1365870982,0.0089798607,-0.145271495,0.5205239654,0.1002962738,0.0326508507,0.37821877,0.1075446531,-0.1473995,-0.0629663318,0.0565607883,-0.0082267206,0.0059154364,-0.1218897626,0.2556420267,-0.0212328024,-0.0183687899,0.1203053519,-0.097508274,-0.2274171412,0.1760930717,-0.3408681154,0.1228426918,0.3797631562,-0.0701436698,-0.2853853703,-0.0001086736,0.1405759752,0.4462239444,0.0706145167,-0.18459405,-0.4417585135,-0.3292541802,-0.2576588988,0.1386142522,-0.4017037153,-0.0440296866,0.118548505,0.1867649555,-0.2359999418,0.2564884722,-0.1184171513,0.1935309619,0.2313036621,-0.2722188234,0.2109432369,0.4602375031,0.3790766299,0.0113808513,0.0496142618,0.3216421902,-0.0529675297,-0.1204490811,0.562353909,-0.1215746328,0.0837367252,0.2450886071,-0.0117531838,0.320433706,-0.0062420312,-0.1649091095,0.5180585384,0.3185868263,-0.4948662221,-0.4512437284,-0.5133804083,0.1050352007,0.4594183266,-0.0621292926,0.3865552545,0.0475593098,0.1687434316,-0.1067017838,-0.0047656014,0.1005403474,0.0102560399,0.3739118278,0.1554258317,-0.1244882941,0.1860373616,-0.019477956,-0.0803717822,0.0637969524,-0.2097226381,-0.0261022337,-0.0222852975,0.1380210072,-0.1774203926,0.1316171736,0.0892143995,0.2456714958,0.3150606751,0.1443891376,-0.0571823828,-0.1413040012,0.3117470741,0.5554292202,-0.1336813122,-0.0731785223,0.0855107456,0.4026112854,-0.087894313,-0.0893845409,0.1603705436,0.0179317407,0.2412939668,-0.4022683501,-0.0895631835,0.2245129496,-0.2601105571,-0.2941981852,-0.1213130504,-0.0415150188,-0.2908771336,-0.2496638298,0.0559084155,0.0048680846,-0.0641784221,-0.290843308,0.1059360504,0.0068274052,-0.2634836733,-0.2163226604,0.1354009956,-0.3099243045,0.1489433646,0.1042680815,0.1488614231,0.37087515,-0.1605480015,-0.10679508,-0.1834675074,0.0414253213,-0.1364021748,0.1206368729,0.1558703333,-0.0061822073,0.309397012,0.0233883373,-0.1726247817,-0.4984369576,0.0189275499,-0.1504622847,-0.0613014959,0.1211765558,0.1209563762,-0.4014960527,-0.1249210164,-0.3572137952,0.3441362083,0.0831248239,-0.1271319836,-0.0298275966,0.0958377048,-0.1046818718,-0.2167442143,0.20299685,0.6037052274,0.094476603,-0.3677794635,0.0785753503,0.0083179902,-0.1182465777,0.1067932174,-0.2318627685,0.4463392496,0.04635581,0.0373966135,0.2166668475,-0.574362278,-0.27188766,0.1112951934,-0.1332582831,0.0907881409,0.2957184613,0.2834581137,0.2771713436,-0.4240314662,0.0544349253,0.2461315542,-0.0672732145,0.1181529537,-0.0978874788,-0.3014930487,-0.0465724804,0.0463174433,-0.2792060375,0.0065148612,0.2964585125,0.2114416212,0.303152591,0.1095388085,0.05622622,0.0741520971,0.0705616847,-0.1719370037,-0.0168912783,-0.0143582625,-0.1421349943,-0.0081006167,0.1352147758,0.0016021794,-0.0393484756,-0.4302903414,0.143793717,-0.091854699,-0.0512564518,-0.3166414797,0.2020543069,0.1330290139,0.2059656829,0.059566047,0.0042645358,0.1732726395,-0.3910719454,0.0728156939,-0.4693699479,0.1274827272,-0.0700266212,0.0972751603,0.2877371609,0.3687362373,0.2272826284,-0.0500230901,-0.0158360507,0.1769267172,0.1354346126,0.6815310717,-0.0163425338,0.4602656662,0.2904715538,-0.5873613954,0.1933742315,-0.2625128627,-0.0854331106,-0.2483108044,-0.3497190475,0.1101983935,-0.0919145346,0.1990024894,0.1812399477,0.0126496563,0.0414172411,-0.1380126029,-0.3803361058,-0.4260339439,-0.1419214308,0.4059378207,0.0526502654,0.1769868135,-0.0444141477,0.3668403327,0.3053131104,0.2437003702,0.0134226372,-0.1738654673,-0.3735981286,-0.2639075518,0.185659498,0.5812264681,0.0588332973,0.3410807848,0.1458032876,0.0462883525,0.0564634614,-0.107605733,0.0538325384,0.0469712839,-0.1568640321,-0.1075319126,0.1514116377,0.2761743963,0.048563648,0.129694894,-0.0437429436,-0.2355324924,-0.5040350556,-0.0668784827,-0.1607832313,-0.3451222777,-0.3923563659,-0.2192247808,0.026634993,-0.335919112,-0.0565615892,0.1736796796,-0.3128882051,0.1817235798,-0.0108183147,0.2305707783,-0.4563739598,-0.0576599091,0.1130955145,-0.2517158687,0.2092413455,0.061892271,0.2074334919,0.2063422203,0.2276794463,0.0069877878,-0.0038602711,-0.4430010915,-0.4584119618,0.1721507162,-0.2430887073,0.223734051,-0.068184562,-0.381262958,0.1325632781,-0.1528228372,0.0077382294,-0.164008975,-0.0976217315,0.0144634107,0.0926716849,-0.0532015525,-0.1190430447,-0.1074228212,-0.3545091152,-0.2291745543,0.1439630091,0.0865277648,0.151639998,0.1420312226,-0.3330469429,-0.0992255136,-0.2470338494,0.4689602554,-0.1464550942,-0.4327390492,0.2442490458,-0.3043130934,-0.2188072503,-0.382504493,-0.0708175898,0.0686381385,0.0416203998,-0.3691650331,-0.3203602433,0.0656622276,-0.3541071415,0.0406556539,0.4220488071,0.1390880942,-0.0038265618,-0.0347152166,-0.043318402,-0.1257904619,0.0180331022,0.0695181489,0.0802453682,-0.0301386248,-0.4453698993,0.2384728044,0.6093105674,0.357383132,-0.0821101516,0.0699076876,0.0932861716,0.2277640104,0.2781896293,-0.1315300465,0.2565012574,-0.1018916667,0.191035673,0.3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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/153","title":"Meta-datasets (GLUE\/XTREME\/...) \u2013 Special care to attributions and citations","comments":"Looks good but why would there be a difference between the ref in the source and the one to be printed? ","body":"Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation\/specific homepage\/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.\r\n\r\nLet's take GLUE as an example:\r\n\r\nThe configuration has the citation for each dataset included (e.g. [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/glue\/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded.","comment_length":21,"text":"Meta-datasets (GLUE\/XTREME\/...) \u2013 Special care to attributions and citations \n Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation\/specific homepage\/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.\r\n\r\nLet's take GLUE as an example:\r\n\r\nThe configuration has the citation for each dataset included (e.g. [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/glue\/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded. \n Looks good but why would there be a difference between the ref in the source and the one to be printed? 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/153","title":"Meta-datasets (GLUE\/XTREME\/...) \u2013 Special care to attributions and citations","comments":"Yes, I think we should remove this warning @mariamabarham.\r\n\r\nIt's probably a relic of tfds which didn't have the same way to access citations. ","body":"Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation\/specific homepage\/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.\r\n\r\nLet's take GLUE as an example:\r\n\r\nThe configuration has the citation for each dataset included (e.g. [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/glue\/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded.","comment_length":24,"text":"Meta-datasets (GLUE\/XTREME\/...) \u2013 Special care to attributions and citations \n Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation\/specific homepage\/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.\r\n\r\nLet's take GLUE as an example:\r\n\r\nThe configuration has the citation for each dataset included (e.g. [here](https:\/\/github.com\/huggingface\/nlp\/blob\/master\/datasets\/glue\/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded. \n Yes, I think we should remove this warning @mariamabarham.\r\n\r\nIt's probably a relic of tfds which didn't have the same way to access citations. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/149","title":"[Feature request] Add Ubuntu Dialogue Corpus dataset","comments":"@AlphaMycelium the Ubuntu Dialogue Corpus [version 2]( https:\/\/github.com\/rkadlec\/ubuntu-ranking-dataset-creator) is added. Note that it requires a manual download by following the download instructions in the [repos]( https:\/\/github.com\/rkadlec\/ubuntu-ranking-dataset-creator).\r\nMaybe we can close this issue for now?","body":"https:\/\/github.com\/rkadlec\/ubuntu-ranking-dataset-creator or http:\/\/dataset.cs.mcgill.ca\/ubuntu-corpus-1.0\/","comment_length":34,"text":"[Feature request] Add Ubuntu Dialogue Corpus dataset \n https:\/\/github.com\/rkadlec\/ubuntu-ranking-dataset-creator or http:\/\/dataset.cs.mcgill.ca\/ubuntu-corpus-1.0\/ \n @AlphaMycelium the Ubuntu Dialogue Corpus [version 2]( https:\/\/github.com\/rkadlec\/ubuntu-ranking-dataset-creator) is added. Note that it requires a manual download by following the download instructions in the [repos]( https:\/\/github.com\/rkadlec\/ubuntu-ranking-dataset-creator).\r\nMaybe we can close this issue for now?","embeddings":[-0.1457340121,0.2197993249,-0.065370962,-0.0233403649,0.1595499516,0.2317842543,0.2344495654,0.1404034793,-0.093551524,0.2077977061,-0.1967509389,0.2113592923,0.0908540338,-0.0198894702,-0.0083212974,-0.283608079,-0.0770926997,-0.0565560795,-0.0296263378,-0.1516617835,-0.5537121296,0.192510739,-0.3070515692,-0.0567773059,0.1344804913,-0.1004216671,0.0116874911,0.0735562816,-0.1892565042,-0.4569538534,-0.0369172283,0.2100033462,-0.3256537616,0.1866213679,-0.0000998173,-0.2117406577,0.305957973,0.0414143987,-0.3893639445,-0.3456751704,-0.1688473374,0.0708190203,-0.1088221669,0.0231406745,-0.3398527801,-0.028168736,0.0886452049,-0.2531458735,-0.1794046909,0.1825007796,0.3645730019,0.0986470953,0.0132904667,-0.5131924748,0.0464070328,-0.0297200866,-0.3338403106,0.1761569828,0.3506742418,0.234850511,-0.2275696397,0.3493562043,-0.0850725919,-0.0861073732,-0.1641721427,-0.1617786288,0.2012357861,-0.2212982625,0.2724966407,0.0855337828,0.8510781527,-0.2054710537,-0.1884159744,0.0553838126,0.2368637025,-0.1846892387,0.0259006098,0.0517957173,-0.1399551183,-0.0659550726,-0.1907348484,-0.2145889848,-0.1629605591,0.1234750226,0.0775852725,0.2758606076,-0.0982586443,-0.1178449169,-0.0648626909,-0.0285703354,0.1747392416,0.0444263257,0.0044974941,-0.0994881392,-0.4505813122,-0.1421777755,0.293445915,-0.013916715,0.1805759519,0.0792424455,-0.068962127,0.1509007066,0.2425786257,-0.1074475646,0.1982827485,0.1836605072,0.2357859761,0.1007084846,0.2788398862,0.1521478742,0.0445494503,0.0953177065,-0.1455168277,-0.1416531205,0.0203809589,0.2877107859,0.0293086581,-0.1949759722,-0.2099930197,0.0331568345,-0.010143891,0.1828418821,0.095018059,0.3426792026,-0.0702010691,0.0008310307,0.0004483556,0.1347321272,-0.1633306742,-0.237931639,-0.1635294855,0.0435231775,-0.3320834339,0.0789924711,0.3352038264,0.1977042109,0.0810485184,-0.0731780455,0.6526392102,-0.0325590707,0.0887615308,-0.0946499631,-0.1276352108,-0.0256183911,0.0849453881,0.1249081716,0.1755470335,0.1631166935,-0.1329624206,0.1215442047,-0.097945407,-0.2292851061,-0.1485717297,0.354133904,0.2843053341,-0.345941186,-0.2182160169,0.1816513538,0.0304691866,-0.1242463291,0.0360024199,0.1793025285,-0.5638617277,-0.1198016629,-0.1937254369,0.1882115752,-0.144085601,-0.0578094311,-0.3757381141,-0.0396629199,-0.0179012269,0.0567414649,-0.0736379698,0.1494026929,-0.145664528,0.2531201541,-0.0436911695,-0.4407576323,-0.4889676571,0.1472562701,-0.179641068,-0.2009970099,0.0854589269,0.0793593824,0.4515788853,-0.2775592804,0.1006058455,0.225910306,0.2275553942,0.2256549746,-0.0608956106,-0.1173453256,-0.2582629621,0.1127173305,0.1965082437,0.1305604875,0.0260700583,0.0918694586,0.1809975207,-0.1875294745,0.1367051005,0.1900651455,0.4828184545,-0.0919792801,0.3573668003,-0.1160516739,-0.0181473736,-0.0106611922,-0.2372460961,0.3643128574,0.0819884092,-0.3734312356,-0.3915190399,0.0386187695,-0.0672150254,-0.1480859667,0.2796754837,0.1446676999,-0.2751811445,-0.0765579566,-0.1154840514,0.0268470794,-0.0738224313,0.0028135788,0.0991961807,0.0292198714,-0.0367991515,0.0031727643,0.3169911504,0.1818198711,0.0539361499,0.2323714942,-0.1097493693,0.3216853738,-0.053882625,0.1996225119,0.0129265524,-0.0571823306,0.0831375122,-0.2413344234,0.1716485471,0.314267993,-0.1589084417,0.0413433015,-0.1335089952,0.0982968137,0.243168816,0.01510973,0.1955194473,0.1304554492,0.195156917,-0.2241234481,0.1276551783,-0.319267869,0.0511935018,0.2708053291,0.1071858108,-0.0309547093,-0.2921259701,0.2595498264,0.3622592986,-0.0280064922,0.3814353645,0.2174120396,-0.1518546194,-0.1470986158,0.1664779484,-0.2648390532,0.0843152478,0.3542546928,0.2902134359,0.0116119152,0.0655246228,-0.3486016095,0.0931589082,-0.0934586301,-0.1122279614,0.4130340815,0.2647446692,-0.0552241988,-0.5771736503,0.1376950294,-0.0525519997,0.0329414345,-0.2305874079,-0.2867489457,-0.1552271843,-0.4819123745,0.2490713596,0.0291622411,0.1454894543,-0.109712936,0.4188368618,0.0285632256,-0.0700844377,0.471575737,0.2065003365,0.4535330236,-0.0588373616,0.0771489516,0.2722302973,-0.2450772971,-0.084478192,0.3063847125,-0.2414114773,-0.026740944,0.5109686255,0.054128021,0.2273268104,-0.3986753225,-0.2502072752,0.2745244503,0.2686484456,0.2051804364,0.145625487,-0.1614298075,-0.2069333941,-0.1343294382,-0.1148727834,-0.0758794174,-0.3143543303,0.0239254441,-0.0332681946,-0.1479963511,-0.1009343937,-0.4821455181,0.2255835384,-0.5429046154,-0.0721463412,-0.0649805292,0.1399098039,0.0545005463,-0.1080094203,0.0203583539,0.2380866855,0.2772634327,-0.2891526222,-0.0476002768,0.3393503726,-0.4837678969,-0.3549007177,-0.2117679715,-0.1903628558,0.1838318259,0.0908524692,-0.1896077245,-0.0120774899,0.0065510469,0.0783627927,0.3021087945,-0.0885489807,0.3156242073,0.0954385102,-0.3790741563,-0.2183194607,-0.0315257944,0.043476779,0.0937239602,0.2295888662,-0.1382554621,0.1959206462,0.1474936754,0.2408270687,0.2726469338,0.3439339995,0.1083914489,0.1125005335,0.1874090582,-0.1723178774,-0.2078281194,0.2516317964,0.0688262731,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/143","title":"ArrowTypeError in squad metrics","comments":"There was an issue in the format, thanks.\r\nNow you can do\r\n```python3\r\nsquad_dset = nlp.load_dataset(\"squad\")\r\nsquad_metric = nlp.load_metric(\"\/Users\/quentinlhoest\/Desktop\/hf\/nlp-bis\/metrics\/squad\")\r\npredictions = [\r\n    {\"id\": v[\"id\"], \"prediction_text\": v[\"answers\"][\"text\"][0]}  # take first possible answer\r\n    for v in squad_dset[\"validation\"]\r\n]\r\nsquad_metric.compute(predictions, squad_dset[\"validation\"])\r\n```\r\n\r\nand the expected format is \r\n```\r\nArgs:\r\n    predictions: List of question-answers dictionaries with the following key-values:\r\n        - 'id': id of the question-answer pair as given in the references (see below)\r\n        - 'prediction_text': the text of the answer\r\n    references: List of question-answers dictionaries with the following key-values:\r\n        - 'id': id of the question-answer pair (see above),\r\n        - 'answers': a Dict {'text': list of possible texts for the answer, as a list of strings}\r\n```","body":"`squad_metric.compute` is giving following error\r\n```\r\nArrowTypeError: Could not convert [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}] with type list: was not a dict, tuple, or recognized null value for conversion to struct type\r\n```\r\n\r\nThis is how my predictions and references look like\r\n```\r\npredictions[0]\r\n# {'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}\r\n```\r\n\r\n```\r\nreferences[0]\r\n# {'answers': [{'text': 'Denver Broncos'},\r\n  {'text': 'Denver Broncos'},\r\n  {'text': 'Denver Broncos'}],\r\n 'id': '56be4db0acb8001400a502ec'}\r\n```\r\n\r\nThese are structured as per the `squad_metric.compute` help string.","comment_length":112,"text":"ArrowTypeError in squad metrics \n `squad_metric.compute` is giving following error\r\n```\r\nArrowTypeError: Could not convert [{'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}, {'text': 'Denver Broncos'}] with type list: was not a dict, tuple, or recognized null value for conversion to struct type\r\n```\r\n\r\nThis is how my predictions and references look like\r\n```\r\npredictions[0]\r\n# {'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}\r\n```\r\n\r\n```\r\nreferences[0]\r\n# {'answers': [{'text': 'Denver Broncos'},\r\n  {'text': 'Denver Broncos'},\r\n  {'text': 'Denver Broncos'}],\r\n 'id': '56be4db0acb8001400a502ec'}\r\n```\r\n\r\nThese are structured as per the `squad_metric.compute` help string. \n There was an issue in the format, thanks.\r\nNow you can do\r\n```python3\r\nsquad_dset = nlp.load_dataset(\"squad\")\r\nsquad_metric = nlp.load_metric(\"\/Users\/quentinlhoest\/Desktop\/hf\/nlp-bis\/metrics\/squad\")\r\npredictions = [\r\n    {\"id\": v[\"id\"], \"prediction_text\": v[\"answers\"][\"text\"][0]}  # take first possible answer\r\n    for v in squad_dset[\"validation\"]\r\n]\r\nsquad_metric.compute(predictions, squad_dset[\"validation\"])\r\n```\r\n\r\nand the expected format is \r\n```\r\nArgs:\r\n    predictions: List of question-answers dictionaries with the following key-values:\r\n        - 'id': id of the question-answer pair as given in the references (see below)\r\n        - 'prediction_text': the text of the answer\r\n    references: List of question-answers dictionaries with the following key-values:\r\n        - 'id': id of the question-answer pair (see above),\r\n        - 'answers': a Dict {'text': list of possible texts for the answer, as a list of strings}\r\n```","embeddings":[0.0508040339,0.0322123691,-0.0459481664,0.3747369051,0.3514669836,-0.1831914186,0.1283157915,0.2945096195,-0.0273575988,-0.016604308,0.0504663512,0.7067572474,-0.2432426214,-0.1496466845,-0.2859814763,-0.1268263459,-0.0782126337,0.2369157672,0.2221025676,0.1331424415,-0.106973663,0.3732542396,-0.2818196714,0.0420603156,-0.1871123612,-0.092268154,0.1176642776,-0.01809345,-0.4135136008,-0.6164665818,0.3017502725,-0.2972587049,0.0052366056,0.3161635101,-0.0001209838,0.0202212855,0.3255316317,-0.1212441251,-0.0995187759,-0.362477839,-0.1135787591,-0.2849887013,0.5184355378,-0.2206499279,-0.0529840961,0.1079014987,-0.2984890044,-0.3533740044,0.3960222304,0.4213611186,0.1145917401,0.5141720176,0.17944251,0.015836034,0.2684787214,0.0566137582,-0.2425853908,0.303730458,-0.0190495215,0.0541987233,0.2327842861,0.1645570397,-0.1349526644,0.0857612267,0.0732456818,0.1215908527,0.1143218726,-0.2737191319,-0.3592942655,-0.100736253,0.2150742114,-0.2580164075,-0.2781116366,0.1453799456,0.1172601357,-0.2204061449,-0.1395785362,-0.1348273009,-0.1219677031,-0.140086785,-0.0538091734,0.059328109,-0.2679055631,0.1362895519,0.0089108674,0.2972382307,-0.1583654433,0.0389316566,0.0348842293,-0.0563536137,0.0081363628,0.0158318728,-0.0161510687,0.0318318345,-0.3313934207,-0.3471526504,-0.2688043416,-0.4717374742,0.0875613466,-0.2626972497,0.4471974671,0.1234180257,0.3111550808,0.1956386268,0.1954374909,0.2209554464,0.3190047443,0.0902371332,-0.0424138866,0.0770395622,0.0266692955,-0.0146498308,-0.1296214163,-0.2506048381,-0.0045461426,0.1766952276,0.2899586856,-0.0501544364,-0.4034970403,-0.0299778003,-0.1991444379,0.1590966582,0.1704541743,0.0738310292,-0.0638078675,0.5698382854,0.1854057759,0.3461947441,-0.0511372909,0.0961199626,-0.019367015,0.0788623318,-0.009686959,-0.1923852414,-0.0270005018,0.2162169069,0.1956911385,0.0224326346,0.1642465293,-0.3621160388,-0.0669123754,-0.2479941249,0.128231436,0.0124068372,-0.0250415243,0.0036076822,0.463848561,-0.4866758883,0.0531590544,0.1031382233,-0.255577296,0.0349704623,-0.2557273805,0.079171896,-0.1247742623,-0.045316387,-0.2481400073,0.1151024848,0.2089597434,0.1211374551,-0.1219426841,-0.2758208513,-0.1292758137,-0.3359872997,0.1065729186,0.0315504782,-0.4582893848,0.0349679813,0.1058815569,0.1642006934,0.0140298782,0.3637439013,0.0033610323,0.2000980228,0.1050294712,0.0284748748,0.8762322664,-0.8044187427,-0.0820784718,-0.0090432046,0.001266808,-0.2809941769,-0.0873116031,-0.022015214,0.4402936697,0.0533394963,0.1420265436,0.0503537953,0.1453561038,-0.0085408613,-0.2638967633,-0.1193146557,0.1466754526,-0.1425349861,-0.1730227321,0.1039286926,-0.079743579,0.2684730589,0.1105622649,-0.0664785951,0.0611145757,0.1640529037,0.474242419,-0.2778576314,-0.0457406491,-0.3377385736,-0.3439454734,-0.0655030683,-0.5480741262,0.0769053474,-0.2180027813,0.0058844876,-0.4639632702,0.0206557568,-0.0563175716,0.3359313905,0.1233770177,-0.1026299596,0.0918412507,-0.09350577,-0.255533576,-0.6211904287,-0.2620673776,0.0830386728,0.1390779316,0.3291445971,-0.146387592,-0.3838292062,0.1651629359,0.4464730322,0.2130034715,0.0080681993,-0.0658437535,0.1213375106,0.3540560305,-0.1507598013,0.0106922667,-0.1044697389,0.1438919008,-0.269831419,-0.0107435836,0.2080315799,0.2057911307,-0.2543959916,-0.0049187574,0.5869116783,0.107732676,0.3149624765,-0.2861791849,0.3985180557,0.120303452,0.17695117,-0.0821278244,-0.2799807191,-0.3304685056,0.1522513032,-0.0254360698,-0.1620459706,-0.063786827,-0.1675985307,0.4572305083,0.3228217065,0.0994222239,0.1478133649,0.0465434566,0.0683184788,-0.2657335401,-0.1572823972,0.323928386,0.0054938663,-0.0097191008,-0.0885386541,0.0001503421,0.0225493163,0.2572911382,0.1510605365,0.5275332332,0.1311570555,0.3096666932,-0.1022444069,0.069310084,-0.0438624881,0.0674388111,0.1808807105,-0.3641236126,0.0111573087,-0.0118701011,0.0072927079,-0.238510862,-0.4707314968,-0.0267353449,-0.4714015424,-0.1158625707,0.0100719975,0.1432020515,0.1250333488,0.4857570827,0.0345742926,0.2919036746,-0.111506559,0.0830251873,-0.4020986557,-0.0498573147,-0.0003868992,0.1272578686,0.032095138,-0.1930103898,-0.0936159268,0.1062563732,-0.2555807829,-0.2851400673,0.0597852431,-0.3108468056,0.2366630435,0.3271429539,0.2808583081,-0.3724357188,-0.1666135937,0.3785741925,-0.0288569517,-0.1685851067,0.2441929281,-0.1426271647,-0.0243622791,-0.0988962501,-0.1292325556,-0.3072052598,-0.3354568779,0.0683564991,0.3457452357,0.3730772138,0.0531388633,0.2724443376,0.4034454525,0.0807920545,-0.000171115,-0.0012312595,-0.0164435767,0.3754096627,-0.2786477208,-0.3549692929,0.1033586785,-0.0014872467,0.3966333568,-0.1320361644,-0.1763366461,-0.4355768263,0.0963414907,-0.1231454015,-0.0684964806,0.0432276279,0.1222132444,0.1184096038,-0.1052376404,-0.1198866367,-0.0428601243,0.2057044506,0.2839810848,0.2661149502,0.0998692885,0.0670479015,0.1417685002,0.6796893477,0.5670326352,-0.3287319541,0.1067522913,0.0736637637,-0.0548754036,0.1150363311,-0.1393596083,-0.1889291406,-0.211786121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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"I would suggest `nlds`. NLP is a very general, broad and ambiguous term, the library is not about NLP (as in processing) per se, it is about accessing Natural Language related datasets. So the name should reflect its purpose.\r\n","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":39,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n I would suggest `nlds`. NLP is a very general, broad and ambiguous term, the library is not about NLP (as in processing) per se, it is about accessing Natural Language related datasets. So the name should reflect its purpose.\r\n","embeddings":[0.354604274,0.0250588469,-0.0843049809,-0.3304423392,0.1876242757,-0.2360132337,0.281853199,0.2058823705,-0.1652067304,0.0582918525,0.2198298723,0.294048816,-0.3030344248,0.1246296465,0.3023298979,-0.2793754935,0.2086221278,0.0491361916,0.1632865071,-0.1286140531,-0.1061515063,0.1799212396,0.1566794068,0.4202884436,0.0016413978,0.0405684561,0.2510459423,-0.1025928706,0.0196671039,-0.3666577637,0.0917976722,0.3173364997,-0.0723683611,0.0078898594,-0.000103016,-0.1667028368,0.5983600616,-0.0208175331,-0.1951327026,-0.2738524973,0.349458456,-0.4347401261,0.0873838514,-0.5374493003,0.1049486846,0.2493642569,0.1611560434,-0.2769201994,0.1723022461,-0.08421278,0.2791434228,-0.0322697014,-0.2336917669,0.2664329112,-0.0823114812,0.1612648368,-0.1856753528,-0.1456345767,0.1019584611,-0.0416525863,0.0535839312,0.2429705113,-0.1371386796,0.0111304624,0.4416790307,0.1220082119,0.3055425584,0.0429867581,-0.0890983418,0.1462106407,0.016002601,-0.3754999638,-0.227576077,-0.1618045717,0.0141653046,-0.4211737514,0.0508646816,-0.0911910012,-0.0744974241,-0.0652683526,0.3642609417,-0.1167289987,0.0555218421,0.1869729459,0.0563985147,0.6406509876,0.1287473142,0.1855204701,0.0186784174,-0.2868248522,0.1609270722,0.1716974378,0.2048975378,0.0659400746,0.1589836776,-0.1076443791,0.0360815078,-0.0260845404,-0.1019777954,-0.0282877255,-0.0157899559,-0.0529250763,0.1841489673,-0.1026077643,-0.0229196306,-0.0395875387,0.2494286448,0.06806878,0.3341001272,0.0483051986,0.1616839319,0.0051702829,-0.017028138,0.1136647984,0.0018841785,0.0539529808,0.1125142872,0.0556235351,0.0231455937,-0.0699977055,0.139364928,-0.0318878219,0.012281891,0.2282057405,0.0296439026,0.4047164619,0.1179114431,-0.0512215644,-0.0545108691,0.0529048257,-0.1768298894,-0.0293129664,-0.5875118375,0.1920241714,-0.0093172109,0.5461207032,0.3421474099,-0.2364342958,-0.1096441969,0.0759178326,-0.0507352985,0.1224967614,-0.0358524546,-0.0496277064,-0.4342936575,0.1021536589,-0.2238204181,-0.3176436424,-0.2762625515,0.1289541721,-0.2020074129,0.0938013867,-0.1377503276,0.3023263812,-0.2851534784,-0.0645210966,0.4819487929,0.1596666574,0.13196069,-0.2455682158,0.1019591764,-0.0245489255,0.1727491468,-0.168171823,-0.2812473476,0.0770907551,0.1417668909,-0.4519435167,-0.5630795956,0.0712455139,0.3810745776,0.0145662418,-0.1681337059,-0.0279533472,0.1246093661,0.0463554375,0.5702139735,-0.3770810962,-0.1004941016,0.110865213,-0.2571691275,-0.1666597575,0.0733325183,0.1724896133,-0.0463332757,-0.1781515777,0.2236127108,0.4268013537,0.168226257,0.1578955799,-0.2671319842,-0.1424053609,0.367061913,0.0201186519,0.0713867247,0.1450184584,-0.2018309981,0.7421375513,0.1609519124,0.0481394902,0.0549914017,0.1300477087,-0.0001047266,0.2264642864,0.052703809,-0.04857428,-0.2530002296,-0.1717445701,-0.3851439953,0.2766966522,-0.140701443,-0.3179916739,-0.0011529682,-0.0611749887,0.0840609074,-0.2455073893,0.3142194748,0.1664161682,-0.0728963241,-0.1845241189,-0.3374127746,0.2081885487,0.1162087247,0.0268573985,-0.1347388923,0.0125924638,-0.0472036041,-0.1227353066,-0.0485320874,0.4621073008,0.0617956594,0.0230287537,-0.018729629,0.1212337241,-0.0920708776,-0.1717240363,-0.1780800521,0.352711916,0.1953412741,0.0335730724,0.1522716731,0.217854023,0.0052392827,0.0609577782,0.1654213071,0.0910024494,-0.2310306877,0.172860235,0.1610245407,-0.1268762499,0.2499854863,-0.3104695678,-0.3951842785,-0.1594946086,0.2015538812,-0.1744659841,0.3688205183,0.2622098029,-0.0718516931,-0.0054821074,0.7487162948,-0.102349095,-0.0018591094,0.0346931629,0.0061895815,0.0344388932,-0.0982624963,0.4255813956,0.3692655265,0.2573755383,-0.2147238851,0.0731224418,-0.1584894955,-0.2395242006,0.1479514241,0.2729809284,-0.02106102,-0.0159713048,0.0722971559,-0.0208247695,-0.1442234069,-0.3758417368,0.1088091359,0.0435103104,0.1615836322,0.196914956,-0.2253728807,-0.3355076909,-0.3560675979,-0.1077126339,-0.5679337978,-0.212283805,0.0389435887,-0.319509536,-0.3220556676,0.1848625839,0.0278035179,0.0538053773,-0.3259667754,-0.4077161551,-0.2541850805,-0.2610703707,-0.2709332705,0.101353474,-0.0553875603,0.121204786,0.3438507915,0.075265944,0.0450470969,0.079796873,-0.4759514928,0.0199883431,-0.0811674446,0.2733127177,0.2104487866,-0.0825033486,0.1517145187,-0.4660096467,0.1668881476,-0.2525931001,0.1693013757,0.0579317883,0.152862072,-0.048633296,-0.3818205595,-0.2003627717,-0.4499168396,-0.431799382,-0.1190716177,-0.0393395089,0.0792104751,0.4727215767,-0.575882256,-0.0751322135,0.1555481106,0.2960141897,-0.1504534483,0.3245946169,-0.0218335297,-0.1896303445,0.0030149538,-0.3701182008,-0.4601126313,0.5443202853,-0.0602085814,0.0033655989,0.0939403698,0.062001247,-0.1237863004,0.2940612435,-0.1174602509,0.3699588478,0.3588147461,-0.1884727329,-0.0632570162,-0.1866520196,0.1633792967,-0.0045336215,0.3885166049,0.1221950576,-0.1400718987,-0.0451746397,0.021036813,-0.2248969823,-0.0801913887,0.2310665101,0.3564607799,0.0327313393,-0.0163829681,-0.1170815378,0.0942937359,0.1725271791,-0.119573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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"Chiming in to second everything @honnibal said, and to add that I think the current name is going to impact the discoverability of this library. People who are looking for \"NLP Datasets\" through a search engine are going to see a library called `nlp` and think it's too broad. People who are looking to do NLP in python are going to search \"Python NLP\" and end up here, confused that this is a collection of datasets.\r\n\r\nThe names of the other huggingface libraries work because they're the only game in town: there are not very many robust, distinct libraries for `tokenizers` or `transformers` in python, for example. But there are several options for NLP in python, and adding this as a possible search result for \"python nlp\" when datasets are likely not what someone is searching for adds noise and frustrates potential users.","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":143,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n Chiming in to second everything @honnibal said, and to add that I think the current name is going to impact the discoverability of this library. People who are looking for \"NLP Datasets\" through a search engine are going to see a library called `nlp` and think it's too broad. People who are looking to do NLP in python are going to search \"Python NLP\" and end up here, confused that this is a collection of datasets.\r\n\r\nThe names of the other huggingface libraries work because they're the only game in town: there are not very many robust, distinct libraries for `tokenizers` or `transformers` in python, for example. But there are several options for NLP in python, and adding this as a possible search result for \"python nlp\" when datasets are likely not what someone is searching for adds noise and frustrates potential users.","embeddings":[0.354604274,0.0250588469,-0.0843049809,-0.3304423392,0.1876242757,-0.2360132337,0.281853199,0.2058823705,-0.1652067304,0.0582918525,0.2198298723,0.294048816,-0.3030344248,0.1246296465,0.3023298979,-0.2793754935,0.2086221278,0.0491361916,0.1632865071,-0.1286140531,-0.1061515063,0.1799212396,0.1566794068,0.4202884436,0.0016413978,0.0405684561,0.2510459423,-0.1025928706,0.0196671039,-0.3666577637,0.0917976722,0.3173364997,-0.0723683611,0.0078898594,-0.000103016,-0.1667028368,0.5983600616,-0.0208175331,-0.1951327026,-0.2738524973,0.349458456,-0.4347401261,0.0873838514,-0.5374493003,0.1049486846,0.2493642569,0.1611560434,-0.2769201994,0.1723022461,-0.08421278,0.2791434228,-0.0322697014,-0.2336917669,0.2664329112,-0.0823114812,0.1612648368,-0.1856753528,-0.1456345767,0.1019584611,-0.0416525863,0.0535839312,0.2429705113,-0.1371386796,0.0111304624,0.4416790307,0.1220082119,0.3055425584,0.0429867581,-0.0890983418,0.1462106407,0.016002601,-0.3754999638,-0.227576077,-0.1618045717,0.0141653046,-0.4211737514,0.0508646816,-0.0911910012,-0.0744974241,-0.0652683526,0.3642609417,-0.1167289987,0.0555218421,0.1869729459,0.0563985147,0.6406509876,0.1287473142,0.1855204701,0.0186784174,-0.2868248522,0.1609270722,0.1716974378,0.2048975378,0.0659400746,0.1589836776,-0.1076443791,0.0360815078,-0.0260845404,-0.1019777954,-0.0282877255,-0.0157899559,-0.0529250763,0.1841489673,-0.1026077643,-0.0229196306,-0.0395875387,0.2494286448,0.06806878,0.3341001272,0.0483051986,0.1616839319,0.0051702829,-0.017028138,0.1136647984,0.0018841785,0.0539529808,0.1125142872,0.0556235351,0.0231455937,-0.0699977055,0.139364928,-0.0318878219,0.012281891,0.2282057405,0.0296439026,0.4047164619,0.1179114431,-0.0512215644,-0.0545108691,0.0529048257,-0.1768298894,-0.0293129664,-0.5875118375,0.1920241714,-0.0093172109,0.5461207032,0.3421474099,-0.2364342958,-0.1096441969,0.0759178326,-0.0507352985,0.1224967614,-0.0358524546,-0.0496277064,-0.4342936575,0.1021536589,-0.2238204181,-0.3176436424,-0.2762625515,0.1289541721,-0.2020074129,0.0938013867,-0.1377503276,0.3023263812,-0.2851534784,-0.0645210966,0.4819487929,0.1596666574,0.13196069,-0.2455682158,0.1019591764,-0.0245489255,0.1727491468,-0.168171823,-0.2812473476,0.0770907551,0.1417668909,-0.4519435167,-0.5630795956,0.0712455139,0.3810745776,0.0145662418,-0.1681337059,-0.0279533472,0.1246093661,0.0463554375,0.5702139735,-0.3770810962,-0.1004941016,0.110865213,-0.2571691275,-0.1666597575,0.0733325183,0.1724896133,-0.0463332757,-0.1781515777,0.2236127108,0.4268013537,0.168226257,0.1578955799,-0.2671319842,-0.1424053609,0.367061913,0.0201186519,0.0713867247,0.1450184584,-0.2018309981,0.7421375513,0.1609519124,0.0481394902,0.0549914017,0.1300477087,-0.0001047266,0.2264642864,0.052703809,-0.04857428,-0.2530002296,-0.1717445701,-0.3851439953,0.2766966522,-0.140701443,-0.3179916739,-0.0011529682,-0.0611749887,0.0840609074,-0.2455073893,0.3142194748,0.1664161682,-0.0728963241,-0.1845241189,-0.3374127746,0.2081885487,0.1162087247,0.0268573985,-0.1347388923,0.0125924638,-0.0472036041,-0.1227353066,-0.0485320874,0.4621073008,0.0617956594,0.0230287537,-0.018729629,0.1212337241,-0.0920708776,-0.1717240363,-0.1780800521,0.352711916,0.1953412741,0.0335730724,0.1522716731,0.217854023,0.0052392827,0.0609577782,0.1654213071,0.0910024494,-0.2310306877,0.172860235,0.1610245407,-0.1268762499,0.2499854863,-0.3104695678,-0.3951842785,-0.1594946086,0.2015538812,-0.1744659841,0.3688205183,0.2622098029,-0.0718516931,-0.0054821074,0.7487162948,-0.102349095,-0.0018591094,0.0346931629,0.0061895815,0.0344388932,-0.0982624963,0.4255813956,0.3692655265,0.2573755383,-0.2147238851,0.0731224418,-0.1584894955,-0.2395242006,0.1479514241,0.2729809284,-0.02106102,-0.0159713048,0.0722971559,-0.0208247695,-0.1442234069,-0.3758417368,0.1088091359,0.0435103104,0.1615836322,0.196914956,-0.2253728807,-0.3355076909,-0.3560675979,-0.1077126339,-0.5679337978,-0.212283805,0.0389435887,-0.319509536,-0.3220556676,0.1848625839,0.0278035179,0.0538053773,-0.3259667754,-0.4077161551,-0.2541850805,-0.2610703707,-0.2709332705,0.101353474,-0.0553875603,0.121204786,0.3438507915,0.075265944,0.0450470969,0.079796873,-0.4759514928,0.0199883431,-0.0811674446,0.2733127177,0.2104487866,-0.0825033486,0.1517145187,-0.4660096467,0.1668881476,-0.2525931001,0.1693013757,0.0579317883,0.152862072,-0.048633296,-0.3818205595,-0.2003627717,-0.4499168396,-0.431799382,-0.1190716177,-0.0393395089,0.0792104751,0.4727215767,-0.575882256,-0.0751322135,0.1555481106,0.2960141897,-0.1504534483,0.3245946169,-0.0218335297,-0.1896303445,0.0030149538,-0.3701182008,-0.4601126313,0.5443202853,-0.0602085814,0.0033655989,0.0939403698,0.062001247,-0.1237863004,0.2940612435,-0.1174602509,0.3699588478,0.3588147461,-0.1884727329,-0.0632570162,-0.1866520196,0.1633792967,-0.0045336215,0.3885166049,0.1221950576,-0.1400718987,-0.0451746397,0.021036813,-0.2248969823,-0.0801913887,0.2310665101,0.3564607799,0.0327313393,-0.0163829681,-0.1170815378,0.0942937359,0.1725271791,-0.1195733771,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"I'm also not sure whether the naming of `nlp` is the problem itself, as long as it comes with the appropriate identifier, so maybe something like `huggingface_nlp`? This is analogous to what @honnibal and spacy are doing for `spacy-transformers`. Of course, this is a \"step back\" from the recent changes\/renaming of transformers, but may be some middle ground between a complete rebranding, and keeping it identifiable.","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":66,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n I'm also not sure whether the naming of `nlp` is the problem itself, as long as it comes with the appropriate identifier, so maybe something like `huggingface_nlp`? This is analogous to what @honnibal and spacy are doing for `spacy-transformers`. Of course, this is a \"step back\" from the recent changes\/renaming of transformers, but may be some middle ground between a complete rebranding, and keeping it identifiable.","embeddings":[0.354604274,0.0250588469,-0.0843049809,-0.3304423392,0.1876242757,-0.2360132337,0.281853199,0.2058823705,-0.1652067304,0.0582918525,0.2198298723,0.294048816,-0.3030344248,0.1246296465,0.3023298979,-0.2793754935,0.2086221278,0.0491361916,0.1632865071,-0.1286140531,-0.1061515063,0.1799212396,0.1566794068,0.4202884436,0.0016413978,0.0405684561,0.2510459423,-0.1025928706,0.0196671039,-0.3666577637,0.0917976722,0.3173364997,-0.0723683611,0.0078898594,-0.000103016,-0.1667028368,0.5983600616,-0.0208175331,-0.1951327026,-0.2738524973,0.349458456,-0.4347401261,0.0873838514,-0.5374493003,0.1049486846,0.2493642569,0.1611560434,-0.2769201994,0.1723022461,-0.08421278,0.2791434228,-0.0322697014,-0.2336917669,0.2664329112,-0.0823114812,0.1612648368,-0.1856753528,-0.1456345767,0.1019584611,-0.0416525863,0.0535839312,0.2429705113,-0.1371386796,0.0111304624,0.4416790307,0.1220082119,0.3055425584,0.0429867581,-0.0890983418,0.1462106407,0.016002601,-0.3754999638,-0.227576077,-0.1618045717,0.0141653046,-0.4211737514,0.0508646816,-0.0911910012,-0.0744974241,-0.0652683526,0.3642609417,-0.1167289987,0.0555218421,0.1869729459,0.0563985147,0.6406509876,0.1287473142,0.1855204701,0.0186784174,-0.2868248522,0.1609270722,0.1716974378,0.2048975378,0.0659400746,0.1589836776,-0.1076443791,0.0360815078,-0.0260845404,-0.1019777954,-0.0282877255,-0.0157899559,-0.0529250763,0.1841489673,-0.1026077643,-0.0229196306,-0.0395875387,0.2494286448,0.06806878,0.3341001272,0.0483051986,0.1616839319,0.0051702829,-0.017028138,0.1136647984,0.0018841785,0.0539529808,0.1125142872,0.0556235351,0.0231455937,-0.0699977055,0.139364928,-0.0318878219,0.012281891,0.2282057405,0.0296439026,0.4047164619,0.1179114431,-0.0512215644,-0.0545108691,0.0529048257,-0.1768298894,-0.0293129664,-0.5875118375,0.1920241714,-0.0093172109,0.5461207032,0.3421474099,-0.2364342958,-0.1096441969,0.0759178326,-0.0507352985,0.1224967614,-0.0358524546,-0.0496277064,-0.4342936575,0.1021536589,-0.2238204181,-0.3176436424,-0.2762625515,0.1289541721,-0.2020074129,0.0938013867,-0.1377503276,0.3023263812,-0.2851534784,-0.0645210966,0.4819487929,0.1596666574,0.13196069,-0.2455682158,0.1019591764,-0.0245489255,0.1727491468,-0.168171823,-0.2812473476,0.0770907551,0.1417668909,-0.4519435167,-0.5630795956,0.0712455139,0.3810745776,0.0145662418,-0.1681337059,-0.0279533472,0.1246093661,0.0463554375,0.5702139735,-0.3770810962,-0.1004941016,0.110865213,-0.2571691275,-0.1666597575,0.0733325183,0.1724896133,-0.0463332757,-0.1781515777,0.2236127108,0.4268013537,0.168226257,0.1578955799,-0.2671319842,-0.1424053609,0.367061913,0.0201186519,0.0713867247,0.1450184584,-0.2018309981,0.7421375513,0.1609519124,0.0481394902,0.0549914017,0.1300477087,-0.0001047266,0.2264642864,0.052703809,-0.04857428,-0.2530002296,-0.1717445701,-0.3851439953,0.2766966522,-0.140701443,-0.3179916739,-0.0011529682,-0.0611749887,0.0840609074,-0.2455073893,0.3142194748,0.1664161682,-0.0728963241,-0.1845241189,-0.3374127746,0.2081885487,0.1162087247,0.0268573985,-0.1347388923,0.0125924638,-0.0472036041,-0.1227353066,-0.0485320874,0.4621073008,0.0617956594,0.0230287537,-0.018729629,0.1212337241,-0.0920708776,-0.1717240363,-0.1780800521,0.352711916,0.1953412741,0.0335730724,0.1522716731,0.217854023,0.0052392827,0.0609577782,0.1654213071,0.0910024494,-0.2310306877,0.172860235,0.1610245407,-0.1268762499,0.2499854863,-0.3104695678,-0.3951842785,-0.1594946086,0.2015538812,-0.1744659841,0.3688205183,0.2622098029,-0.0718516931,-0.0054821074,0.7487162948,-0.102349095,-0.0018591094,0.0346931629,0.0061895815,0.0344388932,-0.0982624963,0.4255813956,0.3692655265,0.2573755383,-0.2147238851,0.0731224418,-0.1584894955,-0.2395242006,0.1479514241,0.2729809284,-0.02106102,-0.0159713048,0.0722971559,-0.0208247695,-0.1442234069,-0.3758417368,0.1088091359,0.0435103104,0.1615836322,0.196914956,-0.2253728807,-0.3355076909,-0.3560675979,-0.1077126339,-0.5679337978,-0.212283805,0.0389435887,-0.319509536,-0.3220556676,0.1848625839,0.0278035179,0.0538053773,-0.3259667754,-0.4077161551,-0.2541850805,-0.2610703707,-0.2709332705,0.101353474,-0.0553875603,0.121204786,0.3438507915,0.075265944,0.0450470969,0.079796873,-0.4759514928,0.0199883431,-0.0811674446,0.2733127177,0.2104487866,-0.0825033486,0.1517145187,-0.4660096467,0.1668881476,-0.2525931001,0.1693013757,0.0579317883,0.152862072,-0.048633296,-0.3818205595,-0.2003627717,-0.4499168396,-0.431799382,-0.1190716177,-0.0393395089,0.0792104751,0.4727215767,-0.575882256,-0.0751322135,0.1555481106,0.2960141897,-0.1504534483,0.3245946169,-0.0218335297,-0.1896303445,0.0030149538,-0.3701182008,-0.4601126313,0.5443202853,-0.0602085814,0.0033655989,0.0939403698,0.062001247,-0.1237863004,0.2940612435,-0.1174602509,0.3699588478,0.3588147461,-0.1884727329,-0.0632570162,-0.1866520196,0.1633792967,-0.0045336215,0.3885166049,0.1221950576,-0.1400718987,-0.0451746397,0.021036813,-0.2248969823,-0.0801913887,0.2310665101,0.3564607799,0.0327313393,-0.0163829681,-0.1170815378,0.0942937359,0.1725271791,-0.11957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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"Interesting, thanks for sharing your thoughts.\r\n\r\nAs we\u2019ll move toward a first non-beta release, we will pool the community of contributors\/users of the library for their opinions on a good final name (like when we renamed the beautifully (?) named `pytorch-pretrained-bert`)\r\n\r\nIn the meantime, using `from nlp import load_dataset, load_metric` should work \ud83d\ude09","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":53,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n Interesting, thanks for sharing your thoughts.\r\n\r\nAs we\u2019ll move toward a first non-beta release, we will pool the community of contributors\/users of the library for their opinions on a good final name (like when we renamed the beautifully (?) named `pytorch-pretrained-bert`)\r\n\r\nIn the meantime, using `from nlp import load_dataset, load_metric` should work \ud83d\ude09","embeddings":[0.354604274,0.0250588469,-0.0843049809,-0.3304423392,0.1876242757,-0.2360132337,0.281853199,0.2058823705,-0.1652067304,0.0582918525,0.2198298723,0.294048816,-0.3030344248,0.1246296465,0.3023298979,-0.2793754935,0.2086221278,0.0491361916,0.1632865071,-0.1286140531,-0.1061515063,0.1799212396,0.1566794068,0.4202884436,0.0016413978,0.0405684561,0.2510459423,-0.1025928706,0.0196671039,-0.3666577637,0.0917976722,0.3173364997,-0.0723683611,0.0078898594,-0.000103016,-0.1667028368,0.5983600616,-0.0208175331,-0.1951327026,-0.2738524973,0.349458456,-0.4347401261,0.0873838514,-0.5374493003,0.1049486846,0.2493642569,0.1611560434,-0.2769201994,0.1723022461,-0.08421278,0.2791434228,-0.0322697014,-0.2336917669,0.2664329112,-0.0823114812,0.1612648368,-0.1856753528,-0.1456345767,0.1019584611,-0.0416525863,0.0535839312,0.2429705113,-0.1371386796,0.0111304624,0.4416790307,0.1220082119,0.3055425584,0.0429867581,-0.0890983418,0.1462106407,0.016002601,-0.3754999638,-0.227576077,-0.1618045717,0.0141653046,-0.4211737514,0.0508646816,-0.0911910012,-0.0744974241,-0.0652683526,0.3642609417,-0.1167289987,0.0555218421,0.1869729459,0.0563985147,0.6406509876,0.1287473142,0.1855204701,0.0186784174,-0.2868248522,0.1609270722,0.1716974378,0.2048975378,0.0659400746,0.1589836776,-0.1076443791,0.0360815078,-0.0260845404,-0.1019777954,-0.0282877255,-0.0157899559,-0.0529250763,0.1841489673,-0.1026077643,-0.0229196306,-0.0395875387,0.2494286448,0.06806878,0.3341001272,0.0483051986,0.1616839319,0.0051702829,-0.017028138,0.1136647984,0.0018841785,0.0539529808,0.1125142872,0.0556235351,0.0231455937,-0.0699977055,0.139364928,-0.0318878219,0.012281891,0.2282057405,0.0296439026,0.4047164619,0.1179114431,-0.0512215644,-0.0545108691,0.0529048257,-0.1768298894,-0.0293129664,-0.5875118375,0.1920241714,-0.0093172109,0.5461207032,0.3421474099,-0.2364342958,-0.1096441969,0.0759178326,-0.0507352985,0.1224967614,-0.0358524546,-0.0496277064,-0.4342936575,0.1021536589,-0.2238204181,-0.3176436424,-0.2762625515,0.1289541721,-0.2020074129,0.0938013867,-0.1377503276,0.3023263812,-0.2851534784,-0.0645210966,0.4819487929,0.1596666574,0.13196069,-0.2455682158,0.1019591764,-0.0245489255,0.1727491468,-0.168171823,-0.2812473476,0.0770907551,0.1417668909,-0.4519435167,-0.5630795956,0.0712455139,0.3810745776,0.0145662418,-0.1681337059,-0.0279533472,0.1246093661,0.0463554375,0.5702139735,-0.3770810962,-0.1004941016,0.110865213,-0.2571691275,-0.1666597575,0.0733325183,0.1724896133,-0.0463332757,-0.1781515777,0.2236127108,0.4268013537,0.168226257,0.1578955799,-0.2671319842,-0.1424053609,0.367061913,0.0201186519,0.0713867247,0.1450184584,-0.2018309981,0.7421375513,0.1609519124,0.0481394902,0.0549914017,0.1300477087,-0.0001047266,0.2264642864,0.052703809,-0.04857428,-0.2530002296,-0.1717445701,-0.3851439953,0.2766966522,-0.140701443,-0.3179916739,-0.0011529682,-0.0611749887,0.0840609074,-0.2455073893,0.3142194748,0.1664161682,-0.0728963241,-0.1845241189,-0.3374127746,0.2081885487,0.1162087247,0.0268573985,-0.1347388923,0.0125924638,-0.0472036041,-0.1227353066,-0.0485320874,0.4621073008,0.0617956594,0.0230287537,-0.018729629,0.1212337241,-0.0920708776,-0.1717240363,-0.1780800521,0.352711916,0.1953412741,0.0335730724,0.1522716731,0.217854023,0.0052392827,0.0609577782,0.1654213071,0.0910024494,-0.2310306877,0.172860235,0.1610245407,-0.1268762499,0.2499854863,-0.3104695678,-0.3951842785,-0.1594946086,0.2015538812,-0.1744659841,0.3688205183,0.2622098029,-0.0718516931,-0.0054821074,0.7487162948,-0.102349095,-0.0018591094,0.0346931629,0.0061895815,0.0344388932,-0.0982624963,0.4255813956,0.3692655265,0.2573755383,-0.2147238851,0.0731224418,-0.1584894955,-0.2395242006,0.1479514241,0.2729809284,-0.02106102,-0.0159713048,0.0722971559,-0.0208247695,-0.1442234069,-0.3758417368,0.1088091359,0.0435103104,0.1615836322,0.196914956,-0.2253728807,-0.3355076909,-0.3560675979,-0.1077126339,-0.5679337978,-0.212283805,0.0389435887,-0.319509536,-0.3220556676,0.1848625839,0.0278035179,0.0538053773,-0.3259667754,-0.4077161551,-0.2541850805,-0.2610703707,-0.2709332705,0.101353474,-0.0553875603,0.121204786,0.3438507915,0.075265944,0.0450470969,0.079796873,-0.4759514928,0.0199883431,-0.0811674446,0.2733127177,0.2104487866,-0.0825033486,0.1517145187,-0.4660096467,0.1668881476,-0.2525931001,0.1693013757,0.0579317883,0.152862072,-0.048633296,-0.3818205595,-0.2003627717,-0.4499168396,-0.431799382,-0.1190716177,-0.0393395089,0.0792104751,0.4727215767,-0.575882256,-0.0751322135,0.1555481106,0.2960141897,-0.1504534483,0.3245946169,-0.0218335297,-0.1896303445,0.0030149538,-0.3701182008,-0.4601126313,0.5443202853,-0.0602085814,0.0033655989,0.0939403698,0.062001247,-0.1237863004,0.2940612435,-0.1174602509,0.3699588478,0.3588147461,-0.1884727329,-0.0632570162,-0.1866520196,0.1633792967,-0.0045336215,0.3885166049,0.1221950576,-0.1400718987,-0.0451746397,0.021036813,-0.2248969823,-0.0801913887,0.2310665101,0.3564607799,0.0327313393,-0.0163829681,-0.1170815378,0.0942937359,0.1725271791,-0.119573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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"I feel like we are conflating two distinct subjects here:\r\n\r\n1. @honnibal's point is that using `nlp` as a package name might break existing code and bring developer usability issues in the future\r\n2. @pmbaumgartner's point is that the `nlp` package name is too broad and shouldn't be used by a package that exposes only datasets and metrics\r\n\r\n(let me know if I mischaracterize your point)\r\n\r\nI'll chime in to say that the first point is a bit silly IMO. As Python developers due to the limitations of the import system we already have to share:\r\n- a single flat namespace for packages\r\n- which also conflicts with local modules i.e. local files\r\n\r\nIf we add the constraint that this flat namespace also be shared with variable names this gets untractable pretty fast :)\r\n\r\nI also think all Python software developers\/ML engineers\/scientists are capable of at least a subset of:\r\n- importing only the methods that they need like @thomwolf suggested\r\n- aliasing their import\r\n- renaming a local variable","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":170,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n I feel like we are conflating two distinct subjects here:\r\n\r\n1. @honnibal's point is that using `nlp` as a package name might break existing code and bring developer usability issues in the future\r\n2. @pmbaumgartner's point is that the `nlp` package name is too broad and shouldn't be used by a package that exposes only datasets and metrics\r\n\r\n(let me know if I mischaracterize your point)\r\n\r\nI'll chime in to say that the first point is a bit silly IMO. As Python developers due to the limitations of the import system we already have to share:\r\n- a single flat namespace for packages\r\n- which also conflicts with local modules i.e. local files\r\n\r\nIf we add the constraint that this flat namespace also be shared with variable names this gets untractable pretty fast :)\r\n\r\nI also think all Python software developers\/ML engineers\/scientists are capable of at least a subset of:\r\n- importing only the methods that they need like @thomwolf suggested\r\n- aliasing their import\r\n- renaming a local 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"By the way, `nlp` will very likely not be only about datasets, and not even just about datasets and metrics.\r\n\r\nI see it as a laboratory for testing several long-term ideas about how we could do NLP in terms of research as well as open-source and community sharing, most of these ideas being too experimental\/big to fit in `transformers`.\r\n\r\nSome of the directions we would like to explore are about sharing, traceability and more experimental models, as well as seeing a model as the community-based process of creating a composite entity from data, optimization, and code.\r\n\r\nWe'll see how these ideas end up being implemented and we'll better know how we should define the library when we start to dive into these topics. I'll try to get the `nlp` team to draft a roadmap on these topics at some point.","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":140,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n By the way, `nlp` will very likely not be only about datasets, and not even just about datasets and metrics.\r\n\r\nI see it as a laboratory for testing several long-term ideas about how we could do NLP in terms of research as well as open-source and community sharing, most of these ideas being too experimental\/big to fit in `transformers`.\r\n\r\nSome of the directions we would like to explore are about sharing, traceability and more experimental models, as well as seeing a model as the community-based process of creating a composite entity from data, optimization, and code.\r\n\r\nWe'll see how these ideas end up being implemented and we'll better know how we should define the library when we start to dive into these topics. I'll try to get the `nlp` team to draft a roadmap on these topics at some 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"> If we add the constraint that this flat namespace also be shared with variable names this gets untractable pretty fast :)\r\n\r\nI'm sort of confused by your point here. The namespace *is* shared by variable names. You should not use local variables that are named the same as modules, because then you cannot use the module within the scope of your function.\r\n\r\nFor instance,\r\n\r\n```python\r\n\r\nimport nlp\r\nimport transformers\r\n\r\nnlp = transformers.pipeline(\"sentiment-analysis\")\r\n```\r\n\r\nThis is a bug: you've just overwritten the module, so now you can't use it. Or instead:\r\n\r\n```python\r\n\r\nimport transformers\r\n\r\nnlp = transformers.pipeline(\"sentiment-analysis\")\r\n# (Later, e.g. in a notebook)\r\nimport nlp\r\n```\r\n\r\nThis is also a bug: you've overwritten your variable with an import.\r\n\r\nIf you have a module named `nlp`, you should avoid using `nlp` as a variable, or you'll have bugs in some contexts and inconsistencies in other contexts. You'll have situations where you need to import differently in one module vs another, or name variables differently in one context vs another, which is bad.\r\n\r\n> importing only the methods that they need like @thomwolf suggested\r\n\r\nOkay but the same logic applies to naming the module *literally anything else*. There's absolutely no point in having a module name that's 3 letters if you always plan to do `import from`! It would be entirely better to name it `nlp_datasets` if you don't want people to do `import nlp`.\r\n\r\nAnd finally:\r\n\r\n> By the way, nlp will very likely not be only about datasets, and not even just about datasets and metrics.\r\n\r\nSo...it isn't a datasets library? https:\/\/twitter.com\/Thom_Wolf\/status\/1261282491622731781\r\n\r\nI'm confused \ud83d\ude15 ","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":265,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n > If we add the constraint that this flat namespace also be shared with variable names this gets untractable pretty fast :)\r\n\r\nI'm sort of confused by your point here. The namespace *is* shared by variable names. You should not use local variables that are named the same as modules, because then you cannot use the module within the scope of your function.\r\n\r\nFor instance,\r\n\r\n```python\r\n\r\nimport nlp\r\nimport transformers\r\n\r\nnlp = transformers.pipeline(\"sentiment-analysis\")\r\n```\r\n\r\nThis is a bug: you've just overwritten the module, so now you can't use it. Or instead:\r\n\r\n```python\r\n\r\nimport transformers\r\n\r\nnlp = transformers.pipeline(\"sentiment-analysis\")\r\n# (Later, e.g. in a notebook)\r\nimport nlp\r\n```\r\n\r\nThis is also a bug: you've overwritten your variable with an import.\r\n\r\nIf you have a module named `nlp`, you should avoid using `nlp` as a variable, or you'll have bugs in some contexts and inconsistencies in other contexts. You'll have situations where you need to import differently in one module vs another, or name variables differently in one context vs another, which is bad.\r\n\r\n> importing only the methods that they need like @thomwolf suggested\r\n\r\nOkay but the same logic applies to naming the module *literally anything else*. There's absolutely no point in having a module name that's 3 letters if you always plan to do `import from`! It would be entirely better to name it `nlp_datasets` if you don't want people to do `import nlp`.\r\n\r\nAnd finally:\r\n\r\n> By the way, nlp will very likely not be only about datasets, and not even just about datasets and metrics.\r\n\r\nSo...it isn't a datasets library? https:\/\/twitter.com\/Thom_Wolf\/status\/1261282491622731781\r\n\r\nI'm confused \ud83d\ude15 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"Dropping by as I noticed that the library has been renamed `datasets` so I wonder if the conversation above is settled (`nlp` not used anymore) :) ","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":26,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n Dropping by as I noticed that the library has been renamed `datasets` so I wonder if the conversation above is settled (`nlp` not used anymore) :) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"I'd argue that `datasets` is worse than `nlp`. Datasets should be a user specific decision and not encapsulate all of python (`pip install datasets`). If this package contained every dataset in the world (NLP \/ vision \/ etc) then it would make sense =\/","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":44,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n I'd argue that `datasets` is worse than `nlp`. Datasets should be a user specific decision and not encapsulate all of python (`pip install datasets`). If this package contained every dataset in the world (NLP \/ vision \/ etc) then it would make sense =\/","embeddings":[0.354604274,0.0250588469,-0.0843049809,-0.3304423392,0.1876242757,-0.2360132337,0.281853199,0.2058823705,-0.1652067304,0.0582918525,0.2198298723,0.294048816,-0.3030344248,0.1246296465,0.3023298979,-0.2793754935,0.2086221278,0.0491361916,0.1632865071,-0.1286140531,-0.1061515063,0.1799212396,0.1566794068,0.4202884436,0.0016413978,0.0405684561,0.2510459423,-0.1025928706,0.0196671039,-0.3666577637,0.0917976722,0.3173364997,-0.0723683611,0.0078898594,-0.000103016,-0.1667028368,0.5983600616,-0.0208175331,-0.1951327026,-0.2738524973,0.349458456,-0.4347401261,0.0873838514,-0.5374493003,0.1049486846,0.2493642569,0.1611560434,-0.2769201994,0.1723022461,-0.08421278,0.2791434228,-0.0322697014,-0.2336917669,0.2664329112,-0.0823114812,0.1612648368,-0.1856753528,-0.1456345767,0.1019584611,-0.0416525863,0.0535839312,0.2429705113,-0.1371386796,0.0111304624,0.4416790307,0.1220082119,0.3055425584,0.0429867581,-0.0890983418,0.1462106407,0.016002601,-0.3754999638,-0.227576077,-0.1618045717,0.0141653046,-0.4211737514,0.0508646816,-0.0911910012,-0.0744974241,-0.0652683526,0.3642609417,-0.1167289987,0.0555218421,0.1869729459,0.0563985147,0.6406509876,0.1287473142,0.1855204701,0.0186784174,-0.2868248522,0.1609270722,0.1716974378,0.2048975378,0.0659400746,0.1589836776,-0.1076443791,0.0360815078,-0.0260845404,-0.1019777954,-0.0282877255,-0.0157899559,-0.0529250763,0.1841489673,-0.1026077643,-0.0229196306,-0.0395875387,0.2494286448,0.06806878,0.3341001272,0.0483051986,0.1616839319,0.0051702829,-0.017028138,0.1136647984,0.0018841785,0.0539529808,0.1125142872,0.0556235351,0.0231455937,-0.0699977055,0.139364928,-0.0318878219,0.012281891,0.2282057405,0.0296439026,0.4047164619,0.1179114431,-0.0512215644,-0.0545108691,0.0529048257,-0.1768298894,-0.0293129664,-0.5875118375,0.1920241714,-0.0093172109,0.5461207032,0.3421474099,-0.2364342958,-0.1096441969,0.0759178326,-0.0507352985,0.1224967614,-0.0358524546,-0.0496277064,-0.4342936575,0.1021536589,-0.2238204181,-0.3176436424,-0.2762625515,0.1289541721,-0.2020074129,0.0938013867,-0.1377503276,0.3023263812,-0.2851534784,-0.0645210966,0.4819487929,0.1596666574,0.13196069,-0.2455682158,0.1019591764,-0.0245489255,0.1727491468,-0.168171823,-0.2812473476,0.0770907551,0.1417668909,-0.4519435167,-0.5630795956,0.0712455139,0.3810745776,0.0145662418,-0.1681337059,-0.0279533472,0.1246093661,0.0463554375,0.5702139735,-0.3770810962,-0.1004941016,0.110865213,-0.2571691275,-0.1666597575,0.0733325183,0.1724896133,-0.0463332757,-0.1781515777,0.2236127108,0.4268013537,0.168226257,0.1578955799,-0.2671319842,-0.1424053609,0.367061913,0.0201186519,0.0713867247,0.1450184584,-0.2018309981,0.7421375513,0.1609519124,0.0481394902,0.0549914017,0.1300477087,-0.0001047266,0.2264642864,0.052703809,-0.04857428,-0.2530002296,-0.1717445701,-0.3851439953,0.2766966522,-0.140701443,-0.3179916739,-0.0011529682,-0.0611749887,0.0840609074,-0.2455073893,0.3142194748,0.1664161682,-0.0728963241,-0.1845241189,-0.3374127746,0.2081885487,0.1162087247,0.0268573985,-0.1347388923,0.0125924638,-0.0472036041,-0.1227353066,-0.0485320874,0.4621073008,0.0617956594,0.0230287537,-0.018729629,0.1212337241,-0.0920708776,-0.1717240363,-0.1780800521,0.352711916,0.1953412741,0.0335730724,0.1522716731,0.217854023,0.0052392827,0.0609577782,0.1654213071,0.0910024494,-0.2310306877,0.172860235,0.1610245407,-0.1268762499,0.2499854863,-0.3104695678,-0.3951842785,-0.1594946086,0.2015538812,-0.1744659841,0.3688205183,0.2622098029,-0.0718516931,-0.0054821074,0.7487162948,-0.102349095,-0.0018591094,0.0346931629,0.0061895815,0.0344388932,-0.0982624963,0.4255813956,0.3692655265,0.2573755383,-0.2147238851,0.0731224418,-0.1584894955,-0.2395242006,0.1479514241,0.2729809284,-0.02106102,-0.0159713048,0.0722971559,-0.0208247695,-0.1442234069,-0.3758417368,0.1088091359,0.0435103104,0.1615836322,0.196914956,-0.2253728807,-0.3355076909,-0.3560675979,-0.1077126339,-0.5679337978,-0.212283805,0.0389435887,-0.319509536,-0.3220556676,0.1848625839,0.0278035179,0.0538053773,-0.3259667754,-0.4077161551,-0.2541850805,-0.2610703707,-0.2709332705,0.101353474,-0.0553875603,0.121204786,0.3438507915,0.075265944,0.0450470969,0.079796873,-0.4759514928,0.0199883431,-0.0811674446,0.2733127177,0.2104487866,-0.0825033486,0.1517145187,-0.4660096467,0.1668881476,-0.2525931001,0.1693013757,0.0579317883,0.152862072,-0.048633296,-0.3818205595,-0.2003627717,-0.4499168396,-0.431799382,-0.1190716177,-0.0393395089,0.0792104751,0.4727215767,-0.575882256,-0.0751322135,0.1555481106,0.2960141897,-0.1504534483,0.3245946169,-0.0218335297,-0.1896303445,0.0030149538,-0.3701182008,-0.4601126313,0.5443202853,-0.0602085814,0.0033655989,0.0939403698,0.062001247,-0.1237863004,0.2940612435,-0.1174602509,0.3699588478,0.3588147461,-0.1884727329,-0.0632570162,-0.1866520196,0.1633792967,-0.0045336215,0.3885166049,0.1221950576,-0.1400718987,-0.0451746397,0.021036813,-0.2248969823,-0.0801913887,0.2310665101,0.3564607799,0.0327313393,-0.0163829681,-0.1170815378,0.0942937359,0.1725271791,-0.1195733771,0.29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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"I can't speak for the HF team @jramapuram, but as member of the community it looks to me that HF wanted to avoid the past path of changing names as scope broadened over time:\r\n\r\nRemember\r\nhttps:\/\/github.com\/huggingface\/pytorch-openai-transformer-lm\r\nhttps:\/\/github.com\/huggingface\/pytorch-pretrained-BERT\r\nhttps:\/\/github.com\/huggingface\/pytorch-transformers\r\nand now\r\nhttps:\/\/github.com\/huggingface\/transformers\r\n\r\n;) \r\n\r\nJokes aside, seems that the library is growing in a multi-modal direction (https:\/\/github.com\/huggingface\/datasets\/pull\/363) so the current name is not that implausible. Possibly HF ambition is really to grow its community and bring here a large chunk of datasets of the world (including tabular \/ vision \/ audio?).","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":89,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n I can't speak for the HF team @jramapuram, but as member of the community it looks to me that HF wanted to avoid the past path of changing names as scope broadened over time:\r\n\r\nRemember\r\nhttps:\/\/github.com\/huggingface\/pytorch-openai-transformer-lm\r\nhttps:\/\/github.com\/huggingface\/pytorch-pretrained-BERT\r\nhttps:\/\/github.com\/huggingface\/pytorch-transformers\r\nand now\r\nhttps:\/\/github.com\/huggingface\/transformers\r\n\r\n;) \r\n\r\nJokes aside, seems that the library is growing in a multi-modal direction (https:\/\/github.com\/huggingface\/datasets\/pull\/363) so the current name is not that implausible. Possibly HF ambition is really to grow its community and bring here a large chunk of datasets of the world (including tabular \/ vision \/ audio?).","embeddings":[0.354604274,0.0250588469,-0.0843049809,-0.3304423392,0.1876242757,-0.2360132337,0.281853199,0.2058823705,-0.1652067304,0.0582918525,0.2198298723,0.294048816,-0.3030344248,0.1246296465,0.3023298979,-0.2793754935,0.2086221278,0.0491361916,0.1632865071,-0.1286140531,-0.1061515063,0.1799212396,0.1566794068,0.4202884436,0.0016413978,0.0405684561,0.2510459423,-0.1025928706,0.0196671039,-0.3666577637,0.0917976722,0.3173364997,-0.0723683611,0.0078898594,-0.000103016,-0.1667028368,0.5983600616,-0.0208175331,-0.1951327026,-0.2738524973,0.349458456,-0.4347401261,0.0873838514,-0.5374493003,0.1049486846,0.2493642569,0.1611560434,-0.2769201994,0.1723022461,-0.08421278,0.2791434228,-0.0322697014,-0.2336917669,0.2664329112,-0.0823114812,0.1612648368,-0.1856753528,-0.1456345767,0.1019584611,-0.0416525863,0.0535839312,0.2429705113,-0.1371386796,0.0111304624,0.4416790307,0.1220082119,0.3055425584,0.0429867581,-0.0890983418,0.1462106407,0.016002601,-0.3754999638,-0.227576077,-0.1618045717,0.0141653046,-0.4211737514,0.0508646816,-0.0911910012,-0.0744974241,-0.0652683526,0.3642609417,-0.1167289987,0.0555218421,0.1869729459,0.0563985147,0.6406509876,0.1287473142,0.1855204701,0.0186784174,-0.2868248522,0.1609270722,0.1716974378,0.2048975378,0.0659400746,0.1589836776,-0.1076443791,0.0360815078,-0.0260845404,-0.1019777954,-0.0282877255,-0.0157899559,-0.0529250763,0.1841489673,-0.1026077643,-0.0229196306,-0.0395875387,0.2494286448,0.06806878,0.3341001272,0.0483051986,0.1616839319,0.0051702829,-0.017028138,0.1136647984,0.0018841785,0.0539529808,0.1125142872,0.0556235351,0.0231455937,-0.0699977055,0.139364928,-0.0318878219,0.012281891,0.2282057405,0.0296439026,0.4047164619,0.1179114431,-0.0512215644,-0.0545108691,0.0529048257,-0.1768298894,-0.0293129664,-0.5875118375,0.1920241714,-0.0093172109,0.5461207032,0.3421474099,-0.2364342958,-0.1096441969,0.0759178326,-0.0507352985,0.1224967614,-0.0358524546,-0.0496277064,-0.4342936575,0.1021536589,-0.2238204181,-0.3176436424,-0.2762625515,0.1289541721,-0.2020074129,0.0938013867,-0.1377503276,0.3023263812,-0.2851534784,-0.0645210966,0.4819487929,0.1596666574,0.13196069,-0.2455682158,0.1019591764,-0.0245489255,0.1727491468,-0.168171823,-0.2812473476,0.0770907551,0.1417668909,-0.4519435167,-0.5630795956,0.0712455139,0.3810745776,0.0145662418,-0.1681337059,-0.0279533472,0.1246093661,0.0463554375,0.5702139735,-0.3770810962,-0.1004941016,0.110865213,-0.2571691275,-0.1666597575,0.0733325183,0.1724896133,-0.0463332757,-0.1781515777,0.2236127108,0.4268013537,0.168226257,0.1578955799,-0.2671319842,-0.1424053609,0.367061913,0.0201186519,0.0713867247,0.1450184584,-0.2018309981,0.7421375513,0.1609519124,0.0481394902,0.0549914017,0.1300477087,-0.0001047266,0.2264642864,0.052703809,-0.04857428,-0.2530002296,-0.1717445701,-0.3851439953,0.2766966522,-0.140701443,-0.3179916739,-0.0011529682,-0.0611749887,0.0840609074,-0.2455073893,0.3142194748,0.1664161682,-0.0728963241,-0.1845241189,-0.3374127746,0.2081885487,0.1162087247,0.0268573985,-0.1347388923,0.0125924638,-0.0472036041,-0.1227353066,-0.0485320874,0.4621073008,0.0617956594,0.0230287537,-0.018729629,0.1212337241,-0.0920708776,-0.1717240363,-0.1780800521,0.352711916,0.1953412741,0.0335730724,0.1522716731,0.217854023,0.0052392827,0.0609577782,0.1654213071,0.0910024494,-0.2310306877,0.172860235,0.1610245407,-0.1268762499,0.2499854863,-0.3104695678,-0.3951842785,-0.1594946086,0.2015538812,-0.1744659841,0.3688205183,0.2622098029,-0.0718516931,-0.0054821074,0.7487162948,-0.102349095,-0.0018591094,0.0346931629,0.0061895815,0.0344388932,-0.0982624963,0.4255813956,0.3692655265,0.2573755383,-0.2147238851,0.0731224418,-0.1584894955,-0.2395242006,0.1479514241,0.2729809284,-0.02106102,-0.0159713048,0.0722971559,-0.0208247695,-0.1442234069,-0.3758417368,0.1088091359,0.0435103104,0.1615836322,0.196914956,-0.2253728807,-0.3355076909,-0.3560675979,-0.1077126339,-0.5679337978,-0.212283805,0.0389435887,-0.319509536,-0.3220556676,0.1848625839,0.0278035179,0.0538053773,-0.3259667754,-0.4077161551,-0.2541850805,-0.2610703707,-0.2709332705,0.101353474,-0.0553875603,0.121204786,0.3438507915,0.075265944,0.0450470969,0.079796873,-0.4759514928,0.0199883431,-0.0811674446,0.2733127177,0.2104487866,-0.0825033486,0.1517145187,-0.4660096467,0.1668881476,-0.2525931001,0.1693013757,0.0579317883,0.152862072,-0.048633296,-0.3818205595,-0.2003627717,-0.4499168396,-0.431799382,-0.1190716177,-0.0393395089,0.0792104751,0.4727215767,-0.575882256,-0.0751322135,0.1555481106,0.2960141897,-0.1504534483,0.3245946169,-0.0218335297,-0.1896303445,0.0030149538,-0.3701182008,-0.4601126313,0.5443202853,-0.0602085814,0.0033655989,0.0939403698,0.062001247,-0.1237863004,0.2940612435,-0.1174602509,0.3699588478,0.3588147461,-0.1884727329,-0.0632570162,-0.1866520196,0.1633792967,-0.0045336215,0.3885166049,0.1221950576,-0.1400718987,-0.0451746397,0.021036813,-0.2248969823,-0.0801913887,0.2310665101,0.3564607799,0.0327313393,-0.0163829681,-0.1170815378,0.0942937359,0.1725271791,-0.1195733771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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/138","title":"Consider renaming to nld","comments":"Yea I see your point. However, wouldn't scoping solve the entire problem? \r\n\r\n```python\r\nimport huggingface.datasets as D\r\nimport huggingface.transformers as T\r\n```\r\n\r\nCalling something `datasets` is akin to saying I'm going to name my package `python` --> `import python` ","body":"Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p.","comment_length":39,"text":"Consider renaming to nld \n Hey :)\r\n\r\nJust making a thread here recording what I said on Twitter, as it's impossible to follow discussion there. It's also just really not a good way to talk about this sort of thing.\r\n\r\nThe issue is that modules go into the global namespace, so you shouldn't use variable names that conflict with module names. This means the package makes `nlp` a bad variable name everywhere in the codebase. I've always used `nlp` as the canonical variable name of spaCy's `Language` objects, and this is a convention that a lot of other code has followed (Stanza, flair, etc). And actually, your `transformers` library uses `nlp` as the name for its `Pipeline` instance in your readme.\r\n\r\nIf you stick with the `nlp` name for this package, if anyone uses it then they should rewrite all of that code. If `nlp` is a bad choice of variable anywhere, it's a bad choice of variable everywhere --- because you shouldn't have to notice whether some other function uses a module when you're naming variables within a function. You want to have one convention that you can stick to everywhere.\r\n\r\nIf people use your `nlp` package and continue to use the `nlp` variable name, they'll find themselves with confusing bugs. There will be many many bits of code cut-and-paste from tutorials that give confusing results when combined with the data loading from the `nlp` library. The problem will be especially bad for shadowed modules (people might reasonably have a module named `nlp.py` within their codebase) and notebooks, as people might run notebook cells for data loading out-of-order.\r\n\r\nI don't think it's an exaggeration to say that if your library becomes popular, we'll all be answering issues around this about once a week for the next few years. That seems pretty unideal, so I do hope you'll reconsider.\r\n\r\nI suggest `nld` as a better name. It more accurately represents what the package actually does. It's pretty unideal to have a package named `nlp` that doesn't do any processing, and contains data about natural language generation or other non-NLP tasks. The name is equally short, and is sort of a visual pun on `nlp`, since a d is a rotated p. \n Yea I see your point. However, wouldn't scoping solve the entire problem? \r\n\r\n```python\r\nimport huggingface.datasets as D\r\nimport huggingface.transformers as T\r\n```\r\n\r\nCalling something `datasets` is akin to saying I'm going to name my package `python` --> `import python` 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"I second this request. The bottom line is that **scores produced with different reference tokenizations are not comparable**. To discourage (even inadvertent) cheating, the user should never touch the reference. The `v13a` tokenization standard is not ideal, but at least it has been consistently used at matrix.statmt.org, facilitating comparisons.\r\n\r\nSacrebleu exposes [all its data sources](https:\/\/github.com\/mjpost\/sacrebleu\/blob\/master\/sacrebleu\/dataset.py) and additionally provides [an API](https:\/\/github.com\/mjpost\/sacrebleu\/blob\/master\/sacrebleu\/__init__.py) to accessing the references, which seem to fit within the spirit of your codebase.","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":74,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n I second this request. The bottom line is that **scores produced with different reference tokenizations are not comparable**. To discourage (even inadvertent) cheating, the user should never touch the reference. The `v13a` tokenization standard is not ideal, but at least it has been consistently used at matrix.statmt.org, facilitating comparisons.\r\n\r\nSacrebleu exposes [all its data sources](https:\/\/github.com\/mjpost\/sacrebleu\/blob\/master\/sacrebleu\/dataset.py) and additionally provides [an API](https:\/\/github.com\/mjpost\/sacrebleu\/blob\/master\/sacrebleu\/__init__.py) to accessing the references, which seem to fit within the spirit of your codebase.","embeddings":[-0.2068963349,-0.2239939272,-0.0241725501,-0.1960566938,0.1427302063,-0.2892453372,0.204468295,0.2425775528,-0.5539250374,0.4257799089,-0.2530024648,0.3626277447,-0.013167588,-0.078750357,-0.1742032766,0.1995098889,0.1531567276,0.1220696718,0.5717875361,-0.0796073452,-0.098993592,-0.1696630269,0.0176084861,0.2470569164,-0.3446577191,0.3597022593,0.2996474802,-0.2794865966,-0.1673764884,-0.2797946036,-0.1365122795,0.2227185071,-0.279530853,0.2289049923,-0.0001093052,-0.3120647073,-0.0601100661,-0.0350928679,-0.265420258,-0.4642713666,0.241164431,0.0276452024,-0.055413615,-0.1108257845,-0.0504698008,-0.2427429855,0.04569114,0.069631584,0.3667047322,-0.1348719299,0.1769615263,0.429012835,-0.4011058509,0.154327929,0.1258075386,0.2085044235,-0.0326999277,0.267272681,-0.0411467366,0.3646010756,0.0584651232,0.0565019138,-0.1211043224,-0.1393247098,0.1359600276,-0.0589449778,-0.2566533685,0.0117939906,0.2010882646,0.457672745,-0.0228146631,0.0737749264,-0.2328153402,-0.1242059618,0.0908815861,-0.3574267328,0.0922226533,-0.0738506392,0.1251289546,-0.175254941,-0.0502342358,0.3113988638,0.1208132952,0.063946411,0.1400031447,0.0747234672,-0.0462033637,0.0561071634,-0.0482494794,-0.0372716971,-0.1836458296,0.102579698,0.0456452817,0.0721746534,-0.2898781598,-0.1666918546,-0.1875944585,-0.5826480985,0.1071780995,0.061543446,0.506480217,0.2740062177,-0.1688214391,0.1206374317,-0.1836244464,0.0766869038,-0.266623348,-0.0895998031,0.3690304756,0.0124740377,0.2950677574,0.2305250466,0.2302126139,-0.1735688746,-0.1869598627,0.2104580849,-0.3297913969,-0.2119521946,-0.6489787102,0.0244919956,-0.2098716348,-0.0039327047,-0.1035745293,0.155200839,-0.2394098043,-0.1720015258,-0.0923475325,0.0038391147,-0.1732512563,-0.1808503717,-0.0416512713,-0.2452546954,-0.1640869975,-0.0652837753,-0.0555017143,0.0853482932,0.2829969823,0.0482707955,0.5071291924,-0.1670744121,0.0395633914,-0.1643660367,0.1284259409,-0.0809514001,-0.3301097155,-0.1837484092,-0.1064810678,-0.1753861755,-0.0757575631,0.1259743869,-0.265085876,-0.1970326006,0.219871372,0.1541852504,0.108176291,0.13014552,-0.4073212147,0.0986018553,-0.1138292104,-0.0174881369,0.2405324578,-0.1037751287,-0.1610575616,-0.2548727691,0.2549302876,-0.2436173111,0.1269932836,-0.184952125,0.5565254092,0.2552277148,0.1769501865,0.3732342124,-0.2868903279,0.2685480714,0.0757662728,0.175688982,0.0247070957,-0.0729766861,-0.2471740544,-0.1694583148,-0.1363464594,-0.2782011032,0.0569716506,-0.0022902002,0.4476939738,-0.1601079553,0.0129348934,-0.0264290869,0.1478295028,-0.0873768926,-0.3542244434,-0.5578355789,0.0816820934,-0.1322261095,0.0789171681,-0.0438447334,0.2843369544,0.5939742327,0.3166588843,-0.3041429222,-0.1074525639,-0.282163918,0.5762838125,-0.0202748664,0.071305953,0.1576715857,0.4550969601,0.1679539531,-0.0573650338,0.1576075256,0.1244589686,-0.4581418335,-0.0465322882,0.016293034,0.2220989764,0.0366160981,0.0769572705,-0.1002092585,-0.2054637372,0.1872119606,-0.4104688466,-0.2364171296,-0.5289868116,0.1547191739,0.1777647734,-0.0728960037,0.032057967,0.0623840354,0.136107713,0.3638375103,0.0351115428,-0.0589805134,-0.2578704953,0.3002849519,0.0624148734,0.0660421774,-0.3768453598,0.6890140772,0.3303513229,0.2215900868,-0.2227139771,0.1267667115,-0.1263362169,0.131438598,0.3574001491,0.5529800057,0.0214897841,-0.0761900619,0.0505527705,-0.2885354161,-0.2127612978,0.1523654759,-0.0368538648,-0.2658447027,0.0508229509,0.2942456603,-0.255817771,0.113773331,-0.183015734,-0.0119641284,0.1617596,0.0664129779,0.0226847734,0.0442922451,0.3693778217,-0.1273409277,0.0505143665,0.1427875906,0.0556401312,0.0483270921,0.0977223888,0.1243292987,-0.0088804727,-0.3289555907,-0.142603606,-0.0059857522,0.0289363433,0.1889076829,-0.0027061421,0.3297759295,-0.0355942398,0.016512664,-0.044131849,-0.1556869149,-0.0953174159,0.1654359102,-0.2257624418,0.2198485583,-0.2951077819,0.0351456702,0.0170469228,-0.1059285402,0.2216109484,0.2815543413,-0.1472128183,0.3347761631,0.0994495004,0.6170318723,-0.2452239096,0.0372117497,0.1176237017,0.1728842258,-0.323358357,0.0711140335,-0.1702132821,-0.3984040022,0.2559493482,-0.1248623133,-0.2755087018,-0.1597879082,-0.5918516517,0.0459023975,0.0396379679,0.0232888125,0.141442582,-0.5394570827,-0.4638618231,-0.2547611594,-0.1199979037,-0.2510250509,-0.1145920381,-0.0606336184,-0.0050708628,-0.1690470427,0.0162940826,-0.2947101593,0.1643231362,0.11852213,0.1338732839,-0.2773308754,0.1693721563,-0.2163564712,0.0118126739,0.1174274459,0.1643926054,0.3501387239,-0.212271437,0.1290461868,0.1318792701,-0.1976172775,-0.3267780542,-0.0503234938,0.0647123381,0.4233525395,0.1470820457,-0.2939503193,-0.2734202445,0.3738844395,-0.1731691062,-0.1254466772,-0.2122832835,-0.2605549991,0.0583349876,-0.1946227103,0.1922216117,0.1158105209,0.2132240683,-0.0956216007,0.4491807222,0.1187028587,-0.0395866819,0.6627027988,0.6913072467,0.1017229483,-0.1586233526,-0.0426356196,0.1583472788,-0.0875410885,-0.0045996848,0.0941019729,0.1971177161,-0.1294559687,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"Didn't we have a slide and discussion at WMT admitting that, for production-quality models, BLEU doesn't correlate with human eval anyway?\r\n","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":21,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n Didn't we have a slide and discussion at WMT admitting that, for production-quality models, BLEU doesn't correlate with human eval anyway?\r\n","embeddings":[-0.2491149306,-0.3357783258,-0.0347295776,-0.3980183005,0.1861638427,-0.2238558233,0.3552849293,0.1395354867,-0.3892639577,0.4466587007,-0.0967207775,0.2908814251,-0.1526997685,0.0279197823,-0.1170512065,0.204105854,0.1373981833,-0.009141366,0.6749675274,-0.1052772626,-0.1155280322,-0.2147949338,0.0487757102,0.3603987098,-0.2367162108,0.1981560141,0.3498717248,-0.2481336147,-0.1169564575,-0.2331206053,-0.2685071528,0.1880480051,-0.1370758861,0.0329764225,-0.0001076734,-0.3473198414,0.0011731707,0.0119105773,-0.3443155587,-0.4313924015,0.1730440706,-0.0576675162,-0.0739226341,-0.0539854728,0.1012308076,-0.0790880695,0.1463137269,0.0757335424,0.3050436378,-0.0967882499,0.1885267943,0.329490006,-0.5472923517,0.2496242374,-0.003958114,0.2622883618,-0.0547813512,0.1251739413,0.0734651908,0.287226826,0.0471568629,-0.0566911995,0.0409773998,-0.2722392082,0.0702089295,-0.0097833555,-0.1277758926,-0.1290709823,0.1420107037,0.5171830654,-0.0779175162,0.1853195429,-0.2592955828,-0.16466254,0.1634196043,-0.490164578,0.1457343847,0.0001003969,0.0894526467,-0.108816199,-0.0343419313,0.1923368722,0.1737521142,0.0310319327,0.122849606,0.1572470963,-0.0906207114,-0.011721557,-0.1866638958,-0.0387266576,-0.2872310579,0.1434111148,0.0214997251,-0.0525690764,-0.2434096038,-0.0831254348,-0.1516965479,-0.4444977343,0.0388033465,0.0838735029,0.4004847109,0.2629838288,-0.1080263034,0.1032519564,-0.1735090315,0.163530618,-0.1840124726,-0.2250757217,0.3822994232,0.0213678628,0.3974565864,0.1239967048,0.1788043231,-0.1456840634,-0.1180793121,0.1900229305,-0.3556284904,-0.1888513863,-0.7770127654,-0.0080201868,-0.2706565559,-0.0599770397,0.003641607,0.1757609993,-0.2525804043,-0.0737681836,-0.1152373552,0.0448658764,-0.0611337647,-0.247363314,-0.0125320265,-0.3729315698,-0.208776176,-0.1156876832,-0.1807078868,0.0068790852,0.2841663361,0.1106683388,0.4803144634,-0.1324008554,-0.0027704767,-0.101350978,-0.0555226691,-0.0896622017,-0.3398228288,-0.1305007935,-0.174497366,-0.2144024819,0.057472147,0.1234654337,-0.2911875844,-0.323586911,0.1811128706,0.1271613091,0.122085087,0.1307637393,-0.361689955,0.2228198647,0.0336335003,0.2459506691,0.2967767715,-0.2096000612,-0.0243234225,-0.2422040999,0.1906651407,-0.387170136,0.0432689153,-0.2791077793,0.5192180872,0.2964473069,0.3169187307,0.4025864303,-0.2807274461,0.3707182109,0.0726590082,0.2284861505,0.114445664,0.0976476148,-0.0794755891,-0.0209455863,-0.1425353587,-0.1864728034,0.0257972851,-0.0516279377,0.3966948986,-0.1670402288,0.0254637133,0.0985415876,0.0494866148,-0.1148233861,-0.354819715,-0.5579895377,0.3521686494,-0.1113125458,0.1058301032,-0.0760886297,0.216993615,0.5703735948,0.329774797,-0.3007261157,-0.1977592707,-0.4047829211,0.6597766876,0.0785528421,0.1278491765,0.316111207,0.4889910221,0.0977325514,-0.1304489076,0.1759990007,0.1449162364,-0.3879233599,0.0350165144,0.0397131629,0.2069048285,0.0064133396,0.0390690416,0.0081260586,-0.4279516935,0.1908796579,-0.5212842226,-0.1482566744,-0.5427168608,0.1451872289,0.1458370984,-0.1478000283,-0.000831613,0.1121125296,-0.0124797821,0.2398341894,-0.0534303263,-0.0295754876,-0.2044357061,0.2522409558,-0.0833936855,0.0158802737,-0.4189137518,0.5335503221,0.3664586544,0.1977936178,-0.351101011,0.2762394547,-0.1181578338,0.1622233093,0.3843787611,0.4806502461,0.1684254259,-0.0413100459,-0.0516036674,-0.2080800384,-0.1055176258,0.0972293988,0.0367422998,-0.1439665556,0.1675713211,0.1346848011,-0.1431772709,-0.0929022431,-0.2031818181,-0.0054223579,0.2441968322,0.0609391332,0.1806289405,0.0542811044,0.5331862569,-0.2062664479,0.1067077667,-0.077581048,-0.0115666008,0.0520081781,-0.0053719799,0.1069076881,-0.144276917,-0.3583911061,-0.0926376283,0.0081982678,-0.1586504132,0.1531457156,-0.0389769785,0.343019098,-0.1362837255,-0.1561332941,-0.0443779379,-0.1271745116,-0.1358420104,0.1161138266,-0.2197365165,0.2930183113,-0.0165710635,0.1127933189,-0.0896846876,-0.1608657539,0.2289372683,0.0212383978,-0.1587416977,0.3358259499,0.1481372416,0.7075200677,-0.2578309178,0.1896396279,0.0150311645,0.2776121795,-0.384010911,0.0889866129,-0.1946355999,-0.3346905708,0.2537218928,-0.1041299254,-0.2548139989,-0.0129498215,-0.6315752864,0.0518371612,0.0044754497,0.2070017755,0.1606281102,-0.5352048278,-0.4766362607,-0.1466354281,-0.1487207711,-0.1698218733,-0.0632988364,-0.0788986683,0.0721126348,-0.1897255927,-0.0883985981,-0.2601359189,0.1102479771,0.0771814957,0.1701355129,-0.3146591783,0.0592580363,-0.1035391241,-0.0372062549,0.1083503515,0.1465893388,0.2767812312,-0.1805095524,0.1025464013,0.0277055427,-0.1088031679,-0.2397813648,-0.1181817427,0.0440286621,0.2457215637,0.2073970139,-0.313262403,-0.096177876,0.2511529624,-0.1047439501,-0.1060047001,-0.1914811879,-0.278131485,0.0661624372,-0.1178401709,0.1875624657,0.0107328687,0.2696737647,0.1827258468,0.6394481063,0.1039488018,0.29801476,0.6273650527,0.57050699,-0.0594344176,-0.0164150055,0.0593583062,0.1587062925,0.0083983736,0.0667040125,0.0605471209,0.3135679066,-0.148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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"Yes, there are slides like that at WMT every year :) BLEU correlates with human judgment only at coarse levels, and it seems to be getting worse when people try to use it to do model selection among high-performing neural systems.\r\n\r\nHowever, the point isn't whether BLEU is a good metric, but whether your BLEU score can be compared to other BLEU scores. They only can be compared if you use the same reference tokenization (similar to how you [can't compare LM perplexities across different segmentations](https:\/\/sjmielke.com\/comparing-perplexities.htm)). sacrebleu was an attempt to get everyone to use WMT's reference tokenization (meaning, your system has to first remove its own tokenization) so that you could just compare across papers. This also prevents scores from being gamed.","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":123,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n Yes, there are slides like that at WMT every year :) BLEU correlates with human judgment only at coarse levels, and it seems to be getting worse when people try to use it to do model selection among high-performing neural systems.\r\n\r\nHowever, the point isn't whether BLEU is a good metric, but whether your BLEU score can be compared to other BLEU scores. They only can be compared if you use the same reference tokenization (similar to how you [can't compare LM perplexities across different segmentations](https:\/\/sjmielke.com\/comparing-perplexities.htm)). sacrebleu was an attempt to get everyone to use WMT's reference tokenization (meaning, your system has to first remove its own tokenization) so that you could just compare across papers. This also prevents scores from being gamed.","embeddings":[-0.3135464489,-0.2768908739,-0.0518379956,-0.2917878032,0.0934216827,-0.2474938184,0.28668347,0.2192002237,-0.4277971685,0.3128701746,-0.2075859904,0.1104491651,-0.1642134935,-0.1205747128,-0.1374371052,0.1132958829,0.1067669168,-0.0139779262,0.6649669409,-0.0207699798,-0.1770703495,-0.1872543395,0.0037461594,0.3304761648,-0.3724300563,0.2632550597,0.2985367477,-0.1913367212,-0.1498094052,-0.3490214944,-0.2689947486,0.2102922946,-0.2063372135,0.1252582371,-0.0001022047,-0.3871833086,0.0541020557,-0.0368929841,-0.4439689517,-0.3665273786,0.1544646025,-0.1669862866,-0.1151911169,-0.1171803251,-0.0134163201,-0.1435834169,0.040178448,0.1061420739,0.3251447082,-0.1058374122,0.2449501008,0.3034593165,-0.4683806598,0.1498442888,0.0699931011,0.2710111737,-0.0925994515,0.1770909429,-0.0050246725,0.3587901592,0.0353532992,-0.0747972876,0.0237663742,-0.0775674731,0.0670090392,-0.0880065784,-0.272153765,0.004597412,0.1218435839,0.5070339441,0.0015664555,0.1673952341,-0.3386724591,-0.0811631382,0.1015543714,-0.4909129441,0.0544757843,-0.0133016165,0.152076304,-0.0793089047,0.0021795162,0.281907171,0.2130946815,0.0366321206,0.0277076922,0.1824213862,-0.1342664212,0.0382129811,-0.1514034867,-0.0254612658,-0.447825551,0.113600567,0.0265502501,-0.0206227507,-0.3019562066,-0.1138956845,-0.1809839755,-0.4280613661,0.0257194713,0.0539783239,0.3791999221,0.2382393628,-0.0665691346,0.08494322,-0.1064646766,0.1340885609,-0.2781978846,-0.0645568818,0.3806477487,0.0334777944,0.3261171877,0.1682181954,0.2315180302,-0.11943423,-0.0887901485,0.170956403,-0.3551331162,-0.1987959892,-0.8085628748,-0.0013848902,-0.1617463082,-0.1357214153,0.0229595546,0.2048790455,-0.1913792789,-0.2177047133,-0.1282643378,-0.0339852013,-0.0903071016,-0.1296689361,-0.08550217,-0.3203369677,-0.2990040779,0.0311049167,-0.1111095175,0.2265412658,0.2177430242,-0.066437155,0.3926664591,-0.0662084669,0.0135622285,-0.1394260973,0.0663151592,-0.104272157,-0.4170202017,-0.1082601026,-0.1174196303,-0.1446509361,-0.0133492369,-0.0166644994,-0.21624358,-0.2710017562,0.1235542968,0.1748419851,0.0567967854,0.2153903395,-0.3138505816,0.2010301501,0.0067009279,0.0871113166,0.3875291049,-0.1698922664,-0.1422985494,-0.2447662652,0.1321950108,-0.2628275156,0.0004181841,-0.3629073203,0.4552856386,0.2354488224,0.2896308601,0.4723249972,-0.3027026653,0.2717089951,0.1163449958,0.2523094714,0.1024568826,0.0904901996,-0.1697993428,-0.0509173051,-0.0876499563,-0.2525357008,-0.0231531877,0.060714297,0.4108531773,-0.1739975959,0.0423741415,-0.0023212475,0.1505367011,-0.1329603195,-0.3309361041,-0.5472711921,0.2306468189,-0.0918670446,0.0819598064,-0.1068120971,0.2979665399,0.6883991957,0.3179251552,-0.2438719273,-0.0879760012,-0.3974711597,0.5804697871,-0.0651317611,0.0949912295,0.107622236,0.4525400996,0.1938538998,-0.0534838475,0.2195281535,0.2961948216,-0.3364754021,0.0217113681,0.0140826507,0.2248198092,-0.0177137926,0.1083814278,0.0481035113,-0.3625598252,0.1451941431,-0.4818155468,-0.2320880741,-0.4947704375,0.1124652028,0.2530227005,-0.1261920184,0.0631493703,0.0302841384,0.0864325762,0.3047133386,-0.0851937979,0.0517084897,-0.2287273556,0.1790742576,0.0435498394,0.0120681729,-0.428494662,0.5606502295,0.2933938503,0.1943801045,-0.2939884663,0.2260260731,-0.0991017297,0.1268149614,0.3718375564,0.452625066,0.1289929003,-0.1137036383,0.033375226,-0.255538106,-0.139011085,0.0403778031,-0.0013857797,-0.1774316728,0.1480552405,0.1533436924,-0.1985276043,-0.0082235774,-0.0935473815,-0.1130372509,0.1701189727,-0.0313551277,0.2223936617,0.0351333134,0.4144179821,-0.1636601239,0.1096955761,0.076553382,0.0223658849,0.0529046655,0.1112138629,0.0436987206,-0.0731369182,-0.2892229855,-0.1087961197,0.0376401506,-0.1532531232,0.1441552788,-0.0483636372,0.3560223579,-0.0613275059,-0.1281310618,-0.0646713674,-0.1471245587,-0.0709936842,0.1334352046,-0.1606141031,0.3112232089,-0.0550840683,0.1209589019,0.0132962177,-0.1642047912,0.173934564,0.0952125937,-0.0954497904,0.3084385097,-0.0024335445,0.7073996067,-0.2083141059,0.0458778627,0.0995651111,0.2048485279,-0.2296585888,0.1501955688,-0.142242372,-0.1898702532,0.2541303337,-0.0357539095,-0.2220229357,-0.0826374888,-0.5311999321,0.1318363249,-0.0413252227,0.1282416582,0.205958873,-0.502579093,-0.4749952257,-0.1504636407,-0.0743283704,-0.2248393297,-0.1113214493,-0.0876680017,-0.0031401471,-0.2432128638,-0.0484583378,-0.2018825412,0.0908645764,-0.0008779332,0.1538136005,-0.2386196554,0.0360030346,-0.1215343475,-0.198466897,0.1900894791,0.2075915039,0.1916429549,-0.2571556568,0.1882054508,0.0815550983,-0.1382200718,-0.2136996388,-0.0471130647,0.0370419435,0.3381594419,0.1280739754,-0.3612490296,-0.0142398439,0.3136072159,-0.1650910676,0.0089935008,-0.2673547566,-0.2607000768,0.0362970568,-0.2162789851,0.1994867474,0.003855034,0.1457368135,0.1224850863,0.6416712999,-0.0021231908,-0.026488401,0.5659835339,0.7179704905,0.0041275383,-0.1526336372,0.0798186883,0.2234243453,-0.0822401345,0.0420573466,0.0421513841,0.4057151377,-0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"I do not consider as a sufficient solution switching this library's default metric from BLEU to the wrapper around SacreBLEU. \r\n\r\nAs currently implemented, the wrapper allows end users to toggle SacreBLEU options, but doesn't pass along the SacreBLEU signature. As @mjpost showed in [Post18](https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf), it's simply not credible to assume that people will stick to the defaults, therefore, the signature is necessary to be explicit about what options were used. \r\n\r\nIn addition to the `v13a` or `intl` options for the SacreBLEU `tokenize` argument, which was pointed out earlier, papers frequently differ on whether they lowercase text before scoring (`lowercase`) and the smoothing method used (`smooth_method`). BLEU scores can differ substantially (over 1 BLEU) just by changing these options. \r\n\r\nLosing the SacreBLEU signature is a regression in reproducibility and clarity.\r\n\r\n(Perhaps this should belong in a separate issue?)","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":137,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n I do not consider as a sufficient solution switching this library's default metric from BLEU to the wrapper around SacreBLEU. \r\n\r\nAs currently implemented, the wrapper allows end users to toggle SacreBLEU options, but doesn't pass along the SacreBLEU signature. As @mjpost showed in [Post18](https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf), it's simply not credible to assume that people will stick to the defaults, therefore, the signature is necessary to be explicit about what options were used. \r\n\r\nIn addition to the `v13a` or `intl` options for the SacreBLEU `tokenize` argument, which was pointed out earlier, papers frequently differ on whether they lowercase text before scoring (`lowercase`) and the smoothing method used (`smooth_method`). BLEU scores can differ substantially (over 1 BLEU) just by changing these options. \r\n\r\nLosing the SacreBLEU signature is a regression in reproducibility and clarity.\r\n\r\n(Perhaps this should belong in a separate issue?)","embeddings":[-0.27546224,-0.2523212731,0.015029124,-0.3404414356,0.2117930949,-0.3596958518,0.1853222847,0.3614321649,-0.3344036043,0.3967298269,0.0437221453,0.3229061961,-0.1908613592,0.0206783451,-0.186046496,0.0257372204,0.0594054833,-0.0886750668,0.6912773848,-0.0719551891,-0.1700499654,-0.1265845448,0.008774207,0.1469752491,-0.1182374805,0.3319087327,0.3068475127,-0.2342270315,-0.3007888198,-0.3260027468,-0.0953568295,0.2849736512,-0.2850325406,0.1056501493,-0.0001081686,-0.3039608002,0.0533862971,-0.1401547939,-0.1782739311,-0.4625362158,0.1213937998,-0.2437465489,-0.1138690561,-0.1544534713,-0.0162539575,-0.1707026213,0.0801403299,0.0198843144,0.346075356,0.0222148467,0.1744837016,0.2951719165,-0.4948261678,0.1890507042,-0.0312422365,0.2374393344,-0.0844698101,0.2880865335,-0.0362244211,0.313665539,-0.1438014805,-0.0414782017,-0.0263566021,-0.0947214589,0.1497689188,-0.0572514161,-0.2816728055,0.0068677953,0.0345946103,0.493599534,-0.0878522396,-0.0055465871,-0.2163961083,-0.1053453833,0.0727963522,-0.4867216349,0.1393391937,-0.0989826173,0.13512218,-0.2426536679,0.0057271724,0.1746472567,0.087215662,0.1095076799,-0.0680324584,0.0190903898,-0.1622909009,0.0086817127,-0.0108169317,0.0278702639,-0.48339656,0.2477413714,0.1340903193,-0.0004387455,-0.1850897521,-0.0631029382,-0.0277451854,-0.5770332217,-0.0194555204,0.1404000521,0.3119617403,0.3449689448,-0.2340863049,0.1241531,-0.3108345866,0.1665844172,-0.1009587497,-0.2342416197,0.4526522756,0.1669716686,0.4540477097,0.0995844603,0.2723415792,-0.2017757446,-0.0753199384,0.2687521577,-0.2019764185,-0.1338178813,-0.6423498988,0.0248230789,0.0201711301,-0.1173330247,0.1609819233,0.1125367731,-0.3188554943,0.0448210575,-0.0249752216,0.0525739416,-0.1805677861,-0.0498966724,-0.0631348863,-0.1982997954,-0.2298302203,-0.0254416279,-0.0078387856,0.0413170122,0.2758931816,0.1014923453,0.5700212717,-0.0659460798,0.0850098804,-0.0034184742,0.031156214,-0.1083388701,-0.4364647567,-0.1272356659,-0.0389608517,-0.1705790758,-0.0058359266,-0.0333688557,-0.3677676618,-0.3043262661,0.1216996014,0.1472866088,-0.0167448223,0.1506099403,-0.4524779618,0.1997803152,-0.1081032753,0.0593562685,0.2417585701,-0.1245697886,-0.1582509875,-0.2411295325,0.2471058667,-0.3424717486,0.1747466922,-0.3750631511,0.4064902067,0.1518936008,0.2413390875,0.2540333569,-0.2383617759,0.1914658993,0.1538760364,0.2080127448,0.116146557,-0.1404009461,-0.2718495131,-0.194881767,-0.0188464299,-0.3495473862,0.0538361631,0.0247635916,0.4324914217,-0.1881874502,-0.0882972255,0.0986308679,0.0389308892,-0.1022564918,-0.36290434,-0.6038562655,0.0815442055,-0.0762142763,0.2625020742,0.0183706656,0.1317540705,0.6068987846,0.1456057131,-0.1953678131,-0.0812748298,-0.2756708264,0.6100926995,-0.188805595,0.2301256061,0.1286764443,0.3836823106,0.124755688,-0.1045504957,0.1484696269,0.0867827535,-0.3845891953,-0.1050526574,-0.0811828747,0.3070400655,0.1821350157,0.0624115504,-0.0500540435,-0.4763344228,0.1471545249,-0.5149652362,-0.2080215812,-0.4747824073,0.2136285603,0.1432303637,-0.1589784026,0.1644805968,0.1681167036,0.1220598295,0.3748025596,0.025054723,-0.0488163941,-0.2073414028,0.2976349294,0.0177839305,0.1419627964,-0.4085462689,0.5593419671,0.3624275625,0.1577276438,-0.3639058173,0.1839713305,-0.2214429826,0.1356986761,0.1031718031,0.5868310928,0.1048102304,-0.0521509312,0.0152480276,-0.164219752,-0.2311976403,-0.0265012309,-0.1726716906,-0.2763185501,0.0324626751,-0.0138006862,-0.1483166069,-0.0102157332,-0.1258916259,-0.0296058804,0.2117739022,0.1503810585,0.1223942265,0.2029440552,0.56578058,-0.1657807082,0.0235720724,0.1294998527,0.0724493042,-0.0312146079,0.0524427965,0.1006217599,-0.083846271,-0.2964494228,-0.1408361644,-0.0505328849,-0.2003326863,0.1889407933,-0.0410737284,0.2605827749,-0.1415332556,0.017123526,0.0461693928,-0.0348761193,-0.1211702675,0.1572966874,-0.2206382006,0.2652708888,-0.0518266819,-0.0629634634,-0.0514928289,-0.2756368816,0.2165699899,0.1610555351,-0.2347649932,0.3221915364,0.0509920418,0.7734154463,-0.2841901481,-0.0480795801,0.0800386891,0.1919495016,-0.1521677524,0.0710826218,-0.1971363723,-0.288234055,0.2453038841,-0.1230286285,-0.169133395,-0.0259956568,-0.6473637819,0.0570211597,0.0846215561,0.1302390099,0.2541700602,-0.4206206203,-0.3183861673,-0.1246137917,-0.0045476472,-0.1607361734,-0.0812279433,-0.0185571797,-0.0673048794,-0.099458158,-0.1805820614,-0.3190949261,0.2195562869,-0.0247370955,0.074115105,-0.2699486017,0.1559746116,-0.2022841722,-0.1049693674,0.1877568364,0.3002032638,0.2783682048,-0.1742997766,0.0282519534,0.0802432597,-0.1298372895,-0.2182052284,-0.0646490678,0.02784664,0.5163828135,0.1747506261,-0.4123682082,-0.3798092604,0.2071429789,-0.042192813,-0.0351000726,-0.2209966481,-0.1542415321,0.0020488976,-0.1901153475,0.0419289246,0.0104154442,0.2413175404,-0.0365926735,0.5588755012,0.0531289354,0.0504403189,0.6644158959,0.759304285,-0.0096892109,-0.1742100716,0.0397704542,0.2477197796,0.013521269,0.108068645,0.0331627019,0.3516508639,-0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"Thanks for sharing your thoughts. This is a very important discussion.\r\n\r\nAlso one of the first items on our mid-term roadmap (we will try to clean it and share it soon) is to introduce mechanisms to get high-quality traceability and reproducibility for all the processes related to the library.\r\n\r\nSo having the signature for `sacrebleu` is really important!\r\n\r\nRegarding BLEU, I guess we can just remove it from the canonical metrics included in the repo itself (it won't prevent people to add it as \"user-metrics\" but at least we won't be promoting it).\r\n\r\nOn a more general note (definitely too large for the scope of this issue) we are wondering, with @srush in particular, how we could handle the selection of metrics\/datasets with the most community-based and bottom-up approach possible. If you have opinions on this, please share!","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":138,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n Thanks for sharing your thoughts. This is a very important discussion.\r\n\r\nAlso one of the first items on our mid-term roadmap (we will try to clean it and share it soon) is to introduce mechanisms to get high-quality traceability and reproducibility for all the processes related to the library.\r\n\r\nSo having the signature for `sacrebleu` is really important!\r\n\r\nRegarding BLEU, I guess we can just remove it from the canonical metrics included in the repo itself (it won't prevent people to add it as \"user-metrics\" but at least we won't be promoting it).\r\n\r\nOn a more general note (definitely too large for the scope of this issue) we are wondering, with @srush in particular, how we could handle the selection of metrics\/datasets with the most community-based and bottom-up approach possible. If you have opinions on this, please share!","embeddings":[-0.0818871707,-0.1245273948,-0.0124915289,-0.2027842849,0.1472497433,-0.3798782527,0.2327238023,0.216726169,-0.3655436039,0.433247298,-0.0330452919,0.3025978804,-0.1015592813,-0.023105178,-0.062252719,0.0280110352,0.0301390756,-0.0420776941,0.5825217366,-0.0730280578,-0.2260937393,-0.1260984689,0.1246969253,0.3257729113,-0.1314591616,0.1447937042,0.3365076482,-0.2166612744,-0.3565535247,-0.2769578993,-0.1404503733,0.3231076896,-0.2540121675,0.2577394545,-0.0001069882,-0.4084021449,0.0452209786,-0.0130942352,-0.191257149,-0.4243074059,0.159482792,-0.0473010316,-0.0152656948,-0.1188468859,0.2306433469,-0.0742480829,-0.0054342658,0.0063780774,0.3002331853,-0.0364047252,0.1854625195,0.4392635226,-0.5508165359,0.1971772164,0.034031257,0.3261634111,-0.1520132571,0.26788342,0.2389596552,0.3190007508,-0.0405545309,0.0115692103,-0.0356705301,-0.1282536834,0.1492723525,-0.08414983,-0.1127899885,-0.0924187675,0.0481288433,0.5816909671,-0.0507145151,0.0061406051,-0.2000901103,-0.1411981583,0.1366505027,-0.6313545704,0.167587176,-0.0666436255,0.078595683,-0.1725691706,-0.098167941,0.1102956459,0.0271075442,0.1739035696,0.1366734952,0.14517878,-0.1113369986,0.0330863521,-0.0647934452,0.0700325444,-0.3146150112,0.1770358831,0.0844018683,-0.0167076942,-0.1335647106,-0.1489966363,-0.0528645217,-0.497538656,0.1695331484,0.1334266663,0.2738729715,0.211594373,-0.1975943297,0.1667269617,-0.0983183905,0.1832697988,-0.1140599623,-0.2469255626,0.5300056934,0.1867011935,0.5439881086,0.2406797111,0.1755176336,-0.1962686479,-0.1066871956,0.2655930519,-0.2863332927,-0.257281512,-0.6572712064,0.0352051295,-0.0842982605,-0.1380368769,0.1935329288,0.0698646754,-0.2418748289,0.039669916,-0.1248391047,0.1039018631,-0.1976496279,0.0453195758,-0.0443241633,-0.2019354701,-0.2791752815,-0.0301847011,0.0450530238,-0.0970879272,0.3239656389,0.0443930887,0.522208035,-0.1612733454,-0.0141982082,-0.066585727,-0.1375818253,0.0075469054,-0.4400563836,-0.0836923495,-0.1814333349,-0.1485814452,-0.154213056,0.0515527762,-0.3326386511,-0.3480359018,0.0988398045,0.0916899592,-0.0631538555,0.1043326855,-0.5079072714,0.1652381718,-0.0534167103,0.3217261434,0.3081628084,-0.0437036827,-0.1204959154,-0.2699185908,0.1820601821,-0.2673655748,0.1021958962,-0.3500091434,0.3089857399,0.1232982427,0.2470039427,0.4162701368,-0.2819542289,0.270813942,0.1471827775,0.1384106874,0.1858864427,-0.1708893031,-0.1119875386,-0.1249221414,-0.100658372,-0.2894634008,0.0726195723,-0.0181345604,0.3934051096,-0.322113663,-0.0021629999,0.0995085537,-0.0746621415,-0.093705982,-0.2949598134,-0.6365936995,0.1811713576,-0.0640156567,0.2618018687,-0.0739233643,0.1493518651,0.5474885106,0.16225411,-0.2383558452,-0.0671942011,-0.2915121615,0.6991117001,-0.0566018224,0.1714491099,0.1690240353,0.3516166508,0.0984459445,-0.1594759822,0.0942376703,0.1024674624,-0.3705070019,-0.1538808346,0.0854519531,0.3287083507,0.1383348405,0.0405930281,-0.152090326,-0.4923216403,0.1525607258,-0.5571346879,-0.20369789,-0.5812091231,0.2410837561,0.2647257447,-0.1177807823,0.0340831541,0.1121772379,0.1171182171,0.3213680089,0.0805639327,-0.0825177282,-0.1740756184,0.2082176507,0.0185832959,0.1545181572,-0.2574837506,0.6626389623,0.5278886557,0.292298317,-0.29538095,0.0023913991,-0.201923579,0.1292420179,0.2877389789,0.532376647,-0.0018848474,-0.035863556,-0.0381867066,-0.1980602145,-0.2115884125,0.0169116706,-0.0812572464,-0.1598342508,0.1188878566,0.0781567767,-0.2059928775,0.0583183654,-0.1707789749,-0.0270369127,0.3027314842,0.1237444431,0.0557776839,0.1786347181,0.5540725589,-0.1804165989,0.0339521207,0.0605224185,0.068394348,-0.0285854191,0.0326615758,0.0732317492,-0.0773920566,-0.3613891304,-0.1430312544,0.1695746034,-0.1167992651,0.1390595585,-0.0318722986,0.3440871239,-0.1423461884,0.0464832485,-0.027767878,-0.0538764186,-0.0934589282,0.1594306082,-0.1906055808,0.2088365704,-0.0212829746,0.0130718919,-0.1947479248,-0.1988852471,0.3032418489,0.1695010662,-0.2259410024,0.3890611529,0.2144977599,0.759288609,-0.3860781491,0.1463853121,-0.0089942282,0.1416170001,-0.2407531589,0.0906016231,-0.0337107368,-0.2865643799,0.4852043688,-0.0415745005,-0.1511919498,-0.1072937697,-0.7395250201,0.1265028715,-0.1127201095,0.2133208215,0.0958522856,-0.4332280755,-0.4236867428,-0.1633209884,-0.1294266284,-0.211745739,-0.006442185,-0.1051587239,-0.0112356981,-0.0615267754,-0.1734590679,-0.3504240215,0.0531871468,-0.1200858653,0.1620345116,-0.233478412,0.1778959334,-0.164712131,-0.1721254885,0.1713183969,0.2623412013,0.2695657611,-0.2081600428,0.0154860895,0.0330490433,-0.1602005363,-0.2397180945,0.0083256131,0.1209514812,0.3449020088,0.1680004001,-0.3970922232,-0.5138249993,0.4018003643,-0.1077529415,0.0158451013,-0.2317853421,-0.1203455031,0.0657151267,-0.1335249841,0.0531875901,-0.1063811705,0.2502326071,0.171159789,0.5836225748,0.122861445,0.1070338339,0.5068244338,0.8114039898,0.0053955093,-0.0740964636,0.0504551902,0.4288633466,0.0942585245,0.0926601067,0.1649817228,0.3664279282,-0.0541517064,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"Yeah, I would love to have discussions about ways this project can have an community-based, transparent process to arrive at strong default metrics. @kpu \/ @mjpost do you have any suggestions of how that might work or pointers to places where this is done right? Perhaps this question can be template for what is likely to be repeated for other datasets.","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":61,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n Yeah, I would love to have discussions about ways this project can have an community-based, transparent process to arrive at strong default metrics. @kpu \/ @mjpost do you have any suggestions of how that might work or pointers to places where this is done right? Perhaps this question can be template for what is likely to be repeated for other 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"I think @bittlingmayer is referring to Figure 6 in http:\/\/statmt.org\/wmt19\/pdf\/53\/WMT02.pdf .  When you look at Appendix A there are some cases where metrics fall apart at the high end and some where they correlate well.  en-zh is arguably production-quality.  \r\n\r\nThis could evolve into a metrics Bazaar where the value add is really the packaging and consistency: it installs\/compiles the metrics for me, gives a reproducible name to use in publication (involve the authors; you don't want a different sacrebleu hash system), a version number, and evaluation of the metrics like http:\/\/ufallab.ms.mff.cuni.cz\/~bojar\/wmt19-metrics-task-package.tgz but run when code changes rather than once a year.  ","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":101,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n I think @bittlingmayer is referring to Figure 6 in http:\/\/statmt.org\/wmt19\/pdf\/53\/WMT02.pdf .  When you look at Appendix A there are some cases where metrics fall apart at the high end and some where they correlate well.  en-zh is arguably production-quality.  \r\n\r\nThis could evolve into a metrics Bazaar where the value add is really the packaging and consistency: it installs\/compiles the metrics for me, gives a reproducible name to use in publication (involve the authors; you don't want a different sacrebleu hash system), a version number, and evaluation of the metrics like http:\/\/ufallab.ms.mff.cuni.cz\/~bojar\/wmt19-metrics-task-package.tgz but run when code changes rather than once a year.  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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"While a Bazaar setup works for models \/ datasets, I am not sure it is ideal for metrics ? Ideal from my perspective would be to have tasks with metrics moderated by experts who document, cite, and codify known pitchfalls (as above^) and make it non-trivial for beginners to mess it up. ","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":52,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n While a Bazaar setup works for models \/ datasets, I am not sure it is ideal for metrics ? Ideal from my perspective would be to have tasks with metrics moderated by experts who document, cite, and codify known pitchfalls (as above^) and make it non-trivial for beginners to mess it up. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"@srush @thomwolf \r\n\r\nModelFront could provide (automated, \"QE-based\") evaluation for all the pretrained translation models you host.  Not bottom-up and not valid for claiming SoTA, but independent, practical for builders and not top-down.\r\n\r\nFor that I would also suggest some diverse benchmarks (so split it out into datasets with only user-generated data, or only constants, or only UI strings, or only READMEs) which tease out known trade-offs.  Even hypothetical magic eval is limited if we always reduce it to a single number.\r\n\r\nRealistically people want to know how a model compares to an API like Google Translate, Microsoft Translator, DeepL or Yandex (especially for a language pair like EN:RU, or for the many languages that only Yandex supports), and that could be done too.\r\n","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":123,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n @srush @thomwolf \r\n\r\nModelFront could provide (automated, \"QE-based\") evaluation for all the pretrained translation models you host.  Not bottom-up and not valid for claiming SoTA, but independent, practical for builders and not top-down.\r\n\r\nFor that I would also suggest some diverse benchmarks (so split it out into datasets with only user-generated data, or only constants, or only UI strings, or only READMEs) which tease out known trade-offs.  Even hypothetical magic eval is limited if we always reduce it to a single number.\r\n\r\nRealistically people want to know how a model compares to an API like Google Translate, Microsoft Translator, DeepL or Yandex (especially for a language pair like EN:RU, or for the many languages that only Yandex supports), and that could be done too.\r\n","embeddings":[-0.3010783792,-0.3047813177,-0.0611800104,-0.3138869703,0.1273147911,-0.3273704052,0.3816562593,0.1755339503,-0.4096497595,0.3841941953,-0.1201138794,0.1421944797,-0.0175862517,0.0955633819,-0.0426307619,0.1114330664,0.2059900016,0.0186355095,0.5584244132,-0.1555034071,-0.2218505144,-0.1242716014,0.0931644812,0.4027066529,-0.1611104757,0.1850787848,0.2451403141,-0.2799305618,-0.2717313766,-0.2062995434,-0.3766919374,0.3417181969,-0.101731725,0.1029788107,-0.0001036642,-0.2463439405,0.0133144446,0.0624925233,-0.4220637083,-0.3517745435,0.2042213678,-0.0018871224,-0.1989042163,-0.1293878406,0.1683181077,-0.0654822737,0.0924509913,-0.0769087896,0.4604594111,-0.0036925988,0.1910533607,0.2854010165,-0.4029682279,0.1959451288,-0.0800039023,0.0667359978,-0.043820899,0.0918971449,0.0659917668,0.3166938126,-0.0174476504,0.1742993444,0.1360616386,-0.3998275101,-0.0907147378,-0.0763450116,-0.0710826963,-0.2661143839,0.0737977847,0.632391572,-0.069395408,0.0650852993,-0.517341733,-0.2082910389,0.0953331292,-0.3892348111,0.0115180286,-0.0865085796,0.1589214951,0.0082930885,0.0806455389,0.246793732,0.1504050493,0.0348283611,-0.0706624538,0.1887613535,-0.0210120417,-0.0120099187,-0.0475762039,-0.0762665495,-0.3279489875,0.0747205839,-0.109516494,0.0245236661,-0.3229111135,-0.1526347846,0.1233857051,-0.4519011974,0.0032077837,-0.0374149494,0.3347592354,0.441319555,-0.3616331816,0.0896280631,-0.1255673319,0.3435740769,-0.0596448183,-0.0491025187,0.453595072,-0.0634102151,0.4054988921,0.1880567521,0.0947800949,-0.0840573087,-0.3517459631,0.2669509053,-0.3329097629,-0.1914095581,-0.5132962465,0.0346818268,-0.1944807172,-0.0897806883,0.0164489429,0.1560226083,-0.2040168196,0.1376789808,-0.1604218632,0.0174841601,-0.0186054017,-0.3845176101,-0.0334108844,-0.2106874287,-0.2203496844,-0.0961611345,-0.1874143779,0.0846683681,0.2580227256,-0.0988033116,0.3942308426,-0.2225654274,0.0133917965,-0.2726571858,0.0337810293,-0.2382860035,-0.2118438035,-0.181231007,-0.2084265798,-0.1865707487,-0.0181796998,-0.0470120348,-0.1525343359,-0.289927572,0.2821365595,0.1840290427,0.0047596879,0.1546301246,-0.4161112905,0.5570933819,-0.1702479124,0.3113904893,0.3434077501,-0.1283844262,-0.1843543053,-0.1070308164,0.1684682071,0.0197692737,-0.1154248491,-0.1988828331,0.4583144188,0.1314865053,0.2196232229,0.3778914511,-0.3014671206,0.3738218248,0.1338273287,0.2919662297,0.1472800821,0.2005857378,0.0152869308,-0.0110682882,-0.0741768405,-0.2649732828,-0.013581221,-0.1358347982,0.4207935929,-0.2460363656,-0.0291044172,0.2523927689,0.0322386362,0.0370513424,-0.4075156748,-0.4062932134,0.2187842578,0.020536704,0.1168178767,-0.1498483419,0.2145441175,0.6380792856,0.1009495333,-0.4478616416,-0.1879836768,-0.3772436678,0.4720911384,-0.1045633033,0.1327014267,0.2647719383,0.3468116522,0.2244712263,-0.0971404687,0.272069633,0.2956106961,-0.2033835799,0.1709327698,0.1735499948,0.1529824287,0.1029701754,0.1602182686,0.0174833219,-0.2046836019,0.2456154823,-0.4802745879,-0.2275651842,-0.5535259247,0.0107631469,0.2309435457,-0.1112869754,0.0813828409,0.0827927217,-0.0050035091,0.357495755,-0.1463560611,-0.0156822782,-0.1896278113,0.2357197404,-0.0501512736,0.173104018,-0.2341891229,0.4826675355,0.3344714344,0.2200555205,-0.2113866657,0.1971500516,-0.0720547363,0.1580653489,0.304595083,0.6122882962,0.1674378067,0.066242516,0.1328540593,-0.4178977609,0.0257421639,0.1547708064,-0.1491043419,-0.2237517536,0.2564687729,0.2144478261,-0.1147010252,0.0463883281,-0.1337213963,0.0569404811,0.557829082,-0.1428235918,0.2295326442,0.1989189535,0.5189016461,-0.3050459921,-0.0725493133,0.0159229934,-0.137386322,0.0877700076,0.2015790641,0.1478748769,-0.0273405556,-0.2188295573,0.1252611876,0.0157827772,-0.1527891159,0.1578863561,-0.0572472923,0.2114070952,-0.2554963827,-0.1779022813,-0.0976835787,-0.0542230755,-0.0601527095,0.1648679823,-0.1169072539,0.1615856886,-0.1253595799,-0.0872274861,-0.1981623918,-0.2564946115,0.3206938505,-0.2945397198,-0.1807955503,0.2102748603,0.106284596,0.5638193488,-0.3794335425,0.0047400771,0.0382160768,0.0092616407,-0.3987799883,0.0889996737,0.004158393,-0.3560526371,0.3400898874,-0.1060509533,-0.3122848868,-0.0148054091,-0.6370803714,0.1654572934,-0.0358800516,0.0560886189,0.0953678638,-0.5258800387,-0.34286201,-0.0420559756,-0.080934085,-0.0219958797,-0.1218088865,0.1219160482,0.0355795287,-0.2740758061,-0.1570341438,-0.3323710561,0.1546514183,-0.0127200242,0.2127460539,-0.4659501016,0.1562655121,-0.1724230349,0.0156060383,0.1617344916,0.0289321616,0.2153354287,-0.1713782996,0.1383953094,0.0528838113,0.0204353072,-0.1943559945,-0.0703226849,0.0171600785,0.244169876,0.1987923086,-0.2404878139,-0.153113544,0.2164021134,-0.0376479924,0.0399203189,-0.1564818323,-0.0306669958,0.0451151244,-0.1633165479,0.1383824944,0.0089752218,0.2126473188,0.152288273,0.4314678907,0.0553292222,0.4029931426,0.5166801214,0.418168366,-0.1030318886,-0.1397066414,0.141138643,0.2512727976,-0.1293347776,-0.0605842769,0.138382256,0.3226554096,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/137","title":"Tokenized BLEU considered harmful - Discussion on community-based process","comments":"Very important discussion.\r\nI am trying to understand the effects of tokenization.\r\nI wanted to ask which is a good practice.\r\nSacrebleu should be used on top of the tokenized output, or detokenized(raw) text?","body":"https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.  ","comment_length":34,"text":"Tokenized BLEU considered harmful - Discussion on community-based process \n https:\/\/github.com\/huggingface\/nlp\/blob\/7d1526dfeeb29248d832f1073192dbf03ad642da\/metrics\/bleu\/bleu.py#L76 assumes the inputs are tokenized by the user.  This is bad practice because the user's tokenizer is usually not the same as the one used by `mteval-v13a.pl`, the closest thing we have to a standard.  Moreover, tokenizers are like window managers: they can be endlessly customized and nobody has quite the same options.  \r\n\r\nAs @mjpost reported in https:\/\/www.aclweb.org\/anthology\/W18-6319.pdf BLEU configurations can vary by 1.8.  Yet people are incorrectly putting non-comparable BLEU scores in the same table, such as Table 1 in https:\/\/arxiv.org\/abs\/2004.04902 .  \r\n\r\nThere are a few use cases for tokenized BLEU like Thai.  For Chinese, people seem to use character BLEU for better or worse.\r\n\r\nThe default easy option should be the one that's correct more often.  And that is sacrebleu.  Please don't make it easy for people to run what is usually the wrong option; it definitely shouldn't be `bleu`.  \r\n\r\nAlso, I know this is inherited from TensorFlow and, paging @lmthang, they should discourage it too.   \n Very important discussion.\r\nI am trying to understand the effects of tokenization.\r\nI wanted to ask which is a good practice.\r\nSacrebleu should be used on top of the tokenized output, or detokenized(raw) 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/133","title":"[Question] Using\/adding a local dataset","comments":"Hi @zphang,\r\n\r\nSo you can just give the local path to a dataset script file and it should work.\r\n\r\nHere is an example:\r\n- you can download one of the scripts in the `datasets` folder of the present repo (or clone the repo)\r\n- then you can load it with `load_dataset('PATH\/TO\/YOUR\/LOCAL\/SCRIPT.py')`\r\n\r\nDoes it make sense?","body":"Users may want to either create\/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.\r\n\r\nIt appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.\r\n\r\nA notebook\/example script demonstrating this would be very helpful.","comment_length":55,"text":"[Question] Using\/adding a local dataset \n Users may want to either create\/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.\r\n\r\nIt appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.\r\n\r\nA notebook\/example script demonstrating this would be very helpful. \n Hi @zphang,\r\n\r\nSo you can just give the local path to a dataset script file and it should work.\r\n\r\nHere is an example:\r\n- you can download one of the scripts in the `datasets` folder of the present repo (or clone the repo)\r\n- then you can load it with `load_dataset('PATH\/TO\/YOUR\/LOCAL\/SCRIPT.py')`\r\n\r\nDoes it make sense?","embeddings":[-0.3090123832,0.3447009623,-0.0485156663,-0.1978272647,-0.0229685567,0.0207580794,0.1884503365,0.1586086899,0.1726605445,0.2399257869,0.0062487917,0.3857524097,0.0464574918,0.2451036572,0.3998591304,0.0255310666,-0.0244459994,0.070049651,-0.1732779294,-0.1373276114,-0.3640020192,-0.2514444888,0.3551029265,0.2580877542,0.1759759635,0.0368698873,-0.0416280627,0.4159365594,-0.1249530241,-0.4263148904,0.3296694756,0.2230904102,0.2731530368,0.5622919798,-0.0001124956,0.0487476103,0.0971881226,0.091562666,-0.3861498237,-0.5210891366,-0.4382898808,-0.1007484943,0.3260928094,-0.3913037777,0.0804672316,-0.275677979,0.2364041507,-0.6570729017,0.2124788761,0.334617734,0.1648003459,0.1231686771,-0.2467631102,-0.2063307464,0.1589167416,0.4795048237,-0.1128517985,0.2791862786,0.4473838508,-0.1877632588,0.2337213159,-0.1688640118,-0.0824528709,0.1349943578,0.6063190103,0.0902402252,-0.0422737189,-0.1100494266,0.2116334736,0.2509287894,0.7525584698,-0.4354602695,-0.4185048044,-0.1471859962,-0.1568042338,-0.0201355182,-0.0830314532,0.1686895937,-0.1593190134,0.3493252397,-0.2758412063,-0.379021883,-0.3844536245,0.4024395049,0.0384801887,0.010832279,0.2473139465,0.1539154202,0.0587831475,0.0556046069,0.292114526,-0.2781497836,0.4651041627,0.2353744507,0.2413295805,0.1399903148,0.1555410177,0.030088963,0.2262865901,0.0460031852,0.0414500348,-0.0955523476,-0.3153201342,0.1158604622,0.1871736497,-0.1279511005,-0.0290783346,0.286554873,0.1502451301,-0.1007895246,0.2262394279,-0.1162564084,-0.2261371017,-0.1064446196,-0.1391726881,-0.0561173148,0.3408097029,-0.3458411694,0.167849198,-0.205803141,0.0207194872,-0.1034033746,-0.0321571082,0.1876645237,0.0221797042,-0.0610919669,-0.1767026633,0.2906900942,-0.017483335,0.0387220569,-0.0492266379,0.0404996872,-0.1510084122,0.2130775005,-0.0267855357,0.0682646632,0.3417430222,-0.2578414977,-0.0675443783,0.1835035682,0.3229322433,0.054926496,-0.0518588163,0.1016049609,0.0698422268,0.2327794433,0.0810619667,-0.2629656792,-0.3304671943,0.2878436744,0.0846007392,-0.512612462,-0.2862553,0.117992498,-0.1039315686,-0.3487535417,-0.0508175157,-0.1008580253,-0.0907144323,-0.1254523396,-0.3346060812,0.0529804528,-0.2649670243,-0.2240497321,0.2608115077,0.4978345037,-0.6259242296,-0.0063599949,-0.2309514731,-0.1024579108,0.0164717808,0.0993955135,-0.5138141513,0.4445572495,-0.0029953418,0.0540964566,0.4258202016,-0.55223912,-0.2102416903,0.3178724945,-0.3017854691,-0.1860096008,0.140557155,0.2060299963,0.048614312,-0.0618532263,0.0390185602,0.4804842472,-0.1015206277,0.1506304294,-0.1494632214,-0.4203814268,-0.083973147,0.1870279312,0.1132033616,0.2471871376,0.3971060514,-0.1074997783,0.1809024066,-0.3337996006,0.0671204701,-0.1638286114,0.566373229,0.0118478071,-0.2202934921,0.1150465161,-0.467292726,0.3003493845,-0.1240665317,-0.0614873096,-0.1178948656,-0.3920164704,-0.5102698207,0.0492816567,-0.1401500553,0.1149683595,0.0089603104,0.2614189088,0.1601195335,-0.0975656658,-0.4219164848,-0.1015219837,-0.1182910055,0.1643473655,-0.041011896,0.2645519674,0.0092755565,0.2183258086,-0.1034366637,0.0791499466,0.0184264518,-0.10625723,0.0431232154,0.4312104881,-0.1817670465,0.3442499042,0.3935149014,0.4217561483,0.1872560978,-0.0497749075,0.303897202,-0.0257912911,0.2131172866,0.1154727861,-0.4095541239,0.0820389912,-0.1498382837,0.3559699655,0.077401787,0.0688540936,0.1919163615,-0.1492986381,-0.2090765685,-0.0831065625,-0.1376328021,0.1600797623,0.306304723,0.0021112622,-0.2828633487,0.2838191986,-0.2052478492,-0.1391828805,0.1246972978,-0.0414204374,-0.1701616496,0.1188541725,0.1547646075,0.6942638159,0.2962358594,0.1909158379,0.2645978034,0.2782218754,-0.1092056707,-0.156890735,-0.1746372283,0.009422401,-0.1520839334,-0.0372975692,-0.1935871094,-0.1481100768,0.1039798111,0.0082543362,-0.0298509616,0.2094391435,-0.2639261186,-0.0796205029,-0.4523699284,-0.3008153439,-0.4785174429,-0.3388303816,0.1356676668,-0.294691354,0.0774368048,0.0399066582,-0.2128749043,0.0000551179,0.0094636651,0.3449990451,-0.4868342876,-0.4742683172,0.1611764878,-0.2804466784,-0.0841309726,0.0790378526,0.5324950218,0.2608543038,0.40120399,-0.3653788865,0.0251296554,-0.4508334994,0.1964248717,0.0116490582,0.2860533595,0.3462802172,-0.0934295431,0.1495042741,0.19244048,-0.1640766114,0.1141793802,0.0246386826,0.2139932662,-0.4466781914,-0.0829798952,-0.0619533695,-0.0256237648,-0.3897651434,-0.4546720088,-0.2127760351,0.2069655806,0.3351814449,0.2323654145,-0.1442607939,0.264713347,0.3041373193,0.0645773634,-0.0183743499,0.1228377521,-0.4090555906,0.2555212677,-0.3162050247,-0.287995249,0.1857584417,-0.057696227,0.090583995,0.1595321596,-0.3731519878,-0.3830951154,-0.0730348602,0.4951599836,0.2606619,0.2412657142,0.3888583779,-0.0440204777,-0.0119954329,0.0503383987,-0.3888966739,0.2352429628,0.0624073818,0.0916620567,0.263176024,0.2989981174,-0.0743815675,0.8578977585,-0.1455602944,-0.1107754335,0.1480316818,-0.3166589141,0.2758001089,-0.1176741496,-0.0122438986,-0.1448895484,-0.0289503355,-0.3250271082,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/133","title":"[Question] Using\/adding a local dataset","comments":"Could you give a more concrete example, please? \r\n\r\nI looked up wikitext dataset script from the repo. Should I just overwrite the `data_file` on line 98 to point to the local dataset directory? Would it work for different configurations of wikitext (wikitext2, wikitext103 etc.)?\r\n\r\nOr maybe we can use DownloadManager to specify local dataset location? In that case, where do we use DownloadManager instance?\r\n\r\nThanks","body":"Users may want to either create\/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.\r\n\r\nIt appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.\r\n\r\nA notebook\/example script demonstrating this would be very helpful.","comment_length":65,"text":"[Question] Using\/adding a local dataset \n Users may want to either create\/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.\r\n\r\nIt appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.\r\n\r\nA notebook\/example script demonstrating this would be very helpful. \n Could you give a more concrete example, please? \r\n\r\nI looked up wikitext dataset script from the repo. Should I just overwrite the `data_file` on line 98 to point to the local dataset directory? Would it work for different configurations of wikitext (wikitext2, wikitext103 etc.)?\r\n\r\nOr maybe we can use DownloadManager to specify local dataset location? In that case, where do we use DownloadManager instance?\r\n\r\nThanks","embeddings":[-0.201106742,0.1899249405,-0.003256344,-0.224240467,-0.1611983478,0.0761197284,0.1614465714,0.0639025941,0.3210599422,0.2902062237,0.1616271585,0.3396971226,0.0044046212,0.17841582,0.3171544075,0.0068329861,0.0992788449,0.0957340449,-0.3213845789,-0.0826752335,-0.4165589511,-0.2904864848,0.1600189358,0.251401633,-0.023760464,0.0018262565,-0.033562541,0.2927275002,-0.1446347088,-0.429789573,0.248342976,0.0722853988,0.2206123918,0.4754655957,-0.0001182133,-0.0005177689,0.1042939499,0.1279481351,-0.3687489629,-0.3679015636,-0.5386099815,-0.1528670937,0.3830012679,-0.3592471182,0.1713761836,-0.2725833654,0.3547175229,-0.5175001025,0.1106041372,0.3297718763,0.0845135003,0.01644684,-0.3243149519,-0.0814011768,-0.0194652434,0.3938502073,-0.1172908619,0.092780441,0.5590108037,-0.151700139,0.1013459563,-0.1845551729,0.0188766383,0.0987392515,0.73269701,0.1822927743,-0.133181721,-0.1888921559,0.265627265,0.2752160728,0.978749454,-0.4884048104,-0.3078885674,-0.1967763007,-0.1440268159,0.17021586,0.1675825864,0.0440687016,-0.2567725182,0.3383143842,-0.2870934606,-0.3765918612,-0.3952593207,0.3760219514,-0.0366000086,0.1612858921,0.2452769428,0.1619727463,-0.0502662919,0.100068979,0.376927793,-0.4633914232,0.533257544,0.2898092866,0.2012470514,0.0762998387,0.0185708217,0.2050853223,0.1075656563,0.0456787981,0.093530573,-0.115116179,-0.2197816968,0.1503829807,0.1955340952,-0.1072269902,0.0580995679,0.0583123267,-0.0129448092,-0.1241884157,0.2272546291,-0.2052033693,-0.0919414535,-0.1886608005,-0.0670248047,-0.1223851517,0.1509943902,-0.1734285653,0.1557186991,-0.1623838097,0.1865758598,-0.044490926,0.0042570811,0.0590122454,0.0116429953,-0.0666119158,-0.2304314077,0.2163502872,-0.10072992,0.1422902346,-0.0023032848,0.10835886,-0.1681965142,0.2011038661,-0.014153772,0.1593428403,0.3369290233,-0.107068643,-0.2198595256,0.2733101249,0.3086557388,0.0411727354,-0.058824297,-0.0002561307,0.00132361,0.4736920595,-0.0012951762,-0.21119003,-0.3106511235,0.2809778452,-0.0066266754,-0.4388183653,-0.2618158758,0.0476111062,-0.0542419292,-0.3454589546,0.0852806643,-0.0601730831,-0.1355484575,-0.2552620471,-0.247264728,0.0612340458,-0.3180578053,-0.2794026136,0.1472722292,0.4335565269,-0.6197720766,0.1046252996,-0.3968454897,-0.0975053906,-0.0415219069,0.0253652614,-0.5023852587,0.644315064,-0.0937030166,0.1674820185,0.5209028125,-0.5649005175,-0.359269768,0.3398555219,-0.4539186954,-0.1321177036,0.0977254957,0.2600652575,0.0936468244,-0.109752059,-0.0989375859,0.4697247744,-0.112903282,0.2188141644,-0.1347548366,-0.4183905423,-0.0843808427,-0.0157877374,0.0025854514,0.2211381197,0.3674027622,-0.0033046179,0.1627334505,-0.2260329276,0.2156279534,-0.1381243616,0.3959729075,-0.0033242169,-0.4116642773,0.05300951,-0.5292719007,0.2987862527,-0.0821761191,-0.2978417575,-0.061711669,-0.2491993606,-0.4075194895,-0.0099819209,-0.1845238209,0.1506024748,0.0044379039,0.2695951462,0.2963570356,0.0519347936,-0.4162099957,-0.133428067,-0.0967299938,0.1747345775,0.0542285517,0.115951024,0.1357028633,0.2156604379,-0.2228408903,-0.1024402231,-0.0811592117,-0.0526875108,-0.0750327036,0.4124063253,-0.1210081875,0.3154419959,0.3610307872,0.3907224834,0.0826732293,-0.0380303599,0.3107642233,-0.0806855708,0.2622424364,-0.0040192534,-0.4382065535,-0.08983206,-0.2542896569,0.3789340854,-0.0098817246,0.0602177344,0.0066504758,-0.1252065301,0.0367149115,-0.1321915537,-0.2798055708,0.1747564971,0.368437469,0.0706078857,-0.038574338,0.2817542851,-0.0782720298,-0.0912842751,0.1970616132,0.0334147401,-0.1778915823,0.0533279665,0.1861423999,0.6401782632,0.3523538113,0.0190552641,0.3730781078,0.2265361547,-0.0659944788,-0.1597479135,-0.0807786435,0.0429389887,-0.2280526906,0.0812424123,-0.3045521677,-0.1317316592,0.2525743842,0.1863312721,0.0300050098,0.1881730855,-0.1252739131,-0.1555030942,-0.5117800236,-0.1336259395,-0.4786206782,-0.1955774873,0.1407467425,-0.3628897369,0.0657002181,0.161475718,-0.1322312951,-0.1513798088,-0.0597913526,0.3881176412,-0.4753079116,-0.3518950045,0.1394779831,-0.1854702234,-0.2374277711,0.0315733068,0.6115478873,0.4463932514,0.33825019,-0.2882735729,0.1297725886,-0.4036678076,0.2660573423,0.0261176154,0.2925158739,0.3645951152,-0.075043954,0.2648330927,-0.04500857,-0.2584792972,0.0405011736,0.0190757979,0.2235125601,-0.4360733628,0.0972263515,0.0809806734,0.1280534416,-0.277589649,-0.4195917547,-0.0940385833,0.1597174108,0.0941893235,0.1976951361,-0.3276100457,0.2694450319,0.2887520194,-0.0299179982,0.0139780194,0.177430138,-0.5027784109,0.143203795,-0.3212492168,-0.3227063417,0.1215478629,-0.0575171374,0.0851605013,0.1391380578,-0.3284092247,-0.3233733773,-0.1079703346,0.4302555025,0.2456706315,0.2824125886,0.4292845726,-0.0518874153,0.0977899283,0.01058493,-0.1871977895,0.2799861133,0.0446822867,0.122363016,0.4100353122,0.1412941962,-0.021201428,0.8346470594,-0.0451971404,-0.0149274031,0.172093451,-0.1510705501,0.1046918929,-0.1567704529,0.0099297296,0.028863512,0.164471224,-0.1726556718,0.26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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/133","title":"[Question] Using\/adding a local dataset","comments":"Hi @MaveriQ , although what I am doing is to commit a new dataset, but I think looking at imdb script might help.\r\nYou may want to use `dl_manager.download_custom`, give it a url(arbitrary string), a custom_download(arbitrary function) and return a path, and finally use _get sample to fetch a sample.","body":"Users may want to either create\/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.\r\n\r\nIt appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.\r\n\r\nA notebook\/example script demonstrating this would be very helpful.","comment_length":50,"text":"[Question] Using\/adding a local dataset \n Users may want to either create\/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.\r\n\r\nIt appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.\r\n\r\nA notebook\/example script demonstrating this would be very helpful. \n Hi @MaveriQ , although what I am doing is to commit a new dataset, but I think looking at imdb script might help.\r\nYou may want to use `dl_manager.download_custom`, give it a url(arbitrary string), a custom_download(arbitrary function) and return a path, and finally use _get sample to fetch a sample.","embeddings":[-0.3848367631,0.426192522,-0.0582928807,-0.062286865,-0.1617802233,-0.0290353093,0.1196396947,0.0627896935,0.225906685,0.2672970891,-0.1065552235,0.3037168384,-0.1359498352,0.2354285568,0.3147620559,0.0563904569,-0.1727443933,0.0608874746,-0.2876501977,-0.1493894309,-0.4367071986,-0.1395202726,0.3666732311,0.0160693154,0.0315403603,-0.0743042752,-0.0310812704,0.4312176406,-0.1881704032,-0.4960770309,0.1156177148,0.0801915526,0.3331568539,0.4255272746,-0.0001110298,-0.1615571827,0.1255319417,0.0057520876,-0.3756665885,-0.2831614316,-0.4848709106,-0.0562204309,0.323540777,-0.3018951416,0.167841509,-0.1485852748,0.1979242265,-0.6253156066,0.1218306199,0.2712398767,0.2022996396,0.0922849402,-0.43065539,-0.3477675915,0.1746295989,0.4100430906,0.0017399631,0.3648223281,0.5526695848,-0.1044886857,0.041821409,-0.1705145836,-0.1301363111,0.090017952,0.5631837845,0.1887885332,-0.0688499361,-0.2714115381,0.2703507245,0.3865032494,0.8187440038,-0.3395529687,-0.2601167262,-0.1455864012,-0.0715409219,-0.0685554668,-0.0204075594,0.1074622124,-0.2264563143,0.269659549,-0.1549880505,-0.2469827384,-0.304423511,0.2749157846,-0.0413943343,-0.0181669779,0.2146755755,0.0636865571,0.1867269427,0.0977482572,0.3312122822,-0.2048025727,0.3516189456,0.1316544563,0.2721080482,0.0842457563,0.0519595481,0.0258223396,0.1119041592,0.1751177609,0.1289793253,-0.1803898811,-0.2821822762,0.0377658159,0.0307112504,-0.0086092046,-0.0257195737,0.1011313871,0.2328979969,-0.0870419145,0.161633715,-0.0817736015,-0.1669723094,-0.0070390333,-0.0731824115,-0.0299173594,0.0432662331,-0.1567643732,0.0560780875,-0.083348088,0.1362375617,-0.0285525918,-0.0489856414,0.3104062974,0.0150913829,-0.2721305192,-0.0798953325,0.2013561279,0.1322698295,-0.0393841267,-0.0412744544,0.0199241992,-0.1046452373,0.2363117486,0.147088021,0.1627573073,0.2879635394,-0.2300513238,-0.1614208817,0.2307295501,0.4086382985,-0.0029009632,0.1960332394,0.064433001,0.2319631577,0.3098809719,-0.0775888711,-0.3319489062,-0.2738490701,0.1951435953,0.3463362753,-0.477000922,-0.4734295905,0.1855375469,-0.1103097498,-0.4235783517,0.0389089249,0.0183148254,-0.2762017548,-0.089792572,-0.2889486551,0.2255990356,-0.1902697235,-0.2724776864,0.1481330842,0.6925089955,-0.7137827873,0.1149784029,-0.3483240008,-0.0896897987,0.0683882684,0.1120432988,-0.3913522661,0.3761354089,-0.1457839012,0.2146044075,0.4088515937,-0.6111477017,-0.1721384227,0.3494308889,-0.4038502872,-0.1963203251,0.2511584461,0.3222974539,0.4519783556,0.066643618,-0.0624450967,0.3906957507,-0.0220193658,0.1316788346,-0.1510139406,-0.305136174,-0.0586398244,0.0699212626,0.2266173661,0.2623304129,0.4574160576,0.0484189354,0.1695820093,-0.1636099368,0.1698448062,-0.144837454,0.4279435277,0.0851072595,-0.3243453503,0.0992945433,-0.4985300601,0.2049115449,-0.1063704491,0.0182877183,0.0512507632,-0.3985586762,-0.4555648267,0.0084442375,-0.0241559129,0.0394430272,0.0740873367,0.4098875523,0.111603789,-0.0808666795,-0.3878562748,-0.1711488217,-0.0823819265,0.0974678844,-0.0757940486,0.134936586,-0.0323764905,0.0282301623,-0.1085282415,-0.0606007166,0.0247230791,-0.1131200939,0.031650383,0.3478105366,-0.1357044429,0.2376403511,0.4116074741,0.5253426433,0.2657344043,0.0615926273,0.4397469163,-0.1330983788,0.2462782711,0.037404716,-0.3912294507,0.0947273076,-0.1505209953,0.2330704778,0.1097806469,0.1505803466,0.2660111189,-0.2243696302,-0.037805669,-0.3007338047,-0.1880720407,0.1886543036,0.2170049101,0.1190810204,-0.3386602998,0.30360502,-0.2050985545,-0.1305982172,0.2531043291,-0.0729730576,-0.1957225502,0.1222395748,0.265190959,0.719281733,0.257367909,0.2213434875,0.4219477773,0.1621901691,0.0460409932,-0.2819206119,-0.0837263688,-0.137734741,-0.2034482658,0.2263743281,-0.1388097107,-0.1602101028,0.1037717015,0.0657160878,0.0637394339,0.1325288117,-0.2115382999,-0.2972091734,-0.5328249931,-0.4521932006,-0.471578002,-0.2514738739,0.1549419165,-0.2584532201,0.0688430518,0.1870961487,-0.2059519291,-0.0985751599,0.0135416742,0.3428137302,-0.5756483078,-0.3648793399,0.1012740433,-0.1248009875,-0.0904797688,0.1322442591,0.5557697415,0.2368973345,0.4106332958,-0.2774346471,0.1927794218,-0.5172348619,0.2275174409,0.1171501726,0.2604715526,0.30322963,0.0053092577,0.0604221113,0.230099991,-0.3323422372,-0.1318967938,0.0288817026,0.1702225208,-0.4290680885,-0.2287544757,-0.0765331984,0.0283667129,-0.3620597422,-0.386800766,-0.1854599714,0.108247824,0.3310581446,0.2041646689,-0.2248843163,0.1045309231,0.2954856455,0.1064800695,-0.0391984172,0.0378971286,-0.295904547,0.2268674225,-0.5191518664,-0.3212379217,0.2769160867,-0.1316582114,0.1797231436,-0.084203355,-0.3934152424,-0.4176706076,0.0114080608,0.4556054175,0.1660173833,0.1292218566,0.3921355605,-0.0643505752,-0.0798713714,0.0666899383,-0.3398392797,0.3800733089,0.1752509624,0.1266064048,0.1159877554,0.2497957796,-0.0579179563,0.6440163255,-0.1830210686,-0.0704624951,0.2175628245,-0.3027667999,0.3039292097,-0.1950046569,0.0160667337,-0.1849907488,0.0593994446,-0.3080708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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/133","title":"[Question] Using\/adding a local dataset","comments":"The download manager supports local directories. You can specify a local directory instead of a url and it should work.","body":"Users may want to either create\/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.\r\n\r\nIt appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.\r\n\r\nA notebook\/example script demonstrating this would be very helpful.","comment_length":20,"text":"[Question] Using\/adding a local dataset \n Users may want to either create\/modify a local copy of a dataset, or use a custom-built dataset with the same `Dataset` API as externally downloaded datasets.\r\n\r\nIt appears to be possible to point to a local dataset path rather than downloading the external ones, but I'm not exactly sure how to go about doing this.\r\n\r\nA notebook\/example script demonstrating this would be very helpful. \n The download manager supports local directories. You can specify a local directory instead of a url and it should work.","embeddings":[-0.3328148127,0.3316176832,-0.0937174559,-0.1150168031,-0.0208442621,-0.0157956686,0.1370421052,-0.0018574436,0.2311633378,0.2999218702,-0.2155137956,0.3249565363,-0.0286778808,0.2468104064,0.256311506,-0.019887669,-0.0942598656,-0.0035388255,-0.3042692542,-0.0223879162,-0.3524807692,-0.2041261196,0.2866287529,0.0946041495,0.1109767631,0.0382468104,-0.1240868047,0.3648514152,-0.2470917106,-0.5490741134,0.1527569741,0.1418217719,0.2310725302,0.437787205,-0.0001046002,0.0189364236,0.0589842759,0.0831919685,-0.3596253693,-0.3652914166,-0.4895199537,-0.1823952645,0.2321391404,-0.3313549757,0.1816500872,-0.2091437876,0.2616906166,-0.4603243172,0.1957477331,0.2347235978,0.2444289774,0.1967382729,-0.3012832999,-0.277736485,0.1513493806,0.3050220907,-0.0953642949,0.3225678205,0.480772227,0.0270891711,0.0487667248,-0.1207921654,0.0089251734,0.2245352864,0.5624610186,0.1625576466,-0.0116053196,-0.1680624634,0.1289849132,0.2235559523,0.8339118958,-0.2505113184,-0.2758901715,-0.1794393808,-0.1428814977,0.0745863169,0.1766538024,0.1667137146,-0.2424696088,0.3480572999,-0.2073036879,-0.3318106234,-0.4209996462,0.3002486229,-0.0823545232,0.0901305154,0.148043111,0.0346384458,0.1784930974,0.1505463272,0.3084104061,-0.2626871765,0.4867874682,0.1493736058,0.3469060063,0.0186692104,0.0091671869,0.0597288497,0.0581502803,0.0049298303,0.0845637694,-0.2019646764,-0.2501284778,0.1012817323,0.194765076,-0.1616328359,-0.1639881432,0.0453511216,0.1654997468,-0.1145339757,0.2640037537,-0.1346506476,-0.04747748,-0.1054486111,-0.2436897457,-0.04822696,0.1499397308,-0.2155676484,0.1587688476,-0.1933264285,0.143144086,-0.0827113613,-0.1080665141,0.1716115475,-0.0643270239,-0.1997919977,-0.1747719944,0.1752836108,0.0788822398,-0.003174942,-0.0325122513,0.0397609323,-0.1757469475,0.1771128178,0.092284672,0.1191456839,0.2410404533,-0.2445326596,-0.1945561469,0.2696327865,0.3167909682,0.0556184538,0.0812009349,0.0831421614,0.149827823,0.2602837384,-0.1244604066,-0.2472457439,-0.2761300802,0.1556815803,0.1210807711,-0.4608033597,-0.346748054,0.2225116342,-0.0693982542,-0.4279256165,0.1279610246,-0.0919031501,-0.3035586178,-0.1095278934,-0.3762881756,0.2411279082,-0.2823703885,-0.2036432922,0.061444737,0.4221624732,-0.5632275939,0.0680270419,-0.3140809238,-0.1536068022,0.081208393,0.0673325583,-0.4035091996,0.4359643161,-0.0880141854,0.1984979063,0.4498338401,-0.3739206195,-0.248434633,0.2898443043,-0.4758916795,-0.275029093,0.1604036838,0.2817880511,0.2360337824,-0.1272835732,-0.0010094335,0.4449621439,-0.0301013403,0.1336784959,-0.1492882669,-0.3828783035,-0.1846954525,0.1658445001,0.0935244188,0.2740213573,0.4902617931,-0.0867759064,0.1048768535,-0.2554169893,0.1711494178,-0.1441415399,0.3760519028,0.0958574936,-0.3978231251,0.10250251,-0.4131017625,0.2748021185,-0.2554860711,0.0173003785,-0.0387647673,-0.3529527187,-0.4677009583,-0.0808891878,-0.0483333506,0.1280551106,0.1403770745,0.3553267419,0.1730312705,-0.042150069,-0.2173710763,-0.1355109811,0.0085204896,0.0731845647,0.0879561082,0.1203848943,-0.1296135187,0.1671655625,-0.0807571933,-0.1476216316,-0.0683528632,-0.0872266889,0.0538804159,0.3684260845,-0.3002171814,0.3217330277,0.4200192988,0.3561283648,0.2749055624,0.0052998383,0.3201113939,0.0622730963,0.2744009793,0.0814503059,-0.445967108,0.0579807535,-0.0812322944,0.123615317,0.1355774552,0.1881991774,0.3140018284,-0.1872361451,0.0499427095,-0.1889361441,-0.1364404112,0.2132397145,0.2067412287,0.1099324375,-0.2441462129,0.3518172503,-0.2140483111,-0.0292856954,0.151651457,0.0076449458,-0.2025773823,0.1463209689,0.2122214735,0.7142144442,0.3711208999,0.2744206786,0.5198994279,0.310362488,-0.0486618765,-0.311712265,-0.0655756891,-0.0484361984,-0.3203289509,0.1223462224,-0.1544592977,-0.1924382299,0.002362818,0.1162005588,0.0746062472,0.1901597679,-0.2044833899,-0.2033991814,-0.4943545461,-0.4443024695,-0.3851741254,-0.176476419,0.1432518214,-0.3896022439,0.2325002253,0.2980713248,-0.3325253725,-0.0755388886,-0.0236256495,0.4244071245,-0.5079961419,-0.2385831326,0.0261594914,-0.1457481384,-0.1004976183,0.1987191439,0.6238669753,0.1396591812,0.4617751539,-0.3050397635,0.1750437319,-0.5360557437,0.2101890594,0.086243324,0.2917476594,0.267960012,-0.0695425346,0.142178908,0.1346641034,-0.1786262542,0.0194231831,-0.0481260866,0.0891558528,-0.4775236845,-0.0579296574,-0.0515679717,-0.0693865716,-0.4508682191,-0.4622930586,-0.2281161696,0.1067738831,0.2294006944,0.2104959488,-0.1482412517,0.1822174788,0.2864893973,-0.0247466024,-0.0642585307,-0.0540307835,-0.4249995053,0.3460866809,-0.4094592035,-0.3963734806,0.2634205818,-0.0048495037,0.0607354268,0.127043575,-0.3452188373,-0.2589051723,-0.0152641898,0.4125747979,0.2224730551,0.3128193021,0.3615954518,-0.0700532943,-0.1416071951,0.0418695509,-0.1188239977,0.2748989165,0.1181513742,0.1624618918,0.1214215234,0.0242045149,0.041386649,0.7666494846,-0.2841893435,0.0296369959,0.3063009083,-0.2404668629,0.2930563688,-0.1120333746,0.0548743047,-0.0382472873,-0.0130241495,-0.22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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/131","title":"[Feature request] Add Toronto BookCorpus dataset","comments":"As far as I understand, `wikitext` is refer to `WikiText-103` and `WikiText-2` that created by researchers in Salesforce, and mostly used in traditional language modeling.\r\n\r\nYou might want to say `wikipedia`, a dump from wikimedia foundation.\r\n\r\nAlso I would like to have Toronto BookCorpus too ! Though it involves copyright problem...","body":"I know the copyright\/distribution of this one is complex, but it would be great to have! That, combined with the existing `wikitext`, would provide a complete dataset for pretraining models like BERT.","comment_length":51,"text":"[Feature request] Add Toronto BookCorpus dataset \n I know the copyright\/distribution of this one is complex, but it would be great to have! That, combined with the existing `wikitext`, would provide a complete dataset for pretraining models like BERT. \n As far as I understand, `wikitext` is refer to `WikiText-103` and `WikiText-2` that created by researchers in Salesforce, and mostly used in traditional language modeling.\r\n\r\nYou might want to say `wikipedia`, a dump from wikimedia foundation.\r\n\r\nAlso I would like to have Toronto BookCorpus too ! Though it involves copyright problem...","embeddings":[-0.1203030646,-0.0427291431,-0.0033754625,-0.0371080488,-0.0014099005,0.2495970875,0.2591406405,0.0787601024,-0.1759214103,0.1513458639,-0.1105382219,0.3519927561,-0.2013259381,0.3885519207,0.2654998004,-0.3297358155,0.1974350363,0.3152870834,0.1295732111,-0.3444743752,-0.2801202536,0.1701482236,-0.1321317255,-0.0258767437,-0.2297755778,-0.089881368,-0.2280260175,-0.170895949,-0.1450006217,-0.2987955809,0.1079742983,0.231218949,0.2329899967,0.3308149576,-0.0001178023,-0.129299745,-0.0035570574,-0.1993113309,-0.1886769086,0.0027899798,-0.1899906099,-0.0180445425,0.0683518425,-0.1603112966,-0.2583996058,0.0763194486,0.1850883365,-0.1007540375,0.0858062133,0.343433857,0.10101863,0.0015432481,0.0614229962,-0.0526098087,0.1530392468,0.1496795565,-0.3662744164,-0.0291989185,0.6788930893,0.0175581891,0.0982917249,0.3453907967,0.2276426107,-0.4172407985,0.4147026837,0.0883816779,0.1217895448,-0.510502398,0.0668936521,0.6966727376,0.7019848228,-0.2438404262,-0.3179569244,0.0559540316,0.2425318509,0.1563386321,-0.0448534824,0.2160191983,0.2315979898,0.2801142037,-0.2702526748,-0.5717220902,-0.2372366488,0.2509011626,-0.0221447758,0.5618988276,0.0817074627,-0.1417115331,-0.0033399714,-0.0242395122,-0.0552432351,-0.0694842413,-0.0548124015,0.1566242576,-0.0169132166,-0.6129307747,0.0411336236,0.2980645597,-0.092630215,-0.3859571815,-0.0651953295,-0.0231579095,-0.4463918507,-0.0368636101,0.1946208179,-0.156463176,0.2730415761,0.2248607427,0.0094607463,-0.1019401476,-0.1289753616,-0.0116830179,-0.0784892812,0.4305069447,-0.2459729165,-0.1600877792,-0.0489459783,-0.2302552909,0.1001332998,0.2012497336,0.1455676854,0.0592588224,0.1095893085,0.1507774144,0.0652142018,-0.0314450525,-0.2356081754,0.2275933921,-0.0804409906,-0.1276546568,-0.003691592,0.206608668,-0.125838846,0.1776729971,0.2078405768,0.0144338217,0.1936472654,-0.0902615339,-0.2388184667,0.3665217459,0.1816204786,-0.1185825467,0.16314964,-0.1314094514,-0.1708646566,0.1456835568,-0.1973750591,0.1564054042,-0.5758010745,0.1816931665,-0.4668942392,-0.3915768862,-0.5993227959,0.141055882,0.1419877559,-0.5276520252,0.2362723798,0.8123851418,0.064204134,-0.5009546876,0.3067885339,-0.0126405172,-0.4330202341,-0.0228663236,-0.0258893203,0.2668816745,-0.6962561607,-0.1327310801,-0.3971264362,-0.0115828551,0.0968688577,-0.0610348433,-0.3117049336,0.261877656,0.1716336161,0.2281699479,0.0753216147,0.2323235422,-0.1851024777,-0.1036177576,0.0874260664,-0.1750548035,-0.1340025961,0.2259523273,-0.1113839075,-0.0359231196,-0.0478876196,0.3111105561,-0.1302880347,-0.0960222334,0.0157367457,-0.4841164947,0.318810761,0.3002837896,0.272297591,-0.1253351569,0.0118789161,0.2225655764,0.0961411893,-0.110868372,0.1566894799,0.2868021131,0.3329593241,0.1994375437,0.0469315462,-0.3570227325,-0.1672731191,0.0165462494,-0.2720005214,0.1726179272,0.2039352655,-0.3268772662,-0.0627511144,-0.1004491746,-0.035023246,-0.1459440291,0.0689660385,0.0011789732,-0.0445479229,0.350902617,-0.1993529499,-0.4671763182,-0.2539493442,-0.2026211023,-0.0942984521,0.131910786,-0.1313410103,0.0148439491,-0.1028534845,0.3714749813,-0.1952738315,0.0429734103,0.0932413712,0.0261615906,-0.0709653571,0.2398361117,0.5780489445,0.2656174302,0.3966757953,-0.340257287,-0.1551364362,0.2055173814,0.0773994699,-0.1563781351,-0.3925898373,0.2254051119,0.5341241956,-0.0376358107,-0.0605878048,0.0535709634,-0.1059387699,-0.0207489822,0.1491303891,-0.3045207858,-0.0067129848,0.0497156717,0.0128582735,-0.0142342448,0.0356460363,0.2842810154,0.4608014524,0.0758397952,0.2276088148,0.2138712853,-0.093118906,-0.1594765633,0.1642937064,-0.6020973921,0.0047437302,0.1380546838,0.0668518692,-0.1709865779,0.2515239716,0.036465317,0.257309854,0.1864288747,-0.4395307004,-0.1851505339,0.0494308583,0.1068220288,-0.2343508005,-0.1974150687,0.0767159909,0.100461185,0.1266270876,0.0548214391,0.0118365353,0.2088557631,0.1573198736,-0.3849427998,-0.185869813,-0.0615806282,0.440664053,-0.1395955533,-0.1214400232,0.0538734049,-0.3997397125,0.7127889991,-0.0584130622,-0.1999062151,-0.0424081981,-0.4109727442,-0.2314403802,0.199538663,0.0768301263,-0.1943102032,0.3325208127,-0.1586371958,-0.0028328951,-0.1757369339,-0.6405138969,0.0662712306,-0.2856981456,0.106942758,0.1301275045,-0.0954240635,-0.4952315092,-0.6794479489,-0.0619290061,-0.1864671409,-0.1427813619,-0.192903474,-0.0497932509,-0.1747597009,-0.0376340449,-0.2620522678,-0.2260607034,-0.0736269429,0.0616546385,0.1563372761,-0.1010700092,-0.0249270294,-0.1283663958,0.1685890704,-0.1529408842,0.1515389979,-0.0851017162,-0.0959619284,0.3563258946,-0.1869836301,-0.0367288254,-0.0954204723,-0.3324646354,-0.0849369615,-0.119814001,-0.5708934665,0.0190490633,-0.3292813599,0.2812100351,0.1669231504,0.2473456115,0.2538973391,0.2934454978,-0.0000012368,0.0077695264,-0.2143929154,0.0833687261,0.4451698661,0.2463767081,-0.2431333661,-0.4042190015,-0.4259373546,0.9085752368,0.1068758518,-0.0638644919,0.3335602582,0.1069840416,0.1849312335,-0.3890581429,-0.3985509574,0.3437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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/130","title":"Loading GLUE dataset loads CoLA by default","comments":"As a follow-up to this: It looks like the actual GLUE task name is supplied as the `name` argument. Is there a way to check what `name`s\/sub-datasets are available under a grouping like GLUE? That information doesn't seem to be readily available in info from `nlp.list_datasets()`.\r\n\r\nEdit: I found the info under `Glue.BUILDER_CONFIGS`","body":"If I run:\r\n\r\n```python\r\ndataset = nlp.load_dataset('glue')\r\n```\r\nThe resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:\r\n\r\n```python\r\nmetric = nlp.load_metric(\"glue\")\r\n```\r\nwhich throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets?","comment_length":53,"text":"Loading GLUE dataset loads CoLA by default \n If I run:\r\n\r\n```python\r\ndataset = nlp.load_dataset('glue')\r\n```\r\nThe resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:\r\n\r\n```python\r\nmetric = nlp.load_metric(\"glue\")\r\n```\r\nwhich throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets? \n As a follow-up to this: It looks like the actual GLUE task name is supplied as the `name` argument. Is there a way to check what `name`s\/sub-datasets are available under a grouping like GLUE? That information doesn't seem to be readily available in info from `nlp.list_datasets()`.\r\n\r\nEdit: I found the info under `Glue.BUILDER_CONFIGS`","embeddings":[-0.1725947261,-0.2484223098,0.0658995882,0.3159227967,-0.0194631834,0.0501955673,0.3762942553,-0.1403564811,0.7129033804,0.1203574985,-0.281144768,0.5193799138,0.0818037093,0.0636687577,0.2311221063,0.0292404853,0.0766592845,0.1667412966,-0.1115129814,-0.1865060031,-0.4865836501,0.3221149743,-0.3004097939,0.2563777268,-0.1656361967,-0.0245170966,-0.1407751441,0.1467231661,-0.108386524,-0.2313846946,0.4261036217,0.4168219864,0.1703007817,-0.1210095137,-0.0001230205,-0.0875958875,0.6260707974,-0.1760393083,-0.1699001342,-0.2148626596,-0.6149526238,-0.3502326012,0.4615489542,-0.2409356982,0.121970579,0.3319019675,0.3726021349,-0.1849743277,0.0137700653,0.0481360778,0.0500271097,0.5045632124,-0.3903687894,-0.0787917599,-0.0217167828,-0.1140737087,-0.0577430613,0.6744437814,-0.0432515889,-0.3659249842,0.0440582149,-0.0565739125,-0.0486897714,0.3303422928,0.3373464048,0.0048554521,0.0214751195,-0.4663365483,-0.1145747453,0.5373286605,0.207733199,-0.1874137819,-0.1875194311,-0.306093961,0.1226772144,-0.0138648329,0.2320161909,0.311306864,0.0170250237,0.0447694771,0.1531568319,0.0357270986,0.1865292341,0.3109622598,-0.1849832684,0.5227513313,-0.1189951822,0.2692030966,0.2278757244,-0.0366255753,-0.1399613768,-0.3156047761,-0.2983661294,0.1015648022,-0.3652482033,-0.0635139495,0.0324664265,0.5708153248,0.1710638106,0.007658991,0.2041781992,0.0784858391,0.065491356,0.3932666183,0.3801899552,0.124688603,0.4200635254,-0.0918590575,0.0543452762,-0.0628205836,-0.102940686,0.1195995882,-0.3057979941,0.0590026192,-0.1013354659,-0.2553478181,0.1419204175,-0.0149535704,-0.4296116829,-0.2354436517,-0.093179971,-0.1769378632,0.1591098756,0.2330816537,0.0021019669,0.1385073811,-0.3767081499,0.3707320392,-0.1587339044,-0.0748586357,-0.138585791,-0.0942317322,-0.3390645385,0.1342394501,0.2060617507,-0.0339633897,0.2835069895,-0.1634259075,0.0305538792,0.138215974,0.1435979456,0.0048819045,0.1341758668,0.2735054791,0.307185322,0.0825897828,0.0443132818,-0.4303753078,-0.3361852467,0.0561994314,-0.2327112406,-0.2623212337,0.0821429417,0.0417890511,-0.6383655667,-0.0251228437,-0.6208206415,0.3129177392,-0.0949081033,0.1121767014,-0.0261938348,-0.1705806404,-0.2382139862,-0.2332176268,-0.0261172801,0.3035363257,-0.0929569378,-0.3242765367,-0.2786442935,-0.2820478678,-0.098320514,0.0907391831,-0.1283429861,-0.018978525,-0.1342946589,0.06959676,0.4640235603,-0.1827951819,-0.3434996605,0.1787410825,0.0483872779,0.0153116807,-0.0854013339,0.1239225566,0.0589953214,-0.0509528853,0.4281704724,0.4943158329,0.0499853827,-0.0940643623,0.0150875002,-0.0785889849,0.1891945302,0.0466191955,-0.0622825809,0.2793145478,0.0416076593,-0.0561059751,0.0146746729,0.341131717,-0.4115764499,-0.0953838304,-0.0471466668,-0.2374942303,0.1808024943,0.0512195081,-0.4635836184,0.2720180452,0.0998440608,0.0805387944,0.2446931005,-0.1569918841,-0.1589941382,0.1996584535,-0.0505542904,0.3557608724,-0.0723996907,0.312301755,0.1252313852,-0.2424221486,-0.2602823079,0.6475881934,-0.2580867112,0.1739526242,-0.1627800763,0.1830734015,0.2841739953,0.042868495,0.0041607409,0.2706499398,0.0965240225,-0.0510825478,0.0416191444,0.3302467465,0.2279451787,-0.0203640107,0.1437880844,-0.0672411025,-0.0037752101,-0.0256039631,0.1983072311,0.0699307695,0.0402127951,0.067323871,-0.0200360883,0.3690297604,0.0603734516,0.2613092959,-0.0212721918,0.0521854237,0.1354755014,0.1276657432,-0.5614256859,-0.2807866931,0.0287649464,0.082913734,0.5041057467,0.3679535687,-0.3384752274,-0.1453409046,0.4069750607,-0.060414087,0.1469535232,-0.1286323071,-0.3089123964,-0.0977878347,0.2786588669,0.4591606855,0.5872238278,0.1190103218,0.0607877187,-0.2157073617,-0.191721797,-0.2105320394,0.0080239661,0.2727658451,0.010636013,-0.2120886445,0.3194173872,-0.1029439196,-0.4509445131,0.3798750043,-0.0225264635,-0.1781170666,-0.2922337353,0.0603942722,-0.0216050968,-0.6061503291,0.0375328586,-0.24335289,-0.2842607796,-0.4053376913,-0.1251335293,-0.1692967415,0.0879081711,0.1985606402,0.2002056241,0.4580504596,-0.4821966588,-0.1907385141,0.0049896152,-0.0888826028,-0.1672079861,-0.0923187062,-0.0698061809,0.2826128006,0.1935006976,-0.2446638495,0.0474125408,0.1971144676,-0.1925168931,0.329113096,-0.1074580848,0.5722259879,0.1572949886,-0.0895838812,0.0413080975,-0.1016609296,0.1898089051,-0.1826377362,0.033904884,-0.2854732871,0.0542666055,0.0052303951,-0.1092132106,0.0582989752,-0.5250714421,-0.2473227084,0.0293148477,0.1289645731,-0.0038830021,0.1676345617,-0.4640073478,-0.0216197148,0.1260849386,-0.0622136071,-0.2979718745,-0.1379568577,0.2206344604,-0.1228186637,-0.1601770967,-0.0570614971,-0.3852192461,0.1511649191,0.0822596699,-0.0723710805,-0.5356999636,-0.153814733,-0.0764898434,0.3229403794,-0.0198661089,-0.0556149371,-0.1602959633,0.2289456278,-0.1405753344,-0.3786470294,0.0012552876,0.3115509152,0.1157942042,0.1630406976,0.0895351171,-0.1561243087,0.4338520765,-0.1202090532,-0.3725539446,0.2616612613,-0.1179798171,0.5720015168,0.1639204621,-0.1120459288,0.01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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/130","title":"Loading GLUE dataset loads CoLA by default","comments":"Yes so the first config is loaded by default when no `name` is supplied but for GLUE this should probably throw an error indeed.\r\n\r\nWe can probably just add an `__init__` at the top of the `class Glue(nlp.GeneratorBasedBuilder)` in the `glue.py` script which does this check:\r\n```\r\nclass Glue(nlp.GeneratorBasedBuilder):\r\n    def __init__(self, *args, **kwargs):\r\n        assert 'name' in kwargs and kwargs[name] is not None, \"Glue has to be called with a configuration name\"\r\n        super(Glue, self).__init__(*args, **kwargs)\r\n```","body":"If I run:\r\n\r\n```python\r\ndataset = nlp.load_dataset('glue')\r\n```\r\nThe resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:\r\n\r\n```python\r\nmetric = nlp.load_metric(\"glue\")\r\n```\r\nwhich throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets?","comment_length":75,"text":"Loading GLUE dataset loads CoLA by default \n If I run:\r\n\r\n```python\r\ndataset = nlp.load_dataset('glue')\r\n```\r\nThe resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:\r\n\r\n```python\r\nmetric = nlp.load_metric(\"glue\")\r\n```\r\nwhich throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets? \n Yes so the first config is loaded by default when no `name` is supplied but for GLUE this should probably throw an error indeed.\r\n\r\nWe can probably just add an `__init__` at the top of the `class Glue(nlp.GeneratorBasedBuilder)` in the `glue.py` script which does this check:\r\n```\r\nclass Glue(nlp.GeneratorBasedBuilder):\r\n    def __init__(self, *args, **kwargs):\r\n        assert 'name' in kwargs and kwargs[name] is not None, \"Glue has to be called with a configuration name\"\r\n        super(Glue, self).__init__(*args, **kwargs)\r\n```","embeddings":[-0.1694328934,-0.1131431013,0.1923100948,0.1447666287,0.1055869684,-0.1608262062,0.3246065676,-0.2416682988,0.4928927422,0.2975996435,0.0066172071,0.5925444365,0.0851709545,0.0806321874,0.1479725689,0.0871441364,-0.067354098,0.3852500021,-0.23223418,-0.1193545908,-0.4771755338,0.2173205316,-0.2149813771,0.2020166218,-0.2388361394,0.0284569897,-0.0420778804,0.2567212284,-0.1525195092,-0.3225887418,0.5009709597,0.4004223049,0.048006732,-0.0071245264,-0.0001277717,-0.0651666522,0.6264618039,-0.2052075714,0.0044319453,-0.1768510491,-0.579139173,-0.3032614589,0.4434992373,-0.3734254241,0.0086701922,0.5683819056,0.2329163998,-0.1811600924,0.1121183187,-0.0361293517,0.0350640565,0.2749432027,-0.3470981419,-0.1134784892,-0.1467447728,-0.0202015154,-0.0451229811,0.8158906102,-0.2364584059,-0.4017541707,0.0876230225,0.0711732581,-0.1125771478,0.2932894826,0.5203597546,0.053309124,0.2042470425,-0.3093051016,-0.0650203153,0.5827364922,0.1253231466,-0.3110702932,-0.0930168554,-0.2010276914,0.2905486822,-0.3094304502,0.3871704638,0.1643277705,-0.1318155974,0.1253418028,0.0307994727,-0.1639344394,0.1551112086,0.2616843879,-0.1995371282,0.3903371096,-0.0643599406,0.2511925101,0.156022504,0.0305497944,0.0102956006,-0.4243236482,-0.2983722985,0.0067564799,-0.10550116,-0.0589881539,-0.0094818156,0.477258116,0.0725090951,-0.1048418954,0.1459956169,-0.0078614382,0.1422875226,0.3124381602,0.1497373879,0.1795002073,0.4674269557,0.0466182716,0.1715822667,0.081407927,-0.1534212679,0.0676890388,0.0424037576,-0.0310727488,0.0605365895,-0.1745251268,0.2021006346,-0.1210566685,-0.0995601937,-0.1149303168,0.0140529908,-0.1394963861,0.0938220173,0.3139578104,-0.1833715737,0.2045091093,-0.2903826833,0.425496012,-0.2119328678,0.0371902063,-0.1563826501,-0.0690169781,-0.2381932139,0.3441038132,0.2139392048,-0.0218732934,0.2847322822,-0.1093067005,0.0480423309,0.1019340605,0.2422238886,-0.0931617916,0.1004795283,0.3126707375,0.1332187355,0.0953530222,0.0670015663,-0.5786064267,-0.2952351868,0.1635297686,-0.3343775272,-0.3553062677,0.2480690777,0.0628692061,-0.708633244,0.0345576443,-0.4189407229,0.2075431347,0.0170405768,-0.1871376634,-0.1002299637,-0.1505235881,-0.2170741707,-0.3105587661,0.1083692759,0.4598320127,-0.1007290632,-0.3130807877,-0.1742125303,-0.1292668432,0.0828600004,-0.1217455417,-0.2707915306,0.1485442072,-0.1997079998,-0.0768881589,0.6406319141,-0.3604440093,-0.3108129203,0.0916327387,-0.0039357585,0.1509331316,0.0407514088,0.1100211963,-0.0455563627,-0.0431681164,0.4982181489,0.3668849468,0.1026644483,0.0229516774,-0.0810709968,-0.2251185328,0.0689027458,-0.0152990492,0.0260320511,0.3120307028,0.0078258645,0.0009660467,0.0331266336,0.1660285294,-0.4011260271,-0.021597825,0.0519483946,-0.2292003185,0.0509481281,0.2154651582,-0.3800513148,0.2970751226,0.0964184925,0.1881465614,0.1825900525,-0.1498969048,-0.0632588044,0.0635967255,-0.1701562703,0.2489450872,-0.0731581971,0.2950997651,-0.0338743962,-0.1346231401,-0.2492021471,0.540307343,-0.3059586585,0.2340622991,-0.2367593944,0.1333027035,0.2101884633,0.0561619699,-0.2033628672,0.1293233335,0.0567595251,-0.122350499,-0.0068923528,0.4079770744,0.2588035166,-0.0673183724,-0.0300810356,-0.056121897,0.0861489326,-0.0556969047,0.0633730441,0.0601268969,0.1221924052,-0.1220948771,-0.1145645678,0.4290385544,0.1284840107,0.254658401,-0.0721655264,-0.0801663324,0.0726670325,-0.0405235142,-0.6427885294,-0.3399472833,-0.0774796456,0.1088022515,0.3928149045,0.3404849172,-0.1719955206,-0.3024609983,0.4272810519,0.1237078607,0.1707774401,-0.1698995084,-0.1243429556,-0.0518664233,0.233638823,0.4314949512,0.7753482461,0.1059535295,-0.119349733,-0.0878806338,-0.252744019,-0.1804577857,0.1540833861,0.1202417016,0.2086475492,-0.1038482413,0.225382641,-0.0485094711,-0.3323350549,0.2384047061,0.0789695233,-0.237333253,-0.3911013305,0.0130334143,0.037892051,-0.4379425645,-0.0596230589,-0.1697868258,-0.2356129885,-0.2868682146,-0.2006106824,-0.1280094832,0.0884272307,0.2005221397,0.1486909837,0.3373689651,-0.4282748699,-0.5522496104,0.060178142,-0.1075852513,-0.2194833755,-0.1756982356,-0.1533198953,0.0817978829,0.0827822983,-0.2737563252,-0.0729155093,0.2294443995,-0.3642032444,0.3472115099,0.0735491365,0.5990024805,0.2174128741,-0.1311398894,0.1475277245,-0.099021472,0.3466440439,-0.0230739973,0.1609727442,-0.1113976613,0.0236246232,-0.0147377988,-0.2133246213,0.083220005,-0.3554655313,-0.2513581216,-0.1286612749,0.2116760761,-0.0156708062,0.3157366216,-0.1783219427,-0.1097887307,0.0914314911,0.0718853697,-0.1312550902,-0.1693262905,0.2335375547,-0.0578243844,-0.1815220714,-0.1683603376,-0.4493404031,0.2137179971,0.0865022764,-0.2035409808,-0.6979241967,-0.1644452661,0.1394333541,0.1271078438,0.1680632532,-0.002659562,0.0592408292,0.1707666516,-0.1051993743,-0.4053518772,0.1375135779,0.2413716316,0.2316518575,0.2321867496,0.1489290744,-0.2921331227,0.2373870462,-0.1976976842,-0.3449137509,0.3137609065,-0.1818259358,0.7343224287,0.0804427266,-0.0825398117,-0.06008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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/130","title":"Loading GLUE dataset loads CoLA by default","comments":"An error is raised if the sub-dataset is not specified :)\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']\r\nExample of usage:\r\n\t`load_dataset('glue', 'cola')`\r\n```","body":"If I run:\r\n\r\n```python\r\ndataset = nlp.load_dataset('glue')\r\n```\r\nThe resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:\r\n\r\n```python\r\nmetric = nlp.load_metric(\"glue\")\r\n```\r\nwhich throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets?","comment_length":42,"text":"Loading GLUE dataset loads CoLA by default \n If I run:\r\n\r\n```python\r\ndataset = nlp.load_dataset('glue')\r\n```\r\nThe resultant dataset seems to be CoLA be default, without throwing any error. This is in contrast to calling:\r\n\r\n```python\r\nmetric = nlp.load_metric(\"glue\")\r\n```\r\nwhich throws an error telling the user that they need to specify a task in GLUE. Should the same apply for loading datasets? \n An error is raised if the sub-dataset is not specified :)\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']\r\nExample of usage:\r\n\t`load_dataset('glue', 'cola')`\r\n```","embeddings":[-0.1234988421,-0.2316408008,0.1299612522,0.1788021624,-0.0260566305,0.0349783376,0.4479486942,-0.2386648804,0.6000370383,0.1970048845,-0.1358669847,0.5252872109,0.1504832804,0.1636874825,0.136348784,-0.0717558712,0.0006625268,0.1845783591,-0.2312728316,-0.1488693208,-0.5079345703,0.2756892443,-0.3465511799,0.1943188459,-0.1970527023,0.0579489991,-0.087690033,0.1988683343,-0.1275700033,-0.2630568445,0.5758833289,0.1909217834,0.231526792,-0.0689435974,-0.0001251231,-0.0415128693,0.5903518796,-0.2156631202,-0.0600046255,-0.2415104061,-0.6910247207,-0.3064957559,0.5161479115,-0.2989683151,0.1165501028,0.413461864,0.2769863009,-0.2617215514,0.0181751121,0.033144556,0.061910592,0.4095025063,-0.3501492739,-0.1138348579,-0.1835411638,-0.2181191593,-0.0447623059,0.8315686584,-0.141043663,-0.3725712895,0.0942742974,-0.0238960367,-0.0733997524,0.2983854711,0.4873611927,0.0610513911,0.1099266782,-0.3827915192,-0.039928399,0.4658715427,0.2723920047,-0.3111106157,-0.1319310367,-0.2014098018,0.0698197782,-0.1846679151,0.2438081205,0.2750248313,-0.0093281763,0.1062035561,0.0593750142,-0.0998046547,0.1585218608,0.2778344154,-0.2515942454,0.4114090502,-0.0927316546,0.2883330286,0.0994251817,0.0096532451,-0.0466165245,-0.4762985706,-0.2465341389,-0.038551949,-0.3014807403,0.042502448,-0.0595233031,0.3228663504,0.0277488157,0.0063540041,0.2096154392,0.0352419354,0.1282928586,0.3927739859,0.304125011,0.2012167871,0.4864655733,0.0798439384,0.0103814853,-0.1019407436,-0.0146400519,0.0106120808,-0.0358130187,0.0316042155,0.0298986714,-0.1671725065,0.0716416985,-0.0642414317,-0.3528066576,-0.1836000979,-0.0994028077,-0.1517701298,0.0608579703,0.210056603,-0.1752351224,0.2339484543,-0.3219704628,0.5184216499,-0.2057177424,0.0290766004,-0.1946338117,-0.0567978807,-0.2896016836,0.2027562559,0.1402870864,-0.0241539683,0.3323242664,-0.225214988,0.0736501217,0.1254807413,0.2058135867,-0.0744053721,0.0132715581,0.2799834013,0.3132457733,0.074169077,0.0476088338,-0.4513415694,-0.2670765817,0.2466621697,-0.3530212343,-0.3260582685,0.1507556587,0.0712697655,-0.6081245542,-0.125782758,-0.5980647206,0.2011917681,-0.0330206305,-0.0957994461,-0.125877291,-0.1952590793,-0.2787999511,-0.3485414088,0.0488444194,0.3585067987,-0.1044200361,-0.2893980145,-0.1159591973,-0.1736197919,0.1300078481,0.0689161271,-0.2402690798,0.1021810845,-0.1363590956,0.0296844188,0.5617924929,-0.4231231213,-0.3428970277,0.1687544882,0.0245062672,0.0866550356,-0.0903093442,0.1507560164,0.0957414657,-0.0008755716,0.5150431991,0.3908320069,0.1038895845,0.0325811356,-0.0275330525,-0.1966059059,0.1574236304,0.1146840379,0.048747357,0.3507826924,0.0906138048,-0.0561980754,0.0775560662,0.2598617375,-0.3014622033,-0.027242735,0.0217312295,-0.171361953,0.0665832087,0.1311919689,-0.3722841442,0.2801187634,0.1047654822,0.1512985826,0.3379476964,-0.0076634968,-0.1042713001,0.1097762957,-0.1633646339,0.195718959,-0.0947120413,0.220807448,-0.003428662,-0.1852053851,-0.2477206588,0.7136369348,-0.2071398348,0.2480587512,-0.3053863943,0.2926473618,0.1744356602,0.0416671261,-0.1472857893,0.2093914151,0.0162382126,-0.0904520825,-0.0169345699,0.4237202406,0.2346641123,-0.0982944146,0.0180235934,-0.1781243682,0.1551904529,-0.1540311277,0.1796039641,0.0569764003,0.1660908461,-0.0245173275,-0.1312085688,0.2916741967,0.1013282016,0.2943254709,-0.0451603346,-0.0105011053,-0.0684616864,0.118326731,-0.5679361224,-0.321479857,-0.0514614061,0.1028148755,0.3370561004,0.3288535774,-0.1660836339,-0.2440533787,0.369302243,-0.002039029,0.1345632374,-0.0618418343,-0.2000744194,-0.1146795005,0.2307555825,0.4542492032,0.780992806,0.1426455528,-0.0287820864,-0.1476744711,-0.1922801286,-0.2099156529,0.1177241728,0.180589959,0.0754990652,-0.1641794145,0.2235136032,-0.0315686688,-0.2807987332,0.33404392,0.0296085179,-0.1554500163,-0.3178513348,0.0475432277,-0.1117244437,-0.4101986587,0.0206767414,-0.2049304247,-0.1609475464,-0.3836710453,-0.2601151168,-0.1813657731,0.1334932595,0.1927686334,0.1012802944,0.2962879837,-0.4001510441,-0.4196333289,0.1201057658,-0.0600765683,-0.2728140354,-0.1278371066,-0.2320308536,0.2656525075,0.111680612,-0.3139145672,0.0864860266,0.1176205128,-0.278377533,0.3208665252,0.0015642951,0.5977897644,0.2011018991,-0.0882596076,0.128596127,-0.1251037866,0.3491458595,0.0017467653,0.1210567877,-0.1546037048,0.0655654967,-0.02046551,-0.03798474,-0.0115168812,-0.4604470432,-0.2555038035,-0.0506604277,0.1130422205,-0.0491565764,0.2361868918,-0.1544955224,-0.1646802276,0.0937767923,0.016965868,-0.1407371163,-0.1789999008,0.1827668697,-0.1058648154,-0.2507736683,-0.1905013323,-0.2628196776,0.2189360112,0.1154186428,-0.1900068671,-0.6095163822,-0.2136401385,0.0528340675,0.1841755211,0.1485554278,-0.0460244454,-0.1051724032,0.2209769934,-0.0925368965,-0.5195083618,0.1842223555,0.3203352988,0.1941149682,0.2446210384,0.2085207999,-0.2775198221,0.2897791862,-0.0849903449,-0.3120298684,0.3616764545,-0.1904333234,0.7031662464,0.0129345711,-0.139397949,0.0729867518,0.200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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/129","title":"[Feature request] Add Google Natural Question dataset","comments":"Still work in progress :)\r\nThe idea is to have the dataset already processed somewhere so that the user only have to download the processed files. I'm also doing it for wikipedia.","body":"Would be great to have https:\/\/github.com\/google-research-datasets\/natural-questions as an alternative to SQuAD.","comment_length":32,"text":"[Feature request] Add Google Natural Question dataset \n Would be great to have https:\/\/github.com\/google-research-datasets\/natural-questions as an alternative to SQuAD. \n Still work in progress :)\r\nThe idea is to have the dataset already processed somewhere so that the user only have to download the processed files. I'm also doing it for wikipedia.","embeddings":[-0.1264829934,0.118322663,-0.2279913723,-0.291672945,-0.0624212101,0.2136778831,0.2895945609,0.1539695859,-0.0013700712,0.1868419945,-0.0968561098,0.3380507231,-0.2951551378,0.1478503346,0.5057772398,0.0203669779,0.1696388274,0.0553913526,0.2871233225,-0.0953791589,-0.2698328197,0.0765784606,-0.1687618196,-0.2782278955,0.1056088507,-0.1428774446,-0.0722409263,0.1143308133,0.027486071,-0.4552854896,-0.2237554342,0.2545962334,-0.1129967123,0.2231184244,-0.0001096427,-0.2789277732,0.2226358354,0.112318866,-0.3725150228,-0.1047181562,-0.0413002409,-0.1243512332,-0.0853371993,0.0625224411,-0.0817401633,-0.1325051785,0.3818160295,-0.6124858856,-0.0268362463,0.3899215162,0.122078225,-0.199852109,-0.190206483,-0.1897235811,0.3511377573,0.4608781636,-0.2872081995,0.1540661454,0.3880565763,0.0529771447,0.2539665103,-0.0058899093,-0.0131472936,-0.2195429206,0.2016466409,0.1184510663,-0.2630254626,-0.7430915833,-0.0995844826,0.2322649062,0.6047319174,-0.0574460588,-0.1416893601,-0.1579467952,0.1478342265,0.1208298281,-0.1835998148,0.3654572368,-0.3039933741,0.0501606464,-0.0627103373,-0.6823121905,-0.1605512798,-0.0132761523,0.332806319,0.0657699406,0.1374448985,-0.0156134404,-0.0974986404,0.0450189225,0.2130221277,-0.0901884958,0.0328702033,0.2595645189,-0.3627632856,-0.5124523044,0.0899482518,-0.1171922311,0.3360565603,0.3056876659,0.0137720397,0.0356598198,-0.0117772799,-0.1386509538,0.2288579047,-0.4270021617,0.3070000708,-0.0288840905,0.2250990868,-0.2379026413,0.0268371664,0.0528682731,0.1061939001,-0.0000614115,-0.0319908895,0.0561245605,0.5262048841,-0.0002247452,0.1605979651,0.0187635701,0.1277631223,-0.1333844811,-0.1766401231,-0.0510196127,0.0360507779,-0.2246823609,-0.1994835138,0.3086479902,0.2672205269,-0.4665071368,0.0621694513,-0.0965680555,0.1323516518,0.4155916274,0.4051395357,-0.2413119674,0.169897154,0.0360411592,-0.0391495638,0.1876740903,0.3271625042,-0.1256587803,-0.0916793495,-0.1870537549,-0.000780109,-0.2132493407,-0.1154736876,0.0056673461,-0.2466759682,0.2631090879,-0.1965469569,-0.2714584172,-0.4861047268,0.1716238558,-0.0501413532,-0.3752221167,0.0850140899,0.583350122,-0.2131745964,-0.4421684444,-0.0384575091,0.3340290785,-0.3543671966,-0.1925550848,-0.1020908952,0.3394127488,-0.2921144962,0.1228870004,-0.2250705659,0.17884399,-0.1179101095,-0.0456080697,0.0647220686,0.3149306774,-0.088625446,0.0736361966,0.4298567176,-0.0873505324,-0.1254737377,0.0056469161,-0.3732474148,-0.1461568326,-0.2327250093,0.4704762399,0.0164358076,-0.0243042242,0.1361232847,0.620953083,-0.1872262359,-0.0575591065,-0.0665313378,-0.2913662791,0.2745901048,0.4272626638,-0.0473464541,0.1423153281,0.4425697625,-0.4155982435,-0.1925052851,-0.0963218063,-0.1358813792,0.0439686589,0.5920690894,-0.0666090399,0.048606135,-0.3970466554,-0.6555466056,0.0584423877,-0.5253989697,0.4157306254,0.1717705131,-0.3447129428,-0.190633297,-0.1787100285,-0.0554477647,-0.0577215739,0.1324526221,0.0833631903,-0.210131675,0.3074828088,-0.2489636242,-0.0875366628,-0.0911918357,0.1868052036,-0.0371299572,0.2840295434,0.0193647444,0.0078887753,-0.0581408702,0.101112023,0.0558958165,0.1696424037,0.1618481278,0.147439912,-0.2663387358,0.3004567623,0.7892981768,-0.1025377437,0.0999988914,-0.433324337,-0.0779074207,0.2329799235,-0.0746343285,-0.0570602901,-0.5146198869,-0.0429202132,0.1678945869,-0.1031115949,-0.2638575435,0.1069280803,-0.0922851861,-0.0043124165,0.198727712,0.1549771726,-0.2380850166,0.3278709054,0.200280875,-0.3042111993,-0.197980538,0.2825926244,0.3525802493,-0.0804718658,0.2441768944,0.1669814289,-0.0494250208,-0.2179890275,0.3993863761,-0.2034090459,0.3370552361,0.1952108741,0.1115920618,0.2344420403,0.035474468,0.0314401388,-0.0775914565,0.039846383,0.0043040374,-0.0123272175,0.3985608816,0.1814754307,-0.1965930611,-0.0778731927,0.0317704566,0.0802367106,-0.0097108316,-0.1559304148,0.0805395395,0.019641934,0.1897779554,0.3993789554,-0.2257104963,-0.2347687036,0.3144590259,-0.1198318526,-0.1439290643,0.0316768438,0.0501617342,0.8729278445,-0.0273239724,-0.5442200303,-0.0153269405,0.0397060923,0.0890801772,0.1639343798,0.2189647704,0.0372849554,0.5934820771,-0.1173360422,0.2164494097,-0.617814064,-0.3918794096,0.1022077575,0.0896863565,0.710786283,-0.1210797578,0.1686468422,-0.0542224012,-0.0059996038,-0.1753214896,-0.1285113394,-0.1234858558,-0.3988273144,0.1177428067,0.1473958343,-0.1068145633,-0.5827357173,-0.2150991261,-0.2387956828,0.275292784,0.0514024273,0.0896681324,0.1455763876,-0.0885479748,-0.0370756611,0.0952314511,0.1224287525,-0.1380489469,0.0188020095,0.1814914942,-0.3380017877,0.020610299,-0.0145539213,-0.0074496451,-0.2079558372,0.1188184544,-0.4295735955,0.1003184393,-0.0406882092,0.5455446839,-0.0113011291,-0.1634618342,0.4637937546,0.0664937198,-0.0375897437,0.0745828524,-0.2076437324,0.0464941859,0.4402966797,0.0109204203,0.3696685135,0.1138623878,0.0918771029,0.7774419785,-0.0929285362,0.165503487,0.1381973475,-0.2433730811,0.3671025038,-0.0329846963,-0.2301270515,0.257126927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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/129","title":"[Feature request] Add Google Natural Question dataset","comments":"Super appreciate your hard work !!\r\nI'll cross my fingers and hope easily loadable wikipedia dataset will come soon. ","body":"Would be great to have https:\/\/github.com\/google-research-datasets\/natural-questions as an alternative to SQuAD.","comment_length":19,"text":"[Feature request] Add Google Natural Question dataset \n Would be great to have https:\/\/github.com\/google-research-datasets\/natural-questions as an alternative to SQuAD. \n Super appreciate your hard work !!\r\nI'll cross my fingers and hope easily loadable wikipedia dataset will come soon. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/129","title":"[Feature request] Add Google Natural Question dataset","comments":"Quick update on NQ: due to some limitations I met using apache beam + parquet I was not able to use the dataset in a nested parquet structure in python to convert it to our Apache Arrow format yet.\r\nHowever we had planned to change this conversion step anyways so we'll make just sure that it enables to process and convert the NQ dataset to arrow.","body":"Would be great to have https:\/\/github.com\/google-research-datasets\/natural-questions as an alternative to SQuAD.","comment_length":66,"text":"[Feature request] Add Google Natural Question dataset \n Would be great to have https:\/\/github.com\/google-research-datasets\/natural-questions as an alternative to SQuAD. \n Quick update on NQ: due to some limitations I met using apache beam + parquet I was not able to use the dataset in a nested parquet structure in python to convert it to our Apache Arrow format yet.\r\nHowever we had planned to change this conversion step anyways so we'll make just sure that it enables to process and convert the NQ dataset to arrow.","embeddings":[-0.2953702807,0.2493468076,-0.2129656225,-0.2288988531,-0.2177684903,-0.007762617,0.1600123793,0.2642006576,0.0725733489,0.2330668569,-0.1219623908,0.4617756605,-0.3594296277,0.3047490716,0.6277964711,-0.1398961693,0.109149985,0.1171826348,0.1310335547,0.0052731531,-0.317882508,0.2159188092,-0.2996281385,-0.1805972159,0.3880342245,-0.2200264335,-0.0794761404,0.0181822274,-0.0265263487,-0.5745174289,-0.3112954199,0.0473093987,0.0476757661,0.040043842,-0.0001121784,-0.2186170071,0.174267292,0.06726899,-0.4605724514,0.0060868203,-0.0046946011,-0.2376577556,0.1306650788,0.1122267917,-0.2582050264,-0.4748965502,0.3113420904,-0.5965595841,0.151903674,0.5919366479,0.1091722175,0.0871775076,-0.1037180349,-0.0694532841,0.3513969481,0.054879088,-0.3796557486,0.0306329634,0.287867099,0.0260598753,0.0931188986,0.0747287571,0.1412568241,-0.2341369092,0.0540361963,0.1074217334,-0.2296620011,-0.5888482332,-0.1962655783,0.2285999805,0.5203668475,-0.1335051209,-0.1554478258,-0.1241949126,0.1033762544,0.0856694356,-0.2679816484,0.4884107411,-0.3231521845,0.1151645854,0.0538653061,-0.5424550772,-0.2480218709,-0.0013973647,0.2195855975,0.2661553621,0.1762827635,0.0917402655,-0.1932388097,-0.1051129922,0.3093812168,0.1191854104,0.0989951715,0.1564710885,-0.5393491387,-0.4150444269,-0.0024330709,-0.1480288506,0.2973623276,0.3861842752,0.1967416853,0.0959154814,0.1803384125,-0.084174104,0.2228099853,-0.4225268066,0.3411365449,0.0574572906,0.0475361161,-0.2187972814,0.0957734585,0.0870952979,0.098573409,-0.1406960487,-0.1046526283,0.0877152234,0.402862668,0.1496584564,0.0862818137,0.0376020521,-0.0851916671,-0.0380429663,-0.1958857477,0.1254626513,0.085096769,-0.1955902576,-0.1212322637,0.4383507669,0.1599331796,-0.5843251944,0.0355459787,0.0209727865,0.1136966199,0.2827502489,0.3090487719,0.0690916926,0.0580869094,-0.0036550178,0.2363467515,0.2372781187,0.29205814,0.1680350602,0.0874804184,-0.1461002082,-0.0065881619,-0.3226618171,-0.0357294939,0.0072311522,-0.2910197377,0.2138700783,-0.2787839472,-0.2301320434,-0.4146444499,0.0961111709,0.0838122815,-0.3740875125,0.0682291985,0.4454984963,-0.325990051,-0.3123779595,-0.0269231871,0.1304176599,-0.2292339057,-0.236409381,-0.0510749854,0.2156182379,-0.3670797348,0.0269612279,-0.1518088579,0.2874581516,-0.1973644048,0.087819919,0.0525866225,0.1360738277,-0.1900932491,0.02233533,0.6011225581,-0.2211753428,-0.0514523238,0.0367660411,-0.2813921571,-0.1744874716,-0.3171973825,0.3433481157,0.1696347147,0.0938416496,0.0310205203,0.6536281705,-0.0924195722,-0.0391355455,-0.039010603,-0.227211833,0.199558422,0.4691130519,-0.1735947877,0.0021728354,0.365701139,-0.5542101264,-0.0867786556,-0.1103414893,-0.054489959,-0.0242103953,0.4067526758,-0.2000357509,0.1616885513,-0.251570195,-0.7552241683,0.0615149029,-0.7966159582,0.4462405145,-0.0178984851,-0.1940711886,-0.25786376,0.0020970935,-0.1257833838,-0.1862559766,0.1563038826,-0.0292648375,-0.2549140751,0.223178193,-0.2163216472,0.0227223746,0.1051910967,0.2016458809,-0.1075953543,0.2680731416,-0.0149880806,-0.039805036,0.1759323627,0.1753741652,0.1201355383,0.1826214492,0.2836145163,0.1856957078,-0.282862246,0.2047657818,0.5085203648,-0.122134909,0.0383016057,-0.5743350983,-0.0077638873,0.3941744268,-0.2608610094,-0.0196069311,-0.3717058301,0.33925578,0.2843263447,0.1068695635,-0.3091539741,0.2991065681,-0.0987705365,0.0346748345,-0.0478970148,0.1723697633,-0.2749818265,0.3048970997,0.1159953177,-0.379665345,-0.2862068117,0.2668562829,0.3423602879,-0.0679126307,0.3512673676,0.0810949281,-0.084396556,-0.1377175897,0.4772306383,-0.1296388656,0.1275278181,0.2070845962,0.1430482268,0.2882402539,0.0135387015,-0.0293025617,-0.0542867482,-0.062663354,0.0930217803,-0.0140057923,0.5086836219,0.166303426,-0.2894741595,-0.0866594762,-0.1338922232,-0.0666268617,-0.1769340485,0.0182868522,-0.0329752751,0.1535910517,0.0673317537,0.4362247288,-0.2621743679,-0.3076348901,0.4359920323,-0.2957397401,-0.1318915635,0.1456815302,0.1299084425,0.7573097944,0.1741632521,-0.5157591701,-0.1659678221,-0.0387541354,0.0553572103,0.18978028,0.0895486251,0.0912854597,0.5234966278,0.0052641835,0.3065762222,-0.5940279961,-0.3140815794,0.1414429992,0.069832623,0.6522313356,0.0418383628,0.1898486465,-0.2482081503,0.036749661,-0.1394643635,-0.1734526753,-0.2007226944,-0.1394867748,-0.036491517,0.3687531352,0.000965357,-0.7123110294,-0.3366532326,-0.3728920221,0.2275636792,0.1590357572,0.1532221735,0.0394760035,-0.186305359,-0.0669428557,-0.021684425,0.0722760931,0.036198739,0.2218100727,0.3625462353,-0.4247932732,-0.0341427363,-0.0311215539,-0.2124654055,0.0582344793,0.2095229626,-0.3300026655,0.2758338451,0.0560238436,0.4175790548,0.038538821,-0.1555835307,0.4633901417,0.0285181478,-0.0280735157,0.054876592,0.0071400232,-0.0003175375,0.4249238968,0.0339552462,0.4035647511,0.171717152,0.0728459805,0.8291463256,-0.101885356,0.2454564571,0.1981819868,-0.3811302185,0.192614466,-0.094975315,0.0792257935,0.1595588177,0.0482387394,0.0363795869,0.2618745565,0.277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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/128","title":"Some error inside nlp.load_dataset()","comments":"Google colab has an old version of Apache Arrow built-in.\r\nBe sure you execute the \"pip install\" cell and restart the notebook environment if the colab asks for it.","body":"First of all, nice work!\r\n\r\nI am going through [this overview notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb)\r\n\r\nIn simple step `dataset = nlp.load_dataset('squad', split='validation[:10%]')`\r\n\r\nI get an error, which is connected with some inner code, I think:\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\r\n<ipython-input-8-d848d3a99b8c> in <module>()\r\n      1 # Downloading and loading a dataset\r\n      2 \r\n----> 3 dataset = nlp.load_dataset('squad', split='validation[:10%]')\r\n\r\n8 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    515         download_mode=download_mode,\r\n    516         ignore_verifications=ignore_verifications,\r\n--> 517         save_infos=save_infos,\r\n    518     )\r\n    519 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    361                 verify_infos = not save_infos and not ignore_verifications\r\n    362                 self._download_and_prepare(\r\n--> 363                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    364                 )\r\n    365                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    414             try:\r\n    415                 # Prepare split will record examples associated to the split\r\n--> 416                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    417             except OSError:\r\n    418                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    585         fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n    586         fpath = os.path.join(self._cache_dir, fname)\r\n--> 587         examples_type = self.info.features.type\r\n    588         writer = ArrowWriter(data_type=examples_type, path=fpath, writer_batch_size=self._writer_batch_size)\r\n    589 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in type(self)\r\n    460     @property\r\n    461     def type(self):\r\n--> 462         return get_nested_type(self)\r\n    463 \r\n    464     @classmethod\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in get_nested_type(schema)\r\n    370     # Nested structures: we allow dict, list\/tuples, sequences\r\n    371     if isinstance(schema, dict):\r\n--> 372         return pa.struct({key: get_nested_type(value) for key, value in schema.items()})\r\n    373     elif isinstance(schema, (list, tuple)):\r\n    374         assert len(schema) == 1, \"We defining list feature, you should just provide one example of the inner type\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in <dictcomp>(.0)\r\n    370     # Nested structures: we allow dict, list\/tuples, sequences\r\n    371     if isinstance(schema, dict):\r\n--> 372         return pa.struct({key: get_nested_type(value) for key, value in schema.items()})\r\n    373     elif isinstance(schema, (list, tuple)):\r\n    374         assert len(schema) == 1, \"We defining list feature, you should just provide one example of the inner type\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in get_nested_type(schema)\r\n    379         # We allow to reverse list of dict => dict of list for compatiblity with tfds\r\n    380         if isinstance(inner_type, pa.StructType):\r\n--> 381             return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))\r\n    382         return pa.list_(inner_type, schema.length)\r\n    383 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in <genexpr>(.0)\r\n    379         # We allow to reverse list of dict => dict of list for compatiblity with tfds\r\n    380         if isinstance(inner_type, pa.StructType):\r\n--> 381             return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))\r\n    382         return pa.list_(inner_type, schema.length)\r\n    383 \r\n\r\nTypeError: list_() takes exactly one argument (2 given)\r\n```","comment_length":29,"text":"Some error inside nlp.load_dataset() \n First of all, nice work!\r\n\r\nI am going through [this overview notebook](https:\/\/colab.research.google.com\/github\/huggingface\/nlp\/blob\/master\/notebooks\/Overview.ipynb)\r\n\r\nIn simple step `dataset = nlp.load_dataset('squad', split='validation[:10%]')`\r\n\r\nI get an error, which is connected with some inner code, I think:\r\n```\r\n---------------------------------------------------------------------------\r\n\r\nTypeError                                 Traceback (most recent call last)\r\n\r\n<ipython-input-8-d848d3a99b8c> in <module>()\r\n      1 # Downloading and loading a dataset\r\n      2 \r\n----> 3 dataset = nlp.load_dataset('squad', split='validation[:10%]')\r\n\r\n8 frames\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n    515         download_mode=download_mode,\r\n    516         ignore_verifications=ignore_verifications,\r\n--> 517         save_infos=save_infos,\r\n    518     )\r\n    519 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)\r\n    361                 verify_infos = not save_infos and not ignore_verifications\r\n    362                 self._download_and_prepare(\r\n--> 363                     dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n    364                 )\r\n    365                 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n    414             try:\r\n    415                 # Prepare split will record examples associated to the split\r\n--> 416                 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n    417             except OSError:\r\n    418                 raise OSError(\"Cannot find data file. \" + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or \"\"))\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/builder.py in _prepare_split(self, split_generator)\r\n    585         fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n    586         fpath = os.path.join(self._cache_dir, fname)\r\n--> 587         examples_type = self.info.features.type\r\n    588         writer = ArrowWriter(data_type=examples_type, path=fpath, writer_batch_size=self._writer_batch_size)\r\n    589 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in type(self)\r\n    460     @property\r\n    461     def type(self):\r\n--> 462         return get_nested_type(self)\r\n    463 \r\n    464     @classmethod\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in get_nested_type(schema)\r\n    370     # Nested structures: we allow dict, list\/tuples, sequences\r\n    371     if isinstance(schema, dict):\r\n--> 372         return pa.struct({key: get_nested_type(value) for key, value in schema.items()})\r\n    373     elif isinstance(schema, (list, tuple)):\r\n    374         assert len(schema) == 1, \"We defining list feature, you should just provide one example of the inner type\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in <dictcomp>(.0)\r\n    370     # Nested structures: we allow dict, list\/tuples, sequences\r\n    371     if isinstance(schema, dict):\r\n--> 372         return pa.struct({key: get_nested_type(value) for key, value in schema.items()})\r\n    373     elif isinstance(schema, (list, tuple)):\r\n    374         assert len(schema) == 1, \"We defining list feature, you should just provide one example of the inner type\"\r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in get_nested_type(schema)\r\n    379         # We allow to reverse list of dict => dict of list for compatiblity with tfds\r\n    380         if isinstance(inner_type, pa.StructType):\r\n--> 381             return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))\r\n    382         return pa.list_(inner_type, schema.length)\r\n    383 \r\n\r\n\/usr\/local\/lib\/python3.6\/dist-packages\/nlp\/features.py in <genexpr>(.0)\r\n    379         # We allow to reverse list of dict => dict of list for compatiblity with tfds\r\n    380         if isinstance(inner_type, pa.StructType):\r\n--> 381             return pa.struct(dict((f.name, pa.list_(f.type, schema.length)) for f in inner_type))\r\n    382         return pa.list_(inner_type, schema.length)\r\n    383 \r\n\r\nTypeError: list_() takes exactly one argument (2 given)\r\n``` \n Google colab has an old version of Apache Arrow built-in.\r\nBe sure you execute the \"pip install\" cell and restart the notebook environment if the colab asks for it.","embeddings":[-0.0670081377,0.0724993795,-0.0823872089,0.1897586733,0.267567426,0.0750563815,0.2924349904,0.3888992071,0.0351886898,-0.1877863556,-0.1114239022,0.2747960091,-0.0162892751,0.0643214062,0.2407227159,-0.2864512801,-0.1546422988,0.2549157739,-0.0124721359,0.1232353002,-0.2655300796,0.1166809052,-0.3398716748,0.3442742527,-0.0802765116,-0.1929238886,-0.0296067595,0.3408279419,-0.2490498424,-0.5879958868,0.3756798208,-0.1130219474,0.2241377831,0.1312045604,-0.0001051211,0.0366863534,0.4223832488,-0.0525458157,-0.4962961674,-0.2144066691,-0.2681881189,-0.1776150465,0.2765499055,-0.5071807504,0.3534993827,0.0584660582,0.1453215331,0.172617957,0.4719077647,0.3926600218,0.3172274828,0.4065113962,0.1332868487,-0.0378221758,-0.0242808741,-0.0132257333,0.0424994789,0.3613389134,-0.0597006828,-0.2916482091,-0.1058744639,0.1836791486,-0.0739921257,0.0822866559,0.2149083763,-0.0782490224,0.1761753112,-0.2062117159,-0.2290777713,0.0764528736,0.310865581,-0.3317778707,-0.1878501326,-0.2332880795,0.1658896357,-0.286799103,0.2173205912,0.2049746066,-0.2205486894,-0.0430262759,-0.1023475379,0.0367132388,-0.1697550267,0.2968499064,0.1530664116,0.1422135085,-0.0430708043,0.0544458181,0.1611634493,0.0255567972,0.1349268258,-0.1027902737,-0.0119791068,0.3151762187,-0.3757234514,-0.2236605138,0.0898646191,0.1502705365,0.0355190895,0.0423011705,0.1994195133,-0.1857152432,0.0624949113,0.3097718954,0.3490634561,0.2397695631,0.3099018633,0.1482294202,0.111098066,0.2224611193,0.0778009146,0.040816877,-0.0988591313,-0.2621631026,0.239755556,0.0685242489,0.3718980551,-0.0113207232,-0.4528195858,0.0513457023,-0.2397605479,-0.0740910098,0.1411995888,0.2162542045,0.0511003211,0.0173275508,0.2893837094,0.0570257194,-0.2770854235,-0.3184550405,-0.1165348962,0.2498125285,-0.3269898295,0.0069971788,0.1883507669,0.0352810137,0.308734715,0.0512832366,-0.0036526511,-0.1259551197,0.2600570321,-0.1636987627,-0.2521975636,0.1093505993,-0.0698628202,-0.1896069795,0.1693443358,-0.2162506133,-0.0904491022,-0.0141093191,-0.1204682961,-0.3169983029,-0.2073783726,0.2719335258,-0.1719684303,0.0835194215,-0.173584044,-0.1093321443,0.2492954582,-0.3055495918,0.0167168751,-0.3455445468,-0.1564008296,-0.1015432626,0.0918391347,0.3969186246,0.0699042231,-0.1808037758,-0.2149003297,-0.2028656602,-0.0220352262,0.2457960546,-0.2600751817,0.622546494,-0.1959222555,0.2986060679,0.6382418275,-0.3533470035,-0.2437379956,0.2599744499,-0.0882795528,0.0478558242,-0.2249871194,0.0980274081,0.1081014648,-0.0667474642,0.3803537786,0.510879159,-0.1190070361,0.0474854112,-0.1602506638,0.0501506105,0.290140897,0.1346265078,-0.0792289376,0.1688669324,0.0797369778,0.6542676091,0.0420449451,-0.0276956894,-0.1052355617,-0.0001355516,-0.0991272107,-0.1206663325,-0.1381555945,-0.2238782793,-0.5490896106,-0.0626133382,-0.4242883027,0.0873748809,-0.013877809,-0.0138693107,-0.4102626741,0.0395544954,-0.2429813296,-0.1580200195,0.233346194,0.1386093199,-0.1312867552,-0.1246051863,-0.0883770362,0.3406808376,-0.3272353113,0.0233292747,-0.5331027508,0.2011083066,-0.2686674297,-0.1432891786,0.1279355735,0.2801431417,0.0276369806,-0.0509063974,-0.1709376276,0.4143425524,-0.0930758566,0.0388934501,0.0303059649,-0.1917450279,-0.0529045127,-0.0611756183,0.0195933953,0.1188744232,0.2759116292,-0.0820460692,0.1410550177,0.2947699726,-0.0729208514,0.07114885,0.0039901254,0.1084019095,0.1370729059,-0.1486365795,-0.0938241631,-0.0202759448,0.3909056187,0.2834856808,0.2081480324,-0.0288621206,-0.2282043695,-0.1797693074,0.4322507083,-0.0689732358,0.1922805607,0.2028542161,0.1720713526,-0.1804119796,-0.0145860836,-0.0284012984,0.541462481,0.0389190987,-0.3035569489,0.2416815013,-0.0952259153,-0.0702836663,0.2528953254,0.06303069,0.1794157326,0.2073055506,0.1111282334,0.1453695595,-0.3022251725,-0.2434382737,0.1295956373,0.3865086734,-0.3687125742,0.3285272717,-0.0910153762,-0.5053382516,-0.1362622678,-0.0972985923,-0.1995762736,-0.3681539893,-0.2554415464,0.197640568,0.0036706775,0.0604116842,-0.0738964379,0.1609850377,0.079625167,0.0558333285,-0.0202164091,-0.2739411294,-0.1665540338,0.0571618229,0.4820735753,0.0963714942,0.3322121799,-0.2350907773,-0.2184936702,0.0778685883,-0.3148480058,0.0317500941,-0.0699617192,0.2388782352,0.3108826578,0.21828942,-0.096302405,-0.2679318786,0.3181727231,-0.0013899575,-0.3025505543,0.1445501298,0.0671869069,0.0353902094,-0.1147907376,-0.4487812817,-0.4314314425,-0.4160797298,0.168674171,-0.156861797,0.2440702766,0.4337669313,-0.2115263343,0.3001113534,-0.1510654837,-0.0303636268,-0.1450505853,-0.0210048556,0.139748618,-0.2263175845,-0.3450107276,-0.2016336471,-0.0866467878,0.2221891433,-0.2155761123,-0.4385277331,-0.0517624728,-0.0057823048,0.1955138892,-0.2977797985,-0.1599109322,0.2887176275,-0.0905646086,-0.1065410152,-0.016771609,-0.1417757273,-0.0614282452,0.0821514055,0.3034346104,-0.1351955384,0.4471290112,-0.1231755018,0.2869756818,0.0732987374,-0.1082974225,0.3293793499,0.1539563686,0.2039957941,-0.2005158216,-0.4926657081,0.2181332558,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/120","title":"\ud83d\udc1b `map` not working","comments":"I didn't assign the output \ud83e\udd26\u200d\u2642\ufe0f\r\n\r\n```python\r\ndataset.map(test)\r\n```\r\n\r\nshould be :\r\n\r\n```python\r\ndataset = dataset.map(test)\r\n```","body":"I'm trying to run a basic example (mapping function to add a prefix).  \r\n[Here is the colab notebook I'm using.](https:\/\/colab.research.google.com\/drive\/1YH4JCAy0R1MMSc-k_Vlik_s1LEzP_t1h?usp=sharing)\r\n\r\n```python\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('squad', split='validation[:10%]')\r\n\r\ndef test(sample):\r\n    sample['title'] = \"test prefix @@@ \" + sample[\"title\"]\r\n    return sample\r\n\r\nprint(dataset[0]['title'])\r\ndataset.map(test)\r\nprint(dataset[0]['title'])\r\n```\r\nOutput :\r\n> Super_Bowl_50\r\nSuper_Bowl_50\r\n\r\nExpected output :\r\n> Super_Bowl_50\r\ntest prefix @@@ Super_Bowl_50","comment_length":17,"text":"\ud83d\udc1b `map` not working \n I'm trying to run a basic example (mapping function to add a prefix).  \r\n[Here is the colab notebook I'm using.](https:\/\/colab.research.google.com\/drive\/1YH4JCAy0R1MMSc-k_Vlik_s1LEzP_t1h?usp=sharing)\r\n\r\n```python\r\nimport nlp\r\n\r\ndataset = nlp.load_dataset('squad', split='validation[:10%]')\r\n\r\ndef test(sample):\r\n    sample['title'] = \"test prefix @@@ \" + sample[\"title\"]\r\n    return sample\r\n\r\nprint(dataset[0]['title'])\r\ndataset.map(test)\r\nprint(dataset[0]['title'])\r\n```\r\nOutput :\r\n> Super_Bowl_50\r\nSuper_Bowl_50\r\n\r\nExpected output :\r\n> Super_Bowl_50\r\ntest prefix @@@ Super_Bowl_50 \n I didn't assign the output \ud83e\udd26\u200d\u2642\ufe0f\r\n\r\n```python\r\ndataset.map(test)\r\n```\r\n\r\nshould be :\r\n\r\n```python\r\ndataset = dataset.map(test)\r\n```","embeddings":[-0.1308908612,-0.288844794,-0.0395002961,-0.0892400667,0.1618371606,0.0317668915,0.0189053081,0.2528122365,0.372751385,0.1373792738,0.2324204594,0.6317356229,-0.0919273198,0.1015725359,0.1246013343,0.0128668025,0.1166422889,0.2223274559,-0.0682751015,-0.0151178883,-0.455271095,0.1827889383,-0.3452918828,0.3195168376,-0.1788795739,-0.0197323374,0.0069232848,-0.1686056405,-0.150720045,-0.1539473236,0.0808377117,-0.1376018077,-0.1134997159,0.3354356885,-0.0001208801,-0.1301519424,0.3381017148,-0.0655615479,-0.2692093551,-0.7517463565,-0.3737787306,0.1315031052,0.1735256761,-0.1138142198,-0.1845378578,0.2965347171,0.4201627076,-0.2645874619,0.1648977846,0.3474882543,0.1447360814,-0.1399454474,0.0086326376,-0.0474374108,0.0732381865,0.2379055172,-0.1838501692,0.2487777621,0.3222712278,-0.3623713851,0.392286092,0.118521519,-0.0787296072,-0.1327400059,0.1144522801,0.2852788866,0.2475356609,-0.2126544565,0.1879269779,-0.0303253382,-0.0482015125,-0.2822025716,0.033467032,0.05807136,-0.1231693774,-0.5438141823,0.0971134081,-0.0498420335,-0.0030328662,-0.062092647,-0.204874143,-0.0853242427,0.2212250531,0.6745916009,0.1022634059,0.9548653364,-0.116338104,0.2279542983,0.3377545476,-0.099991098,0.0041731172,-0.0862776488,0.067313768,0.376660198,0.069268167,0.1327460706,0.1648412198,-0.0948004797,-0.2642460465,-0.6372027397,0.1999678314,0.010157818,-0.1684218198,0.3996458948,0.020142179,0.2257401496,0.4909593761,0.3822185695,0.1175036356,0.0059167417,-0.2318648845,-0.1034115329,-0.0083997566,0.0281142052,-0.3582536876,0.1919237375,0.1363413334,0.1164997518,0.0773293898,0.0594358034,-0.4156877398,-0.0138432207,0.0812271386,0.32006827,-0.0052135736,-0.3745110929,-0.0273591131,0.1046239659,0.070869796,0.1158993021,-0.1659529507,0.1215727329,-0.6696619987,-0.1169036627,-0.0992712975,0.145935297,0.3452649713,-0.1467160434,0.0364236012,-0.0586835556,0.0628519133,-0.2476706505,0.4761578143,0.0555966832,-0.1343472749,0.4023709893,0.3105251193,-0.5544050932,-0.0962489694,0.4196369052,-0.1514539719,-0.0798978359,-0.1413527727,0.0859790742,-0.1877979487,0.0859404057,-0.3463787138,0.3524733782,0.3490440249,-0.3453374207,0.119552128,-0.1349669546,-0.6410399079,-0.195941776,0.1157927066,0.0416710228,-0.3937189579,-0.0446945652,-0.2229492366,0.4102565944,0.0790214166,0.1991873682,-0.1474320889,0.4485391974,-0.1345473379,0.4181539118,0.3669397533,-0.3884114027,-0.3279597461,-0.0438183732,-0.2272126973,-0.1566497236,-0.3628614247,0.0593842678,0.0771250501,0.0182608292,0.3440069854,0.0222455766,0.0512007624,0.0110291466,-0.0668046698,-0.1032929271,0.2660877705,-0.094947353,-0.0731077194,0.1685606688,-0.0119153848,-0.0485044308,0.2499534935,-0.1668195575,0.0707987323,-0.1735836267,0.2821959853,0.0722110048,-0.174994126,-0.0447287112,-0.1231511161,0.0357340761,-0.1855429709,-0.0179974753,-0.0927846208,-0.0023257572,-0.157992214,0.0448443182,-0.164195314,-0.0482518822,0.0877189264,0.0776939988,0.0191209968,0.0309899244,-0.0614586249,0.109588325,0.1743949205,0.1306148171,-0.5375675559,-0.1109388694,0.0721308738,-0.312258482,-0.3377268016,0.262784332,0.2720017433,-0.0913677216,-0.1593883634,0.1826328486,0.0240379274,-0.1603485346,-0.0225018281,-0.2081052512,0.1836200356,-0.1446668357,-0.0301504731,0.5182896256,0.2055364549,-0.2129921168,-0.0350321531,0.1274992675,0.1038670465,0.2728381455,-0.0886465237,0.0565799288,-0.0319980644,-0.0433970131,-0.0903621167,-0.1823167056,-0.0143293645,0.0340844467,-0.0532754324,0.0036533964,0.0778258592,-0.1119713336,0.4006410837,0.0231645592,0.3708415031,0.0209428277,-0.1153282151,0.1659086645,0.1863711178,-0.0547924601,0.5865674615,0.0131229572,-0.0254018661,0.1955808252,0.0845860764,-0.1995898783,0.1008771956,-0.1404303163,0.066596806,-0.2407288998,0.128489241,-0.1666507721,-0.0924958214,0.056909997,0.0684152171,0.2624983788,-0.2354493439,-0.0236500502,-0.1048641726,-0.2389103025,0.1042293683,-0.4910825789,-0.1033510417,-0.5496193171,-0.061219532,0.2564050555,0.0575671531,0.2079385668,0.2172444165,-0.3307384849,-0.0395485573,-0.4727929831,0.0556713194,-0.3682712615,-0.2575454116,0.0285215862,-0.2591771185,-0.1270316094,-0.0272990596,0.1257539988,-0.076078482,0.0362054817,-0.591994524,0.1960734278,-0.1373319924,0.0644340292,0.1637121886,-0.229882285,-0.5939878225,-0.4168896675,0.3337340355,-0.0796898231,-0.0332238451,-0.1684315205,0.0010464024,-0.3191904426,-0.310695529,-0.052027043,-0.1261584014,-0.3103807867,0.4487834573,-0.1254446954,0.2938332558,0.342856735,-0.1564956754,0.3314344585,-0.1706441194,-0.3483972251,-0.2987544835,-0.0064586718,0.3493944705,-0.3635314107,-0.148914963,0.0703558922,-0.1867864132,0.18225573,-0.0701196045,-0.1043677777,-0.2003175616,-0.0184744895,-0.0312952399,0.3904739618,0.2745307386,0.0836583078,-0.0805731341,-0.0049564466,-0.1555367857,-0.5929970741,0.278050065,0.6513228416,0.5089289546,0.1921819746,0.2489903569,-0.0075511946,0.5325362682,0.161803931,-0.2125650644,0.2115820795,-0.3211400807,-0.0320385434,-0.1424285471,-0.4234916568,0.0245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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/119","title":"\ud83d\udc1b Colab : type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'","comments":"It's strange, after installing `nlp` on Colab, the `pyarrow` version seems fine from `pip` but not from python :\r\n\r\n```python\r\nimport pyarrow\r\n\r\n!pip show pyarrow\r\nprint(\"version = {}\".format(pyarrow.__version__))\r\n```\r\n\r\n> Name: pyarrow\r\nVersion: 0.17.0\r\nSummary: Python library for Apache Arrow\r\nHome-page: https:\/\/arrow.apache.org\/\r\nAuthor: None\r\nAuthor-email: None\r\nLicense: Apache License, Version 2.0\r\nLocation: \/usr\/local\/lib\/python3.6\/dist-packages\r\nRequires: numpy\r\nRequired-by: nlp, feather-format\r\n> \r\n> version = 0.14.1","body":"I'm trying to load CNN\/DM dataset on Colab.\r\n\r\n[Colab notebook](https:\/\/colab.research.google.com\/drive\/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)\r\n\r\nBut I meet this error :\r\n\r\n> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'\r\n","comment_length":63,"text":"\ud83d\udc1b Colab : type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array' \n I'm trying to load CNN\/DM dataset on Colab.\r\n\r\n[Colab notebook](https:\/\/colab.research.google.com\/drive\/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)\r\n\r\nBut I meet this error :\r\n\r\n> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'\r\n \n It's strange, after installing `nlp` on Colab, the `pyarrow` version seems fine from `pip` but not from python :\r\n\r\n```python\r\nimport pyarrow\r\n\r\n!pip show pyarrow\r\nprint(\"version = {}\".format(pyarrow.__version__))\r\n```\r\n\r\n> Name: pyarrow\r\nVersion: 0.17.0\r\nSummary: Python library for Apache Arrow\r\nHome-page: https:\/\/arrow.apache.org\/\r\nAuthor: None\r\nAuthor-email: None\r\nLicense: Apache License, Version 2.0\r\nLocation: \/usr\/local\/lib\/python3.6\/dist-packages\r\nRequires: numpy\r\nRequired-by: nlp, feather-format\r\n> \r\n> version = 0.14.1","embeddings":[-0.2273421288,-0.023100283,-0.0340336785,0.1723956466,0.0568722859,-0.0996569172,0.2892366946,0.1857061237,-0.0378169827,0.0181524269,0.0521476455,0.5537141562,-0.2503296733,0.0401885919,0.0903182849,-0.0751916692,0.0834185407,0.258730948,0.1037930027,-0.1277628094,-0.1502255648,0.1527535319,-0.3422175944,0.1068625599,-0.1202139482,-0.0682862923,0.1318603307,-0.0488690846,-0.0279800203,-0.4014903009,0.4233409464,-0.2384961843,0.1887197942,0.0650445744,-0.0001270101,0.0349720083,0.3435460329,-0.1084538177,-0.355566293,-0.3720389307,-0.054751683,-0.3115216196,0.6195546389,-0.1986419708,0.088460274,-0.2497655153,0.050522767,0.1458174437,0.1289491057,0.2576676607,0.0944548026,0.2429156899,0.2680660784,0.0369408764,0.3030516207,0.1586951762,-0.3170281649,0.8054688573,0.1521752775,-0.0587130897,0.382936269,0.1836001426,-0.292855233,0.3461164534,0.2409609109,0.0738360882,0.1236388534,0.0077660764,-0.024109859,0.0774950683,0.2109683603,-0.1711868197,-0.1291289479,0.1297546625,0.2963559628,-0.0789032876,-0.0245266836,0.3779229522,-0.1516014338,0.2225867808,-0.0472658351,-0.0163547583,-0.1646301001,0.2858141661,-0.0288101323,0.4169096351,-0.1104350016,0.2412788719,0.0684108511,0.1585405916,0.6358198524,0.1410775185,0.0020679836,0.0277524069,0.2584057152,0.2047149241,-0.1335516125,0.0248839911,-0.1483647078,-0.0341440775,0.4241458774,-0.0339717269,0.1124347597,0.3809981048,-0.1169970557,0.2114268839,-0.1434601694,-0.0326790698,0.1138976887,0.1078684628,-0.2203160524,-0.0274381042,-0.1393719316,0.0647004098,0.1614550799,-0.2065222114,0.431833595,-0.1551369578,-0.2683421671,-0.2181916088,-0.6080505252,-0.0528738946,-0.0578332469,0.3415149748,-0.2353737801,0.1496099532,-0.0124659156,0.1326818317,-0.2515012324,-0.0333122015,-0.0183452722,0.1814994514,-0.2161078304,-0.1013979167,0.1927607954,0.143014133,0.2412786335,-0.2968501151,-0.015250206,0.0636128634,-0.0285659172,-0.2196339965,0.3401510417,0.4856801331,0.2516511381,0.06944561,0.1663389951,-0.3547950089,-0.270447284,0.4337829351,-0.4250109196,-0.3489814997,-0.3356326222,0.01754486,-0.2543338239,-0.073577866,-0.6186476946,-0.1014759764,-0.0699049011,-0.3670096397,-0.078219384,-0.2841028869,0.0642401576,-0.3631058335,0.0506600216,-0.071565561,-0.4645743072,-0.1888604909,-0.0797881633,0.1347500682,0.3948203921,0.1182053462,-0.1718877703,0.0022878549,0.1080621406,-0.0528102703,0.5477809906,-0.1122525558,-0.4989821017,0.0723102242,0.1771580875,-0.1638492942,0.1235975251,0.1389488131,-0.0887546241,0.078792803,0.1198111773,0.2991141379,0.2241226733,-0.031194346,-0.3039538562,-0.2280001044,0.1243885756,0.1765549928,0.0341269746,0.0752119794,0.1721445918,-0.1110548824,0.0998981372,-0.1193781942,-0.1763902605,-0.0833880082,0.4347991049,-0.2393099964,-0.1224682555,-0.0388059244,-0.1566177905,0.0591565333,-0.0501139611,0.032014966,-0.3557682931,0.0814519748,-0.1497117132,0.2907872796,-0.083903797,-0.1440961957,-0.0464455634,0.1238604635,-0.0979486555,0.1587922126,-0.1432317346,0.0955498591,0.0379814878,0.0007210464,0.040151082,0.4449893236,-0.2541424632,-0.3926926851,-0.1927075535,0.226912573,0.0311912671,0.0506366491,-0.0731994063,0.0104083186,-0.1712086052,-0.236125201,-0.2261316031,-0.0824178606,0.1172009781,-0.4324964881,0.2024841905,0.0100899572,0.240073055,0.0297513362,0.4912329614,0.3507786691,0.0764851272,0.2080870569,0.1864064187,0.1246459708,0.0424841605,0.2603189647,0.2518983185,-0.0253212322,0.0110155623,0.115363799,0.3049418926,-0.0108530624,-0.3738047183,0.0419913903,0.3567649424,0.052017875,0.3117726445,-0.0289514475,-0.4617649019,-0.0467014909,0.3583218455,-0.0207948927,0.4363690913,0.1499262005,-0.0362185761,-0.1294074506,-0.1859571636,-0.1250617057,0.2619031072,0.091383338,0.430811733,-0.1021552235,0.2690778375,0.0374647975,-0.2176920027,-0.4670942426,-0.0732046142,0.2502478659,-0.181112811,0.3723154068,-0.2413871139,-0.3120823205,-0.2413733453,-0.6629678011,-0.0929108709,-0.2774499357,-0.1045342311,0.351057142,0.3144078553,0.1537207067,0.0195632689,-0.1171601936,0.0267101247,-0.2134560943,0.0538363643,-0.2400348037,-0.5079825521,-0.0529078059,0.2017836422,0.2395821214,-0.0820834786,-0.0431232676,0.0429058,0.0961140171,-0.2651324272,0.1731713414,0.0140371248,0.3970383406,0.0427309796,0.0957200006,-0.564878583,-0.4018689692,0.2459059954,-0.2879649699,-0.1525113881,0.1136663482,-0.1439387649,-0.2021039128,-0.1280095577,-0.0126290526,-0.5180642009,-0.2005015761,0.2381270528,0.2255993783,0.2146229595,0.4167945385,0.367084384,0.2150843143,0.3779624403,0.2771554887,0.0209961869,0.0206607785,0.3824847341,-0.1830014288,-0.4004779458,0.081339635,-0.2128242254,0.198611632,0.1585609168,-0.3589545488,-0.4112590551,-0.0249733888,-0.0041604419,-0.0937071666,0.0064305365,0.0887785032,0.1368145347,0.126965493,-0.0449938364,-0.1077996641,-0.2498589158,-0.0095360894,0.035297215,-0.0117583796,-0.0443688855,-0.151064977,0.7354260683,-0.3515334427,-0.1901317388,0.4313213825,-0.1782525629,0.2434347123,-0.1233844459,-0.428332746,-0.0222853068,-0.0430831946,-0.043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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/119","title":"\ud83d\udc1b Colab : type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'","comments":"Ok I just had to restart the runtime after installing `nlp`. After restarting, the version of `pyarrow` is fine.","body":"I'm trying to load CNN\/DM dataset on Colab.\r\n\r\n[Colab notebook](https:\/\/colab.research.google.com\/drive\/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)\r\n\r\nBut I meet this error :\r\n\r\n> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'\r\n","comment_length":19,"text":"\ud83d\udc1b Colab : type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array' \n I'm trying to load CNN\/DM dataset on Colab.\r\n\r\n[Colab notebook](https:\/\/colab.research.google.com\/drive\/11Mf7iNhIyt6GpgA1dBEtg3cyMHmMhtZS?usp=sharing)\r\n\r\nBut I meet this error :\r\n\r\n> AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'\r\n \n Ok I just had to restart the runtime after installing `nlp`. After restarting, the version of `pyarrow` is fine.","embeddings":[-0.2835429907,0.0447813608,0.0172028225,0.2262002379,0.1537768245,-0.0492425673,0.2736166418,0.1078590304,-0.0629185885,0.0287807398,0.089954637,0.4489680529,-0.3376855552,0.2220071256,0.2667759061,-0.1032789871,0.0401548296,0.3651624024,0.1162208095,-0.0348985642,-0.1869616807,0.1577655524,-0.4699403346,0.093881458,-0.2458846867,-0.1728018373,0.0579281226,0.0365408249,-0.0127620483,-0.4258046448,0.4096870124,-0.2087133527,0.2067955136,0.049235519,-0.0001234279,-0.0009604987,0.3468475044,-0.1835941374,-0.380650878,-0.2610020041,-0.0870168731,-0.0719363689,0.5392192006,-0.1955817789,0.0109692076,-0.2137584835,0.0638213009,0.1740896404,0.0559797697,0.2633334696,0.1195242032,0.139874205,0.2437542379,0.0178375617,0.1536610126,0.1872360259,-0.2336520851,0.7389292717,0.162230134,-0.1054732352,0.36913082,0.0969696939,-0.2743653059,0.2058781385,0.3785057664,0.06716723,0.0230651107,-0.0656105727,-0.0372657552,0.1121522561,0.3022116423,-0.2212343365,-0.002140336,0.1088491306,0.4271304309,-0.2622922361,0.0225428641,0.3496264815,-0.1930697113,0.1046563014,-0.1546211243,-0.1691000164,-0.1028717309,0.2732112408,0.0031538589,0.2862986326,-0.0820575356,0.1322266757,0.2594425678,0.1244037822,0.5737629533,0.1832592785,-0.0675802603,0.0166732483,0.1255553961,0.1166650206,-0.1151927635,0.0048528053,-0.1534453928,-0.2217015773,0.3211809099,-0.1065697223,0.1329921782,0.3500325382,-0.0483986437,0.3419393897,-0.0776829645,-0.0088762799,0.0688883886,0.0841941833,-0.3237350583,0.0261882674,-0.2106641978,0.0583889484,0.155764237,-0.1791136414,0.3839304447,-0.1505262107,-0.1758469343,-0.2368793041,-0.5335425138,-0.0388216041,0.1062474921,0.4182965457,-0.1107630804,0.0536781214,0.0200206507,0.0310178827,-0.1992997229,-0.1668626815,-0.1107777283,0.312034905,-0.349057585,-0.0040702019,0.2279451638,0.1600241065,0.1629523784,-0.2321518511,0.0262949038,0.0044045448,-0.0706854612,-0.2387650311,0.230105266,0.5033140779,0.165932864,-0.0782710835,0.1211165339,-0.3699175417,-0.191652149,0.5548939109,-0.3915857971,-0.494168669,-0.3462758958,0.0253200568,-0.3040960729,-0.1140843108,-0.7277987003,0.0179966483,-0.0531854145,-0.3441113234,-0.15384835,-0.3110789955,0.0408108942,-0.2952149808,0.135038808,0.081266351,-0.4829117954,-0.1567835361,-0.0012833568,0.0355808325,0.4002494812,0.0959426835,-0.2945821583,0.1581172794,0.0410972238,-0.0093365349,0.4867193401,-0.0099859219,-0.38403368,-0.0328746997,0.1367969364,-0.1818748564,-0.0236539785,0.131323576,0.0161779411,0.1076828092,0.0527130589,0.3254430592,0.1134636849,0.0453519225,-0.3021544516,-0.1602824181,0.2848570347,0.272996366,0.1056291163,0.2323065102,0.0395339988,0.0595537499,0.1080310643,-0.1597004682,-0.205755353,-0.1156278551,0.4424152374,-0.259305656,-0.0949249566,-0.1033822745,-0.200037688,0.0993315503,0.0219705366,0.1486597806,-0.2642741799,0.098718375,-0.0018053843,0.2425141782,-0.0544146299,-0.0932020172,-0.0472624265,0.1068520397,-0.1139084846,0.0855095237,-0.2942034304,0.1732182652,-0.0855095536,-0.059121605,0.1172953025,0.4961355627,-0.221056357,-0.4288746417,-0.1441909969,0.2088763863,0.0196503736,-0.0017652157,-0.1202741787,0.0807258189,-0.1485061496,-0.2879565656,-0.2266209424,-0.0763427243,0.0713797435,-0.3917328715,0.1217708066,-0.2625131309,0.1892559826,0.0111224782,0.6011775136,0.2312677354,0.0353085808,0.0962717757,0.1675921679,0.0979729146,0.1538674533,0.0913398787,0.4108107686,0.0572190322,0.0587004013,0.0357715562,0.1198372245,0.0239027534,-0.5300848484,0.0398728959,0.4848031402,0.0172754433,0.2846191525,0.0506549031,-0.3280873299,-0.0911349952,0.2563597858,0.0343006924,0.4824927151,0.1402957737,-0.2031363547,-0.0206070263,-0.165212214,-0.1110758409,0.260269165,0.0133426962,0.5372642875,-0.1529492736,0.1179368421,0.0928597376,-0.2979283035,-0.5027729273,0.0765952989,0.4357075393,-0.1862333268,0.249039039,-0.252643317,-0.4363840818,-0.1391378045,-0.4521755278,-0.0466637239,-0.174242124,-0.1480227411,0.2751824856,0.3541748226,0.1759913266,0.0556834601,0.1341069043,-0.036357563,-0.0861927941,0.1157094687,-0.2787898779,-0.4251944125,-0.0438833944,0.3219062686,0.2654452622,-0.1445865333,-0.0429668278,0.0609350614,0.2248010486,-0.2154298723,0.1809117198,0.0597249344,0.3478369415,0.0290864334,0.1726853997,-0.6037281156,-0.4763202965,0.1973819137,-0.2548491061,-0.1514302343,0.2011635602,-0.1185197905,-0.2979263067,-0.1392681599,-0.0399456732,-0.5196630955,-0.2402305007,0.1384040415,0.0515491664,0.2625725269,0.4227849841,0.2467351109,0.2516053319,0.2856173217,0.2782490253,0.0416850634,-0.0272825621,0.206046313,-0.2266697288,-0.3076445162,0.1467666179,-0.1775440127,0.1536619216,0.0756456777,-0.5399981737,-0.3905145228,-0.0119577246,-0.1464466751,-0.1086419597,-0.1064012274,0.1131096035,0.1331327409,0.1270314604,0.0461503081,-0.2463925928,-0.1610321999,0.1056986079,0.0864791498,-0.0364234038,-0.0304755699,-0.209296912,0.5693787336,-0.4026015699,-0.2776995301,0.4051408768,-0.1440754384,0.2460667193,-0.089598462,-0.5039294362,0.0884152278,0.0083330488,-0.0451650918,0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/116","title":"\ud83d\udc1b Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323","comments":"Sure, [here is a Colab notebook](https:\/\/colab.research.google.com\/drive\/1uiS89fnHMG7HV_cYxp3r-_LqJQvNNKs9?usp=sharing) reproducing the error.\r\n\r\n> ArrowInvalid: Column 1 named references expected length 36 but got length 56","body":"I'm trying to use rouge metric.\r\n\r\nI have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.  \r\nI tried :\r\n\r\n```python\r\nimport nlp\r\n\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"test.pred.tokenized\") as p, open(\"test.gold.tokenized\") as g:\r\n    for lp, lg in zip(p, g):\r\n            rouge.add(lp, lg)\r\n```\r\n\r\nBut I meet following error :\r\n\r\n> pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323\r\n\r\n---\r\n\r\nFull stack-trace :\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 3, in <module>\r\n  File \"\/home\/me\/.venv\/transformers\/lib\/python3.6\/site-packages\/nlp\/metric.py\", line 224, in add\r\n    self.writer.write_batch(batch)\r\n  File \"\/home\/me\/.venv\/transformers\/lib\/python3.6\/site-packages\/nlp\/arrow_writer.py\", line 148, in write_batch\r\n    pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1550, in pyarrow.lib.Table.from_pydict\r\n  File \"pyarrow\/table.pxi\", line 1503, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323\r\n```\r\n\r\n(`nlp` installed from source)","comment_length":22,"text":"\ud83d\udc1b Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323 \n I'm trying to use rouge metric.\r\n\r\nI have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.  \r\nI tried :\r\n\r\n```python\r\nimport nlp\r\n\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"test.pred.tokenized\") as p, open(\"test.gold.tokenized\") as g:\r\n    for lp, lg in zip(p, g):\r\n            rouge.add(lp, lg)\r\n```\r\n\r\nBut I meet following error :\r\n\r\n> pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323\r\n\r\n---\r\n\r\nFull stack-trace :\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 3, in <module>\r\n  File \"\/home\/me\/.venv\/transformers\/lib\/python3.6\/site-packages\/nlp\/metric.py\", line 224, in add\r\n    self.writer.write_batch(batch)\r\n  File \"\/home\/me\/.venv\/transformers\/lib\/python3.6\/site-packages\/nlp\/arrow_writer.py\", line 148, in write_batch\r\n    pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1550, in pyarrow.lib.Table.from_pydict\r\n  File \"pyarrow\/table.pxi\", line 1503, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323\r\n```\r\n\r\n(`nlp` installed from source) \n Sure, [here is a Colab notebook](https:\/\/colab.research.google.com\/drive\/1uiS89fnHMG7HV_cYxp3r-_LqJQvNNKs9?usp=sharing) reproducing the error.\r\n\r\n> ArrowInvalid: Column 1 named references expected length 36 but got length 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/116","title":"\ud83d\udc1b Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323","comments":"This is because `add` takes as input a batch of elements and you provided only one. I think we should have `add` for one prediction\/reference and `add_batch` for a batch of predictions\/references. This would make it more coherent with the way we use Arrow.\r\n\r\nLet me do this change","body":"I'm trying to use rouge metric.\r\n\r\nI have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.  \r\nI tried :\r\n\r\n```python\r\nimport nlp\r\n\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"test.pred.tokenized\") as p, open(\"test.gold.tokenized\") as g:\r\n    for lp, lg in zip(p, g):\r\n            rouge.add(lp, lg)\r\n```\r\n\r\nBut I meet following error :\r\n\r\n> pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323\r\n\r\n---\r\n\r\nFull stack-trace :\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 3, in <module>\r\n  File \"\/home\/me\/.venv\/transformers\/lib\/python3.6\/site-packages\/nlp\/metric.py\", line 224, in add\r\n    self.writer.write_batch(batch)\r\n  File \"\/home\/me\/.venv\/transformers\/lib\/python3.6\/site-packages\/nlp\/arrow_writer.py\", line 148, in write_batch\r\n    pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1550, in pyarrow.lib.Table.from_pydict\r\n  File \"pyarrow\/table.pxi\", line 1503, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323\r\n```\r\n\r\n(`nlp` installed from source)","comment_length":49,"text":"\ud83d\udc1b Trying to use ROUGE metric : pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323 \n I'm trying to use rouge metric.\r\n\r\nI have to files : `test.pred.tokenized` and `test.gold.tokenized` with each line containing a sentence.  \r\nI tried :\r\n\r\n```python\r\nimport nlp\r\n\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"test.pred.tokenized\") as p, open(\"test.gold.tokenized\") as g:\r\n    for lp, lg in zip(p, g):\r\n            rouge.add(lp, lg)\r\n```\r\n\r\nBut I meet following error :\r\n\r\n> pyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323\r\n\r\n---\r\n\r\nFull stack-trace :\r\n\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 3, in <module>\r\n  File \"\/home\/me\/.venv\/transformers\/lib\/python3.6\/site-packages\/nlp\/metric.py\", line 224, in add\r\n    self.writer.write_batch(batch)\r\n  File \"\/home\/me\/.venv\/transformers\/lib\/python3.6\/site-packages\/nlp\/arrow_writer.py\", line 148, in write_batch\r\n    pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)\r\n  File \"pyarrow\/table.pxi\", line 1550, in pyarrow.lib.Table.from_pydict\r\n  File \"pyarrow\/table.pxi\", line 1503, in pyarrow.lib.Table.from_arrays\r\n  File \"pyarrow\/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n  File \"pyarrow\/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 1 named references expected length 534 but got length 323\r\n```\r\n\r\n(`nlp` installed from source) \n This is because `add` takes as input a batch of elements and you provided only one. I think we should have `add` for one prediction\/reference and `add_batch` for a batch of predictions\/references. This would make it more coherent with the way we use Arrow.\r\n\r\nLet me do this change","embeddings":[-0.0529837795,0.1234703138,-0.01561666,0.2224005014,0.2445526421,-0.2058066279,0.0348038748,0.2737767696,-0.2766898572,0.2284607589,-0.0470915511,0.5116131902,-0.0322659165,-0.5062631965,0.1793055087,-0.2050362378,0.0496256724,0.2373667061,0.3766706586,0.0053776717,-0.2605624497,0.3032194376,-0.1880754679,0.1640558392,-0.2273846716,-0.0277239792,-0.2081149369,-0.0000733137,-0.3736788034,-0.5136927962,-0.186548993,-0.1602295339,0.0929599479,0.2773670852,-0.0001235569,-0.1549440473,0.149187848,-0.0811496451,-0.0515882559,-0.2576403916,0.0698857605,-0.4877816439,0.2812328041,-0.3927665353,0.2623868883,-0.5535238981,-0.2693206966,0.120566301,0.3221309185,0.3828631043,0.0593123138,0.1814615875,0.1997821927,-0.1504540741,0.4799138904,-0.2872894108,0.027054552,0.8451249599,0.4745265245,-0.1043577269,0.0101642683,0.2115784734,0.0698365495,-0.008395087,0.4609832466,-0.0085368883,0.3220505416,0.2491591722,-0.1645759642,0.2763655186,-0.068574734,-0.45033741,-0.2426938415,-0.1303236336,0.2197299004,-0.8170561194,-0.2259736806,0.1794520617,-0.0250247847,-0.1299295276,-0.1462098211,-0.0006306816,-0.1770025939,0.3596486449,0.1693440825,-0.0147993406,-0.0118942177,0.1285102665,0.1847855151,-0.0393699519,-0.2734961808,0.2802458107,-0.3638677597,0.1397887319,-0.3178015053,0.1936397105,0.0414396375,0.0906878859,0.2250478268,-0.0171071608,0.4222173393,-0.13881585,0.0712493807,0.1222760528,-0.2013107091,0.6905140281,0.2165013701,0.1341722608,-0.3007668257,0.0094200037,0.0774845406,-0.2331835628,-0.1528362483,-0.4813553989,0.0929708183,0.2240232825,-0.0433338583,-0.0264970958,-0.3014723659,-0.0460714623,-0.3360155821,-0.1674297005,0.0867106766,0.3681385517,-0.0388477556,0.0624952391,0.1104905382,0.4761027694,-0.0439197794,-0.1354417205,-0.0921947658,0.2260674685,-0.5083327293,0.2136933655,0.0997615308,0.3832262158,0.1763621569,-0.073057346,0.2946143448,-0.3448928595,0.087014392,-0.1961714923,-0.0883425027,-0.2993743122,-0.0402428135,-0.0425539501,0.030005252,-0.4934621453,-0.0675721541,0.1587438285,-0.3589678109,-0.1973158568,-0.1149041355,0.0197511129,-0.0926990733,0.083531037,0.0243349411,0.2035319954,0.3013353646,-0.396302402,0.1792049855,-0.1033511907,-0.0777109861,-0.2220205814,0.1725850999,0.0104020853,-0.3534255326,-0.0505670086,0.4460203946,0.4284258485,0.219517529,0.5122495294,0.0170855112,0.4416695237,0.0511136688,-0.0548066832,0.8497806191,-0.6675328612,-0.2030715495,0.1031758562,-0.1517938375,-0.3849717081,0.244235605,-0.1403275728,0.0424472056,0.0017697939,0.5252922773,-0.1263539046,0.0111441053,-0.2413899004,-0.2935152948,0.1216426417,0.0770450234,-0.0832394958,-0.2776447237,-0.0424484909,-0.0174259041,-0.2651268244,0.315461129,-0.185546279,0.094031401,0.4362598062,-0.1067258939,0.1817872077,0.1525934935,-0.0213390924,-0.0370824598,-0.187252149,-0.7418581843,-0.1519334614,-0.3425426781,-0.3136158288,-0.3647011817,0.1320697665,0.3085981309,0.0722291172,-0.065481782,0.024303494,0.0326466747,0.0266355537,0.0122129973,-0.3676487803,-0.2076220512,0.0716270953,-0.1128770486,-0.1675752699,-0.1139455065,-0.3280251026,0.2027525008,0.5103162527,0.3298405707,0.1804865003,0.0027423869,0.3516777754,-0.1016335711,-0.0717662424,-0.1064506024,0.0596692003,0.1854149103,-0.2278652936,0.0522937067,-0.2333274633,0.2378190607,-0.2452935576,-0.1454570889,0.1176463068,-0.247784391,0.2879173756,-0.1668803096,-0.1577744037,0.0029124611,0.020116711,-0.2688775361,-0.1750835478,0.2672446668,0.2518250942,0.2535242438,-0.0183255989,0.023780413,-0.2061971873,0.4903864563,0.2489996552,0.0117215551,0.1085847393,-0.167279169,0.0327808559,-0.033713907,-0.458175391,0.2858690619,0.3511742353,-0.0713107288,-0.0535919406,-0.0323589295,-0.3229382336,-0.1007634327,0.1633617282,-0.0217885561,0.4516548514,0.4819218814,-0.0829909071,-0.0522987023,-0.2824621797,0.156576857,0.0922544301,-0.3211486638,0.1029351428,0.0322973467,-0.0499642231,-0.1294693947,-0.6580086946,0.1291842908,-0.1266931444,0.0177926142,-0.0792982504,0.1163699999,0.2354958206,0.3414893448,0.1112762541,0.1976393759,-0.0952695683,-0.0614368916,-0.3655001521,-0.0988830477,-0.0739974678,-0.0825133026,0.0339725949,0.1985394806,0.0282311291,0.0514330678,0.0575689711,-0.3675534427,-0.0267351288,-0.0360474586,0.103749834,0.1836600006,-0.1784429103,-0.3185208738,-0.0348143652,0.4909922183,0.1498887837,-0.0801961944,0.1976710111,-0.2899659276,-0.1214714423,-0.0277298242,-0.1028282419,0.0555745959,-0.6066274047,0.4826376438,0.2180045247,0.0196515191,0.1390763074,0.161531806,0.1337260157,0.1015784591,0.1502329856,0.188487187,0.2855364382,0.3695700467,-0.2453286499,-0.3526135981,-0.0996478871,-0.212758705,0.0100670941,-0.0160778034,0.0084737213,-0.3637005389,0.0287342574,0.1017535329,-0.1553876251,0.0992477313,0.3726509511,-0.1118815467,-0.0563923642,-0.2225678712,-0.0112460554,0.2974607646,-0.0985563323,0.338360697,-0.1027389243,0.3412115276,0.0712851882,0.3087331653,0.2600158751,-0.0898883343,0.2145285755,-0.2634678185,0.1250749379,0.0214319285,-0.0775774866,-0.1835475564,-0.0070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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/115","title":"AttributeError: 'dict' object has no attribute 'info'","comments":"I could access the info by first accessing the different splits :\r\n\r\n```python\r\nimport nlp\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\nprint(cnn_dm['train'].info)\r\n```\r\n\r\nInformation seems to be duplicated between the subsets :\r\n\r\n```python\r\nprint(cnn_dm[\"train\"].info == cnn_dm[\"test\"].info == cnn_dm[\"validation\"].info)\r\n# True\r\n```\r\n\r\nIs it expected ?","body":"I'm trying to access the information of CNN\/DM dataset :\r\n\r\n```python\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\nprint(cnn_dm.info)\r\n```\r\n\r\nreturns :\r\n\r\n> AttributeError: 'dict' object has no attribute 'info'","comment_length":42,"text":"AttributeError: 'dict' object has no attribute 'info' \n I'm trying to access the information of CNN\/DM dataset :\r\n\r\n```python\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\nprint(cnn_dm.info)\r\n```\r\n\r\nreturns :\r\n\r\n> AttributeError: 'dict' object has no attribute 'info' \n I could access the info by first accessing the different splits :\r\n\r\n```python\r\nimport nlp\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\nprint(cnn_dm['train'].info)\r\n```\r\n\r\nInformation seems to be duplicated between the subsets :\r\n\r\n```python\r\nprint(cnn_dm[\"train\"].info == cnn_dm[\"test\"].info == cnn_dm[\"validation\"].info)\r\n# True\r\n```\r\n\r\nIs it expected ?","embeddings":[0.1249497235,-0.4288868904,-0.0755954757,0.6906054616,0.0929051787,0.0969590843,0.269435823,0.0933502242,-0.0262442082,0.3825279176,-0.1993025392,0.2700487673,-0.0093511529,0.1719216257,-0.0433850512,-0.210740611,-0.1371984631,0.1066760346,0.0732271969,-0.2672348619,-0.2168749422,0.0030046206,-0.2907536328,0.0620870106,-0.243092075,-0.0518735573,-0.1551669389,0.2124351561,0.1969709694,-0.3359895647,0.068953611,-0.2129002362,-0.0541770272,0.1591783911,-0.0001246873,0.2159624696,0.3259490728,0.0212656278,-0.4290561676,-0.2216002643,-0.3981970549,-0.3013712764,0.3539854288,-0.4983978271,-0.0128480112,-0.2380619645,0.1509514451,-0.2423049957,0.4023640454,0.0351480581,0.0804313198,0.0912048742,-0.0358201005,0.1423137188,0.0212015919,0.2588219941,0.2370947301,0.1636524647,-0.2527903616,0.0627270564,0.1914429963,0.425129056,0.0440268442,0.3171995282,-0.1918286681,0.5741863251,0.1710591316,-0.2567908466,0.0959282145,0.2467696667,0.1706724763,-0.1224946007,-0.4816110134,-0.3179220855,0.1392760724,-0.2829391956,-0.0797850713,0.2207458317,-0.0475859083,0.1868588924,-0.2426729202,-0.0948003083,-0.2555106282,0.34411937,-0.3710780144,0.2993047535,-0.1208858192,0.2345955819,-0.1352393031,-0.0211658757,0.059795592,-0.0942324921,-0.0770025775,-0.0593116246,0.0755457431,-0.1603479981,-0.01686172,-0.3014565706,-0.3027259111,0.2690218389,0.0478206091,-0.1880151033,-0.0445899889,0.0476634391,0.4869711697,-0.000109636,-0.0382666774,0.6492273211,0.1455626488,-0.0843479037,-0.3494222462,-0.1001153514,-0.0615426525,0.3459921181,0.0822181925,0.0042108246,0.6258033514,-0.0217756461,-0.3278491497,-0.0295965802,-0.3862670362,-0.3342377543,-0.0254537836,0.3758757412,-0.2405271232,0.2571175992,-0.0810012892,0.1662137806,-0.1648440957,0.0016576174,-0.2084467858,0.1559554487,-0.2232463211,-0.0478444584,0.0609903559,0.1417697221,0.3283350468,-0.1531536877,0.137735337,-0.3344838917,0.0545238182,-0.1950105727,0.1769057661,0.272721231,0.1509708464,0.2214309722,0.3293368816,0.0471366122,-0.1581557691,0.0576178357,-0.2800487876,-0.393958509,0.0948265791,0.125705868,0.1640482694,0.1380668581,0.2096283287,0.2284054905,0.0393176824,-0.217241779,-0.086212799,0.1584498584,-0.2489687502,-0.3260108531,0.0923232064,0.3571796417,-0.3308548927,-0.1999840885,-0.1797043085,0.0452080742,0.2457810342,0.2800107002,-0.2833347917,0.0341763683,-0.0315481387,0.0204127356,0.5325303674,-0.5967894197,-0.4960958958,0.2557237446,-0.0120022874,0.0755669326,0.3176009953,0.2206098139,0.243834272,0.174723804,0.2448198795,0.3377783,0.0100636194,-0.2427791357,0.1311953366,-0.018845398,0.5743771195,0.024627924,-0.1827917993,0.1722548008,0.179322198,0.2157419771,0.3071216941,0.1184071898,-0.1560167372,-0.0024089026,-0.0152380494,0.0578808635,-0.0360682309,-0.2658931017,0.0435572006,0.1698504686,-0.0126617411,-0.1012088358,0.0056865183,-0.1509738266,-0.2884414792,0.0347993784,-0.2785722017,-0.491494,0.0957370102,0.2937062681,0.1070410609,0.0497993976,-0.3838412464,0.1931142658,-0.1739235818,0.0594511293,-0.2641113698,0.2616459131,-0.071247302,0.1353574991,-0.2566051781,0.561059773,0.2033924013,0.1142710522,0.0233576857,0.2034628838,0.2300905138,-0.0946370885,-0.1387583762,-0.1457545161,0.257663995,-0.4002567232,0.0830498561,0.1750844419,0.2735494971,-0.1525911093,0.0078616152,0.3907418847,0.3698183298,0.4349882603,0.2422243506,0.2169765234,0.0280832835,0.0046050744,0.142694205,-0.4672394693,-0.1400223374,-0.2043556273,0.1088456437,-0.1078538373,-0.3479730487,0.286169678,0.4567412138,0.0138227446,0.0307955127,-0.2911535203,-0.3108551204,-0.0372543037,-0.078284055,0.2733258009,0.4840683341,0.2343032658,0.0746500492,0.0855285451,-0.0124238692,0.0048267907,0.1198433116,0.1941518635,0.2420474291,0.1419252902,0.1857102364,0.0966679379,0.1877738088,-0.272166729,0.2104979455,0.3406482637,-0.3003823757,0.1629361212,-0.4099977314,-0.4880042076,0.0144571178,-0.3423974514,0.0018421698,-0.5070488453,-0.0561672412,-0.0347428843,0.1162312776,-0.0149056157,-0.0840855092,-0.1331908703,0.0325239599,0.2032119483,0.1661236584,-0.3341940045,-0.2673010528,-0.0231218804,-0.0567047298,0.2377941459,0.0999237671,0.0278999768,-0.189251259,0.0388244055,-0.084839575,0.2069585621,0.0250350237,0.2025088966,0.1381623596,0.2134179175,0.0887719393,-0.1765597314,0.1649943441,0.2929334342,-0.3445080519,0.2239868641,0.1372479945,-0.0419559702,0.0637377724,-0.3967239261,-0.6464790702,-0.2083216608,0.1116249487,-0.0307405274,0.3225349486,0.3150753677,-0.401143074,0.1219071299,0.0677896291,0.3995665014,-0.3273166418,0.0046430989,0.0294392891,-0.2837055326,-0.1603331566,-0.0964622349,-0.2722267807,0.1293747127,-0.0154739451,-0.4079187512,-0.2342712432,-0.3203280866,-0.2996292412,-0.0103180036,0.046045877,0.2568389177,-0.0417731553,-0.075642392,0.0562416837,-0.0911545753,0.0311829057,0.1106230691,-0.0718172714,-0.1088000983,0.0765248314,-0.0211603157,0.2974599302,0.3613356352,0.2205090523,-0.092190899,-0.3419997394,0.1614146084,0.1079079434,-0.6722040176,-0.1568453014,0.032436084,-0.1027038842,0.219003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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/115","title":"AttributeError: 'dict' object has no attribute 'info'","comments":"Good point @Colanim ! What happens under the hood when running:\r\n\r\n```python\r\nimport nlp\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\n```\r\n\r\nis that for every split in `cnn_dailymail`, a different dataset object (which all holds the same info) is created. This has the advantages that the datasets are easily separable in a training setup. \r\nAlso note that you can load e.g. only the `train` split of the dataset via:\r\n\r\n```python\r\ncnn_dm_train = nlp.load_dataset('cnn_dailymail', split=\"train\")\r\nprint(cnn_dm_train.info)\r\n```\r\n\r\nI think we should make the `info` object slightly different when creating the dataset for each split - at the moment it contains for example the variable `splits` which should maybe be renamed to `split` and contain only one `SplitInfo` object ...\r\n","body":"I'm trying to access the information of CNN\/DM dataset :\r\n\r\n```python\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\nprint(cnn_dm.info)\r\n```\r\n\r\nreturns :\r\n\r\n> AttributeError: 'dict' object has no attribute 'info'","comment_length":115,"text":"AttributeError: 'dict' object has no attribute 'info' \n I'm trying to access the information of CNN\/DM dataset :\r\n\r\n```python\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\nprint(cnn_dm.info)\r\n```\r\n\r\nreturns :\r\n\r\n> AttributeError: 'dict' object has no attribute 'info' \n Good point @Colanim ! What happens under the hood when running:\r\n\r\n```python\r\nimport nlp\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\n```\r\n\r\nis that for every split in `cnn_dailymail`, a different dataset object (which all holds the same info) is created. This has the advantages that the datasets are easily separable in a training setup. \r\nAlso note that you can load e.g. only the `train` split of the dataset via:\r\n\r\n```python\r\ncnn_dm_train = nlp.load_dataset('cnn_dailymail', split=\"train\")\r\nprint(cnn_dm_train.info)\r\n```\r\n\r\nI think we should make the `info` object slightly different when creating the dataset for each split - at the moment it contains for example the variable `splits` which should maybe be renamed to `split` and contain only one `SplitInfo` object ...\r\n","embeddings":[0.0860448629,-0.2287642211,-0.0386711396,0.5566271544,0.2393110842,0.0820070282,0.2878679335,0.2890189886,-0.009584601,0.1997731477,-0.026269732,0.3800343275,-0.2866004109,0.3110379577,-0.1329599321,-0.312898308,-0.0617810637,0.0819354728,0.0685543194,-0.1877292842,-0.2176921517,-0.0186453275,-0.2146317512,0.0927675217,-0.3512641191,-0.1529839337,-0.1275163889,0.1742342263,0.1626358628,-0.3590449393,0.2186698765,-0.0824093074,0.0676054358,0.1456326097,-0.0001200967,0.1501245797,0.3937638998,-0.0132749826,-0.4775072932,-0.1808106154,-0.3652683496,-0.4784733653,0.4104360938,-0.4280000329,0.0141701633,-0.1955828071,0.061412517,-0.2636317611,0.2931585908,0.3056741059,0.1423611492,-0.0766032413,0.0717008263,0.0712740049,-0.0116626862,0.2337168306,0.1357189268,0.0999801829,-0.1666555256,-0.1774171889,0.2367302626,0.3461387157,-0.057550244,0.2939850092,0.1243030056,0.5018519759,0.2756029963,-0.3079268336,-0.1120869219,0.2656952441,0.3015528619,-0.1667890847,-0.4109053612,-0.402497828,0.3017542958,-0.3622320592,-0.0417770557,0.205669567,-0.1510541141,0.1317649484,-0.1509331018,-0.162933141,-0.3153492808,0.4602555335,-0.1868041456,0.1968062371,-0.0741694197,0.2910074592,-0.1482135206,0.0615462661,0.1554133147,-0.004855847,-0.0361904167,-0.064291656,0.1266234815,-0.1908593476,0.0237800386,-0.3372946084,-0.230488047,0.1992776692,0.0300014876,-0.2213547528,-0.0144096715,0.1017793939,0.4677263796,0.0468038544,0.0606660657,0.543048203,0.0864747465,-0.0984051377,-0.3730103374,-0.09964858,-0.1022699848,0.2678650618,0.0410548337,0.0573246442,0.6547595263,0.1710321009,-0.2175796479,0.0475258753,-0.291619122,-0.1841300875,0.0196716096,0.4202069044,-0.3758158088,0.3166821003,-0.0023546722,0.2598299384,-0.1711176187,-0.1351026297,-0.1672390699,0.1883716136,-0.1644888967,-0.0454338044,0.0658375174,0.2751301527,0.1876869053,-0.1203981861,0.1502534896,-0.2108028531,0.0118785836,-0.1996326745,0.1468948424,0.2428472042,-0.0314818099,0.1773031205,0.3328693211,-0.1857574582,-0.2288423777,0.0898406729,-0.3860589862,-0.4279601276,-0.0504821837,0.1436499208,0.0643262267,0.001024521,0.1183593422,0.4320581853,-0.0101195741,-0.2594801784,-0.1091433689,0.0236568023,-0.2464594245,-0.329369843,0.1277022958,0.3755084276,-0.3210703731,-0.1966145337,-0.2238684297,0.0265125725,0.2153689116,0.0777888522,-0.294102788,0.3019200861,0.157410264,-0.1787673086,0.5169852972,-0.5471842885,-0.408711195,0.2692839503,0.072537303,0.1466144621,0.1654193103,0.1860263199,0.1700222194,0.1644580811,0.2275981903,0.5364189148,-0.1210606396,-0.1559235752,0.1025620252,-0.0822920352,0.4554801881,0.1035026759,-0.1737706363,0.3286602795,0.1283843666,0.3014736474,0.2027495503,0.0885989442,0.0090019833,-0.0317896008,0.0372528955,0.1113415211,-0.142565012,-0.129997924,-0.2095660865,0.1428060383,0.1349204779,0.15136531,-0.1725528538,-0.1128297523,-0.14405711,-0.034804374,-0.2341735959,-0.5324178338,0.1261230409,0.0717895478,-0.0386830643,0.0219994839,-0.4613384604,0.3799110055,-0.1166355982,0.074172087,-0.267467767,0.1598748565,0.024300117,0.0912194103,-0.2332624048,0.4407404959,0.0516265556,0.0769978166,-0.0225503724,0.2596832514,0.1651898474,-0.2301405966,-0.0918694288,0.0093692252,0.1660282463,-0.3179225326,0.1193693578,0.1209877506,0.2671662867,-0.1914663017,0.0119792027,0.3789295554,0.3295072913,0.4914184213,0.0843806192,0.2783165574,0.0613161698,-0.0651727021,0.1059273928,-0.3576053381,-0.1298234612,-0.2990133762,0.1558816433,-0.1758946329,-0.3190110028,0.0923191234,0.3692837954,0.1103923321,0.0295224693,-0.039108187,-0.2058977187,0.0333850421,-0.2275181413,0.3525831401,0.5222452879,0.2023171037,-0.1882100403,0.1882207245,-0.0132415779,0.0064639673,0.1345752031,0.2327063233,0.3685837388,0.0253146086,0.089261502,0.1285708994,0.2245946378,-0.5728698969,0.18870157,0.5563440323,-0.3044978976,0.1860845089,-0.1966259629,-0.426761061,-0.0486705042,-0.3421123028,-0.0448546223,-0.3559026122,-0.103885524,-0.1297439635,0.0746125579,-0.0035421327,-0.131395027,-0.0159502942,0.0439389907,-0.0229033977,0.1642870754,-0.443105191,-0.2144471407,-0.0498669632,0.045073919,0.1782763898,0.1592863947,-0.0440746322,-0.0607202239,0.1528818458,-0.048673138,0.0986357853,-0.0524646044,0.3286854029,0.2460146993,0.2065265924,0.0840366185,-0.2817554176,0.2767193615,0.0393044353,-0.1586167961,0.2133670747,0.136747092,0.1096086502,0.105488278,-0.5136440396,-0.62358284,-0.274356246,0.0848078653,-0.0427183509,0.3290720582,0.4674614072,-0.0300893597,0.079797782,0.1688726693,0.4912845492,-0.1182611957,-0.1812795103,0.0237383693,-0.3036913574,-0.1763626784,-0.1564724147,-0.2721151114,0.0525374189,-0.0739744976,-0.4470912814,-0.2999522984,-0.2285856009,-0.2480921298,-0.088008076,0.060194239,0.1543685943,0.0106969262,-0.0379680768,0.0280986261,-0.2343032211,0.0801368356,0.1232224703,-0.0749570727,0.01107259,0.1864682436,-0.1427345574,0.1945560277,0.1889092326,0.0911906809,-0.0227014516,-0.2615151703,0.1633200943,0.0860735402,-0.6142836809,-0.0744868666,0.0496811159,-0.1890935451,0.17656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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/38","title":"[Checksums] Error for some datasets","comments":"Fixed with 06882b4\r\n\r\nNow your command works :)\r\nNote that you can also do\r\n```\r\nnlp-cli test datasets\/nlp\/xnli --save_checksums\r\n```\r\nSo that it will save the checksums directly in the right directory.","body":"The checksums command works very nicely for `squad`. But for `crime_and_punish` and `xnli`, \r\nthe same bug happens:\r\n\r\nWhen running: \r\n```\r\npython nlp-cli nlp-cli test xnli --save_checksums\r\n```\r\n\r\nleads to:\r\n\r\n```\r\n  File \"nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/home\/patrick\/python_bin\/nlp\/commands\/test.py\", line 61, in run\r\n    ignore_checksums=self._ignore_checksums,\r\n  File \"\/home\/patrick\/python_bin\/nlp\/builder.py\", line 383, in download_and_prepare\r\n    self._download_and_prepare(dl_manager=dl_manager, download_config=download_config)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/builder.py\", line 627, in _download_and_prepare\r\n    dl_manager=dl_manager, max_examples_per_split=download_config.max_examples_per_split,\r\n  File \"\/home\/patrick\/python_bin\/nlp\/builder.py\", line 431, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/datasets\/xnli\/8bf4185a2da1ef2a523186dd660d9adcf0946189e7fa5942ea31c63c07b68a7f\/xnli.py\", line 95, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(_DATA_URL)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/utils\/download_manager.py\", line 246, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/patrick\/python_bin\/nlp\/utils\/download_manager.py\", line 186, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/utils\/download_manager.py\", line 166, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum(path)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/utils\/checksums_utils.py\", line 81, in get_size_checksum\r\n    with open(path, \"rb\") as f:\r\nTypeError: expected str, bytes or os.PathLike object, not tuple\r\n```\r\n","comment_length":32,"text":"[Checksums] Error for some datasets \n The checksums command works very nicely for `squad`. But for `crime_and_punish` and `xnli`, \r\nthe same bug happens:\r\n\r\nWhen running: \r\n```\r\npython nlp-cli nlp-cli test xnli --save_checksums\r\n```\r\n\r\nleads to:\r\n\r\n```\r\n  File \"nlp-cli\", line 33, in <module>\r\n    service.run()\r\n  File \"\/home\/patrick\/python_bin\/nlp\/commands\/test.py\", line 61, in run\r\n    ignore_checksums=self._ignore_checksums,\r\n  File \"\/home\/patrick\/python_bin\/nlp\/builder.py\", line 383, in download_and_prepare\r\n    self._download_and_prepare(dl_manager=dl_manager, download_config=download_config)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/builder.py\", line 627, in _download_and_prepare\r\n    dl_manager=dl_manager, max_examples_per_split=download_config.max_examples_per_split,\r\n  File \"\/home\/patrick\/python_bin\/nlp\/builder.py\", line 431, in _download_and_prepare\r\n    split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/datasets\/xnli\/8bf4185a2da1ef2a523186dd660d9adcf0946189e7fa5942ea31c63c07b68a7f\/xnli.py\", line 95, in _split_generators\r\n    dl_dir = dl_manager.download_and_extract(_DATA_URL)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/utils\/download_manager.py\", line 246, in download_and_extract\r\n    return self.extract(self.download(url_or_urls))\r\n  File \"\/home\/patrick\/python_bin\/nlp\/utils\/download_manager.py\", line 186, in download\r\n    self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/utils\/download_manager.py\", line 166, in _record_sizes_checksums\r\n    self._recorded_sizes_checksums[url] = get_size_checksum(path)\r\n  File \"\/home\/patrick\/python_bin\/nlp\/utils\/checksums_utils.py\", line 81, in get_size_checksum\r\n    with open(path, \"rb\") as f:\r\nTypeError: expected str, bytes or os.PathLike object, not tuple\r\n```\r\n \n Fixed with 06882b4\r\n\r\nNow your command works :)\r\nNote that you can also do\r\n```\r\nnlp-cli test datasets\/nlp\/xnli --save_checksums\r\n```\r\nSo that it will save the checksums directly in the right directory.","embeddings":[-0.0627012923,0.4912028015,0.0221911166,0.0352558196,0.1421761513,-0.1060484871,0.2264277935,0.5860058665,0.0751720741,-0.0241698995,-0.1206225306,0.3043448329,0.1317296326,-0.1162971184,-0.0193382669,0.267116785,0.1346074939,0.1131177247,0.1047997475,-0.1083303615,-0.1602604091,0.0813890547,-0.0319696777,-0.053366188,-0.1634702533,0.0185005739,0.0922335386,0.1823868304,-0.2103388906,-0.3178810775,0.1828085035,0.2279720455,0.1180133522,0.1371126771,-0.0001219498,-0.0516471677,0.3258031607,-0.0858075172,-0.3152633905,0.0974388048,-0.2606828809,-0.3950639367,-0.0185791161,-0.4255894721,0.37205109,0.3203902245,0.0684392527,-0.0244946051,0.2928688824,0.2679957747,0.1374267936,0.5577475429,-0.1816007644,0.0434500761,0.1389746815,0.0707661435,-0.1192683354,0.2082669735,0.2688108087,-0.0789423734,0.1626288891,0.148846224,-0.3349234462,-0.3724066317,-0.0468121246,-0.1594828665,0.4003409147,-0.0175617374,-0.1278445572,0.2270002663,-0.3293230832,-0.2747108936,-0.2557941675,-0.1852754354,0.1262196153,-0.4058308303,0.3126735985,0.3615929782,-0.1322477013,-0.3856508434,-0.2356782705,0.3625898659,0.1786392778,0.1741923839,0.2103234828,0.3407019973,0.260371536,0.1711850762,0.2275183797,-0.0792823508,-0.217236042,0.0797711089,-0.2267632335,0.1732077897,-0.7504251003,0.0182806868,0.1907794774,0.1760831624,0.198428303,0.4816954732,0.195856899,-0.10183236,0.066376932,0.2116571963,0.0479924604,0.1656826884,0.5243514776,0.3478689194,0.4528412521,0.1817325801,0.0125049716,0.111005716,0.1427177042,-0.0208090693,0.0522172265,0.2191821039,-0.1132478863,-0.2716455162,-0.4418676496,0.1631049812,0.1084636375,-0.1463720649,0.470731169,0.2857308388,0.068439275,-0.0357059985,-0.1219876334,0.4323750138,-0.099094063,-0.100993596,-0.1348179877,-0.1447826028,-0.2988794148,-0.0133951716,0.3063559234,0.0413430072,0.2411156595,-0.1284284443,0.1660990715,-0.0986437798,-0.0269741975,-0.1339724809,-0.219033137,0.1573349684,0.0276565365,-0.1535784751,0.0760046393,-0.1846380979,-0.1296412945,0.1298684925,-0.1619165987,0.1030448377,0.0288048442,0.1467627883,-0.3563662469,-0.0005756225,0.2564969957,-0.3233272135,0.3759012222,-0.3198791146,0.1601622552,-0.1939737648,-0.0694036558,-0.2678243518,0.0827553272,0.1281574517,-0.0813919008,0.0126430159,-0.1524866223,-0.2828461528,0.2618383467,0.3076504171,-0.0190558992,0.1066120788,-0.2534818649,0.3117203414,0.6236253381,-0.5391952395,-0.3489595354,-0.103822574,0.2453111261,-0.2230147272,0.0535941944,0.191665262,0.2072511315,-0.3625619411,-0.4169750512,0.2400106043,0.0361243151,0.2027406842,-0.2288201153,-0.0826072171,0.2481996417,0.0433604345,-0.0329150036,-0.0492722131,0.1717432737,0.1826472133,0.2804702818,-0.151297465,0.1541961581,-0.0797671005,0.3585596979,-0.0942279845,0.0306893755,-0.1185662597,-0.2086607218,0.1897599101,-0.6152535081,0.1698651761,-0.0682334527,-0.1599045098,-0.2258606553,-0.076962322,-0.1803464442,0.099957794,0.1058685854,0.2672636807,0.2386163771,0.0891887695,0.006184604,0.1085713282,-0.6231620908,0.0500973426,-0.1666647792,0.3818546236,-0.109388046,-0.4351024926,-0.0777804404,0.3842042089,-0.0393123589,0.211449638,-0.2845964134,0.4408490956,0.0782611892,0.0158712585,0.0451843925,0.1725321412,0.0855831727,-0.216851294,0.0500729978,0.6702576876,0.2047982663,-0.0931499153,-0.1812061667,0.1928213537,-0.0809176266,0.0449230187,-0.0232682955,0.0919856131,0.2223759443,-0.2604334354,-0.2387338877,-0.2169543505,0.1933977008,0.2466154695,0.3714800179,-0.0275688227,0.0609269477,-0.0080922544,0.2202794701,-0.0130476607,0.1423615962,0.0530194193,0.4494906366,0.0485340543,-0.1523383409,0.4089898765,0.440344274,-0.047346212,-0.0382508226,-0.0022969323,-0.1980335712,-0.1626869887,0.0150017915,-0.1399281025,0.1665215641,0.2899983823,0.2119664103,0.0607976951,-0.1362030357,-0.0183633827,-0.0110555505,0.2668966353,-0.2982129753,-0.064503096,-0.3084881604,-0.467023313,-0.1548491269,0.0802347362,-0.4626788795,-0.4291225076,0.038176991,-0.091297403,-0.2140588909,0.1140495762,-0.3381961882,0.1988053173,0.0887017027,0.1420766413,0.3029599488,-0.2489279211,-0.1107928976,-0.0527097583,0.4460258484,0.216684252,0.4916332066,-0.1002859771,-0.1132466942,-0.1128514335,-0.3865994513,0.0523086786,-0.1023961753,0.1059584245,0.3008179963,-0.0112966755,-0.3379214704,-0.3600876331,0.3471969962,-0.1742161661,-0.1782204956,0.3479773402,0.0316541046,-0.0400286242,-0.2230281532,0.1262023896,-0.1741138548,-0.3688487113,-0.0119620208,0.0021293128,0.2077801377,0.1534917057,-0.2540101111,0.2515192032,-0.2722303271,0.1960293502,-0.0344826356,-0.2537587285,0.218549028,-0.103799589,-0.5492884517,-0.2848454118,0.0491536111,0.0365566164,-0.0146186268,-0.3372529447,-0.0577521324,-0.2105458826,0.1740269214,0.3083756566,0.0900353342,0.1295183897,-0.2349826396,0.054937344,-0.3663519621,-0.0224972423,0.2733776867,0.0242557637,0.6932616234,-0.104063876,-0.0190503821,0.0218197126,-0.1689798832,0.5697202682,0.0551941022,0.4835469127,0.2954323292,0.2614176571,0.1312999725,-0.1737048477,0.3005037308,0.2075375319,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/6","title":"Error when citation is not given in the DatasetInfo","comments":"Yes looks good to me.\r\nNote that we may refactor quite strongly the `info.py` to make it a lot simpler (it's very complicated for basically a dictionary of info I think)","body":"The following error is raised when the `citation` parameter is missing when we instantiate a `DatasetInfo`:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/info.py\", line 338, in __repr__\r\n    citation_pprint = _indent('\"\"\"{}\"\"\"'.format(self.citation.strip()))\r\nAttributeError: 'NoneType' object has no attribute 'strip'\r\n```\r\n\r\nI propose to do the following change in the `info.py` file. The method:\r\n```python\r\ndef __repr__(self):\r\n        splits_pprint = _indent(\"\\n\".join([\"{\"] + [\r\n                \"    '{}': {},\".format(k, split.num_examples)\r\n                for k, split in sorted(self.splits.items())\r\n        ] + [\"}\"]))\r\n        features_pprint = _indent(repr(self.features))\r\n        citation_pprint = _indent('\"\"\"{}\"\"\"'.format(self.citation.strip()))\r\n        return INFO_STR.format(\r\n                name=self.name,\r\n                version=self.version,\r\n                description=self.description,\r\n                total_num_examples=self.splits.total_num_examples,\r\n                features=features_pprint,\r\n                splits=splits_pprint,\r\n                citation=citation_pprint,\r\n                homepage=self.homepage,\r\n                supervised_keys=self.supervised_keys,\r\n                # Proto add a \\n that we strip.\r\n                license=str(self.license).strip())\r\n```\r\nBecomes:\r\n```python\r\ndef __repr__(self):\r\n        splits_pprint = _indent(\"\\n\".join([\"{\"] + [\r\n                \"    '{}': {},\".format(k, split.num_examples)\r\n                for k, split in sorted(self.splits.items())\r\n        ] + [\"}\"]))\r\n        features_pprint = _indent(repr(self.features))\r\n        ## the strip is done only is the citation is given\r\n        citation_pprint = self.citation\r\n\r\n        if self.citation:\r\n            citation_pprint = _indent('\"\"\"{}\"\"\"'.format(self.citation.strip()))\r\n        return INFO_STR.format(\r\n                name=self.name,\r\n                version=self.version,\r\n                description=self.description,\r\n                total_num_examples=self.splits.total_num_examples,\r\n                features=features_pprint,\r\n                splits=splits_pprint,\r\n                citation=citation_pprint,\r\n                homepage=self.homepage,\r\n                supervised_keys=self.supervised_keys,\r\n                # Proto add a \\n that we strip.\r\n                license=str(self.license).strip())\r\n```\r\nAnd now it is ok. @thomwolf are you ok with this fix?","comment_length":31,"text":"Error when citation is not given in the DatasetInfo \n The following error is raised when the `citation` parameter is missing when we instantiate a `DatasetInfo`:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/info.py\", line 338, in __repr__\r\n    citation_pprint = _indent('\"\"\"{}\"\"\"'.format(self.citation.strip()))\r\nAttributeError: 'NoneType' object has no attribute 'strip'\r\n```\r\n\r\nI propose to do the following change in the `info.py` file. The method:\r\n```python\r\ndef __repr__(self):\r\n        splits_pprint = _indent(\"\\n\".join([\"{\"] + [\r\n                \"    '{}': {},\".format(k, split.num_examples)\r\n                for k, split in sorted(self.splits.items())\r\n        ] + [\"}\"]))\r\n        features_pprint = _indent(repr(self.features))\r\n        citation_pprint = _indent('\"\"\"{}\"\"\"'.format(self.citation.strip()))\r\n        return INFO_STR.format(\r\n                name=self.name,\r\n                version=self.version,\r\n                description=self.description,\r\n                total_num_examples=self.splits.total_num_examples,\r\n                features=features_pprint,\r\n                splits=splits_pprint,\r\n                citation=citation_pprint,\r\n                homepage=self.homepage,\r\n                supervised_keys=self.supervised_keys,\r\n                # Proto add a \\n that we strip.\r\n                license=str(self.license).strip())\r\n```\r\nBecomes:\r\n```python\r\ndef __repr__(self):\r\n        splits_pprint = _indent(\"\\n\".join([\"{\"] + [\r\n                \"    '{}': {},\".format(k, split.num_examples)\r\n                for k, split in sorted(self.splits.items())\r\n        ] + [\"}\"]))\r\n        features_pprint = _indent(repr(self.features))\r\n        ## the strip is done only is the citation is given\r\n        citation_pprint = self.citation\r\n\r\n        if self.citation:\r\n            citation_pprint = _indent('\"\"\"{}\"\"\"'.format(self.citation.strip()))\r\n        return INFO_STR.format(\r\n                name=self.name,\r\n                version=self.version,\r\n                description=self.description,\r\n                total_num_examples=self.splits.total_num_examples,\r\n                features=features_pprint,\r\n                splits=splits_pprint,\r\n                citation=citation_pprint,\r\n                homepage=self.homepage,\r\n                supervised_keys=self.supervised_keys,\r\n                # Proto add a \\n that we strip.\r\n                license=str(self.license).strip())\r\n```\r\nAnd now it is ok. @thomwolf are you ok with this fix? \n Yes looks good to me.\r\nNote that we may refactor quite strongly the `info.py` to make it a lot simpler (it's very complicated for basically a dictionary of info I think)","embeddings":[0.2855120897,0.0732322857,0.1477971971,0.1601161659,0.2827774882,0.2352983654,0.2543569803,0.4805189073,-0.4466493428,0.16624026,0.403686285,0.3917041719,0.0535713695,-0.203330785,-0.0758434907,-0.3508134186,-0.1062086448,0.2190288305,0.3720492125,-0.0522521213,-0.3091630936,0.1654009521,-0.3137940764,-0.0903745443,-0.2572643757,-0.1726606786,-0.2614687681,0.2065153718,-0.3102674484,-0.5589748025,0.1857405305,0.063550368,0.0325638615,0.0146645615,-0.0001185848,0.0302900337,0.3959799111,-0.1021313891,-0.2354656905,-0.2480834574,-0.5433374643,-0.2237135321,-0.029977249,-0.3961987495,-0.1640129983,-0.0125304721,-0.1367189437,0.1232259646,0.1175461933,0.2436022162,0.1624839008,0.0612453744,0.0864233896,-0.3654048443,0.0225184187,0.4103301764,-0.0947481841,-0.2211684287,-0.0262204297,0.0773107931,0.0846517608,0.2534255385,-0.196715951,-0.1238166988,0.4814615846,-0.0951773897,-0.0258708559,-0.0744219944,-0.0097684907,0.1676390171,0.5024431944,-0.2813969254,-0.5132006407,-0.0163041782,0.2781536281,-0.0212765168,-0.0074698143,0.0304121226,-0.0812080204,0.0895343795,0.2815128863,-0.2978960872,-0.0270233434,-0.1198625565,0.0443734415,0.2116338611,0.0791531727,0.1897022575,0.006169613,-0.1191703305,-0.1341097206,-0.0683485717,-0.2433660775,0.1042299494,-0.0947714001,-0.2623224258,0.3013670146,-0.1240373775,0.3263944983,0.2747790217,-0.0126671297,-0.3645196557,0.1062225774,0.1398010999,0.2596132457,0.0484170578,-0.0095627923,0.2912044525,0.3808363378,-0.2724874616,-0.0530267395,-0.0224923305,0.1467389613,-0.0760321692,0.1871124804,0.3163543642,0.6639338136,-0.2459217012,-0.2278142571,0.0181359351,-0.4110317528,0.0834341347,-0.0681851432,0.1200495139,0.0531694703,0.1811803877,0.0788510516,0.1461924911,0.0442111008,-0.3349076211,-0.3145993948,0.2601854205,-0.141174674,-0.1581093818,0.0291049238,0.3195924461,0.3016165793,0.0878599286,-0.2863543034,-0.1176711768,0.0519980602,-0.3555636108,-0.15103136,0.2481955886,-0.1378612816,0.0533519797,0.3309005797,-0.3365867436,-0.207349807,0.5098431706,-0.2286172807,-0.3707093,-0.1757764667,0.1305076778,-0.2939586639,-0.1857750118,-0.0267878566,0.1469056308,0.0339358598,-0.2264343053,-0.1586720645,-0.1596159935,-0.1011698768,-0.1808520108,0.210424751,0.7028290629,-0.2065771967,-0.2376949489,-0.1792689413,0.0066007464,0.3217360973,0.0637401119,-0.2051242739,0.4242260456,-0.1891367584,0.309779495,0.4551816881,-0.5058348179,-0.264592886,0.0924514309,-0.090154022,-0.1495330036,0.3402810097,0.0750064477,0.3930941522,-0.3331330121,0.1752074659,0.1514261812,-0.1647031307,-0.0128044328,-0.2054263502,-0.025216924,0.2687395513,-0.0305352192,0.1777623147,0.4301315248,0.026665451,0.23543787,0.0071865981,-0.0845381394,0.09592361,0.6712453961,0.4971642196,-0.0815782472,0.0407697707,0.0891511142,0.1425515115,-0.0065857228,-0.0049517527,0.5135893822,-0.4911940694,-0.3580025136,-0.2441371828,-0.0477071702,0.0384993032,-0.128395915,0.0801430494,0.1488384604,0.2277501523,0.153040424,-0.1139558703,0.0341832377,-0.3096472323,0.1729861945,-0.2149274796,0.0709076151,-0.0962565392,-0.1759302914,0.2825001478,0.4030955136,0.3565137982,0.1395501345,-0.2577136159,0.2078028768,0.3365240097,-0.0840731561,0.3318354785,-0.0264528077,0.2214430571,-0.5601446629,-0.106444627,0.1312949508,0.2259838134,-0.0394202173,-0.1677945852,0.1209984645,-0.0971575826,0.1369831562,0.0983100757,0.2548840344,0.2972361147,-0.1475794017,-0.2540400028,-0.1345717907,-0.4073702097,0.3577827811,0.0442956835,0.1807568073,-0.3323710263,-0.06579981,0.2814854383,0.0483954698,-0.083436124,0.0813504905,-0.2417711169,-0.0494092368,0.0345413573,0.504362762,0.558821857,0.2867323756,-0.1355875134,0.0329405963,0.0807511434,0.0325014032,0.1965010613,0.172426492,0.0140458588,0.1181470826,0.0886255354,0.264751792,-0.0481837392,-0.3537189364,0.2952138186,0.4577527642,-0.2807684243,-0.150439322,-0.3010823727,-0.3772722483,-0.2303933054,-0.1566966474,0.1558211148,-0.2895430624,-0.1114748344,0.0278519038,-0.226184845,0.2793827951,-0.2176556587,0.0751801282,0.1010244191,-0.2260205597,-0.0099929068,-0.1933918744,0.0566482544,-0.0408326052,-0.0482504629,0.2714774609,0.2608515024,0.0312365536,0.0936894119,-0.4852143526,-0.499157995,0.185065195,-0.2016006261,0.2247467786,0.3787457943,0.254168421,-0.056391865,-0.1754277796,0.3002544045,0.1890562177,-0.0711904615,0.2082998604,-0.2221645266,-0.1809340864,-0.1976263523,-0.4478684068,-0.1524800062,-0.3519616425,-0.324232161,0.0325689949,-0.1595597714,0.5114241242,-0.0376598351,0.107879214,0.4586960673,0.3906828761,0.0442582294,0.0350523777,0.3263026476,-0.1154825166,-0.3304879963,0.1488804519,-0.2420458794,0.050730776,0.1023525372,-0.1610190421,-0.4054076076,0.085415177,0.2321101427,-0.0968379304,0.2345744967,0.3909744322,0.2886578441,-0.1030012518,-0.1418347955,-0.2641219795,0.0721986815,0.0509948917,0.2560450733,0.161925137,0.1512395144,-0.0068999906,0.3923106492,0.1909050792,0.0996040404,0.221127972,-0.0202333014,0.1393013299,0.0169179533,-0.0862640515,-0.037109077,0.2619738877,-0.1297571361,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/5","title":"ValueError when a split is empty","comments":"To fix this I propose to modify only the file `arrow_reader.py` with few updates. First update, the following method:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n        name,\r\n        name2len,\r\n        absolute_instructions,\r\n):\r\n    \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n    # For each split, return the files instruction (skip\/take)\r\n    file_instructions = []\r\n    num_examples = 0\r\n    for abs_instr in absolute_instructions:\r\n        length = name2len[abs_instr.splitname]\r\n        if not length:\r\n            raise ValueError(\r\n                    'Split empty. This might means that dataset hasn\\'t been generated '\r\n                    'yet and info not restored from GCS, or that legacy dataset is used.')\r\n        filename = filename_for_dataset_split(\r\n                dataset_name=name,\r\n                split=abs_instr.splitname,\r\n                filetype_suffix='arrow')\r\n        from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n        to = length if abs_instr.to is None else abs_instr.to\r\n        num_examples += to - from_\r\n        single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n        file_instructions.extend(single_file_instructions)\r\n    return FileInstructions(\r\n            num_examples=num_examples,\r\n            file_instructions=file_instructions,\r\n    )\r\n```\r\nBecomes:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n        name,\r\n        name2len,\r\n        absolute_instructions,\r\n):\r\n    \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n    # For each split, return the files instruction (skip\/take)\r\n    file_instructions = []\r\n    num_examples = 0\r\n    for abs_instr in absolute_instructions:\r\n        length = name2len[abs_instr.splitname]\r\n        ## Delete the if not length and the raise\r\n        filename = filename_for_dataset_split(\r\n                dataset_name=name,\r\n                split=abs_instr.splitname,\r\n                filetype_suffix='arrow')\r\n        from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n        to = length if abs_instr.to is None else abs_instr.to\r\n        num_examples += to - from_\r\n        single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n        file_instructions.extend(single_file_instructions)\r\n    return FileInstructions(\r\n            num_examples=num_examples,\r\n            file_instructions=file_instructions,\r\n    )\r\n```\r\n\r\nSecond update the following method:\r\n```python\r\ndef _read_files(files, info):\r\n    \"\"\"Returns Dataset for given file instructions.\r\n\r\n    Args:\r\n        files: List[dict(filename, skip, take)], the files information.\r\n            The filenames contain the absolute path, not relative.\r\n            skip\/take indicates which example read in the file: `ds.slice(skip, take)`\r\n    \"\"\"\r\n    pa_batches = []\r\n    for f_dict in files:\r\n        pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n        pa_batches.extend(pa_table.to_batches())\r\n    pa_table = pa.Table.from_batches(pa_batches)\r\n    ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n    return ds\r\n```\r\nBecomes:\r\n```python\r\ndef _read_files(files, info):\r\n    \"\"\"Returns Dataset for given file instructions.\r\n\r\n    Args:\r\n        files: List[dict(filename, skip, take)], the files information.\r\n            The filenames contain the absolute path, not relative.\r\n            skip\/take indicates which example read in the file: `ds.slice(skip, take)`\r\n    \"\"\"\r\n    pa_batches = []\r\n    for f_dict in files:\r\n        pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n        pa_batches.extend(pa_table.to_batches())\r\n    ## we modify the table only if there are some batches\r\n    if pa_batches:\r\n        pa_table = pa.Table.from_batches(pa_batches)\r\n    ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n    return ds\r\n```\r\n\r\nWith these two updates it works now. @thomwolf are you ok with this changes?","body":"When a split is empty either TEST, VALIDATION or TRAIN I get the following error:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/load.py\", line 295, in load\r\n    ds = dbuilder.as_dataset(**as_dataset_kwargs)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 587, in as_dataset\r\n    datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 158, in map_nested\r\n    for k, v in data_struct.items()\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 158, in <dictcomp>\r\n    for k, v in data_struct.items()\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 601, in _build_single_dataset\r\n    split=split,\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 625, in _as_dataset\r\n    split_infos=self.info.splits.values(),\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 200, in read\r\n    return py_utils.map_nested(_read_instruction_to_ds, instructions)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 191, in _read_instruction_to_ds\r\n    file_instructions = make_file_instructions(name, split_infos, instruction)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 104, in make_file_instructions\r\n    absolute_instructions=absolute_instructions,\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 122, in _make_file_instructions_from_absolutes\r\n    'Split empty. This might means that dataset hasn\\'t been generated '\r\nValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.\r\n``` \r\n\r\nHow to reproduce:\r\n```python\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n\r\n    def __init__(self, **config):\r\n        self.train = config.pop(\"train\", None)\r\n        self.validation = config.pop(\"validation\", None)\r\n        super(Bbc, self).__init__(**config)\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, description=\"bla\", features=nlp.features.FeaturesDict({\"id\": nlp.int32, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": self.train}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": self.validation}),\r\n                nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={\"filepath\": None})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        if not filepath:\r\n            return None, {}\r\n\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"id\": idx, \"text\": line[1], \"label\": line[0]}\r\n```\r\n\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc\/data\/train.csv\", \"validation\": \"bbc\/data\/test.csv\"})\r\n```","comment_length":379,"text":"ValueError when a split is empty \n When a split is empty either TEST, VALIDATION or TRAIN I get the following error:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/load.py\", line 295, in load\r\n    ds = dbuilder.as_dataset(**as_dataset_kwargs)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 587, in as_dataset\r\n    datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 158, in map_nested\r\n    for k, v in data_struct.items()\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 158, in <dictcomp>\r\n    for k, v in data_struct.items()\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 601, in _build_single_dataset\r\n    split=split,\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 625, in _as_dataset\r\n    split_infos=self.info.splits.values(),\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 200, in read\r\n    return py_utils.map_nested(_read_instruction_to_ds, instructions)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 191, in _read_instruction_to_ds\r\n    file_instructions = make_file_instructions(name, split_infos, instruction)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 104, in make_file_instructions\r\n    absolute_instructions=absolute_instructions,\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 122, in _make_file_instructions_from_absolutes\r\n    'Split empty. This might means that dataset hasn\\'t been generated '\r\nValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.\r\n``` \r\n\r\nHow to reproduce:\r\n```python\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n\r\n    def __init__(self, **config):\r\n        self.train = config.pop(\"train\", None)\r\n        self.validation = config.pop(\"validation\", None)\r\n        super(Bbc, self).__init__(**config)\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, description=\"bla\", features=nlp.features.FeaturesDict({\"id\": nlp.int32, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": self.train}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": self.validation}),\r\n                nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={\"filepath\": None})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        if not filepath:\r\n            return None, {}\r\n\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"id\": idx, \"text\": line[1], \"label\": line[0]}\r\n```\r\n\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc\/data\/train.csv\", \"validation\": \"bbc\/data\/test.csv\"})\r\n``` \n To fix this I propose to modify only the file `arrow_reader.py` with few updates. First update, the following method:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n        name,\r\n        name2len,\r\n        absolute_instructions,\r\n):\r\n    \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n    # For each split, return the files instruction (skip\/take)\r\n    file_instructions = []\r\n    num_examples = 0\r\n    for abs_instr in absolute_instructions:\r\n        length = name2len[abs_instr.splitname]\r\n        if not length:\r\n            raise ValueError(\r\n                    'Split empty. This might means that dataset hasn\\'t been generated '\r\n                    'yet and info not restored from GCS, or that legacy dataset is used.')\r\n        filename = filename_for_dataset_split(\r\n                dataset_name=name,\r\n                split=abs_instr.splitname,\r\n                filetype_suffix='arrow')\r\n        from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n        to = length if abs_instr.to is None else abs_instr.to\r\n        num_examples += to - from_\r\n        single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n        file_instructions.extend(single_file_instructions)\r\n    return FileInstructions(\r\n            num_examples=num_examples,\r\n            file_instructions=file_instructions,\r\n    )\r\n```\r\nBecomes:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n        name,\r\n        name2len,\r\n        absolute_instructions,\r\n):\r\n    \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n    # For each split, return the files instruction (skip\/take)\r\n    file_instructions = []\r\n    num_examples = 0\r\n    for abs_instr in absolute_instructions:\r\n        length = name2len[abs_instr.splitname]\r\n        ## Delete the if not length and the raise\r\n        filename = filename_for_dataset_split(\r\n                dataset_name=name,\r\n                split=abs_instr.splitname,\r\n                filetype_suffix='arrow')\r\n        from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n        to = length if abs_instr.to is None else abs_instr.to\r\n        num_examples += to - from_\r\n        single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n        file_instructions.extend(single_file_instructions)\r\n    return FileInstructions(\r\n            num_examples=num_examples,\r\n            file_instructions=file_instructions,\r\n    )\r\n```\r\n\r\nSecond update the following method:\r\n```python\r\ndef _read_files(files, info):\r\n    \"\"\"Returns Dataset for given file instructions.\r\n\r\n    Args:\r\n        files: List[dict(filename, skip, take)], the files information.\r\n            The filenames contain the absolute path, not relative.\r\n            skip\/take indicates which example read in the file: `ds.slice(skip, take)`\r\n    \"\"\"\r\n    pa_batches = []\r\n    for f_dict in files:\r\n        pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n        pa_batches.extend(pa_table.to_batches())\r\n    pa_table = pa.Table.from_batches(pa_batches)\r\n    ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n    return ds\r\n```\r\nBecomes:\r\n```python\r\ndef _read_files(files, info):\r\n    \"\"\"Returns Dataset for given file instructions.\r\n\r\n    Args:\r\n        files: List[dict(filename, skip, take)], the files information.\r\n            The filenames contain the absolute path, not relative.\r\n            skip\/take indicates which example read in the file: `ds.slice(skip, take)`\r\n    \"\"\"\r\n    pa_batches = []\r\n    for f_dict in files:\r\n        pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n        pa_batches.extend(pa_table.to_batches())\r\n    ## we modify the table only if there are some batches\r\n    if pa_batches:\r\n        pa_table = pa.Table.from_batches(pa_batches)\r\n    ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n    return ds\r\n```\r\n\r\nWith these two updates it works now. @thomwolf are you ok with this changes?","embeddings":[-0.2972342372,-0.0131540569,-0.103067942,0.2931129634,0.0207595415,-0.0219010543,0.6005375385,0.4749840498,-0.0488540977,0.4204655886,0.0286984555,0.2306589633,-0.3276998699,0.1830051839,-0.4715830386,-0.2210291326,-0.0620970726,0.4033246934,0.0316321403,0.0022019364,-0.2077705711,0.1781739295,-0.3489648104,0.2001751065,-0.4412805736,-0.3235142529,-0.0962038264,0.0336118527,-0.1655961424,-0.5333554745,0.2795565426,-0.315582782,-0.2108621746,0.4383441806,-0.0001089997,0.2471223772,0.3216657937,0.0212966055,-0.2349680364,-0.1730071604,-0.2148883939,0.0468612611,-0.0701180547,-0.1868547648,-0.1008067131,-0.2197800577,-0.3655616343,0.0298773181,0.2869431078,0.2550378144,0.1966028512,0.1572323292,0.0249177329,-0.1763806641,0.0856723338,-0.105694443,0.0970320776,0.0854190886,-0.1803481877,-0.2269463241,-0.1018456817,0.2128955573,-0.0124384062,0.0009043886,-0.2380745709,0.1348021328,0.2368194163,-0.3553718626,-0.1338815093,0.0872519761,-0.0105639342,-0.0024150251,-0.1980995387,-0.3114365339,0.0216911975,-0.3575952351,0.0597179197,0.2921065986,-0.1286965311,0.124095358,0.228433311,0.1736139357,-0.2168529481,-0.0249617305,-0.3034926653,0.3541932404,-0.031331487,0.0348216146,0.2476863563,-0.0247122962,0.0857289061,-0.0367395617,-0.218579337,0.115537703,-0.2246417701,-0.3038747907,-0.1532901824,-0.3125609457,-0.0919000655,-0.0317804776,0.027630873,-0.1767440885,0.1064907983,0.4088112414,0.304340452,-0.0534912385,0.3710521758,0.4308705032,0.0564235486,0.1503341496,0.0145205986,0.02915808,0.0300026834,-0.276969254,0.0414252691,0.1946728826,0.4200007617,0.0776329115,-0.3998927772,-0.0317487083,-0.2662963867,-0.0253004767,0.2187399864,0.377743125,0.0436375402,0.1135604605,-0.059801314,0.1989057064,-0.0594728105,-0.4426795244,-0.3030682206,0.3334758282,-0.1042798758,-0.3177535534,0.0193641763,-0.0207902119,0.1957095265,0.2191386372,-0.1739814132,-0.0592373982,0.1233527735,-0.4605067074,0.1437606066,0.3618825972,-0.0033455135,-0.2083796561,0.2578766048,-0.2094171792,0.0035285249,0.2536199987,-0.0682576448,-0.3559485078,-0.0321290009,0.337768048,0.1614764482,0.1738892049,-0.116377309,0.1625697762,0.3533759117,-0.2079333812,0.024277322,-0.085838072,-0.2200120091,-0.2932094634,0.1470615864,0.3619220257,-0.3413058817,-0.133083865,-0.1345353574,-0.1824868917,0.0983030871,0.0809265226,-0.1797881573,0.3938573897,-0.2386934012,0.5446959734,0.4584326446,-0.2361291498,-0.0809233189,0.3233044147,-0.0367279463,-0.2104673386,-0.0367180035,-0.1798265278,0.1973543465,0.0377613567,0.1241152808,0.0272634588,-0.2770866752,-0.1476682574,-0.2296878099,0.0351514332,0.3037281632,0.0866832137,0.2002630979,-0.2132453322,-0.0600378513,0.0495811887,-0.0122905448,-0.0569790304,0.1059752181,0.2511630356,0.2062113136,-0.2719920576,-0.1631948948,-0.1595900804,-0.2165904641,0.1562371105,-0.4682896137,0.0489555225,-0.1579335034,-0.110664919,-0.1586215347,0.1668361574,-0.3561002612,-0.3218687773,0.244806394,-0.0483381748,0.0161141399,0.1176600084,-0.3002466261,-0.0519229732,-0.1977204233,0.2331566364,-0.0668309703,0.4000051022,-0.2685908675,-0.2137828469,-0.1045412198,0.1410851032,0.1222287789,-0.0281012356,-0.0013925924,0.5620461702,0.3317559361,-0.1843186319,-0.1320330203,-0.4199966788,0.218624562,0.0801085904,-0.0030808279,0.043158669,0.1956190467,-0.1354965121,0.0702928603,0.4215479791,-0.164619267,0.099870339,0.0960523933,0.110236004,0.0731214285,-0.1226166189,0.1938954443,-0.1256381422,-0.1501625627,-0.6316099167,0.1066081002,0.0297481883,-0.1727101952,-0.1840963364,0.3182244897,-0.0157901887,0.1076047346,-0.0694290102,0.08997076,-0.0430511348,0.0124480771,0.4540242851,0.3595222831,0.1093105897,0.0691784099,-0.026388742,0.1020538881,0.0043796748,0.23595047,-0.0829031691,0.212587297,0.4033504426,-0.2370361388,-0.1057201624,-0.0774152577,-0.0378504284,0.2675491273,0.3059709072,-0.0998275802,-0.164961651,-0.1096301898,-0.0298333131,0.0824984685,-0.0792456567,-0.205833748,-0.3919481933,0.0027045219,0.0255116206,-0.1684821695,0.2499877661,-0.0448413081,-0.0304837208,0.3654894531,-0.0320747755,-0.087266393,-0.0385822132,-0.3025594652,0.1351388246,-0.0923736244,0.1497679502,0.1493162215,0.2277183682,-0.1718711555,0.1362490058,-0.1538341492,0.1338604391,-0.2160104364,-0.0281831641,0.456261605,0.1762324274,0.1133828387,-0.2925429642,0.3600921035,-0.1658401489,-0.2999567688,0.1651653945,0.3752211034,0.1846021414,-0.3102334738,-0.2788729072,-0.3881157041,-0.4727134705,-0.0969036743,-0.1738376915,0.1903258264,0.1340660453,0.2356193662,0.2885960937,0.1857780218,-0.271815896,0.0637562424,0.1844049394,0.0797061399,-0.1597715169,-0.1220592856,0.1154370606,-0.3134942651,0.4794272482,-0.2793750167,-0.3259856701,-0.0821823329,0.0239235349,0.2465979904,-0.1440027952,-0.1793990135,0.2091835737,0.2842224538,-0.1756088287,0.0167780239,-0.0508549362,0.2462119609,-0.1252524406,0.0307019632,-0.0990921035,0.3232720196,0.2634009719,0.4122442603,0.2139680237,-0.1056070551,0.0975423306,-0.0699536577,0.182574898,-0.0814448372,-0.4792007208,-0.2696222663,0.1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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/5","title":"ValueError when a split is empty","comments":"Yes sounds good to me!\r\nDo you want to make a PR? or I can do it as well","body":"When a split is empty either TEST, VALIDATION or TRAIN I get the following error:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/load.py\", line 295, in load\r\n    ds = dbuilder.as_dataset(**as_dataset_kwargs)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 587, in as_dataset\r\n    datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 158, in map_nested\r\n    for k, v in data_struct.items()\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 158, in <dictcomp>\r\n    for k, v in data_struct.items()\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 601, in _build_single_dataset\r\n    split=split,\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 625, in _as_dataset\r\n    split_infos=self.info.splits.values(),\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 200, in read\r\n    return py_utils.map_nested(_read_instruction_to_ds, instructions)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 191, in _read_instruction_to_ds\r\n    file_instructions = make_file_instructions(name, split_infos, instruction)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 104, in make_file_instructions\r\n    absolute_instructions=absolute_instructions,\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 122, in _make_file_instructions_from_absolutes\r\n    'Split empty. This might means that dataset hasn\\'t been generated '\r\nValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.\r\n``` \r\n\r\nHow to reproduce:\r\n```python\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n\r\n    def __init__(self, **config):\r\n        self.train = config.pop(\"train\", None)\r\n        self.validation = config.pop(\"validation\", None)\r\n        super(Bbc, self).__init__(**config)\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, description=\"bla\", features=nlp.features.FeaturesDict({\"id\": nlp.int32, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": self.train}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": self.validation}),\r\n                nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={\"filepath\": None})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        if not filepath:\r\n            return None, {}\r\n\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"id\": idx, \"text\": line[1], \"label\": line[0]}\r\n```\r\n\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc\/data\/train.csv\", \"validation\": \"bbc\/data\/test.csv\"})\r\n```","comment_length":19,"text":"ValueError when a split is empty \n When a split is empty either TEST, VALIDATION or TRAIN I get the following error:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/load.py\", line 295, in load\r\n    ds = dbuilder.as_dataset(**as_dataset_kwargs)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 587, in as_dataset\r\n    datasets = utils.map_nested(build_single_dataset, split, map_tuple=True)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 158, in map_nested\r\n    for k, v in data_struct.items()\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 158, in <dictcomp>\r\n    for k, v in data_struct.items()\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 601, in _build_single_dataset\r\n    split=split,\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/builder.py\", line 625, in _as_dataset\r\n    split_infos=self.info.splits.values(),\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 200, in read\r\n    return py_utils.map_nested(_read_instruction_to_ds, instructions)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/utils\/py_utils.py\", line 172, in map_nested\r\n    return function(data_struct)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 191, in _read_instruction_to_ds\r\n    file_instructions = make_file_instructions(name, split_infos, instruction)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 104, in make_file_instructions\r\n    absolute_instructions=absolute_instructions,\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/arrow_reader.py\", line 122, in _make_file_instructions_from_absolutes\r\n    'Split empty. This might means that dataset hasn\\'t been generated '\r\nValueError: Split empty. This might means that dataset hasn't been generated yet and info not restored from GCS, or that legacy dataset is used.\r\n``` \r\n\r\nHow to reproduce:\r\n```python\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n\r\n    def __init__(self, **config):\r\n        self.train = config.pop(\"train\", None)\r\n        self.validation = config.pop(\"validation\", None)\r\n        super(Bbc, self).__init__(**config)\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, description=\"bla\", features=nlp.features.FeaturesDict({\"id\": nlp.int32, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": self.train}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": self.validation}),\r\n                nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={\"filepath\": None})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        if not filepath:\r\n            return None, {}\r\n\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"id\": idx, \"text\": line[1], \"label\": line[0]}\r\n```\r\n\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc\/data\/train.csv\", \"validation\": \"bbc\/data\/test.csv\"})\r\n``` \n Yes sounds good to me!\r\nDo you want to make a PR? or I can do it as well","embeddings":[-0.2972342372,-0.0131540569,-0.103067942,0.2931129634,0.0207595415,-0.0219010543,0.6005375385,0.4749840498,-0.0488540977,0.4204655886,0.0286984555,0.2306589633,-0.3276998699,0.1830051839,-0.4715830386,-0.2210291326,-0.0620970726,0.4033246934,0.0316321403,0.0022019364,-0.2077705711,0.1781739295,-0.3489648104,0.2001751065,-0.4412805736,-0.3235142529,-0.0962038264,0.0336118527,-0.1655961424,-0.5333554745,0.2795565426,-0.315582782,-0.2108621746,0.4383441806,-0.0001089997,0.2471223772,0.3216657937,0.0212966055,-0.2349680364,-0.1730071604,-0.2148883939,0.0468612611,-0.0701180547,-0.1868547648,-0.1008067131,-0.2197800577,-0.3655616343,0.0298773181,0.2869431078,0.2550378144,0.1966028512,0.1572323292,0.0249177329,-0.1763806641,0.0856723338,-0.105694443,0.0970320776,0.0854190886,-0.1803481877,-0.2269463241,-0.1018456817,0.2128955573,-0.0124384062,0.0009043886,-0.2380745709,0.1348021328,0.2368194163,-0.3553718626,-0.1338815093,0.0872519761,-0.0105639342,-0.0024150251,-0.1980995387,-0.3114365339,0.0216911975,-0.3575952351,0.0597179197,0.2921065986,-0.1286965311,0.124095358,0.228433311,0.1736139357,-0.2168529481,-0.0249617305,-0.3034926653,0.3541932404,-0.031331487,0.0348216146,0.2476863563,-0.0247122962,0.0857289061,-0.0367395617,-0.218579337,0.115537703,-0.2246417701,-0.3038747907,-0.1532901824,-0.3125609457,-0.0919000655,-0.0317804776,0.027630873,-0.1767440885,0.1064907983,0.4088112414,0.304340452,-0.0534912385,0.3710521758,0.4308705032,0.0564235486,0.1503341496,0.0145205986,0.02915808,0.0300026834,-0.276969254,0.0414252691,0.1946728826,0.4200007617,0.0776329115,-0.3998927772,-0.0317487083,-0.2662963867,-0.0253004767,0.2187399864,0.377743125,0.0436375402,0.1135604605,-0.059801314,0.1989057064,-0.0594728105,-0.4426795244,-0.3030682206,0.3334758282,-0.1042798758,-0.3177535534,0.0193641763,-0.0207902119,0.1957095265,0.2191386372,-0.1739814132,-0.0592373982,0.1233527735,-0.4605067074,0.1437606066,0.3618825972,-0.0033455135,-0.2083796561,0.2578766048,-0.2094171792,0.0035285249,0.2536199987,-0.0682576448,-0.3559485078,-0.0321290009,0.337768048,0.1614764482,0.1738892049,-0.116377309,0.1625697762,0.3533759117,-0.2079333812,0.024277322,-0.085838072,-0.2200120091,-0.2932094634,0.1470615864,0.3619220257,-0.3413058817,-0.133083865,-0.1345353574,-0.1824868917,0.0983030871,0.0809265226,-0.1797881573,0.3938573897,-0.2386934012,0.5446959734,0.4584326446,-0.2361291498,-0.0809233189,0.3233044147,-0.0367279463,-0.2104673386,-0.0367180035,-0.1798265278,0.1973543465,0.0377613567,0.1241152808,0.0272634588,-0.2770866752,-0.1476682574,-0.2296878099,0.0351514332,0.3037281632,0.0866832137,0.2002630979,-0.2132453322,-0.0600378513,0.0495811887,-0.0122905448,-0.0569790304,0.1059752181,0.2511630356,0.2062113136,-0.2719920576,-0.1631948948,-0.1595900804,-0.2165904641,0.1562371105,-0.4682896137,0.0489555225,-0.1579335034,-0.110664919,-0.1586215347,0.1668361574,-0.3561002612,-0.3218687773,0.244806394,-0.0483381748,0.0161141399,0.1176600084,-0.3002466261,-0.0519229732,-0.1977204233,0.2331566364,-0.0668309703,0.4000051022,-0.2685908675,-0.2137828469,-0.1045412198,0.1410851032,0.1222287789,-0.0281012356,-0.0013925924,0.5620461702,0.3317559361,-0.1843186319,-0.1320330203,-0.4199966788,0.218624562,0.0801085904,-0.0030808279,0.043158669,0.1956190467,-0.1354965121,0.0702928603,0.4215479791,-0.164619267,0.099870339,0.0960523933,0.110236004,0.0731214285,-0.1226166189,0.1938954443,-0.1256381422,-0.1501625627,-0.6316099167,0.1066081002,0.0297481883,-0.1727101952,-0.1840963364,0.3182244897,-0.0157901887,0.1076047346,-0.0694290102,0.08997076,-0.0430511348,0.0124480771,0.4540242851,0.3595222831,0.1093105897,0.0691784099,-0.026388742,0.1020538881,0.0043796748,0.23595047,-0.0829031691,0.212587297,0.4033504426,-0.2370361388,-0.1057201624,-0.0774152577,-0.0378504284,0.2675491273,0.3059709072,-0.0998275802,-0.164961651,-0.1096301898,-0.0298333131,0.0824984685,-0.0792456567,-0.205833748,-0.3919481933,0.0027045219,0.0255116206,-0.1684821695,0.2499877661,-0.0448413081,-0.0304837208,0.3654894531,-0.0320747755,-0.087266393,-0.0385822132,-0.3025594652,0.1351388246,-0.0923736244,0.1497679502,0.1493162215,0.2277183682,-0.1718711555,0.1362490058,-0.1538341492,0.1338604391,-0.2160104364,-0.0281831641,0.456261605,0.1762324274,0.1133828387,-0.2925429642,0.3600921035,-0.1658401489,-0.2999567688,0.1651653945,0.3752211034,0.1846021414,-0.3102334738,-0.2788729072,-0.3881157041,-0.4727134705,-0.0969036743,-0.1738376915,0.1903258264,0.1340660453,0.2356193662,0.2885960937,0.1857780218,-0.271815896,0.0637562424,0.1844049394,0.0797061399,-0.1597715169,-0.1220592856,0.1154370606,-0.3134942651,0.4794272482,-0.2793750167,-0.3259856701,-0.0821823329,0.0239235349,0.2465979904,-0.1440027952,-0.1793990135,0.2091835737,0.2842224538,-0.1756088287,0.0167780239,-0.0508549362,0.2462119609,-0.1252524406,0.0307019632,-0.0990921035,0.3232720196,0.2634009719,0.4122442603,0.2139680237,-0.1056070551,0.0975423306,-0.0699536577,0.182574898,-0.0814448372,-0.4792007208,-0.2696222663,0.18031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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/4","title":"[Feature] Keep the list of labels of a dataset as metadata","comments":"Yes! I see mostly two options for this:\r\n- a `Feature` approach like currently (but we might deprecate features)\r\n- wrapping in a smart way the Dictionary arrays of Arrow: https:\/\/arrow.apache.org\/docs\/python\/data.html?highlight=dictionary%20encode#dictionary-arrays","body":"It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.","comment_length":31,"text":"[Feature] Keep the list of labels of a dataset as metadata \n It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata. \n Yes! I see mostly two options for this:\r\n- a `Feature` approach like currently (but we might deprecate features)\r\n- wrapping in a smart way the Dictionary arrays of Arrow: https:\/\/arrow.apache.org\/docs\/python\/data.html?highlight=dictionary%20encode#dictionary-arrays","embeddings":[0.1531038135,-0.0619831346,-0.1409771144,0.0409961455,0.1746009439,0.1912366003,0.2029764354,0.2303460389,-0.0681677684,0.0453065932,-0.0701301843,0.5808182359,-0.3312079906,0.1982669532,0.0061332081,-0.1212565675,-0.0128118554,-0.0272393189,0.0372485667,-0.0533793308,-0.1979878545,-0.3048540056,0.1820646673,-0.0400751047,-0.2287419736,-0.0672599226,0.1197463796,-0.3560012281,0.0509823933,-0.683021307,-0.1670466661,-0.0050464547,-0.0410597622,-0.1845041811,-0.0001063943,-0.0223298315,0.2254130542,-0.0063495575,-0.3393560648,0.0036049434,-0.3735336661,-0.252666384,0.2441437989,-0.2920621037,0.0635793507,-0.4220448136,-0.2341690958,-0.3475583792,0.0337780938,0.0731797069,0.2945555151,-0.0099700466,-0.0633411705,0.198744297,0.4615858495,0.3243832886,-0.4491933882,-0.0578366034,0.2165960819,0.1747603565,0.1609930098,0.6276299953,-0.1296104938,-0.2748526335,0.4868668318,0.0433791801,0.053918384,-0.296800077,-0.0868220106,0.0617774017,1.0295807123,-0.3937677443,-0.3564585447,-0.1362314075,0.0429887623,-0.248009935,-0.0468659699,0.1938485056,0.0802288949,0.3612401783,0.1168773398,-0.3947039247,-0.1558552384,0.0775375143,0.0123679871,0.5747609735,0.0079296753,0.006995487,-0.0464662872,-0.1192316413,0.1567903757,-0.1131044254,-0.078289628,0.0232752524,0.0664356947,-0.2106243074,-0.1160638183,-0.2866198421,0.2796501815,0.498293668,0.0537300706,-0.1367437243,-0.3827081621,0.1011157855,0.2913881838,-0.0538900159,0.5860031247,0.1966705918,0.2218898088,-0.3975260258,0.0835797042,-0.0928669795,-0.0077863103,0.3977659643,0.5791301727,-0.036672432,0.4260240793,-0.2016580999,-0.0679505244,0.152439177,-0.2635843158,-0.1534396708,0.1068716496,0.2219767272,0.1317170113,0.2757698596,-0.124416247,0.0658217221,0.545681417,-0.106338717,-0.0713844672,-0.0793322474,0.0519186817,0.1652719229,-0.0826038718,0.0689334869,0.4504217803,-0.1267027855,0.1890272796,-0.064078778,0.2548243701,-0.059005443,0.2395191789,0.2200737596,-0.3890978396,0.2038203627,-0.0463676862,0.1786973476,-0.3816194534,0.3463989794,-0.0534163564,-0.2060552239,-0.1616810411,0.2132520229,0.1692087203,-0.1648845226,-0.1126016676,0.0359026939,0.0138854189,0.2280050516,0.3043180704,0.1635724306,-0.0215761587,-0.398511678,-0.2363695353,0.0136657599,-0.3425946534,-0.0080059143,0.2239617854,0.1316719353,-0.2075072825,0.2535156012,-0.2940642834,0.1013360992,-0.0034681449,0.0235082712,0.3565423787,-0.4369632602,-0.2732064724,-0.0279662944,0.1864790022,-0.1035629436,0.0481274948,0.4568344951,0.1580266356,-0.2445974052,-0.0531273261,0.1354698092,-0.1033434644,0.1129797995,0.0414424762,-0.0668416023,0.1230785027,-0.0180224981,-0.3156913221,-0.102321066,-0.1786926091,-0.1581577808,0.2403324544,-0.0035172047,0.1564743668,0.4551902115,0.4405421913,0.1935774088,-0.0041880249,-0.2268556058,-0.3842477202,-0.0581124127,0.2511888444,-0.138198331,-0.3032629788,-0.6113615632,-0.1767916977,0.2631886303,-0.0754908696,0.0055043357,0.2138353884,-0.1993266791,-0.2779326737,0.0624175295,-0.2574976385,0.0297128856,0.0468112342,0.1070219353,0.0149146356,0.1145128533,0.0927567706,0.0290389154,-0.1173444837,0.2132788002,0.0408873037,0.0829877034,-0.0345223844,0.145313099,0.3163555562,-0.3232550919,0.1213525236,0.4607732594,0.3032994568,-0.5314986706,0.0622610189,0.1226724684,0.0218913965,-0.0518396385,-0.2709649801,-0.0260816962,0.1027444303,0.3627811372,-0.0343949236,0.2423748374,0.1144367009,0.1110746562,-0.3544942141,-0.2036374658,-0.3353032768,-0.0867559314,0.135872066,0.1299476922,-0.3200688064,0.484347105,0.6123461127,0.0640560612,0.2658021748,0.0981492102,-0.352642417,0.1297808439,0.2959808409,-0.1598252803,-0.0144370655,0.3183603287,-0.0008385434,-0.0637735426,0.0819299892,0.1354631335,0.3058955371,0.2960719168,0.3277533054,-0.1924916208,0.0786267519,-0.0426282175,0.0444987938,-0.3453595936,-0.158987388,-0.2152090371,0.0066773626,0.0380661525,-0.2455751896,-0.112329334,-0.0882889852,-0.5619063377,-0.4075858891,-0.3165303767,0.0344989039,0.1153464913,-0.3554001153,0.3305261731,-0.3464923799,0.0453386232,0.0523465686,-0.2609804571,-0.1679685116,-0.3869166672,0.1574691236,0.0538238436,0.0889512673,-0.0401540585,0.5392515063,0.3545880616,0.159402445,-0.6103399396,-0.2528915405,0.1790262461,0.0170618836,-0.2253209203,0.1091634557,0.1069799885,-0.1393186003,-0.0697339848,-0.0014452366,0.0785157382,-0.0372331813,-0.0896277502,0.2730149329,-0.0314972065,-0.0204621516,-0.3882804215,-0.4076157212,-0.4055927694,0.3017730117,0.1276456714,0.1697412729,0.1785371453,-0.2694990337,0.0260159299,-0.1096279845,0.0952151269,-0.1074527949,0.1441744864,0.2464603335,-0.1360030472,-0.0080136368,0.0874869153,-0.1389940232,0.17115435,0.0423163511,-0.0191368796,-0.1977375746,-0.0855146572,0.3730426431,-0.1065643579,0.1364077777,0.2442816198,0.3777547777,-0.1017339379,-0.0099786222,-0.1178264394,0.2335264981,-0.0354419388,0.089684315,0.0194216222,0.0804448053,-0.1720803976,0.4238517582,0.0187355634,0.1440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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/4","title":"[Feature] Keep the list of labels of a dataset as metadata","comments":"This should be accessible now as a feature in dataset.info.features (and even have the mapping methods).","body":"It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.","comment_length":16,"text":"[Feature] Keep the list of labels of a dataset as metadata \n It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata. \n This should be accessible now as a feature in dataset.info.features (and even have the mapping methods).","embeddings":[-0.0496049151,-0.0984715894,-0.1723788232,-0.0273312796,0.1680133641,0.1654706001,0.3242646754,0.2305074632,-0.0478426032,0.2778837681,-0.2302711606,0.492888689,-0.1844331771,0.2717520297,-0.0722042844,0.0264097042,-0.1009540185,0.0530562438,0.0414123982,-0.0347817875,-0.3080717027,-0.1635333449,0.1349450946,0.0643810108,-0.237500295,-0.0460765511,0.1412658542,-0.195451051,-0.0466829576,-0.6009489894,-0.1726687551,0.2099639624,-0.075079076,-0.1280405819,-0.0000924084,-0.1215024292,0.1399082392,0.0204979815,-0.2751190066,0.0876628309,-0.4400596917,-0.0634489655,0.1277536601,-0.3124091029,-0.030301543,-0.1620437503,0.021702636,-0.2083199173,-0.1103460044,0.0456910953,0.4085198641,0.0035763024,0.000951154,-0.0858163834,0.4976731837,0.1833201498,-0.4111306965,-0.0564074665,0.2255617231,0.1022936627,0.0467004068,0.5869672298,-0.0641263872,-0.1524451226,0.3551450074,0.0096309436,0.1121637821,-0.3324599266,0.0456431322,0.102847904,0.9027412534,-0.3083907068,-0.2470870912,-0.1317891926,-0.0125023052,-0.1988036931,0.0801150128,0.0526473895,0.0992975682,0.1977138817,0.0551455878,-0.3937343657,-0.1392278224,-0.0967758074,-0.0178305861,0.4943385422,-0.1232156828,-0.0832449645,0.103380844,-0.2010066211,0.1059985161,-0.0191349257,-0.0495446175,-0.0015812089,0.0675469264,-0.2441413254,0.0989901572,-0.1532108039,0.2423734367,0.3187515438,-0.0798826888,-0.0288356263,-0.3631315231,0.1258137077,0.209936142,-0.0072107795,0.4834262133,0.1031986848,0.2926647365,-0.2207577825,-0.1145367622,-0.1516707838,0.0008645065,0.2730922401,0.3092392385,-0.0071154884,0.3624818921,-0.3352712393,-0.2529607117,-0.0697743148,0.0482400991,-0.1187166423,0.1934691668,0.3123272359,0.0354918763,0.047175996,-0.2396335304,-0.0242575929,0.2669627964,-0.1388695389,-0.2123287916,-0.1292961538,-0.0829126015,0.0255032964,0.0194381252,0.0227207169,0.3629740775,-0.1533164829,-0.0136335632,-0.0287381373,0.1597516388,-0.0463826209,0.2886559665,0.1537839919,-0.2408829629,0.1265989691,-0.0432015248,0.0505391397,-0.3477985859,0.0889652073,-0.2663639188,-0.2532556951,-0.2703177035,0.3592046201,0.1903045326,-0.1899776608,-0.0029621404,0.1976010948,-0.0891306251,0.0178295821,0.0847133398,0.1344020367,-0.1058998704,-0.2697375417,-0.1391242892,0.0331959985,-0.3472645581,-0.0254422724,0.1159432456,-0.1032407358,-0.0487671383,0.1077130139,-0.3057391047,0.0840149745,-0.1304382533,0.1342795938,0.2191059738,-0.1828376204,-0.2081879526,-0.0410217308,-0.0110607604,-0.22094585,-0.0191750973,0.3735283017,0.1456188858,-0.2916695476,-0.0252589881,-0.0990169123,-0.072570242,0.1471474916,-0.0109407846,-0.0757462531,0.1559707969,-0.0825422555,-0.219349876,0.1205210015,0.0938543454,0.108762823,0.0994633436,-0.069374688,0.1036927253,0.4537687898,0.4328600168,0.1737988144,-0.0954231545,-0.2658981085,-0.3110403419,-0.0317870229,0.062756367,-0.0624678358,0.0312345717,-0.5854985118,-0.1437659562,0.1872646958,-0.014401502,0.0042471611,0.3816923201,0.1099592149,-0.3010726571,-0.0189692788,-0.2340240628,-0.0605973862,-0.0038256675,-0.0431618318,-0.0519668497,-0.0022551881,0.0235307403,0.0451432839,0.0022891283,0.135599494,0.1382024139,0.0514880531,-0.0487503,0.2879352868,0.2015812844,-0.1524143219,0.1315054297,0.4913338125,0.267747283,-0.3744827211,-0.1273801625,0.0072628055,-0.0027249097,0.026938403,-0.3749998212,0.0071977749,0.1368162632,0.1164636984,0.1178022772,0.1408797503,0.216995582,0.0723883733,-0.1767579913,-0.3400178552,-0.2923746109,-0.1043406054,-0.0824241117,0.0987219289,-0.477979064,0.5590272546,0.7271511555,0.1874971688,0.2642273903,0.0688684508,-0.2115924507,0.021986587,0.3479870558,-0.0654762313,0.0490362495,0.4268992841,0.2220215499,-0.0561690293,-0.0363544859,-0.0129512409,0.2276465297,0.1589504331,0.1350108385,-0.1153644174,0.1157374904,-0.0805163011,-0.1274996698,-0.2985969782,-0.0738099664,-0.0514241382,-0.0466167927,-0.1763063222,-0.3657177389,-0.1905111521,0.029067006,-0.2515901327,-0.3413636386,-0.3003245592,0.2787344158,0.2025237679,-0.3914099634,0.4054634869,-0.2809786499,0.180066824,-0.012809217,-0.0476994738,-0.0472429618,-0.2777006626,0.0947754681,0.2540524006,-0.0370873697,0.079837583,0.45062235,0.1521618217,0.2697623372,-0.6478309035,-0.3487976193,0.2086029351,0.0181230512,-0.0781253949,0.1263718158,0.1510206908,-0.144692868,-0.0811225548,-0.0505003259,-0.0534176379,-0.1017615944,-0.1608781815,0.1139837503,-0.089772895,-0.1069203094,-0.4496890008,-0.1781235337,-0.5046770573,0.2615948915,0.0235694367,0.2280349433,0.179664582,-0.2393412888,0.1410049647,-0.1516529322,0.1815710217,-0.2869572937,-0.1689717621,0.1275098324,-0.268332094,-0.1008078903,0.1138846576,0.0540677682,0.0919373706,-0.0693853572,-0.057525456,-0.197721526,-0.1064798087,0.3291487992,0.1518095285,0.1072690412,0.3619349301,0.317442894,-0.2667703927,-0.0292521175,-0.226419583,0.277375102,0.0564540923,0.1877759546,-0.0833340362,-0.0693659559,-0.1662255824,0.2725535929,-0.0823972896,0.0848290026,0.0662150308,-0.0174633972,0.2688052654,-0.1162871644,-0.136541307,0.0221418776,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/4","title":"[Feature] Keep the list of labels of a dataset as metadata","comments":"Hi,\r\nI hope we could get a better documentation.\r\nIt took me more than 1 hour to found this way to get the label information.","body":"It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.","comment_length":25,"text":"[Feature] Keep the list of labels of a dataset as metadata \n It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata. \n Hi,\r\nI hope we could get a better documentation.\r\nIt took me more than 1 hour to found this way to get the label information.","embeddings":[-0.0012369895,-0.1648714691,-0.1444353759,0.0895124599,0.1662273556,0.1849589199,0.3181912005,0.0862610564,-0.0790702775,0.3192556798,-0.1647137552,0.5471563935,-0.200616017,0.2491293252,-0.1022710279,0.005948856,-0.157364741,0.0784152895,0.1580577493,-0.1261553466,-0.1806293577,-0.1693413109,0.0251516663,0.0729987621,-0.357034713,-0.0805934444,0.2116435468,-0.2055696845,-0.175666973,-0.5510213375,-0.1353230625,0.0937980413,-0.0786830112,-0.2112112939,-0.0000956425,-0.0900051296,0.2437764853,-0.080670476,-0.1377635747,0.0274874195,-0.450014025,-0.0306218527,0.1355545819,-0.2670372725,-0.0036933725,-0.0796642378,-0.0997258052,-0.2003891319,-0.2043870687,0.045795612,0.3885522187,-0.0868356749,0.04931321,-0.0398686752,0.5041478276,0.253072679,-0.4126293361,-0.0206843466,0.2439529151,0.1936398596,0.1692546904,0.6854000688,0.0045043714,-0.2017082423,0.3443518579,-0.0197564326,0.0436132103,-0.4302619994,0.1069989279,0.0780438334,1.148717761,-0.2577243149,-0.1661703885,0.03488601,0.013556974,-0.2583199143,-0.0203066878,0.1106656119,0.0717397258,0.1847582757,0.0073038996,-0.3757540286,-0.2281251401,-0.1638335288,0.0684202462,0.5441606641,-0.1537513584,-0.0204880033,0.1099063233,-0.1912332475,0.0578916371,0.071008563,-0.120502539,-0.0050507295,-0.0082447464,-0.1972566098,-0.0211164281,-0.1875525862,0.2372861058,0.4003069401,-0.1271066666,-0.095906429,-0.2914321125,0.152131781,0.1925049275,0.0913550705,0.6101221442,0.0721338987,0.1942231208,-0.3319151103,-0.0696623251,-0.1991147995,-0.0890012234,0.3325089812,0.3371145129,-0.0426591523,0.3909405768,-0.4748960435,-0.2659723461,-0.042298682,-0.0798114538,-0.1603301316,0.19779706,0.3338942826,0.0164155792,0.0565250404,-0.3122140169,-0.1739887893,0.3510822654,-0.2051125467,-0.1845497191,-0.0596301965,-0.1094477922,0.0480876528,-0.073043339,-0.0498026721,0.4779724777,-0.1632076651,0.0568900257,-0.0626576021,0.1679087281,-0.179626286,0.2364334315,0.2492837161,-0.1773935407,0.1520492584,-0.1059272513,0.0674741194,-0.3431613147,0.1716541052,-0.3339016736,-0.3239254355,-0.328152746,0.3036516309,0.2867658138,-0.1956075579,-0.081105046,0.1323769242,-0.078157641,0.1557225883,0.1960145384,0.1929073781,-0.0881436765,-0.357396245,-0.2089812756,-0.0679304376,-0.3548083007,0.0634033903,0.1835010946,-0.0971572623,0.0263426118,0.1660446525,-0.3402350545,0.1804993749,-0.0747926459,0.152998805,0.2817381322,-0.304576695,-0.1977692395,-0.0991869122,0.0565673783,-0.3040274084,0.0313151367,0.357199192,0.1371988356,-0.2525860965,-0.037988916,-0.119259201,-0.1827456206,0.1229213178,0.0769780204,-0.0729242191,0.1534150094,-0.0192936305,-0.3171247244,-0.0344871543,0.0391572975,0.12291646,0.202541247,-0.0204833131,0.0531006716,0.595230639,0.5330174565,0.1924235523,-0.0073460038,-0.1274936199,-0.2925902605,-0.0048785121,0.1233347654,0.0435006395,-0.0476257652,-0.5623975992,-0.1672822535,0.2020263374,-0.0502532795,-0.1356932819,0.3412768245,0.0692978352,-0.1592821032,0.0307356305,-0.234834671,-0.0835729614,-0.1016063467,-0.1031384096,-0.0791600943,-0.0126527939,0.0224931482,0.0734020025,0.0195239559,0.0770109221,0.1528437883,0.0858817697,-0.0826209188,0.2503280938,0.3642770052,-0.191136539,0.0896234885,0.4628484845,0.202989012,-0.5049231052,-0.1265562475,-0.10458868,-0.0068428423,0.074111253,-0.3349622786,-0.1465952992,0.1267175674,0.1205837429,-0.0052573672,0.1091275066,0.1501394361,0.0851366669,-0.1811238974,-0.2047689259,-0.2610884905,-0.122795172,-0.054593306,0.1272089481,-0.4433560073,0.6448712945,0.81930691,0.1280813813,0.1116261333,0.0814267173,-0.156567961,-0.0312818065,0.3874503076,-0.1714462042,-0.0298576802,0.3746444583,0.0640372112,-0.120387584,-0.0335312001,0.0954781398,0.2456456423,0.1229071245,0.2739792168,-0.1923180372,0.0251686499,-0.1045745313,-0.053110417,-0.3405828774,-0.037565954,-0.024300104,0.0644434541,-0.1721327007,-0.3638795912,-0.2382689118,-0.0409300476,-0.3866483569,-0.3366088271,-0.2427175641,0.1281423122,0.1403514147,-0.2408833057,0.4542850554,-0.2238083333,0.2266671658,0.0122665986,0.114090085,-0.0426628329,-0.2075577229,0.0608816408,0.2083187401,-0.0507894829,0.0117369825,0.4336638153,0.1324025244,0.2497446537,-0.5540035963,-0.3498836756,0.2959485352,-0.0070695118,-0.1748352796,0.0527373217,0.1413435936,-0.1761558354,-0.0824228302,-0.1549874097,-0.0203714985,-0.1302684546,-0.233631894,0.107998386,-0.1518330574,-0.123505421,-0.4730251729,-0.3118480444,-0.334597379,0.2471403182,0.0232421719,0.2466925234,0.252849251,-0.3076382279,0.2085217386,-0.1585282683,0.1713126302,-0.2391826808,-0.1494201422,0.0749913082,-0.2606092393,-0.1155941561,0.2041754723,0.1375212818,0.084371984,-0.2092921883,-0.0700515434,-0.2334060371,-0.0555215925,0.2725538909,0.0570352077,0.1138924956,0.3041983247,0.2385294139,-0.1843321323,-0.0857886299,-0.2510438859,0.356795907,-0.0861879438,0.2458522916,-0.0341413543,-0.1150797084,-0.1341923177,0.3058535755,-0.0768330172,0.0881058425,-0.0944919884,-0.0230640844,0.2096022367,-0.061435461,-0.0265810397,0.0251490269,-0.0652383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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/4","title":"[Feature] Keep the list of labels of a dataset as metadata","comments":"Yes we are working on the doc right now, should be in the next release quite soon.","body":"It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata.","comment_length":17,"text":"[Feature] Keep the list of labels of a dataset as metadata \n It would be useful to keep the list of the labels of a dataset as metadata. Either directly in the `DatasetInfo` or in the Arrow metadata. \n Yes we are working on the doc right now, should be in the next release quite soon.","embeddings":[0.0434394106,-0.0362067781,-0.1563860625,-0.0524546467,0.122303389,0.2208477557,0.2759690583,0.1788536161,0.0087437974,0.2267382592,-0.1574053764,0.5626071692,-0.2707872689,0.2366517782,-0.0232619904,0.0165955648,-0.0474637486,0.0209634155,0.1306467503,-0.0336884186,-0.2415300161,-0.1698799729,0.1512940228,-0.053203024,-0.2328320593,-0.0394080877,0.1785150617,-0.2916420698,-0.1576616466,-0.5575762391,-0.1708637029,0.2196195722,-0.0708721131,-0.1691180617,-0.0000974572,-0.127391696,0.1770223379,-0.023043802,-0.1470951587,-0.0159274116,-0.4960756302,-0.0295409262,0.1479242295,-0.1194488406,0.0284162294,-0.0955274627,-0.0997408554,-0.3222854733,-0.183641836,0.0122567974,0.3661126196,-0.0640486255,-0.0614348017,0.0069510075,0.4805946052,0.2897692919,-0.4747666121,0.0097427862,0.2417715788,0.1432863623,0.1520195752,0.6420621872,-0.0295930393,-0.2510326505,0.3693681657,0.0115175229,0.0201355666,-0.3688250482,0.0242838394,0.0691424683,1.0715801716,-0.3872752488,-0.1921420395,-0.0374775976,0.0414030552,-0.3004676402,0.0241514053,0.0755021051,0.09690658,0.214543134,0.0616907664,-0.5372310281,-0.1946929991,-0.0914590582,0.0487755053,0.5409656763,-0.0751591995,-0.1670041233,0.1555884331,-0.1719274372,0.1570845395,-0.0032776885,-0.1599087715,-0.0129470052,-0.0063076848,-0.2228289694,-0.0958382264,-0.1572147161,0.2525980175,0.4223838151,-0.0732687041,-0.0907001048,-0.3351193964,0.1235924363,0.295575887,0.0198883619,0.6246948838,0.0946136788,0.2880927324,-0.4285324514,0.0352327786,-0.1624549031,-0.0993715748,0.3854803741,0.2950223088,-0.0776431784,0.3249313235,-0.4315893948,-0.1809603125,-0.04522264,0.0039805737,-0.1701399386,0.1890342236,0.2550528646,0.1284542531,0.1242187545,-0.2564404905,-0.0217115339,0.4035780132,-0.1800673902,-0.114364773,-0.1187709272,-0.0754052177,0.123991631,0.0000380957,-0.0456723347,0.3899737895,-0.1539159268,0.0750819892,-0.0887620822,0.1447477788,-0.0744216144,0.2771876752,0.1985365301,-0.3157619536,0.1249477193,-0.1547252536,0.0169929005,-0.3274433613,0.2830433547,-0.2704171836,-0.2312248945,-0.4020668566,0.2658469081,0.2844426036,-0.3097572625,-0.1067962945,0.1678143293,0.0092426715,0.091192238,0.2200801373,0.1911843866,-0.0632189438,-0.2448177338,-0.3248766959,-0.020181708,-0.3555791974,0.0212505255,0.1612933874,-0.0053835595,-0.1127403751,0.1281253695,-0.2790532112,0.1148020327,-0.0166740548,0.198962599,0.215004921,-0.3138137162,-0.1832923293,-0.140606612,0.089103654,-0.2974984944,-0.0027125869,0.3696067631,0.1509943008,-0.3287335336,-0.0952910036,-0.1117072329,-0.1460364312,0.1848975122,0.0566564873,-0.0997147709,0.157763496,-0.0430928208,-0.2826045752,-0.0194783807,0.0270490441,0.1429832429,0.1487599015,-0.0547486246,0.0540669486,0.5306127071,0.5022466183,0.1811080128,-0.0339322388,-0.193663612,-0.3577806354,-0.1424719691,0.1433421373,-0.1000240296,-0.023233626,-0.5768047571,-0.1213204712,0.142217055,-0.0736330152,-0.0558607951,0.2889319062,0.0115037095,-0.3270288408,0.1184311211,-0.335976392,-0.1061220169,-0.0946167037,-0.0481744744,-0.0669817477,0.0370242558,0.0716620609,0.0317729451,0.0576182678,0.1245610192,0.0394380726,0.042498406,-0.0618643463,0.2732851505,0.3557746708,-0.1737657636,0.140822202,0.5224788785,0.3935517073,-0.4445533454,-0.0970880389,-0.0675663874,-0.092899926,0.005917463,-0.4204341471,-0.1387980729,0.0643701181,0.1765261739,-0.0108582927,0.0879018828,0.1306762546,0.0643801913,-0.2210184932,-0.2471513599,-0.3081650138,-0.1115167513,0.0108659621,0.0825834423,-0.4147004187,0.6170598865,0.7159015536,0.1884166747,0.1927088052,0.1729831696,-0.1423829943,0.0155096315,0.3298496306,-0.132500574,0.0031863532,0.3548626304,0.1074496582,-0.1206543893,0.0597314164,-0.0086461874,0.2671056092,0.141038999,0.2787774205,-0.0590398572,0.0770412982,-0.0987058207,-0.1839791536,-0.223000899,-0.0659468621,-0.0925993472,0.0474029146,-0.1751674265,-0.4039583504,-0.2162207812,-0.0144815538,-0.4419514537,-0.4217895269,-0.2748585343,0.2110532075,0.2215564251,-0.3491579592,0.4842574596,-0.2305960208,0.3170522153,-0.0948730186,0.0737082437,-0.1031873599,-0.3096196651,0.1940344125,0.168467626,0.0646788701,-0.0462103449,0.4901993275,0.2274284065,0.2941660285,-0.6255378127,-0.3937917352,0.2680423558,0.1080576032,-0.2010456026,0.0211653765,0.0601061098,-0.1602723598,-0.1049769372,-0.1912260801,-0.0714137927,-0.1114473119,-0.2610792518,0.1679469198,-0.0486777611,-0.0943757668,-0.412730366,-0.3196935356,-0.3933529258,0.3229835927,0.047798086,0.1956526041,0.2367887348,-0.2738618255,0.1109074652,-0.2667829096,0.0560957827,-0.2118016779,-0.1464274973,0.040188659,-0.3039529026,-0.0693357587,0.1233008057,0.1781906635,0.1063380837,-0.1853940785,-0.0211831313,-0.1820605099,0.0329698324,0.2561916411,0.107886672,0.0982023552,0.2715085149,0.30254969,-0.1601308286,-0.0782744735,-0.2235371023,0.3034810722,-0.0258755721,0.2311404049,-0.0255304892,-0.1827392578,-0.1518349499,0.248751238,-0.0658458993,0.121062085,-0.0589036047,0.0601909719,0.2668934464,-0.1118384823,-0.0111772427,0.0304732341,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3","title":"[Feature] More dataset outputs","comments":"Yes!\r\n- pandas will be a one-liner in `arrow_dataset`: https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.Table.html#pyarrow.Table.to_pandas\r\n- for Spark I have no idea. let's investigate that at some point","body":"Add the following dataset outputs:\r\n\r\n- Spark\r\n- Pandas","comment_length":23,"text":"[Feature] More dataset outputs \n Add the following dataset outputs:\r\n\r\n- Spark\r\n- Pandas \n Yes!\r\n- pandas will be a one-liner in `arrow_dataset`: https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.Table.html#pyarrow.Table.to_pandas\r\n- for Spark I have no idea. let's investigate that at some point","embeddings":[-0.4236600101,-0.061805103,-0.2500792146,0.0073686498,0.302544415,0.1241440624,0.2948102653,0.3709729016,-0.146498695,0.0166035481,-0.2502678633,0.7553219199,-0.0170536079,0.3141035438,0.2778237462,-0.1931229085,0.0942965299,0.1060331464,-0.2115752995,-0.0662083998,-0.2025430948,0.0692163706,0.1552253366,0.1883412302,-0.3077115119,0.0542252064,-0.2953985035,-0.3888697922,-0.0519564971,-0.4054460824,-0.0291128717,-0.0066005536,0.0361478627,0.0782574043,-0.000105874,-0.0859250426,0.2928694189,0.0189206824,-0.0985230654,-0.1385447383,0.0015435003,-0.2957569063,0.4588668346,-0.2712087929,0.0664705336,-0.4761643708,0.1820202321,0.1816089451,0.3071880639,0.1816480309,0.3140155971,0.2772028148,0.0876397192,-0.0478978492,0.4405783117,0.1783980727,-0.1809587032,0.2180856317,0.035754513,-0.1531905979,-0.2881404161,0.2587610483,-0.0024940812,0.0705226511,0.1081708595,0.2521232665,-0.0431856662,-0.2616991699,-0.01423347,0.2416135669,0.5697915554,-0.4277185798,-0.2202006131,-0.101631999,-0.0081955735,-0.1537102312,-0.0653768033,0.1404832602,-0.0338151343,0.159204334,0.2372844219,-0.148344636,-0.1802538931,0.0324250311,0.0428766832,0.567420423,0.0350545309,0.0761028975,0.2279883325,0.2201832831,0.2787986398,-0.0737675801,-0.0235871468,-0.0989851728,-0.1824269742,-0.0830923989,0.0241274517,-0.1216841117,0.0777469501,0.1878507435,0.2261743397,0.2018346637,0.0698906407,0.1147333905,0.1123695225,-0.2256430835,-0.056435246,0.1779745817,0.3263490796,-0.2604716718,0.1060847715,-0.1591226906,-0.0053732484,-0.219436273,0.2726423144,-0.0774719715,0.0982927978,0.1101387367,-0.1355004162,-0.0550318398,-0.0814183578,-0.1464684159,-0.155445084,0.2199733704,-0.0913158059,0.0208556242,-0.1420201659,0.2134454995,0.1208521575,-0.0601051189,-0.1055602953,0.0647919104,-0.0890365019,-0.0799133927,0.0018244983,-0.0327111855,0.2158912271,0.0448193289,0.03563587,0.4225840271,0.1090576053,-0.0523547567,0.1695752144,0.1312266439,0.0576992407,0.0786736831,-0.0038996153,0.0589622185,-0.2611331642,0.0690426007,-0.0885624662,-0.0840344429,-0.4620334208,0.2259626389,-0.0248054769,-0.200250417,-0.3925516605,0.4292556345,-0.1183581427,-0.0206985641,0.2050465941,-0.0127050057,0.1893754601,-0.3556747735,0.0144588277,0.1952510327,-0.5921708345,0.0633017942,0.0456995703,-0.1552539915,-0.0064759669,0.0487558916,-0.2209919989,0.0197519101,-0.1579330564,0.29813537,0.4278999567,-0.0897898898,-0.1441705823,0.0430087447,0.0285683107,-0.1067304537,0.2111125886,0.2186039537,0.1423521638,0.271194905,0.0414086394,0.0939130038,-0.1085531414,-0.1136269495,-0.0003044448,-0.139150694,0.1358144879,-0.0325311013,0.3364757001,-0.1443516761,0.1346239001,-0.4724796116,0.263956368,-0.00486856,0.0789878666,-0.0003532473,0.4330609739,0.2121476084,0.1850803643,-0.3109677136,-0.1032641381,-0.0511830486,0.1801474541,0.0830069631,-0.3254921436,-0.3626220822,-0.0088911122,0.2873338163,-0.0360436514,0.034847606,0.2639262974,-0.1477839798,0.1388918161,-0.021156868,-0.072000958,-0.0205897335,-0.0253083408,0.1436189711,-0.0922502726,0.4329188466,-0.118888095,-0.2623587549,0.0483898148,-0.2049252838,-0.13349244,0.1074999869,0.0801312253,0.3656724393,-0.0655185953,0.1203395724,0.2987856269,0.31216079,0.1776942015,-0.3076853156,0.0522706099,0.0754826665,-0.0292481426,0.1495366991,-0.1281578094,0.346421659,-0.0796327218,0.0508350097,-0.0992205292,0.2070183605,0.2490616143,0.2576343715,-0.1731717288,0.0004901166,-0.2870499194,0.1379067749,0.1510300487,-0.0820539072,-0.5496090651,0.2932040989,0.3700991571,0.0327968001,0.3454669714,-0.0250067133,-0.401443541,-0.1105823889,0.5292955637,0.0525189526,0.0905274898,0.4710197449,-0.0126012042,-0.2390518636,-0.4071599841,-0.137552157,0.1281996667,0.2362999171,0.2711461782,0.0875641704,0.1468279809,-0.1913910359,-0.4449611604,-0.1730959713,-0.1262284517,0.089934364,-0.2430396527,0.1468863636,-0.1808814257,-0.2581594288,0.0371693932,-0.1066882983,0.0025671637,-0.3480190039,0.2545681596,0.1055138856,-0.2233173102,0.2132592946,-0.0406424962,-0.1390573233,0.0891872868,-0.0437626615,-0.4960609674,-0.2942949831,-0.0096845673,0.2562079728,0.020722039,0.0339410342,0.2570437789,-0.036506407,0.2048762292,-0.4996928573,-0.3178368807,0.0938514993,-0.2857542634,0.2574905157,0.1663200557,-0.0129058249,-0.2927206457,-0.2263267338,0.2496741563,-0.2605257928,-0.0466354154,-0.0209565405,-0.1518792659,0.1266611665,0.0478392355,-0.1570951194,-0.0969642028,-0.3295823336,0.2770689726,0.2057902813,0.0709744468,-0.3107313216,0.4204165339,0.1273530871,-0.0021180054,-0.1289887279,-0.1200925931,0.2234212309,0.5940716267,-0.2815119922,-0.2537537813,-0.0943998173,-0.2281398326,0.1199764907,-0.0334272273,-0.1439121813,-0.2743971646,-0.0952565372,0.5558851957,0.0151029443,0.2108995467,0.2947681248,0.2981844842,-0.2353915125,0.014389283,0.0349209607,-0.0512359925,0.1449640095,-0.026564721,-0.1152961478,0.3901530206,-0.1239051446,0.6111701727,-0.4408535957,-0.1811609864,0.3107129931,-0.3898284733,0.3139994442,-0.0590240844,-0.0743202418,-0.4171909988,-0.3363457918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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/3","title":"[Feature] More dataset outputs","comments":"For Spark it looks to be pretty straightforward as well https:\/\/spark.apache.org\/docs\/latest\/sql-pyspark-pandas-with-arrow.html but looks to be having a dependency to Spark is necessary, then nevermind we can skip it","body":"Add the following dataset outputs:\r\n\r\n- Spark\r\n- Pandas","comment_length":28,"text":"[Feature] More dataset outputs \n Add the following dataset outputs:\r\n\r\n- Spark\r\n- Pandas \n For Spark it looks to be pretty straightforward as well https:\/\/spark.apache.org\/docs\/latest\/sql-pyspark-pandas-with-arrow.html but looks to be having a dependency to Spark is necessary, then nevermind we can skip it","embeddings":[-0.5538601875,-0.1469933093,-0.2703717053,0.0605323277,0.2588167489,0.1869446039,0.2700168788,0.4753178656,0.0849914774,0.1245463192,-0.0857295692,0.6752998233,-0.1100908369,0.3995358944,0.02501739,-0.2358332276,-0.10844253,-0.0002741395,-0.2984122932,0.0295078866,-0.2555410862,0.1111409962,0.2057302296,0.106230326,-0.3432416022,-0.0539068729,-0.2266675979,-0.2679175436,0.0396039635,-0.3622856736,0.0098006399,-0.0368889011,0.0572327338,0.2427306771,-0.0000990341,-0.1879158467,0.2158855051,0.0625416562,-0.0935503468,-0.1431126744,-0.0893051028,-0.2987647355,0.499340713,-0.32741943,-0.001760466,-0.2864935398,0.0751593783,-0.04923236,0.2396622151,0.0574651584,0.3537534475,0.2032936215,-0.0505121388,-0.0931669399,0.011724608,-0.0443656407,-0.070485726,0.0733082592,0.0989308506,-0.0770636648,-0.3784050047,0.3305183053,-0.0419841483,-0.0166584868,-0.0480699576,0.0941856578,0.1756498963,-0.2204088122,0.0856401324,0.1887379289,0.4700126052,-0.3086578846,-0.273935169,-0.2869238257,-0.1321463734,-0.1571960449,-0.165258348,0.2294098437,0.061391741,0.2063027918,0.1382098645,-0.0838116929,-0.259355247,-0.0061442731,-0.1574677974,0.4023459852,0.1091234013,-0.0698471963,0.2539541125,0.065071553,0.090452224,0.07416673,-0.0713789165,-0.0784911439,-0.2035108805,-0.2129990608,0.055978369,-0.2913092673,0.0196784977,0.1325111538,0.1347487867,0.2045744061,0.021805767,0.1224140972,0.1788022816,-0.2655582726,0.072490029,0.2539013624,0.2871673405,-0.2409293056,0.1024187282,-0.1289289296,-0.0519943759,-0.2499616891,-0.0389688984,0.0761710703,-0.009905586,0.0484883711,-0.242231369,-0.0391204469,0.0224282481,-0.0806409717,-0.120958887,0.3000917733,-0.0717795864,0.0651730448,-0.0388911031,0.1913029999,0.0923086181,-0.0686771646,-0.1064642146,0.1821031868,-0.018811876,-0.1073064059,-0.0210037772,0.0676506981,0.2616631091,0.0312227253,-0.1493034661,0.3017959297,0.1890586466,-0.023165673,-0.0656429827,0.089925915,0.1306271106,-0.039786581,0.1047995016,0.0877235308,-0.2037502974,-0.0406651348,0.0616203696,-0.2846140563,-0.5234966278,0.3075758815,0.0326080211,-0.2044956237,-0.238145262,0.5409141779,-0.1498934776,0.0261262842,0.0736213773,0.1660899371,0.1113569811,-0.2901499569,0.0518780835,0.3494935036,-0.6666471362,-0.0364144258,-0.0275494326,0.053966336,-0.0140019711,-0.0442776121,-0.1260465533,0.0602844059,-0.2055065185,0.0935079157,0.3954156935,-0.1698444188,0.0932532847,0.2512268722,-0.1106658131,-0.1646519452,0.2606687844,0.2550144196,0.2499963492,0.2460156977,-0.1078328639,0.2227351069,-0.1699499637,-0.064878419,0.048115544,-0.1972186863,0.1155436337,0.05259661,0.387093395,-0.0340709463,0.221007064,-0.2865006328,0.3103354275,-0.0058452999,0.0196932796,0.1366246194,0.3954476714,0.22259444,-0.058859475,-0.4964853227,0.0338161886,0.0449425727,0.1493114084,0.0796359181,-0.2523971498,-0.3368175328,-0.0894295946,0.1325584203,0.0984070748,0.0260067414,0.303365767,-0.2732362449,-0.0066490863,-0.0025284952,-0.042477794,0.1733339429,0.1019584239,0.0758345351,-0.1531866789,0.2987890244,-0.1364869624,-0.1301478446,0.1363058239,-0.1347593963,0.0113679273,0.0759536847,0.1194988862,0.3512020111,-0.0334719419,0.1418432146,0.4699138701,0.3609049916,0.193582803,-0.0714738369,0.0232465882,0.0834303871,-0.1012845635,0.0524637662,-0.1680175513,0.5050488114,0.0285401754,0.0658488646,-0.0646449924,0.2344291806,0.3114738762,0.1539533585,-0.1260727197,-0.1162480488,-0.2289201617,0.0588110834,-0.1082051024,-0.1587497592,-0.5301884413,0.3296276927,0.1728732139,0.0653062761,0.2982239723,-0.0369402841,-0.2525236309,0.0748354495,0.5008333921,0.2263532579,-0.0477875732,0.5283264518,-0.0352791362,-0.1022377089,-0.2568717301,-0.0895506516,0.2588435709,0.1919327676,0.0886877179,0.024516983,0.0556643084,-0.1531887949,-0.3598842919,-0.2306941748,-0.1705384254,-0.0150525235,-0.1025401726,-0.0200323071,-0.2984920144,-0.391721487,0.2167875916,-0.1473570466,0.0638981611,-0.3715459704,0.1317882091,0.2747735083,-0.2373939455,0.3069716692,-0.2229562253,0.1047448516,0.2739242911,-0.0615652278,-0.1887937337,-0.1890191883,-0.0125589827,0.2616713047,-0.0673963949,0.1524406374,0.4395678937,0.0669552833,0.2104679495,-0.5713222027,-0.2410607636,0.0058486396,-0.1094828844,0.1648474038,0.2017240971,0.0509246401,-0.2241855264,-0.2447012961,0.1638811082,-0.1372713894,0.0477252379,-0.0188017413,-0.0957382917,0.0998383239,0.0703514144,-0.1633325666,-0.1662694663,-0.5500902534,0.0828612894,0.186529398,0.1722971052,-0.377078414,0.2456801385,-0.0064157583,-0.0042385207,-0.1589741558,-0.1405054033,0.1762330979,0.5741929412,-0.2814626396,-0.2942431271,0.0368870944,-0.2076482326,0.0857882202,-0.1217321232,-0.2171171457,-0.2361728847,-0.1228895411,0.4963285327,0.0257592537,0.1940105408,0.3538024426,0.3066567481,-0.1938488036,0.0947411582,-0.1446152478,0.0669935867,0.1638549119,0.1552448869,-0.1388577968,0.5101762414,-0.0036213815,0.3623269498,-0.1596821994,-0.1353380978,0.2480469346,-0.4136278033,0.1829379201,0.0529923774,-0.1954563707,-0.4070477188,-0.2088878453,0.079246603,0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2","title":"Issue to read a local dataset","comments":"Ok, there are some news, most good than bad :laughing: \r\n\r\nThe dataset script now became:\r\n```python\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n\r\n    def __init__(self, **config):\r\n        self.train = config.pop(\"train\", None)\r\n        self.validation = config.pop(\"validation\", None)\r\n        super(Bbc, self).__init__(**config)\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, description=\"bla\", features=nlp.features.FeaturesDict({\"id\": nlp.int32, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": self.train}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": self.validation})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"id\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nAnd the dataset folder becomes:\r\n```\r\n.\r\n\u251c\u2500\u2500 bbc\r\n\u2502   \u251c\u2500\u2500 bbc.py\r\n\u2502   \u2514\u2500\u2500 data\r\n\u2502       \u251c\u2500\u2500 test.csv\r\n\u2502       \u2514\u2500\u2500 train.csv\r\n```\r\nI can load the dataset by using the keywords arguments like this:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc\/data\/train.csv\", \"validation\": \"bbc\/data\/test.csv\"})\r\n```\r\n\r\nThat was the good part ^^ Because it took me some time to understand that the script itself is put in cache in `datasets\/src\/nlp\/datasets\/some-hash\/bbc.py` which is very difficult to discover without checking the source code. It means that doesn't matter the changes you do to your original script it is taken into account. I think instead of doing a hash on the name (I suppose it is the name), a hash on the content of the script itself should be a better solution.\r\n\r\nThen by diving a bit in the code I found the `force_reload` parameter [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/nlp\/load.py#L50) but the call of this `load_dataset` method is done with the `builder_kwargs` as seen [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/nlp\/load.py#L166) which is ok until the call to the builder is done as the builder do not have this `force_reload` parameter. To show as example, the previous load becomes:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc\/data\/train.csv\", \"validation\": \"bbc\/data\/test.csv\", \"force_reload\": True})\r\n```\r\nRaises\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/load.py\", line 283, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/load.py\", line 170, in builder\r\n    builder_instance = builder_cls(**builder_kwargs)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/datasets\/84d638d2a8ca919d1021a554e741766f50679dc6553d5a0612b6094311babd39\/bbc.py\", line 12, in __init__\r\n    super(Bbc, self).__init__(**config)\r\nTypeError: __init__() got an unexpected keyword argument 'force_reload'\r\n```\r\nSo yes the cache is refreshed with the new script but then raises this error.","body":"Hello,\r\n\r\nAs proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:\r\n```python\r\nimport os\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass BbcConfig(nlp.BuilderConfig):\r\n    def __init__(self, **kwargs):\r\n        super(BbcConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    _DIR = \".\/data\"\r\n    _DEV_FILE = \"test.csv\"\r\n    _TRAINING_FILE = \"train.csv\"\r\n\r\n    BUILDER_CONFIGS = [BbcConfig(name=\"bbc\", version=nlp.Version(\"1.0.0\"))]\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({\"id\": nlp.string, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        files = {\"train\": os.path.join(self._DIR, self._TRAINING_FILE), \"dev\": os.path.join(self._DIR, self._DEV_FILE)}\r\n\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": files[\"train\"]}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": files[\"dev\"]})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"idx\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nThe dataset is attached to this issue as well:\r\n[data.zip](https:\/\/github.com\/huggingface\/datasets\/files\/4476928\/data.zip)\r\n\r\nNow the steps to reproduce what I would like to do:\r\n1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)\r\n2. create the `bbc.py` script as above at the same location than the unziped `data` folder.\r\n\r\nNow I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:\r\n```python\r\nimport nlp\r\nfrom bbc import Bbc\r\ndataset = nlp.load(\"bbc\")\r\n```\r\n\r\nI get:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 88, in load_dataset\r\n    local_files_only=local_files_only,\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py\", line 214, in cached_path\r\n    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/zipfile.py\", line 203, in is_zipfile\r\n    with open(filename, \"rb\") as fp:\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```\r\n\r\nBut @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\".\/bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"\/absolute\/path\/to\/bbc.py\")\r\n```\r\n\r\nThese three ways gives me:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 124, in load_dataset\r\n    dataset_module = importlib.import_module(module_path)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 965, in _find_and_load_unlocked\r\nModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'\r\n```\r\nAny idea of what I'm missing? or I might have spot a bug :)","comment_length":353,"text":"Issue to read a local dataset \n Hello,\r\n\r\nAs proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:\r\n```python\r\nimport os\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass BbcConfig(nlp.BuilderConfig):\r\n    def __init__(self, **kwargs):\r\n        super(BbcConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    _DIR = \".\/data\"\r\n    _DEV_FILE = \"test.csv\"\r\n    _TRAINING_FILE = \"train.csv\"\r\n\r\n    BUILDER_CONFIGS = [BbcConfig(name=\"bbc\", version=nlp.Version(\"1.0.0\"))]\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({\"id\": nlp.string, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        files = {\"train\": os.path.join(self._DIR, self._TRAINING_FILE), \"dev\": os.path.join(self._DIR, self._DEV_FILE)}\r\n\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": files[\"train\"]}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": files[\"dev\"]})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"idx\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nThe dataset is attached to this issue as well:\r\n[data.zip](https:\/\/github.com\/huggingface\/datasets\/files\/4476928\/data.zip)\r\n\r\nNow the steps to reproduce what I would like to do:\r\n1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)\r\n2. create the `bbc.py` script as above at the same location than the unziped `data` folder.\r\n\r\nNow I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:\r\n```python\r\nimport nlp\r\nfrom bbc import Bbc\r\ndataset = nlp.load(\"bbc\")\r\n```\r\n\r\nI get:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 88, in load_dataset\r\n    local_files_only=local_files_only,\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py\", line 214, in cached_path\r\n    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/zipfile.py\", line 203, in is_zipfile\r\n    with open(filename, \"rb\") as fp:\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```\r\n\r\nBut @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\".\/bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"\/absolute\/path\/to\/bbc.py\")\r\n```\r\n\r\nThese three ways gives me:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 124, in load_dataset\r\n    dataset_module = importlib.import_module(module_path)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 965, in _find_and_load_unlocked\r\nModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'\r\n```\r\nAny idea of what I'm missing? or I might have spot a bug :) \n Ok, there are some news, most good than bad :laughing: \r\n\r\nThe dataset script now became:\r\n```python\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    VERSION = nlp.Version(\"1.0.0\")\r\n\r\n    def __init__(self, **config):\r\n        self.train = config.pop(\"train\", None)\r\n        self.validation = config.pop(\"validation\", None)\r\n        super(Bbc, self).__init__(**config)\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, description=\"bla\", features=nlp.features.FeaturesDict({\"id\": nlp.int32, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": self.train}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": self.validation})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"id\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nAnd the dataset folder becomes:\r\n```\r\n.\r\n\u251c\u2500\u2500 bbc\r\n\u2502   \u251c\u2500\u2500 bbc.py\r\n\u2502   \u2514\u2500\u2500 data\r\n\u2502       \u251c\u2500\u2500 test.csv\r\n\u2502       \u2514\u2500\u2500 train.csv\r\n```\r\nI can load the dataset by using the keywords arguments like this:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc\/data\/train.csv\", \"validation\": \"bbc\/data\/test.csv\"})\r\n```\r\n\r\nThat was the good part ^^ Because it took me some time to understand that the script itself is put in cache in `datasets\/src\/nlp\/datasets\/some-hash\/bbc.py` which is very difficult to discover without checking the source code. It means that doesn't matter the changes you do to your original script it is taken into account. I think instead of doing a hash on the name (I suppose it is the name), a hash on the content of the script itself should be a better solution.\r\n\r\nThen by diving a bit in the code I found the `force_reload` parameter [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/nlp\/load.py#L50) but the call of this `load_dataset` method is done with the `builder_kwargs` as seen [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/nlp\/load.py#L166) which is ok until the call to the builder is done as the builder do not have this `force_reload` parameter. To show as example, the previous load becomes:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc\/data\/train.csv\", \"validation\": \"bbc\/data\/test.csv\", \"force_reload\": True})\r\n```\r\nRaises\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/load.py\", line 283, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/load.py\", line 170, in builder\r\n    builder_instance = builder_cls(**builder_kwargs)\r\n  File \"\/home\/jplu\/dev\/jplu\/datasets\/src\/nlp\/datasets\/84d638d2a8ca919d1021a554e741766f50679dc6553d5a0612b6094311babd39\/bbc.py\", line 12, in __init__\r\n    super(Bbc, self).__init__(**config)\r\nTypeError: __init__() got an unexpected keyword argument 'force_reload'\r\n```\r\nSo yes the cache is refreshed with the new script but then raises this error.","embeddings":[-0.2719613612,0.1254656762,-0.0766852647,0.256811142,0.3318964839,0.0312691405,0.2642410994,0.3003315628,0.3266820014,0.2098683715,0.1191096306,0.2447595894,-0.2942002714,0.1220210195,0.1568034738,-0.1723625809,-0.1275069416,0.3326410651,0.1501982808,-0.3661575317,-0.27604267,0.1413950026,-0.1191338152,0.1533625871,-0.2043232173,0.0607061796,-0.036645785,0.4012748301,-0.2069403231,-0.2449603528,0.1894469708,0.0119974464,0.2054079175,0.122409679,-0.0001004647,0.1904105544,0.2995985448,0.0436597355,-0.1575898379,-0.364929229,-0.0587465651,-0.3323357999,0.2688184083,-0.3986444175,-0.3326914608,-0.1401537061,0.0905135646,-0.4378168285,0.4685541093,0.3634622395,0.3068140745,0.1048855186,-0.0699259415,0.1463657767,0.195211947,0.152147904,-0.0873053744,0.2656306922,0.031915091,-0.2012286931,-0.1960862428,0.1223725975,-0.2222957164,0.0449407585,0.2718259394,0.1370849758,0.182052508,-0.1032298878,-0.032771349,-0.0018344095,0.3769168258,-0.2492007166,-0.2248517573,-0.13559407,0.0572931319,-0.2591866851,0.0394319892,0.342464596,-0.349896282,0.3455857933,-0.2791967094,0.1082817465,-0.3008818924,0.2382965088,0.1193012074,0.3104308248,-0.076890111,0.1048834771,0.0907402039,0.2240543067,0.1786538064,-0.1133791059,0.2185333967,0.2561581731,-0.0302851815,0.0967433602,0.3372043669,0.0830674171,-0.0235993508,-0.0186839495,0.1047985032,0.0249034651,0.0130941896,0.3297557831,0.1096323282,0.1400410384,0.1735097319,0.087680243,0.0717137232,-0.0622796975,-0.2097682357,0.0256421678,-0.1461680084,-0.283438772,0.0297457296,0.2404010743,0.4468615353,-0.1885111183,-0.1420501471,0.2193927914,-0.2050481737,-0.0884669423,0.1696829945,0.4059306085,-0.0357793234,0.2412724048,0.1384448409,0.0997360051,-0.3508428931,-0.1973964125,-0.3274970055,-0.0029654084,-0.2937141359,-0.0887341276,0.3137897849,0.0290612634,0.3492821157,-0.0983311981,-0.2490224689,-0.006291463,0.1585656404,-0.2560243607,0.1404405981,0.138631627,0.0651841164,0.034821,0.2272037119,-0.4230361283,-0.1299738437,0.2165416181,0.0924713016,-0.4230499268,-0.1779252738,0.3226445317,-0.0838615969,0.1769313514,0.180574581,0.1790369749,0.1407811642,-0.036249388,-0.2524119318,-0.0215898771,-0.459605664,-0.2430503964,0.2107181102,0.3516464829,-0.4640030563,0.0853260383,-0.164036572,-0.1117871031,0.3510994613,0.2487842441,-0.3300900161,0.4454449415,-0.1017358303,0.1409201324,0.6580390334,-0.3004144132,-0.3807192743,0.6322368979,-0.3527038991,-0.1465917528,0.0498830862,0.0073254914,-0.0018453643,0.2033673376,0.2166674733,0.3950749636,-0.1738853306,0.1002998799,-0.4054657817,-0.152086556,0.1437031329,0.1029602066,-0.0783379376,0.1322496831,0.1525419801,-0.05174575,0.497027427,-0.1389278173,0.1745269895,0.3185678422,0.2489011884,-0.0089441948,-0.1572516412,-0.0782212839,-0.2163548321,0.04727301,-0.1540113091,-0.0111839129,-0.1268217266,-0.1966494024,-0.3752773702,0.1024699733,-0.4161543548,-0.0244457163,0.2532124221,0.2463398874,0.0947190672,0.0092416257,-0.0782992616,-0.1027992368,-0.1485022753,-0.2201791555,-0.3223364353,0.2954417169,-0.3488661051,-0.1378353983,0.1312768757,0.3829959631,0.1777834296,-0.1002675742,-0.052053079,0.2365419269,-0.0244584288,0.0325130597,0.0145486901,-0.1853820533,-0.0120613975,-0.279640317,0.1465234011,0.0235882532,0.348398447,0.0077597764,-0.3682603538,0.377537787,-0.0371985137,0.0586498044,-0.0751952454,-0.0948600769,0.3058986366,-0.2762518227,-0.0493951924,-0.0236396324,0.2183714211,0.0369678438,0.1693601906,0.04129586,-0.3924960494,0.047164917,0.3549182117,-0.072537221,-0.1731103212,0.0018169068,0.0174868628,-0.0824427828,0.164626956,0.2774141133,0.3839050233,0.2610786259,0.0662332624,0.1700644791,-0.2135237604,-0.1831107736,-0.054769367,-0.0941865519,-0.027594151,0.3564697206,0.1723606884,-0.1707368493,-0.3508794904,0.0280967355,0.155304268,0.3651978076,-0.1970971674,0.2237509936,-0.2249526829,-0.2432209104,-0.3664650917,-0.0820040554,0.0938911885,-0.3452377021,-0.1890164614,0.1978519261,-0.107149139,-0.1096806899,-0.1519416273,-0.0034873104,0.0685041398,-0.1828078628,0.0319254473,-0.29726246,-0.4607281685,0.1170991212,0.3691140115,0.3623128831,0.4312880337,0.0150138075,-0.1073622257,0.0393667519,-0.0697979704,0.076720871,-0.100895606,0.4542248547,-0.0569396578,0.0416780375,-0.2283503711,-0.4602437317,0.5433725715,-0.0930568278,-0.1630839258,0.003641258,0.1127770245,-0.2605376542,-0.2423969209,-0.5966646671,-0.376578927,-0.3775201142,0.2393841147,0.2145242095,0.2878387868,0.3024253845,0.0545422919,0.227584064,0.2486261129,0.0220368877,-0.1577897519,-0.2745801508,0.1092553139,-0.3437816501,-0.3863990009,-0.0418258309,-0.2934792042,0.2532537878,-0.177119717,-0.5053718686,0.106159687,-0.2085334063,0.175987035,0.1183275729,-0.0756837502,0.3462190628,0.0716120824,-0.0953156427,-0.1507954448,-0.1724995971,0.17420201,0.0590066873,0.0601394363,0.060319189,0.2799823284,-0.1291005462,0.4796222448,0.0056985766,0.0647447631,0.2293583602,-0.1257217079,0.0351171568,-0.0269690529,-0.5060185194,0.0983495489,0.1094464958,-0.324074626,0.239359051,-0.03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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2","title":"Issue to read a local dataset","comments":"Ok great, so as discussed today, let's:\r\n- have a main dataset directory inside the lib with sub-directories hashed by the content of the file\r\n- keep a cache for downloading the scripts from S3 for now\r\n- later: add methods to list and clean the local versions of the datasets (and the distant versions on S3 as well)\r\n\r\nSide question: do you often use `builder_kwargs` for other things than supplying file paths? I was thinking about having a more easy to read and remember `data_files` argument maybe.","body":"Hello,\r\n\r\nAs proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:\r\n```python\r\nimport os\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass BbcConfig(nlp.BuilderConfig):\r\n    def __init__(self, **kwargs):\r\n        super(BbcConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    _DIR = \".\/data\"\r\n    _DEV_FILE = \"test.csv\"\r\n    _TRAINING_FILE = \"train.csv\"\r\n\r\n    BUILDER_CONFIGS = [BbcConfig(name=\"bbc\", version=nlp.Version(\"1.0.0\"))]\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({\"id\": nlp.string, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        files = {\"train\": os.path.join(self._DIR, self._TRAINING_FILE), \"dev\": os.path.join(self._DIR, self._DEV_FILE)}\r\n\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": files[\"train\"]}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": files[\"dev\"]})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"idx\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nThe dataset is attached to this issue as well:\r\n[data.zip](https:\/\/github.com\/huggingface\/datasets\/files\/4476928\/data.zip)\r\n\r\nNow the steps to reproduce what I would like to do:\r\n1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)\r\n2. create the `bbc.py` script as above at the same location than the unziped `data` folder.\r\n\r\nNow I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:\r\n```python\r\nimport nlp\r\nfrom bbc import Bbc\r\ndataset = nlp.load(\"bbc\")\r\n```\r\n\r\nI get:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 88, in load_dataset\r\n    local_files_only=local_files_only,\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py\", line 214, in cached_path\r\n    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/zipfile.py\", line 203, in is_zipfile\r\n    with open(filename, \"rb\") as fp:\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```\r\n\r\nBut @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\".\/bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"\/absolute\/path\/to\/bbc.py\")\r\n```\r\n\r\nThese three ways gives me:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 124, in load_dataset\r\n    dataset_module = importlib.import_module(module_path)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 965, in _find_and_load_unlocked\r\nModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'\r\n```\r\nAny idea of what I'm missing? or I might have spot a bug :)","comment_length":88,"text":"Issue to read a local dataset \n Hello,\r\n\r\nAs proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:\r\n```python\r\nimport os\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass BbcConfig(nlp.BuilderConfig):\r\n    def __init__(self, **kwargs):\r\n        super(BbcConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    _DIR = \".\/data\"\r\n    _DEV_FILE = \"test.csv\"\r\n    _TRAINING_FILE = \"train.csv\"\r\n\r\n    BUILDER_CONFIGS = [BbcConfig(name=\"bbc\", version=nlp.Version(\"1.0.0\"))]\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({\"id\": nlp.string, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        files = {\"train\": os.path.join(self._DIR, self._TRAINING_FILE), \"dev\": os.path.join(self._DIR, self._DEV_FILE)}\r\n\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": files[\"train\"]}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": files[\"dev\"]})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"idx\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nThe dataset is attached to this issue as well:\r\n[data.zip](https:\/\/github.com\/huggingface\/datasets\/files\/4476928\/data.zip)\r\n\r\nNow the steps to reproduce what I would like to do:\r\n1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)\r\n2. create the `bbc.py` script as above at the same location than the unziped `data` folder.\r\n\r\nNow I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:\r\n```python\r\nimport nlp\r\nfrom bbc import Bbc\r\ndataset = nlp.load(\"bbc\")\r\n```\r\n\r\nI get:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 88, in load_dataset\r\n    local_files_only=local_files_only,\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py\", line 214, in cached_path\r\n    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/zipfile.py\", line 203, in is_zipfile\r\n    with open(filename, \"rb\") as fp:\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```\r\n\r\nBut @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\".\/bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"\/absolute\/path\/to\/bbc.py\")\r\n```\r\n\r\nThese three ways gives me:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 124, in load_dataset\r\n    dataset_module = importlib.import_module(module_path)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 965, in _find_and_load_unlocked\r\nModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'\r\n```\r\nAny idea of what I'm missing? or I might have spot a bug :) \n Ok great, so as discussed today, let's:\r\n- have a main dataset directory inside the lib with sub-directories hashed by the content of the file\r\n- keep a cache for downloading the scripts from S3 for now\r\n- later: add methods to list and clean the local versions of the datasets (and the distant versions on S3 as well)\r\n\r\nSide question: do you often use `builder_kwargs` for other things than supplying file paths? I was thinking about having a more easy to read and remember `data_files` argument maybe.","embeddings":[-0.2719613612,0.1254656762,-0.0766852647,0.256811142,0.3318964839,0.0312691405,0.2642410994,0.3003315628,0.3266820014,0.2098683715,0.1191096306,0.2447595894,-0.2942002714,0.1220210195,0.1568034738,-0.1723625809,-0.1275069416,0.3326410651,0.1501982808,-0.3661575317,-0.27604267,0.1413950026,-0.1191338152,0.1533625871,-0.2043232173,0.0607061796,-0.036645785,0.4012748301,-0.2069403231,-0.2449603528,0.1894469708,0.0119974464,0.2054079175,0.122409679,-0.0001004647,0.1904105544,0.2995985448,0.0436597355,-0.1575898379,-0.364929229,-0.0587465651,-0.3323357999,0.2688184083,-0.3986444175,-0.3326914608,-0.1401537061,0.0905135646,-0.4378168285,0.4685541093,0.3634622395,0.3068140745,0.1048855186,-0.0699259415,0.1463657767,0.195211947,0.152147904,-0.0873053744,0.2656306922,0.031915091,-0.2012286931,-0.1960862428,0.1223725975,-0.2222957164,0.0449407585,0.2718259394,0.1370849758,0.182052508,-0.1032298878,-0.032771349,-0.0018344095,0.3769168258,-0.2492007166,-0.2248517573,-0.13559407,0.0572931319,-0.2591866851,0.0394319892,0.342464596,-0.349896282,0.3455857933,-0.2791967094,0.1082817465,-0.3008818924,0.2382965088,0.1193012074,0.3104308248,-0.076890111,0.1048834771,0.0907402039,0.2240543067,0.1786538064,-0.1133791059,0.2185333967,0.2561581731,-0.0302851815,0.0967433602,0.3372043669,0.0830674171,-0.0235993508,-0.0186839495,0.1047985032,0.0249034651,0.0130941896,0.3297557831,0.1096323282,0.1400410384,0.1735097319,0.087680243,0.0717137232,-0.0622796975,-0.2097682357,0.0256421678,-0.1461680084,-0.283438772,0.0297457296,0.2404010743,0.4468615353,-0.1885111183,-0.1420501471,0.2193927914,-0.2050481737,-0.0884669423,0.1696829945,0.4059306085,-0.0357793234,0.2412724048,0.1384448409,0.0997360051,-0.3508428931,-0.1973964125,-0.3274970055,-0.0029654084,-0.2937141359,-0.0887341276,0.3137897849,0.0290612634,0.3492821157,-0.0983311981,-0.2490224689,-0.006291463,0.1585656404,-0.2560243607,0.1404405981,0.138631627,0.0651841164,0.034821,0.2272037119,-0.4230361283,-0.1299738437,0.2165416181,0.0924713016,-0.4230499268,-0.1779252738,0.3226445317,-0.0838615969,0.1769313514,0.180574581,0.1790369749,0.1407811642,-0.036249388,-0.2524119318,-0.0215898771,-0.459605664,-0.2430503964,0.2107181102,0.3516464829,-0.4640030563,0.0853260383,-0.164036572,-0.1117871031,0.3510994613,0.2487842441,-0.3300900161,0.4454449415,-0.1017358303,0.1409201324,0.6580390334,-0.3004144132,-0.3807192743,0.6322368979,-0.3527038991,-0.1465917528,0.0498830862,0.0073254914,-0.0018453643,0.2033673376,0.2166674733,0.3950749636,-0.1738853306,0.1002998799,-0.4054657817,-0.152086556,0.1437031329,0.1029602066,-0.0783379376,0.1322496831,0.1525419801,-0.05174575,0.497027427,-0.1389278173,0.1745269895,0.3185678422,0.2489011884,-0.0089441948,-0.1572516412,-0.0782212839,-0.2163548321,0.04727301,-0.1540113091,-0.0111839129,-0.1268217266,-0.1966494024,-0.3752773702,0.1024699733,-0.4161543548,-0.0244457163,0.2532124221,0.2463398874,0.0947190672,0.0092416257,-0.0782992616,-0.1027992368,-0.1485022753,-0.2201791555,-0.3223364353,0.2954417169,-0.3488661051,-0.1378353983,0.1312768757,0.3829959631,0.1777834296,-0.1002675742,-0.052053079,0.2365419269,-0.0244584288,0.0325130597,0.0145486901,-0.1853820533,-0.0120613975,-0.279640317,0.1465234011,0.0235882532,0.348398447,0.0077597764,-0.3682603538,0.377537787,-0.0371985137,0.0586498044,-0.0751952454,-0.0948600769,0.3058986366,-0.2762518227,-0.0493951924,-0.0236396324,0.2183714211,0.0369678438,0.1693601906,0.04129586,-0.3924960494,0.047164917,0.3549182117,-0.072537221,-0.1731103212,0.0018169068,0.0174868628,-0.0824427828,0.164626956,0.2774141133,0.3839050233,0.2610786259,0.0662332624,0.1700644791,-0.2135237604,-0.1831107736,-0.054769367,-0.0941865519,-0.027594151,0.3564697206,0.1723606884,-0.1707368493,-0.3508794904,0.0280967355,0.155304268,0.3651978076,-0.1970971674,0.2237509936,-0.2249526829,-0.2432209104,-0.3664650917,-0.0820040554,0.0938911885,-0.3452377021,-0.1890164614,0.1978519261,-0.107149139,-0.1096806899,-0.1519416273,-0.0034873104,0.0685041398,-0.1828078628,0.0319254473,-0.29726246,-0.4607281685,0.1170991212,0.3691140115,0.3623128831,0.4312880337,0.0150138075,-0.1073622257,0.0393667519,-0.0697979704,0.076720871,-0.100895606,0.4542248547,-0.0569396578,0.0416780375,-0.2283503711,-0.4602437317,0.5433725715,-0.0930568278,-0.1630839258,0.003641258,0.1127770245,-0.2605376542,-0.2423969209,-0.5966646671,-0.376578927,-0.3775201142,0.2393841147,0.2145242095,0.2878387868,0.3024253845,0.0545422919,0.227584064,0.2486261129,0.0220368877,-0.1577897519,-0.2745801508,0.1092553139,-0.3437816501,-0.3863990009,-0.0418258309,-0.2934792042,0.2532537878,-0.177119717,-0.5053718686,0.106159687,-0.2085334063,0.175987035,0.1183275729,-0.0756837502,0.3462190628,0.0716120824,-0.0953156427,-0.1507954448,-0.1724995971,0.17420201,0.0590066873,0.0601394363,0.060319189,0.2799823284,-0.1291005462,0.4796222448,0.0056985766,0.0647447631,0.2293583602,-0.1257217079,0.0351171568,-0.0269690529,-0.5060185194,0.0983495489,0.1094464958,-0.324074626,0.239359051,-0.03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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2","title":"Issue to read a local dataset","comments":"Good plan!\r\n\r\nYes I do use `builder_kwargs` for other things such as:\r\n- dataset name\r\n- properties to know how to properly read a CSV file: do I have to skip the first line in a CSV, which delimiter is used, and the columns ids to use.\r\n- properties to know how to properly read a JSON file: which properties in a JSON object to read","body":"Hello,\r\n\r\nAs proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:\r\n```python\r\nimport os\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass BbcConfig(nlp.BuilderConfig):\r\n    def __init__(self, **kwargs):\r\n        super(BbcConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    _DIR = \".\/data\"\r\n    _DEV_FILE = \"test.csv\"\r\n    _TRAINING_FILE = \"train.csv\"\r\n\r\n    BUILDER_CONFIGS = [BbcConfig(name=\"bbc\", version=nlp.Version(\"1.0.0\"))]\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({\"id\": nlp.string, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        files = {\"train\": os.path.join(self._DIR, self._TRAINING_FILE), \"dev\": os.path.join(self._DIR, self._DEV_FILE)}\r\n\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": files[\"train\"]}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": files[\"dev\"]})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"idx\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nThe dataset is attached to this issue as well:\r\n[data.zip](https:\/\/github.com\/huggingface\/datasets\/files\/4476928\/data.zip)\r\n\r\nNow the steps to reproduce what I would like to do:\r\n1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)\r\n2. create the `bbc.py` script as above at the same location than the unziped `data` folder.\r\n\r\nNow I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:\r\n```python\r\nimport nlp\r\nfrom bbc import Bbc\r\ndataset = nlp.load(\"bbc\")\r\n```\r\n\r\nI get:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 88, in load_dataset\r\n    local_files_only=local_files_only,\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py\", line 214, in cached_path\r\n    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/zipfile.py\", line 203, in is_zipfile\r\n    with open(filename, \"rb\") as fp:\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```\r\n\r\nBut @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\".\/bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"\/absolute\/path\/to\/bbc.py\")\r\n```\r\n\r\nThese three ways gives me:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 124, in load_dataset\r\n    dataset_module = importlib.import_module(module_path)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 965, in _find_and_load_unlocked\r\nModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'\r\n```\r\nAny idea of what I'm missing? or I might have spot a bug :)","comment_length":66,"text":"Issue to read a local dataset \n Hello,\r\n\r\nAs proposed by @thomwolf, I open an issue to explain what I'm trying to do without success. What I want to do is to create and load a local dataset, the script I have done is the following:\r\n```python\r\nimport os\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass BbcConfig(nlp.BuilderConfig):\r\n    def __init__(self, **kwargs):\r\n        super(BbcConfig, self).__init__(**kwargs)\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n    _DIR = \".\/data\"\r\n    _DEV_FILE = \"test.csv\"\r\n    _TRAINING_FILE = \"train.csv\"\r\n\r\n    BUILDER_CONFIGS = [BbcConfig(name=\"bbc\", version=nlp.Version(\"1.0.0\"))]\r\n\r\n    def _info(self):\r\n        return nlp.DatasetInfo(builder=self, features=nlp.features.FeaturesDict({\"id\": nlp.string, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n    def _split_generators(self, dl_manager):\r\n        files = {\"train\": os.path.join(self._DIR, self._TRAINING_FILE), \"dev\": os.path.join(self._DIR, self._DEV_FILE)}\r\n\r\n        return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": files[\"train\"]}),\r\n                nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": files[\"dev\"]})]\r\n\r\n    def _generate_examples(self, filepath):\r\n        with open(filepath) as f:\r\n            reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n            lines = list(reader)[1:]\r\n\r\n            for idx, line in enumerate(lines):\r\n                yield idx, {\"idx\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nThe dataset is attached to this issue as well:\r\n[data.zip](https:\/\/github.com\/huggingface\/datasets\/files\/4476928\/data.zip)\r\n\r\nNow the steps to reproduce what I would like to do:\r\n1. unzip data locally (I know the nlp lib can detect and extract archives but I want to reduce and facilitate the reproduction as much as possible)\r\n2. create the `bbc.py` script as above at the same location than the unziped `data` folder.\r\n\r\nNow I try to load the dataset in three different ways and none works, the first one with the name of the dataset like I would do with TFDS:\r\n```python\r\nimport nlp\r\nfrom bbc import Bbc\r\ndataset = nlp.load(\"bbc\")\r\n```\r\n\r\nI get:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 88, in load_dataset\r\n    local_files_only=local_files_only,\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/utils\/file_utils.py\", line 214, in cached_path\r\n    if not is_zipfile(output_path) and not tarfile.is_tarfile(output_path):\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/zipfile.py\", line 203, in is_zipfile\r\n    with open(filename, \"rb\") as fp:\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```\r\n\r\nBut @thomwolf told me that no need to import the script, just put the path of it, then I tried three different way to do:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\".\/bbc.py\")\r\n```\r\nAnd\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"\/absolute\/path\/to\/bbc.py\")\r\n```\r\n\r\nThese three ways gives me:\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 280, in load\r\n    dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 166, in builder\r\n    builder_cls = load_dataset(path, name=name, **builder_kwargs)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/site-packages\/nlp\/load.py\", line 124, in load_dataset\r\n    dataset_module = importlib.import_module(module_path)\r\n  File \"\/opt\/anaconda3\/envs\/transformers\/lib\/python3.7\/importlib\/__init__.py\", line 127, in import_module\r\n    return _bootstrap._gcd_import(name[level:], package, level)\r\n  File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n  File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n  File \"<frozen importlib._bootstrap>\", line 965, in _find_and_load_unlocked\r\nModuleNotFoundError: No module named 'nlp.datasets.2fd72627d92c328b3e9c4a3bf7ec932c48083caca09230cebe4c618da6e93688.bbc'\r\n```\r\nAny idea of what I'm missing? or I might have spot a bug :) \n Good plan!\r\n\r\nYes I do use `builder_kwargs` for other things such as:\r\n- dataset name\r\n- properties to know how to properly read a CSV file: do I have to skip the first line in a CSV, which delimiter is used, and the columns ids to use.\r\n- properties to know how to properly read a JSON file: which properties in a JSON object to 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